diff --git a/SInCohMap_openEO_radar_mask.ipynb b/SInCohMap_openEO_radar_mask.ipynb new file mode 100644 index 0000000..17e5b1c --- /dev/null +++ b/SInCohMap_openEO_radar_mask.ipynb @@ -0,0 +1,12499 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "820cbabb-356b-462b-ae3b-b07cdfd050c9", + "metadata": {}, + "source": [ + "## SInCohMap data access and processing examples\n", + "### Author michele.claus@eurac.edu\n", + "### Date: 2021/12/20" + ] + }, + { + "cell_type": "markdown", + "id": "b0f31cf0-02c3-419d-b85d-3213aaab7d80", + "metadata": {}, + "source": [ + "## Useful links:\n", + "\n", + "SAR2Cube website: https://sar2cube.projects.eurac.edu/\n", + "\n", + "openEO main website: https://openeo.org/\n", + "\n", + "openEO Python Client documentation: https://open-eo.github.io/openeo-python-client/index.html\n", + "\n", + "Getting started guide for openEO with python: https://openeo.org/documentation/1.0/python/\n", + "\n", + "## FAQ:\n", + "**Q: I receive a 403 error, what does it mean?**\n", + "\n", + "A: If you get a 403 error, it usually means that the connection with the openEO back-end dropped. Please re run\n", + "\n", + "`conn = openeo.connect(openeoHost).authenticate_oidc(client_id=\"openEO_PKCE\")`\n", + "\n", + "and re run you code from `load_collection` onwards.\n", + "\n", + "**Q: I receive a 500 error, what does it mean?**\n", + "\n", + "A: It is a server error: something went wrong processing your request. Please check carefully that the area and time range you are requesting are available in the datacube (you can use `conn.describe_collection('COLLECTION_NAME')`) Currently the error logs are not passed if you run your request as a synchronous call (i.e. using `.download()`). If you run you process as a batch job you will get a more informative error message.\n", + "\n", + "**Q: I receive a 502 error, what does it mean?**\n", + "\n", + "A: If you get an error similar to: _[502] unknown: Received 502 Proxy Error. This typically happens if an OpenEO request takes too long and is killed. Consider using batch jobs instead of doing synchronous processing._\n", + "The message is already explaining you the problem: you are using a synchronous call (.download()) to run a process which is taking too much to complete. You need to use a batch job in this case." + ] + }, + { + "cell_type": "markdown", + "id": "85e70cd4-871a-47b4-92f2-47a63ca5c305", + "metadata": {}, + "source": [ + "### Import all the libraries and utilities functions included in the eo_utils.py file" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0205484c-3eff-4825-bdae-78357b775897", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "from eo_utils import *" + ] + }, + { + "cell_type": "markdown", + "id": "fd0781cb-38dc-4795-a39a-8a687f6a3fdb", + "metadata": {}, + "source": [ + "### Connect and login" + ] + }, + { + "cell_type": "code", + "execution_count": 220, + "id": "a4fa0ed5-6678-4ca7-9442-c8521cfa0c90", + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Authenticated using refresh token.\n" + ] + } + ], + "source": [ + "openeoHost = \"https://openeo.eurac.edu\"\n", + "conn = openeo.connect(openeoHost).authenticate_oidc(client_id=\"openEO_PKCE\")" + ] + }, + { + "cell_type": "markdown", + "id": "convenient-survivor", + "metadata": {}, + "source": [ + "Please check to have the latest openeo library. openeo >= 0.9.0 is required" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c2ab09c2-cd21-436d-b040-0c3439e0dfa5", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'0.9.1'" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "openeo.__version__" + ] + }, + { + "cell_type": "markdown", + "id": "1e195c12-b3f9-408f-83c5-e1a6b7949e5c", + "metadata": {}, + "source": [ + "### Get the info of our account:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2eeb9e3f-17d1-4581-a0fd-d0af76aba43b", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'user_id': 'dfd05cf2-30d1-4139-9cda-493787936318',\n", + " 'name': 'MIchele Claus',\n", + " 'links': None}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "conn.describe_account()" + ] + }, + { + "cell_type": "markdown", + "id": "b1c55e6f-4f31-4aee-96ce-d2a4bf4fcd29", + "metadata": {}, + "source": [ + "### Discover the available collections:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "53a63f29-33a3-4703-8b96-510299a8fd93", + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " " + ], + "text/plain": [ + "[{'stac_version': '0.9.0',\n", + " 'stac_extensions': ['datacube', 'scientific'],\n", + " 'id': 'ADO_CORINE_100m_3035_ODC',\n", + " 'title': 'Corine Land Cover (CLC) 2018',\n", + " 'description': 'CLC2018 is one of the Corine Land Cover (CLC) datasets produced within the frame the Copernicus Land Monitoring Service referring to land cover / land use status of year 2018. 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In: Journal of Climate, 23(7), 1696–1718. doi: 10.1175/2009JCLI2909.1.',\n", + " 'providers': [{'name': 'Eurac EO WCS',\n", + " 'url': 'http://www.eurac.edu',\n", + " 'roles': ['host']},\n", + " {'name': 'Slovenian Environment Agency - ARSO',\n", + " 'url': 'https://www.arso.gov.si/',\n", + " 'roles': ['Processor']},\n", + " {'name': 'Zentralanstalt für Meteorologie und Geodynamik - ZAMG',\n", + " 'url': 'https://www.zamg.ac.at/cms/de/aktuell',\n", + " 'roles': ['Producer']}],\n", + " 'extent': {'spatial': {'bbox': [[4.0563685981112565,\n", + " 42.85381219788743,\n", + " 17.360182784195487,\n", + " 50.310634900383555]]},\n", + " 'temporal': {'interval': [['1978-12-31T12:00:00Z',\n", + " '2020-12-31T12:00:00Z']]}},\n", + " 'links': [{'rel': 'licence',\n", + " 'href': 'TBD',\n", + " 'type': 'text/html',\n", + " 'title': 'License Link'}],\n", + " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", + " 'extent': ['1978-12-31T12:00:00.000Z', '2020-12-31T12:00:00.000Z'],\n", + " 'step': '1-day'},\n", + " 'X': {'type': 'spatial',\n", + " 'axis': 'x',\n", + " 'extent': [4.0563685981112565, 17.360182784195487],\n", + " 'reference_system': 3035},\n", + " 'Y': {'type': 'spatial',\n", + " 'axis': 'y',\n", + " 'extent': [42.85381219788743, 50.310634900383555],\n", + " 'reference_system': 3035},\n", + " 'bands': {'type': 'bands', 'values': ['SPEI_6']}},\n", + " 'summaries': {'constellation': ['N/A'],\n", + " 'platform': ['N/A'],\n", + " 'rows': 167,\n", + " 'columns': 202,\n", + " 'instruments': ['N/A'],\n", + " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", + " 'gsd': [],\n", + " 'eo:bands': [{'name': 'SPEI_6', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'assets': {}},\n", + " {'engine': 'WCPS',\n", + " 'stac_version': '0.9.0',\n", + " 'stac_extensions': ['datacube'],\n", + " 'id': 'ADO_SPI_12_ERA5_QM',\n", + " 'title': 'Standardised Precipitation Index - ERA5_QM SPI-12',\n", + " 'description': 'The Standardized Precipitation Index (SPI) represents a standardized measure of what a certain amount of precipitation over the selected time period means in relation to expected amount of precipitation for this period. 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In: Proceedings of the Eighth Conference on Applied Climatology, Anaheim, California,17–22 January 1993. 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In: Proceedings of the Eighth Conference on Applied Climatology, Anaheim, California,17–22 January 1993. Boston, American Meteorological Society, 179–184.',\n", + " 'providers': [{'name': 'Eurac EO WCS',\n", + " 'url': 'http://www.eurac.edu',\n", + " 'roles': ['host']},\n", + " {'name': 'Slovenian Environment Agency - ARSO',\n", + " 'url': 'https://www.arso.gov.si/',\n", + " 'roles': ['Processor']},\n", + " {'name': 'Zentralanstalt für Meteorologie und Geodynamik - ZAMG',\n", + " 'url': 'https://www.zamg.ac.at/cms/de/aktuell',\n", + " 'roles': ['Producer']}],\n", + " 'extent': {'spatial': {'bbox': [[4.0563685981112565,\n", + " 42.85381219788743,\n", + " 17.360182784195487,\n", + " 50.310634900383555]]},\n", + " 'temporal': {'interval': [['1978-12-31T12:00:00Z',\n", + " '2020-12-31T12:00:00Z']]}},\n", + " 'links': [{'rel': 'licence',\n", + " 'href': 'TBD',\n", + " 'type': 'text/html',\n", + " 'title': 'License Link'}],\n", + " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", + " 'extent': ['1978-12-31T12:00:00.000Z', '2020-12-31T12:00:00.000Z'],\n", + " 'step': '1-day'},\n", + " 'X': {'type': 'spatial',\n", + " 'axis': 'x',\n", + " 'extent': [4.0563685981112565, 17.360182784195487],\n", + " 'reference_system': 3035},\n", + " 'Y': {'type': 'spatial',\n", + " 'axis': 'y',\n", + " 'extent': [42.85381219788743, 50.310634900383555],\n", + " 'reference_system': 3035},\n", + " 'bands': {'type': 'bands', 'values': ['SPI_6']}},\n", + " 'summaries': {'constellation': ['N/A'],\n", + " 'platform': ['N/A'],\n", + " 'rows': 167,\n", + " 'columns': 202,\n", + " 'instruments': ['N/A'],\n", + " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", + " 'gsd': [],\n", + " 'eo:bands': [{'name': 'SPI_6', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'assets': {}},\n", + " {'engine': 'WCPS',\n", + " 'stac_version': '0.9.0',\n", + " 'stac_extensions': ['datacube'],\n", + " 'id': 'ADO_SSPI_10d_SNOWGRID',\n", + " 'title': 'Standardised Snow Pack Index - ERA5_QM SSPI-10',\n", + " 'description': 'The Standardized Snow Pack Index (SSPI) represents a standardized measure of what a certain value of snow water equivalent (SWE) averaged over the selected time period means in relation to the expected value for this period. SSPI is computed the same way as the SPI (using gamma distribution), except for being based on daily SWE timeseries instead of daily precipitation. It is calculated using the average SWE over a period of 10 and 30 days. The value of the SSPI index around 0 represents the normal expected conditions for the average SWE in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus, while the value of -1 is about one standard deviation of the deficit. SWE data used as input for the calculation of SSPI are derived using a modified version of the deterministic snow model SNOWGRID-CL, with downscaled ERA5 data used as model input data.',\n", + " 'keywords': ['SSPI', 'standardised snow pack index', 'ERA5', 'SNOWGRID'],\n", + " 'version': 'v1',\n", + " 'deprecated': False,\n", + " 'license': 'Licence to use Copernicus Products',\n", + " 'sci:citation': 'Olefs, Marc, Roland Koch, Wolfgang Schöner, and Thomas Marke. (2020): Changes in Snow Depth, Snow Cover Duration, and Potential Snowmaking Conditions in Austria, 1961–2020—A Model Based Approach Atmosphere 11, no. 12: 1330. https://doi.org/10.3390/atmos11121330. EDO Indicator Fact Sheet: Standardised Snow Pack Index: https://edo.jrc.ec.europa.eu/documents/factsheets/factsheet_sspi.pdf',\n", + " 'providers': [{'name': 'Eurac EO WCS',\n", + " 'url': 'http://www.eurac.edu',\n", + " 'roles': ['host']},\n", + " {'name': 'Slovenian Environment Agency - ARSO',\n", + " 'url': 'https://www.arso.gov.si/',\n", + " 'roles': ['Producer']},\n", + " {'name': 'Zentralanstalt für Meteorologie und Geodynamik - ZAMG',\n", + " 'url': 'https://www.zamg.ac.at/cms/de/aktuell',\n", + " 'roles': ['Producer']}],\n", + " 'extent': {'spatial': {'bbox': [[4.0563685981112565,\n", + " 42.85381219788743,\n", + " 17.360182784195487,\n", + " 50.310634900383555]]},\n", + " 'temporal': {'interval': [['1978-12-31T12:00:00Z',\n", + " '2020-12-31T12:00:00Z']]}},\n", + " 'links': [{'rel': 'licence',\n", + " 'href': 'TBD',\n", + " 'type': 'text/html',\n", + " 'title': 'License Link'}],\n", + " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", + " 'extent': ['1978-12-31T12:00:00.000Z', '2020-12-31T12:00:00.000Z'],\n", + " 'step': 'P1D'},\n", + " 'X': {'type': 'spatial',\n", + " 'axis': 'x',\n", + " 'extent': [4.0563685981112565, 17.360182784195487],\n", + " 'reference_system': 3035},\n", + " 'Y': {'type': 'spatial',\n", + " 'axis': 'y',\n", + " 'extent': [42.85381219788743, 50.310634900383555],\n", + " 'reference_system': 3035},\n", + " 'bands': {'type': 'bands', 'values': ['SSPI10']}},\n", + " 'summaries': {'constellation': ['N/A'],\n", + " 'platform': ['N/A'],\n", + " 'rows': 167,\n", + " 'columns': 202,\n", + " 'instruments': ['N/A'],\n", + " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", + " 'gsd': [],\n", + " 'eo:bands': [{'name': 'SSPI10', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'assets': {}},\n", + " {'engine': 'WCPS',\n", + " 'stac_version': '0.9.0',\n", + " 'stac_extensions': ['datacube'],\n", + " 'id': 'ADO_SSPI_30d_SNOWGRID',\n", + " 'title': 'Standardised Snow Pack Index - ERA5_QM SSPI-30',\n", + " 'description': 'The Standardized Snow Pack Index (SSPI) represents a standardized measure of what a certain value of snow water equivalent (SWE) averaged over the selected time period means in relation to the expected value for this period. SSPI is computed the same way as the SPI (using gamma distribution), except for being based on daily SWE timeseries instead of daily precipitation. It is calculated using the average SWE over a period of 10 and 30 days. The value of the SSPI index around 0 represents the normal expected conditions for the average SWE in the selected period based on the long-term average (1981-2020). The value of 1 represents approximately one standard deviation of the surplus, while the value of -1 is about one standard deviation of the deficit. SWE data used as input for the calculation of SSPI are derived using a modified version of the deterministic snow model SNOWGRID-CL, with downscaled ERA5 data used as model input data.',\n", + " 'keywords': ['SSPI', 'standardised snow pack index', 'ERA5', 'SNOWGRID'],\n", + " 'version': 'v1',\n", + " 'deprecated': False,\n", + " 'license': 'Licence to use Copernicus Products',\n", + " 'sci:citation': 'Olefs, Marc, Roland Koch, Wolfgang Schöner, and Thomas Marke. (2020): Changes in Snow Depth, Snow Cover Duration, and Potential Snowmaking Conditions in Austria, 1961–2020—A Model Based Approach Atmosphere 11, no. 12: 1330. https://doi.org/10.3390/atmos11121330. EDO Indicator Fact Sheet: Standardised Snow Pack Index: https://edo.jrc.ec.europa.eu/documents/factsheets/factsheet_sspi.pdf',\n", + " 'providers': [{'name': 'Eurac EO WCS',\n", + " 'url': 'http://www.eurac.edu',\n", + " 'roles': ['host']},\n", + " {'name': 'Slovenian Environment Agency - ARSO',\n", + " 'url': 'https://www.arso.gov.si/',\n", + " 'roles': ['Producer']},\n", + " {'name': 'Zentralanstalt für Meteorologie und Geodynamik - ZAMG',\n", + " 'url': 'https://www.zamg.ac.at/cms/de/aktuell',\n", + " 'roles': ['Producer']}],\n", + " 'extent': {'spatial': {'bbox': [[4.0563685981112565,\n", + " 42.85381219788743,\n", + " 17.360182784195487,\n", + " 50.310634900383555]]},\n", + " 'temporal': {'interval': [['1978-12-31T12:00:00Z',\n", + " '2020-12-31T12:00:00Z']]}},\n", + " 'links': [{'rel': 'licence',\n", + " 'href': 'TBD',\n", + " 'type': 'text/html',\n", + " 'title': 'License Link'}],\n", + " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", + " 'extent': ['1978-12-31T12:00:00.000Z', '2020-12-31T12:00:00.000Z'],\n", + " 'step': 'P1D'},\n", + " 'X': {'type': 'spatial',\n", + " 'axis': 'x',\n", + " 'extent': [4.0563685981112565, 17.360182784195487],\n", + " 'reference_system': 3035},\n", + " 'Y': {'type': 'spatial',\n", + " 'axis': 'y',\n", + " 'extent': [42.85381219788743, 50.310634900383555],\n", + " 'reference_system': 3035},\n", + " 'bands': {'type': 'bands', 'values': ['SSPI30']}},\n", + " 'summaries': {'constellation': ['N/A'],\n", + " 'platform': ['N/A'],\n", + " 'rows': 167,\n", + " 'columns': 202,\n", + " 'instruments': ['N/A'],\n", + " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", + " 'gsd': [],\n", + " 'eo:bands': [{'name': 'SSPI30', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'assets': {}},\n", + " {'engine': 'WCPS',\n", + " 'stac_version': '0.9.0',\n", + " 'stac_extensions': ['datacube'],\n", + " 'id': 'ADO_SWI_1km_4326',\n", + " 'title': 'Copernicus Surface Soil Moisture - 1km',\n", + " 'description': 'The Soil Water Index quantifies the moisture condition at various depths in the soil. It is mainly driven by the precipitation via the process of infiltration. Soil moisture is a very heterogeneous variable and varies on small scales with soil properties and drainage patterns. Satellite measurements integrate over relative large-scale areas, with the presence of vegetation adding complexity to the interpretation.',\n", + " 'keywords': ['surface soil moisture', 'ASCAT', 'Sentinel-1', 'ADO Project'],\n", + " 'version': 'v1',\n", + " 'deprecated': False,\n", + " 'license': 'CC-BY-4.0',\n", + " 'sci:citation': 'Copernicus Service information [2015-2020]',\n", + " 'providers': [{'name': 'Eurac EO WCS',\n", + " 'url': 'http://www.eurac.edu',\n", + " 'roles': ['host']},\n", + " {'name': 'TU Wien',\n", + " 'url': 'https://www.tuwien.at/en/',\n", + " 'roles': ['Producer']},\n", + " {'name': 'VITO NV on behalf of the European Commission Joint Research Centre',\n", + " 'url': 'https://vito.be/en',\n", + " 'roles': ['Provider']}],\n", + " 'extent': {'spatial': {'bbox': [[3.691964285713869,\n", + " 42.995535714283896,\n", + " 17.156249999999197,\n", + " 50.55803571428218]]},\n", + " 'temporal': {'interval': [['2015-01-01T00:00:00Z',\n", + " '2020-04-19T00:00:00Z']]}},\n", + " 'links': [{'rel': 'licence',\n", + " 'href': 'https://creativecommons.org/licenses/by/4.0/',\n", + " 'type': 'text/html',\n", + " 'title': 'License Link'}],\n", + " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", + " 'extent': ['2015-01-01T00:00:00.000Z', '2020-04-19T00:00:00.000Z'],\n", + " 'step': 'P1D'},\n", + " 'Long': {'type': 'spatial',\n", + " 'axis': 'x',\n", + " 'extent': [3.691964285713869, 17.156249999999197],\n", + " 'reference_system': 4326},\n", + " 'bands': {'type': 'bands',\n", + " 'values': ['SWI_002',\n", + " 'SWI_005',\n", + " 'SWI_010',\n", + " 'SWI_015',\n", + " 'SWI_020',\n", + " 'SWI_040',\n", + " 'SWI_060',\n", + " 'SWI_100']},\n", + " 'Lat': {'type': 'spatial',\n", + " 'axis': 'y',\n", + " 'extent': [42.995535714283896, 50.55803571428218],\n", + " 'reference_system': 4326}},\n", + " 'summaries': {'constellation': ['Sentinel-1; ASCAT'],\n", + " 'platform': ['Sentinel-1 A/B; MetOp A/B'],\n", + " 'rows': 848,\n", + " 'columns': 1509,\n", + " 'instruments': ['SAR Scatterometer'],\n", + " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", + " 'gsd': [],\n", + " 'eo:bands': [{'name': 'SWI_002', 'center_wavelength': 0.0, 'gsd': 0.0},\n", + " {'name': 'SWI_005', 'center_wavelength': 0.0, 'gsd': 0.0},\n", + " {'name': 'SWI_010', 'center_wavelength': 0.0, 'gsd': 0.0},\n", + " {'name': 'SWI_015', 'center_wavelength': 0.0, 'gsd': 0.0},\n", + " {'name': 'SWI_020', 'center_wavelength': 0.0, 'gsd': 0.0},\n", + " {'name': 'SWI_040', 'center_wavelength': 0.0, 'gsd': 0.0},\n", + " {'name': 'SWI_060', 'center_wavelength': 0.0, 'gsd': 0.0},\n", + " {'name': 'SWI_100', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'assets': {}},\n", + " {'engine': 'WCPS',\n", + " 'stac_version': '0.9.0',\n", + " 'stac_extensions': ['datacube'],\n", + " 'id': 'ADO_TCI_MODIS_231m_3035',\n", + " 'title': 'Temperature Condition Index - 231 m 8 days',\n", + " 'description': 'The Temperature Condition Index (TCI) is based on the Land Surface Temperature (LST) MODIS satellite data. The LST is based on 8 day MOD11A2 (v006) LST products. The spatial resolution is 231 m after regridding from the original 1000 m resolution. The LST is masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetatated areas are masked using the MODIS land cover product layer MCD12Q1 FAO-Land Cover Classification System 1 (LCCS1). The final product is regridded to the LAEA Projection (EPSG:3035). The TCI is calculated using the formula TCIi = (LSTmax,i - LSTi)/(LSTmax,i - LSTmin,i) * 100. The TCI expresses anomalies of the LST. The data is provided as 8 day measures. The time series is starting from 2001. The TCI values range from 0-100, whereas high values correspond to optimal vegetation conditions and low values indicate unfavorable vegetation conditions.',\n", + " 'keywords': ['temperature condition index', 'vci', 'modis'],\n", + " 'version': 'v1',\n", + " 'deprecated': False,\n", + " 'license': 'CC BY 4.0',\n", + " 'sci:citation': 'Kogan, F. N. (eds) (1995): Application of vegetation index and brightness temperature for drought detection. In: Advances in Space Research, 15 (11), 91–100. Kogan, F. N. (eds) (1997): Global Drought Watch from Space. In: Bulletin of the American Meteorological Society, 78 (4), 621–636. Kogan, F. N. (eds) (2000): Satellite-Observed Sensitivity of World Land Ecosystems to El Nino/La Nina. In: Remote Sensing of Environment, 74, 445–462.',\n", + " 'providers': [{'name': 'Eurac EO WCS',\n", + " 'url': 'http://www.eurac.edu',\n", + " 'roles': ['host']},\n", + " {'name': 'Eurac Research',\n", + " 'url': 'http://www.eurac.edu',\n", + " 'roles': ['Processor']},\n", + " {'name': 'NASA',\n", + " 'url': 'https://modis.gsfc.nasa.gov/',\n", + " 'roles': ['Producer']}],\n", + " 'extent': {'spatial': {'bbox': [[3.99537296130427,\n", + " 42.87349365119738,\n", + " 17.523924303829347,\n", + " 50.32636203215727]]},\n", + " 'temporal': {'interval': [['2001-01-01T00:00:00Z',\n", + " '2021-01-03T00:00:00Z']]}},\n", + " 'links': [{'rel': 'licence',\n", + " 'href': 'https://creativecommons.org/licenses/by/4.0/',\n", + " 'type': 'text/html',\n", + " 'title': 'License Link'}],\n", + " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", + " 'extent': ['2001-01-01T00:00:00.000Z', '2021-01-03T00:00:00.000Z'],\n", + " 'step': 'P8D'},\n", + " 'X': {'type': 'spatial',\n", + " 'axis': 'x',\n", + " 'extent': [3.99537296130427, 17.523924303829347],\n", + " 'reference_system': 3035},\n", + " 'Y': {'type': 'spatial',\n", + " 'axis': 'y',\n", + " 'extent': [42.87349365119738, 50.32636203215727],\n", + " 'reference_system': 3035},\n", + " 'bands': {'type': 'bands', 'values': ['8d_tci']}},\n", + " 'summaries': {'constellation': ['Terra'],\n", + " 'platform': ['Terra'],\n", + " 'rows': 3606,\n", + " 'columns': 4430,\n", + " 'instruments': ['MODIS'],\n", + " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", + " 'gsd': [],\n", + " 'eo:bands': [{'name': '8d_tci', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'assets': {}},\n", + " {'engine': 'WCPS',\n", + " 'stac_version': '0.9.0',\n", + " 'stac_extensions': ['datacube'],\n", + " 'id': 'ADO_VCI_MODIS_231m_3035',\n", + " 'title': 'Vegetation Condition Index - 231 m 8 days',\n", + " 'description': 'The Vegetation Condition Index (VCI) is based on the Normalized Difference Vegetation Index (NDVI) derived from MODIS satellite data. The NDVI is based on 8 day maximum value composite MOD09Q1 (v006) reflectance products. The spatial resolution is 231 m. The NDVI is masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetatated areas are masked using the MODIS land cover product layer MCD12Q1 FAO-Land Cover Classification System 1 (LCCS1). The final product is regridded to the LAEA Projection (EPSG:3035). The VCI is calculated using the formula VCIi = (NDVIi - NDVImin,i)/(NDVImax,i - NDVImin,i) * 100. The VCI expresses anomalies of the NDVI. The data is provided as 8 day measures. The time series is starting from 2001. The VCI values range from 0-100, whereas high values correspond to healthy vegetation and low values indicate stressed vegetation.',\n", + " 'keywords': ['vegetation condition index', 'vci', 'modis'],\n", + " 'version': 'v1',\n", + " 'deprecated': False,\n", + " 'license': 'CC BY 4.0',\n", + " 'sci:citation': 'Michele Meroni, Dominique Fasbender, Felix Rembold, Clement Atzberger, Anja Klisch: Near real-time vegetation anomaly detection with MODIS NDVI: Timeliness vs. accuracy and effect of anomaly computation options, Remote Sensing of Environment, Volume 221, 2019, Pages 508-521, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2018.11.041, (https://www.sciencedirect.com/science/article/pii/S0034425718305509)',\n", + " 'providers': [{'name': 'Eurac EO WCS',\n", + " 'url': 'http://www.eurac.edu',\n", + " 'roles': ['host']},\n", + " {'name': 'Eurac Research',\n", + " 'url': 'http://www.eurac.edu',\n", + " 'roles': ['Processor']},\n", + " {'name': 'NASA',\n", + " 'url': 'https://modis.gsfc.nasa.gov/',\n", + " 'roles': ['Producer']}],\n", + " 'extent': {'spatial': {'bbox': [[3.99537296130427,\n", + " 42.87349365119738,\n", + " 17.523924303829347,\n", + " 50.32636203215727]]},\n", + " 'temporal': {'interval': [['2001-01-01T00:00:00Z',\n", + " '2021-01-03T00:00:00Z']]}},\n", + " 'links': [{'rel': 'licence',\n", + " 'href': 'https://creativecommons.org/licenses/by/4.0/',\n", + " 'type': 'text/html',\n", + " 'title': 'License Link'}],\n", + " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", + " 'extent': ['2001-01-01T00:00:00.000Z', '2021-01-03T00:00:00.000Z'],\n", + " 'step': 'P8D'},\n", + " 'X': {'type': 'spatial',\n", + " 'axis': 'x',\n", + " 'extent': [3.99537296130427, 17.523924303829347],\n", + " 'reference_system': 3035},\n", + " 'Y': {'type': 'spatial',\n", + " 'axis': 'y',\n", + " 'extent': [42.87349365119738, 50.32636203215727],\n", + " 'reference_system': 3035},\n", + " 'bands': {'type': 'bands', 'values': ['8d_vci']}},\n", + " 'summaries': {'constellation': ['Terra'],\n", + " 'platform': ['Terra'],\n", + " 'rows': 3606,\n", + " 'columns': 4430,\n", + " 'instruments': ['MODIS'],\n", + " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", + " 'gsd': [],\n", + " 'eo:bands': [{'name': '8d_vci', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'assets': {}},\n", + " {'engine': 'WCPS',\n", + " 'stac_version': '0.9.0',\n", + " 'stac_extensions': ['datacube'],\n", + " 'id': 'ADO_VHI_MODIS_231m_3035',\n", + " 'title': 'Vegetation Health Index - 231 m 8 days',\n", + " 'description': 'The Vegetation Health Index (VHI) is based on a combination of products extracted from vegetation signals, namely the Normalized Difference Vegetation Index (NDVI) and the land surface temperature, both derived from MODIS satellite data. The NDVI is based on 8 day maximum value composite MOD09Q1 (v006) reflectance and the land surface temperature (LST) on 8 day MOD11A2 (v006) LST products. The spatial resolution is 231 m, therefore the original 1000 m resolution of the MOD11A2 LST is downscaled to 231 m of the MOD09Q1 reflectance. Both products are masked to the highest quality standards using the provided quality layers. Missing pixel values in the time series are linearly interpolated. Non-vegetatated areas are masked using the MODIS land cover product layer MCD12Q1 FAO-Land Cover Classification System 1 (LCCS1). The final product is regridded to the LAEA Projection (EPSG:3035). The VHI relies on a strong inverse correlation between NDVI and land surface temperature, since increasing land temperatures are assumed to act negatively on vegetation vigour and consequently to cause stress. The data is provided as 8 day measures. The time series is starting from 2001. The VHI values range from 0-100, whereas high values correspond to healthy vegetation and low values indicate stressed vegetation.',\n", + " 'keywords': ['vegetation health index', 'vhi', 'modis'],\n", + " 'version': 'v1',\n", + " 'deprecated': False,\n", + " 'license': 'CC BY 4.0',\n", + " 'sci:citation': 'Kogan, F. N. (eds) (1995): Application of vegetation index and brightness temperature for drought detection. In: Advances in Space Research, 15 (11), 91–100. Kogan, F. N. (eds) (1997): Global Drought Watch from Space. In: Bulletin of the American Meteorological Society, 78 (4), 621–636. Kogan, F. N. (eds) (2000): Satellite-Observed Sensitivity of World Land Ecosystems to El Nino/La Nina. In: Remote Sensing of Environment, 74, 445–462.',\n", + " 'providers': [{'name': 'Eurac EO WCS',\n", + " 'url': 'http://www.eurac.edu',\n", + " 'roles': ['host']},\n", + " {'name': 'Eurac Research',\n", + " 'url': 'http://www.eurac.edu',\n", + " 'roles': ['Processor']},\n", + " {'name': 'NASA',\n", + " 'url': 'https://modis.gsfc.nasa.gov/',\n", + " 'roles': ['Producer']}],\n", + " 'extent': {'spatial': {'bbox': [[3.99537296130427,\n", + " 42.87349365119738,\n", + " 17.523924303829347,\n", + " 50.32636203215727]]},\n", + " 'temporal': {'interval': [['2001-01-01T00:00:00Z',\n", + " '2021-01-03T00:00:00Z']]}},\n", + " 'links': [{'rel': 'licence',\n", + " 'href': 'https://creativecommons.org/licenses/by/4.0/',\n", + " 'type': 'text/html',\n", + " 'title': 'License Link'}],\n", + " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", + " 'extent': ['2001-01-01T00:00:00.000Z', '2021-01-03T00:00:00.000Z'],\n", + " 'step': 'P8D'},\n", + " 'X': {'type': 'spatial',\n", + " 'axis': 'x',\n", + " 'extent': [3.99537296130427, 17.523924303829347],\n", + " 'reference_system': 3035},\n", + " 'Y': {'type': 'spatial',\n", + " 'axis': 'y',\n", + " 'extent': [42.87349365119738, 50.32636203215727],\n", + " 'reference_system': 3035},\n", + " 'bands': {'type': 'bands', 'values': ['8d_vhi']}},\n", + " 'summaries': {'constellation': ['Terra'],\n", + " 'platform': ['Terra'],\n", + " 'rows': 3606,\n", + " 'columns': 4430,\n", + " 'instruments': ['MODIS'],\n", + " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", + " 'gsd': [],\n", + " 'eo:bands': [{'name': '8d_vhi', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'assets': {}},\n", + " {'engine': 'WCPS',\n", + " 'stac_version': '0.9.0',\n", + " 'stac_extensions': ['datacube'],\n", + " 'id': 'ALPS_SNOW_MODIS_map',\n", + " 'title': 'MODIS SNOW ALPS DataCubes',\n", + " 'description': 'SNOW map derived from daily MODIS observations (Aqua and Terra) for the entire Alps arc. The input data are the atmospherically-corrected reflectances of MODIS MOD09GQ, MOD09GA for tile h19v04 and h18v04. 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The map has two bands: - SNOW MAP: Snow cover classification map with four classes [0 = NO DATA - missing or corrupt data of one or more input bands; 1 = SNOW - pixel covered by snow; 2 = NO_SNOW - pixel not covered by snow; 3 = CLOUD - pixel covered by clouds], QUALITY FLAG: Snow cover quality map [NO DATA - missing or corrupt data of one or more input bands; QUALITY_INDEX - higher values indicate higher likeliness of correct classification].',\n", + " 'keywords': ['SNOW', 'MODIS'],\n", + " 'version': 'v1',\n", + " 'deprecated': False,\n", + " 'license': 'CC-BY-4.0',\n", + " 'providers': [{'name': 'Eurac EO WCS',\n", + " 'url': 'http://www.eurac.edu',\n", + " 'roles': ['host']},\n", + " {'name': 'National Aeronautics and Space Administration (NASA)',\n", + " 'url': 'https://www.nasa.gov/',\n", + " 'roles': ['Producer']},\n", + " {'name': 'Alexander Jacob',\n", + " 'url': 'http://www.eurac.edu/it/research/mountains/remsen/staff/Pages/staffdetails.aspx?persId=37007',\n", + " 'roles': ['Processor']},\n", + " {'name': 'Bartolomeo Ventura',\n", + " 'url': 'http://www.eurac.edu/it/aboutus/people/pages/staffdetails.aspx?persId=15903',\n", + " 'roles': ['Processor']}],\n", + " 'extent': {'spatial': {'bbox': [[4.216446958373531,\n", + " 42.817152675995985,\n", + " 18.99055634355496,\n", + " 48.601848359993454]]},\n", + " 'temporal': {'interval': [['2021-01-08T12:00:00Z',\n", + " '2021-01-09T12:00:00Z']]}},\n", + " 'links': [{'rel': 'licence',\n", + " 'href': 'https://creativecommons.org/licenses/by/4.0/',\n", + " 'type': 'text/html',\n", + " 'title': 'License Link'}],\n", + " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", + " 'extent': ['2021-01-08T12:00:00.000Z', '2021-01-09T12:00:00.000Z']},\n", + " 'X': {'type': 'spatial',\n", + " 'axis': 'x',\n", + " 'extent': [4.216446958373531, 18.99055634355496],\n", + " 'reference_system': 3035},\n", + " 'Y': {'type': 'spatial',\n", + " 'axis': 'y',\n", + " 'extent': [42.817152675995985, 48.601848359993454],\n", + " 'reference_system': 3035},\n", + " 'bands': {'type': 'bands',\n", + " 'values': ['Snow cover classification map', 'Snow cover quality map']}},\n", + " 'summaries': {'constellation': ['Aqua, Terra'],\n", + " 'platform': ['Aqua, Terra', None],\n", + " 'rows': 2867,\n", + " 'columns': 4901,\n", + " 'instruments': ['MODIS'],\n", + " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", + " 'gsd': [],\n", + " 'eo:bands': [{'name': 'Snow cover classification map',\n", + " 'center_wavelength': 0.0,\n", + " 'gsd': 0.0},\n", + " {'name': 'Snow cover quality map', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'assets': {}},\n", + " {'engine': 'WCPS',\n", + " 'stac_version': '0.9.0',\n", + " 'stac_extensions': ['datacube'],\n", + " 'id': 'wms_test_true',\n", + " 'title': 'WMS title',\n", + " 'description': 'SNOW map derived from daily MODIS observations (Aqua and Terra) for the entire Alps arc. The input data are the atmospherically-corrected reflectances of MODIS MOD09GQ, MOD09GA for tile h19v04 and h18v04. The map has two bands: - SNOW MAP: Snow cover classification map with four classes [0 = NO DATA - missing or corrupt data of one or more input bands; 1 = SNOW - pixel covered by snow; 2 = NO_SNOW - pixel not covered by snow; 3 = CLOUD - pixel covered by clouds], QUALITY FLAG: Snow cover quality map [NO DATA - missing or corrupt data of one or more input bands; QUALITY_INDEX - higher values indicate higher likeliness of correct classification].',\n", + " 'keywords': ['SNOW', 'MODIS'],\n", + " 'version': 'v1',\n", + " 'deprecated': False,\n", + " 'license': 'CC-BY-4.0',\n", + " 'providers': [{'name': 'Eurac EO WCS',\n", + " 'url': 'http://www.eurac.edu',\n", + " 'roles': ['host']},\n", + " {'name': 'National Aeronautics and Space Administration (NASA)',\n", + " 'url': 'https://www.nasa.gov/',\n", + " 'roles': ['Producer']},\n", + " {'name': 'Alexander Jacob',\n", + " 'url': 'http://www.eurac.edu/it/research/mountains/remsen/staff/Pages/staffdetails.aspx?persId=37007',\n", + " 'roles': ['Processor']},\n", + " {'name': 'Bartolomeo Ventura',\n", + " 'url': 'http://www.eurac.edu/it/aboutus/people/pages/staffdetails.aspx?persId=15903',\n", + " 'roles': ['Processor']}],\n", + " 'extent': {'spatial': {'bbox': [[4.216446958373531,\n", + " 42.817152675995985,\n", + " 18.99055634355496,\n", + " 48.601848359993454]]},\n", + " 'temporal': {'interval': [['2021-01-08T12:00:00Z',\n", + " '2021-01-09T12:00:00Z']]}},\n", + " 'links': [{'rel': 'licence',\n", + " 'href': 'https://creativecommons.org/licenses/by/4.0/',\n", + " 'type': 'text/html',\n", + " 'title': 'License Link'}],\n", + " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", + " 'extent': ['2021-01-08T12:00:00.000Z', '2021-01-09T12:00:00.000Z']},\n", + " 'X': {'type': 'spatial',\n", + " 'axis': 'x',\n", + " 'extent': [4.216446958373531, 18.99055634355496],\n", + " 'reference_system': 3035},\n", + " 'Y': {'type': 'spatial',\n", + " 'axis': 'y',\n", + " 'extent': [42.817152675995985, 48.601848359993454],\n", + " 'reference_system': 3035},\n", + " 'bands': {'type': 'bands',\n", + " 'values': ['Snow cover classification map', 'Snow cover quality map']}},\n", + " 'summaries': {'constellation': ['Aqua, Terra'],\n", + " 'platform': ['Aqua, Terra', None],\n", + " 'rows': 2867,\n", + " 'columns': 4901,\n", + " 'instruments': ['MODIS'],\n", + " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", + " 'gsd': [],\n", + " 'eo:bands': [{'name': 'Snow cover classification map',\n", + " 'center_wavelength': 0.0,\n", + " 'gsd': 0.0},\n", + " {'name': 'Snow cover quality map', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'assets': {}},\n", + " {'engine': 'WCPS',\n", + " 'stac_version': '0.9.0',\n", + " 'stac_extensions': ['datacube'],\n", + " 'id': 'wms_test_true_full',\n", + " 'title': 'WMS title',\n", + " 'description': 'SNOW map derived from daily MODIS observations (Aqua and Terra) for the entire Alps arc. The input data are the atmospherically-corrected reflectances of MODIS MOD09GQ, MOD09GA for tile h19v04 and h18v04. The map has two bands: - SNOW MAP: Snow cover classification map with four classes [0 = NO DATA - missing or corrupt data of one or more input bands; 1 = SNOW - pixel covered by snow; 2 = NO_SNOW - pixel not covered by snow; 3 = CLOUD - pixel covered by clouds], QUALITY FLAG: Snow cover quality map [NO DATA - missing or corrupt data of one or more input bands; QUALITY_INDEX - higher values indicate higher likeliness of correct classification].',\n", + " 'keywords': ['SNOW', 'MODIS'],\n", + " 'version': 'v1',\n", + " 'deprecated': False,\n", + " 'license': 'CC-BY-4.0',\n", + " 'providers': [{'name': 'Eurac EO WCS',\n", + " 'url': 'http://www.eurac.edu',\n", + " 'roles': ['host']},\n", + " {'name': 'National Aeronautics and Space Administration (NASA)',\n", + " 'url': 'https://www.nasa.gov/',\n", + " 'roles': ['Producer']},\n", + " {'name': 'Alexander Jacob',\n", + " 'url': 'http://www.eurac.edu/it/research/mountains/remsen/staff/Pages/staffdetails.aspx?persId=37007',\n", + " 'roles': ['Processor']},\n", + " {'name': 'Bartolomeo Ventura',\n", + " 'url': 'http://www.eurac.edu/it/aboutus/people/pages/staffdetails.aspx?persId=15903',\n", + " 'roles': ['Processor']}],\n", + " 'extent': {'spatial': {'bbox': [[4.216446958373531,\n", + " 42.817152675995985,\n", + " 18.99055634355496,\n", + " 48.601848359993454]]},\n", + " 'temporal': {'interval': [['2020-12-31T12:00:00Z',\n", + " '2021-02-27T12:00:00Z']]}},\n", + " 'links': [{'rel': 'licence',\n", + " 'href': 'https://creativecommons.org/licenses/by/4.0/',\n", + " 'type': 'text/html',\n", + " 'title': 'License Link'}],\n", + " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", + " 'extent': ['2020-12-31T12:00:00.000Z', '2021-02-27T12:00:00.000Z']},\n", + " 'X': {'type': 'spatial',\n", + " 'axis': 'x',\n", + " 'extent': [4.216446958373531, 18.99055634355496],\n", + " 'reference_system': 3035},\n", + " 'Y': {'type': 'spatial',\n", + " 'axis': 'y',\n", + " 'extent': [42.817152675995985, 48.601848359993454],\n", + " 'reference_system': 3035},\n", + " 'bands': {'type': 'bands',\n", + " 'values': ['Snow cover classification map', 'Snow cover quality map']}},\n", + " 'summaries': {'constellation': ['Aqua, Terra'],\n", + " 'platform': ['Aqua, Terra', None],\n", + " 'rows': 2867,\n", + " 'columns': 4901,\n", + " 'instruments': ['MODIS'],\n", + " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", + " 'gsd': [],\n", + " 'eo:bands': [{'name': 'Snow cover classification map',\n", + " 'center_wavelength': 0.0,\n", + " 'gsd': 0.0},\n", + " {'name': 'Snow cover quality map', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'assets': {}},\n", + " {'engine': 'WCPS',\n", + " 'stac_version': '0.9.0',\n", + " 'stac_extensions': ['datacube'],\n", + " 'id': 'wms_test_true_full_wrt',\n", + " 'title': 'WMS title',\n", + " 'description': 'SNOW map derived from daily MODIS observations (Aqua and Terra) for the entire Alps arc. The input data are the atmospherically-corrected reflectances of MODIS MOD09GQ, MOD09GA for tile h19v04 and h18v04. The map has two bands: - SNOW MAP: Snow cover classification map with four classes [0 = NO DATA - missing or corrupt data of one or more input bands; 1 = SNOW - pixel covered by snow; 2 = NO_SNOW - pixel not covered by snow; 3 = CLOUD - pixel covered by clouds], QUALITY FLAG: Snow cover quality map [NO DATA - missing or corrupt data of one or more input bands; QUALITY_INDEX - higher values indicate higher likeliness of correct classification].',\n", + " 'keywords': ['SNOW', 'MODIS'],\n", + " 'version': 'v1',\n", + " 'deprecated': False,\n", + " 'license': 'CC-BY-4.0',\n", + " 'providers': [{'name': 'Eurac EO WCS',\n", + " 'url': 'http://www.eurac.edu',\n", + " 'roles': ['host']},\n", + " {'name': 'National Aeronautics and Space Administration (NASA)',\n", + " 'url': 'https://www.nasa.gov/',\n", + " 'roles': ['Producer']},\n", + " {'name': 'Alexander Jacob',\n", + " 'url': 'http://www.eurac.edu/it/research/mountains/remsen/staff/Pages/staffdetails.aspx?persId=37007',\n", + " 'roles': ['Processor']},\n", + " {'name': 'Bartolomeo Ventura',\n", + " 'url': 'http://www.eurac.edu/it/aboutus/people/pages/staffdetails.aspx?persId=15903',\n", + " 'roles': ['Processor']}],\n", + " 'extent': {'spatial': {'bbox': [[4.216446958373531,\n", + " 42.817152675995985,\n", + " 18.99055634355496,\n", + " 48.601848359993454]]},\n", + " 'temporal': {'interval': [['2020-12-31T12:00:00Z',\n", + " '2021-02-27T12:00:00Z']]}},\n", + " 'links': [{'rel': 'licence',\n", + " 'href': 'https://creativecommons.org/licenses/by/4.0/',\n", + " 'type': 'text/html',\n", + " 'title': 'License Link'}],\n", + " 'cube:dimensions': {'DATE': {'type': 'temporal',\n", + " 'extent': ['2020-12-31T12:00:00.000Z', '2021-02-27T12:00:00.000Z']},\n", + " 'X': {'type': 'spatial',\n", + " 'axis': 'x',\n", + " 'extent': [4.216446958373531, 18.99055634355496],\n", + " 'reference_system': 3035},\n", + " 'Y': {'type': 'spatial',\n", + " 'axis': 'y',\n", + " 'extent': [42.817152675995985, 48.601848359993454],\n", + " 'reference_system': 3035},\n", + " 'bands': {'type': 'bands',\n", + " 'values': ['Snow cover classification map', 'Snow cover quality map']}},\n", + " 'summaries': {'constellation': ['Aqua, Terra'],\n", + " 'platform': ['Aqua, Terra', None],\n", + " 'rows': 2867,\n", + " 'columns': 4901,\n", + " 'instruments': ['MODIS'],\n", + " 'eo:cloud cover': {'min': 0.0, 'max': 0.0},\n", + " 'gsd': [],\n", + " 'eo:bands': [{'name': 'Snow cover classification map',\n", + " 'center_wavelength': 0.0,\n", + " 'gsd': 0.0},\n", + " {'name': 'Snow cover quality map', 'center_wavelength': 0.0, 'gsd': 0.0}]},\n", + " 'assets': {}}]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "conn.list_collections()" + ] + }, + { + "cell_type": "markdown", + "id": "519e361a-cc24-483a-b498-36aeed492e94", + "metadata": {}, + "source": [ + "### Discover the available processes:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "912c8bbb-cb35-455d-a825-7ec9445c3c7c", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " " + ], + "text/plain": [ + "[{'engine': '[ODC_DASK]',\n", + " 'id': 'dimension_labels',\n", + " 'summary': 'Get the dimension labels',\n", + " 'description': 'Returns all labels for a dimension in the data cube. The labels have the same order as in the data cube.',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'The data cube.'},\n", + " {'schema': {'type': 'string'},\n", + " 'name': 'dimension',\n", + " 'description': 'The name of the dimension to get the labels for.'}],\n", + " 'categories': ['cubes'],\n", + " 'returns': {'schema': {'type': 'array',\n", + " 'items': {'anyOf': [{'type': 'number'}, {'type': 'string'}]}},\n", + " 'description': 'The labels as array.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'ln',\n", + " 'summary': 'Natural logarithm',\n", + " 'description': 'The natural logarithm is the logarithm to the base *e* of the number `x`, which equals to using the *log* process with the base set to *e*. The natural logarithm is the inverse function of taking *e* to the power x.\\n\\nThe no-data value `null` is passed through.\\n\\nThe computations follow [IEEE Standard 754](https://ieeexplore.ieee.org/document/8766229) whenever the processing environment supports it. Therefore, `ln(0)` results in ±infinity if the processing environment supports it or otherwise an error is thrown.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'A number to compute the natural logarithm for.'}],\n", + " 'categories': ['math > exponential & logarithmic'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/NaturalLogarithm.html',\n", + " 'title': 'Natural logarithm explained by Wolfram MathWorld'},\n", + " {'rel': 'about',\n", + " 'href': 'https://ieeexplore.ieee.org/document/8766229',\n", + " 'title': 'IEEE Standard 754-2019 for Floating-Point Arithmetic'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed natural logarithm.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 1}, 'returns': 0}]},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'coherence',\n", + " 'summary': 'Compute the complex coherence with SAR data',\n", + " 'description': 'Compute the complex coherence with SAR data, given the specified time delta.',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A raster data cube with exactly two horizontal spatial dimensions and an arbitrary number of additional dimensions. The process is applied to all additional dimensions individually.'},\n", + " {'schema': {'type': 'integer',\n", + " 'enum': [6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 66, 72, 78, 84, 90, 96]},\n", + " 'default': '6',\n", + " 'name': 'timedelta',\n", + " 'description': 'Temporal delta in days between acquisitions on which we want to compute coherence.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes', 'math'],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A data cube with the projected values in the requested projection.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[WCPS]',\n", + " 'id': 'cos',\n", + " 'summary': 'Cosine',\n", + " 'description': 'Computes the cosine of `x`.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'An angle in radians.'}],\n", + " 'categories': ['math > trigonometric'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/Cosine.html',\n", + " 'title': 'Cosine explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed cosine of `x`.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 0}, 'returns': 1}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'lt',\n", + " 'summary': 'Less than comparison',\n", + " 'description': 'Compares whether `x` is strictly less than `y`.\\n\\n**Remarks:**\\n\\n* If any operand is `null`, the return value is `null`.\\n* If any operand is an array or object, the return value is `false`.\\n* If any operand is not a `number` or temporal string (`date`, `time` or `date-time`), the process returns `false`.\\n* Temporal strings can *not* be compared based on their string representation due to the time zone / time-offset representations.',\n", + " 'parameters': [{'schema': {'description': 'Any data type is allowed.'},\n", + " 'name': 'x',\n", + " 'description': 'First operand.'},\n", + " {'schema': {'description': 'Any data type is allowed.'},\n", + " 'name': 'y',\n", + " 'description': 'Second operand.'}],\n", + " 'categories': ['comparison'],\n", + " 'returns': {'schema': {'type': ['boolean', 'null']},\n", + " 'description': '`true` if `x` is strictly less than `y`, `null` if any operand is `null`, otherwise `false`.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 1}},\n", + " {'arguments': {'x': 0, 'y': 0}, 'returns': False},\n", + " {'arguments': {'x': 1, 'y': 2}, 'returns': True},\n", + " {'arguments': {'x': -0.5, 'y': -0.6}, 'returns': False},\n", + " {'arguments': {'x': '00:00:00+01:00', 'y': '00:00:00Z'}, 'returns': True},\n", + " {'arguments': {'x': '1950-01-01T00:00:00Z', 'y': '2018-01-01T12:00:00Z'},\n", + " 'returns': True},\n", + " {'arguments': {'x': '2018-01-01T12:00:00+00:00',\n", + " 'y': '2018-01-01T12:00:00Z'},\n", + " 'returns': False},\n", + " {'arguments': {'x': 0, 'y': True}, 'returns': False},\n", + " {'arguments': {'x': False, 'y': True}, 'returns': False}]},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'aggregate_spatial_window',\n", + " 'summary': 'Zonal statistics for rectangular windows',\n", + " 'description': 'Aggregates statistics over the horizontal spatial dimensions (axes `x` and `y`) of the data cube.\\n\\nThe pixel grid for the axes `x` and `y` is divided into non-overlapping windows with the size specified in the parameter `size`. If the number of values for the axes `x` and `y` is not a multiple of the corresponding window size, the behaviour specified in the parameters `boundary` and `align` is applied.\\nFor each of these windows, the reducer process computes the result.',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A raster data cube with exactly two horizontal spatial dimensions and an arbitrary number of additional dimensions. The process is applied to all additional dimensions individually.'},\n", + " {'schema': {'subtype': 'process-graph',\n", + " 'type': 'object',\n", + " 'parameters': [{'schema': {'type': 'array',\n", + " 'items': {'description': 'Any data type.'}},\n", + " 'name': 'data',\n", + " 'description': 'An array with elements of any type.'},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'context',\n", + " 'description': 'Additional data passed by the user.',\n", + " 'optional': True}]},\n", + " 'name': 'reducer',\n", + " 'description': \"A reducer to be applied on the list of values, which contain all pixels covered by the window. A reducer is a single process such as ``mean()`` or a set of processes, which computes a single value for a list of values, see the category 'reducer' for such processes.\"},\n", + " {'schema': {'minItems': 2,\n", + " 'maxItems': 2,\n", + " 'type': 'array',\n", + " 'items': {'type': 'integer', 'minimum': 1}},\n", + " 'name': 'size',\n", + " 'description': 'Window sizes in pixels along the horizontal spatial dimensions.\\n\\nThe first value corresponds to the `x` axis, the second values corresponds to the `y` axis.'},\n", + " {'schema': {'type': 'string', 'enum': ['pad', 'trim']},\n", + " 'default': 'pad',\n", + " 'name': 'boundary',\n", + " 'description': 'Behaviour to apply if the number of values for the axes `x` and `y` is not a multiple of the corresponding value in the `size` parameter. Options are:\\n\\n- `pad` (default): pad the data cube with the no-data value `null` to fit the required window size.\\n\\n- `trim`: trim the data cube to fit the required window size.\\n\\nSet the parameter `align` to specifies to which corner the data is aligned to.',\n", + " 'optional': True},\n", + " {'schema': {'type': 'string',\n", + " 'enum': ['lower-left', 'upper-left', 'lower-right', 'upper-right']},\n", + " 'default': 'upper-left',\n", + " 'name': 'align',\n", + " 'description': 'If the data requires padding or trimming (see parameter `boundary`), specifies to which corner of the spatial extent the data is aligned to. For example, if the data is aligned to the upper left, the process pads/trims at the lower-right.',\n", + " 'optional': True},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'context',\n", + " 'description': 'Additional data to be passed to the reducer.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes', 'aggregate & resample'],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A data cube with the newly computed values and the same dimensions.\\n\\nThe resolution will change depending on the chosen values for the `size` and `boundary` parameter. It usually decreases for the dimensions which have the corresponding parameter `size` set to values greater than 1.\\n\\nThe dimension labels will be set to the coordinate at the center of the window. The other dimension properties (name, type and reference system) remain unchanged.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[WCPS]',\n", + " 'id': 'arccos',\n", + " 'summary': 'Inverse cosine',\n", + " 'description': 'Computes the arc cosine of `x`. The arc cosine is the inverse function of the cosine so that *arccos(cos(x)) = x*.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'A number.'}],\n", + " 'categories': ['math > trigonometric'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/InverseCosine.html',\n", + " 'title': 'Inverse cosine explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed angle in radians.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 1}, 'returns': 0}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'merge_cubes',\n", + " 'summary': 'Merging two data cubes',\n", + " 'description': 'The data cubes have to be compatible. A merge operation without overlap should be reversible with (a set of) filter operations for each of the two cubes. The process performs the join on overlapping dimensions, with the same name and type.\\n\\nAn overlapping dimension has the same name, type, reference system and resolution in both dimensions, but can have different labels. One of the dimensions can have different labels, for all other dimensions the labels must be equal. If data overlaps, the parameter `overlap_resolver` must be specified to resolve the overlap.\\n\\n**Examples for merging two data cubes:**\\n\\n1. Data cubes with the dimensions `x`, `y`, `t` and `bands` have the same dimension labels in `x`,`y` and `t`, but the labels for the dimension `bands` are `B1` and `B2` for the first cube and `B3` and `B4`. An overlap resolver is *not needed*. The merged data cube has the dimensions `x`, `y`, `t` and `bands` and the dimension `bands` has four dimension labels: `B1`, `B2`, `B3`, `B4`.\\n2. Data cubes with the dimensions `x`, `y`, `t` and `bands` have the same dimension labels in `x`,`y` and `t`, but the labels for the dimension `bands` are `B1` and `B2` for the first data cube and `B2` and `B3` for the second. An overlap resolver is *required* to resolve overlap in band `B2`. The merged data cube has the dimensions `x`, `y`, `t` and `bands` and the dimension `bands` has three dimension labels: `B1`, `B2`, `B3`.\\n3. Data cubes with the dimensions `x`, `y` and `t` have the same dimension labels in `x`,`y` and `t`. There are two options:\\n 1. Keep the overlapping values separately in the merged data cube: An overlap resolver is *not needed*, but for each data cube you need to add a new dimension using ``add_dimension()``. The new dimensions must be equal, except that the labels for the new dimensions must differ by name. The merged data cube has the same dimensions and labels as the original data cubes, plus the dimension added with ``add_dimension()``, which has the two dimension labels after the merge.\\n 2. Combine the overlapping values into a single value: An overlap resolver is *required* to resolve the overlap for all pixels. The merged data cube has the same dimensions and labels as the original data cubes, but all pixel values have been processed by the overlap resolver.\\n4. Merging a data cube with dimensions `x`, `y`, `t` with another cube with dimensions `x`, `y` will join on the `x`, `y` dimension, so the lower dimension cube is merged with each time step in the higher dimensional cube. This can for instance be used to apply a digital elevation model to a spatiotemporal data cube.',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'cube1',\n", + " 'description': 'The first data cube.'},\n", + " {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'cube2',\n", + " 'description': 'The second data cube.'},\n", + " {'schema': {'subtype': 'process-graph',\n", + " 'type': 'object',\n", + " 'parameters': [{'schema': {'description': 'Any data type.'},\n", + " 'name': 'x',\n", + " 'description': 'The first value.'},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'y',\n", + " 'description': 'The second value.'},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'context',\n", + " 'description': 'Additional data passed by the user.',\n", + " 'optional': True}]},\n", + " 'name': 'overlap_resolver',\n", + " 'description': 'A reduction operator that resolves the conflict if the data overlaps. The reducer must return a value of the same data type as the input values are. The reduction operator may be a single process such as ``multiply()`` or consist of multiple sub-processes. `null` (the default) can be specified if no overlap resolver is required.',\n", + " 'optional': True},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'context',\n", + " 'description': 'Additional data to be passed to the overlap resolver.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'https://en.wikipedia.org/wiki/Reduction_Operator',\n", + " 'title': 'Background information on reduction operators (binary reducers) by Wikipedia'}],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'The merged data cube. See the process description for details regarding the dimensions and dimension properties (name, type, labels, reference system and resolution).'},\n", + " 'exceptions': {'OverlapResolverMissing': {'message': 'Overlapping data cubes, but no overlap resolver has been specified.'}}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'predict_curve',\n", + " 'summary': 'Predict values',\n", + " 'description': 'Predict values using a model function and pre-computed parameters.',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A data cube to predict values for.'},\n", + " {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'parameters',\n", + " 'description': 'A data cube with optimal values from a result of e.g. ``fit_curve()``.'},\n", + " {'schema': {'subtype': 'process-graph',\n", + " 'returns': {'schema': {'type': 'number'},\n", + " 'description': 'The computed value `y` value for the given independent variable `x` and the parameters.'},\n", + " 'type': 'object',\n", + " 'parameters': [{'schema': {'type': ['number']},\n", + " 'name': 'x',\n", + " 'description': 'The value for the independent variable `x`.'},\n", + " {'schema': {'minItems': 1, 'type': 'array', 'items': {'type': 'number'}},\n", + " 'name': 'parameters',\n", + " 'description': 'The parameters for the model function, contains at least one parameter.'}]},\n", + " 'name': 'function',\n", + " 'description': 'The model function. It must take the parameters to fit as array through the first argument and the independent variable `x` as the second argument.\\n\\nIt is recommended to store the model function as a user-defined process on the back-end.'},\n", + " {'schema': {'type': 'string'},\n", + " 'name': 'dimension',\n", + " 'description': 'The name of the dimension for predictions. Fails with a `DimensionNotAvailable` exception if the specified dimension does not exist.'},\n", + " {'schema': [{'type': 'null'},\n", + " {'type': 'array',\n", + " 'items': {'anyOf': [{'type': 'number'},\n", + " {'subtype': 'date', 'format': 'date', 'type': 'string'},\n", + " {'subtype': 'date-time', 'format': 'date-time', 'type': 'string'}]}}],\n", + " 'name': 'labels',\n", + " 'description': 'The labels to predict values for. If no labels are given, predicts values only for no-data (`null`) values in the data cube.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes', 'math'],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A data cube with the predicted values.'},\n", + " 'exceptions': {'DimensionNotAvailable': {'message': 'A dimension with the specified name does not exist.'}}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'add_dimension',\n", + " 'summary': 'Add a new dimension',\n", + " 'description': 'Adds a new named dimension to the data cube.\\n\\nAfterwards, the dimension can be referred to with the specified `name`. If a dimension with the specified name exists, the process fails with a `DimensionExists` exception. The dimension label of the dimension is set to the specified `label`.',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A data cube to add the dimension to.'},\n", + " {'schema': {'type': 'string'},\n", + " 'name': 'name',\n", + " 'description': 'Name for the dimension.'},\n", + " {'schema': [{'type': 'number'}, {'type': 'string'}],\n", + " 'name': 'label',\n", + " 'description': 'A dimension label.'},\n", + " {'schema': {'type': 'string',\n", + " 'enum': ['spatial', 'temporal', 'bands', 'other']},\n", + " 'default': 'other',\n", + " 'name': 'type',\n", + " 'description': 'The type of dimension, defaults to `other`.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes'],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'The data cube with a newly added dimension. The new dimension has exactly one dimension label. All other dimensions remain unchanged.'},\n", + " 'exceptions': {'DimensionExists': {'message': 'A dimension with the specified name already exists.'}}},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'sqrt',\n", + " 'summary': 'Square root',\n", + " 'description': 'Computes the square root of a real number `x`, which is equal to calculating `x` to the power of *0.5*.\\n\\nA square root of x is a number a such that *a^2^ = x*. Therefore, the square root is the inverse function of a to the power of 2, but only for *a >= 0*.\\n\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'A number.'}],\n", + " 'categories': ['math', 'math > exponential & logarithmic'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/SquareRoot.html',\n", + " 'title': 'Square root explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed square root.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 0}, 'returns': 0},\n", + " {'arguments': {'x': 1}, 'returns': 1},\n", + " {'arguments': {'x': 9}, 'returns': 3},\n", + " {'arguments': {}}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'gte',\n", + " 'summary': 'Greater than or equal to comparison',\n", + " 'description': 'Compares whether `x` is greater than or equal to `y`.\\n\\n**Remarks:**\\n\\n* If any operand is `null`, the return value is `null`. Therefore, `gte(null, null)` returns `null` instead of `true`.\\n* If any operand is an array or object, the return value is `false`.\\n* If the operands are not equal (see process ``eq()``) and any of them is not a `number` or temporal string (`date`, `time` or `date-time`), the process returns `false`.\\n* Temporal strings can *not* be compared based on their string representation due to the time zone / time-offset representations.',\n", + " 'parameters': [{'schema': {'description': 'Any data type is allowed.'},\n", + " 'name': 'x',\n", + " 'description': 'First operand.'},\n", + " {'schema': {'description': 'Any data type is allowed.'},\n", + " 'name': 'y',\n", + " 'description': 'Second operand.'}],\n", + " 'categories': ['comparison'],\n", + " 'returns': {'schema': {'type': ['boolean', 'null']},\n", + " 'description': '`true` if `x` is greater than or equal to `y`, `null` if any operand is `null`, otherwise `false`.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 1}},\n", + " {'arguments': {'x': 0, 'y': 0}, 'returns': True},\n", + " {'arguments': {'x': 1, 'y': 2}, 'returns': False},\n", + " {'arguments': {'x': -0.5, 'y': -0.6}, 'returns': True},\n", + " {'arguments': {'x': '00:00:00Z', 'y': '00:00:00+01:00'}, 'returns': True},\n", + " {'arguments': {'x': '1950-01-01T00:00:00Z', 'y': '2018-01-01T12:00:00Z'},\n", + " 'returns': False},\n", + " {'arguments': {'x': '2018-01-01T12:00:00+00:00',\n", + " 'y': '2018-01-01T12:00:00Z'},\n", + " 'returns': True},\n", + " {'arguments': {'x': True, 'y': False}, 'returns': False},\n", + " {'arguments': {'x': [1, 2, 3], 'y': [1, 2, 3]}, 'returns': False}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'neq',\n", + " 'summary': 'Not equal to comparison',\n", + " 'description': 'Compares whether `x` is *not* strictly equal to `y`.\\n\\n**Remarks:**\\n\\n* Data types MUST be checked strictly, for example a string with the content *1* is not equal to the number *1*. Nevertheless, an integer *1* is equal to a floating point number *1.0* as `integer` is a sub-type of `number`.\\n* If any operand is `null`, the return value is `null`. Therefore, `neq(null, null)` returns `null` instead of `false`.\\n* If any operand is an array or object, the return value is `false`.\\n* Strings are expected to be encoded in UTF-8 by default.\\n* Temporal strings MUST be compared differently than other strings and MUST NOT be compared based on their string representation due to different possible representations. For example, the UTC time zone representation `Z` has the same meaning as `+00:00`.',\n", + " 'parameters': [{'schema': {'description': 'Any data type is allowed.'},\n", + " 'name': 'x',\n", + " 'description': 'First operand.'},\n", + " {'schema': {'description': 'Any data type is allowed.'},\n", + " 'name': 'y',\n", + " 'description': 'Second operand.'},\n", + " {'schema': {'type': ['number', 'null']},\n", + " 'name': 'delta',\n", + " 'description': 'Only applicable for comparing two numbers. If this optional parameter is set to a positive non-zero number the non-equality of two numbers is checked against a delta value. This is especially useful to circumvent problems with floating point inaccuracy in machine-based computation.\\n\\nThis option is basically an alias for the following computation: `gt(abs(minus([x, y]), delta)`',\n", + " 'optional': True},\n", + " {'schema': {'type': 'boolean'},\n", + " 'default': True,\n", + " 'name': 'case_sensitive',\n", + " 'description': 'Only applicable for comparing two strings. Case sensitive comparison can be disabled by setting this parameter to `false`.',\n", + " 'optional': True}],\n", + " 'categories': ['texts', 'comparison'],\n", + " 'returns': {'schema': {'type': ['boolean', 'null']},\n", + " 'description': 'Returns `true` if `x` is *not* equal to `y`, `null` if any operand is `null`, otherwise `false`.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 1}},\n", + " {'arguments': {'x': 1, 'y': 1}, 'returns': False},\n", + " {'arguments': {'x': 1, 'y': '1'}, 'returns': True},\n", + " {'arguments': {'x': 0, 'y': False}, 'returns': True},\n", + " {'arguments': {'x': 1.02, 'delta': 0.01, 'y': 1}, 'returns': True},\n", + " {'arguments': {'x': -1, 'delta': 0.01, 'y': -1.001}, 'returns': False},\n", + " {'arguments': {'x': 115, 'delta': 10, 'y': 110}, 'returns': False},\n", + " {'arguments': {'x': 'Test', 'y': 'test'}, 'returns': True},\n", + " {'arguments': {'case_sensitive': False, 'x': 'Test', 'y': 'test'},\n", + " 'returns': False},\n", + " {'arguments': {'case_sensitive': False, 'x': 'Ä', 'y': 'ä'},\n", + " 'returns': False},\n", + " {'arguments': {'x': '00:00:00+00:00', 'y': '00:00:00Z'}, 'returns': False},\n", + " {'description': '`y` is not a valid date-time representation and therefore will be treated as a string so that the provided values are not equal.',\n", + " 'arguments': {'x': '2018-01-01T12:00:00Z', 'y': '2018-01-01T12:00:00'},\n", + " 'returns': True},\n", + " {'description': '01:00 in the time zone +1 is equal to 00:00 in UTC.',\n", + " 'arguments': {'x': '2018-01-01T00:00:00Z',\n", + " 'y': '2018-01-01T01:00:00+01:00'},\n", + " 'returns': False},\n", + " {'arguments': {'x': [1, 2, 3], 'y': [1, 2, 3]}, 'returns': False}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'if',\n", + " 'summary': 'If-Then-Else conditional',\n", + " 'description': 'If the value passed is `true`, returns the value of the `accept` parameter, otherwise returns the value of the `reject` parameter.\\n\\nThis is basically an if-then-else construct as in other programming languages.',\n", + " 'parameters': [{'schema': {'type': ['boolean', 'null']},\n", + " 'name': 'value',\n", + " 'description': 'A boolean value.'},\n", + " {'schema': {'description': 'Any data type is allowed.'},\n", + " 'name': 'accept',\n", + " 'description': 'A value that is returned if the boolean value is `true`.'},\n", + " {'schema': {'description': 'Any data type is allowed.'},\n", + " 'name': 'reject',\n", + " 'description': 'A value that is returned if the boolean value is **not** `true`. Defaults to `null`.',\n", + " 'optional': True}],\n", + " 'categories': ['logic', 'comparison', 'masks'],\n", + " 'returns': {'schema': {'description': 'Any data type is allowed.'},\n", + " 'description': 'Either the `accept` or `reject` argument depending on the given boolean value.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'reject': 'B', 'value': True, 'accept': 'A'},\n", + " 'returns': 'A'},\n", + " {'arguments': {'reject': 'B', 'accept': 'A'}, 'returns': 'B'},\n", + " {'arguments': {'reject': [4, 5, 6], 'value': False, 'accept': [1, 2, 3]},\n", + " 'returns': [4, 5, 6]},\n", + " {'arguments': {'value': True, 'accept': 123}, 'returns': 123},\n", + " {'arguments': {'value': False, 'accept': 1}}]},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'load_ml_model',\n", + " 'summary': 'Load a machine learning model',\n", + " 'description': '...',\n", + " 'parameters': [{'schema': [{'subtype': 'uri',\n", + " 'format': 'uri',\n", + " 'pattern': '^https?://',\n", + " 'title': 'URL',\n", + " 'type': 'string'},\n", + " {'subtype': 'job-id',\n", + " 'pattern': '^[\\\\w\\\\-\\\\.~]+$',\n", + " 'title': 'Batch Job ID',\n", + " 'type': 'string'},\n", + " {'subtype': 'file-path',\n", + " 'pattern': '^[^\\r\\n\\\\:\\'\"]+$',\n", + " 'title': 'User-uploaded File',\n", + " 'type': 'string'}],\n", + " 'name': 'id',\n", + " 'description': 'The STAC Item to load the machine learning model from. The STAC Item must implement the ml-model extension. Such model can be trained with processes such as ``fit_random_forest()``.'}],\n", + " 'categories': ['machine learning', 'import'],\n", + " 'returns': {'schema': {'subtype': 'ml-model', 'type': 'object'},\n", + " 'description': 'A machine learning model to be used with machine learning processes such as ``predict_random_forest()``.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[WCPS]',\n", + " 'id': 'tan',\n", + " 'summary': 'Tangent',\n", + " 'description': 'Computes the tangent of `x`. The tangent is defined to be the sine of x divided by the cosine of x.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'An angle in radians.'}],\n", + " 'categories': ['math > trigonometric'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/Tangent.html',\n", + " 'title': 'Tangent explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed tangent of `x`.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 0}, 'returns': 0}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'add',\n", + " 'summary': 'Addition of two numbers',\n", + " 'description': 'Sums up the two numbers `x` and `y` (*x + y*) and returns the computed sum.\\n\\nNo-data values are taken into account so that `null` is returned if any element is such a value.\\n\\nThe computations follow [IEEE Standard 754](https://ieeexplore.ieee.org/document/8766229) whenever the processing environment supports it.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'The first summand.'},\n", + " {'schema': {'type': ['number', 'null']},\n", + " 'name': 'y',\n", + " 'description': 'The second summand.'}],\n", + " 'categories': ['math'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/Sum.html',\n", + " 'title': 'Sum explained by Wolfram MathWorld'},\n", + " {'rel': 'about',\n", + " 'href': 'https://ieeexplore.ieee.org/document/8766229',\n", + " 'title': 'IEEE Standard 754-2019 for Floating-Point Arithmetic'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed sum of the two numbers.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 5, 'y': 2.5}, 'returns': 7.5},\n", + " {'arguments': {'x': -2, 'y': -4}, 'returns': -6},\n", + " {'arguments': {'x': 1}}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'array_element',\n", + " 'summary': 'Get an element from an array',\n", + " 'description': 'Returns the element with the specified index or label from the array.\\n\\nEither the parameter `index` or `label` must be specified, otherwise the `ArrayElementParameterMissing` exception is thrown. If both parameters are set the `ArrayElementParameterConflict` exception is thrown.',\n", + " 'parameters': [{'schema': {'type': 'array',\n", + " 'items': {'description': 'Any data type is allowed.'}},\n", + " 'name': 'data',\n", + " 'description': 'An array.'},\n", + " {'schema': {'type': 'integer'},\n", + " 'name': 'index',\n", + " 'description': 'The zero-based index of the element to retrieve.',\n", + " 'optional': True},\n", + " {'schema': [{'type': 'number'}, {'type': 'string'}],\n", + " 'name': 'label',\n", + " 'description': 'The label of the element to retrieve.',\n", + " 'optional': True},\n", + " {'schema': {'type': 'boolean'},\n", + " 'default': False,\n", + " 'name': 'return_nodata',\n", + " 'description': 'By default this process throws an `ArrayElementNotAvailable` exception if the index or label is invalid. If you want to return `null` instead, set this flag to `true`.',\n", + " 'optional': True}],\n", + " 'categories': ['arrays', 'reducer'],\n", + " 'returns': {'schema': {'description': 'Any data type is allowed.'},\n", + " 'description': 'The value of the requested element.'},\n", + " 'exceptions': {'ArrayElementParameterConflict': {'message': \"The process 'array_element' only allows that either the 'index' or the 'labels' parameter is set.\"},\n", + " 'ArrayElementParameterMissing': {'message': \"The process 'array_element' requires either the 'index' or 'labels' parameter to be set.\"},\n", + " 'ArrayElementNotAvailable': {'message': 'The array has no element with the specified index or label.'}},\n", + " 'examples': [{'arguments': {'data': [9, 8, 7, 6, 5], 'index': 2},\n", + " 'returns': 7},\n", + " {'arguments': {'data': ['A', 'B', 'C'], 'index': 0}, 'returns': 'A'},\n", + " {'arguments': {'return_nodata': True, 'data': [], 'index': 0}}]},\n", + " {'engine': '[WCPS]',\n", + " 'id': 'sinh',\n", + " 'summary': 'Hyperbolic sine',\n", + " 'description': 'Computes the hyperbolic sine of `x`.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'An angle in radians.'}],\n", + " 'categories': ['math > trigonometric'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/HyperbolicSine.html',\n", + " 'title': 'Hyperbolic sine explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed hyperbolic sine of `x`.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 0}, 'returns': 0}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'subtract',\n", + " 'summary': 'Subtraction of two numbers',\n", + " 'description': 'Subtracts argument `y` from the argument `x` (*x - y*) and returns the computed result.\\n\\nNo-data values are taken into account so that `null` is returned if any element is such a value.\\n\\nThe computations follow [IEEE Standard 754](https://ieeexplore.ieee.org/document/8766229) whenever the processing environment supports it.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'The minuend.'},\n", + " {'schema': {'type': ['number', 'null']},\n", + " 'name': 'y',\n", + " 'description': 'The subtrahend.'}],\n", + " 'categories': ['math'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/Subtraction.html',\n", + " 'title': 'Subtraction explained by Wolfram MathWorld'},\n", + " {'rel': 'about',\n", + " 'href': 'https://ieeexplore.ieee.org/document/8766229',\n", + " 'title': 'IEEE Standard 754-2019 for Floating-Point Arithmetic'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed result.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 5, 'y': 2.5}, 'returns': 2.5},\n", + " {'arguments': {'x': -2, 'y': 4}, 'returns': -6},\n", + " {'arguments': {'x': 1}}]},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'array_interpolate_linear',\n", + " 'summary': 'One-dimensional linear interpolation for arrays',\n", + " 'description': 'Performs a linear interpolation for each of the no-data values (`null`) in the array given, except for leading and trailing no-data values.\\n\\nThe linear interpolants are defined by the array indices or labels (x) and the values in the array (y).',\n", + " 'parameters': [{'schema': {'type': 'array',\n", + " 'items': {'type': ['number', 'null']}},\n", + " 'name': 'data',\n", + " 'description': 'An array of numbers and no-data values.\\n\\nIf the given array is a labeled array, the labels must have a natural/inherent label order and the process expects the labels to be sorted accordingly. This is the default behavior in openEO for spatial and temporal dimensions.'}],\n", + " 'categories': ['arrays', 'math', 'math > interpolation'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'https://en.wikipedia.org/wiki/Linear_interpolation',\n", + " 'title': 'Linear interpolation explained by Wikipedia'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'An array with no-data values being replaced with interpolated values. If not at least 2 numerical values are available in the array, the array stays the same.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'data': [None, 1, None, 6, None, -8]},\n", + " 'returns': [None, 1, 3.5, 6, -1, -8]}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'eq',\n", + " 'summary': 'Equal to comparison',\n", + " 'description': 'Compares whether `x` is strictly equal to `y`.\\n\\n**Remarks:**\\n\\n* Data types MUST be checked strictly, for example a string with the content *1* is not equal to the number *1*. Nevertheless, an integer *1* is equal to a floating point number *1.0* as `integer` is a sub-type of `number`.\\n* If any operand is `null`, the return value is `null`. Therefore, `eq(null, null)` returns `null` instead of `true`.\\n* If any operand is an array or object, the return value is `false`.\\n* Strings are expected to be encoded in UTF-8 by default.\\n* Temporal strings MUST be compared differently than other strings and MUST NOT be compared based on their string representation due to different possible representations. For example, the UTC time zone representation `Z` has the same meaning as `+00:00`.',\n", + " 'parameters': [{'schema': {'description': 'Any data type is allowed.'},\n", + " 'name': 'x',\n", + " 'description': 'First operand.'},\n", + " {'schema': {'description': 'Any data type is allowed.'},\n", + " 'name': 'y',\n", + " 'description': 'Second operand.'},\n", + " {'schema': {'type': ['number', 'null']},\n", + " 'name': 'delta',\n", + " 'description': 'Only applicable for comparing two numbers. If this optional parameter is set to a positive non-zero number the equality of two numbers is checked against a delta value. This is especially useful to circumvent problems with floating point inaccuracy in machine-based computation.\\n\\nThis option is basically an alias for the following computation: `lte(abs(minus([x, y]), delta)`',\n", + " 'optional': True},\n", + " {'schema': {'type': 'boolean'},\n", + " 'default': True,\n", + " 'name': 'case_sensitive',\n", + " 'description': 'Only applicable for comparing two strings. Case sensitive comparison can be disabled by setting this parameter to `false`.',\n", + " 'optional': True}],\n", + " 'categories': ['texts', 'comparison'],\n", + " 'returns': {'schema': {'type': ['boolean', 'null']},\n", + " 'description': 'Returns `true` if `x` is equal to `y`, `null` if any operand is `null`, otherwise `false`.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 1}},\n", + " {'arguments': {}},\n", + " {'arguments': {'x': 1, 'y': 1}, 'returns': True},\n", + " {'arguments': {'x': 1, 'y': '1'}, 'returns': False},\n", + " {'arguments': {'x': 0, 'y': False}, 'returns': False},\n", + " {'arguments': {'x': 1.02, 'delta': 0.01, 'y': 1}, 'returns': False},\n", + " {'arguments': {'x': -1, 'delta': 0.01, 'y': -1.001}, 'returns': True},\n", + " {'arguments': {'x': 115, 'delta': 10, 'y': 110}, 'returns': True},\n", + " {'arguments': {'x': 'Test', 'y': 'test'}, 'returns': False},\n", + " {'arguments': {'case_sensitive': False, 'x': 'Test', 'y': 'test'},\n", + " 'returns': True},\n", + " {'arguments': {'case_sensitive': False, 'x': 'Ä', 'y': 'ä'},\n", + " 'returns': True},\n", + " {'arguments': {'x': '00:00:00+00:00', 'y': '00:00:00Z'}, 'returns': True},\n", + " {'description': '`y` is not a valid date-time representation and therefore will be treated as a string so that the provided values are not equal.',\n", + " 'arguments': {'x': '2018-01-01T12:00:00Z', 'y': '2018-01-01T12:00:00'},\n", + " 'returns': False},\n", + " {'description': '01:00 in the time zone +1 is equal to 00:00 in UTC.',\n", + " 'arguments': {'x': '2018-01-01T00:00:00Z',\n", + " 'y': '2018-01-01T01:00:00+01:00'},\n", + " 'returns': True},\n", + " {'arguments': {'x': [1, 2, 3], 'y': [1, 2, 3]}, 'returns': False}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'save_result',\n", + " 'summary': 'Save processed data to storage',\n", + " 'description': 'Saves processed data to the local user workspace / data store of the authenticated user. This process aims to be compatible to GDAL/OGR formats and options. STAC-compatible metadata should be stored with the processed data.\\n\\nCalling this process may be rejected by back-ends in the context of secondary web services.',\n", + " 'parameters': [{'schema': [{'subtype': 'raster-cube', 'type': 'object'},\n", + " {'subtype': 'vector-cube', 'type': 'object'}],\n", + " 'name': 'data',\n", + " 'description': 'The data to save.'},\n", + " {'schema': {'subtype': 'output-format', 'type': 'string'},\n", + " 'name': 'format',\n", + " 'description': 'The file format to save to. It must be one of the values that the server reports as supported output file formats, which usually correspond to the short GDAL/OGR codes. If the format is not suitable for storing the underlying data structure, a `FormatUnsuitable` exception will be thrown. This parameter is *case insensitive*.'},\n", + " {'schema': {'subtype': 'output-format-options', 'type': 'object'},\n", + " 'default': {},\n", + " 'name': 'options',\n", + " 'description': 'The file format parameters to be used to create the file(s). Must correspond to the parameters that the server reports as supported parameters for the chosen `format`. The parameter names and valid values usually correspond to the GDAL/OGR format options.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes', 'export'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'https://www.gdal.org/formats_list.html',\n", + " 'title': 'GDAL Raster Formats'},\n", + " {'rel': 'about',\n", + " 'href': 'https://www.gdal.org/ogr_formats.html',\n", + " 'title': 'OGR Vector Formats'}],\n", + " 'returns': {'schema': {'type': 'boolean'},\n", + " 'description': '`false` if saving failed, `true` otherwise.'},\n", + " 'exceptions': {'FormatUnsuitable': {'message': \"Data can't be transformed into the requested output format.\"}}},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'filter_temporal',\n", + " 'summary': 'Temporal filter for a temporal intervals',\n", + " 'description': \"Limits the data cube to the specified interval of dates and/or times.\\n\\nMore precisely, the filter checks whether the temporal dimension label is greater than or equal to the lower boundary (start date/time) and the temporal dimension label is less than the value of the upper boundary (end date/time). This corresponds to a left-closed interval, which contains the lower boundary but not the upper boundary.\\n\\nIf the dimension is set to `null` (it's the default value), the data cube is expected to only have one temporal dimension.\",\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A data cube.'},\n", + " {'schema': {'minItems': 2,\n", + " 'maxItems': 2,\n", + " 'examples': [['2015-01-01T00:00:00Z', '2016-01-01T00:00:00Z'],\n", + " ['2015-01-01', '2016-01-01']],\n", + " 'subtype': 'temporal-interval',\n", + " 'type': 'array',\n", + " 'items': {'anyOf': [{'subtype': 'date-time',\n", + " 'format': 'date-time',\n", + " 'type': 'string'},\n", + " {'subtype': 'date', 'format': 'date', 'type': 'string'},\n", + " {'subtype': 'year',\n", + " 'minLength': 4,\n", + " 'pattern': '^\\\\d{4}$',\n", + " 'type': 'string',\n", + " 'maxLength': 4},\n", + " {'type': 'null'}]}},\n", + " 'name': 'extent',\n", + " 'description': 'Left-closed temporal interval, i.e. an array with exactly two elements:\\n\\n1. The first element is the start of the temporal interval. The specified instance in time is **included** in the interval.\\n2. The second element is the end of the temporal interval. The specified instance in time is **excluded** from the interval.\\n\\nThe specified temporal strings follow [RFC 3339](https://tools.ietf.org/html/rfc3339). Also supports open intervals by setting one of the boundaries to `null`, but never both.'},\n", + " {'schema': {'type': ['string', 'null']},\n", + " 'name': 'dimension',\n", + " 'description': 'The name of the temporal dimension to filter on. If the dimension is not set or is set to `null`, the filter applies to all temporal dimensions. Fails with a `DimensionNotAvailable` error if the specified dimension does not exist.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes', 'filter'],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A data cube restricted to the specified temporal extent. The dimensions and dimension properties (name, type, labels, reference system and resolution) remain unchanged, except that the given temporal dimension(s) have less (or the same) dimension labels.'},\n", + " 'exceptions': {'DimensionNotAvailable': {'message': 'A dimension with the specified name does not exist.'}}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'median',\n", + " 'summary': 'Statistical median',\n", + " 'description': 'The statistical median of an array of numbers is the value separating the higher half from the lower half of the data.\\n\\nAn array without non-`null` elements resolves always with `null`.\\n\\n**Remarks:**\\n\\n* For a symmetric arrays, the result is equal to the ``mean()``.\\n* The median can also be calculated by computing the ``quantiles()`` with a probability of *0.5*.',\n", + " 'parameters': [{'schema': {'type': 'array',\n", + " 'items': {'type': ['number', 'null']}},\n", + " 'name': 'data',\n", + " 'description': 'An array of numbers.'},\n", + " {'schema': {'type': 'boolean'},\n", + " 'default': True,\n", + " 'name': 'ignore_nodata',\n", + " 'description': 'Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that `null` is returned if any value is such a value.',\n", + " 'optional': True}],\n", + " 'categories': ['math', 'reducer'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/StatisticalMedian.html',\n", + " 'title': 'Statistical Median explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed statistical median.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'data': [1, 3, 3, 6, 7, 8, 9]}, 'returns': 6},\n", + " {'arguments': {'data': [1, 2, 3, 4, 5, 6, 8, 9]}, 'returns': 4.5},\n", + " {'arguments': {'data': [-1, -0.5, None, 1]}, 'returns': -0.5},\n", + " {'arguments': {'data': [-1, 0, None, 1], 'ignore_nodata': False}},\n", + " {'description': 'The input array is empty: return `null`.',\n", + " 'arguments': {'data': []}},\n", + " {'description': 'The input array has only `null` elements: return `null`.',\n", + " 'arguments': {'data': [None, None]}}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'reduce_dimension',\n", + " 'summary': 'Reduce dimensions',\n", + " 'description': 'Applies a unary reducer to a data cube dimension by collapsing all the pixel values along the specified dimension into an output value computed by the reducer. This process passes a list of values to the reducer. In contrast, ``reduce_dimension_binary()`` passes two values, which may be better suited especially for UDFs in case the number of values gets too large to be processed at once.\\n\\nThe dimension is dropped. To avoid this, use ``apply_dimension()`` instead.',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A data cube.'},\n", + " {'schema': {'subtype': 'process-graph',\n", + " 'type': 'object',\n", + " 'parameters': [{'schema': {'subtype': 'labeled-array',\n", + " 'type': 'array',\n", + " 'items': {'description': 'Any data type.'}},\n", + " 'name': 'data',\n", + " 'description': 'A labeled array with elements of any type.'},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'context',\n", + " 'description': 'Additional data passed by the user.',\n", + " 'optional': True}]},\n", + " 'name': 'reducer',\n", + " 'description': \"A reducer to apply on the specified dimension. A reducer is a single process such as ``mean()`` or a set of processes, which computes a single value for a list of values, see the category 'reducer' for such processes.\"},\n", + " {'schema': {'type': 'string'},\n", + " 'name': 'dimension',\n", + " 'description': 'The name of the dimension over which to reduce. Fails with a `DimensionNotAvailable` error if the specified dimension does not exist.'},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'context',\n", + " 'description': 'Additional data to be passed to the reducer.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes', 'reducer'],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A data cube with the newly computed values. It is missing the given dimension, the number of dimensions decreases by one. The dimension properties (name, type, labels, reference system and resolution) for all other dimensions remain unchanged.'},\n", + " 'exceptions': {'DimensionNotAvailable': {'message': 'A dimension with the specified name does not exist.'}}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'apply_kernel',\n", + " 'summary': 'Apply a spatial convolution with a kernel',\n", + " 'description': \"Applies a 2D convolution (i.e. a focal operation with a weighted kernel) on the horizontal spatial dimensions (axes `x` and `y`) of the data cube.\\n\\nEach value in the kernel is multiplied with the corresponding pixel value and all products are summed up afterwards. The sum is then multiplied with the factor.\\n\\nThe process can't handle non-numerical or infinite numerical values in the data cube. Boolean values are converted to integers (`false` = 0, `true` = 1), but all other non-numerical or infinite values are replaced with zeroes by default (see parameter `replace_invalid`).\\n\\nFor cases requiring more generic focal operations or non-numerical values, see ``apply_neighborhood()``.\",\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A data cube.'},\n", + " {'schema': {'subtype': 'kernel',\n", + " 'description': 'A two-dimensional array of numbers.',\n", + " 'type': 'array',\n", + " 'items': {'type': 'array', 'items': {'type': 'number'}}},\n", + " 'name': 'kernel',\n", + " 'description': 'Kernel as a two-dimensional array of weights. The inner level of the nested array aligns with the `x` axis and the outer level aligns with the `y` axis. Each level of the kernel must have an uneven number of elements, otherwise the process throws a `KernelDimensionsUneven` error.'},\n", + " {'schema': {'type': 'number'},\n", + " 'default': 1,\n", + " 'name': 'factor',\n", + " 'description': 'A factor that is multiplied to each value after the kernel has been applied.\\n\\nThis is basically a shortcut for explicitly multiplying each value by a factor afterwards, which is often required for some kernel-based algorithms such as the Gaussian blur.',\n", + " 'optional': True},\n", + " {'schema': [{'type': 'string',\n", + " 'enum': ['replicate', 'reflect', 'reflect_pixel', 'wrap']},\n", + " {'type': 'number'}],\n", + " 'default': 0,\n", + " 'name': 'border',\n", + " 'description': 'Determines how the data is extended when the kernel overlaps with the borders. Defaults to fill the border with zeroes.\\n\\nThe following options are available:\\n\\n* *numeric value* - fill with a user-defined constant number `n`: `nnnnnn|abcdefgh|nnnnnn` (default, with `n` = 0)\\n* `replicate` - repeat the value from the pixel at the border: `aaaaaa|abcdefgh|hhhhhh`\\n* `reflect` - mirror/reflect from the border: `fedcba|abcdefgh|hgfedc`\\n* `reflect_pixel` - mirror/reflect from the center of the pixel at the border: `gfedcb|abcdefgh|gfedcb`\\n* `wrap` - repeat/wrap the image: `cdefgh|abcdefgh|abcdef`',\n", + " 'optional': True},\n", + " {'schema': {'type': 'number'},\n", + " 'default': 0,\n", + " 'name': 'replace_invalid',\n", + " 'description': 'This parameter specifies the value to replace non-numerical or infinite numerical values with. By default, those values are replaced with zeroes.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes', 'math > image filter'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://www.songho.ca/dsp/convolution/convolution.html',\n", + " 'title': 'Convolutions explained'},\n", + " {'rel': 'about',\n", + " 'href': 'http://www.songho.ca/dsp/convolution/convolution2d_example.html',\n", + " 'title': 'Example of 2D Convolution'}],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A data cube with the newly computed values and the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged.'},\n", + " 'exceptions': {'KernelDimensionsUneven': {'message': 'Each dimension of the kernel must have an uneven number of elements.'}}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'aggregate_temporal_period',\n", + " 'summary': 'Temporal aggregations based on calendar hierarchies',\n", + " 'description': 'Computes a temporal aggregation based on calendar hierarchies such as years, months or seasons. For other calendar hierarchies ``aggregate_temporal()`` can be used.\\n\\nFor each interval, all data along the dimension will be passed through the reducer.\\n\\nIf the dimension is not set or is set to `null`, the data cube is expected to only have one temporal dimension.',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A data cube.'},\n", + " {'schema': {'type': 'string',\n", + " 'enum': ['hour',\n", + " 'day',\n", + " 'week',\n", + " 'dekad',\n", + " 'month',\n", + " 'season',\n", + " 'tropical-season',\n", + " 'year',\n", + " 'decade',\n", + " 'decade-ad']},\n", + " 'name': 'period',\n", + " 'description': 'The time intervals to aggregate. The following pre-defined values are available:\\n\\n* `hour`: Hour of the day\\n* `day`: Day of the year\\n* `week`: Week of the year\\n* `dekad`: Ten day periods, counted per year with three periods per month (day 1 - 10, 11 - 20 and 21 - end of month). The third dekad of the month can range from 8 to 11 days. For example, the fourth dekad is Feb, 1 - Feb, 10 each year.\\n* `month`: Month of the year\\n* `season`: Three month periods of the calendar seasons (December - February, March - May, June - August, September - November).\\n* `tropical-season`: Six month periods of the tropical seasons (November - April, May - October).\\n* `year`: Proleptic years\\n* `decade`: Ten year periods ([0-to-9 decade](https://en.wikipedia.org/wiki/Decade#0-to-9_decade)), from a year ending in a 0 to the next year ending in a 9.\\n* `decade-ad`: Ten year periods ([1-to-0 decade](https://en.wikipedia.org/wiki/Decade#1-to-0_decade)) better aligned with the anno Domini (AD) calendar era, from a year ending in a 1 to the next year ending in a 0.'},\n", + " {'schema': {'subtype': 'process-graph',\n", + " 'returns': {'schema': {'description': 'Any data type.'},\n", + " 'description': 'The value to be set in the new data cube.'},\n", + " 'type': 'object',\n", + " 'parameters': [{'schema': {'subtype': 'labeled-array',\n", + " 'type': 'array',\n", + " 'items': {'description': 'Any data type.'}},\n", + " 'name': 'data',\n", + " 'description': \"A labeled array with elements of any type. If there's no data for the period, the array is empty.\"},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'context',\n", + " 'description': 'Additional data passed by the user.',\n", + " 'optional': True}]},\n", + " 'name': 'reducer',\n", + " 'description': \"A reducer to be applied for the values contained in each period. A reducer is a single process such as ``mean()`` or a set of processes, which computes a single value for a list of values, see the category 'reducer' for such processes. Periods may not contain any values, which for most reducers leads to no-data (`null`) values by default.\"},\n", + " {'schema': {'type': ['string', 'null']},\n", + " 'name': 'dimension',\n", + " 'description': 'The name of the temporal dimension for aggregation. All data along the dimension is passed through the specified reducer. If the dimension is not set or set to `null`, the data cube is expected to only have one temporal dimension. Fails with a `TooManyDimensions` exception if it has more dimensions. Fails with a `DimensionNotAvailable` exception if the specified dimension does not exist.',\n", + " 'optional': True},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'context',\n", + " 'description': 'Additional data to be passed to the reducer.',\n", + " 'optional': True}],\n", + " 'categories': ['aggregate & resample', 'climatology', 'cubes'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'https://openeo.org/documentation/1.0/datacubes.html#aggregate',\n", + " 'title': 'Aggregation explained in the openEO documentation'}],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A new data cube with the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged, except for the resolution and dimension labels of the given temporal dimension. The specified temporal dimension has the following dimension labels (`YYYY` = four-digit year, `MM` = two-digit month, `DD` two-digit day of month):\\n\\n* `hour`: `YYYY-MM-DD-00` - `YYYY-MM-DD-23`\\n* `day`: `YYYY-001` - `YYYY-365`\\n* `week`: `YYYY-01` - `YYYY-52`\\n* `dekad`: `YYYY-00` - `YYYY-36`\\n* `month`: `YYYY-01` - `YYYY-12`\\n* `season`: `YYYY-djf` (December - February), `YYYY-mam` (March - May), `YYYY-jja` (June - August), `YYYY-son` (September - November).\\n* `tropical-season`: `YYYY-ndjfma` (November - April), `YYYY-mjjaso` (May - October).\\n* `year`: `YYYY`\\n* `decade`: `YYY0`\\n* `decade-ad`: `YYY1`'},\n", + " 'exceptions': {'DimensionNotAvailable': {'message': 'A dimension with the specified name does not exist.'},\n", + " 'TooManyDimensions': {'message': 'The data cube contains multiple temporal dimensions. The parameter `dimension` must be specified.'},\n", + " 'DistinctDimensionLabelsRequired': {'message': 'The dimension labels have duplicate values. Distinct labels must be specified.'}}},\n", + " {'engine': '[WCPS]',\n", + " 'id': 'arcsin',\n", + " 'summary': 'Inverse sine',\n", + " 'description': 'Computes the arc sine of `x`. The arc sine is the inverse function of the sine so that *arcsin(sin(x)) = x*.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'A number.'}],\n", + " 'categories': ['math > trigonometric'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/InverseSine.html',\n", + " 'title': 'Inverse sine explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed angle in radians.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 0}, 'returns': 0}]},\n", + " {'engine': '[WCPS]',\n", + " 'id': 'resample_spatial',\n", + " 'summary': 'Resample and warp the spatial dimensions',\n", + " 'description': 'Resamples the spatial dimensions (x,y) of the data cube to a specified resolution and/or warps the data cube to the target projection. At least `resolution` or `projection` must be specified.\\n\\nRelated processes:\\n\\n* Use ``filter_bbox()`` to set the target spatial extent.\\n* To spatially align two data cubes with each other (e.g. for merging), better use the process ``resample_cube_spatial()``.',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A raster data cube.'},\n", + " {'schema': [{'description': 'A single number used as resolution for both x and y.',\n", + " 'type': 'number',\n", + " 'minimum': 0},\n", + " {'minItems': 2,\n", + " 'maxItems': 2,\n", + " 'description': 'A two-element array to specify separate resolutions for x (first element) and y (second element).',\n", + " 'type': 'array',\n", + " 'items': {'type': 'number', 'minimum': 0}}],\n", + " 'default': 0,\n", + " 'name': 'resolution',\n", + " 'description': \"Resamples the data cube to the target resolution, which can be specified either as separate values for x and y or as a single value for both axes. Specified in the units of the target projection. Doesn't change the resolution by default (`0`).\",\n", + " 'optional': True},\n", + " {'schema': [{'examples': [3857],\n", + " 'subtype': 'epsg-code',\n", + " 'title': 'EPSG Code',\n", + " 'type': 'integer',\n", + " 'minimum': 1000},\n", + " {'subtype': 'wkt2-definition', 'title': 'WKT2', 'type': 'string'},\n", + " {'subtype': 'proj-definition',\n", + " 'deprecated': True,\n", + " 'title': 'PROJ definition',\n", + " 'type': 'string'},\n", + " {'title': \"Don't change projection\", 'type': 'null'}],\n", + " 'name': 'projection',\n", + " 'description': 'Warps the data cube to the target projection, specified as as [EPSG code](http://www.epsg-registry.org/), [WKT2 (ISO 19162) string](http://docs.opengeospatial.org/is/18-010r7/18-010r7.html), [PROJ definition (deprecated)](https://proj.org/usage/quickstart.html). By default (`null`), the projection is not changed.',\n", + " 'optional': True},\n", + " {'schema': {'type': 'string',\n", + " 'enum': ['near',\n", + " 'bilinear',\n", + " 'cubic',\n", + " 'cubicspline',\n", + " 'lanczos',\n", + " 'average',\n", + " 'mode',\n", + " 'max',\n", + " 'min',\n", + " 'med',\n", + " 'q1',\n", + " 'q3']},\n", + " 'default': 'near',\n", + " 'name': 'method',\n", + " 'description': 'Resampling method. Methods are inspired by GDAL, see [gdalwarp](https://www.gdal.org/gdalwarp.html) for more information.',\n", + " 'optional': True},\n", + " {'schema': {'type': 'string',\n", + " 'enum': ['lower-left', 'upper-left', 'lower-right', 'upper-right']},\n", + " 'default': 'upper-left',\n", + " 'name': 'align',\n", + " 'description': 'Specifies to which corner of the spatial extent the new resampled data is aligned to.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes', 'aggregate & resample'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'https://proj.org/usage/projections.html',\n", + " 'title': 'PROJ parameters for cartographic projections'},\n", + " {'rel': 'about',\n", + " 'href': 'http://www.epsg-registry.org',\n", + " 'title': 'Official EPSG code registry'},\n", + " {'rel': 'about',\n", + " 'href': 'http://www.epsg.io',\n", + " 'title': 'Unofficial EPSG code database'}],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A raster data cube with values warped onto the new projection. It has the same dimensions and the same dimension properties (name, type, labels, reference system and resolution) for all non-spatial or vertical spatial dimensions. For the horizontal spatial dimensions the name and type remain unchanged, but reference system, labels and resolution may change depending on the given parameters.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'fit_class_random_forest',\n", + " 'summary': 'Train a random forest classification model',\n", + " 'description': 'Executes the fit of a random forest classification based on the user input of target and predictors. The Random Forest classification model is based on the approach by Breiman (2001).',\n", + " 'parameters': [{'schema': {'subtype': 'vector-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'The input data for the classification model. The raster images that will be used as predictors for the Random Forest. Aggregated to the features (vectors) of the target input variable.'},\n", + " {'schema': {'subtype': 'vector-cube', 'type': 'object'},\n", + " 'name': 'target',\n", + " 'description': 'The input data for the classification model. This will be vector cubes for each training site. This is associated with the target variable for the Random Forest Model. The Geometry has to associated with a value to predict (e.g. fractional forest canopy cover).'},\n", + " {'schema': {'maximum': 100, 'type': 'number', 'exclusiveMinimum': 0},\n", + " 'name': 'training',\n", + " 'description': 'The amount of training data to be used in the classification. The sampling will be randomly through the data object. The remaining data will be used as test data for the validation.'},\n", + " {'schema': {'type': 'integer', 'minimum': 1},\n", + " 'default': 100,\n", + " 'name': 'num_trees',\n", + " 'description': 'The number of trees build within the Random Forest classification.',\n", + " 'optional': True},\n", + " {'schema': [{'type': 'integer', 'minimum': 1}, {'type': 'null'}],\n", + " 'name': 'mtry',\n", + " 'description': 'Specifies how many split variables will be used at a node. Default value is `null`, which corresponds to the number of predictors divided by 3.',\n", + " 'optional': True}],\n", + " 'categories': ['machine learning'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'https://doi.org/10.1023/A:1010933404324',\n", + " 'type': 'text/html',\n", + " 'title': 'Breiman (2001): Random Forests'}],\n", + " 'returns': {'schema': {'subtype': 'ml-model', 'type': 'object'},\n", + " 'description': 'A model object that can be saved with ``save_ml_model()`` and restored with ``load_ml_model()``.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'log',\n", + " 'summary': 'Logarithm to a base',\n", + " 'description': 'Logarithm to the base `base` of the number `x` is defined to be the inverse function of taking b to the power of x.\\n\\nThe no-data value `null` is passed through and therefore gets propagated if any of the arguments is `null`.\\n\\nThe computations follow [IEEE Standard 754](https://ieeexplore.ieee.org/document/8766229) whenever the processing environment supports it. Therefore, `log(0, 2)` results in ±infinity if the processing environment supports it or otherwise an error is thrown.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'A number to compute the logarithm for.'},\n", + " {'schema': {'type': ['number', 'null']},\n", + " 'name': 'base',\n", + " 'description': 'The numerical base.'}],\n", + " 'categories': ['math > exponential & logarithmic'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/Logarithm.html',\n", + " 'title': 'Logarithm explained by Wolfram MathWorld'},\n", + " {'rel': 'about',\n", + " 'href': 'https://ieeexplore.ieee.org/document/8766229',\n", + " 'title': 'IEEE Standard 754-2019 for Floating-Point Arithmetic'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed logarithm.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 10, 'base': 10}, 'returns': 1},\n", + " {'arguments': {'x': 2, 'base': 2}, 'returns': 1},\n", + " {'arguments': {'x': 4, 'base': 2}, 'returns': 2},\n", + " {'arguments': {'x': 1, 'base': 16}, 'returns': 0}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'resample_cube_spatial',\n", + " 'summary': 'Resample the spatial dimensions to match a target data cube',\n", + " 'description': 'Resamples the spatial dimensions (x,y) from a source data cube to align with the corresponding dimensions of the given target data cube. Returns a new data cube with the resampled dimensions.\\n\\nTo resample a data cube to a specific resolution or projection regardless of an existing target data cube, refer to ``resample_spatial()``.',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A data cube.'},\n", + " {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'target',\n", + " 'description': 'A data cube that describes the spatial target resolution.'},\n", + " {'schema': {'type': 'string',\n", + " 'enum': ['near',\n", + " 'bilinear',\n", + " 'cubic',\n", + " 'cubicspline',\n", + " 'lanczos',\n", + " 'average',\n", + " 'mode',\n", + " 'max',\n", + " 'min',\n", + " 'med',\n", + " 'q1',\n", + " 'q3']},\n", + " 'default': 'near',\n", + " 'name': 'method',\n", + " 'description': 'Resampling method. Methods are inspired by GDAL, see [gdalwarp](https://www.gdal.org/gdalwarp.html) for more information.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes', 'aggregate & resample'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'https://openeo.org/documentation/1.0/glossary.html#resample-changing-data-cube-geometry',\n", + " 'title': 'Resampling explained in the openEO glossary'}],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A data cube with the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged, except for the resolution and dimension labels of the spatial dimensions.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'filter_bands',\n", + " 'summary': 'Filter the bands by name',\n", + " 'description': \"Filters the bands in the data cube so that bands that don't match any of the criteria are dropped from the data cube. The data cube is expected to have only one dimension of type `bands`. Fails with a `DimensionMissing` error if no such dimension exists.\\n\\nThe following criteria can be used to select bands:\\n\\n* `bands`: band name or common band name (e.g. `B01`, `B8A`, `red` or `nir`)\\n* `wavelengths`: ranges of wavelengths in micrometres (?m) (e.g. 0.5 - 0.6)\\n\\nAll these information are exposed in the band metadata of the collection. To keep algorithms interoperable it is recommended to prefer the common bands names or the wavelengths over collection and/or back-end specific band names.\\n\\nIf multiple criteria are specified, any of them must match and not all of them, i.e. they are combined with an OR-operation. If no criteria is specified, the `BandFilterParameterMissing` exception must be thrown.\\n\\n**Important:** The order of the specified array defines the order of the bands in the data cube, which can be important for subsequent processes. If multiple bands are matched by a single criterion (e.g. a range of wavelengths), they stay in the original order.\",\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A data cube with bands.'},\n", + " {'schema': {'type': 'array',\n", + " 'items': {'subtype': 'band-name', 'type': 'string'}},\n", + " 'default': [],\n", + " 'name': 'bands',\n", + " 'description': 'A list of band names. Either the unique band name (metadata field `name` in bands) or one of the common band names (metadata field `common_name` in bands). If unique band name and common name conflict, the unique band name has higher priority.\\n\\nThe order of the specified array defines the order of the bands in the data cube. If multiple bands match a common name, all matched bands are included in the original order.',\n", + " 'optional': True},\n", + " {'schema': {'type': 'array',\n", + " 'items': {'minItems': 2,\n", + " 'maxItems': 2,\n", + " 'examples': [[[0.45, 0.5], [0.6, 0.7]]],\n", + " 'type': 'array',\n", + " 'items': {'type': 'number'}}},\n", + " 'default': [],\n", + " 'name': 'wavelengths',\n", + " 'description': 'A list of sub-lists with each sub-list consisting of two elements. The first element is the minimum wavelength and the second element is the maximum wavelength. Wavelengths are specified in micrometres (?m).\\n\\nThe order of the specified array defines the order of the bands in the data cube. If multiple bands match the wavelengths, all matched bands are included in the original order.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes', 'filter'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'https://github.com/radiantearth/stac-spec/tree/master/extensions/eo#common-band-names',\n", + " 'title': 'List of common band names as specified by the STAC specification'}],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A data cube limited to a subset of its original bands. The dimensions and dimension properties (name, type, labels, reference system and resolution) remain unchanged, except that the dimension of type `bands` has less (or the same) dimension labels.'},\n", + " 'exceptions': {'DimensionMissing': {'message': 'A band dimension is missing.'},\n", + " 'BandFilterParameterMissing': {'message': \"The process 'filter_bands' requires any of the parameters 'bands', 'common_names' or 'wavelengths' to be set.\"}}},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'resample_cube_temporal',\n", + " 'summary': 'Resample a temporal dimension to match a target data cube',\n", + " 'description': 'Resamples the given temporal dimension from a source data cube to align with the corresponding dimension of the given target data cube. Returns a new data cube with the resampled dimension.\\n\\nIf the dimension is not set or is set to `null`, the data cube is expected to have one temporal dimension only.',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A data cube.'},\n", + " {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'target',\n", + " 'description': 'A data cube that describes the temporal target resolution.'},\n", + " {'schema': {'subtype': 'process-graph',\n", + " 'type': 'object',\n", + " 'parameters': [{'schema': {'subtype': 'labeled-array',\n", + " 'type': 'array',\n", + " 'items': {'description': 'Any data type.'}},\n", + " 'name': 'data',\n", + " 'description': 'A labeled array with elements of any type.'},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'context',\n", + " 'description': 'Additional data passed by the user.',\n", + " 'optional': True}]},\n", + " 'name': 'method',\n", + " 'description': \"A resampling method to be applied, could be a reducer for downsampling or other methods for upsampling. A reducer is a single process such as ``mean()`` or a set of processes, which computes a single value for a list of values, see the category 'reducer' for such processes.\"},\n", + " {'schema': {'type': ['string', 'null']},\n", + " 'name': 'dimension',\n", + " 'description': 'The name of the temporal dimension to resample, which must exist with this name in both data cubes. If the dimension is not set or is set to `null`, the data cube is expected to only have one temporal dimension. Fails with a `TooManyDimensions` error if it has more dimensions. Fails with a `DimensionNotAvailable` error if the specified dimension does not exist.',\n", + " 'optional': True},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'context',\n", + " 'description': 'Additional data to be passed to the process specified for the parameter `method`.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes', 'aggregate & resample'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'https://openeo.org/documentation/1.0/glossary.html#resample-changing-data-cube-geometry',\n", + " 'title': 'Resampling explained in the openEO glossary'}],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A raster data cube with the same dimensions and the same dimension properties (name, type, labels, reference system and resolution) for all non-temporal dimensions. For the temporal dimension the name and type remain unchanged, but the reference system changes and the labels and resolution may change.'},\n", + " 'exceptions': {'DimensionNotAvailable': {'message': 'A dimension with the specified name does not exist.'},\n", + " 'TooManyDimensions': {'message': \"The number of temporal dimensions must be reduced to one for 'resample_cube_temporal'.\"}}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'predict_random_forest',\n", + " 'summary': 'Predict values from a Random Forest model',\n", + " 'description': 'Applies a Random Forest Model to a raster cube objects. The raster data cube necessarily needs the same bands as the predictors in the model. Otherwise, an `IncompatibleBands` must be returned.',\n", + " 'parameters': [{'schema': {'subtype': 'vector-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A raster cube with the bands corresponding to the predictors.'},\n", + " {'schema': {'subtype': 'ml-model', 'type': 'object'},\n", + " 'name': 'model',\n", + " 'description': 'A model object that can be trained with the ``fit_regr_random_forest()`` process.'}],\n", + " 'categories': ['machine learning'],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A raster data cube with the prediction of the target variable based on the model.'},\n", + " 'exceptions': {'IncompatibleBands': {'message': 'The bands provided do not match the bands that the model has been trained for.'}}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'sum',\n", + " 'summary': 'Compute the sum by adding up numbers',\n", + " 'description': 'Sums up all elements in a sequential array of numbers and returns the computed sum.\\n\\nBy default no-data values are ignored. Setting `ignore_nodata` to `false` considers no-data values so that `null` is returned if any element is such a value.\\n\\nThe computations follow [IEEE Standard 754](https://ieeexplore.ieee.org/document/8766229) whenever the processing environment supports it.',\n", + " 'parameters': [{'schema': {'type': 'array',\n", + " 'items': {'type': ['number', 'null']}},\n", + " 'name': 'data',\n", + " 'description': 'An array of numbers.'},\n", + " {'schema': {'type': 'boolean'},\n", + " 'default': True,\n", + " 'name': 'ignore_nodata',\n", + " 'description': 'Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that `null` is returned if any value is such a value.',\n", + " 'optional': True}],\n", + " 'categories': ['math', 'reducer'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/Sum.html',\n", + " 'title': 'Sum explained by Wolfram MathWorld'},\n", + " {'rel': 'about',\n", + " 'href': 'https://ieeexplore.ieee.org/document/8766229',\n", + " 'title': 'IEEE Standard 754-2019 for Floating-Point Arithmetic'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed sum of the sequence of numbers.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'data': [5, 1]}, 'returns': 6},\n", + " {'arguments': {'data': [-2, 4, 2.5]}, 'returns': 4.5},\n", + " {'arguments': {'data': [1, None], 'ignore_nodata': False}},\n", + " {'arguments': {'data': [100]}, 'returns': 100},\n", + " {'arguments': {'data': [None], 'ignore_nodata': False}},\n", + " {'arguments': {'data': []}}]},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'save_ml_model',\n", + " 'summary': 'Save a machine learning model',\n", + " 'description': '...',\n", + " 'parameters': [{'schema': {'subtype': 'ml-model', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'The data to store as a machine learning model.'},\n", + " {'schema': {'additionalParameters': False, 'type': 'object'},\n", + " 'default': {},\n", + " 'name': 'options',\n", + " 'description': 'Any additional parameters to create the file(s).',\n", + " 'optional': True}],\n", + " 'categories': ['machine learning', 'import'],\n", + " 'returns': {'schema': {'type': 'boolean'},\n", + " 'description': 'Returns `false` if the process failed to store the model, `true` otherwise.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'load_collection',\n", + " 'summary': 'Load a collection',\n", + " 'description': 'Loads a collection from the current back-end by its id and returns it as processable data cube. The data that is added to the data cube can be restricted with the additional `spatial_extent`, `temporal_extent`, `bands` and `properties`.\\n\\n**Remarks:**\\n\\n* The bands (and all dimensions that specify nominal dimension labels) are expected to be ordered as specified in the metadata if the `bands` parameter is set to `null`.\\n* If no additional parameter is specified this would imply that the whole data set is expected to be loaded. Due to the large size of many data sets this is not recommended and may be optimized by back-ends to only load the data that is actually required after evaluating subsequent processes such as filters. This means that the pixel values should be processed only after the data has been limited to the required extents and as a consequence also to a manageable size.',\n", + " 'parameters': [{'schema': {'subtype': 'collection-id',\n", + " 'pattern': '^[\\\\w\\\\-\\\\.~/]+$',\n", + " 'type': 'string'},\n", + " 'name': 'id',\n", + " 'description': 'The collection id.'},\n", + " {'schema': [{'subtype': 'bounding-box',\n", + " 'title': 'Bounding Box',\n", + " 'type': 'object',\n", + " 'required': ['west', 'south', 'east', 'north'],\n", + " 'properties': {'east': {'description': 'East (upper right corner, coordinate axis 1).',\n", + " 'type': 'number'},\n", + " 'south': {'description': 'South (lower left corner, coordinate axis 2).',\n", + " 'type': 'number'},\n", + " 'crs': {'default': 4326,\n", + " 'description': 'Coordinate reference system of the extent, specified as as [EPSG code](http://www.epsg-registry.org/), [WKT2 (ISO 19162) string](http://docs.opengeospatial.org/is/18-010r7/18-010r7.html) or [PROJ definition (deprecated)](https://proj.org/usage/quickstart.html). Defaults to `4326` (EPSG code 4326) unless the client explicitly requests a different coordinate reference system.',\n", + " 'anyOf': [{'examples': [3857],\n", + " 'subtype': 'epsg-code',\n", + " 'title': 'EPSG Code',\n", + " 'type': 'integer',\n", + " 'minimum': 1000},\n", + " {'subtype': 'wkt2-definition', 'title': 'WKT2', 'type': 'string'},\n", + " {'subtype': 'proj-definition',\n", + " 'deprecated': True,\n", + " 'title': 'PROJ definition',\n", + " 'type': 'string'}]},\n", + " 'north': {'description': 'North (upper right corner, coordinate axis 2).',\n", + " 'type': 'number'},\n", + " 'west': {'description': 'West (lower left corner, coordinate axis 1).',\n", + " 'type': 'number'},\n", + " 'base': {'description': 'Base (optional, lower left corner, coordinate axis 3).',\n", + " 'type': ['number', 'null']},\n", + " 'height': {'description': 'Height (optional, upper right corner, coordinate axis 3).',\n", + " 'type': ['number', 'null']}}},\n", + " {'subtype': 'geojson', 'title': 'GeoJSON', 'type': 'object'},\n", + " {'description': \"Don't filter spatially. All data is included in the data cube.\",\n", + " 'title': 'No filter',\n", + " 'type': 'null'}],\n", + " 'name': 'spatial_extent',\n", + " 'description': 'Limits the data to load from the collection to the specified bounding box or polygons.\\n\\nThe process puts a pixel into the data cube if the point at the pixel center intersects with the bounding box or any of the polygons (as defined in the Simple Features standard by the OGC).\\n\\nThe GeoJSON can be one of the following GeoJSON types:\\n\\n* A `Polygon` geometry,\\n* a `GeometryCollection` containing Polygons,\\n* a `Feature` with a `Polygon` geometry or\\n* a `FeatureCollection` containing `Feature`s with a `Polygon` geometry.\\n\\nSet this parameter to `null` to set no limit for the spatial extent. Be careful with this when loading large datasets!'},\n", + " {'schema': [{'minItems': 2,\n", + " 'maxItems': 2,\n", + " 'examples': [['2015-01-01T00:00:00Z', '2016-01-01T00:00:00Z'],\n", + " ['2015-01-01', '2016-01-01']],\n", + " 'subtype': 'temporal-interval',\n", + " 'type': 'array',\n", + " 'items': {'anyOf': [{'subtype': 'date-time',\n", + " 'format': 'date-time',\n", + " 'type': 'string'},\n", + " {'subtype': 'date', 'format': 'date', 'type': 'string'},\n", + " {'subtype': 'year',\n", + " 'minLength': 4,\n", + " 'pattern': '^\\\\d{4}$',\n", + " 'type': 'string',\n", + " 'maxLength': 4},\n", + " {'type': 'null'}]}},\n", + " {'description': \"Don't filter temporally. All data is included in the data cube.\",\n", + " 'title': 'No filter',\n", + " 'type': 'null'}],\n", + " 'name': 'temporal_extent',\n", + " 'description': 'Limits the data to load from the collection to the specified left-closed temporal interval. Applies to all temporal dimensions. The interval has to be specified as an array with exactly two elements:\\n\\n1. The first element is the start of the temporal interval. The specified instance in time is **included** in the interval.\\n2. The second element is the end of the temporal interval. The specified instance in time is **excluded** from the interval.\\n\\nThe specified temporal strings follow [RFC 3339](https://tools.ietf.org/html/rfc3339). Also supports open intervals by setting one of the boundaries to `null`, but never both.\\n\\nSet this parameter to `null` to set no limit for the spatial extent. Be careful with this when loading large datasets!'},\n", + " {'schema': [{'type': 'array',\n", + " 'items': {'subtype': 'band-name', 'type': 'string'}},\n", + " {'description': \"Don't filter bands. All bands are included in the data cube.\",\n", + " 'title': 'No filter',\n", + " 'type': 'null'}],\n", + " 'name': 'bands',\n", + " 'description': \"Only adds the specified bands into the data cube so that bands that don't match the list of band names are not available. Applies to all dimensions of type `bands`.\\n\\nEither the unique band name (metadata field `name` in bands) or one of the common band names (metadata field `common_name` in bands) can be specified. If unique band name and common name conflict, the unique band name has higher priority.\\n\\nThe order of the specified array defines the order of the bands in the data cube. f multiple bands match a common name, all matched bands are included in the original order.\",\n", + " 'optional': True},\n", + " {'schema': [{'subtype': 'metadata-filter',\n", + " 'description': 'A list of filters to check against. Specify key-value-pairs with the key being the name of the metadata property name and the value being a process evaluated against the metadata values.',\n", + " 'additionalProperties': {'subtype': 'process-graph',\n", + " 'type': 'object',\n", + " 'parameters': [{'schema': {'description': 'Any data type.'},\n", + " 'name': 'value',\n", + " 'description': 'The property value to be checked against.'}]},\n", + " 'type': 'object',\n", + " 'title': 'Filters'},\n", + " {'description': \"Don't filter by metadata properties.\",\n", + " 'title': 'No filter',\n", + " 'type': 'null'}],\n", + " 'name': 'properties',\n", + " 'description': 'Limits the data by metadata properties to include only data in the data cube which all given conditions return `true` for (AND operation).\\n\\nSpecify key-value-pairs with the key being the name of the metadata property, which can be retrieved with the openEO Data Discovery for Collections. The value must a condition (user-defined process) to be evaluated against the collection metadata, see the example.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes', 'import'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'https://proj.org/usage/projections.html',\n", + " 'title': 'PROJ parameters for cartographic projections'},\n", + " {'rel': 'about',\n", + " 'href': 'http://www.epsg-registry.org',\n", + " 'title': 'Official EPSG code registry'},\n", + " {'rel': 'about',\n", + " 'href': 'http://www.epsg.io',\n", + " 'title': 'Unofficial EPSG code database'},\n", + " {'rel': 'about',\n", + " 'href': 'http://www.opengeospatial.org/standards/sfa',\n", + " 'title': 'Simple Features standard by the OGC'},\n", + " {'rel': 'about',\n", + " 'href': 'https://github.com/radiantearth/stac-spec/tree/master/extensions/eo#common-band-names',\n", + " 'title': 'List of common band names as specified by the STAC specification'}],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': \"A data cube for further processing. The dimensions and dimension properties (name, type, labels, reference system and resolution) correspond to the collection's metadata, but the dimension labels are restricted as specified in the parameters.\"},\n", + " 'exceptions': {},\n", + " 'examples': [{'description': 'Loading `Sentinel-2B` data from a `Sentinel-2` collection for 2018, but only with cloud cover between 0 and 50%.',\n", + " 'arguments': {'temporal_extent': ['2018-01-01', '2019-01-01'],\n", + " 'spatial_extent': {'east': 16.6,\n", + " 'south': 47.2,\n", + " 'north': 48.6,\n", + " 'west': 16.1},\n", + " 'id': 'Sentinel-2',\n", + " 'properties': {'eo:cloud_cover': {'process_graph': {'cc': {'result': True,\n", + " 'process_id': 'between',\n", + " 'arguments': {'min': 0,\n", + " 'max': 50,\n", + " 'x': {'from_parameter': 'value'}}}}},\n", + " 'platform': {'process_graph': {'pf': {'result': True,\n", + " 'process_id': 'eq',\n", + " 'arguments': {'case_sensitive': False,\n", + " 'x': {'from_parameter': 'value'},\n", + " 'y': 'Sentinel-2B'}}}}}}}]},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'radar_mask',\n", + " 'summary': 'Compute Radar Mask from DEM (Digital Elevation Model) and LIA (Local Incidence Angle)',\n", + " 'description': 'Computes the Radar Mask taking into account Layover, Foreshortening and Shadow. It returns an array with the new bands as integers: 1:layover_mask, 2:foreshortening_mask and 3:shadow_mask. This process must be used in an apply_dimension process, since it acts on pixel level but modifies the existing number of dimensions (bands). You need to use rename_labels to assign the band labels afterwards.',\n", + " 'parameters': [{'schema': {'type': 'array',\n", + " 'items': {'type': ['number', 'null']}},\n", + " 'name': 'data',\n", + " 'description': 'An array of numbers with two bands (DEM and LIA), expected to be indexed in that order. If the given array is a labeled array the bands should be called DEM and LIA, no other dimensions allowed (no temporal dimension)'},\n", + " {'schema': {'minItems': 1, 'type': 'float'},\n", + " 'default': 0.2,\n", + " 'name': 'foreshortening_th',\n", + " 'description': 'The foreshortening threshold, between 0 and 1.'},\n", + " {'schema': {'minItems': 1, 'type': 'float'},\n", + " 'default': 1.0,\n", + " 'name': 'layover_th',\n", + " 'description': 'The layover threshold, must be greater than 0.'},\n", + " {'schema': [{'type': 'string', 'enum': ['ASC', 'DSC']}],\n", + " 'default': 0,\n", + " 'name': 'orbit_direction',\n", + " 'description': 'The Sentinel-1 orbit direction.',\n", + " 'optional': False}],\n", + " 'categories': ['cubes'],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'An array with with zeros for the masked pixels and ones for the valid pixels.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[WCPS]',\n", + " 'id': 'tanh',\n", + " 'summary': 'Hyperbolic tangent',\n", + " 'description': 'Computes the hyperbolic tangent of `x`. The tangent is defined to be the hyperbolic sine of x divided by the hyperbolic cosine of x.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'An angle in radians.'}],\n", + " 'categories': ['math > trigonometric'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/HyperbolicTangent.html',\n", + " 'title': 'Hyperbolic tangent explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed hyperbolic tangent of `x`.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 0}, 'returns': 0}]},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'apply_dimension',\n", + " 'summary': 'Apply a process to pixels along a dimension',\n", + " 'description': 'Applies a process to all pixel values along a dimension of a raster data cube. For example, if the temporal dimension is specified the process will work on a time series of pixel values.\\n\\nThe process ``reduce_dimension()`` also applies a process to pixel values along a dimension, but drops the dimension afterwards. The process ``apply()`` applies a process to each pixel value in the data cube.\\n\\nThe target dimension is the source dimension if not specified otherwise in the `target_dimension` parameter. The pixel values in the target dimension get replaced by the computed pixel values. The name, type and reference system are preserved.\\n\\nThe dimension labels are preserved when the target dimension is the source dimension and the number of pixel values in the source dimension is equal to the number of values computed by the process. Otherwise, the dimension labels will be incrementing integers starting from zero, which can be changed using ``rename_labels()`` afterwards. The number of labels will equal to the number of values computed by the process.',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A data cube.'},\n", + " {'schema': {'subtype': 'process-graph',\n", + " 'returns': {'schema': {'type': 'array',\n", + " 'items': {'description': 'Any data type.'}},\n", + " 'description': 'The value to be set in the new data cube.'},\n", + " 'type': 'object',\n", + " 'parameters': [{'schema': {'subtype': 'labeled-array',\n", + " 'type': 'array',\n", + " 'items': {'description': 'Any data type.'}},\n", + " 'name': 'data',\n", + " 'description': 'A labeled array with elements of any type.'},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'context',\n", + " 'description': 'Additional data passed by the user.',\n", + " 'optional': True}]},\n", + " 'name': 'process',\n", + " 'description': 'Process to be applied on all pixel values. The specified process needs to accept an array and must return an array with at least one element. A process may consist of multiple sub-processes.'},\n", + " {'schema': {'type': 'string'},\n", + " 'name': 'dimension',\n", + " 'description': 'The name of the source dimension to apply the process on. Fails with a `DimensionNotAvailable` exception if the specified dimension does not exist.'},\n", + " {'schema': {'type': ['string', 'null']},\n", + " 'name': 'target_dimension',\n", + " 'description': \"The name of the target dimension or `null` (the default) to use the source dimension specified in the parameter `dimension`.\\n\\nBy specifying a target dimension, the source dimension is removed. The target dimension with the specified name and the type `other` (see ``add_dimension()``) is created, if it doesn't exist yet.\",\n", + " 'optional': True},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'context',\n", + " 'description': 'Additional data to be passed to the process.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'https://openeo.org/documentation/1.0/datacubes.html#apply',\n", + " 'title': 'Apply explained in the openEO documentation'}],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A data cube with the newly computed values.\\n\\nAll dimensions stay the same, except for the dimensions specified in corresponding parameters. There are three cases how the dimensions can change:\\n\\n1. The source dimension is the target dimension:\\n - The (number of) dimensions remain unchanged as the source dimension is the target dimension.\\n - The source dimension properties name and type remain unchanged.\\n - The dimension labels, the reference system and the resolution are preserved only if the number of pixel values in the source dimension is equal to the number of values computed by the process. Otherwise, all other dimension properties change as defined in the list below.\\n2. The source dimension is not the target dimension and the latter exists:\\n - The number of dimensions decreases by one as the source dimension is dropped.\\n - The target dimension properties name and type remain unchanged. All other dimension properties change as defined in the list below.\\n3. The source dimension is not the target dimension and the latter does not exist:\\n - The number of dimensions remain unchanged, but the source dimension is replaced with the target dimension.\\n - The target dimension has the specified name and the type other. All other dimension properties are set as defined in the list below.\\n\\nUnless otherwise stated above, for the given (target) dimension the following applies:\\n\\n- the number of dimension labels is equal to the number of values computed by the process,\\n- the dimension labels are incrementing integers starting from zero,\\n- the resolution changes, and\\n- the reference system is undefined.'},\n", + " 'exceptions': {'DimensionNotAvailable': {'message': 'A dimension with the specified name does not exist.'}}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'sd',\n", + " 'summary': 'Standard deviation',\n", + " 'description': 'Computes the sample standard deviation, which quantifies the amount of variation of an array of numbers. It is defined to be the square root of the corresponding variance (see ``variance()``).\\n\\nA low standard deviation indicates that the values tend to be close to the expected value, while a high standard deviation indicates that the values are spread out over a wider range.\\n\\nAn array without non-`null` elements resolves always with `null`.',\n", + " 'parameters': [{'schema': {'type': 'array',\n", + " 'items': {'type': ['number', 'null']}},\n", + " 'name': 'data',\n", + " 'description': 'An array of numbers.'},\n", + " {'schema': {'type': 'boolean'},\n", + " 'default': True,\n", + " 'name': 'ignore_nodata',\n", + " 'description': 'Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that `null` is returned if any value is such a value.',\n", + " 'optional': True}],\n", + " 'categories': ['math', 'reducer'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/StandardDeviation.html',\n", + " 'title': 'Standard deviation explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed sample standard deviation.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'data': [-1, 1, 3, None]}, 'returns': 2},\n", + " {'arguments': {'data': [-1, 1, 3, None], 'ignore_nodata': False}},\n", + " {'description': 'The input array is empty: return `null`.',\n", + " 'arguments': {'data': []}}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'not',\n", + " 'summary': 'Inverting a boolean',\n", + " 'description': 'Inverts a single boolean so that `true` gets `false` and `false` gets `true`.\\n\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", + " 'parameters': [{'schema': {'type': ['boolean', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'Boolean value to invert.'}],\n", + " 'categories': ['logic'],\n", + " 'returns': {'schema': {'type': ['boolean', 'null']},\n", + " 'description': 'Inverted boolean value.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {}},\n", + " {'arguments': {'x': False}, 'returns': True},\n", + " {'arguments': {'x': True}, 'returns': False}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'min',\n", + " 'summary': 'Minimum value',\n", + " 'description': 'Computes the smallest value of an array of numbers, which is is equal to the last element of a sorted (i.e., ordered) version the array.\\n\\nAn array without non-`null` elements resolves always with `null`.',\n", + " 'parameters': [{'schema': {'type': 'array',\n", + " 'items': {'type': ['number', 'null']}},\n", + " 'name': 'data',\n", + " 'description': 'An array of numbers.'},\n", + " {'schema': {'type': 'boolean'},\n", + " 'default': True,\n", + " 'name': 'ignore_nodata',\n", + " 'description': 'Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that `null` is returned if any value is such a value.',\n", + " 'optional': True}],\n", + " 'categories': ['math', 'reducer'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/Minimum.html',\n", + " 'title': 'Minimum explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The minimum value.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'data': [1, 0, 3, 2]}, 'returns': 0},\n", + " {'arguments': {'data': [5, 2.5, None, -0.7]}, 'returns': -0.7},\n", + " {'arguments': {'data': [1, 0, 3, None, 2], 'ignore_nodata': False}},\n", + " {'arguments': {'data': []}}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'linear_scale_range',\n", + " 'summary': 'Linear transformation between two ranges',\n", + " 'description': 'Performs a linear transformation between the input and output range.\\n\\nThe given number in `x` is clipped to the bounds specified in `inputMin` and `inputMax` so that the underlying formula *((x - inputMin) / (inputMax - inputMin)) * (outputMax - outputMin) + outputMin* never returns any value lower than `outputMin` or greater than `outputMax`.\\n\\nPotential use case include\\n\\n* scaling values to the 8-bit range (0 - 255) often used for numeric representation of values in one of the channels of the [RGB colour model](https://en.wikipedia.org/wiki/RGB_color_model#Numeric_representations) or\\n* calculating percentages (0 - 100).\\n\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'A number to transform. The number gets clipped to the bounds specified in `inputMin` and `inputMax`.'},\n", + " {'schema': {'type': 'number'},\n", + " 'name': 'inputMin',\n", + " 'description': 'Minimum value the input can obtain.'},\n", + " {'schema': {'type': 'number'},\n", + " 'name': 'inputMax',\n", + " 'description': 'Maximum value the input can obtain.'},\n", + " {'schema': {'type': 'number'},\n", + " 'default': 0,\n", + " 'name': 'outputMin',\n", + " 'description': 'Minimum value of the desired output range.',\n", + " 'optional': True},\n", + " {'schema': {'type': 'number'},\n", + " 'default': 1,\n", + " 'name': 'outputMax',\n", + " 'description': 'Maximum value of the desired output range.',\n", + " 'optional': True}],\n", + " 'categories': ['math'],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The transformed number.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'inputMax': 1,\n", + " 'inputMin': -1,\n", + " 'x': 0.3,\n", + " 'outputMin': 0,\n", + " 'outputMax': 255},\n", + " 'returns': 165.75},\n", + " {'arguments': {'inputMax': 255, 'inputMin': 0, 'x': 25.5}, 'returns': 0.1},\n", + " {'arguments': {'inputMax': 100, 'inputMin': 0}},\n", + " {'description': 'Shows that the input data is clipped.',\n", + " 'arguments': {'inputMax': 1,\n", + " 'inputMin': 0,\n", + " 'x': 1.12,\n", + " 'outputMin': 0,\n", + " 'outputMax': 255},\n", + " 'returns': 255}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'and',\n", + " 'summary': 'Logical AND',\n", + " 'description': 'Checks if **both** values are true.\\n\\nEvaluates parameter `x` before `y` and stops once the outcome is unambiguous. If any argument is `null`, the result will be `null` if the outcome is ambiguous.\\n\\n**Truth table:**\\n\\n```\\na \\\\ b || null | false | true\\n----- || ----- | ----- | -----\\nnull || null | false | null\\nfalse || false | false | false\\ntrue || null | false | true\\n```',\n", + " 'parameters': [{'schema': {'type': ['boolean', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'A boolean value.'},\n", + " {'schema': {'type': ['boolean', 'null']},\n", + " 'name': 'y',\n", + " 'description': 'A boolean value.'}],\n", + " 'categories': ['logic'],\n", + " 'returns': {'schema': {'type': ['boolean', 'null']},\n", + " 'description': 'Boolean result of the logical AND.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': True, 'y': True}, 'returns': True},\n", + " {'arguments': {'x': True, 'y': False}, 'returns': False},\n", + " {'arguments': {'x': False, 'y': False}, 'returns': False},\n", + " {'arguments': {'x': False}, 'returns': False},\n", + " {'arguments': {'x': True}}]},\n", + " {'engine': '[WCPS]',\n", + " 'id': 'sin',\n", + " 'summary': 'Sine',\n", + " 'description': 'Computes the sine of `x`.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'An angle in radians.'}],\n", + " 'categories': ['math > trigonometric'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/Sine.html',\n", + " 'title': 'Sine explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed sine of `x`.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 0}, 'returns': 0}]},\n", + " {'engine': '[WCPS]',\n", + " 'id': 'ndvi',\n", + " 'summary': 'Normalized Difference Vegetation Index',\n", + " 'description': \"Computes the Normalized Difference Vegetation Index (NDVI). The NDVI is computed as *(nir - red) / (nir + red)*.\\n\\nThe `data` parameter expects a raster data cube with a dimension of type `bands` or a `DimensionAmbiguous` error is thrown otherwise. By default, the dimension must have at least two bands with the common names `red` and `nir` assigned or the user need to specify the parameters `nir` and `red`. Otherwise either the error `NirBandAmbiguous` or `RedBandAmbiguous` is thrown. The common names for each band are specified in the collection's band metadata and are *not* equal to the band names.\\n\\nBy default, the dimension of type `bands` is dropped by this process. To keep the dimension specify a new band name in the parameter `target_band`. This adds a new dimension label with the specified name to the dimension, which can be used to access the computed values. If a band with the specified name exists, a `BandExists` is thrown.\\n\\nThis process is very similar to the process ``normalized_difference()``, but determines the bands automatically based on the common names (`red`/`nir`) specified in the metadata.\",\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A raster data cube with two bands that have the common names `red` and `nir` assigned.'},\n", + " {'schema': {'subtype': 'band-name', 'type': 'string'},\n", + " 'default': 'nir',\n", + " 'name': 'nir',\n", + " 'description': 'The name of the NIR band. Defaults to the band that has the common name `nir` assigned.\\n\\nEither the unique band name (metadata field `name` in bands) or one of the common band names (metadata field `common_name` in bands) can be specified. If unique band name and common name conflict, the unique band name has higher priority.',\n", + " 'optional': True},\n", + " {'schema': {'subtype': 'band-name', 'type': 'string'},\n", + " 'default': 'red',\n", + " 'name': 'red',\n", + " 'description': 'The name of the red band. Defaults to the band that has the common name `red` assigned.\\n\\nEither the unique band name (metadata field `name` in bands) or one of the common band names (metadata field `common_name` in bands) can be specified. If unique band name and common name conflict, the unique band name has higher priority.',\n", + " 'optional': True},\n", + " {'schema': [{'pattern': '^\\\\w+$', 'type': 'string'}, {'type': 'null'}],\n", + " 'name': 'target_band',\n", + " 'description': 'By default, the dimension of type `bands` is dropped. To keep the dimension specify a new band name in this parameter so that a new dimension label with the specified name will be added for the computed values.',\n", + " 'optional': True}],\n", + " 'categories': ['math > indices', 'vegetation indices'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'https://en.wikipedia.org/wiki/Normalized_difference_vegetation_index',\n", + " 'title': 'NDVI explained by Wikipedia'},\n", + " {'rel': 'about',\n", + " 'href': 'https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_2.php',\n", + " 'title': 'NDVI explained by NASA'},\n", + " {'rel': 'about',\n", + " 'href': 'https://github.com/radiantearth/stac-spec/tree/master/extensions/eo#common-band-names',\n", + " 'title': 'List of common band names as specified by the STAC specification'}],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A raster data cube containing the computed NDVI values. The structure of the data cube differs depending on the value passed to `target_band`:\\n\\n* `target_band` is `null`: The data cube does not contain the dimension of type `bands` any more, the number of dimensions decreases by one. The dimension properties (name, type, labels, reference system and resolution) for all other dimensions remain unchanged.\\n* `target_band` is a string: The data cube keeps the same dimensions. The dimension properties remain unchanged, but the number of dimension labels for the dimension of type `bands` increases by one. The additional label is named as specified in `target_band`.'},\n", + " 'exceptions': {'NirBandAmbiguous': {'message': \"The NIR band can't be resolved, please specify a band name.\"},\n", + " 'BandExists': {'message': 'A band with the specified target name exists.'},\n", + " 'RedBandAmbiguous': {'message': \"The red band can't be resolved, please specify a band name.\"},\n", + " 'DimensionAmbiguous': {'message': 'dimension of type `bands` is not available or is ambiguous..'}}},\n", + " {'engine': '[WCPS]',\n", + " 'id': 'xor',\n", + " 'summary': 'Logical XOR (exclusive or)',\n", + " 'description': 'Checks if **exactly one** of the values is true. If a component is `null`, the result will be `null` if the outcome is ambiguous.\\n\\n**Truth table:**\\n\\n```\\na \\\\ b || null | false | true\\n----- || ---- | ----- | -----\\nnull || null | null | null\\nfalse || null | false | true\\ntrue || null | true | false\\n```',\n", + " 'parameters': [{'schema': {'type': ['boolean', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'A boolean value.'},\n", + " {'schema': {'type': ['boolean', 'null']},\n", + " 'name': 'y',\n", + " 'description': 'A boolean value.'}],\n", + " 'categories': ['logic'],\n", + " 'returns': {'schema': {'type': ['boolean', 'null']},\n", + " 'description': 'Boolean result of the logical XOR.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': True, 'y': True}, 'returns': False},\n", + " {'arguments': {'x': False, 'y': False}, 'returns': False},\n", + " {'arguments': {'x': True, 'y': False}, 'returns': True},\n", + " {'arguments': {'x': True}},\n", + " {'arguments': {'x': False}}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'divide',\n", + " 'summary': 'Division of two numbers',\n", + " 'description': 'Divides argument `x` by the argument `y` (*x / y*) and returns the computed result.\\n\\nNo-data values are taken into account so that `null` is returned if any element is such a value.\\n\\nThe computations follow [IEEE Standard 754](https://ieeexplore.ieee.org/document/8766229) whenever the processing environment supports it. Therefore, a division by zero results in ±infinity if the processing environment supports it. Otherwise a `DivisionByZero` error must the thrown.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'The dividend.'},\n", + " {'schema': {'type': ['number', 'null']},\n", + " 'name': 'y',\n", + " 'description': 'The divisor.'}],\n", + " 'categories': ['math'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/Division.html',\n", + " 'title': 'Division explained by Wolfram MathWorld'},\n", + " {'rel': 'about',\n", + " 'href': 'https://ieeexplore.ieee.org/document/8766229',\n", + " 'title': 'IEEE Standard 754-2019 for Floating-Point Arithmetic'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed result.'},\n", + " 'exceptions': {'DivisionByZero': {'message': 'Division by zero is not supported.'}},\n", + " 'examples': [{'arguments': {'x': 5, 'y': 2.5}, 'returns': 2},\n", + " {'arguments': {'x': -2, 'y': 4}, 'returns': -0.5},\n", + " {'arguments': {'x': 1}}]},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'fit_regr_random_forest',\n", + " 'summary': 'Train a random forest regression model',\n", + " 'description': 'Executes the fit of a random forest regression based on the user input of target and predictors. The Random Forest regression model is based on the approach by Breiman (2001).',\n", + " 'parameters': [{'schema': {'subtype': 'vector-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'The input data for the regression model. The raster images that will be used as predictors for the Random Forest. Aggregated to the features (vectors) of the target input variable.'},\n", + " {'schema': {'subtype': 'vector-cube', 'type': 'object'},\n", + " 'name': 'target',\n", + " 'description': 'The input data for the regression model. This will be vector cubes for each training site. This is associated with the target variable for the Random Forest Model. The Geometry has to associated with a value to predict (e.g. fractional forest canopy cover).'},\n", + " {'schema': {'maximum': 100, 'type': 'number', 'exclusiveMinimum': 0},\n", + " 'name': 'training',\n", + " 'description': 'The amount of training data to be used in the regression. The sampling will be randomly through the data object. The remaining data will be used as test data for the validation.'},\n", + " {'schema': {'type': 'integer', 'minimum': 1},\n", + " 'default': 100,\n", + " 'name': 'num_trees',\n", + " 'description': 'The number of trees build within the Random Forest regression.',\n", + " 'optional': True},\n", + " {'schema': [{'type': 'integer', 'minimum': 1}, {'type': 'null'}],\n", + " 'name': 'mtry',\n", + " 'description': 'Specifies how many split variables will be used at a node. Default value is `null`, which corresponds to the number of predictors divided by 3.',\n", + " 'optional': True}],\n", + " 'categories': ['machine learning'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'https://doi.org/10.1023/A:1010933404324',\n", + " 'type': 'text/html',\n", + " 'title': 'Breiman (2001): Random Forests'}],\n", + " 'returns': {'schema': {'subtype': 'ml-model', 'type': 'object'},\n", + " 'description': 'A model object that can be saved with ``save_ml_model()`` and restored with ``load_ml_model()``.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'power',\n", + " 'summary': 'Exponentiation',\n", + " 'description': 'Computes the exponentiation for the base `base` raised to the power of `p`.\\n\\nThe no-data value `null` is passed through and therefore gets propagated if any of the arguments is `null`.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'base',\n", + " 'description': 'The numerical base.'},\n", + " {'schema': {'type': ['number', 'null']},\n", + " 'name': 'p',\n", + " 'description': 'The numerical exponent.'}],\n", + " 'categories': ['math', 'math > exponential & logarithmic'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/Power.html',\n", + " 'title': 'Power explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed value for `base` raised to the power of `p`.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'p': 2, 'base': 0}, 'returns': 0},\n", + " {'arguments': {'p': 0, 'base': 2.5}, 'returns': 1},\n", + " {'arguments': {'p': 3, 'base': 3}, 'returns': 27},\n", + " {'arguments': {'p': -1, 'base': 5}, 'returns': 0.2},\n", + " {'arguments': {'p': 0.5, 'base': 1}, 'returns': 1},\n", + " {'arguments': {'base': 1}},\n", + " {'arguments': {'p': 2}}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'lte',\n", + " 'summary': 'Less than or equal to comparison',\n", + " 'description': 'Compares whether `x` is less than or equal to `y`.\\n\\n**Remarks:**\\n\\n* If any operand is `null`, the return value is `null`. Therefore, `lte(null, null)` returns `null` instead of `true`.\\n* If any operand is an array or object, the return value is `false`.\\n* If the operands are not equal (see process ``eq()``) and any of them is not a `number` or temporal string (`date`, `time` or `date-time`), the process returns `false`.\\n* Temporal strings can *not* be compared based on their string representation due to the time zone / time-offset representations.',\n", + " 'parameters': [{'schema': {'description': 'Any data type is allowed.'},\n", + " 'name': 'x',\n", + " 'description': 'First operand.'},\n", + " {'schema': {'description': 'Any data type is allowed.'},\n", + " 'name': 'y',\n", + " 'description': 'Second operand.'}],\n", + " 'categories': ['comparison'],\n", + " 'returns': {'schema': {'type': ['boolean', 'null']},\n", + " 'description': '`true` if `x` is less than or equal to `y`, `null` if any operand is `null`, otherwise `false`.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 1}},\n", + " {'arguments': {'x': 0, 'y': 0}, 'returns': True},\n", + " {'arguments': {'x': 1, 'y': 2}, 'returns': True},\n", + " {'arguments': {'x': -0.5, 'y': -0.6}, 'returns': False},\n", + " {'arguments': {'x': '00:00:00+01:00', 'y': '00:00:00Z'}, 'returns': True},\n", + " {'arguments': {'x': '1950-01-01T00:00:00Z', 'y': '2018-01-01T12:00:00Z'},\n", + " 'returns': True},\n", + " {'arguments': {'x': '2018-01-01T12:00:00+00:00',\n", + " 'y': '2018-01-01T12:00:00Z'},\n", + " 'returns': True},\n", + " {'arguments': {'x': False, 'y': True}, 'returns': False},\n", + " {'arguments': {'x': [1, 2, 3], 'y': [1, 2, 3]}, 'returns': False}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'multiply',\n", + " 'summary': 'Multiplication of two numbers',\n", + " 'description': 'Multiplies the two numbers `x` and `y` (*x * y*) and returns the computed product.\\n\\nNo-data values are taken into account so that `null` is returned if any element is such a value.\\n\\nThe computations follow [IEEE Standard 754](https://ieeexplore.ieee.org/document/8766229) whenever the processing environment supports it.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'The multiplier.'},\n", + " {'schema': {'type': ['number', 'null']},\n", + " 'name': 'y',\n", + " 'description': 'The multiplicand.'}],\n", + " 'categories': ['math'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/Product.html',\n", + " 'title': 'Product explained by Wolfram MathWorld'},\n", + " {'rel': 'about',\n", + " 'href': 'https://ieeexplore.ieee.org/document/8766229',\n", + " 'title': 'IEEE Standard 754-2019 for Floating-Point Arithmetic'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed product of the two numbers.'},\n", + " 'exceptions': {'MultiplicandMissing': {'message': 'Multiplication requires at least two numbers.'}},\n", + " 'examples': [{'arguments': {'x': 5, 'y': 2.5}, 'returns': 12.5},\n", + " {'arguments': {'x': -2, 'y': -4}, 'returns': 8},\n", + " {'arguments': {'x': 1}}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'mask',\n", + " 'summary': 'Apply a raster mask',\n", + " 'description': \"Applies a mask to a raster data cube. To apply a vector mask use ``mask_polygon()``.\\n\\nA mask is a raster data cube for which corresponding pixels among `data` and `mask` are compared and those pixels in `data` are replaced whose pixels in `mask` are non-zero (for numbers) or `true` (for boolean values). The pixel values are replaced with the value specified for `replacement`, which defaults to `null` (no data).\\n\\nThe data cubes have to be compatible so that each dimension in mask must also be available in the raster data cube with the same name, type, reference system, resolution and labels. Dimensions can be missing in the mask with the result that the mask is applied for each label of the missing dimension in the data cube. The process fails if there's an incompatibility found between the raster data cube and the mask.\",\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A raster data cube.'},\n", + " {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'mask',\n", + " 'description': 'A mask as raster data cube. Every pixel in `data` must have a corresponding element in `mask`.'},\n", + " {'schema': {'type': ['number', 'boolean', 'string', 'null']},\n", + " 'name': 'replacement',\n", + " 'description': 'The value used to replace masked values with.',\n", + " 'optional': True}],\n", + " 'categories': ['masks'],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A masked raster data cube with the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'aggregate_spatial',\n", + " 'summary': 'Zonal statistics for geometries',\n", + " 'description': \"Aggregates statistics for one or more geometries (e.g. zonal statistics for polygons) over the spatial dimensions. The number of total and valid pixels is returned together with the calculated values.\\n\\nAn 'unbounded' aggregation over the full extent of the horizontal spatial dimensions can be computed with the process ``reduce_spatial()``.\\n\\nThis process passes a list of values to the reducer. The list of values has an undefined order, therefore processes such as ``last()`` and ``first()`` that depend on the order of the values will lead to unpredictable results.\",\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A raster data cube.\\n\\nThe data cube must have been reduced to only contain two spatial dimensions and a third dimension the values are aggregated for, for example the temporal dimension to get a time series. Otherwise, this process fails with the `TooManyDimensions` exception.\\n\\nThe data cube implicitly gets restricted to the bounds of the geometries as if ``filter_spatial()`` would have been used with the same values for the corresponding parameters immediately before this process.'},\n", + " {'schema': {'subtype': 'geojson', 'type': 'object'},\n", + " 'name': 'geometries',\n", + " 'description': 'Geometries as GeoJSON on which the aggregation will be based.\\n\\nOne value will be computed per GeoJSON `Feature`, `Geometry` or `GeometryCollection`. For a `FeatureCollection` multiple values will be computed, one value per contained `Feature`. For example, a single value will be computed for a `MultiPolygon`, but two values will be computed for a `FeatureCollection` containing two polygons.\\n\\n- For **polygons**, the process considers all pixels for which the point at the pixel center intersects with the corresponding polygon (as defined in the Simple Features standard by the OGC).\\n- For **points**, the process considers the closest pixel center.\\n- For **lines** (line strings), the process considers all the pixels whose centers are closest to at least one point on the line.\\n\\nThus, pixels may be part of multiple geometries and be part of multiple aggregations.\\n\\nTo maximize interoperability, a nested `GeometryCollection` should be avoided. Furthermore, a `GeometryCollection` composed of a single type of geometries should be avoided in favour of the corresponding multi-part type (e.g. `MultiPolygon`).'},\n", + " {'schema': {'subtype': 'process-graph',\n", + " 'returns': {'schema': {'description': 'Any data type.'},\n", + " 'description': 'The value to be set in the vector data cube.'},\n", + " 'type': 'object',\n", + " 'parameters': [{'schema': {'type': 'array',\n", + " 'items': {'description': 'Any data type.'}},\n", + " 'name': 'data',\n", + " 'description': 'An array with elements of any type.'},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'context',\n", + " 'description': 'Additional data passed by the user.',\n", + " 'optional': True}]},\n", + " 'name': 'reducer',\n", + " 'description': \"A reducer to be applied on all values of each geometry. A reducer is a single process such as ``mean()`` or a set of processes, which computes a single value for a list of values, see the category 'reducer' for such processes.\"},\n", + " {'schema': {'type': 'string'},\n", + " 'default': 'result',\n", + " 'name': 'target_dimension',\n", + " 'description': 'The new dimension name to be used for storing the results. Defaults to `result`.',\n", + " 'optional': True},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'context',\n", + " 'description': 'Additional data to be passed to the reducer.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes', 'aggregate & resample'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'https://openeo.org/documentation/1.0/datacubes.html#aggregate',\n", + " 'title': 'Aggregation explained in the openEO documentation'},\n", + " {'rel': 'about',\n", + " 'href': 'http://www.opengeospatial.org/standards/sfa',\n", + " 'title': 'Simple Features standard by the OGC'}],\n", + " 'returns': {'schema': {'subtype': 'vector-cube', 'type': 'object'},\n", + " 'description': 'A vector data cube with the computed results and restricted to the bounds of the geometries.\\n\\nThe computed value is used for the dimension with the name that was specified in the parameter `target_dimension`.\\n\\nThe computation also stores information about the total count of pixels (valid + invalid pixels) and the number of valid pixels (see ``is_valid()``) for each geometry. These values are added as a new dimension with a dimension name derived from `target_dimension` by adding the suffix `_meta`. The new dimension has the dimension labels `total_count` and `valid_count`.'},\n", + " 'exceptions': {'TooManyDimensions': {'message': 'The number of dimensions must be reduced to three for `aggregate_spatial`.'}}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'product',\n", + " 'summary': 'Compute the product by multiplying numbers',\n", + " 'description': 'Multiplies all elements in a sequential array of numbers and returns the computed product.\\n\\nBy default no-data values are ignored. Setting `ignore_nodata` to `false` considers no-data values so that `null` is returned if any element is such a value.\\n\\nThe computations follow [IEEE Standard 754](https://ieeexplore.ieee.org/document/8766229) whenever the processing environment supports it.',\n", + " 'parameters': [{'schema': {'type': 'array',\n", + " 'items': {'type': ['number', 'null']}},\n", + " 'name': 'data',\n", + " 'description': 'An array of numbers.'},\n", + " {'schema': {'type': 'boolean'},\n", + " 'default': True,\n", + " 'name': 'ignore_nodata',\n", + " 'description': 'Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that `null` is returned if any value is such a value.',\n", + " 'optional': True}],\n", + " 'categories': ['math', 'reducer'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/Product.html',\n", + " 'title': 'Product explained by Wolfram MathWorld'},\n", + " {'rel': 'about',\n", + " 'href': 'https://ieeexplore.ieee.org/document/8766229',\n", + " 'title': 'IEEE Standard 754-2019 for Floating-Point Arithmetic'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed product of the sequence of numbers.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'data': [5, 0]}, 'returns': 0},\n", + " {'arguments': {'data': [-2, 4, 2.5]}, 'returns': -20},\n", + " {'arguments': {'data': [1, None], 'ignore_nodata': False}},\n", + " {'arguments': {'data': [-1]}, 'returns': -1},\n", + " {'arguments': {'data': [None], 'ignore_nodata': False}},\n", + " {'arguments': {'data': []}}]},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'load_result',\n", + " 'summary': 'Load batch job results',\n", + " 'description': 'Loads batch job results by job id from the server-side user workspace. The job must have been stored by the authenticated user on the back-end currently connected to.',\n", + " 'parameters': [{'schema': {'subtype': 'job-id',\n", + " 'pattern': '^[\\\\w\\\\-\\\\.~]+$',\n", + " 'type': 'string'},\n", + " 'name': 'id',\n", + " 'description': 'The id of a batch job with results.'}],\n", + " 'categories': ['cubes', 'import'],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A data cube for further processing.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'rename_labels',\n", + " 'summary': 'Rename dimension labels',\n", + " 'description': \"Renames the labels of the specified dimension in the data cube from `source` to `target`.\\n\\nIf the array for the source labels is empty (the default), the dimension labels are expected to be enumerated with zero-based numbering (0,1,2,3,...) so that the dimension labels directly map to the indices of the array specified for the parameter `target`. If the dimension labels are not enumerated and the `target` parameter is not specified, a `LabelsNotEnumerated` is thrown. The number of source and target labels must be equal, otherwise the error `LabelMismatch` is thrown.\\n\\nThis process doesn't change the order of the labels and their corresponding data.\",\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'The data cube.'},\n", + " {'schema': {'type': 'string'},\n", + " 'name': 'dimension',\n", + " 'description': 'The name of the dimension to rename the labels for.'},\n", + " {'schema': {'type': 'array',\n", + " 'items': {'anyOf': [{'type': 'number'}, {'type': 'string'}]}},\n", + " 'name': 'target',\n", + " 'description': 'The new names for the labels. The dimension labels in the data cube are expected to be enumerated, if the parameter `target` is not specified. If a target dimension label already exists in the data cube, a `LabelExists` error is thrown.'},\n", + " {'schema': {'type': 'array',\n", + " 'items': {'anyOf': [{'type': 'number'}, {'type': 'string'}]}},\n", + " 'default': [],\n", + " 'name': 'source',\n", + " 'description': \"The names of the labels as they are currently in the data cube. The array defines an unsorted and potentially incomplete list of labels that should be renamed to the names available in the corresponding array elements in the parameter `target`. If one of the source dimension labels doesn't exist, a `LabelNotAvailable` error is thrown. By default, the array is empty so that the dimension labels in the data cube are expected to be enumerated.\",\n", + " 'optional': True}],\n", + " 'categories': ['cubes'],\n", + " 'links': [{'rel': 'example',\n", + " 'href': 'https://processes.openeo.org/1.0.0/examples/rename-enumerated-labels.json',\n", + " 'type': 'application/json',\n", + " 'title': 'Rename enumerated labels'}],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'The data cube with the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged, except that for the given dimension the labels change. The old labels can not be referred to any longer. The number of labels remains the same.'},\n", + " 'exceptions': {'LabelsNotEnumerated': {'message': 'The dimension labels are not enumerated.'},\n", + " 'LabelNotAvailable': {'message': 'A label with the specified name does not exist.'},\n", + " 'LabelMismatch': {'message': \"The number of labels in the parameters `source` and `target` don't match.\"},\n", + " 'LabelExists': {'message': 'A label with the specified name exists.'}},\n", + " 'examples': [{'description': 'Renaming the bands from `B1` to `red`, from `B2` to `green` and from `B3` to `blue`.',\n", + " 'arguments': {'data': {'from_parameter': 'data'},\n", + " 'source': ['B1', 'B2', 'B3'],\n", + " 'dimension': 'bands',\n", + " 'target': ['red', 'green', 'blue']},\n", + " 'title': 'Rename named labels'}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'or',\n", + " 'summary': 'Logical OR',\n", + " 'description': 'Checks if **at least one** of the values is true. Evaluates parameter `x` before `y` and stops once the outcome is unambiguous. If a component is `null`, the result will be `null` if the outcome is ambiguous.\\n\\n**Truth table:**\\n\\n```\\na \\\\ b || null | false | true\\n----- || ---- | ----- | ----\\nnull || null | null | true\\nfalse || null | false | true\\ntrue || true | true | true\\n```',\n", + " 'parameters': [{'schema': {'type': ['boolean', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'A boolean value.'},\n", + " {'schema': {'type': ['boolean', 'null']},\n", + " 'name': 'y',\n", + " 'description': 'A boolean value.'}],\n", + " 'categories': ['logic'],\n", + " 'returns': {'schema': {'type': ['boolean', 'null']},\n", + " 'description': 'Boolean result of the logical OR.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': True, 'y': True}, 'returns': True},\n", + " {'arguments': {'x': False, 'y': False}, 'returns': False},\n", + " {'arguments': {'x': True}, 'returns': True},\n", + " {'arguments': {'y': True}, 'returns': True},\n", + " {'arguments': {'x': False}}]},\n", + " {'engine': '[WCPS]',\n", + " 'id': 'e',\n", + " 'summary': \"Euler's number (e)\",\n", + " 'description': 'The real number *e* is a mathematical constant that is the base of the natural logarithm such that *ln(e) = 1*. The numerical value is approximately *2.71828*.',\n", + " 'parameters': [],\n", + " 'categories': ['math > constants', 'math > exponential & logarithmic'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/e.html',\n", + " 'title': 'Mathematical constant e explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': 'number'},\n", + " 'description': \"The numerical value of Euler's number.\"},\n", + " 'exceptions': {}},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'max',\n", + " 'summary': 'Maximum value',\n", + " 'description': 'Computes the largest value of an array of numbers, which is is equal to the first element of a sorted (i.e., ordered) version the array.\\n\\nAn array without non-`null` elements resolves always with `null`.',\n", + " 'parameters': [{'schema': {'type': 'array',\n", + " 'items': {'type': ['number', 'null']}},\n", + " 'name': 'data',\n", + " 'description': 'An array of numbers.'},\n", + " {'schema': {'type': 'boolean'},\n", + " 'default': True,\n", + " 'name': 'ignore_nodata',\n", + " 'description': 'Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that `null` is returned if any value is such a value.',\n", + " 'optional': True}],\n", + " 'categories': ['math', 'reducer'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/Maximum.html',\n", + " 'title': 'Maximum explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The maximum value.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'data': [1, 0, 3, 2]}, 'returns': 3},\n", + " {'arguments': {'data': [5, 2.5, None, -0.7]}, 'returns': 5},\n", + " {'arguments': {'data': [1, 0, 3, None, 2], 'ignore_nodata': False}},\n", + " {'description': 'The input array is empty: return `null`.',\n", + " 'arguments': {'data': []}}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'apply',\n", + " 'summary': 'Apply a process to each pixel',\n", + " 'description': 'Applies a *unary* process to each pixel value in the data cube (i.e. a local operation). A unary process takes a single value and returns a single value, for example ``abs()`` or ``linear_scale_range()``. In contrast, the process ``apply_dimension()`` applies a process to all pixel values along a particular dimension.',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A data cube.'},\n", + " {'schema': {'subtype': 'process-graph',\n", + " 'type': 'object',\n", + " 'parameters': [{'schema': {'description': 'Any data type.'},\n", + " 'name': 'x',\n", + " 'description': 'The value to process.'},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'context',\n", + " 'description': 'Additional data passed by the user.',\n", + " 'optional': True}]},\n", + " 'name': 'process',\n", + " 'description': 'A unary process to be applied on each value, may consist of multiple sub-processes.'},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'context',\n", + " 'description': 'Additional data to be passed to the process.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes'],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A data cube with the newly computed values and the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[WCPS]',\n", + " 'id': 'run_udf',\n", + " 'summary': 'Run an UDF',\n", + " 'description': \"Runs an UDF in one of the supported runtime environments.\\n\\nThe process can either:\\n\\n1. load and run a locally stored UDF from a file in the workspace of the authenticated user. The path to the UDF file must be relative to the root directory of the user's workspace.\\n2. fetch and run a remotely stored and published UDF by absolute URI, for example from [openEO Hub](https://hub.openeo.org)).\\n3. run the source code specified inline as string.\\n\\nThe loaded UDF can be executed in several processes such as ``aggregate_spatial()``, ``apply()``, ``apply_dimension()`` and ``reduce_dimension()``. In this case an array is passed instead of a raster data cube. The user must ensure that the data is properly passed as an array so that the UDF can make sense of it.\",\n", + " 'parameters': [{'schema': [{'subtype': 'raster-cube',\n", + " 'title': 'Raster data cube',\n", + " 'type': 'object'},\n", + " {'minItems': 1,\n", + " 'title': 'Array',\n", + " 'type': 'array',\n", + " 'items': {'description': 'Any data type.'}},\n", + " {'description': 'A single value of any data type.',\n", + " 'title': 'Single Value'}],\n", + " 'name': 'data',\n", + " 'description': 'The data to be passed to the UDF as array or raster data cube.'},\n", + " {'schema': [{'subtype': 'uri',\n", + " 'format': 'uri',\n", + " 'description': 'URI to an UDF',\n", + " 'type': 'string'},\n", + " {'subtype': 'file-path',\n", + " 'description': 'Path to an UDF uploaded to the server.',\n", + " 'type': 'string'},\n", + " {'subtype': 'udf-code',\n", + " 'description': 'Source code as string',\n", + " 'type': 'string'}],\n", + " 'name': 'udf',\n", + " 'description': 'Either source code, an absolute URL or a path to an UDF script.'},\n", + " {'schema': {'subtype': 'udf-runtime', 'type': 'string'},\n", + " 'name': 'runtime',\n", + " 'description': 'An UDF runtime identifier available at the back-end.'},\n", + " {'schema': [{'subtype': 'udf-runtime-version', 'type': 'string'},\n", + " {'title': 'Default runtime version', 'type': 'null'}],\n", + " 'name': 'version',\n", + " 'description': 'An UDF runtime version. If set to `null`, the default runtime version specified for each runtime is used.',\n", + " 'optional': True},\n", + " {'schema': {'type': 'object'},\n", + " 'default': {},\n", + " 'name': 'context',\n", + " 'description': 'Additional data such as configuration options that should be passed to the UDF.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes', 'import', 'udf'],\n", + " 'returns': {'schema': [{'subtype': 'raster-cube',\n", + " 'title': 'Raster data cube',\n", + " 'type': 'object'},\n", + " {'description': 'Any data type.', 'title': 'Any'}],\n", + " 'description': 'The data processed by the UDF.\\n\\n* Returns a raster data cube, if a raster data cube is passed for `data`. Details on the dimensions and dimension properties (name, type, labels, reference system and resolution) depend on the UDF.\\n* If an array is passed for `data`, the returned value can be of any data type, but is exactly what the UDF returns.'},\n", + " 'exceptions': {'InvalidVersion': {'message': 'The specified UDF runtime version is not supported.'}}},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'gt',\n", + " 'summary': 'Greater than comparison',\n", + " 'description': 'Compares whether `x` is strictly greater than `y`.\\n\\n**Remarks:**\\n\\n* If any operand is `null`, the return value is `null`.\\n* If any operand is an array or object, the return value is `false`.\\n* If any operand is not a `number` or temporal string (`date`, `time` or `date-time`), the process returns `false`.\\n* Temporal strings can *not* be compared based on their string representation due to the time zone / time-offset representations.',\n", + " 'parameters': [{'schema': {'description': 'Any data type is allowed.'},\n", + " 'name': 'x',\n", + " 'description': 'First operand.'},\n", + " {'schema': {'description': 'Any data type is allowed.'},\n", + " 'name': 'y',\n", + " 'description': 'Second operand.'}],\n", + " 'categories': ['comparison'],\n", + " 'returns': {'schema': {'type': ['boolean', 'null']},\n", + " 'description': '`true` if `x` is strictly greater than `y` or `null` if any operand is `null`, otherwise `false`.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 1}},\n", + " {'arguments': {'x': 0, 'y': 0}, 'returns': False},\n", + " {'arguments': {'x': 2, 'y': 1}, 'returns': True},\n", + " {'arguments': {'x': -0.5, 'y': -0.6}, 'returns': True},\n", + " {'arguments': {'x': '00:00:00Z', 'y': '00:00:00+01:00'}, 'returns': True},\n", + " {'arguments': {'x': '1950-01-01T00:00:00Z', 'y': '2018-01-01T12:00:00Z'},\n", + " 'returns': False},\n", + " {'arguments': {'x': '2018-01-01T12:00:00+00:00',\n", + " 'y': '2018-01-01T12:00:00Z'},\n", + " 'returns': False},\n", + " {'arguments': {'x': True, 'y': 0}, 'returns': False},\n", + " {'arguments': {'x': True, 'y': False}, 'returns': False}]},\n", + " {'engine': '[WCPS]',\n", + " 'id': 'cosh',\n", + " 'summary': 'Hyperbolic cosine',\n", + " 'description': 'Computes the hyperbolic cosine of `x`.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'An angle in radians.'}],\n", + " 'categories': ['math > trigonometric'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/HyperbolicCosine.html',\n", + " 'title': 'Hyperbolic cosine explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed hyperbolic cosine of `x`.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 0}, 'returns': 1}]},\n", + " {'engine': '[WCPS]',\n", + " 'id': 'arctan',\n", + " 'summary': 'Inverse tangent',\n", + " 'description': 'Computes the arc tangent of `x`. The arc tangent is the inverse function of the tangent so that *arctan(tan(x)) = x*.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'A number.'}],\n", + " 'categories': ['math > trigonometric'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/InverseTangent.html',\n", + " 'title': 'Inverse tangent explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed angle in radians.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 0}, 'returns': 0}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'absolute',\n", + " 'summary': 'Absolute value',\n", + " 'description': 'Computes the absolute value of a real number `x`, which is the \"unsigned\" portion of x and often denoted as *|x|*.\\n\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'A number.'}],\n", + " 'categories': ['math'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/AbsoluteValue.html',\n", + " 'title': 'Absolute value explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null'], 'minimum': 0},\n", + " 'description': 'The computed absolute value.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 0}, 'returns': 0},\n", + " {'arguments': {'x': 3.5}, 'returns': 3.5},\n", + " {'arguments': {'x': -0.4}, 'returns': 0.4},\n", + " {'arguments': {'x': -3.5}, 'returns': 3.5}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'mean',\n", + " 'summary': 'Arithmetic mean (average)',\n", + " 'description': 'The arithmetic mean of an array of numbers is the quantity commonly called the average. It is defined as the sum of all elements divided by the number of elements.\\n\\nAn array without non-`null` elements resolves always with `null`.',\n", + " 'parameters': [{'schema': {'type': 'array',\n", + " 'items': {'type': ['number', 'null']}},\n", + " 'name': 'data',\n", + " 'description': 'An array of numbers.'},\n", + " {'schema': {'type': 'boolean'},\n", + " 'default': True,\n", + " 'name': 'ignore_nodata',\n", + " 'description': 'Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that `null` is returned if any value is such a value.',\n", + " 'optional': True}],\n", + " 'categories': ['math', 'reducer'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/ArithmeticMean.html',\n", + " 'title': 'Arithmetic mean explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed arithmetic mean.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'data': [1, 0, 3, 2]}, 'returns': 1.5},\n", + " {'arguments': {'data': [9, 2.5, None, -2.5]}, 'returns': 3},\n", + " {'arguments': {'data': [1, None], 'ignore_nodata': False}},\n", + " {'description': 'The input array is empty: return `null`.',\n", + " 'arguments': {'data': []}},\n", + " {'description': 'The input array has only `null` elements: return `null`.',\n", + " 'arguments': {'data': [None, None]}}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'normalized_difference',\n", + " 'summary': 'Normalized difference',\n", + " 'description': 'Computes the normalized difference for two bands. The normalized difference is computed as *(x - y) / (x + y)*.\\n\\nThis process could be used for a number of remote sensing indices such as:\\n\\n* [NDVI](https://eos.com/ndvi/): `x` = NIR band, `y` = red band\\n* [NDWI](https://eos.com/ndwi/): `x` = NIR band, `y` = SWIR band\\n* [NDSI](https://eos.com/ndsi/): `x` = green band, `y` = SWIR band\\n\\nSome back-ends may have native processes such as ``ndvi()`` available for convenience.',\n", + " 'parameters': [{'schema': {'type': 'number'},\n", + " 'name': 'x',\n", + " 'description': 'The value for the first band.'},\n", + " {'schema': {'type': 'number'},\n", + " 'name': 'y',\n", + " 'description': 'The value for the second band.'}],\n", + " 'categories': ['math > indices', 'vegetation indices'],\n", + " 'links': [{'rel': 'related',\n", + " 'href': 'https://eos.com/ndvi/',\n", + " 'title': 'NDVI explained by EOS'},\n", + " {'rel': 'related',\n", + " 'href': 'https://eos.com/ndwi/',\n", + " 'title': 'NDWI explained by EOS'},\n", + " {'rel': 'related',\n", + " 'href': 'https://eos.com/ndsi/',\n", + " 'title': 'NDSI explained by EOS'}],\n", + " 'returns': {'schema': {'maximum': 1, 'type': 'number', 'minimum': -1},\n", + " 'description': 'The computed normalized difference.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'filter_spatial',\n", + " 'summary': 'Spatial filter using geometries',\n", + " 'description': 'Limits the data cube over the spatial dimensions to the specified geometries.\\n\\n- For **polygons**, the filter retains a pixel in the data cube if the point at the pixel center intersects with at least one of the polygons (as defined in the Simple Features standard by the OGC).\\n- For **points**, the process considers the closest pixel center.\\n- For **lines** (line strings), the process considers all the pixels whose centers are closest to at least one point on the line.\\n\\nMore specifically, pixels outside of the bounding box of the given geometry will not be available after filtering. All pixels inside the bounding box that are not retained will be set to `null` (no data).',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A data cube.'},\n", + " {'schema': {'subtype': 'geojson', 'type': 'object'},\n", + " 'name': 'geometries',\n", + " 'description': 'One or more geometries used for filtering, specified as GeoJSON.'}],\n", + " 'categories': ['cubes', 'filter'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'https://openeo.org/documentation/1.0/datacubes.html#filter',\n", + " 'title': 'Filters explained in the openEO documentation'},\n", + " {'rel': 'about',\n", + " 'href': 'http://www.opengeospatial.org/standards/sfa',\n", + " 'title': 'Simple Features standard by the OGC'}],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A data cube restricted to the specified geometries. The dimensions and dimension properties (name, type, labels, reference system and resolution) remain unchanged, except that the spatial dimensions have less (or the same) dimension labels.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'geocode',\n", + " 'summary': 'Geocoding SAR data',\n", + " 'description': 'Geocoding SAR data given the desired projection and resolution. The resolution can be either 10m or 20m or 60m, to align the with Sentinel-2 grid.',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A raster data cube with exactly two horizontal spatial dimensions and an arbitrary number of additional dimensions. The process is applied to all additional dimensions individually.'},\n", + " {'default': 4326,\n", + " 'name': 'crs',\n", + " 'description': 'Coordinate reference system of the extent, specified as as [EPSG code](http://www.epsg-registry.org/), [WKT2 (ISO 19162) string](http://docs.opengeospatial.org/is/18-010r7/18-010r7.html) or [PROJ definition (deprecated)](https://proj.org/usage/quickstart.html). Defaults to `4326` (EPSG code 4326) unless the client explicitly requests a different coordinate reference system.',\n", + " 'anyOf': [{'examples': [3857],\n", + " 'subtype': 'epsg-code',\n", + " 'title': 'EPSG Code',\n", + " 'type': 'integer',\n", + " 'minimum': 1000},\n", + " {'subtype': 'wkt2-definition', 'title': 'WKT2', 'type': 'string'},\n", + " {'subtype': 'proj-definition',\n", + " 'deprecated': True,\n", + " 'title': 'PROJ definition',\n", + " 'type': 'string'}]},\n", + " {'schema': {'type': 'integer', 'enum': [10, 20, 60]},\n", + " 'default': '10',\n", + " 'name': 'resolution',\n", + " 'description': 'Desired reolution in meters.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes', 'aggregate & resample'],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A data cube with the projected values in the requested projection.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'fit_curve',\n", + " 'summary': 'Curve fitting',\n", + " 'description': 'Use non-linear least squares to fit a model function `y = f(x, parameters)` to data.\\n\\nThe process throws an `InvalidValues` exception if invalid values are encountered. Invalid values are finite numbers (see also ``is_valid()``).',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A data cube.'},\n", + " {'schema': [{'minItems': 1, 'type': 'array', 'items': {'type': 'number'}},\n", + " {'subtype': 'raster-cube',\n", + " 'title': 'Data Cube with optimal values from a previous result of this process.',\n", + " 'type': 'object'}],\n", + " 'name': 'parameters',\n", + " 'description': 'Defined the number of parameters for the model function and provides an initial guess for them. At least one parameter is required.'},\n", + " {'schema': {'subtype': 'process-graph',\n", + " 'returns': {'schema': {'type': 'number'},\n", + " 'description': 'The computed value `y` value for the given independent variable `x` and the parameters.'},\n", + " 'type': 'object',\n", + " 'parameters': [{'schema': {'type': ['number']},\n", + " 'name': 'x',\n", + " 'description': 'The value for the independent variable `x`.'},\n", + " {'schema': {'minItems': 1, 'type': 'array', 'items': {'type': 'number'}},\n", + " 'name': 'parameters',\n", + " 'description': 'The parameters for the model function, contains at least one parameter.'}]},\n", + " 'name': 'function',\n", + " 'description': 'The model function. It must take the parameters to fit as array through the first argument and the independent variable `x` as the second argument.\\n\\nIt is recommended to store the model function as a user-defined process on the back-end to be able to re-use the model function with the computed optimal values for the parameters afterwards.'},\n", + " {'schema': {'type': 'string'},\n", + " 'name': 'dimension',\n", + " 'description': 'The name of the dimension for curve fitting. Must be a dimension with labels that have a order (i.e. numerical labels or a temporal dimension). Fails with a `DimensionNotAvailable` exception if the specified dimension does not exist.'}],\n", + " 'categories': ['cubes', 'math'],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A data cube with the optimal values for the parameters.'},\n", + " 'exceptions': {'DimensionNotAvailable': {'message': 'A dimension with the specified name does not exist.'},\n", + " 'InvalidValues': {'message': 'At least one of the values is not a finite number.'}}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'pi',\n", + " 'summary': 'Pi (??)',\n", + " 'description': 'The real number Pi (??) is a mathematical constant that is the ratio of the circumference of a circle to its diameter. The numerical value is approximately *3.14159*.',\n", + " 'parameters': [],\n", + " 'categories': ['math > constants', 'math > trigonometric'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/Pi.html',\n", + " 'title': 'Mathematical constant Pi explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': 'number'},\n", + " 'description': 'The numerical value of Pi.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'filter_bbox',\n", + " 'summary': 'Spatial filter using a bounding box',\n", + " 'description': 'Limits the data cube to the specified bounding box.\\n\\nThe filter retains a pixel in the data cube if the point at the pixel center intersects with the bounding box (as defined in the Simple Features standard by the OGC).',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A data cube.'},\n", + " {'schema': {'subtype': 'bounding-box',\n", + " 'type': 'object',\n", + " 'required': ['west', 'south', 'east', 'north'],\n", + " 'properties': {'east': {'description': 'East (upper right corner, coordinate axis 1).',\n", + " 'type': 'number'},\n", + " 'south': {'description': 'South (lower left corner, coordinate axis 2).',\n", + " 'type': 'number'},\n", + " 'crs': {'default': 4326,\n", + " 'description': 'Coordinate reference system of the extent, specified as as [EPSG code](http://www.epsg-registry.org/), [WKT2 (ISO 19162) string](http://docs.opengeospatial.org/is/18-010r7/18-010r7.html) or [PROJ definition (deprecated)](https://proj.org/usage/quickstart.html). Defaults to `4326` (EPSG code 4326) unless the client explicitly requests a different coordinate reference system.',\n", + " 'anyOf': [{'examples': [3857],\n", + " 'subtype': 'epsg-code',\n", + " 'title': 'EPSG Code',\n", + " 'type': 'integer',\n", + " 'minimum': 1000},\n", + " {'subtype': 'wkt2-definition', 'title': 'WKT2', 'type': 'string'},\n", + " {'subtype': 'proj-definition',\n", + " 'deprecated': True,\n", + " 'title': 'PROJ definition',\n", + " 'type': 'string'}]},\n", + " 'north': {'description': 'North (upper right corner, coordinate axis 2).',\n", + " 'type': 'number'},\n", + " 'west': {'description': 'West (lower left corner, coordinate axis 1).',\n", + " 'type': 'number'},\n", + " 'base': {'description': 'Base (optional, lower left corner, coordinate axis 3).',\n", + " 'type': ['number', 'null']},\n", + " 'height': {'description': 'Height (optional, upper right corner, coordinate axis 3).',\n", + " 'type': ['number', 'null']}}},\n", + " 'name': 'extent',\n", + " 'description': 'A bounding box, which may include a vertical axis (see `base` and `height`).'}],\n", + " 'categories': ['cubes', 'filter'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'https://proj.org/usage/projections.html',\n", + " 'title': 'PROJ parameters for cartographic projections'},\n", + " {'rel': 'about',\n", + " 'href': 'http://www.epsg-registry.org',\n", + " 'title': 'Official EPSG code registry'},\n", + " {'rel': 'about',\n", + " 'href': 'http://www.epsg.io',\n", + " 'title': 'Unofficial EPSG code database'},\n", + " {'rel': 'about',\n", + " 'href': 'http://www.opengeospatial.org/standards/sfa',\n", + " 'title': 'Simple Features standard by the OGC'}],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A data cube restricted to the bounding box. The dimensions and dimension properties (name, type, labels, reference system and resolution) remain unchanged, except that the spatial dimensions have less (or the same) dimension labels.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'clip',\n", + " 'summary': 'Clip a value between a minimum and a maximum',\n", + " 'description': 'Clips a number between specified minimum and maximum values. A value larger than the maximum value is set to the maximum value, a value lower than the minimum value is set to the minimum value.\\n\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'A number.'},\n", + " {'schema': {'type': 'number'},\n", + " 'name': 'min',\n", + " 'description': 'Minimum value. If the value is lower than this value, the process will return the value of this parameter.'},\n", + " {'schema': {'type': 'number'},\n", + " 'name': 'max',\n", + " 'description': 'Maximum value. If the value is greater than this value, the process will return the value of this parameter.'}],\n", + " 'categories': ['math'],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The value clipped to the specified range.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'min': -1, 'max': 1, 'x': -5}, 'returns': -1},\n", + " {'arguments': {'min': 1, 'max': 10, 'x': 10.001}, 'returns': 10},\n", + " {'arguments': {'min': 0, 'max': 0.02, 'x': 1e-06}, 'returns': 1e-06},\n", + " {'arguments': {'min': 0, 'max': 1}}]}]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "conn.list_processes()" + ] + }, + { + "cell_type": "markdown", + "id": "df5eb0e8-8ee1-449b-bc86-dcbd8035f895", + "metadata": {}, + "source": [ + "## Select the AOI\n", + "Use the rectangle selection tool to select the area of interest" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "8711d5ce-4958-4fc1-a457-06dd3d5e1b88", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "55ab6c17c42c40b1beb45e36dfe1fade", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map(center=[46.6, 11.3], controls=(ZoomControl(options=['position', 'zoom_in_text', 'zoom_in_title', 'zoom_out…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "center = [46.6, 11.3]\n", + "zoom = 10\n", + "\n", + "eoMap = openeoMap(center,zoom)\n", + "eoMap.map" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "id": "68ebc058-7c34-4362-82eb-076f969b641b", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Coordinates selected from map: \n", + " west 10.993881 \n", + " east 11.306992 \n", + " south 46.568444 \n", + " north 46.739061\n" + ] + } + ], + "source": [ + "bbox = eoMap.getBbox()\n", + "print(\"Coordinates selected from map:\",'\\n west',bbox[0],'\\n east',bbox[2],'\\n south',bbox[1],'\\n north',bbox[3])" + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "id": "3dfa661f-c493-4e85-8a71-dfe70eecb6fe", + "metadata": {}, + "outputs": [], + "source": [ + "spatial_extent = {'west':bbox[0],'east':bbox[2],'south':bbox[1],'north':bbox[3]}" + ] + }, + { + "cell_type": "markdown", + "id": "a401ea5c-7c6c-40e8-bb4e-a133156f9f32", + "metadata": {}, + "source": [ + "# SAR processing" + ] + }, + { + "cell_type": "markdown", + "id": "a1a06e33-674d-4890-a3d9-be0d06c54ceb", + "metadata": {}, + "source": [ + "\n", + "### Load the datacube" + ] + }, + { + "cell_type": "code", + "execution_count": 273, + "id": "d0775a26-949c-4210-b737-27786a8490f1", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Authenticated using refresh token.\n" + ] + } + ], + "source": [ + "conn = openeo.connect(openeoHost).authenticate_oidc(client_id=\"openEO_PKCE\")\n", + "\n", + "collection = 'SAR2Cube_SInCohMap_S1_L0_117_ASC_SOUTH_TYROL'\n", + "temporal_extent = [\"2018-06-01T00:00:00.000Z\", \"2018-06-30T00:00:00.000Z\"]\n", + "\n", + "S1_slant_range = conn.load_collection(collection,spatial_extent=spatial_extent,temporal_extent=temporal_extent)" + ] + }, + { + "cell_type": "markdown", + "id": "a1d95578-e9bd-4f92-b807-9ba1d1b9d0ad", + "metadata": {}, + "source": [ + "Compute the intensity" + ] + }, + { + "cell_type": "code", + "execution_count": 274, + "id": "bb999d3f-bae8-4036-ad2d-500b8139f3df", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "i_VV = S1_slant_range.band('i_VV')\n", + "q_VV = S1_slant_range.band('q_VV')\n", + "S1_INT = (i_VV**2+q_VV**2)\n", + "S1_INT_VV = S1_INT.add_dimension(name=\"bands\",label=\"VV\")" + ] + }, + { + "cell_type": "markdown", + "id": "ed1fa191-24d2-44c3-a6ad-6c5cb529ef70", + "metadata": {}, + "source": [ + "Compute the Multi Look over the intensity and convert from linear to dB." + ] + }, + { + "cell_type": "code", + "execution_count": 275, + "id": "16506231-9885-471a-b5cc-3695aa60bc01", + "metadata": {}, + "outputs": [], + "source": [ + "range_looks = 19\n", + "azimuth_looks = 4" + ] + }, + { + "cell_type": "code", + "execution_count": 276, + "id": "737fc78a-bc12-4efb-b859-64b6e974fb7a", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "args_aggregate_spatial_window = {\"data\": THIS, \"boundary\": \"trim\", \"size\": [range_looks,azimuth_looks],\"reducer\":S1_INT_VV._get_callback(mean,parent_parameters=[\"data\"])}\n", + "S1_INT_VV_ML = S1_INT_VV.process(\"aggregate_spatial_window\",args_aggregate_spatial_window)\n", + "S1_INT_VV_ML = S1_INT_VV_ML.apply(lambda x: 10*log(x,base=10))" + ] + }, + { + "cell_type": "markdown", + "id": "1017563f-0e84-4a60-ac5d-2781cc2fbcd3", + "metadata": {}, + "source": [ + "Compute the same Multi Look over the coordinate grids for geocoding" + ] + }, + { + "cell_type": "code", + "execution_count": 277, + "id": "ab3b8435-18a1-4d38-97e2-50b44bbc7428", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "lat_lon = S1_slant_range.filter_bands(['grid_lon','grid_lat'])" + ] + }, + { + "cell_type": "code", + "execution_count": 278, + "id": "43c3bd8d-1053-41ec-b8a0-ba46f2d396a0", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "args_aggregate_spatial_window = {\"data\": THIS, \"boundary\": \"trim\", \"size\": [range_looks,azimuth_looks],\"reducer\":lat_lon._get_callback(mean,parent_parameters=[\"data\"])}\n", + "lat_lon_ML = lat_lon.process(\"aggregate_spatial_window\",args_aggregate_spatial_window)" + ] + }, + { + "cell_type": "markdown", + "id": "20618d81-a393-4b92-af12-ac5872eaafe9", + "metadata": {}, + "source": [ + "Compute the Multi Look of Local Incidence Angle and Digital Elevation Model for radar masking" + ] + }, + { + "cell_type": "code", + "execution_count": 279, + "id": "058bfd1b-9e0a-4e6a-a535-b80a8df6b21b", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "dem_lia = S1_slant_range.filter_bands(['DEM','LIA'])" + ] + }, + { + "cell_type": "code", + "execution_count": 280, + "id": "e9f13c50-31d8-4b63-80c5-407337d90c51", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "args_aggregate_spatial_window = {\"data\": THIS, \"boundary\": \"trim\", \"size\": [range_looks,azimuth_looks],\"reducer\":dem_lia._get_callback(mean,parent_parameters=[\"data\"])}\n", + "dem_lia_ML = dem_lia.process(\"aggregate_spatial_window\",args_aggregate_spatial_window)" + ] + }, + { + "cell_type": "markdown", + "id": "f41f5073-2e0c-411a-baa8-82f4fecee522", + "metadata": {}, + "source": [ + "Remove the temporal dimension, not required for radar_mask" + ] + }, + { + "cell_type": "code", + "execution_count": 281, + "id": "af50df57-98ef-41f7-8df7-5084001d0faa", + "metadata": {}, + "outputs": [], + "source": [ + "dem_lia_ML = dem_lia_ML.reduce_dimension(reducer=mean, dimension='DATE')" + ] + }, + { + "cell_type": "markdown", + "id": "b0705bfd-069e-4777-b7ad-c43635306c5a", + "metadata": {}, + "source": [ + "Use the **radar_mask** process inside an **apply_dimension** process.\n", + "\n", + "**apply_dimension** is used when we need to apply a process over a specific dimension, similarly to reduce_dimension, but the specified dimension is not reduced/removed. Instead, the number of output dimension labels (i.e. bands) can grow or shrink." + ] + }, + { + "cell_type": "markdown", + "id": "8c62cb86-7ebb-4103-bd94-f2414cb2f1dc", + "metadata": {}, + "source": [ + "Use the list_processes method and search for radar_mask to get more details on I/O of this process." + ] + }, + { + "cell_type": "code", + "execution_count": 282, + "id": "ed829262-5e5e-481f-a8a9-7e8a2a883019", + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " " + ], + "text/plain": [ + "[{'engine': '[ODC_DASK]',\n", + " 'id': 'dimension_labels',\n", + " 'summary': 'Get the dimension labels',\n", + " 'description': 'Returns all labels for a dimension in the data cube. The labels have the same order as in the data cube.',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'The data cube.'},\n", + " {'schema': {'type': 'string'},\n", + " 'name': 'dimension',\n", + " 'description': 'The name of the dimension to get the labels for.'}],\n", + " 'categories': ['cubes'],\n", + " 'returns': {'schema': {'type': 'array',\n", + " 'items': {'anyOf': [{'type': 'number'}, {'type': 'string'}]}},\n", + " 'description': 'The labels as array.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'ln',\n", + " 'summary': 'Natural logarithm',\n", + " 'description': 'The natural logarithm is the logarithm to the base *e* of the number `x`, which equals to using the *log* process with the base set to *e*. The natural logarithm is the inverse function of taking *e* to the power x.\\n\\nThe no-data value `null` is passed through.\\n\\nThe computations follow [IEEE Standard 754](https://ieeexplore.ieee.org/document/8766229) whenever the processing environment supports it. Therefore, `ln(0)` results in ±infinity if the processing environment supports it or otherwise an error is thrown.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'A number to compute the natural logarithm for.'}],\n", + " 'categories': ['math > exponential & logarithmic'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/NaturalLogarithm.html',\n", + " 'title': 'Natural logarithm explained by Wolfram MathWorld'},\n", + " {'rel': 'about',\n", + " 'href': 'https://ieeexplore.ieee.org/document/8766229',\n", + " 'title': 'IEEE Standard 754-2019 for Floating-Point Arithmetic'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed natural logarithm.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 1}, 'returns': 0}]},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'coherence',\n", + " 'summary': 'Compute the complex coherence with SAR data',\n", + " 'description': 'Compute the complex coherence with SAR data, given the specified time delta.',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A raster data cube with exactly two horizontal spatial dimensions and an arbitrary number of additional dimensions. The process is applied to all additional dimensions individually.'},\n", + " {'schema': {'type': 'integer',\n", + " 'enum': [6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 66, 72, 78, 84, 90, 96]},\n", + " 'default': '6',\n", + " 'name': 'timedelta',\n", + " 'description': 'Temporal delta in days between acquisitions on which we want to compute coherence.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes', 'math'],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A data cube with the projected values in the requested projection.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[WCPS]',\n", + " 'id': 'cos',\n", + " 'summary': 'Cosine',\n", + " 'description': 'Computes the cosine of `x`.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'An angle in radians.'}],\n", + " 'categories': ['math > trigonometric'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/Cosine.html',\n", + " 'title': 'Cosine explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed cosine of `x`.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 0}, 'returns': 1}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'lt',\n", + " 'summary': 'Less than comparison',\n", + " 'description': 'Compares whether `x` is strictly less than `y`.\\n\\n**Remarks:**\\n\\n* If any operand is `null`, the return value is `null`.\\n* If any operand is an array or object, the return value is `false`.\\n* If any operand is not a `number` or temporal string (`date`, `time` or `date-time`), the process returns `false`.\\n* Temporal strings can *not* be compared based on their string representation due to the time zone / time-offset representations.',\n", + " 'parameters': [{'schema': {'description': 'Any data type is allowed.'},\n", + " 'name': 'x',\n", + " 'description': 'First operand.'},\n", + " {'schema': {'description': 'Any data type is allowed.'},\n", + " 'name': 'y',\n", + " 'description': 'Second operand.'}],\n", + " 'categories': ['comparison'],\n", + " 'returns': {'schema': {'type': ['boolean', 'null']},\n", + " 'description': '`true` if `x` is strictly less than `y`, `null` if any operand is `null`, otherwise `false`.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 1}},\n", + " {'arguments': {'x': 0, 'y': 0}, 'returns': False},\n", + " {'arguments': {'x': 1, 'y': 2}, 'returns': True},\n", + " {'arguments': {'x': -0.5, 'y': -0.6}, 'returns': False},\n", + " {'arguments': {'x': '00:00:00+01:00', 'y': '00:00:00Z'}, 'returns': True},\n", + " {'arguments': {'x': '1950-01-01T00:00:00Z', 'y': '2018-01-01T12:00:00Z'},\n", + " 'returns': True},\n", + " {'arguments': {'x': '2018-01-01T12:00:00+00:00',\n", + " 'y': '2018-01-01T12:00:00Z'},\n", + " 'returns': False},\n", + " {'arguments': {'x': 0, 'y': True}, 'returns': False},\n", + " {'arguments': {'x': False, 'y': True}, 'returns': False}]},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'aggregate_spatial_window',\n", + " 'summary': 'Zonal statistics for rectangular windows',\n", + " 'description': 'Aggregates statistics over the horizontal spatial dimensions (axes `x` and `y`) of the data cube.\\n\\nThe pixel grid for the axes `x` and `y` is divided into non-overlapping windows with the size specified in the parameter `size`. If the number of values for the axes `x` and `y` is not a multiple of the corresponding window size, the behaviour specified in the parameters `boundary` and `align` is applied.\\nFor each of these windows, the reducer process computes the result.',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A raster data cube with exactly two horizontal spatial dimensions and an arbitrary number of additional dimensions. The process is applied to all additional dimensions individually.'},\n", + " {'schema': {'subtype': 'process-graph',\n", + " 'type': 'object',\n", + " 'parameters': [{'schema': {'type': 'array',\n", + " 'items': {'description': 'Any data type.'}},\n", + " 'name': 'data',\n", + " 'description': 'An array with elements of any type.'},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'context',\n", + " 'description': 'Additional data passed by the user.',\n", + " 'optional': True}]},\n", + " 'name': 'reducer',\n", + " 'description': \"A reducer to be applied on the list of values, which contain all pixels covered by the window. A reducer is a single process such as ``mean()`` or a set of processes, which computes a single value for a list of values, see the category 'reducer' for such processes.\"},\n", + " {'schema': {'minItems': 2,\n", + " 'maxItems': 2,\n", + " 'type': 'array',\n", + " 'items': {'type': 'integer', 'minimum': 1}},\n", + " 'name': 'size',\n", + " 'description': 'Window sizes in pixels along the horizontal spatial dimensions.\\n\\nThe first value corresponds to the `x` axis, the second values corresponds to the `y` axis.'},\n", + " {'schema': {'type': 'string', 'enum': ['pad', 'trim']},\n", + " 'default': 'pad',\n", + " 'name': 'boundary',\n", + " 'description': 'Behaviour to apply if the number of values for the axes `x` and `y` is not a multiple of the corresponding value in the `size` parameter. Options are:\\n\\n- `pad` (default): pad the data cube with the no-data value `null` to fit the required window size.\\n\\n- `trim`: trim the data cube to fit the required window size.\\n\\nSet the parameter `align` to specifies to which corner the data is aligned to.',\n", + " 'optional': True},\n", + " {'schema': {'type': 'string',\n", + " 'enum': ['lower-left', 'upper-left', 'lower-right', 'upper-right']},\n", + " 'default': 'upper-left',\n", + " 'name': 'align',\n", + " 'description': 'If the data requires padding or trimming (see parameter `boundary`), specifies to which corner of the spatial extent the data is aligned to. For example, if the data is aligned to the upper left, the process pads/trims at the lower-right.',\n", + " 'optional': True},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'context',\n", + " 'description': 'Additional data to be passed to the reducer.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes', 'aggregate & resample'],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A data cube with the newly computed values and the same dimensions.\\n\\nThe resolution will change depending on the chosen values for the `size` and `boundary` parameter. It usually decreases for the dimensions which have the corresponding parameter `size` set to values greater than 1.\\n\\nThe dimension labels will be set to the coordinate at the center of the window. The other dimension properties (name, type and reference system) remain unchanged.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[WCPS]',\n", + " 'id': 'arccos',\n", + " 'summary': 'Inverse cosine',\n", + " 'description': 'Computes the arc cosine of `x`. The arc cosine is the inverse function of the cosine so that *arccos(cos(x)) = x*.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'A number.'}],\n", + " 'categories': ['math > trigonometric'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/InverseCosine.html',\n", + " 'title': 'Inverse cosine explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed angle in radians.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 1}, 'returns': 0}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'merge_cubes',\n", + " 'summary': 'Merging two data cubes',\n", + " 'description': 'The data cubes have to be compatible. A merge operation without overlap should be reversible with (a set of) filter operations for each of the two cubes. The process performs the join on overlapping dimensions, with the same name and type.\\n\\nAn overlapping dimension has the same name, type, reference system and resolution in both dimensions, but can have different labels. One of the dimensions can have different labels, for all other dimensions the labels must be equal. If data overlaps, the parameter `overlap_resolver` must be specified to resolve the overlap.\\n\\n**Examples for merging two data cubes:**\\n\\n1. Data cubes with the dimensions `x`, `y`, `t` and `bands` have the same dimension labels in `x`,`y` and `t`, but the labels for the dimension `bands` are `B1` and `B2` for the first cube and `B3` and `B4`. An overlap resolver is *not needed*. The merged data cube has the dimensions `x`, `y`, `t` and `bands` and the dimension `bands` has four dimension labels: `B1`, `B2`, `B3`, `B4`.\\n2. Data cubes with the dimensions `x`, `y`, `t` and `bands` have the same dimension labels in `x`,`y` and `t`, but the labels for the dimension `bands` are `B1` and `B2` for the first data cube and `B2` and `B3` for the second. An overlap resolver is *required* to resolve overlap in band `B2`. The merged data cube has the dimensions `x`, `y`, `t` and `bands` and the dimension `bands` has three dimension labels: `B1`, `B2`, `B3`.\\n3. Data cubes with the dimensions `x`, `y` and `t` have the same dimension labels in `x`,`y` and `t`. There are two options:\\n 1. Keep the overlapping values separately in the merged data cube: An overlap resolver is *not needed*, but for each data cube you need to add a new dimension using ``add_dimension()``. The new dimensions must be equal, except that the labels for the new dimensions must differ by name. The merged data cube has the same dimensions and labels as the original data cubes, plus the dimension added with ``add_dimension()``, which has the two dimension labels after the merge.\\n 2. Combine the overlapping values into a single value: An overlap resolver is *required* to resolve the overlap for all pixels. The merged data cube has the same dimensions and labels as the original data cubes, but all pixel values have been processed by the overlap resolver.\\n4. Merging a data cube with dimensions `x`, `y`, `t` with another cube with dimensions `x`, `y` will join on the `x`, `y` dimension, so the lower dimension cube is merged with each time step in the higher dimensional cube. This can for instance be used to apply a digital elevation model to a spatiotemporal data cube.',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'cube1',\n", + " 'description': 'The first data cube.'},\n", + " {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'cube2',\n", + " 'description': 'The second data cube.'},\n", + " {'schema': {'subtype': 'process-graph',\n", + " 'type': 'object',\n", + " 'parameters': [{'schema': {'description': 'Any data type.'},\n", + " 'name': 'x',\n", + " 'description': 'The first value.'},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'y',\n", + " 'description': 'The second value.'},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'context',\n", + " 'description': 'Additional data passed by the user.',\n", + " 'optional': True}]},\n", + " 'name': 'overlap_resolver',\n", + " 'description': 'A reduction operator that resolves the conflict if the data overlaps. The reducer must return a value of the same data type as the input values are. The reduction operator may be a single process such as ``multiply()`` or consist of multiple sub-processes. `null` (the default) can be specified if no overlap resolver is required.',\n", + " 'optional': True},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'context',\n", + " 'description': 'Additional data to be passed to the overlap resolver.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'https://en.wikipedia.org/wiki/Reduction_Operator',\n", + " 'title': 'Background information on reduction operators (binary reducers) by Wikipedia'}],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'The merged data cube. See the process description for details regarding the dimensions and dimension properties (name, type, labels, reference system and resolution).'},\n", + " 'exceptions': {'OverlapResolverMissing': {'message': 'Overlapping data cubes, but no overlap resolver has been specified.'}}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'predict_curve',\n", + " 'summary': 'Predict values',\n", + " 'description': 'Predict values using a model function and pre-computed parameters.',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A data cube to predict values for.'},\n", + " {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'parameters',\n", + " 'description': 'A data cube with optimal values from a result of e.g. ``fit_curve()``.'},\n", + " {'schema': {'subtype': 'process-graph',\n", + " 'returns': {'schema': {'type': 'number'},\n", + " 'description': 'The computed value `y` value for the given independent variable `x` and the parameters.'},\n", + " 'type': 'object',\n", + " 'parameters': [{'schema': {'type': ['number']},\n", + " 'name': 'x',\n", + " 'description': 'The value for the independent variable `x`.'},\n", + " {'schema': {'minItems': 1, 'type': 'array', 'items': {'type': 'number'}},\n", + " 'name': 'parameters',\n", + " 'description': 'The parameters for the model function, contains at least one parameter.'}]},\n", + " 'name': 'function',\n", + " 'description': 'The model function. It must take the parameters to fit as array through the first argument and the independent variable `x` as the second argument.\\n\\nIt is recommended to store the model function as a user-defined process on the back-end.'},\n", + " {'schema': {'type': 'string'},\n", + " 'name': 'dimension',\n", + " 'description': 'The name of the dimension for predictions. Fails with a `DimensionNotAvailable` exception if the specified dimension does not exist.'},\n", + " {'schema': [{'type': 'null'},\n", + " {'type': 'array',\n", + " 'items': {'anyOf': [{'type': 'number'},\n", + " {'subtype': 'date', 'format': 'date', 'type': 'string'},\n", + " {'subtype': 'date-time', 'format': 'date-time', 'type': 'string'}]}}],\n", + " 'name': 'labels',\n", + " 'description': 'The labels to predict values for. If no labels are given, predicts values only for no-data (`null`) values in the data cube.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes', 'math'],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A data cube with the predicted values.'},\n", + " 'exceptions': {'DimensionNotAvailable': {'message': 'A dimension with the specified name does not exist.'}}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'add_dimension',\n", + " 'summary': 'Add a new dimension',\n", + " 'description': 'Adds a new named dimension to the data cube.\\n\\nAfterwards, the dimension can be referred to with the specified `name`. If a dimension with the specified name exists, the process fails with a `DimensionExists` exception. The dimension label of the dimension is set to the specified `label`.',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A data cube to add the dimension to.'},\n", + " {'schema': {'type': 'string'},\n", + " 'name': 'name',\n", + " 'description': 'Name for the dimension.'},\n", + " {'schema': [{'type': 'number'}, {'type': 'string'}],\n", + " 'name': 'label',\n", + " 'description': 'A dimension label.'},\n", + " {'schema': {'type': 'string',\n", + " 'enum': ['spatial', 'temporal', 'bands', 'other']},\n", + " 'default': 'other',\n", + " 'name': 'type',\n", + " 'description': 'The type of dimension, defaults to `other`.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes'],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'The data cube with a newly added dimension. The new dimension has exactly one dimension label. All other dimensions remain unchanged.'},\n", + " 'exceptions': {'DimensionExists': {'message': 'A dimension with the specified name already exists.'}}},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'sqrt',\n", + " 'summary': 'Square root',\n", + " 'description': 'Computes the square root of a real number `x`, which is equal to calculating `x` to the power of *0.5*.\\n\\nA square root of x is a number a such that *a^2^ = x*. Therefore, the square root is the inverse function of a to the power of 2, but only for *a >= 0*.\\n\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'A number.'}],\n", + " 'categories': ['math', 'math > exponential & logarithmic'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/SquareRoot.html',\n", + " 'title': 'Square root explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed square root.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 0}, 'returns': 0},\n", + " {'arguments': {'x': 1}, 'returns': 1},\n", + " {'arguments': {'x': 9}, 'returns': 3},\n", + " {'arguments': {}}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'gte',\n", + " 'summary': 'Greater than or equal to comparison',\n", + " 'description': 'Compares whether `x` is greater than or equal to `y`.\\n\\n**Remarks:**\\n\\n* If any operand is `null`, the return value is `null`. Therefore, `gte(null, null)` returns `null` instead of `true`.\\n* If any operand is an array or object, the return value is `false`.\\n* If the operands are not equal (see process ``eq()``) and any of them is not a `number` or temporal string (`date`, `time` or `date-time`), the process returns `false`.\\n* Temporal strings can *not* be compared based on their string representation due to the time zone / time-offset representations.',\n", + " 'parameters': [{'schema': {'description': 'Any data type is allowed.'},\n", + " 'name': 'x',\n", + " 'description': 'First operand.'},\n", + " {'schema': {'description': 'Any data type is allowed.'},\n", + " 'name': 'y',\n", + " 'description': 'Second operand.'}],\n", + " 'categories': ['comparison'],\n", + " 'returns': {'schema': {'type': ['boolean', 'null']},\n", + " 'description': '`true` if `x` is greater than or equal to `y`, `null` if any operand is `null`, otherwise `false`.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 1}},\n", + " {'arguments': {'x': 0, 'y': 0}, 'returns': True},\n", + " {'arguments': {'x': 1, 'y': 2}, 'returns': False},\n", + " {'arguments': {'x': -0.5, 'y': -0.6}, 'returns': True},\n", + " {'arguments': {'x': '00:00:00Z', 'y': '00:00:00+01:00'}, 'returns': True},\n", + " {'arguments': {'x': '1950-01-01T00:00:00Z', 'y': '2018-01-01T12:00:00Z'},\n", + " 'returns': False},\n", + " {'arguments': {'x': '2018-01-01T12:00:00+00:00',\n", + " 'y': '2018-01-01T12:00:00Z'},\n", + " 'returns': True},\n", + " {'arguments': {'x': True, 'y': False}, 'returns': False},\n", + " {'arguments': {'x': [1, 2, 3], 'y': [1, 2, 3]}, 'returns': False}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'neq',\n", + " 'summary': 'Not equal to comparison',\n", + " 'description': 'Compares whether `x` is *not* strictly equal to `y`.\\n\\n**Remarks:**\\n\\n* Data types MUST be checked strictly, for example a string with the content *1* is not equal to the number *1*. Nevertheless, an integer *1* is equal to a floating point number *1.0* as `integer` is a sub-type of `number`.\\n* If any operand is `null`, the return value is `null`. Therefore, `neq(null, null)` returns `null` instead of `false`.\\n* If any operand is an array or object, the return value is `false`.\\n* Strings are expected to be encoded in UTF-8 by default.\\n* Temporal strings MUST be compared differently than other strings and MUST NOT be compared based on their string representation due to different possible representations. For example, the UTC time zone representation `Z` has the same meaning as `+00:00`.',\n", + " 'parameters': [{'schema': {'description': 'Any data type is allowed.'},\n", + " 'name': 'x',\n", + " 'description': 'First operand.'},\n", + " {'schema': {'description': 'Any data type is allowed.'},\n", + " 'name': 'y',\n", + " 'description': 'Second operand.'},\n", + " {'schema': {'type': ['number', 'null']},\n", + " 'name': 'delta',\n", + " 'description': 'Only applicable for comparing two numbers. If this optional parameter is set to a positive non-zero number the non-equality of two numbers is checked against a delta value. This is especially useful to circumvent problems with floating point inaccuracy in machine-based computation.\\n\\nThis option is basically an alias for the following computation: `gt(abs(minus([x, y]), delta)`',\n", + " 'optional': True},\n", + " {'schema': {'type': 'boolean'},\n", + " 'default': True,\n", + " 'name': 'case_sensitive',\n", + " 'description': 'Only applicable for comparing two strings. Case sensitive comparison can be disabled by setting this parameter to `false`.',\n", + " 'optional': True}],\n", + " 'categories': ['texts', 'comparison'],\n", + " 'returns': {'schema': {'type': ['boolean', 'null']},\n", + " 'description': 'Returns `true` if `x` is *not* equal to `y`, `null` if any operand is `null`, otherwise `false`.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 1}},\n", + " {'arguments': {'x': 1, 'y': 1}, 'returns': False},\n", + " {'arguments': {'x': 1, 'y': '1'}, 'returns': True},\n", + " {'arguments': {'x': 0, 'y': False}, 'returns': True},\n", + " {'arguments': {'x': 1.02, 'delta': 0.01, 'y': 1}, 'returns': True},\n", + " {'arguments': {'x': -1, 'delta': 0.01, 'y': -1.001}, 'returns': False},\n", + " {'arguments': {'x': 115, 'delta': 10, 'y': 110}, 'returns': False},\n", + " {'arguments': {'x': 'Test', 'y': 'test'}, 'returns': True},\n", + " {'arguments': {'case_sensitive': False, 'x': 'Test', 'y': 'test'},\n", + " 'returns': False},\n", + " {'arguments': {'case_sensitive': False, 'x': 'Ä', 'y': 'ä'},\n", + " 'returns': False},\n", + " {'arguments': {'x': '00:00:00+00:00', 'y': '00:00:00Z'}, 'returns': False},\n", + " {'description': '`y` is not a valid date-time representation and therefore will be treated as a string so that the provided values are not equal.',\n", + " 'arguments': {'x': '2018-01-01T12:00:00Z', 'y': '2018-01-01T12:00:00'},\n", + " 'returns': True},\n", + " {'description': '01:00 in the time zone +1 is equal to 00:00 in UTC.',\n", + " 'arguments': {'x': '2018-01-01T00:00:00Z',\n", + " 'y': '2018-01-01T01:00:00+01:00'},\n", + " 'returns': False},\n", + " {'arguments': {'x': [1, 2, 3], 'y': [1, 2, 3]}, 'returns': False}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'if',\n", + " 'summary': 'If-Then-Else conditional',\n", + " 'description': 'If the value passed is `true`, returns the value of the `accept` parameter, otherwise returns the value of the `reject` parameter.\\n\\nThis is basically an if-then-else construct as in other programming languages.',\n", + " 'parameters': [{'schema': {'type': ['boolean', 'null']},\n", + " 'name': 'value',\n", + " 'description': 'A boolean value.'},\n", + " {'schema': {'description': 'Any data type is allowed.'},\n", + " 'name': 'accept',\n", + " 'description': 'A value that is returned if the boolean value is `true`.'},\n", + " {'schema': {'description': 'Any data type is allowed.'},\n", + " 'name': 'reject',\n", + " 'description': 'A value that is returned if the boolean value is **not** `true`. Defaults to `null`.',\n", + " 'optional': True}],\n", + " 'categories': ['logic', 'comparison', 'masks'],\n", + " 'returns': {'schema': {'description': 'Any data type is allowed.'},\n", + " 'description': 'Either the `accept` or `reject` argument depending on the given boolean value.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'reject': 'B', 'value': True, 'accept': 'A'},\n", + " 'returns': 'A'},\n", + " {'arguments': {'reject': 'B', 'accept': 'A'}, 'returns': 'B'},\n", + " {'arguments': {'reject': [4, 5, 6], 'value': False, 'accept': [1, 2, 3]},\n", + " 'returns': [4, 5, 6]},\n", + " {'arguments': {'value': True, 'accept': 123}, 'returns': 123},\n", + " {'arguments': {'value': False, 'accept': 1}}]},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'load_ml_model',\n", + " 'summary': 'Load a machine learning model',\n", + " 'description': '...',\n", + " 'parameters': [{'schema': [{'subtype': 'uri',\n", + " 'format': 'uri',\n", + " 'pattern': '^https?://',\n", + " 'title': 'URL',\n", + " 'type': 'string'},\n", + " {'subtype': 'job-id',\n", + " 'pattern': '^[\\\\w\\\\-\\\\.~]+$',\n", + " 'title': 'Batch Job ID',\n", + " 'type': 'string'},\n", + " {'subtype': 'file-path',\n", + " 'pattern': '^[^\\r\\n\\\\:\\'\"]+$',\n", + " 'title': 'User-uploaded File',\n", + " 'type': 'string'}],\n", + " 'name': 'id',\n", + " 'description': 'The STAC Item to load the machine learning model from. The STAC Item must implement the ml-model extension. Such model can be trained with processes such as ``fit_random_forest()``.'}],\n", + " 'categories': ['machine learning', 'import'],\n", + " 'returns': {'schema': {'subtype': 'ml-model', 'type': 'object'},\n", + " 'description': 'A machine learning model to be used with machine learning processes such as ``predict_random_forest()``.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[WCPS]',\n", + " 'id': 'tan',\n", + " 'summary': 'Tangent',\n", + " 'description': 'Computes the tangent of `x`. The tangent is defined to be the sine of x divided by the cosine of x.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'An angle in radians.'}],\n", + " 'categories': ['math > trigonometric'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/Tangent.html',\n", + " 'title': 'Tangent explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed tangent of `x`.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 0}, 'returns': 0}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'add',\n", + " 'summary': 'Addition of two numbers',\n", + " 'description': 'Sums up the two numbers `x` and `y` (*x + y*) and returns the computed sum.\\n\\nNo-data values are taken into account so that `null` is returned if any element is such a value.\\n\\nThe computations follow [IEEE Standard 754](https://ieeexplore.ieee.org/document/8766229) whenever the processing environment supports it.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'The first summand.'},\n", + " {'schema': {'type': ['number', 'null']},\n", + " 'name': 'y',\n", + " 'description': 'The second summand.'}],\n", + " 'categories': ['math'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/Sum.html',\n", + " 'title': 'Sum explained by Wolfram MathWorld'},\n", + " {'rel': 'about',\n", + " 'href': 'https://ieeexplore.ieee.org/document/8766229',\n", + " 'title': 'IEEE Standard 754-2019 for Floating-Point Arithmetic'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed sum of the two numbers.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 5, 'y': 2.5}, 'returns': 7.5},\n", + " {'arguments': {'x': -2, 'y': -4}, 'returns': -6},\n", + " {'arguments': {'x': 1}}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'array_element',\n", + " 'summary': 'Get an element from an array',\n", + " 'description': 'Returns the element with the specified index or label from the array.\\n\\nEither the parameter `index` or `label` must be specified, otherwise the `ArrayElementParameterMissing` exception is thrown. If both parameters are set the `ArrayElementParameterConflict` exception is thrown.',\n", + " 'parameters': [{'schema': {'type': 'array',\n", + " 'items': {'description': 'Any data type is allowed.'}},\n", + " 'name': 'data',\n", + " 'description': 'An array.'},\n", + " {'schema': {'type': 'integer'},\n", + " 'name': 'index',\n", + " 'description': 'The zero-based index of the element to retrieve.',\n", + " 'optional': True},\n", + " {'schema': [{'type': 'number'}, {'type': 'string'}],\n", + " 'name': 'label',\n", + " 'description': 'The label of the element to retrieve.',\n", + " 'optional': True},\n", + " {'schema': {'type': 'boolean'},\n", + " 'default': False,\n", + " 'name': 'return_nodata',\n", + " 'description': 'By default this process throws an `ArrayElementNotAvailable` exception if the index or label is invalid. If you want to return `null` instead, set this flag to `true`.',\n", + " 'optional': True}],\n", + " 'categories': ['arrays', 'reducer'],\n", + " 'returns': {'schema': {'description': 'Any data type is allowed.'},\n", + " 'description': 'The value of the requested element.'},\n", + " 'exceptions': {'ArrayElementParameterConflict': {'message': \"The process 'array_element' only allows that either the 'index' or the 'labels' parameter is set.\"},\n", + " 'ArrayElementParameterMissing': {'message': \"The process 'array_element' requires either the 'index' or 'labels' parameter to be set.\"},\n", + " 'ArrayElementNotAvailable': {'message': 'The array has no element with the specified index or label.'}},\n", + " 'examples': [{'arguments': {'data': [9, 8, 7, 6, 5], 'index': 2},\n", + " 'returns': 7},\n", + " {'arguments': {'data': ['A', 'B', 'C'], 'index': 0}, 'returns': 'A'},\n", + " {'arguments': {'return_nodata': True, 'data': [], 'index': 0}}]},\n", + " {'engine': '[WCPS]',\n", + " 'id': 'sinh',\n", + " 'summary': 'Hyperbolic sine',\n", + " 'description': 'Computes the hyperbolic sine of `x`.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'An angle in radians.'}],\n", + " 'categories': ['math > trigonometric'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/HyperbolicSine.html',\n", + " 'title': 'Hyperbolic sine explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed hyperbolic sine of `x`.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 0}, 'returns': 0}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'subtract',\n", + " 'summary': 'Subtraction of two numbers',\n", + " 'description': 'Subtracts argument `y` from the argument `x` (*x - y*) and returns the computed result.\\n\\nNo-data values are taken into account so that `null` is returned if any element is such a value.\\n\\nThe computations follow [IEEE Standard 754](https://ieeexplore.ieee.org/document/8766229) whenever the processing environment supports it.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'The minuend.'},\n", + " {'schema': {'type': ['number', 'null']},\n", + " 'name': 'y',\n", + " 'description': 'The subtrahend.'}],\n", + " 'categories': ['math'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/Subtraction.html',\n", + " 'title': 'Subtraction explained by Wolfram MathWorld'},\n", + " {'rel': 'about',\n", + " 'href': 'https://ieeexplore.ieee.org/document/8766229',\n", + " 'title': 'IEEE Standard 754-2019 for Floating-Point Arithmetic'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed result.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 5, 'y': 2.5}, 'returns': 2.5},\n", + " {'arguments': {'x': -2, 'y': 4}, 'returns': -6},\n", + " {'arguments': {'x': 1}}]},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'array_interpolate_linear',\n", + " 'summary': 'One-dimensional linear interpolation for arrays',\n", + " 'description': 'Performs a linear interpolation for each of the no-data values (`null`) in the array given, except for leading and trailing no-data values.\\n\\nThe linear interpolants are defined by the array indices or labels (x) and the values in the array (y).',\n", + " 'parameters': [{'schema': {'type': 'array',\n", + " 'items': {'type': ['number', 'null']}},\n", + " 'name': 'data',\n", + " 'description': 'An array of numbers and no-data values.\\n\\nIf the given array is a labeled array, the labels must have a natural/inherent label order and the process expects the labels to be sorted accordingly. This is the default behavior in openEO for spatial and temporal dimensions.'}],\n", + " 'categories': ['arrays', 'math', 'math > interpolation'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'https://en.wikipedia.org/wiki/Linear_interpolation',\n", + " 'title': 'Linear interpolation explained by Wikipedia'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'An array with no-data values being replaced with interpolated values. If not at least 2 numerical values are available in the array, the array stays the same.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'data': [None, 1, None, 6, None, -8]},\n", + " 'returns': [None, 1, 3.5, 6, -1, -8]}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'eq',\n", + " 'summary': 'Equal to comparison',\n", + " 'description': 'Compares whether `x` is strictly equal to `y`.\\n\\n**Remarks:**\\n\\n* Data types MUST be checked strictly, for example a string with the content *1* is not equal to the number *1*. Nevertheless, an integer *1* is equal to a floating point number *1.0* as `integer` is a sub-type of `number`.\\n* If any operand is `null`, the return value is `null`. Therefore, `eq(null, null)` returns `null` instead of `true`.\\n* If any operand is an array or object, the return value is `false`.\\n* Strings are expected to be encoded in UTF-8 by default.\\n* Temporal strings MUST be compared differently than other strings and MUST NOT be compared based on their string representation due to different possible representations. For example, the UTC time zone representation `Z` has the same meaning as `+00:00`.',\n", + " 'parameters': [{'schema': {'description': 'Any data type is allowed.'},\n", + " 'name': 'x',\n", + " 'description': 'First operand.'},\n", + " {'schema': {'description': 'Any data type is allowed.'},\n", + " 'name': 'y',\n", + " 'description': 'Second operand.'},\n", + " {'schema': {'type': ['number', 'null']},\n", + " 'name': 'delta',\n", + " 'description': 'Only applicable for comparing two numbers. If this optional parameter is set to a positive non-zero number the equality of two numbers is checked against a delta value. This is especially useful to circumvent problems with floating point inaccuracy in machine-based computation.\\n\\nThis option is basically an alias for the following computation: `lte(abs(minus([x, y]), delta)`',\n", + " 'optional': True},\n", + " {'schema': {'type': 'boolean'},\n", + " 'default': True,\n", + " 'name': 'case_sensitive',\n", + " 'description': 'Only applicable for comparing two strings. Case sensitive comparison can be disabled by setting this parameter to `false`.',\n", + " 'optional': True}],\n", + " 'categories': ['texts', 'comparison'],\n", + " 'returns': {'schema': {'type': ['boolean', 'null']},\n", + " 'description': 'Returns `true` if `x` is equal to `y`, `null` if any operand is `null`, otherwise `false`.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 1}},\n", + " {'arguments': {}},\n", + " {'arguments': {'x': 1, 'y': 1}, 'returns': True},\n", + " {'arguments': {'x': 1, 'y': '1'}, 'returns': False},\n", + " {'arguments': {'x': 0, 'y': False}, 'returns': False},\n", + " {'arguments': {'x': 1.02, 'delta': 0.01, 'y': 1}, 'returns': False},\n", + " {'arguments': {'x': -1, 'delta': 0.01, 'y': -1.001}, 'returns': True},\n", + " {'arguments': {'x': 115, 'delta': 10, 'y': 110}, 'returns': True},\n", + " {'arguments': {'x': 'Test', 'y': 'test'}, 'returns': False},\n", + " {'arguments': {'case_sensitive': False, 'x': 'Test', 'y': 'test'},\n", + " 'returns': True},\n", + " {'arguments': {'case_sensitive': False, 'x': 'Ä', 'y': 'ä'},\n", + " 'returns': True},\n", + " {'arguments': {'x': '00:00:00+00:00', 'y': '00:00:00Z'}, 'returns': True},\n", + " {'description': '`y` is not a valid date-time representation and therefore will be treated as a string so that the provided values are not equal.',\n", + " 'arguments': {'x': '2018-01-01T12:00:00Z', 'y': '2018-01-01T12:00:00'},\n", + " 'returns': False},\n", + " {'description': '01:00 in the time zone +1 is equal to 00:00 in UTC.',\n", + " 'arguments': {'x': '2018-01-01T00:00:00Z',\n", + " 'y': '2018-01-01T01:00:00+01:00'},\n", + " 'returns': True},\n", + " {'arguments': {'x': [1, 2, 3], 'y': [1, 2, 3]}, 'returns': False}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'save_result',\n", + " 'summary': 'Save processed data to storage',\n", + " 'description': 'Saves processed data to the local user workspace / data store of the authenticated user. This process aims to be compatible to GDAL/OGR formats and options. STAC-compatible metadata should be stored with the processed data.\\n\\nCalling this process may be rejected by back-ends in the context of secondary web services.',\n", + " 'parameters': [{'schema': [{'subtype': 'raster-cube', 'type': 'object'},\n", + " {'subtype': 'vector-cube', 'type': 'object'}],\n", + " 'name': 'data',\n", + " 'description': 'The data to save.'},\n", + " {'schema': {'subtype': 'output-format', 'type': 'string'},\n", + " 'name': 'format',\n", + " 'description': 'The file format to save to. It must be one of the values that the server reports as supported output file formats, which usually correspond to the short GDAL/OGR codes. If the format is not suitable for storing the underlying data structure, a `FormatUnsuitable` exception will be thrown. This parameter is *case insensitive*.'},\n", + " {'schema': {'subtype': 'output-format-options', 'type': 'object'},\n", + " 'default': {},\n", + " 'name': 'options',\n", + " 'description': 'The file format parameters to be used to create the file(s). Must correspond to the parameters that the server reports as supported parameters for the chosen `format`. The parameter names and valid values usually correspond to the GDAL/OGR format options.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes', 'export'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'https://www.gdal.org/formats_list.html',\n", + " 'title': 'GDAL Raster Formats'},\n", + " {'rel': 'about',\n", + " 'href': 'https://www.gdal.org/ogr_formats.html',\n", + " 'title': 'OGR Vector Formats'}],\n", + " 'returns': {'schema': {'type': 'boolean'},\n", + " 'description': '`false` if saving failed, `true` otherwise.'},\n", + " 'exceptions': {'FormatUnsuitable': {'message': \"Data can't be transformed into the requested output format.\"}}},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'filter_temporal',\n", + " 'summary': 'Temporal filter for a temporal intervals',\n", + " 'description': \"Limits the data cube to the specified interval of dates and/or times.\\n\\nMore precisely, the filter checks whether the temporal dimension label is greater than or equal to the lower boundary (start date/time) and the temporal dimension label is less than the value of the upper boundary (end date/time). This corresponds to a left-closed interval, which contains the lower boundary but not the upper boundary.\\n\\nIf the dimension is set to `null` (it's the default value), the data cube is expected to only have one temporal dimension.\",\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A data cube.'},\n", + " {'schema': {'minItems': 2,\n", + " 'maxItems': 2,\n", + " 'examples': [['2015-01-01T00:00:00Z', '2016-01-01T00:00:00Z'],\n", + " ['2015-01-01', '2016-01-01']],\n", + " 'subtype': 'temporal-interval',\n", + " 'type': 'array',\n", + " 'items': {'anyOf': [{'subtype': 'date-time',\n", + " 'format': 'date-time',\n", + " 'type': 'string'},\n", + " {'subtype': 'date', 'format': 'date', 'type': 'string'},\n", + " {'subtype': 'year',\n", + " 'minLength': 4,\n", + " 'pattern': '^\\\\d{4}$',\n", + " 'type': 'string',\n", + " 'maxLength': 4},\n", + " {'type': 'null'}]}},\n", + " 'name': 'extent',\n", + " 'description': 'Left-closed temporal interval, i.e. an array with exactly two elements:\\n\\n1. The first element is the start of the temporal interval. The specified instance in time is **included** in the interval.\\n2. The second element is the end of the temporal interval. The specified instance in time is **excluded** from the interval.\\n\\nThe specified temporal strings follow [RFC 3339](https://tools.ietf.org/html/rfc3339). Also supports open intervals by setting one of the boundaries to `null`, but never both.'},\n", + " {'schema': {'type': ['string', 'null']},\n", + " 'name': 'dimension',\n", + " 'description': 'The name of the temporal dimension to filter on. If the dimension is not set or is set to `null`, the filter applies to all temporal dimensions. Fails with a `DimensionNotAvailable` error if the specified dimension does not exist.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes', 'filter'],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A data cube restricted to the specified temporal extent. The dimensions and dimension properties (name, type, labels, reference system and resolution) remain unchanged, except that the given temporal dimension(s) have less (or the same) dimension labels.'},\n", + " 'exceptions': {'DimensionNotAvailable': {'message': 'A dimension with the specified name does not exist.'}}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'median',\n", + " 'summary': 'Statistical median',\n", + " 'description': 'The statistical median of an array of numbers is the value separating the higher half from the lower half of the data.\\n\\nAn array without non-`null` elements resolves always with `null`.\\n\\n**Remarks:**\\n\\n* For a symmetric arrays, the result is equal to the ``mean()``.\\n* The median can also be calculated by computing the ``quantiles()`` with a probability of *0.5*.',\n", + " 'parameters': [{'schema': {'type': 'array',\n", + " 'items': {'type': ['number', 'null']}},\n", + " 'name': 'data',\n", + " 'description': 'An array of numbers.'},\n", + " {'schema': {'type': 'boolean'},\n", + " 'default': True,\n", + " 'name': 'ignore_nodata',\n", + " 'description': 'Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that `null` is returned if any value is such a value.',\n", + " 'optional': True}],\n", + " 'categories': ['math', 'reducer'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/StatisticalMedian.html',\n", + " 'title': 'Statistical Median explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed statistical median.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'data': [1, 3, 3, 6, 7, 8, 9]}, 'returns': 6},\n", + " {'arguments': {'data': [1, 2, 3, 4, 5, 6, 8, 9]}, 'returns': 4.5},\n", + " {'arguments': {'data': [-1, -0.5, None, 1]}, 'returns': -0.5},\n", + " {'arguments': {'data': [-1, 0, None, 1], 'ignore_nodata': False}},\n", + " {'description': 'The input array is empty: return `null`.',\n", + " 'arguments': {'data': []}},\n", + " {'description': 'The input array has only `null` elements: return `null`.',\n", + " 'arguments': {'data': [None, None]}}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'reduce_dimension',\n", + " 'summary': 'Reduce dimensions',\n", + " 'description': 'Applies a unary reducer to a data cube dimension by collapsing all the pixel values along the specified dimension into an output value computed by the reducer. This process passes a list of values to the reducer. In contrast, ``reduce_dimension_binary()`` passes two values, which may be better suited especially for UDFs in case the number of values gets too large to be processed at once.\\n\\nThe dimension is dropped. To avoid this, use ``apply_dimension()`` instead.',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A data cube.'},\n", + " {'schema': {'subtype': 'process-graph',\n", + " 'type': 'object',\n", + " 'parameters': [{'schema': {'subtype': 'labeled-array',\n", + " 'type': 'array',\n", + " 'items': {'description': 'Any data type.'}},\n", + " 'name': 'data',\n", + " 'description': 'A labeled array with elements of any type.'},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'context',\n", + " 'description': 'Additional data passed by the user.',\n", + " 'optional': True}]},\n", + " 'name': 'reducer',\n", + " 'description': \"A reducer to apply on the specified dimension. A reducer is a single process such as ``mean()`` or a set of processes, which computes a single value for a list of values, see the category 'reducer' for such processes.\"},\n", + " {'schema': {'type': 'string'},\n", + " 'name': 'dimension',\n", + " 'description': 'The name of the dimension over which to reduce. Fails with a `DimensionNotAvailable` error if the specified dimension does not exist.'},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'context',\n", + " 'description': 'Additional data to be passed to the reducer.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes', 'reducer'],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A data cube with the newly computed values. It is missing the given dimension, the number of dimensions decreases by one. The dimension properties (name, type, labels, reference system and resolution) for all other dimensions remain unchanged.'},\n", + " 'exceptions': {'DimensionNotAvailable': {'message': 'A dimension with the specified name does not exist.'}}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'apply_kernel',\n", + " 'summary': 'Apply a spatial convolution with a kernel',\n", + " 'description': \"Applies a 2D convolution (i.e. a focal operation with a weighted kernel) on the horizontal spatial dimensions (axes `x` and `y`) of the data cube.\\n\\nEach value in the kernel is multiplied with the corresponding pixel value and all products are summed up afterwards. The sum is then multiplied with the factor.\\n\\nThe process can't handle non-numerical or infinite numerical values in the data cube. Boolean values are converted to integers (`false` = 0, `true` = 1), but all other non-numerical or infinite values are replaced with zeroes by default (see parameter `replace_invalid`).\\n\\nFor cases requiring more generic focal operations or non-numerical values, see ``apply_neighborhood()``.\",\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A data cube.'},\n", + " {'schema': {'subtype': 'kernel',\n", + " 'description': 'A two-dimensional array of numbers.',\n", + " 'type': 'array',\n", + " 'items': {'type': 'array', 'items': {'type': 'number'}}},\n", + " 'name': 'kernel',\n", + " 'description': 'Kernel as a two-dimensional array of weights. The inner level of the nested array aligns with the `x` axis and the outer level aligns with the `y` axis. Each level of the kernel must have an uneven number of elements, otherwise the process throws a `KernelDimensionsUneven` error.'},\n", + " {'schema': {'type': 'number'},\n", + " 'default': 1,\n", + " 'name': 'factor',\n", + " 'description': 'A factor that is multiplied to each value after the kernel has been applied.\\n\\nThis is basically a shortcut for explicitly multiplying each value by a factor afterwards, which is often required for some kernel-based algorithms such as the Gaussian blur.',\n", + " 'optional': True},\n", + " {'schema': [{'type': 'string',\n", + " 'enum': ['replicate', 'reflect', 'reflect_pixel', 'wrap']},\n", + " {'type': 'number'}],\n", + " 'default': 0,\n", + " 'name': 'border',\n", + " 'description': 'Determines how the data is extended when the kernel overlaps with the borders. Defaults to fill the border with zeroes.\\n\\nThe following options are available:\\n\\n* *numeric value* - fill with a user-defined constant number `n`: `nnnnnn|abcdefgh|nnnnnn` (default, with `n` = 0)\\n* `replicate` - repeat the value from the pixel at the border: `aaaaaa|abcdefgh|hhhhhh`\\n* `reflect` - mirror/reflect from the border: `fedcba|abcdefgh|hgfedc`\\n* `reflect_pixel` - mirror/reflect from the center of the pixel at the border: `gfedcb|abcdefgh|gfedcb`\\n* `wrap` - repeat/wrap the image: `cdefgh|abcdefgh|abcdef`',\n", + " 'optional': True},\n", + " {'schema': {'type': 'number'},\n", + " 'default': 0,\n", + " 'name': 'replace_invalid',\n", + " 'description': 'This parameter specifies the value to replace non-numerical or infinite numerical values with. By default, those values are replaced with zeroes.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes', 'math > image filter'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://www.songho.ca/dsp/convolution/convolution.html',\n", + " 'title': 'Convolutions explained'},\n", + " {'rel': 'about',\n", + " 'href': 'http://www.songho.ca/dsp/convolution/convolution2d_example.html',\n", + " 'title': 'Example of 2D Convolution'}],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A data cube with the newly computed values and the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged.'},\n", + " 'exceptions': {'KernelDimensionsUneven': {'message': 'Each dimension of the kernel must have an uneven number of elements.'}}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'aggregate_temporal_period',\n", + " 'summary': 'Temporal aggregations based on calendar hierarchies',\n", + " 'description': 'Computes a temporal aggregation based on calendar hierarchies such as years, months or seasons. For other calendar hierarchies ``aggregate_temporal()`` can be used.\\n\\nFor each interval, all data along the dimension will be passed through the reducer.\\n\\nIf the dimension is not set or is set to `null`, the data cube is expected to only have one temporal dimension.',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A data cube.'},\n", + " {'schema': {'type': 'string',\n", + " 'enum': ['hour',\n", + " 'day',\n", + " 'week',\n", + " 'dekad',\n", + " 'month',\n", + " 'season',\n", + " 'tropical-season',\n", + " 'year',\n", + " 'decade',\n", + " 'decade-ad']},\n", + " 'name': 'period',\n", + " 'description': 'The time intervals to aggregate. The following pre-defined values are available:\\n\\n* `hour`: Hour of the day\\n* `day`: Day of the year\\n* `week`: Week of the year\\n* `dekad`: Ten day periods, counted per year with three periods per month (day 1 - 10, 11 - 20 and 21 - end of month). The third dekad of the month can range from 8 to 11 days. For example, the fourth dekad is Feb, 1 - Feb, 10 each year.\\n* `month`: Month of the year\\n* `season`: Three month periods of the calendar seasons (December - February, March - May, June - August, September - November).\\n* `tropical-season`: Six month periods of the tropical seasons (November - April, May - October).\\n* `year`: Proleptic years\\n* `decade`: Ten year periods ([0-to-9 decade](https://en.wikipedia.org/wiki/Decade#0-to-9_decade)), from a year ending in a 0 to the next year ending in a 9.\\n* `decade-ad`: Ten year periods ([1-to-0 decade](https://en.wikipedia.org/wiki/Decade#1-to-0_decade)) better aligned with the anno Domini (AD) calendar era, from a year ending in a 1 to the next year ending in a 0.'},\n", + " {'schema': {'subtype': 'process-graph',\n", + " 'returns': {'schema': {'description': 'Any data type.'},\n", + " 'description': 'The value to be set in the new data cube.'},\n", + " 'type': 'object',\n", + " 'parameters': [{'schema': {'subtype': 'labeled-array',\n", + " 'type': 'array',\n", + " 'items': {'description': 'Any data type.'}},\n", + " 'name': 'data',\n", + " 'description': \"A labeled array with elements of any type. If there's no data for the period, the array is empty.\"},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'context',\n", + " 'description': 'Additional data passed by the user.',\n", + " 'optional': True}]},\n", + " 'name': 'reducer',\n", + " 'description': \"A reducer to be applied for the values contained in each period. A reducer is a single process such as ``mean()`` or a set of processes, which computes a single value for a list of values, see the category 'reducer' for such processes. Periods may not contain any values, which for most reducers leads to no-data (`null`) values by default.\"},\n", + " {'schema': {'type': ['string', 'null']},\n", + " 'name': 'dimension',\n", + " 'description': 'The name of the temporal dimension for aggregation. All data along the dimension is passed through the specified reducer. If the dimension is not set or set to `null`, the data cube is expected to only have one temporal dimension. Fails with a `TooManyDimensions` exception if it has more dimensions. Fails with a `DimensionNotAvailable` exception if the specified dimension does not exist.',\n", + " 'optional': True},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'context',\n", + " 'description': 'Additional data to be passed to the reducer.',\n", + " 'optional': True}],\n", + " 'categories': ['aggregate & resample', 'climatology', 'cubes'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'https://openeo.org/documentation/1.0/datacubes.html#aggregate',\n", + " 'title': 'Aggregation explained in the openEO documentation'}],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A new data cube with the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged, except for the resolution and dimension labels of the given temporal dimension. The specified temporal dimension has the following dimension labels (`YYYY` = four-digit year, `MM` = two-digit month, `DD` two-digit day of month):\\n\\n* `hour`: `YYYY-MM-DD-00` - `YYYY-MM-DD-23`\\n* `day`: `YYYY-001` - `YYYY-365`\\n* `week`: `YYYY-01` - `YYYY-52`\\n* `dekad`: `YYYY-00` - `YYYY-36`\\n* `month`: `YYYY-01` - `YYYY-12`\\n* `season`: `YYYY-djf` (December - February), `YYYY-mam` (March - May), `YYYY-jja` (June - August), `YYYY-son` (September - November).\\n* `tropical-season`: `YYYY-ndjfma` (November - April), `YYYY-mjjaso` (May - October).\\n* `year`: `YYYY`\\n* `decade`: `YYY0`\\n* `decade-ad`: `YYY1`'},\n", + " 'exceptions': {'DimensionNotAvailable': {'message': 'A dimension with the specified name does not exist.'},\n", + " 'TooManyDimensions': {'message': 'The data cube contains multiple temporal dimensions. The parameter `dimension` must be specified.'},\n", + " 'DistinctDimensionLabelsRequired': {'message': 'The dimension labels have duplicate values. Distinct labels must be specified.'}}},\n", + " {'engine': '[WCPS]',\n", + " 'id': 'arcsin',\n", + " 'summary': 'Inverse sine',\n", + " 'description': 'Computes the arc sine of `x`. The arc sine is the inverse function of the sine so that *arcsin(sin(x)) = x*.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'A number.'}],\n", + " 'categories': ['math > trigonometric'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/InverseSine.html',\n", + " 'title': 'Inverse sine explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed angle in radians.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 0}, 'returns': 0}]},\n", + " {'engine': '[WCPS]',\n", + " 'id': 'resample_spatial',\n", + " 'summary': 'Resample and warp the spatial dimensions',\n", + " 'description': 'Resamples the spatial dimensions (x,y) of the data cube to a specified resolution and/or warps the data cube to the target projection. At least `resolution` or `projection` must be specified.\\n\\nRelated processes:\\n\\n* Use ``filter_bbox()`` to set the target spatial extent.\\n* To spatially align two data cubes with each other (e.g. for merging), better use the process ``resample_cube_spatial()``.',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A raster data cube.'},\n", + " {'schema': [{'description': 'A single number used as resolution for both x and y.',\n", + " 'type': 'number',\n", + " 'minimum': 0},\n", + " {'minItems': 2,\n", + " 'maxItems': 2,\n", + " 'description': 'A two-element array to specify separate resolutions for x (first element) and y (second element).',\n", + " 'type': 'array',\n", + " 'items': {'type': 'number', 'minimum': 0}}],\n", + " 'default': 0,\n", + " 'name': 'resolution',\n", + " 'description': \"Resamples the data cube to the target resolution, which can be specified either as separate values for x and y or as a single value for both axes. Specified in the units of the target projection. Doesn't change the resolution by default (`0`).\",\n", + " 'optional': True},\n", + " {'schema': [{'examples': [3857],\n", + " 'subtype': 'epsg-code',\n", + " 'title': 'EPSG Code',\n", + " 'type': 'integer',\n", + " 'minimum': 1000},\n", + " {'subtype': 'wkt2-definition', 'title': 'WKT2', 'type': 'string'},\n", + " {'subtype': 'proj-definition',\n", + " 'deprecated': True,\n", + " 'title': 'PROJ definition',\n", + " 'type': 'string'},\n", + " {'title': \"Don't change projection\", 'type': 'null'}],\n", + " 'name': 'projection',\n", + " 'description': 'Warps the data cube to the target projection, specified as as [EPSG code](http://www.epsg-registry.org/), [WKT2 (ISO 19162) string](http://docs.opengeospatial.org/is/18-010r7/18-010r7.html), [PROJ definition (deprecated)](https://proj.org/usage/quickstart.html). By default (`null`), the projection is not changed.',\n", + " 'optional': True},\n", + " {'schema': {'type': 'string',\n", + " 'enum': ['near',\n", + " 'bilinear',\n", + " 'cubic',\n", + " 'cubicspline',\n", + " 'lanczos',\n", + " 'average',\n", + " 'mode',\n", + " 'max',\n", + " 'min',\n", + " 'med',\n", + " 'q1',\n", + " 'q3']},\n", + " 'default': 'near',\n", + " 'name': 'method',\n", + " 'description': 'Resampling method. Methods are inspired by GDAL, see [gdalwarp](https://www.gdal.org/gdalwarp.html) for more information.',\n", + " 'optional': True},\n", + " {'schema': {'type': 'string',\n", + " 'enum': ['lower-left', 'upper-left', 'lower-right', 'upper-right']},\n", + " 'default': 'upper-left',\n", + " 'name': 'align',\n", + " 'description': 'Specifies to which corner of the spatial extent the new resampled data is aligned to.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes', 'aggregate & resample'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'https://proj.org/usage/projections.html',\n", + " 'title': 'PROJ parameters for cartographic projections'},\n", + " {'rel': 'about',\n", + " 'href': 'http://www.epsg-registry.org',\n", + " 'title': 'Official EPSG code registry'},\n", + " {'rel': 'about',\n", + " 'href': 'http://www.epsg.io',\n", + " 'title': 'Unofficial EPSG code database'}],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A raster data cube with values warped onto the new projection. It has the same dimensions and the same dimension properties (name, type, labels, reference system and resolution) for all non-spatial or vertical spatial dimensions. For the horizontal spatial dimensions the name and type remain unchanged, but reference system, labels and resolution may change depending on the given parameters.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'fit_class_random_forest',\n", + " 'summary': 'Train a random forest classification model',\n", + " 'description': 'Executes the fit of a random forest classification based on the user input of target and predictors. The Random Forest classification model is based on the approach by Breiman (2001).',\n", + " 'parameters': [{'schema': {'subtype': 'vector-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'The input data for the classification model. The raster images that will be used as predictors for the Random Forest. Aggregated to the features (vectors) of the target input variable.'},\n", + " {'schema': {'subtype': 'vector-cube', 'type': 'object'},\n", + " 'name': 'target',\n", + " 'description': 'The input data for the classification model. This will be vector cubes for each training site. This is associated with the target variable for the Random Forest Model. The Geometry has to associated with a value to predict (e.g. fractional forest canopy cover).'},\n", + " {'schema': {'maximum': 100, 'type': 'number', 'exclusiveMinimum': 0},\n", + " 'name': 'training',\n", + " 'description': 'The amount of training data to be used in the classification. The sampling will be randomly through the data object. The remaining data will be used as test data for the validation.'},\n", + " {'schema': {'type': 'integer', 'minimum': 1},\n", + " 'default': 100,\n", + " 'name': 'num_trees',\n", + " 'description': 'The number of trees build within the Random Forest classification.',\n", + " 'optional': True},\n", + " {'schema': [{'type': 'integer', 'minimum': 1}, {'type': 'null'}],\n", + " 'name': 'mtry',\n", + " 'description': 'Specifies how many split variables will be used at a node. Default value is `null`, which corresponds to the number of predictors divided by 3.',\n", + " 'optional': True}],\n", + " 'categories': ['machine learning'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'https://doi.org/10.1023/A:1010933404324',\n", + " 'type': 'text/html',\n", + " 'title': 'Breiman (2001): Random Forests'}],\n", + " 'returns': {'schema': {'subtype': 'ml-model', 'type': 'object'},\n", + " 'description': 'A model object that can be saved with ``save_ml_model()`` and restored with ``load_ml_model()``.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'log',\n", + " 'summary': 'Logarithm to a base',\n", + " 'description': 'Logarithm to the base `base` of the number `x` is defined to be the inverse function of taking b to the power of x.\\n\\nThe no-data value `null` is passed through and therefore gets propagated if any of the arguments is `null`.\\n\\nThe computations follow [IEEE Standard 754](https://ieeexplore.ieee.org/document/8766229) whenever the processing environment supports it. Therefore, `log(0, 2)` results in ±infinity if the processing environment supports it or otherwise an error is thrown.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'A number to compute the logarithm for.'},\n", + " {'schema': {'type': ['number', 'null']},\n", + " 'name': 'base',\n", + " 'description': 'The numerical base.'}],\n", + " 'categories': ['math > exponential & logarithmic'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/Logarithm.html',\n", + " 'title': 'Logarithm explained by Wolfram MathWorld'},\n", + " {'rel': 'about',\n", + " 'href': 'https://ieeexplore.ieee.org/document/8766229',\n", + " 'title': 'IEEE Standard 754-2019 for Floating-Point Arithmetic'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed logarithm.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 10, 'base': 10}, 'returns': 1},\n", + " {'arguments': {'x': 2, 'base': 2}, 'returns': 1},\n", + " {'arguments': {'x': 4, 'base': 2}, 'returns': 2},\n", + " {'arguments': {'x': 1, 'base': 16}, 'returns': 0}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'resample_cube_spatial',\n", + " 'summary': 'Resample the spatial dimensions to match a target data cube',\n", + " 'description': 'Resamples the spatial dimensions (x,y) from a source data cube to align with the corresponding dimensions of the given target data cube. Returns a new data cube with the resampled dimensions.\\n\\nTo resample a data cube to a specific resolution or projection regardless of an existing target data cube, refer to ``resample_spatial()``.',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A data cube.'},\n", + " {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'target',\n", + " 'description': 'A data cube that describes the spatial target resolution.'},\n", + " {'schema': {'type': 'string',\n", + " 'enum': ['near',\n", + " 'bilinear',\n", + " 'cubic',\n", + " 'cubicspline',\n", + " 'lanczos',\n", + " 'average',\n", + " 'mode',\n", + " 'max',\n", + " 'min',\n", + " 'med',\n", + " 'q1',\n", + " 'q3']},\n", + " 'default': 'near',\n", + " 'name': 'method',\n", + " 'description': 'Resampling method. Methods are inspired by GDAL, see [gdalwarp](https://www.gdal.org/gdalwarp.html) for more information.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes', 'aggregate & resample'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'https://openeo.org/documentation/1.0/glossary.html#resample-changing-data-cube-geometry',\n", + " 'title': 'Resampling explained in the openEO glossary'}],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A data cube with the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged, except for the resolution and dimension labels of the spatial dimensions.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'filter_bands',\n", + " 'summary': 'Filter the bands by name',\n", + " 'description': \"Filters the bands in the data cube so that bands that don't match any of the criteria are dropped from the data cube. The data cube is expected to have only one dimension of type `bands`. Fails with a `DimensionMissing` error if no such dimension exists.\\n\\nThe following criteria can be used to select bands:\\n\\n* `bands`: band name or common band name (e.g. `B01`, `B8A`, `red` or `nir`)\\n* `wavelengths`: ranges of wavelengths in micrometres (?m) (e.g. 0.5 - 0.6)\\n\\nAll these information are exposed in the band metadata of the collection. To keep algorithms interoperable it is recommended to prefer the common bands names or the wavelengths over collection and/or back-end specific band names.\\n\\nIf multiple criteria are specified, any of them must match and not all of them, i.e. they are combined with an OR-operation. If no criteria is specified, the `BandFilterParameterMissing` exception must be thrown.\\n\\n**Important:** The order of the specified array defines the order of the bands in the data cube, which can be important for subsequent processes. If multiple bands are matched by a single criterion (e.g. a range of wavelengths), they stay in the original order.\",\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A data cube with bands.'},\n", + " {'schema': {'type': 'array',\n", + " 'items': {'subtype': 'band-name', 'type': 'string'}},\n", + " 'default': [],\n", + " 'name': 'bands',\n", + " 'description': 'A list of band names. Either the unique band name (metadata field `name` in bands) or one of the common band names (metadata field `common_name` in bands). If unique band name and common name conflict, the unique band name has higher priority.\\n\\nThe order of the specified array defines the order of the bands in the data cube. If multiple bands match a common name, all matched bands are included in the original order.',\n", + " 'optional': True},\n", + " {'schema': {'type': 'array',\n", + " 'items': {'minItems': 2,\n", + " 'maxItems': 2,\n", + " 'examples': [[[0.45, 0.5], [0.6, 0.7]]],\n", + " 'type': 'array',\n", + " 'items': {'type': 'number'}}},\n", + " 'default': [],\n", + " 'name': 'wavelengths',\n", + " 'description': 'A list of sub-lists with each sub-list consisting of two elements. The first element is the minimum wavelength and the second element is the maximum wavelength. Wavelengths are specified in micrometres (?m).\\n\\nThe order of the specified array defines the order of the bands in the data cube. If multiple bands match the wavelengths, all matched bands are included in the original order.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes', 'filter'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'https://github.com/radiantearth/stac-spec/tree/master/extensions/eo#common-band-names',\n", + " 'title': 'List of common band names as specified by the STAC specification'}],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A data cube limited to a subset of its original bands. The dimensions and dimension properties (name, type, labels, reference system and resolution) remain unchanged, except that the dimension of type `bands` has less (or the same) dimension labels.'},\n", + " 'exceptions': {'DimensionMissing': {'message': 'A band dimension is missing.'},\n", + " 'BandFilterParameterMissing': {'message': \"The process 'filter_bands' requires any of the parameters 'bands', 'common_names' or 'wavelengths' to be set.\"}}},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'resample_cube_temporal',\n", + " 'summary': 'Resample a temporal dimension to match a target data cube',\n", + " 'description': 'Resamples the given temporal dimension from a source data cube to align with the corresponding dimension of the given target data cube. Returns a new data cube with the resampled dimension.\\n\\nIf the dimension is not set or is set to `null`, the data cube is expected to have one temporal dimension only.',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A data cube.'},\n", + " {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'target',\n", + " 'description': 'A data cube that describes the temporal target resolution.'},\n", + " {'schema': {'subtype': 'process-graph',\n", + " 'type': 'object',\n", + " 'parameters': [{'schema': {'subtype': 'labeled-array',\n", + " 'type': 'array',\n", + " 'items': {'description': 'Any data type.'}},\n", + " 'name': 'data',\n", + " 'description': 'A labeled array with elements of any type.'},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'context',\n", + " 'description': 'Additional data passed by the user.',\n", + " 'optional': True}]},\n", + " 'name': 'method',\n", + " 'description': \"A resampling method to be applied, could be a reducer for downsampling or other methods for upsampling. A reducer is a single process such as ``mean()`` or a set of processes, which computes a single value for a list of values, see the category 'reducer' for such processes.\"},\n", + " {'schema': {'type': ['string', 'null']},\n", + " 'name': 'dimension',\n", + " 'description': 'The name of the temporal dimension to resample, which must exist with this name in both data cubes. If the dimension is not set or is set to `null`, the data cube is expected to only have one temporal dimension. Fails with a `TooManyDimensions` error if it has more dimensions. Fails with a `DimensionNotAvailable` error if the specified dimension does not exist.',\n", + " 'optional': True},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'context',\n", + " 'description': 'Additional data to be passed to the process specified for the parameter `method`.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes', 'aggregate & resample'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'https://openeo.org/documentation/1.0/glossary.html#resample-changing-data-cube-geometry',\n", + " 'title': 'Resampling explained in the openEO glossary'}],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A raster data cube with the same dimensions and the same dimension properties (name, type, labels, reference system and resolution) for all non-temporal dimensions. For the temporal dimension the name and type remain unchanged, but the reference system changes and the labels and resolution may change.'},\n", + " 'exceptions': {'DimensionNotAvailable': {'message': 'A dimension with the specified name does not exist.'},\n", + " 'TooManyDimensions': {'message': \"The number of temporal dimensions must be reduced to one for 'resample_cube_temporal'.\"}}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'predict_random_forest',\n", + " 'summary': 'Predict values from a Random Forest model',\n", + " 'description': 'Applies a Random Forest Model to a raster cube objects. The raster data cube necessarily needs the same bands as the predictors in the model. Otherwise, an `IncompatibleBands` must be returned.',\n", + " 'parameters': [{'schema': {'subtype': 'vector-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A raster cube with the bands corresponding to the predictors.'},\n", + " {'schema': {'subtype': 'ml-model', 'type': 'object'},\n", + " 'name': 'model',\n", + " 'description': 'A model object that can be trained with the ``fit_regr_random_forest()`` process.'}],\n", + " 'categories': ['machine learning'],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A raster data cube with the prediction of the target variable based on the model.'},\n", + " 'exceptions': {'IncompatibleBands': {'message': 'The bands provided do not match the bands that the model has been trained for.'}}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'sum',\n", + " 'summary': 'Compute the sum by adding up numbers',\n", + " 'description': 'Sums up all elements in a sequential array of numbers and returns the computed sum.\\n\\nBy default no-data values are ignored. Setting `ignore_nodata` to `false` considers no-data values so that `null` is returned if any element is such a value.\\n\\nThe computations follow [IEEE Standard 754](https://ieeexplore.ieee.org/document/8766229) whenever the processing environment supports it.',\n", + " 'parameters': [{'schema': {'type': 'array',\n", + " 'items': {'type': ['number', 'null']}},\n", + " 'name': 'data',\n", + " 'description': 'An array of numbers.'},\n", + " {'schema': {'type': 'boolean'},\n", + " 'default': True,\n", + " 'name': 'ignore_nodata',\n", + " 'description': 'Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that `null` is returned if any value is such a value.',\n", + " 'optional': True}],\n", + " 'categories': ['math', 'reducer'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/Sum.html',\n", + " 'title': 'Sum explained by Wolfram MathWorld'},\n", + " {'rel': 'about',\n", + " 'href': 'https://ieeexplore.ieee.org/document/8766229',\n", + " 'title': 'IEEE Standard 754-2019 for Floating-Point Arithmetic'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed sum of the sequence of numbers.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'data': [5, 1]}, 'returns': 6},\n", + " {'arguments': {'data': [-2, 4, 2.5]}, 'returns': 4.5},\n", + " {'arguments': {'data': [1, None], 'ignore_nodata': False}},\n", + " {'arguments': {'data': [100]}, 'returns': 100},\n", + " {'arguments': {'data': [None], 'ignore_nodata': False}},\n", + " {'arguments': {'data': []}}]},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'save_ml_model',\n", + " 'summary': 'Save a machine learning model',\n", + " 'description': '...',\n", + " 'parameters': [{'schema': {'subtype': 'ml-model', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'The data to store as a machine learning model.'},\n", + " {'schema': {'additionalParameters': False, 'type': 'object'},\n", + " 'default': {},\n", + " 'name': 'options',\n", + " 'description': 'Any additional parameters to create the file(s).',\n", + " 'optional': True}],\n", + " 'categories': ['machine learning', 'import'],\n", + " 'returns': {'schema': {'type': 'boolean'},\n", + " 'description': 'Returns `false` if the process failed to store the model, `true` otherwise.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'load_collection',\n", + " 'summary': 'Load a collection',\n", + " 'description': 'Loads a collection from the current back-end by its id and returns it as processable data cube. The data that is added to the data cube can be restricted with the additional `spatial_extent`, `temporal_extent`, `bands` and `properties`.\\n\\n**Remarks:**\\n\\n* The bands (and all dimensions that specify nominal dimension labels) are expected to be ordered as specified in the metadata if the `bands` parameter is set to `null`.\\n* If no additional parameter is specified this would imply that the whole data set is expected to be loaded. Due to the large size of many data sets this is not recommended and may be optimized by back-ends to only load the data that is actually required after evaluating subsequent processes such as filters. This means that the pixel values should be processed only after the data has been limited to the required extents and as a consequence also to a manageable size.',\n", + " 'parameters': [{'schema': {'subtype': 'collection-id',\n", + " 'pattern': '^[\\\\w\\\\-\\\\.~/]+$',\n", + " 'type': 'string'},\n", + " 'name': 'id',\n", + " 'description': 'The collection id.'},\n", + " {'schema': [{'subtype': 'bounding-box',\n", + " 'title': 'Bounding Box',\n", + " 'type': 'object',\n", + " 'required': ['west', 'south', 'east', 'north'],\n", + " 'properties': {'east': {'description': 'East (upper right corner, coordinate axis 1).',\n", + " 'type': 'number'},\n", + " 'south': {'description': 'South (lower left corner, coordinate axis 2).',\n", + " 'type': 'number'},\n", + " 'crs': {'default': 4326,\n", + " 'description': 'Coordinate reference system of the extent, specified as as [EPSG code](http://www.epsg-registry.org/), [WKT2 (ISO 19162) string](http://docs.opengeospatial.org/is/18-010r7/18-010r7.html) or [PROJ definition (deprecated)](https://proj.org/usage/quickstart.html). Defaults to `4326` (EPSG code 4326) unless the client explicitly requests a different coordinate reference system.',\n", + " 'anyOf': [{'examples': [3857],\n", + " 'subtype': 'epsg-code',\n", + " 'title': 'EPSG Code',\n", + " 'type': 'integer',\n", + " 'minimum': 1000},\n", + " {'subtype': 'wkt2-definition', 'title': 'WKT2', 'type': 'string'},\n", + " {'subtype': 'proj-definition',\n", + " 'deprecated': True,\n", + " 'title': 'PROJ definition',\n", + " 'type': 'string'}]},\n", + " 'north': {'description': 'North (upper right corner, coordinate axis 2).',\n", + " 'type': 'number'},\n", + " 'west': {'description': 'West (lower left corner, coordinate axis 1).',\n", + " 'type': 'number'},\n", + " 'base': {'description': 'Base (optional, lower left corner, coordinate axis 3).',\n", + " 'type': ['number', 'null']},\n", + " 'height': {'description': 'Height (optional, upper right corner, coordinate axis 3).',\n", + " 'type': ['number', 'null']}}},\n", + " {'subtype': 'geojson', 'title': 'GeoJSON', 'type': 'object'},\n", + " {'description': \"Don't filter spatially. All data is included in the data cube.\",\n", + " 'title': 'No filter',\n", + " 'type': 'null'}],\n", + " 'name': 'spatial_extent',\n", + " 'description': 'Limits the data to load from the collection to the specified bounding box or polygons.\\n\\nThe process puts a pixel into the data cube if the point at the pixel center intersects with the bounding box or any of the polygons (as defined in the Simple Features standard by the OGC).\\n\\nThe GeoJSON can be one of the following GeoJSON types:\\n\\n* A `Polygon` geometry,\\n* a `GeometryCollection` containing Polygons,\\n* a `Feature` with a `Polygon` geometry or\\n* a `FeatureCollection` containing `Feature`s with a `Polygon` geometry.\\n\\nSet this parameter to `null` to set no limit for the spatial extent. Be careful with this when loading large datasets!'},\n", + " {'schema': [{'minItems': 2,\n", + " 'maxItems': 2,\n", + " 'examples': [['2015-01-01T00:00:00Z', '2016-01-01T00:00:00Z'],\n", + " ['2015-01-01', '2016-01-01']],\n", + " 'subtype': 'temporal-interval',\n", + " 'type': 'array',\n", + " 'items': {'anyOf': [{'subtype': 'date-time',\n", + " 'format': 'date-time',\n", + " 'type': 'string'},\n", + " {'subtype': 'date', 'format': 'date', 'type': 'string'},\n", + " {'subtype': 'year',\n", + " 'minLength': 4,\n", + " 'pattern': '^\\\\d{4}$',\n", + " 'type': 'string',\n", + " 'maxLength': 4},\n", + " {'type': 'null'}]}},\n", + " {'description': \"Don't filter temporally. All data is included in the data cube.\",\n", + " 'title': 'No filter',\n", + " 'type': 'null'}],\n", + " 'name': 'temporal_extent',\n", + " 'description': 'Limits the data to load from the collection to the specified left-closed temporal interval. Applies to all temporal dimensions. The interval has to be specified as an array with exactly two elements:\\n\\n1. The first element is the start of the temporal interval. The specified instance in time is **included** in the interval.\\n2. The second element is the end of the temporal interval. The specified instance in time is **excluded** from the interval.\\n\\nThe specified temporal strings follow [RFC 3339](https://tools.ietf.org/html/rfc3339). Also supports open intervals by setting one of the boundaries to `null`, but never both.\\n\\nSet this parameter to `null` to set no limit for the spatial extent. Be careful with this when loading large datasets!'},\n", + " {'schema': [{'type': 'array',\n", + " 'items': {'subtype': 'band-name', 'type': 'string'}},\n", + " {'description': \"Don't filter bands. All bands are included in the data cube.\",\n", + " 'title': 'No filter',\n", + " 'type': 'null'}],\n", + " 'name': 'bands',\n", + " 'description': \"Only adds the specified bands into the data cube so that bands that don't match the list of band names are not available. Applies to all dimensions of type `bands`.\\n\\nEither the unique band name (metadata field `name` in bands) or one of the common band names (metadata field `common_name` in bands) can be specified. If unique band name and common name conflict, the unique band name has higher priority.\\n\\nThe order of the specified array defines the order of the bands in the data cube. f multiple bands match a common name, all matched bands are included in the original order.\",\n", + " 'optional': True},\n", + " {'schema': [{'subtype': 'metadata-filter',\n", + " 'description': 'A list of filters to check against. Specify key-value-pairs with the key being the name of the metadata property name and the value being a process evaluated against the metadata values.',\n", + " 'additionalProperties': {'subtype': 'process-graph',\n", + " 'type': 'object',\n", + " 'parameters': [{'schema': {'description': 'Any data type.'},\n", + " 'name': 'value',\n", + " 'description': 'The property value to be checked against.'}]},\n", + " 'type': 'object',\n", + " 'title': 'Filters'},\n", + " {'description': \"Don't filter by metadata properties.\",\n", + " 'title': 'No filter',\n", + " 'type': 'null'}],\n", + " 'name': 'properties',\n", + " 'description': 'Limits the data by metadata properties to include only data in the data cube which all given conditions return `true` for (AND operation).\\n\\nSpecify key-value-pairs with the key being the name of the metadata property, which can be retrieved with the openEO Data Discovery for Collections. The value must a condition (user-defined process) to be evaluated against the collection metadata, see the example.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes', 'import'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'https://proj.org/usage/projections.html',\n", + " 'title': 'PROJ parameters for cartographic projections'},\n", + " {'rel': 'about',\n", + " 'href': 'http://www.epsg-registry.org',\n", + " 'title': 'Official EPSG code registry'},\n", + " {'rel': 'about',\n", + " 'href': 'http://www.epsg.io',\n", + " 'title': 'Unofficial EPSG code database'},\n", + " {'rel': 'about',\n", + " 'href': 'http://www.opengeospatial.org/standards/sfa',\n", + " 'title': 'Simple Features standard by the OGC'},\n", + " {'rel': 'about',\n", + " 'href': 'https://github.com/radiantearth/stac-spec/tree/master/extensions/eo#common-band-names',\n", + " 'title': 'List of common band names as specified by the STAC specification'}],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': \"A data cube for further processing. The dimensions and dimension properties (name, type, labels, reference system and resolution) correspond to the collection's metadata, but the dimension labels are restricted as specified in the parameters.\"},\n", + " 'exceptions': {},\n", + " 'examples': [{'description': 'Loading `Sentinel-2B` data from a `Sentinel-2` collection for 2018, but only with cloud cover between 0 and 50%.',\n", + " 'arguments': {'temporal_extent': ['2018-01-01', '2019-01-01'],\n", + " 'spatial_extent': {'east': 16.6,\n", + " 'south': 47.2,\n", + " 'north': 48.6,\n", + " 'west': 16.1},\n", + " 'id': 'Sentinel-2',\n", + " 'properties': {'eo:cloud_cover': {'process_graph': {'cc': {'result': True,\n", + " 'process_id': 'between',\n", + " 'arguments': {'min': 0,\n", + " 'max': 50,\n", + " 'x': {'from_parameter': 'value'}}}}},\n", + " 'platform': {'process_graph': {'pf': {'result': True,\n", + " 'process_id': 'eq',\n", + " 'arguments': {'case_sensitive': False,\n", + " 'x': {'from_parameter': 'value'},\n", + " 'y': 'Sentinel-2B'}}}}}}}]},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'radar_mask',\n", + " 'summary': 'Compute Radar Mask from DEM (Digital Elevation Model) and LIA (Local Incidence Angle)',\n", + " 'description': 'Computes the Radar Mask taking into account Layover, Foreshortening and Shadow. It returns an array with the new bands as integers: 1:layover_mask, 2:foreshortening_mask and 3:shadow_mask. This process must be used in an apply_dimension process, since it acts on pixel level but modifies the existing number of dimensions (bands). You need to use rename_labels to assign the band labels afterwards.',\n", + " 'parameters': [{'schema': {'type': 'array',\n", + " 'items': {'type': ['number', 'null']}},\n", + " 'name': 'data',\n", + " 'description': 'An array of numbers with two bands (DEM and LIA), expected to be indexed in that order. If the given array is a labeled array the bands should be called DEM and LIA, no other dimensions allowed (no temporal dimension)'},\n", + " {'schema': {'minItems': 1, 'type': 'float'},\n", + " 'default': 0.2,\n", + " 'name': 'foreshortening_th',\n", + " 'description': 'The foreshortening threshold, between 0 and 1.'},\n", + " {'schema': {'minItems': 1, 'type': 'float'},\n", + " 'default': 1.0,\n", + " 'name': 'layover_th',\n", + " 'description': 'The layover threshold, must be greater than 0.'},\n", + " {'schema': [{'type': 'string', 'enum': ['ASC', 'DSC']}],\n", + " 'default': 0,\n", + " 'name': 'orbit_direction',\n", + " 'description': 'The Sentinel-1 orbit direction.',\n", + " 'optional': False}],\n", + " 'categories': ['cubes'],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'An array with with zeros for the masked pixels and ones for the valid pixels.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[WCPS]',\n", + " 'id': 'tanh',\n", + " 'summary': 'Hyperbolic tangent',\n", + " 'description': 'Computes the hyperbolic tangent of `x`. The tangent is defined to be the hyperbolic sine of x divided by the hyperbolic cosine of x.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'An angle in radians.'}],\n", + " 'categories': ['math > trigonometric'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/HyperbolicTangent.html',\n", + " 'title': 'Hyperbolic tangent explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed hyperbolic tangent of `x`.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 0}, 'returns': 0}]},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'apply_dimension',\n", + " 'summary': 'Apply a process to pixels along a dimension',\n", + " 'description': 'Applies a process to all pixel values along a dimension of a raster data cube. For example, if the temporal dimension is specified the process will work on a time series of pixel values.\\n\\nThe process ``reduce_dimension()`` also applies a process to pixel values along a dimension, but drops the dimension afterwards. The process ``apply()`` applies a process to each pixel value in the data cube.\\n\\nThe target dimension is the source dimension if not specified otherwise in the `target_dimension` parameter. The pixel values in the target dimension get replaced by the computed pixel values. The name, type and reference system are preserved.\\n\\nThe dimension labels are preserved when the target dimension is the source dimension and the number of pixel values in the source dimension is equal to the number of values computed by the process. Otherwise, the dimension labels will be incrementing integers starting from zero, which can be changed using ``rename_labels()`` afterwards. The number of labels will equal to the number of values computed by the process.',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A data cube.'},\n", + " {'schema': {'subtype': 'process-graph',\n", + " 'returns': {'schema': {'type': 'array',\n", + " 'items': {'description': 'Any data type.'}},\n", + " 'description': 'The value to be set in the new data cube.'},\n", + " 'type': 'object',\n", + " 'parameters': [{'schema': {'subtype': 'labeled-array',\n", + " 'type': 'array',\n", + " 'items': {'description': 'Any data type.'}},\n", + " 'name': 'data',\n", + " 'description': 'A labeled array with elements of any type.'},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'context',\n", + " 'description': 'Additional data passed by the user.',\n", + " 'optional': True}]},\n", + " 'name': 'process',\n", + " 'description': 'Process to be applied on all pixel values. The specified process needs to accept an array and must return an array with at least one element. A process may consist of multiple sub-processes.'},\n", + " {'schema': {'type': 'string'},\n", + " 'name': 'dimension',\n", + " 'description': 'The name of the source dimension to apply the process on. Fails with a `DimensionNotAvailable` exception if the specified dimension does not exist.'},\n", + " {'schema': {'type': ['string', 'null']},\n", + " 'name': 'target_dimension',\n", + " 'description': \"The name of the target dimension or `null` (the default) to use the source dimension specified in the parameter `dimension`.\\n\\nBy specifying a target dimension, the source dimension is removed. The target dimension with the specified name and the type `other` (see ``add_dimension()``) is created, if it doesn't exist yet.\",\n", + " 'optional': True},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'context',\n", + " 'description': 'Additional data to be passed to the process.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'https://openeo.org/documentation/1.0/datacubes.html#apply',\n", + " 'title': 'Apply explained in the openEO documentation'}],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A data cube with the newly computed values.\\n\\nAll dimensions stay the same, except for the dimensions specified in corresponding parameters. There are three cases how the dimensions can change:\\n\\n1. The source dimension is the target dimension:\\n - The (number of) dimensions remain unchanged as the source dimension is the target dimension.\\n - The source dimension properties name and type remain unchanged.\\n - The dimension labels, the reference system and the resolution are preserved only if the number of pixel values in the source dimension is equal to the number of values computed by the process. Otherwise, all other dimension properties change as defined in the list below.\\n2. The source dimension is not the target dimension and the latter exists:\\n - The number of dimensions decreases by one as the source dimension is dropped.\\n - The target dimension properties name and type remain unchanged. All other dimension properties change as defined in the list below.\\n3. The source dimension is not the target dimension and the latter does not exist:\\n - The number of dimensions remain unchanged, but the source dimension is replaced with the target dimension.\\n - The target dimension has the specified name and the type other. All other dimension properties are set as defined in the list below.\\n\\nUnless otherwise stated above, for the given (target) dimension the following applies:\\n\\n- the number of dimension labels is equal to the number of values computed by the process,\\n- the dimension labels are incrementing integers starting from zero,\\n- the resolution changes, and\\n- the reference system is undefined.'},\n", + " 'exceptions': {'DimensionNotAvailable': {'message': 'A dimension with the specified name does not exist.'}}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'sd',\n", + " 'summary': 'Standard deviation',\n", + " 'description': 'Computes the sample standard deviation, which quantifies the amount of variation of an array of numbers. It is defined to be the square root of the corresponding variance (see ``variance()``).\\n\\nA low standard deviation indicates that the values tend to be close to the expected value, while a high standard deviation indicates that the values are spread out over a wider range.\\n\\nAn array without non-`null` elements resolves always with `null`.',\n", + " 'parameters': [{'schema': {'type': 'array',\n", + " 'items': {'type': ['number', 'null']}},\n", + " 'name': 'data',\n", + " 'description': 'An array of numbers.'},\n", + " {'schema': {'type': 'boolean'},\n", + " 'default': True,\n", + " 'name': 'ignore_nodata',\n", + " 'description': 'Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that `null` is returned if any value is such a value.',\n", + " 'optional': True}],\n", + " 'categories': ['math', 'reducer'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/StandardDeviation.html',\n", + " 'title': 'Standard deviation explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed sample standard deviation.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'data': [-1, 1, 3, None]}, 'returns': 2},\n", + " {'arguments': {'data': [-1, 1, 3, None], 'ignore_nodata': False}},\n", + " {'description': 'The input array is empty: return `null`.',\n", + " 'arguments': {'data': []}}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'not',\n", + " 'summary': 'Inverting a boolean',\n", + " 'description': 'Inverts a single boolean so that `true` gets `false` and `false` gets `true`.\\n\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", + " 'parameters': [{'schema': {'type': ['boolean', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'Boolean value to invert.'}],\n", + " 'categories': ['logic'],\n", + " 'returns': {'schema': {'type': ['boolean', 'null']},\n", + " 'description': 'Inverted boolean value.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {}},\n", + " {'arguments': {'x': False}, 'returns': True},\n", + " {'arguments': {'x': True}, 'returns': False}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'min',\n", + " 'summary': 'Minimum value',\n", + " 'description': 'Computes the smallest value of an array of numbers, which is is equal to the last element of a sorted (i.e., ordered) version the array.\\n\\nAn array without non-`null` elements resolves always with `null`.',\n", + " 'parameters': [{'schema': {'type': 'array',\n", + " 'items': {'type': ['number', 'null']}},\n", + " 'name': 'data',\n", + " 'description': 'An array of numbers.'},\n", + " {'schema': {'type': 'boolean'},\n", + " 'default': True,\n", + " 'name': 'ignore_nodata',\n", + " 'description': 'Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that `null` is returned if any value is such a value.',\n", + " 'optional': True}],\n", + " 'categories': ['math', 'reducer'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/Minimum.html',\n", + " 'title': 'Minimum explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The minimum value.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'data': [1, 0, 3, 2]}, 'returns': 0},\n", + " {'arguments': {'data': [5, 2.5, None, -0.7]}, 'returns': -0.7},\n", + " {'arguments': {'data': [1, 0, 3, None, 2], 'ignore_nodata': False}},\n", + " {'arguments': {'data': []}}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'linear_scale_range',\n", + " 'summary': 'Linear transformation between two ranges',\n", + " 'description': 'Performs a linear transformation between the input and output range.\\n\\nThe given number in `x` is clipped to the bounds specified in `inputMin` and `inputMax` so that the underlying formula *((x - inputMin) / (inputMax - inputMin)) * (outputMax - outputMin) + outputMin* never returns any value lower than `outputMin` or greater than `outputMax`.\\n\\nPotential use case include\\n\\n* scaling values to the 8-bit range (0 - 255) often used for numeric representation of values in one of the channels of the [RGB colour model](https://en.wikipedia.org/wiki/RGB_color_model#Numeric_representations) or\\n* calculating percentages (0 - 100).\\n\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'A number to transform. The number gets clipped to the bounds specified in `inputMin` and `inputMax`.'},\n", + " {'schema': {'type': 'number'},\n", + " 'name': 'inputMin',\n", + " 'description': 'Minimum value the input can obtain.'},\n", + " {'schema': {'type': 'number'},\n", + " 'name': 'inputMax',\n", + " 'description': 'Maximum value the input can obtain.'},\n", + " {'schema': {'type': 'number'},\n", + " 'default': 0,\n", + " 'name': 'outputMin',\n", + " 'description': 'Minimum value of the desired output range.',\n", + " 'optional': True},\n", + " {'schema': {'type': 'number'},\n", + " 'default': 1,\n", + " 'name': 'outputMax',\n", + " 'description': 'Maximum value of the desired output range.',\n", + " 'optional': True}],\n", + " 'categories': ['math'],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The transformed number.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'inputMax': 1,\n", + " 'inputMin': -1,\n", + " 'x': 0.3,\n", + " 'outputMin': 0,\n", + " 'outputMax': 255},\n", + " 'returns': 165.75},\n", + " {'arguments': {'inputMax': 255, 'inputMin': 0, 'x': 25.5}, 'returns': 0.1},\n", + " {'arguments': {'inputMax': 100, 'inputMin': 0}},\n", + " {'description': 'Shows that the input data is clipped.',\n", + " 'arguments': {'inputMax': 1,\n", + " 'inputMin': 0,\n", + " 'x': 1.12,\n", + " 'outputMin': 0,\n", + " 'outputMax': 255},\n", + " 'returns': 255}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'and',\n", + " 'summary': 'Logical AND',\n", + " 'description': 'Checks if **both** values are true.\\n\\nEvaluates parameter `x` before `y` and stops once the outcome is unambiguous. If any argument is `null`, the result will be `null` if the outcome is ambiguous.\\n\\n**Truth table:**\\n\\n```\\na \\\\ b || null | false | true\\n----- || ----- | ----- | -----\\nnull || null | false | null\\nfalse || false | false | false\\ntrue || null | false | true\\n```',\n", + " 'parameters': [{'schema': {'type': ['boolean', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'A boolean value.'},\n", + " {'schema': {'type': ['boolean', 'null']},\n", + " 'name': 'y',\n", + " 'description': 'A boolean value.'}],\n", + " 'categories': ['logic'],\n", + " 'returns': {'schema': {'type': ['boolean', 'null']},\n", + " 'description': 'Boolean result of the logical AND.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': True, 'y': True}, 'returns': True},\n", + " {'arguments': {'x': True, 'y': False}, 'returns': False},\n", + " {'arguments': {'x': False, 'y': False}, 'returns': False},\n", + " {'arguments': {'x': False}, 'returns': False},\n", + " {'arguments': {'x': True}}]},\n", + " {'engine': '[WCPS]',\n", + " 'id': 'sin',\n", + " 'summary': 'Sine',\n", + " 'description': 'Computes the sine of `x`.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'An angle in radians.'}],\n", + " 'categories': ['math > trigonometric'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/Sine.html',\n", + " 'title': 'Sine explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed sine of `x`.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 0}, 'returns': 0}]},\n", + " {'engine': '[WCPS]',\n", + " 'id': 'ndvi',\n", + " 'summary': 'Normalized Difference Vegetation Index',\n", + " 'description': \"Computes the Normalized Difference Vegetation Index (NDVI). The NDVI is computed as *(nir - red) / (nir + red)*.\\n\\nThe `data` parameter expects a raster data cube with a dimension of type `bands` or a `DimensionAmbiguous` error is thrown otherwise. By default, the dimension must have at least two bands with the common names `red` and `nir` assigned or the user need to specify the parameters `nir` and `red`. Otherwise either the error `NirBandAmbiguous` or `RedBandAmbiguous` is thrown. The common names for each band are specified in the collection's band metadata and are *not* equal to the band names.\\n\\nBy default, the dimension of type `bands` is dropped by this process. To keep the dimension specify a new band name in the parameter `target_band`. This adds a new dimension label with the specified name to the dimension, which can be used to access the computed values. If a band with the specified name exists, a `BandExists` is thrown.\\n\\nThis process is very similar to the process ``normalized_difference()``, but determines the bands automatically based on the common names (`red`/`nir`) specified in the metadata.\",\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A raster data cube with two bands that have the common names `red` and `nir` assigned.'},\n", + " {'schema': {'subtype': 'band-name', 'type': 'string'},\n", + " 'default': 'nir',\n", + " 'name': 'nir',\n", + " 'description': 'The name of the NIR band. Defaults to the band that has the common name `nir` assigned.\\n\\nEither the unique band name (metadata field `name` in bands) or one of the common band names (metadata field `common_name` in bands) can be specified. If unique band name and common name conflict, the unique band name has higher priority.',\n", + " 'optional': True},\n", + " {'schema': {'subtype': 'band-name', 'type': 'string'},\n", + " 'default': 'red',\n", + " 'name': 'red',\n", + " 'description': 'The name of the red band. Defaults to the band that has the common name `red` assigned.\\n\\nEither the unique band name (metadata field `name` in bands) or one of the common band names (metadata field `common_name` in bands) can be specified. If unique band name and common name conflict, the unique band name has higher priority.',\n", + " 'optional': True},\n", + " {'schema': [{'pattern': '^\\\\w+$', 'type': 'string'}, {'type': 'null'}],\n", + " 'name': 'target_band',\n", + " 'description': 'By default, the dimension of type `bands` is dropped. To keep the dimension specify a new band name in this parameter so that a new dimension label with the specified name will be added for the computed values.',\n", + " 'optional': True}],\n", + " 'categories': ['math > indices', 'vegetation indices'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'https://en.wikipedia.org/wiki/Normalized_difference_vegetation_index',\n", + " 'title': 'NDVI explained by Wikipedia'},\n", + " {'rel': 'about',\n", + " 'href': 'https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_2.php',\n", + " 'title': 'NDVI explained by NASA'},\n", + " {'rel': 'about',\n", + " 'href': 'https://github.com/radiantearth/stac-spec/tree/master/extensions/eo#common-band-names',\n", + " 'title': 'List of common band names as specified by the STAC specification'}],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A raster data cube containing the computed NDVI values. The structure of the data cube differs depending on the value passed to `target_band`:\\n\\n* `target_band` is `null`: The data cube does not contain the dimension of type `bands` any more, the number of dimensions decreases by one. The dimension properties (name, type, labels, reference system and resolution) for all other dimensions remain unchanged.\\n* `target_band` is a string: The data cube keeps the same dimensions. The dimension properties remain unchanged, but the number of dimension labels for the dimension of type `bands` increases by one. The additional label is named as specified in `target_band`.'},\n", + " 'exceptions': {'NirBandAmbiguous': {'message': \"The NIR band can't be resolved, please specify a band name.\"},\n", + " 'BandExists': {'message': 'A band with the specified target name exists.'},\n", + " 'RedBandAmbiguous': {'message': \"The red band can't be resolved, please specify a band name.\"},\n", + " 'DimensionAmbiguous': {'message': 'dimension of type `bands` is not available or is ambiguous..'}}},\n", + " {'engine': '[WCPS]',\n", + " 'id': 'xor',\n", + " 'summary': 'Logical XOR (exclusive or)',\n", + " 'description': 'Checks if **exactly one** of the values is true. If a component is `null`, the result will be `null` if the outcome is ambiguous.\\n\\n**Truth table:**\\n\\n```\\na \\\\ b || null | false | true\\n----- || ---- | ----- | -----\\nnull || null | null | null\\nfalse || null | false | true\\ntrue || null | true | false\\n```',\n", + " 'parameters': [{'schema': {'type': ['boolean', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'A boolean value.'},\n", + " {'schema': {'type': ['boolean', 'null']},\n", + " 'name': 'y',\n", + " 'description': 'A boolean value.'}],\n", + " 'categories': ['logic'],\n", + " 'returns': {'schema': {'type': ['boolean', 'null']},\n", + " 'description': 'Boolean result of the logical XOR.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': True, 'y': True}, 'returns': False},\n", + " {'arguments': {'x': False, 'y': False}, 'returns': False},\n", + " {'arguments': {'x': True, 'y': False}, 'returns': True},\n", + " {'arguments': {'x': True}},\n", + " {'arguments': {'x': False}}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'divide',\n", + " 'summary': 'Division of two numbers',\n", + " 'description': 'Divides argument `x` by the argument `y` (*x / y*) and returns the computed result.\\n\\nNo-data values are taken into account so that `null` is returned if any element is such a value.\\n\\nThe computations follow [IEEE Standard 754](https://ieeexplore.ieee.org/document/8766229) whenever the processing environment supports it. Therefore, a division by zero results in ±infinity if the processing environment supports it. Otherwise a `DivisionByZero` error must the thrown.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'The dividend.'},\n", + " {'schema': {'type': ['number', 'null']},\n", + " 'name': 'y',\n", + " 'description': 'The divisor.'}],\n", + " 'categories': ['math'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/Division.html',\n", + " 'title': 'Division explained by Wolfram MathWorld'},\n", + " {'rel': 'about',\n", + " 'href': 'https://ieeexplore.ieee.org/document/8766229',\n", + " 'title': 'IEEE Standard 754-2019 for Floating-Point Arithmetic'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed result.'},\n", + " 'exceptions': {'DivisionByZero': {'message': 'Division by zero is not supported.'}},\n", + " 'examples': [{'arguments': {'x': 5, 'y': 2.5}, 'returns': 2},\n", + " {'arguments': {'x': -2, 'y': 4}, 'returns': -0.5},\n", + " {'arguments': {'x': 1}}]},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'fit_regr_random_forest',\n", + " 'summary': 'Train a random forest regression model',\n", + " 'description': 'Executes the fit of a random forest regression based on the user input of target and predictors. The Random Forest regression model is based on the approach by Breiman (2001).',\n", + " 'parameters': [{'schema': {'subtype': 'vector-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'The input data for the regression model. The raster images that will be used as predictors for the Random Forest. Aggregated to the features (vectors) of the target input variable.'},\n", + " {'schema': {'subtype': 'vector-cube', 'type': 'object'},\n", + " 'name': 'target',\n", + " 'description': 'The input data for the regression model. This will be vector cubes for each training site. This is associated with the target variable for the Random Forest Model. The Geometry has to associated with a value to predict (e.g. fractional forest canopy cover).'},\n", + " {'schema': {'maximum': 100, 'type': 'number', 'exclusiveMinimum': 0},\n", + " 'name': 'training',\n", + " 'description': 'The amount of training data to be used in the regression. The sampling will be randomly through the data object. The remaining data will be used as test data for the validation.'},\n", + " {'schema': {'type': 'integer', 'minimum': 1},\n", + " 'default': 100,\n", + " 'name': 'num_trees',\n", + " 'description': 'The number of trees build within the Random Forest regression.',\n", + " 'optional': True},\n", + " {'schema': [{'type': 'integer', 'minimum': 1}, {'type': 'null'}],\n", + " 'name': 'mtry',\n", + " 'description': 'Specifies how many split variables will be used at a node. Default value is `null`, which corresponds to the number of predictors divided by 3.',\n", + " 'optional': True}],\n", + " 'categories': ['machine learning'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'https://doi.org/10.1023/A:1010933404324',\n", + " 'type': 'text/html',\n", + " 'title': 'Breiman (2001): Random Forests'}],\n", + " 'returns': {'schema': {'subtype': 'ml-model', 'type': 'object'},\n", + " 'description': 'A model object that can be saved with ``save_ml_model()`` and restored with ``load_ml_model()``.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'power',\n", + " 'summary': 'Exponentiation',\n", + " 'description': 'Computes the exponentiation for the base `base` raised to the power of `p`.\\n\\nThe no-data value `null` is passed through and therefore gets propagated if any of the arguments is `null`.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'base',\n", + " 'description': 'The numerical base.'},\n", + " {'schema': {'type': ['number', 'null']},\n", + " 'name': 'p',\n", + " 'description': 'The numerical exponent.'}],\n", + " 'categories': ['math', 'math > exponential & logarithmic'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/Power.html',\n", + " 'title': 'Power explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed value for `base` raised to the power of `p`.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'p': 2, 'base': 0}, 'returns': 0},\n", + " {'arguments': {'p': 0, 'base': 2.5}, 'returns': 1},\n", + " {'arguments': {'p': 3, 'base': 3}, 'returns': 27},\n", + " {'arguments': {'p': -1, 'base': 5}, 'returns': 0.2},\n", + " {'arguments': {'p': 0.5, 'base': 1}, 'returns': 1},\n", + " {'arguments': {'base': 1}},\n", + " {'arguments': {'p': 2}}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'lte',\n", + " 'summary': 'Less than or equal to comparison',\n", + " 'description': 'Compares whether `x` is less than or equal to `y`.\\n\\n**Remarks:**\\n\\n* If any operand is `null`, the return value is `null`. Therefore, `lte(null, null)` returns `null` instead of `true`.\\n* If any operand is an array or object, the return value is `false`.\\n* If the operands are not equal (see process ``eq()``) and any of them is not a `number` or temporal string (`date`, `time` or `date-time`), the process returns `false`.\\n* Temporal strings can *not* be compared based on their string representation due to the time zone / time-offset representations.',\n", + " 'parameters': [{'schema': {'description': 'Any data type is allowed.'},\n", + " 'name': 'x',\n", + " 'description': 'First operand.'},\n", + " {'schema': {'description': 'Any data type is allowed.'},\n", + " 'name': 'y',\n", + " 'description': 'Second operand.'}],\n", + " 'categories': ['comparison'],\n", + " 'returns': {'schema': {'type': ['boolean', 'null']},\n", + " 'description': '`true` if `x` is less than or equal to `y`, `null` if any operand is `null`, otherwise `false`.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 1}},\n", + " {'arguments': {'x': 0, 'y': 0}, 'returns': True},\n", + " {'arguments': {'x': 1, 'y': 2}, 'returns': True},\n", + " {'arguments': {'x': -0.5, 'y': -0.6}, 'returns': False},\n", + " {'arguments': {'x': '00:00:00+01:00', 'y': '00:00:00Z'}, 'returns': True},\n", + " {'arguments': {'x': '1950-01-01T00:00:00Z', 'y': '2018-01-01T12:00:00Z'},\n", + " 'returns': True},\n", + " {'arguments': {'x': '2018-01-01T12:00:00+00:00',\n", + " 'y': '2018-01-01T12:00:00Z'},\n", + " 'returns': True},\n", + " {'arguments': {'x': False, 'y': True}, 'returns': False},\n", + " {'arguments': {'x': [1, 2, 3], 'y': [1, 2, 3]}, 'returns': False}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'multiply',\n", + " 'summary': 'Multiplication of two numbers',\n", + " 'description': 'Multiplies the two numbers `x` and `y` (*x * y*) and returns the computed product.\\n\\nNo-data values are taken into account so that `null` is returned if any element is such a value.\\n\\nThe computations follow [IEEE Standard 754](https://ieeexplore.ieee.org/document/8766229) whenever the processing environment supports it.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'The multiplier.'},\n", + " {'schema': {'type': ['number', 'null']},\n", + " 'name': 'y',\n", + " 'description': 'The multiplicand.'}],\n", + " 'categories': ['math'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/Product.html',\n", + " 'title': 'Product explained by Wolfram MathWorld'},\n", + " {'rel': 'about',\n", + " 'href': 'https://ieeexplore.ieee.org/document/8766229',\n", + " 'title': 'IEEE Standard 754-2019 for Floating-Point Arithmetic'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed product of the two numbers.'},\n", + " 'exceptions': {'MultiplicandMissing': {'message': 'Multiplication requires at least two numbers.'}},\n", + " 'examples': [{'arguments': {'x': 5, 'y': 2.5}, 'returns': 12.5},\n", + " {'arguments': {'x': -2, 'y': -4}, 'returns': 8},\n", + " {'arguments': {'x': 1}}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'mask',\n", + " 'summary': 'Apply a raster mask',\n", + " 'description': \"Applies a mask to a raster data cube. To apply a vector mask use ``mask_polygon()``.\\n\\nA mask is a raster data cube for which corresponding pixels among `data` and `mask` are compared and those pixels in `data` are replaced whose pixels in `mask` are non-zero (for numbers) or `true` (for boolean values). The pixel values are replaced with the value specified for `replacement`, which defaults to `null` (no data).\\n\\nThe data cubes have to be compatible so that each dimension in mask must also be available in the raster data cube with the same name, type, reference system, resolution and labels. Dimensions can be missing in the mask with the result that the mask is applied for each label of the missing dimension in the data cube. The process fails if there's an incompatibility found between the raster data cube and the mask.\",\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A raster data cube.'},\n", + " {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'mask',\n", + " 'description': 'A mask as raster data cube. Every pixel in `data` must have a corresponding element in `mask`.'},\n", + " {'schema': {'type': ['number', 'boolean', 'string', 'null']},\n", + " 'name': 'replacement',\n", + " 'description': 'The value used to replace masked values with.',\n", + " 'optional': True}],\n", + " 'categories': ['masks'],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A masked raster data cube with the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'aggregate_spatial',\n", + " 'summary': 'Zonal statistics for geometries',\n", + " 'description': \"Aggregates statistics for one or more geometries (e.g. zonal statistics for polygons) over the spatial dimensions. The number of total and valid pixels is returned together with the calculated values.\\n\\nAn 'unbounded' aggregation over the full extent of the horizontal spatial dimensions can be computed with the process ``reduce_spatial()``.\\n\\nThis process passes a list of values to the reducer. The list of values has an undefined order, therefore processes such as ``last()`` and ``first()`` that depend on the order of the values will lead to unpredictable results.\",\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A raster data cube.\\n\\nThe data cube must have been reduced to only contain two spatial dimensions and a third dimension the values are aggregated for, for example the temporal dimension to get a time series. Otherwise, this process fails with the `TooManyDimensions` exception.\\n\\nThe data cube implicitly gets restricted to the bounds of the geometries as if ``filter_spatial()`` would have been used with the same values for the corresponding parameters immediately before this process.'},\n", + " {'schema': {'subtype': 'geojson', 'type': 'object'},\n", + " 'name': 'geometries',\n", + " 'description': 'Geometries as GeoJSON on which the aggregation will be based.\\n\\nOne value will be computed per GeoJSON `Feature`, `Geometry` or `GeometryCollection`. For a `FeatureCollection` multiple values will be computed, one value per contained `Feature`. For example, a single value will be computed for a `MultiPolygon`, but two values will be computed for a `FeatureCollection` containing two polygons.\\n\\n- For **polygons**, the process considers all pixels for which the point at the pixel center intersects with the corresponding polygon (as defined in the Simple Features standard by the OGC).\\n- For **points**, the process considers the closest pixel center.\\n- For **lines** (line strings), the process considers all the pixels whose centers are closest to at least one point on the line.\\n\\nThus, pixels may be part of multiple geometries and be part of multiple aggregations.\\n\\nTo maximize interoperability, a nested `GeometryCollection` should be avoided. Furthermore, a `GeometryCollection` composed of a single type of geometries should be avoided in favour of the corresponding multi-part type (e.g. `MultiPolygon`).'},\n", + " {'schema': {'subtype': 'process-graph',\n", + " 'returns': {'schema': {'description': 'Any data type.'},\n", + " 'description': 'The value to be set in the vector data cube.'},\n", + " 'type': 'object',\n", + " 'parameters': [{'schema': {'type': 'array',\n", + " 'items': {'description': 'Any data type.'}},\n", + " 'name': 'data',\n", + " 'description': 'An array with elements of any type.'},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'context',\n", + " 'description': 'Additional data passed by the user.',\n", + " 'optional': True}]},\n", + " 'name': 'reducer',\n", + " 'description': \"A reducer to be applied on all values of each geometry. A reducer is a single process such as ``mean()`` or a set of processes, which computes a single value for a list of values, see the category 'reducer' for such processes.\"},\n", + " {'schema': {'type': 'string'},\n", + " 'default': 'result',\n", + " 'name': 'target_dimension',\n", + " 'description': 'The new dimension name to be used for storing the results. Defaults to `result`.',\n", + " 'optional': True},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'context',\n", + " 'description': 'Additional data to be passed to the reducer.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes', 'aggregate & resample'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'https://openeo.org/documentation/1.0/datacubes.html#aggregate',\n", + " 'title': 'Aggregation explained in the openEO documentation'},\n", + " {'rel': 'about',\n", + " 'href': 'http://www.opengeospatial.org/standards/sfa',\n", + " 'title': 'Simple Features standard by the OGC'}],\n", + " 'returns': {'schema': {'subtype': 'vector-cube', 'type': 'object'},\n", + " 'description': 'A vector data cube with the computed results and restricted to the bounds of the geometries.\\n\\nThe computed value is used for the dimension with the name that was specified in the parameter `target_dimension`.\\n\\nThe computation also stores information about the total count of pixels (valid + invalid pixels) and the number of valid pixels (see ``is_valid()``) for each geometry. These values are added as a new dimension with a dimension name derived from `target_dimension` by adding the suffix `_meta`. The new dimension has the dimension labels `total_count` and `valid_count`.'},\n", + " 'exceptions': {'TooManyDimensions': {'message': 'The number of dimensions must be reduced to three for `aggregate_spatial`.'}}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'product',\n", + " 'summary': 'Compute the product by multiplying numbers',\n", + " 'description': 'Multiplies all elements in a sequential array of numbers and returns the computed product.\\n\\nBy default no-data values are ignored. Setting `ignore_nodata` to `false` considers no-data values so that `null` is returned if any element is such a value.\\n\\nThe computations follow [IEEE Standard 754](https://ieeexplore.ieee.org/document/8766229) whenever the processing environment supports it.',\n", + " 'parameters': [{'schema': {'type': 'array',\n", + " 'items': {'type': ['number', 'null']}},\n", + " 'name': 'data',\n", + " 'description': 'An array of numbers.'},\n", + " {'schema': {'type': 'boolean'},\n", + " 'default': True,\n", + " 'name': 'ignore_nodata',\n", + " 'description': 'Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that `null` is returned if any value is such a value.',\n", + " 'optional': True}],\n", + " 'categories': ['math', 'reducer'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/Product.html',\n", + " 'title': 'Product explained by Wolfram MathWorld'},\n", + " {'rel': 'about',\n", + " 'href': 'https://ieeexplore.ieee.org/document/8766229',\n", + " 'title': 'IEEE Standard 754-2019 for Floating-Point Arithmetic'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed product of the sequence of numbers.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'data': [5, 0]}, 'returns': 0},\n", + " {'arguments': {'data': [-2, 4, 2.5]}, 'returns': -20},\n", + " {'arguments': {'data': [1, None], 'ignore_nodata': False}},\n", + " {'arguments': {'data': [-1]}, 'returns': -1},\n", + " {'arguments': {'data': [None], 'ignore_nodata': False}},\n", + " {'arguments': {'data': []}}]},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'load_result',\n", + " 'summary': 'Load batch job results',\n", + " 'description': 'Loads batch job results by job id from the server-side user workspace. The job must have been stored by the authenticated user on the back-end currently connected to.',\n", + " 'parameters': [{'schema': {'subtype': 'job-id',\n", + " 'pattern': '^[\\\\w\\\\-\\\\.~]+$',\n", + " 'type': 'string'},\n", + " 'name': 'id',\n", + " 'description': 'The id of a batch job with results.'}],\n", + " 'categories': ['cubes', 'import'],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A data cube for further processing.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'rename_labels',\n", + " 'summary': 'Rename dimension labels',\n", + " 'description': \"Renames the labels of the specified dimension in the data cube from `source` to `target`.\\n\\nIf the array for the source labels is empty (the default), the dimension labels are expected to be enumerated with zero-based numbering (0,1,2,3,...) so that the dimension labels directly map to the indices of the array specified for the parameter `target`. If the dimension labels are not enumerated and the `target` parameter is not specified, a `LabelsNotEnumerated` is thrown. The number of source and target labels must be equal, otherwise the error `LabelMismatch` is thrown.\\n\\nThis process doesn't change the order of the labels and their corresponding data.\",\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'The data cube.'},\n", + " {'schema': {'type': 'string'},\n", + " 'name': 'dimension',\n", + " 'description': 'The name of the dimension to rename the labels for.'},\n", + " {'schema': {'type': 'array',\n", + " 'items': {'anyOf': [{'type': 'number'}, {'type': 'string'}]}},\n", + " 'name': 'target',\n", + " 'description': 'The new names for the labels. The dimension labels in the data cube are expected to be enumerated, if the parameter `target` is not specified. If a target dimension label already exists in the data cube, a `LabelExists` error is thrown.'},\n", + " {'schema': {'type': 'array',\n", + " 'items': {'anyOf': [{'type': 'number'}, {'type': 'string'}]}},\n", + " 'default': [],\n", + " 'name': 'source',\n", + " 'description': \"The names of the labels as they are currently in the data cube. The array defines an unsorted and potentially incomplete list of labels that should be renamed to the names available in the corresponding array elements in the parameter `target`. If one of the source dimension labels doesn't exist, a `LabelNotAvailable` error is thrown. By default, the array is empty so that the dimension labels in the data cube are expected to be enumerated.\",\n", + " 'optional': True}],\n", + " 'categories': ['cubes'],\n", + " 'links': [{'rel': 'example',\n", + " 'href': 'https://processes.openeo.org/1.0.0/examples/rename-enumerated-labels.json',\n", + " 'type': 'application/json',\n", + " 'title': 'Rename enumerated labels'}],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'The data cube with the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged, except that for the given dimension the labels change. The old labels can not be referred to any longer. The number of labels remains the same.'},\n", + " 'exceptions': {'LabelsNotEnumerated': {'message': 'The dimension labels are not enumerated.'},\n", + " 'LabelNotAvailable': {'message': 'A label with the specified name does not exist.'},\n", + " 'LabelMismatch': {'message': \"The number of labels in the parameters `source` and `target` don't match.\"},\n", + " 'LabelExists': {'message': 'A label with the specified name exists.'}},\n", + " 'examples': [{'description': 'Renaming the bands from `B1` to `red`, from `B2` to `green` and from `B3` to `blue`.',\n", + " 'arguments': {'data': {'from_parameter': 'data'},\n", + " 'source': ['B1', 'B2', 'B3'],\n", + " 'dimension': 'bands',\n", + " 'target': ['red', 'green', 'blue']},\n", + " 'title': 'Rename named labels'}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'or',\n", + " 'summary': 'Logical OR',\n", + " 'description': 'Checks if **at least one** of the values is true. Evaluates parameter `x` before `y` and stops once the outcome is unambiguous. If a component is `null`, the result will be `null` if the outcome is ambiguous.\\n\\n**Truth table:**\\n\\n```\\na \\\\ b || null | false | true\\n----- || ---- | ----- | ----\\nnull || null | null | true\\nfalse || null | false | true\\ntrue || true | true | true\\n```',\n", + " 'parameters': [{'schema': {'type': ['boolean', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'A boolean value.'},\n", + " {'schema': {'type': ['boolean', 'null']},\n", + " 'name': 'y',\n", + " 'description': 'A boolean value.'}],\n", + " 'categories': ['logic'],\n", + " 'returns': {'schema': {'type': ['boolean', 'null']},\n", + " 'description': 'Boolean result of the logical OR.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': True, 'y': True}, 'returns': True},\n", + " {'arguments': {'x': False, 'y': False}, 'returns': False},\n", + " {'arguments': {'x': True}, 'returns': True},\n", + " {'arguments': {'y': True}, 'returns': True},\n", + " {'arguments': {'x': False}}]},\n", + " {'engine': '[WCPS]',\n", + " 'id': 'e',\n", + " 'summary': \"Euler's number (e)\",\n", + " 'description': 'The real number *e* is a mathematical constant that is the base of the natural logarithm such that *ln(e) = 1*. The numerical value is approximately *2.71828*.',\n", + " 'parameters': [],\n", + " 'categories': ['math > constants', 'math > exponential & logarithmic'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/e.html',\n", + " 'title': 'Mathematical constant e explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': 'number'},\n", + " 'description': \"The numerical value of Euler's number.\"},\n", + " 'exceptions': {}},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'max',\n", + " 'summary': 'Maximum value',\n", + " 'description': 'Computes the largest value of an array of numbers, which is is equal to the first element of a sorted (i.e., ordered) version the array.\\n\\nAn array without non-`null` elements resolves always with `null`.',\n", + " 'parameters': [{'schema': {'type': 'array',\n", + " 'items': {'type': ['number', 'null']}},\n", + " 'name': 'data',\n", + " 'description': 'An array of numbers.'},\n", + " {'schema': {'type': 'boolean'},\n", + " 'default': True,\n", + " 'name': 'ignore_nodata',\n", + " 'description': 'Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that `null` is returned if any value is such a value.',\n", + " 'optional': True}],\n", + " 'categories': ['math', 'reducer'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/Maximum.html',\n", + " 'title': 'Maximum explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The maximum value.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'data': [1, 0, 3, 2]}, 'returns': 3},\n", + " {'arguments': {'data': [5, 2.5, None, -0.7]}, 'returns': 5},\n", + " {'arguments': {'data': [1, 0, 3, None, 2], 'ignore_nodata': False}},\n", + " {'description': 'The input array is empty: return `null`.',\n", + " 'arguments': {'data': []}}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'apply',\n", + " 'summary': 'Apply a process to each pixel',\n", + " 'description': 'Applies a *unary* process to each pixel value in the data cube (i.e. a local operation). A unary process takes a single value and returns a single value, for example ``abs()`` or ``linear_scale_range()``. In contrast, the process ``apply_dimension()`` applies a process to all pixel values along a particular dimension.',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A data cube.'},\n", + " {'schema': {'subtype': 'process-graph',\n", + " 'type': 'object',\n", + " 'parameters': [{'schema': {'description': 'Any data type.'},\n", + " 'name': 'x',\n", + " 'description': 'The value to process.'},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'context',\n", + " 'description': 'Additional data passed by the user.',\n", + " 'optional': True}]},\n", + " 'name': 'process',\n", + " 'description': 'A unary process to be applied on each value, may consist of multiple sub-processes.'},\n", + " {'schema': {'description': 'Any data type.'},\n", + " 'name': 'context',\n", + " 'description': 'Additional data to be passed to the process.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes'],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A data cube with the newly computed values and the same dimensions. The dimension properties (name, type, labels, reference system and resolution) remain unchanged.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[WCPS]',\n", + " 'id': 'run_udf',\n", + " 'summary': 'Run an UDF',\n", + " 'description': \"Runs an UDF in one of the supported runtime environments.\\n\\nThe process can either:\\n\\n1. load and run a locally stored UDF from a file in the workspace of the authenticated user. The path to the UDF file must be relative to the root directory of the user's workspace.\\n2. fetch and run a remotely stored and published UDF by absolute URI, for example from [openEO Hub](https://hub.openeo.org)).\\n3. run the source code specified inline as string.\\n\\nThe loaded UDF can be executed in several processes such as ``aggregate_spatial()``, ``apply()``, ``apply_dimension()`` and ``reduce_dimension()``. In this case an array is passed instead of a raster data cube. The user must ensure that the data is properly passed as an array so that the UDF can make sense of it.\",\n", + " 'parameters': [{'schema': [{'subtype': 'raster-cube',\n", + " 'title': 'Raster data cube',\n", + " 'type': 'object'},\n", + " {'minItems': 1,\n", + " 'title': 'Array',\n", + " 'type': 'array',\n", + " 'items': {'description': 'Any data type.'}},\n", + " {'description': 'A single value of any data type.',\n", + " 'title': 'Single Value'}],\n", + " 'name': 'data',\n", + " 'description': 'The data to be passed to the UDF as array or raster data cube.'},\n", + " {'schema': [{'subtype': 'uri',\n", + " 'format': 'uri',\n", + " 'description': 'URI to an UDF',\n", + " 'type': 'string'},\n", + " {'subtype': 'file-path',\n", + " 'description': 'Path to an UDF uploaded to the server.',\n", + " 'type': 'string'},\n", + " {'subtype': 'udf-code',\n", + " 'description': 'Source code as string',\n", + " 'type': 'string'}],\n", + " 'name': 'udf',\n", + " 'description': 'Either source code, an absolute URL or a path to an UDF script.'},\n", + " {'schema': {'subtype': 'udf-runtime', 'type': 'string'},\n", + " 'name': 'runtime',\n", + " 'description': 'An UDF runtime identifier available at the back-end.'},\n", + " {'schema': [{'subtype': 'udf-runtime-version', 'type': 'string'},\n", + " {'title': 'Default runtime version', 'type': 'null'}],\n", + " 'name': 'version',\n", + " 'description': 'An UDF runtime version. If set to `null`, the default runtime version specified for each runtime is used.',\n", + " 'optional': True},\n", + " {'schema': {'type': 'object'},\n", + " 'default': {},\n", + " 'name': 'context',\n", + " 'description': 'Additional data such as configuration options that should be passed to the UDF.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes', 'import', 'udf'],\n", + " 'returns': {'schema': [{'subtype': 'raster-cube',\n", + " 'title': 'Raster data cube',\n", + " 'type': 'object'},\n", + " {'description': 'Any data type.', 'title': 'Any'}],\n", + " 'description': 'The data processed by the UDF.\\n\\n* Returns a raster data cube, if a raster data cube is passed for `data`. Details on the dimensions and dimension properties (name, type, labels, reference system and resolution) depend on the UDF.\\n* If an array is passed for `data`, the returned value can be of any data type, but is exactly what the UDF returns.'},\n", + " 'exceptions': {'InvalidVersion': {'message': 'The specified UDF runtime version is not supported.'}}},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'gt',\n", + " 'summary': 'Greater than comparison',\n", + " 'description': 'Compares whether `x` is strictly greater than `y`.\\n\\n**Remarks:**\\n\\n* If any operand is `null`, the return value is `null`.\\n* If any operand is an array or object, the return value is `false`.\\n* If any operand is not a `number` or temporal string (`date`, `time` or `date-time`), the process returns `false`.\\n* Temporal strings can *not* be compared based on their string representation due to the time zone / time-offset representations.',\n", + " 'parameters': [{'schema': {'description': 'Any data type is allowed.'},\n", + " 'name': 'x',\n", + " 'description': 'First operand.'},\n", + " {'schema': {'description': 'Any data type is allowed.'},\n", + " 'name': 'y',\n", + " 'description': 'Second operand.'}],\n", + " 'categories': ['comparison'],\n", + " 'returns': {'schema': {'type': ['boolean', 'null']},\n", + " 'description': '`true` if `x` is strictly greater than `y` or `null` if any operand is `null`, otherwise `false`.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 1}},\n", + " {'arguments': {'x': 0, 'y': 0}, 'returns': False},\n", + " {'arguments': {'x': 2, 'y': 1}, 'returns': True},\n", + " {'arguments': {'x': -0.5, 'y': -0.6}, 'returns': True},\n", + " {'arguments': {'x': '00:00:00Z', 'y': '00:00:00+01:00'}, 'returns': True},\n", + " {'arguments': {'x': '1950-01-01T00:00:00Z', 'y': '2018-01-01T12:00:00Z'},\n", + " 'returns': False},\n", + " {'arguments': {'x': '2018-01-01T12:00:00+00:00',\n", + " 'y': '2018-01-01T12:00:00Z'},\n", + " 'returns': False},\n", + " {'arguments': {'x': True, 'y': 0}, 'returns': False},\n", + " {'arguments': {'x': True, 'y': False}, 'returns': False}]},\n", + " {'engine': '[WCPS]',\n", + " 'id': 'cosh',\n", + " 'summary': 'Hyperbolic cosine',\n", + " 'description': 'Computes the hyperbolic cosine of `x`.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'An angle in radians.'}],\n", + " 'categories': ['math > trigonometric'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/HyperbolicCosine.html',\n", + " 'title': 'Hyperbolic cosine explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed hyperbolic cosine of `x`.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 0}, 'returns': 1}]},\n", + " {'engine': '[WCPS]',\n", + " 'id': 'arctan',\n", + " 'summary': 'Inverse tangent',\n", + " 'description': 'Computes the arc tangent of `x`. The arc tangent is the inverse function of the tangent so that *arctan(tan(x)) = x*.\\n\\nWorks on radians only.\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'A number.'}],\n", + " 'categories': ['math > trigonometric'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/InverseTangent.html',\n", + " 'title': 'Inverse tangent explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed angle in radians.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 0}, 'returns': 0}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'absolute',\n", + " 'summary': 'Absolute value',\n", + " 'description': 'Computes the absolute value of a real number `x`, which is the \"unsigned\" portion of x and often denoted as *|x|*.\\n\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'A number.'}],\n", + " 'categories': ['math'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/AbsoluteValue.html',\n", + " 'title': 'Absolute value explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null'], 'minimum': 0},\n", + " 'description': 'The computed absolute value.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'x': 0}, 'returns': 0},\n", + " {'arguments': {'x': 3.5}, 'returns': 3.5},\n", + " {'arguments': {'x': -0.4}, 'returns': 0.4},\n", + " {'arguments': {'x': -3.5}, 'returns': 3.5}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'mean',\n", + " 'summary': 'Arithmetic mean (average)',\n", + " 'description': 'The arithmetic mean of an array of numbers is the quantity commonly called the average. It is defined as the sum of all elements divided by the number of elements.\\n\\nAn array without non-`null` elements resolves always with `null`.',\n", + " 'parameters': [{'schema': {'type': 'array',\n", + " 'items': {'type': ['number', 'null']}},\n", + " 'name': 'data',\n", + " 'description': 'An array of numbers.'},\n", + " {'schema': {'type': 'boolean'},\n", + " 'default': True,\n", + " 'name': 'ignore_nodata',\n", + " 'description': 'Indicates whether no-data values are ignored or not. Ignores them by default. Setting this flag to `false` considers no-data values so that `null` is returned if any value is such a value.',\n", + " 'optional': True}],\n", + " 'categories': ['math', 'reducer'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/ArithmeticMean.html',\n", + " 'title': 'Arithmetic mean explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The computed arithmetic mean.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'data': [1, 0, 3, 2]}, 'returns': 1.5},\n", + " {'arguments': {'data': [9, 2.5, None, -2.5]}, 'returns': 3},\n", + " {'arguments': {'data': [1, None], 'ignore_nodata': False}},\n", + " {'description': 'The input array is empty: return `null`.',\n", + " 'arguments': {'data': []}},\n", + " {'description': 'The input array has only `null` elements: return `null`.',\n", + " 'arguments': {'data': [None, None]}}]},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'normalized_difference',\n", + " 'summary': 'Normalized difference',\n", + " 'description': 'Computes the normalized difference for two bands. The normalized difference is computed as *(x - y) / (x + y)*.\\n\\nThis process could be used for a number of remote sensing indices such as:\\n\\n* [NDVI](https://eos.com/ndvi/): `x` = NIR band, `y` = red band\\n* [NDWI](https://eos.com/ndwi/): `x` = NIR band, `y` = SWIR band\\n* [NDSI](https://eos.com/ndsi/): `x` = green band, `y` = SWIR band\\n\\nSome back-ends may have native processes such as ``ndvi()`` available for convenience.',\n", + " 'parameters': [{'schema': {'type': 'number'},\n", + " 'name': 'x',\n", + " 'description': 'The value for the first band.'},\n", + " {'schema': {'type': 'number'},\n", + " 'name': 'y',\n", + " 'description': 'The value for the second band.'}],\n", + " 'categories': ['math > indices', 'vegetation indices'],\n", + " 'links': [{'rel': 'related',\n", + " 'href': 'https://eos.com/ndvi/',\n", + " 'title': 'NDVI explained by EOS'},\n", + " {'rel': 'related',\n", + " 'href': 'https://eos.com/ndwi/',\n", + " 'title': 'NDWI explained by EOS'},\n", + " {'rel': 'related',\n", + " 'href': 'https://eos.com/ndsi/',\n", + " 'title': 'NDSI explained by EOS'}],\n", + " 'returns': {'schema': {'maximum': 1, 'type': 'number', 'minimum': -1},\n", + " 'description': 'The computed normalized difference.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'filter_spatial',\n", + " 'summary': 'Spatial filter using geometries',\n", + " 'description': 'Limits the data cube over the spatial dimensions to the specified geometries.\\n\\n- For **polygons**, the filter retains a pixel in the data cube if the point at the pixel center intersects with at least one of the polygons (as defined in the Simple Features standard by the OGC).\\n- For **points**, the process considers the closest pixel center.\\n- For **lines** (line strings), the process considers all the pixels whose centers are closest to at least one point on the line.\\n\\nMore specifically, pixels outside of the bounding box of the given geometry will not be available after filtering. All pixels inside the bounding box that are not retained will be set to `null` (no data).',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A data cube.'},\n", + " {'schema': {'subtype': 'geojson', 'type': 'object'},\n", + " 'name': 'geometries',\n", + " 'description': 'One or more geometries used for filtering, specified as GeoJSON.'}],\n", + " 'categories': ['cubes', 'filter'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'https://openeo.org/documentation/1.0/datacubes.html#filter',\n", + " 'title': 'Filters explained in the openEO documentation'},\n", + " {'rel': 'about',\n", + " 'href': 'http://www.opengeospatial.org/standards/sfa',\n", + " 'title': 'Simple Features standard by the OGC'}],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A data cube restricted to the specified geometries. The dimensions and dimension properties (name, type, labels, reference system and resolution) remain unchanged, except that the spatial dimensions have less (or the same) dimension labels.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'geocode',\n", + " 'summary': 'Geocoding SAR data',\n", + " 'description': 'Geocoding SAR data given the desired projection and resolution. The resolution can be either 10m or 20m or 60m, to align the with Sentinel-2 grid.',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A raster data cube with exactly two horizontal spatial dimensions and an arbitrary number of additional dimensions. The process is applied to all additional dimensions individually.'},\n", + " {'default': 4326,\n", + " 'name': 'crs',\n", + " 'description': 'Coordinate reference system of the extent, specified as as [EPSG code](http://www.epsg-registry.org/), [WKT2 (ISO 19162) string](http://docs.opengeospatial.org/is/18-010r7/18-010r7.html) or [PROJ definition (deprecated)](https://proj.org/usage/quickstart.html). Defaults to `4326` (EPSG code 4326) unless the client explicitly requests a different coordinate reference system.',\n", + " 'anyOf': [{'examples': [3857],\n", + " 'subtype': 'epsg-code',\n", + " 'title': 'EPSG Code',\n", + " 'type': 'integer',\n", + " 'minimum': 1000},\n", + " {'subtype': 'wkt2-definition', 'title': 'WKT2', 'type': 'string'},\n", + " {'subtype': 'proj-definition',\n", + " 'deprecated': True,\n", + " 'title': 'PROJ definition',\n", + " 'type': 'string'}]},\n", + " {'schema': {'type': 'integer', 'enum': [10, 20, 60]},\n", + " 'default': '10',\n", + " 'name': 'resolution',\n", + " 'description': 'Desired reolution in meters.',\n", + " 'optional': True}],\n", + " 'categories': ['cubes', 'aggregate & resample'],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A data cube with the projected values in the requested projection.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'fit_curve',\n", + " 'summary': 'Curve fitting',\n", + " 'description': 'Use non-linear least squares to fit a model function `y = f(x, parameters)` to data.\\n\\nThe process throws an `InvalidValues` exception if invalid values are encountered. Invalid values are finite numbers (see also ``is_valid()``).',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A data cube.'},\n", + " {'schema': [{'minItems': 1, 'type': 'array', 'items': {'type': 'number'}},\n", + " {'subtype': 'raster-cube',\n", + " 'title': 'Data Cube with optimal values from a previous result of this process.',\n", + " 'type': 'object'}],\n", + " 'name': 'parameters',\n", + " 'description': 'Defined the number of parameters for the model function and provides an initial guess for them. At least one parameter is required.'},\n", + " {'schema': {'subtype': 'process-graph',\n", + " 'returns': {'schema': {'type': 'number'},\n", + " 'description': 'The computed value `y` value for the given independent variable `x` and the parameters.'},\n", + " 'type': 'object',\n", + " 'parameters': [{'schema': {'type': ['number']},\n", + " 'name': 'x',\n", + " 'description': 'The value for the independent variable `x`.'},\n", + " {'schema': {'minItems': 1, 'type': 'array', 'items': {'type': 'number'}},\n", + " 'name': 'parameters',\n", + " 'description': 'The parameters for the model function, contains at least one parameter.'}]},\n", + " 'name': 'function',\n", + " 'description': 'The model function. It must take the parameters to fit as array through the first argument and the independent variable `x` as the second argument.\\n\\nIt is recommended to store the model function as a user-defined process on the back-end to be able to re-use the model function with the computed optimal values for the parameters afterwards.'},\n", + " {'schema': {'type': 'string'},\n", + " 'name': 'dimension',\n", + " 'description': 'The name of the dimension for curve fitting. Must be a dimension with labels that have a order (i.e. numerical labels or a temporal dimension). Fails with a `DimensionNotAvailable` exception if the specified dimension does not exist.'}],\n", + " 'categories': ['cubes', 'math'],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A data cube with the optimal values for the parameters.'},\n", + " 'exceptions': {'DimensionNotAvailable': {'message': 'A dimension with the specified name does not exist.'},\n", + " 'InvalidValues': {'message': 'At least one of the values is not a finite number.'}}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'pi',\n", + " 'summary': 'Pi (??)',\n", + " 'description': 'The real number Pi (??) is a mathematical constant that is the ratio of the circumference of a circle to its diameter. The numerical value is approximately *3.14159*.',\n", + " 'parameters': [],\n", + " 'categories': ['math > constants', 'math > trigonometric'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'http://mathworld.wolfram.com/Pi.html',\n", + " 'title': 'Mathematical constant Pi explained by Wolfram MathWorld'}],\n", + " 'returns': {'schema': {'type': 'number'},\n", + " 'description': 'The numerical value of Pi.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[WCPS, ODC_DASK]',\n", + " 'id': 'filter_bbox',\n", + " 'summary': 'Spatial filter using a bounding box',\n", + " 'description': 'Limits the data cube to the specified bounding box.\\n\\nThe filter retains a pixel in the data cube if the point at the pixel center intersects with the bounding box (as defined in the Simple Features standard by the OGC).',\n", + " 'parameters': [{'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'name': 'data',\n", + " 'description': 'A data cube.'},\n", + " {'schema': {'subtype': 'bounding-box',\n", + " 'type': 'object',\n", + " 'required': ['west', 'south', 'east', 'north'],\n", + " 'properties': {'east': {'description': 'East (upper right corner, coordinate axis 1).',\n", + " 'type': 'number'},\n", + " 'south': {'description': 'South (lower left corner, coordinate axis 2).',\n", + " 'type': 'number'},\n", + " 'crs': {'default': 4326,\n", + " 'description': 'Coordinate reference system of the extent, specified as as [EPSG code](http://www.epsg-registry.org/), [WKT2 (ISO 19162) string](http://docs.opengeospatial.org/is/18-010r7/18-010r7.html) or [PROJ definition (deprecated)](https://proj.org/usage/quickstart.html). Defaults to `4326` (EPSG code 4326) unless the client explicitly requests a different coordinate reference system.',\n", + " 'anyOf': [{'examples': [3857],\n", + " 'subtype': 'epsg-code',\n", + " 'title': 'EPSG Code',\n", + " 'type': 'integer',\n", + " 'minimum': 1000},\n", + " {'subtype': 'wkt2-definition', 'title': 'WKT2', 'type': 'string'},\n", + " {'subtype': 'proj-definition',\n", + " 'deprecated': True,\n", + " 'title': 'PROJ definition',\n", + " 'type': 'string'}]},\n", + " 'north': {'description': 'North (upper right corner, coordinate axis 2).',\n", + " 'type': 'number'},\n", + " 'west': {'description': 'West (lower left corner, coordinate axis 1).',\n", + " 'type': 'number'},\n", + " 'base': {'description': 'Base (optional, lower left corner, coordinate axis 3).',\n", + " 'type': ['number', 'null']},\n", + " 'height': {'description': 'Height (optional, upper right corner, coordinate axis 3).',\n", + " 'type': ['number', 'null']}}},\n", + " 'name': 'extent',\n", + " 'description': 'A bounding box, which may include a vertical axis (see `base` and `height`).'}],\n", + " 'categories': ['cubes', 'filter'],\n", + " 'links': [{'rel': 'about',\n", + " 'href': 'https://proj.org/usage/projections.html',\n", + " 'title': 'PROJ parameters for cartographic projections'},\n", + " {'rel': 'about',\n", + " 'href': 'http://www.epsg-registry.org',\n", + " 'title': 'Official EPSG code registry'},\n", + " {'rel': 'about',\n", + " 'href': 'http://www.epsg.io',\n", + " 'title': 'Unofficial EPSG code database'},\n", + " {'rel': 'about',\n", + " 'href': 'http://www.opengeospatial.org/standards/sfa',\n", + " 'title': 'Simple Features standard by the OGC'}],\n", + " 'returns': {'schema': {'subtype': 'raster-cube', 'type': 'object'},\n", + " 'description': 'A data cube restricted to the bounding box. The dimensions and dimension properties (name, type, labels, reference system and resolution) remain unchanged, except that the spatial dimensions have less (or the same) dimension labels.'},\n", + " 'exceptions': {}},\n", + " {'engine': '[ODC_DASK]',\n", + " 'id': 'clip',\n", + " 'summary': 'Clip a value between a minimum and a maximum',\n", + " 'description': 'Clips a number between specified minimum and maximum values. A value larger than the maximum value is set to the maximum value, a value lower than the minimum value is set to the minimum value.\\n\\nThe no-data value `null` is passed through and therefore gets propagated.',\n", + " 'parameters': [{'schema': {'type': ['number', 'null']},\n", + " 'name': 'x',\n", + " 'description': 'A number.'},\n", + " {'schema': {'type': 'number'},\n", + " 'name': 'min',\n", + " 'description': 'Minimum value. If the value is lower than this value, the process will return the value of this parameter.'},\n", + " {'schema': {'type': 'number'},\n", + " 'name': 'max',\n", + " 'description': 'Maximum value. If the value is greater than this value, the process will return the value of this parameter.'}],\n", + " 'categories': ['math'],\n", + " 'returns': {'schema': {'type': ['number', 'null']},\n", + " 'description': 'The value clipped to the specified range.'},\n", + " 'exceptions': {},\n", + " 'examples': [{'arguments': {'min': -1, 'max': 1, 'x': -5}, 'returns': -1},\n", + " {'arguments': {'min': 1, 'max': 10, 'x': 10.001}, 'returns': 10},\n", + " {'arguments': {'min': 0, 'max': 0.02, 'x': 1e-06}, 'returns': 1e-06},\n", + " {'arguments': {'min': 0, 'max': 1}}]}]" + ] + }, + "execution_count": 282, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "conn.list_processes() # Search for radar_mask" + ] + }, + { + "cell_type": "code", + "execution_count": 283, + "id": "78b3b021-dedb-4041-beb3-a2dff2275efa", + "metadata": {}, + "outputs": [], + "source": [ + "radar_mask_node = PGNode('radar_mask', data={'from_parameter': 'data'}, orbit_direction='ASC',foreshortening_th=0.1,layover_th=2.1)\n", + "radar_mask = dem_lia_ML.apply_dimension(process=radar_mask_node, dimension=\"bands\")" + ] + }, + { + "cell_type": "markdown", + "id": "ae3f2f93-0908-427d-a8a3-afb066408ed2", + "metadata": {}, + "source": [ + "Rename the output bands with a meaningful name" + ] + }, + { + "cell_type": "code", + "execution_count": 284, + "id": "43c071d5-f066-4970-886f-b460219f9e93", + "metadata": {}, + "outputs": [], + "source": [ + "radar_mask = radar_mask.rename_labels(target=['layover','foreshortening','shadow'],dimension='bands')" + ] + }, + { + "cell_type": "markdown", + "id": "d8f07a2e-b2a2-4ec4-b192-74aa96cb1b68", + "metadata": {}, + "source": [ + "Merge the intensity and the coordinates into the same datacube" + ] + }, + { + "cell_type": "code", + "execution_count": 285, + "id": "9457845e-6c7c-46a2-9820-a0b6dbf1c6ff", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "S1_INT_VV_ML = S1_INT_VV_ML.merge_cubes(lat_lon_ML)" + ] + }, + { + "cell_type": "markdown", + "id": "029fd079-0058-4220-b29a-446aec8900fd", + "metadata": {}, + "source": [ + "Compute the average over time" + ] + }, + { + "cell_type": "code", + "execution_count": 286, + "id": "7cef9f4b-4e51-40c9-bbaf-2374fb6c533d", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "S1_INT_ML_VV_MEAN = S1_INT_VV_ML.reduce_dimension(reducer=mean, dimension='DATE')" + ] + }, + { + "cell_type": "markdown", + "id": "36eaf863-51c8-45a2-8ed6-a5da73b1f8f2", + "metadata": {}, + "source": [ + "Merge also the radar_mask output" + ] + }, + { + "cell_type": "code", + "execution_count": 287, + "id": "b2d28d46-c78a-4578-88c0-9e51d01e3a72", + "metadata": {}, + "outputs": [], + "source": [ + "S1_INT_ML_VV_MEAN = S1_INT_ML_VV_MEAN.merge_cubes(radar_mask)" + ] + }, + { + "cell_type": "markdown", + "id": "9029a44d-a1fb-4ebc-9dcc-f5738ee5da95", + "metadata": {}, + "source": [ + "Geocode the resulting data. We choose 60m resolution for the pixel size and the local UTM zone as projection.\n", + "\n", + "We can choose only from 10, 20 or 60m for resolution, for being able to align the data with Sentinel-2 grid." + ] + }, + { + "cell_type": "code", + "execution_count": 288, + "id": "ad8f8963-4ae6-4d1d-9f02-d56a781c78cc", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "args_geocoding = {'resolution':20,'crs':32632}\n", + "S1_INT_ML_VV_GEOCODED = S1_INT_ML_VV_MEAN.process(\"geocode\",args_geocoding, data=S1_INT_ML_VV_MEAN)" + ] + }, + { + "cell_type": "code", + "execution_count": 289, + "id": "7a9513e2-200c-4d09-8157-9690db027b46", + "metadata": {}, + "outputs": [], + "source": [ + "S1_INT_ML_VV_GEOCODED_NC = S1_INT_ML_VV_GEOCODED.save_result(format='netCDF')" + ] + }, + { + "cell_type": "markdown", + "id": "348e8781-ddaa-4a58-848d-9a07544cc825", + "metadata": {}, + "source": [ + "For jobs requiring to process many dates and a big area in the spatial domain, we need to use batch jobs.\n", + "\n", + "The job will run in the background and when it will be marked as finished you can download the result." + ] + }, + { + "cell_type": "code", + "execution_count": 290, + "id": "unknown-triumph", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Batch job created with id: d1084a2c-8e37-4b18-a81d-b577ef94a7fb\n" + ] + } + ], + "source": [ + "job = conn.create_job(S1_INT_ML_VV_GEOCODED_NC,title=\"SAR2Cube_South_Tyrol_radar_mask_ASC\")\n", + "job_id = job.job_id\n", + "if job_id:\n", + " print(\"Batch job created with id: \",job_id)\n", + " job.start_job()\n", + "else:\n", + " print(\"Error! Job ID is None\")" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "fd1ff66a-6d2c-4a60-a2bb-6bd824d75001", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'a888eecb-5f19-4bf5-b8ae-c14f1c3403df'" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "job_id" + ] + }, + { + "cell_type": "code", + "execution_count": 221, + "id": "4e07bc47-f848-4a7d-bd20-70e5b72b5262", + "metadata": {}, + "outputs": [], + "source": [ + "job = conn.job(job_id)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6b5e1560-8379-4130-b7ac-2507120c494b", + "metadata": {}, + "outputs": [], + "source": [ + "job" + ] + }, + { + "cell_type": "markdown", + "id": "1234d1b0-cd4f-4451-9d13-ffa5bc3b88a7", + "metadata": {}, + "source": [ + "Once the job is marked as finished, you can download the result.\n", + "\n", + "Either via the download link provided in the following visualization:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8698677c-832c-41ce-ad73-374dc0a76a7d", + "metadata": {}, + "outputs": [], + "source": [ + "result = job.get_results()\n", + "result" + ] + }, + { + "cell_type": "markdown", + "id": "bc879de8-b042-4cd2-bb95-a1d456b636c5", + "metadata": {}, + "source": [ + "Or via python code specifying the target location:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c3817cd7-2b38-41a5-8a2b-db09ad6072c5", + "metadata": {}, + "outputs": [], + "source": [ + "result.download_files(\"./radar_mask_ASC/\")" + ] + }, + { + "cell_type": "code", + "execution_count": 295, + "id": "3bf0fb3c-5fb5-4a8c-be37-4c4f5cc006f9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+       "    shadow          (y, x) float64 ...
" + ], + "text/plain": [ + "\n", + "Dimensions: (y: 982, x: 1225)\n", + "Coordinates:\n", + " * y (y) float32 5.159e+06 5.159e+06 ... 5.179e+06 5.179e+06\n", + " * x (x) float32 6.523e+05 6.524e+05 ... 6.768e+05 6.768e+05\n", + "Data variables:\n", + " VV (y, x) float64 ...\n", + " layover (y, x) float64 ...\n", + " foreshortening (y, x) float64 ...\n", + " shadow (y, x) float64 ..." + ] + }, + "execution_count": 295, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "out = xr.open_dataarray(\"./radar_mask_ASC/result.nc\").to_dataset(dim='variable')\n", + "out" + ] + }, + { + "cell_type": "code", + "execution_count": 307, + "id": "93792c1f-709b-4906-a534-522140dab4e2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_args = {'cmap':'Greys_r','add_colorbar':False}\n", + "fig, ax = plt.subplots(1,4,figsize=(40,10))\n", + "out.VV.plot.imshow(ax=ax[0],**plot_args)\n", + "ax[0].set_title(\"VV ASC Intensity [dB]\")\n", + "out.layover.plot.imshow(ax=ax[1],**plot_args, vmin=0,vmax=1)\n", + "ax[1].set_title(\"Layover ASC mask\")\n", + "out.foreshortening.plot.imshow(ax=ax[2],**plot_args, vmin=0,vmax=1)\n", + "ax[2].set_title(\"Foreshortening ASC mask\")\n", + "out.shadow.plot.imshow(ax=ax[3],**plot_args, vmin=0,vmax=1)\n", + "ax[3].set_title(\"Shadow ASC mask\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 308, + "id": "5029c190-3119-4786-bce6-194ef6e1c383", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Authenticated using refresh token.\n" + ] + } + ], + "source": [ + "conn = openeo.connect(openeoHost).authenticate_oidc(client_id=\"openEO_PKCE\")\n", + "\n", + "collection = 'SAR2Cube_SInCohMap_S1_L0_168_DSC_SOUTH_TYROL'\n", + "temporal_extent = [\"2018-06-01T00:00:00.000Z\", \"2018-06-30T00:00:00.000Z\"]\n", + "\n", + "S1_slant_range = conn.load_collection(collection,spatial_extent=spatial_extent,temporal_extent=temporal_extent)\n", + "\n", + "i_VV = S1_slant_range.band('i_VV')\n", + "q_VV = S1_slant_range.band('q_VV')\n", + "S1_INT = (i_VV**2+q_VV**2)\n", + "S1_INT_VV = S1_INT.add_dimension(name=\"bands\",label=\"VV\")\n", + "\n", + "range_looks = 19\n", + "azimuth_looks = 4\n", + "\n", + "args_aggregate_spatial_window = {\"data\": THIS, \"boundary\": \"trim\", \"size\": [range_looks,azimuth_looks],\"reducer\":S1_INT_VV._get_callback(mean,parent_parameters=[\"data\"])}\n", + "S1_INT_VV_ML = S1_INT_VV.process(\"aggregate_spatial_window\",args_aggregate_spatial_window)\n", + "S1_INT_VV_ML = S1_INT_VV_ML.apply(lambda x: 10*log(x,base=10))\n", + "\n", + "lat_lon = S1_slant_range.filter_bands(['grid_lon','grid_lat'])\n", + "\n", + "args_aggregate_spatial_window = {\"data\": THIS, \"boundary\": \"trim\", \"size\": [range_looks,azimuth_looks],\"reducer\":lat_lon._get_callback(mean,parent_parameters=[\"data\"])}\n", + "lat_lon_ML = lat_lon.process(\"aggregate_spatial_window\",args_aggregate_spatial_window)\n", + "\n", + "dem_lia = S1_slant_range.filter_bands(['DEM','LIA'])\n", + "args_aggregate_spatial_window = {\"data\": THIS, \"boundary\": \"trim\", \"size\": [range_looks,azimuth_looks],\"reducer\":dem_lia._get_callback(mean,parent_parameters=[\"data\"])}\n", + "dem_lia_ML = dem_lia.process(\"aggregate_spatial_window\",args_aggregate_spatial_window)\n", + "\n", + "dem_lia_ML = dem_lia_ML.reduce_dimension(reducer=mean, dimension='DATE')\n", + "\n", + "radar_mask_node = PGNode('radar_mask', data={'from_parameter': 'data'}, orbit_direction='DSC',foreshortening_th=0.1,layover_th=2.1)\n", + "radar_mask = dem_lia_ML.apply_dimension(process=radar_mask_node, dimension=\"bands\")\n", + "\n", + "radar_mask = radar_mask.rename_labels(target=['layover','foreshortening','shadow'],dimension='bands')\n", + "\n", + "S1_INT_VV_ML = S1_INT_VV_ML.merge_cubes(lat_lon_ML)\n", + "\n", + "S1_INT_ML_VV_MEAN = S1_INT_VV_ML.reduce_dimension(reducer=mean, dimension='DATE')\n", + "\n", + "S1_INT_ML_VV_MEAN = S1_INT_ML_VV_MEAN.merge_cubes(radar_mask)\n", + "\n", + "args_geocoding = {'resolution':20,'crs':32632}\n", + "S1_INT_ML_VV_GEOCODED = S1_INT_ML_VV_MEAN.process(\"geocode\",args_geocoding, data=S1_INT_ML_VV_MEAN)\n", + "\n", + "S1_INT_ML_VV_GEOCODED_NC = S1_INT_ML_VV_GEOCODED.save_result(format='netCDF')" + ] + }, + { + "cell_type": "code", + "execution_count": 309, + "id": "b8af1f99-a1df-4ed2-8051-0961b70e128d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Batch job created with id: 5848dec0-4aab-4cd7-a67c-3481ebd28e7a\n" + ] + } + ], + "source": [ + "job = conn.create_job(S1_INT_ML_VV_GEOCODED_NC,title=\"SAR2Cube_South_Tyrol_radar_mask_DSC\")\n", + "job_id = job.job_id\n", + "if job_id:\n", + " print(\"Batch job created with id: \",job_id)\n", + " job.start_job()\n", + "else:\n", + " print(\"Error! Job ID is None\")" + ] + }, + { + "cell_type": "code", + "execution_count": 310, + "id": "070bc913-3eb8-4936-b23a-29892c9c38fb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 310, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "job" + ] + }, + { + "cell_type": "markdown", + "id": "052c49d1-a428-40a1-8bd2-eaf5a738f5b7", + "metadata": {}, + "source": [ + "Once the job is marked as finished, you can download the result.\n", + "\n", + "Either via the download link provided in the following visualization:" + ] + }, + { + "cell_type": "code", + "execution_count": 311, + "id": "a9fec0e4-2253-4065-84ab-cdd5622f70c2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 311, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result = job.get_results()\n", + "result" + ] + }, + { + "cell_type": "markdown", + "id": "c7804db0-62eb-4750-90f8-c6a42cff8207", + "metadata": {}, + "source": [ + "Or via python code specifying the target location:" + ] + }, + { + "cell_type": "code", + "execution_count": 312, + "id": "0927fe45-c8ce-493c-8e6b-e24f5c8cb973", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[PosixPath('radar_mask_DSC/result.nc'),\n", + " PosixPath('radar_mask_DSC/process.json')]" + ] + }, + "execution_count": 312, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result.download_files(\"./radar_mask_DSC/\")" + ] + }, + { + "cell_type": "code", + "execution_count": 314, + "id": "66db9d93-e470-4684-8934-f791486bbc3a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset>\n",
+       "Dimensions:         (y: 982, x: 1224)\n",
+       "Coordinates:\n",
+       "  * y               (y) float32 5.159e+06 5.159e+06 ... 5.179e+06 5.179e+06\n",
+       "  * x               (x) float32 6.523e+05 6.524e+05 ... 6.768e+05 6.768e+05\n",
+       "Data variables:\n",
+       "    VV              (y, x) float64 ...\n",
+       "    layover         (y, x) float64 ...\n",
+       "    foreshortening  (y, x) float64 ...\n",
+       "    shadow          (y, x) float64 ...
" + ], + "text/plain": [ + "\n", + "Dimensions: (y: 982, x: 1224)\n", + "Coordinates:\n", + " * y (y) float32 5.159e+06 5.159e+06 ... 5.179e+06 5.179e+06\n", + " * x (x) float32 6.523e+05 6.524e+05 ... 6.768e+05 6.768e+05\n", + "Data variables:\n", + " VV (y, x) float64 ...\n", + " layover (y, x) float64 ...\n", + " foreshortening (y, x) float64 ...\n", + " shadow (y, x) float64 ..." + ] + }, + "execution_count": 314, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "out_dsc = xr.open_dataarray(\"./radar_mask_DSC/result.nc\").to_dataset(dim='variable')\n", + "out_dsc" + ] + }, + { + "cell_type": "code", + "execution_count": 316, + "id": "698fd690-183e-40e5-8e7c-f829f82c8d3d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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x8QHkukjyD0dRlB2KooxXFKUh1O/S+gBmCpucELalh5Po/sdgxtgaxlhu6Lt4CE59F1cUD6rsvtIvAHoyxuoC0EIVz3RnjKVAdcrZUs1myvWLRPIP5aIX/iiKshKCmAEAGGNNGGNLGWMbGWO/CjefbwYwW1GUvNC+WWe5uhJJecyF6uRzI9SnrTMjPv8QqqJ4AAA7gK+rc/CQsOUKAL9GfqaoLjoLAfwJ9Ut5F9QFzNXVbENViXwCKwfqYqGFcOMrSlGUim78nDqYoqxXFGUEVNvnRVDFPFUpF1D79QYA/QB4FEX5vSplhjgONbBHNKqgjiWKovwKNU3H5dUoQyKRSKqNXBtJLmKmQXUE7KIoigOqXTKg3mSAoihrAASgPl11PU4JfnNw6ikkohGAo6H9SqCuL64L7fe1ojrfAOqa6b/CGiZaURSLoihiStaaPH0+AkARgFJPPCmKchhqKo+Wobd+ADCQqWm3zgSV9esyRVEGQA3o7YT6VB2xA6q19XdMTVcrkUgkZxW5LpJcQBxD6bXIMeF15HriMNRUm+IaxKQoytHQfZ4nFEVpDtWtZxjCU8xXqQ4hsXQSQmuicupRVr3mRdTLqijK/1Wh/OpwHGrKCKqrGaccrKsEY8wGNU1HWffKvIqizIaaQq051BQXPqjp1WqdkLj8bahu0XVCD5Ftw6n11glFUW5WFKU+VLen1xljTYVDjAYwkjF2z5mon0Qi+Wcg10WSiw1FUXZCFby1rGRTMDVl+xdQHQ8TQ9/F3yL0XYyK40GV3Vf6G6qAeSqAlaF7Siegpm/9LXTfqUrI9YtE8s/mohf+lMNbAO5SFKUD1JyLr4feTweQzhhbFVJtVkndLJGcBeZC/bK+GWW7+fwKIB/q3P6kqk9MM8b0IWXvx1Dzwb8Uen88Y2woY8zOGNMwxgYDaAFgraIoCoD7APybMTaBMeYIbdODMfbWabYTUNXBDVkoR3xoUfM21HypCaH6NQi5D1XWPgNj7AbGWJSi2lAXQn3CrbxyG4tvhIQ+JVBtF6vj9gOoAcCpjLGGTM0V+2BFG4eciJoD2F7RdhKJRHKGkGsjyT8NPWPMJPzooIqjvQDyGWOxAB4rY7+5AF4DUEQWwyGr4s8A/De0NkqGuhb6SNhvAVRnmxsgODNCXcPcGnIDYowxK62xatIoxlgsY+wGqMKeZxVFOckYi2GMPcEYaxpak8UBmAg1jzugrmEOA/iCMdYstE0dxtjDjLEh5RRVHcrtV8ZYImNseEh05Iea3jRsLRYSQT0M4AfGWJNaqI9EIpGcLnJdJDkf+RjAo4yx+NB3/QyEr0UimQN17ZIMAKH9RoT+7sMYa8VU18NCqIGo8u6ViHwGYChjrB9THYKmQf1+X13BPpH3Wj4CcAVjbCBjTBtap/VmjDUsZ/+a8nmonG6h+0tP4FRgrkIYY0bGWAeoD4/lAXg/9P49obqaGWM6pqbJsAPYrKipUGcAmM0YG8lUR0Q9U50BnquF9lihiqiyQ3WZACFIyRgbLfRhXmhbcUyPQX2obSpj7PZaqI9EIrl4kOsiyT+G0D2RafSdyRhLgvoQ15qK9wSgOvYYoX4XF4ViZuJD3J8BGM8Ya84Ys0C4N1LF+0q/QBXIUFqvnyNeV9Y2uX6RSC4CpPAnAqaqHbsBWMgY2wLgTZyyk9UBSAPQG+rF/h3GWPTZr6VEEo6i5vxcDfWLckkZnytQA1XJOJXWoSLGMMZcUMVCS6Ba+nVQFIWeFiuEGoA5FNrmOQC3CQGwz6EGtyZC/fLNBPAUgMU1aV8EK6CKX04wxnJC7/0LqhvOGqamkPgB6pPlVeFGAAdC+90KNZ98WTwD9SZaPmPsfuH9uQBaoeIbamXxNlTr7D8AbALwZRnbvMYYc4XGYh6ARxVF+a6a5UgkEslpIddGkn8o30IVo9DP41Ctm81Qn7RaAzV1aCTzoN6EiBT83gU1fdY+AL9BFfe8Rx8qirI29Hl9AN8J72+AKtx+DepNjL8BjK9Be/4IrRf+BjAZwL2KoswIfRYAkAJ1fVQI9ekpP5UTSlfWH6rbzvLQNuugWiuvrUFdIpmJ8vtVAzUoeAzqk6O9AJS6WaMoyocAngSwgqlW1hKJRHJOkOsiyXnMUwA2QHVj3gr1PsNTFWz/CtT7Pd8zxpxQv6O7hD6rC1UYUwjVfe8XVOGeh6Iou6DeU5kF9Xv/CgBXVPLwWdi9lpAz4Qio95yyoYqTH0At38NWFGU71PXbJ1CfwHcCyIK6RiqP6aG+yoV6L2gjgG6h1KWAuqZ8EeoT+DkA7gBwtaIo+0JlvgQ1iPeo0LY7oQbgTrc9f4XK/h3qPbhWAFYJm3QCsDa0XlwC4G5FUfZHHOMQ1ODZvxhjk0+3ThKJ5J+PXBdJ/oE4oa6H1jLG3FDXR9ug3reokJALz1SoAp48qI7PS4TPv4N6f2QF1Hs3KyIOUeF9JajrMTvU9OllvS4PuX6RSC4imKoHuLgJ3Tz+WlGUlowxB4BdiqLUK2O7OQDWKIryQej1jwAeVBRl/dmsr0QiOX9gjI0DMEVRlB4VbHMZVJGPH8AYRVGW1UK5EwC8DMAEoDktxCQSiaQ2kGsjiaRsmJoGIgtAe0VR9pzr+kgkEonkzCPXRRLJP59Q8DofQFpkQEkikUgkp5DrIolEIpFIzl+k408EiqIUAtjPGBsNqPmoGWNtQh8vAtAn9H4cVLtCGWyXSC5SQpaMt0O1NC0XRVFWKopiDuWmP23RT+iY74eOZ5KiH4lEciaRayOJJIzbAKyXoh+JRCK5OJHrIonknwNj7IpQygorgBegOiUdOLe1kkgkkgsHuS6SSCQSieT84qIX/jDGPoZqI5bBGDvCGJsE4AYAkxhjf0BNKTQitPkyACcZY38B+AnAA4qinDwX9ZZIJOcWxthAqNaGmVBtFyUSieQfgVwbSSRlwxg7AOBuVMHiWSKRSCT/DOS6SCL5RzMCarrRY1DT0VyrSGt8iUQiKRe5LpJIJBKJ5PxGpvqSSCQSiUQikUgkEolEIpFIJBKJRCKRSCQSiUQikUguQC56xx+JRCKRSCQSiUQikUgkEolEIpFIJBKJRCKRSCQSieRCRHeuK3AuiYuLU1JSUs51NSQSiUQikQDYuHFjjqIo8ee6Hhczcm0kkUgkEsn5gVwXnXvkukgikUgkkvMDuS4698h1kUQikUgk5wcVrYsuauFPSkoKNmzYcK6rIZFIJBLJec/gwYPBGENJSQlMJhMMBgO0Wi1KSkpAaUONRiPi4+NRUlKCmTNnVrsMxtjBWq62pJrItZFEIpFIJFWDMVat7aubZl2ui849cl0kkUgkEknVqGxdNGLECPzxxx84cOAAALkuuhCR6yKJRCKRSKoGrYsSEhKQk5ODkpKSCrevzXXRRS38kUgkEonkQqRPnz5gjKFNmzZQFIWLb0pKSlBUVASNRgONRgOtVst/09+0He1TXFzMfweDQV5GSUkJgsEgioqKoNfrkZKSguLiYhQXF8NgMMDpdAIAioqKoCgK9Ho9GGPw+/0wGo246667MGvWrHPVRRKJRCKRSC4iqivCORcwxqp9M0cikUgkEomkupxP66Lnn38eBoMBU6dOxezZs3HnnXcCkOsiiUQikUgkZ4fK1kVmsxler7fMz+rVq4cTJ06UWrMYDAZ06dIFLpcLx48fR/PmzXHdddchISEBu3fvxj333AMAuPnmmxEXF4fExMRK61hb6yIp/JFIJBKJpIYMGzaMC2A0Gg1fIBgMBq7iLS4uRklJCd/OYrGgqKgIRqMROp2OC3B0Oh1iYmL4viaTCS6XC8FgEMFgEAaDAXa7HRqNBp07d4aiKPB4PPD7/SgpKeEOPPRbq9VCr9dDp9Nxpx5AFfQwxrgIiDEGn88Hv9/PRUFarZb/XVxcDI1GwwU/jDEUFRXxYxUVFUGn0/Fj0bE1Gs3ZHg6JRCKRSCTnmPMp0CSRSCQSiURyLpHrIqB///5o27YtACA1NfXcVkYikUgkEsk541yti7KysmCxWLB3714cP34cu3fvxsGDB9GuXTu0aNECl1xyCXJycrBy5Ups2rQJ0dHRSE9Px9q1a3H06FGMGjUKqampyM7Oxvz585GSkoLLL78ciYmJqF+/PtauXYvi4mJ0794dv//+O4YMGYLmzZsDAI4fP47ffvvtrLZXCn8kEolE8o+gX79+8Pv9XHBCAhSdTge9Xg/glDtNgwYNYLVa4fV6uXAmGAyCMQaDwcDFOsXFxXC73QgGg9Dr9SguLkYgEODCF61Wy8snwQyVbbPZUFJSgsLCQng8Hmi1WgQCAQDgoh8AvL4k5CksLITFYoFOp4PJZOJOPV6vF0ajEbGxsfD7/QDAxTpWqxUGg6FU28mFR1GUsM+AU4IkRVHg8/n435HbKoqCQCDAj0ViJeozvV7Pty8uLubpv8S+kUgkEolEcvaRwSaJRCKRSCQSlaqui9q0aYM//vij3M/T09NRWFiI7OxsFBcXo2vXrrjtttuwadMm5OTk4O+//8aJEyfw+OOP44MPPsD48eNrqQWlueSSS1C3bl389NNPZ6yM0+XYsWNo27Ytfv/9dwwdOvRcV0cikUgkEgkurvtFt912G5KSknDttddi4MCBGDhwYKltGjRogM6dO6NHjx5o2LAhtm7dCrfbjZSUFDRp0gRt27ZFYWEhHA4HLr300rB9+/btC5/PB5PJhPnz52PTpk345ZdfYDabsXjxYvzwww8V1i86Ohr5+fm11l4p/JFIJBLJeUX37t0BnBKvkLiEMQatVst/a7VaWCwWLkZJTEzkYh0S8ZjNZp7KCgAKCwsBgAtcyGmHPqdUVuSOIwpoiouLedmUGkur1UKn06GoqAjBYJCLZxhjfHv6IdFRIBCA1WoNW1yRcIZceIqKilBSUgKz2RzmvkPtDwaD3NGnqKiIl0cCJ9quItcdsSxy9CE3oEhIACQKpKgfyU2I0n1ptVruAiSRSCQSieT0uZhuyEgkEolEIpFURG2tix5//HF8+OGH2L9/f9j7t9xyCxwOB3bs2IHDhw8jOzsb3333Hf+8R48e6NKlC/bu3YsTJ04gNjYW48aNw8CBA3H8+HHs2bMHhw4dQlxcHBISEmqlruXx5JNPIi8v77wW/vTo0QMAsGTJknNcE4lEIpFI/nnI+0WV88UXX8BisWDQoEE8nlUWiYmJsNlsAICoqCgkJiYiLi4ODocDAOBwONC1a9cy9zWZTACAXr16YdGiRdi+fTtSU1Px999/Y82aNRXWb+jQoZg/f35Nm1cKGZWTSCQSSbl07dqVfxFqNBruNBMVFcX/LioqgsFg4GmqSARD4hNyyBFTQnk8HlitVjgcDiiKAr/fD5PJBJ1OB7vdDgBcOFJUVMTLKi4uhtlsBmMMFoullLCFHHtMJhMXsZA7DaA67Wg0GgQCAfj9fthsNi7iof3py59+G41GlJSUIBAIQFEULsQBwI9NwhexrOLiYu7ao9Fo4HQ6ufiHBDp6vR5+v5+/JmGR3+9Hfn4+Fw9Rui7GGPx+Pxhj3GlHq9XC5/NBp9PBarWGHUtcxIiOPsAp0Y5er0dJSQl8Ph9vX6TASaPR8G31ej3cbjevG6UiM5vNcDgcCAQC/LO7774br7zySu1MRolEIpFIzgPkTRXJ6VCbedslEolEIjnXnI11Uf/+/XHs2DH89ddftXbMxMRE9O3bF48//nipz3w+HzIyMtC+fXtccsklCAaDqF+/PnJycgCo91I6d+6MiRMnQqfT4eTJk/yYiYmJPK0VACxcuJD//eyzzyI3NxfPPvtsrbUjLy8PPXv2rLXj1TavvvoqD5Y1adKk1OdyXSSRSCSSfxLyftH5S1paGlq1alXhGJHoBwDq1q2LQCAAs9mMmJgY/n5lYzx27Fg4nU7ceOONSE9Px59//oljx46Vu/29996L/v37IzMzs9bWRVL4I5FIJBcIXbp0ARCefslgMCAhIQEWiwU2m42nkCKhit/vh8vl4mIMjUYTJk4BwN8zm83QaDQoKipCfHw8Tpw4AavVyrcxmUzw+/0oKiqCVqtFTEwMFEXB0aNHYbfbuajH6/WiuLgYNpsNRUVF8Hq9cDgc0Ol0cLvdKCkpgclk4qmpSGSi1+vh8Xjg8Xhgs9l4mquCggLYbDa4XC4u3KH6k0jFYDAgGAxy8Y1erw8TuQDgghuNRgOPx8MdcnQ6HT8mueBQH5KrD6AKkcgRCFBVvJTyCwAXGtExSLBD4iSdTsfFTMFgEAaDge9rMBi4qMfj8XBBEKCKg6jd5HSk0Wjg8/lgMBhQUlLCBUWUposWIKLrDzkfUR+JqdBKSkqg0+m4gEir1cJgMPCyaO5QO8mRiByDvF4v4uLieBuo3hU5DkkkEolEcrrImyoSiUQikUgkKqezLqpXrx7y8vLg8/lqsUanx6RJk9CkSRM89NBD8Pl8MJvNtXbszMxMXHbZZWV+dt999wFQ7/n8+eefSEtLw0svvYQDBw5g27ZteP/99/H+++/j+++/x4ABA1CnTp1yy/nzzz8BAPfccw+aNGmCgQMH1qrwZ/369bj88sv565iYGEycOBEZGRk4ePAg/vvf/9ZaWdXl6aefxl133cVfv/baa+esLhKJRCK5+KjpuoiyO0j+Wezduxculwu5ubmIjY2tdHuj0Yj69evz+GhV0Wq1uPPOO/HTTz/hyy+/rHDbqVOn4v7774fb7Ubz5s0rTQlWVaTwRyKRSGpAx44d+eKBUi0ZjUZYLBbUq1cPDocDRqMRdrudp4kKBoMoKiqC2WyGXq+HxWLh+4iuMZRyiX7o9ejRowEgzLFF/E2ikkAgAI/HA7/fj7y8PNhsNrjdbp6Kyu/3c7ccErsUFhbCbrfzYxUUFPBji4IZEv1otVqYTCYEAgHEx8fz9E6iKIcEIkajkTvF+P1+FBcXw+FwcAcgnU6HYDCIQCAAn88HvV4Pm83GRSOU6ouELyRuIUcdarcobNFqtfw1/abxos+8Xi8CgQASEhK46w2VRy5G5FxDddHr9dzViPqHhFg0zlQvg8EQNjZRUVG8D4LBIBcOGQwG3oZIxyBxzDQaDRISEng/kLCLXJLEVGN0PDGFGKUDi0xBRk4+1C6dTsfHi+Yf9TkAuFwuXpZWq4XH4+H9IIp+qJ8lEolEcnEgRTgSiUQikUgkKhfauujpp5/GuHHjsHbtWnTr1u2c1iUxMRFNmzbFq6++yt+r6EnpM4XP58P48eOxatUq3HjjjQDU+zS5ubn44Ycf8O6776JHjx4VCpI6dOgAABgxYgS6dOmCvLw8LFiwANdff32t1PHLL7/E7Nmz0a1bN6xevRp5eXkoLi7GzTffDABYtmwZNmzYUCtlVYcmTZpg2rRpAIC33noL48ePxx9//HHW6yGRSCSS84PTWRdRnIJiTBqNBidOnKhSmZW5p+h0OkRHR6NBgwaoU6cO7r77bixYsACffvppjesrOT9xuVzo0KEDrrrqKrz11lswGo2V7lNd0Y9ImzZtsGzZMrjd7jI///e//4177rkHsbGxWLJkCf73v//VuKxIpPBHIpFc8LRv3x4AuPuIyWSC0WiE2WxGVFQUGjduDJfLhezsbOTm5kKr1SIqKgoGg4GLb8xmM3c4KSws5E86mUwmxMXFcbEG0atXLxQVFcHpdHKxCYk/zGYzrFYrT88UCAS4mEQUZIjCIVHwI7qyiM48olADAHeiERdOXq+Xu7GQi4vT6eRlknNLcXExd9zx+XwwGo18v+LiYv6UVyAQ4K4vkYIerVYLs9mMkpISuN3uMPcZSkdlNBp5v5Gzj9ls5g4/ALg4xOv1QlEUWCwWnv5LFNSQ6IccfmjMdTodd8oxm828j0WoDbQ/9bXH44FOpwsT/5CASXTN0Wq1XKBErko+n4+7C1ksFhQXF6OoqIgLf6hddCwSEOn1ei6cER2PaHuj0cjnALk6ie5BVBez2YyTJ09Cq9XCarXy9omuP+J8IeEOzbVI8Y/FYuHbUV+IbkJiijPaTlEUBINBWCwW7r5E7aG5IpFIJJKzz4UWbJJIJBKJRCI5U8h1UeW0bt0aGo0GXq8XAHD11Vfjq6++QiAQOOt1mTJlCqZPn87vUQDAunXrzno9AGD16tVYunQpBg0aBEC9tzRu3Dh069YNu3btQl5eXoXCn5EjRyIpKQlNmjSB2WyG2WzG0aNHa61+J0+exP3334/Vq1fz92bOnInx48cjOTkZ+/btw8cff4xx48aF3dM807z44oswGAzIzs7GW2+9hcGDB5+1siUSiURSMedyXXT77bfjyJEjWL9+PY4fP4769eujW7duWLp0KVwuFxhj6NmzJ5KSkpCamgqHw4HExEQYDAaYTCbodDqsWLECv/76a7nCVr1ejyFDhiAjIwMulws5OTkoKCjA0aNHUVBQAKfTidTUVHTo0AE+nw/169dH06ZNERMTg5iYGOzfv/8s94rkTGK1Wrn4xuVy4ffffy8zflibHDx4EN999125op9GjRrhySef5K+zsrJgMplqrXwp/JFIJOcFbdu25aIBcngxm80wGo0wmUzo2rUrV2GSYw4JE8aNG8eFMoqiwOVyYdeuXfB4PPD5fNixYwcAcDGL3W6HxWLhZRgMhrA0VyRcIXcaEgmJKZIorZToMhOJKPKhdEokQBEXWKLQp7zfkTDGwlJTkYJZdAgiAYjo+gIgTMhDf5OFIbWHyqA2ivUU6xC5DQlKAoEAv0kj9hHVTewTcqEh5yStVouSkhIEg0GejktMNUWiEqoDjRnVTWwvjVdk/UmkQn3m9/vDXIWAU6IVsa/FsYgcF6PRyAU6BoOBi59E1xtqmzguoqCGUnoVFBTw+ul0OlgsFi52ovRkgCrMovRqYnqzSNGPOF6R74v9QuItarMoPqP3xeMHAgEwxmA2m0ulVSvvvJBIJBJJ1ZBBKolEIpFIJBIVuS46s1DKg927dwMAunXrBofDgffff/+s12XZsmVhwRAA2LVr11mvB3HixAmsXbsWXbp0AQAMHz4cOTk5+Pvvv1G/fv1K9+/SpQuio6P5a3o4r7aYNWtWqfd+//13pKWlIT09HcOGDUNMTAyysrJqtdzy6Nq1K39A85NPPkFKSgree++9s1K2RCKRXCxUd10kih8iadSoEdxuN06ePFnu/mlpaTh27Fi5x6gKrVq1woMPPoicnBx8//33WL9+PRo1aoS2bdsiJycHP//8Mzp16oQOHTogJSUF6enpcDgciImJgcVigdlshtfrxfHjx1FYWIht27bBYrHAarVCr9fDarXyB/Ivv/xyNGjQAPn5+cjKyoLT6UR2djYKCgpw/PhxdOjQAa1bt+aioPbt28Nms2Hnzp3IzMyscRsl5x+dO3fGTz/9xF/v2bMHgUCgVlPIRnLw4EFs27at3M9NJhPcbjd3FGrWrBnat2+PPXv21Er5UvgjkUhK0bp1awCn0hiRIw6JcMxmM4LBIBewUHCfxBtarZZ/fuLECcTHx6Nu3bqwWCyIjo6G2WxGbm4u8vLy4Pf7YTAY0Lx5c562iIQXZN8nLkzImUYsm4QjJFDIzc2FwWCAy+UCcEooQraAer0eDocDUVFRpZx3SPSTn5/PxUButxsxMTEwm83w+/1cVBMplAFQSmSi0+nCRBUk+CBhBQllAISl+hKFP6LQQkzvRaISsUyqk9/v520h9xkSuJDohcokJxk6Bj3RRWmcqN46nQ5GoxGBQIArUGlcaE4Eg0EumKJUVUajESUlJfB6vSguLuZiERLBUF3JKUesN7nbiOm9qJ9FdyHaV2yTXq+H1+sN6ydRjELb5+fn83keHR3N5wu5DBUVFfE+0ev1YYIhGk/R7YecjEgoRG45ossR9Te1wePxwGQywWQycQclnU4Hh8MBm83GBUFms5lv5/V6YTQa4Xa74XK5+GdUL3EeUh3IdYjmSqTLEwmfqF6i8IeEWYFAgAuz6Ok2Gn/q10AgwMdZIpFI/gnI65lEIpFIJBKJSm2si9q0aYOsrCwcP368FmpU8zpMmzYNLVq0wNGjR7F582YcPXoU6enpuP/++89Zvc42FosFycnJAMCfnt+0aRPuuOOOcyL8WbduHT744AOMHz8eAJCdnY0vvvii2sf59ttvMWTIkNOuz4QJEwAAK1euRM+ePREVFYWoqCg0adKkSvsvWLCA3wsCVDelSGHT6VDWA1f/+c9/0K5dO7Rq1QqPPfbYWRP9UNlJSUlYu3Ytpk6dCgA1Gj+JRCK5UDgX94vefPNNGAwG/PXXX8jJycHixYtRWFiIoqKisO3IeW7Lli04duwYSkpK+L39Nm3a4IorroDRaMSPP/6Ijz/+uNzyXnnlFVx66aX4/fffsX79euzfvx9WqxV//vkn9uzZU6XUW1u3bsWnn36Km2++GaNGjUJaWhouv/xyeDweHDt2DFFRURg6dCg6deqExMRE1K1bt8y+HTRoEBo1aoT8/HxoNBo0adIE9erVg9VqhcFggNVqxYgRI3jcyel0wuPxIDc3F263G8ePH0dGRgaaNWvGP/P7/UhOTsbixYtx8ODB6g+I5Lzk4YcfRv369cOEPwDw+eefIyUlBY0bN+bCstpi/fr1mDdvHo9Nl4Xb7cbrr7+Oa665Bg0aNAAApKen11odpPBHIjnPadGiBXeioZRCJMIhoYKYSkkUJRQVFcHn88Hn8yEqKgoWi4WnCNJoNCgsLITX64XT6eQpsgBV5UuQYKKoqAh16tRBvXr1uHDH6/WioKAAhYWF0Gg0iIqKQmxsLEpKSuB0OpGbm8uFGlqtFiaTCRaLhae+0mg0PI0RoCodyc2EhAokOqE6kKMKpc0iYQW1v6SkBCaTiadSoj4oKiqCwWDgrimBQAA+n49f1EUXFKPRiPj4eJ6qi45hNBphMBgQCATCBCzUTySoKEuwI4p5RPGK+COKNCJdf0jIISI6/UQ6tJCQg1KUUb+JkMCF+pVSUNGxi4qKYLfbeeo0sX1+vx8WiyXMHcbv93OBjMFggE6ng8lk4sIcSidF4hOqV2R6LDG/Jo2xyWQKcz+iupNAiOYIuQRZrVZ4PJ6wPqV5Qk5HNPdI6EWiHjq3RKGR6KxEUJovo9EIv9/P3yNxWElJCZ8v4jwQ05+JabdIaESiJUp7Bqhp0KKiovi5T3UiMY/X6+XlUeovca5FirtERyXxx+fzwWazhbkZ0ViZTCYUFxfD6/VCo9EgPz8f2dnZSElJgcFg4DeyyDmpqKjorFpJSySSiwcpwpFIJBKJRCJROdvrovHjx+OOO+5AXl4eDhw4gP379+Pzzz/H5MmTudtyWfzyyy/o1asXf/3UU0+hefPmVRZPnAn++OMPPPHEE3jxxRfRsGFDdO/eHVFRUXjuuefOWZ3OBQ0bNgRjDPv27eMptebPn49ly5adszpNmDABTqcTd911F1atWoWtW7dWed/+/fujf//+6NWrF8aOHYvvvvuOuxhMnjwZXbt2xeTJk6tdp8WLF2Pfvn3o1q0b0tLSqryfKPoB1Ict77nnHsycObPadagqx44dwzPPPIO33noLjz/+ODp37oy///4bubm5Z6xMABg2bBj69+8PABgzZswZLUsikUjK4nTWRWazGYwxeDyeUp8NHToUOTk5WLt2bbWPO2DAAPTv3x/r1q2rkhAyNjYWDzzwAPR6PTZv3oz58+eHfT5ixAiMHj0aMTExAFTRLolwPB4PXC4XT2F1991349dff0WbNm1Qp04dfr8+GAyiUaNGuP7663HixAkEg0H07dsXsbGxmD59Ovbu3RtW5s6dO9GrVy9cfvnlaNmyJQ4cOIAGDRogISEBP/30E0aNGlWl9KAPPPAACgsLcdddd+Gqq64CANhsNnTv3h3R0dHo06cPmjZtWuExkpKSEB0djSNHjuDgwYNIS0tDw4YN0bBhQ0RHR8PhcIQ9hG2z2QAATZo0QUlJCTweD8xmM7RabSnBh3T7+ecwatQoTJkyBYFAAF27dsWaNWv4Z+PGjQOgrjdHjx5daylJKcXpO++8U+420dHRuPzyy9GoUSP4fD643W7s3r27VgVnUvgjkVSD5s2bA1AXEJRXknJLkgsNpaDKy8uDwWCAw+HgQgIS1hgMBmRlZcHlcqGoqAiJiYmoV68eYmJiuBCAUk316NEjzEknNzcXhYWFXECg0WgQHR0d5jpCIoRgMMgFEFarlQf/SSRhMBi4+ILKJFEBOcNQmiWDwcCFCYD6RJDf74fP54Ner+dlarVaGI1Gnhec2g+ogoOCggLuCuPxeFBSUgK73c6FIqLwh8Qqer2eC4BIkBIphqHtNRoNFz1QvUi4IKbAcjqdYemVSKxAaZWoD0hARG2jOpD4h8qk/heFO2KaKRJgUH/TZ+R8Q4h1pN+i04+IKPqhfhHfB8DnJKVPIzEOub1Q35JAw2g0cgEOjSX1DynCaZ54PB5eJvUnY4y7zpBbDtVH7CODwRA2jhqNJkwFS64zOp0OPp+Pz1etVhvm7kN1oflH+3q9Xrjd7rA5Qg481CfimAaDQRQVFSErKwsWiwUxMTFhadDEMaQ5IAqUqH8MBgM8Hg+CwSC0Wi1PxUXj6nK5+FynsaJ60TlXUlLC5zCJokgUR+ekKKghMRsdn5yBxH6PFJmJx6a6Ud/QtYbqRucGUNoVy26382tLVFQUFxRROr6qqP0lEsmFjRThSCQSiUQikahcDOuipKQk7lbi8Xhw9OhRpKWl4d5778WUKVPK3Oepp54KC6Q0aNAAl112GV5++eWzUueK2Lt3L0aOHAlATUHRrFkzLFy4sFaObTKZeMr585nY2FhkZWVh5MiRYQKbnJycc1grYOrUqWjatCl+/PHHau33yCOPoHfv3gCA2bNn4+TJk/j777+xf/9+dO/eHXFxcUhISKi2C86LL74IADh69Gi19iuL6dOnn1HhD6AKlZ544gm8/vrrKCwsxMmTJ9GjRw8cO3bsjJTXt29fPPHEEwDUuSMdEyQSyfm6LhowYAAmTZoErVaLrKws/PXXX2jatCmef/55/Pzzz6WcN7p3746HHnoIOp0O//d//4dFixZVuazJkyfj2muvRffu3TF37twqCX+aNm2KtLQ0dO/eHaNGjUKDBg24KDk2Nhb9+/fnoh8ASE5OxqhRo3iMhBxsrr32WiQnJ6OwsBAnTpyAxWLhD8gXFRUhKSkJdrsddrsdNpsN9erVg1arRUpKCh544AGsWLGCl/H2228jOTkZV111FZKSkrhoGAAGDx6M66+/HtnZ2Vi7dm2l64e8vDzEx8eHvde8eXM0aNAAjRs3rlK/2u129OvXD2vXrkWTJk1Qp04dJCQk8IfOy0Oj0XAhUCSBQKDCdGeSC4MmTZogJiaGx/KTkpIwaNAgtGzZEmvXrg1b65Kz5aWXXhqWlrUmZGdn46OPPqpQ9POvf/0LV111FY4cOYKuXbsiLi4Oubm5cLlcKCwsPK3yRaTwR3LBkpGRgeLi4jD3G61Wy91FSL3qcrnQsGFDHjDXarVwOBwA1H/WFEWB2+1GSUkJzGYz7HY76tevD6vVCpPJxIP0RUVF6N+/P3w+HxdEnDx5EidPnkR0dDRXAx85cgQAeECcyjUajfD5fMjLy0NMTAwcDgcPkpPVHO1HwhKfz8cFFIFAAG63O0zMkJOTA6/XiyZNmoQF9EUhA6UDIvcaEppQGh9yYQHC7WFFwQY5lJAQhlL50N/0mhxKSFXs8XgQHx+PmJgY2Gw2LhYi8YdWq4XL5eKpt2h8xHRE1NckBhIFLuQaJIpcqL+sVmtY2qxgMBiWcisYDMLpdPK+sNlsYWmcyHmFxD2BQIALTzQaDUwmEzQaDb+JI9ZZdPYh8QoAuFwu/hmNB5VPx4h0p4lMp1YWkQ4uVH/qT1rwKYrCc56ScITcdABVvBIIBHg6L1EURMIacn0xGAzc6YnSr5EQhtI+Ua5MEpiQ2IfKJXEK5TcnByZqK23r9/vDRE40XtR31F4AfNxoX+pLEvrQPBJFPOL2gUCA/200GmGxWLiQiURktHgUxTBGoxGKooQJkOgzKpuEQSTao7Rg5DpE7RGFO+SkRW5V5PLj8/m4IAgAF2HpdDrenxaLhbsaFRcX84VvpOiH5ozZbA4TnRHUTtGZiJ58oPPfaDTCarXC6/Xya5XBYKhw3kokktrnfL2pIpFIJBKJRHK2OR/WRQMHDqySa8qwYcMwY8YMHDp0CLt378aRI0fw+uuvV6mMm266Cffffz9atWpV5ucjR47EkiVLwv7HOx02b97M/7ZYLNz5pH///oiOjkZ+fn6pfXr37o3rr7+evz569Ci2bt2KevXq1UqdaoutW7dWy1mmImbMmIERI0bg4YcfPqfOOVXBbrfD7/cjOzv7XFelFDNmzAh7WK4yGjVqxEU/gPowosPhQGpqath2a9asQWZmJi699NJq1+m+++5Do0aN0LZtWzRq1Ag9evSo9jHq1auH6667Dnv27OHp1c4EL7/8Mp5//nnEx8fD4XDglltuwWOPPXZGyurbty/at28PQB03iURyfnGm10UxMTHIyMjAyZMnceTIEf5Q+PnI8uXL0bNnT/z73//m723ZsgXvvvsunn/++VLbr1q1CpMnT0Z6ejqWLFlSrbJuvvlmdO7cGW63u8rX+65duyI1NRV16tSBXq/HjBkzsHfvXnzxxRdgjCElJSVs+/j4eHTu3BnBYBBHjhyB2+2Gz+fDsGHDkJeXh/T0dMTHx8NqtSI7O5vHBerXr8+/Yxs2bMiP1759e7zxxhvo0qULX9ft2LED7733Hjp37hwm+gHU+MCTTz6JlStX4rXXXsOKFSswadKkcts3b948zJo1K+y9OnXqoE6dOlXqHyIlJYXH6ex2O8xmc7X2j8TpdJaK7UguPJYuXYqTJ0/i0KFDPPZJa59NmzZh0qRJ2LJlC9/+/fffR1paGiZMmIC6devWuNz4+HhMmjQJL774Yrki8Tlz5qBPnz5IS0tD/fr1Aaix6mPHjnFdQW0ghT+Sc0p6ejoPhlMaKp1Oh5iYGO5OQ0IL+iHXkWbNmnFbOkq7BJxaxBiNRhQUFHAHGApAkxinuLiYu7mUlJTA6/XC4XDAZrNxIZFOpwsTgFBZwCnnFjFVFdWX9iHREAXbA4EANBoNdymhwDgJZ9xuNywWCxcukfMGcMrNg/YlQYfP5+MCC9E9RgzqU0omUYBAgghKpWQ0GrkQiMQH1Gc6nY6ndqLt6dhGoxGMMf7lGuk6pNfrYbfbudCJ+o8+I1s96jsSyZDYQOxPv98f5qYjigrEtFqiC09kGi2aIzSXyJlFTIclplCibWnOEKILEImtqB70mZjui8RcNEaRi22xfpFpwmjbSBGFmHpKFGyQQIPmgigeor6k7T0eDxfDiH0jziGxPEoVR84/JPoQ3aC0Wi3cbjcCgQCfzwC4mEisqzi/aa6JQidRmCK6OlFdDQYD3zeynyKdj8j9Rsx1K/YvORIFAgE4nU6e9ovKFvuFoHlU1rwQ60R/U1+T2IjOPfpnSBQxiXOc3MUi5zJdM0mcRinnAoFAmNhPnJ+iww/90DkoukeJ1wlxbonpyUShniiKovkukUiqz/kQpJJIJBKJRCI5H6itdVH37t2xdu3asP8Fzwa33nor7HY7Pv/88wq3y87ORvPmzdG+fXucOHECWVlZWLx4cZWcRbp27VrqyXSRkSNHwmaz4aOPPqp2/cuiPFfXhIQEdOnSpUyRy//+9z8cOnQo7L3Nmzeja9eutVKn8xHGGGJiYnD55Zfj119/LTNtyPmC1WqF3+8/L4O01RXFHDp0CDt37kSzZs0q3C41NbXG6Tw+/fRTmM1mdOrUCSkpKTUS/gDAlClT8NNPP51R4c/69euxe/du7qxQljCvttixYwd8Ph9MJtM5d4uSSP6p1HRdVB0BZXVo1KgRGGM4ePAgTCYTrrzySthsNhw7dgzHjh3jbhrnI9u3b4fH44HFYkFubi6aNGmCpk2b4u2330ZcXFyp69jOnTuxc+fOapeTl5cHQL1XHrkWKgtyRczIyOCxPKvVit69e+OLL77AyZMnsXz5cgwbNixsP7PZjOTkZADqw+c0VywWC6KiogCAC67pwfyKHE7S09Nx7bXXYs6cOfy9ZcuWYfv27WEiISIpKQlNmzZFcnIy+vXrh+HDh5crkiooKEBBQQGv1+kQHx/PHzA/XVwuV1iWBcmFSUxMDJo2bYpWrVqVSnGakpKCbt26hQl/AODLL79E06ZNMXr06NMq2+FwoH///vjwww/L/LygoADffPMNbrjhBv6e2WxGXl4ev1bUBlL4I+E0adKEi1X0ej3i4uK4gwuJYAoLC1FSUoKEhATuYiOmZyJBik6n444f9JsCxuTEoygKevfuzcU0R44cCRMKREVFoU6dOoiJiQkT/WRlZaGgoICXS84X5OpCYhpywxCD8iQq8Xg8YSmVAFUolJiYiNjYWCQmJnIBi+jqQe2h8ihgrigKTy1EYiGz2QyDwcCFMX6/nysMGWPwer3Q6/VcCEHplMRUOowx/gXv9XqRn5/PhUy0YCsqKsLJkycRGxvL3VgIURgiCgrE9F5040sUifh8Pm55R2Or1Wq5uwiJI0jIUFJSwp8eycnJQXZ2NoLBIE9PRA4zfr8fbrebjz+Jkkg8IAqtROEKOe2QeIraGOmEE5k6i8QMJA7xer1cSCT2BfVNSUkJLBYLn9s0V2kcGGP8y18UxYjCNJpXYgolEnCR0EwU9ZQl+BHbQkQKTERIlEZzh8oWRRwkPqM2kwBGTOlG/UOuVmLKKppboqiJjkvnot1u5+3z+XxwuVyIi4vjY0HbU/2KiopgtVqRl5fH5wCNDYnQtFotdwoiRxsSyomiQDo2nRNiH4upqqhfaE6SBSadh3SdILEfifWondSPYroxGncSiInzifqa2kFjotfr4fF4eLo+EucFAgF4vV5YrVae+oxEOKK7EV1HaEysVisX/IjzhfqE0rtRSj9yNiLRGp17dK0i1zBxbopOQdSndH6LfRE5ryWSCxkpwpFIJBKJRCJROVfroptvvhmdOnXiaaQ6d+6MN998Ey+++GKVxSxJSUno2LEjXnnllTNZ1VKYTCY8++yzlQp/1q5diyFDhqBx48Z47733EBsbi379+mHu3LkV7jdmzBhcf/31+Omnn8rdZs6cOVizZk2Fx3nggQdQt25dTJs2rcLtgPKFGEajEZ06dSpT+EMpkkSuvvpqxMbGIjk5+R+ZEuiJJ57ArFmzSgUczkdiYmJ4EO6fwIwZM/DZZ58BAG6//XY0aNAAjzzySKntTidll9frxcqVK7Fy5Uo88MADaNCgQVjalarQu3dvtG7dmqfOOxP89ddfuO+++9C+fXskJyfjr7/+OmNluVwu5Ofno27dunjnnXfAGOPjIJH8E6nquig5ORmHDh2q9n3SlJQUdO7cucrn0UcffYQ2bdpg3bp1yM7OxsGDB7Fs2TL07t0b6enp6N69OxhjmDlzZqXrkqqSkZGBsWPHIi0tDYcOHUJ2djb69u2LqKgovPXWW7VSxpkiGAzC6/XC6XTip59+QkpKCqZOnYrNmzfX6rpk69atGDhwIEpKSpCenl6pC+Cll16K5s2bw2q18vcCgQBcLhd3Vvzwww/Rpk0bTJw4MWzftLQ0vg2JeijmabfbYTKZEBsby2O8kem2InnwwQeh1+u5O09RURE++OAD9OrVi8cJRbp06QJAnfPPPPMM6tevHyYcElm6dCnGjBlT5mderxfff/892rZty8VM5VEbgh/C6XTi+PHjtXY8ydmnZcuWKCwsRJ06dWCxWHimGyI2NhZjxowp5ay6YcMG3HDDDYiLi0PPnj15rKu65Obm4s8//yz3c7PZjLS0NDRo0CDsPa/Xi71799aozLKQwp/zlNTUVBgMBsTGxqJBgwYwGo2w2WwoKCjgAXeHw4GioiK4XC5oNBp4PB7unEMBWo/HA6fTyYOxFEDX6/VQFAUmkwlarRZJSUmYMmUKF5lQGZRa6siRI8jJyYHZbOapewwGA0wmE4xGI8xmMw+W0/6i2wqJZURhgpgWiYL2lM6GxEa0gKJ6U51JREIB6uLiYvh8Pp6qiIL85ETi9/u5IMjlcsHlcnFRCgXLKc0XXRTITaikpAQ+nw9+vz9MVEGB/IKCAi5WcDgcvGxKywOAO4eQ6EAUD9DnYlolEiCI6ZXcbjfvdwC8TxljKCgogM/nQ+PGjcOcRug4omAj0hlGTFUlpsmi4D4JXkwmE3d5ofEgtxAS8NATQiQmslgssNvtfOzE9EtiqinR2YZEJJHuO5RyjNojHk90JxLdTSitEgkYqCxRgEFzkgQkNCdJKEGCGtHNpKioqFSqJVGsFCluoTlqMpm4aI0+E88ZqqPYFvF3eep82lc8x2iOFBQUhIliRGGIz+cLc/+hdtGco5RkJMYSU2QZjUbExcXxdGL0m+YtXRvoXCHBiugkI9aHtqN56HK5uCiG+kjchl5Tv9Bx6fwgxxsRmhvifiTgIYEitZeEQIWFhTyloCg8onGldHc0l0UxkqIoPDen6I5EbY+Ojobb7eb9DYBvHxsby+crCRVtNhtsNhufwzRmNC4ajSbMklJ0dNJoNHC73VzYIzo20Xyg6yGd1zSPRdckagMJE202G0+J6PP5+LVfPK8lktpCzieJRCKRSCQSldpeF02ZMqXcAM2sWbNw11131Wp51aF79+4YOHAgADUI9v3338NiseDkyZNVPsYnn3xS4xu4p8Mnn3yCmTNnIj09Hbt37y53u6SkJIwdOxY//fQT5s6di969e1cq+gFU55FbbrmFp9sqCxL9jB49GitWrAjrt1deeQUejwe33norli9fXuV2ffzxx7juuuvC3lu3bl2ptA0A0KZNG/zxxx9h791www1wuVzweDy466678O6772LHjh1VLv9C4UIQ/QBqeo2srKxzXY1aIzMzE1OnTkVUVBTeeOMN2Gw23HXXXXA4HGHb9evXr1bK69atGxo3boz27dsjJSWlWmmuYmNjAQDffvstjhw5ggMHDuCvv/7CokWLKt03KioKP//8M55//nksWLCg3O3WrVuHdevWVblONeXuu+/mKTIcDkepp+klkjPJ2b5f9MILL+Caa66Bx+PB0aNH8ffff8NoNKJLly5o3rw5MjIycOjQIezatQtXXnklxowZg9zcXPz99984ceIENmzYgOPHj2PXrl04ePAg0tPT0bBhQ56aac+ePQgEAnjyySfRo0cPTJ06tdI6zZw5EwsXLuRikG3btiEtLQ1dunRBw4YN0ahRIwDqPf1Dhw7VynUhIyMDrVq1Qrt27cJSJ2ZmZmLUqFFo3rw5VqxYUa74A1CvZedCeLpnzx4YDAYcOXIEX331FQoKCtCjR49KHV/uu+8+bNmyBXv27MHhw4crLYcEPEeOHOGx1/IwGAwYNmxYKSeezMxMrFq1iru2DR8+HFdeeWWZx4iPj0edOnV4fAxQ7+nbbDbodDo4HA7k5eVVSfiTnJyMqVOn4scff+TC0fj4eBw7dgyNGzeucN/mzZtj6NCh5Y79tm3bMGjQoDJdf7Zu3Ypvv/0WixYtwuuvv16r4p6K8Pl8cDqdZ6UsyZmhXbt2lY5hhw4dcN111+Hjjz8Oez8YDGLChAm466670K9fP6SkpMBut1fZMS07Oxvz588PS4scSb9+/dCpUyckJCSEvZ+RkVEq48zpIIU/VaBp06ZhwhnRkcThcMBoNPILOAluKLBNgWoKVterVw82m42LdrxeLxevmEwmWK1W6HQ63HHHHdBqtSgoKMDBgweRnZ2N7OxsOBwOHmDPycnhAWa32w2bzRbmVELOKGLQnRw93G43rxcFnH0+H3fsoTyQ1CYK/AaDQe5kQ8Fk0SWDnFVEQQ/VCQB3PqH6kOiGnEVIOEHBdXKjoOA2QYFn0QkIUIUAJPYgdx0SM5AQx+/3o6CgAH6/n7vaUKCfxoGEJ2JqG1EkQn1htVphs9m4cwlwSvREc4WOYzAY4HA4EAgE+Dwh0YPJZOJzSnQjEkU6tC25iADhIhen04kTJ05w1xWLxYKioiLk5eXB5/MhJiaG153mqN/vR2FhIU8jJArIynLOoX1IFEE2wDSHyGWFxFlxcXFITk5GMBjk84m2I8cgGkOC3qMxoHEV+5X6QpxjZTnoiA5WJNQgUQSJb8Q5Him+oZRWNB4kCiEBmJgCSnSFAU6lKqMySJRGQjK6nmg0Gp5mLTK1VuQYi6mxxLkm9p+YaozGUHSLEZ2atFotXC4Xv6aIopiCggLen6SMJWEa9SUJBwOBAGw2GwKBQJggi4Q7dFwaTxIEkfCMjmGxWKAoSphzVqQLj+goQ30ibiM621C5opBKTNVH/UntonlM84naQ9dpujZFR0fztGWR4ixR/EUiOVFAQ6JNeh0IBMLEQHT9zc7Ohtls5mMHAHa7nY8ziXDENG0GgwFRUVG83jT/qL/E6zoJvzQaDex2O59LOp2OjwMJeahd1DbqX7PZzK+BwKnrCl23SbQn+WciRTgSiUQikUgkKudyXTR69GhMnz4dBw4cqLE1+f/+9z80a9YMHTt25K46Irfccguefvrpc/YEbOvWrbFt2zYAwIEDB2AwGJCfn48ff/yxWsd57LHHcOLECcTHx+PQoUNYv349tm7deiaqjNjYWFxzzTW49dZbER0djVtvvRX33XdfudsfPnwY/fr140GS1NTUKpfl8/l46oby6NatG2bPno05c+aEiRJSU1NxxRVX4ODBg3jjjTeqXOaPP/6I/v37Iz4+HlOnTsXgwYORl5dXZuBuzpw5+PPPP3HLLbcAUFNH3HHHHXjnnXfw1ltvndHUQ5KqER8fj8OHD2PatGllujNdaJATD+Hz+XDzzTdj/vz5YQLA6OhoNGvWrEapW0ScTif++OMPLnCbOnVq2IOhlfHaa69h8ODB/HUgEMAjjzyCF154ocL9CgoK8MILL5QKXJ0L0tPT0bt3b/76xx9/rFDsKPlnU966qCI3e5Ho6GjUq1cPcXFx+PXXXwGo6SQbNWqE5s2b4++//8bq1athMpnQrFkzNGvWDNOnT8fXX399Rl2tRNLT03HzzTdj7969aNKkCVauXInff/8dAwcORPPmzbkDfnFxMQ9Cx8bGonPnzgBU4QagikF++ukn7Nu3D/Hx8UhNTcWAAQPgcDj4PdnCwsIqiWM2bNiA6dOnc4egli1bIj4+HomJiWHbdezYEbfffnutCH/q1q3LBX8iiYmJSExMRIsWLWA2m8sVfyQmJuKRRx7B66+/ftrX4uqydetWDB48GE2aNOHiyW+++abMbR988EG4XC6kp6fj1ltvRTAYRF5eHubOnYsZM2aUm0o2KioKSUlJ2LJlC954441KRZ3jx4/HTTfdVOp9p9PJxQRJSUno169fhS5z5X3/UAyQHigWYw3l0bRpU0yaNIm7QpLZRFW47LLLyj3vn3rqKWRlZWH27NmlxPk2mw0ffPABAoEA2rRpg3vuuadK5Z0uFotFCn8ucMSYdXlYrVaMGDEChw4dQmJiIo4ePYq1a9cCAA4ePIj7778fgPrwQkZGBlq2bImhQ4eiffv25R7T7Xbjtddew8svv1zuNs2bN0ffvn3Rtm1bbl5ANG3aFCNGjMCXX35Z1aZWyEUt/Dl48CCuuOIKHD9+HIWFhVxAEB8fj7S0NBQWFoIxhpYtW8JgMCA6OhparRaFhYU4efIkd0ygxbwY8CZhDIlITCYTYmJiwtwgrFYrD/ZTCikKxNI+oiBADHoHAgGYTCa4XC6eToqcGABw5w9FUbg7haIo3LUGAE89RIIQSklTWFgIn8/Hg+9iqpfo6GguUiE3GAquU9oc8UJNbjvk0EPiCQBcbHDixAnutEFiHhJNkUApKioqLEWOmGJJrHsgEEBUVBSCwSBPk0MBe3KooPEpKSmBx+PhaWpIYFFSUgK3280FNiSeIoHXsWPHEB0dzQULdrsdbrebixlsNhvvd2oLHUsM1JMLDQmEqC3kPkLbkxCAgvFerzfMsYSERTqdjn/pWq1WGAwGeL1ePl+ojiSCIXELfdnTgoDG02KxhNWVxpPmg16v5y4/JGYhVxvGGNxuNxdhUftEERONCwkjREcd0Y0HOOUKROMkuuSIv+lcEZ196NyhelGfis5KYjo0EsaQiEl0QBGddURHJJr/YsoyQhSNUf+SUwzNSRLhif0rpqoSF0di+jYaM+oXqi8di4QcYoomEt2R8InqW6dOHZSUlCA3NxfFxcXweDx8fAgS6JDoQ3Scoflht9t5GilK21dSUsLFYtRPtJ84d6kvaV6K42mxWMIctKgPaBuxL0hcSOI6yldPfU/1ozbRdUUUqtCY0DGKiorCREUk1hHFWDRXyGGJ+loUP1IZjDH+HQOAn7vkpkU5PUXXJHEOmM1m/h45+Wg0Gi5KKiws5ONH5dF3DI0pCbgiHZVE1ypx7ouuUUVFRYiLi+NubyQcojklnjeSC4uNGzdKUY9EIpFIJBIJTq2LfvzxR6xcuRJPPPFE2Oc///wzLrvsMvTv3x8//PDDOanjiRMn0LFjR3Ts2BGpqanYv39/tY8xcuRIAOpTvdOnT0d+fj5MJhNuv/12vPTSSwgGg+fU9r5u3bph/btz5040bNiQPxBUVR599FG8+OKLmDp1KvR6PfLy8pCQkFBusOZ0yM3NxZw5c3D11VcDAEaMGFGh8AcApk2bhg8//BA9e/bE119/jfHjxyMQCFTo5NGoUSN0794dS5YsqfDYq1evxgsvvIB77rkHLpcLzz33HIBTrsIul4v/D1oVdu7ciaNHj2LXrl2YNWsWsrKyMGXKFFitVu5qK9K7d2/cdNNN2LdvH5o1a4bU1FQ8+uij+OKLL6Tw5zwgLi4OEydOxF9//VUj4U+XLl3wxx9/hD3wdz5B95+++eYbDBw4MCw9yYIFC3D8+HFs2rQJhw4dwqZNm7Bx48bTKm/AgAHo27cvnn322TI/nzp1Kvr164cRI0YAAO64446wzw0GA26//fZKhT8AMH/+/NOqq8g777yDrKwsPPzww+jYsSM2btxY5dREu3fv5veNjh8/XqG7h+TCpTr3i/r06YObbroJa9euRX5+Pnbs2IGRI0diwoQJ+OKLL/D777/D4/Fg/Pjx+Oabb7Bhwwa0bdsW/fr1Q9OmTREMBjFv3jw0bdqUCx0yMjKwadMmfP311xg0aBDq1q2LNm3aoFGjRvD7/di5c+dZE/7897//hcFgwOWXXw7GGIYNG4bk5GS0aNEiTHSh1WpLubeINGzYEDfeeCN27twJn8+H1NRU7k5GfT1kyBD89ttvWLp0aaX1WrhwId59912MGzcOer2+lOgnLy8P8+bNq3RNUhUGDRqEli1blnJTi2TYsGG466678MYbb4StuQYMGIBbb70VGRkZOHnyJKKionD48OEKA+e1zapVq7Bq1apyP4+JicE999yD2267DXa7HQUFBTweZLFYEBMTg549e5abcrWgoABXXHFFlesjuiYReXl5WLVqFU9P2b9/f3Tt2rXKxywLeoi3qsTHx2PKlCnYv38/1q9fXyXBEKC6v02ZMqXc7wRyMmrYsCGaNGnC369bty5Gjx6N+fPn49FHH0X79u3RsWPHUmmbahuK10suXEaNGsVTzlVE3759kZubC6vVipiYGFx//fVcT0CQqPuzzz7D0qVLsWDBgjJTz2VmZuLrr7/Ga6+9VqFwbPLkyWjXrl2pNHl79uzBzJkzK03NXB0uauEPoCpmycGEArVZWVk85ZNOp4PL5eLimUAgwFM6kThDTJdDqarI6YLEExaLhSspxaAoOZJQkJVSr9DxyF2DnDVExwrRbYQC+eTe4fP5eCCZFuler5fXOdK1hOqdl5eHvLw8Lnyhz0kgZLPZYLVa4fF4wtx2yGGChCWi2wNdLEXnCAo8kyhDPKkoEE997Xa7w1wp9Ho9dwfy+/1hDh5RUVG8TlQGiSJI6UcCJRKskCCAhBBut5unQhMD5wB4YJ2EQBSALyoq4sej8RSD66J7ENWPxptclsTUODQ+orCJhF3U1zS/SOBD/UkpiwA1P2BcXByOHz+OQCDA56A4d8R0SqJDE6Uf83q9Yf+8O51OPtZ0HEonZzQaueMUCUpIJEPtpvqJIhRywhFde0R3JXpfFBGJri8kwIh0zKE2UBo16ltKh0RlioI62o/ON7pBIIqLSKBFdaJxooV9ZKou2ofmFR3T4/HwY4siKNHpSBRDUX1E8Qh9ToIuUSAiui/R/LXb7dDpdNzNxWAwwGq1ciGb2Wzm1whAXfCQoE8UE1E9yImMxHd0Dnu9XsTFxYExFuYyQwIwmic0NidOnAj7x0an03HxmChoIqEN9RsJ6Ogcc7vdMJlMpW5OkNuZ1WqFyWTi5wwdS0xPJYpcCCqX5iUJwMTrmzj3aJzp/CZxo+hkRiIcEvTRd4vRaER+fj7y8vJQp04dfj5Sm0mcRukWya2N+pjcl6hu1Ock/MzPz+diP7PZzMeUxpy+K8TzjupMxyQxLC24qb9IVEfXrareJJJIJBKJRCKRSM5HBg8ejL59+6JFixZc+EP/e/Xq1QsAKgzonGnEG5spKSk1Ev5kZmYiMTERy5Yt4yKMSZMmYerUqXjppZfwwgsv4IEHHsDzzz9fW9WuMqmpqYiNjQ1zjkhNTUV0dDQefvhhPP3001U6zvTp0wGAP60MIMwV+UyxcuVKdO3aFY0bN670af3Y2Fhs374d/fr1w6+//oorr7wSAwcOhF6vx4cfflhqe71ej0OHDmH27Nn8ydSKeP3113H33XdjwoQJWLZsGaxWKy677DK89tprmDJlCjp37lzl1DyrVq0KSw/26aefYtasWbj77rtLjcnq1atx33334YMPPuAPGH7xxRfwer04duxYlcqTnDmSkpJ4uo7qng8dOnTA7bffjtGjR+Pw4cM4duwYBgwYcCaqedosXLgQCxcuLNNlOyEhAbfddhscDge8Xi9GjRpVrdR3kWzYsAEbNmzAhAkTUKdOnVKpVJKSkip8ahxAKdev1atXo1u3bjWuU2W0bdsWI0aMwObNmzFx4kS8+OKLuOWWW7hzSGVYrVZkZWWhbt26WLhwIT7//PMzVlfJhUH37t0xYMAAdOvWDYmJifjggw/Qtm1bNGrUCPfeey/atWuHlJQUxMfHo2HDhujUqRN69OiBZs2awWAw4NixYxgyZAhPiUKpfjp06ICGDRsiOTkZdrs97P5tnz59KhXC1haXXnopWrVqFVZ+q1atany8Zs2alftZu3bt8P777+PSSy/FgQMHKj3WyZMncfToUaSkpPD3fD4f/vjjD7z77rt4++23a1xPYujQoViyZEmVnc2mTZuGOnXq4PHHH+fvLV++HAMGDMBVV10FrVaLlJQUHpsrL/3s2eaFF17gqdMAlArY//HHH+WKfmpCpFALUNerY8aMwVNPPYVDhw5xZ6izyY033ogbb7yRr92q87/PlClTcOzYMX5uTpo0CR06dMDixYuhKAquvfZanDx5Elu2bEHz5s0BqGvi2267DfPnz4fb7cbrr7+Oa6+9FkOHDq00XdrpQPNPcmFy//33cze1yoiPj8fo0aN5Fqbp06fj5ZdfLvdBiB07dpQ5NxRFwY8//ohvv/22whS/9erVw6hRo8p0SEtLS0OzZs1qVfhTtSvzPxRyxRFTGhF+vz8sjQ6JCijgS+IecuUBTgXZjUYjzGYzd2QQnUoiU/KQu48oZBCDuxRAJccMMeUQBc1FcQTVkQK8ongnMj0RCQ1E8VAwGITH44Hb7eYiCyqb2ksOMdQXJEYQy6L2iW0Fwt04IgPskfWjv6ldJF4Qj0tCDyqb+puEUCTQofROohsHia7I4YJcUkTxB+0jpoyiRSUJSkShCjljkDiF6iCmghLTd5FTRmTqIrHfIsUsoluNmP6I9qVtaX5QOh5qk5i6jepEc5DERSQGipxTPp8PPp8vLD2d2WzmIiASqlB7yDGJ3J3IAUSsA/V9ee491J5IIQaVI75flsiA5oDojBUpphHbKY5jZF+J6ZUi08yVVx+an3SNEec4oLqniOnExPqIdRAdh0REERfNfXG+0Q+1XUzbFilEJHEKpVSjtH4iJPAihyfRfYuOodfrERMTUyrVkyhYorYSYoo0EqrQ9uJ5Jgq1xP4Q3ZHE84UES+TsReMlznma9+J5RNdVqhf1QzAY5K5bVEdxTolQWSTWo7lOn1Fb6HwW3ZhIJEbbim2m7wUSX9H3iHju0TlH0Pym45Gwko4rzmcxnZvYz3R9tNlsiI2N5f/siOe1KMQUhWsSiUQikUgkEsmFSMeOHQGoKSaI3r17o1evXjh+/Dh8Ph8yMzPPSd2uuuoqjBo1CoAq3q9J4EGj0fAgA6XTAsCDc507d0b9+vWRlpaGqKio2ql4NaAHHkRXGLoH16lTpyrf/Kd7QiJn2mkmISEBbdq0AQDs2rULsbGxFW7fqFEj/hCiy+XiIpnU1NQy2zlq1CiMGTMG+/btQ3Z2Ntq1a1fh8V0uF1wuF8xmMzp27Ii///4bBoMB27dvR0FBAZ/rVWXt2rVh/bpixYqwOURs3bqV3wi3Wq04evQo3nzzTcyZM6dSK37Jmefee+9FbyFFU3W47bbbkJaWBrvdjubNm9f4OGeTjRs34uDBg/ze74EDB/Dee+/h008/xYoVK5CZmRmWdut0oONSqiLihhtuQFJSUoX7Uv1uvPFGdOvWDRkZGbVSp/Lo1KkToqOj8dtvv2H79u38obSqct1116Fu3br49ddfeToaycWN1+tFdHQ0d7AZNGhQWFrK9PR0pKSkwGq1Ij09HW3atEFqaiq//xobG4suXbogOTmZi34AVWydnp4Oh8NR6p5jYmJiqZRBZwpyzz9bOJ1OXHLJJVXaNiEhodSa48CBA1i5ciW+/vrrWqlPs2bNqiz6AVRXSUpzJkLXtoYNG/IA/JAhQ86o0LE6bN26FVlZWeV+TiYJtcVHH32EvXv38tcUW3A4HBg2bBgA4PPPPz9n11mz2YwmTZpU638C+j+CaN26Na688kp06NABl19+Oe68804MHjy4lLCiUaNG/O81a9Zg8+bNpQTjhYWFp9Ga0ojxE8mFhcFgQEZGRrWuS3FxcbDZbGCMoXv37ujTp0+52+bl5ZXpNkuOdps2baqwrM6dO6NevXql/qej/8MiReKny0Xt+CM6o4iOJIA6YFarNSyNF4AwYQk5VoipaKxWKxfJiGltKHhNwVMxAEtQKhxRuJGXlwe/3w+r1cr/jo+PD0sbFAgEuAiDXDwo6AqcCjqLQVhRzEMCEr/fD4/HA6/Xy4PSVqsVOp2OizvsdjufnLS92WzmaZRIOEBiII/Hw/uIgviiWEg8WajOoniE+g1QF4wU3BbFBn6/H06nkwehzWYz9Ho9t95zOp3wer1ciEUuQwC4gw4JZKgsEvJQQFx0RTKZTCgoKOB96HQ64XK5woRcYuCd5o44Vyj1FAXmqR/oGNQ2UQBFQXoSidBr6luqq06ng9Vq5cF7sisjBxBRSEPz2Ww2898khCA3JRL9kNhBFPOQewoJxqhtNG5utxu5ubn8H0Zxf1GwQXNGdE8pS3wRCfVRpBuOuPCnc5XaQgIgukFFxyZ3EhIxia4ulLqPhFrkkENiNFGwI4qrRFGOmE6NHJxoO9E9iARS5LhC7aS+ovdE0QvVVXTYEusQFRUFl8vF+9Pn84U5F5WUlMDpdHKhj8FggMPhgFar5eNL84aOS9ca2obceah/KOWUeD0j8RzNLbPZzBdoJG6hMSMxDPUJtZXGgoSRItT3LpcrrM9oXOlcozaJc5VSz4lua1QX+k2p7KjtJIKMFHVRuXR9Et1+6LwlpzmPx8PPL1EsZTabuSMZXffo+mIymfiihOYnOYjRXCSXIDqe0+nk14CSkhKe3lIUV9G40g/NXVGU6fP5cMkll4SlgaTvIPqbUoHR3JZIJBKJRCKRSC5EdDodbrrpJgDAjz/+yN/v1KkTOnTogO7duyMjI6NUUPdsEQgEkJ6ejhMnTpRKFVNVxo8fD5/PB5PJhBtvvBH9+/fHI488gokTJ4IxhnvvvRdDhw5FTExMKaeMs8G+ffvwySef4I477uAOEnTvZ9WqVaX+J4zk7rvvRr169cq0fBcDNb1798aePXt4GgWR4cOH18hBwGKxoHXr1vjf//6HO+64o0LrdwB4/PHH8fPPP2PHjh246qqr0KVLFwQCAZw8ebLMdrZp0wb33nsvcnJyUL9+/VIPNJbFkSNHkJGRgTlz5qBXr14YPXo0jh07Bo/HU+YTqBUxadIkxMXF8bnz7LPPlhmIyszMxK5du3j6io8++ggrV67kn48ePRq7d+/GH3/8Ua3yJafPtddeiyuuuAL5+fnVdi4bNWoUbrjhhjD3A51Oh0GDBlUpHU1tMnnyZGRlZVXpPO3cuTPq1q2LLl26oFGjRpg1a1bY53379sX333+Pbt26nXYqFdElbdGiRTy1V/369bF48WI0b94ccXFxiImJCdtv0aJFiIuLQ58+ffDhhx/C6XTWenAzkuuvvx5Lly7F888/D6/XW2lKp7vvvht169bFQw89hKeeegpjx44FoKYt27p16xmtq+TCYNWqVcjMzOTOJOnp6WFxsPr16/O/Y2Nj0bVr17DvMZPJVKZALvIBVZHPP//8rAXtf/3117Pqcr5nz54qiT1SU1NxySWXhKXfOnz4ML799lvMmDGj1lIyRl63qkL37t3D1lT33nsvXxuIqadGjBiBYDCI1atX10pdT4eZM2ciOjoajz32WJmfjxkzBlartVQ64JqyYMEC+Hw+TJ06Fa1atcJff/2Fbt268dgSoK6Nr732WqxatSrsPDpfSUxMxOjRozFv3jy88MILuPHGGwEAEyZMQFRUFOLj4/Hnn3+GmRcAqnMVcfDgQbz99tuIiorC4MGDcckllyAnJwc5OTk8205tQPFuyYVHt27dTksw2LdvXxQXF+O3334rV+w3ffp0XHXVVbj11lsBAG63G6tWrcKnn35aoRubzWbD2LFjy4yTPf3009i7dy/Wrl1b47qXxUXt+EPBfQpYUlCWFhkFBQU8mE3BVnLNIIcIcjyx2WyIiopCbGws7HY7jEYjrFYrrFZrWDogINzxg4KwFGynYwPqhSYrK4sHoin4CgAej4f/TWIKr9cLt9uNvLw8nvJHFAJQm+k3BX/pHwiPx4NgMMhT/5BDhs1mC1P6U1CcguvkHESuKuQmQT8+ny/MZYeci8TgMAllyMmIxEuia47oyiKKPagNJDowmUxhqW2cTmdY+jHxi4AEUzSulJ6GRDEkBiEBCLmh2Gw2mM1meDweHiSnMRFdmkgUIYo+SL0c6dACgLeZRCwklqC5QeNNfUJpuEhwY7fbYbVaubCCRBok0hHFNVQ2iZmoX8jdiAQwJEqgukcK2MgdS0yHRvO9oKAAmZmZfFFJ+9F4EtTvkQ5OJDwobyFdlsNOpOjG7/fzNpBYhMRsNDb0Q+WI7lZ0rouOMZHOM9RuUXQnuoTRb5PJBIfDgejoaDDG+Fyjc4b2IcEM9YsoQhPFe9RfkW5BoguVmMKL3G9EYQsJ8BRF4Sn+SCxF16HIdGY5OTlwuVxcYELzRhRFis4xVGcaU6oDzSsSZpH7lOg6JIp+6HOtVstdzUTRl3hdjXR1IlGPmGqLIBEYCWzEOkSKyYqLi/l57/F4+JOYYqozEujQOAHqQoBEQCT8IQEPXQ/ElH80P6kP6Ty1Wq1c9CO6IInXRDqGeO0kERE5d9HTnm63Gz6fDxqNBk6nEwUFBVywSdckmhcmkwlxcXFhjmoAuAsYXdOp3YqihD2VI5FIJBKJRCKRXEgUFRVh69atyM/Px3XXXcfTMDz99NO4+uqrsX///rMe4Bah/1d79eqFL7/8skbHWLRoEVasWAFAfRr7559/xnXXXcf/txk1ahS2bt1a4ROQZ5ply5ahZ8+e/PW///1vAMDPP/9c4X7jxo3Drbfein/9619lOpFs374dXbt2xeDBg/HVV19h2bJlZR7nuuuuw7hx46pd7wMHDiArKwtpaWmVin6In3/+GVdeeSVeeeUVNG7cGFlZWfjhhx/K3PbBBx/EI488woM+VXEC6NevH/r06YMdO3Zg0KBBuOyyy/gDj6tWrapy26xWKwYPHoySkhKMHTsWb731FlasWIGpU6eWWaaYwmTRokX877i4OLz55pu15rAiqR6ffPIJunTpgg0bNgBAtRxerr322lIpTwDgyiuvrLX6VZVWrVrh2muvrfL2J06cwOLFi0uJfgDVuerFF1+E0+lEcnJyrdVx+fLleOONN7B8+XK88847GDlyJDp16oQ+ffpg4sSJeOKJJ7BkyRJs3LgRX3/9NXr27IkVK1aAMQaHw8Ed3dq2bVtrdRJp0KABPv/8c36f7sMPP8TixYsxffp0DBo0KGzbf//735g5cya8Xi8WLFiARx55BMnJyVi2bJkU/Ug4a9aswXPPPRcWvK/IiaEq4tWKcLvdNV4L1YQWLVqcVZfzIUOG8GBzRXTs2LHUtauwsBD79u2rNdEPoF4zqovD4UDDhg356/r165frchF53TmXPP7449i6dWuZLoWtW7euVccfAPjqq68wZcoU3HPPPVi6dCmeffZZrFmzBt988w3f5sCBAzyN7YVAly5dsGPHDi76AYCmTZvy8W/dunWYww8AvPrqqxg+fDieeeYZfPfddzCbzfjqq6+wefNmbNmyBc8//zz+/vvvWq2n3++v9KECyflHVFQUkpKSTnvs+vbtixtuuKHcz5cvX45XX30Vq1evRmFhIdasWYOFCxdiz549FR73iSeewBVXXFHq/RMnTmDr1q21LvoBLnLHHzH9FKCe2GKQnwLOoqhFq9WGOSHQokSv18NgMIQ5LJBAggLU5JgCnFroiAFaqouiqGm+nE5nmIiBAsgUuBWDvfSe3++Hy+UKc6agALToFEOOLcXFxfB6vXC5XNwBiOonBt4pqE0BXxJRUPliyjBypSHHDjEQXFJSwvtXTBUk9iO5kFBfWa1Wvi+1n4Q1gLowtFgsPKhPIgASVJG4hYLqZrOZuxpRm6h8EoWQSIPaI6YDs1gscLvd3OnH5/PB7XbDZDJxkQe1neaEVqvlYizRWSMylRHNExKQkPhAHEtyLqK5BahiBJvNhuLiYvh8Ph6U1+l0XBQVCATg9/tLCQZofEUHKJqTJHygMaRtSBhBfRqZOozEESQCIscl0amKhEWiU404zyLTWtG5FYkodqF2kYiEhDPkhELnfUlJCZ9XNM9E9ysSe1AfRM4rcnuiNE00N2g8RBcj0clHdIeh84TGheaIKIyJTGNF7adtaH4SJPQhwR2d8yTUE1NTieIcOpedTid3+aLxpvlE5weJHWkMRScaUfAVeQ4YDAY+t2kekojObrfD6/WGiWuoTJqnYhtJNEWfiWnCqA7UVhpjEjSJqb/oPKNrpNFohNfr5eecKAwl9xoSulE7SMhD4kGqh6IocDgcPHUijTWgupLRNUt0AKL5V1hYGJaWjoR7NpsNJpMpLLWbKCQV06SJ5yQJHt1uNxcgkdtQUVERGjZsCMYYd0eja4BWq+XfRfQkR+T1kuac+D1AY2u1Wmv0j6BEIpFIJBKJRHK+sHjxYnz66afIycnBX3/9FZby61zSrVs3vPnmm3j//fexe/fuGh8nNzcXx48fB6Cm+nrooYcwduxYTJ48GQDwww8/oFmzZvyJ/XPBJZdcEhZsyc3Nhcfj4WKF8mjdunWY4CSSbt26ISsrCzk5ObDZbGjRokWZ282dOxffffddjeqenZ2NpKQk3H///XjhhReqtM+cOXNQt25dPPbYY0hOTkavXr3QqFGjMoVJL7zwAvr27YvBgwfj1VdfxYEDB8IcqMaMGYPdu3eHuQTk5eXBZrMhPj4e06ZNQ1FREbp06YKxY8fihx9+wBtvvIEvvviiwjq63W4UFBQAAJ599lkA6v/Tkf//NWrUCJ06deL/Tx4+fDisLjk5OcjNzcWgQYPwf//3f1XqH0ntIjokVzV1HgBMmzYNK1euRMuWLdGkSRP07dsXAHD55ZefkXpWxFdffYW5c+eibdu22LJly2kf71//+leN9rNYLGjXrh3Wr19fKh3E7NmzS21fUFCAP/74o0y3q0mTJoW9JsHjN998c0bus8ybNw933HEHPvzwQ17f0aNH45lnnilVl2nTpgFAKYeL2g7ASi4cpk6dioYNG+Lrr7+GoihYv349fD4f5syZA7vdjueee67ax/R4PNi1a1elaSyJgoICJCQkVJiWqTaZMmVKtcSStUHz5s0xYMAALF++vMzPtVot0tPTS61V8/PzwwRYtUFKSkqN9hOFPvfdd1+529lsNowaNYq7PZ5r5s2bh8suuwx9+vQJE/oYDAbccMMNePnll/l6+nQJBoPYvXt3pev7VatWYf/+/Wd9jb57927ExcVVmsI2ksq2F8+nzMxMtGvXDk2aNMGll16KoqIidOzYEZ999hm2bNnCXTGvvfZaWCwWHk8+XcRUY5ILg5iYGKxfvx4PPfQQ/9+kpmi1Wvz+++8VbrNjxw4MHToUTZo0QYcOHSpNn5ieno6rr7661PeF3+/Hww8/fMYE0xe98IecEChIyRiD3W6H3W6HxWKByWRCVFQUD5RT0JyC1BRoJjEACQ5Ed5dgMMjTToluNGKgVnT1AU4F3CmgCpxKhUPvkeCHRBZUR4PBwIOxdrud18lisXAhB4Aw0QAFwCmVl+hCQ8IM+hFFNaKAiZwl6FgkKBDdTOjYovMGvef1esNSb5GogFxRIlMpUQA+KioKHo8nLIBOgW6j0Yjo6Giu9iMXFBIp6PV6/jeAsNRSohCK+pX+CS4sLER+fj7y8/PD9qX5QHnZKX0bCRCozqJ4gAQhgCrwcLvd/H0xBRTNUepbKk+j0SA6Ohomkyks1RrVxefzwev1wu/3c1efsgRl9AVJYhkSubnd7lKOIqJ4xWw2c3GJRqOB1+vl+5tMJhQXFyMvL4+ngyMRkuiUQ2If0b1GdLkRHVci012VdV5Te0ilS+VFpgEjYY947opCChKFUN0AhKVZIycmGmMSldDnVBc6JvU1tVd0eqLtREch0X1KdGoiVyESvYhOQaIzGPWjOE8ZYygoKODXg0AgAK1WC5fLFSbOsVgsvFxKJ0WuMzabLSytGwlFRAciSr9H9aNzyel08n1sNhu3T6TUiWK/0RjGxMTwz0hUKF6PxBSCJIqicaDyqV/FfhfnNYk3SWAnpmET3ZEIEifReU1iQa1WC7fbzftXq9WGpd4rLi7moisS6xUVFfH+JlEU1SsmJgZWq5V/L0WKesRUklQ/2ka8XgLgfUfXQDrfaT7QHKIxiYmJ4WJJ+u6gNIV0zpPIrLi4mDtZuVwu1KlTBw6H46za3kokEolEIpFIJLXNRx99hKKiIkyePBlHjx6FzWaDy+U619XC6tWr4fP5ykxNVV0mT56MYDCIgwcPAgDuv/9+LF26lKc627x5M95+++3TLqemPPjggxgyZAh/3bJlyyoFV8oT8hQVFWHjxo2YPn06bDZbWHqJsqip6AdQA0IdOnTAsWPHqiz80ev1YYH9N954Az6fD+3bt8eOHTtKbT9kyBB89dVXGDZsGObOnYtHHnkECxYsAKD2wZtvvolRo0Zx56CcnBzuFG00GvGvf/0LH3/8MRo1aoScnJwqt23KlClYvnw5Pv74Y37cvLy8sG0OHTqEN954A02bNkX9+vXLfKL1P//5DyZOnIi0tDTs37//rKVpkajExMTwe4XVEf4cPHgQr776Kn9N//unpKSgY8eO2LBhA/r371+uY1VFXHbZZdBoNNiwYUOVrrcbN27EzJkza0X0czr89ddfiI2NxZIlS3jqq5rSq1cvbN++HS1atMD+/fv59Vl0S6hNvvrqKzzxxBOYOnUq1qxZg3Xr1mHhwoW455570LJlS75dhw4dEBUVVeYxfD4funXrhvXr15f51H3//v2xevVqmUblH8j48ePRtm1bpKSkID4+HnXr1kVmZiYWL16MRx55pNrH83q9ePfdd+Fyuaos/Klfvz6aNm16VoQ/iYmJZaYQrU1mz56NI0eOoE2bNrjssstQv359+Hy+ckU/gNoHUVFRpR6c9ng8OHHiRK3U64YbbkBSUhJOnDjBU8VWB7o2PProoxU6QAGqMMjn81UaWD8bPP/883jttdfQvn17/PDDD2Htrl+/Pu677z488MADZ6UukyZNwqFDhxAVFXXWH7gtLi7Gf//7X2zatAkdO3ZEw4YN8Z///KfWy0lMTMSYMWMQHR3N46YdOnTAZ599hvz8fCxbtgyjR49GTEwM9u7di19++QUpKSlchFwTiouLq7UOlpwftG3bFj///PNpu8YBQFZWFtasWVPpdvn5+di4cSM2btxY6baFhYWlRG/79u3jD/CcKS7qVF8AeMCYUvsYDAYu+KG8ghQQFsUgouhFTCtEYgFRXEBBbnLViBQAkPBATMkFgDut0PFEkQJ9TsF3CuCK6WXI1YEC3SQ8EutEx6VjAwhzJRFdYSJFCtQPYh9QkDnyhwQ3kS4U4msAPOgNIEzcIW5HYgY6nlgPEnpQeiNR8CQG+8U2UdvFvqD3SBgkbkNpo7xeb1jaMRpXcuUR08KRIwiJjMRUQ/QUiJj+ShSQiX1FLjPUr+KYiUIasY+Kior4P8qiaEdMYUbCFDFtEgkSRGclcT8xjRi5p5D4g7an+pJrCLkPiW444vwVhS9Ud1HoE0mkKxCNkZgGS0yBJSKmPirvuKI4j44b6W5EbaS+Jfcm0YFI7DNRwBPp0ELjQWVECrTEOojOQOL5UJ4YShQh0Y000cWoqKiIuzBR+jhRgCO6GInXP3HOkluZ2Kfk/CM6pFEKQbo+kbuV2Efi9tSPJD4hFyfq80gxGJ0/ZQnHxP6jutD74n7iGEWmposUeYrXRwBcaFdYWMjbT8InEmqSc5d4raEy6PpsMBh4yi86zwhqu+juFen2U1abRecr+pyES5EOUyR8olRsJPYhoY84n8R881QnEjhJJBKJRCKRSCQXKvS/0/Dhw7Fjx45qBzjOJOKDYqfLzp07edqyjIwM/PDDD3jrrbeQlZXFU8ycS3799VcMHDgQAJCUlBT2/0d5REdHl/n+Tz/9hF9//bVUSoHaTIFBiPdRqkqnTp3QunXrsPcyMzP5fa+y+N///gdAFV2IwhlKUR2Z9uOTTz7B999/z1/XqVMHs2bNwtdff40///yzSvWMiooKS7d2/PhxHDlypNR2n3zyCQ/a7dq1q9TnX331Fb788ktcc801ZaYKk5xZHA5H2L3kbt261eg427ZtQ1ZWFhRFQePGjQEAt9xyS42OlZGRgVtuuQV33nlnhSkXiLy8vDIddc4mPXv2RHJyMux2O1JTU0/beeCdd97hQjpRaEOpGWub3bt3w+VyYcKECbj00kv5+7/++iscDgd/XZHrXefOnXH77beXG3i95ZZbzliqMsm5ZdOmTTh+/DjatGmDpk2bolmzZkhPT0eLFi3KFYpVxOHDh7F27Vrs27evzPRK5SHO1TNJZmbmGT2+2+3GsWPHMGfOHLz55pv4448/+EO6Fbmm0AO4kTidztN2wiA6d+6MiRMnwmaz1UioK8YkKiM1NRWdO3eudhlnCq/Xi1WrVmHdunWlPrvsssvQtWvXs1KPJk2aYMqUKViyZAkOHjyI/Pz8s1IuoK5rk5KSsG3bNnzwwQc4dOjQGUuNlZCQELb2zcjIQOPGjcMMCRwOB7744gts2rQJP/3002kJS/1+P3Jzc0+73pKzy+bNmzFnzhxoNBo0bdr0tI516NChWk/dd+LECaxduxZ//fUXT/3822+/nbH1HHFRO/6QY0edOnW4wIdSZJnNZjgcDlgslrBUNRSUJpcTSvtFweaCggLuzAKAp9IB1IsHOWYAp1KmiGIACh5TMJVScNlsNlgsFhQWFkKj0XCHH/pCp7owxmA0GuFwOFBUVASHw8FTfzkcDjDGkJWVxetEQier1cpTzwDhohAKeouOImazOUwgJKbfIUGMKEgQRQEAwsoRXUqKiop4uhgxpYwY4Kd65OfnQ6fTISYmJiytWiAQQEFBAerWrcv7lILYYqCcFkzk3EFBcWobbSc6GRUXF/N0OYWFhWHuLiRWiIqKgsPh4CIQEmfRF5XH4+EOPuRoQk9bFRQUhLkpUdkGgwFutxter5cfl9xOqN/EsaC5p9Vq+cKO+shgMHDXDxKTkIsQufKQiweNIdU/UthFrknUxzQ3acx8Ph+fa5mZmbDb7Xwuu91u3n8k/hAFJ6JrkCgCoXlDZYg30MR6ifWMFH1RWiOab5Giusj5QuMoConEY5IDipj6TBRCkWCC5h1dZyIdoejcp+1EkZd4TopCH/osUmwnCjlEkQf9TenFaEzJ0Yy2E5+qIsckSg8FnPpnisQmBQUF/NomiowCgQAcDgcCgQAsFgvsdjsCgQCioqKgKAp3qiJnKTp36FpGAi0SKZGjFDnnUB+SsFIUOtL5INZHTCNHfU/jKfaJRqPhc1cca9rfZDLx9lLf0HjTOUbzmdKZBYNBuN1uXhad53QO+f1+nn6LrE+pz+x2O68LnY/kRkRjQOe0ON5iW6j+9F1B3x0nTpwAY4y7rnm9Xuj1ev7PNR2DhF+5ubn8CRK9Xs9t3knQZLVaoSgKXC5XrS+WJBKJRCKRSCSSc8Hrr78OnU53Xj0JOmPGDMyfP79WjvXKK6/wvydNmsQdYwCctnPF6dKtWzcMHToUrVq1Qrdu3dCqVSusX7++0v3q1asHQE1FpdPp0L59e9StWxeXX345tFotRo4ciYkTJ/LtyVGjNiHxkd1ur7L7yaZNm8ICD2QFX1Y6IOK9997DtGnT0Lx5c9xyyy1wOBzYt28fnn76aXz11VdYtmwZJk+ejP/+97/4+uuvcffddwMAnnvuOTzwwAN48cUXcfDgQWzevJk/8Lhv374KAyh33303JkyYwF/v3bu3zGAYoN5IP3z4cJntz83NxSuvvIIFCxbguuuuQ+vWrTF+/PjKuklSS2zZsgXffPMNEhISkJycjE8++QRNmzYtlaqqMtq1a4fU1FSeigMAmjVrVqPUO4cPH0bbtm1x7bXXwu/3V+k6dy7Tc3Tr1g3z5s3jr7t06YLbb789zBGpuqxcuRLff/89evTogdjYWOj1+jMWXAXUe9V79+5F27ZtMX78eKxYsQJbt27Fgw8+CK/Xi4YNG2LLli0V9nPPnj0BqCkOy0pNmJGRwV1/JBc+3bt3x2OPPYa1a9di48aNiI6OxpAhQ2A2mwGo38GDBw+u0bHT09ORlJSE//u//8PVV1+NQYMGVWm/pKSkGpV3vnHo0CHs3bsX+fn5+Pnnn+FyueB2u9GqVStMnjy53NRpsbGxZd6HzczMrLU0VOPGjUN0dDTS0tJqtD8JGtetW4eDBw+WEiaL1K1bFz169KhROWeS2bNn47LLLgu7/965c2fceuutVXILqSm//PILli9fjkaNGmH48OH4z3/+g0GDBuHWW2/FvffeWyuprqpCly5dUK9ePRw/fpyLj05XcFEVhg8fji5duiA7Oxs7d+7E/v37ERUVhZkzZ+L48eOw2WxISkrC2LFjuZNhddi3b1+ZAnXJ+U1+fj42bNiApUuXok6dOqd1LI1Gg/bt24elTa4NBgwYAJ1Oh44dO6Jp06bw+/3YsGEDkpOTkZiYiE2bNtW64+lF7fhDaVQcDgcSEhIQFxeHxMRELoShz0gUI7pNeDweuN1ueDwe7n5ArgdFRUUIBAI82EriAqPRGOYwIqbeiUy7JQb0g8EgCgsLYTabuTMKBZjpgq7X62G327lDBNWfUpXZbDZYrVbodDpYrVYuYjIajbDZbDAajXxhBiBM5BAIBLjDDQk5yD2I3HBIbOD1esNSwIhtofep/tRPFMSn9pOYJdKlRQzO+/1+mEwmOJ1OnpqI+qO4uDgsbY3oHkJlkIsF1Y8C5mL6pkjHoZKSEng8Hj7mHo8nrJ+io6NhtVr5OJFAhAQIPp+Pp/gSXTA0Gg0PnIsqdkoBRn1JYw+oN41oPEk8RKIgr9eLQCDAnVdIdEWiBxpnv98fJiohZxLR7YPSQImCFvF1MBjk/UNzkfrO5/OhsLCQ3yjyer3Iz89HXl4eFz+JDlkknKA553K5+FiLoq9Ih62yRC/iuJR3jtFNDHIziXQ8EecBLeRIYEH9JCI6vpCoglxaKFUYtY/SVtF1gdJoUftE8QldR3w+H68jzWUSwpAQSEzlJNZFFK9RX5MgSUwzGJmyjdLOUepBEoeQ8IX2zc/PD3OuIsGWKBwTnW+MRmOYqI/6ieqt1WoRCARKucrQOURiOjGloMvlQm5uLhfe0P6iaxqNHx2XxIJi2jy6/olzSEzdptFoYDKZYLFYYDab+flN4kFCTMPocrng8XjC2p6Xl4f8/HwEg0EuCKXPKU0hiVBJjEjpBkkwRGNIbaT60dyjOtHY0pym7yfqf0oHR45lYopIOrfpmqnX6+F0OpGZmYnCwsIwN6Tdu3cjPz+ft52EoBKJRCKRSCQSyYXO0qVLz4tUAyK1JfqJRBT9nA/8+eef8Pv96NmzJ2bMmIGUlBRs3769wn1iYmL4w2C7d+/G/fffj759+6JDhw4A1P8xx4wZE7ZPSkoK/33rrbfWSt0dDgdWrFiBvn37Vjnlkc/nC3PdMRqNGDZsWKX7Pf300ygoKEDfvn3x9ttv8zYcO3YM33zzDdxuN2655ZYwt4Dp06dj27ZtaNu2LUaMGIHHH38c7733HkaPHl2pA9KaNWvCgqwajabMwOLgwYMxaNAgrF27NswhKJJHHnkEfr8fmzZtwoQJEzBgwIBK2yypHR5++GHs378fAGA2m1G/fv1qH6OoqAh79uzhqd8AoHXr1jVKu7N06VK0a9cOBQUFMBqN+Oijj/DEE0+c06B+RY5bzz77bFjwWqvV4s477zztMl966SX+95QpU/jfI0eOPO1jl8WuXbuQn5/P02Zcc801PKX74MGD8dBDD+Gpp56q9DgTJ07Ehg0b8PLLL4e937ZtW3z00UdnpO6Ss4vNZsOVV16JAQMG4NFHH8Xw4cMRGxtb6l55Ta4lxPXXX4+bb74Z8+bNq1I6lcOHD+O9996rcXnVITU1lV8za5vCwkIcPHgQf/31F39vw4YNmDhxIu68807u8FcW9erVQ0xMTKn39+7dW2upvk7X2V10p6lKesl69eohMTHxtMqsbZYtW4a///4b69atw2+//YaSkhK8+uqr+Pe//31Gy42KisIDDzyATp06AVD75tixY9i1axf27t17RssmtmzZgttuu42v9/Lz8ysUptcmjDHUrVsXrVq1wujRozF9+nQUFBTgrrvuAqA+xP7MM89g/vz52LRpE+bOnVstEfPvv/+OlStXnqnqS84gV1111WmLfgD1f67aOE5ZFBUVYc2aNfjoo4+wcOFCAEDHjh3x7LPPYvPmzbjtttsqTX9YHS564Q8FWUksExUVhYSEhDBxDAkoxNROYpomEsN4vV7u0EHCC+BUyiMKRvv9fi70oG0i3SeCwSAXJFDwnRwmNBoNzGYzCgsLudBGq9XyvMxWqxVarRYej4eLJshRgrYV3SJoUUYimcj0NqIrD/14vV4eJKZgtZiiSFzoiS4iYiCfHHWoLyk1DDkZUZBbFOdQueSyYjabuSsSCSfMZnOYcxK1kQQNYrohSslDjjlUJo0J/Y5MDSSm0CFxjMlkgs1m445FJDAg0RQJckj4JIoJSPRD84fqQuVT/Uit6vP5oNPpeFo6cgqhJy+oHRR0F11i3G43n8f0m9okinvIcUoUadC24tzw+XxcSEJCHnIvKigo4OWQMMjtdsPlcsHpdMLlcsHr9fKn2aj9JOYgUZgoFBNFG5HCMjH1VWT6MtGtCEBY6jUSkIjzPzINF9WPRBeiUw/VV0yLJs5vMSWY6JwiOgqJAgqqK7WTrhsGgwF2u50LTmhcaA6LghVRIBR5XtJ5QNcHo9HI+6i4uBh2u533DaWrojJo/lG/U39QKitqL11rSIRHfeTxeLigjc53xhgXu9BcFgVMJSUlcLlc3G2MnNjo/BPTD4pzg+ZKpKMajQNd10nMR8ch0SL1rdvt5tc6vV4Pi8UCm83G03WJ5wOJicilKDLdGJ2z5BIlCnWMRiPi4uL4NYy2Fa/Z4rlK3zP0PSJea6nddF6I11rR3Y3mJc0nl8vFBWmR81g8r+ic0Wg0qFu3Lho1agS73c6Fan6/n5/fEolEIpFIJBKJ5MKnXbt25yT44nK5wgJdOTk5lT6Re/nll/P/D8X/ScS/27ZtG+ZeYTQaMXfuXJSUlJRKtVVTrr/+evTr16/a+9ET8YDqRFSVdGvz58/HO++8w1+PHj0aOp0OJ0+exM0334z+/ftjwoQJpdIYlOXM0aJFCzz77LMYN24cF1BF8t133+G3337jDzeOGTOGp4sTadmyJZo2bVpmOSL79+/HvHnzEBMTg+eee67WxkBSNcaPH493330XWq0WcXFxtXLM6qS4i8Tn83GX5RtuuAEzZszAa6+9Viv1qoznnnsO119/PTp37gy9Xo/XXnsNN998c5nbDhgwoExHirS0tNNO++J2uzF9+nTMnj0bNpsNgPog6KxZs07ruOVx8803o0+fPjh8+DBiY2Nx++2346qrruLX0qricDjQoUMHtGnTBunp6fz9kpIS7Nu3r7arLTkH9O/fP2zeDxo0CJ07d67VdKitWrXC2LFjsWjRokqFzgUFBZg2bRq/D3ym2b9/P9asWYOsrKzTSi1UFnT+UMpEwul04ocffsCePXvK3TchIaHUOu3o0aNVTuFZEWPGjMGwYcPw7rvv1vg8pod5qV5VSdtK2Uqqw2233Ybbbrut3JSvp0tBQQGefPJJLF++HM8++yzuvvtuvPLKKzh8+PAZKY945plnsGPHDjRt2hQ5OTn8gdx3330Xb775Jo4dO3ZGyweABg0ahGXlANQUlIcOHTrjZZdFbGwsxo4dixEjRmDy5MlISEjAlClTMHLkSMybN6/KTq0HDx7EypUry3WulJzffPnll7VyHDHGeTb46quvsH37diiKgl27dp22sFLkok71BZxKhwSAOyuIoh5RKEBON6LKn5yAyPmCJgalxhEDtSS0IYcgusFAQVhyBaGAt81mK5XOhhxbKCULBXVFVyLa1mg0csEIYwwul4unp8rNzeUODiUlJSgsLITRaITVauUBadHVRLQTJcGP3++Hw+EIc8qIdBqhH3KIIfEOpQkjhyIAvO6iyIUEDiTKoMA6lWkymVBYWAi3283TRdntdp5Gym63w2g0Ij8/nwe/yRUoMsWaoihwOByw2WxhqYFEpR0JX6Kjo7kLCol7aGxEIQqJVcQfElBQHeh1IBCA2WxGQkJC2IJGdD6iFD9utxuBQAAGgwEOh4OnCAsEAmEuTKKQhH6L/3iTUCDS1YfqXlYqLeCUIpu2EdNlkUMVfSYKXkgQUlBQwN2PLBYLF3aQopLGis4FEmfQfBXbRvMq0qVFFLuIAp6SkhKegokETiSSEftbFO2QgEhM6yaKakj8I7rrkKOU6GZEYyj2O80r8Xyh9lD7xZR5JOAT5y65vYguYuK5Qv0lHpOuNwC44wz1O11D6Nyn6xI5h9GYkICI0nZRGi66nul0Ovh8Pvh8Pj6/aZ7R+SmmQRPzytN81ul0cDqdsFqtXARG5xk59dA1icZAdKuJTNtF40PXKuonn8/HxS7idYwsUkmcI4q8yHFLXPCSCIecv8gxiZx2qM9JeBQfH8+vSdR+SodWWFgIRVFgt9sRFxfH606p2kj0RNdS6mdqM50b5JZE856+l8j5h75jyGkpEAjw7zKqL7WFzhWdTofs7Gz4/X4+DyjFpOgcVdlTohKJRCKRSCQSieT854YbbkCvXr3w008/hTl6nC1mzZqFgwcP4l//+hcKCwsrfbpYdIt58cUXy6xz+/bt0blzZ/z73/9G7969kZeXB6fTiUOHDmHq1Km1Uu8tW7bUaL/8/HzMnj0bqampePTRR7F58+Yq7bd06VJkZmbigQceQHx8PD744IOwVG2RAZCEhARccskl/PVLL72E+vXro0WLFujevTsWLFhQoVPRlVdeiS5duiA5ORmtWrVCdHQ0WrZsiW3btvFtYmNjYbPZsGLFikrrv2XLFjz11FNYsmRJldssqR12796N1atXY/jw4WctXUhlHDp0CPHx8Tx4O3z4cCxevBgbN27Exo0b8c0339RaWb1790avXr2wY8cOjB8/HnFxcXA6nTh8+DBatGiBTz75BLNnzy6135AhQ8qte01TviQlJWHBggXYvHlzqWuR0+nEE088UaPjVobT6cSWLVuwc+dOFBQUoF27drjssstqfLyePXvit99+w/z583HvvffWYk0l55q9e/fiiy++QMuWLfl907JSTJ0ul156KSZNmoSCgoIKt/N6vWjfvj13UTgbPP3008jJyUHv3r3RqlWrWj12fHw83nzzTTidzip9dxL16tUrJdzcvXs3Vq1addp1euihh9C6detSrk7V4dixY/w+8b59+7Bu3TokJiaW6VJEUOyzqowePRqPPvooAFVctGTJkhrXtyLmzZuH5s2bhzkznWk+/fRTFBUVYdq0aTh58iS2bdvG48svv/wycnNz0aJFC6SkpGD06NFnpA4ejwfNmzfnQp/GjRtj3LhxaNSo0RkpryokJSXh0UcfRUJCAkwmEx566CG89957OHz4MDZt2gS3242GDRuGZbshduzYgaSkJOzduxc//vjjOai9pLZYsGABrr/++tM6hmjGcTYIBAJISEiAx+PBzp07a/XY58dK/hxRloJKTLdDgXQxAA4gTMhAwWpRHEGBZwrAUhCWxD8kaKB0OpGiC3JHIcGJwWDggVsSEFCwWUy9JIoJSJhAzhXkpEEuH0VFRVyYQAF2RVFgs9nCUlyJ7RHbSO2mttF2oqNIeem6yLlEDA5T4JucJkiwIIoxxFQ2AHjwGkBYqiNRhCSmTBL7uCxIVEMCCbHetA/NBXLbobH0+Xw8gE6iGVHMJS5QIh1qaMwo7RoJwTweT1j/igF3cmMR51Sk2EV8kk2c0yQIEMsW5w2Nt5gmS3SkogugmOKL5p/oKiI6OdG8A8AFCyT8EUVh5OJE857eJ5eiyHON6iWm+6I6R85J2l4U0YhCnsg5T22g80pMA1XWfqJDFI1tpGisLAcYsczIdGLib3KGKi4uLpWmTXQzUhSFjw9Bx4zsD+DUeULXNZoLVLbf74fZbA4TMgGnnr4qS3xDc4rqJIoDSZQmisvoPBEFR2L9qP9o3hNUT/H8FvtbTM8lultRf0Y6kYmCSdGRia7HkXOAxpr+JlEVOZrR9Smyz0XxD/UJib3ofKF0eF6vFxaLhQtPI+cY9QO1XRQ9ieeEOJ/oOiumAKO+Eh3NaL6IAioxdSAALkIVBbA098itSCKRSCQSiUQikVzYpKamon379vj999/PSflHjx7FG2+8gUsvvRTp6emlXGtEEhIS0Lx5c/66Xr166NKlC9auXRu2ncvlwooVK3iKgM2bN+OOO+4AEP5Q27lgzpw5aNasGTIyMqolgPnhhx+wcuVKDBgwAAMGDEDPnj2Rnp6O3bt3l7m9xWIJS1EUDAbx5ptvom3btkhKSsKsWbNQv3596HS6Mp/mzsnJ4eKLli1bwuFwcNHPxIkTsW/fPtjtduh0ujLTgEUyZMgQbNu2DStWrKhWsFNSO9hsNgQCASQnJ9dYtFKbzJ07F82aNcMNN9zA37v88svRsmVLNGjQoNaEP3a7Ha1atcK4ceP4nGWMweFwoEWLFgCAZs2aIS4uLkw8V69ePWRkZJR5zIMHD9a4PiaTCS1atEBGRgYWLVpU6lwQnb3OBG+99RaSkpLQtm1bJCcno3HjxoiNja22sEOn0yE+Ph7t27dHvXr1qnQNkFwY7N27F+vXr8e+fftqXfQiotfr0bp160qdYaKjo6vtCnO6bNu2DZs2bcKll156Ro6v0WjQtGnTan0XJiQkwOFw8NfFxcXIzMysFVei7du3o3nz5lVKz1UeBQUFPNWX1+vlD/JXRnWEP3TP2+l0QqPR4JJLLsGOHTtqWuUKOZuiH2LdunVYs2YNcnJycOTIkbDPPvzwQzRq1AgZGRkYOnRorZ8TPp8P69evx/fff8/f69OnD5o1a1ar5dSEFi1a8Jhip06deNq/hQsXolGjRmjTpg369+9fygXqt99+41koapKaVHL+sGjRInTs2DHMabC6iPG8s4XVakUwGERqamqtunZd9MIfEsBQ0JICvhQwJVEMcEqAIzo40JddpFBCTBkkuniYTKawYD0tXEhwQqIEo9HIg7HkguN0OhEdHc2dgywWCxdTiMILCqqTsw4FsIPBIMxmM/Lz8xEVFQWz2YySkhKcPHkSMTExKCoqgsfjCWuX2FfUJqPRyFPVULli6i5xW7EPKCBN6X5EgYzYz9Q+EoWITi0kMqDjUj9RAJucOEhUJTqgiA4WtKigMaPUbiRAEtMkAadcPOjY5Fik1+u560ZMTAxiY2P5GIouRTQ25LJEc4TqYrVaeR1oHojCFDGwLzqUUIDd4XDA7Xbzfvf5fDylEqAuFkncRaKM4uJimEwmPpfF9lIaHxLKGAwGWK1W7l5DKefEY1N7RScaURxGn9Nvg8EAr9cLp9PJX2dnZ8NsNvP5SWmiyF2GzjOaS1QeiSZEkYg498Tz3efzwe/383RR9BnNR9GdRxTo0HFJICE6C0UKw6iuka5AYsow0ZWF5iiNDTlWUZkkaHM6nWG5SUVRB81dmnuRIhEql+agmAqNnMUozV5xcTFfHHo8Hj5PaA75fD4UFRVx8RZZudK1lNT71K+io5J4Tvp8Pn7eUSpFWmx5PB6eNovOHfFaKx6PrtuiKDJSeGUymXj9aX8SktH5Jdr5iW5WotKX5gTNaSqL5rbf7w8b+4SEBOTl5YUJaETRHo079afNZuN1iRTYmEwmfg0V5xM5K5H4kY4tQsJG6nOj0cjFpOTEBID3d6RLGH1/ieI5g8HAUy3SdVE8B0QHPYlEIpFIJBKJRHLhotfr0bJlS/Tu3Rvbt28/K1b411xzDb744ouwgM+4cePw4YcfIjMzs9z9rr76arRr1y7svZdffhndunUrte3EiRPRv39/zJgxA/Hx8bVX+Vpg586dNXr6MxAIYO7cufB6vWjWrBneeOMNnm5s06ZNePzxx/kT8Ha7HWlpaXzfYDCIdevW4eeffwYAvPHGG7jmmmuwZMkSPPfccxUGz0SXH71ej5tuugkdOnRAVlYWDhw4UGka6HHjxqF///645pprsHjx4iq397bbbuN1lZwe27ZtQ3R0NObPn49PP/30XFcHTz31FK699tow4Y/JZELjxo1rNRXcyy+/jAYNGqBx48al0usQbdu2xeTJk/F///d//L2RI0eie/fupbbNzc3FZ599VuP67NmzB2+++SYefPBBPP/88+jevXspN+XZs2fD6/Xi/vvvr3E55fH555+Hva5Xrx7S0tLQrl07JCUlYfjw4WHXjbI4fvw46tWrBwC47LLL8OCDD+Luu++u9bpKzj69e/fG8OHDsWjRIjz33HN45JFHzmjgf8SIEZW6zJyrBw8XLVqEFi1aoGHDhuWmxawpe/bsKVe0WxZarRYJCQlhjj/5+fnYunVrjcpPTk7G3Xffja1bt2L//v145JFHsG/fPjz44INVCox//vnnSEpKQmpqKhISEgCcysoAqPGKuLg42O32Co9TXFwMl8tVpTpbrVa0bNkS0dHRqFOnDj799FMUFBRg5syZePrpp6t0jPOVpKQkTJo0CY8//jief/55ZGVllSmIOnToEA4dOoRff/0VAwcOrNU6mEwmnnUEAKZMmYLbb78dUVFRtVpOTRDdfK666iq0a9cOR44cwahRowAAY8eORXJyMjp16hS2n9vtxpQpU85qXSVnhoULFyItLQ3//e9/a3wMi8XCY8ZiDPZMQuLzq6++GtnZ2dW67lfEGY3IMcYOMMa2Msa2MMY2lPF5M8bY74wxP2PsfuH9jNA+9FPIGLsn9NmnwvsHGGNbQu+nMMa8wmdzKqtfSUkJV5ZSADhStECpvUhgQqlg/H4//H4/D/yKPxRgJREHBbj9fj8XTtA2ousOiS0oiO/xeLggIj8/PyyoS2lZhD4LE2JQ2ikS6Wg0GsTGxsJoNCIqKgp16tThopqsrCzuVuF0OlFQUFDKGcRiscBkMnHVm9FoDEtrRb9JQAKE53KO/GKnlDckGKBAPAXuaRtyhiHRRiAQCBNnUVkkWvL7/byvzGYzD5CLY0P1p8B6dHQ0HA4H7HY7gsEgFxyI+wUCAbjdbp5WjMQMFDS3WCxcnEXOUBS0pzpTO8mpg8qw2+2w2+084O/1ennwneamXq/nqc9MJhPv/0AgwIUz1L8UnKdUPiTkinRQEt2IxHkvupfQ5+QKEhMTEya0Et1q/H4/8vLyUFhYGJb+h1xgKF0XlQOA18dmsyEuLg5xcXGIiYmB1WqFXq/nKYi8Xi+OHz/O04iJrkmi0Ipei6nqRAEFjQXNATElFZ3j1J5I8Q+1p6x/Nkj8I7qsiIIecd9IQZzoskPzk9oDgNeN+trv9/OUTlQ2zRG6tohiCzou1YeuDXSNI/EJKeG9Xm+Y0CoxMZGfLyTwEheW1N/UL9SvosgJUC2DxblPQiW/3w+9Xg+73Q6z2QyDwcDPK9qejkeiHhKlif1Jx6XrBPWD6LojCiHpM/F6FhUVxeecOCaR4jjqa1Ew6vP5uMCQUtKJoie6PtJ3CAlldDodv8ZFzlv6XnK73fB4PPB4PGHiNmoXzXvRdYp+AoEAfD4fHzfxWmyz2bjoR3Q1inQ8E88BKg84lf5SdBMT57NGo6nV/N7/BM73dZFEIpFIJBLJ2USujWqO2WzG999/X2F6hNrkjTfewLRp09CjRw+kpqae0bJGjRqFNm3a4J133inz5u1NN91U5n5jxozByJEj0a9fv1L/h1AqhEhycnKwZMkS/Oc//6m19F7nAx999BFGjBiBxx57DB07dsS6devwxRdfoHXr1nj11VfRsmVLWCwWHDlyBFdffTX+7//+j7v6isGThQsX4oEHHsBtt91WrSfmY2NjodPpYLVacfjw4SqlX3n44YexdOnSaol+EhIS0KdPH9x0002lnuKWVJ8VK1bggQceqFSkdTbp2bNnme/XRuoaQBWpde7cGYMGDQp7//bbb8cdd9yB119/nbv3jBs3Lmybrl27hrlrELGxsWEp9moC3ZNp3749Zs2aVeY2TZo0Oa0yqsrx48excuVKvPLKK7j//vvx0ksv4eTJk+Vuv3btWnTt2pW/pnvvksq5ENZFjRo1wuTJk/HCCy+gX79+tS54iSQ+Pr5U+qpI8vLycOedd57RepRFfn4+VqxYUeH5UFO2b99eaVpTEZvNBpvNxh/c/fvvv7F9+3bs3bu32mU/9thjeOONNzBlyhTMmDEDV111FXr06IE6depU+fthxowZ6NOnD0aNGoWVK1cCQFgsD0DYw9flkZmZCafTWaUyhw8fjl69evEyKJZlNBoxePDgs3bNPBMcPnwYo0aNwlNPPYXjx49X6oI0aNCgMr+fTpfhw4ejW7duSE5OxuTJk9GmTZtaL+N0SUhIQJcuXcJSES5fvpzPQ5HzJbWppHbYvn37ae3vcDgQGxt71tYsiYmJ0Gg0iIqKwtChQ2v1GnU2ZnYfRVFyyvksF8BUACPFNxVF2QWgLQAwxrQAjgL4X+izMbQdY+xFAGKSz72KorStasXIQYMsgsnBgQLEYuobFnJQoOCsy+XijgcUPBbTx4hOJyRSIQcKCooqihKmIKOgLwklCgsLeaBbdB8ikQVd4En4QCm0qHyhP7kzEAXYSfSh0+kQGxsLp9MJq9XKRSc04UjIYDabw8QIJJAQ3U6oDymILKaXos8ICviTAxL1r5j6hsRN1Ba/3x/mYkMuGXS83NxcmEymMPEGCQciBS8ajQY2mw0AeLBfDMqL9SYhgc/ng9fr5V+aojsMiYNIBCCWRX1N5ZK4i5x+aJ5RX3s8Hmi1Wp7yiwQjJNAgkRgJVbxeL1wuFw+803wyGAyw2+287uTUQvPZarWW+eVGZVBKrsj0dHa7PcxtREyTRK4jJOwgwQQ5w9Dx6YccfcjVJzo6mgupqG7BYBCZmZkoLi7GyZMnUVJSgqioqDDBAzn+RKaJikw5ReXTOESm36I5GCl6YCHXE7ENJJAQxW/UdrqGiG5PoiuQeF0hsQSJk0gsIYqnSEhCxxedpMR+Fo8vlkvzlMaQzjnqf7FdXq+Xz0m9Xs+FOXS9oLGkax4Jqqjv6DM6D6gt4jVSFH6JzmsknCFhpXge0zWUzlE656gPRQFSZAoumqe0H7WfPqP0dSRmpHEXxTCiUxG9JwodbTYb3G53qXSM5OKTlZUVJjyicQ0Gg2HnNl0baI54PB64XC7Y7XauOhbdv+gcoXlH5yqNE4kfSTBJC14aH9GVSrx+RF63aZ4pisK3oe8rOhbNOfEc0mq1mDx58hm3or7AOG/XRRKJRCKRSM484jpUAkCujWpETEwMgsEg8vLyzlgZdrudB1tOnDiB2bNnY9iwYdwN5kwxcOBAvPXWWzAajTh8+HCV96tfvz4effTRMlNalOcGs2TJEu5+cz4zdOhQ9OrVCwcPHsTs2bOrvN8nn3yC1q1bo23btrjqqqsAqE/x//zzz8jOzsbOnTuxb98+jBgxgt/zEoNyNU23lZmZiXnz5iEjIwMPP/xwlUQaUVFRpZxGyqNz585c3NW6dWvMmjWLpw+RnB4JCQlhrsfnGjFtH+H3+7F69epaOf6TTz4ZlqooPz8fL7zwAr9mREVFobi4GHfddRcuueQSpKSkYPr06Th06BB69eqFgoKCMt0OTido1L9/fwwfPhwAsG/fPixbtqzUNnfcccc5G6fPPvsMfr8frVu3RpMmTZCWlhbm+NKlSxfMmXNKQ5KVlYWlS5eWezy5LirFeb0uqlevHtasWYMBAwaUcs84V/z666/nrOxffvkFLpcLHo+nVlMr7dixo1prPHKJLykpwZIlS/Dpp5/i888/5/GvqjJjxgzcd999PP7FGEOfPn2g0+nQqlWrSh16ADU28dtvv2Hjxo245557sHXrVvTo0YPHPwkx1WhZFBUVIScnp1S8pzxuueWWUqnXdDodZsyYAZ/Ph3Xr1mHQoEHnlbi1Opw8eRL33HMPEhMT8dlnn2H58uUVbu90OnH48GEkJSXVWh3i4+PxzDPPYN++fefN+V8ex44d4+lLp0+fjgEDBpTa5mw9QCE5O6xfvx4HDx6s9NpSHhaLBUlJSYiLizsrqd/GjBnDr9FerxcFBerXc22si86ppE1RlCwAWYyxoRVs1g/qIiQsOS5To4rXAOhb0/I1Gg0KCwuxd+9eBINBuN1u7pJDqY3E4C8FkUV3H/pSpS9WCsyLPyReoeCqOGgmk4kLakhIRAICUgsXFhbC4XDA5/Ph/9k76zipyvaNX7M1PVt0d5fSDQoqoQKKYGARIoJgF8Zrd4EFqIAvIOKLLSGSiiItKd0s2zG1M7s7vz/2dz3eZ3a2l9JzfT772YlznvOcp2bm3N9z3V6vF7GxsXA6nSogL2EWBvdl3l3COtLxw2DIz1VsMBhgNpuRkZEBj8ejgIq4uDhERkYiKytLBZQDgQCcTqfahkBLUL9o2osBaQAquO3xeBRYwu3oUiPhB+lqROiJrkgM1PN1uonYbDYFGBC+kGmA2O/84w9aplWTrh65ubnK3efUqVOq/z0eDywWCwKBANLT0xXkJGGSYNiDbSFTaBEOYGA9JydH5TdlKjVuJ6Epnrfh/1P+EIrw+XwKNGAwnv85xuhmFBUVpdI3sc1dLpeCH9g/nAt0iZHpwFgH1pvjm2PAaDTCbrcrMEQCb4TTCHsEAgH1WM4vl8ul/hwOB5KTk9W4tFqtGnedYLiHczJ4PvLY0tUpFJRUGHEe/Lp0VuFYk+3K+SadsIKPJ9eG3NxcdYddYQASy5Fp9Qj+SKBDOsjQtYfrAdcfzis6+nCuAvl3RTL1HtdG9jXXKgJieXl5GmciOdfoGMX1SLYHz522xUzRx7nBuSNBOIJFHOvA3wAawSaCNwA0ICbbTfafz+eDxWJRgBPdeWTaQwnzSPcypkfjesU5mJeXpz4f6M7FPuQa6fV6ERcXp/qVfUDQkuL58k+eL88nNzcXfr9f4zAWPG4lhCrTpxGyMpvNmvOV44/rDQErmQqNaQb5A5efefw8SkpKKlAfXaF1vr8X6dKlS5cuXbp0XUjSvxsVLqvVikGDimqWsqlx48ZISEhAVlYWHn74YVx22WWaVDYDBgzArFmzMHny5EJddMqrevXqqQvxpXEXatasGeLi4mA2m/H9999j8ODBAICff/4ZixcvPit1paZNm4ZJkyadlbL/85//IDk5GQ899BCA/PMpTQqwxx9/HBaLBUuWLEGvXr0AAPHx8YiPj0dMTAw6deqEdevW4cMPPywW6mrcuDHGjh2LjIwMHDt2DBs2bCjUkv7DDz9Eo0aNMGXKlGLBn+uuuw7Lli3DZ599VqJzatmyJR5++GEYDAZUq1YNS5YsUe/VqFEDp06dKlE5ugoqLi7uggF/bDYbatWqVeD1Y8eO4eeffy53+c2bN8ejjz6qnnu9Xtxwww2aYGpGRgYWLVqEm266CfHx8TCbzahcuTLuvvtuZGZm4sknn8SDDz6oCawGAgF88sknZa7XNddcg+bNmwMAGjRogCZNmoTc7s0338Tu3bvx/vvvl/lYZVFqaio+/fRT9fz+++/H888/D7PZDJfLhRUrVuDaa69V71etWhWjR4/GggULzmk9/4m6EL4XvfLKK8jJyQkZRD8XklkPgPxAb0WBgGWR2+3G5Zdfjs6dO6Nv376YOnVqucvMyMjA/v37i9yGN2XGxcVh9uzZyM7ORs+ePZXjD+OKJU2TBQA33ngj/vOf/2hes1gsaNGiBcLCwlT6vsKUmZmJd999F7t27ULLli1hsVjQqFEj1KpVC36/H+np6Rp3pN9++w1t27bV9KcU42Ql1bx581CrVq2Qrhm8ufmee+7B8ePHL4h0lqXVM888g7fffhu9e/dW8OUbb7xR5D4///wzhg8frokVl1e9evVS3ycvZPXt2xcff/wxwsLCcNNNN4WM9Z0Nty5d50+nTp3CiRMnygz+APmuP/Hx8RVYq8K1fv169OvXD23btoXb7a7QmxjOaqovAAEAyw0Gw2aDwVDWZHkjAYT6ZtgTwJlAICA/BesbDIatBoNhjcFgCOkFajAYxhkMhk0Gg2ETg6Ner1elcHI6nSoQzmC2DCjTLYLOMgz0Fjhx4WzBILdMiSID/wBUEJWBcgId3C8iIkKlUJJpWCTwI9P5yG1YLoO+FOEZo9EIIP/DlEFdOmewDJfLpdLFEOKQbheifVVbSVeT4G0YwJZlyXQ6hBYIysiUVKwHISuZwornEhYWplxSgp1VZL2CU4AFu70wHVxycrL6ohQMcni9Xk2qtmCxDmw7nosEmfiY/cPHEqySQIp0TwGggaQ4lghMSACGTiBsM557cD8SQJEuIjJlFP9kyh+ORelmQlcm6QjC8nlOMrWTTFUl24xzTvZ/MPUYDPhIkEa2VXDKLjkWQo3ZwuhK6XoUvC1BDrZfqD4MBoyCQR65tvBYwXWT7SnPI9gVKPgvGCbj9l6vV7UP1wAJD3F8WiyWkABUWFh+qrDg9Y2wmjwX6XAmU+CFAlcIDfKcCfbwuWxnOTeBv+erdFUiCMeUaZwDLEemvpNtHAwMEezk+cl1iG5w/MwIvnDG82e7SSBHuhTFxMTAZrNpxkdRsFvwmJTtwu2DUzIGr5NyHeYfXZUkqCr7UX4+yuPJNHC6AFyA34sA7XejMtZJly5dunTp0qWrLLrgvhtdLN+Lxo4dW+JtS3MH+oABA3Dvvfdi1KhR6NWrFxo0aFBgm+PHj5frgmpxkneTE94piapUqYKcnBwcP34cn3/+uXq9Xr16FVm9AurQoQNuvfXWAqmCKkq33nqrJm1QWY7jdrvx119/FXjd6XRi/vz5SEtLC/l+sO666y6MHDkSt912G8aOHYuJEydizJgxuPLKKwvcNd2iRQvVJ8WpadOmSEhIKPH5fPrpp1i9ejWaNm2K6OhojTPUpEmTcMMNN5S4LF1aWSyWkNc2z4fi4+PVdV4AOHDgANLT05GUlKTuii6PgsfmqlWrQjoobNiwASdOnEBGRgb27NmDX375BQBw8uRJ/Pnnn9i2bZtm+wMHDmD58uVFHrtZs2Zo3LhxyPcuvfRSzfPCAqw9evQoFLw7l9q3bx9Onz4NIP/z4bfffiuwTUUGnf/huii+F8mUkOdaW7duxZ49e+B0OnHkyBF88803JXaLO1tyuVxYuXIlVq5cWSE3P2ZlZRVbTu/evdG/f3/ExsaiRo0aGDRokPq+x2u4pVVhadvCwsJQrVq1YlNHRUVF4ciRI/j888/x3nvv4X//+x+io6PVWt6+fXvUqVNHbf/dd99h06bCv26npKSoG4VLoh9++KFQgDk8PBxdunTBnXfeibvuugtxcXElLvdC0enTp7F69WpUrlwZjRo1wm233VYsoLBjxw58++232L179zmq5YWlIUOGoEePHoXGJkozvnRdHAoFjJdGRqOxQMros6Xdu3dj69atKt4dijMpq86240/3QCBwymAwVAHwk8Fg2BsIBAom0ytEBoMhCsA1AB4L8faN0H6JOQ2gTiAQSDEYDO0BfG0wGFoGAoFMuVMgEJgBYAYAOByOAH9AnDp1SqVTYZCW6ZRo2WkwGDRuCHTHAP4OZtMBgiAGHTDoPEOIgWUAUIFeOrYA+T/Kc3JyYLVaYTabVSokum8wOGu1WjVAg0w5xCCvDJBLhxM6DHFgZWdnw+VyISwsDElJSXA4HMpNhy4ceXl5KlUYg/GEO+h0IQPiEi5gIFgCMoR6zGazxsGH7ULXD4PBAK/Xi9zcXGRmZip3Dgbq7XY7XC4XPB4PrFarciMJDlyHhYXB7XYjLCwMMTExqr/oZiMD4IFAAJmZmUhPT4fX61Vp2Ziuh+OBdWNdJRAmYRIGzCVpzUC7dP6JjIyE1+tV9WRQXboHBad64mKUnZ2tYA0AKmUP+waABiaSkA2hA44bk8mknJcI53AcESJin0rYiG1PkCclJUUDwck5A0DjimK329VxmVYqOzsbDodD1TUrK0tBQDLlkQRtJCQhXZN4DoRBioKBgp2NxBqime8SEpFp6AhF0FWFAIQ8jixPwi/cVoIWRqNROdJwH7YpoT2ZJor9zrVFOs7w3CQ1T+AwKioKWVlZClzJzs5WdaarT0xMDNLS0hAIBGAymVR6uvT0dJW2i+OL58PxaDKZFDjE+ZeZmalZt7gWmEwmjYuOhM8k7MNjEmpj+izp9EPgJzs7W+NawxRaPBbXJgkkyjVLOnSxz3hcQkx0V5L97HA4kJmZqdYhmf6Mfejz+WCz2dTx6KITGxur1hXakXJtYF2DQT4JvHEsMUUk1zz5mcV1SKY0k+uDTI/G8+Z50LWN6znXcZ6/TD2p68L7XgRovxsZDAbdY1uXLl26dOnSda50wX03uhi+F1ksFtx+++149NFHS5T+YO3atXA6nZg5cybmzZtX5LZXXXUVBgwYoJ4HuzwD+Q40v/32G1577bWz4qRTuXJl9bhBgwaw2WwlumO9bdu2+PLLL7F3717UrFlTvd6wYUMMGDBA4wpTkapfv75KUV7RqlGjBipXrqyBl5555hmcOHGi1IHOr776qgAwtnbtWrz11lvFOuTcdtttGDVqFC6//HL1WuPGjdGzZ36c2OPx4NSpUzhz5gy2bt2KY8eOwWKxoFevXnjttdeKLLtmzZro378//ve//5XqfO6++25cf/31yMzMVL/XK1WqhDvvvBONGjUqVVm6/lZsbGzIdHnnQ7Vq1VIg4CuvvIKXXnoJ9evXr7D0IsHnWRic4vP58P777+Oll15Cs2bN8M4776Bv377Ys2ePCsIOGDBAXf974oknCsBAwXrggQeQnp6unLykpMsakO+wE0offvghVqxYUeRxzoW+//57XH311Rg3bhyaNWumWX+B/JiLnv69xLoovhe1a9cOSUlJqFSp0jm72Y9p71599VVUqVIFn376KWrXro309HQcPHjwnNShOK1atQoLFizA2LFjywVHnTx5slhnvy5duuCxxx5DWloasrKy1PGOHz+OTZs2FesYFEoStAxWSUAZk8mk4k8JCQlISEjAsGHDFEReqVIlLFiwAGvXrsW1116LpUuXokmTJqhVqxZq1KhRoLzk5ORSORy2aNGiyMC53W5H8+bNER0djTZt2hTrcnihyeFwICUlBRkZGWjfvj327NmDm266CZ988kmhzkhvvvkmgL/d/v9tstvthaan8/l8OHz48Dmuka6zrR9//BENGjRA48aNER8fHzIda1Fi5pFq1aqV6qaEssjtdmPWrFno1KkTHA5HhaaLPKvgTyAQOPX//xMNBsNXADoBKPGXFQADAGwJBAKaX88GgyECwDAA7cWxsgFk///jzQaD4SCAJgAKxUbpCgNAAQYMlpvNZgVUyGAng6SEZqTricFgUHAIA6YMQjN4L+0IGZiV7jR09GGQNi8vDx6PRxFfzLHOYzDATfcFBtqBfHs9BqL544MgktfrVUFkp9Op6MaMjAxERkbi9OnTyMzMVBdYCB4w/Znh/51aJKTAoC8D4gz8EqogRCChJNrssb1lMJ9tTgcQ5pZnkD02NhY5OTmIiIhAVFQUbDabqgvbOTc3F5GRkQpWkrLZbPD5fIiMjFSgFiEp5mZNTk5WUIvJZFKQAPOeR0REKCCJY0G6r8iUXzyH9PR0jSsOYSjp8MK0VxKakK4kHG+sA/tZuiWxLjLQz4uCEoJgfxKSIGzAcyBIwnR17DfCTiaTSaVa4/jjf4IF7Cf5Y0C6jvAcCLd4PB7lxhIIBGC1WhU0Z7fbceLECZWmje0e7Cok1osC7jrcTs5r+Rr7LtgpS0IP8rher1e5RnG9kFAQ68aygreTEIn8TxDDarWq/pH1IXDHOS3TfFEsX0IYoaAmgi8EEDnnuSZy25ycHLjdbgWKEQDhPHU4HHC73QqC8nq9ar5IRycJOkVFRan1jgAK6y3HLdcxtqXRaFTjTYJtBNUIXErgh2MqEAhooB+TyYSIiAjlACQdbOjIxR9RdCbi/GKbyjHDtYqgEuvCNgkEAnA4HKrvA4H8dHfSiYj9R+AwEAgoCFOSx3KtYZ9xbBL6YvvwPbYbITvOWQkWETYKnlNcD1gOHfDYrvLiGftdV74u9O9FunTp0qVLly5d51L6d6Oyye12KwC/OPDn5ZdfRvv2+c3w448/okqVKkhMTCx0++A7zAsLiixfvvyspNZo3769JviTlZVVIugnPDwctWrVwosvvohZs2Zh5syZ6r2dO3eeNejn4YcfRp06dWC1Ws9K6rNTp04hLS1NAyScPn26UBCgKC1ZsgQPPfQQ6tSpg0mTJsHpdGL27NklSouVnp6O5cuXIzExEXXq1EGDBg0QGxurfpeazWY0bNgQDRs2RLdu3QD8fRf1jBkzCpQnb8S699570atXL0yZMkVd3yyJmjRpAqfTqXFWSU5ORs+ePdX1Q12lV9WqVS+YdN0yHXl6ejoyMjKwbdu2YqGakkqCjfv37y8yPdeMGTPQrFkzXHPNNdi7dy+mTJmi3lu0aBEmT56MBg0aYOXKlVi0aFGRx/38888xYsQI7N27F6+99ppmTX788cc12x45cqRQyO/DDz8EANSpUwfHjh0r8phnWytXrkRUVBSaNWtWIO1hjRo10Lt3b3z88cfnqXYXjy6W70WJiYmYNGkSGjZsiFGjRqFZs2alqGLpdPLkSWRmZuLOO+/E77//DiDf7SouLg52ux1Dhw7F77//js2bN5+1OpRG2dnZSE1NLQDAlUZHjhwp0tWsefPm6NevH8xms7pWnZycjJdffhmzZ88uc/qiyy4rexY4gsRff/21eq1mzZro0aOH5nud0WjUfF+Kiooq1LWmQ4cOaN68OaxWK55//vli69C0adNCUyNKRUVFoVWrVli7dm2JAPoLRVWqVEFycjKWLFmC+Ph4fPrpp9i8eXOJvn+OGzcOL730UrHp2v5NOnHiBDZu3IiqVaueFXhf1/nRhAkT0LhxY8ycObNM7j/Dhw/HkCFDsH79evTp06fiKxik48ePIyYmBi6Xq0j4srQ6a6m+DAaD1WAw2PkYwBUAdpaymGASmeoHYG8gEDghjlfZYDCE///jBgAaAzhUVOEEbiIiIhRRRZBAAg8Moss0NKHcbIxGI2w2GywWiwqoyrQyhDuioqLUhzJfj4iIgM1mUzCP2+1WwWMGgWNiYhSUJJ12GCAnYJSTk6McQBjsZWCZMAH3yczMVC4c/OHt8/mQkJCggCM66tjtdjgcDlVPBv0lZCNBIDphSAcNCa7QxSM7Oxsej0fBRBKaIojgdrsV2ECHIgam6cATGxuL6Oho1TcEkdguERERsNvtiImJQXx8PKKiopRzEOvH88/KysLp06eRmJiI06dPK1DDZrOpPifwwHNn0BuACuKznRiAz8jI0MAnLEMG6iXAIrflY5n2TKbuMZvNChBhuZGRkTCZTGpsWq1WdS7SfYb1lM4eEtJgXxOOk33Gx7yoEx4ernKCB/e97H/pzsPXeP7h4eFITk5GZmammlsulwtms1mN24SEhAIpogg/SdAnuB3l+JMOOBKoCgULybEkHXqCU9/J9FyEuiQwJF1VpFNRsOMPxwHBNvYHQTX2M9cQCf0E14H1k65BEvrgWA1eV8LDw2GxWGA0GtUaw3VLroeEhiR4SEcujhuv16uOw/3oHMV60kpPtgPfl25SOTk5qj0kaMe5QHCO23q9XrWO8rgxMTGw2+1qzkRFRaljR0VFaeYl10iCMBKy4n5MbZebmwu32w2n06lZw06ePKn63uVyaaAgziOuiXRZy87OxuHDh7F7924kJCQo6IpzT64b0p1Lgk+EkghN5eXlqfSWBLjkeux0OuFyueB2u1V7hXIP8ng8mrWCgE/g/53SCERyLOi6OL4X6dKlS5cuXbp0nSvp343KriZNmmDq1KnFpuNp27Yt7r77bvV86tSpIdPYSK1YsUIFZ3fv3o3rr78+5HZPP/00HnzwQYwbV9ZMJKHVrVs3zU0OcXFxGDFiRLH7dezYESkpKQoCefLJJ9V7derUQaVKlSq0nqzbY489hquvvhomk+msOVqMHz9ek4pg165dKtVQafX666/j3nvvRVZWFr766iusW7eu2H369OmDjh07Ii0tDa+99hoGDRqEbt264ZprrsHs2bML3Y832ATb5bdv3x5//vkn5s+fj0qVKqFz5874+OOP0bJlS4wfP77E5zJ+/HhUq1YNK1eu1Ly+b98+1K5dG7fddluJy9L1t6pXr45du3ad72oAyF+DBgwYgMmTJ+Pll1+u0LLtdjuWLVsGIB947NOnD+bMmVPkPo8++igWLMj/yDly5AiOHDmi3uvRowe6deumScsXSjfddBO6dOmCr7/+Gk2bNsXw4cM172/dulXzfMmSJUU6q9WsWRMrV67Eo48+WuRxz7YWLlyIO+64AwsXLgz5/rBhwwp1XdCVr4vpe9GECROwcOFCnD59WjMPKlIHDhzAqlWr0KpVK3Tv3h0bN24EkD/mL7nkEpWW6vLLL8fy5csV5Hy+1bVr13JBPwAwYsQI3HvvvYW+f/nll6NVq1aa12JjYzFlyhS1rpVF7dq1K/U+v/zyC55++mn07t1bA/0A+enIgstkbIeSmQNCyWq1okqVKkXW4YEHHsB1112Hhg0blgge9ng8GDBgAN555x2MGzeu2LQ+8kbk8yWbzYbq1avDaDTiiy++wAMPPIAlS5YUCfNLzZkzB88888wFk8rzQlBGRgZ27dqF999/Hxs2bDirAKOuc6v9+/dj0aJFePvtt7FixQocOHCgVPtHRkae05sIbDYbAoEA2rRpU2FlnjXwB0BVAL8YDIbtAP4A8EMgEFhqMBjGGwyG8QBgMBiqGQyGEwDuBzDVYDCcMBgMjv9/zwKgP4BQ325D5SrtBeDP/z/elwDGBwKBIm+BoUNPtWrVUL16ddSoUQPVq1dHlSpV0LRpU9SuXRtxcXHKDYFBZAb+pRiwli4tfr8fPp9PQREejwdZWVlwuVwqcBwctGfAmq4q0lWH6Y0IGDCITrccj8ejAs5utxt+vx8ejwcpKSnq2HSpoIsFz0uKwfjExERkZmYqNx2CP9HR0cqpJ9jRRTrNMHAu24upqAAoFw22EZ2FGEQnlEE3Ie7D93w+H3Jzc9WXA5vNpoLzwN9wAoPiLDcuLg6xsbEKomAbsD5paWnYv38/Tp48CZfLBb/fD5fLpRxXgL/T7PB8CGIEfxGgkwldNNiPDocD1apVQ3R0tAKeCAdJ+AeAZvwR+GG7EOyxWq3KvYS0t0xBxfRK0dHRiImJKeCcItuaMBPHmXSeITTA/QksmM1mBWSxfJlPlm5UEkoh0MVjEVAg6MHxaTKZVDlut1tBbLzLkeOLIJ8EethfEpKSsERwvWSqJOn2I4Ed9g/HunSM4vG4DhD8kSCLbE+2TTD8w7qxDwmtEHaTbcZxFQz/Sfcs2ddcm7jeEHjkXOQx2QZ0fwqur+xXwmBsf457jufIyEhYrVZl+el0OhXQxXKioqIUMCbHOCVTFUqIRY5ZrgOyTqwHz4nzgCAc11m5PklnrOzsbLWuOJ1OZGZmahyaDAYDMjIy1OtutxsZGRnweDxqPzr8SBiJx/H7/cjMzFTnxDpznHg8HpVCzefzIT09vcA4llAW1yH+0dEoGM6UadM4NvgXFRWl1lmOQwmQSbcubidBULmdhGd1Xfjfi3Tp0qVLly5dus6h9O9GZdS+ffuKDVAD+U7G8uYlu92ONm3aYPz48Rg0aFDIfT777DMYDAYsWbIE7du3R3p6eqHlT58+PaSbS3nUq1evAq917ty50O2rVKmCIUOGoFOnTti5c6dyvZB3eDocjrMSFK9ZsyasViuee+45AFBByYqWw+HQXHzeu3ev+o1bVi1evBiDBw8udrshQ4bg/vvvx1133YX33nsP//3vfzFkyBAcOXIEP/30E9q2bVtgn/nz52ue33DDDZrnp06dgtFoxI033oh3330Xffv2xeTJk/HQQw9h1KhRIccAZTAY8MADDyA2NhYNGzbE+++/H9JdZeLEiXjggQeKPT9df6t169b48ssvUbVq1WIBwXOlU6dOYd26dXj33XcrvOwRI0agTZs22LZtG0aMGFGs81V0dDRee+019O7du9BtSgJATJo0Ce+//z4eeOABHDt2DFdffbXmfV53paSjVSidPHkS33zzDWbPno1hw4ahatWqxdbhbGr27NkhnUqsViseeyxU9ildQhfd9yJ5g29FasWKFWjXrh1uuukmpKenIy0tTV2Xvvrqqws40yQmJl4wzi3JyckVUs5VV12FDh06hHzvkksu0aTe8ng82LhxIxYvXozTp0+jX79+pT7e4MGDNalWS6qwsDD8/PPPId+bP39+yOD5qFGj8Ntvv6kb84tyKMrNzS0A+AarY8eO+PLLL3HfffcV+R2Cql27NgYOHIiJEyfivvvuw7Bhwwps07lzZ5WirEePHgVAq3OtSpUqoX79+hg8eDAWL16M77//vtSA2YwZM3Drrbfi0KGL8r6HCtcll1yCLVu2YNiwYejUqROef/75AjCuLqBnz54q5fDFBPC+9957eOSRR9C/f3989tlnpd6/bdu2iImJKdU+4eHhGDlyJNq3b1+qfY8cOYKePXvilltuUVBreXXWcMVAIHAIQIFfgIFA4EPxOAFASL+lQCDgBhDS5y0QCNwe4rX/AShdQmjkBy1pHUrHC6a0kpCCwWBQgXcpBn8JLQQH8BlMZwCcUANdJAgXSJBCghjBTjIy7ZN0GWEwmQ47DPz/f9toAto8h1AELOsuoQ2CDXSSoCuPBBa4HcuQLhwy4E1YiMenq4jcnq4jshx57mwbAidsBzqGAFDtTDcOwlHBkADLZRA9MzMTKSkpyimJ9WagXdaT9eA5BAM0HB8MzOfk5MBut6uxwL6j45M8DusXDD/w/GWb8j8AVQ9CG3IM8HEwRS3BF+n+QcmxBECTHo11pcMQ3VII7vBiFNsm+DHHqtls1jjzEHAjtMU5lpmZqVxrMjMz4XQ6NZAD+zi4v+T5s3yOm2DnLtnm3E86LHG8BPdFsOiqE2y/LueN3C/YCYmgCx2D2D/BAJp02SGIIvuWUA/HF2EpeVyCdMF1km3HdFry3NnOdAaT41jWh+CPhJJYP+7L7eW6KP8H95GcL9J5httwvSUgyDlhNBphNBqVe0+wQxLXXR6T504IiM48XEfkOUlbcrku8py4fbDDFsHAYLiPZchxIOvDbdkfMiUbxyadeHieMrUXjycBSQm/Bc8Htq10EOJ/2R/SLS947v2bdbF8L9KlS5cuXbp06ToX0r8bnX0dPHgQO3fuLHD34OjRo3Hq1Cn88MMPIffbt28fFi1apAk+h9LZuMtfpoOgigqixcXFYdy4cTh8+DAOHz6sXq9fv75muxMnTgTvWm7FxMQgLCxMpYg/W+mRGBijKuJu7c2bN2PkyJHo168fVqxYUeh2kZGROHHiBNasWQOr1Qqv14uTJ0+q9xs2bAgg//fgX3/9hcqVK+Oll15CmzZtVJBs1apVmjJPnz6NgwcPonHjxujduzfuuecehIeHo2XLljhw4ECB665SVapUQc2aNXHHHXfg22+/LdRddvfu3XA4HLjssstw4MCB854G6WLQvffei4EDB8JkMmHTposuA2KpxfR5c+bMKTBGQ8nj8aBOnTrlvgOcTv7p6enIyspC3bp1Ne9fcsklGveJkqTsmT59Oho2bIhx48ahY8eO5xWwSU9Px5EjR0JCgVFRURdEWrILVRfr96Kzcc3v1KlTcLlcIVMYNWzYUAM85OXlXVDgT0Wl/Tx58mShQGJwYJg3ZH/zzTewWCyaz+mSiGkMyyKDwYDMzMxC3z9+/HhI18Xo6GhERERg165dWLx4McaOHRty/1OnThULVpcnPU61atUU4CPVsWNHNGzYEH/++Sf8fj86deqE1NRUpKWloWXLljh27FiJ3XYqSjabDdHR0TCZTKhevTpGjRpVaje8NWvWYMWKFRXu2Hmxqnnz5uqxw+HQfN/Wla9OnTqhTp06OHLkCDp06FCi70wXmpYtW4Ynn3yyVO5dtWvXxj333INjx45h48aNOHToUMgbL3gDfVZWFu6//34MHjwYu3btwnfffQej0Yg//vijWLh8y5Yt6NmzJxo0aIDmzZsjISGh1OcYrPPvU3YeFRYWBrvdjtjYWA0Ywh+OhBGYciU7O1sDtwBQoAPT40i3HwaxPR6PSotC1waCOgziGwwGmEwmeL1eTSCageuoqChkZWXBYrEo1wsAClAKBALwer0KiJEAD12DmHKGAXufz6dS8PA14G/oIyIiAl6vF2lpaapOVapU0bgQsS342Gg0ahyMGCDmNkxTw/fYzsHgE52QJEgV+H9XnsjISBiNRgV9yHQ6VqtVOSzZ7XbExcWpFFjS0UWCVbm5ucjIyIDb7cbp06dV+jPpZmIymZT7D9MayT+CY+np6bDZbCrAzzs1srOzkZubC6vVqhkHDodDgRM8JsuUUIME0CT0IGELCUiw36UbDssJCwuDzWbTuKfI8cuUdGxTQgcSUAsGlLKzsxUQRojA6XQiKytLjQ1enGJdWRfmL5R1YUo0pnFLSkpS+7Gt3W43Dhw4gPj4eNSqVUvNJQmLENZh2/Ex4Ytg6I5tF5y+LRgykSBJMHzFMuiEwvpwTrDf/H6/BlQJBnZYN8J8PHcel/0qxwDbNxAIqDR2nOsci5wfbCP2ncPhUCmeAoGAgrs4dgk/5uTkwOFwKMiF7WSz2RAeHo7MzEw1xjweDwKBgBrnHo9HtZnP51PgHuvOH0esN+cWISq2H1P9yTbn2KNzF8siLEWHJJvNpj6QpXMN1z4JTzHtXGZmJtLS0hS8REc2ti3hKn5WsCyTyYSYmBjs2LFDA+sQOJVrkcViUfWn8vLyEBsbq9Yorh+sJ9dzmQ5OuvPw84AgE+vEtY1iWXQkk4Ak/1gu1xY+57xlu9CNjmNVpq3TpUuXLl26dOnSpUvXudW4cePw+++/a17r0KEDfvvtNzRs2BAHDx4ssM8rr7xyrqqnUdWqVdG0aVP1fMWKFdiyZQv27t1b6D579+5FjRo1MGDAANx6663q9Y4dO2q2OxuB5lq1aiE1NRWbN2+G3+8vtY18STV9+nSEh4fjhRdeUL8Jy6tp06YhKSkJa9euLXK7RYsWITs7Gxs2bMCZM2cQExODe+65Bw6HA4sXL4bT6YTdbseUKVPw4YcfokGDBti7dy/uuece3HfffahZsyYOHToEq9WqCYbu3LkTV111FWrUqIFp06ap13mtpDD17dsXXbp0QWxsLK644gocP3485HZz5szBnDlzcPToUWzZsgVDhw4tZQv9+1S3bl2cOXMGK1euxIYNGwrd7uGHH8arr756Dmt2dvT222/j9ddfx9tvv12i7X0+H5599lkNYFiYunTpggYNGhRwvwLyobS1a9ciNTUVe/bswXXXXafe69mzJ+68807N9uPHjy82Jd/hw4fRsmVLxMTE4Pbbb8exY8fwwQcflOi8zoY+/vhjvPDCCxpngLS0NLzzzjuFzlldF6eCb6gtj9atW4fNmzfjkksuwe7du0NuU7t2bfTr10/jTBMWFoZevXrhgQcewCOPPILTp09XSH3Kqu3bt+Pmm28uVxlerxfbt28vNFhM6FaqT58+sNvtuOeee7Bnz55SHa9169YFvjcVp927d+PHH3/EQw89VOR2P//8My655JICrx88eBBnzpzBDz/8gB9++AEejydkejOj0YgzZ84UeYzyjMGYmBgMGjQIzz//vHrt9ttvx2233Ya6detiypQpcLvd8Pl8qFSpEiIjI9GyZUt89tlnZXIRKauOHDmC1atXo3HjxsjOzsaff/5Z6DwpSqdPn8bcuXNRo0YNDB48GGfOnDnvTnEXgvx+P1auXIk///zzfFflgtPo0aNx5swZGI1GdOvWDY0bN65wx9ezrQ0bNmDhwoWlXpu5LuzatQv33ntvSPexypUr48cff4TZbEbjxo0B5McFBw8erNwi5foSSjNmzED9+vVx2WWXIT4+JL9bap3NVF8XvAwGA6xWK2JiYlC5cmXExsYiOjpaOQDl5OQgKysLGRkZyMjIUJAMQQ666MiUXYRemN4pPT1dpX2REAUfS/cLOtbQjYLlM8UMgRtCRXI//vAP/H/KHAaCSbwykM2gNABVT0IfhGri4uJUqhmmyWE9CL3wmHQPIcjEY0oYiKACc3ZKOMVsNsNutyMmJgYOh0PBA4RyvF4v3G43srKyVFsTFIiMjFTtSqAhPT1d9Q0D7DExMcqpiMdnQJ/n4fV6kZWVpY7HPua5E8RgnzAwHgx/hIeHw+12q21lOiq+z1RNVqtVAS/A38F3Bu35mnRGIrwQHNyXAANBND5mgN9oNMJut6vjmkwmlSrM7/erdFIyhRH7Mdh1Bfjb+cfr9cJqtarUSbm5uQr6CQQCKvUYQRK2A8+H40WCHoShCBRwTNjtdoSFhSEjI0NBK3JuEegJTl0m03BJSIp/rA/Pmf3F7eVcK8xlSW5L+IF9zXktnWWkK5KEeIKhMqvVqv4Id8hzi4qKgtlsVv3J12QaMNZNpt+S55STkwOTyaSgEM5tQouE9QCo9GysB9uKoKDL5UJERARcLpcGUpGuOBKKCgZDuB1BS65PbGteBJSgohxLbrcbmZmZ6nWbzabah/1AMIXtFDyPZcpCtgPnAeeTx+NBWloa3G63WjNcLpcC/Jjyjk467AsJ8nE+S9c2rm0yjSOhwhMnTiA7OxtJSUlISEhQqfDMZrMGtpFp6rg+Sic3nptMC8YxItP8SScwmdZOtiHHvoTXuB9hr6LSA+jSpUuXLl26dOnSpevsacOGDSHvPu/atSsmT55c7vLff/99dOrUqdzlAChwsXPp0qV45JFHsHhxqKwlf+vNN98EABWAeeuttzBgwADNNo8//niF1FGqSpUqSElJwcmTJ0MGqypS77zzDpKSkpCbm4uWLVuGvDu9tPr8889LlDLs22+/VUG33r17o1+/fhgyZAhuvPFGfPTRR8jMzERMTAx8Pp+CtNauXYuhQ4eqsfHMM89g5MiRqsyHHnoo5F3q2dnZRToHHDlyBEajEWvXri0RAPXWW2/hxx9/xJQpU3D99dcXu/2/USaTCS+99BJmz56N9evXY/To0YVu27NnT0yYMEG55VyMqlGjBo4cOYJx48YVG7AO1tatW0t0faNbt26YNWtWgdebN2+OmTNnqrvlN23apHE0Gzt2rAoaUdu3by9R3b7//nt06dIFvXr1Oq/QD5APFi5YoM0qFRsbW+TY0nVxqlGjRmjXrl2FlLV//3788MMP6NOnD6ZPnx5ym6ZNm6J169Yh3xs1ahTMZnOF1KU82rdvX7ndGg4ePIg//vgj5HsdOnQI6Y4I5DuG3X777aU+nt1uD+nKU5R2796NL7/8stjtjh8/HnLd7Nmzp2a9mzt3bkjQqUqVKpg4cWKRx9ixY0fIFIMlVdu2bdV3hKioKDRs2BAdOnRQgJnFYkFMTAyaNGmCv/76C3369MENN9xQZCras6HFixdjwIABmDx5MtasWYPNmzeXqZxff/0VDzzwALZt24bly5dfMG5Z51Pz58/Hyy+/jB07dpzvqlxwSklJQZ8+fTB69GgMGTIEt99+uwZavli0Z8+eYn9TFqbk5ORC3WM7d+6MatWqadaz7t27o3bt2nA4HLjuuus0To6hdPLkSXz33Xd4/fXXS7SulkT/evDHbrfDYrEUCM57PB6kpKQgLS1N46DDP5m2i6ABoRwGVhk8D5V+SwbupauIhGEIRAB/AzuBQEDBJDJwz2AvFRERAYvFogkGE0bx+XwKymA6LOksREszlmuxWFRwmU4PDNZLpwgGxk0mkwIeJGDBYDdfJ4hjMplgsVgUkCJTetEZhG4jdPrIzs5W7c66E/7h9jL9D6EJnhfbMSMjQ9m0hkpPFezAIwP50s0lEAggMzMTXq8XycnJyolDAjQEw3JzcxETEwObzabGCR1kZD/KFD5sL7Yr24ZphuQHNMEPgyE/bR2BnOjoaDgcDtUWbD+OJ8JSHON+vx9RUVGwWCwwm81qG4/Ho9JzsQ4AVLky+B8VFaWOLWGPiIgIBSERyuB5sw3obpWTkwOLxYL4+Hjk5uYiPT1dASSEoLKyspCZmanaIzjlmhyDEvThnOI2ANQ8lnMsOJVX8HMJa8lzlKnxJJzD8cmxyHoQLJHgBcev1WrVuLHwPa5dBFvYLjLtGduRQB/nrNvtVmANy6lSpYqah9KhJjs7WzmkSdcqmd7N7XarNZTrg8ViUWNLwmuRkZGaVIgce9yPY0GmApN9JV2j+L7H40FWVpYaexzjMl2hfCwBLvY/2ykQCCArKwunT59Wc8Hv9yuYUqbIotMP5xLdnDiu6TjGNVSmgWNfZmVlqR9CdHDiZ4B0UOM6w7Ei12bpTMX6SdhQrlcyDZv8fJNrdigYiWOMMJ8E5jg3JOQWHR2N6Oho6NKlS5cuXbp06dKlq2L1zTfflCiQ+sYbb4R0zRk7diyef/55DZBRmEJdsBw/fjxuv/12dO3atWQVLkYLFizQQB/BgePCNGHCBKxZswYA0L9/f0yZMkWlmaJOnDiB+++/HzfccEOF1BXIdyhKSkpSaUY+++wzTcqAilavXr1w0003YeLEiTh06NBZO05xcjgcGDp0KObOnYsffvgBv//+e7Hg05kzZ/DII4+gZ8+eKjAbKjWHx+NBampqyDIiIyPx+++/45prrsGNN96IHj16FFvXt99+GzNnzsSkSZMqtO//Sbrlllswfvx4rF+/vtg7od955x08/fTTFZbK5nzou+++w59//omZM2eWar+7774bV111VYm2rVq1KvLy8vDf//4XV155pXr9q6++wn333aee7969G8eOHVOgYrAzlc/nw3vvvVeqeu7fv79U258tvfTSSwXcn6dMmVJsAF/XxaFmzZrh0UcfxahRowqknSqL/H4/Dh06hPXr1wMoPF3WihUrMGPGDLjd7gLvZWVllSvlU0Xpt99+w19//VXm/VNTU7F8+fJCwZ++fftq3LSk/vzzTyxZsqTUx+zZsydq1QqZQa5QnTlzpkRuiu+++y6efPJJda2fio6OxrvvvqtuXN68eTN+/PFHzc3H1F133VXk+puQkFCum06lUYLP58PChQs1aynjqC6XC2vWrMH8+fPRt2/f8wYUz5kzB88991ypU7pJ7du3D127dsXSpUuxd+9eZGVlhZxX/wadPHmyWFepf5OaNWuG7t27A8h3xJo2bRqWLFmCrl27wul04tdffz1rLqdnUy+88AKmT5+OhQsXljpt6/79+0OmuI6IiEDz5s3hcDgK3bdt27Yl+r2+aNGiErtQlkT/evCHKY/cbrcKQtO+jUFRBkAlMCChA8IGhGD4YSED4wzGEtJgkJRBaOnYI1O/AH+DBBkZGerYQH7gmSAPA9kSGiEswsAsYRSCKRI2iY2NRWxsrEotxIC0BAgMBgOcTqfmfCUkwccyTRDwdwqenJwc5TxBoIRwEtuasBRdNHw+n/qyx7KZukumkJIuJYQNYmJiYLfbVXvTgYJ/7KusrCx4PB5ERkbCbrfDZrOpvmA7EKYhEBDsGsN+ZbofpqoKTgEVCAQUxMH9OE4AaNIPyUB9sDMNx4F0gJKB++joaMTGxir4ga4n0uGDX1iY3ollcG4QpqATDNPNZWRkIDExUQPZSEDK6/UCyP8CZ7VaFcRGxxGmOTIajQr4slqtKqWTdJiScIKE5+jsY7PZEBsbi5ycHBw/fhwnTpxAQkKCxnGK5xLspBOcXivY2Uc6c7GcYCgreB/gbwcgmbqM/WcymRTgxr6Q8J/clsAG20s6ObEeMv0Z4R/CjBxXTO2Vnp6O5ORkjfMLATqLxYKMjAx4vV4FHsoUgRTHDkE7ivOUoBNTYUnQxOfzqfPlucj9mCZQQmyEk2TaNUJV0n0HgFpbCJMF9wGPwXaXawHbPjs7G2lpacjLy1PwZ3BaKwJwEpbheGO/EVYyGo3weDxwOBwK+JLwDNdwAArK4pikM5Pclikjw8LCkJ6ejpMnT8Lv96v0bDItnUzHJ0FMAmN0NuK2rL+cD3JNkO3P/pPpvOT6I52UCPnp0qVLly5dunTp0qWrYrVixYpiU8AAwNNPP40OHToUuKBvMpnwxBNPoG3btkXu365dO2zevFnZm48fPx633HILXn31VZjNZvTt27fsJxEkAi1btmwpNMWFlN1uR+fOnZGYmAgAeOSRR0Jul5ycjIcffhjjx4+vsLpWrVpV3dVfpUoVACg2vUZJL/6G0vHjx/HFF19oHELOtb755hv07NkTAwcOxJgxY2AwGLB//36YTCa89dZbhe73+uuvw+FwYMmSJdi6dStWrVqFOnXqFNjO5/PB6XSGLOPqq6/Gxo0bMXLkSPz000+FBobbtm2LcePGaYCLp556CsOGDdNvSgmhrl27Ijo6OmRAI1jTpk3DnDlzzn6lyqiePXuWaDuZJqikmjp1aonBvpSUFFitVtx8882aYyUlJWmC67/99hv279+P/v3746qrrlLXo6m1a9eWGbKyWCznNeXRkSNHCqSZjI6Oxv3331/gZkpdF5/atWuHPn36qM++8urUqVP4448/SgQe5OTkhAzSp6amonr16hVSn/IoMTERjz/+OCZNmoR58+aVev+4uDhMmDChUDfF1157rVAo6JtvvsG3335bquNNmDChxOkw9+/fj02bNuHYsWM4cOBAidaYyMhING/evICrTEJCAr755htNes/jx4+HBNWbNWuGBx98sNBjMOZVVv34448al42dO3fiu+++w8aNG/HBBx9gy5Yt8Pl86NKlC+bPn48bbrgBVqsVPXv2LLVT0oUkr9eL+fPn44svvsDvv/+ORYsWlRqI+CeoevXquPTSS893NS4I1axZE3fffTfuuecexMfHY/jw4dixYwdef/11/P7775gzZw7efvvtErsRXmhatWoVBg4cWOpxvnXr1pAuqUOGDEF0dLQmThosg8GAK6+8ssBNKWdb/3rwR4I9DJDLVFoMWkt4I9hdRTqvEExgIFW6tjBYLfdlYFS6pLBMBsmZAoxuEzI1lHQY4v4M6JKsl2lcmLImLCxMgUASxKADjIRWWB4D0dL5SAbked6yfSX4kpubqwhZgkHSMYJlyzYl1MN6SJBJuloQNCDZTZcaGQCXzioEtoLTBckyWH+WTdee4Eku68JjMPBPWES6O0nnnmDXGRlMD06hI8uSz2V/cHvCBXJcSYcUuqywTzle5Hhkm0uHGYvFouA4tqMc0wRmmPaN9r90OAn1J0UHJ7Y7jyHHuTwnOugQPktJSUFSUpICSHgOwWMyGHDge1LBjjyy/blf8Lji/1DHlNCFHMcS9JH1kw5F/JP1kOcg0zAR2CH4I1M78ZzoRkVog25grCNT+gWDZ3xfpjmU6fLk9vziznkjxy7Lo0MQv5xLMIvnJesv21PmkZbONHLuyHOQEFBwf/FYXPvp2sMfDuwLueYFjxWOX5kiEfjbvUf2F4E8ris8T9ab/UmHJ553dna2+izhmizhyOCxLF2o2BZ0BpIQW3DbyrEr3X9CrUEA1GefBOworve6dOnSpUuXLl26dOmqWE2bNg379u0r0bYul6vQO4MJzRSmJk2aoG7duujcuTPCw8Nx55134uabb1aAf6NGjSoskEvngJIG2Hv16gUgPyhUp06dQlOAdOrUCatWrcL7779fIfWMioqC3W5Xd5iX9GaH9u3blxhOuFDldruxbt06zJkzB4cPH0bTpk1htVpxxRVXIC4urtD9du/eDavViqZNm6J3797qbmIpXtsJpbi4ODRr1gx9+vTBjBkzCg18du7cGaNHj8a4cePUaytXrsSpU6cwcODAUp7tP1tDhgzBiBEjSjx/P/3007Nco7IrPj4eY8eOLXa7Zs2aISkpqdTlb9myBT/99FOJtnU6neo6EW/qBfID4LGxsep5SkoKDhw4gDp16qBbt24Fytm6dWup60m53e4SwZNnU6FSptSpUwf9+vU7D7XRVZHijbwVpfT0dBw/frzY7Ro0aIC2bduiZs2aBd5LS0srAM+dL61fvx4rV64sEPcoqYxGo/p+E0rffvttgXXM4/GU2mnIYDAgNjZWs04VJq/Xi6SkJHzyySeYN29eoe58wYqNjUXNmjULXBs+ceIENm7cqHntiiuuQIsWLUKW07x580LBLovFogGISqOcnBwcPHiwwOvLly/HrFmz8OuvvyIpKQknTpxAXFwc2rVrp5wLfT4fJk2ahDFjxuCSSy65aOHirVu3YuXKldiwYUOh6Yz+yZIx2n+zoqKi0KtXL7Rv317BnY0bN8YVV1yB1atXY9euXdixY0e5nKYuBAUCgVKnyQt100WnTp1w9913Y9SoUYiJiSly/4YNG+rgz7mU3++H0+mE2+1W6Vrcbjc8Ho+CgJjWiICJdIth6ikCNgwYM5BOsIYBV+k8YbVaFZwiA9UMxlosFthsNpUn22azqf0keMIfxZmZmYiMjITNZoPNZlMwhMfj0bjS0KmC9WYaocjISHi9XmRkZMDlcin4BMj/EuByuZCZmanaigFw6fYj01ZJdwiZQsvr9SoIxGg0KgCCTkNer1e1U05OjgJM3G63On/W3e/3axxQ6GpDt4/o6GhNuh+ZRoiQgHRMiYyMhMViQUREhMbFg+3MNGIej0edG8Epq9WqysnOztZATBLqYFkUwRW6kQSnaSrM9Yf9w8dsf+5vMpkUHMA+JIzBtFhsWwkFEcKQ8IMELWw2G+Li4pSjjNyGTiiyToQmoqKi1Ni02WwqzRfHAOEy9q0cMz6fT41LuqgwrZXdbofBYFDjxmQywePxIDk5uYDjjzwPjjEJ8XBbKQm7cR7LNpJuXiyH44yS4JQEqOiExD+O01BwkEzrFQywSDDGZDKpuUEnJ5mejVAQz4cOTXRQ4nnJscAfKSaTSUFYhAcJorCvgb8hM7oJ5ebmwm63Izo6WjNugfwfDdKNS84Vq9Wq+opjRLYlXaRcLpeaU1xf2X5yf8KNEoSUUCDXfjm3mYZQjhmOC67v0u0K+Bt+o5sW1xF57nQ7Y5+SMpbgj9FoVOs/56uEEHNzc+F0OnHy5EkkJyerFHhcD+Uf+zYmJkaTqozjR6ahlHMm2CWL6xXLY7+xPWRaMcKDfr9fd/zRpUuXLl26dOnSpessatCgQSXeLlTAm641hem7777DvffeC7PZjLlz56Jjx46alAstW7bEtm3bsHTp0tJVPITo9mI2m/Hss88Wu/2YMWPw/vvv48SJE+jcuXOh7gO1a9fGjTfeqLmjuzyqUaMGqlatijNnzqBLly5466238Oijj+Ldd98tdJ+qVatizJgxGDBgAJo0aVIh9TjfSklJwerVqxEIBNCiRQu88cYbhW67ZMkSzbWX4PQwOTk5SEtLK/Su2aZNm8JisWDw4MFYtmxZocDatm3b0KlTJwwbNkwBBqdPn8Z3332HW265BW+++SbuvPPOspzuP0q1a9fGpEmTkJSUVGYXqT59+lRo8B9AmftmxYoVuOWWW4rc5o477sDGjRuLTU0XShMnTsTOnTtLtO2+ffvgdDpx/Phx7Nq1C0C+G1nnzp1RuXJlNGvWTG27Y8cOXHPNNZgyZUqBcsp7R32XLl3KtX95tXjxYrz//vv49ddf1Wvh4eEV6rym6/zI4XBoIDYABVK7lVRZWVnYvn17SKcXqSpVquDBBx9Er169EBUVVeD9du3a4aGHHsKjjz5apnpUtHbv3o2VK1cWCqkWJ5n6NFg1atQokDrrt99+w8KFC0tcfuPGjdG3b19EREQU6rQnZTKZ0K1bNzRq1AhTp07F3LlzS3ScxMREPPbYYwUcNho0aFAg+F6/fv1C07XZbDYMGTJEPY+Li8OCBQswY8YMFZ8qi/Ly8kLCRsuWLcOMGTMwb948DB8+HO3bt8dll12mSaXWs2dPPPXUU5g5cyZWrlyJTZs2YcKECWWqx/nUt99+i5dffhlz5sy5qFN5lkWHDh3CxIkTcfnll5/vqpx3RUVFYfz48ejevTuaN2+Oe++9F9dddx1uuukmxMTE4M0336yw31HnUwMGDMBTTz1V4ptnABQwAqlXrx6mTJmCyy67rETpLlu0aIHhw4eXuq7l0b8e/PF4PApYYEAbgMbhhq8RRgCgCWoy6EmYhYF2ulwwOMwAO0EJppAB/gZ+WHZYWBisViuioqLg8/kQCARQrVo1VT4dgfilKiwsDE6nE16vV0FDEkQiDMSgt3Q3ysrKUj+s+SFpNBpVqii2h8vlQnJyMjIyMhSk4fV6NS5JwYAUg+BMYxMXF6eCxdxOBokZRGZ5/M+US0yjQ3iBwALPKzIyEpUqVUL16tVhsVgU6JCUlISUlBSNUxHLM5lMKjhOAEfWnaAQx4nX64XT6YTf79cAQ8Fp1thPTJlGZWRkwOl0KieQyMhIREdHIyYmBlarFdHR0QqIYUoeCURIgIQQCC+e0LXJ5XLB6/UqqIRwhvwSLh19zGazorvp/ENIgjACIQWOJQlqMdjP8c1xLSEvHoNpq6xWqwLaCCVxvnFeZmZmqjYi9EOIiOBMVFQUTCYTYmNj4XA4EBMTA6fTiYyMDFVHmbIL+NtZRs47KekqJV2nCH8QPpI0sHThkfNdQj8SXiGgRWcmQh3Bjk/SoYbHJ3jB48m54vV6kZWVpcA4pjKUcAYANWa5L4E39iHHDo8rHbC4LskUTxw7EkLj2mmxWDSORrm5uUhJSdG4q8ny2A6EauTxWf9gZzYJbbF96eDFPwJEbE8ej+sgL3ZxvBF0YV2ysrLUZ0Wwww3/OP+5rhKmImQkgSo6LvEcWS+OlVCfS0xzyHSJKSkpSEtLU2NcQqQSHOQcZOo+rnvsN6/Xq9Zp7s86MoWc2WzWlMk6yTWFnxls3+D0bbp06dKlS5cuXbp06ao4Pf3004WmtwrW/v378eOPPxYItLz55ptF7ufxeDBu3DgYDAbcdNNNIbdp06YNevTogXbt2hV712NRkqkcigp6AcAtt9yCvn37YuPGjRg4cCAWLVoU8iJuWlpasQHF0qpevXqoVKkSpk6dqtrPZDKhfv36BYKh1NixY9GlSxfY7fYSp9W4GPTiiy9i5syZAIDbb7+90O0+/fRTjB49utCUYH6/H1arNSQAAQDPPvss5s6di7Vr1xZ5V/0ff/yBXbt24eDBg9iwYYN6/eDBg+jbty/uu+8+vPnmm5g8eXLxJ/cP1rXXXouGDRvio48+wrXXXlvq/Rs0aIDvvvsOmzdvxpo1ayqkTp9//nmR6VwKU5UqVdCuXTsYDIZC3SKAfBClZ8+eGDlyZKnKnzJlCl588cUSb79t2zYkJydj4MCB+PXXX/H000/j5ZdfBpDvHCSvo6xduxaRkZH49ddfC8CT5XXG4TX+iRMnlqucsmrFihW45557MHbsWPzyyy/q9QEDBuCee+7B4MGDz0u9dJVfsbGxGncdp9OJuXPn4oUXXihRui6pgwcPYtOmTcVu17VrV7Rs2bLIbXr06IHOnTuX6vhnUzNmzEDPnj0xffr0Uu9b1Hep5ORkjcPe3r17sWTJklJdf+3bty8WL16MZ555RvO9xePxhNz+2LFj2L9/P8aPH4/Zs2eX+DhAfhrUhQsXIiMjQ70WFxdXIGD+/PPPF+r8tHbtWnzwwQcA8p3DHnnkEQwbNgxjx45F9+7dy+z2FBUVhUaNGiE+Pr7I7TIyMnDo0CEcP348pCtOTEwMGjVqhFtvvbVc34PPp9xuN1566SVMmTIFn3zySZnc8S4WHT16FIsWLcKoUaPw3nvvFcju8G8U451Ur1690KBBA+VKWFw644tF69evx/Hjx3Hy5MkSA6sPPPAAOnTooJ5Xq1atVDe6e71eDTR4LvSvBn8aN24MACqAK51wJMQC/G35JYPkhDmk4wODtPySQ6eZYEcRpuHh/jJAyiAwgYrw8HBUqlQJdrtdBeUJJTEwz8A1AOV6w/oy6MxAMQPrDNgTDjKZTIiLi0PlypURFxenguYy7VN6ejrS0tJUQFrWWQI9PB+fz6fS3oSHh8Nms6n0NQz0s3yK5RL48Pl8mqA+gQeWIfvFaDQq6jw7OxsZGRnIyspS4AMdPWSKJrpdENAC/rZipYuMTAGUnZ0Np9OprFsJ6LAv+cWLMAJTexHAcTgcAKCAKQbko6OjER0dDZvNptxRuJ9MSUe3FekQYzKZEAgEkJaWhtTUVDX+CMVERUVpHJ3Yjqy7hCMIjtntdgWHyNRbdCwhkMUfkgS8CL3RpcZqtaqUSYRdWF/OEbYdj5Geng6n06kgD0Ib0iFKgi+EcNje/JDi+JNp3ahgR6Vgsb5sZwlAheqb4D+DwaCB/0Idi2M3Ly+vgKsM20aOVQIt0kWMkApho6ysLE06P8JAMrUbxyIBQ85DthPnLKEfwmWc4wAUfMI60jmG+9FdifOdwJTRaERWVpYCbjjHuW7IFH90vgmGpghhct0iNCOBOPYRx5tsV7YX18Dw8HCkp6crSIcuVTI1Gsc4fwBJRzAJHsm5Kd2TOHa4PpvNZkRERMDj8RRIj8W2I2AVHh6ucTPjZ0dubq6a8+wfObbkuDMajQpE47yXnzXsGwmpRUREwGq1KoeuUM5GgUAANptNgaqcD9K5q6x3XejSpUuXLl26dOnSpatwVa1aFStWrMCrr75a4n1uv/12dOjQAQsWLACQH+CZP39+sftVq1atwN3awbJarVi1ahU+++yzEtcnWDLFV3F3yVeuXBnR0dF45plnAORbrgen/zh06BAuu+yyIp1oyqKaNWuqwM7p06cBALVq1cLx48eRlpYWcp+hQ4fCYDBg69at+Oqrryq0PuVVzZo18d5772HNmjV47rnnSr3/o48+qn4rFwb2eDwefPrpp4UGJs1mMwYMGIC33noL7733XoH3s7Ky8NFHH+HKK6/UBA9D6a677sLJkyfxwQcfKLjgzTffxPDhw5GQkIDo6Gg8+eSTePXVV3HPPfege/fuJbpj95+iqVOnYtq0aahbty7279+P9evXl7qMQ4cO4emnn9bAVeXVCy+8UCS4Q40fPx4PPfSQep6YmIjs7Gzs3bsXu3fvLnS//v37w+/3o27duiWu05gxYzB16tRSuTgkJyejT58+yiGoU6dOAPKdgO677z7s378fANC2bVvcf//9AIDvv/8eS5YswdGjR1U5L730UomPWZj69euH559//rxCNnv27MG8efPUc7PZjOnTp2P27NmoU6fOeauXrrKrSpUqGgDzxIkT2LFjB6ZOnYqnnnqqVADKTz/9hB9++KHQ95955hmMHTsWnTt3LjKdJJA/9y60VEU+nw/Lli3DunXrSrXfY489VqiL2dNPP61A50AggFWrVuHbb78tcdmNGzdG27ZtC0C0c+fOxeDBg/H5558X2Oevv/5Sa8nq1atLfiLIB8WaNGlS4HhXXnllgeeFOTcOHjxYOf7cd999uPXWW9XNp1dddVW5PsOzs7M1wENhGjx4MPr06YNatWoVuk27du1w6623lrku51uLFy/GO++8U+Rn6T9BFosFSUlJZfr+808VY+DBiouL+8c4lVIxMTGoXLlyidMx2mw2XHrppep5rVq1VMq/kmj58uWYM2dOqetZHpUt0eQ/SDJ9jnQFoXOCdKMB/g4YM8gaDPYEAgHlmiDfC07ZRDcVr9erSTXFoDNBBzqkWCwWFVCn64/dbtcE7LOzs5GSkqJccMLCwmA2mxEIBOB0OhXgQ0cXwkEMTBOskE4hBDWys7ORlZUFn8+nIBK2UTAERQAhGPrJy8tTwAj/TCaTCo4Df4MabD8GpOkIAuRDID6fD263W9Wfzh88Lh1HmH+TAAb7XLrASJcMphfjIme1WpUbjQzGEwIym80KFEtISEB4eDhiYmIQEREBn8+nSeHEbdk/bEOmxyGZLJ2NZECedaUrC7fjawSmWAaBHglhZWZmIiIiQgMncXsACvDg+KZDinQCYXtbLBa43W54vV7VJoSZOKcIx0ioIywsTAFFMv0a+yYtLQ3p6ekqfRrhE5mWTbq/sG/y8vLgdDoRGRmJ+Ph4BR9wbrPdZToi2QahJNPdBc9hznkJsASnFeO8kGCIPB7PXR6HawadcbivBIAk/MY0hQR7uC1T63FOEfCwWq0a1zCj0aj6LC8vD0lJScjOzlZjhOfAdHzSiUjCfnJNZT8TYJPrIOddbm6uShvH+vCPtK3FYtEAfnL+EgyksxDnfDAcJl2rKPZNeHi4mpPcxmQywev1amAhzjWOeV7QlF8OZN+wrTneCFdlZmaq9dxgMCjnMJ6vdAAiRCPf4zoAQPWvx+NREBHwN2waDEpJpyL2Cc9bfg4STuL6IcEySlqoBqcd5HYyFWOwHaIuXbp06dKlS5cuXbrKrzNnzuDMmTNl2nfdunUIBAIYNGgQHnjggWK379ChA0aPHq1cKwpTTExMue50lnfqr127tsht33rrLfTq1QvZ2dkAgJEjRxZIO3T8+HFs27YN27ZtK3OdQql69erqpq5jx44BACpVqqQC+sFq1aoVateuDQDo1q0b7rjjDjz22GMVWqfy6LLLLsPw4cNRuXLlEqX8CFZaWhq++OILXHbZZZg4cSLWrVuHxYsXq/fr16+P+vXrY//+/SUKrk2YMAG1a9fGwYMH8cEHH6gA5xdffIH777+/yFQDrVu3xpgxY9CgQQPUr19fE8A8efIk3nnnHbRp0wZ16tTBqFGjEBsbi7S0NNx3330hg53/RMngeXmCXsW5hZVWO3bsKNF2EydOLOBctmHDBlSvXr3I/YYNG4aUlBT89NNPJa7T008/jR9//LFY2CxY0rWiUqVKAPIDRVdccYU6z9atW2PAgAEAgFGjRqFnz55ISUlBq1atUK9evVKloShMaWlpiIiIwMqVK8tdVnk0b948PP7442odBID4+Hj07t27XLCornOvsLAw1K9fXwNx7Nq1C1u2bAEAvPHGGzhx4gRq1aqFyZMna/o8WEePHsWSJUtw6NChAu899dRTcLlcGDt2LOx2O5KSktCgQYMi68Ybsnlt90LR999/jz/++APffPNNiVPwxcTE4JVXXsF///vfAu/16tULf/75J1q1aoXt27fju+++K9V6ER4eHtKtomHDhli/fn0BIAfIByxPnDiBVatWYdWqVSU+Fs+F35molJSUAt/NDhw4oLmpNVgtW7bE119/jWuuuUYD+pQ0eF+YvF5vgdRpwWrfvj06d+5c7Bg0Go2oX78+WrduXeLPtAtRa9aswe23347KlSuf76qcFUVHR4dMGfhv1jXXXBNyLkVFRaFhw4bnoUZnTx6PB02bNi3x9uvWrdMAj9WrVy+Vy5jVakX37t1LDU2WR/9qxx8AmmBzYQ4OfM5gPAOplAx+SyAgeH+CCgwIM+Asy5J1IWhA2o5wj3TYYEBfpl0hpEFwiOJ2dLqQ7g4McrNuEnoiWOJ0OhVEQohIAhHB7UjQg+fCtpKOPhK0kk5LbBtZ7+C0QtyP7U5AgHVwOp1ISUlRMIhMnxQMvAS787DehHYIVcgUVqFgGQlq0CUoGEKQbZWXl1fgy4V06JAphOT4kG0RDIrI96SDCttHAiISZglOgRZ8bNaNx+M50fmH7UgHluB0WtyPdWS5BFdkSj22K/tMAngS2JHtwnElXahYJtuAz0PZ94V6LRjkkZIuYLLfJXBRmJuQHLty2+D3ZZ3kcwmVSDcczn3+cT4Q8JDgEsvhXOOfx+PR1FmmoJJpqGT7sx6yf7nGyDnHehE4km3P+RDcdixTpiCT5871V0J8wVBXqDWZ9ZZtm5OTg6ioKFWf4P3puMTHcj2ScJOc8wRk5JxhGW63WwMdyXrxvOS4l1++2J5RUVFqDQo1DrkusH/lNrIdWb78k+sm+0OmLeN+hH7kuOb/YDBMly5dunTp0qVLly5d51+ffvop/vrrrxIHS5KTk3H48OESbduwYcNCU4IVJxm479OnT7Hbp6enq8DKJZdcol73er1ISEjAkSNHylSP4sQbUQBo3LcLczutVKmS5s7Qq6666qzUq7SqVKkSWrRooVKxAyhRypVQ2rBhA+bNm4eTJ08qmIGqV68exo8fj0mTJuG6664rUXlXXHEFbrzxRvTv3x9AfqCzUqVKBa6dBKtx48YYNGgQatWqpW7UorZt24Zp06bhww8/xMyZM7FgwQIsWbJE3fn7b9BVV12lCcDSsepi0nfffVcA3tmyZUuRgEH9+vXRuHFj1KhRAz179izRcS677DJUqlQJc+fOLXNda9eurRx/LBaLxm1o165dSEtLQ0ZGBr777jv4/X7MmzcP06ZNw0cffVTmY0qlpqbixIkTpU6/VNHKysoKOdaKS6+j68JTXl5ege8Dp06d0rhVLVy4EO+++y5+//33IstKSEjA9u3bNa9ZLBbExsZi4MCBmDx5MmrUqAG73Y4aNWoUWzc60ZfGieFcKTExEcuWLSvVPjVq1CjgZAjkgzR16tRBamoq9u7di61bt5aovJiYGMTGxsLj8eDAgQMFgEZmTQgFcNeuXVuB1qUVjQWk3G43kpOTNa/98ssvhaYTysrKUuNu9erVpXKVKkperxfp6enFulrWq1cPtWvXLtH35tjY2Ivezezw4cNITU0939U4a4qMjCxzerh/qpKTk9Vvm2D90z6rs7OzCz3XYOXk5ODIkSM4ePAghg0bhubNmyM+Pr5Uqb4qVapUpFPY2dC/3vEnNzdXpRRiUFMCC3QNAaDAEKaekanBKBmcD3aTYYoVGVjmhwUDq9L5RDp00EnI7XYrBxyPx6MCt3TYoPtFYmIiwsPDERsbq47PVCwMKNPNhq97vV7lQiNhDTqG8Nyzs7ORlJQEi8WC6OhomEwmDczCIDPhCwaYCSrR5Ui2t9FohNvt1sAqhEHcbrdql+zsbFgsFtV2dHRhO9P5gvWn45GEWAj1BIMQbBemuHK73RonGwba2U9sF7pcEALxer2w2WwKiqH7TyAQ0HzJIYjDlGEMshOckfCCBCMIsAQDUMHbEtLi+UoIhnCH2WxW41KCHQQXgqEA7p+Tk6PaiO4yRqNRjSnpIiXT0xH4kumGXC6XAthycnJgMpnUGA/OsygdknhRjefj9/ths9lUSiT2E+tHaIPuJzKNXSjwRj6X40RCL4TOOAbk+5y/wfCP7EeWI9Nnye3k9pwvdLWRcBfXMenMxO2ysrI0rix+vx9ut1u50HCM00HGbDYrmESmh+K4k/OObUB3GNbJ4/GgatWqakzJ/o2IiIDdbkdaWppaQ+XaKdOY0dEoeL6aTCYFmyUnJytXLMI40k2KfSvXNTlP/H4/MjMzNW5JXH/Z/lzHOGfoUBT8I4P9FB4eDofDodqN61VcXJwmxRfnfkREhMZRi3OIrjlGoxF2u12R6BxPLMtoNMLr9SIyMhLR0dEaoJTjiMAfxXkkgaFg2InPufZxLrPOMu2ghOBkn1bUDzFdunTp0qVLly5dunRVnLxeL5YtW4YOHTqUeJ+vv/66RNtVr169zK4/q1evVq4/Y8eOLfLOyPHjx2Po0KHqYnT37t3Ve+vWrcNdd91Vqrs5S6M33ngDBoMBr732Gk6dOoX4+HjUr1+/QACTqlu3rsaNKDMz86zUq7SqWbMmZsyYoVyqd+7ciQ8//LBMZX3wwQcA8lNATZ8+HWPHjlXvtWvXDsOHDy9VeUajEVWrVsXTTz8Np9OJkSNHYu/evfjiiy+K3O/o0aN45513ULduXfTp0wcGgwF2u10F9VwuF9auXatxlDp27Bh69uyJadOmlaqOF5tGjhyJuXPnIj09HUB+0P18yGw2Y9SoUZgxY0aZ9g/llnXppZfC7XYjJiZGnZ/UddddhzZt2gDId9eZM2dOkU5glSpVwj333IMHHngAK1asKFM9AaBHjx6a5xI62rp1K7Zt24ZTp05h+vTpAPJTOH788cdYuHCh2q569eplBrQOHz6Myy+/vEz7VrR+/vlnXHrppZrA+aBBg/D222+fv0rpKpMmTZoEr9eLKVOmID09Hdu3b0dWVhaqVKmCxMREAPlO4C6Xq9AykpOTsWTJEg1c0KFDB8ybNw+nT59GrVq1NNBLSdzi6FDucDgKTbt5PjV79mw8/fTTJd4+NTU1JLT37bffwmw2o1u3bli+fHmJ1/J+/frhwQcfxIwZM1C/fv0CN0FnZWWhffv2ClaU6tWrF7766ivUqFGjQLymODH+JZ+vX78ep06d0myXlJSEw4cPo3Xr1gXKsNvtKg3YM888g6NHj+I///lPqeoRSiaTCV26dEGdOnUKhY4AoE2bNmjZsmWJymzevDmGDx+Opk2b4sSJE8V+b7kQlZKSgrVr16JXr17nuypnRQaD4YL5Ln6haNGiRbBYLHj77bcL/I77p4E/KSkpJd42IiICDocDPXv2xJtvvgm73Y5Dhw6VCEalGjRogI4dO4Z0cDtb+tc7/hBcIPQCaCEc6SLCoKff71epggipEGhhAJkprggeSEcKGSwlCCTdUwh18D273a6cGhhUlx/MdIxhkJzpYTIzM9UCRvcYpkGy2+2w2+2wWq0qoJudnQ2Xy6VS2dCJgtCL2WxWIM6ZM2dw/PhxuN1uBf4wYMz9eDw6ewQCAWRkZMDtdsNisaj0MtKxRjpIMPDv9XoVaJCdna1xGaGjUWRkJCpXrow6deogJiYGkZGRyMzMLABe0Ykn1DGBfDjKbrdrgA3COEyDxgA60w0xaM9jcRv2n4SoCD7I9ETBUIV0upHuPlIyGC/HmITWSLnLFFwmkwlWq1WlkJNOREajUUEzJpNJk/5H1odtxvRQMmVSsCONnBsej0fVw+v1IisrSwEyTCdE0AT4O+2bx+OBx+NRLkiEepjGimOF0JbNZlN1joqKgs/nUy4rTqcTaWlpChQKnp8cV9yefxL8kA42HN8cH3L8sl5SEsoqzFUp+Diyfixbgl65ublwuVxqLElIiZCKJFC5ZjGlnc/ng8lkQnp6OjweDywWi4LPuJ7Y7XY1ltg/HMu8eCnnQVxcHKKiohTMx3khATqXy6WOA0CBYIQI6VhmsVjUWkAYhuPE4/GotjCZTOqODo5F2XbBoA6fZ2ZmIjk5WfUnx4B0EmL/cjwyTSIl09oReJOOXgTtJCCTkpKiwB62M8e3TMUF5OcRlWATgSYCX16vF0lJSSrVY7DzEvuA+0rnL67XoZyppKOYdGWT41E6P0kAVI7d0v4g1KVLly5dunTp0qVL19nXhg0bcM0115R4e5/PV2QQj0pKSsL7779fpjpt3LhRPS7OZWPBggVISEjAE088gVtvvVXjQvrJJ5/g8OHDWLp0aaH7z5o1q0SBxML07bffwuv14sSJE9i5cyfatGlT6IVxu92u+c0V6g7+86FRo0ahffv2CgD79NNPy+0AM3PmTCxduhQPPvigeq127dohXZZLIrPZjE8++QT9+vXD5MmTi9y2VatWuOmmm/DZZ5/hxIkTaNy4MRYvXoxHHnmkyP3+97//hQx0/lNkNpsxb948vPXWW+r66dKlS/Hzzz+fl/p88cUXeOmll0q0bZUqVRRUVpi6dOmCrl274ujRoyGhHwC4+uqr1c1U69evLxL6iY6OxvTp0zFw4MAyr2WUdLQ4depUgXRyO3fuxFVXXYWNGzfiueeeQ3JyMmJiYjQBycceeww///wzZs2aVaY6nDx5smyVr2AtX768QDrEDh06aFyQdF0cysnJwX333YfffvsNO3fuxG+//YaOHTtiw4YNCmID8r8PFKY1a9bgf//7n+a1Sy+9FA0bNkTv3r3L9DkZHx+PJk2aFEgrdaGotI7oFoulUIBp4cKFmDx5Mn744YcSl9e+fXtUq1YNzz33HC6//HIV3N+1axdmzZqF7du3o23btqhfv75mvwMHDuCVV17BihUrULNmTQXglFQJCQlISkrCzp078cwzz2Dw4MEYOXJkge8F9evXR9euXQstZ8yYMbj55ptx9dVX4/bbby9VHYpSlSpV8OGHH6JevXoh32/cuDFatWqFqlWrlqi8Tp064bbbbsMrr7yCUaNGFZoN4kLXmjVrzncVzprS09OLXJ/+rZozZw4+++yzAlCezWa7aMdxKMXFxWHBggX49ddfC3VrlXriiSewcOFCxMfHIy4uDh06dCjVTS52ux3t2rUrVzrs0upf7/jj9Xo1KWeAvwENghR05oiKioLJZFKOMxK+oKuI1+tVabAIKkRGRmocVeiQIVN0SScgOqrQGYMuJwRZJDwUCARUoJ5pviIiIhTYYbVa4fV6ERsbq4LedrsdZrNZBW55PAbnea7B7igyVYzRaFQB89jYWBX0Z6BZpp6hTSAD/wSquGBIBxoZMJYpe3jMiIgI5WoSHx+vYKCwsDDExcWp8/b7/fB4PApmIshDSEH2BfB3urZAIKCC7wyoy6A2A/myz/knARS/34/jx4+jcuXKiImJUa4/Pp9P49IkHUK4P/uV7UG3Grm4ynRSLEO+L+snU2sRapGpfKRjhxz7MqgfEREBk8mkgQ7cbjfCwsJgs9k0qb54DJliiKCPBKUcDoeaG1lZWcp1hqAPt7VYLKrvLRaLckriOHM6nXC5XArckCnFCOwRYklPT1eADuexBBWCXX34mhwzEsyRrkByHsl0SewPiuuKfE6YQioU/MPnwe5NdKCRZRCk4tzhvAag2jIzMxNWq1XNKYIrnMcEiQjw8Nh0CuLaEB4erhxzCBSyruwPv9+PjIwM5cLFNUuuh2wvtgfdiCSMyb7kWuJwONRYluuHTBUmQRXZZ263GydPnoTT6UTlypVhMBiUq5ism5xnfr9fgXuc97I/ZV3orEYHNoJkycnJyMjI0HwGcN3hWhMREQGXy6VctLKzs2EwGJCRkaGOC0DBWwkJCWp95LoqHaHYN1yPuc4wHZlMcybTxfHzh/WS4BlF2JDjUG4jXZ106dKlS5cuXbp06dJ1cevQoUNo2rSp+m356KOPol69erjyyitVoCj4GltpNGzYMAD5QajiUmK0atUKUVFRBe74/v333wsE10OpcePGeOedd3DXXXeVqa4NGjRAWloa9uzZg8qVKyM1NVXj0iElA5gZGRlo0KBBmY5ZkQoLC8Mdd9yhfu+5XC68+eabFVL2999/j+eeew4//PADDAYD+vTpgy+++AJbt27F0KFDlatTSXTttdeiVq1aaNOmDdatWxdymyZNmmD06NEYM2YM4uLisHfvXsyYMQPNmjXDjTfeCIvFgvvuuw9vvfVWyP2nT5+Oyy+/HNHR0QVSn/wTZLVaUaNGDVSrVk29duONN1bI3e7NmzfH1KlTYpWs4AABAABJREFUcfPNN5do+2uuuQaDBw8GkA+EHT9+vMjt8/LycOONN+J///tfoc47kyZNQmRkJC655BLUqFGjQMCqUqVKaNeunXreo0cPTWr2YIWHh6NFixa4/vrrS3ROhaldu3YaN5s333wTb7zxhmabn376CTExMTAajXA6nahZsyb69++vccewWCy47LLLAAAPPvhgoXDTha7Vq1dj9erVaN68uXotJibmgnEk0lV6devWDVWrVkX16tVx7bXXol69epgwYQJWrVqF//3vf3j44YcxePBgTZ8D+Z83X3zxBXbu3InY2Fg8+uijaNSoEbp166a5xlpa8UbmCzHVF5APwLz77ruoV68emjZtikqVKhXppGEymXDvvffi3XffLddx7777bjRq1Ehd3w92qjhz5gy++eYbfP/995g2bRqio6M173u9Xuzduxfz5s3D0aNH0apVK/z000/4448/NO5+hcnr9WLgwIHqmn5hmjdvHnr37o0xY8YUeC81NRVPPfUU9u7di+XLl1coQJ2Xl4fly5eHTA9rMBjQpUsXNG/evNRAGY0UmjZtir1791ZQbc+dVqxYgTNnzpQYeLoYdPLkSXz11Vd48803S5zC+N+mN998E263G3fffTccDgeOHj2KLVu2lBngvxDVo0cPvPPOO7jzzjs1jrGFKSIiotTAY7Bq166Njh07FkhVe7b0r3f8qVevnoIwKAnf0N2HAduoqCgFNDCYmpeXB7fbDafTqYE4jEajcqywWCywWq0K7JDpk+j0INMjeTwe5eJB8ILHBKAC3ywvIiICZrNZlVOpUiWYTCYFInCbsLAwBWlImCA3Nxc2m005W0iXB7r8EFwhwJOdna1chRiEpwuP3W5X7coAOZ1XGHgngEFHGJ5bsFMHwQ+mKWPQOioqCjabDXFxccrpxWazwel04uTJk0hKStKkqpJuLDyOdAxhAJ6Bcwk70X2EKZqMRiNiY2PVeXk8Hk2/0w3HbDYrdxU61PA/xxydSiQgJl062H4yjRzrK2EPgg6EbQg/uVwuDXBmt9thMpk0+8n0QoQWCPkQCKGjCVNByTRZBDeYgotQSHZ2NjIyMlRaJwAKaCP4wTHo8/mQnp6ugCKOe84Rpg1j2jSz2Yzo6GhNWij+aKerEwGW7OxspKenKwcitmUo8Eem8uLcJ5jHdpLbmEwm5QjE8wtuR5nySLoyBacAC4aK5JiV/cv60y2Ma4Lst7S0NJw8eVKNKebTZd2Sk5MVpME5JV1mACjQjVAPgRKDwQCr1armB8W5SuhKgjjh4eGIiYlR23OeyP0lHEMwxev1qpRYnF9cf6TDUbCLF8uSzjcSCMrJyUFWVpYCeYC/3ZBkX7HNeQ6cu3QrCnZu4nHZ1lyrOF5YBwlNORwOtQ7YbDa1PZ3OcnJykJGRAZfLpUnPRdCGd+wBUE5OPG+uq5UrV4bD4YDFYtF8/kRFRaFSpUqoUqWKqk/wmsnPFuloxLlOJyKuB9KtLRhY06VLly5dunTp0qVLV8Xruuuuw6uvvnpOjjVixAjccMMN6vnmzZtx991348yZM+o1Ou+URe+99x6AkqX0mDhxYoG70gEUCt8Eq1GjRvjrr79KV0GhmjVr4vTp0zhy5Aj8fj+effbZQsGURo0aAchPo3HFFVfglltuwdSpU8t87IrQ1Vdfjbi4OPV8wYIFFVb2b7/9hoiICPzyyy+YM2cOWrVqhZUrV+KVV17Brl27NGmoixKv58ydO1fjIBSsunXrYujQoYiNjQUAPPvss3jjjTfw4osv4pdffsF9992HN998s9DUdgcPHkS7du3+kdAPkD/+6tSpo54nJCQgPT29QlJzb9iwAd26dSvRtgMGDMBHH32kng8dOrTYfZKTk7Fjx44iobQbb7wRQD5UJx12qGuvvVYTsI2NjS0UAgPyA8yXXnppqZw0Qumjjz5CixYtEAgEMG/evALQDwAcOXIER48exR9//IFly5bh4YcfxsyZMzXbbNiwQT1+8skny1Wn863g9I27d+/GokWLzk9ldFWIjEYjHn/8cQX/GQwGPP7446hZsyYmTZpUAHTNy8vD0qVLVfqjq666Ct26dcOwYcM0cGJZtHPnTkydOhV//PFHuco5W/L7/Zg8eTKuvfZazJ8/H4cOHSpy+9zc3CLdyUqqkSNH4v7778fUqVPV9WOptWvX4rfffgMAtGjRQvNeXl4eatWqhVmzZinXsfbt26NOnToYM2YMXnzxxRLXozhH+DZt2oRM87V371489dRTiIiIwLx58yrcNZGZLUKpY8eOaNu2LWrXrl3qcnnjdKNGjdC4cePyVvO86Pvvvz/fVahQxcbGwmw269BPETpy5AjefvttLFq0CPv378dbb71VIWn1LiRdf/31WLJkCfr27XvOjhkbG4tmzZqds+P968Efpu5igFdCIDK1DKEWmXpJOnUwtRcdY0wmkwoMM5DNICvBExngl8cjBMHj0kFEpqwCoAmo0sGEQWM+J+wRyj0EgIJL6AzDenE7ABowilCUdDVyu90aQMJoNGrAIZkuhudBoIOQEQEhivVlezLdEuEEGaC3Wq0qPZWEd9h2so0lDCLTpwW3LZ2PWHeZEissLEyTMktuQ+AgJydHgV+EGyRMJr9M8PxkP8kUXtK9Q4I/hVGWMs1PqPHC+hOskH3OxzJlj3Sg4uvB80SOYTnWCI0R2mCfE7QITgsUynGH45RQBedZeHg4zGazSgUlUzMFpzgiQCHnkWxD6QQTfGwJQsiUXxLcCYaIZJsXBRcV1ZfBAJBcn2S7cFs5f9hvWVlZymFJuuaEchCSzjV0bCHAk52drekjpv2TY0eCItwv+H3CdNItSZYTDF5x3POPIE9wf7Lfg+EpKdk3PDfa1MvUWlzzWVYwJMYxIFO6BfcX1zfWi+kX5eucA1arVa3bXFPZn/zhQVhN9rF0BuNY4J0TcvwTnmRaxFAyGAwF0qrJMS5dwYKhIOmgxHoEj/vSWtnq0qVLly5dunTp0qWr5BoxYgRGjhx5To61Z88e/PLLL+q3KC9gJiQkqOBUee4IXb9+PQCgTp06ylWoMG3ZsiXk63FxcQXuVg/WmDFjYLPZkJKSUraKAnA4HMo9Njs7W+PQESxCF3a7HX/88Qe2bNmC3r17l/nYFaETJ04AyHdIOnPmjAYuqAgdPHgQWVlZaNOmDZKTk7Fr1y4AwMcff4wVK1bg2LFjJSon+DdnKPn9fnz//fdYs2YN/H4/qlWrhvj4eBw8eBCzZ89W2zVp0qTQMioCgrlQValSJQ34kpiYWCHljh07Fna7vcRz/rrrrtME9ovqD6lKlSqhWbNmhUKFvGZ9+PDhkFBZqGBz//790b59+5Dl9enTB7fddluJ6laU2rZtCyDfKa2otGqHDh1C7dq1cfXVV+PKK68skH5EOjNdrMFjavfu3Zq5tmfPHmRlZZ3HGukqj6KiojBo0CB06tRJs8Y4nU7ExsbisssuK+ACeOrUKezYsUNlZahbt2650m5KHTp0qMI/y86W+vbtW8AJKVhMj1VeJScnq2v7NptN896hQ4cwc+ZM9X0oOBWNx+PBihUrsHHjRnWjatWqVVU5w4cP1wDh5VHbtm3RsmXLAq8fPXoUixcvxrXXXqvW1YpWYa42derUQY0aNdR4LY3y8vJQrVo1jB07FhMmTEDPnj3LW81zrn379pXru/KFpvDwcHg8nvNdjQte/F23Y8cO/PzzzxX2vfFCUfXq1ZGenn5O3axoDtCzZ88K+8wr8nhn/QgXuFJTUwFApcJhwFpCAHTXYaojAjkMyNKxhkAMHSRkwJspwqxWK6xWqwJkbDabSuXCIDPT93g8HgXkyB+6BGdMJpM6Ph174uLiEAgE4HQ64fP5YLPZFDQjXUcIY3g8HgWiOJ1OBakwoEs3CQaSefzo6Gg4HA7k5eXB6XQq+IZgE2EWCRrwTh1ux20AqHZle8n0Mmwn4G9IQJ6LxWJBpUqVlNNIVlaWSq0jjxUVFaUceugcQ2cfnivbmW3P1xwOh0qRxr6UKYe8Xq8CrnjOJpMJPp8PLpcLLpdLtTPdSgKBAKKjoxEbG4vo6GhNqiLpvhKcYklKQlCybZj+hwuKdJfia0ybBfx9YS4YTiH043a7FbjAfdjXwZAMgQy6A8XExCA2NlYBDmxvOhlx/HLeSUcRghlMs5eenq6cT+hmFBcXp4AipjEijMc65eTkIDIyUqUXk+cqobJg8CQY5JB/wdCPBAIlfBU8d4KBHwmscJtgRzC2q9yXLka8uChhOYI+XGvYrhxbhE4IlNDdiOfFcU2gUY4t1qUwGI1ONE6ns4DDGX8gsD6cQxKikesO3YboKmU2m9Xc4dpmMBg0kJFsP+kcJv9ycnKQkJAAr9eroDs5Z5g+je0VCASU61Z0dDTCw8ORm5urnMak8w/XZ7qrZWdnw+FwaPqYLj9WqxUxMTHqsyEqKgpOpxNpaWnK5YhrBs8pOzsbbrdb9QeQ/4OazmvsV/YZL3pbLBYNeCrHH9cRi8WC2NhYBQbKdpRjk3NAwm+sSzBcWdI7OXXp0qVLly5dunTp0lU2uVyuIu+Sv/vuu/HMM89g+PDhpS772WefxeTJk9VvSCDfaYfXThigHjp0KG699VZkZ2eXy5GCziGRkZEYMGBAkdtOmzYNf/75p3q+fv16TJs2DR6Pp8gAzUMPPYQPP/wQNptNwShlUb169bBlyxb06tUL0dHRGkcVqREjRqBTp07qefXq1fHaa6/h9ddfL/OxK0IHDhzA9u3bMXHiRHTv3l3dyV8R2rlzJzp37owuXbrghhtuwEsvvYRff/0VQH4/PfXUUxUa7F+9ejXuv/9+9O3bF/fccw8A4Morr8T06dPx8ccfY+/evUhNTUWrVq0q7JgXi4YNG4bRo0ejUqVK6rWtW7cW2G7cuHGlLpuOW3a7vdhtY2JiMHr0aM1rAwcOLHTeULVr11bQz7PPPhtymwULFuDUqVOYMWOG5vW6desCAObMmVMg/VeTJk2Uw1iw7rjjjiJBvpLq5MmTyMvLw9NPP41PP/200O2cTicaNGiAZ599FpmZmXjsscc070dFRSn452KHZHbu3InDhw+r63i9e/cut8uLrvOnhx56COPGjVNzjfr++++RlpaGjh07al73eDz47LPPsGDBAmzZsgVr1qxB48aNy+SmEkoulwsNGzaskLLOpqpWrYo+ffoUgHCCVa1aNRW7LKt443ooAHLbtm2YP38+evbsqa4ZB6cBS0hIwK5du9C1a1f0798f3bp1Q+/evdX3wkaNGuHll19Wjnvl0fbt2wus1UA+JPXjjz/ipptuOiuu8mlpaSGdbRikv/TSS8t0XJPJhMaNG+Oaa67BlClTzhq0dDb1008/4ddff1Uxh4td5UlH/G/T9OnT8d5771UIfHghqXHjxrj99ttx//33F3tubrcbO3bswJdfflluJzmHw4HXX38dP/74I2bPnl2qtMdlUUTxm/yzlZiYqHGrkYF4BlkZwHY6nYiOjlZuGhIiCQY06GQjIQKmNuLrTJWSl5enwBCv16sciFiWTIfDYCoD3jK1CsEHr9ergRromuPz+RAREaF+JDDVldvtVvARj82gLh2DeKcVASkCNQBUGhpCMRQBmaysLBWAJ0BFMIROIzLFEOEBptgyGo1IS0tTwW4G6mUw2mg0wuPxaFJBGQwG5dLD9DsSiuHr/E/wi0AQHZPy8vLUubDOPK4EkHh8thvHj3TOIJTi9/vVHTcEYUI5zrCuubm5sFqtGscZ9j+hHz7meMnOzlZwD9uIcAEhHnkHnSxTOprwxxjrbjQa1TlwzMi+I7zBPua+dD4hYBIeHq6gqaysLHi9XtjtdsTHxyMlJUVBPHRhIVTn9/sRHR2tIBKbzabqwnHh9Xo1849gB+cdYRjOfQnaBLu6BDvR0LFGgiRsN4I+dIwpzL1IwhLBTlcACoA+wY5BTqdTpReUsF5ERIRKgSVhHp5Xbm4uIiIi1BjnvlyHJGAoU3KxTlzz5DkD+bl6uWbJucxz4JdD6fLFVH3sK0JF0pmHaxnrFhkZCbfbrdrH5XKpunB88RwkhCX7iWOIbkhc06STFOcuU+JxPWJbcN7IlGQyFZlMC+f3+2E2m1G1alW43W44HA5ERUWp/1xzXC6XSnkoxxPPNTc3V7UHxxn7me9xzjgcDng8HpXbWs5JCetQcnzJdZ7tIR3IQu0nHeQk+CM/T3Xp0qVLly5dunTp0nV2lJiYWKRbydChQ9G/f39s2LCh1ClVrrrqKnTs2BF79+7FsmXLMGTIEIwfP169L9Nq7dixQ7lCl1V0DTpx4gS++eabIrf1er147bXX8NlnnwEAvvrqqxLBNHFxcQgPD1fpscuqtm3b4pNPPlF3wY4bNw5PP/10ge169OgBv9+vbkLq1asXwsLCQsIX51IZGRm48cYbsWfPnrN2jISEhJD9WKVKFRw7dgw//PAD2rZti+rVq6Nu3bpwOBzwer344osvcOzYMdSuXbvQ9GmFyWAw4P3330eDBg1QvXp1GAwGtGnTBo0bNy5XareLUTabDZMnT0avXr00r992221YvHgxvv32WwDAc889h2bNmhUAZ4rTwYMHkZqaWiLwJz09XV0TpurXr4/hw4eHTIFFHT9+HD/++CMGDhxY6LWFadOmITExER988IHm9SeffBJNmzbF4cOHcfr06QIB7c6dO2Py5Ml45513NK8/9NBD5b67vUOHDmjQoAEOHjyIefPmFbrdnXfeqemfSy+9FE2aNFHrQ82aNTF//nzs27cPjRo1KhaIlIqLiys3NHA21Lt3bwUE7tmzBwkJCee7SrpKKYvFguuuuw7XX3892rVrp3kvLS0NJ06cQOvWrTVrQ05ODnbu3IlNmzbBbrejSZMmaNKkSYH1qSzKzc3Fp59+iqlTp2pSj16oKiyt1NlQbm4umjRpEhKINplMuPvuu7F8+XIsXLgQzZo1K7CenzlzBvv37weQ7xx04403FnBrO378eIVAidu3b8dXX32FRx55RPP62rVr4XK5Coy1itLatWuxatUqzWthYWG49dZbMWDAAJWutbx69913MXToUGzduhXHjx/H9u3bsW/fvpCw04WirVu34vPPP4fRaET//v1L5MJ4Icvn82Hfvn3nuxoXjVauXHm+q1Dhatq0KV566SV88cUXBVJRBmvr1q2YP38+wsPDcc0111TI8W02mzIsOZv614M/dEFgcJ5AgIQggPxFITU1Fbm5uRrLOxmMl2ABA9XBaWeYGorQAeEHuvwEp3digFe68BA8MJlMsNvtCkhhQJsgAaEJo9EIt9utbMyys7NV8D83N1eBJ36/H0ajUQXwZVoc1p+OIAQbCAEkJSXBZrOpPIkMTgcCAURFRalzY4CawJHFYtEE5tmWrBOdK9xutzovs9kMm82mIZUjIiLg9/sVDMFUOgR+2JcEq1iW1WpVQW4G/30+n9qP5yGhHzqaMCifnZ2NjIwMGI1GFZhn/7G9JOxCoMlsNmvSRwUH5JmiSUIsDOQHgyYMtBM+AoDo6GiNOw9Tj4VK9RQs2d/8QCeAI1OfEfLgmKKjk0yLZrVaVeo6nieQT0z6fD4kJSUhNzcX8fHxqg0ImbDeTB9HeMnhcCAuLg5+vx8ZGRnKOcbj8SgoSTreOJ1O1UYEISIjIxWExr7x+/2atFhy7konqmAHFCAfAuF5En5gO4VaL6SDkwSuCCrxNbkPnYzoCOZyuTTOOwRweA7h4eEK/pKuUIRocnJykJSUhLi4OEX/EyhhmVwPCI5wfNG1h/Xl+XHOM91hVFQU0tLSVPtzvLOPQwFvdC3jfJEQDdcfrptcswGotZXrknT9IQjFMUcYks5pEqJh38l9OW+8Xq/6YCaEKB3DpEuTXLM5b7gft8/JyVGuYISNZMouHpfuQRzXhPzYFxz//HwgEJeRkaGOxzEm+1SmUJTAX6hxKiGeUBfcuK+Ef0IBQ7p06dKlS5cuXbp06ao40fG5MJUHbtm0aRM6duyIhx9+GNdddx26du2qcWcYMmQIvv76a/X8r7/+Qv/+/fHhhx+W6XhutxvHjh0r8UX56Oho7NmzBxaLBa1bt0b37t2Vs0xh4h2WZ86cwenTp8tUz2bNmqFevXpYvXq1SllVrVo19O/fHz/99JNm2xdffBH79+9XcEFKSgpWrlyJgQMHatJQnQ+dTeinKC1ZsgQOh0OBYwaDAe3bt0fHjh3x1FNP4dSpU/jrr7/wzDPPlLpsCa/w5k2/34/du3cXu+/o0aPx8ccfl/qYF6oaNWqE6tWrh3wvLS1NPU5PTw/pulK3bl0cPXq00PI//vhj/PHHH2jatGmR9XjyySdRuXJljQMxACxevLhI6IfifgcPHgz5/qZNm7Bp0ybNa6NGjcL111+P6Oho9OjRo9Cy//Of/8BgMGDatGnq2kdFpLSgw1rlypXRunVr7Nixo8A2Xbp0wY033oiEhAQ8/PDDaN26NRo2bIgaNWpg69atmDRpEoYPH45evXphw4YN2LBhQ4E2LEqXX345xo0bhw8++ACLFy8u9zlVlE6ePIl169bhjz/+CAlL6rrwNWzYMFx99dVo3bp1gffy8vLg8/mwe/dupKSkqBRgERERiI6ORk5OTgFIrzz65Zdf8MMPP+Dll1+usDLPtlJTU/H555+jbt26aNCgQZHpZkaOHInPP/+8TMex2+3IysrC5s2bCwS4A4EAfv75Z8yfP1+BPC1btiwACB08eFDjjti6dWvN50V6ejr27dsX0lGoLAoFwXz//fcIBAK49tprK+QYwQrlVnT//fdjxIgRFZpe0WAw4LLLLsNll10GIN+hKjU1FR999BHefffdC9bR7auvvkLTpk3RokWLCnPnOl/KyMgocapZXf9MNWrUCJGRkRg9enSxqb66d++O7t27V3gdateuXWj62orSvx78MRqNiI+P17hZMIgsA92EPDweDyIjI2GxWBQYwIAqnR0YCA6GfoC/QQoGYplKx+12K2ccAh8ECPgag8Z8LyYmBg6HQx3L5XJpIAEGiRl0lvkL+ZoM3tP9BIAK2rIteGwJIrhcLnWcpKQkdVeXyWQq1BGJP1BcLhfi4+PVjyq2gdlsht1uVwFon8+H9PR0jfuJw+FQrjDBqZLoHkOXHrYFwZ3U1FTlGCJdV6TrD0VAyGw2w+v1qiC4vFPL5/PB4/EowIeAAKEq9h1BCKZGYx+w7QkIcDwRJGCd8vLyFPxEoEemlyIcxv6RwAnHG91FgmEW/peuHXyf58RzJ2jBuSPv5iNMI91z5ByS7iF+v1+lU2L/EBzi8+TkZAX+0EWL8BLBL7/fr+5e8fl88Hq9cLvdKv0X3Zc4ltkuhP2Yjow/SthnnO+hgCzpcsI2k23s8XgUxCYdbCi2ZfAdmWzrUOmVZF8zvR/dXXw+nwZS4xzjOiLrKB2L2PdMuSUBL5/Pp+AWuQYQ7CDsRXiIsA8dwjhWCOTxNZbPMcjzlLBMKNiRdTSbzTh16pRy+jGbzQo05BpAiJCuXhRBMrqrSYhLrpMc32azWQN/0i1Nzk2Z2kueB2Ektj9hKs4ZrpGnT5+G3W5XP47YXn6/H+np6eq8WYY8P9ZZjhW2uXRw4nbJycnK4Yllcnxx/Mh92Bcsl2NfgoNyfLJNJBzGNbQ0F6Z06dKlS5cuXbp06dJVOjGFc3R0NDIyMgq8v3HjRpw+fbrI4HdhOnz4MABoAhVSl156qQb82bRpE/r06VPq40g99dRTxab/AfLvmBw2bBg+//xzzJw5E507dy4W+gGA5s2bA0C53F+6dOmCqlWrquDBmjVr0Lt3b8TExKB9+/bYvHmz2vb06dN499138c4778Dv92PFihXYsWPHReFKcDYl3aICgQA2bdqEAwcO4N5778WYMWNw+vRp/PjjjxpApbQKNR8Kk8ViUXDWPwX+6dChAypXrhzyPV4jfumll/Doo48q5ywgP1jcsGFDfPfdd8UG+Xbs2BESapF65JFHQrpNFJZqK1h16tRBTk4Obr755gJ9Ewq2A4Bbb70V0dHRxZYdHR2NZ555BnfeeSdOnTqFbdu24dFHHy1RvYrSgQMHkJSUhDfeeKPQ9vn9998xfPhwhIWFoW/fvvj2228155KUlKRc0Ki5c+eWuA4//fQT7rvvPtSsWbNM53A2tWnTpgpxetF1ftS1a1e0b98+5PW+2NhYdOnSBbt27UJYWBhuv/129O3bF7fddhuqVq2KOXPmaG6qL48CgQA2bNiAmTNnVkh551I33ngjrrrqKjzxxBOFBp8DgQB++OGHMpXfqlUrLFy4EPv37w/5fYc3lp48eRLr168HkA/+BOvEiRPqu6DFYkHLli01KcqOHj1a7GdASUX3v2C98sorpfo8L60Ip1GjRo3CXXfdVWFOP4XJarXCarXitttuAwDMnDmzQsDTipbX68XWrVuxf/9+VK1aVZNB5GLToUOHzhv0ruvCUGJiIs6cOVOi7yAnTpzA2rVrcfToUdStWxctWrRAlSpVCjhIlladO3fGxx9/jMsvv1w5qlW0/vXgj8VigcPhgM1mU7nJg1P7MDDLVDAEgRjwtdlsKhjLFFgEOigJCjAYTYcGpnih0wyPQ2eZiIgI5ZxBQMfn88FmsyErK0sd3+v1KgAlIyND4/Ih3UAsFosK6jLtEd2HCBMAUDAMH9MthxeTWLfc3FwkJiYqN5asrCwN3MAAOdMLSVCFqXuAv10rGEynswnLsFqt8Hq9Crxi+xIAYZozv9+PiIgIOBwOmM1m1Sb8oysNA+bsS0Ii7FPpnuF2uzXBdZnOKywsDBaLRbU/AAVNBAIB5dLD9uB2BCokGMB24OsGg0FTlgzYsw4ySM/H3IZ9TSBHBvblWGf5hLSCy2GdCIQw3RchHQb86YAiwQ4CFXyN44EOTXTeIVTF43Lcy/FIgCsjI0O1Z1paGoxGoxpPdHaR/c1+la9ZLBaNE4p0zuH4ldCN7JfgNFIEYggpcW4EwxTsg+D0R8GQkXTBkmmTcnJyVHo0zid5HLk9j0UATqZeIsAlxydBGQkRScCF/ZKRkaFJTceUWQTb6MpD8MjlcinoStaL85Rjk32XnZ2t2pYOTuz/vLw8ZGZmwufzqTWW6xj7i/3INVuOb6a5k9APnXToeJabmwuTyaTGrgQuJQTF8cT+k2uddNQJBAKqruyj9PR0eDwetY7yB5PD4UBmZqaC/DgX5ZqSmZmpmbfSKY0OZoQVWQe2i4S+ZIo5eW5y/Et3n+D0XXK88bOL/cyxJdtYly5dunTp0qVLly5dZ0dMG2U0GlGtWrUCaVPef/993HLLLSWCaYJV3F2xbdu2VY/tdjtuv/32Uh8jWHPmzCnRdtdddx0uvfRSXHnllcjJydEASKH04IMP4vjx4+pO9e+//77MdWzbtq3mN9Xvv/+O7t2749ChQ5g3bx6GDh1aILCwdu1a9Xj06NGYPXv2BZ3e4XwoPT0dbdq0Qf369eHz+fDEE0/g559/xpIlS876sd1uN/78809Mnjz5HwP+1K1bFxaLJeR7s2bNQmJiInr27Akg3zWDevvtt9GgQQPUqlULU6ZMwZEjR7B582YcP3681HW47LLLQkI/27dvLzaFRIMGDXDo0CGMGDECTZs2LQCw3Hzzzfjoo4/QunVrFZgG8t23SgM6RkdHo3Xr1mjdujWqVq1abvBnwoQJeOuttzB69Gj897//LXLb9PR0XH/99XjooYfwww8/aMCfzz//vMxOHyy7W7duiIuLK3MZunQFy2w2o2XLlqhXr17I98PCwjBq1CgMGDAA0dHRyMrKUjftlgTGK4lOnDiBdevWoV69eti3bx9SUlIqpNxzrXXr1mHp0qWw2Wwh01idOnUKtWrVKhOocMUVV6BGjRqoVasWunbtGjLzw86dOzWubgMHDtS87/F4cObMGQXduN1uTJ06Fa+88go6duyI3Nxc/PHHH9i5c2ep6xdKI0aMwNVXX13g9alTp2Lo0KGoUqVKhRxH6vjx41i6dKl6ft999+GBBx44p8Bk48aN8fTTT2PcuHFISEjAwIEDL7gx/euvv2LQoEHo1q3b+a5KuXTs2DH9u/e/XJUrVy5xdopt27bhwIED2LVrFxYtWoSqVati7NixGDZsWLnrUbt2bXz++ed48cUXsXTpUrhcrnKXKXVxJ+WrAAUHnUP9MchLWEO6QDCIajKZlGuJTDElg8ahnEIYxJfwgCw/GLyQdabDDQO6hHsICADQBG8JZTAwazQaNQFuBmcJ3zCQK117GHCWQV8JKRG+ke3Gx1IM0tMRQqY3kuATgQ2r1QqbzabKCwsLg9lsVqnamHpMwhiEpKRjDQPTPA9uy2OxfdjGBJvYh8HnHxkZqeCiUOOKgX+ZVop1YvvIFEyUdOSRMIoMznMMSAAhlMuUdBOSZcttZMowmTpMpmDjYwkGESQjDGQ0GlVaMwlWyfEbXD/OPUDrLEW4SLq58HxycnKQmZmJzMxMzfhmu0rYQ84Bvi5hOG4XDPTwmKHe4z6yT4C/7Yf5fjDgU9iHiiybwAedXzi3uZ3sI+mixOMFtzOdn+Q2fM9oNCrQh4857gltSPcmmZqM5dL5R8Jh3I/1lIAS92dbyfqwb4C/XWbkuXAtIhgmx1FhfcV25HG43vLYdHqS4zAYCiTcwnEm+zzYUamwsS5BOAKfEqCS6xJhJjkH2eZyLku3H85Bro9yrZL9IKG8YNc6+Tj4s4rnKsEfOSYJfLGeXOuC10ZdunTp0qVLly5dunRVrFasWIEZM2Zg7NixhdqR22w2TUqdooJv8m7r4gIP8fHxmmNQxVmnV4SWLVuGhISEEqWXsFqtuPnmm3HXXXep18qTAq127dqau89btGiBiIgIXHrppahevXqBlBpA/p3sTDfVuXNnDB48uMzH/yfL7/dj3759GDx4MIYOHQq73X7Ojn3kyBE0atSoUFjmYlOlSpUKpBJYv3499u/fj6ZNm6Jv377qZrHly5cDyL+u1adPH/Tu3RsAcOedd+Kuu+7ChAkTcPfdd6NTp06lAkkKc9Y6cuRIkfvFxcVh0qRJAPJdhb788ssCbje//PILkpOTVV2pdu3aqfMqrRYtWlSm/aQaNmyI1NRUzJs3r0Tbb9++Hfv378eGDRvKfexQInShS1dFiDeny+ufwapatSpatmwJo9GIRo0alQk8Lk4ffvghZsyYUeLUoBeiXC4Xpk+fjt9//x1A/s2eMvgbERFR6lRTBoMBNWvWVPEnh8OBKlWqFIB+/H5/AXeZYIc4j8ejbtinVq1apUBrl8uFhISEIlNClkb169cP6aSxbt26Yj8zyqply5bhm2++Uc+ZgeJcKzIyEnXq1EGbNm3Qpk2bc3784pScnAyHw1Hmz9YLRYmJiQXGtK7zo7PxuVASSeOT4tSrVy+MGjUKY8eORceOHQGgQlN0tWnTBsOHD8ekSZMwaNAgNGzYsMLK/tdH5OiQwAC6yWQqAFEwtQ0DsgyUA387PURFRSEmJkalk5GOIzLdDwPRBD4YbGaKMaZ6kamCsrOzYTKZkJ2drcoOCwtDSkoKMjIyVH08Hg/8fj9q1KiB6OhoFYglhARAA6sQ0jEajSr9TUxMjAIAGNwlXERXBx4nEAgodw6Wf/z4caSnp6Nt27aqzizDbDbD6XTC7/crlxiXy6W28fv9CAsLU24pMu2TzWbTOMtERUXBbrfD6/XC5XIpdx6CRAAUcEJnF5/Pp86fk1t+6WGbW61WuFwulRIMgHKtYOodOt+wLtLVRjpjyHrY7Xb4fD64XC6YTCaYTCY1JuSYYrA+GP4hOMF+kP3KY0j3D7m/DPLzf3DgnmNTQmjSBYjbE4Jg2iKCWTKlD7chKCTbmnABHXx4/hKwcjqdiIqKgs1mUxCBhBIyMjKUUwsvyFitVpVWiudHcEbORZaTkZGBQCAAs9ms6sp24DayLzg+pQMN6xsKCOJj+Xow+BOctinUNhIE5Nhin3CNkCAN25Nzxel0atYxprDifKQbDduB4AjXRJma0O12qx92EmSR64UE0tg2hA69Xi+sVis8Ho8GcuHYdbvdGpgRgFqvCPjRxYeQmfyhKdPKET6SKe9yc3PhdrvVOp2dna1pQ+mOwzaU/cf5L6ElzpVgOFRCN3yek5ODxMREldZRtrXJZILX60V2drZKC+bxeFSfE+qx2+0KyATyL67b7XbExcWpzzM5/9l3JpNJ/XhkHXNzc9Ux6NJD2LEwCEgCavKzjf3Fz1Cz2Qy/36/qoEuXLl26dOnSpUuXrrOjTZs2YdOmTUhMTMQrr7wScpsPP/xQPTaZTPjuu+9w8803F3DwuP/++/HAAw8gMTERl1xyiQpGFaYGDRqgcePG2L9/P06fPq1+k99222149dVXNdu2b98e7777Lo4dO4apU6fi4MGDZTxjqOsrHTp0KNH21apVQ506ddQd9RkZGSq1RWlltVrRtm1b/Pbbb+o1AlczZswAkO+mMmrUKKxYsQJAfhC0YcOGePDBBxEREYHevXuXOX3Hv0W7du3Ca6+9hkWLFuGJJ57ACy+8cFaPZzKZMGjQIJjNZjz66KN46qmnzurxzoWkWzqQD9vMmjULixcvRqdOnVCvXj20bt0ajRo1UkHVWbNmacA1OuFcddVVAPLnTkpKCvbv34+DBw9i586dOHLkCDZs2BASMPH7/UhNTS0ACxUX2LznnnswadIk7N+/H++//z4AFEj7lpOTg7fffhsPPvggZs+erV7ntZ7S6uTJk3jxxRfLtK/U/v37UalSJXTu3LnYNRTIB3PeeOMNbN26tdzH1qXrbCs2NrbEQKbZbMaECROKTRlYUu3Zswfvv/8+srOzsX79eo2T3sWqjIwM3H333ahbty727NmDxo0bo02bNqhduzZ27tyJjRs3lqq8l156CTfddBPOnDlT5PVYt9utSUtqt9sLuNzExcWhcePGqF+/vsZV7cUXX0SLFi3QsmVL/Pnnn+X6PifVqVOnkA5xs2bNKhesXZSeeuopnD59Wj3ftm0bUlJSCk2TebZlMpnQp08frFq16rwcvzDVqVMHV1555fmuRrnF7DK6zq+aNGmCcePGYenSpeq30rmSw+Eocaouh8MBh8OB48ePw+VywW63F0gNWB5FRERgxIgRGDFiBDIzM/HNN9/gzjvvLDGYVGTZFVC/i1ppaWnqQ5ABXRmwJdhB0IUXMRjsZIol/qiQMIAMBBNGIEDB8jMzM1XZfD/YxQXIJ2gZ0CWglJ6eDq/Xq4LIABQ4Q0BIpq1hoDgvL0+ltsnMzERsbKxKU0OISaZmITDAADuBHAaGpeNHRkYGXC4XbDYbmjRpolLn1KhRA0eOHFGBYrPZrOpMOMPlcimYxufzKQjB4XDAarXCbrerFF1MEeb3+1V6r7y8PBXEJtwA/O28IlMQBUMd7DemoGJAnmALy3U4HIiOjlZwASEij8ejgSUkoCFTHrFdGWDnmJGwB+ETeXyz2awmPNuc9eUxJVjDsUeClWNAuhAB0EASHAMEGVgX6QBEql86ZEm3IgIB7FfpTCOdklh3upqwPI/Ho8AOHovtw3RrXq9XQQdGo1EBYXSaCQaWgh282EZOp1OBYTIlGfc1m81qfEiYJ9hRiACFbC+WQfcTzncem+0pYadQwA+PQbG9OK8k4EJwhRAg+yk9PR3h4eGIjo5WZXGOE/Lj/ONYYZ9IWJHAjNFoVOcaFhaGypUra9pZQjkSWJHrCcESmWrQYDCoL1+cqzx/o9GoaS+WH+oOE8I/LJPAVnh4uHIjCzVeZLtL9yFuL6FQAj/SXUgCQdJZR26bmJgIl8ulWXc4TtLS0hT4yTbkZwv3ycvL06SelCm6WI5cWzkOOU4JO3EdkmOI9Wab8PNAzlk5HiVYSPHzUaZek+nmdOnSpUuXLl26dOnSdfaUlZVVoruELRYL/vzzz5BpexITE1GjRg1UrlwZ1113Hfbs2aNunJDasWMHqlatiqpVq6Jbt27Yv38/OnTooK6v3XHHHZg7d64m7djmzZvRqFEjdOvWDXXr1sXQoUNx5swZTbndu3dHamoqDh8+rBx6qTFjxmDWrFm44YYbcM899+Dbb7/VuBgVpZYtW2rAg99//13j2FMauVwuzJo1C1OmTMHNN9+Mv/76qwDUUK1aNQwdOlRdzB44cCBSU1OxY8cO1K1bF2azWU81UIxWrVqFVatWoXnz5rjiiivg9/sVTDZ+/HjExcVVCKRBTZgwQV3MHzNmzD8C/AkOoD7//PP4+eefkZGRoUkpJbVly5YiU/ZFR0cjOjpaAwd5vV506dIlJPgzduzYkA5B9evXR4cOHbBp06YC79lsNowYMQLh4eGYNGmSAn+khgwZgnfffRdJSUlo2bIl6tatq1wnVq1ahR07dqB169YAgK1bt6J69eoqzV8o+f3+Cuvze++9F/v27SvgplGYHn74YYwYMaLQ1Em6dF1IOnXqFDZt2oRKlSoVOaco6SJYXvn9fnz00UfqRkOpESNG4LLLLsPRo0cVMCOBjgtdAwcOhMFgwG233YaUlBSMGDECv/32W6nO4dFHH8UjjzwCAMXCVomJicqFEMj/DjljxgzltEY99NBDGDlyJK688kpNyrFbbrkFAEo0BkqqSpUqaZ4fP34cn376KcaNG4dWrVpV2HEoj8dToH137NiBjRs3olmzZhV+vJKqTp066N69OzZv3lzgu/D5UvXq1RETE3O+q1EurV69Gg899ND5roYu5EOFV1xxBTIzM88Z+FOjRg2cOnUKlStXDgkYFqWjR49i79696N+/f4WBrMFyOBwYNWoUjhw5UiEOZ//6VF90dnG5XCqgz+A9XRdMJpP6b7VaVUCcwXEGuAkrMJAsAQcJcYSHh8PpdCIlJQVnzpxRFmN0rZGprxi8ZvCbgXo6AdEhgkFdBmuZeoyOKgzqE0DJyMhAXl4erFarSnUj00oxAE/QiO4yBJrsdrtyY2FQm+48dOtJSUlBVFQUoqOjERcXp9yE4uLilAOPx+NBZmYm0tPTkZaWhszMTE37x8TEICYmBg6HAzabDTExMbDb7codxOPxqBRjGRkZSE1NVRekZFowmaaH7QRAtTchFRlIB6AAIrYB20VCJNLiUgb0U1JSkJqaisjISOXewQA+0/wwBZjNZtMAOGwDOXYIdxAiYz2lOw9BM7qbyLrLfaRrDduEkA3PheOOIAZTlUkgjeOOrjyyPJnuTDqbBKefI5TEtEuENggtEFSgE4nT6UR4eLhKbRQqjRGdlYLnHsWxIecd2126LUn3JjkPeJ4yPZMsW/YhxxgdqXiOhFgkPFLUH/tajjcJrPC50+lUY5pADucI25hj2GQyaeAPOQYJ/GRlZannhHikwwsBLpYXFRUFq9Wq5qFsO7pcsS85L9k3bB/ux7bi+KBDGQFB/hF+4trFtuVrHHcEmHh8znc6Eknojc47wU5Vsm8prn/B23N8A/l3cKWmpmrmJT9XCAVKqIZ9JIEdt9utwKvMzEzNmKarGIE12Z8sTwJK8lxluki5HvIzI/h8eTx5zjxfgqxcT4xGY8gf5Lp06dKlS5cuXbp06SpaU6ZMQc+ePUt8V2Lfvn1x2223FbtdamoqJk6cGPK966+/HkD+b/XPP/8cV199dUgXii5duqBr16645ZZbsGzZMrz44ov4z3/+o95v1qwZNmzYUADMmDhxIrxeL7p27Yo5c+YUKHfYsGFYs2YNtmzZonm9bdu2ePLJJ2EymfDMM8+gYcOGJYZ+ACgAAMj/3Vlet52ff/4ZERER+O9//4vGjRtrACcgP0Xajh071HOLxYInnngChw8fRmpqKjIyMvQbJEqoPXv2YPv27ejTp496bfLkybjnnnsq9Dgy+Fe9enWMGzeuQss/HzKbzerxyy+/jE2bNuHYsWNF7rNw4UL8+eefBV53Op2YM2cOtmzZUgAaNJlMuP/++0OW99lnnxV6rML2cTqdWLBgAQCEDL42aNAATz75JGrXro1LL70UwN9rFyUDlFlZWXj++edDHsvv98Pj8WDmzJn45JNPCq1rSTVy5Eg0b94cu3fvxqFDh4rdfurUqbj++usr5Ni6dJ0Leb1efPbZZ/j6668L3MR6NrV+/Xrs27cv5DXGGjVq4MYbb8S4cePwwgsvYNGiRSG/Y1zoCgQCWLlyJfbs2YONGzfi119/LfG+3bt3x0svvVTi7UOlYVy2bBnWrFmjSTcGQJkPhFLw95/y6NJLL9V8LuzcuRNr1qzB7NmzywxrF6WMjAzlZie1bdu2Cj9WaXT77bdj6dKlmDdv3nlLhxSsDRs2lMjB7kLV66+/joULF5bYKVTX2VV8fDy2bt161iCaUPr999+xadMmxMXFqfhaSZSTk4ONGzdix44duOqqq1C/fv2zWEvgySefxCWXXFLucv714E+9evXg8XhUehkZ+CRgQ+DGYrGowDWD03RmsNvtsFgsiIqK0gTHKcIHhD4SExORlJSk4BcG6+muIJ08oqKiYDabYbFYYLVaVSDcbDYraAKAghIkAAHk/8CRwVzpyGEymRQcwHL8fr8CjOg+QXiFLkgEYhg0l85GfM/tdqtzoSuPTGfm8XiQmpqKhIQE9eFNhxqDwYDY2FgF/dBCUtbR7/croIEORhKAovsFASX5x3LoykNIQqZeokMIv1ASkuLdbnT6IaTE85KgQnp6OrKysjQAC/vA6/UqMIBBcuk4Q1gkOIVSMJzExwQppNuH7Ff5mOOD9Q52u2K7SCiE9ZbHJSjE+nMsEAYhCEdJRyDpMkQHFsJOZrNZ49rE9svJyUFmZqYaw7LNCOpJx5Rg1xw5J2WaNNn/fI/lEuSSYIiEJ+R8k85CPp9PjVM5zqTDVyjYpjDoR7qMyT7husX6p6WlKShROtZwjSP4x/Mg2CXTVxEgSkhIUGsMIRXgb8cdOk9xfeSdXwaDQaUB5JgC8n8cEryh2w3Hg9PpRFpamgIpOWaDU7CxL+R45XrM9ZRtTMchrmXZ2dkal6rgdue45rgkVMT6yDbneJXzjH9sR55DUlIS0tLSVD35GUHghmsIj8mxwvqw3WW6PavVqrYHgCpVqqBKlSrqfORniXRnk3OOYI5ME8fy+cfPDemIJduD84WfR3RG4uPIyMgy22zr0qVLly5dunTp0vVvVVxcHBo2bIglS5Zgw4YNJdqHAf2GDRuW6ZgTJkzAtddeq55HRERgwIABIcGjkSNH4tChQ1iwYAHatWuHsWPHYuDAgZpt6tSpgzFjxqg0WABU2iYAuPLKKzF8+HC0bdsWNWvWxFdffYV+/fqhcuXKaN68OW666SZ06tQJALB9+3bUqFEDS5Yswb59+9C4ceMSn1d0dLQqBwAOHjyIH3/8scT7h9IVV1yB2NhYAMBtt92mHlMvvPCCJr3ae++9h3379qFGjRpIS0vDunXr0Lt373LVoTy67777KhycOZvasWMHmjdvDiA/uNmsWTPUqFED7du3r7BjLFiwAOnp6er5nXfeeV7v+K8IMbBx6tQpfPTRR9i+fXux+yQmJuLSSy8tEMzdsGEDPvzwQ3Tq1Ckk0HLTTTehRo0aiI+Px+zZs/HLL79g2rRpuOGGGwpNWdCtW7dCYcWPPvoIGzZswNGjRzXQFwAcOnSowM1Yixcv1mwjr0X26tUL8+bNCxm0XL16NQYNGlQoGFQaRUdHKzeB66+/vkjnJCB/XXjuuedw//3349lnny338XXpOldauXIlVq1ahczMzLN+rKSkJHz44Yfo3r07hg8fXuB9s9mMu+++G1dccYV6LSoqCv3798fHH3+snGkuFh07dgyvvvoqevfujaVLl5Zon6eeegqrV68u8TGys7Pxxx9/qOfXX389evXqhXr16qFKlSoF3DCcTqcmO8jZUlZWFmbPno2lS5di0aJFmDVrFv744w/07dsX0dHRFXqso0eP4p133gnZxikpKQXgp3Mtm82G2NhY9d3nQtAnn3xSIOXmxaC8vDzcdttteOedd87rd29df+uHH37AtGnT8NFHH52zY/r9frRv3x533nknqlatWuL99u/fj2XLlsHn86Fq1aqa+PjZUEpKSokdI4vSvx78oQsFAE1QUwb1GSAlVCMD8oRtGAiXLgrBgyAsLAxer1cFv+mowXQuMhgdDCgwQMtjSMBCpmHhazwPunsQCiHEI8EFtgPhDQIdBAJkQFxuI+sngScZRPZ6vcjMzFRBY7YJnT34GoPU2dnZGucYui0FE+TSnYWPCdIEOxXJP+lME+xkIYP30p2E5dG9iI5PMk0Wj0cggSL0wT5i28lxxXoQRGGZhAIIjchzlzCIDMIHp4wiUCDHY/B/qVAQigRiZHof6eQj3aykW5BMdyfLlkBCcPokKhhgk4AL6yD7mfWXKbnYFmxD2RcczzKdkXQOoiTMQck+lm0SXGcJUASDPKEU/H4oYKmw/QhpSAt2ng9hEkJ+0tmI5y7hL4J70iFLujYRGpPOOhIElEAPjyfdjzh2JDBF5yXZ7jLVG//keJLjVJZP+Es6ZUlnqODjyMdMDyZTVQW3tYSO5DgOHjsAlPuUXJc4brj2y7Rasu9kG1qtVhiNRpXSy2KxqLWR4FuocRH8n/VgHwSnwZPjSX5+SDeiYDCNa5n841jgWNKlS5cuXbp06dKlS1fJ9dxzz6Fu3bqwWq2oVauWuhmqOFWvXr1QN5/iFOpu6k6dOoW8G1MCEVu2bMHChQtDpvmJj4/XQDeXXHKJJsXQuHHjMH78eAwZMgS9evXSAD3jxo3D3XffDSA/LVBOTg569+6Nxx9/HB6Pp8TnVbNmTU0qisTERJUSqKxyOp04dOgQPB4PLr/88gJ3jga3Rd26dXHnnXdi0qRJuPnmm2EymdCsWbMS92tFq2vXrhg0aNB5OXZZlJKSouAqmWpEjq3yqlmzZprAYr169dClS5cKK/98iDcKeb3eUgURcnNzsWnTJo3Dg9FoxF133YVJkyahdevWBYKiERERmDhxIgYNGoR+/fqha9euGDFiBO68806NW7GU3W7HmDFjQr6XnJyMjz/+GJ999lnIgLYEIpkeUEqOEyDf9StUWrHY2FisXbu2QlICXX755WjUqBGA/OvBzZo106REC9ZVV12FRYsWhXRV06XrQldqamqpPovLokAggC+//BKzZs0qdJu2bduie/fuGoczasiQIRg8ePDZrOIFocmTJxe6zoaSy+XSpButXr067r77bmVsECy3210q4Lo8SktLw6xZszBjxgwsXrwYTqczZJ3Kq82bN4f8bLHZbLj66qtLnQrobCgsLEylIL0QtGXLlhI52V1oCgsLw+eff47ly5cXgIR1nT9t2rQp5Peys6GwsDD88ssvJd4+OTkZf/31F1wuF44dO4Z9+/YByAdRz7akQUN5VPJPhH+ocnNzlXNIqHQtDJSaTCaNE4pMY0IohKANfxhJMCEsLAx+vx9Op1OlFmOw2+fzqfQ10rWG9ZGODFlZWcp5hCm+CLv4/X5YrVYVUPZ4PMjKylLuNIReCAZI9x/plMOAL2EgtkVkZCTsdjsiIyNVyh22j9lsRlRUlHKpYHDd5/MhKysLbrdbwUF0PaIrCZ1JvF6vSj1ksVhgNpthtVrVa9LNRgavAah2p+MLg89sR4ruGW63W4EQdCcxm82w2+0qyE6Qx+PxKOcKq9WKsLAwOJ1OZGdnq/dsNhuioqLgcDiQmZmJvLw8eDwehIWFISUlRUFbPE9CX16vV30xY3+wvmxLn8+nSdEloRI+Zn/xP6EEOlcFgwkSWpAOJRLMIUjhdruV4wshN/YbA/7BziDS2YiuPxLSkK4jPGcJznAMESKhWwlBDqPRqNxLJExBsIUuI4QbCJqxHpxDhM3YttI1i+AC50tRYJJ8XYKCBOckaEMwTwIiBJrkuYRyDJPwiqyHHDMca7wII53GbDabmoPZ2dkFUrJJqC8mJgYGgwFpaWmqzzlWCQVxPMgfFzxPjvHgtGbS2SYQyE8f5nQ61bylIxDnqoSagL9z1EsXoEAgP50hxwTT0knoh+3Aeki3GpYn+0KufWxXOUaZ5k0CNDwGj5OSkoL09HR1fLYtYZ/k5GSVPpKORFy3ON4sFota2yU0yvWR88PpdCIjIwNxcXGa8RMMuknoSY5tOY64fvMHPOeFHG9yDZZtLV3EeIyKzOetS5cuXbp06dKlS9c/XUOGDMGECRM0r02fPr1EabymTJmCVq1alep4CxYswNGjR/HRRx8hKSkJlStXVu8xkPbXX3/BbDarlAPDhg1D165d0bNnTyQmJmLixImIiYnBzTffrCk7LCwMY8eOxbJly3Do0CHMmjVLA7v069cP/fr1AwCsW7cOzz33HC655BLUrVsXEyZMQIsWLdCvXz9MmzYNV155JSIiIrB79+5SnV+nTp2ULfsbb7xR4Oaqsujdd9/F3LlzMX78eDz++OMFAB5CBy+//DJatmyJ0aNHo1OnTmjatCkCgYC6Drh48WJs2bIFTzzxRLnrVBqdOHEC11133Tk7Xnm1ZcsWWCwWjBgxAvv27UNGRoZyV+HYKq969OihudZy9OhRnDx5stzlnk+ZzWakp6fjhRdeKPUNOaNHj8all16K1q1b49VXX0WPHj3Qo0cPAPmOVsuXL0f9+vXRqlUrdOvWDd26dcNjjz2GQCCAw4cP4+abb0bbtm3RsGHDQh0L4uLi0KNHD/Tt2xcHDhwokEJs5syZhdZPBoI3btxY4P3p06eja9eusFgs2Lx5M9atWweDwYCJEycqx6DGjRujQ4cOuOmmm4pMSVac4uPj8fLLL6N+/foqSPvXX3/hyy+/DDk2W7VqhVmzZuG1117D2rVri02/pkvXhaj9+/cjMTFRA9ZWtAwGA7Zt24bNmzcXus2YMWPQs2fPkO/FxcWd08/Wc60qVargjz/+UNeCS6rDhw9rAu4dO3bEyJEjMXLkyJDOPm3atEGTJk3w+++/F5ryqyL1v//9T/P8iiuuwNixY8vljJaZmYkff/wR9evXR3R0NJ555hlNSlbqhhtuuGC+H/Xu3Rsmkwl+vx+7d+9W8MH50ubNm/HRRx9hxowZ57UeZZHdbscDDzxQABLW9e/Qzp07sXv3bnz99dcYMmRIkdvu378fGzduxLvvvguLxaKJawV/T61oOZ1O/PTTT6X+rRtK/3rwJy8vDzabTQU66XwhXSEkKACggLOBBDFkuhfpgsDUVsnJyQr2YaCXYERERIRy5QkEAhp3GgaWmTaKkAID3D6fTwWTw8LC4PF41HEILTFwLV0kACjgiDQZA8Ssg/zhKwO8hAFycnIUBMTXpWMEg/m8uOLz+VRg3Wg0olq1avD7/Th+/LgCY3i3lMfjQXx8vKor04XJFEISNjGbzcqRx+Vywe12axxOCJzYbDZERETA5XLB5XJp3FCkaxBBCbaJdA6RjjxMCRcREYGMjAwFvBgMBiQmJsLv9yMuLg42m02dY25uLrKysgqkw5LuGoRBAC0IwjpIRxMAmjr6/X64XC4Fr0hoIdgJh+UTrpCQG8c+3Zdkaq/w8HCVpo4wBcc2U55J2MNgMKj3g116CICxH/i+BJN4cUzCSRwrHHNy7koHGpkDWDpahYWFqXki08EFAy2sg+yXYEBQQhYElLgfgR/2m3ROYb2lqw7ryXbgGJbjQK49cg7wuIRuJMzHdcLr9ar1TjpPcc5aLBZkZ2cjLS0NkZGRiI+Ph9lshsPhUKn1CPwRHszIyFBtTjgxMzNTpUtkW0hYJiIiAk6nUwE7ck2U66lMwSXduji+DQaDWjusVqtyOZLHlI5sAJTbmwTgpMsZQRZCUtJtSEI/0pWI9XS73SrVn7wgxbYmfMX3CD7JuR4WFqYutKelpcFut6v1k+PLZrMp4NPpdCI6Olqz1plMJng8Hs345LFkWjopfjZx/XQ4HBqIinWUbkehHNaAv93kdOnSpUuXLl26dOnSVbwGDRqkHCOkbrnlFvz888+YO3dukftbLJZS24OPHDkSQH6Q/ddffw15QfSTTz7B8ePHMX/+fAD56cQaNmyI4cOHY9GiRQCA//73vwXAHwBo3rw51qxZg+Tk5EJTJ2VlZeGZZ57BypUrlSPQ6NGjUblyZcycORO1atXC2rVrS3Ve1OzZs9GiRQu0bt0aU6dOrbCgVXp6Ol5++WVYLBY8+eSTmvfeeustdO/eHXa7HfXq1cOUKVMwd+5c/Oc//1E3EQJ/g092u70A7HU2Va1aNQ3gdaHryJEjSEtLw/z585GZmYlTp07h1VdfxYMPPognn3wSd9xxR7mPMWvWLLzxxhsK3PB6vSGBkotJdrsd27dvxyeffFLqfRMTE7F06VJUqlSpwHv9+vVDVFQUHn74YdjtdixZskS9t3XrVowcORL79+/H559/jpdeeqnYY3333XfKtefIkSPYtm0bjh8/jvXr12tcKaSqVKmiHl911VWoXr26xrXn66+/xs8//4yrr75agX9r166Fz+fDf//7X6SkpODZZ5/FwoULywX9AMDUqVMLOBe1atUKDzzwAEaNGlUAPPjiiy+watUqTTpAXbouNh09erRCXAkKk8vlwu+//15soP6aa64p0u3mfKdsOptKTEws082Whw8fxs6dO9XzFi1aqMehruFWrlwZzz//PKZMmYLdu3fjP//5T0ho5mwpISEBH3zwAfr164fq1aujVq1aIR2eitKiRYswd+5cbN68GSaTCSkpKSG3483+wU6O50udO3fG2LFj8eijj57vqgDI/63Qp08fDB8+XN0kfDHIZDL9o9cCXYVrypQpaN68eYnT5kVHR2P16tUFUm3XrVu3SBfH8sjlcuHMmTNISkrC4sWL8fXXX2PhwoXlKlMHf/LyEBMTg6ysLBUU5ZcWn88Ht9sNs9mMQCCgIBvCJQyasxwZ2GdQlUHetLQ0BZnIlFcyrVRWVhYCgYAKYssgswQrGHyVKZKkc0ZkZKRyx2Hw3GKxKMCBH+B0+YmMjITT6dQ4+EioiedGpwngbwCIEAPdbJiKh5AFA/N0gHG73cjKylIOR9Ipg+4VdMLJyclB9erVVT3ZN9LJRaYwkk4yDIJLZyLpHMLAud1uV64mQP6Hu3QL4bmxTwheSRcT6cLCcSLTfuXl5SmnDpPJBKvVikAggPT0dNX3DocDZrMZPp9POfwQoOB5SXCFziaEs0wmE3w+n/rA9fl8SEtLg81mU24h0pEj2C2F5yShMoIC3M9sNmvahfAI+5bzh2AC4QzuL0GM4JRffI3lSoccHjstLU0DgEjoSqZlknANAAVZsa8sFguioqJUOxOECQY5QjnzcL5xjoRK3SWdZLi/nEsEmNiWcsxKtyOOIbqF8Viyn7g/XaR4gUrCUjxvAkF0OMrJyVEXPGNiYtRaxTWC5xcTE6P6jUCbnBtWq1Wl8+MYZFtGRUXBbDarOcW1kW0SERGhUnxxvHOsS3cyCbdxHnB7rguEDglwcRyzD3gM6e4mc8Kz3lyDJPzDfpVrOusgf1yy31JSUuDxeP6PvTOPs6n+//jrznrn3js7Rva1CFmTPYosIUVZkhZSIm2S+qZVu1ZSWUJESkIkJYnIkqwh+zoYY7Y7d5vt/v6Y3+vjfc7c2e9Y6rwej3kw9577OZ/z2e6d+3me1wtOpxMRERFqjLI+clzY7XbYbLY8Tk90IiO0w/MTOouKioLFYkFkZKSmTzgeXC6XGtuyLeh+FRYWpomCY704pwnFSTCR0V+sp8vlUqAcgDwuSIRm5dplyJAhQ4YMGTJkyJAh36pWrRq+/fZb9f2EVEBAAEaPHl0o+AMg382M/BQfH49KlSrh/fffx2OPPZYH/ElPT8dHH30Et9uNbt26oWbNmqhduzaioqLQpUsXBf78+OOP+Z6jXLlyPgECKiAgAP/884/msb59++L5559Hq1atkJKSgq5du2LlypXFujZq7NixJXpdUfTCCy/glltuwQ033KAeq1evHvr374+RI0eiatWqOHbsGP766698NydHjBiBGjVq4PDhw9izZw8OHTpU4mstTG3atMHAgQPzfJdxuevs2bOoUKECoqKicOrUKfz0009o2bIl+vbt6xfwp02bNppNrFatWuGZZ57Bs88+W+qyL5WsVmuxQUC9unfvDiDXkWv16tV48cUXccMNN2Dt2rWoUaMGjh49irNnzwLI/e6pc+fOmjVs8uTJ2LNnDz777DP88MMPqFKlimauALmuY263G40bN0br1q0xZMgQZGdnIz4+HgMHDsT69esB5PaJy+XCjh07MHbsWE2EzyOPPJIHwJs7dy4SEhJQu3Zt9ZjL5cKwYcPg9XoRGRmJHTt2AABq1aqFY8eOab4nKqoGDhyo+f2vv/7CmTNn0LNnTyxcuFCtqY0aNcL48eOxd+9ejBw5stjnMWToclKTJk3KZBP09OnT+PPPP/G///0Pu3btKjDm6b777ruiINay0LJly4odZ3b99deje/fu2LhxIxITE7Fo0SJYrdZ84WwgF/4pX748bDYb7rzzzosK/gC50Ts33ngjwsPDMXHiRPTt2xexsbFFem1SUhImTZqEmJgYrF+/HgcPHkS/fv18HvvGG2+UCvpZvHgxYmJi0KFDhxKXoVePHj2QlpaG7du34+TJk9i8eTOOHDlyydysvv76a8TFxeHmm2++JOcvic6cOYP09PRLXQ1Dl0C33XZbsY7fsWMHfvvttzyP9+7dG02aNPFTrXKVkZGBpKQk7N+/H7t378aJEyfwww8/+KXs/zz4k5WVpYFxZCRRZmam2uQEoDa1GR3ETU690wk3UYEL7i0ulwtut1ttgHLTnvALARluAEuHFr3zBaET6TgDQEVtZWVlqU111leCD3TokfUk6MDnuTGt37CWkIGMaJLxUtnZ2Qoo4mtklBFhFQCKoA0KCkLlypWRlJSk3IHi4uIUHME24fGsl4RGgoKC4PF4NNmXCQkJcLvdsFgsqp8IsPC1sbGxCAkJgcvlQnZ2NtLS0lT8jnS7oYuQjB0KDw+H1WpVjk4hISEwm82qX2UcFWEsPkfKlJFg3CCnGw4/ZHCjnu1IIIJwgt7JJCQkBGfOnIHL5UJERATS09OVaxI35WUEFfuIbi8SaKM7UU5Ojop5Yzl8LUEilsnxw36REVZ09ZFOIdJBia9h5JHFYlHxbYQXWC8CMxSvR4IIdDLhuJFuNHRc4TVJuISAjj5Si2uGhJJ4br34nKwPI+DY3hwL8ho4H2V8GMeujGNj2XoAjZAaXcB4XoI3ci57PB6UK1dOA9NwjSEQIh2VOH4J+mVmZiIhIUGdi/M+KysLERERyM7OVu5aHJsSZOPr2N+ETaxWq4oeZL3k2E1LS1PxehTXcZYbGRmp1on09HT1WhkZSDBQwl50nGIfBQUFITw8XLPOsW/YtxL4zM7OxpkzZ/LAboSEOJY51+TaQAUHB6t1kJAY+4HwmtlsRnh4OKKjo/PEy2VlZSE1NVUTUUZAkP0o5yhdf1wul3rfI3AngSjWU87bnJwcWCwW1X5sH9lOBvhjyJAhQ4YMGTJkyFD+iomJQVJSEtq0aaNckfVKT0/H119/XWhZ4eHhSExM9PnclClTcOTIEaxatQrbtm0DAIwZMwaVKlUCAPTq1Qtbt27FkiVL0KNHD/V37Nq1a9VNI4wbq1q1KurUqZMnCuns2bOIi4sr4pVfkNVqRdu2bTXXuHz5cjRp0gStWrVCVFQUFixYgCeffLJE7iVlKV/Xy5sxgNw7R10uF5o3b15gOQQsgNw2Lwvwp3bt2vjkk08A+I5Hupy1ZMkSnDhxAj169ECFChVw5513YuHChWjWrBl69uyJZcuWlar8hx9+WOMg8Mcff+Cnn34qbbUvqSwWC86dO1eqMsaOHYtNmzbBbrfjhx9+wP33349q1aph1KhRGDx4MI4cOYI2bdoAAHbu3JkHXDx16hROnz6NtLQ0zJkzB0uWLNF8/7F79248+OCD6nebzYY1a9agefPmKF++PG6++WaUL18e+/fvx4QJE9C8eXMcPXo0jxPQrbfemgf8+frrrzVryksvvYTIyEhERkaqx/jd7O+//47ExEQsXLiw2HEyCxYswOjRozWPTZo0CUeOHMHDDz+MjRs34uTJk2jQoAFcLheaNWtWrPKLoujoaMTExODQoUN+L9uQIb0CAgIwYMCAYruuFKaTJ09i/PjxmDVrlnosv5jCypUrK8fCgmS32/1VvctOkZGR+bqiFaTq1atj4cKFOH36NM6cOYO2bdsiLS0Njz/+uAaU9KXDhw9jyZIlJa1yqWW327Fo0SLExsaicePGKFeuHKKionD48GFUqFABNpstz2tiYmIwZ84cnDt3DtWrV8fOnTsRFhamPqdRQ4cOLRXM5nQ6MXv27CKNy+KKUWwAMHXqVLz55puXLLZqyZIlCA8PR6NGjTTue5ezvF5vsSNPDV35CggIKPac3rx5s89YvUmTJqF3796oWLEiqlatqvksWVKFhITg5MmT+P7777F7924cOHDAZ9xiSRRQ+CH/bkl3BW5c0mWHkIXc5JRxLhL6IYxDGEBGKvG1MiJFggXyfGFhYcohQ8aLAcjjdCJdTvi8FMuWcVg8t4QcGJck3V+ke4rebYIb1hKKIoQkY18krCCvQW7gEyphDBHdeSR0A1xwNaIIArHeEhoh3MANdYfDoZx0AKhyuZFPAIuAQkBAANxut3K+kHATr4kb5HqIhJv0rB+hAAm6mEwmZGZmKtCGr+MYIZTEfmJbsx8JvDDqJzQ0VJUlgQYZHyXhMZ6fkm49cizzMbajjB3jWNGPd9nX+UkfkSfHmIwwk/AZ20I6l7AeEtCRLlD6eSfhNI4pgiZyLrMevhyJ+DoZkUb5gn9kvBrHsn5OSFBCDxPJuuiBDekgJEFAlsfzcQyZzWYN1Ef4RIJcJpNJAxPJdtP3FaO9COsRuuJx7AuCJ2w7Kjs7Gx6PR+MmBEAD4si2I1iZkZGhIEp9FB6hMbfbreBJrpcScPMVmSbXROkUJN8b9KCXjLRimVw35HXy3Po5wjEtYU6OeQl18r2BklF7Eu5k/7J+BA85XvVRXHJ+8fV6qI3X5msusP5yDsqxLseoIUOGDBkyZMiQIUOGfCs2NhYDBgxAtWrV8j1m37592LhxY5HKyy96o3fv3njggQfUHd0PPPAArrvuOs0xffr0wZdffolVq1bh0KFDSE1Nxc6dO/OUdeLECfz66695vhhds2YN/vjjD5w5c6bYkVqNGjXKE1nx559/qv/Hx8fn+d7rcpDJZILVatU8lp2djWrVqqFt27awWCzFjkNo0aKF3zdUgVw4on79+vB6vVi3bp3fyy9LTZkyBfv27QOQ6z7z3XffYe/evTh58iQaNmxYqrKvvfbaPDDGsWPHlNPMlSreIFkanTp1CpMmTcKePXtw9uxZzJ49G1lZWQgLC8NVV12loB8A+bp6tW/fHnFxcSpm4fDhw0hNTVWbpFLp6enqTuvMzEzcc889GD58OB555BHcdNNNiIqKQpMmTdClSxfN6xITE1G5cmVcddVV+V5L1apVNb8fOXJErW9erxeNGjXyuWlcmLZs2aL5Hu3qq69G586dUa9ePZhMJtxwww3o0aMH6tWrhxkzZhS7/KJo1KhRaNWqVZmUbciQXtddd12JIN/CFBQUVORNz1tuuUUTUfVfVGpqaoldX0JDQ1GjRg20atUKgwcPxrp164p04+bp06eRkZHhE1K/WNq2bRu2bduG2bNnY+HChdiyZQsOHz6MgwcP5vuaRo0aoUOHDtizZw82bNiQB/oxmUxo3759qepFZ/7U1NRSlVOYGjZsWCbzrzjatGlTiaCzSyWafBj6b6lDhw6oXLlysV5TkHPutGnTMGvWLKSlpZW2akoRERE4fvw4duzY4dc59Z93/JEQDDdg6XhA5wxuqOsdH+QGKTeJ+bjctOfmNoEGGdXDTVuPxwOr1YqIiAjlYiHhAzqvSFiH9ZaOHzJyjHXn8/KLBunUwnoziosuHQRy5DEOh0M50gQGBiIsLExzHrr3yBgmbg4DuV9AhYeHq3Yn/ON0OpXDRFBQkIrEokOGhGAIFRASIoxEWID9BkBBLqmpqQgNDYXNZkNWVpZyHmEbsp15XYwfohh3ww15t9utvthhOdxkN5vNqh1MptzIMcIDbEu2tcVigclkgsPhUHE9FotFRZ2xXSX0wSim0NBQBR4lJyfD5XLB6/UiKSkJwAX3Fl4rgSUJ1kg4S45btqeEnTwejwKT+HrpRCWjhtgurAf7wmQyqbZke0kgScaDyTkqHYhk3Jt0wGKbBwUFqZg3RkyxrhT7kOVKWIHXyXNJ1ylen94NTM4rXxAEAQv+8SLXHP4ry2MZbEMJ9sgvEzjn+Tg/wND1i3UODw9XbcYxwyhDji8JDUkoimUQLmH/cA7qnZEyMjJgNpvhdDpV+2ZmZqpxz7Em1wWuI1FRUTCZTApc83q9GpegtLQ0pKWlISYmJs8YzsrKUudkOfIPIBlTxfI5R+kuxLrxC1b2j+xf1kVGp/G406dPK0cfukyFhoaq/pHHsl/Yr5y/pIUltJmdna0clCIiImCz2WCxWBQkKoEmQlESsCTMRUcoOZYl2CfnrR6MIszI9wXpksdzc3zJ+US405AhQ4YMGTJkyJAhQ77VtGlTfPnllwV+ifjkk08WCdS47777fN6t/cILL6gvPu+9916YzWbMmDEjz92vdevWRUpKCkaMGIHU1FTUrFmzWF+Y8m7kmjVrYvDgwcVyznj++eexdu1aHDt2TD1mNpvV/1977TV8+eWXRS7vYomOy1JVq1bFpEmT8NRTT2HDhg3FBn8sFguWLVuGQYMGqRglIBeg2LhxY4k3L5555hkEBwdjyZIlePnll0tUxqXS6dOn4XA4sHbtWvTv31/9Dbtu3Tr06tULb775ZonL3rNnT54+7NmzJ4YOHaockq400bWbjjalkdfrxaZNmwDkriVVq1bFfffdl+e4/CJ36Mx+22234c0330TLli2xbNkytGrVymekytq1a/HII48gPDwc4eHhqFOnTp5j9HPq4MGDaNeuHd5++20kJCRgx44dOHbsGHbt2oWDBw9i9+7d2LJlCx544AEAufE4n332mXI/++KLLzBu3DgMHToUkZGR+Pvvv3H8+HFs3LgRZ86cKbB95s6di06dOqmybTYbnnrqKc0xW7duxZo1a/Dxxx8XWFZJ9dxzz+GNN97wa5lvvPEGvv76a9VGhgxRDocDp0+fVnsb/lJaWhqOHj1apGNfeeUVVKlSpcBjTp8+7YdaXd6SgHRJVb16dQQHBxcpCikuLg6///47UlJScPbsWezcuRPHjh3Dq6++Wup6FFUJCQn45JNPkJKSor5zrlixIvr06YOnnnrK53sGABw4cACvv/46li9fnuc5r9eLv/76SzlblkSHDh3C1q1bERISgp49eypHTX+rTZs2mDlzpoJpL4UOHDiAFStWoHLlyldE3N6/2fnLkG9FR0fj8ccfz/eGGF9yu904fvx4vs/TRXL48OGlrh9VoUIFVK9eHTVq1EDHjh0xbNgwvwBAl9+tMhdZ0jWHrjCZmZkKSqCTCkEMCfTITWwAGjccuUHsdDrhcrng8Xg0kA1jnbKzsxX0wbsxUlNT8zifMC5MukxQPLcETqTrB3AhDotAAF0keExWVpb6okluVPN36Twj3X0kjETggi42Ho9HXbsEFujcERAQgPT0dLWJTDgnJCQEdrsdKSkpcDqdqp3kpjQ3mMuXL48qVaqoiC2CGtJdSP4wakk6CnEjnY4ndOsBLjjDSBiMsADhDI/Ho64DyIVHHA4HUlJSVJwUY3OSkpKQkpKiXI0IpyQnJyMxMVG1ixyf7G+ei/CThCSkU1BgYCCio6Nhs9nyAF9yXMn+lBv9ErQiKMV+ls41wAW4heOK7a93A5J9Qqcm6UTEcU2XGHlOnoftwXqwPI5T/rGRmJiovrQJCMiNaiM8wygqvUMPISJacrtcLs2awLYiLCNdpPTl8HrlWsHxL8EL9jHXDM5jxl/xORmHRqcbtglFAEOO1cDAQJjNZgXn8ctBp9MJp9OJ6OhoDVxGwJAxU0DuHQBhYWGa+c91LiQkRM1XjkU55wmgyDESHBysGSvBwcGwWCyIiopCTk4OUlNT1frEfiI8yfNxnWPbuFwuBf1ER0fDYrHA4/Hg0KFDSEtLg9frhdVqVU5qnC/BwcFqrEnXLLPZjMjISAUOcq5xzoSGhsJqtWr6IzU1FUePHsXJkycV6Mc5yfoxWkyOCcJwbH/2P9uI85sgWmRkpIJ+ON4ltCrBUkJ20nWIZcn3HwmnsS0kTMTxxjWL6xbbRzoZ8bqDgoI0zmmGDBkyZMiQIUOGDBnyrWbNmiEgIABRUVE+n//rr7+K5c7iK+qlRYsWAHI3wbp06YKPPvoIAPJs2FmtVixduhSffvopUlJSsG3bthJFKB05ciTf6ylIb775Jlq0aAGz2Yy+ffti4MCBAHJdQH7//fdil3ex9Prrr2Pz5s15QKqsrCyYzWasX7++2HeA33TTTfj+++8xaNAgtGzZEmPGjMGXX36JnTt34tdffy00jsOXpk+fDiAXcrgSN0FeeOEF3HjjjZob9TZv3oxmzZrh1ltvLVXZ+gi5mJgYjBgxolRlXkrFxsYiODi42M5bRdGTTz6p+X3Lli1ISEjId3PlwIEDyMrKQqNGjQDk3k39/fffAwA6deqU5/gtW7YUe9Oje/fuePzxx1GtWjW0aNECQ4cOxSuvvIKvv/4aa9aswZYtWzBu3DgAubBer169NGtb3759AeT2+/Dhw/Hhhx9iwYIFag2ibrvtNs3v0dHRmD17tk8XcCB3PZ4zZw5++eWXPFFk/tJNN92ExYsX47XXXvNLeS+//DKGDRuGcePGlcgBydC/XwkJCTh37pxfonOSk5OxYsUKHDx4EGfOnClyXN2PP/5YKNh48uRJ5RT3b1V+a09x1LdvX4wfPx7XXHNNgcfl5OSgU6dOiIiIQLVq1XD99dfjgQceKJP4wsKUlJSkudH0zJkz+PTTT/PAlUeOHMGIESMwZcoUTJw4EXa7Pd/PT2vXri2xS15aWhqmT5+O3bt34+eff/YLkFWQ6tWrh++++w4tW7ZETExMmZ4rPy1duvSKcEZMTU0tEOYw9O/U008/jV69ehX5+EOHDmHWrFn44YcfCj3Wn+8rERER6Nu3LyZMmICaNWvmceMtqf7z4A83cUNCQpCZmQmHw6HcXuQGqnS+AC44PwAXImu4wclN6szMTLhcLqSlpcHhcGgihySUQ0eG0NBQBbvIjVeCDawbbYS5WcwN3KCgINhsNkRERKiNejowcCM2JCREbRrTbYgb6ykpKQgNDUVUVBSysrLU5jPjqghe6DeL2QYulwuBgYEK6nG5XLDb7bDb7eoxtlVmZibsdruCK7jpTBchum/Y7XYkJyfDbrdroqy4SR4cHIwKFSqgWrVqqFKligJd6LrDayf8kpCQoPpHuoWYTCbYbLY80UpsW8JgBI5iY2NhsVgQGBgIp9OJlJQUxMTEwGazqf612+1ITExERESEcvFxuVxISUmBw+FASEiIihgjqJCenq76X7aJBLbo0CFdeBj5Q3ApNDQU4eHhiIyMRHh4uBp7dAAizCThFRk5RMiEEI90+dFHK3FMyx8Z0yTdPwgfSHcZXpOEQiSwxjHDscx6AtCUTweo9PR0TWwSAS/pgsMvI+Q44LimY4oc66yD0+nUOO/IqCpeH8ExOtX4gnXohMP1goAEgTkeL4E3CSLyeT7GdpJtyrWNTlNyrnO880telsFxlZOTo+ZpYGAgbDYbHA6Hmh8EzzguGY/H8cFxRcCG102YTAJetHE9d+4c0tLS1JePdA5LTU1VH7rpkiPHxvnz59UfetHR0Qq2iY+P18BxZrNZXW9YWBgiIiKUq5iEvKSblnTn4ZiUTkd8ncfjwf79+xEYGIiIiAj1nkH3LL430EmNjkscU1arFSEhIQr4Yh+ZzWakp6ercRsWFgar1QqbzabcfvRwGddmug3pgTqO8eTkZI1LHMu2Wq3K8Ugf2chxQqCI7ymyfTgnCaDpY8MMGTJkyJAhQ4YMGTJ0QY0aNULPnj0BANu3b0dycrJ6LiMjA+PHj8dXX31VrDIHDhyYx6EhOzsbf/75J8aPH4+vv/66wM1cs9mMbt26FeucvlSczagffvgBr776Kk6dOoXt27dj0aJF+Oqrr9C/f38AuW4e0gnoYqigyCC9Fi9ejDvuuAMpKSmax7Ozs7FmzRrceuut6m/j4uj666/H3Llz8dNPP+G+++7DVVddha1btyIoKAgjR45EdHR0scrj9w+LFi0qdl0uV/36669IT09Hv379SlxGdHQ07rjjjjyPN2rUCP/73/9KU71Lprp16yIkJARRUVG48847S1QGQRm95LhLSkrCZ599hvbt2+PEiRNo27ZtnuM3b96MpKQkWK1WTJ06FQAwf/58APC5wdGgQYNi15WRNXrxO9wWLVqgevXq2LFjh8+57cs5JCQkBI888gjGjh2Lrl27olOnThp4Z/r06Rg1ahSGDBmCoUOHal77/fffY+HChRg0aBCGDBmCl156qdjXVFR9/vnn+P333zU33JZEzz//PIYMGYL//e9/+PTTT5GZmYm6dev6qZaG/k1yOBxwOBx+iTRKS0vD1q1b0axZMzz++OMal7v89NFHH6FGjRoFnj8hIQFr1qy56J8dLrbWrVtXqCtZYWrcuDGqV6+u+c7dl/TOeLwZtE+fPti4cSO+/vrrYn12Kgt98MEHmD17NnJycnD48GEsWrQIn376KUaOHInPP/8czz33HDZs2IAaNWpoXhcYGIjGjRsXGTzTKyIiAtdffz0A4OjRo9i8eXNpL6VQ9enTByNHjrxksV8bN27E4sWLizRnL6WOHDnyn3D/MqRV+/btixQRnZmZiV9++QVdunTBiBEj8qxzer3++uvo3bu3v6qJgIAA3HDDDcjKysK5c+f8Vu5/PuqLX0RIdxoJBsiNdHnnAjcy9XFM3OTkH9MEajIyMlSElYRoWBadPrh5Hhsbq6hVnptOCxywpKpZL25yWywWtemamZmpbFPpOsONa8bLcPM6MDAQFotFEy9mNptV23BjmhvBAQEBSEtLQ2xsrIq3oZsK24TuNXQHIUwkY9TYNoQ0goOD1XkCAgKQlJSkIAKLxaKZsNxcj42NVW4VdCshCEHQghvVst/ZxgRFQkNDlcOGBGIIUhCMCAsLQ0hIiHI4oqsMY9rYtoSGCGuxPcqXL6/6RcIjWVlZcDgcKt5KOuNI8Iptxh/ptsFzRUZGIjIyUhPZFRwcjJycHKSnp+eBM9xuN+x2u4ork7FVhH9YRwkE8Ed/Zw/HAse4jLPSx8BJFx9eK/tFgkYEeGSEVnZ2toqKYnRcWFiYcvdhnT0ej3Ip4eMyLgqAgv5k7BP7lzAYgR6OLZfLpUAQ1pHQSGBgoPojhG470umIbcG1QH4xSmiIgA+fk45dMqIuOzsb4eHhcLvdag2ggxjrI6MAo6Ki4PV64XA41PpC2IdxaRaLRQE4Mv6K40HGZ+kjwdhPvGZegwSH+HxycrJq9+DgYLVuZWdnw+FwaNy42GZyLGRmZiIuLg4Wi0WBQG63W0FuPI7zBYCaq16vF06nU8FaBFk4B6WDGgEqrvF0fyJ4JB21OH4BqPlEMJLrJduUazkhJb5XWCwW9UcX4yb5HC3v9a5rPCfbSkYMhoSEICMjQ0Wmsc/Kly+vifvTv+fJ8S4hWAJzejCNTkUEmBiLaMiQIUOGDBkyZMiQIa0GDx6MunXr4ty5c2jfvj369u2LWbNmAQBWrVqFCRMmFLvMrl27okKFCur3L774Ah07dsR9992HJUuWYMeOHflukkt17twZq1atKvb5qbFjx+aJu9HLbrfjk08+wYsvvqicSb777jt0794dAPDbb7/B4XAgISGhxPUoqc6fP4///e9/mD9/Pg4fPlzo8adOncIzzzyDTp06oVGjRoiLi1OxGcHBwXj99ddRo0YNPPjgg+o1GRkZeO+993Dw4EF88MEHsNlsOHDgAMqVK6fgCpPJpL7fOX78OAYPHgwA6u+74uj666//18X2JCUl4Z9//kGnTp0wdepUPPbYY4V+aa9XcnIyfvrpJ3Tu3DnPc6NGjUKdOnWwY8cOfPDBB36qddnr559/xo4dO3D33Xdj48aNxX59586dcdttt2HdunV57ug/fPgwUlJSYDab0bFjR9jtdoSEhCAuLg59+vTxeXxCQgIqVKiABx98EOPGjcORI0cAADNmzMhz7k8//RS1atUqdp0LUlpaGsLDw/N1Ejp27Bjq1auX5/E6dergrbfegtvtRkpKCk6cOIFPPvkEaWlpeWAfasGCBSr28GJo2rRpWLFiRanKqFGjBh5//HFER0fj/fffR4MGDeDxePDtt9/6qZaG/k1iaoQedi2q0tPTsWfPHnz88cdYvnw56tSpA7vdXqT3p3vuuQeDBw8uFHyNjIzEnXfeiQ4dOqBr164YM2ZMiep6uevQoUN47bXX8NFHH5X5jZdmsxnLli1D+/btsWPHDlSpUkWt1TfccAOqVKmC1q1bX3K4eNeuXZg8eTLWr1+P7777TvPc8uXLUa5cOdSqVUsTK9ehQwd07NixRG6KFEHJsLAwOBwOpKWlqajLstKQIUMQHByM1atX48SJE9i0aVOJ52VJtHDhQtx+++3o1q2bz+jOS620tDTEx8cXOULQ0L9DdevWVS6ThSk4OBgul0t9Li1IL7zwQh7XS38pISEBe/fuxf79+/1S3n8e/JGb6tzoBbROIr5is3gMN7H1dzIRutHHtHBzW56f4AYjqAgxsNzg4GAFjMgNf0Z3eTweDdzBf7mxzU1aOpHw2rhRS1cRbmg7nU5VHtuHAIfH41HwjXSa4KayjD6TThF0VSJQAVyIPJIOSIRvJFiSkZEBp9OpIod83cHA83JzX0JV+kgp1k3/r4RMWKb+GAIHMmaK44Z1zs7ORmpqKhISElRsDjfsQ0NDFSAkITECRXTkAS5Q1Hrggm4hjD5iH3MsEkKSsW98va8IKm7eS4hDjmEJx0igiu1LSMLX73ytlB4iYP/I+Dj5uHSW4pzRjxEZ+wRAub7Ia5dznU4nsl9Ztozqk/FyvD49nCPdXeR4Yb3ZvoQ+gNz4LI4JX2uIbE/+7guQkufh8fpYPo/Ho9YU+brAwECNYxTbm3Vk/9MlSI5D2R6sA+EnABpAUT+v9GOB7UM4Rj7Ha2F9CL/I8W61WhVEQwDH4XCoD5uEnQgvcqwQ5KHDDqFIeY10ReOYlpBNYmKiBi4kVJeZmamxy5dQHK+VcBZfTyc39h/7h+tBfmOR/8poPYJafJ6OTHI8SNCV6znbRcJ80kVKvg9yvMtINs4/1l/vPmTIkCFDhgwZMmTIkKG8uu6663D06FFs3LgR6enpqFWrFhwOB7Kyskp8t3BoaKj6uw4AevXqhS1btqhYmz///BMbN24sFPwpDfQDoMA7x3fv3o1y5crB4/FgxYoVmjgi3pWbmpqKzZs3Y/369ahatWqp6lISZWRkoHPnzti7d2+RwB8AmDt3Lvbt24dmzZqhRo0aWLZsGe6++258+eWX+OCDD/Dpp59qjne73VizZg2sViuys7ORlpaGzz//HK1atcoTKcTjZf3y06BBg/DPP/9g69atmsd/+eUXNG/evEjXciXp5MmTuOGGG9CvXz/MmzcPa9asKXYZ+Y33ihUr4o477kDt2rXx/fffl9gN4FLo888/x5AhQ0oUXTJ8+HBERESgdu3ayM7OzgMPLVy4EDVq1MCuXbsA5DoPWK1Wn249vPmIGjVqFE6cOAEA+Pvvv/McX9Kolfx07tw5LF++HLVq1ULNmjVRqVKlPADQL7/8AgA+4R8gd8O7YsWKqFixImrXro3ExMR8z+cLZipLffTRR6WOXKpVqxZiYmLw22+/YenSpWjXrh2+/PJLvzi6GPp3ymQyoXLlysV+3YEDB7BhwwacOXMG586dw/nz53H+/Pkiv75y5cpIT08vFPwJDQ1FjRo1UKNGDaxevbrY9byS9Pvvv+PgwYNl7tAVGBiIX375Rbk0DRgwQANpVq5cGTfddBM2bdqEU6dOlWldCtJHH32EmjVr+txAX7VqFWrVqpVn/FSrVg21atUq1XfY4eHhMJlM6Nu3L2699dYyh36om2++GbVq1cKpU6fQsmVLfPPNNxct4s7hcGD//v1o3bq15qaDy0Uej0eTFmHov6Grr766UAcziokiRdHNN99cZvtcTqcTmZmZqF69epH/7ixI/3nwJyAgQDnE0DFGv9kp3WIkLMONaG6mSgCAm9N0VeAmqNPphM1mU2ACN6DT09MREBCAmJgYtdEbEBCAsLAw9aUNIZmQkBAFP0gwA4CKe2Lskdls1kAYrAvdXggUSZiIm9JhYWFIS0tDVlaWJv4rLCwMAQEBaiOdsAUdOuTGL88bEhKiYpzoKsL6mc1mBAQEqC+m6LDCTXKCCYys8gVI0ClJtqvsRwlNSSCDIqAlnW1km7H9w8PDlTOTBCgsFguCg4ORnZ2NEydOIC0tTZVnNpsRFhamrsVisai6hoaGatqDbet0OpGcnKzGAGN4pEMVYSs6f/AagoODER4ejqioKI3TCnDB/YMxUoRJ+Ec14QXppkOAgA4qPDfhDp6D/S7hKI4DCcXIyCReAyEMjl0AmnOwXLoTEZxivQmEeDwe5S7FchhtRACCv0sYRbYL4QsJ5RD6YB/L9QGAgu8kNMHzEVYj3OTxeJCWlobg4GBYLBblqkPITQJYXHvYHnJtke3IczqdTuXEwznFa6ebji8oKygoSDlNZWZmwmazaeLNCO9JCIygiX5sETLk/7nmyHEox2VgYCCioqIQEhKi3IfYR5w/vMb09HRERkbCZrMhPT1drcuBgYFISUnB+fPnVZwhxwzHZWpqKsqVK6fWRDmuLBaLGoeyjuw71p9wVFJSEo4fP46oqCiYzWZYrVbVr+wbuhbRaUq/tnG95Jdf8nWcx3T3oWMOI8j4I9c3zhX2Pd28OO9ycnKQlpaG1NRU5OTkqEgv9idBTJ47ICAALpdLM94kqMkxwPEp4UFeL+eqBAoNGTJkyJAhQ4YMGTJ0QTfeeCPGjh2LyZMnA8h1yRk9ejRsNhs2bNhQojKHDx+Odu3a4f7770eTJk2wcuVKDBs2THMT1RNPPIEePXogLi4OkZGR2LhxI6ZOnYqqVauicePGykG0NBo2bJjPx5OTk9G0aVNUr17dJ3w0e/ZsdO3aFUePHsX333+PdevWlbouJVVcXFyxoaP4+Hh07NgRixYtwqJFi1CzZk18+eWXcDqdeTaBIiIiMG/ePMTExAAANmzYgHPnzuHaa6/1WbYvJ5vAwEA8//zzePnllwEAjzzyCCZNmoTnnnsuD/jDcfZv02uvvYaFCxfiww8/RL9+/VCrVi18/vnnxSpj27Zt+Pvvv32CKxEREWjTpg0eeughjB071l/VLnOtW7cOI0aMKPaGV7Vq1VQ82OzZszFu3Lg84I90rgJy4+4mTpyI0aNH46677sLGjRtx/PhxALnf2e3btw9t2rQBADVWAeB///sfevTogUGDBqnHTp48ifr166vfp0yZgqZNm6J169bFug5q1qxZqt8OHz6MKVOmoE+fPppjRo0aBbPZXCS3qJiYGDVnfennn38uUT1LKrvdXuoy+vXrhwULFuCxxx7DsGHDEBUVhUmTJvmhdob+jerbty+GDBmCihUrFvu1CQkJWLZsGRYuXFis1z399NOoV68eOnbsWOz35YJAvctZNWrUKJJTSUREBH777beLEs136tQprFmzBl6vF3Xq1FHxVtQDDzwAk8mExx57TH0nf7GVmZmZr2vG3r178fzzz+d5X2zUqJHP6MniKC4uDu+99x769evnMz6yrFShQgUF3dSqVQsHDx68aOAPkPvZtVGjRn6JCfa3nE4nUlNTiwUXGrryNWHChDwmLvnJ5XIVKS7xjjvuQIcOHUpbtXwVFBSEOnXqoHHjxn6Jz/vPgz8mkwkOh0MT5SJdHQiCSBjE7XYr0IQuCiSy+Ee8hE8I0EgXBQCw2WwK4qFFanh4uAIGQkND1Wa73CSXIJGMTJIgQ0BAgIrtItAgoSUg9w8DXgcAtbEdERGh4nb4JhgcHKwgILYZwRK2Gd18GPPFdjSZTOpOJLYlN5qDg4NhtVphtVrhcrlUX/Ba6LgRHh6u4A7pjiLBgsDAQAURSbcJ1o2wAesBQLNpLcESCfVwY52OJDyGm+9A7p0f3MSXbktWq1VtgrPdZeySBJt4Lo4POqzY7Xbl8EPHIKvVqtyZOI5lOQSR2O8c33R4kbFlhDxk+0h3GbYz66p3XtEDMnxcgh6yv6Q7CecX3Z54LWxnAg2yz9jW7B/pxEIAj45AHNeEMDj2srKyNI5ajDrjtUhghG3IehC4kG5CADSQhYRH6IQjI8qAXHgrLCxMzWHOG16zLxcf1k26uhAOIczFNcpisahrIExIKAiAGgsEPmhrRycaub4QWGL8n3Qek2AS25dgHPtVundJFyP2H+9uZBQb+59lELokiCQdkzIzM1X7SFc19gfbwOFwICQkBFarVc3BkJAQxMTEKHBLrrXsJ0Jgci4lJiaq9YaAIt2E2B5Op1PNS/newvMkJSVp4DRGZHEupaSkICYmBtHR0WpdI0TIc7F8l8ulIgPpBMY+lA5QqampcDqdGhiNMJ/NZlNrld1u1wA/PBfnKvuVc4frG8eWjNmTc8SQIUOGDBkyZMiQIUMX1LdvX+zfv1/BGM2bN8fRo0cxe/bsYkc4SdntdtSvXx9vv/02XC5Xvnc83nDDDXj00Ufxwgsv4MSJE6hUqRJeeeWVEp9XrxkzZmDixIl5Hp8zZw6ysrJw6NAhn+4pmzZtwieffAKz2XxJoR8AaNy4cZHvGKW6d++OCRMmYNiwYXjmmWewZcsWfPHFF9ixYwduvPHGPNEPq1evhtPpxF133YU2bdooOMKXpLssNWTIEPTp0weHDh3Cd999h2HDhiEgIADt2rVDWFgYjh8/XmwI5krTrl27sGvXLgwdOhR9+vTBwIEDceDAgWKPn2eeeQbffPON+j5OKjAwEL169bqiwJ+KFSvCYrEUaUNDqlmzZprfi+rc8PTTT6Nly5aYN28eVq9ejVtuuQVALkhUs2ZNn6+pV68ePB4PYmJikJSUBCB3E7FLly4AgH379mHkyJGYOXOmT/Dn5MmTBW6wZmZmIioqSv1+/PhxeL1eDB8+HF9++aVm89ftduOff/7BNddck295LpcLBw8eRPny5VGhQgWfG0sdOnTA77//fkXcBDVs2DAcPXoUQ4YMQZ06dbB582ZUqlQJzzzzzKWumqHLWE2aNMmzThRF6enpWLlyJVauXFns13bo0AGdO3dW310WVefOncOePXuKfb6LrT59+uD+++/Htm3bsGLFCvTo0QPnz5/HRx99VOhr165di23btqFRo0a44YYbyrSe8+fPh91ux8CBAzF//nzccsstKh4rPj4e8fHxGDJkCA4ePIj333+/TOtSUumhn5CQEPTq1UvzXuFLKSkpOH36tAZMlQoLC8Pjjz/up1qWTE2aNMHMmTNx2223Yfr06aV27yyKfvnlF1x99dXo0KGDz8+pl1JMkjEcf/5bqlGjRpGPzcjIwN69ews8xmq1YtSoUaWsVcGqVq0aQkNDcfz4cfz666+lLq9o2NO/XC6XCy6XS7kVSKCBcAfBEbfbjYyMDOWkkZqaiqSkJOXawk1obpR7vV7NJirBF94dQECDed10YSDcQPiGX/rQsYNuDnT8oXh+AAogoHsD68z664EFQipyQ1neEcaImOTkZLjdbuUEwk19fUwSoQqeW9aL10lHo6CgIERFRSE2NlYTURMcHKwBJ7iRzR+94wUdcBinpf+d7c/XhYaGIisrS4E1Mj7IarUqKELCLG63G5mZmXC5XAoyIThC+IBRW8AFIIL1AaCJtWIMGn84jjgO3G43kpKSkJCQgLNnzyI5OVnBEV6vF+XKlVP9KJ1VeC10bnG5XHA6nWqsO51OpKWlISMjQ4EmEtSRoJmEp/g8YQJCQvIPWn28GF+vj3bjOTk36NTD3ynZbxxDdOCRwFRQUBBsNpvGlSQzM1MD4EgnFMIUbD+n06nagOOCwBPdhfSxXBJq0j9PRyPOAwk9hYSEICwsTPOcxWJBZGQkwsPDFWDDWCauTRI8knOFY0/2KaENukHJcZKRkaHalBAMQRpKtqvH40FycrJqF/YlYRcJrXG+SWcpuZYSYpLRUXI88HeOMbpjEWZkLCLXB/5I5y2+lnAK+5cAXUZGBlwul5qvcnyaTCY1xzkGOCbsdruC6yTYZDKZ1Lxn3QGtExqvUTpBsX8JnkVFRSEsLEytr+Hh4QgJCUF4eLjmh23lcrmQnp6OpKQk9V7Ec/N9ye12w263Iz09XTOPJHjIvuP7luxfrsXsa8JN7Beuk1xnJOQoz2fIkCFDhgwZMmTIkCGgd+/eaNiwIbp27YqvvvpKPd63b19s3ry5VNAP9c0332DgwIF44YUX8j0mJSUFr776Ko4fP44777wT1atXL/V5pTIyMpCUlKRcTpOTkzFs2LAiuWFMnDgREyZM8Gt9SqLMzEwFIxRVM2bMwMqVK/Hhhx+iZs2auO2229CyZUu8/PLL+OCDD/Dll18CyI07mThxIkaNGoX4+Hj1HdKpU6fw4osvKrcUqbp162LdunWqDACYOXMmmjZtivbt22PTpk2oX78+fvjhB1SpUgU33HADlixZoo5dunRpHqeTf5O6d++ONm3aIDU1FS+++CLeeuutYr1++fLlPl/Dv5fr1aungJQrQXFxcbDZbHlirQrTvn37MG7cOPV7cZx2rrnmGgQGBqJTp0547LHHcMcdd+DTTz9Fp06d8n1N1apVNRDB7bffDiDXneeRRx4BAHz22WcYOXIk3nrrLc2cnD9/Ps6dO5dv2c899xyGDx+ufvd6vWjatCmeeOIJn9DOxIkTkZKSkm957777Lpo0aYK2bdvinnvuwaZNm/Ics3z5ckydOjXfMi4H3XvvvRg4cCCmTZuGpUuX4sEHH8SWLVuwdetW3HXXXZfthr2hy0Nbt27N4wJWmPbu3YunnnoKr776arFdqmJjY5GQkFAid6vU1FRs2bJF89g999yDxx57DI0bN1Yu65dKTZo0wTfffIOpU6eid+/e6NKlCyZOnIhHH30U/fr1w9VXX12kcux2O7p164Y33nijTOsbGBiIv//+G8ePH8e6deuwePFiBYeeOnUKU6ZMgc1mw+DBg3HvvfeWaV38pYyMDKSnpxd4zOHDh/HSSy/h9ddfL/Z76sVWaGgoevbsiY4dO16U89ntdmzcuLHYkPHFkMfjKbRvDf37VJy/3Y4fP46dO3cWeMzYsWPzuJv5W6GhoahcubLiSkorw/FHAAEEBIALTjLcPCdAAEBtLHPTlhnbjGGRcAiPoWMDYQ5upmZlZcFisSgHFzpI0L0hMzNT/Z8OCtLlg2AHN28pbqzLjXnCN4yCoduHBCgIDDCuhZu6x44dQ3p6OnJycpCeno7Q0FCkpaWpjV8JN0kAhG0nHU0IQjBah5ARkAsMxMbGqg16XjNBAwlVMBaL5wWQx1VDgiYsR0ZZEbLhZjnhpMjISGRnZ6tNfhmhQ6CBUWVS3CBnPXNyclQZhA/Yn4GBgZpMQBnjxI10tk14eLi6BsZEMRIsKSkpT3zT+fPn4fF4VDwa3VrYdqmpqTCZTIiLi8vj9CMdpAhGSQBAP958ud/o55iEO+T1yh9erz5GSrpd8foIubAODodDjUUZD+b1euFwOBAeHo7MzEzVNxSBDxnbx9fJx6Triayz/ho5BrmeMCKLkUlURESEmpey7+T1cx5z3eB4la5dfJ5luFwuuN1uNa5YZnJyMmJiYjTAE91b6ChEOIfRVMzxlsAQoRCCdwSF9HcxybGbmZmpialjvSXgI2PnWD/WheshHdFSU1ORmpqKiIgIBc9w7QwICIDdbld9rRfHA6Edl8sFs9msxgW/YGe7SOgnODhYxYRxPmVkZKjIRdmuABTUSKctugIRUjKZTAo6pRuTXE/Yruxjs9mMiIgIBf3wfcblciE7OxvJycnIzMxEZGSkBiriOKZjm9lsVqAjXcE4v9LT05Urk4RV5XrKOSjnsOxPgkyEfjhWDBkyZMiQIUOGDBkylKvY2FjMnDkTSUlJeP3119XjN910E7755hu/nOPEiRM4ceJEkY5966238OKLLxZ6t2Nx1bNnT+zYsQPPPvssNm7ciD179mDGjBl+PcflqscffxyzZs1C8+bNMWbMGPz4448YN24cnnvuOYSEhOD8+fN47rnnsHDhQnz77be45ZZb8NBDD6Fjx464/fbbYTKZ8O677+L5559H+fLlkZ2djSeffBLVq1dHs2bNkJSUhMqVK2ucWJKSkrBr1y6cO3cOt956K4Dc6Iphw4ahZcuWOHLkCHr16oWQkBAsXrz4ErVM2Yt3ytavXx9PPPEEDh06VCwIQ79B5HK58MYbbyAkJAS1atVSzttXgsqVK4ewsDD1/U5RtW/fPg0s88gjj2DkyJFFeu306dPRvHlzdOvWDR988IHP72f279+PypUrqw33l156SbkSvP766ypu5eWXX1Z3PW/cuFGBBldddRWGDBkCINexZubMmXjyySd91qdVq1aYOHEixowZAwDo1KkT4uLi0Lp1a9x00024+eabMXr0aHX8woULsX37djRt2hRVqlTB9ddfj6uuugo1a9ZEZGQkqlatipycHBw+fBiHDx9G27Zt8zhs2Gw23HXXXfnGHV5KDRo0CPv378esWbPUWA8LC8O3336L/v3746677tLcBGzIkC8xvYLfWRZFoaGhag+tOLJYLGpfpKDN0AMHDmDZsmUYMGAArrrqKvV4nTp1sHbtWhw7dgw7d+7EiRMn0KdPH9SvXx+jRo3CjBkzcO7cOZw8ebJETkSlVY0aNdCrVy/1Xb50/Wvfvj2efvrpPNGK+SklJQXPPfcc4uLi8MADD/itjl6vF2+++SaysrJQu3ZtzJgxQ7ko/fjjj3jooYcAXNh3A3Kd44YMGYKVK1delkCIXqNHj8aTTz6JO+64I89zKSkpGDJkCNavX48HH3wQ8fHxqFSp0iWoZdFltVrxv//9Dz179sTp06exbds2PPfcc2V2Po/Hg1q1apVZ+SWV2+02wJ//oA4dOlTk8bhhwwZs27bN53P3338/Dh06hJCQECQkJMBms/mzmnkUGBgIh8PhF/DHcPzBBXcSueksNzm5gcoNTG7ISqcbbpRys5uADTdI5eapdD9gdAt/ZGwNXSAIvUiQJTQ0VG0sy013/p/lSAcLGWNDMIHXK+OECBOFhYUp2MbpdCoHCoINcmOf1+vxeNTGt9w8lmCIPkKG52M5hKOks4mMrJGQiGx3Wa78P8/LOrEOPC831wkQEDpgn7FtJOzAdpWuKtL5hdAMnVtYV+kqxH7Qu2gQDmC92d8Wi0XBTtKtSeZP89plrJW8PgIWOTk5Kv5Jwi4yYkv/I51Q2BZ6hycp2eayn+Tzsh30r2GbyDEr+5r/J5zAtuRxrB8BFwkq5VcHjhfpZsRzSQcighestx5ukjFLEpJg/9IRR99esi2k65ge6pN1I7An56aMWeOaJechzycdzuTckdcqxTnidDrzxPmxPpyv+nHlC5TSA2OyLnIsEMpk20lIi8dwzeDc1Tss6QEvjhH9Oij/YNU7aOnnAtuddSVAxPpIWE5eF0FMCV1yPZH9Z7fbFZRI5yWOAQmXEsgELkRuyXrT6YrvSQQeJdDHduYxso/kHJbziOuAfJ/Tu8XpneMMGTJkyJAhQ4YMGfqvKy4uDjExMahTpw6OHj0KIPfmo7i4OL980VdcVaxYETabDUeOHPFruTabDW63G9u2bcOJEyf+U38XHDlyBKtXr8ZNN92EqKgoOJ1OrFmzBgkJCQBy/07fsGEDgNyYqqysLPzxxx/qBpe2bdti586dWLt2Lf7++28kJSUhOTkZkyZNwmeffYann34ajz76qAJ8AKjnPvnkE/XYrl27UKNGDfTo0QN33303APwn+mHOnDmYNm0azpw5U+w7dPXxACdPnsRff/2FyZMnY8qUKVi/fr0fa1q2YtR5SRQTE1Oi182ePRufffaZ+t3X+b/++muNq84ff/wBIBfS6dmzp3o8MDAQDRs2zPN66d4RHR2NrVu35jnmxIkTcDqd6NGjh2aeAMDZs2exePFiNGjQAJUrV9Y8l5KSgj///BPTpk3DpEmTMHXqVMyYMUOt1aGhoZqNn+TkZF/NUOyIwIulBx54ACNGjAAAzXUEBwfj22+//U+sD4ZKL35/WNTxcv78edjt9hLF7YwaNQr9+/dHjRo1fN5wvGXLFhw/fhwOhwOTJ0/G4cOH8xxz7bXXonPnzrj77rsxdOhQ1KlTB3FxcahTpw5uv/12NG3a9JLNWd40nJ+uv/76AiNAfWnfvn2lrZZGhw8fxo4dOzB58mQsWLAAq1evVs81aNBArSURERHo0KGDeq527dpo1aqVX+tSVmrevDni4uJ8um5+//336r3f4XBcESAT1bhxY9x8881o0qSJ3509qfDwcNx4441lUnZpxf05Q/8tHThwIM9jqampef7OPnHiBA4ePJhvOQ8++CCuueYa9OjRo8zmj15M6Smt/vPgjwQMuLmpjzYh3MMPNQRyJPDCcgAoOya3263Z2GWUDkGO0NBQREZGIioqClFRUYiIiIDX64Xb7ca5c+fgcDiUuw7BDxkJZTabER0drYmzkq4pjMGRAAyQ6xhD6MDj8WheA0C9wTF6KScnBzabTX0IYXl0kUhJSVGb/tzslm4RemcTtpV0/uGGuMfjgcfjUZALHwMuLNTSDQXQglo8H9tLgi/c9Kd9n91uR1pamiaahhv2vE6CVYzIYlyPzWbTRJGxTXJychAWFobw8HBFpPNYtjGvgRvwLEO/yc5rjo6OVuOS0UXyehjhQxcSQlrMjCfwQ3iN41VCJdyc5xhg++kBHDn2CX7wOvJz+5Hzi2Lf6ePJCBwQfmJ/cjzKucky+VhKSoo6X3Z2toq3slqtmnGjB7Skiw7jiiQ4oZ+7+nIkNCPjoghaMD7M6/WqiDvCZVwzZNtJQE+uK9IBiX3P351Op4rfCwsLUx+4eRzrT+cZPi7nImEU1kfCaIQ+oqOj4fF4kJqaqsaR7AMeR1iF5XB9YJ15/Wwjwo1cl6S7F+c95wqdfTweD5xOp3KtITgTFham+jwtLU0DHWVmZsLpdKq122q1KigGgCYaUV47243Xy/nB9Z5jgnGJvEYCkhaLRTmIEeZhu8XFxam1kEAO52JKSgpOnTqFrKwsuN1u9eNyuZCSkoKkpCSkp6fD5XIpFyMg948Qjmc6AfF6nE4nAgMDVaSYjCbjdRJyklCiXMvZRwSQOFZDQ0MRERGBwMBAuN1uOBwOTUyeIUOGDBkyZMiQIUOGcuOCgAsbxuXKlcMDDzyAqlWrFtmlx59q1qwZzGaz311g+Ld9dHQ0nn766XwdOf6tysnJQdOmTTFx4kQAwOrVq/Hpp58CyIW/evbsia5du2LBggVYunQpJk+ejI8++giTJk1CmzZtcPz4cfTr1w+NGzdGu3btMGfOHBw9ehRfffUVGjRogCeeeEKNJQCIj4/HmjVr8PXXX2vq8eKLL8JsNqNixYpwu9348ccfL14jXCKtWbMGM2fOxNKlS3HnnXcW67XLli3T/L5ixQosX74cV111Fe677z4/1rLsVbly5RL/PT5//nz1//zgFl86cOAAFi9e7HPz/dixY5g+fTo++OADzePffPMNNm7ciPDwcBw7dgyJiYkAgKlTp+K3337LU87kyZOxbt06AMDvv/+OefPm5Tnm7rvvxq233oqwsDBUrFjRZ10///xz/PHHH+jTpw8GDRqU5/nExEQsXrwYkydPxk8//QSn04nbbrsN27dvx9q1azF06FDs378fR44c8RlBNG7cOAwZMgT169f3eX5/yWw2Y+vWrUUa62azGR06dNA4V7377rtwOByYM2dOWVbT0L9I3Fcpyvoya9Ys3HXXXWjSpAn+/PPPYp9r2LBhePPNN9GxY0dUrVo1z/N79uxBmzZt8NJLL2HkyJFo3Lixz3KCg4NRpUoVNG3aFDVq1FD7DS1btkT//v3RvHnzYtetNBo/fjx+/vlntGvXrsDjGjdujPfff1+5nBVFhw8fVqBxaTV9+nSMGDECCxYsQEJCApYuXaqeu/vuu/HUU0+p3xs0aKCpZ/Xq1TFgwAB07drVL3UpS0VGRqJ169bYtGmTAlPdbjfGjx+PIUOGIDo6Gm+88QZmzJihAVSvBHGPe/Dgwejdu7ffy69evTratm3r93L9IY/HUyLg0NCVrcmTJ+O+++7TuNlu3LgR33//vYoBW7FiBT788MN8o0379++PZs2a4c0338R1112nMWYoK3Ev8Pz586Uuy4j6+v9NXX1UEh1xCGrI6B7gAoTCyCFCGYxtSUtLU89xU5gbqCEhIRr3Fm58Z2dnIzU1FVlZWUhPT4fZbIbVaoXFYlGwAzfmASj3GTq+8EsVbl4zxsvhcCgISRLEVqsVZ8+eRWZmJiIiIjSOFsHBwUhOTlaDjHAKr4ngADeb+RxBKQkO0AFFuqlwc1nGDUknGRkzJaOtCFJJmIfuHfq7SAgeEL7gpjr7T/YXwQhev4z8IRjAa/R6vQqyIjTEqDa+kRKkoMNPaGio2gCXkVcsLygoSJ2Tj7Es1sPtdmvKlc4e3GDnGJCxZWxbXofD4YDNZtNEx2VmZiqgiWVQnAPS1UPvKqN39+Eckg4qencg+bjeuYl9Ice5dHiRpC77j4CUHEscj/r4L85HPkarUbalzWZTi7l0ppFQFsENCUboRYiI0U7sBwJyPK+EniQIxfNLpxjpqgPkwixutxtJSUkIDAxEbGwsLBaLiu2zWCyqjSU0xLIZO+ZyuRQ0xbbjXCHwRlcyOpHJeckoPI4l2Vb6vtRHrBHW47pAIJHtzDnJ/5vNZrWmmc1mNbc59whQsr05B81mM9LT01Udg4OD4XA4VJ/4mneEPaOiotQY5Pyw2WwKqiHYw/lHIEuurRyPTqdTlUeIiv3EuRMYGIiYmBgVeRYQEACr1arGQkpKioLKKPYD11b+SDCHQCJj8Th++ce67Hc90CYdoeSYZbQX5z7/JYxYEitfQ4YMGTJkyJAhQ4b+bYqOjkZUVBRatGihfh89ejRSUlLw3nvvwW63+9ws16tDhw5o3LgxJk2aVOo62Ww2dO3aFSEhIahbt67POyRLqvXr1+OHH37A2bNnsWDBAr+VS73//vvIycnBrl27cOzYMRUJdLmoXLlyAIChQ4eqyJ/XXnsNr7zyCgICAvDZZ5/B7Xbj1KlTMJlMWLBgAX7++We0bNkSwcHBGDZsGJ577jlkZ2dj//79mrK3bt2KDRs24LHHHkPHjh2xd+9ejB071mc9EhIS8Pvvv6NixYr48ssv/TJurhT9+OOPGDhwIGrWrFlkR6uVK1fiwIEDqFu3LjIyMhRU0rBhQ7Rq1UpFWl8Juuqqq0rsTJCamoovv/wSd999tyZSrqgaOnSoZk7OmTMHGzZswKeffoqZM2dqnHZq1KiBcuXKISAgAI8++ijmzJmDdu3aISgoCF988YXP8j/66CO0b98eNWvWBADVZ1S9evUwbdo0AEBUVBTi4uJw9uxZTRlnzpzBxIkT8eqrrxZ6jWPHjsWPP/6IX375BbVr10bt2rWxatUqvPLKK1i+fDnq1KmDr776SuMY9cYbbwDI3YB69NFHi9BqJZPb7cavv/6Kb775Btdeey0OHz6c7/cw7dq1Q2RkJK6//nrUqFEDjRo1wrPPPltmdTP079T58+eRnJyM9PT0AqNPHA4HDhw4oGJUjh07VqzzVKtWDdHR0QUe8+uvv+LUqVMICgrC3LlzSxTFUq5cOdxzzz3Yv38/Zs6cWezXF1dDhgzBmDFjEBERgc6dOxd6fMuWLdGkSROEhoaqda0g/fnnn9i3bx8qVKhQ6rrGxcX5BBsBaJzbAGj2vKiuXbvC7XZj7dq1mtSKy02rV6/GsWPHEBsbi99++w29e/fGggULVCRvcnIyHA6HMgy40tSlSxd06dIFBw8exMGDB1VUmz+0e/du/PTTT2jbti1iY2MveRRafHw8YmJiYDablUGCof+W9u7di6ysLDidTiQlJSEiIgJLlizB7t27sX37dlSvXl0TaSs1ZswYhIaG4sEHH1SGLBdLp06dyvM3X0n1nwd/GOMlYQO9Mwk3uSW8IuNLGLslYSCn06lxv+GPhA6YbUqnjoyMDNjtdvUHJDfDZfnABZtU1oHOEwRSrFYrQkJClEsEARHCQDExMcqlh24tPKfH40FERIRykCG4Eh0drTZ26UZhNps1m8eEjAiy0AFEOhBJRxvZzkCuLRw31gkH0cmIG/smkwlut1tBQawP+4UwAR9jBE5KSorqC8JA0vFEOj4RcpGwhXREIuwg3Um40c9rpJsIHYwIKzkcDlitVgXTSFJQnoPuJRxPjN8hoMA3rczMTOV8wg17buJHREQoUIh9C0DVXX4QI3TB+utjr9hW0oGGQJfsY45DjlX5nASR2LaEZ2SUEIEMGUPla9zIuCfCIHTu4Ru6dIOR55RwBceDBER43XJs89oILemdrWRf0vGH45WSYIk+povjVcbBMRKM18o24niTYy8rK0utF4TwpAOR1+tV6wgBm9jYWLhcLkRERKjygQuZvBLgIhxnt9thsVjU2ON1Z2RkICwsTF2LBMXk/7nGsmyv16vaXPa97HeueXIOcl12OBwICwtTY4vnp2MV259QIdsnJydHzQO63fDccq0irCnL5uM2mw1ZWVmKHOcYl+s1254xYGFhYWoNIYTE3/l+JCFKOpelp6fD6XTimmuuUWs21xr2Ba+LkFNOTg4sFgtcLhfCw8PVusJxzVhFuUazHySIJyE5toHsV66FHCtcl2XspSFDhgwZMmTIkCFD/3W9+uqrqFWrFurUqaMe+/DDD9UNSikpKUW6w2/06NF+u3s1PT1dfSfm76gvf0dN6OX1ejF8+HDYbDbs3bsX1157bZmer7h67LHHMHTo0DwbXStXrkT37t0B5Lpv1K5dGwCUU8+zzz6Ln376Cc8991yB5U+dOhXvvPMOevbsiZo1a+YL/gBA+/btER4ejipVqqjH+vbti+zsbI3T09y5c+F0OvP9MvxK1GeffYZz587h7rvvxpdfflmk14wZMwbXXXcdqlWrhk2bNgHIjfr44YcfrhjoBwAqVKhQImgHyB2nycnJePrpp1GtWrViv/66664DkLvGHD58GE888YRa35YvX44dO3Zo7rK22Wz47rvvkJycrDYMX3jhBbz66qs+y6fzx1VXXQUA+PbbbzFu3Dj1fKNGjdClSxecPn0aV111FTp16oSvvvrKZ1lz5sxBRkYGlixZgv79++cLzaxevRq///476tSpg4oVKyonofPnz+P8+fP46quvNHWg7r77bjgcDp/P+UtjxoxBWloaMjIyMHz4cCxatCjfY1NTU7Fq1aoyq4uhf7/OnDmD+Ph4pKWl5euoBeTG6m3btq1YrmFStWrVwrx58zBo0CAF00odPHhQOdCMGjWqRNAPVblyZbRp0wZfffVVmQIqd911Fx555BGV1FBUhYSEYMqUKWjbtm2h7nPHjh3D5MmTNbFbRdX+/fvVGl2tWjVs2bIFGzdu9HlsSkpKoZ9Ho6Ki0K5dO9xxxx1Ffg++FFq/fr0myvO1115DZGSk5pjvv/8ew4YNu2iRP2Wla6+91q/gDwB899136Nu3L5o1a4aPP/4YAwYMQKNGjfx6jqLotddewyeffIKXX34Zd955p4I/DP33dODAAbRo0QINGzZE/fr18c033wCAcoyUatOmDQYNGoRjx47h9ttvR+vWrS92dZGYmIi//vrLb+DPfz7qi+CC3Nyku01YWJiKv3G73RoXDm6wEkwBLriyeDweBcVIqICbrXytzEP1eDxwuVzKnYewAp1+pOuCjMgCoIn+YpQQN3cJzPA66BDB1/P6nU6ngg0Y0eJwONSmdbly5RAVFaXqEBUVhejoaAX+UNJVhOVxE5v15kY+oQbWhaACwRHWnb97PB4kJyfDbrfD6XRqwAQp9hGt3Ai86MEa9jfLYEQbY3gCAwNhNptVe7KtGe0lwQCn06kAAkJHbFs6uJw/fx5BQUHKAYOb65mZmXC5XJpNc73zCMcXwRO6GLlcLgVcWCwWjdtLfHy8xk2FwINsc451wjcEQiheD69BxjfJa5Pty+MkpCNjoHwBM/oYL5YnwSzprMMxrx9z+sg5jgcZnSUhHuACQEQwRDpLsd48Xu/CIwE3GWHFcc5jOK7YVnrgS0J6BL24HvBDtKynPr6KdSYEmF/GMtergIAART6zfgRYOPdkX8vYOxn9xmg5j8ejgDa98xHHCr9MJgAjndW43vF51oHzhe3L9YDADuMQCUvxHBwfBFskuEI4iW0ZEhKi+ka6LbFN6dDGdqVTGMe4jOojdEn3JLneJSUlKZci6awkQSC6D3m9XnVHS1BQECwWi1r/jh8/DqfTqXGDk450Xq9XtQv/+AoLC1MRaDJ6ku9zcp7RkYlrOwEf9pOEnzgvZFQg207CQ0Cu3ashQ4YMGTJkyJChXOlvgjH071elSpXQokULdO/ePc+dg8HBwZgyZQr+/vtvpKamFlhO8+bN0b17d3z22Wd+qdfQoUMRERGB77///ooCGoBcJxt+mV+/fn3lqnM56ZNPPsHKlSvV72FhYejSpYvPY6+55hoAuRuqRbm7dP78+ViwYAGysrLQoEEDn64BchPUbrdrLO9r1qyJHj16AAAeffRRvP/++/jrr79Qo0YNNGzYsFQbqJeLli1bhueeew7jx48v1pxZunQpJkyYoAGgvvvuuwLhqstR5cuXx/bt24v9OrpDV69eHZs2bcLo0aOLXQYj0/7++280btxYAzUuXLgQH3zwAeLj4zU3CoWFhSnoZ86cOflCPwBw4403AsiFcQBg9uzZmDVrFrZu3Qogd0wvXbpUgUHt2rXDmDFjfJa1f/9+HD16FImJiahTp476DsiX2rdvj5EjRwIABg4ciDvuuAMA0LNnTyxbtsxnvE50dDSeeeaZMo9CeeihhzB16tQyhy79qaCgILz77rvG56IrTCkpKUhOTi4UCjxw4EC+0EhRtGbNGjz22GMYO3Ysjh8/rnkuPj4ec+bMUVCRr7i+4ooxYGWpW265pcSxYkFBQbj33nsxa9YsBQ3np2+++UaBq8WRzWZDeno6Zs+ejVdffbXAeNAzZ87gzJkz+e5DUDVq1MCNN95YJHejy0VutzuPS9yOHTtKNZ4vB9WpUwcjR470O7xUqVIlREREIC0tLc9n34slmnGcPn1a7Wd4PJ48zlSG/lvavXu3gn586dZbb0X//v3xyCOP4O2330bTpk0vYu0u6MiRIzh69Cji4+MBlP77ov88+CPjuvSRPvxXggUAFEAgN0olbEJISEafSHcgGTmkhyq4gS3dPWT5PC/BAzq68DhulLOOcjNbxrDITVkCHNLtg0BSYGCgirHiMaGhoYiIiPD5JYDezo/nl5FAErRiLI50c+HmsXRVYr0IuvAPQxlzw/Nx89vj8Wg2obk5LmEvwgeyDyQExs17wgvycZ6Pm+Isj89zbBEEoeuS7B8pPZQiIQvZX6yr2+3WOKCwX7iZHxoaCqfTqSAjfRSaHFMSxOC1ycek8wtfI9taliehFDkmZLvKa/cVJcTn9W5MvsauhHEoCczJ8aQvR+9OwvrrYUDpYKOvg2wn/XXyh31GdxZfX6hKqIfXQiCQ7Sx/ZN3ledluchyxDrLN9W5dBNrYFtKxBrjgZiOvX84fSkI2EgwjACjHiDxWXgMdouQ4kW0so7hkfeV8J7QixxbLZt14vH4NIETDOS8deQglyTkh4xVp+y3bneu6HNdy7hJmk2u0xWLROPcQHjt9+jSysrI05+M1yPki60kQSrYFgUAJoemBvICAAM2YlTCnfO+S7c8xItczINfRzZAhQ4YMGTJkyJCh/6qqVKmC8uXL5/v8nDlzMG3atELvnN65cydWr16NLVu2lLpOjRo1woABAwDk3pF9pWnHjh04ffq0+r1x48aXsDa+9cMPP+CXX35Rv7tcLp8x4QCUA8fZs2cVrFCY9u3bh19//RW7d+9GmzZt8jzfuHFj3HPPPT5fazabsWrVKlitVgwfPhw9e/bE3LlzsXDhQowYMUIBDf8GOZ1ObNu2DW3atEFcXFyJyqhatSqaNWvm55qVnRo2bIjw8HCcO3eu2K+NiYlR/y+KC5kvMbawoPjCefPmISkpKc/jBw4cyDfii9q5cycAKNeMffv24ZNPPsGMGTPUdxO8UTcnJwd33XUX7r///gLL3LBhA4YPH45Ro0YVeNyuXbuQmpqKqKgoXH/99WjcuDEeeugh3HLLLQWu802aNEHPnj0LLLs0mj9/Pj7++GOcOHGizM7hb1111VXo2LHjpa6GoWIqMjISbdq0KTT26PTp0yV2+6ECAgIQFxeXZx/q4MGDah1o2LBhidd2qejoaJ/OQv5U3bp18/0cUFQ1atQIffv2LfS4HTt2FLtsi8VSaLwaxRST/FzSqMDAQFSpUqVE7nGXm06cOIH09PRLXY1SqWPHjhg1apRmT6A0Cg4ORps2bRAaGqpuXN61a1eh48Lfkns6cl/WkCFfqlq1KqpVq4arr74a1apVU/tYlyrOLysrq9AbgIqj/3zUF50rpAsFNzylY4vH41FOEQDyuFZwY56RXTwuIiJCuXUEBgYiPDxcEy0j3SfoWCFjmOj+wQgablTTrYfwAZ0ruMDShYN1kS4T3KzlMQQA0tLSAEDBB0Cuw03FihXVtXk8HlSsWBFhYWF5oKSAgADlrgNcAEGk2w8/2JhMJgVnMHZLRnERppFwg91uVxbYPJcEHCS4IWOg5CY4gQdfUVPsV+nYIutP5ySWKZ2J3G63GhN0GuEfx3FxcSrOiC5GEgqT44l9xTHBDfjQ0FDVFsnJyepNjK+NiopSixLdOjweD9LS0pCcnKzcmUJCQtQdcRLKkGCaBD3oBEMHItaN/ax/85TwmnQAknFJPB/HpIRw+C+hCTmOWEc5njl/6TQjgSN9tBfHC69Fgnwej0dBEnq3HoIfjFeTsWLS3UjvciLBB7YL1xVGUekdkuR1ckywLnKdYbsHBwcjMTERLpcL2dnZyq2FfUdHKbojcW7ISCqCRWFhYQgPD0dCQoJyoZGRbdKxKDMzE1arVbW1jCeUc5bnk2uFhMskYML+5DllFB/LYxnsE87BjIwMeDweBAUFKac21pPtypgvAo2EWeg8Jp2TGMnHecc1my5MvB6OVbruEPhzOp2aWK2MjAxYLBY1prjWyjkUGRmp4EabzYaIiAg1T1JTU3Hu3DlYLBY4HA6cPXtWRY1xLjJm0Wq1qvakYxkBIrlWh4WFaeYkcME9jvAS+5BOSmxv6Vgk1249qCXfIwu7E8WQIUOGDBkyZMiQoX+zevbsiVq1avl8zul0Fvku4szMTPTq1csvdapWrRp27dqF4OBgxMbG+qXMi6kVK1agbdu2qFixIsaNG4dnn30WAwcOxPz58y911ZR+/fXXPI+tX78+j/PHunXrcP78eQwePBiVK1fG+PHjCyy3S5cuuPbaa/Hhhx9i2rRpGDNmDMaNG4d169Zpzlm3bl18/vnn2LlzZ54NwAkTJqj//+9//0O1atWQkJCAqVOn4tChQ6hatWqh8MWVopdffhkvv/wyfvnlFzgcDvTu3bvYZQwcOBAnT54sg9r5X5999hkGDhyIJ554Art37y726+l4sWTJklLFgWRlZeGnn37K9/mnn34aVatWRf/+/TWP//PPP4VGUX399dcYP348rr/+evXY5s2bsXnzZowaNUoT/Tdnzhzce++9KF++PCwWS76xHzNmzMDx48cRHR2Nt99+O99zHzhwAO3bt8fOnTvx1FNPwe12o3PnzujZs6f6jsSXJk+ejHPnzqFChQoFXltpVNJot0uldu3a5fveaOjy1QcffIB+/fr5fC4nJwdffPEFfvjhBxXJVxo1aNAA9erV0wCJKSkpmDlzpoqqHD58uF8gBunAXha6++670bJly1KV4XA48Oeff/qEJvVasWIFWrduXazIpaioKDRs2BAVK1bEmTNnCjz21KlT2LRpE+rVq1foPG7YsCF2796N6OjoUsNgl1J//PEHbrjhBrRv3/5SV6VUevDBB7Fq1Sq/OPM8/fTTGDRoEBo0aIB//vkHAPDFF18gPT0dVatWRceOHXHLLbcU6KbnD2VmZioolzdSy5vmDRmiRowYgeeeew7nzp2D3W5H/fr1L3WVYDKZ/BpL9593/OFGvXS4kRvxvtw1CBnI+CIAavOdkAedICwWCywWi3Jc4EYzcMFxiGCA1WpFXFycJp6Gm8HSUYGb/zLGSW7UAxccLWR8TUZGhvoDRDqJ8PpTU1PhcDjU7wQwSEzShYLRPbwe2Z6MnZLXzR+LxQKbzYbw8HBkZ2fD5XKpdna73Zrr5GYyQQxCKHa7XTkr6a+XG9FsU27K6x1EpAsSwR6CMXrXIsJCISEhqhzpgMFxwzFBQMDlcimHocDAQLjdbqSnp2ugAek+Q/H3zMxMDUglI90CAgIQGxuronsIP3Djn2PL6XQiPT1dxZ5xnCUnJyvoQrqacFxKmIKwB3DB/SUjI0PF2kmYTIIFcuMfgMaBR3+90k1J7+wjXUzk83L+Sici+RqKbSPrQyCBfaXvc+kWw3I55iUcqB9zHBuc7/rxSTDPbrcrMI8uTexfzmmekzFgcswRjnM6nUhLS1NADK9ROg0xzo2Qmj7CjnOLgGJ2djbMZrPqMzrMsGzOJZPJpCBEOmxJ2EmOC0Jy7Ce5tsp25jgjVCMBFJ5bwk8sg25gcpzIuC/Wgc+xvdluhCzZP+np6RoIi/OPbSKhuYyMDAWOEZ6TayvhPcahyT5iv6ampiI0NBQVKlRA+fLl1RhISEhAcnKymtfZ2dmIj49XkBHX4HLlyiE8PBxBQUEIDw9HuXLlEBMTg9jYWERFRcFms8FmsykXN4vFogHt+N7A9VzvMCX7kuOc73FyrhDo5Ov4XnL06FEYMmTIkCFDhgwZMvRf1W233ZbvcxaL5ZI47ixfvhxPPvkkkpOTERkZ6deyyzoqAwDatm2Lzp07Y8WKFZg1axa8Xu8VcTe5vo5nz57FkCFDAOTeyHXs2DH88MMPBZZx0003YdSoUSouZOnSpZg1a1aefty7dy8GDx6M6OhoTJgwId9Nl6VLl2Ly5Mnq99atW6Nbt25o0qRJcS/vstbPP/+MtWvXYsSIEcXefB01atQVE4fH75RnzJhR4qiXQ4cOYcSIEaWqx7Fjx7BgwYICj5k3b16ex66++upCYcTdu3fDbrfDZDJh2rRpmueWLl2q+b1Ro0ZYv349EhMT0adPnwLLbdmyJYYPH45ff/3Vp4sWtWvXLvz888/Yt28fnnjiCfVdyuOPP45XXnkFn3/+ObZu3ZrHfWfJkiUFnt9f4ve7l7uuv/76K6auhi5I3pCsV0ZGBnbv3o2FCxfivffeK/W5WrZsqYEs7Ha7et8HciNL9fBgSeV0OvO9rtIqNjYWgwcPLrWbRHx8PDZu3Fjo5wQA+P77730CyIWpfv36RXbi2rlzZ5FcKAMCAlC5cuXL0p2xOFq0aBGmT59+qatRakVGRuLHH3/ECy+8UOqyGjZsiAYNGuR5fNGiRZg0aRJSU1PVnlBZyuv14oYbbsBrr72Gdu3aITo6Oo9RgCFDN9xwAwYOHIgqVaqgadOm6NChQ4FujRdLx44d8+v+2X8e/GHUlHTBSU9PR3p6OhwOh6KsuEhkZ2fD7XZrYAVu+NvtduX4IkEROrJwoxW4EIfDKB9G7bAukZGRynmBG/10xpFxVNKhBrgAF0h3EB7L18n4IUZmSYCBC3FwcDAqVaoEs9msnIEiIiKQlZUFt9uNtLQ0BbVERkYiNDQU4eHhsFgsCv4JCwtTG+9sExkNw01vti1diiTkJCNy+OGLQIp0zZH1l+5NBHvobiEjoghPyLaVQIKMMyIUIOsXFBQEq9WqXE3oJGK322E2mxEeHo6QkBCkpKQoiEfGJElohFCCBEsyMjLgcDjUc4SnpNNIcHCw5roBaKANr9cLh8OB5ORkJCUlKWcQCfVIwEqOOX2klYxVYt/JtpMxajK2SO8GIiUf18dwSSBERn7xWD0QxOgqAjcy4oxtBkADoLhcLgVQSCCL5RCmkWNJugJJ8ImgF9uDQB7bkeNQH6dHEIdjm8/T5UqOYQkdORwO9Tq6CPHcvBaOFzrPEGrKyspCYmIi3G63Ghdc2wi8EA5ke8p4LDlfOLblY5QcL2wfCVOyPSVg4nA4VDuwXD5HeIrrLNdTRt8R8goODlbtajabNWuQdHPyeDwKBCIUybVIwj8Em9j+0mlK/lHIfiAgybUXuAApBQcHIzk5WTlPEXKMiopChQoVEBkZqd4rMjIylNOUhBdppcoyCXIRMoqJiUF0dDQiIiIQHh4Om80Gr9erjpNOSHQso3ucjPCS7xtcgyXgRUl3H7k2002LfWXIkCFDhgwZMmTI0H9NXbt2xXXXXVfgMaNHj75Itckruo76S4MGDcJTTz3lt/LyU//+/dGsWTMcP34cQO4XuW+99VaZn7ck+v777wEA3bp1y+OA8M4776gveg8fPoyqVavmcQTSa/369ahVqxY6dOgAANi/fz/GjBmj3A+oTZs2Yd68eXj44Yfx1FNPoUuXLkWqb0JCAiZPnowTJ07g6quvLtJrrgS9+eabmDhxIp599lkMHTq0WK9t06YNOnfunO/zTZs21ThSXEpFRUWVOKILyP1O8cSJE0hISChVPcaOHZvnRju99JAOAFSqVAldu3YttPx169YBAIYNG6ZxtNBvyjZr1gydOnVChw4dCl1rz5w5g/nz56NJkyZo165dge48t9xyC5o2bYqPPvoIAPDaa69h7dq1ePHFFzF06FC0aNECrVu31tzUuWfPHnTq1AmVKlUq9PpKo+XLl5dp+f5SmzZtsHnz5ktdDUPFlN1uh91u9/kcv2f212b78uXL1ffDQK7LzI8//qh+Hzp0qN9ctNLS0vLcEO4vPfjgg+jWrVuJXrts2TKkpKQgJycHiYmJOHLkCOLj4wt9XXZ2NtatW1ekY6Xi4uLQq1evAuFHavHixfjhhx8KjRiMiYlBvXr1UKdOHb/D5gXJn59vqT/++KPAGMsrSXTrKU07HT58WO21ce+EysnJQaVKlS5Kn1ssFjzwwAN45JFHUL16dQQEBCA5ObnQGGVD/y116tSp0L/LL7a8Xi+OHDmiHLP8of88+EMnC7rwEEzhZmt6erra/OYmrT6aJycnBw6HQ0U40alBRjHZbDZN9JGMWeL56RhDIIAABjeFeS4grxsDN+vlJjvdJ6xWq4IAZMSRjN/h5i9wwY0DgAKQ3G43goKC4HK5EBAQoDacGf8VGBiIqKgo5SQhXX8YM8ONd24ukw6XcAXPz2uQrjPSPYhuE/JDJMEC9qsEa+ikwo1xOngwgoob/vo2lb9nZmYqFx/ZfoRlgAvxUmwT9h3tF6XLDgANECHj0HgcYRKek/E+fBOlownhBl472zQiIgImk0kDBREUcblcSE9P1zj3sP0JzLDPwsLCFDjD8SPdbQgLSHcWwgK8FikZwyYdePiY/NHDOPooLT4m440kJCT7iXVkuS6XS417Qhx6Jyb+n8emp6crgEXGUskMUf6enZ2tYBMJXHi9XuU0RKcaAiBcAzjX5LXwh/3gdrtV/7C/OKZ4noiICOUIxLoR2HI6nWqupKamamK59B/YuG5lZmbCbDareSxhHxnVJSEtvXuYvt8k8MZ5yvElo/dk+5rNZrXeEEwkvCnXMiAX8LRaraqdZHmEoFgHzsOwsDAFGbndbs26z7nNdUGOHbfbDZvNptYX6U7FMiQcRCCM0WIs0+12q3plZGQoeDAqKgoVK1aEx+OB3W5HamqqBs4xmUwwm82IjIxEeHi4umbWUYJIXF8Jx3FcyPc5rjuyvyR8Kv8g5mskDMgvuC5VPqohQ4YMGTJkyJAhQ5dS5cqVw8iRIws9rnXr1hehNr7Fv6/8pXnz5uHrr7/2W3m+1L59ewwYMADBwcF4/PHHUaNGDaSmppbpOUujmjVrolu3bnjsscfyPPfuu++q/+/duxexsbGoU6dOgeX98ssvSEhIKBQQoqpUqQK3242XXnoJy5cvx4QJEzB8+PACX/Pkk09i0aJFhTq2XInavXs3evToUazXNGnSBF999VW+z3/66afYsmUL3njjjdJWr9SqUKFCoREtBen3339Hp06dSh2PsWjRoiIdpwcIVq9ejRUrVhT6OhlrJYEeX1E9GzZsQP/+/XHmzBkEBASgYcOGBZb9zjvv4NVXX8WWLVsKBFOys7Px559/4ssvv8Tzzz+PXbt25anjt99+i/Xr1yM+Ph7vvfceli1b5hcnlIKkhwAvVzVs2BBz58691NUwVEzlB/XY7XakpaXh9OnTfjtXixYtNFBlvXr11PtXt27d/Pr5KS0trVBYsaTasGFDnsjNoigzMxO7d+/G66+/DqfTiejo6GLdXLlw4UKsWLGi2E5GN998M26//fYiHfv1119j+fLlqv9/++03zJs3DykpKeqYsLAwVKpUCYMHD8YHH3zgN5emgtStW7diQ75F0XXXXXdFOEwWRTabDV9++SVGjRpV4jK4V8ab67nPQCUmJpa2msXWvn37MH/+fLzzzjslmndXih5++OFLXYUrSh07dsSAAQMuKnxYFGVkZOD8+fM4duyY38r8z4M/TqdTAylwk1ZGm0inEODCZjKBCsIPdOUg7EHIQAIN+rghQAuQEJCQzgnSEULWT9ZRRrCwztxY1jugABfgEG4q6yNy6EDE4/Wbu/zjT8IeMj6Hm+cyAotwjHSPkY4/eqcYQkG8Zhkho4934vGUHrDSwyFsf+kskx/RLcuXjkDyPKyXdMxguXTsIbCgh7Zk3+nhFwnMSFcbbrzrx6a+jLCwMI0rDcEGrzc3Wi0lJQXp6ekalygACo5gm8sNf1kfKb1jEuvAusprkG0rf9fPMz3ko4dyZLyQdGHRO5LQ+Ug6SXGOsq9km8q6yf6XgI+MPJLxW3rnE9ZRRlPJ8SmPLeyuAtaBEA/dwji/pDMRz0V4Rb4WuDB3GfEk+wyAiqiSx3NsECAh9ELQiHXU93F+basfKxzz0qWJbaKf47xuCb0QNKHrj4R0uDbLGDHpTMNzyR8JG/H8EsKULldsI84zfdyc7Fu9W5u8RrYnnXL4uNVqRUBAgHISy87OVpFxdC3ieQig6nPl5RwHoFl/eZ3yTjS2s6/1ivWS4njjMRKO80fWtiFDhgwZMmTIkCFDV5ratWvn0/5er5CQkDK5K7ko4o1L/hSdOMpKrVu3VrbsERERBTqxXA5KSEjAAw88kCc668CBA5rfGQHtC1yQcrlcSE5OzuMelJ8sFguSkpJQr149dO7cGYMHD9ZsyOWntWvXolq1ann+trzSlZKSUmyHiEGDBuX7XP369VGvXj3ExMRcFm63ZrMZ586dK/Hr09PT/VibwrV06VL8/vvv6vfVq1cjOTm50NfJuLbw8HCfj1MtWrRAt27d8Nlnn6FZs2boWEiMTXx8PHbt2oVq1arh+uuvL/DYHTt2qNghX/r0008xbdo0BSdaLBbUqlWrwDJLq48//rhMy/eHKlasiNDQUGzduvVSV8VQMeV0OvOAgU6nE1988QXmz59fqvVHr1atWuWBk4OCgmCz2Qqdm8WV3Dfwt7Zu3YqVK1cW+3VutxuJiYlYtmwZDh48iBo1aqBFixbFKkOaChRV5cqVQ9OmTVGvXr0i1XH//v04c+YM1qxZg7lz52LdunV54p0qVqyIDh064Pbbby9z1zMAuPHGG8vEtXDv3r1l5gx1qVQaEIJ7ZcePH8eOHTvytE1ZwXT5KS0tDd9++y0+//xzbN++/aKe+2Kqffv2qFWrFipWrHipq3LF6MYbb8S11157qauRRzk5OUhOTi41cC/171qhSqBTp04p9xdufhOE4aLFOBq3262cbtxut3J/YTwOPxgEBwcrICg9PR0pKSnwer0KiAGgARRycnLgcrngdDqVSwQ3TKVzCKEauekqN825mRwVFYXKlSsjKioKYWFharHlxrYEFbhRTjcIbuqHhoYiMjJSXSvrHBwcrFxCWAePx6PqSScLuv5wY5suEHQ4YVvS1YKbxQDUpjfrzXoBUO4orCevSQ/9MMaM10gggjCShIkkmCEBDrotcUxISAOABiayWq0K+rHb7bBYLCpmKC0tTcEIdNbRxx9JdyNu+LOfCDhIkIpfvBBokZCDbLewsDAVz5adna2cThITE3HmzBkcOXIEx44dU+3Ktqd7jHRJIlwinT4IhugjgfTuIIyrkkCEHJeA9sN1fm4+EvSR/cY5zDgk9o+EhDiGeR6ORUYjSbBDDx1J0MbtdsNutyM9PR1Op1OtF3pHMAmecU5IIE7CPzJuTrqBSfiEfcNry8nJURakFosFVqtVjVnGvwUEBMButyMgIEDBP3LusFzZv5R0n5KOMBEREbBYLJoP72x7/RrD+Uh4kf3L8/iKgJMxbPKPLZfLpX6XrltcuwiweDwetUbJD5r6ODi2Mx3BZH8zFtBqtQKAmhuENBn5SPcoCSHxPUOOPY5PCZNaLBYAUG5vnEd8X3E4HMjKyoLFYoHNZlOxXenp6UhISMDJkydx5swZpKamqjtSvF6vckLK7w8QuXZzvPH6pAuYnM8ejwcOhwMul0utYRKaYjtyDLP/+d5hyJAhQ4YMGTJkyNB/VTfeeGOBG7z8fH777bdf9M12ijdXXe669tprsWLFCsyePRsPPfSQ5jlfTjqXkzZu3Ig777xT8+X82bNnMXbsWM1xTqcTJpOpSF/if/vtt/j111+LdH5+l3nnnXciKSkJiYmJGtAiP02fPh0ZGRkljie5XPXhhx9i6NChePPNNzFixIhCj+/WrRteffVVPPLIIz6fHzNmDLZv344777wTzZo181vsTEk0cOBAXHfdddi3b98lq0NxNXjwYEyZMkX9XtQIBgm+vfLKK+r/0glI6oYbbsCKFStw5MgR3H///QUCj7NmzUKLFi2UU8CSJUvw3nvvYejQoWjYsKH6vggAjh07hlWrVuVb1rp16zB79mzExsaqx66++mqsXr0aQO53fGXhSnG5q1u3bvjjjz/+1Ruz/1ZZrdY88+evv/7CSy+9hHHjxvkV5goKCtLcpLho0SK8/vrreOCBB/Dggw/67TwA1PeaZaERI0agf//+xXY/ycrKwoEDB7B37158/PHH2LVrlyb6rCgqasynVGBgINq1a4cHHnigUIc0AFiwYAGWLVuGZ555BtOnT8ehQ4d8XqvJZEJERASWLFlS7DoVRyEhIbj77rvRtWtX9OzZ069l79mzp8yjvoobz1Za3Xvvvdi2bVuRgXKps2fP4qeffsLMmTPxySefwOl0ap7fuXPnRYV/mPxyKZyGLpZGjRqF119/HV26dEH37t0vdXUue8XFxWHPnj146aWX8jhSXQ5KT09X0dX+0uX/l30Z68cff1Sbvtx814M/3NAl/AHk/tGcnJysXDEY5QNccAKiewKPkaCKftOVf4Qz6ob10buHMMZIH6MkXS0YNyM3aGU5wIVYI8JFQO4butVqRUREBKKiohAZGYns7GykpKQgISFBvZbXlJWVlSe6i9AK42v0jj90s2Aby4gobpSzDPYLXVNYX77O4XDA7XZrXEmAC64b+TnheL1e5Yahjx3i9RFEYjkSAuIGPdssODhY/cHndDpVuYzHYn3lGAAuxENxrEmnDMYweTweTWQQoYWIiAgF5TCWia4rgYGByqo7KytLRYHR/cntduPYsWPq7hmPx4Pz588jNTVVAS0yYkk6oBBe47jjGOTYk1AJADVnOGY4/gll6B1dAOQZ2wQn5PnkeaTjDY+TY14CQnw8ODgYqamp6ku1kJAQ1T4S9mHdQkND1bUwXkm6pRAa04M/PB/j/xgpKB2KJCQkxxWBJM55QjthYWEK0mA0YWBgoAJJpBsXQUMCXzabDeHh4ep6OH+47oSEhGjgIwI1bBcJZkkQjuuAvAb9XPHldiVBPOlmxrnL1xIUkrAiABWPxddx3ZB/qOmj4mT0oFyjuC5nZmYqYFI6PLGdOc8IghGQkfGBFMe4PkpQrtUcD4RnJARJp5+KFSsiJCQEaWlpSEtLQ3p6ugK5srOz4XA4kJaWpiIZ2Ud6pzjpQiTjzeQ6pAcp5VrFMa8HEwmR6WFVlqmHuAwZMmTIkCFDhgwZ+q/oww8/xIkTJ/J9/v7770dKSgrOnj17EWulVVltcvlbe/bsQVJSEoYMGZIHpuKmVL9+/dTfxpeTNmzYkOcxt9uNn376Kc/jqampRQJ/xo8fj3HjxhXp/N9++y1q1KiBW265BXa7HQ6Ho0ibSl6vF48++ijq1q1bqEPKlaRNmzZh7ty56N27d6GxagAwduxYLFq0CPPmzfP5fI8ePfDcc8/hl19+QYMGDdC1a1d/V7lIev/99zF79mwEBATgzz//vCR1KKnmz5+P//3vfwByI2Zq1qxZ6GvWrl2r/i+P128ox8fHY86cOQCAvn374vz582jYsGGRNssI1/Xu3RtPPPEEpk+fjt9++w3btm3D4sWLiwwdtmjRQgOERUZGKlho0aJFeOqppzTHf/jhh36HGi433XLLLQXG5xm6fBUUFKRxCMnOzkZCQkKZbLRv3boVf//9t/q9Xr16GDBgAJ566qkSQQoFqUKFCmUWAbN161asXbsW5cqVK9brTp06pTaEp0+fji+++AJHjx4tVhl0SCyuQkND0bdvXwwePLjQY+Pj4/HFF18o6HTjxo3Yu3evz2OnTZtW5uDMoEGDULVqVdSuXbtM3pPnzp1b4Of7kmrs2LGYN28e5s6di99//13dFJCUlOT3c0ldddVVaNKkiU/HvMK0fv16TJgwAXPnzsX69evzPL969Wr8888//qgmEhIS8MUXX+Ddd9/Fxo0bfR4TGhqK8PBwtS/2b9M999yDV199Fe3atUPDhg2LHV37X9R1112H+vXrX+pq5Kvjx4/jr7/+8muZ/3nwB8i1ruMmv9x8luKmNjdFHQ6HilHihnJYWBiCgoKUe4rcBAcuACnccOe/Ho8HQUFBiIiIUC45LFu6UHBjWh9HQ+CBkAahIm7OSuiFWYsAFKghHXbMZjPKlSuHqKgoBAQEID09HQ6HQzmDyLgkgiBBQUFqo53nlUCEjI3Sx3nxNRLY4GY4oQ3p+GKz2RAQEACn0wmn06ncKdhHAFSZ8lxsJ9mfhCH4uwRPuNHPcqS7kmxbRupYLBa16R0QEIDQ0FAEBwfD7XYjLS0N2dnZCjYAtM4zbFtukrvdbuVYIiEUuXlvMpkQExOjAYY41ggs6CEJvtmdO3dOtS/hMH7RaLfbNS5EHNsEZyTExXEno91knBX/lcAAr9Xtdqs+Zd0lra53BpIuP3K861195HFy/HAssl6BgYFwOp3IycnxCSyxj6TDDNvX4XCo+DbggjOOHvyRcIsEi+SPPF6CX+xHAiK+5nRqaqqCmQi0ECCkiw1hPt4dQVcpOhVJtyHpVhMQEIDw8HDlZsX+ks40hCEdDgesVqtaVwh4ETyRrjAcx7LP+Zgc51wrZZQXx5MEcDIzM2E2mzXgFNuW7jTSgYpOTwRkJBTENpdrhoQECWPKuSHdwKSrlXSxkvOTfS4drEwmk4KXfEVjud1uBQlxreY6w3IISXJcSlBJzl+z2QyLxaJxBWPbyL5nH7FOsu19OVrJNVSCXhxX2dnZl4XduSFDhgwZMmTIkCFDZanevXsjLi5O/T5lyhRMmDABUVFReY5dv349nnnmGQBAVFTUJf0y0p+24mUtbt77UrVq1TBgwAAMGzbsItaoaFq2bFmeWGWbzeZzMy4xMbHQqK/i6ptvvsH69evRp08fVK9eHR06dNA4pOSn48ePY+HChRgyZAhefPFFdcPjv0VNmjTJ47rkSytWrMDkyZPzjUc7e/Ys/vnnH0RFRSEiIgJ9+/b1c02LpvDwcHi9XowZM0YDxVwpev3117Fv3z5Ur169UIeGWrVqaTa7evbsqVlHGTXkcrnw+eefY8yYMTh48KByl3nrrbcwatSoQuu0YcMGnDlzRrNPEBMTg7p16+K2227T3HRXkP7880888sgjeO2117By5Urs2LEDr7/+OqKjo9G5c2d88MEHAKAcn++44w58/PHHGiekf5MaN26MG2+8ETNnzrzUVTFUAqWlpWl+T0pKwv79+8vkXHv37tVEh1177bUYOHAgqlWr5vdzlStXDhEREX4vF8iFCIvbRhkZGVi1apUmFnTq1Kn45ptvilXOH3/8UazjpWrVqoU77rgDTZs2LfRY6d5lt9t9Qu0ZGRmoW7dumTrj9e/fXwNldu7cGXfccYdfz/Hyyy9jwIABxXZfKkherxeJiYm4++678cwzz2Dz5s1wOp2Ij4/HV199dVE+rzdu3LhYx99xxx14+umnUbly5Xzd9nbt2oXvvvvOH9VDVlYW9u/fj5UrV+b7mcxkMuWJB/w3af369cpVKSgoCJ06dbpkwPmVosIi//w5j0uiHTt24Pz5834t0wB/kLuRz41wOvdwo1zGYhEWYWQSN2blRj2P5+Y8kJs3zk1aQikAFDDj9XphsVgQERGBiIgI2Gw2mM1mBY8QLCDMQMBHH3tESAC4sIHMTeeQkBB4PB643W5VR0IBdA1hJBEjwlJTU5GcnKxcIKTrjNlshs1m08QDSTcIbvpKSIQbygQtJBwBXAAoAG0UmowF4jFZWVlIS0uDw+FASkqKiqYhcARo4RG2kYRF6HIiHYOkM5L8P6/BV59zTLBP+UdaZmYmUlJSVLuyTuwXOvpIxx/pVELwQLoOZWVlaXJZZdvo47B4DWwTwiJ8U6STC5BrjxsTE6NgKNaD4zM/8EcCP2xXCS9wrObk5CA9PR3p6ekqkoiQB+EJ1lf+q49NkvNBD25IVxfpbCKhIB6TlpamYBkJpEl7cwk4sB3oXsRcbnkuwhIS/JHtJqEl6f4koRXp0KXvV4471sHpdGoi6TgHCIYQNtL3D79slO1HyIzxcKGhoWjSpImCwjIzMxW4xAhDr9eLtLS0PFFdEmpj+8s+pSOVvE59HxKm4zXrY+FYZ67Z+hg5toPL5crjjMZ25bznWOd4ZCQXAT4JWTLei85CXm9uFGJqaqpqfwkSJScnIyUlBS6XCw6HQxNJJ0E5jhP5/9DQUAXKARf+qCYwFR4erhycGAdmtVpx/PhxFd8IQDNH+X+2gYToOPf1DlxyndOvf3JMSng2JycHbrdbtSPHoyFDhgwZMmTIkCFD/0Z1794d/fr1w8KFC7F582Zs3rwZ06ZNw+DBg3H33XdrYpIB4Nlnn8WTTz6Jt99+G48//jg8Hg+qV69+0esdFxeHSZMmoX///vlGGF0KPfzww/jtt998Pvfjjz/63Fj4/vvvcfz4ccydOxfTpk0r6yoWW0OGDIHJZMK2bdvUYwMGDMCxY8fyHJucnKyJEfKHjh8/jhtvvBG7du1S3wEWJ5q5adOmuO+++y5qVMPFEG/kef7553Httdfme9w777yTr3NBq1atEBAQgPr16+PDDz/Ejz/+iPHjx5dVlQtUdHQ0zp8/j3fffTffDbjLXdKZpyAdPnxY833UsGHDNLATv79YvHgx3njjDSQkJODVV19VwNsLL7yAq6++WhO/5UuLFi1Cu3btMHjwYIwbNw5fffUVduzYoTaJ3njjDWzfvh2TJk0q9No++eQTPP/88+jWrZvaRE9OTsb777+PqVOnAsh1j5o2bRratGmDgQMH4uGHH/5XbmDeeeedsNls/7o15b+icuXKaW6wPnToUJHiI4ura6+9FosWLcKdd97p97J9qWLFin4Hb6lHHnkE9957b7Fek5GRgT/++AMOh0PzWHG1bNmyYr9Gqlq1avm6/l111VU+H+/SpQuaN2+e5/GQkBA0bdoUnTp1KlWdClL16tU1YNg111yDG2+8sURuNgVpw4YNmDVrlt/KM5lMGDhwoFrzFy1ahG+//RZz587F3r17kZSUhIMHD5apq/748eMLjK6Uio6ORqdOnXD33XfjkUceKfAmhiVLlvj8zFtcVahQAddeey127NhRYDRSdHR0nj2rf4vq16+vgZ5iY2MxcODAfx2c70/Vrl073+eysrLw3nvvYfLkyWXurJWfyiLy1AB/ABU7pYc8CEBwE1k6KdDhQzow8DlAe8eUdEwAoDZQJSwiHXv0sIrceNa72Oj/r4/SITgjnVXoBsO6cDOYMBA3vgkJeb1eBSJJFxJ9Hp4vRwjpUKS/Fgl+8P8SvOAGMttWAhyBgYEKzJLOHmwPQg6+ItGkM5IETXz1F5/X942sY0BAgIpzI1EaHByMjIwM2O12BZgQ6NH3j4zLkg5P+vpJKIRvXByHrAthBr1rB0ErPeRAiIrAg4RM9CCKbE99f0poQ16bhCAYXyYjn+S8kA4iErzimNC7XMk68PrlG7reyYXi+OdY0Lu98LVyTNMdieuBhLP0r5frgL5MffvxR7azHjrTjxfp4sTXEwiTzkn69iB4Idue80COH4JKdrsdFotFxYFxvrPtCCAFBASoO5xkbBbPRRiMbaIfw/p25HiVEV7SvUbfzvroNykJGMk1Qb/u6F2W9AAW21i+hm1GAImwp3TqkRAowTU6ZEkHL9kG8hr5h53X69Xk40o4SEI74eHhClqV7SzXFl9gmXyvkWNEXr98rZybcq5IRyfOFdlWhgwZMmTIkCFDhgz921StWjUMHz4crVu3RnBwMKpVq4brr78e/fr1Q3h4OADg66+/xsaNG3Hs2DE4HA7MmTMHmzdvBpC7afDTTz+hUqVKF73uDocDVatWxeLFiy8r14VmzZqhdevW+T7vC2jgxtO2bdsu6kZy//790aRJk0KP6927N06ePIkZM2YAyL2xI7+NFbvdrtlQ9adCQkJw/vx52O32AjdMfMkfGzaXq3r16lWkyC9fuu2223DNNddg3bp1SEtLQ3JyMnbt2uXnGhZNMTExsNvtl+Tc/tLWrVsBoEgwpIwVCgoKQrly5dS6GxMTAyDXaYffp8THx6NDhw7qNV988QVGjhxZoEtYdnY2Dh06hHnz5mHSpEmYOnUqZs6cqWJLbDYb6tevr+JYiir5PbSEhurUqYM5c+bgxIkT+PbbbxEfH1+o+9GVqEaNGhU7rsjQ5aPY2FgNkHb27NkyiW5q27Yt6tat6/dy8xNviC8LdevWLU9MaWGy2WwFfh4qqkoLE4eGhiI2NjZPDFqTJk3QoEEDn6/p0KFDvq5MISEhZer4k5iYqHHPMJlMqF69Oq655hq/n6s0bkq+tH//frXvs2XLFkyZMgXbtm1DUlIS1q9fj0WLFmn2CMpCN998c5EiZ5OTk3Hw4EGEhobimmuuQatWrfI9ds+ePTh27Fip44WZmnP+/HmkpqbmubnC7XbjwIED+PXXX/0WL3Y5KTg4GG3bts3jYFOtWjXUq1fvEtXq8leVKlXyfS4oKAhmsxk7duzAmTNninVjhD+UkZGBM2fO+L1cA/wBFLhDkIQfvuXjjOSSEUzZ2dlwOp1wOBxwuVxwOp1qE9zlcimXDJZDZx9u1tLZIiwsLI8LhIwHklAIALW5LF1opOuIBAS42ZqVlYXU1FTlMkOnCr7OYrEgJCQEZrMZXq8XSUlJCijJyclRbh904pAbycAF1weCRTJqSkIW0uVE1pWuQnLjWMbqMNpG71zBL3T0DjMSppExN3QsoXOHjKzhtcoNcgkO6N2XJKjEuCG++QDAmTNn4HK5YLFYVBuyX2QsUWhoqOpn9isfCw0NVY4mwAVXKUIEFosFVqtVtY2EU4AL8Ttsk9TUVFitVlitVoSHh2vaje1EMIHQgoxuk647bAsJsbDdZd9Ktx3Gs0k3Ks43AhQyNk5CH7J9pHOOHjzSQydsB4ruXXRU4fN6wE6CM6mpqXA4HPB4PDCZTKqPCZTIuSmBO/3jbDOCdKGhoQoekW5VEsDyBUTZ7XaNc5C8Runk5PF4kJaWBpfLpQHB6FZDKMRXDNmWLVvgcDh8uifxXBEREQoqYz3MZrPqn5ycHOWuxLYiCCPhSNmGvGYJsmVnZ8NisSiAUc5Pk8mk1q+AgAC19mZlZcHlcsFut2v6ST82OXcILnEMs60AqDHNa2Q9AgMD1Zjl+wffG+x2O0JDQ5GZmYmTJ08qlyTGOcpr8Hg8mrlPByeHw6HqTPiUkF5oaKhyh2Obly9fHjVr1lQxgJxnHFP6+Ek+zn7Vz12OORlFyHKlI5eEx7xer8a9jOPnSv/y0ZAhQ4YMGTJkyJAhqdjYWHz11VcYMWIE+vTpgyeffFLzPDeOXn75ZYwYMQKtW7fGihUr8M033+Cmm27ClClT0LFjR4wePRovvPACevXqddGvIT09HQcPHsyzIdS5c+cyidAoqjp37ozg4GCMGTMmz3Pjxo3zecd2nTp1ULNmTfz88895NqbKUs8//7yCeQrSvHnzEBUVhd9//x3Hjh3D8OHD8z02PT09z412/tKtt96K9u3b47HHHsPixYvL5BxXoiZNmoRffvmlwGO6d+/u83Gv14vp06cDyL1jt127dhgwYIDf61gU1a5dG1u2bLkk5/aXVq5cCQCoUaNGoZtYvuLMtm7dqnEMI4zQvHlzPPPMM6hSpYqKd3vppZdQoUIFPPzww0Wqm9PpxK+//ooPP/wQb775puY5RjeWRDIOZ/fu3ViyZIn6/fHHH1d39t91112oXLlygWXJ79YuZ914440FRjcaury1a9cu5YYPABs3bszXFa00+umnny7qJmxISEix4ZyiaMCAAejVq1eJ5ufjjz8Om81WqvPfcMMNpXo9ANSsWVMDzowePRqffPIJbr75Ztx7773o27evZn368ssv83WxSE1NLbULUUHasWOHJh4NAFq0aIFRo0Zh+vTpfnXlM5lMiI+P91t5VqtVfc+ekZGB3bt346uvvsK8efNw++2348MPP8SJEyf8dr78tGLFCjz11FOFHvfNN99gw4YNKFeuHO6///583foyMzPxzTffFBuS9aXIyEhUqVIFH3zwAQYMGIDHH38c8fHxSE1NxUcffYTBgwfj/fffzzcK7EoW92jkDQLbt2/Hu+++q3EVNaTVihUrCnwveeihhzB37lwsWrQIO3bsuKiumcePH8e6dev8Xu6V8WmwjEWohC4F6enpmrgawiXcgOYmKTdECUgQdmF5hD6A3Lt5uOkv4RQ6P/CHjjvc2JVxPRJMkK4hMvpIRvpQrKfdbtcAL4ybIhQTEREBi8WijtUDRdwc5ya73k1FH+9jMpngcrngcrmUIw435/UATE5ODlwul8a9hm0qASbG74SFhSnohWXoXSwkAMT6ud1uBXGxfIqwBh17WE8CGWxvCRPJ8zByKTAwUEVqsU8lGEPgw2q1IiwsTJUpYRBGzjF2ifAPr5GAgNVqRUREhAIk9M4wBAy83txII0a0sQ9DQ0NRvnz5PM5MBFEkxCV/pDOJjNCS8BCld7iRrjWM2JOgF62W5d0v8hzsK/4eHBwMm82WZ06wDI4hxj7x2vVzmuNO72SUlZWFlJQUBfBFRkbCarWqdtHDPayvHFd6BxWeR7px8doJlOjBJgmiOZ1OhIWFITMzU0EqdORim7jdbjidTvUcxUgoi8WixrwE8uhUwznD5xhvxXpwLMt1hu0tYRBCLaGhoQp2k05OPFY6H8lxxvNxvMrXSaCKfSZj6tim/EDEtuYckhChjBrkvJNjREJnEkJjW2VlZSkIFMi1vaUjEsdQeno6AgICEBYWBpvNphzLnE6npm3o2iTXLto1hoWFKdgPgOZ9IiAgAJUrV1YAJ8eLx+NR45fzQu/oIwFT2W6ECblmEtySUJB+3SHwIwFQaUtryJAhQ4YMGTJkyNCVrt69e6N///4YN25cvsfs2LEDEyZMUL/Pnz8fISEhePrppzFixAg899xzAHK/sPV3/EBRdfLkyTwxX0uWLMH69evx4IMPXpI6LViwAIsWLcJDDz2E/v37a55bvXq1z9dERUVh5syZyM7O1mxGlrXefvtt1K1bF7feemuBxy1atAgZGRno2LEjWrdujQULFuR7LP+GLgtlZGRg7969mD17dok2X6ZMmaLG7b9Jc+fOLfBv1qZNm2LSpEm47bbb8jw3c+ZMjBw5Eh06dMDbb7+Nb7/9Nt9xWlaqWbMmvvjiC1SqVMknDFMW2rBhA7755hu/lyudfho1alTgsStWrNDctAUAdevW1bj6PP744wBygSDe/f/kk0+iRo0aAIDHHnsMDRo0wNdff12sOKF58+ZpIiFMJhMGDRpUYNRJUfTyyy9j5MiRAHLjPBYuXIhVq1YhLi4O9957LzZt2oRXX30139f36NGjVOe/GGrRogXsdnuRoElDl6cmTJiAZ599Fjk5OUhKSiqTmBIg12lOgnEXQ2URvbp3794SAw/79+8vNfzkDxejChUqaGK9atasiXr16qFjx454/fXXMWPGDIwYMUI9v2/fPsycOdNn3ZOSksrUnXH79u3KWZOqXLkyWrZsiaFDh2L8+PGavavSaObMmfj555/9UhYAXH/99QW6XMXHx/sVNMpPTZo0Qfv27Qs9Lj4+Hl9++SWSk5PRrFmzfCFpALj66qtLPRadTiecTifCw8MRHx+PRYsW4dy5c3C5XGpfQ3/T879NkydPxo4dOwDkRiB//PHHWL58+SWu1eWrSZMm4f333y8wNtXj8WDx4sWYOnUqli9frnGULEt5vV5s3brVcPwpK8kNeLpEOBwOmM1mZGVlaRwOfMWV8DHG7NDZh64pAQEBCrgBLjgscGNWH71EBwo6OUjwg//qY7P4O6EhbmIT7nE6nRonDpbjcrmU44/VaoXJZFIOGYwPCggIgNPpRFJSkgIFCP4Q5pAOIjLaiaBNamoqUlNTVd0BqE1xOnno24Ib1txoZl+xXeiexA1r9oF0fWE7S5iAfSo3sCUoI11sAGg20AkssA15vXQkCQkJQUZGhnoDpqsM+4nuHgR9WG/Z//JaOV4k0CX7W7qEcENfuhgRuKIji8VigcVigclkQnJyssZBJCwsTIEM7AvCE3oASA94yXpJ9xQeI11FeOcaAQHpLCT7nsdL1xL9vJNAWnh4eB7Yi/V2OBxwOBwa+IOvl4CRfj2QIBLnIh1beA2MxJNAiT4yidcqr1EPGRE8zMzM1AB3jNSSLkl07eIxUVFRCnqJjo5WEAjnJscio6CysrJgt9vVtQUGBsLtdisSOjAwUIEmdMqS5YWEhGjAPOkAI0EqXntaWppmnMtILPaXHihxu91qvWGfSVcazicZ3ca2ZD/wWglU6QE+/tB5iXXTx2FxHsk7M/T9SIc3tml2djbCw8NRrlw5BaYlJiYiMTFRE3tGWIiRhWzDkJAQWK1WeDweJCUlqfFLN6CUlBSkpaUhIyNDrf3y2gkucgwT6JJRhjIGTa53hI9cLpcGlJT9w/aR/S9jJPk8nyutrawhQ4YMGTJkyJAhQ5eTOnXqVOgxtWrVwvbt2zFo0CAAuZEzgwYNUpvZXbp0Ucfu27evbCpaiD744IM8oIzL5UKVKlVwzz33FLu8hQsXljqm6tlnn8V9992HihUrYuDAgZrnGEct9fTTT+Pjjz/GNddc49fNl6Jozpw5mDFjBm699VZ1419+at26NT788EOcPn26wOMGDBiA7777zp/V9JsiIyPx/PPPIzo6uszP9f777+ONN94A4B+3gtJo27ZteOONNzROLAAwaNAgrF27Fo0bN1bfI2/btg0JCQkXtX6vvvoq7rnnHrjd7nwj5PytnJwc9OvXr8Boj5KIkXJer7dQsOjnn3/O4+qgV9u2bTF9+nSMHj0a69atg8PhQFxcHP744w+MHTsW2dnZWLZsGRo3bow2bdoUy5FDD658/vnn+O233/DHH3/grrvuKnI5UtOmTVPvGdIRY+PGjejRowcqV66MatWqoU+fPj5fX5YuGv7S3Xffja+++grJycmXuiqGSqhmzZqhZcuWMJlM2LlzJ9asWVMm5zGbzRcV5gVKH4vlS3feeWeJnPyysrKwaNGiUscjTZ06FTt37ixVGQEBAWrjvHnz5mjQoAGCgoLQqlUrVKpUCZGRkRgwYIAmuvbrr7/Gr7/+mieOqXr16vmuYf5QdnY2/vzzT5w7d87n88HBwfjkk0/8dr6xY8f6zZmqQYMGePHFFwt0njp37lyhnyX9oYYNGxZ6THR0NO68805Ur14dVqsVLVq08AmvN27cGL179y51nSwWC7p27YrXX38dw4cPR/PmzdUekUzD+DcrMTER8+fPx8qVK/HGG28o10lDvtW3b98C3WDj4+Px22+/YcWKFYiNjcWePXuwdu3aiwKdHjp0qMzePw3wBxc2cCXcI50n5DF0AOExhAGkCHAEBgYql4WMjAwkJiZq3DDoJCHBAwl1yM1zbnyzHjKOSO+oQhCCG768a4XAAiGP8PBwhIaGwu12IyQkBDabTcUI5eTkwG63K8cPGYUm3VnkuVhXAJp4NL0DB11oMjMzkZaWpmAIGZMkXSmki5KMNuP/6YgkN6Blm3ITm1CWjEeS0I90vdHDNXqQgVANwQO2R0hICE6ePKne4Oh8wT8cCWx4vV6YzWZYrVbVHux/ti837glaBQcHq/NLxxgCPWazWW3s0+1Fjlt5XFpaWh5IQu/sQQCOYA7bVrarHI+y3fSPyXYnOADkfhBLSUlBUlKSiktjTikdYKQrlATvpEMW4Q2CLRwr8nUyEosQCQEL6aZEmIhj2263w+l0qvaj001wcLAGbtO7IEkXFI5hPWTE87GdOG8IsnCcybmRk5OjQB8ZDWUymZSDFOtiNpvV4xx77INz586pvqbLi8lkUhBMaGioWt9k5JYEF9mHANQclO3I9YPXwbVEOgOZTCY1PwkNEXykuJZmZ2erfuE4YnlcF8LDw5UbkoTKOJYlFCgBSa5t0v1JAkYE9gjYcK6GhYWptSMnJ0dFerGNsrOzERsbq1yoGBvncDhgMpmQlpaGpKQk5cgG5H6IlcBXTk4OIiIiEB4erurncDgU/JOeno7MzExYrVZVFiFPrnPSmS4zM1ONB/YZnd5SUlJUW8m1jdco4VOWLftCjlfpildWd6waMmTIkCFDhgwZMnSx1adPnyKBP+Hh4YiOjlauEPxblzp27BhGjx6N3r174+abby6TuhZXNWrUUDePtG/fHqNHjy7W66+++mqUL1++1PW47777cP78eQ3EcMMNN+Cll17S/K2YmpqKiRMnYtSoUdi7d2+R7k72t5544glMmTIlT/9K3XrrrZgyZUqhZbVq1Qr//PMP3n33XX9WUam0TgaPPfYYpk+fjnfeeafQyKHSqEaNGrj11lvRvXt3DB48GD/99FOZnUuv/NybfLmTzJs3D7Nnz8aOHTuwdetW7NmzBz179lRuMhdLPJ/VasW0adMuyjk//fRTOJ1ObNy40e9lb9u2DSaTKY8bmV7x8fGFxiMEBQVh6NChaNmyJb7//nv06NEDc+bMQcWKFRV8eeedd2LixIm4//77sW3btgLdFqQmTpyogTbpqt6qVSvUqVOnSGXoNWXKFHUnf4sWLQAA69ev14ypIUOGoGrVqiUq/3LQ7bffjo8//vhSV8NQKcT1bt++fVi2bFmZxHFVrFgRTZo0ueib+BUqVPB7mf369cuzf1gUZWVl+SW+cfv27QgPDy9VGTk5OahQoQImT56MJUuWoEuXLnkiyGrXro3BgwdrHvviiy/w999/ax6z2Wx+h0b1Wr58ObZu3YoDBw74dBd64IEH8Prrr/vlXAkJCfj000/9EmMFAP3798drr72W7/MDBgxAmzZt/HKuglS7du1C34dvu+029dn77Nmz+PDDD3067uzYsQM///xzgZ+Vi6NevXrhs88+wy+//IJp06bhmmuugdlsVjey/9v1xRdf4O23375oLo+F6f7778cTTzxxqavhU4W9h1SqVAkVK1bEd999h+3btyM5ORmHDx/GX3/9lS886C/p42n9qTIFf0wm01GTybTLZDJtN5lMf/p4vp7JZPrDZDJ5TCbTGPH4Nf//Gv6kmUymx///uZdMJtMp8VwP8bpnTSbTQZPJ9I/JZOpa1HpKwEDCEtzI5DGhoaHwer1wOBzK5YORMNwM5yY8cMEBKD09HefPn0d6erqKNpKb+BIQ0McZAVCbtxJqKcxxhXUODg5WMIF0DjKbzYiOjkadOnUUBBQcHKw2bnkcN+sJErhcLqSlpWlcIPSOOBLWIBgjnXSkKwbBCeCCE5KMHSI0wg17ugNJtw4AecAIWRe5Ma0vXzo66aUHOGSUGzfD+RjHgMPhwPnz55ULh3Q2YRuFhIQokIB9xD7zer0KKuF18nnpEsVxwb60WCyw2WzKocOXywnl9XoRERGByMhINV6dTqcavyyDEW0yHkiW4au95I90wmIUk3RUIRTgdruRkZEBp9MJh8OhgQz040gPHtFZSEJhdKhhOxLWoAMXgY+MjAwVmSTBCLYD+97tdufpSx5HEFC6NLGPJPTD9iPMQSCQ/Uj3KtZXQhXSnYr1iImJURFnoaGhcDgcCo6TloYmk0lFehFC41iUDk8E99iehMzkHJMgB+EkzmUJTtG9izCZlFz/JNAlY96kMxol5yf72xc4KdtQRk5xPEs3IumqpIcKJVwIQEVvsV5cRxi1xuPtdrsaf3RwIvTCNdZkMuHcuXNwOBwqUpHnoDOXjCxjn0RERKgv0CUwdf78eSQlJaFChQoKwjxz5owGCuOaJ9cvji9CP06nE+np6UhLS4PT6dQ4BHH917/f8HHp9iNfwz4iTGboyvlcZMiQIUOGDBkydDF0pX42Wrx4MdavX1/gMYsWLcLevXsREBCg2UR+9tln8f333wMAli5divHjx5dJXE5J9OCDD6J79+6azaFXXnmlWGW0aNGiVHcht23bFn/88Qd27dqFgIAAzd2Wr7/+Om688UbN8TJeZPny5WjYsGGxHDv8pd27dxf4fHJycpE270JCQlCtWjUMHz7cX1VTqlq1Kn7++WdMnDixxGUkJiZi9OjRCAwMVFBCWWjAgAGoXbs2Fi5ciLlz5yIpKanQODV/KDg4GO+9916xXiPj/vr374+HH34YR48e9XPNClaVKlXU/y8WEDJ37lwVSeVvDR06FBMnTsS4ceMKvZ6iulg0b94c5cuXx9q1a/Hbb7/h4MGDmvVk2rRpmDhxImrUqIF27doVGjMG5G725nenPSPGSiJutNIRx+PxKCdtADh//jwmTZpU4vIvpeLi4hAbG3vR58iVoivpc9E777yDxYsXY+XKlaW44vy1ceNGbNiwAc2aNSuT8vNTXFyc38t86623SgQwmc3mYgPYvnT//fejZs2apSqjc+fO+OijjzBy5MgCwd/u3btrXPrOnj2bZ38AADZt2lSq+hRF3bt3R9OmTfHhhx/6bP+RI0ciJibGL+d6//33MXv2bL+UFRQUhAEDBuDpp5/O95ijR4/i0KFDfjlfQfroo48KhIxOnjyJ48ePA8j9u2bXrl35Hvv999/niWArrSIjI9UNAfz8b7fb/XqOy1GnT5++6LGyUp07d8ZPP/2Ezz77DK+88greeOMNvPPOO+ja9fLZeqhVq5ba+/Ql+TnkhhtuQHR0NCIjI3HVVVehQoUKMJvN6ub5stLmzZuxd+/eMin7Yvw13Mnr9Tbxer2+/iJMAjAagOavTq/X+8//v6YJgOYAnACkz+37fN7r9f4AACaT6VoAAwA0ANANwBSTyVQkiwFf8Ijeaeb/zwEAapNURmsBFyJruDkvrke5W3DTXJ5HSv8FBTdW9e4aLFe/Eat3XOE5PR6PeoznDQkJgcViQXh4OCwWS554HTpdeL1eDXxEaEXG7OijX/RxMNLFiKCB1+tVDjTyemV7y8gkliM3/iU8JY+T7SLdg9hHcjNctqd8vWxLXxCRHC9sH6fTibCwMAW4sK7cGNePMfY5YQt93SScIOuhvxYCL2FhYeo8su3Yn6xXRESEcnSRTlI8F+EUec2+YB+9ZLtLaEpGa/E6pCuIjMziGPIFiPjqJ/3410dSycfYDmwb6RTFc+mBHrYvgSE5nwnZyD5lOXoRsJDAmIxe4nkI0ch25/+l0xfBPAItXKsIYujHNV/Pc3NuEMCTaw8/DMs1A4Ba6zhvCKXo49NYlmxDrkNy7rLeHJf6NZFlybLlPGfby2vn6ziHuIYRpJFlsFxf0Fp+oJuECIELa7aEfPh/2faM2QsPD4fH44HL5VJz02KxqP7XA6HyPUW+H1F095LrBdckOdd8rVsS/CGAJeP99A5Oeic6uYbpHdvYB3qnLkMAroDPRYYMGTJkyJAhQxdRV+Rno4JcLux2Oz799FOcPHkSFSpUQMOGDXHrrbeibt26mDRpkoqyqVatGsxmc6njF/ylYcOGoXPnzprHIiMji7UJJm+aKIkCAgJQuXJl7Ny5E+XLl0e9evXUc74ixKQLxoEDB+B2u3HttdeWqg5loT/++AMLFiwo9Lju3bvj+PHjKobbnzpx4oTm+6vSaOXKlVixYoUfapVXVatWRdWqVZGYmKjcVPbu3Vskl63Sqk6dOrDZbCV2RqpXr57f7vgvjuT3qufPn79o5501a1aZlLtt2zZMnjwZlSpVKhT4ost9YYqJicHVV18NIBdgmz17Ns6cOYOhQ4eq75ImTZoEq9WKBx98ELfddluRyt26davPx/3hfEZNmzYNZ86cUb8vWrTIb2VfbDVp0gQnT57UPKZ3DTF05Xwu2rt3b6Hga0lVvXr1S/J9olxP/SWLxVLiCEh/OB4V1w3x0KFDPl1yiqLKlSvjmmuuUb+3aNHC5z5JWbjF+RJvvPXVjhEREejQoYNfznP8+PFCoyeLq5tvvrnAvvv999/9ej5fKixqLyUlBefOnYPL5UJsbGyBfzNs3749j/tTWUgfLWfI/7r99tvRqVMn9OvXD7169UJcXBwCAwPRoEGDS101pYceeggjR47U7JNKHT9+XLM2RUZGwmq1wmQyKRMB7pGVhVwul+aznb91SaO+vF5vgtfr3QKgoNl4M4BDXq/3WCHF3QbgK6/X6/F6vUcAHATQsoj10GzWc+OTjgf8kCEhg+TkZLUJS3cWuv/QuYEOIwRAPB4P0tPTVaSLjBLjJqkEZFi2rKOvzXE9HMLNdF4HHSgIixAGCgwMhNVqRXR0tAIk7Ha7igLjRjIAtfnMzXvCO3SG4OY6XYCkiw5hgZCQEJjNZjgcDpULarPZ1PO8Tm5C62ERxpbpI70KckGSoIUsT0ai6TfIvf/vuhMaGqr6lK/j5rv8kc5FDodDwSGMnuLr2G4ytoxuONLZhw41+k1/uYFPYEPGMQUHByMqKkq5ykgIg9dKWI0ORRaLRQE2dGih646EuvSSc0Iv1ongjYRW5I+M4WLbud1upKenIzU1FcnJyUhPT9fEqekBBuACcOcLngoLC4PFYskDIwG5iznhDNmvcr5JEM5qtcJms6nIJTmf2K+El+R8lX0m+5FziPFq0u1FvzbIdQjIvdsnNDQUHo8H58+fV2NLQj9c0zgnOY45h/m4hE1MJpPGcpFl8bmQkBDVjuxbPTQoo/w4Dt1uN9LS0tQY57oiY+QkKMNjJDxGBxs+zmskiChfwzHOdTcjIwOpqanqwyrXMukCJp21ZBvKdZh9QEiGbWqxWNSczc7OhsPhUPFwXEOsViusVitcLhdSU1Nx7tw51dYRERHKipL1MZvNypmK45PXKccC4SYZXcd2s9vtSEtLQ2pqKpxOZ544M8YC0sGNc4PjT8av6ddXXmtOTo6qk9PpVPPIZDKpdUDOLUMF63L5XGTIkCFDhgwZMnQ56HL9bFSvXj0MHDgw3+f379+Pn3/+Wf09Pnz4cHz77bf4/fffsWrVKtSqVQspKSmoVq0aPv30U1x//fUlqYbftH79esyfPx8tW7bEHXfckef5okIQFoul1HVZt24dnnrqKTzwwAMwm83o06ePem7lypV5vsyvXr26+vtw6dKl2LhxI+69997LLmp4zJgx2LNnT4HHjBw5Ek8++SQ++uijErl51K1bV/P9mi917drVL8DOtddeW2rIKz917twZbdu2xapVq7Bw4UJcffXV6NChgwLmylJxcXE4cuQI7rzzzmK/dsSIEViwYEGp49RKotjYWAC5Dl1//pnHJOSK1LFjx+B2uzF27Fi0atVKXaNeJ06cKFJ55cuXVy4+S5YswYQJE7BixQpMnz4d77//PoDcDc6vv/4arVu3LrI7xpo1a/KdC5UqVSpSGYVp/vz5+O6771ScTFk4gl0sderUCT/88IPmsb59++Lee++9RDW6snS5fS6aM2dOcQ4vsi5HgLekKleuHO64444SrQdbt24ttvOiL+3cuTPfzW8pr9eLTz/9FAMGDEDv3r0xbNgwTJo0qVhAad26dRVE3rlzZ9x///1o3ry55pikpKQi1cdfkt/B6zV06NAiObwVRf7+7rsw+Ortt9/GkSNH/HpOvRwOB5o0aZLv80ePHkVCQgKys7MRFhaGcuXK5Xvs8ePHsXjx4jKB26mUlBQkJiaWWfmGcvdUmQASExOjGR+33377RY+7zU/9+vXDBx98kC+MtnjxYpw6dUr9Xrt2bcyfPx8fffQRXn75ZfTo0QPVq1eH1Wotk/odOHDAL1GO+amswR8vgJ9MJtNWk8lU0k+lAwDM1z02ymQy7TSZTJ+bTKbo/3+sMgD5af/k/z+mkclkGm4ymf40mUx/MqONG59Op1NtfmZmZiobTb4RcWObrhmnTp1SG7IEDbg5K6EEboqmpqaqKBVCDd7/d1qRYAZBBoIT0kmEG93ABccfPXzAYxhzEx4eriABCScQLLBarcjMzITD4VAuGXSQ4CY4N38JKrAuTqdTbSjzOekYwWiw5ORkZYtFRxO6XHBzWe9EwfLyc8JgWWyj/+9fzaa23uVCgizSIYPgFMvkhj433aVzD9uXbaiHD/i4vGOB7U4oQUJHMtoJgCbmidfM6+HjPB9hD8I2QO6dEoRIZCwZ4QnpCJWdnY3IyEgVd8coJo5BOe7EHNL86EUQgPCUdBWSEAXjvwigEIBxu91ITU1FSkoKUlJSkJqaqkAC6f7D8c86sc4SLiEMJCP4OB4I6UmHF+ko5XQ6FaQXFham+lYPkrHteB0SJmLb8Ti63cgxyflPSEkfZUUAQ8aHORwOOBwOmM1mlCtXTkWF6R1a5A8juOTY5WN8Da8rKysLTqcTaWlpatyxTVlnq9WqIB2Wl5OTA5fLpXHfkXOK9ZKwJduO40PGtElXHZ5bOgyFhoZq2lsfjydBysDAQAUOEvxxu91qHsp5x/EkzyldjnJycmC32xEcHKwAndDQ0DxtRvgvNDQUWVlZOHv2LJKSkhAcHKyJ+pLrfUpKioLvGPlFUIsuQfpIsri4ODVmgoKCEBUVpQFM9WNBAqXsD85B6RqlH6u+XJFkVBzHKtuR4A/bzRCAy/BzEaD9bFTCOhkyZMiQIUOGDJVEl91no6J8LuJNPDKG6ty5cxg/fjyAC7E7Xbp0Uc+HhoaiQoUKaNWqFXr06IFnnnkGzZo1wzPPPFOcay0Tud1uXHfddfk6lcjrKEi33HJLvrE3RVXdunVRv3595ZZxzTXX4KabbkLVqlVx7733+nSrkc4c3377rV/v4PaXevbsWegxEyZMwM6dO0sc/TZ9+nR89913BR5z9OhR/PjjjyUqX2rIkCF44403Sl2OL3Xq1Al169ZVMSAPPPAAnE4nvv322zI5n5TZbIbT6cRNN91U7NcuXboUTqfzokSSScXExKjvu6ZPn45HH330op6/LOVyuVClShX89NNP2LJlC+bP1y/1KDBWRGrr1q15oLc1a9YAgAY6efLJJ+FwOHDLLbcUuIEpJSMHpR577LEivb4oGj9+PNq1a4eXXnopz3PR0dF5X3AZymq1omPHjnniSW666Sb89ddfl6hWl52uyM9F/taDDz54sU7lU6WNxZJq06YNqlWrVqLXNm/eHLfffnup6zB37ly13uUnj8eD1157DSNGjMCff/6JVatW4eTJk7juuuvUd/pFVaNGjfDwww/jqaeeQv369TWvT01Nxa5du1SU4cXQzp0783VcysnJwbBhwzB8+PBSxRSVK1euQECmJGrYsCG4f+1Le/bswdtvv41Vq1b59bxSmZmZBYIPiYmJsNvtaq8oOjq6wOO3b9+OqKioEtcnLS0Nv/32G959912fTlIpKSkldqsyVDSlpqbmG+vcrFmzIv3NczE0efJkZGRk5DseFyxYgH/++QdArsvZ559/jg4dOlyUz1Qulwt///23313CpMoa/Gnr9XqbAegOYKTJZCrWX94mkykEQG8A8q/eTwDUBtAEwGkA7/JwH0XkySbyer1TvV5vC6/X24JfJJjNZhWZQ6jG7XYDuBDrBeQSjtwIJQRC2CMkJARBQUE4d+6cxv1DbroHBgYq+Icb5dzs5UY14QVCE/rYHwIxkiDlRj030QkmAVCb4CaTCS6XS7lfyLIsFosCn+gUxM17AAq64Ma39/9jc7i4Op1OjTOIBB7o9EEHFwI/emcJCXTIjXoJEEgnJAI0jLfiBJaQimwXPifGVp7z6KPSAGjalH0jy+D5CC6wHeioI50z6OYigRT+X7o9+XKIkc5C7B/2JQGvwMBA2Gw2hIeHIzg4OI97jbzmiIgIBQrxmlwuF86fP4+QkBA4HA6kpaVprlFfjj5ySLYxwSO6nBASYT9KCIdRVTICioCKBFn0bkf6+C8AGscfvfsPH+N4IgxF4I/zj3UkjEPoh4CKy+XSzG0eq49B0o8RCS7xefY5j42IiEBERITmOuWcIqRjtVrVmhEWFgabzYbQ0FC1vshz0EGGYJeMkQsKClLOZnSDIgzkdDqVa1l2drbmQxPHL4ERjkEZfUbXM7aTvB451+lCI8ESuW7p21K6IXF94lzleJYuV+xztgFBMtZHQl/68+Xk5GhgGF57YGCgGj+c6x6PB1arFaGhoQpyIRh07tw5BfkQVOTYIRzJMcr3HH4wsVgsyoGNkutITEwMYmNjERoaqsYIv5ySY1SCPzIGTz+3WD77TAJ2vH6z2axx7SJMxjZkn3AdkJGYhi6/z0WA9rNRcepjyJAhQ4YMGTJUSl12n42K8rnozJkzuOWWWzQbldOnT8fbb7+NnJwczZ2FTzzxBN5//32sWbMGBw4cgMvlwoQJEzB16lQAF240u5S6+eab0aRJE/To0QP33Xcf3n33Xc13Tk888USRyqlVqxZq165d4g0uIBdiaNq0KebMmQOXy4UvvvgCq1evxvnz5xETE+PzNe3atVP/X79+PR566CH8+uuvJa5DWahbt24FPj9r1iwcOnQIH3/8cYmimgYPHowOHTqgTZs2fonyKkw9e/bEm2++WSZljxgxAosXL0br1q0BAA0aNMDu3bsRHx9fJueT+vHHH7Fq1SpNxJzUoEGDMHDgwDybAkOGDEHlypWxfv16dOnS5aLe7Vy3bl31/6K631D+BFPKQh06dEC/fv0QHh6OmjVrok2bNujSpQsiIyPVMWfOnCk0igTI3WzUb14vXboU2dnZiIiI0JTXq1cvWCwWvPXWW0UCuX777Tefj999992FvraoyszMxIEDBzTAKbVp06YrwiGle/fuqFevHpYvX57nuUOHDl2CGl2WuiI/F5VGFSpUAADccccd+PPPP3HfffehTZs2ZXGq/2PvvMOjqN63f6dvdtMTekdA6VVAkKooCAKCUlVQEQEBRbEjil1EEKSKCKJ0C6IIqFQFBEHpIEhvoaVuS3//yHsfnpndJLubhPL7zn1deyU7O3PmzGlbns/cj8cqytSSK1aswObNm30+vihSj+3YsaNA0CYkJESzFj700EN477330KZNG6/T8TVo0AAzZsxw+ezjdDqxbNkyzJw5E6dOnfKqzMJo27ZtOHDggFtQpGvXrhg5ciRmzZpV4Ge1/HT58mUsWLDA6/fh/FSlShW88847eb4eFhaGxo0bqxsOikNVq1bFXXfdle8+jAm0bt0a77zzDnbs2IGhQ4e67Pfss89i9uzZPtXj9OnTWLhwIe666y60bdsWX3zxhUsK3fT0dCQkJPhUviHvlFc7m83ma5Ka1xOVKVMm37RvNWrUUClrvf0+vm/fPvz2228+u0udP3++2NJkUsUK/uTk5Jz7/38vIjevqLc2yp0A/J2Tk6M+1ebk5FzIycnJysnJyQYwW5R5BkAFcWx5AB59K2QwnY4HHBBMnyJdOhITExXo4u/vjytXrmjcUcxms4uLC4O0/v7+mvRRJpNJPST8Q+hHpphigFY6LLgDIWQqKQI6dFFhgJZ1AqCC6+fPn1e562Tgm6lgGNhnIB+4GmiXIIsMFvN8rAPTYdHpR9YVgAvsIt1/6FIhwR7CC0xNRqCBfUgoRkIOsh0lUCEBHAn3sBwAmj6SYAKvl05IrBPPlZiYiOTkZA20xLrKa2GwnXAPQQjux36k64xMFyWhmoCAANXGbFfprsLAPK+DYz8lJUWBMFlZWSpgr6+3TCunlx4SAnI/oFosFgXQSEAkIyNDgVsSmpHtwbpzPOnHh3SFYf/JvpQOMEwBxb7nOOR8ZjnsWwn1cDxJNy8JUUgwRdZLAme8Fulyxf7mPGH/SViGYCFhFJneTAIYcjxzDaM7VGZmJkwmk8YlKisrC+Hh4Zrrpwjl0LmL4lyk0xmBIo4JfiEhHMO1grANrzE5OVmzlkmokmuybEsJ/cj+kY41cm7I/qeDTWBgIFJTU1X7E06U8Jwc13RxkmkOgatOY0xFFxISotLAcSwQALLZbJryOe/CwsLUek/4KicnR5OSj65C/OFWglS8thIlSiA6OlrjpCUhG73bj4RRZVpGtj3HKI+VkBrHVEZGhiqD2yS4xf953XR2M3TzfC4yZMiQIUOGDBm6FrpZPxt9+eWX+Pvvv9UP4TNmzMCrr76K9PR0FQB+/vnnAQCffPIJnnvuObRr1w7Hjh3DhAkTlDPQjaSMjAz8/vvv+PLLL/HKK6/g66+/Vq956uxStWpVXLhwoVDBnG3btuGll17Cvn37kJOTo4JQdrs9zx9IW7VqBQCIiorCjBkzsHPnTixfvtznOhS1atasmS+I9Omnn6J79+744IMP1A/Q3qh+/foK4IiKisJTTz3la1U91tixY1GvXr1iKTsrKwunT59G06ZNUbJkSQwePNity0lx6a+//lKBaL1GjBiBRo0auQRQH3jgAdxyyy34/vvv0aBBA3Tq1OlaVBVA7rzzVc2aNUOvXr2KsDZFq4MHD2LFihXYuXMnzp07h4oVK+KHH35wSQu1ffv2Ast67LHHsHbtWk3KGZvNhkOHDgHIdZai1q9fj0OHDqFNmzZqfclPO3fudLu9XLly6NOnT4HHF0ZDhgxBQEDATeH607x5c7dpGIcPH55nGp7/Nd2sn4sKo2nTpuG///7Dt99+i8aNG+PVV1/Frbfeeq2rodGcOXPw119/FcqZRGrgwIGYNGmSW3AvPx09ehQTJ04s9PkTExOxZ8+eAvcrX768uuYdO3Z47cR15swZt3AN5XA4sGrVKixevNjjMvN6P/ZWv//+e4HtP2TIkEKt2Rs2bMCiRYs0saDCKCAgAD179sQbb7zh9vV+/fqhXr16xZ5itGbNmvm+LjORWCwW3HbbbZp+u+222/Dxxx9j3LhxmhS+nmjv3r2YPn067r33XvTv31+lMw0LC3NxcrHb7R6BwIYKrzNnzuT5WpMm1/+e4k6dOqFfv375uk917NhRjScJ0XuihIQEzJ8/Hy+++KJPqYiPHTtWrG4/QDGCP35+fhY/P79w/g/gHgDeYkx9obMm9PPzKyOePiDKXAGgj5+fX4ifn18VANUBFPzJH1BQjDiHxgFDBlEZ4GTw3G63w2azqTc1fRonPdjhDkqQwWkJWBB4kfXSgxDS8UQGyll3Qgv6gDaD9vzAnZqaqnF3kO4rehCFAXS9AwZw1W1DwhmZmZnKOYWgAAEGBrBZN1lHeU366we0qbBkQJsBZwlG6eELvTORdGUiSME2lg430hVIuoAQcGC5EjhieiHpZsT2lWKbEJLQv8Z207vwSLF8CfWwPAbeZdsTFpDtwf3Yx9IRSbYzzyfbVfab3CbhG3mMhGsoPYTD+urHuv76ZeotfZ3lvKJ7iv4aJFwkoSvZ3kz9JK9ZP1/ykhx3eocVCdPpoSwJvvA6JRgjHcD081yem+VJ8EOmzWJZsk14fm7nfOW5CZcR4JGQIrezDJnqi3CehHm4n/xfjgG9443+XLKNeR59P7O+Mh2VHG+y31l/rlkSXqLrDdPpAVCgD9deviZTKMpzsQ/oJiVdc/TOYhL8kXPD399fwaP+/rnp6WQbSOnBH+nqIwEr/XzTwz9yfXTXD/o1XKar+1/XzfS5yJAhQ4YMGTJkqLh1M382atSoEapXr65+K/rhhx/UawxA33333S7H7dmzx6PAy/VWRkaGJhXLL7/84tFx0dHReaYL81SVK1dWAeyzZ8+iWrVqAHKdP/ICTUqWLIkyZcqgW7duqF+/Pho1anRNHVcKEm8QzEs9e/bE/v378dNPP/lUfsuWLVGjRg31vGHDhj6V443at2+Pe+65p1jKbtSoEWrVqoWTJ0/i4sWLOH/+PDZt2lQs53Kn1NTUPNOanDx50q3z1PLly7Fq1Srs378fBw8eRFJSUjHX8qpCQ0N9PrZkyZKFOv5a6YsvvsCWLVsA5F6vfn7v3r27wDLomKx3c2LApmXLlprts2fPxrx58zwCf8qWLZvna8U9H5s3b46zZ88WylHkWqly5cpu09akpqZeh9rceLqZPxcVRnXq1MEtt9yint9yyy0IDw+/1tVwUenSpVGjRg0EBwejefPmhSqLMUJvnevKly9fZGuIdNU6fPiw29+OLRaLeo9r2bKlSrvqiQ4cOICFCxfmm1LMl9+Gb7/9dq+Pcadjx44V6M5Bc4bCaNeuXUXu5nnnnXe6hXvq1auHuLg4t+/jRVmHgtrkypUrOH78uGabdObLyMhAuXLlNI5SnurKlSvYsmULDh48qNnOWKOU3W5XWXwMFa+OHDnitq1TUlIQGxt7HWqk1S233IJy5VwyV2pUu3ZtDB8+3Kfy+f0kMDDQJ6fWxMTEfNP4FYWK0/GnFIA//Pz8diP3Q8PKnJyc1X5+fkP8/PyGAICfn19pPz+/MwCeAzDGz8/vjJ+fX8T/f80MoJ8/4XYAAQAASURBVAOA73Tljvfz89vr5+e3B0A7AKMAICcnZz+ApQAOAFgN4OmcnJy8EVMhBnulo01SUpJaOBnoBqAJkjLYeeXKFU3al/DwcI2bj3SD4BsIXWrkPkzxRXcJAjeUDNgy0E4AgIANg+Y8X3h4uEr/xBRQPJ7XTNCAcI9MZcbrYbvQmQLI/bJlNpuViwphBX5Bzs7ORlBQkEpfFBERAT8/P1y8eFGlz5HprwBooCGZoovB5LygBwbMZVCdx8qAPR1Q+FwCIdLhhqCMHtLhm4p0/wkICMCVK1cUrMVxQlcS1ller3TxkM4ZdODQO8bIscBgvT7dEesaHBwMi8WCyMhI9SbL1GD6FE1sa443Oj6FhYUhKipKtQn7V4Il+r96mEoP53C8yzRhejBOn+pJnwpNXz7Huz7VnT7dF1MuScgOyIX+QkJClLOLn5+fGlN+/9/NR143/8o0Y4SB9OCPBFfo7CNhNX0qr7S0NFUPOqVI0M/hcMDPz0+l4aOTD52aAgMD1dojXZmky4ysF9tB72wm1xuOPaYAk3NQOn6RnqUzklyrOJZpCxoQEACr1arAGLYN/2c/sN/cgYsEkeQ6LJ/roTXWx2w2IyAgQDmZybYmWCfbJTMzE8HBwZr1hcCT2WyGv78/kpOTcenSJaSlpSE4OBhms1mt4ydPnkR8fDxCQ0PVWJLrTnBwMNLS0hAYGAiHwwGr1QqTyaTqGxoaivT0dFitVjWvY2Ji1LlLliyJyMhIhIWFqfSSTqczzx8ZWS5Tikk3tuzsbHWNcu7IsR0YGKjmMMcwywwKCnLr0ESnN2/zQf8f1U3zuciQIUOGDBkyZOga6Kb8bPTss8+idOnS6vlHH32ENWvWqOeTJk3C4cOH3UIcL7744k0RnAWAr776Ctu2bcOhQ4ewd+/eAvcPCgrCLbfcUmjgYe7cuWjdujX69u2L6tWro169emjYsCEaN26cZ6qvihUr4p577sHbb7+tvndWqVJF00/XUzt37sTIkSPdvjZgwADs2bMHvXr18ilYERkZiaFDh2qCKT179kSVKlV8rq8nevvttzF27Fg0aNCgyMvesmUL9uzZg2XLlhW8czEoODg4z4DZn3/+ibJly7rccPXll18iKSkJBw8eRLdu3bBo0SK3xxeH+Luftw4DMTExqFKlSrHfcVwUmj59Op599ln1vFevXhrnHm9Sh+ihzMWLF+Py5cvo37+/Bj6YN28e3nnnHVSvXl0D1rlTfo5UDz/8sPq/f//+ePvtt/HQQw8V2dwJDAy8aRwO6tevn2daNEMAbtLPRYVRuXLlUKFCBc02GTe4nipfvjxee+01fPTRR3jllVdQt25dn8uyWCyoVKmS164SISEh+YKF3mjLli2YOXMmhg8fjrNnz7r9vFa2bFnUq1cPvXv3xrvvvotu3bp5VHZGRgZefPFFvPTSS/jzzz9x8eJFt/uFh4fjjjvu8Pjz2YABA9C7d28MHjzYo/3z04oVK7BgwYIC93viiSfy/LzpiS5cuJBveiFfpYdm6tevj27durl1/bt06RImTJiA0aNHF8m5s7Oz83VOee6559CwYUP88ccfAHLdUGw2m3r96NGj2LJli4vRgScqVaoU4uLiXLaHhIS4OMjl5ORcU/D6f1lHjx51+fx46NAhTJs2Td00fz0lwbO81KVLF4wYMcLrsjMyMvDPP/9g8+bNaNSokdfOm+np6fj333/x77//en1ubxRY8C6+KScn5xiA+m62zxT/xyPXRtDd8XYALnhYTk7OI/mc810A73pbV+lckJ2djStXriinh6CgII0zBYPZhDwIiqSmpsJut8NsNmsCowyqh4aGagATAkD8S8BCDwYAVx0yJOxBQIEBV7qR6IPxMnDLwG5ycjLMZrOCJhwOB2JjYxEfH69cIyIiIlycfRj8DgoKUunKeJ3SBUfCN3SoiIyMVP9L9yAJKKSnp6tFm+CFhEp4vSxbAj3SDUYCD4QyuC/7j+fl9ekJUYJVEv7h/vL/7OxsWK1WFTgHoOnHgIAAOJ1OBV2wbjxetgNdUKTDBsdkWlqauk7pnqMHMJhCSIJTWVlZOH/+vBpTEoaQ6e0IFxHmIhBBJxOZOkw6ghDIoaRjivwxxN/fX1n/yfRJ0tWG/ZCTk6MBfwhj6R1ZZPo6PpeOWRkZGS4AFtNSMUWTHEOE4kiAE9ZgqizCfRzjEhChpDOPhPL0LlRsn7wclQjd8LyEQKQLDceGHL8cT3JeyjmXlpaG6Oho+Pn5KQBHvhlzPQwJCUFGRoZaszhemAqOfUmgjH2SkpKi1gW2O52WHA4HLl26hMzMTPUGzLbjfJF10F+XbE+2kXQM43rLlGYSBKNkejIJQbE+PA/nIiE+6ZAjxyWvVY5xrhsmkwlOpxNWq1VBWRcvXkRkZCTsdrs6B+Gz6OhohIWF4dy5c6pdZNo9vr+kpaUhOzsbYWFh6n0kJCQEFotFM9bcyc/PT0FmBDklRMh217u+cY3mNRK4lDAl04axvTMyMtQ40v8w+r+om+lzkSFDhgwZMmTIUHHrZvxsdMstt+DRRx9Vz1NSUjBr1izNPhcuXMDMmTMV+B4ZGakJyp4/f97X019zPfLIIx67ulSvXh3h4eGFcm4YM2YM6tati3bt2qlgU05ODurWrYsVK1bggw8+yPOu42nTpmmCEmfPnkV8fLzPdSkqVapUCY8//jimTZvm9vWAgAA8/fTTOHv2rE/lp6amolSpUpptkZGR6NevH959t/i+BkyePBnTpk1Ds2bN0KxZs3xTe/ii6dOnq3mzbds2DBs2LM90SkWto0ePonfv3m5f27p1K4YMGYIKFSq4TWk3btw4nDhxIs/+Lg4xLYu3Tgp16tRB6dKl8c8//xRDrYpeZ8+exZUrVxAbG4ty5cph1KhRCqo5fPgwDh065OLm40533XUXqlatimPHjgHIhRz//fdfvP766/j888+xZs0anDx5EuvXr0d8fDzuvPNOHD58ON8y33//fTRv3txtkLxs2bKYN28ejh07BovFghdffBFZWVlISUkpVHCZstlsuHDhAkqXLn1DrHl5yWQyoVKlShg7duz1rsoNq5vxc1FhVKdOHTz77LP5AgXXW127dgWQ+xu/w+HwOQ2UyWRCiRIlFJzsqX788Ud88sknPp1Tr+PHj2Po0KHo2bMnQkND3bZ73bp1sXTpUlitVq/SB166dAlbt24FAPz2228oX768S0pGINcBsWHDhrjlllsKXK969uyJAQMGoH79+mjevDmqVauGF1980eM6udO3336LMWPG5Oso1bp1a7z//vs+p01lTK0oFRYW5gIJ7N69G8nJyShf3nU5yMrKwqpVq7BhwwY8/PDDhQZNz5w5g8jISA3Mo1dycjJWr16N2NhYREZGokyZMprvQP/88w/Onj3rdVqyiIgIt2OR8REqJSUFhw4dyhM6M1T0evHFFzFp0iTcdtttiI+Px1dffYUlS5Zgy5YtRf69wFsdP35csRpFoSNHjihwMykpCStXrsQdd9yBNm3aeJ0SMiEhAadPny723wSuPz57A4hBUgbAuYgxoE3IBYAKMjNYzy9W0pVFHkNnH7PZrHIP8jXp/kN4h4F8m82G5ORk2Gw2pKWlIScnB2lpacq5hRCDdKwgwCL/19chNjYWlStXRmxsLEJCQhQQExsbiwoVKiAlJQV2ux0Oh0OBA3TxiYmJQVxcnHIu4jVIMIQB35CQEPVgwDsoKAg2m02lvpLACtuW/cE3SAbWgaswgIS0CD9I5wnCDnRrkg45DGRLCEumn2K50vWFrxHaYBCe/e9wOFT5DG7LOst6s3+4D8vSu9bw3CyD52e5dONwOp2w2+1q7Onr7+/vj1KlSsFisSiIgm1GkIC20+yv4OBgOJ1OXL58WcEJBI30EE1GRoZmPOqdbAhWSDccPUwjAQ7OQf24ptON1WpVDiVOp1NdN9tMgkDsY14bx6AEh0JCQhSgRhDL4XAgNTUVgYGBCp5jGRxbEtiS0BmlT+cl+1a+8UmQUAJArKOcR6yzdO6RH3AcDgcCAwNht9uRlJSkxj3XK7YLoSiOB5mHleuRdB+jwxWQ624mr5Ntwz5MS0tToAfvViQ8lpKSosAQjn+ZXpDrnxxLdMXhPObYY/0J4hFSkc5K7BtCfDJdFh1zCMHY7XY1Xzj+ZAo1novnZ53ZXgRweD62H9d82U8SlpFtW7p0acTExCApKUk5jtFxiC5CVqtVjb2SJUuiXLlyiImJQXBwsAL4WD5hL44zudZZrVYFPrGPOT/k+4eEVKV7D98f5JrNeUioj8AUz18cdzsYMmTIkCFDhgwZMnQt1b17d9StWxdOpxP//fcfIiIiNG4OVFpaGtq1awcg1wmkX79+17qqRaL//vsv3xRVUufOncPDDz+McePG+Xy+r776St15GR4ejuzsbMycORP79+/HxIkT8001wADW7t27ce7cOXzwwQc+16Mo1bBhQ9x77715pkAzm82a9BveKjs7W6VAkkpISPCqnJ9//lmTss4Tvfzyy3jhhReK/Mf9GjVq4LXXXsNLL70EIPda9GmYilMXLlzA8uXL3b62bds2lC1bNs/UK4GBgejYsWMx1u6q6tati/Xr1yuQYv78+V4dv2nTJkyaNAkOh6M4qlcskuvLQw89pLnJa/LkyR6VUaFCBQwZMkSzbfv27fjoo4/QqFEjvP/++1iwYIEKHhL6yW8d/+CDD/JNczhgwACMGzcOo0aNAnD1d7Ki0FNPPYXnn38ebdu2xV9//VVgaovrpbvvvhvnzp3DN998c72rYugG0Ntvv41XXnkFbdu2vd5V8Uj+/v5o27YtOnfu7NPxJUqU8Hpu/v7770UG/Uj5+fmhZMmSea5BQUFBXkE/ABAbG4sdO3Zg69at2LBhA2bPnp0n2LNw4cIC3S9LliyJpk2b4s4770RMTAyqV6+OoUOHFtrB5ujRowWCnADQp08fn52WVq9ejU2bNhXp7+DlypVzC1LltZ6WLl1apd6aNWuWil/76obTtGlTzJkzR/Oe605RUVEoU6YMLl68CJvNpoHLdu3a5ZIOzBMx9kKZzWYMHz4cU6ZMQdeuXVV64iZNmuDuu+++KVwM/69o9erVeOWVV3Dp0iV89913mDFjBo4ePepz6uKi1I4dOzB9+nQ8/vjjhU5D7XQ6MW/ePOzfvx9A7no+a9YsDBs2DDVr1vSqrJycHJw5c0bB58UpA/xBbk621NRUFYQm+MAULFxc6Egig8wMqvM1wjQWiwVmsxkWiwUWi0UFgLmd8A9TKBEOYEDW4XAo0IGwD+tApwYGW+maItMiyXQtJpMJkZGRiIuLQ2xsLKKjoxEVFQWLxaJxCQoMDESJEiVcAt/+/v4ICwtTgIDFYlHphJhaiuCChGokEEQ4ICEhQbU125KBfIIKDBzr3WIomXbIZrOpNpApiyQQINN5yaC9BKXYtmxf6ZAiHVhkeimWQ8CBZclryczMVHWhkw7T/hD60DvByD4lMELHDOk6QsCB6XqcTqeqHwGLgIAAmEwmxMTEaKAKh8OhPoDIsRseHq7qS9cUAj4STsnOzobdble5MwkC6NNXSWcgPZSld76R6Zl47cBVMIxuVYmJiUhKSkJSUpImNZY8L4EHXpeEavSwCF18CDDxmjkG5Py2WCyq7fVwmnSoAqBcY/TtwboQDAkNDVXtwnkinZyAq7AW24nn5dhhv8v0WdLtisc4HA417+U84PxnWXQZIqBF5yrOWx5/5coVpKenK2DQ7/+npwK07js8N2ER1oewiHRHkqCbdFmTc1Q6jUkwj3NWwkXsC+kMxDWN7c31Vg8Gslx+yJQOOJzbBBuZPoxjj3MvKioK0dHRaoz5+/urc0VHR6u6ALk/aCYmJiIrK0vNEa61ZrNZpUs0m80oWbIkypYti7i4OAUBOhwOVTeuIXwvY3vxfUZCq3xNtg/XkYyMDJXujynnZHowvTuVdECSwNnNYj1tyJAhQ4YMGTJkyFBe6ty5MwIDA7Fx40aVdqB9+/aafWJiYjB27Fg0atQIf//9N3bs2IGFCxdej+oWWrVr18Ybb7zh0d34SUlJhXYOOXnyJBYtWoRWrVrh448/hr+/P1q2bImUlBTExrqYGLjVF198gZYtW3oNQRSXli9fjubNm8Nut7u8VrFiRUyZMqXQgfq//vpL83z8+PGYMWOGV2UkJyejffv2Xv2AnZqaivXr13t1Hk90+PBhrFq1CkOGDEFsbCwGDx6MTz/91KNj58yZU+zQV1BQUJ53z+/YsQO333476tSpU6x1AHIDbHfccQcA4NNPP/UpFcqYMWOKulrFqk8//RTbtm0DkPvbMOEwAJg5cyZWrVrlUTndu3d32bZp0yaVXs7Pzw/z5s1D/fpXzVfyStcHAB9++CH69+/v9jXpgiZByqJyJWjatClq1aqF0qVLIzo6+ppCct6odevWWLNmjXFTmCEAuZ+d+vXrp0mvd6OrVKlS6N69u0tqMk+0d+9er53Qly1bhnXr1nl9roJ08eLFInPBoEJCQlClShVUrlwZALB582bMmTMHCQkJyMjIUEHuM2fO4IsvvvCojufOndOsmWFhYRg2bJgm7aMv2rp1a4HAckREBIYPH+7zOebMmVOkfVehQgW3bkf59eOECRMQEBCAmTNn4ssvv8Tnn3+OKVOm+FyHjh07olq1avnuEx8fD7vdrvqtRYsW6NWrF4Dc98KzZ896DYs7nU7N3OnQoQP69Omj6kJQ7UZJ7/u/pm3btmHHjh2YPHkyEhMTr3d1lA4fPoz3338fe/bscevQ6amcTieWL1+OKVOm4Mcff1TbmzVrhp49e3pdnsPhwJEjRxREVJwywB/kfsFlIJOwBIPfMlUWU51ICEOCMzK1Dp0cGDDlXz6kkwkDr3RP4TkY+GbwFbga+JaOMDJALgPjMt0V6yXrpqc0/fz8lBOQdDKRcBPdWmQwHriaf1XCCwQE+BoDzgxIywCxHnSQ1wZA0158ZGVlKTckAhYy6C6vme0h/8r/KQkeyPaWLkPS4YUgiQQoJCzCMcF2k20iHX1kijfpLMQAvBxbMr0Uj+MYIVCgh9Lo2sE2Itwi21iOC4IpKSkpGihJD7fo4TOZVk4+17c9zyHbWAJWPId0DOJfm80Gh8OhcdGR7k88vxxH7j5cy2uSwIweYGJbhYaGqnRbnJ95nUM6HHE/6T5DME6uL/Ihz61PZSfbDYAGTNSfT9+uErgj3MPzcN0DrrrScE0ErtpGm0wmNeZlH9Dth9ckYS4peWeTnP9S0lVIjhG5Xb+Pvv3kGk3HJMJeEuIMCgpCdna2hv6V7jhsHzkGJYzHOUlgku0l1wE68hAyI4hEJx0gdx2w2Wyq7pzzPC9d48LDwxEZGalsWel6xb6QawPHTmZmJpxOp5o7+vFJeFC2pQQF2T8StJLznPWXa6Bc3/TrhiFDhgwZMmTIkCFDN6MqVqwIANi3bx82btyIxMRElyBmQkIC/vjjD/j7+6N8+fI3jPOMLypTpgyuXLni8x3uvurIkSPq+1mZMmVw7NgxnD592qNjmzRpghMnThRj7YpOZ86cQXJyss/gz4gRI9C9e3eEhoaq72WHDx/GpEmTvC7rn3/+QVZWFpo3b+5TXYpaP/30EyIiInDXXXdh+PDh6NGjh0fH3Xbbbcptq7Bq1aqV2/OmpKTkGWA6ffo0oqKi0K5dO7Ru3bpIUjnlpfT0dBVcyy/9xv81rV69Wv2vH6/bt28vVNm7d+/GhQsXAACdOnVSQazg4GB88cUXec7Vc+fO4eDBg0hJSXF57fvvv8c///zjAvq429cX1alTB71798bp06exb98+/Pnnn0VSblGrRo0aOHTo0PWuhqHrrKioKJQrVw779u273lXxSTVr1kS9evV8Onbt2rUe75ucnFxsziVZWVnFBknw9+PKlSvjjz/+wIEDB3D8+HEFKOvTVeUnd+m4KlasiGbNmhWqjufPn/fo5tRGjRqhatWqPp1j8+bN2LFjh1vo21dVq1YNkZGR6nnZsmXRuHHjPPcPCgpSqYmmT5+OX3/9FQ6Ho1DvPfL87pSYmAin06mMD/r27auBkpOSkryCPxMSErBo0SINzM8Yh5TJZPI6jZ6hotH58+exZ88ej5y0rrUiIiJw++23+wRrUgcPHsTKlSvRtGlTxMXFFdrllNmergUkZYA/yA0qMwWV3W5XjhT8EsWURAyWZ2RkIDQ0VKV3kRCDhBxCQ0MRERGhgrV0+pEpVRhYZnqhlJQUJCcnw+l0qsAtU3zR5YfBbNadQWh9qiA9hCRdXSIiItQXRDpKEE4qUaKECu47nU44nU5NcJvgBvcBoILYISEhyumI52OaGrvdjsuXL6uURISgCA7I1DoMeGdlZWnSofGag4OD1ZsnXWcIZDDYznrK1EjA1bQ90sFGAjXSNYYBa31KLqbVysnJgclkUmWbTCYNUJOenq7ag+AIASDpesL+lSnJCBXI/wMDAxUcxr8EqlJSUpCUlISUlBSVJouABl2nsrOzYbPZFCwiITbpjsMHXUqSk5ORnJysAB8JxOjTeenhGQIJenArODhYA0pwbrFOEvACrgIE6enpmvRmaWlpmjRaEkbgeOEjNDRUAQgOhwN2u121BccCx4DD4VCuQnTrCg4OVueTsJoExFgO24PgBPuK25kOi+VJWIpjgnVl29FtyOFwKKCMUEtAQADS0tIQFBSkXLMINHFOsY1l38sxL+G2jIwMBf5wrsm2kq5IdL3h8RwPMlUa02sFBwcr1zC2mRzznDt6YEyOMbaFhNg49tjeHHM8P9cpmQ5MrqFMRSYdj9g+3N9dakCZjoznYz8RtJJp03jtERERAHLTAHDeEvYBcolii8WC1NRUBAUFISYmBuXLl0eFChVQsWJFxMTEwGq1qvctuhZxTGVkZMBmsymg1OFwIDk5WaX4koBgTk4O7Ha7Jm0f20XOZ44p9gXL5riSa45cR9lGhgwZMmTIkCFDhgzdzCKMsnv3bmRnZ+Ouu+5ye7f6gw8+iNatW+Oxxx5zucnhZtLOnTvxyy+/aFw1PFVhwP/4+Hhs2LABQG6Q57vvvkNbD9OBuHPyuN6qVKmS2+1xcXE4ceKET6BSly5d8Pbbb2Pp0qV47bXXVHsvW7Ysz/Qa+Wnr1q3Ys2ePulv/RtD3338Pi8WCxo0bu01xoVfNmjXRtGlT7Nmzp0jOP3XqVI3jC5Dr+BAZGYkmTZq4Pebff/+F0+nElClT8Oabb6JKlSpFUhd3KlGihPr9wJf0GTer3nzzTfV/x44dMWjQIPXck7R5aWlpmDp1qtvXPv30U7Rs2RLr1q1DXFwcHn30UQBXb4T75Zdf3B43efJk1KtXD/fee6/GBWvDhg0YNmwYGjVqhOnTp2uOOXnyZJG483zxxRd444038O2332Lq1KmFurO9uNSqVSs0a9YMixYtut5VMXSdRGeScePGoVOnTgqkvtnUuHFj3HfffT4d+/nnn3u0X2ZmJjZv3qyBHItS77//frGUC+T+lt26dWsMGzYMSUlJeOihh/DKK69g7ty52L9/v1fwnztwNiAgAHfffXee6TY9UVRUlEdQbu3atX3u6wsXLmDx4sXYuHGj16lX81JqaqoGWHrggQdcXEepXbt2YeXKlaq9d+3ahaVLl+Krr77CmjVrcOXKFZ/qUJBD1759+3DmzBmUKVMGTz75JHr06IE77rhDAVRHjx71OCPA+fPn8fnnn+Oll17CwYMH1XaCE1KhoaGGm9x11Msvv3y9q+BWDzzwAAYOHOgWIvREWVlZeP7553Hp0iVMnDgRgwYNcjE48FZ2ux0nT550gdeKQwb4A2hSmrDRpYsHgRJpHRYREaGC3QzqEuIhKGKxWBAeHq7SJxHAkUCKhHvoXgNcdZWQD+7DoKwMhjNQL1N9MdhKaMZsNiM8PFwFwfk6nSro1BEbG4uoqCiEh4drUt4AUEASr0WCIiaTScFNTG3G601KSkJycrJKa0OIhvXmX+nUIYEUmWItJycHaWlpKujudDo1gW/pxCFBDDo46aEf6QQiXXTY/3IsEHhgXfkIDg5WP+jRuSMlJQVZWVka8Ifpvpjqi2IaJKYV4hsW4QG2kQzyO51OBUtJmMHPz0+lkOO4DAsLQ1RUlKqPhIbYDhxj8lojIiJgs9mQmpqKxMREXLx4UY3D9PR0l3RpFNtSpuHiF2X+MMHzZWZmwmazadyD2C90uGH/yfFIQEb2g4Rx5DyRUA6BE5kyLi0tDWazWYEshB/S09M1zlT+/v6wWCzw8/NTa4ZMDyXhJgn90OmF0Iec3+w7CUGxvzlH5dhnuwPQXAuhs6ysLISFhalUXewHrlWEQoBcEIbwhuw7zrvQ0FBNSjPuz77j61zv2JcWiwVRUVGqbbOzszVQIEEZfTozgo2UnIsSeJQOPBL8kfsQ1NH/4Ew3M/Yx50xaWpqi3uU6nJ6eDpvNBqfTqQBA9rGEpugcRGDGbrfDZrOp4ABT3DHdY3BwME6dOqXGFgDlAORwONSc4dggCBQREaEchAis0kmN+/MYwm0EONmH7kBVtiXnDt/zeM3yvYRtyPFNKJXzgfODY5jpBQ0ZMmTIkCFDhgwZupm1ePFiAFBpBv755x+UL18e/fv3d7kD+ciRI1i5cuW1rmKRKiEhAT/88ANuu+02r44bPXo0fv31V6+O6du3ryYQIwNeXbt29fju9PDwcJQtWxZAbmqeLl26eFWPotbrr7+OLVu2uH1typQpqFixotcpfzp16oSnn34akZGRmjQYp06dwsmTJ32q5++//47Vq1djzJgxN8xNG3379sXcuXMxePBgj1KX9evXD8eOHcMPP/xQ6HOXLl0aDocDtWrV0mz/+OOPERISkifMlZCQgLNnzwLIBb70xxelZPq7/zUnFab7CgwMxIgRI9CiRQs0bNgQI0aMKPDYkJAQ3HHHHXkGqo4ePYp58+YByF1D6CLw2WefqTTs7pSZmYlWrVqp9GtbtmzB3XffrdyYPvzwQyxevFg58tx///3o2LGj5xftgX777bciLa+w6t69Oxo3bozOnTvDYrH4BCUauvlVs2ZNjBkzBvv27cPIkSPx5ptvqnlSlLLZbDhz5kyRuqzoZTKZ0KlTJ58ccypXruzRe3RgYKDPrkKeqEyZMsVWdsWKFTFkyBD069cPdevWRXx8PL777jusWbMGn332GSZPnuxROYw3udOGDRt8Ti3btm1bDB061KN9y5cvj06dOvl0HiAXgnnllVfw1FNP+VyG1I4dOzTPp02blue+V65cwTfffOOy/ezZs5g/fz6+/fZbr89vt9sLTBG3c+dO7Nq1S+O+ExISomDpgwcP4vz58x6dLzU11e3nUWY0oLZt24bRo0ffcO9/hq6/cnJylOuVL/rpp58QExODV1991eVGAF+UmJiIy5cv49y5c4UuyxMZ4A9yqUCZTodOJ3T7kVAGUyCFhoYqxxqCGnRVCQsLU4Fd/q+HAmQKGQZlGbyWbhoyiMuHrKsM4BIkkKmUCM2wPnoRSCJ8QKeG6OhoREZGqgC9hEuYpkfCHAwQSzcjgkGBgYG4cOECLl++rM6nT3MFwCXNE+vP1yQQRGcmOlUQ+iGUQpBBOnSwLSQQA0DjbsJgNa8nrxQ13N/f3x8pKSmqTgQv6JbEQD3Hh3TD0Dvn0PmFqXykC0daWhquXLmi3Dqk8xJBKwkVEcwiKJOZmYmwsDBlyUfYQ5+aKDQ0VIENdGWS6cQcDgeuXLmiICPpJiWBDYrzRjoU+fv7q3HN8cF+JvjBc7PvZTokziXpkCP7kPCOhDr4kOOS/UoohuXK+STBJUIMJpNJXQOhFh4joR+CEdLRivXjdcmUcBJmIUDB8cJ5zfOYzWaN4w0dgDiWzGazSukk4T6z2YycnBykpKQoByqej+ubrJcErWTaqpCQEHVOIBfukYAhYTj2KfuEoBLLYjtwPdSDj2w/PSjFtpZzgWNBzmuuSXLt49zn/EpPT1fQIt3WTCaTAqXopJSWlqbGh5xfEjI0m81qLSfEyHNLgFKmo2Mbh4WFqTRg0jHIbrcjKSkJSUlJSE1N1bwvZGdnKwCQMKP+vYPwj9Vq1YxRQlJ6yFEPO3KO8j3F399fvS/abDY1x2TKOdl/egcuQ4YMGTJkyJAhQ4ZuVi1fvhz//fcf9u/fr7Zt3boVX3/9NWrXrn0da1Z8WrNmDTZu3OiVhX6PHj3w3XffeXWesWPH4pFHHlHPly1bpr47e6vevXsDAB5//HG89dZbPpVRVDp27Bjmzp3r9rVVq1bBZDJ55frh7++PwYMH46677sLs2bPx66+/4vjx48jMzESXLl0we/Zsn+v63nvv4cUXX3T7246nKg7Q6vnnn0eNGjUK3C8mJgZ///03fvrpp0KfMz4+Hi+++KLGvQWASjkXGxuLunXruhyXkZGhHFdKly7tc5qQ/BQXF4e2bduiVKlSatvmzZuL/Dw3snbv3q3Wh3r16mH16tX4+uuvcfvtt+d5zEcffaT+79OnDwYOHKhpQymOoRIlSmDnzp0AgEmTJuHgwYP49NNP3R7j5+eH8ePHw2Qy4cMPP0TLli01N7s5nU707dsXzz//vNr2/PPPuzgB/V9SXFwcVq1ahY4dO2Ljxo3XuzqGroMqV66MIUOGYMCAAepzUrly5QpMGeSt0tLSMHLkSIwbN07Bl8WlKlWqoGfPnl4ft2rVKhw4cMCjfcuXL49u3bp5fQ5P9PbbbxdLuUDuDa1du3ZFuXLlXG4AnTJlisfpy7KysrBmzRocO3ZMsz09PV2lDfNFNWrU8Orz7H333YeHH37Y5/Pt3r0b33zzjbpxoDAymUwa55L83HcqVqyo4mp6/fTTT5g+fTq2bt3q1fkvXLhQYJqjrKwslzR+AQEByv1w165dBbpc2mw2vPLKK6hbty5GjRrl8npSUhLOnj2LrKwsXLp0CZMnT/YJZDL0f18ZGRmFSrnbokULvPDCC0UCqi5ZsgRPPfUU6tWr51Xax8LIAH+Q++WQrj1A7iJF8Cc9PV05+1B0wjCZTJp0XpGRkYiJiVHAjHQUYQCfgWl9eitCPwyqA1BggXSykUFaiuUy2MrgbHBwsIJQ9JLABIPg58+fV44+PF663DBwz3ZyOBwK1JDpePSQDt1cbDabxmmHThISUqILiHTxoLMG244AhQxGy7RP/CtTlLHP2CfsFwbJ6TwDQNVNAkIyBRED2XQ9SU1NhdPpREpKigbKIEwRFhamcq67A374YBCdfUaHH45Hi8WiQDO6p0RGRiIiIkLBaDKVGNuTEAJT1PE6ZF/J4D372+l0IjU1VaVqkxCPzWbTOFixjfKDpCS4RsiBEBgdoAAouIbXAEDjSKMfZ5yzhKdYRwkeSKCFYJ8EMjgfOL85p6TrkkwXRicdjhv5OoEKwkh0QuG+eqcZQlYy/RvBD6awczqdavwTUmL6PL3TFNcqtjfnGesr0y4RxAJyye2srCzlUMP24LEEcCRswzrn5OSo+cZrYHuHhoZqXMj4v59fbgo6jk1Kzg3+ZX34AZPlSNiHaxf3zcjIUHd5ENCRafPYdmx/rpesC7ezL7lecH2QkAzHqnTzkmmxeKyE1UjKs12Tk5OV41BmZiZCQ0PVnLt06RIOHTqEY8eOITExUfUt20vWleNXzjm73Y6UlBTY7XY1FiVkyWuQ81g6xrFstiPTl7Gd9e0ioTXuU1grREOGDBkyZMiQIUOGrrcOHTqExYsXa+5W/frrr7FkyRJ88cUX17Fmxav77rtPfU/0RFu3bvU6xdn777+Pzz77TLONrh4FyWq1YtKkSVi/fj0OHTqk0n0tWrQIVatWRYsWLbyqS1FqwYIFGDNmjNvXAgMDkZCQgNGjR3vkshMZGYmRI0fi7rvvRlBQEAYPHox77rkH9913H2w2m3I6Kow+/vhjn+3ne/fujQkTJmDFihWF+qGd6tatGyZMmIDKlStj4MCBBe7/8ssv44EHHsCLL75Y6HMDwKZNm1ygDAYt/f390ahRI7fH7d27V33fLw7XhhIlSmDlypUYMWIEvvnmG0yaNKnAYNz/NT311FOafg4PD8/XXWnDhg2wWCyabTVq1MDq1avx0EMPueyfmJio0pHUqFEDI0eOxLPPPovu3bvj8ccf1+z75JNP4u6771bw1YkTJ/JNe7Flyxbs3r0bQO6NyMXp7HG9dccdd6BEiRKoX79+kQS+Dd08eueddzB37lw8+uijaNu2bZG8P+Un3nzvKzDsrZo2ber1MampqZg4cWKB+507dw7Tpk0rEvc6d9q8eXOxOk5wrc0LPPFUP/30k0tKuODgYJ/anlq1apULmFKQXnjhBZ9TBVH79+8vtOPZHXfcgb59+6rn+X22NZvNuP/++/N8fffu3ZgzZ44C0TxJR1alShXUqVMHFSpUyHe//fv34/Lly5pttWvXRqlSpRAfH6/e//JSQECA5sZ56rbbbsObb76Jn3/+Gc8++ywCAgKQmpp6U6dUNlS8mjlzJl5//XWfjy9RogSaNWuWp/uYN0pISMCpU6eQlZXlUVraopAB/iA30C+BFgZnGSSVoAoAFQhloJeOEmazWbmu8BgpvYuFDAIDWqcECYVIJxAG3bk/xWPlc3kt7iQhFNaZiyrdMiIiIlQ9eO0SMpLBY3eSwV4ZVJbHcDtBHhl4JyQlr4NlStceAC6p0Vg/Agd0IuL5CBjoXYdYrh5o0bevTNFDdxW6wvBLN12U+Bof7EsGyHmM7H8JeknIgvtLyIQPmcqMbayHvCR8IaEmtqVMBcTzSkBAwh96CEfftzLlFa+b7c7+0P9Aoe8DCZHJsuUccwdTyW16FxPpvqVP7cZ6yX7ng+NUpiLTjwsCKdKlSEI+egiF64gEJXhNTMdGdyb5YyCBOLrrcI2S9ZVOQRxXrCMdsoKCghSkJueFPjcq3WFYb8KS7B/pZMRr0bsFcUzp3XkoOT/kXwnN5QWY0VVIjneZpkw6/cjyWUe2nyxHwmIAlCuXfm7J8SjbRI4jSqZu1K9H7G+uI7w7IicnBw6HAxcvXkRiYiKcTqdm3ujXLL4/yfWNzj9MIybTKrKu7A+5lsj1RV43zyvfB9zNfdmnhgwZMmTIkCFDhgzd7Nq1a5fGoaVMmTL/c24bBWnbtm3o3LlzgSkBpFasWOFyd7inqRy2b9+OL7/8ErNnz8bcuXNRsWJF+Pv7Y/fu3bBYLIUO2BSlpANMkyZNUL58eVSqVAk1a9Ys8NiSJUvi9ttvV3erE3Q4evQokpKSEBcXVzyV9lCZmZlYs2YNmjZtqglO+ao2bdqgR48e+Pvvv1G+fPkC92/YsCFCQkLydX0prGQwKzo62u0+Fy9ehMPhwMmTJ/HHH38UeR3MZjOCgoJQvnx5XLhwAVOnTi3yc9ws0rtB5KXNmzejefPmmm1+fn5o0KABypUr5/YYCTs+9thjmtcqVaoEPz8/tGjRAr169cJTTz2lxh2BofwknRaYAuX/ohi0v3LlSqFcOgzdfGrVqhUqV66MFi1a5OmsVZTib5y8ObG45evvnL/99hsSExPz3efMmTPFOl/Onj2rucG6uJRXSkxP1bJlS7dmBrfcckue63ZBOn36tNeOUPXq1UPXrl19Oh+1atUq/Pfff4UqA9C6/OzatStPKD8jIwN79uzJt6xt27Zh7969OHXqFHbu3OkSB3Knhx9+GE8//XS++1y+fFmTigvI/a5UokQJAMClS5cKPI87mDkqKgq33nqrJs1ecHCwEXMwlKeysrLw6aefFokTaGGVlJTk0dgvShngD3KhDbp4MMBL9wybzaaC40ztFRERoSAfuv3ExcUhLi5OgT+ENKRLClN6SReftLQ0FTDNyMiA0+mEw+FQ6Vv0aV2Cg4MRGRmpUhXJQKse8GDANy/4h4Fxf39/WCwWVKhQQaVzAqDAHz8/P6SkpKh0PIQq2E76BVYG3wEoqCosLMwFuJBvKvogMdtSD1cwqK0PtHM/whC8RrqO0EWG/QJAQSISKmD5ErTRwyES6iAowPIItURFRSkgg9fKD1aEKxikZ3ovls/rkPAKnXBkeizpwCEfbBvCQ/zgq983LS1NAW7SbYjXTrcTbpfAkZ52lBARn7NfWQ/ZP+7gIwla5AUAsS14ndJBh+PX4XAoiEKmwAOuOvrINHZsK73LiXTnku48bF85HySIpq87AA2cxTnBdpfuVSzLz89PzS/ZZmazWa0XEl6hG1FoaCisVqtah+Rcp9OLrCfXI7oI5eTkqLRQ0r2HYzkzMxMOh0M5CzEdFttSAlMybZycQ3qYR4rXrV8TJCSjb1u2L4/hvJR0uASOACgHML7G9HJWq1W5B8l5YLVacenSJSQnJ2uAT0q6XzF1mBzLvCa6XrFv5bzKyclRQJDValXrhclkgsViQUBAABISEnDp0iXN9ennP+sXFhamIK7w8HDNfJbwFddHs9mM8PBwREdHIzo6WqW0lO+Nch5LEEoCa7IfeNyN9GO7IUOGDBkyZMiQIUO+6tZbb9V8Dxg5cmS+N0P9L2rp0qUwm83KeccT/fXXXy7wy+zZs1VqpfxUuXJllCxZEosWLUJaWhoqV66MRx99FKtXr8auXbuuGxDz6aefaoJWTZo0wbp16/DPP/9gxYoVKrVZWFgY7rzzzgLLO3LkCMaOHYsrV64AyAWBgNzvmO7Ahmutb7/9Fs888wwOHDhQJK47+/fvx3fffae+q+Ynk8mEUaNGYdu2bZqUTkUtCfLklWbj9OnTuHjxIl599VVMmjSpyOtQpkwZ9VvPkiVLPIZf/q9p8uTJaNy4MT744IN89zt9+jQmTJiQZ4A0rzSNjzzyCJ544gkAQIMGDTSvbdmyBX///TcmT56Mu+++Gw8++KCa6wW5IQC5afUOHToEIPc3NXeuQ75q2rRpReZ6VVgRhoqNjUVUVNT1rYyha67atWvj3nvvvSbgj4wBeOJeUljde++9Hs11d1qxYkW+r/v5+RW4T2HErBXFrYYNG/oMAZcpUyZPt8QGDRq4OK95I1/g4MICmjt37sTYsWMLVQYAjXvW3r178cMPP7gFX6xWa4Gumfv27cPQoUPx1FNPYdKkSR6lYStZsmSB7y/Hjx93+exepkwZ9Rl/x44d+bofMealV/ny5RU8RDEWZejmUWBgIHr37o1WrVppIK7iUnJyMt55553r6jqYnp6OK1euXPPP6wb4g9wvyQQq+GDAMjk5WQE6KSkpCvSR4E9ERAQiIyM1aZb4gV8G3BmoJwjhdDpht9tht9tVwNhdcJ5gAoOwdNswmUwaZxdA60Ahg+zuAuwy+M4AckBAADIyMlSZmZmZKqWUdPkhJMGAPsEL+ZBlE+iQkId0NKLbiHSw0bvFSOcN2gVKBwoG79mXsg2kI4p0/pDXJN2BGIyngwpTgunfTNmvWVlZCpIiyBMXF4eoqCg1Fmw2mwK/JODEaw0MDFR9LK9HuirJfSUowg+4TFsmxwOQCzlYLBZYLBZ1XGBgoII+uC8BHcJFbEcJAXEuMI2SXqxvVlYWHA4HUlNTNa5VdOXhNRDEIZQkYRM5L5jujq9xPEk3FrYv/+dz+SGAEAudvgjYsX2lA490O5HOVARaOHYlfCbbgf3MY3it/v7+akxKcILrAecD0zWxXej0wjlBKMPf3x9paWmaVGGhoaEKLrLb7cqlSc4hi8WifvyQzjOc3wRUAgICYDab1fpEUIjXy3VTwjxsG9l2euce9gF/tNI75LD9CG7p4R/uk5OTm2JP754j5xnHjEwtyDpLByO2Gc8pgRz2pbxepid0Op0aMIzjm+0gQUzOB6fTqXFFYtmEFePi4lRaOc5ftj/fm+TYYgo7jl++PxEsZIo9Qmv+/v7qWkJDQxETE4OYmBiEh4drAFbZF5yLsn2lkxIAlaKOqeT0zk6GDBkyZMiQIUOGDN1MevTRR/HRRx/h0UcfxRNPPIF3330XAFC/fn2MGzcOU6ZMuc41vDYKCAjwGKoYOHCgVz+ybtq0yeXH+3379uHbb78FAKxZsybPH0wrV66MO++8E8OGDcMrr7wCABg0aBAA4IsvvsCff/7pcT2KSiNGjEC/fv0QERGhttWtWxcxMTFo0KAB7r//fpUOo1evXpg2bZpH5ZYtWxbJycmwWq3YsGGD2r527Vps2bKlSK/BE7366qu49957Nds+/PBDJCQkKLDJV82ZMwejR49Gx44dNQ4p7uR0OjFv3jxUq1YN1apVK9R59fr666/xwgsvANCmw7jlllvcBn6XLFmCBg0aYOHChUVaD6pkyZI4ffo03nzzTfz+++9qe2hoaLGc70ZWUlIS/vnnH/z22284ceIEUlNTNa8vWrQI3377LZKSkjB58mS3wcQ6deq4LXv9+vUqJUxSUpLmtbJly6JBgwZug8H//vtvgfXOyMjQBEY//vjjAo/xVJ07dy4SZ4nC6sEHH1Q3uALAAw88cB1rY+ha6+TJk4iNjb1m57PZbGjRogX69u2bJ5RZlCpTpgz27duH1atXe32s3t1Qr6CgII2zZHHI23RXvuj333/HokWLfDr2/Pnz+aaE6tWrl0cpQPV67rnnvE5FmpWVhbVr13p9Lr3Wr1+PXbt2FaqMpk2baj5bzZgxw607ZkREBDp06FBgeYmJiVi9ejVWrVqF1atX4+LFiwUeY7PZ0LBhwzxfz8jIwMmTJzXvt7GxsahSpQqA3LTJBaU6cpeyr0KFCm5BEX1aMUM3rqpWrYply5bhgw8+wM8//4xt27Zh7dq1amwUl7Zt21bs6SbzU0pKSoEp7opDRjQOUDCDxWKB2WxGdnY2nE4nMjMzYbfbcfnyZVy8eBEWi0Xd2UVnBG6T6aQY8GWQOyMjQ4E+dF6R6XTo8CPhFxm8lUAM60tHB+lko09LJK9Pwi0yNY3eAYLgiQyqE4QgoBAQEACbzaYAIQbKeW36NFuyHnoYicfxPAy4881BAgQycC/T81B8TfYBU5jJgLw+HY1MnSb7UO4r04LxurKzs5GUlISkpCQFDxCaoaML0/XY7XaN6xNTg0mHGwJDPDY9PV3jfJKZmYmMjAxNah673Y6UlBSkpKQoyIXHSMiJAf7g4GBYLBYFi7Dd9ACGBBw4BulswzZISUlRcyQ5OVkBayRzU1JSFPSjT78GQKWu4nHBwcGqjhLoImDDeSMdgOhEwnpx7HBM8X9eE8vkvCEMoZ8PHA8SROOxVF451GWaKAJWZrMZUVFR6sHxr3destvtGlCEY0aeW84jOvzINFVpaWnqbjjWUUJUcu6Eh4drnKUIB0VHR6sxyv6RAJGcgyxfgk8SBpT9yH4lMKdPF8exqJ/7LIfzTq5jEq7iuJLzBtA64lA8lwQseW2EVhwOhxpzPL8E7vSuWHKs8LwhISEaUIZzOzMzUwMe0XGJ4Gl2djZsNpsa3wRx5HhxOp2a9wk9/BMUFITIyEhERUWpsc7nhFcJj/G9Tbqe6VOaMUUZ+0+/PkrginPPnTuYIUOGDBkyZMiQIUM3k5555hmMHj0aX375JSwWC3744QcAwO7du/9noB8g9/tl27ZtC9zPZDJhy5YteOqppzwue86cOW7vQJ8/fz5WrFiBjh07okmTJm6DVf7+/ujSpQuWLl2qYBgGzWbMmFFggKE41LlzZ9jtdg0o0qtXLwQEBLhABJ66IQQHB6Nv376oWrWq+h2DysnJ8TnAVhgNHTrUxVFpzZo16NevX55AhbeaNWsWmjdvXmBg4IcffsD58+fRqFGjIjkvRUCLunjxIo4ePYpBgwbl6UjlLmhVVCpfvjwOHDiAcePGabb37dsXd999d7GdtzhVp04dTSBx+vTpWL16NV577bUCj126dCk6dOiA22+/HR06dMDw4cMxfvx4rF27Fps2bcKoUaMAAN999x369euHd999F/Pnz8c///wDq9WK2rVro1mzZm7LvnLlClJSUvD666/jySefxNtvv61SRrhL12O327FgwYIC69ynTx/NWnrgwIECj9Fr0KBB6r1IqlmzZvjuu++8Lq+o9eCDD6r/z5w5g+nTp+e7/5w5c4q7SoauoQICAoosndSff/6JGTNmYOvWrXkCwCVKlEC/fv3QvXv3a+IiAeTCFd6knAoICMCzzz6Lfv365bvfli1bih3e8wVY8lYlSpQoVLB93759Lp+XqEuXLrmkk/JEf/zxBxYvXuwVEH7ixIkig8mmT5/uNiuLN5LA0+bNm7Fy5UqcPHlSs0/FihXRpk0bjUNpQVq5ciU2btxY4H5hYWGoX79+vvscP35ck/YyJCREgYDJycnYuXOnW2euK1eu4Ndff8Vvv/3m8lr58uVdPm8mJSV5BCsZuv4aMmQIFixYgO7du6Ny5coICwtDxYoVYbFYrknK01deeQUjRoxwO7aKQ1arFbt27cK4cePw8ccfF0vq34Lkmijxf1CEbyIjI1VQkwFdk8mEiIgIhISEaNxsZJohQAsJSBiFAV19GhcGSfmcZckUQgwsy78MsGZmZqoAswQhpJuOTMOld76QAICsHwPRmZmZCsCQrg5BQUHKYYauFDyezkTS4YHXQmBApn+SzkAEQOx2u2of1okgDq9ROr0QCOKD0BRBGr3ltjwnA9ysj0wfJdNKsR78K9MkJScna9IxEb4pVaoUwsLCEBQUhISEBNVfhCciIiI0Y4XwGPuOLjSEI3heuvqwHRhc9/Pzg91u16Tk4T50LuEHC/Z9RkaGgkZk2jRCE0yzxvFJEIRjJi0tDfHx8cqpSLrYELbhfvpxxHYleKF3o5EgiZwXeuCCUBDHiXRHYl3ZZnzO1znWmQKJMIYEPaQDDs/P80hwg/3D7bx2tm1ISIhmu8lkUk5fhGgI0/HcqampyMjIgNlsVs4xwcHBmrsg2TYA1OvsdwkI6WEpwllMZci+lm44iYmJmnlstVoRFBSE9PR0mEwm5drE/mZ78NxyvZH/s62Yh5UPXqest1zzmOqM7S3XQpPJpMqx2WwKZOFaJccU13xeFwEZWUer1Yrk5GRUrFhRAT3h4eGaD+dyPLKdOK55jASd2L42m00BVwA0QBBhPr5XsD+DgoJgNpuRlZWF1NRUREZGKsiHUI1cG+X7AQE+OiIRzpE2uASn5BwiLMg+ofOdHkCUQCnnGtPAcf6zLwwZMmTIkCFDhgwZuhk1dOhQZZk/c+bM61yb66uOHTsWuI/T6cQPP/yAv/76y6Mye/fujdmzZyMhIQGPPvooNm3apF77559/MHjwYADAww8/jBo1argto0GDBmjQoAF+/PFH9O7dG0AufLNy5UqP6lDUevbZZ13SSbRv3x6fffYZNmzYgPHjxyMpKQmHDh1Cu3btsGbNmgLLTE9Px2effYahQ4dqAv69evXCjBkzcPr0ac0P2i1btkRKSgr27t3rdf27du2K06dPu72TXKpp06Y4f/68y/aDBw9i/PjxXp/XnSpUqICcnBw0adIEx48fz3ff+vXrF3nqPb1TybRp0/Dbb7/hvvvuw7Fjx7Bu3boiPV9BKlmypNu5dccdd6Bq1apugxoff/wxnn/++WtRPZ902223Yfbs2Th27BjOnj2L+++/H0AuMOKpLl++jMuXL2vSm8gbswDgm2++wTfffKOeL168GL1798a7776L1atXY8KECZr9bTYbBg0ahGXLlmm2JyQkoH379rj99ttRpUoV1K5dGxUrVsTEiRPx1VdfFVjXTz75BA899BBatGgBIDd4642GDh2K6dOn48CBA2jcuDF2796tfmO6cOECgFxg9eTJk1i+fLlXZRdWjz/+ODIyMtC5c2e1rXz58nj11VexZ88enD59Glu3blUpC6mqVate03oaKh41a9YMZrMZt956a6FdyPbu3YsFCxbgww8/VNvat2+PNWvWaG5GvV6yWq1euTgwW4R0AnSnixcvqlSAxaWvv/4aMTExqFevHqpUqYL27dsX+Tk6duyIixcv+px+848//sBff/2FZs2aubTZgQMH8P3333td5vbt29G3b1+vPhuWL18ezZo1Q3BwcKGh3tmzZ6NLly7o2rWrT8enpqa6rOkXLlzQuKtRrVq1Qvv27T2GvNatW4eyZcuqz47ly5d3gcyB3BhHQc6K58+fR0JCggJ1/P39NWUdOnQIJ06c0LgvXb58GS+++CLmzp3rtkx587485lqk9jNUOA0YMMAl/TG1bNkyl89YxaEtW7agUqVKGiCtOGW1WrFu3Tq89957xXozQH4yHH/+v6SjAYO2ISEhCA8PV84IoaGhGmcMGciUkII72IT76F1RCFnI9FJ8SJcKBs/1Tj6y/pS8BumwwsA468dAswSBZPoulivPI2EcWXcZbJb/c18JUsh0PBLc0af0kg4kEuKR0JJ0EtE7reiD0rKfGaznsTKFkOw/fTlS7EOCLITGJESh7xsAypGD55N9yDah2w+BIgmxMFgvgTDZx/I6ZUokPYAhnVbcXTfLkeNE9jchENaJ9dQ75cj2Y59KOE46vOjFY6TTCusnx5CEemTd2SZ6CI5tJ1Mv6d2q9ONV1knWTY4pCbLIPqDLU1pammoz/ViUcBbvipBOVxKok85H+vqw3rJc6Zgj28VmsykwjO3BsUBnGfaPdA9iW8k5L9PSybkqXXukA48cA+5S8OmvRbZnXusg30jlGGDfyrrJsS+hQj5sNpvG4YpAk6ynTEkowUP9GOe1y9Ro/v7+yl0uICBApQkjPOPn56dce4Dcu0gsFotqT4KBHKv6uSvnXnBwMEJDQxEWFqZ+9JJzliAPz63vLzmfOT+k04+cExKUlHNf/2ObIUOGDBkyZMiQIUM3k2699VYAuZ+jd+zYcZ1rc32lD9bmpalTp3rcVr///jtMJhMqVark1nmDgewWLVrkefeyv78/atWqhT179qgbXNavX+/R+YtLdAahMjMzUb58efz11184efIkNmzYgO3bt6NmzZoel1mtWjVkZWXh66+/Vtu6deuGK1euaJyNQkND0a5dO9x1111e17t69epo1aoVhg0bpkCrvFyJ3EE/1JUrV/J0UvFG9evXx6VLlzzqT/6+U5z69NNPcebMGTzxxBOoW7dusZ7LnU6ePIn4+HiX7RUqVMDZs2fdHtO3b180aNCgmGvmu+hI3ahRI9xzzz1qe4kSJQpVbkFjYd26dbDZbGjXrh2qV6+uea1Pnz4oV66c24BUaGgodu3ahdmzZ2PKlCmYPXs2rly54tUNT3K+egu9MMVcYGAghg0bhnbt2mle7969OwYOHHjN+7x06dJ4+OGH0apVK5cAba9evfDEE0/gySefxIgRI9CrVy9N/caOHXtN62qoePTII48gMjLSxSnNW6Wnp+PLL790cYoqX768csa/3kpJSdH8Ju2J3KUk1CsqKqoQtfJcU6ZMwdq1a/N83yisypQpkyes7YlOnjyJQ4cOuXV0SU9PR8WKFX0qt06dOl6lFgoJCUGlSpVQu3Ztn86nV2G+Rxw+fNgF/A0NDXX72bhcuXJu05Hmp7179+LPP//E3LlzNRCtVGJiIqKiohAeHp5nOVarFXa7HUlJSep9OCQkRL2ekJDg4uakjxXpJTMTALk3GSQmJhaZs5ih4pPT6cwT1mzdujUqVap0TerBmNu1EG+6v17QD2CAPwCudnpiYiIcDody+YmLi0NUVBRiY2MRFxeHihUronz58ihRooQmkKkPTDNYSniFLi+pqakq5QtTf2VnZyvXhtDQUOW6wJQvJpNJpX6SwWkZvNd/sZAAQ2BgoIINGNCVQIBMbcXz8njeLSChAb7GID+Qa+McFhYGi8WiroEQEVMuhYaGqvIZAKfrhkwvxXRTTC0mg8byCxsDyqyLdMWR4JB0C3F3/bxuBtnzA5joGsPXmOaK0I/D4UBQUBBKlCiB6OhoFaSX/RMYGIiIiAhNSjJ5LWxfmaqKLhsEXdLT09UboQSkJFwgf+yQY5HOIWFhYcqtSYoghATN9LAGF2o6tnAsSxhJpqrjcbJO3M7Ucu7mkITxCBpJVyQJ9MixIkEMWQbblNcp68TUZIArNCWBGwn3cLwQnGB76SEiq9UKh8OhttvtdhcQhWNagid2u13zoYhzhMAgx3lmZqYqXw/4sJ3kuQgecV1i2kD2eUhIiHI98/Pz09yhQSBIOpSxLkFBQQokopOZHJscGxKYoehsI8e6HI/cX36hIqTEtYLXpIf4ZCoz2c7sT3fQX0JCgsZNimOF6xfrxXEs3arkusZxQOccunVlZWVpxjMd1AjOSLeegIAAlClTBpGRkTCZTEhISFA/ZMvUfrJefE+hG1d4eLgLJMSxJOE9zmWHwwGHw4HU1FQkJiZqUgnyGnJyclQd2FdsX7pPcW5zLhR3jmpDhgwZMmTIkKGbSdfqhydDhRd/7F+1apULzFGUeuihhzQuCTezDh8+7PG+586dw/79+wEAPXv2dLtP9erV0adPn3zLGTBgAEaOHAmTyYT4+HjY7XbPK1yEqlWrFubPn4/PPvtMs/3SpUuoVasWSpcujUWLFqFDhw6YNm0a7rnnHuVqlJ8GDhyITz75BL1791auJf3790e/fv0we/ZsjRuOw+FAZmYmhg0b5nXg58iRI7h48SLatGmDixcvYtiwYdi2bRu2bduG2bNne1xO06ZNsWbNmgJTQhSkTZs2oXTp0m5TwV0PJSYmIiwsDHXq1PE4TVtR6qOPPsK0adNctn/11Vd53tlfpkwZj5xorpf8/f3Vbwvyd7AGDRrgtttuK7bzfvbZZ+jSpQv8/XPTBb7xxhvo2bMnFi9ejIULF2Lt2rVuj7Pb7Wj7/1N1xcfHIyAgAHfddRdKlizp0XkHDhyIXr16qefeAnocdzVq1MDjjz+u0plRgYGB2LVrF/bs2eNVuYXVo48+iubNm+PJJ590eS0mJgaNGzdG165d8cYbb2DhwoVYv369Suf2+++/G5+LbnK99dZbePrpp/Hmm28WGl4JDAzE+fPnNZBMnz59MHDgQLcuJNdDkZGRXgd1169fX6ALX5UqVQoFzHijU6dOFRtIFRsbizZt2vjsbpOTk4Pff//drfNggwYNXIBHT1W1alWvPxe1aNECH374ITp16oTo6GifzkstWLAAly5d8unYtLQ0l3Sm0nmfys7ORnx8vIpReKrdu3dj5MiRGD9+PPbs2eM2pWXp0qURERGBWrVq5VnO/Pnz0bBhQ3Tr1g3Lli1DQEAAwsLCVD0PHDjg8j1BHyvSSw8GZWZmIjExEVar1atrNHTtlZOTk2c/de3aFU8//fQ1qUfz5s2LDOArSGfOnMHff/9d6HIK87nIAH9w9QuG0+lETk5u2qnw8HBER0cjOjoaMTExiIuLQ0RERJ5OLXoHCgY7WW5qaipSU1OV44/D4UBGRoZy9DGZTCrljHSFIPAgoRSe050bDR1ipAML0xExqK4HAQjt8BwEOxiAZxCZIISsd2hoqHJEkg/WMSsrS0EMehcMwhJ0FiE8QFCFQW/ZzgA0wXiCLnoXI8JVEsBggFz2n3RXkkCCdO7gg+4+7KPk5GRVb+mMAuTCUHJMsY4Eo5iOTPYn0zkRDiO4QTpQwgCEMvhDlnTFkYF2iv1ImCwyMhJxcXEwm80KSpAgmIRpWA9CDtINhi42rD/bTY5b/mU9+QgLC0NYWJg6r3QmYptIlx4J4kjoh2Neuo/wNV4P51xKSgpsNpuLAw3Tb0lYJC9HJiCXpo6IiFBQDMeHTKEk3Zm4D9PZAXAZA/L6mA5KOvSwXvzQxfGSnp6u1hP2vdVqVfNL9iOvlWWlpaWpVIac80zNRniLaeo4VoDcD5U8PyEUu90Ok8kEp9Pp4ubDOa13x/Hz81NrH9cWveONTBOl/8v+kenPCBHxugnPSfCMY0PWj+L4YOo+ptMLDQ1VEA37TkKDhKqkWxPHAK+RoBFBJQI0UVFRLuAWwanSpUvDYrFowDqun1xbpQsP247rtlxD9O1L9zGuo5zThH0I+thsNpWKjnWUkKEcu9KZi+8T0k3IkCFDhgwZMmTIkKGbTStWrMDTTz/tkgamqFW5cmW8/vrrhU6PUdzq378/brnlliIrb9CgQcr5oXHjxm738aTtGzVqhEGDBiEgIACVKlVC6dKlNa8/+OCD18Sh5cCBA1ixYgVeffVVtc1isSAoKAiffPIJ/Pz80KhRI9StWxdDhw7FyZMnMWzYsALLXbZsGSZPnoxvv/0WAFCpUiUFgNx5550u+48fPx6xsbGYNGmS19fw0UcfoUaNGkhKSkJgYCAqVaqEpk2bolSpUm5TSrjT9u3bMX78eK9SobjTlClTcPjwYbzzzjuFKqcodejQIfXb8Y2iBQsWuKRCGzVqlBqHderUUb/X3miaMmWKS92cTicqVqyI7t27F+u5N2zYgB9//BEOhwPPPvusSv/l5+eHyMhIl3qNGTMGx48fx4YNG9S2iRMnwul0onLlyh6dc8uWLZrfRypUqIAOHTp4dGynTp1cQCH9/E9KSsK+ffvw3XffeVRmUalatWoev38FBAQgKiqq0GCgoRtHjz76KIBclzZP3yfyUmZmJk6fPq3Zdt9996Fp06aFKrcoZbFY8MQTT+CPP/7Agw8+6NExp0+fxp49e/JNNzN37lyv4OnCiDGd4lKVKlVw8OBBn4//9ddf8ddff7mAv3Xr1vV4zdQ7jaxYsQITJ070ui4dOnTAokWLXD5beqtjx45h586dPh3bokUL9OvXT7Pt1KlTmvej7du3Y9asWXjssccwb948r89BB6hp06ahf//+ePnllzFjxgwNRPTII49g3bp1uPfee/Mt6/Lly0hJScGePXuwd+9e9b535swZxMfHa+I5Mu6tV6VKlVRcIyMjA8eOHcPMmTN9uj5D11ZvvPEGZs+enS+w6cnND0WhkJAQ5eJbnMrOzsbatWs9TrNXXDLAH0AtGoA2fU9ISIgCWfTuH+6gG5mihgFgPqxWK2w2m4KA6MogA69My8JAKYOzDDhLlxxKD0kwuE34gMFiunro025xn9DQUFgsFphMJoSGhrq43rB+DGb7+/sjIiICZrNZBb/5Q4ZMwyShH/kAoK6VQXAJKUg4QO9mw+tl3aQzEZ152O58U2BQXbp6UBLw0IMerBfBGe6bnp6OxMRE5arBNDyEGwif2O125XhE6z3uw3pKMCQ7O1u5t9jtdkXEms1mBRqwf9gmMn0cJR2ZJGRBFx5uY78TgmAfsE8Jd3CcEITIzs5GUlKSApukM5JMbcTAv7xuCexwO88rr0GCObw+CYPJc+k/GMg+J8Bht9s1zjysN+d5RESEAjMk7S7nunTCYt8DV1MnSSANuAqp8ZzcTz8+pcOQw+FwcbnJyMhAYGAgUlNTFYRD0QGK8Jp+TZNOTRwXaWlpau4QupHtDFy9y4p9Kd2gCLklJCQgMTFRATxsD9lHHI8SqmNdCc0EBATA4XBo2oz7sg56YJF9yHJycnJgMpnUOsly6LLENte7eUm3HumGw+NycnLgdDrVj3pms1n1sRyPEmZkHVhfOVbYDiyb/cN+IYRG17no6GjVPunp6bBYLMjJycGVK1c0QI5+DZAwFR8STKNTk3Qtks5nBIbYdnqITfaRdGKicxD7SMKyxt1bhgwZMmTIkCFDhm5GbdmyBdOnT8emTZtQpkyZYjtPxYoVceLECfXd6EZUlSpV8OqrryoXjtdffx27d+/GokWLfC5TulIEBgaiefPmbs/rrYYMGaJ5PmXKFI+Dc4VRnTp1cPr0aRw5ckRts9lsiImJQY0aNVCmTBmVAmv8+PG49dZbUb9+fXTr1i3fcm02Gz766CP1nGkwjh8/ju+//95l/+zsbMyePRs9evQolPuqTLcUFBTklZPDe++95/N5KavVioceeggffPBBocsqjKKjo5VzAX8niYmJua51KkhNmjTBu+++q55fLxes/DRhwgS3Kb3mzZuHkSNH4ssvvyz2OnTt2hVNmjTBf//9h8DAQA1oqE8BZrVasWnTJs22mJgY9OnTByNHjvTofIcPH8Zvv/2mnu/atQu//vqrR8fOmTPHJS3GkiVLNM9/++03fPzxxx6VV5SaM2eO1+4L19qVyFDx6Y033lD/+wownzhxAr///jtOnjyJU6dOaV5bunSp5n31RlHdunXzTVGk19GjR3Hu3Lk8X4+NjS2KanmkgICAYj3f4cOHC9VnSUlJ2LZtmyY1IgDExcWhVatWecLiVLt27fDxxx+7fPZ78803FeCSnZ2dZ1orvSIjI/H6669j9OjRXlyFq1q1auXzsVWqVEHv3r3V8x9++AEzZ87E6dOn8ccff2DSpEkYOXJkoefK0aNHsWrVKhw9ehTlypXTGB4AgNlsRlxcXL5lHDhwAE899RRatmyJd999V8WqEhMTkZiY6JHb1OTJk7F27VoMGDAA4eHhOHz4MObNm4cXXnjhuqf0NVSwatasWaBLm7v0tcWhOnXqXJPzbNy4EXPmzCkwrWNxywB/kGuBm52drdKiSMhAOt1IAEYGjvXOIxI0cDgcKqUXn6enpyMoKEg57TAgL1NPSUjGXYovCe9I1wsJ0DDIy6Asg7uy7kwBRhcIBu95zQz+s00IZzDg7XQ6kZ6erlK6SMiETiRMA8UAPwDl9ANAk4tRAgPSDYWiYwWvnW1BYIIONAQqZC49vYOLbFOWqXdVkufgh1YGtlNSUhTEwJRsFotFpUszmUwq9RBdfvz8/OBwOGCz2TQpl4CrqcwyMjKQmJiogAQCWdKZiOONYI1MqSTddig9eMXjmYJNAkt099G7B+nTpiUlJam681r0ABpwFfBimawP/xJi0Tv+cJxI2IDjiwCLfn5IIIPwBR1f0tPTkZSUpMZGSEgIwsPDERkZiejoaAU5cYxLdxTWSZ/Gi1CYdL+SdeP4ttvtsFqtmjRJEtCTbkIEMiTQxH5JSUnRpFCiG4/ebUmK6wL7kxCHxWKBw+HQjHGOCZ4/KSlJjVvWje0swSqOt+joaMTGxmqgPbmeylR/PCehljNnzgCAAuz4YFtzTZAiWCIdq6xWq2pLri106EpISFDuPwAUeCgBGa4hdrtdwXdpaWkICQlBVFQUwsLC1JiQ0KJ065H/Sxc3Hsd2YNvRQYvXExwcjIiICOUERBcnp9OJhIQEXLlyBWlpacrBymazaSh5PUTJfpLOS9xPziGey263q5RfXOM5TuVDpvGTD7nOSDcub74IGzJkyJAhQ4YMGTJ0I+rtt98uNteMqlWrokePHujYsWOxlF8UslgsSEhIQK1atRAWFoZXXnkF9erVQ7du3dC+fXu132effYapU6d6VOb27ds1weK+ffu67ONLirWqVatqntPVu7jVp08fDBo0SLOtRo0ayM7ORuvWrbF79251l7JMz/DMM88UeLOEPp3D8uXLcfz4cSxbtszt/mPHjsWKFSvw2muv+RzcS0pKQmZmJr766it06tQJJ0+e9KkcX1WrVi18/PHHBQaYilsLFy7E559/rp6fP38eZcuWvWbn7969O1q0aOHx/hUqVNCkNLhRUqVJDR48OM87vWvVqoVPP/0U58+fvyZ1SUpKwvr16/Hrr7/ihRdeUAEbBoy/+eYbTJgwAc8995yLG0dISIjX6XI+/PBD9b+n69Lrr7/uAp8mJSXdMGncduzYgXnz5uH333/HkSNHPALNfHW+MHRjadWqVWjXrl2h15nKlSvjvffeQ6NGjVzea3766Sd88cUXSEpKKtQ5iloZGRlu4UV3qlq1Kh5++GGXzydSbdq08ToVla86cuQIEhISiq389evXuzjueKtNmzbht99+c+n3smXLonXr1vkeO3z4cAwZMgT33nsvli1bplKw5uTkYOrUqXA6nVi7dq1XDkt9+/Yt1Od03oTvq06ePOkCey5btgxLlizBp59+isWLF2tiBIXV9u3bNTf8SpUvX97nclNTU1XMdvPmzfjggw9cUuQCud875Oe/nJyca/JZ3lDh1atXL7Rp0ybfeNClS5eu2WeY/JzWCqOsrCx888032L59OzZv3owZM2Zc8+9K7mRE4XAVuACgQB9+2ZZBaiBvdxgGjRkI5nP+zzKk04KEA2RAVu9oQjFAK49hGdJZREIg0iWDII1Mt6OHKbgPU8pwYjLIL2ELpjKTrjhyIsv2oGQ6JoILEgaRbU1QgYFr6Zqibx/WhSCThFTYjxIacAcmuHMC4vXoYQ5CNbLN9CnRJKAig+pyrOjT+wQGBqpxo3dZYb0lZCABKX3KKDk+uK8c6+6uW7+/HG/sP+5DJw86ghAy0wf/9WNOziECd3JsSumBAtZf1pvAh358yznAh4QgWBahJAml+fv7q7Gkbx89/ENwgwAE24tADvuPd4TpU8rJOaOfA1IS4AHgknZJXrN05ZFrAMcLr5XXIfuM10KXIblWSfiMz+UclHNd756jH6dy/vIHEj1UqV8DWaa8Htm3HIMS1CS4ZLfbVVoyrsuyjwm2ANC4I3HcMRetfj7o01dJMIZtLVPPyXZwl45Pph8MDg5W9ZRuYOy3tLQ09Trrqof7eG7pMCfno3xwXBH64VqlXxf066d88FrkvOM2b3NfGzJkyJAhQ4YMGTJ0o6lFixa45557iqVs/qYg7+S90cTfieLj49GjRw91g1RoaKgmUNWhQwdUrFjR43JlcN9dOi53qawK0o4dOzTPpRNrceqvv/7Cli1bNNvuu+8+pKWlYceOHUhKSnIbJGzUqBF69uyZb9mPPfaY5vmBAwdQvnz5PO9iTU9Px8aNG9GgQQOfA1WLFi1Camqq+k57rdWwYUO0bNnSK+ilOJSenq6BFK5cuXLNUn1FRkaid+/eHqe9CggIwIgRIzSORN46sVwLNWvWLM82LMp0gp7q008/VYHH77//Hps2bcKdd96JypUr45577kHfvn1RoUIFREREaI7jTbzeSAKk5cuXVy5q+Sk8PBz79+/HpUuX1DY6Rt8IysrKwrRp0zB79mzMmzcPK1euxIULF/I95tChQ9eodoaKU5UrV8YDDzzg4tLji+rUqeN2vbJYLLj11ltvqBSLADS/fRakBx54APXr11c32rtT5cqVPU4bWFglJCS4ZBcpKlmtVtSoUaNIXDaOHTvmFlCqVq1avuPh5MmTCA4ORo0aNXD77bfj/vvvV+kF169fjz179mDr1q24fPmyxpigIDVv3tyjNdudCuvqmZeTz549e7Bx48ZCle1O8fHxeQK4hXE0Yfzz9OnTmD17Nj799FO3+9E8gfr+++9dwCdDN6YGDhxYYGq8qKgoVK1aFYMHDy7272iFSTuYn6xWK7Zs2YLPP/8cv/zyC5YvX14s5/FWBviDq044TNkUHh6ugqQSjmHAVjqXAFfdONLT0+F0OpVDAtOk0OWHEIfD4VBOEMBV6IdBapbJDw5MUSOdLwBo4CK9GwvdRKRDD7+IyLQ7dKOQAXeeNzAwUH1wkeX7+/sjLS1NBdH5vwyIp6Wlwel0qn0YCKYDjnQv0kMI0oGGgIHe+YUBbtYnISFBuSyRGGUKJDpoyH4mjCH7Vg90yVRArD/Pz7ZheigCGSEhIWo/tr90TaKTCF1npBsGf+QjFCSdfuSPKxIuoUsT03+xPfUwlew/6fLhDrSQrjcS/mH7c0zSwclmsyknHcIpelcc1kumQ5NgEt129CCSdC6RUAPBCF6nrL+ckxJs4Tzg2CBMIUEMOabkNcuxIcdPUFAQQkJCkJOTo9Layb6U8IesH8cVIYycnBw1HuiOxeunMwzTPJnNZrW2SHBNwhf8MZYuWHLsMi0f04bJayYgkpqaioyMDJhMJg1UQnBEgm3BwcGq3g6HA1arVT2XbjgSdOJ1sX+tVqty35HjkK/L49iGBIoI8WRmZsJqtSI8PFy1t8lk0qQTy8rKgs1mU0450pWMYzAkJETNO157tWrVEB0drek/Xh+/GLC+nIdMd8V257gIDg5W10UYJisrS7Wl2WxGiRIlEBERoa6J/XHx4kXlcMQ+5diQMKEEm9gXgYGBMJlMmvcbSsI/XEe5tuvhKglcyXku12umCuN1EmDy5kuUIUOGDBkyZMiQIUM3mvr374/KlSt7fGe3t3r44YcBuDrV3EjatWsXWrZsiczMTE0qmaSkJPV9eMSIETh//jwGDBjgcbnyzshmzZpp0iDUqlXLa+jj/PnzmDJlimZbnz59MGnSJK/K8UU//PADXnrpJc22cePGYfbs2Xj33XfRuXNnl7QT//33H06ePImnnnoqz3JbtGiBhx9+GE8++aTatmLFChw/fjzf+kyaNAnJyckudfJU586dw8iRI3H33Xdj8uTJPpVRGC1YsABjxozBn3/+ec3PLdWtWzd06tRJPf/pp59QrVo1lCxZstjPnZaWhjJlymD79u0F7hsXF4cHHngAw4YN09yNf+zYMfV/y5YtNS7h10OxsbH5gm7ffffdNaxNrk6fPo1vvvkGADBgwACkp6ejbdu22LRpE8LDw1G2bFls3LhRpYihBg4c6LUrmXQ2CwgIwCuvvFLgMfPmzUODBg1w55134vnnn0daWhrWrFnjkyNacenQoUP46quv8N5776FXr16a9IRS8fHxSE1NLXD9MnRzqGbNmvjwww+9BuDcqUuXLnj00Uddtrdr1w5Nmza9LgBqfvLz8/PYUe/06dMuN5DqlZ2dfc0cfwBgzZo1sNlsRV5uWFgYWrRogQcffBDlypUrVFl//fWXW8ike/fu6rNzXnUAcj9XVqpUCUlJSQqc3LZtG5o1a4ZJkyYhPj7eK+jAYrFg0aJFPqdR/eGHH3w6DsgFJdyNj6+++qpA0NIXpaen57lON2jQwMWJ0lMxZhUaGgo/P788nbxCQ0PV965du3Zh7NixHqdmM3T9FBUVhRo1ahS4X1BQEB5//HHMmjXL5+8pnuqLL77QgNNFpT179mD58uWYPXs23nrrrWKDKb2VAf7galCWKW5CQkJgsVhU2i/uowckJOjCQDVhF4fDoQKfdNGQbkJMoSPBBQlEsEwGTmU6IMJEErKQrgoyKC/TkxFYkKnFQkNDNa4QDO6yTnp3B4IP0sWI9ZEWnnSmCAoKUvZ1dDpieZmZmQgJCVGpd2RqmsDAQBVUlu3Ma5OOLWwrOnpYrVYFG0nXEelqJK9Rto0e/mG/6R1p6GDCNG4SnpCuSSaTSdPvhJ/okJOenq6C8KGhoYiMjERISAjMZrPGMYguLADU4sHtemck6WwiXT9kW+qhH14zy+J4o0OR/FDtdDqRnJysgVwIuvCa9NAN548esmHfyDEoRciIUIMcj+wHHpuWlqZJTcSH7ENeE+lqCe3RcUXWQ7qlREREICIiQgPsyfqzzXidBCiAq4483JcOUVwDmKqJbisEuTjWCTqZzWbVzvp6UjJFnjsnI4I6iYmJiImJUbCLHlLJyclBSEgI7Ha7Zuzp3XRCQ0MVmMjrl1AInYOkK5eEJ51OJ6KiohAbG6tJWyfHB8cA1x2CjdJ5jP0k11FeO+cR7ZcdDgdSU1ORmJio5gqQ+2GWkBDHSFZWllonKQklEcySjkGst3weEBCgwBuuLUwjxlRifN9h/3Ot4LhITk7WOFXJtZVrESEozkPOAblOcFzonaIIJkrIUrpI8Rjuy/mvH298nYCrzWZDdnZ2sXyRNGTIkCFDhgwZMmToWunFF1/EP//8g0WLFhVL+fHx8fjxxx8LlQLgWsnf31/jWDF69GiVbiohIQFnz55FYmKix+Xdfffd6n+z2awBYw4cOICvv/4aqampmDFjBk6cOFFgeQsXLnTZ9s0336g7pa+F849UUFAQPvvsM5w6dQpff/01Jk6cqF6z2Wz48ccf0aBBAw3Uo9eWLVvw4osv4vfff9dsHzx4MNatW5fv+d977z2UKFECy5cvx6RJk7xO+/X9999jxowZ2L9/P+69916vjl2/fn2hXaxmzJiRrzNIZGQkqlevXqhzeKpFixahZs2amDhxIux2O26//fZiPV+dOnXw7rvvYvHixQpKyUs9e/bEnXfeiSVLlsBisWhurJPt07hxY+zatQvjx48vtnq705gxY9T4HTVqFCIjI132+eijj/Djjz/ixRdfvKZ1c6cJEybgxRdfxA8//IA//vgD8fHxuOuuu7BgwQLNfu7Wm4L0/fffa57369cv3/15N3lmZiYOHz6MS5cuIT09PU/AskaNGujQoYPX9Spqffzxx3jzzTddAt3PP//8De1uZ8h7ZWRk4NZbby1UGSdPnsTs2bMxf/58l9dq166Nhg0bFqr84lBAQIDHn9sqV65c4G+jtWrVcpvytLi0YsUKLF++3GPXIm9ksVjQsWNHdO7cuVDlREdHq3aTjjlly5bNExaoX7++en/mTfkXL17EuXPnNPslJSVh48aN2LFjB06ePOnxTau1a9dG//79vb4WABg0aBD279/v07FNmjTxCqwvCp05c8bt+9yTTz6Jo0eP+lQmY5W8EVqvoKAgDBo0SH0uPnPmDA4ePHjd074a8kx169b1CoDZvXt3sX2/pg4fPlzkacXOnTuHn3/++YaEmA3wB1eD/wQ0IiIiEBkZqYAbCVLoU89Il42MjAzY7XbllkBwgylbGHSm0wRdUyRMwvQqekcFCU2kp6cruEiCBvLB80q4Qw+3SKcZ6byRlpamwCCZ7otuITIAzMAywR/Wic4kYWFhsFgsqn0IfDDoTScIGTjXp/EBoAnkM5ivd7iRgAjrJd2SMjMzNammZP/IdEu8Vkq2p9xms9lUbsCgoCCYTCZNiiAg13I1OztbOXLIa5Mgi3Q4CgsLQ1RUlEqjxXahYwkD+gyqk4wlaOBwOHD+/HlcvHgRVqtVA8vIsaB3MuJ2k8mk4A0CMRIOSkhIUG4xfIO2WCyqbhL+kQ5L7mAjnle2C8UxmZ6eDqvVqpySeO1y7nFM6/ueeUBp+yjBK4Jp8vrlHCNURKBCzlPpNMMH259wD8e3dNHiQzoC0R1LpnRi+YR8srOz1Rjj9bNsee0cg7xWme5LpsBi2exvHi9Td9HxhscTDpLrgslkUmOV7UTnHqb9Yr0DAgLUNbJebC+CUBL8IxRDSecnOSZlGq2IiAi1nx7G4TiNjo5WdbTZbEhNTVWAVmBgIOx2O2JiYtS6TaiQ6xThJa41JpNJAU5yfdC7MBHekY5VhKbYFjk5uXlqOV4zMjIQGhqKkJAQ5T7E8rm2cvzwvYV1kdCPPsWYBBnZVlz/k5OTVV1kWjH2kclkgtlshtlsVmNP77xFgJZrtc1m04BKhgwZMmTIkCFDhgzdjCpZsiSmTp1arClznnnmGVSsWPGGdv0BgAsXLiAhIQFZWVnYvHkz5syZo15r3749/P39vQrQ6e/0bdOmjfo/NDQUlSpVQlBQEJKSkjBz5kykpKTkWdbWrVsxbdo0zbbBgwejcePGiIqKQsOGDYs1bVTlypUxY8YMzbbTp09j2LBh6vv37NmzceDAAQDA6tWr8dprryEnJ6dAqGnz5s0uAMzmzZvRrFmzfI/76quv8Pbbb6Nbt2549tlnPU4ZRdlsNrz77ruYMWMGWrRogblz53p8bJMmTYo9iPnpp59iw4YNxXoOql69emjbti0SEhLQokULnD17Fr/++iv69OlTLOe777770LJlS8ycObPAfSdMmIA5c+a4/MYJAKVKlUKDBg0AAFOmTEHNmjXxwgsvoHnz5pqUYEUpfT2SkpJgs9mUK5FemZmZOHToELp27apSwl9PrVmzBh999BFGjBiBXbt2aW7Gorp06eJTIFIfEAsMDETjxo3z3H/+/Pl49dVX1fPY2FhkZ2fn2XeDBg3CokWLUKpUKa/r5onatWuHjRs3Yu7cuRg1alS+a8q4ceNcQKeFCxdi1apVxVI3Q9degwcPxpAhQwpdTqVKlXDrrbe6hXPbtm17zaFdT5ScnAw/Pz88/PDD+X62uPvuuzFgwIACU/OVLFkSbdu29cgto6i0YsUKFyezolLt2rXRtm3bQpXx22+/Ydu2bfj333/x66+/KteOixcv5hlw3717N3bs2KFJEZZXirVt27ahefPm6NChA7777juPnHOCgoLQpUsX1K9fX6W99VSXL1/G0qVLvTpGqmfPnj6nb/VFixYtwnPPPYe1a9e6AGJly5ZF8+bNvS7T6XTi6NGj+PPPP3H58mWXzwudOnXCI488op6PGjUK/fr1w+XLl327CEPXVHfddZdbuDsvlSlT5pqs73PnztWYl/giu92OhQsXYvPmzdiwYYNP8Pe1kAH+IHehkYFYi8WCkJAQOBwOBY4Q/tGnSwJyP6zTaYSAUEhIiApeM8AeHByMsLAwFUCn2wvhl6ysLI2bjwRXWAeCOXKRlWAKg9VMbSXdZoCrLhR6aILBXwAq0CxdYqTjEQAFEBGiYDCc8A0D06GhoSroT5AkMzMTZrNZuYBIKMbpdCqgRcIdFAPQ8tr9/PyUS0d6ejpSUlIU5COdZ2QqJRkI11tESqcRth3bkn3q7++PlJQUjQOKdNFgkJzXyPIIKOgdaOSYio6ORnBwsIJq2K5yvPHa5UOmGuJYSktLUwCLHnpi+0onK9m27HsCJgQXmMKI8Ab7lvOFfSxhLQmL6F2zzGazSplGcU5xrOldawIDAxEaGgqz2Yzg4GBNSiOZdojgDh2gWAaBCjn2WTb3o5MR5zbHNgCX6+A2zgmn06nGqpxH7H+eg1AP/6frEPtdpk7SzweTyaQgLc53ppuTsFFwcLCagxLmYJ05FvlcQmY2m00Di0hJly698xjLpIsN+zAnJ0e52RCG4RrEeUwYS65xeicuAmsEe3jNdAHiGOK6mpaWpsYK5yb3S0lJwYULF3DlyhWYTCYF5/D9IDAwEGFhYWoO6FOQMb2ZbB+uyRwT7hyk2N4cp9nZ2QgNDUVcXByys7ORmJiI7OxstbZzTplMJrW28viUlBQkJSUhJSVFraecE3L+yD7m2itTljFlI/fhXOG4lesb1wB5ByHnK8ck5wLbO78c1oYMGTJkyJAhQ4YM3ciqXr065s+fX+x3Ix4/fhyTJ0++IYNbUr/99htatmyJhQsXomLFimp7q1at0KdPH/Ts2RP//POPx+V9++23OHfuHLKyspCQkKBxxBk+fDjuvPNOmEwmjBo1Ck899VS+gbM77rgD7du3V89LliyJTz75BGvXrsXOnTuxaNGiYnVpCQ8PR6dOnTQ3j40ZMwaPP/645s70b7/9FgDQtGlTr+qjD/R/8803BaYOAYDPP/9c9ckbb7xRICyUl7y9s9VsNueZwsFTdezYEevXr0dUVJTLa08++ST69++PEiVKoGbNmoU6jyeqX78+li5dit27dyMoKAjnz59HrVq1ULly5WI535kzZ7B3794C9wsNDUXlypXzhXjk7268S3/16tX466+/Cl9Rnd58803s2rULv/zyi9o2depUdOzY0SXouW7dOhw8eBBpaWk+z01f0414qtWrV+Odd97RbIuMjMS3336LNWvWuIWt8pM7UCavNJILFiyAw+FQcNtrr72GSZMmITIyMs+UQBMnTsRbb71VLKlfwsPD8cILL6B169YYOHAgJk6ciGrVquW5f9myZTVuZkaalv9bateuHZ588skiA5Z79Ojh1tFkx44d+QZsL1y4gNOnT19zt/HKlSvjpZdewvjx41GnTp089zt48KDbdFXuFBYWdk2dTfbt24fDhw8XS9kmkwkdOnTw+f25fv36AIDnnnsO9913Hz777DPMmzcPe/fuxddff43p06fneexLL72EDz74AFlZWfj3339x/PjxfB0Pjxw5ggMHDng8hipUqIC+ffvi77//9u6ikJuG9fHHH/cp9VCDBg3wxBNPeH1cYZScnIxatWq5fa/zJeXZ6tWrcc8996BTp0747rvvXICikydPYvfu3QCAP/74w6c2NnT99Mcff2Ds2LEeg1olS5bEyJEji7lWuWvdr7/+WiiHs7Nnz2L16tW488478eqrr+LUqVNFWMOikwH+4Co8QjiH0IV0dQCgtsngO4Ok0gWEAU6CLhI0CQoK0qTVYQBVBs4lwEBXEcIfmZmZGlcP4KobjoQRJOzjTu7gH/lDgR7O4HnksZRMISUhFrp/MOgsU58RspAAlXTVkZCK/nz6esuAOAEfBuWlcxDbVqY0cwcXAVcBE9nGbBdCEZGRkRrHGH2fuusD6c7E12XKJkIUso14TTJllux32X5sawb26QQi20vWi20uYRTZzjyXHJsEQyRwJNO16VOwybrKtuHrhFMkdCNTe8lj9PNFjjk59nhOAnRybri7RvYDIRLWh9cvwSLpEOTuww7bXj9W2W6y3+gMxvIlaMVy9FCOrDMdWDiupCsWzyfHZk5OjnLxIZzCstmWHBc5OTkahzCWw3FINxx9PSWoxPWUfcEyWA93c8/dfJdjiONYP+84/rifPmWYdC8jPCbXHK4jTBkXHh6u1gl3abuCgoIU6CbBLL1zlZR+fsr6s9841yU4x+ti6kDp3sZxK52jpMOTXtKBTL7HcU7JNVe/XujXTIKmcrt0jSIk6M7py5AhQ4YMGTJkyJChm0nHjh27Zi48U6ZMKbYATFHq1KlTOHv2rCY4dfLkSaxcudLrstatW4cFCxZg/fr1OHLkCNavX69ea968ufrueurUKbfwh14y+Hbx4kVs3LgRly5dQkxMDCpVqoQyZcp4XUdPtXfvXsyfP1/j6PHTTz8hICAArVq1Uttq1aqF1NRUlChRQgW2PNGIESPU/xaLBVOnTsWePXsKPK5BgwbquitUqODz3eLbtm3D1KlTPd7/77//RsOGDQsFZoSEhKBZs2bo1KmTy2ulS5dWNzUVVb8OHDgQtWrVcvsa3a2rV6+Orl274sKFC/jvv/9Qvnz5Ijm3XgsXLvRoPfDEHeLixYvqf84jb+7I9kadO3dG3bp183T+ks5Vn332GebOnYsVK1bgp59+8ul8w4cPLzbXJQBYuXIlvv76a802m82Gffv2Yd26dV4HkS5duuTi3rV9+3a3+7Zq1UoDEkp4IC/Q68KFC5gyZYpXdfJU7dq1Q+3atTXbypUrl+f+586d0wAbW7Zs0bzeq1cvDUBq6OZSy5YtC53iS6ps2bJu17NNmzblCymuXbsWCxcuxI8//lhkdfFGly9fRkpKCsqWLev29fDwcI8gXerff/8tqqoVqAsXLuDMmTMAoOIGRam4uDjUrVvX6+OqVKmiOe7YsWNYuXIltmzZgn///Rf//PMPNm3alOfxCQkJ+PXXX3HgwAGcOHEChw4dKtBx6fTp017Byo0aNUKlSpW8TkOXmpqKuXPnYsmSJV4dR9WvX9/rtK2FkcyqoNfTTz+Nzp07FymsJjPE5JUOzNCNq99++w3r1q3z6DsbVVwOhXrt3LkT58+f9/n4S5cu4eDBgwByv/feqDKicLj6pY3pqWTKLavVqpxX9I4PMu0MA6+BgYHIzMxUzj8EUOi4IYPoemcKun8Q9LDZbMppiPBGZGQkzGYzQkNDFSAg01/pJQO20nVE/9Av3Po0NQw2c38ZnGbAF7gKFnBhDggIgMlkUk4ebD+6AhEGoINLeHi4gqZYtqwfXUJk6qX09HQEBgaiRIkSKFWqFCIjI2EymTRBZ5mSic4tPLdsJ+newv6SwIhMC1aiRAk1XthedMPQl8kgOmEIggN0iGH/5+TkqLEn0+xI5yICFwykly5dGpGRkZqUWgQGrFarSi0n3Wr4Zk3XHIvFohxwpKMHr8XpdKrzR0REKMcmjgm73a6x4eU4oVMVgTgZ/JduR0wDJJ1RpCsN5wPnSHp6OpxOp3LjYTnsN/7ow7rL/K+sA9OHSTciAoAWi0UBUf7+/iodlN1u1wAbhC/0gBJhEQmjmEwmREREICwsTLUZ2yc1NVWtF3qHGPnlnj+U8TplXTku5NoioRW5TtHxSI5TmZ6KQExERASCg4M1gBAhH65BdH7hPnTB4jjn2KazWk5ODlJTU5U1u6wf24vjjfOca4neJYviusD1QjonZWZmaq6BQExwcDAsFgvCwsIQHByMtLQ0XLhwAWazGU6nExEREWpd5RiW45tjw+FwuNRNvi9wbMgfgbhG0h1Opu6SqcvkuhsaGoqIiAiVaouvSccemXpRuqPJtVxCkNyf66IEmDg25PuWdH+iWx3d0HgutotMV8c5Vtx34BkyZMiQIUOGDBkyVFyKi4tDzZo18eCDDxb7uTy9I/xG0Jo1azB8+HD1PCIiAm+99ZZPZf3555/o0KEDunTpgtOnTwPItX7v0aMH9u7di6SkJKxYsQLDhw/PNyXWrl278Nlnn2m2PfXUU2jSpAnatWuHAQMGYPTo0T7V0VONHTtW87xLly4ICwvD5cuXERwcjFGjRqFLly6YNGkS7Ha7BgjKTyVLlsQzzzyj7q7++++/cfToUY8CdTExMShdurR6Xpg2kKkzClK/fv0QGRmJadOm4bbbbvPpfD/88AOGDBniAgwAUHeEA8Att9ziU/lSDRs2xBdffJHvPhcvXsQDDzyg7P2nTp2KK1euFPnNLoS8PvroowL39WQMyUCH/A2vMAEQd2revDmaNGkCIHftdNfvS5YswYABAzBmzBgsWbIEH330Efr16+cTOAjkttGCBQsKVW9vlZmZicaNG+OFF17w+tixY8eifv36KniUkJCQ57yaOnUqPv/8c/VcpuTJK8XK6NGjvXJd80ZDhw51AXXq1auX7zHLly9X/5csWVL937hxY8yePRtbt26F2Wwu0noaKn717t0b/fv3R3h4eKHL2rJlC7744gsEBASgTZs2Lr8hZmRk4Pjx4y6QndPpxJdffokBAwZg586dWL58eaHcHHxV3bp1MX/+fHz33Xdu3W1atWrlVUqoa+n8mJmZiVOnTmHOnDkKACpq5QWN5KVSpUph/Pjx6NKli4ur3vLly/HOO+/g66+/LtDVbNeuXRg6dCjWr1+Pb7/9tkDHwvnz53sFoLZr1w7//fcf7rjjDo+PkRoxYoRPrj/Vq1fHM88849M5fRHNKNypSpUq+Omnn7xOI5ufGPPjefMzuDB0Y6pZs2ZerWP5OaYVpaZPn47Fixf7dOyZM2ewdu1a7Nixo4hrVfQywB/k/qjC4CVwdWFhyi0AKlAMaB0ppJMHA78AFCBAuIOpmhjYDg0NRVBQEJxOpyalV3p6ukpXdfnyZSQlJalANd09mD6L7g8yUAvAZSGUjhgSLJKwiYRTGLB355bBYDuD8nSZke4bMg0PgQ8GqplOJjAwUME3TKnE4Ly+LQlDMLjP9EB8TcIkbCcGwxm4ZoBbOl0QgMjL7Uef4kjCU/7+/rh06ZImtZl0o2E7y+A6HZCCgoJU39HhR7omER5gIF+62UigRcIFJUuWRHh4uMa1gw+r1YqkpCSkpqaqVHF84+T1m81mmM1mVS/p5sOxTHCI/UUggNsJ/3CcEKBj+joJasgxwucEYui6Q6ghMDAQdrsdiYmJqk9kmikez7aVfcH2ZbtKhyNeF88hnX70qZoI3xH2IWjDcapPTcXzSecWCbIR8mH9UlJSNIAP5w3HiMPhQHZ2tnL74jVKhyjOc5bL/WR6JQn/EWqUbUYABoACO0JCQjSwjVxf2A4hISFqLZKvZ2ZmasArpnAjPMgfJ2UfyW1sP+kopHfI4Tol1w6ChNLZR8J7ABRA4+fnh/DwcAUJxcTEID09HcHBwYiOjlZzKjk5WbNWsg9ln9AtToJJHJssh+BTenq6mkchISGIjY1FTEyMgsEkDJWdnY3w8HD1HiTT7xHYoUuT3W5XcCTPKYFV+ZCuchJe41oq25gwFa8pICBAA/Zxvkm3H6b4Yrn5WY4bMmTIkCFDhgwZMnQja9SoUfj444/xzTffXO+qAIDPAEVRa8OGDQqSqF69Ot58803MmzfP63KGDBmCcePGAYDGFr5t27bYs2cP+vfvjw4dOmDu3LlYuHChCoInJSW53EFvsViQmJionj/xxBPYunUrKlasiF27dmlSDF0r9e/fHzt27MD333+PJ598EkOGDEFISAhatmyJH3/8Efv37/eonIsXL6JNmzbYvHkzgFy3m6ZNm3p07OrVq/Hrr7+q52FhYfjwww+9vxgvlZqaipdeegnnzp0rlCPP/Pnz0aBBA83Yr1OnDh555BGsWrUKu3btyjPtkTfi92558487rVmzBufPn0eHDh2wfft2vPHGG0UabB41ahRef/11j+d6gwYNCtxnxIgRaNu2LYDcFGn8Te3YsWO+VtOt/vzzTw2MNmjQILf7zZ8/H++++26RnHPjxo3w9/fHvHnz8MYbb/iUeqSw8vZmp/T0dLz66qsArjpEu9P48eM1z/fv34+XXnoJ06dPR4cOHdzeUT906FA4nU6EhYV5VaeC1LBhQ9x7770u2wtyvJJjrHXr1ur/t956CxEREThy5Ei+aZwM3ZgaPXp0kXwe+e+///Dyyy9jyJAhOHToEGrWrIlHH31Us0+vXr3Qtm1bF8By8eLFGDt2LDIzM/HOO+9g8eLF181xPCgoCBcvXnQ7HzZs2KCg5oKUnJys1uri1IwZM7Bw4ULcdtttGDt2LAYNGuRRaklvtWvXLixbtsyrYxo2bIg2bdqgTZs2eP/997FhwwZMmDBBAcwS+s1LYWFhaNu2LapVq4a1a9di3759BR6Tk5ODL774wmMnD6fTieTkZA2Q6a2efvppn44bOHCgz+f0RTNmzMh3nZ44cWKRgfVZWVlYv349XnvtNTz11FMKkjV08ygpKanAz9JALovx7bffYvbs2degVsCVK1c8TkGm165du7Bu3boirlHxyAB/ABVcJ5zBgHNoaCgsFosmLRIhF+mUI4ECPYDDYCwdHrKzsxEbG4syZcrAZDKpoHh6erpy92E9AG0aIwn7MNgug/o8pzsREGA96MBBGIaBXcITDOQz8EsABdCmqwGgATFYB0IhDPxbLBbVdiw7LS0NVqtVObawTJliSAIBMlgtwR8AmvPTCSMgIAAOh0MF6GXaNLaDTClF8cOhdPVgEJx9bjabERsbq6m7dH9hWjhCHg6HQ4EVEtzgMQTEbDab6ns6PrHvGFynvR3rQqBHAkcydRgApKWlITU1VZUpHZB4XHBwsILKCBYQbJLuQ+xj7h8WFoaQkBAN5JGdna1JO0SQiV/8JTBHgIYOLNLxheM5NTUVYWFhCirSO27pU5LJca+fBzyGc4CuNZxfBH6ke5PZbFYAkz7dkR524/k5XqXrFesgATYJtUiXKekgxLoQGvTz81PWlMwBzvPa7Xakp6drUgLSNYZziw5PXD84FwhDyTnIeaaH4OQ4IDQl1w25pnJ8cyzI1GlcO7nOsc8kdElAhlAe+0u6O8nUXdLxiCAg+50uNRwDdC7ieI6MjFTQW2pqqkqXl5iYiKSkJI1zFmE5OWekyw77RP6Aw/6mGw7XA0I88kdreS6CNBL64rpC2EmmzOO6IeegbHcJJcq5ItPRyXkioTG9IxDbS66pHFusG+eBIUOGDBkyZMiQIUM3oz788EPMnTv3elcDQC4QIF12bgQ9+uij+OOPP9C1a1dNmi5P9cILL7hN7fHzzz+jTp06GDBgAHbs2IEDBw6o7QkJCdi2bRv69eunCTTExcWhffv26vn+/fuxZMmSfF2Cilv16tXDvHnzsGXLFjz55JPqWo8dO4aBAwcq6KkgTZo0Sf220LlzZwQFBeGNN95wmwLLnebMmaNxdxk0aFCRuVg1bNgQPXr0cIF74uPjsWTJEuzfv185//qqESNGaNK4WCwWVK5cGV27dlVgUX4AhSeic4Un7gTR0dGYNm1anmBLYVSmTBmcOnXKJR1UXvIk1c6UKVMUaHPmzBnlLvbff//5XlE3qlOnjiYtx8CBA9GsWbMiPYdeHHcDBgzAm2++ma9rU34wjK83LNWqVQvr16/3Ot3L8uXLkZqaisjISI9dUw4dOoTx48fjp59+Qr169dCzZ0+XfcqUKYNNmzbBarV6VZ+89OCDD+K5557DmDFjXGIP77zzToF39Z88eRInTpxAamqqBtC77777ALim/zJ0c6go0modOHAAU6ZMwe+//46MjAxcuHABERER6N27Nxo3bgwAeOmllzB06FCN60tOTg62bduGjz76CKdOnQKQC9dIN7ProUqVKrmFocqVK+cRvOB0OvHzzz9jxYoVxVE9jcqUKYO+ffvimWeeUetI9+7dMXHixCI7h8PhQGJiotdg7t69e3Hw4EFERUWha9euqFSpEvr374+RI0cWeCw/B1SoUAHNmzfHyJEjPYJjqZMnT+Ltt9/GW2+9VWA/OBwOpKSkqBupfVGTJk18ShlUoUIFDB482OfzeqsxY8a4OFpKhYeH4+2331aOf77q8ccfx4wZM9CtWzdMnTo1zzSYhm5srVq1Ck8//bQmzaw7MWtPQEAARo8erdb94lKvXr08dlrVa9OmTdiwYUPRVqiYZIA/gHLksVqtyvWAKZCYVkvCIAzuSwcffQCb2xmEZVA0OjoaMTExKkBKEIXOKHR5kalhSpYsiVq1ainQRwa4CU0A7r+UMtguHW5Yd+k0w8kly+W1h4aGaoLY3JeSUIwEAuTrhDz8/PxgNpuV844EHdi+DIDLdFZ8TabgkmnH0tLSVP30LiQynY1MmSX7ST6Xjiysv979Jzs7GxERERoozGQyaZxRpDsSg+PyWgnxENDgOOA1nTt3TuWxlE41EsgCAKvVisTERLVIsm857uiyQier1NRUNQ4IHxFAkG5SkZGRGliAbiUhISEICwtTkBhdTtjOhEBk33PeSCiJwBPbQwI9dJYJDAxUblgpKSkuwB3rJd2lOHdkX+thPTkHZBowAiAyLRQdWQICAtQHObobpaena9JzyTqwj+gwIx20goKCVD9nZGSodUOelwBScHAwTCaTguZYP54zJCQEUVFRmlRLcu5x3BOCA6AAwszMTCQlJal+5RqgB34kWCNdYKTzF/fh+GRdpMsR5zXTpgHQfDAmDMXxyfMQzpKp9/hgnWSbcIzx3LLvbTabiyOSnId2ux0lSpRAYmIiEhMTFfxy+fJlpKenK5hIOrBZLBYFyknnGz1YxzpxzqSmpqr+4+tsZ849plUjHMcxQpiOkm5wMlUf1yLCdA6HA1arVblwybVKDy4xhRhhQTkWKAKa0g2K18568nqMu7cMGTJkyJAhQ4YM3aySDjLXU926dcPo0aMxa9as610VjTIzMzF37lxs3LgRn376qdfH33PPPQByoR2pMWPG4MSJE4iPj1fbSpUqhSeffBIxMTFo1aoVli5dio8//lh9P5o5c6ZKwQQANWrUwLp1665pCrUaNWpo7sT+8ccfcc8996gb0QBg27ZtGDVqlMdlfvnll3jsscewc+dOALnpr+bNm4fOnTtj1apVHpWxZMkS/Pzzz+p5TEwM+vfv73Ed8tOsWbOwZMkSbNu2DTt37nRxInr22Wfx9ddfFwrM+eabbzSuAUePHkXz5s2RmZmJ48ePo1SpUpp0Zr4oIiIC586d8yitRIMGDTB9+nR88sknqi+LwnXIbDajbNmyBboT9OzZEwMHDkTLli09dlOy2WyYNWsWoqKicOnSJeTk5BQ5+KMP+i1duhTbtm0r0nPoFRgYqAHLatSooVmLbrnlFphMJmzcuBE7duzAjz/+iMmTJ7uUs3PnTvz555/47LPP8NJLL3l8/uPHj6N27do+9f9XX30FwDXNY0FjcNWqVTh16hQeeughl9d+/vlndOvWDRaLxev6SD3yyCMYMWIEhg0bho8//hg9evTQvH78+HH89ddfBabpOnjwIJo1a6bAhylTpuDll19Wr0sXIEM3jxYtWlQo4AHIdeyhm2JcXByqVasGIDeNXYcOHTB48GC3sMeRI0fw1VdfKSAYyB33xb3WFKRbb70Vd911FzZv3oy6deuq7d26dcNdd91V4PEmkwn33HMPIiMji7OaAKDcwrp3746RI0eiT58+AHJT4fzxxx9Fco7Q0FA0btzYaxe2s2fP4u+//9Z8ZihdujRq166d73HTpk3D9u3b8fzzz2P06NHo3bs3GjRo4PVngzlz5mDevHno2LFjvvsFBwcjLi4OderU8dlp6qWXXsKiRYu8Pu6PP/5wSW1b3GKatbzm/cmTJwv1Oc9sNqNly5ZITEzE9u3bPUpla+jG1erVqzF16tR8U8pGRETgrrvuQq9evfDRRx+hb9++HpXdsGFDjBgxAo899phXcPnSpUuxadMmj/en4uPjbypI2QB/AAUXWK1W2O125Y4RFhYGi8Wi3DEIEjDA784xhzAFpQdqZHof6TbDsp1OpyaQXq1aNZQuXRrBwcEoUaKExiFCwkEMxEonBgagJfQjoReZzogPumFIBwwGnVm+dN1giiTpaMLzS0cgBpaDg4NVWzEwLNPzhISEaILG0jVJpqaRwJIEmlhHuhYR/iFgIuEf9qUEBRhs53kIYsk+5PXwizjhAQmtSABIgjZ6VyS6dDAwn56ejosXLyIgIABly5ZFVFQUgoKCNE48bC9/f381/mSarZCQENX+bGc+bDYb0tLSFJAi02Nxm9lsRkREBEJCQtS+sn9YFwIKHH9MlyT7lV9SJZTG9iaYRDCHDiiEPdiOdEeR84r1kWVzjMrjpAsT212O48zMTAWh8PrZzrw+PnJychAfH6/mLee7TNEmHZcIrsn2l6AF56bD4dC44/A4Xh/nH4EYQm6EaPQ/SHHu83zBwcEK2pLp2QgiyVRVhFp4PjnmZZoz9rN0JGIbyevifCPgyJRpdrtdlRkQEKA+xHHt1bs2yT7lMdJtSfYr209vsZ2ZmanSDLKPLRaLxoWLac0CAgJw7tw55VJ1+fJl2Gw2OBwOWCwWNSYIydHVSwJ+HF9sB9aBInBUrlw5lChRAhEREcpBiOCixWLRuCnRWSs7Oxs2m031DY/ROw/xwfWO9dY7/xDwkm2YlZWl4CBeHyXHMeemTBMo3yf4RSA7O/uGCZYYMmTIkCFDhgwZMnSzKjExEVu3bkWlSpWud1U0WrhwIV5++WV06NBB3XnvjTp37ow///zTxXo9JiYGs2bNwoQJE9S2CxcuqB9szWYzunXrBgDYvHkzUlJS8P7772vKGDZsGN56661CB8C90fjx4/Hss8+q57169cLevXthtVpx9OhRZGRk4IUXXoDNZsPKlSsxdOjQPMuKjo7GhAkT0KZNG5c7VBMSEjyqT8uWLdVvDPofu5s3b+7hVeWtJ598ErfffjsCAwNRoUIFNGrUyAWqiIuLg9PpRMmSJX0+z8yZM1VqpLi4OMydO1elPTt27BhKlixZYMqhgvTdd9+hfv36OHz4cIH7rl+/Hp988gk6d+6Mnj17IigoCFu2bPEJfpMqVaoUypQpowlou1OdOnUwd+5c/PHHH7jllls8KrtDhw7o0aMHfv/9d3To0AFJSUnYs2ePz3WdO3cuZs2ahaeffhpdunQBkAupSV24cMHn8j1V06ZN0aFDBwwdOlT99ko3GQDYunUr/vnnH7Ru3Rq33norunTpgnbt2mmcif39/bF37140a9YMTz75pFcpjBwOBzZs2OCTS0e7du2QkZGBBx98UAMK/PPPPwUeS8iuXbt2KFeunNr+xRdfYPny5bDZbF7XR+ree+/FlClT0KZNG7evjx8/Htu2bfNoXr/00ksKCBwxYoRmrb799tsLDLAbuvFkMpk8SrmUn0aOHKn6vlKlSihVqhSA3Pf3YcOGYc6cOW7XqBo1aqBXr16abSdOnPDZyaGoFBISgttvvx0tWrTAu+++iwoVKqBjx47o3Lmzx04oGRkZBcJ0hVX37t0VDBMaGorWrVvj9ddfR8uWLdG4cWOPXOQ8VUREhFtXx/xUv3599OnTR91QDeT+Hp2fa1JQUJDKtDJq1Cg8/vjjaNCggXLt90ZRUVGYOHGiJt7pTpGRkahZsybCw8MLBWvt2LHDq/0zMzOxZs0an8/nq44ePYqVK1dqUsdK3XrrrRg2bBi6du3qU/l2ux1PPPEEHn74YcyfP78wVTV0A+jUqVNYs2ZNgXB6ZGSkcvp56qmnCiy3fv36eOWVVzBmzBgMGTIENWvW9KpeV65c8Xhfu92OGTNm4Pvvv8fWrVu9Os/1lAH+ACqoKtM06dPCyFQmdNBxOBwu6Y/0rjsS0gCuQkYynYsEBiTwwgA4v5zLcuSxrK87qlSmyNJDLjIwLJ/L1Cx61xKWyeC63Cc/mlOmySLwwbIkOCWDz9I5Qw9X8HrpKMN95UOCHzIdk0wZxjaXThUS3JFBcQkqSFcU6VjD59IxRQIdeoBB71xEQIMpnfROQ9LJQ7o2SRcdCX3J/WX/uHNO4TYCHMDVVEMch9L1heVIaMJkMmnSB+nHorsxKWE0Xpd06WFbyPEv21L2gRxjegBPDyGxzVlXef0SiJOSY1emrpJtLtcPfZn61+nwJKEZlsc1Rzpp6V2R+OFTQjjsV64zeocpd445LJ/n5BjVrznSQUe2Mx/S+Yn1IszDNmCby/nDa5djW9bb3dqmh33y6iMAGojPXVpGugtxXCcnJ2vAIbvdrtZ91l2uP5y7ehBJtgn/Umxbi8WCiIgIDcjH9Gh0OnK37st1C4BKX8brdjfmJZDIdpNznNvkOijbUba37Dd5Tu4j1x59nxkyZMiQIUOGDBkyZMg3HT16FLNnz0aTJk2uKchS3Nq5cyeqVKnisn3lypVuLf43b96svsvwuFKlSiElJcXFNWP79u3YvHnzNf0+smPHDk3Ks6ioKKxduxYNGjRA8+bNERQUpL43t27dOt872Pv27YvOnTvj888/x969e9V2p9OpCfTnpaZNm2LYsGEKiNGDWREREYVOy+wuwFOnTh107txZPbdYLEhJScn3rl9vdPnyZURERCA2NhZALgRlsViKJFjqTUAAyA30/vjjj+jatSvKly+vAte+in1y6dKlfPejW4O3ojPO8ePHceDAARw/ftyncgDg7rvvxkMPPYTHH38cTz75pNomxRT1xakrV65g+/btmDlzpgJmkpOT1ethYWEuIE92djZGjBiheb506VL13OFw4JFHHvG4Dr46sVWrVg1BQUEYPHgwhg0bprbXrVsXZcuWzffYU6dOITIyEoMHD9a4iaxZs6bQAFqFChVQtWpVAHD7u9zFixfx559/4sKFCwWm8gCgIE13Cg4OxtmzZ32vrKHrIt44XBjFxcUp4E0PO1aoUAG33347fvvtNwBwSeOlf+/avXt3odazohKBmvvvvx/Dhw/H1q1bPXYuuXDhAo4cOYJjx44VZxWRlJTkkvawevXqeOCBBxATE1PozwV6eZv+qVWrVi4uPYmJiQU61J09exYREREun4/4mdFTJSUl4dy5cx7tGx4erswTfFFQUJBHn+ekMjIyisRh0Bf5+/sjIiIiz9e7deumPg8YMsTPEQWJ39PyS8dKhYaGqpvfZRYKT6VnOPJSfHw8li5dinfeeQfTp093MRq4kWWAP8j9AmKxWFQQ1Ol0atJ3EVrJzs6G0+mEzWZDUlKS+qJkNptVgJZODABUWhS6MjDNDIP30rXFbrfDarXCYrGoVC0Wi0Wzn4RFGFTNyclRKXT08I8+jZV0ugGuuqYwEC7T+dAxh+AT037JAL2fn5/GHchdAJ5/ed6MjAyEhYWpNpaThfuHhoZqAvMSknLn0sNrkdAM96MjB9uK/cuH1WpFamqqS4ogp9OpUiOxbXm9MnXT5cuXlSuP3g2JLi+RkZEqVRNBFwlCAFAwBNOfOZ1OTYBdBvL17ku8ZrZPVlaW6ktZhoSkKD3gxXYgOCIdreiGQ8CA5dCJhBBJdna2crGR4IgcjzIVFwEmAMp9hW1Fx6HAwECVl1o633COSaCBY0lCYHoYSsJbEq6R7inSEYmvhYeHa1y59HAMr4XQDceMTI0lx73dbofD4VBOTYQt9OVJKIVtRzCRawBTQfH6srOzYbVaXVx5+JwOMRzPPLcEDt3Bb4TlpBMUAb60tDS37SOviW5qISEhCoqSY5N1lSCldPDRtzf7UK49chzQSS01NdVt2je9a1tOTg4SExOV4xHbNCcnB8nJySolJJ1wCOpIwI51ZMo8psaToBn7Qrqc0YmH7SLbQcJZVqtV055sG64teliLbSSdfpxOp8YFjuszxxvLlk5wTD0mr5HzkeeRayWvU85NQ4YMGTJkyJAhQ4YM+a6zZ89izZo1iIiI0ASsb3alpaWhRIkSLqm+mjdv7hL88vf3x7Bhw9R3jqCgIDz00EOoXbs2ypcv7+J8MnXqVIwYMUL9pnAt9M4772jSeNFRu0yZMqhcuTIAqLtTn3/+ebzxxht5lnX27Fls2bIF77zzjmZ779698dhjj+Vbj+bNm+OTTz5Bhw4dcOeddwLITTEmnYLMZjPuuOMOr64PyHULAHLTsdHp5ciRIxg3bhzOnz+PsLAwvP322wpQi4mJQevWrQusszfatm2bCihs27YNcXFxKFu2rMdpr4pKs2fPxsSJE/HKK68AyG33wgRNY2JiEB4ejl27duW7n6cuP3rJYOadd95ZKFeY+Ph4REdHo1GjRujatSsCAgLw8ccfq9ezsrKQlJTkc/m+aMmSJfjyyy81a8dzzz3nsl/9+vU1rkAA1G8jADB06FCv2njlypU+1XfcuHGIj49HuXLl0KlTJ1SsWBFA7m+GBQWejx49isTERPTp00eT8mv48OGYN2+eT/WhWrRoka/rxzfffKPG6KxZszBx4sR8y5Mgll6EHQzdXKpdu7ZHzjAJCQl5Blp/+uknleqL8TmpiRMnYtq0afjpp58wbdo0nDp1ChcuXMBbb72FwYMHu5R3rVMfuZP8Xfbhhx9G//79PQZ5VqxYgc8//7y4qqZ09OhRF9gkKCgIzz//PGbMmKExASgKeQuqBgUFaW6gBYBLly7lC7tkZGQgKSnJ7ftvxYoV3QLm+Wnz5s0udchLLVq0UGnqvFVGRgY2b97sFRgdGhqKOnXqXPPPO0DuZxSZGUCvyMjIG86Z1ND1k6eQDZAbkx8+fHiB+5UuXRoREREIDg5GbGxssTik/frrr+jUqRMee+wxnDt3rsD0uzeaDPAHUGlWGLROS0tTwWgAmmBxVlYWbDYbrFYrMjIyYDKZYDabFfCTnJysUvBIaIABV5nOSw8sMPgvPxzQ6YQwDoPowFW4gUFjQiqA1glIujfIY/WpjPQPGdzljxTuHI0kMCRdS1gGgSdej2yL7Oxslc6GTiEyHZcEZfiXcACD7oSxpIKDg2E2mxEQEKBAKpablpYGm82mHikpKeouGtlnbE8+GNRn/yQnJ2ugF/YFAJVex10AnxAEzyXTNrF+Mk2WFKEIfvhi33LcSrCL4yo9PR1Wq1UDzkjAQ4IWfD0tLQ2pqalIT0+Hv7+/SnvHNFCsC88vwRTCAHTEkumlCFnI8ShdbgIDAxUdzTFntVo1Y4vABs9HeEi6JklgRTqZEKAgcMXXCYJJaIHXRLCNEAf7TDoUSZcWCalIpxXZ5hxrCQkJcDqd6nycR+6cWlhfutYAuT8CZGVlwWQyISwsTAPR+Pv7a9KE8TUJyTDNHOsmzyvBOunyJPuf5XEM0w1Hukaxz9PT09V84VpCmIRzMikpCampqRr4RL/e6Z1z5JyTY5LjgentCDICUKn3ODbk2sIxJEHP6OhoWCwWOBwOJCUlqflmt9ths9ncwjCsO9uD45lps9iPTLvGa5BtqncZYnuyPyRIx7kg54N070lLS0NSUpJyM+L1ZWZmIjU1FSkpKcqBivWRrlMZGRlITU2FzWZT12QymRSoyboFBASoH3YlhCmhRUOGDBkyZMiQIUOGbkY9/fTT17sKSkuXLsX9999/vatRJIqNjcUTTzyBBQsWaFJ9jRo1CqNHj8ajjz6q2T87O1s5iDidTsydOxdjxowBkOtkYrfbNfs///zzeOmll4r5KvLX5s2bERcXh2+++QYHDhxASkqKCsp/9tln+TrMhIeHY86cOZptX3/9tUfpmfr374877rgDJUqUwOTJk7Fz504sWbIEMTExmv28vRO/bt26ePnllwHkAjfx8fE4duwYpk2bhjfffFOlZ2jYsCH+/vtvbNy4UX0ff+qpp7xyUdFL3gm8ZcsW1XYHDx5E6dKlMXfuXEyePLnY06TolZGRgffeew9vvvkm1q1bh5iYGLRv396jO5f1io6ORkRERIGpnjy9i1ovvWuGJypTpoxLgLFChQqa1FRArgPI22+/jT179uDcuXOYMGGCmp/XShMmTMDAgQPx4IMPqm116tRxu++JEyc0z+vUqaMJeI8ZMwYNGjQojmoqvfvuu9i+fTs++OADfPDBB1i3bh02btzoUdrEEydOqGBxs2bN1PZDhw4hLCwM1atX97lebdq0QXR0dJ6v//LLL+r/4OBg1KlTxy1gBQC1atVyGSsAMGDAAIwdOxavvfaaT+PS0PVV5cqVC4QcHQ4Hdu7ciZkzZ7oNAN96663qN8/77rtP/eZNhYaG4o477sD999+P0aNHY+vWrYiKikKJEiXUTflU+/btFYB5o6hEiRJ47rnnPAZpKlasqJlbxSUCu3mpKB1/7HY7Ro4c6dUxu3btwtGjRzXbbrvtNo2zmTudPn3aLcxatWpV1K9f3+s66MdjXrr11lu9TmcmtXPnTjz//PPYtm2bR/unpaXh8OHD18Vd/6effkLHjh3x4Ycfqli0Xvv27XPrFGfof09LlizBa6+9VuB+OTk5WLx4MaZNm5bvfpUqVcItt9yCEiVKwGw2o0aNGpg6dSrmzJmjvpsUpE2bNmHTpk2KqZBOYpcuXcKcOXPQo0ePAgH8G1nG7EOuTdyVK1dU0JSpvywWiwZ2kAFym82G0NBQldooMTERSUlJuHjxIrKzsxUo4HA4YLVaFewC5H7gYcBYQgzBwcEK3pCBd+nyo3d54PEMujOgrQ9s52dDJVN7ETDKC/6JiopCWFgYQkNDERgYqNqGxwBa95rMzEzYbDbltBMUFKSCzdnZ2ZpgPJ0jbDabBg5iuYQBGER3OByqngyQ06WHIAXbzGKxICgoSAMpEOCyWq24fPkyLl++rCAWXheD7rzjg85C3EZwguCHDPzzWvVpwfRpzxiY5w9Usu1lYJ/jgKARITICKxKmkcALwRX5QUACYTLQL6GKzMxM5VTCviaYpk85JlOJsa0krCXdjiScwLoQvpDzhAANXWv4A4MElVgOnacIeekBMrovyfYHtKAXxx/HGceQdK4ilJSTk6PgD4IbdEWR6Y2kO5GE8tj3XGPYD7Kf9O3DviHIw1RPhMvoTMN+JcAkHaA4jyWMxjEHQEGPfLBNpTMQr1e6jfHaJPTC8tLT05UzEccRAAW9sA/ZTrwuWQfOIc5z2Xe8NvYp50xiYiKA3LsXCagQBpTp3GR/sB9KlSqFiIgI1T4hISHqh+XLly/DZrPB6XTCbrcjNTUVdrtd4yqlT5FGuIlOS3L+WK1WtTZK6IfQEdcFmaqM6zTPIdOiScBMuprZ7XYkJydr0sux3zm+OA95HrZlQEAAbDabWif5YwzfB+hSxvmVk5Oj3jvZL+xLQ4YMGTJkyJAhQ4ZuVp0+ffp6V0Hpzz//xPTp0693NQqtDh06YNasWTh69CjeffddzWsvvfQS/Pz8cP/996Ndu3aa13jXpclkwrJly1CvXj0Auel+9HdM//LLL0WessJbdevWDV999RUA4PPPP8eHH36oXsvvbu0qVapg6tSp6NSpk9oWFBSEO+64Q11zfjp16pT6jSUmJgaNGjVyAcZWr16Njz76yKvr2bt3r4J3Hn/8ccTGxmLChAmYPHkygFwHHLp71KhRA61bt1bH7t+/Hxs2bPDqfNT8+fPx999/q+fbt2/HiRMn0L59ewDAyZMnVcqMkSNHFhjYLGp99913GDduHAYOHIgFCxbgxx9/xI4dO7yGBiMiIvIFLiimb/NW+d2pn5fuuOMObNu2TUFjzz33HN58802XND+TJ0/GsmXLsGPHDjRv3txtEKZixYpo3bq1T1CUr9KnAKT279+ved68eXPN84CAAAWyeaq5c+dq5rgn6t69O7766ivEx8dj48aNuPPOO7Fjx44Cj9u0aRP69euHF154AVFRURg7diweffRRrF69Gu3bt/fJRSc2NhbLli3D0KFD8w0qy+B6cnIyatWqhXHjxrndt0uXLhrwYfDgwXj77bcxf/58vP322y5wo6GbQzt37ixwn5ycHFy6dAnDhw/HihUrXF6vXLmySgv5yy+/4N9//9W8vnr1ak3qzLVr1+LgwYO4cOGCy3v7uXPn8nWWuh4KCgrCLbfcgo4dO3q0f9WqVV3SJRaH1q9ff03c2a1WK1auXIkzZ854dVxAQICLm5Sfnx/q1q2brxNbpUqV3KahKl++vHJT80RRUVEaF7WCFBcXh6ZNm3q8vzstWrQIzz33XIHuUMePH8esWbPw3HPPeZyOrDhkt9s1DpJSffv2xQcffHCNa2ToRtV7772HTz75BJs2bcpzHz8/P5w6dQqNGzfO97NHrVq1cNttt6FatWoaMK9Dhw4ep8E+cuQIpk+fjl27dmHFihVYvHgxnE4nNm/ejKeffhqDBg26pk6xxSED/AGUAwMD5gx0M+0W03BZrVZNKhWZuiUzMxMJCQkKBGKQ2uFwwOl0atJySdtQACrgLAOjDodDk4JG78IAQJ2XQV6WyWC6hB+k2w+hBsISMqUXg7iEO/g/tzN/dmxsLGJiYhQkoyc4eU5OUgaPc3JykJKSgqSkJE3aLCleLyEePtcDDmw3OogQymGaLAmoBAQEIC4uDhERES7QC9uBgXyHw6HgALanbEe6mRDsSklJ0aRjkqAV+0SmAWKbsE8ZgE9JSVHOJCxLutZIRyi2m91uVw46Eg7iOSUwJtM8cX+ZAo5tT2AkOjpaA8oQkpDQEsuW7cyx4M69SYIFADTghRz7HDOyveX52X5URkaGGgvyjUGOYZmWSOZ+lOXJdEd5tSXHC8/LdtfDV3ywTJlui65B7Hu6Q7GN7Ha7BrBhudL1S6bHstvtaqywvqGhofDz84PZbEbZsmUVFMWy/Pz8FIRns9k0afE4r81ms3JvYn/yHHTSka40hEX0bSzTFAJQa418nW0jYUDZjxyf7sYBHb3YF3SjYRuHhYWpcS/LkW0oQavw8HCEh4er6+d6ZLVakZCQgLNnzyI5OVkDn0ngjutKSEiIKoe5fnl9gYGBqv0452X6OY5BppLkesQf1VhvOd4leMh1nuPP6XQiOTlZudLx/UOWQ9DHbrerMcU1RzqSWa1WBV4GBwerMcC/cs0JCQnRAKWGDBkyZMiQIUOGDN2schewup5asGDB9a5CofXrr79i7dq1SE9Px/HjxzWvrV27FkAuvCKDU6+99hpeffVV9bxt27bq/ypVqri4Snz77bd5BqOvhV544QXMmjULQK4rRnp6uhpLDz/8MCZPnuziwEMdP34cQ4cOxXvvvae2ZWRk4MSJE3jkkUcQGxub77nj4+Pz/eF66tSpeOCBB7y9JABQQMEdd9yBLVu2YMaMGeq1o0ePunUsSE9Pxy+//OITRDdlyhQ88sgjmt8Rz507h/379yuYadWqVVi7di1atGiB999/36MUNMWl6dOnY8yYMahUqZJHEI9UQECAgqjyU2RkpE91czqdGjccT9SkSROUKFFCBfkImOj1yCOPoGPHjhg8eHCe/fz6669j5cqVWLhwofeV91GTJk1yGyDVO0ONHj3axZXEnVNNfrp8+bKLU0VB4jmXL1+OZ599Fjt37sRvv/1W4HE2mw179+7FkSNHcOnSJYwbNw49evTA4MGDNZCcN7py5UqBQfqTJ09q6vfhhx/irbfeQmhoKNatW4fPP/8czz77LB566CGUK1cOP/74o9p3y5YtmD17NsaOHetT/QzdOPr2229dXHf0MpvNqFSpErKzs7F06VKX12fPnq3eI+Pj47F+/XrN+1bXrl01EOfPP/+ML774AuPGjcPBgwc1ZUVERHg99240bd++XQM6FZcOHTqEZ5991gV+LGqFhYWhefPmXqXB6tSpU57OHREREejRo4fb14YNG5bna1WrVkXjxo09rsPdd9+dZ1nudPLkSfz8888e75+XmNp1z549cDqd2Lt3r8s+W7duxfPPP69uer5eCgsLy/cz5v33348hQ4ZcwxoZupE1atQo/Pjjj/m+Z4wZMwZr167F3Llz8wTp2rZti06dOrmAn9JowhP9+++/8PPzw7FjxzBx4kT06NEDzzzzDJYtW+ZxGUWp6tWro0+fPkXm4mWAP4AmbZAMxjOIzWAnA6UOh0MFShn4BeDiICEdamRgmX/1TjCEJ/z8/JCSkqIgBT1IwDL0ji08Vu+uIqEQ6TQh04dJZx99XeRzmbKGac5CQkI0dWP9JAxEaAG4ml5JpgLjPnpgQl4jr5PlEpiQDhgMLDONmuwnBtFJ/slrZj8RHmCfSDiEbUpXFjpqsO6yvRjQJ/ABXIVHeA3SmYUBdwJSvD6ZVkmmUyOMJNtGtpUcZ9LRRO/6I91B5BimS5LsD/3xlLwu2QasozyOZUmQjX+lU48cp9JFhm2kr48E56Sbi6yfvGb9fGIZhBckRCWvSf4vy5P9l1f5ethEXr8enJPjkH1KqEmfgtCdM5QcO2xXSrog8TjOIzrzsHwJMRIwoziGJTDFuURYxF270alGzn3Wl/0n05Hp1zz9eOfaLMcj05xJ0MTdGz8hJ32qOwlCmkwmBT/RrS0hIQGJiYkICQlR++nHtlxXuI90XpIuQ3J9le8J0p2I/+vHinTaYjlyTvGa+F7A1/V9yfcClsX0hn5+fmoe8zjCsna7XQMaEVaUgBnTLfJ9w5AhQ4YMGTJkyJAhQ4aktm7dinLlyrkE4HnH886dO/H777+r7ffcc49yZHWnwt5xXdTKyspSKcz69OmDQYMGaeqfmJiIkiVL5nn8okWLNA4tjRs3Rp06dXD8+PECU4fwBjd3SkhIwLJlywqdXufcuXNu+2PPnj0u6TYSEhJw4MABn87TqFEjANqbwIDc76+1a9cGAMycOROzZ89Wr0VGRqJdu3bXPO0XACxbtgw//PCD5uYxTzVnzhx88skn+e5TGBerxo0bo1WrVl4dU6pUKc2d/atXr8bOnTsRHx/vsu9dd92Vr5NE3bp1ERYWhsaN/x975x0eRbm28XtTNlvTQ+9dRGlKFUQEKwrHo9SjINgBK2JvHI9dQUFQQVAULAifIIgISFGa0pFeI0UIIW2zLW2/P+L98szspoemc19XriSzM++887bdnec399O22LFfmerQoUNIAEvvJrZ58+YgmABAme5n/P777/joo4/KXsm/dNlll6F69eplAtdOnjypXI1q1KiBK664ArVr1y61y4heJQWUQ6WhS01NRUFBAbp27Ypbb70VQ4cOxT333IORI0eiW7duakzI9dzQha/58+eXuA9BslBQTpMmTTQPyLtcLo0bWI0aNTRz4ejRo0WC2J07d0azZs1KXffzUSkpKTh69OhZOVdqamqQw9KZUPXq1eF0Oku9f/369dG8efMiXy/qPV0/dqT279+v3CJLo2PHjgU5SBanTZs24Zdffin1/sXp+++/x/Lly3H48GFs3rw5CK7Zv3+/ikefS23btk2lrQ0li8VyTh2JDJ1/2rt3L06cOFHk62FhYYiJiUF8fDxq164dcp+6deuG/N4hM4+URlyTAoEAMjMzsXDhwlK52J0ptWzZEoMHDw4J1ZdHBvgDaJwPGBSn8wUdd+j2kJmZiZMnT8LtdisAiM4KTCMDFLqWBAIBTXokOkAwvQtTYEmXhoKCAmRlZcHr9SIuLk7VTTq0AFoHjVBBW73rj3SWkSl0GODmb73bj/yRKcCsVitiY2NVKhcJIuXk5Kggtt5lSA9K6KEHmQaKwWNei971iKmBwsPDYbVa4XA4EB0drdJ6AadBLsIGFosF0dHR6vrpOiRdjyQgIAEPgixMJeZyuRQcwQB4VFSUcsTIzc1VN1j0qX8oCRCZTCZYLBYFK9CNicF39pfP54PL5UJubq6qO11A6H7C4LyEZAgNSHiE7S3BHcIX8kM3y5YgAscfx6KEOFhXtpt0QtIDGBzfhA04LwiLcbF2uVwqpZN0gIqIiEBsbKx60oljTQ+x8RpkHaQTEueNnHM8nvvK/y0Wi8ZJRg9fca7KdpdgXm5urjqedWCbyzloMpmQkJCg3GfCw8PhcDjUmM3Pz1c389iXdHoJBAodttLT0xW4wrlBRy1CHBx7brdbtQcBNq5PHo8nCMDzer1qnBEssVgsCoqRTjusG38ID/HDKgERCZVwjLDdWAeubfJGJtuS5wWggYhYF73TkISQuI4yRVhsbCxcLhf+/PNPdV6mu+I6GApuotsNt+vXGjlWZXozs9msICiuazabTY0J9rG8NjouEb4CoNYJn8+nWbfkei4dnOx2u4KuJLDFvpZzjykZZfmst9/vV6nLJPgUFxcXdIPWkCFDhgwZMmTIkCFDhjZv3oxFixYFASr33HMPfvvtN7z77rvqe8kbb7yhSRsVSkOGDDmDtS273nnnHTzwwAMACtPFNWrUSAUmv/76a9x7773YtWtXqctr2bIlHA4Hnn766ZDQBdW3b19MnToVNWrUCHrt119/xbBhw4q13C+tvv/+ezRo0CAordbHH3+Mn376Sf0fCATwww8/hHx6vTTaunUrTpw4oXGqMZlMOHr0KNq2bYu6detiy5Yt+Oqrr7B161YcP34czz33HObNm1csKHYm5Xa7MW7cOOVmUZISExMxYcIE3HbbbUWmz6AuuuiicterQYMGZU67MmzYMA3M0r9/f3To0AHPPPNM0L4lpalhQLdGjRpYuHBhmSGk8qhjx44h4Z1u3bqhVatW6v9bbrkFTZo0CdqvLOnRPv3003LVkbruuutQq1YtPProo6U+Zv/+/SrAevnll+M///kPPv30U/zwww/lqsOWLVuKfT2Uc8ZPP/2EQ4cOITw8HDExMbjkkkvQo0cPPPHEE5g4caK631YZrhiGzh8NGjQIw4YNw8mTJ0O+7nK5sHr1agChU1v26NFDkxJID6vFxsYGvb8kJycHlXPJJZfg0UcfDfmedyGpoKDgrKVBTE9PLzbdaGXJ4/GU6XPO3r17i4WE33jjjZDbv/rqK8yaNSsIipk3bx66dOlSprSmq1evLpXrGlD4/uByucrkKFScTpw4gSeffBLffPMNPvjgAyxZskRdk8/nwxdffFEp56moPvvsM7zyyisqTZJesbGxyM/PR2Ji4jmonaHzUfPmzcPWrVuL3ScnJwd//PFHSPDObrfjxhtvDAn3NGvWDKNHj8amTZswZcqUEutStWpV2O12NGvWrEzuXmdKXbp0QZs2bTBq1Cj897//rXB5BviDwjc5wip+vx+pqanIzMyEy+VSAA+D3idPnlRgDqEHAiUEHTweD1wulwoAMwjMFFUM1Kenp6tgb05ODnw+H7KysnD8+HE0aNBABfvdbrcCCaRDjXTtkfACwQXp7CHdRqQjSijXH/m6HgKSgWnpvMHANgO/EryQEI/H41HBbwkKEIiQDkYy9ZF0CDKZClMU+f1+TUohBrNjY2MBnHYsofMS68OAOvcnoCBTn0nHISo/P1+lbgsEArDZbEhMTFTOQwRV3G43PB6PejpC7wTCshlcJwQVFRWloCReO/uHcA9di3JycuD1ejX9KQEc6fDBtpTOQ/J3KMcZpiEiaMKxC5yGmFh/6ZzE/SMiImCz2eBwOFT7UNJRhv3H4wkT0MGFfUQoRTrF6B16mK7PYrGolErFOb6wnaRLEuEbn88X5J7C89L5hnUL5ShECIx14LGEJBwOB/x+v3LNCg8PVyAZAA0cI8cj3aZoD+71ehWESHiMcAvXAs4P6cAUFRWF/Px8ZGVlqbHHeUxoTI4l9i3LYFovwmA81uPxqLFOIE+mpiLEpx9vhNQIG0VHRytQkH0roUC3261AFzmG2WbSwU2uARLykeNe75JF1yKmezx16pQm7SJQaLmekZGh9mcbhIeHw2azwel0aq5BunZxbISHh8PtdiMrK0sBb0wJxg8wBLXoosW5oU95x7nHtS07O1vBYITv5HsEz8/6M6WYdD/iPOUY5Xxhm2ZlZeHkyZMaByvCj0DhhzGHw6GuM5RjmCFDhgwZMmTIkCFDF5o2b96M+++/v9h93n777WJfLyqFwT9RDRo0wNSpUzXbnnnmGeUEwoD7fffdh759+5ZY3qWXXorGjRvjzjvvxFVXXXVOnEeLCjwuW7YMqampqFq1KoDCwFJJAZGXXnpJA7t8/vnnSE5OxiOPPFLscWlpaSGDh7t27cL111+Pb7/9toSrKFldunTBnDlzkJKSgiuuuELz2p9//omvvvpK/W8ymdChQ4dynad///647bbb0KNHD43TSFRUFPbv348pU6ZoAsEtW7bExx9/DADqe/i50IkTJzBq1KgyuTc0b968VC44jRs3LvK1LVu2FPtENYBypVsLlTLvxhtvDNq2fPnyYsvp3bs3RowYAaDQyWn48OEad4ehQ4di3rx5eOGFFzB06NASU9qVRk899VSRzgPXXnut+vu5554LOV5WrFiBTz/9FKNGjUL37t0rXJ/ixHlSFkeHEydOaJw7TCZTud2U6tSpEwRa6FW7dm3lqE9lZGSU6OazdOlS1K1bt1z1MnT+as2aNSr9o5Tf78ecOXOwbt06AECfPn2C9klLS0NKSgqAwnufDRo0wP79+9V43rBhQ6lSrY4bNw61atWqwFWcH+rZsye6iRSmZ1KLFi3C4sWLz/h5Nm7cWKb3nMWLFxfpJOV2u4t0D8rJycFHH30U5LzToUMHXH755aWv8F8KBZiFktVqxTXXXFPuzzih5PV68fnnn2P16tWYM2cONm7ciC1btuD9998P6Up3LjVmzBgMGDAgyEQgNjYW33zzDX799Ve88MILRR4fExODgQMH4qWXXsJdd911pqtr6ByKsdXiZDabUbNmzZBQYmJiYrFgpNPpRJMmTZCYmKjiXkVp//79+Omnn/Dyyy/jm2++Kd0FnGHFx8fj4MGDleL6ZoA/KPyiGBcXpwLo/GDNgKx0Y2Aw1e/3q+A5oZeoqCjlrJCRkYGMjAxNIJnBczoiZGRkwO12K7CCQFDjxo1Ru3ZtREZGwul0qtdkaihCBxIsIjyjTzEkUx6FcvLRp/iSgJAEf3gOfbolvZOQzWZDZGSkckzx+Xyq/rxeQiyEdxi0lrAP25XXIEEZj8ejnEZ4bZzMdLKQMAFBBOnWwuuRbSlvCEiwBDi9MBESO378uOaJD5lazePxKMCC0IZ0lmL7sQ4RERFwOp2a1Dx8syRsJVPtsBwJIhSVTkuOCensw3Ep3VEoul3J65Pl0gVGOtjIMuSYYVvq01uxj3g+jm9pB0wggc4nLIdQFEGa7OxseL1eVWcCe3qHITlegdNOThI4Yz0IZ7HNJAgmXYCkm5HsL7qnSDcWjk+73a6B+QoKCjSp6ggMcYxIx6y8vDykp6drgCKLxaLK4phm20rnrYKCApw4cQJer1eBa/Hx8bBarQoQsdlsmjnO9ZBl+P1+Nb4BqPVJpnji3wRc6KBGmIzjR7YloaDc3FwFvHFN5nUT3JMpEAn6cEzJMS7drWSKMZ5PrhG8Rtabttmc95xzrHt4eDhOnTqFzMxMtdZZrVbExcXBbrdr5gbnAtf6qKgoxMTEIDc3F1lZWeo9heCa0+mEw+GA0+lUY8BiscBisag2kqnguI4DUFAWIar8/HzlSKd36rLZbGp+SVc1vduPBNukY5LcxmMJkVWpUgUOhwNms1n1vwH+GDJkyJAhQ4YMGfo7aM+ePfjggw+KfD02NhZ33HEH6tSpU+Q+DzzwAL788sszUb0LShERERg3bhyuv/56zXbCMC1atFBB+Q8//BDr168vsqyXX34ZM2fORGRkJHr06IHnn38e8+fPx9y5c8/cBegUFhaGZ599FmvXrlXbRowYoUkn1K9fP7z44ou4//770aRJE1SrVq3YMm+66SZ07txZ/f/yyy+jUaNGyMrKKva4tWvXBqWHAArTiJTkJlMajRo1CsOGDcOOHTswduzYkOkeZsyYgYMHD6r/7Xa7BrIorWrVqoWcnJygNB0+nw+fffYZnnjiiaBjfv/9d8yaNUt9Dz+XatKkCdauXYuBAwcWu9+DDz6IevXqaZySihJdr0PphhtuKDGwEipIXx7pA7DZ2dkluhEsW7YM77//vvr/X//6F1asWKH+f+qpp3DTTTfhxRdfxMcff1yqJ7dLo2effRa7d+/WjP99+/bh9ddfB1DYT9L9R6/27dvjpZdeKnafiqhZs2aYMGGCcjVzOp0lBq6k5s6di9GjR2PPnj0ACgNIZYGmPv30U7z++usYP358qYKv+jRpAIpNdXPkyBEMGjQIkydPPudz0lDlavfu3SEzHURFRaF9+/YKVLzmmmuC9omNjVX3zAsKCtC7d28899xzmDNnDmbOnIk77rgDY8eOLfLcLVq0wJQpU844kHe21KJFizO2xoTSnDlzKuUzQVFyu90lgqihVFQKMrvdXmwKwwMHDuCzzz7D+vXr4ff7cejQIXz88celgsekbr/9dtx2222l3r9q1apBAHRFRdejmTNn4scff8SYMWMwatSoSj1HZWjnzp349ttvMXv2bBw7dkwT27NYLKhfvz5efPFFPPfcc6hfv37Q8X379sWIESNw7bXX/i3gPUPFa+PGjSXu0759e1x55ZVB25OTk0vMKBEREYEqVaqU+P1q06ZNeOqpp85pei+phx56CC+99BL2799fauiwOBngD6ACugzQR0ZGIiMjA16vVwVK6SJCACMvLw/Hjx/XAA8yNVV2djYKCgpUSi9CJ4R7srKylKOQx+OBxWKB3W6H0+lU+zBYLGELBuSlU45MZ6RPycUAugRopFuQHgSScJD8LUEO+SNhB56Lbia8NpkCjKmr6HRCQEgGoyVoo3+NIJGEYiSc4fP5kJmZqVJtESaQqZvMZrMKdgOnoSEJZ/H8hEJkaiiTyYTMzEz4fD74fD4FiBA6Ydl0EJIuKtKBRZ5b/xQHxyOBKO7DYD4D9kw9JAEa4LRjCfuY0kNAoZ584nUnJiYqpx8JPUjHD44XzgO9Q46EBiT8I1Nh8ZwmkwnZ2dnKKYWSUBvHFd1PpKsRy2FdJPAj4SQJSRAWYtvJ8U8XLrrFSJcswiEyXRXHrUxvxPkrwSCn06mADwItubm5sFgsao1gfZh+iSnoQq0BdFUiWCP3jYyMVFaLTL/m8XjgdrsVqEL3oKNHj2pShbFdfT4fCgoK4HQ6FdBCSIRzKD8/X9WBkBNBHfa/BMzkuC4oKFBptdj2ubm5cLlcqt8BqDRWZrMZ8fHxQW/yBHkIF3G+8jfPRVhID7ewbQkycf2RtDpBKL/fj4iICKSmpiorW44puSbLsRAeHq7Kk+NQOhnRfUm+18hzcywTSAwEAmqt4drI9zC6/hDKkY5teXl5alywrlwvpbsax7tsB5PJhOjoaMTExCjXILYLwViZrlA/twwZMmTIkCFDhgwZutDVt2/fIEdZqZEjR2LBggX4448/itynSpUq6Nev3z8++JmXl4dffvkFV199tWb7kiVLUFBQgG+++QaLFi0CUPi983//+1/IcjZt2oTnnnsOgwYNwvHjx/H666+jXr16sNls6NmzJ0aMGIE6deqc8XQDBQUFWLFihUpbUrduXYwePRp33HEHAGDgwIEap5NVq1YFwSx63XDDDXjvvffU//fddx+WLl1aIgyRnZ2tsclfv349fv31V2RmZpbr2vSaOHGiSmk0bty4ItN4yFRD06ZNU/1ZFi1atEjdwP/222/RokWLEo/58ssv8f3332seSjtXSkpKQosWLYp0YLnjjjvw+uuvY/jw4ahdu3apnuYv6onn7Oxs7Ny5E7Vr1y7y2JkzZ1ZaoGP48OEayOi7777Dr7/+Wqpjmd7PbDYjMTERo0ePxoABA4IcYbZv314pdZ02bRpatWqlCSbdc8896u9QwNKaNWuwaNEiXHnllWjTpg169eqFkSNHlvqc9erVK/W+u3bt0jhaASiVyxn1ww8/YNy4cdixYwdmz56Nhx9+GKdOnSr18W3atMHo0aNx8803l2r/Bg0aBG0rLqAXHx+PunXrIioq6pyl3zN0ZlRQUICOHTvik08+CXL9aNasmQIoQqUvCQsL07ggrl+/Hl988QWefvppDBo0qNiUTz179sTHH3+MYcOGVdKVnHuFhYVVOkBSnLZs2RKUarWydPLkSYwdOzZkSsji1LJly2JBrlAAmdTUqVPx+eef47PPPsOrr76Kp59+utTndjgcuO+++/DUU0+VCfwBEBJUqAzl5+dj7ty5mDNnzhkpv7I0YMAAdOjQAe+8807INE1jxozBsmXLgpy/Jk+ejE6dOqFTp0548cUXz05lDZ0zvfXWWyXuU716dXTp0iVk6tXSfCasVatWyM8oerlcrhL3OZt6+eWXsX79+qDPguWREYUDQqZ0crvdmiAtg7cMTsv9gNNABYP2hCPoIMRALAO5DPaHAkEYbKcIDsjAv97JhEFs+aNP0aVP4ULpnVq4LVR5EjKSqYD0Dj3SmYbXS5ckggMsSw9l8As5g/6ybdjWMj0U60Ngij8MYrO96NRBNw9p96x/Eka64ujdagigyA+yMg2OTG8k+0m6D8ny9UAEX2PaHX3KM5nih441EryRfS9dNmSf6aEJCc7I1FcSPNOPR55H7zYVauzwR4JH+jFMoEc630jAISwsTLmiAFDpi/RjWY4J6aAkX+cTX9Iti6/J+kpYTEJ0MvWZbBNZD32duI4UFBSmA2S/Ei7jvixHnoMgDcVxLa9V9hdf4/olxwAhE7YfncvkfOW+LIdjnPAQcNpyWL8+EFrTuzdxTMv95GtMl8jzsXw9uCYBK72rlUxtyGN5jBxvEiaUEA6hLul4FAgE4HQ6lXsXz8O1hOAUATwJUMp2oWRKM85d6Sgk05FJCFLOAzmmZWpAjhPCP4R29OsG21+OU5lGTA/q8L0wPDwcDodD45AmoVHpqiTBTdbXAH8MGTJkyJAhQ4YM/d1VpUoV9OjRQ+NeEUoMstxyyy1no1rntU6ePIkaNWpogv1XXnklDh06hDVr1mj23bRpk8b+nA9BSSegzMxMOJ1OTSCrU6dOuOiii/Dggw+eqctQ2rBhAyZOnAgA6NWrF6pXr45t27YBKExDxoDerl27lDNHcTp+/LhyzalevToiIyNLhIUAoFu3bgqWSU9Px8KFCzFlyhQNiFMReTweLFu2rMT99u3bp+6dhrqBXxrt2rULOTk56NGjB9q1a4d27dppXnc6nSHdR3JychAWFoY6deqgYcOG5Tp3ZSgtLQ0WiyWkS0+VKlUwcuRIdO7cGYsWLcLSpUtLVWYoaHDt2rWIiIhAdHR0kcclJycrMI2qVq1amQOc1N69e7F161Y135jSpzTSj/+hQ4finnvu0TwMGGq/isjn8+H3339X93TlGD5x4gQOHjyogeM++ugjrF69GsDpMV+nTh00a9asVOdr1qwZLrvsslLXb+XKlZp7c8WldAul3NxcbNu2DQsWLFDrTml15MiRMu0fajwXBxZybGZnZ4d0IzN04Ss1NRWHDh3SbPN6vWpdO3XqVEgoujzB1ypVquCaa67BJZdcUq66ns+qjPSGZVFZ0gqWRZs3b8aPP/6I/fv3l+m4LVu2YOfOnSGd6/x+Pw4cOFBiGatWrcLWrVvL/Jnn+uuvR5cuXcr1mUH/3lWZKs7x8nwSU7oV5frPzBWhVJKTi6G/j44fP17iPjVr1kSbNm2Ctpdm/ttsNsTFxZWrbudamzZtKlX7lCQjCofCD+4MSgYCAZw6dQqHDx9WH1YZWLVYLCrYabfbkZSUBIfDAZPJpIEvgMKFKiUlRQO1EPBgoJegit/vV64NTqcTcXFxmmA8oIUtZNqoUG40Mugsg7J66QPvch8ZBA+VGoxOFfr0TxT/Zz1lEJ9AAYPchBgIQBCaIWDBPmD6GpYl3Y1Yjt/vV25KbrdbtbNsq7CwMDidTsTExMBsNsPlcqkf1p3AAwPnPJ7i5DOZTAok4vjgDwP1rIM+vRlls9lgNps1Th5Mz8NAOmEDlsWgOtuN55ZOI4TN5DYJDehdcuR1+nw+pKWlISYmRpOOTY5DtpV0K2G/hRqHdIORKfU4BsLCwtRTKExtlJeXp+am1WpVUItMaSTrz3kmXV4kwMN60/3EarUiOjo6KJUe9/P5fJoUYtIxRTqiMJWRFM9LwEPWxe12qw9BhM/4wZDnp/sKy2Jb6aEu6VrD+vl8PvW/7GO+Ticozq+IiAj4/X7N0zaElNiOnFsc7yybfRsbG6scqMLDwzXwHcuS7l28VvmadMchIMe5znbnPKArDdcBCZp4vV4FbrI9CYzxN12MACh4h+3B7ex7k8mEuLg49WEhPDwcVatWRd26deF0OpWzm8vlQk5ODtLS0tQ6o4cuuaYAUHXJzc1V45xOU/r0Y+wHjjWOcb0bFl3HfD4fnE4nEhISVH9L8I4uXnRH4nE8F9uC63xBQQHMZjOio6ORkJCg+oLtIaHNyMhIeDweZGVlaWBQukIZMmTIkCFDhgwZMvR30JgxY/Dcc88FbX/zzTfRtWtXTJs2rdjjX331VQDAo48+etYDPOebDh8+jBo1auDWW29V22666Sa88847mDRpUtD+Eqpavnw5nnjiCeU8AxSCDQcOHECvXr0wbNgwjB07Fv3790fHjh1x3333oUuXLmfsWoYPH45Vq1ap/+vXr4/U1FRMnjwZADB27FiVJu7tt9/GQw89VKby77vvPuUYXpyaNWuG1157TaVhiouLg81mw+TJkxWUVJkq7mn+H3/8UYFKV199dblcrnJzc3H48GHMmDED1atXx2233YaaNWsCKPxevGbNmpBPEGdnZyMvLw+zZ8/G1KlTy3zeypLP58OXX36JxYsXB73WrFkztG3bFp07d8ayZcuKTGEycOBAzdPL8mFGavHixSU6G2RlZWHfvn3q/9tvvx3bt28vkzOMXldddRV69+6NYcOG4d133w16/fbbb8emTZvw5ptvara/8cYbmv+bNm2Kbt26BR3/2WeflbtuRal///5B8++GG25A27Zt0aNHD9x777149dVX8cknn2DMmDGa/QYMGFCkw5VeV199NT777DMMGDCgVPvv3bsXy5cvB1AIIt1www1lfojq+eefL/E9SK+qVasWm54ylGw2W9A2vduLlNlsRtWqVXHddddVmvOYofNLMTExQal83n//fbz33nto0qQJHnnkkZDj7LvvvivzucaNG4cRI0aEXAsvdCUlJZX4Pl9ZatmyZZGQRkV0+PBhTJ48GT///HO5jp86dSr++9//alLe7N69G7Nnz8a4ceNKPH79+vWYPHlyse6benXr1g2jR49G//79y+3IWb169XIdJ1WcY9+FoPnz5+PJJ5/E22+/HQQDM15r6MJXkyZNyg279evXr0Q4OSkpCTfddBNat26t2T5//vxinXcBIDo6Wn1PuNBUnMtdWWSAPyik0WWqmlOnTsHj8SgYhClzgMIvjQykEnSQKaIYHGYqHwa0CS/QFYKwgt/vh9lsRlxcHKxWq0pVQ9BCghsANCCDdOmQKa2k+w6BGb07ifxbOpxI6dPVSMcfvZOG3lED0LrdEDhgnQlbMODOdsnLy9OkNmLbMWBMWCAyMhLR0dGw2WyaNF12ux1WqxW5ubkq1ZjX64Xf7w+CO5gyKDo6Gh6PRwX6Cdiwj2T6sbCwMOTk5CAzMxNmsxlRUVGw2+0qLQ+hFOnuw4B/ZGQkbDab6j/2lUzPxLrKlE7sQ8IgHHeEPiwWizovt8n6yh+ZfkyftouQFiEAoDAPp0ytxmvkeXgs+58wAcuSIjDH62d5BJQcDodyfOF4pJMKxxIdaggucNxLNxNZNv+X84ZjTc416YTE6yCUIuE5OR/5Gsew3sFHwlCU1+tFSkoKMjIylPMKISS2gz49mL5ueucX6Uzj8/nUPGZ9CHMRJmOKPcJVKSkpiIqKUsdyzcrLy1OQj2zX8PBwBZ94PB7lLiPbJSIiAhkZGaotpAuMyWRST4JJYIfzgdsJ5OidsbhuynWR41+2oRwf0v2LaczYtlz39esh01bxCVLmRuc4SEhIgMViQXp6Og4dOoTk5GQcPnwYBw4cQHJyMjIzMzVjh3OVAKAE2TiemQaSTz7pXd0iIiJUGSyX+3Cu+Hw+5Z4l0zFyHrGt9C53HFOEwTj/gMIbikw7R8cqr9erXILkvOc6npWVhfT0dOWuFh0dbYA/hgwZMmTIkCFDhv42WrRoUVDKipo1a6Jfv34AgM6dOxd7PNO/XH755XjxxReLTNvzT1BycjJWrFgBv9+vAoYzZ85Enz59gtqlT58+6Nmzp/q/SZMmeOONNzSwzdq1a5GUlITffvsNU6dOxWuvvYbly5fj6aefxvHjx8sdhCqNMjIyUL16dVStWhUAMH78eJhMJgUYtG7dWgEWhHJK0pNPPom5c+cCOO3ycvvttxeZBqNKlSrYtWuX5jozMjKC7k9Ulvr374/HH3+8yNd///13BWslJSWVGoCgqlativHjx6Nx48YqVdYVV1yBO++8E0ChU8Gff/6JyMjIIPgnIyND3ds4lzp48CD+85//KOcYqWrVqql7EkUBLjVr1sSLL76ocX8JFZRMTk4ucS05fPiwuudXrVo1TJ8+HfHx8cpVqjzKzs7GkiVLioSr1q9fj4svvlilvKO++OILPPvss/jyyy9VGvVQKg34UlZoZc6cOZoUelR6ejrWr18Pp9NZJET19ddfAyjZwcpkMqFbt2745ZdfSuWORU2bNg3Z2dm48cYb0aZNGwwdOrTUx5ZXn332GZo3b16mY/x+v6Zu1157bbHpKXj/qmXLlnjooYcMx7u/obZv347p06er/w8fPqxSdI4YMQIdOnQIeVxZ35dffvllDBgwoEjnkAtdMTExZ83JqHnz5kHpFStDBw8erBCU5XA4MH36dDz//PM4dOgQdu7cic8//xxvvvkm0tLSSlWGzKhSGi1fvhxut7vcjvULFy4MmeKqLOrVqxf69+9/QQNtK1euxKeffoqVK1cGubt5vV71GcTQhae+ffuiSZMmGDhwIN5++21s3rwZbdu2LXM5K1euxEsvvVTifOnSpQt69eql2TZz5swSU8pGRkaievXqQdli/kkywB+cvuFC4ITBYq/Xi8zMTGRnZyunCQa4zWYzsrOzkZ6ejtzcXOXEQJcFk8kEr9eLtLQ05OXlISMjQ8E/VqtVfamrXbs2qlSpAq/XqwKuubm5yrVDOu6EShmjD8hLxxkG4PVOPTLIzd+hwB+WqQ/oM6jOYLGETGR9pDsMYSkJEvF/CRUxuB8KdAKgYCoJMPCamIZIOvX4fD4VpPZ6vZo6FxQUwGazISYmBna7HTk5OfB4PPB4POo6OCZkcN7tdiMmJgaxsbGIjY2F0+nUpOVyu91wuVwIBAIaNxF9qiTpiGSxWNSY0/eHTJdDAMfv98Pn86k+l2m2CGoQLNG7AEVFRSlwgOCOBMcIJbDOGRkZCijg6xaLRdN/EtCQAJocoxLakmNZn+ZND3Pw79zcXFV/OV8pCcWESlkmy5NtL8e4vq2kk5PeNUWfOkqOK5mOT843ugdxvABQ44Mf6GQfEgyxWq1q3EiQSboAsZ1kerawsDBYrVbVfuwrzqUjR46obRIkoZNUTk6OBjRkmWazGT6fT7NNph3jOdxutwKzbDabgv8Iish2k25WPAfXA+mAw2ulZbd0qpIwC+cTwR32q3R1klBfIBDQzNOqVavCbDYjJSUFLpcLVqsV1apVg91u1wBKdG3LyspCWloaUlNTsWPHDhw4cAAZGRma9UmCVJxL0mGITjlAIWzDecZ+5zX6/X7NvJNzmOOfqbkkMJidna2BGjm+ZJovthHrJwEzQj9utxvZ2dnIyMhAZmam+sAu3cyOHz+O9PR0ZGdnK/DzQv7SYsiQIUOGDBkyZMiQlARNqKNHj6qn1ktKYyLTWgwdOhQbN24sMT3Y31V79+5Fz549sXbtWgVGdOjQAVdeeSXuvfdezb7ffvutaqf169cjMjIy6InOtWvXYu3atep7W0pKCiZMmFDqFFkV0ZYtW5CWloZLL70UQCGI8eabb2Lnzp0AgK5du2LIkCGoW7cu5s2bV6p0QZGRkcoVaty4ccjNzcUff/yBY8eOhdz/k08+weLFi3Hbbbdh1apV2LdvH95///1iXXkqog0bNsBms6FHjx5F7rNy5Ur1nfPf//53mez3T5w4EeSu4nA4lDNO//790aNHD1xyySUKDKJcLpfmQbbzUYmJicjOzsZdd91VZJDS6XTC4XAo5wOn04n27dtr9vntt9/w8ccfl+g2IFMkyGBNWVwRyqojR47g4MGDqFKlCh555BHNa//73/8wYMAAfP/990UeL93AQumuu+7CmjVrsGDBgkqpL1DoxlZcMIsuJsUpKSkJ06dPx/bt28uUtmHmzJn44YcfcO2112LHjh3o379/qY8tj9555x0NUFlavfDCCxrwp3379ho3cb1MJhMGDx6M5557DuPGjUPv3r3LVV9D56/effddDBs2DBMnTlSp8TIyMgCg2HFcq1atUp9j8eLFZ+z97HxRbGwsrr766rNyri+++ELjqlNeMaUnVbt27TLDhFK9e/fGc889h/379+Ott97CnDlzMGnSJGzevLmCNS1avM9fHq1cuVJBbnqVNt2j3W7H4MGD0atXL1x77bXlqsf5pKNHjwZBvSaTqVhnOEPnrzp16oR33nkHy5Ytw6hRo9C1a1c0b9683HDy7Nmz8cQTT+Cnn34qcp/atWujR48eGgc0j8eDWbNmlVh+7dq1S4TCmzdvXuLDOheqDPAHp2+4MG0LUBgEdrlcyMrKUjcLGKiWjhoECiIiIlRwPjY2FlFRUQgEAsoxgiljbDYboqOjYbFY4HA44PF4cOrUKaSnp8Pr9SrQgLBQZmamSjMkA/N0FmFAmU4u/JGQDo/VQ0C8TuD0F2D5RZgBeP1xhAIksCGdbvQAhgzGEypgPkeZQk2m7ZKpqaS7jM/nU+3B+rKOhEJksJrBfwI9LpcLHo9HU0+LxQKbzQaLxYLc3FxNKiUJd8i0aqyvw+FQUJPNZlPAEeEaCf3wegi30F2H4ITJZFKuGrJdCZ2xLfLz85WrBvtIAlAypZd0G5J9yLYmgMAfBvxZx0AgALvdroGJ5PGyz2S/cZxwf6bO4ocnti2BBJlKTQI0LMvlcinQiS5JeqcWlivHMutDOEvCRtI5KRSQZjablRMP3XQICxGUIDhBmInzU4IV3MZjoqKiVH3MZrMmdV1ubi6sVqsGEOGc4RyQdZbzVcIfEtxh++fl5eH48eOIjo5WjmQco9IFhjfjZGo91p/nkc5ALFumpwMKwRXCQVyrJKwkQZ3s7GzlcsZ+42+6RMl+5jl8Pp9y8OEc5fnkGiHrTbcljj9eI9dQi8WiIJ2kpCQFcoaFhcHhcCAxMVF9aDh8+LB6cjI8PFxBMfn5+Thy5Aj27t2LY8eOaYA2/vb7/Qq+Ywo8j8ejxpLePUzCTUx1JsvlOiHHn1w/LBZL0BzTO0HJMSDd6ghquVwu1VcEkAghynWJ5TLtms/nU25FhgwZMmTIkCFDhgz9nfX777/D6/Viy5Ytxe735JNPYuXKlQAKUxeEcgL5p2n37t2wWq145JFH0K1bN0RGRoa8mbxjxw54vV48/fTTaN++PY4ePRq0z5133qkJQs2ZMwc//PAD1q5de0avYd++fTh69Cjefvtt/PDDDwAKg2GzZ89W9WjatCmmTZuGXbt2leqp8hkzZqBr164AClN9ZWVlYe7cuSFTDd1///24/vrr0aNHDyQlJaFPnz647LLL8Oyzzwa5MleW9u7di4MHD6Jdu3ZF7rNgwQJlXd+1a9cyO33Mnj1bc28RAFq1aoWXXnoJ77//PgDg2LFjQY4yWVlZ6h5HedMRnGklJSVh3759mDFjRpH7xMbGYt26ddi7dy8uueQSbNu2TeP4xPkAlBxA379/v/o7MTERQKFLx5lyhAKAJ554Qrnj6PuI+uijj0Juz8jIwFdffVVs+U899RRq1KiB66+/vsIwCdvk9ttvLzYdyebNm9GjR49iz5eSkoLx48eXKi2NXjNmzMCQIUPw6aefon379oiJiSlzGaXV4cOHy32sfOqeTmfFqWfPnoiNjQUABYQY+vtp+PDheOihh7B3714AhfOqOCjsxhtvDJk6Dih01qpWrRqqVauGESNGaJzP/s56+eWXsWnTJjRs2PCMn+uVV14pkyuZ1J9//olvv/0WN9xwA+6880688847+Pbbb3H48OEKpZDcvXs3/vOf/6Bt27Z4//338e2331aovNLowQcfRNOmTct1rMPhwOWXXx60ffDgwbj++uuRmJiIYcOGKQOKULr11lsRExOD5s2bo2PHjuWqx/mkmJiYIHdC+ZCzoQtLrVu3RlRUFGrUqIHWrVsjOjoaQGE64fKmp/vss89w1113Yc6cOUWmAG3RokUQCPfll1+qh/2LUvXq1YNST1IxMTF46aWXMG3aNHTu3PlvOSYN8AdQDg9MdwNAATUMDks3BZPJpAKcMnjK46RTDwOmYWFhajAWFBQoIAQ4DRRJgIKQS3Z2NrKzs9U+gDaNVChHIAmuSPhB72qiv8Ggh374W6YCkyCMTMkUKh2RHk6QAXiCDPziznrKH3keBp85CaWTiv46Gfjm8ey/nJwc5ObmqpQ0LFuCQ7K+MlWT3hFJAl+skwy6E0KSbkaynrwm2WZ6yEr2owRb5DiUgIQeSuM1SKhF1oV/yz7juCJ0INM9yb6WfSb/lu0u4QP2tRy73Fem6pJtz7ozPZeEWCTYIa9dP+4pXh/PIaEkff/o54F0WtGnspPjQz92JADH65bXIOslxzrBFd6UY7/QyUdKrglsL7YdwSTZNnIuMU2dfgywDnIMyOuXDmMEvFhP/TiRblSyLgTReA0SECzOgk9//WwzSq7FbA9eH9P9sS3lvCK8pYfjoqKiULVqVXVTguOYkODx48fVNoKQTG1mtVqRmpqKU6dOqddkGQSp9POP/a8HyOSc4DqnbwOZ9k06T3Huy76R7UyAT44FvZsVxxRTEcrxT3c1ubYAUOkTZf8bMmTIkCFDhgwZMvR3lnR4Lk7yAYcDBw7ggw8+wOTJk8909c5ruVwuZGRkYOjQoSoAWL9+fY2DicPhQPPmzREeHo5t27aFdL3p0aNHkFMQAEyZMkXjdnIm5PP5kJmZiUsuuQQtW7ZEfHw8Bg8erKCHVq1aISkpCVdeeSW2bdumYJjidNddd+H+++9HTEwM2rVrh0OHDmHbtm1B+9WsWVPjAmOz2dCiRYsib6RXhvgU+59//okGDRoU6zZD4CQmJqZUTkdSdJSVstvtuOOOOxAfHw8AKgAhxXtJQOH33latWp1RgKI8stlsmgflQiklJQUfffQREhIS8MADDwSlZcnNzVXtU9z1FRQUaNaZyMhI+Hw+vPvuuxW8iuIl3YSKCsIUlbYhLCwMDRs2LPZeEV2/TCYT7rrrrlKn0QulkSNHAgB++umnkPOM2r17N7xeL9q0aVNimXfccUdQqoqS9Ouvv6J27dpYs2YNwsPDy5VKozSKiYmpEFgg03SU1smLD4V5PJ5yn9fQ+a8pU6YoaE8PbuqVlpZW5Hjo0KEDRowYgauuugpt27YtFiD6u6lVq1ZnJA2XXpdccgkaNGhQrmO3b9+OlStXYuXKlfjkk08wYcIEfPjhh5g1axY+//zzctfp5MmTyM7OVu7xpfm8VBG1bt0abdu2DXKRDKVQqcaSkpKCXAe7d++Oq6++Gg0bNkTDhg3RpUsXtGzZsshyk5OTkZycjOjo6DKnrzwfpY9fAKdjEIYuPCUmJoZ8qNvhcKiH5cujgwcPYsGCBfjtt99Cvh4eHo4aNWpoth09erREJ0W73V7k55KEhAR069YNrVq1QkJCwt/yfcUAf/6S0+nEyZMnlfNDdHS0JkBLBwQGkpnyyev1wuVyqS+TVqtV47bj9XqRlZWlfnw+H6KiohAfHw+Hw6EWO4JA/ElKSkKNGjWU5Wtqaip8Pp8GXJHQCsEVmeJL76JTlPOPDJBLyWCzhDckuCSdiCRcwfNKR5FQ6Z3o3iLTK9F5Q6ZpAgqfYOEXYdZJBpqjoqLUeSV8wmPpxsF+45d/Ql7h4eEqjZRMWcN9eFOO9eQxbEMCRkzZI6EQthH7i9dOEIHOJWw7gmgWi0V9uWW/cxzKPtQH8vVwF/tLDyBxG906ZDqv7OxsuN1uTZorCZpwjNFhSgb89QAQ+5Dn4BiSrjISTOJr7DsCdDabTaXWI3hAkMRsNiM6OhoxMTGIjo5W44B9I91rOK6kw5TeVUY6W+lBKwlV6Nuc5dDNiKCNTKNG6V1ZuLa4XC6V/oswDgEpCYnIa5IfpgKBgHIW4jhkH506dQperxcJCQmqLtJdhsCadCoKBAIqbZYccxKeIUwk56PValUAU35+vnJdYjtwbDBtH9taQpU5OTmwWCyqLnJssy9keew3jlHpniPHAcc954bD4YDdbldrltVqhd1uR3x8PBISEtR1JiYmwuVyITU1VfWjHAtch+j6dezYMRw5cgSBQECzzlssFg1MSTjU6/UqYEauHRxj0q1Ln/aNfScdqhh48Hg8QQAQ+8vj8ah5yTaXY4nn0W/j+x3nI+spwS6+Z0l41ZAhQ4YMGTJkyJChv4NCPXV+5MgR/P777yWmue3Tp49KgZCWloa1a9fi888/x7///e8zUtcLRR988AF27tyJdevWASgEqV566SX1Oh+MKygoKPKGb5cuXXDjjTcGbZ89ezbmz59/ZiouRMihWrVqeP3119GhQwcMGDAAjz76KMaMGQMAOHToED744IMSy2rbti3uuusuDBgwACNHjsR1112HTz/9FF988UXQvi+//DIGDhyo/s/Ly1PAUWUrPDwcb731Fj755BMAwPfff48ff/xRAwHoNWvWLJVqraTUTXpVqVIlZPo8+eS8/qlyADh16pQ6rm3btvjpp58wYsSIMp37TIsuw8WlLThw4AAWLlyIjh074u677w56/cEHH8SGDRtUivKidPfddysnrJEjR2L8+PG44oorzjh0+NFHH6n5WhSYlJeXhxEjRgS5wERHR2PVqlXFuqht3LhR/d2rVy88++yzZa7j008/jTvuuAPPP/887rvvvhL3X7lyJZYsWVKqfV988UWMHTsWHTp0KHV9jh07ppx4vv76a9xzzz2aVBeVodjYWCxZsgTDhw8v1/Eul0ut1QCKDWgDhYAA0778+eefWLx4cbnOa+jCER1/0tLSikxPuXjxYkydOrXIMq655ho888wz6jNSUc5AUkuWLMHSpUtx8ODB8lX8PFKjRo3OaPlxcXHo0KFDuQCj9evXY/z48Rg7dqzadvDgQfzwww+YMGFCsZ8JSlJycjKmT5+uxs2ZBgWtVisaNWpUojvgnj17MGnSJAwfPlzjOMk4jFT16tVxySWXoG3bthg3bhz69++Pm266qciyly9fjt9++w3Lli3Djz/+WLELOg/ENKVSxUG8hs5vXXzxxUH9CRQ+9LBv3z6MGzcOL7zwQrnKnjp1KoYOHYovvvgCu3fvxrFjx+DxeLBy5Uq8+uqrmDBhQtAxn3/+ebFp4+Lj44sEeg4dOoRGjRph7dq1+Oijjyq0Vp2vMsAfFBKkJ06cQFZWFiIiIlCnTh1UqVJFBUM9Ho8m3RZ//H4/MjIyFNDj9XrhcDjgdDoRHR2tgsiEgjIzM2G1WlGlShUkJSUpqEOCOjLIarFYEB0djejoaPj9fqSlpcHtdqvyJPjDtEESxtGn+tJDP/J1PXktAQO9gw5F8IVBaJl+RoJHMuUV68qUWExvJKEqXgPhA7p4sJ2lq4/ezUiek+4VADTADAEDbiN8EBERoUk9ReABOO16QacnQkKsP/eTbjkMoss6siwG/Am0EETgeQgesF7cl44/wGknJL2bCt0+JBxRlEtTKEcaunqEh4fDbrdrUpwBWhccPWBlsVhUeizWVQIY7A+9iw7hDwlGyTbKyclBlSpVNO1PEITp85xOpwIlCCEQNOC4s9vtQcCa7Cs92EAQg6mq2IZ68ESmXaNjl0xdxeM4D7idbihhYWGatFgWiwVOp1OBGRz/+rR5Mle9vv9J4LKN5fyMjo4OAv1485TtQ0CG6ejkeYDTeW/Zj3Q04/gnAMPj/H6/um6O+9zcXM0Hal6/dOfhMXJNkWNJrlESqiIwxv7nfCVsJq/LbDbDZrMpWIcpFwn/MP2X2WxGIBBAWloaTp48qc7NenF/pg5MTExEfHy8sjdlOkDptkRojX1K6Mfr9ar2ZJ3MZrMa42wrwkwc40AhbMP3JDnnZFtz/HD8EuziPJBp4yi648kP6RzTMkUd25kpxOheV948yYYMGTJkyJAhQ4YMnY9asGBBkHPJhAkTcPnll6vP86H09NNP4/PPP0dCQgIAqKd6Z8+ejf/85z8a15Z/ml577TX07dsXHTp0wMKFCwEAP//8s2afSZMmYefOnejbt2/IMr744gu0atUKXbp0OeP1DSV58/iuu+4CUAh6zZ49G7t374bL5cIHH3xQogMCAAwbNgxJSUno3Lkz5s6di5SUlJAPVAwfPhyDBg3SfOebNm1akemTKqr8/Hzs379fOfy88cYb+Prrr4s95quvvsK3336LnJycMoMmzz77bLFBSZ/Pp1JdSXk8Hpw4cUL9HxMTg7i4OAwePLhYd6KzqYSEBLRv3x7XX399ifu2adMmKGj22muv4dNPPwUA1K1bt1jXFQYpO3XqhPfeew9r1qzBhg0bKlD70mv69OnIzMzEmjVrikwJ8f7772Pr1q1B26tWrYqLL744yE2B0qdJGzx4cJnr9+STT6p27NGjR6mOef/99+FyuUp0ZpCO/WXR3Llzcf/992PixIn45JNPNOkLK6rBgwfjl19+Ua5dpZVMf+Z0OjVtXRxomJqaiqlTp6Jz587YvXs3qlevXuZzG7qw9fnnn8PlcgVt/9e//lVsqsO9e/fizz//RFhYWKncvPLy8rBgwQL07dsXu3fvrlCdzwdFR0ejZcuWIQPulaGBAweiRYsWZT5u6dKleOeddzBv3rwzUKtCvfLKK/jss8/OWPlSq1evxvLly0vcb8+ePZg1axYmTZqEr7/+Gnv27MHcuXNx9913K8c4as2aNfj111/RrFkzdOjQAVFRUejTpw86d+5cZPnz5s3DPffcg2nTplX0ks65GN+UcrlclfpeZujsiAAbdezYMeTn5+PkyZPYv38/mjdvjlatWuHOO+8M+fBFaXT48GE8/vjjmDt3Ljp16oQ+ffpg9OjReP3110Pu//nnn6u02aFkt9tDwuY33ngjJk2aBI/Hg2nTpmlS4P6dZIA/KAyA0hLVYrHAZrMhIyNDgT9Mt2U2m1VA2G63w2q1wuPxqCAtA8lVq1ZFfHw84uLiVCBY70BCdwyZMoowBMEfBlgJE506dQpZWVnKOUECL4RpQjn9hPot/5awgARJ+CMhDZkCRgbaGZiXEJJ0hCEEwPYj9BQKepGgjHQ28Xg8KCgogM1mU2CCHqoh9GOxWFQQmml3pEuP3t1Fn3qK7kq8fplGiddqsVg06XnYhlarVV2zTNlF4EDCGvK6pWuPdJXJycnRtJFM8yYdjvSQiXQFItAkz6sHuUwmkwIG6LLicDgUDCTbSjreEBRi+/K8EqYhUCLbn9ABwRaWwb5kGT6fD9HR0TCbzZoUbYRbCEZER0dr5lZCQoIC8KSrDNtZOlXpxx3HhgSqWCfpWkWARJ++jJKgCucFgTOWwbYgvME5zw/00vGJABLHjJzncjtdqSSkxPnKcSnnMiEbOTbcbreCcIDCD2sEaeTaybWLfSJdoQj3EfThGsm1gaL7DduEcB4A1bbMAUsQi+NApsxie3EcEHyJjIxULlx0T2K6OLYfx5ZcP2QdU1NTkZOTA6/XC7fbjaysLNW2EtKUAJHVakV8fDyqV6+u2pvwDN9r5JwoKCiA2+3WuLvxOnlNcp7r13KWLeE5meqMY5Uudkx/yL6kUxPXcen4xvnB9yf+DRSCRgStpBtYfHw8EhMT1dM4JeVeNWTIkCFDhgwZMmToQtL777+PXbt2lfm4OXPmKHfpAwcOqLQCL7/8Mh544AGNg0Jpdckll2DZsmV45JFHynzs+arly5djypQpQUGfVatWYcGCBSGdTwBg1KhR2LVr1zm7kXv8+HEcPnxY41DTsmVL1KpVC3PmzMHx48fxzTfflKqsSZMmqe/tDJjfe++9mrRBd999N0aMGBH0lPqZBp82bdqk0mKHsv4PpWnTpiE/P7/U+1N6+EuvqKgojSuvFF2GgMLv1o899hiGDh0a0iHoXIipykrzfVkfLBs3bhyeeuop9X/16tWLdcP4448/MHz4cCxbtgwAQgbhz5Ref/11fPXVV+jUqZNysgmlolJ+AYUAXShNmjRJ83/VqlXx2muvlapeNWrUgMlkQs+ePTFv3jysXr262BRfUkuWLEG7du00qcxCKT09HTabDd27d1dOb1LXXXddyKfZv/vuO1x//fW4++67g5yQKqrs7OwiQSq90tPTkZ+fj+PHj+O2227TvHbllVcCALp161Zs+paHHnoI33//Pfbu3Yv3338fQNmdvwxd2HrnnXdU6i/K4/GUOA7WrVuHI0eOlPo8ERER2L59O9LS0nD99der9e5C1Ztvvonvv/8ew4YNq/Syn3zySdx9991lfk8GgG+++Sak+2Bl6tSpU2e0fKkePXqETBmqV1JSErKzsxEIBPDoo4+ib9++6Nu3b0gA6sCBA9iyZYsmVWlCQgKGDBmi4H+9jh8/XuJ7yoWiL7/8EldeeSUefvhhHDlyBDk5OXC5XGf1s4ehimnw4MHo3r078vPz1WfQ3NxcbNy4Ef3798err76K3bt3o3Pnzti1axcSExMxevTocp/v6NGjeOKJJ5CcnIzFixcX+514165dmDFjRlC8Eig0BFi9enXI74KNGzdGly5dkJeXd1ZSKZ4rGeAPCgOeOTk5qFatGuLi4pCRkYHU1FQFQWRkZCA/Px9OpxOxsbGIi4uD0+mE1WqFzWZTYAoD4+Hh4UhMTFQph2JjY1GlShVYLBYFldAFhwFS6fyiB3gIzBBI4jkYiDWZTMjJydEcF+pvPfzD6+aXSwks6CGgUA5AwGkHGX3qKQkYyXRKFAEWn8+ncXlh4FjCAwQsrFarCmITNqDDDmEcQj50x+DfDMTrIQleK4Pg+fn5yM7OVo5Ksg7ymuV16aEPEugEB2TKLZn2STrM6FP58Drp6sE+J9wgASTpJMT/ZduzryX8o0/XRDBAgiwEUJj+jCm12CasL52JJJCUn5+v0oZx/MgxQdhBwlYsk3VlfzAlEfsJOJ32DICCoqTlrclkUm40FP+22WywWq0ayE7v/sM24vWyL/R9LcE0PXxFtxPZxxxLHMsEkdg+rIfdblfXm5eXh8zMTJXKiWVJaEzOSTlGCOLIVGqE2ThO2P5A4c0yugtJZyK2H8csxznbhykSWTb7i2NT3sBimUzxFwgEEBMTo0An1o31J5giYSbZ1mFhYWodlv3Penk8HmRnZ6u2kO5FMv0dUHgTjeCYBNkIEWVlZalrTUpKUsBZQkIC4uLikJSUhKSkJHUDMSYmBvHx8YiNjUV0dDSysrI0DmHAacccXjv7lH3P+cDjOE7k2i5TiLHuchzQuY7udXqnL8JLhJb4t1zjpJMaj5EORKw/97Xb7bDb7Rq3rNI80WrIkCFDhgwZMmTI0IWgOnXqYNGiReU61uVy4dixY1izZg3atWungjmbNm0qt9X4tm3bsGrVKtx///3lOv581BtvvFEk3LNixQp06NAhZEq1iIgIHDp0qMi0IhVRnz590KpVq2L3+eyzz9CuXTvccMMNeOihh/D2229j2bJlqF27Nj788ENUr14d//vf/0p1vm3btmH16tUACuGGrl27onnz5spJyOl04q677gpyngJQpkBpebR27VpkZGQgLCwM06dPx+23317iMQcOHMCaNWtw4403FgsJSHXo0AF33nlnsfv8/vvvRT6pHyrVCx/MOh/EQGNpnoDXw016Z7HRo0cXmw6qZcuWmDBhgrr2sxl8S0tLw7hx44rd58Ybb8TDDz9c5Ov33Xcf5s6dG7S9oKAAgwcPRkpKitr2xBNP4I477iiyrKpVqwIoHMdXXXUV1q1bh6+//hq33XabJr1gSUpLSytxn/T0dFSrVg1jxozB8uXL0bVrV83r//73v3HnnXfilltu0WxfuXIlzGYz7rrrrnK5GBWn22+/XaXA+Oijj7Bv374i9/34448xcOBAfPDBB5g3bx42btyIw4cPIy8vT6X7e/TRR4s8/uTJk1i6dKkCZb///ntkZmZi+vTplXdBhs479enTBw6HA02aNMErr7yC7t27Y+nSpep1j8eD/fv3l5jC6YcffsDmzZvLdO7HHnsMEydOxPDhw3HdddcV68J4IchutxeZsqY8SkxMxH333Yc77rijxBR9/xSNHDkSAwYMKHG/WrVqaT5rb9myReOEpte6deuwYcMGZGVlqW3XXXcdhg4dWrEKX0A6evQodu7cCbPZjJo1awaZERg6P/XQQw/hkUceQfPmzbFv3z713eXgwYNYvnw5vvnmG/j9fuzYsQM//PADFixYgBUrVuC33347a3WcN29eUCrYP/74A2+99RYGDhwY8kGLI0eO4OjRo0hKSkLNmjVDOqn+HVS6b1n/ADE1CwC1EDOozOC8yWSCw+FQkA734Q9hFL/fr3F8YNou4LQlJoPO0oEiEAiogKve0Yf/R0REKMcfq9UKq9UKn8+HQCCgzqVPy8Tz6WEhwgwSuNA7+vBYPVygd8yR0rue6NtJ/s92koFqgj0M7NMZRoI4FotFk6oHOO0CI8EXXo8M8ksHFIoQEv+W7ikEIySoQlBDpvOSi4Q+HZS+fWQ769tJAlISNpL/6/tCAlh6lxq5XX+Mvk4SjiDQwr6Q7Sb7U7oKcYxJQER/vfJYPQjAFGPSBUleC5Wbm6v6OycnB9nZ2cq1JSIiQkFDMs0V4Tw6lUgwRo4fAjJyXsp2l+nceO1ynMly2abyWK4pbrdb0548Z0FBgQI5XC6XZjwSsNJDRhL60b8m4Se2gx5G05dDeIfXyLpYLBZNe0RFRcHj8SjoR14j25RrDB1kpLMU1zr9PJGwm6yXdM6Sbco6s29585eOQDKlmHTFkXOATkJ0RZJrENvV6/VqQJucnBw4HA4FGrI/uZ4xXR5TRdJhh2OT7jjymin2Od8nXC6XZp7LviUUJucfARy68ciUdEwhR6iIbUGYSM5r1pVjhvWX/SXXGLmOSIegUO8LhgwZMmTIkCFDhgxdqHrggQewc+fOcjn+NG7cGFarFRkZGTh16lSlPdE8ffr0f0x63T179sBms6FevXrYuXOn5rWDBw8WmUqoorrtttvg8XiKBJKo48eP4/jx4/j5559Rr149dOjQAQsWLEBOTg727t0b8unUorRx40aVFoLfw5o3b46IiAhcffXVaNq0adAxJ06cKNb+vrLkcrlwxRVXoG3bthpnneK0YsUKVKtWrdRtcOuttxabFgMohK2Sk5NDviadlyj5AMu5Fu8X86Gn4kQndN4TkPO9WrVq6NmzZ7HH68ft2Q6G6+eqXo0bNy4WCGvdujUaN24c8rXp06fjoYce0rjYFBfQHj58OA4fPozatWujUaNG+Omnn7Bs2bIzAgxmZmaqv6OiotC6dWskJyerMVu7dm1ERkZi/fr1GDRoEH799Vfs3bsXQOFaEhMTg+bNmyM+Pr5UoFFJGjZsGK655hoAhXDh66+/josuugiNGjUKuX/Tpk3x+OOPK3eqjz/+GLVr10bbtm2xcuVKhIeH45JLLinyfKmpqZqUex07dsSePXuwdu3aCl+LofNTrVu3RqtWreDz+VCjRg20a9cOVatW1bhpnThxAgcOHAgJZ+q1ceNGZGVllcqRBQC6d++Oyy+/HIcOHUJ2djYWLFiA2rVro0GDBpUK0JwthYeHlyrNWWl19dVXo0OHDhX6rHQhtmNxuvjii5WLYXFKT08vFpTU6/Dhwzhw4AAuuugiNX6joqJCAtsXqhh/KErMngMADRo0wMCBA89amlFD5VebNm1Qt25d7Nu3Dw0aNFB9TGfTUaNGYcCAAUhMTMT48eOxfv16NGzYsMygZkV06tQp7N27F40aNVJpvWJjY1GjRo0ij8nMzERmZia2bt2KAwcOwGaz/S3TzxngDwpz0l1zzTVIS0tDTk6OJo2KyWRCdHS0csLg6w6HQ33BioiIgNvtVimsGLjnG3J6erpy5fF4PKosBlr5ZddsNiM+Pl6T9gY4/cWeQWM6T0RHRys3IsJFFotFAxDIYKt0FAGgAuESWKAkEMFgLgPAADSBZwl4FAUCyfRPEpqg84lMJ0bHE5ZFsKmgoACxsbEK7NF/SZd1IIggYRT5WzqH0AlDOnFIFxTg9Bdwtql0fpFtCkCl0mEd9emhpOMIf8uUPAQYWLYEGmRQneVJOE068XA/6ewiQQl928nxRnCAQI3P50NmZqZy+ZCphAKBgNpGwIPnDgQCCuYBoL7A62ET1tftdsPpdConEQI+nHtse54/LCwMPp9PAUA8J+tC0EG2IdMoyfbXj1OObdnPFMcNIQyONUJ9chxzDEnQiUCaHD8EQKKiotT49Xq9qj7ymgjQ6MeLbFfWie3CMUDohxAL56Hsd9l2AFSaPAKNEkSSwBJBMQnryTRxXG/MZjNcLpdmznAO0bWGYCX7muCMHOtyTrrdbjUmKI41AjE2m021nz4dYlhYmGoPn8+n9mFbc186j/HGnMlkUu5vbD+ZooxPEUZGRiI7OxuZmZnw+XzqGvTXL8ckt+Xn58Pr9ap6SQcvtr3ZbFbXxxSVHIuZmZlqzDmdTtVuLIdrhgTCJMhKCIyAJl3IJLDFfgeg9uE5ua8B/hgyZMiQIUOGDBn6u6h///7497//jd69e5fr+OXLl2Pfvn1o1qxZ0L2YimjPnj14+eWXARTeZP87Q0B//PEHUlJS0LNnzyCYYNasWXj33XfRq1cvzJ8/v9LO+eqrr2LgwIElpp3S69ChQzh06JD6nzfSS6tQgc7GjRvjiy++QJUqVdSNbqkJEyaosVCZWrx4MVasWKHKTk5OxsKFC2Gz2dC3b1/8+uuv+OGHH4otY8yYMaU6l9lsxuuvv16sAwy1YMGCkNsvueSSkOnvqlevjunTp2PmzJklutCcafHesXQDKEqpqalYsGABbrzxRoSFhWmgprZt25YIM3Xv3l3zf2Wnj6qoSjO36Bat180334w2bdqo//fv3x/krEN1794dDz74oApE9unTBx999NEZgX4AbTtHR0fjf//7H0aNGoVDhw7h4MGDiI+Px9atW2GxWDBlyhSMGjVKgT8///wzmjZtinbt2mHIkCF45513KlyfmJgY9cBcnz59NCknQ+mmm27CsGHD8PHHHwMAJk6cqHl90qRJqFevnvp/27ZtSEpKQtWqVWEymYJSLTVt2hRff/011q9fX+FrMXT+qVq1arj//vvRo0cPDBkyBCdPnsRll12G3Nxc/Pbbb8jJyYHZbEb9+vXx5ptvlmocfPTRR7jiiitw+eWXo0mTJiXuHxkZifj4eMTHx+M///kPevbsidjYWOzZs6cyLvGsy+VywW63q5RJa9aswdGjR8tV1tChQzFy5Eg0bdo0pGtiabR06dJSp1O8UBQKEg4lvg+XVikpKVi7di0aNmyoPvvt3LkzKEXlhajrr78eN9xwA7p3747ly5dj+PDhIfcLBAL4+eefcfHFFyMmJgYjRoxA3bp1jXSP57GqVq2Kq666CvPmzcPOnTtx00034aqrroLH48HPP/+MhQsXYseOHahVqxbq1q2LRo0aYe7cuXj11VfPOkTz448/on79+mjfvj0AYN++fcWC5osXL8bixYs15gJ/Rxmpvv5SVlaWJsUT4RCm66EDBAAVrLfZbArosFgsyM7OVoFeGbBloDU3Nxd+v189oSGDxBaLBQ6HQwWi6UzCH5m2ihBEamqqqjd/CBQx0B8ZGalJvcVArXQzImygD8xKcEhCJ0WpOPgHOJ0iS8IwMqUZ6yddSfh3Tk6O6gsGmi0Wi+ZLLcED6cIjA+rShYfnz8nJgdfrVQCT7BO9Yw2D2bKuLFfCPdyH18cxIa9bH+TnMbTbla4t0q2HQXo9KETYSKby0oMFBCP0LjwS0mFdJERE2Mbr9cLr9QY5B7GPWEZYWBiioqI0Tk3SvYfXwXajiwwhEYvFAqvVqrleOY/k+JYuUR6PB263Gy6XS+NkJZ1LsrKy1M1P6Woi+4PXw/rrxddkCirCHdJBhm2nL0P2K9uax7MevF5eB8dbdHQ0EhMTNXWVYnsSHpTpzOQbGfuTcA5hRaZ1kpAe3YliYmLU+OR8yc3NhcViUfPa7/drxqHH41FzVbYF08gxHZwEidgOcm7L/cxms0o5xbnI9YFjil8i6cLjdDrhcDiUs5vValVOPZyTNpsNNpsN4eHh8Hq98Pv9CmoDoEnrxWtl3zOdoIQaWTbHMZ2psrKykJWVpfqfaxBTe8n563a7kZ6eDrfbDbfbrdLXyf5iHxMMZRm5ubkKsOL8j4yMVKn5ACiHOYfDoUk3Jl2UeLzVatVcF/eRaQ8BqPccls35W9L7hyFDhgwZMmTIkCFDF4rGjx+PoUOHYseOHeUu47rrrsPx48excePGIoPTFdENN9yAp59+utLLPZ905MiRIJABAD799FPs378fXbp0qbRzTZ8+HU8++SSAwqBmRVWUO41edevWxc033xzytVtvvTXk2FmzZs0ZgX5eeeUV9OjRQ+O+s337dvW9Lzc3F7Vq1aq08y1cuLBU0E9BQQEOHDgQ8rXBgweHdDGpU6cOLr/88qB7OGdbsbGxGvCnpNQfS5Yswc0336zqLVN10bW5LCptoPNsacOGDUX2ZUmaP3++pj06deoUMvVhu3btMHfuXDz55JPo168fXnjhhXKnWCyteL+FcrvdWLt2LfLz83H77bfj8ssvx44dO7Bnzx7MmTMHnTp1UuNi+fLlSE1NhcViQZ8+fSqlPtIZ/OTJkwAKXXyKS/1WXCqb++67T/29YcMGtGnTBp07d8bkyZMBFPaF1Pjx4zFw4EANbNS1a9cioS5DF4bCwsIwcOBAPPDAA7j11ltRv359JCcnq7ETGRmJTp06adIsljatXkFBAWbNmoXFixeXGVjkPfSMjIyzmoKmMlW9enUMGzYMY8eOxRdffIE1a9bgrbfeKnM5r7/+Ov773/+iVatW5YZ+fvrpJ9x///2lcqm7kPTdd9+VCv78/fffcfjw4TKVnZ+frwHWtmzZUirY93xXbm4uqlWrhjp16hSbZnTGjBl48skn8dhjj2Ht2rVIS0s74++7hiqmW265BYmJiZg2bRquvfZaXHnllbjmmmtgs9nQsGFDXHfddSq+lJCQgIEDB2LMmDG4+OKLz3pdf/nlF6xevRrr1q3Dyy+/jGuuuQZvv/12icf9naEfwAB/lOgMwmAsXS7onAFABXjp7gAgCKLQpwmTwAbLZaCWAVYGj6WTigRMCCcwMG2322GxWJCamoq0tDS4XC4FPMgP6TKQLh0mZJCdAWEp/TURptGnLAqVIiiUJMRCkIDH5eXlqS8cvFYJhtA9gulpJJSihwMICMkUTxIeklAUoQW/3x/05YXHyCC1LEO2g8lkUkCWDH4TQCCwwcC8dPthmxFY0o8butPw+uV+erCLr0nARt83ElCSoJMEnFhfgi0FBQWq3XkTQR7D/+nQEh4eDo/Ho9xTOJa5H8EEXg/BDG632+0ICwtT+X2lo40eoJJtqU9fRwCF8AG36ceMbCsJ/PBHfhnWO1/p/w7V5rLtebzf71cuVhKGi4yMhMfj0UBQHDPh4eGIi4tD9erVkZ6erukDOd5ln7DuXFf4w/JZF7kWsB34JSwiIgKZmZma8UlnLNlesg09Ho8qjy5MdM5hXfnlgn3Ba5QOUjIdGQBNf5hMJthsNjX+2e7SsYlrJt1q+CMdfyR0SGBKAjWEjVjnmJgY2O12dWOzoKBAk+aLc5LnklBPQUEBUlNTkZOToyAhq9WqmVc8t1xfOL54Ds7J6Oho2O121WZyDEoHLOnEEwos5Zw3m81qTXS73aoMAMqBjUAP24Htx/HL9YXgJt3t3G53kSCdIUOGDBkyZMiQIUMXktq3b4+4uDi89tprxaaRKUnNmjXDsWPH0KpVK1x//fWVWMNCyYdC/q7av38/LrvssqDtbrcb33zzDdq3bx/y9bKqV69euP3229X/iYmJ6jvhmVZycjJ69+6NIUOGlPqYxYsXV3o9unXrhn/9618AoEm1dPToUaxZswYHDhzAtGnTMGXKlAqfq2HDhvjuu+9CQl2hNGLEiCLdrUoKLBUHM5wNValSRd0f6dq1K6677royHR8fH4+BAwcCKB/4cz66gpXkYrFq1SrN/w8++CAefPBBXHHFFfjuu+8AFF5XSkpKSCeR+fPnY8GCBfjggw8wZ84cjBkzBsOGDau0+lutVvz3v/9Ft27d1LaXXnoJgwcPBlAIa/F/Agz5+fnKKWvixIm49NJLFSyzdOlSlR6pVatWKt1WRSTjAIwjvPLKK5qHHCm6Vfzf//1fyLIk1LN8+XLccccdyMvLw4EDBzBhwgQcOnQIrVq10kBLbdu2RevWrfHGG2+gc+fOqFq1KmbOnIn+/ftX+NoMnRtFRkbipptuwoABA3D77bcjLi4OAHDppZfihhtuKBJGTUpKwsKFC9GhQ4cSz/Hzzz9j48aN2L9/f5nuL1atWhVffvklXnvtNcycORPHjx8v9bHno3hv1+12o0+fPmjevHmpjz127FiFgdcvv/xSOZL9nbRhw4YS06nt2bOnzK6PCQkJGDJkCOrXrw+32420tDQ0b95c41B3oYqxPsYaSpLT6URMTAwyMzM16R8NnV+qXr06OnfujDlz5uCPP/7AsGHDcOONN6q0pZ07d8ajjz6qgXXDw8ORlJSEli1bBsHOZ1p79+7Fo48+ig4dOuC5556rtBTaF7oM8EeIwV4Gz6OiotSCz5QzMoAt0/0AUK8RgtAHrmNiYlSQloF/GWxmwJYBfBmYZXA7KipKgSZZWVlBrj8ul0tBFDk5OcphiAFsBmal801R0I7e6SeUEw6vU+8gQyhAD8vI9mJQmpAC6ygdYniTitAC3VD8fr+mrQgxEPhg+7HtpBOGvLb09HScOnVKpYuSLkJ6YEYfXKdkyhvpNsQAOFNL8fjc3FzVN3xN38aANtUUj5NgD+vGuoZKISbrKF2PJDQiwRHpHpKTk6NgtcTERMTExChggccQGLBYLIiMjFRplwhvcMyZzWYFWXFM+P1+WK1WldqLc5DOVZyDANS1E1LIy8tT80w/ZvPy8hQ4lJeXpxyAOIbYHpxTMkWV3uWJ1yhT28k1gn+z3+VYlkAQ4Q/iIPUvAAEAAElEQVReN9tHgmGcMxzDdIiR9U9JSVFfxDneeN1sI8IbbGfZ7xK84jigw49Me8V5QLCHbS3nvQRT+CFPpmST/SfdntgmUVFRmjWP52Q7SRcks9mscZ3i2GZ9o6KiVD14Pq6rMTExCsyz2+0K6pTObtLNTb+2+Xw+BQbxvFarFfHx8SpVI8cB60qHtqioKGRnZ6t5zjUoJiZGQULR0dFwOp1qDjFdI8vl2OQ1sG4SupHrEMX3D45TwnLSqYrrJM/FvpbrNwD4/X5kZmYqZyOr1aogVJnuS6awY0ozQksyfZwhQ4YMGTJkyJAhQxeq/ve//6FXr164+eabsWXLlnKXs3btWvz+++8AUCpXk7Jq1qxZmDRpEjZt2lSkY8yFrgMHDqBGjRohX5s2bRoaNWqkbN8rIvYTFR0djTp16lS43NLq559/xrJly0r9fepM9Pfu3buRkpICAIiLi8O7776LRx99FP/73//QqVMnXHbZZXjvvffKXX5YWBh++OEHTJo0CStWrECvXr1KfWyVKlWKfI11Lkp9+/bF9OnTSxWwOhOqWbOmJkDSokULLF26tMTjfvrpJ+zatQvPPvusgoXKE8w938Cfa6+9tkinLj5YNWHCBLXt/vvvx4ABA/Duu+9iwYIFCoKS90Mpm82G7777DkuWLMGAAQMqrc733XefxnnjvvvuwzPPPINLL71UbTt48CCmT5+OtWvXYvLkyZg5cyaefvpplS7ym2++wcyZMwEUgk316tVTT80fP34cv/zyC4DCoGlZ4bBQYvvoU3ZdeeWVuO222zTbNm7ciI4dOxbpDiUfQP7kk080Tnjbtm1T6RYlPHn55ZcDAAYOHIiFCxdi7dq1OHXqFL766qsKXJWhc6ldu3bhyy+/RK9evTRp32JjY/HEE0+gfv36RcI61113HWbPnl2im15aWhp2796tXOFLqxYtWqBfv364//77MXPmTHTq1OmCB1dq1KiB559/Hl988UWR6ZVCaf369di3b1+Fzl2Zzn7nkxo0aFAs+LN582a88sor+Prrr8tU7qlTp7Br1y7Mnz8fr7/+OtLT0xETE1MiZHQhiHEoPnxdkmubzWbDvn37kJmZacAZ56H69++Pyy67DL1798Zll12Gp556Cj169ABQ+L1yyZIl2LZtG2rXro0OHToE9XdaWhpWrFjxt3MDu1AVUfIu/ywxEG2322G32zUBXMI/+uC2TNvDADID8wwG5+XlqTRVMjVTbm6uckXgftKlRzrX8Fz8iYiIQG5uroJWGFjm5JJB7PDwcE0QXi95Lfrt+mC4dKvRB5slQCLBIbmv3qlFugdJoEK6s8g0WXQp4T56aEWCUgCC2o7XJYGXvLw8BbXQ1USOCV6/hKZCQUCyDfVtpge7CItI8ErCZBKk4msFBQVBKbskiKGHMvRjRt+3rAPHjnQbkrAQ21H2Neso3Zv4Zk9gg/WVYIFM/8UPCHoSVM4dzhfpxBKq7VmebH/ODX3f8LccM7IO+jEq6yKBF1muBHj0fcf/Zd+wH3kOua7I9F4ESeiiJCE74DSwKNtBD/xwPwkjMdWdhOp4HMthv+oBEjlXub7IdmMdo6KiFIQkx6weruP+rC/PI8vU/52bm6tgSzqDSXco6W5DuEeOGTkGQt0gk+0g08VJFyD9jwSc2L5yPY6OjtY4TpnNZs36x3pKxya9oxPHBftTzhWZflA/32XqPl6fbHf2NQD1nsTr4JrL+awHzwidsWyCQ9nZ2Zo+Pdc26oYMGTJkyJAhQ4YMVVQ7duxQzgwV1ezZszFmzBj1Xa+y9eeffyIvLw+dOnXC9u3bsX///jNynnOljIyMkA8EAYVuQE6nEwkJCRU+z9VXX635Pzw8HLGxsRUutyxKS0uDx+NBdHR0ifueOnUKdrsdbre70s5/7733onXr1gAKXWb69euHWbNmqdfT09MrVH737t3RvXt3uFyuMjuaFBdoysrKgt/vL9INp0uXLqhVq9Y5C1Lo+/Oiiy5CTExMicd99NFHqFu3LoYPHw63241OnTqhevXqZT7/+ebKW79+/SJfO3XqFNxuN9atW6e2DRs2TLlv6Z88l7r//vvh9/tx1VVX4c477yzyAdiyKjw8HJdeeimaNWumtrVr1w4mkwkNGzYM2n/KlClYtmwZRo4cqQGDFi5cqNmPD2BSMv1MRVMNms1m1KhRA1u2bAlyYfn999+DQMdDhw4VWx5BpOzsbPz666+a1+rXr49WrVoBKHRdoeS4dzqdyM3NxaxZs4pNNWbo/FaDBg2KfK1bt24qplOUatSogcGDB+P1118v9jxHjhzBiRMnygVrulwuVKlSBQcPHsS3336Le+65p1Tr7fksi8WCK6+8stTv+UePHq0QcLF9+3bMmDGj3MefzyoJxDl27Fi5P/9PnDgRTZs2RYMGDbBs2TLs37+/zM5B56NkzDEqKgoWi6XYFKKzZs3C1q1bcemllxb5+d3QuVGnTp1w77334sCBAzh16hQSEhJw4sQJbN++HVu3bsX8+fPRrFkzOJ1OVKtWDUlJSZrjt23bhgMHDmDPnj3n6AoM6WWAP0IScsjKyoLP50NsbCzy8vJgtVqRkJCA6OhoFSCnM4U8LioqSpPyhK8zwM6UXElJScjNzUV8fDxcLpeCihi05/H8zTQ3TInDNDEej0ctlOnp6QgEArBarSqgLK9L724RyhlG/i9dT0KBC6H+limQ9OAP660PvstjKAbbCQ8wkM86+Hw+9cU9IiJCBcDp8iGD/DKgLsEeBq9lejEJpbAeMlAuHYQYXA8FUslz83UJBkgoieeVaYxkyhwJBhSVUkqCNPJHnlv/Px09cnNz4fV61bXr00ZJ+ET2p+xnAg50sqGjClBos835QrcZ9tnx48cRFxeH+Ph4pKamqvNJyIBjWT9W9F8aeH2cnxIIS0hIQEFBgfrwIeE0maZIPyckREEwT7r8SHcqtp1+7hIWobtRREQEsrKyEBUVpamH1+vVtD9Q+IWcEIaEctgOTEUo4Tb2HaEYAl2yXwkqyjkXHh4Ov9+v5pmEtAgi6tc7ABqwRY4h9hHBL6bAknAM01exHoSvOB55vL5PJWDE4+T55JpBJx2OLZ6PT7aZTCZNii22BeefBOBkSj2ZmkvODa4r8jpY34SEBE2aNunERviM5UsQkX0uU5PJa2cZZrMZDocDmZmZyvWKAKnf79cAXPp12GQyITMzEw6HA/n5+Zrgg9/vh8ViUZAe25ProNfrVf3E/TMyMlBQUIC4uDi1xpyrpygNGTJkyJAhQ4YMGaosPfjgg5VW1q5du7B371712V2vxo0b4+DBgxVyzmzXrh3y8vLwyCOPYPHixdi0aVO5yzrfdOzYMbhcLvTo0QN79+5FcnJy0D4yuF4evffee7jnnnuCttetWxdr166tUNllUXZ2Nvbu3YsqVaqgWrVqRdrop6WlYdy4cZUK/bRt2xYvvPCCZlvVqlUrNWCan5+PyMjIcqUxojNKKG3btg3p6enFwhL6B/LOpvSBEwClAgHpjPLII4/gzjvvxKBBg8rlHnC+Bd6KcrM5cOAANm7ciLvvvlulx2rSpAnatm0bcv/Zs2erv2vUqIGXXnoJUVFRsNvt6NixowZaq4hGjBiBf/3rX3jiiSfUNt5z51PyADB37ly43W5s3rwZzzzzDIYOHapeW7duHT799FNNuceOHUPTpk3V/1u3blX3Yy666KJy17ddu3b4/PPP0bhxY0yZMqXIVHhz585VbkQStAqlrl27IhAIYOvWrdi5c6fmtbFjx+KKK64AUJiy7IEHHkCXLl3QuXNntU8gEMDo0aMxZcqUEt0iDF2Y6tixY4lwZUZGRqnSwyQnJ2vSxpVWgUAAhw8fRsOGDXHixAl899132L59Oy699FI0aNBA/b4QdfHFF2PDhg04fvw4Nm3ahE8//RSbN28Oue+hQ4ewZcsWNb9Lq7y8PHzyySeYMGHC3zaw73a7sXfvXjRu3FgTQ6P27dtX7s/ju3btwq5duyqjmuedZHzC4XCo+F4o7d27F3v37lVOcIbOH+3btw/x8fFo3bo1bDYb8vPzMXjwYCxfvhzTpk2D2+3GihUr8Pvvv+PPP//EzTffjNq1a8PlcmHevHlYt26dBlI2dO5lgD9/icFfACpwzZzAMTExKuULU5sw7YvH41FQggRHIiMjVcoeAAqOYNqt/Px8WCwWnDx5UhOMDgQCsNlsKqBqNptV2h9CIHTvsdvtMJlM8Pl8yl2CufacTqcCYwhiEHChY4S8dkrvUKMXA9gSeNA/ISJhIQb0GZSOiopCTk6OxvFDpsHiNgnnxMbGas5rs9ngdrvVdTM4znZhYJz9SICEsAmD9iyfTh7sK9k2+pRuejhJ/i3bTr4uASwqVDoxjgt+0JXOQBJa0jt66EEhOrRIEIb1kGVKGEi68ujrqgeVCBIwbRXrw/aV7iHS3YWSKfN4rMvlUmOKgJgExuQ4k+4o+hRsHA9MKxYIBOBwOBAXF4esrKwgdxxeFz+4RUZGqrRSMv2WBM947RKmk20v24rtLuElOtVERETA6/UiKytLtWsoVyXZboRV2CbMRy/dffRzV9bJZDLBZrNpoCm2uUwtFRERAafTCY/Ho86bmJgYcq7yHDwuJydHtQcAta5KCIn9KfuPbSZhPNmOEqbi2sjr57rGPuR8kU5hcq2g/H6/gqqYipF9azKZ4Ha7NcAdQSuOQbrC8bxMkyUBHo5/zg+fz4eMjAy1lrFuVqtVjVv2h5z3VqtV0y6sE9d5ns/v9+PUqVPIycnRgJ7sF16jhIAoOkvx/KxTVFQUbDabuj6+zrXRarUq9zkJ5QUCAWRnZ6uxwP4yZMiQIUOGDBkyZMhQoVtF7dq18eKLL4Z8/eabb4bNZsN///vfcp/D6/WiRYsW6N69O/71r3/hvffewxdffFHu8s4nHTt2DBkZGZg7dy62b9+Odu3aaV7/8ccf0aZNG8TFxZXLkebFF1/EyJEjQ74m3SvOli677DLUqFED7dq1Q506dXDJJZdgwIABsNvtAAofBpwyZUqlB1RSUlLU91OpynQ9ku4mpdEff/yBiRMnolevXsW6xPzxxx9ITU0tFvwJde/zbCmUI1VxT8s/99xzOHz4MD755BMAhdDFZ599Vu6Udufbd/TatWtr/i8oKMB///tfTJw4MShtW+PGjZGVlRXkmrRp0yY8/fTT6v9jx47h+++/x6233orJkyejU6dOaNu2LTZs2FDh+nIMyjp8/vnnuPXWW9G8eXOsWLECycnJ6N69OxwOB2666SY1X1euXImff/5ZpfiSOnXqlGaN2blzJ1JTU1G9enU0adKk3I5eHo8HVatWRSAQwMGDB4vcb9WqVejduzdOnjyJlStXFltm3759ceLECU2wt1mzZpg4cSKuuuoqta1evXp4/vnng45/++238fHHH+OBBx4IWmMMnf9yOp145513StyvOKgnMzMTK1aswGeffVaqc86fP7/MKS1NJhPatGmDmTNn4vjx49i6dSv27duHxx57DAAwZMgQPPXUU2jSpEmZyj1f1LRpUzRt2hT16tXD8uXLiwR/gMJUsz6fDxaLpVRlHz9+HNOnT8dTTz113rnEVabGjRuHiIgIPPHEE/j9999Rt25d1K5dG263W0FVRnoqrfTZP6xW67mukqFyKiUlBaNGjcKtt96KOnXqoHPnzujfvz9q166NmTNnYuDAgUhOTsby5cvxxx9/wOl0omHDhli1apUGfjZ0/sj4RPWXZICVAVm6UzgcDuUkQzHgSnjC7XaroLeEMCwWiwIQcnJyFGBE95/8/HxYrVYFXxDAYJAWOA3SMEDMALrFYlGveb1e9WXV5/OpwCzryoWYAXLp2qAHVbhNwheUHmaRbh2ANi2Nvt6EQ2TZ0h1DnkMex/Zk21qtVuTl5SEzM1O57xBuIvRABw0CVAQ+JOghU1vJOsh2l05AdCkJBbRwP3176tNBSWcY7sPzcQyyHeW52U6yLfm/HgTgF3eCJKy3rAMABZRJBxvgtNsJ3UNcLpeCXdjOEuySAIDejUi2m4Q86CwDnLaEZrtERUUhNjYW2dnZ6sMD+9Tj8cDhcGhgKo5rtgv7jWn0OD/phuXz+dS4YBn6sS/7VAJ98rr1IIocL9J1Sc4HCb15PB5kZ2croISwGvflHObfEv6T41OCMdwu6yPBH73zVVhYGLKyshTsEco5h+CHdC/ieZkiju49ANS6yXNLhyOWw3rm5OSoPmdfcQxJ5yOCg7xOzmcJhfF1Cf3ogTkJE/F1uuvI9Yl1l6nb2KYEYnguvWsXf3OMyHXb6/Wq9HMEzCwWiyqHzm4SJORYNJvNsFgs8Pv9Kp81r4HHsmzWlam2OH8oOhTJJxvZDpmZmWrMZWdnq3FB9yKuRTJ9G92jJFjEayagCZwG7AwZMmTIkCFDhgwZ+qercePG6NmzJ2w2G3r27Ik333wzaJ+33367Us71+++/w+/34+TJk9iyZUullHk+aP/+/bjvvvvQr18/DBo0CA8//DCWLVumrvHLL79E7969ceWVV+Lbb78tc/lZWVlFvhbKqeVs6NixY+paLrroIrRs2RKXX345AGDGjBmVNmaoQYMG4bnnngsZkC9N2rHSqizgz4EDB9CjRw8cPHgQnTt3RosWLYoEIfLy8nDy5MliyzuXzrShrrsooGPw4MEYM2YMcnJyFPhz+PDhcrkkUXy47HxQv379UK1aNaxevRr16tVDXFwcJk2aVCQYuWjRIlxzzTVo3LgxWrRooQJPCxYswIEDB9R+NWrUQOPGjbF+/XpMnToVa9euRZUqVSpc35deegmRkZGIjo7WlDd37lx1v6pr164AoFzdbrzxRtSvXx8XX3wx3njjjSKdEXbu3KlJm5Weno4TJ06gevXqqF69OurXrx+Ukqs04v2d0aNH46233ipyv+bNmwModAeRbRlKX3/9NZxOp2bculwuvPrqq1iwYAHefPNNmEwm3HHHHUHHHj16FI8//jiAQreys51C0VDF1Lp1a3z//fcVTj+3fft2LFu2LKRrXyjt3r0bW7ZsQcuWLct0HrPZjLp166Ju3bpo3769BkT75JNPkJubi2nTppXKeeh8ldPpLBFMXrFiBf74449SQ06//vrrPyKwn5eXh3feeQcXXXQRVqxYgQYNGqBXr1748ccfsXLlSsOlphjpTQoMXZhavHgxli5dikGDBiE+Ph6dOnVCjRo10KhRI1x++eVwuVxo1aoVVqxYgRYtWmD79u2lAj8NnRsZ4M9f0jtNeL1e5ObmIjo6GhaLBTabDXa7XdlOejweBZzQDYgBeZmSK1R6KgZCGVwmqENogNCPTBlExxBCATKNDZWZmYlAIAC73Y6cnBwFGdAZg8FXgkgMjvP/UCrK+Yf7y0A3YRp9qij+LR2HeF4eK88hU+MwWE1IgAFlXj+BJqZDki4m0qGFwXkGrQks8Br1DjjcLuEQXq90Z5EKdTxhJH2qJgmESOCCEAWPJWhAUIpwghy3DPrLOjC1kh6ikG40Em7gfg6HA0lJSfB4PCq4z9cJSknnFAm3yHbnaxIU0ANIvFYqKioKeXl5sNlsGpvAsLAwnDx5EhaLReMKxHkj5xhBI3n+QCAAj8ejjs3Pz9dAdrw+CfVIBxk5bmTfsb8k3CHBHTlP5FjhWGa/6h2qWEfpMEXnotzcXGRkZGhcVSRcJceqhGjy8/OVMwz7j2OK7SfBLen4wmsnAEbQSqYgk+4+BHjYvlwL5RcnOa/5uh7Mke5H4eHhymVNwlPSwUfCPHSVkm3K/TlmCfsQOuL5uS7xmuQ1S7iFrlWcRxLyk65AhNDcbrcCzlg+HbMkXMS6ut1uNUakc5HP54PX61V15rF+v1/1o1w/aQct5x3roV/3bTabeu+wWCyqf3Nzc2G1WlU5BK8I1HFscV7QeU2CU3oQ0pAhQ4YMGTJkyJChf7L27t2LOXPmoF+/fmjTpk3QAymVrd69e8Pr9eLUqVO48847z9h5zqaSk5ORnJyMtm3b4tixY3jllVfw9ttvK/Bn9uzZ+P7775GWllbmsnv06IHbbrutyNcrAxyoqHbu3InRo0ejdevWqFOnDsaMGVMuZ6Pi1KpVK03KISmbzVZp5ylLe65Zs0a5laSlpcFut2PDhg0YPXo05s2bF7T/iRMnii3ParXim2++wfbt24NSmp1phQrQFpUSh1CETAWWkJBQIViiNGnFzpaef/55PPXUU5g+fToaNGiABx54AFdffTUSEhJCuizMmjULM2bMUKmrevfujWbNmuHuu+/G9OnTsXfvXgCF8FrHjh0xYsQIlZ5P7x5UVg0ZMgR33HEH6tWrh8zMTI3j0u233x70oOhNN92EevXqYeXKlSU66Nx111144403ghw7/vzzT7Rq1QqJiYlo1KhRucAfOjIcOnSo2P0aN26MZcuWYceOHcXud+utt6JGjRoAoFKwAYVAz9GjR1GzZk14vd4i14qlS5eqv1988UWsXbu22HXX0Pml7t27Vxj6SUlJwapVq8oEJY8ePRoTJ07EqlWr1Pgrj8aMGYOJEyeq9WXGjBno3Lkzhg4dqu5zXmiKj4/H2LFjERERga+//jokXOjxeLB+/fpSgz/Hjh2D1Wo9r94vzpQI1i5btgzNmjXDwYMHMW3aNON+dhGKiorSxHjOt/Shhsouq9WKmTNnIicnB48++iguvfRSNG3aVMXf4uLi0K1bNzRu3BirVq06x7U1VJzCSt7lnyM9yMHgqD4ILcEU4HRQmsADA+1MCwNo3V4YwGbgPScnBz6fT+OSo/9h/eRPZGQkoqKiYDabFQgjnWIkiCMD9DKlj9frDZnPWl9vCebIfWUdpTtKKPBH76rDIH1R5wjlaCLdY/jFgQ5JEoCQMIHeiYZwlXSskHXVu2CwHWR7SOkdk/ROL/L6WR8CEhwHsnzZRvoxKcsI9bc8f6i68X8ZjJdlSxAlPz9fPf2j34/nlOXowSLpTCTbmRCDvm04JnJycuD3+4McVwi7cJzxtVDXqZ+vTFFE1yLp1qWXvq30/SyvV55ftof+pi0hC44vPRwogTUJaXF/likdU2Q/6NOphWoT7qMHmKQjjtwuwSOOBa6J0vGF1yxBOpn2jefQt69cx7gO8HW5JoRaH/TXqIeWJIAl+0r/QZ1gpH5MMw2g7EeWI2EiCXsS1grlMEQ3Hp6TdeT52JYSEiSgQzFVpD5VokwxFwoA00N/3MY+1o9hlin3lZKuPnx/ISzEsUq4U0KKcowYMmTIkCFDhgwZMmSoMHCdk5OD9PT0Mwr9UGvXrsVVV131t0uncuLECUyfPh0nTpxA06ZNNS4AH3zwQYmB9lC6/fbbi02fxHQ951rLly/Hu+++i/Hjx1c69ANoU0PrVZmuCHzIsjSS4zcnJwd79uxB7dq1UadOnZD7u1yuYstLTEzEDTfccNa/r1qtVjidzqDtRQUYIyMjcfToUQ20ItOPl0eh7iWdK4WFhSkXqQMHDmDHjh2oX78+OnbsqNmvTp06aNWqFS6++GI0bNgQQGHKs6SkJKxcuRJ5eXlo1KiR2r9Bgwbw+Xw4evRopdW1TZs2Ki2ZdOYBgD59+mj+37RpE3766SdMnTq1VGXffffdIWEuOpBFRUUhJiam7JX+S3ygsDg5nU6cOnUK77//frH7eb1eTJ06VXP/WMrj8RTrKiVTu61atarY9cbQ+afiXPFKo/z8fKSkpCA5ORl79uwp07GHDh3Crl27KnR+oPC9XuqXX37Bzp07K1zuuZTNZsPAgQOLXSd27dpV4jpAJSUllTkd54WsNWvWAChso+XLlxvQTzHiw9SMzRYFLhs6f9WqVSvUrFkTQKHb31VXXYXWrVtj8eLFWLt2LQ4ePKiJKVWrVk1BgwcOHCjXwxWGzo7+Xt/2KyAG3yVoYbFYEBkZqdIbBQKFaUskpMGbMw6HQxNolg4OBHzCw8OVa4ff71fn8ng8iIyM1ASbZSoimR6H0AgDuUyFZDabkZCQgMzMTE1KnsjIyJAOD3RxkJLwgZQ+4E4YATgdVGbdJfQgP7AzYM6AsIRT6DhCeIfnlMF+XrcMtPM1mV4tLCxMpReSgXi6gOTn5yM7O1uBVnr4QdZX3zYSxtC7lPC65fF6gEM6hTCFkgyUA9CMlVB0OduE++shBAnR6GEcCRZkZmYCKPww6HK51Jf9nJwcpKWlaVxMWHe2LftdBvIlrKEf+9wWap9Q7c0UZDJNW0xMjAIM2P8FBQVwuVxqfkr4Q7Y5YSE6n9DNhuODc1s6+RAOozsQ55oekpPjVcJk0t5Q1ofjhHUI5UrDOeLxeDTQT3p6OkwmE+x2u5pbEpaSAJDs99zcXMTFxanrl/PT7/fD4XDA6/Vqrot/M4Ug68O5RJjK6XSqdmZ7EubJysqCxWJR7kwmk0mT65XrWiBQmMaKrjoSsJHzj+NG3uST80q6pPG65byVqfRkubIdvV6vAs8kBMNxwXVEQj8S3JGOU3LOpqenIysrS31RkmCMvo4StuI1FhQUpnOUjl/yWiXUqJ97crucYzL1ntlsVu5AhONkG0uYSDoQURw/nIs8J9cz6WZmyJAhQ4YMGTJkyJChQl100UVwu93YunXrWTnfVVddhccee0y5s/5dNH36dABAx44dcdttt6Fbt26oVasWcnJy8MMPP5SrzHr16oV8+AQAjhw5EjI127lSQUFBiel4yqPbb79dpeHR64svvsAzzzxTaecqS+o0eU/gnnvuQWRkJPbv369Snul15MiREsvkd9ezqaSkpJBputq0aYNvvvkGt956q2b7W2+9ha+++gqHDx9W22rWrKm5z1IWnU/fz8ePH49atWpp3CkIykhXmObNm2PIkCG4++67kZeXhyuuuAK1atVCo0aNkJCQgBUrVuD222/HH3/8oY65+eabceTIkXIBgFJOpxMPPfQQ2rVrh6VLl2LZsmWIi4sLSmElocA//vgDffv2LfU5unfvjjZt2qjUYFIcxzabDbVq1Spz/R0OB2655Ra4XK4S3YKSkpIQGRmJf/3rX8VCEAsWLEC1atXw1ltvqfvMUn/++SfS09OLTEcnAS1DF56mTp2K22+/HVdccUWR75fFKTw8HCkpKfjxxx9x7NixMh8/ZcoU1KtXDw0aNCjzsdTYsWORn5+P8ePHAwBmzpyJ48ePY/LkyRUq91yroKAAXbp0gdlsDjmHv/zyS7Rs2RL//ve/SyyrTp06qF69uua95+8sCSvu37//HNbk/FdcXBwSExMRGRkJj8eD7Ozsc10lQ2VUmzZt8NJLL+HkyZPYtm0b6tSpg82bN2PVqlVYvHgxevfurfYNBAqzqsTHx+OHH37AuHHjzl3FDZWoM+r4YzKZDplMpm0mk2mzyWRaH+L1ZiaTaY3JZPKbTKZRYnvTv47hT5bJZHr4r9feNJlMu0wm01aTyfR/JpMp9q/t9Uwmk1cc80FZ6hoXF6fSk0RFRcFisSAmJgYOh0OTSoiBV5vNpkk3RbiGN0/CwsLg9XpRUFCgwAKCNuHh4XC73fB6vcoCzW63IyoqCm63G5mZmcjMzERWVpaCVAgcBAIBREZGwuFwwOFwIDo6Gg6HA/Hx8YiJiUFCQgKA04H/3NxcZGVlweVywe/3w+fzwe/3w+v1wuVyab5MFOWAImENCUjwmgn88LqliwTF7RLQ0IMaEt6R0ATbnMFy6QzCfSS0AUAFmnkcnZHcbrdqTwJY0qVIXz/+zb6Vzh7se5YhA+Ssn/7/goIC+Hw+uFwuBVUQGmGZkZGRmpQ6MuUbA/2six5WYVsTKmB78bfL5VKpogib8TyE3LKzs5Gdna1SB+lBBukwIlOeEfigOE/i4uLgcDhgNpths9k0oI2sL92ACPhIpx/CEWFhYQpOkHWS185xWVBQoElvxT6OiIiA1WqF1WrVwFGc5zyW9SEEQSBLn5pL9oVMHcW+kQ4pHFP5+fnK+YtjkcezDrTQ5DrDdiK0wX5jWzP1lmxXk8mk4By2Gevl9/uDnFkkWCbBRp6L7S/TFcr5LuE6m82muXY9MMK1kSAjxzBBLVk2xzDbjPNRrgF6+IfHhoK1JBgjASnCUBx7EhCU/SMdc/ihlu1lMp12ZiJE5PP5FBhG6FPCSJw7cqxJuEgCmxzDBLDYN6wj1z6WyzWF1yXF9pbgKx2QpJsX56NchzlGwsPD1XyyWCxq7NO1y+fzqXY0dGF9LjJkyJAhQ4YMGTrT+qd/NurXrx/i4uKwevXqs3bOt99+u1gHhgtZu3fvBlAYtB46dGiFyuJDR6E0Z86cMqUluRAVHh6OG2+8MciFmlq2bJlKt1VRxcXF4dtvv8ULL7yAjz/+GL/++iuSk5ND7pucnIyff/5Zsy03NxcejwcNGjQIGcQ8evQovvrqqxLrkZ+fX+G0NWVRtWrVQjoyxMTEFOnEow+8JiUlldvxhw+8nWvdd999uO222/DMM8/gyy+/VNuvuuoqVKtWTd0bq1GjBn7++Wf06dMHsbGxSExMRK9evTBixAhcd911AIBRo0Yp6Oeee+5Bz5494XA4sHTp0go7YrlcLqxbtw49e/bEu+++i549e+Laa6/F8ePHNfvJ1HJ2ux1xcXGlPofNZsOePXtCulSlpqaq/ioP+LN06VI89dRTOHHiRFCd9YqLi8NFF11UqkA/7x+FSgOUkZFRrKtIRdI0/V11IX0uys/PxyeffIIVK1aU+3q7d++OKVOmaJz6SqsvvvgCs2bNKve5qffee0+z5v7000949NFHNdDhhaYuXbpg2rRpGDVqVMjX9+7dW2p3lsjIyPPG5dDQ+SWn06mc+m6++WZMnToVb7zxRoVSkBo6u5o6dSq+/vpr1K9fH3fccQe6deuG7t27o127dujTp49y5tu1axc++OAD7NixA3v37sX3339/1utarVo1vPzyy/jwww8xePDgs37+C01nw/HnqkAgEJxQslBpAB4E0EduDAQCuwG0AgCTyRQO4CiA//vr5cUAngoEAnkmk+l1AE8BeOKv1/YHAoFW5amk0+nUUIkyrZR0qpGpqaxWqwrEMzBKJ49AIACv16sC1FarFZmZmZqgts/nQ1xcnAqc+v1+9abr8/k0gdfc3FzY7XYVPA0PD4fdbteARVlZWSrwLJ1CCHCEhYXBarUqaIPlBAKBUuXmlg4U/K0PTEtgh+1HhxsJb0hoQwbtJWQl3SsY1CZYwMA64R69C5Gsk9lsVqBTTk5OEGAh3UVkmiAZqNaDJhIG4P88P11VpPsR687cl+xfAJoxEhkZqV6Li4uDx+NR7iNhYWEKnOFYkn3AtqRbEK32JLBESCksLEyNd7rOhIWFqbRzrLvFYlFtY7PZlH2rhB/YT7xueWOINxIKCgrUh8SsrCxVT+mIVFBQgNjYWA1IJsW5lpGRgapVq6o65uTkKHiE7U93Fvab1WpV9aXDltlsVlaE+mOlG5AER3h9BH74m/Oa+4RyfqHbEIGkiIgINf64tuhdsth+BF6kAxPPJYEp6VAk5xrdevLy8mCz2VQ7cA5J+I3gBsdlZGSkxmlHzlfZNyzL7/drrg+AWuckxCbBG7fbrXGPYnvyWnw+H6Kjo9Xa5vP54PF41HySc5VjuSjoR7YZHX3k/vJ/OrBxG9cbma6O8IzsBzm3WS7fAyIjIxEdHa3Gg+zHrKwszfoMQAMDsk8k7Ke36ZdgnIQU2b8SUuP/XGOYNpLjRbanhM04f+UaJOE09pHewe18shE/x7ogPhcZMmTIkCFDhgydJf0jPxt16dIFPXv2BAA8/PDDePvttzWvy++Yhkqnw4cPY+bMmWjdujX69OmDKVOmlNvZqKjjfD7f3y5VWijpXZr1slgslXau9PR0fPvtt/j222/VtgceeCAozVBOTg569uyJvXv3hqxvixYt8Nprr2H27Nma1z7++GOYTCbccsstRaYnM5vNeOmll3DppZdiyJAhZwWIqVatmgqY6VXacVuWFGl6ZWdnV2r6q/Lo3Xffxd13343evXtj8eLFmtdq1KiBZ599Funp6Zg0aRKOHTuGQCCAxo0bF1me7LcPP/xQpfhat25dhev6wAMPYNeuXTh06BCuuuoqLFu2DKdOnQraT0I7fHiqtJo/fz7mz58f8rXjx4/D6/Vi5syZeO6558pcf9bjp59+KnFfzu/SjI/s7Oyg+/xUfn4+xo4di44dO2L48OFBr4eHhyMhISFkO/7DdcF8Lpo6dSp8Ph9q1qxZ7NwsTh06dMCgQYPKBdRW1lr9+++/4+GHH8aiRYsAAHPnzsWvv/6KWbNmoXPnzpVyjnOh+vXro379+kGgbocOHdCmTRsAwKlTp5SRQCgxtmTIkF4JCQkagPn6669XcRxDF44ee+wx7Nq1C88//zxq1aqFevXqoW3btmjbti3CwsKQmpqKhQsX4oMPPkDfvn0RFhZW6vSllaG4uDhMmDABV1xxBerUqYO9e/eqhz0MFa0z6vhTkgKBQEogEPgNQHGI6dUo/BCS/NcxPwYCAb7brAVQdsw9hJxOJ5xOp4JoZICdLh/SSUIGQ+12uyb9CmEbpiuSweJAIICMjAzExMTA6XSqchjYpyMPHT/cbrfG6YRBXQbrCfNIEECm7JI/dF/wer3Kfi07OxsulwvZ2dmawDugdZ1gkJmAgIRrpFMQP+gzoCxddXisvHnFoDShmKioKNhsNlitVuXWIqEa2f4yAM8gtx4kYT3ptsHgfFRUlKbdWD+6MPF8LFu6E7Fstpesg2wT7ivrnp+fD7fbrWAYvkb3II/HowCY+Ph42O12Ne4IUxBUYdvyutiWZrNZua7wXLx+jp+CggJYrVZ1k0FCEoQM6PxBACYqKkpdE11fJKRDAI71AAC/3w+/369uWkp3JI57Ou/IsUVXHbY9UwgRkpPwC+ESmc6I4AaBL84XzqusrCzlfKSfMxJIIaThcDhUP7CtCEpItxXCInonGul2I2ExXrc+fRXPTZgCKHxSKDo6WgPp6QE1CWFQfPqNZXItYN8QGJTQioRLOD7luJbuP3Kd5JzgOCG0IiESQk50gWIbydRZBNA4niWMxzlqs9lgsVg0sI4sR4KaEnqRfcL1l448dGkDoBkz+rVN9i/rR9hFAmBcawlvSZcjth/Xez1cpn+fYVtyHrFe7C8J8eidgCQIx/cmCXvReU6vyMhIjUuXlNlsVmsURXcj+d5B1yz2taHidT59LjJkyJAhQ4YMGTrX+jt/NqpevTrq1q0LAEGOHZGRkdi8eTN+/PFHdOnS5VxU74LU2LFjMWjQIBw8eBAdO3bE5s2bkZiYiNdeew0DBgwoU1n/93//h5dfflmzLT8/H3fffXfIAPbfTUUF8qlQqekrU6GAFv39RKnOnTvj8ccfR1RUFBo2bBj0+qpVq9C3b1+88MIL+O2330KWIR8OOxtKSUnBc889h19++SXoNZnyqjhVxIUhKysLbdq0OWff0wcOHIgBAwbg8ccfD4J+AGDGjBn47bffNOnmSnKgmTt3LgDgkUcewcqVK/Hkk09i2bJlOHDgAHr06KGeXC+rateujeeeew733XcfHnzwQSxbtqzIfX/99VdMnjwZANR95spQcnIy0tLS8Ntvv5V6fABAs2bNMHnyZLRp0wbLly/HiBEjit2/SpUq6u/SpBB0uVzqgUG9duzYgRkzZuDIkSNFuot07NixxHMYOq3z8XPRkiVL4Ha7y3282Wwu99yMiYlBdnY2Dh48WKRTXGnUrFkzfPjhh6hfv77a9ueff2LQoEG455578N5771UKQHi2deWVV2pS9VDJyck4efIkpk6dGvI9SIoP3xoypFdSUpIGYPb5fEhNTTXAnwtQkydPxnPPPYdly5YhOjoaDRs2VFBXeHg4evXqhXvvvRdjx47Fhx9+WKE1v6y69dZb0bt3b9SpUwcAEB0dXaYUwf9UnWnwJwDgR5PJtMFkMt1TzjL6A/iiiNeGAlgo/q9vMpk2mUymFSaTKeTdEZPJdI/JZFpvMpnWnzx5Um1nYJI3XBh8l8AHg/D6L0UMfMtgrd5twufzacCTyMhIBfsAp5+m0UMaDFBLZxXWLdSPdKxh2YRk6FzBsnguBoLpDMRrkgFumeaLr+s/1Mt2YTCeQWn+sBw6iRDoYDojwg7yR39O6abCdpf1kXWV4I0EMJjWTfanHpiSMI/e4UTWQx4nz6/vG8IyBLb0qc3Y79w/PT0dGRkZmuMJEoSSdDphe8t2kXViMJ51ljAZx7MEnQhc8AurPhVRSdKnVWMdEhMTYbPZNPAM55keagFOp7SSUJcEO/QOIzyXTIsmwR05bkM52BCYkKmhZHvL/yUAJyX34esy9ZisL/+WwKEEJiTYwnOyXAkr6tcoCZDIuSiBPAAagESmOeM5OHZDQTCsj3Ry0reBvDb2p4S4JGhI0E3OFeA06EXwj/WWbSivWdaV2+T4Zj/z2vV9w3rLMSrbRO4f6qlI6VAGnAYFCfPI9xW5TQKF8nX9dUogi/OV59W7GPFa5H68bgmvSbiSa7Osl9lsVum99KAlj2cfyDYu7XrxN9d597kI0H42KmedDBkyZMiQIUOGyqPz7rPR2fpcdOTIEfUZPSUlJej1Fi1aoHv37soVyFDp5Xa7ceLECTRp0gRdu3bFtddei8svv7xMZXz00Uf47rvvNNvCwsKwa9euyqzqeSt+Hy0KgjnTbq5FOfOEuucCFLoGBQIBWCyWkOmDdu7ciW+//Rbjx48vFuw5my5ba9euxYwZM0Jea2lhkYq4T+Xl5eHuu+8u9/EVUUREBB544AH88MMPmD59epH7rVq1ClWrVlXHEJYsShs3bgRQeA9+3bp1GD9+PD744AOkp6fj3nvvxciRI8tV38OHD2Pfvn1o0aJFSEhJavr06Wp+5OTklArSsdlsIYE1qdTUVHg8njKn+br66qtV4L8048rpdGLNmjXKyac0kvf5QsntdhcJ/rB/DSldcJ+LTp48WWFHmPKkrwMKYb8NGzbgp59+qnA6v7p16wali0xOTsbkyZMvWPAnLCwMDzzwQBDw9+eff+Kjjz7CokWLigUqjx49ipkzZ2LDhg1nuqqGLkDxoWyKsU9DF6bmz5+PJUuWwOv1alzA4uLi0LhxYwwaNAi9e/fWpDQ9GyIbQZnN5kp1Hv276kyDP50DgUAbANcDGG4ymbqW5WCTyWQGcDOAoISdJpPpGQB5AGb8telPAHUCgUBrAI8CmGkymYI8UwOBwEeBQOCyQCBwmSTDmK+SbjMUA6fScUW6/0h3Drp/EOIATg9Mup4UFBSolEl6pwy6t/j9foSFhSk4iE+c0A2IwXVCAAUFBSpIy0AtUzTJ4K3NZlPuDgR9PB4PfD6f+pHpsmQgmO1A1yEJqchgNIEinoNlEPwgPMIPhBEREbBarart+YZB9x+LxaIJjBf1JZz11Ke24g8A1YcErgg3sI3Yr/wBoPpJuj0xkC3dTSRwwt/yRwbUQwX2WT+eJz09HX/++af6UsZzS2cQ9qs8vwTEJPBA0Ek6h7jdbmRkZCjXHtZRQhisp95VRe+UJM/FlF9MCUanKY4L9hFT5bndbg28JaE5tk8gUJgyrmrVqookZvsTYJLgFevC8Un3LQlDyDaSv3kcoRS3261Jb8b+KApq47llufLmFF2b6LRDKE+60XCusW/Z3pxTTHsmxz/XIaZsk249XE9YBtOT8Xr5wczv9ytil1AegZ/s7GzV3wQTOQZ4TWx/6cJl+ssVR8IjbBeLxYLIyEiNO1ogEFBrQlRUlFpXOTZyc3NVOVyP2AcSkmHf87qlkxW3SRCKTjV8yk8+Tcg0WxKWYrvLm6Ey9RXbiMCbx+OBw+GA3W5Xaw7XIwKPnKNc/1hfi8WiytavQ/KaOaZ5jfoxzzHC9tSDRNLpjG5XElhln8pjOTblvOO4k+UaT4gonXefiwDtZ6Oy1MeQIUOGDBkyZKiCOu8+G52tz0WrV6/G3Xffjaeeego//vij5rXc3FwkJycjPDwcDz74IG655ZYzWZW/nfr27YvWrVvj1KlTeOqpp9CqVSvceuutZS4nMjIS06dPx8qVKwEUfvcqLh3G30k5OTkYOXIk/vvf/4YM6J5px59jx45h7NixOH78uNrG+43FKTIyEmPGjClyzqSnp2PVqlVYunQptm/fHvR6dHQ03njjDVx22dn7WhQXFxe0LSsrq1THFgVIFaft27dj5MiRaNCgAdq3b18kTHUm9corr6BevXq44447NKmx9Hr66adVIP7aa68N2VYAMG/ePCxbtgzvvvsuAOCDDz7AM888g4KCAmzYsAHbtm1DZGQkOnXqVO46b9q0qUQ4h4qPjwcA7Nmzp1TpsoYNG4ZZs4LexjTasWMHPv300yJTgRWlzMxMdd9m3759Je6/f/9+fPzxx3A4HFi8eDFWrlyJiy66qNhj/H4/HA4HmjRpEnJMnjp1CidPngzpNt2kSZNSXsk/Rhfc56JAIIA//vijLNUMUvPmzdG+ffsyH/fTTz+hZ8+eeOedd7Bt27YK1QEoXHPeeeedoO379+/Ha6+9VqI7zvmoxo0bo1WrVkFw3vTp05GXl4cePXqEPO6nn37Cgw8+iEmTJp2Nahq6wBQfH49atWppnAcLCgoqDOAZOjuqXr160Ht7amoqXnnlFbz77rshHX0OHjyIHTt2nK0qKjFrBmWxWAxouBQ6o+BPIBA49tfvFBTmFW1XxiKuB7AxEAhoMDKTyTQYQC8AgwJ/fUMJBAL+QCBw6q+/NwDYD6DUnx4JRDDFFG0qJeTC4LU+FRCDrAx0mkwmRZ0xMMrj/X4/XC4X/H6/Oo4BWo/HA5fLBYfDAavVqoAGpvxiMF8G3xlcZsA6MjJSBc0JzbCeBJiY4kdCOQy8EyzR/8igOwPtADTtwdcJEOnTF8kUNXrHnPDwcAX9MP0THZj0KY9kYJvXxWC0BH+A024bdKmw2WwqbZMEWwhysHy2i0w/pXeYkYBRKAcf+cM2AKAgDAmQFeUkJc/DvtCDN9LtQ6bTkvsRgqAkWCNddrg/24vH2Ww2Zd8nr0d/HpZNhysuynIcsa1cLhf++OMPlTKMYJNMF8a+ILTFfud2eW0SQNA768h+JJwUqo/1bi5yfsn5wPPKa5bHyf30c4ggH4EXmWqKc0s/v4HTgBPLkW44hEdMJpM6RjqLETaUN8qsVqt6nWmgeF6CgxEREWq94Bd0wkdy7QROO8dIGFGup+wjmfqLqa84Bvx+v1o/CVryh2ny2HeECHmtEtTRuzdJJx3OXwkySpiHa2B6eroC0jiv5HrF+nN/Oe+AwifM3G430tPTVYpFCRRKNx6uIVzzCARZrVbYbDYFk+bm5qq1guOK55Rpydxutwby4Thkyj6mJpMgkUwbSKcnph+UayzXdo4/Ca5JFyE9TCQBxX+yLqTPRYYMGTJkyJAhQ2da//TPRl27dsWHH36Ixx57TLO9fv36qFmzJoDCNBZPP/30uajeBS23243Zs2eDTt/Vq1fH0KFDy1TGqlWrMHjwYBw6dEhti42NLfaYmTNn4sUXXyxjbc9P8YneUN/jygOclEXTpk3D1q1bg1yIi3OVCAQCWLBgAcxmM5o3b47ExMSQ+z3xxBPo0aMH7r///qDXbrnlFowaNQrSIf5MaeLEiZgwYUJImCUzM7NUZehTuEyaNAkTJkwo9phFixap4872k/k9e/ZEnTp1sH79etSsWRMtWrQodv9rr70WN9xwAwDgiiuuKHK/r776Ct27d1f/z5o1C23bttXss2LFCkybNq3c9b7jjjvwf//3f6Xaf//+/Th8+DDmzZtXqv2bN2+O2NjYYsHC/Px8vPnmm1i9enWpygQKnePeeOMNVa5cy0o6DgAaNGiALl26oFq1auo9KZTS0tLw9NNPY/LkySoVh9TMmTPRrl07LFq0KOi10sJU/xRdqJ+LTp06VZ7DABS6yixfvhybNm0q1/G5ubnYsWNHqVLTlaS4uDjce++9eOihh4Je+/PPPy9I1x+gcP2QKZmohx56CM2aNQt5TEFBwVl39jB04ah27dqoUqWKBgRPSkrCM888E/LzlaHzR/Xr18dNN92E4cOHo2vXYLb0qaeewtixY4Oc+rZv335OwJ+MjAwNJB4ZGYnY2FhNalJDwTpj4I/JZLKbTCYn/wZwDYDfy1jMAOisCU0m03UAngBwcyAQ8IjtSSaTKfyvvxsAaAyg1O/4gUAAOTk58Hg8QelWGIAP9WSJ3I8OIdJpRAIbeXl5cLvdyMrKgtfrVe4/UVFRmoArnSDoykLHkaKgH/6Eh4fD4XAgOjoa0dHRiImJgdPp1KRN4hd2GaTVgxEysC+BF0IiDD7rgRVeM6ERtgmDzNIZRbq6MJgeERGh4B+CMWFhYep/li3hFG6X1yahD7raeDwedW7CEawznXOkq48e9tE/AcM662EWvRsH6yED9QymcwywPL2DjIRbZB1k2dI1SLrRELri9Xo8Hng8HtXeNptN4zzCdnA6nQq6IIjDsc/UY4RgJEwkxw9BCP0YkU4g+r6kZNohgiA5OTnqnBI6kDeaZJtLiEcCO/pUZmxTPZijB0Wkq5FMp6Z3VZLjX9ZFgmGsq8Vi0YAg0rGLa47+muUaJAERs9msgf94bjk3mB5NXr88n7zuqKgo1e90OqI7FkEbvUsM3aAkrKafFxyTetjOZDLB6/UqUIjrHz84Sncl0sZ+vx/Z2dkKxmFbE26RwAvnkh4OpOMZ1wL2bV5eHlJTUzVpBqW7mR6UkY5DbAcJyIQCAOW6IgFIu92OmJgYOBwOBf8QOpIuahKKY/2zsrLgcrk0aRylkxC3Szck9hE/yHGucc3OysrSrMvS8Ug6/nCdZ/v6/X7VfoQA9YDgP1EX2uciQ4YMGTJkyJChM6l/+mejvn37ok+fPrjrrruCXjt48KDmQaq2bduic+fOmn2MYGnJGjFiBEaNGoX8/Hx88803mDp1arnK+fzzzzFq1ChMmDABX331VZH7tW/fHq1bt8bBgwfLW+XzTvK+mFRUVBT+/e9/h0yrVVlyOBya+z7yYZdQmjp1KkaPHg2v14vRo0dj/fris/UdOXIEY8eODXKcMZlMmDt3bplBsbKqatWqGD58OKQbPeX1enH99dcXe/z9998f5GAza9YsWK3WYsGlRx99FD169MDu3bvRoUOHYs/xxRdf4Msvv8QjjzxS7H6l1dy5c7F27VoVVH/55ZeVO04oxcbGon///oiJicFNN90Ucp/Dhw9j5syZQdurVaum+X/x4sX4+OOP1f+tW7eG0+kssc59+/bF888/j82bN5c6mDlq1Ch07twZb7/9don7vvvuuxg2bBhWr14d0hGnrHrggQewdu1aTJkyBSNHjkT16tXVa6UFI/TjYv78+RqwSq/evXvjvvvuCwkWUCkpKTh27FjQdn0//ZN1IX4uat++PR5//PFyu+EFAgEsXLgQ33zzTYmObiWpIvCRlM1mw5NPPokxY8YEvZafn4+MjIxKOc/ZFO+567VkyZIgZ73x48ejX79++Oyzzyrs5GTo76ujR4+G/PwCFL4HPvvss2e5RoZKo4SEBNx+++249dZb0bdvX/Tt2zfIjAIAXn/9dXzzzTcas4MaNWqU+Nn0TCglJUWzvkdERCApKalYINkQUP5kwCWrKoD/+2vgRACYGQgEfjCZTPcBQCAQ+MBkMlUDsB5ANIACk8n0MIDmgUAgy2Qy2QD0BHCvrtwJAKIALP6r7LWBQOA+AF0BjDGZTHkA8gHcFwgE0kpb2UAgAJfLpcADGZwmVMEPILwBIx1S6EIhA9Qy+Ml0MB6PB36/HykpKcjLy4PdblepZUwmE+x2u3LwkM5AEt5h4FZ+4ZUuKFarFRaLBWazWTmKMJhrs9lUkF3CD/wy7/f7VVmEOBiwlQFsghF6FwcZ4JdAkHSmkFANnUukG01ERIQCEWT6G56HMIJMfyWlh24YoCfEwaA46yNT6Mg8zDxeP070MA5BJ16jBGP0LjQSRuF52ab8m+eVwXm9iw2hKHmt8hyshx46Agpv0ISHhyM7O1uNO4I/bD/CDBKeoEuUvC59H8vrk/0uzy/rJa9V76okAQmTyaRSUbFeNpsNWVlZGqcZlqcHEySIJMcTx5kEoGTfyBRz0hFGQj+sn3SBkb8Jk0jwKT8/H06nE5GRkQqSkSnL5BrDJwq9Xq+6Hrah7PuCggJERUUpECcyMlKlBuS+dE7iNbHvCfsQ8PB6vWpNoyNRVFSUph0lXMX5xHPJFHT5+fnq2uS45vzxer3KkUhCV3pAj5AO0x7yeikJa8njPR6PWodl33B8cI1j+jSTyYSUlBTUqlULPp9P7cd5w3Ho9/uVsxvryTWU26TTmEwFybaR/W2z2VS/0yFOrs1yTeQ4ZFmEoOR45hpBVyUCedJRi/tybhcUFMBqtWpSORLC4rjn+0N4eDisVisCgYBaS1gGz8d2kcDeP1wX1OciQ4YMGTJkyJChM6x/9GejgQMHolGjRvj000+DXqtevTruuecefPjhh+p756uvvqqeyqxZsyZWrVqFO++8EwsXLjyr9b7QlJGRgYcfflgT8C+rFi9ejMWLF5e437p169CyZcsKBy/PJ82cORMbNmxAy5YtUbduXTzwwAMAgMcffxxer7dEx5aKKD8/H++//z769++PevXq4ejRo8WCP8DpVOdOpxNmsxl33XUXDh8+jKVLlwa5BR08eBDTpk3DHXfcEVROy5Ytz3iq6qICZkChc89XX31V7PyWwRi57a677sKPP/6IK664AikpKahbt27Qfl6vF61atSrW8adPnz7o06cPLBYLlixZUsLVlE6pqamoXbs2du3ahV27duHqq6/GokWLcPnll4fc/6uvvkL79u3x6KOP4tVXX8Xnn3+ueT0vLw8zZszQbLvuuuvw+OOPBwF4v/+u5SfGjRsHk8kU8ml36rLLLsOYMWPQtGlTDBs2rMgUJv369cPs2bM1Y+zw4cNFlivVvn17PPzww5g4cWKp9i9OI0aMwPjx41W5ej3zzDP49ttvizzebDZjyJAhaN68uWY77xUWpfXr1yM/Px+dOnUqFhQ4efIkPB4PbDab2paQkIBHHnkE27Ztw549e4o89h+iC+5z0bhx49CgQYNyOy+YTCZUrVoVe/fuLdfxUkuWLMGBAwfQoEGDCpdVrVo1PPzww9iyZQtmz56ttv/222+48sory5WW7FzKZrOFBH++//57NG/eHDfccAOio6PVA8Fr1qwp9Rpm6J+p1NRUDBkyBC+99FKQI19mZibS0ozbz+ej0tPTceutt6Jhw4aw2WwYNGgQkpOT8cEHHwSB8NOnT0f16tVRo0YNbNiwARMmTCiT62BFlZiYiJdeegnXXXdd0LqelJSEJk2alNsp7p+gMxaFCwQCBwC0DLH9A/H3cQC1ijjeAyAIFw4EAo2K2H82gNmhXiuN6KzA4LPezYbBbQkGANrgLmEApo4xm83qixgDwiw/EAgo1xc+wcKAKgBkZ2cjOztbOVhQEr5hXRhQJljA3wSACE1YLBb1xZWBeRmwpiMK6yDdV+gsoXd1YSBXQkkS3NEHuXkeGfyXzh+8RgaJWS77hy45MpWMhCHoAsQv++wrPhnEehCA4A0Z6UAi3V1Ydz1Ao3ev4c0HCbOwXbm/3uVEtpu8DrldurTwOOmuIZ12ZHtINx0JFxUUFMDj8ahUQBKoys3N1Ti66B1P6GYioQ89FEbQh5KQlB7I4n6EByTIwPFMW2cJ1QQCAdhsNkRGRsLr9WpSJbE+nIP8Msl2Yio8k8kEt9sd1J8SFJI/EkCSbamHmCS8pIebOG5zcnIQERGhvjRnZWXBarUqQEhCWEzzxLHt8/k0EI/D4UB6errGXYXQC9NLsY4FBQXqeK5DnGfy5gTXOp6H8CHXPwlUSfiG/c80gxxTck0k2EioiOeVawGvV7oe8dq5fkkoiesvfzj2OS85NuQ5+VuCnuw7PrlBmIngDq9DPyfYfqy33+9XX6YiIiKQmJgYEnKU40Q//tjOERERyMzMRG5urhojnHcyxReBOM65iIgIzc1mOSeZe53nl+970uGKc9NkMikQKC8vD1arVdWF44dwWlZWliYdGcc/++Cfrgvtc5EhQ4YMGTJkyNCZ1IX42WjIkCH45JNPKlKE0nvvvYdLLrkE8fHxSElJUdtvvPFGvP322zh+/DhOnjyJHTt2oFq1amjTpg0eeeQRjB07FkePHoXT6UTHjh0N8KcEHTt2rMTUR5WpvxP0AxSmBjp06BAWLVqEAQMGYNiwYZp7AGfy4Y5JkyYBKJx3y5YtK9ZxhIqPj0dycjI2bNiAtm3bYvLkyfD5fHjooYfw0UcfBe2/c+dOXH311Vi4cKHGGQVAiZBRRRUqxZdUampqka8NHToUY8eODdpOl+TVq1fj4YcfxvDhwxWsJWW1WktM81W9evWgh50qovj4eMTFxcHtduP111/HihUr0LZtW6xatarIYy655BJ06tQJzZo1w/PPP4/s7Gw4HA71+i+//BIEzPzf//0fMjIy8MQTT2D69OlFlj1lyhS8//77aNmyJbZs2RJyn1q1aqFp06YAiu+Pe++9Fz179gzp4FacEhMTcdVVV8Hr9ZbpuFBauHAhrrvuumL3ueyyy9C4cWMcOHAgJDg2YMAAvP/++4iIiMDy5cuxfPlyPPPMM4iMjCwR7Ni0aROGDx+OOXPmYNu2bSFTVH788cdYuXIl3nvvPVx00UUAgGbNmuGdd96Bx+PBlClTynDFfz9diJ+LfD5fhdOt3HTTTfB4POjfv3+Fytm5cyc2btxYKeAPADidTrz44osIBAKYM2cOAOCbb74p8zw/18rKysKRI0dCOpxt2LABgwYNwmWXXYZGjRrh0ksvxVtvvVXsemfIEPXbb79h0aJFuPTSSzWOb5mZmaVOWWro7IqxL/IGsbGxuOWWW/Dpp58GgT8//PADWrRogZSUFCxatOisp/5LTU2FxWIJ6SgXHx+PWrVqqZiloWAZkbi/pHdj0Lt5EGggZCDhF+C0040EWvg/UPg0BVOvyLRgQGFQNDIyEhaLRdXB7/drnC9kXeRvmUpH1l9/bdKRRg9sAKdTEulBEm5jAF2/jywv1I+sG6ERBt1l2jLplMNz6/vDYrGoH0JSvA55LfIYBqaZFomBdwlL6Z1mpKTDSajtbCPpesR+k049oVJEyZRQ7Eu5j75N5Dn0TiASHAjlrKFvK/01SfiF4IAecAgFwujrwrEgr4nn18MAEh4hVMT2C9Wfsp501WLfyutnnQn2cP5K0EKfDkrODf316N2a5P9Fbed5WB892BIWFqZgNl6vvo4E8+h0o4eurFarGsNyTtNdJTc3V0FG+rksASPZX4G/HGEkSCj7EYCmveVrHM8SbmG7EvyQa4A8px5mDNW20v1HOouxL6XTjwSIAKh0iHJucd2R6Qhzc3ORmZkJq9WqHHSkExRvjHD+6tuJ9eX10y1HD5Pqxyvbnf3MfZjeSx7L9GL6dViuEayXhDUlMEaxHeTc4jXy+jif+CGKkB7f/0wmk3r/ojudnAsSytOvr4YMGTJkyJAhQ4YMXUgaPHhwpZW1f/9+HDhwAG3bttVsv/jii1GjRg107doVFosFkydPxrRp0zBv3jyNk8KxY8eMz9eGzqpMJhMWLVqEDRs2ADjtgH0mZbfbERUVhaysrFLtn5ycjEmTJmHdunVqm8ViQcOGDTUPVVJ5eXnYsmVLSLAlISHhjKY0kA7GoVQcmDNs2DCNawpF8Gf8+PHYsWMHtm3bhh07doQsQ58mTC8J2FQG+NOlSxc4HA7Y7XZceuml8Pl8xUI/QCGoUr9+fcTExGDo0KGaOgHAxo0bg1wpLBYLqlWrhquvvjpkn1OLFi3C3r17iwXKtm3bBp/PB7fbXawbTf369Utsz1AaOXIkBg0aVObj9Hr44YfRs2fPUu37wAMP4MEHHwz5WrNmzZCSkoKVK1di4sSJyM7OVgBcTExMiWW73W7ExcUVCc0lJydjyZIlapxK2Ww21K9fv1TXYOj80dy5cytcRmZmZokgYmkl79FWhurWrauZ2zVr1qwUUO9sKjc3Fy6XKyioTxUUFODXX3/FzJkz8f777xvQj6FSKy4uDvHx8UGfR1wuV6XNaUOVr3Xr1mk+O8ksOHodPHjwnEA/1OHDh0O6Lebl5cHhcJQqZes/VQb485ckGJKbm6vcMmSqEroqEPrJyckJGehnWTJdUkZGBlwuFyIjI2G322EymVRansBfrj4Oh0PduMnOzta4bsjyJfTDVFgSKNDDN2azGU6nUxOs5vEyIJ2bm6uuka/l5eXB5/NpnID0gXN5/foy6e4RGRmJqKgomM1mBVywnfkjYSCZGoZlRUVFISYmBk6nU31BZuBZpsCRIA7rYrfbVX5w9oHFYtFACHa7HQkJCeqpFoIMEmKgWK+cnBwFFMggOqEDCTVIIEL2k2wrBuxlSjKWz/pImIJADseC1WpV0JGEVvTjhO1kMhWml+NrUVFRqF69Oux2u7peOuXItpJvCBIG42++4UtAKlTwXzppsV+Y/ornIqglXYK8Xi98Pp+yb2afsH2Ymkw/DqRDjMPhUE4urAvnt2x/gkISIJKgjQQsZH9LMIw3xOR48ng86kM3YT99v3k8Hvh8PphMJsTHx6uy8/LyEBUVhczMTNUmsn3Dw8Ph8XjUmGJ96K7EffQ3QTim6ZLFtTE3NxeRkZEKItQ/GcRr4vkk8AZArZWUBPwImbH/JLAj+5ugCc9BJx460DCFGa+Vjj0EVTwejxrzPCdTohGwOnToEE6ePInw8HCkpaXBbrcr9x66ZRGaohsOr1OmOmP9uQ/nFtcNWU/53iMBqezsbHg8HtVuOTk56j3J7Xar9yCeR8KrXAO4RrD/uE5wLDgcDg34xPUrMjJSAwzl5OQoxzr+7ff74fV6VZ/Y7XaVulL2nd5d6tJLL4UhQ4YMGTJkyJChQhngxoWl1q1b46GHHqqUsgYPHowOHTpg2bJlmu3/+te/kJmZiSFDhuDHH3/E3XffjTfeeAMDBw7UpJt49913ceONN6Jhw4aVUh9DhkrSzJkz8eCDD8Ln82HlypW47rrrsHPnzjN6zqpVqyInJ6fUaWDS09Px66+/Ii4uDjt37lT3XAYPHozNmzfjyy+/DILtAOCRRx7BQw89pHH0+vDDD/Hyyy9XynXoFRYWViJIUVzKpPfeew8vvvhi0Ha6h506dQpAYZoGuhrr1a1bt2LPz/rl5+dXOO3ZbbfdphycgEJnmeHDh5d4XL9+/ZSbSMuWQUYomDZtWtC2PXv24ODBg/jss8+KdW1KSUnBlClTcM8996Bdu3ZBrzdv3hyPPvoo0tPTcffdd2PXrl0hy6lfvz7q1aunHGz04pPq9erVw7p16zRPrj///PNo1qxZkXUsSTExMVi1ahXGjh1bagjv4YcfxjvvvBPytWuvvRZ79+7FlVdeiVmzZsHv96s51KRJk1KVv3v3bvznP//BL7/8ghtvvDHkPkUBCJdffrnxuegC07hx47BixYoKlbFixYqglH3l1fz58yvVaWTr1q3YvXu3+j86Ohrx8fGVVv7ZkNlsRpMmTVCjRo0S9zXSexkqiy666CI0a9YsyIDgpptuwltvvYUXXngBl1122TmqnaGi9M4772Dy5MkqHVtBQYGK5+g1e/bscwb9AMDatWs15/f5fJgxYwZGjhyJl156SX3evZDUrl079OrVq9Sfq8r7ucgAf/4SHTgYgHe73SoVFwOe0sVABpWB024J/KHLAwPNDI4ybY7D4YDFYtFACgxsEzrKzs5WFLF0fOAPA/EyoK53BNKDQjLlDesmoYhQqakobiMcJdOYSQBKX2Z4eDgsFosCf+iCYTabNall9AAJnZBYTkREBGw2G6KiojT1IgjA/QgnMHUVg+Oy3QgeMEDu8/mQmZkJj8ejgRT0jkQMqgPQuCCxPPkUDEGfUA5EocAetiXbmNchASg9fMLrjoiIULCZ2+1WfSv34/XwHJGRkWjUqBFq164Np9MJh8OB+Ph4NTbj4uJgNpsRGxuL2NhYTX/pHYf0bj/sOzlHpCOJdH1i3xCikfPNYrHAbrfDZrOp9HQSYCMA4XA4NO0TERGB6OhoBSjIOUpLO5/PB7/fr4FfCCJJpyD2D//n/GV9+JqEquQ1Ep4jREG4Kzs7W0F2hEf4t959KDc3F9nZ2XC73WrMcPyw/eT8kyn8fD4fsrOzFeREwIjtyHErx4aE9vROO3Qu45oi14ZAIKDqyLzAEqwiDMnxTCcZ/Ry22WwayIzAjYScOOa5tsg5zraRaax4fopzOywsDCdOnMDevXuRmZmpoLeCggJER0cHAWVcp9kG7AMJ8RDgcbvdavzpXX/YPqw/X+faQJiM+3MMcDzJtYDgqN1uV2VI9x7pGMW+4BpFqI1AD9uRx8k6cJ57vV4FpUlwz2azqbkq3xvYF1ar1XhyxJAhQ4YMGTJkyNAFq1mzZqFHjx4VLsfhcODmm29Gamoqjh07pra3atUKrVq1Qq1atRAREYG33noLzZs3x4cffhhUxqJFi1CjRg28/fbbFa6PIUOlVVJSEiZMmIArr7wSK1euPOPnO3DgADp06IDHH3+8TMcNHDgQrVq1wvXXX48JEyagatWqaN68Ofr164ebb745aP/Zs2fjvffew4EDBzTb+R25slWtWjXY7fYiX/f7/YiPj0fjxo1Dvv7VV1+hWrVqQdv1T0Vv27atSCeaklyU+L0+Nze3QkEfk8mEqVOnalKp1a1bFyNHjsTcuXMVxKhX27Zt0aFDhyLL/fnnn/H7779rtnXu3Bm1a9fGd999hyVLlpRYt9TUVDRr1gwPP/ywZnu7du0wb9483HnnnahRowa++OKLIssYN24cAASNHeqxxx7D6tWr8euvvyIhIUFz39Dr9aJmzZol1jOUHn74YSxZsqTcQdV+/foFbeNDa9Qff/yh7uFIN54vv/wSr7/+OurVqxdUxubNm7Fs2TK0adNG8/4m5fV6sW/fPs021/+zd+ZxNtX/H3/Ncufus4+xky1E9iUipFApoUgKaUOpJNrTTvs3KRUlSxRaRaWSlF2RJZF9Z/a5+2y/P+b3+nifc+8szDDUeT0eHubec87nfM5nu/ee9/O83tnZxbazoXNXS5cuLdPx1157LR5++OFyqcvMmTOLBNtOR1WrVtU4jf3111/43//+p9bPUCnzzjU5nU5UrVq12M8cQ4ZOR7t371YpQfWqXbs2LrroonJxDDRUvjpy5AheffVVzJ07FydOnEBeXl5IKP5cUKNGjRScvW/fPjz//PMYMmRIucGiFaGaNWvitddew+LFi0NC7eUlA/z5f0lnBAanfT6f+ls6euhBEn3qHwbMbTYbwsPDkZaWpmAGOs7QuYZgAwOjQOEXYK/Xi7y8PHi9Xk3gVQ8AyS8YEvCRr/l3ZGSkggYYvJbOLbJMtgXPLd1XpHNNKPhDpp5iudItRP4toRbpUMEgPwPW3I+BasI3Ejwg1ESgyuv1wmQyITo6WgW2gcJAfVpaGlwulyZlVSAQQFZWFtLT01X96U4k25bXKFMXySC8Pg2OdEGSDkh6SIQOGmxneU4JRrDdeRzHKiEDnlc6U3k8Hs0NC5PJBKvViuTkZFxyySW47LLLEBMTg5ycHISHhyM+Ph4JCQkK9HC73ZrrlKCXnEMcj6xXKECIUBJdoAg9cK7xGumyRcCKQA/bhOAIAQwJ4bEN2TYS2OOTJRLYkHOKfS5hKTk/ZTtIAE0CbxII4nyRYJSce5yTbBeZ7knvYCThnqysLDXO9PMUOOlixvlCeISgBuvBf36/H5mZmQrcILwi4S1CUdLBRQJtcp5JwFGfiottISEWtqUc41yPOJfZljw/55IUYSKCLOwL6ZAl+y4lJUUdQ1cxj8eD2NhYzdyV5ctxwbrKdY1zTA/ESRCSLj36dUSOOzkOfD6fAgLp1CPHm9Vqhc1mU25GbGe6uHm9XoSFhWkgKZ6LrnCRkZEawIrOWqw7r9nlciErK0uBd/K6pAsXIVjWNTIy8rRvahkyZMiQIUOGDBkyVNG69NJLy+WpR5fLBafTiW+++Ubz/o033ojDhw9j48aNePjhh5GYmIgvv/wSd9xxh9pn2LBh6Nq1Kw4dOoS+ffuiVatWuPXWW8tcJ0OGSqP169dj3rx5Z/Wce/fuPa3jAoEAfvvtNzgcDmzfvl09FVycC+2JEyc0r/1+Pzp06BASsimLkpOTlbtwKJnNZjz11FPFOtaESoukv08XHR1d5PElBeN4H2TSpEklpuQqTg888EDQfeL4+HhUr14dNWrUwPXXX4/HH39cA1Xa7Xb06dMnpCvS2rVr8e2332LKlClB21555RV88cUXmDp1aon1at68uYINunXrpu5V9OnTB2+++Sbq1q1bpDON1B9//AEASEtLQ8eOHTXbHA4Hhg8fDq/XixMnTmjuybz33ntYtWoVPvrooxLPIRUbG4tnnnkGr7/+OpKSknDw4MGQaTCkQo2j119/XZPKbuLEiWjWrJm6FwwUgmMHDx7E5s2bMXjwYLXvgAEDMG7cOHTq1Cmo3MmTJ2PVqlWIioqCy+UKWZ+rr74ab775prq3nZubi7Fjx2LMmDElN4Chc06JiYllLqNbt27o06dP2SsD4JlnnlHzsqy64IIL0K9fP40r2F9//YV169Zh3rx5+PjjjxUEtH379lKtGRWhatWqGfdjDZW7du7cib59+xbpynjo0CHlKmPo3FJOTg7efPNNzJ49Gz6fr1TpPCtCQ4YMQVhYGPbs2YNx48bhueeeOy+Ay+LkdDpRqVIl1K1bF59//jlGjx59RlKdGuDP/4vBUUDrYEMYgf/zS6kMVEt4hIFYAi4mkwnp6ekq6ExXFcIO0jEBQJBDhNPpVAFw6XQCQFMvveOH3h1IHwSWMIN0XZGuHDLdkvxfAg4SHCgJANK7/UgIhlCATIfEYDLBIMITMu2S/poJXDmdTk2OSQle0YlJggDS4YJuGzKtkHREktcloRzZJjIFF2EACVUFAgGNu490i6FTh3SZYptIiEGOU9YrlFOHhLukU4rNZsOGDRvwyy+/YNeuXahVqxasVqty2LFarQrakKASgQ3pDkPxb6ZVknWV44Jzje3o9/tVyjfpfpWfn6+sZQnDSUgCOOliJEEqQj/czj6V497n88Hj8SgATKaIk2NPD7FxbkhgQ7qpSCCQEJiE5aQ49yTAIse7TNWVm5urXMk4l+V2vp+Xlwez2azgEJvNBovFouyZ2TZyDQMKb4o5HA5VnkzpRXcsCUtJqEm//sjUfkyrxzFEAIn9I6+BYuo/jgGm/iMsKIG6UO2qB6f0cyY/Px9utxsHDhzAwYMH1boRGRmpnkiic49cTwlFMS0i+4Drl0y3xfqxHizDYrHAYrEo6IcuU3Lc8/x0PAsEAmreRkREwGq1qvakqxP7jOupTFvHOSfnvwRUubZyHEjIjGOY181/dP3hXOf6S9hLgqmcH4FAAMnJyUH9ZciQIUOGDBkyZMjQ+aAff/wRF154YbmU1bFjR427gdPpRI8ePbBgwQJ06NABVqsVL730Etq0aaPSVtA54+uvv8bGjRvRtWtXPPDAA3j11VcxcODAcqmXIUP/Ng0bNgytW7dGly5d8M8//8BqteKpp54Kue/q1asxdepU9VBely5d8OGHH6JOnTrlWqfKlStr7scUJT6Io1dYWFjQb+tQgRDp3qJXmzZtimwHoPD+9Mcff4wJIVKKlVbVqlXDG2+8gcsvvxzffvutev+XX37Bhx9+iJYtW2L27Nm46KKL8Pjjj6vtXbp0CQkB+Hw+LFiwAL169cInn3yi2fbSSy+hQYMGePzxx0uVgm7jxo34+eefARSCWLfffjv69euHKVOmwGKxYOjQoXjrrbdKLOfzzz/H0aNH0aRJE7z++uuabTabDZmZmbjzzjvx4Ycfwmw2Kxirbdu2mD17tqZdSqOWLVuid+/e2LNnD3bu3InWrVvj7bffLvaYG2+8EQDw2WefYd26ddi3bx+qVKmCu+++W+1Tr149AIUAU61atQAAe/bswdq1a5GXl4etW7eqfQkajR07NmT9d+zYgZycnGLHX2pqKtxuNwBg1apVeO+990pz+YbOQendm05X48aNKxeICCgMFm/ZskXdxy6LWrdujRdeeAHvv/8+7rvvPhw/fhzdu3fH/fffj6lTp+Kxxx7DAw88gI0bN56zAWlmdjBkqLyVkZGBP//8U63nUnfffTfuvffeCqiVodJox44dePPNNzF9+vSQqVMrWr169VKOOPPnz8fq1asruEblp02bNgEohEv/97//4bbbbiv3cxjgz/9LH6CUqZck3EIYCDjpqMP3ZYBXOjEwtY48hsFrwjtms1n9Lb8kMGBOdxOeW9ZP7yqhl/59HsPAOoO8MkDOoHFRZUqnHQnghPpbD+dIqEcPzeiD5bJNWabf7w/5xY3nYrou+SOa23ht7HNep75/ZH/qHUK4Xd+23EdCOBKKYPvqU+rI87NectyxHjyHTIUm99Ffq4RYJCzDvxmsP3z4MPbv34/09HTY7XZ1PrYhoQYCLzyXHOOyXNZBuqvox4F8LSEgWW/gJATDvmMb6+EfKQnASNcUCbFIwEKCPXrIR44BWbYeAGO5EqLjfhIEkfXlddG5JtT1yPlBdyOCLgSK9OsQAJVii6CJw+HQuMCwjtJlRjoDUYQSJVwjxx/L4fvSyYjHyPnNOnEsMc2XnFtyLnKdotOTHpLTw0tFieNB1jU9PR2pqakaiEsP88g0bmw/Xr9M90hwSz8f5LyQ4CbfJ1imnyOEwCQAqD9WXpvsU5lXV45Ptrd0lwJOruU5OTkh55MEr+T+XItZXiinKgn9cR0hjGnIkCFDhgwZMmTI0Pmm1atXo0mTJuViy52SkqJx0bjiiitQv359bNiwAV6vFxs3bkTjxo1x4YUXYtu2bQCAdu3aASh0w6hfvz769euHhQsXwmKx4PLLLy9znQwZ+rfK7XZjy5YtCAsLw2WXXYYuXbqE3G/Xrl1BEAV/+5anZOqY4lSUKw8f2JMK5epS1D1dAOjfv3+xDhvLly/HtGnTSlXPULrgggswevRojBgxAm3atNHAU1u3blXr365du+BwONC5c2dcc801AIBff/0Vf/75Z1CZdEQLpauvvhpbtmwpMuVWKMlUVJdccgm6deuGqlWrYufOnfjoo4/w5ptvlljGtm3bMHfuXCxevBjbt2/XbDt+/DhmzJgBl8sFm82mXOqBwgcmTwdycLvdMJlMmDlzJrZt24b09PQS74ulpqYiPz8fW7ZswbRp0zBr1ixVVoMGDQAABw4cQFpaGr777jvs2bNHHXvixAnEx8ejUqVKAICbb75ZAQQXX3wxWrZsGXS+w4cPw+/3a+5P6eVyuRTYZtwnOr/1/fffB7mlnY7cbne5wSmbN2/Gl19+iR9++AEbN24s0RWrOJnNZnTp0gX9+/fHrbfeqtbNevXqIRAI4O2338bs2bPVPVA69J9Lmj9//jkZ2Dd0/isuLg5VqlQJmUrOYrGgX79+xX4XMVSx2rt3L1auXFnR1Qgp+f2iVq1aCko+3/Xrr79i2rRpWLdunXpPxsPLS0V/A/uPye/3q6A6nS2kO4TevYRBTBn8Z2BXupJ4PB5NoJbb7XY7vF6v5lh+MSBcYTab4ff7ERUVpXFvCA8Ph9PpBHAyWEu3B+AktEIIRQb84+LiEBkZCa/Xi0AgoJwiXC4XrFarcrlgUJ9f0mVQXw9KyLRTeiCGAWYeHxUVFQR4yCC+HpKR7/MHr9vtDglthIWFKccYs9kMq9UaBI2wHrwu6dwjy5HvSdiEwIhMWQQU/hgnwCCBE7Yd20Kmf2L/6AEZCQSEAht4HWwX6ZYi4RYJWtFdhMcT6GD6n6ysLOTl5cHhcMBqtarUYHTZkbAby5RAmh4Ek7ASA/96wIP7s28kUES3IjrKEBSh9KnS2MbSMYpwGOccr9lkMmm+gEvnGfaxhDdk+iOCDRLukmOM5bAfOd6lOxbHVlRUFDwej3LCoiuVPA/XGc5TAj1sj8jIyKA5xmvKz89XLjbsawKGXq9XsyYBUC5BhLbYL7ye3NxclbaJbRwKIpKAC69VAi5ut1vVlemrCI5wDeM1cZ2SYKB0VgJOrlES5pLjgv+kQ05aWhr++ecf+Hw+9cXUbDarGyZcd3ndkZGRmvK9Xi9cLpcCM2NjY9W169dkgkQcexJ8lKnxOAeioqIUIJiTkwO3263aXY4jOS8iIyPVTRWLxaI+Q2QaRzruFBQUqHHEduZaJvtV7/bEfaOiolQ5cqzI85jNZgURcjxbLBbVroYMGTJkyJAhQ4YMnY+aPXs2Lr/8cjRs2BCbN28u15t08fHx2Lx5Mz799FMAwIsvvojY2Fi0bNkSH3/8MYDg1D5paWlo1KgRVq9ejfbt28NisRTpEGLI0H9dNptNORJbrVaMHTsWr7zyimaf7OxsbN68Genp6SoVV926dfHtt9/iscceKzdXktIGt1esWIHXXnsN77//vub9pKSkoCAbXYqoNWvWFAsYRUdHF5sK7KeffipVHUNp6NChGDlyJNq0aRNye05ODjweDypVqoQvvvgC9913H5o2bYoHHngAixcvRmZmJhYuXIhBgwZpjps1axaWLl0aVN6oUaPg8XgwYsSIUtdx3LhxGD9+vHrdo0cPAIUPwBG2pNtaccrJyVEpqtgntWvXxuuvv47rr78eL7zwgtrXarUqgOb48ePKZae0atKkCT788EM0atQIXbp0QUpKCgCUmCJp3rx52LFjB77++musX78eERERuOuuu3DTTTchMzMTI0aMwK233orMzEz1FDq1bNkyvPrqq2jRogWWLFmCkSNHaraHgpd27dqFgwcP4v3338fBgwdx/fXXB+1DF2mg8PNPPoRt6PySzWbD8ePHkZiYWKYA/+7du4tMGXQ6ootYr1698PTTTxe5HpVGERER6jvZc889B5/PB7fbjT59+qBly5ZYuHAhKleuDLPZjN9//x1///03br311nPmHui4ceMqugqG/qVKT0/HV199hYYNGyI+Pj5oe1xcHG655RbMnDmzAmpnqDTat29fRVchSF26dMGQIUPU6zZt2qBNmzZYsWJFBdaqfLRz507s2bMHkZGRiImJQc2aNc8ILGo4/vy/cnJyNLllJbQhQRTuk56erpx8QsE//JuQCgOyTHXF4CgDpV6vF0ePHlWOCDabTTnDELDw+XzIyMhQ6cCYtouwkAQGKPlaBs6ZboZBaIvFogLoDNYGAgF4vV4FX0gARu9Co09Npf+ffzMdjEyNo/+nD3DzHPn5+fB4PAoG0Af5GViPiYmBzWZT1yGD0gQSmBJH1ofXJesrU4IRJJHQj0yhI91+CAAxeE9owul0qmuX7h4SRNLDU16vFz6fTwNisAwJp/BvlsdrNZlMCrxh6ixCDHzahMDL8ePHsXfvXmRlZSEzM1MBCGxbeV2ES2RbSXiA9eQYk444lHS0keCTBIRiYmLgcDhgMpmU84jValXls/68fs4zjicJKtGdRP6YlH3GMUM3HdmW3Kaf63K+y/Ej56B0i2F/ASfT3/E4l8ulaVcJkhQUFCAzMxNer1eBZmwLAljsi5ycHAWWpKenIz09XQNqmEwmNW/ZPrzhw2N5PQQ+9H1TUFCA7OxsBeYQ9rBYLKoObGdCkkyvRsgJ0MKBHKeEIrOystTTTHK90Ts+yb7juTl+OWe4tmRlZWHnzp3w+XyqTbxeL+x2O+Li4hAXF6fATEJWBQUFsFgsCpZMT09HTk4OwsPDYbPZ4HA4YLPZNGAkxxzXe85Z1pdrtoTM2C+5ublwu91wu92auSTnmclk0jibeTwetU7Y7XYFeMn1MTw8XAGebCf9fONaKOco1yGz2azaTKYQ47hmejKr1YqYmBh100u6j52rtreGDBkyZMiQIUOGDJVG7777Lj755JNyD1BOmzYNHTt2VK9XrlyJJ598ElarVcE8c+fOhc/nw8GDB/Haa6+hW7duGDBgAIYMGYKWLVsa0I8hQ8XI4/FgxowZAArds15++WUFYeglnVsiIyORmJiIpKSkcqtLqOBYKDVs2DCk4zkfQpLKzs5Wf/fq1QstWrQosXy6z5S3EhIS4HQ6MXLkyJBr5ejRo7FgwQKsXbsWixcvRrVq1QAAHTp0wNChQwEUpqU6cOCA5jim5tJrxIgReOmllxSwU5KaN2+OCRMmaPohLS0NgwcPxoABAzDhNNObMdXJ+PHjlUMb9eeff2LChAn47rvvUL9+fdSoUQMXXHBB0H7Fic4JKSkpCvq57rrrSkxBWbVqVaxduxbr168HUNg/hG5at26NF198EfHx8bjgggvQvXt3dZzVasWNN96Iw4cP4+jRowCC0zqFhYWhSpUqQefcsmULWrdujbZt24YcZ3l5eTh69CiOHz+OyMjIkGUYOj9Ur149XHTRRWV29YiKiiq3VF9SK1aswPbt29WYL6sqV66MCRMm4KqrrkLHjh3RtGlTPPHEE6hZsybsdjvcbjemTp2K6dOnq3lTkdqyZUtFV8HQv1yTJk3CmDFjQs4xh8MRMg2YobOr+vXrl+p74bmi22+/HfXr11evPR5PSFep81XkCfg7/0zMEcPx5//l9XqRnZ2tLFzz8vI0MIKEQSQYY7FYghxwgJOBaEIT0u1BQhsWiwUejwdut1sDMUgXFxJfdGohuMH3GKTl/4RKJDwgU+3Q1YeOEQAUBMT0NQBUMJfXAGidPWSbSMcK1ku6ufA68vPzFfzC8mT7AoWBZp/PpwEQCA3wiQBJwUnHoYiICBXMjoqK0jgXMYgvIRQG2fXQhvwnoQ/pXMKAuAQYQqXk8fv9yr3F6XSq/uM5JaAkQScJP0k4hddIoMxkMmlScEm3FovFogAGmS6JQA/hGLqEcDww+C9dawhAEBaRbiMSAJKuI+xP2iLr+1m+xzrKsZafn4/MzEzY7XZ1rQUFhc5VWVlZ6rrZNtIlKTo6WsF5BCzy8vLgdrvhcDgUdS/ntB7+keAQ99PDWXrQR++ypa+bXCc4F10ul4I49OsNwS26L/G8bCvWlfOf/cJ5wb7m/tHR0Rq3pkAgALPZDI/Ho3FMkv1EYEOWZbVaVTnsG958km5brAef6OLaw2tgeQUFBQrIA6DmCevJ9qeLEYEsCc8Q0svJyVEpDOmgA5wENrl+hIeH49ixYzCbzUhOTkZYWJgCKrOyshAXF6ec2+Qa6XK5EBMTg0qVKqn6yPaWMBzbMyoqSq3/7CtCgTIlHNtYDzexTL/fD4vFoqy9OQfkHJXrnwS1ZDoyWd/8/HwF9fAY3lyUYBLXNumgJlO25eTkwGQyqTaXYBw/V8t6I8CQIUOGDBkyZMiQoYqSw+HAggULUL169bNyvmXLlmHFihUYM2YMpk+fjkqVKsFkMuGXX37Bk08+iYSEBIwZMwZZWVl49dVXz0qdDBk6n/XII4/AYrHg/vvvBwB8+umn+P777zWuLADw2GOPoXPnzpg0aRKWL1+OhIQEjBs3Dj169EDnzp3LVIevv/5apbQqST6fTwP0SMnf1uvWrcN1112nXlut1lLBiXp4qKxq164d/vjjD7z66qvo06cPhg8frnlYiWnSeW596gaLxYKhQ4figw8+wLXXXovY2FgAhamqoqOj0alTpyLhn4suugjz588vsY5t27bFm2++qRydqJEjR+KTTz45jasOVt++fYMgpC+++EL9/dVXX8Hj8eDKK68ssawmTZqgT58+2LJlC6pXr44//vgDzz77rNq+dOlSbNiwAbVr10ZMTEyRDiOrV69Wf+fk5GDBggW44IIL0LdvX7Ru3Vptu+qqq9TfH330Ea6//npcc801+OOPPwAUpgXRq3nz5jhy5IjmvTVr1mDAgAFISEhAw4YNg1K0/fTTT2jfvj0aNGiAuLg4HDp0qMS2MHRuauHCheVSTr9+/fD3339j4sSJ5VIe5XK5sH//frjd7nJLK1evXj1ERkaidu3aAArvndOV0Wq1IjU1FQ899BD++OOPcnOKO1UdPnwYr7/+eplSNhoyVBpVrVoVTZo0CfmdIjIyEtOnT0dSUhKmTp1aAbUzNGrUKFx00UUAgJkzZ2q+D5yLuvnmmzXfRYBCx8eXXnqpgmp0ZnTs2DF8+OGH8Hq9+OCDD8q9fAP8+X95PB7lqhMqdRH/ud1uZGdnIzo6WrmeSBCGImTCQDXhGemwQxiCzgyxsbHKqYaS0AGBjoyMDISHh8PhcCiogOUAJwP1rL90xZApXgikAFCgBoPHBDhkgJjnkMAGg8YMBBMuYQCe55UOIKw7cDJIzv34v9Vq1aTIIZCVlZWl6i1hJgIRubm58Hg8sFqtCAsLg8PhQHh4ONxutwr2y9RV+rRBEtCQDjQS+mAgXNZdDx9FRUUFBdfT09Phdrs1dZfnlIF+CY8QGLNYLMoBhvszDRRBIv1YYL9Ipw2ZToqBfv745vkJ2jBVnXS7IZxmNpuVo5EElhISEtS2jIwMzRiQYAvnjr6dJXxAhx4CIDx3IBBAfHw8XC4XwsIKU7wxvVt6ejry8/MRExODrKwsBctJ2E0P7+Tm5sLpdCrIg+eWc1C6R/FapWOOHmjgNul6w7EggTTpKiXdtPTQm1xfqOzsbA20Icet7G+2r9lsRlJSEtLT0zVjhMCLxWJRqZx4nNls1kB4BQUFqj/i4+ODnHj4JI9Mbcf5p4fn6OLEtpCwZVpaGnw+nwZg4VzQr3Ec8+xjub6xTtnZ2XC5XGq8s73y8/MVMcz/CwoKYLPZNE/MshyTyYSkpCTExcWp9uIxMgUf32NfEo7Sp6lj/0dFRalUhRKi4/X7fD7VBpw3nGfyetge8rOEY57tRyiHazavlWNernfSzUzCd/xMku3NsvlajkG/3284/hgyZMiQIUOGDBk6r+VyuTB58uQzUnbz5s1xyy234MEHH1TvDRs2DNWrV8fBgwcBAC1atMDnn3+ODz/8ENdccw06dOgAh8OBwYMHY968eTh06BAaN25catcLQ4b+i3rggQdw6NAh1K5dG/3798c///yD5ORkHDt2TO2zevVqpKamYujQobjxxhuRlpaGv//+Gy6Xq0znbteuHXr16lXq/f1+PyZOnIjly5cjNTVV8z7v6QDAr7/+qgEvpBtxSZo4cSJefvllTfmnqyuuuAIffPABnnjiCSxatEgF8A8dOoTXX38du3btQvXq1dGsWTPcfvvtIcvo0KEDWrRogRo1asDpdAIA5syZg+XLl+PXX38NeczDDz+MRYsWFVmvyMhIvPvuu9i3bx+uv/56NG/eXG3z+/148803yw36AYBrr70Wa9asCbnt6aefRsOGDfHnn3+WWE7Dhg3x2WefoX79+sjNzUVGRgbGjx+Pv/76S+3j8XjQtm1bNGjQAI0aNcILL7yAxo0bq+28N7Ny5Ur1Xnp6OsaOHYtKlSrhiiuuUO0MALVq1cLAgQPRrFkz3HDDDQCAmjVrqu2hUoKESlm2bNkyAIUPPLZp0yYI/KF27NhRYjsY+m/I6XTilltuKRL8kffYT1WrV69G586dy9W5jdAPxfv+jRs3RnJyMvbu3Yv3338fjRo1wgMPPFBu5y2tqlatCrvdrmIzhgydKTVu3BgtWrQoEia22+3o0aOHAf6cZdWtWxeDBw/G8OHDUblyZWRkZCAqKgp///030tPTK7p6IVWpUiVcfPHFiImJ0bx/plyjunfvjn79+uHAgQP4888/i/0uWd5atGjRGT2fkerr/0VIR6bZ0cvlciEzM1OTWotBTP3+MgCvhzwYbGYQFoAm5RLLoyMGvzjwnEw3Q+gm1D/WgYFvmWpGpleSjgz69uCPSJnOTDp4sJ7ynAwG85++rQhLESrQAxgUA96yHZkmSKYEkilp9OAFg9oSmtL3SShXDVkf+a84pyP9cRIokv1BuENCSwA09dGXx/1lX0jgSgbp+TcD9PrrYr+EghP4LzIyUpM2Sm5jm8qxpAfeJKQgXZfkNYVqQ3ktch85hlg//m2z2RQ8kp+fj6ioKFitVthsNuVII9MryTpKKIN9FggEQgJdcjzI69X3k/wXalxKRyP98WwnuVbQ7SbU2ASg4EFKv3bJY3gc0+TJNiA4Rsm0cnIcSIhPAjgyXR6dX/hPOuzIeSQdrICTDlBhYWGadGZy3OnnjbxOQj90mCKkw7WET8nl5+crhzWZnkoP1DHlGOcar4cpzeTaLD8L9HNYlqevu5yH7BeOQV4X20yWL0E0jg+57ujnqUxlKMewdCeTY4/1lmNNP7bpYCTdv1gnvXuZHLcG+GPIkCFDhgwZMmTofNaZAn8uvvhiDBo0SBOAXb58OcLDw3HixAkAhY5Db7/9NlJSUjBs2DDUrVsXQGHQ9ZJLLgEA3HbbbWekfoYM/Zs0ZcoUvP322wgPD0eVKlUwcuTIoH127tyJDz74AMePH1e/u//+++8ynddut4e8B1uUYmJiUL9+/RLT+OmD4QMGDChVGq+EhAQMHjy4XFwwqlWrhhYtWqBatWqoUqWKeuCT2+rUqYM//vgDb731FmbNmlVkOREREWjfvj3Wr1+vrnvTpk347LPPcPz48ZDHlBQ4adCgAXr06IEhQ4bg4osv1mz79ttvgxyfyqLBgwcXG1CLjo4GAOVmVFJZTHWRl5cHj8eDnTt3htx3x44d+PLLL4MAm++++w5///03du3aFXRMSkpKSJjtjjvu0LgRyfGXlZUVtH+o1HV79uxRf1euXDlknQ0Z0ovfa0JJOlOdqnbs2BGUOvBMyel0olOnTur1kiVLNOkjz6b0zmaGDJ0JxcbGIi4ursjtHo/nnEh791/TgAEDcN1116FGjRrqQfZmzZqhQ4cOFV21IpWamopjx45p3C6PHTuGuLg4zffKsuqKK65Aw4YNERsbi/bt22PIkCEhIebzWQb48/9iEFOCCJTf74fL5VJpg6KjozXOI4QO9GIKlEAgAKfTqVLCMEhNZ5WIiAiYzWaVHoUBaBkkZ1l0tKGDgoRr9P/L43mNUVFRKtBtNpvVPxnUZTobnoeBf16nHnaREAodYGQKGz2MQBhAQigSlJAuFFFRUfB6vcjIyEBWVpYCFCQURLCHbQZAtWNmZiYyMjLgdruVAwiBKz1good69Kl79JAL4RoJJwBQ7h5+v1+5d8gAv3Sc4Xu8JpkKTp6H49Dn82nS5RAkkna5epCH5cv0XQzY050IgHK7ysnJgdfrVXUniMDrledzOBwqdzfHmtvths/nQ0ZGBvLz8+FwOOB0OhEVFaWACdnuEvzh36yrHnDiGI6MjER6eroGLsjNzYXf74fdbofH44HH41GgDSEg6Zwk0zDRYYZgh37usF8lgKW/Bh4jXVI4bpi+Sd830gmL/URQhOmy9H0p3ZrYD4QtpPMT5zjHFOEdugRJkM3v9ysnF9Y5MjISZrNZQSt8zXaMjY1V64fJZILdbld14XrFfpAuWBIqYZ+yPwjbpKenqxsL0jlGD+PJfmL/0OHJ6XTCarWioKAAf//9N/bt2weXy4WcnBwEAgFER0cjMTERCQkJGrepsLDCdIhM48i+JLxERyumYAsF/RDstFgsam2XKR7l03cc41yjWE5RQI6EtyQMStiNwJPZbFbvMS2Yfh0i8CRdw/Rrhkz/xnFDYMpms6n3vV6vWiukC5wsRw+OGjJkyJAhQ4YMGTJ0vsnj8ZyRcmfOnInZs2dr0prv3r0b48aNQ8eOHfHss89i4cKFsFgsmDlzJnr06KF5aIQ3SHv16oUGDRrg7rvvRsOGDc9IXc9VNWnSBNOnT9fAU+eyfvzxR0ybNq3UaZ8MlZ94n9FsNuOqq65Chw4dlAOw1CuvvKL+vvzyy7F27VpNSq1TVdWqVU+7vlIyhThw0vUbAO655x7079+/VOXS2bg8gik33XQTevfujZiYGAwbNiwoJdrIkSOxcuVKDB8+HL/88gvmzp2LrVu3apyWgMKgz/79+1FQUIATJ07gxIkTZU6BsG3bNkydOhU//fQT1q9fr3E3Wrp0abk5YnTt2hUzZ84s9oGnffv24cCBA3jooYdKLK9Zs2YAgBMnTuDYsWNo3749VqxYUewxMt3Zli1bcN1116Fly5YhAZ/8/PyQqbu6deuGli1bAihMTSdBLQn0UH369MHXX3+teS8zM1N9LrVs2bLcUiwZOve0atWqcivLbDaHfL9Dhw4YNmwYbrnlltMqd8eOHdi6detZcbmwWCxo1aqVgi+XLl2K559//oyfV+rPP//Eiy++iIcffvisntfQf1MLFizAlClTitweHR2N66+/HjfeeCNatWp1Fmv235bX69V8VwQKAcr+/fuja9euFVSr4pWXl4elS5di69atSElJwYIFC/DEE0/gueeeK7PrJlWrVi0MGjQIv/zyC55++mlER0ejWrVqMJlMpYKyzxcZ4M//SwaMKQZoo6KiEB0dDafTiWrVqiE5ORlxcXGIjo5WkICERSQ0wlRTgDZ3MoPFDNBKZxoCATI1Um5uLiIjI+FwOOBwOJCXl6dS13i9XhVg5z+mBmKaIDrkmEwmDfzD19LtRaZ2Ibwj68FyCaKwfAmyUCyfwWMJL0jnnVAuKGxbv9+vgsryfQIG+rRd0t1HunUwmM666101CAURbpGpsDgeZDtJ9yR5/TyWAXmCXdItg4Fxti/rL8EcQiYcKwQ39M4whMs4dli+BKLY57J8tgshDaZiomMP25EQiYR/eF6WSyAnIiICLpcLJpMJNptNjaOoqCiV6qdOnTqIjY2F1WoNckKR0AFhEQk9MFUUb2xyDnAMcy5brVYFuNABSDrSSLFNc3JyYLFYFNAix6OETGQKLgm26UEe2W/8R4hFD4MBhWAeQROTyQS/36/6hWuMPk0d1w2eRz8WZTvyw166v7COhKQIZfEYwj4cjwSSCHtZLBbVnuwr6fRDCEwCILKfJDRF5yGXywWPx6NxQuL6J52xOBZZZ5bNOmZnZ8Pr9SItLQ27d+9Geno6UlNTYbPZFFQYGxuLxMREVR4hSwBqnZFzKhAIqDaRzj4S2DKZTJoy2I7S4Y1zi3Cg3rlJrjHyBh7bwu/3q/ZwOp2IiYmB1WrVgFjS0Y3zU8KBMjUk3ZX040K6FknnNK6RvDaOVf24Zl/IfWQgw5AhQ4YMGTJkyJAhQyf10EMPBTl7zJ8/HwsWLFBBdD6AI3X48GEsXboUffr0QaNGjfD1119jzJgxQc4W/3YNGzYMgwcPPm/An8aNG2P48OG4//77K7oq/0oNHToUV1xxRZHbDx8+jEAgAKAwjd769euLLW/v3r247rrr0Ldv31MeYzabDc899xyqVKlySscBhQ9P3nfffejWrZt6jw8rUR6PB+3bt8ezzz57yq5kfCj1dMV0DJdffrm659CqVasg8AcoBJ/at28PABg0aBAuvvhiPP300/D7/XjjjTcwd+5czJw5E9988w369OmDGjVqICkpCW3bti3y/Ndccw0mTJhQYj2fe+453HHHHWjXrh06d+6MtLQ0jB49uthg5akqPT0dXq+3WJCKD7rt3r272LJiYmJw4YUXAgD279+Phx56SJPOrSh98cUX2LdvH7Kzs/Haa6+VuP8rr7yC++67T/N0vZTezYepJ6WaN2+Onj17omnTphqHEaadbNiwIVq0aFFiXQydnyqvYKzf78eXX34ZcltYWBj69euHu+6667RdbLZs2YIFCxZg06ZNZalmqcRsHdTs2bODAM4zqcTExCCo0pChM6kPPvgAb7zxBlJSUkJuT05Oxrhx48o13Z6h4rVnz56QsO6VV16Jfv36VUCNSqfNmzfj+++/xyuvvIJ7770X77//frmW37dvX3Ts2BFJSUlo3Lgx6tSpA7vdruJq/xYZ4M//i4CJz+fTwB0M4ubm5mpABT3IIp+2YFCVjg8M4jMATjFwTDcSAiD6NE38IcryCSllZ2cjKytLueG4XC5NkD0QCMDr9WochOR16R1/COlERkbC6XQiOjo6KMWSHkAgaBQqzRjLY8CZwWLpeKR3M2H7ASdde9xuN9xut6oDQRfuI2GOsLAwFdSX0AVw0haVzj8S3qALiIRwuI8+VY0+lZOss4SfCNLIdEgysC5BG+mqIR155D89MCED9zw3oQ/2LwEAmbKLDi+Ek6STU35+vnIHIVBCBxSm2PL5fPB6vfD5fEHwFwDl/BIZGamcsVwulwIQsrKyFPRFMMLhcKB69eowmUyIi4tTbWU2mxEbG6v61GQywel0qrZLS0tDZmamagOCNRwrXq9XA8VwP0IaPIbgBd2hHA6HgkD0bjwEYHiNethNwiMyTZZ0B5Jjl4AHb9pIJzGOc6/XC7fbjfz8fLWf/rxFOXHJcS4dbNhOcoxK8Co8PFy1J8eR1WqFw+FAdHS0Borj+dmWMuWcdG7SrwP6uc+xxXMReuL4k25GXKv0jlcpKSk4duwY9u/fj2PHjiEjI0P1PduGwJrFYtE4DuXk5GjWcgm5cf1n38g+1YOM0vEGgOpj6Z4m3dm4hsp1RIrjvaCgQIFlTFdGVyHOeTp4SViL5ckngnltTNvG+c15J92tuE4wrZccIxJYpVtYIBBQ6xnXXbaPBJkMGTJkyJAhQ4YMGTrfVKNGjbN+zjlz5qB169Z48sknUblyZRVop+bNm4f8/HzcfPPN+Oeff7Bq1SocOXJEBV3Pppo2bapcKs62HA6H+i15Lmv48OHo1q2bCn507NgRN954YwXX6t+liRMn4sMPPyzRoWvYsGHIyMhAQkICGjZsiMGDB6Njx45F7h8fH4+UlJQiIYmiVKVKFTz44IN46aWXTuk4oPC+xLPPPqsJIlerVk3joDJhwgR89913ePzxx0+5fODkfcXT0csvv4xff/01ZLsdO3YMd9xxh+Y9mUqhRo0aGDFiBLKzs7Fu3ToMGjQIY8aMAQC89dZbyomnqBRfAHDhhRfioosuOqU6b9u2DTNmzDhlSMpms2Hu3LkYP348br311qDtTE9RqVKlIstIT09HdnZ2sSklhg8fjg0bNqB+/foYMmQIFixYgE8//bTU9Zw6dSrWrl2L2bNnl7jvZ599hjfffBOHDh0K6VSUkJCgeV0UTBAZGYkff/wRd999t3qPa1yNGjUq7HPB0JnXFVdcETIF3KnKbDajXbt2RW4/evQo2rRpg759+55W+V999RXuvPNOXHfddUEp8cpbderUCXJdPBtuQ9SmTZvw22+/nbXzVYTuuOMOTJgwAbfddpsBFp4DcjgcaNq0abGOKbt378ahQ4fOXqX+46pRo0bI72ZVq1ZFy5Ytz+l5M3/+fEyaNOmMpIjr2rVryLSSzz77LD799FM8/vjjZQLizxUZ4M//iylmpEsCA9SRkZHKxSI/Px8pKSmaAD4D5oQCGJxloJOAgwwQS0cZoNAtw2KxaFKrSFceACrQLJ08GFhlyifp6kMnIAlwyLQ4kZGRyqVDAjo2m00FxGUaIQAKZGLAWqbzkpANwQjCEQy0M4DPgLGEaCS0wACyy+VCVlaWJj2MTOuld7hhGxNKkSm+pGOR/DtU+iC2t76dJVRBpxzpHCTrQLiC77NvOZb4T9/vckxKAImvZd2kI4/ZbNak+pF9nJ+fj+zsbE0qHrqoyDRyYWFh8Pl8CqqQIIPZbIbD4VDpv3w+H06cOKEABNbZZrMhNzcXFosFiYmJKt2QzWZDTk4OMjIyFHhksVjgdDqRlJQEm82mrqFq1apwOByw2+3qejjuMjIy1M0bvUMTnbD0c0wCERLmIbhE5xy6unAtkONSP7/k+JZAhXSPYhmRkZGw2+0aGIRlm81mxMTEqPaWoBHnWXh4OKxWqzov9yE8ok8XxvFNAENCGgSB9KmkuI8E1iRsRsCEN1EJgfB6mBpQOkmxTD0AI9uG8zcvL0+BinI/pqiSUJ1cX+Sam52drUDIo0ePYvfu3YiOjobNZoPJZFIOXEwDRkiLYIrX61X9ybnJ6+b1pqena9J2hYWFwWKxaPqHblfSDYj9LeXxeDRrGNsdgGpLgmB05eGcpIuZdFSji5AEyugQJP/xs4nAI/uTY0t+vgHQQIoS3CH8yfpJoJGAlcPhQGJiImJiYmC324PawJAhQ4YMGTJkyJCh80mLFi0q1/L0EE8orV69GosXL8aDDz6ISy65JMiBgXXq2rUrtm/fjqFDh+Kyyy4LmcLlTGvAgAH47rvvgh5UOZMKCwvD22+/jTvvvBMRERHlkraovGWxWDBkyBB07NgRI0eOxNdff43vvvsOM2bMgMViMcCfclZSUhI8Hk+Jc2DRokUaMGXWrFmoU6dOkftHRkbC7Xafcn0uuOACjUPPqcrlcuHEiRPqddWqVTWu8mFhYYiOjj7t8keNGhUEeJRWhKVCuSC9+eabmDZtGjIzM9V7Ep5899130bRpUyQmJga5MzFYdezYsWL7cfny5WjZsiXi4uKK3OeKK67Ajz/+iHvvvVe99+CDD5Z4bXoNHToUAwcOxMSJEzF16tSg7UePHkVqaipq1qxZZBlfffUVrrzySsybN6/Ic0ybNg1169bFU089hZkzZ2LixImnVM+rrroK33zzzSk5Lnfv3h0vvPBC0Puh1vKiUpklJSWhXr16uOWWW/Dkk0+quWS329G0adNS18XQ+acDBw6USzmVK1cOOX9+++03fPfddzh48CBuv/32Yl3AStK+ffvQp08fvPfee2WparG69NJLMXbsWHTp0kW9VxzAWJ5as2YNrrrqqhJd7M431a1bF6tXr8ann36KiRMnYuzYsXjqqacwffp0/Pjjj/jf//5X0VX8T8vlcmHq1KnYuXNnkft06tQJl1566Vms1X9XnTp1wsiRI4t0WAoLCztra9Lp6Ew9vNKuXTs0b948yNSDat++Pcxmc5m+U58rMsCf/xfBCvm3BGUIFeTn52us+fSQiAyyS3cXPaCiD7gz6E+AQsI2sl5AsLOH3jWDxzP4LqGZUHWWLiayHHk90o1COmfoX4dqA5YtHSgkVBFKBEsYpJbBaH0qI+mgIgPP+mvQO9vI9pd9rW8j6VgiJd059O48brdb1UG6ZfBv1lGmDJPHs26sk3Qc0o8d2V8EU6RjlNwur12CFPI1nVs4Tun+IVPSMe0cwTHpKiQBJbYBAST2E+tAFxim2SNII1M76dMTybaV1yrbTroh0cGEAAfrTEBFuh7xWDpnhXLBkY5M0kVIpucLCwtT8JQEBEM53XCO8nwy3R+ldxiT9ZJzM9T4lWObcIZcg/TzQJ5PwjnyeuW52Td6dy2Z0kx+mMp2lGOQaaBkaikJYcrzyfaU52RqMXkDTKYrY6ouvfuYHAtcZ+RnAPtR9hXrThhKPlUaCgaTYjkyBSH3pSQoKt2EZFmyLhKo1ENC+nJZdzk+eX7ZJtK5SPaxhIDk3Al1jSaTSZOGjJ9xhgwZMmTIkCFDhgydj4qIiChX5wIZiC5OGzZsQEREhCaIRG3fvh1AoRuJvIlbXuk3TkU///wzUlJS0KZNm7N2zoKCAk3Q/1y0aR89ejRuv/12jBgxAlWrVsWePXuwcuVKbN68GQCKhU0MnbpiYmKQk5NTKheKBQsWaO7zlgRL6FN1l7Y+ZZHf79e4F5Un3BYfH48+ffqc8rypUaMGevToEdJhy+v1Ij8/Hz/++CMArdOFBIRk8F66Y1SpUgVt27ZV6dmL0549e/Dzzz8rd6BQopNIWR9EknXMy8tDx44dUbVqVfVeQUEBsrOzS+zv1NTUIrf5/X6VqutUHYkAIC4uDjVr1iwxlZhehw4dQkpKSlA6olBOQ8U5OV933XW44447cNVVV2neP500d4bOHxXncnWqKsoRZNeuXTh06BDWrVtX5oD1vn37MH/+/GLnYlnVpk0bzRp3tqBku91eZFD7fFbz5s3Rrl07XH311ejbt6/me1NcXByuu+66CqydIaDw+9QXX3xR5PbKlSvjhhtuOHsV+g8rKysr6EERqcTExP/kw9m1a9cutl1ycnLg8/mKBJzPJ/37PgVOU2FhYbBarRr3Az2QQWeNSpUqaQLW+mA84QUGXuVgkWlPGOCm2w6DsHqnESAYapGuLRLSILRB1xIJzgDQBLOZeolBWb3bDa9Jpq6hI4oEBEwmk8YRRf7PdqRLCsEL/s1rlgsNnUrcbrdK1cQgM39chAJi2GZ6sIXXSUBEBr65n9xflisD3vrxQoeMpKQkFTAn3HPixAn15ZHl5eTkaFyI4uPjER8fr8aLdI9hObIP9A5Tsi5SEnyS10rXGKYsknCFHkTh8XRLiYqKQm5uLrxeL2JiYlCpUiU1ZpgWLjY2FnFxcaqvXC4XMjMzYbPZEB0dDYvFogCh/Px8ZGVlIT09Xf3LzMxEeHi4mi8mk0m5+xBSAAC3242MjAzlMqKH6cLCwjTzmNdus9ngcDjgdDrhcDiUMxTLCeWuFAqIkTCK1WqF3W5XrmC8AUTYyGazaVI8Ma2UXkzbJ+cZ3V+kowu/uEswJyoqCna7XdOP+vnAfuY41KfG442XgoKCoPHIccf5SniPbj9cEyRkyHmqh2w4tkKlvkpLS1Ptwb5mH5jN5iC3GQmEhYeHY+fOneopMDoBcb2Ji4uD3W6H1WpFbm6uWg8AaNzQOKZlGjGPx6PWHgI2ci30+XxwOp2IiYlRfUXHNLp5yT6nKw/XB6aF5PWyPeTc5rWwD/VuadyHa7lMgSbdk7gGEkQk5CPblnOUY0WuJxKs4xch1kXuz3bhDVN+DrBtDRkyZMiQIUOGDBk6X/Xcc8/hs88+K9PT5lKPPfaY5nXDhg3x6quvBu337rvv4rvvvkPt2rU17//+++/IzMxEly5dsGvXLvz000/lUq/T1Q8//IDOnTsroOVsSbqwyBRIFa2qVavinXfeQZ8+fXDppZfi5ptvhtlsxsCBA/Hqq6/innvuAVCY/uimm25Cq1atKrjG/w5VrVoVaWlppU7JtW7dOgBQ91CLU0npw86Ejhw5ogmGx8TElKujl9PpPKXf6iNGjMDKlSuLBFOWLVuGQYMGYc2aNQAKA/YUnz5/9dVXNcBe69at0bNnTwCF18tjZIqzUEpNTcWoUaOK7bdFixahT58+WL58eSmurmhJpwKHw4GvvvoqyMlp9+7dIVNIlFZerxebNm1Cz549kZ6ejjfffLNYByG9rr76atSqVQt33333KadwI/xD/fLLL3jyySeD9ituDlSrVg2dOnUKStnUqFGjYs/tcDhOKxWeoXNDQ4YMKbeULEUFXL/88kssXrwYjz/+eLmsfz/88AOeeuoprFu3DidOnFCxhPLSxRdfjNtvv125mQ0fPlzjfnYmlJWVhdWrV4d8QPN8V3p6OgKBAGw2G+rXrx/0YGmtWrXw+eefV1DtDFE7duzQOBTqdeGFFxrfdc+C/vzzT/VgCBD8uV2vXj0cPnz4bFcrSAkJCWc15Vjr1q2LTeOVnp6O1NTUU07pey7KAH/+X2FhYfB6vSqIzICp1WpFdHQ04uPjVYBfnyKHx0tJ8IdPTdDFw+v1IhAIKCiDKb0YkM3Ly1PuCFarVblYBAIBeDweuFwu5Xoi3VWAk1RaRkaGCspKNxHWmamhGBCWMJAsTzqc6NNjsVzp+iBdQfQpawgpSNcVINjBiOBPdnY23G63xtFEOl9IYEgPaeTl5am0VjK4TZcN/mPdZQomfboavUML60gYhVATg+cZGRnIzc1VhDX7wO12Izw8HHa7XYEwslyOGXmtrDMD+XpHFu4n6yHHqYSlWF8JpHAbf+ATVAgEAjCZTKq/rVarGpdWq1VBBEyXRAAoPz8fHo8HaWlpCubJyspCdna2AtUIDfALGtNtER4hQEPggh9MBDOYdqmgoABJSUnqvOxbACqlEK+H2/x+v3L/4WtCPBzT0qFJgmkELiTcZjabkZSUBIvFohljHIPsT8J4cs2gpIOVhD7Yz/r9CU/oXWJ4TunyI+vPdHUcO/wRwDSEcq2gEw1lt9s10JKsq9vtVnONkJsEkySkp4ea6EKUlZWlxivHo7x2maJOznWOf66Nqampam2NjY1FVFSUSuXGfvd4PGocyDbjmuhwOFS9COlxvDkcDpU2TMJYVqs1CGBkO8lUdPn5+XC5XJrrYZo2CUJJVzS2B9clOWbkuqGHNSUYxnR5Eirk8VzLJOAjx58EWeV59fsxPaPf71druLxpqh/bhgwZMmTIkCFDhgydb1q1ahUOHz4Mt9uN6dOnl0uZe/fuxTPPPKNeX3DBBRg4cCAGDhyo2S89PR3vvfeeJpizb98+3HXXXZgyZQqef/55HD9+HD/88EO51KssSktLC3KNONOSLixlSalU3ho4cCAGDx6MSy65RL03depUbNmyBT6fD3/88QeAQvBi2rRpqF+/fkVV9V+lDz/8ENdee22p96cTg8fjwf333x8E2FEWiwVNmjQ55bRFCxcuxP3331+sK01xateuHV555RX1+tlnn0Xfvn1Pq6yiFBsbW2wwRColJQXVq1cvcrxWrVoVn3zyiXr9119/qb/DwsLw0ksvYcyYMZpjIiMjMWrUKOWgwwek9PuFUp8+fbBw4UJUrly5yH1++OGH0wYS4+PjMWTIENSqVUvzflRUVJAj0ZYtW9CoUaNSt6XU5s2bUVBQgM8++wwrV64EAPTr1w+jR48udRkMqLZv3x4PPPDAKZ3farVi06ZNKlVLqFQX8fHxp7XG1qhRo1hXmN69e+Pmm2/WOCgZOn+0ZMmSUx5vRal///4h3z9y5AgmTpxYroDOlClT0LZtW3Ts2BHTpk0rt3Kp+vXr46233sLy5ctx/fXX4+DBg+V+DqlHHnlEk77y36JBgwbhxRdfLBFQ7dChA+6///6zUylDIeX1eotML+Xz+fD7779rYGBDZ0YFBQWYN28eNm/ejOnTp+Pll1/WbN+4cWPFVEyoR48e2Lx5M3766Sds3LgRS5YswXPPPXdGXVBLcu08ePAg1q1bd9Z/x54JGeDP/4sB0piYGA0EwyA7XSOYLgjQptORcAAAFSQPDw+HxWJRLhPyOOnKQrCDoAKdgAoKCuB2u5GdnQ2/369Jv8N96S7Bc3s8HmRmZirHBZmeRToHEbTQu51IkICBYgkFMWick5OjAACWQ1BEwjY8LwPM8lwyiMwyc3NzkZ2dDZ/PpzmGxzH4LlMJsb76lFmEdCTIEyplkUxbQ9cTCRfR5US6/zCon52dDZfLpdrZ7/cr0AAo/MAjVAZAA8xIuEKmIiMAQTDA6/Vq0gwB2vRM8tr5w7NSpUoqJVd0dDRMJpOCHAhgsd2p8PBwREdHK1ApKysLmZmZCj5zuVxISUlBenq6BvjIy8tTc4MOUDk5OfB4PIqUPHHihHoKj+NGOpgwxRbHpx74kn0BFFrS8QenTDkGQAFK4eHhmpRkvG6CCnSf4TVERUUhKSlJOWlJeEyOJUJUOTk5yM7O1oxPCaswZRgAdU1yLMr9WUeuB3RmojsNx6DJZNLAQhwnPAfbS8KIdKGxWCwoKChQa0N4eLgCNVgPCZfk5ubCZrPBYrEEOczQAYofhKHANH3KQenwo18n2E68Tp5LD51wvLMd2U+cwwcOHMDhw4dRuXJlWK1WZGVlIRAIKODRYrEoxye2G9+3WCwawIhtRGc0jle5dvh8PqxevRrr1q1Damqqqhdd5CTgSKcqwkk5OTkKOJLrtHS4ksfzOnlewp1y3unbjvNSAqbsK7ppcdzJusmxIyFOjn+2m/zRJe3HWX99aj9DhgwZMmTIkCFDhs5X7d69G/v378cPP/yAGjVqYOLEiWUuc/To0RrL8SVLlmDIkCG499570atXL82+3333HRYvXqxeT548GevXr8fIkSNx8cUXo1WrVmjcuHGZ63S+6bnnnsPgwYPV63PBZfSCCy7AmDFjUL16dQWVeL1eDB8+HM8995zar1+/fjhw4AAA4Pvvv8e8efMqpL7/Nr3//vvYsmVLqfe//PLL8cADDyAuLg7t27cv0pUhNTUVnTt3Pq0AcVhY2GnBIFT//v01ac1TU1Px0UcfFftk/alo2bJl+OOPPzBq1KgS96UzT1GqUaOG5vWaNWs07mYPPfRQyOBLr169MHbsWDz88MMYO3YsgEIHm5IUFhaGevXqaVJxladatWqFGTNmwG6349VXX8Vnn30GoHCtkSmsRo8ejccffxxVqlRBvXr1Sl1+ly5d8PPPP2PJkiXw+/346KOP1LZKlSph/fr1pS6refPmAAoBzFNN9zV79mz07t1bBQR5/0zqiy++CJnerSRZrVZ069atyO3fffcd/v7777P65L+h8tW8efPw8ccfl6mMY8eOlbvzTmm0c+dOLFiwQON4VV5q0KABOnfujPvvvx8XXXRRuZcvJV3U/k26//77S+W06ff7ceTIkbNQI0NF6ZNPPsGLL76oceKktm3bhtWrV582BG3o1PTOO+/g9ddfx8MPP4wtW7aoubFo0SLNb5GKUnZ2NgKBAGJjY9GsWTP07NkTrVu3Rvv27c/YOR9//HF89dVXIbcdPnwY3333HdLS0vDUU09h3Lhx6NWr13mbjtkAf/5fUVFRiI2NhdPpVOm5aCcqAQS9S4t0nZCQQn5+vnJZIKRCGMThcKjyGfC2Wq1wOBxBTg50JSGcwICt1WqFxWJBfHw8YmJi4HA4FHjDNFk+nw/Z2dkKTCE8QuhHpnFhoFkPLxCmYLoYOmswYM02kA4jTIXD+siAtkwHpHf6IQTAuvJHoN41RMIX0iVJggXSoUVK9pO+zgxU652KpBsKARuel3CM3pGEbjoSaiB0JOEFCSDpHYh4PulCwzZkH/L6ZL8VFBQgLS0NGRkZ6ppiYmLUDQafz6dx0QFOpldjYJ/gWk5ODjIyMrB3716VdovQCOvhcrlQUFAAu92OzMxMNZ70LjK8Vl6/HqLiWKMbjNlshtPpVJBUfn6+chqKjIxEZmYm/v77b3i9XsTHxyMuLk7NX/YD28xutyuHK6YZKygoQHx8vCatWocOHVSaJ85f/TiQbUVAivAPnYQIGEmIS4JL+muXDi9ynptMJrUuEQySAIzsP5lySZ5PjiG6D7GOFOcyxxzXHgm8SMAROAl56N22CHSx7UKlnZPbpAuMzWaDzWZDWFgYLBYLoqOjlfsO3WMIF/LYnJwcpKamIiUlRdU9NjZWgTzZ2dnweDzKgUvOT14jx4XsIwJXTqdTQTNMQcd2JjCkT4HFzwqZYovORvwBy+vQOyKxH/TuONK9jOMmNzdXk4JNQmRerxdut1uNRf5jyjMJCXHu5eTkaOanBD45lyVEql83+ZqfUYQLWX+OL0OGDBkyZMiQIUOGzke9/PLLmDBhAkaMGIElS5ZoHDhOV0uXLlXpnqj09HSsXLkSmZmZKi0zUOjgcPHFF6vX7733HoDC79qrVq3CuHHj/nOOMQkJCRg9erTGmUKf/qEiVKtWLdx555246667ABS6jcyePRsffPABOnfurHGUISRRvXr1CnMr6tChQ5Bzyb9Jn376abEuCBkZGZrrLyq9VO/evdVv/lNV9erVNeDOqSouLg4rVqxQr/fv34/XX3/9tMvTKzY2FvXr10dUVBQ6duxY5H79+/fH0KFDiy0rJiZG83r27Nl44YUX1L3I999/H9dffz3Gjx+Pt99+GytXrsTu3bsRERGBe+65B/fdd59Kb9WpU6cS6z537lw0b94c+/btK3Hf01GDBg0AFD5kuHTpUjU++JAwUAj7vfHGG7BarYiNjQ1yBypOiYmJ2LlzJ8aNG4dvv/1Wvd+qVStERkZiyJAhpS6Ljjlut7vM7iJWqxXXXHON5j5OWZwaZKo0vWbPno2OHTvim2++Oe3yDVW8Vq9erQHXTlXJyclITExEs2bNyrFWpdPmzZvx448/Vgh4VFYdOHAAb7/9Np5//vmKrsoZUVEufHrVqFGjXL6bGyqbvv32W3z33XdB77ds2RLXXHNNEFBq6Mzpww8/REpKCj7//HP88MMPmDZtGkaNGoWFCxdWdNWQkZGBrKwszXt16tRBtWrVzuh5//77b+VsKLV48WJ8/PHHuO222zBo0CBMmjQJn332GX777Tc8++yzZ7ROZ0IG+PP/YvCVQXrCCwzGM1AvHVb0AUzpksAgp81mU2lrZAoXpuSStlEEBViODALr028xFRFwMi0OAA3II9P20MlB7wDEQDKDvIDW6YeBXBkcZmBXAj/SaUcPfOjBHraxXtLthg4kodIc6d1OJAQhzy0BI+mMIv9JCEi6EElgSO+II52KGOjXu/HIeupdifTtRxBCXy+9440+zY6+7nQbYh8zHRzHgOwHjgmOS0ALPLGfJXRFtxpeA8cFr4/H+Xw+zdjQu4fINpTbeE10oNEDI9J5ifVm2jimOIuNjdWMC84VOuYQuqG7D4EP3hj0eDyqXWTatVD9L52s2A/yOlgGQRg5BvROPfrxJuclXVr07aUfs7IP9W3KftVDb9LpR46xUE5foQA+/VoogTXZJrIcOV7l+JbgCK+bLjZ0O9PXk+sFoUYCW4TWKAnR6F2zQqVTozuQbAM5L+QYkNcl0xpyfnAMEGSUY1n/OcJ1XV8fOZ6k8xqviy5WcnyGhYUpWIrHSTBHXhfXMP045rzh3/r25Jhi+YTyeA2Ep7gOcMwZMmTIkCFDhgwZMnQ+qlatWmjWrBnCwsKwbdu2cnsy/LrrrtM4ZGzfvh1z5sxBfn4+brzxRvV+VlaWSpVy4sQJZGdnAwC2bt2K9evX480338T3339fLnU6XySdXKmKftigVq1auOuuuxATEwObzYZjx45hzZo1mDJlCoDClBX33HMPhg0bhi5duqjjGjVqhD59+lRInatXr457770XN9xwQ4Wc/0ypffv2SEhIQGJiIgYNGlTsvtLFpLg01Xw481QlIb7TUXR0dNA9n0AggBUrVmD79u3lth71798fI0eOLHL7gQMH4HK5ii1jw4YNId+nA0B2djaWLFmCyZMnY8qUKXj//fcxa9YsAIX3HmTKrtI6WBQUFGDPnj1B6b5uuukmJCQklKqMULrvvvs0qRetVqvmXj5hvTvuuEOtPbw3VVotXboUU6dO1bzXunVrXHDBBQCgSRVYnHr06KEgJZ/Pd9quCvxsMZlMuPPOOzVgZVngxAsvvDDIDQoAunfvjksuuaTIcWPo/NHcuXMxe/bsIuHJ0mjgwIFqHJ9NtW7dGs2bNz8nXANPVXQs+rdq3bp1pd63evXqmu9Whs6+mMkilC6++OJTSsVqqHyUl5eHqVOn4v3338f+/fsrujoACr+n6MGfv//+G6mpqWf0vHa7Pei7ZU5ODrZv3469e/eiefPmqF69OoDC7zyVK1c+Lz8XDPDn/0V4ICcnR6UJIhQQyk0jlNOPfK0PAJvNZgVksNzU1FSkpqYq95HIyEgkJCSoAHFeXp5K5cJgqtfr1QSL6TISHh6u4B46STAAT2cOOv/I1Fcy/RfrQJcKOmZIuEKmOiM8QWcH/vCVQEQoyIbBXwnLsM1kKhvgZJCeEJbZbFbQALezvjLVkB6QkI44escmCTrpj6UY1JZwUUFBgXJAstvtQYFztoMMegNQ7amHppgSTgINEhrhexLAIpxCFxqv14usrCwFzzDdFiEgwhB0L2HdCMfogQbCMawjIQyv16uBGrKysnDw4EHk5OQocEP2hRxrBA9k+3Mfj8ejrptjmRAG62ez2WC325UDjtPpREpKCtxuN5xOp8YmnXX2er1qLrFdCDIBUHU+duyYgkYIAUnAiftyrMu5zn0iIiKUSw5hCQm0sG0kzMP5GB4eDpvNpvoPKLw55PV6NU5Acv7La9LDSvp9OY+ioqKUcxPHM0E/6UAkz0ngh+NMDzYSRpH9LcEgrhES/pDtajabVV9nZWUpcESuW1yb5ZwIBALK7Ycp7mw2m/riwPaR6yrXOjnGJagj1zzZpjItHMcTy+K5pNsb51l2drayCmcKQ45dCflIlx3pwCbXIM4D/dpKVyI6C2VnZ6ubgnT9sVgssFqtCtCR4BTHi1wPOB74ecC0b7wuuT6y/9l+rC/rJfcxZMiQIUOGDBkyZOh8VF5eHho1agSz2Yz77rsPy5YtC0rHdapKTk7GmDFjcM0116j3OnfujLvuugtvvPEGJk6ciAceeABAoUX+k08+iZycHPz0009q/5YtW+LRRx8FgDPmeHGuSj7gQukfpDjbGjt2LAYOHKgAhFtuuQWvvfYaDhw4gIkTJ6J///548MEH8cEHH2DRokXqOJPJdNYdm6KiovDkk0+iRo0aGDduHObOnYvJkyef1TqcSbVq1Qrr16/H4MGDi03NFxERgeTkZPU61H1BKiMjAzab7ZTror/feTqqVKkS1q5dq17/9ddf6NevH1q0aIE9e/aUqWyqQ4cOaNSoUchtjz76KOrXr1+s61F6enqQixlFB5quXbuiQ4cO8Hq92LZtG2bMmIG//vpL81CSVEnQFnX33Xfjp59+Qr9+/RAXF4fXXnsNs2bNwt13312q4/Xq2bMnnn32WVx66aU4ePAgxowZgy+++AJpaWnq3gnHgqw3H04sjW6//XYsWbIkCHrp1KkT3n777VLXdezYseXmljN8+HCMHz8eVatWRdeuXZXbxscff6yBoE5VzZo1C4Ibb7vtNrz99tuIjY1FbGzs6Vfa0DmhlJQU/PDDD/j0009DpvMrjTp27KhJoXe2lJCQgAsvvPCsn7c8FBcXF+S09m/SF198ga1bt5Zq361bt2LTpk1nuEaGipPT6SwSdi4oKMCFF16o3OkMnT2tXLlS8x2yInX77bdj+vTpGofJAwcOoF+/fvjggw/O6Lnz8/OxevVq7Ny5ExkZGcjMzMSCBQswefJk3H///ejRo4fme35BQUHI1HXnugzw5/9FiCAmJgaRkZGw2WxwOp3Iy8tTLirAycAsg7NFKTw8XAE3+fn5GucDBvhzc3ORmZmJY8eOwe/3Iz8/H06nEwkJCQgEAggPD0d6ejrcbjf8fr8KngYCAQULACfT8zCAT7cUBlqZesvlcsHlcqlUXTKtFEEcBruZFob1ZmCc4A/byGKxqNQxdOeQYIPeNYRuRzLVGGEXCS7JdDIStgG0N3aka4t0INE7jADadFL8m30qQYxQbkZyG9ucbhoSdOF+OTk56tyEAqTTiT5oLmERCZPpwTKCQISvJJQhHaICgYACKQge5Obmwm63axyH2L7S9USf8oiggUzpJF0+CA8QPmG7yDpL9ykJk/CHMvuVZfr9fuWIJW8oEMapVKkSqlSpgtzcXLjdbpWCzOv1wuPxKCiNY4fvyfR20o0HKIQ6jhw5goMHD2pSSwHQgGp6lxbCNJwj0dHRSExMVJAagS69y43sP4vFEuR4wzG2Y8cOBXPJY+WTjaFcpCTsRkBIOk7RVYfXwHMTUmR6Quk+JNNzSVCQbShdvwAoUEU6iLFseR02mw0mk0nR4NHR0RrQLT09HXa7Xa1FctwxNyxTawFAZmYmXC4X8vPzVbo0jgmOSQKD+vkt1w1eF9erQCAAv9+v1mdCYtKBjeumdOdJS0tDdna2SpdYFIwo1zK5rnM/meaL/3w+H1wuF3w+H0wmE3JycjQgD+e3XMs5vvh5wbkpPzu4r8lkUnNJApJsT/085vkkvBgIBFR6S1p7GzJkyJAhQ4YMGTJ0vmnRokUKsElISECXLl3w4osvlqnMY8eOYfHixXj99ddV2p64uDj07NkTjRs3RmJiIp5++mk0btwYAHD48GFs3boV27ZtU2V88MEHxUIN/2bpXYb5XkWpb9++KsXP7t27ceutt2Lp0qVIS0tD/fr1MX78eM1T0BIOOHjw4Fm/IR8IBNCgQQO8+OKL6r7E3XffXez9TqmyOKmUl7p06YInn3wSQ4YMQcOGDTXbpkyZgsWLF+Pw4cMh005QeXl56gnjgoICrFq1qsh9//nnH0RHR+Oll17CqFGjSl3Pe++9F8OGDSv1/qFUs2ZNNGzYEL169dI89Obz+bB48eIypWGSCgWtPP7443juuecwa9asYvs9Li6uSCByz549cLlceOKJJ7By5Ur1fqdOnXDnnXdi165dGDRoEJ5++mmNW80999xTqtQgy5cvR2JiIi688EIsXrwYDzzwAJo2bYr58+eXeKxeVatWxYwZM1Twcvfu3QqsMZvNyMvLw5VXXonXXnsNt956Kx588EHN8aV1hXI6nSFBqtdffx1JSUkAgCNHjhRbhslkwssvv6yZtzabTePUc6pauHAhjh07Bp/PhyZNmgBAmV2ltm7disOHD6vX11xzDV577TUFPBrgz79H8+fPL3YdLU42m63MUPXpaM6cOWVyKqpIRUdH/6vTJ11++eW46KKLStxv69ateOONN5Cenn4WamWoKDGmoNfOnTtx4MAB7NixQ/NZYOi/p1atWqF58+bqdVZWFn7//XfNd9szpXvvvRe9e/dG27Zt0b17d4wdOxbffvstevfujb59+wal5Q0LC8Ott95abMrgc1EG+PP/0qcDojMCA7t04NCnypHwiN75JyIiAna7XQM+yCc8JDzk9/tVYD8hIUEFTulkwfyiVqtVgTnSjUK6TkjHCJnWhc4nbrdb1UPCPwQjpOMPA/cEggj8kMSX8I/dblfuDixDggEREREKDpHQj94diFABHYZkSjTpDsR21KcZk8FufWoyOtbI9GYM/IdyJpJwi75+Xq9XjRmCWRKukP+4YNDVSAbuGRj3+/0q9Q6vo6CgQOP6JJ2NeKwEf6TFdUREBGJiYuBwOOB2u9WXVzlm9JCUvo2ZPkn2aUREBGJjY9GiRQvUqFEDNptNWbO5XC5N+0l3JLrFhHKDkbAAXbfoGhIREQGPxwOPx6PgjsjISDidTlSqVAlutxterxcZGRnYt28f3G43oqKi4HA41PyS4A1TeRFUk2CY1+vVgEzcHhkZGQSZsZ0IwvGHus1mQ5UqVRAdHa36CziZzovtw36WwAnHKx152AcSkJNjXa45nGcca7If9AAJJVPqyXEl1zeuEXJM8D1ZNlD4xS4QCGjmAdcP6XTFayHgpa93XFwcXC4XDh48iNTUVCQnJytnM47VzMxM7Nu3T0FfBOdcLhdSUlLg8XjUGuJyuTQuOtLxStZfAl1sH7/fD5fLpdIzejweBWrq0xJynZTrCtcuh8MBn8+nWaelOxgdmKRTloTF2PfsG5k2km3g9/vV5wLbmWVKaIoOcW63W8F4ci6wfEKjEnaTaxE/t9h2XIvCw8M184Lrg96+0ZAhQ4YMGTJkyJCh802RkZHK2cHj8aBZs2bKDeF0NWnSJPz666/o168funTpgo8//hgPP/ywcg11Op245pprcNNNNym4QQbUunXrhvHjx6Nnz54hy7/zzjvRvXv3MtXxXJX+wRyg8PcMU6KdTQ0fPhyffPIJLrnkEtx+++2YNWuWSl8EAGvWrMGKFSuKPP7333+vkFRtgwcP1tzkjoyMLDXQsmrVqiLTOZwNNW3aFC+99BLGjBmDqVOnhgQoSnst9913n3KBLi7AuGfPHlStWhUPPfSQAvJKq/JwsXA6nZgyZQr8fj9WrVqFbt26AQAmTJhQrFPRqUg/pwDgiSeeCPl+KF199dVB78XHx6Nv375wOBw4duyYZtuKFSswY8YM1K9fH+Hh4ZgwYQKWLl2qrueSSy7B+PHjSzzvX3/9haSkJDz//PNo3749AGD//v3YsWNHqeotVbNmTQ0AtXXrVuTl5WHSpEkYPXo0IiMjlfPa66+/rnk6nA/SlUa5ublF1m/79u0AENRepVFsbKxKVXE62rVrFzp16oTPPvtMAXW///57UGrFU9Gnn36KhQsXAiiEud544w2NS0lZQCVD55YWLVqEOXPmnPbxPXv2DILpzrQqV66MvXv3ntVzlocKCgqwevVq/P777xVdlTOm559/XpNiMZT279+PRx99FNOmTTtLtTJUlEIZZvz4449o2bIlWrRogZ07d1ZQzQydC6pfvz4iIyORlpam3ouOjsaFF15Y5t/Up6KMjAxs2LAB06ZNw8yZM4t1vKxbty5efvlljTvouS4D/Pl/TZgwQbldMHhL4AI4mS5GpvDSAycSpCH0Y7Va1Zd/mT5Jn0orNTUVx48fV/CI2WxGZmYm8vPzVSotBn4lmCPTJvn9fo37CIO/FAPGhB/0bj+EcQhG6OEVntdkMiE+Ph6xsbFwOp2wWq0K/LHZbJpUMnrXHRlgl4FkQkd00yAYwGsgMMB2lI5GEqiQUJB0zZHtRoADgKZ+Eu7Rtxn/Zl35LyoqSuO+pHc+Yf/o6wicpF9zc3OVGwb7UNaH7cixRCcY6YpE8RibzQaHwwGTyYTo6GjUqFFDueEwbZLcn9fH//UQGwEKQl4EGsLCwpRbCyEhOR8kpMBz6cEfOYfoBiTHUXZ2tgIbWA+6XjkcDiQlJal92S52ux0Wi0XjLiP7meUQTNA7FLF9ZMoxzhU9IMa+BE46Ax06dEilLZPzULa1BNUk2MExTdco6Uoj+1yOK65ZnLvcX+/4QhiE9eH+EvCRczOUe5Dcn+NdujbJuSwhETmn6O7F8cM1UQJsJ06cQFZWFmrWrImkpCQ19vPy8uB2u3Hw4EHs379fra82mw0RERFIT09HTk4OrFYrPB6Pyr9us9k0Xzz1bjsS2CNw5/F4kJKSAq/Xq8BMQkwyFZeEtuT7HEesM6EY7i/Hm3RBkmuW/JyR7mJMsyjTjHk8HhQUFCA2NlbNSa6p8vONKdP4P8uRQCkADRQk1zV5XbxuwnoSSCTcxX+BQOC8zIlqyJAhQ4YMGTJkyBAA9OjRA1dddZX6Dv7BBx/gmWeewWOPPVbmsh999FGkpqaiRYsWAIALLrhAc3Nx0qRJmD17Ni6++GJYLBaVbqVVq1aIi4tDfn4+Xn75Zdx5552ach0OB+69995/VfomKXm/iXr33XexfPnys16XGjVqIDIyEomJiZg+fTomTJig2V5SIP6nn34qN3DjVNS4cWPNfaW0tLRS2ex36NAB9evXx9ixY89k9YrV5s2b0bZtW1x++eVo3759mR1v/vzzTwBQD1+GkkypdaqORzVr1jy9iul0/PhxvPPOO2jevDkSExNVuSUFRovT3LlzMWLECEydOhXVqlUL2s40XaVRy5Ytg977+uuv1d833XRTkDvF7Nmz8ffffytXpHHjxmHcuHEACh1vSuvEceDAAc3r003PkJCQoO5lzZ07FyNHjsSuXbtUG//6669qX31Kkw8++ACffvppqc4zc+ZMLFiwIOQ2Ov6cOHGi2DK+/PLLkPV/4403MH/+/CJTrpSknTt3Yt++fWjVqhW6deuGLVu24NChQ6dVFgC0a9dOtWn16tVRt25dzfbSpkczdH6oVatWZTp+7NixGDx4cDnVpmQdPXoUu3fvPmvnKw+dOHECr7zyCgYPHqxxgvy3afPmzfj888+L3L5o0SI8+OCD+Oqrr85irQwVJRlTl8rPz4fP5ztn0k0ZOvvq378/3nnnHdx+++2oU6eOet/lcuHQoUMV9tB4XFwc6tWrV2wq33379pUJqD7bMsAfIbrmMDDL4KUETKRDhwwc83j9P7o/AFBBdYruOBLykGlQsrKyNE4NFIPGMnCsdxIKZW0sA9z6ekoXDwbeJYighzTCwsJgtVo1rkCEavh3qPPo68H6yzRM+vrq0/GwX5j6RjqnhOoHmZ6J/Sn7XLrS6N/Xl8v6Ev6iE0uo8aAnW6WziwSKeH4CKEXBFnpXFPlPOgARumF9o6OjYbfbEQgE4PV6Ndv0baAHiUKBY4RI0tLSkJGRofkRLftcDxTJ9tM7y8j2l2Oe85FjmzADUy8xlR5T4PHa+EVC7+gjz8ltoUAaPURG6Ee2tV5cM5jiT7r0yHbR10WWRahK1kkCIfp20vcTYTnp4CTPJ8E2ubbJa2ddQ60jdCMi5KGHoNgX0sFJjjM9OMW1Qq5nkZGRcLlcypnM4XAowI7rVHZ2NtxuN9xuNxwOhyqL5yE8SZAOKLxxx3VJtolsL7YDYbxAIKCAIo4/rnFyHWaZ0l1MQpYANM47LE+/xujrJevK/pDnlfUmWCNdeNgWBDslnMR5TKBPQqmEezh+9XNXppjTfx7xujlvCByyPoYMGTJkyJAhQ4YMna+y2+2oWrWqeu33+7FixQq0bdu2zE8orl27Fj/88AN69+6N5s2bq/tFUuHh4UhOTobX60WbNm0AAA0aNIDb7cby5cvhdDoVOETVrVsXycnJqF+/PhITE8tUx3NRoX6bO53OkO+fSXXr1g3x8fHqN1AoValSRQXyQ6miAo5du3bV/PbfuXMnXC5Xiccx/U9Zg7vloQ0bNmDTpk2a907HLYLggd/vx2233YYLLrggaJ+srCwFoRQXIAil8kpl1K5dO/Tv3x9AoaNX5cqVAUDdJzsdHThwANOnT8eKFStgNpuD0i0cP3681GWZTCZNQGfAgAFo27YtgEKwbNOmTUFjrKCgAEeOHFHH7d27V6WaWr58ebGp2qSKg7ZORXywSa+8vDxkZGTgzTffVO/Jtfrw4cOYMmWKSh1XnNq2bYs+ffoUeW2MBRQHL40YMUK5PklZLBbUqlULNpvtlMepVGpqKhITE3HnnXfi6quvLlPQa+vWreq+f0pKynmbVslQyWrcuDEuvPDCMpVRuXJlXHHFFeVUo9Jp/vz5ymnrfJDVakVcXFxFV+OMKz4+vsjUUB6PB99++61yEzsX1bhx4wp1RzzbkrEs6mx/Lzd0booxPym/34/169fjiy++qDCA0WQywel0FunW6PP5kJGRoR6GPx9kgD9CdNfgUwf6YD1wMngNnAzycl8JpshAqclkUmloZFCdIBCDpwCQnp6ugtgulwuRkZHK6YUOJQRA+MMiEAggIyND4xCiDy7La2SQlgF4GRiW6bckvCGdPiQQYLPZEBMTo9x+mCKNgIA+wK2HTuj+wfQ5Mp1NqBsmEuQg6MEJWRQEEyrIHqpfQ8E4+hQ4dERiWh+/36/S2khIQ0IV8ng6oDBlFOECtrm+j1iGDMZLZyl9/xIgys7ORkpKCtLS0nDo0CGkpqbC7/cjOztbA8joxy2D/XpwRg99eTweHDt2DMePH0dWVpZqB33fyrbWp13jPrwOObYJzhAAYrl0LPJ6vfD5fMjMzFTp8DjPAKjt7CeCEbJfACioRA+4yLbgnJBzSII2hF3MZjPCw8Ph8XiQlpamoCQ5V+XYoPOJBHmqVauG2NhY1VbsTz2MI8eprHteXh48Hg8CgQDMZrNmfEuQkf/bbDYN/CehPwldSTCQ7i4Oh0MBWExVReBE9jVfy5RnHINsGzkfc3Nz1VN6MTExyu2KLkEHDx7E3r17sW/fPpW2jw48/JCW7kAcw/n5+XA6nWo8SNciCVHSncfj8aic6DabDU6nU6Wfk+nvOB7MZrNKR8b3OA5PnDihSb0o4ZlAIKDSunEc6AFNrqUcN/wn0/Zx/SZow3UpPDwcXq8X2dnZ8Hq9Ghcp7mMymVT9ZcpGuW6xjfRrk0wJVlBQoM7JG478bOIx5XUTzpAhQ4YMGTJkyJChs62NGzeq77MFBQUYPnw4MjIycNFFF+G2224rc/mPPPIIatWqhRkzZuCbb77BY489pnHJAAp/8z399NP46KOPABSmNvjrr78wd+5ctG/fHh9//LFm//379yMuLg4RERGYNm3avy5AJH93SukfxDqT6tKlC7799lsMGzYMDz/8MJ544omQ+/3111949NFHcfTo0aBta9euLTXYUN7atm0bPv74Y/z22284evQo3n///RKPSUxMxIABAwDgnEzp/OCDD+Kuu+5C69atT+k4gllJSUl44403QoI/mzdvVhBMcSCXXhEREeXm+ANAPYx5+eWXK+eh66+/vsjxV5KGDRuGTZs2oXfv3oiIiMD111+v2b5s2bJTKq9Tp07q72+++QYnTpxARkYGPvnkE8ycOTNo//r166s079SJEydgNptxySWXoGfPnujbt2+J5w31IOzpiPeZTpw4oXGSSklJwc6dOzF//nwACAoSZWVlaVyhitPcuXMVQKeXbIvs7Oyg7bGxsUhMTMRbb72luf+nV40aNcoE64SFhSErKwudO3fGhAkTTjt4u3DhQrz88svq9Y4dO0I6GZUXHGeoYrVt27ZSwW8lqWfPnujVq1c51Kh0mjFjBqZOnXreOP9s374dGzZsqOhqnHFdfvnlCh6V2rJlCyZMmIApU6acsw+b2u12jBw5Er///jtuuOGGiq7OWdHRo0fx008/YcmSJdi0aRNyc3MRFRUV5PRn6L+nZs2aaYDOnJwcvPTSS7jhhhvw1ltvVVi9wsPDVZw+lA4cOIC9e/eqNNzng0qXcPY/IrPZrJxnePNA717CzpdpbvQgA/8xGM5AK10yJOTBwHFUVJQKuvv9flgsFpWSSh9Ep+uErJtMYwScdP2Rr/WuNzIQLyEXQhoSLggFOElggRADg+kk+OV5Qzlb5OXlqYC+Hg7SOw+xDvJaGZyW16CvqwRqeD28RrajPl0Wz802NJlMCvrJzc1VgXOm7tFDL2wHmbpJ1ovnke4cfE/WoaCgQKWx4uJDEETeyGJ7SDgjJycHLpcLERERCqCyWq0KfNA7zkhQgrAN4Rter0xd5fF4FKEpf5jrr1H/o1D2XygHHb/fr9KI8cer1WpV70uISLqScA5JSCUsLAwxMTHw+/1wu91qPlqtVsTGxipAR+/+ImEKuRbo21uOncjISAQCAfV0IR1Y7Ha7JlUXnWdkCjmORdaF4Jx0C5JjN1Q6OvYbgRqLxaKsFfWQUU5OjnLuAaDam/vwxgXXKrmmcVxHRkbC7/cjKytLAUR0eYmJiYHNZgsaZxLkkqm92O/5+fnKPjomJga1a9eGz+dDQkICXC4X/H4/9u/frxk7UVFRCvRharioqCiN+1BkZCRiYmI044xwG/OJc54SJiLMJV2UJAQnQUHuK1P75eXlqbRjFotFrWP6dZBjmWOIc47rixyTEtyS85XlyD7jtXMMEJBiSjh+trB/ZfpDq9Wq0oSxDkydJlPeEZrz+/3qPY5tpkojoETo0ZAhQ4YMGTJkyJCh81W7d+/GpEmTMHPmTCxbtgxr167F8ePH8cMPP4RMjXOqys3NxbPPPouaNWvC6XTiyJEjQQ4+hw8fxrp16/DTTz8BAH777TcMHjxYpbfRQyXp6el48MEH8corr+C6667D22+/jZtuuqnMdT1XpHe7pcor+F8aJSYmqvsm3377LbZs2VLkvpMnT8bAgQOVQwtQ6KTy2muvnY2qhtSyZcsU1FGrVi3s27evxGOGDRuGDh06AAB++OGHM1q/09GHH36IqVOnnlKap6ioKA0Y5/P58Nhjj+Ho0aOaJ5D379+PP//8EzVr1jwlkKdRo0blsk6EEuu9f/9+zT1GavPmzXjnnXfw4IMPBqVXopKSkpCUlIRGjRoBAK699lpMnz5dbT8V543du3crOBEofMJ7/vz5aN68uSaN3E033YS2bdti8+bNuPnmm5GYmKjSewEnXbFr1aqFZ599FgcPHsRnn31W7LnpbPbFF1+gSpUqpa6zXryP06pVq6D0YRJckoHMrKwsfPLJJ6U+x4ABA1CvXr2g95s2bYrFixere3b6Ph00aBCeeeYZ9QBwcUpISEDTpk3h9/uLXZuK0iuvvIJPP/0U8+bNK1N76udKUlKSxvWfKqt7nqFzR4sWLVLOZKerX375BUuWLCmnGpVOdrtd41h2Lsrj8WDmzJl4+OGHz6tA9OnqyiuvRNOmTTXv/fXXXxg9evQpQ6lnU5GRkRg+fDh69eqFOnXqYOLEibBYLJg1a1ZFV+2MasOGDdiwYQMaN26MoUOHokGDBnA4HIiNjT0l90BD/x5FRUVh1KhRuO+++zTuV2vWrMG0adOQkpJSgbUrFOPujKNK7d69G7///jv27t179it2mjIcf4R8Ph8sFosGzmHQ3efzwefzadw/CBZIaECmUQFOupgwpYp02JGBdAZMASjYQwabJThks9k0lqOEHPiDgMFgBrylWwuD8zw/fyDQLYLXJ11/pPuN3plGHwSXqb9k2i8CTrw+1kOmpuHkysnJUecgKEFXEQktSecTPfTD8hmQZj9KsIWBdcI7slyWycA224SBcOAk3CLTmsn/5fjh/gyCh4WFqXEkHX/06bbYdjK9md45h68JH3DsEKYiWCYBFr2Dj94JidAT6yLLIiAREREBu92utuutziTgIM8pYSzptMJ9ZXqtQCCgzuVwOBTE4PP5kJ2drcYOxwzHPI9jfaOjoxEbG6v6ICYmRp2TiznrJGEifWosPbwlnbLYRmFhYSp/NucT68l5IeEMwhDSaQbQpgJkP0u4i30j5zNdjywWi4KWOIY4H6X7DwAN3MHjCe3IvqT7GMdwTk6OWhPZB3QR4rno/sJ5x2swmUzIz89X4FVeXh5SU1Nx5MgR5ObmonLlyqhWrRrsdrvqd94Q8Pl8cLlcatyyXoR+uF9OTo4Cd+Li4tQ5JajFduIxMtUb558EtPQAJeeW1WpVbkAS8GJ9uI5INy/Zrmwr9iFvMEl4UZ5bXx+OC16HXHP1jkbsJ7lOEwLjGiHTuekBITqxMTWh1+tV6zfrIN3F9GtXWSymDRkyZMiQIUOGDBmqaM2aNQurVq1C3bp18cgjj2D//v245ppr8Oijj5ZL+TNmzMAzzzyDVq1a4ZVXXglyati9ezdWrVqlXjscDrz33nvFpltKSEhQAMTAgQP/VU8c6x1/li5dih9//BHff//9WatDlSpVsG/fPqxZs6ZUgXUCM9TkyZNPCRY4kyoN9AMUuojwAbBRo0YFPQxW0Xr11VexdOlSTJkyBePHjy/VMbVq1VKpvoBCMKFbt26oXLlykFPWL7/8AqAwFQ3ddorT2LFjMX/+fFSqVOkUrqL0GjBgAF5//XUAwMUXXxy0/fjx41i2bNkpuVhcfvnlmnXlVNyT6tSpo0mLCAAvvPACLr30Utx1110ACtelESNG4P7778f06dPRrVs32O12DB8+XB2jf5CwWrVqQa5mev3xxx8AgJUrV6J9+/alrrNedrsdOTk5OHbsmOb9d955R/NUunzA6YknnsCECRNKLLtu3br49ttvsX79esybNy9ou9lsVg+pAdCkbxs3bhzmzJmDunXromHDhiWeq2rVqvjwww9x9913l7hvUapduzbee+89TJ06tch9Fi9ejHnz5mk+n6SSk5PV3926dcPrr78eEq4ozTUZOj+0YMEC/P3332Uq459//imn2pReFZVq5lS0YMECjBgx4j8B/QwePBg33HCD5lpXr16NF1544ZyGfgDglltuwU033aTWOsI//5V74/v27cP+/fuxZs0afPjhh9ixY0dFV8lQBYlmKnqAeObMmdi/f39FVEmj/Px8lYEmlHbs2HHejV8D/BHyeDzweDywWCwKnJDpmtxut6LspasIgKCgLh0UJPgDnAQgGHiVZL50lklPT1egC8tmEJbBXgZzpasQYRYJm7B+DBrrHVEkFCNBJD3wQ0BAAipSsq4yHY1MK0Pl5OSo9pTQCwEOQlDS8UK2sz7dknTC0DtqACdTN3FfOjvxevSQA1D4xEz9+vVhs9k07hV2u12T4kkPtEgHIrar7Aueg21IQEkGyGV6L6/XC6/Xq9pJD1xJkIMB+4iICJW2h2OG/SBBEtmPbDfCMyxTgmCyfeksRLcX7ifbMBTkA5wEE/T76uEmjmeg8GmbhIQExMfHKxjP7/fDarWqPmHbsS7yWuUc5RxhWi3Wn20iUxhJRyRen7wWtjfHF0EUh8OhGbc2m03Tr7K/WR9+wEggRMJo3E+6MsnrZKo99pt0mJHzXI55l8ulGVP6Y2TfcPwAhR/Ybrdb4+DD8cf5yzFH9yQCQgUFBfjnn3+we/du5ZBks9mQl5cHm82GqlWrwuFwKCtopqoymUyIjo5GQkICYmNjkZCQoElbmJKSoua2TIcVHR2NsLAwdW7WJyoqSq050pEoJydHtSPbXq47/McxwjnGfXNzc+H1etWXGoJrLJ/znmNWrp8ElHhOrtXS+UzOFQl0BgIBtQ7ItZIAGUHL/Px8jasc24pjhOOf2wklSRci1lOupdxOVzSZ4o7j10j1ZciQIUOGDBkyZOh817vvvovw8HA0aNAAAPDTTz+hd+/e5XqOzz77TKV+WrZsGX7//XccPHgQq1ev1qREd7lciI+PR8+ePUOWExcXh2uuuUaTPuWZZ545q6kzzqTkg3dr167F6NGj0b17d4wYMeKMn7tp06Z49NFHUbNmTezYsaNUkEEoZ59JkyadieqdUc2fPx9paWkACgOz59rvvGrVquGSSy7ByJEjQ6YHAQoDcg0bNlTQUt26dTVPIVOfffaZgmoo/tYNDw9Ht27diqxHrVq1sGnTJlx55ZVo2LDhGUtzce211yIpKQkLFizAnXfeGbS9Zs2aaNGiBbZu3VrqMu12Oy677DL1urRQGHX99dejRo0a6vV1112H8PBwDBw4EAMGDMCjjz6qSQcGFKZSkPeOv/jiC/h8PvU6LCwMN910U5FuwgkJCbjnnnvgdrs1aaVOR3a7Hbm5uSWObfkQZKjxo1evXr3wyy+/FAvL6cuRzlWn+1kTHR19WscBhaDbjBkz4PV6kZ6eHrR96dKl6NOnD2666SZs2rQpZBlHjhxRkNwff/yhgZmk6tevf9r1NHRuye12Y86cOUHxo1NR7969z5hTWlFyuVxn9Xyno/MBTiovffLJJ/joo48we/ZsbN26FWvXrsV9992H2bNnV3TVStSAAQPQpk0bzXu7d+/+z4A/brcbv//+O6688kq8+eabFV0dQxWoRx99VOPoCBR+ryxNeuGzofDwcGU+Qh09ehRr1qzBvn374HK5zru0igb4I8TAKCEBGVAHoFw9AG06KOnMoH8agYFaBvAZcJaQggwoM9WM1+vVpHdh/aSjBIPD+qCwhF6kg4Wso95ZRsIN0ulHOgTJc8p99aAR68l2lEFqnpv7E5bQp1Fi+aHSaOn3J8whISe91am8frarvFZ5TWxLk8mEmjVrwuFwqB9yDPbLtmVgXpalL1umS2J99PCYvBZ9yil9eifWRbooSRBAgi4yVZGEFGT5LEMPfBFQkWNZ9hGgdReRY6CoPpNtIN+TsAnrqh9zTOvEbQUFBcpliv0izx0IBODz+RSUIOtDCEGmddODQnqYjOXq4SzCJ7LvZYov1lGOCXnt0klLtpEc1zyGNzblvJBzTAJnvDa62khwiOudvj8BaOY020VCUNxOEI59z7rJNVM6FLEvCPwRIAkLC4PVakV0dLTGhYc/tgis0NmM49hqtapr5lrCcSLXHLYV/2faKUJLsk9luio5z+T6WdQ/9n9ubq4CMiVIyf30Y451k3NS72Cmr4P8bJCOPxIM1cNJEu7Mzc0NcgXiflQohyKOQSk5pkKtQ7Idy/KD35AhQ4YMGTJkyJChc0E///wz/vnnH3Tu3BkA0LhxY7Rr165cz7F7926sWbMGQGEwfPr06Sroodf777+PzZs3hyzH4XAoR1qqdu3a6N69e7nWt6LUvn17ze/UswmgOJ1ODB06FG3btsWuXbtKdUyTJk2C3uvSpUs51+zM688//8TBgweRm5uLTZs2Bd2vqmjJoFooMMfhcKBPnz4YOXIk7rnnHgAIcvWhmMpclt2jRw/1WqZtk7Lb7bjvvvtw8cUXo2vXrqd1Haei/Px8XH311SG30dn5VOeHbBO9801J6t27N6677jr1mk4uDocDd955pwaUI9iiDwJ17do1KOWCdOLXq3v37iowXFZFRUWV6v6FTMNVkvOVw+FA//79kZeXVyzwp3cyk+fQ378rrcoj9XpqaioWLFigoD9q7ty5Gvdtvfbt24d58+YhNTUVQGEKyuzs7JDnkM5Ahs5/7dy5s8j5WhrVrFkTHTt2LMcaFS+73X5euE5Jd7p/u3JycvDWW29h9erVmDVrlkq1e66rQYMGuPDCCzXxKI/Hgz///POcSGt0tnTgwIEiXVQM/TdUrVo1XHvttUHfl88lB52oqCg4nU7NQ+z5+fmYNm0aZs6ciaNHj+LEiRNq3/NBZf/W9y+S1WpFdna2AiWAk8H5yMhI+P1+uN1u2Gw2FRjOzc1Vabz0gICEA2QKKwZfGWRlkJqOFF6vF1lZWcq5gV/q6aIggRp5PgaNgUIXIFkHwkYMsjM4LZ1NpKMJASUJCrAd6HRDuIDnlk4psl4MCOtBJTpFkKaTwfu8vDwFc7BODCBLWIllS/cYpjOSzibcj84WEqThdvmaQfT8/Hw4nU7lAEUHEY/HA7PZrEAt6cTEwDrbITw8HH6/XzNu2Ed+vz8ouM8feKFSZ7Ed9AAT/+ePTLY7YQmOUy5MHM+EL/T95Pf7g1yVeG4JmxCSCAsLU09rSDcatq2EMnis/Fs/b1gngngc/3Q+kj++w8LC4HA44Pf7VdooOWYzMjLUGI+Pj9fAMPzxLJ252HecZ9LVS7pumUwmjeuNdHOh61VycjKysrKCnLTotsW24dxnu0g3FZ47Ly9PpfGSYBbrbzKZ1JyS4Jl0F2J7MS2g2+1W41j2i5yDYWFhsNvtsFqtKuUh53x0dDTMZjOys7Ph8XiUqw3r6PV61dpGCItjxWQyITExUdMuZrNZpYmKjo5GamoqoqKilEuN1+tFdHQ03G43AoGASknm8XgUzGI2m2G325XbGK+HY53/yzVJwp5sTwmsyKdI2bZciyUcJmFIj8ej1g2uY5zflITVWDb7W6Zp1ANG0i2Kaztw8gYL56Bci1k++4TXx3Ej10qWI9PDSfBKgmW8DunOJj/3uFaxj8vyg9+QIUOGDBkyZMiQoXNB+fn5OHDgAAYOHIglS5YgJycHP//8c7mf53//+x+aNGmCwYMHY8iQIUXul5CQgKNHj4bcduDAARw9elTjorB9+3asWLGi3Ot7tnTNNdfg77//xmuvvYZrrrlGve/xeDTOGGdaNWrUQP369VG/fn28/fbbpTrmm2++wRVXXAGgMJj/119/4frrr8fSpUvPZFXLXZmZmejZsyc6dOiAzz//vKKrE6RffvlFzdHY2Fi8/fbbGDlypNpevXp1VK1aFX379kVubi4++eQTjavEDz/8oIHjLrvsMqxevRoHDhyA2+1W0B+AoKf5ZR1atmwJoHygi5J0yy23FLktOjoaTqfzlOths9lwxRVXYMeOHfjuu++KdE8KpR49esBms+Gjjz7CVVddpUk1JWGsN998EwsXLkTdunURERGBWbNmqW1z587VPNiZk5Oj2a5X06ZN8f3332vK//nnn08LrouNjS0VKHXbbbepv/WpGfVyuVzYvXs39uzZgyVLlhS5X0xMjLqf5fP5cOjQIQDArbfeWiQE4fP58Mknn6BOnTpBTkpAIVDz/vvv46effsLcuXNLvK5Qev755wEAzZo104yFvXv3qr+zsrI0xxw/fhyDBg3CypUrNe/v3bsX9erVCzpHixYtTqtuhs5NMf7A+9CnKqfTiTvuuAN5eXlYuHDhaddj6NCh2LlzJ3777bdi93O73Thy5Ijmnvu5qPMl8Fxe2rlzJ3bu3IlvvvmmoqtSat16662oXbu25j2bzYYhQ4Zg3rx55/X38FPRgQMHKroKhipYR48excGDB9GkSRMFLR48eLDY73NnW/Hx8UhISNCA/sePH8f69esxbdo0zb733HMPLrnkEvz222/IyMjArl27sG7dOo1D5bkgA/wRiouLU8FJBp0JCACFX1Z8Ph8yMjIUkCP/l64f8okjn88Ht9utAqV8LzIyEtHR0YiIiFDB96ysLEW9S1BHggIWi0XjcmGxWFSwVkIgXq8X4eHhGhgEOAnQMAgrASXpHCFdgSRswgCvDIzzf56fAWcGhqXzEF8HAgFlmyphBekYwWtkQFmflkq62eh/kEk4hueVzkUSciAUw75hMP+ff/5BSkqKSqHDvmCaIDqYACdTPgUCgSAnm4iICPh8PpjNZgUCMZBOOEPWm5LAFftV70JDWIXAjAS2AGigLJbJtpdwjqyvHB9MG0RARTpUsS9NJhPMZrNm3HBfQhM5OTlBDjYcj3rwR+94Il1ipDVyWFgYvF6vSqNFOEcutJxDBQUFmrRkXq9XnYdtweuSDk/sP5k6CgCys7ORl5enIJPs7Gxlx+vxeNS4ttvtSE9PRyAQUP1gMpng8/kUdMF2Yb9JdxQJ1hHw8fv9GkjKbDZr3FwkHCLLY3uybJvNpuA76fbCY6OiohATEwOr1arWQYJAYWFh8Pl8CAQCCAQC6trpbkRAi2UHAgFkZ2fDbDYroMRisSioh+fMzc1FdnY2UlJS1HWZzWZkZGSolFl8n2PD5/MpYK1mzZoaQFKOPTku5fvytQSR2C8EsaQLnIRn5JoUFhaGrKwsVSfCRlwjJJDJzxS5RkmAU/7P9uFayjaVc5hjjiAZz8H5Kz+D6JhEhyXOAf018kc6509+fr7m2uSTC0z1xjVSAqoEs87mjXhDhgwZMmTIkCFDhs6EXnvtNfTu3RsdO3bE888/j0WLFmHOnDln5FwbNmxAly5dMGnSJIwfPz7kPldddRUGDhyIRYsWhdz+0ksvwWazoVWrVgAKU6mUFKA+V/Xiiy/igQceQFZWFpKSkjTb1q9frxwlzoaqVKmCI0eOIDY2Fp988kmJ+69YsQL79+9Xrzdu3IhrrrlGkw7pfNKRI0fKFIg9k3rttdfwwgsvqNd6MK527doq7VB2djYyMjLwyiuv4Oqrr0blypUxZ84cpKen44YbbgAAVKpUCZUqVQrp7NW0aVO0bds2yIFApteraDkcDkyYMAExMTGndNyYMWMwcuRIpKenIzExscT9/X4/tm7dikqVKqF69ero1KkTNmzYEJS+iSBAfn4+3n33XWzbtg2//PJLUHn6dCg9evTAsmXLQp67RYsWePzxx9XrH374Abt27UKbNm1QtWpVHD58uDSXrJSYmFhkyp+XX34ZDz30ENq3b48XX3wRQOFafddddxVb5qOPPooxY8YUm/LkwQcfRGZmJsxmM7xeL/r164cdO3bg5ZdfxtixY0Mek5+fj3HjxmHy5Ml4/fXXFfjz7LPP4u6770ZSUhK6deuGyy67DF988UUprr54TZo0CTVr1lQp8ORT+xkZGZp9v/rqqyDoBwA2b94c0nluzpw56NChQ5nraOjcUHk48XXp0gWRkZHYuHFjqd319Bo+fDj279+PtWvXlug+snDhQmRnZ5/yenmm5fP5sG7dOqxduxYPP/xwRVfHUDGKiooK6fAIFLo1Pfvss7j33nuLdOs0ZOjfpLy8PPz222+oW7cumjdvDgD47rvvzinwx+l0Ijk5Gfn5+XjjjTdwwQUXICMjAxs3btTsd+211+LGG29Eu3btcOmll2LPnj24+OKL8fjjj+ONN96okLoXpXMXXa0AhYWFIT4+XgVQGRynWw8hGqZxkUFvghQy0Em4hYCHyWSC1WpVgVMGvCMjI1VAmAFjQigMGDPozKC0TOXEOkonHwJL3EaYR7qlMNgs09DwPbpzyDIkhCIdbihZR+nEI+vK/SQAJNMcRUREwGKxaAARBvsJOAFaeEkCQDKdkj6FDY/RW7WyDoFAAF6vV4EsFosFmZmZOHr0qAqgsx1l2jf99bEu0qEGgCqfqdy8Xq+CPyTswn6SKZakc4mEo3h9ZrNZgVtyP1671WpV4IMEuiScFRYWBr/fr8aSTElEiEoG+fXXbTKZFHDBQD9TaUm4iXWTdZGggUw5RCCEZYVKf1ZQUKAgN/ZTXFwcbDabpi7s68jISAWnmEwmOBwO2O12NXYkPMe6SdiK8ILH41HgVbVq1VQfWK1WeDweZGdnw2azoXr16hqQjW0nXVAkkMH3JIylh1VYT5bDcSVBENZXQmB6sErv3qR3k/H5fPB6vcjOzobX69WMeQlmOZ1OBQOFhYUhMzMT6enpql4c89nZ2di/f7+ChwjEsU9cLhdyc3Nx4sQJBcoBhTep+DRDXl6eSjHG+ZKWlob09HRERETAarUqCI/rFYEitpnJZILX69U4r8mxyTnOMiTcyP1YntwWFhYGt9sNl8ulAEibzabANIrtq3fCkum4ZP/I/uJY4mcOy+I8ke5y+vWKfcH1nZ9zFotFjV/OYen+I6Ekq9WqgQ4BqM84SqY/lCkN6c5lyJAhQ4YMGTJkyND5KpvNhtatW8NsNmPQoEG45ZZbMGPGjDN2vlatWqFmzZrFOgqNGzcONWvWRN++fUNuX7RokSatjN1ux/DhwzW/788Xffnll3jwwQcxbdo0BVp4PB6MGTMGDz/88FlzGI2NjUXVqlWxbds2TTqjohQfH49LL70UgwYNUu/98ssvSElJwR9//HEmq/qfU3JyMrZv34477rhDvUd7fqpmzZoKHDt27Jh6ALNr165o1qwZdu7cWWpHhdq1a2vcTzp27IilS5cGgWkVqfDw8NMOYlssFlSpUkWT/iCUNm/ejH/++QetWrXCk08+qd6X0E8gEMAzzzyD/v3748svv8S6deuwbdu2kOW1bt1aPWBGLV++POS+zZo1U240VJs2bXDnnXfCZrOdFkhSvXp1ZGZmBr3fq1cv9O3bFxERETh69CgyMjLw008/YdiwYeqeX1EaMGAAxo8fj6effjpo2+jRo/Htt99ixIgRqFKlCgAgJSUFDocDN9xwQ5HQDwCsW7cOkydPBlCYJtLn8+H555/Hk08+GfQEuh7MOR199tlneOONN5CZmYlJkyYpRyIgeK5deOGFIcug27f+vXfeeafM9TN07mju3LkluuyUpMjISHTp0gXPPPPMaadVTU5OxrXXXqtxfitKV199dVA6u3NBFosFu3btwvTp0yu6KoZKUCAQwEsvvRTyMwQodBF87rnnznKtDBmqOH355Zf4/PPPVZq7BQsWVHCNtFqxYgUmT56M5cuX448//kCfPn0wYsSIoP0OHDigvt9XrlwZDRs2hN1ux/PPP48xY8agTp06Z7vqRcoAf4QYmCTcQypZpr+yWCywWCzKvUOmDCI0IcEEgjN8OoRBYLrYMDAKQKUN8ng8cLlcmjQ/DNbLOhEEkKAA4Q2mGyKcwcBtURCGrLOEHiRsJCEMAhSEU/i+dB2R9deXKd1+9FCCDHgT+iGUIZ1L2B6sj96CUQbn2TYMRgOFoEpcXFxQIJpQAl1L6MhBdxnpEkKwifCOBDkYIJcuLLm5ucq5hkF/up+wvwBoIAWgkOqWgJQEbiTYwfaXgICERmT/SucQmW5Opn+TAIUecJBuPOxHpsKSacg4FuT4ZYotWU9Kuo8A0Iw7j8ejfqQS2MnLy0N2djZcLhfi4uJQp04dxMXFwW63qxsbTA3FdH0FBQXqeIfDgaSkJCQnJyMhIQFRUVEKwKDkHPT5fMrth65dbN/IyEhkZWUhIyMD2dnZyM7ORmZmpqY8CdsQtNCndKLDkHTsoZsUwQ22qQRV5NiSc1GmWaLDEFOTWa1WNYf1czUnJweZmZnIysrCiRMn1PXQtYVwC68jPz8fbrcb2dnZcLvdqq0JoBw7dgxutxtWq1Vdd1RUlHID4nhyOp1qrXU6nQgPD4fNZlOwFeEfukwx7Zp0oUpISFAAHm/Y6V3L6GYlxzfbLzIyUgE0HAOcF1xL5BpH6CczM1ONDR5DlyMJhcobQhw/TP1H4EnvsCVdhjgGJHwp6yWBIo4hOlZxvDC1JMeBvEb5+cFxYzabNXNfAj88n0w3JgFOuaYbMmTIkCFDhgwZMnS+yuPxYNWqVfjtt98wZswYfPfdd2UOahWnO+64A/fff3+RAW+g8Dt2ZmZmkSmHAOCff/7BwYMHARRew/fffx/0UNT5oNWrV2PKlCl49NFHNfdPeI/hbKhTp074559/cP3112PFihUlpunq16+fJh0QtWnTpjNVxf+0KlWqhOjoaM17x44d04ArNWrUgNPpBAANtAAU/tb+7bffsGjRIuzcuVOltS9KsbGxqFu3rnrdr18/dO/eXZV/rmvBggVYs2ZN0PvLli3D5MmTS5WO5MSJE3j88ccVBPftt99qtvP+R9euXfHUU09h/fr1CA8PLxaaHDVqVJDjzrx589CjR4+gfQ8dOoT7778fwMnUW3JNuOSSS0q8Br0SEhKQnp4e9D4fnoqNjVVPqw8aNKhUzg3NmzcPShkBAO3atcPdd9+NSy+9FHXr1kXXrl0BFI7TW265RbkKFaWkpCSYTCZ06tQJDz30ECwWi3ryXO9uUp4uzMePHw9yc5Kgz8cffxzSBWnkyJEYN25c0Bwxm82oXLlyudXP0Lmh5cuXlwtwNnDgQLz00kunBTHOnDkThw4dwtChQzFq1Khi9128eLH6vnSuKS8vz3BSPw/Uvn179OnTp0iA2OVyIS0tDYmJiejXrx+qVq16lmtoyNDZ1e7duzF79mz8+OOPWLNmTdD3xHNBb7/9Nnr27Imvv/4aAOD1eoP2+eOPP3DrrbcqqC8uLg5A4YNBr776Kpo2bXr2KlyCDPBHiA48hGfcbrfGBUOm1SHgQJCCDi4ej0cTnC4oKIDT6URsbCwcDgdsNhusVisSExMVVMG0LD6fD+np6SpYb7VaVRBWOufIdC4yjZN0a+B2fSoaOsiwPL7PYLTcl9dACMDv98Pj8SinGvkjSjqWyH/6tGHyfQCqHUO5kxBWoKMSYSbZJtLZREIroSAgls196bJUUFCA2NhYVK5cGfXq1cOFF16I8PBwBTmwzoS6eCzPxXb3+/0qBQ6vjVAFr4ftwRtskZGRcLlccLvdqjyONxm41wf3JRBAZykJZ8mUTgRS5PH6tE58nzAInWuYJk6mGNKfnwCBBMKky450r6FDDcEglks3GT1EIK9ROu+EhYUpFxWCNVarVeUsJyyWnJyMmJgY5b5EB5r8/HxERUUpqITXFRUVBYvFomAobpd953a74fP54HA41HxMTU1VTkCcH16vF263G8ePH1dP2MixLR1V5JiW1ythPLaHHAuce3qHLQlp6cc81wiTyYSoqCjExsbC6XQiKipK1Yd15Ly1Wq1wuVzweDwatyM5rwl/ud1uDYyXl5cHl8uFw4cPK5iNzkHSkczv96v616hRQ6VBJLhDuJJp1JxOp7qZFxcXp8aq1+tVT4XpU5exHSRsyPFIV7eoqCi1foYC5SToJ+E3r9eLtLQ0jYOVhEN5rJzPEtiTny+ybNaPc0HOeQl1sQyZxo//2N50uOJ6xZRqGRkZqq04Plg/mf6Q5cXExCjLbb0zGeeQdCSTMFJJTwkaMmTIkCFDhgwZMnSu68UXX8Rtt92GzZs3F+vCUF565513ioQPxo8fj27duuGCCy7A999/X2QZ3bp1U+m9bDYbevbseUbqejbF3xb8fXS2lJCQAJvNhl9++aVEKOKKK67AkCFDgtxIcnJyglJDUc8880y51fW/qFq1agX97nz33XexZMkS9bpmzZrqd++ePXtCljNt2jS0bNkS1113XchURVIEf8LCwtCtW7eyVP+s6+abb0b79u017wUCATz44IMYPXo0OnfurIIgemVmZmLPnj3o3LkzvvrqK5WG58iRIxrnl08++QS9evVS7fjee++hd+/emDp1apH1uuuuu9CjRw/ccsst6p7KDTfcEDJNYUpKCtq1a4fOnTtjyJAhAIC9e/eq7Xo4pTSKjo4O6fpRuXJlRERE4KmnnsKll16K2bNn49ixY8WWdffdd2Pz5s2w2+1B25o1a4alS5eidu3aajvBHwDo3bt3iXVNSEhAvXr10KxZM5U6kONbAp5hYWFwOp3lBtf4fD5Uq1ZNvX7sscfwyCOPqNe5ubma9IYA0L9/fzz33HNB9+2pCy64oFzqZujc0Zo1a7B9+/YylxMeHo7OnTtj2rRpIedScZo8eTJmzJiB5s2bY9SoUcW6MhQUFOCZZ545JwPT8mFqQ+emOnXqhCeeeAJ33HFHkGsdZbfb4fV60b17d9x111348ssvVWpRQ4b+rdq9eze+/vrrcz5VIaHvpk2bolevXhq4HwD++usvrF+/PuSx+hS1FSkD/BGSaVMI10hHBenKIcETGdyVaVUYZGZAmkFls9ms3CxkmhbpRiPdU/ha/pOuLtKFgfCIPl1QqLRB8n09aKL/m/+kWw/bR183vXOQrIt0/ZGAijyPlEy1pneQ0NdVpqXSp+aR186/pUsT+95isaj6EnCga4keqNE74PC6WE8JzehBANnPbEvuy2C57KdQIAfLkG0s6yYdffRgV6gxQLEt9CnG9P0joSsJLkhHFAlLFNUP0tFEPxb0PwTlsVarFQ6HAzExMQq+4hcqOt9ISE7OscjISJXuSDpiFRQUwGq1Kmculs1juR+hvYiICDVGpDMWgRcCQHRLiY6O1jx5xn6XddC3Leul72e2u/5HvHSHke3FfmB7s/4cs6yD3uGL6xn/SSBISjrjEEgkCEk3MwKKctxx/hE649jhGCCQ4/V6NWAS9yVMSUcdCbrJucD1WIJ4lBx/EpSS66XsD+nOJfuC7mD6f9J5S/YT+0/2if78PJ/sV33by7EpXdp4nXog0GazqdRqGRkZQWnICB/JMSCPl4AZ96dTFrcV5eyjX78NGTJkyJAhQ4YMGTrfdM8992DPnj2Ijo4ul6fYT1d169bFwIEDldNPcels5s+fr0n50qhRo9NKf3Muib85Qv0+PZNyOp0IBAL47LPP8Oeffxa771133YU6deoEPfXtdrtx5MgRzXtxcXFITk7Grbfeiu7du5d7vf8rCuW0k5iYqHGPkY4RWVlZRZblcrlw6NAh5cpb0jkfeOABNGvW7FSrXKEiNCfTLq1Zs0aTgm7lypXqQSepY8eOYdeuXSGD+vPmzVNuaIcPH1ZB9KuvvhqXXXZZia4VgUAAK1euxOzZszXnLSqd1h133IHhw4dj0aJFAIDU1FS17XRSjkdFRYWso8lkQnx8PAYOHAigELopSS6XCzabLcjBCCiEkpxOZ5EB4tKooKAAiYmJqr4rV65UMJL+XvCIESMwevTo0z6XFB+KpPTXV7t2bdSrV0/zns1mU0/Ih1KlSpXKpW6Gzh3t27cPhw8fLrfyOnTogKuvvvqUjsnMzFQA9QUXXFDiZ+wPP/yA5cuXB6XKq2iVNgWloYrTlVdeiWbNmiE2NrbIfcLCwtC1a1c0atQINWrUwMUXXxzSzc6QoX+btmzZUmz66nNJ1apVw+23345Ro0bhtttuQ5cuXRTYU9TvgrJ8lytvGeCPEIO2ABQw4Pf7VbCaAWUGNZk2hU8XBQIB5fbB1Ex0VwBOppCy2+2Ii4tDXFwcnE6nojylq4J0T+APHJl6S6bTYsoiBpgJFwEnwRLpbMGAs3SFoPTAgAz+ynRBdE7x+XwaRx99miEJCdDVg9fKdGXSLSQUEELoQbpdSNcg6cgiXWTkMRTrpE9hw9d0wXC73cjLy0NmZiZ8Pp8CkJi+Swa9JTBBNxDp1sHrIYwgHVry8/MVqc1UVrwm/ouKilJBdY4PwhJse5l2jfWREJo+2C6ddwi30fVEpu+Raa/0adMkxMB/hIzo+iH7U7r86I+VsBP7m/0rxybfJ1Rit9uRkJCAtLQ0ZGRk4Pjx40hJScHx48cRFhaGrKwslY4rOTkZdrtdzQ2v14uIiAhER0crkMdqtSI6OhpOpxP16tVD+/btERcXp0AIoHABj42NVfsSEpOwlR6AMpvNsNlsSE5O1qT2kmNetk1YWJgamzLtlh7q4Fih5FyRjmSybLrV8D26VPEcHJNyfThx4oSC5JgCLz8/X5OijtfFsefxeLBv3z7s3bsXBw8ehNPpREJCgvqBLx28uNZxjkgYz2w2a9qCawXnsdPphMlkUk972Gw2NdYkhBQWFqaZm/LmsB6YYj3kvJH7Et7k2pOTk4OsrCzVb6wn5w4/L9g33M/n8yEsrNBpyuPxaByv2EfsM85/zmk9KMTtBNr4GSbTw7F/rVYrkpKSEBsbqwE6WTeTyYSYmBgFhum/tMi0cISvHA4HLBaL6i9eB88pbfgNGTJkyJAhQ4YMGTqfdeWVV+L666+H2WzGO++8c1bOOWjQoKD3du3ahU2bNuHaa68FAIwZMwYjRowIefz+/fsxadIk9dpkMmHq1Km44oorzkyFz4IYAONv2bNxvr/++gsTJ07Evn378MsvvyAlJaXI/bt06YJ+/frhoosuCtq2c+fOoCD56tWrsXDhQtSqVQtPP/00evbsec65X5wPsFhSUlLI91u1aoXly5ejSZMmyvHE5/NpnGmoW265BTVr1gQAdS+pOJnNZqxZswavvvpqGWt/dvXrr7+qvwmi/fnnnxg3bpxmv4kTJ+Kyyy7DgAED8MMPP6j3v/322yLXkNGjR2Pnzp0AgJYtWwIA/ve//+HRRx/FsWPHMHPmzFLVcdSoUbBYLEhLS8PKlSsxZ86coIBqbGwsOnXqhFtvvRUvv/wyAODZZ5/FmDFjABSmnyrKTWHEiBG48cYb0apVKw3EY7FYkJubiwceeACNGzdW72/cuBH33XefcokPBfPoNXv2bLRr1y7ktk6dOpV4fHHaunUrRo8ejXr16uGhhx5CamoqXnzxRQXQ6MGfm266qVzWlRo1amDatGl46qmn1Hv79u1T97WzsrIwefLkoJSGy5YtC5k+g2rSpEmZ62bo3JLH49GAhWVVYmJisalNi9LRo0cBFM7t7t27q9SERWnixIn49NNPzymHnaioKOO+6jmsBx98EPfdd5/GCa0oNWzYEE8++SQaNmyIqKgoDB8+HOPHjz8LtTRkqOJ0PqU5rlq1Knr06IEHHngA06dPxzfffINnnnkGBQUFOHbsWNBa7HK5zqn12QB/hAhAZGVlKUhFAiqEKyQ0oodRmBJLWu/p3TucTifi4uIQExODqKgoZGdnw+VyweVywW63w2azqZRFMk0MA60SsGFAW5/ihyl9uC0qKkoTcNZDBYDW6YL1ljCGPnUMoR+m3yHQonf1CeVQwoC0x+OB3+/XBONZZ4ITbG+9OxFhLL07DN2B9CnEpGsF66kHUaSzD6+LoAPbhH0h+zUQCCjXCwbpMzMzFTjG/QiCSNBAwk/8x3Px2uTTLdKVRfY9+xSABqQhcCBBCIIVetiEwIB072E7y76U10N3I45XCccRDGFd2Y+hnGykY4x056Grld1uVxBEWFgYsrOzkZ2dreaoz+eDx+NBamoqEhMTERERgWPHjin4RzoT5eTkKBCB85Sp9QiRmEwmHDlyBPn5+bBYLKqNfT4fTCYTzGYz8vPz1WuHw6HaIBAIaNKmSQCNIEhsbKwCXtjeeuCObS1hMbmmcF+9ywr7Ojc3FykpKWo94vUD0Lh3sU7sV30qP9l+nBsEW+T6SNDj6NGj2Ldvn7qGpKQkxMTEICEhQd0w4xogx0pERIR6Csrr9SqYTt6osNvtGpccrhUmk0kzV/TAoISxuI5IcIdAD52Z5PpBdzOORdaVN5pdLpdKIcfzShcvmT5Nrje8doIxHNsSfJTrlFzfZH/J/elkRnCKsJzX60VWVpaaM/n5henu7HY7srKykJqaqupgtVoV7Mb0Y3Is8Lz8bCHww3VAtjHHmHQwMmTIkCFDhgwZMmTofFabNm2wePFixMTEqNQ2Z1JjxozBRx99FNK1QgbhV65cWWzqqQkTJmDixInqddOmTTF16tRzypb8VESXZT6Ed6Zlt9tRtWpVVK1aFb/++muJgcxatWoVuY0P1VBdunRBgwYN0LFjRwCFgM3nn3+OlStX4rfffsPHH39cPhdRBl199dWYM2eOxi3nXFRRDizVq1eHx+PB4cOHFTji8/mC+vGOO+7Am2++qaz++eBXcbrkkkvQtm3bslf+DMvtduOjjz5S68SMGTPUtgYNGgAohAJr1qypUkZRW7ZswcKFCzFlyhR1D+Tdd98t9nyffvopgMIA57hx49C7d280adIEbdq0wciRI0tV5+rVq2P9+vXo2LEjOnbsiKVLl2LDhg0akCaU+8fw4cPx3HPPYefOnUhOTsasWbOC9rn99tvRtm1bvPjii2jSpAnWrVuHpUuX4r333kNCQgJuu+02tG7dWnNPavXq1Sq919tvvx0ESek1aNAgjBgxokhI8HQABimv14u2bdtiypQpaNSoEZxOJxYsWIC1a9di9erVaNSoUdAx5QFhHDhwAO+99x4WLlyoILk///wThw8fxoEDB/D0009jwYIFmmPq1q2LiRMnFjmf/H5/kWnlDJ2/uvrqq1WKvi1btpS5vKioKLRs2VJ9ByitpENfIBDA8uXLSzxm0qRJQeO4IsUHPg2de3r77bfx1FNPhXQdLK26d++Ovn37lmOtDBkydKq68847MXz4cDRo0ACZmZnqfZvNptKqrl69OiiVqQH+nMNyOBwoKChQQW0ZaGUwmXAHA+N6IEOCMTJFlUxZJeEh6YSTm5uLmJgYOBwOFXil8wRT7AAnQQAGVGV6KwkYyXpxm3QN0TvzyICz3pWF1xXKwYdAjXSr0cM04eHhKjjMtExMjUNogUF8BtMZWKZrBgPgDLjTcUkCHaxrqHRH8tokoMVrZ7De7/fDarWqiUo3GAI3EqiSLjYEC2Q9JCijd0BivXleQg4yjZAcjxwj8toY7Jdggt6Bh2mSuF0CXgUFBSq9Gcc0gRXZB7IN+L4EEuQ1EcoihKB3atEfy9cAVH/rwSrWISoqSgEjJpNJA6i4XC54PB7ExsYiPT1dQTtAoaVnWlqaJm2VfDrQZDJpUvXl5+cjIyMDhw8fVlBPVFRU0LpAgIhtXbNmTURGRipQKCoqSq0Hbrcb6enpcLlcyqWGP1JYT/3Y5XwiUKYfz3Rskn3F8tgenGusL9cMuQ5JSIvlyrWD9YiMjITH40FBQQG8Xq/m5kdkZCR8Ph/cbjeysrIUxJOUlIQqVarAbrerMc5+YF05Lun6w7kRCASQmZmJyMhIJCUlwWw2q/7Spy0rKChQUFEgENC4gkm3M85Btqd0CwsPD1fp46SDk37d4Zj1+/1wuVxwu92qfTmmKOmsxTVfOvmwzbne6+e/hBEBaNyY9GWZzWYFsEmwj8fK9R4ohN2cTqem3wlRyXEmQTmWFRkZqRzrmG5NfuawbKbNk2BkeeWTN2TIkCFDhgwZ+jfIgKPPPzVq1AjPP/88rFZrkSluy1OHDh2Cz+dDnz59grbJgPfXX39dYlDtkUce0aSvqVOnDqZNm1ZudT1bMplMSElJwaFDh5Rr85nUFVdcgeeffx5OpxO7du3C4sWLSzzmo48+wi233ILHHnssaFuHDh3w+++/Y+nSpZg4cSIGDBgQtI/FYkHlypXRoUMHjeNIRWnVqlUYP3685ib4uSiZXl2vLVu2ICsrC9HR0QgEArjvvvswZcoUzT7Lly/HRRddpOCIuLi48xaO02vFihUYOnQovvrqKwBAv379ABQ+1Uw1atQIc+bMwapVq7Bu3bqgMr744gusXr0aZrMZ//vf/4o935IlS7B8+XLk5OTgySefRPv27XHFFVec0hh65JFH0KZNG2zfvh379u1D586dceDAAaxZs0bt88orrwCAgnDGjx+Pfv36YerUqbj99tuxc+dOmM1m9OrVS1P2sGHD0KdPH+zduxd16tRRKYDuuOMOAIVg1M033xyUyiw7OxtdunTBo48+WmyawU8//RSff/55yHRysbGxWL58OerUqVPqtgil1q1b45577oHVakVWVhZuuOEGREREoFatWkW6DMXHx5dbyqDk5GSsXr0a7dq1w4MPPoh69erh559/Djk2kpKSinX0yczMxKZNm4zvRf8yzZ49G8uXL8f06dOxcOHCkC5rp6rk5OQi3d2KkgzS1qhRo1SAxbZt2/DOO+/gwIEDp1zHMyE+RG/o3FOLFi3KBP14PB789ttvIT93DRkydPZUt25dTJgwAePHj9d8PwYK0381btwYGzduxN9//63ZdiZTQ57O96LIM1CP81YMpMqgPQPiwEkogW43bHDptCMDxTk5OSqQKwOwMlAvIQv+LdN0MfgtA7N83+/3K3CDQWq6OEirY+kCIyEf1o91kqm1JCBDgIewhoQ/CGaYzWYVyGeqHQm/sO5ms1kD3BBkAU6mtyKU4fV6VZvRSYYONvyBkpWVhby8PA3wIJ0+JAjF8vVQDutHh6e0tDTk5uYiIyNDpX+SabrCwsKCnGz07a934GGbMvBO0ITb5M1CmUopLCxMuWWE6h/CSMBJFxi2A+tEcITnJmBBYKigoAAej0cF9yUxz7ahu40EF3htEszKzc1V56LTkHTDYl/xOqQDCttA36Y8hwRs2Hd0mnI4HIiPj4fFYoHX61VfgiMjI2G1WhU4FRkZqZxkCPLoLcE5hgjrWK1WDRhls9mQlZWlcW0pKChAXFwcEhIS4HK5cPjwYTUvZBu4XC7k5eUhJSVF9QvnBvtDjmGTyaTagACPfr2Srl2EvNgHTE1H5eTkwOv1qhthEmDjXJfnl2sZQSU6K3Euy3HNsgixxMfHKxjHZDIpUJAApARSAGhunPNGrtlsRu3atRXUkpOTo9xo2GYEmmJiYmA2m+Hz+UKmNWP/cp3heSTUSEhRn0ZLpq/j+x6PB16vV6V6YxtLlyfpviVTXkkHKK4B0rlMfmbIduJ6wLWI4jwl5CeBSH6G8Ty8HtnXnPf87JCfLTINZXh4uEpZyXYgDMXPJV4f007KsRQVFRXyppchQ4YMGTJkyJAhQ+eLcnJy0KRJE7hcLtx+++1Yu3Yt5syZc8bO98knn+Cmm25S9yWkVq5ciYEDB2L79u345JNPSlXeRRddhMsuuww9evTAoEGDcNNNNyE1NRX33ntveVf9jCknJwctW7ZEzZo1VTCvatWq6Nu3L6ZMmRL0QFxZ9eyzz6Jdu3Y4ePAgunbtWuog4OzZswEA9913n0q9QzVp0gRNmjRB9+7diy1j165deOaZZ06v4uWotLQ05eByrioqKko5S0gdPnwYTz75JKZPn46qVavC4XDg888/D5luaseOHZrXycnJZQrmnSs6cOCAcvgZNmwYAKBXr14YMWJEEDgXGRmJatWqoVq1aoiNjUVGRobaNnnyZDVmu3fvjsTExBJT3lWpUgX169fHHXfcgfr16+PEiRPYs2fPKdV/+vTpylnmww8/RHJyMo4dOwYAeOGFFxAfH6/SfD333HO4//77FdT1+eefo1mzZnjmmWewZMkSAIX38l577TW88sor2L17N0aNGhV0zuKcNYrbFhcXhw8++AB+vx8XXXRRyM+HK6+8Ep07dy7l1ZdOmZmZSExMDPlZIdWvXz+89957GDp0aJnPWa9ePcydOxcHDx5ULk5NmzZFmzZtsHr1as2+OTk5qs9CKSEhAe3btw86ztD5r2effRZA4Xpw9dVXnzK0o5fH4zllGKdPnz5wu92w2+3o3Lkz4uLi8OOPP2Lfvn3FHvfbb79h2rRpGDJkSJlBvbLK7XYXCxsaqjiV9UGA/fv3Y9u2becMZGbI0H9VjRo1QnJycsht9evXx5gxYxAeHh6UNtXn8ym30HNBhuOPEB0sLBYLnE6nJo0OA7gyRZZ0ztFTVwzaMlAr3V4YbCVIxCA5IQA91EHARw+uyLIBbfopCelIQIBlShBD1kkfaA51Xrk/XzPQzAC8PnUW/2daGx7D8/B/tiXhGb2DkXQhYloqGdCXDiQ8f1HXJeEfwgwEEwgO6N1XKP17euckfXoyCVJwLEk4SaaCkiCFBIr0QIBMDyXbULaFhNM4DuQYIVhE0IjOPvprBaBxAZJtLd2H2Kasq0z7o29zCTHJY2V/y7L17SfdlAjqSQiG73O8y/bJycmBy+VCZGSkSo3EfVgHq9WqKVP2CV1RpMLDwxEdHY0qVarAZrMp1xnZhvL6ZL9LAFBes34uy76WYBTFGw8SyuL6xfNLVyNej34cyfPLMcOxwTRfrBsATcovh8OhUrPJfuP5WUfpWiMBQbnGSuiRgKGE3eS4tlgs6lr144/XxHbgeeVYkddOdxpek97Ni44/sp9ZtoR95P76m8+8Rp5HliHBOD3QJ/uLkqne5HrP/Qmx6lPPEdaS8vl86jr0zm2yP5iqjv2rh/skWMYxHh4eXuYf+IYMGTJkyJAhQ4YMVaRWr16NWbNm4euvv8bGjRuVy+WZ1Pvvv49NmzYFvb9x40bk5+djy5YtpXbR2LdvH2bOnInFixer4Pv5mlpAPsE/ZswYDB8+vNyhHwAK/Ni7d+9pBWXi4+NP+9yrV68ulcOQoUK3n1CQjs/nw8GDBwEUpmyLjIyEy+VS24cNG1Zk+qGYmJhyc0epKPn9fsyZMwe//vorGjRooEnjNXz4cFx//fVFHtujRw/1d3h4OK666ir1Oj8/v1johzpy5Ah++eUXXHrppejTpw86dOhwytcwcOBA9ffVV1+Ne+65R72ePHky5s6dCwBo3749Dh48iC+++EKzfd68eRq3mby8PCxcuBBmsxnXXHNNUIo4+YDUqermm29Gjx498O6772L9+vWabXTvCgWolVU5OTml/iwqr+DUb7/9hrfeegv9+/dX78XHx4dMdXjdddcVm9ps37592Lp1a7nUy9C5qcOHDyM9Pb3MzginM3/099Dr1atXInhL/frrr1i7di0A4OjRoxUG3xjQz7kpPmB/uiooKEBGRob6nmLI0Pmi/v37Y9iwYWjatGlFV6Xc9NdffxW51sbExKBu3bpB0A8ApKSkIDU19UxXr9QyHH+EsrOzFXASExMDp9OpnEtkmiqZwksGW4GTQXMGxWVKFgn3MGhrNps1YAVBFgZ2XS6XcreQAVzeyJDOGQxy001Huqkw4C4D5UwxJYPUMgWZDP5LNx4CAxLMYT1sNptKHSVdYWQgX6bE4TYZlKdLiUzLIyGEiIgIlTNPBrD5BU7/v2xvCXbIgLzL5VLpyljP/Px8dUNAQhd8LVPASVBIXpuEN6Trhn7s0IUjLCxMwUe8Xhn810M+LJ8gBNuH44O5XyWcpocfJCzDcS4hHykJwUioQgIe0hVJAiqyXwiB6eEV1jMU+MLxHhUVpQh37sM0R0zX99dff6nymFbKbDbDYrEo1x3OTavVioyMDJWqimBEcnIy7HY70tPTVVo6AAqe4PGBQADR0dGwWCw4cuQIrFYratSogYMHD6o0VYmJicjNzUVKSooGomHqMra3vOb8/Hzl9iXTVLHP5Hscy9nZ2WpNIQRjs9mQm5urxi1dhOQHmMPhUNfDeoSHF6Zqo3sQUAhHhocXpsNiX3N/l8ul1srY2Fg1VwKBAGw2m1pLPR6PgnvYFlwX6Zglxx3bISsrC1lZWar/5Bjzer2aNFeyTWQ6NFkm13o5nyQIxJRtHHPsM9qq0hGKDjtybnKtl9ch3dN43XSeslgsCkSSdee+XFeys7MVPMM1gtcjwRw5z/jjQ84rq9Wqzi+fUuPnB8cdXab0c53l6+EjCX95vV4FrTKFG+vUokULGDJkyJAhQ4YMGTJ0PmvGjBmYMWMGHA6HBiCYNGkS9u/fjw0bNpSra8E333wT8v0nn3wSc+bMwQsvvHDKZWZnZ2PJkiVo3rw5kpOT8d577+HOO+8sa1XPqAYPHgynMIMp3QABAABJREFU04l33nknaFtYWBjef//9cj3fq6++isqVK6NatWrYvHkz5s+ff1rllOTAIeV2u/HKK6+gefPmqFatGh5//PEzah//b1K9evVQrVq1oPc3bNigILcqVarAarWqdDOVK1fGI488giFDhqBLly5Bx1arVq1MAb1zQevWrcMjjzwCAHj00Uc1cFSrVq3QqlWrIo99/PHHkZaWhlWrVmHSpEkaxwvpBFQa9ezZE0AhQHequueee9C0aVPUrVsXN9xwAwDgiSeeUNvp1HTbbbdh5cqVOHTokNp25MgRzJo1C3feeadyCXM4HFi2bBmqVKkSdK6CggIMHToUs2bNKnX9unbtivXr1+ODDz7AbbfdhtzcXCxbtkyzj81mw9dff42UlJQSAcKdO3fixIkT6NChg7rXW5x+/fVX9OjRAw888ECp6puWllaq/UqjPXv2oEmTJlizZg3atWuHSpUqoX379kEudH369FH3C/XaunUr3n33XZViz9C/Uzt27MCuXbvQvHnz0wbrAISctyVp3rx56NSpE4YOHQqbzQar1Yonn3wS4eHhJX53+Omnn3Dw4EEcOXIEZrMZ1atXx7XXXnu61T8tHT58GCtXrjyr5zRUsuLi4vD4448Xm8awJPl8PuzcuRPbtm0rx5oZMnRmNWDAAIwdOxbJycn43//+h7179/4rPsPHjx+P48ePqzSypZHP58OhQ4cUIHouyAB/hLxeL3w+H2JjY5GXl4f4+HgcP35cBTIBqKA507kwQCuhHAnOUBKYkMFZACrATKgmJydHBZwDgYBKO0MQRJ/aiaAJ9wvlVKOnmvXQjkxdRbBApuKh04Z0jpHghQxS+/3+IEcS7sNr5HsANOcJCwtTPz4YMLbb7RpHFrokRUREqHNxf1kP+T//lqAEj2Eap/z8fGRlZakUNgxWS1BH9rcEu9jGhKPy8vIUQEPoh+fm+JCpc/6PvfMOj6Jc2/i9aduyabTQu/SOVEGaCkr7pCmKHRQ4duzt2I6C9QB6FAsKiiiiNBGkKU1AUDoIUkMJLW2zNWW/P+L98szsppLQnPu6ciU7O/POO2/L7jy/uR8JIDB4npOTo0AL6QYjXZxkX8t0OoQd6CzFMcLxwaeVsrKy4Ha7VX/RqUW2kSybgIGExWS99CARQROOc+lSpR8/nEMSdOL29PR02Gw2BecwnRMdR3Jzc5GcnIyEhAQ1P9je0pWLYFp6erqCgq699lq43W4cO3YMZ86cwZkzZxAIBFCjRg24XC4FmnBM6B2K2LfMGR4eHg6PxwOHwwGr1Qqn06ngFJk6MDIyEn6/X3PtEm6TMBXHEdMMxsXFqTrIMU5AhY5EHI96sCWUi5EcjxIM5NrEfs/NzUVsbCwyMzOD5pQEsQgnBQIBlRqRZdMFJiwsTPUn4Re6NIWFhaFq1aqq/snJycjIyEB8fDzi4+MVWGO1WtWTebJ95VrsdrsRGxurxgXHf6iUhNwnLCxM44AloTyn06n+JnTEPpUuNxx/0lVHrsuEJAkO6ucc12e2iVzPJfQp4SnCZOxDpjLjdcv0jnLNoIORXLslyENASA+vyuNZLs+Vk5OX1pD9wvUtvycpDRkyZMiQIUOGDBm6lNStWzdMnjwZ+/fvx4EDB7B9+3Y8/vjjAID169dj9OjR+OOPP8q0DrGxsZg1axZ2795d7GPnzZuHefPmoX///nj55ZfRs2dP/Prrr1i9ejX+/e9/B6X/udC67bbb8NJLLymAQa9HH3201M9Zp04dDBw4EF6vF3fddVeQe0dR9d133xXZVemxxx4LCTYZKlw1atTQuCtlZWVh/PjxGkCkUqVKWLlyJRYvXozXXnsN9913H+Li4jSgiFRZOLOcLyUlJcHtdmPRokVqW5MmTYpVRtOmTfH999/jyJEjQakPmNodyHvqe/DgwRpnnvyUmpoKq9UKj8dT5HpMnToVANC5c2cVcN+4cSN69+6tcR364Ycf8p0/H3zwASZPnoz+/fujR48eaNmyZcj9cnNzg1K+FaT169ejadOm+PDDD/HTTz/B6XTigw8+0OwzePBgPPLII6hTpw7q1KmDdu3a5Vve7t278cADD+Dee+/Frl278J///AdVqlRBrVq1MHr06JDH/PLLL5p7X4XpscceQ/369XHbbbcV+ToL0siRI3HfffehVatWeOutt/Diiy8G7fPbb7/l6wowYcKEkKn3DF1+OnLkCJxO5zm7gd9xxx0qfWFR9frrryMsLAz33XcfgLz/Ge+//z6OHDmi0gDmpz179uCRRx4BkLcO1a9fH40aNcKpU6fK1Nn8zJkz+O9//4svv/wS+/fvL7PzGCqZXn/99XOG5q1WK0aMGIFt27aplJWGDF3s+uOPP7B9+3a0adMGDRs2vCygH+qtt95Cw4YNcd1116FcuXKFuimuW7fuokuHfGk/slDKYtCSzhTA2VQxBGzS09Ph8/mUI4w+hYvemYUpYvTuM/p0RmazWQEYDFx7vV4FOhA8YDCfAWG6udClISsrC2azWUEnoQAY1kmmcZHAjz41mXT9IfAhj5Viqi/+MKBOlwy6QDDVkEzvRFcVBonDw8OVg4tMpSX31x9PlwoJUcg6yXIAbdoqulMwSG42mzVpoujKw/aSYAbbNSwsTKWMk+NDQjgRERGqXMIU+nEo/+a52e8EITgm2c56gEhCYdKlinUlEKC/Ngnk6N1mAGi2yfEQFRWlcTmRY4qwFl2zODb1bkahQLKIiAjl5OJ0OuF0OpUbk91uR2RkJNxuN9LT05GSkqLszb1eL3w+n6YtPR4PTCYTEhISUL58ecTExCA3NxfR0dFwOByw2WwKWDh06JCqH6/BZrMpmIXXlJ2dDafTqbF3J3jBdk1NTVU2wRIgYv85HA4FvrCdCTDRiYpzR+8OxDEGQK0dnAdRUVEKOGJKKY57trnVaoXb7da4S/H62McE2YC8Gzu8LpmSimOPaxOhskAgoCArziuLxaKpC/uJbV2tWjVERUXB5/MpOIhj2Ww2K7CMfVexYkX1tIaETABo5qNcz0Ktg3L8y/nk9/uRmZmJtLQ05TpEqIbtwzaRTkL6lF/SbUuua/I9XhfnD9tLusdlZ2cjJiZG9YF0R2N9srOz4fV61XHy/5Jc5+R4kuuWnJe8ToJUfJ/jzG63w2w2qzWAx9BBjMcRJDLAH0OGDBkyZMiQIUOXg/r27Yt69eqhX79+eOCBBzBlyhT1Xp06dQp00SgtrV69GkOHDg1KRV0czZs3Dw8//DCqVauGDh06IC0t7aJMbTRt2jS0bdsWXbp0wapVq8r8fFWrVlXuMVu2bCkx9AMA7777Lj7//HOsXLmywFQO33777UUJ/ZQvX/5CV6FIqlixImJiYtRrj8eD48ePa/aZNWsWRo4cibFjx+LJJ59UDiQnTpzQ7JeYmIhPPvkE3bt3L/N6l5WuvPJKtGnTBr/++isA4JNPPkHdunXz3T8/Ryu73Y4GDRoEpYeST0O//PLLhbbVqVOnkJubi5UrV2LkyJFFvQwlh8OhgUPatGkT5KZ13333KXcnqdq1a+P2229Hv3798N5776F+/fr5OnHNnTsX69evL7Q+zZo1w9y5c3Hs2DE899xz2Lp1a77OITfddBM6duxYaJkAkJ6ejiNHjmDatGlo3LgxvvjiC0yYMAHffvttvsfwvEV1UXE4HCHTcZ2LTp8+jSeffBLPPvusxg2f+v333/N1ierTpw/i4+NLtT6GLk6lpqaWCljco0ePYh+TlJSEt99+G7/99pvaFhERgQ8++AB33HFHkctZs2YN3nnnHSxYsACfffZZmULevF8rYxyGLh7t2LGjRC52oTRs2DAMHTq0VMoyZKistWfPHqxatQoHDhzAhx9+eKGrU+oaOXIkOnTogGHDhhWYTjs1NRVffvklli1bhuHDh6Ndu3YXhVPoha/BRSQ6o7hcLhXol04+kZGRyMjI0IAr2dnZ8Pl8KjAtgR4Glunoo0+5IgPi0dHRKm80U+wwSC3dXsLCwlQw1WKxKIiEHwAYKLfZbBpnIZYjXVr0UAcDzSxPgit0rWCZMiiulwww0+GBZUpXF1k260OnCDpTEGxisNhms2mOkVBPVFSUAqP0jkcSdJHuF2xTOgqxPaOiouD3+zVOT7Jcggd02GB7SIckCU/J/pMwhYRdgLNABsEgCXeYzWY4HA5ER0crmIxgk3ThYT0Ilvh8Po1jCIP4Ho9HjWO2P8EXQgwS8OE++uuQLlPcFgrMkmOQv1lXs9ms5o6EftjnYWFhCvpJTU1Vc0pCBYFAAJmZmXC73erDMMcEfwKBQNB1HzlyBPv27UNSUpImjVRaWhqSk5MREREBh8Oh6sx5y+ulU5Tb7YbL5VJ9ybWB5+bY4viQDlXA2RRpQJ61N0E01lu+7/P5lLOUHF8crzLFmR42kU4yLIdwEecpHXvk3DSbzcjNzYXZbIbP54Pb7Vb1li5h7FOfzweXy6VSmbGtOBa5H+FGUsHh4eEq3d/Ro0cV3BIbG4u4uDjExMSo+crzms1m2Gw2VUeOtZycHPj9fs1NcI4V1lW6IzF9FgGkrKwseL1eeDweZGZmIj09HampqRoHLzmm9TBRqDVTgptMxSVddORawHWP8Cf7mSnHeCPF6/Wqcc8y2D6ZmZnweDxqfznmmCKPzkZybY6MjFQOWRy/EpSU8CTHDdtSXgevXf4/JURmyJAhQ4YMGTJkyNClLIfDke9n2woVKqBx48Zlev6bbroJd911F6666iqMGTPmnMpavnw5XnvtNTidTjzzzDOYO3duKdWy9NSyZUt88sknGD16dJnfUH3xxRexc+dO1KxZE5988gnmzJlzTuWtWrUKd9xxB66++mq89NJLIffZsGEDhg8ffk7nKQt9/PHHGDFixIWuRpFUoUIFzYMmmZmZGjcYqlq1ati8eTNmz56ttunBn3vvvbfU3FAulE6cOAGXy4Xly5cDADp27Ij27dur90+dOoVNmzap13/99VeBYNrChQuxefNmAMD27dvx73//W71XvXp1VKxYMd9j+/Tpg927d+Opp55CuXLlcNVVV+HWW28t1vU4nU5NnwFARkYGAGDQoEH46quvcM0112gcjqgDBw4oZ7QxY8bkm8Jh0aJFRa7Xtm3bMHnyZIwYMQJvv/12SPeR2NhYPPjgg+jatWuRygTyUqidOHEC8+bN02w/duyYuhcn97355ptx6NAh3HXXXbj//vuLfB6muystffvtt3jnnXfyff+PP/7AwYMHNekxqUOHDl0UgTJDZa8zZ86UCsTSt29fDBo0qEj7jh49Gg8++CCAvDR677zzDlavXq3er1GjBp5//nmNi1lh+uijj9CvXz+8+uqrmDZtWrEczIqj+Ph4dOnS5ZwAb0NlI4vFgkGDBpUo9VworVq1CsuWLSuVsgwZOh9avHgxnnnmmXN6OOJi1okTJwr8Pu90OrFo0SKV5vV///sfpk6dig8//BCLFi1C7dq1z2NttTI+UQkxqMmAOMECCWPQWQeAJo2QBDQYsJXBXj0MI90emA6FT1SxXJnmh2JQXoITDORK0IQBWIrBVu4vHRtkoFkCQBIUYJ1kcFo60+ilD3JLyEHv9CPdMKRjj2wL6XYUFhaG6OhoREdHayCX/EAkvQsQ95fuH4QoJCTBvpK/+TeD9qGC9dL5hD9sCxkM5z76fuB181zSkcRkMoV8ekPWM5SDjmwLgk4SJmAbcYxIyEl+8ZLpkPSBfT3kVNC41ztgSWCIY4ZQiAR7CBAQ6JGggr4P+L6sB+tAUMLtdsPpdCIjI0PjKAVAAzsRVGFd2P8SxtC7yTA1F8uSoJfP54PH49G4Ysn2kC4+0gFJD5/pxyDbSIKF7D85t+U4JjBCKESWJdcn4CzQxrUwlHisdPWSkAjBH4J2EmaS84ftxba32WxqzkunLQkMSrBJgkVcQ/m+hNZkGi4JxbAOvFZClexrSo5HOWfk3JFtKl179PNTzjMJCMp0dexL3uyR/3Nkm/D8odzS5HkAqP8pEphk+7GNOPf0/xfkGs1zy/9LHP8EnApaqw0ZMmTIkCFDhgwZupS0evXqIDcRqcTExDI9f7t27RAbGwuz2Ywrr7zynMtbuXIl5s+fj71796JixYqoU6dOKdSy9DRo0CD06dMHwFmH3LJQu3btcPfddyMmJgYVK1bE4cOHg2CDc1F+LhsVK1a8oGml8rs5nZaWplxxLnbxngHl8/lCph5Yvnw5Jk+erAH3fD4f+vbtqxxxmG7+UtVff/0VtC06Olrz+syZMxqobeDAgQU6xiQlJWHlypU4c+YMNm3apJmHCxYswLp16/I9tnfv3vjhhx9w8uRJAHkQUkngyPfee0/z+u6770aXLl3QqVMn9OvXDy6XK6TjD4AiuXJYLBY4HI4i12fJkiUhQRaqU6dOuPPOO4ucggvIexhQD/hwu9/v19xvmTlzJmbOnAkAaNu2bZHPAeSto+XKlSvWMeei48ePY8aMGUFgqdPpxOrVq3HmzJnzVhdDF07p6emlAsnEx8cXybWqRYsWaNmypeZz0vLly7F27VrNXKpduzb69u1b7Hqkp6fjt99+Q1JSUrGPLaqioqJCrgmGLqyuv/56tGrVqlQecM3KysLRo0eNddDQJaWjR48W6EZ4qatNmzZo0aJFvp/hjhw5gq1bt6JFixbo1KkTYmJiUK9ePVxxxRXo2rUrBg8efJ5rfFYG+CNE5wsJ1VitVhVApYsGA9RMoyKddaQDC3DW9Ufv9MM0RAwgM7jO95kiRzqMMACck5OjXBgkMEQYgIFlurcwUM3URHoQR0IoHo9Hk1pMwlB01+ExMgjO/SSMwLoxIC/L0oMODDBbLBbY7XaVEomOJAz+MwhNApvOJLIsXoseKJGBajqBEFhhqh6Wk5WVpWAHtq+EeAiOSDcmKioqSo0LQgaEAoCzUI/emUjCFhIa4zkIaTAdmoSqpCQwQ5cZ1pv9QRcTtoF0pKLbkoQBQkEFbF/CG3J80iHGYrEoMIBtzPEsYQHpbCP7jO3M7fwCznbyer2a1GcE4ywWS5D7kb7e2dnZKn0TnXri4uJgNpvV9bAPA4EAKlWqhMjISJU6i3MkKioKdrtdOfw4nU6VGkq6ttAFJycnB263WzmwZGVlITU1FSkpKapPOc/Ypvq5kpubq/qPY0pCJBKck7Af251zhx9M2dZ0ueI6x+vnHLbb7WrdczgcGthIgkIWi0XNaekgxvrpXXfMZrOCKunQY7FYEBYWBrfbjbCwMMTExCAuLk6tfXS34bop20D2Occ2kHdTg0AngR05j1hnii5O0mGJ18S62+32kGnDOIb06xLfk20m95EOZhaLBV6vV9kJ8jqZmk3OHc5tCfPJ9Y7HSghIpoiLiorSQHR62FCm85LQKuehx+NR5+bY4/9M+X+B7W7IkCFDhgwZMmTI0KWu6dOn44svvsj3/SuuuKJMz3/77bcDyHt6feHChedc3vLly3HLLbegVatW6N27N/bv33/OZZamfvrpJ9x55504ffp0gYH2c1GnTp0wefJkleLL6XRi6dKl2Lt3b6mdY+zYsSG3OxwOXHPNNfkeV5ZpcGrVqoVvvvkmJPyzdetWdOnSpcgphC6kKlSooLlH5nQ60aNHDzVXpNLT0zVt+thjj+Huu+9WQbeC3GsuZm3fvh1z585F/fr11bY6dergzTffRPXq1dW2adOmoVGjRnjllVeU80WjRo3yTeuWlZWF9evX46GHHkLXrl3xySefaN4vKJXVoEGD8MADD2D8+PHKMahatWpo3bp1sa/v0KFDmDlzJn799VckJydj4sSJWLhwIUaMGAG73Y6YmJh8197mzZsXWv4vv/yi4KSCNGDAgHznMvXbb79h9OjRaNGiRbHug7Rt2xY///wzOnXqpNl+9OhRXHfddRpHomeeeUb9Xdzg87Bhw/DDDz+ct3s0Bw4cwBtvvIEFCxaobRkZGfjoo4802wxd3tq3b1+Qw1pJ1blz5wLfv+222/Dss8/illtuwf/93//hiSeeQHh4OE6cOIH//Oc/+PHHHzUA4zPPPIPevXsXux5r1qwpM8eL3Nxc9OjRo0jrkqHzp169euG5554rFihakLxer8aFypChi1Xdu3fXuBiW5cMYF1obNmzAwoULQ8Kq2dnZmD17NqZNm4aHHnpIgaNRUVFo1aoVrFYrXn755QuWwtkAf4Sk001YWJhy5IiMjFSBfAbaGTQl9MAgPtNt6aEG6dJBOl8GwRnEBqBxCSH0IdM6sSwJaBDSkCmgIiMjFXBAmCgrK0sDBeidUmQ6I9ZNpg2S8I+EeRjYla5DDJITpMjNzdWAGnRk0QfrbTabBvLhNbIPfD4f0tLSVPCbAXgZzJauSdIlg8AE24EwBNtPOpNIpxXpVsT24P4S/pLQmJSEpGQAXLY9xx7bhmXqYTIZRNdDIXqnGJbJYDuhJAkwyH7isQQiJIAkHXBknzH4z7JkG8p68H26CplMJgVgsGz+jouLg81m04AlVqsVFosFNpsN2dnZGkCN55Gpv2TbMmUTnbwiIiLU/CYQ5XA4EBsbq+obFRWFcuXKqe0ulwter1ftwzkunbl4Hq/Xi7i4ONjtdoSFhSEuLk7BMzLNlVx7CEllZWUhISEBDodDgW1yfPCc+tRMbAeCI6yfHBdZWVlwOBzqfba/hJ1cLpfGNYnXx2sjUCLBHunSwzFCUIRAH9c91p0glN/vV/3mdDrh8/lgNptRqVIlhIeHK0jKarXCbrerp+7kXJdziefkeeLi4hAXF6dgNPYJ6yvXfLYDkHcTkE9ZMaUW1y7CRJynTE0mITk5D2U/85zSzUi/DnLdi4qKwunTpzXwoISjeJ3SHYjzSEKh7D86FzE1JSWdkuR4Yt35/4bgD2EruXZJwC83N1edh+OP/xvNZjO8Xi8MGTJkyJAhQ4YMGboc9M477wQ9jEOVpb33uHHjkJCQACDPoaI0A0K5ubk4ePBgqZVXEl133XXq+qhVq1Zh0aJFsFgs6uGI0tSwYcNQt25dBf0AeQ4ha9euLdXz/PbbbyG3v/fee5gyZUq+x82bNw+//vorXn/99VKtDwC89tprqFOnTlC/16lTB88++yzmzJlzSXyPmzhxIm655RaMHz8eM2fORPPmzTFu3Lh8HYtjY2M1EJnH41Gpo86nE0ppafTo0WjVqhUGDhyo2d6oUSOV5obasGGD+vv9998vtOxjx45h/fr1CAQC2LlzJ1atWlXkev33v/8FAPz444/417/+pbZLEKk4uvnmm9GpUyfMnz8fQJ6TUYUKFdT7Dz30UEi3plDOVZ9++im+/vprbNu2DW+//Tb+LdKXhVJkZCRmzZqF7du357tOdu/eHd988w2aN2+Ofv36Ffm6qBo1aqB58+ZIS0vTbM/NzcWGDRuwZ88etU0/four9u3b45tvvin2ceeiTZs2KSDjqaeewqOPPnpez2/owmrv3r3YtWtXqZRVr169At+/8sorccMNN6j7yaNHj8ZTTz0FIO++76RJk/DTTz+p/d1ud4lAON4fLwsVJ32fofOnHTt2FMvJrTA5HA6899576N69e6mVachQaapKlSp44IEH8Pbbb6NDhw4XujrnTcuXL8e8efOQkpKi2b5jxw7Mnj0bV199NYYNG6Z5QIJAoNlsxp133on77rvvvNYZAC5dz9IyEAOYMuUMA8x+vx9paWkah5PMzEz1mmlt6BIUFRUVlO5EOu3IIDWBAgaVnU4nrFarBmgJBV3w3JSEixhUNpvNsFqt8Hg8mpRM0l2G1yPdH+hGIVMfSccRmbaG5bAOEsbgtqioKLhcLmRmZgY5qRACYL0YGJfpd3JycpCenq76xO12K7BBn2aKQXO2J0XoyufzKTcfQjU8jyyLgXTCKgCUCwrhArfbHeScwr6RkBb7g+OJ+0iog+8DZ1NDMb2PTI/D8UinDz1swCA7gSaZKo2/6dJBSQhHXgf7j+0qxwG3M9jPgD+vi/WR6YY4biSUFB0drXHCYt9zXNIlx2QywWazKecVjlV+IJfjm6BETEyMxuGEsJbH41H9HR0djbCwMGRmZir4yW63Iz4+XsE6LCMhIQFpaWnwer1q3tOdhWOKIA3nbkREBGw2m2buyraik44E8eS8ktAfy2dfsq24n4QFAWhAP8I1cjyy3hIecbvdCrIiPMe5IwEttg3HE8cq53RsbKy6aSbfk4ANxwnHPt19YmNjNQCg1+tFfHy8Wjt4rEx/RahND2TRsUjCeTwn+4f9wDHl9/vh8XjgdDrVuJH9Kv83yPmi/y3BGUJfEs6R2+Sazv5PT09X80COCa5JeuiHx1ssFk2attzcXI3DFIFQCQ9xfMm5HREREZRSTqa8k+eU18wxQzhVzk9CtYYMGTJkyJAhQ4YMXQ46fvw4du/ejUaNGmH//v2a9FgJCQno3r07VqxYUerndblc6u/q1avjnnvuwdKlS0v9PBdCn3/+OQYNGhQy1Vi5cuVgtVpx6tSpUj2nzWbDTTfdhHbt2mmcXkrDSUmvL774IqSzT7t27fI9ZuDAgbjqqqsAAD///HOp16l69ep49dVXgyC2I0eOYNu2bZg4cWKpn7MstHHjRmzcuBFfffUVXn75ZbU9P3eJXr16oV69emjTpg2GDx8Ol8uFQYMGYe3atUHg2cWolStXolq1aqhTpw42bNiADz74IOR+iYmJQSCMhEoaNmxY6Ln27NlTou/yjz76qILp9E4aiYmJWLNmDbZs2aLcYIqjG2+8MeT2EydOhAzCP/LII7jjjjsAALt378bu3buxZ88ejB8/XvMwVUHKyspCRkYGWrdujVmzZgW9f9ddd6FKlSoYMmRIsa6FcjqdyhE8v/ZmyrbHHntMQW0NGzZEly5d1D65ubl44YUX4Pf7UbNmTTRv3lytIXoNGDAATZs2xfbt20tU5+Jq7969peqiZujS0+bNm5GamnrOTnaFrdOzZs1C06ZN0a1bNwB5qTZHjRqFtWvXYvny5Vi0aBHCw8PRokULVK1aFXv27MEvv/xS7HrExcWVKgRCrV+/PijYbOjiUGZmpoo9lJYSExNx7bXXlsn3BkOGzlXNmzfHNddcg9q1a6NVq1aoXr16maY4vFjUokULtGrVSvP/Jj09Hb/88gv8fj8efPDBAh0X9+3bh927d5+PqmpkOP4IEeqgAwmdHuiC4XK5VLA5KipKwTlMpyTT6gBnIQyCJHqnDpvNhvLlyyM+Ph6xsbEIDw/XQEfS8SZUuiIZKKZjDc8rXVj0zkF60U2Gx9HRhttlSheZLksG13kOs9msAAPgrJtRSkoKUlNTNV+8GLCW6YmioqKQkpKiAu6yzgRs9BNJutLIwLO8Jj0ownNK+IXnkc4qvH6CFoRupOOF2WzWHCOvjduzsrJU0JwQgnTRkfWVLjwSVpBuQvJaJTjE3zJQL+EtpumRsBDTIUnwR4JU+rEmwQDZxvK65bVzu9yP/c05JgEtjh/CCoQbCGcQqsvOzobf71dpvSRoER0djcTERAX50KnE7/cjIyND9QXdX3iuffv2qXrIVF2EI06ePKk+2Pn9fuWAw7Flt9sRGxurwBYCgTL1lwSVCFw4nU4FxXFOMqUWHWBkv7PPeR3SEYhrhHRfkWuEhM0oCdqZzWa1DkmIg8cwHRj7PiwsTI05ACqFYSAQgNVqVbAWnclIv3KN4TjIyclLTwhAAUAJCQkoX768BsjUr6ts41BAXW5uLjIyMnDs2DGNa49MfSVBSo6xtLQ0DbAk11nZD+wXveOWhJO4Lsj5KdPusS30N1jlOsD6RkREqDaV4CThJM5dgnxca9jHhBE5pmUfM6WidC7idXCus67y/whBLkJRPE6uKex3AnBGXmpDhgwZMmTIkCFDl4sIyx85cgRPP/00PB4PHn74YQB5gfX83IDOVTIwtXTpUrz99ttlcp4LodmzZ+P6668P6WLE7/lMx1RaKleuHBISElClShUNIPHtt9+W6nmAvBRLR48eDdpepUoVtGnTRrNt+PDhePPNN/Hkk08CyLtPQbeC0tRVV10Vcgz5/X4MGjSo1M93PvTcc89h8uTJyMrKwvHjx0Puc+LECZWixWaz4Z577sGnn36KnJycixr8SU1NxWuvvYabbroJ+/fvx+bNm3H33Xfnu3+oazl9+rT6+4UXXij0nOvWrStRXZn6QK///e9/SEhIQKdOnTB69GhMmDAhpCNPfrriiiuUK5Me8jl27FjIYzp06ACfz4eFCxdiz549uO222xTUVhToBwCGDh2Ku+++OyT0A+SlC5TQWXH0119/Yfjw4WjTpg1atmyJAwcOBO3z9ttvY8aMGQC0QNtrr72Gxo0bq9djx47FK6+8ggkTJmDs2LGw2+0FnptllqaK05+G/llauHAhVq9efc6fkV588cUC31+5ciW+/PJLjTNW9erV8dBDD6F9+/YAgB9++AEzZszA0aNH0bBhw5CpIQvSrbfeimeeeQa333473nvvveJfRAidPHkSt956Kzp06IDly5eXSpmGSlejRo0qdnrFwpSamoqtW7eWapmGDJWWtmzZgu3bt8NqtaJZs2aazxyXs9q1axfk4rtp0yb8+OOP2LlzZ6EPZDRu3BgtW7YsuwrmIwP8ESJQEBYWBp/Pp8h6OntI1wW6JdClhEFYBmsltCJBGQnuMAAu0x3R+YLBWuCsA48EOQCtA498X0Ireuci+UNJIEO+H2q7HviRQXMJpfCH7UioKpR7kCyfMIX+fLLNZTBa70IEQAO/SAcO7k9oR25nHWR/ScnjOUZk2+r7RH+c/jr0bjoSppHvySA9FQq84fl5Dj14oG/rUKnUpIOJvv5S+mvU15eSTjvcjxAKxzhTFxGOIhAhHa7k+GK6IpZLRxF5nQCUywnBLpZB+ECOUbafhK1kqj7Wwe12q7Gpd6Xi8Xqogv1y+vRpZGRkqGuXfcexJOcl0yJREqiQsJx0FpJlcJxK6dNJyTGU3zzg9cv95M0QCcvRbUcCMIT1JJgkQRg5x+U84rUnJCQgOjpac+NVtqt8LftLjnkJ6hAWk/CKfg1hKi2Oy1BzQLpayTGibz/9OsZ9CdDpU5bJfpH14zXo13N9P7Ie8prl/x7uJ2FQOZ6lgxCdqfSuRcBZaEsPXPEcTDUm5znBL44TQ4YMGTJkyJAhQ4YuBw0aNAj169dH+fLl0aBBA3g8HqxevRoZGRn4888/y8SdBYAmxczatWs1aXsudc2bNw8rV64M+Z7NZkNOTk6pP0wQGxursWin5MNBpalQ5e7fvx/btm0DkHeTuUqVKmjfvj2GDx+ubq5HR0erB2YMFS6mipcOWaEUExMDq9UKIA92qlatWsjxcLFo+fLlmDFjBo4fP45Dhw4hKSmpQLeWxMTEoG16eE4GxkMpOTm5RHWV9w8pp9OJL7/8Mmh7v3790LZt2yKV26pVK/X3zz//jP3796t1ITU1NeQxFSpUgNvtxpw5c/Dbb7/B6XQW28XokUceKfB9mSqwuOKDuGfOnMnX5cNkMingSQadk5OTsWLFChw8eBCBQCAobWBSUhJ+//33fNsmv3R456LmzZsbaYoMhZTX68WuXbs0AGJxdfr06SJBMX/88UcQ+NasWTN07NhRs8++fftQq1YtNGrUqFj1aNCgATp16oT69evjzz//POdUpIcOHcKMGTPUGlmaqVwNlZ769u2LatWqlWqZjRo1CnLGM2ToYtGJEydw4sQJZGZmIjU1FTExMRe6SudFVqtVZXqhdu3apb6zFcXNp2fPnqhVq1ZZVC9fGeCPEC2DmQZJBjOZtgSACogGAgG43W64XC4V/PT5fMpRQkIkBANYpt1uV0+GEbzQO8IAUClzoqOj1QBjeiEZQNZDORLckZCSHuQBoHHNkAFrvqcPYEvAgXWnewVTB7FebrcbTqdTA6YwwC6DzbJsuiKFCvbLfaQTDt0lZDos6f7BYDPBE16zhGt4zdyPdaZLDp066PoRCARgs9kUxCAD5Nyf9WC7M7ivd6iRQJEEPKTrhgRoOAb0TkwSOmN/S5ceXj8D9xaLBREREYiKigpyHZEuQrLfJIwjoRz5m/tK9yhea3Z2tkqHFxkZiaysLOWmxT40mUyIiopS0Ai3s06cQyZTXso9urmwHU6cOIH9+/cr1xzOA7PZDIfDAavVipiYGMTGxiI7O1sBarm5eem8YmJiYDab4XQ64fV6kZ6erm4gSAcgXk92djYyMzORnJyM5ORkVVZOTg5SUlJw/PhxlRLL5XJproPjkWMDOOuaQzjEYrGoNiPMIV1d2JYEPCQcJMuW79OViOLYIFgVaj1hvbgOEbySoBb7lOdinU0mk4IA/X4/3G63AlSYforA0+nTp5GWloa4uDjExcXBZrNpzqNfx/ROZaw/wSO65FitVg3oEmr+paenq5SA3I9rv5y3HN+hIDzWhWNDrlmEXwgXydR2siyeH4DmwwXd5qxWq3LHIjCq7089iEcYk9cUGRkJj8cDj8ejWXu5huthLtm+ErDU/98glEeIT0JIWVlZRqovQ4YMGTJkyJAhQ5eFhg0bhnHjxsFsNsNiseDFF19EQkICpk+frr5TlpVq1qyJ7OxszJ07t9jpcS5lxcfHw+fznXNgTeqJJ57AL7/8ggoVKgDIc77YuHEjtmzZUmrn0Ov7778P2vbTTz/h2muvRcWKFTFmzBisW7cOo0ePxowZMzQPT4wePbrM6nW5qXLlyvB4PIUGTitVqqTStCQkJODnn38Oerr3fGjp0qV48MEHC3SfWbp0KQYPHqxAn02bNqF58+b5uqvcddddIWEVvZvMSy+9VGDdSpJ65KGHHsLVV1+t2bZmzRr07t0ba9asCbo3MG3aNPTr169IZROG83q9ePDBB3HllVfi8ccfB5CXuioUIDdt2jRMmjQJf/31F1555ZViX0+VKlXyTRsHAHa7HXXr1i12udS8efOwZs2aAvdJSUlBTk4ORowYgY8//lht//rrr3HnnXfivvvu07jbUwMGDECbNm3w448/hiy3oOsqqQYMGICJEycqqM6QIamvvvoK8+bNK/Hx27dvR/ny5QvcJzw8HN27dw8CEGvVqoX77rsPNWvWVHWZNm0agDygY/DgwUWqw3XXXYdrrrkG7du3x8svv4yrr776nFN+7dixQ9XF0MWpRo0aodvf6eNKU2FhYbjlllswYsSIUi/bkKFzVW5uLtatW4eXX34Zt99+e77Oh5ebnnrqKc1ns927d2P27Nk4evQoLBaLiskWJIfDgSuvvLKsq6qRAf4IEVhgYBo46xATExOjAqIMgBIqoIMC95egiR5u4T4yPQ+dTgj9MKAq4RgGeKVrBVNQERwgoCHLpnhOCe3onR6kW4w8TjpQyAC5rDODyBI48Hq9yMzMVGAGg80EqdhG0qmCx0r3DwkpZWdnqy8wFosFdrtdQVTSPYUBdumAJMETCTEwGC1hGga9JZhAMCIqKkq1M69FwjmyHc1mc5AbDdtc39YANGmy5PUzxZLH4wkCdGT6J+4vf3NMs86EGngt+tRg0rlEwj/sF9n3vFa+lu41rJseXOL7st/Yvh6PB263G263Gx6PRwF4HOfsG4vFotxZWDc552T9ZP3Zz3T/CQQCyMzMxJkzZxTEkZ6ersCNjIwMpKenw+fzBYFd7B9eowRsPB6PBgbjGJFAnQRzJKwTCATU9coUSrJ9eT1MQSjnKm8uc4yzzfUQn961if3Ia5VQCser0+kMSstFCIspvXg+Amp06fJ6vXC73fD7/WpOSQckzi/CUEw9xfRWEmTkOivHE/uajj5RUVGwWCxqjeV41wNQsu18Ph/S0tIUsMQxpIfaZGpHPXzH/w9+v18DHsoxw+uVMCHBJv4t0wNKwE4CYWxHlqe/OcjzERzSQ5dsC7aZhCklvCSvkaLrkP7/DM/LdUYPRwF5T/cZMmTIkCFDhgwZMnQpKzIyEmPGjAnpTtGwYUMAec4PvXr1KrVz1qlTR6Xzeeqpp/DFF19g1KhRhTp1XE6Kj4+Hx+MpVfCnfPnySEhIQO3atfHHH39g8eLFuPLKK3HttdeW2jn0GjlyZNC23377DVdddRXWr1+PwYMHo3r16liyZAnGjRungvUnT57EW2+9VWb10ks+kHcp6ujRo3j77bdDghBS5cqV04AihaVFKitt2bIFbdu2DXJwph555BFcc801mm2///474uPjFbiml0wvcPjwYXUPQe/4s2LFigLn1a5du4pyCRqFmkOtW7fG2rVrAeSlttI72/Tv379IZRPMcjqdOHDgAFJSUjTuIRKKkXrhhRdKBDEBefc8BgwYkO/7jRo1QsWKFYO2nzx5UuPSFkrTp0/HE088Ueh6vn//fgQCAXzxxRcAgM6dO2PixIlYsGABfv31Vzz99NOwWq35pn4L5f4EANdff31QqsFz1fXXXw/AgBUNhdbmzZsxbdq0EruCN2/eXOPaE0rh4eH5uiyEhYWhTp066vUnn3yChQsXIi4uDgMHDiz0/98111yDQYMGKYeg3r17o0uXLsW7CJ08Hg8WL15cpuCxoXPXrl27yuze9pYtW/Dnn3+WSdmGDBVH11xzDR5++GH06tVLrYfr1q3Du+++i/3791/g2p1fPfjgg5g9ezb27t2L2bNnq8+RzZs3R6NGjTSZW0Lp6NGj2LRp0/moqpIB/gjZbDYNuEN3CqfTCZfLpUmfIz+UyLRBhF/0AVMZbOdEycrKUpCRyWSC1WpVrg1Wq1VBJRIMAs4GjKWDDdOEydQ7EooAoKmbDOAyAEwAQUIIdPSh2wgD8wwgM32L3EbwITU1FW63WwXOeW16yEh+oWV7eb3eoHoSZJDpZnw+nwKLGFxmwJpty+A7zyMtftlHdBrJzs5WcIB0yiGglJ2dDbfbrcYCoRUG1vXpfwgJSWBKAk96VxYgGNLQwzey3dj3vHZCQRKOoJMOYQKv16sAEll3vViGBCukiwjrxN96eCssLAw2m00DW0goKCwsTI1z2W7A2RRPVqsV0dHRGkADgErDxDaQ7lyEMaQTFucpx4bFYlGvc3Jy1GuWa7FY1BjksXTXkXNDOhQBeTa7HMOZmZnwer0KViKMxnkkU55JJxnOFavVqhyZpLuK1+tVN630Ll7sFzl+OAYl4Ef4Q4J2ERERan6x7Vg23Y4kPEc4hGtQZGQkfD6fxt2JZVksFgVaeTweREREIDo6Ws0Dh8OhIDCOadmuencstpFMLyUhNN6wo5ONhHYIfXG95Xav14vU1FSEheWln+M1SfiHay/rwdeyvVke212uW5yHehCG9aIrFMuV66MENfXQKPtE1o2QG9cXOS94DOFHObclNMc1kfXUtwnLl23L/wdcj6RDFfvWsKY3ZMiQIUOGDBkydKnrxhtvRNeuXQvcZ86cOVi6dGmpnfPWW2/FuHHjAACLFi1C7dq1/3GfrePj47F9+/ZCg+jFEZ/4d7lcaN26tXrSvqzSa4wdO1a5lUjNmDEDgwYNQq1atWC32+H1ejFjxgwAwHfffQcAKsXP+dLmzZtx4403ntdzlqZGjhyJV199tdD9Vq5ciXvuuQeHDx8+D7XKX48++mi+T/pPnz4d77zzjmZb+/bt8dRTTwU5AEvVqFEDQF5ftm/fHkOHDg2ZfmnUqFEhnSrS09NL7AYTyr1GAkcdO3bEZ599pnlfgkoFidfFBweBvPWB6tq1K3766adi1ji0YmNjsXfvXnzyyScF7nfFFVfA4XBotu3ZswdXXXUVJk2aVOCx+Tk2SbVs2RI//PCDSi8BAD/++CP69+8Pu92O6tWrq/9LEyZMCHLaSUxMDHJgkioIaiquWrRooSBYQ4by044dO4Ieii6qEhIS0L179wL38fv9+PDDD7Fnz56g906dOhUEAX777bdYs2YNUlJSCgWSdu/ejWuvvVaT7iYU+FdUrV+/HnfccQcmTpwYFCcxdPGprOCsFi1aoEGDBmVStiFDRVHLli3xn//8B1OmTMFbb72Fu+66q9C19nLXpk2b8MADD+Dxxx/H/PnzMXLkSDzyyCNo2rQpGjduXGh64OHDhxf6ObC0ZYA/QmazWaVh8Xg8KgDKQLtMv0V3BbozyJRXMujKoKmEPwAEpZiSKb9kCh6W5ff7VcCVksFaGUxm+QwMM1BMUEE6k9AVR59iitcinUb0aVskzMMfv9+PtLQ0pKSkwO12BzmbECRgEFifJocBZjpQ6NPHSGiI/SQBBuCsK41MC8YgPIEJulXIYDldQXht8hjux3pJpwwGs9muMgjOc1ksFlV3Cf5wfwmHEX6QYAPHjnRz0jvOsL8p9i9dmXjNrBchgsjISA0QIgEUCf9wXLFd9S4nEmyT7iQWi0VzjfIYk8kEu92ucboi8CJBqfT0dJw5cybI0YlwkJxTEsiQqZIIKpjNZnVzVIJ47KucnBykp6fDZDLB4XAoiIPpoiIjI2GxWBAfH6/mD9uIoJuEqSSMpP/grne7omOP3W7XWMXR6cnlcinXHAnKSCCFaw/bl3WWKZskgCdhKo4nrhcc41x3CFrJdIPyGgkPyT72er0qxRrdcvx+P+x2u+ojpvKSc02OJQlySRCFbci+ZB0ISNEtjc5WBPu4xkrQJTMzU90sys7OVsdZrVbN2i3BL65rEkBiXeR6QEc0Oeb04Jd0d/J6vZq0ZPp1UK7tcg3hWNf/n9CXQbhSujqxnoTXpKuRXBP5I6E6/u+U0J9+TWZdOK8MGTJkyJAhQ4YMGbqU9fXXX2PHjh3q9fHjx4OcKzp06FCq51y7di0aNmyIG264AfPnz0dGRgaqV69eque42PXqq6/immuuwYYNG865rDvuuANDhgzRgBHnQ7feemvI7fXr10e9evXUa4vFggkTJmD16tUq7Yjf70flypXPSz0feughREREYPfu3eflfKWlyMhItG/fvljHbNy4EQsWLLioUxLJ+33Ua6+9hgEDBiAnJ0fjdkOFhYWhSpUqAIA///wTycnJmDNnDiZPnhy078yZM0Oe99lnnw2ZKqwo+uyzz4LugxFiA/JSmQ8fPjzouPwgJsrhcKgAO+9lAdD0X9WqVdGqVatCgzGFaeTIkVi1ahW+/vprfPnll/nuFxERgU8//VRzb2jSpElo1qwZ9u7dG+SwJDVz5kw8+uijBdbDZrPh/vvvxxtvvKH53/Kf//wHv/zyS9D+cXFxWL58OaZMmYJnnnkGN954I1asWJGvmxQA7Ny5s8A6FEdbtmzB5s2b4fP58Pbbb5dauYYuL+njWcVVUUDBbdu2YerUqZrPbAAQHR0dBOEuWLAAs2bNKhI0mJSUVCqw6ObNm/Hcc8+hQ4cO+Oabb865PEPnR/v27SsTx83w8HA888wzGDJkSKmXbchQUfT000/jscceQ61atWAymdCqVatS/057KapFixbo2LEjrrzySnTv3h2vvPIKJk2ahCZNmmDnzp2YMGFC0P8ZqYLS+JaFDPBHiA4f0kWFwWYCIYRiKAnG6J0nCDpQMj2NdLBg0F6mTGI5ADRpYCRUondPkcFVCYNI2Ea6aMjj9e9LGEjvtsE2kXWRQXmZ1kYPjkiwhJIBbQaT9YFquR/bTMIy+rRhsk8Ij+jdOgBo2p3XL2EIQJt2jDASr0GCQqyvTMnE4/WuK6GocemII/tVtrPeIUlei/6DsgSz9GNRBu1lu8v+CdXX+rryPLJsWWf9PJDggRw7LINtQ8CF/UwnHtYhPDwcMTExiI6ODuqD7OxsjRMVt8u5R9cmeYwEdOhoI+ci0xfxPTkneN3SgUi+T/iKII9sf85TQhYEN1gXQjwSPuO40vcj21bOBV67vr/1T4QRtuF52c6Ei+R406fdkvNBur0QTuH1S3iQx0unMjreSHcYfTouvXge6XYm073J9mWb6CFJk8mkUsvJMc739WOb22R6Mh6jd/OS9davuayfvE7OF84f/djVr//6m2iyT1mGBPMouX7K4yS0pP9/wPEt560EOvXrvP7/lH5NN2TIkCFDhgwZMmToUtb27dvV3zt37sTXX3+NAwcOqG01atRA+fLlS+18+/btg8vlQq1ateB0OvHjjz9i1apVpVb+P00xMTEYNWoU4uLisGrVqgJv1pamCBoVRVWqVEHnzp3Rr18/AHkOKsePHy+rqilVr14d119/PVatWlWqMEBpKlSaPQB4/PHHcdNNNxW7PD58dbEqlAME79Vs3bo1CDwE8hxw4uLi8OeffxY6blatWhWyjCpVqijnqeLq6NGjQcCLDNRfffXVIVNPdevWrcByr776agX56J3XpbKzswtNv1CQoqOjceutt6JZs2aYOXMmpk+fnu++o0ePhtlsDjo/7+HlFyD2er2YNWsW9u7dW2Bd3G43srOzkZSUpNk+adIk/PrrryGP6dChA4YMGYLbb78dI0eORHR0dIHnSE1NLfD94urTTz/F4cOH0a5du1It19Dlo8Igv8IUHx+v4MaCtHnz5qD0STVr1sSVV14ZtO+WLVuK7BZ26tSpolU0H7lcLsyfPx9ff/31OZVj6PyqYsWK6NSpU6FrakmVnJxcZqnEDBkqTKdPn1YPxwN5IHGlSpUuYI0uDlWuXBnDhw/HXXfdBYfDgcjISOzfvx+RkZE4efIkNmzYgK1bt4Y8Nisr67x/nzLAH6HMzExkZ2fDbrcjPj5eA3wwMGuxWBAdHa0cNxjg1AeYgbNwCgOs+lRcdCDx+/3weDzweDzKcUG6LdD9hJLpVgCt0wXLJvRB8II/dIFhQDY7OzvIhUhCMwy+SwhEBnL5fiAQUGnRMjMzkZaWFpQCh9LDQlKhoBPgLMwk00ZxH4IbhDK4vwRC/H6/SqkmgQn+pguM/JLGYLZ0r5GORwz66/+WsBTdOPTgkGxvuqewXyRApHfXkX0vXUg4nqQriQRypAOOvG4JBUk3INnPhGhCjRP5I/tKwgEej0e5xkhXEAn/SBcijp+MjAy4XC7k5uYiLi4OsbGxav+KFSsiLi4ONWrUgMPhUO3B/ucNGzpX0U0lLCxMzTW6ukiojqANUxwRhjGZTCp1HFOF0QKZc8pqtcJutyM392z6N4vFgri4OJjNZvVhUDozRUVFqb6RDkWcy3RkofNObm4uUlNTg2AK1kNCQtx+4sQJlcIq1Dwn0CRzubMedAiT4Jw+NRTroIeVPB6PBgiKjIyEw+GAzWZT65Xf74fP51NjnS5ADocDFotFXZNca+SaxDR9st04Vjn+JBSodyxjfVNSUlQ6RwlRcsxLeIrHBQJ5qbusVqsaa2z7UFAUxXrR3Y1uXJGRkUhPT1euUfrUYfJYOe/0UB/3k+1Eybmqh0wlJMd1nNulqx3do9jmBPMoORZYPte9UK5XhgwZMmTIkCFDhgxdqnrnnXewb98+AHnfx8aMGYPZs2er9xctWhTShaOkOnDgAPbv348BAwagd+/epVr2P03Dhw9H/fr10atXLzRo0ADXX3+9SqNWlrLb7UUKUoY6DoBKQ1bWev3119G1a1c89NBD5+V8xVXXrl0xbdo0/Pe//9Vs79OnD+66664SzY2EhIRzdocpS4X6Lu1wOLBp0yZcddVVIY85c+YMIiMj0bp1a0yZMqXA8ocPH46EhISg7Y8//nhIOKeo0o9ZWdc5c+Zg//79Qcc8+OCD+ZaXmJiI+fPno1atWgDyd/wBoO6RlVR33nknmjRpAr/frwE9QymUE5cch7t27Qp6CPTUqVP497//rXFBouho9Pbbb2PVqlWYOHEili9fHuRM5nK50KJFi3zrFRcXh/r166N3796oVq1agdeQlpZW4PvF1aRJk3Dw4EEMGzasVMs1dPnoXF3WYmJiCkxfRy1atAjffPONxsEuNjYWt956Kzp37qzZd+PGjZp1pSCdS7qnpKQk3H///Xj++ecLBf8MXVyKiIgoFsRdHOXk5GDp0qUFwmcXM6Rs6NLXjh07cOTIEfU6MTERrVq1Qp06dS5grS6sqlevjpYtW6JatWpo1aoV+vbti6ysLHzzzTfo1KkTPv74Y8ybNy9kmr5AIIBly5Zh48aN57XOBvgj5PV6lcsInSmkGwyD0F6vVwO8MNjLwLyEP2R6J/4tXTwIdURERKiUPEzDwv0kOCNdLPQOGHonCr4vg7SERxhszs/9QgaB9a9l+jLWKzs7G06nEydPnlQBdAlAUTLgrq+3vC69w5CERdgO0lmJH8gYpCZQIdszKytLXTchIcIg7G95Ln4hI+Ql92V/StcL9g3BDwnZEODIyspS8ILNZoPf74fT6YTL5UJUVJTmGni8TN2kd/gg3EOATNaB7wNnXYv4ntvtVpCTdOzQpxyTUJn8oXuNdGWRY08CCwRv5BgGzqYgki43rIPT6URaWpqCLejERViCVOXevXtx9OhRBVqwroFAAAkJCahXr56qp0wfZTKZ4PV6VQotCVGFhYXBZrMpeEi6l3BcZ2VlITk5WT05RACEbUCwIzY2VkFxTGMlHXD4mteUkJAAs9ms9mVfEJChm09kZKRmLPG8ercZ7sPzcWxJmIvwGduE0BLHP+eaPtWdvIHC+SvTG7KPOZbDwsI0KZ4iIyOV8xIBlvDwcNjtdpWKi2uqflzxtQTaJGQpx7WEV/RrDdctAmZcNySME8oVSjo76R1z5FokIT2OPc4fi8WimVPZ2dnqCS7p9KN3ztGvy5zbegcqtqscuxJCZT/LtYF9yBSKBJ1kPQnGcc1he8m1Xf7w3BwbwLl/sTdkyJAhQ4YMGTJk6GLQ+vXr8eGHHwI4m9br/fffR3JyMoCzD3SUplasWIFt27ahQ4cOGDZsmPHZugRq1aoVnnzySfzrX/8CAPXgw/l4uvqGG25QfycnJ+Puu+8u8rHp6ekKBihLXXfddRg6dChSU1PhcrnK/HwlUWJiImJjY/HAAw/gP//5j9r+xx9/wOv14uTJkyUqU+/YcjFJnyKgc+fOqFixYkhwRmrIkCFwu93YtWtXvvs88cQT+aZH83q95wRE8aE5KiwsTIFsQB4cwjWT6tu3L+Li4kKWJyGXrKwsbN68GQkJCVi3bh1efvllzb7nEpQH8taGjRs3YujQoYXuW65cOfV3amoqxowZoxmbu3btCppPFSpUQNWqVYPKGjVqFH744QcsW7YMDzzwAK666ircfPPN+OuvvzBv3jzNvgkJCZp15Vz0xRdfoHbt2qVSFpDX1x06dMAHH3xQamUaurxEgO9cVFD6y4SEBAVofP3111ixYgWOHTum3m/fvj1uvPHGEp87P7et/JSRkaHcv5588klMnTq1xOc2dOF07NixoLW4tLR7926sXr063wdnW7VqhTVr1mDJkiVlcn5DhjZv3qxxrklLS8OmTZvgdrsvYK0urJKSkrBs2TINFHr48GGsW7cO+/fvx5dffoknnngCrVu3DjrWZDIhISFBxf7PlwzwRyg+Ph5ut1s5UPAmTVRUFGJiYpT7hd/vD4ImGEhlUFc6LUiHDLpPcBudQqxWK8xmswKLZKoeCQtERkZqoA79kwsM5DL4LwEZQgtMaaN37QGCU5AxmC5dLhjwl8BIamoqnE6nSmmlTw2lT1UjXZIkUBEqFYwsT+/MIveXgXKbzaaC+Lw+tqE+9ZMUHUTYRzKtktls1hyvdyzKyclRxG14eDiio6NVIN3n86nzMUjucrlw6tQppKSkICoqStVPugWxDhKOkG4ibFOCRdIlKCwsTDndMFUWywvlyEEYSqZ8knCPBEb4N9M36dP/5OcMJceEdLMhJME5FxYWBqvVqt53uVzw+XyqD06cOIG0tDSkpKQgKytLOadwLoWHh8NqteLIkSNISUlBUlISkpOTFdxH1xyOIQk+REVFITo6Ws1xCVywHdLT0xU0xHnt9Xo14zw7OxspKSnIyMhQ41W6cUngLjo6GnFxcQrKkmOTjjd0lLFarcqdSgJ0BOA8Ho8ap9yP0BodjrKzszVwI12VCGdwLSMcxzHGMc05wLWALj5msxk5OTnIyMhQbRcVFaWBqywWixoXWVlZsFqt8Hq9mv7lmsrxJNOfsY5cFy0WiwLR5HpJaIXXLx24WI7X64XL5YLH41FrpMfj0YBUHKdyTQvlXCPd3Oh2JEEYzhXWh/XlWDl9+rQ6F9c1fSou/dzndr1TEduec0k6gHENycrKgtvtRlhYmHJXAqDGH9uKbcgPJ1xDCAdxvHDdk7CcBH+ki9HF/BSlIUOGDBkyZMiQIUPF0RtvvAGXy4Vly5YByHPlmTVrFgCgS5cuaNiwYame78EHH8Sjjz6Kr7/+Gm3btsWIESNKtfzLXWPHjkXdunU1Qfo//vjjvJ2/Z8+eAIC1a9fi+++/x6efflrkYz/55BO89dZbZVU1AHnuIA899BAiIiJQvnx5NG3atEzPV1JVq1ZNASXXXnut2p6cnIy+fftqArtFVaVKlTTftS82SfDns88+w6JFixAfH4/169cXeFxRnjAeMGAARo8eHfK9AwcO4ODBg8Wqq1STJk2Ctsl7Au+++y7uu+8+zfvh4eEKzNOrXr166u/+/fvjtttug8ViQf369TX7jR49WqXIK67q16+PdevWISUlBQsWLMDcuXPVe/k5Lejvc+jHUiAQUKnUvF4vPv74Y8ydOzeks8jvv/+O7Oxs9OjRA6dOnYLX68XmzZuxadOmoH1vvPFGVK9evdjXGErly5dHr169SqWsHj164M0338S7776Lxx57DM8++2yplGvo4ldx1tFq1arB6/We0/liY2NDgoLlypXDgw8+iDFjxqhtixcv1sA6DocDvXv3LjHw9ssvvxR532XLluGll17C9u3bVZYDQxePTCYTWrVqVaR9+/TpUySnqZKoSZMmuP322/HCCy+EhNJatGiBmjVrolevXurhA0OGSkN169bFv/71L0ycOBEDBw5U21977TU88MADQZD2P01z587Fk08+iaSkJDidTqxfv159/69duzZuu+22fI9t164dnnvuufNVVQBlDP6YTKaDJpNpm8lk2mwymYK+aZhMpoYmk+lXk8nkM5lM48T2Bn8fw58Mk8n00N/vJZhMpiUmk2nv37/jxXFPmUymv0wm058mk+m64tZXUleEFPTpvKR7AnAW6pFpi2TqIBncB6ACpfzN/aTbCR1qWA5BDtaHAADTVxFSYcCZrgoSsmGQXr6WgXW6OeihE+miIWEoXrvf74fX61VwigSEpFuNDJLLYLbeJUYGh/VuP6wfy9MDJoQBWCeeS4IhrCMhGbY53S8IVsjypasKYRB5bjoL6R2OLBaLStsmU0yx706ePInIyEhUrFgRdrtd9YMM8BOUIBikd/sh7CGdUCQkBpy1tg0EAgpsIxwj3WQIPOndpST0IyEgggyEGQiSyHrI+hLMiYmJUX3BsRXKWYpgEeV2uzXOSadPn0Z4eDgqVKigSbtF55Q9e/bgyJEjSE9PVw48dDOJjo7W1Fc6mhCOIYAXERGh2obABmEnzkn2u37Msmy2P9cQOuFERUWpvifcJB17CKKZTCYFzERERMBqtaq6cX/uy/rzGNZZrgkcvzKdIQAFzEknGY6F6OhozToBQAPIEfwJDw9X61BYWJgCKdkenF8+nw+xsbEwm83IyMhQ65FMdSjXXZPpbLo1wkAEmmTaK/YTc9nzeiQgGAgE1HwkCMn36EpEJyQJyrGdJbAoXZR4nXqnKLaZnFMsy+PxIDU1Vd3Ek33PsSLhH851mW5NjjvOabmWcy7K/z96l6Dw8HC4XC5Fb+uduWTKNZZNZyjODzoBSZBPgqc837lYbV/qutQ+FxkyZMiQIUOGDJWlLpfPRr/88osmWPDjjz/C6XTio48+0qSVKE3t2rULCxcuPGdHi3+aWrdujalTp2qChD/++ON5ObfD4VBB9cWLF6tg5KFDhwo91ul0KveQNm3aoFu3bmVSx4cffhi9e/cGAMyaNavQ9EYXSu+88w42b96MU6dO4dSpU5r3Dhw4gB9++KHYZZYvX760qlcmouM0kDeWoqOjYTKZsGHDhhKX2aJFCwwcOLDAYOe5piaYMGFC0LYzZ85oXs+dOzcohdUzzzwTEnRjqons7Gz10BnvvUjxHkZJlJqaihYtWqBcuXKYPHkygLP3r//44w98//336N69u+YYeZ9j6tSp+Oqrr4LKJZA2adIkPPzwwxg4cGDIFIP169dHYmIiHn/8cXTu3Bn9+/fHxx9/HLKu+aV5K4lOnTqFP//8s1TKuu6662C32/H8889j5MiRePnll0O6GxnS6lL+XNS4cWO8+OKLIVOd5KcffvgB69atQ25uLhYuXIicnBy4XK4iQzEejwdmsxkVKlTQbB80aBBeeukljBo1CgMGDFDbFy9ejFWrVmlgxgYNGqB///5FrjNVpUoV3HPPPQDOZo0IJb/fj1WrVqk1rU+fPmjdurUGKDR04XXnnXfinXfewTfffIOaNWsWuO/KlSs1/5NLW3fccQf+/e9/46OPPsIzzzyjtleqVAkNGjRQn1dGjRqFxx57rMzqYeifpZiYGNSvXx8tW7ZUMfBTp06VyEXzctU777yDp556CuvXr8fSpUtV7PTAgQN46aWXFOAdSqHg7bLU+XD86R4IBFoGAoG2Id5LAfAAgDflxkAg8Offx7QE0AaAG8D3f7/9JIBlgUCgPoBlf7+GyWRqDOAmAE0A9AbwvslkCkcxFR4erv5ZM7ibn3uJPv2KBH1k0Fd++SBQI2Ee/uihCZYjvzzI8mSKG5kahq/1AIIMFFOsoz4oq79mCS7IwK9JuM3oj9E7+Mjr0JetVyjXHwafZTBc3zYSlOK5mSJJ7ybEwDvbmwCHPuWYhBZke8l+DtVuubm5KoUQ+4sgUk5ODqxWqwqesww5TtiHMnjO+slzECDTB/H1DkE8N/uQbSWBAL0k4CDHs2wbCa+xDdlf0lmJY0SfZkoPNkiAgdsIlEg4iSmG6OzEbbINpaMQ32eKJT3Uxn7Ozc3VuG7RsUkPMsj24TXLcmSqKH353GaxWJTjjXQQYl8SkJHtKd2T2E6yDaR7l76OegCPc0COM8I9ss/Yb3L8ESqR4COBMzrscNzz/NKVhuATARu2N+eihNmkU40ci6yDhBRZNusgxyrrxLWET1bJ9+U6AEADiOnHKSVfc0zKNtavF3zN1GqE9/Tv69dB2eayPL0bkX4d5tiXbUjJVGZyvrIv2faUHoClM5RcP/T/+yT0xjHxD9cl9bnIkCFDhgwZMmSojHXJfzbasmULqlSpol63bt0aVqsVHo+nNIrPV5MnTy7U7cPQWVksFsTExCA6Oho2m01tLyxVUmmpWbNmqFSpEgBogo47d+4s9NiUlBQFS9xwww0YNWpUmdSxU6dO6nyrV68uk3OUhgKBAD766CNERUWptCnnKjkmiqKkpCQkJSWVyrkLk/5+F1P8ud1u7N27t8TlduzYEZ07d4bFYlH3MPU612sMtQ5ec801Qdu2bdumeW2xWDRuTlR0dDSAvCCLhL7090/yS5NSFKWnpyMiIkKTHm3YsGEYNGgQmjRpgu7du2vaqnv37ujRoweAvLnz1VdfITU1Nahcn8+HtLQ0LFmypMBx27dvXyQmJuLgwYPYv38/lixZkm9aGaYxKg253W6kp6eXSllNmzbF0aNHAZyF1rp27VoqZf8DdMl9LipfvjxGjBiBcuXKFSslZUpKCrZu3YrDhw/jt99+w6lTp7B37141dgoTH0KV6QOBvBRiV111FRITEzWuWl6vFwcPHtSAnuHh4ahTpw7q1q1b5HoDeetns2bN4Pf7ceLECXUefeB3z549WLp0qfq8lpaWhn379hXrXIbKXg0bNkStWrVQv3595c6Yn1wuVxCsWhbyeDyIj1ecHipUqIDy5ctrYrtdunRB27ahlgpDhoonxm/k+GLs0tBZff/995gyZYomHVq3bt2wYsUKrFixIqSTo8vlCvqcW9a6oKm+AoHAyUAg8BuAgjD8ngD2BQIBPgIzAMDnf//9OYCBYvvMQCDgCwQCBwD8BaBdMeuDqKgoTTDW7/fDbrerYDedDCwWCywWi4IBmGpGgkB0iZHBz8DfzjR06pEBXzp52O12lVJHH4iVgWk6fPCHX3IIccj0WAzEMqguXTskmCABIj28QeiA5zeZTMjIyEBKSoqqozxe/wOcdUVh2TL1mR76YHCe18W/Q8FKEjxhIJptwuuWrkKsq9yfsAEnJwP47MPw8HCVzkm2J4PxEgwA8r4wnTx5Up3XbDYrpxY6qxD8ka4x0o1JQlYcZ2wfAjHSXYQwjMViQVhYmEqhxLHo9Xo1KbHoXJMf+KOHfeRYkcCPHNeEC/Swlelvx5asrCyUL18eNptNAwrpv6Tn5OTAZrOhfv36aNCgAeLj41U6PI5FQhmsv9/vVymcmM7OZDKpdmCfMs1RZmamBmYD8j5USecSznNCgRLA4pynQxHrIh1zWFeOYbYZnWbcbrfGzUhCJrL9eCzHK7cBUG5GdAWSLjM8n3RkYZsTHjSZTKr9bDabBhTjOkhxzMh+Y38zxVpsbKwmnRbBHpPJpMZmeHg4bDYb0tPTlcMZ0+WxL9jGXCckjCnXD8JZdATitXA8yHSFADSpqjg2uRboYSOOMSkJlQFnYVA6X3GeyrkrgbCcnBw4nU6kpaUphx39uibHGQEtCfHIecXf7EfpKsftrC/bgnWUYB7dlNhWEnyUIjxHsZ8lUMgyJYzF+VFaN5IuR11sn4sMGTJkyJAhQ4YupC6Vz0azZ8/G9u3bcd11eQ/L9+zZExERERg+fHhpFB9SN998M+6+++4yK/9yVGJiIhITEzXbjh07hjlz5pyX8w8aNAh2ux179+7F7Nmz1fZJkyYVemzNmjXxyy+/YNq0aXjmmWfQqFGjUq9f+fLlFbzw0Ucf4f333y/1c5Smfv75Z1it1nNOPeBwOLBlyxa8+eabhe/8t1JTU9G8eXNNP5al+FAYFR8fj127duGLL74Ief1FTT9jsVgwbtw4eL1e7Ny5E/379w9Ku7N27dpzqnvfvn2DtoVy13jllVeCtjVt2hRjx47VbLPb7fjkk0/QokULBT2FuqcZKvBSVGVlZeHJJ5/EunXr1LZBgwYpF58zZ87g3nvvxcMPP4xBgwZh7ty5qF69Ovbu3YsHHnggyCUpIiIC48ePR8uWLfHGG29gyZIlAIArrrhCsyZVqlQJX3zxBdLS0vDggw+qtJFAXko7+ZrlljRFUSilpqYWGbgoTHfddRe+/z6PO3nvvfcAAPfee68RQDxHXayfi+644w6MHTsWTZs2RYsWLYp17PTp0/H+++/jyy+/xLx58zBp0qQir61RUVGIj48Pcvzp2LEjGjduDEALFVaqVAn9+vVTACF1zTXXaJyBKKa/BKAc4imbzYaBAwfir7/+wty5c/Htt98iKSkJn3/+Ofbt2wePx4OVK1fiwQcfxEsvvVSk6zFUtsovVeM111yDvn37omLFiliyZAlWrVpVYDlVq1bVADllpYMHD2LHjh3q9fbt27FixQoN2Hro0CHlfmfI0LnI4XAEQZQyDmkoT263G7NmzVKuu0OHDsWCBQvw66+/oly5ciHdwLZv337eH9aJKHyXc1IAwE8mkykA4MNAIDClBGXcBED6Y1YKBALHASAQCBw3mUzEiKsCWCf2O/L3No1MJtMoAKOAYCqeIEhycrIKgNpsNpXGye/3qyAwA6oy7RHLIAQi3UFkkJYBd0IBErjhOfWTSu+yQpcGQiIEkqTLDAPYMthNQEE6cEh3GH1dJXREZw5+oeIXz1AuKxIYkU4WMpjN9pLAB0EYvTuPbAN5nHzN9pb1IMzF4HpYWJiCN2T/MVgtXYMkvMH21gNSfr9fAR8SyAHyCG6Z1ke6svA6WSdeG4EF2UYSHmC7crzp3Zb0LhzSgSo7O1uT9ojXR6BAH+CX4IIeapKOIQQvWBeZSki6fEjHm+zsbERHR8PtdiuQRwJnTL1Ws2ZNhIWFwel0KtCK7ci6Z2ZmqtRSXq83CFaTsAPhBr62Wq0aR5Pc3FxN2rXIyEi1WBMKY7lyfnHMEDRh/xKG0afhkqAcx6NMdcV+zMrKUv3udrthNpthsVg0AFJubq6C7wgAcT2g5LiVkA4dkAjCMKUcy+W4Yf3Zn3a7HYFAQLUNgULOH7vdDqfTifDwcFVvpswC8m5SmUwmpKWlwWQyISYmRjM35NjPD4iS658EYrh+8KkyAj2c616vFykpKfD7/Ro4y2w2q7HDtVe647B8thvnu5zPXq9XA8ZJQE4e6/F4cPz4cYSF5aUP5HjgGKVk+Zzn8v8KQT+5vkpXHbmGSfH6CCvJdYjtLtcl1kmuAVxL+Z7X61X/u8LCwhRYJaFEjo+ytEO9BHTRfS4CtJ+NDBkyZMiQIUOGzqMuus9GJflctGnTJnz99dcYO3YsFi9ejClTpqB79+5o3Lgxbr31VnzxxRfFuqCiKC4uTgPjGypYVatWRa1atZTjDvXrr7+eEyBQHP3f//0fgLy0RtIt5Mcff8Tu3bvRsGHDAo+XbhnFdacpisaNG6e+yz377LOlXn5pq1KlSoiMjMRNN92EOnXq4Pbbb8fp06eLVcajjz6KBx98ENWrVy/yMYFAAG63G2lpaZqUcWUp3its2bIlXnjhBXTo0AHLli3Dgw8+GLRvs2bNULt2bZQvX77Q9vjiiy/w0ksvYfTo0ahSpQqWL1+Ol19+Ga+++qraZ9GiRSWut8PhwA033BC0/bvvvgvatmfPHs29NorBeyo3NxedOnXCDz/8gN9//x2HDx/G/v37S83xZ/jw4ejQoQMeeOABzfZ27dqp+2x16tTRpByLiIiAy+XCvffeixUrVgSVmZ2djb1798Lv92scxp555hl0794d+/fvx4EDB9ChQwcEAoGga3799ddRoUIFDB48GEDevZzmzZtj2rRpxXJXKUynTp0q9hzKTyaTCU6nEwBUyqXx48eXuRPeZaBL8nPRsmXL0KdPH9SvXx9NmzZFlSpVVGq7wrRx40YFy3366af466+/EBcXhz59+qBly5aFHl+7du2gNXznzp1o2rQp4uLiNO5b/fv3Dwn4XHHFFbj22mvx5Zdf4sSJEwDy/s+++uqraN26NdauXYtrr70Wa9asUXPc5/MhOTkZM2fOxKJFi2CxWJCUlIQPPvgA27dvR5UqVfDVV18Z7j4XiV5//XX069cPu3btwoEDB7B161bs378fbrcbAwYMQKNGjfDbb79hyZIlhTrpVa1atUhj81zVuXNn1KhRA4sXL1bzacaMGahfvz4eeughxMXF4bbbbkNqaiqef/75Mq+PoctbjONkZWXhjz/+wIIFC7Bt27bz9oDEpagqVaqgc+fOsNvtsNvtIb9P+P1+LFiwAD///PN5rVtZ3yXoHAgEjv39gWKJyWTaHQgEVhb1YJPJFAWgP4CnirJ7iG1BOaT+/sA0BQDatm0b9D7tAaWbDADExsYiPT1dE2yXEI38ciLT3kj3GhmcZcolCVcw2GqxWGA2m5GRkaHqwC8s0t1Bgh3cxjRKBF/oSMLguNyXQWF9Sih57RJq8fl8Kh0Ry5QuPAAUlCLTjTFAzZRUEoaSQWHWK/C3rZj+C5+EJfQON3I7g9qEBgAoWEQ6UURGRqr3GXxnUJ3OIQxis14MshPwYAoqOVYYENenBpJBfenEIvuTAAbhE9lfhFVk0F7fB6wP+5t9TAtZ6cYhYQS9C5SUHtqS29kvBMIIThA+4nn045w3Slwul2Y/jg+masrIyEB4eLgCbmJiYhSEwnlEyMnlcmncU6SLEtuPv+Pi4hTwwvRh0hmFYyYsLAx2ux0RERHqC6uELngtPp9PtT/bXQJGdLeRknOB18+bCFarVR0v3WkiIyOVaxSQB7XQwYf9yXkhr5vrCuctAOU2xmMJoXg8Hs3YJdTjdrvVXKPrD8ebdKGRjkdy/eP8p8MV9wv87YZFJyb2D+cE11LOFYKN7K+oqCh1DrkGcL7ynByHTqcTHo9HATEEnli+BJ/0859jS7rhSKhKXqN0fWN/0/0oLS1NwW0cq3IN1N/oYn0kfMR+zM7OhsViCQIu5f+KUOCpyWRS67nZbNas4zKdnIQw2V4c77wuCfXxN6FQjivuExkZidjY2KDr+wfpovtcBGg/G/19g8mQIUOGDBkyZOh86KL7bFTSz0Wvv/66+i7RvHlztf3qq6/GmjVrcODAgaIWVST973//K9XyLldVrlwZx48fxzPPPIMmTZoEgT+LFy8+b3WhM8fUqVOD3gt1LyY/ZWZmhnRHOReNGDECd911FwBo7jlczKpevTpMJpMCXEoCLPTt27fI0M+mTZvw8ssvw263Y8eOHYiNjT0vT/wDefcpvF4vBg4cqILWe/bsCblvmzZtkJWVVaT2uP/++7Fo0SJ8+eWXAPJShrz33nto2rQpbr75Zpw8efKc6j1kyJAgRxqfzxcSjqlYsWLIeyG7d+/WvN6+fTtGjBiBRo0aoXv37gDy7hPpn1IPCwtD1apVi+1gM3r06JDjP79xEhERgWPHjmHChAkhr4tyOBxITExEv379sHTpUowePRo333wzIiMjUb16dVx99dVYsWIFevfuHXTs9ddfj2bNmgEAbr/9dqSnp2PIkCFo2rRpsa6tMNWoUQOzZs3CG2+8gQ0bNpxTWY888ghMJhMee+wxpKen49tvvz1voNwlrkvyc9Eff/yBJUuWoHv37ujSpQt69eqFadOmFbXaSuvXr8frr7+ORYsW4euvv0ZcXBxq1apV4DHffvttUEB1zZo16NWrFxo0aIBevXqhc+fOWLNmDRYvXoz+/fsHOZGFh4fjyiuvxP/93//hgw8+AJAXK2rTpg1atGiBl156CY0bN0ajRo2QmpqKzZs345tvvkGNGjXw/vvvq3jEsWPHkJSUhE8//bTY126obBUZGYlq1appwMozZ85g2rRpuO6667B3716sWLFCObIVpA0bNmDZsmUhIbLSVvXq1fHYY49hwoQJOH78OACgUaNGKu4VExODcePGYenSpVi5sshLhSFDQapQoYJKVRgXF4e0tLRC3a/+6erVq5dKkxxKOTk52LRpE6ZPn34ea5WnMk31FQgEjv39+yTy8ooW1y6wD4DfA4HACbHthMlkqgwAf//mt5AjAOSn8GoAioYWC9GtgkFbACooazabNQ4HdKGQcAslXTVkEJ2QBEEY6YBDR5GIiAg4HA4FmBBOYIBffgEh4CPdfOgYwjrIL078W6YhY90lQMDAMSGizMxMOJ1OuFwu1QZ60COUe490U9E7AEkQR9ZRuh/xtYQQ+DcdWXi8vA7W3+VyweVyaVxw9K4ZEswCoHE9YSBd/550HNGnwmKqHR4vnWfkvhLMkOnfZBBd76Akz0sQhD82m005VDG1knTj4LVLByN9vdje+nHAa9FLQlt8zR/pUCTLcjqdOHXqFNxuN3w+n8axiMfIFGrsH8IztIUlIAYATqdTpTRjPTmfQqWSi46ODkobJZ2HACiHGp/Ph/T0dDUf5JwlECfntIR1WJZ0BeN8DgVKcb7FxsaqPmaKLoI2coxxnBGAo9uQhHo4DyXAwr6W/S9hHAnV5eTkqL5iXQjMyXRt7AsJeEjYSs5tjovo6GjExMRoUtjp5x7XKs5ZCbsBUKAkx7/VatUAO3IOyj6S4B3rxj6W0JIezOK4kfAPzxMeHq6e3JT7cO1yuVzIzMxEZmamZm7I9GY8H2EkPTSnX38AqLkt3ZHkuXksRYcu9jn7lPXheOV4Yzv4/X4FL5nNZk2d5X78XyL7h+NbD0r+03Qpfi4yZMiQIUOGDBkqK11On41yc3MxYcIENG7cWDm7uFwuvPfee6UO/Rgqutq1a4dly5ahTp066Nq1a1CahxEjRpyXejAN3PHjx7Fz507Ne+PHj0eDBg2KXJbVatV85yoNPf3005pUKaHSM11smj17NpYuXYrt27fjzJkzJSojJiam0H1yc3Px1VdfoW3btpg7dy5mzJiBLVu24Nprr0W1atWKdJ4//vgD77zzTomBKjpc33DDDer79K5du0LuSzeajz76qNByhw0bpkmp07x5c/z+++/KzeZc4Y+ePXsGbaPrsF76NDpAXtu/+OKLGDlyJADgxRdfDNnmeugHAD788MMipzyTioiIQFJSUpH393g8eOCBB/Df//433326du2Ke+65B0AexONwOJCSkqLWI6/Xi7lz5+KVV14JciDr0qWLBp5699130apVqzJJJdm+fXv06dMHp06dOqdyWrZsif79++Pbb79V21wuF26//XZYLJZzreZlrUv5c9GPP/6IrVu3ol69emjVqlWJHFHKlSuHO+64AyNGjMC0adMwb968Avc/efIkJk2apHHSAvKcUlq1agUAqF+/Pq666ioAwOHDh/N1MUtISECfPn1UGrDU1FSsWLECKSkpuO6661C9enX069cPo0adNT/673//q6AfAMVaOwydP8XHxyM2NlbTV0DeeBsyZAjq1asHl8uF7du3F6m8G2+8EVdeeWVZVDVIXq8X+/btU9APAPz2228aN/2DBw+qB9YNGSquunTpgtdffx2ffPIJHn74YURGRqJSpUqoVq0aqlSpcqGrd1Grbdu2qF+/fr7v79y5E/PmzcOhQ4fy3Sc/tWrVKuRn46KqzMAfk8lkN5lMDv4N4FoARVs9z+pmaK0JAWAegNv//vt2AHPF9ptMJpPZZDLVBlAfQLG/ochgLYPxhHLojMP9pJOCdEUAtLCK/M2gbSj3FJnihdukkwq3yWCuHsABtKmHJJjCY6RLhYSWZHCZ9WUZWVlZKtgur13v9hHKyUfvesOyZXBdf928Ntl2smw9lEJJVyAACuiQx/Ja5fn45VvCT7I99FATISvZRrKPWQ6BBpnGiOeWx/G1BJD040FKtjlhBbqw8DoIhhCWYNtLqC2/dmMd5LiRv6Wku4t+DEkgimMvKioKfr8fbrc75ByRv+n2Q5BFutpIhy05VmQ/6F2TJEAl05NJeI1rQFZWFnw+HzIzM5WzEMvWzx/Z7/LaZZvLtiMQyPICgQBiYmLgcDiUMwyvi+5GXItk+iXpABP42zlHtoGc73wtQRZZNwnwEOKQKQc5niTIqJ+fslz+zXOzDyR0JOE9npvvyTVJjkNZFp2qoqKi1A9BOD3IxmPkWqk/J9uL4pyUc1e/NkixHeQaI+dqTk6OSksnx5DcV7/O6cFH/TXJ1/q1TUq2oR7Wk45vsr/kuJXXJdtE9pd+XBBWk9elX/f+SbpUPxcZMmTIkCFDhgyVhS7Hz0aTJ09G69atUbVqXqYM2n4bujAymUxwOBzo1q0b2rdvDwBITk7WBEfq1at3Xuqyfv16/P7779i6dWvQe9dcc02xygoPD0fr1q1Lq2qoUaOGph2Sk5PPKb3T+dSUKVNUSu+SaPHixUhOTi5wnx9++AETJkwI2j5mzBi0adOm0HNkZmZi2rRp8Hq9IR1tiqKcnBzcfffdaNu2rdqWX5Bv7ty8Ja+gJ5CBvPQEVatW1YBoCxYsQK1atdR8OVdokWuhXn369AnaFgoICgsLQ3x8PBwOB2666SbcdtttyuWnMJV07f3666+DUhSFSqfF+xobN27UwFOh9Ntvv6l2jouLQ8eOHdXrXbt24aeffsJ7772H5cuXBx1bqVIlDSzD1C5lpUAggLFjx55TGU2bNkXlypXx+++/q23t27dHrVq1cP/996Nu3brnWs3LUpf656I9e/bgwIEDCAQCqFy5MmrUqFHsMsqVKweHw4GmTZvC4/Fg165dBa7vBw4cCAJ669ati86dO6t5Ex4ejurVq6v6/Pnnn/mun06nU2VNYPnyf4TD4dCAuqUN4RoqG/Xv3x/dunVDYmJi0HuVK1cGkJdWkpB2YapRo8Z5AyIsFgvatGmjAQC+//57Bbbm5OQgNTW11NI0GvpnqWnTprj99tsxdOhQlCtXTm23WCwICwvTrIeGgpWYmKhMKkIpKSmpyGkvpW6//XaMGTMGd955Z4nrVpapvioB+P7vwGAEgBmBQGCRyWS6DwACgcAHJpMpEcBGADEAck0m00MAGgcCgQyTyWQDcA2Ae3Xlvg7gG5PJdDeAwwCG/F3eDpPJ9A2AnQCyAYwNBAL5kxMhxMAsF9KcnByVugeAJvBP2IGBfgak9YFtmZ6Fv6UTEMvj+yyTwXxZD75mPaKiopSbCd0YmFbFZDLB7XYjLCxMpSxiHWUglvXUu9YAUA4fTNsiA+3y2uhSwt+8HkofJNc7goRKQ0YxJZoeSKFTRSAQgNVqRYUKFZSTjGx7h8OhAtr64LmEIZj6xufzKdckWW9eF/fjOXh+AhH87fP5YLfbVdsTNsjOzlapiSTcIAP5Mm0Qxxrrzf6RLjlMk8QvyXp3Duk2wz7k+egqwj6QrjIFpQzieViGhGwkfMBxzmsKCwtT7ZKZmancW/hhnMASXaYIFLG/nU4nTCaTorR5jV6vVwMd5Afu0RmGKa3YptHR0ZpzEs5IS0tT1+F2u5GdnQ2Hw6EBOeS4JKDE6+Zcl5Ac4Sez2awBjywWC3w+H8LCwpCcnKzmM//RSrch9jW/YHA8Wa1WuN1u5ObmqicUJPjDecZxzpRiJpNJAVl8j+NIpsLKzMyExWKBx+PRjDdCkQQOmeM8JydHjQ+upXTc4TmZbspqtcJisSjoSQ9WcR0CzqYU5FokgRym9WOqKbl2mEwm5QLFHzlGIiMjNan0+OVRgixyjZRzgX1vt9sViMe6Z2dnK9cklqN3JeI6I12rCNlwDvH/h1x/2AYSxNGDQ/xNoE2uE/x/QyBOusYRmpQOXEDe2u3z+TRrGfeTjnCy72Q75Qcn/QN0yX0uMmTIkCFDhgwZKkNddp+NkpKSMHLkSM3N+X/9619Ys2ZNaZ7GUCEaMGAA/vzzTyxevFg5esfFxeHbb7/FoUOHEBcXh7vvvhsAMGPGjPNSp7S0NLRp0yYoEBobG6tcCYqjkSNH4vHHHy+Vuo0fP17dZ8zJycGHH354SaT6AvLqm5KSgp9++qlExz/99NM4deoU3n777aD33G43PvzwQzzyyCNB7z388MPo1q1b0Hav14vly5ejY8eOKg2Y3+/HfffdVyxXJ73i4+MRERGB3bt3o2HDhsjOzkZqamrIfTdt2oTjx4/jr7/+KrDM66+/Hjk5Odi2bRuAvPsUhw4dwosvvogXX3wRr732Gv74448S1xmAcg6SGj9+PGrXro2bb74ZGzZswOHDh9V9ifw0evRoJCQkICEhoVjnL+69h4YNG+Lrr78OSnHWsGFDzetFixZh2rRpaNKkCSZNmlRgmSNGjMCRI0ewevVqDB48GEDe/wU+/f35559j/Pjx+R4fqg0LS310LoqOjsajjz6K8PBwPPzwwyUq4+abb4bb7dZAEbGxsahfvz4mTJiAnTt3Yt++faVV5ctJl/TnIo/Hg9WrV6NJkyZo3rw59uzZU6hjj15WqxV//vknmjZtigEDBmDWrFm49dZb0blz55D7169fHz179tTAqvv27UNqaiqysrLUfd327dvj6quvxvTp07F06VKsWbMmZFq9jRs3al7Pnz8fdevW1cC2RuqbS0u33HILXn755XzTNTJOGRUVhd69e8PhcBTqnjN58mSMHj0aV1xxRanXN5TuuOMOVK1aFddeey0AYP/+/Vi+fDmuvPJKeL1erF+/3nCbMlRsNWrUCPfeey/uuuuuoM9Lf/31F3744QfDtbYA1a5dG40bN1Zx91CKj48PcnIsTLVq1cLgwYPRuXNnFbcsyCAkP5UZ+BMIBPYDaBFi+wfi72Tk2QiGOt4NoFyI7WcABHuF5r33KoBXS1hlFehlQDszM1MFTRmIJjTBwLM+jRCgdbWQaVn06W6k5Adi7se6SGggJydHBYbpbMH9ZV1k3Ti4JLASyq1CbvN4PHC5XBoXIf5mSiHmmCaUpE/1QvcIurYQJtCnGJP1kM4QMg2TrD9hAEIEJpMJMTExCAsLw8mTJ5WbhsVi0Ti1AGdT3MinJWTQ3WazATgLFsk2428CPnQYodjOPp9PQQvSVYXwgN5Rg+PO7XYrhyB5jQz+SwiGE57wAMeJHF8EBiIjI1VaH6Yx0jv8SMcYi8US0rVG30eUHmSTQJuEJzgPCLtIECEiIgJWq1XVmRCCx+NBfHy8Si8UHR0Ni8WiYIq0tDSNtaF05JJzlm3G6+f4YJvGxMSoNEzsY/0TAzk5OYiJiVFgEK+DawJvVHDM5uTkIDo6WgEbHGMyRZhM55WWlqbqSgBFtgvrwHEl4TcCNtyHkI3e6YspAZkmzmw2q6fOJMTI/pL/mGR6PDrXlCtXTgPU6ec2x2VGRob6wqV382G9CZ3JOUlQRO96JseiXHdCzUlZFgAFcMk683r1Y91kMmmczjhfJUjFfpV9yz7iGCakxraTKcPkPAzlJsS5JVP38W+mouO6IB2x9O3D45gK0e/3ayAgtr10b5N/A1BQFcEiQoM8L9c+umXFxMSoa5Zt9E/Vpfi5yJAhQ4YMGTJkqKx0uX420qe26NOnD+6++2588sknZXlaQ0I333wzevToAYfDobn3M2TIEABAmzZtFPgzefLk81q3w4cPa16//PLLJSonLi4Ot956K7744otzqs9dd92l2gUAlixZgjfffPOcyjyf+u677/Ddd98V65jOnTujUqVKmD9/PrKysoJcaQ4cOACr1YqePXsGpWWj4uLigrYtXLgQL7zwAjp16oQuXbqo7SUBVkKVPX78ePTr1w8NGjTAvffeq5x9QmnUqFHo0aNHgWXWqlVLU0blypUxYcIE9O3bF19++SWefvrpc6ozAJQvXz5o25EjR/D+++/j22+/xdtvv41Tp05h9+7dePLJJ9U++vszJXHmWrp0Kf79738Xef+xY8fimWeeCeniIF0i5s6di4EDBxapzOrVq2Po0KHo0aOHBtTq2LEjOnbsCACF3iNZsGAB9u3bp0mbdT40cOBANG3aFPv378e99+o5kvzVrFkztGjRIgj4WLVqlUqH8fjjj6NKlSpFSkf3T9Ll8Llo9erVaN68OYYOHYoOHToU+/gtW7bgl19+Qc2aNdGjRw/s2rUrX+cwIG99bdu2bZBL3bfffouqVauqOjRp0gTdunXD9OnTAQBfffUVKlasGOSep3dmOH78OJYsWYLBgwejTp06+Ouvv/D9998X+7oMXTglJibmC/1InTp1ClOnTi1Syqx69eoVO5h/ruKD/36/H5UqVUKtWrXUdd13333YsGEDZs6ceV7rZOjSVoMGDZCamor09HTN59qsrCx8++23+Pnnny9Y3S4FHTx4ELGxsQXuwwcBijM3O3bsiOPHjyM6OhpNmjRB3bp1sWfPnmLXr8xSfV2KYrCU4AjT7DBIzMClz+dTAXd9KhZKBlp5vAQJ9C4JgUAAPp8Pbrdb41Zit9tVMFm6KEi3B3lulkcwQAaY5TUx+MsgtkwNRQcOGYRm4NZsNqt/NCxfD6qEAmYIYujrKR05CGGwndluMsgs4QPZjpmZmThx4oSCFyQsxTbgdVCsGyEe6RqSn9sP24nXJF2LCH4QrpCwBb/ISTcd1kGek+CHbFMZhGefyb6VsAjbn+mxZFkcR7weOa4IXbBvmTKJbSx/ZPtJMID14ZgiYKRP58RxxvRxABTc4XA41BN5dGdxOp3IycmB1WpFxYoVERcXp8Yxz69Pm8W2ZJsT+OG4c7vdSE1NhdvtVjAb5z3BEZ/PB4vFokCjyMhITVooCfoAUC4+bFe+JkTndDoVMMf87NKxi9tlO+shE5lGTjoYsV057piiS8I40oWI85PQlOxXs9ms+og3SQkhcb4zHZkE8CSUQtCPcBKdmTj+pAOWhGUkrMW11ul0Ij09HR6PR/W5TBelT6Mm5wQdt2j9nZKSosY+25oQJftdzje3242MjAxNHTkOeH28JrkusP0yMjI0zlKc01w/2ZasEyExCT1KiIiwp1z3OZe47sgUYvp0YhzfeniS5zebzRpnO73rGABNmi/9Gs6+d7lcyvlIQqEchwVZIBoyZMiQIUOGDBkydClryZIlmtexsbG4/fbb89nbUFmoSpUqqFChgvo+m56ejv3796v3u3btCgA4efIkDh48eCGqqMRUDSXRuHHjsHDhwnM6/7/+9S8NePDNN99c1rb+06ZNw88//4yZM2di8eLFsNlsQe4pp0+fRqdOnXDixImg45kGQaZDAIDdu3fjhhtuwMaNGxETExP0INm5aNWqVbjhhhuwcuVK9V3+q6/0mXy0WrBgQUinIqlnn31WszZZrVbExMRg/vz5uPXWW0ul7qG++9OpvWbNmkhMTESzZs2QkJCANm3awOVyYdKkSbjlllvw2GOPYe/evSU+99SpU4vlttakSRM4HA4MHz5cAyEBZx9wnDVrVpGhn4SEBCQlJWHu3Lmw2Wz5wguhUpxJ7dy5s0RPep+ratWqhV69emHUqFGaFHOFadu2bVi9ejWmTJmi2S77omvXrmocGLq8tH79eixbtgxpaWklSm8CAMuWLcOWLVvQqVMnDBs2rEDwBwCaN28e5Jw3c+ZMzJ8/X80dr9eLLl26KBBo2rRpmDlzpuZhYgC48cYb0a5dO822pUuXYtmyZUhKSsKZM2fQokUQm2XoIlZR/w+kpKQU2QWyadOmaNq06blUq1jKyMjAnj17FGxUvXp11K5dW71vtVrx1FNPlWkaSEOXjyIjIzF58mR89913eO6554Jg9qSkJBw9evTCVO4SUiAQwH333Ydff/01331yc3OL9Vn24YcfxpQpUzBy5EiYTCb8/PPPJXbzMsAfIQaTGfikKwJBHwaS9WlzJCTCACiD2wzcEooAoEktw6Ap3RekkwMD1kynwg8rhAcYRGWdGDSWjh7cxkCtyWTSBHP5moFj1oMBXXktMg1MZmamSkskoQW2B4+TKWYIDfC6ZfCejikMUss+kZbDvA55PcnJyUhKSkJaWprm3DJ9F0EmAgGsq9ls1qQbk0CRTLvG8ZCVlYXTp0+rYDz7WEJh0gGGEIN09pGOMOwHgi0y2C5/JBwk3YQk4EXIQfYj94+NjVXtKMcp/2ZKKcIIeuBIAj6hwCsJUEhnIbYhXUF4DOeBLI8fXhISEmC32xVYkpWVhaioKEUzx8TEKFhNusDIctm3hBw4FzweD7xerxq/WVlZ8Hq9ypGG8EN4eDhiYmLgcDjgcrmUW43X68WZM2cUtMR5I0EmjkG64rBN2QYcfxyfMh0fAAWlAFBuLhIy4rzjPoRnpKsYx6seSOT7bHsJv8jUgn6/X8FQhE4AbVo3ud7IMUKgSY5b9q/L5dLkZuY4zczMVP3Neca1l4CdXDf1zkRyHea8IBRltVoVqOTxeNRamZOTA4vFoq5bAoOcqzabDXa7HRaLRcE6vCb9+shrppxOp7pWOW8lQCX7iOfneNFDivyfoIcoWS7Hnx4a089HQn6Eq2RqMwlRcRxwnHJc8nVOTg5Onz4Nr9erGTter1dBiNIJjOVKWM2QIUOGDBkyZMiQoctNX3zxBdLT0zXbZLDJUNlLD2UMHDgQzz33HADgtddewx133AEAmDNnznmuWbC6hUgXVVS1aNECffr0Qfv27UtcRvPmzdXfBw8exNSpU0tc1sWs8PBwzJgxAyNGjEBERIRKwVejRg1Uq1YNaWlp6nv2iRMncODAAZw5c0ZTxowZM7Bp0yYF9axevRputxsA4HK51H52ux0//PBDqaXd0EMf8v5NaSoqKgo2mw2nT58ulfKY6gzIc5UH8q4lMzMTJpMJR48eVemuunfvjmnTpiEqKgoZGRmYOXMm3nzzTRw5cqTE5y9Ouro2bdqgTZs2iI6OxlNPPYUxY8Zo3j969CiOHDkSBLPkp+HDh2PFihX48ccfC03ll5OTgxtvvLHAILJ0si+KJk6cWKz9C1Jubi5mzJiBG264ocjH3HTTTdi0aZNmm94BqHv37qVSP0MXn+bNm4cPPvigxO4jCxYswLJly1CrVi2MHTu2UJCyWbNmIf8PLly4EKtWrcKyZcuwZ88e1KxZE+3bt1f3JD///POggG2vXr3Qv3//oLJWrFiBzZs348EHH8S0adNKdF2Gzr9iY2Nx3XXXFWnfhISEfFNo6vXtt9+e15RvJ06c0KRHrFatmsaJDgD27NkT5CppyFAohYeHK/MRKX7erFOnDho3bnwhqnbJ6YcffsAtt9yC3bt3h3x/69atRU55+eyzz+LFF19EdHQ0gLwHVLZu3ao+QxdXBvgjxECu3lWGwU4Gg+UHDgkcsAw9DCEDvBLgkGAFtzHQqg/e8lzAWQccSgZX9bAIt8ngOCXryfNJkEVeF49jvRlglk4dEsyRQIl0uNFDJfLc+hRA+uuScIG8VgAK5JBBa9aHUA+hC32qLQAaiES2C9uCfagfLzIFEceOHuriaxlUl840LEvvHqWHNmQd5PESguA1SWcZpoHiPnqHFJYtQQ5Zj/wkgQU5zglH8Ee6XrH/9WmhZJtKZxOOWx57/PhxlaeX/Svfl3/TdYfjk8AC6yNddNxuNzwej4JMCA3JtsnKyoLL5QrZ54RjZH2BsymZuF22sewv1p2QhgT9mCKK++nXJ+lyRYhI9o8eLJFzSF6HdOPh2OKckiAH4RQCKuxznkv/tIQ8p3TxoiQ0ItcSgiwEIfVwl34+yn7Xpzjz+XxqbZWpEnk9sn2la5XZbFbgkB6Mkdch13vpfCT7RQ/B6Ocgj5egldxfQlRck+RaHGrOym2ybejopQctZX9xHMi1Rq4N8v+ULIOgrEw5xt8SqjJkyJAhQ4YMGTJk6HLUunXrVDCbcrlcmiC4obKVTO8FANu3b8eMGTNgtVpxyy23qKekt2/ffiGqp5FMCVUSpaam4rfffivx8XKs6kGXy0nR0dEaO/7NmzfjvffeQ2xsLGw2G2bNmoWff/4ZBw4cyHdc1KtXT90bnjx5MhYtWqTuZ0jI5IMPPsCHH35Yat979fe95INIpa2wsDAFM52r2N67d+9WzlSBQAAejweRkZH48MMPldsNH0b0+/2aB4WKMiaPHj2qecCMKg748+ijj6p1oWnTpkEuPKdPn0Z0dDRq1KhRpPKGDh2K5s2bo2fPnhg2bFiB+95+++0YOXJkEGwkVZygz5EjRzB//vxS68ewsDDUr18fN9988zmVowfK9P8nDV0+SktLw4oVK4LSbxVHBw4cgNfrVfe4C1JCQkLIFH179uzBlClTMG/ePCQnJyMsLAzVqlVDzZo1AeQFVfXzJCEhAXXq1Akqa//+/ZgzZw7Wr19fwisydCGUnp5e5P/FFoslyO2pIB05cqTM/hfrlZGRoUlBxgedpU6fPo3k5OTzUh9Dl7ZMJlPIlHa//fYbduzYoXmY3VDhOnDgQL4g4O7du/NNG6yXx+OB3W5XrzMyMs6pH4qHjF/mYsooGRAljAPkPf0gXYEYDKWbBV0c6KLAACuhAeDslzQZ7JZwiHTt4L5ms1kF/GUglbAI92dZBAcY3GYKGJmainWkS00omAc4G3hm2S6XCy6XS10Xg7tM1SQdY/RpZiRwIAEgCT1QbCMJ6TCALsuRwAzbn9ulexK3SecKvXMGwQa2Cx1gCM7INDp0IpHuIQBU22dnZyvnGAmzSMnUW2w7CR/wPbaf3sFJtpMEmbifz+eD1+tFWFgYrFarxpFGAhzSLYawgux7PRhAsd2Zzklel2xjtgdfy7HA9mJfMNVcIBCA1WpV9Y+MjMSxY8c0Kb44ruV55LgCztoK8xg6EDmdTpjNZgQCAWRmZmrqJtO5WSwWBS8xVRUdewKBgJoHhB04V2RqJ44/XiM/FMrUYdyfY4lpprj2ELSRKeIiIyPVFxQ6AuXm5sJutyMrK0ulKpPtQucrzmWz2Yzc3Fzl7sNywsPDYbFYcOjQIdhsNtWHrIfNZlPrhn7O6MFEQl5sO3ktnPOcGx6PJ8hJx2QyKTcs1o0p8+S8lmCQnB/Z2dk4cOAALBaLxrmIN3K4jx76kSCOrLeE92S6OfaxTPnFPpFrENtcrlWsv4S66PQmwSGZoox9IlOFSfAOOJv+TYJ4nAeyXhxzWVlZiIiIULCZBOekuxXnA1NRStCQYzYsLEzzJZ0wn8ViMcAfQ4YMGTJkyJAhQ5etxowZg7p162q2LV68GD/++OMFqtE/S7GxsQqyOnnyJCwWiwo4P//886hevbra9+OPPy7Vc3fr1g0///xzsY45ePDgOaUNkd9zS6JVq1apIGebNm1QtWrVy9Lev2rVqqhYsSKAvAcHDx06hBMnTqBz5844c+YMPvvsM6xduxblypULCZs4HA7ExMQgNjYWq1atwtSpUxEXF4edO3eiVq1aSEtLU/seOnQIZ86cCQLQSir5cBVdy/m9uzQ0YcIE9fBZs2bN8M0335RKubVr18bBgwfx/PPPo2/fvgCg7jv4/X78+OOPQW7A99xzj8YlpDDHn5UrV+Lqq68O+eCidGEqSLGxsbjqqqtQoUIFzJw5E82aNQtaw3ft2gWLxYIOHTrg008/zbesNm3a4IknnsCAAQMA5N0H0TuQ6VWnTh3UqVMH3333HaKjo0Om2iuO89G7776LpUuXYsuWLejYsWORjytMeie7/NSwYUOcPHkSKSkpmu09evRQf2dlZeHtt98utboZuvi0cuXKczp+zpw5GDNmDDp37hz03qlTp7B161b07NkTAFCxYkV06dIFNptNA/K43W6VFjExMRHt2rXDlVdeiaioKDRu3Bjff/+9goAok8mEq6++GldccQX27Nmjtq9fv96Afi5Rvfvuuxg6dCjq169f4H6HDx8OSnmUn7p164brr78+5EPJZaGIiAjNZ5MZM2YoNzaTyYSMjAwcOHCg1JwGDV3e4kPuzHAC5MXkDh48iBUrVmDLli0hU7Uayl9PPPEE4uLiMGTIELXt4MGDWLBgAdauXVukMj777DO0adNGgdaZmZma7xfFleH4I5Sbmwur1Qq/368J/jLoCZzNvRsqkMvgKQP/DITSbUIGxRmclSli6C4BnA2WM5UXzyvTUvH88lx6sEYGgBmQZsoawgHSjUUfQGegmO4p3E+CI0wzxXRFhFQYnJduR9JNSO9WE8olgu0vwQwCANxHvs++iYqKUpCD7F/2I4/lkyYSYpJQCZDnuMK6E8SSjizyHGw3mXpNvsdjZBoeBuaZoov9znaQqd2k24dM9cU+5nsECGSwn2X5fD54PB6V9opjQLrosA4Eg2SaoVA/3Jc/0h2F2wjI6SEtjm+z2azqJ4/Lzs6G1WrVpMjKzMzUwEYStpBpnghesJ0IcAFQ6Y8kgEH4z+l0auAv6TjDscG+kOmV9PNT72hEQCM+Ph6VKlVS4B3ngdvthtPpVCmTcnJyVFomPaQRHh4Oq9WqHJ4kXCUdZjju2KZczzIyMuDz+VTfcW4QKsrKykJ6erpqM9mfHMNyPeOc099wocOLdEwzm82wWCzIycmB0+lUdef453gwm83qJhfHPMuQ44zXK9eV9PR0HDt2DGfOnIHL5VL9ZbVaYbfbNXNVAmOca1FRUbBYLKoePC/7yePxaNZQ9gvbSe9YxjGnHxNyTWQdpTsZ202+pmOP3W5HbGwsYmNjERMTg+joaLUf5w9hOQkH8XpNJhOio6PVhzyOI7/fr4hirn0ej0elxyPwRLCM/c5xKSEq+b+R/yeK8sSOIUOGDBkyZMiQIUOXovg5XaqoaQYMnbvq1asHh8MBAHjrrbdwyy23qPfuvPNO9ffhw4dDumgQDimJXn311QLT9YRSSVKhfPbZZ5qHnW699dZil0HJYDwAjB07tsRlXcyqX78+KlWqBCDPJenEiRMAgNmzZ6N79+7qpnwo6GfcuHHYsWMHGjRogPLly6NKlSpISUnBY489htatW6N79+7o3bu35hiHw1Fq33uzs7Px8ssvY9CgQepBr+K42RSmQYMG4fHHH8dTTz2F+Pj4oBRNJVXt2rXhdDrhdDo197oltCQfGvN6vVizZo2mDAnBrF27FgsXLsSWLVsA5DlwXH311SGDUy6XK+TT7KHUr18/VK9eHU6nEz/99BMWLFgAQLsW8MHEli1b5tuv1atXx8yZMzFkyBC8++67GD9+PJYsWRIUtFmzZg1efvllTJ8+HZs3b0ZSUhKys7PRu3dvTJo0KWTZxXEni46Oxl133YXvvvuuyMcURRkZGUXa79lnn8XAgQODtt9///3q799//720qmXoMpXb7caCBQs08A31+eefY+rUqTh58qTa1qxZs5Djjvrrr79w5MgRXHHFFXjggQfQo0cPDBs2DA0aNNDsl5qait27dxtuF5eRTp8+XSCwSf3222/Kna4w/fzzz1i3bt25Vq3IatGiBT788EO0bNlSbZs5cyaef/55AHmg+8GDB4sMvBr6Z4vmEPLzWCAQQEpKCo4ePYr169cX+0GGf7pSU1PxxBNPKPju0KFD+PHHH4sF01999dWaFGtZWVkG+FNaysnJQUxMjHL9IOABnAUKbDYboqOjFRzBIL9MGcPAeVRUlAIB9OmO5G99eiO9Mwhf88udnJyEA4C8J30kREOQg0FjBoR5fSwvVIoYBs/pEMPAsEyBxTZgnRmUZqCa18/AuIR/CLlIWEmeW7YnzyvPSTCCYlsxmM12Yr2la47dbkdkZKQCsiRAAEBTr9zcXHi9XgWrsP0IsBCosNlsyq2F21hnfcoq9inrKUEOGTCXAXoJoUknEtaJ+zNoT2BFjif5Wzrx0K1JAmR68Id1ye9H9r/cl20qXVMkyCTdjdjfctyyDdPS0tSYJ3Qg3YnkOCA4wjEuxxWvj5Ad+4ljh20RExOj3IAkEOL1elWfSfCJ55ZAFsex3MZ+9Pv9Kj0d6xYK0qDriwSlCABxHHD+032Ic4RQE+EuOXb8fr+6ySkdjKRT05kzZ1RdHQ6HBiTLycmB1WrVzAnpVgbk3bSREAshQavVipiYGADQuBpxnFitVnUtbDsJ+nHcSoCQcA4hKACw2+2IiIjAqVOnlGsRAR453zlPpHOPXIckHObz+eB0OuH3++FyuTTrpxzrEuwj3Kd3+OG6L93JpFMa68a/+VrOLwn52O125TIm4U3ZdoSZeK0EiKS7EMc6+4vrlPwfxvFPEfzhNqbP43yjk5R0HDNkyJAhQ4YMGTJk6HLUxIkTsWHDBs02u92Oe+655wLV6OLXY489hhtvvLHExz/yyCO49dZbccUVV6BRo0bqYZWVK1eqID4ABX4AwJdffhlUTv369bF+/foSBXOee+45dOrUKQikKUzFAX+++uorrF27Fs8//7yylHe5XPjiiy+KdU6pWbNmaV7/3//9H5o1a1bi8i5W1ahRA+XKlcPXX39dbEcjv9+Pb775RkFBmZmZmoAzQRSpuLg4jav5uahv374YOnQoMjMzYbfbSxX6AYDjx4+rv7/66qtSC2bWrFkT6enpSElJUTBkVlaW5l6uHgIitBcWFoZnn30Wzz77LIC8ewxvvPEGbrjhBpUygeV8+OGHmvPOnz8fHTt2LPIT1u3bt1fnbNq0KVasWIEDBw4olyIAeOaZZ/Dnn3/i8ccfz9dtKSkpCQkJCXjrrbewcOFCPPnkk7j22muxdetWzX4ejwfPP/88brvtNrRq1QqdO3fG8OHD8eyzz+LOO+/EAw88EFR2Tk6OZswVpH//+9+YNGkSZs+eXaruD08++SSmTJlS4D4PP/ww+vXrFxRkb9++Pa655hr1+lzdYAz9M7RkyRKsXr06aPuRI0cwZ84cbNy4EYFAAAsWLIDFYlEOQFJ2ux2PPvooXn75ZVx55ZWoXLkygLz53rVrV806vXv3brz00ku49dZbcfjw4bK7MEPnXQsWLCgUoLzjjjvw5ptvFrnMxYsXn9eUhTExMbjttts0LnJ0rKpXrx46dOhw3hyIDF3aGjp0KE6cOKFx8rPZbOjYsSPq1at3AWt2aevAgQOYOHEijh49irlz5+K7777TxKYL05w5czTQelhYWJArXXFkrAZCDJDKwKlM9SL3kQ4NTI/CYK4eyGAgVZZPMZArA7Esi/AOA7gEHwggeDweFWh3OBzKnYLn4/F61xk6N3AfCT7wt4SOCM/IcvmPhHWSAX7uRziGbcS6yzaVEJR0huGPbFN5Lrr1MP0Q+4x1lS4qrDsBCkI2TFVDMUAv3VEiIiIQGxurAUzohiIdhWSAXsIA0ulDH/zn+eQ+dHChAxDrHBkZqQmgSzBEn+KMwXWLxaJJvUanFXkthA5k6iD9WNU7+kgwSTquyHJl28tt+jEiQRHZXhy7rK/b7YbL5VLpy+SckKAcy5POV/rxT3cU6b7EfTlWCcVJuEc/L3iN7A/ZDgTM9O3CNne5XMp+lH1AqMntdqsyCF1I1xQJCXH8y3EgQQ8pn8+nAYCio6PVPn6/X5XLdGu5ubmIiYlR7jcS8NG3m0xpRWCQ7SWBG7ajy+XS9GNERAQsFotK6cUUbxwzrLN0tZLrF89htVoVkMT0Zayv2WxW7STTdnE8cuzLfpLzMzc3FxkZGXC73fB6vcoVSroMUSxbujJJSScgCQCx3/XAGI9hG8r3uBawHzlX5XFyDZBfamX5FNuBaRL1wCHFfmYbyDRx3JaWlgav16vOyXKNJ2cMGTJkyJAhQ4YMXa46fvw43n333aDt999/P6644orzX6FLQImJiXjuuedKnPKqcuXKmD59OlavXo2nn35abdcDDIsWLUJubi4++eSTkGm+9u7di1q1aqF9+/ZBDi6Fieelk0xR1alTpyLtd+DAAQwfPhydO3fGwIED0bp1awBQ6ZdLKn0KoYkTJ2Lbtm0lLu9iVbVq1WCxWPDKK68UO/3RxIkT8eabb6rUXdHR0XjrrbcKdEeqVKlSqTrdZmdnw+12K5f60hQD3LNnz8bw4cNLrdx69eohJSUFJ0+eVCm9PB6PSgE1duxYTQAzMjIS0dHRAPLaT84NPvBFnTp1Co8//jief/55jBgxQnNev9+P22+/HYmJiUWqZ6VKleD3+3HnnXfi0UcfxU8//YS1a9eiT58+ap/vv/8e1atXD3IECw8Px9NPP40lS5ZgypQp2LFjB7766issWbJE7TN//nzNMW3bttW8TkpKwqxZs/DOO+/g4YcfxoQJE0LW89dffy3S9QB5Abw33nhDk9qwNNS6dWvMmTMHL7zwQsj3Z86cGRJQGjBggOb+ux6ONWQolDZt2qRAPKmBAweia9euGD9+PO68804sW7YM+/btQ6dOnTSAGZA3v9u0aYMqVaoUer4VK1bg3Xff1cCQhi4P3X///YU6MjqdTk2quMI0cODAcwrKF1e5ubk4c+aMcgCJj4/XQBoPP/xwqTu9Gbr89PTTT+Pjjz/Gf//736DUoj6fL2i9NVQ8vfnmm3j//ffx5ZdfYunSpcU6Njc3Fz179lTOm1ar1QB/SktZWVlwu90qGCvdYGRqL77PwChdMmSgmIFbCUbIALHeyUECJzLYKoOwBEGkWwXTrehBI+mqINPoMFVLKKcdABpYSAIEdH+Qrj569yCmgJGAhEwDxmC4dKuxWCzqb+kgw/aQMAWD1wxC83x07gGg8hN6PB6YzWZ4vV5VT/aXy+VCeHg4YmJiNKl6CGSwvtymD5Sz3larVdXT7/er9gGgnG0kGKZPtaN3xNGDV/yRsALHqQQX5Nij6OwhxybBMjnuCBoQPgs1NvRpvqTLiL5/5PiWr6UIkpQrVw5xcXH5Oj7J9FYSxpH1km0UCAQQFxen0jgRFMvOzlZuWBwrPp9PATN6+E1CYhy/HO8Oh0M5BXFuAVBuMwRQYmNjNXOHY1cPlrFvWQfOn0AgALfbrWAv1o1OSKyPzWaDzWbTONnwWgjxsO+9Xq9qD9lPBH14U8put6v0XnSQyc3NVaAh54yERuQaI1OBMXUX24BtJNtAuuPQoYj7cWxImESCmYSmpPMa1+5AIKDagOsD6yJhNq4nck23WCwqjRW3ezweuFwu5OTkKOtMmYJQQoZy7SBwJ0FS/f8I6eLEurE82cYSqOLcZ1tImIZtrJ9TvFaONzkegbOwE9fQtLQ0DUQn68TtXC/YD7xGOksR1pPrAZ2uDBkyZMiQIUOGDBm6HPXVV18FASDNmzfH3XfffYFqdHGrcuXKOHXqVLGdWKiqVasCACpUqIBGjRoBQEg44pZbbsHq1atxzz33YP/+/UHvE04A8gL9nTt3LnIdLBYL0tPT8fXXXxf5mMmTJxcZMFq2bJn6e/DgwQqQAPIck0oq6USVkpKC//3vfyUu62KWx+NBRkYGTp06VaLja9SoocCFKlWqoGXLlkhNTc13fwm0lJb69u2LHj16qPuPhemKK67AqlWrMGPGjAL3y8zMxPr16zF48ODSqioAoG7dujh69ChOnDihoJ309HScOHECr7zyCiZPnqzZPywsDK+88gr+97//ISoqSjMfTSYTHnvsMXTr1g0+nw8dO3bEDz/8EBKuGjRoEK666qoipfq66aabMGTIELhcLpVmLBAIYMOGDejUqZOChzZt2oSYmJggZ7KcnBzExsaiV69e6NWrF7p27RqUKk0PHMTFxeUL/L3zzjuYPn06fvrpJ7z//vua93766adiPTk+aNCgIu9bVLVp0wYDBgzAfffdFwRcAXlBw6SkJE36wYULF6Jbt27qdSAQCHJBMmQoPy1atAhr167VpOZs27YtevfujZUrV+Lzzz/Hjh07sGvXLtStWxfNmzfXHH/48GHs2LEjKBODXikpKUFBcEOXj4ri4OR0OosMJd5///246qqrzrVaxZLH40Fqaqq631+jRg0N4MqsCvo4nCFDUvx+ZLPZNA+k7N+/Hw8++CDmzZt3oap22eg///lPiQFnv9+P9evXIzk5WcWJSyoD/BFi+inpRBIKCNCnnZHia5nWh69DOaLIgKsMckvoQQZoGbiV55PBWOkGAUBzjL7++nrL9ySkJK9X/yNTW8l6yQC3DBzLcngOed08twSspCOFPkAt0+NI9xVKBr8JATA9GlNj8Ufft/p0QtIRRgI0EiyQcBPrKN09GNhn2+mhAVlvCdvo+0yOL9lerLvsb/kjx5bsDwkb6ce0BBH0P7Jf8tsm+02ez2TKS5HG9pFlyHFMqEACb2xP1stiscBmsymIiS4xBIbcbrcCV3JycjQwT6gUT6E+JPEc+jbWu7JEREQowE6OcdlPEt6Q546KilJAGW/kyBSBciwRQuOPhP0kPCfXEgIxss0JYRA80qe+Yx/IG0sEnTg2Qo0VXp9+DPA3U2zJsSXrxnaKiorSHJNf/xBokes357x+LhOC4XWHWrsogjCEV1gexxjPLevBea93BNOv1XoolOeXYx+AxtFKzmc5d/VrrnRDCrVe6+eT/B/DcwYCAc2XBn195Pou+0APaLLNeO7StiY3ZMiQIUOGDBkyZOhi0+bNmzWvs7KygpweDOU9eBIbG4uMjIwSB9344I2UDBJSKSkpIZ1+qGuvvVbzujgPLGRnZ2Pv3r1F2pcPYA0aNAhDhw4t0jEdOnRQf9eqVUvz3pVXXlnkeupVt25d9feuXbtKXM7FrqlTp2Lq1KnFdmSiNmzYgDNnzmjAi4LGR2m4/axevRqnT58GkNfnt956K2w2G7Kzs0Omg9KrZ8+euOqqq9C/f/8C95s3b945pYvLT5zXHo8H33zzDQ4cOACPxwO/358vBNm9e3dceeWVIe8vtmnTBlarFV9//TX27dsHIC81nV6HDh3CtGnT1ANbBYnp/6KiotCwYUO1/fDhw4iPjw/aFioFBu85Tp8+PeQ52rVrF7RNlqvXqlWr0L1796C1oWnTpkUCvs6HUlJSQq53PXv2RJ06dTRgU9WqVTXX6/P5Qq7Phv7ZqlWrVsh18/Tp0zCZTLBarZrtTNkFACdPnsSxY8dgMplQoUIFxMfHq/eys7ORlpZW6OeLrVu34uDBg+d2EYYuWhXl81mVKlXQtWvXIpWnjx2cD/F/DRUbG6uBwLOysjQP8BoyFEp6YwUgDxpbsWKF+sxp6MIqMzNTZSqSEHxxZYA/QnSI0EMk/Ds7O1u5wXARZVBY77AAaB1J9AFtCTYwCE3oyO/3q/OEh4fD5/PB5XJpUv4AZ51TmDJKusmwHAIIMiAug7L6IDsD1Awgy/eBsyl5uA+DuXqYhG0j0/HItDsS4GEgWDqvAFCOR3r4iseybLrf8BxZWVkKRggLC1N0nAza8ws6nzph+jMAQYufBHQAICMjQ006puSiAwrTdTENl4Q7pOMGP3BKeILjQcJAdE+Rbh96EES6kujrrU+BJV1sCCmxXH1f61NYsZ0lxKB3MNL/SLhB/6Xd6XQiIyMjCJZjmfJcbFez2axJV8bXFosF0dHRmnRk3M/r9SI1NVU5ycTGxsJqtYacE5y3bD+ZWotuUnqggu1vNpths9ng9/tVGiu2A9tdwhuco3Thke5OANQ4kmm9WB+eLyEhQWN3LOeByWRSTkB6uE26U3F8Z2VlISUlRZ0nNjZWuYnl5OQgOjpaMxbkGOTconOQdNXhPlzTCGdFRUWpsiR8Q6CGc5vnleNSrp28Fpmqi/1isVg0ABgdeKKiopCZmQmXyxVyfZHrlNfrRXp6OpxOp1qPQ6VIlH/L8W4ymRAVFaX6STpqSQckjlm6e/n9fvVhTDo15Qejst85puQ4kK5T+nWe6zhw1lmLdTKbzWr8ybVEpvfi2OG6Kx2XJMzJa8zJyTEcfwwZMmTIkCFDhgxd9tq2bZv6/rhhwwYMHToUr7/++oWu1kWnKlWqoGLFijh27FiJUwIzeC8lvydL5RecB6AJDH7//ffYuHFjkeuwZMkSfPrpp0Xad+TIkfj999+RmJiIBg0aFOmYpk2bolmzZrjnnntQo0YNzXvXX389hgwZUuS6Ut26ddM4sUyaNKnYZVwqOnToEB555BEAeekwli9fHnLcFKTu3bvjpptuwvPPP4+PP/4Ys2fPznffUDBacTRhwgQMGTJEfae22WzKOaZatWoYN25cyAeipBgQzMnJweuvv67csPRasGBBkPtOaahq1arKxevdd9/Ff/7zHzgcDuzbty/fNFy8p/fCCy+ETMlWvnx5LFq0CCNHjsRLL72Exo0bB+3z888/B7nl5Ce6/Njtdo3D1/Lly5GTk4MuXbqobdHR0WjatKkmJeHMmTPxxhtv4NFHHw2Z/qpz584qLZ/UuHHj8h0jX375JUaPHo1x48bhueeew8CBA9GsWTOMHj1as9/s2bOxYcMGnDx5Muh+cFkrMjISu3fvDtru8/lgs9lw1113ITo6GnXr1kXlypU1IMaWLVuQkZFxPqtr6AJq2LBh+Oqrrwr8/HPHHXfgf//7X5CjFpA3h3r27AkACrz88ccfcf/996t9tm7dit27d+P06dNo0qRJ0Lqwbt06/PXXXyHPvXnzZrz66qvo27dvkdcNQ5eebr755iLtV1Rod+LEiUGAf1krPT1dpcoEgJUrVyIpKUm9zsjIQHp6+nmtk6FLT06nMwi+zcnJQYMGDVC/fv0LVCtDAHD33XdjxowZGDNmDGrVqoWKFStqPj8VVxGF7/LPkXQQYSBbOiUwsGmxWOB2u1WAlEFaQhISIuCxgDbIy5QydFTghxcCPISMcnJykJ6eHuS4IcEIAhjSjYFBcH2wn2KwXQ97SOcNbmewNjo6GlarFREREThz5owGYGJ58stGKKcgOj7I1/qAvnTFkO4QDCrzfYIUvHbp8iKDy3SLIVhhs9nUeenO4vf7Vd9yu4Qj2K5MgybTf3F/Xq8EK6Q7CAPtDIyzbaRLh3T3IYTA93mMdNnQu/VIgEIG22Wb69OESThDwlsSLGAZsq9YV71rCLezX6QLjWwvCQEw7ZV0S5FQCVMtsY/4DyoUUMPrZLo21i8qKgoJCQmwWCxwOp3wer3Izs5W84JjU6bWI1gEnH2aS7YjITiCFj6fT1271WpVTjwyHZ0eKpTjSDrx0KGIYzcrKwvR0dEKYLFYLMjMzFTzQ6bT4loVGRmJjIwMtZZER0erMck5xTq6XC6YzWakpKQgJiYGsbGxKt0Yx350dLRKn8exIIFEm82muQ5eF9uEP4TlCKnIffVQItdh9pFMKSXbh5JtGhkZqUn7xTZiyjQJGPG3LIvgDZ8U49oioRk5J6RLm1xbuUYTBpSwEIFBufZ5PB5NP8n1Lb81Vjr9yDGqnxd6pzC5NhFglGnTJHzJ+nBuybWGoB3/lq5aTHWoX5sNGTJkyJAhQ4YMGbocVb58eQwbNgz79u3DCy+8gPLly2POnDkXuloXpdLT0/HQQw+pAHxJVL58+aBtFosFvXv3xqJFi4pcztKlS9Xfn3/+ebHqcP3116tUUIXp//7v/9C0adNilQ8A7733XkgXE4vFgjFjxmDWrFnFKu/nn39Wf0+fPr1YacouVZUrVw7XX389kpOTi+3+s23bNmzbtg3fffddofvGxMSUtIoA8p6+ttvtGD9+PGrUqIExY8Zo3i+K/X9YWBhmzZqFIUOGYMSIEec9jZvFYsGhQ4fU68TERNSpUwcrVqxAnTp18j2uRYsWGrhGKiYmBjNnzsSwYcPyPb5t27Zo1aoV/vjjj0LruHHjRnWPp3v37vjpp5+wfv16+P1+BRH861//QmxsrALuFi5ciP3792P9+vWoUKECmjRpgg8++CCo7KFDh6J27dpBTiUA0KhRIzz33HMYN25c0Hs5OTn4+OOPERERgdOnT8NmsyE9PR1PPPEE7HY7WrVqhaSkJIwdOxZAnnvQa6+9hoEDBxZ6veei5ORkAHn9WL9+fXz33Xfo0aOHZp958+bh+++/x6hRo7B69Wps375dE7R66aWXQgJShi4/mUwm3HfffXjkkUdQr1497Nu3DwsXLsTKlSuD9h0yZAiuvfZa7Nu3D/v379ekSKlbty6sViu2bt2KyMhINGrUCBs2bNCs34FAANu3b8fq1avx6quvBgEZmzZtwrp161C5cmWNUxCQB4LK/4WGLk/pnRJD6fPPP8cTTzxRpPKaNWt2Xh1/cnNzceLEiaDPLf369cPixYvRpk0bOJ3OAlOQGjIEAJ9++imef/55OBwOtc3hcKBmzZpo2rQpVqxYETJdsqGyU3x8PL777jt07dpVs66UL18eHTt2hNVqLZFTouH4IySdYhgQpySgEwgEYLfbg9IR0dFFgjcyzY6Efvg+AQPgLBDg8XgUCOT1euF0OhXwIt1WCCVIcIHlEVCRjjIM4krXDn2aJT1wwYB5QkKCOi+BBq/Xq1L+6N1dpMME21OmvqEklMJr0ENKbDs6p7A8CQiFh4fD6/Wq65Z9QWiCAXAGqAkPREZGwmKxBLlp2Gw25dQhYQP2PZ1U6DijT+0l20QCZQQkCOVIwIZjhEF1uotItw0G0fXwANtbtjOvmWAB68XxLV11JGgk0/hIJxR5Pfp0RXxf37/SXUS2AbezTgCUI4wcOwQI/H4/HA4HYmJiEBUVhZiYGNjtdpUKiuObQAnhgrCwMNhsNkRHRyMQCCjnH7Z1VlaWBhIjECFdotjvVqs16Fp9Ph+ys7NVLlUeZ7VaER0djfj4+CCYjWNYzj0CGwQ8pBsQxyn7zmQyITMzUwFMeliPcIgeCOPxElAiZEIwjjBRWFiYxm1Hgi1cF+R1cZ7oHcZYloSluG5JgJH9R9cal8ulcaCR41QPvOhdcOTfZrM5yMWM6avYlhJSITiVk5ODzMxMBcNwPvI65DXoz8u5qgcZrVYrbDZbEPAloSiXy6Us/QKBsw5fwFnwiO3E/xNyzOpdhyIjIzVzWJ5brglcK6QrGh3jOC4jIiI0kCrXGOnaJEEmPUCZmZmJtLS0Ej/Ja8iQIUOGDBkyZMjQxaQnn3wyJNBz+vRpfP7556hVqxaWL1+O999/HzVr1kRCQsL5r2QxdP/992PixImF7ldY6qDi6OTJk+cE/VSqVEm1q0zDBAAvvvgiHnvsMfTu3Rs1a9YstKzBgwdjy5YteOGFF7B48eJi16UoqYUAoH379sUuGwC6dOmC3r17h3wvLS2t2OUxAJ+Tk4PXXnutRHW61FSpUiXEx8fj2LFjZXqecwV/nE4n9u3bh/Hjx4cMwBQlADB+/HgkJycjKysLTqcTZ86cOac6FUc1a9aE0+nE9u3b1bb4+Hh8/PHH+Y41r9dbaDqeyZMnFwj9AECTJk1w5513FrmuTAHTtm1bXH311Wq7y+XCsGHDMGnSJDz//PNqe5UqVZCTk4O+ffuiZ8+eWLt2bchyu3XrVuC8GjVqVIH1ys7ORq9evfCvf/0LHo8HJpMJu3fvRv/+/RX0AwC7d+/WuFSURWr1FStWoEqVKhg8eLC6F9S9e/cg8Ofuu+9Gnz59AOQBXLfccovmwVn9Gm3o8lW1atXQoUMH1KtXD9nZ2fjhhx9CQj8A8Msvv+DgwYPo0qVL0JiaNm0a+vTpg4cffhi///47fv/9d+UkJrVx40YMGTIkXxeWNWvWBLn47dy500ht8w/RxIkTNQ/6SmVlZWHFihV48803iwQE2+12DB48GM2bNy/tauarsLAwtGrVCk2bNtW4PkZERGDv3r1ITU1Fenq6Af4YKlTly5cPCSRXrVoVDRo00KQANnR+xLbXx9WTkpLw1ltvlTg9qgH+CDFAyg/M0gmFwVZ+SDWbzQoCkNAPA7MyUGqxWACcDUQzqCrdVugiwwA0g/1MbyQDw3RQILghg98sW6bDYR31AXSZCoZBcAkmyGByTEwM4uLikJubi8OHDyMjI0OdVzqYUNLpha/ZpjJdDKCFVlgXmSJLwlNyfwmf5ObmqnRoANQXC+muJK9VumfYbDZYLBblikLISzrayL6z2WwKpmBwm+ciPEFIQqZEYjvwGL2jjwQYJOSgd5tin+tdTugk5HQ6g9xkeG49fEV4TJ5fD4vI1D5sfwn/SFBMf065v4QMTKa8FEo+n0+VwxRRBEMIVkhwIT4+Xjnw6OvKsiMiIuBwOFC+fHlNerKwsDB4PB5kZGQooIPuI3pHI8JSLNNmsyE+Ph4JCQnK+YrtTYhPzk8Ayl2qYsWKqlyOL5nCS44tPTDC9UCuIRTPSyBFjgcJ/wBQ9eQNo4iICFitVnXenJwcte5xzWEaJ65jVqtVrVly3PF9eU7OT+k4JW82cTxxbZOuTcBZpx3pTEQojJBJfpCanJthYWGoWrUqYmNj1Zjyer3weDwqjZl+bWCbOJ1OZGZmKpiQbc41yWw2axzLZNvzR583VQ+sybFAIMvpdCoHKT1QJOcT21Xux/8jhDpl/7Bd5HzWz2/OE56PEJgEHyW4J92gOG7lWs/6yjmcmppqOP4YMmTIkCFDhgwZuiwUFhaGnj17hrRGf+211zB79mw888wzAIDHHnusSE4hF0omkwlPP/00rrjiikL3HTRo0EXj2lCrVi311Optt92Ghx56SL3Xrl07TJgwAd9//z1uueWWQsuqUaMGNm7ciJdeeqmsqgur1XpOtu2hlJ6ejpkzZxb7OPb1nDlzyhyEuVjkcDgQHx+vSZdRFrLZbFixYgV2794dEmZZvHgxtm3bplxU9HK73SrNVFxcXND7hQEy1P/+9z8MHToUPp/vvM7Zdu3a4cyZMzhw4IDaFhERAbvdHjIVybZt23DTTTdh3bp1pXL+m2++GRUrVizSvjt37gSQ12fVqlXD3XffjV69emnSBUqwZt++fZg+fTratGlTYLmtWrUKeY+UcjgcWLJkCaZMmZLvPhs3bsQnn3yCHTt24PXXX0fVqlWD9rFarWjcuLG6b/XBBx+UqnvXkSNH0KNHDwQCAbjdbk3/vfXWW5r/fytWrMgXMFuzZg1eeeWVUquXoYtbSUlJ2LhxI1JSUrB169YCgYrFixfj559/RoMGDXDNNdcEjfO1a9di+fLlWL58OR544AF89dVXQWUU5lDx008/Yd26dfD5fMjIyMCUKVPwxBNPaOBEQ5enwsPDce211+br0BMZGYmOHTvmmyJWr//7v//DddddV5pVLJIiIyPx0UcfaVLWxsfHIz4+HhaLBampqXA6nee9XoYuLd10000qFawUH0QP5aJqqGyVnp4eEkKdN29eyLSqRZUB/gjJQKXcRkCEAV++L4POEkBgcJQf8GV6Ku7DIL8M4srgNmEQAj6yTL0jj/4H0Ka7CZVaRqakKUwM9BJ8YvCfbcNr0Ttt8FysJ9tQD5ZI4IXH6+ssg/K8NkIhDH7TjUPvPEPIR9aP7ctgtvwSR8chfdvoASfZ17KPZfuzHvm5cegBHQkDySC7bAO9ExVwFjqSKa8kJCbHox5wkZIwgKyvHvzRj8NQbj/6/gs1PlgeXZM4piQEJgG2zMxMpKamasaRhMhkG+lTGsk2kk+ZSNhBAhPyqRQJ6hHYkHWQ18JrdLvdcLvdOHHihKaOhIrYj7J+Epqgk5Fsc71zD8eHvH7pLgWcBYT8fj8yMzM1cBlBEZkyS6ZmAqBgGwkayv6V7mH68SvrRCcdPShFqES2D/tCgpF62Ez2d6gxLMvhuko3NNYBgGZ9J+RD8IbrG8/F/eV1ybaX84R9I/dne3GNl9fAc0kHOL1CwXd6WEz+SNgv1Hjl+KD07lyybaSjD/tcSg8whkq9p3eCM2TIkCFDhgwZMmToUtamTZuQnJyMtm3bBr3ndruxevVqdOrUCUBeoL4oqQYulOh4XJhrDR9maN26dcgnRs+n7rzzTtx3333q9eHDhzFp0qSgAKDFYkGDBg0KDMIDwMyZMzF//vwyqStVuXJlJCcn49ChQ6VmpX/kyBH8/vvvxT7uzz//hN/vz9ex5HLU+vXrsXLlyhI/PVtUTZ48GVOmTMHUqVOVowy1evVqTJ48GVOnTs0X2BoxYgRGjx4Ni8UCi8WC9PR0zffoojqn7Nq1C16vF1dccQWGDx9e8gsqpuLj4+FyuTR1LleuHKKiotCwYcOQx9C9uzRUvnx59OrVq0j7ZmRkAMi7HzJkyBDcc889uPfee/NN3Tdt2jRs2bKlwLVy1KhRIYEtvbp164bBgwcXuE9OTg5WrFgBILSzV1hYGGbPno2TJ08CALZu3Yp58+YVeu6iSvZJXFycJjh+4sQJTaB5//79IeGtQ4cO4b333iu1Ohm6NJScnIyTJ0+iQoUKqF69OqpUqRJyv7179+LgwYMIBAJITEzE8ePHQ+63e/fuEjsEnj59GsePH8eff/6JpUuXYurUqViwYEGJyjJ0aalGjRqoXbt2gftYLJYige9AnqNdYmJiaVSt2DKZTJqHaa1Wq7rXzgdzDRkqSOvWrQuC39PS0vDLL7/gm2++wapVqy5Qzf65opGHXnXr1kWzZs1KXK4B/ggR9mBAVwaIJfgjA+IMdPr9fhXMlmLAXe/SwCAvzyXdT1guXTiio6NV0FSWI9PpEGYg/KIP8PI8LIOgiwwkyyCtDOy6XC643W6VSksex7Ll+aSrjAQhWEc9GCWD8NKBgmWzXkwZRAiAzjBAHphis9k0bjjsG5PJpJxF+A+S/cz2YxCdMAZ/s77sKwm3sH8kvMMy9dCXhGlkwF+2layvbEeWx7FFOCEU1EKXFoIrUVFRauxI2Es6IemhJJlWLBSgxfaXkjCKHBeUhL2kU43cxrRCdFs5ffo00tLSNKmaTp8+Da/Xi4iICOVCFRcXp3GbCQ8PV8fL8a1PKxYdHa1uPERERCAuLg5VqlRBbGyscrfJyspCZmYmMjMzUalSJURHRyM7O1s5L+ktdCV0kpmZieTkZBw+fFgDxHD+STcburRIKIdp9CIiIuDz+ZT7D69FgjESqpBOLxJcycjI0KRpku5iBMV8Pp+C6bxerwJyrFarcqnhuSQEKd2pwsPDYbVa1Rhk/8mxR5CK6aS8Xq+qK+tAxyfOP9aTddLDXvr5o4cUCdXQ6SkrK0sBPmwjrte8dgkEchzJ8S7nr6yDBLck6MLrZwpBug4ROpL9nB8YKOcq21W6Wsl95DrOtpPrLZ2gmFJND0yaTCblNibnD9cWPSjH9Zlrh/x/wlR5bAdDhgwZMmTIkCFDhi51LV68GPPnz8fNN98c8v0PPvgA5cuXR926dTFlyhSVKuViVKVKlbBt2zZ8/vnnBe5XpUoVVKpUCceOHStzeKIwPfnkk7jjjjtUPdauXYvc3FwsW7YsaN/bbrsNFSpUKLC8Y8eOYe7cuWVSV8rtdmPv3r1o164dxo8fXyplLly4MAguKYoaNGiAuXPn4u233y6VelzMslgsGDp0KIA8YGz58uWlWr5MvQHkARAzZ87EhAkT4HA41D2QTz/9FF26dMGCBQvwzjvvaObRypUrMW7cOLjdbnTq1AmNGzeG1+vFzTffjP/+97+a79HFSZ+dlpaGJ554AomJiXj00UdL54ILUe3atTWuRImJiTCZTOjfvz8+/PDDoP3Dw8Nhs9kUhFMUuVwu7NmzJ8hhZsKECZg1axbq1Kmj3MAKknzKOjExER06dMDgwYNRuXJlzX5ZWVmYPn06Pvvss6CUQdQ999yD7t2748MPP9QEmt9///2Q6QMjIiIQHx9fqMvYhAkTcPr06ZBP4rtcLowbNw5paWmYO3cuPvroI5w5cwZvvfVWgWUWVTJAWKVKFUyaNEm99vl8Qa5V48aNw6ZNm3DkyBF1ry07O7tYfWvo8tCSJUuwefNmVK1aFV26dEHXrl1DArhutxs//fQTtm/fjtOnT+d7z/BcIdWpU6eiRYsWGDRoUKm5ixm6+NW5c+eQzpxSe/fuLRD8rlOnDl5//XWMGTMG3bp1K1L62LJQdna2JtUdYUyTyaTiGYYMFaSNGzfi+++/x759+wDkgbkvvPAC7rjjDmNdvEA6ceJESPe5jh075pviuSiKKHyXf44YcJWAj9/vh91uR3Z2tgpCm81m+P1+9bfeMUHviMEgv8/nUwFTBlul24hMxUUwQ7qXyBRfDOozyMrALXDWiUimuwGgnD0Y3OZ5JIhDuEbvKHT69GnExMQoAEbveiFBIF6f3h2HddMHw1kOf+uD1zIoTjiLAXlepwxM87rYXgQGCDN4PB4FDOkdO6xWq2pPPUAgIYacnBwN+MD2lWAA21q6ErE8CQzpXVI47mQqIB4vxwsA1X/S5cdsNmsAErY7zyNBBwJOoSAfvUMIASlen3QB0QM2egcn9oEEgCTYxJsgEqYwmUyw2+0auMTv96u0U2FhYbBarfB4PKocOrrw2ggk5OTkaJ6M4dyNioqC3++H2WxGXFwcYmNjVYqn/2fvvMOkqrKvvapTxY50NzkLAgaSBEUQGREjo4I6hgF1BowY0DGMPxVzQMxiAhXBnMaECRFQEFCSoJJzaJqmc8UO9f3R3zrse+p2gkZAznoeHqpuOPfck7rq7rfWlnVKTEzEzp07Y1JuScBBOu3ogIbL5VKQSXx8PEpLSxWYwXHGdpNjhY48sl3obiWvzbanZPoqjstAIKDGPd13ZLtyLauoqEAoFEJiYqKCEZnWqrKy0gJi8X44/l0uF0KhkAKnfD6f+gDKehOSlO3Ge2TecglDSbBQQnRybMt/vA7LIqjF8yS8GIlE4HK51DoaFxenHroRiuH4AarWNrfbrda8uLg4tdZJ8I31k65wANT6ynvj3xK6DOlzVYcKWZ4cx9KBivvlusw+ZFnShUh3q5Jrsu6MJZ3G9PSLBJgID8mxzXozBaJsTyMjIyMjIyMjI6NDXR988AG+++47nHXWWba/HmfKGwB44okn8Ntvv/3ZVayT1q9fj4EDB9Z6XJs2bZCenl5j2o4/Q3FxcUhNTQVQlarnxRdfVPuefPJJnH766THnXHvttdiyZQsmTZr0p9VTV05ODp555hnk5uZi3LhxuPbaa5GRkVHrefqPQqS++uqretfj+uuvx/Dhw3HyySfX+9xDUc2bN8dNN92EjIwMvPjii1i2bFmDlT1+/Hicd955aN++ve3+M888E0OHDsWYMWMQDAYxevRobNq0CfPnz8crr7yCDRs2YOrUqZg+fTq8Xq/lR5hUenq65Tt4XVN9AVW/7m7ZsiXWrFmDcePGNRgQUp1mz56NwYMHY/DgwWpbMBjE448/jsWLF+Pxxx+POYfPdP/44w9MnjwZ559/PlJSUmq8zsSJE3Hfffehbdu2OO6443D00UejXbt2ePTRR5Gfn2/7Yz2ppk2b4r///S+uu+66Ot1XYmIi3n33XWzevLnaY6688kp06dIFACzOOAsXLsS1116L9957D+eff37MeTfccAMWLFiAL774otqyb731Vjz33HOYOXMmFi5cGLP/mmuuUdu//vprOJ1OXHbZZWjUqFGd7q86bdy4Ub1+88030axZM9x2220AgKFDh+Lpp5/GDTfcoI7x+/047rjj0Lx5c/Tt2xctWrRAcXFxjfdm9NdUYWEh5s2bh/79+6Nx48Y48sgj0bVrVyxdujTm2AULFuCrr77C7Nmz//yKGv2lNW3aNAwYMACjRo2y3b9ixQq8/vrrMX9XMzMzceuttyIQCGDr1q0YNWoUMjIyDijEmJCQgMsuuwyvvfYa5syZg6ysLPh8PiQmJqKwsNAAlkZ10uTJk7F161Z069YNn332mW36RKM/V0cccUTMttTUVBx//PEYOHAgZs2aVe8yDfgjRKiEqZ4yMjJQVFSkArvhcFilaaHrikx/xQC7DOAzCCodgViGTHnD9wQ+6PZBcETCIYQo+I8ONroDBrAHUJCuNdFoFE6nE6FQKAbakc40wB5IhVAAvzTJa0l4AYByyvD7/eqaEk4gICHTnUkHCwIi0u2jsrISoVDI4l5D0IXHsg1cLhfy8/ORlpZmCbqzrcrLy+HxeNQ29iPvMSEhQbnKkJjlsRKO4P3THcrj8ai6Eg4iUCDhH9ZJOkbJgD77IBAIqC/8hL3Y3tIRRPaJDPDbOYNIdyMew/GoOwiVlZVZ0oRJBxEdbpFuUQQ9pIuKvG/OAemEJdMxVVRUqA9bnGcul0vBLtFoFKFQCNFoFH6/H+Fw2DIWpEOUTIXE99zPBwF0j/J4PBbghm3FugUCARQXF1scXtiO0uFKn08cHwQwkpKS1H3Jecr+ZfsQFJTjl2M1KSnJ4trENYHpuOQDDl6jUaNGSExMVKCU7OuUlBQ1lmQ6L84LOTY4BwmryPUuISEBfr9fQVV0zuF9OBwOuFwuS914z2xvmeKM40xfbyQo4/F41EOisrIyBAIBi5sV60XnJJYh3b0kLET4TzqySeCRNpoSpJP3IsFLthvvjeOb44XvWQfpFqavmRK6k+sEX0uAk2NY1kOCYWVlZQgGgxZAk23EdmT5CQkJ6liOGwJB3M+/iexXzhsJ8RHa02FZIyMjIyMjIyMjo0NZ8+bNQ05ODi644AJb8Ee6Ufzyyy/4448//szqNbhmzZqF119/HUVFRQesDqmpqfD5fAoMWLVqlQXm+fbbbzF//nz07dvXct7dd98NAAcU/AGqYDGg6jvbq6++iltuuQUbN27EU089hSFDhsRAS0uXLsUTTzyBo48+GhdccIElZVxRUREWLFhQ7zpce+21WLJkyWGT5qtt27bIzMyMcXHZF3Xp0gX3338/zjvvPBQVFSE5OdnWqn/16tXIzMxEbm4uhg4ditGjR6OiogLbt2/Hl19+icWLF+Ojjz7C+++/jyuvvFI9B4yPj8fLL7+MLVu2YOjQoZYy9TQNten999/HF198YZuWsCF0yimn4I477sDGjRvRvXt3XHzxxXj99dfV/qKiIixZsqRa0Iyu2vfddx+SkpJw5pln1gr+9OnTB6WlpVi+fDmWL18es7+m5w7dunXDe++9hw4dOmDevHlo2rSpbSqYvLw85OXloVGjRsjKysL48eNtARY+B+rQoYN63iy1adMmAKg2fVhKSgruv//+GuGY1157DampqQgGgxg0aBDmz5+PQCCg9jMdGDVjxgycfPLJ6NWrF0477TRb4KguKigowJgxY5TTjz6HCDrp2rZtGz788MO9uqbRX0eLFi3Czp07ceyxx+K4447D5s2bbcEfALjrrrv+3MoZHTZ64IEHcNxxx6F79+4x+yZNmoTJkyfHbD/jjDMwaNAg9OzZ07K9tr9Nf4befvttjB49GpmZmWjcuDFKS0tRWFhY788GRoenli1b1qAAvNG+qVOnTjjxxBNt97Vp0wZ9+vTBjz/+WO9yDfgjlJCQgJKSEkvgmQF/BmUjkYj61QWDqgyC0tWHqXEI7jAgD8ACCBBCiEajyhFEujzIND16oJ5BWemEweMkfMDAtIQVUlNT4XQ6UVRUZAEPpFsDoYyUlBSLM4t0lpFpxmQaGDplSLcfeX3em3S2kOCQPJZWr9K5ha4RiYmJKuDM4/lli+4SDDrLFDrJyckWcEICN4Qe6Dgi74EwAfuF/UbYhW3g9/vVawI/hMYkLBKNRpULC/fJ1GM6UMWyCCLIND7BYBBlZWXK0k8CWdIthvcp+106JsmUQRw7rI90P9HBHzkG4uLilOsQIRPpBiLdngjEEX6QoBO3JSYmIhKJqOswRRNBGoJBEmBwu93qON4HwS0AChySc4aOQKFQSAF4ycnJahyw/uwH1p310sexdDgidORwOGIACQmnSbhPOthImEuuPVy3WF5SUpJaWwh3+Hw+NX/o7sMHEYT/OLZlOkDdiYr1IagoXbbYtgT0OEYkdJKQkICsrCx4vV7s2rULkUhEjQ/ZZtKtRr9XOQ/lGhQXF4eSkhI1TqRrGOEYzg2uxbzPSCSi1ixeR6Y4IzBEYJD9T1BK/uqO/UaQjpJrNAAFINFBSLYZxyzHh/63g/clHbLk+NPBP7YBr8eUerwnQn7S7Y5lsn15HsE1glJyjMh+ZJo2AmJy7hB8MzIyMjIyMjIyMvorKD09HQBw1lln1XjcVVddhUcffRRff/21LRxwKOnRRx+1fP7/s0SHWaZjYCrhJUuWxBx78803Y+7cubblfPzxx9i4cSNuuumm/VfZOoqQz4wZM/D000/j119/jQF/3n//fUydOhVA1Q9fpEPJhg0bLM4wddXTTz9t+c76V1RGRga+++47LF26FDNnzsTKlSvx6quvNlj5xx57LDZs2ICZM2di165dNc7r22+/HUAVNPHBBx/ghBNOQOvWrVFYWIgNGzbgkksuAQD88MMP2Lx5M+6991706dMHffr0sS1vb4J7gUAAc+bMqfd5ddGpp56KQYMGAagCnexSFgCISadO8TkLUPWccMyYMWjVqlW17kS//fabbaC2rurfvz/i4+OxadMm/Pvf/8aOHTtQUFAQc9zUqVPxf//3f2jXrh0WL15scRKXOuecczBq1CjlQqaLLkFerxerVq3CkUceGXOMz+ertd5PPfUUNm3ahMzMTGzZsgU7d+7E0qVLsXnzZixZsgRr1qzBli1bAFT1N6Gorl271lp2dRo1ahRGjBih3jdp0sSyf+3atXtdttFfXwsWLMDcuXORmZmJLl264MsvvzzQVTI6DNW5c2e0bNnSdt9PP/1k66LXu3dvWxeOg0HNmjXD66+/jlmzZqFRo0ZISkqC1+s95L9fGBkdjvrXv/5V7efj5cuX49dff92rH9Eb8EcoPj4e4XAYwJ7gqnTzAKBSajGwqbvrEFYAoIL8ekov6e4BWNNgycA2jwUQkxpFdxXR08FI6EgCDoRLQqGQJVWPBGCkQ4aeUovl8jgJPcj2YjBeB0zkILYDUKS7BYGasrIyCyjEYLTT6YxJt0UoR9ZbwkXyeL3NpLuJfh96HxB+sBsj8ly2uw6ASYhId34hzKKPAf1/3clDliUBM4IUMgUX21svU3dwknXTXYR4nr6NxxLeYTlyXBIwka4khBDkuJXzieOC9SOcwTRDBDNcLpfFQYn14din4wzbSLazHM/JyclIS0tTx+Xm5iIuLk45zHCs2i28hF5kv8o218E32QcSJqLka/Y5HZn0+S/hGQkxynMBKPBDQiqEQySsJ+/Jbg2TTk4Ew+zWJ915SdZZd/SRbSZhOXm+dHhiijcJCFF0TmKf87pyfnJc8hrVuafJtpJjxQ6+setbvR+j0aiCEAmSyetynMn+0cvkdtl+chxIcRthJ71OfM225dor01BKYFUCSnwt4THpuMV5Lh2ljIyMjIyMjIyMjA51nXrqqWjWrBmSkpJw3HHH4ZdffrE9bsOGDUhOTsaxxx5bLZByKEl+j/izdMUVV2D37t3weDyWX2DLVDRUTU42p512GnJzcw84+NOyZUtkZWUBqHItAmJdOwBg5cqVAKqcjlq0aAGg6rtdKBTa6193T5w4Ec2bN9+rcw8VNWvWDEcddRR+/PFHLFiwAMXFxcp5pSH0zjvv4KeffkLfvn1tx6CdduzYYXku+Pe//x1HHHGE6vfPP/8c6enpeOCBB2oshz9sOljUoUMH9bqkpAR5eXm2x8kU4lL6s90PPvgA//znP6u93jfffLNPbjKJiYkoLi7G559/rlzY8vPzkZqaqp4Rrl+/HnPmzEEgEMCKFSuwceNGbNiwwba8xo0b429/+1u112NQ+ZVXXkHLli3x8MMPxxxT1/SJP/zwAy655BIceeSROPLII9GpUyfk5+dj+fLl2LhxIz7//PMYwEs6z+2NZDBZdy0yqWWMalJFRQU2bNiAHTt2wOfzHfKuh0aHpmbPnq2eg+tq165dTPpEh8OBjIwMuFyuP6N69dbWrVuRkZGBM844Q8VAhw4ditzcXNxzzz0HuHZGRkZ1VXZ2Nnr37m27b/v27ViwYAFmzJixV2X/+T/ROYiVn5+vnBf0oDADm4FAAOFwGE6n0/JhVzreyOAzXYCkS4UskwFf6bpCMIBggw5D6KKbBiGAuLg4uN1u5ToUiUQsoJLf70dpaaklmK47c7hcLqSkpCAtLc0CcEgXBx14oisKA8QSOGGwV384pKev4fl0EHG73SgvL0coFFLnl5eXIxgMqnaXgAPbSsITBI7oHmPnskJIS9aF9adbBd1oCLXQZQaASoPGfmZdKAbQ2W7sVzqKyMA5gScJcUnAQoIydCphvRlsl/AUy6BLEp1fJHyhO3awrhLG0QP4EmbRRTDK5XJZxgDvle3k8/mQlpaG9PR09UGF13Q4HAiFQvD7/THjj+3JFEmEvdLT0y1jX0/JxtR8Pp8PqampSE5OhtfrRUZGhvq1YHx8PDweD5KTkxWMUVpaqtJa2bn9ULKP5HrAtmI/cMzpLi1ut9sCVlRWViqoSbaLdIUBoOa+HbzH8mmdzDYuLCxUD4voSMV+0l2VWHfdhUq2LesgQSjWgfDI7t27sWPHDgQCAUsaQK4ZdL+yS0Unx6yEc+gcJNdMuk2VlZXB7/cjFArB5XKp9mWd5Hx2u92qbeVc4n1wLLOfOe64xnJuy7pLRzY5d3hd1sHn86l75z7psKS7d/EYznvZF4STZBtRBH6ku5sEi+gSJUGl6kAoAJYxwTReBEaZ7o3jjf8SExMtue6NjIyMjIyMjIyMDmXNmzdPfa6//vrrY/Zff/31+Oc//4lt27Zh5cqVGDFiRLWuEEb2atasGYYPH46mTZti6tSpMalmqgMA3nzzTds0WC6XC61atcLYsWP3S33rqkGDBiE7OxvBYNACKklXkcLCQnz33XcAgOOPPx5HHXUUAGDXrl0YOnQo3n333b2+/rZt2/b63ENBLVu2RGVlJXJycrB69Wp88sknDX6NTZs24d13361XurWbb74ZI0eORFJSEo4++mj1A1QqMzOz2l/+UvsKcjS0XnzxRfV6/fr11YJQ1f0ISLo1U7m5uZb3u3btAgDMmTMHY8eOrbPT1cCBAy2Q25NPPonjjjsOc+bMsaQWGjRoEF566SX1/t5778X//vc/9f65557DRRddZHuN9evXW5y4dPFe3nzzTTzyyCOYNWtWjBNyr1696nQ/jzzyiAVAys7ORqdOnXD++efjP//5D/r37x9zDp2ApKvFhx9+GDP2aqs/YE1zU15ervrFyMjr9eLSSy+N2f7ll19i9uzZeP31123hViOj/a1QKIRrr70Wv//+OwAgJydH7bvssstiPpf36NEDRx555EH5/HrLli146KGHcM4551g+KzRr1gy33HKLbTozIyOjg0/dunXDU089hQEDBsTs8/v9mDNnDn799de9zpxhwB+hl156CT6fTwWAJTjBYLhMNSSD1sAe+Ec6HTBgzaAnYO+SIh1OZFCZ/6RzjbyGDnkAVda/aWlpyMjIQHp6ugqIM00NsAcuITghtyUnJ1sAFV5fQkuEfBisZv1leiwJ0FRWVqqUQ3b3IqEDAhW0zJYBeJYZDocRDAZVvVgW4QamNKIzEEEaBuOZ6kbCOHQ6kUCHhAMkSEK3HulmQqgEgHIl4TkMtMsgOR1bZABewhO6A5IOdshxxbHGYDshH16T9ywdPeTYkYACgJjxJ4EK+a86GA3Y84sd9quEjuiwRNArISEBXq/XAr8R6OK4jYuLU2ABUwZJYM3hcCiYRc5FSqaPAqoW0EgkgvT0dGRlZcHtdiM5ORkZGRlwOp0oKChAUVERdu7cieLiYgXg8Iuy3g7x8fEq9RulAz5yTdGBjoSEBASDQQuBTqAtKSlJ1Z19yjHNczmX2R4yDZYOexEASU1Ntaw7dLwikCjHtHR0sfuVFscW+4VjFICaA9yv9wvL5nrJe5RrnwTYCM2wHeVDIo7J0tJS5OfnIxqNKoBQth/LSkpKUnOGH5gltEOnH6YK5D1wfNN9SV8by8rKYhx6mCJQjgO+93g8CpixS+/FdpRgle4axX6QoJgEqYqLi1FSUmJx4iorK4PX61XgmxyTcoxKkEz+A6DSh8m2YfpDCdxxnB2sv5gwMjIyMjIyMjIyqq+2bNmCn376CUCV+0/79u3VvjZt2uDOO+/EUUcdhYkTJyIxMRETJkxAUVFRg9fjkUcewahRoxq83INBbdu2xb333ovLLrsMiYmJaNasmdqXk5NT7TOJSy+9FG+88UbM9kWLFmHt2rWYMGECsrOz91u9axN/4blu3ToL+JOWlqZeb9q0SY2XLl26qHvPzs5Gt27d8PLLL/95FT7E1KRJEwSDwTo7qTS02rVrhyFDhsRs79ixI/7v//5PvdfdcbKzs2v9zmyXlupA6uuvv1ava3I/qs4pLBKJIBAIWLZt375dvf72229x4YUX4rLLLsPVV19dbfnXXXcdHnvsMVx33XU44YQT8Oijj+Kzzz7DTz/9hFmzZuH111/H1Vdfje7du+O9996znLts2TIVfNm+fXvM2vHMM8/gq6++wvnnnw+PxxNz/9X9Ivv++++P2TZmzBg89thjlm1OpxOPPvpotfdGXXDBBWjVqlW1+y+88EJ88803lm1vv/02+vXrh7///e+4+eab8eSTT2L48OH44osvar0eAMtzKPkj6EgkYpsix+jw1MMPP4yBAwdaHPmAKte6L7/8Eo8//vgBqpmREfC///0Pt956K1avXo033ngDwWAQW7ZswcSJE9XnrFGjRqF79+444YQTLJ81Dzbl5eVh5cqVMaluN2/ebFzYjIwOESUkJNhCP0DVZ63WrVujbdu2lmcL9Sp/Xyr3V1RycrIFZpDBTUIHdJyRIAcdH1JSUuB0Oi1BaBmEl2l6JAAk08PIAKse6NWhIenYwGA7LX+Z4oapcCoqKuDxeFTZPC8cDlsgJQm3RCIR+P1+Ffh2u90oLS1FUlKSAiy8Xi/8fr/lHiTIRGccQjo6MCJfM9gPVH1JXr9+vUqHI61sGUxmW8fFxVmAArrBEMDhl0sCDXTG4LXohMJ9lZWV6noM/AN7HIkYTI9EIirQzcA374EQgUwXJhWJRBQMEwqFFNzCvuF+OjXJ1Fh8r6fhYTuyLwHrF2s9FRAA5Vpil3pMBvplKiTpPKKDBzrQxTHI8a+nTJNORyxPwh7RaBRpaWnYvXu3BWIJBALq3ujukpycDJfLpZy5HA6HJZ0V2y0UCiEUCqlfp/j9fgSDQcTHxyM1NRV+v1+BChwjvGeuETr4xb6WcFVycrKC1NiOnAf6nKVrjYR8OEb5P8eUBGU43vQUTFyT2BfSySUcDiMpKQnBYFCNfYJWRUVFas6Gw2EEAgGkpaWpuUMYSUJlss9l37NePp8PZWVlCowiACPbSoJCLM/v98PlciloUAee7NyP6CbFa0iXI44djm0JvnF/eXk53G63grDoEiSBTgIzOsAkCVweq6/PujOQw+FQUJ6EdFh/6cimu+0kJiaqfpbgl4S12MaBQAClpaWWMcH662uHvh5y3vA1nco4XzlPJOwj24TrLcEmu/XQyMjIyMjIyMjI6FDVtGnT0LdvXyxevNjiorJx40YkJiZi9+7daNq0KTZt2oTVq1c3+PV/+OEHnHjiibaOQ38FtW3bFpmZmbaQzltvvVXjuRMnTkRaWhoefPBBAEBRURFGjBiBM844A+PHj8fbb7+NdevWYfTo0ful7jVp2bJl+OOPP/Djjz+qbSeffLLlmHXr1qnX7du3twTd//3vf2Px4sWYOXPm/q/sIajs7GwUFxcfEEcSt9uN+fPnY/HixRYoJi0tDY899hgyMjLUNh3iadKkScx35pUrV6KwsBB9+/YFgBhI5kCrY8eO6rUEdnTxeZfu/BONRmPuKTc3F6FQCEuXLsXQoUNrTW9244034qabblJQTElJCZKTkwFUuSy3bNkSJ510ErZv347u3bvHlNevXz8cffTRAFAtEDhu3Dh899132Lp1Kzp37mzZZwfAzJkzB3fffXfM9hUrViArKwtLly5FZmamSuF38803Y+XKlXjttdeqvc+ZM2fimmuuQaNGjWz3H3PMMejQoQO6du2KdevWqXpt2bIFW7ZssawX/MFtfeRyuVQflpeX19l5yeivrWHDhinQcdWqVVi0aJFl/6xZsw5ArYyMrPriiy8wZ84ctG/fHmvWrEHLli3x6aefAgBGjhyJ//znP8jKykJeXt4BBcNrUn5+PjZt2oQ2bdogMzNTbS8rK8OGDRuqTUlpZGR08CguLg5HH310jWmXmzVrho4dO6Jt27aW74N1vkZtBzgcjuscDkf9PwkeotKpfQYsmYZGupXwSwkDt3QniY+PRzgcVpaZDORKJwY6QujBXj2tl542THeRkI4xss4VFRUoLi5GXl4evF4vKioqUFRUpAL9/BKpwwEMGjNozTrSIYRBX0JADJ7r6aqAPS4VOpQiryUhEN4v961YsQKbN29W2/RUVKFQSLn+EBIBYHHnoOsFIQ+mFyIwIdtQBu3LyspUHzPwznpJoIZfdAjP8N5kKjHCBzoYlZ+fr+CBpKQklWJIuhcBUHXnvbGdCf3INFyy3VkXXpt15vjie+mAwj7h/esp5qTLjUyJJyXHrXT64T/pPEOXKZmGSvYlfz2Sn5+v6up0OuHxeCxzlXOIgAznCeekdNnR08+Vl5ejpKQEpaWlFlBCukKxLYCqByx0FiIERbiOKek4xtLS0uDxeBS4wr60SwclISKOM/aJTKMk4TgeEw6HlVMQ5y3nbkVFhQIV2QeJiYnweDzIy8tT7cq1QAJsCQkJKtUaATbWkfudTqeChFiGXA+koxbhIY4v/i9ThfEfU67JMS/HE2E83XmppKQEJSUlak4lJSWhrKxMwV+y7ryuHA/SzUu6ZHFs0rlHbiPQxLkoXYjs0jTaAZ9sc+msxP91UEzWUwJFEgTkHGKfMl0d25bQps/nUy5N7DcJZHHdkXOY7/V25GveE+EkOulJF7JDXYfbZyMjIyMjIyMjo+pkPhdVgT/z58/H8uXLVTD5zDPPxCeffIIZM2bgo48+Qrt27VBaWgqfz9eg177uuutw4oknAoBy8TzUpd9H27ZtbQPUOTk5GD9+fK3lPfTQQygpKUEwGMR9992H33//HYsXL8bEiRMxaNAgjBo1ygJi/Fl6+eWX0bVrV0ydOlVtGzhwoHodjUaxfv169f6tt97CDz/8oMCKjRs31ivF1OGmZs2aIT8/H5s3b/7Tr52VlQWfzxfjNpSWlhYzvmVqNyAWxvjuu+/QuXNnS6qykpKShq3wPioUCuHxxx9HUVFRjWnInE4ndu7cGfMsUf6AiSopKcHNN9+M008/vVboB6hy+JJOOIR+dDVr1gxnnnlmzPa5c+dix44dAGCB8aQWLlyIVatWITc3F9dddx369u2rAJzc3FzceOONKpUMAIujt64rr7wS3bt3V1AiUPUMpjbXn1mzZmHu3Lkx25cuXYqlS5eisLAQLpcLM2fOVCBTdarOLU2XTHfz0UcfYcKECQBgwJ9qdDh+LnI6nVi9ejWOOOKIah0MjIwOBpWUlGDp0qWYNGkS7rnnHrV9+PDh6NChA9LS0nDEEUcctM+umzVrht27d6NNmzaWzwu7du3C8uXLq3XWMzIyOnj04IMP4oEHHqjxmOzsbDRv3hwtW7asNQWwneqygjUB8LPD4XjP4XCc5tCx/L+YJCQBwBJkBWABIBj0B/akN5FON5QEDiTQw0C6dA6SMIB0epDX1tOsSAcd6STBenBbZWUlgsEgAFjgDgl/SIcTupDINDN0EWL6LHmP0q1GbtOlO/3I9FWyTbdt26bqS0BEQjeEG1iWnoLG4XAoAIv3KevL69i5Ksn+01NKUdKthXWUZUh3I+lsw3smuCLL1WEAORZkH/P+CRZI+Eevj3T5kfctASg59qTjTk39ZvcFUYcb2EacA3KcsA31vtHLI2gh0ynpsBmhEEILHNO8R3nPcqwQoiGsIQEtmWZK9jkhIDlv5L2x3m63W6XdS05OtuyTsJ4+dllPrhe68wvrbDc+pBtNRUWFWpP01FuyDF5Td36S41mer/e9bEeCJdLdScIwsn+ke5Ucn3KMyNRfsn/sxpGcA5FIRN27BFlke8trcn7qblh6WjIJtMi1Tu6T9ZPbZXvp67d0TdIhOzlWdUlgTDpQybJkeTK9HyE6mT5POiJxnNj1DcuWYJPd+sIy5Bys64OlQ0CH1WcjIyMjIyMjI6MadNh/LiorK8Nvv/2GLl26qG3t27dH37598fTTT+PGG2/E4sWL8c477zR4WhT57II/pjnUNWbMGFx00UVo27YtHA4HMjIybNNNr1y5Ejk5OXUqc9OmTeo7eseOHfHjjz/i+eeft1zzQKisrMyS5qtt27bqdTgcRkFBAZo0aQKgCkx45ZVXMGfOHABAhw4dqnX9MAJSUlLg9/sPSFqskSNHKsd0KY/Hg1WrVllccaTTTefOnS1QyoIFC/DUU08BsMIXfFZ6sGjz5s34+eefkZSUVKMb0fbt2/Hmm2/GgD98piEVCAQwceLEGDCqOtXHBemII46otn4A8P3331d77sKFC9GuXTtcccUVuPrqq3H99dfjwgsvRKtWrfDCCy/EPF+uTU6n0wLPZGVloVOnTtUe36pVK7Ru3Tpm+6pVqzB58mS8//772LZtm/pxZE2qa4BYlrNmzRr13IjP34xidNh9LsrJycGWLVsQFxeHpk2bqr9bRkaHglq1aoU2bdoc6GrUSVlZWfjXv/6FESNGWFLDlpaW1gjeGhkZHTzatWtXrZ9bnU4nUlJS4PV6LY6vdVWt4E80Gv0/AB0ATAZwGYA1DofjIYfDsXfJxQ5yMajJILdMPaMHfYPBoEopRBhGOjcAexxVJFAgg7UyoMu0TjJYr4MHeiCXgVeZjqisrAx+v18dx4A+yw2FQigrK0NJSYlK+SPTMfGDe35+vnLUiY+PR1paGnw+nwoYe71eBAIBBQfpwWEZ8JX11oEQCeBIkEeHI+iqAexxoggEAhYIgmVKV6OysjLlMpKQkGBJpcS2o+sIj+U9M/AvISN5PT5Yk4CC7AvdOUOmJUpNTbWkQeMXKenoI0EP+SWY6ebY9np7yXZiXzC473K54HK5lAMH0+/o0FRNZLMcn3Kb/J9jV453fvGUAEJZWRkKCgqwfft2lZ5LpsxiX3i9XrhcLovjC52COBf8fj+KiopUeiy2CY+VTk8cw/rDUaa34vX1cSvdb3R4hPAcVVBQoH655HA4lANNSkqK5bryfDlmCPAwHR/TgUmQory83JLWjGM+Pj4ebrfbAhDyGoTo6IrDsZeQkACPx2MB7pj+Srobyb6RgArnqQ7PESjRgSLeN8U6EUhxuVyqruw7lqs7GxHU8nq9cLvdCIfDqi5sf9mPUhIuAqDqYDdWJUzJerBOOvSjg3h2MJ1ci2R7SlckudZQ7Bu2K+ugw1U8l+0pAR95n263G16vV615er/INZXXYZvyfM5LOq6x72V7y3X+UNbh9tnIyMjIyMjIyKg6mc9FVfr6669x/PHHq/crVqxAIBDAmjVr0Lt3b3z++ed45513Gvy68jtGbUHeg1lnnnkmxo0bh+HDh+POO+/EtGnTMG/ePPz666/o0aOH5Vh+v3jooYfqXP7kyZNRXFyM+++/H3PmzMHSpUuxY8cOTJ8+HUuXLsW4cePw3//+t0Hvqb5KSkpC79691ftQKIR169bh8ccfxy+//IJJkyahTZs2+Pzzz5Gbm4t27drhxRdfPIA1PniVkZGBpk2bYuvWrdi0adOfeu2ffvoJ9913H8rLy5GXl2fZ9/vvv6NXr1746aef1DaCLdnZ2fjpp59wzjnnoKKiAlOnTkXfvn3x+eefA7A62GzcuHG/30d9FQgEkJiYWCOM9vTTT+OWW26JgUXi4uIwePBgXHjhhfW6Zs+ePTFlyhTceOONNf4a+tdff7UARJdccontcR988AEA4Lzzzqu2rLvuugstWrRA9+7dMWLECNx9992YNm0aunXrhkgkgg8++ADvvvsuFixYgF9++aXWe3jppZdi5vE//vGPao8/55xzcNRRR8Vsf/HFF/Hcc89h9OjRePLJJ+F0OvHee+/hxx9/rLa8ukBxenowYM8zZ8YfjKw6HD8XhcNh5TB15JFHGtcfo4NePp8P1113HU444QScd955lpSVB7vuuOMOnHLKKQCAV155BQ8++CDWrVu3V+mAjIyM9p9uu+02fPPNN1i0aBEuu+wytf23336zOETaKS4uDtnZ2UhPT7dAfnVVnX4OFI1Gow6HIwdADoByAOkAPnA4HN9Go9Fb633Vg1gejweBQECBFDJoLp01kpKSFHxRUVGhUqZEo1FLyiYGYxmU1oEX7mewVbqfMMAsj5MpX5i2SnckIfQj3Tyq2y8dX7ifgXIGuwEo9xyPx6PACsIIEurQXW9YJsux++UDA9W8H54j3YgIJ3g8HpX+KSEhISadGmEJtrHP50MoFEI0GoXf70dSUpKCJmRwXt47AOWCQTFtGFBF0HKyVVRUxLhBVVRUqC+csr95TxIo4/HScUY6uXAbz+W4Y2o53j/BHZ7P+jDoL51D7BxJ+D8hCva9BNbsfqCgu9VIuEJ3qpGgEH+pJO8zHA4jFAopKIFwgMNRlSKLdsjsZ5liinBMWVkZ3G43kpOT4Xa7sWvXLiQnJ6t0V0w3xfmamZmJbdu2obCw0OLyw7pTvDfZN7KNdRgvGo2iuLgYgUAAqampqo5Mh1RcXKyADlmOnE9yHvK+OIfY30DVw0CCGiwzGo2qtIM8VsIh3CdTQ3EOpaSkKOtlwmEylZ0E9TiXOC5TU1MRFxenwCkJ1FVWViIlJQVxcXEqVZrufMN6JCYmWpybOBYqK/ekHQT2pGMjKET4ietwcnKyAjNZF85FCa/JlHDSyYn3Kee5nLssQ7aNHRwj+1U6nHF+EQqTqcVYZ+nUwz7TAbNQKKTASEJarAcBn9TUVFVnAo+6Qw/vJyEhQQGQLEu64dHNTM6PxMREOJ1OC/Qj/25xDarrL8oOBR1On42MjIyMjIyMjGqS+VwEfPXVV3C73Rg0aBCWLFmCBx98EG3atME555yD7OxsdOrUCe3bt2/wh/L6DysOVT333HNo06YNysvL1bOYJk2a2LoGzJo1C9OnT8e3335rW9ZZZ52Fc889F4sWLcLEiRMBAE899RSKi4sxefJkNG7cGJs3b0ZBQQHOPPNMnHXWWfjss89w55131gsmaki1atUKgwYNsjh5pKWl4fHHH8fu3bvRtWtX9OzZEwDUD4oAYOvWrQekvge7GjdujEaNGmHVqlV/uussnxeGQiHbX99nZWUpOGbBggX48ssvAVSBP3zu8/PPP+Pee++1nJeSkqJeb9iwocHq+9BDD6F///64/fbbbVNI1VUFBQXYuXMnnn32WXTr1g3XXHNNtcfqaQ9btGiBu+66C7fddludr3fKKadgypQpaNasGUaMGFGtC9KuXbswePBgdO3aFV9//TUcDgdyc3Ntjz3ppJMAVPVFYmKi7Y+WsrKyLM+BgKp1eOTIkTj22GOxZcsWfPjhh7YBnX/+85/Izc3F119/rbaFQiEkJibis88+w9lnnw0AWLt2bY33befu9sMPP6jXEyZMQHl5OVq1aoWxY8fiqaeeQlpamgV+uuqqq3DWWWdVex3qkUceidkmn5cdbO5TB4sOp89FGRkZuO2221Ta0dWrV2PFihUHuFZGRjXr7LPPxrBhw/Dwww9j9+7de5VK52BQXl4eXnjhBWzbtu1AV8XIyEhTo0aN0KZNG1RUVCAtLQ2dOnXC+vXrsXr16jo9E/B6vcjMzNyrlNS1PhVwOBzXOxyORQAeAzAXwDHRaPRqAD0BDKv3FQ9y0dFBBoUl1CABFYI5BCtCoVBMCh4JTOiBUnkNpskhJMG0RjIoS/cSCfXIL7AM5rL+DNbKlF4S7pCwjnTJ0cEUoCpYHAgEsH37dhQVFSnoR94LA8QyGM57k44YuoMR611UVGRx5pDgB6EoQiF0NqJbTyQSUXXiNRITE5UVlkwBxHLj4+Ph8Xjgcrli3EicTqd6T9CmvLwcpaWllv6UKdwk6MD7koFuCWOwfaS7DevOccB7dzgcFrBBurEQuGAQX3fqkO5KEjjja7pMSRcdtjHdTuSDRPlaTwkk24XOSTJFWVJSkoJtGjVqBLfbbRnfrEdSUpLaTlCL4FUoFILf70dJSYmCPwiTSIDK4XCguLhYzcusrCx4PB44nU6Vdqtx48aIj49HMBhUjkDp6elISUmxgD8STmFb62uE7oDEsRYKhVBYWKhAGAIVkUjE4gyltyPdffia9yGduRwOB3w+nwXOAqBgROkYRRgjEAiofqG7j5zvvI/MzEyEQiGUlJQo8EdCH7LOlZWVcLvdSEtLUw8fCFGxLQoLC1W/se/oPEQHLt0hSq6Xcq5XVFSgtLQU+fn5iEQicDqd6qGDngIsFAohKSlJfXiX+2T9CTE5nU4FALGd5fyUqcDsICG7uSLXQDlGJDDGe6QjmO6WJueIbH+OQ+k2J915OC+4diYlJSm3L4/Ho46Rf1O4Puk224DVlY7tynpyHLH+hKzk3yyCW+FwGOeff35M+YeSDrfPRkZGRkZGRkb7V3Y/sjhUZD4XVamiogLbtm1Dz5490bp1a3Tv3h1AlZtDYWEhrrjiiv3yS1y6/KxcuRIvvPBCg5e/v3XaaaehXbt2SE9PB1C3dGWvv/46JkyYUO3+Y489FieddBL69etn2f7qq68iNzcXlZWVeOWVV9T2zz//HIWFhSod+4HQiSeeiNdeew0ul8uyvUWLFujatatlG7/f5uTkqLRfRlY1adIEKSkptaa9uOiiixr82rTjLy0ttXVUyc7ORkpKCp577jn07dtXPePLyspSfVtQUGCpu9vtVrCQ/kO1fVViYiJOPPFEdO7ceZ/KmTt3Lnr16gWg9lRkt956q3IykqorvNisWTPccccdaNasmdpGaErX7NmzccQRR+DSSy9FZWUl/vjjD0yaNMlyTFJSEh599FHluLV+/fpqnYrHjx8fU8/4+Hicd955uP/++/Haa6/h6quvtj339ddfR8uWLWO233DDDXjrrbfU+yeeeCJm/QKqxpZ0BZO6++67Le+ffvpp3HzzzQCAd955x7K+paWl4amnnqq2zaTsHJy4TmdmZuKjjz7CPffcU2s59ZX5XHToKD8/HwUFBQiFQgiHw3j++edrdTIwMjqQio+PR48ePdC8eXP4fD7b9ImHgoLBIHbt2mWgHyOjg1DMXhMfH49OnTrhySefxA8//ICnn34aO3fuRE5OTq2fl2X2jvp+LqrLJ+pMAOdFo9Eh0Wj0/Wg0WgYA0Wi0EkDtaPghJjpzMDirB5KBPeltGPSUwAuBG74nCJKYmGjZx8AoO41wiYQMKAZVdfcUCfPQ2YWAhAQS6JhCKIKpvWSqGjpnyHuQ0AGBKF5fprCxcwzifsIkBGvkMTJNDQPjhAUkzECIgvdFCIbHsI7BYBCRSEQFvSsrK1UwX6bhIsBDNxWZwobpagiqyFRgclxwGwP/dq5HPJ4P4jheKMIb8r7ZPtKxg64s7AcJYRBu4j3W1ZWHfUbQhk40XEwkOKaLY0gG83kPDOoTbiFoVVFRgVAohEAggOLiYhQVFVngCb6ORCIIBoPqC3ZSUhLcbrdK+URnH/ZBo0aNkJ6eDrfbrVyzwuEwcnNz4ff7VUq7LVu2qPERDocRFxeH9evXY+3atWoRBqpAETqdSMhDOhgRpGKbcRzL+cB2I+jAsS+BDLlgS1BCrgcsNxqNIhwOW9YS6ZBDAEzmhqSzFyESur4Q+OB9SCclrgGEzAKBgMVNS4JqHFvhcFiBPRLkY5vJXyOGQiGVnk6uBbw+z6HDzu7du1FaWqquo8OUPF6OI8IptMHmukr3I6aH4/kEbrhWc45I+E2u6RJMkn8fCNewDVi+vD9ZhpyfhKcIkXLuyzRrEjSV40WuG1zjuc5wjvN4Ce3IdZHzWv59khAg+51zkunB5NpfVlamvuRL2Ef/x79dNf2C7RDRYfXZyMjIyMjIyMioBpnPRQBuvvlmdOjQAUceeSR+++03FBUVqX3Nmze3BesbQk6nExs2bMB//vMf5OTk7Jdr7A8NHDgQzZs3xzXXXIMff/zR4v5Rm6ZOnVrj/oceegi9e/fG8ccfj59++gkvvfSS2jd79myVVkhq+vTpAFAjULQ/JQGGumrFihX46quv9kNtDn1lZWXB5/OhuLi42mNuvfVWvPXWW3j00Ucb7LqpqamoqKhAcXExSktLbedk06ZNbaGkzMxM9TovL8/izpKZman21wYz1VfPPfcc8vPzY2CYvdGOHTsAVD3HtANXqKlTp2LBggUx2+u6DhQWFtpCQlu2bEEoFLJsO+WUU7BkyRL07dsX69evR9euXWPSLp511lm49dZb1S+q7SDNxo0bo1OnTpYURsXFxbaAQ2Zmpu24qqiowMUXX2xJ90BJt4msrCxccMEFMcdcdNFFaNy4ccx2AJb0cVJPPfUU4uPjce+99+Kqq65S2+jEXpMefPBB3H///THb+QzV7XajadOmKC0trbWsw0yH3eeiH374AcuXL8e2bduQlZV1oKtjZFSj+vfvj969e6NVq1YHuir7pMLCQuP8aGR0kCo5ORlpaWnqBwEAlPkEY6RMC/ziiy9i6dKlMWWEw2Fs27Ztr348VCv4E41G745Go7YJkaPR6B/1vuJBLunkoTsyyIAw9/E1A6s6uEMwgAFSGSjWgSIZHOV7XTJwLOsoHRuqC07L9xKYkcH36u5Z7pNOL9KxRdZZppGSZch6yfRBdENhHzDwz4C6rB8dTNgXsv0kkMR6SBccQgsMpuvghW7XKttGlq0fx4C/7tCkpwCS+3XIiuXKdpPto8Mg8toyQF8T/WcHAPHe7SwNZf15TQlKybHGftNdUsLhsErDRRiIab0kvEYRqpL9J8crr03QoKSkRF0vHA4jGAzGgA7BYBDBYBCBQAB+v98CQEgXpJKSEksKPTleq3NN0uexPl/0+5P9rJ/H8aCPeaDqIYDuikN3H2CPyxDnKAEmu+sQ5NAhGtaNwIjT6VTQIM9l3dgnTOUVCoUsUJC8J+koJAFI6Z4jxxOBQcJHvD+uC9KlSDow6WAWYTnpoCbnJY/n2JR15/ihW5LsZ7lOShBStqsEfvhP1lGu39I9jOL6pI8jfd7q44rH6X9n9D6Q48JujQas66K8Lzs4ldeSbaiPPXkf0qb8UNTh9tnIyMjIyMjIyKg6mc9FQPfu3XHhhRcCgHJClb/gS01N3Wc3jerEH6NV505xsKpx48YYM2YMUlJS0LRpU1vX6YULF2Lz5s2W7XW9z27duqFt27bo27evJZ3N6tWrAVQ56Uj98UfVUD3jjDPqfS8NId3ppy7Ky8tDfn7+fqjNoS/+OEz+OErq6quvxujRo9WPgfr06WPrbFJfeTwevPHGG/j4449RVlYGv98fcwx/vKfXTY4B/Tyv16t+tCWBoIbQpk2bMHPmzAYrr7KyEoMHD8aVV15Z43EZGRlYsmSJ5YeQdXGgAYAxY8agb9++KCwstDyD2LFjB7766isFIAFVa/KwYcPw2muvYcaMGbZrSNOmTS3v7dr42muvRf/+/S3PMrZv345XX3015tizzz4bl1xyScz2yspKDBw40PY++cwPqFr/du3aFXNMTX9HfvnlF9vtX3zxBXbv3o3zzz9fuf7UBMRJMQ2dLh0aoiO7UZUOx89F69evx4YNGzBnzhysWrXqQFfHyKhGtWnTBllZWXUCIA9mBQIBW2dBIyOjA6/ExESkpqbGfMdjvK2iokJ91502bRo+/vjjGJA6EonA7/fv1Tw/dBOA70dJVxvdkYJOHeFwGA6HAykpKcpBQwZPeZzuCiLTofBaMhBs5zYkHULi4uKUO4UMKLN+PA+wgjDSQae6lDrytQz06zCJHTgig/hS8t7oXsH7ZOoiaVcV/f/OHhUVFXC5XMpZRgdK+GVFwhvSrUUCAPHx8Sq1jezP8vJyRdYB1i9Z3EanHdZDOnCwrXlvugsSsCctEvtdjhPeF+9TunroqYtYJ6ZoIuTBcvVxZgcc2fUZ29/n81lSbLHv9P6U4JtsZ8IVBCgI+3CsStcSHk8YR457PcWQBEzYnpWVlSgtLVVptIqLixXwwtRGbC/CTE6nU1mHJyUlITk5WY07CbZUVlaioKDA4iCjg3U6xKWDcnLsSWcfbg+HwzFjiGXx/qTDlsPhsDgx8Rw+xCIMU1lZlQ6Mrl8SoJKwHevDcaJDLWVlZQgEAnC5XPD5fMpph+3EdqCbj8PhUCn16JDDa+jrgnSzotMUj/N4PGr8Op1O5OXlqTHJBzN073G5XPB6vUhOTlb3TjCO7cbxwGvQoYZzln0UDocVEBYIBCxAp+4SRIBSjmXA6t7FNuX15ZrJbWwH/uPckesAx4cETSUQxrnLB5VyrZV/a+R8ZTtJ8FL/eyEhH8Jfsu9kUIHtI9MDMp0d3X8kcMQxEwgEDnnwx8jIyMjIyMjIyIjatGmTAn2aNGmC008/3fLdOikpCSeccMJ+ubbH40EkEkFJScl+KX9/KTs7G9deey1OOukk2/0OhwN9+vRRQBVVl1RgAPDwww+r19OmTVOv+cvo9u3bW47/6quvsGrVKrRt2xZXXHFFna7RkKJjbX20atWqmB9eGFUpMzMT8+fPr9YdauTIkWjfvj0cDgfuvPNO3HjjjQ0yh3bs2IF77rkHfr8fxcXFto4/Q4cORbNmzZCXlxdTZ0rfd/755yMjIwN5eXn7JWVgQ6biDofD6NatW62uH2PHjsV9991nCXJ07Nix1vKHDBmCRx55BJdffjn69euHiy++WO1buHAhzj333BiXmqeffhq7du3CNddcY1tmp06dLPXfvn27ZX92djZuuukmvPzyy5btLVq0wMqVK7F06VLlTg5UgVrRaBTXX389zjvvPLWdz9DoLCQ1Y8YMPP744wCq1r+BAwfGHPP888/b1h9AtenFZsyYgVtvvRWpqalo3rw5Lr/88pj1rzq9+eabePLJJ2O26+uVhLeMDk/NnDkT3333Ha699lr873//O9DVMTKqVieccAJOOeWU/Qbk/xlaunQptm/fjm3btmH9+vUHujpG+6CzzjoLDzzwAIYOHXqgq2LUwIqPj1cxUyoYDKKoqAhlZWV44YUXcMopp6Bfv3749ddfcfzxx8eA4bt27cLGjRv3yjm4bt+YDyMxIC7BGga7uY1Elu7YIAEFyuVyweVyWdxtgD1AiQyKAnvcFnSQiEFlCQMwKC7BAxls1gPHlLyGhEp4HR1osIN/JIDA9EoUg98MAEvwSAbcGQB2uVyWYDV/KcDUN7weXTuAqoc9TqdTgQAJCQkoLS1V6aBYb95/YmKiJaUR7zkajarUYbL/eIzT6VT9npycrFIfSfcR9qUErxjcLysrs6R9IuTAQL2ESRITEy3l6GOP5Uj4RoIbhClkX8p9eiofXpNuRXIOSLcZee2ysjLk5eWptpMAB4+V9ybdQSQoxPZjGigJJ0jogG0o24+psyorK9VYkb/YiYuLU64+fAjKMcMyPR6PegAj+1ICF3Kcy3mlu2fJ+2S9pJONDnpJ+Ex3YcnMzERBQYEFJOFcl0ALgSaCS26325LiKRKJqAcNHIuhUEi1Ncc0yyVQSKCR10hISIDH41HtWFFRYRmnHG+EYlgPKhgMWoAltgHdfDh35TisrKxK2xcKhdQ1pOMSARmew7nr9/sV5Md7omOPDsjI9YygE1BlK02oUMJaPJ/jhOms2AcSkqPTENcYjhkCghKaczqdai5yDeWaos9Lth/XSra9y+WKAR0l/Me66Y5chA7pMKWPVV6HY1D+XeA45Fxxu92qXbkOlJeXKxBMAoIELo2MjIyMjIyMjIz+KnrwwQdx4oknAgD69euH3r17W9J8FxUVxQSSG0oej0f9KOZQUHZ2Nho1aoTs7Gz13as6nX/++Xj//ffx5ptv4rTTTkOjRo3gcDjQrFmzWtvzvffeQ9u2bfHdd9/htttuU9tvuOEGVY+OHTsqB6BffvkFAwYMwGOPPYbRo0fbung0tNLS0lBYWIgPP/zQAgfUVfsDAPmraMKECTWmbUtJSbE86+IPyhpCCQkJaNSoEXw+H1588UW88soreP/99+H1ejFt2jScc845AKrgowEDBqi0TzKFk3R7eeKJJ3DTTTcBAAYNGmQBhA5G7dq1y/LD0poUCoVQUFCAtLQ0AECXLl3QoUMHrFmzJubY/v37Y926dXC5XCgoKMDChQuxfv16/P7775g8eTK8Xi82btwIAOjRo4fl3IyMDEyaNAlOpxMTJ06MKVuCP8Fg0OIY1LFjR3Tu3Nk2DZnP58N9992HYcOGoXPnzjjmmGPQsmVLdOvWDSeccAKefvppRCIR9YOq4uJiJCcnY9GiRTFlbdy4EWlpaViwYAH69OmDv/3tb+jduzcWLlyojikoKEBJSYktKGjn4k69+uqrGD16NMaOHVvtMXZq3bo1tmzZErOd/QVUpVdbuXJlvco1+mvqlVdeOdBVMDKqVSeffDL69OlzoKux1yorK8NHH32EV199FSeccAI2bbI1FzM6RNStWzeceeaZGDlyJFq0aGH7GcXo0FQ0GrXwDQBQUlISA/eHQiEce+yx6NGjhyWuWlxcjFWrVu31Zyzj+KOJwVcZLC0vL1dpbOhwwOAsA+UEWoA9Lj5OpxNJSUnw+/0KUJHBT4rXoDuKLIspdOgSwnox6K07yADWVEOEE+Q9SWcM6fYjHTJk2hkGdKVbkQx0s74MZutAkgx00w2EbVRUVKQC4h6PRwX26SohQR+6pYRCIdW+Ekby+Xwxbh4ScuKDN+nQw/v3+/2qregiVFFRAbfbbQlq859MMyTbDoAKurOOelsRAtPdQmT76kAX60ZgQqbIIkxE8IPjk/1IsEI6u0gHKbovSZhB9hsddgoKCpCfn4/du3ejpKRE9aesoxxjHOOynhIqkC47so3YNoRC2GbRaFTNQ8JLnDcc92wDlldYWIhgMKggCjq7rFmzBsFgEKWlpSr9GFOPSWhJys4FTD+O7S0ddFhfCQPp7cz6s191IFBPVSedh1g3oOqhL9tdOg1JWIratWsXwuGwcmzR4RCfz6eAN+6XYBDbnusgy+a6x7lDQIXAD/utsrISLpfLMu/prBUKhZR7FAE1gju7du1SbUQox+GoSgnGerC96KYlJfuM1yLApu/X107OQbsUeBL4snOLkv0n11G5BktAVAeU5PrJ90lJSXA6nQq4kn8POJblOpOQkKD+/tAq0O/3IxgMWq7FMcs62bUF5wPT63Gesq/l3zIJTvGcuv5S18jIyMjIyMjIyOhgFh0U5A+9JPSzevVq3HDDDfjkk0/2y/WTk5Ph9/tRVFS0X8pvSLVr1w6tWrXC22+/jSFDhljazE7NmzcHAFx66aV499131fYRI0bUeq0nnngCb775Ji666CLLdpnSaNCgQZZ9ubm5+Pbbb9GzZ08MGDCg1mvsixITE/Hzzz9j7ty5OO200/aqjGuvvRbPPPMMrrjiiph7MapZF110kQWCaMj5k52djeXLl2PlypVITU3FF198AaDK0efZZ5/FU089BQAYMGAAmjVrps5juql3330XzzzzDICq7+ESCNq4cSPef//9Bqvr/lCvXr3w7LPPIj09HS+99FKNbkL5+fnIzc1VoFObNm1wwQUXxBx3zz334LvvvsP8+fNx9913Iz09HSNHjsQFF1yAo48+Gl6vF4FAAD///DMA4PPPP48pw+/3Y968eTHbXS4XsrOz1fu0tDT07NkTbdq0QdOmTfHjjz/iwQcfrPYeevTogTZt2uCLL77AI488gmuvvRb9+vXDo48+CqAKyJk4cSJuu+02ZGZm4rPPPsNXX31lW9aVV16JF154Qb3XHYb+9a9/VesO9uOPP1ZbRwC46qqrFDA5adIkTJ061fIj3uqkB6gAKAhq4cKF6NevH77//vtay6mPunbt2qDlGRkZGQFAy5Ytcfrpp+OII4440FXZawWDQeTl5WHbtm14//3398oJxOjgkNfrRZcuXdCuXTu0aNECzz//PO6///46fc8xOvgVDAbx7bffYsqUKfjll1+wefNm7Nq1C9u2bYs59qSTTrJ8FgWqwOrff/8da9eu3avrG/BHk3RLkemLgKqAdlpamsrLRncJCZQwmC3Ty8jAu3SckO4rvCYDraFQSIEI3Mdgtgz265APoR7WmWmwpPuNvKYd/COBJx1skIAPjyPUJN2DJNzjcDhQWlqqgtrSuYNBaN4nUxVVVlaipKREpReSKWcYWJZOOXRWkqmeJEwjQSa2C91PCA2wjwieMNDP6xAE4j0RhmDb6sCH7CvZR3YpnrhfTyEmHV9k+7Oe7EOeI8EeCfewvhJMkc4qoVAIwWDQAk+Ew2GUlJSguLhYBfeZZ5ztKR095FjR21uOG/aNHNMcKxw7Xq83JsURx6NMqSb7SF6X/SWhIJmeqbi4WMElwWBQjWOmmeOckPcnJV2ZeIzcTpBEup3Ic9kXcls0GkVJSQkKCgoUFCHHNN1XnE4ngsGgAt0413gc1xgJR8l+kW46EnAhGFJUVAS3243i4mLlwMX1hNAdsAd6k33I9SUxMVG57fDYcDis6iYBGAlw0QkmEAhY0mpxXeNxdBJjmfKXeWxfYI8TlA7QEN5hPbi+EKSRYJ8EG3nPEm7UHdTYJ3Jt4BxgeayHXLfl+XLcyDXI7m+GhP44Fthefr9ftRHvgfOff5skmEO4Svap7m6nA0nyepxDLEPWnf/kOmBkZGRkZGRkZGR0qKu4uLjaz7ebNm1Cjx49MGXKlP1y7bi4OMybNw/nnnuu7UPEg0Vt2rQBUBV4fvbZZ9G4cWP06tXL9ti77roLoVAIAFBYWKiC3NJJYNiwYTjzzDNrvGY0GrU4/VCffvqpet29e3fb/cuXL8f06dPxr3/9q+Yb2wedccYZOOKII3DCCSfU6nxUnfr06YMxY8Zg8uTJCiYxqpuWLVum0sEDDQv+bN++HQ8++CCGDx+OPn36KIfhTZs2YebMmWjSpIk6duDAgQruuOuuuzB48GD84x//UPszMzMtgYABAwYclFCEBJgKCwuxY8cOHH/88ejfv7+tYwy1cOFC9O3bF/fff796DjR48GC1/4ILLsDixYtx+eWXIzExES1btlRuPnfffTfefPNNzJ49G9u2bcOLL76In376CQDwySefWJzBgaoAW79+/SzbEhMTsWLFChx77LGW7ddffz3mzp2L6dOnIysrC0cddVSN93/PPffEbLv99ttV+rirr74ajzzyCJxOZ0xgR5ff78e4ceMAVAEwI0eOhNvtRocOHXDuuedWe964ceMs6SR0LV26VKU627hxI0aMGFFj6jCKABNQBV2ef/75qr/j4+P3Kk1hTerevTs++uijBi3TyMjICKiCfqv7/HmoqLy8HMXFxQe6GkYNoA4dOqB3795ISUlR266++mq0atXqANbKqKGUm5uLcePG4bLLLkOvXr1wwgkn4IknnrBNz6c7VZaXl2PNmjVYuXLlXqdTNeCPJhmUBaxgjMvlUqmk5IMdGQB2Op3weDwqhQshFQZdZRoWGXiXcIqEAuwgAT2YKusuA7UM8Mp66mXwXhk85nveI4O8dhatMtgtQQg7twsGhvV7pgMS6x6JRCxpaqR7CZ09eG0JUUnQRKahsguYMzAv249gAuEf3cWD7SPP0V2N2K66u43etjKgL1007Nxk9LroDkbS4UhCCPrYoHSnEQI1HKeEK9i+dJpi3fR21OEl2dbS4UbCEzxOtr2EwqLRqOpvOYakU4osQ38v+4YwClNi6fCC7AOOQR1208e37G8deNL7Xs4Vu7Ei5wxBLGCPO4scF9K9S45hjn3WXcJl+lzktvLycst8AaBAHTrzEILiuNHTM0nHJjuYQ8491okPXlgfti3rxPknr8v757b09HS1nWOT64Z0OdLnp+wXuV8CbHKt0yEVCTfq7mfyXvWxYifZL1wz9HbT53N17SthTgm4yXrLOUSQkG0lx5IclxyHsh76PykeJ9tMzmm7c4yMjIyMjIyMjIwOddX0GTcajaofz+wPVVZW4rXXXsPu3bv32zUaQmlpaRg5ciTS09PRt29fywNuYI8TKwDMmTMHmzdvBgAcf/zxuP766wFUBa2pFi1aYPTo0bVe1y51k0yJsGrVqpj9JSUlyMnJgdfrVenb9odat27doOXJ1FBGdZMErvbnPO3QoQPatm2r3suUUYmJiQo62bx5M2bMmGE5t3nz5nC73er9yJEja3zWcCDUvn17XHfddRZ3LbZnWVkZhg8fjksvvbTGMnbv3q3OkWBUfn4+kpKSqp0vCQkJyMjIQHFxMZ599lkL7DN//vyYlGGXX345jjnmGADA8OHD0alTJ7Rv39627GbNmqFbt2411pvq3LmzrevWr7/+GrPN6XRaXOF0ffDBB5Yxcs455+D666/HMcccgxYtWlR7XkZGBk4++eQa60nnuYKCAgBAVlZWjccD1v64/vrrMXr0aLUtMTHRMj4bQh06dECjRo0atEwjIyOjNm3aoFevXjWmRTwUVFFREROjMTo0lZaWFvOdiDFqo7+etm3bhg8//BCzZ8+2bHe73ZYfIwBVn5+LiopQWlq619cz4I8mCeswCB0Oh5GUlISUlBTlQCOBDX7QZRqw9PR05U7D4LmEPRg0JvQiIQRCPnSeoGuIBEokCKSDSgzsulwuCxwD2AeiZTCYwV8G/gkOSQhHAkkM6MvgPdtQuoLQUUWm05EqKSlBaWkpEhMTkZycbKlneXm5asuEhARVp/LycvWrGaaqio+PV4CRhAoYcGYKL5ZHkCASiSAuLk45ZNBpSd4Xr02QRKY5kvfMNpGBeztAQAepolTY9N0AAQAASURBVNEqVyiey3aVIIqEH6RjESEICVCxTB0+0QP5dFlhqi9CFtKBhQ4rOpgmgQOOazm+ed8SrJFwjQ4rVFZWWlxyJAglgTbpIqPDVZxTugNQXl4eCgsLVd3tABDZVvo4T0xMtAAhcu4wRR+36SCUBHsk0CLHF9tCAj902+EHOo5Dlp+Xl6f2S4et0tJSlUqJx0voiusb5wHHXXJyMjweDxo3bqx+aUW4jCnWWDeuBTJFoQ6Rcc7KPuIax34oLy9HTk4OAoGAWmuZco5ONAAsAFpKSoplXUlKSoLH44HX61Xjh1AQ4TEJ9ci5JNuUfc1Ug3ZwJcdyQkICXC6XghElPKODb3Js6W49EizlGLcDBSUkJ8cVXxPkkSAg1xLp8iXXYafTqQBVCfZwrkYiEQVDSrKYcKDuFse/mXKcs346lEonOiMjIyMjIyMjI6NDXXauMZTdD6gON11yySVo3749Xn/9deVkojvc/PDDDwrsefDBB9GuXTsAwOjRo/F///d/6rgrr7wSa9aswaZNm9C2bVt07NixxgC6nVauXAmg6nvNE088YXsM4Qs9TVhD6qyzzmqwsvLy8iyOSEZ1k4QL7NIZNYS6dOmCJUuWYN68eRg1ahQA4K233rIE7XJzc23Pve222zBz5kyVAgyo+kXwwQR5jRs3DpMmTcIdd9yh3GSAKkelt99+G8ceeyxuvvnmWuu8fPlybN26FUBVyrPbbrsNl1xyCcrLyy33X53++OMPbNy40bJtwIAByjmH6tmzJx588EF07NgRZ555ZoOlqMrKysKdd94Zs/2WW27BO++8Y/l19xFHHFFrWq5NmzZh/PjxAKrAn0ceeQRvvfVWjS4As2fPtk1xJtWpUycAwHnnnYfMzEzLj4xr0pIlS/Dll1+iZ8+eOOWUU9T25ORk5ejWUOrfv/9BB7cZGRkd+iooKGhwh7IDocLCQmzYsOFAV8OoAZSSkmL7w+q6QLlGh6705wOdOnWK+XwXDoexdevWmM+29ZGJvGlKTExEJBJBIBBQAU4GdWXwG9gTcKVbBC0uSebrbh12jg0SAABggQtkcNcuVRLBEwmGEG7Rg8oMtMugtO6AIQEKBsoZNOY1dfcLmf5GupHwusFgEA6HA06n0+KIEY1Wpekh3BAMBlFeXm55AMTgsLx3AkkMPIdCIZSUlKCsrEwFtiV4QPG6TMNWVFRkAZlcLpdqU0IWenuzHdiWhBEqKytVCjAdwCJAIPuKY0OCX0AVgCH7hCCZ7rIRFxcHl8tlAW3YD4R/5JjgOQy8s32kk4oOCOkOM+w7uxRf+j4dEJJ15D1LoIn7CQ7wV3l6ii2CPoRP9L6VcIsOiOn3KO9VB+N4PzK9Gq+jgxy6a5AsQ+8H6fglj5NOUk6nU/UJwRVCXex3HstjuEbJ+nCccxz5/X4kJSWpNYxtxHoGg0HEx8fD5/MhJSXFcj3Wh6AI4Ufel+wDrgUJCQnqeJ/Ph0aNGiElJUWtT5zbgUBAXZu/8GS6N4JBbGv2IeddMBhUZRH+YftwbZEAF9tCjh+uAXJe6KCOPF+OITrA6deQUJEEXuT44f1UByrKcUFAT4eq5HxnX0lxrZRrs+4cJEE4zgc5l/XXkUgEoVBIAWdyznEccR2Tf2fk/Uqo0sjIyMjIyMjIyOhQ1pQpUzBz5syYz+JGVSl6evbsicceewyvvvoqAKvTidQxxxyDvn37AgBOPPFE9ewFgKVtX375ZXz88ce48cYbMWPGDMyZMwdLly5Fz54961W3UCiExMRE/PjjjxYnFoq/xOSPZRpaXbp0aZBUE4RHPvjgA7zzzjv7XN7hJK/Xawms7Ny5c79cp1WrVvB6vWjSpAnOPPNM+Hw+zJs3zwLJ5OTk2J6bmZmJtLQ0BcIBVakF96c7UX114YUXon///gCq5jcdZZYtW4YXX3xRHSdTgdlp7dq1yo0rJSUFjzzyCKZNm4Z3330XGRkZtdZj+fLlttvtUgKeffbZ+OGHH9ClS5cGdZYZNGgQWrZsadk2f/58XHTRRRg3bpx61unz+Wr9IdRzzz1ncTkDal+PdLcoO61cuRKhUAh/+9vfkJeXh0suuQRAFXBZk7p164bTTjvNsi0ajeLhhx/Ghx9+WOt166P+/fsjJSWl3uu6kZGRUU0qKirCfffdhx07dhzoquyT8vPza0yhaXToaPv27TFgdGVl5X77/mF0cKpDhw4xP4opLy9HUVGRAX8aUh6PxxKIZfC0srJSuR8w8ByNRuHz+eD1euFyueD1ehGJRLBr1y7lRkEnFQZzJShTXeoomVaKwWLprMPgKQAFUejOP5QEgPi/vKYegJUOKDIYDFjdhaSTi53DCQEagi8MMNPtgQF8ggi0do6Pj4fL5YLb7bY4WMgUXNLuLBwOWxw66PoB7HH00V1pGEinW4/H41F1SkxMjGlbpj1imdKxSTppEAKSbcLrSrcdvpb3x7rK88rKyiyBfXkP7A8dNJBpfuR5BAk4/ngc20HWnZLOPqybhH7kmJDwDx1dpEMT247wC0EvurxwTsj7kWCd2+1GamoqkpOTVRl8GCghJgCWOsrjJAilj207dxcdhpJtoc8X9hnHlw79SEcZ3XFIOq5UVFQoCze6ssTFxakHAxwT4XBYrUmVlZUoKSlR458pu+hURghGAj9yLgJWG0GHw6HK4NoXFxcHt9sNp9OprglUwWq8B9af84r18fl8yh2H/xISEpTDlgQA2efSHY39wLZPSEhAMBi0OOxIII3SoUY9PZedmxTnhBwvcszIcSPXcB3elOnK2N469MN7ktfn3wgdKJPrB9clOW4BxLi0sX5264t8L9uF6wDvl3NPQozSZUzCP5yXHDey7Qn8cR13OBzKWtrIyMjIyMjIyMjoUNXs2bPx0EMPYfr06Vi6dKn5jAugX79+6Ny5M/7+979jxowZyMjIsNjY83uBlG5vruuGG27AsGHDAAB33nknWrdujZtuugmNGzdGly5dcN1119WrjnTb6NevH4YMGRKzf9GiRfjqq6+wbNkylRaoIRUXF4fU1FT1/l//+tdelTN9+nSMGjUKH330UUNV7bBR48aNkZmZCaDqueK2bdv223WonTt3oqKiAvPmzcNrr72mtlcH/shzKb/fj8LCwgav597oxhtvRLt27TBq1CiMGzcOmzZtwuDBg9X+n376CUBVmr633367xrKCwSBWr14dsz07O7tOdakudcu6detst2dnZ6N37951Kruu2r59e7XB2KlTp+L+++9XkEw4HIbX662xvNr2SxUVFeGzzz6r07EPPPCA5Xnnt99+izVr1lhciWpTfn4+Hn300f3iNNahQwcAiAGfjIyMjPZFzZo1w+jRo+vkInewKhqNIj8//6BP8WtUN/3yyy9YsGCBBejOzs5G586da/1uZPTXUePGjZGWlmbZFo1GEQgEqnUFrYsM+KOpqKhIgQgSZKATCaER0nder1cFfRmYLygoUCAIwRQGcykJTMjgqYRpdKCGAV3pFCMD4xKwIFgi06xwn50YDJfBXBnQp9gu0nlCBr7Lysosjjm8PoP5ZWVlyskmLi4OwWBQOSrR9YMOHtKxguWyLVmew+FASkqKakcZKJcpaXS3I6DqV16EKOhO4vF4LLAV7w+AckFh3/CLpQxw81qyvyWEw/ISExMVxMH9PI+SoBDP4fijA4ju7COdbyQsJCECwj8cF2xTOe50RxpKB5Ak0GDn+iPvQ0/14/f7LY5V8l5ZV8IcdNKiQ4wc1/o9Ew6j8wzbTQIKOvzDuSTbSHd9ku2s/zpHniddVVgHCfHIOcg2k/MnHA4r4CIQCFjAN8IZDocDRUVFiIuLUy5BdK9xu90KZuNDVensIlOhlZWVKViELmdOpxNZWVkKhpMAJJ18OA55Lzrswn4jiMO+SUxMtLga0Y0pLy9P9ZNMK8X1V6b64txmHnGuK/K1Pk4l7MM+lOs41xkJU+mS8I+8pp7aTs5xO7CSY4Twk3Qb4nV5n/Jc9iH7S4KjrBOBSVlfjg39WDnfIpEIiouL1fxiH9DViDnoOZ/4t83v98Pv9yMQCKi+BKDWUqagozsRy5XrkZGRkZGRkZGRkdGhqldffRX33HMPzjzzTHTv3t0SBC0tLT3sgIy2bduiZcuWmDFjBjp06IC0tDSLY09lZSU+/fRTXHDBBViyZEmdy33qqafwxhtvAKh6JrVu3Tr873//U/uHDh1qcQmqTZMnT8batWsBVLnv2On000/HhAkTFHC0r7r55psxaNAgAMCKFSvw3HPPAah6LvXbb78pB9z6aNq0aZg0aRK+/fbbBqnj4aRWrVrhvffew6RJkzB16lT8/vvv++U60lVo9+7dyMjIiHm4n5+fH3Neo0aNkJmZGfPjzoaAftq2bVvnFE816ZprrsHPP/+M1157Dffeey/ef/99BINBNaf4fHzRokV1Gt9btmxBOBzGqFGj8Oyzz+Kzzz6rczqTO+64w3b73XffXfcbEsrJyYlp+9okYT47PfDAAxg+fDgmT56sUsDVpFAohOeff75O1+aznLqocePGKCoqUu9PPfVUvPnmm7agmZ38fj9uuummatt8X9StWzf1q/fq4hdGRkZGe6Pt27dj8+bNB7oa+yS/348dO3aov0//+te/MGDAgFpd9YwOXs2aNQsLFy5U73Nzc5GTk/OXSEtnVLsSEhLg8XhiHH8CgQB27NhhG6Osq0zkTZMOrQB7nBQAWAAD6dTBQHsoFFK2wQycA1agwi74CuwJhuppZWQwmcF3O9cQYA/EIF0jWG89OC3L1x1p5BccHWCRQI8sh4F0HWyQQAbhHAkFyDZh0F5CGLqbDQC1n2nNeI8SspEuGlLyOAajCSPpDkm8T5bHstiOcvJJ4EPel+7sIttFBsJ1GEUCKrr7DPuB7+2AHf2f3fn6F1n9fEq6/uj/JAwltwFQgJFsc45F2R7y+vo8YNvroJS8Fu9DtosEkVi2Dv3o8I+U3TaWq89Ruzmj/6N7juwvCT8QeuEccTgcMV/ceQ7HvZwn0WhUrT1UKBRS8BHvla+lMxTvh3OYgIcc8/o4lPNcv3+5PnL+cA2VLldcy5xOp7onOS51+E7CanZjm3NKX0Nq6mO2o7w/fc3Q+0C2gQSM5Dqh102fn2x7O/BHgoe8L33syPEj70v2v6yL/rdIXp+wD+dYJBKxhackFCkdkwgiSmhUrg1224yMjIyMjIyMjIz+anK5XOoHAh999BGeeuqpA1uhP1nXXXcdkpOT4Xa7bVNZff/995g/fz4++eSTejuseDwedOvWDQCwZMkSvPTSS2pfWloajjrqqDqXNXv2bEyZMgUA0Lx582qPW7FiRb3KrUkXXHABrrrqKvU+Ly8PQNWPD4888sh6p4sLh8NYsGBBg9TtcNSsWbPwzDPP4MUXX8STTz65365D15bS0lIsXrwYXq83BlILBAIx5/l8Pvzxxx+YPXu25cdRdsfWV2PGjMGYMWNw3HHH7VM5LVu2xLJly9T7hIQELFmyBL///rsKQm7evBmdOnWqU3nBYBBr1qzBpEmT8Nxzz+Hll1/GypUr96mObre73rBUeXk53nzzTcyZMwd//PGHpf1rktfrVWtUTfryyy+Rnp6OJk2a1Hjc1KlTUVxcXKdrN23atE4p0QCgdevWymmcKi8vVz+uq0485+eff67VwWlv1b59+/1SrpGRkREALFy4sM5r+sEon8+HrKwspKWl4fLLL8eVV16Jq666Ctdddx0uuugis4Yegvr111+xYsUKlJWVoaioCHPnzsW33357yENqRnUT45X6d4Py8nLbHwbUR3X/ScxhIkIgDFYTHPD7/RZIQDpmNG3aFBUVFcjNzUVcXBxSUlIQFxdnOUfCETKozc5NSkqyBNJ1xxhgj4sEz2GAW3cpkcHZxMREC6Ai3Wj0ALsOh+iOE/Ia0mmIEAEfcMkgv3QFImhDN5/k5GREo1HlhMLAs3QoYV0qKiosIBXr7vP5lGOPBEAIEUWjUQUwSABC1qe8vFxRdU6nU7WrDvxEIhFVBwmTSIBFum3QGUOHArhfwhEypRKlgxZsdwmd8FwJAvBabEMJYMhgvZQOqUigRYI4hCRkSiE7GIofothuOmTicDhU2i72Aa8l4TS2K12JJJhGmIZjTt6XvmjKsaG7FOmwmwSOJHilp3PS5w7f0z1GgmMul0s9BJap6ST4wf53uVyIj49HaWmp6jO3263aiGOQY5426YQ1MjMzVXnx8fGqL5j2y+l0oqioSM1FOt4EAgE1l5lajeezfZj6jusPUz7pgItMHRWNRuFyuRAKhZRbUyAQUPcQjUZVirLKykrk5+db4BXeh0y353a7VV24LkookWAhx50c4xKokWALf4HG/pPrlrw/utjokBnHYzgcjmkTHcwEoFx4WEeWIceqHOvsU+k0pteR6yj3EUzS60pwKhQKqb6QZVFsX5muTLYpz+EY43iQkCfXYq7H7FPz6y0jIyMjIyMjI6O/mh555BHMnj0bLVq0wDPPPHOgq/OnKjk5GR07dsTYsWOr/az/7LPP4pNPPgFQ5ahTF1VUVKC4uBjp6ek444wzsHTpUlRUVOCrr77CggUL0KRJEzRt2hRnnnmmBUKoSTt27MADDzyA+++/H926dUNGRobtw9UlS5bgb3/7W53KrE1HH300evfujREjRuCNN95Anz59AFTBRX379q13eYsWLcKOHTsapG6HsxYtWrRfy8/KysLPP/+MkSNH4o8//sCAAQNi0lLZjb1Nmzbh5ptvxvPPP4+BAweq7b/99ts+1adr16646aabAFQBIPuiyy+/3JKOacqUKZgyZQreeustdOrUCSeeeCI6dOiAGTNm1Km8cDisnsusXr0aq1evxmmnnYbS0lLlDF9fBYNBXHnllXj33XfrfM5XX32FW265BUAVzFlXJx0A6NWrV60pqj788EMsXrwYTZo0wdy5c3HWWWdVmyYyFArh008/RcuWLdG9e/cay33qqacwYsSIGo+Ji4vDEUccgRYtWlieYyYnJ9foyjx+/HhMnz4d3bp1w0svvaSe/+u688474fV6sXz5cqxbtw4///yz7Q8qqxPvsbryjYyMjPZF8fHxKCgosLjxHWo6++yz8e2332LXrl3o1auXAu0feOABTJ8+/QDXzggAxo4di0gkgsWLF2Pt2rU1pmtatmwZPvjgA/Ts2RPTp0/H888/f9CkdDXa/6rOuCQvL2+f4S8D/miKi4uD1+tV6bpcLpeCcmRqG7prMAWVdI0BoILxEobQnRbKy8tVYJXBct3lRT4wIYFPmIKpZBjoZWCVgWsZxNXhIJlKhiCLhIGk04YMTnMb24D7wuEwIpGIAk1YXmJiIsLhMILBoIJl6OrjcrlUAJ+/WmFZrHNcXBzS0tKUcwlThfn9fuUSEggEFNAg6+bxeCyOPqyX0+mE2+1GJBJRaYcCgQA8Ho8FgkhMTFT3xHZiYJ//PB6PBQwhgCDTN5WXl6u0S3Iis14MjDNoznqyvyTEQpCIkrAPr8X+l7COBH5kX9q5tOgQjGw7CSfYbZNjh+MqPj5epcnTHVwcDodyetFdTyS0RDgmKSlJjXkJQPGedacYtgPbjmOIMIOcm4RcdDchHeyx+9Iq70vOW5bBXJ2sD1NsSZCIoBh/AcbUSRzXBIZcLhdKSkosMEl5eTlKSkqQlpamAI3MzEzk5eVZwCyfz2cBTzjmmUqLY9rtdsPlcqk+Li8vRyQSUV++CR9JIIj9L9PqEWbi+OOaWVJSgqSkJCQkJMDr9ar2B6oerMg5wvmUkJCAUCik1o3CwkK13hKIIginu2vJtU7OBc4XXl/CRPIPLlP48XweK/ucr51OJyKRiKUMOTfsxpKcU1wfJQjI1GkSPtPnk3wvt3HM8W9YXFycJXUa6yfnMq9HIJL3WxPMI8ey7uRVWVmp/iYyDZ8Bf4yMjIyMjIyMjP5q2rFjx2GX3gsAevbsiXA4rNxz7NIILV26VEE/1R1jp48//hjffPMNnnzySdxxxx0oKyvD+PHjAQB9+/ZFdnY2+vbti3nz5tW73mvWrEGHDh1w7rnnYvLkyTH7n332WaSnp2Pw4MH7lE6rTZs28Hg8yM3NxeLFi5GUlISjjz4aQFXKqdpSBNnp66+/3uv6GP05io+PR3Z2NgoLC7Fr1y4AsE2n9NBDD+GDDz6wLSM7O1u9/vzzz/c5JVmbNm3U67q6yVSnd955x3a73+9Hx44d8eCDDyISidTJHWbZsmVISEjAnDlzLNt79+6NnTt3YuzYsRg4cKCClnTxeYOd+KyoJrBFSj6r8Pl8ePbZZ3H++efX6tADVEE9ddH111+Pd999F8nJyTjllFPw/vvv2x533333AQCGDRtW7Rihfvnll1qvW1lZiTlz5qBLly64//778X//938AUK1T0aZNm/Dss89iwoQJAKqcsqrTv//9b9x7770qtlFSUoInnnhC3UNdRCAyMTERF154YZ3PMzIyMqqLEhMTD2noh7Jz7CstLbWkcTQ6MOrcuTP+9re/4cQTT8TcuXPx7LPP4ssvv6zxnDlz5mD8+PGWNMZGh49ycnJQWloKn88HoOqz2rZt2/YZ/DGpvjTRWcLhqEqzwxQm0nmBDygk2CEdUBjkZLocQhkyXQr3yeAs98ltDOYyWC2dOiQoxACs/kVGD87LoKwMNutBbF5XgiIMfstjCHVwn/yCJKERGcCPRCIKXPJ4PBZIg24gtBCl04/L5VJB5/LycgSDQVUHj8eD8vJy9a+srEy5WPDeZF8R+mAKt8TERAUe6YANj5fBfunqIyEb6VAk21I6kUi3GQlhcezJduAvWuS9SdAF2OM2Q2gkEokgHA5bHIl4HGEzCcfwWhTHMbcxZZ3T6VRQlISNpCuWTibyGnJeSGBHOv3wXnltjjsew/oTnrJLOcT71eeRHMPSsUSHQCRkJV14eB+yb+X80p2IZBuzDePj4xEMBi31l3XnGKIzEOcUxyfnQjQaVfCG2+1WZRQWFiIvL0+5BbEtCgsLEY1G4ff7FewBVME1BODC4TD8fr8Cf2S6McJsMr2gdH6RoJRsf/ZheXk5AoEACgsLUVJSgvj4eBQWFqp24P0lJiaq+tARTLpk8XU4HFYATHFxsWWtlm0tx4HumhMIBBQsqK+/cl7orjb8X94vxz/nMdcW6cLFvmUaNTk3ZNl6PQh6sf3ZF1z7JTAq5w3/vuj3xj4rLi5W843wFf93uVzwer1IS0uDz+dT1+Mc0h3D+JprBP+mEYzkOOZ5wWBQ9WltNtJGRkZGRkZGRkZGRoeGzjzzTEydOtUCKeh67bXXLO/XrFlTa7lz5szB2LFjMXXqVHz66adYu3YtZs6caTkmNzcXn376qUqdVR8RXBg2bJjtfv7wZfjw4fUuW+q8884DUAVxPPjgg7jzzjvRqlUrAECHDh1wxBFH1LtM82vg+un777+vNfDS0MrKysLHH3+MU089VY3PtLS0mOdKHTp0wOWXX15tGUDVj6LeffddTJ06dZ/qRABpzpw5+zyGOnfubLt99+7dyMnJwVVXXYW0tDT84x//qLaMU045BUuWLMGxxx6LLl26xARkGzdujIKCAsyYMQPJycnVllOTQ0w0GsWvv/5ay93skQSi8vLy8M033yAlJaVO59Y1vePcuXMxbtw4HHPMMXUCo6LRKB555JFqYSugbuAPUOUKsWjRIqxYsUJtO+aYY2KOW7hwIbp06aKgn5o0fPhw3HnnnZZ4SWpqKkpKSmo9t1mzZmjcuDG6d+9uSQtnUhkaGRk1tD744AM8+eSTh3S6LzuVlJTs1edgo4bXKaecghNPPBEpKSk4/fTTMX78eFxzzTW1puM00M/hq61bt1rcP3NycpCTk7PP65QBfzQx5YuEQ4A9wWCn0wmv16uC0ykpKUhOTobH40FycjLS0tKQnJysUtfwX6tWrdSHWQZUGcyNj4+3AA7SDUKHGFhH3c2HgVseJwPwLIuQAK8pgQMJAUmnGAZ8JeQhYReZQsouBQ6DygRX2J50uJHAFIPJdM2RwAa/PPDaEmQh7BCJROD3+xUwQkBGlkuAQQI/4XAYCQkJMY5O0WhUlS3z7BFSYN9KmEhCUjKdEwBLf+gpwHRHqGg0qsqnu4ocI+wTjk3CGmVlZQiHwzEwBu8J2AOQUOx7uU+6Fkk4g+NeBw7kr/V0AIhgUHx8vMXNhSAP7XwlDEdgQwcI5PiSsI28Dx14kLAd/+mwFfuEEBVTr+nAkHQkkoAUx6Z04aFoR0zHHR4j+5tzjW4vdPci5EFQRo5rut3Ex8ejqKgoBkJZs2YNXC4XysvLlbMV7yM1NRVut1vVheCMhDoqK6tSjNFZiy4wFRUVKmWX7j7FduS/3NxclJWVIRQKoaSkBIFAAE6n0+ImxLR/BEHy8vJUXXUIyev1Khck9h2BPN431zjZNxLSYyoujkl9LOljSPapBNA4/iVYI8cCnawAqGvKtZ3rGt1zWL5Mr8Vj5P3INZplyfWegBOvxToCVakM09PTFahDSUctr9erxkZlZaXF4SwUCin3NfY175frIvuD4BKdgVgX/q0wMjIyMjIyMjIyMvprqHHjxujWrRuaN2+O3bt32wbh9YeXtblXAIDX60VpaSlCoRAuvvhidO/eHTfddJMlNfS+aOLEiQCAfv362e5nIP3vf//7Xl8jLi7O4lIydOhQ3H333ZZj0tLS6l3uo48+utd1OtyUmZmJ3r17Iycn50+9bk5OTgzUMWPGDFx88cVYtWqVZfurr75q6xbDsVFUVIQVK1YoN+m91YYNG/D666/jpJNO2qdyAGDSpEm2gazbb78dffr0wc8//wwAGDJkCHr27Blz3LBhw7Br1y5069YNK1euRF5eHoYNG4bBgwerY9xuN0KhkPpRbnWqyVH4zTffRI8ePep8X7pjQkpKinJDrk3//Oc/lYuOy+Wq8dhJkyZh+/btiI+Px0svvVTj8R999BHuuOMO3HDDDdUeU9fnLGeccQbatWuHG2+8UW078sgjLceEQiG899576keytWnLli3qB7xSu3fvrvXcpk2bYsqUKXj88cfRokULtX3jxo11uraRkZFRfbR27dp9ds872NQQaYGM9l39+vXDpZdeaoGFjzrqKFx00UVo27btAayZ0cGsnJwc5Ro8evRo9O3bFx9//PE+l2vAH00SmCAkwO38PxwOIykpCWlpafB6vZa0RwxSM2hP4IDwCIPNhHQINMh0RNIVQgakJWChO0fI/TJAzX126ZjsoAk+QJEQBQPfTqdTBf1ZZ8ItEhoKh8MKVGBgX7q9sJ0Y9GY6NbrKsOxAIKBS3xCEkHCFTFdGFxDWHYAKpstAuA5nuN1uJCYmwu12K1ihsrISPp9PBbGlI4yEK9iWvG95nOwjOZ74WrrksP3s3Gzi4+MtuaylixHbHoD6EkygSIcfdAcTCRoBe2AN7pPpmvhaAk8c2zqEwGvJ++K9E2ig5Pl0RJFwiUyLJN2RJPwj74Fjgn0txwPL5ZyWMIUEcPQ0RXqf6nNPdzyyg4+kuxXHKgEql8tlWWOYCkuWz7HM6+jwSiQSUTAixxHXk3A4bOlDOycjumQRNgGg3HAcDofFiYdAC2EgOb7ZzoSmysrKsHPnTgQCAbhcLvh8PotzEB2NUlNT0bx5c8TFxWHr1q2qPhJy4/gjmAlU5SAnvMJffunuNhIGYrnSTUgeK8eNdFnS4S/OE44XCUbyfDkeZL041wj4SIcolldYWKjgGLYlx5V07ZIAHcuUbmRc41kXtr1MEanDlQTKuGYWFhaitLRU9QfXKOluxHVSwnVy/BPuY9tImMnIyMjIyMjIyMjIqP7q3r17rb8c/TN05pln4q677lLB2mAwiBNOOMHW4eOSSy6xvO/atWut5ZeUlMQ4gMyaNQt33XUXmjVrtg81rxJBkJSUFJx88skx+7/77jsAwDfffLPX1+D3/IaWy+XC4MGDcemll9YpBdHhrBYtWsDtdiu3mwOpDRs2AAAKCgqwa9cuS4oqr9cbczzBn0aNGmHcuHH7fP0dO3ZU6y5UH11yySVYtGgRCgoKbPfv3r0bf/zxBxYsWICdO3fiqquusqQZu/vuuzFt2jQsW7YMO3fuxP3334/HHnsMpaWluPbaa1UqPKDq+VRGRkaN8E1lZSVmzpxZbWBNd1mqSYFAAJmZmep9YmJinVxvqHvvvRdAVTqvhx56qMZj6RB06aWXWtqnOiUnJ2PkyJG2kM3kyZMtjjnVqVevXkhPT0efPn1w/fXXAwC6dOliOWb69Okx7mo1ic+2dNUlnVybNm3Qpk0bDBo0qM7XMzIyMtpbNW7cuEE+Qx5M2rJlC1avXn2gq3HYa+TIkejVq1fM9hNPPBHff/89nnnmmQNQK6ODXStXrkTfvn0xbNgwvPLKK9iyZQtmzJixz+WayJsmCRgwaMn0JACU8wGDqTJ9S0VFhUq1JOEJyg56kKljpAuPDjTIc2Rd+eWFr+U2wPrlRk/TpddJpp2SwXwJojBYzH28d8IKDBjrgIYsk/CSDusQbCAgJIPyPF5CRjIYz2Azy2BQnW2gA1ASAuLxMm2RTNUD7Pl1moRA2F/SgUkCJ/o1OSZkW1QHZsm6s+/1sSDbma9lqi7WzW486H0it8l+twNaahrXUnI7z9OdSdj2BC3kuNQBIgnq6I5FUhKK4HuOAbYdQaNIJGK5N3m8PNZuP8eePlfsgCLCbwSypHuWHAtlZWUWOIbXk2MOgAXwiEajCpqTbcO5yNcsg+dIZy3CQpR0vpIOWYR/JIDI8wmzsJySkhIL+CXdvViuXXo+ua7agWXBYBDBYNACcHFt0OcVX0tYRR8/EviSUJReloTN5BiQYI4cC5QOUHI/AULZr/Iasq14DoEpHQjV55peD7ke6Pcv4VR5ru6uJe9FXlvfJ8ebnCfy3rhOGxkZGRkZGRkZGRnVX2lpafj3v/99oKuhgtCEkHbv3o3Vq1fb/kpRT2elB5rt9MEHH8QEMr777juMGDHC4mDaELJzJNm5cyeKior2OeXM/gjGVFZWwu/3IyUlJQaqMrIqIyMDFRUVdXYv2d/Kzc3Fyy+/jLfffhtff/212q6nRZI/AgRwUIERLVq0wLp162oEaiZOnIhXXnkFU6ZMwY8//ojdu3ejc+fOcLlcuOSSS/DUU09h4MCBaNSoEX799Ve88847SExMxJAhQyzPCOUz2uqUlpaGrl27Nsi60KRJE4uzzpQpU/D888+r9wsWLKjReSkSieDkk09GixYtMGDAAEsf6po5cybWr18Pj8eDa665BiNHjqzx+HXr1iE/P9/W3cfpdKJ79+613R7at2+vXv/973/HddddZ3EnKCoqwoQJE7BkyZJay6pNNaVgo5KSkmJSqdGV3MjIyKihRROAv4oCgQAKCgrq5LBmtH/VsWPHaj+rJCcn71VqXyOjvZUBfzRVVlaq4HY0GrUEoxlkdjqdSE5OVkFYBuwjkQjKysrg9/uVwwEdMugUVFpainA4DGCPC0pCQgKysrLQvHlzFYwH9gRwJYTBOjJYLiUhFRkQlvCNdKRgIF4Gs3muDNAyAC2D8xJMYKC+rKxMfclieiYCKXQjoZOITBnFcuhiIoEHusAQdJF1qqioQDgcVnVyuVzwer2qXQnFhEIhlZpIOv54PB7l/kFYC4ByGNFdctg+kUjEFq6R/c1gumwrlsv+k31KeEGmc5KggHRtYfvRDUSmGtP7Vqbu0gEY6Zgj66v3O/fJsvWgvQQR9DHJ8tjnEhqQsIBM4yZBCwm8yXHHe5Bi+3OfvGf9fisrK9WDH3nP8sGCDujIOssxooNB+vzlOG7cuLFyz+E/zlHCLAShZGools1743ks1+12Iz093eIeJF8T5OH/TK3GuSGBPh5HoMzhqHIp8vl8SE1NRWpqqkpnyPkeiUTgdDrh8XhQUVGB7du3Y+vWreqDNOcZASXCIEwDxV+50VGK80OOfYJt8fHxCAaDymmmvLzcMh4lrCLXQrlNwjPyfqWzl5xzcizp0JmEm/ia857ruwR5ZApJuR7zGI/HY0lBJse5hHIkHGgHNLHt9DEp28jtdqs+TU1NhdfrVanlCEFJsIplyTWEwJd0tuNr/j30+/2W9GMcC0ZGRkZGRkZGRkZG9Vfz5s3x3//+90BXA02bNkX79u1x4oknAgAWLlwIwN5dIzs7G8OHD1fvly5dWmv5n332Wcy2DRs2ID8/H2PGjNnLWu/R0KFD1euxY8daXEaoRYsW7VOqLwCW9DUNpfLycgwbNgw33ngjrrnmGlvHIqMqzZw5E2vWrKnWnebP1syZM/Haa6/hhhtuwLRp09T2Nm3aYNKkSQreyMrKUu7GB5teeuklvPHGGzUes2zZMkyePBn33HMPFixYgGeeeQY//PADlixZgrfffht33HEHrr32WqxYsQIrVqzAli1bMHz4cGzZsgWNGjUCAPW8qKSkBBMmTMCVV15ZrYNWXFwcfD7fPt/biBEjLGsVAAs0dsYZZ9ToPuRyufDZZ5/hnHPOQb9+/fDEE09Ue+yiRYuUm9CYMWNw66231pjSDKh6HjR27Fi8/PLLWLt2rdqem5uLZcuW1XguADz22GMoLCwEUAWTPfvss+jWrZvaP2/ePMybN6/WcqT0Z7NUXX7w1atXLzRt2tSybceOHfW6vpGRkVFdlZWVVSNgeagpJycHy5cvt3WCM/pz1adPnxr3d+nSBb179/6TamN0uMuAP5oYRE5KSlLpc+j6w3Q4cXFxSElJgc/ng9frhcvlUv8YkC8uLkZ+fj6cTifKyspQWFiIoqIiS6C9rKxMBZrj4+Ph8XgUjEIARoIF0pVCQiQMMhOs4Adr3ZVCBl4p+Z5lSWBIAi4MzstyCBcwBU9cXBxcLpeCcqRbj4Re6E5CyIFppGQ6IwIwhAMI5yQkJCgIQK8L0xBJoIN1JnTk8XjUuQy6E8aKi4uD3++PaXed1mR6H16D40Z3YrE7T4I27C8J2kgnExn81105ONYkoCGPkcCLdKeR7iu6644Ee2RqOglBsJ3ke32sSdBApgCSKb8kwEYIRH7BlffA43mshB4k2EFYQYIarC+P53zjP85HO9iK80JCZxIiSUpKioGLWD+2L1Pf+Xw+5XAjYT6OH9aL15FtIaGvcDiMxMRE5Q7EMU3ohinEJDBDSJDgC6Gl8vJy+P1+dRxQZcXLsc36ynHK1HherxdutxsulwtpaWlwuVwoKytDfn4+tm7dqhx/CKBxjjMFGesTDAaxY8cOFBUVWe6T7URwknPA6/WqNVC2nQ5MylSAsh8JJLKPpIMZ+1GOMUKHoVAIpaWlKC0thd/vRygUUmsk56BMh8W1Ss41CdhJgIb9GggEVJ+yXhyHOhRJ2bnv6M47uvMPIUX+Yzo3jgPZvrL9ZLvQJcrtdlsgQYJoXD/4dyASiahr66CZkZGRkZGRkZGRkVHd1bRp0wMKMbRq1Qrjx4+PSavzxRdfqNfbt2+POe/qq69Wr1988cVaUy/Z2eUDwK233mopa291wQUXqNdNmzbF+PHjLfszMjLQo0cPHH/88XtVfuvWrfHvf/8bxx577D7V005JSUkYO3YsOnTogB07dmD58uUNfo2/krp164bp06cf6GrESKbsO+6449CiRQvs3LkTQNWYdLvdan9NLjN/tgoLC+u1Bt1zzz24+OKL0ahRIyxfvhzj/n/astNOOw1z585Vx/E5AcGfQCCApKQklJSUYNmyZTU+S3A4HDj66KPRoUMH2/0LFizApk2b6lRf/VmufO92u6sFXSiv14uWLVsCQK1r1cSJE7Ft2zZ1ndrSO3z55Zd44YUX8MgjjyA1NVVt37VrlyqnJv3444+YM2eOZZsMgu9NSrzq+sTOmUhX3759Y7a1a9cOV1xxRb3rYWRkZFSTGjVqhIKCgoMGBG4I5eXlqc8NRgdOXbp0gcvlqvGYpk2bYtCgQX8p8Mzo4JUBfzQxYEsHGwAKOikvL4fL5YLD4UBOTg78fr+CYQgKpaamIikpCR6PB8nJySrQnJ+fj4KCApUmLBgMKuinoqICRUVF2L59uyXYL90npNOIhGP04LJdmiDel+5WoTur2DmV0AWD8AIDyQRf6ATEa1VUVMDlclkC+DK4TXCD53Gf0+lU4BSDz/Hx8SodE9uRwebk5GQVcJf3ygA6gQyW43A4EAqFVFkybRgBJAapeS3p5sHrEM6QsEAoFFK/OJHOLzJXt4QngD2/epD1Z98T4AoEAgomIFjEMiQARvBLd4XSARieL91m9PfSrUN3PZGqLq2RnSSQIKEi1lM6vsjrSKBHjhUJp0lJFxJ5Xc4jCRHRxUQ6k+jwjnTC0uvG8mU9JIwkncNYXl5eHnbv3q0gQglLSScXQmqRSESND5fLZel7CfDwH+cdASqHw6EeEvFY/s9xS3CG9eB4klCITM0l4R+Xy6XAH6fTiUgkgsLCQmzbtg3hcFg518h1hPOTawrrTtcYCURJMEqOM+nwwzrLFHzcbuc+RciFkKHsW7q5sR+4tkajVc5vhH3C4TBCoZACEjlv2ee8H5bL9pL1ku3NcVZeXm5pb7l2c73huJF1lPfMa3JN4/myPrKfed8SvAyFQmq9ko5Fcg0hyMk+Yx/IuvA8rpl0vWPf1/YrNiMjIyMjIyMjIyMje40fPx5nnnnmAbt+69atccstt1hcd0pKSjBr1iz1ftKkSTHnyTQEX3/9Nfr164cRI0bYOnj8/vvv+PDDD22vP2PGDDz33HP45ptv9uEugFNOOcXy/rTTTlOv4+PjMWHCBKSlpe21i8jYsWPxyiuv7FMd66K1a9ciLy9vv1/nUFYkEsGaNWsOdDViRMCF2rlzp3I7ad26tcUx5VBO45GdnY3Nmzdj5cqVFuDO5XJZ7svpdKKiogKZmZkAqu5Zwk8yPbuu1NRUvPbaa2jevLnt/r59+9oCidXpzjvvVK/l86WJEyfWK3V5XFycJVWYnR5//HEAQOfOnZUjQG0BRL/fj3PPPVetnzt37sSQIUMUcFSdzj33XNvUhtTegD/HHHOMbX1rayeXy4XOnTvb7jOuP0ZGNWvMmDHo37//ga7GIaPnn38ev/76K2666SY0a9bsQFdnn5Wbm4t7770XQ4YMwXPPPXegq3PYa8CAAbUek5SUhOzsbPUZx8hof8qAP5qSkpIQCoUQjUYVKELoQLp3hEIh5SrD4woKCrBt2zYEAgEVVN69e7dKDSUD1Ax+AlDASUlJCSKRiMW5RDrl2AWyGXSVgIcdjKBDCzxfprXi8dL5h4AB75vBXwZu9RRIBBecTqflPctm0J6BZwnQSNBApuKh2xBBF7YRQQKCJAS0WEdgz5dC9iOD2oSMpAsO3YUY/CackpSUhPLychXUZ5CeqZmkQ4cMpktggW1MEEQG8/mllWmbgD15kNm2bEP2I7dLZx6ZEotjiG0mU0jp7jt22+V9se84NnQQTY4Zu38SQuJ4k+CN7sbD+6Kk25V+Xd6rBCWkywmvJce3HJMSDqJbi3SD0eecbHtKd1yh8xTHOevRpEkTNG7cGB6PxzK3pBONdHCSzkQS8pGuUuwPt9uNSCSC4uJiBanwmGAwqNJn0UEHgHLf4bjndel0xvuUAF1cXJwCxgiLyNSIOTk5KCoqUu0r3W4kVMZrE4whtESwhG3P1wR2gD2wHNtIzikJu8nXbFeHw6HqzDL0+sl1Ua5n3E/3n127dlnGIseKTGHFcgnY6c5tZWVlah3iMZWVlepviBxngUBAucZJpzM51wCoNUE6/bCd5PE8li5putsV4S4JCEp4EYDlvQSM5HyXf/e4LRKJWKyyjYyMjIyMjIyMjIzqp99//73e55xwwgm44YYb9um6Xbp0QevWrWO2u1wui9PGPffcg9GjR1uO+fjjjy3v16xZg6lTp2LVqlXIy8uz/DigVatWNdbjueees6Snqa8SExPRuHHjmO3jx4/HmDFj8NBDD+Gf//yn2l5dcLo6HXPMMRgxYsRe168+qquLidHBp+zsbMv7Xbt2qe/rjRs3Vqk73n77bdtUdAdCtQEpdqJbfo8ePSzb+ZydopsOU5xt2bIFKSkpaj9/gFWTRo8ejUceecR2X1FRUZ3qe+SRR2LkyJHqvUyhIlME1lXXXHMN/vvf/+L000+33f/UU0/hrbfeAlDV75MmTao2pRmVm5uLuXPnqmc9I0eOxOTJk+H1ems8r1evXtXCUeXl5fVOFzNq1ChMnjwZnTp1qtd5ADBkyBCkpaVZtm3duhV5eXlYvHhxvcszMjpclJGRgauvvhpXXXVVtS5nRla1atUKWVlZB7oaDabs7Gx06dJFpW40OrDSXVCrU0ZGxl59jjIyqq8M+KMpEAhYoAzpGAPscV9ggJNOHDJtF2ELghcEOqSTgw7i8DgZrJbnAHsC/XapjiQYpEMQOgzEgLDulCOlO07YARAEfxjQlsCOTNclYRXpVCQdNySkIeuk15sQjwQqZLsznZd0v9HhE3k9AAo4oGRKMvaVnvaG4IOEnnRoSr9vjicJy0hIIS4uTqUP0mEF2TayXWQqLrldBt3lWJIuPvo2CV7JfxIskGAR61KbqoPWpKOLrLOe7svuf33Myl/8SGBF9pmEhmS/6OARx4R+Hbt5pe+XdeA4jYuLg8fjQUpKigWy4AMOCSHJ+S77V46zxMTEGEcbtqXf71fnSZcYtpH+axvWg+1OcEnOU3mf+vhgvQgGEniS5etrJt1o4uLiFJDH9ZP3SOhROuXIfpJjW85Vu7WSYhnSMU1vQzmH9HEigR59bdDngwTTZP/I8SfXQEKV0i1Ih8ukA5G8d94/AVReT6Y606+rj1W9P3XpZRCQlNtYT1m2XHP1v3t2MKqRkZGRkZGRkZGR0f5Teno6Lr/88pjtdUnHQl177bW2v5ROTEyMAX10t5vq4Jk//vgDq1atwvfff6+C3bW57GzatAnJyck47rjj6lx3qfbt29tuv/jii3HFFVdgwIABlu+29QV/jjrqqJiA9v5SSUnJn3Ido4aXDmnIdF4ejwcff/wxli9fjvnz5//ZVatWY8aMwcUXX1yvc5KTk9GiRQsEg0HL9oqKCsuzFqDq2QFdfnJzc+HxeNQ+6bpcnYYOHYouXbrY7pOQUW2S7s31Oa86jRw5EqNHj0aTJk1s93/33Xfqddu2bescRJSqqKiIaWNdNUGV+jPZ2jRkyBBceuml1f4N6dChA/75z39Wm9akRYsWMdt27dqFd999F8XFxXWuh5HR4abWrVtj1apV+PXXXw+qNJAHo0aOHInevXujuLi4VnD0UBHjHUOGDEG/fv0OcG2MANT5M39NKUuNjBpSJvKmqbCwUDlJSAcSftivqKiA1+tVqUvKysrgcrlQWVmJ1NRUpKenw+PxIDExUTkzMJWR2+1G06ZNkZycrALkOlAhU8xIFxmCETJYKgPOdi4rBD5YVwZpZUBcBqhrcmqRrkWsDx0bmCJGuoPo0AoA5UxiFwiWjhtMxcNzAKj6854kpJCQkIBwOKxS8LBsgjkALP0h3Uj0NFeyrnQPikajCtags5DT6YTH47G0M++ZbUunD57HNEzSiYPHAXucnwKBQIxDhgzMs910UEeCPLrjD+soU3zJNF8SgqquTdgP/CKouzLZ/ZMOVxKOYxvrab/4PwEGuo7oIIZ0AOI2CSGw3blPplhjX8nzOUd4rpxfPE7ej2wPSq4XdPOhQw4ArFu3DqWlpUhPT1dp7AjPSciFsKF0yElKSlJgoJxbEgDx+/3qgYh0LmP7SmcbYM8vpQgvMv0g56h0auH40evKVHx0GqqoqEBqaiqSk5PVdSSoSLEuhYWFKm0W1xXZbhI+ZEox1oHH+v1+i/sW20wfZ9KpSo5lHfKTLmYcK7x3OW+57nOMSSBJuuBI0FF30nK5XKru+jyUEFhl5Z7Ucbq1Na8bDAZRXFysxgnP1+/fLuWfBBMBxLh9ybnJB148hmsO10iuDfz7KOcF/5bGx8fX+is0IyMjIyMjIyMjI6OG1RdffIHPPvsM77zzjmX7zTffjBkzZtR6fnZ2Ni699FI8+uijtvvPO+883HbbbZZtP/30k3rdtGlT2/NeffVVPPvsszj11FOxZMkStf3CCy+ssT7Tp0/HhAkTaq23nVauXGmb+qlZs2bo1q0b+vbta9len8DK3//+d0vasPqoPqmIqJ07d+7VtYwOrDIyMmIcf2TKtscffxwffvghkpOT6+xU82foP//5D84666x6nUOHmWuuucayPS8vzwLhffrpp7jooouwaNEifPzxx1i/fj0yMzPVM4v8/Pxa3YO9Xm9MCjXq448/xuTJk+tU58TERPWMrSFSlXfs2BHnnHMO3nzzTdv9r776KtavXw+gCnyUqRN1JSQkqDaS0A2fz9Wkmpx5wuFwvSCCe+65p8b0Jg8++CCuueYa9YxXV58+fWK2/fTTT7juuutqBZiMjA5nLVmyBOeeey4effTRvfrccLjI5XLh2GOPxZw5c3DxxRdbQNJDWX6/H1OmTMGUKVMwfPhwdOzY8UBX6bBXfn5+rccUFhZi7dq1xqnzEFWHDh1w8cUXo3fv3ge6KnWSAX80OZ1OSyBYuj/IgDOdF4qKilBQUGBxfKErR1JSEsLhMNxuN9LS0pCRkWFJkSOBCglpANbAb3UuEbo7g3RyYCCazgyUDHzzvZ2LCn9FwffSBYTAAkEap9NpCXyzLjLgTHhFBqwZIGcAXsIoTqdTtQcBBQbhw+GwKjMhIUG55OhgU0VFBUKhkLoOy+QXDum8QriCMAED7MFgULU57yEcDqv2AGCBctiObAs72EJ3BSIQIF2KpKuKdPWQsBPbQ15fbtfhLQmxyX/6OJCgg0wjJtP2cLzSZam6L8ES+GA5ukuR7vwj9xPgkU4wEiyQ58q2snPikfvlONShDh260ueVBLCkswz7Rh8Hss7yV4sc6wQF2RdMM5eQkKAcceS8ZPvLOcdxTYinsrISJSUl2Llzp/pQyzWLdSGswesDUOnG5D/pNsW+kWBLIBBASUmJWgu8Xi+Sk5Ph9XotcJXuQAVUOaxJ6IftToCE7R6JRNRaSPiH13Y4HHC73RbQUXdpk+kGpYuRDk/KsS7T67EtCCWmpKSoOsgyuTayTAnHyfRh7A8J9LA/pSORnLdlZWUKciorK0MwGFSQj7yXcDis1lMJB3JscX1zOp0qlRfbS675cmxxjktnIh7PfpJpI0OhkIIwCWzJOc5tRkZGRkZGRkZGRkZ/ru666y7079/fEvxu3bo1TjzxxFodJtxut0rBM3bsWIwfPz4GGLrrrrss72+55Rbs2LEDv/32G+6//37bcr/99lu8++67AIDjjz9ebW/Tpk2N9Xn33Xctx9dXPXv2rPOvbufNm1fncseOHYuBAwfuVZ3uueeeep+Tm5u7V9cyOrDKzs6OAVT0wFFaWhq8Xi8KCgr+zKrVqD59+tTL8ad79+5ITU0FgBioZNWqVTjmmGPUe7/fj06dOuGdd97BSSedhLVr18Lr9SrHrR07dtTJCSY1NRUZGRkx26dMmYJgMIj8/HyEQiG8/vrruO+++yzPCCkJ/gCoNfWWrnvvvRePPfYYvv32W8v2QYMGYfjw4bbn3HXXXQpsatWqFW655RYMGTIk5rg+ffqgR48eOOeccyzbo9EoXn/9dTz55JO25Xfp0gUtW7a03VdeXo4xY8ZUe66d6gKk1RTgvPbaa2O2/fzzz3W+vpHR4az27dvj22+/xXvvvVetq9bhLo/Ho+KMfyUVFRVh8+bNuP7663HTTTdh27ZtB7pKh7WaNm1apzRyHo+nQRwEjf58XXbZZZg2bRoeeughnHfeeQe6OnWSAX80MVBK15FwOKxcLcrLy5GYmKgCwnTtqKioQDgcRmlpqcXlJykpCampqXC5XEhNTYXH40Fpaakl1Y0evOdDB+mcowffGejV07NIZwYJBDHdlp6SRp4v4QcJM9AdwufzWdLusK4EY6RrENuKHzoYNGZAXDqBEOLRwRVCCiyXMAPPI/TD9iCURXBLAjq8LveFw2H1ywGWRacP1lmmSdLBFpfLFQPd8Pput1uVx/YAoNpEutDofSvdjqRLEPdLGM2uD+X/BEkIVulOPbqjil4nSk81RZiMoEkoFEIwGFT/h8NhC1ghy9XLklCD7HOCGvKLt7x3CTJI4MvuXnTQQx6jzxfpEsT9bE+7uSXbi/NEHst7k64/BDekcxPrJIEjwmmca5wDhDrknCOg5na71dpVVFSEcDiMjIwMeDweCygknYAKCgoUkFRRUYGUlBQLmCIhMr09gCpwJxwOw+v1KpCJD4NdLpeaPzxfglocD9IJitsJ86WkpCAxMVGNXQnEEDxJSkqyADic03IsEriT0t20JCBGyYfAnJPSYUuCTHKuyfWQ65M8luNfwoqEeAh68T5kmxOCLCgoQGFhIQKBgMUdi397eK24uKp0aoFAQLky0ZlJgnUcZ2yTSCSi5rN0wKMDF13rZHsT0pRzhePI7XYrRyPCbnraOSMjIyMjIyMjIyOj/S+PxwOfz4crrrgCd911Fy677DJ06NABBQUF2LBhg+XY0aNHY8iQIQr22bRpE9544w0AwMKFC3Hrrbdi8ODBlkC81+vF7bffrt7PmzcP/fr1w4MPPojNmzfXWLdLL73U8r46ZyFq+vTp+Pnnn3H00UfXfuM2KikpsTz/sNOnn36KxYsX48MPP6xTmQkJCcjKykJxcTG+/vprrF27tl51GjFiRJ2P5XOtnJycel3D6OCQy+XCM888o6A3ANi9e7flmLS0NLjdbmRnZ2Pw4MG25cj59mdIXydqU1paGioqKrBp0yZMmTLFsm/27Nlo3749Zs+ejTfeeAPHHHMMrr/+erjdbrhcLqxZswaFhYW4++678eijj+KUU05BZmam7XVWrFihIJP27dtj/vz5timo7rjjDpx99tkIBAL45Zdfqg0Iu91uBSwBVjem2hQMBjF//nzcdttttsBQjx49cPXVV6Ndu3aW7S1btrSsSePHj0e3bt1izq/OtSI7OxtDhw6tNvVWjx49qnVf/uqrr9T6Xlfp47W6Yy644AJ07do1Zl8wGIxxK5k9e3a96mBkdLgqGAyq5892oKMRLO76fyUVFhZa3B5NurcDK8bjalNSUpJJ83WIShq0HCqgZd0TeR9GkgCMdIqgy4LL5QIAS/A6MTERfr9fgRtut1sdm5CQAI/Ho4ARBlUBayor6fYixaC9dDWpDgjS3WQk7CABCXkuA7s6JAJUfYigc4QOYgBVv3gg8CIdKNxuNxwOhwVgoGsPU3MxkM9FT7qDMPAdiURUkNjv98ekhQqHwwo2CAaDCAQC8Pl8Ch5xu922jjSEguikQdcNpt1hO8THx6O0tFTlaZTORxIikeASA/n88sj8obxfCRsAe+AC6XIj4RHZj9JVQ0+fJMevfp50L5Ggg+5qpUMtTKvGbbwf6awkAR/5v0y5JNMdAXscXAgKSNCLfUc3J9ZRQkNyHMvXsu4S+tEl98u5wDYOh8MxoBLP00EgOwcgAMpByuFwKDCKD0wJpch/cXFxCAaDCs7iHOL16JRCuI7tA0CtPXFxcQiFQggEAsjMzERycrLqf0IhAFQ6r9LSUiQkJCAlJcXi5MNxKf+Qce4SRiHwVVFRAY/Ho9YSjmvdgUkHFQlZArCsaUztxvFBcf2wG7dyfvDasr6ce7p7mlxzuJ/jnedxbaB4Lc5l9rdMqaivESyrsrIyJkc84RyCTBIG0scq70+Hs3hNt9uN4uJiNfYqKysRCoXUGiidfuheJ/9mELKS41/2JQEqHbyToKPsH9nOhIzi4uLg8/n+MnmVjYyMjIyMjIyMjA4VffLJJwgGgyrwe9999wGochmxA1T++9//onHjxvjwww8VlDNv3jxccMEFFleGWbNmYejQoer9mDFj8Mgjj6j3GzZsqBMsoAeG+/Xrh7lz51Z7fGlpKT744AM89thjOOOMM2ot3061gT+vvvoqPvnkkzqXN3XqVHTu3Bk9evTAkiVLsGjRonrVp3///rUeEw6H4XQ68dprr2HZsmV1Si9g1PB66KGHcNxxx+HUU0/dq/OXLl2KpUuX4sorrwRQ9X1ah0syMjLgcrnwyiuv4N///rdtOWeddRb++OOPeo3TP0sXXnghbr/9dsTHx2PWrFno3bs31q1bp4CRhQsXory8HAMGDMDvv/+OiooKFBYWAqh6pjZhwgRkZGTg/PPPr/Vaw4YNw4MPPojWrVurHyw1a9YsBjgsLS1Vz5v5LM1OiYmJlmB6SUlJne87Ly9PBWJvu+02BAIBS+rCO+64A3l5eXj77bct53Xr1g1ut9uy7fTTT0fnzp1x2WWXqW21pauxC3RnZmbiiCOOqPacZcuW1VimneoCHV5zzTW48sorUVxcHAMnDBkyBM2aNVPv8/Ly6g2WGRkdrtq+fTtOPfVU9czWKFb6j7z/KsrPz8eOHTsOdDWMUJUi+K677sKoUaNqPfaTTz7BM8888yfUyqihRVYhKSkJXq8XLper3k6Qf7b+eitfAyguLg5ut1s5pjBIDUC5/+hBf74mmCFTqFRUVCAQCCgXC0IPwB5XET19k+5QorunEAig7ILgOtAhXWH086pLfyMD8hzg8rxQKISysjIVRAZgCZLL1F8M/nO7dNohnCDrzbbiawbGddcWui6xXgxWy4A/g+by3mWAnXWTAJTuAMKy9Q8MunuLDsBI+MEOUJHgh3Tt0V1WJPxSneRYlCmzJITBtpcfCnXwR7avfi8y/Zp0qeE/ADGQDs9nO8nrSGBDAk8EzmS/yZRssl76/cuy9XrIucX62oE8+jyxcxbiufo/Oc4JMhFokWn9pAuTHCMSVOF2Oycs1o3zQ84Tj8ejjpPQCJ1a5Da6XclxLPuS8IscM1zPCG7pUB7HoBzThFBYLiEYffwAUKASz5WOaCxfplYjbMM2kftkP8n+1ueiLF9fLyVMRLENZf3lWJdzXDrjcJ8EgthW7Ad9LMvrE8jj3yI5Z+S1uT7Ka3Ctru7vAccDXcPkmJPjVPaXBABZjlwDuAbxbypBMSMjIyMjIyMjIyOjP0/Jyck4++yzY9w3CRdIZWZmIi0tDS6Xy5JOq0WLFnA6nepHXkBswLhJkyYYPXp0veu3Zs0ay3s7hwhdv//+O7p3717va8nza9L8+fNrLSMhIUGlSTv33HMB7Pn1dVxc3F6nYFi/fr1tKp2cnBwsWbIEeXl5ePnll03agAOk4cOHV+s+Ux9x7vHZsZTX61XPIOzGAtOsH6yOugMGDFCONccccwxGjx6NMWPGYPjw4ejSpQvWrl2rYKeUlBRcccUVGPj/0+TFx8er13VRRkYGSktL1Xu68NuJzzZCoRA8Hg9mzZoV075xcXEWd5z6BHi++OIL/PDDDwCAP/74w3aOVlZWol+/fujYsaPa1qRJk5jjBgwYEPMcsjZg0eVyxZR16623VtseK1aswBNPPFFjmXYqLS2N+aGtneLj45GamoouXbpYtjdt2tTyvi6pw4yMjKwy0E/14rP0v5qKi4sVJGt0YJWVlYUrrriiTsdOmzZtP9fGaH+JGXAYJz0U0gcaxx9NDL56PB6kpKRgy5YtlmB4MBhESUmJxX2irKxMuXMwcCwdNvgBv7S0FNFo1JJqiuJgYQBVBlIJP0i4Rf5P6bAQA70ycC3BBcIEhJNkupaysjI4nU713uPxqFQ+kUhEleV0OhEKheDz+RQ8QFcS6Zoj4Q66mrCOMqAsg9MejweBQEClymHb0a1Efrmg8096erol0B0IBJCamqrStsk0OwRK6OpB4Eu6ech0ODrgFI1G1QMvHquDAdJ1RvYDg/SyXxnQ5wcSHUSRfcly7YLnPFYeYzeuWIea4BkJQElYhO2rAwQylZMsT4JHOgzEMSldn7jN6XRanFFYVmJionIDYnvo7SLTN7H9dUBDgg8SSmJ/yy/XBEl473bOJ7qzEccpQUKOQzqncG4TKASq1oKCggLVjxKek21EyINtJd1ZWEe6vMj2SEhIUEAdz6fzTyQSUfPc4/HA7XbD5/NZxiP7iqmj0tLSLO3AeSUda4CqNZGpDnWQjK5mEoYqKyuDz+ezuN0Q6IlGowpKYdnp6enIz89X6f143xzzdNNhP/I1Jd15JJApU/dJINLu4YbsU45TCVAx7ZmEb/iBQTpOyWuzDenWQ4CKACkdejh+U1NTUVFRgdzcXLVWcq47nU7Vv4S55BrFcS7nsYQr+XfL7XarNUa2g0xp5nQ6LWteJBJRrkS8ZyMjIyMjIyMjIyOjP0/Z2dnVOkUce+yxmDNnDjZs2IBff/0VW7duVUHidu3a4fTTT8eXX36JuXPnWoLrAHD33XfjrrvuUu/j4uLw0ksvYdOmTfj666/rXD8dPjrvvPMwceLEGs9hOq3jjjsOv/zyS52vRX333Xc46qijbPdVVFRYUinYqXnz5ujevTteeeUV9RwN2JMWqHv37njjjTfwz3/+s8512rRpE9LS0vC3v/0NV199NW699VbL/tWrV+PSSy9FdnY2ACA3N7fOZRs1jOLj45GSkqJSS+2L6DCel5eHgoICyz66oYTDYfz6668x53bs2BHZ2dmWdHsHk6RLTo8ePdCjRw8AVc9wiouLsWHDBrRv3x5AFVR48803W87X3W9qUps2bSxzwel0olmzZli+fHnMsfwRa2ZmJqZMmYLly5fHOAM5nU41xwDUq43/97//Wd7bpcTKzs7Ghx9+qNphy5YtaNGiRcxxDocDGzdutGzjmKlOI0aMwODBg7Fz506sWLECiYmJuOSSS2KOmzlzJj7//HM8+eSTtd6TruOOOw4XX3xxtWnFdIXDYfzvf//D3LlzcfnllwNADLRp1jIjI6OGFJ9B/9W0bt064452kIhZNGoT4+dGh6YikQiKiorg9XrRuHFjNGrU6KCHlQ34o4luLwy0+nw+ZaObmJiIlJQUFaQuLy+H3++H3+9HIBBQgVKCLeFwWKX4KS0tVYFQCRjwWrqzCmCFGXTQg+cw0CzLko4xMjWUBCp4fCgUUg4qPJ6BXP7PIHVCQoL6tUkgEFD1IixApyOmLGIZdD4i9ABAufnwPF6D4FVFRYVKmRYOhy3wC1OFsT28Xq9KOSadYwgAhMNhS/ostg8hBNYHqPpFCOk9n8+nHtpI1wsJWnCbDNInJSVZUrnxurxHma5HQihyLLC9Odak05B0FWEb6M4l0n1FtgmBNEq629j9YkR3KpJwGMt1Op0xLkwSJpDOIJw3BFFkKjH2O8cc5xfPk+mEdGcVOb6ly4wEZ2S9uF2/b1lP2cbyD7juxqIDd4Q4CN+wvdhWhH7kmOOawdRyEm7hmiQdbwi9SECKwBznBtcZ3qcE/xyOKrcu/npJrksejwdJSUmWa8v7JZRD0I7zTq4H0j6ebaHDb9KJSq5nnFu8j4qKCsuDBEJSPp8PLpcLeXl5ChCLi4tTafy4tugAVHl5uQXm4/0DexzKZD9zbkrXIYI2XIucTqcFXmIqLa537MtgMKjO5fnyvnTAj+OP2yVMU1FRgfj4eIRCIXXf/DsFVJHITJXIeRuJROB2u1X95LrFvuX/cg0NBAKWY3UQSr4m4MN7J0jK8zmmDgUy2sjIyMjIyMjIyOivpEaNGtW4v3///irVlPyeAOz5rvD111/j3nvvxZNPPombbrpJ7V+6dCmaNWtmCZT/97//hc/nw4cfflin+t1www0x9RkyZEit8FBd0mNVp379+lW7b+nSpbWeP3fuXOTl5cW4azD91nfffYdBgwbVq0633347Nm/ejNzcXOUkJDV//nzk5uaaIPkBVOPGjZGcnIzt27fjtNNOw+rVq7F+/fq9KisxMRHPP/887r777pi0bc2bNwdQBUSsXr065twjjjgCqampmDBhQp0csv5MXX755bjqqqtQXFyMvLw8NGrUSMGECQkJyMjIiEn9tC9q3Lgx/vjjDzz11FO48cYb1TXs5Pf74Xa78fjjj2PcuHHYunVrTODO4XCgcePG6v3PP/+MvLw8DBs2rNa6tGrVyvK+OoDQ6XQiKysLWVlZ6N27d7Xl9e7dG0cffTTWr1+P22+/3QJa2ikhIQEtW7ZEy5Ytcdxxx1V73Lp16/YK+hk3bhyuuOIKtGzZss7nuN1udOjQAV988QUGDRqEdevWoVOnTpZjGgKkMzI6XNWtWzccd9xx2LJlS72g67+iWrVqhRYtWiAcDqOgoED9mP+voOLiYmzcuNGAPweJ9BSt1cnhcFhcBI0OHQ0bNgxjxoxBp06d4Ha70bJlSxxzzDF7/bn/z5LJtaGJgV4Gr+nCQSAmGo3C5XKpLyiNGjVCeno6MjMzkZqaqpwNduzYgZ07d6rAcnx8vAr4y6A3g+uUDjHIVD/SpYJAiXS0kEFzCflwHwOwLIvOI6yLdJQgFCQdJQDEpPQhWBQMBlWgWLrM8B4TEhIUFATscYDgwyTCOdJlIj4+Hl6vV90roR8GvpmKhuluCATw2OTkZEvwnfCPTD3DFGwSCKHTEduE7e10Oi1pyQhAsFwJuRAWkECM7t7B9pPQl97fdAHSHXW4XQInbFfZxjxWuglJ5xuZZkqXDrRIKIHtzTLksWwbu/RNrBdBCOnaQhcbtgGBGQk/cSzqIJBeTwm4yBRQcv7YuSVxu3SWkXNfQjBsVzn3eG/y/iU8wtRVEt6QZTJXJN2uZH0AKDgoISEBwWBQuWzxPM4hulvxHI43wkHRaBQFBQUWJ6BgMGgZU/wnU7rJtYZzk3OZ5RP0Y3uyjxyOKocwQkp8n5qaqu5Brl9sW16DfR8Oh1X7Eijhmq2DTrIvCZnJeSnHgBwvcgxJcc7zGhKirKiosIxfOo9x7eG6oq/jvLbdug5AucaFw2HVziwPgBoPvGYgEFAOaIRsUlNT4fV6FfTDa0kQEtgD73COud1uC8Qo/y5xvspxRrBHplvk/bEdCGXV55d7RkZGRkZGRkZGRkZ7r/vvvx///ve/a3WJkJLPqWbNmoVvv/1WvX/iiScwZswYPPzww2pb9+7d8dhjj1nKGDBgQK0BaindJSMpKQn/+te/6nz+3qhDhw7V7nvvvfdqPb9169bo2bNnzPZffvkFn3zySb2hn9LSUnz22WeYN28e+vTpExMYB+oGJBntXzVt2hRutxsjRozAHXfcYftMra46++yzsXDhwhjoBwDS09Pxyy+/4IMPPrA9t2XLlvB6vTjmmGNqBVKGDBnSIKnJ6qrevXujqKgIpaWl6NOnD+6880617+uvv8by5ctRVFQU89xlb9WlSxc0b95cuevExcXhueeew3nnnRdz7IYNGzBq1CiEw2EUFRUhKysLy5cvjwni/OMf/8Dtt98OAPjPf/5TpzVh+vTpeOWVVyzbduzYEeP8Xx+dccYZ+P777/HLL7/Ua02tSZWVlXuVhtDn8+GGG26oF/QjdeONN+Kzzz7DN998E1OGTCFpZGRUd917772YMmUKJkyYgLPOOutAV+eA67jjjsPTTz+NBQsW4Oabb/7LQD9AFUi6du3aA10No/+vP/74o07HOZ1OdO7ceT/Xxqih1adPH/zrX//CCSecoGDy5s2bo0OHDkhOTj7AtatZBvzRxIBmWVkZCgsLLSmcuJ1OCgkJCUhLS0P79u3RuHFjpKSkICUlBW632+I4wSAtnUBkoFW+BmCBPmTgmecRhpCpnFgvmapGwhy6cwX/MYAdjUYVNCBT4TBQXlhYqALOgBX+cblcysVFQlKEOnh9AgH8x/PofsGAOIPGPN7r9aqAOQGYsrIyC0QkXVok2MNzZGoj2W5Alc0ag9w6rCPhEPahbDtCKbKN2e4SJJKOLBxLfC0BGnkuYRPZjxIY4DGEM3Q4SDqr6PAP+5l1lDAHZfceQAwYIl2LJACjH0P3q1AopCAalse6sb/kuGEdysrKVNo3CdlI1x+K41E6+vB+dWci/i8BLPalnBOcU3Iu6fNTzj3pwCQhLr0MOV6Bql/AJCUlIRwOw+fz2cInABT8wnnIuUyYjS5eXB/cbrdyYolGoypVlA4dhUIhlVOeAJNMS0YHGMJYoVAIOTk5aqxz3fP5fBa4h+VIkI59KB8oyzkoYUmZ7ozX4rEy5SLrSZhPzhn2AdNvybmrO1vJuc/2oQORnKPsn/LycnWfvFf+z3FPJyK68nA+cj7z/iRYSdiNUKPsM7r2yPEj5xivxb9HdHKS41KOXcJ4TqdTrc8cJxLQs4PsOH74d0ims5OOTVzX6pID3sjIyMjIyMjIyMho33ThhRdi5MiRGDBgAF555ZWYVFp11aOPPhqz7dVXX435Mc2ECRPwzjvvWLZ17doVp59+ep2uI901qFNOOQVvvfWWSnlUkwYOHFin46Tmzp2LZcuWxWzftGkTnnvuuRrPrQm06NmzJ4YOHVqvugBVAQQ6H/fu3RutW7e27C8pKcFPP/1U73KNGlZNmzZV34V37dpVa0q46tSzZ0+MHj0ab7zxhu3+9PR0bNy4EWPHjrXdT0eovLw8/PzzzzVe6+GHH8aCBQvqBQDuq+666y784x//QF5eHv7+978DqHLDGjZsGLp164ZTTz0Vl156KR5++GF89tln+3St0aNH4/LLL0dhYSGAque4GRkZSElJsT2ez2hKSkqwaNEi9OvXD998843lmF69elnWJTs4S5f+vCMjIwNz5szB0KFD1Y+D90aZmZnVBg0jkQgmT56MWbNmYcuWLXUqLxQKqbaqj0pLSy3X+OSTT+pdhsfjQceOHWPAyxEjRlhc44yMjGrX5ZdfjrvuugvHHnssUlJSMHjw4Bqdvg4HNW/eHBkZGX/JNF95eXlYuXLlga6GkdCsWbNqPSYYDBqnzkNQBQUFMW6E6enp6Ny5M4499tgDVKu6yYA/NmLwVwZZpUNDUlIS3G43vF4vmjZtiqZNmyqwg44SEmiQII1dQFmHKHSIgf/zNYPPdo4NEv6xS1tEERJg8FymnGFQXe5nEJfBerfbDbfbbQEepKORPF8G06WjD8+TbSDbhhCI7pQBwAJyyMAz9xEQ0s/R+5n3y3JYpgyQyzRb8roSHND7UwcJAKhxIJ1I7GAhWZastwRY5DiRIJE8Tx87+hjUAR0djJH9YvdegmR6iiQdDNBBI9k+8kuxdHyR9yGvpW+3G9/yWParHcikz0E7yEYHm3Tp/amDLPxftjvHgrw3HTST9yCBQOlAJI+nu0soFILL5VLv6QbDctj+TI0lgY5wOIxAIKAADjk/CaEQegsGg6ioqLA4wuiOSrL/6cYl20aOR/mP8BcBI9m/XGPZbjLtmb4OyusTONHnmz7n5PXkuJAgpT5eJJikQ5eEjJhiTK4zso3sAD7eL4ExuRZIuE8HSHmshIskeFid5H1KSFTOX1l/WRf91xORSMQCpMnzme7PyMjIyMjIyMjIyGj/KTk5GaNHj95na3k7Z4evvvoKRUVFMdtfeukllJSUWLbVNQB19NFHx2xLT0/HKaecUid3iSuvvBKjR4+u07Wol19+Ga+++qplW2lpKX7//Xf1o5jq1KZNm3pdi27UNUkG1LOysmKcUktKSpSjidGBk5xTpaWle+3mkp6ebutIA1TBES6Xq0bYhOn7SktLazyuVatWaNu2Ldq1a7dX9dwb/fjjj1i8eDF++OEHZGRkoEePHgCABQsWwO/3o7KyEgsXLsRbb72FZ599Fu+///4+X9PhcMQAd9U5PfBZh5znLpdL/QiY8ng86nVta4J+PABcd9116Nq1K3Jycvabo80PP/yAF154AS+99BK++OKLOp3DH5nVVx07dlQwVW5uboy70b7qkksuadDyjIz+6tqxY4cF4mvatCm6d+9+4Cp0ECg1NXWvYfeDXZFIBKFQ6EBXw0jozTffRE5Oju0+v9+Pn3/+Ge+//75xajoExR/F68rOzo5J83ywab9G3hwOx0aHw7Hc4XAsdTgcv9js7+RwOH5yOBxhh8Nxi7YvzeFwfOBwOFY6HI4/HA7H8f9/+7v/v7yl/7/8pf9/exuHwxEU+17cmzrLYDcDtn6/H6WlpQpQYJ7g5s2bw+12W4K1dHfxeDzw+XzKOeH/11EFqaWjC//pKZPsQA4JB8i0VwzylpeXK4cH6ebCILl0iZH3KIPEdOWRKW/oJMHz3G43PB6PxWmI7jUSgGAdJaTCYyVgpYM/0inJ4/GoL050AaHjCZ1NZBsBe+xBZaCczhqsG11CuJ2B6bKyMrjdbst9EIqQ50swiuAF+zYQCKgANyEiiu0sg/cSPpKQlEyNQ2iAwfiEhASVvofHyDHB++Y/HpOUlKTqIMEdCRjYgTDsX7ar7Es5tnRxn/xHuIBfNAmhSIhIgmK8fx18YFsRNtHTNMk5o4MGMtWSHKPyvjkGObckgGYHHVU3Z/k/x0JaWpqyWJauSUzTpbcFYRemd3K5XGquAVBfujmePR6PBQYJBoNqfeF980Oi0+lUDjXl5eXIz89HTk5OTJomjnOuB0VFRSgrK0NSUhL8fj9CoZAF2OM447zWXZfi4+Ph8XhUii72n9PpVIBTcXGxAkfYJpz7Eqzje6ZCk6APHZuCwSCCwWDMPOY8kGNA1le6OPG9dPsCqj7E0dmG5fE8glI6bCnHBtcOXpPXCoVClnRwEoqT7crUWewfjj25LkjXHs4/CWixzQKBgCq7tLRUzfOysjLlVsTxyr8VLJ/jORqNqlRnHLucQyUlJZY0moebDsXPRUZGRkZGRkZG+0vms9H+VevWrdG7d2/bVFS1aerUqViyZAkAe2ebjz76CA899FDM9lmzZsW4/tDpoyadc8456Nq1q+2+rKws9O/fv9Yy2rZtW2PqLjv973//wzPPPGN5lnHvvffi6aefrvXcE044oV7XWrt2LcaMGYPPP//cdn9FRYUljddRRx0VAy1s3779sP0udTBJQja7du3aqzLef/99PPbYY5bnQ1IdOnRAamoqVqxYUW0ZkydPRjQaRUpKSo3j4uabb0ZqaioqKirw1Vdf4ZRTTtmrOk+bNq1ODl6DBw/Gm2++qeqen5+P5cuXAwB+/fXXmON37NiBqVOnVhs4q6vatWuHwYMHW7ZVl2Z869atqKiosMBQl19+OR566CGLM096erp6XVv9vv/+e0v7XHbZZRg3bhzef/99fP/997VCmG+99RZeeOEFzJ8/Hxs3bqwTLAhUgWiLFi3CO++8g02bNtUpKMxn6/VRv379MG3aNOVEdu6558a4JO2rfvkl5qPAYSXzuciovkpJSbGkKUxJScHFF1+MgQMHHrhKHWB5vd6/VHovis/tS0tLD3RVjIQmTZqEq666ynZfZWUl5syZgzvuuANvvfXWn1wzo31V3759ERcXF/OjljZt2qB9+/YHqFZ1U0Lth+yzTo5Go3nV7MsHcD2Ac2z2PQ3gq2g0OtzhcCQB8ABANBq9kAc4HI4JAORPjNZFo9Fu+1LZcDiMzMxMtYDKAK50nygqKkJCQgKSkpIQCARQUFCAkpISlWKFwVK6OPALmIQF5C8IAKjANs9jMFY6JchgPp1pdBcSOkvoTgvSnUeCLsFgEKmpqYhGo+qPotPpVO4fvBe/36+CzAxMM6DNoC7TwhBA0J1lZF3ZVuFwWAE9BFcISDDA7PP5UFpaagn2E6qSUAfbmnUiTJGUlGRJVyYhEBks5372IYP4PFe6IUn3IrY5x1AgEEBqaqqlTNn/bC++lnUiBMRzdecPh8MBj8ejoDMCAXZuIKwf20xPK8RxpKenknABtxF2sHORka95rl4ffSxwn9yuj2WOY84V3ivvh3WU7yW0wbR4bFfdUUvvF74G9rhi8VyOU96/nYuQXn8Jy3AcEnYhxEMoRaZD8/l86rq6OwqBE+mw43A4lOsYIQzeN8EeCSWxTUtKSpCdnY0tW7aodgwGg8jLy1O/qORc5FoWCoVQWlqK4uJieL1eNbdCoZBKZ8j5zP5l3Tg+6OLF9ubckuOLjmJJSUnqoUVcXJyCmng+U5TxWLY1+0+ukbx/tq2E+rheSYcbCWUBUCmsuI/tL4E7jgP+zSCwJeEljg3p5MM5xmNKSkosa5iEdOS44n7CkQSw5N8iWT+5TY5vtjvvkW3I/+U8kWCcHI8c04S5JLTF61RUVFiAosNUh9TnIiMjIyMjIyOj/Szz2agBdf3116NJkybYunUr2rdvb3F3rqvC4TCeeeYZrFixAjt37sRJJ51Ur/MnTpyIUaNGobKyEldddVWdfhGpBzFKSkrw0EMPoV27dhg1alTMD6rsdO+99+L9999Xz1vqoylTpuC0007D+PHj8cwzz9R6/vnnn49jjjmmzuV/9913+P333/Hcc8/B7XbjrLPOijlm586d+O2339R7pnECqr5nLl68GBs3bqzzNY32n7799ltcccUVaNWqFe699956n+/z+TBw4EBkZmZi48aNyMuzLoFvvvkmNmzYgFAoZIHB7Orx0ksv4aqrrsK0adNwwQUX2B537rnnYvbs2QgEAjjjjDP22gUsLi4OAwYMwKZNm/D777/bHvPll1/i2GOPRfPmzS3bn3/+eQwcOBBnn302br/9dttzJ06ciPvuu2+v6ladFi1aZLt98+bN2LJlC8aNG4fjjz9eAY7jx49Hr169cPLJJyMzM9MCDtUEeZWUlGDQoEGWbfwRXmJioi3cJbVly5b/x955h0lRpV38dM9M54nEIUgUQSQpKqIIinyisMiKOYGKGbOy6oqYF7OCgIIoRhAQcWUXVFRQUBCUoARBkkiGyZ1npr8/Zs/lrdvVMz0wCEid5+FhusKtWzd1d72/Pq/B7aZ27dq47rrrTFMt6pLp2/744w/k5+cjNze3yvOqq1WrVmHbtm0AKu73u+++q9Hyi4qKsGDBghot8wiV9bnIUlK64oorMGzYMLRu3dqwvUuXLrjkkkuSSkH0VxR/iPpXUzQaRVFRkanjpqVDq0RpL9PT07F792713mnpyNEzzzyDO+64w9Q9rF69emjUqNEhqFXy+jPAn4SKxWK7AOyy2Wx95HabzZYB4EwAg/53XARARDvGBuASAMZP1Qcov9+vfgXBoDWDswwI+/1+FBQUqEBqIBDA7t27VbBbpmyR0AIAg3OEdJ3QA6uAEfrQQQzul38zgCxdVQjzyOAx0/QwSMvzGRyW58pzgIo3zpycHBU8JuzCoLVMa0PQgQAA3UYIE0nHIwlLSQce6cDhcDiUgw7LpmQ7MticlpaGcDiMtLQ01dbSTQaosOWSIBOvz2MkXKADLARpdGcl/tqGx0sYhgF3AhayLrL99b6WbiEOhwMul8sAHLBObAvpQKX/I6wg700fp+wLCYvRVUW2L6+ng0Dy+rJtJPhQWlqqxgzF49mXhKP0sQBUwAV0uiEUJtNyyfP0FEty3Mj/5bzisRLyoeMM68E5oo9Xve8I39AxhU5i3C/dorKzs5GXl4fU1FQFjBQUFCjXI4fDgZKSEqSlpSEQCKC8vBzZ2dkK9iH8wv6ULmNsU4/Hg6ysLAXwyHWH9SVA4nQ64XQ6UVxcrGyQZb9IGIXrDNuCcA3HSHFxsWGuyV9RSViSawLLlfOM81lvazk+nE6nAqZkuioCMxw/Ho8HKSkp6mEMISEJosm5J9cItiXngnTmcTgcqm3k3NaBNdaX5TudTkO6MLaDhD1ZT16DfWG321WKt2g0qhyh5Dot1zFZH8Ja7Ct9bZUAknw/JJxFYEm6B7EOBPA4Pjk2/4pfwGpCh+PnIkuWLFmyZMmSpUOlv+pno9TUVFxzzTXYsGHDfgdl2rVrpxw0gIpUPjfccAO8Xi/uvvtuAPEwDVDhOkP36kRauXKlcl148sknccEFF+Css87C119/bTjuoosuwrZt2+KCv06nE0OGDMExxxyTdBqYOXPmYO7cueoX6vPmzcOIESMwcOBAtGvXztRdSNesWbPw/PPPo7S01LS+lemee+5BcXExXnzxxSqPbd68OSZNmlQtt4xvv/0Wjz32GOrVq2cK/QAVAXu6LOXm5hqAqT179qBz585xMIWlQ6e33nprv89t1KiR+sGXhH6effZZlJSU4Oyzz0b9+vWxZs2ahOAKAOTk5CiHGZZnptzcXEybNg0LFizAeeedh5ycnP2q9913342dO3fGbbfb7WjYsCG2bNmCYcOGoWXLlnHHTJs2DQBw/PHHo0GDBqZBsFGjRmHPnj1o164dmjVrhlatWqF27drq2V919dRTTyUESfx+P0466ST8/vvvaNq0Kd566y1s2LABTzzxBC655BIUFRUBgOFHS4nAKsAcMAqHw+o5VlXSUw7u2bMnaXDT7/cjOzsb+fn5WLZsGbZt21Yl+CN/WJys8vPzVTrC33//vVrnJiOfzxf3g0pLRv1VPxdZ2j998MEH6NKlSxz443K50Lt3b7Rp0warV68+RLU7NOrQoQNOOOEEQ9zzr6KCggKsWbPmUFfDkqbU1NQ4x59YLAa/3w+fz1fpdy5Lh6/KysoSpgzMzMxE06ZN4Xa79zvd78HWQU31BSAG4HObzfajzWarTqLt5gB2A3jLZrMttdlsb9hsNv3nCN0A7IzFYuvEtmb/O36ezWYz9QG22Ww32my2JTabbYkZqZ+dnR1nj0oARIINfr8fe/bswc6dO1FSUoJoNKpy/dJxRgZwZbCX6V8YUJYBVx7DwKp0U9DhBAk58H/5pYCuD9KJhLCN7sZAhwd5DRl8B/YBBHT2ASpyFzudThXkB2Bwi5COEGwLCZ0wHQwD0rItJMgiXSvoFkK3Igl0cHtpaanBnYOAgQy8O51Og2uO3EfpwAn7gUFsbuM98P4IJ/A8CekQjGIKIB3Ykf0nYSOOFfmFUaYN4zGynrwHee+yXmxXtrtMT0S3Erary+VS/c19sj50OmJqvFAopMafHMNlZWWqb9gWhAmkcxHHqJ4Cj6ADAZOMjAz4fD5D6jHZnrJN5dyScI4OBPFv+YWTc4v3IMEOOcfkMXKfhMDkeOHYdzqdyM7ORkZGhnITk+m5QqGQAUorKSlRDltOp1MBPx6PJw54oTNZNBqFz+eD3W5X19m1a5eakzxfuu0whVQoFEJeXh727NmDSCQCt9utwC2OI7nm6an+CJAA+9YhOS+Z4ozHs4/C4bBKtyZTEHINkvMK2PdQhuODbS7TXcmymPpLjkMJXwIVX5jcbje8Xi+8Xq9qG31sUyyH7cc2pCSsI2FAtoV0m+J2WXeCbLwmr8/9EkqT67qEq3TAjePW4/EoWJT9JWFSeZ8sX6YPJJzGVGCyP3k8yzxKddh9LgKMn42qUSdLlixZsmTJkqUD1WH32ejP+FzUsmVLvP7665g5cyZuv/32ap177LHHIi0tDYMHD8aSJUvw8ccf45FHHsHZZ5+Nhx9+WEE/gDkIMHv2bPTr1w+DBw9OeA2ZWujDDz/EE088YSiXCoVCeOGFF1C3bl3D9kWLFmH06NH4xz/+kfR9NWjQwJByZ8aMGQCASy+9FF26dMH111+fVDmPPvooUlJS8OmnnyZ9baAioH3HHXckdez//d//GVxXkxGBnrlz56JVq1amx2zevBkbN24EAJxwwgnKRRoAtm7davjf0pGtpk2bqmBC586dcemlFYYcGzZswCOPPIJatWoBADZu3KieNZvp3HPPVWmXMjIyDGmpqE6dOqGoqAgbN25E48aNEQgEMGTIEJx77rnVrvfTTz+NQYMGxW3Pzs7G8OHDsXTpUixZsiQu3R/FNF833HCD6f6CggKMHTsWt956K8477zyccsop6NWrF2666SY89dRT1a5vVcG2QCCA999/H1u3bsWgQYPw+OOP48ILLwQAXH/99Rg/fjwCgQCuv/569OnTB6NHj1bnFhYWYvLkyfj++++xfft2g+sOdd111yUMGuk68cQT47YlA8GMGzcOvXv3Rn5+PoCK9XvdunVVnLXPfbu6ysvLw5w5cxKmZjwQ2e32pNIs/sV1VH4usrT/eu+990yBzGbNmqFPnz4mZ/x1NWjQILz99tvo37//fgOuh7M2bNhw1IFcR4JKS0vx66+/4ptvvsHatWsRCAQQDofxxhtv4JprrqnUudHS4SudE5ByOByoVasW6tSp8yfWqHo62ODP6bFY7EQA5wG4zWaznZnkeakATgQwNhaLdQLgB6D7gF4OYJJ4vR3AMf87/h4AH/yPdjYoFouNi8VinWOxWGezjnnrrbcMsAKwL6gqYQECK3TPkUF97pfOObrDgoQyJOChwxp6ii8JbphBDBLAkeWwfnpaJh7jdDrjBrN0HAL2OfHQwYd1I2TAesrtPI/XYfCXku4RhDnM6i7rQWhK9pN+bxLUopsHAQqZzkdCP2xfvR1k28sxweC6BLRYDu9ZfomS44iOSLqjUyKAR44B3eFE1ln/W29LXbJs2Zb6NaTrDR1OpMsU21EvR7qnyH/l5eUKGiHoIN1H9HvT74n10NMn6fNK3oMZ3KO3i4Sj5HyT4JV0gqLMXIZkeayfdG2SQAQA1YYSgGEb8njZrjLNnhxLEqxwu91xTkUcm4RHwuGwcpFh3SQcw+MJ0hQXFyv3IjmnZX3lWJBtoYNt0u1Muk7JcS8BFgnImPW3BHFYJoEYwkMSDCLsw3bRx6/sF26TgI0c43K8mq39cqzokvektyHPlZCoHB+ybLl+6PNRjmHZbvKfTE8m0/8RUOTaJscz6yTLlWujBBNlnyWTc/4vqsPucxFg/GxUjXuxZMmSJUuWLFk6UB12n43+jM9FdevWRWpqKrxer2mwtzJlZ2fjjjvuQFZWFk466ST07t0bAwcOTBhIlyopKcHPP/+MOXPmYMqUKQmDvgUFBQCA8847D4MGDcKcOXOwcOHCuOO+/vprtGvXDrfddlulThjJqH379ioVTlFRkXIz+vXXXwEAp5xyStJllZWVYf78+QdUn8q0efPmuB8MVqXTTz8d/fv3R61atRKm/GHgHkDcMXKfpSNfEuoCKlLHAcDEiROxZMkS9cOhXbt2VVpOgwYN1N+nnHIKTj31VNNj5A/7uO7sb7qvs846K27b3r17UVRUhPr161ea0urLL78EUAG5JAPE7N27Fz/88APGjRuHUaNGKRceqcrgFTPHnFNPPdUAL7366qsKzJOaOnUqHA4HTjzxRAwePDguXfnHH3+MsWPHYvz48VixYkUcCOjxePB///d/Vd2iklm7JfODqXA4HJcqrrKUZJTZM9FkVFxcnHTd9kf16tVLGpb6i+qo/Fxkaf+1YsWKhGmEGjRocFS5jUQikb/08+bt27cr1zVLh5fmz5+P8ePH46233sKCBQuwbt06bNy4EfPnz6/UudHS4SvGqxPJ6XRW60cgf7YOKvgTi8W2/e//XQA+BpDsN/U/APwRi8UW/e/1NFR8eAEA2Gy2VAAXAvhQXCsci8X2/u/vHwGsB2D+M5oqJIES/s9gqwRL6PQTiUQUiMBAbyAQMDie6A49DKBGIhFEIhFDUJQBXQZVZRowXoMOFUylIiGCrKwspKSkIBQKKSjJbrcjEokYHDokdCDBHx5PV5uUlBTDhwRCB0wlI8EABoUZeA6Hw8rtgs5C/Fvep3T+kIFvCVsw2K6DIiUlJapdCFyJcWFwsWHqJOlCJO+brho8l/WUYAPbQNZXOsDQkUOOHx0m0oEZHSziNQkq6ft5jN5WMrgvJVP8sP50OpFBepbJ/iUUIv9JJxvpXMI0VhI84fgmOECwoLS0VB3PYyWsJV1/JCySkpKigBMJEPEhhn6P0gVIjkt9rJmBRgTFeKyE6uRDE9bLDL7S99ERxefzwePxxPVpKBRCOByGy+VCJBJRD1yZbotQSSgUQn5+vkr/JesrAReZDqq0tBSpqakqLVN6errqk2g0ilq1asHhcCA9PV05DzmdTgQCAQSDQZSXl2P37t3YsWOHum+6b8k0dUxdJl2HOEZ0Nxo6w/AaHJPSTYvpr1JTU+F2u5W7EVDxgCMcDqu+5r2xj+hQxrZxOByG1Hact9I5C6iwSQ6HwyqllnR94xjU3dXk3NFdpyRcw3Gtr1PyPYcuTnQOk3V1OBwqtZieglA6ktlsNoRCITU/eJ9ma4lcx+jIFQqFVP9QhMM4l3TnKzm3XC6XAse4Xwf0LrroIhxtOlI/F1myZMmSJUuWDr70z2hHg47Gz0Z33nmnwfFl1apV1Tq/ZcuWuOeeexQoEA6H8dVXX6Fr165Vnjtr1iyMGzcOAHDXXXcZvmtLXXXVVZg/fz6GDh2KM888E2VlZRg7dmzccX6/Hxs3bsQjjzwSF3iurs4991zk5eXh5Zdfxty5c/HDDz8AAB588EFVp+ro2muvPaD6VKZZs2bhkksuSdohCACGDh2KKVOmoE6dOgnT96xfv1793axZM8M+mdbN0sFV//79MXz48IN6DZnGDaiARGrVqoVQKITnn39ebacDVCIVFxcrF520tDRT14eGDRtixowZWLBggSGAkUz6KV0jRozAhRdeiDvvvDNu3/z585Gfn19peqqhQ4ciGAzC4/GgTZs21br2zp07UVhYGLf9119/xZ133onx48fjq6++wubNm1WaQ/3ZKACMHDkSAwcOVK/XrVuHrVu34scff0QsFlMprOrVq4d69eqhWbNmaNCgAbp3725IHVNWVoZvvvkGb731Fvbs2ROXjqxp06bVSjVjlsYvGcDwmGOOiXO2WLt2LQAY3KLefvttjBkzRr0uLy/frwB5UVGRelYJVO6qdMstt2D48OGYPn06Fi9erBzEzbRjxw6EQiEsWbJE1cv6XHR0fC6ydGAKhUL4+uuv4ff74/b16NHjqHH9OeaYY9ChQ4f9em87nMV1PBwOY9GiRXHpdS0dHgoGg3jvvfcwYsQIzJw5E08++SRGjhyJjRs3WrDWESrGJBPJ5/Mph84/S9X5XHTQwB+bzea12Wzp/BvA/wH4pfKzKhSLxXYA2GKz2Y7736aeAOTTkHMArInFYn+I69Wx2Wwp//u7OYBjAWzYn7pLKEWmoJIOIDrwIQGS/90DioqKDECLhBtYBoOnPFdPecRrAzBARgzqSoCE6WcIA9E5RAdI5MMdghkEhGSAnpAMYQ7uj0ajKCgoUE46DM7LVFHcXlxcbAisEybhPTGATfckgjuyrXgPDodDARBMISbBApaTkpKiUj/J/qPNGuELAhGJ2luf2HTqkYAM/2baIB3kYb8SNKLYPjKQL1NuEeiQ7S77Tnd/ke4kMrBfWlqqxgnHIdPShUIhNYZ1KC0ajSIUCikQhf/4BU0CMqyb7B8JS8h0dbL+0rWH9yzTMfFe5D2xfwiuBYNB5OXlxaWUkhCOhMUILklowsyVSJ+zhFnkh0fm6uY58voOhwMul0uNSwmvsZ0IpRB2IbjGMUmHHkIvnANcU8rLyxU0KPtCgjY2m03NqUAgoO5Nh4W4PmRmZiIzMxPp6elwOp1qHLCOJSUlCAaDaswwlZVMW8a1kfNPQlXSHYbjnyAexybXL7YX5yPXooyMDANgxj5m+dFo1JDmj/3Htpept2T5uosYoRWuUWxr9jH3Ea7iOGIZ+jiU40S/ljyW5brdbng8Hni9Xvh8PjVmXC6XSs0m749KS0uDy+VCVlaWGmMypV+ih/uyntFoFKmpqYb3EAm+ejweg/OPDjjq70spKSlq/ZTw4NH2K64j+XORJUuWLFmyZMlSTeto/GxUu3ZtPPnkk4Z0McmCPz6fDzk5OWjRogXq1aungq1paWmYMWNGwl97U+FwGPfeey+ACsimstRZOTk5OP3009GjRw+VUmDSpEmmx7733nsoLy/Ha6+9ho4dOyZ1L2aaPXs2vv32W9x999244IIL1PbLL78cQMV3kOqklnnkkUcq/e5TXellffLJJxg1alRSzhpUVcEoGRiQ4A+dmiwdfJ1++ukYPnw4Nm/efFCvo4M/bdq0QYsWLWC323HMMceo7QMHDqwUMBs3bhzeeOMN9Xrv3r2mx9x0001YtmyZ4Tmnx+NBjx49qlXvdevW4bfffsONN8ZnIPrll1/w3XffYceOHQnPLy0txYoVK/D555+buuxUpjZt2sSlFeT2KVOm4MYbb0TPnj3x6KOPxv1oU2rPnj1x7jqyHd999118/fXXqFu3rkrfdcwxx+Cee+4xnCPnc35+flxqRcJHZorFYti4caN6Xrpt2zbcdNNNcceZBfKlZs+ejf79+yMvL8+wfc2aNQiFQrj33nsRCoWwfft2DBo0CHv37lVB5P2FaqZPn67AUyDevYp66aWX8Nprr+Hxxx/HgAEDcMopp6B79+4YMmSI6fHz5s3DxRdfXCXs9lfW0fi5yFLNaOnSpdiwIb7r2rVrd1T86PKee+7B9OnTMXTo0Go7aR7umjdvHu655x6MHTsWc+bMwemnn47c3NxDXS1LlWjChAmYMmXKoa6GpRqQBOZ1uVwu07Tah4sOpuNPPQDzbTbbcgA/APhPLBabbbPZbrbZbDcDgM1mq2+z2f5AhZ3gwzab7Q/bPkvB2wG8b7PZVgDoCOBpUfZlMFoTAsCZAFb873rTANwci8XysB+SgWb55Z4ggwxuM4jJgPv/7gtlZWXw+/0quCvdGOQXDwarGcAFEBc09nq98Hg8BkhFOt7w1xQMJgcCAQULED6gC4TuYGK32xEOh1Uwn/dPGEAPrksogo5DZWVlClSRaWJ4rHSWISxBOAKoAJoYrAf2pUmToAfBBD2FFoET2ZbyFxXSfUM61EjwQfabDFzLFF4sWzrHcD/vxcxNRo4hWQ95jzKNkYQwpHsQAQO6aLA8CT/pbiJsU/lrinA4DL/fj2AwiEAgoCwQ2U4SBGI9S0pK4Pf7FUAk24DQA0ETOcYkPCTTOfGeJZTEvpBgiISfdLhFii4rEq6Q7lMU6043Fel0JGE+maJJzlPWgfOK7a+fy7aXTigEYfx+P/bs2aPmmwR3JAxj5kTkdDoVOEeAhY5MbFc+eNBTs0lAh/2UlpaG+vXrqzEEVDz4SU9Ph9frhd/vRyAQULAG+4z3y3tMS0tT40cCe/JY2Y6E5PSxL1OP6fOD65K8HwJzHCtcRwjVsQ4EiwgUcY5xrErAk3XmPtl/cl5IiEuHewh6SXcduSbK8S7XBAnRyfHv8Xjg8XjgcrngdrtV2Xx/4bzSgVSOJ4JC0nmI9ZVzSM4Rea/S5YrjkHXU3wdlGSyX74fsm4NlB32E6Ij9XGTJkiVLlixZsnQQdNR9Nmrfvj18Pp/6Tg8AZinozdSpUyfMnz8f3bt3N7hYeDweTJo0yZDyR1d5eTlefvllBZY8/PDDaNKkSaXXCwaDAIAhQ4bgm2++QdeuXQ1ADjVt2jQ888wzcLlcaNu2bVL3YqbPPvsMV1xxRdz24uJiFcy67LLLki5v+fLlBueUA9W//vWvOBcYn8+XdP8lo3Xr1gGogEJat26ttv/+++9YvHhxjV3HUmItWLAAnTp1wsSJEw/qdfSgXdOmTRUM06tXL7W9WbNmaNGiRaVlZWdnq7/NwB8p+YzszTffTCpFoK5XXnkFTZs2xXXXXWfY3qVLF7z11luVntuiRQu0b98en3zyiWF7ZmYmjj322ErP7dSpU0IHnccee0z9PXHiRDzzzDPIy8tDaWlpHGS1YcMG1KtXD5dddpkBslq7di3C4TBat26NUCiE888/H40aNUpYHwn+FBcXx/2wqbL0fMOGDUPPnj2Vg9GWLVswe/bsuOMqc/x56qmn0LdvX9N9v/zyC1JTU7Fr1y78+OOPuPnmmwFUODa98MILAGB4hl0d6e5uhKOkfvjhhzhQCqgAXRs2bGgKfa1duxYzZ87ERx99VO06/YV01H0uslQz+vLLL03nVWpqKs466yzTFI1/JTVs2NAUDD1SFQgEsHDhQjzwwAOYP38+vvjiC3z++efYsWMHzjnnHHz//ff45z//eairaSmBqoJ2LR0ZkmYbZmKs7nBVYv/NA1QsFtsAIO7nOLFY7DXx9w4App+iY7HYMgCmuUNjsdggk20fAaiRT4cSLpBuBQxiSmcQ3b2HgVvpGsHgMAP9MvAqg9PczsAo4Q8CHNKZhmAPA/n/awNEIhEFoTA1EFPe0HGHAfP8/HwVkC0oKFA56XjPuosRJaEbfglhmW632xC0Z2qhSCSiAu68X9aH5+upnujiIeEpHQJiujR5TjQaNTxIk+4j7D/2FdsqGo0anD/YPzKQD0CBAjokpKfQoSuLHEd6kL+srMyQOofHUQRopBOQdHGREBbHHetGQIT/05osGAyipKREtSl/6cHzWJ4ET+Q9y/6X8AbvVbfRZTvRnYawiLxfCSPIsc9zCZXIsuS9sy4SiguHwwqE0Rdojj+Wxf0ET3iP0t1F9pF0MeF2tj8BJKZIkuOE41vmm+UHAYIohN8kUMH+czqdyM/Ph8vlUinzZF0JYcm+4LXpgiXvS6aAYuo1m82GjIwMlJeXq7HBuc3xVadOHTXG5HbWn+WxPSXUJddK1pNzVo4dOs5I1x+ZVoqgD+cm+4broVwbzGA8OS/ZHhKi47og1x2uo7L+XLckKEY3HI4JjjkJ6bCeOhgl54yE4OT7D/+XTj5y3eAcomsb1xpuk3ARx48cpxJ4ZH3o/sT+lnNJgkwSgpJAmRz/HE9mc/OvriP5c5ElS5YsWbJkyVJN62j8bHT88cfHbXvppZewZ88ezJw5M24fv/cAwLfffouvv/4at956a9xx6enplV7XbrfD7/fjwgsvRO3atXHGGWdUevyECRMwefJkdOjQAVdeeSW6deuGDz74AI8//nhcwH79+vV46KGHkJKSgqeffhrvv/9+pWVXV9OmTUN5eTk++ugj9O/fH08//TSKi4urPO+1115DKBQyDT7vj3r27Ildu3YZtv3tb3+rkbIpphg66aST0Lx5c7V9xYoVWL58eY1ey9LB1SWXXIIff/zRkL6NqlevnilQkpmZifbt26N9+/aG7bqbS2VKFGx6/PHH8ccff+Ckk04ybN+fVE8TJ07EgAED8Pe//x1ffvklNm/ejNzcXEycONEU3pPq168fwuGwSkVFtW3bFqNGjcLVV1+d0AXtgw8+wL333mvq5jBjxgzD6+effx4PPvgghgwZgh07duDZZ59V+7Zs2YI6dergvffew6WXXqrm3dKlS7F8+XI0bdoUvXv3Ru/evSu9F/k8o6ioKG5/ZetUw4YN8fvvv2PXrl2oVauWeh6vq7L++f777xP+sGrHjh2IRCJo1qyZYb0PBAIoLCzEf//73yrfB5KVmZOZ/pxL6tFHH8Wjjz6KPXv2oFatWli5ciWWLFlicMI7WnU0fi6yVDPavn07fvrpJ1xzzTVx++rWrYu///3vWLBgQVLpA480NWjQAG3atKlREPtQa82aNXj99dcVhPzcc89h8ODByMvLw65du9CkSRMMHz4cv/32G+bOnWua5tOSJUsHJhlbNZPb7YbX6/0Ta1Q9HUzHnyNaslPN3D8oOQCk4wrdILhNOkyYpWySAA8D8twfDAYNab2k44qsnwxis24sW/6T7g90dwgGg3FuELJM3pcM7DLYy/REMiURj5HOOhIi0cEN6YqhX5uv+S81NRVer1ddRwbnCUUQTJDtK9tbQhzSGUaCXKy3rCevpbeTvAe2q7xvCebIcSX/NyuPfSWdUeT5sr6y/wgsSccjuu8QmACgUqzpcIoEYmR/yNf6nOA19XthWrVwOIxQKGQoi8dJaE72uUxjxPt3uVyGPpD7ZNvo6dV0uEHOI7MUbbJ8vX3ZN9wn20TCDbJtZN9ItyZCHoRjWEeCfdwvAQ/+LQEqgoWy3+R4k6no2AYEpHg801vRmUV3NCP8wuuxzyUgyW06ZGT2QEKOcQnjEK7i2iLrLP+Way7FftHTUwH7nHHkeNDXb5YnARner96XctxI4Ehfs6RLnCxfh2XM/umSMKK+rkggVY4Ds4cuuquUXgcJSnFd5/XM3hPltfXUb2wr6eAl+8ySJUuWLFmyZMmSpaNBtWrVittWWlqa0LJfT/Hzww8/7Pe1Bw0ahBtvvBHXXnttlcf6/X7MmTMHo0aNwq+//goAaNSoEVq2bIlLL700Dh4AKoLmDRs2xOmnn77fdUyk2bNnIxgMomHDhqbXTqRoNGoKW+2PcnNzlSMPVZVrUnU1cOBAXHTRRahTp44B5tq6dWuNXsfSwZfX68WgQYNM92VmZiYEPczEH4glknTpNVPz5s1xzTXXYPDgwRgwYIBh3/5+L1++fDmaNWum0vtt374d33zzDT788MNKz2vevLkpyNSsWTPUqlWrStjmP//5j+l2/UdFTZs2hcvlUvCkTMXAFFwpKSmGeZafn4/x48cnnb5PXjPRM69EKikpQe3atRWolSgNYGVB+oyMjIT7eK5ZGq7//ve/GDduHGw2G0477TRcf/31hh/P1oTk89lEojtVYWEhxo4dawWuLVk6QK1fvz4hLNiuXTu0bNnyT67Rn6MdO3YgMzMzznXtSFYkEjG4xtWrVw8+nw/NmzdHly5dAFS8b1x11VW47bbbcMMNN6Bjx45V/hDAkiVLyasqZ0TGug5XWZG3SqRDIrpTAl8z0Fu7dm3lCGGz2eDxeBS4IAPoEthhAJRBWKZHkjAGU9josAadL3g96UACGN0sCAUQQHI6nUhJSVHllpaWori4WIEiTJklnS8YBJZwRjgcVkBHMBhU901owe12q/bhL9WcTqf6ciOBHEIHetCd+xkwppMFoSLZHgzQy7RodL1wOp3KXUSmI2K76E43ElqQwX55/xIUkamaJCSjO8TIe5aLhw6zyPFC4IVlyMC8/BeLxVRaLjotxWIxBINBFBQUwO/3G5ycJFTB8mTKJO6T4Il0H5IAg0zzREAjNTUVoVAIkUgEgUBAlZ0IcpDlS7CLIF1ubi7q1asHt9utUkYx9ZBsTwmw6C44cv4B+xyVpAMLy5PwiO74Q4iBZbEe0WgUgUBAud9IZx6uA+ybrKwsdR+sI51c0tLS4HK54HQ61YMevW3oskXohsfRmUUCFnQNoiPSrl27DL9KstvtyM7ONkAqXq9X9Wk0GoXX6zUAOXQk4j1KaFG2MVMflpSUKFBIAoX8cC7hGJ7HLy3sJ/YF5z3HHV1k2I9c5wKBAEpKSgxly77lfUhQUK57OkQmwRY5j7kucd0kUMg5pwOBOnSkzwP9AZwO9UkHNY5z+b7BMiVkpoM5OvgjIUU93ZdsfwlEyfaR1+JYYNtKMJT/Etl0W7JkyZIlS5YsWbL0V5Pdbsdxxx0Xt3337t1YsmSJYdtdd92Fs88+G7fccguuuuoqtZ0P+/dHzZs3x7nnnptUGQRaIpEIFi1ahE8++QRnnnkmPB4PPvjgA9NflE+ePBkrVqzA7Nmz0adPn/2up5kCgQDGjh2L9PR03HnnnUmft3Hjxv1KZWSmnJwcfP7554Zt5513Xo2UTT377LOYNGkSHn/8cUPwZsWKFTV6HUsHX7m5uahVq5YpeNGkSRPUrl076bKqAiL4vToQCOC6664zpP4CKpykcnNzcfLJJ8fVp23btnj88ceTrgs1a9YstG3bFvfddx9uvfVW9O3bFz169KjU6QUAunfvHgfQARVpEI855hhMnTq10vNlai6pf/zjH4bX27ZtQ3FxMUpLSzFs2DD8+OOPmDZtGgAYUqnpzksTJkzApk2bVAquyiSBLD6D1XX//ffjiy++MBz38MMPY+jQodi5c6eCkMzSfAH7ICUzMYVZmzZt0LBhQ7W9du3aGDZsGLKyskzba/Xq1ZgzZw7S09MxZMgQdOjQ4YBcQMxcpnQ3eDNxnBYVFWHz5s37fX1LlixVqE6dOigoKDDd16VLF1xyySV/boX+JJWXl+P6669PuI4eierSpQsuvfRS3Hfffbj00kvRoUMHU6Cyb9++GDZsGF5//XVccskl1YKKLVmyVLlkzNZMjHUdrrLAHxNV5vQh3Qt06isUCsHn8xkgHwDqtXRokYANAJUGiQCLdL+QaZIIjch6mAVvZQCXgWIGp2lBJSGkUCiEwsJCFZh1OBwKmJEAEoP2MiDMoHMoFFIOIi6XywCNML0RUwLJ+vI6hG9Yd5kSR3dUIdAi05gxWO1yuVSaK34Bttvt8Hg8Kk+9BDlYJ7adHjgH9jmF6ACUdGfR3Y4IF0hJ5xAJ8MiypCMMx4McO/La0lkjGo2isLAQeXl5yM/PR1FRkQq8E+qSsJIOy3CcyPFr5sLCfwzeS+hHwgISdOHYkGNUOolwHPKew+GwASLhPebl5WHbtm1xqdkI0rAvODZ5HsehPj847uR40/ue53LsJXJIoZjyKhgMKkBGuuNIEMnpdMLj8SjgB6j4ZRhTs6WlpSnwRYImsp9YV90dR0I3QIX9uwRswuGwGlspKSkKporFYti6dStKS0vh9XrVvNm5c6cqn+n8CHSxzTn/5dgmFMgxEIlEDONI7w/Oa7YfAT9CixwfNptNuahxHHEeE7oiDMX1QsI0+trCesj0XRwb7FeOb/0c9q3+K69gMKjqQihMOjtJ0EeuEbJd5FiTc4G/yCI0KdNu6QCRXF/MgDt9O9d1uQYx3RrrqDv6EEKS+/leJ9/XdOcfS5YsWbJkyZIlS5aOBpWXl+Pjjz+O296mTRv861//wr/+9S/cdttt6NGjB2644QZ8+umnaNGiBf7+978DqPiMfcIJJxz0ekYiEfTv31+9fvnllw3pxex2O/r27Wt67ooVK+Dz+XDbbbcZgKWa0KmnngqgIlVQMrrooovQvHlzPPPMMzVy/eXLl8e5jXz0Uc1nSElNTUXjxo0N277//vsav46lg6unn34at956qylA0rBhQ1MgaO7cudi8ebMB9ggEAti2bVul13I6nfjxxx+RkZGBJ554Ii7tVGlpacJg8Mknn4zy8nK0aNEiibvap0WLFmHRokU47bTTMHr0aLzzzjtJnXfsscfip59+itv+t7/9DcuWLcOWLVsqPZ9roA7EzJ8/3/C6pKQEPXv2xFVXXYXs7Gy0atUKXq8XV155pWF969WrF4YPH24495prrkHPnj1x4403VpqeXP/RGp+hST3//PPYsWOHeu10OtUxffr0QceOHfHbb7/h7rvvNr2GWQoxbt+yZQs6duyIzz77DK+++qrad9lllyngUQJBUoFAQP3NZ2r7q+3bt+PTTz81pGhLxv2Iz+vlDyYtWbK0/+rfv78CAnW5XC5ceOGFf3KN/jytWbMGQ4YMMbjkHOm6/PLL8dxzz+GDDz5ICAvzmXwgEEAgEEiY7tOSJUs1L8Z2D1dZUbcEYlAXMKYFYjBZprvh9lAopHL4MshJ1weZkkl3RZGuNXStkGmAuE86x8j0LnTwKC0tNbgAyfvg9SVoIh0qXC6XCvSyTgQp6GAh0ylJOEd33ZH1ZoBed0my2+0IBoMqsM72ItTj9/sNrjkyQC/bCKj48ML+YECZDjEEBQgPsG8kwEF4Rfa37nQjA/7SuUPemzyGkuVK9xlCBRJG0OEtumZIxyY5LngeXZcImxQXF6O4uFiBNjo0pae5kvckv7jKNtfBH+naIftOuhnpABBBFTq8cFxxDNKRiW2pQzOxWAyBQEClvYtEIgiHwwpmo4sVx5QEZXRJ1yHZprId6FKiQ0DSgUuHVQCo+mRnZyuAR443lpeamgqfz4esrCwEAgHlEsP7Zwo+AlDSzaW8vFzBdYRqeH25JhGI4prFutpsNvh8PrjdbjVn+HdBQQHWr1+PrKwsRKNRg1MUnXU4fjlO5LohHYmks5ace6wP700CTAQFA4GAAebh3JfzRzqEcc7TwYvzQwIrrAtBGbZDenq6ckPiekD3IN31Rgc+CR2Zrblsg4KCgrgHHyxTwmbyfUIHyijOR+lwJCEcWYZc46Rrjw5TJnIxI8zE+2AfSrCK5etuZ/p19WtwvyVLlixZsmTJkiVLR4umTp1qmq7r3HPPxQMPPIBXX30Vn3zyCY4//nj1q91du3YBqPguM3ny5INex7y8PNxzzz2GAPa2bdswePBgzJw5E3PmzEHdunXhdrvjzl2zZg2ACiece++9t8bq1KVLF3Tu3BlAxfeRYcOGVXnOtGnT4HQ6q3QgSVZmwNHIkSNrpOyqNHHiRCsw/heQw+HARx99hFGjRilHp2XLlql5s3btWnTp0gV9+vTB3LlzAVT8mKiqVG933323Sp8lgQ6pyhxsHnjgAcyfP79aIFtpaSn69eun3Ih08CaRIpEIfv75Z/X6448/xowZM9CmTRv897//rfL8Zs2aAQA+//xzXHfddco9iOuk1NKlS/Hhhx/iyy+/BAD07t0bb731luGYM888E23btjUEy/Pz8/Hjjz+iuLi40h8ryR838rmMWfqt7du3q79tNhvq1KmD8ePHY/LkycjKykqYog2AijFI/fDDDxg8eDAaN26MGTNmoHHjxob1Wj4PTpT6huviTz/9hIcffrjSOlSlYDCIAQMGGNojGfCHzxT9fr8pCGfJkqXq6ZVXXkm4b8mSJXHr319N69evx7hx4w51NWpcdrsdDRo0MGybO3cu/vnPf+KKK67AU089heeffx5jxoyp1CXOkiVL1RNjqolkt9trPFVqTcoCf0xkFjiV6aP4WjqKSGBAd2lgcFeCHLqrDHMPMzgqIQx+GCagwNcEI3ht1kkGgiXoI51/7HY7vF4v0tPT4fV64Xa7UVpaisLCQgVpyGC9TP3FOoXDYUOQn6nDGMjnawAKLpLB/mg0aoBfQqEQQqGQAgtkahgJPLANCa4QWpJAAdP8sA0lSMTzuI91lH3F+5Gwke5Mo4MvdHGSsIiEX6Szj+52I4/l+NGhCOmoozt2SEeVcDhsgGUIRbC+EmqTkII8JxEcwCC+/Ken/eI26bxD0MftdsPj8ShIR3cNYR8QBJLtTriG6fBisRgyMzPh8/niQDY5bnVXFqZJ0mEoOqdIyCnRl1/p+sQymSrK5XKpf1lZWQZXHZfLpdogJycHdevWRevWrdVawDHJe+U443hg+zgcDmRkZCArK0vt4xplNtb0dkhJSYHP50P9+vVRt25dZGZmwuv1IhqNYv369aquJSUl2LBhA/Ly8gxrCdcrrkWsW05OjgLl5D/Of5fLpaAbti1BKs45uoPJfuA+rj3S6YdjnOsGoRg5ltLT01FcXGyYX4FAQDmEsc4SYJLQFPuQ9dDnNceuXNd5rkxpJcccxyglgS0JienwmQR1zMAqjk/+r4OVchzItYj3yPWV7a+PPTo2yXuQEKgOXMkHPtKBiGuAJUuWLFmyZMmSJUtHk2rVqlXpfj1wPGLECAAVzhS33367YR8djWtS9evXx9VXX60C7NSECRPw6KOP4qeffsKbb76JwYMHx53brl079fm/devWyM3NrZE6XXnllYZfVN5www049thjqzxvxYoVaNu2bY3UwQwsAIDffvvN8Hr16tU1cj2p448/HoMGDarxci39uapXrx7q1KmjfoQFVKT6u+mmm1BWVobt27djx44d+OabbxQIEQgEDI4x1MUXX4wOHToAAF588UWVvqq8vNz018djxozBiy++aFovp9OJ+vXrIzc31/THR4kUiUTw7LPPYuLEiUkFW1u1aoUtW7Zg0aJFattxxx2HCy64AOvWrcNXX31V6fldu3ZFTk4OAKBbt2546623lNNNZQ5jV111FZYtWwYApgDdeeedZwpkmkE3UjK1V3Z2Nmw2G3r16oXTTz/dcJyeqm3w4MEYPHgwfD5fpeUDiHNqWr58Obp27YqpU6fC4XCgbt26AGDo8z179iAQCCAvLw8vv/xywrLD4TDGjBmjoLEDUTQaxbPPPot58+YBSA78sdvtePvtt3HxxRfjl19+OeA6WLJ0tOurr77C77//brqvVq1aR4UbzEknnXSoq/CnqFatWigqKsLkyZPx1FNPYcaMGTWylluyZGmf/H5/palQGXdLBFkfalngj4n0FCt6IJb/M6irp4sBYAgAMxCqf/A1c1SRYpCWQVmCJdJ1RX4pk39LtxNZN/nFRMIbBGpCoZCBZuP9yOvJILK8tu6cI4PcMjBMSEF3QiKYIVPdyHvUnXMY+Obf0n1CBuF5bdZbBvf1X3/JAL+8N91dRDoCybKk440+jlgP6W4iHXdkOfo/fRzqfcv74r1JEE13CNIhArN2kOXKe0wEBLE8fUwT0ODfTCUlQS39vuT1JagkxxvnhYTECErJlEfynvW20+8j0X1TOuEpryHdanjPEoyQMITL5UJmZiYyMzMNzjQSZpNAkbx33TXFzFFM3occXxIsAaDSjKWnpyMjIwMulwuRSASFhYXqvgOBAAoLCxEIBFBaWqoekpg5t+jjTNaddZXtK+eK3r4SxmOZEoTUU8SZHSvbyOFwxOVf11289FR1ck7q4KfZwzDdCUiOUYfDocaHPF+fk2bvJTo8w/MIBcr7N1s7zN67Eu2XoKGEEyUsyfVQriOyfNkWcr90HJLzzZIlS5YsWbJkyZKlo0l6Khhqw4YNpts7deoEAGjcuDGaNm1q2KennqopffLJJ6aB2B9//BHdu3dHx44dcfzxxxt+gXzrrbeiX79+mDt3rnKoPe2002qkPnqKs0aNGiVV9vTp06uECQ5U+gPhN998s8p0RdVVMBjE7t27a7RMS3++0tPTlZMXVVBQgJycHCxduhRLly5V2xlEiEQicalLUlJScOONN+K2227DrbfeaoDwYrGYKfgzcuTIOEhNl9vtRr169QzbEqWNYd1HjhyJsWPHKuBDSnfLad68OQoKChREl56ejuzsbAAVYF2iNZDij+YAoE6dOujWrRu+/vprVXYi7dixA3Pnzk3ohuTz+eLS6wEVAZ/vv/8+YdDHzNX5hhtuUBAWVZUDg/48VCoUChle06kaqHheJ39sSwWDQZSVleHDDz+s0sWpMkej6mry5Mlxz++rUiKg0pIlS/un9evXm26vX79+nGvMX1E6tP5XFWMdAHDbbbfhlltuwbXXXnuIa2XJ0l9LkUikUpCZBiQHki71YMoCf0wkU9jogU09yM7gqN7BElZggFQGumWAHIByCwH2DRo6YbhcLvh8PoODjgQqZPkyHRAABQvpjic2m005sKSlpalUYUVFRcodh+URJqH7igz+y/+lM42eNob76ExBNwjetwzCRyIRg7uGBIGkA4o8jwAI750BZqDCTpeBfzpxyHrLVF6JguMEVXTgSk9pZAZmsR1KS0sRiUQM6btkmh/Wnw4edNYg1BMOh9W5rL+8Ds+TEJkOwkg4SQfN9EA9P0QwBZVMNWf2Twb1Jfjj9XqRmZmJjIwM1b9cOFkf2TYSjGK96Awk+4j1S0lJUefT8UgCMixHOgtRuiOKnOeyzXQoQtZL9oXH41H3w2vKdklJSYHH40GdOnWQnp6OcDisfsFD8t7r9SpnI85Ljitel+uOx+MxjG05lqTzkoRXeN9erxcpKSlIT09HTk4OIpEIioqKYLfb0aRJEwSDQUWLl5WVIRQKwe12G9pFwojS8UWuTRR/0cYxSccyOf7oVsX+5T2npaWpMcAxKccm1wA6/chUblyfvV6v6jO/32+Az9h3OkjFeWLW13Ls6gALAS8CMnqKMJmyjP+bAZ0S2NLHLceurKt0e5JjIdG6Jp2L2IZ04uF7DN93uPakpaUZ6kQAlmuzBOfk2qDDWnItsmTJkiVLlixZsmTpaFDr1q0TOv4MGzYMixcvjtvep08fAMC8efNw1VVX4ccff1T7HnvssYMSOKXLkJnatm2LM888E1dddRUWLlyIr776ChMmTMCrr76KMWPG4NVXX1XfI4cNG4Z33nkHjz/++AHVh3AAZbPZkgpwVJUi6UDUrl07uFwuHH/88QCAhx9+GK+99hpGjx6Nu+66q0avlZubiy5dutRomZb+fDVr1swwlv/73//ihRdewIwZM3DyySfjk08+AVDxPIGA4I4dO+K+N//zn//EOeecgxtuuAGjR49WacMAICsrC7feemvctcvLy1FUVFRp/erVq4d27doZto0YMQIDBgxIeE4sFsMPP/wQ547Tt29fLF++3ODM1blzZ6xZs0aBMO3atVOONQsWLMCmTZsSXic1NRXnnXeees1nhz///DOmT5+OOnXqoG/fvgnPf+SRR3DhhRdiwYIFCY/56aefDHN37ty56NGjR8I0DvI5jd/vx9tvv40mTZqgdu3ahuN0xx9dOgwmpUNf6enpSElJwc0334znn39eOcTJVFmbN29GKBTC2rVrTctMT0/H3LlzlZt4TeqTTz7ByJEjDT+8M9NJJ52knOEtWbJUc0r0GXPBggUGuPSvqhEjRsQ5pf2VtGTJEtx66624//77sWvXLpx33nm45ZZbcOGFF1bqTGLJkqXqq6SkpNLPM4xtWuDPESTdyUIGL81cRHRnDn4pY3CYQXHCDtJFgkCB7vIgXXiYKofptWjdKtPlABVfOngNu92OQCCAoqKiOEiJNlVMpcQAPgOzDL7L1C8M3IbDYTgcDnUuv+gQUmHQmPdnt9sRDocRDAbh8/lUKiXeH+89NTVV3Y/uBKKDLWxDOgxJWETeK/fZbDZ4vV7VV/I4CXYAUJOV/SID6rKuDNyzbhK40ccPQQT5pZBBfi4MDLzz3nV3GwmCyFRfMnWOzWZTfeN0Og1tIJ2edCiLbS3LlNAP//H+EsE/EiQwm1Mulwsej0eBAwSDeL9lZWUIBoNqfEoARIdx3G43nE6ngojYPxIQkn/LtjRz7qGjFlNMSaBCutaYwUgSZAmHw0hPTzdANwRSCIMwxV5qairy8vKwfPlyAPvAJKb8Y30AKKBF1l0fL9wfCoXUL3yky4yUTFdls9ng8/kQCAQQiUQUZLhp0ya1vjgcDpSXlyMYDMLlchlSoenzjpLOMWzD3Nxc9QslzmX2EaEtpsaS9Q4GgwYXM4fDocYAAUY5D9gO8s2ZgF84HIbf7zekfZNwG9uN98A5I+eADgPJccJ+0V185HiTfajPE7O/JeynA5+8toQ0ZT/ItU5KrzPHBdOxyXroaQL1MqXbGMti+3OM8D1EAkWH6wcjS5YsWbJkyZIlS5YOhk466STTVDMAMGvWLDz33HOYM2eOYftpp52G9PR0rF+/HtOmTcMXX3yh9s2bN08FzmtSl19+ecJ9H3/8MdasWYOUlBQ0btwYZ511Fq677jqMHj0a//jHP/Dbb78pwKBjx464+uqrMWzYMJx77rn7XZ+nn346bpsOA+nq3Lkznn322f2+ZlUaPHiwSh8EVLgv3XLLLQgGg5g+fbqpA8qB6P77749zfLJ0ZCk3N1ela1+zZg369OljSHtFZWRkKBhk1apVcftbtWqV8Br16tXDk08+aersUFUwtHbt2mjTpo16fdddd+Gyyy6LSzGYjIYMGYITTjjBMO9POukkbNy4Ub0+/vjj1bOLFStWVFreTTfdZAB/+KNCABg6dCgAYODAgQnPLy4uxmeffYYHH3wQGzduNIWgOnXqhEaNGhm2VRZIle5tL7/8Mu655x6UlJTElZHIgYOS4JaZ5HP/rKwsHHPMMahTp47huU1GRoZKHZafn49QKBTnFkSdf/756Nq16wGDP2budS+++CI2btyIX3/9tdJz6dTEHwhasmTpwOVwONC+fXvTfWvXrj1oLpGHk2bOnKkg2r+iOnfujAcffBDnnHMO6tSpgwYNGsDj8cDn8+Hxxx/Hd999V+PwuSVLR5sGDhyovpdXlqKb8T+z2OvhIAv8MZHuvMKgOmAMglK6UwgDvwxoM8BNQIXHR6NRBTgQnAD2pXEpLS1FMBhUQVN+ICcIIQPqAFRKGQJKMuhNKMflcinXGxl8JUhEeIFfLNgOtGqWYAitZwkmOZ1OBRPQPYTOGgQZGOhl0J51ZhqZRCl12H7RaBQlJSUKHHE6nYhGo6p8tjlBGzrCSBiJXxBZngQKpHuHBIL0gLtuhyqhGQn/pKamIhQKqbrJwDzhCwbszcYg21KHUGRwX8I2sp7SFUqm6ZEwgoQBJJhC8IJ9RBhFOlzxn95est3kPt4n25t9kZGRoVKASaBL3idBE4fDAY/HA5vNhmAwiJKSEhQVFSEQCBjSNkmnFD0dlIQmZBtyLvBctqGEHGTbcVxJVyuCQxkZGcpRR0IVTqcTDocDfr8feXl52LlzJ37++Wc1N9huWVlZcLvd8Hq9cLlcChzSnXVkG8t7lO4+sq8IhhHwk/BOcXExdu/ejUAggPz8fOTn5xscmjiPWQb/93g8CpiR7VFaWqrIWIfDoaA6PoyQ7cryOZb5kIvApIQj5T07HA7DOOc57GOZog+Agou4ZtBxjPOD98q6cUxI1zU5Pij5HsE0YnJd4NjgHJUOPHJc6ukUdecqjjl5fQlwsXx5jv5PX7N04FSONa51aWlpBshLn0cSkOT9yvVOOh7p7kOWLFmyZMmSJUuWLP3V1aBBAzz55JM48cQTTfeHw2Hk5+dj6tSpuPnmmw0OH82bNzcEz7/66ivs2bMHAJCTk3NQ6nvxxRcn3Ddr1iw0aNAAs2bNMmzv27cvFi5ciNtvv9007c6TTz6Jf//73/tVHz3VFwBMmjQp4fE9evTA66+/jjvuuGO/rpeMLrjgAhx33HHqtYSAAODCCy/Ezz//XKPXPBrSdPyVNXXqVGzcuBF+v79SCMfj8ajnrb///nvc/ieffLLKa3366ae46KKLDNvMypJKS0tDz5491etOnTrB6XSie/fu+Mc//lHlNam+ffvi7LPPBgB8/vnnanvr1q0NdWjSpAmAimfjP/zwQ6Vl3nTTTQbQJDU1FXPmzMHFF1+M9evXIxKJoFatWlXCcd9++y06d+6Mnj174pZbbsGTTz6JWbNmYcWKFSgqKlIOOslIT4sGVDj/6DBmZU5GQOL0j5RMUeZ2u9GvXz907txZbfv000/Rr18/5aTEtYmu4ro6dOigflxYFXSUSLNnz8a4ceNM97399tu45ZZbKj2fsQE+H7RkydKBKxKJ4MsvvzTd97e//a3G0q8ezqpdu/ZBAeIPJzVu3Bh33XUXXnzxRYwcORLp6elwuVxo3rw52rVrZz1rt5RQt91226GuwhEhp9OJ+vXrVwn0SKOVw1GHZ60OsSRwIF1ipDsD055I5wcGj3WHEUIfDNTKwDIBHH7olnAHUPEAiNsZdJfuMtIxSA/CEq5h4DUWi8HhcKjrl5aWKrDD6XQqyEiCMzJdFgCVgszj8cDtdqOsrAyBQEAFivlrCN4b4Qe73Y5QKKS+RLG9HA6HIcDNa9P5hMcC+9Jpud1u5aJD4Id1ZX314DePYXol1i8Wi6kvGqyTmRuQBBuk2Lcy/RjhDLP0VLKvJOyjX0+OP7apdJ1ie0jnFPnGLlN9SbcYCQjoixPBAgkhEJyiJHAixzZfyzRJEmpgG8r25baGDRuirKwMmzdvRiAQMLg+yfvVXWa4XTrfyDpKyIFzhnNbjhO9nqyXPpdl+UxtJK/F+6djEV1yOP/cbjcaN26M+vXrY8OGDfD7/dixY4dKqyRBMLfbjczMTLjdbjgcDrhcLrWP5dLFyW63qxRcQMVclWsW+1GCIcFgUAEtHAd//PGHgmL4wIPwYDgchtfrVZCNdKqS/c/yOQckPBOLxbB37141/wiIcLzwGDlO5JyhO4/et7we+4ZrLe+da4Cc3+z/UCik4Ey73Y6cnBzlqsV7lRAfnbd4TX1cEebkOq23CcexhJMoXku6vemQEe9JgqVyrdDbRv+bx7A8fT7Kc3hsSkqKAUCV81zOTTkHeD7HHkEx3qPucGbJkiVLlixZsmTJ0l9ZJ598Mv75z38atv3yyy/45ptv0KFDB3Tt2lVtX79+PW688UZ07NgR3bp1Q8eOHXHWWWdh2rRpAIAvvvgC8+fPR//+/Q9afVNTU9GiRQtTp4q0tDRkZGRg3LhxuPDCC9X2pk2bomnTpjj11FNNy2SgulWrVglT0CTSjh07sGHDBgNQdO211+KZZ54xPX7u3LnYsmULfvnll2pdJxkNGDAAHTt2VNACNWnSJINTUl5eHoYNG4YZM2bUyHX5/MzSkavCwkJ0794dmzZtikvhJMXvzwAU5Ce1Zs0aBAKBSlNEnXjiiTjttNOwaNEibNmyBQCqdGGJxWL45ptv1OvFixfjkksugcvlQocOHSo9V2rAgAHKIZ5zvVWrVmjUqJEBgmEasN27d2PdunWmZb3xxhvYvHlzXAoyoCK91RNPPIGpU6diwYIF+Pjjj6uEbICKuZmXl4clS5aobXwm1qVLF9x999146aWXqiynXr168Hg8BjAnHA6jdu3a6N27N2bPng0ACshJpMrSXU2cONEAI9ntdrz88suGY1auXKlSQA4aNAhvvvkmACMwRF155ZV48MEH1Ws6UFVXDRo0SFjvysY2AAwfPlw5/ewveGTJkiVzSdc2qqSkBC+88AK+/fbbQ1CjP1eBQAAzZ87E5s2b0aJFC/Tq1etQV+mgSv8cIH+4bgFAlqT69u2LQYMGIS0tLe5zhCWjduzYgd27d1d5nDQxORxlOf6YSAZAdQBIHqNDAdLhgOfLY+nUIQeEDMbLa+qwhUzbw0CwDlzodZTQBIOtOmQj3VR0JyOex8A96yyBAwmBAMa0XHTj4LXS0tKUe410j5H1kLCFTstJVwoG6xlc1l14pNOGTPdk5pIiA9+sq+xfeZ/SvUWCIvKa8m/5JkvXInmulIRoJLgj05PJuktITB+LEnzSYSb5j8CJdH9iai/dMUgCLjrko/eTHJPyH8cTy6L9rNvtRlZWlgGAkXNGh3wkoCPHhmwbvpaAhZlTk4Ql5L3IsSCPldLdl9iPEsKR8ys9PR1ut9vQXwQiOJ55TTovSUBCzl8JfsnxwT4k8CXnMO+T6fwImnBbog+H0pXMbEzIvtfBR5ZPly3p+kSwLS0tDW63Gy6XS5Uv1yI51vTryWNkWkaOaVkvfU2Xrm5MGcZ11KzNJeiirzmso1zf5FgyW5/0/To4I92d9H3yfmQ5sk3kuJDzX9ZBnq+3q3RvkteV670sm+0g4R79nuX1zMA6S5YsWbJkyZIlS5b+aqpTp07ctjVr1mDs2LEYP3481q1bZ0h58uabb+LVV19VjjG6401VAfwD1axZsxKmp6H7UHVAAKkhQ4agd+/e1a7PO++8Y9jWsGHDSs/ZtWsX6tevX+36VaVjjz0WV199ddz2Tp06oUePHoZtNe3QU5UziKXDX3TyqSyg0Lx5c8NzFDMxZXxl6tGjB2677TaVpqCytFUAsHnzZsyfP1+9/vXXX7FmzRoUFhYmTFFoJs7NYDCoth1zzDFwu90qxVZ6erqql1narTZt2iAnJwcDBgyoNIVXq1atUK9ePTRu3DipdSXRulVeXo4tW7agXbt2cemnzAAaoOLHtkOGDMGVV16ptvGHYDfffLOhzapq+0Tq2LFjlcfIZ+fyOps3bzYc5/V6cf311xu27a9rXCQSqRRYSqQTTjgBV111lXrN+WDJkqUDV1ZWlunnjpSUFCxevPgQ1OjP14YNGzBmzBi8/fbbcWvg0SAZL7VkSapt27Zo3rz5QXOL/SupoKCgyvS4wD5TjsP1h+0W+GMiM0ACgAFkkIHaWCymUk6VlZUplw7psmIGQRBckC4PMhgfDAaVQw+BmdTUVHWMhH1kgJ8BZn7JiMViCAaDCiJh0JppxnToqLy8XKWlCQaDCAQCBlcdmXonPT1d/fogFouptFBMASQdM1JSUlBYWKjKcblcBtciHst60hmEbS/TpckUTHQ1ko4WvK/S0lIFNDidTtVedFlhe+quPRIEIUggHUAknCWdLiTcwu1sZxksNxsHEo6R4APTvsny9TElxetzLMgAPPfRRYZ95Xa71T+PxwOv16tSOLEd+OGB/whxyFRlZjCSBCLkBxDe59atW7F+/XoFRbndbtUehGIIkMl79Hq96ldQEhSS4AHrokMr3C/rTbGvdCgIMAIask8lQEeXK7rzcKzTXYcPNbxeL9xuNzIyMpRzjN/vRygUQjgcVvbOTqdTbXO5XHC73WpcR6NRBINBtT6w32V6MEIwnJvRaBR+v1/NV45RuvvI9IbS6Yv9LiEifZscZ1zHJKRUXFys7oVz1G63q/WCbcK600WHawT7VwKG/BeJRAzuSXqKPx7D63J7OByOq79805bnsHyOK64fcgyxLDlmdEcuOV7YVrwO98s1iPfK1xJ+099L5BohoR8JCiUCfqQ43/heQMhMuliZgX16/SS0KusVi8UM6SgtWbJkyZIlS5YsWfqrikCIDCCPHTsWv/zyC95++20sXLgwDmxZu3YtXnjhBQAwuOhceeWVhkBzTWvLli2Vul3k5uZi9erVGDZs2H6Vf/vtt2PMmDHVOmfjxo147LHHDLDEggULEh4/ZMgQ3HDDDejfvz+GDh2KPn36VLuejz32GK6//vo4UOCcc86Jc/sBKvpr6dKlhm16Wo1nn30WU6dOxapVq7Bz585q/xDiYIBMlv5c7d27F926dUsIs0ybNg2ff/65+j5uBsWwnKp04oknYujQocjNzVXbEjnrABXAjnSA+eKLL9CpUyecc845mD59epXXo5hua8eOHWpb69atEQqFlPtQbm6uClBv2LAhroxZs2bhu+++Q1ZWFlq0aJHwWjabDQ8//DBatmyJ888/H4899ljCY3NycvD666/jnHPOMd1/ySWXYOPGjXHzMlGKtJNPPhnDhg0ztG8kEkGzZs1w6qmnGrZX5fqTmZlpur2wsLDS8wAjEMhng/fcc4/B0QgARowYgbPOOku9HjVqFG688cYqyzdTSkpKpY5TiTR//ny0bNlSvbZgRkuWak4FBQVxaUeBCkjxr+58o2vdunXYtm0bli1bhm3bth22gfmaVjAYTJjm0dLRrVdeeQUrVqww/Q5jyai1a9di9erVKiaWSJFIBEVFRUlBQodCFvhjIplKRQ/uSzcMGTjNyclR8I8MTDPgLFMqsSy6TKSlpSkAgecCFYt1NBqFzWZDcXGxIVjNAGw0GlVQhHS5kGllWFYsti81UyAQQCgUUr/AIPTBe2J9SktL4ff71fkOhwNer1cFbtPS0hTkxOB4WlqaSp8lUyqxzna7HeFw2NCeOghAoADYB2LI9FkAlHUsv3BINySgwsGEbcKgNCEFAi8EM9hvrBOD9zKND++N9ZcuJHqwWzohyWA520JCBNJxxyxVmUyHRPhJgkASHpCpjXSXEu4n6ON0OtX4czgcBscV1p3AgHROIjATjUbVeNLdsOT80CERQitMs0Toobi4WNWdAJK8F0JAvCfmRScYItuaY08CD3JOcBzoji2stxxnsh3Ly8sNbjk6eMWxFAgEFOyTlZUFm82GnTt3Ys+ePSguLjbMlaKiIjVGnU6nGrdyzHFs0c0nGo0iHA4jEAjEgVES9tGdb9hugUAAOTk5SE1NRSgUUiAQ5yWwz4mI45TOPKybnJcEQTgW9LpFo1GEQqE4eIf3I2E9zgO2LVNr6esxxwbHIq8rgSO2B8c4007J9Uu6ebF8jk/COGw/6X4mASLZ/0ybKNc4WXfpmCMddbjWS8iJY5BpziScpDtwSUcgHfCRzkGyPhKYk4AO24Ttqjsu6deV//i+wfqyfyUQKGElS5YsWbJkyZIlS5b+yrrgggtUsHXKlCmIxWL47rvv8NVXXwGoCICfeuqpuOiiiwypooB9rh5Op1O5zAwdOhSNGjU6aPWtDKgBgLPPPhsdO3bE3//+92q5WOzYsUM9QG3WrNl+1W306NHqbxk81sXvaLfddhueeeYZg8NEMurTpw8eeeQRvPHGG5g6dSoAYMaMGXjppZdw3HHHmZ6zePFiQ5B+wIABOPvss9XrYDCIV155BZdccgnatWuHrl274qqrrsIDDzxQJRRAyZQ/lo5crVmzJuG+k046yfCMobi42PS4evXqJXUtm82GGTNmYNasWRg9enSl88bMTRwAlixZgkmTJiV1PWAfzCHTlDVt2hRFRUXIy8sDALRo0UL96lwPQnXp0gVNmjRJONd0DRkyBEAFJJXIqQwABg8ejFNPPRXXXXed6f7Nmzfj6quvxgsvvKCczQBU6pThdDoN85LP2D0ej+FX9VWlv0r0C/xdu3ZVeh7rQPE5+t///vc4wEkfS99+++1+OxFJp/Nk1KJFC/z8889xgJPurmTJkqUD07PPPmuALoEKIOjf//73IarRodHevXsxfPhwdOrUCXfddVdCgPOvpvz8/KQ/U1o6uhQKhfDwww/j66+/PtRVOexVXl6OVq1aGVwJp06dikWLFmH37t3qs5OMuR6OssCfBGIwNhKJGIKUdHsAYNgeCoVQUlKiHHEoBlEJmJSXl6OkpMQQxNVT2DCwzCC4maNKWVmZCqhKaITwC4PHDLay3tFoFD6fTzkJMTjv8/kUBJKamopoNGq4j5KSEgSDQUM6MwZzmeOdXzYIn0jXGga1+Zpl64FwCUxJxyIJ/vALhtPpVG3KPmMbpqSkKMBBdwUh3MIgPVMiEaIh2CCBHF6DYAmPkW4cbJfS0lIFVsngvQQM6EDEPuN9yfRC0rFE9j8hCv6SQ7oMsc31wD/LpmMM/xGIICghv7jpgXqZWkrCFtxmlhpJdymSbj+sp4RJ2E6pqakK/klLS1MONnSH4TzT6yjHmLwPPV2aBE0kNCEfsEh3Iv3hB+e97NOUlBQFvLhcLjgcDnVtOpsUFBTg999/V+uF0+k09CPnP1OgsZ3ZrhyjmZmZ8Hg8qFOnjnJIYh2cTqdqTwlKsR5lZWXIzs5Gfn6+IsG3bNmiwDfOrfT0dJSWliI9PV3Vj2NcApDyf5mGzO/3K/CH7cp+4FpK8E2OG7ZnNBpVaxPXAgkE6q5MBPI4/wlesQ0IvNFVSU/txfWBQJzdbkdJSYlqMx2M0SFQuebJecj1j2u6dCqS/co5rbtQEczi/OL6J9tLB9zkGkDJ13xvC4fDCIfDBhtQ2Z7sC85JXofQH9dC6fgjwSr2hV4HWb4lS5YsWbJkyZIlS39FnXrqqahduzYuvfRS5WzRokULlJWV4c0331THbd++Hc899xwAKIcfqffffx8AcPXVV+Mf//gH2rdvf1DrfeaZZ1Z5TCQSwZw5czBx4kQAUMH8RNqzZw+6dOmCn376SW277bbbql23J598Uv2qskWLFgZ3Eik9NY9s72Qk3YyOPfZYvPjii7jgggtw1113JYSu9FT15513nkp5tHz5cvz222/Ytm0bgIrvWBs2bMCkSZMwcuRIhEIh9b3fTGVlZRg/fnylTkyWjnylp6fHgRGJHF90p5Rly5Zh2bJlKCgoiHM2aNGiBXr37o1bb721Uuff+fPn44svvtjP2u8Tr79z5061rUGDBioYmZOTg+HDhysHq7Zt2+Lqq6+Gz+fDe++9h379+lX7mu+88w6mTJmi0iOa6cILLwQA9OvXDxdccIHpMQsWLMC///1vjBgxQm2T65autLQ0pKenq9cEf1JTUw3rU1VrJNOe6dq+fXul5wHGscDnTt26dYPNZjOk/dF/kX4gv/p3Op0J3ajMdP3118elrASsVF+WLNW0Jk2aZHjf+OOPPzBq1Kg4R8KjSVu3bsWiRYvw888/G96X/mqKRCLYu3ev+qxpyZKuBQsW4OOPPz7U1Tisdeyxx2Ls2LHo37+/2lZWVoabb74ZXbp0wWmnnYYbb7wRe/bsQWpqqjIWORyVWvUhR59koJ/wAgCDE4UEASTkQdcLpvWRDhQAVPqqQCAAm80Gl8ulAAQCOgyOMxUWg++sj4SEGGSmSw6D08A+0t/pdBoC7gzO7t27VwE2qampKkVRLFaRGiwUCim4yOFwGFLFEIohwMNtDGgzWE7whscAFQ85CEVI0EQCSoSfeB6D8nRq4TGElBiYJrDEfqBLEfuMsIsEBgjWSGcMmYJNT5nFdpSBcglNEEohTCAdkXgvMrWbFPexXAbkJTAgf1mhpzQiRMCxKB2E6HJE2Enev/zyb+biIdNmEX6STkSyL/XgvrxvWU8Jf0gHEunek56ersZCIBBQ4AeBEdk20o1Ef5jBNpHtwf6UwJMcx1Ky/tKpifUiQFVWVoaSkhIFmni9XoRCIdhsNng8HmzevBm5ubnIyclBSUkJvF4vysrK1Jj1er2qj3l/fADI+c21wefzwev1qu1Op1OtGYSiABhS81HRaNTgIsZfIzHFIOsRiURgt9vRqFEjbN68Wf2SiY4u7A/Wn+MqGo2isLAQdevWBQDDusS6SiCPY5UQGecPoTS+gTLtYWpqKjwej1pnABgch/jabJxL5zHOJY47uoix/9m3EnSU6yjXIY4xnivburS0VM05/f2D652EguT8kNAc1wcJ/nBsynkgAShCT3ItkPOM2zg/5BrNvmA5ulMP68hjWBfpSCfXMgJCvFemaLRkyZIlS5YsWbJk6a8gWqj/+OOP2LhxI+6880507dpVfScCgG7duuGnn37ChAkTDOdOmDABI0eORG5uruEZFAAMHz4cl156KXr16vWnpGqozq/C33vvPZx11lno378/Vq5cmfC42rVrY8yYMTjxxBPVtlNOOcXg4JOMysrKMG7cOAwdOhRLly5NaK3++OOPG15v3bo16Wtce+21htRqAHD33XdXed59992HFStWoEWLFjjhhBNwxhlnYOXKlahduzaef/55/PLLL6bnBYNBnHnmmejevTvGjh1resyLL76IoUOHJn0Plo5M1a1bN84BJdEv9/VnVmeffTby8/PRuXNntGzZsloOPdS9995b7XPMxOcM0q2Gz5QbNmyIl19+OW6O9ejRA82aNcOll14aB9FVpo8++gjbt2/HF198UenaVbt2bZW2z+v14q677sInn3xieux5550Hr9eLkSNHYtOmTfD5fIbnibqkWw+hw7S0NDRr1gy7du3Chg0bqkwDUbt2bdPtTI1WmfgcUHdjnzFjBvLy8rBhwwZs3LgxDhpN1lHJTIxrJKtEP/xyOBzKJdySJUsHrq5duyrQcMuWLXjzzTfx6quvHuJaHVp99913+O6775CSkoImTZrgueeeUyDoX0nhcBi7du1KChi1dPQqmRSiR7PWrVsX52j466+/ol69esjLy8P69etxyimnIBwOw+PxwOv1wufzVQl4HwpZ4I+JZLCV4AKwL2URgRd+0WLKIn6RcbvdBicGBpv5JYHuFQzIM1DMAD6PY75cfvFjUBXYR/HLVC0yGCzTZbE+PIdB+7y8PLhcLtjtdmRnZyMzMxNpaWkqBRiBlczMTITDYQUIEYwIBoOGVDepqanKPYJfOhhg111ZuI/3xWMI7+hOJXSYIAQgA9nhcNjg8EEXCx26YQBfD8oTYJD2qKwXj9UnvO4QQ+iAgAjbisfI9Ea6sw/vSdaLrh0y0M7+01MTSThJpi2Tzkwejwcul0u55cjrVBZ8lymKZPo7OeZke8i2IighU0HJtqIDDduD801eu6CgQNXb4XAowI4wjQ4/6E4i0gGIdZRzk/WU+/X6sxy2KYEzrg0cP7Vr11agTiAQQHZ2NoqKilBUVIQ6derAbrerL/u0PPZ4PIY1pqioSKWu0x1XOA9isRjcbreCYQj20R1IgjjhcBjBYFA5AdlsFam+CgsL0aRJE4NzFdupVq1aSElJwcaNG5Vtsd/vh8/nU+5m7CvOWQmdEIyqW7euAQpjX0QiERQUFKjrSLAlFArFpb1jGjq2NdcCOZY4Nzhu5Ljw+XwqdRchFYfDYfhbXz9lai8CUBIAlNci1CLTc8lUY3oqNJnOTMKUXJ+ku5CEEDkeCTqawXZyvPA6Mt2h7lwmz5Hve5ybLEdChRLUkikSJWzJ8+Tckm5f+n5LlixZsmTJkiVLlo5knXXWWTjvvPNwxx13oKCgAHv27EGjRo1M3QxOPPFE3HLLLXGQx9ixY3HnnXfGBfTXr1+POXPmoHfv3gf1HqjqBHNvuOEGvPvuu1i1alWVx55//vlx5+6PHnvsMQwdOrRS56P8/HyD00Xfvn2TquOoUaOUC0l1tXXrVuTn5+O3337D8OHD4XQ68fjjj+Ozzz7DqlWrEqZsAoDVq1fjmGOOMd03c+bMKqGf+++/Hzt37sTixYuxadMm5Tpi6chSw4YN49aMRH0pf4AD7AOElixZgiVLluChhx5CnTp1kh7Py5YtU6kFD1R0oDnzzDPx0EMP4emnn0Y0GjVAHjt37sSjjz6q1kE9/dbAgQPRtGlTdOrUCeeff37cr6nHjBmD1NRUjBs3Dj/++GOVdbr44osNbVsVXDRixAiVQqwqMe1av379cNFFFwGoeO7/5ptvIi8vDwsXLsT8+fOxZcuWhGnGsrOzTbdv3ry5yuvbbDY4HA4Eg0HD+4fH44HH40GjRo3inNw2b95scDWqrqSzeTIySyEHQP1IzgJ/LFmqGW3evBnhcBjfffcdvv32Wzz33HPw+/2HulqHhcrKyrBhwwbMmjULDRs2jANQj3RFo1GUlJQclgCCJUtHkpYsWaK+Z06ePBmffPIJVq9erfYXFBRg165daNeuHerUqYOMjIzDct5ZUbcEkoFjBmNloFW6O8jgphmwoQc4ZVolCRqUlpYqBwsGVxkolmmTZEogBmll0Jr1lu4TMsDOwCtTAhEQYYCYzjAsTzoGcT/rq7sASUlnCJkCiOJ9yqA26y7ThMl2ZoBZhzekg4V0oWBfSKcXWT9ZTxmwZvvp9yPrLl1U9PQ6el1l/7HtWIYOFbHeMjhO6IN1MgNvJEwl202OVTk+9HvTnXt0xx/pFCLvxUxmzjtyHkjnE9kG8nq8Jt1fJDQhIQY5V1mGBBl4bTNnI7lf72Md/JLjg33NecqHBh6PRwEdBQUFyMvLU848HMMlJSUGwM/n8ylIT3dQ0l2cmJ6J0J0Eamw2GyKRiAIRpXMSgaGysjKV5g2AglTojEMnHXnPHo8HeXl56gu5HEe8d72dU1JS4h7MyLWAMA3bmX0s75ttKucuz5VjX8JLZuOY7c7+kvOTfSXHn7yW2X69fDmeeD39fUICNjpMqLedDp/pAI3Zcfp8Y93leibrL8cbyzGDdCjpqsX70H9NJvtZQmtyLst70uejJUuWLFmyZMmSJUtHqpo2bYo6deoAALKystCyZctKU5hI5xtq7ty5CYOflbnp1LQ6deqU1HHNmjVD69at9xsWkKnlqyN+J0lJSVEBd1179+41vG7ZsmVSZbdr1w7nnnvuftVr27ZtWLNmDT755BP88ssvKCoqwpYtW7Bo0aJKoR9q165dcd8x/X4/Pv300yrPPeOMMzB48GDcdttt6Ny5837V31LNqF+/fgkhrqqUkZERB94leu6mb9fXjuqmt/v888+rdXxl4vOmVq1aqXnOH3LedNNNOPvss7Fo0aJK04qtWrUKI0eOxLhx40zdC+bOnYsxY8YkBf0AMDivARXpbxKpcePG1UqD5Xa70alTJwwcODAuVVtOTg58Ph9GjRqFd999N2EZid4vkvllvny2mewzloULF2L9+vVJHWsm/hAsWZ122mkJy7GeC1myVHPaunUrFi5ciB9//BHLli2zoB8Tbdq0CW+88QZ27959qKtSo2LsMBQKHeqqWLJ0RGvHjh3YvXs3Zs+ejcmTJ8d91mS8j0YkVqqvI0wSuJCOLTrsIp0m6MQhIRqn06lSE/FDsYRxuI/BdEINTIVDuEQGaBnAdzqdKsgvg8MysC3L5nl0BWnevDlSUlKwbds29SsSpvuiqwrTETEAzgFdUlKCSCSCjIyMOHcj3ptM42Sz2QzgigR0pBuSdAviL0IYNHa73QbYRrpz8N5YBoEMtnt6enpcyi4JBDClGUEICaBIqIDXJExB0IP1YL3o4EE4g04msVhMpSuiHatcLFi2y+UypD3jm7Y8nl/0JUClQw76WJXjNVHgXUJE0sVEOv/oIBHnjAQMeO9mUBXvU0IpHPc6fEMHnZSUFGRlZSEQCCinIAnZsQ4EFyQMIccmy5Vtw3aW40uHPGS/ynHGXzPFYjGVuqu4uBjFxcXwer3IysoypChjOV6vF3l5eahduzZ27doFl8ulUmnpIFkgEECtWrXg9/sRjUaRlpYGt9utHp5wbMdi+9ILEuRJS0tTKas4F2w2G/Lz8xXo4/V61frEPsrIyEBpaSn27NmDYDCI7Oxs2O12wzVlXxFyKy0tRXFxMXbu3InmzZsb4BS6YNntdvWmSPcw2Zdy3ktYTEIk0WgUNltFukSOEwmN8Z9Ml5eWlqYePMl1g5ARzyFMxDnM9mWKRgnKAFBtLvdJUIbXkSCpHJdybeL5nKsy/SOP5VrKftbHtZzf+niW85wOVg6Hw9C+7E/ZrzoUSLiSfZGamqre/ziO5HsP68uxw7pbsmTJkiVLlixZsnSkq3v37sjKykrq2HXr1uHDDz+M2z5z5ky88cYbuOGGGzB+/HjDvqlTp9ZYGp6qlCz4c8011+CEE05Q6XXeffddNGvWDGeccUZS5zdu3Ng0jY38HmWmCy64AECFW+zOnTvj9nft2jXO3aJp06ZV1uekk05Cp06dkJ6eXuWxZpoxYwa2bdsGAHjggQfw6KOPYs6cOUmfv3r1ahQVFRnAAa/Xi44dO1Z57tatW9GvXz907doVe/bswbffflvt+luqGdWrVw8zZ85Et27dqp1OYe7cuejbty+aN2+O9u3b4/bbb49z9qEkdGF2zC+//IJAIIBgMBiXPkzX8uXL8cADD1SrromUlpZmCID069cP7du3R58+fZCTk6Ou4/f7FSwpxWcUS5YsAQDMmjULU6dOxSWXXKKAqu+++w5Tp06tVr1OOOEE9XckElHlm2nLli1YsmQJjj32WLRu3brKsk888UR8+eWXCV17WrRogRkzZlRaxrnnnos2bdrg559/NgBC69atq/L6fDYJIKn6sk4HotmzZyedqvHOO+9Ejx49TPfJHxJasmSpZvT000+jZcuWWLp06aGuSlJq1aoV6tSpg99++830c11Ni5/NcnNzMXjw4P2GdQ83hUIh5OfnW+CPJUsHqG+//RaxWAxvv/226f6ioiIUFxcjHA7D5/PFZRE6XGRF3RJId1aw2+2GtES6k4nD4YDT6VRATHl5RcovfogNBoMqYB8IBAy/bpJAQVpamnLL4LVlaigGf6UjgwwoS6DB4/Go4C0DxyyPgVy/34+ysjIFU5SXl8Pj8Sg4paioSNVb3jvPk0Fm3W2GaWb4f0lJiSFIzaCv7mDC+wwEAigpKTFAOCyb19RT8vBeGTCXqWgYXGfgXQcSCHTwGhL0kWANjyecJdueQJN02yAE5XQ61T/2IftVQgpyO9uJ9ZAADlDxBS8UCimYy8wpRoJC0lVGjnV5HOsgXYl4XekUIlMW6cF8/i/bQr8ftifbhrCD7pZCC9lIJAK73Y6GDRuqvpLtJJ1g5DXlvcl+kYCXdJnRf3EigQeS05wrsVgMmZmZqFWrFrKysmC32+H1etX8jUajKCgoQElJiYIjgIpfBKWnpyvIhUAKX0sQheM7OztbpV/i+CkqKlLrDe+NoBj7yOfzwW63o7i4WLU3UPFAJhgMqlR5HMtMFZadnQ2v16vsggsKChAIBBQoIh16uI2AS3FxscEdxu12KwiR6wnHZyQSMQBfEhqk25iEKdk/dEdjv0hHKn2tk/8IFck1V6bCkuCL2+2Gx+Mx7CMkKeFN1oNp2piajsfRApljkv/LdIist3ROkzChXC/l+sp9co3kuNXnhkwXp0tCSawjxwrf+3THIglQ8j2TkCbHlHTrkg5ROjxlyZIlS5YsWbJkydKRqq5duyZ9bJMmTdCrVy8FsEg99dRTeOqpp+K2L1q0KOm0MweqZAGmxx57DI8++qh6fc011yQdCAaAk08+2XT7smXLKk0DNnnyZAAwPL+R2rRpU9y2q666qsr6nH/++cjIyMC2bduSSq+jS7oKff3117j55ptNwaZECoVCcc4m3333HT7++OMqz50+fTq2bNmClJQUXH/99Zg7dy5efPFF3HnnncnfgKUaUYMGDdCuXTvMmjWr2ueWlJTg66+/xoQJE5CRkaG+h5uJzxh27NiByy+/PG7/nDlzcMopp6Bfv3646aabKg0Evvfee5XCdtVRv379DK+7deuGq6++Gjk5OYbthYWFaNSoUdz5s2fPxn333WfYdv/99xtSI1aW5i+RLr74YtVmBQUFVbrdPP744zjppJOSAox8Pl9C6AeoSOF25plnxgGJUpdeeinuv/9+PPTQQwb4cMOGDVVePxqNol69eli8eDFeeumlKo/fsWMHPvnkkyqPS6QLLrgA8+bNS3p9qywlmAX+WDrSdfzxxx/qKsSpuLj4iIF+gAoQ8f3338f333+Pzz//HK+//jqGDBmS0NWxpjRmzBhccskl+/WZ73BUIBBAfn7+oa6GJUtHvNavX58Q+gGAFStW4PHHH8dtt92GBx98ED///POfWLvkZUXdTCQhC8ISidIySWiA4ALT1hDg4QMJCYkw/zJBCwZFnU4nXC6XOtbhcBicchiUlW5Degqd8vJyBaXo9QQqgslOp1MBN3Q6IRRQXFxsgJPoDMJAuQxMEwRhuayThKMYIPb5fCrATjkcjjhXIwnXsE0lbML9BB7oIsLrSYCK15BOSTpEBexLm6Y78PAcGdRm4J/tzr6WaY6kW4vL5TL8k+4+EmLiGCAwIuES2ZYcD7rLjT5GJSzAcSwddvSxIce6BH2kww/bQUIY/FvCFWwD9oPuDqM7P6WlpcHn8xngETlfUlNTDU41sn/08SJdTnSnEulAIl2n9LRpEtDgeOJ9EXohMBMMBtVcITDndruVO05hYSHC4bACb8LhMHJyclCvXj01JjiXWCeKfehyuZTjFduU19fTXrndbmRmZhrAqoyMDPj9fuTn5yM/Px8ul0vNrWAwiFisIqUXoSY5h1wuF7Kzs1FUVAS/329wp+F8ZzvzmnTAYdvrbjucS+wPgkDSFcput6u1lPdNSIfzTkIqcozK+SH7n/OT1+PaJesq1w7p2kWHKZ4vx7sOw8n7k3Aay5bQTCgUUvAU1zLWSU+vJ0EmwmU68EPJMUyQLBqNGvpAthXnHeEy3qOEEuV7o3zf4npHAEtfX+SaKCXHuiVLlixZsmTJkiVLR6I6deqEY489NunjHQ4Hhg4dikGDBsXt27lzJ7Zs2YInnngibt/o0aP/tOBE3759kzpOT6lFKKcyxWIxfPrpp1izZk3cvi+++ALt27fHVVddVanTDZ/BXHfddXH7dCBo6dKl2LVrV6V1GjFiBPr06QOgIuWRGXxVlS644AIMHDhQvV69enW1y/j1118Nr1977bVK0yFRc+bMwVdffQUAaNSoEbp37467774b7dq1q3YdLB2YGjRogOXLl6NFixamczxZ1a5dG8FgENOnTzftR869UCiEzz77LG5/hw4d8Mknn2DOnDkYN26caSqpgoICzJw584AgEF29e/dO6rhOnTqhW7ducduXLVuGF154IW77iBEjsHHjRgAVoM2NN95Y7botWrQIQEXar/fff1+luU+kevXqmcJJyWrp0qX47rvvkoJ3KLfbjSlTphhcmvRUge+88w6++eYbBXP169cP3333XVJp/sLhMO6//348+eSTSddJaujQoRg/fny1UuQkcq0CrGdClo5s1a9fH5deeil+/PFHXH311Ye6OkesMjMzkZKSgmbNmqFXr1648cYbceaZZ+K4446r9LzOnTvjlFNO2e/r7t27F4sWLcL06dOxatWqpNKyHs7as2cP8vLyDnU1LFn6S+uBBx7A559/josvvhgbN26s8jvmoZQF/iQQA6Ey5RKwL5jKoKhMsVJWVoaMjAykpqYanEcYrKYDgsPhgMvlUkFdmXpGQhzSlUQ66jCIGo1G4XQ6lbMNwY5wOKwAD9ZZgikyCO5wOJRbkYSBSkpKVMopCdQwIO/z+QwgCduMwV3pGkNgiGmxIpGI2scUW7w/GSQH9qWRkfVmWhoAhn3y/ni/BKnYXno6I0qCOBKuko5L7BOZKks6CMl2lsAI25eOJ2x3wgQSHJPp0fgwSw/O6zCObCvem6ynFB1eZHnyfLldTxXEvyXcIwEgfTvHvRz/ElLQHYEIM0g3H4IrdK8KBoMoKipSoI8cP3Leyr9le0g4hJCUDvpJ6IiSMIrX60WzZs1Qv359eL1e9SWWbjAEdWrXrg273Y6CggKEw2GEQiEUFxcjEokgFAph27ZtaizzCzuBOd01qby8HOvWrYtLx0S4KBqNwu/3G1KBcS5I2DA/Px+lpaUqJVMoFFKwosPhUK5KvJ+UlBSkp6cjKysL6enpChLiPPR6vUhLS4tzqSJ8KB2zmPOSkJHf71cOR5yD7CPOJ6fTqdYLm82mnKFisQpHML/fb0hfx3rxNYEc3Z1Mrjesu0zDRlBGrr2EZqS7F/uAEA7/SQCG98W1SndH45iTY1iuofqxHBu6m5m8vgTf5PgGoOakDuHwmuxjOX64HslxxzaSjl9y3nF+yrrrcJf1yy5LlixZsmTJkiVLR7oIjADJuTNQXbp0QZMmTeK2Dx8+HP/85z9Nz/nPf/5T/Qruh5K5j8zMTNx5551xzx0SwUljx47FF198gaeeegr9+vXDqlWrDPsnT56Mc845BwBwxhln4OuvvzYtp0OHDur5jg4a5eTk4J133lGvCwsLcc011yS8hwYNGuCyyy7DXXfdhVNPPRUA8Pbbb6Nnz54Jz0mkr7/+GpMmTar2eVJ6+qFmzZoldd6gQYNMIYpk0/5YqjkVFhZi/fr16Nq1K7Zu3brf5cyaNQvjxo1Dhw4dTF1/ioqKAADp6enw+Xxx+wcNGoRWrVrhzDPPTJi+bvv27bjsssuSSieVjNxuN84++2xD/RLp1FNPxR133BG3XYffpLj+RaNRzJ8/v9r1k6nFPB6PKWApdfnll+PEE0+s9nWokpISnH766TjxxBOTBgGbNGmC3r17KwehKVOmGPrv008/xcCBA9G9e3f1fMbj8aBBgwYJy4xGo5g6dSpWrVqF+++/H++9995+3xOf4TGtYTKqzOlZd4i3ZOlI0t13343LL78cLpfroLvT/BWVnZ2NMWPG4PHHH4+DLOvXr19pmtZ77rkHL7zwAnr37o3c3NwDqsc999yDTp064bzzzsPAgQOPWHhm+/btcc6RlixZqlk1b94cgUAAK1euxB9//HGoq1OprE9XJpLgAB0+ZDBcgjP6Px3UkAFiCRPQzUKCCCxXBtB5vgy4SlCGkBHryyByKBSKSysjg84MIMtUTQSIGJyV6WtYjnT4kI4UvD7Pk245PF7CTxKuYSBfprVhe8l0QtLZiPv1tFV6IFkCJxI6keXofS/vR4Ih0llGd9ORbSLdPGSgm8FzOV7kr9FkMFz2kxx3uvuPHBNsHzn2ZHuwLmbpvnSHFF1m5ej11d1a5D8Js+jtK8vVz6U4XqQbEcuQdZPwkg6+SFcX/X5YLzPnFL2egUAA5eUVKfH4CyHpCuV0OpXzjtvtVpCKhE2KiooUJEToRx//OsxExxn9fgiBRCIRBZLp6wlBp1AohEAgoMZdcXGxGg+cm2x3witpaWlwuVzweDyG+5SQl7wO+5BwEceXz+dTTkA6UMb71IE8zl+5Xsj1NBwOG/pUrnUsU4cJZZ3YrhK40o+RaxAhLwm30GlKH4NsU92VyWx+yfkn1yspriH6+4+E5/hazk19Tsh2l9CdrL8+Twlf8Tyu2bLf5L2xbL4/6eu93s+WLFmyZMmSJUuWLB2patGiBQDA7/fjnXfeqdTdQCozM1OdKzVz5syEgfhVq1ZVy2lhf7R9+/Y4KMdMhYWFWLBgAQYMGGDYrsMr1JgxYzBu3Dh8/vnnpvslQGW325GVlWWaPmf58uUAKr6vBgIBw74rrrjC4HrxzTff4Jdffkl4D+eddx5uuOEGOJ1OtW3r1q1o06ZNwnMSacOGDepHffurnTt3qu9Ou3fvxn//+9+kzjv33HPRvHnzuO3Jpm2zVHN65513UFJSgvXr1yfl1pRIY8aMwffff49QKGT63ZljX/54Tsrr9SISieDmm2+G0+nEihUrsGPHDsMx4XAYfr9/v+tItWjRAna7HR06dFBwzaxZs5RberJauHBhXB2lGFgPBALVAk+oKVOmGMCiyy+/HFdccUXC49u3b29YG6qrtm3bAqhYKxOBjInEHxv/3//9n9qWn59faeoJXSUlJQiFQvjiiy8wduxYTJgwARMmTKhWPXQFAgHEYrFKnTEaN25seF2Vq48F/lg6EtWtWzecdtppaNy4MVasWIFly5Yd6iodcbr00ktx+eWXm7pmZmZmIjMzM+G5p512Gjp27Ig2bdrsV/pHXZFIBAsWLMA777xT7fX6cBF/EG7JkqWDp7Fjx2L8+PGYMGFCjYHzB0vWpysTMUjqcrkUWU+IQwaLgfgPsHRSYEBaBjh5LsERBpIZrJcBZr5m0FimuaJk0F8Ge5muRbpWSHcIYJ81MlMNSdcNAMr9A4ByiJGpZlJTU+H1epUjj0wxRXcOCWfwHKYuk2lwCDPIVDC6ewgAFWDndUKhkAqSSyCCEBMhDAl8xGIxBIPBOCiG/SudMHhPdOJhOTKllEzjpn9ZYT9LMEA6eHi9Xvh8PrhcLkO6HJbNsaG71fB6ZWVlBqcNjk/p/qRDIHSgkq5LEirTYYNEcJuExmT6I/YxnY4cDkecw4gc63wt6yhhBrad3+/H7t27EQwGEQqFDO5NehoyHTSQEJCcO2aAhQ5mSZBLpnDavXs3cnNzUb9+fQXvcM7VrVsXderUgdPphMPhQHl5OUpKShToI4Ecus2kpKQoiEimguNxEjKR8A3rxIc+clzz3nhtzmeO9aKiIpSXl8Pv96v0TnSmAaBSifFBR0ZGhkohRiBIulvJPqhduzaysrKUww8df4qKitRYpksO6yThHZlqy+VyGWA16VDGtpdjSTpfxWIx5YCmO+Ow7kzPRuhRtr90LpOS4A/TpgH7Ur1xbnLcc35wbsq+SktLg9PpNFxHznsJ9HAf1xumDtPXPpmGT0KcEvqRa45cWyRkxzYnsMX68D1F9pW+DtMtTq4xZrCfJUuWLFmyZMmSJUtHqnr16qXSDCxevBiPPfZY0rAGf2Rmposuush0++jRo/crDZXZD1wSKTc3F08//TRuvvnmKo998skn4+pz//33mzqUnHDCCZg2bRq+/fZb07LMXEsWL16M448/Pm57OByGx+OJ23fPPfcY0vd4PB688sorpinBFixYgH/84x/KoYSaM2cOTjjhBNM6VqZvvvmm2ufo+v777xX4MHny5IQQla4FCxaoHxRJNW7c2LRdLR08/fLLL4aUbweiBg0aoKioCOFwOG5fQUEBAGD+/Pn4/fff4/bXr18fDRs2xBlnnIFGjRrh5JNPjoNqJk6cWCP1nD9/PpYtW4a77rpLPUMPhUJ48cUXky4jEolg1KhRmDNnTsJjWrZsCQDYtWuXuv/q6NFHH8XMmTPV69zcXLz++uumx6anp+P000+v9jWkcnJy0K9fPwDAnXfemXDtM1OLFi2waNEiQ+B71apV+OijjwAk55Szc+dOXHTRRejTpw++/vprvPjii3GwZHVVUlKC1NTUhODPkiVLDGnEUlJS8Pe//z1hefJZlyVLVPPmzTF06FD07t0btWrVOtTVMVWrVq3QrVs3uFwu5Obmmq7TliqXx+MxpDWUOuaYY9CqVauE527fvh0ZGRno2bNn0ulpk9VFF11UpSPc4aZoNIo9e/ZUCs9asmTpwLV06VJ8+OGHpt+7DobatGmDMWPGYOTIkcoZN1lZ4I+JGBhneh7d2YDptPSUUAzwS5hHByYYVNbBn2AwaHDH4T6Z8osBf+kmEwgEVABdwh3APmpepmIiUMEy6EzCcqW7EQAVMOd+GQRmm9BJxCyFkx40d7lcBpeN0tJSA1ChB515DRl0Zr14z0yXJOEB3jfLZYCdsAIlHXUkuKA75+guG7IMBvXZxwQT5H4JEgQCAQVrSVhGBurZ59JZRdZTgjPSQYlQBcEeHbDhr+KCwSACgUCcS4wOSfGe+T9hJh2ekKCODh+wjqyDHviXAIyexopt6XA4UFZWhpKSEuWUpadvYlszPZ4sT3/Qyb6V6fVkv0vQR5bvdDoVMOf3+1FQUACn06lgO6fTqcpmnzB1G++JUBSvm5WVBafTCY/Ho+opnWUIFBUUFKg+YNsSXmJKQo4ZmZqK81Omk5JgoAQO2TZMj5eWlga32636NCcnR9WbZbKtUlNTUVpaitTUVEP6PzrG/PrrrwgGg3Fzo6ysTPWp7iTFMUDIkDAb54zb7VZtzzRVgUBAwYpy7eIYk5AM0y7SKl4HZCSoJZ1u5HgmuCPddVimhAfZ1ly7JLgl0ybK9w45p1k/3WlLtqUZ2MNr6nOL9ZGAD89lexJKk+OAc0T+zXWc/wi8RaNRgysS28/M3cmSJUuWLFmyZMmSpSNNX3zxBebOnQsACvh5+eWXkzr3o48+wuzZs033/fzzzwnPmzZtWpyraVWaN28ebrjhhqSPf/DBB+OcfMy0detW2Gw23HLLLWrbxo0b8dZbb8UdW7t27YTluFwu0+0tWrTAZZddZtjWtGlT9Z3ylltuweWXX45mzZrBbrdj586dhmN79uyJO+64A/379zdsv++++9C1a1fTX5kfc8wx1Xag2L17d42AP7/88gt+++03AFBAWTL68ssvsX79+rjtGRkZBwwvWDp0qlOnjnrGrGv79u0oKytTYIkuuuPk5ubiyy+/hMPhQElJido/ceJEvPLKKwdcx3bt2qF+/fpo164dLr30UrX90ksvxYsvvmhI+VVZCpJIJFJpyoQGDRqoezr22GNx++2371d9hw8fbnjt8/lM16uzzz47LvVMdbV9+3b8+9//BlDxjO+pp57Czz//jB07dpg6QUv95z//MbiXARV92rx5c/To0QN//PFHwnWT2rlzJzZu3HhA96ArEokkhE8nTZqEk046yQB+fvvtt+jVq1fC8vjsypIlqnbt2nj77bcxYsQI3HfffRgxYgQWLlyIDh06HOqqGUQQEah4v9Y/Z1iqWj6fL+FamJWVhfbt2ycEgzge+D5Z03rkkUcwYcKEajvXHSpFo1EUFxdj7969h7oqlixZqiGlpqaiV69euPTSS9GvXz/Dj1uSkQX+mEg6uhQUFMQ5rsigOgOj/KAaiUQUPBGJROKCnnyAQCCFzkAM6hJG4DEsl9dkkDUcDiMYDCIYDCrHGGCfY4m8DoO0EnKw2WxwOp3KqUPeHwO8sgwzkMTr9SoAo7Cw0ODAwnNZZ4JKDNrzutJJScJRrI9MGSPThzFYLVP96OmrXC6XAXbgvRAi4WvZJtJRR0IfMqDOfiFEJB0v5DEyQM9jXC4XMjIyFKTBcmS6NXnvOqDCNpSQCq8hx6VMzSPdliT44ff7UVxcDL/fr2CDyoLxEjbRwR+eJ8/XXXVk2+quOtwu+7a8vFxBUqy/dD+RfW7mRCRBDAl26X0t5xkdnORxHDN0vOEH0y1btqg1oKSkBOXl5eoBJMFAt9sNj8ejnG9Yb7vdjuLiYgQCAWRnZ6v1gP8TDOM4pSMP20C2twQMU1JSEAqFVL9SMv0eXWeKiopgs9nUPbD9PR4P0tLS4PV61RpBZxsJdNlsNoP7jKyPDpHR+YUuPRwDTHuW6Fybzabmq1x/5DrCMmWdioqKUFhYGJeaUII4LId9xrXVZrMhFAopEEeOddZZuraVl5erOSz7SIJEcq6zTbm+8Lq6a5hcH9mv+tzR1xvpwESAR6ZvZN/J9y/Z9uxP6X5UUlKigDe5Rsv3PtZdQpAAVNo7XltClKy7JUuWLFmyZMmSJUtHsl577TX4/X589dVXAICvv/46qZQP1XHDkNq6dSt++OGHarn4vPvuu9i8eXPSx+/evTvpXzs/99xzGDFihGHbPffcE3fcq6++mrCM0aNHJ9x34YUXGl7ffffd6u8hQ4bg/fffx4IFC/DTTz+hS5cupmV06tRJAVqJ6ncg2rJlS9Ip3qoSx9Hxxx+fdKqu3377DStWrDDdd/LJJ5tuHzhwIE4//fQq4QFLh061atVCw4YNTV1wNmzYoH58ZiZCMqmpqcjJycHdd9+NDh06IC8vD4888giuvfbaGqnjP/7xD9PteXl5cDgcePrppwFUpCB5/PHHsWbNGtPj9+7dW2lavuOOO86Q+u+aa65B69atq11fv9+PGTNmGBxrzAKlMqi/v1qwYIHh9WeffYb27dvjtNNOw+23314p/GM2L1u2bIn58+fjiSeeQP369ROeG4vF8M477+DZZ59NKm1jdTRp0qS49R6ogDW6du2KvXv34rPPPlPbK0vVQ5mlqrN09Oq+++7DGWecAZvNhlNOOQWDBg2Cz+dDbm7uoa6aQSeddJL62+1244ILLkCDBg0OYY2OHNlsNlx22WXo0aNHpfO/efPmpq6PWVlZao3Oy8vDypUrD0o9fT7fEQMmMlOGDsBbsmTpyBXNHCKRCJxOZ0IQMpEs8MdEkUjEAJsweEpXBh1sYECTbhd2u10FdxkQBmB4EMBjS0pKFMRjlp6JgW7pAEKgiNt4HWBfmpZIJGJwx6HKyspUUJtggQzgMgjM+rvdbgWM8Dy2Dy35eC2CP9J1RortlpWVpdxN6M5BxwwJB3Fws0x+KaJDEoPKsr0Y1E9LS1Ptx/aXUAn7RQIpEtCRriC8PsuUaYd4rB54l+OH8AL/lo4t7CPpmCP7mQF46a7B+rBvZeon3p90e5G/BtRBHQmpyfR10m2E/xNc4D/dcUTvB/6TDlPSsUiCVWxfAgY8lxAH21qmDZIQAecO71/WzezLtISQ9HNk+iObzQafz4datWqhQYMG8Hq98Hg8KCsrw969exXgw/lAdx+m/uJ9yTZPSUnB77//Dr/fjzp16igXrEAggNTUVOV+w3krx6ScszIllUyrJ8e0TBNYq1YtdX4oFILH4zGMKcJNtJt3u90GqFCOPQJA0k1HOltJFy6uBeFwOM7Kno5dbCPpTiX7RqYM01PUcT6lpqbC4/EgPT0dbrdbOQBJOMputyMYDMLv9xu2maVWM4MJJUDHdY7Hu1wu1aZy/Wa6NKa9k2CTHCvBYNAAp3GtDQQCqh3NHIj0NYdtL2EuAj50kJLvMxK845oh0w7IFIt8n+H7IO+H9ZHrkc22z2VNrh0SYrTAH0uWLFmyZMmSJUtHunr16oWVK1fixx9/VNvee++9Ks87kF+HX3XVVdVK07N+/XpceeWVSR9fq1Yt1K1bN6ljx40bh2XLlhkC1X6/Pw6EufzyyxOW8fHHH+OHH35Qr2WqgrZt26q/s7OzcckllxjOtdlsyM3NrdQNoFGjRujevTuACsegmg4gmrnt7K++/PJLAEAgEMBxxx2X1DnvvvsuLr74Yrzwwgtx+0466aS4B8XHHnssJkyYgNmzZ+Ozzz5Ds2bNDrzilvZb55xzThxAk5KSolKomzlTzZs3Dw888IDhB19SI0aMwMiRI9Xrxx9/HG+88QZ69OhRYylMateujZ49e5ruy8jIQHZ2Np555hl89NFH2LRpE3766ScMGDAAGzZsQHl5uSFNwtq1a5GXl5fwWs2aNTOM46KiIuzZs2e/6j148GD06tULQ4YMwTPPPBM3f5s0aZLQSak6atGihen2TZs2YfTo0Zg8eXK1y3z//ferhAy3bduGiRMn4pNPPql2+VUpURpEr9eLWCyGTp06YerUqWp7VeCP/CGmJUtAxdpGpaenIzU1FVOmTEnokHgo1K1bNwP4A1TM95oCKv+qeuWVV3Dvvfdi2bJleO+993DWWWdVmuqvfv366NixY9z2s88+W0FWq1evTpg2tyZ0pICJBQUF2LFjR7V+FGDJkqXDX2RSGF+sjizwpxIxoCkDwTIdjgwIy+Cn/F+mhNGD8QBU0FcGs+W5dGBg0F86kjDwKlNkAfsgCD2FEc/j8TKtjXSzkOldeI5sC7N87RIgSE1NVe4U0uWI+2TwV8JTbCM9PY+EARhcJhBDuEAGkBlQlml99PuUwX7pRiTdNGTd5Wvp0KGfo7uDyHaXoIqelkd38ZBjisfIftD/llCJBIoIDOiwhN4uOiSjW1vrYJMEWeR5ZpANP4DJOSDbTd6v/qVP7wPp3KO3mRl0lAgsqOxYlsk0UOnp6cjJyVHjhZCUz+dTgJzD4VAACx2XOMb4pZz1J9ThcrkMIIff7ze4yMhxLQEt1l+6csm25/ij8wz7XY6LcDiM7OxswzgEYGh7tgPPleXr88HMaUympuL4k45Hcv7rc0COI15fQnH6eqy7a3Ht4jrBtiPYIt14JAAk+0MfK7wO74FtI0EX6YAj20P2i4QHZd/SdUuvE9ds2fYS6OK9S3c5OYbMxjqvoTs10c2IY10CQPpcJ4wo6yuPM4MM5Zohj7VkyZIlS5YsWbJk6UhV06ZN49x0Vq1aFef2oKtNmzb7fc0NGzZg4cKFSR9/yy23oF27dkkfb7fbEwZ4dbVq1crw62tZRymzX21TM2fORGFhIQDgjz/+SBgQP+WUU1CnTp2k6mUmm82Gs846a7/PT6Tdu3fXWFl0RFm5ciUWLVqU1DkNGzbExo0b8dNPP8Xtq1u3bhzotG7dOuTl5cHn8ylnY0uHTrfffjuaNGli2Jaenm6w9Ne/OxcXF2PUqFEJy3zttdeQkZERV2ZlaQSrq7p168Lr9Zru8/l8uO2223DLLbegtLQUu3fvxh9//IFVq1bhk08+QTgcNjjD/P777wmvk5WVhfT0dMO2QCCArl27Kmej6mjv3r1YtGgRRo8ejVGjRsWlIOvcuTMaN25c7XJ1tWrVqtL933//fbXK27t3L1577TWMHz++0uMYAK5p9e/f35DWUcrhcODtt9/Gli1bDNurClLxB2OWLFHLly+Pg/oOt/RFq1atMnVW6dChgwXSJpDb7UazZs1w3XXXoX379pUCP1L6+5jH40HTpk3V6wYNGiQNSVdX1XXXOJTy+/3Iz88/1NWwZOmw19lnn10jn/H+LEnzh2TXTcoCf0wk0y0RvgGM7ioSTAmHw3HuCywHMAaO6eIAAMFgELFYDB6PxxCgpyMN6wDsc/twu91wOp0oLS1VxzCQzSCv3W5Xv7bSg9YMvMqAOB0ieB2g4s3N5/MZAtuEGkKhkEq9I51zZEA6EAgYgsiRSASxWAxut1sFx+nyI0EkQkdsJ26Twe9oNKqcYXSHGBlY5/1TeioaQkUsV8IWOuCigzuUTCvF13oaII4hGeyXX2xk4N5msykYTN63Dhgw6E+HGZnCiHAV74NtGQqF1DGEQGTgn+0lg/SsK/uZ90cIg+NOtrUcc0DFF0Cv1wubbV96ObY32473yRRXHo/HkFqMY1NCCIlgKyAeYJFzSAeIZN1ZVnZ2NrKzs5Gbm4tmzZrBZrOhsLBQOXRlZGQoa91oNAqXy4WUlBTl3MX5vXPnThQVFSkQT6ZRkvVo2LChIS0U1wqOf7o5cU7J9UTeI/uVx9E5KyUlBS6XC16vF4FAQH0Y9Hq9SE1NRSAQQDAYVA9t5LrDOUGQSMJrnKscg7LdA4EAQqEQUlJS4PV64XK5DA+r2K/S/UpCInJtkJAL20yONQm82e125VTG+RGLxeD3+1FYWKicy3geoSUJJUoHKDlG5NyWfSBTysn243gj+EQ3J4I+XOsdDodaD+VaxjkuXXIouvs4nU64XC51v5xL0hGO98T75sMV6UjHNpbuYhLkkiCpfL+KRqMGVyvOY5ahw55msJAlS5YsWbJkyZIlS0eiOnfujB49euCLL74wbJ81axbGjBlT6bndu3dH165d9/vaVQV/pS677DKceOKJSR+/adMmQ2qsytSjRw+0bt06Lk3Pyy+/bHhdlcPRO++8A2BfWnJ+nxk3bpw65vrrr6/2Q0+pK6+8EkOGDNnv8xPJzJFlf7V371788ccfKCkpSfqcefPmoUWLFvjqq6/wxx9/GPY1b94cp59+etw5BNOaN29eqVuSpYOvJk2aoKSkBEOGDFEAkMfjwSuvvILnn38e5eXluOuuu+LgoKrEoOWvv/6KrVu3onbt2jVa78aNGxtSZum67bbbMGbMGFxwwQWYN28etm3bBgBYvHgxtm/fjiuuuEIde9111yUchzfddFPcetK3b19MnTo1oatOstq6dStmzJgBAAokuvzyy2skeB+NRhWYJCEuqrprWa1atTBy5Ei0atUqoavDq6++igceeACrV6+utKxOnTpV69rHHnssrr/+eoNLk9S6deswfPjwuO1m9y1lt9urPMbS0aVt27Zh7dq16nU0GkXLli1NnV8Olfbu3avc+aT69OmTEI472pWbm4tatWpVCoHrCgaDcLvdhveuLl264G9/+5t63axZM5x//vkHJW3pkZI2KxQKxc0bS5YsxWvixImYPn06XnvttWo54R5K+f1+BAIBuN3uOBCyKlngj4kYRC4vL1dpvoB9aYF06EYG3+m4wCCpdP7Q3TlcLhd8Ph9cLpdKh0JXCh0CYPmEWhgQZlCZaVoYxCYMIwPzvC6DrQzGStcU1p1pc2SAl0HdcDiMUCikAsSENMrKylRKH6bykvfLIDfLcjqdSE9PV0BRNBpV15CuLtFoVAXJy8vLkZ6ert7QCUHoKa34BYrlEfhh4FmmnZL9KO9fd6NhP7PtpeOKnnaJsIFMbaU7+EjoRo6NYDCIcDhsGJPyfAmNSAhNB3B4HsdxIBBQTic6NEPpjkASqpHpehjwl8BDImcRACoFk8fjMQAKHKPSSYnpmQh1lZWVKZBMwlh0VtGdTwg18CGH/DIt3U0kLKLfr8/nwymnnIJGjRohJSUFxcXFyMvLQzAYVCm9CMBxrJSUlKjXwWAQe/fuVSCQLJtrS1FRkQJMmjVrhszMTAXtSScd9gvnI9cgmUZKjiU5d8rLyw0QYDgcVhBYJBJBnTp1kJmZqVJp6YCIHA8SqiOYlpaWBo/Ho86Tc4XQB+/Z5XLB7XYb3Iqk64ycExxfrK+sj3QJMnPe4ljw+XxqrMo1knNYnqfDP3K8SFhQBz91Fxy5zkoQU77m9bmN41h3c9PBGN4bQSEJXHI+cHzQcU32hwQPJbDIa8iUltJ5jeXznEAgYFgvCB1JKEi2L+e8XAdln1uyZMmSJUuWLFmydKSqT58+aN26NXr06BG3b/bs2ZXCG7Vq1cI555xT5TUGDx6ccJ/uNFRTKioqqtKxiBo3bhymT58e56QzduxYQxqiFStWVFrOe++9h9WrV6NFixa477771LOwzMxMdOjQAVdeeSX+/ve/V/NOjHrjjTeqBUAlo1AohF69eqFXr141Vua8efPQoUMH+Hy+Ko/t1q0brrrqKhVAX7p0qWH/Cy+8gHfffTfuvE8//RSlpaXIyMjA8ccfj379+lnOP4dIbrcb999/Px555BHlmLJt2zbMnTsXtWvXht1ux4svvlgt1y6gYu5MmDABJ510EgKBgOG5Y03os88+Q9euXU1d4aVcLhfmz5+vXh9//PFYu3atwVVs1qxZpusokNg5JxQK4ddff61+xTUdc8wxyM7ORv/+/bFw4cIacwUrLCxUTiUvv/wy5s6di5deeglAxby97rrrql3m+eefj+HDhxv6ctiwYXjjjTcwadIkvPDCC5g5c2alZdx+++2YOXMm+vbtm/R1y8vLkZWVhU2bNiV9zsMPP6ygHv1HtFRKSgqysrKSLtPS0aHly5erv+12O4455pikXQj/LC1evDhum8fjwZVXXlnj6USPZDVo0AATJkzAq6++ipNPPrla5/LH4Z07d8bZZ58NAPjqq6/w6aefquf7QEVmBqb+qkn98ccfcfG5w1F5eXn47bffsHXr1kNdFUuWDmsxlp2dnV3jMPzBUl5eHubOnYtvvvnG9H2nMlngj4kkpCNhEofDoYLd0hHFbrcbHHe4jSQ8g8l63loGagnRyHQrDMIyCM56MQAuoRMZnKVrBN1VGFSWzjF8IBAMBlUQl0Fv/g1UgEE+n88APkkQgCAMg/rhcBjhcFi5bNDph8eUl5cjGAwa4ChCCZFIRAW+JUzBtgiHw4bgNu+b0A6wz3lDBv8JRehuTAxASwcYCavwOtLBQ/atHrSWqcsIZkiAi045HCvcJ91E2D8ul0ul/WGZ7ANel/fM+2UddXBBAg0cCzKwz2vL+5cOOTKIT3iAY50ggJ4+SH/N9vR6vQpakE5RbB9CPqWlpSgqKkIwGFRtxDZj2+tOQBSBCb/fb5g/PFa6qUgwTd6r3W5Xziwulwvbtm3Dzp07FeRC0C0tLU25b7G/S0pKsHv3buXmFYvFlINTIBBQkB+vUVxcrIAUn8+n5rdcY9g3wWAQABRwJMcW5wbhqNTUVNVPXAf4YZXzi3MmMzNT2e+yPwggSocoXjMQCKCkpETBWOxTpvgLBoPw+/1xjk6cO3LcE56RkBGBSTrHcP7LvqI4LsvKytS44rrhcDjg8/nU8XTGYX0JFQJQ58m1VI4nHeCR15eQlYTZuIaZwTt2+75UbBzz8jjpjCPrxXEvoR193BYVFan1Q65HetpEHUSU81um+uLa4XA41H6OMSnpfMb9hPfke4ucu4ke/liyZMmSJUuWLFmydCTohx9+QDgcNnVUycvLM029JNWlS5cqr3H11Vcn3Pfss89WXcn9UPv27Su9rq6XXnoJDz74YNz2//73v+rvhx9+uMpy6Bgh08OsWLECr776Kt5777247yDVVVVpZ/ZH3377LW666SZkZ2fXWJn33HMPrrjiiqRcf3w+n7Ks/7//+z98/fXXhv1z5swxPe/8889X7Tl06FA8//zz1QIBLNWMfD6fArz4fEiKrtdABSxYHbndbhQXFyuXagni1ZQ2b95seG5upp07d2LevHkAKlwfunXrht69exvcty666CK88sorcecOHDgwoUPEnj17aiQFUM+ePbF48WKcddZZaNq0KXJycg64TKDCyWnZsmWYM2cOzjvvPHTv3h133XUXbr31VgwaNKjaIJeZgsEg3nnnHdxwww244oorkgJz/H4/3njjjSoBIaloNAqfzxeXyiuRrr76ajzxxBOIRqN45513cO+996KgoMD02IMRsLd0ZOu3334zxJyOP/54dOzYEQMHDkTz5s0Pce0q9MMPP5imVmrQoAFuuummQ1Cjw1OtW7fGRRddhPPOO6/aaf1SUlJw4YUX4t1338ULL7yAU045BQAwadIk/Pjjj+q4Tz75JC69bE1o2bJlKv3q4axt27Zh/fr1h7oaliwd9mJmlqysrBr93nawdMEFF6BDhw6YMmUKrrrqKnz33XfVOt8Cf0zEAC8DuAzUA8aUQA6HAy6XS7nOyACtdAKS6VokQFFWVqbcQwjLlJWVKQsnBn8JjDCgLd1AZMoaliVTMUlHCn6BlM4QrJ8EangenVO4nc47LCsSiRjS8cjjYrGKFGI8h5AH4Q15fZ/Pp2AK6eDCdpSQDu9FBtTpfuF0Og1uEgQhABhACT34zP28ngRbZACe2yS4JJ1wpIsQ+0am9uI/imNMpnQqKyuD0+lU95KSkqJoRKDiQw8hCDlO5T/pDJXIkYf3I8Vj2UYEPuR4kvWQzlhmjj/yPuU4I0QgU3lxHwGTaDSqUpNJZxWv14ucnBxEo1E4HA54PB6DQ46ENujgJMuQ6dKkU1JZWRkyMjKQkZEBj8cDn8+HNWvWYMGCBdiwYQNCoZBKXRUOh5Urk9PpVE5XBGLoDFRWVobi4mJs3boVW7duNaT7YjsSGAoGgwqCS0lJUWOakIV0bGFqLj7AIbQSiUQUcAPAQL9zzObk5BhguVAopMYaxybdfGTqLAmqSagkHA6rFF906YlGowoolH0h11TZBtJ9iaAKXX44/6SLEM/hPUhIT6ZILCoqUmm9JPBIYEuCKlwTOPYkkCTnig4KyrEpx58sm9fW05FxP/uJa5qcPxIScrvdBiBMh4CkG5PuMsVy5JiXdWe/ScBQzmXpgMd66W5HEqbjNWUaSbmd9bdkyZIlS5YsWbJk6UjWrFmz8NFHH+HDDz803c8UMomUyMlCqnv37gn3jRkzpkonnf3VgAEDkj72hx9+wDnnnINjjjnGsP3VV1/Frl27sGzZMmzcuLHKcqZOnRr3+umnn64RV4+DpVNOOQWbN2/GlClTaqzMXbt2Jf1wd9asWepX3rfffjvuvPNOw36zdHIdOnRAz5491etAIIDbb78d77///gHU2tL+iG4zQAUsqOvKK6/ELbfcgnA4jNzcXJx33nlJp7cqLS1FixYt8MEHH6Bx48bVcmupjvRni7pCoZB61jBgwACVZkrChZMnT447z+PxYOLEiQnBn8rSjFVHrVu3RosWLdC5c+caKU+qbdu26NmzJxo1aqS23XXXXZg4cSJ27NhxwOWvXLkShYWF1TrnzTffNE3LlUhdu3bF7Nmz0bFjx6TWcWBf+sPS0lKsXr067jkxUPEc6tVXX8UHH3yQfOUtHRXavHkzioqK1OvWrVvj3nvvxSOPPIJzzz33ENZsn+rWrYs9e/aY7rvkkksOynpyJKp169ZIT0/fr3MzMzPRsWNH1K5dGy1btlTOitu3b8eMGTOwcuVKrF27NiHgfKBasWLFQXPWrEnt2bPHAn8sWUpCmzZtwvbt25GdnY26dese1rGp+vXr44EHHsAzzzyDPn36oH79+tUuwwJ/TCQBCOlKIp1zJLSQ6EuO/NWDBFXkoGJwVDqx6EFlCf0wuC1fs3wZjNWvQUnoQcIretoX3iddc2QgG4CqK119otGowdGG0oEiCRvpwW39+vxfh0hksJ4BewmOSKhIul5I8MfsWnI729UswK07W0hAADCmhJMOTbpThwyuyzFEkIj7CJZJsEqvN19LhyLCTGYOPLI9zbbLOuqAhD5eEs0BWaZMoybHHPtLglgSUCDAwGMIuhAy8Pl88Hq9hjbQxwfLS3SMvGeXy6VAGLoOEaAi8EM3oVAoZICTJOzHfnO5XCgtLUVJSQmKi4sNkIfss7KyMrhcLmRmZiIjI0P1mz6XJAjHOcc5GAgEDGNCwkVsP8JFEniSc0OOId2FSUJkXBeZGor9xrpJEFA6henQnnRXk6AJYRnp0iWBFd19TM5Bnk+nIJleSsJysmy5FpnNF33s6OuTfnyidUGOSbPyKV5Hh1Dl/cpxK8EfAplybZTHsB4SGOIYkO85so4cT3I86uAh100dDmT5+hpjyZIlS5YsWbJkydKRrnr16uHYY4/FunXrTPdX5Y5Qr169A67D559/fsBlmKlZs2ZJH9ulSxds27Ytzn3jm2++wYcffoiFCxcmXZZ0uWEwpyba6WApMzPzkAcit23bBqAihVKTJk0M+3QYCwDWr1+vgqrLli1DQUEBvvrqq4NfUUtxWrVqlXqmksiRZ9euXSgtLcWgQYNw00034Y477kiq7FgshnPPPRf9+vUDAOzevbtmKm1yncrUpEkTXHzxxejQoQNatWqFjIyMuGMkGKNvMzseQI2l/qlbt26NlJOssrKy8P3332PlypUHVM7mzZsxceLEaoM/ulJSUtCwYcOE+3v06IE2bdoAQNJpb+hOJZ8V6+Nk1apVmDhxYjVra+lo0IYNG0zBvubNm+PMM888BDWKV//+/RXgpqtJkybo0KHDn1yjw08pKSlIT0+Pe+a9P/L5fGjZsqV6vXbtWqzplV/vAAEAAElEQVRbtw67du1KGkisrgoLC9X78+GsnTt3YufOnYe6GpYsHfbKy8tDNBqFx+NBVlYW3G73oa5SQhUVFaG4uBhpaWnIzMzcL4ciC/wxER1WCJOEw2EVVGXqGZm6SzraUHS/YMCcQAH/MQAvnQ/oxMEAK1MVsTw6azidTpWqhu4pEqJgcNcMnmEQVrpn6HAL606XD6ZoYtosOuMEg0F1336/Hx6PR0EYDBrrjki8F/7PdrXZbMrRgu0Qi8VUEFu/D8DoOqM7adCRSO8T3i+AuPQ6DIbrYJV0p6EcDgecTqcaB6xzMBhEYWEhiouLDXCUDKS7XC7D+TpUwXrJoDrhDb2/WG/eM9uN9dedbcxcRWTbSKCJ/+R5sp10xx4JrOnOLDIFmxxnBAUk6CGBOR0OCwaDKCoqgtfrhd1uh9vtRnp6OrKzsw1OLLJ92G90yzEDJwAotx6g4sNdIBAwgCMEXfx+P0pKShAKhbBnzx4EAgHV7tJFhenNjjvuODRt2lSl6pMgjkzp53Q6Ua9ePfh8PsOX40gkosZYOBxWY5sfPmU6LOmew3ZnX3k8HnVdj8cDm82GQCCg0uxJuEyCWkzfxfHMtYf3EggEEAwGDSm03G63SjvI/pRpBzkGJeTGsSBBEelGJkElCdXJeUNHMZZRXFxsAH04BnUoUwJJEoaRY0WuqfIcHivHO1PF6YCPrLOsJ8uXYJB0F5LgjKwn5yLHQWlpqVpXuGZL2e12tWbJdaOoqEiNJ76fyLJ5PxxfLIPtyrRlEkzj9XRQits5HixZsmTJkiVLlixZOlLl9XrRuHFj/Oc//zHd/+WXX1Z6vs/nO+C0Fffffz9GjBiBSZMm4eeffz6gsqTatGmDKVOm4PLLL6/y2NNPPx0jR440BRfuuOMO3HLLLUlfd8SIEZg+fTp++OEHjBs3DgAO+xRUicCEP0tr165NuK9du3ZxY6ykpAQrVqzAmjVrMHnyZOTk5GDp0qWYNGnSwa6qJRP17dsXd9xxB3744QfT/dOnT8dLL72EcDiMCy64AGeddZYhBVgiPfzww5g5c6Zydz5YwdFk9MEHH+Crr77CaaedZrqf8JpUVQH+lJSUGgFHrr32Wjz33HMHXE6yWrJkCUpLS3H99dcfUDmTJ0/G6NGjD7g+3377LWbPnp1wP1N0vf/++0mnvWH6QT7Di8Vi+OmnnwxjMDMzE7Vr197/ilv6y2rp0qX46KOPTPdddtllpkDrn63K1qeysrJKYbqjRZ06daqRlIZUs2bN1PvZ2rVrccIJJ+Crr77C9u3ba+waUvn5+Ye14yRQ8bx+5cqVWLJkyaGuiiVLh73OOOMMnHnmmcjIyDA4bh6OCgQCWL9+PQoLC9G0adP9et+zwB8T8YOphHmYQkXCFRICkgF/CRUwUO5yueByueD1ehW4QyiGAVdCIEwLJkEGOi3QUUe6fehOMnQnkUFm6cIA7AOACPmw3hLE4P/SdYavZVqh9PR0FbyXaYoIKellSmcJpjJioFs63TBgTMchSjojMTgv4Q6Z5kZCJUz9xX6keG0dTuK9xmIx1X8EGwhdsT50f2FqIVlOMBhUbixut1ulG3I4HAoykgFzCREQZJCggnTgka5PvB7hgz179hhSRElgiPVnv3I8cgzorir6uJZwAvta/yUHUzYRIiOIJZ2ZeFw0GlVwiYSSeE2WwT5lajce73a7kZ2dDa/XG1cPwlcSajIDoAjhBAIBFBYWoqSkRMFF/GAp5438Asu6yvblPqfTiczMTMOviHS3HbrxSMcqgoasL1OCsf1cLpdhfhIu5Hji+OSY4brj9XrVHJXp5XhNCSPSOUfP2c424HHBYFCBgFznSM0SDuE8kQCbnIOyv3kfcu2gZKpEAlC8j3A4DJvNplLAMd2gDjfq80zCRmZuOXIdleNGurHpbmbcr7ulyWtyzeNaoKfBknCUBOekexHHbXl5OVwuF9xutwJ7+F5BpylCjNK9TqZV43sPQSvOXx7ncrnUXJDrqv6eJ8Ek9quEtOT6bcmSJUuWLFmyZMnSkaoNGzbgX//6Fx566CHT/fn5+Rg8eHDC85csWYINGzYccD0efPBBXHHFFejWrRuCwSAA1EhAeMCAAQl/0S41ZswYPPvsswd8PQB46qmnMGDAAJx66qlqm3QBOtxUXFyMpUuXHrLr33HHHYaUSVLbtm3DAw88YDrGFi9ejMaNG+OJJ56Ay+VC27ZtccIJJ+DUU09FTk7Owa62pf/p0UcfRcOGDTFq1CgMHTo04XELFy5EZmYmgIrg57Jly6ose9GiReo7t9/vP2gBzPLyckybNg2LFi1KeExqaipycnISpr8xA3+YEqwyrV69Gk2bNq1W6ioz/Vlz2O/3Y/78+QAqHHsWLly4389F6tWrp57PHIjsdnulqXhCoRB+++033HTTTUmXSaDnmWeewQsvvIBXXnkFvXr1MrjgZWVlITc3d/8rbukvq7PPPhtnn312wv233nrrn1gbc40dOzbhvldeeQWvvPLKn1ibw1Mnn3xyjTofde/eHQMGDEDfvn0xZswYNG7cGB07dsQrr7yC6667rsauI7VixQps374doVDooJR/oNq5cydWr159qKthydJhrczMTPzzn//ETTfdhJ07d+KFF17AsGHDqnTmPdTasWMH8vPzUbt27f1yv7XAHxMxIKunswqHw3A6nQrWYcCfgVQJM0jxlxgMzDscDuX4It19gsGgCvTSqYEBUx0EKi0tNaTikU4KKSkpBogikSOODKibwREEEBjg5RcKmcaHgWu3260CvnT+SUtLU/AEgRwGkSXIJN19JAgi085IyIlQAIPnElDRU2mxbSScojsgEYJhcF2WzfYg0MTAuewbCZdICItgDYP4dHpyu93w+XyqDEJHEgahi5S8bwknSEiB5cjgO7+0sTx5X6FQCG63W7lOyfGqO6tI+IvXkZJ9I9tXL5fX5Qcl6RoiAYNoNGpoewIReroxio4znIcEYMzS4EkHJf2eJURRUlKioA39vuW12WcADK5bMjWWHMucm7rLCccOoTCHw2EAgoCKL9oOh0OV6/F4DKmfpKsW5xjdxXhNXt/r9SqAw+FwKLcgtj9TD7JvJBDCeRoKhdS44nGE29LS0pCdna3WUK5X4XBY1VmCiKw3/5Y2mrwfud7K9US6ZXHtpAsaXYcIS/r9fsN9yjFEdzMJ6khY0MwVTa4tcj2U/8wAI+n+xWsQntFBIc4rXl9fxyWoJ+eABIck/Me2lNAfoR+OCdmuZWVlKnAgx5s+fqUTk3QH0ttIpheTQKslS5YsWbJkyZIlS0eq0tPTTdPUUBMmTEj4YFH+GKImVFhYiGeeeQYfffQRnn766QMu7/PPP0/KCePOO+884GtVpj/ze8MDDzyA1157Dd9//32Vx+bl5WHLli1YvHjxn1CzeD3wwAO47777TPdt374dTz75pKkLVI8ePfDAAw9g5MiRBnDghBNOwOeff46ePXsetDpbMqpBgwa4/fbbqzyubt26ylnK5/PB5/OpdEqVid/n+V39YMhut2PhwoXw+/1JO8Lo2rp1a9y2pk2bVnne3/72N5x++ul49NFH455VVEd/1q++t23bhuXLl6vXt99+e0JwryrVq1cPDRo0OKD6XHfddejUqVOl6S5sNhv8fn/CVHS6br31Vpx22mlYuHAhnn76acMzUAk5+Xy+w8K5xdLhp7Zt26Jjx44J9zdu3LjGPz9VV2+99RZ++ukn03133nknunbt+ifX6PBT48aNa9zV67333sO0adPQvXt3OJ1O9OvXD3fccQf69et3wA6aZpo8eTJOP/30Q+qYV5m2bduGX3755VBXw5Klw1qFhYX4/fffUVJSgmg0imXLluHrr78+1NWqUtu2bcMHH3yAadOmKWi8OrLAHxMxUAlABUntdrsCdQCoQDG/WNAJRoIldrsdkUhEgQQM3Mp0Vn6/XwVZ6fZDsMThcCiXEwmy6IF+OnwwsErACIAhWCyD7gzgyzRGDPYTYiD4EwqFFJhA8In3zSAvXY+4jaAMoQyZ3qasrAx+v1/dR3FxsUo5pAesWV+ZIkh30KBjje7kwqA5wSvpsiGdZHgdGcDXASBem7CEBJZkeh6Hw4H09HSkp6er42S6IgIUEtbi9eggRVCE9SLQoAMkPE/WmaAM+1KmbJJQSiAQMIVQAMRBFTpEI+sMGOEfPUWQPq8I0Em3FOk8ImEvlsdrSAiCIAdhokAggPLycrWAS8BNtrMOlVD62JFgh5zr0t1FAhdOpxM5OTlo1qwZsrOzVZ/Q7UnOMwmRMCUTYRaOSY4TrgHAPqiD6wTbjC5ZFNco9p/sV5vNhqysLHVNn8+noEPCV36/X80LniNtpCV4KNuSkAjbXzo+8TyOTbandKKSqRPl/CAgImEZ9hOwb+1le3IMEq4kcMh+ZPnsP5mOS7r2yHEoATDOLbmeSqhF1kGuXbrrmp52S7+uTNnFNV5CO6y3Dt5ImJRp5OTYlnWQc06+D7Ac1lHOD7YD5yProAODuhOWBBnldSxZsmTJkiVLlixZOlJVq1YtnHPOOfjss88qPe6rr74y3V5ZsFXXq6++ildffbXK4x577DFcdNFFeO2115IuO5G6deuWFASiuzakpqbiyy+/xBtvvHHAdQD+vKC83+/HhAkTcMstt+DFF19EUVGR6XEFBQWIRCJ48cUX8eKLLx6yX1uXlZUldOdJTU1Vqcylrr76ajzyyCOYMmUKHnrooTg3pYkTJ+Lnn3+u1KnKUs3I4XAgOzs74TiTys3NhcfjUa9TUlKScmZav349gIpnJZW5uhyI+GPZnj17VpneMJHM0tVlZWVVed7pp5+O9957D8CBuZz9WS5Xu3fvNtzrkiVLkgZqpPLy8vDcc89h8+bN1T736aefxiuvvIKJEydi3Lhx6tmZHF9SHo8He/fuTarsZs2a4ZFHHgFQca+6ysvLsXbtWvWcu3Xr1tWuv6W/vqZPn27qalZSUoKpU6fioYceMo1n/JmqW7cudu3aZepIuGPHDtP336NNDRs2VD98rynREEBXkyZNcMYZZ9TotaiNGzdiwYIFlbraHSqtX78emzZtOtTVsHSE69hjj8U555xz0D4nHg765ZdfsGTJEqxZswalpaVHxL2OHz8eDz/8MJ566qmE6YArkwX+mEg6SwAwwAgyqM6UTtLdQXcUkWVI6EQGlbkfgAGgAPZBL7rjiB6IZQBbB1f0v+W5sp46bMGy6NQhwRdZHtNmEWiQfwP73I5kvRkglscTaqH7iR6Ep3TgR6bjkdCQBI1kW/I+dehEtgPvldc1c71hUF93VWJQnsFyHs9f10iQS94b21o6m7BedGSR7ks6/MDrsA6EqDiuJETB9E10aNEdqjieZABfv3e9L+R4TNRfZu0mXVQkYMVy5DkyRZCeiojAlO7UZObwU9V+CQyZpS2SY0Luz8jIQJ06dQyQUlpamnKdkWNPjicJWhHWSE9PjwNOWJ6E4LgGEUbh3JPrkQ4/ScCN7kihUEjBNXIO8p6lW5c+V2S7yrmip1xLBMLI9pXuNHqZ8poSaJTnS8hEHz/yfjhmuU0648g1TY4HfZxzuxwD+hyR6ayky410iJPtyPPY1wR8ZHvqjj+yLmwvzgkJf8m+kvenQ1Z8zetKUE0vx+w9D9gHFOnSzz3UX9QtWbJkyZIlS5YsWToQtW3bFvXq1asy+Lp48WLTVDbTp09P+lpNmjSpVsqrZFIBVSWv15tUmgb9V+8XXnghevTogQsvvDCp6xx77LG4/fbb0bdvX1MXkz8rxcIff/yBPXv2AACOO+64hIHwdevW4eeff8bWrVsxYcIEFBcX/yn10/X7778nDC5mZWWhbdu2cdu7dOmCDh064D//+Q8AYNeuXYb9H3zwAa6//no0bty45itsySCPx4NAIKDAlaqOle5M0WgUl1xySZXnFRQUqB9uJQPS7I/44zwA+5166txzzzUttzrq2bMnWrRosV/X/7NcxaQDPkVXpuros88+2+9fqw8YMACXXnop+vTpo56X80fKuvr374+pU6eaQjxmqlu3LjweD+bNm4eXX345bv9bb72Ft956S7VBnTp19useLP21tXv3buzcuTNuO39wvT9zpqZ1xhlnoEOHDqZgyxdffHFYQiJ/pviD3D/Lmcnr9R6wA1plGj16NP7444+DVv7+ygLMLNWEmjdvjmuvvRYDBw481FU5aNq+fTvGjx+PKVOmYPLkyYfsu9ufKQv8MRFTohDCkYFdh8OhgsR0U5COHToAQccOBkJlKhZCAXxNwp7XYxC6tLQUwWAwLvUVr8sAsgyW881VQix8LdPhmDlN0IEiEokoJxCW43A44PP51Bu33+9XEA/hFV4nFovB6/UaXDVkwJnHSVcT2Z6lpaUqtZYO9UhJZx0ZyNcD2xKq4vF2ux1ut9sUNuGXHhkUl1ACwROCAuxv3X2orKwM4XBYtaUEE2QQn24uEiiz2WwIh8PqOoR1pIMH/3GsylRMrLeEZjie8vPz48AgKR0Kk64mcsxQ8l70fRKKYHtIxxwd3JLQlQ5ScB5kZmaqeRgMBlFcXGwoT4cv5DyW40P/W96b7AdCG4SWZPsQkmI6KY4ft9sNr9erUuFxfhD80J2cbDYb0tPTkZGRYUgpx3WCdWQasF27dqm1SqaL4mt5L6wzyfiioiIUFxertIC83/T0dLhcLuXKJeEzlkfIiHNCprdjO0mnH5lXntvlNSVcQ2cqtivbwGz9Y7o6zifZL3qKLOlQRJckuhIRIKNDk+5Ew2tKCFMH1SRYpEOUvLdgMIiCggJVZ32esUwJxxHe0eeoPo7lGstxqoOEEliTayWPZ/vKdVyfp5S8fwkuAjCsU1KyPAv8sWTJkiVLlixZsnSk65tvvsHOnTsxe/bsSo8bPXo0nnnmGcO233//Ha+//nrS17rsssuwcuXKpI8n2HEg+vbbb5NyDpKQSEpKCh599FHY7XbMmzcvqetceOGFGDlyJKZNmxb3i8a+fftiypQp1av4fuq4447DPffcg0suuQQXXXSR+k4NAE899RQ++OADrFixAl9++SU6d+5cpdPTwdaXX35pmsoLqHASWbJkSdz2l19+Gb/99pty5+b3fKAC+lm0aBFKS0sxfPjwg1NpS0oFBQUYOHBgUuBPbm4u7HY7vvzyS8yZMwdNmjTBk08+WeV58+bNw2WXXYann34ac+fOrYFax4uB+DZt2hjcogFg2rRpWLRoEXbt2mUYaz/99BNGjRqlXg8aNAi9e/dWr5999ll06dKlWvVo1aoVxo8fj+eeew5169at1rnPPPMMHn30UeTn51frvOoqFovFpVxbuXKlaQAqFAoZ4BnpDJQISkxGpaWlqFevniEFD93jpVq0aIG33noLw4YNUy4+Valt27bwer24/PLLTZ3u3n//fUyaNEndb2VpMi0dvYpEIqbpNumO7vV6D0GtjBo6dGic2yH11ltv/cm1OfyUm5uLOnXqmLrzULNmzcJvv/1WI9dzuVy46KKL8Pbbb+Ohhx6qkTKlli1bhszMzBov90AUCoXw+++/H+pqWPoLqH379mjXrh3uvfdePPvss4e6OgdFO3bswIwZMzB+/PhDXZU/TalVH3L0iYFQGbiVH4BloJbBTjqsSOBBAh3cL11hCALx10sOh0PBPgAUnCOBE1mmDAanpKSowK2e6olQCOsuA+j8XwIddIThP9q2Op1OdT2Xy4VQKKSAlIyMDOUaQhcR1lMGyxnQJmzDtpXpihwOhwpEs278m5KBdZlyjf3Cfby2DiawHSQwxHbieTKoL4P7rIu8JwazzVKLhUIhBUfwuoSbYrGYgkL45Y+QF9sdqAikS/hDrxMhCfY507PJNrDb7SrtVHFxMUpKSuDxeFQ6JDl+JSQmU2zp40ZCHbLd+FqmmpJ9xzEmoSxZBiVhEIrpjpxOJxwOh+HXfyyPc0COaemww3rIOuluVLoLi3SkkpAZUPGwTKYe0x1qCP5xPpeVlRkeiHAspqamwu12KxeejRs3qjbhGsT7Z11CoZABJiPgIdP9sW0IkknYA6h4aMDxyX6R6eQkdMJrsK04H2gPrKd6kv3pcDjiHNQklMM1VUJXLINrkHTSkmOH4tpNqDAcDhvGiNvtVn1gs9kMbmPczvuSawfnMc+XgBrHAqEbtge3RyIR2O12BUJyvZNAnpw/cl5xbMq+lM5BrAfbU65bEuyTa4aEDnl9eT0CdXL9kPerl8X6Ms2ZfD9JBNnJa1qyZMmSJUuWLFmydKSqX79+pj+k0TVt2jS0adMGN998M4CK70Zt27bFli1bkrqO3+9Hr169sHr1alOgQ9cFF1yQVLlbt25FcXGxacqV9PT0pFK8XHfddQpCKCsrw+bNm9GmTRv8/PPPaNWqFTZu3FipowZ/Ge90OtG8eXPcfPPN2LRpE77//ns89thjqF27toIEbr/99krrEgqFUFJSgoyMjDgIIRm98MILhu/Db7/9NjweDz744AOsWrUKAHDMMccAqPj16KHUnj17sHLlSpx//vlx+5YtW4Z///vfcdtPP/105Obmwufz4ZprrkGbNm3UPn4/e/DBBw9epS3tl9LS0jBmzBjcdttteOWVV3DOOeckdd7y5cuxfPlyfPTRRwelXpwnEydOxPnnn4///Oc/2LRpE5o2bYqVK1fi+eefB1Dh6tW1a1dMnDgRQMU8vffee3Httdcqx4znnnsOF154ITZv3oz777/f9HqlpaX46aef0KxZM1O3mLPOOgtnnXUW7HY77r333mrdy969e9XzvIPlUpGVlYXjjjvO4AC3bds2/P777waHrlAohGHDhmH79u0IBALweDzo168fxo8fj+bNm1caTK9MAwcOxPHHHx+33eFwoHHjxsrxDKh4tuZwOLB9+3bTVGxmatWqFYqKiipdGzdv3oxu3brhlFNOUT+M05WdnY0PP/xQpaqzdPRp9erVGDVqFJo2bYq//e1vAKCeq//zn/9EvXr18K9//Qvffff/7J13fBTV+safTd3d9NBCB+mKUgSjNAVFxYLYwIKgIhbsoFwbtqvXevGngqKIFAugXIqoYEGaijRBREA6UkNJ35LNJvv7Iz6Hd87OJpuQEMB5Pp98kt2ZOXPmtOzO+53n/fm4181ms5k66lGbNm06jrU5MdWyZUukpaUZ4jlS27ZtwzvvvIOWLVviqaeeOuaUrg0bNkTDhg1x9tln46OPPjqmskIpXOez46WCgoIg10ZLlsorp9OJtm3bomHDhli3bl3Y/+8tnfiyHH9MRLgDgAIopOsBcDTFFYPKeqBfui/IVD5AyRcju92u3GEYLKYDhwQ59IC4z+czBEtlwFo6e8hgsQwky0CvDGrzWNaT8AiDutK9g84/0dHRClRifQkasY6xsbHqXBISkO4ldO/gPjJ4rDurSHhKd46QgXDd3UX2rSyPgAgD3T6fD36/3wBNEH4hpMRzsz3ptMLro6SFK7fL8xP+iYqKQlxcHOx2u4J7CBrk5eUhJyfHABpIpw49nRIBCTqXEHRif3q9XgXGEBpxu90qVZYEAmRbS4ciGdjXYSHd9YfjVzroSKADOJrWyOFwwOl0wm63G1x2pKsIyyEQJUEJnodtT/BAhx14nBz33E+OTTl+ZDvLa2Hb+v1+5Ofnq76i9NRRrIPdbleQlqw34Q3ptEVwMBAIIDEx0TDGExIS1BgrKipSYJd0SmId5Jzl+EtKSlI3QzlXueYRHCEcpsNR0tVHzgPCb7GxsYiOjlbrHNvU4/Go65LrANs4JiZGwTUEHnlNMl2fhP3ooMbxLt1t+L5sX+kkJM/Nc0mHIdZP9j0lx6gEwThHOKflvNZBJZYvYSP9nNIpTra9GThj5iYmx64EdehuxDFFGIxucPr/CHmNvDau3ToEJfc1S7XGNdeSJUuWLFmyZMmSpZNZzz//fFgBi3379uGee+4xHFeWU5Cu//znP7j55pvD2ve7774La79ffvkF06ZNM93WtGlTXHvttWWWMXDgQMPr6dOnAwBGjRqFH3/8sUzHnoceesjw+t1338WsWbOwevVqdOjQATVq1MDHH3+MBx54AF9//XWpZS1evBjdu3fHgAED8NRTT2HJkiVl1l+XvF8xa9Ys9O/fX0E/AE6oJ6w/+eQT3HHHHcjNzTW8f8kll6BLly5B+990000oLi7G9OnTg2CQLVu2GPb79ddfDWCQperToEGDcO+998LpdBrcbB566KGwU+pVhThXPv30U8ybNw8zZszAc889h9tuu01BP0DJ2Jo8eTJWr14Nv9+P9PR0DB06FDfeeCPuu+8+jBs3Dnv37sWFF16If/3rXyHPN2bMGJx//vkYPnx40P2EQ4cOwefz4dChQ0HpB8PRmDFj0KVLlwrP71WrVuHee+8NGYj1eDx47rnnTFN0PfHEE7jvvvsUaPnTTz/h9ddfx5w5czBo0CB4vV4sWrRIPdTmcDjKXb/TTz9dgafUzp07MXr0aGRnZ6NFixbq/eHDh2PBggVwOp3lCgK2adMmrED0xo0bMXnyZMyZM8d0+/nnn4/evXsH1dfSP0efffYZHnjgAXz//ffqPafTiQ4dOmDIkCG44ooryu0KVlkKBAL4/PPPQ26X7mX/VNWqVStk6q3MzEx88cUX+PLLL/Huu+9WOphav379Si2PGjNmDAYPHnzCpNdi5gJLlo5FzZo1Q7NmzZCUlIQePXpgwIAB1V0lS5UkC/wxEQOrOsRCpwwCH4WFhSqFE10SZGCax/DLCB1h9IA4HV9kCiSZXzc6OhpOp1MFoxnQBo66MzAdlHSy8Hg8yM/PN4BLenAWQFCwnGBATEwMIiMjVf3pWqG7qPAYAgsMThNSYhBYgjDSiUJCPAwG0/mEwW3poiQD2vo1yacyWDZhBQa4+UMQQgIhMiWUDLZL9wx5Xl6X3I/nZhuECorr6YwcDgdiY2MRHx+v2l4HEVgm25v1ZbBfgkccPxKOcblcBjCHrk6EVyQwxR8J/hBeoHQHoNKgA13cJgEAlkEwiUAXoS7WqaCgAIcPHw6CM1hfCWBIYIl/E4CRkA77VzrWSFhL9rl8j+3r9XoV1MLydACJadjYV4To8vPzkZeXp8Y715bY2FhERkaq+e90OlX9+PSNHJuESrxer0o75vP5FJzBa5BuNjVr1jS4SwFAXl6eulbpfEaHL8JxiYmJSEpKMrgNsR5c36SjT1FREZKSkhToJuexdJaRc0hf9yRko0MyuksQ+0iOKd1Nh3UlgMZrkfAZxze3yzHM/czgMo5BnleOObYlxbEmy+A5JEDDeSrTu8ky5Bqmu3PJOREIBOB2u9X/DVlf+f9FusXJtI4ADA5MZmkU2V5c4yVkRDc5S5YsWbJkyZIlS5ZOVvXs2RN9+vRBu3btwj6GKU7MHggoS5s2bcKQIUPCcvtYuHBhUEoZM1188cV49913TdOILV26NKyAzO233254TVcPoCT4U1YQPjU1NSh4Ybfb0axZM9hsNhw6dAh79+4FUAI/hdLhw4cxYcIEbNq0CbNnz8aLL75YKkRQlnbv3h0yMH2i6LfffsOECROC+u+0004zhba+/vprNGjQABEREXC5XFi+fDm2b9+Ol156Cf/+97/VfuPGjUOHDh2qvP6WyqeUlBSD080bb7yBli1bVlt9fD4fXnnllbCBxE6dOmHOnDmIjIzEtddeiy+//BJjx47FPffcg0svvRQjR44s9QGhm2++GWvWrMG//vUvgzP/22+/jW7duuGOO+5ArVq1MHjw4Apdz86dOyvsTLxq1SpkZGTgyJEjpqm7cnNzMXPmTNNjv/jiCxw4cEDdX2nevDmSkpKQn58Pl8uFpUuXGpzi9bRc4WjXrl1qHaWWLl2Kn3/+GTk5OYZx1KxZMzRv3hwbN27EmjVrwj5Hs2bNsGfPnnLXTSoqKqpc/1MtndravHmzwcVdgtZ9+vRB+/btj3udIiIi0L17d9Ntubm5VeawdjLp888/x8cff2z6OXT27NmYPHkygJL5vnPnTpV+9Fjl9Xqxffv2Cn3GLks///wzpkyZUunlVlQyHmTJUkXVrFkz1KtXT8WXLrroIrz77ruw2+3VXDNLxyoL/ClFDHICwU4P0glHAi4ADPvpjjMyQCyBCgZeGfyVTj/yh5LgjHTEkCmNAAQFr83qI88vXSZk8JlBf9ZXigHdiIgIQyojCUIxEC1TXLH+et3odMRzyqC+fg0sW3fckPtIJyQJbMgypIsLANWnsp1lcFxCITKtmQ4eSVhId72R9SA8xLRnDocjyGGHgXSOTelCJYEHtkN0dLSqI8ERmX6I0JFMdaSDP3IsyPKldNecUGNWSvabPLfZWOaPdLFhO+h9LttCdx7R+1iHufSxIc+ru5VIlyH9eMIb0oWG55Xgh4SGZBkSJOF4kONKzjEJjnBNCQRKUotJmMpszgJHnXIAKBCDcJBsGx7D+vGaWDdeF8EzHRhjmTLVoVm99LagpBOOrL/Z+JLQmdk4lv0sXYQk8CPXE9aD9ZbjQnfcknXW5wnbj/NOB8P0/fmeBDJ1OEheI+E9fS2Ubc82k8fo6zxTK+rtK8e3hBxlXfi3DqeyHhKMZfo7S5YsWbJkyZIlS5ZOVnXs2BFpaWn47bffwj6GQeWKBuuLi4sN6VhK07fffltm0JYuwOvXrw/aFuppbanu3bvj/PPPD3r/wIEDAICcnBwsWrSo1DImTJhQarC4QYMGeOCBBwCUOGGE+h4RERGBXbt2Gd775ZdfTL9rhSOZjudEl5lLSceOHYPey8jIQH5+vkrtNn78eHz00Uf46quv1D49e/ZEQkICAKBx48ZVVGNLFVFcXFyQ20tkZCTOPvvsaqoRMHbs2HLtz9R+TZo0CdrGdO2h4JtatWqhdevWaNu2reH9zz//HJs3b1bu7cfiBFJRZ2LWfeLEiZg9e3bQ9qSkJINbk1TDhg1x6aWXKqhh1apVyMnJAQDs2bMH77//PgDjvbzyimu9lNPphNvtxrfffou5c+fi7LPPRnJysnLt/vDDD8uV3iYxMTGs9JCl6a677rIcBywpbdy4MSQUUq9ePdM0pVUtm80W0lXG7XZX+DPHqaYlS5YEOYbl5+dj/fr16nNzeno6OnXqBKfTWSnn9Pl8SEhIUJ9hqkKhUhRWh6z76paOVfHx8YiLizO8FwgEULdu3WqqkaXKkgX+mEg6wkj3g6ioqKB0TSTuGTSX7jkyfQoDpUynxIA7nX94voSEBBQXF8PlcikYRKagYrCWX4L4t3SoAI4CEaTzpPON7kjBYLEMEBMiiI2Nhd1uV85GEiSKiYlRzh106XE6nSgsLFSBf4IkRUVFyt1IpqxhXc3qLyEh6d4jHSak646EaWTQm/AFYQXpvKFDWizb4/HA4/Eohx2CFDJgTzcVnofwgIQJZMow3V1IunUwjRvbPC4uDikpKYiPj1duLdzG1E8ej8cAE7AN+aQc2yY6Olo5xyQmJhrqK6ECjiWOA/kjoQMdLpPQkZnrjw4ccE7I1HDS7YnjnWCAHBNybDkcDsTHx5s6CzFdmEzpxHHH/pdQiIRBdCCFdZXQhoQg9LVDzj+ej2LZdM3h+aOiohAfH4/k5GTY7XbVb06n0wBWcI5x3OXk5BigFb/fj9zcXJXGjeNPAjtyzvODtdPpVO5kHGd0W5KQGds9Li5Otb0cN6yXTGvFcc95k5ubq/pRH38ybRnXBZmGi05scr7KuR4K8JLjUsJ9dFiT5UZHR6tzS0CIZXFM0FKzoKAgyCFISkJuhPikk5kE8TjuJBAnIVO2s56ey+v1qmth+/O6+FrOh6KiIuTn5xvczQj8yHEtf0vwjv3K66O7E1O8ybVaBwDZ73TQs2TJkiVLlixZsmTpZNV///tfrF27Vn3PLksRERFITk4GAFx99dUVCtaPGDECd9xxR1j7XnnllXjwwQdL3WfatGnIz8/HxIkTg7Z17NixVIcd4KhjhO7Q0LVrV6xatQrvvfceli5dWmoZkydPxv79+0Nuj4qKwsiRI1VKo5tuugkjR44M2i81NRW33XabaR0roooeVx1asWKF4fXatWvx5ptvBu33yy+/YNOmTbj66qsBlEBXzz77LH766Se1j/zbDB6ydHx0+umnB71Xp04dBYesWbMGbrcbL7zwQlgp+apKu3fvLtf+b7zxBgKBAD799NOgbVOnTkWXLl2wevXqMsspKCjAwoULMWnSJDVX161bh7vuugtOp7NCTiAOhwOLFi0KO1WifuyXX36J1157DTt27DA8qAeU3KcM5dbmdruRlpamXl933XXq7/Xr12PGjBkAjkJJvNdXHhUVFQWl4UpNTcW8efNw5513olevXliwYAFWr16NO+64Azt27MDrr79eKrCkq1atWrj++uuRlJRU7vpRV155Jdq0aYP8/PwKl2Hp1NGuXbswb948020tWrTAhRdeeJxrVDKXmNJUl9vttoDZvzVjxgw89dRTKk6Vn5+Pb775xrD2b9y4scIua2Zyu92oVasWHn/8cdx///2VVq7Upk2bqqTc8kpmWLB06mnAgAHHxXmzRo0aBvDH7XZjz549J0xKO0sVlwX+mEimnJHuDBL2YRCVQXcZRJaBWabcYUDV6/Uq55zIyEjExMTAbrfD6XSqwHNhYaEKykrHB5lySjq1ECACgl1PKAlwMHDLoLF0GgKg6iUBILfbra47OzsbhYWFCkzgFw4Jr8jUV7wGnls68LC+Mj2NTLWUmJhoAEOkG4t08NCdVXgdBAf4voQIZJofCc+wPgQUZP1l3dh2dLjQ4RkCFOxrBsvlWCLUI9uBoI6EJgCo8UUATdaHdXG5XOoDFcstLCyEy+VSKZ8kQCBBKMIMclxzbEsAIhT8ozt/yPf0uSVdbYCjYAHrQNBLQkQ83u/3Iy8vz5B2SAdxCNuxHpwjodKZyTFm5trCL9e6O4vuvCVBNLkWSJBFunrJsZqTk4OsrCy43W5ERETA4XAgMTFRgUBsE/aX3W6H3++H2+1WgBGfjOJ1ENaTTl2EDevUqYODBw+qdG+E+1gfgiPsa7vdro6lm5RMV+d2u2Gz2YKgJsKDCQkJSElJUW5WTHvmcrnUukgAh9Aj5yfXMOmSxDkl3+caKgEa9rGch1wbJUjG/bldAj+y71lHto1cY+WY4HrDMce0V3KuyzpKxzCOL+mgw/PKNiAkRwiRgCjXYh3g4TjIyclBTk6OAUyU80Wul+xfCXLqIA+P5/hgudINT/4P4hyviD21JUuWLFmyZMmSJUsnkpYuXYp77703rH15jwgoCbj26tWr3OebMGEC0tPTy1W/UFqyZAleeeUVADC1dHe73dixY0eZ55g8eTKGDx9ueG/79u2YMGECXnrppbDqGY5Dx4gRIwAA06dPx2uvvRbkfJSTkxPkXJSWloZzzjknrDpIZWdn488//yz3cdWltWvXqr9zcnIwcuRIU9eR4cOHo3379rj++usRHx9vWha/xwHA2WeffUxBfEsV08CBA/HDDz8EAT1169ZFUlISHn74Ydx9993qOzWBwpNBvXv3hs1mQ8OGDU2379y5MyzXmA8++AC9evXCXXfdpd5bsWKFuhdSEecZj8eDe+65Bx988EG5j5VwzJQpU3D11VcHAUSh0gMdOXIEP/74o3pdo0YN0/04LysC/gAIck5JTk5W98cyMzORlJSE0047DStWrMC6desAAL169cKyZcswd+7cMsvn/5EXX3yxwvd7ioqKkJOTg6uvvtqQ4snSP1dz587F9u3bg96Pjo5G3759cdpppx33Or322mtBcB9QkmbzhhtuOO71OVE1Z84cvP7668jOzsbKlSvx9ddfIyMjQ23fu3cv3njjjXI5i5WmtLQ09OrVC48++qj6zFjZ+vnnn037/nhLxjEtnVpq1KgR+vbti3nz5mHGjBno0aNHlZ3rzTffNHzfOXLkCDIzM5XroKWTV9bqYCIGOW02mwHSkY4JhAj4hYLBeAlUyGAqg/1er9cQ1I6Li4PT6UR8fDwiIyORl5enABtCFxSD06wXA9Pcl44bDAozkMwArnTE4Ad7vi+D2JGRkSrIL911eL20L+V5ZeCa55QOMLp7CqX/c2KbyVRPemodBsAJUejOHhKwkampuC+D4jExMabphgjK0NEoPz/fkDJJOrTokAvdgbxerwFOYF0k8CThGXkduptRRESEAbYAjGmPpAORBHeAEptZns/j8Sh4KycnR7kFybqzbQiNMEgvQQP5o8M/OuyjO/+wffX95dggJECQi2Ac+5/9WVhYiNzcXMO4kOmmOBfZ/jyG4jWwvjo0JiEiXouEPAhhSNhJwjmyzXTojPtKhxa2VWZmJvx+P2JjYxEfH6/6jfUjQBIbG2tINcc6BQIBxMXFIS4uzpCOi/NUOn7l5OQoSKegoAAxMTHKmUvCUIFAALGxsYiNjVWgWl5eHvx+vyqXzjcSRuF8Ky4uRkJCAuLj4+F0OpWDGG2G9bRuMkUUQSo5duQ4lWOY4IueepHH6g40BHzYt9I5iPNYd7/hOkmXG0JMcv5ISE7COhKAka5TPIcEOyUUSck1XHfeIVDIehCikufnPvwfwddc2/m3HONcY2TddPjKzNVHwmZynY2IiEBhYaGC1awnEyxZsmTJkiVLliydzKpZsyauvvrqkE9+m4kB5ZUrV+KTTz6p0Hk7d+5crv1zc3MNr9euXYt//etfGDNmDPbu3QsAuP7664OOczgcYQU3fvjhB/Tv3z/o/XHjxoWdEuHrr78ucx8dTqpZs6bh9Zw5c4LSDt15552mUFNOTg4GDx6M1157TT0AI3Xw4MGgFBUnsjZu3IiRI0dizJgx2LJlS0jHkrp16yI2NhatW7cu1SVm69at2LJlC+655x7r5n81KDU1FXFxcXjggQcwefJk9b7X60VCQgKOHDmCtLQ0/Pe//8WMGTPgcDiOKb3V8dT3338PALjqqqtC7sN1CQC++OILjB492nBP78cff8RTTz0FAIb75kBJaqyioiIsW7YMs2bNqlAdzdaMslSrVi3197Zt2zBv3jz8+OOPBnglPT09pNPbypUrAZQ4/JhBSytWrECXLl2Qn5+PL774otz1A0rAnw0bNijnn+bNm2PdunVYsGABHnnkEezZswf79+/HhRdeqICqSy65BKeddhquuOKKsM+zefPmCqdM43ojH2az9M/WF198oVIE6qpduzZuvPHG41yjknk6depU022NGjUK+nzyT9bTTz+N++67D19//TVmzpxp2Na0aVMMGDDAsH5WlmrWrFkl7kuLFi06IT4fWuvjqatOnTqhadOmqFGjBjIzM6u8ry+++GL1XbZhw4a47bbb0K9fvyo9p6WqlwX+mIhBTa/Xq1LfAEcD9jJVjwxeSkcdBrN5HLcTZCF4wtQ9/OEHYwbUuS+dWqQLkHQQkQCQnmaH5yYYw8A0A7O6AwyPZTBdBpoLCwuV/Zfb7TakTCLQxGuWYAuDv9L1R4IfZuma5DbdJUg6xhAwkG0iYSAAhkA6r1sPOrNedrvd0D4EZnTnIYIQEiIBoILvHEvSzUMCPux/6Y7EukrohABMbGyscveQMIocE9yfeb8JA3i9XuTm5iI7O1u5EMl2o1OIhGg4LiSIVRr8I/vVDP7RJR2NdFcWjuOUlBTUrl0bCQkJBlgNKAFAjhw5ooAX1ocgm4Si+L4cE+xL/sh+keeSY0rCcxKkkUAEAMO8k22gpwqUc5FgIZ2mCGclJCQY0t5FREQoFxyeg9fLbYFAAAkJCYiLizPAM8XFxerG7cGDB5GcnKwgpJiYGFVHOTd0hxo6+wAwQHGESngulsO2Y5o/6WojAREJfLE8fV1iH0jnLdknHOdcmyR0UlhYqOYOHc0iIyPVWk4xDZWcG+xnu92uoEiCTFFRUQq2oTh/5G85b6SDlMfjCXLmke3OuSnHk4QY5XEE3aTbEN/j8XTLkv3H9IEU559eZznH5TklREXHOPYlt8tUexyr+ppvyZIlS5YsWbJkydLJpNtvvx1nnXVWWGlpqMceewyZmZno0aMHRo8ejSeeeAIDBw6swlqWPJ0s9frrr+PVV1/F559/rt5755131N/btm3Dtm3bYLPZ8Mgjj4R1jo0bN5aZFqw0uVyuMvfRAy162ho9yN+rV6+QAcH169djypQpGDlypCmAtXnzZvz2229l1ulE0f79+/Haa69hyZIlpboffP7559i6dStmz55tAEqkRo4ciVatWqFWrVqW1X81yWazYfz48WjWrJkhcPnFF19g6NCh+Oijj/DFF1/gzTffRHJyMgYPHhyWI8uJoMLCQqxduzZo/kpJN68tW7ZgzZo1hpRi6enpIYNSGzZsQGZmJurXr19hJ6S4uDgsWbKkXMHdtLS0IHes1atXY+fOnep18+bNTV1/evXqhSeeeAIAQsIEBD6ffvppPPbYY2HXS+rll19G3759VWrFhIQEnH766ejVqxdq1aqFvXv3KrgoIyMDderUQZcuXdTxzz77bKnlv/XWWwCMDmTllcvlQnR0tHpQ0JIlj8eDJUuWhNw+cOBA9OzZ8zjWqEQffPABFi1apF67XC68/fbbuPfee4McCf/p+uSTT5Tzj9TAgQNx5ZVXVsk5HQ5HyJSPqampFS73p59+MqRErS7JeIGlU0d2ux2pqalo3ry5yiYjU3FVhWrXrm14SOScc87BW2+9FZTG2dLJJQv8MRGD9zI4KdPzAFDwhwxS62laJCjCgKeUDHrTtUHCKRKkkc4mMhguXYgkOMEAvnSy4D56Hc320wEOvkfx6Qf5D8bsOqWjCQEYvS5mjigUQQcJPUiXHZ5DOquYXaMsS7p8SHcRliXThvG6ZMovCR2YOV2wH3UnmFCOPjrwozvNSCcllsd2ku0j+0eCKhT/WcjjeW1erxder1elijJz+ZHSx7gOLIT6keKc0VNRyXPI8uXxMkWbhOx0dxhZRx0g4TaZsqo0SUBJlsG/ZVvJvpb9o8NsdGaR/UknIJvNZgBL5HiX4JG8RjmOOW5kGxLg4Nri8XgMKevk+iOPl1CInDccfwSTpFuUbAcJ7enzRX+PY13WOyoqSrkE6eCZ7ppEOE66PsnxJSE7OUbkeJDlsm8iIiJUCj7uK1OT6WNInzvy/4F8T0JZZnMk1FqjzzlepwTT5PXraR25XQJvEugxg/Xk+YCjjmNm8B+dhGQaSTnvSivfkiVLlixZsmTJkqWTQU6nM8hNpyzVqFFDOUBceeWVGDx4MIYOHVoV1VPatGmT+js3NxfffPONYftVV11luI7NmzcrKKRFixZhnePXX3+tUOCN3xFat25d5r66o4kO5ugB9Y4dO6JBgwamZe3atQtAyfce+WQ++2bXrl0npdPN1q1bkZCQgHr16pluX7RoEdatWxfkkiLF728JCQlo1KhRldTTUul688038fbbbwc9pAPA4DDmdDrhcDjUfes6deoc76pWSIWFhcrhxkxut1uNw/r162Pfvn0GUCg6OjrkmnH48GHk5+cDKAn8VkTffvstxo8fX67gvcPhQEpKiuG97du3G1LYxMXFma5Jffr0USkcnU5nqec51nspbrfbNIXWTz/9hD/++MMAKrVq1cqQduySSy4ptexdu3bB5/MZIK3yqrCwUD2kJ+/LWfpniw9zmqlp06bo0KHDca5RiQvXihUr1Ou4uLiQ/3stmeuMM85AWlpalZStf76T719xxRWGh6rLK36GrE7p8ThLp4ZSU1Nx1llnKRes5ORkJCQkVOk5malDyufzVdnctFSi/v37V+n3LCvyZiLprsBAKAOnTI8inRQIA+mOL9yPwVXpQEFXBLrG2O12OJ1OJCQkwOFwICYmRoENTF8j0y5RMk2N/uGfQV7AGITWXTLMXCYCgYAiCmUbMH2N0+lULkQs0+PxGOAE2R4MPtvtdlVPCVDIQL/+mvsyWGzmyML+MksvI11CdOcQgi+EdXhOpvChXC6XchGRgJcMdPNvCRqwH5kWiBCHhJBkHc1SREknDQn+sD25jdfAMgkmsLz4+Hj4/X5lY00AhO+5XC4FVxBIkRCEmVuNGZyju7jIPpbgAstj+0iXF86b7OxsHDp0CC6XC8XFxYiJiYHT6VTprOx2e1DZsj9kfaXrig7ucCzJ6wJgcKDR+0t3+6JzD/vS7PplP/LGjAS0AoGAYUxyjhHEYSoujkHm9eaXH75mSi32jc/ng8vlgs1W4uLl8XgQGRlpaFeWw/JZVl5eHnJzc+H1epGfn6/gJJlTnNdfWFhogA7p6iTBJB3E4drCeSvdYmQKNqYm45rDMQRAraV05CHUwnXaZrMpSIeSAJBcR8zGc2FhIVwul3ITYlt5PB61NvO6pRORvrbKVFssgxClBHzkONSBSW7juXgtXBflWGVb8hroLiTHP8ctUwrKOaGPX/YHy5AgE3/kOs/xLOEfntMCfyxZsmTJkiVLliyd7Hr22WfLffP/oosuUoEhp9OJli1bokePHuUOUH/22Wdh78vUOsXFxZg0aZIhmN2wYUNMmzYNP/zwg3pv+vTpWLduHYCSh76GDBlS5jnefPPNkE9Wl6Y1a9bg66+/xllnnVXmvk2aNDHUZejQoXjttdfU67vvvtvgAvLVV1+ZpszJycnBsmXLAJTAQc2bN1fbfv31VwwbNgy//vprua/lRNCaNWuQnZ2NHj16mG7PyMjAp59+imuuuSZkoJRgSWRkJC6//PIqq6ul0pWXlwen01mqM06DBg0UmJGcnIzFixcfr+odk9LT00t1OsvMzITL5cL8+fMxZswY/PDDD0Hp6y6++GLTY30+Hx577DEsXrwYKSkpGDJkCHr16lWu+m3btg0LFiwIK9UhlZCQgLZt2xre27hxI1atWqVeR0REoGvXrkHHrlu3Dn6/H5s2bcKUKVNKPY8OF5VXhw4dMsA91Nq1a4PW+p49expApHPPPRfDhg0LWXbjxo2RnZ2NHTt2VLh+Xq8XMTExyM3NLRVQtPTP0SOPPILnnnvOcB9aKjY2tkw3qqpQYWEh3nrrLUyfPh2//PILMjIy0LFjxwp9Fvon6vLLL0eXLl0qlFoxXKWlpSkXfKAEqnjjjTcwatQo3HDDDRUud+HChdXuiGg9VHvqqWnTpnj33Xdx1113obCwEAcOHEBSUlKFIeZw1aBBgyDwx+PxKIjaUuWrcePG6NatG5YtW4Zff/0VU6ZMwSOPPGLqCllRWauDiej0I1MBMXBKMdDMIKgEALjoMvDMoHNxcbGi6hlMlemeYmNjVVofHp+bmwu/3w+v12twDmEQtaCgwACfMPgsnThkoFaK+zBoLQEEBpRlEJmuG3l5eSoNEcv3+XwG8ESmnZJAhlnKK7aHTJulO3qwHSXEwfcBGGAXlifhAqbikfCMbBMJFLFfYmNjVVosBrAZ5JdOT2w/HWhimdLNiftJYEoCPhJSiomJgd1uR2xsrArIc38JCURGRirQw+l0GoL+chwWFBSoFEUEuGSwXjr/EPyRsJneN2ZuJmxLHUAwcyXidehuOBKYk2nI6PzidDqVc5EOUOnl66m8WK68Hu7L69ABMY59gi1MZ6U7/URFRal0SOxHHeKgJCjGa5b7yjpHRUWpccCxDkCNC7YPx6FM8ef3+5GXl6fgLl5ffn4+CgsLkZycjLi4ODidTnUNAAzuTxwneXl56qmbrKwsBYzIa2BaPL0P9DbT25qQnN1uVy5DstyioiLExsYa5pgEa+jyI/tLrgNy7ZAwjUxRJWEsfd3iHPB6vUGp/1geIc3S5owcf/yb0kEhrt/SRS2Uuw7nAPucazPTkhUUFCjwS7rJxcbGGhzf5Lqvr5E8J69bOs3JducYlKn8JNQoYU7LttmSJUuWLFmyZMnSyawaNWrA5/OV60nzDz/8EI8//njQ++edd165zn366aeHve9XX32FrVu3YvXq1Zg0aRIAqJQ0119/Pex2O2rVqoX58+dj3bp1+OSTTwzARzgQzO+//25IeVGWpk2bhjlz5qBdu3bo06ePITATSjt37sTs2bNRr149TJgwAbt27cLIkSOxd+9eACUg1VNPPaX237x5swKYqLfeegtffvkl5s2bBwDo1q0bmjZtqrYvXboU7777Lj788MOwr+VE0xVXXIFp06aZbrv00ksxdOhQrFu3LmRw/uGHH1Z/9+7du8oDDpbM1aBBAzgcDtx8880YO3as6T6NGjVSIAi/3+sPDp1oGjt2rGGMmcnlcuH9999H3759VUqVL7/80pAScOHChSGPnzlzJnbt2oWWLVti3LhxOP/888tdz5iYmHIHvZo1axb03vr16zF69GiVErJ169Zo0qSJ2h4VFYVbbrkFycnJePLJJ03Ti917773q7yZNmhyTU4Xf7ze4me3atQujRo3C22+/bbqvrieffDJk2a1atULt2rWPyX3F5XLB5/MhJyfHAn8sAShxLSzLga+6Umvt3bsXN9xwA8477zwsW7YMTZo0OeHX4BNFX331lfr8VlVq0KCBYV32er1o3rw5mjdvjptuugnXX399hcpdvnw5fvnll8qqZoVkpfk69RQbG4v27dtjw4YNePfdd3Ho0CE4nc4ynQCPVSkpKQZ3P6Ak1mbmDmipcnTuuefizDPPRL169dChQwfccsstuOqqqyo1rZsF/piITikM1AIliykdUwKBgMEdgUCJDJoCULCPTL0lA7Q8l5kLhgRMZHob4Ggwm4CGdBiRQIEMnHO7WcoobpNBfgZ+eS6Z4ob70SmEwV2ZAodiO+kOHRLMCZXmiQFi6QIkg8dsIwlgSOggIqIk7Q8BEj6pId2E2K50JaLjiHQPIXBAeMbr9RocLBgEl+0qg+B0tJHwEUENCb7IcSCvIyYmBvHx8YiJiQkKvBPWYIDf4XAoeEICIKyPzWZTjigSUGC/eL1eBbew7WSbS9efUBCQWd9KsEICFTJFkoSv5Fhje/p8PuU4IyEpeax0ieE4ooMO56J0kZHgjgTN2C9S0gVFOk5JJxmOUZ5XQiESPJFzS45NnoNuSzJ9FN14oqOj1binO5jMkUv3psjISOTl5RmugY5TxcXFSEpKUnOb7l4pKSlqbZGOTBKU0SE8rhdcj2jHy/aUgIvT6VRzgTek2CeE7OSY4zzhzR1eO89JiI51MnN70gEjznW5prEeHFuEZmSaPY5BOn/JsRAIBOD1euHxeOD1eoMcbbhmSpch6camQ5nSTU5Cb5xPhAlZL85zCXzKHLCHDh1S8KF0muJc0EElCcnpdeO4l+uQ7vDEfSTopMN2Xq/X+pJiyZIlS5YsWbJk6aTWTTfdhHbt2mHNmjXlOu7ll182pH9ZtWqVwXEnHLVr165c+x85cgRLlixRde3duzcWL16Mvn37qn1effVVtGvXDpdffjkuvPBC9X64we/p06eHnbbsiiuuMJw7HC1btgyNGjXCVVddhdtvv129L9MbySdW77333iCnj/r162PgwIHYtm0bAKBv376Gm6zSneNklUw9omv+/PnYsmULsrOzDfcRpJKTk9Xf6enpuOmmmyq5hpbCUf369dU9i9TUVNPv5mlpaYYUED6f74RPNXPgwAFDujIzTZo0CSNGjDA8gLtixQoFq/3555/473//W2oZ+/fvBwDDg5HlUUJCAj766CM899xzYQe/pHsY9eGHH2LEiBH4/fffAZTMLwkiDRw4EL179wYAU0eT66+/Hm+99ZbhHMfiKDJy5EjD2nvOOedg9uzZyM7ORp8+ffDoo48qV6IXX3wRGzduVPuuXLnS8L9L6r///a9KWTFp0iRTCCocFRQUwO/3Izc3t1yOS5ZOXdWtW9cA/ekKBALK2bA6lJaWhldeeQWXX345Nm/efEp8jjheGjFiBGbMmFFl5cfFxRk+FyYnJ+PSSy8FAPTo0QP9+vUzxGnLo+p22GN8pyodkywdXzVr1gy1a9fGtm3b8MYbb+DRRx/Fyy+/jHHjxlXpeRMTE4Mcf1wul8oaY6ny1apVKzRu3NjwXrdu3fDJJ59gwoQJIZ1byyML/DGR0+mEy+VCIBBQqYSKi4vV0xPSxUKmW6Gki4r+z4OBbwkIyH0Z7OVxDITbbDa43W5DGiQJ6hAUAI5+UZAuExL+0dNLRUREwOPxqLKk+4geVOZ7DKZThAeAoyCRHmDmNVPSnUiWr0MDfI/i37wueS0y4M5jmVqHgXfZBhJkkUFoQgIMoDPIDpRAGW63Wz15IFO9xcTEGAAUBsIJ09C9ieCSnt6HIAEBB92hSIIrAAyplHhNhEGKi4sN6dfY/nraMMICNpsNBQUFBrcfjjcdjjGDfiTUw/NJwEYHgHheCQ/wmtnWBHrq1KmDGjVqICIiAtnZ2Wqs6OeQ9WO5OtDA/TguOeekA48sX4INABR0IR1jJAjD46Kjo4PWB+lkI8ezdLIhRMK5wzRTNptNubcQsiCQlpmZqSARur3k5+cruIbnpfsPx4cEpSIiStJ7MV2X3++H3W5HQUGBYc7Y7XakpaUZ0jz5/X4FneXk5BhgD6DEHj4mJkbVUUKScjzKscA+5NxhXTnn6EhE0bGKfSQhI/YHQSEJ3HF+czwQCpJ9zBSPnF9cA6VTFY/XQSPWjcAhnYl4A0um6+IPHabYjlyb9Xmkn583tAgt+Xw+5ObmBgGFhKz0dGesh1xPdGBNjmlZRx3IksdL0I3zTc4pS5YsWbJkyZIlS5ZORvXv3x9//fVXhY5duXKl+rsiQelZs2aFve/ZZ5+NVq1aGQK4Z555Jnr06GEIQD/zzDMASq7rtNNOU++XltqFateuHW677TbDdYVShw4dyvVE49NPP40PPvgA9evXxyWXXIJ33nkHADB+/Hj1vY2ST/2/9dZbQcHAa6+9Vt3cbt26NTp37gygJAWW1+vFb7/9Fna9TlaNHj0aF198MS666CLT7Z988on6+/PPP8ecOXOOV9UsCX311Vd46aWXsGrVKhw8eNBwz4mqU6cOVq1ahQMHDgAAWrZsqdLYnajatWtXhV0e/vzzTwBAvXr1TCEbqS1btgA4eq+2vFq/fj0+//xz5ObmKtfrstSoUaOgp+apP/74Q/0tU4JxzZo7dy6mTp0adNzhw4dVynkAWL16NTIyMsK+DiouLg7PPfccXnnlFaSlpQEoeUjs4MGDWL9+PYAS57lXX31VOUzVqFHD4DSwYMECU+goOTkZ/fv3R5s2bQDgmNx65P02CX5Z+udq2LBh6n+1mWw2G4YOHWpw0jqeysjIgMPhwGeffYann366WupwsqpHjx7lBtnDFbNI9OnTB/369UPTpk0xbtw4da/e4XCgR48eGDBgQIXKDydFbVWqadOmmDx5Mr755hsrLespoPT0dNx4440ASlzOdu7ciW+++QYzZ86s8nMzlkwxFmfBt1WnI0eOmH42TU1NRa9evYKgoIrIAn9MFBcXh9jYWIObDHA07ZWEEnQnD/6Wzju6246EFSQ4IcuXQWMGgplqS37ZYyBWlsmgq+7CIoEHGTBmGbL+0hlCD9DKoLPu8sK/pUuM2TmB4NQ2+nl5vFl6GdneUrJ8lse6y5tBeh+YlSEdhCQoRChGB4VkO8h+kIFxmcJJQlnyujgGoqKiDO8TOJHOHfzhGKMrEceEDNDzmiRsobuNmPWd2Y/sO7N+kOXpTj86IKQDcPo44T4E5gjGMK2Wvj/bW6bsknVnnSXco88fM+BBB4fMrkUfCxLokeNKb3N5DK+XcAihER2UkyAdXbX4mmOBKdz4fmFhocHdxqweBGTk2OPa5fP5kJqaqlKtyeuLiYmB1+tVT4NK2IPHs2/0Npd10MeWhPMIKUmYkccQKJLrL9tEBwfZNxImkn3POcI6sE4EiPSxobuSSeCMbUDoSW9Tuc6brcs6vCbHrmwrHssb36yXy+UyjA99PPO6zP5fyLEu57k+5vmeLFf+z5N1lf87Sls7LFmyZMmSJUuWLFk60fXrr79WOA0SA9hAydPs5VXdunVNP5ebid8PCgsLVWC2Vq1aQfu1bdsWPXv2RKtWrQzvh+Mu0bp1a7Rv3x5r164tc9/ypt0ZM2YM3nnnHbRr1w4dO3ZU71900UVBbacHkMxcAK6++moAJUEbQkBz5szBN998U2Y6kVNBO3bswI8//hgSHpDpK1asWFFtKVQsAbNnz0ZsbGzIp66nT5+O9957TwUPnE7nCe34c+ONN4btCmYmjlm73W5w+jJTbm6uuudwrKmxpk6disWLF2PTpk2l7puQkIDExETTbUeOHFF/16xZU91zopPJd999Z3pc9+7d1Tp18OBBfPnll9i1a1e5r+Puu+9WQUWgJLC4b98+wz506eH9wKZNmyI1NVVtl+clPASU/D+SbgFutxu5ubnlriPF+3/Ww2KWgJI0n2WpqKjI8PngeCoQCGD69On4+uuv8fnnn1dLHU5W1atXz/TzaGUoMjIS1157LQYNGoShQ4ciMzMzKDNCzZo10bRpU4NzXjhq0aKFAZCvLsXHx6v4gaWTWwcPHsShQ4dKdeSsKukZGRhDstJtVp2mTJmCr7/+2nRbkyZNcPPNNx/zOSzwx0TJycmIjo6Gy+VCQUGBclHgIkrHArrB2O32INcSBoflkwXSQUIP8FIySE6HBrp8BAIBZGdnG2AYnoN1YkBVwjpyH8ITEgaQ7huyboQDGFBnmioZ4OZCYJa2i8F4SehLoEI6JklnGR5fUFBgOFYCKTLArYNZEiKRx0rXHpajp31iWax3RESEIfUX0x7RhYWQDduLDiIy0C/TZ3E80R2E7W/mFqOn87Hb7XA4HKqvCXDINpTOQoR/mEZJutnI65HggHRzkjCFWYovHSKQknCHdPkpDbKR1yHHFseCz+eD3W5HSkqKId2SnEu6y5ZMoSfLlPXVU9RJGMMMUpEwiQQldJm1i5y3fC3nuryW3Nxc5ObmqjlA2IVjjWn+IiIikJiYaHC8kkSux+NBRERJKi49rZasp+wnrjdMh0XbSD3dH8ugMxrTctFlR1oqFxQUwOVyoaioSDlj6WuGDrW4XC4UFhaquc512OPxBKWqk3NMulbJ/pEAIdtPuqxFRUXB6XQaXJkkOCTHgRwDEgaV6ybHoNPpVEAj20+6ghHw4rXzeiTEJ+eSBAm5lnMs0J1IusDJY7le0TmKfSjXTvaNbC8JVElgSB4v24XjlGsYxwdTrbHNBg0aFDR3LFmyZMmSJUuW/qkKF+SwdGLo2WefrfDTkDLIWx73Huq9997D//3f/4W1L79vfvDBB1iyZAlWrFhhGiCrUaMGPvjgA5x99tmG97t06VLmOaZPn45FixahYcOGZe4byhHDTD6fD1lZWVizZg02bNiA66+/Xm1r0qQJ5s+fb9g/Pj4ejzzySKnn+s9//oM2bdrg4osvVu8tWrQI/fr1MwTnT2VdcMEFKvWQrvz8fOWWcuONNxoC/JaOr+rWrYslS5bgpZdeMt2+atUq/PjjjydNqo82bdqgU6dOFYIdAWDz5s3weDyIjo4u82noX375RTkhVfR8QIlz2IgRI9CrVy/06dPH4L6jKykpCbVr1zbd9ueff6p7Pk2aNFFrZUpKStB9YapmzZp47rnnAAC///47Ro4ciXnz5lXoOho0aIAWLVoAALKysnDrrbcaQCAAOPfccwEAP/30E4CS8TVp0iRkZ2fj559/Nqy3I0aMwIIFC/DBBx9g3LhxhhSBLperwuAPx3JsbCy8Xq/1ucgSvv/+eyxdujTk9pycHEyYMOG4OGOE0k8//YQDBw7g6quvtv5nhimn04n27dsb1o7KVnJyMpo3b466desiIiLC4LwGlMQ56tevjw4dOpSr3C1bthjg0upUXl4eMjMzq7salo5RO3bswNKlSzF69Ojj7j6al5enoLicnBxMnToVkydPxu7du49rPf5JysvLw6hRozB37lzT7Zdccgk+++wzNG3aNGhbuJ+LLPDHREVFRbDb7bDb7Ya0MDJoycAyAAX5+P1+5OXlqbQ0hEYcDofB7UCXhAGio6MRFxdnCMIyWA5ABb4JyRBokeVIxxPg6E0eBsL5N4P78lwy7VBMTIyCmxjclYCTdFzh+aU7EWELnl9vA5m2jNv52m63q4Axg8gS9tGBEl6nBItksJ7ADMEACZXoaW9kMJ+BdIIydNvxeDxwuVzIz89XEIJMYcQxQsCA44gAj3TdMQPACIwRkJBp3QgE8RyEA1hnurHIL0iEhKRLDFNRSXjHDB6T16bDPmauTTrEVBrwY7PZYLfb1Zdjtq90QqKLEduTIIXu5MK+I2wgYTgJJci6k16VEInuhsI663OAbaI79shxRLEddViIYJbP5zOAKGYwFMcV+5LzLzIyEomJiSgqKkJmZqaCueSxhGaYko+wImEZgmisZ3x8vLpGpvBjGQcPHlRwke5QxDmSm5trgHqKi4vhdruRn5+v6ktYSK4hOljCtYAAEsct116uu7qzDs9t5rJGMIdrJ9uE7SO3y3VE1lMfK4Sj9PLZ/nKNlf0i/6/o41LOJ1kHfS1nmbIvuN3n8ynwivNCB9j4/4PXJmEh2Qa8BraDBAvlvNBdiOT81PtCOgpZsmTJkiVLlixZsnSy6a677kL9+vUrdOy8efPUjfrSAlqhNGvWLNx666244IILykybxe+o0dHRqFmzJjp37oz09HTTfc2eYI6JiQkrjcDEiRPDgnp+/PHHoPeWLFmCLVu2BDmbyJvOEyZMCDqODkZSTFsDIMi9CCgJgL///vvo0qULdu7ciSNHjmDRokUAjn4f+qfr/fffB1DiUlLeJ+EtVZ7mzJmD++67r1QnqrS0NAP4cyK7pOTn56tgh3RlB4Drr7++THDwjz/+wMGDB/H222/j9ddfL3Xf3bt3q1SMleWCtHPnTgUTmak054XNmzerfkxOTkbt2rUxceJETJ06FZGRkSp1vRTL2r17NwYPHozJkydXqN516tRB165dAZQ8ZNizZ08sX77ckP4RgAowrVu3Tr33wAMPYNOmTejatavBeaVLly7o1asXhgwZgh49ehjK8Xq9eOaZZ3DBBReUu64JCQlYuXIlcnJylBuSpX+uunTpgieeeALdu3cPuU9SUhIuueSS41grcz355JP4+OOPcccdd+CHH37AAw88UN1VOqGVkJCAVatWhYSQK1Opqalo1KgR/vrrL4OLSVFREaKjoyuUJu7999/H4sWLK7GWFdORI0dw6NCh6q6GpUrQzJkz8dprr4V0AKwqHTlyBJmZmcjJycHs2bMxZMgQQ9pfS1Wjpk2blupeef311+O7777DG2+8EdaDNbos8MdEBQUFhg/cdJ6Rbg0McPLpLkIBEvJgkFWS+4RgZKC3sLAQHo9HBVUZhKbDDgPSDOADJV8mCBjJoC9w1J5LghkM1BIw0N1xWA/CJbwxRPgHgHKJ4HUwwE3pEAgdhni8DEjTqYJwQXFxsYJcGLjXc7XLYLVMFyMhKHk9DEw7HA4DLOHz+eD1ekO61cjgNNtRB5ykS4t055CACgPvEmaKiYlRzj06BEOxPlFRUaoNoqOj4ff7kZ+fb4Aq2K4SUGKgn+OHbcO251gwc+6R0ASdjTimebzu/KNLBxh0+Ee6q0RGRqKgoAC5ubkoLi5GXFycGi9y3nDs+v1+BVqZQQRyjEgwR44hjl/uy3HIeSKBDQnpSKcr/YfQjAQluL+EgswcgvTjCJLI19JhjK5Tcm7LlEoExtxut2p/Ob84J2XfcbwSJmI5LpdLlUvQjG3G8S/HINcorp/sY6YAc7vdhhu5rL8E+lh/AkgEc7jm0bUsIiJCrVdsZx36kuuadEHj3OSYlmAXx5rX61XQWUFBQRAcJseEhGrYXjoYJgEd6T5GmI/2+xIW5DjmNXBc6Wu7/L/gcrngdrsNYA/hMh3g4TEy9Zs+TqUrEtuJ1yaBRglF6fCfhDTl/z/ryS1LlixZsmTJkiVLJ7N27dp1TKDIDz/8AAAVsvM+cuQIiouL8dVXX+Gcc84pdd/CwsJjSnUDAHfccQcuuuiiUvf56quvwkr19fzzzxteFxcX45lnnkHnzp1x7733GratWrVK/d2tW7cyy87NzVXpeJo2bapS1+jq1q0b2rRpg4EDB6Jv377Yv39/mWX/0+Tz+bB27Vrl/mPpxBSdDKiioiKcfvrpJ6QL0AsvvIDly5fj6aefNqSWeOqppzB16tQyg69r1qzB3r17sX379rDOx5SKZaUFK48OHjxoCukAJY7boVLjHT58WDmKnXbaaZg2bRpuvfVWtTabpXN77733AADTpk3DmjVrKlTfCy64ALNmzULnzp0BAI8//ripm4BMH3nllVca3JkHDx5s2PfMM89E8+bNQ57zpptuwuOPP14hB7X77rsPF154IQBYLhb/cLVs2RJPP/10WODenj17jkONShdjUPfffz969uxZLnfDf6IyMjIwfPhwDB48GDt27KjSc/l8Pjz99NMYNmyY4fNwVFQULrjgAgwdOhRjxowJ+gxammbOnIkPP/ywKqpbLh06dMhaKy0dk3bu3Indu3fD6XSidu3a1tp1nNS0adMyUx3ye6yeji0cWeCPiQiF0FVCpqeRaU1iYmKQnZ1tSEVD6MHv9yMyMhJutxtRUVEqUMqgNAO8BQUF8Hg88Hq9hi8ODKYyRRO/xPH4uLg4FSBnQFU6g+hghwxsM/gvnSQk+KE7cPB6JMhBSRBEisFoh8MRBNEQknG73QokkGAIIQoGwGV6LF4nA/4MiMuAsgzMFxQUqDJ5/Qz6ezweBUJJsEtKD1Y7HA6kpqYqxxJem3TyYDvJOsvUWna7XaX+kTCMmWSQ3ul0qmNjY2MRHR2t6mD2VAkBD+nkIgP6dARyOByw2+0KxpFOOWwvtrOEJfR+14Eg3UlIAj8SVGDfZGdnIzc3V6Wk4pghUAEcdc7h39JZRO8L1l3OBUIM0vlEwiYSxpDjSYJF+tggkGLmEqQ7/Mi2ktAI66+nO6MTD+tot9tV3mw6WPH6uS/Px5RVBDWYAgqAgkg4dggf8joCgZI0X5w/rJuECuUYJVTkcDjUWOe8I0jJc/J8ErCSABFTXwFHgUq2Ja9DrsPsZ+nwI8EYM2hNtgvPJZ2c+CPHNcc/gUK6wsmxJMeRBF5YDh2FpGNRREQEkpKSULduXdjtdkN6Pa5V7FsdlCEMxjFIOM7tdisIi65CcsyyPAn4yXEu9yXUxHEqUzLKlGlyndclz6uXZ8mSJUuWLFmyZMnSyaqpU6fiq6++Qvv27St0/FtvvQUA6NmzZ4WOX7duHZxOJy655BIMGzYs5H4JCQnYs2fPMbkn9OvXD3PmzAkJ0gDA6NGjwyprw4YNhieun332WSxatAg5OTmYOHGi4d7Y999/r/4uy+1i9uzZ2Lx5M/bu3Yv4+Hi0a9fOABL89ttv2LZtmyo/OzsbP/30E37++eew6v1P0k033YSYmBjcf//9uOGGG6q7OpZKUWpqKr7++msF3UVHR2Px4sW47LLLqrdiJqIDuw7HEHpJTU0t9fgjR45g69atpqkPzER3npSUlArU1lyXXXYZ/vOf/6jXbrcbW7duRU5OjkpNH0rZ2dkAgMTExKBr4H0iKaY2NHNiAxDW/541a9YgJycHGRkZGD16NN55552gfZo1a6ZczwCgb9++hr7YvHmzYf+OHTuWGawKBAIhAalw9Mwzz+DKK6+s1L6zdHJp27ZtYcHVy5YtM4Bq1aHTTjsNLVu2BACV7m/KlCnVWaWTRvv37y/V1a4y1KJFC1xzzTU477zzDO9HRkaiefPm6NatG+66664yU0hKMc5XkYB8ZamoqAgHDx6s8vazdGpr165d2Lt3L4qLi1GnTp0qTb9n6ah++OGHMlOG05SARhnlkRV1M9GBAwdUYJPOFoWFhQYoguCCw+FQYAPTcNlsR1MV0UkhEAgoEEMGTBlM5k0PBkfpUsPcwXRDiYuLU/CLdJ2h0wzBA4IFMkUMf/NDE/elaxDf57HS1UdCQwAMwV7dKYJB+FCuJwzgs3zCBrKehDWkOwQl3Tyka4S8XgAGlw+CVAzMS3iA0FVBQYHBfUWmvpHv0clIpiCSbSXrTLcmHscPBQQldBccSgJk7H9CERyX0slEAlARERFITExESkqKoQy2oewvHcTRUyVJ1xSOV441wgJ6Sivd8UfCFlykuGixbQlSxMfHIy4uTv1NSIDt1ahRI2VjTnBDglvS4UZCahI64vyS41y2uYQf2A4SYpLAjjxWOrHIY3WoTYJkEvBh+5mVX1RUhLy8PHWTJD4+Xl0Dy8rKykJCQoLqP0Im7Ft5LQRomO6NwAjXB6b4omsUt9P9jCCP7GuO9xo1aiA1NdUALXGM8vxut9vgLsRrLyoqUkCgbA+5HnHtk7CQBO1YH45ZHeTRU6lxXOrjgNfkcDgMcFxkZKSCfvR1SF+bdSiT18u2lmtmTk4O8vPzDWuzvh7IuSbrqKdwlGnRZN1YrnRTk3OZ7ayvD3ItZHk83qyu8ou5/J/ENuAY1WE6S5YsWbJkyZIlS5ZONo0YMSIslxszMTDv9/srlDJs5cqVAIB//etf+Pe//x1yv++++w5nn302LrvsMtx2220VqitQ4nSxbdu2kNu3bt0aVjm33XabSuH10ksvBdV92rRp6u9HHnlE/b1o0aJS06KNHTsWnTt3RlxcHCZPnozbb79dBeAAYPHixWjfvj2uvPJK7Nq1y3KyCaFzzz0XHTp0AFDS51b6sxNbEydOxL///W/DPcWaNWuqtO1VrZiYGMyaNavUNWzatGl44403EBsbi/j4eENwNT09HU899RQiIyPLTFsIADt27FAB9tI0bNgw3H///QCAuLi4SnP9ycrKUvdegBIHs3POOQcvvfRS0L1dXbm5uSG3md0b8fl8ePzxx3HdddcFbZsyZQoWLlxY5nXl5OTA6/Vi9OjRGDFihOk+jRs3DmpTPRWbvn9Z93Lk/eiK6KabbsLChQuRlZVV4TIsndwqKirCl19+WWZgtHHjxsrRqjqUlpaGZ599NsiZ6Oyzz66mGp1cOnDgAA4cOIDffvsNOTk5QeYCx0vjxo3DyJEjw97/6quvxlVXXVWt4E9OTk6FnNUsWZLKysrC1q1b8dZbb2H06NFBsK+lqlFGRgauvfZazJ49O+Q+WVlZKhNJeWWBPybih3QJu0iogCBMcXGx+iLFf0p6UJkwkEwFIwO20qFHujJER0ejoKAAeXl5yvmHkk4rEh6Q4A5w1C1GpqiS0IF0EpKQBo/Vg7MyzYvf7zf9EkBIRIJEuqQrig5V6OnHKBncl2XIALsEW+QxfI9/S9iFaXykK0xpYjswcC6hH1lXHThiHem+owfY9fRisp/kdUmQS143xw/LISijB+H1gLx0I9EhLtbL7Lok0CPbXJcZsMC66mOE4IjD4YDT6URcXBzi4+MN88LpdCqgie/JttEhECk5pnVgTNaV1yrBNlm2metPqC+8sv0kAMH2C5VWTLpA6ecguMR+lu0q+4xtzvULgAJnZN0I/vA99o0OLso1ivXQXa44L3QIR65PPKdMg8Xrlan/5JjR1wr2l+w/2V/SOUn2qZwjPKeZkw7bQh/nZsCK7B95Tn2e6Cmw5N9ut1ul59LL1Otm9p5MCSZhN16HdMOSc1yvk96u+hrA42W7SWCKx8m5osOfZnPXkiVLlixZsmTJkqWTVQRYKqI2bdoAKLFY37t3b7mPl5/VU1NT0a9fv5D7ut1uLFmypNSbi2Xpl19+KXX7rFmzwi5r3759AEpci3StWLFC/S3TydhstpAuE8XFxSr4kZiYiOTkZFxyySWGfbZt24b8/Hx8//33WLRokZXeK4Tatm2r/t6xY4ch3ZqlE1PJycnIzs5W60ggECgV3KhMtWrVCueccw4SExND7tO7d2/ceOONOHjwIObNm4ddu3YBKHHGuOeee9TT5WauN7rcbrdywi5NgwYNgsPhAFByXywpKSmMqwlPvG8GlLR9VlYWfD6femA4lMrrgOPz+bBhw4ag97t3744LL7wQ8fHxYaXkiIqKUg+amckMEisNYAq3LY/lfo/b7UZCQkKFj7d08istLQ3nnXdemRDjvn37SnXaqmr16tULXbt2DXq/IkD3P1Xjx4/Hhx9+iBkzZpQKSFaV/H4/lixZUq5jmjZtiiZNmlRrWk23210t7WXp1JLP58PHH3+MMWPG4JNPPqnu6vyjtHPnTixdujTk50O73Y6srKwKPYRhgT8mio2Nhc1WkrbF6XQCgErLBRhT/NBRwePxwO/3w+FwwGazKTCG7zNFk5kLDT9My/QvUVFRiI2NRVxcHCIjI+F0OpXzBh1+SM/LoLN0bmCwlYFfBp+lG1FERAS8Xq96j+XL4HJxcTFycnIMsA+DvwAMTjd0zpFpvKR7iZ7aisF9mZJJutvwy5yEB2JiYlBQUBAUmJYOIASbGJxmuRKKARDkXKIDT5R0L+H+dPMhfCFdXzhe2K6yLyQkIQEels8+4g/Px+uLiYlRzjiEe7xeLzweD4qLi1X6N4JJ0m1GtiX7U4Iwsh106IuOIhL0kHCSDjjIcW4GcrFtZL/TmjA5ORlJSUlISUlBbGwsvF6vIeUR+0RCHBJk0R1zZH/GxsYaxr8Z8GXmKMNzsq91qEpul/uZgUUSuiAsIschj5PXwWM4R6WDEp1tWAavQY5nto0O+BUVFSEzM9Mw5vgj01ixLQk3MtUh94uIiEBKSgoSExOV2xDTGLpcLpWezmazKecgXiudm6SjUG5urmHOyvEk12A9HZu8PrmGcd3kOss1Xk85JecGcNQ5jGuUDuXIuujwkVyL5DyXjmjyte4AJtdKSgd3eB7eZOLayWsijMm/JQypj3npSiRBUjnXuZ3ru4S/WDeuS/J/kdmaIueHJUuWLFmyZMmSJUsno5588km0bt26Qsf+3//9H/x+P8aPH1/uY+Pj43HPPfcY3ps+fTruvPPOUo/Lzs5WztLlVY8ePUrdHm6qLwBYvXo1AOCqq64K2vbuu++qtDORkZGYNm0aXnnlFdxyyy0h0zDk5uZi586dAIAGDRqgfv36hgdlAGD79u3q71tvvRW//fZb2PX9p8jpdOLaa69Vr7/44gvs2LGjGmtkKRzNnz8fPXr0UE9py3tDVa09e/Zg4MCB2LhxY8h99u7dizp16iArK0u5dsXExGDu3LkYPHgwgJJ7EEyFVZqOHDmCWrVqlel08/777+O5554DUHK/ozwpXMqSy+VSQZq0tDQAwOHDh5GQkFBqaqrSArRmoFZWVlbQ/xen04mhQ4eiXr16iIqKMoB6uurUqYOFCxeiY8eOpim+AKB169Z4+eWXg97X10+pcICGyMhI3HPPPaWmoQyl+Ph4HDhwAGvWrCn3sZZOHaWmpqJdu3al7rNr1y7cd999+OKLL45TrYK1ffv2IGjR5XJV6LPdP1UzZ87EW2+9hczMzGpxGczNzUXHjh3RsGHDsI/JyMioUPqdytSRI0ewZ8+eaq2DpVNHf/31V5n79O/fH9dccw1OP/3041CjU1/8zhpqLUlISMCLL76IN998EwMGDChX2Rb4YyKCIbobBVPd2O12Q9qYqKgoBbzIMiRUExkZqVIkMfjMf2TR0dEqrQ7LiYqKQo0aNZCWlga/34/8/Hz1xU13bZEpW+RTBwBUIFgGr3mcdKOQUIj8cqinIrLZbCpFkM/nU4NSBqAJPjHIS3cIpmxiuQyusyx+caIDCdud6YuY/szv9ytYgIBGUVGRAl0kECChFj1tDVNiJSYmwul0KljBzOmI1y9TBuluFhJCou2rx+Mx9Ld0epHjRAa/pbsK+5OAF11ReE2st4QGWAeOOzkW7Ha7aiPpzCLTQOnghO4wIq9BQkRm7ibyNeEEQjw8Rpbr8/lw8OBBBX3FxMTA5/MhISEBcXFxyMvLg9frNYB5BKAIYXH8+f1+NGvWDE2aNFGglXRu0a9FAhUyJR/7RAJm0tVEghQSHuN78jz6uGJdmGqKa4wOksjxyzaRLk0RERFwOBwGaE6Odc45WQeOEaZUY8on7ltQUKDmq3SSiouLU2OOafNsNhvsdjsSEhKQlJSktrOfmRqP+zudTuVaxPkuxxLHu9vtVpAWQTrCJ1wD9JRech7q4I9s+9jYWNjtdgPcxLWNqb08Ho9y4mEfybVTdw2S5+b+vDbWJyKiJK0d680xwLKlE5icgxxPLIt15fou10SeOyIiIgjmNAPRZDo4uj7xtQ7G8TwSONJTt+lObBzLMsWY7BdLlixZsmTJkiVLlk5WRUVFlftGHJWcnAyfz4fly5eX+9i2bdsGPWUcExODu+++u8xj3333XezZsycsdw0puvSEUnluAt9+++0AgMsvvxwXXHBB0PZXXnlF/T1gwACMHDkSkydPVg4euvLy8lRKmObNm5tCAW+99ZYhiPj000+HXd9/ioYOHYrzzz9fvU5NTa3G2lgqrwidFBYWorCwsFQIpbKUlZWFhQsXlroPQb/mzZvjvPPOA1ByD5v1W79+PQoLC7F+/foyz+dyueBwOMp0nfnwww+D7o+H0j333INPP/20zHNTmZmZykGH4M/+/fuRlJRUKhTjcrnw559/msKXOmgzfvx4NGzYEK1atVIPJQMlDg+///67en3aaaeFPF+rVq3QtWtXPPXUUyH3WbBgAc466yzDe++8806p6SPDSZsWHR2NkSNHBjmvhaP8/Hz06dOnTJDV0qmtDRs2YNy4caXu07hx46Dxe7z1yy+/4LPPPjO8t3v37lJdtiyZa8OGDThw4MBxP29qaiouvPBCvPXWW7j33nvDOmbChAkVTvVbWdq/f39YsIalU0eDBg3C/fffX+bDGFWhSZMmYcKECRg6dCjOPPPM437+U1EEf0pzWXQ6nejVq1e5nSst8MdEcXFxykGHUAxBgOjoaANcwwAyAQyKAc+EhAQVPJXpjSQ8EhMTo1IcSTAoOjoadrsdKSkpSEhIMASQIyMjFSgj068QMNHdVXSgQwZbZZCWTjJ0iSgsLERUVJRqE+nwIKEOGSgmbEQ4gfWVkA7bl9t4DIPJMpDN8zE4rp9fd1XR0yvJv3mtDocDcXFxiIuLU25MsbGxBnCG12OWOkmm02H/8hyRkZGIiYlRbUPYhRCY7BNeo3Q84nY5Tgh8cBzKMRgXF4caNWrAbrcrqIN9IsEevT10F6ZQ6XhkO1O8Nun+o7s26XAQ24DjSrZBREQEkpOTER0djaKiImzZsgXbtm1Dbm4u/H4/vF4vEhMTsXfvXmRkZBjStRGAiIiIQEJCgnJxiYqKwpEjR5CUlKTGG28SSgBKzgX2H+skgREJwHHMSWBIwhY6/CRBITmueIw8n942xcXFsNvtcDgcqFGjBoqKipSjlpzjPp9PwUMSduMaBRyFwZxOpwJa2G5yvQNgAMxYN73OcXFxQc5lXDtk/VkXQi9Op1NBbzx/UlKSAnGY3pBOY7wpIlOS6VChHI/ShYb9wXnJY/QUfXw/NjYWsbGx6m+mSmMbEM5kP8k0WnI8BAIB9QSYnM+6q4/cX29zHaBhHxJwIghJWJTrr6wT21uCRRKM1Me0XJcJQ8rzy/VAd1vS95FrBUXQiqqOJzksWbJkyZIlS5YsWaos/fTTT7j++usrdGzHjh3hdDrx4osvlvtYs7Rb33//PTp06FBqIBgAHnnkEZx77rm48cYby3XOsgLj33//PQYOHFhmOc2aNVNpgRISEnDZZZcF7TN//vxyBc34naNOnTo47bTTVOogqaioKLRo0SLsMkOpPE+En2waOHCgAa566KGHcMMNN1RjjSyVRwRw4uLiMGnSJLRs2bKaa1QipkS02+24+eabMWnSJDz77LNo1KgRli1bhhkzZsDn84WVfo9O8GeeeSZq1qxZ6r6891BYWFiq284ZZ5yBVq1aoWnTpmFdz549e5Q7Ud26dQGUpBJMTk5Wr800ZMgQnH322SotoZSezogPF7dv3x7nnnuuYduff/6p/m7WrFnI8/GhWjM3poiICMydOxf16tVT740aNQoTJkwoM/DNlIu7du3CAw88gNtvvx3PPvssZs+erSAvKpy0bJYshdKECROQmZlZ6j6XXXZZqfPueGj16tWGVC0NGzbEOeecU401OnbFxcVhyJAhuPnmm5Genq7iBTVr1sSQIUPKXV7Tpk3xyCOPYNasWcqNTdfcuXOxaNEi/PzzzwaXxuOhzp07o1+/fmjWrJkhNhhKvXr1qna4q1WrVtWaasxS1ctut+O5557Df/7zH/z444+YPHkynn322eOeSjA+Ph516tSBw+FASkoKrrvuOjz55JNhfe+zFFrJyclhPWSxefPmcqe0rFLwx2az7bTZbL/bbLa1NpstKCm0zWZrbbPZltlstgKbzfaIti3ZZrPNsNlsm2w220abzXbe3+8/a7PZ9v5d5lqbzXaZOOZxm8221Waz/Wmz2cqPlP+tGTNmGAAMmS4oEAgY0mwxwAwYXRM8Hg98Pp9yvtAD0Xrwlw4ThCL45BWD9MBR9x3prqCDBBLE0APvMjgr3U0Y2OYPy5EgCmETCZEwsM3AP9uATym4XC5VDoPz0mGCKZwkNEFooLi4GHl5eQZ4RTrhyH5gfWWfMNAvgRcGxWWKLgJX8vyy3WS/Mx2ThJwkGCRf60F6mc6J9ZEQlQR5JHRDlyTCLayzDpKwHyVgQScVfnmU4A4D/DJ1kfxQo8M+Zn/rYI+8DjkGmbpJwk9y/PHcqampaN68OZKSklBYWAiXy4WDBw/C6/UiPz8fhw8fht1uN7SbHNMEhOT4drvd+PPPP1WfSNcsCf/I+WUGNUjQSQJssi/5I51cZDvp7cXtEnzgazPww+FwoH79+qhZsyYiIiJU6iwAKr1eXFycYdyxDKYgBI6CFxIOi42NVfAP5ybry3lDZzKuQZwzEh5jX+vjX9pMc71zOp0Gxx0JK7HOAJTrD9tHd79huRx/dP/Rxy0ANZd4zRLiI0Aj1xyWwfLk3Nfd0czWYrY3b7bIehNyY32lG49+fjmO5P8cAGoN1oEfee0ScGLd2WYFBQUKqGT5XAsJOPl8PsM5uZ8OKso5JIFJHYhiGku2wT9JJ+vnIkuWLFmyZMmSparQqfDZ6Pvvv0eDBg3Qr1+/ch/75ptvAigJHjz99NO47bbbwj72jTfeUH+73W6MGjUKL774Ig4fPowff/yxzIDT3r178b///Q8///yz4XtNaSrrGt94442wHBpk6pft27eHfKJ/8uTJYdULKHnqf+jQofjqq6/wzDPPmO6zdetWlUKsorr88svx6aefVih9zYmutLQ0dOrUyfDerFmz8Pnnn1dTjSyVV8OHD8fYsWMBlASL8vLyqrlGJe5ko0aNUq/PPvtsDB48GM8884x68GrHjh1hA2Y+nw9paWmYOnVqECyji0ESv98fEh6oWbMmGjZsiIyMjLDAI6AkHQbhHbrfZGZmIi4uDo0aNSr1WJfLBZfLFfS+DN7Wrl1buZg0a9YMHTt2VNucTie6dOmiXjdv3jwkjMhzmTlorFq1CldccQWys7Px6KOP4oUXXsALL7yAO+64o9T6A1DA1V9//YW5c+di4sSJeO6553D11Vdj/vz5hn0TEhLKLM/SUZ0Kn4sqUz6fD+vWrSt1ny5duuCiiy46TjUKVr169XDXXXcZ5nBcXFyFofATQf3798eiRYvw7rvv4uOPP8Z3332H3377Dc8++yxeeeUV9OrVq1zl9erVC9OmTcOzzz6Lfv36oW/fvjjnnHNw+eWXG9KLHjlyBPfeey+6du2KcePGHXeX+NWrV2P48OFhnffXX38tE0qrajVt2hQzZswIgkMtnTpKS0tDvXr18Pjjj6Nr164ASj4HHA9HR6kaNWogOTkZkZGRSE9Px3XXXYehQ4cqENhSxTRz5kzceOONeOGFF0rdr2vXrhg+fDhuuummsMs+Ho4/PQOBQPtAINDJZFsmgAcAvG6y7U0A8wOBQGsA7QBIPP2Nv8tsHwgEvgYAm812OoAbAJwB4FIA79hsttAeSWWIQVfAGNAFjgbnJSRASUiIvyMjIw1wBMtncJ+AhwRwJNDBHwIqAAxBfT39lJSEZSQkIcvmfvJa9dQ4MhDNeks3DYrXK9tKB06kg5IEQLg/wRDWgxAP206HS9iGst3MXH/0Nud+BLYkTGPmVML66Wmh9PJDgTF8j32hgzSyzXUwjO42fE0QSLrWyP4CoOAwHSLgOGUfymNkf8p+k9dkVm99u5kriOxTHUaz2UrSczFdlJ56LioqSjnaSLBJB2kIZvHcsq/lGNEBCrM6y37ibx220vtdwkj6eNDHCs8r3ZbYX/o2jr/MzEy1D9cCCevJ/mJd6CKkO77YbDYFpOjzkWON5y0sLFTb6WBF9zN5raEcx2QbyznF/tRTFEoRlpNz2sz+Ts4bfR3Ut3Mf6cYm04hxrIQCe+S41gEgs3VBn0vyt/xbrlF62WY34zlOuQ6GumY5fuV18Tok7CnbTgKL0mFKrw/LlWAkz8995bwHjKnK/oE6KT8XWbJkyZIlS5YsVZFO+s9GO3bsqJDluUztMmjQILRu3TrsY5leBgAWL16MDz74APn5+fB4PKhbty7S09PDKmf8+PE4fPhwWPtKZwgz7dixA61atSqzHAmXpKSkmH5/A4A2bdqEVS/qjjvuKNUGfffu3WFfayhlZGQgISEhrFQ3J5sYUJDiA32WTh7JNFLlfTq4KsR7V6EUGRmJKVOmYN68eWGVx3sOeXl52Lt3b6n7ynvcoRx/4uPjER8fj5ycHINjR2nKzc1V5dHRZuDAgbDZbKZuY7ro5iMl73PdfPPNCt6Mj483rL233norrrnmGvU6Li4u5NPigUAAmzZtwp49e0LWYc2aNXjrrbfw9ttvl1lvig8o5+fnK+cjec6//vpLOWGUBWdZMtVJ/7moMrVp06ZSt9eqVavcnxcqU7179w6CZgGcEOBlRTR48GDcdttt6NSpk1q/ExISUL9+fTRp0gTdu3dH06ZNy5V25pxzzkGHDh3UA8v169dH27ZtkZ6eHrLv9u7di9zc3ON239jtdmPBggXo2rVrWA5S2dnZ+O2333Do0KHjULvQqlmzZrWkfbJ0fMTPKFJ8aP94Ss92RIX78Iil0Dpy5Ah++eWXMtP2tW/fHldeeWXY5VZrqq9AIHAwEAisBGBILG6z2RIB9AAw4e/9fIFAILuM4q4CMC0QCBQEAoEdALYCOCZPPQZDXS4XPB6PIaApg8V0LZDgCV0lJJBQUFBggFUkxMAALeEfui1wXwlE8OaQz+eDz+eDx+MxfDmJiIiAz+czgA4yrYuEXaRstpKUQCyXsACdPpxOZ1Ad2UYFBQWGgHd0dDQcDocBwuC1SmhFAi1M/UN4gMALbwJ5PB4UFBTA5/Mp1yMpGdBmHdgP0g1DpgnSQQAZvGZZ+j46VCL7Ug/cS0cfupHowIqECOT5JOBDtx/2A0ENurbo7kzsy1D/BLidY5ntoUM1EnrRoR/+1q9FurzIdpRQi37jiOP94MGDCjJh6joAyqHJ5/PBbrcjJiYGUVFRCASOOkTxHHTDYZ/zmvx+P5xOJxISEhS0IvtDzmGZvkpCE/JaWLbeFrqLkhznOuTDsuTclaCgfD8QCGD37t3Yu3evai8JLxGg4Tny8/NVesCCggKVtonjJTIyUq0TTqdTzfeIiAjlKgWUfPDl+mKz2ZRDjFzLJKjCtpDjWne14TrmcDjgcDiUixDddjhGYmJiDP2iA0nSVUbCRPp+HEM8r3TNkrCSHOvyerguyLqF4xDF1GLSQY11lXNRh/70ea1DTTwf7apjYmJMYVGuqzrkpPcb54KcSwTupOuYXm85l1lfM/BKhwRZJh28LB3Vif65yJIlS5YsWbJk6XjqZPlsNH78eNx///3lPm7QoEHq78aNGyMjIyPsY7/55hv1908//YQDBw7gr7/+UjcNH3roIQwdOrTMciZNmoTPPvsMWVlZZe5bVgoxoCQQ/PDDD5e6z+7du1UgOiUlBbfeeqvpfmYgSmk655xz0Lx585Dbt2zZUq7yzLRq1SrMnj0bffv2rRT4JzIyEk2aNDnmco5VixYtwsqVK4Pev+uuu3DddddVQ40slVcXXHAB6tata3B/eeGFF6o9DUNOTk6p2wmRAMDYsWPx6KOPlrp/dnY2XC4Xbr755jLPvX79erzwwgsoLi5Gv379cPnllwftU6dOHeTm5uKTTz4pszwqJydHrZnx8fG4+eab8dprrwEoWcvLkhlgRGCobt26eOWVV9Q928jISAUBDRo0CK+99pohZWHt2rVDpjDctWsX0tPT4ff7MW/ePAwYMEBtI5SQkJAAn8+HgwcPlllvii4+u3btCgJ/Ro0aheuvv96wxo8YMQL9+/cPu3xLoXWyfC6qTE2fPt00XR1ls9lwwQUXHL8Kafr2228N6feo//73v9VQm2NX8+bNcfrppwe9n5SUhP79+6NFixbo2LGjYT0pS/PnzzdAoLVq1UK/fv1w99134+qrrzZ1L/n0009x0UUX4eabb8bvv/9esYsph5xOJ0aOHIl58+Zh2bJlYR3z6aef4vPPPy/X5/fK1tatW03hTkunhux2u+FzElAC/iQmJh7XNG+pqamGVMBAydprufpVjuLj400f6pcKBALlSvFW1eBPAMC3Nptttc1mK9vv96hOA3AIwESbzbbGZrN9YLPZ4sT2+2w22zqbzfahzWbjf4b6AHaLffb8/Z5BNpvtTpvNtspms60qjciUjg8MTBLgYJoSh8OhAux0wmBqFN0lhI4JBAkIAgBH3RIYcOfxDNAyWE1ogROKAXVCDAysyycadOAHCE4jo0tCPQz8SjBHpoXh0yMEdHh+whm624TuLCFdSKQLjR7gZn9kZ2fD4/EEQRq8NglU8NpYtgSJJJghXxMYMHMVklAM20i68OguLzyW8AphDUJg7F8Z2Jdjgz8EFXh+CZ/INDwSTOGx0hWIx7BOEnaSIJQcO8BRGEUfKxL44bWwbWTfeL1euFwu5OXlITc3N6htuU92draqp4R1iouLVTooXi+vjamiOE7YLzExMYiLiwtyHMrLy0NkZKRKM8T2lC5PZlCSdCrhPJXjmzJzhpJjW/ah7lbEcRYTE4M6deogMTFRleV2u9WTdnJ/zlOfz4c6derA5/Op9zweT1A/UxwfgUBJ2qWIiAh4vV4D9MS1AICCG7lmcUwxtRePk9fElIVcmyQsx/HG8c25QSAEKAGOZJ8CgMPhUEAY+5bbOB4cDoeCnKS7jL4W8Yd1kCnQdFcorq2c7+wX+f9BrrGEfuR6zvpynEg4SboCEeiRZerwGetZVFSkgEzOG1k+wSkeQ0mXOK7n7B9CQHLtI6Ql1xQJj0qAUwck9XaR4KVsz3+QTrjPRYDxs1E56mTJkiVLlixZsnSsOuE+G1Xkc9GUKVNCutaUpokTJ6q/d+7ciffeey/sY6+99lrMnTsXQImDTkxMDA4ePIjXXnsNL7/8Mk477TTcc8896N27d5k3ZZ966ilceOGFZaYai4iIKNPZqLCwEA8++GCp+wwcOBBffvmlen3FFVeY7vfHH38EvWeWIidchQM3laW+ffvi0ksvRbt27dC/f3+0a9dOPcFeEd1+++34+eefsXbt2moLEiYlJSEyMhKHDx8OerK+du3auPvuu6ulXpbKp/r16+PPP/80pMy66aabDA+xHm81bdo0KPWTLt4v6NSpE6655poyAx85OTnqHnVZ+uabb+Dz+ZCQkIAxY8bg6quvDtpn+fLluPrqqw1rUjiS8M64ceNUQKysVF8ATPukW7du+O9//4tZs2YFuSS1atUKY8eOxf/93/8FBQATEhJU6i2pCRMmYNWqo//GLr74YkRERAS5dJTX0Uu2u1kKMQDYv3+/cldr2LAhXn/9dfTu3btc5/kH65T4XFSZWrRoET799NOQ27/44gtD+tPjrf379+Orr74yvJebm4v27dtXT4WOQY8++iiuu+46NGjQwHQ717nY2FhcddVVYQHaqampuOyyy4Le7927N2rVqoVffvkl5Oez1atXY/HixQgEAjhw4MBxeXg0ISEBaWlppqYJZlq1alXQulzZeuGFF/Dhhx8Gve/z+bB+/XqsXbu2Ss9vqXpl9nnH6XQe0/eP8iolJSXIYEKPN1mquKKiorBx40asX78+pEPl/v37DZ/rylJVgz9dA4FARwB9ANxrs9nC9R2LAtARwLuBQKADABeAx/7e9i6AZgDaA9gPgN+MzUZZ0H+DQCDwfiAQ6BQIBDqVloOOLjsM7svFXkI20dHRyM7OVgF2TgD5ZZlgBYPndJiQKWPokOJ2u9WXHAZgZXlRUVGw2+2IjY1V9fN4PMqt4e9rhM1mg9frNQAurDfdOnhej8ejwAa6UUhQATjq1sJgut/vR0FBgYJfpEOI7lbCYDVhFwmh8IdtExERAafTaUi/RfgDOOraIdtWBqr1wD6vXXdX4vvyGtnvsl9kKh15Ldwmg9388CFBB14DUHIDzOPxKAcV6YJi5kYkHYnMxLbn3zyXdJmJjY1V0AOhJb/fr7azfc0gBtkmsm6yHeSPBH6kA5DcznEuASEJCXk8HuUEIt2hJBDFOcRj6AYkoQ05D+RYKywsREZGhmp7jhVKjhEJDOnwmBlopjtU6aCGhFgkCKG3NYERCSOxTvHx8bDb7arfeU6uBzK9m4RBCEfJbQRkvF6vIUUT+0pCebwuwkJ2u90Au9H1i3Oc9WO5EgxiPeSY4pyRMBPXzdjYWJUGTq5j7DeOEZmqS6bHCwQC8Hg8qlwJ9bBt2YZRUVFB41dfB3UHMH09AUq+JDidTrUusf2k+w+BStn/LIfXJsvkmgtArTv6+Xn9vCY5hiRsGBERgZycHNhsNgUHSSctucYQYmJ9OZ6kGxavjZIQorwGOZ7Zd+F+kTqFdMJ9LgKMn43CrI8lS5YsWbJkyVJl6IT7bFSRz0VJSUnIy8sr91OPBw8eVJ+bmzdvXi7XoFatWqn9u3btijVr1mDGjBno1q0bjhw5gj179uCss87CnDlzykw3kZ2djTVr1mDSpElYu3ZtqYGVli1bhtzmcDjQqlWrsBwvJPQUCuYh2AQcvSf23XffYfjw4fi///u/Ms+hS3eNrogiIiJQp04d2Gw2vPXWW5g1axb69etXobJ69eqFvn37om7dumjXrh0uu+wytGvX7pjrWF7l5OSge/fueOGFF7Bs2TLcddddhoD+WWedhUsvvfS418tS+eR0OjFlyhT89ttvhvePR6A0lNq1axfSjUZXZmamuv9Xmnh/67zzzkPbtm3LLFc6DlVmkEzeq5VpONLS0sp0AzODbS644AIMHz7cNE1jzZo1MWzYMFNXDIfDgRo1agS936pVK1XHrl27IiIiAh988AFWrlyJV199VcFF5U1HJGGGUC4X2dnZ2L9/vwGOCscxzhKAU+RzUWVr6tSp2L59u+m2pk2bGlIcVof09H2JiYmmoOGJrPvuuw/PPfccWrduXSaACQAvvvgifvrppzL36927t2lb2O12FBQU4I8//jCkr9W1f/9+dOjQAV27dsX48eOPi7tNbGwsevbsGda+jNNWlfLz87Fv3z7ce++9QYBqTEwM2rVrZ3D6s3Rqyev1mn6OO97QTXx8fMgMQpaOXZ988gkuvvhitG/fHhdffDEGDhyIl19+GdOnT8f69euRmZmJjRs3lsv9rErBn0AgsO/v3wcBzEL4doF7AOwJBALL/349AyUfXhAIBDICgUBRIBAoBjBelLkHgFzlGgDYV9G609kgNjZWOR0weEs3CwIrTB0kIQE69shAPGBMESMDtnSBkQ4+MlAu3Sni4+ORmpqqUl/JIDhgTMHCwHUgEIDX61VwDb9MSfhEukUwwPt3m6sfM9CEx+jOMxLCkWl+9B8GuhlcZooqAkr8MkLggvsVFRWpwLruahEZGWlw/pDBZ3lumQJMtpuEGeTNIbYL4QNCDrLPGUBnnzFAzvbxeDxBfacDKmZiH8igOgBDUJ7XI11MpFsQHWHkazru6LCShJnYxxz/ZpCPhMm4je0n0ykRstDTJbF9WQZQ8k+F/RQZGakAFzpuMfVZXFycAkM4Rthe0rlKjhWCW9LZhNcp4S/9H6mEn7i/DkLI8S9dT+S5eE06pBYTE4O8vDz1pYHlOp1O2O12ZeMnxx7XJ84zt9ttAGB4Y4Tn47nYD1xb9PnBdpT/2Jmei33ENpQwDOcsz0WXHgn3EGThBxgdZuEclqnTeJwEJ2Vby3ki1zGWJ9cyCf5x3nB+cd12Op1BfWc27jkGdCckuW4STvN6vabwEF/LdFvsDwmeceywvryGwsJC1Rdcx9gHhA4jIyPVuJJrJdtA9oNcN+W6QPBHzjUJMEmoTp8z7D+2Adfnf5JO5s9FlixZsmTJkiVLla1T5bPRrFmz0LRpUwwZMqTcxy5dulT9fe2114Z93LJlyxAXF4ctW7agVq1aOP3003HttdciJiYGr7/+Os4991zk5eXB4XCUCybp0KGDoU5SP/74I/73v/+FPNbj8WDLli0q3VhpWrFihUrdMXbsWNN9pk2bpgCUNWvWYOTIkZg1axbeeOMNPPzww9i1a1eZ56HmzZuH999/P+z9Q2n27Nn44Ycf1OumTZvi4osvrlBZP/zwAzZv3gyg5Ptb69atsWjRIkybNq1abqQvX74cDocDf/zxhwouFRcX44svvijTtcVS9Wv8+PGYP3++AUCULsnVoXBAG967yMnJUQ92hVKvXr3w+uuvw+Fw4LHHHkN+fn6Z5efm5qr7JHTWrgyFAj1TU1OxYsUKTJ06NeSxlRkkttvtKk2YlMPhwL59+/Dkk0+q+et0OtGiRQtcfvnl6pjypKh57LHH0KZNG/U6lEtHXl4e7rrrLlx99dXq/4EVmA5Pp8rnospWXl4e/vzzT1NoLjo6ulJSb1ZUPXr0wFVXXRX0fu/evU8qaDY7O9s0DaGZtm/fjr1794a17x9//IEff/zR9H9RUVERunTpgp9//hlfffVVSCe34uJibN++HYWFhcctvVG46QkDgUCZKS2PRXQC6dy5M1q1ahW0vUePHhg7dqzp/wFLJ79cLlfQgwt+vx9utzsIOKxKJSYmBqX6kjFcS5WjoqIiLF++HJ988gkef/xx3HDDDfjjjz/gdDqxa9cu07SSoVRl4I/NZouz2WwJ/BvAxQDWh3NsIBA4AGC3zWbjanYhgA1/l1VX7Hq1KPMLADfYbLZYm83WFEALACuOof6m7zF4TOcJBoilK8Pf16COk24HLEcCMxIQkAF2Cf1I+EfWgx94CGRQMsAt6yeD3noAXAbQdQBFOqEw8M7gsExlwx/9GvTrlu3Bc0v3HAnqSOcS3SlEBuFlm5o5csg66D/cpge6CS3xNb+EStck2f68XglbSCgBKAmMy5RMss3LIwl26G5GZu3AoLtsex3k4TbZNrLuej+Hcvdhu/FHd/Zh/cyANTnm6SzDv6XzlYSAnE6ncufiWNFhDTkXuM32t6OMbBN5/Trhro8XeRylu7WYQV26IxYlYQ69Pznf+SNBLO7P9YPOXoSmAoGAgq/kueScKi4uVi5hOvzn9/tV3Tj+Ce0AMLjnAFBgoXQOk2nKZB3M5qsOFvIa5DzTnZ1k28q1T7Yz2006IoVqawkKyf1leaHmig5AskyzLzl6/+rOZQTnuC/rL6FBuYbI42TKLrvdDp/PF7Q/r0XOF9kuZnWX++jAmPzfol+j3h9m7Xoq62T/XGTJkiVLlixZslSZOpU+G/n9fuzatQtnnHFGuY9dtmwZsrOzASBkagUznXfeeTjzzDMNgdfffvsNU6ZMAQDs3btXfV8LJxWD1PLly03fr1evXpnH+v1+/Prrr2GdZ+XKlQBKUvyYadOmTVi5ciW2bNmCDRs2YMyYMeqYpKQkU/cLyufz4eeff8b27dvx66+/mqZIqKi2bNmC3bt3K6cis0BMuFq2bBkA4NChQ9i0aROSk5PRoEGDco2FytKaNWtQp04dtG/fHr///jtyc3PVQy6WTg7FxcVh6dKlWLNmDQAY7tNUh/QgkZn8fj+uueYa5TJR2r3R+++/H7169VKvr7jiCtOAuxQfugQqD7hhmnkzxcTEoFGjRiFTvyQlJVVqgNZmsyElJQXnnnuuIeVXQkICdu3ahRYtWhgciQDg9NNPVxAU//+Eo759+xpSQpYWdMzKysLOnTvV/6jExMRqARpPJp1Kn4sqW5deeinS09ODHh70er34/vvv8d1331VTzUqAOv3zUSAQwPr167Fp06ZqqlX5lZeXB4/HE9a+kZGROP3008Muu6CgwNSVyel0olmzZmjatCnOOOOMMl12CgoKgmILVaXSHC6lcnNzsW3btiqrh91ux9VXX406deqEBE6joqJM3eIsnfzSH64GjDHC46X4+PggME/Gmi1VnYqKirBnzx7s27dPpVENR1X56b8OgB9tNttvKPnQ8FUgEJhvs9nuttlsdwOAzWZLs9lsewAMB/CUzWbbY7PZiN/fD+ATm822DiVWhP/5+/1XbTbb73+/3xPAwwAQCAT+APAZSj7UzAdwbyAQqPDoZ4qtv+uJoqIiuFwueL1eFBQUqAAnU3M5HA6VaoVAkExF43a7DWCEDIjLgDNdSbhNh3t4rN1uR61atRAbG6s+aOvQhfxyJwPCXDD0QHtRUZFyb9CdMgiy+P1+5OfnG4AJusYw2C8XIwayZZoYGSiXDkLSfUM6SrD+0smF2wOBkjQ+dAWh84meoswsuC/rI8EnQl3yGtieBE0SExPVkysyyC3hAjoXyX6Qrh7SbUnCF3rQXF/cCb0wJRRdqXguCf6EglNYJwIVOpwmxwzHDbdLmEamQpOOPz6fT4EhTAHFa+Zrs3PwH4ZM5+R0OtVvpiXy+XxwuVzKKSopKcmQBis/Pz8IPGIfcj6yj9l2dCiSKZ4k2KDDTuxved3STUe6pnBfmSpPzmuWX1RUhMzMTGRnZ8PhcKi1hyQv08TxOLoWsS9ok1lQUKD+GettymuTblqsZ3Z2dpBTFc9Pl63ExEQFlnB++Hw+wzVLyEimaNMdz8zSRhUWFhrAoYSEBERERMDj8RjANQn/yWMldMP1knaEPB/XGI5/s3EuYUxCZbLvdYhMptlim+tjA4By/pGAjAR8uA5wjLjdbtXWEpzjnOL/Cf5mObIteE10BeL6HR0drfqO52NZbCv2u5xLcl3W5y/LkO0p1yMdAvongT84yT8XWbJkyZIlS5YsVbJOmc9GXbp0waBBg3D++efjmWeeKdexL774ogpkp6WllZrugOrQoQNatGiB999/H+ecc9QMYN68eVi9erV63b9/f9x6660YNmwYnn32WdN0MGYaPXq0afqXssCfG2+8ETVr1sRDDz0U1nl48/KOO+4IuU/fvn3RsWNHvPbaa/B4PMol6PLLLw/p3rFy5Urs378fXbt2RefOnXH22WdjxowZYdUpHL388stIT0/HRx99BABIT0/H8OHDcf3115e7rBkzZmD27NnIysrCp59+CqAktVZZMENVaPPmzZg0aRIWLlyIjz76SAFgl112WVjj0lL1a/r06bj11lvVPbfCwkLceeed1dZ/4Zz39NNPx7Rp0/DFF1+o+y+h1KRJE8Prt99+25B6ykxut1vd49cBmIqqVq1aZcI7Zg4kw4YNw/Lly48JFjTTkCFD8Nlnn+Hyyy9HXFwcXnrpJaxatQorVqwI6fjG1Fv79+8P+zz16tXD+eefr17n5uaWuv+mTZuUqxmhQkul6pT5XFTZat++PVJTU4Pet9vt2L17N3bv3l0NtSrRt99+GwS15eTk4LPPPsPOnTurp1IVULiOjQDQuHFj9OrVKyxXt8OHDyMxMTEkDHnuueeqMkeMGGH4XGtWxzVr1mD79u1V6rIDlKSqbNasWZn7ffrpp5gwYQL27duHdevWYf36sFi9cumBBx7Ap59+GvJzb1ZWVtgOTJZOLpFHkDre0I/T6TTEwimv11vtaRZPFjVt2rTCx958883o1KkTRo0apT5ThaMqi7gFAoHtgUCg3d8/ZwQCgRf/fn9cIBAY9/ffBwKBQINAIJAYCASS//479+9tawMl+UPPCgQC/QKBQNbf798SCATO/Pv9voFAYL8454uBQKBZIBBoFQgE5h1L/RmY5sBmahUZeI2Li0NkZKQKnDKILuEfCdtIVwzpSAIcdUtgqisGSTmxGRiXAeaUlBQFQtDhIxAoSekVFRWlgsh/tw28Xi8OHTpkCELLOss0MwRAZACcAeHi4mJVx0AggNjYWIMjhQ7XFBcXqy9Z0mVDQjEyGCxT78igtQysA1Dwi4R3pPONBBH0ejENlgSfZNtKsCgQCKg0bARBGDSPjY1V52Kd2dd2u12lCoqOjobH4zEEwr1erwrOFxUVqb8JZ+iwjnSoodsNf/iefHpFfuhkeQQV+EWaEBuvQQbyS4Mh2B8SkOK+BH78fr9y44mKilJOR3rqL6qwsBD5+fkqJZ2ECBwOh6pzYWEhXC6X+sdH8Mxmsynwx+fzwePxKACD481msxkAEqY5k09B6U5OvC62p5zDfC1dueRc5XaeT8J+0i2M5/b7/Wpe8OlB6d7icrng8XgM10wgj+OUACKBJbfbrYAdu91umJvSJYkQGc9HqIaS4CPL5HzT5zL3IyxSWFiImJgYgw1hVFSUGr9yHeL8Yto8Xh/nIM/BNGXsBwm8yLRivHEkoUA5zjl2JSjD+c/1SB4jnxbj+NKdv3juwsJCdc1MzRYbG2tIMSeBKIJnMmUe1xqWzzWVfSThJ0oChdIhSa6r0dHRQbbMMr0j1yS6QMmUcOw3OW65v8/nU/2vQ5HSCYzX/E9K9XWyfy6yZMmSJUuWLFmqTJ1qn41+/fVXtGjRAr179y7XcXl5eVizZo269xPOE8Z089ADAC1atDC8Xrp0KSZPnozGjRvjmWeewZEjR8Kq04EDB/Dee+8Fvf/ZZ5+VetzUqVPhdDqRmZkJoARQKk0MSCcmJuK+++4LuV9+fj5+//13w3sXXnih4fXkyZPV3yNGjMB5550HAKoula39+/dj6tSp6rvgv//9b7zwwgsVKuv5559H7dq18dZbb2H37t1ISEhA//790atXLzRq1Kgyq12qatSogd9//x0bNmxAVlYWPvnkEwDA999/r1KuWTo5RCjF6XSiZ8+eYTnvVIXChQ2jo6ORmpqqHnAsbb/y6vDhw+peUmW1Q1xcHFauXIkFCxZg48aNpinHpPsO5XK5qixNTXx8PNLT07Fu3To89thjuOmmmzBx4kSMHDmyVLjyySefDAKqQkm6rPF+alnaunUr1q9fj4MHD6JLly5hneefqlPtc1FlqrS0funp6aU6AFa1BgwYEHT+5ORk3HnnndW29lZE+/fvL1cK1R49euCSSy4pc78DBw5g2LBhmDVrVpluPb1798bjjz8ecvu4ceNw6aWX4o477sCcOXPCrmtFlJSUhCuvvDKsfT/44AN8++23ePXVV6ssNWp0dLSCp/Ly8tQ9/aysLGzatAk7duyokvNaql7l5ubC5/Oph/WBozEwPXZcVXI6nahbt25QDMftdoftEvZPVZcuXfDBBx/gxx9/xIwZM0wB1nBUEdDxH/WofXnEgKUESAhWMNhNEES69XC72+2Gy+VSNwEYvJZOEwzeMkhLiIUpomSKl8DfzhMyzY/D4VBQEgPFLIcBah3UkC4tdA6SaV4omUJLBrcJONHVg+fjMdLxR7roECiSLj8EfPTgL9uA9WL7MrAsg/ESwJCgCgPvZjaxuvsQ24ZlED6QIAgdRxjEJ0TAa5UOPAQ52GcyyK87rjBATvhE7icBLFk/9p2Ef3h+OX518bpYH9mGEv5h3WTAXgbw+Z6EQqTTD1192F6JiYlo1KgRkpOTDSANf3OssS7yPARb8vPzkZmZqcYK515xcbFyyPF4PApCkf2SkJAQBEG5XC7lICTTJclrl/AO68r24bVK4EJCaXLeyfIJjMixT2iM49jr9So4SXdTkgCbHPt0HNOBGQDKGYnXI+cnxwPbih8gCefIucnjOAdsNps6r+7ekp+fb4Cl2I+EumRfcF6zXVhPpgfjMToQJ9OdyTHF+krnH+moQwhHH4PyQ5MOE0p4S9ZbppCTYBjrKOcQxxLLk3XlPvJDG/cnLMR1EYC6Dtke/FuHcuSaLGE1AnF0IJLOQRzjHAdsD0qOQeAo+MO20Oukf7Fje3Mts2TJkiVLlixZsmTpZFd+fj72798f0l2hLM2fPx8LFizAkiVLytz3+eefV3/v379ffZ9q3Lix6ROFS5cuBQD88ssvmDhxYlj1efzxxw1O2Cy/NKWmpiIuLg5XXHEFAGDixIlYs2ZNyMDXkCFDDOcrjxvEk08+CQD4/fffsXbtWvz4448YM2YMPB4P1q1bVy4Xi4pqyZIlWLJkCXJycnDFFVfg1Vdfxfjx4/HUU0+Vq5wnnngCERERyMnJwccffwwA6N69O+bOnavgm+OhYcOG4d5770VSUhKAEpBr+vTpeOWVV45bHSwdu6KiorB+/XpDyr7qctoNlZYklAoLC0t1CaqIY09mZqYCVCorbd2mTZswcuRIXHTRRejYsaMpYJiXlxcE/+Tk5FRZkCwlJQX33HMPTjvtNAVJuFwufPfdd5gwYULI4xwOB95///2wzkFoKTc3F//5z3/w888/l3nME088gd69e2PDhg3o3LlzWOexZEmqe/fupp8PiouLsXDhQowYMSLowcbjqfvuuy8IcnS5XFixYsVJExTv06cPXn/99TJTbUmdeeaZBgewsjR79mzMnDmzzP0uu+yyMsHxjRs3IiMjI+xzV1TlaY8XXngBn3zyCRYtWoR9+/ZVWZ3Wr1+v4PA77rgDDzzwAO699171ALmlU0t+vx+DBw/GRRddhCeffBIHDhxQMSk95l1Vcjgcpt/lsrOzS4UyLUFlD0pJSUFsbKz6jnU8ZIE/ISSDuQwMS6ADgEp/Q5cVgjtRUVHw+/0qgE8HDoIDhIAkhCPT5TAVi8/ng91uD0o7xjrZbDaVdorBZAZyCSZIRwnCIQQQZLCb1yxT2jCQKx0o/H4/EhMTlTOIDBQzWMz6MwAswR0ABhBCptWiywbbSjr80HGCwWIAqi1jYmIU6CDdlWRfyXJ4rXpwXgdRpJMPt+fn56vrkAFrpgGz2+2qLySM5fP51DUQjGH76S5LuoOMhB64ne3Aa5LQkJkY2GebSeBKh67YbrrLjQ6zsFy2F1OX8YeuO3l5eerDB91OuOjFxcWpfpfjiBBCUlISatasqUC7mJgYxMXFoVatWiplV35+PrKzs+F2uw1whXRQYR/KMcbxRaBCh8Tk9bJNZNommUZJAnFy7PNYCaNwbMq2l/vJcSvPI+tMxxumKXM4HKpfIyMj4XA4VNly7OiAIAEetpHT6URRUZG6GcJ2DwQCat2SEKR0NdJhD16fBH3oDKQDKEwpJSEeu91uSOPHdcnMeYnjRq41bDM60HA9Jlwj4U5Z91BuTjL9lQ4icR8JfhG0k2kL9TSQcvxw7aYTk9PpVK5hLCMmJkYBdbxW9ievgeOA7cG/mRZMuhKxn+XNL/k/j4ARIVbZT3L9lY5KfJ915piUQJF0//qHpfqyZMmSJUuWLFmydAqra9eu8Hg8uPHGG8t97MSJE9G1a9cy93vggQcwcuRI9XrEiBG44YYbAJQEVcye+F28eDGAkqfib7311rDq4/f7MWDAADz44IP48MMPAQANGjQo9ZhXX30VADBmzBgsX74c7dq1Q9u2bUOmrZI3i+vVq4fXX389rLoBRx9wmDNnDjp27IgFCxbg/vvvxzPPPFPl6R+k3n33XeTk5CAhIQETJkxAr169yu0ylJOTg6lTpwKAIUDvdDqRnp6OBx98ENdcc02l1ttM//vf/3DnnXeq9vP5fLjhhhuwdu3aKj+3pcqT3+/H9ddfjx9++EG9V12pGMrrdnH33Xdj0KBBIbfz3p4U72mHUk5OjgJ/ynKbqIj4QKfUlClT0K9fP5XOkDp48OBx6Yv4+HhDcKlhw4al7h+uUx0fOB07diyefvrpsOtz4MABrF69Gh07dsTZZ58d9nGWLAEln3suvfTSoPcjIiKQnp5+XFPe6HI4HKZOVlFRUdi0aVM11KhiuvDCC3HNNdegdu3aYR/jcDhw3nnnlerSJlW/fv2wQJqYmJhS/w8AJWvK8fisV79+/bD33bZtGwDgq6++wurVq1FcXIzs7GwcOnSoUutUq1YtxMXFYe3atZgwYQI+/vjjKnO3tHRiKBAI4M8//1QP2ssMF8dDDocDtWrVCnq/d+/eFUpz/E8TY7kOh6PMNLGVKSviFkIy/Y6EW3QXkFAwiwz+y5RbAAxADX+bOXrojivAUbcaWS8Z6AWOumvobj8MKMvgunSSka4/ursDrwOACuTKoDUDyvIaZLkSJNEdMvTj6GqjU4vcT6Yqk+eXTi3SoUI6U7BO/K072ejOLjrsJcEdme4HgCG4LUEcAhs60KOn4pEOJex3+VueSwez5Gu5rw6ysJ4EdaRLh2wrCRXIOoUqVx/LvD49RRP7geeQc0WOGQmpxcfHB6Vn0qE5vi+dY9gPBC3k+JDuO3qd2A7SWUqeX5ajjw95rBkkJethtj/L1GEvvY3ldRA2ZN3NnJP0PuP80p2k6GIl1z/ewJAOXnrd9f7ldZjNe9nWcl7oDlsSLtTnBOtsBo3ItuGYkA5N0r1Hjjk5JyUUJPeTawKP0wExWSdep+wjszkJGPOzSmc4jhu5L9M5yrrqkKNeX3k97H99XZT/dyQsKOeCLFOfJ3IO6eOU77M/OHf1dd6SJUuWLFmyZMmSpZNVO3bswMGDB8NK16WroKAAkZGRZQZFL7zwQvXw2e7duzFr1iz89ddf8Pv9mDVrlukxmZmZFQp4z549G2+//TbeffddACVPGYfSOeeco9Jrpaam4pxzzgFQ8tR7dna26TH16tULqme4Sk9PR3Z2NrZv345AIKCAp7fffjvsMipDeXl52LlzJzp27AigJOXYnDlzMGTIkLDHwZQpU7By5UoAJU/QS0VHR+O2227D0KFDK7fiJtqwYcNJFai0VLpkSqmy4JiqUnmCUoFAAKtWrSp1H7N7QGVdm3xIsirAHwAGh+jMzEx8+umnpilz6FZ+PCRhwd27d2Pt2rWlAhLhpmUDSk+7FEr79+9HamqqqSudJUulqU2bNiHdupxOJ9q2bXuca3RUHo8He/bsCXo/NjYWrVq1qoYaVUw5OTkVcoypVatWmWAh5fP5wnZ9b9++vSnoKbV3796wyjoWnXnmmRVy6Pjrr7+QmZmJpUuXIjc3t1LrxIeELf2zlJ+frx7MlsYJx0OMa5npwgsvDPruYskoGWvr3LkzLrnkklJTfpXmfFkeWeBPCBE8YDCTQVa6aUhHCQbqZRCcDgg+nw+5ubmGwKgMPtORQQ+AyiCpBCQI9ABQ6XASExMRFxenQAcGoG02G7xer4Ic/H4/4uPjDQH24uJixMTEqPMXFBQEOcGwPBncj4+PR3JysjqvLJOOGtI5RzoGSWcUwksyKE8IgXWQAe+IiAgDdCOD1zI4z/7SA/UyYG8GcMgy6WYhnUgKCwuRn58Pr9cblJZHOl3QHaWwsBAul0u1q3R+IqzFtiksLFRuHi6XS3151QEj1l0GzyVwIyEB6ZRSVFQEl8ul3DakU4gErvhaQjMSkJCAGyXrA0C1Px1D3G63AnRkijaZ8i4yMlKV4XA4kJSUhPz8fBw+fBjx8fHqQz7HhAQH6O5DF5Ho6GjlJiTHIttGQkNybHFcEpJj/0vXFik5vmTaLgmQyJRQOjChQxVyzkgnLo4tCevZbDY4nU7UqlULqampyvKY+3If3uzhcawPHVwk0EjYiusSzy9Tk7ndbgQCAdWndCqTsKEEjySIJNOPeb1e5UrGduPYlH0s1wJ5k4bl6vOP5/X5fMjJyUF+fj48Ho8hrRnBt+LiYrUesZ+ZKk4HXbgeyj4sLCxUT3exnrJOpK95jPyQJCEbjhM5vnQXs+LiYng8HrhcLtVvbG8JQ8p5KKE11ol9SciL60lhYaGaT6yXDkDqoJsOCkqYUQI+vAZCk9zGuluyZMmSJUuWLFmydKpo/vz5uPbaa8t93BVXXIE9e/Zg9erVpe63YcMG9fdzzz0Hr9eLMWPGICoqKsh1gvrtt9/w2GOPKXCnPI5EDMZ7vV5MmTIl5H5t27ZF8+bNgwLLBw4cwBlnnGF6TKdOnQyvywr6Sy1evBgrVqzA7NmzDe/T2eN4qGXLlvjXv/6Fbt26YcGCBQCA559/Hueffz4uuugibN68OaxylixZgkmTJgEALrrooqDt7dq1Q9euXSvtRrClf4ZkUOF4umBJlQdy8fl8qFu3brnPUVawWj40pqcvrCzRxeeHH37AgAED8M0335jul5GRcdz64j//+Y/hdYcOHTB58uSQ+z/33HOllieBnYqkUtyzZw8+//xzzJs3r9T9+vXrhzZt2pS7fEunrmbOnIkjR46Ybtu5cycWLVp0fCuk6aOPPjKdE9XltFYRbd++vULpqerVqxeWWyVQkvoxXLeLFStWhPzsSC1cuLDK2zgmJgYrV67EvffeW67jhg8fju7du2PKlCnYuXNnpdaJ2V8s/fN04MABFRciu3A8RM4hLy8vaFtSUhIGDhx4XOpxMoptZ7fb0atXL7z33nuYOHEizjrrrKB94+PjMWzYMHz11VdYuXIlunfvfkzntsCfELLZbCpgGxUVBYfDEeQQAkAFyhmoJvDhdDpVIN/j8RgC19IRRQZTZVBbOvtIoEV36SksLITT6URcXBwAKPiBrikM1jPtDBDsOCRT18i6yaCx3W5HQkKCIVVQfHy8gi54TkJG0kGD7aY7BEmXCulYxHPIYLl0S2GbMOUQAS2zPmQ76TefpBML6yPBH/kjASi/36/SS8m0VjyOLh2EhtjnBGgIVTFYLqGU3Nxc9fSH2VMYenA9EAgooEh3GGLf2u12lQ5KtnEgEAgCIeQ2CfboMJHcLvtFAlMyzQ+hJn7ZpmOU7ppis9kMqfF4TFZWFtxuN2JiYhAbG6sAIUJx3Ndut6NGjRpISEhQqcTi4+MNAB/7iDAXr4/Qhuw72Z9sdx3cYR9IOEu6JElAgtcox1NsbCxq1aqFmjVrIikpSV2PnCOUhC4I4rndbhQWFhpS+HG8SYApKioKbrdbPUXKfdkGvM7i4mLk5OQY9iF8wu1y/WC78RxyzWIdYmNj1XgAoFJucc1gHxBGCwQC8Pl8imIGYAASKdkvutOYvlbosJWcdxJyiY2NNYxpOdZlf8q+JtAiITk9ZZ6cY7IuwFGAScJHEuJkXVk+06VJQIdAD88n24OvJRRFIEmWz7rw2gjTMXgg12EJHepOY3IdkJAQr4G/ZZpJS5YsWbJkyZIlS5ZOFU2aNAkHDhwo93FTp07F/Pnzy9yPwS+fz6fSQi1fvhxz585Fu3btTI9ZsWIFXnvtNSxbtgxASTqd8mr79u2YOXNmyO0ff/wxfv31V8yaNQsjRozARx99BABo1apVyBReHTp0MLx+9tlnw65PVlYWLr30UmRlZYV9TGVr8+bN+PHHH1FUVGRIh7V06dIKpXu77rrr0KdPH9NtCQkJIbf9E9S/f3906tTJ1PHln6Qbb7wxrMBpjRo1VFqGwsJC3HfffWjfvn3VVs5Ehw4dCsvt4LfffsN1112H7777rtT9Zs6cia+//lq9DgQCIR3FKK/Xq+55VNWDRx6PB9988w0uvPBCfP/99yH3y8rKCgkwlFe//vortm3bFhJ2TEtLw2uvvWZ474knnghZ3p133lnq+aR7iZnDSVmaPn06Hn744VJBrTp16mDs2LHlSvto6cRU9+7dMWLEiEopa926dVi4cGHQ+xkZGZg5c+Zxc9EyU0xMDLp27RoELc6fP/+EHscOhwM33ngjnn76aZUCdvfu3eUuJyYmBp06dcLZZ5+NSy+9FBdccEHIff/666+wy23Tpo1pYFxq165dBhi+qlS/fv1yf/bw+XzYtGkTli9fjnXr1lXovEVFRdi8eTMyMzNRWFiI7OxsbNiwAa+88grGjh0btH/79u3x73//G6+99hruv//+Y4YGLJ142rx5M7Kzs1WcXjo7VqV+++03XHzxxbj00kvx/vvvB8XHqjPd4omuhIQEJCQkGN5zu92mLnb5+fk4ePAg7HY7Vq5caeocWR6V7pn2DxYDkoQGZPCSAXugJODKnMUMjjPIKl0XCNhIeIJBVrN0YQAMQVYZdNeBkYiICNjtduTk5CgIgE4WdHUgxMMgNYPMPIcMikuHF1lHCYJIMIjuHXa7XZGGEmKSAWwZqGYgGyhxt5BuLLwOtgOD+wxwM+AsQQDZpnwt6yrL4t/8LZ1KeKzubCNTLdHpg9Z6ZnABQQi9rwndSBcMbpPuHJR05pAOHRK00h152DcMvjscDuUYkp2dDafTqVJoSShG/si+0t/juSh5nATZJJgjnWNYR45nu92OjIwMeL1exMXFKQCDUE1+fr5qU90Fhf1WVFSk9ouLizOMGToKsY4FBQWG+si0WnT1opOLDmqwHQg1EHCRUIzsBx0AkumcmHeb/Zibm2uAbaQDE+vGunBtolvWkSNHkJycbHiCScIsbrdb9TVdkeRcjY6Ohs/nU0ANb45wPzkeeAzHmN1uN7jOcB5z3EdGRqq+tNvtBqBGuj6xregSxfFJF7W4uDh4vV4DKMlzcT2TbRUbG6sAFX0MyGPZnjL1HeElCfiwDwlLyjVGd95hO/MaddjQZitxbGI5DofDANMVFBSo9Z2/OVcKCwtVm+hOQdyX9ZHzUoJUOlApYTxeH/8PcgxJiE6OHbk26OuE/L8iAUMJeFqyZMmSJUuWLFmydKpo69atGDJkSLmPC9fthimf5FOeDz74ICZPnoz27duXCucsWbIEt9xyC7p06YIxY8Zg7dq12LVrV5nBdgBITk7G66+/jkceecR0u8/nw6xZs7BixQr19L3T6cS1116Lw4cPmx6Tnp5ueO1wOHD55Zfjq6++Uu/VrVsXb775Jvr3719mHatDn376KXr27IlrrrlGuWlUJHgGADNmzMCFF14YEsy67LLLMHXq1OPqalTdatu2LYqLizFgwABceOGFuPLKK7F06dLqrla16b777sP999+PLl26lLpfvXr1ULNmTQAl3+9feOEFlQ5n8eLFGD16NObMmVPl9X3xxRcxdepUnHPOOWjYsCEee+yxoH1WrVqFzp07h1Xefffdp9yxgBK3n7JAGj5IBhy9/17ZCjfAWlxcXGmw4ty5c/Hmm28iPT0dt9xyC2666aagfR566CE8+uij6nVGRgbcbrdpmpjo6Gikp6djw4YNpk/10/GnoKAA27dvr5Rr0JWRkYGsrCw0atSoSsq3dHzUt29fDB8+HIWFhXj77beP2Wmrbdu26NatW9D7M2fOVJBxdcnn82Hu3Llo1qwZ6tevDwD47rvvgqC7E0UJCQm47rrrcPfdd+OMM85QRgKZmZmIiorCK6+8ghYtWqBbt26oXbt2WGV2794df/31Fy677DJs2bIlpAPTzp07kZ+frzIWlKamTZuiefPmiI+PLzW14Lx588JKC3Ys8vv9FXZW2b17NzZv3qzu4ZdHixcvxhVXXIGmTZsiPT0dDRs2xN69exX0L5WQkIBbbrkFV155JVq0aAGgBCJ444038PTTT1eo7pZOPG3btg0vvvgiGjdujClTppj+r64qXXbZZRgxYgTOOussQ+xaxrssBSsxMTEI/GnWrBm6du2KdevWISMjw7BtzZo1GDx4cLlccEPJiriFEIP5DofD4F7CfyTZ2dlBXxgIoyQkJCgXDIoOJXTGoCIiIhT8IUEGCfcQUKALkIQqGKyPiIhAUlKSSr0lHRz8fj8yMzMNUA2BAun0o7s9+P1+eL1eBTkw7ZF0Q2EwmiCFDOzqi4AElWTAme3AYLd0J5KwCx1rCGuYucYQsNLbmMeY7c++k7CUTPHD/qBbBfvT4/HA6/WqlEU6NMA+I2TjdDoNNB8hEOmipP/QOYNjT9bLLH0Qr5MuK0CJ5VpKSgoSExPhcDgMIBHbhm3P6+EYYR8xDZnH41FjWW7XHVaks05sbKwCMFwulxrPhGZcLhcOHjyIvXv3oqCgQDmacIzyhy4w/MLAnKZxcXFwOp3qmKioKAU4ATCk7CJlabfbVRuxn9i+dF8CoGAeAgrsEz0NlHTo4WuOM85HCcBxvsXHx8Pr9eLQoUPYs2ePgl2kQ5AE06SLC9vYZrMp6E+OaX3NkvOBv6WbDX/znxHr4nQ6VV/JucBUapy7nB+y3zkemN4pPj5eHSfbRM4Z9gf7lVAR1zaWz/HGc3Jtk6AWr58/7AvpmiYda2SbSWcbQi8A1DjhXLPb7QYYUQJS0u1KrrWE8VgfWVemh/R4PEHAo3RVIrjD8STBGpl+i2uiXAtl38j1SJ6PY0LODZYn20quTxKkkvuyj/X24bixZMmSJUuWLFmyZOlU0auvvlpqSqxQ6tOnT5kpUDp16oSWLVuq1/zc3b9/fwwaNCjIQUfX0qVLsW/fPkRFReHee+/F+PHjMWfOHPUwW2mKiYlRDiKh9OqrrxoCPoMHDwYABSDokg4/f/75JzIzM/HHH38Y9hk4cCDS09PRq1evMutYHdq4cSPuuOOOoBQ67dq1Cyu4pes///lPyCBXr169KuQkdLKqdevWGD16NJYsWYJrrrkG+/fv/0dDP0CJk084T3j//vvv2Lhxo3rN+2gOhwPnnXeeCvZWtTIzM7Fq1Sq88847ePzxx4O2+/3+cgNI8h5Xbm5umelpmjRpou4B1ahRA1988QXefPNN3HHHHVXeDn369MHChQvx0EMPqffKcigKV++++y6ysrIwf/58U/cHAKZrycSJE0OW+c033+DXX38Nej8pKUmBPz6fr9KuwUy7d+9G7dq1LfjnJNWDDz6IUaNG4fzzz8e5556Lm2+++ZjLfPbZZ/HJJ58Evb99+3aD21516fXXX8dzzz2HnJwc7Nu3Dy+88AJ++OGH6q6WqYYOHYqRI0finHPOMax/qamp8Hg8WLBgAX755ZdypdBq2bIlHnjgAfTs2RNt2rTBaaedZrrf2rVrw3bErFevHjp27IgOHTrg+uuvD7nftGnTqnwM8AHximr//v1lpqQ0U5cuXdCvXz9s2LABEydOxPPPP28K/aSlpeGJJ57AoEGDFPQDlHxub9KkSYXrbenE1JdffomxY8ceV+inUaNG6N27N7p3746kpCTDtoiIiCCwxdJRNW7cGPXq1Qt6/5lnnsHy5cuD0oNv27atUqAfwAJ/QopfCuieIwP6NpvNkIYGMLraMMiqp76REIkeZKVbkB405t8MDEvQQJYTGRkJu90eZBPFID/TqjCoDhwl8hiw1p1mZNBbQiZ6CiIZ9Ka7Bq9ZD/zKv6XrB+vIfWRqHYIiOsAjASM90C+D1GbONLJsGYyXP3J/Xp90GZHlyjRbsh0JTEloh8FyPUgu0w6ZQUBsK+lYwvaXEAr7Q/afBH54LgmHyX7V3XR0lyG9j0urK+vGc9LFRIIQtCsk9EPwRYe09HHJuhMykX3F1EuyDklJSSoHqlk/yzHLusp5IueT2TG6K5AOZci5IKEr/vB8co7I88k5xHYgqKWfQ9ZBjhO+T2BHuq9wbElwToJlBDSkUxNFuEU/txnsIdcB6ZLE+cU6xMTEKBcxeX0ybaJsG1l2KCBNQj9mTk4SPpKpDnWYhufUx7feBxJO0tcI3TVH7qevNewfuRbyNfeRktfE33J/PU2aXJNln8hz6deut5c8XoffZPvoqQktWbJkyZIlS5YsWTqV5Pf7KxSw5M360tLx8PssUPKgBj9r01GnTp06aNiwYcjjzYIPDocDF198cdD9IF1JSUnlthx3uVwqyJOSkhK0XUJC06dPx/Tp03H48GF06tQJqampAIAzzzwTGzZswOLFi8t17uMlh8OBxo0bB70/dOjQCkE6u3fvxpw5cxSkJZWamoozzjijwnU92TRs2DA0a9ZMwQuzZs2q5hpVrzp16oSUlJSw09pMnz5d/a1/95Zu6dWp8jwp3r9/f3Tv3t0APnm9XmRmZpZ63F133aXmTatWrXDxxRdjwIAB6NatW5W7Z918883o0qWLwWGnsh5+iouLU/d2fv75Z/X+r7/+qlJxHTp0KOi4JUuWhCwzKSkJzZs3D3r//vvvVynmeM+1qpSVlQWHw6HcrSydXDr33HPRpk0bAEB8fLwhRdyxaOHChUFzXX4mqm7NnTsXv/32GzZv3lzqHKtu8cFeMyUlJaFRo0Y4cOAAtm7dih07doSVqtHpdKJhw4bw+XxYs2ZNSEew8qQ6dDgcSExMxFVXXaUAcDNt2LAhCBivbK1duxZbtmyp8PF5eXmGmE+4crlcpp8vdZ1//vno0qVLEGTv8Xhw8ODBcp/XkiVdXq+3VBjQyuYQWn6/39RlEShZO0NtqwxZvRJCMTExSEpKMsAJHMR2ux21atVCXFycwa3H5/OpoCddV6Qrg/wbgME5JTY2Fg6HQ8EXPp8PBQUFiI6ONvxD1mEFAht04eB56Dgkg/k6YCThCD1NTkFBATwej7p+XguhlYiICHg8HuV0w9RmPp9PfQFgUFgXnVxY7+LiYlUXAAr+AIzQB11b2EbSQSgiIkJNFumMxEC/hJJkap5AIKDS3rDddHgoIiJCLXASFJIwlplTBuvgcDgMDjN2u93gKGIGRUnYQAbeZbm6swmvl33AMcT0V6yzw+FAQkKCgo9kHSU8JsEDOo5IyIHS3Wj4I1PN+Xw+BAIB5ZqSk5OD/Px85S7kcDiUiwydXaQzFMcTcNRZi24hhYWFcLvdajz6fD643W643W4cOXIEWVlZqi7SUcfhcCApKUmNbZ4jNjYWTqcTSUlJiI+PV+0h0xIRDOKcl8CDhHak2w/bXzq2cO7QUSkqKkrNWx1+k3ALxxtdp+TYkYAcxzyvj/2op5Vi20hwkfOsoKAANptNuXHJ1HQyTZeEXOT6xLbn+QgpAiUfQgmD0TFIQn4cn3SM4hpBZyeOa3088jzSUYhzWTr66LCdBJ3kXON6I9clXpcEZeR8CAQCyg1IzmO73Q6Hw2FwUZJrEwFIOl+xLhIik6Abx7NM+aen4NL347ojYSnZPvxb9oMEAXUoVLpqybWMddHhSjmerQ+HlixZsmTJkiVLlk4l/f777xV6wrZv377YuHFjqU8uyyfIR48ebXg/Pz8fw4YNKzXN1AcffIAzzzwz6P1x48Zh3bp1mDZtWshjX3rpJfz5559lXIVRQ4YMQVpaGgDggQceCNret29f9feaNWswY8YMpKSk4LvvvsPKlSvx7bff4rrrrsOrr75aZSl6jlXDhw83bbc+ffqEfOq9LA0cOBD9+vXDwIEDg8AE/cnQU00OhwOvv/46nn/+eVxzzTWoW7cuxo8fj+Li4hM2dcrx0NixY/HTTz/hjTfeQNeuXcM6Zt68eVi3bh2AknsD8+fPx7Rp09Q9lnbt2lVllYNkBhdGR0er9DiladCgQXj99ddx6NAhDB8+XL2/c+dO7N271/SY0aNH46GHHjK47QAl95zr1KmDQCCA/v37o1mzZuW7kDD16quv4uabb1au+QBw9913m0KQobR169aQ/xN+/PFHfPDBBwBgcKP44osvcMMNNwCAwf2B+uyzz8pM7/jLL79g/Pjx6vXTTz+t0kzSDb6qtH//fiQkJODBBx/E2WefXWXnsVQ1WrNmjQHQ0R9UrIiaNGmCPn36BIE+PXr0QJ06dY65/MrQgQMHcP7552PAgAHVXRVT1axZE08++SQefvhh08+BQEm8s1u3bvjoo4/Qu3dv3H///fjmm2/CPseWLVvKhLTDSS0LlKxpbdu2xYgRI9C3b19DykJdZm5QlaH9+/fj5ZdfRrdu3RRI26lTJ4waNQq33npr2OXk5OSU251l9+7duP/++/Hyyy+Xue+dd96JHj16BL2flJSEu+66K+yUbZYshdLBgwdx8OBBZcRArVu3DuPGjcNLL71UTTU78fX888/jkUceMf3cVNXwuRVxC6GaNWuq1DEADCBJYWEhnE4nIiMj4fP54PV64ff7VVotBoGl24vuFKPDKDJYDpRYZ8rAtqwHA6aFhYUKWmEwloF2Bob9fj8cDgdSUlJUkF9CSgBU8FW6Q3i9XkMeZDqrSKcWpvcCSv4hmzlpyGA/QQR5XkI/Pp/PAOzoabwk0CDhKZbtcDgUwCEdcGSQWod/uI3txcC1LIPXyfaSgJLuXiGdaAg26eVx3BD4ovsHA/DyHGwXCYKxHrw+CSvIlF1M21VQUKCe6LPZbMjPz1dBfxm8Z/ok6agk0yPpjjUEQHi9+hd42VdyXERElKTjysnJwYEDB+DxeNQ8Yao66a7DVEo6CCT7UwIhAAwwHoGUvLw87Nu3D8XFxcpFJjY21pDySTqScMwTRCKwpI8lfRywLfS5JQE9/hQUFCA7OxtZWVlwuVyq/XVwwszhi6/r1auHBg0aoGbNmkhISDAAKewDlkVgT3fm4fXSOchmK0lr5ff7kZ2djfz8fAQCAeTl5ak5IevAMSddogAYoB+Hw4H4+HjD01wcR5zfrIfuBlNQUIAaNWqo+nL+684/cm2Q6zXHtgSiCN0QvJFtzLlHEElfx3SYUa5vEmqR6wDnktPpRFRUFPLy8pCfn68gH45pOe/YFjw/x7V0IZJ1l/9bdAcf1kf+/+D/Mqb+kmNLQjuE87hmUbrDm2wLrg9ch3geub7LVJGWLFmyZMmSJUuWLJ0q+umnn3DkyBE8/fTT5Tquc+fOmDlzZqn7XHPNNervUaNGGbatX78eWVlZIY/t3bs3brnlFtNtaWlpaNu2LQYMGICePXua7vPMM8/gk08+CXKZDqUePXrgX//6l3ptFmi67rrrAJS4UixatAh9+vTBggULkJycjNNOOw2JiYnYvHkzFi5cGNY5q0NvvPEGnnnmmaD3O3fubJraKFw988wzyMzMxJQpU/Dzzz+rJ+ibNGlywgYWj1UvvfQSbr/9dowYMQKjRo3CpZdeisOHD+PKK69UsMo/VQsXLsQtt9wSVhBQii4tKSkpaNmyJfr27YvIyEiMHz8erVu3roKahhbvbW7ZsgWPPfaYGtPhwB0HDx5EVFQUdu7cib/++kulmuK9Ol1NmjTBww8/jDfeeCNkmbfeeis++ugjlcKqsiUduyIiInDrrbfinXfeMYBLZen777/HK6+8Yrqtbt26ylWMa2lRURE+++wzQ9pHszU9VLtR6enpuOOOO9CkSRP1gC0l7wVXhTIzM+HxePCvf/2rXMF1SyeGvv32WyxatAhbtmzBiy++eEz/B6mdO3di27ZthjR/GzduxNtvv42MjIxjLr8ydaI6rNx666245557SnWFBErSS9FlZuPGjeVq36KiIuX2FEobNmwIu0w6PwIl0HOotHHfffddlbj+1K1bF507d1avzz33XNx111145plnMGDAgLDTuebm5pYb/HnnnXfwv//9L6x9S3MZjY+PxyuvvKLiKpYsVVRHjhwJAlX++OMPzJs3r8yUq/90ffnll5gxY0aQ41ko05TKkgX+hBAdcui8oKcSAkr+ockPwLrbCoPtCQkJQRAEYR/COwQKGKzmeaUjgnRtkCmdGKCWTi0yKG+z2ZCQkKDcXCSUwMC3dJpg/aWrDVPNsC7S3Yh1KigoUIF4tpUMrBcUFBgcOiREAxy1BdODwwQiGEAntCGhGhnUl695PQxay+A4nTPoMCSdJ+jOxPq63W61PT4+3gBkMWgvz8GbEna73eBywsC/DKLLPudkz83NRXZ2NnJzcw2Qghxfsp0llECwiPvRrYhBeAkzyHaSx8v0TrxGjl1eq3RPonSIoqioCF6vV4EERUVF2LVrl7KdleO7Tp06BmArEAgoJyymKpOOOxLgYn05Hzj25ZhluYTlfD4f8vPzkZmZqVL3sV0kmGe321GjRg2DaxLPS2ctCc3JHzo9sW0oQnlutxu5ublqbkhohGXIv+X12Gw2NGjQAI0bN1ZOMiyH84awHh2O4uPjDedj30owJDo6Wo0Vr9cLj8cDt9uNmJgYw9iQY4SuQKwvU3VJV5eoqCjExcUpdzD2I4EWgoa8GeT1epW7UHFxsXKVkWOL41aH31iHhIQEAxBIGEbCdKyLTBEn+4FzRIIr8fHxSEpKUv3BuS7ng1w3WQeCMhIiJfjGusk5SchKzk+Cf2wX6UIm3YakyxHL5bhhX0vQjNcm1zP+X2HdzdpEBxC5dsqxLp2H2NZmjkOWLFmyZMmSJUuWLJ3s2rJlC/bs2YPnnnsOnTp1Cvu4HTt2KHecUCI88/XXXwdtq1GjBq6++uqQx1555ZVh1WPmzJl48skng95v3LgxNm/ejDVr1mDgwIFlltOoUSOD89Ejjzxi2N6mTRtceumlAIDVq1fj9NNPx+WXX64cKo4cOYJzzz0XF198cVj1ri653W689dZbQe+XlX6oLO3btw8PP/ww7rrrLnTt2hWdO3dWZd5www2GdEengnr27IkRI0ZgzJgx6r3XX38dDRs2VCk+6DjyT9SMGTPw2WefleuYOnXqlJoK53in+5o9eza+/fZb3HrrrXjzzTfx66+/AgBat24dMhgdHR2N++67D5999hmWLVumgk4Emrp16xYEQQJAvXr1wqpTZGRklQREX375ZTz77LPq9fPPP4+JEyeW+/7H999/j2nTpuGXX34x3R4TE4O2bduiT58+AEqu58Ybb0Tv3r3VPjo4mZaWFnb6pZ9++smQRgxAhVLWlEf/+9//cOONN+L//u//qvQ8lqpGa9euxeDBg3HTTTfhqaeeqrRy33jjDfz0008ASsZlecAISzDcLy9NLVu2xBVXXAEA2L59O7Zv3x52OsYmTZogPT3d1GmMmjdvHr755hvk5OSEV3Gh0j57fv7551UCJErYLCMjA5mZmSguLkbDhg0xceJEvPrqq+jfv3+pZeTm5qrjwtG+ffuwadOmsFMqbtu2LeS2nJwcLF++POwUa5YshVJWVlZQuq/k5GQkJSVVU41OHtWsWRMNGzYM+kwuzWCqQhb4E0IREUfTO5FmJ3Ti9/vhcrlU8JwpumQQVqbSkYFc4Kj7RlFRkXJxIfgTGRmp0hEBUO4bDN7KMhh09Xq9BiiAP4R8CNfoMBDBBlkuA8j8kYF+mSKLdZVuFtJJg5LvM+AdCARUGjFZb8IchBhYJ5k+S7qWcHLwfZkiSTqkEPZgOzO1EYPxEriQaXFk3QEoxx6+p7vESPhHd86gcw0BMtZFgirSCcbr9cLlcsHr9SInJ8cA8vBaJPyjwwkcP3Jf2cccW6G+dMr0QxIciImJUSAOU5ZR0vVIuhbxOrxeL7Kzs7Fjxw41HqUjjc/nU+dg/dh2TqcT8fHxKoe1hLeAEqteSVoTYCFAxzIJHERHR8PlcsHtdhvGtKxHamqqcgeiIxHdkVim7DeKwA1BGV6rnhIMQFDfyLHEvyXwJ0Gn6OhopKSkICcnB16vFxERJenuOLckoGMGDcn+pWsWz0HQzel0qn7knOT+HOeBQEC5BfF88tp5LTExMco9KT8/Hx6PR80Jn88Hj8ejxibHMME8gjt66kHp9qPPffm+TFFYUFAAt9utQCSuG1yPPR6PIUUYr1e6/tBGma85PzgH5PjmWqG7EPGaOPYk+CNdhnR3H7lesA4SWuRx0l1Jd5zj8RyLXKfkvC0qKlJznWsbgSy5duv/BwigyvMQIGSfyGu3wB9LlixZsmTJkiVLp5ICgQAOHz4MADjvvPPCPm7p0qXKvSGUPv/8cwDBDhkPPvggWrRogauuuiqkk8OCBQvCqkdycjIeffTRoBu5+/fvx+TJk9GyZcuwnuBftmwZNm/erF7roMpTTz2F+Ph4ZGRkYO7cubj++uvVk+KBQEAF9E7Up+fL0v33328IvpdXH3zwAW644QYMGjQIQAlINGHCBAAlT6FfcskllVLP6tagQYPQpk0b9O/fP8hNSr/GWbNmHc+qnfTKyMjAn3/+iUOHDplul/cRqlqRkZHo2LEjpk6dip9//hlerxdLliwBUJKO5KqrrjI9rrCwELGxscjJyVEpu3RQp3nz5rjtttsM75XHxadWrVrluBJz9ejRAwsWLMALL7yAtLQ03H777ZUSDFu0aBGAEnBo3Lhxpvt8+OGH6NKli3o9atQoQxpFPVienp4ednqkevXqBaWEK69zRXm1adMmzJkzp0rPYalqFQgEsGrVqkov97333sMrr7yCt99+2wCJWgqtJk2aGGJj4eiSSy5B27ZtAZSAg6WlkJVKSEhArVq1cMYZZ4Tcp6CgAOnp6RVaHy+44IKQ2yZNmoRly5aVu8yyxEwQQEn6rX379sHtduOMM87Addddh3vuuQfNmzcvNYXj4cOHkZGREQRNhNKOHTuwfPnysOsYKt0lUPL/deDAgQYXOEuWKiKyEFIOh0PND0uhFR8fD4fDEfQ9x+fzhb0uVEQW+BNCDIoy2ElYhECBhA701EjS9UR3CZF0J+EFig4RMojM43TJdEh6qhUZcGZgXxff43YGXwk9SLcIlss687UeGJeBeEp3qZAuLSxPOs9IFwjpakHIhz8yMM9zm4EsMmjP9tdhLOnEwmuTbj/69bBOEhZi2fK65H46kKMH3+X7Okzk8XgMrjt6ih1ZlgQG2K4y/ZhcYGR7SdhLXhshDu5DhxICTHLssR7SnYgQEMdRXl6eoW3Z5zxeOpPIdiRQQAhHd8IBoGAT6Y4i4S/ZvoSRZD9JByeOJz1FFGEiCTzI+SHHnbwm+Z6sM9cNHdbgD/tQbyO2SW5uLo4cOaJSQEVFRamckfIYgho8v5xDEmgh+MTy5bwnlCWdwFgfusPIeSVBJY4NCTWxDAnw6WOTay6AoPR4EqKS7S/nOuevHAeEOBMTE5GYmGgAnjjH5FjU10C5VurznmNJwoJyzZTwjxzjEszR20DOabldtpdcP2S5cgxIdyd9TEoo1Aymko5o0mlMr6t0Y5JOT4TQ2KfSvSvcJxgsWbJkyZIlS5YsWTpZlJubC6DkCb9w1a5dO+WMG0oM5uo3DhnYP3LkSEiHj7POOivsuiQlJSkHCcrn8+HNN99EdHQ0mjZtirp165ZaxiWXXGJw/JF/AyUpE4CSFB4bNmxQweVDhw5h8eLF2LVrV9j1PRF10003oWPHjhU+/siRI3C73ejatat6b8WKFQCA5cuXhw1yneiy2+0YNmyYAVwwU3Z2NjZs2HCcanXqaNq0afj2229Ntx1Px5/4+Hj4/X5DcFQGK1u3bh0yjSDd8el4JdO/AMDFF1+MZs2aqdd9+vQpNRCqq7SAbbgaMGAAunTpgoKCAjgcDiQkJBxzmQCUS8O8efPw1Vdfme7TuXNn1K5dO2QZcg0BoB5qrKhcLleFj7Vk6Vj00Ucf4e2331ZAnKWyddttt+GBBx5Aly5dwl6XGjdurMCfP//8EwcOHAjrOJutJONIaSBA165dy0w3Fkp2uz1kispdu3bh999/r1C5pamoqEj9z/H7/cjNzVVxF6AkFpWQkFCqu15RURFyc3PDvv8dGRmJ/fv3h13Hstqzffv2uPzyy8Muz5IlM8lMEBQ5CUulKyYmxmCeQfEB+6qSBf6EEAPADGjLwD+D2LLDZIopGaCmGwYAUycPGQy22+3wer2GgLpZvaTDg0x5JNO9SGCF+0p4gjAOwQYCG16vVwXG6aTDdE2FhYWGVGN6XaQrBb9EsL1IBDJYTqhEAlISpJGOEmxnuk7I9DBMtSTBHxmgZwoe4KiLDcvQwQoJ0PA97s/+lvCQ3v5yDDBIL/tQQivSwYngA9uWDk6FhYXIy8tTKY8I1Ohpy3QAim3Gxddut6t0TOxvQhzSQYnXKqEdOQfolMIfHZyx2UrcqZhSLhAIIDMz0wCIcSxwPEhAhk460lmK/c79JcjB46Ojo+F2u5GTk6PGN1PB8bccsy6XC7m5uYa5KAEW2Q8SlvD7/Yb209PD6U5T+msdnouIiEBcXJxykZJjRgfvdGiQ9d6zZw9ycnLU+uF0OlU6La4DHo/H8MN+16EYwigul0uNZzoHEfjhe0y/V1xcjIKCAiQkJAQBTHTXkVCPdLjiPgQC5XWyfPZ7VFSUcsGS7SvXZOmkxDWroKDAcJ2RkZFITExEamoqUlJS4HQ6gyAygjlm7jucV0yBRocr1l+mFJPzUN+H6cQAqDEmxwpvwLGNZQpB1k9PXSZv2nGsS7hUjmUdNuT/HOk8x5RsEvTxeDzK8Ue2mWxDubZwvSN8KJ3WeG75hcmSJUuWLFmyZMmSpVNBDIwOHjwY3333Hd57772QTjxUenp6yMAuAHTq1AnDhw8HALzwwguGbfy+NmrUKOU2JHXbbbfhwQcfDFm2mc34yJEj0atXL8N7+fn5WLt2LRwOR9A2XSNGjDAEf/SUXaeddhoA4M0338SiRYuwfv16fP311/jf//6Hnj17ntRpXtq1a4dzzz0XQ4YMOaZyJk2aZHDrmDFjBhYuXIj9+/cjPz//WKt5QuiTTz7BVVddVSaYtmTJEtOHIv/pGjVqVKnOPZ9++mlISO94gj8OhwOBQMDg1vPDDz/A5XJh69atGD16tME9nvrss8/w2muvYe/evWpdpTMYlZaWhn379qnXY8aMwdlnn41hw4bh5Zdfxrx580qtm4SGKqLhw4dj2LBhsNvtuPbaazF27NhKScWn3yv58ssv8d1335nuS1dqM91+++0qjeQNN9yAiy666JjqVR6oypKlytbevXuRkZFR3dU4aVSvXj08//zzuOqqq8J2+mrTpo1yLPR4PPjyyy/Dhn/o9h8KfHc6nXA6neFV3kSlOWNOnjy5Uh8uLSwshMfjQVJSkvo8u3nzZkMq14yMDNhstjLbdteuXaXCPMXFxTh48CC+//77kC59Zrr88svRvXv3UvfZt2+f9dCtpWOWx+MJYhXS09Px9NNPY+XKlejRo0c11ezEF+O1uvLz86s0DV8wamQJgDG9lO5cIZ1FGMBlgJaWlxImKSgoUF9gCHkwQEqwhoFq6SBBtwiCAawXg9JM4+R0Og2wjwweS8cJ6VLCYDzT3DAwK4PurBthJr7HAL/P5zOkXZIwDEGOyMhIQ+ocBq55nAwcs24MbOvb9AB5dHQ04uLiVPBb5itle/GLLNMfMYgt0yuxHgyGy/RmTqcT+fn5CgqREBXLLioqMrjQyHQ/MlUY68P0SUwzJWEBv98Pr9cLp9OpiEnp2EMRkOG5CLlIdyGZeqywsBBRUVGIj49X9STUwfPq7k+E1tiXEjKQ44RtztdSHKNMq8QPS/zAIesAQLWx7qbE+vFa2BZMueXxeOB2u1XqqNjYWAMYwTnDeRgdHQ3f/7N35nE6lf//f812r7MZy4x9SxFljSgqEUqoEEqJ0EKLSsu37VMqWqiUyhYpsiRkrSwRWUN2QpasY8zMvc/6+2N+r6v3Ofe5Z6fUeT0eHuY+9znXuc613fd93s/zemdkaCAbiuen1Rr7h0CM/hr149pqtSpYS14Ly+YxnMsE/vTlyn5gPfWOLufPn1dpmbKzs+F0OtU4ILTBPmUbss04p2UqMAImBG3YntnZ2fB6vYiNjVXX6/F4FEDkdDrVvJWQCm8gSwiKdZKptyQQKN28cnJy4HA4kJ6eDpfLhbJly6p0i1yHZf9Kdx2jNYSgoIS2pKOSnBscJ3rXHwlTWSwWBeFwTeSclmuFXF/Zd4FAQMGE8vwcRyxLOo/xhr7+mvXgkBxDekciCaWx3nIeSFArPDwcPp9P49gjYU9uk+CgXMcJOfGauVYBUJCRKVOmTJkyZcqUKVP/VlWvXh3Vq1cHAMybNy9kALpGjRrYu3cvvvjii5Blbd68GTt27EDbtm2Dnq5855138OGHH4Z82vnFF18MSo8j9fDDDyM2NhZXXXUVatasiTZt2qBx48aYN28ekpKSNDbk/fv3x9q1a/HVV18FlWO321G9enV07NhRgT2U3src5XJh9+7dmDFjBgDgscceAwCVwvtCWp9faDHFW506dTBo0CCMHz++WOUMHz4c27dv12wbPXo0xowZg1WrVmH9+vUlruvfLY/Hg5YtW2LmzJlBziRUSkqKmfonhO6++25cd9116Nixo+H7lStXRnx8vOF7pQGnFFZ02JbAWmpqKs6ePYujR4/i0KFDQceEhYWhSZMmiIqK0rxfp06doH0PHz6s/q5UqRIqVKiAZ599FgAKnH81a9ZEjRo18McffxT1sgBAAyw1bNgwKDVWcWWUXmfmzJlFTiFosVjw0UcfYdiwYWjZsiX69u1b7Drl5OQY9pUpU6b+eUpMTERSUhKsVmuRjouKisKNN96IunXrYu/evZgxYwbq1auHPn36FHhs2bJl0axZM2zcuNEQRF+wYAHWr1+vXB+LqvwA+Y0bN8Lv9xcI2hdWbIeffvoJycnJ2Lt3L6KionDFFVcgJycHI0aMwNSpU9Wa+Prrr8Pv92PHjh1YsGCBpqxjx47h/PnzIc914sQJfP/993j++eeLlOJ20aJFOHHiRNB3bimbzYbU1NRCl2nKlJGM4GyLxYJy5cqhXLly6veuqWBJeJByuVw4efLkBYWpTcefEJJOEzLIKmET2o0SKJHpa/h+VlaWcsOQKacYQOV2BoBlgJ6OCfpUNjJ9knRUYB3p0BEIBDQBXummI624CDCxDDp7EASQUA8dO/SuNnSJkOm6uL+8Fr1bh3SpYTBZukwAf7mAMEUM8wdKtw55TrYzoRTpLMLrl+42vHYZEAegvhixLmwbuuFIyIbjhD9mZUouKYJj0dHRiImJUa9lOi0G2aWbiSzTyO1JAkB2u12RhE6nEzExMYiLi0NsbCyio6PVWGNf8trp0kHp3Tw4vrhNjh/pWANAgU3Hjh1TjkMZGRmIi4tDfHy86g85rgnASIJUwiCBQEDBcayDdD8ipODxeOB2u+H3++H1etU8zMjIUCm+eIweZGA5XAO8Xi9SU1Nx7tw5REREKBCGIBShl+joaNhsNthsNtVHtGvjnNRDGDk5OfB6vUhJSYHP5wuCNrgPAEOoQw8LUlxjZJol9p0EXiT0w9eZmZmw2+1qvHG8hIWFKVCNTjwulwterxfh4eGw2+0a5yiuG/p1RzrQcM7QjYpzhe1EnT17Fl6vFw6HQ60hhKQIJaakpKixIOE/CaVwO+G65ORknDt3Dh6PRwNv8RgCZjwH55jX60VmZqZam+W6TgBNrhH6/uMY1a8NEgrVuz5JyE7/ecB+lmNZgkpGTlTse+n0I13L9ACqLEPfTqw3r8Hr9ar+4Wckgbjw8HDlGCTLMWXKlClTpkyZMmXq36S0tLSgbS1atAi5/8MPP4y6devinnvuCbnPoEGDcM011wCA5vcSkPc7NtQTg6+//nq+AYGVK1diwoQJeO+999CvXz/ccMMNKjgRExODDz/8EI0aNVL7b926FXPnztU4CIWFhaFatWoYOHAgfvrpJzzxxBNB5xk1apTmdUxMjOFvgX+Dk828efPU34MHD0azZs2KVU5WVhamTp2q2bZw4ULYbDY899xzJaniP0p//vknJk+eHLQ9MzMTzz77LCZPnoxp06b9DTX75+v2228PGiNSlStXDpne5amnnsLatWsvVNU0IuA1evRozfZTp06hUqVKhse0a9cO5cqVw5IlSzTjXZ82EAD27NmjeX306FH1d0EPHJUvXx7VqlUr6BIMNXLkyCAHttKS/poA4KabbipWWXfddRd++eUXXH/99SWqk8vlKjAlpSlTpkpfcXFxeOCBB9CpU6dCu5RVqlQJZcqUKTL4A+Sts0z5evDgQSxcuFDjdBNKCQkJqF27dr5r6qefflpoByG98kuhythdaSo5ORmLFi1CZGQkunTpgk6dOqn75GXKlNF8vlSqVAnPPPMM+vfvH+RqdOutt+a7/sbFxcFqtapUwUXR+++/jw0bNoR8v2rVqnj22WfzdQc0ZaogFZSxoWbNmhepJpeejMCfmJgYdOzYMeT3uqSkJDz00EPo1KlTUIrbwsqc8SHEFDUMwEoYQzpEWK1WTQCU4AUdG3gM8FdAHoBK3USnC+mcIuEUBlBlwJbQAcuUqb4AqHow4CoDvdK1R6YtI0RBdyI9XAJAA8kAUOCGhGIIwkggQ5/Djh+QFotFQTwWi0Xj9kGARsJIbEsJjHBfCfhIsa50gZFpdgiDyCC0DESzDrwuusjIQLgEqFimBGqky5B0f4mKikJsbCwcDoc6Z0ZGBjIyMtT1MZgvr599Lh2HeP3SAYTj0Ol0KiiD26KjozUOPjJtD8ed1WpFdHS0Zl/pKMVrkeADz0EXlOTkZDWP2CYxMTGIjo5GmTJlNDCD1WpFbGysxsWFkuNCppNim7lcLoSFhcFms8Fut6tz0dEHgIKfWHfZpnooQjpKRUZGIi4uDhUqVEBcXBxiYmKUu5IEP7gvHWoSExM1Dj7SAUeuK0yvB0B9KWSded2yPThvY2NjERMTgzJlymiAOgmcyLlAOEemYdODRJmZmYiKilJuSASdvF4vsrOzUaFCBXWdbrcbHo9HrRVyDBLMYVvm5OTA7XarcmQb+Hw+BAIBNRbltXJsEnIkqMI2kXCiHjriOiPBI84Vn8+nXLx4vRLg4z+ORaYMY/1luj5eK514ZFo6XqNMQcZ+4bpDcX7JY7hd74LE7bINOEflZxXXN3msfp3jHJH11ANhegBQpjPkNgkQZmdnIy0tTUF28nOR60l4eLgC5Upi8WrKlClTpkyZMmXK1N+pUDfRjSCcUOBPRESESl0QKqARHx+PV155BTExMdi4cSPeffddzftlypQxPO6JJ57AU089FbL+CxcuNHyCW8IAAwYMwOuvv655Pzo6GkOHDsWdd96JihUr4vnnn8cvv/yCZ555BhUqVDB86vPRRx8N2tasWTNcfvnlIet3qSg8PBxVq1ZVrx9//HEcPnwYX3/9Na688krlZlRaCgQCqF69er7pff7p6tq1q3JkAYzdTX788UeMHTsWzzzzjOGTxqbynG7ommWkhIQEnD17VpMKi6pVq9ZFS/e1detW9OvXL2j7mTNnEBcXF7R9xIgRmD59OuLi4uB2uzVuPPr1ZfTo0er9hIQEpKamap6glvcXjRQdHV3oFDh6de3aVQMZlaaM1tErr7yy2OVVqlQp36B5YXT+/HnDuWrKlKnSV5kyZfD666/jqaeewvvvv4/3338fc+fOxc8//4x169ahf//++R5frVq1fN0e81N4eDg6dOigUgN+/fXXWL9+fYHrKQBlRBBKU6dOLTbMO3jw4JDOQ3TXL00dPHgQr7zyCt54442gVF1hYWG44447cOONN6JmzZrw+/3weDxBMOnw4cPRo0cPnDhxIqSbDx9mZ4yoKOrUqRMaN24c8v3MzExs3brVTJdqqkTyer344IMPMGbMGKxevTroQY2qVauGdJj8LysiIgJly5Y1dNm02WwYM2aMSuUtlZ6ejiFDhmDu3LnYtGkT1q5dm+88N5IJ/oSQhHBkkBdAyP9lIFmmbpFOHzIVjgyAA1Db5ULM4wlIyDRhMkDOwLisrwzesiwJA8lgMz+QJRChBwMkYCNBGx7DgDIdRvQpZlgmg+wSYtI7COmBHlkO4QPZ7kaSfSCdTuR55L5GT5tJtyNCBHoHDBmcl//ktfAc8rXVaoXNZtMcL1MjyXaWx+khFb3rlLwu/TVJgEzCXdI9iACS3tFIP2YkPCLHCr/opKenIyYmRnNdhHzsdru6wcB5ox9v+n41GpfsW14X3XjktUqHGKMxVlDaJ/YT+0rvIiXPIee4XAf0443b2DYRERFITEzU1I3/JFzHcRgZGYmYmBiV8o11l/0inYCkyw7LkmOedZOOTzLdFJ3OeG1M/6QfRxJIkWkFCaDo+1a2iXQ9k/Xn2qJ3NmLdCWKFGuty/ssv0RIilOOJ/cpjpFuQvv4cT3IfmTJP78yjX2fkDyG9G5RMVWc0p+Vnklzb9O1rNJZkmfp1UV8vWY4EAGV95OcZgUlCpPp1S6aWIyRrypQpU6ZMmTJlytSlqCFDhqBbt25ISkrSbGcKeKly5cppXnfu3BlNmzbFtddei4oVKwIADhw4YHie2267TbliVKhQQfOe3W7HoUOHDNOIdezYMV84ZO7cuYZPXe/bt0/zWp+6pnr16qhWrRoefPBBPProo2jTpg0qVaqEKlWqhDyXUcD57NmzOH36dMhjLgU1aNAADz/8sIIaoqKi0KJFCxw5cgSTJk3C4cOH0bx58xKd44EHHlB/x8TEIDExEUePHi3w6dt/qq688krcfffdaNWqldpmlPZj/fr1l+w1/lP0xx9/YMKECSFTCVysQGDt2rUNnYfOnz+Po0ePau6TNG7cGHfddZdaM3fv3q05hukAgbw1U7oIxcfHq/sRhZXe/bwocjqdWLNmTbGONZJ0j5BOazyXESR1MeX3+w0/30yZMlX6ql69OqpWrYoHHngA0dHR6qHnpKQkOJ3OAh1c+DB4Sc5ft25dAHn3hffv32/oaKmXz+crMN1WcVOV1q5dO18Xxfycb4qjMmXKICcnBwkJCUFQQ/fu3dG/f38MHjwYgwcPRtu2bVGuXDnExsaifPnyaj+v14tdu3Zh0aJFhq6Wfr8fp0+fNkyNVpCqV6+OihUr5tvefr8fO3fuLHLZpkxJ/fHHH/jggw/w6aefarI9UNHR0UGuNqbyfrc5nc6Q8S+32224Tnu9XixZsgTZ2dmoVasWWrVqhS5duhQJ2DfBnxCSQVg69zDAqQcvcnPz0sJERkYqOIAuBwz+y39+v1/j/kKoQgIVfF8GkZmeikFmCeEQouHfdF9gvSnWTQa4WR+73Q6r1arcUmTKGKaBkWAQYSSmVGKwXIJJEoJwOBzKcYRuKQA0QWMZPJZglD6AT6cI6bjEerJMgit0SeHEkBCVkZuODKgzhRPfo7uIBJV4Xrqt0OlHwjjcR4INFosFsbGxsFqtan8JHMgy9IF6unzQnURCJ/rUc9nZ2Sr9FeEy9gGdkNhvTE9FQIKBejrccKzLutLhiam4mB4rKysLsbGxGmcTn8+nUpHRGYhtER4eHgQL6Ockz80+1cNmnC+yXXJycpCSkgKPx6NxROHciYqKQkxMjGas8/+srCx4PB6kpaVpUncRRrHZbAr8kFBYRkaGgpA4Zzl22CeZmZmqXenORMti9iHTI0lQiemm0tLScPLkSeXOI4+TwAyP5Xhh/3IsyTnK+c8x6Xa7ERERoQCNsLAwpKenIxAIBDlBcb3TOyJx7eB8kRCZTL/G+nD9kjBhenq6Wt+4jsl/8jrZtvzHceB2u5Gamqrmk9vtRlpammoLuvzQGYlWqPKmEesn5zPnfiAQQEREhCYFofzc4BrNVH9cu/g++5F9LB2TJDgqAVB57fIHnwQRpeOcLINPTLI8fmboIVO5nvJ6pLOSBII4f7nOSlCQ1ylBLSPY0pQpU6ZMmTJlypSpS0EVKlTABx98gFmzZmHjxo247rrr1HtGMM1nn32mef3666+jW7dueOaZZxAZGYkjR47g559/DjquefPmeP7559XrMmXKaL77M5Xw1q1bNcf16dMHHTp0yPcaQqVsWblyJd544w11L0y62QB5T35HRUWhU6dO+L//+78CzwMArVu31rx2u92YO3duoYJI/2SFhYWhYcOG6NixIwDgoYcewo033ojPP/8c+/fvR3p6Oq644opCtZGRWrVqhbFjx6Jv374A8lI7RUdHG6Y7ulSUmJiIOnXq4PPPP1fb9OmeXC4XPv7444tdtX+dVq9ejSlTpiA2NtbwfbfbjbvvvvuC14P3ZvTq168fOnTooLkHeNNNN6lg8+OPP45XX31Vc4yEX3w+H1JTU9XrKlWqaO6RATB8ylrW64UXXsD06dOLdD3x8fF48803ce7cObz44ovYu3dvkY4PpXvuuUcTuJZQaa1atTB69GhMnDgRP/30U6mcr6hKS0sL6VhhypSp0lXDhg1RuXJl1K9fH927d9fAk/JeupGioqJQsWLFEjlw1K1bF927d1fr6fLly7Fr164Cj2vTpk2BaQXnzp2Lw4cPa7YV5vtgTEwM7r77bgXM67V48WL88MMPBZZTkNxuN7777juMGTMGR44cMbx/nZSUhKuvvhq9evXCs88+i7p168JisaBOnTq4+eabFVD60UcfoUmTJhg3bhwOHTqEl156CRMnTsSPP/6I4cOH47bbbkPz5s3xzTffFLme1113Ha644op894mJicFDDz2EOnXqFLl8U6akGLMCgh8ar1atWtCDMKbyvpfmB+cdP3485IM/L730El5++WX1evjw4fjkk08KfW4T/MlHdD6RQXSCBU6nE1arVcEeBCEArRuE/GHD4CkBEkqCCsBfqcEYNJXnZcBcBnGZNkYGwiUwY7FYVOBdD5GwznR4IUDBwDMAzTGZmZmaVFvynBLYsNvtsNvtKhDMQDphCLp7MFUQnUEYUCZIIR0ypNNPTk6OCtITKDIKUutdVWSKKyPXH7YZ25dplRgU17tyyPbn/xLSIIQQCAQ010NghunH2LbSwYbjSpbH83IMEGiSQIJ0CuJNQLfbDZfLBb/fr0kHxTRFhCMsFgssFgsCgYBmTERFRSloi33IehL2Ybmpqak4efKkSovEdifkQRhLXgshDKZWk9coAQHZfjJtFceivC6+z3Zm2iEJB4WHh8Plcilog3NHXj/rEwgE1AccU/UxHZ90CnI4HBpgjPOU881isSA+Ph6VKlVCTEwMHA4HnE4nHA4HoqKiEBcXp4EjeA5CQITt+IVYOrZwXuhdjGQ6OrfbjYyMDENnMrad1WqFy+WCxWJRqZiysrKQnp4Om82GyMhIlcaN81WCP4R0JCTIMUbHJwmcsN04FyRMxPVSrosSSpTAj1w/ZD8DUKAd65+RkQGv1xsEchIAioqKgs/n0zgZsS7sT5kukAAbgSf2h95tjJ8fNptNrQtyDssUj/I92V4SFJROOhIYZLtLiIj1ZRtJxyI6O0kXHrkWck1hOjPZzhJs4pyTn2m5ublq/vAzjmuxBANNmTJlypQpU6ZMmbpUxOBnVFQUqlatiuuvvx6dO3dGuXLlDFN9ff3115rX7733HsaPH48uXbpg/fr1WLduHb7//nv1fs2aNfHZZ5/hwQcfRP369dX21157Lcilo2bNmnjiiSc02+Q9JyP5/f6QTxcvWrQIL774Ivbt26d+M3bq1Em9P2rUqHzLNlJSUhKGDx+OTp06oVevXti3b19QCrFLUTt27MCkSZPQqlUrXH755Zg3bx6Sk5ORnZ2NqlWr4qqrrgIATVqromjdunVYs2YN3nzzTQB/pcS6+uqrC0zz8U9Vw4YNUbNmTcybN09tGzdunGafhQsXGs4jU0VXeHi4xiVHqn379rjxxhsveB2YQt1I+mCvXJfOnj0btL9MFx4IBODxeNTr8uXLIyMjQ3MvPj/wZ8mSJZg8eXLBF6DTLbfcgj59+qBTp05ISUkpdjodqYMHD2LWrFn47bff1DYJke7YsQMff/wxBg4caOiedDHUuHFjrF69Gr169fpbzm/K1H9BCxYswDfffIMGDRqgVq1ahvukpqZqoEe9kpKSULly5ZDQZ2F1zTXXqPm+cOFCLF++vMBjypUrh+7duxe439ixYwHkgb4LFixAenp6oepUqVIl3HfffYbvdevWTaUnK4lSU1Mxb948fPrppwCA8ePHo2vXrnj11VcNU2dKWa1W9OzZE/fcc49m+7Zt29CzZ0+MGDECAwcORPv27fHOO+9gxYoVOHfuHDZv3lzkek6fPh3z588vcL+oqCjccsstRS7flCm9GP/RmzbEx8eHTD39X1bFihVRuXLlkO+fPHkypCtn3bp1NfcAHA4HOnbsWGjAygR/QogghoRZCAQwwBsZGalAhcjISBWc1qctksFnWS6DxTIYKt0xeAyD5dJRh2Uzb6Y+9YyEJujwoL/xw3pJtxCCQkbWqPKpCZZF0EKCFZGRkYiNjVXBarp/EIZg/Qm98Bi2qXRqkQF3PQhCKEqmYpJpuGQQnO2jTxMmgRIJ9WRnZyvnGpfLpfqazjp0RcrNzYXFYtG4MEknJp6fAXKODwbHZRotCW9JFxnWTw8zyfaR4IB0EHG73fB4PApkkW4+BI2kGxLrznoyoC/HDOEVOp2wfen0kZqaivPnz6sxI+cJr1GOdfa3BDfkdekhMwmWEJLQp20inODz+RQgQTcbuvbYbDb1geTz+TQwHdtZzg1CgARppINNfHw84uPj4XA4kJ6eDo/Ho4HXZKqx+Ph4ZcvpcDiQk5MDl8uFPXv24OjRowgEAgpOkX0uxzHPK12ngDwbTz04BuRBL2FhYQrWMUpRJteAQCCgABC6gnm9XmRmZsLpdKJChQoIDw+H3+/H+fPnNXNfQmkcMwSZeB2yfhkZGXA6nWo8U1z/ZH9wHFESppMgEJ9MJWzItVuON6/XC7fbrdqE/SMhmaNHjypXKLolERbinJeuYQR/ZDmyz+T/Ejji3APyrPzkOsI25flkv+uBLc4jAj/8/OI6K8eLhKckVCpBLv16yn24/vEcfr9frSlyjktHPOkSxHFHoFAGEUyZMmXKlClTpv7rMl0RL02NHDkSc+bMwaZNmxSkIaVPTfDll18qAKZt27Z45ZVXNE9v33DDDejRowcGDhyoOe7999/XvP7www/x/vvva9LdAMHpcYzqq3cJ0mvgwIHo06cPAKB379757qvXyZMn8fjjj2u2jRo1Ct9++y3GjBmDV155BSdPnixSmf9U7dq1Cy+//DL2798Pt9uNPXv24OGHH0ZMTAy++OILAHkuJsXV2rVrceTIEQDAmjVrsH//fpw+fdrQIaqkmjNnTqk8LZ+ffv/99yDo7KGHHlJ/Hzx4sFgwxr9RtWrVKvFnQmJiYr6gSLNmzbBgwYISnaMgeb1eQ4hHr3Hjxqlg8NmzZzFjxgzN+wkJCRrHH7fbrUlFSFduCf7kl3pCQkNFkdfrhcPhUGBOKLAKAPbu3YuPP/4435RgWVlZWLBgAapWrar5rFi6dClGjhyp2TcmJgbPPvvs35Im0WKxoHz58hfdqc38XmTqv6K+ffuiU6dOuPPOO/H000+HBH+OHTumvhcYqWLFiqhWrVqJ547D4cDgwYNVas7Zs2fj0KFDBR5XtmxZTTpPI40ZMwZr167F66+/jlOnThUa/AHynG70aWgB4NNPP8X+/fsLXU4oORwOTfodusP169cvyKHQSPXq1VNOjVKMl5WmfvnllwKdmA4cOIA5c+aEfD9UGiJTpvSKiopCQkJCEFRtsVgK9T3vvyajNIFSp06dCumkuG3bNnz77bcqW0kgEMCGDRtw6tSpQq3tJvgTQgyyEkKQjioAVOA/OjpapfMhFCBdaRhopjMKA6B+v1853khXB8I2TJ8k3Wrk+flDRu+cIkEiGXhmQF+f8kruJx0gCGHIcqVDAwcX4RcACryRji5OpxNOpxPx8fGwWCzKcYbXbLfblauHPngvQRxK7sOAO9uBgWYJwuhdfZhqh9cpXTRkYF4PD7GNGdgP5cbDttMDCnxPBsQJaMh0ZHpXIelMpHf9kTCIhMvYNh6PB6mpqSo1k3Qf8vl8GqhNpgVyOByIiYlR9WG7SyiG7S2dPAhunTp1CjExMYiMjFQ/sBnwlwACxxRTI3FsyjkhxyIhK0J2Xq9X41xFByjWm+4mPp8PXq9X9T+hMubHptUa3Z0472Vf0+GHIJuE8zIzM+F2u1GpUiVUq1ZNAVEERCSU5HQ6YbFYFGgUHR2NiIgIuFwupKamKpCKzjQy7ZIcy3pXr/DwcHUjJzIyEmXKlEFsbCzsdrsaa1xv6L7FeSKhH15PWFiYSsfm8/mQnJysoEfOVwAKrJJrnwTr6JrFdYRAE8E6vi9TonFNYn0J7NBViu9nZ2erp8akC45MuyVTmck5ynaT7cs5xe1nz55VLkQEaVi2TEXGMct6sU25Vunnr5GjkXSikmkQIyIi1NjTrzMSACVwI1P9ybWEzkJyDdV/TgF5N82Yi1nWS56XbS3XN7ahBMTYRjyvrKM8Tl6HKVOmTJkyZcqUKVOXimTwmbJarahRowaaNGkS9N7IkSMxa9Ys3HbbbQCAOnXq4NVXX0WdOnXwyCOPBNl8N2jQwPDJSf19hhUrVmDEiBFB+7377rv51v/+++8v0Llnw4YN6mZl+/btg65n4cKF6rW875Keno5WrVqhZs2aOH78uCa4brVakZSUVKjA0aUit9ut3IsGDhyINm3aYOrUqVi6dClGjRqFpUuXAgAefPDBYpU/YsQIlTYjJiYGtWrVQmJiIhISEkrnAoRSU1Mxa9asYh1bo0YN9O/fHw0bNgwJQtSsWRMLFy5E48aN1bbq1aujZ8+e6vW3336LH3/8sVh1+Dfp3nvvxdq1a/HII4+UqJykpCSNS45ezZo1w7XXXluicxQk3pcoSB6PR7muG82XWrVqaSCmNm3aYNWqVeo174/JlAr5QTnFBX/++OMPPPzww+o1U8VTPXr0UGDlkSNHMGTIkKCUZXLd3rp1K7799lscO3ZMs71Dhw646aabNNfgcrnw448/qs+gp556KggOutAqSoDelClThVOXLl1U+tf85Pf78fvvv+ebYjApKQnVqlUrlXq1bt1aQeg7d+7E7NmzCwQPT58+XSiH9+uvvx5z585Fdna2eoC4MKpfv75KsSq1YMGCUlkPmTWD96vvuusu1KhRo0jubm3btsUDDzxQ4roUpPbt26v0mKHUokULDBo0yPC9++67D1988QVmz56Nd955J6SbkqnS1ZVXXomvvvoKGzduxPjx49GzZ8+LMl5KqtzcXE1MlnI6nUWaw/8VORyOfL+DM4YcSgsXLsQzzzyDI0eOIDU1tUDHMSkT/AkhmaIGgCbQy0ClTB2ldzcgIMB9gL/S7AQCAXi9XhU0l9BQZmamsuqTUA/dZgjoMEWW/ssA32d5+nx7+kCrhAkYBJdOFnSJ4fkIgLDcjIwMhIeHw263q1RETOtksVgQExODmJgY9aOLLiE+n0/jSCGD1QwOs01lKjXpOsH6EyRh0F1et94RhSmgpPOShLwY3Cf4IGEnmUKJf4eFhal+ZGq4QCCgUpwZfcmREBnhDcIFEkaS40ym25Epd/QOQ/p+5vkkFJaVlaUC/LKOkZGRsNlsCvjgNVK8Run6QccppqVKS0uD2+1WDjvsH0Imcr7oQa3s7GyVRox9w+uT4FpUVJRyMCLUQbCJzk9ybEjwgucknONyuZCVlQWr1YqYmBjEx8fD6XRq+lMCKdKdi2mgON7Onz+vnGvCw8NRpkwZlClTBvHx8ep8p0+fxqFDh3Dq1CmcPXsWZ86cQXp6uhqvsbGxsFqtSE9PD0p7Jtchjm3+T3jI5/MhOjpafWGWTjAcr7Jf2ZcE4jhHLBYLbDYbsrKylGOUy+UyhOjcbjfOnz+v3J3Yvpw3kZGRKvWfhMVkKi62MdcWuitJdyAJ10goj2sI1wm6ickxwHbUQytGwElubl6KPLfbrdqEc4djWX4W6FMIynVVX76c+0wDxrlP9yeZ0oxri975SbYV5wTHKOezzWbTuJFReqCOfeXz+TTOYAT6uL4YwUv8R3Ed5mcWxzDXHzmmWV5mZqb5xdCUKVOmTJkyZcrUJae0tLQgWCc/tWjRAj169FDpDz744AM4HA7s3LkT27dvD9o/lEOMHvKZN29e0H2Hhx56KMjSX+8AXbNmzQKfPC5XrpxKJ5WUlKRJn/D8889jw4YNAPJch/iQBgDMnz8fM2bMwG233Yabb77ZMEBk9KT2v0Fffvkl9u7diypVqgDIAwSWLFkCt9uNiRMnlrh8l8ul2nrw4MGlnvLnwQcfxIQJE4p17N13340xY8Zg5cqV+Pbbb9X2hg0bom3btvjwww9VOjs+vQoAjz/+OMqVK6de9+3bt0QOSf8Wffnll3jsscfw8ccfl6icqlWrFhhIlqDMhVBCQgIqVqyY7z5OpxNdu3bF2bNn8eeff2Lt2rVB+9SoUUPjwiAfZgPynHhkunWWG0rFfQhp586dmjbT33v/4YcfFLh58OBBVffly5dj1KhRWL58OSZPngy32w0gL70iHYHmzp2rUvoBQPPmzfHrr78GuTJlZ2fjkUcewdtvv43nnnuuWNdRHEnHd1OmTJWehg0bptKD5qeDBw9ixYoV+c7DBQsWaNJpllTt27dXDj5jx44NcmOTSklJwbx589R3xIJ08OBBnD17VrO2F6T4+HjUq1fPMKA+ZcoUjYNmUZSZmYkhQ4bg0KFDePbZZ/Hoo4+idevWmDZtGlq1aoWHHnoIq1evzjdQT7lcLjRo0KDQaXmKq2HDhmHFihUF7hcbGxv0QEGrVq0waNAgdOnSBd27d8fTTz+NSZMm4YUXXrhQ1TX1/3X69GmUKVMGVapUwTXXXIOpU6fi3XffzRdW/ieID8Dr55geBDKVJ2ncopfMZJOfYmJiVIruo0ePFvrcJviTj2RgXToySAcdvduKPp0RA9TSCYdBZH5As2yZWoXH89x0zdC7L8j66QO0MvgsQRn9DxteAwPXvCHE66RDj4RL9CljCBnItDWEOCwWi+ZGFNuF55CAhnT0YaCYwXrpkiOPlfCShFxYhrxe+bcMQsu0XDLAL6EIWbbFYtEE++V1yDJln8g6SMcWQJsfUbpksD301yxf83+96xDhAJ7PCPhifaWjlISNeIx0c5HXxHaIiIhAeno6zpw5owF7JMSlrwfnDOEj2f9y3OnbQe+AI8eiHFs8jxwbcgzQVYfbCP8wrZN0Q5JtLFMAEmqIiIiAx+PBmTNnFOTCNEb6eZibm6toTpnWiS448vpZvv7DUz+HWYbdblfAkn5uUhzXRgARAT8JfDGNlwRIJEyUlZWlABU5H1im3mVLOk1xH9ZXf61yvBJ+lMCbBOQo6ZjDsUrgT6ZcNGpHvse1TD/WeM2EiCR4KB12eB3yNcewHsiUzmyyv/maTmyyr+WNFv2aqU/NJ/uZ7xv1k/wMMIKNZBl6kErOXYJa/GfkGmT02WXKlClTpkyZMmXK1KWm4gTL69WrB+Avh4hNmzYZupuEugl41113Bblz9OjRQ/O6RYsWQb91fvzxRxw6dEjj1FBQ/ZOTk7Ft2zYVvNFblScnJ2Pt2rX44IMPNPWtV68eGjVqhKlTp2pAFerUqVP4+uuv8z33paqTJ09i586duOaaa3D55Zejdu3auPrqq0v1Rv6mTZsA5KXMcrlcpVZuSUWQrEyZMhqwp0KFChg0aBA6duwIm82Gyy+/XPPwh348ly9f/j8P/jAAOnv27BKXlR/4Qsn7CBdCNpsN5cuXz3efQYMGoU6dOvD7/di9ezfOnTsXtI/D4ci3ridPnlQPoFLSjScjIwMHDhxASkoKgOKDP9WqVQtKwyjldDrVeTlHo6OjkZycjLFjx+Kzzz7Dn3/+iS1btgCABnwDoLZTderU0dxXDw8Px1dffYVPPvnkoqfdAoKd50yZMlVyGa15Rjpx4gT27dtX4H5ffvllqX1HiIuLQ/PmzQEAf/75Z76pYo8ePQqPx1OkhzwTEhIK/IyQslqtSExMRPXq1YPe4wPjRZXb7ca6devw8ccfY8qUKahUqRIGDBigYKyTJ0/i7NmzSE9PD/peq9fevXvxww8/YNasWSol5IXSgQMHFFQdSikpKThw4IAG+Gc6tssvv1zzPSE1NTVfhxJTpaNz587h9OnTiI6ORoMGDXDq1CksXbpUAcH/VEVERCAuLi4o1ZfkAkz9pVDfl1JSUrBu3TqsWLEi37SNQN53SrvdroxUCiuzN/IRHXjowsAArwz+A1AfZAyE+v1+TTocQhBWq1Wl75GBfZmixel0Ijw8XDkDMchNRwcJwrBerI8EdOi+wYCshI5koJjXKR1nGMjNzMyEx+NBRkYGbDabxh1EBufpLMHrzMrKUvBDdHS0CuzzWugORGcRCUixrgzw0x2JcIgMnkt4RToGGcEqMohNJwv+wJMQAd166PRStmxZlC1bFvHx8eqcnGB6hx6ZMo114t+UdMqQAJA+KC5hEdk/MrguwQTZBpmZmUhPT0d6ejpcLhfS09MRFvZXminCIHQt4dgkwEOwieNVgldM1ya/vNF5Z+/evfjtt9/gdDoVQUzbXKbH0jtOETbhPJNuSdLZivNJDzixLXhdevcR3nxkX9hsNgVsxcfHo1y5cpoxRben6Oho1QcS8AoLC0N6ejpSU1M1EAXr4/F4UKZMGXUDVYJ+EqDguGPdWc+UlBSkp6cb3hTRQ06sF+dkUlKSctviGJXrEAC1ZkmAi6CR1+uF2+3WuAX5fD64XC7VNvyCyOM4Rnw+H9LS0jTAG68tNzdXpczyeDzIzs6G2+1W81K6nunddNj3VEREBHw+n7pm2faRkZGIiYlR5LGE9PgByXaWc5jjRrrfZGVlBaXpkuChhHro1sM2k6AQISfOMz34IyE7li3hGym2F9dC3kCX8zMzM1M5bbF+VqtVA2yx3eTYlX2mXxul25oespJQnQSHJHzI9ZR9IW/SsazC2L+aMmXKlClTpkyZMvVPk1HAoSA1btwYFStWVO4vL7/8suF+I0aMMHxiuW7duvi///s/9bpFixaIj49Hq1atYLPZULZsWdxxxx2aY3766Sd06tQJzZs3R/v27TFnzhxkZ2dj7ty5BdZ30KBB2LRpE8aOHYs5c+Zo3vv0009x/fXX49ChQ5ob1c2aNcMdd9yBN954AydPnsTx48fVe2fPnsWwYcMKPO+lqnvuuQcdO3bE2rVrcfLkSdSpUwctWrQAADz33HOwWCyYMWMG6tSpU+xz9OrVC8eOHSvSU58XQ5UrV1bBomnTpqntr776Ku6++27UqVMHVapUwZo1azRBw5YtW2rK2bt3L1avXl3i+hQ3tdrfrYYNG+LQoUPo3LlzqZQ3f/58nD17Nt9A5YV24V2yZAlat24d8v0ff/wRo0ePxvLly3H8+HHceuuthvvp4UMg7z7H6NGjAQDbt29H2bJlkZCQgKuuugqzZs1SaeWOHj2Kvn37omnTprjxxhvx4IMPYsCAAcW6nrZt26J+/fqG76Wnp2PRokXK4ejkyZMA8hw4evXqhT///BOzZ89GRkYG7rvvPmRkZGDQoEHo27evKuPMmTNB5ZYrVw7Tp08HkHevZvDgwQDyHmq8mDK6X2XKlKmSqzAp9Hbt2oVZs2ap71w9e/ZE9+7dUbVq1aB9jx49ioULF5YKqBcIBNCkSRM89NBDePXVVzWxNr0aNGiAu+66C506dSp0+b/88kuRXHoiIiLQqFEjTJo0KQgkb9euXZHTEaakpOC5557D7bffDiAPfjl48CDq16+PpKQktZ4TSs2vrtnZ2Zg9ezbee++9QrselURt2rRR3zONlJaWhtWrVweBxC+//DKefvrpoPjTH3/8kS/YZar0tGbNGnz77bd4/fXXcfvtt+Oee+75u6tUoMgj6MHpU6dOmW6ABvJ4PCq7E5WTk4Pp06fjiSeewPz58wssIyoqClarVcXNCysT/AkhAhkyPdYgNQABAABJREFUkMlgOQc4g+oy9RHTFUm3FQAacINAggQC+D8DyNLNAshz3ZAuFkwdRBG4YbCV55P764O0/FsfpOZ+WVlZKuUT24GB28jISM17AJSzT3h4OGw2G+x2u4KQ+I8uKFarVdWVQWHZljL4zXZKSUnB2bNnFSAA/AXKyOuj9E5LAFTKLhmAJ4jFdpIpwHJzcxEXF4fY2FiNy4cM8EuIhWCQkduFdNiRDhp0zZF9xvbX95t+HOkhAtabAfdAIIDc3FzY7XbExcXBbrdr4BcJ/+hBIgk7ZGZmwuVyqf5wu91wu91wuVxIS0vDmTNn4HK5FOzF9E1AHpUonW9YX+kWwusi7COhFMIfhO9k2i0JLEVFRam6s32sVqt6IkiOYYvFgmPHjmHnzp2Ii4uD1WpFamoqTp8+DbvdjpiYGPXFR4JzVqsVsbGxiI2NRVxcHCIiIpCRkQGn04mwsDAFwBBwoRtOZmYmfD6fSsskISYCK4RjCAVyXvNaWReuHxKOYUonzlm+z/5g+jK5vslxyNRlrBMBHPYN+8vv9yvQi31SpkwZREZGKshM9jOhwLCwMJw/f17BKhyXEnpjG7NN+L50VNPPX64BvAauqxJq4Xau2Vxf5Vpo5Mom24p14BiTzjr6OaN3+ZHv6dcmXj9BI7mmSSc4bg8LC1PgosPh0KQplOsT95fucVyjJcQp13q73Q6Hw6FJk8b2k4CVbDd5TbLfOb4CgYBKGxYIBFQ6McJFcl9TpkyZMmXKlClTpv4LslgsuPPOO3H//fcDCP2E9+zZs/HRRx8Zvte5c2f069cPALBhwwbUqlULS5cuxebNmzFr1izExcWpfb1eL0aNGqXOtXHjRpw6dQo7duzAzp07C6xv48aNMXDgQI1rhpF4U9Pr9eLRRx/F0qVL1XsHDhxAWloatm3bhtdffz3fFBGXuhYvXoy0tDT8+eefcLlcWLp0qUqd9eijj+L//u//0KtXL/Tq1avY5zh16hQGDRqEN954o9TgkNLQkCFD1G/Pzp07Y9iwYZg4caJKD0JVqFABtWvXRq9evTBkyJCgcubPn2/oglUU9e7dG2+//fYl+dR61apVER8fj6uvvrpUyuOcNIJJqFAB3NLSvHnzkJ6eHgS8tW3bFsuXL8fNN98MABg/fjxuuOEGwzKaNm2KsmXLBm2PjY3Fgw8+CIvFAo/Hg6ysLNxyyy1YtWqVxg0tOTkZO3fuhMvlwo4dOzBp0qSgsm677TZ8++23eO+999Qaa6QqVaoEPe0u69OoUSPlesW18Y8//gja9+jRo3jppZdgsVjwyiuv4OGHH0anTp3QsWPHoH2vv/563HnnnahcubLaJp2tL5b0DxObMmWq5KpXr16BQHBaWhrefPNN/PDDD3j00UfRpUsXPPzww5gwYQLWrVuHLVu2qO+WVIUKFUplvpYtWxY9evTAkCFD8PLLL2PSpEkhnUkiIyNRp04dtG/fvtDr0/Tp04sE/NpsNiQlJaFly5a4++67Nd+XlyxZgnXr1hW6LCDPcahevXoqFnTkyBGkp6cjKioKtWvX1rgd7dixwzCFLRUREQGn06nSPF5orV69Grt27Qr5/saNGzF//nyN20///v0xcOBAJCYmBu3frFkzvPLKKxekrqa0mjx5Mu6//3689tprhfpN9k9QIBAwnPsnTpwoMIX0f1Fer1e5TFLh4eG4+uqrC532Ojc3FwcPHsQvv/yS73d5vcxvaiGkB3+k6w+DuHRByMrKQlRUFJxOZxBsA0AF5hnwZ9BYpsDh/nSPYBCa52awlKCG3kkGgIJsCDYEAgEVYGZ99W4NMsjMoDavgddLEIPvyXqxnWQ6KtaPBJp0dSGg5Ha74ff7kZ6eDo/Hg0AgoGAiq9WqHFP0DhtMkcRz6lM+STGIz2A/oSXpskKIQdYf+Cv9FP/FxMQgOjpa4+DBOsmAPYPvdGyi24V0QiFMwX4iHGGz2TRBeZ5DwiwyfzX7QoJndG1hm9LhiEAQz0EQgsF4noP9J6Esn8+n4CyCUSdPnlRuUHSZKVu2LMqVKwe73a5ACTqw0EGJ18Gyc3Nz4XQ6Ub58edhsNg3wI+Em/djluTkGZXouOTfZTux7QjV0naH7lMPhQHh4OPx+P7xer7pm4C8Ai/COBG3S09NRsWJFXHbZZWrcEnTiOAoEAspFSJ9rnO3t9XqRnp6uQKacnBx4vV4FcuihMM5HzglCWMBfcAevlfkiZWoonldCa2znqKgoeL1e5TJGi2KuOZGRkaq9+LfD4VBrDsdKWFiYgszYrjyvTMlGWFLeNCGEJutF+JHjl+uS3oWGFCzbiW3I9UC6dnGs8Rg6lUmYjv3PdY1OR1arVR1r5IQj688xJ+siy+cYYRkc7xJm4jzmcRzT/JyR49Xv92tuxkioUsKUbBO5n1zf9ZJrjhyLcn/9dbDNWX8JJnFN/KfnrzVlypQpU6ZMmTJlSi+63IbSggULQr7Xvn17dOjQAQCCgjRSBEaMJH8fEuKpX78+2rZtq7b/8ccf6NatG5YsWaI59syZM6hWrVq+9acOHz4Mq9WKH374Id/9+HT1I488gnHjxmneO3fuHF577TU0btw4yDXo36SyZcvimmuuwahRozBkyBDlNPLhhx8iJSUFVapUUQ5PgwYNKtG5KleujKpVq+KDDz4ocb1LqunTp+Pw4cNISEhQ2+677z689957IR1VbDYbvvrqK4wYMUKz3e/3Y8qUKSWu06BBg1CmTBn079+/xGVdbC1cuBDfffcdvv3221IrMzExsUBnmIEDB6Jdu3YacLC09Msvv+COO+7AgQMHNNs7deqEtm3bqvtFBw8eDPnE+P333x/SIc1ut2Pr1q3o1KkTUlNT0aZNG814BPIgG/359apWrRq6deuGYcOGGboLUdLRGUCQ+8Phw4fRrl07PPDAA5g6dWq+59y7dy8AoHbt2hg3bhzmzp0bcn22Wq144okn1Gu/33/BoS29wsLCCpU+zpQpU4VXixYtcMUVV4R8/9ChQ3jnnXcwffp09OzZEx999BFmzZqFG2+8EfHx8ahSpQqaNGmCBx54QJM+c+rUqXjxxRexcOHCEtfRZrOhfv366p6vTKlopK5du+b7HVevd999t9h1e/TRR/Hss8+q1+vXry9yGfHx8bjzzjvRvn17AH+5EdarV099nwPyXDT3798fdPyGDRtw+PBhBAIB/P777/nCQaWtMWPGhISd9u7di2XLlmm2+f1+lVLUSFdddRXeeeedUq2jqX+HGI8z2m66AQbrxIkTOH36dNB3tZo1a+Lyyy8vVBnPP/88rrvuOjzzzDOFcuulTPAnhGRnMAgrU2wxGCxBIEI8DI6Hh4cr+EUGoKVTi0wvI90UGJxmYNflcilYQroyMBWSDEzLNGQM4jLYTrcMHsu68aYRg8J0mSB8wG36lDAECvx+v9qPrh0ej0el5SGYQpDE4/Go96WzB52CCAzI9mCAmYFtGcDWTx4GqAkSEcog8GOz2dQ16QEa6UjD9rRarepHnz54zfalqxFhAOnAIl1GJLhDByRes8PhUPAPQTEJeUhnIgIEhD8yMjLg9/tVm0sXE16HPJdMHRQIBFR7SkgtJydH/fDmeE5OTsbJkyc17RYREYEyZcqgfPnyiIuL04wp9rtM/SNBB0JnMTExCnLRp6jieZjOiOmnOJ8I/nC8sE7SHUs/TjkmCc0EAgGVB9btdiMtLU19mEmXL+nIZbVakZ6ejj179ijXlPj4eMTFxSkYJiIiQgNcca5LECMzM1PtK9OcAdqnaeQ6IscBoSS66fCmgwTOuBZxPvB9AGpdkU4tXq9XwVtOp1MDrejTP8XGxsLhcKj1UDrNREVFIS0tTYGIRqAc1wy2KeeuTD/HekdFRcHtdiM9PV0DI3I8sL31bm1yjWM/8ljZtlw/+R7bODc3V81RmWbMCJSR85PrgFwzOE5l2kSWw7Gpdy6iJAgnncKkO5d0aZP9JOsp100J73Ce8gubdIHjeNQ7AMlUe3JOso3lPA4LC1PjNCsrCzExMahUqRJMmTJlypQpU6ZMmbqUdP78eezbt8/wPb/fj4cffjiks2XXrl0B5DlB5Af33HvvvSHfY7AYyAMvxo4dG7TPF198YQjsvPDCC0hISAj6rWGkli1bwu/3Fwjs8GnGcuXKBb33yiuv4JNPPgHwV9qbf6N69+6N6dOnY8GCBahfvz4ee+wx9d6mTZs0+1apUkXBX8UR+6NWrVr4+OOPi11OaWjLli34/vvvi3xceHh4EGTy448/GgbUiqIWLVqowOelCij07NkTe/bsKbXyEhMT4Xa7Q65JNpsN48ePx4IFC4JSr5WGZs2ahQ8//DBoe1paGoC8NTE1NRVHjhwJWUZ+AZLIyEhceeWVqFq1akgXihMnThSYhkKmN8kvlWP58uU1qW70adQyMzMxdOhQVKhQAX369Mn3nHFxcerhNAAhnYQofRuNHDky3/1LQ6NGjcLMmTPVa6O0QqZMmSqeHA4HqlatGhK6ZFqYUaNGweFw4PPPP8fGjRsNnRi3bNmicYSYNm0a3njjDcTExFz0NDxVq1YtEvizZs0azJ8/H7/++muQQ0Zh9L///U/9XbNmzSKlDgPyUrW+8847mD9/PhYvXqzuZ9erVw+33HILmjZtCgDYvHkzfv7556DjvV4vxo4dizvuuEOl871Y6tKlC2rVqmX43tChQ4M+J/7444+Qv2Goxx577KJ8vpi6tHTq1CmkpKQEfe85ffp0yO9f/2WdOXMGp0+fhsfj0WwvX7486tevf0Fge8oEf/KRTOcC/BUkl6mb9OlbZGCbxzCYKtNSSXhG78bDoCkDqLm5ufD5fBq3HQZzJfwiwRISdvJcsr68BulsoYed6BginUH0KaBknfROLfKcbBc6F/GcMoAsAQsZzNan5dGn4+Lx+r6TgBUhGwIlMrAvgR9Zf33/0ZlHtrtMhyPrIV0u6Gwj35cBfpliiKCETOdFSYcmfTog1pOQgb6NpaOPvn3lMawH29br9SqHJXm9/CHK8gkgEL6QQBr7SI5x6YaSnZ2tXIfYV3o3GnlzQl5LKMn+lHOB8B7bXdZRpoTTAxiyX7kPQRa62RCaIrgl+0ACJvnND3nNcn+eX39jVm7nGJdtwLaSsJO8RpnuS5Yp3ZmkCwzrQrhRQkxyrZSgEqEvbmd9JEjJscE5pu/H3NxcWK1WzXyU6bPkXJFjSJYjASr5Wq7Pcr3Ur+XSLUff/vrX8vOB66w8jueQ857H669J/zkjHXokSMpj9GngjMqS5ck6G80r+bliBAFxzaIDErfL9ZXtrO87/jNzwJoyZcqUKVOmTJm6FJWUlAQgGGY5efIkTpw4gdWrV+PYsWMhb4QW9LQ0XTCMVLduXc1ro+/UetiE4u95pjQIpfDwcPTt2zfItdZIfFK9SpUqQe/l5OSotDf/Zq1atQp2ux2dO3fGgQMH1BPiDRs2NEzhoU+BVRSlpaUp95Lff/+92OWUhj777LNSSd/m9/sNg2lF1dVXX63G+IYNG0pc3r9BZ86cwVdffVVgigC73V6gm1lxZLFYULt27aDtdKkOBAKw2Wz5rhP5OfBQvG9kpPzWU+ro0aOausl7bPrzSEd2/fp7+eWXo3PnznjggQc098qMFBERgeXLlxdYN0qfSiNUHUtLBw4cwGeffaY5b35OEaZMmSqaoqOjNQ/q65Weno5z587BarVi6NCh6Nq1q8pkoFfXrl3RokWLoDSXu3fvxvLly3Hw4EGVfvBi6MorrzQEwkPps88+w6RJk4qV9shqtaJLly4A8r7/JicnF7kMIG99kw+o2mw2JCYmokGDBmrb2bNnNcAmAGVwsGTJkot6nzsuLg41a9YMcrmT0rtJNW/e3PAzWcpiseCWW24plTqaunRU0HdAn8+Hc+fOKYMBijFwU1oxs4x+TbDZbChXrlyhvtsWVyb4E0KEEOjuwcEswROjNE8M5BJWYEBXQg8yHZF0qpAuOrGxsbDZbAr6SUlJQSAQgNVqVY4zDJwzkM7z6V1reF7+oAL+ggn0wWF9udJlham6pEOJdJyIjIzUpMCRqXwIRdA5SQI2DAhL2IDBZLr0SAefQCAAj8cDv9+vnCOMICAJkPDapBsSXYD0rhkSYpCQQnh4OMqWLatcg6TzhuxfOu/QSYfuRnpoB4A6L514pDuRBKmAvyAkCQbQmYOADiEj6STFVFt0u6HLEF2VWI4eRsjKysKff/6p0lrx3MyjKgP5mZmZqiwSjGFhYRqXF5nqjlQ654HL5cL58+dhs9nUD2jZD3pogGOMbWkErunTEukhKLar3+9Xqdlk2+nBhdzcXDXevF4vbDYbypYtq1yaeIOCqdbYLnJOyb6T80+CP+Hhf6UGLFOmjDo3j5OwCOcGoTY5LqUzVXh4OOx2O6KjozUACtcE/ZNKHJOyPXNycuBwONQ6wz5kai8g78sxoUWmQjt//rxKkSZdfggB2u121XacA5wXdrsdTqcTCQkJqFChAux2u+ZY2VZsQ5kukOVI2Equw1x/pKMW1wf+SOL4oYUyj2FfSRhK9hHhSc5zvQOPXNOio6PVfKc7EF9LUEb2Ma9BXqseLJSfLVzP+NmlB7/kzTHZVhxLXGPZ7hEREUHtLOvFMc82ksAm5xEd4P4LQQBTpkyZMmXKlClT/z75/X74/X60bt1aEwDgb6WbbroJzZs3x2233Wb49HJERATuueeekOW/9957AICNGzdiypQpWLVqlXrv9ddf1+w7Z84cjSuP1+sNmdqBwe3x48djzJgxIc9/9dVX46abbkJYWFjIG5NdunRB+/btkZSUhIyMDHz00Uchy/u3a+fOnVi8eDHeeust1K1bFw0bNsSAAQNgs9lQsWLFoP0ffPDBEsE///d//4e0tDT8+OOPJal2ieV2u9GvX78SlzNz5kyMGjWqRGXUqVMHt912m3qtf3jqv6qxY8dizJgxQcFgI10IR95ly5ahTp06QS46DRo0gMfjQfXq1WGz2YKeiKbCwsLyDWpSvPdipMLcd6hQoQICgQCmT5+OZ599NmTwNi4uTt3vb9++PcqWLRu0T3R0NHbt2oVp06ble84pU6YUyd1JprMBgPr16xf62KLq448/RsuWLXH48GHVN/IeoClTpkou3jfXP1xKxcfHo2PHjnjkkUfw0EMPYeLEiWjZsiWio6OD9q1duza+/PJLbNu2DT/99BPGjBmDFi1a4JFHHkGnTp2wZMmSIGDlQqps2bIYOnRoofdfsmQJxo0bh02bNuHcuXNFPt/IkSNRq1YtfPnll/mmqvL7/Rg/fjzuv/9+rFmzpsBya9Wqhe7du+PGG28EAHz77bd46qmn8N5772HlypXqnjld7C6mbr31VrRr107zILVUamoqFi9erNlWtWpV9RmWnxo3boy33367VOpp6p+thg0bYvbs2di8eTNGjhyJevXqhdx37969QQ+9uFwuM75joNTUVKSmphqCmk6nE5UrV75g5zbBnxBikBXQOvzIFF4M9koXHeluQMjC7/crNwsGTEny6lMS6UEguqEAeT9SeH6mdgKgUiexftKdg9fCa2A9JSzE65GQiwx+5+bmwu12K0iAQIvP51PQA0EEBv6lc4mshwx4h3K3kUFvBroJq8iguXTVYEBbD77wetheDEYDUGVK+If/GKxm2/r9fgQCAURERMDhcKg+Zl0IBRBaycnJgd/v1wBN0gFEwj8SBNPniSZkoYeG9H1LsEcCCRJ+YRuzj5kyiTCCvEaWk5ubiyNHjmjSJoWFhSkSnZLggN/vV7AWIQwJtrCtCXWxTH7ZYH+xH3heCWUwfRr3l9aN0u1Fwlh6FyH2C+ESwiJ8T0IP0dHRsNlsqg7p6ekKZKE7ktvths/nw8mTJ3H06FEcP34cp0+fhsvlUm0p4SoJjOidZ8LCwhTMBfz1NKYeGpJOMhwHPp9PnSMqKgp2ux0xMTGIi4tDQkICnE6nOh/nOGE+9mVubq6ax9zGlEwEsTg+vV6v+ls61+Tk5CiilSARgSBZBucj+4LrCf+3Wq3q7+joaDXGAGj6janCKDkH5Da2E8cYwRo5p+R44fwjmcu5xmvkWi0dnbh2se04lghiyfGlh0flnJZuWVxbWS+ZtlDvCsS/CUjRcSk7O1tBkgRuJEAl68/rlPOOayFhUp5fn2qMkBnnjGwPuojJss38r6ZMmTJlypQpU6YuVV133XXo3r07unXrprmBLgPnp06dwurVq0MG3IcOHRrSsSEzMxMzZszAnDlz8MADD6B9+/Yq+Prbb78F7d+jRw91/2jevHkh600gqGvXrujVq1fI/Ro2bIjo6GhkZ2djy5YtWLduXVC6njlz5uDLL7/EwIEDMXLkSBw8eDBkef8FzZ8/HyNHjlTpeB599FFYLBZDOKdSpUoYPnx4sc81e/ZsvPPOO5gwYUKhoIgLqdJID/Xaa6+VuIwDBw5gwYIF6nVJ2vffpvT0dHTt2hWHDh3Kd7+iuDMUVitWrEDZsmVxww03AMhzO5syZQouu+wyJCUlISEhId9gtNPpLFTatkAgEDL4WZD69OmDGTNmwGq1FvgEdmpqKnJyclCxYkVMmTIl5H4DBw4s1LmNUvaE0lVXXYVXXnkF4eHhGDt2LDp16lToY4uq/fv3q+B7VlYWfv31V9x77735pqg0ZcqUsS6//HLD9SkzMxPJyclBDhpSHTp0wNtvv40aNWoUeJ7w8HDUqVMHbdq0QZs2bXDNNdeo95YtW4bPP/8cy5cvx65du4p1HUVVhw4dcN111xXpmPXr1xcr3Ve9evXwwQcfAAA+/PBDrF692vCzZdu2bXj88cfxxRdfFOiER7Vq1Uqtt36/Hx9++CGee+45HDlyBG63G3a7HS6Xq8h1LqlmzJiByZMnh3z/t99+C/oO+t577xUaAHvqqadUm5r69yoQCKBMmTKoVKkS6tatiyZNmoTcd9euXdi/fz/8fj8OHDiA119/HS+//HJIx8X/srKysuB2uw3XhvDw4JTHpSkT/Akh6Qyid1GQzjjyPZm+RjqKMFhN4IGBTgm8MIDPlEMSDqGrQlpaGrxerwr2yicP9DabLMcIwJGpfxhEZ6CZIENOTo6CQ2QaGr/fr9xACLkw8B4ZGamC9DwH24LBZgnAEJygcwnL0UMrdrtdAxjpXXn0bhIS9pH1kCltGFTXB7DlzToJdNGhgtfL9pfpySRwIV1IfD4fvF4vXC4XXC4X0tPTkZ6ejszMTFV3CYAQAOK5gL9AC1kvCYcR2GFAnftLVxEJmrDNCTfQxcbj8WgAKb/fr6xys7OzFagl5wnHSHZ2trpuXoPdbteklXM4HGr8st31c4x9qLe41M85WQeZqssoRROvWTpi8W85VzmGCYTINHMcS5x34eHhqk8Jhvj9fgW7EK6QwJZ0oJLrgx6O43jzeDzKNUnWXT+GpTOX3nFGQmHSFYtlSpiMMBz7W8Ic0o0mKysLPp9PzQX2O/uGUAmhH64lXNvk+iHTa6Wnp8PlcsHpdCooj+dIS0vDuXPnNOMwPDxcjU/Wg33POktHsoyMDHg8HgVZcn4T6tFDeaw711JCnNJ1KBS0yLIIYuphHkJAHKMSCiIkxnlDSE/uxzL0Kdmke5T8XCKIarVag4A7CcXJ8aFvS7k2s07SMclqtSI6OlrjLMV1mZAUr5/rFqFTU6ZMmTJlypQpU6YuNR08eBCLFi3C4MGDNduNUi9JR1Wp6tWro2fPniHP0adPH/XU8p133qmC36FSrbz88stYuXJlvi4T0hmoQoUKqFChQtA+zZo1w6effqoehKlVqxZatmyJBg0aoFq1agCAJ598ElFRUXA6nVi0aBFeeeWVkOf8r2jatGl488038fbbbyMjIwONGzfGlClTcO211xruf+utt2LFihUYP358sc43ceJE7NmzB5dddllJql2s87700kvq9dKlS0tU3rp16woEUgormXasY8eOGDZsWKmUe6nL4/Fgw4YNOHnyZL6AXkJCAq688spSPffKlSuxf/9+XHnllbDb7fjll19w//33Y8mSJfD7/ejWrVu+ASM+EFqQTp8+HRLaKSh9xdKlS9UT7AW5HvE+U7ly5fIFkvT3NUMpEAhogLWC9H//93/Yvn07hgwZUuhjiiMZdxg9ejSaNm2K2bNnX9BzmjL1b1OrVq3w6quvYvHixdi0aZNyjZGaO3cuevXqhUcffVSl8SwN1ahRAzfccAOmTp2KSZMmYcuWLRg+fDjatWuHRo0aoXPnzti7d2+pnc9IjRs3xp133omGDRsWOk3gnDlziv29om3btgqQGj16NBYvXhwUS9q1a5eKiW7YsAGnT58usNxly5YFOWlmZWVh2rRpePrpp7FhwwasXbu2WHUuqTZs2IBt27YZvvfTTz9h3bp1mm0nTpzAl19+Waiyw8PD8eijj6Jz584lraapf6i6d++O5557Dg0aNIDNZkPNmjVRq1atkPtv2rQJGzZswP/+9z8MHjwYL7/88kWs7aWjGjVqqAdCjL5XyzjzhZAJ/oSQdL/ROzbwBwcDvwQYLBaLCrQTRODfBDgISRAWkQ5ADIJTdKVgkNXn88Htdisnj4yMDLjdbg1EwaAw68ZzyDQxLJPnkE40TqdTk06KdWPaMY/HA7fbrbaxbgy+0xFCpmaS4A0DzhJukQ4xMmDNOvMcbCvpAkRwQ6auYZotgj8M8svguHRgkX0q4RAp1olQCgP6fM3j2e8yjVhERATcbjfOnz8Pr9erzkcQQba/hDYkiKR3A5FtlJOTo66VQXYJFBCekM5M0hFHBvQ5VgKBAI4dO6ZSghH6kOmECEEwhRhBAtZfOthICIMgh/7a+Q8Izo+tbyN92xC0MfpRLc/PfpFgT25uXjq98+fP4/z58wqe4hghjCIBlbCwvJRmqampCvqRjlOyzvLLpR70M3J2kWAP4RuWIQENOZa5ZnFO05GL9SIUJs/vcrk084NAnwSU+D/HtnQG4vqQnZ2txn9ubi68Xq8GoJFjjv3KJxy4bnA9sVgscDqdQe5IaWlpCqqSwE5OTo4Cr7i/bCvpikPQhHCbdA7j+JGADq+PY5pOU9IhzePxaFJn6WFR6f4mxzn3Bf4Cv/h5owfEZP/LdFoEq+SY0Ke44/9er1eTyi4sLEzNQz1oGRkZqZn3BI8kAMU1m/OJa55MU0jJeSGvSX4WmUS4KVOmTJkyZcqUqUtVERERqFOnjmZbdHQ0BgwYUKjjly5diq+++qrA/aKiojQ33du0aWO437vvvou2bdvmGzBZuXIlTp06pf42etr5999/N4SVrr32Wqxbtw4//fQTnn76aaxYsQLHjh0Lsnz/r2vChAno0aMHXn75ZdSoUQPly5c33C8qKgo33XQTunfvXqg0THqdPn0aDzzwADZu3FjSKhdJTZo0UU8D165dO1/nqILkcrkwderU0qoa7r//fs3rC2mjf6nJ5/Ph+uuvx9dff42srCx88cUXQYHMfv36adIKlobOnTuHvXv3olevXli1apVKT/XFF1/goYcewk033aR5yFAvv99f4ANDgwcPxjfffKPWLX3asIKcjFJSUrB161YAQMWKFfOdj0ztNWHCBMTGxobcr7APOT355JNFStkXFRWFBg0aFHr/4ko6rBfWFcOUKVPAXXfdhapVq+KRRx7BpEmT8NJLL6F27dq47LLL0Lx586A0S8nJyVi4cCF27twZBKmURAkJCWjdujXuu+8+9O/fH8OGDVMQZVZWFhYtWoR33333gsI/FosF7dq1w4oVK4oECIwbN65YQXGHw4FPP/0UiYmJmD9/PsaOHYvt27dr9mnatCliYmIAAF9++SVWrFhRYLlz5swxTAu2YsUKTJ48Ga+88grOnz9f5PoWRomJifm+f8cdd6BRo0ZB23fu3IlFixYZHvP9999j/fr1hTr/oUOHiuXAZOqfr/DwcAwePBj333+/GmdJSUm44oorQo6706dPY+XKlRg5ciRWrlx5Mat7Salp06Zo3bo1qlatGvReTk4OfD4fqlWrhptuuskwdWNJZYI/ISRBBOm0wKAsnXvoNCOD8NK9hR/kdEYg2CGdXqSbgnRl4XESlJGBYQIPegcTCbXIc/Ec8guEdKWQDjr52b3pU7zIoLMedJCBbtZbBqbldeuPkUCKHnaQzi56lw4G0OW5pXuFdOgAoAmqy7pJwITnlEF+6QqVnZ2tjpVuGrJM1k2ODwksUdKhRg+R6B1C9Kly9G0BQFN/6TJFcInjRPZFVlYW/vzzz6Dxy7pzzLANeD6WLZ159Gm2CIlI0Ed/fXK8GL1m+8k+1LeVUV+zPhLmk3CFkQOLBOGkCObIOkhoRJ5fXwd9vSVkw+08B/tKXpM8Xva7dGCRbSpTTulhOUJQsh+NRMBHnp9106fWM2pP1pd/Z2RkwOFwKEs7QoD84OPYJLTCNUmOfV4v20A6NMn5yP25Zsr20o9hI1iL/+vXTwJH+jEn6ykBJn2f8bV0fZLjR8KcLJPXLB3D2OZ6tyt+VkkAiTAm037pISMJHcp1UA8byjYxel/Cc0yPBvz1+SHrWpo/ak2ZMmXKlClTpkyZupgySl9Qvnz5oBQvoVI4hHpCV6/69evjiiuu0Gzr3Lmz4c3Ewuj48eMAgB9++MHw/VDl2u12VK5cGW3atEFiYiLGjx+fb4qBf4oupJV6KC1YsADLli3D/v37C9y3TJkyRU6FcTHUv39/w+3Vq1fHZ599BgC48cYbDV2jCqsjR45gy5YtxT5er8svv1z97Xa7/9Xp56677roCA4JGcjgcKuCrB/wsFkuxILT8dM8996BVq1aoXr06mjdvDiDviXGmxAOg+Vsv3nPKT3TNCg8Px5kzZ7BgwQJNaoX4+PgC04URYIyOjg4JjF133XXKsaNFixYh7/ex3oUVHxjWA0umTJm69JSYmIihQ4fiwQcfRN26ddX9T4fDgerVq6N69eqGx3k8nlJ3RSeoCOStWUy5SP3555+YPHmyAsIvhJjSsShpQffu3Ys///yzWOfr0KEDnnvuOdx9993YtWsXjh49qnm/QoUK6NKlC1q0aIHk5GRs27Yt38+Y1NTUkM6dF1oWiwWPPvpovqkjlyxZorn3Ttnt9pDfgWrVqqUcPAtSTk4Ojh07VrgKm7qklJOTg7S0NM33FafTiYoVK+brlFhYaOy/rPwcfbKzs1GrVi08+OCDGDRoEK6//vpSP78J/oSQz+fT/LCQYA+DtDk5OfB4PPB6vUEpURhstdlssNvtKsAqXWVkqikJ5RB6YdCXAXE6arhcLhWMZTom1lOmpcrIyNC4+zAYK51uZNCXkIfD4dDALKyLw+FAuXLllNWzdE+x2WyIjo7WpO5hHbhw8FzStUcGl/ljSTrqSFiFfUBnCRnQZn2Z1sjr9SonGr7PsmRwW0rvRCTTkNGNhGmQ9GBJZmamahOCFPKa+L/f7w9KyyX/EUZgG8kvHXpQRo5TBt7pwKIHEWTaJOl4xHpERETA4XAgEAjg/PnzSElJgcfjQUxMTFDwn9dChyumNuP70sGGrkwS0gKgSZcmHYKkyw3PIaEFCT7o07dxLsj5Kv9JNxg51jg3Of+Y9kkCJhJ44j9CFEblSpCCKf441tl/EjSR4ntMESdTkcl95PokQTd+0ZOuPWxvmf6L9ZRzlO+xTeR6wXIiIiIQHR2tWRO55uTm5rl0ZWZmwuPxaOacdAPiHLFarShbtqw6h9frVbnSgbwn/1JSUpTLDvAXYMhr47zUuxJJAIZzi081cJ2V6xvHFdd1QpVsfwkRcU1leRJU1K9ncp2lOJb1QA/HkvyskFCW3hFKrgF6kIav6chD2IfuYIFAQDMm2bYy7aEehtKvyXIsso4ShqVzEK/NYrHAbrertVuOXVOmTJkyZcqUKVOmLkXprdA//PBD7N27Nyh4a2T/f/r0aYwbN65Q5+nduzeaNWum2TZ37ly8+uqrRavw/9fXX3+NtWvX4qOPPtJsr127Njp27IhZs2ble/wDDzyAl19+GTNnzlSpyP7Juu+++zB//nx8+OGHBabxKU1t3LgRH3/8cUjASurRRx+9CDUqvNq3b49JkyZh6NChatvs2bMxatQofPPNN+o3/xNPPFGi80yZMqXUwJ+bbroJXbt2Va+3b9+O+fPnl0rZ/zR99tlneO+994oFtQ0bNgzdunXDjh07sHbt2iDHJf390pLq1VdfRcWKFdXrn376SaW/8/v92LBhQ77gG+9f6mXkQBAREYGvv/4a8+fP1wSyK1SoUGBKPDpnOZ1OtG7dOuj92267DYsWLSoQIKLcbneh9rPZbOjQoQMmTpyIhx56qFDHhNIvv/yC1157rURlUEaBZFOmLpaqVauGUaNGYfTo0SFT+P0TVaFCBdSoUQNPPfUUGjduHPR+27Ztccsttxgee+rUKQ2wWBqS63mTJk3w8MMPa95funQp3nnnHQwfPhwnTpwo1XNThINbtWqF0aNHF8rpEgA+//xzZGVl4ciRI1i/fn2RHhx94okn8MADD+CKK67ADz/8AJ/Pp96rVKkSvvzyS3z//fcYMmQIJk6cmO+1M5vC36EuXbrgnnvuwQcffICOHTsa7rN161a8+OKLQXBO7dq1kZycbHiM3+/XQGH56YorrsCoUaOKVnFTl4yOHTumWXecTidq1aqFunXrhjymtAHFf6NoZmCkqKgoJCUloWXLlmjdunWpf+8GTPAnpBYvXhwErMi0PNnZ2bBYLJrtDGRLKIUBWSAvOCyDuRJIAP5KXSMDuDKIzKcuUlNTFXARHh6u0tQQKuGxLpdLwT+RkZEIBAIatxPCHHQvoluHdKhhvvYyZcqgbNmyiIuLQ0xMjIJcmMpKfvBmZ2fD5/OpdDgScpDpr/TBcCAvAC7TeuldcwjisAy+T2CDaWWY6kz+k21GGINi/dj2Rue1Wq2aoLd0DOF100VHtgfrxm3sJ4ISMmBOOMHhcMBms8HpdKq2YnodHsNrJbghXYb0Dh4OhwMOhwMREREK9vH5fArasVgsOH/+PNLS0rB37174fD5VB4IGHM8EJuQ1SughMzNTgSYS0NCPEdaN/c624nvS8Yl1IJTCMjivCKywHWX/yXGmB9okbKd36uH410vvFqOHI+T80rvQ6B1R9ONfli3hP70bD9+XbjNcm6TLD5BHd+sdr+S1E2xhKjgJnRES8Xg8iI2NhdVqVWMyIiJCQY96By+/368B+2Q/yfY/ffo0zp07B6vViuzsbHi9XgDQrIvyf30KK0ImLpdLfQmX7aB33LHb7bDb7fD5fEhPT9eAa3KuBgIBzbiVgB+BPY4PXg/7WI4jIxcr/s024XVxXZNzV65LElqi3G63ckPSQ0dynElXJNaba6lMzSVdh2Qbcl3Wrwl6CJJ1tVqtsNvt6jNC/s2UbWxL/TWZMmXKlClTpkyZMnUpacqUKerv0aNH45VXXoHP59NsB4CaNWtqXmdmZmL69OmFTmPQs2dPDbQP5H2vv+uuu4pV73bt2uHHH39UgFLVqlXRt29frF27FmPGjMn3Zq/X68WUKVPw5ptvFuvcf4c2btyILl26YOjQoUEA1YXWRx99hFtuuSUI/jl16hR27NiB9PR0AEDXrl3x9NNPX9S6Galbt26oWLEibr75ZgB5rj9ffvkl3nrrLXTv3h3Dhw/HsmXLsGjRImzbtq1EKYfOnj2LDz74oLSqjt69e2tgvPDw8GI7BvyTFR0djXnz5uHaa68tlKNUfnr11VexePFizbbSDujoA4+ff/65uj9x9uxZOJ1OpKWl5VsG7xcBefdCHn/8cUyYMCHoQaLc3Fw4HA6cO3dOk54qMTEx33UNAFavXq1gnR49eqBevXrqvaeeegpTpkxBXFwc7r333nzLARBUr/y0e/dutGvXDqdOnSpx8Gf58uUYN25cyGBTUWQG9kz9nTp69Ch+++033H333Xj++ef/7uoUWtdeey18Pl/ItE9Vq1YNCTqeOHECJ06cKFaKq8Lo7Nmz+PTTTw3fCw8Pv+BgdFRUFO6//3706dMHgwYNKnD/1157DX369MHgwYPx66+/5uuwZqQOHTrgiy++wFdffYVffvklCBaNjY1F06ZN0b1795DueWfOnFHpKi+2GjdujLZt26JWrVqw2+148803cdtttxnuW7FiRcPPnd69exvu/9577ynnxsKod+/eeOaZZwq9v6lLR0ePHsXhw4c12ypVqqRx0DRVdKWnp2uAw1Ci4UppywR/8pH+w4QuCT6fTwVY7Xa7JgUUAQ2CJfxbuvxIRyCZZoYBeIIP0hVBun5YLBZkZmYqlxWLxaKcQRhIJpTEGwgMuhM2AaDcc/g/f1wQOJDl2+12dS0Oh0PBBNJphOAQ09/oU8gQ1pEpePQfSBLS0MMfUVFRymVIOqjIdD2EP+goIuvH19LVQg+VSChC72rBfxIq4TXExMTA6XSqH0XSHYZ1ZYCf8ArBMl4b3aEIVRFSYNk8npAEYR+OEZZDeECfji0QCMDj8SA9PR3Hjx/HyZMnFYSWnp6Oo0eP4sCBA4iPj4fP50NCQoJadDg+CY9JgEGOX9ZJzh/pvsN25g9Q1pV9JtNSsa34N1/zvBxjdJeSkAPHsBxPsq0AaMA1Of445gk1cLxI0EECgPrxrwf4pKOJdCuS0ISck3ooSZYpr13CIxwzeleh8PBweDwe5QrDcaGH7iRERHthAj8cQxJgoqML3Vu4Lsk604lKAkbyern2UBJaDAQC8Hq9yuKUgE5kZCTS0tLg9/uVk43L5VLb2B5yzvOchOWcTieioqLg8/kUoCRdidxutxoHrKsEx2TbSkceuV06sMlrluuChMM4ziW8x/ksQT6K651cJ+UYYzvwtVyvsrKy1I0sWY48B4/hZwn/2e12OBwOjUudBI702yIiIhTACED9cCVERZk3k0yZMmXKlClTpkxdyvroo4+wcOFCPPXUU7j//vvRrFkzvPHGG9i0aZPaRx/M3bRpE6ZNm1ao8vv27YsaNWoYvlcct4/KlSujefPm2LBhAwAgISEBM2fOxIMPPlio4Pjvv/9e5HP+3dqwYQM2bdqEffv2YcGCBX9LHb777ju8++67WL58Ofbs2YPU1FR06NABN998M1auXAkgdGqti6EuXbogMTERzZo1w/r161WAp1GjRrjnnnvw3HPPqX1HjhyJmJgY1K9fv0Tn/Oqrr4qUDqkg/fzzz5rfmi1btsTIkSNLrfx/itxuN5YsWVJq5W3evBm9e/dWa1ZpBp179+6t3H2opKQk9fepU6cwZsyYAoMj0j2H9969Xq+6T5iQkAAg7yHYChUq4MSJE/j999+Vm1TZsmULXNsAYOfOnQCA5s2bo3v37mr78ePHYbPZ8H//93+oV68ezp49m285qampBZ6Lio+Px8mTJ/HSSy8hMTERp0+fLvSxeiUkJCA5OblUwJ/rr78ew4cPL3E5pkwVV1999RVatmxZKNe8v1tVq1bFRx99hBkzZuDll18O6aZy4MABvPfee4bv5ebm4sCBAwWuL8XV8ePHDdP3NG3aFMOGDbsg59SLa/Vnn32Gb775psCUtbNnz0b79u3Rt2/fYp2vatWq+Pzzz3H77bfjhhtu0HyXAfJSUX722WdBD38fPHgQP/zwA+rXr48mTZpg9+7dxTp/SdStWzd07txZva5Xrx769esXtF9YWBiuvvrqoNRdLpdLpbA00htvvKEBZAvSiBEjMHr06ELvb+rS0J49e4LS3VksFjRu3BhXXnnl31izS1vnz58vlPMjDQFKWyb4k4/oWgL8Faim048MbNJFJTIyUgWf7Xa7AgYcDody7pApZhj0ZTBWphCS8A5BEQbJrVar2t/hcKigsYQhrFarSsnF9C7SwcPv92sADkIQwF9uDwAQExOD2NhYTUocAKo8meqI5wG0Di4yNY6EL4ygHxnA1geV9SCVBCAiIiJgt9s1Dkt0wyFkIwEMmc5GXrOEf/TgjwSy+M/pdCqHE7fbDb/fr97TO3DI4DvdLjwejwYY4d8yZVFkZKSCgvQuHkapyVh/6QpDh6GUlBSkp6crQEymaIqIiEBCQoICjuLi4lSaHkIWEkRjm0gHJ/Yb24rADOcNITmm02I9JLDEccJ+4w0HvTuSdBQifKFPG6TvA/YDy5PtxPnIvmH9ma5ID//IvpDuS3JcsmzWjeeR7i98zWuT414P+UgAjXNUXj9f0wWK55UOY9yHf3OcSyiRY5QwmwSb3G63KpdrBp3DCDcyfRzryjIIELEdeBzXDp6fjlTSTU1CWtKlR84t2dZco+S45PhgCjvWCQDS0tKQnp6OQCCgWQ9lv/C1HEMsWz8vOG+kgxnXbTkGJDwWHh6u0pZJ0Ef2Ed3d2A5sS84rlq9fQ2Q6Pn176F3MOPajo6MRHR2tnH709WLfcD5LNya9Ixj3lzAY150L8eXGlClTpkyZMmXKlKmLpaFDh+L2228HAJX2qGrVqmjWrBm6dOmCevXq4YUXXtAc8/XXX2Pr1q0Flp2YmIiBAwfmu490pXjttdfQs2dPNG3aNOT+N910E5xOp0pr07BhQ1SpUgVt2rQx3N/n8+HHH3/EuXPnAKDQaW7+KYqOjsbw4cPRpEkTlZqsbdu2F70eY8eOxTPPPIN27dqhUaNGAIBp06Zh8+bNePHFF7Fr1y7UqFEDtWvXvuh1A/JcqdavX4/HHnsM1apVC3KYkiooZVJhtHHjxlIPIH333XfYvHmzen327Nm/5Sn9S02HDh3C6dOnERsbCwBBqQqLqxEjRmD69OlB2+WatWXLFkyePLnAsmQg/I033sC7776LlJQUdX+EAc/jx4+jUqVK2L17N/r164fhw4fD7XbDZrOhefPmBZ5n1apVAPKC01dffTUAYODAgVi1ahXmzJmjAJ3y5cuHLMPv92vSjIXSlVdeiW+//RZOpxNvvvkmrrrqKowaNQrLly833F/CUdnZ2Zg6dSp++ukn7Nu3T20vX7487HZ7odOM5adHH300pFuEKVMXS0ePHsWPP/74d1ejQB07dgxHjx7VPORqpM2bN+frcHbgwIGglE2lpSZNmqBPnz5B210uV7FA8pIqMjISTZo00cCgRhowYABiYmKKfZ59+/bB6/Vi586d6rtsfjpw4AD69OmDHj16IDk5uVCuHaWtGjVq4LbbbtOAUSdOnMCuXbuC9q1YsSJq1KgRZGKxdetW9ZlmpDNnzmD8+PGF/sy3WCx48sknC/xdYurS0vr167F9+/agudG8eXO0atXqb6rVpa/Tp0/j22+/xcSJE7Fp0yYcP37ccD+9WUVpyQR/8pEMkjKIKgPAwF/wigRhZKoh6QQkXTuke4feFUI6VBB8IOggXRVk2Xa7XdWJdZRwEQO8Mv0PAI0zDP/JdFh05ZDpgyQIIMEKIwefUClcpOMFJSEbozQ/eohDli/hDQlCyGuWkmnW9DAFA9gSFmGfsQ5M/WWz2WCxWFQ/yWOkqwr7RAb7mVJIgjyyDWVZsp0ID1CsjxGwJN1oJORDiILXyjHIdGB0stK7k/AY2a569xj9WJaOPkbtKvtED0XI9tDPPe7HvyUgI/eRY8ioTyW8IQEgHsP0fRJO0J/HaHGWgJC8FqO2kn0Vam4YvW9UvnQpkueUbcRxYiS/36/2JawCaNdAeR5ZR16TBGM4rySEI+cI95fgH8cl95UwCf/WO4jJ8/GapfuRTF9F0EzOx6ysLHi93qC1ln3DunDcy/R9EnKST8ZJiCs/m2c5L+Q5+VkigbmsrCzljiTHqxwvEiIklCMdjOSY068lHBtG7mH68Sbnp74v9Ncmz6kfAxcij6kpU6ZMmTJlypQpU3+H9E/bPvjgg3jkkUfUE87U9u3bC1XeDTfcgCuuuCLffRggCg8PR48ePTBw4EA8/PDDGDJkiOH+5cuXh9/vVzd4ExMT84V5fvjhB0yfPl0FPmJjY1GuXLlC1V+vOnXqoHXr1sU6trhyu904f/48srOzsWPHDtx9990a8ODvUEZGBtavX6/Sjq1btw6TJk3CsWPH0LJly7+lTrt27UJcXFyJgmtF0Y4dO0o9uHnVVVdp5uDZs2exbdu2Uj3Hv1V0RwZQKm4xAEIGB/ODZoz0xBNPoH379gDy1rtx48YBgHKKBvICnwBw7tw5zRg+deoUXC4XgLz1uSBw8dixYwqaqVSpEh588EEMGTIEPXv2xKefflqoVIFLly4NSm+mV6VKlVCtWjV07NgRFosF9957L+rUqQMACsCS2rt3ryYl27Jly/D+++9j/Pjx+Pzzz9V2PrxYWoHqUPf1TZkyFazk5GRNWkIjXXHFFfnC2V6vV60FpS2Hw4HLL788KIXP/v37LxhslJ+qVauGQYMGYciQIejYsWPI/f74448SnUemXQvloKnXqVOnCkxBeSFVr149VK5cWbOtVq1aht+h+cC6XqFAA6mdO3cWue979OiBChUqFOkYU/9cpaamIjk5Oei7WXR0dIFQnqnQSktLw9ixYzFu3DisWrUqX04iv7hhcWWCP/mIrg10TPH7/crJhx0l3Xk8Ho8GuggLC1PHMejs8/k0P6AYdAagArSEK3isTEcl4Y6srCzlMCMDqz6fT7lvZGdnw+v1aiABm82mbP1kWiKmkWI6l6ioKFVvBp3pGESXFoJBerBAAky8Tv5PgEAPyUhnDr1rjQzgs235v4Sz2HZsG6/Xq9o8P1hDOrcEAgEVvJfnJRRlsVhgs9kQHR2tHJEIzLCvGCxn2iQJ4LDP/X6/CuQTVNJDShKoYtsyZRe/SNIlhI4cMrUYx4PX60VaWho8Ho+CWei0EhERAa/Xq66DXxQ4fiSsRhcWCcfonZlk/0iIiqAYU1IRTpHHs94SipLuIdIFR56P29l+EnCRY0jWU+4rnaBsNptKZ8e0RkxtxzrKMc/yCQjpIS890GU0fvUOQBKiIewix5dsF9ZfOrZI+EJCbgA0rlkc5zKlGtN46QEui8Wi1iTCQHa7XZNiTEJ7cp1jX1qtVo1DEuvM+jAHMtcbutTQeYppo9h/4eHhyhmN6Q4lGCX7n+mqnE6nxtEnNzcXPp8Px48fV9fFa5FORnpXIY5vrjkSLjKaw7J/pdsV1wqjcSrnBNue7m98X5Yl4VOeNxAIwGazqWsNCwsLAvs4fjneJehD6WEfo39yvMu5IsFSjg/pEqaHAE2ZMmXKlClTpkyZuhTVqVOnoIdCbr/9dgwZMgQffPCBZntB8EuPHj1wzz334NFHH8335vp3332ngtRfffUV6tati3bt2mHAgAEYO3Ys7rrrrqBjUlJSNDf5rVYrxowZE9IZpVGjRqhbt656Grx8+fIh3YEK0ksvvYTFixcjMTGxWMcXVxMmTMDBgwcxZMgQ7Nu3T5M64e/S5MmTcebMGXTp0gUAMGbMGHzxxRfo0aPHBQv65acff/wRd9111wW5+SxFV56pU6eWetldunTRBMquvPJKQ3cDU8EqV64coqOjAQDp6emlUmaFChXgcrmwfv16HD58WN0nqlWrVpHKefvtt9V9Db/fr9JoHTp0SDnrNGzYEACwdu1aVKlSRc2hvXv3Yvv27di6dSteffXVAp0N9uzZo1KjtGrVChMmTMDVV1+N7777Dhs2bMCdd95ZYH3HjRuHG264Id992rdvjw8//FBdV4sWLVSQ1gisfOaZZ1QaMiAvULdz505Mnz4dGzZswNmzZ5GRkYG4uDiUKVOmUK4WhdGFXg9Mmfo3affu3QW6fTVv3hxdu3Y1fC8uLg5PP/10ECxemqpXrx4efvhhjYkAACxcuLDIZRGqLK4aNWqEW2+9Fb1798Z1110Xcr8JEyYUKX2iVFpamibd1Y033ljgMR6PB0ePHi3W+UpLl112meE4aNu2LaZOnaqJ/ZQrVw4Oh0Oz34kTJ7B+/foCzzNz5sxCpx6m2rdvj2+//bZIx5j656pbt24YMGBA0PbHHnsMI0aM+Btq9O9RWloatm7dikAgAJfLZfj9OiUl5YKkdzTBn3zEAK4EZvQOItJ9gpBOdna2Jj0XA90Mysp0RHQKkpADj+F2ACrNEM8LQME/evcOGXhlsFsGuTMzM+H1ejVAgIQ4oqKi1LXq0xgxLQuD/QyKs2wJEEhoRqbAkQCTBDGk2wT/MUUNYSuZvoYQjoQ+mI6N4IDX61WggpE7kGwzli3hBulywuuKiopSqW8sFgscDgecTiecTqcGEGD9CVfoQR95Pv0PKRnwN2ozQjhMLUU4Qjp1WCwWdV0nT57EiRMnVFvKa2db8XqkIwzHoIRqWA86HOkD/3yfY4Nl8ZojIyPhdDo1qZoAqPIlSGE0NghKSPCF2zMyMpCamqpJ+WYEU7AvJaggxynPyetPTU2Fz+fTwC36Mll/WSdZB6MUY3pnHwlasN/1EJ10v5FlyPEtr42S0KCsD9c5pirkHOL6wuuy2WwauIX7yBRS8twyFRn7g9cSCAQ08B+/nBIsi4qKQnR0tCqboI6ER+TaKFMasm2lIxDPzbkRCARgt9sRGxuLtLQ0pKWlKYiTfZmeno60tDTlAiTT6knIhXOUr7km8drZN7Iuct2RgI90BmL/SFDNyL2M6wvnF4/luS0Wi1q/5fqkX6dZFq+FDmH8LCusWA/OU3kurudy3TafHjNlypQpU6ZMmTL1b9FTTz1V6H1vvvnmfF1nWrdujS+//DJfwGbPnj0KGqlZsyZ69eplWKdu3bpptk2dOhV9+/bVvB4xYgT27dun+c3hdrsxceJEnDx5Ev369dO4aBjdIC5I9913H+6++25ER0cb1vVC6r777kOlSpWQkZGB8ePHo0GDBnjmmWcuah30+uWXX7Bp0yb07t0bbdu2RUxMDMaNG4eXX34ZBw4c+Fvq1Lx58yB4rTSVnZ2NJ598Etdccw3WrFlTauX26NEDV111FTp16hT0npmSonBKSEhQgeDExESNu0xxFB8fD7/fjxEjRqBly5Zo2bIlDh48CABISkpCzZo1Cyyjdu3a6j4OxfvOQJ5Twf79+5GSkoKRI0cCyHNzcDqdKoick5OD2bNnY8SIEfjmm28KPKfP59Pcp9i7dy8+/PBD5TgR6un33bt3q79XrlxZ4HmaNWsWBPgdOnQIAAxdt2bPno1XXnlFU0/eA1q1ahWuv/56jBs3DtHR0ShbtmyQ49Ann3yChx9+GB999BGWLFmC33//vVBuFgWlLTJlytRf2rJlCw4cOICMjIyQ+1itVtx8882oXr160HuNGzdG3bp1L2QVAQC9evXCQw89pNk2adIk/PDDD4Uu49SpUxg/fnyB7maFUVxcHLp374758+ertVxq1qxZWLBgQZHKPHjwIJ5++mlce+21GtCYrpeh5PV68fPPPxfpXKWtxMREJCYmarJPSF122WW444471OuEhISgh2p37dqF1atXF+p8Y8aMwZEjR4pUx1atWmk+k0xdumrWrBkaNWqEtWvXYt26dQCAuXPnYtmyZX9zzf49eumll9CwYUM8/fTTWL16Nc6ePYuZM2fit99+w6FDhzRpiktLJviTjyIjI5GRkaGCmNJZR7oYyCC8dESQ7i8S2mFgXMJADFjLgP358+dx7tw5BdhwX5ZB+IR1kpZudMsgAML/6Y6RnZ2tAB6+zx/3TAvDALnf71fBeJYhA/DSgYjBZpZHeIGwAMEU6fQiA+J6FxcCRgSf5D9Zbz3Uwx8/cXFxSEhIQExMjOo/vTONBJICgQB8Pp9ynJHQDfeX18zrkQ5A0n0nEAiodtG7YujT8QDaVG+ErrgPxyKPz8jIgMfjUUCRDOSz/zweD5KTk1U92O6BQEBBAUwnpHcrkq4ybAc62uTk5KhUSawr21z2ix6CktCJzWaD1WpVqYuysrI0UJmca9yf189+4Hk55ggu+Hw+NeckqKN3zpEpmnjtEo5LS0tDenq6Zmyz7+Sck+3A1/q0XNK9Kb80aexzOdZ4DP/JmxByDLMMCQ2xXn6/Hy6XCy6XCx6PB+HheSkCpbMPf9ATfONawL6l45dM+cZ1QT8HZT2dTqealxyndP/Jzc2F3+9X6aucTidiY2Nht9vVtXGdYz9JWJJjRAJnXKNYDwnccbyHhYUhLS0Nfr8fPp8PcXFxGjCK0J50bZNtxXaQ1yznoISu9Gki5VquB9LYDzLVY0ZGhnIak2NFjil5PrY1/+fcl2OMawadfTi39PWR16b/ESHbQz/u5fhnG+nXeq4TpuOPKVOmTJkyZcqUqX+DiuKMMHXqVOzZsyfk+yNHjtTchHe5XDh9+rRmH0JBMTEx2LRpk2E5LVu2xOOPPx603Sj90auvvoo+ffoo559FixZh4sSJuPbaa9GqVStNIITOEkXRN998o5yL77rrrqCnky+kvF4vYmJiMGvWLMyePRtdunTBO++8c9HOb6SsrCzs378fvXr1wsKFC7FlyxbMmzevwNRuF1IykHQhNH78eAV/lKa6deuGn376ydApKT4+Hv369Sv1c/7bVL58eeVanpSUhDNnzpS4POm+fvr0aeW6kJaWps6Vn26//XZs3bpVvaajOe/FJCcnY/Xq1epeS926dXHPPfcAyHMk4PbJkydj7ty5BZ7vzTffxLJly1CpUiW1be/evZo11Age2rlzJx5//HHs27cPAEKOt8aNG+P777/HqFGjDFP9JCcno1evXqhfv37Qe/J+EBD8ebN//36kp6cjJiYGiYmJGocLIC/ly6effoqhQ4fi1ltvRZMmTXDzzTcXmBLMarXilVdewQMPPJDvfqZM/Rs0aNAg9OnTB7Vq1SrWvcrMzExs27atQIgiKSkJQ4cODdq+atUqbNq06YI7bcXExAQ9iJmcnJzv91K97HY7Zs2apWAhn8+n1sCiqmzZsqhbty66dOli+B0oOTkZc+fO1aTsKkjh4eH4448/gtwsO3TogG7dumHevHlBgFZubi5WrVqlATkvpvr06YNRo0ahffv2Kn2lkfRuQHpYNDs7G7/++muh0wonJSXlC6uF0quvvorRo0ejQ4cORT7WVOmpdevWGkC6qHrrrbdwww034KmnnsKvv/6KZcuWYdWqVcV22TJlrKysLEyYMAGff/45zp07hx07dqBZs2Z47LHHEBsbixkzZuCtt94qtfOZ4E8+kkFYTh4J98g0WXIbwRpAm7aKx+tdPAjhyGC62+1Genq6crUgXMIyIiIiYLfbER8fr+AJPpVAl4ZAIKDcVQiiSPiHYE1MTAysVqsm8MyAM3+kERDi+wwY8xop/s1r4T8ZNCfII9NS6VPpsN1lQFkP6RA6YN2lI1B2drZKC8T32XYSLCIwoE9pwxRhst76/pTgEtvVbrer9Ed0HJHOK/L8EuyRbRQWlpfqikCP7BfCBgRpCHToU7Yx4M8fw263W8FPhCEIYXB8yfFLSIrbCBEQoiCkIduGbh50DuK1yv6V4AOPkUCWPlUZz3f+/Hmkp6cHQS+cPwQzWL7b7UZqaqrGyUrCR3r4h/2udyGR10iAQoIdEraR/SvHrQSP9K4uerBCrj1yfMoxJ8+lH5s8p0wjpT93VlYWoqKilEsVy6W4bvALn91uV6CZ3v1JpsSTawNTSWVnZ8Pj8ShAi+sYHV84j+mOFhsbi+joaLUP5xH3Dw8PV8Ak+56QZUJCgoKJ2A7SYU1Cb3TKysjIQHp6ulp/5bVIJy+6/OiBHrn2SMiLY1x+XnCbnKuc6/p1Rrpf8X8JM/H6OL/kuOM10wGM/W+z2TTrkx7Q4v8SzNEDrpRc0+RnH+FESgJJcs6Fhf2Vtk2fqtKUKVOmTJkyZcqUqUtVRXlar6B0Ma1atVL3gM6ePYtXXnkFrVu3xr333ott27Zh8eLF6inr9evXo2zZsiHLat68eaEgm23btmHWrFmYP38+Tp06hYkTJ2LDhg0A8p6cfumll9S+U6ZMKZRbhJTH41HAUYsWLS4qjDFnzhxs2LABQ4YMAQANTPB3immH7HY76tSpgzZt2vytDjVNmjQp8jEnT57M9+l5qebNmxe5/MKoQYMGKFOmTMjAR1GChf9VPf/88/jyyy/V69JoM/29jj179iAnJwd33XVXyNSCUtdee60Ggmnfvj3uuOMOzX2Hn376CZGRkUhISMDbb7+ttrdr1w5PPfUU2rVrV+j62mw2dV+K2rJli2af559/Pui4+fPn47fffkOZMmUA5Dm6GemWW25B+/btMXz4cLRs2TLo/TfffBMzZswI2r5gwQKsXLlS02Z6N3cgL11ETEwMEhISkJKSonFvrlGjhmZfl8uFLVu2FHg/pl69enjuuefy/YwxZerfIIvFgrp162LEiBFYu3Yttm/fjhkzZhTZofDXX38tFODau3dv9O/fP2h7YSDFkioqKsoQGD969GihARCr1Yq+ffvirbfewqeffopPPvkEv/32W4nrJtdfqcWLF2Pjxo0FpmtMTU3F9OnTQ7q8/fHHH1i2bBnOnTsX5Kx/5swZ7Nu3LyRMfyF15ZVX4tZbb8WAAQMwbdo09OjRI+S+FSpUwGeffYYVK1YgMTERNpsN4eHhOHbsGJ599lm0adOmSH0RGxtbKBc+Iz3yyCP4888/i3WsqdJRkyZN8Ntvv2HVqlV477338Pjjj2vS3hYkj8eDnTt3YsuWLdi9ezd69eqFsWPHXsAa/7c1ZcoUNGnSBN9++y0yMzNx4MAB3HDDDWjVqhWee+45DBs2rFTOY4I/hRCDoNL5RbosMDgqnX30aZEIMOhBFikGUHNzcxV0QhhCBkwp/Q8obiOgQEcV1kMCNzJlkt7thQFemQJI72zCMqXlp1EQWTog6SEIPTwjQQWeX/8BrG8ztr/8wcNzsH+MfsToA/V6ByC2oZFk/eXfMkjOv6VDjt7RSAIBhFwIQXCs8BimM6KzihxX0hmEbSahCovFooAhvSOHHk6QbSJdQGTbMLgv21y6eTCQLx1y9I4g0sFG36asuxw7BNv0ziksk9CBHMt6AIZjNNQ/1lE60QBQ0AePDzUHZTmyb/V9bQSSybGtdwiSYJjRGJTH6suX+3O+S8ctmRqLZRBEkW423K5vC3ndst78l5WVpVJl6SEhXgP7kymlOH5ZT45bOU71Y0sPp0hnMDl2ub9+nsjUcHowkP9zLOnHnvxflqFvV55Xvi/HuRE8pF8rCObIesi1W9ZH1l0CjnLMc83QX4/+s0C+z3aUa7M+haFcV/TwnR52k/uYMmXKlClTpkyZMnUpq1WrVgXus3//fvj9flx11VWG6VyoRo0aITExEQAwc+ZMLFq0CAcOHMBXX32F9PR0TJkyRe1bu3btfM/5xx9/KKedwojnWbFihWb7qlWrcOrUKQDAxo0bC10e1aJFC1x++eUA/gqwXSzVqVNHBeX/STp69GjQtssuuyzfNHAXUr/++qtKaVQYpaWlYcaMGYUeD06ns5g1C63IyEjExsbmuw9TKJnKX19//bX6u7AwVygZ3bM+f/48srKyCv0UuR5W8fv9QQHGo0ePIjU1FQ0aNEB6erraXqFCBfTr1w+DBg3ClVdeWeC5ateurdZcqZSUFM1rozR8+/btw2WXXabgmFBzSH89UpmZmYYQwLZt2/DVV1/hs88+w+TJk9V2u90eBLp5PB71kJXX69XctwkVTC/Mg1j6e5ymTP0bVbFiRSQmJiImJgZJSUm46qqr0KJFC40DWGGUkpJSKDC6YsWKaNiwYdD2yy67LOh+bGkrIiLC8DtYcnIyXC5Xocqw2Wxo2bIljh49inHjxmH16tU4fPhwiesWqr0zMzOxZ8+efN3osrKysHXrVkycOFGzXhrp1KlTQRCRy+VCcnJyiT//iqqwsDDccMMNuOyyy9R31fx+I1A+nw9+v189mJyZmYlTp05h3bp1mD59eqHPv337duzcubNYdQ8EAprPXlMXX5GRkahbty7atGmDPn36oH///qhVq1aRy8nKysKCBQtMp5+LIJ/Pp3EWq1Spkpr73bt3LxK0Hkom+JOPGOxlEFwGfvWBd4vFAofDoUk/Q9giLCxMpYthqhzprsIAuXTICQ8PR2xsrHK8YABcBr8lDBAIBDTuE3TIiIuLU2nCGOCVripRUVHqnPKaMjMz4fP5NKmvJKTB9mCaGOnQkp2djUAgoEllRvEHBR0sZKoXCSdERESoFDd0vZFQjQR7mDaIAW4g70eJTIElzy+D6BLYAbQuHRkZGfD7/RpYBfgLdJAOMrwWOhDZbDY4HA6UKVNG494i3TpYlux/6QiiT+0UGRmJ6Oho5fQj25MBd31g32KxwOl0KoeerKwsldpIOrTI9pSpyej+IsEeOW55vHTRMRpjbFs9AETASsJMEtrgPrwujmE595hmjvCGdPXRz9mwsDCNy5ScXwSpHA6HctEixKBvW7a9HkbR110PTTHlEs+ph89keWwnCWDpgQk9iCTbVo4v/d9AntW53+/XpHqSIAjnosvlUmMgOjpajQtChXIe6EEXrncE2WRKMDoC8dyBQECl3mJd6OrFunPsScCLjkB2ux0OhwMWi0WTPo9tRUtogmlMnxcWFqbWOJ6D9eP4iIqKgt1u1zj+cDzq3bbYL3TAkusVxyS30blNgmcchxy7sn0JbXKMSuBJrofh4eHKAU62HcU1S45lIziN45+v9es5x2UgEIDH49HAZfq1TvaZnEdybTVlypQpU6ZMmTJl6lKWUZohvRYsWICPP/4YNWvWxKuvvhpyv2XLlsHj8WDmzJkYOnQo9u/fr977+uuvMXv2bABAYmJigSlzipqiZdKkSXj66acNv6d/8cUXyMzMDEojUxh5vV5UqFBBvb7tttuKXEZx1bp1a1x22WWYMGHCRTtnYTRr1iz1t8vlwty5c1GhQgUMGDDgb6nPtddei4cffrhQ+44YMQK9e/fGU089pVJO7Nu3D4MGDdLsd/bsWQDAsWPHMHjw4NKtMID69evnmxLD7XbjxIkTpX7ef6MkICjdYooj3i+Q7hGEdIwAGyPpA1dGwSiPx4M9e/bg9ttvx7x58zTvXXnllahWrRoaN26c73luuukmbN++HTfeeGPQe/pgqB62OXXqFKZNm4Z169YpJ7H4+HjD87Ro0SJkHaKiokK2y8mTJzFz5kyMGzdOBaqrVasWlBLn2LFjcDgcqFChAs6ePasJXocKphcG/AGKlsrSlKlLUc2bN0e9evU07lZ0EyuKdu7cif379xcI/+gf8gWAhISEC55yE8i7L2z0ebxx40bs3bu30Ot/06ZN8frrr2Pfvn2YP38+PvroI6xevbpEdcsP1Pzpp5/ydbKJjIxElSpVCuVadOTIEQWzU2FhYfB6vUHA54XW008/jUceeQQtWrQoEvR15swZ+Hw+xMXFwWKxwOfzFbvuH3/8caGc+PSKiYm56KCUKa1kfCcpKQlXX301rr322mKVZbo3XXwlJSWhZcuWCvZr2bIlxo8fH/K7ZGFlgj/5iEFwmRIpLCwMFosFADTpYSIjI2G325Gbm6tJ3SIdLfRgAD9ELRYL0tPTVcA9NzcX0dHRSExMRPny5TVBYwZ4JRxy/vx5lboGyMuLGR8fj/j4+CB3Ev7wYrBcul1IaIbXGggEEAgE1JMCBBbk+ZiuhcFuSsIMMtCrDzAz+M99JWCSkZGh4AEJdMhyeD0MmMu6Z2ZmauhdCWTo3UKYngr4a8Gkuw3HAr+Q6YPYHBdMx0OgKi0tTXMsgQNCBhKekfVj3Qj0SEDG4XBoQICoqChkZWXB4/GolFdA3pc4m82G6OholbqMEIYciwTUCIAAUMCRdDFifaSDCIEClqUHfvTwj3TFIQgn+5v7yCdJZJ9arVbD9FpyXsj3peuPhK+k+5VMBZebmwuPx6PmB+EUvfOU3s1Fwi7612wjCU/IY9lf+i/7dG1hH+idlAjMcP7q55ceDuPfBPY4ftivgUAAWVlZCnbhNQYCAQWoREVFqXXL6/VqHMRkHaTjl7w2gnzAX45irFdYWJiC0vx+PwKBgGadkuCMhPasVqtKRShT7ElHq7CwMLhcLgUwyvUF+CvVnWw3rmVcG6SjEOE7jg9eo0xnReCSY4195vf7VR15vHQm00OBPJ6pulgPua7KdYLj1e12az67KNZTOpMZAZUS/uP6JWEdthXf17sdybErU6Wx/mwT2YamTJkyZcqUKVOmTF3K+uqrrwrc57vvvsMLL7wAAIbuDpTX68Xp06cxatSooPc++eQT9ffp06eD3j948KAmQB4WFoZ7770XL730UpGfXNdr2rRpOHDgAK677roiH7tjxw78+OOP6nWtWrWKVU5xNHnyZAwYMKDAJ8D/Dn344YdYtWoVFi1ahCeeeAI7duxA165di1RG586dC5XOLZSYVg4Ali5dmu++Bw8exOzZs/HSSy9hyZIlWL58ORo0aIC9e/di+PDhWLRoEf744w+43W5kZ2ejTZs2yMzMxJNPPlnigKCRGjdunC/8lpaWhvvuuy/ftBmm8iQdaUr6BH9YWFjQPdkzZ87A7XYXyskACAZoQsEn69evR69evdTaKjV16lS1NkdEROCtt97CQw89hBYtWigQkS45cn10u90AEDRm9UH5pKQklQroyJEjAPJgSwmjJSYm4umnny7yE/hZWVl4//33sWbNGgB5kBNTPFarVg2NGjXS7L9jxw7k5OSgQ4cOGDZsmKb94uLiDF1/0tLSEAgE8q1HWFgYnnzyScyePVtz39+UqX+LGjVqhAYNGqBatWpB91FjYmKKlOru/Pnz2L9/P3bu3Amfzxdyv3379mHRokWabSkpKRfNXeuaa65B3759Ndv27Nmj0v0UVgMGDMCIESMA5MGHn3zyiVqzqKKCpKEC3qtWrcKOHTvyPTY6OjporatUqRKWLFmCKVOm4KmnnkLPnj2xb9++oLWvdu3a6NixI5o1a1ak+pZEffv2xVNPPYUGDRoU+dhz584hIyMDKSkpsNvtSE1Nxe+//16serRt27ZYTpxhYWFYsGCBJiuMqb9fFyq1rqnSV+vWrYPmf3JyMqpUqVKick3wJx+lp6crOCErK0uTZgmABmjga717C+ET+aFNOEjvoCPTJ0k3DRkYlgFk6ZjidDpVcNbpdCq3IJbBc9CNxmq1KscPGUSXgBID4ZmZmSoAzx9u3M6AL6EXfToiGaTWp/PSwy5yu4RBCKtIMIptwNc8n75deI0ysEzQQQ+UEFIhqBEIBFRAne4/Ho9HA1roARAG/DMyMuByueD1epXLDpBnxRodHa2pp3QZkiLIwmuXxwBQLiQ2m0398JL7h4eHKxcVp9Op0sdlZWUFOT3JPgf+SvfEcS0D/voUX9LxhpAEoR/CcZJWZrvJ9GWEWgiUyTRE0oGG5RIu4Pim+wnHEuEhCVFIOEVfTwld+Hw+Bf/Y7XbDvmF9ZD30wIYe/pHv5eTkaKA22ZbyOPmeLJPbpPuPkfWuBErYlw6HQ40ZQhfSEYZgY05ODux2O4A8O2XOE73DlpzXEhzjefk+108CS16vV/WBTENHpy8J1Nntdg2MwrGrd+KS0ElmZia8Xq8CF+kkJJ2c5NiVcyAnJ0fNF33bE75h20mYTd/HEibiP1qbSycn6ZDDNjPqd33qM3le3uTlOfnZJYFEo/U5MjISgUBAfc4RsuRaCEB9jvG4zMxMBWdxvZb1JYAogSmOZznX2b/mjwNTpkyZMmXKlClT/wbp3ReMtHr1avUAQnx8PO68807D/V544QXUqlULvXv3LlIdAoEArr/+ek1g5Oabb8aECRPw2muvhTxfYbVz5068++67WLt2LQDgrrvuKtLxkyZN0qSRuOmmm0pUn6Loiy++yDcI93fp8ccfx7Jly3DFFVfg6quvxvjx4zFt2rQilVGnTh2MHTu22HXQww2hUhWtWrUKb7zxBnr27Km2ERAZNmwYFixYgBMnTmDz5s1466230Lt3bzzzzDOIiorC4sWLi12//KQPMEr5fD74fD6MHj0aM2bMwCOPPHJB6vBvkXTkKmmqB7vdjqysLJw/f15tc7vd8Pv96l5TQdK764QKSqampqJKlSqGzj4DBw7Ed999h0ceeQS1atVCv3798Mknn+CHH37Axo0b8f333+Ojjz4KOm7mzJno169f0PYbbrghaNuwYcPQtGlTtZ5ZrVYNhHTdddfhnXfeQVxcXL7Xq9eMGTMwdepUzTamBzx06JAGqrrpppswceJE2Gw2NG/ePAgySkhIMEx3eP311+P777/Ptx42mw1Vq1ZVMQ9Tpv5tatWqFTp27BgE+FStWhWDBg3C5s2bsWDBAs17vH9upKysLKSkpOQLvLjdbsM1bf78+UWsvfa8hRWzk+i1YsUKbNq0qdAPaTocDjz88MPo0KEDgDxXyg8//FDBk4FAAMePHy90vQAYrr0A0KRJE3Tp0iXkcW63GwcPHlQQJtWtWzc0bdoU999/P959913MmDEDjRs3Ru/evYNS4Xbo0AFffvllkepbEt15552FdsHTi+P1888/x/3334+FCxcWG/yZOXNmgVBVKIWFhaF9+/bFOtZUyfXnn38Gzf2iOpWZ+ntUpkwZNGjQQAP5nDt3Dtu2bSt2+j3KBH8KEAEZQjmRkZEq9ZRM5SUD3QxM+3w+ZGRkBDlUSOcemTZHAkMEAxjkZdDUZrMFpZthLuuoqCjExMSgQoUKKFeunAqg8zqcTqcKtBMQIdBC0EO6UNAtgm4xBBWkCw4dPKKiohRMwPrJ6w0FLgAIAkMk+EBbWNZTWvVJwIJ9EhERoSAF9pF095FwBOtFWIeuIRKUYrtnZGQoBw2CT3oCnJJOLXp3F8ITFosFmZmZQenMpAg30AWFIAz/pltRVFQU3G63AhWkewuBMu7PunG8yPbg2MvIyIDP51Npqdg/PI5jQ3/NdAWi84r+KRCOC+keEhcXh7i4ODgcjiC3EaYrIrjFOUZwQPYf21i6Bsn21AM0egiIUATTNzHFnd/v14xhtpceuGEbcFzJtpUwir7eEtzQw0L6f2x3mf6N55MuYEZjk22Tk5OjHI4koCJTwbFvmTIrJiYGLpdLASEEX2w2mypXAiyRkZHqZmp2drYaD9LxisAIr4EgFuEVrm8cR7L9CS6yDyVck5ubC7fbDZfLpQGBuMZFREQgEAjAYrFooDfOBzluSP1zDeLYZl+yfeQ6Jl20eA0SxpLuN/p6G4Fk7Ofw8HC19st24xqiP5b9Q3jHyHmO/aYHC+U4k59vgUAgCKbjeqGHyPRALNfSQCAAl8ul1heOqZJaF5oyZcqUKVOmTJky9U9QQU/r8rdWmzZtAOT95vjpp58M92UKqGuuuabQ5w8EAnj99dcxaNAgXH/99Wr7G2+8AZvNhkmTJhkGuIuqzz//HJ07d0ZkZKR6yruwmjNnDn7++WcAwN69e4t8fGlKnxbo79SECRPQsGFD3HLLLZg8eTJee+21Qh+7ePFijB49uliOPwkJCRg+fHgQ6KMfJ2vWrMGBAwdw00034fPPP9e8d8011+Dw4cNYsmQJAGD06NHo3r074uPjMXv2bBUMulCp3Q4ePIg5c+Zotq1fvx6HDh3CrbfeitGjRwPI+80+YsSIYqc/+C8oOTkZb7/9No4dOwa3211giqz8ZLPZ1L1iSj4QVBj5/X6kpaVh8uTJ+P3330Ouhxs2bMChQ4cMXYoaN26Mzp07o3PnznA6nSptYkxMDKpXr4727dsbOvH8+uuvQdANANxokA7smmuuwfLlyzWBtj59+qBZs2aIi4sr0jouZfSQ1M6dO3Hw4EH0798fc+fOVdsrVKiQb4qcUK4l+/fvL9Dxh5IPTpoy9W9RtWrVcP/994ecpzabDZUrV9aAMDfeeCO++uorrFu3LshNrmPHjvj4449x++23q5idkerWrYuWLVsGgZDffvutShtYFG3evBk9evQotLPhmjVrsHLlSsNyUlJSipRyKjo6Gv/73//QqVMnAHnf9b7++mtkZGRg7dq12LNnT6HLAhAyLWifPn1w1VVXGb538uRJPPLII2jTpo0mZZXT6USbNm1Qvnx5tS08PBxOpxOHDx8Ogl38fj/WrVtXpPqWRHrXp6KoX79+eOWVVwDkOXKOHDmy2HDmd999h1GjRmH79u1FPjY5ObnIcJep0tOmTZuC2v+KK67A5Zdf/jfVyFRhdeWVV6JevXqaOPrvv/+OX3/9tcRlm+BPPpLBZunyIgOp0ilCpkdi8FS6qEgAQMIWXJD16YroMiThC/klOzIyUgOrREREwOl0aixTGVwl8CF/XPFa9HXQu08AUIAKy5KuGYRWZDoiOmEYAT9SdJ2QaWJkABuApk3kPhKS4Hb5WoI7+vRPRjCS3sVHD7pIKEv2q16hHHy4jX2ld/2R+xHA4jF015BuP3IsEN5gW7H9JGTDH3L665TnpRMHQTC9q5E+DZf8W7ZnqH6X7Ud4ginp5L4SXpHtLevB9/WuRUZlccxKAEsPAUknKfaN7A/uI11m9G3A1/q0WxJMkj8U5Dg0khEMZORUw33159TXS7aZTLvFduZ8kXOC7U0QjGURKiNgRqhQulgZ9YW+zbh22O32oFRW7A/pGiP7RW7Tz0+WS3CQ169PrSUddGSbG8E4ch+2hR7kkf9kWbJv9PNGnld/bgAaVyRZjh44ZXoxzi/9OOR5eCzbzah/ZB/yNZ3qJPwjr0meWzqDccxxnZJArHnTyJQpU6ZMmTJlytS/RUZuClL8DXH11VcDyHNKOXfunOG+/A1Gx9DCaNu2bfjmm2/QoUMHw+/Z+qfVS6JBgwahYcOGqFKlClq1alWkYxlkKUrqjAuhjh07/mMeQsjJycG5c+dQrVq1Ih/bsGFDAFApgIqiOnXq4IYbbsCxY8c02/Wps6ZNm4YvvvjCsIzKlStr7kNyHvDp1XPnziEQCATBOaUpPfCxevVqTJs2DefPnw+q26233nrB6nGpa9q0adixYweioqIwaNAgDBo0qNhlxcTEBP3m50N3hXX9zcjIwA8//IAZM2YgJSUl5PzYt28fvvjiC8yZMwebN28OGs9A3nrTvn171KtXL99z8r7diRMnDN+XrkhSejefhIQEPProo3jsscdCHlOQjBw3du/ejblz5+LQoUOa7cePH0daWlrIsqKiokJ+nhTW2cNisaBv374FtqEpU5eSWrZsicsuuyzf+5N+vx9nz54FkAcK9e/fH+3atUOjRo2QlJSk2XfXrl2GTjp6RUdHo1q1akEpWH/99dcipdqi9u/fj3nz5uHXX38tMFVjeno60tPTcdlllwW99+eff+L333/XuLUVRvXr18fNN9+sXv/yyy/4448/8Ouvv+L48eNFSvdVvXp1QyDz2LFjIb/rlClTBtWqVQv6fJFZUajMzEykpqYiLi4O1atXDzrHhUhLGkolhTNuv/12dOzYscT1qFChAipUqFDoVJxSbdq0wb333lviOpgqnk6ePBk0XxMSEkqc3tnUhVeZMmWC7h+cOnUKJ0+eLHHZJviTj2w2m/qRIl1I+I+pa4C/0p8wOO71eoNcbyQcQ0hDOi1YLBZkZGTA4/GoVCphYWEqVYvValVgkXRhYZmxsbGIj4/XBHtZLwZ6CfBIxwjp5EPXDgJCdLnw+XxwuVzweDwqcJuZman+DgsLg8PhgNPpDEq9Q8nANt9j3QkbSRhDH1CXqcj0MAqdOAKBgMYhRKYOYttxXz3cJNsS+Cu9DduGUAxdMvRplCjWWaZCYrowAhcycK8Pguv7lymzMjIylDOL3W7XQFyEAlgWIYDMzEy4XC71oR0IBFQbSLiI4zAsLAwej0e1FfuR+7FudPdgu3Af1oP76McA68A2dbvdsNlsiImJUWCATO0loTojaIJzh20r4SgJOPAfXUt4XXIc2mw2REREqKeR+MOX6b6kQ08osIX9TycTCXixfelaZQS58ZoAKBcVuV3CPdKFSfaTPu2Svm5cW+gcJccpb8DIp7Byc3MRGxuLyMhI9cOF5fp8Pk0aKLYbARS2F+cA/+b4yszMhM/nU/nU+Y9tLR2g2Bacf4Re6OzF8clx5PP5VHvzyw/bVoJncp3hexIqYrty7LKt2WbAX/OPgA7rwr7nfJDglByDcq7LzwqZwk8CmtINSJ6foBP/1gOKHGvSGYvroVGd5Pik2xjdk+Q8k6nXWC89iCZBJdl2BBtNmTJlypQpU6ZMmbrUtW/fvgL3qVq1Ku6++24AQPny5Q2hmYoVK2LatGnqAYmXXnqpUOd/4IEH0LZtW8Myd+zYUargT8OGDdG9e3ecP38e77//fpHcZugMU758eXTr1q3U6lQYyQDdunXrMHz48It6/lC65557UL58edxyyy1FPpZuPfmlEerYsSP69OkTtH3Dhg3o2bMnDh48qNlOl56jR4/i1VdfxaRJk/J1Z5LH80l5Am6VK1cusV19QWrSpInm9Z49e/Dqq69i+/btqFq1qua9wqaZ+q+qXLlycDqduOmmm0r0W71ixYqae3xAHjgiH7QsSDt37kT9+vWxcuVKrFmzBtdff72hC1FycjL+97//YcCAAbjmmmswadKkoH2aN2+Ot99+W+P6YKTk5GT07t07KP0V782WK1euUHUH8twYXnvtNdx3332FPkbKyHHjo48+Mly39u7dG5TeRspisYSELWXKsPzUsWNHTJ06NWi+mTJ1qapz58547rnnCkyLExMTg549e+Kdd97BL7/8gr59+yI+Ph52ux3VqlXTfM4cO3YML730UoGgS0REBK666qqg72wpKSn47rvvVKqswurMmTMAgI8//hjz58/P9/jY2FjcfPPNhqkLAWDu3LlYs2ZNodcGIA9kevDBB5W73+TJk9GiRQt8/vnn2Lx5c1BKrfxkt9sNnQ9TU1Oxa9cuw2NsNhsSEhLQqFEjzfacnJygzzKu5+fOncOpU6c07x07dkylVLzQevrpp/Hkk0+WqIw///yz0K5t+alp06aoX78+KleuXORjo6Oj8dBDD5lQ6N8kt9uNPXv2aGJAdrs9XxdAU/8MVa9eHeXKlcOBAwcwZswYzJs3D5s3b1buuCWRCf7kIwZPGRiVqbP0bgZAXmDU4XAoipQOI3TRkelO9HCO1+tVKVN4LKEKbpNuLtIdiGWVLVsW8fHxGoiD5wb+AiGYw5NQEVPn2Gw22O12WK1WjcsJHTx8Ph/cbjfcbreCNxjIB6DS7/Dv3NxcTVosvTMJ68Z/EtgxctrQgyTS/YNty7Q3Ho9H00ZMiSMBI1kXCV9JaIhgA/dh6i3pihMK/GD6MNn3dLwICwtT160PvEvQiXBNeHi4SrnGYD3L5fVHRkYqZyK+RziLbcF0Zfp0XQQsfD6fqod00mHZHNPSZUUPCLD+nDsShmC/s1yXy4UdO3bg5MmT8Pv9mlRJeqCIdeL7HH8sT6YtIuAjz8XjJbDA961WK2JjY9UccDqdCm6KiIhQNzElFGEEIvH6WT7nkbwmCX8YOb/IuSqBPTn3JVQkzysBJdZXpgZjH0v4iO3E1IJsW4vFotoxLCwMdrtdASPsT0KJ8nplu3I+SsBRQjEcJxEREbBarcrxR0Jw7DfaRHNuSkCRbmcANGM0MjISx48fV/Xyer1qTrD8zMxM5azGtpUQkGwnud7IdVjWVTqzsW1lX0vAhnCPHCtWq1WtwZTeQY7rA2+aSUiK9eZaxzFEqFFCSJyf7K/c3Fz4fD5DdzWChVwH+HnIOunhKblGyDbi2iehq+JY0psyZcqUKVOmTJky9U/RU089hWrVqhV4897n86nUDtRLL72Etm3bqtdNmzbFkiVL1O+LBg0aYNiwYfmWy9+4X331FUaNGhX0/r59+9ChQ4dCX09htHv3bgwdOhQ2mw0TJkwoUkBn1apVyM7Oxs8//3xR023dc889mqe4z507hwcffBB9+/a9aHUIpcWLFwOASs1dWA0aNAjNmzfH7NmzUadOnZD7PfLII+jZs6fhex6PB+PGjdNsmzlzJt544w1s2LAB//vf/zSux3q53W488cQT6vXGjRuRkpKCGjVqoH379ihfvryCgC6EEhISgpxgtmzZAiAvWCufePb7/cVyUvgvqWzZsuqBOP39rqKofv36OHbsmEqtBQAOh0PzwF1Batq0KXbv3o2cnBz8+OOPuPzyywt0bJowYUKBayYArFixwnAs7N69G7Nnzw5a0yZMmIClS5cWqt56yYf6iqKirAWNGzdGdHR0yPfDwsJCtjvvERZWpnOzqX+Lhg8fHgSKhFJcXByefvrpIBeNWrVqBX3+zpo1q1CpWuLi4nD11Vere+vUzJkzsWrVqiKlbZKfszNnzsSKFSsKPPfNN99s6AR27tw5LF68uMgpuuLi4jB48GAFgKSmpmL37t349ddf83UkM1Lnzp01rx9++GGMGDECNxqkWwSA06dP45lnnsHmzZs128+cORO0nh8/flx9/+zSpQtGjhwJIO8zLyUlpVgOisXRlClTsGbNmmIfn5OTg99//13zOVtc1ahRAw0aNAhyRyqsDhw4YOi2Z8pYTz75JPr374+rrroq38/uwmrDhg04fPiwZlvdunVLXK6pC6vs7GwsWrQIBw4cwBdffIE77rgD7777bkhH4KLIBH/yUW5uLqxWKxwOB6KiouD3+zUuCzKAy0Annxyx2+3KwYGB06ioKBU4ZZCZP56Z85POD4RQ6K7A/aTLCm8EMbguQRoGnAFogBoJAlksFsTExCinoPj4eFgsFgU70DFCwhYej0cFz/XODWFhYXA6nZoUQPzHYLV0YeHxrB+D0frUXwDUUyLSAUbCDNnZ2ZpAMh2VWLdAIBDSulRCFgy8x8TEIC4uTgMCcT+/34/09HS4XC4Fq8h6yrQ3Eu7Ru6Aw8O5wOBRsoP/HMgHA6/XC5XLB5XKpa2N76AEX4C9nFL/fD7fbDavVCp/PB4/Ho8Yg60TIhWmb2L6sF9uPABCdg/jlUDqQGMEJhAvYv3JeBAIBpKWlafpSOoiwLLYbx4FMv6V3QJEgA+sn4SQJzElHIzopSXepQCCgtulTicm+5liS64EEHqSDjBE8JF1S5HhimfpxKFNb6dcjurAQ3GFd+LSBnG+Ekdjv+jR+2dnZCmBzOBwKriKconfJknCQHFtWq1VBcxLe4Y1N+eSX3imLaQ953dHR0bDZbAosjIyMRFpaGs6fP68BXNLT09UYZL3ZLlwneN1sN+lEI91s2Layn/VgG9uDLkg8Tr/2SAcoXpN0yuE6zx94rCv7hcezP6XTjhzfdDzy+Xzwer1q7rDd2e8E3uRNJTkeec0SrmM7ejwe9TklIZ9QTl2EPPXp0UyZMmXKlClTpkyZuhTVrVs3dOnSBR999BHWr1+PIUOGhEyx8P3332Pw4MEat4uOHTviu+++w4ABAwAAkyZNUqmbgLzv+jfddFPI87dv314FlUMFfocPH14qluFSU6dOxWuvvYbvv/8e06ZNK/Rx1apVw7hx49T9soupVatWYdmyZep18+bNUb58+RI/bV0aOnToELZs2YK1a9fmC9noFR4ejrNnz2Lw4MGoX7++Gkd6denSpUjuSp9++il++ukn7N27t8B9LRYLPv/8c+VMtWvXLpw4cQJOpxPz588HAKxdu7bQ5y6qUlJSNA4AGRkZygXJ5XLhk08+UY5DMgW8KWM5nU7NPZ7iqmHDhtizZ4/G7WnHjh148cUXDR159HI4HPD5fPjmm2+Qm5uLpUuXYseOHejevbvh/jabDW+99RYefPBBdY9dujwsWbIEH374obrPMnny5CDnnG+++SZksLxt27b5rsUXQoV1p4qOjsbUqVPzrV8gEEBKSorhez6fz3B7KLePyMjIQsMSpkz9U9W1a1c0b968xOVUrlwZFStW1Gxzu90h55teHTp0QJs2bYK2z5o1KwhiyU9t27bF448/DgBYtGgRZsyYETJlIdWyZUvccccdhu999913xXKduPXWW/HYY49ptm3ZskV9LhdWcXFx6Nevn3q9aNGifL/L7tu3T6UYlcrNzcX69evVsYcOHcLzzz+v6pOVlYVTp05h8uTJmDFjBubMmYPt27cXqa7F1ZVXXlloBzwjhYeHw2q1FhmqMlK1atUM06sVRoFAAIcPHy6yS9V/WRUrVsRzzz2HSZMmlUqqtjVr1mhgw9WrVwfNQ1P/PH366ad48cUXcdttt6k1qbQyY5jgTz6ScAXwVyDUKGApnSeAvKd0nE6nSs8kYZjc3FxNYJqOL4QwoqKiVGCVgIwEeXg8nUAYxE5LS4PX61WLPvBXGhYGpBnstdlsKvBPxx/eLGJgVzqVMIDLY2QqKtk+0dHRcDgcKvDNfwxWy9Q9rD+vTUJEwF9BdroLyTbRlycBgIyMDNhsNg2kwXRQbH+2jfzBL4EkGdiX0BRhHSDvBxBBHL2rEcvj2JBBdglTEYiRx0hAhWPD7/crNym2HduIZdKBg44d7HO73a5SxfFaCBCwveWNJYJjsh8I8dABSu/swjaWUIKEf3jtRu5OvEYpmU4N0DqesJ1knXhO9pestx7UkyCMBHEIFbFtuT/TpenbSrq0EB6R40APQulTIMkyJPgjARJ92jqWQ8m6si/k+NI7i0m3K8JXhDBku/O62Z/sI/aD1+tV7c15zPRhBCaBv55W5LgNCwtTN6IlRCP7TAJhXq9XfdhxzSxbtixiY2OV81hYWBjcbjeSk5ORlpamxjqvx+l0qhtG7AOusXQGk3OXFqDSGUvONz1QKV3h2JdyreIY0MNgsk9lGxBylC5c6enpCuaR9ZLzQ14D18CwsDD4/X7l8MZ/XAv06ynXOI4Deb28frqeEQDVO/vI65Xrtlyn9enETJkyZcqUKVOmTJm6FFWxYkWMHz8ejRs3xk8//QQgL8XCjh07DPefPHmy4c1dh8OBbt264YUXXtBAP0Ae2LNt2zYMGzYMPXr0CLLgP3LkiAZmkb/l//e//+GLL74o1RRf1Ndff423334b9957b5FuUB49elTdU6lcuTJatGhR6nULpT///BNDhw5Vrz/++GMAecDUkCFD0KVLl4tWFyPde++9eO+997BgwQJ07dq1UMdMnToV0dHR6NatGzIzM9GuXTvD/RwOB77//nuNu1RB+uGHH7Bw4cIC9/N6vahZsyY2bdoEIO93ONNk2O12pKSk4P333y/0eYujnj174rfffgOQdw/R5XKp93788UeVhk86Opsy1rBhw/DEE0/A5/OVKJhYsWJFHDhwQJPuZu/evYV2+eL9IKZGbN26NWJiYkICJxUrVsR1110HIO+eSHJysgYSmjx5Mh5//HEcOHAAa9aswVdffYVFixZp0rz8/vvvyvmBatWqFQYOHBiUMu5iqDDgHZAHGRTkuhYdHa2gAKnPPvvMEH5cu3Ytbr/9dsPPs4kTJ2LRokVBLiWmTF0K6tq1KwYMGIDHH3+82O4mUlarFb169cJrr72m1qcKFSoUuuyrrrpKpceSmjZtGubMmRMSzDPSgw8+iAceeABA3ve0+fPn57uOZ2dnh0wBePLkSfW5XhRFRETg9ttvD1ozp06divfeew/Lly8Pma5Lr7Zt26qHzo8ePYrvv/8+5FrXqFGjkN+dFi9ejI0bNyogq3bt2uq9kydP4oMPPsCAAQPw2GOPYebMmaUOy4fSvffei9atW5eojEcffRTTp08vcV08Hk+xvx9ZrVYzzVcRlJSUhNq1ayMpKQm///57kVLqhdLWrVtVer7k5GQF3pu6dJSamlqq5ZngTz6i84ze5QH4Cx7Qu2xIRxcZ/Af+CtJLUIYuKoRMpBOJhDEYSCVkIFO7SJCBcBHL57GEJOQ/CRNJBwk6N/B9Cb/o03HJtmAZBJj04JKUdLnQgxT69mdZevcgvVMKYSXZPtJBhQHr/GwS9XWR9ZHuLawLn37RB95luxAOkOXw+iWIJM/LcUdHEQDKuUSWoXe1ycrKUgAG20KCWkzNJAEC/Vhj2+vrxHOECtjrIRY5P2R7yuNZhr486aJDuEkPBxnVoyAbXNlW8polrCNTKkkgRu84Ja9NzkfpwsO21TsY6eEf2V7SwYl9KGEv2efyXHr3HznP9WAR24pjRtaXx7LOEiSUacf0/QNAgV3yHKwzkDeuYmNjg1x1WLb8m+NTglB0HiKUxHp4vV6kpKRoACe+T7CJ40WmIZRzWtZTpiaTdTT6LJBjWrahdPzRzyGj+STHpQQhJZDIftanztOPJcKA/EzgeJLpyfRuQXLNl/vLsmVfG8GO3C7XaY4vua8RNGjKlClTpkyZMmXK1KWmq6++GgkJCYiJicGZM2cA5LlOJCUlBe2bkpKCn3/+2TClApAHn/To0SNo+8qVKwEA/fv3x6BBgzBkyBA88sgjuPbaaxEREYHff/9dualkZWVh9erVOHbsGDweDz766KOgFE7/BB04cAAA8Ntvv2HDhg1/Wz1kKocBAwZg4MCBhn13sbR3714sWLAATZo00aSDy08+nw+pqamoXbs20tLSUK9ePcNgfNu2bdGqVSu0a9cOCQkJha7Txo0bC9wnKysLa9as0aRBki4De/fuxY8//ljocxZHp06dwpw5cwBAA5pQvMcVHh5eaBeV/7JWrFih3JuLq+joaLUuFkd169ZFYmIisrKy4HA40Lp1a5Vip2bNmkH7Hzt2TAWEt2/fjt27d2Pbtm3q/SVLlgAALrvsMpUKDtDCNR06dAi6Z9y4ceOQTloXUgcPHsT48eMLtW+1atWC7qsYOYXccMMNQdtCOSjxYTkjsJP3q0yIztSlqCpVqmDgwIGG86E4atSoEdq3b4/bbrsNl19+OYC874L79+8v9P3OUCl59u7dq4ETC9IVV1yhcQ/avXs3jh8/HnL/ffv2hYTVgbz0QUVJ5UqVK1cOTZo00WybPHkyxo4di/Hjxxf6s6FatWq47LLL1Ov9+/eHbI/Y2FhUrlzZ0Pnyzz//xIEDB3DmzBlUqFABcXFxQftER0dj6NCh6N69Oxo0aFCo+pVUV199dak8EFunTp0S11nGHoqjBg0aGAJspoJVpkwZJCQkwGq1qlhtaSg1NRUulwvh4eGIj48vlTJNXboywZ98ZLFYVNoY6YrAgKaEJuh0AfyV4oagSW5uLrxeLzIyMjRuEy6XCw6HAzabTW2nAwytVTMyMuD3+1WaF6aikosxU7VERkYq6IM0sAzkEgKRwV/gLwCA55DQgN1uV3W02+2wWq0qHZIM0rNtmCaMUIxMg8O2ojMNnYX4o0oPJtENhmnTAoEAfD6fph8IBISHh6ucxLw+mU6KgXiXy4Xz58/D6/VqIAlegwQ8ZKBbDwHQaYl9SucRGdCnWA+mxmF6MOmyIYElGQinI1N4eDg8Ho/aX0IN4eHhsNlsig7ltWZlZSEhIQFVqlRRdWMf0rXF7/ertmYby7El4QDWSYJgHLcybZR0MqFYvnT74fWy/tI1RQ9i8IOQ52EdOb64L0EpOsZwP6vVqtqRkIMEn6TDjgSyJLQg5wrHinQ3kUCEvGZ9ii5ZHs8l3WKYXk3Wj8rIyNDAIBIakU4xdG7RQ0WU3MY2yc3Nhc/nUy4xhNo4/4G8J+dsNpsGfuONM6fTqcYXxyj/Z1/IuSDnqn5OZ2RkqPEtHaiioqKU0xbPLwEeef1se4KIdP6x2+1qHeHaQhiIbWOz2dRaLLfLfpJ9xfaXa63sQ/38ljCPdKmSa4aEF7m+EeiT41Aez7rQtUem2pLzjWkrCQD6fD7NOsw68nOLn038DNE7d8kxyP7mWJJzhOuVHvwpaj55U6ZMmTJlypQpU6b+Cbr11lvVd/ERI0YAyLPnNgpKLF26FNdcc416rU+hU7lyZUMni19//RXbt29H/fr1ERMTg/79+2P06NH4/vvvsW3bNixbtgx16tTBli1b8Mknn+DGG29EixYt0LlzZyQnJ/+tYE0oLV++HNnZ2X9b3cqUKYPIyEjcfvvtalujRo3QuXPnkCkviqsRI0bgoYceKtIxb7zxRpHSexw6dAjDhg2Dw+HAp59+avj76pdffsETTzwBq9WqApOlpfDwcGzdulWzbf369ervMWPGlMrTzAXp9ddfx6FDh5CWlobvvvsOL774onqP57darUEpWf4LGjRoEDp06FDo/WvWrInc3DxX9xtvvLFY5zx37hxmzpwZ8v1QwAmQ9zT8p59+ijVr1mDcuHHwer1YtmyZukd56623Bh1DUOWPP/7AypUr0bVrV0ycOBFA3nj0eDzo2rUrdu3ahcmTJ6vjrrjiCvW30Rp82223FduZ7JtvvsGGDRtw6tSpkGnTDh06ZOjosXXrVhw+fLjAczzwwANYtmyZZl4fO3YM11xzTVC6mpiYGDzzzDO4++67UaNGDaxfvz4kCMj7o6HqHR8fj19//RVTpkwpsI6mTP1TVLVqVdSrVw8tWrQo8OHhoshqtSIpKQmXX345nnjiCQwaNAgTJ04stIPDt99+a7h90aJFWLRoUaHrERUVhX79+uHee+8FAHz++ef45ptvDPf1eDyYO3cuEhMTUaZMGcN9Dh48iG3bthkCtfnJarXiuuuuC4JRjhw5ggMHDhQaiKpduzYee+wxvPDCCwDy0p8tWbIEJ0+exObNmxXcmZaWhh9++AFOpxPly5c3LGvRokVYvnw5EhMTUb169aD3n332WQwdOhQ333xzidJcFkUxMTElLsPn82H//v35Al6FUUpKSomve/jw4UHOpaaCVbt2bVSuXFmlmSutuMj/Y++8w6Mo1zZ+b8pmSxoplNCRLlIUAQHpCBYQUVSOIiqIDVDBdr5jOdaDHQVEARUbiIogooKCIChNujTpvQRC2vZks98f8X59ZnY32YTQdO7rypVkduadd962s/v85n62bduG/fv3IykpCRkZGQbo/g9XTOm7/HMlg7F6BwwGVePi4uD1euF0OjVBbwab+SPTZ0nHDqvVqoLdFosFPp8PLpcL8fHxyl0jJydHBcxZJwkm2O12mM1mBRB5vV4EAgHY7XaNc4vekYKvMXDLm3oJwjCAzXoTMJKpf2SgvaCgACaTCTabDT6fDzabTePKIl1TJLDBALh0HtL3QyAQgMfjUYFu2T8EAHw+nwIAZHoh6T7BOvr9fiQkJMDn86GoqEgBMdKdQu9YEggEVPoaoPhLvaysLKSmpqr+DeVuxPPx3OwPCcTI8oFiKMTtdiM/P1+NCwmiFRYWKvCI7ZGfn4+EhAR1PkINhDMYvN+0aRPi4+NRtWpVNQ78fj+cTifsdjucTifMZrOGDmUfWywWNf4IzLCNOPZDXb/cj+3EscD2j4qKUm2kh9MIWbA/7Xa7AiL0Kefo7BQXFwebzabageeXrljSzYoghQQuJLSmr5ccG7JPZV1YZzne5RNEHC/yXPI4CVHx2gl+6IEe7q93JpMABseB2+1GUlISDhw4oNY3zuu8vDxER0cjKSlJAUR+vx8+n0+1j9PpVF+Ysk4StisoKFAppbxeryr70KFDah3hWsO5I9tEAlV0+SkoKFBfmFSpUkXBTkAxmS/BTP0847w4fvy46mu9A5psR/YZ2046ikloiukFOT84Tjg3uW4SutGn/yIQI8cSwUgChjyO1yOhHulqJKEynj8mJkbNAc5zvcMU681xpYcL5RgnsMTr1TsZsR25FkuwS74PyGP1a4YhQ4YMGTJkyJAhQ+eDunbtqv7mwxIAcNddd2HLli3q/8mTJ8Nut2tSEHzzzTdYuHAhXn311bAuQMBfgegZM2bg5ptvRoMGDXDRRRdh5syZaNasGZo1a4Zjx45h4sSJeOaZZwAUpy04U2kKyqPVq1djx44d6N+/P5599tkKLTstLU3j5BNKw4YNw6BBg5CYmKjZXlRUhBtuuAETJ06ssPoMHjwY27ZtwzvvvBPxMWU9/1tvvYWaNWsiISEh7HlGjBiBgwcPYvTo0ZrtaWlpeOqppzBy5MgynVMqKioqCHZbu3Yt3nrrLdStW1c58ZwJDRo0CMOGDUOzZs1w8uRJtX3atGlYtmwZmjdvjs8+++yM1edcUZUqVTB27NiIHVoyMjJgNpvx1FNPKdexsqqk9CX3338/xowZE3ZszJw5EzVr1kSnTp2wd+9eAMXfl+Tm5iI6Ohpt27ZVqfqoFi1aYMSIESgsLERWVhb8fj82btyI6OhoDB8+HEBx+sOpU6cqh4u77rpLA4KFSq0VLrVYacrPz8eoUaOwf/9+NG7cGJ9++mmQA8a0adMwcuRItGrVCrVr11agEgCNy0VJOn78uCal0O7du9GmTRtUrVo1pDPSyy+/jKKiIuTm5oYN9gPFqYpee+01dOzYMeTrcXFxqFu3LtatW4du3bph586dKsWfIUPnqq644gr07NnztJSdkZGB5557Dl6vFwcPHkR0dDSOHTtWqsvejz/+iK+++irs6+HmYEl65JFHcPToUSxYsABfffUVHnzwwaB7HrvdjjFjxiAQCODFF1/UwLJSd911Fxo3boxmzZqhY8eOEbffgw8+iDvvvBPr1q3DSy+9pJz/1q1bh3nz5qF79+6lllG1alXceeedKCgoQGZmJn744QeMGDECU6dOhcvlwqBBg7Bt2zYsWbJE3TuFe59bvHgx4uPjEQgEQt5nvfzyy/jyyy+DgMnTqYULF6Jp06anVIbVakWfPn3Qp08ffPzxx+Uu59ChQ8jKyirxfaE0bd++Hbt37y738f8UVatWDZUrV4bNZsOAAQPg9Xqxdu1aHDt27JTK3bdvH/744w9kZWVh9uzZZUoTaOjvJwP8KUEyOEpnAxks5T50t0lOTtZAMwzUEmKRv91utwrIOhwOAFCuFA6HA7GxsQrAIGQhIRTCAwzW06GCQXQJL9CZxmazISYmRtWDUI5MpcQPC9LlAtA6ndBBRQI6MnUYIRPp8CAdUiTowrJ5TXFxcRo4RtaBbed0OjWOQBLEYKCZYAHLlXAL24cOJ3QtYZBbBuz5I9OX6evs9XqRlZWFgoIC9eYYKo2NrI8MgPM1jhuCW/oAPACNcwqBMpablJSEw4cPIz4+XrVPXl6ecv6JjY3F8ePHVV8QKmC7+Xw+WCwWmEwmlQ+9Ro0aGmCBfS2D/9zGOcLyQkEp0mGG5eiBAzpiybEnXXOioqLUWJYuJexfCcrIduTfEphg20vwgmCbBBfYVxJAkmmiWCbbQabPkqmZ2O9yXEgQTtZXjiMJ/sj25HVJ9zE5ZiQAI6+PDl55eXlqXkVHRyMhIQEOhwNOpxMJCQka6AaAct+RMJ6EjFg+j4uOjtaAWRJwk44w0lmH44PwENesEydOBJHncixJAE6fois+Ph4+nw95eXlqzeQ+7DPpHCRhLf0Y5TUShtL/zzIIIklgjD/SPUi2o8lkUmuShBbleGE/cIzJ9UXChxwHCQkJah2WYvk2m00Bh+w7Caax7+U8kE4/ckzr53lhYaFyL+J1si0LCgo0TkGGDBkyZMiQIUOGDJ1v+uqrr3DRRRcB0AaMt27dqh7K2bZtG+6//37l4AkAw4cPx4oVK7BmzRoMHToUDRs2LPWp3zlz5gAoTpM1dOhQAMVP5zqdTnTt2hW7du06HZd42vTZZ5/h6aefRteuXcsNFuiVnJyMsWPHYvv27SUCRbt370a1atU0wbhjx47h0UcfrZCnr6UmT56M//73v8jIyNCkv6pIlRQwpG677TasWrVKAxZ06tQJfr8fXcI4urz66qt4+OGHSyy3b9++eP755/H6669rtq9bty7IBehMaNmyZVi2bBmAYhctqiyOCX9HVa1aFVFRUahXr15EQTmm1AJQKkhXHqWkpAQ9NEndcsstaN++vfpunLrjjjtQVFSEAQMGYOLEiZq1Y/jw4RgzZgxq1qyJ7Oxsdcxjjz2m/n7llVdQs2ZNjB07Vm3TB1xDpdvh97wjR45EzZo18cgjj0R0jSdOnFAgTO3atbFx40YEAgFccsklah8+TLpgwQJ069ZN8yBf7dq1kZqaiqysrLDniI2NxSOPPKIAn40bN6Jv377qmLlz5+Jf//pX0HFRUVGlBncbNGiA2NhYHD16tMQUiP3798ell16K//u//8Mnn3xSYpmGDJ1tDRw4MCLXu59++gkLFy5Eq1atUKtWLdSpUwdJSUkayC6c4uLicMEFF+Cee+6JKN1O9erVcfPNN2P8+PFBr9ntdo0rWaRq3rw5Hn30USxYsAAbNmxQLmihZDKZ0L9/f3z66afYunVr0OtbtmzBli1bUL9+fVSuXDniOsTGxiI1NRUNGjRAmzZtcPDgQezevRs+nw+vvvoqbDYbWrRogf79+4ctg/Ga2NhYTJ48GYcPH0ZmZibefvttTJ48Gf/+97/VNVCtW7fG1Vdfjf3792PWrFmae6+5c+dixYoVId/X8vPzzyj0A6DCnF5MJhP+7//+D3FxcZr7vLJo2bJlGDx4MC6++GLUrFkTQ4cOLVNqWADYv3+/iikaCq/KlSurlHSVK1fGqFGjUL9+fYwcORL79u0rd7nHjh3D0qVLMX36dMUbGPrnykj1VYKkYwRz7RECYtCYQefU1FQVIJeuMjLVD0GNnJwcFQxnIJ3wEJ1J6FaSkJAAq9Wqgroul0udg3BQXFwcYmNjFQBEGy/pflFYWKgC+gRmpHONTOFClxS73Y74+HjlKCRTxcg3VJkiS6a04TH8oMZ99EFeCcP4fD7ldsG2l6AJ05m5XC5NwJ2wEV2XbDabAg/YDtKhiG4aPp9PwTSsv4RA2B4y1RTbWqamYtsSZuDxgUBAtSXrp09/xt9er1el3yJ8BECl1UlMTFSOKrIfoqKiVJo2ugAx6J6bm4vMzExkZ2erJ/7YnxwDHNfAXw5UMg0YIQ99Kjaen8AU20Omb5Mwh6yzBBoAaFJa6R1JZFsSaCkoKFDHhypbwmwy9RJ/2P8FBQUqPZ50imFfS0k4RQJB/J/nI0DC8jjHJaRBhUoBJUEOma5KD41J+EhfT457mcpM1pkAU0pKinLkka42TI1GwJDrBl3PCM5Jly1CWxwHhPgkTKOHkQKBYocy6b5DII197HK5EB0djby8PLVGWq1WzfwhIGSz2TQuQCyTazLHNOcgoRSWxWNDjVFeo2xvtrVebAuuF9I5SEIwPCfblT8cPxIO45iU41TWkWsuzyvTJLINZPo4OZ71zkd6cI7nYv3kOqhvQ/lbjmk5R2WdOecNGTJkyJAhQ4YMGTrfNGPGDBVQWLVqlea1N998E0eOHMGnn36Ku+++GwCQl5eH999/HxMmTMCaNWsAAL1798bTTz+tjvP5fNiyZQvy8vLUffn06dMxbdo0tU/fvn0RCATQp08f1KpV67yDfgDg/fffx6pVq0oM+JRVOTk5+Pnnn3HbbbehdevWYfebN28eFi9erNn2ww8/4KOPPgpyETlVvfHGG1i0aBGGDx+Oxo0bnzXb/fbt2+Pmm2/WbBswYIAKvOl1ySWXYODAgSFBCKnVq1eX2NZnU4cOHTrbVThnVKNGDcTFxaFevXoR7S+hKQnSlFW33357SLAsMTEx5ANAbdq0wauvvgoAcLlcGqeL559/HhkZGUhISEClSpUwbNgw9drPP/+Me+65p8S63nffffjwww8121544YWg/Xr06KH+tlgssFgs+OOPPzB//nw8+uijQWt9OEnXtfnz5+OOO+4ISvsjg7579+7VQD6VKlXCFVdcUeI5HnvsMXTq1En9v2fPHhU0HDp0qOZ9ozx68MEHNfBUONntdnTp0gUPPPDAKZ3PkKGKVlJSkub/Tz75JKJUmjExMVi+fDkGDBiAtm3bomPHjrj55pvh8XgiPnck0A9QDCCGSzN6+eWXlxsa7tKli0rxOHnyZOzevTts/S+44ALcdNNNJZYXFRWFKlWqlLketWvXxgsvvIClS5fi+++/V9ufffZZbN68uUxpxDIyMtCiRYsg96KOHTvi22+/xa233oorrrgCt956K1544QV069YtqIzTAbOWR4MHD8bAgQMrrLwNGzYoN7vy6MSJE1i2bBn++OMPpKSklPl+1eVyGW4/EUqaJ1A5OTlBscjyaPLkyQb0YwiAAf6UKBnMl2lMpPsNX5eOMAzw6wOoBFvoZsI3NgbcCfRwkjNYSkiDgV8JA9CtRDrbSHcHvQsGA+oAVJCdkJIEJvQOEfqgt3wyQ+/sIs8rFywG/Vmevq3lNet/JITFtmfd9c5MBGX0dZTBdLa1TJkjHYsIRsj2ZDCdgXx9fTweDxwOhwIZWCbbgf2nBzhYH4/Ho0AJvZNHXFwcrFarpj1YbiAQgNPpVKnNGJD3eDzqQyVThVmtVo1bjs/nU2OW/SDTLsl+koBKqLEgx1yoMaEHKOR2CWeFS5emh1xYZwn4yPrpx46slz59liyX84rXxDEpQYZQcATHOI/Tgxn68+rbJhQYxOsL165ynHGs6eso95djzmazKbqY65fb7dZARrK9ZFtJNyNCeXKOch5K5xjgL4er2NhYWCwWBelxjhGc4noo+18Pe8nxQHcxCQLKeUawj2sk561MYyXBJ70TjX7OUhIwk/0n11G5ZkvwRfaLfrt8D5Ftqt9Pzh0J8ejBN7mmhBqDck7qwTLZB1x35P88hxyPeviR+wMIem8JNR8MGTJkyJAhQ4YMGToflJ6eroIPF154oea1X375BdOmTcPGjRuRmZkJoNgCX59e4MSJE5onqPft24f3338fs2bNwtKlSwEUQylSc+fORW5uLo4fP17h13SmdODAAWzcuBHNmzcv0xPkpWn+/PmoW7cuOnfuHHYfp9OJzMxM5QwDBKf4GTlyZMiAUVmVn5+PNWvWoGPHjrj//vsjTt9TkRowYEBQKi6gOMXEjz/+GPIz2WWXXYaMjAy0adOmxLIPHz6MOnXqVLhTkqGKFftHD3+F07Zt2zTO4+XVv/71Lw2gQ4X6zhAArrvuOuUus2HDBnz99dfqtaysLDgcDtSqVQv79+/XpLHy+/0lus2kpaWhoKAgKOCflJQUVA8J/sTHx8Pr9WLs2LEKJEtNTS3pkpXosC2lD+rJh6BcLhcOHz6sCfLXrl27xHPov0OUAe3u3bvjrrvuiqiuQGjnifj4+CBYKZRSUlIwYMCAoPfB0iTTrBkyVNGqWrUqWrVqpdm2bNkyrF27ttRjmzRpooFad+zYgdmzZ2P+/PkVXk8AqFmzZhCkBBTfN4Z6/45EsbGx6hrmzZuHDz/8EPPmzcNvv/0Wct/S3sePHDlySu8HaWlpqFmzJmrVqqW27d69G4sXL8aePXtCrpmhxAeLpVq2bIlu3bqhTZs2aNWqFSpVqgSTyRSRQ9PZkNVqRfPmzTXueqcqCX6eigh4lRX88fl8BnASoULNIxlzMWRIr9jYWNSrVy8oo0dJMsCfEiTBHwZMZXC0qKhIQSaxsbHwer0KnCDgIwPj0qKUriUulwsAlGuLy+XSuPu4XC4UFRUpYIPBcoJAFotFBfsJIMkBwDrHxMTA6/XC4XAgKysLOTk5Cniha4cM2MqAOOvHa5Dpb+gAIYPVZrMZNptN42ojU32xLBkkjoqKUm45hAZC/ZZBf6fTCZ/PFxT4tlqtMJvNqi94DqZY4+/c3FyVBo0AkHSzsNvtykEpLi5OwTvSFYM3GoFAAHl5eTh+/LiCfyQEQvcNm82maRs6cbA+DocDBQUFGoAsISEBSUlJiI+P16RfMpvN8Pv98Hg82L9/P/x+Pxo0aACz2YyjR4+qD4snT55EdHS06her1Yq0tDTlrMRr53mZho7pxyRwwfEl20D2jRx3nCdyPoUCcfQgB0EN+QGW9WCbsR3oNiPHP8ECCT7weAkYSWhBpqKSYAvrRUhE1lteqx7SkBCUTHEnQTEeGwokoni8nIMyzRrrJ2EPmZZQppmSEBKdrmJiYpCYmIj4+Hg4nU51k52YmAir1aoZ3+wHCVVJRx9ZZ84Nrom8Vo4l9pXFYoHdblfwD8cJ4TlZZ85nmTKR48Fms6l1ke0gARWmN6NDGsuOi4tT9WTfsA0ljCVBKDmuuV3CQlISIOTaIdP66fua65l01+HaQUhPjgU5RrjGRkUVpzqU62xJqbTk8SxD70Ak5yPbSYJHrBt/uI/8IMb9CTKyvfSAqCFDhgwZMmTIkCFD54uqV6+O+Ph4eDweXHnllZrX5s+fj4cffhhz5szB559/DgD46KOPggLPbdu2xahRo+B2u/H444/jm2++wWuvvYbbb78dXbt2xe7du4OC9Y888gg8Hk9E6SrOVV1yySWoW7cu2rRpU6FPPO/fvx/z5s3DAw88oFLV6FVUVIR7770XHTp0UE8nDxw4EBaLBUCxe8Vzzz0XsTtKaZo4cSIuv/xyDBs2rFQHj4rWSy+9hA8++ED9X6tWLfXE/ezZs8MeN2LECACIqG8aNmyInj17nlpFDZ1WMV1Hw4YNIwosvfLKKxg3bhz8fj9yc3PLfd6LLroINWrUCNpOp3uppk2b4vHHH1f/u1wunDx5EkBxUHfcuHE4cuQI3nvvPSxcuBAXXXQRmjRpggcffBA///xzifV47rnngpzTAGDUqFFBD3g98MADyukqKSkJl112Gd555x04nU4AWjekkrR3796gbfpgtVyfjh49ipYtW+Lqq68GUBzEnTt3btjyR4wYodI+AsBVV12FSZMmqf9btmwZNrWPXllZWbjppptw4MABzfaaNWti69atQX0VSomJiYiOjsbQoUNDuojpVa1aNbz//vtB752GDOn14osv4p577onYXc5qteKee+7B008/jRtuuEEDmhDA3rhxY4llJCQkqIdlpaZNm4bt27eX7QJKUVFREebMmRNyrd29e7daBwHg+PHjOHz4cInf80p17NgRXbp0gd/vx7PPPov7778/pCuMyWRCjx49ULNmzbBl/fzzz+WGkKgaNWpo7oOmTp2KK664Aq+++mrEzkb79u0LWquioqJw4sQJDBo0CL1791bQSlpaWkTr0ZnWY489hiFDhlRYedu2bcP3339/yv0DAPXq1SsXkJSTk4MdO3ac8vn/CXI4HEEp0bxe7zkLqhk6+2rVqhWmT5+OFStWRHzMaQV/TCbTXpPJ9LvJZFpvMpmCfPRMJlNjk8m03GQyeU0m08O615JNJtOXJpNpm8lk2moymS77c/srf27baDKZZplMpuQ/t9cxmUzuP8+13mQyvVMB9dcE7mXwmYFlBqgZOGXwlsdzfwbnbTYbEhMTkZqaitTUVJWSisHuvLw8ldpGn2IpPj4eSUlJqFSpkgqUM5hMRx/CG8Bf8IUMVjOAy0CtDPryOAlByOCxhApkiht9SrHCwkLlbsM6MGWWTKFUUFAAp9OpUmQxzRcD5YQ49IFhnr+wsBD5+fkaJw6/34/Y2FhYrVYV/Oa+BQUFyM3NRX5+vqqnx+PRBKwZOOe184eQFesTCl4JBALweDzIzs5GXl6exgmFcBide+Lj4zXBe4fDgby8PLXwu91u1SfR0dFISkpS6cJsNpsCwwgYZWRkqH6Mi4vDli1bsHPnTnVN2dnZsFgsqryqVasiMTERJpMJubm5CuBgmwBQ/UFJQEbCDhK8kQAOx1yoOSWhDjnG5XiVwAVhBJkSiv2tBxUIb8hxQ0BJOjfJ+nI/h8MBt9utxgvP5/f7YbPZkJycrAHt9K5CclzIa5ZzSB4XDhgJNbb06dX0wA3PwTVBzgsJIzG1WUFBAex2O9LT02GxWFR6Qb/fr6Afua5xHrC9CA3yRwIicm7zGI/Ho+lHACrFIVOLcV3itbCdOU8JRzocDgXJsF7SEUfCRnL9YdpECTkWFBQgPz9frTscn/q+IRgjHcLY7npYSK5V8oevy/6Q7ce/uZ5LKI5wpt45Rw8jMQ2bhJj065mcvwR09NCPvF6ul3JMSgiNc5RjVe4j25rtyPEk6/lP0fl+X2TIkCFDhgwZMlSROt/vjejGsG7dOgwfPjzsfu3atcPatWsxZcqUoNdWrlyJ7OxsvPPOO3jppZcwevRo9VpRURF+/fVXdO3aNei4X3/9Fb179w76zF2RGj58eKmpH8qr5s2bo3Xr1rBYLLj++usrtOzXXnsNRUVF+OWXX7B+/XoF9ITS5MmTAQDNmjVTsIvT6cTBgwdx2223VUh99uzZgw0bNsBsNmPAgAEVUmZpqlOnDhYtWoSHHnoIH3/8sdr+0EMPhRxPehEqizTQGkmZhs6eUlJSsHTpUnTq1CniVNuVK1eG2+1G8+bNFThUVu3atQtVq1YNSnlz/PhxOJ1OTWBd/71A79690a9fPwDA+vXrceDAAfzxxx/IysrC7t27kZOTg59++gmvvPJKia40rVq1wm233YZ3331Xs33AgAG48cYbg/a3WCxYsmQJfv31Vzz//PNYt26dei05ObnE9YQqLCwM6ch27Ngxzf9JSUmw2WyabevXr0dWVhY2bdqETZs2aV574IEH8PLLL+O///0v3nrrLY3r0Y8//qhJQ1ZSAF8vh8OBuXPnBqXcSUtLw5EjR4IClOF055134u233w4JXOpVv3591KhRA++88845GZg/Wzrf74sqWrVq1cL111+PF154AVdffTUuuOCCEvdv0qQJ3nrrLfznP//BXXfdhauuugq33nqrZp/169fjk08+wdatW8OW4/P5QqZR/fzzz/Hzzz9H7E4TiXbu3Ik//vgj5Gs9evTQpPN7+eWXcdlll+Hdd9+NKGVVamoqGjdurO5XDx8+jM8//zwkzNe4cWP07ds3bFl79+4t0VktEpnNZtSqVSvIzWzu3LkqBW5pysvLw8KFCzXbNm7ciH379iE5OVl93x4VFYUWLVqU6lx4NkTzg4pQdnY2fvjhByxZsqRCymvZsmXEDxbk5ORg0qRJWLJkCbKysrB///4KqcPfXe+++y569+6N2267DYsXL0Z+fj6qVKlSrlR6hv4Zatq0KWrUqIE6dergzjvvjOiYM+H40zUQCLQMBAKhPi2eBDASwKshXnsTwLxAINAYQAsAfDf+EUCzQCDQHMB2AP8Wx+z681wtA4HAPadacenkoQ8mMzjLN0rCQF6vF16vV+PiIcEgu92OxMRE5VARHx8Pm82mgSCcTmeQYwrBg4SEBMTFxSlYiMFfnkMG4BmsBaAcLyQUQLCDMIzb7daACRKckAFdvWOKhCvoJMJ96P5Adw+en9sCgQDcbjdcLpdy3mF99QCQDBQDUECPx+NRAWoCNhIO4bUS6GD/ETpgsFu6XsjgNftXOgPx+njdlIQTGChnebxuOl7Y7XZERUUpZyc6keTn52vcMtgXFosFSUlJiI6ORl5eHo4dO6ZJQxUIBJCZmYmjR4+q/QhU0WlIls3xIQEjOiDRmUXWgf3JOUB4gG0knYjYD3IMh2orOabCWf3yvLxWQhAej0cDEemhA44P6dpCKIwwCsehTC+Wl5eHnJwczTjnvIyLi0NSUpJylNLDNQQ7eG3ydT0EIdtPAisS2uGPHrqS59JfO9cAmWqK186UXgUFBahSpQpSUlLUTTHTDHJ/v98Pi8Wi5jVTyUm3KrabBITohMYxTkCH64K8BulaxlR5OTk5SExMRJUqVRAXFwe3243s7Gw1T1hPzl+ug7JtJUCkT3/GMWQ2m5VzWF5eHnw+n6a/5Zjmeitdbvh3uDRe+nEvAR2uaVwPgb+ALM4hlh0bG6vWKwlMsQ317lYSWJNwjwQ3Taa/nOkk9Mn1SUKXJlOxWxrf26Qdpb6/5flCuSOxHLmd71f/MJ2390WGDBkyZMiQIUOnQeftvdHFF1+MadOm4YcffsCECRPC7ufxeIKCztSTTz6JoqIijB8/PuTrixcvxrFjx4KCXRdeeCGKioo0T7JXtPr164e33377tJT9wQcf4LvvvgNQDJekpaVVWNk//fQTPv74Y1SuXBlNmzYtEV6aPXs2Nm/eDKA4zRCVmpqKQCAQFJQvr7766isAxQ5P11xzTYWUGU5du3bF22+/jYcffhirVq1Ceno6gGKHjXvuuSdk6qVwSktLKzXl2cmTJw3Hn3NcSUlJZU4NWKlSJcTHx+Onn36K2OVGr5UrVyIjIyMoiPi///0P3bt316QFofsWFRcXpwK2DRs2VN8htWrVCm+88QYyMjJQtWpVxMTE4Jtvvglbh/vvvx+HDx/G+vXrAQA33ngjFi1ahNtvvz3supOeno727dvjkksu0WyPFIRbuHAhFi9eHLRd78ZQrVq1kGv4nj170LRpU822Dz/8EK+//joeeeQRPP3005rX3nnnnaBAvt1uj6iuANTDYhL8+eWXX/DEE0+o7+kiVXR0dESuBffffz+aNWuGWrVq4Ysvvoi4/H+Iztv7oopUgwYNFKxoNpuRkpKC1q1bY+jQoRoYRiohIQGNGzdGjRo1EB0djbp16+L6669H+/btAfw1Lz766KMSx3UgEECvXr1CvnbvvffiiiuuwPTp008p9RVVpUqVsO+hzZs316QAu/POO9GmTRvs2rUronNfeumlmDBhAlasWKHcJ+fPnx/S8cjlcpUKNo4fPz4oNWpZ5Pf7UadOnaC1df/+/Zg1axZcLhcOHDiABQsWhIWrWrRogV9//VWzbni9XnWvQ8XHx6NVq1YqfeS5pFWrVqn+OFVt2rQJv/76a4WU1aRJE3Tv3j3i9K3Jyclo1aoVevTogc6dOxupviJUQUEBli1bhj/++APHjh1Dfn4+KleuHHEqUUP/PE2dOhWHDh1CZmZmxO5oZzXVVyAQyAwEAr8B0CDlJpMpEUAnAO/9uZ8vEAjk/Pn3D4FAgHezKwAEe4ZWkAh5yAA8A89/1lPjTkEHFgaiQ6XBkileEhISkJKSopxkGGjluXkeHhsdHa2gBQZkPR6PJngrXX5kQJVuOwwqy3RNTH2Vn5+P3NxcDUxCQIlBWr3LBcuU0AFhGgbWef2U3iHF4/GodpUuMwx6x8TEIC0tDYmJibDb7QoC4jXk5+drnIKkGwlBFgkaRUdHq/Rm0tFCpi/TQyiyzoRw6FLCtDX6NFIEMCSIQhckuj/Z7XaYzWYkJCQosCslJQVFRUXqWjgG6PbjcDiQnZ2t6uX1epGVlYW8vDw18Xmz43A4EBVVnJuT4IUcv3R5oTgG5T68bgIyMvWVbAuOe4IS/JswhB6qkuOCbS3Hl3QC4Rzj//n5+ZrUXRwTss76Mrkf202OI+Av8ILXzjHEv30+HzIzMzUAGfuWsIyE0iQAQTBPuqZQEhDR/62fKzLdkqy7XF+YAovrk4Q6qEAggD179mD58uXweDwq1RXwF7zG/zm3+EUA6y77To59jnHOT6YvNJlMaqzwevh/YWEhnE4nAoEAKlWqpBx8An+6aO3cuVPdPJpMf6XokikKuZ6xnSTIJF1u5DzmfDCbzWp+sK1kX7C/JfwpJaEfuebpIUxZH7kvgUkCWHR9o3sPHY8kQCjHiL5v5fwM5dbD9Ur/HsXrZN8RQNKPSemEJtP86dOFyfHPv71er3qyQYJyhop1rt8XGTJkyJAhQ4YMnUmd6/dGx48fxwUXXID//ve/Je63fv16TQoWqm/fvhg9ejRsNltQ0JtavHgxdu3aFZSypVq1avj888+xb9++cte/JHXt2hVNmjTBmjVrMHDgQDz00EMVfo533ik2F7BarfjXv/4Vdr9mzZqFTLlRkqZNm4Zt27YhKysLt99+e9gg+LZt27Bw4UJ4vV689957AIrTW1WpUgVLliyBy+Uq03nD6bfffsOyZcuwa9cu3HHHHSqV0OnQokWL8PDDD2PNmjVo2rQpOnXqhLp162LOnDmwWCx47LHHSi0jMzNT/V1ayof69eujUaNG5XaFMXT6NWrUqDI7azHYHAgEyh3M27JlC2JiYoLAH7/fH+Sm4XQ6gxwsqlSpgiuvvBIbN27EE088genTp4dMP/f888+HrUOfPn2waNEi9f+VV16JLl264Kqrriq1/vrUO4RxSnL72LFjB3r37o2ffvop6LWsrCzlsr5+/Xr06tUrZBD9jz/+QLVq1RTw2aFDB1x55ZUhXToWL16Me++9V7ONaW5KU05OjgLCUlJSVDozp9OJTz/9VNWV30EDxamSZHvqFRUVhcTExBLP+/jjj2uAzISEhIiclAyd+/dFFaVatWph+fLl+Pjjj9GkSRNYLBb06NED7777Ll588UVcfPHFIY9btWoVPvjgA83cvfjii3H99dejcuXKGD16NOx2O66++mpNdgm9kpKS0K9fP9x///1Br/n9fqxcuVJlXThVJSUl4dJLLw25Rn///fcaZyKv14vY2FisWrUq4jSMUVFRqFq1Krp3766cdj7++OMghy+Px6MyjJSkd999V3OPIFORlSaPx4OCggLUq1cPEyZM0HznPHPmTFx99dUYPnw49u3bF1Q/qaZNmyI9PR21a9dWTmih7kE8Hk9EzkhnUgkJCXjooYeCnKjKo6KiIixfvjwkaFoeXXPNNaW6aul16aWXYvz48SX2l6HQcrlcqFatGtLS0pCdnV3q3DP0z1bXrl0xcOBAHDp0KKL9Tzf4EwDwg8lkWmMymSJ/pASoB+A4gA9MJtM6k8k0xWQyhfqUfieA78X/df/c/2eTyXR5qIJNJtMwk8m02mQyrY70aQe92w1hHwadGVSVqbJk0JVuDdJhhhM5MTERKSkpKogKFNve0SmHwVHpqGA2mzXwDvAX6CPTkjFFkUx3wwA3rwuAcpmh8w8tPBnclYF66ZrBID9/eH4CL7wmwklsNwI+BQUFiIqKUu4t3Mb24jXYbLagcxIooZMIoSu+ybBtGNxn3xDS4DXJlDYMeHO7hL54rDw/nZe4jaAFr0OmY5JpfSScFBMTA5vNBqvVqlKAJSQkICEhAXa7XQEODMgzFZh0PSGgQucjOU6AYpqd9bHZbPB4PIiJiUFCQoJKicb2kP3NNpCADa/JbrerdGX6lFN0MpFOTRxnMsgv24VjmPuwHLoGsT/p6iSBFulmo09/JGE6nkMCEIFAAPn5+cjOzsbx48eRlZWF+Ph49YGzqKhIQResu9vt1rSN7GvpMiOhJwn7yDRneshM/i3npxyXoQAqCXTINGUSvJDH0Q3K7/cjOzsbTqdTjWmmj+NaxnpFRUUhISFBbZOOV3rHLP7tdrvVmEpOTobVatX0VUxMjALX5FwhEJibm4vjx4/D4/Goucu1U6bPkiCXXDvl+iz3ITwpASEJAEoXHdkfsk8kvCWhKzqY6VPG8frk+4B8f2A/cX+9Q5N03dKvLbK+nA/SFYoOXvwhxCed2Dh22PcSHvX5fKr9+f5EtzWuuzJdoEy9p0/HR+iS6QzZN/8gnXP3RYD23qgMdTJkyJAhQ4YMGTpVnXP3RmW5L5o2bRouuuiiMlRbq3vuuQfvvPMOmjdvHnaf3bt34+eff1ZPq8tzP/jggxEHeMuitLQ09O7dGxkZGahfvz7S0tJw4403BjlQnKqWLl2qgt79+vVDgwYNgvaJiYnBJ598gg8//LBMZW/duhWtWrXC5ZdfjssuuwyPP/542H03btyI2NhY5Ofnw2azqSD6nXfeGZSiqLz6/vvv0aFDB7Ru3RqTJk2C2+3Gu+++i6uvvrpCyqdeeuklAMXAReXKlVGpUiWkp6djxYoVyq0kkn6kC9K4ceNKTe3BoF///v1PpeqGTqMItZVFBH/y8vI0Qd6yaNOmTTh+/HjE6SuysrI0/99666346quv1Pevbdu2xZAhQ4KOK8mVqnLlyrj11lvx7LPPlugUkpmZiR07dsDtdqtteocfrlEzZszArbfeipdeeinIrUYer1dOTo6CaY4cOaL+1mvnzp2IjY1Fx44d0bhxY/z666/o3Lkz3nzzzaB9Q0GjkThcbN26FQMGDEDHjh1x1VVX4ejRowrwMpvNGpcRxgnmz5+Pu+++G9OmTVN9tW/fPnz66aeaskt7X9q7d6/m//j4eFx22WWlQob/EJ3X90UVpYyMDA14GxMTg6ZNmyIpKQmvvfYaxo4dG/bY999/H19//bUmRV2fPn3w73//G0OGDMHo0aOxe/fuUsFem81WYsqjJUuWlAl6KUl16tRBr169UKdOHc32rVu3YtmyZer/DRs2YPr06Vi6dGmZnbJSUlJw++23AwDGjh2LZ599Fl9++SVWrlwJh8MBk8mE5ORkLFy4ED/++GNQ+kVq7NixmDhxImbMmIFXX30VBw8ejLgOVqsVAwYMwCuvvIKhQ4fihhtu0Ly+ePFizJkzB3v37i01nVqtWrUwdepUPPvss+jQoYPGwe3AgQOYMmUKevbsiYkTJ0Zcv9OtqlWr4r///S/69OlTIbDjvn37sGrVqnK/R0s1bdoUV1xxRbmOHThwIJ588smQY9hQeJ08eRI+nw/R0dHIysoKugcyZEjK6XTi999/x++//x7R/qc72tYhEAhcDOBKAPebTKbQd9fBigFwMYCJgUCgFQAnAM2ndJPJ9B8AhQB4d3kEQK0/9x8FYNqftLNGgUBgUiAQaB0IBFrrLeBC7Ktx6GHAXAJA3E8vvUMJ95eOGAzo84fgCCVTEcnUQgyay0A4AQyZtkk6tMhgtoQRWE95rQzM8jUJdcjrlbCB3vFBAkLcV+96xOuPjY0NctyR7SDbV++AQhEeIoQh6yWBKIvFoqmT/JEOLvq+5DY9fERJdyJ9X8hrkql6GPTnOSVMRBcoOd5cLhfy8/NViit5HvYbj3G5XMqWT96oERbSAzF6Jxyg+IMegS3WmeOBv2X6KX1/6dtM3+Z6qEoPv+jTW3F8ybLZz3pgRsJvvFYJFcm+dbvdcLvdynGFLiasC6EG/XjQu7xIqEY6I8nj9O4n+vGlX0vkPAk1D2U5+tc4D/Sv83y8tvz8fDV+CeJxTPBYjhXZLvr+1bteEVCk05aEBLmeMXUUxy7BGFkOgTaOBYJf+muXLktyjZPzmuNQPybYb7IfShtjlEyZx3Yn2MN6c72Q65yEZOQ8ltcix7KExcKNZR6r73e+pgfm5DjWQ2psO+kYpZ+3EuqR7SPf9/TvCdzO9U8/rv8BOufuiwDtvVGZr8iQIUOGDBkyZKj8OufujcpyX1TWp2KlLBYLGjVqhMzMTOzfv7/EfbOzs1GtWjXNtnr16iE9PR2dO3cudx3CKRAIqPQzdevWxeLFi1GrVq0SAaXy6o8//gBQHPAP9R0d3WHr169f5rI9Hg+2bt2KvXv34vLLwzLwOHToEBwOBy688EJ06tRJBfoyMjJw5ZVXlvm8JSknJwfz589Hw4YNcd1115VYr7KqTp06mjRe0qWlcuXKmn1Le/ginANVKG3ZsgVA8Zg09PcRHyZ0uVzKCaasOnz4MPLz8zUQSUnSgzALFizAH3/8gZycHBQVFeHmm28O6epQWioyq9WKQYMGYejQoSHXGaA4gPrxxx/ju+++0zxNLaEipuHIy8vDp59+igkTJmDt2rWacvTf1Ui53W71/WJJqbhyc3Ph9XoxdOhQ5TiSm5sbEkR87bXXgraV5rgDFH8vZrFYsH37dmzYsAEA1IPCsbGxmjSH/F5u+vTpWLx4MY4cOaLSJP32229BKcwyMjLQr1+/sOfeu3evxr3IbDbj7rvvxsiRI0ut9z9A5/V9UUWoXbt2iI6ODutouHTp0lLL2Lp1K44cOaL+z8jIQPv27VGjRg20a9cOixcvxvbt20stp6TY4aZNmyJOuRKJGjRoENINUL63ZmVlqX0iqb9eCQkJCsQcN24cJk2ahLlz58LhcMBqtaJ3795o164d6tWrh5o1a4YtZ+rUqZg9eza++eabEmFHvWJjY1W8ymw2B93bUtu3by+1bStVqoRLLrkkaL3Lz8/HzJkz8fHHH59zbj89e/ZEx44dK6y8EydO4MCBAxVSVv369csNXyYkJODyyy/Htddei+HDh1dIff4Jkvc8NMowZKiidFrBn0AgcPjP35kAZgFoE+GhBwEcDAQCK//8/0sU37wAAEwm02AA1wC4JfBnJDEQCHgDgUDWn3+vAbALQHgsNwLRnYBuNE6nUwMg6NMhMS2L/ABtNpuVk0t8fDwSExNhtVqVcwNTFjE4LJ096NpitVrVBwJ+gGBAnXAPxfoWFBTAbDbDYrFoICU6vejPJZ0dpLsK3Sj01oX6oDLrQmiCwV4G/WX5dMShQ0VcXBwcDgdyc3ORl5en3DJCAR+yvSVsIN1XZEodu92OSpUqISEhQbnhSJcafYCbKXf0UAXPJ4EqCYPFxcWp1F1Wq1WdJyoqSgE7hHzoCiJhC9YpJiYGiYmJiI+P14AQPp8Px44dw8GDB+F0OhVQQOjA7/drxqff70daWpr6n2ni5NiMi4tT6dD8fr+COqxWq2o/flFENw+CQTwvIRcJD0hxG0EL+Tr/16fJ4m+LxaIZ2zyWY5R1iYuL07iaEEyS9WOqO74m57hsOzrayLp6vV7k5eUhLy9PuZSw/tJthW0m4RVetwR3ON54TaEgII4TPbzD1/kjXWn0UIqsT6g5S7cs1pPgWmFhoRonTIMlU9TFxcVprAeluxaPk64+drtdUfo2m00DrOXl5SmamSBIbm6uWh94w1O5cmXlymMymRScqHf30cMlnOfcT5/CSjqwSZcmHs/95VyXjlbSVYdjgu1TVFSkHLyYzkyfIo7jlO45pLz15bIPpKMRpQctpUsRj5VzSc5BQkrSsUcP53A8SICSddQ7TxH4slgsyslMAm+c74HAX+nN9GDd313n+32RIUOGDBkyZMhQRep8vzfq2LEjfvzxx3Id27lzZ1SrVi2iL+y3bNmCmjVrqnRXdevWRY8ePTBhwgTMmzevXOcvSVlZWRgzZoz6fun333+HxWJB9+7dK/xcO3fuBADcf//9mqfapSZPnozGjRurYFFZNXr0aMyYMSPs6/PmzcOECRMwdOhQ1KlTR+NO8sADD4R1CDkV/fjjj/j0008xYsQIzUNl5ZHFYsG8efNQp04dDB48WG0P52YQ6kESvdavX4+TJ0/ioosuQtWqVbF27VrMmTMn5L4PPvggHnvsMeUSdDZ0zTXXhHSMMlQ+JSUlqWBqpCkFQunAgQPYtWtXyACy3k0H+MtR55dffsHmzZtx9dVXo1WrVujRowduvvlmBd7odemll4bcLt2t6tSpg7Zt24ZdR9LT0zF79mzccMMNGjeRjRs3qr8JexIoOHDgQBDAE+rhPOrEiRMKmCnJBenYsWNwOBzo2LGjckX7/vvvcdddd6l9nE4n7rjjDgXeUTExMRg3blzYsim73R4EUUn3EumeMX36dNxzzz3KeW3lypXYs2cPAOCiiy7Cpk2bMGnSJKxYsQJ79uzBuHHj8PLLL4c9N/ejzGYzbrrpJvTq1avUev/ddb7fF52qUlJS8P7772PhwoUhAYl9+/ZFlFZyxowZmDNnjord2e12tGnTBlFRUbjiiivw6KOPhkyzp1e9evXCrjvLli0LmdKvvLr00kuDXAAHDBigga779++vUqNOmzYN9913H8aMGYMff/wRmzZtKvUcrVq1Uvdyubm5+PHHH7FmzRocPHgQiYmJaNq0KcxmM+rVq4f//e9/YcvZu3cvPvvsM6xbtw6rV68u1Z0nnNq1axfS+fDzzz8v9f42OTk5aD1ftGgRRo0ahYceeghLliwpV51OlxISEvDwww+jTZtIp3TpslgsCqA/Vc2ZMwczZ84s9/GdO3dGjx49MHr0aA2Ebii8Tp48iXfeeQejR4/G448/XqHriaHzQ/37969wR13qtIE/JpPJbjKZEvg3gCsAlP4OBCAQCBwFcMBkMjX6c1N3AFv+LKs3gMcA9A0EAupTrMlkSjeZTNF//l0PQAMAkT+iEkJMh8OnHBiElqluQrkvkJqvVKkSUlNTkZ6ejrS0NKSlpcFut8NutyMpKQkJCQkIBAIqHQ4dX/g3YQ0GT6VLRCAQUMH1P68ZUVFRsNvtcLvdcDgcKj2MDGQTIgnlBsFrJOEvU5RJ1xFet94Rg3CFBAV4HdIZQkI0BBhiY2MVsGQymRTMQbhB/8FJOlVIRwkGkb1eL8xms0qblZSUpNJmAX+BCbJ+DIKzL2Wwmv3KNFqyr+Pi4hAXF4eEhASVC5WphKKjo1FQUIDs7GzVJwSp6OrDtpGwCQPkdGDJycnB0aNHVe5l9n9BQYEaNzw2Ly8PqampCmaQQXqg+AMV+6Zy5cpwOp3qRpgQiN1uV3XTgy4SQOC1yjGrdymRkIreJlLCMwA0biQSQJH9TzCLYIuETCTww+uR9WE5Xq9XpU1zOp0qDRrToknIiQAD25ZP6HCscnzyOmXqLwl68DoImMnUWDxeOr1IoEfv/CMdYuS+XJdku+phRUJy0s2M/Q5Ak0YOKL6J5DlNJhNsNhvMZrMqizCfy+VSMA5BOq6dMkVUUVFxyjp+2REbG6vaQ6bJ47ltNpuCaZgmi2OHYBPHNwESubboQSuuT9KRh3OC8A/HItuPQI8E3PR9JOcx5zdBQK77EgSTbmysC+cn024RcGI/FRYWqnkWKsUX+0k/FtgO0umI41u+LlOnSbiO9fR6vZr0XhK0kvCb2WxW7cUyufbJcUsAqDR73b+L/g73RYYMGTJkyJAhQxWlv8O90UMPPYRnnnmmXMfOnz8fAwYMwJo1a0rdd+/evUhJScGFF14IAHj++ecRHR2NZs2alevckahy5cpwu90q6P/BBx/guuuuq9Bz9O3bF7fccguA4jQBoVStWjX0799fPVBVHn377belpnp48skn4Xa78dlnn2mCRW3btsUTTzyBFi1aKPCqovToo49i6dKleP3118tdRv/+/fHRRx9h/PjxKkVGKD355JOYPHkyVq5cGRGg88EHH6Bbt264/fbbsXHjRrRq1Qp9+vQJue+aNWvwxhtvlBuCqwh16dIFS5cuxYoVK9CuXbuzVo9zUYMGDULDhg3LBJhVq1ZNjXd9Wqayat26dWjUqJEmZc4rr7wScrzwe6Jx48ahVatWAIq/R1uzZk1YMBAALrzwQgwaNChoe5MmTSKuZ506dVRKwFdffVV9J3bNNdegU6dOuOCCC5QTGr+bBbTfqU+YMAE9e/YMe449e/aoY6tVqxbW9eeaa65B1apV8cQTT2DChAkh97nrrrswderUoO1//PFHRE5iycnJeOGFFzBt2jTlyCTX2BtuuAErVqzAtddeiw8//BCTJ09Wr504cQJ79uxBIBDArFmzMHXqVNx999247LLLlAtJaWlsFi9eHLStevXqpwxCns/6O9wXnaq6d++OjIyMoAfQqVq1aoV0xdFrz549mDVrVsiUKNHR0Rg+fHhQmqlQqlmzpkr/GUqTJ0/Gk08+iSlTppzyd5t2ux29evXSOHvt3r1bA7fUqVMH/fr1U9smTpyITz/9FE6nEzVq1Cj1HDVq1MDdd9+NN998E7fddhuA4lhHqDa98cYbS01Hmp+fj127dpXbWadLly548cUXMW3aNDz//POa1yZNmoT169dHXFZmZib+97//YcqUKeWqy+lWfn4+Vq+u2Gx5F110EaZNmxZxOs3SNHbsWLz99tvlTh1GePeZZ55RbnWGStbMmTPx5ptvBjnnGfp769Zbb8WkSZPwzjvvYPbs2ejSpUuFn+N0Ov5UAfCLyWTaAGAVgG8DgcA8k8l0j8lkugcATCZTVZPJdBDFdoJPmEymg6a/LAVHAPjUZDJtBNASwIt/bh8PIAHAjyaTab3JZHrnz+2dAGz883xfArgnEAicUqJNBoIZ1CUI8WfdNWmdCPAwKB4dHY2kpCQkJyfDbrcrx5/4+HgF/VitVjgcDuTl5WncGhiIJlTC4LIM2vt8PnV+CQrExcVp4B+Px6PgCQI1wF9pguhwIV0zCP8Q+pHwD119GDzmsQwgM+DNfRio1qfvkm4l/B/4K9jOYD2BCDoHUWwf6b7DehJEkMFnk8mE+Ph4zXl4/QRwgGLbVZfLpV5nW/H6pBUp+4ygl0z3JVOBsY0YLGc761PkyGsmxOBwOJCdnY38/HxkZ2cDKH5qKz4+XqX8Yr9GR0fD4/HA4XAoICMxMVGdm33I/k1OTobb7dYE9C0WC1wuFywWC6xWqyYNE9O/yQA+8JezDH/0gI+EA9heEnCSEAvBEM6rvLw8BQBJBxReO/uex9NNRI51PbxEQMzpdMLhcKgxKsdKUVGRZoxxLJnNZhw/flytBxL+08N0ErjjtctxL/taghayDB6vTxXHMURohOsRy5euW1zL6N4jXWbYPh6PR5POi7AG9yWAxrWJc5QQIeccx4s8h+yjvLw8ZGdnK4AqEAhogKDY2FhYrVY1Lwg/yrVOpm/jDyE4Ob8kEGT60/XH7XZrgCWTyQS73a72ZZvI8S3bWW7jNUm3MfYD5+ORI0dw+PBhuFwuDWwjITQqKipKgYNynee6KddLOT7kl0zSgUeudWxPXodcO2Xbcv3Sr51c+yREJusv4R9CWnJMc2zJfpX7SAepv7nO+/siQ4YMGTJkyJChCtR5f2/Utm3bU/pi+9tvv40opdKRI0dgtVoxd+5c/Pbbb+pp7xtvvBE9evQo9/lLUrNmzZCYmIgLLrgAlSpVwnPPPYd58+YhKSkJl19+uXo45lQ0Z84cjB8/HpMmTcJ9990XMlh95MgRmEwmTaD9dMjv92PAgAFo2rSpJq1YdnY2unXrhp9++gmLFi2q0HMWFBTgnnvuCQvrRKKvvvoKX375JebOnVvqfsOGDcNnn30WUdDU6XRiw4YN2Lx5M44cOYLc3Fz1+T2UrrzyyhLdAU63Lr74YlSpUgWtWrUqMcXQP012ux3XX389li5dWqYAap06dRSUciqOP3Xq1MFNN92EFi1aoHfv3mp7TExMyPRfkydPxsGDB/H5558HOQOXVI+WLVsGBedffvlljB49ukz17d+/P4DiFDJMwfHBBx/g22+/xdy5c1WdpbuFrOcff/yhXK1D6ffff1cOPYmJiWjfvn3QPvfdd58CISVsKL8TvvXWW0MCDePGjQtKued2u7Fs2TJN6j+g2JG7Ro0asNls6vtrmbKncuXKqFSpUtADnNTevXvxxx9/YO7cuRpYh+4T/C4vnBYsWBCUyiclJQX/+c9/cMcdd4Q97m+u8/6+6FSUkZGByy67rMTUgCaTSeNGVZKWLVum5o1eNWvWDOk6pldMTEyJqaw2bdqE559/HosXL64QaK1BgwYaIGnz5s2aWBSAIAh6+/btqFatWshUgHo1bNgQnTp1wrBhw9CpUydUrlwZAwcODAk2R0VFYdSoUaWWuWDBAo0zWllUrVo1NG/eHAMGDECPHj006VUzMzPx/vvva9zBwunYsWNYsWJFRC5OZ1M///yzAlwrShkZGZqUlKeiWrVqoW7duqXe4588eRJz587FwYMHQ75etWpVvPjii+jQoUOF1MuQob+THnnkEUyaNAl33XUX0tPTUbt27Qp1AqNOG/gTCAR2BwKBFn/+XBgIBF74c/s7gUDgnT//PhoIBGoEAoHEQCCQ/OffeX++tj5QnD+0eSAQ6BcIBLL/3F4/EAjUDAQCLf/8uefP7TP/PE+LQCBwcSAQ+KYirkOmQ2IqL5nuhIFQBmXpXMFAMh1wuJ90iJBgCcEMBkQZONa7JMh0VvK80tGFNxpOp1N9UGGQ2mw2Izk5WQV1AahAtHSwcDqdyuEoFLjAALEMvktIgk4dMugrr5l1J6DgcrkUkOHxeOByuZQDkLxO4C/ohynUEhMTYbPZlEOGhHek40RMTIyCEggq0EGEwFB+fj7y8/PVa4R7JABFYEE6t/C6TH86igDF7kkEDSS8JQEffVofBuZZH4I8Xq9XOYjY7XZNGiTZ5xaLBQkJCQoasNvtatyYzWYFZ3BMxsfHw2azweVyaZx0GPiPjY1VbSQhOJZJOE46C+mdZvRAE9sJgLpmCYbp95VwDNuMDkd6aC0QCGjSKnHssZ9kexOuICTBsmQKOkJhBHwIpuTn52tAJTmepUMVy5VwBvdln+v30UNQEuaQ81G/pshypOMNf8xmM+x2u+pXQlIEQUwmkyYlFNtRAiP8n/tzDEgAhkBdXl4e8vPz1VqVnZ0Nl8ulvjzi9XFucl0hgMJrtNlsMJlMSEpKQlJSkgb2kSCXdBST7kj6dGCc+xwr0i1LQodsA9alpH7QgzBynMk0WXJu8Dzsn0qVKiEpKUk56rDdua5aLBZVP5YpQVTWX663EqqTMJZ0iGL7ENjk3JeAnnRKk9cugR/2m4RJ9Y5dvFbZjhyv/wT9Xe6LDBkyZMiQIUOGKkJ/h3uj5s2b4+KLLy59R6GPPvqoTC4UANR3AmlpaZpAVYMGDUp0lzgVvfHGGyq4PXLkSOTm5uLWW2/FY489hu+++y5k6p7y6IUXXsDdd9+NypUr4+mnnw65z8yZM1G1alUAwJgxYzBixIgKObde69atw+DBg5GRkQEA2Lp1Kzp37owZM2YgJSUFrVu3xuDBgys0kLF3795yOeVUq1ZNpfP5/PPPQ+5js9nU3/369UNycjJuu+02rFq1qtTyR4wYge+//x4zZ87EJZdcgiuuuAIffPCBAiP0mjNnDgYMGKAJ2p1J1a5dGwBw+PDhoNRH/2RVqVIF6enpqFy5Mho1aqRcdEpT9erV1d+pqal45JFHynX+qKgoVKpUCTabTbl7Pfzww3jwwQfx7rvvBu0/bdo0DB8+vFznGjNmjPr7nnvuQdeuXXHZZZcBKHYS+u6770otw2Kx4LHHHsPw4cM1gfD4+Hg0btxYBUPlQ1k5OTl4//338fPPP0fkSvbbb7+pv0O5U0nnDLp6MN4AFH8/s2nTppBpfUIBE19++SV69eqFffv2qW0LFy5Ebm4ugOLvazweD6ZOnRq0tjocjrBOHrt27ULjxo0xb948zbhi+sb4+PgSHUg+//xzzVo0d+5cZGdn4+mnny7RYeXvrL/DfdGpqEOHDhE5tmVkZESU+vP6669H586dw74eCahjMpmwf//+UvdbvHgxnE5nqftForS0NHTp0gU2mw033XRTEPjjdrs1a43M3hGpLBYLatSoAafTGRZEOXHiBGbNmlVqWb///jvWrVuneeC/rDKZTGjUqBFefPFFfPfdd+redty4cfj4448BFKcm08OCe/bswfjx49G3b19ce+21EaXPPZv68ccf8c03FTvNNm/eXGFOQmlpaahSpUqpDpeff/45RowYgffffz8smJ+YmIhJkyZF5KxlyNA/SceOHVP3YEAx68D71YrU6XT8+VuIgUoGmQGtg4IEefgag+HcTnBABjwJZvDNWwIILFceA2gDuaGCzABUwJuSgWWeW4IBPJcUg9Ssk4Qh5DklZCBTxMjAPOutd7iQZcrjCeUwkM+2ZNksT/5IqEKmMSKAJc/NMmWAXAI3BQUFCjrihyoZ/GbwnPWSQAT7htfF11hPnpP9yHbgNcnfEvLi9vj4eAU9SUciAMqNJi4uDlarVdWT449lSmiNIJh09vD7/ep/n8+ngDLCIaw3pR8f+lRIEv6R9Q3Vj+xLOVa5TbaX3h2HY4PtwjEggSyZ5orHsB88Ho/G4UeOSelkRFksFgVESXhFD8/pQRG99MCIfqzKsaffh9KnStNDU2wzveOKbEt9nXmt/E2YJpTjDOeavl5Op1PBZBIiJOTDcqTzkLw+npt1pOsUISCuySyH16Vvw1DrDvcjbCQBRsJQcXFxSE1NVSBUqHEZajzLdY91lCnz5PXKdU22P+c+55ZcWyTQpIfo5HgINYfY3hKw4/FyzMvUdXJsyTkggTv5m9CPrJP+fUeOU9nnhgwZMmTIkCFDhgydb6pXr15ETyJTVatWRYsWLcKmTCpJ8sEfKjo6WgEPFa3Y2FjlXCEDcZs2bUJ8fDzS09Mr9HwrV67EoUOHQjpENG/eXH0WbN26dURPtZdXEqyqXbs20tLSNAHzoUOH4u677z5t549UDz74YKluBddccw2A4s9fy5cvR4cOHXDBBRfghx9+KLX86tWro3fv3gj8+bDQqlWrcOTIkRKBr6ioqIjBkooUXdaBYgjjyJEjZ7wO56r4oCRQvIZEmo5FAiQ33nijGktl1e7du7Fr1y4AQN26dXHjjTeif//++PLLL/H2228H7b99+3Z8/fXXIcsqbd4TIomKisKdd96pXEG2b9+O3NxcfP/99xHVuWfPnmjevLn6P1T6Hvnw0rhx4/Duu+9i0qRJ+Oyzz0otX75nMHWYlAzo88G5O++8Ex07dgRQ3KahXBa6desW0kGodu3aGDlypIK5CgoK8M033ygXE7PZjBtvvDFkcDY7OzvsfOL2+Ph4zfsTHVbi4uJKTT/DsQEUuz0RQCgtTZihv6f4MHNpio2NLRVMaNWqFXr37l0hDxpGcp916NChCgF/vF4v+vTpg2HDhuG+++7Dtm3bgoAavRsagHK5yCQmJqJy5crYvXt3EFwEAMuXLy/VTZDau3cvFi1ahE2bNmkc0SJVdHQ0kpOT0bJlS3Tr1g1169ZVr23evBk7duzAmjVrgtqYJg0SqDyX1blzZ5UOq6K0c+dO7Nixo0LK4gPvpX1Pz/bu2bNniXM2Pj4eF110UYXUzZChv4uOHz+uAX+A4s9dFZWyjzLAnxLEYKXf79e49ugD/dI1hsCH2+2G1+tVEAsDwDJQTXiE2+hCQdCAbhR6sEe+GTPYyn0YbJdAjnRfkSIYIYO13M/lcinHDgnKyMCtBHakCxDdKdiGhAlkKh29G4zdblegDKEBAjgej0elY+IbugQc2B90qQgEih2UmMaK18U6A1DOFIE/XTVYJ7/fD6fTiby8POWWREk4iD+sGwP17D+6xRAcCAX+SICMksH2zMxMNSYKCwuRlJSkyuMNHdshPj4e1apVQ1xcnCqbdbHZbCgqKoLD4UB+fj5OnDihXHOsVisSExORmJgIq9Wq+pGuKIR9eL0SbOJY5zhk38txLq+rJGcbCenIMSbnIh13CGroX+NY9Hq9qm0JTXAft9utPpCyLLrK0FFFD/ww7Zl0sIqLi9P0N+eCbHvp2CPrKtuE4yoUDCbbjZKwBuvJsS1Tx8kyJYBEyIllyX5j3WWqpujoaI3bixy/0oFJjl1e48mTxe6w+fn5ymXKZrNp2oxOX4SIOI/ZlizP7XYr+MdutytnGrprSUCQbSjbgeOB16CHOflFIc/btGlTNGrUSPUPx5Kcs5yX0mmN44Tn4Bjg0xdOp1M5hsnxzzbmekVwje8v3Eem6NJ/eGUaNgn8sG50O2OdmOZMD5nK9xnOczqA8bfValX7y/lotVoVjKWHryQYpG9HjmNDhgwZMmTIkCFDhs43FRYWRhxMBoAZM2YgPT29RBeEcAoXfG3RokWZy4pEDz74oAq8yIDCtGnTsG7dOnTv3r1Cz9e7d28MHjw4KPiUkZGBa665Bj/88AMCgQCmT5+OHj16qIB4RYvftRw+fBgmkwlz5sxRTiUA0LFjRwwYMAB16tQ5LeePVA6Ho9Qn8Zmig+lO7rjjDmzduhXPPvtsqeVXq1YNQPET9gQo1q5dGxJUoD777DPceuutkV5Chalx48aoVKkSgOI0Q2vWrDnjdThXJZ/cz83NDemGcO2112Lq1KkYNWqUCkjKNSotLS0k/BKpPvjgAwBAeno6PvroI1SvXh0DBgwImaoqknLCadiwYQCAAQMG4JJLLlFz9LXXXsNVV10VMo1CqAB69+7dccMNN8Dr9WLXrl245pprMHToUIwbNw7ffPMNTpw4oQl0FhUVYdWqVZg2bVrQ98ih9OOPPyqYsHPnzkHpGiXUU6lSJbRu3Rq33347rFYrsrKy0LJly5DpxL7++mtccMEFOHDgAB599FHlUtKpUye88MILCpzid+u//fYb1q1bh6uuugrvvfdeyDV13bp1YQPKGzduRF5eHsaPH69JI7dy5UoAxd8FXXLJJSW2xbJlywAUO+HNmTNHpelJTk5G5cqVSzzW0N9PJpMpIuiLqVFK0oYNG8KmISqL0tPTMWjQII0LWjj961//wh133IEPP/yw3Omc4uLi0L59ewwcOBAPPfQQ6tWrh71796rXc3JyEBMTE3T9CxcuDLmelaRGjRrhiy++wGOPPRb0vWxWVhZ27Nih3BZL00cffYSePXvi1Vdfjcj5rCTt2bMHKSkp6NKlC3r16oXZs2fj4osvxtChQ4Pew+x2O9LT0yscRj9dGjZsWESuVmVRUVERGjZsWCFl0SCgJM2bNw/vv/8+HnvsMVx22WUlztldu3ZFlNLYkKF/krZt24b9+/dr7hnr16+Ptm3bVuh5DPCnBCUmJgZBDoRxGDAlmOH1elUQnqmqmGrL4/Eokp7ACR1aCN0AfzlGmM1mBVowDYsMZDOQLwEKCQcxrZOEfiT8wqCrhBT0AVsJEejhAQkzSPcVfbonvcuQ2WxWkAzwF3gUCAQUAMAgP11CpHsSXTroSCPrRDiJaXuY8ovtHh0djcLCQpXqiG0lA/Sst8Vi0aTxku4U0oWDgXQCB4FAQAXIZZooiinjmC7M5XIpCEW615hMJuTk5Kgc6l6vV40FCSsACBnAB6AZJwR5vF4vsrKykJubq9oyJiYGXq9X8yGe+zKoT7tIghgyRZuEyvROUxRfD+UAFA6A4jjidUnYi21O2IVgksfj0YBT0hlFphOT6dliYmKQnp6OhISEoPnCOnJOEurgnOc8JvjAsSrTKcm5yzaSKQJl+8h2k/NH7wrDsSrnooTPOJfZpqyPHH/SRYZAhxy7evcXQOv4IoESs9ms/mcbcoxxrDCFngRY5Don20bvikXgyul0IhAIqDzhnNME/NiWElhiXxGo4fwmMCTTuMm0bbt378bmzZuRn58fBOkwhZ9cn2w2GxISEjSAqHQtY9o+9g/BT3k8r5Hzj9CNXD/kuJKOR5QE6CSsxHHE33TzktCo3l1KziG+VzGNI+c/1z4JYUkoyufzqbalcxOBIe7HehgyZMiQIUOGDBkydL5p1KhRmDJlSkT7zps3D+np6TCZTOV6MvfYsWNB29atW4fHH3+8zGVFonfeeUd9VtOnuZg7dy66deuGpk2bnpZzSx0+fBizZs3C5ZdfDgB477330LZtWw2MU5GaPn06PB4PxowZg+uuuw7PPPMMtm7dqtnHYrGETXl1pvTpp59i0qRJIV/r3bs37rrrLvUF8owZM3DzzTejV69eEZe/efNm7N27Fw888ABOnDiBhg0bYvTo0SU+2e33+1G5cuWQgMXpVMOGDdVnyr1796qHkE5VAwYMQMuWLSukrLOl+fPn45VXXsHKlSuRn58fMhVUtWrVcO211+K1117DnDlzYLPZggCvzMzMctdh0qRJyqWBKeXLo9IcHYYPH45Ro0ahQ4cOmu9KfvnlF2zZsiVkQGXOnDkhg+WHDh3CDTfcgNdffx2LFi3Ce++9h5EjR6Jv3744cODAKTs2rFixAkBxoOeKK65Q24cMGYKvvvpK/b9+/XpERUXh6NGj2LRpkyYVxC233IILLrhA/c/vl48cOYJFixZh8+bNIc998OBBrFy5En379sUnn3xSYj2XLl0a9rWcnBwcPnw4yM1Bvr+1bds25JijfvnlF/j9fqxduxYAVPqyxMTEiEALQ38vZWZmKseokuR0OlXavXAqKirC3r17y5wCK5SaN2+OO+64o9T9lixZgqlTp+Lw4cMV4v7jcrlwwQUXKCAuJycHn3zyCSZOnKhxIgSADz/8EGvWrClTuq2UlBRccsklIaGR1NRUXH755WV+P1+5cmVIMLEsaty4Me644w7cfffd+L//+z90794dDocDe/bswbp16zRgER9qD+WKea5pwIABFR7YB4AnnngCv/zyC1auXIn77rvvlMqiiUE4rVu3Dk8++SSAyJywunbtipEjR55SnQwZOld19dVX46677kLPnj3L5Ea7Z88erF27Ngj0riiAjzLAnxIk4RtCB3QAiY2NhdfrhcfjCXIWsdlsSE1Nhc1mQ35+PhwOB1wulwJyCFwwmEqHHAIHDMR6PB54vV7lGEE3HwaNCY/QXYiAAgPbZrNZAQISkJEODAxM00lCOuAQMOFxMlDNIDigBTpkShrpAGO322G1WhU0BfzleiTPy3p6vV44HA643W4UFRVpAsUyXZCEfghm8DwEHTweD6Kjo2GxWBTUIEEZtoPP51Opn+jixH6jAw5hFwarCSEQEJHOGewLmVaN5dKRiIF+BsYJqhA4cLvdcDgcCu4iWGC32zXwF0EHwigcJ2xnQJvzVaY1kunKCI8kJCQo5xI99MXrl30gx4MelAIQEizg3xKUYMot9jf3ISwh3aMIYUgXGwk06VM4sY1YV/Yt24zXJcewhH+4n91u15yTbcx2BP4CSmR6Iwkx6Z1/5LXK88u5KmEL2X4cVwTP9OWwfnoAisfSwpH9R2iQIKMcKxLAYtl0hqLbDIE7ua/H44HT6dS4RnEt5X56aItjCSgm+IuKihT8w334pRHXAAnQyeNlG+rXWbodcWzFxcXB4/Eo8E4CMnoISPabvm31rjah+ovrltlsVukF2Q+y/fWgonR0k1CndHPiuiX7Vj/muLZKJyTpCsT+Y9tJWEeu+2wbuRZwTSV8JeEwWQ/Od0OGDBkyZMiQIUOG/s5KTk6G1WrFo48+inHjxpX5+D/++CNo24kTJ/DNN99URPWClJaWpgAKfcCradOmMJvNZwzwmDp1KvLy8lSQYfny5act3dZ3332HAQMGYNy4cZg/fz7efvvtkKDWjTfeqElFcaa1e/fusI4CI0aM0EBBQ4YMwU033YT4+Hg4HI6Iyj98+LAG0BgyZAjat2+P1NTUsMcMGDAAP/30k0q5dKYkHQkivb5INHbs2HKl5TsdqlOnTolp1krSpEmTEB8fj5o1a2L16tVBDg4ZGRkaVyCXy4UZM2Zo9unbty+GDx9evsoDeOqpp/DWW2/h559/LrMrBRUKfpRKT0/Ha6+9hiFDhgAAtmzZgldffRVbtmwBgJDz9aeffgrpgvTTTz9h7ty5IdORbdu2Da1atTqldFS//vorHA4HTCYTOnXqBAAYPHgwJkyYgPr16wMohpK+//57zJw5E9dffz1mz56NSy+9FAMGDMCQIUPw7LPPqoc4H3jgAaSkpAAohgXq1KmDVatWhXQguvPOO7FkyRIAoVNIUnl5eZg3b16J17F48WIsXrwYvXv31qRe2rlzJwCgTZs2GrBJr8zMTBw/fhyNGzcGANVXiYmJFZ4Ox9C5r3nz5qmxWZJq1qwZ0XpYs2bNUt1LIpV0tSpNJ0+ejNgppyTVr18ft956K2688UYAxZDL66+/roEDqdzcXNx000245ZZb8Mgjj2D9+vWa2EN51KhRI3Tu3LlMx2zbtg1r164tt+MR1bBhQ/Tp0wedOnXCpEmT8PDDD+Omm27Ctm3bcOjQIbUfv8vWp805F/XFF1+clpRksbGxSE9PR+vWrXHzzTef0thj7Cuchg8fjtWrV+O2225Ds2bNIirz4osvxieffFIiBGro/FLXrl3/8XBurVq10K5dO7z55pv4+uuv8dtvv+GXX36J+F5548aN6l7p6NGjeOeddzB58uQKraMB/pQgp9OpgpvS3YXuETLwDUClo7Lb7co1hu4/dBlhEJlggtVqhd1uh81m0wQ/pfNNdna2Sj3FYCrBEW4rKipSUILNZlPON9K5hM4x0mGhsLBQA6zI6wP+Si3GQLAEoWRQXcIKLINBZZvNptokOTlZkwaMXyRER0erawGgSSPF9DB0vOAPb2DMZrMqn7ANQQ66VBD84XXGxcWpNiDUQViH1+33++FwOJCXl6eAGOleI8uX7ht6txjZv7whke5M7Be6ReXn52P37t0KLpMwBH8IeunHYXx8PDweDwoKClSAnfAYgTXWhamEODb4gZDtQ3qeAX8J/chxLPtbwjJsLwkISHcoPTwQCAQUmEVQh4BNIBBA3bp10aRJE42bFc9NaELCPqGcgrjN4XDA4XDA5/Op1FF6pyr9cbI8updINxV5PXIs8G+OdwmMyOvnGOD4Y3vxWD1oIqEqjnvpPiXXEgnr6MEmQiIEG2NiYtR8N5vN6uaZZfK6AaibQfmFR0FBAbKyssA0VYTbsrOzNSnZOHf5N+c+55McU7GxsUhLS1NppgiasJ85JuT1SsXExKg1REJGekBM9hnddiRgI9esUA5FelBMDwvJucL+JAwK/OXSI0FQPbQUypGH4nuBBI4kiEMnL8I9EnjiuinHP+EluirJ9wk5l+U6xD4gKEr4h6kveV4JXZb3iz9DhgwZMmTIkCFDhs62XnrppYj2a9euHVq3bo2PP/64XOfZtWtXUBC3cePGFZ4ygBo5ciSqVKkCABpnn3r16qFPnz544oknMHXq1NNybr3cbjcOHTqkAvpvvvkmmjZtioyMjAo/1+HDhzF37lz1/5AhQ3DzzTcH7de2bVvcf//9FX7+kpSSkoLWrVuXul/Xrl01/7ds2VKlPbrtttsiOtf27dtV/wN/fe9Z0tOoTqcTEydOjKj8itQbb7yB559/HtOmTcN7771XIWU+9NBDyMjIwMUXX1wh5Z2q6tWrhx9++KHc8E98fDwsFgtsNhtOnDihea1OnTrw+XyYMGGCgmC++OILLFiwQO2TnJyMtLQ03HnnneU6/+uvv44HHngAXbp0KTX9UzhFutbZbDYAwL59+/DII48A+Mt9Xyo3Nxfff/896tWrp9l+4sQJ/PLLL2HL37x5M9xu9ykFV1euXKnS9zRo0ABvv/023nrrLU3AtX379vjwww9V2rV9+/ahZcuW+OyzzzBlyhT4/X5MmTIFzz77LMaOHQugeN7efPPN+PLLL7Fx40YcP3486Nzs/48++ghPPfVU2Dru2bOnVBeNe++9F2PGjMGsWbNw1VVXAQASEhJUqsDk5OSg9UivnJwctT4RcDWZTCWmFTT099W3335basq8tLQ0ld4xnJ544gkMHDiwQuqkvy8oSS1atEDv3r01Dz2eiho1aqTKMpvNaNSoUdh99+/fjxkzZuDIkSNITk7WfF9cHiUmJuKiiy4qc2rTESNGoEuXLhgyZAieeeYZzJkzB+vWrSvz+Zl+sEaNGhg4cCAqVaqE7OxsDeSzfft29eDu+aBvvvkmJMhfEcrKysLmzZtx9OjRcpexc+dOPP3008pl6uTJk6p9Fy1apNIzPvXUU2W6H7nllltCAmuGzk8NGDAAy5cvx2effYZrrrnmbFfnrIif661WK6xWK+rXr48OHTpEDLgtX75cwc779u3DBx98UOEAowH+lCLpfKEHCQCoIDmdVAiUyKC0TP0jU9EQbpHuHsBfziB8jcFTvsYgrj7oS7cTvYMCg9HSgYKBWr0LiYQBeI0lQQN6EEEfdKdLjQQ9CNew7HDXwnrJtETy3PKaGbyX4IbsC+Av15no6GiVak3CHSyLEA/hDrpmhIJC9GCITOsk24Ap4ggDSLcd6WJDQIxiCjnpvCMhAcIvBDcCgYAmfRjbTqY4kqmgJFwgQSgZ1Jf9qb9p1LsB6ftGSkJTeiBI31byBrmoqAg1atTApZdeisTERI3TEABNSiEJInBcSoCB18X/CW7pISUJXEiIR9Y3FGAir1POCf4vf+uPkUCFLF9fjmxH/uY4kOuOvq7hFMoVh2NMlivPSbEvJJTi9Xo1sBDhNfaVhJPYzxKa0sNgbBOZ5k8PSOnrp28v/vD8PJbzjyCM/JFtL92gZPpBWQfZl3rwSjru6MEilsv1h7/l/qH6UbaT/pyyT0PVQc6fUIARj5F1YR0l9Cn7Ilw9CQER/pHzkf1bXqtvQ4YMGTJkyJAhQ4bOppKSksqUcupU0h+cPHkyKBhWqVKloKB1RWnNmjWa/5nOZffu3Th27BjS0tJOy3lD6eqrr1aQ05VXXomNGzdiz549Kv3X6dTatWvD9tull1562s8vFR8fjzvvvBNNmjQJu09KSkqJKVASExMjOtehQ4fUgyHAX046JX2hvWvXLvz6668RlV/RGj9+PN5++20cOXKkQsrjk+yna36VVXQKHjx4cJmPlam5+f20VGpqKjweDyZMmIDp06er7frA9W233YahQ4eWo/ZalTcFTlnSKADQpHyrXLly0OtZWVlBaQyBYhBFD0dJ7dixA3PmzFHgTnmUlZWF/Px85OTkICUlBTfddJNmbrpcLrhcLhXkX7p0KY4cOYKqVauq79caNGiAHj16YNCgQeq4hIQE1b+h0g2dPHlSrWcdOnRAenp62DpGmjLP4/HAZDIhIyMD0dHRuOaaa1TA/tdff8V3331X4vFut1u5Bcl2lw5Chv45Wrt2LQ4fPlzqfqVBLXRBqQhFeu8WExODjh074sILL6yQ8+oVHR2Nhg0bluo2tn//fuUGdqpKS0srM/jjcDiwYcMGvP/++xg3bhwmTZqkcekpj/i98rJly5CZmYnc3FwsXrwY7733Xonr9bmmCy64oELcoEKJ5gWnohUrVmjuBdxuN7788kssXrxYuV716NFDk2YyUrVt2/asOmUaqjgRBk9ISPjHvldXq1Yt5L0lOYfSdPLkSWRnZ8Pr9SqzjoqWAf6UoFABTgaCeSMtA8PJyclISkpCYmIi4uPjFbDBsuhYA/wFVxQVFaeW4o2yhEmio6M1qXFYDgAFqOidZqKiopTDDIO7DGLzeOlYxMB9dHS0ciORAVk9dCBhJDoOyWCuHtBhbkgCJzL1EsvzeDwKDpJpbaKiotSHHel2IqGU6OholZJKXmt8fLzKPV5UVKRSX5lMJuV6JB2OWBeCNdKNwu12q36Q6d9kuh7p4hTKOYRWeXT3kACYTEPl9/uRm5uLxMRE5bIhg/aBQAAWiwWJiYmq700mE1wuFywWC/Lz8+H3+9WHLADK/Ycp5ST0w/aLi4uDzWZTMIYE3ujGQTiCx0k4TYISeucbPTCj3y7BIwmvcUwEAgGkpKQgPT1dpX7zer2Ii4tD9erVUb16dQU7hQKTuL+sn0xrJcEN6aIir1P/Exsbq9yaOG94Dfq0TpzPnK9sV9aX7S1dZUK1mQSRZLn6sURQhi5GfJ3XJ8vj2IqKKk6bxbbnnI2KilJPUcg0gdL9h+ctKCiAx+PBsWPH1BrJ9IQErJgGjH3DerNtOCfpRMY20Kce49zkeJXzUkKNcqyxHNaTQEooeJBprji29UAZ+5dp+7i2y7kgYSe5BptMxan6+MM1hufhmz3rJm8WQn2wldAY68UxSFcvriNMTcj2oYubfr3ivJMwEr8glOs8x6MEF+m+Jd8zmNaRY4agnkwJZsiQIUOGDBkyZMjQ+abc3FwcP35c8/lbrxEjRlTIubZu3Yrt27drtvn9fuUGUdH65JNPVADh0KFD+P3339VrX3/9NYYPH44ePXqclnPrVa9ePSQkJKBnz56YNWsWFixYgCZNmlQIhFCafv31V8yaNSvka40aNUK3bt1Oex2o2rVro2bNmnjuuefC7lMaiNazZ8+IznXo0CF4PB7lurFnzx6cPHkSlStXDgvDPProo2fts92xY8cqDDqKjY1Fly5dAACzZ8+ukDJPVfyuuTwBzkAggP79++PZZ59V38cAf6W+qlmzJjIzM7F161bNE/nS8Yn7JyYmlurgUpFq3749Hn74YfTq1avMqQUl1BMqSPnbb7+FdNDav39/ie4Un3/+OT744IMy1UWvwsJCtG/fHv/73//g8XhUmi7qwQcfVJDX66+/jhdffBEZGRlB89dsNmuC8unp6ejYsSNq1aqFd999V6XQAorfQ2rUqKHapbQ0HXr4syRlZmbimWeewfr16/HCCy+o7V9++WWpTik5OTkqhaBM58bv9A39s5Sbm4svvvgCu3fvDrtPpUqVSnUb8fl8FQb+RAqt3H333Rg0aNBpAzsAoF+/fqU6bCxbtqzE9iuLKlWqdEopL7OysrB27dpTcqEBigGU/Px8HDlyBH379sULL7yAOXPmYOzYsRg1atQplX2m1KlTJ3Tt2vW0pbyKi4uLGNgsTdnZ2fD5fMjMzMSPP/6Ibt26qbV9wYIF2LdvX5nLtNvtmDt3Llq2bFkhdTR0dlSpUiXFHRQVFWkMPv5Jql27dtDnodzcXJXhqTTl5ubi559/xuOPP47HH3/8tDw4YYA/JUiCPww4SzeIoqIi5OfnK4q2qKhIpbVi8JS/ZboTpmliwJsuFgA0aVhYlkwZwzIJEDBgLp1kCP8A0AAZ3E+6n8hgMQPuMuhNyXNTfr8fLpcLbrdbk8JFwlAyFZZ00iEoQHhCwj/SWYkph2TwXabRYcBeQjDSEYYAAgPWDN4TtGIb0GlHuuDIv3ldDFoTOtA7gkhHGeneIx1xTCaTJiUZIQu/3w+n06mAC4fDodqHriny2ngt7C/2iXTM4biiwxBTxHm9XgWj2Gw2xMTEqFRZMsURKUXazZI+lO5J0gGFwX59Kq9wsA/3NZvNqt6EBHitQPEH70OHDuHkyZPw+/2oVKkSoqKi1BOPEqYym81ISEhQcweASjPE9GHZ2dlqbsr0W3K+h6q3Pi0e5zShEzkG5NzRgygUIRpCIxJuk3OB+8p1KZQLl4Q8eAzHvYSAOEa5H9tC7xwDFD9lwzlTVFScUtDn8ylQiM5YDocDhw8fxvHjx9VcoRNVbGwsqlWrpiA9tqU8r3QCYr/yNa5bnGsEVmQKPjn+9C48cj+CchKs4ZrCuefz+dS6RriL4Izdbtek7OPf0i2Ix1Dsr8LCQgXG+P1+WCwWuFwuleaQY0OChRL2keNCjgmWLddhuU7rnafkj8PhUCCc7BcAKlUb11WZapFAHd8DCIKyDnK8yzWS7SJfk25EhgwZMmTIkCFDhgydT9q6dSvuuOOOkK9de+21ePTRRyskJdSvv/6Kn376SbPtlVdewZgxY0657HD67bffABR/D9CvXz/l+jN27Fg89dRT2LNnz2k7t9TXX3+tUhDFxcUpYKFr16647777Tvv5Z8yYEbQtEAhg4cKFQX1yOlWtWjVkZGRoXFn0Kg386dSpU0Tn6tGjB6xWqwIjfv/9d2RnZ8NkMuHWW28NeUzv3r0jKvtcV58+fVCvXj2cPHkSixYtOtvVAVAM/oRK0xWJCgoKsGLFCqSmpirnJqA4TdOGDRuQlpaGzMxMzTHyITCpCy+8EO+++27ZL6CcatasGV555RXMmzevzKmfZKqyJUuWBAVEN23ahPbt2wcdt3v37nIFNSNV27ZtsWLFCrz00kvIy8vT9Ak1atQo1c6jR4/GvHnzMGnSpFKBgpiYGGRkZKBy5coqRWNOTg78fj+2bNmiwC8+ABpOubm52LBhQ0TXY7PZ8J///Acvv/wymjVrpnF0iCStW15ennI7kg5F/9Rg4j9JCQkJ6Nq1a1CQ9KOPPsK3334b9rj4+Hj07t07LByWnJyMDh06VFg9t27dGtF+s2fPxubNmyvsvKGUkZGB+vXrl+iy4ff7sWnTJuTn51fIOTt06IB+/fqpFH5l1ZEjR8oEEoYSYzfUK6+8gjfeeOOUyjyTatasGfr161diqrZTVUZGBp5//nn079//lMtKTExEdna2BvyXzlfTpk3Djh07ylxu06ZNS4TXDZ37SktLU3E6Ov7QJfOfpEaNGgU5URYUFGDPnj0RO1suXboUY8eOxbx5805DDQ3wp1Qx4Kl38SC4QkcFBogBaKAW6fhCaIRBaAaMXS4XAoGAgkAk5GO1WmGz2RSU4/f7FZxBeMRiscBqtWpseOmuIEEGBunpOiMDwtKphYFYBp65H4PjMljv9/tV0JfXBUDBIixTD//wWO4rIQC+zvZ1Op1wuVxB7ihsBz2kIR2B2Gf8TdCKQA2vSS9eP9uOvwkluFwuOJ3OoCA2601oiFAE+z4QCKj2J/BCNw3CKfzNPuQHbgmGsL8KCgrgdDpV37PcgoIC1dZ8TYJIBA0IVcTHxwf1gXSxYXsSUOKxHIdynOidmfSwj961BoCCLjgueQ6CcB6PB6tXr0ZWVhYKCwuV41FeXh5OnDiBxMRETeo2CXNIoIWwGgAFGRE4Yb/LdtavBbLu7IfCwkIFP+mBFnmsBHX0AKHewYfSwyzSZUXOV+kwQ8hQgiRyveH/Eqzi/xK84mtMv0ZoTM5ni8UCk8mEgwcPYu/evYiJiUFaWpqmjQoKCmC329XYZzuzb3mzwHbiPNGDTnIu6x2CON7kOOSxEiriuemcRfhOujHJ9G8sz+/3w2w2Iz4+Hna7XTOHJYzndrvVlzaynbifzWZTa4SEkPT9QZiPsJF0Y+J8ZL9zveCckeMqOjpavS6BNc5lrqFsHwm4cf3m+4x0SyKsyXVKuh7p1wGWJ/uBa7icExWVe9uQIUOGDBkyZOjvoFBOj4bOTV111VV45513grZXq1YNH330Ee69915MmDChQs41a9YszQMGPXr0OG1PlttsNmRkZAAoTpPz+eefY9GiRVizZg1ef/11dO7cGVOmTAkZOK9oLVmyBK+99href/99jWNJdHQ0nnzySfz666+n1f1n/fr12L59uwYcMJlMuOKKK5TNe+vWrUOCEhWpqlWrok6dOpg5c2bYfRYsWIDvv/8eu3btQk5OTtDrkT5ROmHCBMTGxqqnWQ8ePKiCPs888wyuvvpqzf6jR49G/fr1S3S/Ol/EgPHevXuxZcuWs1ybYlWtWhU2my2iFDjhlJWVpRkTGRkZaN68OaKiovDjjz8GnS9cUD0qKgpTpkwpdz3KorLCPtTDDz8cBOX98MMPmv///e9/o1+/fkHHHjt2LOhhsorSiBEj8MUXX6j+zM3NDRmcb9y4MRo2bKhcMiJ5epxKSUlB79691QNdffv2xb/+9S8sXrwYAHDrrbeiXbt2JZaxc+dOLF26NKLzuVwufPzxxyEdRi6++GKMGzcOw4YNC3t8Tk5OSIhBn2rOuC/6++nll1/Giy++iG7duilQDQC2bNmCWbNmYdu2bWGP/frrr8OCLTk5OZoH109FbrcbBw8ejGjfQ4cOYdmyZTh8+DB+//33U3a5CaVGjRrhiSeewJo1azBu3Liw+82ePbvC3r/atm2LGTNm4Oeff8aQIUPKVcYXX3wRcTuGUkpKymlzyjkTGjRoEK688srTvo7FxMTg8ssvVy5qkah9+/b49NNP8dRTT+H2229H06ZN8frrr+Pyyy9Hnz59Qo6jhg0bonbt2mWun8PhqDA3KkNnVmlpaVixYgW+/vprXHXVVbDZbOjcuTMmTJiAn3/+Gddee+3ZruIZ1bhx47Bp0ybNtrS0NIwbNw7PPvvsGalDaetJxbwL/k1FuEEG52WAt6CgQKVvko4eQHFgml/ISCiDMAP3ZyorBmBluimg+EaXQX26oOhdfxgslkFuprYqKChQcIl0iWDaKpnehY4oMhDsdrs1KaII2uhTJjHwzGAxr0XeqPO6GZg2m81wOp0aaEKCIgSJCBxYrdaw7jESStG7qgDQBMMJZhG24v5ut1u5zvDmkYAS681gNl0vZOouCWDIPmZbM+AuXTPYx7GxsXC73XC5XCpFkoQRZJ0l3KN3gyFExGumoxGBJbr7cBzTuSUhIQEOh0PjTiPT/XAch3L3kUCBTCknU55xPwCaMiXsoh8ffC02NlbVn+njoqOjYbFYlPW4HLesmz5VkwTV4uLiEBcXp3FhIcQir0sCcfJHP65kn8txQklHGvmahFtk2QROZJoq7kO3Ffn0C9cJ1lu6/FitVg3AwnJZXyleC4/nuaKiomCz2RSMFhcXp/rC4/Hg+PHj8Pl8SE1NxeHDh2E2m2G1WpGdna3mmSTz2W4SiuQY4vgM1Wb69pNrDNtPprFiPTmWCRWxbfTOVCxHwluEVli2fr2VDk9c3yVgSSiG8GZBQQGsViuio6Nx/PhxtV5yzBK45HuIdErj3wQF2QZy3kt3HZbp9/thtVo125lOTUJVfF2Oab53FRUVp6Xk33JucX/pUiTnPd+/CFnKvpVrsyFDhgwZMmTIkCFD55sWLlyI1157DUuXLsWXX36JhIQEdOrUCd26dcNjjz1WapqTsmjVqlVYu3atSnnjdrtRrVq10xJcatKkCZo0aaL+j42NRWpqKlJTU3HxxRejb9++yMrKQnZ2doWfO5TefPNNWK1WVKtWDVdeeaXaXrVqVVStWhVZWVmnFUZo1aoV6tWrh9atW6NmzZp49tlnkZiYiBtvvBHjx4/Ha6+9hho1amD79u3Ys2cPNm7cGBIIOxVNmTIlJKgg9cQTTwAAUlNTUbduXSxatEgTVB87dmxE5zpw4AAaNmyowJ/8/HzN095dunTRODL07t0b//73vyN+yvVc1R133IHmzZvD5/Nh9+7dIeGps6G6devC6XSeEvjz9NNPa/6vUqUKjh07hnr16qkH5KgaNWqEhU0uuOACXHDBBRg+fLhyaz5dys7Oxrhx48qUMtHv9+O2227Dli1b8P3336vtercKm80W0gEr0rQ+5VGdOnVUiqKEhATk5OQgNzc35L4ejwcNGjQAgDJBDM8//zyAYvjhwQcfDAJ4HnroIdSvX7/EMtatW1dm16NQaf66deuGbt26qXUplPLy8mCxWNT/Y8eOxYUXXgigGIAqCf4wdH6rVatWuOSSS3DFFVegW7duWLVqFZ566ikcOnQIixYtwty5cxEdHa3mAbV8+XJ8/PHHSEtLC+uCVlHfMUZHRwc5opWk9957D8uWLUOrVq0wfvz4CqmDXna7HQ0bNgzpFkatXLkSc+fORdu2bSvknGazGampqWjQoAGuvfZabNmypUyOL1lZWZg/f365wKHc3FysXr36lMChs6l+/frhuuuuCxrHp0NFRUVYs2aN5n6tNNWoUQNVqlTBv/71LwDFfbV06dISHSSTk5PL5coWHx+PkSNHwuPx4KeffsL8+fPLXIahs6PatWujcePGIQE8q9VaYakVzxfNmzcPfr8f48aN0zh5paSkoH///vjuu++wYsWKs1hDw/GnRMmAOwOtMujLgHZcXBzsdjusVqsmGCpT6+gDyHRHKCwsDHK6kYFkCQTIgKqEXyTswIAu8Bc8oIdDJJTh8/k0IIN0ytGfX/8jHUR43QQFJMAhpXfkMZvNqi2k8xDrrwdtpKNFKMCH22R7yDrLbVLcpgdI9DCBBEE4LmTQmmXI/ud+HEO8Xp5X9ndBQYFyAOGYkS4pMoWOBIf07SEBEToQERBgvxG2IGhBOEIPVkjnGAnGyD4KB7bIfgkHz+j7JlQZ+tRUBNJk/8v2lu4n+mv2er0K1gKgXEuk00w45yJZX9lGBDskJKgfw+HGnoSz5FyVY0ruRyAxHASnb1vZV3I+6q+PY465KPV1kh/E2a5+vx9ZWVkKWCMYlJCQELK99HCT3pFJP2eBv9YguhHJ+rMO/C3HDddWiuOC417fBrKtJHwkpV9r9W3OlHUsWw/gEBYkUFNQUKDWC66Bsu/keND3pR6Q0wNrcpsc7/Ja5PuRbFtuJ5gl33/0MJqsk34t5PUTbNLXQR4X6ksiQ4YMGTJkyJAhQ4bOda1atQo33XQTWrZsCaDY0aFq1aq48MILKxz8ALQpJ1asWIF169ZV+DkAYM2aNaUGu9xu9xkFPdxuN9auXRvyNQkpnQ65XC5s2rQJU6dOxRdffAGfz4fjx4+rpz337t2LevXqoXv37rjppptw8803V7gbk8vlwuTJkyPaNysrC6tXrw6CwujiVJrY99LBR8IhenBg+fLlYfvmfFHPnj1x1113oUaNGli4cCFycnLOmc+piYmJ8Pl8FZa6BSgO1Ph8viDoh+fTO67opf8+9nTorbfeith5hoqOjkbz5s2DgJpwDkZ6hWqPipIcT3wYNhw8FR0drQL75WnrkydPYtq0aUHbMzIyVGqtcDpy5EiZz1epUiXs378/JIygT4ch5fV6NU78b7/9NjweD+rUqYPhw4fj3nvvRc+ePctcH0PnthISElC7dm3ExMSga9euaNSoERo1aoTmzZurfbZt24a9e/cGHXvkyBH06tULI0aMwI033hj0eqjMDuWV/rvoSLR161bNg/qnSzabTYGEobR+/foKPV9UVBT69++PYcOGYfjw4WU+/vfffy/z+rp//34sWLAAX3zxRZAz3fmiRo0alTtNWlkVFRWF7t27l8nxh6lEKWYFCTePTCaTJkZUHnXu3Fnj4Gno3FelSpXCwl5RUVGl3lf8HZWamhoU6/L5fEEOm2dLBvhTgux2uwrIMh2NPshusViQmJiIlJQUVKpUSQWzgb9cSKQ7D51wpAsM3R+4qMpgLSERfRom/qYIdjBFj0yjwlRTMnjOlGEej0cD8tBJhIFappyRZeoDuhJSIuAiYSkG5GUA32T6K8WU1WrVQBxsA0JMJpMJXq8X+fn5yhXH4XAotw8JFUjJFDSsK6AFDhhwZgCe9ZbXKqEE6ewhA+8cG7J/eD5eq+yvUCDXiRMnkJeXB4/Ho8qgGw+dUNjXhYWFcDgcGjCJH8o5lgoKCuB2uxXwQEcm9hlBDZkyjc5N0rGIbUZIzGazqVRI0qGEdQsFsUhIRe8EJIEBHitzt0oIJioqCvn5+eqpGLvdHhJq4L4cb5x/LpcL+fn5MJvNQTBTYWGhukaTyaTSoUlnLQmtyXR4st6yvUONHzqpyDIJrkjHIII+EvyRN12yfdnnXAN4PMdYbGwsbDYbEhMTYbfbYbFYEBMTo9ILMr83017xCyDOA/aFxWLRwB4nT55EVlaWcsoKBAJITU1FSkoKEhISYLfbYbfb1XyTkJtM96QfL1xPuF2uhdyHfxOm0YOO+vURgAZyYx1kCkLpCsSbGa4X0iFKgoIS7GSaMP0YZDler1cD8bjdbuVUJq+XdaKDGtfgqKgolfqQ1862kus424hjhuNd9p2sF1MMcixLJyu5xkooU/+bbUZHLp6DKcrYDzK9GLfJtcaQIUOGDBkyZMiQofNJw4YNQ5UqVTBkyBDcfPPNePzxxzF58mQ4HI7TkpZg+vTpOHr0KNasWYMXX3yxwsuXCue0sH79ehw4cAAOh+O0O37o9cEHH4QMwlksFpWi6XRr27ZtmDp1KhYsWKDS5zzwwANYuHAhYmNjkZKSgrZt22LQoEEVfu7p06eXaf///Oc/GD16ND7++GMsXbo0YucQpmKoUaMGnn/+edSuXRtZWVnqc1vz5s01qZTmzp173ru4tmvXDpdddhkaNWqEUaNG4ZFHHjnbVVI6ePAgcnJyyvQUf2mKi4vTBFJbtWqFOXPm4Prrr4/oye0aNWqU64n/sqigoKBc6UQA4I8//tD8z7R8pamsTjeRavDgwXj44YcBAEePHsXWrVtx4sSJsDAXv/+rXLlyUNqy0uTz+bB8+XL1cB6VlJQUEZCoT18RiaZMmYJLL70UvXr1Uq5DVJUqVcIel5ubi8LCQhV43rFjB44cOYIrr7wS999/P95++21cd911Za6PoXNXTZo0wX333afG4kUXXYTY2Fi0adMGN9xwg9rvu+++w8aNG4OOr1+/Pp555hn85z//CQmFmc3miOd7aYqOjsall15a5uMYfymPNmzYENG9VXJycol1mzt3LpYsWVKuOoQ7X4MGDXDVVVehS5cuZV7/P/roo4jTjVJ+vx87d+7EggULynTcuaIuXbrgqquuCpnO8HTp9ttvxxtvvBHx/jVr1tTMF30MVK+UlJQygUWhdPz4cRw/fvyUyjB0ZlW9evWwTpBkJCRA9neX1WpF9erVg+6pzGYzmjRpcsZgv5JkgD8liPALUNxpDJh7vV74fD4VSGcwnU4OvLFmgBMIDsISSjGbzcolQgZL9al/CK4QctAHlAOBgAqMS9DFYrFoHHVkqpiEhAQkJyerL00kLGSxWFSdvF6vAjykAwwD3aHS8git20YAAQAASURBVEjwx+PxKLiGgAzfQMxmMxISEhT8BECVrwdP3G438vLykJeXp0AmfVvwN8/tdrs1QI7ValWpz/x+P7xeryZ4zuA6U7iFCkZLiInHyWMZKJfORBKy4M2fBF4CgQCOHj2KvLw8mEwmJCUlBVHlHF90V2F7yfbnuTweDwoKChTYFQgEYLFYlKsL3at4fbxejjGCDNHR0bDb7arfTSYT4uLiVMowCbmwLfQppfT9JNtJD36wLaRTESVBBKb9ksANgRd+2cTxyfNnZWXh5MmTSE5OhtVqVeNbAkaEnzweD9xut2Zu6KE7CS/oncBCORaF+uF4kuOG1yPHsxxvsj2ks5HeLYYQEusrYRK73Y6EhATEx8erNH7yGmSqL64dUVHF6b4IlxQUFODEiROwWq2w2WywWCyqLomJiQoSS0xMVONfrjPAXxAb6yyvQc5rwmgJCQnqCSkJVXEsMJ2iBOVkP/Ia5biUYArHnCTbJdAogS7ZVqwP1wxZL14z1xtup/OUdMnh2ssbJTlHAai1WMI5BJ4kOCrXYz0wKts81NzjWNIDeWwrQo169yY5XiUcxbHFNYMQrd4RLhS8Z8iQIUOGDBkyZMjQuax27dqp9NNVq1bFtGnTEBUVhQceeAA33HBD2BQup6L58+dj1qxZGDdunLqvPx265pprcMkllwRtz8nJwebNm9GmTRsMGzbsjKX6onbt2oUtW7YEbV+6dGmZA0qnorvvvhu33367+j8nJwf33Xef+t9isWDgwIEVcq4pU6aUmCqnJH3++ed4/fXXcdttt6FTp07Yv39/RMfRKaBr1674z3/+AwB4+eWXccstt+DRRx9FRkYGunbtCqD4s1zHjh3xf//3f+Wq47kgi8WC7t27AwA2b96Mbdu2nRNP61LDhw/HsGHDKrTMSy+9VJN2ZeDAgejTpw+ys7MjAn+WLl2K9evXo0uXLhVaL73KG8DXQ1LJycn497//XeIxRUVFpy3VV4cOHdT3VP369cNzzz2HQ4cOlequ07lzZ1x88cVlOtdbb70VMs1kaakCgWJoqLzpKTIzM7Fs2TIcP35c4/xTvXp1pKSkhDyGD8BKwOvAgQOafc53qNCQVv3798edd94ZtD02NhZdunRB06ZNARQ7+/z4449BzoLNmzdXrmTdunULCrw6nc6gMXQquvPOOyOaO1Jff/01Dh8+XOb7tLVr1+L111+PyK0nLi4OTZs2LdHJb+LEiRqnyIpSjRo1cP3115fpmOzsbHzyySdlPhcfnj4f1bBhw1JTK54ODRo0CA8++GCp+8XHx6NOnToakMfpdGLHjh1h7++zsrLw3XffnRL4n5iYqOJEhs4PlQR7mc1mNG3a9B/l+lOzZk3UqlUr5DWnp6fjnXfeUenzzpYM8KcEEcCJiYlRwWZCPYmJiUhISNCkMJGwjD4oKoOodJSQAAQDqjyvTFMkA9R6xyHe/BL88fl86odBfgIfPKeEf6TrCQBN/QkKEADw+XwKsGA7REdHa8ABCS4UFhbC4/HA5XKpY2VKL9Y7MTERNptNOcno05M5HA5kZ2cr4ErCIXpog3/7/X643W7k5+cjNzdXwUcMhBN0MZvNmvYn+CJT9Ejx3IRkeL0S8tG7vxAS4TlNJhOcTqdqW7/fj/z8fOUGQjAoLi5OtTeD5U6nU7UBr1c6vcTFxSl4RUIAvE5eo3Q1ItATExODhIQEBUGw/+V18toIUdHaTwIEvG4JSOl/9HARj9W/rt8u4QSXy6XSohGkAKCuVfZfXl4esrOzUVRUpIAOzgWCPryJpBMX4TyOO1lnWQbbijfzBEk4juQ1sK3ksTJ9mxzHeihCzmHpNsR95Vogxx1fd7lccDqdcDqdyM3NRW5uLtxut3LFkmseb94kuCEhKgA4ceIEzGYz4uPjERcXh6SkJLVOcl7SsYp9KWEpuZ6yXVl/uS7RfUpei+w7rpXSyYdrW2FhIY4ePYqcnBw1zuWYDHdtMn2bBBn5P8eGBN4IthDo4dyR7lK8fo5VWVe65fC69WkEJeDFNY5zXaZClGNfwpMS5JJlybGid19ifxD2kuNajk3OOb3rkoSfpNMP95Prd2k24oYMGTJkyJAhQ4YMnWtasWIFfvjhB/W/yWTCs88+i3Hjxp3W895333348MMPT+s55s6dq1wmli9fjqNHj8Lj8SArKws///wzjh49iqVLlwY5SpwJrVy5Mmjbddddh7p1657xukht375dk4qrVatWFQJrNGjQAPfff/8pl1MWzZ49WwO++Hw+7Nq1C9OnT8crr7yC3377DQUFBRg8eDA2btyI1157DR06dDjrfVBeDRs2DJdffjkKCgowa9ass12dkDrdjgcEbObMmROR21HlypXRpEkTVKlSBa1btz5t9Xrsscdw7NixUy7nk08+wZgxY0p09PF6vacF2ASgCZISrMrNzQ3rekBwpjzB0Y4dO+Kzzz4L2t6rV69Sj92/f3/EgGA4OZ1OBcUCxWmd0tLSQu6blZUFh8OBli1bok2bNgCAnTt3avYJ5zJg6PzUkSNHwrqX1apVC3379lXfzy5atAi//fYbtm3bFjK1aJ06dXDllVdqtlWrVq1CHy5MSkrCI488UiZXwczMTHz22Wf4+eefNXOhJO3cuRPffPMNPvroI3z66ael7h8XF4fk5GTUqFEj7D6fffYZ3n333YjrHakcDgdSU1PLnNL0o48+KhNY6PP51Hf656PS0tLOqNuP1PXXX4+hQ4eWuE96ejqqVq2qWWNPnDhR6nvAY489htGjR5fbvb9Tp06n/XOMoYpVae/Dt9xyC3799Vf88MMPmDRpEkaMGIEePXqcodqdeTVp0qTEzzwul6vCnOfKKwP8KUES2GDQ2el0KmDEbDYrWIUQR7igK4Oc+fn5cDqdKkArQQLpMCFdHBhEZnCVAWqZboo/ElZgeh86crAuPJc8B/BXWhYJvvA6CftI1x/pfmGxWFTwXF5vYWEhXC6XclDhj2zjqKgolQ6I10VIh9dFiIhBcqbN0Tu/SPiHIFJhYSHy8/PhcDg0QXmCUQkJCSotUVFREdxuNwKBgHKFAaDaiqmQWG+Zto0BeACqvWRg2+fzqQ9uMq0aUPx0GJ10ZL8QODGZTHC73Zry9e5J0p3GZrMp5yIZuJeEtNfrVe5JdF4iOESoRLr2EOqRTh0y/ZR0Cgnn6CF/JHAl3Ys47+R29qfZbIbVag1KfyRTLLFszhWPx4P8/HzVLnFxcZoxQ5hBuvUw9RvBGM49znk63BDA4LzlvJbp0OSc1rvU6G0T5f6ET2RKK/Z1uNRIcr2QLl2cTxJUoyMU7dnZX9L5iWn4pEsW+5qp6PTzgnNI9qGEnOjeA0DNGb0DklxH2KdyTQ0Eit3LHA6HmgMEV6QjmRxT+nGohyh5bpkajvsVFBSodUw67bBePp9PgWg8VqYX07ehHKMA1NynQxPhJJfLpdkm5xTrTOhMgqASJCVAxPbk2sH5KoEb+f4if3itXDP5w75lO8my9P2lh6d4PdIZyJAhQ4YMGTJkyJCh803dunXDyJEj8dxzz+Hf//43Xn755bNdpQrTCy+8gM2bN2POnDlo3749+vXrB7fbHdFTxKdT69evV+lxCgoK8Oyzz2LGjBklBr/OlO677z4sXbpU/d+xY0e0aNGiTGXce++9eOihh9CtWze0atUKTZs2xcyZMyu0nqUFogoLC5WjD1AcjJW6++67ceONN2LMmDFo1qwZgOIUaOfb5zqm47vlllsQFRWFlStX/i2CUe+9916ZYRymY7Lb7WWaS9OnT8ejjz5apnOVRYFAIGTAvyTJ752pe++9FwDCAihA8fekZT1XpKIT/rhx45QbT3x8fEg3m19++QU2mw2VK1cu0c0jnNq1axfkkJGcnIzLLrus1GNDpVYqq/bv34///ve/2LBhA4DigGGlSpVC7nvo0CHlBDJ+/HgAwXDn6UibaejsadWqVUGp+KiYmBi0aNECHTt2BFA8b7p27YrevXtj3rx5QRBNVFQU2rVrp5nXR44cqfCUM1u3bi2zG9iUKVPw3nvvYfXq1WFT+kktX75cAdfjx4/XPDQaSmazGWlpaahZs2aJ+7355puYP39+5BWPQJUqVULTpk3LfH8DAA8//DB+++037N+/H3PmzCnRXS8zM/O0ubCdCfFh87Oh9evXhwTlpVJSUlC5cmVERUVh1apV2LdvH7Kzs7F9+/YSj7vsssvQpk2bUwLsmjdvjueee67cxxs6s9q+fTvef//9kPdXVL169dCzZ0/cdddduOGGG0pM83m+q1atWiWuvbt27Qr7PnemdH59IjvDks4cDNJ6vV64XC5NQFsGPqVjgt41gf8z3QsBBwmr8Lfe4UO6A8nyZIoYfcoW6fBDoEMf9Jbl8dx6+CKUE4SEjfx+vwpusyw9kCRBINmurI8EkaTDjAwoM3BMpw06G+nbmfWWMBWdc/TuKgzIM1gv03bx2mX/st7yOn0+nwaGku0vXZvYDj6fTwFFdN+QQBIdQXw+n3LRkG43BFJkaiaCPzKQL119pOiEAkBBTjy3HM+8Rn1qKv0c4TXo01zp54BUKPhCv519LsuQwBCdRmT/6evI/qCbEgE9eWPCuSzdVWQ9PB6PSo0mXaFk+ip9e+hddOTcDPW/PF62n3RPCbW+6I/lfux/2TdyH9af7SZdjNgmehCQ10bQhe5chIAk4EPJNUDWSV93/fjSr2tyPEooiP3CdpZtyHXLYrEowCjU2gNAAzVyDunHk2x3/brPuvI4n8+nUg263W6Vhg6AZp3knAOgcTvTp/Li/Jc/+nbUw016SIxlynaXAJ+skwR69O9HbB+uOVzT9H3FeujbSf8+It3tDBkyZMiQIUOGDBk6nxQdHY2kpCQcOnQI48ePx9ixY892lSpUK1euxOzZs7Fz507s2bMH8+fPx8yZM9GgQQMMHjz4rNUrJydHBef/+OMPjBs3DhMnTtQAN2dLhYWFWLRokfp///792L17d5nKyMjIwJAhQ3DPPfdg4MCBSEtLiyhoWBYNHz681H3Wr1+vPrvVqVNH89rWrVsxe/ZsZXG/Y8cOjB8/Hrt27arQep5uDR8+HLfddpt6kGzTpk3n3TXoVbVqVdxwww0YNGhQmY4rb6Cc382dTiUkJJRp/5JSQ+3cuROHDx8O+Zp8gLOiFQgEsHTpUo0r2JAhQ9C/f/+gfWfMmIFdu3ZhxIgRqF69ernON3LkSJUyCQDq169fYqoOKjMzs1znk/rxxx8xefJkzXc/4cZIdnY2nE4noqKikJycjLS0tCCHp0hSzxk6f5SZmalJBadXtWrVgsZ9bm4uzGZzSAcsl8uF9u3ba9xnVqxYUSFjmfJ4PNi7d2+ZjsnNzUVcXBwqVapUqltGfn4+du3ahW3btqltpX1PGhMTg/T0dFSrVq3UuqxcuRInTpyIrOIRyGazIT09HcnJyWU+lq4g69atw5YtW0pMd+hwOEocK+e6mPnlbKhTp05o3LhxifvY7XbExcWhoKAAq1atwscff4zMzEwcPHiwxOPq1asXBISXR/369dO8Txk6d/XFF19g8uTJEUOEbrcbFosFLVq0OGuuV6dTNputxOvKz8+v0Peg8uj8TJB4hsSgPG/66QRitVpVsFw6YDBQS6pewgMMiHq9XuTn5yM2NhZWq1UD3vBGmAF2nl/CJ9xX1o9/S0CCwWGTyaRcOWQgl6/zNQamJRAkA7a8NjpL0AWDaaMAKBiDLjx6UACApky9+0lhYaEKftNxRV6nhEvMZjN8Pp/qCwIzwF/px1im0+lUQJHVag0JB/FLFL3zi4SQeM3y9UCg2HmIoITf71cfZvRAEmEAv98Pm82mUndJ9yc6/BD6oTsNxxjhAofDoa7R6XQGOTrRaSk+Pl6BRmazWQFGXq9XOdfINElsE4Iw0u2K16G/Lgl2sI/1aepk6jQJKoRyXZGwk1RUVJQCnux2OzwejwLbcnNz1XbKZDIpJyYAChKSkJGEGOS8Y3twPJ88eRLp6elqvJhMJlitVgVTsN68dgmZyDWA+8q2kL/Zz5yDEuSSwAvngJzv8nx6QIhziO3FsU2XK7vdDrvdjqysLE0fm81meDweFBQUqHnEL/aYOopjkusB2zE2NlY5kMXHx6txzbpHRUXB5/OptmC7yPVLQj0S2uO8kw5V8tx2u105rCUkJCgHIrYn9+N6Kx3JOCYtFotaZwjISWhQjmk5D+ikJMEttg3XH16DxWJR9WLaOh7n8XjUPCS4JtcVWR+OBT2EKt26JFzD9Y/jhnOc49Nisaj1Rjp5UbwWzi/OFwlQSvCNv4uKtKn25HuZkerLkCFDhgwZMmTI0Pkmv9+PHj16oGXLlmf9y73TpSeeeELz/3//+19kZWXhwgsvRN26dbFnz54zXqeNGzfi0KFDWL16NZ555hmcOHGiQgNap6oPPvgAXq8XNWvWxKZNm8oM7TRo0AAXXnghatSooT6LVmQKLbPZjIEDB2LMmDGl7rt7925YrVbMmDEj5OuPPvoomjdvjnvvvbdE2OJcVYMGDfD0009j1KhRGDduHL766quzXaVTVq1atZCQkFDmYGNKSkq5z3m6gzqvvPIKWrRogVtuuSWi/UuCd1q2bInVq1eHdNKRKd0rWmPGjNGkN4qJicHdd9+NCy64QG0bNmwYMjIysHDhQnTt2hVPPPFE2Hl17NixEp+mf+2113D55ZfjuuuuAwCsXr0a27Ztw6WXXhr2mNzcXA14cKqSkIZ82FBq//796v2zVq1a+O233zB48GAcP35cAT8VEWA2dO4oMzMTW7ZswcSJE1G3bl00bdpU08c2mw0DBgxAw4YNsX//fkydOhU5OTkIBAIhwZ8HH3wQ9957L5YvX66c6l5//XVYrVaMGjWqQuqckJCAJk2aYOvWrREf06VLF5UCU+/ApdeWLVswf/58NRd69eoV0QOSF154YUQuXW+99RZSU1MrLG0oYyYlpSK86KKLsGvXLrhcrqDXeG95ySWXoLCwEI8++ijMZjMcDgcOHz6MGjVqICoqCjt37iwxPeO5rqysLJw8efKsOFI2b94cb775Jnbs2IH169eH3CctLQ2pqalwOBw4duwYpk+fHhH83LNnzwpJ8ZmQkIABAwbgmWeeOeWyDJ1+FRQU4NChQ3A6nZoYbCj16tULvXr1wrZt2/D8889HlL7wfFKlSpVKhNKdTicOHDhwBmsULAP8KUWxsbEa54UaNWoogEA6HRA2sdlsGgiEwU19mhO32x0EwOgD8zLorXdLkEFx6UAhHUVk+hfpxhHKQYUK5QSkB4wI0fB/vYMQYSBCS4RleL5QQALtC+nAIz/YEKSQwEhBQYECQRhwlsANbzzy8vJUaiEJ8ci6E6zKzs6G1WrVpDajI4e8Bj1gJQPd7F+mWZNBeovFopyHmI7N7XYrR6bY2Fi4XC7ExcUhPj5elad3/QgEitOQxcfHq6fcvF6vJs0Pz89rpNtNXl6eSkOUmJgY5KpD0Ee6cEgHHun+I6EVCYrJccI+1zv6sK9l/djX3E+OSY43uhV5PB5Fy0unFoIdLNPlciE/P18BFgRzWJacE3LuSPCH4EZubi6sVqtKNWa329X1FxQUaNpBjm+ei+dhKir2FSWhM7lNul3px7Cc8+wnznceB0CTdowQSEJCAiwWi6rzyZMnUVBQoGBAk6k4vRzXO6fTqSAbq9Wqcf3Rn5/rmM1mg8Ph0EAxsp19Pp+aF0wdxWv1eDwaJydaCbK9CRBJVyvpkFRQUAC73a6BUvSuaVzb6eokgbOioiIFZ0rXJCn5RRrnPdtfrtESgpGwDccD10nZLkVFRSqFIt9LAKj6m81mBZixLL07l3T7YZ/wddlu0mGIdeS6K9+7eKzFYgFQvL4SVpTwpXzPkOu9XCPk+51c3w0ZMmTIkCFDhgwZOp+0f/9+7N+//2xX44xq3LhxZ/X8v//+Ox5//HEsWrTotAXpT0V79+7Fiy++WO7j69ati6ysLMycOROvvvoqHn74YbRs2RLPPfccDhw4gLVr12L16tXlLt/n86FevXrIyMgI63xCzZ8/v8TgzoQJE8pdj3NBjRs3xpYtWxR8UVpw9nxQ3bp14XK5ypwe5aabbsK3334b5O4Uic4E+PPll19GvH9+fj4++OAD7N27NyigaDabMWvWLKSlpaF27dqa16R7d0VLQj9A8XeZDocD+fn5ePTRR1GjRg2NGxDTct11111477331PYTJ06gUqVKeOSRR/DRRx+VeM5+/frh7bffxsGDB/Hiiy/i3XffLRH8OXr0aIWmpuB386U5/vA9NC4uDnXq1EGXLl00/VCeMWno7Or2229HfHw81q1bhzVr1gSlO5o4cSKA4nVHnyL10ksvxaWXXor+/ftjyZIlmDp1KoBip7muXbuGDLbGxcXhggsuwD333AO73Y69e/fiq6++qjDwp3fv3jhw4EAQDF2Sqlevjs6dO5fqipOZmYkNGzZg1apVatv8+fOxbds2XHTRRSUeazKZkJqaqjEnCKWsrCxMmDChwsAf6vfffw+5/ZprrsHrr7+OQCCArVu3Yvfu3Vi+fDm++OILzX5r1qxBTk4OunbtipiYGIwZMwY//PAD2rZti7p16yI9Pf28Bn/27NmDdevWoXnz5mfl/G63Gw0aNCgR/ElISMC+fftw7NixILe1ULryyitx+eWXl9mJL5SOHTuGzZs3n3I5hs6MqlevjsqVK5cK/UjRhOLvpipVqpTolHny5Mmz/lDQ36/VK1ASUpHON8Bf7jYulwtFRUVITExEbGyscmaQaYEYSGWam+joaOTm5qrAdnR0NOx2uwryM5ArQRMZkJbpdSS8Ego2YEBVBmG5v3Q8CeWuAgQ7iUjgQzpFAFCuHxJakk45sgw9aMH9+DsuLg4+n09TbwIG0inC7/fD5XIpsEHCGlFRUTCbzSpnskxhQxDB7/erVDxsV14j92FQm05Esk7sM+mqwTah6w7b2u/3w2KxwOv1KrcaHms2m2G1WpGXl6fai/8TJuNNXGxsrIJOvF6vct6gkxCBBTqF8DyFhYWw2Wwa0IBQhYQBCgsLFZgWCpKQ8IQEv1gWwZZQsA8lgSGCSNxPfriTEA/HJZ1jZIopjiGCJYSX+HQcy6J7DccGz613w2IbSThJOrhwzvLvwsLCoLRzbFOOCenCJSES/byV104IUK5H+jbRj3mWJa9NP5+ZttDv98Nqtap1jFCiHA8Wi0U5HHk8Ho3DC8ce25PXTZepuLg4uN1uNXccDgdsNhvy8vIUlMO5oE97yH71+/0KSHO5XAoU4nVw7uvbj3OFtppWq1UzprkOEnDhuJVOYHwiiZAZ24zzgGM+FDDHc3Bft9utoCoJd3LuyrqxPfV9xz6SIJ4cg3Jt4xyT66Z+LkrolGNRjks5luVY5VMdPDfrrT8X217uJ/uKfcDrM2TIkCFDhgwZMmTIkKFItGDBAtx7773Ys2cP5s2bd7arU6FKTU3FtGnTMH78eOzYsQP79u3DsGHD0KZNGwDA8ePHUbly5VM6x5QpU0qFfoBiF4KzmdbtdKpmzZqoWbMmPv/8c/Tq1QsA8OKLLypHqfNVVatWRW5ubkQBPKktW7bgww8/xNNPP13mc1aqVAldu3bVpLmraJXFJbh69eq4/fbbASAI/PH5fHjhhRdCBsA9Hg/uvfde7N27F99///0p1RcodvC56aabMHDgQE0AqHfv3rj77rtx6aWX4scff8Q777wTdOzo0aPRuHFjHD58WD2YChQ7+ezcuRPdunWLqA733nsvAOCrr75C586dS9z3wIEDYQP55RH7zO12B4EfUrm5uZr/DfeH81vXX389Xn75ZaSnpyM/Px8HDhzAZ599hueeey5o33COMFSNGjWQlpaGEydO4JdffkHXrl3Ve6FeNWvWxMSJE+H1evHJJ59UKMRXuXJl9O3bF2+99VbEwdz169dHBEX+/vvv+PXXX4O+F33++ecxYsQIdOzYMeyxJ06cwHPPPRcW+unXrx8OHDiANWvWlOgQFqlcLhfee+89LFiwAOnp6Vi3bl3QPrfffjsGDRqEBg0aAAAaNmwIoBgsDKVdu3ahY8eOSEpKUmvBokWLTuv7SUXppptuwpAhQ7Bu3Trs378f+/btw6pVq+D1enH77bdj5MiRQYDpmVIgEFB9H07p6emIiYlRAGZpadVSU1PRtWvXCnNha9OmDd577z38+uuvJaZ8M3RuyGQylbnvmdXm76RatWqhbt26JcJvJd3znCkZ4E8JYlAyPj5eAQlMD8OAvgyISvcIOjXonXMAqAC21+tV5dF9RTox6J1xGCDluRispouD3skGgAIrGOCWwA0hDUCbdoXXzu0ylZMMOMuAr3SykMFh1lXWm8CSbD8Z6JcBav4twRD9eRk4p9MPISig2B6SwX+Zmol9RYCEwWlCD/xAxfrS2USmNZMOOPLvQCAAp9MJp9OpcVuS7kFMfyRdL5KSkpQjEAAFTLA9uWAQFCJwQZiAdeOYYKolCQAR+CFExHFMGIH9xHaXr/PcdA9iPTk2pOuNHEOyzVi+PA9BC3mDG85el+4gPJ7AjQTCWEe3243c3FzYbDbV7tLxRF6T7DtZR7avnFOEWqKjoxEfH68B8eQ8J+wg3YokxKMHCznOeSzL4piVMAlf04N77Bv9PGdbc9xarVa1ftC1hu1JcZ2jAxcddjhnmO5J9jWBNc4dwj+BQAA2mw2HDh1SbS/TrklYhW3FOceymHaM55Mp3OjYI+ct6yFTaLG95HrCsckxQOBLOuKwXaWDDrfHxcUhOTlZrSdsS5naj+MR+AsiIhDENYyAFeFQ6aBESYcuOd44hrjeyLRocXFxcLlcQW5HBIXkexgl1266/XAcst/kj1wvZJ+yHnLsu1wuBUGyr9lnhgwZMmTIkCFDhgwZMhSJ7rvvPrz00kt48803zyj4E4lLzqnq5ZdfhsPhwPbt2wEUgz5S6enpePHFF/F///d/5T7HQw89FNF+t912G5YuXVru85zLatu2LdLT07Fo0SI88MADAIBvv/32vIZ+AKBatWrIyckpsxOZzWYrM1DGudCwYUNMnz4ddevWVd99VLR+/vlnFBQUYMCAAWU6LpwTRqgn0C+88EI8/fTTePTRR8tdT6mOHTuidevWQUEvl8uFOnXqYMmSJWEddqZPnw4AGDFihCZN1qeffooDBw7gyiuvLFNdpkyZUmr/Hjp0qMLWt+TkZPWAIB/aDSev14vDhw8jOTk57BP0+u/HDJ27OnjwIHJzc5Geno6EhAQ0bdoUPXv2xCuvvBIUDN25c2eJqTqTk5PRsGFDnDhxAj/99BPatWsXFvyh4uLi0LFjxwpLkZmdnY1p06bh+++/L5ODw4YNGyLar3v37vB6vUEOXp9//jk8Hg8uu+yysIFzl8uF6tWrh00pM3r0aPz2229o1KgRrrnmmojrrldeXh5Gjx6NXbt2YdeuXWHfX+644w7873//CwkZPfHEE0hOTg5776IHAM919erVC8OGDUO3bt3QsmVL+Hw+pKam4siRI9i1axcOHz6MevXqnbX6mUwmHDx4ELt37w75empqKqpVqwaPx4Pjx49H5KzUq1cvdOrUSbMtNzcXc+bMQf369ZVTXVmUmJiIwYMHR5R+1tDZ1ddff43169fjp59+inhse73evx34k5GRUarrkYyxni1Fne0KnMsi+GM2m2GxWGC322G32xUIIIPHDGr6fD54PB7lIsMAK6EhWS6AIPhFBlD1Th5SLFcCR7LeEswheFJQUKAcO7ifBEv4w/L1QV0eo6+f3pGCx4dy6JHn0EvvbiED8xL6YX2k84m8DkpCN0ylJesg+0UPt+gl25F/S/hHQhw+n09BOzyfdHuRQABfI7BksVg0Djx0EdFDNkwVJrfpU/JINyJZhpR0+pCQQ0mAlwSr5LVJeEOCK6H+1kMGcg7o219CTRwneqcSSrr7sD30kA3TR0lYS8IKPF6mhpJvUHrHHn0ZrEcoGE62uSxP/8O6yHEZSrJd9WVKmInzkcCOzMHr8/kUPCcdZwjfEDqTUI4e2JNtzOPY9gR42Eb8EojnDbf+yPbi/3pnL14n54OcCxKq4rZQXxJwH/mlTyiIUc5L2fec0wTxCFqy7lxjJLQmnZl4jKyPHFMSOpPrsYQzZTvJPpIAqFybuX+o37IerGOocchy9OugHkyTZenXAZZLRyhDhgwZMmTIkCFDhgwZKk12ux3t2rXD9u3bK9SdIhINHz78tJ9j0qRJmifdGQzbs2cP8vPzAaDMQf9w6t+/f4mvZ2VllRiwP5+VkpICv9+P1atX4+DBgwCC0zGdj7Lb7fB4PMjLyyvTcY899hgGDhwY8f65ubn45JNPFLhit9tRtWrVMp2zLBo3bpwmXdTy5cuxZcuWsC4SFB860svpdKr5JFWR30/wSXcJ7gDFT4KbzWZMmjQJu3btKrGMuXPnatLMM8C/YsWKMtWlQ4cOqFatWon7eL3esN/Zl1V2u119z1Qa+LNnzx5MmzYtbOA53Pd5hs5NrVy5EqtWrdKAHDVr1gwJ4uTk5CA7OztsWTExMUhPTwdQDLns3bs3IriwXr16YdPLlVUmkwlbt27Ft99+W6bj2rZtGzLGFEoXX3wxmjZtGrR99+7dmvmvV82aNcOm46xatSqaNm2KFi1aoHXr1mjRokUQSByJ1q9fj0WLFuGbb77BokWLwkI/3bp1Q79+/cI6CyUkJODKK6887akhz5QaNmyo0oSmp6ejSpUqsFgsqFu3Lrp06YI+ffqc5RoCPXv2RJMmTUK+lpiYCLvdjsLCQjidzojAK6Zfk8rMzMTMmTOxcOHCctfzdAHDhipe+/btw8yZMyMG9WhK8XdSUlJS2HtLSs9ynA2d/Rqcw6Izht1uh9VqRVxcHCwWi8axRjrbFBQUwOfzqeAunWLMZrMGNoiNjVWOQNKRhgAK4QQZfI+NjQ0LBRDqkKl5rFYrLBaLcutwOp3Iy8tTKcYYcJdBdQaYZRBXAiDyf16PdATihyNeBye2dAOR7SadM6RrBR1xpBMFz0sHkdjYWOW6Ip0xWI7sm9jYWE06LOZRzs7OVm0h0znpwQ62OwP6cXFxajyYzWbVPqyrdCCS/Wq1WhEVFYXjx4/D4/HA4/FoXKBYhtvt1kBbLpcLDocDiYmJCthwOp1wOBwqxQ/HG6B1KeKYk/ux3Zj+jM4urD/3sVqtmh/OA14TxT7l2CeIJt1R6C4j3Xr07jesAx2QJGzDY+X4keCOhN8kJCLTDHEu6ecj+5VjTzoI8YcOShwHElCTx3ObHoiTgJV+XEmIRd8WeuBCQhUSJJTly7aW60RBQYGaW3SAkW3HOSXLMplMcDqdmrmrhx0JgplMJpUPnen1JPDCvyWISJccPZwjwUHOKTpVcS3i2snX5Bhg6jKCR0lJSRoQi/tKsEWmdissLITX61XuRhxDEvorKipS48XhcKg0j3IdyM/PV/OCa5eEPllfvWubhI2kO5ocH3q3MXk9ch3nesC2l+OM80Cusxx7PIZ11o9Rgl3y3KHgLQkFcn5L8EmCZ4YMGTJkyJAhQ4YMGTIUierWrYtZs2ZhxowZFV4201MAwOrVqzF79mwMGTIEQ4YMqZBUGZFIpjyIiYmBw+HAE088gSuuuAIjRoxAnTp1Tvkc9957L9q1a1fiPv/+979x3XXXnRNfYFe0mjdvjlWrVuHw4cMq1dLw4cPP6hP64fTUU0/hlVdeKXW/2NhYpKWlIS8vr0QXDb1uuOEG3HfffaU+wUzt378fHTt2xGOPPQa/34+TJ08iPj4eHTp0iPicZZXf78eOHTvw1Vdf4Y033kD79u3Rrl27sLAWv4dYtWoV7rzzzqDXL7vsMnz66adB248ePVphQaq8vDxER0drXGz69u2LL7/8EidOnMBXX32FsWPHlljGnj178MknnwAANm/erLZnZmaW+Yny0gLuJQEGZVW1atU0Duglfefz0UcfYcaMGWFTY4RzMzF07uqWW27BFVdcgYkTJ+Lo0aOoVasWunfvHnLf3377DatWrcLx48eDxmB0dDQyMjJw5ZVXIiUlBd9++y02btxY6vn1sF0oRQob0AwglEtYSRoyZAjsdjt27txZKuBXtWpV3HfffUhOTtZsP3LkCE6ePBn2uIULF2Lx4sUhX+vevTuOHz+O9u3b44477kCTJk3wxRdflJrOiVq8eDHmzJmD9u3bo1+/fiWmjzSbzZg2bRr69u1bYpktW7bElClT8Mwzz5TZve1c07hx4/Ddd9+p/+X4iImJQaVKlUotY9myZaWOjVNR5cqVMWPGjJD9UqNGDVSrVg1ut1vFjEtSmzZt0KdPn6B7pAYNGuDmm2/Gyy+/jB07dpS5jjk5OaUCvIbOLe3ZswdbtmyJaF8+9P53UknuhNS5EOcyUn2VIpvNBovFooEWKAmrkOIneAP8FbyVAViZpoUDX6ZT0kM4hDL0rh4EOWSgVabOoaQTBoPLDFhLiCGU048si29eDI5zXwbJ3W63KoNt5ff7lbONDOiTuGYAnrAQz8frkilv9MCUDFDziQSCImxb/vBJjYKCAuTl5cHn86l2cLvdKrAt4QIZEJeQhQQ8JDQl6UWr1araQp7f7/droCvCWrwOAhQydZPf70d+fj7S09PVGImKilLpclJTU1VfhnKt4W/5unQ/kvuzTRjMZ10IQ7AfCD3IsUegKpQzlXT5kHCVhEdkexJIoHh90pnF4/FoXFpkuraioiKVCs1msyEvLw8JCQkaiIf9wjmjhxRk+i62B+cK6875y3aRqaF4PXJf+ZqEZvhbusHwGP2bhOxHCeHoLW/1YB3LlWsRIUbOz8TERFitVnUjz/PbbDYEAsXp6zg3eX5CZIRGmHaO4Fhubq5aD10uFywWizqe0I7b7YbNZtPAJSxfwiQEV2JjY9V6azKZlMMaoTTOI3memJgY5OTkqHMQ/pJjl3NLD5SF6jcJ9LEfWCYdiDweD7KysmCz2RAfH6/Kk/CWPi2eBBD5OtM06scA68m2kO5OHD/yHPoPvXL+yTkgxxQdljhGeF4Aan2WY1G+n+jfy9hGhBlNJpNa/9nuhgwZMmTIkCFDhgwZMlSanE4n3nvvPTz55JP45ptvIk6nEU6PPvooNmzYgG3btuG+++7DHXfcgd27dyMrKwuXXHIJmjVrhieeeAKbNm3Ce++9V0FXEawxY8Zg48aNmDZtmmb7k08+icmTJ6vtK1asQKNGjRAfHx9xAC+U5syZU2paq9atW6OgoAD169dXqcf+Ljp58qQCP3r27Amg+PvFSOGXM6mqVatiyJAh+PDDD7Fp06aw+yUmJmLs2LFYuXJlmcr/8ssvsWXLFkyZMqXUVB1+vx9Tp05V9ejXrx8aNmyIFStW4Prrr8f+/fuxZMmSMp0/Uj3yyCOa//kdQyg99NBDuOSSS3DbbbeFBHmOHj0a5OyzdOnSoDQmpyJ+XyOBm6pVqyI2Nha33nprROBBQkICOnfuDAD4/vvv1fYuXbqUGUQoTQUFBUhLSysTNBZOqamp+Oqrr1C/fn1s2rSp1MDu1q1bMXjwYNx+++0YNWqU5rUzBVwaqljxu25+f966dWvUrVsXe/bs0ez33HPP4bnnnkOPHj1w6623YvDgweo1u92Ot99+Gx6PBxMnTsSbb76JX375BQ0bNowIrNBr2rRpqF27NgKBANavX4+hQ4eGdW7Yv38/XnrpJZw8eTIoG0Ek+vjjj/HLL7/A5/PhX//6l3KHCaebb74Zu3fvxuuvv662ZWVl4fjx46hevXrIY3755Zew7wkzZ85Eu3bt0LdvX9SqVQv5+fmYMmUK0tPTS4RulixZArfbjd69e0dwlcXy+Xw4efJkRHP1pptuAlAMXu3fv7/M71fnkhYvXozLL78czZo1K/OxO3bswF133YU+ffqc1jRXDocjJPRZu3ZtWCwWFb8tzd2xcePGaNGiRcjXbr75ZgQCAYwcORL/+9//0LJly4jrFxcXp4HdDZ37YixHZtoIp1q1amHQoEFo3rw5tm/fjnHjxp2hWp4emUwmVK1aNQjSlHK5XGFB5jMpA/yJQIQDpGuB/M1As6SSZVBTBv55nIQrGGCPjo5WZUgXFn3wWTqOyGCtdM6QQANvsoC/oBpZpt7Gk0F8WW99QF4PGUinFxko1juF0DVFHifBExlUljeHJpNJOezQlUQGxtkuDFRL9xk66/j9frjdbgX5BAIBeL1eBXaxL6W7j6wn6yjFMUFQgM4VNptNk95NuqfQGUXvwMNrS0hIQExMjIK26NrE9iQ0ZLVa1ZNf0iGnqKhIHcexRuBKQlG8VpmSjCma+OGX9ZawloQ/pDsIzyvTDIVyT5LjXz+O5WvczvrKYwmNsA4ELjhOCHURPJOOUBKaYD/LVGpsE9aH7SrnhR5qkNa9EnLTX5u8RoIrPp9POS/RqUa6H+mdVvRtKd1h5BuunLucH+yPwsJCeDwe1QZ+vx+xsbHwer2Ii4uDz+dTbixsOwm1SEnoiGAPy+N12O12nDx5EnFxcWpe0nWI55FzT44djj+2N28uOC8cDocmTZ/80ke6QNlsNnU+OVfYdhwTekBG9gHXCY5B/dpJEMjtdqt+jYuL04x1PWgn68JrkNcvQUCOQZ5LnjfUdcsxoXfkkfOU6yjPI13LCLVxDWU/yLaScI8EiuQ5ZTtKhyO+VxjgjyFDhgwZMmTIkCFDhiLVN998g2bNmpX4BHokql69Ovr374///ve/yM7ORkZGBgCoVAbbtm3D9ddfH/GTreVVzZo1cf/99+P//u//gl5zuVxBqVAeeeSRU/5SuTToBwA6d+6MAwcO/O2gH6DYRQcAfvjhB1x00UUAisGfM50+LhJVr14dTqdTpSQLp6ysrHKnK0tLSysxkEH9/vvv2LZtm/rf6XTCZrPBZDKhX79++O23/2fvvOOjqtI3/kwyybQ0CCWEDoKoSEcQLCAozYoo9kUFG1Ys6E93rauytlUQFBTFviquIIoIioLSVIqoNOkttNTpM8n8/sg+x/eeTJJJMgnF+3w+80nmzr3nnnvalPd7n/fHWgN/dNHVniosLMTtt9+O119/HRdffDHefPNNXHPNNeUCAvxd1ufz4bbbbos72BcIBJCYmGg4f2JiIvbv3x+zi01RURHmzp2LHj16GFKp9OrVK651BYDRo0dj+PDh6NOnD/bs2VOjsubNm4d58+bFvL/H48HatWvVTYcSaqospYapI0/JyclITU1VN4gCpfOsVatWZcAfasmSJejUqZO6uVPKbreja9eu6N27NxwOBwoLC6sM/ni9Xjz44IPYtm0bWrZsiV69euGss84ypNhyu9349ttvMXnyZGRmZiq3reroyy+/BFDqqJaenl7p/pmZmXj66afRp08fTJo0STn5fP/99+jSpQv27duHd999V7VF48aN4XK51E29uvx+P7799ls0b94cTZs2RX5+PpKSkvDRRx+ha9euOO644wz7b9q0CStXrsRll11W5Wvt06dPpQCArl27dsUtHdvhUP369dGhQ4cy7Rir2rVrh08//bTWoZft27dH/QyXmZmJlJQUBW1Vpo8//hj/+te/yl2PL7/8crRp06bKoKbD4cAHH3yA2267DTNnzqzSsaYOj6ZPn45vvvkGvXv3Rv/+/TF69Ohy923Xrh3atGkDq9Va5RSlR6Kys7PRvHnzCj8vHzx4sMbfT+MhE/ypQNKVQ0IwElghQEHHERmAZxoYOmLoQVfCAgzAynQ4PL8MmAJ/Qg/8n2CRdCghiBQKheD1epXjD4EOBnPpdCPPo6dlYbkyrRcD90x/JIO+vBYZcI9EIup4ghZ0LGLdGcyWqXWk045MrSWdeHgdhFYikQjcbjf8fr9KIRMKhWCz2VRbsf4Wi0UBOjxeB5Nk+0Rzx2C9ee6EhAT4/X6Ew2FVXmJiogIB3G63ApNYt8zMTBQXFyvwh6Qtv+ykpqaqfrfb7crJRodt6IRTXFyMtLQ01c9+v98w1jj+KH18ShcYPV2VbAP+lX0pgR3gT9CMc4KvSzhFdw/i6xJIoZsPJZ1+2P6SvGeOYKfTqdIs+Xw++Hw+JCQkqB8lpKMPgRJeO6+J4IaE9+RfHfzRHVIkfKNDQ3RJIiQiHWVk/SScpZdVGQzE/ua6JR2CCIMVFxcjLy9PjQXOA7fbrea0zWZT6axkO8n1S7royLWKwA9hHY4Lti3HPF+X64j8si/HDfs8MTERaWlpBsAQKHW4kfCldKpiGWx/jk3p3KSDaPr4Zt9w3LGP+F7AOkqIUtaf6yr7kc+5PhGi4jVGg3j0MSX7X0JzrG+0ccL5xGtnXaVjm3Tz4lpPdzQdSJPnl4CUBDu5zsnrj/edaqZMmTJlypQpU6ZMmTp2dejQIdxzzz01KqNBgwYYP368CqDrgcZgMIjzzz+/WqkLqqqdO3diz549Ue+E3bZtW5kA50MPPYSHHnqo1uvVq1cvrFu3rtbPc7j0t7/9Tbn9AKVpSB599FE8/PDDh7FWZdW4cWPk5+cjPz+/1s6RlZVVaXD6xx9/xCmnnFJme0ZGhrrRsi5TpYXDYUObRCIR5OXlYc2aNejUqROmT58OAHj++eexfPlyLFmyxHA8oSGfz4c5c+bEvX4HDx5EMBhEVlaW2jZt2jT8+OOPVSrntttuA1D622dmZib++9//onXr1nGtK1DqLhQIBHDuuedi69atmD9/ftzPUZn4u7b+G1FGRkatjn9T8VVqairS09NRv359pKamAihNO8SgaTTwzev1YvXq1di3b1/UdJYtWrTAHXfcgVNOOaVaNw/6/X6ce+65mDRpErZv3w63241hw4bBbrcjOzsbdrsdhw4dwgcffGBw16qpli9fjj/++APdu3evdN+kpCT06tULy5Ytw4oVK+D1ejF//nzs27cP33zzjWENq1+/PrKystC9e3dce+212LlzJ3799Vds3LhRwZlff/01GjdujK5duyItLQ2nnnoqJk6ciAsvvBDNmjVDUlISfv75Z/z666949dVXsWLFiipfX48ePXDjjTeiQ4cOVTquXbt2yM7ORps2bbBly5Yqn/dwq2PHjujVq1eNwMR27doZ0svWhrZt22Zo3zfeeANbt25FWloa0tPTEQgEKk3zBZTWNS8vr0Kwp7pAqtPpRL9+/Uzw5yhRSUkJtmzZgvT0dJx++ukV7rts2TLMnDkTzz//fFT3xaNN6enpOO6448pNXxYMBrFp06Yqf86rDZmRtgrEwej3+1WAnAFpCcLojh58yMC4nuoH+DPYKoPBMrAsg83lQUAyoMz6MUjL4C1dd6SzEI+X6WcoCTVI1xoGzwkQSccHCf7IOstrkZCFDGjL80o3IRkwB/50XpHtJNuCKb8YeJcQkSSfWR5BAD2ALZ2YJLSi95/sE/YlYSQZpKc7DduSMJV01aHYTpFIRDmrMDDO8xBkIriQnJysvhTRPYXtIsEP1kv2kw4RSNcR2V9UeUF+OaZlX+ptJsddtDkjJR1JZDtIBxsew3ROBQUFCqyiew0BCh5H0Ewf/xI2IuxQUlKi0lBJRyv9unXXGB2Ukvvp44fb5XiQ1x0NitKBH1kf2V7l9ZMEM9hX0jWJAJtMQcj+4PgkkCTT8UmIj5CRBI54bq4fdBfiuiXnuw6nSBhIwolckwKBgBoTvAYCY3Iey77S25LnkPM/2tjU54Fcuzj+gD+d28pbP8obEzIlGOvC+kUbV3pZOpimn1/OfznPZDvoaf04P9j/cl2WYKyEUOU4k3OedeV59fcCU6ZMmTJlypQpU6ZMmapt3X777Rg2bFi5r//+++91Av1QCxYsiLp97ty5+OOPPwzbunXrVhdVQr169SpN0XM066STTiqzrVOnToehJhWLrta1KafTaQhgejyeMmnPpNO9lN1ux3//+1+0bNkSLpcLWVlZdTZuduzYof53Op3Yt28fXn/9dTzzzDNl6qgrFAohLy/P8BttPFVYWIhQKKQcxIYMGYJWrVphypQp1SovGAyiefPmOOmkk8q4VcdTY8aMwU8//XRYwB+fzxfVvaRevXom+HMUyel0wul0IiUlRY3V448/Htdeey06deqEbdu2RZ0H69atw969e6OCP6mpqcjOzq7274cZGRlo1aqVSjd26NAhfPvtt9i8eTO6du2KZs2aIRgMxuSGVxWdd955VQIimzVrhoEDByIhIQE7d+7E+++/j4ULF6KoqMiwX25uLvLy8nDFFVegf//+SEhIwKZNm7B161ZMmDABO3fuRH5+PgoKChAKhdRvvCUlJVi6dCl69+6N5cuX45NPPoHdbsfKlSurdX0dO3asNrxyxRVX4JRTTsGvv/6Kbdu24ccff6xRCtO61K5du454NzKv14uCggIF9tSvXx9du3bFGWecgQ0bNiApKQmHDh1CYWFhpWU1b9681sCN/Pz8uKSYNFW3InNQkVJSUpCeno4RI0Zg48aNWL16dd1Urpb0+++/4+uvv8YFF1wQ9fWCggLs3LnTdPw50sU3xYKCAiQnJ8NutyvAwO/3qw8uutOPBHLkBKDrAl0spAMCHWAYWJV5imXQVocwZD0ZfAdK7UQLCwsVvCCD8nTqSUxMVAF46ewiA9iEaegCIp1j+IGBikQiKsUYP6QzsMtjSkpK4PV6VWCY5TH4y+uUH+IkiCHdWXhuXiP/Mo1QOByG3W43wALS/SM5OVm5IRFYkWCDDMDL4DbPybrJvtEdkOjiRPeMQCCgvlSmpqYq6p3ghwRPJJjA9qS7EeGJ4uJi2Gw2+P1+AzxCmCnaeGG7S+iM16mDQTLlGa9JB0rYbxJ6kRCALFOOl2gQg/4/QQ62CceovBbWKSkpCU6nU417plBzOp2q/hx7FosF6enpChJjWRznbF+LxaLa1uFwKOiN81iHKbg2yDkn1wQJDck0U9ymQyhyTsoy+Le89FAEXWTf6iCRdACTLjucX9Lpy2q1qrHNsZ+UlKT6nnPE7XajpKREtRVBNUJYnHcFBQUoKSlBenq6anMJshEGkq4//J/nZP1ZN44Ppg2j6450byopKVHrH2EjtkEkUupARKhOBywleMftXPMlsBgKhVBUVKS+5NIlSY4Dzi05l1iWTMfH88j5qb+mg2hc5+QY4vn0McZ95EOH/aSzkZ4STF//OfaSk5OVexPrzjVbB4FYrr6umDJlypQpU6ZMmTJlqnbUoUMHbN68uVYC3UeLpkyZgptuuinqaz/99BMaN26MBx54oNbrceWVV6J79+4YN24cxo4dG3Wf1157rcy2O++8s5ZrVqoGDRrUepqzulRKSoohqHjeeeeV2edIDOQNGjQIV199da2eo1GjRur3s7Vr1+KOO+5A69at0bFjR9x1110oKioq45hDzZw5E1999RXeeOMNnHfeeejbty/++OMPnHXWWbVaZ6DUTeOaa65BSUkJXnzxRSxfvhzLly/Hiy++aNjv0UcfxWmnnYbHHntMbXv44YeRkZGBa665plbqtmPHDkyYMAGvv/46rr32WkyePBkvvfRSzMe/+eabWL16Nf7973+rbZ07d0b9+vVrobalatCgATIzMw8L9AOUBs2CwaAai1Tnzp3LTRFl6shTgwYNUK9ePdSvX1/15UknnYSTTjoJAwYMwFtvvRUV/Nm7dy/mz5+P7OxstGzZ0vAaAbrqKiEhAWeccQbWrVun0vq98cYbhn06duyIX3/9tUbnkbryyisxceLEct0hytOgQYMwaNAgBAIB7N27V6X9krJarZgwYQIuuOACtG3bFgAUgLNq1Sp1jR6PB/Pnz0fbtm2VC8WkSZOwePFiZGdnV9vd6N1334Xb7cbIkSNjSmUWTeeffz6A0t/SFy1ahNGjRx814M+WLVtwxRVX4Ntvv62y21FdiTezM27Svn17ZGVloXHjxgpG++2332JaWz0eD+bNmwe73R53Z7/t27cfla5Pf3UxW01F6tixIzp27Iji4mJ8/PHH1UoleKRp4sSJyM7OxpgxY5CZmWl4bfXq1fjhhx/g9XoPU+3+VNXedf5iomuI1+tV7hc2mw1JSUkqQOxyudTiSVCBQWqgFMBhEF26Nsj9ZKCfgXGn06lAGxl4BqBgEsIbFIOseXl5yM/Ph9/vV6laJDQhr49Bbwl7sBxOXOn2IesunTckAEMIgCAMr4Xl+3w+lQ5LuonwWpkiS57PbrcjMTERgUAA4XAYwWAQPp9PBZMJ1QSDQXg8HhQUFJS5buleQoDLarUqQIQBdhmg1p08ZN+y38LhMDweDwoLCw3XxWvy+/1wu90Ih8NIS0tTZXD8EEBgf7NfgD+hDZvNBo/Hg9zcXJX/VKYlkv1B4CQYDMLhcMDlcimwQ8JE7FsZxGf7SNci3bWDHxj4YH8RLCCYwbKY8o59xH6KRoSyDwhxcQwROGBf0EGJ7cxr83q9alw4HA7DmJaQmXS4kW4xvB4J3XFuSscT6eiju8jIdFd0fGFbSPcZ3UFFQiHyC4EEeGR92CYSMNIhDglNsU05P1NTU9VYikQi8Pl8CozRYTaXy4WMjAyVYk7WXXf44TgCSr+EuFwuNfbpwsU2KSgoUOn2mJqQkBzhH5k2Tc4JWjLbbDY1H4qLi9WHjkAgAJ/Pp1LFEeSS7cPyOD4lRCjhQrY1xzmP5RzUgUEClQTSWC7nqRwvnHvsF84/2fdy/MtxIdc4jke2MbfJa9VdnjgudDcrCZnJ9VKWw/dCPpfAH//nPjwf21WmnOSaot+5YsqUKVOmTJkyZcqUqfiKd9B//PHHWL16NW699dbDW6HDpGnTppUL/WzatAl33303BgwYgC+//LLW6xIMBnHbbbdh4MCBVTouOzu7lmpUqtatW6NRo0Zwu91HxF2r8dLUqVOxaNEitG/fHuPGjcPxxx9f5rtoamoq/u///k/dqHckqKioCJMnT67Vc9jtdgU97dq1CwsXLsT06dORkpKCQCCA3r1747777iv3+LS0NKSlpcHlcqF58+bo378/evToUSYoEm+99957AErd+letWqW262Djaaedpn4Do7KysnDrrbciOTkZgwcPjprGrCZauHAh/vnPf2L69Ol45ZVXcMstt2D8+PExH9+rVy+88MILBvciHYaoDZWUlMSU+qU29M033+Dyyy/H+PHj8fLLL6vt0RxgTB25qlevnoqZRdPxxx+v/r/pppswePBg9Xzu3LlYuHChep6fn49PP/0UBw4cqHG9mjdvbji3rnhCP0ApyFQTJxObzYZLLrmkzPYuXbrg1VdfxV133aWgH6lnn31WHffpp5/i5ptvxjnnnINly5apfdasWVNt6KdLly448cQTccMNN1Qb+pHi7956ytXK1L59e9xyyy01Pn91tW/fPnz22WdlXBmPJD3++ONYuXIlTj/9dDRo0AB79+7Fnj17VOzgl19+wc8//1xpOQsXLsS4ceNw9dVXG5z2aqpwOIy1a9ce02llj1UxDh6LEhMT0aRJk1quUd0pGAxi//79hm3bt2/Hf/7zH7z11ltq28CBA9GiRYu6rh4A0/GnQjF46XK54HQ64XA4FCySnJysUmpJhxa+Lt1eGATmFwwJ2fBBdwSCB7rLj3SaYcBUd22g9WtRURFsNpuqm36MdDMB/oQbZFoZ6cYiA7pS0mFCghD6dckAsgyAS3BBwjR6oJrAVTAYhN/vh8fjQSQSQUpKCkpKStQiI91HUlNTlQ2uvC4JILHOrGM0VxS2uw426a4sdHkBSr/sEggoLi6G3+9XYAGhLrqhAKV55FNTU+F2uw0uJ8FgEKmpqQgGg8q9iIsp+4iwBR1OZJ/wOu12Ozwej4IQokEBso94/fI6dWBC9pmElAhPEICRriwcv/r5dNhFAleEWQhvSBBLOqCwDI/HA6/XqwAVQgXSIYjgkITAuM3hcCj4gk5ecmwBxhRxdDLhOTjP6FojHX9k+iNKv27dUYn9SKcZHRyS9ZQuRPpY1tOZsX3YDmxTQhq01iUEQ3CGcJAc/9LpRQemeA1ynhcXFyMjIwMejwc+nw/5+flqzng8HqSnp6u5JOcf10W2JYE3WXdZN45DCfxJ+2bOHzme5bWzfAnKsc8IuXH8EYTheGe/cs7LtpDSXW6ke5JsS931x2KxGAAflq0fL9dk6Twnr0dCnrw2uR/7V4e8EhMT4XA4VF10YI3nttlshvcSrhH63Kwtu1BTpkyZMmXKlClTpv7qys7Oxp49e/DCCy+gdevWKr1ReWl7jmVdf/31GD16dLmvT5w4EYsWLaqz+vCGla+//jrq63feeSfcbncZ158ePXrgggsuwF133VUr9br55ptx8803o7CwEHv37q2Vc9S1rFYrOnXqhMTERFx//fW47777kJ+fjwceeABWqxUtW7ZEly5dkJWVhSuvvLJMqqhjXY899hj279+PTp06GSCjrKwseDwe7Ny5s8Lj09LSsGPHDqxYsQI9e/aExWLBN998g7fffrtcN6t4KD8/H0uWLEHbtm0N6Wb+/ve/48QTT8R1112ntvH3DmrYsGFISEiA0+nEu+++i9mzZ5ebvqG6euuttzB8+HAAqHIqnUaNGgGAAcJp3Lhx/CpXjkpKSnD55ZejU6dOGDNmTJ3eub5t2zZs27YNX375JbKysjB69GjMnTsXr776ap3VwVTN5Xa71c25wWBQ3bRJZWVl4brrrkOfPn1w2WWXIScnB1deeSWWL1+OZcuWITs7GwMGDEDz5s3x22+/4cYbb8TQoUPRokULdOvWDYMHDy5TZix1+u677wzwS21q1KhRuP7669U8rq46d+5cZlskEkHLli3LdU9fvXo18vLyanTeaLriiitgt9vRrVu3uAXxI5EI5s2bh/nz52Pz5s1VOvbCCy/ExRdfjJ9++gkrVqyIS32qqoYNG9a4j2tbjKvOmTMHc+bMQdOmTdGnTx80b94cu3fvrhLouWTJEtxxxx14/PHH0bFjxxrXzWq1YseOHdi2bVuNyzJVt5JGJ7GoXr16ZTLCHK365ZdfcO6556q42/79+zFz5kxMnz5dxd3OPvtsvP7660hISMDmzZuxffv2WnOYjCYT/KlATMeUmJio0tQwUMkAMqEA6QjjcDgMwXaZ7qi8QK90gJBBWuDPgK6cTCxbOkwAUGllGJRl0F66DREmsNvtBgcIlks3FJ6TAVoG7VlfPRWVdB+KBjDQWUIPtnO/pKQkQ4oqGUSnYw4dWwhKMYis18NqtSIQCKg6A0aHHAmPROsXlpGcnGwAB/ia7sTh8/ngdrths9lUMJywCB1vPB4P/H4/nE6nchli3xCaSExMhN1uh9frRWJiInw+H1wuF7xer3InslgsKCoqQnp6ugrAMz2TBFxYb4vFgrS0NAM0oDtJRRt7EuqQfSvLYF/z/JFIRI1BwhE6pMYxJZ2wJCAjz6HPDb0ObF/+LSoqUg4uutMU68rz0TGG45vjJS0tDZFIRM0fAGrs8lokQMRrttls6tzFxcXqeLYj53s0cVxK5xeZ0on7RHtjJBgnHZrk9fKHDQlL0YmM84P/E/DhdbMP2afcrrsU8dpYd7YHxyEBHeZ29vv96stfSUkJMjIyAJTabvt8PtVnoVBIAT0lJSWGeS/XLTlu6bLFNYzOQdLFi2s157EEuWR/6S44BNp4PdyfaxOd4aSbGx/sX7mOSxAvMTGxzNoogRwdrJEQGKFKliNhMjln5HG8PqZXkw5REiKS5ehtSJCRdZdly/ZlH0pQSfaDPJcpU6ZMmTJlypQpU6biI96QctFFF2HUqFHo0KEDUlJSAJQ6iEydOvUw17DutW/fPjzzzDMYO3ZsmVQymzZtwsSJE+u0PsFgEP/85z/L/a3g3//+d9R0Sc8++yzC4XCtgT+dOnVCSkoKHA5HraYVqku1aNECLVu2xPPPP69SDezbtw8bNmwwuEvwd6G6TIX3+++/Y9OmTXGHTqqqV155pcy2zMxMuN3uSl16161bh9GjR2PKlCnKOSc1NbVOQJW+ffuidevWBneq5557zuAYAwBnnXUWbr/9dpVuq7Cw0PB6vXr14l63Xbt2qf+rGojnjWSynvq6VRtKSkpC586dsWrVqsOarqKwsBAjRozAnDlzDlsdTFVPRUVF8Hq9KCoqigr+tGzZUqWiAoC2bdti6NChWLt2LbxeLz755BMMHDgQl19+OZYuXYr9+/fjzTffBAA88cQT6NGjB5o2bRpzffbu3YtPPvmkTp0OzzrrLPTp06fS/fjbd3lq1qwZUlNTDWvwmjVr8MMPP2DAgAFl9t+1axfGjx8fdxCmZcuWKjVpvPTrr7/irbfeqjZou23bNrRv3x5XXXUVNm3aVCuwU0Vq1KiRupn5SFYkEkFWVpZ6vnv3bnz00UfVLu/TTz+Fx+PBV199FY/q4YknnsCJJ56IUaNGxaU8U0em0tLS0KRJE+zevftwV6XGmjlzJlavXo3evXujefPm6NSpE7755hv1fa53794YOXIkmjdvDgDq/Wr//v2455576qSOZqqvCjRnzhwkJyfDbrdHdTJgsJjBa5lWimmQJFAhA9XRAqwyDZGEieR5ZaCaaciCwaDhwdRLwJ+wS7TgPM8vA76A8Q4Iwi/JyckKfpLORoQGPB6PagfpAKQDH0yfI12EpKMIr5P7MqWXdO/hm2kgEDAEvnUnEoIDbAeCALJ8Pe0M07jR4pbXK0EgmdKJcENCQoKCL2Sqq0AgoByJZMor6VZDJxWeIz09HWlpaYaUQ0w/lZycrNJlcRywTJmmiu0qwSgJNbD/KLaf7Dv2hQS0eO0yrRHBGT7nOSWIIMtj20gXHXl+6SCjAzO625CsP52VCOqxPaWblLyGQCCgrpVQCb9U2+12OJ1O9YWafSnbRgIQPL/uVEOnIum2Io+VafPYloRxOCalK5MO10nwT3f4kWm1ZHo2AkThcFilA2Q/yTVCrnPSnUU6tMj/JXgj57285oSEBDgcDtjtdjVf2N68Vp/Pp+pEsM3n8xnGMa+P1801mgQ7YShCmFyfOebk+NLdi7gOyrEuxz/7TI75pKQkOBwOeDweFBUVKUCJ9eVc4zpP8Zxcj+XaxXEpoSUdxJHgXrTncl7xdTl+OA74ICzIdZHXoDuz6e9NTH0pU9253W643W7DXGC7sb9ZFvvZlClTpkyZMmXKlClTNReD10zrMHLkSPTo0UNBP0BpQL6200UdiZozZw7eeOMNQ8qQnTt34tlnn8WMGTPqvD5ffvklJkyYUOE+brc76va333671vrwuOOOA1D6/Z6QxNGudu3aISUlBVdddRXat28PANiyZQt++eUXw34lJSXIz8+v07rVq1fPMD/rSkOGDIHL5Yr6WlJSEp5++mn06dOnDCBTkb7++muMGzdOBanpol/b2rp1axlIRU+x07NnT0MKN72fayO9W05ODoDScaWnhahIjz32mBoTcg3Q05XFS2+++WYZwOZw36Dl9XpN6OcoldfrVb+TxzqOLrjgAgVlAsBHH32Er7/+ukwKoGXLlmH9+vVVqk9CQkKdQyGRSKRSYPL999/H3XffXeF+WVlZyqlRavHixcjNzS2zPTMzE82aNat6hcvRyJEj0b17d5xzzjno0aNH3MoFSoGPmrjrLVu2DB988AE+/fTTOu9foDRlXdOmTaucoqyuRUOCeGr+/Plx+3yYlJSEgQMHGlL+mTryxfhlrA6yDofDAKAd7dq8eTPeffddPP3007jiiivw+eefq9cuvvhinHPOOWWOufvuu/Hpp5/WSf1M8KcS0R2DIA4dGeRDusEwwCyDt9FcGvTnuhtDeXf7yEAuoRAGbFlffV/9OO4nXVb4mg5UAGXde6TbinRr4V9eTzR3I9ZTXqeEJaQjC+tEZw29vWSAW6a2ks48vD7d0UYG8xncJyhCQIKgA4P9EuCS9eG59TRisv68bnn9fC7HiwQL2F4MxEt3EXldMtgvHakkxCNBAdme+jiUbadfs2xDHWCRDkjS3UP2k4R/9DGlg0f62KUkyKZDDhKoku5csgx5XpnCi3CFvDYJ78k5p89NCd/IVGZM0VbRnJb9SUlIQ2+naM95jMypKdtPP5Z1keCMnPey/eUYDgQCZVJL6fWQ/RztmgmRWK1WZGRkwOl0qh8tpIsSwR/COtIZSkKLEjyTadQsFosBjiMIJ+Eh+ZBrQHn9Kx86/ETHG93iUB9/0dpDX7f0fi7vfUS+Vt6Y0a9Tn7f6+4lcr6RDlJxrLIuOXex3CU7xeMJDEjiVa63sY1OmTJkyZcqUKVOmTMVHXbt2xahRo9C+fXt06tQpasAoEokc8akJakv5+fmYN28eli9fjn379uHXX3/FxIkTD1uaiMp00UUXRd0+c+ZM7Nmzp1bOmZaWpv5PTU1Fq1atauU8dakOHToAANq0aaO27dmzB4cOHTpcVVIqLi4uF/CqLWVnZ+Puu+82pMKSGj9+PK699loAVUsL+PHHH2PevHnqO384HMZtt91W8wpXQ+FwuEwwWIIzOtBkt9vjXgcGW+fPn6+cxyvTkCFDDP0if4uranqjWLRnzx5Mnz69TD/XFmRk6tgXf+fjDcqxqFmzZjj++OPV81WrVuGXX34ps0Zv3Lix0tSDUgcPHsSyZcvqNG3l1VdfjSFDhiA9Pb3cfX777TfMnj07apxGymazITMzs8z2pk2blknR5PP58MMPPxhc7Gqqbt264aabblKZFeIln89XbvwzVu3YsQMrV67Ejz/+GKdaVU2NGzdW6YuOZMmMKPHU119/XaW5WJFyc3PLwLqmjmyVlJTA6/Xiq6++wsaNG6OCiFJJSUm1AlgfaWrevDlOOukkNGzYMOrrvXr1qvL3qqq63AEm+FOprFYrioqKUFRUhMLCQhUcJRAQCATgdrvV4knXHYvFohxEJIRQXuBeBmH1ALAOBTFQSnJaHsfArIRNZABfBvKlk4Qe9KWLhQww05lCur3IVGIMBEsoRYIZTIMmU/cwEK+nbpJBdAkxSMcMmeJKfkhieXTnoZOS3hZ00KFDEl077Ha7+mBKFx1uk/2gtzthITrw0O0nEokoRx6/3w+r1Wpob34YlinW7HY7UlNTldMSU6AlJSUhHA4b7Gd5XrYJwZhwOKzqk5eXZ3De0CGsaKmB6Mxhs9lUG0kwQm+D1NRUpKWlKbcimcJIjgUZ/Jd1kH0u54F0TmF72O121a+8loKCAuX2w+vX5wxdYQAYXieow7lMwIRtS7CDfSXhDglLyQ96gUAAHo8n6rymmO6K4n4SbuI55PiWZRL8kDbUcv5JAEf+L2Edntvn8xncgzguA4GAoZ6sow50cXxw7skPlZxvHINpaWmoX78+XC6XSo1Gpxk6+DB9m8PhMICFHCcS3OJ1JCcnq/WR402OI+niI/tSd8nhOCjPfU0eK1PrOZ1OBcbI/WWbSqhOQpMSzpROZDJdG+vDPtbnjL5eSpcsCSVKSEdeK/9y/ZAQIOcEy/V4PPD5fIZ+lut2JFLqhMW24LosIUmWVRd3AJoyZcqUKVOmTJky9VdQgwYN8Nhjj+GBBx4oN1XTG2+8gdWrV9d95Y4A7d27FzfeeCN69+6Nvn374sEHH8SOHTswf/78Oq3HW2+9FdN+t99+O0aMGFFme4cOHdC1a9d4VwsA1I/V4XAYBw4cwLZt22rlPHWpc889V/3/22+/4fHHH8fvv/9+GGv0p6666qo6H38tWrRAdnZ2uYGEbdu24YUXXkBOTk6VnRQaN26Ml19+GXv27MG5556LoUOHHhb3mMcffxzTp083bGOqeQBlApZpaWlxr+eBAwfwxx9/4Jprrolp/6ZNm+Lzzz9X/cLfSwHg3nvvxcUXXxzX+n388ccYMGAAFi9eXAY+qw3IyNRfQ263G3l5edi1a1fMDmqZmZkYMWIE+vfvD6AU0n399dcxa9Ysw34bN27EggULKkxDFw6H8ccff+Cxxx7DwIEDceGFF2Ly5MnVvp6qqH79+rjwwgvLDfoCpTGDhx9+GB988AHWrFlTqRtYx44dDc/HjBmDhx56CK1btzZs/+STTzBmzJi4ud9kZmaiX79+GD16NP7973/H1fEnEomgVatWNQYup02bVqmzUm0oISEBzZs3r5N0lrqefvppfPDBBzHvb7PZDKkw46XZs2fjkUceiUtZbdu2rXDOmDrylJeXh0ceeQRXXXUVlixZUilclpycjAYNGtRR7Q6fzj77bPTq1avctW3jxo0xOUC2bt0a559/Pho3bozevXtj+fLlWLVqFS699NKY6mGCP5WIoEVhYaFK9VNcXKwAhNTUVDRo0KCMY0ZJSYlyj5GBZkJAMgjLYGs0MEBCMdLlh+4bOhwgHWhk6h5Znu60wjQrOiQg09BIAEK6ykjXFElwSwAlEokoiIQgFN09eB4JRUkXIb7ONmN7EuwIBoMKfmDdCR+wrhJm0oP4PDfTeBFMiuaUobt56Cl1pCtJKBSC1+tFQUGBCnqXlJSoL006qENnDBk0T09PR7169eBwOOB0Og1ARnJysrpGAhk2m02NVwIB7CPpiGK1WlX95DXpbcJ2IQwl95F/Od7p3pKUlAS73a5e09PDcaxJyECCCNIBR44DAljsZwmuHThwANu3b0dqaqpqY/YD4TJ5fXa73eCmRECLxxEQIWjGOvMNTHdB0l2NdIhDAhdyjZBQkyyXbSKPkeNXnofzjtcix7ucU3r7SpiI7ZKamgqXy1XGHUjWjdfNdYDlStckgnN8XcIeLAso/cElIyNDgVVMVcgxCpTCUXl5eWr8BwIB1RfSdQaAAvW4PrNfS0pKDDCfXM90YEYHz+R8YN319F866ENx3ZLAjDxGT8fHccR+lmNcPrh26ONA9rXebxKuA0q/hBcWFhqO44N1DQaDBjcxPc0jy+T7kt42ABTMRYepSCSi0rBxXCQnJ5vgjylTpkyZMmXKlClTNVTz5s0BlKb4at68eYU/rnbu3LmuqnVEa/PmzVi1alWdnvPSSy/FW2+9hcGDB+OEE06odP/FixfjrrvuKrO9W7duePHFF+NeP44jALjsssswceLEuJ+jrnXdddehX79+6vkvv/yCRx99FM8///zhq5TQt99+i0mTJtXpOVu3bo169eqV64Lxzjvv4Omnn0ZRURE2btxYpbIXLlyIZ555RqURO+6447By5coa17k68vl8hucyJYvumOV0OqOCktWV0+lEt27dsGfPnpjTfOmQFH8fAlArzltOp1PVTQ+en3TSSVi4cGG5rlCmTJWngoICrFq1CuvWrcPOnTtjdjRr06YNLr/8cpWOcffu3VH3++KLL7Bhw4Zyy1m3bh1ee+01PPzww1izZk3VL6AGslgslUIWNptNvSetXLkSW7ZsqXD/Hj16GOb/7Nmzo6Y7O+mkk9C9e/cq17k8nXnmmWjXrp16Hk/HH6fTiaZNmx617pMZGRmoV6+ewSGxrvTee+9h1qxZFcJvALBp0ybceOON+Pe//11roHPDhg1V1oWayOl0YvLkyVFBd1NHtmiwUdn6YLFY/hJA8fTp07F58+aorx04cACLFi2qdO4CwODBg/HEE09g+fLluOaaa9C0aVN06dIF/fv3j+oCp8sEf2KQzWZTri1erxderxcJCQlo0KABMjMzkZmZqQLNLpcLycnJBncVBrwZAKY7hw4+AH8CJLprjoRtAoGACjLrAWkCEQw4838JzEh4RbrfMCAsHWkcDocBbGA9pGOLBItYb+nGAhhTR0kHC3mdDJ4zgKzDRrJMBpol/CQhE72tGXS22+1wOByw2WxqX/YRYRq2pQShpLuK7BcJQbA8XkNhYSG8Xq9yMQJK7x6R8JfNZlPBdfaHLN9ut8PtdqtrKy4uVg4pcn8JQzFgT5BIXhvbT/YR+0Gm8pKOM3TR4eKtAwisCwEyu92uwBoJwfEhx144HIbP51NjmmCAhLgIHzkcDuU8I6Edj8eDgwcPGtyZWHeOe/YpoRfCSSyDD8AIdOlzSzppcV+OabYNt+swj5xvsmzZ9rJ9JYDCtUKHKuS8ZF9Ec/fhubmPhEM4FvlXglIci7K++nWzfzhfpBMNx76+TvFa5FwIhUJITk5WcA+v2Wq1Ijc3F36/H8XFxfD5fPB4PGVSgLFeHNcSDEpJSYHdblegHIFB6YCmt4uEOXVgkvOC4yscDqs3bOkmxA++EoiR4ysYDBoc1CToE22uyTkhxwnnnmxjHSzjmsm1ASh9b5OQYHFxMYLBoFq3CPXo678cL+wvCZ3pNrVyO69Dwj82m+0v8cHPlClTpkyZMmXKlKna1Omnn46ff/4Z559/fqX7lheEdjgcGDduXLyrZkropZdeQq9evfDSSy9h3bp1le4/YMAAvPbaa2W2P/XUU+jWrVvc69e3b18Apd/jlixZgn//+99xP0dtacKECRg7dmyZ7bfddhusVit+/PFHrFu3DkuXLq1y2otBgwYZ4KGjXevXr0c4HK40/c25556LW265pcrlezweXHTRRZgyZQqys7PRpUuXuEI1sUpPr9WrVy/MnTsXTz75JG6++WbDa8nJyRWm5qmKsrOzsXjxYtx5550488wzYzpm/PjxhlR0QOnvXCNHjsSQIUMQiUTinprO5XKp1Bsej8cQMzj++OPRr1+/qDcqmzJVmXbs2IFIJKJuso5VQ4YMwZVXXlnhPnl5efjhhx/g9/uxd+/eMqme0tPTayV1XywaP348rrrqqgr3ycnJwaJFiwCUwonfffddhfu3a9cOp512mnq+b9++qC5xHTp0wKmnnlqNWv+pc889F3379kWHDh3Qo0cPAywZb1ksljLpyo4W2e12pKenG37/rguVlJTgzDPPxIIFC7Bjx44K9w0EApg6dWqtQOJU586d4/abfqtWrfDcc8/VeZuaqpkYZ61snWcc8q+g5557roxbHQB89913mDNnTqXHZ2VlYfjw4ejYsSNatmyJnj17qtduuukmPPvss5WWYYI/lcjj8ShXiqSkJHg8Hni9Xng8Hvj9ftSrVw/p6enIyMhAWloaXC4XXC6XAfSRQV+ZukqmeSKgQucXqXA4DI/Hg0OHDsHtdhtcdni8TDUjA7QM+EpQhJLuJZRMhUSHI+7Da9FhBwI2DPbKoD9gTOMknSNkgFtCQjIILsEfAArOkDAO6yMBBglaSKcc2S9JSUkGAEheqx7o1+spoQduk44ZdIkKh8PIz89XKW7oelRUVKQgBtn2EhJjH9ExhddL2IDBfx3CktAC3amkgwrHonRl0dtSB5skuVkelMB0ZDLFnXSD0l1WJHATDAbLONHQBcRms6GkpAS5ubnIy8tDUVGRAeYJBoPw+/1IS0tT1y3HDoEPXg9hC6Yx83q9Kj0b55cOvEhIh3NUd82R40WOR31MSZcetp2ENKK5KekAk+4EJJ2EuI8EhKQkIJiYmAiXy6XSo0lIR4dUZP3keCGsw3aXcKEEWzgeQ6GQKpcAEMeXy+VCSkqKame6+zB9os1mg9vtNrjrSFiSa41cd61Wq5qTANT6SRhQtiVgTHFHMCaagw5hJYIy0jlNru96mrBoc51jTvaRDgDJNZWADlMK0tUrmjOR7Es5t5mOTa8bIVe5Nun7cH7a7XbDOJeOUhaLxQB7RltTpROcmTvelClTpkyZMmXKlKma6dxzz0W3bt2Ql5eHgwcPlrtfMBjEv/71r6ivnXrqqbj00kvRokWL2qrmX1Y333wzmjZtilAohO7du+OJJ56I6bhwOBzVLWXt2rX4+uuv0aRJk7jWs0uXLgBK06xUBoUcSXrttddw33334cknnyzzWmFhITZt2oT3338fnTt3jvpjfEXKzMzEZ599hjlz5hhShh3NWrVqFS655BJ8+OGHFe5XVbcfyuPxYOnSpWjUqBE2b94Mn89nSLNVV9Lvqu7UqRMGDx6MBx54oEzqH5vNFlc46cknn8Txxx8f8/6PPPII7r//fsO2xMREXHDBBUhKSsJdd92FL774Im71A4CtW7eq33T4O7auaNtMmYpFRUVFVQZ/GjdujJycnEr3mz17Nu655x488sgjZVx9cnJysGTJkirXNx6aP39+pekxQ6EQFixYoJ6vWrWqTDxQqn379ujVq5dh2zfffIMDBw4Ytu3bt69Gaa8SEhJw7bXXYu7cufjkk08qTFdTE+Xk5OC9997DH3/8cdSCP8FgEPn5+XFxu6mKEhISMGjQIDRt2rTcMe7xeLBo0SLDGKst3X333fjjjz/iVl6LFi2OCafJv5JsNhsyMzNjggR1GPtYVY8ePXDKKacYtu3evRtvvvkmli9fXuGxmZmZuPrqq9GrV6+osd19+/aV64YnZYI/lYhuLDabTaUlIaRAhwg6nJBsY7ATMAbiASNAIsEgmeaJkqlSiouL4fV64ff7y6RmknCGDBTzr3QOkWnApIONTLvF7dIBQ0pCDDIIrp+T+0pARAajZd3046J92InmfiHbSdaFz6VTkIQTpAMRA/B6GjZ53bpri+7WxG0MwnMbA+gytQ+BneLiYvj9fkP7RwuOc0zx3BJuohsHnXL0wL8EdWSbyfRz0dxtZF3k+NH7Wvab7koj4TBZrn6s7rRCh6CkpCTlcBUIBFBYWIhQKFTGNai4uBhOp9PgOsN+juZ4JduRbiWBQEC9xv/lWJKSwJ4Oa+huKDw+GtSmly37T471aDCXTIfG64kGpuiuP7xmPuiSxPVNh94kPCSBHzmf5byV9dHbmYCL7BvCH6wj5yDHhUz/lZycrJ5zbElwivXgGiPbTXfIkjCMbDMJqMlt0dZvzgvpFKWPCX2tBmCAYPiQ81vvf32MyPWG103wTwe/5DHyOuU+OvhEyR92dPAp2nZ5Lh0ckw+9LSVAacqUKVOmTJkyZcqUqaqrY8eOcLlcOPnkkwEAS5YsqTCYn5ycbEjdIPXjjz/il19+qfQuYlNV13XXXYfzzz8fq1evjjntCVVeSrCVK1figgsuiEf1lAgSeTyeuJZb2+ratSsAYPXq1WVee+211zBjxgwsXrwYoVCo0vGtp8nLzs6GxWKBy+WqFZelw6Vly5bVavlpaWl45513MGPGDCQkJKB///44++yza/WcuqoSlOXNjPHQnj17MHPmzEpT/khVFGAfOHAgbr/9dsNd3zVVTk4OpkyZgq1btwJA1JuRgdLfuzp06BC385r666ioqEjFRWLVunXrsGnTpkr3mz9/Pl5++WV89tln2LNnj5rrJSUl2LdvH3bt2lXtetdEX3/9NebNm1fhPs2bNzcEhVNTUyt8z3U4HIY0nACwfft2HDp0CPn5+QBKf2edO3cuZs6cWe26d+rUCR06dEBqaipOOOEE9KsFlztCuBMnTlR1Pxo1cOBAZGVlHRaQoVmzZujQoUNUMHfr1q14//338cILL0T9PBRv7d27NyYHk1hVXFxcI3jNVN1LNw8oT6FQqEz61WNVBw8eNHymi0Qi2Lx5cxl3umjq0aMHTj75ZOXGqGvXrl0xQfl/DW+lGohuO7SsItghg6IlJSVIS0tTbhZ0Z2FKIgBRg+QSgGEQV09LlJiYiKKiIni9XgQCgTJBY5nyRQbjGXAnhEDHDAbCeawM+sp0QRJAYP3lX0oG0WXaHL2eEiyS6Zxk8Nrv96vJL51bpEuKDBBzm7wueRwABWTJa4z2kMF+9qkMavM8fr/f4CgSiURU6iCmx2E/+f1+9aGTziE2mw0ejwfJycnIzc1VaakoQgp0RbHZbAaYhucjFCEBoEgkotJXFRQUqP5lexNGkQCPBFSk+0hSUpIBpqDk+JFjm4CcdChiGRJsk64lTD0koSX2EevJ1Gl0u0pKSkJKSooBZLBarUhJSVHHSbhDthPryvOyjg6HA/n5+XC5XAiFQti/fz8sFgsaNmxoKItimigJick3Nt2tieNJAhE6mKNDc3Icsh/o0CJfl25MErKRLlz80UJCMbJv6aAk+yDamiLBH/afDptJCIz9I91mpCuOnKty7SN4ReAxEAigYcOGSE9PRzAYhMfjQWZmpqHN6CamAyr69RAYYno/3ZFHpqOTc4KQoL7+SeCPaRHp0MS2l30v1xIJQck1TPa7HFdyLEmYiXfNSNegaA5QlByD+vxgn3Kd5jyJNi4laCTbXPa3BP8kTCXHFueJmerLlClTpkyZMmXKlKmqqW3bttixYwdeffVVNGjQAO3btwcA3HXXXcjPz8eoUaPgdDqjHlteELeoqAg33HBDrdX5r6omTZqgUaNG6Nu3L84777wqHbt69WqcffbZ+OCDD8q89uSTTyInJwevvPJKvKqK1q1bIycnp1xXqCNVDJpOmzatzGtvv/12zOVkZWVh2bJlaNWqldq2du1a+P1+pKSk4LPPPqtxXf8q2r9/Pz799FP07t0bVqsVr732Gh566KGoKWpqS2632/B7V2WKt4NWvHTbbbfFvcyPPvoIK1asUM/z8/MRCoXKAEhTp05FQUEBli1bFlMqSVOmKLfbHXOqqEAggNWrV+Pxxx+vklPJ3r17sXLlSqxfvx7r16/HU089hVAoFFMqzdpQs2bNEAgE4Pf7y4X5/H6/WmtcLhcuv/zycoO8VI8ePdC7d28FbBYVFeHUU09F69at0bNnT7Ro0QIPPfRQjep+8cUX48QTT1TPqwJsVUX79u3DsmXLah0+rQ2NHDkS55xzDi699FKkpKQcljqwz998803ceOONaN26tXqtXr16CIfD+PTTT+usPuPHj0fz5s1x8cUX17isxMTEKsPxpg6vpGlFRSooKEBhYWEd1Ojwa8KECfD7/Spd8tKlS/Hmm2+Wm2qbqlevHi688EKcddZZUV+PRCL48MMP8dZbb1VaB9PxpxIx4C6hBRmoJQ3PgCedRQgI6S4+LLO8ADsAQ5Dc4/GoLymcQExRo7vQAH8GlRl8leCHxWJRKX30oDOvjWCATI3EejJIzmvSoQhCIwzCSwcNBoCtVquqA8vktRJO4vllkF5PFSP/JxAkwQjp4GO325GSkgKXywWHw6FgIJvNpp47HA6VnkoHcWT6Hp4PKA2S+/1+lWqKaeDo5BOJRODxeFTZhHhYfwkTSCcWurA4HA7lxCKdUOgwEggE1L683pKSEuWMwn18Ph+8Xq/qO9l+EsKQbjkcY9K9SD4kDEIoQLqqcN7IuSPBDsJFTDemtwPL9Hg82LdvHwoKCpCcnIzU1FQkJiYaUoPxzUU6x0h3EcIjBNp4fQRM0tPT0bBhQwWa0F2L45VtJOevBIg4/mRao8TERNhsNgVlSJcpHfjRXbV4PgllyTkm54ScbxIa0/taB/cklKc7wsjUXdwm/9fTN0lYUQI9hHkkFKhDT4QS5R1VOnTHeUDAjiAZ+0h3cuL6ZLPZDNfL/Qj9RHOr4XO5NhDaY//xeI7D3Nxc1WYEAGWaRLpy8TnXbtkWcp2hdFqa/S7bWqbZiubQo88H6XSmuyvJFHZ03QqHw/D5fAgEAmVgHZkmjfOB44PnkGCoHEs8B78IS+jJlClTpkyZMmXKlClTsenpp5/GqlWr0KdPHwX9FBYWqjuo6eKga/bs2bj77rvrqpqmUJo+q2HDhvj++++rfOyePXvKdUmhe82dd95ZwxqWqkGDBmjatCnuuecevPzyy3EpszY1aNAgjBs3DgDUHedXXnlljcrMycnB7Nmzy2w/77zzsHr1aowdOxZ///vfa3SOv5p4oxRQ92mj9u/fH/Md5kVFRYc1rdVzzz1neF6d9aIqkmBo69atcdddd0WFD5KTk9GwYUOcd955+Oijj2q1TqaOLbVt2zbm9HkEAz///PMqn2fWrFno0qULJkyYgDVr1uD333+vchnxUteuXXHyySdXCP18+eWXCkj1eDyYMWNGpeVGIhF069bNcJNlfn4+Vq1ahalTp8YE/cjg/ODBg/Hiiy9i3LhxOPvss9G2bVsMGTKk0jKqq6KiInzzzTe46667MGHChFo7T23q3HPPxbRp03DdddcdNugHKP2dv1GjRkhLS8OqVasQDoeRm5uL5cuX4+OPP640lVC8FQwG8fXXX8etvCeeeAKDBw+OW3mmaleMtVem3Nzcoza1X3X04osv4u2338bOnTsxY8YMvPnmmxXu73A4MHLkSAwePBhNmzaNus+aNWtihvpMx59KFAgEDPACgQUJTMjgsnSAieY4IR1wZOCdf202m3IWImnPgC3PwW10d4kW5JXn8fv9BnhG1kU6WkgwgueTQAEDvjI9md1uVw4ohIzodmG32w0uFCyPAIzX61XOEgyoSwhBd1aSrhQAFCwgIQSCEBJiko4d0uWHoJaEpHQYgHWXsBPPYbFYFNRDCMThcCinH0I6qamp6libzaba2mazqXYmvAGUTnK2tWxvghzhcBhOp1M56kgnFzp06NfJMcRrk/0ZDW6RDlHS2Uf+1e+W0R1BWJ50gSGIRfiBMIM+H2Rb8ws6n8s5J8cnoQ59HOuuRQQ2/H6/IbUTwTWKY5ewhZyDHCfsX0JX0sGEMBoBCZl+ivtKIILtocNZvE7poiPdjSQYwusMBoMK6mC9o7kKcc6xzdiWycnJZeyQo0EcXGtkiiquUXIfjie2E9uXY9Fut6u2SUhIgN/vR3JyMrKyslBYWKhob851no/zUqbK4jXqwBTPSQiMKQAllMM1jOVxreV7gBx3gUAAHo/H0P4pKSlwu91qLWBaMt3RR65PLK+yuuvzjW2pA6lcX+T84IPOXHKeSpCHIJ6cK9IFiOOD4066EHF+y3bVHYzC4bBaA0OhkAIMgdI7XEyZMmXKlClTpkyZMhW7+vTpg+zsbMO2tLQ09X95rj6tW7dGgwYNcPDgwVqtn6k/1bJlS1it1pgs3nUNHz4cAJCenh71B/Np06bhoYceUneV1kTt27dHZmZmldIjHU5lZ2fjgQcewPPPP48pU6ZgwoQJOPvssw03QFVHt99+e5ltPp8PoVAI119/PebOnVuTav/ldMcdd2DXrl144okn6vxO/n379sHj8ZTrfkZt3boV9913H2bNmlVHNSsr/cbhpUuX4ttvv62xi0d5atSokfr/0ksvjSmd14gRI3DGGWdg0aJFtVInU8eOsrKyKgRgpPLz83Hfffepmzyrq1WrVtXo+M6dO6N9+/aYM2dOtVPStGrVCi1btiz39cWLF+OTTz5Rv90CwLfffouFCxeif//+UY/Zt28fvvvuOyxYsMBwXFWUnZ2Nf//738jMzMSqVavQv39/lbrS7/cjLy+vVtPwMMVXdcCuWJWdnY09e/bUWvmMB1TmzlTbSk1NxdVXX42LLrpIAUhbt27Fxx9/jGefffaw1Ck9PT2u5Q0cOBBffvllXMs0VTtiPLYy0Yjhr6RrrrkGTZo0QX5+fqXfSy666CIMGzbM4DgqdeDAAXzzzTcxpfkCTMefSuX1ehXxzwAo8Kf7AmESBjVDoZBhAEuIQQbe9ZQ0SUlJsNls8Pv9KCgoQEFBAYqKilBQUGBw6JEpaVgnBqZ1qIBgSkZGhgI7ZFBXQiASDOFDwiAM+jNATFBABycikYgCXzwejwoQS6cJBuqZgotfnnV3Hwa1ZbtLqEI6jzCYLVN2sX7SpYMuLDrowmsE/gSnZJBeOrywze12O5xOJzIzMxWExWuRDifS9US2BVNNESBhm7IteW28Zr/fr9wxZDCe9aMLjtfrRVpamrqjJjExES6Xy9BWbGsJBUiQg2OBbjksWzoAyXbTnackIFFSUqKOJ2gkXVQIcPE1Qi48TgJZHA+y3ixDh7iKi4sRDAbLjHe62sj5QxiBx/p8PuTl5UV1RpJp11hX6SSjA3VyrOptLWFAtoEOoOnAB/tfwjbSsUemT2Nbd+nSBS6Xq4xzkRyDEgAJh8MKxpFzQu9rCYHIfejkJR1pZHuwzSSgxfUlMTERqampcLlcCh6ULlEE4oBSMFOHjAj0sU1kHbn+SHcbrhm8frp3+Xw+dSzPqTu/0S2Maw4dt5KSkgwuU3paLK653Fd/r9ABOLa3XAflmOEYJbjD/MKcExIw1IFTi8UCn89nmJcsW4dXpbud7HP2oRzbErakK1k0Vx/OA/ajKVOmTJkyZcqUKVOmKtbtt9+Ohx9+uAz0A5QGirmP/P2HWrRoEfLy8tC2bdtar2dNZbVa8eCDD+Lmm2/G+eefjwYNGhzuKlVbTZo0wd69e7F69eoqH8sg1ogRI6K+PnnyZEO6npqoZcuWcLlclVrRHynq2LGj+p550kknASj9bnrvvffG/Vw7d+5U31sP553+NVF5Y6gutGjRIlgsFhw4cKBOz9uhQ4eYXHySk5PjHrisqrKysrBr1y71fOvWrfD7/Zg6daphu9TKlSvxxRdfVOt88o7y8oLlgUCgDIxxpKZDM3Vkye1249dff40pBcyWLVtiAoRqW8OHD8dzzz2HCRMmVPsGxQYNGqBevXpRXztw4AC+/vrrMvBL586dK4SFdu3ahY0bN8Yc8NV1yimn4OGHH8Yll1yCs846C3fffbeCfoBSx44mTZqgTZs21Sq/Mu3duxdz5sypNRezNm3a4Pbbb8fHH3+Mn3/+GRkZGbVyHovFotKKHgmSn0VKSkpw6NChw1aXefPmRU1JW13l5ubGrSxTtSsZE69IJSUlMad+PJa0d+/eSqFKq9WKM888Ez179ix3n02bNlXpe6QJ/lQiBn8JWkRLn8KArwzMysBtecCPfC7TKPl8PhW4JVTEgKuebkp3g5DlULTbkoF7CdbwOAkY6A5FukOQDAzrQWB57RJQ4DHcRlBCh0lkoFn/K9uPIIteDwac2T4yEC3LkZLXw7+yHgzcy9dlGxFwYKov3RmFwX+2t81mAwAV9Jf9Jduax0rXGDnWdLBGd1DSgQ0+9FQ8en/qKZrkQ7pMVSQdpJJl6AAMQQ0CZ4Qz9C/ncnxJiEHPJanXV/aXbGfpqiShqGAwCLfbDY/HE7X9JFQmr1UfQxISkq/z+qOtCbLuElCT+xFaYv2jtbV0pkpIKLWAlPVkGjnpCKS7EeljRF8jZPtyGyXnJ1NhSbCJ9ZWpAbmNaeJYvnThYTkADCmoKD3tG+slx44E+eR4ZrtGW+v0McX+4VzmuZOSkgwfdGQ9JNTFOrEd5bmirX28dh4jt8sUcxwT+hiT66t+fo5lfZzqY4Jl6Gsbt8ntutuQXFcIh8mxo9/ZZsqUKVOmTJkyZcqUqejq2rUrrrnmmqivMQB8+umnR319wYIFmDZtGn755Zdaq1+81KRJEzRu3BijR4/G6NGjcdtttx3uKlVbLpcLeXl51bpTf8eOHQBKnZrK06uvvlrtukmNGDECiYmJR1RgqyJt2LBBBUI7deqktp999tm46aab4nquPXv24O2338bq1avVzYxHm+644w507979sJy7fv36CAaDaNeuHRo3blxn573uuutiAlWSkpIqdQWqba1YsQLvvfeeep6Xl4eXXnoJkydPLvc32HfffbfaaY2ka0V5Qda5c+eWAX8KCwurdT5Tfy253W4cOnQo6k2AUh6PBwUFBcjMzKyjmpWvBg0aIC0tDR06dEC/fv2qVQZ/b4+mvLw8bNu2rcx8q1evXoUB8aSkJMPNzbHqpptuwrBhw3DqqafilFNOqfLx8dCKFSvw9ttv44UXXsD69etr5RxDhgzBoEGDcPLJJ+OEE04wfB6It6rTD3WhgoKCwwr+rFq1Cu+88w7WrVsXl/KGDx+OHj16xKUsU7Urxg8rkoxVmiqrli1bonXr1hUC6Lt378a2bdtiLtMEfyoRHSACgUCZoC0Ag1OEDm9wQMsgrR5kDgaDKrDt8/ng9Xrh9XrVmwhTrchJIR0cZBCVwVuCIoQm6PjicDgM7hbS2YcTVHcEkW4XuoONhDnYFtLZR7oA8bhAIAC/34/i4mI4HA7lKiJBBgCGsiU4I51JZF0Ij8hzS0emaNCP/KsDHdJNQwbXZduXlJQgPz8fe/bsQUFBAex2O/x+v+o7CX6wPK/XC4vFArvdjoSEBDgcDkN/SWcgCbiwLrJ/uC8ANZYI/tAhRYc3ZFuwjtGC8wQv2CfS7YjSA/US7JLbeF7Z/hwTPCY5ORkul8vQxwTgCGfI80oghACGdJeRkIIcf/L8vC5pMyf7i25HLF/vS7aTzWYrA/7o+0UDaiQYox8bDYTToRAJ7un7RSIRdV1WqxWNGzdGWloaUlJSkJSUpNyqJFgo1zfphiOdnuRr0qVHuu7QTUo6OLH//X6/AfxhG+rPCdP4fD41npxOp+HHR6/Xq1x1uNYSgItE/ky1pqf+Y5vTGUkCKHzI9YQuTjqUlZycjNTUVOX2RWpZd6+R1yahHR24k/0rATk55nkdsmyu+TLVG+tBMEqCqzoYWlxcDKfTqaBTj8ej+ltCmdLJLJpk20nXMtaNbmvcl45c+npsypQpU6ZMmTJlypSp8vXoo49i1KhR5d6V3adPHwCIGkD7448/8MUXX+Cdd96p1XQO8dLOnTtx4MABdOvWDSeeeCLuvfdevPDCC4e7WtVSeno6tm7dWq1j6QxQ0V2e8Ug9NWbMGAwfPhx+v7/OXVmqq6lTp+K6664DAAwdOlRtHzhwIKZMmYJx48bF9XyvvPIKPB4POnbsiJUrV2LUqFFxLb82Zbfb0a5dO/Tq1euwnL9p06ZwuVx44oknKgUB4iWr1YqBAwfGtK/NZjvs6VsmTZqk5l4wGEROTg6KioqwZs2aco85dOhQtdKnbdiwwZAS5vfff1c3Icv6XHTRRYYb3gDgtttuw6RJk3DTTTdh8ODBVT63qb+O+Ntzefr9998xatQonHXWWdi0aVMd1syo+vXr44477sDgwYORnp6OnJwc9RtmVSV/95YKBoPYvXu3AdS74oorcMYZZ6BFixYVfi5r1qwZunTpUqU6ZWZm4v7778fHH3+MO+64Ax07dqzahcRJOTk5mD9/PvLz82vtHCNHjsTQoUORkpICh8OBq6++Ou7nmD17Nl5//XW0a9cu7mXHQ+vXr6/wvaIipaSklOtSVRUdOnQI06ZNi0tfd+/eHa+88kqNyzFV+5Kx1vIkDQxMldXgwYPRqVOncm8qiEQiWLFiBRYvXhxzmZV77f3FlZycjGAwqIKt/LAinVSAP4PtBCRkkFQ60LCshIQEFfRk4Ja2WAQMWAZhDQaTmV6IAWkJhBACikQiyjFDBmSBPx13GKAtL/isO1NIZwaCORIEkE4y0uWEUAYnNt0/GFwvLi6G1+st48ohA/ESNJBBeAk7yTaQgX4Jg1C8BqYYk5CSLEPCWhIKIjTC1whosV2kFWowGFQfzLxeL1wuF8LhsIIwWB9CIbKtCBiwXnTGCYfDBocOfgmTaZU4tiQAI8EnfWyU59wkoQ6ZlojXyucSVojm6iKdTWRdWA+bzabgDb/fr1I8OZ1O+Hw+dW2EcXjNHJehUEiNJ55fOorIdEwyLRKhH5vNBqfTicLCQpVSLRAIwO12q7RpnOM8rwS02A4SwNDhMrY71xGbzWZYNyTQx+26244+NwAY2lefh2lpabDZbCguLkZaWpphTBcWFipXI33+SRCI9Q6FQgaIitcpU/DRZtLv9xvmrt/vh81mM7QP3X0krMP+Y4o5i8WCtLQ05Obmwuv1qvEo6ys/OBCAc7lcChTiGOQ4kPtHA3EkmKU7Vcm210EgpqZzOp1qfWN5ci6yPWV/sz6yfeS1yTVArssc+9K5Sc45jgWOAY4RCTtJgFLCPsXFxep9Rr7XyBRmcg5y/dJd12Q7s484lvj8aLxb0pQpU6ZMmTJlypSpmqply5bYvn17TPu2b98eDzzwQIX7dO3aFUCpHXj//v2Rk5ODf/zjH2jZsiW6deuGP/74o8Z1rktt3rwZf/zxBwYMGIDt27fjzjvvxF133XW4q1VlpaamYu3atdU69v3338cjjzyCjz/+OM61Muq4444D8Kf7wtGidevWoW/fvjjzzDMN2yORCObPnx/38zE9eUZGRq0GMuOtpk2bIi0tDfv27Tss54+WmrC2VVl6QHnDld1uR/369euiWhWK8FE4HFbuDfJ3YV0ZGRlo1qxZlc/z3XffYerUqer5rl27UFRUpH6bCYfDymVNv2lxyJAh6n+/349+/fph+fLlVa6DqWNbjRo1QufOnSvcJz09XbnaHU5NmzYNF154oVoPWrduXe2UdgcPHsTSpUtx4oknIjMzEw0bNgRQ+tu52+3GwYMH1b7Z2dmYOnVqpWnF6tevj65du+LMM8/EggULYqrH6NGjVfqwihwDa1uMZdWm9PeXkSNHYuXKlZgyZUpcyj/55JNxzjnnlIEgjxTt2rULP/30U8zfJ3RddtlluP/++zF37twauWsuW7YMy5YtQ0pKCsaMGYPmzZtXuyyPx1Ptz82m6lb9+vVT61t58R2aq1SWDuyvqPT0dJx11lkVumFu3bq1ymldTcefSsSgNGEDmc5EBl6DwSB8Pp96eDweeL1eg5MNA9l0Y2HAlYF4BrllMDZaqiCem4FkGQTWHXOAP4PYBAJsNptyX4jm8iFBEAnWsJ4y1Y3u1iDTy8hz83/poAGUgg90dJGuMgw4M12VBBkkaMFr1VN68aGnEAL+tBaTLiYy+M/riQYEyO3yWgkqWK1W+P1+5OTkIBQKwW63K7DEarUiNTVVjSP5xU2OBelcw+OSkpLgcDiUi4qEBHh+GYwndEDHFDoMSahD9jPbly5JOvggHUMoXrscs6wrHYMkpCDdQiRgRLiIKbd4rXROYp3YXnTHkucmaOX1etX5OXbYhhL2YdvxuiRURiCjpKREpfuic4och5Se+kwfn3pbyWNkvzFNk+70ox/LdpagG+vL8cFr57EFBQXqzp3MzExYrVbVZiyX84FzRAInhEnKW/8kMBMIBFR9ogFecs5Ggwv5QaGoqAjp6emwWq3qB4/CwkLDmLbb7epDN9uc7mdpaWlIS0szwDvsP+kuJtc9CTNRBLB4XrkGcp653e5yndFk3eQ4kRAOxzDzocr6yPVHb3uWRwhQgln6g2NdlhGJRNS8kmu+HMusI9f/UCikxqB8n5FQlEwzyHK4xtP1Sb43mMS3KVOmTJkyZcqUqb+a+OPejBkzDGmaRowYUW6g7Oyzz8aBAwcqvCs8KysLw4YNw5gxYzB27FjMmDED06ZNw0MPPYShQ4ceVUAHALz33nu4/PLLsWPHDjz99NP46aefcNNNN6F3796Hu2pVUuvWrasdwNi8eTPWrFmDtm3blrtP27Zta+SGtHjxYrRo0QIAMGfOnKNunIwYMQIZGRmGbRaLBUVFRXE/1549e7BixQo4HA6kpaXFvfza0ubNm2GxWLBr167Dcn4J4YwcORIjRoyo9XNW5OBz//33Y9KkSer5+vXrVdq4wymmGwuHw2r8RiIRXHXVVbj22mvL7P/8889j9OjRVT5PgwYNDH0SCoUMzkHSpaU86AgoBaZmzJiBpk2bVrkOpo5tDRo0qFzHrYKCArzyyiuYOXPmYXX6oU488UTDb/0nn3wy+vfvH9Ox3bt3x4MPPohXX30VY8aMwbPPPouLL74Y48aNww8//KD2s9lscLlchvlUFXj0uOOOw7Bhw2La98QTTzxi5iSzkNSWBg0aVObzUWpqKu699964naNevXoVOlcdbnk8nmqD/cOHD8dll12Gtm3b4tprr8XFF19c4/okJSXV+PPRpk2bqp3G0lTdKDU1FZMmTcLEiRMxYMCACmFrxqGO1FR5h1MXXnghzjrrrHJfLygowMcff1zl+WCCPzGIAIoEcQgfMBDt9/vh9/uVg4Z0w2HAU09lJYl5BnsJIMjgcWJiogIjJDyhO0EQbNABGJmiSgaQZYBbBn1ZN5nGSNZdBuol6CHBnGjpjOjqwhQ6ANRxdMqIBvSw/fVUXhKQks41rI/uZCPhHboyUdHgH3kdBLMCgYDBeYl9lZSUhPz8fCQnJ6OgoEC5qKSlpRlgAxnEZ2odjg+WyRRFMiUbjwX+/PJFwMLtdpcBuPhhla5DEo6S/S37XcIi7EvWXQc4ZB/Jvwzoc77ozkgSbCFQJ2EH9iUdsZKSkhRARQCJ45CAh+w/OgbZbDYFhhBwk/NCr7+EMXjNcg7SoUSCFVK6CxD7Tm9Pzl+ZXko6o0jnFQlU6ZCM7s4jj5VzrqSkBDk5OdiwYQNyc3NRVFQEn8+n3HvkOJZQj2zTaLCXBA3lvCVMFAwGFbCju+XoQJDuDMNxYbfb1drq9XpRWFio5h/bOz09XZXJecO7JJiiUbphyfbUXYuk843sy4SEBLW2E/Chg47T6VTjlH0uAVEd3GE9uJ5LgI/rmnzOMSnnrw6gRnNYk+sIXXwCgYChLnS1kgCRDmgFg0GDSx3fz+S4kONBOpjJ9xO+/7Hf2IZ8PSkpSQGSpkyZMmXKlClTpkwdy6KV/quvvoply5bhzDPPxMCBA3HBBRfghBNOwGuvvYaFCxfi3HPPLXPsyy+/jL59++Kiiy6q8O7pcePGYeXKlZg8eTLuv//+Ktfx2WefxTXXXFPl4+Kt7OxsTJkyRX2ne+CBBxAOhzF58mRMnjz5MNeuajruuOPw0UcfVfv4//73v7jooovKfX3z5s0V3ilamU499VRcdtlluPPOO6PCBUeSbrvtNtx5552Gbeeff37Ufe++++64n//iiy/Gww8/rG7oO5rUuXPnw+bMIt10XnjhBaSnp9f6OaV7jUwd//PPP2PChAnK/SgcDmPIkCHKAWfs2LF45plncOutt5abVrG2JG9u4+8uoVAITZo0wU033VRmf/k7XlXkcDjKHMuAnNfrxY033qi2V3aj1vHHH48PP/ywWvUwdWxq9OjRuOGGG6I6zYTDYcyaNQu33XYb7rjjDuTl5dV5/QjFtG/fHv/4xz/QoUMHw+vp6emVOv44nU6MHj0a06dPx8MPP4wrrrgCPXr0UK8vX74cq1evVvOqpKQEGRkZCrIFSlPuSQegipSQkICBAweiX79+Fe7Xp08fTJ8+vVpAYDz1/fff4+GHH8aZZ56pUpbWhph5QGrTpk348ssv43YOeRP+kah9+/Zh/fr1VT7OarXivPPOUylAXS4Xxo8fj+HDh9eoPuFwuMYuldFSFps6ssQsLbEoOTkZdrvdECMzVQqZDh48uMzNC1KzZs0yODTGKhP8qUSRSCQqEBEKhRQIQnhDunjIoKju4KH/rzvtSMcQ+b+EdRh01915eLzuuCAD0bKOOqyj1022A1/TQQH93DqwIAPq8iEBFekeIkEBGQyXZeoPGdCO9ldvbx1iinatsgzpWKODFwRDEhISlG2ZxWIxuM7I43WnDFme3p6EZyTgJV2BJETC8m02m/pQyfPqKaN0yKSi/uP40YP9so30cS/TkOngFa+ddSbkpI8NpvdimxEcItxF4EWOcTlf9PrpYzhaH8t+ZV0I/UhnEwnR6fNDHivngHxwHMrxL4+Xj2g/6EYbt7Kt5T4ejweFhYUKnKErGYEO6Toj66e3R7Tten2iQUlyHES7PrldOtikpKTg0KFDCIfD8Hq9ygWMbU+wiz+2EfDinGH6QMJfrKcOykRbcyWUAvyZolGuq3Je6+OICgaDap3W3XbkuCUsJ1MORoMR5fjVQSYJM8lyZdouCaESwtHrrPdHOBw2tBf3l+9b+tiVY6q8/fXxK+tjypQpU6ZMmTJlytSxqiuvvBLXXnstOnbsqH5ob9y4MW644QaMHTsW6enpqFevXrlpH7Zt24Z58+ZV6MjSr18/bNmypVr1a9u2La644orDmhaCysjIQNeuXQ0pHOhaq9+McyTrjjvuwO7du2tUxoYNG3D88cdXuM8rr7xS7fLZnkfD97Lk5GRcddVVhm1ZWVlR9129enWt1OHLL7/Er7/+etjcc6qrw+Vo43A4ykBSFY21sWPHxuW8HNcLFizAZ599hqVLl6KwsBDffPMNABhgIP5u1KdPH4waNQpXXHEFrr/++joPUsmbuuj+43K5cN1116n3jNoS22D79u14//331fZYAt7dunWrtXqZOvrUuXNntGvXzrBty5YtKCgoUG7gh+P9xmKx4IorrsCtt96Km2++GUOGDMGAAQOi7lsZkNO1a1cMGTIEJ598MpKSkpCSkoK2bdsqECU3Nxe7d+9WTlq//vorvvjiC6xatUqVkZOTg9zc3Jjr7/P5VOqwaGratCluu+029OrVq1ZddmKR3W7H3r17a/080d7Xtm/frtb5eOlIA39ycnKwf/9+AKVwKFNDVkWdO3fGCSecYICnTjrpJJx++uk1qttLL72El19+uUapRZs3bx4X9yFTtSdmOIollZ/ValU30Jv6U+3bt0erVq3Kfd3n8+HHH3/E5s2bq1z2ketRdoSIgWI620g3EL/fb3A0IRwgg++6DZwebOYx/CJBglSWweecRHq6IZnWis8JU/CcEnaQDjMy8E7pgf5oLil8jU4t5cEQhDQAlEkZQ6cXth238xw8JwPP0ulH7iMdaWR7SfcYBqN1RwtZVz3Qzn0YMA+FQsplR/+Rad++fbDZbPB6vWoMuFwuBQoEg0HlUMNjA4GAcr7gPoAxJRrToultw+08T25uLmw2GxwOB5xOJwoKChQ8wfRjdrtdnZvjg+eK1seyf+VrhJDY/mzXUCikxi/bj6/zGAIEdJuRzkxse6/XC4/Ho1IrcTvnHGEPthXrpDvJ8DokHMW7fZKSktQc4Jig24+cexJ+8Hq9sNvtagzy2jhH5NzmOeT6IaE7Xg/Hvw5DSJhE/i/PIyEbOQd1BYNB7N69GzabTY0HAlcSCiMwI12U5LVFmze8Rjk2OT4jkYhhTEv4kPtybss1TkI9zA1KV6vU1FT1Y5HP50NaWpoau0yhyNRVfr8fTqdTgWPhcBgulwuZmZk4dOgQ3G43QqGQIddwUlKSIQ0ix1R6ejoCgUCZFGq8aywhIUGNW6YhA/5cu3QARk9dCECNPT2tnt6vOvCjp1CjuGZx/eJ6Q1ksFng8HjWm5RiT9ZYgl3RuYh2l85asnxy/cpzJsRsNsjRlypQpU6ZMmTJl6ljW2LFjy9xZ7nK5MHToUMO2li1bVljOokWLcMEFFwAovbM5MzNTOWokJCTgt99+q1b9Nm/ejLy8PAwZMgSPPvpotcqIl37//Xc88sgjhru2d+7cCaDUOSnajT9Hmj7//HP07NmzTAC0qvroo48MaXiiadGiRdUqm5BXUVERfvrpp2qVUVdq3Lgx+vTpo4JJXbt2RUJCggIkdEl4Id7q27dvrZVdFXXp0qXWAKd4KSsrC40aNTJsKy/of99992HgwIF4+eWXa3zewsJCHDx4EDNnzsRPP/2EzZs3Y+3atcpxg4FS/g7FY0pKSpCdnY3MzEz1m3Z1dMIJJ6CgoAB79uyJ+RjWyeFwoG3btli3bp367SaeCoVChlR4LpdLzSP+lgQA119/vXKqq0h2ux1Tp07FDTfcENd6mjo6tX///jJOEEuXLsVrr72G9u3bHxaXHwC47LLLcMstt+DEE0/E7NmzMWjQoHLB0W3btlVY1rJly/Djjz9i2LBhyqmre/fuuPDCC/HOO+8AABYuXIiNGzeiR48e+OCDD/DMM88YguSbNm3Cjz/+iHbt2kV1rtH18ssvV+geuHv3bhw4cKDScupCWVlZBnC7thQNOhw4cCAaNmxYY9cZqqCgALm5uUdM+jSg9H2LznXNmzev8vEnnHACrrrqKpxyyimG7U6nE2PHjsVPP/2Ed999t1p1y8/Px6pVq7B8+XIMHjy42u6IvXr1woMPPoh//vOf1TreVO2KMUbG4ypTSUlJraTgPZrVpUuXMr8LUH6/H/PmzcNrr71WrbJNx59KxEAng/0MptIFJBAIqJQy/MGBXwoYRNXBFJarQyYS1OH5dNcQCddIOIQBa4fDAYfDYfhiIutltVoVbMSgre6EIt0kZL05kXmsTNskQZtoTiG6cw9BBbaddKGQrhR6u0l3D14P20E6ZUhYQoIJPK8MpssAv0ylJgGc8qAgpgDKz89HYmKiSqfDMeP3+1W9pRiIZwow6cohj+d1yv5inamkpCTVjhyf/LJmt9uRnJyMcDgMm82GkpIS1d4y0M5z6+48dFkhqEBYLRwOG9KlEfCSr+mAgoS05HhjSjm2VzAYxKFDhxSYwrmgt79M4SVdXDj2OF7lc6YTslqtyglGjoNwOKzGEsW2CIfD6kuLPq51iENvRwJf7Ee+JlO8ydRT5Tnt6E5V0VyOWC/9xwmZJotrmQQ8JJAUzcFIn9/6XOMYYAosfW1hO+ltpTvJJCQkwGazYceOHSplG+cFbQF5l6esr8PhQP369RWEY7VaVaqw4uJiOJ1OZUEYCoUUIMQ2kfAP25qvyXbntYZCIRQUFKC4uFjNMc5B6VQlAS+Zck72LcekdAmjJDSlS/adhDsl7Mb5KN+nCA7K1HbsI6fTaQCcOM9kHSTMx3lFyEuvhxzzcuzqivePWKZMmTJlypQpU6ZMHWkaM2ZMuT/uSfn9fvzrX/+qcJ+PP/5Y3T3frVs39OvXD/fee6/6DaImjjg+nw9du3ZF+/btq11GvKSnauCd3U2bNsWpp556OKpUJbVr1w6fffZZhQ5NsaqwsLDSfarj1NSnTx9s3rwZw4cPP2xpoGLVgAED0K1bN0yYMAEA8J///AcffPBBufvHyznmSNWdd96J77//Hh07djzcValQ2dnZhjQGgUAAf//733HyySeX2XfXrl1o1aoVfvjhh5gC4RVp69ataNCgAaZNm4Y77rgDaWlpCAaD6vcH+Rsfg1Zut1v9dljebzGxaPTo0fjxxx9x3HHHVem4P/74A0DpGi7fLyqDEMpTOBzGmjVrUFBQYPgtJjc31wBmNGjQwOBU4vV60b17dzz33HMxn2vDhg3VqqOpY08bNmzAH3/8Aa/Xq7atXr0aa9aswdSpU2uU+rImuuaaa9C3b1/Uq1cPf/vb38qFfoBSR5WKVFxcjGXLlhnmZkZGBoYOHYrOnTsDKIWpFy1ahP379+Occ86Jmsb1+++/j2nubNy4MSaI8Jdffql0n7pQ48aNY/rMW1OV54bYpEmTuKXjzMnJqfYaHG9xHbfZbAiHw3j33Xfx9NNPV7mcP/74Q2U40JWUlITHH38cY8aMqVFdGzduXKM+8Hg82Lp1a43qYKr2xFhTrC4+CQkJMX2X+Svp73//e7mpUpcsWYIPP/xQfbevqkzwpxLpAepQKASv1wuv14tgMGgAMPT0MMCfbjF68Fims2JQneCChICi/S+ddCT0w4deBwkjEB7Rg7PRnEZ04EB3VdHTuRDWkWmceDzrxEdycrJyGPH7/fD7/cotQ0I4rDfL4XMZJJeATHnwhwRboqW10lN4sUw9dY7sA7fbDY/Ho74QMsBP8AIofRMmwAJA/S+vlX8ZjJdpuaRbEN10JEhGEEemD4tEIqp9Ob6cTqfqQ/nFVbrdSCcf9qcEWKQLjoSVeF0ETdif+viIBrQQkCCcwLklQQybzaYAIwJCcuxKMIXlJSUlGfJVy7ZkWjYdHmN7RCIR5frCvpbn0QEHfUxF+2GAc1Z3VpGAlWxPvY3k+kAIRv6QKwEbCehIhyf2Kd+UZfvT2UnOU/2auJ3rjGwv9g2vpaCgAH6/3+DSJcenTGXH8cp1gXU/dOgQUlJSkJiYqCCdvLw8Qxm8JgmgBYNBpKamqrWF6b8SExPV+MrMzERaWpphLPF4CYIxnWMwGFTnTEpKUvOQ1yjnJFM/yvLk+s86yzWS64och3LNAWAA7zhX5XsJx4Zc43mcBBjltcrtst859rlNtjH7keBrcXGxaie/329YQ2TdWH/53sdt0UA1U6ZMmTJlypQpU6aOJZ144okxQwixuHe888476NmzJwYOHAi32421a9cCgPrcXl1oZ/z48ejevTuSk5Nx6aWXVquMeEq6qlx55ZV44IEHAJSm4IhXQCeeysjIQKNGjXDFFVdg48aNeOmllzBlypQal5uVlYX777+/wn2uv/56LFiwoMpln3rqqdi9e/dREVw5/fTT0axZM+Vu1K5duwrBisWLF9dV1Q6LGjduDIfDEZMjy+FU06ZNlSMZUPr70fHHH4+uXbuid+/eBpfxpUuXol27dujTp0+NXZUikQjWr18PoDQIPG7cOLRq1QrffvstgD9v3E1ISFAwUFFRkXLXiub4HqsWLVqExMTEKqe6Wbp0qfpfpvaqDEIoT8899xy6du2Kc845B1dccYXa3rt3b4NLRL169VRqIKaKPOGEE5Cenh7zuY6UwLipw68PP/wQF154Ic4991zcfffd+Pe//42ff/75sDn9AMATTzyBwYMHx7RvMBiMKU3R7t27y6Rp6devHy688EL1/IcffsCaNWvQpUsXdO3atUwZn332WUxpqVq0aIGTTjqp0v3mzp2rAMLDqaSkJDRo0KDWz/Pqq6/i999/L7Pd7XbHJa1Q79698cADD1QZ4oynioqK8O2332LkyJG44oor8NRTT2Hu3Ln49ddfq11mKBTCqlWryv3s17p1azzyyCOG9+477rgjZlc3q9VaY4ekvXv3qu83po48+f1+FBQUGNKmVqTjjz8+prRgfzU99dRTmDNnjmGb2+3G3LlzMXPmzGqXa6b6qkQyiE7AIxAIwOv1Rk1fI+EdbqMzigSDosEm/MtgPCWdKKS7AyUBBqbNiQbwMNjPMiUowW0SlOA2CeHI/ehgIl2RpMsH8CcYIIEVfmlioNvn86nUU9xfh5d0SZcK6YYUiUQUkCWpVQkRRYN/eN2sm4SkpPNQSUkJvF6vcitiaq/U1FT4/X7lVsL+JgwRDAZVkJ3uGxJSSkhIUNatdrvdENBnHSiOAd0hiv0ht8m2khCGTCGmuyNxHLKush/lfhIMk/0aCARgs9kU6BDNRSkSiagUSfK6PB6PweWI9SgsLFTQjz6/ZL/J/uSYJOxESecV2d+EMeQYkW1DdxSpaJAPz8vxp6c6Ypoxvb2l25PuCMN+kq5Bst3kc5lWTe6blpYWFTjhcwmD6euQnIt6uTwmEAjA4XAoNzRep+wLgiL8cUXCZkwZZbFY4PP51BhiGUAp6U34iNdCVx3p/BOJRFS+aKfTaYC3iouLkZeXB6/XWwbQ4XWy3/g60+QRVgT+tKWWKd/kWCJcJt1/5DzS21COzWhuTvJ9JhpAJ8sIBoMKApXrL8c757XT6TTMATlOJUTGcvWxyXOxXgSECGrJ8S3rKuegHPOjR4+GKVOmTJkyZcqUKaXuh7sCpuKne++9V90BHk0fffQRmjdvjt69exucMSpSbm4ucnNz1fM9e/YgPz8fqampOP7449VvNLHqscceMwSGzz33XHzyySdRAyp1oQ8++ABDhw5FWloaAGDy5MlISUnBoUOHMHHiRBW8P5JUr149vPvuu/D5fHA4HJg0aVJcys3JycHbb79d4T4zZsyo8t31WVlZGDRoEN59911s3ry5JlWsEw0dOhRWqxUfffRRucGqe++9F8888wyA0lQsx7JCoRAmTZp0xANOjRo1irquzZgxA0VFRdi1axd2796Ns88+G1u3blW/KZ1wwgmYN29ejc49c+ZMtG7dGh07dkSnTp1wySWXYNasWQBggAe5zhQVFakgVk0cf4DSdX3Tpk1VOkYCooQEHnroIYwfP75adcjOzkYkEsGKFSuwa9cutb1Dhw747LPP0KVLFwClUAF/H2Kdo6XQqUgV/Y4fJ5mfi44ieTweLFy4EAsXLqyV8lNTU3HWWWdh2bJlMUE6GzZsMPz+XZFyc3MrBdn69euHyy+/XMV+qCZNmuD000+H0+mE1+vF/Pnz0aFDB7Rq1UqtPVL5+fn4/vvvMWrUKDRs2LDc89nt9phAmt27d+PQoUOHFVQBStfS/fv31/p5BgwYELXd4gH9pKSkYNy4cRg+fHiNnDSrq/z8fMyfPx/Tp083OGBW5HQYq+666y5ce+21FTpFZmdn45133sGNN96InTt3wm6349FHH0XHjh3x2GOP4eDBg+UeGw6H8cknn+CSSy5BkyZNqlXHnTt3Yt26ddU61lTtq0mTJirbRnkqLi7GgQMHsHr1anz99dd1WLujRykpKWjWrJlh2/bt27FixYpY1rFyPxeZjj+ViGAHAENKExmUB/4MysrALI+RjhryGBkY5X46aCMD9TKAzuNlmqFoTjaA0T1EAgEVSdZTL0vfFs2dQndK0utBMSUUHWp0d5Hy6iThBP2apTtRtHaN5qoSLYgu048xoM3+osOFz+dT6XMI/bB9CQnoTkPR3HWkY4eeEof1JXAg4Q8ZWNdBFRlY18dGef0tnW2iuSJxu/7lV44tSra1Pk5kejcJN8mUThzn/KFSAiA6iCSvk3ATAINrCV9nv5Q3X+TYktsJoRAI47n1tpLOPBI2kfCJ3m7RpLd9NKBOH7OyTSjdWYV1oaSbkHTrkucsr46yraSrkcVidLCRddTHhQSg5Bzl3VYSCJKAG9tYtoHeX/J/2TaEA3Vgp7z5obcBjyWwpAN0Okilr7f6WNGde/ia/qVCuiRFg2p4rQTn9LaX8KW+5sp20seWLF//gixBJfaXXJ/065bXpcOE5Y0zU6ZMmTJlypQpU6aOdvXs2bPC11955RVMmzYNADB79uxqnWPJkiUqYNuoUSO0adOmSsfr4EezZs3QqVOnatUlHtq3b59Kf9G/f38VmF+7di2+++67w1avilS/fn04HA6ceeaZ2LFjR1zLpstNeQqHw1UGjTp37ozGjRtj+/btNalanSgjI0MFj4YOHYorr7yyzD7FxcVYs2aNel7Tu82PdE2aNEmlPTuS5XK5ynX5TU1NxQknnID+/furbUwPJJ0GqqtJkybhlVdewfTp0/Hee+8ZAu/ytxq6JgWDQUMKrOrCLA6HQwFoUhW5Haenp2PAgAHqeWZmJm655RZcdNFF1aoDUHrDF6WnWE9JSVHXJ9Oq0V2oqnfmjxw5srrVNGWqyjrnnHMwevRojB07FjfccAO6d6+YC9u8eXPMaWY8Hk+ln6FycnKiOg/m5+fj22+/VeuYz+fDnj17MG/evHJB6vXr18cEyTRu3DiG2peuHYdTbrcbM2fOxMSJE2v9XMcdd1xU8Ke4uBgtWrSoUdl01asJAFoTMcZSG3Bvbm6ucnmrSH369FEOoP/973+xbNkynHPOORg+fHilx7788svVhpT8fj9ycnKqdAODqbrVDTfcgOuuu67Cz9putxurVq3CK6+8UqXUoX816Zld8vLyagxOmuBPJSouLobH40FhYSG8Xi88Ho9yfJEADmEOHR6gCDcwyA7AAFAwuMr9dACDLih2ux0pKSllUl1JsERCN7I+vNtLuu9Ec52QLj2yHvJ1li1hBtkmfI1uR9L1iO3q8/ngdrvVnVCEMiTgpAe+9S9cMogs24/n4/XqqcrYRjxewlMWiwWBQMAAp9DFJBQKKdcfOj/RxcRmsyEjI0Ol5SLxzf6kgwmdfySs4/P5FPCiw0lpaWlwuVwG9ymmrKLohkNXKgIehC/4kCnRdIDHYrGo16RLkOx7vsbXZaogno8fGtguEl4pLi5GIBDAoUOHlDOIxWJRY4FgFL8IE5pJTEw0lCfHmOxDm81mcEziPJQghgSv5BjjeOC4kZAJ0ytJWInXJduV7cdxx9RaemommU5KghbcxrHKMlg/2UdScmzIsc7XeF1MUSjrQlAtFAoZ0pjpa4LsR/3LP1M1SUCH9ZT9xf7ndUvXMf4lSJeZmalSAObl5cFqtcJutys3mWAwqFJLSfiN12W1WuFyuQyOXRIOkwCkXDflB0pCXmwzHifhLwmEcQxwLMr1VU/hJ4Er6XrEOklXM44FHQZk23O+s06cV3K8ynXParWqH3/knOJ5ZFtGg3MkKMa6Wq1WNYZ00IqS8J18sB4m+GPKlClTpkyZMmXqWNRNN91UYXqG77//Ht988w1mzJiBzZs345FHHqnWebZt26bSRWRnZ+PMM8+s0vEzZszAp59+qp5v2LABu3fvrlZd4qE77rgDp556KoDSO8eBUlejBx544Ih0OGnbti3atGmDpk2bIjExMe4uB1988UWFr1999dWYPHlylcocOnQovF4vPvroo5pUrVaVmpqKYcOGYfTo0eq3IqfTiezs7DL7/vzzz5g/fz5WrlwJAHWSZuRwav/+/dizZ8/hrkalys7OVtDJF198gR9++KGMY1NiYqLqL6bHateuXY3PnZOTg8WLF+PFF1/EPffcU+aclAwcc70BUMbNI1atWbOmTIqS9evXV5iyr6CgwABgZmRk4OWXX66y844UgUmgFKRatmyZApt2796tfoeR84lOWffcc0+V3gNGjBgRN5czU6ai6YQTTsCyZcvw6aef4uqrr8a5556Lv//973j11VfxxBNPYObMmfj8889x9dVXlzl2yZIlCiauTG3atMHf/va3Cp1K1q9fj4ULF5YJzi5btgzPPvusYduiRYtw1113levesHHjRvz888+VpsypCBykLr74YkMav8Ohn3/+GTfeeGOduO49++yzUd8Ht2/fXmMAu0ePHjjuuOOqDYDWVMFgEB6PJ+ZUSlXRrFmzFJxWkQ4dOqTivRs3bsSDDz4In8+HCRMmVJqOc+PGjfjqq6+qlQYyJydHpeo0deTpqquuwp133ok+ffqUyZAilZaWhoYNG6oYnqmyWrduHcaMGaNu8CgsLMT27dtr7MRqgj+ViBAIHV4YlGYQm6CJdOVhQJv70KmFb+7RAp/6g4Fom80Gl8sFp9MJp9MJl8tlcIpggJ3ACGB0+JCBW7rryHRQDPjrgI+sN4O10nlEpsGRriUMbIdCIdVeDAQzOC6D3/n5+ahXr54BEmDAm2lkynOOkX2UkJCgQADWXQJAMvAtXS4YsGZ7S7hDAiJsVwAK1CEMwgA7g+0Oh0MF/xMTExEOh1UfJScnq37VITBeG8sl3OVyuZCWlqaeE46QaYrsdrtqA6Yl4nXpcArrwYeEgmT/y3pRMl0VobNwOAyfz6egOPZ9MBhU+7PdCwsLUVBQoNqZ5/H7/SgpKVEQEeeJDvboriA6dKan35OONRKc4Hk5V+WxdIQJhUJR3Wh0uEG628jUShy3uttPRdaQcg2QkA3nLR96Wio597gv68w6SQBE/qWTkt1uL+OQJOebHBsSqpKwn9yP48RmsxnanWAU20Oum7QGJBjn8XjUWLLZbLDZbEhKSkLDhg0VuMIfZAj7uN1u7N2719CfchzLNUHCinJMst8kTMO20MGfSCSi0ihyzskfhuR45HMJPLGtdScwucbyGNYtOTlZrXesu6yrXFck1CP7kw5m8j1MAlBy3PMY3amJ7ce1gGObsJSE1uQcYjsS5nI6nSpFmylTpkyZMmXKlClTx5IGDRqEO++8Uz3X71wNBAJ49NFHAZR+rm/YsCEuvvjiap3r9ttvxzXXXKOe9+3bt8rgw9SpU9X/c+bMOeyADdtr5MiRWLNmDf7xj38ckemb6tWrh5deegnjxo1Tbb506dK4nmPJkiUVvp6Xl4ezzjqrSmUOGjQIK1euhNvtrknValUXXXQRJk2ahFtvvbXSfZkKY9OmTSgsLDS4/xyrGjZsWEzB4Fh1xhlnxN0pSUIlu3fvxuTJk6M6ZfTp0wdt27bFoUOHAKBM6oN4izdPAqWAGQB89dVXuOWWW9R2l8tV5XKzs7NxySWXlNnesmVLuFwuXHXVVeUeu3r16iqfryLJ3yB/+uknvP/+++qGTZmGrGXLlup/GWwlhBWLfD4fDhw4UJPqmjJVrk4++WRcc801OOWUU3DBBRfgggsuKPP68OHDMXToUIwaNQrnnntumTK+//57g6NXebJYLDjttNMq/Tz23//+F4sXLzYAFF999VWZ1DeVASgWiwU7d+6sMHXSrl27DGtTefr222/VGnq4xBt260J5eXlYunQp8vPzDdtjgVoqUvv27dG5c+eokHFtKhKJ4LvvvsPDDz+M008/HZ999lmtnOf8889XcYeK1LRpUzRs2BBZWVkAgE6dOqFx48bIyMjA5MmTMWLEiAqPdzqdylGvKlq3bh1+/fXXKh9nqm5EM4zKZLFY0LRp02MexK+JiouLsXz5crz99ttYtmwZtmzZgn379iEUCtWoXBP8qUQ+n08FVGXAkgF4wiwAVNBXuknwuPIC5QyaSscZPhg0d7lcSElJUW4XDAgTTCBcIiEBBpP1lDB8TUI70ahVWV99uwxQs64SzmFQXAbSGWiX9fP7/fD7/SrlDMX6MkAvzyfPIZ8T8iEsRNcWto+ejknCEhKMkO4+BJfo/CP3iUQiqj8kiAWU2rmmpaWp43guq9WKSCSCwsJClJSUwOl0qmvkNXm9XsN4stlsCs7heODYkLCQw+FASUmJCp7rbkm8TjoDSYcaBu3l9bHfpEuIHFMS1uC+Pp8PPp/P4AYiQQn2d2FhIYqLi+FwOGC1WtUbhQTXCDdwzLHOnFMSHKEjEK+X80im/OIYkbAe5zbbm+0gnXpkejGWRfCHQArHNseZPI903ZF9poM7ehtLYIXtJ0EgCePpc1wHc3R3J9l2Enzj+JCAm1wjZP+znXXXKPYjXXn0uSpTTOkgCuc5xz2/JEhnoJSUFCQmJsLv98PlcsFut8Pr9SrIKBwO48CBAwqSYflcK5OSkgyQJkEzWVfdhYkQklwX/X6/SvnodDrLuDBJsE5ep1yPdKBMT48lQSmr1WqAtNgvycnJBkczzjfOkWh9z74jbMc5LMcyxzlBt/LqnpycrEBH2UZ+v98AXEpgTjqxybks11BTpkyZMmXKlClTpo4V3XvvvTj++OPV8+eff96Q/uX333/HggULAAD3338/pk+fjnfeeada53r77bcNdwf26dMHgwYNqlIZ8jvM8OHD0bp162rVJZ5q27Ytbr31Vlx77bV4/fXXD3d1ourEE09E27Zt0bt3b1gsFmzfvh3z5s2L6zlkiqJomjNnDr799tsqQSDt27fHt99+W8Oa1a769++Pli1bGsCE8sS5dNZZZyEtLQ1jxoyp7eoddmVnZ2P+/PlxK++9997De++9F7fyAGDx4sXYtWsXwuEwcnJy0LBhw6hAzYcffoglS5YoF4HGjRvXasoaCbyNGDECq1atwtlnn23Yh8HOWGW1WjFnzhz885//LPPa5s2bce2111Z4Q+CWLVuqdL7KpAeOGJMIhUIG16XjjjsOgLFNWrVqVSXnEJ/Pd9iBA1PHnl577TW89dZbGDZsGIYOHVqu+4oEFk8//XT87W9/K7PP4sWLsXHjxpjOm5CQUKlTSVFRET799FP8/PPPKCwsxKJFi6r83n/BBRfgiy++wIMPPlju+9yePXswd+7cmB1aPv300zJu/XUpHcKpTbVs2RJDhgxBRkaGYbvdbq+RU0+PHj3QrVs3g2tabeunn37CuHHj0L9/fzz22GP4/fffMXPmzFo51/fffx8T2Gmz2XD33Xdj+fLlWLhwIU477TQ0atQIQCkE9Pzzz6Nt27blHh8MBpGXl1eluvl8Pvzwww9x/xxtKn7at29fzDcN5Obm1jht1V9Br732Gs466yx069YNd999d9R9Lr30Uvzwww9YsGBBpd/RTfCnEjGYyiClDMJyGwPwMhgvU+3wL0EQGVzlG5B0aGEAnqm8GEAm1MF8w3R8CAQChiCr7mZD8X+6w7CO0klFT8uiuxYxOKunNiPcozvl6E4rBCBkWirpCsNjWZYOUknwgcdI5wr9vGwTQiCyr6SjCff3+/0qpRuD1zJtWCAQgMfjQWJionLX4Z0S/DLlcDhgt9sNcBYhi5KSEvUaf4yRqZ0keCDT4jCgz/ay2+0KhGHfEKRhv0gAh+2enJysXFPYDwRspEMSISqmhyIkINurpKREBfJZf45Jng8o/RDMlG5ut1sBQAR2fD4frFarIX2ZnAt8TliEMAghFI4Zme5IXp+E6yTwIdNOsW8TEhIUyMA5y/3lnCesIMEhWSfpKsP5K2EfzgW9jyUEwzLknNRhLiker7sfSfhHghusG9cetjnbShfnHdtWrhG6O5B0iGGbc/9ogAeBFLlm8UMh543P51OgTiAQgMPhgMvlQlZWlnK5KioqQn5+Pmw2G4qKilQ/SZhI0uw60CLhFUJKEiBj3yYlJcHhcCAcDisYT0IyEugjICQBKTmOdFcyfSwlJCTA6XSiuLgYBw8eVG5j0fqJ41SOTZ7X4XCo+U84R7qOSScpr9drgF4l7MdxyDVIulNJeFK6fpWXdlHCTib0Y8qUKVOmTJkyZepY0iWXXILOnTtjwIABhu0lJSX44osvsHfvXvj9fnTt2hUtWrQAAIwbNw4vvvhitc+Zl5dnSC3Trl07DB8+vEplPPTQQ+pzf58+fXD99ddXuz41VceOHdG0aVNs2bIFO3fuRNeuXQ9bXcpTUlISXnvtNfTo0cPgTrJgwYIy7k411euvv25wj4qml19+GQ8++GDMZe7cubPSFGKHUwkJCejTp4/6Tl5Zm9Kh6uWXXwZQmq7lWFd2djaaNWuGIUOG1LisjIwMpKWlVcnlJRa99NJLGD9+PPx+P/Ly8pCeno5ffvkFu3btMuxns9lUQJH1qSigWFPJ859yyino0qWLer5t2zbceOONVYagwuEw8vPzozopdO3aFbt37zakEtMVb3BGdzdJSUnB4sWL4fF4sHPnTrWdrhY5OTlq2/HHH4+UlJSYz1VUVITc3Nwa1tiUKaMuueQSXH311XjqqacqTJsqlZSUhJycnDIg7KxZswwpTStSKBTC559/Xul+7733HmbOnInXXnsNjz/+OH7//feYyqc2b95c6XtbdnY2+vbtWwZuKU/vvvtupS6B8VY4HMbatWtx5513xgxXxUPbtm3DO++8o9qQMMLChQtr9Fvzaaedhp49e8aljrEqEAhg06ZNdfIb+ZYtW3DbbbehsLAwpv1btGiBfv364brrrjPEI3bv3l1hSsz09PQKU0FF06JFi/DGG2/USoozU/HR3r17UVBQUOl+e/bswRNPPBFXQP1YVkWOdHRPOvnkk5GVlVUplGiCP5VIuk1I4ER3ZgBgCA5LVw3AGMgnUCLf1Bl0lmmHpPMPHVnk+YA/HTZk8FQGe3XYQLqHRLtW/Y1FD+DLNDgSxJHACo+j+w4l2066neiQlAzWS8BAls9t0dwyZBBfBtzlOfX+0h0z2G7S6YnuGzLQTTiL5UigS9ZLXqusOyEN1jWa64fuyhQIBNQYkW5BcpwS/tFdZGT9JVQlzy/7Wgbu9b6W7SKBGlkW25BORgQmpOsL29Xn86nrlPMhmtuQHJ90G5LuSrIfZSo3lh0NtmFZhO6km5V0VypvnkRzuJFOQLL+MqWTHM+y/SWQwf8lGCOPj1YvWbaEySTMJ2GgaNKvSYIy+vyT/SKdivR5wLrxeqVkejzexSDrFggEYLPZDJAbXay8Xi/y8/PVeKUblyxTH8eyrvqaKMcgP9Cy33huuV7LdYzAFmE2OW7leiPdo2T7RBtvPp9POZDJNuRx+lov02+xj7mmcg2RY0g6VklnITkOdMBL9mM0oEdu16Efeb36vDZlypQpU6ZMmTJl6mjXmDFjcNNNN5XZ3qpVK2zevBnvvfeeCrqecMIJOP3005Genl7p3eUVqVevXujVq5dhG10cYtXKlSvV/wkJCejRo0e161NTjRo1CkOHDkUkEsGBAweq5DxRV0pPT0fjxo1x3XXXGRxMaiM9Qf369Su1yt+8eTNOO+20mMtct25dnQboqqoGDRoYHF++/fbbCoOqp5xyCgBg/vz58Hq92L17d63X8XArJSUFXq83pgBMZUpNTYXVaq2VYJvL5cLBgwcRCATw1VdfYdq0aZWmMUhKSlJp3mtDFbkQvPXWW4bUh1URb9LVxd/0KnLtiHfaPb2NFyxYgKlTpyIQCKBdu3YAoBy29fPTdTtWyZT0pkzFQykpKYbgZlXGo8ViibrGxPqeFw6HccIJJ1S6X0lJCdasWYMVK1Yo17mqKDc3NyZgrl69ejGvh2vWrMEvv/xS5brURF6vF0uXLsVLL71U7bWzuvrmm2+wfv16hEIhbNiwASUlJTV+T2zdunW1UlTVRLwRt660ZcsW/PTTTzUqIxQKYd++feW+brPZqvw+XlJSYqaNPMLl9Xpj+rzy888/x93J8K+qSKTUEKa4uNjAJJQnE/yJUXRL0YOhdHegO4S8C0WCFgQ+6NYgwR8GY/kFgA4hNptNQQh0qwCg3GIYwGdKIpbLwDTLZBoaghh0ciD8IWETPb0P/+d5GHxm/QklSJiB7hyEPWR9JDwAlOZ5DIfDcLvdBgcMgj9OpzMqIAD8CQmxLjyPBKb0/3UAQl6jdMUhWMB9eB4G+gEYguc6KEPgSYI+hA8I6TBFER1DmMKI59DdSFgunTakaxLPwbL0FGN0LpHXKF2h6MBDKIF94Xa7cejQIfh8PgWs8VzSAYcQFNvNarUqi1dCOcFgEE6nE6mpqcjMzITNZoPX6y3zIZzjUkINElgB/gQj2C9erxeFhYUGkIUuL9LhSEJPcm4SFJJAFvtBugjxGukwI+Eh9j/djegWxH3kuGWZXEd0EEjOXc4b1lum1pMuKnJNkv9LGJDjyWazwW63w+PxGMCXio6X6QR1ty/dsYp1ZTn88s9+kWuo7jhExxkJyqSkpCAjIwORSAQulwv16tVDYmIiAoGA6of8/HwcPHhQjduUlBS1hiQlJSlnKpmeUYcGpaRzEuvHueTz+ZCXl4eUlBQ1fmVZ7D+g1Cqfa6YOsAWDQfVBSbp38csp51lBQYGCmfLy8hAMBg0OQxLGYrvL9xGHw6HGLesqncRYd7YRYSrpthbtPYJOWXIsS6iHZbKesv05T/lXjilTpkyZMmXKlClTpo521atXLyr4849//AM7d+7Eiy++iIMHDwIA3n//fXVHZE104MCBMneDd+rUCVdffXXMZcydO1f9v2/fPixatCjmY+MZoK9fvz4uv/xylSaN6QViSfdUl2rTpg1atmyJTp06ASjtg1AopBxn4qlwOIzZs2eX+3qHDh3w4osvVinweP311xtu2DvSNHLkSAP4c88996BLly7luiNMmDABANCzZ08kJSXh66+/rpN6Hk41bNgQeXl5ZdxzqiOfzwe73V4rri0zZsxA69atMXnyZCxbtgzvvPMO1qxZU2FwLykpCfXr1497Xajt27eXceYpKSnBU089hYcffrja5ebl5UUFDpxOJ+x2OyZOnIgffvgBjz76aJl9ZLpGXQ8++CC2b99epbroIM6SJUtQVFSExo0b46GHHsJ9992HZs2aqQB3Tk4OLrvsMgBVc2ooKSnB888/j08++aRK9TNlqiKdfvrp1T62cePGUbe/9957WL16daXHOxwONRcq07fffov//Oc/5b7epUsXfP7555g4cWKZ9Cx79uzBhg0bKj1HvXr1lENkZfJ4PPjiiy/qNL0OU8VGIpEK17Ha0H/+8x+88cYbePfddzF79mzs3r27Rm5xvXv3xmmnnVbnv1OvXr0aCxcurNNzjhw5skYpX08//XQ8++yzUdNiJicno379+jG/jxCa7du3L1atWoX58+djwoQJuPXWW9GnT59y57SputXAgQPx1FNPxeTEmpycHPO6ZapipaSkwOFwqFhdZfPKBH8qkR6kl+4ITFMUDofLgC02m83gGMNj6NjAAKseVCboo4M/DOoSLCFwk5CQYChPd7Ig6EECrLi4WMEMEkrg9Un3FpmeRzrgMCAtISW2lQwkS4CC8JGeIgwo/SLH9DOEdyTooqcSYrCZwA8f0tmC7jMS+JDAhO5OIV2MCJKwTXXwobi4GF6vV10nXZfkuCCBzrYDoOAmXlMoFDK0D2ExWTe2qwzCy/2Ysko6KRFm4Y83sl043gixEFzToa5QKKRSdMngvgRUpDONdNFhnbxerzqOY9vpdKq7SEKhkCHdW1paGsLhsCGtGNuQC5l0BOJ4JVRAiILPAaj5wzqyDaloriN6f8vrcjgccDqdcDgcCo5gujmfz6cAqfz8fAMoEc3dh9dFiI/bZQo4Oa+iOVxJZyXdtYnlyXEhx0EgEIDb7UYgEIjqjlWeM1Y0ZyHd+YVgB6Erjlf2GcvjmklJoMvhcCAxMRFNmjRRdtNs87S0NLjdbgXVuN1uFBUVKdiQNpWyXtLVh+OGiuZYxLkjASxCdbRllkAPUJpGkdBecXGxyifMdma7yDaz2WxITU01uANJMJNrtd1uN1hncu5wfZGglDxWrsPJycll0qrpcBihNpmSLZqTlBwPbCeZ1k4ChHpbss4cbyUlJSgsLDTBH1OmTJkyZcqUKVPHjF555RX1GX3s2LEASu+s3bRpEwBg0qRJOPXUUwGUBnNOOeUUPP744zU655YtW/DUU0+V2X711VejSZMmlR5/yimn4JFHHlHP33zzTfzrX/+K6dxjxozBzz//HLcfd3Nzc1FYWIgTTzwRw4YNw5gxY7BgwYIqB71rWx06dDC07YIFC3DJJZdU6mRSHa1evRo333xzua+vX78eRUVFFaYR0rV48WLccsst8ahe3NWiRQtceeWVhm3jx4+vMP1Kv3790K9fP5x22mnYvn071q1bVxdVPWzKyMhA48aNceDAgbgEeA8ePIhLLrkE9957bxxqZ1Q0wOyiiy7CjBkzyj0mISEBzz77rHJyqg3p80XelFVVZWdn4/HHH8fpp5+O0aNHl3k9PT0dTqcTxx13HPr06YNIJIKmTZsa9pGptnRt3ry5yr+bRHPgke5kl156KSZOnKi2DR48GD169MDJJ5+M119/PeaA7YQJE/DKK69UqW6mTFWmZs2alblRM1YNGzasXHDnvffei8nZ7JJLLsGwYcOqdX6pU045Rb0/6QDz6NGj0bdv30rLsNvthpSilenzzz/HnDlzqlzX6ioQCMQ9TWRV9Pzzz+Paa6/FY489hv79++P//u//ql1Wz549q5TmMB7Kzc3FokWLEIlE0LJlS5x99tl1ct6DBw/iuuuuq5HT0KhRo6JCn1VxjfN6vXj88ccxcuRIPPXUU9i1axcGDhyI++67DxMnTsS8efOwfPnyWv08YCo2nX/++bjooosqdZwBSp2zHA5HHdTq6FNVU1hnZmaiUaNGsNlsMd20YYI/lUimM5IfNKxWqwpqSpcG2i0BUJCHHmBmoF26hiQlJanALv/qaZhkIJYiuMHzykAt3Sqk04eEciQ0QHcbHgugDKjDLz+JiYkqXZPcl2XZ7XY4nU5lOaUv8HQPYn0Y4JdOGXo6IbYdYZFAIGBoa16DdHthAFpCFwxGS0iK2wiOeL1elX5H9h8hA/Ytg+gMxvPLEIEe1oNl8RwMhOtuLjyOEIlO7rEd/X6/crdxOp0G0EWf9DJtj3TXIIwRCASUY5Tf71cPj8cDj8djcIWKlp6J/SXLZxsQYqBLDt2bWJ7H44l6Z40EH6RTk56SKSEhwQDFAVD95/f7De4k+vXLuSTHDdvMYrEYgCE9jZJ8lJSUKHCK8JQEKmTbSWcvCaYRUOK18zwEVFhGNEcd7qvDK5wXHPcESCRsxDHAD3dyPsvrluCQhNzYH7JfJGhESIpzXAItbGt5PdyHcE/9+vWRkZEBl8sFl8sFm81mSNdlsVgQCASwf/9++P1+OBwOWCwWNY65L6+D65fsC32ccIxLoEWuncFgELm5uQbnIDn/+T7AcwNQTlycq9JhiZCeDsJJpxyORzpmyXNHG49yzEnx2rnGyrZnXTj+5FzhdUlXKAkVSdiSsJcEf3QYTdaN5UgAzpQpU6ZMmTJlypSpo12jRo3CL7/8gtdffx2TJ08GUJpGh448X375JSZMmIB58+bh119/xdatW2t8zpNPPhmXX355me39+/ePCe5YsWIFfvvtN/X8nXfeKddZBfjz+2avXr3w7LPPokOHDsjOzq5GzaNr7dq1OPHEE9GkSRPUr18fEydOjFvZ8VL79u0N6bdmz56NWbNm1cq5xowZg2uuuabCfWbPnh11DJSn1q1bq3Q/R5Luu+8+XHvttWVS19HVpqJUag8//DB69eqlILtjWfn5+di/f7/6TSQemjlzZlzKiUVWq7XCO/gdDgdat26NRo0a1Vod9HQw//d//4e///3v1SorLS0NZ5xxBp544gmsWbOmzOupqanKiRkA7r33Xnz11Vdl9isvAOt2u6uU6ggoCzYBxpu7unfvjvPPP1/d1AuUvmcsW7Ys5nN4vV4cOnSoSvUyZaoitWnTBm3atMGGDRuwatWqapXhcDhw8803l4HrgFL4+osvvqi0jA4dOmDUqFHVOr8UYeY2bdpg6tSp+O677/Diiy/i2WefxVNPPYWhQ4fGVE7nzp2rdN7qQozVUW5ubp06DFWkmjoOVbWdayq3241ly5YpUGvEiBGYNm0aJk2aVCdOl1u3bsXLL79cozSfp556KmbNmoUuXbqobTabDS6Xy/D+Up6+/PJLPPnkk5g1axaefvppbNmyxZAWMyUlBS1btozZhctU7Wnz5s0qXXVlatiwYRk32r+62rdvj6VLl+Kbb77BqlWr8Pnnn8fk+rt9+3b1+W3//v2VpsOrVfDHYrFss1gsay0Wy2qLxVImYaDFYulgsViWWiyWgMViuUd7LcNisXxssVjWWyyWdRaL5dT/ba9vsVjmWyyWTf/7W08c84DFYvnDYrFssFgsg+J0DSpIyoC9dL6hCKNIcIVQDtPRcHtycrKCe6IFTemaIHO1SbAF+DNArUMCMuWVDLDLICvrw3IY0JcBfJnOhzBKKBRSkIceZJbpWoqLi5GSkgKn06nKkUFsBoNl0DgalEQIQDr7ECiRLj3SNQKAoQ98Ph8KCwtRVFSkQCHpqCMdjtg2hGEIaTE1GvszFArB6XSq53TSkEALj9HdkwgThEIhpKSkGJ6z3dk2EkYg9OR2u5GWlgaXy2XoIwlySfhJggWyv2XfEUaiWw3TMhEYYqogjiMdBNBT1TEfKdskPT1d5eMlqMH2peOJBJ84vlhPCSJwHEuIQIdOCOEwXZQcx6wj24LlSxckzjsJGsk7fngcAbJAIKCume0mQR1ZT+mMIuE+zicJVvA4fa7xNemgwmuXbi/sJ/6Vc5Xzy263q7rqQJwOWhGWkf0v56t8yOtLSUlRdzVxvLhcLgXIyHHEtZQAZFZWluozrlPFxcUKjPN4PCgsLITb7VbluFwuJCUlwe/3l4G1eK2yj3lu1llfY+WYs1gsKCwsNDi1yfcKjlvpLpaamqq2eb1etWZxf84/CclI1yk5fuQYlqnmKOncI8EgrnmRSERZS+vrPwDlcsbj5PuSfK/iWJFOQvJ9jeuYntZSjmcJVUknpGNdx8LnIlOmTJkyZcqUqXjpWP1s1KpVK5x22mkoKCjA6NGj0ahRI3i9Xnz44YfIz88HAEyZMgX3338/Bg8ejM6dO8flLum1a9dGDb5ardaY031Nnz5dAQS33357uft1794db7/9Nt5991289tpr6jt/TVIr6NqwYQOaNWuGgwcP4tChQ7jkkkviVnZN1bx5cwBl05BUlOqjptq0aROee+453H333eXuM2PGDPTp0yem8hwOB3JycvDLL7/Eq4pxUe/evXHttddi/PjxZV4jpDFy5Mhyj+/Xrx+aN2+OIUOGxHQ38tGuv/3tb0esa1NlatKkCRo2bIiCgoIKXT1qM00E12Sg9EbL6dOnV7usjRs3qvTp0SR/cwJKU39Fc9TRb+oDStMB5eXlVRn8kddHVQR0AkC3bt2qlLrR7/fXSnq4v6KO1c9FsSo9PR1NmzbFmWeeiR9++AHz589Ht27dql3evn37osIvPp8vZoeqc845J2bnq/K0Z88eFBUVwel0ol27djjjjDNw++234+677zbAw5WJ6U9j0aBBg9CjR4/qVLda2rp1K37//fc6O19tKj09vU7Pt3LlSuWYY7fbcf7556Nly5YYO3YsZs6cWeV1v6pyuVzo16+fwQ2uOjr//PPxySefoE+fPmpcM15QkWbPnl0GuD106FBUEOmUU05B9+7dcdlll2HWrFm47777MG7cOJx77rk1qrup2LVt27ZyP+fo2rVrF9avX1/LNTq6lJGRgczMTGRkZKBLly4YOnRozN+fx48fjwceeACrV6+uFHCsC8ef/pFIpEskEon2TpML4HYAz0Z57UUAX0YikQ4AOgOgP+v9AL6ORCLtAHz9v+ewWCwnArgMwEkABgOYbLFYarwqSncG4M+gP+ETAAaYRE+nI4EflpOcnFzGzYcPGVCWQM7/rlEFviUsQ8n0UPJDDY8nUEIgQ74uz8HrJrSSmJiogtMy/ZXcn/8zaC2dfiQkwXZgXeQ+ss1ZrkzNpMMPEoLQrxWAIfhM151ox8g+4zHSzUk6VhAGYp/S1YPp01hnBrgJC7Df2EeyvWWgX/4vA+Tcn+3KD5wcc7K/JfwjA/USBJDnkRAUr1uOVxmgl2NVtrkOgxC6kFCb1WpVIBzPwfa3/A9ii/blUwIx0lVIgi76uI3mmKK7Z7G+0tWKr0kYQvYFj2F9JNQmHWaipemS5cv6yuvUn/N8+niX44b/669H+yuvRdZDQoP6uXUYQ0Il0cYrxwyPczqdcLlcZdYfOX9YLtPXSaBEjhOv14twOKz+UpwPhIqcTmeZNGKyX2RbSwcmCdfI9pFtwe0ytaM+FvkewbWeY8XlcsFqtUYFuPT5JftJtr0Et2TdZB14jXKc6q/LuSTHmL6+yvVXAnk6nMj1Uz8uGkzGc+pOYfpYO4Z1VH8uMmXKlClTpkyZirOOmc9GLpcLTZo0wWmnnQagFA7Jzs7Grbfeqm6MiqaSkpK4pbD6/vvvo25v2rRpTM4u27ZtwxdffIEff/wR3bp1K/euZ5vNhnbt2uH8889Hx44d1faaBg2k8vLyEA6HMWzYMKSkpBxRab5uueUWjB07FllZWYbtFcEL8dAnn3yCwYMHV5i6LS8vL6ay2rZti/z8fGzcuDFe1YuL2rRpg8zMzKipAc4880wAKNPu5em8886La91MxVculwtz587FzJkzK/w9INYAU3UkHXESExORmZlZ7bJKSkrw22+/Ydq0aQAQtSz9fUD+5kNFW0emTZuGJUuWVBlmi+YEFW8XkGAwWKM0MabK6Jj5XFRVderUCbfddhtuuukmZGVlxeQUUpF8Pl+56fOWLFmiwDifz4d9+/ZF3S8tLa2M+1xVlZeXFxdXtszMzJihlD179iA1NbXG54xV+/btw+7du+vsfLWpr7/+uk7P53a7sWfPHgClTowypVv37t1x8cUX1+r5u3TpUqH7XlXUunVr3HTTTbj11ltVTLAi+f1+fP755wZorHPnzjjttNOifqfIyMjA8ccfj/79+6N79+7o2rUrevbsiREjRsSl/qYqV/PmzWP+rPTRRx9h/vz5tVyjo0t2u73MvKgKXDpx4kTlJlyRDmuqr0gksj8SifwIwJD82mKxpAE4A8Dr/9svGIlE8v/38gUAmAB4BoALxfYPIpFIIBKJbAXwB4AaJ/2TKYcktCKBAgmXSHCHDhJMi2WxWJCamoqUlBS4XC4DmCHBDpkiSw8EizZCUlKScs5gIDgcDqt0MSxTpsgKh8NwuVwGEEVre0MQuaSkBG63WznhsHzCRzIYzMA968k0RhK8ke4TbCPgz/RK8pqlG4fuvCKD/tzGc/N4QixM4UVXHf16pROLdJlhSi22Ebf5fD7lZuJ2u5W7DduC0AHdcmw2G1JSUgxpw9h2hYWFCIVCKjUOoRU69Mj+T0pKQlpamqEcn88Hu92uXH14fXydfcD+JsREqIl9zGsMBoMIBoNl4DPp6iFTPOnQmsXyp8sSIQ9+AWTKJ4/HYxijdBOR8I1MmySBDX5gkMCODrtEIhE1RiVsIPtZAjEE02S/EJ4i3ME+4RxISPgzzZjb7YbX60VRUREKCgrUtbEuOrgnwQnWTwfldDCE4086HUlITXd+YTnyGLldgi9yfMjXdHhQznWZfk2HmCwWi5onHLN0wEpISEAwGFRfrOS8l2PRYilNGUg4CIBy3woEAgiFQigqKkIkEoHdblfroNVqhcPhQGpqqqFfdTBGwidMRce5wnGuu6axvpFIBCkpKbBarQgEAsptiGtaYmIiioqK1LF0+UlMTERKSgqKiopU2/F8ejvyurg+Stcr3iEm6yf7Sp8P0kWIr7Mc6QrF6wf+hKRkSjAJQ7JtdahHrg06UKXDUdKVTM6Tv7qOhs9FpkyZMmXKlClTdaWj6bNR27ZtMWrUKCxbtgx33nknAKBly5Z4/vnn1V2sl156abxOV67eeOONqKldrFYrbr311pjKuPjii3HGGWegc+fOGDt2bNR9lixZghkzZiAlJcWwPZ5po7Zs2YLc3FyMHj0ab7/9Nr799tu4lV1TtW3bFpMmTcJxxx2ntkVz1oi3li1bhh49emD58uUYPnx41H1+/PHHmBxSBg8ejB9//BE//PBDvKtZI/Xp06dct5Gzzz4bvXv3LjPuylNtB8uqo3PPPRdTp0493NU4IrR+/Xq89NJLlYIotZkmQqZZfOutt2oMwt12223w+/0YMWIEfvrpJyxdulS91q9fP+WORkVzctDb4/XXX8cjjzwCADGPfSo/Px8nn3yyYVu83Xl8Pl/MwCFVUweVv6qOps9FVdG9996Lxx57DF27dsW9996LU06JTzWCwWC5qQL9fj8+/vhjzJo1C88++6yKxUTTlVdeWaN67Nixo1ywqCp6/fXXy6QnLE9r166tknNXTbV58+Yap9g6UvTmm29i+vTphhhPbWrdunVYsWIFAGDAgAFlPsP179+/1s7duXNnjBo1SjlZxkNXX301evXqhYSEhKhuP/v371cZNF555RXDZyKXy4UvvvgC/fr1izp+TzrpJLz77ru44YYb0LRpUwwbNgwXXHAB/va3v8UEQ5iquU466STUr1+/0v3WrVuH5557rg5qdHSpQYMGZaA2mVmqMoXDYWzZsqXS/Wob/IkA+MpisfxssVhuqMJxbQAcAPCGxWJZZbFYXrNYLGyNxpFIZC8A/O8v372bApDJ5Xb9b5tBFovlBovF8pPFYvmpsjxo/9sfJSUlsNvtBmiFEAUBABksl045NptNBXdTUlKQmpqKtLS0Mg4PMlhLsEEHYRhslel5GEzXA7Q6ZAP86WxBkEYCH/J6GawNhUIoKChQabJkCi2eR6bXki4pEgqwWCwKxGAwWcI8TP2kQ0R6eiXpPCHTNNGNSHer4P9Wq1X1A8+pQ1fRXE0SExMVJAVAOY0wiM/9JChDkIXni0QicLlc8Hq9CoRh6hy3241QKASbzaYmN8eVTLnDvia8FQgElOMU4SLmy0xOTlbXzfEpx5AsWwbtZXoxmRJKh390pymWwXKKi4sV2MO+5Hk5hmQqIf7VgR+Zho3PKVkXHUDiXCP8w7HC69BdXDgvZAor1kkeKx+8zoSEBPj9fhw6dAj79++Hx+NRuUflmJIOQvL8oVBIpQeTgBavXYI8/J9trbe7dICR7i06DCTHvqwb1wwCcrx2HRghqMNz6A4usu8i/3O9KSgoQFJSEho0aICEhAQ11uVcl2OH62Y4HIbb7cbu3buRlJQEr9er0khZLBYcPHhQzSHCTxxvqamp8Pv98Hg8CiRim8k0ZHIt4iMa3BaJRJSrE+c8xxvT/gFQ9bZarSq1Gc+Zlpam6kd4j23M+exwOAzjjGuIXOP09GJ0TWI/6D8ayWtkv3Atl3NYvo/I/qerlQR+KAnAco4S4uI1yfWD55PjV75HRbvT7RjUEfe5CDB+NqrKxZgyZcqUKVOmTNVQR9xno+p8Lho2bBgWLlyI6667Di1atED37t0BlH42Zkqia6+9FlOmTKnCJVZf5UEFl112Wczpsvx+P5566qlyYZbGjRvjvPPOw4oVK9T3YADo2LEjWrduXeU6R9Pq1atVCrSuXbvGpcx4icCP/A7z448/1sm577nnHjRv3hzjxo2L+vp//vMfXH/99ZWWM3jwYAOUcKSoX79+mDdvXrkBt3nz5sVc1q5du+JVrbjp3nvvjdscOVZUWQCpNtOc0GUgPz8fH330UY3KkvW84YYb0KpVK9jtdgwaNAhnnXUWJkyYUAZ40X8HmTVrlsHR45NPPsHo0aPVc939qDIXkcmTJ+O2225Tzx9//PG4p00MBAI488wzcfnll8d8TMuWLfHdd9/VKZhwlOiY+FxUFXXu3BlDhw7F3//+d7z44otx/W1w2LBhuOqqq8p9fcyYMbjwwguxY8cOAwSo67TTTsMVV1xR7Xp4PB5s3LgxKpgdqwoKCvDee+9V6ZiqpBGrqTZt2lRn56ptdezYEd27d69VtznK7/djxYoVOHToENq0aYMuXbqUeZ+oLfA9KSkJN910Ey644IK4lz148GCsWLECV1xxBT799FP89NNPOHToEDZu3IhRo0bh0ksvxQMPPIC77rrLcJzD4SjXITWaUlNTlUPkzTffjGHDhsX1OkyVVcuWLSvdZ9OmTZg9e7bhO6KpUkVzNY2FU6mqajvK1jcSiXQDMATAWIvFckaMx1kBdAMwJRKJdAXgwf9sCCtQNLuAMt6YkUhkaiQS6RGJRHo0bNiw8opYrbDb7So4zQAsQQkZMKazDQPRDPimpKQgJSUF9evXR0ZGhgEGAmBwJgH+DKZKOIbP6YJB5yEGoOWHIq/XC5/Pp4LhOrhAMOV/7aGuRQIUDEYXFBQYgv3Anyl59LRMDO6yXtLNg4FsCawwyO5wOGCz2VTwn8fIQLZ0kUhKSlLBf0IwMpCsp5Wx2+1lUl/paY70IDTL0APWzMfKtE4pKSnlAk8EXXidVqsV4XBYuWpIgIluUewPAjqECOgSJYPvwWAQkUgEbrdbQT+8NsJJ0l1DuiURDJGpqUpKStS4ZDvLR7Q25Ln48Pl88Hg8ypVEprvjvoWFhQaHGplajG0vnZfoNkSQie0sQR+6CdElhQCUdE7SvzgQ7gmHw1HTdMnjJHjH5x6PBwUFBepLtnRcknOT41aCa8XFxTh48KByiGK+U7qDyTHIesg5qG/XAR25L+eTHHfSYcUiXG+Yhkr2CfeX7aa3oXSg4TY6JBHA4piSjkesW2JiIkKhkHLekbAJHYIIrBB2I3jFNYLuai6XCw0bNoTX64Xb7TaMdQJgXNulk5t0FZPtKIFOjik6uJWUlKi1lICbXHskVMYxU1JSotIOyjbmvOP1EDaS9ZFjUsKBLEOOD7nesk95fHnrt8fjURCmHK/sJ7ouSdc2HaSTkBrXq2jgINc46c5Vmz/mHUE64j4XAcbPRjHWx5QpU6ZMmTJlKh464j4bVedz0XXXXYfmzZujW7duUV8vKCjAm2++iUcffTTWIqutgQMH4sILL4z6WqNGjTBmzJiYy/rHP/6B++67L+prTz75JM444wyceeaZ2LmzNG4YCoXw1ltvVRg8q4q2bduG1atXo7CwsMrBrtqU1Wo1pDejvvjiizo5/+uvv44lS5agZ8+eBiCAKiwsxBlnVDyVGjdujJ49ex5RLkoA0KRJE3To0AEvv/wyFi9eHHUf3TGlIr3wwgvxqpqpWtSPP/5Y4Z36jRs3xkMPPYQLL7wQ7du3j+u5N27ciHA4jNTUVAwePLja5fTt2xcff/wxAGDkyJHo378/du7ciS5duuDTTz/F3Llzox4nfwc5+eSTVapISgbu27RpU+b4//znP7jlllsUJKnLarXiqquuwj/+8Q/89ttveOihh3DLLbdU+foqUocOHfDAAw9UKVVM9+7d0bJlS+WQZ0rpmPhcFIucTieuuuoqnH766VVyO6iKmjRpEpPb4vfff4+VK1eW+3qrVq3Qq1cvXHHFFdV2I9q0aVONXH/27NlTJfezVq1alfntN95at24dHn30UVx00UWYOXNmrZ6rLnXSSSfF1QGnIuXm5mLOnDkASgEzHXT3eDz473//G/fzpqam4o477sBll12GWGLk1ZHP58OTTz6Jiy66CD179sQZZ5yB8ePHY+7cuZg9ezZeeumlMsccPHiwRtD266+/jptvvjlurmGmjMrKyqr0c/iqVaswatQo3H9/ZW9Bfz2lpKQgOzvb4IQViURqJU1irYI/kUhkz//+7gfwX8RuF7gLwK5IJLL8f88/RumHFwDYZ7FYmgDA//7uF8fIFbkZgD3Vr32pmAKF6WV04MBmsxmC716v1xA05z6pqakKIGIQXgIjgDFllXT7kYF2AgIMwjLQLGEjmRJHOmHQaYZwBGGc/7WlejAo7PP5DNCNdDGSgW8GhgmXyDROsp0YrJYAB/BnAJ4BeuncwXPzuUxBRUBGD3azz1hfHTKQEAsDzoQT2A8sj+ACA+vsL+YuZn2Y/kteF919WBbTKdExBCid7BIWIGTAcxN8IbDFtvL5fIZxR8iMkAxTyck2kY4sBAEIq+njmnWX/8t2k+3E5wRoOMZk6iCOB4/HY3Ck4XZel2yrxMREBVkQ8JLuKzpQwPHNsU2QgeeUEJSE0TjvJKQiH3I80qWHad+8Xi+cTiccDgcsFouadxJGk7AD54jH41FjyG63q+u32+1wOp0GMCoa5VweUMRtOvTF69RdsyRUyLkhr43nkmCMnlKPdZQACNcQlsMxzmtjWRxjXPN4LOtBKOngwYPqdbfbrWApOT84TtPT01UaLq43dOqRYBLPLddg2e9y/SgpKUF+fj7y8vKUA1woFEJubi48Hg+CwSAcDocCejhGOD+4vhFU0sEgugZxPodCISQmJqr99bHIdpRjQf4v11rOd+nOxH1ZttyHa5p8H5LrqKwLIVLdCSwaHCYdlOR6oYNDx7qOhc9FpkyZMmXKlClT8dKx8tkoNTW13NcWLFiAhQsXxuM0MWnBggUV3l3Zv39/3HrrrTj//POrfY4TTjgBl1xyCSZOnAi/348pU6bg7bffRk5ODt5///1qlxtNS5Yswfbt248o8GfYsGGG3zqAUthm9erVdVaHESNGYNq0aVGdfZ544glMmDChwuPPP/98JCYmYt26dbVVxah65plnKkxZcd5552Hp0qX45ptv8NZbb9X4fFUB3epCTqcTPXv2xMCBA+vsnOPGjVNAypGqCRMm4Jtvvin39dGjR+Pxxx/Hhx9+WC4QVl3t2LEDe/bswZQpU3DPPfdUq4xJkyZhx44d6o7tW265BevXr8eAAQPw66+/GlK56youLsZ7771nuKGY8vv9GDp0qHquB4T/8Y9/YNWqVZgyZUqF7zMOhwOPPvooTjzxxGpdX6yqyp39J598Mpo3b47PPvusFmt09OlY+VxUmS677DJcddVVGDVqFCZOnIizzz671s7Vq1evSt2o1q9fXy48B5R+zrv99tvxzjvv4OOPPy6TpiUWrVu3ToHS1VHz5s3Rq1evmPf3er346aefKk2lWBO1b98e+fn5+PTTT+HxeGrtPHUpi8WCpk2b1mqKSSn5ub1nz55o27at4fW1a9dWmIauurr33ntx44031up17tq1ywDx/P777/j0008rPMZqtdZonjRu3BhPPfVUhfPZVPU0ePBgjB8/vkIAe9euXXjyySexZMmSOqzZ0SPGNaWrV0FBwdEF/lgsFpfFYknl/wDOAfBrLMdGIpEcADstFsvx/9s0AMDv//t/NoC//e//vwGYJbZfZrFYbBaLpTWAdgBW1PhCAIOLjwwIM12KHmTXn8uAp9wmrrfMQ4dZ+D8ABbVIcEI63dDpQabAkYFmPU0SJYPHdNzgeaV7iwyU83okkCPbRD+PBFvkNTHALNtFP5btJ9MDETiQgWYeK0ECnkt3TqEkpCFhCOloweMJHBGESU5Oht/vNzg0cfKyLP0aZN2ka4i8DjneABhcc0hsc7xJlyMCFXpgX4JU7AfpZCKdefRxImEZvf4cZ3TvkNCVBFgAqPRGenvrwIpM/8PxLYGhaCABxTKkQ4sEv3THIr1/dBHckGOFoFAkElGwESFA3TWK/af3PR1rmCpOAnp0byqvTlKyfXW4RfYTQQ59LWIZ0pFFplyT4F+0ukj4h3VgmVyLmM4vHA6reuhjV7YLr4HuPgQRAaixL1NY8XyEfzh/udZIsE6CVHKO6+uIBOfYfvn5+apMAi/hcFhBebrjTygUKrOWS1CM/U3QSU/xJttVti3nSzToR58nel9F259rGgAFKMrxIt9b5LjTt+nziNcox408Rm6PVt9jTcfS5yJTpkyZMmXKlKma6lj6bLRt27ao23fs2IGpU6di0aJF8ThNzNqwYUO5r1mtVlx//fUYM2ZMtR03161bh5ycHAU8vfTSS9i2bRsyMjLKDW5XV1u3bkXDhg2PqFRf0e4CLykpwZYtW+qsDnv37sWiRYvKBIiA0rvHK6tLmzZtapRypDpyOp3o2rUrTj311HL3ad26NZYvL41bL1++vErpHqKpLtJ0VEXt27dX9v41ge+qooYNG+KEE05Av3796uR81VUsgfSkpKS4BymHDx+OFi1aYNu2bdUKkLdp0wZnn302rrnmGrzzzjsASh1w1qxZg02bNuGrr76q8PiWLVuiW7duSExMhNfrNTifrFy50hBMr1evnuHYgoICfP/99wBK3T0Ot6oSnD7uuOOQkJCALl261F6FjjIdS5+LKlKTJk1w9tln44477sCAAQNq+3QASoHlyrRly5ZK33MIhVTHHWznzp3Yv39/5TuWo1AohKysrJj3379/P3777Tfk5eVV+5yxnudYktPpLHNzf20pNzcXv/zyC4DS97dmzZqVeS+Ml4umVGJiIk455ZRaf984dOhQlVMYZWdn19jZr7CwEG63u0ZlmDIqIyMDffv2xamnnlqhQ9S6desqdE8zBbzzzjv4448/VDptt9uNgwcPxv08tbmCNQbwvcViWYPSDw2fRyKRLy0Wy00Wi+UmALBYLFkWi2UXgHEAHrJYLLssFgu9om4D8K7FYvkFQBcAT/5v+9MAzrZYLJsAnP2/54hEIr8B+BClH2q+BDA2EonEBWn1er0GVxQJJ0inC0IgDODzfwkoSNiATiM6FAP8GeCV7jNy/2g/0DBYm5SUBL/fD5/PB7/fr1wV6Cyhp1+S56JLBl0s6KhByIlAiYRHZL3oWiIDujJYzRRIdAWS10HAQoIEDBLLtpdpcHicdH6RsI1sa7Ylz8kgczAYhMfjQWFhIXJzc5VbR0JCaZow+YbLlFN2ux02mw1Op1OlHmM7y3rwPAQrCJAwtRHbjZACABWED4fD8Hq96trZ/nl5eQoyAEp/yOCxhG6iObJIYMJisag7SQKBgHKaocOPhJiiwSJyLNOhxOfzqfRm0gmE4ABhNDr58PpZX77GtuBz2feEngjGsA8l0CPdfHiNdK+KlspM7y8pec0S/qFbCZ1mXC4XXC4XUlJSYLFYVJ9I8IRjVbpYsRzWi+Pf5XKp9pOQEyXnhZyf7AsCJxKg0d1YJHzD53LdKi4uVo5J0qkmGjjCPuY+0pUrGAwqwElajLJeEviQayfLdLlcCIVChvb2+/1ITExESkqKcmSz2+1ITU2FzWaD1+tVKRWl6xnFukrgitfEetA1yOfzoaCgAPv27cPBgweVy5jVakVmZqa6ZjnnEhMTcfDgQeU0lJCQgEAgAKvVitTUVMOaa7Va4ff7FUgmYS26BHE7gSYJ6kgAKto40cez7s7D9ueaz+thm3CeyrL01Iqcl3wP0V3vJFCovx/K8TxlypQyc/AY0zHzuciUKVOmTJkyVSf6+XBXoJZ1zHw22rRpk+H5wYMHceWVV2Ly5Mn46KOP6jzd0LvvvluhTX6XLl0wZMiQGt1h/8cff2DYsGEASj/Pz5w5E+FwGOecc061y4ymb775Bunp6QbXi8Ol+vXrY+HChfjkk0/UNgblMzIyMGvWrDp1Jvrwww+xefNm9O3b17D9+++/LzfdG9WrV68apRypjrxeLx566KFywbQWLVpgwIABeO211wCUpmCqiYvSwYMH8cwzz1T7+NqQHMc6xFFbysrKwqFDh7B9+/ZaPU9NUzbFmmok3nDh2LFjAQB9+vRRUFasmjhxIlauXImbbroJL7zwgoI8A4EAZsyYAQC4++67cc0111ToaFRSUoJJkybhsssuQ5MmTdT2lJQUw34ySBsOh5GTk4Offy79qHDo0KEq1b029H//938xg665ubk4ePAgHnnkkaqAnebnoqPkc1E01atXDx988AGWLl2K6667rtYdqKRGjhyJ008/vcJ9Zs2ahW3btlWaHishISFqms3K9Pvvv9foPe3FF19U60qs+uabbxRYEk8dOHAADzzwAAYOHIgffvgh7uUfTmVkZNSZ28+aNWvUZ8ozzzwTnTp1MjiBFBUVYcGCBXE/b3FxMZYuXVqrUJjX68WqVauqPOale151lZubW+vA219Njz32GG6++WaD65jX68WaNWvUc7/fj5UrV6KgoOBwVPGo0dq1a9GtWzf0798fo0ePxvTp07Fq1arqFlfu5yJreS/UVJFIZAuAzlG2vyL+z0GpjWC041cDKJM7NBKJHEIptRztmH8C+Gf1aly+6OIgUzVJoEIGUyVQIN1//lc/FWSWjgoSVqEkFEIR3uA5GIxl8JwuNHa7XTltyCC8y+VCSUmJAXZgcLe4uBjBYFAdA0AFmCXcJGEcwhAM8koYCvjT7YagDmEOHkvAg+cgwMB9CQrJYLzFYlEpsEpKSuBwOAztLIEIHiPTiPGvDDiz/Xw+HzweD7xeL1JTU1XdKZmezel0wm63o6CgQKVeo3ML6+hwONS5JYDAfibA4HQ61YdKpi+ikwgBGY4nq9WqgCemF0pIKE0dZrfbEQgE4HQ6ARiBKB1CoksP05jRsYbn5/HR6Gam3iJ8QLjG7XYjFAqpdHTS9chisSiAhP3EY6UD0P+3d+fhUZVn/8C/J8tkJgsJIYEkhCVhJ0FAqGwir/JSQKqCvLVaK7aKLa2orbZXXVqr9q20r0sr2iIIrYpWRLGACPUXLWAqhRAIS9ghBMISSMieWbKd3x+T+/GZSQIJJOTEfD/XlYswc+bMmfOcM3mS85379m8hpx9/UkHF4/HAZrP5hGn016hXL5L2fBKGkcfJ+iU0Ic+hn296wEUPp0g7JzmXZDk57/wrUsk5pYeYJDgigSE53yToIcEWOa/9q+PIWOrHshzzsi/1gJtsl/4+owfk9ICQHoTSzxkJqchr0yvCCL0KjIyZHrrTAyx66Ms/qCfbJ2MeHByMsLAwVFVVITQ0FKWlpQgICFAt7c6dO4e4uDg4HA5UVlaipKQEsbGxyM/Ph8vlQnBwMBwOh0+ARbZb3of8K6hJ8Cw/P1+d3zabDS6XSwWOJHwk+0yqE1VWVqp2dxIadblciIyMxNmzZ2Gz2eBwOBAUFISKigp4PB6filr6zwKPxwPDMNS5qW+/3hpRAm4Oh0ONvf4+KO+/Mo76sSLHoywrj9erZQk95KlXhpL3PanupB9/co7L+SqPkXX5Vw76uvo6zYuIiIiIrtTXaW7kfyH6f//3f9u1NdXq1asxffp0/PCHP2xymcDAQNx444345z//2eL1P/roo5g6dSqee+45ddvu3buxceNGXH/99Vi3bt1lbXdT9u7dixkzZuBnP/tZq663pcaMGYMRI0b4/H70/PPP4+zZsxg2bBjmz5+P5ORkpKamIju7WUUarthPf/pTPPvssz7VB9auXYvnn38e//d//9fk4/r06YOTJ09ejU30sXXr1ibvmzFjBlJTU3Hw4EEAwMyZM6+o0tM777xjuYsPzz//PGJiYvCjH/2oTT7d6y8qKgrx8fE4f/58m1ZluOmmm/DHP/4RiYmJl90uSw+8NGfZ1mrjcebMGQQGBmLJkiWq0nRzrVq1Cvv37/dpszVz5kysXr0aaWlp6rbly5fj448/bvJC5JAhQxqtSOK/L7t3766+93g8PmGu73//+zhy5Aiio6MbrMfj8TRZ/Ur+jvT2228jMzMTffr0wWOPPYZ9+/ahd+/ePq0sFyxYgL59+yIlJQVdu3ZFz549ERAQgIqKCvzxj39EXFxco8/fmAcffBCvvPIKBg8efEVtXb5Ovk7zosbceeeduOmmm5od8mtNAwcOxB133HHRVoHV1dWYOXMmBg0ahGHDhuGOO+7A4MGDG102Li4OP/7xj5Gbm4tt27ahqKioWduxf/9+fPjhh+jVqxeSkpJ8zumLSUtLw9GjR5u1rG79+vUYMmQIbrzxxkY/8Hy5YmNj4XK5sGnTplZbp1VERESgS5cul16wFXz88cc4fvw4bDYbRo8ejcRE31N77969rT6nFgUFBThw4ECbnY+nT5/GoUOHLmuuc/PNNyMjI+OyK8INHz4cX375JXJzc7F7924sXrzYcvPBjiQ0NBSjRo1SH4AXa9euxebNm/Hkk0/i9OnTSE9Px9NPP90mrem+bsrLy7Fr1642bRPdZsGfrwv9gqh+m/8PS//WVhLUEPpFTakIIUEZ/UKvhB305/avetOlSxdUV1fjxIkTKgSkV+SQMIAEK6QqjdArL8gFYwlYAPC5mAxAVQ+RCh4hISE+FWT09fpXQZLXoYeF9H0m29LYPpWAimyrtAmSIIRUp2ksnCIXq/UL4KZpqtCGVD6S/VpaWgqPx6P2hR6IkDGtqalBZGSkT2hJAhISKpDta6w6EuANGUh4obq6GiEhIWqbZOylZVRVVZUKEsj+Cg8Ph91uV9soY6EHB/QAh94KSW995XQ6UVFRoV6LhNX0tkP6/vMfT73Kj1RzkdfhcDhUlQ/92JewEABVFUqqJ+lVY/QqMP4hIdmHethHxkeOPznWJKCkh51kTPQQjVRx8q8IpR9L+vZI4Ekq9OgBh+DgYBWukO2rqqpqcBxKhR9pESVjqYe6pNKNvg8bC8pI8EsPIulBHj284z+G+vuSHCcyRlLJSl5vWVkZHA6HOj71YJR+vuivU99GeQ55H9EfK+S9Q0JgUvEqKioKXbt2RWVlJQICvC3SIiIiEB4ejqqqKsTExKhzsKioCCEhISgrK1NhHT3EV1tb2yBg4398S0hFqoB16dLFJ1AjgTEJ9sl6ZBvk+HK5XD7vT1JmskuXLqpSkR76k/dp2Qb9PJT3FVm/bLMci3r40v+9VH+/9q/0o1fU8g/fSNU3u92uHiPHRlBQkAoLejwevPHGGyAiIiIi6owef/xxAMAXX3yBsrIyS8yN33rrLezZswc333xzk9VypkyZguXLl7c4pPLhhx/i2WefbfB7x8qVK/H444/j1VdfxenTpy972/098sgj6N+/f6ut73L88Ic/RFRUFNLS0nD69GkcP34cSUlJsNvt+Nvf/gYAuOaaazB58mRs3rwZOTk5OHXqFGbNmtWm27Vt2zZMnToVDzzwgDruJk2ahLFjx2L48OE+n8TVORwOn5ZCVjBp0iSfYMTq1atx7Nixyxr7Xbt24ZVXXmnNzWs1y5cvxz333OPTwqmtxMfHo7CwEMuWLWvT55P2M4899hgWLVqEY8eOtejxoaGhSEhIaPby/fv3b5Xgz3333YfBgwfj1KlT+PTTT1v0WIfDAafT2aBy8erVq7Fo0SK8+uqrPtXgSkpKsGrVKsyePdtn+by8PMTHxze49nDo0CGf8BDg/VuS0+nEpk2bkJyc7NNmsqioCE899RSmTJmC2NhY9OvXD926dUNNTQ0effRRhIWFITExEUOHDkVsbCwGDBiAqqoqfOc730FMTAxWrlwJwPuz49lnn8Wzzz6LPn36ICYmBsnJyUhISMCf/vQnn+2JiopC9+7dMXr06MsKux4+fBiHDx9u8eOoY5o6dWqDC8dX08XaTIq9e/di7969GDZs2EWri40YMQJ/+ctf4Ha78cQTTzQ4N5qydu1arF27Ft26dcOKFStw0003Naul1ODBg5vVrsyf0+nE1q1bcerUqUbblF6O/Px8pKenY9++fa2yPquRDxq3tZKSEnz44YcAgAkTJmD06NENjrmNGze2SWh34MCBmDNnjk/1ltbkdruRk5OD7du3X9bjq6urcdddd+F3v/sdhg4dipiYGMTExLRoHePHj8f48ePRtWvXiwbh6dK6d++Offv2Ydy4cTAMAzk5OUhOTobH48Hrr7+O0aNH4/33328wZ6H2xeDPJegX/SXQo1eIMAxDpealkoJ/ey//agZ69Q15nNyvt1CRUIceGgkMDFQX3iUU4l+NQa9q4vF44HQ61fPpVS6koo9Ug5DHy/+lYoq0qdEvKstj9QohEszxr7wj2ypBGbmYLKETWUYPP8m/sp0AVChHns9ut6uwh145qbEQkQQ2JAjicDhU1RG3243i4mLU1dWhqKgINTU1iI6O9qnQEhAQoKrrOJ1Otb9k2/Q2U3pYTJbTqx/J9kkQBYAKjOhVTuS4crvdqoKOzWZD165d4XQ61cV+CUHJ/nQ6nQgPD1fHqx7ekYo5sh/829HJcR4SEtJge+V1ymMlmCDt4UzThNPpRFhYmHpdMs4BAQFwuVwqgCPBIKk0JceaHiYBvqpUIq9T7pPXLNskoQX/AIdsv3+lHNkmOf7kSw9N6eeEHmSRdfgH2fT9JFWj9OCNnEN6RSIJMelhOanuIr/Q6xXG9EpCsqycq/q5o5+Dcq7KbRKu0r/Xgyay32Wd8ngZYz2IplcCktepv4fp+1eOQzmXZDul7R8AnwpGMqYSJPNvPxYbG6uCVQkJCaitrUV5eTncbjcqKirUuR0ZGQkAPi2/pBKYvp/08ZXbXS6XqoolFXr0amQSGpPKOHplLqnWJUFDCSPJeuR81Csq1dXVwW63o7a2VlUgi46OVvtcbwco2ysBPP39SH9/ffvtt0FERERERG3n1VdfRVJSErZt29bsiz9tbcuWLdiyZQtycnKaDP6kpKRgxowZLQ7+nDx5Ejt27MAdd9yBZ555Rt3+/vvvY/ny5Zg1axZee+21K9l8H1u3br1opZi2FhkZiddeew01NTWYP38+AOCPf/wjFi5ciFmzZmHdunXIyMjA448/jpEjR6JXr14YPXo0kpKSMHjwYOTm5qoP67U2+R320UcfRUJCAk6cOIGnnnoKNpsNGzduxDe/+U1kZmY2eJy0yLaSa6+9Fhs2bPC57bPPPvMJ/uTl5eGTTz7BvHnzLrquxYsX+4QirCQrKwsjRoxo1XBcUw4cOIDvfe97bf48UrUiLy9PVSFvCafTiRUrVuCuu+5q1vKtdQF77Nix6vvx48djy5YtzX7s/PnzMWzYMMyZM6fBfdnZ2Rg7dmyDNpC/+tWvUFpaikGDBiE+Ph69e/fGpk2bsHr1asTHxyMxMREDBgxQoRx/586dwyOPPIKlS5eqv0/pXn/9dbz++us+t/Xt27dF58K9996rvs/NzUVubm6j7yGA98J1SUkJwzt0SXa7HQUFBbhw4UK7VPwBvHOeb3/72/jggw8uuWx+fn6zfm7b7XaMHDkSQ4YMwYEDB5q9LRcuXEBmZiauueaaZlX9iY+Px7XXXouePXu2+GfHpk2bcPTo0VZ731y8eLHP3O/rJiQkBAUFBfj3v/+NHj16oH///q1aLUn85z//UdXOBgwYgKSkpAbLZGRktPrzhoWFYe7cuW0W+gG858WECROQmpqKnJycy1rHwYMHMXv2bDgcDkyZMgVr1qxBaWmpus7TXNOnT0dmZiZuueWWVqsU2Nnk5ubi888/R2pqKjweD7Kzs3H77ber4/ell15q0fsfXR0M/lyCXk1DLtrrIQC5oOvfOkgupvu3XNEvMuttbfSQgR4u0J9LLirLRfyoqCiUlZWp/+uVfvT2W3JhWA81SHBCnlcPnch2y4V2CWvIBW4APqEGaUum7zMAqvWYBEz0AIZsqwSC5Hn0H6R6FQu9zZAEaxprC9RYlR8JEMhrkmBUbW0tiouL4XQ61S9MEpICoCpyiMDAQDgcDnXRX6qS6JVVQkJCEBwcjMrKShU0kIou+nok9KOHxiSIUFlZqYIzst+rq6tht9vVc3g8HrVeqcwiF/+lmoy0ntKrq+hVX/SKNf4BCFmPjKceiJBAg348y+uS8dSPRQlK+be80o8V+VdCLHLs6W3g/INdtbW1ahv116VXxZFjXA/+yLEix4V8ybnn34JKqqX4V4fxr6Ijx5gELvQKKhIEkRZQEu6RMZfxk2NUWoDpYSI9SCfnin91Lf0c0t9z9O3Wzwv9uNDDRPr7h2yrrMvlcqG2thahoaE+gR+5Xw9oyf7Qq8rI6wO8FXVkf8tj9PCQBHRkfxYVFfm0hZPjzm63o7i4GGVlZSgvL0d5eTlM00RoaGiDKkX+x5E+znrgqbCwECUlJepTXFKhSar0yDEk7wkVFRXquJcApoQz5TU6HA784x//ABERERERfX08+eSTABpWj7WCDRs24ODBg422q7DZbLj11lvxhz/8ocXr/fjjj7FgwYIGtx89erTVgz/tKTAwECNHjlTVkT/55BMA3rDXk08+idTUVPzjH/9ATk4OTpw44ROyGDBgAL788kucP38eqampbbJ9AwYMwLp165CSktLgQlzXrl3xwgsv4MYbb2zwOGk9bxUjRoxA37598eijj/rcPnnyV91pli5dir179yI7O/uiwZ+8vLwG4QeruRqhH928efMwY8YM7Nq1C7/+9a9bff2vvPIKEhIS8Ktf/eqyWzxIJYzJkyfjkUceQa9evTBy5EgMHToU/fv3Vx9UBdBqVUPy8/Oxfft2/OY3v2lR6AcAXnjhBVXpyN+UKVMavf3gwYO4//77fW6LiIhAeXl5s57zkUceUd/7V+FvilUDcNR5zJs3D3PmzMHYsWPbJEDRXHa7Hbfccgu2b99+yfNi06ZNmDx5MoYOHXrJ9fbs2RPXXnttiy58DxgwAP369WtQ6aspQUFBGDRoEG688Ua88847zX4ewHttaePGjUhNTb3i0FVFRUWbto20gqysLDz44IMAvO/Pzz33HObNm6eq4beGAwcOqGo/gLdqXrdu3VBWVoaIiAgYhoGCggJ8/vnnrfacorKyEuvXr8ePfvSjNm1pFh4ejmeffRYXLlzAl19+ednrcblcan5QWlqKa665BsOHN+iIeFEJCQk4d+7cZW8DeT/cUVJSgpCQEGzduhVLly5Vv1sy9GNNDP5cglyw1y+a6xeq9eCELO9f9UWvViL8L8brF/P9l5Pb9cAEAHVRXCb7spxejUguQku1Eb1Sjh6wkOWkEoUEH/RqIXpoQw8eSFUgvd2SvCa99ZXcr7cA0i/06/sRaBg4kX/1NmT+YSY9QCJjpQenZL9JkEMPGEhIRoJcevUR/bn1oI0+QdNbQcn+1v/wp4ddZJ/J//WwjN5yTa/Soleq0QMVsu0SNtBfqx6y0oMgsq9lX8q4+x93/rfLePp/qkT2j/wxTA+86BVJ9P2hHyfyWmU/SKjJP7yln0v+FXj040Zeq14JR9+H+uvUx8c/nKYHm/QqRPrY6FVy9Mo8+tjJ8tXV1eoY0qshAVDniexHqRajb6ceVPF/Hv21+Fcm0ve9Hpi72Lpl/+qP11tP6e9N/lW7ZH16+EkP8UmLQT1k5R+ikvcoGRtZ3r+tncPhQF1dnQrb6e2z9PXplY50enUieU8sLS3F+fPn4XQ61b6srq5WgbeioiKsX78eREREREREorHfk60gKyur0eAPcPmVM+ST+4MHD8bBgwfV7YcPH0ZKSsplrdOKHnnkERQWFqr/6xcOtmzZgttvvx0JCQlISEjAiBEjfB575MgROBwOpKSkwOFwqL+LtKbS0lIsXboUkydPxkMPPdTgfv9tAqA+VNgW23O5BgwYgOrqamRlZfncfubMGQwYMACAt+XF/v37cerUKZ9l/P92d7mtJb7O5syZg1GjRmHo0KFtEvwxTRMLFiy47NCPvp7CwkKsWrUKgYGBmDhxIl5++WWUlZVh27ZtSEpKQvfu3VvtAmxAQAB+85vfNKg01Vz5+flXvA3NDf0QdVQ9evRAampqu4Z+xLBhwzBmzJhLBn9OnjzZ7PO7oKAAqamp+O53v4tdu3Zh//79F10+ISEBt912GyZMmIDo6Ojmbjqio6MRHx/vc92jufLy8lBYWHhFwZ+DBw/iiy++8JkTfd2Vl5cjJycHBw4cwMiRI1ttvbm5udizZ48ay/DwcAQFBeHMmTM4f/48kpOTUV5e3mYtOjdv3ozdu3dj4sSJbbJ+0ZrHypIlS5CUlITAwMCLBn9OnTqFuLg4n2u28mFtujKffvopwsPDVQCwqXbCZA0M/lzCe++9h9mzZ/tc1NbbV0n7JflFU36BlmCGXCj3vxDf2EV6Wbd+MV9/jB54ME1TtWOqrKxUIZTg4GD1qR1ZToI7VVVVKpQh1VAkxKEHReT5pdqKBECAr0IV0v5GD3JIcEMnLXCkaohUGtJbDwHwCf40tl79l3gJC8gbtt62R7ZRnlvf/3r4QgIbHo9HrS8wMBBdunRRzy3ttiS4YrPZEBoaqqpA1dTUoLKyEmFhYSow4Ha7VZUgCSfoAR99/8m6TNNU7a4aq/wk4Sq73e5TnUf/gSWvS9apB9T08IjsC9le2UbZf3poTa+Qoh9PUvFHfqGX1xUWFoa6ujp06dJFhaMAb4nEiooKta1SJcbj8aiKRzL+Ul3LvxWV/lgZH6ko1Vi7LXm8vD4J0UhlJf9KOfqxr1eokWo/+nZLtRo5B/xDTf4Vp6RKDgBVgUi2W45/OdeqqqpUsOjkyZM+rcL8Ayv665WAm39YTl6HXtXHv/2Z/7mth2b8g0/yHqdXY9IrWunhIf04lTGSNnGlpaVqW0JCQnDhwoUG73uitrYW1dXVKCsrU/tPPp1YWFio3u+aKj1MRERERETUmb388suYMGECevfuDQDYtm2bKvHfq1cvfOtb38K6detatM7ly5cjPT0dx48f97n9wIED6oLWlXzC1wrCw8Px9NNPIzw8HIA3ZKN7+eWXcfvtt/ssP3bsWJ+2ZGvXrsV3v/tdbNiwAZmZmfj5z3/eqtt47tw5rFmzBmvWrMHs2bORkJDgc39UVBQWLFiAJ554AgDQr18/bNiwAaGhoSgpKWnVbbkSw4YNQ2ZmZoNKOH/6058wadIkAMDf//53dXt5eTnCw8NhGAbuvfdeLF++HIA3HPTYY49dvQ3vILp06QKbzYaYmBj06NGjTT75XlxcfMXrmDlzJrp3766OzdWrVyMyMhLnz59X4Rz5oFxr+NWvftUq6yGipi1fvhwjRozAzJkz23tTMGLECMyaNQvvv//+RZerrKzE5s2bYZom+vXrh9TU1CaD0nfeeScA74dEf/vb314y+HPmzBkUFxc3+Hl9KVFRURg5ciSGDx/eICQrmprPrVmzBrfddhuGDBnSoucURUVFePXVV/GXv/zlsh7fkfXv3x89e/ZscLt/kMT/OlNTQbfKykpVKfKf//wn7HY7rr/+ehiGgd27d2PLli245557fNqctrbx48er61tXwu12NxnEra2txebNm1utVa9U3Jw9ezYA7zFZXFyMvn37qmtxLpcL69atw4gRIzBs2DCEhYWhpKQEcXFxmDZtGv75z3+2yrZ0Zvo1XrI2Bn+aQQIXeosYCT3IxXOHw6EqVEiIRMItspx/dR/5Xr9dryIirZH0HxR6OEMCLPJ8EgDQq+/oF/n1UImsS4Itsh4JXuhthSQsoV/E17dVgiP+FVj0SkJ6+EfCI3pwQIInUo1I2ofJ9ksYxTS9bcfcbneDVkl6ezIJp0jlHYfD4ROmcbvd6ge03tYoNjYW5eXl6jliYmJUkd2MAAAoYklEQVRUyy+Px4MuXbqoiizS7kxCDYGBgeqX06ioKJ/2RcBXn6ryeDw+rbBkf+mBq5qaGtUSSva7VDqRbZBfdPX2WHolFAkM6ZWHZBxkealYJceKXjnJv0KTPF6eU2+BJ8tJkEc/jvXzAPAGsuS1AVDVj/RzQcZKwkF6BSr/gJd+bujnkt76zT/Io79OPdDiHxzSKw6Fh4er80gCK4Zh+ATtZLv0oI2Mlb7tMu4SKvNvmVVTU6NCLvr4Ad4AkgSJ9Io7/hWHZPulLZXebkseI+Q1yfuX/F/OQ//2XQBUwE2qGPm/5/z1r38FERERERERta/MzEy89957+OUvf4ny8nI8/PDDSEtLUyX+Z8+ejcGDB2Pnzp3417/+1ax1mqapQj/f/e53VSjjhz/8IQDgO9/5TocP/gwdOhSRkZHq/5GRkbjtttuwZs0aAN6gQ05ODpKTk9Uyd911l89FjhdffBHFxcWYNWsWJk2ahHXr1mHTpk1tsr1jx47F4sWLMX36dJ/bH374YRX8GTlyJAYMGICamhrLtD0wDANjx45t9GLlt771LQDAjh07fG6fNm0akpKScM0116i/jdXV1WH+/PlsbdSIhQsXYsqUKRg8eLBlxr0p/m1k3nrrLZ//t1boh4iujpycHCxcuBDf/OY3ERoa2t6bg3HjxqFfv344duzYRZf74IMP8MEHH2DmzJlYsmTJJdd75syZZrfuKigoaNZy/oYOHYprr7220eDPPffcg+uvvx75+fkNPhybmpqK8ePHN7nezMxM9OjRo8lwU2VlpeV/drSV8PBwOByOBre7XK4GgXAA6tpeU/syKysLaWlpGD9+PJKSknwCPjU1NXC5XDh58iROnjzZei9CEx0djVmzZjVaEVJIoQMAcDqdOHbsmE/VrsrKShQWFuL48eOIjY1VlT6rqqpgs9kAeCvvHD58uFWrocbFxeGGG24A4K1i9cUXX2DChAlITEzEhg0b8Je//AV1dXVITk7GoEGDkJiYiO7du+OLL77A0qVL8YMf/ABpaWmttj1EVsbgTzPIBXy73Q6Hw4GgoCB4PB643W51cd1ut6uL5nrFC3m83jqpsTY7eoBEb1GlBzD0cIt+0V5CC1LhRoIjEjTQL+bX1dWp0IwEHKTFjd7+SoIJEuCQSj1SIUfetCXkoLcuAr5qWyRfeoBF3wd6WzI98KKHYiRoII9zuVwqbCCBCXktbrcb1dXVqKmp8akeI+EYCQLJ9/o41NbWquo9Ho8HNptNBWcCAgJQVlaGhIQE9RpsNpsai6qqKrjdbnTp0kWFiuQ16CEUt9uNqqoq1U5MnldCWxKc0V+T/L+yshIREREqSetyuVBRUaHGRm8lJvtWD6NJmMM/8KJXqvEPcMhrkGo8/hVg9CCR3tZND9d4PB7VLk8PtUnQRpbVx1jGSV6PbJsEc/RwjtDDaPptwFct+SQIox/7crtMTPyrTclj5fVIFS2n06m2TX9OeQ0SGJKxkPNI3gekapLdblfnlISxwsPDERERocJxcr+cL9LSSo4jOSYkGCfHrYQWly5dCiIiIiIiIuqcli1bhl/+8peIiIhAaGgoVqxYoUI63//+9wEACxYsaHbwRwQHB+MXv/gFjh07hm3btuHIkSMYM2YMXC4Xunbt2ipVQFqb/M3iUr7xjW9g+/btcDqdqurMypUr8dJLL+HJJ5/E/v37sXfvXpSUlCApKQlRUVG47bbb8Oc//xmHDx8G4L3AM3/+fOTk5OCll17C+vXr2+zCZ15eHubOndugak5oaCg+++wzHD9+HKNHjwbg/XuSf8CivYwbNw4DBw7Egw8+2OC+48eP49ixY1i5cqXP7Vu2bMGWLVvw7rvvqqDVCy+8cMlKC53VkiVLmnXhmoioLWzcuBErVqzAfffd196bgl69emHKlCmXDP6I9PR05OfnX7JNVmpqKn7yk59g/fr1yM3NhdPpbHS5p59+GpMnT27xdgPeqn3XXHMNAGD06NEq4HPrrbfigQceQJ8+feB0OhsEf370ox+he/fuDda3Y8cOvP3221i4cCESEhLw0ksvqQpGwu124/Dhw8jJybmsbe7o9OtEurq6OhQUFPiEf+Q6VN++fQF4QzP+c77rr78e99xzDzweD3r06OFzX2FhIc6cOdOKW9/Q//zP/2DGjBnq+mF1dTXy8vJUiH3fvn3IzMzEvffeC7fbjYyMDOzcuRNZWVm49dZbUVxcjL179+Lo0aM4cuQIysvL8bOf/QzdunXDqlWrcOeddyIsLAz/+c9/Wv0DAPn5+bj//vsRHR2NlJQUrF69Gtu2bUNeXh62bNmi5vaNdYV46623EBUV1arbQ2RlDP40k4RW5AK+hD8koCHhAD0AIBUz9PZeEliR1lpSjUOqpej9kPUAhzyfPL/+Q0e2S4IfEiiR0IC+LvkDh7TLCQ4OVtsiQQyXy+XT1kxvo6W3I5LwC/BVQELoVVQk/CNhIQlwVFZWqnZJNptN/V/CCnrQQUIXbrdbBTckkCXPB3h/WBUXF6OmpkalcfVqNdKOS8bD4/EgKCgIVVVVPiEMUVJSgsjISFXpxL8Kk4ReJMAi+0g+gaIHRkpKSuB0OhEUFASHw6ECSgEBAXA4HKoNkgRSampqVLBDvqqrq31apoWFhflUVJLqOQ6HQ+032S8SbpFt1ysESVBJD4vpFaTkWJX9JMe1XgHGv4qQ2+1W54RMdv1blflX19Er3wQHB6tzQrZNb10l51NtbW2jfzjTq+XoYSF5XXo1H9lX/q349OCUrFMCcnK7XqVIb3Glt+rTX/Pf/va3BttKREREREQk9A8SEbWGI0eO4PDhwxg4cCDmzZuHZ555RgV/hFSz2bZtW7PXW11djX379mHatGnYtm0bbDYbqqqq8Pzzzzf6Sej2NnLkSPzhD39AdXU1ZsyYcdFlR48ejfXr1yMzM1MFf2w2G5544gk8+eSTALytiX7729/CMAz069cPwcHBKvSjW7RoEex2O/r06YMHHngAb7zxRuu/OHgrDpw/f77BBT7/i4zl5eUoLy9vk21oqeuvvx69evXCkSNHGtz3u9/9Dn/+858vWkVhx44dGDNmDLp27dqWm0lE7Yjzoo5vyZIl6N+/v6rY0V4Mw8C4cePw//7f/2tWmOXChQvYs2cPgoOD0atXr4u2SEpKSsLWrVuxaNEi/PKXv2xw/7x58zB//vxLhogac+7cOTz//PP461//irvuugv33nsvfv7znyM7Oxtr167FpEmTMHHiRMyaNQtZWVl4++231WMXL16MtLQ0pKam4u6770Z8fDwAYNeuXVi4cCEA7/xBghv+rz8nJ6fRn9Ffd/369UNlZSXcbjciIiJ87pPrsVKRUN6fPB4P+vXrBwA4e/Ys6urqMGDAAJ/HPvbYY6qTiO7cuXPIz89vq5eDwMBA3H333UhOTsZzzz2H2NhYDBw4EOvXr8ezzz6LnJwcLFu2DOnp6Thz5gxiYmKQkZGBrKws7NixA0OGDEGfPn0QGRmJ/fv34+jRo3C5XHj33XcRHh6OlJQU7Nq1CzExMSgoKGgQRG8Nb7755mU9rqioCEVFRa27MUTt6FLzIgZ/mklvG6RXwZEdLAEJ/xZe/m26pOKJHvzR23Tp1YIaCyHI7RLG0SsCAWgQhAHgE7KQaiF6KEGCJABU6y+pGCKvWW9BpJN1S2hDf616RRk9fCOBHgmjyDLyeHn9sr3+QRs9hCThDf1+/8CTXsVGbnO73T6tpPzbR+nPqVevcbvdCAkJ8WnRJdst4SYJR0n7N2ltJi2h9GpAst+CgoLgdDob9AfV2zVJQEV/bXqrM7lNP2b0EJMEc/z3pb5v9GNcWr9JsEeeW8ZPP9b180PGUkIyenjI/zj2D9zox4++DySMpB+Xsj16JR49sKbf718tSA/x6eEffd/o5FiR1lymaV72RIOIiIiIiIjoajt69CgGDhyI4cOH4+zZsw3uT0hIQGpqaouCP4C35dWAAQPgcDgwaNAg2Gw2TJo0CWvXrm2tTW8Wu92O73//+3j99debXOb06dPo3bu3ap/dFMMw0Lt3b3z66aeX/MTyqlWrMGnSJJw8eRIJCQmNLuNyubBw4UJERkbio48+arPgD+D9pPPNN9980WWkArEVfPrpp/je977X5P0lJSUXffyhQ4fgdrsbXFgjIiLryMjIwPbt29s9+AMAsbGx6N27d7Or2Kxbtw579uzBiBEjcMMNN6Bnz55NLhsREdFkZZGzZ89edtWR7du3Y9WqVaioqEC/fv0wfPhwXHPNNcjOzgbgbUkFeNshpaamIjk5Wb2+9PR0pKenY86cOYiOjlbrvHDhgs9znDp1SnUkEE6nE0VFRaioqLis7e6oxo4di29/+9u4/fbbGw1qSaeRoqIi1ekC8AarpSBAWVkZ8vLyEBUV5bMOp9OJnTt3Ijk5GdHR0SpUVFZWdtlt4JqjtrYWR48eRZ8+fbBjxw7k5ORg0KBBqKiowJtvvomQkBBs3LgRe/fuRW5uLmJjYxEVFYWdO3cCAA4cOIADBw40uu6Kigps27atxb9DEFHbYPCnGQzDgM1mU62dpJKPVIvRQyzAV2ENeawekAC8b7Iej8enqo20ypKwgrT28q8yJM+vhyH0IIUEkvTnlrCE/NCW4IJ/iCQwMBA2m021DZKqLvIHAXluWbdeeUXCHRJo0oM++v/1dlESwJBtDg0N9WkXpbegkkCPBG0kDKO3opIAiN1uR1lZmc9ERSq31NbWqmo7Ho9H/bEnJCREfTItMDDQJ+Ckt8hyuVwIDQ1V65Vtl7ZKAFSoSCrfXLhwwSe8Je28JGCl3yb01ybBItkHepsum82GoKAguFwutZ0SIvNvxSXhGLlPr+wj9/sHd/Re6VKhSAJLchzKeaAHt2ScXS6Xquokx7J+XrlcLp/EtASrXC4XampqVIUst9sNh8OhHm8YBkJCQlQY5+WXX276BCYiIiIiIiLq5D766CNMmjQJgwcPxsMPP4ySkhKfC1BRUVG4+eabsWzZsmavMywsDCtXrsS5c+fw61//WlXjnT59OrKyspCXl9faL6NJKSkpePzxx1FcXIz333+/0WXOnz+PpUuXYsGCBVi6dCmWLl2qWkXpTNPE+vXrsWLFCgDeCzn63y4WLlyIw4cP47XXXsOuXbtw9uxZnDt3DhcuXMCiRYtw7ty5BuusqKhARUUFXnjhhVZ6xY2bMWOGzwerdCUlJYiIiEBpaWmbXlxqid27d2PEiBGX/fiFCxdi4cKFrPhDRGRhpmli2bJlqKmpQd++fXHttde2W2AzKSkJo0aNwqZNm5q1vMwFfvzjHyM5OfmiwR+51tOYNWvWoKqqyqdrRnM4nU688cYbqoLKddddh4iICMyaNQulpaXo3bu3auUZEhKC2bNnw+124+mnn1brsNvtuOWWW3yuQd11110oKCjAiy++CMAbLvryyy8RFxeHxMREhIaGoqioyDLzhavpF7/4BUaNGoVu3bqp2zweDwoKCpCVlYW4uDgUFRWhuroau3fvBuC97nn27Fncf//9MAwDOTk5+Pzzz3HmzBkcPXoUcXFxSEpKwsCBA7F27Vq888476NOnD4YPH47Y2Fi43W4V5Gor999/v8//5fk+/fRTn9uLi4st2a6XiJqHwZ9mkDBESEgI7HY76urq4PF4AECFM0pKShr80PavniL/AlDBH2lVJMEfvWWXfzBGb4sFwCcoJOENCQlJFRc9SCQBCv/qK3pVHMA7EdBDIYZhqGo10rpLDxj57yd92yWgo1eAkfBKcHAw3G63el2hoaEqHCLbrldqkdcsLaRkGXk+aQslrcCk2oz8X9pQyffy2JqaGtjtdp+KTVJ9JyAgAE6nU42XtLsKDw+H2+1WrcekUhIAVfKxoqIC1dXVqp+nrF/aieljJ9vk397Nf59LmliOATke9GSxHu6RqkMyxhK6kiBPcHCwCtfolZdk/xmGoV6H2+32acOlt8uSsocejwcffvhhy08yIiIiIiIioq+BwYMH44knnkBubi727t1rmd+R161bh9tvvx0DBw7Ec8891+gy1113XYvWWVlZifT0dAQFBWHixIlwOp24cOECfv/731/V0A/gbfn08MMP4/XXX0d1dTU++uijRpfLzMxEQUEBfvCDH2Dv3r2NBn8A4KWXXgLgDTH5t3h46KGHAACvvfYaAKigT3h4ON566y3MmTMH58+fb3S9V+N4eOihh5CQkICkpCT07NkTgwcPRkxMDKZMmYLevXsjMTERx48fb/PtaC7/6teXgxeoiIis7cCBA3j88ccRExODDz74oN2CPzabDbNnz0b//v2RnZ2NP//5z816XHZ2Nvbu3YsxY8Y0uYzL5Wqyot6MGTPg8Xgu2i6sMcePH/dp4ZmVlYXExERMmjQJJ06cwLRp05CSkqLuT0xMxD333IM9e/aoOYfb7UZpaSlyc3Nx8uRJnD59GhMnTkRkZKR6XG1tLSZNmoTQ0FBkZGQgJSUFDoejU7bZmz17NmJiYjBkyBD06NEDCQkJcDgcSE9Px5YtW9RyPXv2RGxsLI4cOYLKykoAwKhRowB4r5V2794dixYtavJ5CgsLVbswIqLWwuBPM4SEhKigioQ/amtr1e2maapAi04q6DTWAguAmgSEhIT4VMHRy+kBX1Xj0dt0SXBDgiNS0cYwDFWZRX8u2W4J2chXVVWVz2NdLpdP2Cg4OFgtI+uR1yzBGHm8bKc/PQAkoR7ZZnkeu92u9oP+uiTsItsHQAWkQkNDERwcrLZTr0oTEBCgEtYSzpIwk+xrqZJjt9vVdtfW1qpqNXqQRgJajVW2kccbhoGysjIVgAoMDERlZSVsNhs8Ho8KhkkIym63o7q6Wq0zJCQETqcT0dHRqKqqQkFBgU8ZQAkd6RMyOY78jym73d6iT+kRERERERER0ZU7ffo0pk+fjtjYWGRkZODgwYNt/gne5jh37hxmzJiBW2+9Fe+++65qC6FLTEzExIkTkZ6e3qJ119TUYPr06Zg4cSKWL1+OCRMm4MSJE6216c22du1aPP3007j77ruRlpaG8vLyBsts2rQJmZmZ2LdvH1555ZVLrlMu8Gzfvh19+/ZFVFSU+vvO1KlTfT4lXVdXh6lTpyIjIwOrVq3CY4891kqvrGUkkNSYzMzMq7glREREvgoLC7Ft2zYMGjQI8fHxV/35k5OTkZycjHHjxmHFihXNDv5s3boVw4cPR2lpaYPrM6JLly645ZZb8NprrzVoi1RWVoaKigqfdlsAkJ+fj7i4uEbXZ5omDhw44BNu3b17N4YMGYLU1FTMmTPHJzxbVlaGL7/8EuPGjcODDz6Io0ePYteuXQCAuXPnonv37hg0aJBq+5qWlubzfBMmTMC9996LlJQUVFRU4N///nenbd9UWFh4yfnw6dOnVSUmf7W1tY221iUiamsM/jSD3rZJKtKEhYWp4AcAFSaRAIweVNEDJPIlpDKLBFj875fAjaxTQjQSvJFlpcoLAJ/qOlKdJSAgAJWVlWo9ISEhPq2g6urqVAswCcjI80nbLdM0VaBFgjl6yEVCNnoKWMJH+rbqlY8cDocKR0kVGwCqzZMEeWw2GxwOh6pII5VvZJ+bpgmn06namun7yDAMhIWFqX3idrtRXl4Oj8eDqqoqhIWFwWazqYo9AQEBKuQjgSIJdQUEBKC8vBwJCQk4c+YMwsPDfcJYERERKCgoUFV0AgMDsXHjxtY6FImIiIiIiIjIwsrLy7Fy5Uo8+OCDGDFiBGbPnm2J4I/4+OOPsXPnTtxwww0AvBeJIiIi1N9ybrzxxhYHfwBv1eMNGzbgzTffxCOPPIKsrKwGF72uhtGjRyMhIaHR0I+44447MHXq1Gatr7S0FNu3b8d9992HiooKxMXFoW/fvoiOjkZCQoLPsnFxcYiKisKwYcPU35iIiIjIV0ZGBsaMGXNVgz/5+fmIjo5W16BM04TD4Wj246Wt0+nTp5sM/tTU1KBXr17YsmULsrOz8Z3vfAdnzpwBAKSnp2PZsmW48847MWTIELhcLuzcuRN79uzB0KFDMWbMGBw+fBgulwtjxoxBXV0d1q5di9///vc+VWGOHDmC7OxsDBs2DMePH0dwcDDCwsKwb98+fPbZZzh48CBKS0vRtWtXJCcnq+AP4G15ev78+SbneV9++SWSk5ORkpKC995776JBYiIisiYGf5pBwjzAV6EVvR2VhEyk6ossp1fcERIc0sM90nJJb5MFwCcoI4EcCdfolXP08oGynB5+kWo38pjg4GDY7XYVWAGgtkmvOqQHe/TqPPK69e3R6S2zJHGst+SS56urq1OvW28TJuuT7ZfATnh4uM/ysj4JJp07dw7FxcUqpGOz2dS+kCCPtLCS8ZJqPrI+qS4UHR3tU9Ho73//e4uPGyIiIiIiIiLqfIYMGQLA+3cJ+d4qTNPE0aNHVfDnH//4B/r27Ytx48bBZrMhOTn5ita/fft2zJ07t11bL8lFtqa43W6sWbOm2et76qmnVHgrNze3yfZgpaWlKC0tbZdqR0RERB1FTk4Ojh07hv/6r/9St1VVVfl8MNxfTU2NT2eNi91eXFyMrl27qv+XlZUhLy8PBQUFGDZsGABvu9LQ0FBER0ejqKioWdt94sQJFBYWNnn/8ePHER4ejsjISERHR2Pw4ME+c5Lt27dj0KBB6Nu3L/bv348vvvgCeXl5KCkpQbdu3bB161bU1tZizJgx2L59Oz7//PMGraByc3ORnZ2NrKwspKWlYdy4caiursbWrVuRnp6OoqIijB8/HmlpaU22Pb2Y5cuX4/Tp0z7txYiIqOMw9ABKZzN69GizOSVu586di8DAQFVxxjRNnD9/XgVLampqVBhFgik2m03dpreGkpZgEm6RUEtwcDCCg4PV5EbCPrW1tT6VaCR0IyEdm80Gl8sFj8cDj8ejqv5UVFSowIuEdGpraxEYGIjIyEiEhISo57bZbAgODlZVdqQCjlQKcrvdKCkpUdsjz2EYhqrWExwcrKoEyfey7foxVlVV5RM4qqmpQV1dHaKjo9GlSxdUVVWhsrISHo8HlZWVcLvdiI6ORnV1NRYvXtxKI09ERFZkGMYO0zRHt/d2dGaGYXTeiSEREZFF1H8gh/Oidna586KePXsiNzcXQUFBSE9Px6OPPmq59ko//elPcd1116FPnz6YMGECACArKwvx8fEYNWpUk20LmuuTTz7BjBkzWmNTiYiok+O8yBpa++9Fd999N37961+jR48eyMrKQnh4OL7xjW8AAM6ePauqAdXU1GDdunVYv3494uPj0adPHwwbNgzdu3dHQEAA9u3bh5tuugk2mw3FxcXIzs7GoUOHcN111yElJQVHjhzBoUOHsHXrVpw5cwYPP/ww4uPjkZeXh0OHDuEnP/kJVq1ahWnTpjVru5csWYIHHngA58+fx7///W8cOnQII0eORNeuXZGRkYH8/Hxs3boV//rXvxp9fGhoKHr37o24uDh88cUXqKurw4QJE2CaJvbs2YO6ujokJyejsLAQ+fn5rbOziYjoa+NS8yIGf5rxx5f77rsPAGC321UYxuPxqPZS0k4rMDAQISEhqm1VQECATxWg6upqnxZUEuKpqqpSoR95nFTskfCMhG30CjsSNKqpqYHH44HL5YLb7UZNTQ1cLpeqDFRVVaWq9UjLrKCgILU9/lWEgoODfV5HbW0t3G43PB4Pli5d2kajQUREnR3/kNP+GPwhIiJqf7zAZQ1XMi+64447kJiYiD179uCzzz5rzc1qM//93/+NkydP4vDhw+29KURERArnRdbQ2n8vioiIwIsvvojDhw9j69atmD59Om699VacOnUKpaWlmDZtGk6dOoXPPvsMb731lk/LKsDbXnPUqFHo2bMnunfvjnHjxuH06dPYvHkzDh06hKioKMTFxaFPnz44ceIE0tPTkZeXh7q6OkRERKB///7o1asXPvnkEyxevBhz585t9rb36tVLtfQiIiK6mhj8uYjmBn/uvvtuhISEICQkRAVzJAzjdDpVoEbul0o/0oZKWl9JkEfCPxLuqa6uVtVzbDabWlb+leCPVAtasmTJVdg7REREVxf/kNP+GPwhIiJqf7zAZQ2cFxEREbU/zousoS3mRYGBgaitrUVMTAxGjhyJI0eOoKCgAMHBwejWrRuioqIatLoiIiLqzC41L2rYFJMaePfdd9t7E4iIiIiIiIiIiIiIiIg6POlyUVhYiLS0NJ/7SkpK2mGLiIiIOrZOXfHHMIwCACfaeztaWQyAwvbeCFI4HtbBsbAOjoV1WG0s+pimGdveG9GZcW5EbYxjYR0cC+vgWFiH1caC86J2xnkRtTGOhXVwLKyDY2EdVhsLzovaGedF1MY4FtbBsbAWjod1WGksmpwXdergz9eRYRiZLHtpHRwP6+BYWAfHwjo4FtQZ8Di3Do6FdXAsrINjYR0cC+oMeJxbB8fCOjgW1sGxsA6OBXUGPM6tg2NhHRwLa+F4WEdHGYuA9t4AIiIiIiIiIiIiIiIiIiIiIiJqOQZ/iIiIiIiIiIiIiIiIiIiIiIg6IAZ/vn6WtPcGkA+Oh3VwLKyDY2EdHAvqDHicWwfHwjo4FtbBsbAOjgV1BjzOrYNjYR0cC+vgWFgHx4I6Ax7n1sGxsA6OhbVwPKyjQ4yFYZpme28DERERERERERERERERERERERG1ECv+EBERERERERERERERERERERF1QAz+EBERERERERERERERERERERF1QAz+WIhhGFGGYXxoGMZBwzAOGIYxzjCMZwzDOG0Yxq76r5vrl+1rGIZLu/11bT2jDMPYaxjGUcMwFhqGYdTfHmIYxvv1t28zDKOv9ph7DcM4Uv9171V/8RbT2FjU3/6QYRiHDMPYZxjG/2nLP1G/Xw8ZhjFVu51jcYVaMhY8L9pWE+9R72v7O9cwjF3a8jwv2khLxoLnBXVUTRznnBe1g5b8LK6/ne//bagl48Fzo2215Odx/fI8N9pIS8aC5wV1VE0c55wbtYOW/Cyuv53v/22kJWPB86JtteRncf3yPC/aSEvGgucFdVRNHOecF7WDlvwsrr+d7/9tpCVjwfOibbXkZ3H98jwv2khLxuJrcV6Ypskvi3wBeAvA3PrvbQCiADwD4OeNLNsXQHYT68kAMA6AAWADgOn1t/8EwOv1398J4P3676MB5NT/27X++67tvT8sOBY3AvgMQEj97d3r/x0KYDeAEABJAI4BCORYtMtY8Ly4ymPhd/9LAJ6u/57nhXXGgucFvzrkVxPv/8+A8yKrjAXnRR1jPHhuXOWx8LufcyNrjgXPC351yK8m3v+fAedGVhkLzo2sPxY8L67yWPjdz3mRNceC5wW/OuRXE+//z4DzIquMBedF1h8LnhdXeSz87ue8yJpj0eHPC1b8sQjDMLoAuAHAMgAwTbPKNM2Sy1hPPIAupmn+x/QeWW8DmFl/923wHuAA8CGAyfWJtKkA0kzTLDJNsxhAGoBpV/ByOrSLjMWPAfzeNE1P/e3n6x9yG4AVpml6TNM8DuAogOs4FlfuMsaiqfVwLK7Qpd6j6vfZHQDeq7+J50UbuYyxaGo9HAuyLM6LrIPzImvh3Mg6ODeyDs6NqDPg3Mg6ODeyDs6LrIPzIuvgvIg6A86LrIPzIuvgvMg6OC+yjs44L2LwxzqSARQA+JthGFmGYSw1DCOs/r75hmHsMQzjr4ZhdNUek1S/7GbDMCbW39YTwCltmVP1t8l9eQBgmmYNgFIA3fTbG3lMZ9TUWAwEMLG+VNdmwzC+Ub98U/uPY3HlWjoWAM+LtnKx9ygAmAjgnGmaR+r/z/Oi7bR0LACeF9TxcF5kHZwXWQvnRtbBuZF1cG5EnQHnRtbBuZF1cF5kHZwXWQfnRdQZcF5kHZwXWQfnRdbBeZF1dLp5EYM/1hEE4FoAi0zTHAmgEsDjABYB6AdgBICz8JacQv33veuXfRTA3w1vcs1oZN1m/b9N3Xexx3RGTY1FELzluMYC+AWAlYZhGLi8/cqxaJ6WjgXPi7bT1FiIu+CbiuV50XZaOhY8L6gj4rzIOjgvshbOjayDcyPr4NyIOgPOjayDcyPr4LzIOjgvsg7Oi6gz4LzIOjgvsg7Oi6yD8yLr6HTzIgZ/rOMUgFOmaW6r//+HAK41TfOcaZq1pmnWAXgDwHUAUF/y60L99zvg7fk3sH49idp6EwGc0Z6jFwAYhhEEIBJAkX57I4/pjBodi/rbPzK9MgDUAYhB0/uPY3HlWjQWPC/aVFNjIfvtdgDv+y3P86JttGgseF5QB8V5kXVwXmQtnBtZB+dG1sG5EXUGnBtZB+dG1sF5kXVwXmQdnBdRZ8B5kXVwXmQdnBdZB+dF1tHp5kUM/liEaZr5APIMwxhUf9NkAPsNb984MQtANgAYhhFrGEZg/ffJAAYAyDFN8yyAcsMwxtanNucAWFP/+LUA7q3//n8A/Ms0TRPApwC+aRhG1/ryh9+sv61TamosAKwGcBMAGIYxEIANQCG8+/VOwzBCDMNIgncsMjgWV66lY8Hzou1cZCwA4L8BHDRN85T2EJ4XbaSlY8Hzgjoizousg/Mia+HcyDo4N7IOzo2oM+DcyDo4N7IOzousg/Mi6+C8iDoDzousg/Mi6+C8yDo4L7KOTjkvMk2TXxb5grcEYSaAPfC+GXcFsBzA3vrb1gKIr192NoB9AHYD2AngFm09o+Gd1BwD8BoAo/52O4APABwFkAEgWXvMffW3HwXwg/beF+391cRY2AC8U79vdwK4SVv+qfr9fQjAdI5F+4wFz4urPxb1t78JYF4jy/O8sMBY8LzgV0f9auL9n/Mi64wF50UdYDx4blz9sai//U1wbmTZseB5wa+O+tXE+z/nRtYZC86NLD4WPC+u/ljU3/4mOC+y7FjwvOBXR/1q4v2f8yLrjAXnRRYfC54XV38s6m9/E5wXWXYsvg7nhWwUERERERERERERERERERERERF1IGz1RURERERERERERERERERERETUATH4Q0RERERERERERERERERERETUATH4Q0RERERERERERERERERERETUATH4Q0RERERERERERERERERERETUATH4Q0RERERERERERERERERERETUATH4Q0RERERERERERERERERERETUATH4Q0RERERERERERERERERERETUATH4Q0QdkmEY3zAMY49hGHbDMMIMw9hnGEZqe28XERERUXvg3IiIiIjIi/MiIiIiIi/Oi4g6D8M0zfbeBiKiy2IYxv8CsANwADhlmuaCdt4kIiIionbDuRERERGRF+dFRERERF6cFxF1Dgz+EFGHZRiGDcB2AG4A403TrG3nTSIiIiJqN5wbEREREXlxXkRERETkxXkRUefAVl9E1JFFAwgHEAFvWpmIiIioM+PciIiIiMiL8yIiIiIiL86LiDoBVvwhog7LMIy1AFYASAIQb5rm/HbeJCIiIqJ2w7kRERERkRfnRURERERenBcRdQ5B7b0BRESXwzCMOQBqTNP8u2EYgQC2GIZxk2ma/2rvbSMiIiK62jg3IiIiIvLivIiIiIjIi/Mios6DFX+IiIiIiIiIiIiIiIiIiIiIiDqggPbeACIiIiIiIiIiIiIiIiIiIiIiajkGf4iIiIiIiIiIiIiIiIiIiIiIOiAGf4iIiIiIiIiIiIiIiIiIiIiIOiAGf4iIiIiIiIiIiIiIiIiIiIiIOiAGf4iIiIiIiIiIiIiIiIiIiIiIOiAGf4iIiIiIiIiIiIiIiIiIiIiIOiAGf4iIiIiIiIiIiIiIiIiIiIiIOqD/D1+meYROBjmzAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_args = {'cmap':'Greys_r','add_colorbar':False}\n", + "fig, ax = plt.subplots(1,4,figsize=(40,10))\n", + "out_dsc.VV.plot.imshow(ax=ax[0],**plot_args)\n", + "ax[0].set_title(\"VV DSC Intensity [dB]\")\n", + "out_dsc.layover.plot.imshow(ax=ax[1],**plot_args, vmin=0,vmax=1)\n", + "ax[1].set_title(\"Layover DSC mask\")\n", + "out_dsc.foreshortening.plot.imshow(ax=ax[2],**plot_args, vmin=0,vmax=1)\n", + "ax[2].set_title(\"Foreshortening DSC mask\")\n", + "out_dsc.shadow.plot.imshow(ax=ax[3],**plot_args, vmin=0,vmax=1)\n", + "ax[3].set_title(\"Shadow DSC mask\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "openeo-platform", + "language": "python", + "name": "openeo-platform" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}