diff --git a/README.md b/README.md
index 9bed9748..19c2164b 100644
--- a/README.md
+++ b/README.md
@@ -141,25 +141,29 @@ Native GeoTIFF and Cloud Optimized GeoTIFF reader/writer. No GDAL required.
 
 | Name | Description | NumPy | Dask | CuPy GPU | Dask+CuPy GPU | Cloud |
 |:-----|:------------|:-----:|:----:|:--------:|:-------------:|:-----:|
-| [read_geotiff](xrspatial/geotiff/__init__.py) | Read GeoTIFF / COG / VRT | ✅️ | ✅️ | ✅️ | ✅️ | ✅️ |
-| [write_geotiff](xrspatial/geotiff/__init__.py) | Write DataArray as GeoTIFF / COG | ✅️ | ✅️ | ✅️ | ✅️ | ✅️ |
+| [open_geotiff](xrspatial/geotiff/__init__.py) | Read GeoTIFF / COG / VRT | ✅️ | ✅️ | ✅️ | ✅️ | ✅️ |
+| [to_geotiff](xrspatial/geotiff/__init__.py) | Write DataArray as GeoTIFF / COG | ✅️ | ✅️ | ✅️ | ✅️ | ✅️ |
 | [write_vrt](xrspatial/geotiff/__init__.py) | Generate VRT mosaic from GeoTIFFs | ✅️ | | | | |
 
-`read_geotiff` and `write_geotiff` auto-dispatch to the correct backend:
+`open_geotiff` and `to_geotiff` auto-dispatch to the correct backend:
 
 ```python
-read_geotiff('dem.tif')                              # NumPy
-read_geotiff('dem.tif', chunks=512)                  # Dask
-read_geotiff('dem.tif', gpu=True)                    # CuPy (nvCOMP + GDS)
-read_geotiff('dem.tif', gpu=True, chunks=512)        # Dask + CuPy
-read_geotiff('https://example.com/cog.tif')          # HTTP COG
-read_geotiff('s3://bucket/dem.tif')                  # Cloud (S3/GCS/Azure)
-read_geotiff('mosaic.vrt')                           # VRT mosaic (auto-detected)
-
-write_geotiff(cupy_array, 'out.tif')                 # auto-detects GPU
-write_geotiff(data, 'out.tif', gpu=True)             # force GPU compress
-write_geotiff(data, 'ortho.tif', compression='jpeg') # JPEG for orthophotos
+open_geotiff('dem.tif')                              # NumPy
+open_geotiff('dem.tif', chunks=512)                  # Dask
+open_geotiff('dem.tif', gpu=True)                    # CuPy (nvCOMP + GDS)
+open_geotiff('dem.tif', gpu=True, chunks=512)        # Dask + CuPy
+open_geotiff('https://example.com/cog.tif')          # HTTP COG
+open_geotiff('s3://bucket/dem.tif')                  # Cloud (S3/GCS/Azure)
+open_geotiff('mosaic.vrt')                           # VRT mosaic (auto-detected)
+
+to_geotiff(cupy_array, 'out.tif')                    # auto-detects GPU
+to_geotiff(data, 'out.tif', gpu=True)                # force GPU compress
+to_geotiff(data, 'ortho.tif', compression='jpeg')    # JPEG for orthophotos
 write_vrt('mosaic.vrt', ['tile1.tif', 'tile2.tif'])  # generate VRT
+
+# Accessor methods
+da.xrs.to_geotiff('out.tif', compression='lzw')     # write from DataArray
+ds.xrs.open_geotiff('large_dem.tif')                 # read windowed to Dataset extent
 ```
 
 **Compression codecs:** Deflate, LZW (Numba JIT), ZSTD, PackBits, JPEG (Pillow), uncompressed
@@ -493,10 +497,10 @@ Importing `xrspatial` registers an `.xrs` accessor on DataArrays and Datasets, g
 
 ```python
 import xrspatial
-from xrspatial.geotiff import read_geotiff
+from xrspatial.geotiff import open_geotiff
 
 # Read a GeoTIFF (no GDAL required)
-elevation = read_geotiff('dem.tif')
+elevation = open_geotiff('dem.tif')
 
 # Surface analysis — call operations directly on the DataArray
 slope = elevation.xrs.slope()
diff --git a/docs/source/user_guide/multispectral.ipynb b/docs/source/user_guide/multispectral.ipynb
index 60ff5f4e..80679336 100644
--- a/docs/source/user_guide/multispectral.ipynb
+++ b/docs/source/user_guide/multispectral.ipynb
@@ -41,7 +41,7 @@
    },
    "outputs": [],
    "source": [
-    "import datashader as ds\nfrom datashader.colors import Elevation\nimport datashader.transfer_functions as tf\nfrom datashader.transfer_functions import shade\nfrom datashader.transfer_functions import stack\nfrom datashader.transfer_functions import dynspread\nfrom datashader.transfer_functions import set_background\nfrom datashader.transfer_functions import Images, Image\nfrom datashader.utils import orient_array\nimport numpy as np\nimport xarray as xr\nfrom xrspatial.geotiff import read_geotiff"
+    "import datashader as ds\nfrom datashader.colors import Elevation\nimport datashader.transfer_functions as tf\nfrom datashader.transfer_functions import shade\nfrom datashader.transfer_functions import stack\nfrom datashader.transfer_functions import dynspread\nfrom datashader.transfer_functions import set_background\nfrom datashader.transfer_functions import Images, Image\nfrom datashader.utils import orient_array\nimport numpy as np\nimport xarray as xr\nfrom xrspatial.geotiff import open_geotiff"
    ]
   },
   {
@@ -132,7 +132,7 @@
     }
    ],
    "source": [
-    "SCENE_ID = \"LC80030172015001LGN00\"\nEXTS = {\n    \"blue\": \"B2\",\n    \"green\": \"B3\",\n    \"red\": \"B4\",\n    \"nir\": \"B5\",\n}\n\ncvs = ds.Canvas(plot_width=1024, plot_height=1024)\nlayers = {}\nfor name, ext in EXTS.items():\n    layer = read_geotiff(f\"../../../xrspatial-examples/data/{SCENE_ID}_{ext}.tiff\", band=0)\n    layer.name = name\n    layer = cvs.raster(layer, agg=\"mean\")\n    layer.data = orient_array(layer)\n    layers[name] = layer\nlayers"
+    "SCENE_ID = \"LC80030172015001LGN00\"\nEXTS = {\n    \"blue\": \"B2\",\n    \"green\": \"B3\",\n    \"red\": \"B4\",\n    \"nir\": \"B5\",\n}\n\ncvs = ds.Canvas(plot_width=1024, plot_height=1024)\nlayers = {}\nfor name, ext in EXTS.items():\n    layer = open_geotiff(f\"../../../xrspatial-examples/data/{SCENE_ID}_{ext}.tiff\", band=0)\n    layer.name = name\n    layer = cvs.raster(layer, agg=\"mean\")\n    layer.data = orient_array(layer)\n    layers[name] = layer\nlayers"
    ]
   },
   {
diff --git a/examples/user_guide/25_GLCM_Texture.ipynb b/examples/user_guide/25_GLCM_Texture.ipynb
index 9ff23695..4d196bce 100644
--- a/examples/user_guide/25_GLCM_Texture.ipynb
+++ b/examples/user_guide/25_GLCM_Texture.ipynb
@@ -282,7 +282,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import os\nfrom xrspatial.geotiff import read_geotiff\n\n\nCOG_URL = (\n    'https://sentinel-cogs.s3.us-west-2.amazonaws.com/'\n    'sentinel-s2-l2a-cogs/10/S/EG/2023/9/'\n    'S2B_10SEG_20230921_0_L2A/B08.tif'\n)\n\ntry:\n    nir_da = read_geotiff(COG_URL, band=0, window=(2100, 5300, 2600, 5800))\n    nir = nir_da.values.astype(np.float64)\n    print(f'Downloaded NIR band: {nir.shape}, range {nir.min():.0f} to {nir.max():.0f}')\nexcept Exception as exc:\n    print(f'Remote read failed ({exc}), using synthetic fallback')\n    rng_sat = np.random.default_rng(99)\n    nir = np.zeros((500, 500), dtype=np.float64)\n    nir[:, 250:] = rng_sat.normal(80, 10, (500, 250)).clip(20, 200)\n    nir[:, :250] = rng_sat.normal(1800, 400, (500, 250)).clip(300, 4000)\n\nsatellite = xr.DataArray(nir, dims=['y', 'x'],\n                         coords={'y': np.arange(nir.shape[0], dtype=float),\n                                 'x': np.arange(nir.shape[1], dtype=float)})\n\nfig, ax = plt.subplots(figsize=(7, 7))\nsatellite.plot.imshow(ax=ax, cmap='gray', vmax=float(np.percentile(nir, 98)),\n                      add_colorbar=False)\nax.set_title('Sentinel-2 NIR band')\nax.set_axis_off()\nplt.tight_layout()"
+    "import os\nfrom xrspatial.geotiff import open_geotiff\n\n\nCOG_URL = (\n    'https://sentinel-cogs.s3.us-west-2.amazonaws.com/'\n    'sentinel-s2-l2a-cogs/10/S/EG/2023/9/'\n    'S2B_10SEG_20230921_0_L2A/B08.tif'\n)\n\ntry:\n    nir_da = open_geotiff(COG_URL, band=0, window=(2100, 5300, 2600, 5800))\n    nir = nir_da.values.astype(np.float64)\n    print(f'Downloaded NIR band: {nir.shape}, range {nir.min():.0f} to {nir.max():.0f}')\nexcept Exception as exc:\n    print(f'Remote read failed ({exc}), using synthetic fallback')\n    rng_sat = np.random.default_rng(99)\n    nir = np.zeros((500, 500), dtype=np.float64)\n    nir[:, 250:] = rng_sat.normal(80, 10, (500, 250)).clip(20, 200)\n    nir[:, :250] = rng_sat.normal(1800, 400, (500, 250)).clip(300, 4000)\n\nsatellite = xr.DataArray(nir, dims=['y', 'x'],\n                         coords={'y': np.arange(nir.shape[0], dtype=float),\n                                 'x': np.arange(nir.shape[1], dtype=float)})\n\nfig, ax = plt.subplots(figsize=(7, 7))\nsatellite.plot.imshow(ax=ax, cmap='gray', vmax=float(np.percentile(nir, 98)),\n                      add_colorbar=False)\nax.set_title('Sentinel-2 NIR band')\nax.set_axis_off()\nplt.tight_layout()"
    ]
   },
   {
diff --git a/examples/user_guide/35_GeoTIFF_IO.ipynb b/examples/user_guide/35_GeoTIFF_IO.ipynb
new file mode 100644
index 00000000..5038dbdb
--- /dev/null
+++ b/examples/user_guide/35_GeoTIFF_IO.ipynb
@@ -0,0 +1,1024 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Xarray-Spatial GeoTIFF I/O: Reading, writing, and accessor shortcuts\n",
+    "\n",
+    "GeoTIFF is the standard raster format in geospatial work. xarray-spatial has a pure-Python reader and writer (no GDAL) that follows xarray naming: `open_geotiff` to read, `to_geotiff` to write."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### What you'll build\n",
+    "\n",
+    "1. [Write and read back a GeoTIFF](#Write-and-read-back) with `to_geotiff` and `open_geotiff`\n",
+    "2. [Write from a DataArray accessor](#Accessor-write) using `da.xrs.to_geotiff()`\n",
+    "3. [Windowed read via Dataset accessor](#Windowed-read-via-Dataset) using `ds.xrs.open_geotiff()` to crop a large file to an existing spatial extent\n",
+    "4. [Stitch tiles with write_vrt](#VRT-mosaic) to build a virtual mosaic from multiple GeoTIFFs\n",
+    "\n",
+    "![GeoTIFF I/O preview](images/geotiff_io_preview.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Imports: xrspatial for the accessor, plus the GeoTIFF functions directly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-22T15:14:48.207138Z",
+     "iopub.status.busy": "2026-03-22T15:14:48.207038Z",
+     "iopub.status.idle": "2026-03-22T15:14:47.871598Z",
+     "shell.execute_reply": "2026-03-22T15:14:47.871052Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import tempfile\n",
+    "import os\n",
+    "\n",
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import xrspatial\n",
+    "from xrspatial.geotiff import open_geotiff, to_geotiff, write_vrt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Synthetic elevation raster\n",
+    "\n",
+    "A 200x300 grid of fake elevation with geographic coordinates (WGS 84). We'll reuse this throughout."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-22T15:14:47.873347Z",
+     "iopub.status.busy": "2026-03-22T15:14:47.873065Z",
+     "iopub.status.idle": "2026-03-22T15:14:47.966077Z",
+     "shell.execute_reply": "2026-03-22T15:14:47.965296Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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KTgDKS5MCBQoUKFCgQMEJQBGCK1CgQIECBT9AhEIhRCKRk3IsvV7/b2s4/v+CE35piseWAgBWeN04I0YdFwnF8RAoG2WUyYRFwmunKRKBU0uH3jY4iIl+DQBArSZ/FFOGCb11HgBAZoENn0YGAQBzjVZ8FCQn56mDWmgy6DyhRAIAUB8OI68lCgAwlFrRF4sBAIxqNXLCRJrpjRokEkPa9i8+aAIAZJydQ8eSJN5valyPgW4q+WAcZsW+Y8o/TI7oAICPpTdosVtNtbZmWKyIgr7XqlTo76D2p2Zb4Oungp66FAMM1GwEfDRoX4h4sMBu52vyi+vK1OkQ2EP92FJhwlgX9dNzej8A4IqUFBSKIqJRXwyRMLU/EZfQn6zh42W56HjBND3WeKh/rRr6u1WtxlytBQCgMWsQHKBraTZLKBcTIeyJoNNEbS6Q6P7tjof4XsYkCbtE5fmLDUk4oqZ7UWgwQBekc6s1KoQG6fuQnfbrj8WQKo7xbG8vHKJNRpUKmTrq53PMSXzvS0SpEkN9APFSanOaN4FNeurHKrMZe0U7yo1GJPdTfzyj8uKe5HRxv+gcO4IBbPFTP96YloYaUXB1rNEEn0RtbvyiG8mTqW7YVrHtxceUH2nY1w9HGvWRw2lC/X4qeLuzWMtt7YvF+PNG4UZ+pS4JLguNyxUuF9/D82x2DHRT+2v39KG1ju79BT8jR+kPQj6sFvfvpfwCrPXT8XJ1OrjjcepzvR7xJjpGRkkSj+lNov21oRAWOhy0n16Pxs+7AACVs7LR30LbdKZrUS3GvFVN7XRoNNB8SoWGw3PSMNZH3++0xjHeQ59TMszoVVE7jGo19+l4DfVRrRThcRKTJFwnarId3NqFsikZAABXPI50DY2J+v1UvHfYmFQ+lq01jPQ8KwDgyL4+jJhM+y3r7+fnwbTaGGJR+pw1ne67Ox6HqtoLANgzTMdjbUbCgFajxP3hF/2YoZbb0Ie8Kmpn3BPBYm83AOCRnBy+lrZIhMdricEAew+N83eM1IdXpKaiSdTjM6rVsHXReE1KNqKjkdpkGZGEerFNoRgveVodJGEd9abLxffNqFLhg79RGaL0XCv6p9Czo9xoxEq3m/rASn00wWBCSy1915inhVHczxf7+vCgKGG0ye9HqZjrXVFq+3izGU/39AAAnFotxn8hxs/0JH52lH45yGM+elU+JojyNmU6av/ezzvQPJbm6QSLBZZ+OrbVYUAD6LNDo+E2LxDXF5MkFOloTuwIBjBaos8D3QGkFdkAAKqohKhOxdvL836aic7XJ8VRs7oFAI3L8vE0DjaFB9ElP+c7gcIKms+hAH33gKcHt8aTqG1pJrwTpPszymhEpZEegkFI3E8WtRqDYtzlijHQsLsXt9ppnr5YWMjfS3EJtVG6x68ODOCBDHLO12hvxHeJUCiEoqIidHV1nZTjZWZmorGx8f/7FyeFaVKgQIECBQp+YIhEIujq6kJrayuSkpK+1bG8Xi/y8vIQiUT+v39pOmHLgc2rfw0AODTZgjNd9EatL7IgTTAI63w+jDHRG3rv7n5kFVCkcHh3L9KyKTLJzKfvDmzrRqWIOP2eCLT59Pd0jRZ7QhS1pbaEsZs2wTQfnUOXbUJ/tRsA4EgzcoRaMjoVy7XE9lzndHKUPspohGovfc4f7gAAHDTGOXqYb7fjTVGhvMRg4Oilrd4DZxZ9vrKvFQDwTlExMxMWSYXDuyg6eywjyJH0RJURD3spSr+yR49hY4h5k9mn3fooJsYpmgoFYjBkmrh/tcKleJXbjbEikouJW1NiMMAnGLF3tAHMslE/6hoC2JNN+0UlCXPFxND5YsxwqAQbITMlAGC26bF5VSMAIHleFh4Rkch9biu6S6lNZWJiRBoH8YyZos9MnY77borFgvPt1KeJhARB9OGp7m5mO5YVFgIAGsJhTDbSNYUGY4hbKYJVeaMw2Kk/pHACn0UDfL0ARXTydbfbVShN0Ljr1CVgEZG0uieM38ZpHIw1mZm92zZI4+G29HSU+UQx2FQd8rR0jKbDLoQCdC0F49NwdAfdz5GTaNBtGvQzS7FtcJDZuBKDAaqOEB/jyCSK9M9XW7BVQ8yCzFLqUgx4qpsYi8s9RoQLqW+1KhWzDbk6HXKoqdCl0HVLvhgS4jqaDruQPpHG1wceD2aIsnFJI+3wHpTZWis0cj/6KJLesbaFWZjTLi5BIk5jqaXOje4iOs+YuB46G82teICYlz0IM7PSFA5jumAyRplMzEY1fNSO3BJiPQ7najBHT3OlWTXE/Mr97B0Iod9G++mag9jkpDYtsNux4aUaAMA511Oh2yN7epEl5owumICXmomwJDEjNs9iwwd+YgXmqMzwuuheZIjnTXUwiJF6ulcHIyHk0VDEr0O9uN6ZBoDGqMzyXOule+JwGvGZlu7JrkAAL/YRs/JqYSFUn9DYmHnhMKwTLOLpNhvubm8HAFwoWMk8nY7HX05YDZXoW004gY/D1JCZMQO6WugYW/LV3F8z+uizL88ASzNdU3uuDlnNNKbMw22wifHYurcP7jLq8waZtdLrMdVM393X1YlfJdG17pRCGNZDbbou0YNb0omJ2Szm6BSLBfPFc6MrFmMWz6hWI1M8299zuzE/MfSsMttorEVCgm20aPHFB80AgJFn53EfNIXDzIy643FmYiZYqJ2GiASdcWiOFXTTtlqdGqYcMx9DvodWuwHLfcTKXpGSAoDG7U6J+muy1oTXBt0AgIl1UajEykb2BCd0Yl7c5O4EAFyUnMxje1cggEr6GcChHd0YPIP6LkOnY/Zr/5ZOtE+iMXamTuz3aRv2TKVrydBqMVUcLyZJMLRRm5zZFjzhpt+EX2fdh+8SXq8XdrsdroGBk/LSlJySAo/H862P9X8dCtOkQIECBQoU/ECRSCQ4CPs2x1BAOGGmqb3hcQCANt+Mtk0UPefOyEBc7C61Bnm92GrX4wMzRUBn+vVoSaNoojxI0dSnmhBmBCjq6E3RoEkI1Sq7JWZEes0q9G+iCC9/ZiYAwKnSIDRI52g3JJDmpRvpsmuYqfHs6kfrSIpSXu3vx+ImihRa6twAgJGLSrC0l978bxZRF0DRv6xFudiQBLWGjidHCbeaU7H/C4pSRp6Ry0yBVa1GkYquZXF3O+4Rld9rQyEkH6AQKVpFUbmtLoBVWRRN3ZaezlHYCpcL84XWydIfxT4Lff+qYMFuS0/nyLDEaIRdRJzP9vTwdf8hN5fbf0dbGxpGjgIAtMaITVnn9bJe5OHMbHQnqB9bN3ShdA5pHqKSxJHrRB1FlpJOxWzPFlOUzweQBg0Aupp9OJxJ91YL0hwBwBFxrBERDS7sJd3BIqcTGrH/S319uELo4LQqFaoEU5ncTW12ZeiwVzAMs6xWZni22uPcjjkqM/pFEGz3xpmtCR7TZjmS9icSWOsllqI/FsOFIbFjlhEH5fMIFq92Vw+ey6D2T7BYmHlTbx3AcKF9cfeF0JCt4fZXeKlNVrtB/D3I7Kq7Lwitjvqop22Q+3B7jopZlB+30rGi4TikicRebPP7caWO2t9W70FbPbFLIyZlsO6jPhxGUVywGmY6xp5gED1vUvRvtumQnkv3Klplh71B6PjMWhjNWj42AKTnWRGL0LzS6tWwCgbrL2E37nDSfGmIRbBHsFGFO/2s+7Ml03WXjU1jDUzB+DSYQP3yD7cLC010LXGDGssEmyMzNXFJQvdWmm/5wx2IOencNo0GbqFPccfjPBemW608puWxdmdGBkLimXRzSwtuE3O8KKxmtk2tUbF2Tz5WbyzGmkyAGEy5jw7o6NxrvF5mJ0oMBrSJ51anYF8nWiysuQlJEm9bHQwyU6lVqfi6R4nxPtWjwQtCv7jR72cN4RLxLJGPV+Ch9k/sO4q9I4id66+h+/aeM8pao85oFGHRB2VGIzJbqZ3egRACY6j/9bvcAICMqWno30VMrX1CKrOrNpUayx7aBQA497oR/EyqGAB8mYItFz+kaT0xpObTtaqiEiShQYoH4ohFab+HA/24HfSMO2Cj/dqi0SHGKJLAkQS109Ic4lWGWTbbEHun1sLvpvtSYxJ60NYIj+3nBgcQFdd9m93JusZYNMFjVNaXdjX74KogligmSTAIFrXES+wuAFTMysbiNlppmGi28P2Un782jQYqwdBqzBrUCbY6aVwKj9eMvjg+tdL4+HHK3fguITNN/X19J4VpSnU6FaYJCtOkQIECBQoU/GCRiMeREAH6tzmGAsIJM03NNY8CoKhVjk6/+LAZsy8rBQC896cDOPc6in72qyIooYAe3Q41Z07FWigKi4TiSC2nqKN5ew+vjQcDMRQI7VFHoxe7x1J0JmuG6sNhFEToLd9jAmeybHi7HjMuGkbHGAhDlUyRkEVSQSUYo0ERofS0DcLdS5H26GlZrGt5LzGI02WtUG+EWQGDU2SW9YXgcFJk2NXigyWPopTqYJAjxsIDB9BUSRlQ9vVaXFlyFADwVF4e9+NTIlOlUK/HeVHab50hjPKDFDEPjk1Crsiy0glmpSVNgw+ETmu61cpvum+6XMyiNEUiHFVv9vs5+pV1EJk6HWsU/iyYNgC435mJtwfp2Ou8XtwroluZybm3vR13ZFDYV2I0sqbJqdXirnYS2PwqM4vZklAigXPM1Ka+TmJU/O4IM4ityWrOIrNEgW6NYDUA2EXyYrfIcqoOBpEh7vFoSc8al3SNFn4P3c9Wo4QMNx3D6jBwFkxlDf3dMC55SONmMnHfzTBYjsvK8TVRpG+26bhvZE1QaDCG5TZq3N2ZmawBu8U4lGFXv7+ft5czdVKzLWgVGU15ZQ54xLhrOuzCmFOJ3Tu4tQt6oeswjXIAAHo393A7nNkWziKNShJrCBurB6Aro/EqHR1Efz51ToXQfQV8EfzzxUMAgGG3lUNaR+Nu1JTM4xjhgF/cT6Hh2762FaPPpPHavL0H9gnEvuSotdgSpPs50q/maHzs7BxmdmSmc4rFwizpKKMRM81W0Ud92Ps5sbWF15fAUUvHKx1LGpKmSAQFGmp/nzT0kG4Ih7FOMIT+RAIPZ1LfbQ8FeDzKTE6pWo9W0PVt9vtxTtwk+iMKUwFdoy2u4nmv1VHfB41DDGookeA5cndmJu5oo3E+zGDAzSZiRlQ2LcJ9ND836MJ8PpmNyNTpWJM4VW/mOVZlMuFNUf9OZrbaIkPZhiUGA8+9mCRhTBcdozlHy4zLI11dnGEnP3vaIhGcHaAx0JCiQimdDq7Uobg4lEhwVp10iPrzZ1Y3Pxeuczq5H+1B8Lg8HA8jrWcoYzermOZ351E6RnquFYfj1AdZrgRSMojxUmlU2LOBdF9PF0WZTZeZtCkWC4ziWt9zuzmzz6hS4cs/U9ZgWraFf2O0KhV2in7aJ/5/pcmBVi2NlZywGp9K9P10qxXxbro/ocEYM01defTsMe/zImcSjbve/QPMVh02JzgLtKfVz3Mls8CKbWuGsvQAYNTUTBytJpauZvfQMzX7yiKep60ftiF5Hj1TR5tvw3cJmWnq7uw4KUxTRla2wjRBMbdUoECBAgUKFCg4IZww01QX+hMAIC+mQYvwlXE4TXBmW3gb2YcpCAmvLdkJADj9klJmHKpmUIQY8EVgEtqTezs6WNPQFA6zvmluUhIC6yk6HjONNE2HdvQgv8wBgNiLqMggSe6OMlulKrbA0EVRj1qjgs9JkavMhAQTCfbgiUcS2BSmtk2wWNhrCABfo9lKkcnalBimtdDfG4YZMC1K33dbhzKhlvX34+a0NP78fBZF7LInz2uGADNDa7xejigXpaaytmK8R82+LzLDttHv58zEt1wuzkKJShJ2i4hL3hagCFVm5LR6ei9+yTfAWqONPh9HqNXBIG7SUZ/WmBKoFRqRK5Mpoh52sJr9WtaWlrL2x6LR4HkRjV+akoJhzRS5541MRrWsMxH/16lU+IPIInu1qAh5gu/5RXcbZlmJLWmKRJAsziNHpLe1tuKt3EIAQCgQZZ+d21pbca243kmdwOWaHj72TpE1d6qXztGbrkVBgPo5Eo6jeiuxRDMvHAZXF/XdZ8YIivbT59SpdP8cGg1UXrqmDaoQJnupbR2NXo5KD6apOFOupc6NQ+U0puUsmt7NPRyVxqIJfLmRou7R0zJ5vH7yRh2u+c1EAEByJm1bKzIzAaCwIoVZkLeX7MJpF5UAoIzRHWtJb1E8KoX1G61Cm6QeY4ehnq7py43tPPfyyxzMoibiErNjMqtwaEc3DojrGNsQRdkEYi97W/1oSKF26La5+BhWuwFb8+j7mb3C68lpxCsJYp1udDo5My8WjcPiEBmCcQl1e2j8yNlYc2PteLO4GACxDc/0Uj/8KjOL5/RrhgAuFzqYt1wu1uDMG6RjfGqNYpbo/0VNTXghQffTOMyK7WJsVJnNuFdkvr2UnQ8AeMPvhkHMx+5YjFmii5KT2XspV69nzVKJwYCNYs6e00tj7QZdHz4uKRXXmsA9vcSq/cxjZmY9Jkk8193HLHnI87EioeNssDVeL7O8RrWadTJGtZpZPfm5ZlCr+VmwJCuLfbv6YjFc3kiZsndmZmKxnp59a9S035l2O8/Na70mDBYQy1JoMGD7u8SUZxcnQTWCnluueJxZlLSA0LMmDfmH9W/txcgZxKx89OJhzL+yDAD55Y2eRt/3tNH1W8Yksy7K0h7m52T+cAcOGof6Rr4XfbEYZ1IfPSiynken4g0d7XeV2saM0uq/HcbIX1QAAMqiWnzyRh0AoHx8Gt8feeUgFIjBOyD822bnYOcnNK9sp6ZBJ9hQvVGD7CLqA7mdzmwLa0PtQbC2Mn7ED6mEfpsGdw9AM9YBABhhuhXfJWSmqbOt7aQwTVm5uQrTBEXTpECBAgUKFPxgkUjEkUh8S03Tt9z/h4QTZpo8fU8DIPZm/dsNACjClSPpnjY/WsfQ2/WYLgnr3joCAJh1wTBmoOS3eZ8ngoHpDgAU0csu34UVyVi/oh4AMGVeAd6PU0QySqx1P9fXi6fScrhNssfHgc2dHGGMnpaF+3uITfhZxMoRscwY9XUOcmZG6sYBeGaTrmBGRI/Ho8QujTGZME6wSr7hwv02qoXfTNfRHo0ip48G0UGHhKUiG2a61YpLBIs1mEiwj8t4wfCY1Gr2ulnj9XK2WOgY12QArN841v1Zjiy7YjE+hlOrZZbrkuRk1gRc19TEmYHyMcqP0SMt6+9nn6PLUlJYy1V+8CBWDCNt2BThpfLmwADGiPa/73az5uHFvj6OcpsqK7GkowMAReByJC0zaRclJzODuMBuZx1D6Jihp2sO4m07XYvMxmW4E4iEqf0v6P2s47A0BNknSGfU8HWFJAk5CRoTg0KalKzW8DHa1XGYOukc7zxXDfOdwwGAtCBZdF3+Gup7h9OIzmbqc3dfCEVCp3S0eoC9WQpW9qJ8AkWuXc0+lJ5NzGKW8Cg6enCAGSC/J8wZaknn5iDwkdD2VKQwyxPPEz5nm3twcIzwy9o1iLRZNGClOj8fr3pbF0fuKRlmREIU8X7xIWXMZRbYWFsViybQmkz321LjR3sJXWvyHh+zZqtfJg3J3N+MRaqPxv7Gd4+yK3fZ3BzWiJitetYEDabqkCNctV9xU/R/ucXBWUpSih7L+kn3cbHHwMfbEg2wdm+F0Pjk6vXMolYMUJYkAJxvd+A9jxsAMcIy8zPNZMFrHtpXdsA3qtU8dncFAsz8bvb7MbuZxtu1NheWCJds2aetzGhEhRjblxw9infEPJga1uGZGJ27ymzmDLWVbjePaVmnuKmsDLsFm7XZ72fn672BAM+FBXY7zxXZx2xRUxOzwHdpkqFLp3as83qx1kfjsVBv4PYBQ+yLfNxgIoH3heP2LJuNnyc3trTgIvFMqgmFmIVrE3NmTB84C/ORri78KpXG2qqAl1m8WTYba4mK3RJnvzm0QzG3/ExS1fiRJFYRanb3oK+D9stdVIwRgv0+pKf7mqfTMYvdFR3KzE31JdAuFjCCW/tROpPG+XM//xw3PzYNwBDbIxWasf158vtKyTAxG2QZk4yg8PTLq3Li47+Svk/W7mUW2LApl85xdUoqtn1MeqX+7gB04jej6MICRPbSMbpbfEhbQL89tkahlQpEeaXlcJKEkX7hHadRMZOclGLkrE2L/WZ8l5CZpvaW5pPCNOXkFyhMExSmSYECBQoUKPjBIhFPnITsOcWnScYJM02Hdz4AgKJu+W1eq1MPvfFX2TH4GWkUUjJMxzkQ287LOe5Ytv0+9rKJhGPsZVO3tw9h8YbucBqRI7I0VmmI0RhzKAyvqJnmzDbDPkawOoe87AfS0+ZH2nyKIsuNRrQfGapxBwBrQ37MBEXxsWictVXHujSravzYmT9UiwsgBkWux3ZnbwdnoWwbHMQjOXR9K91u1iacbrNhuYhip4rItyYUYh8gVzyOd0SEXajXM4tSHQrhwU5iIeTjPtLVxVqPYzNt/PEhd/PpVivWCIaq3GjESNGOlwQLJp8fIL2I7H+0orgYU/UU5Qa1wITDxDjIUXCV2cztKTcaOZPoxYICjtIvTUlhXcRTeXns6C1/1xuL4lMvsTYPZGfDIybwRp8Pf8jN5X6U2yezWXPiRliFBqY2GsYwEAvxit/F0bNVreb7VhDRoFpL/SFnb8aiCVwZovY/3m3FnRnEgr1ZXIzmLdS+UVMysTUiImLBfvRu7GYH4NSNAxguMrzUOSYk0emgMWuwfRUxOyqNCkmnEbuXIVhIR5oJH75CUfCc6yvw/uN7AQCTzsjD8Co63kB3gNlQW6pwst7ahVFTSMe3adCPok6RXZpvZG8yvWEo3nn597swR2SPsrZvvIO1XK31HnY6//z9o6g8pwAAEO8O8TzsEqxabomdWSIAPD9aD7o4o89q17M26cieXr4WuR5jPBDHbqHfaj8lifVlz+h8eCiL5mb1ti7u07Ce2rlrcBAl4t53RqOsr8vV6fi+rPN6RW4c6XlkzY/MUnRFo8dp9+T5G0okmF1pCodxbZDm5EsmYob6YjHW0vnicTwhxu6bxcVcM22Nx4MXxbyoCYWYMdpUQozlpwE/s683p6Uxw+ZPJPj7i5KTmSWSmapdg4P8XPAnEsy8hRIJnh8z9wbwzig9X7ec5fq4aOf5Dgcz1CVGI7Pzqz0eXJ9KrNN9XZ3sizRMS8eKq6mGGkC1FI91qr/KSnNMrVYhKIbbao8H88J07z82CE+3lgRXGLjgZ6M4I9OQa+ZruSg5mX2tZL1V4N12jDmFriMWTcApKkdEQnEcFm0qqkhGn6jtmVlgY/+8YBq1vz4UQlkv9affE+b6ge37B/g3obPMhKlqeh7u30LPAgCYMIf6tnprF/v4mW06ZrGTUow8N+v398NslV3u6bqTM0zoFu7u2UVJrNVKxCWccnYhAGBvIoyWv9HKzMW3/BnfJWSmqfloA5LE+PpvH8vnQ0HxMIVpwjdgmvqFWeDoM/Mw2EqDePfGdpSOpuUtY0sYe4Sw86KbRjN97/dEENtED4+4eJFqGxh68QoFYrx0lpRigFq8jCQSCU7jHCMGa0qGGRVCFN7f4odeFIZUOfTY+C4NzPNuGwOPKEh6sMONktE0ieT06+k/LYeou4t96hhyo3S+Qr2ef5Aso1NgFcdwZdC52yIRtK4hgeDdCwv4QZqr1/PLyoPZ2ZxqXR0KcWq9bI3whmuAl9kuSk7m4r19sRgv5S1KTeX9lg9Qv0WlocKR1cEgL29VWa1ccqGyu4EfvE2RCL/QyMtz4y1m/nyj08mp0U2RCD+8DSoV5iTRMcaIci+v9vfjz2L5YZrVyj9OTZEIl0PYNjiI95NpaepwIsHLJ73CWDMugR/+VrWaC4TekZHBD2mjWs1UvWxQd9AYR2acjlGmM/AL+pRkC7d5pErPZUi6bRJy24QJokgV9g6EcFe3KBR9bgreCdKPRsIdRfFI+izpVNBGaRu5/MGkuXn8IM09p4jLxDTv7oWouAB3X4jF3acuLOKgwTGLXnb1Bg3bW0iuCBfkrd/fh+pttIScXZTEZTVWyUtkl5TycnP/a82IiheX4UiDoYTmja9tkF900rIsyB9OP3CNh6l13avb0TaPXuLiBwf4pclqN6B1K/V/R6N3aHlhLP1gXQagr5P6OSnFgA4xB0OBGC8BJiUbOW3+0rFp6BCp522Z9DjJ7ohyGaWJpiRM7SIR7h2Zmdgilg91s9NwSw/13W/CNA9KCs1c7LXQqeflKH88zktB02pjvCx5YePR40oNATSP5eWyG9PS+Ac6fMiLt1NooNjUGn5ZkudEWzSKszdTYHJL5R5e3n6kqwvP5eRxO+RCvzWhEL+4PDtAgcn8pCRsE0vTrw4McNuOhEO8ZDgTJvzKS/defrm7NyuLBdEfeb3Y1EXnfmc0MPMQBSmD08qOC1IWNTVxOwCgwmjk+bMnEODjjd0XgmsWtfn+zCwExYutWzzPd1vieEQUnX3QkI1LzHQvXvG7uNhuUyiCYQ3CrDjXCtmd9kxRuN0+xsQvGpsSQWzU0bi8y63DeUHa5siXHSzPkMf2uB8PY5uRljoXXlyyAwBQPDKVX9CNZi1GiLH7xYdNOPW8YrG9GwCQF4pj3YfUF7MuGMbPV0ujh4v3hvZ4Ua+j+y1bnxzZ34+4mNPHvhwNr0pDqphjm944AnevCOTGp8E2iX7rulbQ+UaenQefi/oluyiJf2t62vy8FF/iNCJa9P/3S8YPDcrynAIFChQoUPADhWJueXJxwstz1dt+BwDYu6mDo8jikaksYs0uSmK2Jy3XisFK2sZ5NMTCwHefqwYAXLxkAtPm7/2lGpEwRYPzryjjZb3Du3vRfgq9oecJ5qXUA2xe1QQAOOOy4XD3UZjSeNiFVmERcOFtYxD2EAuxeVUTvAso2qhqonN0l5q4MG+fFOc05JgkMfPj6w6i2zFUTBMguvpJEcHefkz5lX3r23GgiqhffyLBy2wxSWJ6Xn4znW618neZOh0zSlOsVi7s+rbLdVwJBAAIJxLctjVeL+8XkiRmsRZnZLAdQHUwyEyMjAlmM/f5i319zEo9edSK+0tpvxKjkSl+2ZBzQX09zhbnHvyX+kOySd0Es5mX1N4cGMAUwWLJ5SJuTEvjAqgP5uSg8yDdq325ambY7sjIwOV6Ok9YFBv2JxJIHqRzGux69Av2T2/QMBVeMtrJn7X5Zl766/6YaPjR0zK5bIkz28xmpcHuILNEuSV29JZT1C+XQvn49TrMuZjS+zvTtdj9yAEAQPn4dKQLY1OjWcfHmHBaLouw5WUGg1kHjVhOMFt1OLST7vGsC4bxkrQswAbA0a5ao+K/1+/vP2a5wMAWAe+/eAj/8eJsAMCGN46wAeDBTXTdVrsBUrmV+yJbLHV7hpnQvWJI9CqjQkTlVoceNbuIXQr4h0pUTL1oGHrrKHqOhOI4UEBjaWyHhC0i0p92ViEAoL3RyzYhVruBjUgP6GLM7JS1yItswBZascM5cRNa6XTI0OmgE6ymLa7ClzER8QfVaDDT/HBoNFwS6ZYA7diarWVR8rEC6wezs7nY7g3NzdhSXg4AzKIuyc7mY9k1Gp4ft7e2MmszwWLBHPn7tjZmf18tKgJA411mxN7u1GF2Gs2rUUYjH++pnh7e/jrBFgHA0gJaMn3b5WLrgEK9HjcKIbtRpcLNrcR0T7ZYYBPXKF/TLJuNr3uhw8HPrRf7+vj7WTYbG71eJJ5TNaEQF7c2qtW4xE/zoztLB0sNzavuUhMz0x8vr0XwHGJ+ZJnAj1s1iIym8VWpMWBATf2l7gmz5UtGSMWMqrWctk3XaJl96raqmOnvafOzHUAkFEfAT8+ylAwzL6/J7OapC4vZYDKzwMbPgkM7elA8ipjkGZeVYtMbR47br+rUbBzcQfNx2lmFPD92fNKKaecWAgD2fd7B8/vie8ezBYlseNlS62ZG7IsPm7nNKRkmlhWEQ3EuTm93fj+WA0drDsP2LZfnfD4fissrlOU5KOaWChQoUKBAgQIFJ4QTXp7bI964T7u4BOvfJluA+v39nNbcUufGoBAARkeYIa2ktXg4TUNCPhE9N2zs5KKnJaNTOXo+Wi2rRQBXdwAXWIjtqNlN0eAX1QMo+AmtaXfUeY+LwEfMJ81M2BPBRmHKlnpJvigRCTQME0UW1/eh/UyKAjK1WmZIqoNB1td0bupgcetHyXRNi1JTYRIR23kNDfhwGLEQ42blYKR49Xymp4dTn6dYLKxjkFPwb2xpYa3ELKuVdQmpWi3rg8622znClks5bPT5uCDpkuxsZm1CiQTebqX+fzBbc5y+Q46wZfHltsFBjj6dWi0eSiYmIKOsH596hywC5LbKfaFVqfi6x5rNrKFwx2KsF1nj9XKU7tBouAhvodCZPNXTw6xUKJFAuITaVg6wLmSWzYZXhE6sELTfHL0FMA5FzI4kOsb8JDMiQjNwV08HHs0lquINr5v7fMyCfG5/hiirI7kirAfba5NYzxPwRaHbTGPPNZuiwjHTstAgxuPkuXnQn0VMgEajwievU9Qai8Zxw5PTAQB/unErrr53NACwbQZArBIA7Pq0DYUVFPluW9PCEWpHoxclQhdoMNN3h3d0w++m+xYJx2ARRXOtdgOzqwt/OhLvPLWPtgnFsWctzbed6+n/Op2a59jwsWl4768HAQDTzipgm5CS0c4hVkwwcDvfb8RpF1Obn793Py68jT674nFmBZoOuzDVQfPNGw5BfW0hAMAuIlBXkREv+ymivz6QjHt8NH+nWK0sUG4oUGGOKK+SJ+ZP2BNBhSijEvBEuChrskaDLMGo7kuEUTNI7YhJ0pBuSBSMrgkEcCBEfTTBYuHx2hqNcmmhGVYrzwuZ+Zpw+DAnPzzT08MM9ASzmQXiK91u3CLYntvS05nlkcfcOq8XL++bCQD41YQvcGMaHa8zGmVDyKZwmHU38rln2WyYUkMJA3IiCQBcmpzMZq1NkQgba2botOiOxrgdAGkr5b64u70dLxyle1+e3ovqMjJ5fHagj+esQWjHptpMMNrU3J5mCz0nC7VaBEWJmelWK5ehGjEpA1vEeW6N0/3WTTBxgsixSS12XxQyv+GNJrBjLTGck4dTua2OFi+PqcKKFLR10LPHNMqB9X+m8Xr6JaVY8ex+AMBpF5VgQGixRk2l51dPmx/5ZfQM7Djq5USI+VeUMbPVvLsXB74Qz+Uzqb/Wv13PYu2a3T2wOsQcc+ix9nXS4O3f0sn6uVfu2sbJRE1CNzj1rEJseIeSAeZfmQ+bYJccTiOevYvMnUdOTuV22IdcZb5TJBIJJP5lleC/cwwFBEXTpECBAgUKFPxAoWiaTi5OWNP05K1XAKB15S5hRnndfaNxaAdFkXqjhlM4X1yyg9/QG6r70ddO0f2Mc4m90erUHDF3NHoxaW4+fy+XhsjMt8E+l6L+SiNFX689sYe1FyMmZfAxYpEEl2rRTEzm9ORwW4DXmeXMq31OYEaCIoKetkFmv1Q2LUdhm/1+Np6Ui8EetCawVUR9accwVHsDAYwXkWZckphRydXrOYtM1u1EJQkTxbZN4TCn/ZcbjceVCnAeU0AToFTnJ7Opb3/W3srHdWq1nDFjVauZxZpeW8up2HKq88vNdjw0nKK6YQYDbheR4XSrFW83EqOnSz6AF4W2QmazakIhTlOuDgahEUzNZSkpbEXg1GpZczXdamWdlfz32zIyWDvyTHI2ru6nc79VWIQHumibq/1mSIXm485dbjTiMgdFkd6BELqtKr5Wi5cmsSnFwJlcZUYjRsXo3PX76RgD3QHWK1jtQxl4o6Zk8phJyTDDA7rPcmaZWqNihlStUcEoWCC/J8xjyt0X5IyZmt09zA6Nmkp933HUizShCYqG42ys9+5z1awFdKQZ0VInMvZOp/nh6s1FaiaNGXdviA0hO456ecwXViRjy2qK3McK8z8AnGlUvbULqYJJS80wcfs/WNaJGecm87ayBkS+1oHuAKd1A+B53FLr5ug+EopDMg+ZEvZu7OZrkf9ePJLYs542P95z0vPiYo+B2WG/J8x2B8tdIuM0GGSGdIHdzpmat7W2sh7Pqlbz/Fjl8bD+j/WBFguPh1lWK+aJcbmoqQlzha5jgcPBY0wuTXJHZiazpVekpPDc3Ob3Mwvsj8eZCVra28v2CKx5MpvZZuORnBxma5/s8OO1Urovc2w2NqGUmdg3BwbwcqtgmGJW3F5C/bErEGA2aprVgrEmkW3X0cEGsLItyUVHj/LnFS4XG2uWG418jQ6tlsvHyKzaks5O1jdpVSqeu1FJwsdC39gWieBnGupHi9OI9sPU/pwK2m+F243xQjNaVOWES2gIbbkWrP97LQDg1CuHQ3LR/JDZoowKBxq2UX/t29LJGsKmwy4cmURjfsyh8DFzxYRPxSpHpbDkiITjrAUcc2o2FxH+5PU6XH7PeABA3Y4eDAqd68evEUucV5rE4/nzlUdx+iWkCfziw2YsvGEkADLAHS8yYdNzrWirp+uWLQeyi5K4nEv5+DTO9jaatUgWbK7DaYRLXO+M8x7GdwlZ01R3YP9J0TQNrxytaJqgME0KFChQoEDBDxZKGZWTixNmml5+4HoAwKDXDElyAyATr9KqUwEA/Z07oRIRajQUxylnE2Mx0B08xiCMorvcYQ72cWqpG8pAKBnt5Gh8y4dNcKRR9HXqlWQe11/j4TVwd1+QM+0yC2y8lu3MtnBZgHggzgZnWrE+/9zgABf8TNdo0Sq8hGKShAKhp3h+oI91EecLpmOW1crZW4ZcM+uRFtjtzGz54nEkiyh4aW8vZ5zIkbFTq+VINFenw/IdlwIAZo9dzv1cHwrhbAdFdf0xavvbuy/E1RPfA0C6Cpn5eaY9ivnOhNg2xl41/kSCM3t2tpF+AOoI8tIoU6vcaGStx0XJyaxjOhAMMhMmH8ugVuGWNGKwLm1sZE1KudHIZoGtkQjrNM5xOJgJk89xelISgiIavz0jg3VFrw4MMKP3Yl8fM2uni0imxGAYKsGhseKBQRo/mTodrheZdvX7+1kTZHEYmNVLFX3+U3USklKozc3xKJdRiYTiaKl10/EKbKybGz52SHggs6inXTQMrzxMEerIyUPGraFADHs3kaahaGQKzv/pKADA0nu2AgBOObuQM9TGnJKFz94nrV3ZMd5GVoce+zbTZ62I8gvKNOxl43AaOYOn6tRsnkv7t3Qxs+vuDfJn2UyzsymMnGK67q4WHxaKth092I8RE4n16Gnzs9+NrNeYNDePr1+lUeGLDygjcM/GdmaJUi8vgOcfxAg7sy2QZlD/jxVatEM7ujnr7kCGCmNddL8f13hwVyZF96EGP4zDROFjwepMlozMcoUCUdjF/JfiEhdGrQmFjvNW+khoCGXdXbnRyKVYFjgczB7NOibSfrW/nxkteexf53SyZmhJZycuFeapi1JT8WAX+SqVGQ3QYUhveEMajZWljTRes5JbmeHdGwgwy1tlNh/nhVYpxrzs6TbFYuH5urU7A1nJ1LdvFhVxttszPT0IxekZdnWaHXeLfnyyh54nP3Om8bU4tFr8QbR5WWEhe8pdmpLCjK/MRNk1GnSLc48ymYY0i/E4JrcPjcHtZur/mTBhn4auRW5zZXMcG3Jo2xlNCWZfI6E463kCvggbtsqrCY40I4qE7q71oAtb19BYG/REkCPG2szzijhj2jsQ5vEhP9etdgOsQvPnSDMx07r6b4d5rngHQjh1IWlhZT2uz21AXin9XWfUwCd+V/o79Zh1IY3nwzt72MstGIgxy3tAZPCpVAYUjaAxnFNix6Ht1KaxM/Vc7mjEpAwsvWcbAOCWP7yC7xIy03Toy92w2azf6lg+nx8jxo1XmCYo2XMKFChQoECBAgUnhBNmmh7/+Y8BADnFlZh4Ou3y9jOHUFrlAEBlGC74GUWzL92/C2k5FMnllTnQ20oRUNWplOUUiyY4Wh83KwdvPrkXABDwR3HaRfK69gBHv7Ira/GoFI4qPn+/kaOK9W/X44wfExuVNtyOne+Tpb+8PwDWm6RNT+dI9NiSBdOtVhiaKJKwldiYEZJ1TvfFHNgnSIgDwSCShcbiTZcL60ppPXxJZydnsyzr7+dSHyuOKZfiF4yLPx5Hlzh2dTDIeoRZdXWcjSPrnDJ1OmwSUeRYk4m1EjubJmFG8S4AwKY+M9BzGgBg9sgPkKk9fuVVq1Jh+baf0D+cm/HpZIpg5xzsxfXZWm7nZHHufXLZB4eD21kfCvG5L09NwWv9FCnfkZGB200UkR3Sx9EgtpEj98c7+1FpoT5fU1KCe0X5CatazRoR2SsGAPfb3kCAWTqtSoVFIiuvOhhkLVdaQIKUpOP+kovKBnwUBafnWrksiCnDxOvRe9a2wWimf8WiCY5g5ei0q9kHv9BBJOIS8sscACgLTma23H0hmARLKsUl5A+nbWRmyJY8AqmZnbytwzlUcHXrR9QHM87NY58yOUJvrfdwgeB/vngIeaVTAQA6Qy1H6//40z5MmU9s7rq3jmDKPNIFyv4w3oEQuxIXj0zh70ODMWbTdqxtxYKrKbNK9qw5srcPKRlDHjlZws34yN4+nnspGWb2XjLb9Nymw2rq89SWMF+ndyCEwBg6RjiRwHihJ/SbVVjSSX3wdC6xW4uamtjj60pYec4+YPBwoVy7RoOzVPT54UA/64keFPPn7rY2ZpVubGnhTK4XetwoEpmYd2dmHlcAGyCNYeExruKyR9qv3v01DFOo0Oosmw39YvtCg4HPfSw7I2uvFqWmYonQ9FWZTEPfO514QHx/sRjnmVotVgg2KJRI8Lw3qtXc/ppQiBnaN10uPl71CGKSb24d0jouLSjgLN5cvR6HRfviksT7rRJ/X5Sayted3R5Fby7d4+uamlj31PKPZkwT2aM7jTFMjtCYl/2ymsJh1jQGB8J4XyWc+y0W2HtoTJhtOjQdpudgwWR6Ru75ZxOmn0OeVX/73U5c+FvSIGncUWajqrd18XM+4Iuy55+smevrHGRWEyDXcACompHNLPCE0/KwXWTuyR6DWz9qQ14pfW6pc2O2+N1pr/fw/O7rHERUZHY7nCakinkh6wMLK5J53uxc24oF19BcioTiePnBLwEA51xTxmzuOdc+he8SMtNUvXsnbNZvyTT5/Rg1fqLCNEHRNClQoECBAgU/WCjZcycXJ/zSJBcI7W49iESC3HQrJqZwds2ez4yIhCjSOXtRGUfuDqcJf/+UsqU0Woooi0ZEOHKXdCr2r9Hq1QiHhpyCOxq94twUHXkHQpy5ZLbpuBZW8agUtIqoOlxoQvI8alOe0ch+LEnC3dbSH0VtG0U8uSV2XJ1CUUVLnZuZgtZYFDeF6M28P4uiiu3hMKoHiX1Z5HQyW6VVqZg9eqq7m6PBmCThhmZao1+RTlHaHZ5OtEco8nJqtXj5MNUiK8r9guvXGVUqjoJl9gkYytBZ5/Oxwy+cm7Gph6J8JPTQFbwFANjQXgYMTAIATB35Drfzpbmki7quuRkPdlGUNTcVWOelyPCi5GTIiVOyRmSj38+14Gr6spHmoIjtU6+Pv/cnEtigomj2rqZ2jqDla7o+3cGMnewiDAA3p6ezZiNTp+NIWY6G78jIYC2I74s+tOfStrFcPfeHVqfFTnHMtJoAR50yK9JW72G9UtWMbM7E6Wj0ctQ6vCoNHqFpCAqvMb1Rg7MW0jhXq1U8Fs+6qpx1dX2dg7BYifVw94WYzZGdiOv2VKO/i/po5JRM9ndJxLIxrJIi81g0zkzq5ytJ82S06NjhfsZ5RfC5msW1hLFtDfX/qCmZXHuuZHQqOyUXj6Lx7B0IsddY/f4+GC1a8X0YRw+STsxo1rFeRHZd9g6EeE5rdeqh+na5VnY27usMsMfVlnQJxYLJK5LZugwT9n5O7ZFOc8Ih5sSjPT08pmMxCVeKuecRte5eSs/DEcFWbQiHcX6JAwCwwDukPVrn9eKgYF8W2O0o89GxnxFanSPhMNaK+bMkKws3NYi5VJTBLtr14TCPR5nBcmq17IZ/b2Ymbmyhfr55wRL8eTUVWp11we/5GC/29XH2nMyWrnS7Wcd4T1MAKj3d+9OTkrhm3RWNxIIDwONCd3Rlaio29NO4vDhDy8+WY+dKdTA4NO8x5G8m+zstdDh47l7U0MBt+0NuLmuP1ni9rIc8X/Rh0uoexC8gPdyG1DjOoW5GocHAvm45RUlw2al9G/tcKBHaQ9U20hjOrkjmeep06HCFhs7RfMiFvXtJU6Y3aFijKmPqeUXYLFYFrvntRBwSrE1fxyCvSoyYlMH+fd6BMOr20DllTeCxxzRZdZwNLc8TAEjPtaBA1Gbc8A7NseLKZFTNoHMYLcPRWtfA59glvM6yi5L4OVK/T4dNTQf5WgBiWWUNYFuDh5m0+v19uOHBidQ+pwn1+/uh4IcDhWlSoECBAgUKfqBQmKaTixN+aRoxidahy8ens0NrVuFovP8CvaHnleqweRVFNG0NHs5mGzcrZ6jadIDe4Gt2x+FxUXR9aEc3rz2brXr21Knd04uzr6JIX65pVx5Ix851FL1dclsR/MKl1u+JsBZly+MHcMV/jAMA7F3fzm7jclZeu12F6GHRznoPsoVmI2u4HT5pSG9ULtrRJqK9Y7N2js2Mi0kSZ6csKyxk/dKDOTkcfV3ZR+vzE8xmLG2gKK00rRHPjqfrumnPBCCDtnk4Jwe/EZofmYWZYDbz50tSkrG0m85n1MQRUtM5pqa5MMVCzMKrmnr0+mmNfmvzGPp7wT75VmK61YqtInI/3+FgT5oVLhdcPopgnyCJGFa//xvceNEjAIDBeCuMaoqy/pyfz7WzVrrdrOs43+HgSu9y9pBVrcaf80lzs8bj4ey4NwcGOOJ/NCULb4ToGmUt1GSjmaN/R5UZJhMxOU6NhjVbW99vZJ+vW9MH8KiGIk15PDizzagyU0SpN2p4POSW2DkzxmzT4/0XSId04U0UOcpRIwBsXtXIbGhf5yB2CeaUPJboXiy4ugLLHyUn4dMvpT4c+LgHiTj1wZ6N7RzZ1u9vhdlG47Glzs0aidOET80rv9+HvFL6e8P+fmZtu9v8GDmR5tLoaVl4QziCS3GJ2yezpXV7e4f8qRx6no8717ehXOil9EYtPnuPrqViAu2Xmm3heWV1GGA7lcZU7SsNOEBJQKwLAYCi7T40zyRWY4xeuH3HIjy/S48E8UoGzbcFdjv7ADWFw6xjM9XTGMg+JR0ZoL9v9vvRE6fv2yIR9mF6rqCAM1fvzczE0gAxD+fYiLl7a2AAJsFsPdjVhdtz6VprQyEejzWhEHwh6psYAtweWUvUFIkwy5Kp07Gm6X23mVmbxv23YeFsynpdKjyfFjoceK+DngtpSV3oHaDnVyzDxazRdKsVpwvNlcxQd0WjzAyvNKxFNEh9fmOeEWvF+N/amYcZ2XSvet35WGkl9mVPP7F1FyUnkKWlvls3fDgzc0s6OphdagqH2cNK1gdeNSaMpeKaqoNBlCXTPayKmfhe9U1xQlVP9+qOsgye66OmE8u90efD5B56Lrg8g3hHZKhd89uJzObqjRo0i2zV2g30fDNZdVwXsq3eg+ptxLxZ7QZmnYxmHQ5up/ZNW5AG4tXAlSg+e6+Ts7InnJaHur30WW/QsKb1r7/dzp5gWYV0f6S4hA0rGrhtMnuUU2JHR4PIxnOq+bcpuygJkUxaMaiYQGMjEo4hEo7z3+VrTc+18vnefa6aMwG/LyiO4CcXSvacAgUKFChQoEDBCeCEs+d2r/8tAGDdP1px6nkUMX/yeh3GC3+YjgY9etqbAAAzzink/b7c2M5RsJwxd3B7D077EdW06mz08vq00aI9rg7XRHFs2a340I5u1lLEognODlr405H8OeCPcGbF+hX1rHGRGaX9WzrRO4/YnkuTkxEVEfgHIR/XmFra28tZcPLfD0oR3Cg0SuMtZtZjVAeDuOFLamftTA/eE6zTx14vH+MRoV0IJRKc2XNJSgo8grl6tX9ozduh0eCZI8RUXFlMkeXynhBmOERtrVCINRRtkQj8f6KMOOsv/s7RuDse5yw32Tdm+cAA6zHKjUaOuve0j4Rl+3nUd2f+hjPX5Oybi5KTObI8z+HASyKqnmKxcDQ+PykJF+6iezw17zBHtnIm1Eq3m7VlC+x21iyt8XoxXmhK5iYlQeqlNg+mCldvcV6A6ojJ11So1zPzNrVVwoECOt40qxXWAI2xdW+Rr9KCayqO8w9b+TzpEpouzcBtohjUk7dux8TT6V5NOI3uZc3u3iEvsVo3Gg+5AQAjJ6ezX9HeTR2s/zGatTzW5Cronc0+GISGatenbRgn3IVL52TjwCoaS30dAWbFOptERfj8CA5uJy1IRp6ZmabsoiTWGGUW2JjlSkox8PiW6y6m51qRJ3SDrq4A661yS+w8V46NjuX9a3b3sn5r9LQsvLeUovFhlWYMCu1SRq6Vmbif/Go8nvXS+L3BSGOnrcHN9e3k/gNIeyX3zfakOLOMM0SZSqncyuyTPx6HT0S3g4kEs49d0ShnlNWHw+yCL+sAQ4kEj41CvZ491BwaDWegnmm3MztcdYxnUm/NTwGQb5rMZPoTCWaYdw4OIuQpo21y2lmntHOA7vvVOXG8XEt/h7kFD5VQ+3cFAny+USYT6/H6/v4Luj9nrkKRk+6b+xjX8Vk2Gz8bGv1mXJxB46A+FILjX7JjVw4bBvtq8iI6Z+Sn7LNWHQyyp5NWpWLGS9Yjpmm1PI+P1UyVG40YpqW++0NfD9fOqw+HcUuUnmGyvtQ7EMJuO11T4r0OnCVWCFpr3TyWpszLZzd+edx+9l4Pxs92AKC5JPv1ZRTYMFJ4ia15tZazsp+/dwcqJhILN+/n5Nq95bUjrBnKLbHjaDV9Hjcrh/3ZDu3oZkZIZoDaj8YwcjLdt5pdvaz5mzQ3j8do9dYu2JLpfjbX7MG511GmYs44em48e+NnyBPMbtMhD2YuLARALK+7l56vo6dl8fyd86PH8V1Czp7b/flGWL9l9pzf78f4U2cp2XNQNE0KFChQoEDBDxaKI/jJxQkzTX++axEAch9OFVGk1wXkD6e3+bzhyWhvIIZBb2iBWk1v18c6d8vRZ8AfgVpENdFoHJfdXgUAePqXe/jtv7djEHNFPSA5WrHaDcwgzL+ijLOiJszJ5XPEognOJIqem8mR5i4RwY4xm5lx2ejzsTPwsv5+9kc6J2xEIp0iEtkt99WBARwQ7EyyRsOM0QSLhaO29kgELwgmpuIYx2wZf8jNRcFndNwZ+TXsOnzTvgzYnF/ydjIT01p7FV1r1T+gEzoNdzyOTS0UyWVl7ENn+zTaKWUHXimhqPuOtjb01tH9yiojF9rOA7cDetJBQD8AODcDAM7PjLBOAwC2Nojjib9PTAJ2isyqLIOKs432BgIYKSLi651OPCWylxalprKW64F2YkvyDEM1rYxqNevBplgsfLxtg4PMaMl1wZYWFHA2olOrRXgHtT+zwAazlY73mTbMdQBL9QY0RuncmsahzCNdMd3XwyubkS+yaKJlFqiqaVx5B8LMvpSNp0j24LZu9iuS2SQAePPJfeyrlFWUhBaRlZmQorCISHnCaaSLOrwrAZ+LaJT8smT2hTpaPYBZFxArsPKvBzkDR9Z05JbYERoU9cLSTMy0JqVWwNVN2wx6OzmS7mzywyD6cdxMYkC7mn3sgbP80T3ILaG5ZLL249xrKUp//Yk9fG5Z63V4dw9nJBUMd7DeqrvNz1qm7KKk42rVybXxXBm0re/zXiyroDnxdG4eM0qpcTV7ZvndERwQzMmZNopcG6MRdnR/PJKMO/XEZt2Wns7sTG8shi2CMVrocOBSkY12bH1EmRk6PSmJGZelvb2spds1OIi1HprLsndTSJLQGaT2z07WcL22PXtuAWJ0fWMnPow97dR3pVkHmNldu+dKAAAiKfjNnJcAkF7viEdoWdxVQNIh+txzGs4Z8z4AYFUzaWSuHtbOzvdS3ykwppGjvObD3yF74YP0WaXCmaL9T+6Yj2dnbKBDi2sdZjBwTbsFDgeuFbqv5wsK8IZwHt8dCDCLJdebOxAK4rJk6ru729tZZ/WZsxBmG13fS74BZvdUHSEkCd2TVk99d2RfH1LG0TFM7hiPJZlBBYhRlV285ZWHT1fUY7IYfzIbCdDz/mzhefTCb7YzQ7vurSM8XuWM0/4uM9Jy6F7WfulBRh612TsQZuaqs8mPMUJ/JTNKfneEf1fMNh3aRZalMzsbRSNkV/oY66LaG5xoPbIbwNBcOfW8Yp6z3oEQsovsoh19GD6WPKeKRng5Y3fszPvxXUJmmnZu/PSkME0TZ81RmCYoTJMCBQoUKFDwg0UiLiER/5ZC8PgJcSv/X+CEmab3ltL6+6A3HQMi2tXpB/mtO3+4A4d3uQEAuaUmxMX3HY1e5A6jN3A5MomE4lxdXae3w2CiqCEWTbBbsTPLwhkUso7plLMKWEsRiyaYufr8/aMYPpYYgq5mH2f6ma166ItoX71L+O8YtPg4QVFyldnMldSzJA1HRqu8HmZDZI3F2TUdeKKQIqRFqamYVkvVu29PT8elIsq95OhRriu1rL+fncLl2lUAOGLbNTjI7IxVreYoeKXbDWnXUgBA3tRbAACth6/HQ6euBEARc2vrbDqYfgDaQ1dQf2RXY37VPwBQBpvM1sgsEQL5nKHz9LQtuLWRItvni53YEyRWZulHDwLDn6LtRVZemqOF+6h6xAikfiZqTOUe5Ui7PhyGXbBjH3u9zKDJTsMxSWLGb5bVyi7MewIBfCD0J1Um05Dvjfh/TSjELEVIkvjzjWlprLkqNxq5Cv2uwUHMCFB0KesnhlelsUahbk8vu24nEgl88jqxlnrj0L2X60udd9sYvLD4CwDE/HSKGloOpxEmwb4YLFp0C42RM8uCAyLqXPhTYiMa9vcjFKTIWG9oYX+n1GwLDm6lbUtGO9HXOSjOTX1bWG5GU80QU2YS1xcNu3DOtb8EAHz4youIRqgPKiZlcHX3aITGkdFsQUDofHzubpxxGaVD7ljbiulCc2i1G9h5WfY8C/ijPK/UahWmzKfMxI5GL+u9jkTCaP0n7Tdpbh47oMsP1lMXFrO3TuooB9dYu8GWgmaVuJ+JBMJf0Bh8u5zG1+KMDNbM3ZGRwfqbmCTxeNaqVLghjeb6WJOJGRr57/PtdtyfSczgVc1NrNG7rrmZGckSg4HH0qp+wbLGrLg+l55ZRpWKNUMOjYarA3QGdbg+k/qmOhjE1sPn0L4JGtulZW/hyCDNA5U2wC7ZfbEYn/v1t+9G+rxfAxiaH5u2/QdQuIzbIdeIvC0jg+fYhTUDrGkyqlTHuYbLkGs3tkYiuLONGM5HcnNZq9QWiTBDLvetQaViFvuejg48kZvL28oM9NV+M2diepBgfeKwKPVRS50LljHUzzZ3jO9993gbRnaKPjVr4R0I82eAxtT6FZRpN/+KMtbr7dvcjzk/onG3fkUbyscJJvJQBCoVzRWrg/rFbNOxtqqj0cuMUVah9TjHf9kvzSyuPzUzwv12ZH8f8ktpzvo9LpSOoXvYdNjFTFNPmx86PbWpuJLmscWmQ7tgqxr29bMG0mjWYdBL1335nZVY9hBVbbhv+dv4LiEzTVvXfQyreIb8d+EfHMTU0+cpTBOU7DkFChQoUKBAgYITwgkzTc/few0AwOdSY8x0BwDSYMhRvD01C2NnUhS269M2Zoy+3NiOaJhOkSQYmaIReuz/gnRHeqMFP7qFMhTWvFrLb+ujpmQyK3BsJtSxa9+9IkIfOyObo4qeNj+vVdfv70fl3ZR5UdBN6+ndWTqYDlI00ltuhkFEYXFJYt2NrCkChryGXuzrw5Jsyhrc6PNhvnjbvru9HetERL8oNRXnCSbprYEB1vbIeqo3i4sxXTBU1mPqSi3f9hPcP5PcvHcFAkORoYj0dg4OItR6PgBAlfsupLYLAAClwz7g2myjTCbOGioxGLCpi9g2RKjP8zL2obWfMhaTk4/AVXsD/V0/gMoy0lg4tVpsODwXADCj/BMAVNMuy0bs3nVOJ2e0aVUqbv+ljY2IRinaSzNGWEeydc/1AIBzJr7MWolFqalD7uCDg8zSObVaLB8g1iBPR1Hk0t5e/nuhXs9R8rbBQY7c68NhXGaldngHQsx8yuNhnyGGqXoal35PmP/e3erHHsGQONJMzKh8vpI0MomEhFHCAXvzqiZ2sB9elcbRsVan5mycgC/CjKisqXNmW7gGXigQZZ+mgC/K9bTeevoAJp6efdx+AV8UBhO1ecz0VHZBziwcDRndLU0Y9NL3ldOysPczYq6yCqkNPW1+jBAZSN6BEM9TAAiLTKKK8Wms8Xr7GdLcZBcPg8lCx511QTGzA7V7e5lV+/HisWgRjuWRUJxZPVn7ddicQGodfed3R/h7vyeM3qQhV+6Jfppnliy61pgkIdxF8+1xyY1UMbYrjEb2HVrocPA43+z3H+eXBtA8kOdsTJJY81MdDLImcYbVyt5kD4o53RSJ8Dzti8U4U9NXdyNQ/FcAgE2bOC7TTGZPN9Wfwn175YhdfL49ncQ0w9zCzK1OF2TH76fyiIW8rrl5aM5seQAY8TsAwC05OmaEIleUY/g/KMPuitRUnP3hAgDAa/No7l61/FY8cdkTAIBtfj/e2DcPAPDI1E2YKliG6mCQn3Eyg/7mwABWl5A/WI5HYj3b7IYjGGmiOTbVYmW94d2ZmdAFaQ69F6HxMLWTLx8FI5LhEZljWw1RjPcMMbjyXJBrCgJDjFFPm591hYUVyRjoFh5vdj3PsUM7u9mfSWazjGYta68SiQS0OhpTJaNT8cnr5Jum1qigEvUKM/Lo77bkSvS27wFA819mmrOLktBwgPorEg6h4yhlj1ZMTEE8RhpBn+uQOJ8ESaLnbHvDEYyZPhMA0HZ0F7KEM7nZpuPajHnD/wPfJWSmacvHH54UpmnavLMUpgmKpkmBAgUKFCj4wUIxtzy5+MbZc0ZTNvq7KLRQq8PMBtXs7oHNQZGtq7eTs5uKR6Wi9kuKIH65lKq1P3v7ZlRMmAMAyC1tRe1uyrIinRJFOvu3dCJDrCfbRZbQQHeAI432BgfyyynS0enUyJtNb/O63ghrLEovKmT/lvMEE3J4dy/Gi2wMrwF4WuiK7ndmIpGgrri+qxXPpdA2cauITFRqHAiJCBZgtsSqVmNWXR1/lrNTCvV6jup8Il2z0mhCSHT3HcdUY9/Qa2I2J1enQ999Z1Ofv0CR0JKODuzsoGjwkZH9uLuWIi+jrRGhfsrSuLGsCUtbKVpSGXvYdViOEFt9yZidJmrnpabiZlFbKyRJkHm18x0OvNFAbBSsxKag5u6hzJ/sfyLLQhHqkqwsjvhXdRugM9E9XJqfz6wSV5KPRtkv6p6ODlwvtBc3GJPxStgNgCJfmVWSPXn2BoN4RkTjSWFga4Lar1WpmIWbY7Ox31VfLAbPu6TlGKqMHmG2JJGQ2AW8rd7D2WcyawIMZXjmltixXeh9TGYt63lWPHuAM8fKxk2DSnXkK/vKLsKJuMTjtavFh1NEpfhP/9GFrEK6Vx2NHo5E5Uh70B9FUGSZRcNqFFaMFMdogt5AbXakpaGllvyRLUlqqNTHR3+unm7Mv4IY3IHuAGu1mg672FumYX8/VCpqh84g6sbp1OxHs39LF8pFNmH5+HTOVv1yYzt7oe3d1IHTL6GxueZVYlEv+FklZy4dsQO2/TQOUicMsYyWhiBvI7sxbzfHmGX1JxI8lzLianwcJqZpjsqM3Wq6nytcLkwTTNNYkT1oVauxUbBSbw4MsC6nymzm+fbmwABn48lscFMkwvMgWatlfdADrR4YddR3oeZLmJUtNxqZkZZ1VZlaLVo9NO6y7N3s4+SLqaFS03Vn6nTodNGYhpbaeWeBipnrOR/NZVYK1nqkZVMW6/ykJGbN+mIx1jqtPkbbJzNzazqcSLY3c388InRKrZEISgWjdZ3wnCsxGLC0gMbl+D39iEwg5u3clqNYXUTPglfcAzgnTOPuny8ewpariF15CDSX3jOHcJ6Pjutwmph5zCm1Y8s/mwAAI8/Ow9o/k0eanA2XX+bAX+4m3eCE0/KwSejkTFbKTANIp+T30P1ub4jCaKV+NIgxY7LpEBYsatBvgUY7lBGnEeyRFJdYkyVraaVEPvq7aP5EI0acd30hAGDFs3UwmuJ8jG6xfVaBDZPnERu97k36/QsH3cwuZxTYkCaqTuxY2wqDiea0JPViithvzIwl+C4hM02bPlp9UpimGWcuUJgmfIOXpvuvvBgA0aG2ZBoQAZ8PV99LD/QX7tuL0jHUmY40Iy8p9HcHMO/HJEL96O91fDzZzr54VAoXA/7s/aNsP/Ducwf4x0Q2B5u+oAh7xA9Sb5ufxa3bPm7mMim5JXZOA22fmYzJ7XR5dXvoR91qN7BQXJVm4IemUaVCmY4m/ivuAS4zcETsV1usZ7FpKJHAA6Lw6KdeLwuRl/b28pLVao+Hf/zlh+tOtx5bKumhenNLCy8xrXC7Ee2lF0pj2lYueLu8TRgEekcgOedTPtY8O/XzWwMuPrZWpcKRnSKlVT/AadKqQbqOklN/ydcajZqQu/RHAIDrH/oY922hl7SHpn+Ie1aTSBWZa6i/vpyArAsfpr5oOJsf9Ahl4rKxq/jc8nUvampiUft79BuLGSkxXj5ZYLezzYNRpWIx/IPZ2bwEI79Y+uNxXn48x62HppD206hU6Bc/vkffakJhuSgLUuVAqZ7uobwcNdAdYDFn9dYu1IgX9ItvHY2gl35QOxq9XCj63b/Qgz2r0Mov0SWjU3lZr2S0Eyufr6bzGbUIizT1zEItG6HKL2mhoAannElj+IuPuqA30PmKR6byy9T6FfXIEstuRtEv1du2YNLcMwEAez77CPZUGmuS1M/LCHqjhpf7dm+IQW+isWkwCgGzc6jEi8FUCKOZxmj5BB8HKdnFSVyiSE7WKBqVCpf40UvPtfJSpLzUBwC6S3KRd5C2Wf92Ay9dpl5OP75jwlq0i2d067stnCbeah0qbnv6kSNsEyC/zBRodJh7lM53RUoKi7gXNjTwS0xfLMafF7e1IizukbxMd11zMwcMXbGhl7ApFgu/aL/dG0aljfaTU+/vO5iDrAwqS9MZlvglB9ufxRVn/gYAsHzvGZgxYjUAYFPdLBaAz61YC4DS/+XEi1KTBkc6qSA3zC1Az2kAgLFl72PPEbq3V47eSMdtcUBlPcrtlJcXfTE10ugUON1mwxvd1OYia4Dn/bFL1nIx5Ac7O6ER4u5kjYblA7k6Hfe/XPB3ndfLL7L3ZmXB1y+W33UavCQCmhKDATn7KVConJ6FXrF9rIXGgKs3iGLxTHU1+vjlv3RONvqr6RjFo1LYbqKvQ5Sm2t3DhrAGsxbtDbRf8chUbFsjilQ3eDgwV6tVnJBx3rX0Yv/6E3sw73IyJd257lWep57+BAcbfR3tmHUhPQc/WEbL73MvvRwb3yVhdtEIAw58QWL/sTOz4OqhcWkwdXNx68KKZF5eNFnoZbL96AEuEJyeZ2WD2IHuAA7toOdaf1cIY6bT3Dnn2qfwXUJ+afps9fsn5aVp5oLzlJcmKMtzChQoUKBAwQ8WSsHek4sTZppWPv9nAEB/115Onc4tccDVQ2+wJaOroDcdAAAc2NIJg4minmGVUXz2XhMAMg4DgITkgk8cIyOvBFoh/NUZOpkxcqSZsHkVRQUX3USReNNhFwvFMwtsvEyyY10vzr22mPerFunchRUpXMyx6lQ6tzPLghc8Q2VL5PT3+9Iz8WgvRRvXJ2w46qBIrV0WcwcCnEr/595ejs4ezM7mZahdgQCbNR4r6JaLZK4pLcX8I7Sc80hODpaIwrzTrVZe0srU6fBCDTFo55fQsliVyYT7GsWg1Q8AfdPps9aP64dT377w+Y18TZdNe5ajSzkqf/jLCWxYiY5zgdwVAADL2l8h/bwHAFAULxcyRYgYEnhHYGrZxwCArQcvxMQRtN9tGRloEmLZe7adClsh2R3Mt9t5SUQWo99S5OP++kNuLhsY3piWhhvFMmGOTsdCe3kJYUlHBxYIJuA9t4tL14yJ61GtpXNk6XTM6I0ymdDwLkWo8hJAzKlnu4lOmwqlahprK57dz0zmmldreTlAHl+t9R6c8guKVHf/tZYZntovPYjFaOzOvaQUm/5JYy2zUMumr/IyltmmhyWJjtt4aD9UwvDVkZoJo4j8QoODSMslJqazkVgfT38fhlVSoeX2hoOIiui5avocxIR5Z+2XW1i0a3OUoqulCQAQ8LsBUKkTeZkkKcV4XOq0bA2QWWBjBjYsxL22ZB0Lt1tq3bysMW5WDjavauL95EK/uaUmFAgxuTPbzNctG4pu8vt56WyYVo/bO2i/y1NT2WRWLpdSGwrBIBiS04J6uFIpplvW389L3cFEgs0t/YkElyr6s2AsdwUCzDoBwKrdlwMAJo5ehvEWakdDKIzbxHKxbF1hVKtxoZjft7e2MtszwWLBhgZaAodzM80dALb8lfAdvQLHwli0HCEXsUuOdWdhUFgjRO0qYpsA2Mr/xHNdbqc7HueyLa3RKLOos2y2oSLC4TAzuE/94z/gPP1uOqforxvS0njeXFgdwQejicW+orGRn0Pz7XYI0h4fe2gZa1lhIayCtSoxGPCmsIcoMRhgEd+nHAnibC2N80dycthKQWaGZ9lsCAszVo1Zg95GevZ0NHqZiVFrVPy7ITM2Y2fn4IO/kb1CdlESPn+f2LaUDDPbD5x+SSmzS1n5Nl5eO7xLWA/YHRhWKextjnrhcdEzydsXw5wfFQKgOb31I2r/7Atprn25sZ2PZTCZodEOWdIYTDSm+jrbj0usiMeo80ZNIQPgpsMHYXXQmDFahHEwwMuFADFU+zZTX//yT8/ju4TMNG14/92TwjTNPu8ChWmCwjQpUKBAgQIFP1goQvCTixN+aeppI5HeoDcEjdYBAKj9sh8OJ0UjgcEd2PMZRXuJhASvi9gEnV6Pa++bAGAopbrpsIoNyQCgt50EpM5sC6ea7lrfhukLKMXz0E5igI7Vk4TDcS7qmJJh5qjBaNZhgij0azTrWA8li3QlnYpN5eYarRhQiyg3GsaNIjK0Qw27cLDKEhGuP5HgopvueBwZOuq6lW43M01VZjNrdI640vBIKUWJskBzWk0NQsE03k8+35LOTo4YFzocUDVQdDy9irREmTodcv5Gmoj2a1YNsUBJh/BC9TgAQFHVY2jsofIqb2y5iTVJT8bJ+h8dC2DZNxkA8NOrnsGTOy4CAAxOew2NbZQynZWzBYty6HpXeyjqa6y7DVvdVXSMkj+zZuPybTfg4pmP0vfWejwo9BSFej3uFQza9YUUYT3T7cb9OXStVzQ1sTmeUa1mZs6p1TLzIDNw9xudWDxA/fkQUvCGSJN22rWIx0TZHEnC+H7qu7b6br7PMgvTcdSLdFFg14EhTF9QhGah+bn2T9NxaA0xILs+pf8bLDr0bqRxN+G0XNb23PrHSXj8558DIObTmU3XXTVDg9V/I6bILgoOa3VqZBV6RNtM0IqxVFgxkpkhg8mPRFwuikvjMhqZAVfPfgBAadVUNB0mnVX1ti0wCJGqweSAu5faH43WAqB95aQJR5oJ3a1DBpky+1Uy2skFhw98EYPB5BBtpXsls28ARdflEyjq7m718zFyS+ywJlOf/2hxFfaspT6TmaizriqHbZDmVYXRiPCXxLQerLKzKPnNgQFMaxE6McEuI8mIdwQrO99owhqvsA4Jh7lw7SybjZnFkSYTM5iyhueKlBSepyNNJnx6JumNFjVF0RAiFmLtnisxfcZ7AIBn2olhuDhDhak1JAweZTLxM2JDcxlmD6M5tOHAj1FaRtYgR1pPharw7wDAFiChwRygaREAwL3wXrb7QN90IGUHABCT2zUfALBJfIe2i3DzGW8CIHG7zDRpAax8n/RUlt5e/OGiR+h8RTv5Hsnt9MTjuLCG2I7r87VY1k9z5Tqnk/vr5rQ0NhqtHzVK9EsTJ2ms9njYyHakyYTdYr8rtV0sQh/bIaGxkdh7SWiXwmeZsAfUt+OFYBwAxpyWg7ceo2SWrhYfa9tkiwBff4iZTr1Rw+xwS60bp5xVCIDK+siJO+/85RAKyojlkJeLdPoB1O+nz8UjU9DRJDRlVQ7+vRnoDiAaoW1kXVXAF0FaDo3FaLgfEWo+PP1hGEz02zWs0sFmymk5xbCn0nhsO0q2EhUTU3FkL/XzoCfA86q/I4DR02geJRISZi4c0gN+H5DiiW+9vCZ9S0fxHxIUc0sFChQoUKBAwUlDLBbDvffei6KiIphMJhQXF+N3v/vdcYyVJElYsmQJsrOzYTKZMGvWLBw8ePC444TDYdxyyy1wOp2wWCw499xz0Sbc7r8vnLCm6Y0//gwAoNMPh6uH2B5PvwppOUKX4DIhLUdkNB3s54K9U8+8EJtXrQQA5JfTGn7TIQ+vCxeN6GOWyN0XgkmkIQNAWRWxE2/9ibJarv3tRNTvo4izfn8fR8HDq5zMMDQddnFGRkejF8HhgmHa7aY2lDlYn1K/vx+WyaSTkTPLAErTlbUE8rr98oEBXCY0D1VmM+t2plutnAX36sAApwLvCgS4ALCMdV4vZ/Cs83o5qmuNRDBV6Bs2tBZxir/j3TsAANff9DIeb6R+SUvq4oyZlW43pyqv/uT3ePiC31N/DQxgj0ewaakUYazd8DiQ+y4AYP7wbfjiFSrRohHRK0BR6VP/IAO2RSJj7oWa4UOFfrvmA0bSBowtex97GmcAAC6u2Im3+4ltg3cEskQZCDmLqSkSYUO/KVYrG/mtcLnYEuHSlBT+Xi5bUWUyIUPsV2kycZ9faUvGfX3UDrtGw5mOaVotsxPyueOBOJcI6Wrx8dj454uHODIcXpXGWTIRkQF3aEcPG15+ubGdU4uba0KIiXYkp2tgsdP9bq/38Bi0JFEKfmdTNdsaAGBdUe2Xfp43kpQFeypFs3IGT/GoSpSNo9A36B+DPZ/9EwBFwbJBrFanh7uPmJ/03HwubVQ+fiIAYqVGTzsVAFC3ZzfmXU5Rfm/7cPhc2+haauNITqNrkVm3WRcMQ61glIpHpmDLh6QRq5ySCZ9I/Z50ywgEd9KYCPijsIo+kDWG42blcPZi02EX3h9FbPSt8STEM+ke7woEeOx6OonRsNoN2CRsJaZYLJyyv9rjYZPHUCLB1hTrvF5mlRrd9Cx4Yji4aPZYk4kzWxfvzUdpDjFG7nic56mcyfnCF1egcgxpTjK1WqztFUx4Qo9kGzEWwUQCNqHziUkSXHseoj6ftAQAYFOrsXOAxpdx8y8RGkf6P1jrgWoqvIvsf0JbfxYAIG32nQAoW0+3iVhbx5y70CseReVmLWrcQj8SyB/KXLXWo1yYtyaL9vgTCbZRsKrV2FVBBW9HHjrEbPlzfgc+y6NnzpOCEV9WWIjne4nteTA7m+0aYpKEOW46R1ZxElxdQ/q4VgjbiG5qaEejF/nCxuLz9xs5i8yRZuT5lIhL/LyWNafuviDPmZZaNzSizFZ7vYd1d1a7nvWo2z8Jw2AU81QYtOoNGqhkoZaUy3Ozrb4OJouwYFFbWetnEMVz4zEdLKJrfa4hFsbqcECnp3EsSQ7YHHTdAX+U2yQ/C1w9FiSn07bhUAyhQRqXRssAgkK3VViRjC/EHPq+yqh88vYbsIhx/t/FYCCAMy6+7IQ1TQ899BCefPJJvPLKKxg5ciR27dqFq6++Gg8++CBuvfVWAMCjjz6Khx56CMuWLcPw4cPx4IMP4vPPP0dtbS1s4vn9s5/9DKtWrcKyZcuQmpqKxYsXY2BgALt374bmGBPq7xKKpkmBAgUKFCj4gSIRT5yEgr3fbP+tW7fivPPOw9lnk51NYWEh3njjDezaRUubkiThqaeewj333IMLLqDl7VdeeQUZGRl4/fXXccMNN8Dj8eCll17C8uXLcfrppwMAXn31VeTl5WHdunWYN2/et7qm/y5O+KVp0EssSyzSjlBAL77rg9FKb922ZLDNvCVJBXsqRSHbP3kXqVkUbcvZRb1tfvi9xFb1tFn5zT23xI76/UMRv2zIlyUi9P1bOuHuJWaifEI6RzR1e/vYfLC90csMQlezD1UFFPkdEpFvoxVwaikCKR6Zgr4jFClk+SKwTyDGIljtRsMwiojvSqP16L5YjBmemTVHMddOkahRrcaCetK73CwyeQBiUR7spAhVZktqAjFUWuizU6vlUg0P5eRg8SFq5y2lnXhmN+mNQhdT9FkTSsLcDIp41jZOwkaQFkIDYNUB8nxJPuUXaAjTPaoOhWA0UD/tCtC1Gt1uxCeSvmPN508AY/cCAH6VloaHD1HfPX6kCrMXLAEAvLDtSrqQlB24uIAYkFX2pxHa97C47lVQZVKplfHmHKwQWpS7y3swxUIMzSVHSRc11Wrl7Kc/9/QwU6BVqZiNi0kSa51uFlqvmlAIJqFzMqpUzApsCg9y+Yt7OzrQLqJLm0bDrN+GNyhLsWpGNvweOq4zy8Ks5o8Xj2WNjlY/VA7FdiZlG5aMduLVx7+kvs0w4/BOWXthhE5PYyk91w6Hk87X0yohFKA+LxtH5TMGvfXIGUb+W0f2fg5PPx17+NgyDHpJs9Tf3Qytno5RMZGuu7e9H+vfpmg8FKhBcjqNwZnnW9DZRGO0u6WavZf8Hjfyh5OerfEQsUgLb6jA2je2AABUKolLCx3Z/zGb8E07O5V9miYLvUnQF0HJaDrHrk/b+Hu1RoXiUXTfGt9p5uym/OEOxCKC4XBQH3ZbVchMpns1OsWILD1F6LXhMNYJFihTq2WWUZtCEePbPhff4zcHBnicxCSJPZ2qg0HcKIwZFzocnIl2cQ7N4z/3DHKW2QtNSWjKoLF7cekgHBqavy+0GKG10/ddInvuzhlv4PGPiTk6kLIDH88hVmrebg2iQlcUqtqPnOXEajTc+RHyxtP3Nbvo/0g6hKISYnP7fT7E5GLaw1cOmcWqI4iLudApykshkM/ZcK5YHDYtnaNmxxJUTibvtQP6vbBp6VovTUnBCx00Bh8RBcmTNRruz+d6e6F5lzRLr812YZtgj66XXHjcTHNdzrR7x+Vipvbu9nb2WzvHYkejheZVf8cgsyw+jYQlraQ5lPcrSDFiu5naM/+KMvZpqt7WhVPPo9+EREJCzS6ab3IWXVKKkY2IJ8/LR+NBYi/NVh2zsvu3dLLOsLfdx1pYWZuUX5bMbfP0+5hxrZjgZC3sRTfl4vP36Vo02kxxrKNw9Qgz499NxJtP7gUABAcHEYvS75vDaUFXCx0jEZfYMFfWYQ0fq0V6LvVXV7MPkGjsBn3dfI0D3UH86BdD5Y/+r8MrmHwZBoMBBjGWjsX06dOxdOlS1NXVYfjw4di3bx82b96Mp556CgDQ2NiIrq4unHHGGccda+bMmfjiiy9www03YPfu3YhGo8dtk52djVGjRuGLL7743//SpECBAgUKFCj4v4VEIo5E4lv6NIn980SFBhn33XcflixZ8pXt77rrLng8HpSXl0Oj0SAej+Ohhx7CZZddBgDo6qKX24yM40XyGRkZaBZBUVdXF/R6PZKFLObYbeT9vw+c8EuTWkT8408zYftaihY1OjW8fRRhmMxaFI+kt9B4LAqAoqWsoiS4e+hNVM5ki4Ti6BeusC01fuSVUrR0rPuruzfIWXALfj0WAPD50sOsR+pp9XOUv39LJ7NOGo0Ku9a3ieO5OYth7Fw61jqfD4H1FPFUTslgH5EVz+7HxaJA6yOZMUxX0bF/001sUYnBgK0ie+v2zBTOmFtgt3MGT3UwyNlgs2w2Zj1kV+s8XYCLfAJDRUaXdHTgN2V07gcaVSgtfU/8nSLSVUfGDmXMWevR20aZdNr6s4BsYuxcYQN7y2gAZD1CbFXfr94BAISK66GS5WvFf+XMnodr0lmzNL/qH1jTIwp2jl3ObawRuoToF3/CZWf9FgDwRrcO852iaPHBVCBGbtDTS3pwdg1Fok8XUZ9v9vu5QOgUiwVLhYZiUWoq9+N0q5U9pWQGbpPfj7eKyedobyCAIyKSXmhKYg3aKJMJg2spGtTNz0dWlMZHwSXE9mxe1YgJp9FE7zZKSNcQo7fi2f0wW0Whz7iErR+Tj45DsE95JXbO9untGOQMnqbDA+z4XT4+jaPgtGygt4Puty2Z/MrUGhU6m4j50RlS0dlEWrWUjNPQ207sipRIoKWGmACTsBeyJedj+FhinQa6A/D0C5+pplSUjqFtVerRaKklJmzqmUUYXiXPBWLg1r5Rx8VLPd4IM7QjJ6ajT2iI9m/p5CxDo5nG2q71rZg4l5jCM348nDUpX25sZzaufHw6e+18ubEdZwjHf1kXFfgoglLhe+UOhFGfoPkx3Wo9zsFevs/y/LgiNZXHybL+fr7HG30+zjRN1WrZY2ml281jRtYedkajzGr+qiQAgOb3k93duF3spzK3YZSRvpe1S4+H+5FcS3Pddc5mzD8ixKaRU3DF1eRRt3Ttr9Fwuch4q/slWoX3UvkE0hIW6vVYs+vn9PeRg1C56F7MKN4Ff3w7AGBPZyk0ghmN73waACBNvBXy81K38l745rwGALj99D/iyS7a9s6sVM6Ce+HQKC6v8pJg448MaqDS0t9XFBfjOjUdA8jHEdHPE8xm1Io5JN+H05OSsLCB2MbVJSXsI/dqURHOttL8VmcZ8FgPzbEsT4T7vyJB/fyntABujNJzr6fTz35dhRXJzHA6sy2wTSJmyreW7mt2URIX1j68s4c99UZNyeSstQXXVHBWZu4wO3o7xFWVCZbVHYbPTeNSperCyMn0A5tVeBYAqljwzxcPIR6T9S90zzRaFQwm6vO/P7ybXe2LRpyNz96jvvN7dPx7A93Q+JZ9zPZv6eTr02gzkVNMz9H+7iGmrLPJwM78FRPxveBkmlu2trYep2n6OpYJAN566y28+uqreP311zFy5Ejs3bsXt912G7Kzs3HVVVfxdiqh65UhSdJXvvtXnMg2/5NQmCYFChQoUKBAwX+JpKSkExKC33nnnbj77rtx6aWXAgAqKyvR3NyMhx9+GFdddRUyM4kE6OrqQpYo6QMAPT09zD5lZmYiEonA5XIdxzb19PTglFNOOZmX9Y1wwi9Nfo8bAHBweyq7eVce4+nS2aRjB+LWI4NcuykaHkrOCw3S9iOnZuLL9RQyVJ6SgQVXU6bHnxZv5iwMd28Qr/yeIumxM6lTu9v8GCYKrlZv6+LspnGzcniduXhUKg4LtmD+FWVY+VdKYZSLN869pBQxUfero9GL/cMoWvJclw+XcLK9Mc+JahGRyVlYFx09yrWdmiJhvFhYCIB0N3KkXGY04uZW8rNakpXFEa+cqXNpSgrGf0xOz2nFK9AbIGqhPEniunYqYxTlRtJkyW7GALjOFaz17PMSq/oT15jD0Z+ia9RfqJ9dlbAuoRpZja1T6O8pOyA1/YQ/Z2WR10tn8zwk530EAFjz5Q0oHfkSAGDDkZkAgInFm1kr8fNzf4dndp5DxwhlYk1iGX32juDMnjcHajDVTlGA7NLs0GiGChVnZDDD8ObAABcL3TY4CCFLwZsRGkcvFRTAJgrKNkUizMwBQE6CIkd3LAbrTLqfN3S24r4Q9Z2cJTd6Whb2aSiynaAzwxWiz6ddVMLRbGuDB4Me4U8zkR4I+7cM6TGqZmSz9mLS3Hx88UETAKClzo1Tzi4EAGxZHUVWITEVMjvjc+ngzKE2x2N6blPrkS/gd1M7zDYbUjIoau7rorGjUrkQi9B3A90DPJcsSR3Y8gF99vTXsL6j4YAJ3oE2cc5c0UMd8Ih5mldqx/CxxMjs/bxjqDZeIIriUamin2hu5pc5sOolmpvzLrdyEd6LbhqNvk66QQe2dWH6OXTd7t4gR+M5gu3NH+7AwR3ETHQ2ejHrAqrVtW1VM+xzRUFbn4SyFNr+2QEa+06tlv3KTk9K4rk3xmRi3VookWAdU65Oxxlj8v/9o6ug3UrPgiyhWwKA90tKMG8T3c8ZJX60iuOVO+j/Uy12hG58EQDwRmMeDBa635qdZ2KpYy8dxFrPma2WgyMROJN8mtpWLAEA1Jz2IjT9dK1xezeqZlH2695AmFnn8wtb8F4f1eC8eOIbAIB3P3wSdjmTbtqn7Pi/Lu1tzHcQa1MfDjPzVp6/DYV6+j4u5sQUiw7uOD1vqoNBuFzEtK5M6mY2d4bVinniB09m5prCYWwcTkzhgvohv7VyoxHW/XTdT+TmYpEYj4mQBIeBziPr2n6alsT+eum5Vh4nw6vSsO4tYq7k+QOAGVwA+Ox9qvqQlmXhVQZntoUzMf3uCOuDJMkCq91Bfb2b9pt9UQm2izp1IyefDp+btGifvfcKsgpJ16UzRDD3Mqqduucz8sMymLXQaokN1WhbcLSatLQN1cuRX0qVALwD/YhJdN0aTRCOVMqKnXvZvQCAg9t/hoFueoaMmxVGr1g9GfREoBGrKpLkRXdLIb5PfB/mloFAgFenZGg0Gj5OUVERMjMzsXbtWowdSytJkUgEn332GR59lLS848ePh06nw9q1a/GjH1Gt1M7OTlRXV+Oxxx77VtfzbaAwTQoUKFCgQMEPFN9H7blzzjkHDz30EPLz8zFy5Ejs2bMHf/zjH3HNNdcAoGW52267Db///e9RWlqK0tJS/P73v4fZbMaPf/xjAIDdbse1116LxYsXIzU1FSkpKbjjjjtQWVnJ2XTfB07Yp+nNJ+nNrrlmD0eniUQhjlZTCuGUefk4sJXetI1mC+b8yAGAMnDkjKVBIbwvKDezPmLvpg52IW6pdeOUs4l5sNr1+HQFrbVniAh9oDvAUUpSipGjmJ/+bjLefGovAKrO7cyiKOyUswuhMtDbblhkUO3RRrnWUk5fHHrh2/GRNoQLTRSFhfUqBNvpWsLCVyZTq2WvmEydDlc0UqRzelISMyBd0Sh7Dcn/BoC9Qmu0NxBg993Nfj/WtlLkm5x6kD1f0sbdyz4t8FIl7xtLO7D0M6otVzr2cc5aWe3xYGu3ENLV/RJakSEYK15Hvi7AEEOVdIg9lozJBxDeRjoNKW3PUE06ALeX0Lr8c0J3FP3iT8ifuZjuyTEZOtHmS/D0NMrOunVfBi4bRveqKxbjPpCr1K/zerFOZF3cm5WFP4tjz7Ja8YbIusvT6Xj7wmNqzy0TjN5qjwdXJWgc1NokbscVKSmsg7Hv9XG0KqPx8ABXYA8lEmjYSGzQqCmZiEXpQfDJ60eYLZHZy45GL3/38WutUKnoHo6YlIG6PcSMTD+nkDUNzXVuZmBlBsg7EGIdn96g5c9anRq9YnwZzGZEw3LWHd3XcCiGria6lyWjq7B/yya6vlQDfO4obxsNEyvQUL3nGIZWtCHNiGiE+kJKdLMGKeCPoGoGaW3kLCcAOO1iiqK3rmlmV/FEXILRouXPMjP348VV6GkbFMdws8OzrE9Z82otzr2Oxq7fE8F4oU3cNjjIrKvJHYNd6Klk5mjX4CB6Y0N1u+SadTFJYpf5m9PShsaPzYZd4t7fJDJXb29t5XM4tVredtQx7uFd0SikBLX1SqHLW96UjsoMYiz6YjF07qRoN2/iXWjb/BQAIHf6bWhtFhk7jr3Q7LsLABAfS7UbS+0eHNlHHjTQD0A3jFhbDQDnM6TjuPh3H2KZcDeXdUWNA7kwbbkWAFBw3gOo6SCGpDLnIA4M0n0rNWlwpJeeF1cW9mB5HWVLbptO89WkUjET5Y7HOZt1UWoqVosMwTcGBlArnMDdoi82+v34SPx9qsXCPmeby8pwnRDkPpyTw2xVezSKWxw0rnwaeu71V7sxUEr3stJkwjI5Q1Kng+fPlDXo7gtyfUe5nmFXs489m0ZMymDtj1anRlIKPQMioTiPu/Lx6di1ntjY3BIHH8NkpXkQDXciHKSxO/XMVHYE7+scxPjZ5I1lSybmdOO7DSgbR0s8rp79vEqSSBh4Po6edirq9uwW5xuO3nZatZDb5u4NcUbpvs3dXKfOajfgiNBnTTurAJtXNwEAfrOM6nN+V5B9mlb+7a8nxadp4TU/PWGfJp/Ph9/85jd477330NPTg+zsbFx22WX47W9/C72Yn5Ik4f7778fzzz8Pl8uFyZMn49lnn8UoMUYBIBQK4c4778Trr7+OYDCIOXPm4C9/+ctXBOnfJRSmSYECBQoUKPiBInESyqh8U58mm82Gp556ii0Gvg4qlQpLliz52uw7GUajEc888wyeeeaZb3T+/0mcMNP057sWAQAcqSXo76Q1eUmywNNPb9TJ6Rnwuiiqm3leMQ5sFYxEuB+hoEZ8FlqoqenM8Ozd1MHsQO2eXlSMp4ixfn8fZyDIkWxWURJ7N3Ue9XIWg3cgxHoRtUaFvZ9TVJqeZ2UWS47y1TkmpGvoXTHgi8LrojaZrTq8lqBo6iqDA7vVpPuQI6/7UjLY5XxVwMs6ppAkcWS7bXAQN7dQdkbNyJEc4cnuuzWBGC5LpWva7Pej5Jjsure7xW3oOQ3lJR/S9j2FAACbow6+vZShg+K/ojSV9AGhRAKth6+n770jgHJy9062dXL0Hm2+hP6euwLnJFMktKo/CtOn9wAAgnPvw6+yqf8f/vBeYDQ5gsvZdUZLO7sgn26zsWPwRQ4H3hTR7LE6E+nodThfOCEvEQK/F/v6kCP6aKPPh7RjsqVmG6hNQe1Q1pzMImVqtcwi1YfDzGBdmpLCbtEb/X72dQpJEmcYTdRR5BsJxaEVTsNffNAE+1nEsmgBxHeK9gdicPfSuJKzMI0WLY5WC98Ym46rpI+cnAy/cMa2JI2G10Vu9a11btYy6fTUTo32+AfVkKbJi+nnLAQAtNUfQVs9zaeUDBqrJquXNR12ZwF/7+k7wE7blaeci43vkT6javoc7N386XHnysi1wiccna12B4pGUFv6OgdxZC9dV2lVCrNiclQODLFtA91BZnYjoTha6qi/jGYdysdTn8eiCXZ1lo/RXGnmLNJ1Ph/mDdK9f80QwG2CETqwuZN1hn/MpPu9JDubtX1d0SimC5f8hQ4HM0ov9vUxI3nhrhRMzSP3eZcYDxPNZnbdj0kSjxOtSsWZq/XhMGdiVog5mKHTYU0bjQ1VUg0W2IWucPsNuP3UZQCAJz/8HSDqzaHnNPxmDjFJD+ycOtTxsmu3Yy8zxdAPYPxdpAPa/XQrbzo1VdRS1GqxqpOeCxOdA9i5fxEAYEbV37GpS3i/DUxCchE5St+Wns7PFpl1XtzWhiXCu+z53l72qjKqVKyncsfjPJ/uFkLcyUYzftVFz8s1Xi/eLCI260AwyE7i061W3i9Tp+NnX+0G2m/UlEzsVxHLlazRwNBEcym5NIlr1jV+0MpjV2ZFOxq97LE00B1k5/4zFo/Gl28Qc91S50YO13QbRKtgR9OEt59Gm4mAj57RspM3APzoF6Pxjz+RF1phRTJ2rB3qd4B+rzLyCwEAR/btxNxLqc/XvvF3VE5N5/bFouI5GjEiq5DGa5/QLiXiEsyigoXPrUVI9FFSSirsqTS++rvUmLaAnq8TT38A3yVkpmnFX/8Ci8n0X+/wbzAYDOKin/78hJmmHzKU2nMKFChQoECBAgUngBNenisUGWfRUA9aj1BUkVNcAluyiOqiAdgSFGFv+aADGXkU1dkcJnbolnUQjYciMJjoTbxqRjb2baY1/olzcrHnsz5xPHDdLjnyfefZVuRRUggmzMllbYnFoWf31zMuG85tTp/oRNc2Yr9kH45IZwgJEaXErRocFnYPY0MS5nUTo/Jx7iBmWSjykzPgPg758ZRgjLTHfL+8J4SGKlqr74vF2MU45+MC1hCdU0TR0bLCQsytI1ZhUWoqaxty9Hrcnktv70/p30VNFznI3lhKkdyyPgnzpxPTtKZuCibkUcT/ntuN5DKql+UKG4Ae8m9yARzlVpa9DwA40DkCazaRrsIkomwAyDOo8PA68paxTfgFZ7lF1dT2LJ2OWaR1T12BvquIJt0VCOBewSQZVSrcsIv6oKj873ivfoK4Rron5UYj6y0qjEZcIaLjqCThcJxYm/pAmH2mZCbBodFwpFpuNGJOXLhvH/ahrYj6f6HDgaB3SK8zKpMiqg9foor1IyZlIBSgv7c3ejGijzRgA90BlAmH30M7ujnDa+VfyfdKb9Cy30zAF4U1mQZK/nAHZwK11W/Cwp/S+nvTYRcW/pSybuSotrAiGe1Haaw5c7zM6nS1+FD7Jbmpl4xORU8bXdfYmUZxrCC7a0dCQY5gJSkDAR9p6TLyDgMSxTzegX5k5lP7O45ShJ5f5kCvYKuMpkwkpdBYM5p1PG+sdgOzufIclOctQHNz+aPUH9POzsahHXKtOyvvpzdqsOJZ8qWSNWCG0Rb4qt0AgAuHOVDrJAbh+rAdrw4Qy1U13oE6wSxepKZ+6RQMEQA8akpDowiOVd4o9kZobFg1GswXLND5xfVwaKjPZGbrtrY23CtYlL5YjFmW+nCYGZK9gQBCnjIAwF2F1IZLPxjKVB01/glmNW0j/oBdATq2rb0dPsEWWo9q8eZYuhZDHTFN4SQbkiffBIB83XaG6O9pzkNoeUkcvG8E0EYeam22B7idU9NErb7IUB8AQLKddEUuYxdrJ+9r9eBGodu8aTfdd6T04y3Rty8WFPB8u/aIB1sq6ZlkVatZ6zRtO13f05V9WCx0lvfZ0vHQAD0vbwpZcX6EMjIfycmBUWRC6ZqDaBdMq8wwbogHkH+Y7o/KqEWzqKawz+eD4QN6BpSMdvJvyKEdQy7bJrGKMH9uHp77NWVL/+NXO5jJzCmxo/MoMUlVp2bDJ7Spsi6velsXKqfS9UXCBvR3Equz7KFdXJ8usyCf2a3uVtrf1dONGefSc6h2N7BhBbG2xSNHo6+T5tApZxWgZjf1h1anRusRukeX3FYJAFj9t8MwmEiDG/DVQ2+U9VR2qDV0vq0feXnlY+L3pF2WToIQXPqW+/+QoGiaFChQoECBgh8oTqYjuIJvoGl68BrSxoyclMEeLJVTMtHfRRHnuFlzsPrlvwIACsqSOBuno9GLg9spSskqpPX+ge4AZ0RYkpzIKqRzmGw6DLqH3GLl6tXtYh3b4tCj+guKpkadksKVtxfeMJKj/6oZ2XzsWDTBmRofvkLMw/CqNM5+cGZbOINo4xg9LhXOsu+nRDkqnSV0FRlqLa5upaiv0GBArmCa2o7RXmTs82NNKV3rWwMDnDUn11fbEwjgYsGihCSJM8peLCjAhYeob9KsAxgl1p+tIrpzarWcvVJmNGJNHXkvTR22haPPcqMR/1jxKwBAOLcd5aOXAgAGHyV/Ev8vX+eoe+uR2ewCjr7pQIpwOdb6WcvESNkBqOmePF2UilubSbcy0arnbEKtSoVoo6hVZ+zi412WMeS5c+0B4TSe086M0oXJDlzvpIjy1f5+ju5vSKFocV8khLWij85MSmJdxaLUVKiidN0vePpxvZ0iRq1OzfdT9igyGIcYo/1bOjFJuF0bLFo83k3j+PRDMa5/eGAbMWxn/6QcezdRhHhwRzfGCP3crk/bmDntbvOjW3jVDHpjSMuh88h/9wyE0HSI2jNmegY7ChsMGtZF5Q9P5u03vkdj2J6SilGnCA3YO43ILCCdibu3B7ZkYv1SMszsLRONJmASmWuy/q94VApHuABgEcyVwaBBPEZjcFglsPUjYsVO+xExFls/bOJ9pswrwObVxGyNmpKJccIV3e8JY/8W6ierXY/5VxBrI2cSRkJxZnZf/8MeLLiGfNgcThPXloyWWZA2QA/i38bpuzOT7DjTZONzPB6ledwXi+FSMW/edLl4XpyelIRnBPu7Rnjh6JIPcP20tw9OQ+Uw0nodaB+JV8YRw5ar02HOdurzyhzKiDrQOpbH7UO56binjuYKtH72RZs/eiWab5oMADi8eHConpxgeCvL3ufnxuef/B6/voDY4QdX3ot5c2lufuz1QuqZRfvJczDpEJJ1NJ5dzecB2f+k791VgJ+eo3Ds5aoAtszPuQ6mzAI/mJ2NaauoDuXt01/nTMHpViu0wvtqbyDAmamyRuzGtDQsamoCAGwsK+O52ReL8TPOodUirY2eAZ+8UcdZcJoKaoOuOch6paSRdgyKNhnaQuzyXXp2HnQiLVh+VielGNlR/4r/GIs3/kjP1wt/nsvZ1Z+8UccMVc3uHsTjx/9cpWVbWEuXnmuFp5/Gv04/yPrE9Dwrn6dIZLtp1Crs/0Kwbguy0HbEAQA4sm8XZ4EWVqSgvUFUQ4h4uHpEWwPNaZmdBkhXK8+3pBQjvHRoTDjNyd8v/vNr+C4ha5reevYpmL+lpikQDOKSm25TNE1QmCYFChQoUKDgB4vvw6fph4wTfmmyiCyBWDQBUUoKRw/2w5lFEf2n/3gRjnSKDvyeCHsoFVYkw++liD4apYhBfmMHgJbafvjdxNQ4nEZYcinir9ndg8uXULGeIw+RV8YFPxuFGQsoWulo9GKFiFhefWwPOx7X7+9HeJIDAJDVHIFTVHSX69fpXVFIKRSN+Op9eGckRa33pqVhtZ4iiHKtkVkPWce0wG7n6K4mFOJML6tazc7Xo4pNaAsOaS82l1EEvuKYLDNZo7OqV4U0M0WAN7e2Au7ZAICS1C3Y4BIDVE3HQiAf+yZSRseYTcmw7JkBANjWfB67DockCeHyzwAA51RswhFqHlqvJS8luEqxy04eJRNL1rPmoTdmpSgWVDH9iW76XCUik75YjKPZp7q7OfsvBrDOYbPfj/Or/snby+7My/Pp+rXb2/HKaJU4RozriN0iJeEn7XQPs3Q6johv76CocEl2NpJF5l6hwcDZeje2tHD2jy+RQLVghw6MMmJULrWbzkCsz54Q9WP9/n5MP4dYm8bqASwUdeqOtPjYxXjkRNJ37Fjbytlw4UCMsz2Dg1bWYWg0Kpy6kKJutVrFXi8y/B4Vxs8mhmr3hk5UTKAxqtaoOJuzs0mNw7tprshzbFglZQoBwPjZWehupe8joRAcTmJIAr4I+6VpdWruA1lnEvQXwWQNc1t8wnU7Z3w6WmppvIYCKTCLMd0gGIHCihSOtNevqMcvnpgGAFh6zzZmxEZPyxo6T5oe20TdvjHzRK2+Og/rus6+Ywzs4h6+53ZjwgiKUg8tO4K8iylSX6SnfmkIh7FVuMHDBGSCrvs8hwNPC1YwJEmYIjRNd7e3s+9ZoYGi+aUNZTAm0/XB2IX5HBUfhENDOph1Ph8gdFQHNj1Jfy5/jLVGxvzNsCU1AQCmWa1YgzUAgDV9auChDQCAuXYTM60u4Yk2xWLBC0eFT1jpG1i+mFz1S+75JT72Dt0LWevIrG4oExlm6v/yso+xbZAesBcNa8BG314ANK8mZFI/72w6FSELPRM3NIwHAEz3bQSM9Iw7HAodl0knM+EP2dLxdtyPY7HC5eJtl/X1wSGYsptSnDz+f9nXgblp1I9j7xwFpzhE1343teHDZlz5G9IxqqISevcIreYYJ2fHqTpCWJ9E11Usxo5Wp+axtn9LJ86/kZjMmt09mDKftEIjJqVj/dukMcouSmLt6hV3jgMAbF7diMIK6seGAz6Eg/SMUKnUsCTRXO/rbIElicZmPELMcByATmRTRkMxNB0mxlFv0MCeStu21bdz9lw4KPHKh8x8ffJ6HVIFQ21PNvCcUKkzUFJJ5/EOhLHwhiHfoe8D34cj+A8ZSvacAgUKFChQoEDBCeCENU1P3051y4KDKs4SkBJ+aLQUgYQCAa4LFAmH2FtGSnSgfAKxJBvfpYghHpfgSKW/ewf64cymCCMe64EjjY59ylmFvA4tv9n3dQ5yZBIKxNj3pqXWjf4LKOKcVB9Dq9COuPuCGCGYA1lvMWV+Pus+/O4wTBkUcW70+bju1d5gkP1P5DX++nCYHb5n2WzMstzR1sb6gadyc3GpcAq/OzOTmSmZeVnn9XJ22qLUVJQt/xkAIG3SbazDWOFyceV2OdNIC7D3jOQvZpfvrNQGdLYSQzW7dCu3I0enw5od5CCu7aMoMnbKHfisnFiRP/f24u0GivKLMvezDmPnrl/ixpmkhXqpT2RbqVTsoH6ew46lreI9u+0izB27HACwdvutKB/3BwDALWlpzGLJbF1fLIY7U2gMnNtyFGcKpqA6GMS14lq3Dw7iejPd5yd9FHXfbnPCpaf+aopEUCUypEKJBJLV1KbOWBRd4rpLDAasdLsBAOOOUH+px9iR5aNjHK0eYC1R1pk5iB8gZrGr2QejhVgNWR9x+iWlrH94+Xc7MUJk2pWMTsW2NRTxf7mxnTPmjuyzIzm9g88DABWTMnBAHE+rU2PUVBrzOv1MHN75DgAgkZBYh5FbQpmfPa0tKCinMbN3Uwdrhj57rweT51GbOo56eUx7BkJc9y0eJ46tveEIs2OzLhiGfZvousvGqXFwO33WG43QaOk+y/W9Jj80DuE1xITU7e2D2Ur9suCaCrSLLKaWChOSv6B+NsxMQ+97xCrJPmanLixGSy39Pb/MgYCPxsNOYwzjB6lNz0oedMboHv1M6Nrc8Thnpa72eHB5mO739qQ4a/f8iQRr/mpCIXa2lufjtQc1PD/OyetkZnekyYiDQbrGQr2eHbXLcyljq6b+LEwcTozSzs//iPLpt9H3X94BFJNWc25akHWKfVuehiR8k2SGShuIQTXjdj6HPO8tGg1qRcaqFEoHOs6l/dLXD+0v9FRZOVt4v8MjRyLzQ9I05RWtYjfyrIKPWdclZ5qu9niQeYx3nKz/m2618vflRiP3nfxcM6jVnHW3rLAQHwkN4S3p6QiKenLNdhX7ny10OGAK0b0IGul+RyUJoQain2LRBK8kVGuj0O9yA6B5I2dtJhK0f/7MTBz5mJjm/o7BIV1UioE1sX/8xeesTRo3K4cd7+v2UlbbQHeAff72b+nEj35Bmcdt9R5moGp296C/i545ziz6rTm4fQP7P/kGwuzQP/vCYjQdpv6QpBwkp9PnUCCGDjH+5WzcwooU9AqG2t0bREklsX7hoNC6AfD061E2jp6D8y5/At8lZE3T8icehdlk/FbHCgRDuHLxXYqmCYqmSYECBQoUKPjBQtE0nVycMNO0YQU5Re9a3waDid7gB7197D1TdWo2R+nhoBljT6Voo7nOBYNRdiamCLCt3sNrxWqNChNOo9pUmQU2bF7VBIAi1A0rKJsidox/y7X30dr5/i2dvOZutGiZUfpyYzuqTs3m7Ztz6NxZDRRdF49KYe2J1W5g5qonHoM1QF1RrY3iNeGhdI/wInq+txfVIlq0qtXsGHwkHMbdzbSOfn2mmRmqP3R3M3Ml+8p0RqMwiQhx8RuL8f4iqv+20u1mbUbZvkag/mYAQNHop3i/GUKXkKnTMZvii6kxVjAke3wqIEbbnJMR5khU1iOt6o/CspYyeAbn3c+V1FXpGyG5KTpLTj0IVytl4Mh6i1smfoRnjlAflGbUYk6SyJiBivu47Ziae1VmM7YJ1/BnakmXMCO/BlcI/6r33G6uJ/d8by+zXAscDqwW1yVn+Ey3Wtlh/dKUFMw10vV1quIIH6Ko793MoVplP/Yah1yHRfZWx1Evu8hXzcjmGmxHD/Zzpt2sC4Zxlo88LtsbvbAIliUpxcgs6eT5BRgUbNWIiRk4epDGyYFt3dAJnyK/e0i/kltKOhOzdSjrrq9jEN4B2sZg0aJhHx3DYCI2zmxLQuMh8j6aePo8dLdsBwCMmpqJLaupnXN+lMmZaDW7e5h1lb2Sho124sgeisZb6twoHkn9X1iRgmTBrg56IqgT2+jFHB0xKR0Fk6kdG16q4Yy5Lze2Y9IZpPWw2vXMpn3yRh3rxGSGIZGQsC+X2lFoMGCpqP925d4ErA56Boyaksm1yx7spOfGQocDBe10/9pzdcyWvNrfj10VlIH3595e3r7KZGJWUx6XNrWG91vocBzHuMj1FPeXlCP1ELlF+wJCgxRJQVoKMQS9++9G5XhiBao3/gG5gnVqPXgTULiM7osjgtvEnL18A2kMkf86zyvErOwOPqN4Fzt7vzIqgYe7iMmrOSqMe9LXc5bc+YUteG/bT+l7az1Uue8CoBqLcvarQ6NhxkvWVp5us+EB0S8hVyUeGkHj5E2Xi3VdqVotawTljF7tMTXrmsJhZqKOdeAPr+7EZJFdaYhI2JugczqP0vNQrVbxvLE69Jy97HeHecwbzTpmh6bMIw2Y3xNhtnTCabl47Yk9AICp8wvYEd/vCcMvMqqNFi1/ls9ntunYlXv7x5246tdjAADr367nMd10eIC1hzKL5O4NIRoR1Qj8/TzOu5p96G6l35Ux0x08x+IJCfZk6o+ikZRN2XrkE36GjJqSyf5TAX+Uf9O8A2F+Fk09k+qLfleQmaZXHvv9SWGarvqPXytME74B0yRTqiqVmS0CTj1vIta+SZO3v7ufqXyNNglNh2mwqTQqXl6TX7ACvij/kGl1al4aaKv34KyraCniw1dqMXYmPWh2riMKd/aFxSygzSywcVpqX+cgAn6aTDOuKeOHQHJjCFFR/sIpJsXR6gEWtVntBmzwk2BvutWKfhc95CYW2DDJREsDb7jpATDKZEKZeGjFAV56u7CuC08U0g+SOx7HpUePAgAezMnhH4s3BP39RG4uNosXilsu+D1WCMF3dTDIS383ZpmxVP1HAEBjE03Ocyo2MT2+dt8lmDjqdQC0jGhUUx+MtSWwp5d+OPtig1jVS30u2wUgYcMFl5PodXkfcMtIEj4WGnKx2Evb6F+8EVff8eZxbX6msQgqx35xXA0KRYmQj71eXCSWFEOSxJYIG30+fkl8dgw9RFK1afhAlH2YYbXytn2xGPfH3mCQxefyw/r/sffmYVJV57r4W/PcNXRVj1U90N3QQDPPAooCggqCilPUqNFEjSYxUaOJ5ognmmiiR40aNdGIx1lwChIxgKKAIIPN0AxNN/Q8VFd1DV3z/PvjW/vr5t77u4dz9Zrn8db3PD6U1VV7r732Wqv29673fT9NMsfbLt50GtsStJDOymlxoJbuxXLF8I+k3qRii4kg6B6XVJp4YQvlsjCIArQnm3yomUgPuNvXt/FWlrR166oxs/3AsX0eaMTDaXl1wYji0HL+fHl1AT94afUqPndz4x4AVNZEIsXKFTJevC0OHT+MSEmC2R5BZT2Zk1qL2tDXTt/rbg1i7Awal0d2DyARox9tiz3EP07SVkU2k+M55qw1IyxMAacvdOKfr5PB6snDg4gM0Ri84yn6sT/RNAj3IRrzRouaH3I6jwf4HHqTihOki2+ZwNtv0jxvjMewTMyVD55pwm/Ej6RtgR47ZDTXfe4o1owoewHQPS4ViVB5N6C10VxfZrFg9jGyDLnZ4cCDgrhcq9FAemR+T8xTp1qNuBhf4UwG7eKhokSpxBzxoHBBxwneAn98/0w6QMERFiLA2MplVpSzb8MqK20fvt/w52H7kFgWVx2mOVta9wYAoF6rg9NOc+VNvx+pRnrw2ta9Cph8OwDg1qfvwwM3vwAA+FBFwo1PPUZOUuzK3uFtO2WYCwu3J5M8Fxp0On6AktYNu1LJJWEalWEiu4MesKQHq/3RKD8USd+fltXgOlEMuV6rRUisjT8vKuJ55VtihyJM9yUQTGCCJNapof6qicpGmLFmMHMxrUO7N3Xxet160MuJwqFdtC64as388O3tjWBokNp2dN8AlzUymNSc2MrlMsTE1lhEjOdj+wZ4+27CGXbeOq+fXoTGz+gzo6coeG4ZCqgPs9ksCmx0jyvHFPFDmEzugkZHwoxDO0PQG2mNKCyO8PZcNLweACVNkgXCkd1ujJ4yXFpISs4iQ1bUTvwfbFy+5cjmvgEieC5PBJcivz2Xj3zkIx/5yMd3NPLbc99snPb23B9//D0AVDTXJBCeaDjJUCwwLHf29MSgVFGGKpMlebtMyiqUKjkX23XWmvl7m99q4Sf+6ec48c83KCOWthaO7/di9GTKEKcvdGL/55TtZrNZLswbtCmhaCNURltjxMnNlEVJkPAn61qxZS6d7wF7CZrF9sTJei1nX93JJBOvpfIMTwwMMMxdpdHwFtTWcJgL03anUlgtsrb+VAoWAYVLZUP6UykuGrrO72cy5p3FxYxKjdfpsPHYmQAAk5MK995ot+PxXjrfNUUjTCzPvB9WDWVvq8vK8LMvCZpePOYzPs9LHYQOyIwnubBwW9OPgVraGvx5iY2h/mPxOG+XSWiPRaFgy4QnXC7e7muNx7kMTDyX4wy8Sq3GK50WAMBDoymTW2AycRFeqdAoQNsnEnl9sk7H55buw53d3YwIdKdSTBKuS8h5G87bG2EERDJwBMD2BK5tQYbe2476EAvTfR1zdhlUMbqWt/90gLNEaYvJ546yjB8ALryRkJ+nf/kF21iMarAxuvL3F44wkiqZ3jXt6oejnIj6bYc3cukRrV6FlgM0NrpammEtIsRILrZzkjENxs+ibP1kk4/nRC5ngkxGCELVWCsGuunz85ZX8fbcc/fuAkDbBRKyNRJRKq0yIOSnfiwsyTJSJiFtMxe7sPFVsqaYcvs4GE7Q2Ah4YsOliOIZ/oyz1szXK52vRZ6C5hi1rXaiHY96yQLgF1YHb6kP+eNIlQ3PNwAwHQwhOonm2FitlsfXMrMZN3cQUjDbaGR0skqt5m3t+8S8W2mxMGL5xMDAKTYhEupUq9Wyaa0k3PDE1by9/fOKYWL5o2430rtoruSm3IvFhQIdjsfZSuHYEF3HNSVyntMWpZKvq0SlYiL7zq6xXGRYmqNOlYq/t6nPwZYEDm2SryWQyWAPAR2EHnfSeqw5SSU9zr/4IawbRVtQ3akUVrbSVuMqq5WLIFsUCrYUkOgFRrmc53qtVoudUt9qNIwIJ0agFD/X2eA3UN+8Ku7P3Y5i7IgRKhX7ey+Xsjq4o4+pEAsursGxfTQOpIK3RouadxlmLq44xaZG+l0Z6A7zlrNWr2IhhzQnBvuSqJtsAQDEIyk2npwwt5TFEZ+/34Yil1ifBDn8iw3t0OhobTnrIhs2rKEdglt+NxUbX6XfnUSsGBodoWJDvjjcYr5detsEcYwORtISMStmnUv3qutEEMViPqo1Cv4du+vPr+PbDGl77sXfPQC99mtuz8XjuOHX9+e355BHmvKRj3zkIx/5+M5GvozKNxunjTS9/DsiJxaWGZCTDMsUMn7tHLE/rdYquFRJV2vwlM8AQCScQvdxytLSqRSWXEXZYjabZT7I5rdamEwnZSu7Pu5k+bW3N8Img2OnOdiaf9HldfxkP7J47yFhcAgAxQKx+HlXFx510jm6UimMS1KbFGY1oysawXPQyeU4O0dP67+NePlYrYkEZ7C3FxezxHmRycR8gznCSO9ng72MrHwYDDJC1RSLcdb5x54QrnRQ3/nFsRK5HGfX95WUYLrEzdirx5XVJPfe+NyNWHDTXwEA731+D2QT7wEA5LovBgA4qv7OSNkrn98O08TVAAglekL0wfpgkMtRWAuJ8/So04k9Ucoin/d4OfPVyuXcvmP9ozGhlLLEJ5xONgSVkKESlQq/KiAk549DHpZJP+UZYI6US61m3pZkLbDKYsFzIkueZzRikpr6f91QAGcO0fO+ucLIWfyjbvew6aUoDr3nk27UCe5Sf0cI9csIcUy2RbhMyjmravHJOsrMpazWaNaw/YWtWI+XHtoLAKibaGdUxuE0Mm8oEUkztyfop/EwbUE5o1JfburEfEGY3v95LxRqOk9bkw8mK/X5wkvFZ/+pQiYtSMl9EcxfRn9//y+HGdlSqOQ4WyA8n6xtRX8noQKjJ1M/H/yij69l3rJqbPs7jZPxswr53ACYvF56EfGqYtu8GDeTuIRrHtrHx3DWmtl2wVlrRjJO43jd04dw9S/JaPDZICEPcw4mTiHDSwgbAIwR9iM3d3bit4KbZMvSOQ5lEgiLMbU1FGJU846smduxQR3nOfmaz8dGqRKi5Dx4kM0a07kcUmJsLC4oYCTzuvZ2RkYlhGdvNMrv2ZVKntObBhWw6kJ8PIk4Pt8eZQ6UhNT8tb0A0BOn5rGKEjwqDDmNcjlfyxVWK6NY0rjdPDSExiAdY8M4O5dXWuf348FyQklX9/bi9+K1SaHAjQJ5u01cv12pxJVK6oMLBjrwQiWJMN70+RjdXWmxcB9IKNKNdjuvJ0fjcRwW/VGr0TBSXqxSIdtD75dUmPDXQe8pfV6ekDOfLxpOMYl76oJyJnoXzi9CcBd9RtplON7owafv0G/GhDla1E6ieXpwRx/zlIxmDd75M80FrTHNCE6R4FUl4xns2UzInKvOgFm3kwVI4/NJxCPH+TNSO6RdjWgoifppNBbLRhXw70csrMGYqbTGKVVy7PtU7ADMsjKSKkU8msb0hbR2nmwaRL8oqaRUy9lyQ6E0IBahc/9mzdv4NkNCmv7ywH3QfU2kKRaP40f3P5hHmpA3t8xHPvKRj3zkIx/5OK047e056Sl6ZAY+0B3GoDBAO954qsJIKugZ9MVhtgmprIOe4CtGW1A5mrK0dCrLe93JeAaLLifp7awlFfCIPWQpw5XOLx1D4nR0Hg+wGZrPHeVsPIbhorhSLDCZ+L1HnU70badzf9mghk7wHBKf9GDZAsqGJIRHK5ejSxTVvDtuxfOgYzhVKkaP4tks8weaYjFWrdwn0I/+VIqz2fe8SVb5vRcIYIJAcDqm1LByTVKcbSiqhDlEyI9GLscF24m7UFf5Gd5oIUO1s298Dt1JwVuZ9htcJ8xDH5e9BwCYrDNyhgvbboTS9HqGUc0qmeVmM3oEYpTK0b28ubOTVXLT9XrOrs0KBRaJjGOjvBXC7w67IhGs/+x+Ot5ZD/B70jH60ik85aE+n6AdloyrZDLOXCUuyJt+PyMPHwYCsAhu2HKLBU8l6Rg3uhXMb7g2qEFCSRwJifMUCSRRMZrGg6nBgh6B/jV/2IZ0kr6nVMsxb3kVAMAnxvPOjR2YvpC4C0MaADnqrwKbho+dy+QQEyqZBRePYnXNycN0/2YudjFPw1FmYEmy0aJGgZgTU+aX4ZO1lEl/tZXeq59eiP3b6LpdNWbOkn/62Fz85+/JjPGcS2vw+QfEw6idZEeRyyiOQRlzWXUBI7S9bUOomWDm7x3YMczVksw3pcw+HEzwMazFOkyeT6hN1/EAZKPpHD3IYE+Ojj1uZjFzv+aKTHzsOeV8fFUqx4heOpWFp4vm07PlLs7Ge6rpugPpNI9RpUzG5T9WD/nwSBFl9GPiwAyBRK7u68NCNc2nuqYmAMRzkub3vaWl+EDYWDzc349DEncvmmZrEMma4wqbjb/3t/X3IzPpEQDANc4otocJcQlkMsx7mq4Hj11p/qtMbcyBvOOrWtS5hOXDRw8iMXonAGAd1mNjXR0AYPyHZ1An2bdjqXO4uLJ03CtsNi7RZFQo+Fqa4nFGVH/WRujea3XFjICP12lZbThdr8ebguv0ps/HSJNZSdfkSaeZL3azw8FzUCmTYaNYfxp0OswQiGlXOjVst7KB0Ev/uSXQC26P5iwHRh+jtcznjjKqFJDn0D6Fji3xmAbdMVx/H63bR3a7mU840BXm8Xh0nweLr6B7by8zMIo1Uu0qjb+yUQWozNH1uav6sHuTUNsurYRvgOb6FLGuH9rRx+rY/s4Q5go0N+RPYNMbNB/PvmQUcjlaw5UqOQZ9NOZnCHSpvyPEc8zbF2F+YDSUxFyBKhcWDxep/ldFfnvum408pykf+chHPvKRj+9oZDPZb0A9l7cckOK0OU2NAj345+vHeS9Ya1Bydm0r1rPfzGB/FJtY+VaIM1fSU/fmt1rFezb2fzGaNewbc/m90+A5TtlNV2uQs+BawUmZuqAcH75EypNL75mKE8LvI53Kcka8f1svK+laD3qxZxQ9F54XF94/JTr0fEUZWc00BzoOEipQMcaCHrlAalJy5lBIBnwLjh/nUie/KnDghgHKsh52OjnT/KvXg9cG6XhX2Gx4spDatKKXFDpp4JSSKxJfpzuZZO+Z7eEwnhZKOo+wWHq+qgzviSxzY/toIE7XB/t2znzRvYrM9QBA7YNLIwrkuuie3NvbC51AiRp0OlbMNWi17HUTymSwoZYyv4eFAd99vb24yUE8jg2BIK4QJpV/7BuEg24hbnY4mOvUnUrBKbhMEqcrnsuxeu66wsJTeBUScnW5qgBbBHphGVGkV0KdmmIxPrc3nYZ/B/VR1Vgresx0DMdAmr3Cju8XWfc55cxvUmsV7J9SOqoAjZ8SokK+ScRhq59GHBG9Sc2Z6LQF5dgi0KBcJoeoUOCV15pZoTPQHWbjPUll9snaVubzTJ5fhi1raTxXjlFjjDhPLJzCx6/RsaUyRKlkkNupUMmZx6HVK5FM0BhNxjOsCty/rZeR4DPOJy6LUiVnnlP9tCJux/vPH8bspcTrGvLF+X3JVNZi13HbahoK+bjdrUFGmKUsn46RQMVoarfkBSUbbcRnfyC/omvvm47YkBgH0RSCNrr3dqUSMcHz+VggPKusVjYzvT9uhmM0IRqBdJpVbrMNBjwiyi69+cR+nHUbcVikeXV0hKrzruJi3NpF8zSSyXAJoydcLjZS1Y4wgZVMY6XxCwBtPieqbVTOqS2qxA9LCElSyIDnTtC8qC+ivnvS5cKSozSmrnRouLzPIpOJi/vuj0aZA6UQYz+SzWLbAPXhi2MVXEblju5uzBFom0Ymw5cCEfb76zC/jNok8QY3Dw2xknCp2czlZXZFIryevFdTw1wtl+BQVnuz+Kua0L/7ezx4oJzu/e3FxTx/PwwGmRu23Gzma1meoTmftKqQGfEzIusVHlcqOeKi+HA6leVxUyq81CKBBHs3vf2nA/jpY+QVdrLJxwW09UYVI5UNs0t4/EumrB3HgtDo6B5OXVDOqHM6lWWlZtVYK49vabyrtQrm7h3c0Y9zVg0rXqXobA7A76H+1Rli6GunfiqtonsS8MYgk9F4OPuSMv7N2L+tl5GyRCKDQsGj+leZW/75vruh02r+6y/8byIWT+DHDz6S5zQhjzTlIx/5yEc+8vGdjWz2GzC3/Jrf/y7FaT80STyOVBKon07ZyPb17VBrKHs444JKzkqT8QwcksrBZcT+zylTkPhKL/+uGZPm0RN/OJjkzLfvsB/rnqYM9Zqn5kIh9sklFV1/RwjLrqdyCprssHvzhuIMqka4Hx/KUPbibxvCyqm0n2+SURbQlU6xgufIl260Cf5JkdMAg7jGdncUtsmEaiRPUsazf+xY7BEZVossdUrpAckjJpzJ4DmhWnllcBDPRujYywRClc7lOJsNZ7MoFLyCaXoLZ9ILTCY8JdChiBioD/b1MfpidTQyUtMSNOPsYkLmple/gTUC5WrQ6ThbldoZyWSQEJnth8Eg/Cl6vcBoZBTIqFCwv5HklZQLj4KrjP6+0mLhLPM35Q78toP6fJXFwtyvdX7/KdkvQAodiTtyT08PnhbXd11hISNob6WGmO8lZbgveL2clddrtaw2UspkmLCAkLnHBwZwY5DGkq3CxG7dK24nz6pQdwRv/4nG1KrbJrC7cDibRaco7LzwhzSmgGF1TTiYxLnfJ6XmW39oZJWMs9aMdU9TiZOa8TY+hqc3AoMYj2POprY1N3pQKjLbUDCJCXPo9dQF5fj8A7rf089xYu4F1B8BL/Vz+1E5+0J9+NJRnnv104rYb8Zo1rD78czFLi7zIvk1aQ1KRAQiptIocGwvcWP0JhVzSuRyOQa6aHxf+RsqT7Tngzb2str67olhDuEYC2f3k88s48KpoyfbMSCQPIlLuPXdE9yPJw4MMu/jwFQdBBUQC0wm9jmSeED3dHfjMT3Nzc6uAGyi3EuxSY2HhXLs/UAA7wWpz89ZVYvnBVorqdNutNuZH3hHdzcWC45OfzrNPmV2pZLH/H1CwbopNITw5MkAyB9M8mm7whbF1hCN5zbfDByzkIoyncsxyusS433Jx2dhyvjXIIU05t/0+7lobjibPYW3BQD7olH8sILasycK9CSFm/SUKbwupHM55ni1qztwo51QJYn/uKiggK9payjEJZ+uttmwWXCojsXjuFBN/eERhbBDJVlc30n9P29MNfs4tScSMLXRMVbVWdEqjleZVMAr+hFCkKz2p9ijrLs1iEPFdF3TgjlGdh697TNccTv1r4Q8Ro0K7H6LkMBVt07kcenti/DrgCfGOxvhYIKRJGmc24r1zPkDhsdgf0cI7b20lo2eYufditcf2w+AkGFpd0KtUTDClE5l2Udq3vJqJBM093pPDvGxRzXQsba+e4L5tulUlttRNdbKfL0zV47icfyvimz2GzC3zHOaOPLquXzkIx/5yEc+8pGP04jTRpqkGjszFpVx9rns+rHY8wntrbtHKB7e/8thzBIO3Ds+bGeeyO5NlFXMXWZHbJYFADAvpjjF/fW6p2lfe2CPlzkbUhagN6n4s/FoirOYGd1JKGfTuf/273uw+A8zAADzV9XgxD7KjiX/J6NFDY9Aov5SlsQNKWpHsEABmdhfv78wgqdASNMeB123OZvlYpfpEQ7Y3vSwEqdeq2W/JQDs8n2F8CW6KKjBjYIzZF53Bn4zn5RtE3Q65kt502lWyUj+KfFcjr1n3vT5GNV5bawBVxHFCwHrEHMxXq2qwsIWUm3d8QX154QxH5xSuPMW4e+yPRxmrsfmoSHcLVQ5krP5jKI0toVpmOwb4WXTnUzitTF0XY+63YzCDabTfL1SZtwaj8Mo+u6ekhJM0FLmdXdvD6t1llsseEoohaQaYUsLCthjZoHJxB5YLijxkuCt3OJwoHMP9fNXW3u4cLPk2D42C9z0hzkAAE9bCKoiQtB2RSLMY2j+tJeLSUvZolIlR+MmGttVY21Qqqj9xxu9zN0JeOOsgtMb1Vw4tONLuo5ip5F9mkLBJHOd9n7SzXyL9qN+dtSWULL6aUWnqEQlb7J3n23C6ATxaGZcMorPl05lcc4qGldaUVvP0xtFpeAajZ5ZhJ3/IIXUzMUuRoEKbBps+7AdACG39J6W53HFaAtn9OFggl3TA544aoSnjr8/yi7NH8UIaT57aQXaxAE92SwmieNZNVlGKmXHwlCKDPzRBN2/EpUKZqGwdWvTKAkMIxIjvY2kgs63+PrwppP4klvF/X6wr4+9m143l+OPKVovVhqNjHxuD4dZ2SrNJRVkzEF6qkODi8ro3POMRvZyumj0fuyP0r2Pj+DwSN+z1r2EBp2Zjyshwka5nBGcKrWazynN+SdcLmwTNTAPRGPDnKfNclxfN8y5YodxpZJ9ln4l5qtRoeA5W6/VstfZ1TYbqwZVMhla5HRdH/moL27T2bCvlMb2/miUuYmTIgoccFGbpwTSgLD5+VIWx5QYvX9IR2O4um8YhQi5NDg7Q39XOBXoFRzV2ol22MuoTVKR6wKbltV12WwOag19T6mScw1FW7EOn6wlFNVZa2akWPotuXb1DLz0b7sBAKOnOBgNLak0oUKsT3/7970IDtL9PvsS2nnwuaNcQHj/tl785j+pePJ7f25irpN5TAGaNxFHbceGLqz4YT0AchMHgFg0jXgkxe2X5mn7UT+vI12tQfawWngZ/iWRL6PyzUae05SPfOQjH/nIx3c08pymbzZOWz235e27AAB7P/Fi6VXE29n8VgvXlftqaw9+8BtCeA7u6IO9jDLsgCfOT9pSpt17coiza7VWcUp2LyFCzlozq+okVd4XGzoYffK5o6zGGDO7GD1HA9wOyXNnJM9EUv5o9Sp2bk6nsvhAR3vnN9rtnH1602lUe2mQfFpAWeTeSITdw2/u7GT+wMa6OkY1LEolNolsdsvQEGfHEjqzcWiIa6ntjIQxViAu6VyO+Q9KmeyU2lpS3L+P/Jien32Q21miUuHBPlJkLTCZsFeoa+4pKcHt3d2nHOO6wkJGbbzpNHM5lDIZeyFp5XLOxiW+RXcyidkCMWqKxbidS81mNAje095oFNfbCHm4tqMdG8Ux3q+he7xmcJA5UmsGB7nuV0sigYtEtq0dUQNrQoqe5TVmNW4T6qfnXRXoSw+r8QyiHQ6lklGusVotKyMVwpfIn8lgYo76oEWe4r47T2Vkn6OJc0tZrXbAScetPhhlJKfIaWSOQjabY47Fvq096DpOSMaEM0rhEGNeGs+tBwdx7pXEJ/n8gzb+XvtRH7vjZzI5VppK82fruydxZA8pQ2sn2pkLmM3k2Eeq87ifHYqnL3Syci+bpfe8vRF2xv/Bb2awsketVTAXqqTSxMo3iU9lK9axs//fXzyC879fz5+VUKe2XAqxPfQZhUqO8YLXIWX/DbNL2EPNVqyDSlQH2BUOMwKSccdx0kJjbKaO3nvW68ENJkJ4BxVZVn3NMxoZJb25sxNaMTbnGo04w0vX9ZSezrfMbEaf4CPtCIfRIJATCS0CiGMkoVESGro9HOZx2ZpI8Oeb4sMO5FtCIUaHnCoVH0Pi5a0ZHESzWBdWWa1YJFDUzaEQponrPhCN8jlv7aC5e7hhNJaJWnEPlpfzHDTK5UiB7medRsu8xvE6HfOoNgvE6RqrjZ26l5rNvD5tD4d5/SlRqRit4npzGg37tC1qaUGzpgoA0FWhYl5XIJ3mNsdyOVbKSetbZTDH9370FAe7wU+cW8p8tiFfApXjhH+e4DQd/KKP1+KqsTaUjqe/+9tC0FVSm3t2e5jTZ7HrWFUnITxKlQKTz6S17GSTjxWjx/Z6WLXd2RxANEznGTV+2HVcmivJeIbnwYmmOM67htb5955rZn8zT18Ec8TuiYRQDbpjuPynE/kc1rk0HoK7vLwLMtgfxdZ3CFm77Y9P4tsMST33H3f8BDrN11TPJRL4xWNP5dVzyCNN+chHPvKRj3x8ZyO/PffNxmkjTY//7GoAwI8fPoMdjOPRNOomE3IS8MaRSVHHzl5ayRnEsX2eU+pXAYQouYUzcNfxAEY1UHa5a2MnVv6IfFe6WoOcuUsKpakLyvH6o40AgMpbR2PwNeJpnHF+Jft99HeEeO/cMMkKRZdwARau48YLyhB4n1CYM1eMQpuCspjWRILRkIf7+5mnJCEu28Jh3CV8UDaHQux3YlQoGHHpT6cZJZqu1zMHR0J4pun17GacxjDvZrbRiMdFnapMLsfZseS78orPh/Ykne9XJaWMLj1VVI4re6kP2hMJzDJSfyWyOUZUPrASSvdsJsi8ou3hMKvrbisq4iy3RqPBAhNllD9xECryxMDAKao2yStpQXMz1lRV8fGYnzEwwFmppHbbFYlwX0zX65mrtTcSYX7KC14v19E6TzXsSyTVDCxWqZjHMfFEip3oz7igEu/9mdygq68f9qGR+rBWo8HaJ4fVcz1Zut9KAB1bqB/lchlzK7ZlY9zOj/9EKrmqsTbmJm199wQreBZcPIq5foUlekZ7KidSHyllMlZ1ffRUEy79GWWlf7x5K753xxQAwLpnDmKxqJE4drpU820vn+PCG8exC7K9zMC+aMlEBhY7jbviKYUINtP7hwVCVVVvhUYrPJHK9Jytb1nbist/SsrC7evbOCOWzjdqfCFn5dFQiq+7t22IIfpdGzuZQ/VpYQZjG0XNQKFG2vhqM6NSrrNL2LfHU6RErZgrKV+CuWESohezKNH8d0LBcucW4VWhDNPIZXhQRevMPkOG0U6JHwUMu8ivtFgYZXGq1TzOd0Ui7HAdzmbxbpiO55hG8zySzeKeHuKvNGi1PI9bEwlGx1aXlvK8eri/n9eLpcIh+02fj98rGeH1FM9msU0cTyeX46hoX49o8412O3P7bu/qwu1i3tdqNLjkBPF5flJUxF5uG4eGuPbcOuH2/eqIOnxv+nw8/ruTSX7dmkigXLRL8ny6c0S9zEfdbjxlFcpPVZo/a4zm+B51Hg9ANorGRKaFrslZa2a1m/QZgDhL1WJtj+dySAg0UyPGbSiTwRYxpxu0Wux7jKoeXPbTSczvmzy/jHmDhQ0W9O4lNG3re4QSG4wq1It5M3l+GeJRQqCS8Qw2vtpM5wkmeWxKc2breydx6a3k+H/ysI/b3zC7hPmGcoWMjxHwxHDmSqHEttKaZTCpGUmzlxlGKP7i/FuXTKQhF+O1bvKv8W2GhDT94ac3fSNI0y//9HweaUJePZePfOQjH/nIRz7ycVpx2khTy/7fASCUSEJtJl0xCh/9fj8AesqXKr5nMznmOmn1SqhFxitlyZ3H/ezafXy/F52zKMs6J6bmulcT55ZyLSIpC/6puxurS+m4Jzf3Ypbwd5LlgN/2E2rQoNOhspGyqCKnkT1DpAywWqVmn5ft4TCrxYzRHOTCFyoZT+OhOO1DS9lbfyo17OCbybD/0Q0nPIhPJN5Kcy6J1QIFWl1ayooeCXHpTiaxSijLXvENwp2i7P9plwuviKwaGK6tJWXaRrmcs90H+/oYgdocCjE640mn2dfpnp4e5k5J2fUyiwW/babsp7TwBDYKFd/eaBT7Jf+pRIKPISng2pNJVu494XYzr+JVn495JrVaLSM86VyOOVBnqajNW5IR/mx7MolqFXGMnvV6GGlSymTs12MWz/IdmRSK49TnO2RxzEpSthuPpjnz1ZjVCAnU6YAhg7ninMk49e2nsjhmDVGbD1uGvW5ivgQyFjpGxh1nfpBJVE8/uXuAuTrP3buLfZNMVUb2/Nr4ajNz7HTlenz5NqECkndL60EvQgJNKXYaOWu94S9nQjNIGbG3L4K1wvdJqs1osWux5HuEPqVTWfZCy2Zz+PBvJJc844JKpETtvJ4TQUy+jLLggT2UiY+e4mDfNL1JxWhvdyuNB4A8bqRMWWrzV1t72I9Gb1RDZVJyf0mO/udcWoMuYUQv2x9EzWz6bkzch0ShCp4vqR2mmYUI7aa51DFBz+M41hSAS6DUe4RnlWlJKXPwevd68Zcy6rtwNstj42aHg72LnnA6GcnTdVKWX1hbwON5jt6A3TF6/XEwyC74tRoNn0dCX69rb8dqwfMzKhTM+VlUUIA1wgtqfTDIaOdbo0bhWcG5khDoKo0G85rpHj/qdHKbvek05h4iBHCpPcv1D+8Va08il8O1u6voWBOOcm03CTEDyGlf4lktLShgPtEYsa7pZDJGwZaazdzmzaEQI1SBTIbVvU8IZPsnQwbmGm0NhfgYl6T1eEtB6+j4HSHIFhGaY/pqCGVn0OveL+h3IDfDiklqakd3axByOc3ZgDfGYyPhjeOYUDJPWkLod9d+L6/xAW+cv+esNTN3KeCJs2Lui390MDdV+p60SwEALz/8FW4QvMDjjR527i9yGlEk5rWEuMYjaebE/vP144w0Wxw6RnZ97ih7Mh3Z7cb51xK/791nCdmun+aATLS5tNLEqtSudArmIUKrDu1yY9Ziul6V5hZ8myEhTY/c+iNoNer/+gv/m4gnkrj7mb/kkSbkOU35yEc+8pGPfHxnI1+w95uN00aa3KlnAAAFCXB2Cgw/uR9vHFY59HeEsGT1VABAeVaBPVuIQzRW+DU17epnLsXBHf2wl+r5eJLzcjSUYjThiJpuWOD9bvaQqZ9WhC+FYmPieS6sE07WM1vT6B1Hx1tkMrFSIy34VkO+BNe9k8tl2Kemv59tNOH3bvKDuqRPgXdK6fMS12Dz0BDzMRq0WuYlLFMY0SHatzcaZW+WhWoDMhrKBu/opnaWKlXsDh7OZLhG1j0lJXhUZH5Pu1ysZnlC+BbVjtiPbtDpWB20NRTCoPCIWV1WxtyEcCbDmajEzZhtMGCxj9qWq9LjBclJOZPB402Eopw4xwuPON47oj9H+s3cVlTEiNJsg4H7YIHJxKqhWo0GC46TaktCdeYZjayuG+kY3J1M8n37aFQt1g0FAAD69+g+hC4qQd1X1BcNs0vwRpxQkqttNkQEPyJkUULTT20irxfKHqWs76PQEJYY6B5GQ0m8kiK080oYWanZiARmqISSUaCaH750FLOFWqbjeABjpghUxJhB8T7Bw5hTwkohU7EOXYfpWk420X2VK2SsIkvGM6j+yRjuDwk1CG5yo2FOMX+G2pniTLtmQSlzeNr2e3FsL2Xr9dMdcNZYABAKJ9tAfXbG+VUAAJVp2PXaolTCpaRrHegOA6U0jsPHhpjjIWXPNXeORV1/ltufcVG/6DxJ5iGWjyrA4d00Nj+Zqmae2+CnNIaLnEYUCMWctzeCN8rpum5Nm3iN6HGq4HmdeDmLbyRH9qvb2vBmNSme3gsEsCih4b5dJxRqZw8pmS/15KCHEWTp3+3hMCO7JSoV1gtU+R1/AK+KY9/c0cHzeqeYazqZDA8LdWxTLMa11jYEgxivo2MvNhWwomyWwcC1HCWX/6c9HkZUx+t0OCzusSedxnKBHq0IafCYmsagNK/f9PuxVnCTBtNpbn+9Vss1II0KBW7SEiJ0QpXGIuHDtl4gxltDIfxQoMvdySTuFOpZ6ZoAQqyluZzqFWrCUi2vIaWhHKNB42YUQWalddKQk0EmUPhwNos97xCfSHc+oTOO1jjz+N70+3G1GA+edBrpTvqtKLBp0KWl6/JsJD8p/QhOUMPsEkZ+ek8OMfLZ2zbEvwMemwKaVvq85LIdj6YZGWo/6se5AqH1uaMosGr52iWuk8R97Wkbgk4gWBPnluLvLxyhPrKoceYKuhcbX21mVd1IVFbiWPncURiEv1thiR5HBehl3jvESvHmRi9M4jOT5q/GtxkS0vT7W274RpCmXz37Yh5pQh5pykc+8pGPfOTjOxv5MirfbJw20uTp+Q8A5G8hKQPkcjlnAdvXt2PlTaR8CwcSSNvpybbzs37mL0m8EblCxk/tCaeWfUnO7pFhm0iMphxOcJY+bxlliI3bejFpLh1r96YuzBeOzvf19uIhR6loRxtnKZkSDUwRypqlekLFYy3Y8Axl1WNuqEVhH2UgepMKjVrKuAwKBUYFqK3ZIpHtKhS4R2RvTzpd7BnUn05zXamRKrESlYorr884Sf/WTHMw5+mmkB4bbHSMKo2GFWpzm5uZIyHVYJus1zPa0J5MciZ6gdmMfYK/Echk2L9mYUaLLQpCGSQex6uDg8wfWmA0srqnO5U6xavGI67rFjuhguPVWlzfRYjAdYWFp/g/zRXfu6u4mBV9wDDCJCkPd0YiuLhf1MurVrFzeXsyyZ/NeRLsFfRlOX1vSULDPJ+AJ4ZIoXDAzuVgcA/7Eg0IBKRhTgkSQtpwY3s7t+USwc86HIuhuovuRYtLyX2ulMlw+BPi0kl14/wtQ8ylGFnbqmK0BfsMdI9nJVWMnISDSYwaT6mmVAeudqIdxbWi2nkwyWjOuJnFjFDNvXgUMtFTF6TO4352BDea1Yx+Fdi0jEA17exH+UriSsiahnBC1FCcIeo0Gs0aNIrq8BPPcyEiasxpnHpGH+cn1egzUV87fNSG1oODzIU6c0U1K6GKnEauTv+cx4PxO+gz2nNLIN/p474BgGYrMC1L7d/+YRsjQ5qZNp4fk/V6nh+ydgk90OM3Q4RWPep08tg1yuU8/nWBNCOEr8QCrAyTOIbrRngwje3P4gYQInZ7cTHPG7tSyQinxOFRymRYW0nrzC09Xcx5ujaqx5MautZFBQWYJM6XyeVwg1CwjRUo16KCAsxJUNvuj3sZ1ZlnNMIguFM6mQzPegnNKRXo33SDgT2d1gwOck2+LaEQHlISQttoyrKP1OZQiOeWVHXgaDyOryoJZWlEgvtrzeAgisV8+6mxkO/tTloucaXFymuZN52GbD+huf4JRnb6dtaaMZChexU45Ge37oiM+rNzj4frhALAeDmtEZ6yekaP1Folz1PpNyPgjfOY2b6+jeuV6gzDysO+jhBUgo9XO7EQx/cTQi7tOGxb3waX+D2avbQSf11N7uBl94xlbmvDnBK8/eQBAMOef6PGF/JvkMWhO0X9JyFJJ5sGee6NmV3M9fx0h4drrCYT1EdqjQInxRy0lxqw62MaG7FwBrOfJp7VuQV34tsMCWn67Q+/D636ayJNySR+89f/zCNNyKvn8pGPfOQjH/nIRz5OK04bafqbl9RzF0Q1p3hTSIq4UQ02GCyUXWaSWfZpOnnYx0iTxNnoPhHgTOHI7gG4zqa/BzIZWN2ilk8mx3yKHuGnM+iOYsZVtIff8nEP3p5Iz3w/6lUjO5GOpzsehaeWMoXZBgO7RdcJlOXIxm5MPI+yjX3vtaF8GWXmmUNB5l5cYbVyFlx6gjLRwgYLe6NM1+tZRXatxsLZ1LZEhDlBl1qtjCrdLLgG8VyOuSwNOh0jSU61mpGa1aWljAJJmbZdqWS370UFBcx5Wuf347dCGXNNWxur3JpiMa6N1TCC67FqhCJOqkd1u9mOL9MxboekVpPCrc3x+T4MBPAzgYJN1+jQnKK+Gf+5BZlzKQuT5YD1Q5Stnqej7DmlkuFNoQRckdLhyRz9faXFwtyR5nicVUOxjgifX+Im3R9wc7/c7HCw/057MonEZ5Rtz1pagdCg8EoRqFXFaAve/hP5NLlurOFjeNJppD4VGX+lCdaxdG7pvg9+6mYVWeuBQVbJDXSHub6bucKIjFDHdWlzzLeQxn48mobOSOcrH1WAHaL+W7HTiIBHKE2zOUwX6JDk9h0OJlA4x8F9IF1roT/DY61Fk0WxQEm1eiWOCI5R94kA3Z9zXMwzObHPw2iPVq9CWE/3uEihZKSs/SjdH71JjSO7Ce0ZPcXBqEBxpYnnkkul4vG1ureXlZqaLwi9GTezCCEDzU1zDHggRG17yFHKtSMLxpvxmODxSZyows4EHKPpPjza34+zvqT+2DXHwBwkyV8IIE6QhNBICsodumGVmeS1BtB9ZZf7VIqRGOl+Ly8wDzs9jx72WLMoFOyxdI3Nxp/fHg5zuyXkbmsohMvFe4lsdlgpmM2y6s6dTjOKJSnZtDIZQuIeu1Mp5kU97fHg7GN0vsq5w2ju1YWFzOFaL/hUHw8N8XFvKyrCzZ3C9V2pZF7T3kiEOVnS+rU1FMLD5TS21/r9cAh0bIHJxJy47lSKkblnSpzIqagfpb+3J5M8d0ODcRjF78DuTV2MJJVVF5yCWkoh1RU1z3OgVNCsju3z8PfsZQZ8EKL1oiWRQNWbNGbOOJ+qUpTXmdEl3Pq1NUYU0JKEIQ1Ypbg/GmX+mGo7jfMxi8vhbiTu4ZAvwRwko1mNgCfO5969ifqxvyPMyr2eJTSurjJYTvFmaqkeRsLNn9Kx568cxff+nH8R0vTAD676RpCm+//2Wh5pwn/joelIjCzgTV0JKKpoMVD0JxApppuxJxJByWc0sWon2pnonZEDO4WkuGI0/Qj53FHUnUX4sGwoxSZ3GqceBhrnCChzkA/QDNhjpAVsil8GvfgR0tk0vFDKHBp+ACkODNfIGbQqTiGFAkSSzO0LAKBtElF7EltDIZyVpol1QDNMxpyREWRIy7Cc+IXKShSKxeVEIsGL1X29vVxC5L6SEt4WKvmKpNrPj7Kz0eXtRUUobab2943R8YI+32BkmXRQLNwL9Ua4hSljOJvlrYhwJoOLzBYAwHvBAJbIabvpkYgXP1fS+yELtXON18s/bu8HArygtyYS3DeLjSaG6qUSFkqZjImdSpmMF4AlQRU/+L7i93F/1Wo0/IM6LUILs1avQo8o4FqjVPPDc58qy1t1iixYcixZQmjlcv57fzqNRvGj/WONBXpRHud5n5fJ9RqZDEUKul7pwS2QyWB5hh6+18oi/EOXzuX4hyM2lMLBL+gH6egM6sOz+mR4pIB+DG84JsOWidSOilf7cNkdkwEABz7pYeL52LklUIHa3yX6MNYUQN2UYcuKhDADVNQZUS6ndgY8MS4iLP2Qf7q2lQndaq2C+2ugO4xElSBmd8b5AWqLMYWCv9OPiUQ6rbvAxYVkXf4sGk3U/6YdfpZ8f/nuSU4aqmV0fQd39GHCAnr4jvkS6BIWBbapNjaTDGezWByj86g1Cnj76H3pwfP9QIDHyeK3fLxtb7BokBNb9M/4vPzD72ikz46e7MBnoDnRnkjw1ptRoUDmC/oR0pxRyKVDlhYU8Dx8XozX24uLkRqgH7JBq4LbYVcqmbB9o93OW9XSw8DyPgUXIV4pDCUBSpBuStNa5nAZ4RbbWy2Fw8alkq3HbUVFvN2XzuXY6uKAJcd2GbcM9eHHR+g+95xB82e5xcJb62/6/Wz3IcuBS6MAIwp4Z7OoE84RhcIAeH8sxgnSihMnmBQ+12iE2i8MH60qxE8Ml7cBgD2KJD+81Wo0/GBsaY5gRwW183pbIT8cuI3D1y2te6neGAYddB9aEwmMbadxV1JpZFqEWqvg7eIasY2dayjgbeGUQ83ila0jytX0nRziklz7swk2BM29TluA85ZXIVFC91LWHsUXIjGZvtCJh0x0r2a90A/dz8kW5lIN9XmLPMXXneiO4j09Xd81qgImw5efWcz3c1pWw8mSRAE4Fo+jsFOIUDI5hKpFqS65HI4h6qM+kwylIRrz1qLb8W2G9NB0/7VXfCMPTQ+8/Gb+oQn57bl85CMf+chHPvKRj9OK00aadn50LwBg+jlObEsQRD4hJOcyCwBOkVcXuQQZWCXHR8LUr1JArkazhpGoRiRgPUrHqxprQ5+KntBLVCrekpK2yIwWNW/XqLQKhuFf8HoZORmv0yEiEJq5Kj1vg6jKKLNyNw6ib4yOP6uSDCtPhlFYO/wELUl9J4tsd05QgbtzlO0+EDUznLshPISYyLxGH4px+xJVOiZ0SlsK0/V63uqq12ohayJUKjXexEaY3nSatxUk2H+RycTmnX92VuBxD213LC0oYIStsiMF2Wjq82ijH6NnkgHdYCcdo80uR41kJOlNMlKT0yu4nItBocDoiGTwSX24w5Rm5CicyeBaI2XBL4f9/FqlVTDCNqYzjRdtlLVd66bs2jXZjusEMfuhoQIuc3NUn+VMrjWRwPzPqB2+JZQlD6bTTOpd3deH8SKTPnNIyf18AsMZY3dyOGuWDA4tCgUo7wVSuRyK2ul8epMKhy10XYFMhkthfPYq2SVoVpYxSV2WykGhpvyiP5WC7yuC+EsrTSxh1hqUXPS3T6AROr2St6NqJxbCNJPua+54mM0mj+0bgOMiQn5KB6ilqTItVKL0SHGlCR5BoN1hSmNKryAzl+m5PMP2D9sw5wrats4MivboVWyz8cnaE1h6DdkduBVZtLxP2fiMS0bxtum8EcVqJSuJ6woLebumpdHDfS6Xy3DYKJDDgSxv10vFUAHgnTT1wVkeOa8Lk+eX4as0Hds1QighoayuuIy3+D1dYd7GeS8YYDQ0ns3y60xjgMfpaGEJETLI2U7jtqIiHpczOrN4ykbtfLC8nBEmSQwwRqXhbdU2TRZjFdSOdyNBzBd9Y46Btza/jEQYqZRI70/HfNCJe3Kz3c6I2OahIdydswAA1qgjfLwugZqUqlS81XWDtRAdGRpHjmgOmwTyVtkYwWNV9Hm7Uolb3HTOGmHjEs/lGK1SZIdJ2uv8flyqoPOptUqmU4SDYmt9TgmTyec2p3F4PM2xKy1WtIjSTfFsFi66hbDYdTzOv5RRm8frdGw5Mkar5T44+FEX6pbQ1t9DfX34YbdAa8XvgMqm4THQnkgg9RGtuQsursEzPrqH16sK0CG232oaCnFQRn0wsmiwtD0/c7ELL/ppjb4krWfBQMIgR4tYZ6TvFTSFmUAej6ZZ9JFOZSFzUN9a5Qoc3NHHnwnOoXZLW8JtTT62F1Gq5Ci+gK41sdvHYqT2oz5uR2X93fg2Q0KafnPNZd8I0vTbV97OI03II035yEc+8pGPfHxnI5vJctHe//P/sv/1iUZEVVUVZDLZ//TfrbfeCgDI5XJYvXo1ysrKoNPpsGDBAhw+fPiUYyQSCfzkJz+B3W6HwWDAhRdeiG6h3v5XxmkjTW/5HgYAnJvWMlGufKyF97df9flwZYoyUYtdx2hUvVaLYsHfkIjDtRoNmzle2CFjVGTj0BCXDvlj0srGexKxUMpYASITjuT5uNKU9bUf9aF4CmW8vXu9qJtkP+U6PlnbyigXAHim0GvVPwcwbgWRCxO5HO4TxTtfctF7bw0FcKmBMpNXQn5MOkh9MG5mMXZtpMy97xwbppygLOyvJUk8KAiWI5EcKdPpSibhFlnWLIOBieqxjggc1dQmCSX6qzqMReLpflJmuLTFe4EAzk1THx3SZThrW2Eyc9+M5FhIXKjxOh2qBgXHxWng8hcWhw5bonROyQZisl7P0ul4Nou5sWE5sEQ+9p9lRV1Lgt9XTKB+kngyvW1DUIkin2mADTIfdbvZcHBvJMIcHKmfnwp4cW2WsmSjRYMToL4tVCrxsWjf8owOZmFLIMuBs+ADCrrWXZEILg9TH+lNKi7/UZdVcUkRpUrOfAQJ8TPK5dznwb2DiE4SQgPZMFFeKZMxwXRdIIBZPXRvHeMsAIAtfz2K3OU0BiobI8xBMk8fRmRa43FGWiS0BxjmlI36xyCLH4oUpxYwzozg60mcMmnM2EsNXNbIXKqHLEV/98mzzGspchoR055K/FfKZIxYpEX7AKDUn2XLkPajfqjm07yqDgO/T9KcvSNL9+0tbYz5LufpTIwuRTIZHkuuMLBNTfdolkB7BtPpUziJD6XpuHdlzGgppDbNNhiYF3iR2YIN/3kMAHDmNSS3PxqPwyoQoHV+Py4VaGfNpEJeW4BhtEAqZ/OOMorlETEHSzWMCFep1TAdJMRoffVwIei5MRVz0aS5clZ8OJs3FetOGUsSEv6cx4NL/PS5kLCdkE8yM7+r7+QQW1wU2DR43kGfua6wEONFqZIv41EYGukzCoFYnqzXctuOxeN8vHQuB+sgzatt+hSvuyNLIEm8wdkGA5uIxrNZvlfhbJZRagCoj9E5JVuTBp2Ofwdc/izbtCTbIthmp/eXmc28Fkmo2jKzGc2fEs8pNNPMffRhMIjwCGsKqR1KmQwGgWK9Ju7lIpMJlVEaGx/IonxfrUkZo1ITc2q8FBXctQD1va1YzyKH1oODMIyj/hhpuxDwxGFYTIhRvVYL91Hqm+KxFgBUNkiyHPAXq+ASIhqNWY33RD9alUpu/1T97fg2Q0Ka7v3eKmjVqv/6C/+biCdTeOj1daeNNHk8HmRGeEM1NTVh8eLF+PTTT7FgwQI88sgjeOihh7BmzRqMHj0aDz74ID7//HM0NzfDJO7hLbfcgvXr12PNmjUoLCzEHXfcAZ/Ph3379kEh5vi/IvLmlvnIRz7ykY98fEeDyqh8vU2l/665pcPhOOX/H374YdTU1OCss85CLpfDE088gXvvvRcXX3wxAODll19GcXExXn/9ddx0000IBoN48cUX8corr2DRokUAgFdffRUulwubN2/GkiVLvtb1fJ34b5tbZjM53v+VeDEAoFDLWRmTTmXxzBDt9dZrtSzfPctPT4c+dxS/L6Es+NcDelTMoA6OdEWYN9F+1IdqUdDzOcFRWGWxwJqkp/k+VRaOKJ0valTghMhi2pNJ5iHVa7W4vYtKmEgKq/cCflwjSnqUdiSxrYSOsSKnx0bBZ5k4t4S5AkGhDFqrirIs+BaHA5okfW+gKwyPk/pBOi9AknaJLyWpjhbqjawQO5qIs6oiHEhyaZdPVQmW70qciEUmE7+ua0ux9DUcSDI3JlelZ2l6cLsHFWeRjcMHIuM5z69CeR0d9/DOft5zj8hy6PiSEKNNNXKWdkuo1Zs+H34vp/7qsSuYd2Mr1iMeoSy9xzD8+dkGA6MdGifdy2PxOGeZ11htaBRoSZVazRlqOJPBGBVlqPEIHatxWy+X3uluDcI9iZCYvZEIc50y7jgr2HzuGG5S0Vh5PkVjp2KMBY94KLu+VWWBTpT3kKVybBRpLzXgP/yE7Fwj1HUveL34uY5e93eE8Wn5cGkLaTw71WpMENyXQ5kE6oSsRuI5AcAWC13LnK4csuJaU8ksCqbauG/8ou8SYiouM5uR8yT4WANVGu4vCV1qTSQwKUHz0GjRsKJHiBS5DwHgM8QwI0x9VFhmOMX2QpqzkunhZ9YMI2bWsWZGJ1O5HCOEB2Ix3K2h9h/ZM4BNk1R8PAA4O6VBziY4c55ho9v2ZBKlAtW4vauLLTDOHlJym5MJanfVWBuu7yO5921FRXC0EjoRDSVZxaRrsLBy7YoRCs+WHcSNmTi3FDHQtSgB5BJZcZ4UDmjoPNK4HduaxHtO+vt5x7N4oybHx5WsHazVJuaAXak1Q6FXnHKMrlSKjS5bEwn0vNEOAJh2TS2P83g2i4RQAm4aT9dxh8WBQQX9/f1AgFGi+RoDPhWI/UK9Efs/J1RmytnljESOLCMjzf9ajQYZUcz54I4+Vip/GAxi7oBAEYVp46jxNlZ7WiNZ+IVVxIN9fXjcQt8LBxPMW+sbo+M1QtCmuI8BQrYkFWjQE+NdiTaXkstQBUR/xXI5fDxCeSgpp92KLB/DlJHh5GHqr0S9kddSCSnr+dyNQ5Np3F2qMPL5bpV78Y6LUOxkPMPqXYkDqpTJmJOmlMngF3PaJJfD0EHH+LgwzevWrWEDMtW0nkkomH0EitQYi2GynObphniI58Lq3l48kqax6Rr9S3ybISFN91y24htBmh5++wN0dXWdgjRpNBpoRqCQ/6tIJpMoKyvDL37xC/z617/GyZMnUVNTg6+++gpTpkzhz61YsQIWiwUvv/wyPvnkEyxcuBA+nw9WsU4AwKRJk7By5Uo88MADX+t6vk7kOU35yEc+8pGPfOTjvwyXywWz2cz//f73v/8vv/P+++8jEAjguuuuAwD0C5FVsfD8k6K4uJj/1t/fD7VafcoD0//4mX9VnPb2XJeVnq8mKDRsbqaEjBUWzpwarwrlQjib5UzgWDzO2Y11tDCos2Vxp4ZQD4dLxU//JS4DBsVT/DulGextI3+nu0sINXHkFDgkp/M5OpLwC7+ozPEQKgVfpEalwgcZQjoKlUrcEaVzulz073y5Dn8V+9uzuuMocVkAAPcEBvDzm0lhdGd3N64K0JO5ej1ld0uvr+OsViuTYXuKMsCxajlnTql4BhvilLFnXu3CuVeSN8g8GyEk3kwGwSRdXzyXw3GDKNWyL4jic+gatw+E2a9kbLf4bH2O9+pPpDzoE/eiOKNCsIjaae5PQCMKsTrPKYfnOLX1AhVl+Y5aE/dzOJjkIpco1UIzlQbm8mNhVBTQ52VGOtbZGgMXu5ys06BZKPcG+6MoFdyw1N4g5s+lrHSgO8woR7yZeBculRxjTHS8zz84CcdS8gECyFsJANJyOe7qIx7ZD/upDb55FtgK6B6/pYjgKuF1ozQYWBV1RZmVVYaaKg0+1BH/Ryro+3EkxHyMuEkJrUAbOpoDXLJnekaJqyPCSLWJUKmqKXp8KtRByyYVok0gfd50mrlO7kN+/LGY+vlKnwY7XaJshuCk9Rz0YWUV9e1RbZwz0eZcknkk3nQac3PUN49ECCV70+dDf4768IpRVrwjCjvPNhjYnHCFT4W3Cql9Xk+Akbeug4SEZMaamCs1qQfQ2oQ6KJdj7lgDwCjcZDv185xuQCmQO6tcgTtF0ejpej0jBdcVFuJl0f+LFhRhxV6aTw1zaE5n5GBuzLFcHM7AcMmee8RcfstZhY9i1KfFYhwd+dLNpqS2Yj0ed5GqUD6QgFcoAV+uyuDadrpvmUyGeWBS9m9NytAxga6lJJNmjyKFSY1kXIxLsxJ1PfR+aYC+J9cqcauKvuezx3BrmPpu2xvHoJ5P47V0VAHGCwQhpZWzalbiRZkscnTuIQSoZbQawQvpnpSoVGxU251Mon4evb9MfF+pkiMs7slSsxnVYs6ePOxDoVD/pufoGR2O53K4BkKdnBHllfYOYm0VrSe3FxVBJzhsExaU8fxI53Ks5Dpho+t7rb8PP0zSseRWLSM8Ky0WVgqGO4dROEQivD5JKFd7Mskoa8iXgFGU29Gb1DAIXqo+k8FbQrEoITx9qRQjhFq5HI/46cfwRrud53c4k4GzhuZNeziMH5oJ9T6apt+BcTOKERO/CY1IwSp4fLUuDSPJ4UASlULt7JUJ77LeNLrEMtSfSjFiNJhOo99OY+KSlB4fCzQxYddhjdjxkPzu3hTXAxDyJZVGWlZsZs+2+0pL0SvxvfCvCblCDrni6+Ej0vf/V0jTfxUvvvgizjvvPJSVlZ3yvmwEPxQgcvj/+N7/GKfzmf/bkec05SMf+chHPvLxHQ25Qga54us9aEjfLygo+G9ZDnR0dGDz5s149913+b0SkTj19/ejtLSU3x8YGGD0qaSkBMlkEn6//xS0aWBgAGecccbXupavG6f90CTxdd70+3GlhS5iyBfHxixli0a5HEaRQTTGoqgSSpKz4mq8YKHP3ChUWkqdDArhyv3xOA3GaIdVKBbxxH9Dyojbq6kDU4Ij8vukG/cWUYe/aI3AKzLwm8c4OKsOZzK4KibUG/4UhoSPi+TdclZXDnPE8QxnOrBBZAsTtDpWIN11WIFpyy0AgGPfp7Z50mm+Pk9biNGET9e3DJfYiISxVHh/7LpWgRNKoTYSCNWk7ixKhLKq78AgnDX0WrXExWqde5U22EyU8R5PUNYayGSQO0l/7+8IYaLwr/H5EtCJ69vzRR8Xa+0LJlFSIRR4gma1NxqFahddq+EsB4aOEzfAmM0hI7LZ8ql29IiM19BN7zlcRuxJEK8l8Uo7ai6rAkClIaxiTHQeD+DePkLkruyWIzWZ+kDKPvt0wLH3iJ9y5spRrIIDALuM+tQGOR4wUgaumUZjZ6fPx3yLq+QmHNNRxjYlooTGTn3QnUqhsofa/Lwpyh5dNcJpuNQdhUEqxrnMxPynkkojlohxYLJqILNRWx3VdC+nyWQ8ZraHw8wXORaPcxa8stqCeQI9SllVmDfCLwoAukep8ZEYo7eYC7EnRX3an0oxD6Y1kcAxMa6kzN2iUOA6kc0G0mn8PC0y94wK/d2CfzKpAFeIPvwoGIRZ7LR/WEnH2ukZwH0pCwDgGW0Yd5pIofaK14ur5DQ2/P1R3C4WqYjg7nlGqRH63CP6yMRIzqKCAkZUw9ks85tq7XY8Wktz75ZW4gQ+XVHBaMJ0vZ5LerxQWcnX3Z1KYomGjt28l9CsyjEWmCyilEwgiaOiEsB8vYYRktuLregTha6dgSw2qOneSsidU63G3BC91spy6BaK20i9ESk1zceKniRCJXSvjGXU5+8HArhc8Hy6W4Osmpo0txQ1DYRurBkc5P7oTiYxVkPf7RcKqo61XcitoB+B5SYTj4Pt4TDzz8ao9HhNlAW5Sk3zZEcmwl5PR/a4oRNriL1Uzy7r6UiW1ZBKlZxdstlbapwOV4vxE+uIICSupay6AOa9NF4njbVCVS7aLMbwihNgCOTgF32MlnjPLcQGsW49p/TggwTNC71RBZWe2vqqGNvLzGYotIJfl8nxOprN5NiLytSfxC2l1I9PBWhefW9IC3VScIycatxWRCpq/9Egtpnpvv27ohB71NS/V8lNaMvR5yUkc7JOhyWgvtgnT+BQPbVtck4Ns5XuVTwSwOJOcnm//wDNk9iFLjRI1QZSKWgEemZXKtnj7S1FFtfIaO79rL8fvxdq6JGlfM6joY2PKoYQzlE7F0SG/eem6/Xsdfb/Yrz00ksoKirCBRdcwO9VV1ejpKQEmzZtYk5TMpnEZ599hkceeQQAMG3aNKhUKmzatAmXXXYZAKCvrw9NTU34wx/+8O1fyIj4f/du5iMf+chHPvLxHQ+5XMYCpK9zjP9uZLNZvPTSS7j22muhHPHgKJPJcPvtt+N3v/sd6urqUFdXh9/97nfQ6/X43ve+BwAwm8244YYbcMcdd6CwsBA2mw133nknJkyYwGq6f1Wc9kOTVJPIKJfjDwPEg7jGZkNVlDK2/lQKF2QJebCYFVhioGw2qcrgtowoYimUGZZYFoNCCWXJZvETkYluHTOGs7MiiwY7o8JJ20bIS6Ang4cGaN97XSCANVVVAMjfSUICVlmt2Centm5NhzDrsKhTNY6UFJtlQ6gSmVyxQsH704kjQyhsoMwjqJZzO8akqIsGusOwiIzSF0/CVCUy8Mvr8MNukUnXVeLIl9Q3Lq0SVWMpA/JvIq6Oa1UNZ9rVk+1oEn4l5sMh1AnPkEAozgpCCS160e/HJaB+bphTgkErZfHFKgOUKnqdSWa5DqAymoZbS9letVJwlNqjsAglWjKYQVhwwIqcRlZOqWJZFMapHyWEzt0RQrlAaoxzS5Fup2y3ptbMfiueKSZMEtfV2+bBl4JbsbyF/jXMKmR35wd8bjwo9rb3benm+nW6SgOejolimoLjM+N4CsrpoLbZNJgqeBqvyP24KkHXFcilMUE4DC+LqhgFMrjoHk+uNEElrrU7lWIn5BZNFhYnnedAOIyz5DR2pfuzLRxGo6gBeJXajNdSlHUvNZuR20XtnLDAxErBj4xhXGMiBHZkIebKDnr9rjLIGef+WIy5Pcfica41dkEr1Sh8pqKCXZqvstlwgKYPWuMhGAWS1O52M0q0T8xNgDJbADgvrsEhC93L2VkDt+lVnw/tRhrbsw0GeAfpu9uTdB0vqSrRsYCO25ZLMYIbz2aZ07Q/GmUPsnWBADvY35Cie5zuS6FQIB2lo81oHDsWABVQfVpwpKrUalQJxajkq9ax1wuF2AaIjTdhklCt+V1yVsyl26PQC8QlPcOOGfup3Zsqabya9w7BNXOYYLpBqB5v1+nwxQZRMPmCSgw1Uv/qxLic1ZREupbGQOV55TAIntLetiFWCV9SasD7/9EEgIoZHxR1MC3CJ8xZawYOU3+uqU3gPA+1eVKxjr3moqEke3t9nKH1rWhbEANiHnw0ToF7SgQCt8uN0aJ24Vdf9LFqOTS1ABbRvxpRa1AbTvJ8bHDq2GsrHEiwH1E6lUX0hFgPxRoYqlTC5KR1qrxMi7YNpDbePDTE7vrbqurgFuq+lwMB2APDhcQB4nQNhkRdwmACrwgFpFEu52oCt8CIlODCSnXlFFYZI9svut2MNPVUqWEVikpNiQ4hgXj1GIB+sS6vFts6TbEYngwO8nGlNhkVCugEr2i9I4211lEAgGtkxJO9LR5nJd5ymR6bknRN0w0GVipfV1iIx8Wa9JMOFV7T0Wtpjs02GHCvTfheyQtY9exNp7mixD0lJbwu/Kvim9ye++/E5s2b0dnZiR/84Af/099++ctfIhaL4cc//jH8fj9mzZqFf/7zn+zRBACPP/44lEolLrvsMsRiMSxcuBBr1qz5l3o0AXmkKR/5yEc+8pGPfHzDce655+L/z9FIJpNh9erVWL169f/v97VaLZ566ik89dRT/5da+H8Wp+3TdDD6BADiXdwmvI+ed1XgjQDxZFYojPg4S5nTnd3d2D6GlGjrAgF+MpOe4F80leIVUIZ4DYxYK6PvdadSuNtAyM8OWZy/J1Unn200YoHgFDzqdrOTsEmhYAdiU1cCXWX0zf5UCuoP6Inftoo27u1KJaNSWrmc61jFeqIosIn9foG8AECZUIIk42l26u2p1cL9Au2Rn/OTBnYg3hWJcGZuOhhC0y4694rbJwEA/G0hfmLvPTmEI7spSylyGXHG+eQ83ts2hJTwWHEI3kKmRMPt/OydE+yxMnVBOauN2o/6oDufsq8JKSXefZYy4jPOrwIAFIw3sxN0Z3MAX8yi7PiyQTVXPD+szcDVS5mflNlnMznOVKNGBfv2zNDrcUD4xhQ5jfwZv1mBSoXktEvcBa1ehdLRlIXd1tXFWaJRoWBujFOtRqiV+l2qTXXysA9bSoYdhZ8QKMWNdjt7DU3qzvLnfe4o2gsJzTws/p7O5dgPyN04iPKpdr5XklJIK5dDfVAo/UQG7jae6t0i8dm2hkLc5vtKS3lMzzMakRD3os9E9ziUzSK8gfpItrSY1TbT9Xpco6M2XzvQxWN3tUDg1ni9fL6VFgtnrXtHKJceLCtjVGxrOMxtkjx0rrbZsLqP6mYZ5XK+b7MNBnZeNp+IwTuKxnxQXOsCo5HRgXV+P6YcpuMmp1sYKStRqRASn0+2RdBIAAFzlALKHKvW+kwyPl5dQs6ozZZoGJWtw0o5ADCU6hkRc0Rz7O7+gS6OS3MShyeFx9R0r+7X2eETPElpXsWjKdROpHt8Ip1Eo0ATqvaEYRCoTMAbx8zzKwAAh7f18fVNEBy9wdYh9iU6ediHBRcTSiEpwQDy+Vrz0F4AwOQz6b6FAwlMFCrSeDTN60j9tCL2nzKaNVwvTzpHeZ0ZbU3DbuVSJQRdsQ7+NjpGkdN4SpsMZxECdfAZckRffv1Y9gdTVxvQvY3WloY5JZBpaMz4Mxm+h4NCPas3qnmuN6cSfK8sCgX74KVTWV5zNCU6GHLU11KNvKcHBhixD2WzOCD63KlWs1/RGz4fe3QtDND5fqPw42qhntscCvEaV6sZdmS/2W6HmW4xMkYFz9mrhbJ6mcWCBoGAFqtUUHqT3HfSGO3f5WFFpbSDUKXRsBp6gcnEc2ylxcII25KCApjEPLyju4s5ggdEkQmtTIYHxfcWmUyM/O4Kh7nepVEu53Xr7VEP49sMyafp32+49Bvxafq3F9fma88hjzTlIx/5yEc+8vGdjX/V9tx3NU77oUmqo1Sr1eI+gRQ85fXgJ3bKeJa0tvB+8gFnHQ6JJ/p0LsdZs6Rw2alJoRa0r//PTBzbg3Ts6woL8XyMsvHuZBKPFFEG5/LRsdRRGRQii19gMrFCp1aj4f3pOQYlfisy7F+VlGDGKvLteS85rPIbIxM+KIcG8WqlGAxaoEoUJWx1pJkTUJ+mNMfWHMEoUSOvKpFFs/B0ip8I458CURp35Sg0v0UZkGt2Cc69kuphSTWLstkcvtpK/KaG2cWYIlR3lWMsXMdtymInesTnn7t3FwDA8dgknN0h6po5jbAKDkU8muZ6YPXTiuBvon5MVpoQj1KuIyFA7Z/0YZxof+tBL+bupSw/YMqgaiwhMVOiabz/YQsA4OJbJgAgR+FpC0mV99X7JzF7KSFi8VyOs+p0Kov2o5QpF0S16CmjvrOXippiVgXa9hPq9NOkBqYC4Y4cTcIt0Krxl9aivYLGhLqT7tWJShWuFshieyLBni5GuRxzQzTWkpoMq4ocLiPcAmGS6ox9WFuLZYIrZC9U4vYYtalWo2GVzNyQEgnh0L1FZLiLYzo4UnQdngIZe7QYFQoea2v9fq6BdyweZ67KzS3EnZms12PyPDqfM5HgTNqfyeBuH43R7mSSkaanREaaGTFntofDzJUIZzLsuVOl0bBnU3siwWiaxLdIhdKYLZDYWQYDfiLQYa1czrUQJ1frMSEpFK9Kuif39fYygvWIyo7YHMoq07kc91eDTse1ta6rLsR+wb+SUIBAPINVXScBADvr6xkFM+r1sIPOU7h3CKPE+MlmaQzHfQmoBFqStqh57J7nyeB+LfX/wi0hLBeu7um5WbQJbyzdDlo3Ji1x4aTwqoqP1iP6DCHCqjvGwyo4UgdHq1EjuGijxtN937uhGwkxH4/tG8BZl9O6UTJiLp3c2ssO/KFgErOXEloVDtL6NG95NaPLx/YNYNIt9QCAIzvcSAh0qa9tCKWCv1Qs+FQHPu9F/TSam9FQEolCuu7P/3IEtROJ7+WoNjGSFI+kkPmExsqFvyAU++aODvw2QeNEDXCFgSO73bxGePuGKy4EPMP1JqW/T1/oxAdC2Zd7vRsX39IAgGqzxUbT9xxdEVyeIhTrIcFru62oiL2lfO4ovlTQ8c4zFTA6tuBgElMW0zW+JwsAAIxhOWp6qV+m15UwuvS4282oXzyXQ7uC7tsNxzrwzihC/aSxf6XFivVD1Ob3AwFWnX4ZCjHq+vE4DZRidyE0wpn9sWJq/72ePjwhPMGe83gYdXquspLH9FitDhdFaK26L0fjNpDN8tzdGgrxnJ1nNLJv2Js+H6Np+fhuRB5pykc+8pGPfOTjOxpy2f+Z+u1/PEY+KE6b0/RFmGrP3dndzVntFVYrVp6gTO7V6mp+6n6wv5+9Oq622ZiTdG87ZR1P1pjYk8auVLJSqEGnwyqhrNgaDnN2/3EtOWu/EfCz0ui69nZGHibrdOyLsXFoiKugb1CWQV1N2bZ8QOz3axTQGiiTa84lh1GnpkGMmU6Z0I6/t+PoGcMV5wHg7B4ZUmPoWCdeb2M1WDiY4NeF0wvZ+VfxYT8sKwmhGXiTkAdnrRklwv3Y2xvhDK/IZcSujaTAy2ZyjAhJn/1k7QmUXkvqP8UOHyvApp/jxLYP2wEAVfVWFJZQNrhvaw8WX14nvksoS/mqCq55pdUr2eelsznAteyON3phFZmoSmTUaq2CFW7pVJYzR4tdhyN7KOOsnWhHTNShU0+2YGAzZWqzFlP25pNnsfs/CcGqHm9DQmTu9jIDo1FagxLtRwktkLxnxs0sxj9fJ++fs64ezXW/+lMpHl8rLRbm+ZQn5OxLVSh4EG5tjjlsh2IxnBOj+313yos7BQehNZHgrFRCcqTzAMA1JivejdCYOiemxh7hH+RNp7kO3WSdjse5xBkKZzIoFOhrBoCk+XCn08wBeT8QwHwvtX+tme7rXKMRT4pq9I86nbizuxsAsMJiwQGJy6XT4bBQS7XG46xsu0+o8jYODXFF+1erq3FfDyGctxcXcxaskcsxOkKr4f3xYbfjCVrqxN+7h+dxrVbL3JEnBgbYs2Z1Xy9+pyW0WVFICFDnHg/Girm0NxFDj0Cdl5rNzDOJHg/hqJN6RLdp2BeqUig893zQxs7x5RFgn5ruRfTdHiRX0DXalUpG03a8S8iW3qTm8dpxPIA6gdR4eiM4vIv6Y8HFo1jx1rSTxmo0lIK9TKp76WfkJxxMMNIkl8twcAchhONmFvO8726lsRHwxljhJlfIMOMSQkW2hkKIraE1oGKMhTlLEufp2D4PI0AWhxZnrqDvpVMZ/CNH863iyxCGhIo1m8nx/HUKDp7FroNGcJP8/VGu97frpeNwXU1rx5iUEq8lqa0rYtQGo1kDlYm+1xSPcy3FLqQZGd3zQRu3KRpKoVklvLbEfZ2s16Plfbo+3fmlvLav8/txXy+hM6tLSzGriT7vnE/zTj6QwEdaGvN3dnezqu4Gu505dt50Gq1inF9nt7N3moSohrNZVg22xuPMR/ptYQk6hPt3PJtl9KhRzJ93Ro3iubs3GmU09MNgEC9UEppeolLhQbFrkc7lWDEqvfdXZwUe9RLiF8xkeCelO5Vih/5wJsM7ImXq2/BthsRpevjHl0OrUf/XX/jfRDyRxD1/fivPaUK+9lw+8pGPfOQjH/nIx2nF6XOahEqoSq1mzsM9PT14U9pjTiaxRjjE7opE8EY1ZTev+XysyBicQU/w9/b2cJZilMtZCbXQZGLFj0WhYOXF/f30XjiTQYnI3JeazaxUG9w7iPB4yjzuLC5mdVZjLAaXOHewkL5nVijwZWzYcyoiBDHPFidww3Y6TzScxLVGQrSkGm3+eAy9AnmoHm/DuFmULb32yFeYKRCV49sHMKeB0K93W4OYl6Y++EJkn+lUFgNdhGQoVHJUCdXXB7IorALZmb7UyX4ykjIum80h+C6hDYqVZZBtoKxpoDuCkMg+e04E0XOCssiqeitnpZKSqFCtRkhkxlJ2ChDSJGXKFaMtnIFL2bC3Lwq18DnyuaMon0kZ1IH329kJvbM5wOezZ4eBS6m22EKZHgWCh6JQyBhR6m0b4mPY1QZk6ih7rlRRv0SQw/graHztj0ZxqYLaf2y/h68hJJfDPED3RecyIi3G1QkVZZGxdA4zRFYKAGvT1KYG5bADfK1Gg3VC2SYhTS9UVnImWnPkMFobiN/xdMqDkgxllAtMJs4um2IxzrAlhOdQJIUtY2gMVGk0uFX4kdVpNLixvR0AkAbgLKU+TUWoPWsGvXhcSe9NOXaMfa0ec7vZkfpEIsEoSyCdhkc4p0uo1P5YjJG0zUNDuEDwojYNDSEjocCFhfCb6fWjdkJFt4bDaBBQvEImY7ThSosV/iz1x6KCAjwmrnG52YK1ScEDO0ZjYOz0IrwWpP4MZzJ8jL2RCKoE70viBgFAJExt99Tr0SjQOpdcjsgOQhX088swVvCR0meUQh2i8ei2K7iv7xQKt/f+dAgtQrW5+PI6RoM8vRHmB7UcHETnGZQt+z6isV9ek2Nkd/rC4TlocWhhNFObh3xxHq8BbwyHBdJqEqo8i12H4/sJNbv+32bALZCk0tYQypZXASCkZtdGwXkTqrsVF1Yg0RLm4x4XHlIBbwzlwofN7jRiluBQebrCjGhFRd/t39YLo2hHOJjEuBmElCnUcqj20zUOlRlwlo/ut24SzYnnvV5cZ6B+sbbFka6ke1VlULM338cztSgUSt+CqTZI0jAJ4VV0xTB7Ca3tL0cDGBsVtel0Wjws0BmLQoHN4wjxbRao5+/Ly4EhQni2jh7NPCVghDrObGZO0HXt7bhJILSrBYJ1dWEhe55tD4exQvAXFxUUsNLa3TqERTa63xJvaiRX8E2fj3dP9sdivFvws8+rMKemHcCpCjtpjbi+q4PnYCSbZb7UApMJT4hxeXdJCX7YQff777X4l4TsGzC3lOX35zjynKZ85CMf+chHPr6jkVfPfbNx2pwmX/rPAOhJXdpX1spknDk6VSrmVVzd1sYKu58dNuAnNYRAST4WC5qbGX26r7SUeUybQyHcJrJxu1LJx5C4IlfZbNCJ7OZoPI4fZCiTCNlVcInnP4Vazt45I58IzzZSFvleMIDRzZTddI7VYZHgsvSn08x9OfF2OyYJhEOqhedNp2GN0N/lcjk6RD2kOrkazZwZxlEtlGjpVJYzP2nAGc0aeGT0PV0gzTymcTOL2QcFAHaKTPS8q0mhd2T3ACuJkokMV2tXqBSczZaNKsD29ZSdzV5SiXkis33/L4fpXumVMAkfKoNJheI5hGQYFQq8+IsdAIBl149jdd9lP50orimGHf+g9rhqzFw53F5qYH8XW7GeuUlv/+kgsqIfr7h9MgAghhxiwk/H2xfhz458rdYq2Fvq0numAgC+fPck+/qMarDhKRkp1VaXljI6WaXR8Pi4RGlCOpXh/geAfYYM9ors8gqbDZpuQkNOFMlZ7bk3GkWfyHLPKxjOSKtGVPD2C3TVpFDw+I9ls5gjMk0AzK2YJJCtCTod8xmMcjmWiixYK5NxhlqiUjFyK3GoLEolpoljPNLfD/dYUjLe4e7BItG+pwcGmAPSnkzy8SRH9HtKSjD7GHn4vFpdzX2wcWgIW6oo5ZUrZFAdaAQA5nHUa7WMsC0xmJARgNDWcBg1wt3cMc7CfR7PZrmG2osJeq9KrebrnqzXM19kcyiES4PUpyGXBp6NhBbMXEwIyommQebl9Z4cYpRoJN9t/+e9+N4dVK/q2L4B9lGTEGGjWYND5dToKQNg3t3sJRX4QozjIqeR1WXHG+leqrUKrkvo7Yuywu2MC4Zd/kfNLMKmF47SdUfTPM6leRXwxHDpbRO5nQ2zCQFpPejFJ+vo3tuK9bDYRziIi3+ldnQc96NcnHvuxaNw4BOaj4Xzi1iZO32hk+eNdFy9SQ2Tlfp263snmU84cW4pWg7QsV21ZuzfRn0uIdDZTA5HS6i/5mTUXONvTyrG3LxMMAmFWMte8HqxVIxBadyOHDOtiQSvo061GlMEP+5QPIYaUV1BV0DnSETSjGKnU1lc2Uv3587iYtSIuaeSyfg34erCQhwS8/4u8VtS29TEtUs/HhriXYa7rUW4y0PXqpXLmW8roUjS/wPEvZou5s9sgwEG8RtTqlLxDotSJmO+1BuCW5nJ5VCjFe7uwSH+fZus0zECutJiwWYxJ1//F/k0/fFnV0L3NTlNsUQSdz35Rp7ThDzSlI985CMf+cjHdzbySNM3G6eNNJUdJOb/3vp6TBcZrD+dxg/FHvPIJ/GrbDac10JqKYtSiaeFB4aUicdzOVZC3VlcfEqWInnBTDcYcJtQIEg1uUpVKs5g7UolIwFLCwqYI+VNpxlBqNJocLPYT37eSvyB97IRzjLmGY2c3VuTMuYJ+LMZtGyhLEXiORjNanYfLp5gxQd/3A8AOOsXEyATqkAJDZJCyto2v0V9ceGN4/DhS0f5s1Vjaa/+ZNMgZ9UVYyysqtu+vp2PJR3bXmZgNY9SJcfk+XRdn39wEgEP9W+BTYNdH4vs7C5CbcLBBDuaZzM5LBUo1rpnDjEiNut7tTiykTgxUrYbDibYx+mLf3Swi/n0c1woPJcyad8WNwqFq/iohkLsF95LEs9p5mIXegQg051K4SyVnv8uZd05lQwdwl9Hug9Gy3Cfy0YbWSFZpVYjLLLZ5zwern1W48uxq7tCT+OkNZGAw0fj63EEuT8fKi1jf5e90Shzfj4Wysv2ZJIz5mUWCyvOVlgseEjw7lLI4edFwwo8iesgZZmTdTrOxpeYC9Acp/Y/6nRi2gE6hlUXYs6GNG7v33IDUPE6AGBpmRc/l7JqjQY3ivFsVyo56/6h3Y65gr8hqY7W+f08l+YZjbha9NGj/f3sTH6j3c4+NBJ69qjTyRykxUoDLugldOMJp5Pb159Os7JVKZMxH0QhOIYnEgksSdDc3GfI4FXBdbynpAR18mH0VUIDpTHlmWJiVdThDV1onEOD5npVAQ7sID5JLpNjflw2m8OY+YQsSAjQ4hvHssfayNqGvW1DiEfoXuzf1ssK1VHjqV+advXzXNr+YRucNRbqT3+cEV93Ng2TQJufvGMHrr+XCiN++i4piCfNLUVQeCml4mmeg7UT7YxidR4P4OxVNQCAL8Xf1RoFZOJHqW6inZ3LrSM8lNRaBfeTw2nEoEB85eL+zTzXxbyV3rZhlE6tVWDd04fo2D+qw+E/UT+duZKQdG9vhNEeo1nDfmuKJcWMXu6NRhlxWWAyQbWLxo95Hq3PkS/p/gKkJBypPNTMpLEtrfHAMKKqlMl4LV4XCDAi87OiImwQ81A5glfnTad5rr/io3NeabXxzkJ/KsW/Dy94vaxgq9VomP8qzZ9USoc5ZnpvdVkZ3hOVLbaGwjgWpj6dYMohJX4e7youZg6XxBu8r6SEnb8f7u9njlSVWs07KQ/29TH69W9lq/FthoQ0PX7nVd8I0vTzR1/LI034bzw0/brnfgA0aaQHEY1cztb4tRoN/7DUarVMhLvZ4eAtA4kou7q0FNOO0uS9yGJh48wb7XbeLtsZiSAifrQkefb2cJiPkcrlMHsXDYSXJw/xD1aJSoU71LSIb5THML2HLu8Zy7C1/y0GmshH5SmGjwcyad76S+dy0IjFUZLYtx70srHjSBLrx28cR6korBucXsCTc4ZMi2P7SI6qEgvmYF8EhQJW37Opi2XGhWUGtIgtvqt/ORVfbiIjwpiQ3ge8cSy7noqe7tvaw1ty6VSWZcsFNg1vVfR3hBj6DwfEw2RfhN8b8iV4O0NvUvHDlkJphaWIFv2xYuFrP+rn602nskwav+2PZ+BZYb4ZCyXZ6FIul6FijAUAuFyEtzcClXYY1NSIRToRz/DrirNK0L/Lw+cEqBhyo9hO0J9TdArUrxKFmJPxDKyzaHxo+hNsACiZ8SlHbCE/4XbzD0F1VxpvF1LfXGq18kO3ZFxpVypxjZXGydsBP4+N6Xo9y6j3RqPYWEtbXY+63bydIT00LTOb2U5jmdnMxNpgJsNGmNv2fx+yWtr6lgjf7SNEFbc5HGzUeZvDgZ1imy2SzfIPxAejajDmCG3DLiygsThWq8OjgrjaFVNhvoXaf11hIf84GRUK3uKT5utso5EfcqSyLgDZgUhS66UFBfxDtcxsxoocXfdGOT3EbQ2HMEMvxnk0ApcwPrzD4sATQXrYutNexFt/UomX8oSc5eyGY2E4JlL/mzLDmXLTzn5+valGjvPouQOp8XTdhX3DRZnVGiU/BETCKUyZP3w90jiWyNjHGz3YKmwLvn/PVBzbR/dNq1fio1fowXHxFZX8oGcr1nOZICmJqZtsx9b36BilFSZeO+qnFfG2tsWuxW4xv6U5o1TJWYChVMl5Tg+6o5ghjGVHlnZKp7KYIx68pG1vi0PH9gmjGmxcgsbqNCDYR+3QlOjQ/CmNXSlRsjh0vA49H/LxWtudTKI8QTcoGk5yMtLdGoS7msaulLjMiCsRstD8bo3HUd1F/aGpMzLx+lg8jrsd9PD/cYSuxZtO8zzQyoeLpFsUw+WagOHtsNeqqzH5yBEAw78Jj7rdPIZbEwkWDxnlck4O5hmNnJisaKYx7NAmcceIgtfS6jTbaGTC98iSW3alkoUVl3xFD6Qq6yGkMjQHPx4zbOtRolJhvSj4fKVDg2Wifd+z3YNvM/IPTf93Ir89l4985CMf+cjHdzTy23PfbJz2Q5O0tbY1FEJbL8HSE1yNp8D7txcROqGVyVAuYFetXI5ygUZJ8KVRoeBCiPOMRs4abu3qwh6RmahkMs5Cfi8sB6wKJcO5K0+cwJXVlF026Eo4w/ir14NBUbR1WdyIfVWUdRYL88VbCwpxsoky6ZYaFR8vfiKMImEB8Lh3ANekKVP2O+nvmrISfJQiNMuxJYBZywl1qptYyGhOeWuctwCaEgFGaIpdRv5XynAnzi3FQDcdr6XRA4f4zMnDg5gqiomeFKVVGuaUMGrl7gjxcduOxFDsEkVG++VIJoTMO5BgVKlGyKwLbBpGYUqrC1AvTAQP7OjDQlEyorBEz9fS3RoQ5y7mLLe3bYjRrOONHlwpChEPdIeZyDoS/ZK2A4d8CUbKPv/gJBvlHds3wEjTjnAYc2dTm2TT6D58EA9jivg8ruoAAQAASURBVNj2a32jHfXXU1mKwr4UfCLjnzCvlOH+HrsCDU7KKGUpQhg9sgz/fbJez1nkmroq7BAE0/lGIzRiHEvGqFVqNS7V0LUeiMXYkuC3ZWU8Fw65K7HGSmNJIkYDYMRVK5PxmH9l87/jh+euBkD2CdLWQdXMN1GiogxWQqjSuRxbC3w0NMQI1JrBQewcFEWq9V6WWp/Vcpy3MCRjyvt6e+FvuR4A4BrzMiaLAsHvBwJY76c59k5tBSO0S0U7tTIZ90U8m2VSu0WhwN3ifIlslhE0o9WKhyLUBw9Y6DpWOgoYUfqh3cHzPx5M8fbh9V0dTD6XkLuXi1woFtYCvb4EXKId3u4wI6OOWXac+Igy+nPlOpSI8dgtZPVhrQInRfHb2ol2RjjHj7Xxwn9s7wBvja99+iAAQKVS8LbZ5x+0cRFoZ60ZZ19C9IK+zhAXujWaNTy+z1xBthLuzhDCfhp3i+6qZVGFtVjH6JdcIRthjEtzbeZiF8+Z/t4QrwuxcAH2bKGtoPnLq9El1o50KosOgYR1Hg8AAEovcvH3LA4d4lHqx0/WtXLB8JO7Bxgpk6gDLYUyZDa0071aWglp26FKrUZYlNbZmUvjArHmeEdpMSlN35WEIO0dfrj1tFYfi8fxqoGQrbub0pjeQP24PxZj09pFZlq3F7W08BpfpVafUvx6kljPf97djZvF78PeaJR/K6Q5WKXRMIn7Tb+fdyLak0k2en3U7eataqRpnC8zZ/Ci2GLuSiahEjsERoUCfTG6vj55Er8pp3m6eWiIzzmllH6PilUm/h0LZzKMXGUAdCeH7QmkufyvCrlMztu4X+cY+aDI90Q+8pGPfOQjH/nIx2nEaSNNUpmJQDqNdMV+AMBkvQESarfAZMJR8SSeyeVYZvycx8M8n41fUZZwZc0JfvoOZbO8N/6ky8XI1Z3d3ZwFSwaatRoN8zieq6jAdcIg8OrCQi5X8YyrAgmB9uRMChwbDHKbACCjkaOxhjKJWT05PBqj7P5XxYXMTbjZ4eCsYpPgf/y8qAgq0HXsUAT5dTKeQfWZlNGc2NqH0EzKZBp/3YglgkD6iY4yrNkDMpYKR8NJANQHrtEWKERHavUqvLZ6D4BhIvXEuaXctjHTHGgWWWZJlRJmwTVIpyKQK6gPpp/jxD/+k8j6E88g3kTbkSRMVso++9pV8PQSub56vI1NNAPeOJoFoiURtLUGFfM7ymvNzKdSqoYtAuxlBkYCdCYV2wSMm0GZ1+cftDHHQsqyAcB4QRlLi42Ng+iP03kk/kRNdxgWwY8ac9UohLojfIzBUrqHqXgGCiX1XX8qxcTNqkEaUzqtAl0Gek8pkzGZ0xTJ4rkKQvS6UinMPkiZ4WOj6P5502n8XRR5bk0keLy6R1hTzCntQnuS+ulRp5PHjMR92Dg0hKsEl+KVko1MYtU6HMw3Khkhay4WiExPMomAIJBP0emYE7i6rAwPyijL3XbyTNRUErdwW38RSq3Ek5H4gSstFrxU9AkA8iJ8qoP6+Tc1Sm7Tde3t+KtAe6R40evFsQChNy/IvZy5P11RgbUCbdsXjTLq9KrPx0TX+300lywKBRPPU80hdLoJiWqcpGW5+gyZFl2H6XhOC123XCGDqYr6ubEgiawgus+sMDEhumXDMBfQ3RnieSMRviW0BQC+2trDhpZKlZwFCvFomgUZEkqUzeaYmF07sZCLQP/jP4/hsp8SUtP4WRxjZ1j52BJaJcn4bcV6qNR037Z92A6dEDRsfWcQxRWEsBnNGiy4mBCt9X8jFKn9qA+Hv6Q1oqAwxuO/wJZEzwm6b6WjzTgkigEXFusZNZYI5rl9AehMw0VzJb6Uzx3Dl4KrNXFuKfZ9QueUCvba+wxQCj5VNJRE+26BVgcTqBhN19pQa4CgN2FcUoFkiq7RraV7ctilwJReem+zMc3o/UPWCOzuYfGDRI6WRAf3lJSwmOEdvx+fuQjxblOksV4gviVKJZrEvHo/EGDeoFk5bOXxs2Y6bvOMcYxaWhTDxqdt3WdgRtVuAEB1QQAAoJQVMAocy2VhktPxZhsMI5BpM5dw2XngWoQnvAQAbHeyTK3GxhHtlHZXHEolHnUSF23hHh3uSBMR/8e0EfOtR3577puNPKcpH/nIRz7ykY/vaMi/AUfwr/v971Kc9kOTpD5Y3dvLyNGuSIQz+2Nf3QnUk3lXvV4JSWCazuVwaNw4AMBiJSl80tCyLHgwnWZL/PtKSxldaorFuCCvxCdRyIA5BspELQoFXhWlWuLZLE4IZGt5ayv2jiX+TDyZZGt+qf1amQyLRdFW21g9HhKDob8zBHk5ZVxqbxwlQg1ybjNdyUbtECoP0b54TYON7QBmnutCu5DK64wqJD6n1z/67Sz+zHkzqHiupyfAGfG6p0/AaKEsMp3KskHm3PMr+aleymSjoRRns+4uPcpHUWZ+8vAgF7/NZnKQZ+l7nccDWPj0bADA7vvI7mD8LANnp0ACY6ZWASClWo/gSpRUGqHRnzokdm3swIU3jgdA2WnfSUI9At4YG2jGo2nYBPdIoVLwdyX+U8VoC5eOmHX7eCgFWhh4+yQ2CeWe6toKuPaJey8y6pnfr0PTe9SHCy6uQVhPY+1oJgFrN2XmYVMOA4JbMt6mRbBCjE1hHLrg4lFwiKz0tcFBRjVXeDpR4qdrfbqiAm+Ooaz6A8FNKlGp8LFAeJaZzZy1lqhUjDq9Hwiwwuy+nh5W3klco3A2i3ckrpOx9RSZ9BstNEYXVx/CngCNxxNTKTtdMzjI6NMbzZMALfWHUT6Aba1n0PHkSTwi0Ko5xXLYlZSBS/PnusJCqEyk+lplsaPfSChdPKtGQszZBp2Oj9HoJyQZ8WpcX9Mjzmdg9dPlJ0/iLYH4tiYSuEfIrheYTNynXJw4l2MzzSUVJi7J06VMYq5OlJ2Q5dBcQZ+/z0CoyJZIBKW7qf017iimrqDxD+AUpdqY2YTkGY9qmLuTFeV7BrrDGDezWHw2xZYVFocWC68eDQB4efUeRpi2/Z3WlnQqiSlnESrVdkSLwT5CYs84vxLVojRSInYUvW2iwLRexeiXdL6RCrdMMouwUIAtvMyOvYKbpDUo8fafDgAAZouyKEO+BLR6UfS7WMYWAO1H/bjqLirf8+Rtn3M5l0ggicQ1NFbG7KXzOWvNjJ6ptQpGqWcvrcTbT9L5RjUUcpurRpjwjp5CvJ3t69t4zYlF01zA2KhSM/LW3xFipM9gpXXoTZ8PF4yqAgB09wR5rW3Q6VgFujUUwvgAzU2vMs1//5Gc1ogp5Xpc3k9rxHS9nnl1T7hcbNtxybpfoH/WzwGAOU97o1GUr7mCvmd8ibmtrYkEPGFqx4yq3bxDIaHBNRoNXhOqvEODhUCc5mxp8QFu/2/bMriohPpryoSXuGDvUwOExm8cGmL0GACeFzy/tiELbnbR+F9eOYD1PdXIx3cn8khTPvKRj3zkIx/f0chvz32zcdo+TROP3A4AOBTUAJ3fAwBo657FT4qGN2olA7OmWIzVPN3JJPYM0FO8lDFbNQne8w1kMmwWtmqEX06JSsXH+714wn/L52Peh04ux4cii19ltbL6p1yuxPVdHcPHFvySGTLKdv3qHIrl9Kz4iMfNXK0XvF48LTguq3t7cekx6pZwgNCZhjklaBF1bguPR7H7n8QhGTezmBVx0xc62YSy/agflXPp3H/58ZfUhkVF/NlxM4sxajxlNM/euwuX3kalMvZs6UZK+BtJ3KCy6gLmahRXmrBDlEtRaxXMkbI4dIgMEepRMyHEBpGSgketVaBNeBsplBnmFg2jT0Bh6WgkYu0AhjPR7tYgrhRlTdY/exgnD1PmaCvWM/ciGkoiJJRAiUiKSzRIfk2amTYcfoEQL3upnj1pqsZaOQOfOLeUlT1Sph0OJOGvpnM4upOIVdDreDYLxwBln58XpHFumt4PWZTsJfR7NbXBbZFzYd4qjQY64Ub3biQIxwizxl0CoZE8kW4vKsISMaZ2hMOMND1cXs6cjG1NF+PhuVsAAL/q6cGHwrNJUuqsGRwc9iYrK2NDyF2RCN6vIV7LwpYWuMSY3tQlMtLeC6Ed/xAAMnSVTCX3HFmF5ZM+oHvRU4jl5XQvSlQqNosNhWkMPF8/XAi7KR7njPgNnw+HxfuedBqLBcdImgeTdTqsFUZ/V1pteMNPx21PJNEjUNsFRiPzTNaaytFsov7tEnN3QViF1fDxMayHRJHqKRb0vE/zZuqCch7Tkp9X/fQiZDSEMORCaQRpmEDRn+DPbP9wGA3Z+u4JnPPELADAxBzNlfajPmxZR2aT0xaUc+HaQ7vccAlFqVIlx9qniOcz4QyaB97e4cLUSpUcVoGmDPZG0N9J8yYypEJkiPrm3CtH4+PXaEybBOJSO9E+QjEaZ0RmyBdnjlFfuxlnrRSGjs+Qck+rV/H1TZxbgu0ftgMAGmaXoG4yjWMAaNlP42fe8io8fx/xHh0CHR/oDjO/6fxr62EvEz5Zliwm9NC82vxWC5egkZAji13Hbe5tCyKxkFAnJQDdDrrWitEWKCvoWrq3ufHZeLreGxQ0djr0OS6hNfnIEfxWoK9zjEZGPldaLAiJ9VwqYHt7UREXaJ9nNLKS7umBAVbJverzMZK50mLB0wLliYn3flNayqq7T09Mw83jiWf5ls8Pf9d5AADdoTm44nv/AQB4qUMs4kPjsLT+c7p/CgXWdtF6d6XLjxXCV+lALMb8xe3hMJdgcY7wJtx0Yhodz74dGDgHALC4biebXjbFYqyU/fd/kbnlX377A+i1X8+nKRpP4ke/+Vvepwl59Vw+8pGPfOQjH/nIx2nFaSNNsvXr6YWxFUudxK8xKRRY20YeJvUlx3FsoAoAYLW2MCJUpVazs7fEhfq0z4GLnJSJWhQK3m9+0+9nZOiOzn6cbdbxZwB62pcUD2O0WuZC3eJwsE/OHvsofKlP8/nM4rt1IosxpMCFHO+KFzAXwlo37Oa9oLkZDwt0q6aXsqOu1iBMwkW3yGXExleaARBvRyrM+eofvsKPH54DgLJHiaekEV4xAW+MSyGUVJqQEshKKp7BhTcS76uzOcCeTBKHYaA7jI5myuIdZZSRA6SS6TlBfZSMx+H3EGqz9Oox2CpKO0gcBqNFw6hUMpGG3ih4XcV6NH5OmZp/wM0lECQ3YwCwlYwXbfuKPaIqRluZx7TjHx2cxU87x8kZcUT4siy+fQKyPZSlHW/0wHW+k4+t6ojxNUrHlrxnZi52ccb8WTTMfiyRrgh/1l5qYITK2xeBv5jGXWmIrvulXIgz2KPxOJaL8bO6r4/ff9ztxotVVQDAar6eZJKRznQux7wKKesFCEkqESiQUaFgxWjXiUvoA/pOzBBK0wadjnlKH9bWYvx2ytxnOI+wS7l0vu5UirPZzaEQlxPqSiXxXDO1c0L5YRxq/AkA4IGzX8E6Mf4ld+RXBwfRtv+XAICOyz/A1W2ETm7zKfHYKEJXFphMuL2LkB9pjpWrVXjHT8eq1WjwnkDEHunvZ3VslUbN5WPmNTdjjei76uP0d71JDdUouiZdIM33x2NToDhAr08e9qFlKn1G4hjK5TK+r9lsjj3D4tE0Oo8T6mGxDztY24p1zFmSEFxnrRk7/tEOADCN8FKavbSCyayb32pBheCwSYhs+1E/z5UzV1Zjy9uE0NZM0MPTQ+N8yllq5spZ7FqccQFd9xuP7wcAXP7TSczdc3dlUFkvqeA0/L2aiYW8HnS1EHI35zw7BroIkTm0qx8XXEt+ZDs/GkL5KKm/BtlrZ/QUOxcJlqoGuLvDmL+8WvS/Cm/9iVCsFTeMY4f9kkoTb7NISsKZi11clkaukA2vOZk0excd2diNMVMI+fEXKmEdpLnwx1wAAPDzdAH3Y8/JIYybRWNjfyyGyYJjdGd3NytXJYX0FTYblzUJpNO8W1CiUjGau8pi4YK8wUyG55BUUumO4mLm5c0yGrBAFGbfEAzilVbi4CFaAZXrPQDAQoGoDqbT7Kv0SH8/GoWPH3ovxPWTPgNAnm2epl8AAEonPI6+nrkAAFnRVgD029bmpXOozM1IpQQ0Kk8CSUJDS01+VuNlpz2PbzMkpOmFh274RpCmG+99MY80Ic9pykc+8pGPfOTjOxt5TtM3G6eNNE0VnKb+dJrrw43X6biwYiCT4aw7kBl2YU4D6HOTz8k1tcQj2BON4haRPbcnEqyweLCsDLd+cDcA4MBVz2DSYeIMPCBcWQGqDQSQYuOlHsqOf14xnCk/MTCAZwU3aUsoxB4ykrJpzMkkBkVdstkGA9c4etPnw22CnxU8MayCkQr27knFYDpI78vkMi6YOWp8IRRVdLzmv3cyz2fvJ93sU5QV6IS3N8pcoWw2B7fgdJRWmDgj7mvXwN1FqIDEO0olTKibTH2byzYgFNgnjlEFhYSkTQpizxa6FoXCw3yo4/sJ7Zp+jhP7BL9p8pllOHGA+lylKURkiNAvZ60Znc0BAFT3DSDESfJYOrZvgPlI2UzulCLD/3iZfKEuvHEcOzJL6hufO8Zo3N5Pupi/YbHrMEook0oqTXhO1LL72YtnAwBaPuvj/rcV65FO0ff8BjkKCWCA3qTCBuFJNff8SigKhSJRDOtYRwSPqalvG3Q6RidfqKrijPe+3l72bLpZuIRbFAoUq6jPr7EVYtVJGruLTCYuTO1Np/k8SpmMx7FUoHp1Xx+7g9/scOCPbYQe1du8ONZPSq6PZ8TgFyjWNcJ3rF6rRYtU3DrmgNVAx41ls4gfegAAUD31QUa/ytVquMV8k9CqK6xW/vsCk+mUGmCS+u+lLy/HgeWfAsApnK15zYSixnM5Rrm0cjkjscCwV83+aJQVhNL1KwGut7XAaITvKxoPrlozvKIOmr1UD7VAXP7+AtUTM11fxT5OqXgGB3cQ32X8OeU48HEXn1viGY6shSjVkOvvCGG0QEWUKgVeE4W1V/xwLPYKjyIA6BNKt2t+SXy9Nx/fj3KBllocOoSE+qztcAiZDM0rZ435FNXZl/+k+TT/Qho7x/Z50DBnJQDg03deA3I0vmYsKmO+0Y4NXXDVCbSjn/pTZ0hi8RU/BQAc2vnSCN5jEXZ8SOPRUa7H1AWEfnv6ItgnuIASD+v790zj7218tRnnfo/GVzySYk7WqlsnstpQqje5f1sv16ErsGl5Ph5v9HKf+txRrnTg7Yug4iyayxLHrXQgzSjex4Ykc/r+WFqO+a3H6Z5ls7hCFHaWEKc3fT5GRuO5HPMK11RV8Xis12pxUxOtI0gbcWUNIeiSsnrFgRymOGicNLbNByz76XuWIRwborUY3auALF2jwkOq1XHn/JIV3OlcDo0nFgIAlo/dxvOmKRZjZDkXmAiXg9SJEt+qPZnkunhzjEZ82ka81EvrjmKtm9aFa0rkjMTKZTfh2wwJafrbwzd+I0jTD+55IY80IY805SMf+chHPvLxnQ257BvwaZLlkSYpTp/TtOmvAIAdc0LsxN3ScRb//bU5LVzX65CnFDIjZeaTdTr0iyd3iadxo92Om/YTqnNlzQn2zuhPp/Ezgfbc06yhDAHAzfP/BgAIpjNcGyyczSI9VWSJfj9nB1em9Eg56KlaF0jj7pioVi5u+sNOJ6MDka4InlVTdvMzmZmVZOlUFpWiwnrP0QAAoHisBSnxd5VNA1mUMvr+EbXglGo5CqyU3fzHzz7HhTcQT0niFJxzaQ2e/uUXAEhpI3GC0qksZ9U3PTUfe96hvusVnkjZbA6lVfMAAO7OHXB30bmdtaPRerARAFA3ycbIzp7NA6gaSzyk442ESpVUVmH8bNqT3/mPv2P8LKmGnBc6I6FxKrUPmQwhC7ksZXrRUIrRniFfnGHaBRfXsHdOeY0ZbpHN2or12PEP4imoRvCOzrmUuDGtBwdZSXSyaZAVVPFompEryV189BQHv9d+1Ael8IDSjCuAI0nt6FJmWDH3XGUlv5bUYvf09LB3i0Euxw9Flri6t5cVYFdYrWiMUXYs+YCt7u3FEjP10ZNOFxa1CPWfUon3jtC9QMlGXGSjz28Ph/8nbtK2potx5RTiAr6x7xLOgkuLD3DW/fjec2Cqehsjo0qjYb7Ve50lWFw+XD29Sry/1u/HoBjznqESoP06AIBs3IMAaK5JDszT9HpsbDofAFBd+y6eEy7gW0Mh+DN0jITg9jXFYtjTSyrA34z14Lc7FwMArpyyns/dn0rhcSOhqFqDCm4FZeNSjch/y1qRK6N5sGZwkHmIlVEZ9qkJnZhnNCIj5tDOLKFWC0wmXgtSe/2oPoPO0XdgECZRw6xUqWIeYutBLzy9dN8kbs+iy+t4/JltGhwQ86rYaWTEtLDMgK8+pbl11kqBGhz1wyvQY41eOazwrLcyR0+lVSAwQOOqyFmB4wfIcX2C8I3a+VE/xk6ndg754lCqafynk1nmdVWNtTLaJnGaDAUp5jde9KPxeOnBr+i+nV2KfZ9S+8tHGbmeZP20Inz8eoD6KUF/lytk8Aje4LSzS3HiMCEgC1fVoPUgIYASUgUAfuEIXjbdDncj/d1Za8Z7WeqDq202Xid7jgbYc+1k0yCrYz9N0GftSiWrnht0OuYbbQ+HeRzblUr2N7pIoEv39PRwtYfGaHRYydnczOpqbzrNfFVvOo02n+BDhmmMwrYb8NJ8fHhGI+7ZchUAwDXhcVjEzkc8m4VCrP+sVO1zQCt8zOKRclxZTtcy3WDAHZtJHW5oM8F1EalY6zQaVphK7Zms07GyNZ7L8XWvPXQ26mo2AKA56xHz9Oj4J/FthoQ0/eejP4Je9zWRplgS37/zL3mkCXn1XD7ykY985CMf+cjHacXpI01fkhPrD0v0+Ov+MwEAU+o+Ysdtz8lVrBiQDY7HmLPuAECcpbiHFGVo/z4AYM5Zd/BTuSeu5u/VWT2cre+PxXBIKBqkbH6lxYKdEUImdDL5sNoomeR98mPxOHbVk/qkJZHgquoS38qiVLICQ1JlAED/a+1omEN79SUVJhz8ou+U69ec5UBlUHg3BZPMQSpyGhET7dQZVNgp1DMrbhyH442UoUrHbT/qY9+kdDILawkhQ/u2dEMrEI6yKjVXP5eUY82NHsw9n9ABd5cd3l46Rt1kJaNjR3a7GREa7EuisJQyC5OFuEl97U2YtYSQu+3r38eMRYTopRLj0HpwPwAgMNiPrDAYmX8hnc/hNHJNrsiQFjIZ9Z1K7YDPTchizQQzWgTqlE0DMxZRRit5VsWjaVRfQsfr+nsXZ/wllcM1xWzFenSNojYPvtwOADj/+/VQmei+7f5HJwoFMlcx2spV4z82JJmjY3An8bGB+k5Sss0zGhlxKVGpWC22rdeJz6ZTdn97VxerPTe2Enr52qwO5ljEczn2a3ll1/eBqjUAAKsuBH+CMmmrJsFcJ0kx9MoXN8I07lEAlIHvbF5CnVuyEdVC4dmeTPI4bBYoS26onlFWU8PvhnkVrT9G3fgX6Rx6PdY2E1fwlBDfe2bFI7hV4uZFK3iOmUo+RyhNc+XJylL87DCNMenvPxnbgpbEsHfXghFqIwl5eD8Q4D5dWlDA81Dyo1nd28u8D6dazdl4vVbLtbzC2SwKjxPiUjqJ0JvD/+zGzMXEB5NqowHEibM4qI/cXWHs2UT30GjRMM9t2fXEVYmGUtgjuEuerjDqhNu1QiHDlrcJwTVZVBjyi7qOojYjABzaSUhI5ZgC5gRqDEpuS397GrPOpeNt+3sXqseLvhEKvlwmB5lAYhVy2Sm+T5m04EsOtkMluFzSeE7GM3y+5q8GMWkeIVfTFzqZK1hg07LKbf/nvbjlz7QGP/2jHQAAQ4GcHfqz2Rzzm5QqOfMJy8da8ORt5E0kvVc11soIbjSUZCT501oFbhb30J/JQCfW19RAHA+kCJmS7mutVosGMYZrtVpG5F/Lhnj8eNNp3g2Q/I5a43Hmmo7kPLUmEsw1XWA0cj3FQ0ENSg2ECEm82ldaR6G+jLyZ3KkU/B7yoULBEaCJUFfT1DsR2v87AMDSefTvRl8a8M2k92q/wsa9P6Dv6TuhPUbvL7/0EUbK/rjuHqSrqO+0QokXD46B1kz8vwzAnmyHuqag3klo4bHuqTAVEVdzaMqz+DZDQppe/Y+bvhGk6epfPJ9HmpDnNOUjH/nIRz7y8Z0NueLrq9/kiv/6M/+vxGkjTbpHiJcTn/x3zkpn1H7CdbNk6gByWXoGM63/FdLiqTt21pNwmei7EiqV802Hyf4VH1tSUChlMt4vbtDpmAMlZTRXFRZyfZ8rbDbcJpROk/V6rPVQdvNijQPjRaY/+0grtowh7xIpC9DK5ewqe1eBA0OUSEAbTEMlMrmYO8ZeQZJv0UB3GMeF71L9tCLmF2x99wRzlgAwF2LS9XWI7CNegaQW620bwpE9lAEuvaoSh3aK/rJ4GJXZ9XEnZDJRuTwnXILPKGXka8qCcsQEUtN7cojRJf+ADhodHXvIl0CRixCEkJ+yAo3Oj1SSMsdsJgOZjJChgDfGvjd97REUllA/Se0JBZOYs4TUQfu39TIHyV5qYFXO5Pll7NNSWl3AysI9mykjHTPVyKoje5kBJRUm7lOppt7EuaXMX5JIi027+lEx2gIAkFnVzEv7+KVjfF/SqQyeLaZ7f53dzqouKRMtUanYlfhVnw/S2rHcbGHkZH80ysiJxPFRQcau9oFMhhVB4WyWkZ9DIRmQpjYvdsSwqY9QiKVl1LdbQyHMEShYIJ1mNHRn11g4ivYDoHptrZupZuONF1FmXKfRDjt8Z7OICCSnyzOWXfXnFEaw88C14CjZCACnKIlkJf8EQL44oX5CJnSNixGb9RYA4GxXG88tqW8tCgXzBtO5HK4W3LD2RAKrBBLQn0phkcg2n/N4cLvwu9H00334siDD822d34+7bHSvhnxxdg+fkFKyei4q+EgjXbktdi2OCy7RkC/OfJz+jhDMk6gdh9a2Me9PQnMDnhgfQy6X8xitGG3h2nCFZQbs2URrh1Yv1pgyA3uTqTUKhAK0FpgLk+g5Qe9Xjzehr53uvd5oglwoVwts1EfNX+1BZf04fk8ma+U2Se74LftzGDuDviehS+7uMErFnPhkXSsmzyfVWkmlCbsFqiaXy3DOpcTjaT3oZfWb0ULHUKrk7L2kUBZgznnUptFT7Lw+RUIpmMUaJ13r/s/7MPtBQmcm5tTs77bw6tHoEDU1jRY1o20Bbxyzlp6qNJ1nNGKK8JH7Y2mcvfYmHW5hr71HnU5sErUcJUS1NZHAtm7iND3W4MNGMe429Tl4DrnUamwT89cklyMk5t6xAI0/l8nPY82iVDKa9WnbBExwEd8zkE6jq0OgvGq6prryfcx13PjWXewY3p5M8k5EbqgeVitxGf1d5zEnEUfuoz6Px/Hi1U8AAG7r7GQFdzibhX+QOKVa6yHmAv6rOE2vP3kT9DrN1zpWNJbA936WR5qAPNKUj3zkIx/5yMd3NuTyb0A99zW//12K0+c0bXmGXvhmwlr3EgDAn5LBpKQn/xKVCi1+4ackT+L6csqOX2pcDFXbDABAw1J6Qm/0lLL64cqxexgJ6OqbgQ2zCQW6YPsozK8lpdm24wsAADdP2oV9EcqQbisq4uz/RrsdT3noe1uGQuzm3Z9KsXeMtLdepVZjeWDY2bs5RxlSTUqJB0J0jNuLiiDrpWxIGiy2Yj2jLD73cEZ8ssnH7ryRYIJVcKXVBTghVCsS38JZa+bMEQCav6Ljzb+whFEsb1+Es1/pHN7eCAwFlK1bHA64O8kvpGxUAeQKUqUplR3Q6MgnZMg3CLXIvrqFT8pZFxWxiqb5q0GUC1foSDiFSJCuUW/MonYiZahH9xJaFPL7GFXrbg0yshULa2CyCj5bTwrX/prO/Y+Xj3FNPYmTtfXdk8zHuOjuKdjyV2q/3qRiBEGrV8Kjp3aYfVLlJpzCHWsvJKSmIa1CWHx2RzjMvIiHSsswIJCijBjWawYHMVlPSNoTbjeMIhucZzSy79Dd3d1wCT6S5Ah+kcXCdbEknh1A6jKJu/Rp81lUcwrAryqG+XGNoj0mhQLvv09jPlV5gD/7/Lg0bjpoAQDUFTezp1lKtDkeKQeO3QMAqPpkO+oeICXOpqOLYdw/EQCgWvFbPp9TrUa5xMkSKjnIk0ARqbsWF2aw9cPVdLxzf80cMKVMNlyXT1zTg2/eifdvIO7F+mAAy83Uzuva24l/CCA4fTRWnSBEYrrBwHNLQoarNBqemxuHhtgrLZPM4qdu4hv91K3F/YU0xn4boPZYJlgROESoiFwuZxRloCvM/mcnDw/yHPrFn87Ea5EAAGDKYUK5spkcYlEaA2abBlvfJR7T0qvH4JN1hPxEQ0meYxLKlYjJUGAXyK9ZzUizTKaAUfTBrCU2rjnprDWjVdTU04nxkcsO8Tj3e+TIpAmxcNVN5mN0tx6Hp5cQmknzyI/s8JefoayasnetXsVq3IHuMHP3zllVy23yuaPsli7NzWQijZmLCQFKxtOMqg35Etym+ulFjBhJNR9HT3HwMUxVRkYcI9448w3TqSw7hfdosozmaHZTe1on67FDrOHtySR7bS2zWPCE8FtaXVbGCL80Xu7p6WGuYMMIJdquSIRRXqNcjpcOU303h/MTPre0qzHFoELjIK1Zml0/Q2Ieqd3Q+T24xpLi265Uon3NbXRfZlF7tNWvIPXFnwAAoxbcySjRnv4KVuMVf6aB9RbiEF5ksbDn2lMdGj7HnGn0u7gzlEC9QNsT2Swj14tMJhgUdD+frRBt+5ZCQpre/vOPvxGk6bIf/zmPNCGvnstHPvKRj3zkIx/5OK04faTpg4/pxdA4OIU6pfuydqB/KQDANPnXCHlJeQS1j7kV/ek0+hJ0il+VEadmVySCT/301C5TRpkr0Z5I0JM+gLvq/Jz9SohAlUbDKo1wNot1fspK+1Mp/FXUKnrE6eTPeNJprkt0dwlxHs4zm7mu0VU2G+yCP9TZHGD+w/hznUgNENKUTFA7vb0RzrxaDw5CdRWpfGzbA0gIl117qQHWWXSNbRu6YLHTnvn6v5HComqcGTUNgmsw2Y5X/9AozpFGqcg0u44H4KyhzG+wf/iZdtYSQm+ioSQy6QmiHY1w1VGbtHoldn5EWfDYGQ50twYAkHINIMSstIoQryKnC21HPhevjTi6l/qjftpM+AdIiRIKUNZUOUbNaM+4mcNolVavZOTNXOhCWQ1lvnPPr+Q6W5KyT6tXMm9q7yddmH4O9Z3RouYsN2RXIbRbHNsglI4TrFB66bh6kxp+NV1rfzqNMSn6jFs77I/SnUyy2kVCU7aHw+z9U6JS8fubhobgFlmrUS5nHoOUce6KRHgsXlM1wDym/dEoc/Ae704yIoRRf2GvpOqpxE1q61yAs+t2AiB+0Lb9pB6FPAnH6DUACJ3hLPjSxwEAjldugedsQh5n1P+dHbcv2G0BTt5Ixyj7kJErDJzDPEPYdgMAXI6j6BLzDgPnAHq6JzC2AoHJ/PrKYppbElrXcuICPDabjtGeSOApN82xpRY9+0LdXlyMbWGBoqjU7IEjVQfYUlULj4zmzbpAgJGH0hgQNVL/DuzxMtpx7X3TARB3TFLibQwGUbclAIDcviUENxxIslt9OJjAfz5MPmSzl5A60+LQMorUetCLxs8I3TBZFYy4hPw6pFM0rmQy4c+zaBWOf0WeWqGABoaCuPisCkoNoTND3jTOufQKAMDBHdvgF+h2UblUS24IdZNt4nsFyIprCQV6MWbqfABUI/LoXhoTaq2Bvy/NU9cYC/z9dC+i4RSrCf/5+nGIJqNyTAGuupvW2g1/I9R23Mxino9b1rbiB8/R+SJHhlixO3tpBXMP33z8AABg3vIqDAn3c4fTCKvgZLYd9SPgpevuvcDOvmfhbJbVlfUxWp+CBQqkmmk8jBpvY0+qy7vbeT6lczncJxBHqQ5ifERNxxOjxuLivnYA5In0VBeNqbsqZfjjDlHLsegTQEnXWG+jNetYbwPuGkvr3vpgEMc6ZwMAzq7Zh0+7iM+KaAUm1GwBAHifuBoAoP3Jy2g7JuYjgJ/M+gAA8NS+hagf/T4dO5qGSkH3MDU4jT8ruZK3JhLY0zmZ3gxM5rm3vLqFr7s1kcDOfbcCAHJX1eDbDAlpWvf8rd8I0rTqpmfySBPynKZ85CMf+chHPr6zIfsGHMFleUdwjtNGmsoOUjZcq9Fgm4+etX5SrsKf//5v9Pczf4GuA1Q3bs6Mx5mnscpqZUSI3VwBdgzPJS3MB1HKZPjtHvJ0mjPmY1YbSYqoK6xWzvK7Uyn2t1nd24v5AkFoisWwQnzGqlQyH0UVE143egVeFTyn/lQKd1uJK3S80YPxQoHzpt8P3dvDqi6AFDUGwQPyHw0ykgNQ/TmA+DdSNvv+Xw7DXkrnlpQ9FruWs+sil5HrOdFr6q+mXW6uBVUzgRQfn3/wDiaLek8Gowo7NojK9EUa2BxjAAAnDx/EwstIidJ+1Mech+AgDfbyGhVa9pNyZNK8EhzaRQicUqlF9XhCro7t/ZLrduWyhLL4PM0I+2mIVI8zwiOUcTXjbYw6hYMJVvyotYr/SdHkrDXjiw3kX1VYZoBGcLV87ihP5qqxNtQIv56E6JfWg17mRZkrjDymDO4kUEr3Xu1P8TEOaNKYoyZE66s0Zc+ZXA4rBf9maUEBcw2cKtUpbr5SfCjUmystFlaI3dDRgR7hGxPP5Yb9WCIp9oKB2oeLZv8FwHAVd4tCwZy60LGfciaqPHYZZAKFSM2/m3mBUjb/VEsplE3kG5Nu+NswipS0QeZ8l+7PV08DE38JAHDpUlyTUVIgzTYYsKlHOEBr+3GzQEOe23kZZLV/BkDcPWluSmiuRi5HnUDrlo7IKPdGhxHhY/E4K48e7e/HhkrKoNdH6b5fciQM7yhq82FjltEjAFheQMjiR6EhRpIlnplLqUJOrM0HPu9lHk08mkJQoJaFxTrm/7ywejeKBceuR/gSWRw6jBceRGdcUInmvYQGbX6rldWZaq0C56yiNr/9p4MAyK9IQkbbjsRQN4nGXTqVxdF9dAxbURmyOeqvEtckKJSEmKjEeFbIZcxH2vepB8466iOzTYvDX9L3Cmw2aPUGcV00lwpLklwjMpeVobKe+sjnjjLnz+eOcV1ImbwAAaEithbRWCypNDGKNH2hk7lLSpUCR3a7xfnSMAiemKTAbZhdzErU3rYhOKqpP9v2e9njSpYDo1jNVmB8mK7xmRz1+Y12O6O50w0GrhjRn0rhcVGH8S2fj9dzifN3XWEh9onXoWwGz3sIPbrJYcdzwjVdq4oh3keu9KWuT9ldXkKGW4Jmrs2YzuVOmcupo3fRi9qnYdj0KwCAfjnVbvTs/QPmzP0NAKDrkUsx/zeENP399V8gIvzGMPU2iOIS8PTPHPZeOkJoFQqOsBoPSRu0+y8EAGQW/4rbcIXVilf2nwsAyF10Nr7NkJCmd/5yGwz6r4c0RaIJXPKjp/NIE/JIUz7ykY985CMf39mQK2TfgE9THmmS4vQ5TW/RHviMiWuwhxJKmNRxVvwAwHV2ypSf64sCrYRM1U16Ei09Yj9YZNrVWjnaeonHgIIjmG8jZGF7OMxV1Z/qkvH+9Y7xlK3M3ZfClS7K2B4sL2dlxo12O/Na2pNJzpof7OvDg4IPMkkoqExyOXxb6HsTz3MxAtXfEWKUyOLQsbeSNFjaj/qYo3TysI8zwKad/Vw9feZiFzYIB99pC8o565QcvvUmFaafQ2jbC6t3o7Ke/FH62g/y+YqcRvaIkdy3w8EEzhCO4K0HB9HXLmo+lZazV0xlfZrrV508lILOQJmmTE6eKa66BHMbMpkctDrKKMtrVIyU/fON41Cp6RoLRBVxlXoIvgE6bjIeQWEJXbdGF+I2SegZQDyMpVePEW31cptHTyYEy2jR4OPXSdF368NzsPFV4nsZzRpGq6TaYmqtgnlkVpEBA1RLSlLdhLNZ3NlN53/C5cKuCPWNxFu72mZjpONGux339VAfLCooYMTots5O9lKRxt8TQukD0LiUFD+7IhEc2kdu99rxDyHedREAoLR6PfqEF0xd9SYANBZThyizRdEncJWTe3OVRoNte38sLqYENy8ml+LnTlAfQRlmZ29UrWHe0RueBKYIn6PGwULg5I/oM8ZWVqOqJtwPgFAiKRsPRe389wmuRhxqOY++p/bhh+OIwyYhVFq5nBG4RQUFXFNsbzTKCFqDTsdIUziTYQRB+p5GJmMvnrutRdgQp3ZMNxhwj7hXDzudrLCTvnfy2eNYKLyIjjd62McpHklhlOACfrK2ldFHe5kB4YDEmxNquHCSx8ygO4qBLnq98NJSfCBUm+NnFaFD+AoVuahve1qD7LE2dUE5ulpoHGTSx3kc1060M5pzbN8AI2FSe47tG8AUsS589UkvVBo69sofjceWdYR2xsNKuOroe+3H6PozqRTGTCUUr/O4H5k0jcu6SQauhxccVGD8LELKju31YLbwSpKcvwPeGK7/N1Ipf7mxk983mtVwCa+z0koTr1VS/biB7jD0Ruq7UQ02HNhBCPTM8ytwaCuh7XKFjNWLujEmHHiTdgkmXUHIdjyX43Gyzu/Hk05ar+/t62WXb61czgjTi2JuWpRKrN1BfJ/lc57iXYH3AwF2BNfKZLhOOJO/OjiIewQ39aZjNP9dZje6/HS+x+qTuFfM79VlZbjnAK1PV9YdxRtugZYbqQ3t2x5HbsbPAADY/wTQQCpX7ef3ID51HQBgSvlh/l0J73wKpl7hg7WKfNWWmc1UIQDAw2evxa/EuTWbH0bcThDV4vn38zFOTngK32ZISNMHL/30G0GaVlz/pzzShP/GQ9N5LQR1frzp98hN+D29mTayuR+SNpI5A4B9O7SKYUheKsS4/subAABKbxmWL1sNAHjv+GTIirYCAHJpPWRKGtS5/nPhcJJk+mrxA96aSPBivSsSwU1iMr0TCGCtmBQvj7bwtt2GYJDlzlIh19uLi+E5EgAAqMaY+MfXrlSiZQtNilENNpYqSw8GJZUm3k7rbRuCT0DvJV1JfuDpbg3g8w/a+HtnrqRFRbITaBJbYgDg6VEgOEg/zLmsDNXjLPR+L1A3iY594hBNNp0xwVYAez/xonIMEbq9/UfgHEUPn4aCdgS8wjTuoJcXd8lAz9MTg7mQXqdTGcToeRSOcg0b3dnLDLyleKKJjlUxuh79He0AgMjQAC+20VASPSfpMz98YDK2r6fPtB/1c3FeyWxPrVXyewd39P1/7P13mJzFlT2On855pqdneqYnZ2kkjRJKowQCJCRAMsEiGWFgDQa8YAuDQaxhESYJYwwYWMDAIpboBUwGCQGSUE5oFCdoNDn3hJ7OuX9/3Krbo/VnvysMi3+wfZ+HR03P+9ZbVW9Vdd1T5557goSBJI2m2fScwFj+6KVPzoBukI4RQpkaDH1FUHimwwitThD4G11IzKCFWS7cAPhYLJpI8Mb5xo4OmMQP/AyTia9fmp6ODR7yBD530w/8yuxsFmN9sD4bS4qofetcfh7zL1Sm87hqDYeZ/CklDjYem81zorRgOzq++D0AYOKiO1D3AW1uglPeh2HfuQCAgJ36Zf6pqzjJp0GhwNGPfkd9YDUAU1YCAHJ1CvQcoR8cGNs5JcS1Z1DalucaS6Cw7QVAx4VLxLHGm3UzcP1EOmZ45kANfjN1DwDg4R3LAABTJ7zKhN29fj/PjzeGk4EZTxYV8YanSq/n43NJlj/fauWjVFcsxhuy+y3ZvNHYHfDDdozucxRTf4aDMQ46AJIbIaNFg/efPwoAuPiXk7BDpPXJKTCjTxwbSQHHKfPzcFxscrR6FR/lbXm/HQYTvfuy6kw01oqgjxorAKCjwcWCrr3tEWTl0rv0DGsQFW3RGSKonLwAANDVvI2P+/o66H1PnqdCTyuNtdovN2HyPPqBdw8FWarjq82DGD+D3oUUujWa4zxf+zr8UAr5ZavdDns+PXuoz3+CKGxZNa2JUl5Bo1HyPLbY9PCJ43lHsZnr2d/p5fQpklhfMSmLZQ20ejUf5U2Zn8epa3oMgKmLrokVGrBWbHqWChqEVaViUeISrZaPyILxOM4V4+7sY8ewq5ycqdv7aV0o0elQF6Qx8MyxPGgyDgGgo3MpqnrvgbF0DAZgYoaHN+5yXO4/fiYHI1XOvoU3Xi83FwDtlHgX2V9Ak0uOTESk9FpUeog3ZsdG0pH5zhUAgMFpseSRW9BBAR7CTJ+Ts5R3/n18n8U4wG05dPxMAMBlE7bhDXHsfWtODh4+QmtfYvEoMdrvwFKbpv8dSx3PpSxlKUtZylL2A7XU8dy3aye9aZJigQnTIB4fSx34q4+uwsRZdPxwqGU+Hz90jDgQFEcHxRsacOmfSKRyw7iHAQD2x1ag9gzyihaV78OGPkEQV3sxVk8exBWnHMYmDyEPs4T3UKXX4x3h0WwcjqFAQ57aQosFUwzkcWWp1TCI47klaWmMOEjPpWV7H14opmsf09iwfyNBqnunGDFLwOy9bR6YfkoI03AdeULm9GQiXbNVi9p/J7jdNCMbOnGMYC8ws5BlVp6Jj8Ok0Fw4GGOBuoLydJSOJ680zaZD7Zfk+VmsGobWQwGR3HQE6O8kInVahh6RED1bp1MxChQJOznlSPHYyYiECfGSHnh6ZhaG+sgr+vE/T8RHa+nvloyxiITp2acsSMNwPyU7njBrr6hzG3wChYlE4owE5JWmQW8kr+/jl+ox8yyCyA9s7cUB4cVK8mh2gRlvPkHk9RkLzXB2i5BqT4SJ5+0NLmycQkjGFeK9q3pDUAsP3AdAoxVJQ/P0yFbS99EsLbJV9DmnJ4KRPCrjlVIKN97p9XJakOUZGRziPMVg4MH/WH8/CzMeE89+V+viUPr1MwKoD5LHvK47CzOye/m+HoFCqBUKDPtojC3IJqRgo7Gd0zAEn/gnVPyK5AnMKjWC2eLh/iJEhegrRsgT3+nzISLIr1dP2If9RfSunj3jQ6wdJI+xNxIhTxjAxMnPwhWlcPk3nyFvGGUFcOTQkXpPKIE3OwiNu2zcHjzTQJ6vIu9DPNxDvXCBOJJ45+B5uDVGZPOW7uk4vZS8/1tzctjLv7Wzk0nr69xurMymxsjUL+VblRhZIPrR5WJEImJU40GR5HllTg5CedS/Em1U2HWINtLnvDnZsEbp7/0dXj6Ocg+FmORsMmtgkUETQlD1+MEBRrMWXFiGP//rLgBA8dh0Ho/xWALn/5z6QF4LUOJsAMgvr0R3CwlhVtc44HVRnTS6MQgFagEAY085F0FxFDxuBnnem995HwoFIdRGiwWDvYQMRcIeDImkvmp1GAPddF9+GdUhFGjlulmzcqAX691wfys8IgjjlAX52LeREGu1RoMxU+j48L3naI2ZPE/P64bRrGXUVqVR8dFaa90wP2fBheXc9x4VIVE6X5wTIKeXW9C2h543YWYO+otoTFuUSlwuxuBLcAEAfuY24OYxNAY+8bixVBxlP9rfz0fE000m7AjTvB+NTMoj8hcmj2BVFz1jeUYG7u2gtqD7R0zrqNbrOXlvj4fGc0bhJwjkk/irSqHDax+J4/Cp9/KRm2LP44i3EJKKUponG3rsQFywvIdmYnCWeJ52iFMSzU5XoOVxQqBC1z2H4bmvAgCvIafbA9h4iEjh5glvM9r74cjHSEQJfVw7OAjE6TfhH2X/KEXwrq4u3H777fjkk08QCAQwZswYvPDCC5g2jeg6iUQC99xzD/785z9jeHgYs2bNwlNPPYUJEyZwGaFQCLfeeitef/11BAIBnHnmmfi3f/s3FBQU/HeP/V+3lLhlylKWspSlLGUp+9ZseHgYc+fOhUajwSeffIKjR4/ikUcegVU4UADw+9//Hn/84x/x5JNPYs+ePXA4HFi0aBE8YqMNACtXrsQ777yDN954A1u3boXX68XSpUsRGxWR+13bSXOalPuIj3S+1Yp3vhSCflVrmFjX0j0dS8oEqbTpFCKnAkDncmjGnpiocIrBwMKBUIYx30HcnoUWCw4LAukKm43PyXVil2tXa7BAhFYHEwms7CD0Yk1+PnMoBqJRRpcKtFomIspULcszMnCBSGnwicfNZ+CbPB4sVtJn70gIB6zULfLvvZEIAnvIk9bqVQxXxmMJ9t7CwRhzF4AkwiTt2IEYSsfTfd0tbvhF2G8sasTYU4So3NEEVGryesZNp5QYm9/9TyaQN+4fgD2fPJfmo7VQiXpYrDmIhAf/ph45hXSG73HtQ8NX9HejJQ1+L/XthJk5CApxzvbGIPLLyYucKLgP7/75CMsuHNreh/xyKlerU7EgX/VsB8JB+uxyBplvIUUux0zNwviZOdwnknvhKLagv4O87ng8zukx5PMs5+VjKqg+ao2S+7ktR8X8oa1eL5O4g/E4BwFIHs1owvGtnZ24VPDj/tDbi3WVJJKoVyqZmyTHCQBGmjJUqhN4dYEEIQ/bvD7mMUDfiww7iZUO190EALCPf5IRrIFoNMn1ePsO1IhEn1ve+S0TT/n+iALq7cRNiur1wJRf0yM0AQT3EZlUM+0mYBNdk7VwFXrqrqV6SA5G1IwLBJq10+dDzyAhCxNz2vBbQaZ92+Xivjnmo/7M0IUYRVo7OMiyCzfa7TwfP3G78VXxGACAzqTGOpGIVZLlXxkcTIaGe73YKxCZc3cEsWs+zd/b7TmMMElxxTSbnseReyiEYYkoWbR8bfnkTDzyz5sBEAokUw3lCL6PTq/mdClFY6yMtM4/rxSfvUHv2OMaZAFZOaYiYWDuuYSW9nVkoekgvYvisWkwW2l8ObsMjOwaLRYEfNTuUoFyDXQpEApQH4VDMZx7FaHtX20ibiYAzFt6Pt7981PimdS+gvJ0bl/NkiKsf5XmwdTTMrD3C0KHbdlKvsZo0cDZRZ/94gdmwqxsnnfbPm7D4svo/TiKzczPHOj2Yd4yQmAlF6y82objh2ldiwSjmH4mrTNOo4JlBCpbIqgrIUTybIOF14sdKoG8K5WYpSdk5f7+XkZiKvR6XCLWX4tKxXNMjocbs7Nx3hGq//WFcbSGqLwLRv2wXtfkxgwrfb/HG2au7AyxLm9xxWHX09+rDQbm3b01PAxPVGACh+/DZac9BACMOnv8WUmkSd+LXEHa1ysUzEl8px+4wpGUvdnvpHXJsO1nAIDAmffjCiH58HLtWdAU/yXZH6J+6waUHISRWHQtvkuTnKZPXr/5W+E0nX3ZoyfNaVq1ahW2bduGLVu2/D//nkgkkJeXh5UrV+L220mqKBQKIScnBw899BCuu+46jIyMwG634+WXX8Yll1wCAOju7kZhYSE+/vhjLF68+Bu16e+1FNKUspSlLGUpS9kP1OTx3Df9D6CN2Oj/QiIy8L/a+++/j+nTp+Oiiy5CdnY2pk6diueee47/3tLSgt7eXpx11ln8nU6nw2mnnYbt24nOs2/fPkQikROuycvLQ3V1NV/zj7CTRprS9t8AgET60o7QLvqSa5/FFuGZL7JYsEF4PaF4UtBu2JOLUishSS2CLwP3eCDvfQDATbkmPHFgFn2v9uKiscTD2Or1YqHwXCeIiLm+SITPyNcUFDC6VB8McqLJCr0ereJFvuty4a1y8rB14u+Ljh3DziqqR9s+J3aUJs/RJUqx570W5gHkzSF33VvvxiciPP7KVacwt6e3zYOSceThvfXkQZz1E/Lw8krT0HyEPLjDO4jHUT3bwdEpnpEwZi8mtK27xY1m4e25BgLM85EikWarDp5h2t+Gg0FkZBN64R4GTOn0+nwjCqgE+rLgwoux5f3/AJBMo2LLMbKkwkC3H4kE9a1SqYJrgN5PeqaWo3hk+LXfE+Zot+OHlJx4dMbCBNa/TtIBGo2So9mKxlqZyyVTw7gGglhwAUUS5pelMQI1/cwCfPku8XVyryxFpaAVGM3JKD8pOKhUKdjTVioVLDR65KMOFI2lOrXlJyl6kmtUqdfjGSEEqFKABfRutNsZdXp5aBAhIXMgEay1g4OccmWK0ciITIVOx2KMtX4/PujKFJVuxyNFhOBIKQxXLAZP68X09+wvUJlODTw2ks78B2iHMLuYxrwMTfbEYpDgc2TP04w0TbUksL+DZComFu7n8Z+pVqNeCA4Keg32bP09oVEAphuNfO08s5m97eGIIpn6peo/+O8yWerDbQm8Oo7m+qrOTkadBqJRRpKCiQSmiPk5NUr3HdJEOS3L+VYrfAcI7Rkz1c58pGfhxq+M1HcKC703VzQKk5ta7vdGOPVOa90QtDohS3BkkCNC/Z4wzxH5XZpNhzOEbMErv/+KEc7WumFGOD3DSmQX0NzLKUrygLZ/3AppEu08ursP0SiVXXVKGs8n91CQ04wYTMQvioQHebwuueKf0PjVu9yWSIjmm95kwkB3l3gGpTrZ+/kGLPsZrUmf/eUYz5ugz4bBXkJnzrqsCFveJ6R4uL+P6yfnRMNXg/jVHwlV/vDFOgwKDtXEmhzmU9kLzKgUUbgSoTu4rYflU1p0cWT20DjJLUtj+Y5pKj33nanQxIKoEn2NeKJoPkJ1KxmXgds8NP5vzs7hcfCM08mov5QQ2On1shDm6HH36MEpHKE6v+pTjpTbs3sVZsykcH/JhXplaIi5dvlaLXzid6f3szWI5BP3E9Zanm/LZlCy+Z0+H5y7H6O/F72GqYWELO4/cjnPhQqdjiNCd4wkcHqG4Co2nAYAeLbmIH4lTjsWWCxY103tqsrqZl7t/rZZHEH7j4qeW/+XX38rSNPiS/74N9/ffffdWL169d98rxfv59e//jUuuugi7N69GytXrsSzzz6Ln/70p9i+fTvmzp2Lrq4u5InoZgD4+c9/jra2Nqxfvx6vvfYarr766r/ZmJ111lkoLS3Fs88++43a9PdaKnouZSlLWcpSlrIfqH2b0XMdHR0nHM/pdP/vzVg8Hsf06dPxwAOkQTd16lQcOXIETz/9NH7602TOv/+aniWRSPyPKVtO5pr/TTvpTZNMxjtjyp9RX0cROs8drcaickqY+cT6u7HoNJKP37DjN8kEoQVvMUfi7l5xhty/gCN/BuwvsVdxxyl78ZmbdpUFGg1zQB4RnvuStDT2xvUKBR4UyXhvsttZF6MnGkGumjyWJ9pN2Osgb0mez//W4WCBvWsmZGHyTkJ4DHOB7hbyOi2Lc1GtEqhMF91fWm1jtOTo7n4WkswuMDNysvCSSvb8/mPNPpz/82oAwPnXUTRA8+Eh1iBy9YfYe+vrMMJsJcQuzabjiJ7SCYTA9bXvx+R5VgCAMe1UbHzrdXFtJooqqeymg7VIs1E/f/Ly88iwkyfq81AfObt9zLGy5RgRFv0Ri8VgzSI0Lej3oXLyXKrrEUovoVD4sEO0Lzu/BJm5hNTs25jDXuuezztRLvgULmeQ2y2RqPN+Np41cN568hB76NU1Dp6Mvg+64RUaULsEt2n24iL27IcqDexF5jYEkCYEQMvPzkffDkKSbC4V7NOoTlLc8nyFgj3R6SYTBqNUxl6/H08cp2vvGqvBvfsoomNJFSUyvjUnhxGqDwYjjOB8MOzDbAs9e0fHONiza6l/w5TOByCuBkDedWmFiERrX4BWA2nFIOjgiKBcyzB2NIjIHiHmytpnADBpFTAwDwBQkrUbK6fTM67cejY0he8AII8YfuLj7Gk9AwDwm7Pv5ejT3kiE0w81hUIsRglvOSwV/w4AuCaL5mNHJMy8rm2TC7BGzLE1BQUc9fRMURHzmJpCIewQiESrQArqR4KM9i60WHConL5v8XlQJRDclZocRoe3vEucm5JxNqwTY+386yawllCZ4AwBpP214jZaiwbtapQ00RwqnEAoRtvBIUZzrVkGVE0jFOjjl+pxzWpCG758rxlKJV2zaz2NE1OaGqa0bPHZz3PFOxJGTiHNpbaGINwiQjAei7N4pYwunXXWj7DvC0rHsfOT15Nj2w0supSQuS/eakKajdCvruOUuFepBpxdIpIuWI/ZSyhyt6fNg+MHqe+6WtwsWJs7MweHtlP9JUJlsGiw7hWab2qNiqMJC88pAD6m9c5k0fBaJedjdoEZbSI9S1ejCyNi7VGqFOgRqY+6rhmD2nSaNwuR1CGb10DI+8qcHEwX4pxbQiFkiGjWvX4/omINX52bi2fEnCwQvKNbOztRIRCJWx0OLBDl5RZuRI8QrNziDvJ8u2j+o9jpjXDZAJBovgYeay0AoL71KkydS3qCWUvuxP4Rui/D4EGBQJI+cIoUQVlGrBs13/YLwVf7mLXo3ED8p+of/Y6fc60jA8+1U12lrentxWyZvqixBoYDhEA1mUwoPINSHN01uQH37joN/0hTKr+FTZM4nktLSzspTlNubi7Gjx9/wnfjxo3D22+/DQBwCF5lb28vcoUuHAD09/cjJyeHrwmHwxgeHkaGQCnlNXPmzPlG7fkmluI0pSxlKUtZylKWsm/N5s6diwaxCZbW2NiI4mJyCEpLS+FwOLBhwwb+ezgcxubNm3lDNG3aNGg0mhOu6enpweHDh/+hm6aTT6OyQZC4ombcXE0KrTkaDe4XEREVo2C6/bvuTuo3HbkcKHoNAHCZna75zONhTkSWWo2HN4qoAn8B7hEJFc0qFZ+pS55JtcHA3ulnHg+fqX84MsK8Cr1SyZ5yllqN64bp+1czyfO9KisLDpG2oTUcRn6I9o1avQp7IuTJlQ8lYMgnb+rLl8l7GzvFjpJxImHpvqQy9uGdvZxgN82mZ6Tpvec6MPYUaq9JeG8qlQL1IoFoybgMjsqp39vPqUP0Rs0o7pEv2f+czmUYc84+HwDQ2XQM2SJlQcexrcgpInXw2i83IU9owEhOhNEcZx5TwOfDnLNpp797Qzt0BmrLsLMHiTg9Z+wpWeJ5Q8xv0upUzDM5sLUXtlz63mBUQ6Gg8hKJXq63TH2RW5qGQsGLatzvhOpculazcRBqob0k1Y4BwDiGPtf+ZzNOPY/QvQ51jFWJs/JM6FNRPw9Eo5yGQ9MdhDKf6nREoCJTjEbmwZlVKlYBz1KrORpsh8/HyvGFwgtecuwYozN7/H6O6Nnr8+GDQqpTbsNh3Cq8IgBcnkRDRycQXZKWhg8OnEcXZn/BSXgNuy5B8Xn3AiANHADYU/8jTB1LiMXRj36H0BzSN0PYBuiFqrwynESk/EUcrSqjfV5uKsNLp5Bnf+VBPSrtxB0r0Go5emmVw4Fa0U+HxL+/ycnB9e2ERlTp9Vgu2u3QaDgqqjcSYf7f9XY7e+Oyn/UKBe41EsLzUtzD/ajqCLA69ZaQD1HRb6p1hJpkLM1HtUAeuupcPA9a64aRO49QoECDh9OaRCJxRjslD+7A9h5MFel4Xvn9VzwGHUUW6E00Tob6/MgX0XPrBU9x7PRsHBHcw3AoBouV7gv4hpFhp4gys9WK44eIf6ZQKJFbQm2R/MdIOBf9nYSSqjUaRobs+VPRcnQnfS4wIyTQU7O1StS9kyNpx56yHJ/9hfSAyicmeYg5RRbsEkroQ31+KBQ0D6/8F/LmXc4g98twvx2hAKF39kIz5oqsBsPpKozspGsk0mQ0a/mzLceI/hjVrf7DduSeTVynAq2WjyR8A0Fs1ND4PjNG7V6nDCBDzMGpHiXujREad73dzqjmVq+X58VdmTRnbunr4nF0OBBgTtMEgwGvCkTv0L5bcNFcijZ8s6UQF5VS/+4Ua3yN2Yw36yh9DIztSbS2fhWWLPgXAMBnH/8O88+m5LySmzg8XAlNevJHXeqiKRyfcgRey8GV0I/SFhwW/NiMC+4HAHT0TebIOEXRG0j4hXbQ0EzmUCkCFpy/+LcAgL+WP4Tv0iSn6Yt3fwOz6Ztxmry+EM44/+GTjp7bs2cP5syZg3vuuQcXX3wxdu/ejWuvvRZ//vOfcfnllwMAHnroITz44IN48cUXUVlZiQceeACbNm1CQ0MDLILPfMMNN+DDDz/E2rVrYbPZcOutt2JwcBD79u1jDu93bSlOU8pSlrKUpSxlP1D7RyiCz5gxA++88w7uuOMO/O53v0NpaSkee+wx3jABwG233YZAIIBf/OIXLG756aef8oYJAB599FGo1WpcfPHFLG65du3af9iGCfg6SNMnxKOZUbIbe1ppF21xfAmPVzDf9b1QKMlLSbgmcT6g2dOe4igkyS25uyWW9JKN7SgU3ACrWo1D4hwa3gosKSGUZ8+zhESdesML7CncmZvLZ+NmpZIjOg4HAqzbszwjA38QnIy37RQts1ER5CiglR0dOFO8oH1+P6NSZRNsrNwtUaShvgCy8si783sizLXp7/Sytsyh7UGEAuTJrXxsPrZ+QN69SkMveNsHLRyp0t7owk9uoUio1roh7N9M+9eA7zgjWod3kAdeNd3Oar9jptg5cs89FEQiQYjQQHcXtMJLTyT8HNGTmUvISjgYQ9dxkZsqQ8VaK1qdGpYMoZkFQGug+o+bli3qNgyd8NC/+qKbrzv9x2UIh6jdBRXp2PO55E1omQ9isSURNqmU3t3iZvXzmYsKkT6W3sX+v7awFpWMvhs/M4e9+H2buuA/ldCZzL1uKGroc7VezxEue/1+5tKki0m1y+fD7QkrAOBZtQfXiMidgWiUkczDgQBHxMn7GoJBvNhGaAS8FXh1Nil7X7X2RswTeRObgkFYhYd9qGU+ppaSJomMHH2ltBRLmwgBWpqejpc/oxxyp592B3aM0oOSdsUofaTI8Z+JFzKEqoqPAVD0qHvrYwCA7FN/jZ5DNwMAcic+ih6peJy1lf4N2/CbSpoT69xu1je71eHA4mPUlhvsdk52/AehsHtnVxfmimunGo2M4t3X08No8o3Z2YxWbfV6uU8lqlCg1fK1aoWCNbMqdDpMDVJ5LeZkol6JUF2kS2Oe3+fWKBaLeeqLx9EheDkZi3OhPEjjv7V+mBXBZTLt9kYXz5WyahsWXkJaXL+/YROjXEaLhhFMmTPRatczWlW/rx9mK30OB2Pobae5Egn7kSv0z9QaJeeclGtBKKBB6XiDuLYQnU20fl3267H4jzXE/cywFzAaJTXPaD0RzwgpceoFNB/72jwYdtI1CkU/fG6aC5PmnopQYJcog8Zo1/ER1O8lDl48HodCQfdNO93KueWqaxw4JJI/lzYSp3Hs9GxWQnfOSOOI5cF2L/NYsvJM2C74TVa7Hj3VFFFZ2kxri1KlQFsxoURAcvyP5jTdkpOD9YIHJ6M3W0dFRZXodPw7cV9PDyfO3uHzwSfm93Sjkct+UeRrhL6X88qVaLWsFfby4CBuEtzCh/fPSCJQEqkdmonTJxInsOXuc9G6gpCt0yt3YGMbKfPPL6rn3HmrOvqwxErr/0WCX3NndzdHpS60WBgpe9flgidIY/fxcgtzHYem/Bu+S5NI06YPbvtWkKYFy36fyj2HFNKUspSlLGUpS9kP1pRKJZTKb0Zf/qb3/5DspJGms49RVMImjye5g9+1mDUoZo/7ADv2EyJkqfoTe5+PfXA3qhfcCiCprv3hyAh6nIQ82LOOwuknD2N2RgTDwpMIxeNYnE472ousGXyf5EIVaLVY1UWIxAKLBU0iGmyr14v78gnNeWt4mDknqwX3anVuLp5wEq/oMXs+jiSo/nqFApoGkRNqkg2RoWTmbwBobxxm9WqFSoGxQkvpy3dbcMZFdNbt90QYBTq8o5c1m9YJ3kReaRrzB3aua8dcoc7bXm+BSk3ennsoiEiY2h1PEFIQDRmw+HJC9PZ+3ok2gYJZMx0I+qlOsVgM6SJ6bqCnC+dfR/17cCs9T6XuRGOtzF2V1FUaOz0bhcJbbW90ceb1U8+j5w32+dEgeFinLMhnHZqhPj/zsKbMz0PTQalGHmXVYRn9VD4pE33thCa4nEFUCB5Ka90w80yUSgUjARJtKKiw4lgp1X+KwcBRkQkFEPKRd78lHkDGbqqzbk4m82Qk56E1HOZoHyCZN+pGu53HRDAexzRxzUejUBH590TUiGWZGi5PRpHVmEzsSe9xaXkuVJpoDB9rWcRco4k5bRzBGRmeSFplAPGUCkgR/J7SpEZUy26hR1PzL6hvPJ+uzXufuRL3T2nEbzeRBlTuhKfQ00O8jvklFPXYGQ7z/GkNhbGun1CK3pkO3CA4S5+5k4r45XryRA2K5OJYpdcz5+TJ/n7sq0rm0NropfdZHwwywiQRP6tKxZGtmyxFrP21yt+PB/SEIKgyddx3CSeNqfc0AVwk8qD5PRHmB8YKDXBupTHY0+5hVMmapccXb9L3F1xH6MzWD1s4+rThKy/yReSe1xXmuk85NQ9BMX56xbis2+Nkjp4lXYuAl/rutAvSGTkdM9WOj9YSEhsK9EKhsFL9ojRPNbpMREI0D0YGQ1AoqX3xWAITZhFx1VHixH6BpI5Ge2WuOK1eDY+L5qNK5YQ9n97rsQNfsh5cV4sbmeJ6Oe8uXTkFjmLqu8/+0gSvi/rUUWxhJExvVOO0S4iDc2gToR+dTSOsZZWoMqMwSmOwty3JHduzwMKaWp8n/DjbIDTexJHNtoCP55ghCrzpozn0xtAQ3hU8oD1+P7oEn/AVwVe6JiuLkaFrWlt5HL0+NMQcqZ6AhvWRmoJB1mfa0EPjyG5th7OX5kRp3l6eN/bpt/HvSobBwwht5en0O1Y/kAe9hU4CFOvuQyBXHPcUvIWLyknfaafXi16xXkRarmAeon38kwAI2eoUSNP1WVm4u4HeW2lWM1q+orx3U2c8iEsE9/Z2x7/iuzSJNG1dtwpmk/5/vuH/w7y+IOYtWZNCmpCKnktZylKWspSlLGUpOyk7eU7TXyhqZPbkl3i3/2LdRFQWktbI0vR01kqq0uuxcSNFHcw+7RYuY8eRH9MHay2WFdMZ+V6/n1GpWr+f1V+DiQQ/R/4bjMeZh7LK4UCr8FyC8TjfV6HX40bhST9TVITJIqdWWzp5RWoko5suSLeiJUJlRI6MYHAMeUvlOh00wsPe9hLxEk49rxR6E3mt60Ne9rZ2behATGQGL5uQiXCIPv/nnw4g00FRK+GwUIh2BqBWU1tsOQ4UVpKXYrUbcGAL1WnCrCQ3QGYcb290MXrT3+llzoYtx4Ajuwj1SMTd8LhkLrsoozaRsMyY3oc8oTQ8aW4uo1/zlpVwzjdzug5TBedK8hxUWiVUgtug1ihZfbt+rxOT5jq4/rKMcDAGaxa1UV576nll+NMt2wAAVzxcgz1/IU/u4LYenCVyZAFJz1WqO3/xZhN+dJfQ5IlGmSez64M2pC8i73icSse6T2k2PetgSS++xZzMJ1eg0TDac4XHgHt11I81JhNze+RYbAqFOH9UKB7HWDEGffE4a0DtGNYwujTfqmS+hbRDIzr8ppDq/PDGaxlRwtBM/nyZXYfXe+id6w2EBFbq9Tgk8lzlZnTw2B6uu4kzsBdmHmcvXa1QMGIktaX2exSck0utUGBYeMzzzWb26D/oysT9Y4R+2e4a+nvVp9wHneEwz7dXSktxfRvN2dedIbxRSf3/m85OLBHor+ShbPJ6OWLuGacT9wgNqF/2dWKVn8bgSJEOsT20XiRmEJLc91Y7yi8uAUA8s9kjSdVqyTdyDQRYzfqUBfnMtZFRndmFJlbXV2uU2PsFvdd5S0sRj0tFeSWO7KLxUT6RXslXmwdRs5gQ9N0bOliBXKFMQ1SgCRptEBkC4VEqiqFQ0jozcxGhXO6hIKM6G99qw8xFxOt0lDSjbg8hYp1NLmQ6aMxbMmgcHd7Zi6Ix1F+Flaej4atPAZDat+RUZuWZcHgnoXfn/LSKeY/bP24T5Y4wX7K/08s55Na93IAMB/VNfmkSISifR+8kEI8j3kXzx+UM8BysmJSFdqHfNNTnx7gFhDzr4sDxKI0riRTqlUpo2qiMnIo0Pi1YOziIBjHflqanMxoluT+m9enwziGk7BHfAH9/3UEr5hdQ9N8CiwX3tlB55s9+gXk/IVVqufZfYbPhsf5+bpeMoraq1XjzOKFcS0oasa6T6i8j5qr1ep4HTaEQnPWUJ1DfWoTgqYRWnZ4ZY96Vd1SWCxl93dGyDIvGbeA+kFytGpMJO/YQ33DG9D/yfY3Vf8J3aRJp2v7pHd8K0jTnrAdTSBNSnKaUpSxlKUtZyn6w9o+Invsh20kjTT9tIbXvl/uDWCTOmDc4DbCsvw4A4Fn2IEfBAWAuRItfDb2GvJCgk3IjoXM5KmdReU2hEGvk3P35z3CZ0OS4Lz8f/ywQI7vY2Y/O+7XX52Nv487cXKxoofPpTWPG4BMRpVGp03F0w2Ug5GU4PanVo1MqkSu8m4OvHYf7R+Q9Tz0eYV6E5AHllaaxXlHjficjOb1tHnQJHtO4adkcdaPWqFiZWHIG+ju9jMLs/bwTY6aSZ26161mnZaDbh5BAq7oEomRK12Kwm3g+0UgM3hGq/6U3T2bUafvHbTCaqS0KpRmzzxaIyQHyZsOhGGeTV2tUGBGfcwrMiEXJa9WbTCgdHxRtpPt62z1cbsWkLAz0EPKw4MJy1H5JvIhTFuTjy/fIM5xzTgkaRJ8lBOpjTteioMIKgPKFHd3Tx9f6PdQWR7GFy5MaWJkVaQgNUH3SbHqOqpt2ZgGjPVeaM9C438llPKck/kn1KN2u4CiujUSdagMB3CL4bje1t/O4kmPjxuxs5i5VGwwchdnhM6HSQt+blUqOANtw4BIsmfKfAMA5qKAdwvUiH96Lf7kdpeeQVkwskcCxLlIgh7EdVxTQu5We+FavF9PF598eLAHafwIA0Ey7iSOMvvz0AcRKP+DnTMw/AgCIiOlcP5CHiXbiZB3qmApLNukEeQZO4WuPBYO4QaBDdQIRCCUSeExE0r3lcnEfRBMJnm/LrVaOUL21s5O9e4l8dUTCuCyDeBxZajU8ov9nGozMhZpiNDKKNT8uVKiVShxW0zwYH1YxauNyBhESEarFY62Maqo1Sh4rkis40O1HdgGhbls+bMXxA4RkeEZC+PENpFT/2V+aeP5Ktf51Lyc1e+q/cuOU04jD89WmLmTlUXnVNQ601hE65nNnIBal/h3qj/O9eSU0/wsq0pmT1XFMD88wrWULLizDR2uJ5/ajawlxaq0f5rlePHY+9m4kNHHy3FzO1dfd4kZM8I2mnpbNP2JS36x2Szf317X3zsKhrVS3knEZOCpQrulnFEAldNE+fqmeypqfzPvl90R4jZh6ej5rjAXjcQzXUf0yxqXzmiqR35baAfiqqD9bQyHMGhLaa8UW+BRUxn09PYy4vDxI7+Sjigp8JiInH2yN46NqQjBk7kYA2NCVj2vLaK5HEwkej3Is7qyqQvkXYr6lHcWyHEKGPjg2lSO0r6jezqcLknc3EI2ip28y36dQ0xxcYLHwGnDs+Lk4XSBJGzc/CEwilW90/4j+Nbbj2VMIybzui6WMAi/KdXJO1qlGI+sJ/iL7TnyXJpGmnZ//y7eCNNWc+UAKaUIKaUpZylKWspSl7Adr32YalZR9DaTp1AbiJm0ZMDL3YqLBwB56fTDIZ71qhYKjCnoCGlYCl99t2X4nUE277lydgu+7JisLl8TIq8v6bDluXvBnAEnvIBiPM3fDFYuhQ/CRKnV6RhbUAHtIhwIBzN1LnlP2QjrDr9Tq8InIFVVjMvG59urubs56r1cokCnq1CfqnDscZ6RjzBQ7MquIx2FKKDjTen5lOra93wqAEKp5y0oAgP+eZtPz4G1vcMFRnIxCkR7j9o/bcExErRhE1JFKnYZxMwh52L2hAwsuoLP6ze81w1EklL97WpBTJLgS1iHm+UgeU2vdMPN8jBYtRyApVcn623IMKBUKylLB++juPhSIzy5nkDVfwsEYMoUHfnh7GIsvJ2/v/eePMrdC6lo17h/AGBFtaLUb8PbThwEAk+6ciOIu8o79njBHAp2xnCJ8/v3ePVgoon1ezI1whNuPtBa8H/bw5/2gOm3xemEQ7/OmLEJQZjXUY+tY0l0JJhJ4WnB+3nO5cKdAON91uZiP0FRNaMS8+npWy16Znc2e7TVZWbhTRG0GEgmME1wnbyzGkXvRUR66VNq+wGrFW0IxfGl6OnvpDx834aMpVOdz9yaj/GbkNXGdpWc/zWjEceEx35mbyzws58B4jtKTkUZqJPVy9ri0uKuY3vc8s5nzyd3pcDDaI/NDPul0YlioQs82mVnlfIHFgiXCw+wMh7lv8jUaTByF6gGASanEoOBQfebxMBp3aUYGPINBbuNxI/WTVAHf6vVyH07zqVC7hZDHMVPs+OItat8Zyyt4DBaNyeBoVRk5pjdqeOwf3NaDHDGvfCNhNB+h8XXqeWWjuEDUvnAwBpNQ6M/MMfD8GOj2IeCzAqCIOckx6m33MI9KRuJZ7XrmW7U3BnHh9aQR9denD3Hk2ykL8tHeSGiV5AxFI3FGcAHg/J8T+vXley1cdmaekZXEL/7lJM4X99GLdQCAuctKsfMTQpSuvnMCXnukFgCw9OpxHMVaPdvByK5EqyfNyWXkt2qanRHvuxJDeLKQsg007OzjtSoeS0CTR33jPEjcMevEDI46XWGz8TvsDIfxjhg/lTod6+N9Iq79TWcno6veeBxeOV6b56HhTBEZ3djI88nZ9BPcUUN8L6n9NRCN8vNqTCaex5F9T5DyPgBEzaiqIhRYjtUSrRYPbyDe0fw592FLL/HZjszUMy/QodEwYtQVieCQj/pGRtEhbMOSAhqji9LScJ+Ith2OKFincNHYzcz5+3XOXfguTSJNezffCbP5GyJN3iCmn3ZfCmlCCmlKWcpSlrKUpSxlP1CLx+NoampCf38/B4NIO/XUU792eSe9aZIci3klSmzy0OfeSIR5DBuPT8NAzmYAwJOFhbjuoBUAUOVo5DKkx3D/kj+hREdezGduN3sgN7a34wOBJMVWfIzeCHFOqo9SrrurMjNZZ6dAq2VkKD+uwkte8t6WZ2SwZx5NJHD/WEIk3lcQ5yGABPNC3hoexoR9dJa95qxC9lKiiQSmiWN1g1D9zZqUibIJ5I27BgKIH6W2qIos7Kkd3dMHtYa87aVXj2NelNR3Orq7j3kHZquWdY60+qRXrdWroBJo1KS5hISEQjH0tFKdLdZMfvHmNBuqKN0c+tqyEI0QQuUdiXD+N8mrcA8FOQ9Xd7MbXpE/6pQF+Yw0RSNxBIQnKr1ThUrB0UgtR8MwWuhdnfev5fj0EdIESiTCaG9Ucbu2f9wKALj+forIGuj24/3n6R2ufGw+zlxO6FhafxwHdyf5C9U1hAa6hwmNKL5zAvo3EjqwZnoZBD0CrXXDWC4i8/o7vZgmPP7yTB1iYozdLXLurc7LY37d6q5krqsqvZ7Hyb15eawMb9pCY+CFCdmMsmSp1Uk9lrY29o5bw2HOhfih14uIjxC2a4uo/g6Nhrk/rw96gCh5nO/0A1c4ItzuVwTHY1FhH9fNGzfwMw4duhoAEJv6Mt9zTVsbVolM4c8r6nheTDeSx/z8wADPN4QdWNNLnD+9QsHRcec2NSHYRxnYeycQz2mnL6m5U6HTceTq+VYr1goe2flWK/M+HBoN657J7PaTDQZsEtylm+zZXDdXLIa1MRprP1OlIbKFyjsk5kykx4c5Ik+a0a5F0Rji2pmtWlT+kvK0tX7u5MixmFnFGlASefGNhBERY75un5Pn0NGePkwR/J3XHtmP2eeUAABzDGu3dGOoJyDKUHBE3PEjQ9DpBvhaOZ80WjMGe+g9y+g6rV6F9MxJAICyCfWM7BaNTapyf/DvbSivpmdKzbMzL6nA208dAkCaZpJPqNHmweWkSNPJc3NZn+2jtUF0NhFHSvKtZi8p4jVkqC/AHC+tXgWrnZ6XZtNxf0lEadBA/EoAyD81B14D3XfHcQtrOZWe6mAuoFqhwIBYJ5dPEbzJYJCVszsjEUYWP3O7GXEs0WqxVoxzmRmixmzGj0WU5V3d3XxacOfc45jXQEjgHwoKcKVoN6y1eLCexveSIlq355nNPMeC8ThyNPTsZ8b/DogTKnhZvg9Vehozdx+iKOTfTDjOnKdgPA67lfqzpj7OuU47nOMwv4DGqzcWw9V2amO1gcpdO9iGdf20bqxrt+GlKfTsaCKB+tw9AIC9/qQ23D/K/i8TwXfu3Imf/OQnaGtrw389VFMoFIj9l4jnk7EU0pSylKUsZSlL2Q/UFErFN+YkKb6nnKbrr78e06dPx0cffYTc3FwoFN+8HSfNafrX7tUAgHu7nKgS3kp9dzVKHYQ2rMzJYe9zq9eL2r9ShunEkjsxWyA7csftUKuTObsCAeaFjI7Qebe8nKPn1jUTz2TwTB8ubaYorTtzc9lzudRmYz2N86xWPl93RqM4W6BYMnpi7eAge9qxNzqx5VzyHh7OzccIyIs88G4rdEvIi58QFAq57R7mHVky9eg6Rs/Q6lXsfR7c1gOLyJUW8kcYdeptIy/NNRBgvk/93n4svJQ4D+lFZvzpetK7mnVWDvo7CSWSOkgAoTUAecSSHxT0RTkL+raPOqBSC45IjQP1+8j7l1wKtUYJn0CR+to87Ekf2N6DvBLSQvIMH2ZOhtSpcQ0EWEvptT/WQW+gnfmcc4sZzcouMDO3xJyu5f6QXr57KIS9X1DE07ylpdiYSWXM71Xgr0+Thz1rSTFmCG0ZGcEz0O3DfqHoPN1oROYw3dd8ZAjp84izFK51obmK+vy8hh5EpxF/Sb5LtUKBgEDmjgQCrNMUBU5AJCWPQXKN5pnNeKKBUA+ovVhWSMiVXqmES4zjDXtuwEdL/woAOLc2DktaK/WjuwQA8Pi4CHvomzwejuKrNhiwo40id04vPcTXSLQrlkhgwwihHqh9DPaZKwEAzublmDj2PeqbaJTnU8Q5G/dMJCTpSdGOTWPG4Hoxfwo0mhMQWskLVCsU+Nkh8u6frRbZ32Mx5vHdmpNzQrSq1GFrCoU4V90UgwEVh4mjJvlgbw4P89x8tbQUhiCNy/0I8TyceDjI+kyGI4RK7S/X4HQ3jbt4LIH+TuqX3jYPlqyg91q7pRt5cwhtMCiV8AkUt0lEyR3Y3sNo6VmXjWHF7+5mN8/Hgop0+LzURqlBplQlf1j8nggr1WcXmPHZXyhXn8GihVYr1LMTTl4P5LW9bR64nDS+8srSMG4G1bOv3cNobm+7h/MwSl6VWqNkzpBWp4JGZCHoaXGjtY5QXgC48AYSlWpvGMasCyl33ra/ENfLOxJmjtS1j8zhjAaNtQMoOo3Wkf49A8xlKltIqJu/0YNEBY2Htg86ONIXuXocELy1WWENOkR0X/94I0xiTEiF72l9wE1q6v+Xrfl4T0FjyaxU8hwr0GrxiZvKuDmbnhFNJHBjB60L12Vl8bqdpVZjheDYXd/WdoJ22l6Re7RE5B31xuN4U+ic2c1DcHppvStNc+FGkblip8+HN4/MpWYVr+f7ZRmvO0PAwDy6r2gTI169kQj2DFB5L4xTMRdQIml7/X5Ge1tDIebevtOrxSMVVPZANMr6U7/8B0XP7d/+r7B8Q06TxxvE1Dm/+95xmkwmEw4cOICKiopvrcyT3jTNrCPSXDCR4MX/jbIyLGyk47fh4UoUZh7nayTJ9ldHTPx9jdg8LU1P503OAouFj0Faw2FOk7KyowOeKC1y68cSpHo4EOAF/w99fXi6SEDooeRi7NBoOOnq+pERFiWUG7fDgQD/SN2Zm8sh7V5XmOUCvK4w/F5aECSR+rnwCGZ/RddOOKuAf4gNSiWevYkStS64sJw3Ci5ngDcScvHX6lQsM3BwWw8KhaDdtg9b+cjBUWRhwrlcjPNK03B0N22ClCoFnGKzYrUbUCzKmDI/D5v+Sv0cDsUYnj/tRiKVvnn7bsw8i45E937eiVhUHGlN1HH90mx63rBIImx2gZk3Zu89V4exp9AC5h0JMRHW5QxgwYV05NbeOMzQv0w/MdibwKyzqN1V0+1MII+F43xEeddgLx7Kow2lFPEbLfPw6sgwZrXFuE46caxyTWsrL47TjUbeFElSskOj4TDlAq0WTwohvKZQiMUY1QoFk6blpr0uGECJltqtUSg4lP9Ggw1TO2jMbzQUotBFP1o32u14oo82FVdk0Zip0us5Ge08s5llEjoO3Yyr575AbQ0E+JnSakwmfDBIfbjEpsZsMebnmc04LNr3rssFnfCajoj2AeAfmw9HRlgK4M3hYawtKQEAPOV08o/QH/r6GGp+TJB+7+vpYXJ4lV7PKYnUAG+ErCoVrhJlTDEY8KbYTM0Q9SzQaNAoxutfi5LCoHa1GtPCNC4HLUrkx2mejvaC5ZFW8+EhZMyiZ1hcUXisVNPQUTeP7YPbenjeyKNwvzeMT1+jTc6Nv5/DwQW9bR4OTDi6u5+PrGR6FaVKwQ5DxaQsnoMLLixDgzgua9jXz3O6vHoqMnPpe5lUuvbLbigU1P/eERdOu4DWgvYGF29+4vEE13V0OiF5fLfgwnJ8JOQA0m16DiYhwU0V11XO2ZrFxdxvqnPpB7zv+ePs9AT8EfxYbLa2ftDCxyxSLHf3px28YasrUWNiF61riQoTPhPSLZepLVAJAnzbPieKp9G8SXhoI66xqBERn58NDjNlozUU4g1IazjM5cnjubscucg/TE7TM0VFnIC91u/HcpEU95ORERwRY36Tx8Prvyxjr9/PG6kenwkX0FKAd5orUJhDgsydWx/DymX3AAAfva1zuznFVms4zL8JdcEgRsRasMBsxn4xt6YaDLym7BDz4wqbjR2JLLWa5/p0oxFvlNGmtqa+niUH/lh4L75Lk5umA7vu/lY2TZNn3fO92zSdccYZuO2227BkyZJvrczU8VzKUpaylKUsZT9QU34Lx3PfV8mBm266Cbfccgt6e3sxceJEaDSaE/4+adKkr13mSW+aJLzf8tWduHYBycEvbWpiEveUHDdqAyLsV6HgsOw7xmRjp492uRIirZlZiwp9cucrvdk3hoYY4ny3vJwTpi4/TgjKpTYbeyBZajXDx6NFBtcODOBWAZmW6HSYN0yeRZpCHNeoFLiiW0gj5AL3RchTuLUsB02b6HmqOZmYrKV2rfeR9zDXbEbJQvIYOsNhjNWQt/jWE4dw8S/pqKW9wYUNAspffNkY9lwl4lJWnckebNPBAZx6HnkjIX+U06/E4wlMOZWg87AgobsGAox4dRwfwWSBZjXudyJDoD1+bxjZhdT/3c1uJp//+3WUvqRkfDr2iWcP9flxzpWE0g10+5CeSWhUb8cBpGcI8rrwZGcuKuRw6Mt/M4UJ4pOqcxmVKhpj5ZQpe7/oxOJfUHny6CS7wMyhzs2Hh7BzHR0bjb28DPlh8t6WpKUx6iQ93/5OL54FeadXh0xoGUN1OxIJYSlo/DybkYdhIdj3/MAAk/yl57g8IwMzNOTtqjRK/vvqvDxOC/KsvQCXD1KdRksPrBRHB1MMBkZDV5ZmYdMYOq7sjUZxjVogLkYjLsqkOSI97WecTqzOo3d5S2cnj1H95MfhilGdXLEYo6vSE6/Q6fBqpY3bJFOSfOJ283HzW8PD2HicBDLteVuZkP6oEAYMHrsB159O4pcvN1ZhsIDa8mFFBeY1kJDjcquVpQN6xDFKaziM6wUCd6nNxqHdzw8MYEcVkbGvaGnBMeH9qwFOrjq8jY4Gs07LRf94GperzOYTiLAGM13rDQah1tIC1hajZ785PIwlx2iels7JQaiX6qZyGFAgkwJPzMB+IesBJAmqcq6pSqyM4HS3uFnocqDHx/NpyYqxCIeoTls/oLKKxlhRv4/qf3R3H8/Z7mY3egS6BAALL6kU9x3C6cvpWFsKzJ5/3QQWvxzoNvM4L6228XF4WbWNUSW5FoRCMUw/g5C+L95sYhFO10AQPoHauoeCTGRfeEklXn1kPwBw0MU5V1bB4aE+8qXrOHhj/Mxs9GgEKm7RIl2gxjEdXRsORbkPMzYPwyPSE+UORDA9QxzjxlVo3Ebo7+axakTFEfDVChrnXbo47CF6hl6h4JOIuWYzp1G5s6sLz/bT2G2ZRGP/tGONfMzrHZUKq0Srxe1dRE5/ps+FR4poPd/iimOVg54pk7VfmpGBY/KUIT2EnUKi4Y7xnXiFQEasOu9evDKYRIQASrPynEB+17vdjDo3hUIsrdEUCmG+WC82eTx81Ch/g9YODuJeMb//MjyMRNgKAAgaQqipJ7TwscJCPs5P2XdvP/4xpW77p3/6J/5OoVAgkUikiOApS1nKUpaylKXsRPu/HD3XIjKFfJt20pymsxpvBUAcEbnjXmCxsBdpVqmwU3j3HZEILhO78QUWC4drlwuS7auDg1gnvGq9UonniulcvuZgL46fQp7HP7e34+NyIm/dK8LH55nNGCd4kc9pvdAJ73MwGoUnTjvGGUYTeyz1wSCWCi9dSia8NTzM5+KWliDeyaK6zdwTgPEMOhCfoNCifi9xMmRYcFm1DZYSM/eHKUGD6D//dIBJql3NbkwQRMqEAji6i7x+k0hAGw5FMWYKeTS1W7qxTRBBz/vZeGz9kF6uc0U+zu6mdkmP+cnbtrOo3qnnlzHa01o3xN7z3GWlmHMu9eORHb3s5QZ85NVVTtYyB+nQjjgsVpFcckkRpyFpOjjIJHPpPV94QzUTu/s7vdwfQ31+Ft47dsCHS35F6It3JMQ8JFkH+RwAePfZI7jqt9PF39uYkFt0cTF09TR+HssihOGXfXq4x1OfFzljnIzX74kw/8xUaGI+m3lU0szpo0TzJM9pg9vNaXNePFaKpybT+6nQ67F4P/XjqxPpGa2hEJdVbTCw+OOtnZ2MJJmVSkagbsnJYe5FreBYvFVWhleGaMD+7JABs3MJ7flszBg4DhDfYmFaGiNNsh16hYIF+2r9fuYY/TjDil1eISUB4D7h5a7p7WWP/eUhcq+7whF80EbjYfA0BXvmzx0+BW/PIR7WJo+Hy5Zo7+Jt+bhoLNWtRKvFj8U8LhmM41Wdn/vW8hXN347JJibPS+mBXw+ZsIWqgyVpaVAJFCyWSKBXrBdT9ckkzxLVXHx1FSPNrlgM61d/BQBYfuNE5hVZ0rV834+uGY8MgSS9fO9eAETyNgnO0LH9SXmCxtoBVAkuTm+bl3lRUrg1HIwxUbyn3YMckWZFkqsB8N8BQkMleip5jHu/6OTEu437B7jsSXNz8dzdxIMbN12DnetpXkjU6uOX6nlOHN3dx9IbH63tx9TTCFFtPjLIPENbjpEFPCXBPK8sDVm51Bd+T4R5gRfeUI3WTKr3RIOBZSik9EY0kUBsMClYK9s4YlMjVucRzzOgXYT9F1SkoyeDrsnopPabysywxKg8lVbJ3B79KCL4Xr+fOXGS4zPNaGSuaWc4zPOtNRRiftNWrxc7+mhN1VtasEkI1RaKefyuy5VMKj00xHNputHIQQzeWIxlCSTP76rWVuwqp7K0H1fBXkYJtBdaLEzcrg0EGIFdX1mJxcfoFEES4NdVVvKzs9Rq7PDROH7WOYDr7Fn8WZLFH8gnXtV3ZZLTVLf/d7BYviGnyRPEuKn/+r3jNP1vWAppSlnKUpaylKUsZT9IO378OB577DHU1dVBoVBg3Lhx+NWvfoXy8vK/q7yTRpoe6KFdcoFWy971PLOZkyuuzsuDU3iRawcHcYXwJkZHEEmeyZL0dBSI3fwbw8OMDN2Ync27/LkJPbYpyHOX8vQfZBZhJI28h5r6ej57HqvXMxIwta4OzwvkaqHFgizh0Q9PJN5ROBjDOyIFhzcW4+c91t+PV0SEUWZMic/D5EGUHycvLK80jaPCsvJMnIy3v9OLmYuIj3B0dz/zcvRGNQ7vIG/vR9eMB0ColfRwR3ul5nQd3v0ziQsG/REO8S+rpj58/JZtzGMCcIKXLJGfpoMDnLy0vcHFEW/yu1cf2Y9K8VlvVDOH6kfXjOe2VEzKZC9eIlh6oxrNRwgtWXHbKejvoHdYv68feiPVY+ZZhSwdEPRFUTWdELuxiwi96N47gJhAqxzFFuz5gvgKM84owLpXiF8zZX4eC/UN9fn5X8lvqpiUhYaIaGsoxF7rG6WlHFEWSSQwxicSRQtQ0FTvxa5iGjOtoRCjnqtzc5nfsKa3l9GoV0soqmhWQz1HnD3W389cqLF6PUer7fT5sFJE7r3rcmFxHfXZdBONL8S1eGMsIR3r3W5Gv1b39LBH7NBomMs0zUR/f6bPhZsd9O6XpKdjaRMhQ9FEAq+XUv2uaG3lxLp7/X6W+5AcqtZwmNt075F8vDCV0MSH+/qwwEJtsas1nCxbpmT5xO3GLsFdOhwIcMTQVL2Bx/8dI73cj88XF2ODqL9FePY7fT72rqOJBEfKfjgygis85OkXVKQz/+eJNJprD2XnoSVB5eaHlGgREWcZdgOLMkYjcUZOphoM2CxC7qcLuYptH7dx+qFoJM7ojKPYAo+L6tHR6EL5xfRutz1M43bOucUsFxCPJxAN03gdPzObuUeugSDO+2fiGw13+jjprUbwqdJteka2DmzrwekiorTp4ACqptE4adw/wPy/LR8QumxJ16FcoMq71rfz/O88PsLo0dHdfZxsd7Dbx2jUrmpCZJa5tCekmunOF+KWe12QNmlukoeoyqT1oe/QcDLdkSmBSQkqr0Mdg2c7oXvZBWakl9M8VCsU8AhJlJFsekZhVMXyI3vsCcxw0hxMs+k4Vc5en4+FUuW4PBwIMD/oSaeT/x5NJBh1ui8vj38/rsrMZEkNmXKlPhjkuQkkE3XvD/hxv4jGfay/nyNXJdJ2vd3OMiM32u28FgBgaYFVDgejv5s8Hkar5DM+dLmY03u+1YoOMQd3+XyMctX6/Yzq/bX8IXyXJpGmhoP3fitI09hJd33vkKb169fjRz/6EaZMmYK5c+cikUhg+/btOHDgAD744AMsWrToa5eZQppSlrKUpSxlKfuBmlKlhFKl/J8v/B/K+D7aqlWrcPPNN2PNmjV/8/3tt9/+d22aThppUu67jh7mcDBKNFojY4HZzGlNOsJhbBOo0h8KCjDSQ9f8c4g4D5lqFRaYyXN5dmAAdwqvtDYQYORni9eDkEBtpNfRN3ESOqLkiXpjMdbWWd7czNEPVXo916kpFGL+kvTs1QoFMj3kRR7d04/qGjov95iUaPucEK390wzsSUtPKP14ALYc8jA6zGCvozMcZk+i4dVmRp2+eKuJo+pqv0ymSJFRObYc4wmCkDK6ZuM7x1FYTnWVyMtXm7rYy58018Eco7IJmTCkUX/t39jFmjST5jrwmfDAZX2AJCfj6O4+nHER8cU6m0YYlSINKOo76a031joZrRrq87MmTeN+J+aIVBRKlYJ1aComZXEbtwpP+td/OhV/FUl6z/pJJYv6OYosHPVUUJEOrRD1cwmRzrQQTkj78HaUyl1hs+G4EDNMs+mAXKGT85/NiC2lsSTH6NrBQeYgTe5NYFs2lf3W8DAjksdCIeY6SRR176h0ItUGA2s9OTSaE7SXJIqy1+/nsSY96QUWC+vCdEYiHI1XbTCwZ+uNxfgaqX3kikbxgRjzi9LSeJz3RiIcSbemt5f5GevcbuYNybQVBRoNJwodiEYxmsf5eAGNidHJUOV4rjGZ2JM2K5XMLfnM7Wa+0ZVKC6zHWwEAn4/L53sTjTTn7zG7eS3Y6fMxr2VldjZyPfQ8lzMITym9t9xhKteUa2QUo7fNg+rZ9C4Pbus5QQjTJbTVlEoFCk+na1S99B6c3T5EQkmU9KCI+rrglxPRcYSQLXNVGg6+RWNTElx7WtOh0dL8zy4w8fe2HCMjUOFQFDN+TBGve95u5vkkuXunnl+GbMGFaj48mBSK1auZT9W4f+BvNKLaG10cGeceCrIgZ355Ota9TGXPXZqD6WeISLOR0An8KgA4vLMPNYtpDenv9HJKlTnnFPO64DklDTMUIoULvTJE2/0YyaN33PDScXguIuTxlGMRWEQC4+wCM1RGasv+DZ2MBBdfRPMnU63GPrHm1vr9HH05EI3izm6qx315eRgW41jOH188joVibs5SGxjdd2g0fIKRpVYzmrOqq4s1xqyjEsXLuTQQjWK/qEdDMIjF6XTfFbZk+i2JUANgrT0AmCrm+nSjkefY9Lo6fl4wkWD+kuQsrrDZWI9wTW8vPhPtqtbrsUn8/p1vtfI8vTDjdnyXJpGmY0fu/1aQpsoJv/3eIU16vR6HDh1CZWXlCd83NjZi0qRJCAaD/82d/719P7ePKUtZylKWspSl7H80qdP0Tf/7Pprdbkdtbe3ffF9bW4tsQa34uva1o+c2bH4Qa5Y9CADY5/efoEEhd+h7/X6OkACS3rt3VBoJeSb9pNOJtnLi/OxJBDmqbu3gIPMipKdapddzFNAqh4O1aUafRzs0GpQPUZM2mSPseUs9muUZGdgrPIZr4xZE7ORlKbqDfC4/PNHMKsbd2wkdq5iUyfyhgW4fe6JpNj2aDtJ5+ZT5eRwJNJr3VLOEPLKGRBi+zXSOPnNRIQ/Evz59GDqh/j2xxoG3M8gDnbqFPJdoJH6CZykRKL1Rwzo07qEQmo+QFxWPJbiuS66gCBGFTonjQocmu8CMOvF52oJ8vC34SIsuqWRulYzcm7moEM/8dicA4NTzyth71hvV7PHLtsu+kUmJJX9CqVIwtySvLI3bcjwahkJEzOVPskEltF68gntituq4j76KBplvMaxNMNfG0+RhDastIR8jlbom8ga7SrTslVbo9WiSfIpgkDkIEw0G5lMUq+j+s5ub8LadPPfnwiOMVP6hrw9bhBeZoVIxn6LGZGK9GDnmLrXZ8KEY+1a1GouJNoSXciLsEV9gtbKCvVQGD8XjjNQE43HmYxwLhbBM1GOd280efWsohAv8VP9IHv17aXMzt+mVoSFEItT/j5dm4i2hYry5cgwjt6+M8sBvtxP66veEcZuHxnAsAUbSakwm5icuTEvjKKsnbH7+brSq+Oi0FGdqqQyFTglFhOapU0FrgVmlYuTLrFRiWKBOb+sD+HGQyog5dMiM0bvfFQ1gWpzQEInOBHwR5E0ndKBrt5OjQGcuKsRQH60Bzgo9JsdoLMl0Q90tbuZF/ceaI5i7lBCEGWcW4D8fJ17k8hsnYfcGQn7Gz8xh1FUmo66aZscXb5Km3IrbpnK0atPBQWx4g+ZbbklSrV/amCl2vPMsIV/TTrdCpaHxMNjtYz6kdyTEqZTq9/XjtBXEe+qrcwEgFFsiRoXBZJJtxTQret4hhLNsQibPX4kud5iBHDFnlP0hBMR6aA8rsB8i5U0kub4+ERxmBX6JBnWOQkAV7/XgrJ9Q3Yb6/FDlUB+1hsOoFuNR8pIWWiw8NppCIU5r8uHICM7VEwL1lHsQV8Zpju0zxZj/NzpqTf6W1JjN/FvzRmkpf44mEnhLfHaI34oNHaVom09jbXpdHf+WrKusZMRoIBrltaPaYODo8MdEVoHni4uTibJNJlzV2gqAxrxcL14fGsK6XlqfEvPvwndpEmlqaXzwW0GaSsfc8b1Dmn73u9/h0UcfxapVqzBnzhwoFAps3boVDz30EG655RbceefXT22T4jSlLGUpS1nKUpayH5zdddddsFgseOSRR3DHHXcAAPLy8rB69Wr88pe//LvKPGmk6U/99wEgnQu5E5+g1yMkbu+JRGAQ3v9yqxU/E2rLdzgcrOwteSZr8vN5V35mWhp+4qZdcKLEyN6LWaViz+NKK3ndN3d3cn1WORz8jNkmE3v8eqWSPenbHQ5kCE9B8qImtERQOoU80bdcLkysJ28qMiUdU/Xk9RwLh5AxKJKhCs9Lr1QiQ0lleRJxtGynevqmpnFS33Aoxvmh6vc5gXmE1hgPkHeUOT0TFh+hKcMmJQxOQha0OhU+eLEOADB5bi4jNTK575fvNnOkUV5ZGhYJT67r2AirdTuKLXjhnn3UX3dMZR0XiUp1No2gQegxTZiZw/mvCirS8aF49qS5DijFOzxWSu+qsiXCnvFgroa91ulnFDAqZcsx4pwrKeLq09caOfJu6T9RYlKfJok2nt6nhK6S3lWk2Qf7GPLIXNEoo0eSk1at1zOfZ5xOz5GJH3ndmNxLnzVlJngOU9mFU7IY+ZQRdYvMFtSF6HOlUstI4HuWEGsQxRIJRkEl3yKaSPCzo4kEe5RTjEbmNCy1WjlqTa1QML9pNMdC8qneGh7m3It/6O3FGhH59sbQEGvBjIx6nuRT3ZuXh5sFF+r1Hg1eGEtjdO3gIEe+vetycbTO6OSmMurUpFRyxN9GRT7ut1Ab/1BQwF6zRODOs1oZKTvfamWu1KtDQ4zQZqnV2CD66ZGCAmSK9q4XcyyKpBZPfTDIOk7BRIL5HXv8fljE+5ZaVqtzcxl9UmmV2PAaaRvVLC6CUWid9bZ5mBN36vmleNNI9T63j9pvH2/ldxh5tYNRzR9dM57HT9PBAZRNSKqGA6RFJJPZmgpN2PWfhBi5h0Konk3IWyQch3MUkizr8fPfzQIAfPQf9SifQO0uGWfDYzdTTspoJI6Lf0npGpzdfs4XKbmJBRXpHBn33F27sEAk443HEigaQ2O0pW4IaadQ2TkxJUJaei9fvkx9ZL+oiLWLtni9OFsgHbpwgnXdxs/I4bks50FGgQlvP0ZI2mk3ToBO5Dz0eyLwFNJ7G6vRceTqOJ0engTVW86JdSMjmN9EY7e6xoFfdBIatyzdymvxM0VFjA5JNHGr18t8uMVpaRx96YpGsUL8PlyakcHo8WduN3OF5BxbV1GBJSK6dLnVesKclXN6SVoa9og5K+fBBIMBfxHj7laHgxFhgOYAAGzyeqAB9fP1djtzEn8m5liJTse/L3Z3HLcHkpF9vxJ57SxKJc+xPxffj+/SJNLU2rQGad8QaXJ7giipWPW9Q5pGm0esCxaxJv+9lkKaUpaylKUsZSn7gdr/ZUXw0fZNN0vSThpp+njkYQCkW7StiLyiGpMJ43S0g21vdGGniExalJbG3KMak4m9cRm5kKVWs4KxWqFgzsZTWfn45wG676rMTI5YktyMGrOZNZ0AcOb5h3p7cW8a7ewf9w/iTLET9sVimC54GNJLdmg0UBwm73JDsYI94nVuN86OisgwUwIzDeTp/6eLPKWqI0HWH4qG4+yh9pTr+Fw+1hfEFwZqq0WlYg6I9H5yggq0N1J5fk+Eo1rmLSthdEmrU2FEcIESwjOWXCuAeD6Sr3DKgnzWWAkHYxxhVzIug5GmdKHOfUBE8AGAbqYN+aLIt546hPFCxVyrU7EiuES2zOk65JVRfwZ9ES6jywRGNwzOMNJzqR6BoRAO6KgtUjW6fl8/R+DFYwlGv8zpWo6O6zIBpjaRz0ygA+oiI9QD1J9d6clJmzscZ6TMqU0wp6FgVM6qRRaqc30wyBGQOd4EFBmEWHwyMsLITjCRYKSiq4T+rlMqGQ2dbzazJlhrOHyCnoyMLru0uZl1mCS6VB8MMl8kV6NhxeD5ZgtzK5pGZYJf0EiowfVZWYwYrentRVCM83lmM3MvmkIh3J9BnL9PIl5G8qSuzOq8PLwhPOn7YUOdoBiaVCpu9zyzmZXJJSflmeJixATicou7j8t7MD+fIwwHolGeh3/o62Nulcw874rF+NkvFhYzR22zOsQI2kA0ym0pDNK/8VgC/Z3JsV4+mdCgF+7ezTpH5Wfnw+Ci8fU6vLgMhN7JCLb6ff2sru1yBuETqt21W7rxo19TNOuzN23BP901AwCgsItcbH1B5t0d3d0HzXQaM6o6D3OhOptGkCv4cz0tbiy8hCJQ5Xzsm2bB2PaoeHaAka2cU3Og6aaxHQ3HmVfUKFTOS8ZlwO+lemYXmLH3cxrD8XiC0eZnfrsT//L8GQCA7R+1Ydoy4knWbaJ5rdYo4aih96AdjmDTX5sBAKddUwVfM407tVaJtAwauzISdXZHgnWjmg8PMrKlN6lZ8X8zAsg/SO/FMMPGSJGcr/Yx6YxUVun1jM7sDwYwQUHz456hPtbSk8ijKxbDXJEH8aH8fD4NeCI7Hy95af25JisLK0QqjBuzs5mHWHWEnlc6J4frk3CGsM1Aa1RnOMzzpkSrxY/30gSQWQCsajWX9erQEC4XvwOvDg1hTykh+Y+4nLhXoLWr8/I476NEsdeNjDDnKV+rRUjMiR0+Hy4Sa86bw8N4QUTp5mlvxHdpEmlqb3noW0Gaikpv/14gTaeccgo+//xzZGRkYOrUqVAo/vsN31dfffW1y08hTSlLWcpSlrKU/UBNqcA3jn77PgXPnXfeedCJjfl55533/7lp+nvspJGmoei/ASDlYHmenKlWo1B42llqNXuaADAVVOlPIl68Ic615Rn4O7ZCtAmlWNconRpXLMZl7/X7cXk67dZfcpEn1xuJcBTcNVlZmBGka7dowxzNszI7m8/Op3YnOErkJSX9fWl6OnvPvZEIzhVn/8r+EEd69EYifA4t9Z/yRxL4TEf3VR0Jwj6LkACLLw6VyAtX6/ejuI+8eEuJmREQiaSdvieE8TOFMnDtAKxZ9LzBvgBmi9xsR8JBRHZSe6Xmiy3HyFE+aTY9Z0kfOz8X+z4gXkXJuAyO4lOqFMwBkZFl3S1ujmaz5RgZqXENBDB+BiFNn/3lGOw/Ia+osJveldWuZ+Xv9kYXDGMJRdnwh4OYupIie3I0Gua2abqDnA9L5qBrbxjG2B9R+wINHpQJ3odrIMDRTTsq1Sj+lNotOVvPOJ3MVZuoN2B3gHgJrw4OMlK51evFDBEJ9U7ch+IvXQCAectIOXsEcaQLZY2rO9o4Kq2yJYKWMrovXaViPtIZAfpujzmGdcLzvbwOeHYseZHnW63MudIrFBzZWaLTMTo0T3jPB6rGIQDql/pgkBFXvVKJM43UrhmN9XhFqHzLcXJjdjajspdmZOD6dkJRgvE4ozpXWm3YG6K5oFEoeN5IxGmhxcKfs9RqRgV7IxH29L3xOHvb4wR6lqVW833X2+08l6YYDPz5Ek0aw/VP+4Z4jvQ1CQTXluSnnacy49mgQGv1enjEnDgrqkedMZnVHqCclbZjyQg3GU0IAPMHqLxoJM46QSPT03Ca6MdXfk8e44rbToEIroNvIMgoUTyWYORKkQDPIZPQImoNh7Hvz/TelqwYw3kO4/EEo8CDvX4UC1Rm94YOjo6Tc8xqN7A22fiZOVDm0/iv/7CdNc12bejAVzPpvisESvbVpi5G0ga6faicSwhi18EhzrEYDsZYQTy/NI2fLc0+Kwu5apqnG7weXosdGg1yBJK3TxnCLDXVSXIvN3o9MO1w0fNmWjChhcbzI5kBnmMfjoxw3lDljiGY5tI4l/Pg8vQMHAjT2pKv0WC9iHAbbZeZrdgRpuu3jtIwkmjQ6PscGg2PiecHBljlfqvXi3cE8i9tgdnC4+Qym43nbIVOl8xDaTLhMiv9lrwocjN2hsP8e1RjNvPfzznehMvEGvFYXx8uEZ+nGo3MEZS/GTOMRuZNWVUqvD5M69dlGTZu44rMTNTUk3J896Qn/6Zf/jdNIk3dHb9HWprhG5YVQF7hbd8LpOl/21I6TSlLWcpSlrKUpewHZ2VlZRgcJacizeVyoays7O8q86SRpv8YJG2mAo0Gs5W0a90RD7AatlWlwjgVeSMve4ZZT8Mbj+OZIkIZOoRX3hUOs4deYzbzGWGWWs0aHqNzCkkPPZpI4DdB2uVeEu/FOqHyqQnEEY2QBxuxqBFoE3pLzgDCk+h6GRnX3ujCAQfVudpg4HpMhQ53u+i8+6edahSKDOWSl3CLv59Risy9bkxcQN7P8wMDuEJDz3A5g1CLKJjsAjPnnpOoVKzVz56j3qhhPk88nmAUyGjR4tgBQhwmCK7Rp683Mvqyf2MX1ymvNKl5lFFg4sij3jYP9EL3qX6v0IU6q5C95y3xAPKOkten0SpZ0ykeS3CdpOlNao6GO1qlY+VcbzwO/xeEflUuzufISYU7gkMa8uCkXtYLGi+/y8khNXZpqc/t+z3Mp2o+PMgeu9S/2ej1oLKXPDlbjvEEjRyZ96pSq+MopN42D3+OlFM917ndzI2rNhgYZZnQk+TGZeWaEPRTnT7S0nvY4fOiREvXrne7uf5Vej1zmiKJBHYJ9GW51cpaMFKP5sORETyST976Bq+Hv1/d08NcugKtFucfp0it/WMoAvHaznbmf4wdBixCi2tFSwsuEVyJC03peHCQxmuJTse8DqmevLKjA08WkvL3Wy4XP9sbj2OW4No1BIOY4FWe0OdPjwzi8hj93ZepYe5SqUKDJ1w0LgeiUa5ffTCI/K/Iq26aQn0+22xm9HjkuAdbsuJ87RX9NP7zJ9ngE5kC5LgM+qNQlAkeYDTKCPVCiwXzdfR9SyLC68FcjZEjwyaJ3Iz7YkFodtJ9OafmoO0Twa1cUsTo0lebumA4h66XCF1xQo1wkMbtS2u+wkX3EeepKxJBvuByhVuSGmTFk2ysIyX5QwsuLGNe4d4vOvHjGydSuxIJaMTcVKoUeM9DCMjsERW3XyLici4CQKdDjYLeKPdNQxHV1bDBCePZVH+NWH+LRxL87KA/yvPYVGaGTkTsKixqJDxUnlyn+lRx6EQZLwwM4Eo/vcOsPBOu7aVosUe0dugctH5u8nhYSdsu+s4ZjfL8L6sPYoxA0w9t6kbBfJrf2So1ozwTBIdPo1DwuNwfCHD0XzCRYFV9TyzGHLwrzRn4KEhcLDkHW8NhLDYR+j37WAOeEfyhZ5xORkCbQiEeS1L7LxiPM0KlH4XUzjWY8OoIXXtpRgbrBi7WmfF2gD5LdLYrHGbV/Vq/HzfpaW4+GhhiZPfSjAxkhKl/DeZ/xndpEmnq7Xr4W0GaHPm/+d4hTUqlEr29vX8jZNnX14fCwkKER6HZJ2spTlPKUpaylKUsZT9Q+78YPff+++/z5/Xr1yNdbG4BIBaL4fPPP0epoEV8XTtppMkffwYAoArFsSsa4O9zBAfBaNYis4J2oAp/jD2d3/Z0szqqRB7shWbWzShyxvjsP1ulRn9MRF6pVBxd4+wgTzYaieOInb6rMZng6xCIUo6GeRglOh1Uom6FWi1Hrk0bERnC7WoYmqnOWxwJjniq0Ok4R57XFea8WBmdtBN9LS2IFT6q5+aMGM6NC5Xjdg9zfj6yRVj7J9ySVA03C87TQ6EhXNJM34VDMc5HFY3E2UtsqRvGOPG99P4Henwc9RKPx/FOnNp9mlPJeitanYqfVzYhk5GT5iN0zm4wazAoNJ0misgbAMhQqnAsnOR4lfeTFynL0upUjN7sSosxkqFXKBBw0zPWx32M4JTodGjfQ+iWROt62z0YM8Uu6p+Ay0n9b8kxsLe31evlaMP0bvrOOxLC4Bh6P5+53bg2TGhPZpGZ+T9renvxmEBUSnQ61lC6RKB/nyf8/I4b3m/H+POo7dJ7BQhFkTo5MvN5lV4Pp+A85Go0PM5Lp2Rhv0CuxvgUcKbRfff19ODekBUA0JZPY39lRwdHxj1ty8cNQ4R6PG7KQYtIzF6l17Ou0DRRz5FYjBHQzzweRnVWdnTgnRKClBsiIUZzptfXM59KeuDnW614QvDy5u4Nwn8qIVv7/X7MFv1cGwiwrtUtJrpfYUn6UZ5OHzIEyvXMwABuSKd6PD0yyAjBaJTr0X5CvuabLVggkLmmUIjHVDgYwxYHXbs8IwPHRF44GfU1EovB0kvvPpGnx8gB8vgT1WnMkdJ1BpnrBwAPD1EbJddrpMGNQBn1uaUjxAhOmk2PvV9QVNrkM/I5wkuitn5PGL1tNJfqStSoPEZzIq80jZXvv3y3GT++bSr1nd+PsZ7kXAaI81QmUJaBaJTHolmp5IjKkC+Kj18ijkvoMoqMO9OrQcxB9dQNRqDJpr5t29XP7Rwz1Y6vNtH4yS4wc0TtlFMJ8W6tG4JivIhyjcfR9norAMB0UQEjOy8MDJwQWQwQ0lYZFwiPP4IdugjXubiLxsZAt4/5ia9mhnClmz7LteedcUrcFKH2pReZGSXK12iQrVLztXJNeRSEtNWYTBxNGTvmxZlxat8fCgp4TuiVStxjJbTqk4iXeUVyztuO+PCvduqL54uLEeqkPm/JUsLeRPXYUgAsC1Gfyt+lQ5ooryElWi1eF0jUtrFjGV3tiES4n6YG1TzXR/P8NEdEnsBTsrjOOaM4Wbt8Pp4f/6jcc/29f/hWkKZsx63fG6RJ6g0qFAr81y2ORqNBSUkJHnnkESxduvRrl51CmlKWspSlLGUp+4Ha/0WkKS5pBaWl2LNnD7KEU/lt2EkjTUcDjwMgbQ5HMXkV2yJ+5jd1qJORb5FEgjVutnm9OFZdDQAcHbHX52N15GUKI0efKfwxzsH2ytAQLtLRjlbqmhyPhjkqxOKLY6OCPIlZbhV7QoOqOCtEry8A8zekx3Ca0cwRW/tyAOcjFDEzcVU1ZmiojIAazNWS+k7FXVEcz6N6bPV6GUGorAty/V7IDuNXIZEzSe/FfSLqQ6Jg0/qSqJNrIIiMcYTEjFYxL9Fqkd9K3naW0D7SmzSo30teZ9tEI6YKB7TOoUS6iHwpGZfBfIhElRl1/3Fc3Ev9Of2MAtaWKq/JYa9oQk8cvYVUJ+1eFysGF5RbAdBkkf3lKDbDb6a2fjQygtN6FFxPqQ5+xR3TGIGS5veGcchCg3ie2QxnCz1bUWiAQujXOIosjLbJPGKGsckIsKVWK2vBvDE8jHtF39rVavS10fUbLMkcWJ5W8oLbclSYpiIvs/nwIP6UQ2U8XlCIwXa6pidbjYwW+l5qUjXudzLf6vCOXpTOoc+P9fWxdzw/rOVQ3iP6GDLqaKw4q+jvNSYTZGbGNb29WC7q5v6iH0dn0bi8NjMLvxcITcaoHHS/NRNy8peIG+f6CYXYlRbjZ1tVKtakusuRyxyKSD+14221n9GZy9MzOKJxfzZQJ/pxvtnMfC+JZj0/MMDaRwmbFgknjamLPF2MLv0mmIZDOdTuWr+fdbCkd90ZiaBQ+GOjcyY2HRxEZrWV/1+WLZHazQgw2mgIJlCnpPGgVyjYu39+YABvCAJnvlKN9T5694VHCGHoqTZhbDfNdWuWnlW7Zy4qZL7a7I4E+gRKsnsGzePLAgbWJcofZ8WNQv35scJCjrLq2diLmYsI4RiOxzhyKt1Nz9ObNDgOqnNhUAGPidq+qqsLD8Zt3B8yctVrpD7MVqnR3ugCAMRKjVA0UVu3OBInrIFSD8pZoed3K1GdGpMJuRH67lHPAFaI92kajMCQQ200QIGnBwhJlX/XKxTcjgylinMRemMxLnvWkBLr0+n7FTYbDnxBiJCjiNa67EIzNGIN7IxEeM2sNhhQnKBxoNWpmFMm1ylfvo5/M27t7MTVB4Qa/Jl2XotlxB1AunAbxG/I22K9/CitEJlC923t4CDzThX+GN4P09g4Oz0dCrEm3esjdOm+vDxud/feAYyfJSKIPR4+4Uiv9WBwMs2FBRYLj8FxfurneCyBPit9lsgwQOvTnUKtH0hGC/7EtgrfpUmkaWjwj98K0mTL/PX3Bmn637QU0pSylKUsZSlLWcp+kObz+bB582a0t7f/DfH778k/d9JI0y/bKRvwmoIC3okrEsDrQjfjMmsGWiJUoYyRGD5Rk5dy2nDSw8gVOimmhIJVgj9SBlgDZKLBgAPrycM7NN2I2cfIwzNNI+/BpFQieJx27d0tbkw/gyKTets82GMXeh9aLUc0zTKZUB0lL9aQRv8e+LIbXhfVs/zsfNZsKu6KnpALyu+ReeFoX/l5ws/8qKXp6XzunaNUYyBB3z/rdKJTeBwParPQJDbkhaOUlHuF1xpNJDCxT3hWJUZWOY5G4sxlktyfjAITju8jD9GWY+RotjqHEqeLqKJ7B/twcTt5PeFJaYzISS/nUpuN+Sttn/egZByhA8M5Gnwg9EyOBAKMGkjv+nKTFT6RlN2sVHLup0VmC54bHOCyA13kEfpytKwLc5ebEJRAIo4/5STflaqE0JK6d9twZAGhbdONRkYZQsLjP6gIs37SOQojdAKlaw2HURglz3ZYmxy+USQ9b34/Gg1rEK3u7sbtCSsAQJNn4MiYifUh5M8kZEfmQTujOQ6DSYyZAiVzhTzxONaKdj9VWIQR8Y60OjUjB8p+qvMHuiCmHqHPPVOSat5qhYJ5DlJRHADn2Nrk8ZyQs+4iVZLDI9GL+mCQr3nX5cJcgdz6RLuPvHIcHcsIkl47OIg1+cSfWed2My+tPhjkfJDS6z47qON8i4ruIHQFgnPSF8RwJs2FcK0LlZOp7M0RP+YmqG9Com7eeJwRhNBAELf4CRpd5XAgx0vtTrPpee2QKOtdnXrkzKb3MPTVEHIKqU2HLHE8LHJSPlJQwH2mCcQZTbhY6OwAyXxysUIDMgX9UqtXcZ7GY5kKXCe0rz4XEbgqhYLR175olDlZJTodz6WrWluZu3f5oI6RYvOoHHoyKrIzEuH1YqfXy+NH9bkTpecSWqXrFZFyuckcngPRKAyCJ1MyzoY9CRrPBVotVC00xy5DHzaXUr0ld/GQJorZRpo/fk+EFb8X+7Ss6TSkjDOa6RM8rd0bOlCzmKKbw6EYoza71rVjxyliHYrFcKeIOgsMhXDBEK3RbwnEzxuPwyLWL3O6Dr91Eu9oitHIvLu5ES3MQhNrW4Dew3STiZHkou1uTBPq5+FgFAOCX2pO10KTZ+B6SBTOOIbG/j6/H5k7af1qnWHGPsEj+43Cij1m6v/FJgtHWW6sonF5aUYGfEdpnOSVpqFPT/1//K9tGPvjEgBAoVqDI7tp3OUUmvl3Y38ezeMZTgXzeEfPWSCp1H7Kgnx84qHnnJP+G3yXJpEml+vRbwVpslpv/t4hTfv378c555wDv98Pn88Hm82GgYEBGI1GZGdno7m5+WuX+bXTqCywWDhFwgSDgSF5XzwOywBN4GPplF4BoB/DlSJ5oTxeORwMonALDfQxU7NgyKdJbUoo0BenyfeZx4PTB2nCyeOvhxQuPJxLEyseS6BDHH60fdABtYauyVmcy4tYMJFgMq9MQnq6yoh9glCZbtNxmPvVvh5eEG8Jp7GAnkzs2aWLQ1Evk3UaeQI9avBwWHntlm4+0nEPBVksUx719R0aRocQVAwmEpzotESrxVOCgPwbgw1O8eObMUKT/p+9vXjKTIuWyxnktAcDiRi0wxHxvBC3JRqJ4/hhCu+VYnwbFUFOchsrNcIk+kijUCDWR++lu9nNpNzenVSf0jk5cB51AQByJ2TgkDjOUe4YQmAWlR0dFSJ87pCGF3KZOuUtt4sXlMFolAVKy3U6TFFSn++LBXnRv72b3s/lLUpOZ2EfSi7osXAcb/rEkaE6CZZON5nQd4h+gGUffR72YaF4tjce5x+yLLWaj7IaLAnesOg66TtrloF/fEPBKLaX0/i6Os2GY3GRmiMQ4E1HiU7HsK3cGFfp9djwPCVDPrLMhksGtNyPMmz+GaeTN3LvGejZy3w6tDdQnzdMN2GuyIBTOCEDt3bSkdxcsxnL0+i+cDCGp300J2WY9XCnjzfXkWIDb2QHenw8nwAgpzjZNwBgiCaPRArbI3gni97lQouFk6uqvDGEQ9TGrmY3h5jL44uxej1v3nKCCnwYk+ljzHDuog3nxHm5eFNslqSMxerubjwqCL69O53YN04cZcdiTOYf51bwUeP4mTl4boTGuQzA6N7eD91M29+8kzW9vbguSm3V6lWcXFUes+eo1ThHQc/YogzhbJGGpyUS5h9+S0yBhEb0YzTKY0YKNHaGwxgW40u9axjRWVSn7NYQjhVSTWaFNZyiSDGN3l9vNIp08YxCjYbL7Y1EeCxFtgzAviCHnzMxkHyHADDQ7ec1a+aiQg7ueLK/n+fe0vR0dgrk+FuZk4PYIF07nK7iNVxvVLMwaPPhQT5SO/X8UnSIPXyF2Age39mHjgnUd6WNQaRPpnbnqjXY6KXNW1MoxOH5IbFZcVboYdhH8zgxI4MdpPMs6SzBIlPbALS+ThbEd+ns7vf7MaWV+kgxPhkwkGj2ofmwTFNVCo+K3pH8bWgNh/laAFB1iOCUEjMHshTNsPN6ETjs4kTv8n3rFQo07KTfuTFT7Wito761TsyAv5HaXTTWyoT53JJb8V1aatMELFiwAGPGjMHTTz8Nq9WKAwcOQKPRYMWKFfjVr36FCy+88GuXmRK3TFnKUpaylKXsB2oJheJb+e/7aLW1tbjlllugUqmgUqkQCoVQWFiI3//+9/iXf/mXv6vMk+Y0VQp5/UOVAVxusgIA2htccAqxsVgkjtyp5OUqtnTjD2ck01xIUx4UMvnjjWiZR7vV/bEAftxKXum71ijODZP3stSejlaR/FYeKz2oyWW5/ikGA3LcgrA3N5dJxHlqLT7y0nPOVBiRERJhwRnkCTkTCQwLj+zoLBOuMJA3+7TfDp9M3qlUIpErji2EZ5k5EMPIOPLYPnz0MMbcREKEN7WaEM2m+ufNyUZnPXlOz2YEsEZFqJiU87eW6wDhpfRGIiz5v6agAJcKT/lQLIppceoDRRbV4UZjNr54iYjdCy+p4FQNsTPtmJAhQoFjI5gXptfpisUQqyWP/sgYal+hRounzC4AwH2adE7Tcbk2HTdF6NqV07LxG4FkrBBHqb5AAAOFgojv9WJWmOpUH0swKfldlwtLBcm50xjGxAjVyaegtu7z+zk9Sa5GCU039VdPeRwKA+3bawf8jFo+lEf9tjHNg9NFQuhAboIRKlO9F/MEodgxShB1VkKPyFhCo2RIdm96BBEh6LfK1YOHDELmwaZiArJVFWMvt6KQ6q6OKWETBFq9UYMhJf19+8etnERVbTTC7qa2aOOARoTrS1J218EhPhoJJhJ85KuJJJArjo0uP6bATSVU/4f6qU39Q15knEbojTkQQE8bjZP1OTHcraH3MtDqg7uA5kI4GMP1BdQuj0AHRrI10AsUrzChwssRQjfONerwmxi97ycsDj5elBIIraEQH5NqrApcp6c/fB72Y4JWJCrWKqHVU9nd442YIML3pbSA0RZDmkB+62v7cZFABz7zeABBrJ0IEo4EAAriB17MLUJ/C60X75UDK8Wc8MZiUIlFW2dUIkMcH3YeG8F5oh7N+wiOyz8zl1GUN4aGeEzdnpGNaFi8K70KSzQnesvlhwN4ZzyN19OdKgQs9Hmnz8dihiVaLZaJcf7hyAgWD9L7loEU03UGKLVUz2Z7EMcFSmGvMGGTCG+vyMpCgZDiCAmU1LfZiamLC7mPForvn3Q6GQnrmKzFRaKuDo0GYYEwD9rp2q35CZwOGmv5hw9hbxWtT/fl5zNaooonEzNbYlTPLT4fdsZorlylyMQGseZmqeIYJ4Ic7AVmRo2D/igqsugdDouAjqpp2RjZQEd2HafakD1IdXtZ6+H1fCei2CF+C2Ii4fuPVXpEp1DfffofjVj6c0rL1Nvi4aTeve0enkNGi4aPUCVi9ogxGy/m03dn7hmCXggJt+Wr0f5XFwAKZjGLtshUP5pIHG4xH5ur9JglxqtaoWBBZF04gXc9VMa5GhUHdUhxT7NajQmz6QTg6K4+9I+netoVCuSMo37e7PVi7n9JefNdWzSRYDrANynj69jq1atxzz33nPBdTk4OentJZiSRSOCee+7Bn//8ZwwPD2PWrFl46qmnMGHCBL4+FArh1ltvxeuvv45AIIAzzzwT//Zv/4YCkd7nZEyj0XDuuZycHLS3t2PcuHGUyFgc0X9dSyFNKUtZylKWspSl7Fu1CRMmoKenh/87dOgQ/+33v/89/vjHP+LJJ5/Enj174HA4sGjRInjEphgAVq5ciXfeeQdvvPEGtm7dCq/Xi6VLlyI2SmPvf7KpU6di7969AIDTTz8d//qv/4pXX30VK1euxMSJE/+udp00p6kv8hQAIguXi5De5sOD7HVr9UnQKmFUMRGuYlImnoq4AACL6+g+nVENiyAF3qEdxgvZ5GW1Nw6zV6E3qllsTnJ1mo8MssejVCW5DcYxFhwQKMQis4XP9iX6BIAJe55YjD3cyVo97u+nne/KnBx88sRhAMDxS7KZh2UQXnQ0kuTRXJJmxWY/1W2T14tbtOQRGy1avOgmD6hAq+XduUSans5PpjLZGPIxWVavUPD5+pP9/RyuKpMG3+lwYI3YobtiMSZrA+D7NrjdWCY4A2qFAvkh+v64SGmy1+9nsT1vPM7PKNFq+TlL09PZS5dpQ1RxSioLANuKKtGjIS9rj8/HKRwqdDrmGHRGItwuGYZ7WcCAQC69774vejFZeNW6OPDs0AA/T953WPCjzEol85zi8QSeEtfelGXnd3tYHUFM9PNU6HBhTysA4L3ycgCUyuGeIirvaYWdxe0+0gbZC3YNBDlFheS1lSo0aD5CaJyl2gp7gjzRHkUMdpEW4fjhQeaM6dK1iPlpfMs+8sZinHzZNRBA15nEtZnfq2Del3u8GVMFmiC5EpvXNnA9yy8sTgYrNAUZHett88Awn+aCodEPkxA2DB2jcZldYMbGdwidnLYgn7lLnsEgJ3PeEfbD9w7xx2QY/LxlJSwxcWB7D4rHUPuKZ2UzH+RJpxM/9tD1w84AVELDRc7dNJseG2M01s7UmlAbp/pXRzXYJWRCZiuTnDEZlKBUKnjudjaNwDKTULViVZIHVFCRjqt7yENcnpGBc/VJ+RMAqOyNc+BJXYWWxVOrDQaej5MHgFdMtF78RszzwW4fsnJprPXHojgi1pPitgiOF1Ofn2k087x/uK8PN4nUDPpRRxfyXV1qszGX8bNRCWxr/f5kOqbh5OIv5RBmtcXQIHiPhwMBDsjIUqtPSF0juU4yKfhwLMYk78vUFn6HsVgCY8UJwMaQjwMkFqmprceUEUaz9m/swjiRHmrXX5vRssAKALjCksHSEe+MuHBakOon319vm4cTZN870o87jFRPvyfCJG5ThFK2AEkeYtvBIeYudZ1iZnmY9W43cj+luVcyzoa/OqitK9OzTpA2AICRvcm8Yo5iC+LZNC41CgU0AmHWpWsRGqFxIHlaBxwK5slZfHHs/byTy5BIoHsoxGO6fl8/i6rKvvXNtjKvy+AMM4dw41+Pw38VrXFXGKyscaQz/ALfpUlOU8/wtyM5kJtx8pIDq1evxrvvvova2tq/+VsikUBeXh5WrlyJ228nwc9QKIScnBw89NBDuO666zAyMgK73Y6XX34Zl1xyCQCgu7sbhYWF+Pjjj7F48eKTqvfevXvh8Xhw+umnw+l04sorr8TWrVtRUVGBF198EZMnTz75ThCWkhxIWcpSlrKUpewHat/m8Zx7lAMAADqdDjrhaP5XO3bsGPLy8qDT6TBr1iw88MADKCsrQ0tLC3p7e3HWWWedUM5pp52G7du347rrrsO+ffsQiUROuCYvLw/V1dXYvn37SW+apk+fzp/tdjs+/vjjk27zf2cnjTR1NP4eACW7lDvxkelpCL1LiJJSpcD4GeS1HXMoMU1wXwZ6klEdHdOSQmHHN1EIqHsoyCk2ets9LJjmGgjwcySqcDxbicJ2EaFXqGYRO0uFBYoh8iTqjHEOSc7wxeEVHoYs40CBkr2UEq2Wz6fH14fYA9+Vr8DEevIYZfTGE0YvoxCHAwFO2zA6fHxVVxdfszInh5GT85uaAJC3+2AWITx3DPQwv+D14SE8XkCeyYLGRvxKeLAXiL8PRKOMVg3HYszTWO92cxlAEtm5KjOTo6wkanO+1crihK5R8OYGjxsWpYr7Q57b35RF7dsfDLBY3c8yMtEWo2es6e3Fc63kcVxf6sb9goeUoVTh8lbiXD1fUsLP8Yjkpmk2PXuXMhEtQCKiMxRUv0c9hCgtz8hAqYLGUXujCwNlInXHYBwhkXZC0x1EHTnumOxTcQoK6d0vTEvjdBTOahO/+2UxA95UUJ3G7/JxPaxZdH95dSa2f9wKgBBEiY7t+6AN+UvIG8+NKPGOCHlfFNCyVIT0yv2eMEwCUR3s9uGoCF8uPbcQgzuIx5SVa2KpC5nMOT3XiLaDhFDF43EUiqidpmAQ9n56Q/0dXk4eGw7GeK5Ibzjoj6ArXSCBej0iIvpJb9Kw3McRc5z5VyYRGWqotnICV6M3hg4Rih3b70JPNb2v0zRGFnEsjanhFLIPhWrBd9vXj8CEZPi1nAcjewdZ2mGkwQ1VJdVV0UrrQ15ZGiOxDw72cUTf7aoM9tYPbuvBe9Opn+7Lz+e0NzLCzapSMSdrndvNKURmGI0sZRExKBnBkVGfnaOSrz7Z38+pn951uVii4TO3m1EZZzSKkKif5PZd1drKf3+zrIzRqp0+HyPXzzidjEBJrubVXiO686nvYokE99f17e0siLpkFApsO+LDV5V0/eXphNIpVAoWKrWq1dyuJ/v78ZxIYmtQKk+IHgOATE8cHynpvtk94DQ388xmjrg8rI6gKiAS/JoVvP7ISMG+SARdbxH6l35hAfMlr7fbMUEhoqs1gFOMaWmlU7KYm1Si1bJQ8icRL69r295vRYXgV+qNyVQm6f00/tJsergGqP6DvX5EREqbvNI0jvgrq7ZhxzqR7HsJ8WFUrghqt9Bv15xzSlAXo2vHKrT48l0KQ68704qL/DSnc4ot8AxSn8pTlcM7exklba0bZnHU8TOzeS3obfNwmp3yiXfguzSJNHUMPfKtIE2Ftlv+5vu7774bq1ev/pvvP/nkE/j9fowZMwZ9fX247777UF9fjyNHjqChoQFz585FV1cX8oRIMQD8/Oc/R1tbG9avX4/XXnsNV199NUKh0AnlnnXWWSgtLcWzzz57UvW+5557sGLFCpSLk4dvw1JIU8pSlrKUpSxlP1D7NpGmjo6OE47n/juU6eyzz+bPEydOxOzZs1FeXo6XXnoJNTU1AMAEbWmJROJvvvuvdjLXjLa3334bv/vd7zBjxgysWLECl1xyCewC8Ph77aQ3TaYy8grH+3NgtdMuWhtWQX9RBQDSTfryPdqh1ywu4uSSZdWZKDqNIgyyhTCl1xRnzsaYKQVoOkieicsZQM908lDPLcvBEREpd+QVKvfSlVOwpYjKGH6hGZpfjANAHmWBgTyvaTEd1DHyRo7oosjKo7qWqancwO4+nCuE+RoSYViFV2ep0LM2TjBXy9EbPdn09+hggnVo7ocNcZFUdpctKeSnVyrxB8Hs3+nzYZVAe9aI757s7+dzfSAZZVWi1aFAkORaR5HTHhVcoxlGIyYbkxEY0usrzNJynaYbjZwe45q2NjwjvEupq3RVaytznq7JysI1bZReosZkwhXtVI+NFcl0GtI7WjswwF5fd7MbrgLyHG/u16N8LHkBV4Vt2Cv4UgPRKGvtyMhJNYAqB72HS9rbsERMuusVZvb+qw0GuMXEXGkkrzwyFMKDYUJnFpRYUCL0aywFatwhIh/mmc1YbiJvb6TXg4jtRG5SpD/I6Ia5wY0F421ct4lbqc5aoxqG2dRumSxYq1ehaAyVaxqfxsllFSoFa1y5h4LI30dcm/jSIqgV9P3hHXRtXlkajmqpH8dolMzRa37oAH5yK8WMafQq9mA/eJE0nUrG2TAoov+sdj0MIlKwSq9HIofGT1qGHmptMp1D/T4aKxKRaTo4gJJx1FbdOD2cQsyw1xDHLhWVfcaROHwCibVV07XWhAp7wjRmVLuHOGWSvsCMsoSILtUp4RL8rXAQUGtp/Mhk24N9AcyZTmjpipYW5s91TDAiW7zjD+xR3CB4YscKRZqYoJ9FIC+12Zh/05RIwCKin6qWFqFQfB8bCbOm0RSB8Pi2DaB+ZhI5lUKkmzw6FjmtiugZ7ZFaaVMMBuYNXpWVxdGUCywW5ikFEnEWRL0+K+tvUpk8WVTEvMHjoRBrxOmVSWRrocWClWJduE7kw3ogOoKzIzQ3z9uVg/WzCT17vrgYe8T8XtPbi7ujVgBA3swcfCnSobzldgEAHu/vxysia/t9PT28Dp2WX4J9oaDoAw9uUNFzvFIv1aKCV6SBMY6xYopAYt8cHsalLnovemcAfiE0mhFS4kAdPVuu1QAw5VISuswMgJ9d6/dzNGE6FBgU/SU5qs8PDOACgeQc3d2HoODHltb5oZhPbXUUW5BeRM+OjYRZHLV3ICn8K9dog1MBlfhdCfqjnCrHatfDnkfrpzyRCEfijMo+v3o3c3OP6lWMEpmVShYYPn54iBOQSyR58hn5zJWadmYBtn9Ea+qO9e3wCDT9vJWTmRf7j7IYwCcI36QMAEhLS/u7dJpMJhMmTpyIY8eO4fzzzwcA9Pb2IndUupn+/n7kCETW4XAgHA5jeHgYGaM4vP39/ZgzZ85JP/fgwYM4cuQIXn31Vfzxj3/Er3/9ayxcuBArVqzA+eefD6Px60c2pqLnUpaylKUsZSlL2f+ahUIh1NXVITc3F6WlpXA4HNiwYQP/PRwOY/PmzbwhmjZtGjQazQnX9PT04PDhw19r0wRQFN8DDzyA5uZmbNy4EaWlpVi5ciUcQuX+69pJc5o8w38CQPwOyTtwDQRYnn7mokJOjun3RFAxiTz3tkYXTvkJnSdafCJBYkuSTNa4fwBnXk2aIgp/LKkEazdgUKAyMnIpHIxh53p6Rul5RQgddAGgKIdxC8lTiA2GmL+kN6pZul9yMOxDMQSEzotDrWaPc7DbxwkxB2vS2XOVGkDXeYwIldB3neEwo0tAElG5UmflVBJqhQLPC20W6TFX6fXMtxiIRvk8/6rMTI5gy1KrOapD/t2sVOLFQkKOQkpgRCSa3Z+RYI7CXn8yzctyq5XVmyX69FR/PyNGH46MMC/ht9kOnN1MnKtXTXmcWFemS6nQ6dh7NqtUmCC89a5IBMeEBzvVYCQNHhCqJPv0UT2hDX1WJXNOmkIhRoHSA8CIOGqPNPswWETfy76PheN42UNIzpK0NNzbS2PtDkcu9+loyw0A+9X0nhOfUX+On5mDngzqo4zOMEdW2XKMrHFz4Ol6TD+D0DHPGOr7nJ4I0mxUH78nAl8O9ZeuM4hckdR3xBlAl4jgLKxIZ3SuYT+999lLilibKZGmgS5M86b58CAGRRTP1Pl5OLid2iX5VDlTM9G9l8poOTKE/MtKAAC77j2AS2+maA9nmhKxQ8Rz6+/0YoyIkJL6VFs/aMUZF9G8e/upekwVuk/jZ+Zw5F52gZmjiQI++q68OpMT5Y583M1et29GOsYJXbTuZjdUwtu2nWLDyAF6R2XVMmoqjDojvZ+5u3TYWUN9UBVQwpcmOFTuGPMNB6UmWjzOaE+VPpmU9k6Hg8fjcDTKauPeeJy5dzv81O4akwlVRyh59IcVFYwMmZVKXGklNO3xQSennXlWzO+FaWl4TGQxuCYri3mRUwwG3NlN3JcstZrHnUOjYd0wmQpkSVoa858C8TgrnWep1Zwq5sOREU7kLTmIw7EYrhWo016/n1EpFZJq6c8PDPCzqw0GXnPOFehxxqi6DUSjWBai95aZZ+K5ma5SoUIsvet1Ia6DjMa1qlS8ngTcEXyeoHlfFwxivuivyTEtNoPaLTWTHnTk4XiU3uU6txvXWaifQ1oF9r/fCoDSicixJvXPVOlaRmF2b+jgZOGeSZYT0Pu2T+jUInFGFnTbqR9NIqWSwaTh8naua+foatP4ND5F6AyH0fAqnVZIHqA5XYuqaTQnjGMsyFLQuIzHEhypWbulmxNyn3p+KXaY6F6JlH/6Hw0oqRIZC/JMjECZrTpOPfXO/V9xZOolKykC/bsyyWmqH3gYlm/IafK4A6jK+s1JR8/deuutWLZsGYqKitDf34/77rsPmzdvxqFDh1BcXIyHHnoIDz74IF588UVUVlbigQcewKZNm9DQ0ACLULC/4YYb8OGHH2Lt2rWw2Wy49dZbMTg4iH379kGlUv0PNfh/W21tLV555RW88cYbGBwcREDM4a9jKU5TylKWspSlLGU/UPtHiFt2dnbisssuw8DAAOx2O2pqarBz504UC9rIbbfdhkAggF/84hcsbvnpp5/yhgkAHn30UajValx88cUsbrl27dqvvWFqaWnBa6+9hldffRWNjY049dRTsXr1alx00UX/883/DztppGmw51EAQChTg56t5MUbLRpGjcLBGBZeQkkk//r0YdQsoSSQrXVDzK2QvAvfOdnI3EQoysxFRax70VDrxOLLxgAAEhoFAiL6QeaV8xoViLaT96NUKRhRMlelsfeZG1EyXwoA52CSZ+EHdFFMH4X2SARk+Jgb79movA9dLtwqoDsZkbK6uxv71dQm+3jrCRosMmJmq9fLUTejNVakLTOm4ZMAeWdvDQ+zXoteoeAoN1kmAC7rlaEhzh/VGgpxJE6FToclx47xtfJ5000m9mIlGlSi1XH+vTtzc3FVaysA8ppk3qsYwKjSLeIZuQcPYrHwLO5wODhaCUjmnnJGI7hWePxZajVH20mv+6rWVo42nK80QGei9+MZDOJt0LVXWm14zzNyQruHO3343BzhPvlVB3mX42fmwKlIRgBKBOpPOQWcj7BYRa5ePJbApCbSmfrCVMhjI+iPcmSk3xNmlEhqrQT9ER53uze0Y/k/TwJAPKZPX28EAMxbWorGWuqPvNI0jr6UUXJWuwFlCwlV6N7ez57t+88f5YifSCTOiUol16O7xY3xM+jaP926je8rGmNFbxuNn1MW5CNb8Ex2RQPIOETIg6xDT5sHmcIDdw+FGPEa+6MiNLxPfLBJc3PR3ugCkEwOrVQpeL4C4LxZkZoMRiczY0rsiwldIb0RCsGjCrjpXWn1Kta4suUYkZ5LaEnvqPkWTSSw/RUauwcXWwFQrjgZUfbjjAxOYrvD6+U8e22xCEo1VI9wKIYpzQ0Akol3V3V1MS+vd9T8e2t4GE8Krt1Vra3MM7xRcONW2Gz4mUBIXM4AZo+0AiDOnElFCMIDejuejtEYXZKezuNcIsqHAgFcJFCbx005eB3UdzVmMy4ViUGXpqfz+L5PoGoLLRbmBN6ak8MK5LkaDfMeN7jdXPYzTifn2rvoOGlxXWqz4bphet+hCiOvT68MDbGW0zyzmfu/uI3eVUFFOqPwaoWCo1UfHupnLtfj6Q5EhNr9G0NDOBSksXJnhN6JL1/H66vGpoMmQp+HlHFYo1SP3jYPj29pzUcGEREq7Wmn2DiBcZpNz4jpV5u6UC5OLba814L55xFvS+r8hYMxhIL0nkP+KKqFQndn0wgjt9s/bkP5T+i+0G763Rk7PxdffUzvPr80jSM5D7zchMSPac6WHg3wfOvr9OKcm2kNCLRR3fZ+3ol4nNoajcR53l9/fw3/puWVpeFYpkCrLH8bffa/aRJpOuz8/beCNFXbb/ve5Z6bPXs2du/ejYkTJ+Lyyy/HT37yE+SL5OV/r6WQppSlLGUpS1nKfqD2j0Ca/v/FTj/9dDz//PMnpGf5pnbSSNO+L/4VAJA7LxuBBtp9l4zLwKa/kqczfmYOdAUiS/i/N3A0QjgUQ5bwND99nTzLojFW1rfoy1LhvV9sB0CaNTJq7ZyfVnEmdblrnzDbwdot2149hulnkrfoydJgZCd5e2Pn52Kt8PymGI0oF/wZo5e8La1ezdmvbVNsjL4YmgOsk+O0qZjTIKPT9vp8OLuRnl030cC8o9GZ2e/q7ubcZvFYAm1CJ+eYQJH6olFWD3ZoNIwG1ZhMzJt4pbQUt3dRdE1fJHmGLj3Hf25vZ4/z5XYrluUnFXElT2O9280epYxcmqo3YG4jeeWr8/I44ufWzk4sEPdt2HMDHl/0OtVjMJk9/qdBaqup0MRRd55YDC8IqPX69naOmIkdGoF7PJUno5WiiQQjVPPMZuaAPJiVi7sGe7memgD17/tC+yiUSGCsKKMhGGQPfYPbDYPgXpypMPI4CWdo8BfBHTmjWeQDnJ6FLf9O7V5yxVjWKLLaDexFvvLwUfziQYpm2yn0XMZMtaNxP9VZda4DLY8SWjX9jAJWCT66p491xcbPzMGGoIiOe4A4NYsuqWA+QzQSx2d/ofG/4MJyLrtkXAZH6a17heoZ8EcwXygs93d6WcfJ7wmzcvGPrqE8XbJs6cUrRP7E45t6uK1ZeSYoBDejeKyVo1XVGiVzPLS6JOQt0aeCinRoxPf9443Qfkle+tT5efB7Rd4uqwKdawlF2byM3s/FB+MYN43mwbNwY8Ym6ueZiwpxrZtQwdW5uSgHoRqyLzyTLPiZGF+f6QtgKaFxtLCxEW+UlXH95JysDwY5klGqYVfp9RxRV1Nfj+fFGDWrVDzfDgcCWCWQZMkD+kNfH8+Z6SYTo2q3dHbyGnGpzcYaRIdGdDg9k9YUyRVcmp6O60X9q/R65uXs8PlYtX5JejqeE+tToahnlV7PUYP39fYyUvbhyAhrq11vt2OHaPdt2Tl4QkTPSX5NMB5ntOpdl4t5Sj2RZFaE2SYTz0OJjkcTCdSI+d8ZDjOPbJ7ZnMzHqNMxN6xAq4XucypDfxb14V+GhnC3gbhEfWYF52M0WrQ8x2w5BihF2fuFPtLRWSZG0Ct0OuwR9ZfRwQBxnSRXTqlUMG9IoqxfvNnE3LgfXTMea++nlBlLrx7HkaRHd/fxHJIRc3qjmufSvGWlrCW49YNW/PzeWdQfTSMcBW616/lk45wriYO7+9MOZBfS79V//ukgporfvOIxVr7WaNEwanb68t/juzSJNO3rewjmNP03KsvrDmJazu3fO6RJWjgcRktLC8rLy6FWfzOsKBU9l7KUpSxlKUtZyn5wFggE8LOf/QxGoxETJkzgJL2//OUvsWbNmr+rzJPecknv+q937cXlt0wBAGz663GUTSAvoOnAINrepCisy2+Zyjv06tkO9iTP/zlBZHu/6GTvoGuTkxXBx8/MZlTpwxfrcP51dH2jiEYyp+tQPJ48qLJqG0fxeWMxpNeQp7P5lUZcILKqN+1wwrKMPM1WPXmLmzxDOFvk7zL7ExjRCTSoUI2JQzIiS8VeluQDTDEaUTyXvIqnu7rYE11bUsJq3Y8VFnI0SEc0greEVyevLdBoOOplf8CPkDgPPy+g52sOBwJoCNKzXxe6Kzd3dDDyFQylY4XwRK+3K7G82c/1WC74Dffl5/NzZP1/29ONG4XSOJCM+PtZVibWj5AXedns53BVJvWX9JLX9PbiigrieqwdHGQveCAaZe/zw5ERrBHnxBPiQ2gQWjAyYqjGZGJPutpgYO7VvlgQKwSva+S4B9pS6t9tQ1S3ueZkXraSwTg2KclrdWwexgzBbVCF4ng7KhSNj/ixfLwVAOCaTO02K5XslfZ3eKEqoXGsVCqYg/Dz303myEnzqJyIlwkUZqHSgLErxgIgz1Za0VgretupTk0HB5mnpBKozlBfgHPFuZwBHuf9nV5MEWN036Yu9qSzhJZMPJZgNLS33cPcquOHPFj6T6RN1tk0wp60WqNEkcgRFxc6NPFYAgsupOi5+n39jHh1t7gZaVp69ThGiiUyHI8nmN/lKLawZ54/FMT+U+lddTe7cXcavfvf7NVh0lxCM6076X33OYOsi3PpBBuOhlwAgM9VQR7nni+diIv7nsinOhe43TgynhC05wcGABE9+mrIzpGyw+NMkJjYyuxsRk5WddF6MzxciacmUN02jRnD43yKRsNRmwssFkaMJIKrVyYzBfyhuRkfVpD+nEWlYlRzg9vNqtzX5plQrqPx+oaoZ4FGk8wh6XRi0xjiZ3rjcUZ21KOE+VTi85L0dEwTyHVrOIz9AnHZUFmJG8QifzgQwHGxJj03OIBPxJoj6+OJx3BZBr2fS202RsKf7O9nxXKHRoMni4iXKblcd+bm8jy+yZrFXLTFumN4SMxpq1rNUXroCSJDzD2pKr40PR27BC+qBFokbPSOhzr9nM9PY1HzvJ97Dq0xM0IxKEUf7Pm0g1GpqdeMw/oXCdk1p2s5orr2y27myo7mzMnTiS/fbeaxeHhnL0fSSSQXAHP79n7eyXPwq01dHCm74MIyRquy8kyM4Kq1J3JlAUKJj+6hNeRfnj+Dc6H6vWFsed8FACgdr+Dfun+URfHNdZq+6f3/KFu1ahUOHDiATZs2YcmSJfz9woULcffdd2PVqlVfu8wUpyllKUtZylKWsh+o/V/mNL377rv4y1/+gpqamhOUxMePH4/jAmT4unbSnKa6PfcCAOr3ObFEeN21W7p5d73gwjJs/aAVAHmrlSJflsGkYURI7vKBpFZTxaQs5nooVQpMmU+7/+wCM/Qiyko+YzR3w2jRcGSPOV3HysUDPT5kCgXZ4VgMZr9Ao4xCjykahaaBzph149PQ9TlxLCbNyWVdklluFTaJqK0JjVRn49QM5k80BIPQic/HQyHsFN7s+VYrIyrXt7fjXZHvRuqkfOhyIQKqz1OFRaxzolcq2fOdYjCw3pLkHVlVKj77L9HpsFrwn9a53dgydiz36dsC2Vlhs3FEj+QPdY7iNlyakcH/H4zHWdNpocXC2lCS62FVqXCV0JDZ4vUgV+QXW5GZCV0n1W9zRjKS7bG+Pka0ZDsWmM14S9RjisHAzyscTirDa/Vq1jaRnu9yq5XrM8FggOkQ9dfYqXYkNPQ+FZEEAmLrf2dXF0fsTRukv4/kaVEaowvqlBF+P55OH5qPiLb6IphzbgmAJL8me0YWhmrp7611wygUSE7tlm6kC+2ijBwj0oWHqjdqOP9bWETzvP/8UQz00Fi7+JeTOXp05qJCDHTTWDuypw/5YkxLL/mBa77AHOGNV0zKYkRMb1TDJZS980rTePwPdPuY4ySf99WmLp5L7qEQe9JAMhq1oCIduzcQ4iA1dKprHGg6SGPnlAX50Iqs8u0NLka2sgvMjApopmdw/8q+U6oUjLDF43FYJ5Knv3ZwEFcIZNHii/O6sE9LY7FpVJ6pdJWK58RFGcnIPVcsxohFazjMyI1c1M+zWrFLcH/m10XwclkyP9wOH/VXLJHk+sno2HPru1FpSkaZSRRl40gA1+dYAQDPHMvDtRU096r0euYnyvl9o93O861Cr8fTAgV7tqiIEbEbs7OZl7lOoEVmlYrnen0wiGvEfAOAlR0dXLYc29UGA0e5yr9X6PWMLt2Zm4s7BfK2yuFgrbnlGRnM95T8x21eL+7R0/PeVPiYFwUQkgcQqjwtTuOnT59AopH6UTOW1qTQUTeGKglJaw2FMENEJ3tH5bg0HQ8wt1WOYZczAMU06tvAlgFMXUS8yJEeP4YzaUx1vN+BqRcSsqXzxRGNUJkSLQXA87igIh1NB6h9GQ4jJtYQ58rjCuGT/yDkyiCiSy3pWuY5WbMMzJUyWjTYuZ7mxJIVY1lV/MzlFfz79eRtxMEdPzOHEV6/J8xobXuji1GupoMDrPu07GeP4bs0yWna1rvmW+E0zXWs+t5xmoxGIw4fPoyysjJYLBYcOHAAZWVlOHDgAE499VSMiDn4dSzFaUpZylKWspSl7AdqEmn6pv99H23GjBn46KOP+P8l2vTcc89h9uzZf1eZJ308t09wlJoPDfMO/cwbxmPbr8gLHjPVzZ6oSqtEi9j9a/UqVlWVZ8hvP9UNez55l71tHj6zvmb1THz5XgsA4lM0HSBvQurAuOdnoFJ4Mev/dIg5G+tebkCeUGmuWVyEmMiLVfteM7wu8g4u+EU11f+zbj5nDxx2YZrIXr/T58OAiMJ6QRPAMnJMEJ9C6MHawUHcnU2ey3vBIGf4/olbjzeU9AxvPI4/CFXhLLUaU+ool5jkAR0LhXCF+Lyqq4ujeeqDQTwhuAYH/H72VqXicGRwGmqmUV+M1oKKJBL4XCoeq1ScZ+6atrZkzrksep5BoWRka7SHfr7Vit+KLPMfuDqgEd8zsqXVsje7Jj+fkZo3hoYw3U79+FhXH5d3vd3OiIHMm3VJRgbf59BoODLv3rQcvB2lOi3XZeAV4QXPkp5qPI7ZLdTPLWOiKJmWKV5KUqn9cCDAkUI3e8zo3E9e3eAi0pnyxmLY/jFFyZx+UQXnuvtqUxePV71RjYYEvcOKGrpvw/N1jF5OP7MALieNUZVKwby7rDwT6yK5nAG8cC/pNyUEglIxKRPjZ1J5o1Gn6hoHWutofhzb7+TrfSLiprrGwUhU0ZgYz7f+Th+Kxlq5zpJvNGZqFt79M0XsyTlhzTIwlyLNpsNnf2nitsj7+ju9jPbIObprQzvrRnlHwvB3UhkqjRLlgnv16WuN7FWPHBhBt4lQJatd6ASVGJAXIn9s3SsNyBMo1q/PKEBHgvr/1uEkx65EI9A6pZIX5+kmExoE+jLdaOQxtdPnw10m8uI7zAk84ST07gIrjYEb2tqYP7S0Jg8VwpMciEaxLJ36rjMc5ryQS0XkG6JmmAVnzhuPY55ZJmcDnmkooQ9hGzrCrVwPySeSyJFaoeC5+eHICO4SaNbKjg7MMtOY7giH8YIY51KDqTcSYRRpwSiUqykUQiIudMUSCeYI1vr9zDHZWlXFz5Co7E6fj+dbMJFgPammUIjRIxlV/OrQEC4JUl8sSU/HYyISuDcS4SjFC7QWRMz0Pk3xONbaaZwuEOuXu24IcycRB8ygULCO1sCAj+eKX6OEUaAyMnOEOV2HqmHqO3eeCTvE2j/UF8C5PyfkdMSmx/FNUjHfgLjo68M7Keo2zabHcYFAj5mSxfzFoC+KT1+j+egaCGCcmIdjBa/QOxLiXKPNRwZx8S9JaT/ojzIylF1gZi23P9+1C5Pm0vp/xnJq6871bZh+BqFjepMa/3bHDgDAlPl5qN9H61PZBBvPt3+U/V8+nnvwwQexZMkSHD16FNFoFI8//jiOHDmCHTt2YPPmzX9XmSmkKWUpS1nKUpaylP3gbM6cOdi2bRv8fj/Ky8vx6aefIicnBzt27MC0adP+rjJPmtO045PfAgCOHx7EsPAcswvM6GxyAaAd/2zBw+hodHG0TmOtk3MKWQQXpKtpBP901wwASZVwgFCnkIjcseeaGJnauS7pmVSeRt7byHEPe+6N+wdw9k2EJB3b3AP7vORZfEQ0z9FB3ltWronzw2k8UehEDqMogCbh2e70+XBmr8gjJP4ejyfwgYm83eFYDBbBaVqRmcnI0OFAgHPLvTU8zCrflwsexwG/nzVkHt2+HBMnvgiAPOkX+5I5cC4TWjvSW3zS6eTIHwDY0UERVE9N7mPeBwC+5vWGybhrMmn+SN7FFq8XdhERt8fvxwXCw360rw+zhVetUyhYT0Z64OdbrbhOaM9MNBi4rXfm5vKzX0I2ikbomvi0SdjgJc9PevOfZ5XgSjdxQaYYjRzRtNxqZW97ocWCwb1U3o5KquevOjowOGUKAOJ/jGkQisGn2JiLEozHcXqE2p2waXHsc3rOzEXkMW/9oBWqM0VetmgU+V/Rs6umZXMeutGe4JfvkeZQZq4JxQLV2fTXZlbMrpqejfq9NGYXXFiO3RsIhTNbtcxv+Ggt9UXlZBOjVc4eHwrLCYXQm9TMCYpG4pzfbb3wjBddUnkCx0IiYh/+ex0jqqNVvifNzWXvXfINrVkG1Ao9HINFwxF93S1u1kiLRuIcNVgldJWikTjOuIg86XeeCeCM66lvI90BrtN//ukgfrqKFpz2xmHmb/QIlfZIgwcGwXcxuWPwewmRaNzvRNup1EcLLZa/icg59m4bDOfQ/F7R0sKRXtFE4gSen4wuW6tzMPdQolK3mLKwvJ/64orMTOb2rM7L48/X2+2M/kp9tBf3L8JFk9cDoCi4HDFPK0fNu8f6+5l75PSbUWii9Ueisi+3ZkORRhD1NVlZjAwtSUvDO2LeLLNamcvkab0YALBm9hY8JXJPluh0jGBdarMxLyhLrWbuVIVOx3NdoqzvuFx4Tzxjhc3GaugP9/YiQ8z7Ox0OzsfmGqU9JdHx3kiEI+2y1GrWiKoLBvG2KHsgGsVnQn1dRg3X+v242kv3NR8Z5DyOroEAo6TrM6MYv8vH3wNAX5uHNY+yck1449FaAKSi7RbZIKbMz+NIufEzc5g3JxHeXevdyMyl/rrk5ilY/zKte+6hEPNfs/KMzLeV68LeLzr581CfP6nHlGXg+ajWKLn+RWOtrMkmObgdx9worqI5bU7XsW7gp681Muoa9EU4GvVndz+P79Ikp+nTrvth+oacJp87iLPyf/u94zT9b1gqei5lKUtZylKWsh+o/V+THHALysrJ2N+zATxppOmPv7wOAHDa+TnQiYin2i3dqBReplavwocvEodn6dXj+Fy4ZkkRqyzLqBylUgGd4EGMm2ZnHaa80jT2gnvbPextLL2akJVoJMZn5FXTsjkSbaTHj8M76IxbrVGxN260aNjDWPwL0nxKDIcxaCGPeafPh1ldVEas0oTCKKEeBxVhzgb+vIK8u1+aM5EwClSksRHnCSTmqsxM2OJU3p5IgL3Lw4EAR51JzzKaSDD3Z2FaGqNSO30+9to2eTwcPSM92P1+P7YJdKbW7+fcedONRuYp9Uaj7AU3VVejeB8hIIiTRzY/y88cKumdAsCDjVZo0hu4fqMjZgBCpSYKjsibgx4UCl2rjlACV9vJy9IpFaz1dE1WFnur0rOfbkoq/3aEwxzltzAtjZGwAq2WNW7OjpJXtEUbZp7G6txcqOqorbFxFgTFsLWqVMgYIU8uYdNy5J1UOU8PJCM1y6ptzJ9zD4UYBepsGuFINMlR2LWhAxMFvykaiXPEzyevNODa1TMBAId39LKS8NHd/YiEqa9nnUWoTfORQdY3A4AR8ez5y0pZjXioz8/5sg5uI+7GOT+twmaBeLXXezFhFqEijmILrP8/9t48TKry3BZfNc9Td/VcPc/QQDODiKKC4gyOaDTRaKIeTdSoiTnqiUk0msREjJroUaOJqBgxakRFQUEGmekGGhroeR6quqq65rl+f7zffru5957f5Rw95rmeep/Hh7K6au9vf8Ou/a5vrfXmUN9sf7+b0a+KhmxWx0nt3/RmO6v5gAm38XufOZN5Idvf72aUTVrTI71+RpQaFuSzY7h0nQDxPiRkqr99nPumYiohqnqTitVzbnmKOTrVGg00QTr35wgz90hCPTTHAqieSceNpNM8N55xOnGbhfrgo7Cf5+Zat5vnmqQWvSE7m5WhSkwoMR8pLIRfXPcys5nVoZKqbY7BwNyfvaEQdOLc840G3OWidr5bmOL1ZppUNFRadwsMBv57mycHKwuFulcuZ7frMrUaM0WbpRqTw/E4I2lVGg0r+1aPjjLafGxSbcr+WAznWege1yE83R4sKOB7j7SOAFLaSY7lN3R3M8ImoUtbamoYPV7n8TAS/tuCItw2QPeQJxwOtIrzF6hU7DIeFX1YpdEg3UdzMb/UxPforDwd/w7I5DIUi/kooUSfrWtntOfQjiHenTBa1fy9zhY3z1Fzlga7BbL7g9+cBgBwDYUwJFDbzevaYbZRf626ewbznhxVFrTspOuVuISJeIrXf67DyPy/wU4f8kpo16J+Tggb19I6zSmS8xrIEe3pPOxBufCFq5phZ+WeayjI9SLPvbYGW9+ltfzPcgT/8CtCmi74fwRpksvlJ9kL/J8inU5DJpMhOUnheaqRQZoykYlMZCITmfiGxv80IvjmzZv/W49/yg9NthxRd23/KPvUDHREcenN9ET97vNHUFZHCMbB7YBOeJ6s/3Mr7xFL1dN7jkWw7OoyAJTxS149coWM+Q96oxpOUbNHci1uWJDPWe2RPSPweyjLKqmxYvZK8vI4vnkQ4SAdo7NlDCXfJTWRQtSe02dpkUzRa28igYMOyqxWaDTYHqfMsDsaQ0mU3j+7nY61c14Yz3RRVvpkcTFWCmOsT30+VgE9PDjIiMqxSISzQym7e2R4mP1mHhka4r4NpFK4VaBLb/XZsF41JNpB1zcZhbEqlewG7FCpsNE5Ub365+VWAMQHOUtU1jbK6Rgt4Ri62i+j/qx/n9V/UKcQHycezLS8HjzZQqqVaaW7AQDJdJrbcb3dzPW51nm9XBfuU5+PPalu7e3FA/3Ez3itkrJIVyLBGXMgmcQHZnr/Bbmf3c1fcbmYR+UXvIulSi3X1tLs7sLBGZSBD8fjyJ9UP0hCJHP1Kpwj+l/y6jF84sTiK6ht3pEwq8xW3dXINaF87ghql5EazC/Qy4JSEyNUo/0Bdr4vrrQgqaGxcI+EeD7ml5oY7ZHUnll5euZKnH5xGWe+rzy6jzlBezb2ISkUbIsuKgMAvP67Js7GswvUrAhq2TXMqjW1RsG+Trs+7uW6V/948Sj3y2TeVM1Mml+dR8ZwdA+NT2A8ykjSZFRKQse6Wz2cMX/2Vjtf67fvn4XmrTRHXUNBnCeUTpIzdvGGIXZu1plVaJRTv3jTabz/7CEAwLS7psAhUA2FQCmUdh3CPlpv7yUDMIo5v3pkhB3qAfLsAoAPSiuxOUpjeLlAT9/zenFPHiGEr42N4ZUy6tP+WIxryM3e78fzdXRsCfntj8UYvZyr1zOC9fjwMEYs9L13emJYaVfz53f6ad5NE6j5iy4Xt3lxrhfDcTrezp0P4axFjwIA9odCPDc9Y4R+TytoYwXeddnZuHwLIZlwrMM2H/X5NdkmVtgdi0QYWZPaeUdfH99ndgWDzBt80uFgZG6OXs+IlhRlhw8zuvRwYSEjcEFZmo+X9sRQk6J+cppT3I8SMqfb7YXmNPrswaAfs8X9/Kg6iXxx7zda1LwWUqISQiSYYL+lKfPyWMHpGgry7sTy62oZ4dn3WT+mChWc9L19n/WjRngClk/NQlgoUA/tGOL5f/iLAKbOJ3Q+HqO2zTg9G31tdN1afZrXQTptwGg/nbvrqIuVcsf2jzLCVCocxscGQ/ybZrVrWbn65lMnGDX74oMe3vn4Z8X/tIemM88886T/37ZtG55//nl0dHRg3bp1KCoqwquvvopyUXHjPxun/NDk99IkWHi+jclxZXVxrHvmMAD6YZFu4m0Hx1BUQTe2klorFwOVihjWzc7BRiGBPvPScp5UZfU2hvU3rDnOWylBAZ2efWUV/4A8c98OrLiFYFRbuQmfrzkhjp3L32s7x4qbxA37xBF64Mmek82EY2udhh9yjuwcRksVtXNVVhaMNrr5DeUK4mQ0yj/848kkHikkkvqLLhdvMzjUaoa65xgMvC0nEcybQyHeknvC4eCtp7f6bHgkQXLbi4vMeH8vbYUezv2MOt/biLlT1vH3zttGg+2y7gWUwhBw8BIoK7YCIFO6x4fpB3qywZyt/C0AQCKtYYLrbdUa3NkjlQZRodxBxm1LTVYA9MC3VsD+gWSSidsP5ufzD9X9hwuwzkg3xJ1778ZfLlgDALwduNHnw507FgEAHpr/Od5S0QJUpGR4QrQzkk6fZPAHAPfm5bHs+Zo8GZNY67RanBA//Lp8PVoLqU2uPSMYmEXz46w0jVV/uRmbRaFo90iIyaHukRB6RGFanV6J+CD9cEs32k1vtnF5kyO7nfzQlFtsxAfPtgCgh608AfF3t3p4i+/ca4kou3djL5O4O1vcuPYeKgq8Z2MfP9w0LMzDZ3+jB3C5WI05hQbeIotFkmxu2bAgn4nbsWgSRwSxVqtXor2Z+mzpKnp46jrihnOA+qJ6RhJKNR1vsMuHkT5q06ILC3GimdaFtKWy4vtTeUs7t9iAz9bR3PC6Ikza9boivH14zx/O4O2aoo9pXc0628HbHdvXnIDiOnpIXm6xQHUrFd6dodbyD/s+I7X9zuwc7IvSOOwaD/D2+4ulpVwm5f78fJ67m0NBNmuUtqzXut18g++Px3GrKBdyq92O3zaR+AT5G3ALXSJvN9+bnw+/SKYOhsIsNMhWKtEo1vc7x34E5eInAQA7/VGcZZkoegtQORFpfT/wxVJcP2s9AKB79kPYPE6fWVtZzEWlP0rRvXOBIRtbxMPKI0ND0Ja+CYC25/kBK5lkgnh7NIrtwtRWSr7uz8/nh6MH8/P5XmVsbsZqUUwbmDCtXSruezCZeDtTK5NhuUhc3vJ4WDjyqSKOgIy+d7rMiHPUdI0lrdRHg/OMWJyi+3Z4lwtjZwhZ//v9yBLbby27Rlgw0N9Bc6271YOKBnro+OytDi4jVFafhXOupjUUiyQ5UfC6wvwANSASGu9oFD43baHNWlKEcGjCUkRK1rPz1bye6udYRXuGccXt0wHQtt+//xsliXWzc7mEkS3HgUiI+mbesmIc2kH3KqlYdVbehNhCq1dywfd4LICWXTSXimutvMZqZyETX3O8/fbbuP766/Gtb30LTU1NiAoAwO/341e/+hU+/PDD//QxM5YDmchEJjKRiUx8QyP5FRhbJv8fQpomxyOPPILnnnsOL7zwAlQCUQXIiuDAgQP/pWOeMhF8/2f/BgDY/PYgTDbKOupm52C4h7LFrqMBzDidsvFj+0d5y6Ss3sbkbmm7o3FxIWe2+aUmzp4bFuZzVt3d6uFtjqt/SBmB1xlhsqzGoMRMUSYCwATBd4oR2gPj4r0o5p1Lmc6OOGUouh0eNAiCb8gfxwd6amcklcKtOZQh+ccieCpG2aBkcveM08kZp12pZHTpvLY2dDQ08GdWCfSlORxm6F/aYlp0/DjmCjj9Xa+Xs+NGnY63H55zOtHnJOL7zAJCSJrGsgEtZTkX2wwMsduVSka2VtlsLEl2JRJI99NWnOYEuZ5Gc8Mon/UIjVX/acCwKF445ReYZqIpoJXJGB2Soj8eZ+L4vmCQkaYVVitnrdsDAc62tXI5trUTSXPtQmr/U6OjuFNsYR6JRBj2bw6FWDq9xu1mhEna7nxwYIBfH4tE+LPfsWYx2jmmSMEWE1sH6jR2PEPbbxKsbnMYcHgLZYPukRBD6G55ijNpZ1+AEaZppxNaenT3CCOco/0BtidQqhRspqdSJxglrZpuR8BLn5EIprs29DLs7xoKwia2vQKeNGRiG2fO2XYuYSJl0fklJpZlu0dCnDEn4ileS81bBzl7njIvj7fUtrxDiIwtJxcKJWXMuQ4jbw3mOoyMjnUcHMOSywj5kbZMuls9bLMRGI8x2fyGB+ZwVr3jwx5ccxeZAWr1KryXpHvAYpdcfC8KtZb6NlipQ61M2HYk09CI0khrPR7eSv1UzNt3vV7eTjscDvP4vOXxYJlYQ1aFAqUKuvl9Hgqw6eXDAnG51GrBKy667jKNBu+JbeM5ra3wt/wrAOCnZ7/IBHCpYPTTHdmw2Wi+3pqTg8cOzAEA5JRs4G3EJmcB6uzUB2OJxP8m+19gMHAh3NZIBPUC7WkOh5Fu+QWNy4yfMzH75d1Xgy6qmf4DsNgqx28FMrTg0DAW2ibWo7SlPpmMfaiK0L/fe5z4WBDLJyO0iXSa16lSJuP3nQKVeqq4mBGsFVYrnhHWBwmAzXx3R0KYGqF1L7OpcegjImOXi7mo1St5DpfUWvG5uNeWd8bwxYfd9JlbK1C2l+ZJ/VKBhn4xwutjsMvH29tH94zyvX/q3FxGeBoWTmzhSVtl7z7fimkL6R6x9OpqvPjwHm5HLCIsMOJJKMTxiirOAQC0H2pGXxuJI6x2HZJJup8rFGH4vdS3CmWabXJ8rgQqp9EWtvRbUzU9m9fNaReU4aVf7gUARINxpplcdGM9mrfSnLnxoRfwdYZEBH+j5+fQf0kieMgXwTWlP/t/ggg+OfR6PY4ePYqysrKTyqh0dnZiypQpiEwSWJxqZJCmTGQiE5nIRCYy8Y2LgoICtLe3/2/vb9++HRUVFf+lY54yp0kyz4tFksgSBSwnE0XVWgXLRJUqBS67jdCX9kNjbCkf9FHGVlgeYCKfXCFj80uvM4yq6YRC2AsN/IQuEXb1JjWy8igjMFo1/H4smmQ+lVwuwxcuykymzMvj/WmdILFm5emwEZQVXeSw4JwTlOnkl1oZwXkgNIrFRmHOJ5AVrUzGVgAbfD5c0Umcju/Z7VgtsjOlTMboUiCZxD3C3DESpzYvs6kYkXmgoICl/Il0GjcLA8lVNhuOaYnftLGLODDovwLV838KAHh/9y2AfTu9rwygOEfwWuRyzijTvjrojhDR2HHJgwBI6t/lIh7ENbUH8UaAkBgEqtCiJHJuOuTAwhwaQymWWyxMBAcmeEot4TBn+XalErVayhLHEkncOo1gz4cGCUF4rqSEr++uvDwuOHxXXh7Coj+qNBo8mkNj+Kqf2rCppoYJr5OLofYk49BKJXuGo2iz0xhtfagZFf9K5Fp5gP4+0u7j+VU3u4xtL3IdRjz/h30AwMVuAaDnKJ3bYFKjSKBIbYdcTEAFwMTTQzuGIBeIUfsh1yTzO1qkVdOzGVEtqbEyN+P4ATNa91HJhZadSeZN1M89AwDQ3XoEYWEQWtGQzUVKp8zNY7QnEkowX2r/lgHUCqk+0jTPR/q6cOujCwAAf/9TC4oq6Bot2WO48NuETuzZ2MdlWaT1OGtJEZv36U0qRtJeeXQfm14uuqAUrzxKfXfahWWoFZxFh7i+SCiBIRXNxZG/96DlPOqvhUNgy4TuZBQpH91+KoWZa5VGw7ycT30+LuwMTFgDdEejLMlvj0aZQC1ZTKggY4QnkErx8e7Pz8dzil8DAB5rmQuEltBxU8QfmpkXQAKEiL3j9WJm5acAgKaus3FzI62PlkgXooK743RNgTGXjCwla4RFG2cCicXUYO0wmrII9UCsFDAThy2cSjHxWlv9J2qzTMbXapDL8YHgQs61xuBJ0vUdc9txaymN7UuuMUiGB98aoHU1ucjvWrebEaV3KyvZhDaSSuFeQZKX+GTrvV7mCj7ndJ5EFJeQmvlaPd6LU5sWDycxV5g4SgW294VCmCFQog2vHmcRwIkeP869tgYAcHTTCOyCNC21bVqxER1il8HnjjBCO2VeLpcvWf9yK2QyWmMavYqLW0uFpE1WFSO0X3zYDYXg7uU4jMwVrJhm49JZPcc2Up+rwQIkhdIGmZyuNZkIIq+YUFujVY0pc/PEsXv4PiJxAntPeNEreJFeZwSVwnLj6J4Rvhcc2DLA1/LPiv9pPk2T45ZbbsGdd96JP//5z5DJZBgcHMTOnTtx77334t/+7d/+S8fMWA5kIhOZyEQmMvENjf9p6rnJ8eMf/xjj4+M466yzEIlEcMYZZ0Cj0eDee+/FHXfc8V865ilzmp64/WYAQCwSweW3U6a65e+diIbpqbximgoFk56uR4SSqHq6HUPdlC1l5VGGeOZKDctPl15dhVce3Q+AuE5SVn1gywBW3d0IYKKUSdCswPu/IBTDYFFzYdGamTn47C3K7nd+1IdbHiHJbjJfg7YPaP89Zznxn+q0WlzXRUjOMyUlbCq5eFiGbfnUFdcYrdgbJzRKIbJIm0LB5RsOhsOMuKywWhl1Gp4+nVGnY5EI2wtIJnZ2pRIbx+m412SbGKkxKhTMc7izLYoCE6EdQwd/Qm2bt5rVPAA4G0x7p+PtWdTPq7q62A7AefQOLJ5Flv3bvNT3xbo4Z7PbvCnATX1k3boAXqF2QyQf8BEicd98KinRH4vhjc0PUd8t+Fccc1NGuyxvnK8lR01IEEAZiaQgelr0xQZviO0HtDIZowPver2s7rs3P5/N+STu2PQ9IeQIRU31zBxW8WllMiwDfabnuBfqRuo7i0IBvbCW2KsgnoZVqWS+2NhOJ1QCFek64sbs6wk5yVep8OlaQlck5DQWTfKcajyjEBFR6DcUiCEu+FRdLW6cfnEZAEJq2pq9AIDK6Y0AgLI6L3M6rHYdl1PIdRhxXPDuUskkzlhxOQAgGiblojlLC+cAlSRqa97KXA/XUJDNALe9341swWOKhrWIhCbMJwHAaJNBraYseXysj69LWksAcGy/E0uFSkniCtbMtDM/KpVMM3o82h/gMhHBUi22/Jyuq7g6zceUeC0Llpdwmw/uGIZGoAIlNdaJshqWOM8TybS0arqdDRXn6PW4Rkn3k8OqBPoE7yaaTjMH51gkwsaZkxEUCclxJRKYIebSxz4fz4N9oRA2C6sOaa3N0U+YkDbodHisl1CdcmOI1WV35ebyHAXA505HckWH3g+oCTkxNfyKLQy0MhnzF3cGAoiEBSooPluskaEvSH+/Pl/O3ES7UonDY4S8310W4/V7Z1MxrqkmI2GpbWVqNV/fk/vOxrOLdtD7Gg2yxfv39fczv1K6f63zehmlGo7HeR0/MjzM41On1eJMsd4+/OsxRmYlnmiVVov4ccGnrNAivplQwTyHkdGccIkWHX/rpr4W86j90BgrqxsW5vNvgqPSwkrU1v2j8EuWIg4jI58SfzCVSvMuw/o/tzLKtf39bi7DFfLHsPMjQv1NVpobSy6r4Dnf3zGOYlECKeyPc3kurV7Jir/OFjer43QG4sn5vW2MxqWSaeQWU39Z7To2bFao5TCaaS3f8MDP8XWGxGl6ufvhr4TTdGPZw//PcZqkCIVCOHr0KFKpFKZMmQLjpILc/9nIIE2ZyEQmMpGJTHxD438y0iSFXq/HnDlzvpJj/afLqACUHQNAfpkSOYX0dB0JJtC6zwsAyHUUY7inGwCQU6SBRkdP2hodKeMKy82s5vF5gNPOJ5VGWb2NC6CO9gcw/yFS6Oz9FXEKLrutAZ+9RfvUy+6YCovgsX/2Vjur9aYvKsD6l4k/cNPvFzGSkZ2kz6o1ClY/9OjTKA0RkqTWKtF7grLIjkoNq+M0AmnaH5ooQ3L/wAAeLyKUK1+l4j36XYEAoy+POnIZSZJQpEsPR1FtoyyszVmOn9ZQf2llMjaKfMnlYoWalFErZTJWlpWp1ZwZPjg4yKiTRibjDFarcyISpPYtyxMlXACcJzKE+3s8sGmobZMXw1KzGe9sehwAUHf6XQCAH+Tk8HUk0mnmiLwx5ucSLTJlCOkQZY8/r0xy30klJaq0Wr4Wh1qNO1sp23u2Ps7XVaXVskpJ6k9/KokyNWXXHdEoI3d2pZLbcac+G/uFgefOYBB3T1LbAcDIe/2oEXyfaJkOns8J/TJY1FAJ/sOWv3dixfeJCyWVG2lYkM9cCXuhgTPKwHiUM9t/vHiUFWyzlhThw78Qx8Vko/lcUJZktU/P8SR8Hhp7a44OkQDlK5GwH2V106itvYQeBH0J5JeW0fitsmHjWpqXKrUbA50CwW2cDd8YfV6tVcAvlH6NQlHqHS3DUDd5z8jkebBkE9oZ8sfQJ3hW519Xy5yM3uP075R5uTBaqc+btw7ydQPAilum8mvputoPuTBfKPAkTp1NrsBQgtaK+4AbOmH+qKs1sUnrqs5OPCy8zirdNAd3mBLYJxCzB4w5uNtLSMDTuUVcJHlDtZyLTc9pbWXUUkKKbrbbTyotJCFKc7MCPCf8ETOhqgBm5hEnqGksGwUWuj8N7X4SMxfdx8cYEiiQSetjM8ltWx+HbBbB++nm31M/N/4I6YAgl2qHMdM0sbYkFGhjXzmrYqW2t3lyYDMN8XsS2tMejXIZGAWAG+yEOpWpNVxWqUhNfTtXb2DfN4dKxcq+faEQnhOFj+8fGGBj0LssdA+pbjvKhZGbQyEsEBn4hvFxNtOs0mgYvYsOh3FPjOaxpHSUpSc8+CKhOHSCd9oeiUB1ZAJF3PcZoT1SCZ7t73fDLn4/YpEEFKqJ0jRRwfPb92k/rvoh/Q50t7qxfX03APC60+gsbLxcNyeXi1j3HvdiRBRpz86fwAYkBbfRomZVqr3QgIEOmTiuCnkl9D3XYBDuUTr21AVWNAkkTOIrldXbMDZM95touJ2PfdoFpWjdK8yUTWbMXUrHrp/7EL7OkJCm57r+DTrTl0Oawv4Ibi3/xf+zSNNXGRn1XCYykYlMZCITmcjEKcQpI01P3nkdACCnqA5BH3F4nANhnLmiDADQ3+FlBU5ZnY35G7Wzc1nFcPZVtBd8dHcShZX0NN+8dRA6se99+sXlnCkAYHWQ5Mb8xYc9mL6IMkSfO8p8i6w8HWf/ez/rZ66HVLwUmOBsGK1qNJfR+XKUSswIUnbzniqMq1X0BN3V6oZ5FikhJMfhJSYTIygbfD7mD70yNsZozQ3Z2VwIc9uBm7Fs9kvUHyLra9DpGE1ZPTrKyElLOMwZ+GQuVNMQ8U2emubBw0+sAgDkfedpLmMAAM5mUgBoRnXIPY98aBr1euZqSQjWsd4F0OZ9DgBYbDRiYx+5ii8v6camD8lDJlHzLqqLd/K1AFRGwt+7gtqxcC8r3043GlmBU6fVcha/vbaWM30pK7+uqwtdLsrArywdwUs2Qhju8Y2wN9bDg4OcjUsu7Wc65Vze46yONmypqeG+k8ZiUVqLziM07xLxFH9eQkiO7R+F8UI634mnjzFacqLJxVluVp6OXbClDFatVSAlyptsfa8LBWXU/pG+Ljgq6RxnrKjAu8+38Lklz5kzLqW+3f3JCArLac7bct04spvma/kUI/tCFVaYWQWaFJ4vPlcCVtEvepMZ6RT1eV+bD9NOI+5Od6ub+UEVU7PRuo/maXY+jXckFIdKTWOYXRBDWzOhENacHMTEWJVP0bF6Lir6q7IhCxteJbRNrpCxg/q8ZcWMUO2dpLoDgNa9tGbPuYrQi70VSvY30w1FWT2k0CuY57MrGGRX6qFOH4+ZpIp6Vhtg9Kg7FuPXO4NBnv832+2srpRQLqNCwWq2SDrN37u8WY3lDurHjmiUkUpprpWp1Xiym9bdQ5VJ/HLjbQCAaXMfw+Hjl9KFqt3AJJf+8hdonBMPvguAlLbHXMI7LmGk/wBomy+BTCrIu+IRnucS+hpIpXjNA8DOHkJWygv3MWdJAeA2MSeqNBrcse5+AEDtmfcAIAWehFDdciIC2cGfAQBevOo3fK+6orMT9+fT/VP697quLnxLeFVVa7VcSWC52czr+K68PFbQrrBa4RkmNFBCKU80uXD2lTTPjRYNrxuVSckeaY4qC47tJ7SwYQGd2zUURJv4zbDYtMxH2vRmGxfp/eSNE4xq/svjC/HH++n+JJUZOv3iMnzwClWDOHNFGZq2UpvzitM8d8PBemh05N8moUGRYIJ/o4I+FaJhmtvZ+aVcRmXxJSXY+RH1RzwWRongPeUVk/fdgS2fQi/mTyqVQm4xtbPriJsLB5fV25iHNXfpL/F1hoQ0PdP50FeCNN1R8csM0oQMpykTmchEJjKRiW9s/E+2HPjviFNGmh75LrnX6o1WfipXaxT8tG60aJBbTNyME017OBufvqiA97KljFOpkuP4Acq6c4o07NL84V8mlBkb1hzHlHm0XyzxlbR6Jdemi0UT0IjXezf24vSLy/nYNY2UcXW1upEzm7Ltzk2U8XhOs3JWt8Xvx416crVtlccxVTgXP+93s1OwlMG2R6PswpsE2DPIlUgwKrNhfBwzRGa7xGTC3X19/BmAvJSkrK9MrcZewd/4dVERZ3h2pZIRHCkLfscVA7yNAICFpQex0yN8XjTj7PW0NxADJO8l3xTMrdlA73eL4p/6XsiMhBACExyQva4srCyk873Tn4UcK2VZzhBlek9VmnBnm/BpUru5VpczkcBMcYylk7JSrUzG1yt5MM3U6di/alVWFh4UdcTWVlSwh0yZWs38jZ+ILPi1sTHmb2nlclYrPed04u4WQoScZ1hxvora2qdMIirm1RThq5SUA63CUbh4YS6i/dTnR/eMMFJ5yc1TmL8kZb4XfrsOv7ltC72+sR4jvXR9g50+disurbGhdR/Nj2t+VIR9n9I8dw3RsbR6JQY6RIHQxRr0HKM14Ro+yp4u+aUmLkgtrY++Nh+yCui1Wp2H8TGaRzqTmhVxQ92dsGRT3xitNsSjhKRKnk/JhBbfuo9Uruv/3MoIjtcZ4XW186M+nLmyDMAEMme0arD9fVKX3vvMmfjFdzZx25ZfT0hf89Yh9lM7tGOI0T32enpwOhzDNAe225Ls5r3tz8cZkQiMxxhNiAqSwPteL0Ymoah3dlCfv1RtYSR2k8/H/JoytRoWsZ5+INaaVaHg9XhSoehUipGtpW1tXNT3/sOEUH8wz8v17QBwQeiNOx5gN35ZzWouyHtXXh5+ufl6AMCVC8np+a2OSub5QRlgpMmWtxueIfJvknVdiXT90wCAnCxSZzr3rEb1QkKMJqPRG3w+tO14CgCw7Kz74BD3rZdHwqgz0ryT1v8N2dmMAq/KymJkSCmTsWP5/QMD/FpCx9eMjWGLqGPnTSYZMQ4kk4yQz1BrMZKicWmPRhkpq1XR34/EIsgZpb8bhIIMAP50+x5k5VJ/zVtWjD5Rc04qqltSY2Xne3OWFrs2EL8sEU8hGqb2ldaakU4TP1OlGUIyQa91BsGPrTBjqJvGsmXnDqTT1C+OKgtzlqKRBPoEKiap+U67oAwjfXSs3rbdsGbTvTMe7WEEraCsglHZZMKDcHCC9wQAGl0c5aJ2nt8d4TXkH49Bb6BdjsGuDsw/V/BLr3kCX2dISNPqrwhpuiuDNAHIIE2ZyEQmMpGJTHxjI6Oe+2rjlJGmT17/KwBg/+YPYTBThpuVn4BXOG1rdYUwmClz7z3uRVxQk6YtzOVMdPPblAktvvRybH33VQCUVUjeMwe3+1Azi9CL5q2DyBOZqMQzUWkV0IiaVuYsDaMDZ1xawbXB9n7az/5NuQ4jWoUbuUl4yYw3mlA6ILmAm9jvQ5ajYRTouq4u9lWRstaeHSPwz6In7HUeD3Nq3vF68WuhpFvjdrO3yVqPBxdZ6Lq/c0B4G5Ue5yx5eyDAmVwkleKMuD0a5bpQxeKzG3feB0juwsPLUTz/bgCUGUpo1AafD/EuynxR+A+YP7gdAOCbfxAAUPB+AYZvoYw47TqNPWIweAmgJ3SpuvodzmyLF/4AANDXcx5kfedTv5z/EPfR0wNxQE6DnKONwRmh/jWpI6wWlLxzbu3tZcSoOxbDLwdoTO4ryGYE6t68PD62hFotMZkY0TtfZUSXgvolzzvBH9KbVOgSlhtlajXGWujzEjfOXmhgJ9/R/gD0RmpnLJpkPtJF361nXxUpE62bnct1pQ7vGsaiC0gB+sbve1BUQdksALQdJD5Sdr6MM1TJE2m4xw+ZnOZMQRm4Zt3l/1KL3Z/QMYK+Tq7vJkVRlYVrZQ11p9h1PDs/hZG+CTWb1kh9UFZvw3GhLguMSxXY9Vw1vqPFDbON0NeKaSoEvfQZW+589LVtBQDMP4/4JM7+AGfonUfc7KNTM9OOd/+deCFLLqs8SUHE3BDhEJ0ciUCRR8cYaRpDVqNAbTsCsNrpfblCxpwsSbm3o1bJ8+RYJMLr46PxcSwWHKkVHR2MOlkVCl5PUmzx+1nlalQoMNR1MQCgvGI9NOJ7k2u3STyhj8bHERSocnc0yudo8+Qw0nRfQTZ+20Xnkx3+KRxCYcqf9et43c3+9h+w2SPGyj0PiyvIQb3lpdvgufxZet91OgDg+in7eJ77u6/CyumEEm95/nsou+EZAEBT65VAyesAyBftcpsVAHCDQBsXfNKIG2eS2/XDhYVYIUpHKGUyVsHN0esRF7f73UKh6kok8Fwpze2HBwd57dVptVgu7l+LXXK8Y6U5c3ZniudmSQ2hVnd7h5ib2KjT4Z0/0ro699pqnrsnmp2s7JT8mOyFBnz8Gt2Hzrrcgr8/R6rCFd+v5M8EvDFek6lUmteY5A1mtet4vrqGgsynSqbSCIp7e+MZhbxb4Ryg8Vt0oQWHd9Jx/Z4OFFcTT8nnHsNIH83zVDKNymn0fmDci5DoGwlpsuX6mKdVMTWbvcmO7R/l2njxSBLWHPqtWHnrH/B1hoQ0/br9AWi/JNIU8Ufwk6pHM0gTMuq5TGQiE5nIRCYykYlTilNGmt5+lpAHnXEG9n1KGc2sswtx4DNCjxoWLoJrkLIDmczF38vK03NGKUVJrRXdrZRhjPYFGYlyj4QQ9FEGoVSpUUziMd4Lr5+dw7yPwgoz2g8Sh6K/w4srbp8OgNQREn/D545AraFsQmrDsf2jqLyW+E/eZBJzVXS8ziNjeC2fMvfbc3JwUDhwSyqg4Xgce0V2lqNUsjv1FTYbc6RedLlwWHxPJ5djvkCdPhVcnY1OHX5aRs+pj3Uq2UNp47afo3weqWFciQQim38LAMhfSu/1HbkdsprVAIC0ew4Ux78DAEiaPIz2TJv9O/akWpWVhcf2LKHOkzgW+RuYF4VIPquAii0jCP31hwCAsTOPA4MX0WcaSJVXbPKwCu79IRNyzJR1Ow88ApS9AoC8bpq6iLPx+OwjOC54AG8I35hIsAh1WTQnkuk0+9MAVGsPAC6yWLhPJT5ZSziMiwRqtc7jYT4GACTbiIdlL9RDLdDHt8M+RvfC4hwmhQJj7dT/gfEoI1SknptQWUq8O8nVur/dyyqfaDSJIzvpugvLzZh3LiGjSpUCO9YTYjHzzDR7jHko+cSZKwtYJSdTyIBUPQBAq9ejr42UjB6nHBExrzQ6WooNC/ORnX8eACDo+xTb/kFIYE6RDio1ZeuxSAQWu6hkH5tQDe76mD4rV+iQX0IZcSKegtFK/KaOQ82s+Ln+JzV4/XfNAMBZ8rxlxUiKbH2k18/csIA3hmiE+q7vhJfdlhOxFHuuZV1O/WLvnKgcrtYoYKmk8zWHQjAdoLEYajSyekxCS7f4/bjRTGP/VnAcV4ix3x4IsEv2dVlZeFBwd4xyOc9NicOz0e/HsdEyAMBZRQPYKfiB03S6CZ+mkB1aHaGdOoGENOh0jFDdkZuLn0h1Iwcu5LpxBbY+5jrZlUriOwFYfNojAIBtgw6g91q68PwNWFhKKO++UAjxvX/iPoFc8LYk9NixDjjxI3pd+A/cWDnA55Ditx89hMcvfgwAKXZVAqGVHL79qSSe278UAFBe9XfmP+0KBvGq8FOq12qxTiBa0j0rkErxeVZYrVjeRmjPmvJy5iYei0SYZ6Xb7UXrTOpzae3ufK8L2nNprcxSatnN2z0SYpVbrsPA6JGEDPUe9zKn6aIb6/HGauqv2kY71ytVaxWQyQjF0ug8fDxJUWfO0mC/cCCfcXo2juym+aU1JhjZGujIworvU5tX37UNABCPARd/l7hcG9f2w2Sj+4XPHUFJ7SwAwPH9e2HJpnURiyYwdf7ZAIDmrVuon6ea2J8qr9QEZx9dt0pTinCAeIH18/KwW3C17nnmNXydISFNj7b961eCND1Q/asM0oQMpykTmchEJjKRiW9sZDhNX238p32aFEoromFCECqnLUBwnPZ/9SY1xobpaX7mmWp88QE9XafTBpz/beL8SI7JC5aXwmSjJ3hnfwADwjsjp9CAXRsoU66ZaZ/Em6Dss3nbIE40U8a54vuV3LZchxGfvE4ZkjlLw7WN9EY1kkZCnVRhyiRONDnZIbr9kAv+6XSOJ0ZGcMsuyqzqryznzOq3wndpfyjECMjNPT28h3+hxYKDQgU3HI9jlfjMiy4XfnuQ/FYWV1FNsUcKC3FDdzcA4LGiImwRleyfd7qQFghPXcUmLBQI1csfPwoA0M69Dap3HwQA+Bd/BO2BKwAApuUPwnniBgDA3IbXsfcovY+UGobjxFMInkf1jlSKJGeXrkSC0R6jXM6KIKtSidaNlM0m6v4GAFC0XYPcMygLvi4ri5G0DcNmLM710mcnTaGdffV4dgb12e1C/TTXGmOuyjteL1ehn+ypY1QoTsqsAWBfMMiKubdKy7n+W9Ko4DZv8PmwRijwXikrY9+eyyOEILbsGmZlZSQY50z16J4R5iw5qiz4+HXyeomFqZ1+Ty/q5tAYx+Mp9hJLxFOMKKVTMpx1Ofk37dnYC4WSMjBLNrUzEkrAJ0qVqdRqRpQS8TjUog9U6gQcVVYAE2hP28EYvE7Kno1WKzse97X5WB1UO2suvE6CtHRGH4a6CWlxVNN1n3VZJbavp74N+Y7D46T+Kp+iYY5XLJJkJEzijcSiSVbG+cdjrPLbsOY4vvvQXADEm5IQuzeebGa+l9TPB7YMwJqjFdek5XFzVFnYe+0J1yiucFI7Okrp3y1+PztZR1IpHvvrsrKY+9bY2soIVALg+SPx5y5qb2eOziNDE1ybBwcG2D3frlQy706qJZfAhGKu6filsJW/BQDw9J2PK6dSHbcN4+Ps1h/76w+h/jZxVIaGqF80h1YiOo8QJd2Om6A8nxygFxgM2LXmLurTc15jVZ110wU0Jtc/xW2b7D3V1XkRplV/BABYZDTgOaHKvCZHg3XifvDEdasBkPv2M+sfpmNc+DO8cGgBAGBu1WfMyzwcCeM2u+BwCfRbJZMxYrTO6z3JdV+KWr8Mu/UTqkZpvUsq30uaEyg5l1AdWzDFVRvmnONAv/D2mrbMgbcep7qhkqdefqkJiRjN51d+tQ/LryPkR6tX8rx0DQV5/gTGY6xclXiAReVmZBcQMuQZbUI6TceWyYaQX0r3drVGgR2iBmlJDaG9OuMo8x6dAymEg9Qftjw911BtO+iD0WIFAMSiEYQCXuoP8fvRutcJmZz6Yt6yYlbjOgeD7BV49pWVOLaP1vKl338KX2dISNPPT/z0K0GaflbzWAZpQgZpykQmMpGJTGTiGxsZn6avNk4ZaXr+we8CAKJhPc65+lsAgLefWY36ufTU3d3qZldhsy0H5izKbpQqFbpbCY2SKkybbCVQqenpOxSIc4Z99hVVeOnnTeIzCvzL46RceOY+QmoazyhglVPV9GxGlI7td3J2bLXr2Pcmx2FE9zF6+l8mqrn3yBLQiQrmg0UqFAhEKS8pRwdof7o9GsVHPsqQnnIQT+OVsTFW1LkSiZN4TNLe//ZAgLkEjTodc3TYH8ZsZi+YoYFF7NAd6bgJdy/4BwBS4DmHhbeS4CtheDkWz1sNgBQwT/+dnL9nnvuv2NvbCABYXHYIB1+9EwDgu/BZYJT2322l7wEAPOOlE/ymhBErK0hd805nFaoLDgM4md9w+CDVGpw243m+vqHW7wEV/w4AeLuqBFd0ku+TRiZjXsqOQIAVhBJScI7ZhJE4HeOZ4mL2pIqk05wF39rbi6Ya4t3c2Eco5StlZayAeScVZHVdlUaD8qTw0UkEWR0EgJGwHHEdmv4J/5T8UhM6WgiVyik0cJ25KfPy0CI4S6vupv7csOY41yjMLjQw0uR1RtiB/PSLy/HpW9SPOr2SVTISfy7gjaK0jopEHt2zC7WzCMVyDgVZGZpfYsKw8IBq3Utronb2XIQFCqkzuFgZqlTJ2TX5Hy8ehcFM6Eo07ORq7NJnfe4ovC5a2la7bMKBvNyMqFC7TZ2by22VsvIvPuxBhUCXhs7Nhu51ScUUZT+1E81OFAkftuA0Ew48QrUhJY+1SCjBx0jEU5zRH9QkoPmC1kRFQxayRDZ+MEb9vC8YxE02oVZNxllRtisY5HG9ITsbA2JeXWGzsQruY4GcuBIJvO8hRO/x4jy8LdZglUbDa/O3A372NPt4Lh33wYEB7PXS36/Mk+GtHupbk/UE/P2ECMHaDISo/+Gex2sB3TcAALTVf0JkaBl9b/d8YCUhxUqZDHeImoi/bNOxGs+kpuuWlLMA3Xs8cUJTFpu1XIcx3nYbr19T3R9YWbi2nNC967q6eO0OJxKMpAWSSa7n500m2Z9NWtNz9HpUifN3R6O4Uy/u22o5Po/T99ojEeYWKkGINAD4BTrWHYuhRngYafUq9gqT+IEAqVkllFfis5bVZzHCGRiPIiBUndYcHb//jxePYnyM5qhGl2TFXs1Mmu+b3mzDaReUAQD2bOxj76/pi/Jx6Ataj1pdH6NAUtv0JjXiMbpukzWB8TFqfzKZRJ1QSR/ZPYbyqbQulCoF2pq9AACzndrmcyWQSlEfLDzfgX2fCS5vOsK7I4HxKKNtP3v1LXydISFNPz1+/1eCND1W+3gGaUJGPZeJTGQiE5nIRCYycUpxyttzkp/RgvNXoK0jj5tTAAEAAElEQVRpP78vZQ0AOHse7h1FQmSAU+cb0d9OGUY6TVlOX9txrgvkdYYR9BHa88nrJ1BSR8eIhe14/QlCnc65mrLCHeu7OZPILzUxryISTPDTvNWu46d8rUGJHFFfTPr7viIZygqpPQ6VCsGDlIFsbR9H7eVlACgr/dTnP+n65xgMnJ09MTyMd/Los/fk5eE1oRJbYbUyQlWm0WAoTNf16sF/oX9LXofMSP0iy92CxUbKYnZV/Rlr3MLhuv1aIEbtV9XSHng8pcY2r+RM7sE1l1CtuFf7s/C9OuLivDIWhPXKXwMApqlUGNYTcuU89FN6b/bvcLjjHLqY3M/wzjD141zHUTSHKcNLffEH3HrZrwAAyhnPAwCa+mZSVg1g2eyXsLH1EgDAC9ZNjBK9PJTEBmHMZdPIsFH401yZQ+OdTIOVhOe0teGnwvF79cgIK+IiqRTzFaQs+lgkAquB0KV9w0E8WUjI4sdBPxIqQlHsSiWOfEZoiH+eBfViOmaXUJa5p3UIzdtIbfWd+2chLXhMIf8Ev0mtUUAlFJeSEmygQ4bbHiOuxOtPNCFsoLFMJdOs/jm6Z4TRpbA/Bp2BkJjzv03vff6OEgWi9lx+mRPb3iNkrnzKLHhGCa0ymCvReYQ4M8uuoXneewIICJTFaq+HwdohzhHH3/5AqE7NzHnQiT4dGwrw/D5j5fepvza9jHSK1qzeqGVF6WRekXskzPwmKROXy2U4spvQjYVZWmQJBVJ/u5e/t+zaGrz9DKGTNfEUTr+4DADQIdyf5ystaIoQylAZou8AwFyZFq2LRX26eBxlcWpf1aTxfnR0AoWUeEwNWu0kVCPKvJwnRkbYRd7ZSXy+g+cdxEUWav8mv58Rl+ZwmJErjC7D8wuoH887Qt9HysiKune9QLlQJgZScvCdQB6DLW83AMDjm4LlWXT73BAiJer5Fgs+/JSQJuVl98Nz8GH6nnYYa5VUhxKR2Xh+Ct1HJNR577FLUFD5Nh13vBQL80T9Spce1Ra6X0TqX+B+XG7JwgtbbwUA3CD7PQCgNRJBZJw4QTZbG94SCFu1RsMocL5SyajTGoFQPT48jHuFUvD+/Hx8Dhq3C3cp4Z1GcyPfqMR60Xf5KhUW+em6JeVYcbURh3U0ltWuBCNGPncEZfXCgTyeZOTWNSgQrENj/J5SJUc0THNUo0tilvDaC/oUuPCGMgBAx+FsmGyEQr//EnFYc4tL8MkbdA9ceWsDjgvV3VtPt7BKVCYLIxQQKldxXKViOlr3fQoASKeLUNFA78ejx3F456hok5rXlS23DDlF1DcjQh1ozdGh8fTLAQBdRzbw+FROmweNjtrpHAry+vhnRTL95YncyQwPnCPDacpEJjKRiUxk4hsaiXQaiox67iuLU+Y0bV9PPJrj+0cRDk3QwuLCuyXoj2Oq8HQ5stsJR1UNfyYcIEVcOEiZfXG1CpEgPfmP9gdYPVQ1PRufrSOOyMILyvjYElKQ4zCyKuH0i8uYq3JoxxBzM7Ly9FyBXatXclbjLyd0oDgAbFNTJmSUy3GWQHsioQTSeuF2PTrKzrj3CVXOvf39WF9FSMAdfX14QU9oyTX+iXpOa9xuVt1ZlAqUqSmDljxV+mMxVt88PjzMnkIfjY9jpciItwQCfAypwnm+UokmUWld2bsAM8+lschWKrGhn5CAcnsnRt8jtU6wtod9mEyfEv8sodUifKZQb0xyAacTXUb/Vv1x4n2BLkHtBow0JmdlJ9n3JrrrGaSLSdlz47SdeNkpVDIGFVY7CBGSMvund1+Kj88j/tY7Xg+KVTQPFDIZ5gqOxa5gkJVMjwiH9RddLjyrIFRhqzmB6kncCwlBiAYTaJNTPxV4UsyFkEJjUePff7oLAFA5dYJD4R4JM2ppy9MhOI3mQYmT5tSbqzuQTBAK4aiynsQlklDS9kMufn+4J4Cuo9Q3NTNnU9vCrVwrcaRXjaJKms+JeApHdlNGfMall6Pr6CeiTWKueuKw5dK1TpmXi47DhOCM9vdCpRHchHQE1Y3E+XMONMNeQKhTOk1qvqnzA/h0XcdEPwhn71lLinj9fvpmG7QGwQE5jeZwrsPI6MDf/nCI0aVp9zcwp8z96Qgft7zexoiD0UrjmkqmoSul9ii8cQTNou5dMsk8v1vtdpiCtGablPT9QCqFM0EoncqkRFi4OAfGY/jATGv2lbEx5hMuMZm41uPKDrrWiywWnndVGg2rRItUKnb/Xj06is3jgm8ToXUs0/ezglVb9AFMr9xBfXv5B3ytaL8DKCQEd27ZHkbCdh4htEFb/iog1KeRhs/Yh+ninDSj1DvH03QcAMvnPQcA2NBdg5zcZjpfyIhHy+iaHmgpxPIyQlF2BALMqbzIYsE960nROm0heUUdDsZxvZ3mpUOtxiaBwCllMvaaW+t2416B8kr3t7tyc/HIENUtlD4HALVaLZZG6f5ltGqwO0H9ZVQokDdE43UkR3Cv5Dq+d8bdUZxoJm5Pw4J8bBXo6qEdQ+ybJHHZDu0YwrnX0u/Emt8c4Ne7Pw6i4TQa46YtAzj3WuKjNm8dQn4ptVFCiXd+5MHU+TRnXIMhnq/jY2akBDqpN5mQTNB9TWugc4R8x1k9m0qm+bVzMIgisb6HusO48gfE03vzqcOonUXIuoSOyeUyhIPUHvfIMKy51F95DiPzn0pq6xANdwMAbvrZi/g6Q+I0/aj1J9CYNF/qWFF/FL+v/3WG04T/BNIUFATTVDKNRJQmqUIZ4pt1jkOGI3voZrrw/BI0b6Uf2lQqzZ8prqXFcuJACPPOpQkYGI8xsTYwHuVCjSaLGhFRPmXO2UTGDvpq4PfS1uCGNSe4xEN+qQmDwragaEUxhpu83O5xcePNrRRbg8YU8tL0Q1blAwJJYYqHMMq30Y/W9QtzcL6YGO3CNG9LbS2aBbT9l6wiXDhK8Gt3NIp3xE16lc2G98fp4WFVVhYTniUbgj85nXi4kG4cjxcVMSy+xGRCizDec6hUXKIlWxAuO6JR1NW8CwA45m3EXre4ubXfwWRUu1KJrsUvUz+/tAh9Z9KNGSvvBQCEu69CsYl+sPrcc6CqpO0CBYDiUnqgaeu4EHX5dJNeWED9/M7z30PkSnq99ZM/ICm2EVD7PApyWkUfaXBrnpXaGoniLS+d57kR6pdnz/wYK56h7YTNd76KpHhO74vFMD9NDwFXjHTio+pq7lMAuCMnB78Qx7rPkM/y8u5olB8oN4TH+eHT6/SxAaMUwfEYZpxGMuTJQgKvK4LTLiRpeiySRCpBbVIXUHsuvqmETfo2vtGOrDwak+XX1XJB25JaKz8wKFVythHIKaQfIbU2Gx+/RtsIpXUWhAJ0w+5va4JGR9fSsmsHamdR+yXbAJMNMNnKqG8P9cE5QD9YJqsKYtcLaq0CLTt3iOtagt422jZyD9EDYlbeCnidVM7ipn+byxYgIX8cm9+mH7LK6dnoEEV2O47Q9fee8PL2oylLgxFBIJ8aUaDzCO19xiMJ/szmv3egWBhrSsazkVAC2sEg95FEUA4e9eHCavqsQibDq3Fas9/TS/eCKH6XED+4KR3aEtQfN5RkY5Wc5nxTOITntpEo5dZL3sVrwm5CmgONOh2v0+5YjK0pTAoF1ou1ucJqZSPYY0pKyBYZzdjQTz/E12RlYd2NotSJr4ySDADFc3+CvhGyEdk7XALDDkpIMIP6PtJ1PVTLaDscI2ei/Enaqnv/+jBUbWfR+7Y4Zs4nG5Atf/8ZAEC77KdwDtMDMGJZeGCQ7hfF1W+gO0b3OP+BJ1C7jLbO79k3BcsW03c39tGaKc5pxav9dI+ry3Lxg+Xu4IRQ4t78fFwZonGbIx5cHGo1Cza63W48WEBr5TSXHCc6aCx655tgE2M4N6VCuFQ8tIq+HToxznYydbNzWNgQnm/lsib/8thp+OvjdB9Ra70AAHuBgYULS6+uRlCsJZncjY5DUomTJD5+je6j0xZOGGRKSbLVrkPT53TvDPrGWYAQHO/nLelkwsNruaaRrAf6TqRRVj8VANC6byeKKmhtphJtvFV/2oWXIBqh64JsQtBgyyUj5SO7t+Pcaynh3P6PNUxPqZiahYEOml/W3DGE/V+OhP1lI4M0fbWRIYJnIhOZyEQmMvENjcRX9N+XicceewwymQx33XUXv5dOp/Hwww+jsLAQOp0OS5YswZEjR076XjQaxQ9+8APY7XYYDAZccskl6Bdgwz8rTnl77vUnCHaORSOIR6nR0WgS0xbQ9pXRosG2f9CWg1KlxtgwZXBavZKJ2UEfwf5zl6pwdA+R7chqn5AfhUrOxO3e416csYLIihJJ1eeOoqTGSu2IJJES0PvMS8r4SXhgj5M/o1Qp0CWI6uZZ1IbnnE6ct0dIyQsM0AmC745CsFR+jdvN5QmkLGxbwM/FMbf4/bjXThLip8acuGcfQbiwb0dwLpExV7S3M6FZKs9whc2G7whDu98t+Qcb2q11uzkb3+mPAs2r6boW3QeAtumk6zPK5ejroRIb10zdgTeGqP05xgmrgpnFTWjy0HaTRCpfXNiPA2/9hMZh7kcsndaWvgkpIlELWxVI2xAFGhmGonTua7JNjPC0R6PwfPRLAIDt/IewRED7+0IhLm0hlcmo02oZbeuORrkMhj+ZxPPCiPB0oxEXiiKjPxDbKOf0A/vLqF8q9wZwcBYhGfkqFRdJ7Y/FuB/vyMnhLR2ZjbJhTSyNT4RxpdGiZml+zwkvZl1AfbD3vS4uxSKhl66hIJdqqJpux9t/pLYlE0EuLTJlXh7sBXStG16LI+ClrHREWFpMO83OUv9EPAXXEKEvSpWct8CAiYK10lZfd+tEZpxKaVA9YyYAYLD7AJv6nXNlFT79GyFG1TOyeGvPORDi40rGm9v+0YecIpqLQZ8acrHNJpcrcM5V1B9ffEhI1LnX1GDNbw/wMSSUeLJR4ZR5ebxFoTepMFZDfSCVJPrzL/fiqh9O5+s2WujcPs2EFcSxSAT3iJvfuw5a56N9ATytp+uu0mhwvc4KAPiJe4jRkvArPcC3RYHvcJhRWa/Y/nrO5cLhPuqvH9S38Rbau14voyjD8TgToiX0Sf7pbxA7m9abYvtqmARJO+87TzNqs+PVO+E/kyw8oHbzGlK0XQMAOPvcf8VnnxAaZBoYQEBshenO+BH8LhIVwNuInJpXAJD4AQA0f/0hdALZmmMw4B13gPv/qVJq852tKtgsNEZ1Wi1/V5r7ZRoN7hDr5lgkwtfXEg7zveXBggI21z1iJ4HCG6oQb2fem5fHFgd1Wi1v4d2QnY3Vo3S/vis3l+9FbESaTiPbT+3pPOLm8T7e7MRCUQi688iEJY0kHlp0QRl2fUzXNGtJERfVDXijXJh3+qICFvz43BEMdkpb5oQsHt0zyihwz3EfDGIruLRuHtqadwKg+drZQucMBeieVFxjhd5I9+2eY3u4zUPdAZRPsQIAopFsNqT1OIegM0jmteL6p2fzVqRWr+TC870nvBNFhpNpHNwutkJXTyql8zWEtD33L0d//JVsz/1xym/+S9tze/fuxVVXXQWz2YyzzjoLq1evBgD8+te/xqOPPopXXnkFNTU1eOSRR7B161YcP34cJkHiv+222/D+++/jlVdeQXZ2Nu655x643W7s378fCnEf+7ojgzRlIhOZyEQmMvENjaQoo/Jl/kv+F7fnAoEAvvWtb+GFF16AbVLt0HQ6jdWrV+OBBx7AZZddhoaGBvzlL39BKBTC66+/DgAYHx/HSy+9hN/97ndYunQpZs6ciTVr1uDw4cPYtGnTV9I3/5U4ZU5TXztl67acXHiclFVk5xdi1wbid5RNmY2qRnqibmtyMulvpE+LtkPdAACzjbK+wLiFn8SXXl2NTW8S76O41sqW844qCyNUUra7Z2Mfc5dmf7cGI1uJQ5Uci8IgiIGOKgvvqVdMzcaRGrH/PkxZzKOOQjSdR9l6oVrNRNGL5HJGRm622/m1RDi+MKXDB1H63lgigbknCFV4pKgIZ1VTRvNBYRVCIxOEyRe66Yn8p1WU9TXqdEzQrtNqceF6QcDW98JaR8jO9XYz3p1DBXQToOzgdKORM+a3js9g9Kg/HkeOka5VKZNx5juc2MsFQGXTqejvNrcVWEZoYbUhibYmQpQKVCp0hcQ0GF7ORX1/Ppeu6eGhISBBffDGwfNw40wq1lyl0SAiLA72heRMcG+PRjn7t4j3lDIZzttPrwcWWRg1WNHRAY24rgvb208i3QOAtsSBhMikpy8qQK5AbWLRGGeGS8qzEB2nsQq6ImjV03gqNhFSoDepJojbjXoYD9AxPCMhtO2QivBamGAqcTBG+wOMOrUfciEmOGeXfK8eXYLb8/Fro8gpIuTNNxaGUnDwJLuNmpl2nq+RUJwLAB/aMYQRwbcP+f2Yvoj4Lkf3UJ9T6RW6PrUmDp+beDvuoTBmnE7H2P2xG3nFhPBIKBMAaATKpzMYcGw/oQMFZTo2pjyydwQmC62JE00utOya6F+ACv5Ka3fqqgp89IsD3GbpM/3tXhSWU6ZvnmrBJz8lwrNMmM1OX1TAhbLbD43AOZf6f4lioqipN0uG1y1CVk5gKeKlOjjEWC4wGOBzUZ8vMZmYm2T8bjlL9RcYDEx4frl1GgDgqdnAI3Fqj1aejedEOZr1VVW4rovuVb91OBg5kVCYm7/1e/yy6VzquzN+hDyBLgWTSTbOTJ//NGZa6D7SHA6jMZvGpTWHxq0losQ8UWR7Z88MLti7c6gGsrYb6RjVL7M9glSqxbXiH9AKpCycSuEHefTjsn58HPsE0mEye+E5TrzAndZmlBfuAzDB5doeCDDZfLnFwtfdMnUqLAeI23Z6tZHLyjyZJoTNARVqtTTXzlLocVhO969AMsl93h2Lsb1IIJVig0wJzdJtHkOBGHurXceI6kiPn0U8FVOzWXiRjNOc2/FhN5Oue497GVFaclkFXKJkzI4Pe5gr1LrXCaUQyUwYySYREHPmyh9MxZa/E/qqUncyKfzo3nHojTJuB0CF25dfT3ywygY33nuB+Jl5JQaMe2iMw/5u6MTvis5ghkZH63rhBbSW9nwSQzpF92K1NohNbxIaLZNZEfS7xfcMUCgn1uc/IxLpNORfEafJJ9aCFBqNBhrNf4xi3X777bjwwguxdOlSPPLII/x+V1cXhoeHce655550rDPPPBNffPEFbrnlFuzfvx/xePykzxQWFqKhoQFffPEFzjvvvC91Tf/VyCBNmchEJjKRiUxk4v8axcXFsFgs/N9jjz32H3527dq1OHDgwP/xM8NCJJUnEmUp8vLy+G/Dw8NQq9UnIVT/62f+GXHKSJNGKrJqsSIWI4RnuKcLl36PSl+4R5zY9g+6ELlcwdmvJTsBjY6yIZ9bQoDKsWsD7WV7XWFWOUSDCVzwHTpeNJLA49/fDAC46EYqsjjaH8BSUQ6l65NB5BTq+X0ps92zsY8VGXK5HJeIve9D2aKUQ8APy04vAODd2XqW2dqVSt7vX2G1MldAyqbm6PVYrqWsaN8kRcq+YJDRkhdCHoRlhHS888UdeHTpnwEALWFqz+PDw3hqKp3vwk1nMepUXXAYzR8RF6L97B/zsVm55/fDI+S/0PcSnwJAcyiE0A6yEbjqgn/DG5MHbAoZYKYPPQ4AWH76rxBM0TPyzg2/AubQ+YbicZg++Al/zT93OwDgZ5/eRG8Y2xnZMtU8h5c7GgAAP6geQosobbHEZMILB5YCAHIq1vGxqkUGsiUQwOJCQg1u7VWwdH1veQ1eC9JY/CAnB2uFSahkxqeUyfAdK53701AARqHa6YsBu0WBzUdDesRNNPZpZxRTI3TssSW0GFU9YcRLKSttPDIOpVANHds3itNEoVmjVYO2g8RNaBMchcLry9HyBJESy+ptWHkrcdV6T3hZJeaoUjMKuua3B1A3m3huWgO15+1nj8Fko/YsWF6KzetozldOm8GlhapnZEFnJKRVkmJ7XWMorSWVVjLRjVSK1Hi5jmKM9hO/r6R2BgY6SI2kUufANUyqILmMMnGdYaKEhXskxDwlo0WNFd+nMexscSPXQe2XlEGRYBzHD4yJz2pYufe9n89hhVRZvQ1eF71/4NkBfPt+4utIvKh5y4qhM1M7wvOtMInyEj2FESim0XqcEZUjmkXzUZpH85U6Vkhe0dmJ50oIOY0n0sw96o7FmCMYSKX4u2vn07mPRyZKHCXSaebX3dLby6hSEhPyegmpOhaJ4OLa3aI9ShzzCcVTwsjqOW31n9DURYo4qN3w5pNB5vUChVnrdmOnU9zgje3Mu7PZ2hBYQGpWBYCIVHZFUqK656Egi9DVDZ0NGCtpBkBr89WW0wAAJseH+Ph8QnmXr7kLjjI699MC1flLVR6rTp8ZHWX+1iNDQ7g7n9bQDd3dyFNRH6hA/axUq/HxOPVBvMiBawfpurfZU/iZjvhuHdoJywSHSsXrV7JGMdXboBCWA+kqA0oER3X6ogLm4HldE0aq7UKxabXrUFhB97rhHj+bRm56sx19bTTG9XOysO8z6puVt05lNKrpc5qvJbU2nsNb/t6JZILmVyySYLuP0y4oZsRrbJjmRuX0bDgHyerD6wyjuJraMT6mRGDcS32k0SLsF+prhxpqLc2lt58lY9fqGbOQnU+/aWqtjpG0nR/1o6DMII7t4QLx/6z4KpGmvr6+kzhN/xHK1NfXhzvvvBOffPIJtNr/WD0ok8lO+v90Ov2/vfe/xql85r8zMkhTJjKRiUxkIhPf0PiyfCbpPwAwm80n/fcfPTTt378fo6OjmD17NpRKJZRKJT7//HP84Q9/gFKpZITpf0WMRkdH+W/5+fmIxWLwCG+3/9Nn/hlxyuq5x7//HQCAJVuOKfMooz64PYh0irIUvUnFe9mnX1SO7euJP1A13c4lWMaG6VTJRJANKAHgqh9SVr1nYx8riSoaspBfMlFEFCBEafl1lPEf2DLAmcsZl5azb8e7/34E1zxERVIV0RT+/ifKCi6/i86x/+M+yM+gDGqWUsuck7ESDYpE5pSVkuNpL2XH53ZRm6fMz2OeQALATRpq/8/9o4xK3ZCdjYuEAmyF1co8JEmF0uYsx3IHZTzVGg1ecNE5Ip5peKiejr0rEMAWYSDJJSBCIeYUvOv1Yu8e4k0gf8K6Pyd/D5xeUUy0/wqUT18NAFykdJtbCa2GsvVIx02A+Sh9Vhlgb6axRALOAGWlUrFg5Z6H8ZDgLv3i7/+K04QPjTORQFBcd75KhWOC83NDdjae7qPrvrWYxmRHIIh8keUbFQrOdhcajMwdu8hiYSRAOlaVVov4ceq7JywB/MZEC2W0P8jqsz0b+5AUPKTp5xfDJBM8DKG4LJ1iw+9uJx+q0y8uZ2Qob5oNrYL31LAgn0sxlH5bKM4eOMBqtrrZOdj6Hs3nRDzFvKfjBzzILqD+dY+EuE2Swmf+8lLs3UjkpbL6LDgHCKEK+gYQFgIpmTyB+jmnAwCy86k9B7YMsDqoano2+yM5B3QI+ugzWr1KcJ+I9+T3UGauMwrUNpJEtVAKdh0N4KzLSdnTdsjF3kzTFxXAKPhNfW2UDQ52t2GG4C5NmZeHgql0TR/96Qgr5ioaslB9IR1vbN/YREkkwUHas7GX+VtGi4bRhJA/hhMG6ruGhAoDGpofkmLrWxYbZgqu4K12OyOuDrUa28WaWOfxMJJkVCiY07NXHCOaTjN69JbHg4iflHnTcoawyEhj/6bbw2jmOwJ5gDyGn5ZQHzSFQtjgFTwU1+lYXnWAjy0hLofHNVyoF1XPACB16Rv7yegSZa+wd9lzXWZGh3HsfkwTPk2HuxbTe8PLGXUuLv0YfU1kUgt9L5bNIHVrezSKLi/dd4stI1hdTP0voW7rvF4uDN4ejeLlHuoXpNSYm9/L7z8ujGObwnR9F1uszF1yqNV4UdyT7sjJYSPSH1jtrOY0WtRIisLryREa7890MUTFz8jiYRn77tWvKEVYrF/3SIhVzZJKrv2Qi7mJsWiC+UYnmp3QG9X8vqRidQ2F+DdBUuDpTWpYc2jcyuqz8NbTNH8S8RimzieEp3WvG9NOo7UgqVnVWgV7rPk8QJYoAZRKpeH30Ot0euJ8/R3jMJjpGFJMW6jn1/s3u5FXTGvv7CurcGgHocOk8qa+WXnrH/B1hqSe+9ahH0H9JdVzMX8Ur03//Smr5/x+P3p6ek5678Ybb0RdXR1+8pOfYOrUqSgsLMTdd9+NH//4x3SOWAy5ubn49a9/jVtuuQXj4+PIycnBmjVrcNVVVwEAhoaG4HA48OGHH/7TOE2ZMiqZyEQmMpGJTHxDI4Evv6X0n/VpMplMaGhoOOk9g8GA7Oxsfv+uu+7Cr371K1RXV6O6uhq/+tWvoNfrce211wIALBYLbrrpJtxzzz3Izs5GVlYW7r33XkybNg1Lly79klf0X49TRpp+fSupP+pmmdF1lNCBaNiLOUI10XvciwGB2lhzdAgLjkTF1Gy0H6LspaKBMgmDuRFFFbRP3ddWiJwiyoQ+eeMEbILXceal5TgontbrBVdktD/AqrschxEa4a3jGgpiunB9VqrlzJfKdRgRn0qZgqTuWut248oRypTMWRrsMNF0OE8+oTZ6rXqCC7FBcCleKSvDOuFn0heLcba7JeBnfkCVVouPxOdVMhlncBK3IV+lYn+VJ4+V4PlGOt8rY2PsBzU0noe1U4X30iTkRXIXNygU7Fb+5GAA00w0fMPxOO7KpX7qjsXwwjBlkrcKH6HnnS6k3YTAQd8Lm4GyQc/AOROlU/qvmOBZFRFfZoHBwNBsfzyObS1C8Ze/AdUWutYrbDasEc7MfUED3quncZZUcN5kEjfbKUsLJJOcrddptcwZq9NqUSf2vqWyDi1Tp+KOXmpbrVaLO3QThZjHQf34zOgol7Gp0mggi1NbJQ5DZ4ubkdGKqdnY/A6V21h4Xgk2aSjjXaEzo1kqbnuQ5nBgPIYG4UHW3z7ODtiHdgzBLrzEPnurnVEWj1OF0lqaY5EwoQAB7zHmPxnMs+CoJt7RkV0jqGgQqhuNgjl9b/+R0L+F5zuY9xH0qeD3UFYtV8iQU0jol9FqRVEFtXXHhz3syi2hYI1nFDLaBgBdR+n6zlyRh4aFhAKt/3MrGhbmcT8BtMYkRMCcpWUEt/eEF8uvI9fk7e93sxpv/5YB5C6l44X30jEcVRbOrod7/NDMovGpVmsQixI6OSBPIi9C6yYp0LGPxsdxgYzmq8cgZ2Q3kEyy51mVRoNLW6lvlttT0Im1JaFLdqWSEarmUIjnbpOzADNzaF419czHs7NpHkj3hZt7ehAfORMAcGXtwZO81aR1/C8v34FLLicu4FvOKOqM1Dc2cYxEOs18n409tYCPfIBWNv4D77SQWvXRebvwQLMoMSXQXpl2FOkW4iBCGcA1C6k47xs7bsf+y4n3tKqzE20eQk6uLAriLVGuZdriuwEAVoWC0bhGnQ5/Ev5n73m9yBcI+kfj4xgR1yWh2D/rG8f1uROcE4kjtqW2ll+7EgksU9Kc/63Pyej1ol66/padI2g8gxSXcrmMdxFikSQjQkf3jDLiOCoUlKddWIpdG2h9183O5c+6hoKMOhksajQJF3BzlhZ+wVcdE+WxImEF5HKJwypjxXU4FMeA8BUrLDejR/g+SaW+ZLIatB38gr8nrWlL9nQYzN0AqNRSXxsdO50OMforRSyShF8o99TqPHhG6VriMWDaQrrn0Lqh3ZHqxn/F1xkS0nTFwbuh+pJIU9wfxboZT36pMipLlixBY2Mj+zSl02n8/Oc/x/PPPw+Px4P58+fj2WefPelhKxKJ4L777sPrr7+OcDiMc845B3/84x9RLJDWf0ZkkKZMZCITmchEJjLx3xpbtmw56f9lMhkefvhhPPzww//hd7RaLZ5++mk8/fTT/72N+0/EKSNNT955HQBSpOWV0JNgJBREOkUZvfSkDgDj7ghcA5RFymRBdu6e8OGo5IK9J5pd7P/iqLSwY+snr7cxiiUpMKYvKsBRUd+uoNyMMcGhqppuZwfZutk5vFetN6k4U5b+NRQbmPcSiyYRE7XK/AY5c4kWGAysDLmzn9CBaCrNDrh2pRJ39tH771VVcYb3y+MWTMsjlKtRp8Org/RM+pc6yp63BwKc9T0+PIxVtgk/ljzxfplajQ6hgpEcyveGQjgm+m5llpEz301+Py61Uvbz3IgXKgVl8VqZjP2n0jErDYpvChcnzSn7BzueD8fjGBbIzyKjAe95KTsbGppLx8rZiYJJBYSlDNWhUuHJfZQ9z637Bw6tJ57GWSt+yQWKJf6EP5XCgEDSHh8eZq6XUibDBlFv7taeHkwV/k5SNuxNJnHvJMLfrQJ1Wm428/uBVIqvVTMcxW4z9YHEWXEe9XI9qhPNThQ3EuKV9icwoqWpH0gmYeigOSahola7jtGqxsWF7Pb7tz8cwlSBQLXuHWVl5yevn2AuhxSF5RZEQqKYrUWDsHjd3erBNMH5ScRTnJl/9FfiVRVXm5kftfuTEdhyRY3FEhOO7KZzlNQZOWv+6K8dmH02vbZkk+fTUPcnzMl657kB9pOKBOOMAp1xaQWf+4k7SDn2/V/MwL8/RK/rZucyX6Sw3IK1q5tpHB5dwJ5Ux/Y7MWvJyX5LaWeU+Y2dLW4kxfmOLLdhlZfW0K7cNJKinyR0xptMMp/tFZcLxyuob53qCZ7S+vFxPCEKQr8yNsZoiORq/YTDgY3is2UaNcYTdBaHWs1q1JZwmL2GJB5dlVaLO5tojG+tP8H17a4/7UWu6bY834d6gYau9XjgEetGuo54sAhXFgW5PeoPCT067arfQIp9oRA8TnIsR4AKgMN8lHmFx3b9CotP/zfujyvEeuuOxbBGqEvjAxfC5PgQAPCCQJQ+9fv5nrXB58Ol4nuPDg1hXSWhgg8ODOCw6K9uO627LcY4o3WPDA3hXfHZAqUK24J0X5ud0sDnoXGx2nW4c4zWhXQvMHzmYm+vRDwFlUCudAlwAXavMwK7UDtLcycSSjDHsKIhi7lLrzy6n9Fh11AQSpVA8uJJLtIu8aKmL8rHtn/QfWH+uUWM0E5bVICOSWtZ4q5KjvlZBTrkFxPP9UTTfuQJPlsskmTuXjwGLBLcva3vdWHJZcR3lDi2KrWRvZuuvaeROX3/ePEIbn2Uagm++PAeVqb+5PmT9M3/7SEhTSua7/pKkKZ3G1dnCvYigzRlIhOZyEQmMvGNjUQ6DVmmYO9XFqeMNP3+h7cAALLz06yk0OpV7HXz4V+PwZJN2UHQ58KUuZT5Hts/inRaKCFEFqnSyHDtPZRtbfl7B2cPIX8c2SIb6Tvhxb3PEMdg/cvk1qrSKhAWKJJ7JMT1rdb/uZX3m2tm5qBA7J33Hfdy9qJUU3YjL9IhJbxn0oVaVncEvFF0NAg/H70eBXH6fJ+S2vb48DBnuM+MjrLr9VqPh7lEr7rHsNJKCME6j4d5EVJW+4zTiU0CWXliZIRVQwqZjPlLpxuNeEzULssRdbisCgU7+R6LRFApEK9nR0dxneTUOylLV8hk2DBMffA7QZ+4f2CA26GVy7G56Xo6R92/I1uceyyRYJ+c28U13d8RZ26GNu9z3GCn85nkCvhT1DfFKjWfuyUcZs6SpCq8yGLBRSIr7YvF2JJ/i9+P5+OE/Dxm9DP6JXFSdgWDjCTcnZOLB4Yow722T4EakYkOJxKMQjjUapypomuU6s3NOceBSJDaE4smsOFVev/aexux4dXjAIAzVlQwr0hSviXiKVbazL2hGgfXktOw5MUCEHevs4UyW7lCxvNY8mva+l4nz8vCcjNONFHmq9YquI6bvdDAn3EN0nxwVFmwcS1dq1KlYhfkgrIk+toIVUsmvFwDLxZJYlzKjkUbIqEETFk0TyxZWhzcTgjttIW5UIi1oNEoGHWS0N5IMIH3/0xctNt+NRXb3+/i63MLt/vCcjOjcIsuKkP3MeJOWWyUrQfGo4xyRUIJWEqozxTRFK/H24f7cb+ozZYTojY8FhpjFPUeaw7zs37rc/J6G47HeS5J3DcAXHftuu5uVmpWajW4xkYo45ITJxiJmaxsbRXzdiAe53PvbL0Y02qpxtwqmw0P7FlAJ7E2Q2bs5HNKCrY79xM6g1gWlk+h8g4bBu1YXkjjvcXvxzTR/r37foSFc58EAHjE+hiIxeAfJGLr3IrtjGivHx/ndl6XlcXroiUc5nUhocR2pZLRtlU2G98Xtvj9jLApQbXtgAmuZplGg6eyCSX6iXsIRYKvtNJq5e816HSw+Kitz8a9+F6KUFxPNvVzmVqNT/5Ka8l8uQM5xwjNGe0PYMFy+n1IxFLs7SXxUntPeJl3V91on/BSGgydxIuS+H8lZ+Zj/a+aAIB3E6z2CQV04xmFvMaSCT2iYeoPjc7K9RZnnUV9uHldOys8rXYtr8d0uggGM3HpamfnIByg82z5ewfqxXqbIfizrqEg9n1Ka8U9EuL12NniRixK43L6xeW8ri++aTW+zpCQpoua7vxKkKb1M5/KIE3IIE2ZyEQmMpGJTHxjI4M0fbVxyg9N1TMoUzrR5ORMOtdhZD8KhULNnk2xSBJH91JmW1xjRUEpZSZ6qT5cpYUdiuctK+b98L8+vh/ZQj0XDSVO4pQAxEnR2Cmbnb4oH5+8Tk7KucVGPrapzIj+uMQdcePgIjq35KuUCiThL6Rj5KtUiAr1UxOiOEe4SZtVauyMU3awz0dZ06qsLIwfF3XErGlcrKfv3T8wgGdEHSujQsFITb5KhZuEYux3I9QXF1ks+Imom/V8aSmqWqgmVKVGc1J1cSQoM3+xlLLFx4eHmacBTPg+WZVKvCOyy2KVChuHhPNsLAtXVpI6aPUIoVXxvX/CzlnkSvyoIxctdf8OAHDGAGc7eWDIyv7KCrstOkJk5mYlMGzaAQAYTgCLjdSfd/X1cRZcpdEwL+Uiq5UzZSkuV5pg66BrfbyoiDlb7+SV4b0ktW99/zh7UUlZ9By9Hu0CCehLxNmTp9OQwFShWHSoVDhrhBCCklojo0PZVxPScWLjCOZeSl49hzf2Y/n1BL25Ryay2QGHCqUjlEkPibqFR3YO44wVxGHwJpPs1u1zR9Cyi8azvCGLlZ3r/9zKvmGS94zXGUG+mPvH9juhEmhWyB/n+ohnX1mFN1eTO7jk+RQYj8GSLarX15uRJ7xiRnr9uPi71EdeVzaf7+1nj+H8b1eK89Bc7O8YZy5Fdp4ec5cWivZHMULLBjlFMeb0vbma5kvtLB0MZurzTW+2Mf9kzhkOXmPukRC77ntGwlAI9Khmpp3P4XXSMQorzMz5eS3oZR7MLwsLGfEVllW4ucCOUBMhD0csMTQa6J5TG9NinfAMqtNqGUnKV6lO8ikCiEsozaM6rRYPDtI95MGCAv7elkCAkRaJYzhHr0dE+mFITPiHPdDrxfcatwIAXhgO8ZxPpNPMZbyxnrzgumMxVvndXRHAk5tILbVs0aPYJj4LazPP7+ViPt+QnY37qZmo0mhwg7hvrMrKwvcfXA4AuPW3m/G+aPMDn13HPmwS+r0vFDqpuoHkDt4ejbIq9UKLBS8JHyZJQm5XKvGAh5S5ZRoN31tWWq04R02o1NsBH+xq6rvrzNloEmuyuoPujS3lKZwh1lU4lUKvcNRuXFyItU82AwBmLSliBZuE1FRNz2bk0ZKlxbK7qH7gJ787BKu4z+c4jDi+n5Cfkf4AhsT6LhUKT1u+nmvF+dxR/l7PcS/O+xZxxkL+GM/HIYFKqdQ65g02bx3k9e0cjKBrF/XHaH+AOVAX3lCL1n00/yXe4+5PRmAQoEuuw4gDWyQPNSUrxSPBOPNp/1mRAPBl/bP/s5YD3+TIOIJnIhOZyEQmMpGJTJxCnDKn6Y3f3waAnuYlTpMtTw+DkTK1klor708vvboafWKP+PCOIVYQSd4bsWgSCVHpOhJM8BN/Sa0NLTvpKb952yBufngegAkXV6NVjTdXU1Z391OLMNpP6IzepILGQZlCIJXC+L4JFVOtUDrt+ZAUFh/OVLEqza5Ush/TsUiEUaI6rRZDgtsi8aN2BYMTVcTNZvgFv6PdDLwtsuAGnY55Ee96vViVRXvxzVL9OoOBfYfaxoqwOJ9QgXyVCm8dJyVHueML5npIHkbd0Sg2OinrfrxSxchVmVrNfKTxZJL9irYHAswnkjLOKo2GeUf5KhX7QkVSKfhDlNmWm70Mwz4j6n7tC4U4K/cmk4z2XJedze8/cMyMZ6fSWGQrlbhStONm4Qhbplazt8v/ytOQeCn5KhXXGpPaXp5UwqOm9rgSCeS56P2OLNlJLsYqP42LwarBW2IsrhLcssB4lPkP5iwtZ4npRguSe+mzB7YMsApO4keoNAokRYaYX2rCcw/sAkAZszRfZXIZ83nOv64Wf7hnO38GIJWclGW6R0Ls9RTyxxAUbZq2IB9+LyGEQR/xZQwWNfsujfYHmROhN6kQDtB8LKpMsLfSGSvKcZBOjdlnCV7ORyEUlNHYL7+uFr++bQsAYMaiAla6drSMQSEqwUvo2Gh/gNWGwUAcUwU38USzk3lP+SUm7ie1VsGKJclpH9vHUL+U+uDBgQHmIM3R66EJUn+M6yYUb9Ic7ohGec10T0JIVnV1Md/teykT2oRdzgafj53AHxVKTbtSifPaBJQGQkykkObdFVYrlpwgJFVCZ+q0Wp77r4xN3D+0cjmjOd9qDaLAROO93GzGyx10zsUOGrdtu36MufOo1mMkneb7yV63EQuzqZ2eZBLXi2uU1uOrR+ewP5pNlcZ8cX8aSyS4zZ+XV2N2B/GGHiks5Da+c5Tc5O+buZc5T/2xGK+hSCqFvjit9e/Zc7jPJb+4FVYrt+MarQVOsd42jI8z52qaTgfdRkJ7Si8uhslFc/czHR23QKXCIh21edObbchZSVyvmVoddn1M97vBTh9zkwJe+t6sJUWMlob8cUYvj+0fZb7Rvs/62etMrVUwh+j0i8sAAOuePczefosvKsP7gv968Y31eO8Fel1UaWJfMak6Re8JL1oEolQ1fcLpOxKMY+kq4p3+9bH9zMdVKOPMaZLao9MrGYlt2pqGLYf6trvVw7zBonIz6mbT+q6f+xC+zpA4TWcd+AGUxi/HaUoEotg86+kMpwkZTlMmMpGJTGQiE9/YSKTTQIbT9JXFKSNNzz9IviXzlhWzi+u8ZcVwj4T4Mzs/Iu+iB/9yNv76K9qkn7ZwQm0nqSeUKgV7YehNKvQItUVpnZ4dXbPydOzPISl4/OMxFFdSmrn/IhsjRrW9Cc5YUsk0TjRRVqTSKlEreBY7DYRGaOVynKWh70VCcbyTogxwpdyAB/yE/Gz0+3G3UI+tjOn4+m6JEMKTSKdZObPW7WbPoKZwmD2KHhkaYlTmxixC2H42PARnQvCtojFscFE2kqMPcFbaN1bJdd/Q+X0AgG3mA8zT2OT3Iy6GbJnJhEUiU67XanGXcODujkaZnyHVo9obmhgnnUyGJr/Y5T70G5jm/BDAhBoIAHM61rjdnLWuKS/HpaK23kqrFfcKROwFlxMjcerfW3NyWM0m8ZxuyM5G0WZqx2tzh/jvDTod1/s7EolwW6XvJTDxVB9Op6ETfWRXKpkjMmcgzfXRvIkENGMTqBJAKJKk1sktNjIaai8woGg6zbWd6zqQcxGhBlLtrQu3haHWUDtSqTSjKVMX5MMg5tqRPSPMtxsbCrK6TEKX7AV6nsO2PB2Cwj1450djqGqk9nmdYSwUCqOmz+m9okoX9MaJivCSuu7onhHmWb3xZDN04rqqptsx71waO0kFNNjpY+fvRDyJjW/SuJXV26AS2XHAG2OXZil87sgkZE7Dx5t9tgMVQrH4+ZoT7AA9mdcoZeCjZRrYOyOi/S4sOI8QxKRVxfUWT9sdhuIcysCl+XBddjarVhPxJF6OC78ltZpv2ouMRkZJu6NRVAs06obubgDAvXl5jAj3x2L82q5UMnL7UX45PodQnwlO1OrRUUZlG3Q69m96v6cU14vqBXVaLR7YQGsFDQ/i8WJa9zvEXHx/LI7v5RPqsdbtZr5U21gRFubRvWOp2cxzTPJYOxgKs6/SSy4XI1u7gkFun1ImYy4UMIGQSd5NDVotr9M7cnOhEWtlrsHA5xuOx1kR92ABoYZnqvT4ODqh0lVO4mRKqkZy1Z+k1BS3RKmPlDIZ32+irghcwq3bnKXhteceCaN0Cq3Tbe8SMlczM4fVqF980MOKs4YF+dizkX5LUsk0c1vPvbaG/f2sOdSIY/tHed61HxrD9EU057tbPXzsOec4sO4Z2qFYcctUAMCeT/pYOTq5niQARryy8vRIp4kXpTN0o1jwqHQGGtdNb7YxQh3yx1mBu+39LuZOXXLzFLTspLG//Pav16BRQpoW77/jK0Gats1+JoM0IYM0ZSITmchEJjLxjY0M0vTVxikjTe7EHwEAbZ8O8l5wSY0VR/cQOnPHb09j9cDxJidzJZLJNEyCLyFlpyF/DB+/RtnDt+6r5mxk05vtvFe95jcHIJMR8nD+twkF8DojsJ1J2W7H37oRv4Syijl6PaJtlC0F/TH4GijDWGAwMMdF4tkci0Q4m7KNJThLScRTGFIRQlCUUqADcf48QA7T6wWHBwC7eXuSSbwmsr3lZjO7Vp9uNE74nIhsuEGn42zQm0xyFjkcjzMf6dLWMUyz0Pck5GW5xYKHhQoIAHaOEVJWbvby9xYYDHhY8IO6o1FG4TZ2zAYAPDu7A7cfp6G+RrgWA5QtStm4J5lEmYb28ItV9O85ZjOWHCcuRWRsNmAkxAKRfORk0etEOs0O3NdlZXHWKfEnjHI5nrXTGG5LTfg4KUC8DQC41K/BdpvwfRL8p9Wjo4zWGeVyfr1+fJzP1xaNsoO4UiZj7kvYR++ptQq45TSu2vEE5GJexk1KVkNufa8TZ1xKCI6llr7/wRMH2dsrEkwwnyfXYeRjbFhznJHWy26bBlkNzbuOvxOXy1FlYRd8rzPMirmaxhx2sNfqlVyb6pVH9wEgNMhRZQVAXCgJHVNrFcxv0uqV7IETrtGjaw2hsRKaW1JjZZTotAvL+Hu5DiP2fUbIyYLzSvDnX+4FAPZNO7pnhK91x4fdUAmk7LQLSvnYPSe8qJ5OyGdxox3jvbT2DMU056LDYYRzaAx1zhheVU4oQyUuWpVGwwhIlVgfl+smMtj69lbm9l2vs6JVTuMZn3S7enZ0lBVo0nG7YzG0RqidNoWS1+xdubm4S7j4T1aoSejSrmAQO4cIrVtZMjyhcDObsVEoyo51n8Hzv84+yPUnJcTYpPWxn1q+SsWK2GORCM/5S60W7A7Q+9La1crlaIvSfWZHIDjBhRrNx5XFNPZKTCgEw9Mb8ayb+m6tuPfsqKnFd3q6ARBiJ923uqNRRm4fLChglE5S/j02PIzXykld+orLhYcL6R69fnz8pNqZs8fp9VMKH+4G9bneSNevN6ngT9Ma83cHmAeaTk7sMnzxQQ/qv0uoTXQPtVlSrAGAqcrEPKvOTwYxW/AC3/33Fpx9BX1vz8Y+lAu+nRQ6gwrNW+neGBiPMm+2u9UNm0D9pi3IxxcfUt9I6NPMy8rR9HdaMw0L8nlud7d62G+tsNzMKGpOsREmC12v5EZ+osmJH/3hDADAE3d8zu3U6JXs75SVp2fEurjmx/g6Q0Ka5u79l68Eado7948ZpAkZ9VwmMpGJTGQiE5nIxCnFKW/Pvf0Lqnqf6zByVvq3PxxErsgu3SMhVrkBwAXfrgNANXuk7EBSPsw5xwFbLj3N9x738v70nLMd2P5+NwBg1d2NzJ2S1BbTljnQ1yyy0+l2aITyLdkdQlB40nhGwlAL7sir+4/hstuoTp6khjMW69AhahUNn5cHlci6tXol+maIStdhFcryqE1SZja5XhUARMU5xrRpRowAnMRBkNQ/kgvvcrMZq4WnU0s4zLyhm+x2RmWWZSdxg50ybCnTu7m7G31BOsbKXECrI85WV0TBleDL1Gr2wLk/Px+NR6mC+tr5hHrsDkZRl0V94Eqo0CfaudBgYH7EWo8H9VodHw8AoqmJSvLXVHdhjp4QnidGOhFJT6jgpEx6snLvJRtlrQ/4R7EtJfhsIN8pANhfWYu2FPXBcW0cVxkp894coHn0Zlk5Lu8i/sMbhaWs5llqNrOfTKNez23dHwrBP0YZ9qEddA7/kixMHRIco0lVyod7/IwYnXZBGfLqqe8+X0OqqplLiri24XBPALZFhCAcXdeN/BLJd0yFpVeT6nHb+104S08KHQld6jjiZpfssvosaAUX4sCWAa7NJlfo4HU1A5jgaQz3+rFnE41rUaWG3zdZ1DALl2/3SJhVqcm9Hhit1AcSR6m71QO1lpZ3+yEXZ8+RUBw7V1A/n56Us9JPqjEnV8hwRHisLbqg7KTzSTyxghITPi+mvrtZJoNX+J5J4/pwQQG2CYRnab4Zq5LUNoMviaCZsu7nXC78PJ8QLak+WWiJlpHfF0tL2e361bAXM8Tae8nlYoTGoVbjlwJdXWKiOfxQfgHe9QIAcPs7D+DKZT/lz0rKvEadjutMSnNnhl4HFBASNUdvYZf/9ePjmCvOHSzezDy+F10ydvleWErrw6qkeQEQ+vqWkxCJHG0czhC17zm/HB9PI87Sqk6a2+FUCheLtRtPp7Grju6d16m70Kijc7/r9SKepL5b1tnON26LQJHyDh9iZG6Tz8eeTvfl5fF9K5JOY+oR8gS7T3x2pk7HqNPPtXZG5vNVKl7HU7vi+J6Z7h0PFBTgsHh/b4gQoxUdcnxMl4Sby+2M8kYNcuy10to7+8pKWDU0T3aJ9eEeCfFvwsILSuERnEBHlYVR1wXLS1ipqVTJmU8k+Yu17BxmDqHepOY6p9YcHd/zp87NY1TLeCHdk957rInn/nCPnxGx0b4AwkGrOLcVGrEL0nFo7CTuFEC/Y1I7F15QxojwnHMcePtZ4lA9+PI5/P4/K5L/9498Lcf4psQpb8+NDZH1/4d/OcY3/9MvLsOHfzkGgOB7aTLFIkk2Djze5ET1dMn0jn7Qch1GFC+km1LPjhEmun74l2P49v20nbTu2cM8qaUtkMolBYgP0sIyZ2nx+u/IUv+GB+bgiw/o4UCmkDHRtX5pEXaupRtyXCysRReUwm+lhdAdjWJ2in4UkkYFX+szo6N8g7WNJ/l863xeek+pxCtia2GGXg+buHEtMBiYmNkfi+FOQRCXLAm6YzEu99Afj2OfgO8DqRSXidDKZGzOKcHV+0IhvknalUqG/SPpNJaIB57lFgu+s4sM5s6q3slbggvE3xt1OtwjiOLPl5by1ki+UolHxI/drXY7m/NJD2M7D34H9y16GwDJ/u8QPyaPDA0hLNo3Eo/zA+KvE1k4M0o/PhIRdlVWFq4HtaPPCNTK6Ifqeb+bb9hLTSa2JZC2EyKpFD/QveP1IpqeKLB7s31CJjzQSuPiHglhZDb9cEnGfEqVHB8E6Oa53GzG5jdIjn7uNTWIg4431htA+0Gau5JBo9cVxvEKao/tcw8LDYZ6/Zh+LT0chVu8/HA2a0kR33hNNurDdCrNhNarfjgdzVvpBz4UiHGCUTE1m20QJCuDWDTJpn+TC5YuOK+Et8ZD/jh6T9C8uuL26bzlJh3DPRJi2XbnETcTaBsW5uMfL9ID9fUPzcGOv4sf7mU0L2t7E3wdidhEMWG1VsFtDvrjCAqybMHKYlTGaXY+E6a5P1Ov5/kQP+6HqpbGRCOTISslLBFiIV4r0rZrlUbD829fMIhacYxchZJ/GCd/z5tM/m9Z33KLBZeLh5c34z5OYpaYTPyA9VBBARd5lh6+HyksxCbxer3Xy38fise5xBEAOMWDnE4uZwK7dK173Ub8tIyubzge52MsNZnwhLAJ0crlOCzG4rXKYv5so1j/dqWSDTm9ySS2eekY00xpfvh5eUDBFgaTSw9Ja3aJyYSrVfT+zL4T/BB2LBLBfK0wDxZ7DM85nXzcNW43H69Oq+X1qAgkea7Fp5pg6powLgWAd8M+pj+EfXEuhB1JpZgKUalU8zl3riMj1eNLLDhvhOa2eyTEoor+9nFOaPzeKJOqASKPA+DfjC8+6OF2xCIJfsCyFxj4dcfhJHKKaNykuV1YbuaEQNpuA2iLr1AYHk9fVMDk7i1/7+Si3VJi7xwKYu7Z9LR4aMcQWypo9Co+Xnm9jekn2QV34+sMaXtu+p7boPiS23PJQBSH5v0psz2HzPZcJjKRiUxkIhOZyMQpxSkjTcnEcwAI9n/j94cAAPc8fTqXagiI7SoA6GwZQ0mtMBf0RpFbTGiBJGUOeKNQCDRo4xsnsPRqMhPT6lU4UELv1x2JIDiTnmhVuyjLsdp1vK0xa0kRI1CuwSBy5wo068g4G44VVpix5xPK9CUE4W1tGOc76cnfU65FsUeShxsw/zihZjsqanA4SdmHtO2USKdxvona85vREfzIRhnPtwZ6cI9AlIpVKiZBt4TDvLUnbcM1h0KcSU/ORD+tqkb2oYMAgE01NfhWFxEUTWJbLJFOIyyO++uiIs6edwWD+K2G2vHz+BhndftCIUapWPZvt/O1NOp0nM2ustn4faNCweUqpLbdkZPDGeeLLheuFhllvVbLaNVGv5/b2qjXsxRZ2vpQymQnZd0SUvBzez7iKlFQGRNyfwl1O91oZFStLxZjOwetXM59oPtrH5quoj5YajZjpp/aIRUTdajV8PdL22x+1M0hpOzonhE4ZwpUSm9E6z7aNt0vEJvrfzqbx7JtxzBnnPmlJobpB7t8jBIVlluw7zOaa1Kx6sEuHyNUuQ4jiyaWXl3FpPB4LIWBDtpKkQoLN55RyHO4s8XNdgJ/e+ogS7/L6m2cjR/YMsDIlGTU5xoKsknlYJcPX3xISKxrMMiya+dgiFHZnCV5PGbSFk3N4TCTbMvqbZzlq7UKNsNs2TWMEiHFljLqY7oUHMN0LQUVZt4y9boiWGuldXWlzcbbPwHxb3M4jDwxd7pjMdxrp7EaSCX4Mzf39OBNOSFo71tjuEYgmE4zXccGnw/fShLKeNCQ5PnjTSZxnUCPn3E6mWwtzX1pfgJkrSF9b18wyK/j6TRMYj2tdjh4vkrroz0aZeL5UpOJt9xfLC3l9WaUy5mELn2/TK3Gy/svBADcOPsDFjm8650QerRHImwS+vjwMKNb0jluyM7m/rwhO5uPbVEoEBTXNkevx4xhmtNlHURa/nRxF+an6VjrkwHeJgyPhNEuAIXxZJLXldGiQVxHr+OjNK4+d5QRTq8rzPf5nHITmjbSespZkodsvyB6H6H1X7ooD8c3knjIXmDgEkC5DiOXH/I6w4wybn+/G2esINK6SjOxM+AXZVQaFuSdVAhb+t5kNEraWncNBXnNhPxxzDnHIa4lwnPbPRJCLJoU161mhEla81XT7SxuGuycWOv2QgO2CluF0f4AfvCHxQAAtfxWfJ0hIU1Tdt/6lSBNR+c/l0GakLEcyEQmMpGJTGTiGxuJdBqniI38h5HMWA5wnDLS9NLPbwZAJVL83olinVJh3qkL8pEUpVF87ig/5VvtWnhdlJEsWE4md/FCLT76BRXs/e5Dc1nuGYskOVvVm9RsUill1Il4ijPwqun2kzLt+eLYiXQaJ0RGX9mQDZ94wJYG3d/ihb2AMlFzlhZvBSkzWWWzsUXAvlCIEZplWqO4pghelBE6oJLJWFr8rtfLvJsNk6Tw9w8MsIHcRcIQclddHZceKdNoOMt9cGAA++rJJG2Dz8eFRZ8RHIWlJhOeFq9vyM7Gx4JLcfVf3TD9iHhMjTodnnbSdU/T6hjRelAUCHao1cxHWu/1ok1cq04uR6uQJx8LJWBS0rhJpNIGnQ5zvdTnL2uCnNmuyspiLkS+SsU+Hq5EgjlZEk/jkaEhNhbcqCzCwXwZH1sqWjySX8sS5ru9QzwmU8UxXhkbw49zCQ0Z7Q+wdPg3oyOoF1m31PfARMmMyQjWskE5zxkpywQos9UaRDka0PVv+/Nx5tQ5HWrYBI+jrViJghZCHmRyGfIEiqo3qtlyw15I/aKYZkHTc2TXcO611ZzBvv67Ji5+27i4kPkZkqme1xnBrg2EDKVSaUZ4vM4wG+zlFRuRLcqhfPFBD8aEpYCU+e7a0MscDJ87AqOVzt28dZCzbnuBgYmzxY2Efrzv9eICGbU/5I+fxAGRjnHbSD8e8FNfGy1qRnxLagQqcsiFnPl0vAKlionxIX+MrxsANKLPpXWXF0jj/hDN4WUmM2bTrQWf2pNcBHpGVIkdKkJu9wWDeCCX5ulfvG4+rlSa5/78fBZhvOv1Mn9ujl4Pf4ruVTlKQgeciTieclB/vTI2hnsF/+/+/HxGHFdYrVgzqcTK46J0y4JjhFA/WFCAZmFV0B6JMHr1eFERn9uoUDASKxWjXuN2MyrdVF/PHMhIOo07s2nsz+9s54LcBbY+Rs0kVPa6rCzux9qDXdgXpPHZ3aDGLVk0Fj3JOFssBCbxJSeXVJGQshudGhim0Dzp+XQImjPp3PF0GnVhYcVhoesInfDzHNCbVLzrYLXrkDeN5sTxzYMwCsl+rUB7k7EU3/uP7hnh+SNXyPg+r1TJYXPQGO74eydOv5iQpuPit8GSpWWEqmq6ndfeZPuazhY3/5ZIJVJG+wInFaCWkOTOFjejTlv+3sGo2bRFBWyzIXH75AoZPn+PdgWWrKxgE86p8/K4/Va7jk00S+t+gq8zJKSpZtctXwnSdGLB8xmkCRmkKROZyEQmMpGJb2wk0vgKkKavqDHfgDhlpOmjv/4IAJVO2SmKMHqdYSwTfKTt73fz07paq8AcoSqQUCYArLRbfl3thKKgygwdKOuPhBLoaKFMTt1oRbmMssCNEcrSFOuH2XgvEUshXEIIg+fzUZZfz1pShLSZvhfsCyIRoyxKetrX6pWc+Q4Xq1HipIzTmavkMiSPFBZy9iUV9NVF0lzM0nfAjSM1QgHmcuEZUX5keyDApnKNej1nftKT6X3mHGxMECLQHYvhFhNli8/6xrBRoEfRdJqtA54R9gRlajVnzDfb7czHaIlE0CQQnI5olJGWa6w2PO2izEoy+stRqviaFhgMWCuy2RVWK1sbBFIpVgVKxxqOx9l8sDUSxqOFlF1v9Pnwa6G6y1Op8HYlKcrWjI1xZn6r4G6s83pZpaSVyRjFCh71oXIGZW+TzfQk1d39AwNc2qVUocLRPQQ92AsN6D3uBUBzTZpLtXNyWe78UpT+/j29jbPZvNIJnont0zHm/xz8YghldZTljgiukVqj5Ew1EkpwgWmNRY2//a4ZAEnypWOXLsrD4fcJHaqbTZn0Fx/2YMllZJq56c12zrSnLyrA1veI86DVq5AjGe4J9dD29V3MvZArZFziQaGScyFfmU3NHBbn9lH0zDTwdQGA1qCCQ5QcOrp3hJV0o/0BeASXy5lIYElAIC0CqfK6IqgXBUZ97ihfX8XULC6QHa818DxfbrFg4BChPBqBTCj6wujInRjL0jSNz5F0jNfCXf39eMtEc2k8i97dHghg5nFCKQ7XaU4yrLxJYwVAykPJLFCukOFVUWrlyjit0426GF4VaND5FgujnRt8vpOQUQlpkc6xKxjka9oXDOI6UbaoOxrl+fjg4CAjXo8XFbFZpsRRagmHGXFdYjKxEWe1RsOosVWhwCPCQFJag3VaLXZJKlKzGZcepvtGz3wbK+L2BYOwCoRqgcHASLj07zqPh8uvSMcFgCKVitfykUk8SwntOjOhYXVm1CDnfun5aIDnvz+dYlS8VKFCw3EqhLtBIVDN3DSjva2RCGaKfi5Tq9ElLGK8zggjNNJxtXoV84PKT8vjvlP5E6xo252VgmWj4I5VWTDaR3OwaDmdWyr1Apxsw9F73Mu8KEeVBX/+BZm4XnbbNLr+Qj2GRDmY8F43c2XPuLSCy3Zl5elhmkfzoO/DfkbCJGR405ttjFyfe00NI01KlZxRLKtdy2jVP8vcsnTnLZAb1f/3L/z/RCoQQ8/CDNIEZNRzmchEJjKRiUxkIhOnFKeMNH36t/sA0NN3eDY9aXuTSWRPUkqZhd1FyB87SU0nhbSvbL6kCOajlFnJFTLoGqwACMHRCEM+a44Wf1bQZ85uoSf1wnIzc6WuwQg21RCfZ4vfj5y9lHHGF9gg20aZpr3AwJ4b/R1eAMBAlRZakV2WjiSxP5suvz8W44zsWCSC8kOE4EioweZkiPkAl9tsUHuoTX6rEm8INVhHNIpzxDGm6XSsupGK7bZHo6z6sioUXPZkvsGADnHs67Ky8IrIlM8TT/RjiQRmC6Tp0aEh5ia96HJx5r7AaGRl208HBhjZkcqpVGm1+ImBMuLPEcb749Qf9Vodbhech6fGnDxWEip1kcXC5RtuyM7mzLY/FmOe0mSVT38sxhmj5LMzU69nT5vmcJhVddf7dbBUmvhark9QW9uEB6Vs2xhqlxEaYUrKWKk53OtHtI7QGc2xALy19L0ClQoOgQr0CSTKVGXC0Q2EIOr0SlaftcnjkDUTf2NjpRwXDlDGaBUcjBMf9kO9lPp5rl6PDuHjFI0kUCs4RiF/DId3EfqVjCcZBVVraFTGdEDwIGX9rsEQG8FWTbdjzW+I01dwRw1UnxCiKCFO0VCC2znaH2B/M6NFA0859emRP7SiagaNW67DyGiUpOC77LZpUAnk94sPemARnjSVC/KwV/AwHFVWLnYsmcaqNUo2uvS5o5y5R4IJ9mwyZ2nhdRIC1XtigiOYXSXUrvH0Sb5REv+su9WD8UpK72eotcwBk9SGvSc8UEyjwe+PxTAjSO3/QXgEjwj+UEFczuqt5W1tZEoJYKWVrkNCggBaxxLSNJxIMJLUoNXynL5CoLovulzszZZIp3luLzIaMUdDx7i+t5vLjGjlclaaSveNjT4fvidQp+zP04ieRe2/orOT18f2QADPFxP/8knBQXQlErhTrOmReJz9olrCYV6HkxV9gVSKeY/XiULFd+XmMo/vuqwsXCcUuK+WlLGi7D61h5W+1aKfDHHg4j5CPddWVDASBQDxbTQnbGfmMurvlCWRJ6dzBwS3NTAeQ38+vdco1/BcfCMyzki94ViAeXNv/JIMIeec7WDj1vZDLp4nWXl6jBTQ+VTN4+y3dPYVVdgcpT6Q7msfPHUIC86jckLN2wbZ68mao8XaJ0mRfMUd09jcVUK7CivM7M2mN6l4J6Km0Y4RI/VjaVrJOxWdR8aYWziiEIprpRJtwqctEkqgTiC0qVQanS1u0Q4dulvp9TlX/RZfZ0hIU9EX3/9KkKaB0/49gzQhw2nKRCYykYlMZOIbG4l0GvIvyWlKZdRzHKeMNI30/g4AFS78/G1ydG08o5A9a3Z93IsxoaCYOjcP2XWUMX7+ynFGayTOU67DyPb5pecXcTZ1+KU2nP59cq8d/GKUM2wp6w7nqJEUhXmHe/2oEmoGv13Fzt2bVRMchAadDgZR6uM9P2VbF2pNbMHvN8jZP+WZkhK0bhnk6xpJ0WekciNzNDq40nSOrJQcT3spC1thtXJxzzl6PW7qIV7L9+x2LBKqOqkMyb5gkHlTG6uroXRRVvdsepzbfKnIfAGw03gCwOUd1OfLzGZ27W0OhxlZ6Y7FJvyW5HJW60geMpLKBqBMW/r79SozDEIV9duREeYjLDNS9vzYyDBzlB7KzsOQjPrAoVKxUqgtEmFF3812O2aCjidlnEq1nFGPw9uH8GwJ9en9+fmsJrzQrWJV3ZkJ+v5BTYKVid2xGHO5CpQq5iCkcjVIDUyoL5Pl1P6+D6mfa2bmIC3KfMgGIyehNhLHaMFIJ96uIO6Rpp3mcLLaAE03HdfnjrAPzUaEuUhs555RLrXSc4YVy/zCNTyXcpG8oTjzrf72h0P47r/NBQD2DgOAujk5zHmQFDf5pSZW9/ncEVYpGUNphALCF80bYxQo12HkMhEnmp3894aFgjumIkQBACLBOKNjVrsWBYL3sVWgT2dfUcncDK1exW1yVFn4WtRaJWf/0So9KzUPfEhcx+rp2XxNewqAuQIVKEopEFXTda3zeLAyJcp7CI6hJ0+F4gj9/dh+J1+Tq0KLHWK+rrLZ0LeZsvuKhiz02ah9BaN0DLNNi5Uj3QCAD0orcSBB4/3xJGWrXalklEhaF95kktfVBp+P0d4rbDZWuQ0nElhTVsavHxsmpHgsQWviIouFOXPLzWbmDVkVCkaB1o+P85y+2EzzqPJIC9aIorkvulz4U57ggyonFSd2OnGhmHc6mYwVuWvFvK3UaPBToZR9wuFgxKh0PI0dOhqLOq2Wy67MbCVe0pqyMuYsXtfVhY0llXxuyRdN5U+gS0P36DKNBuEBWiOSqu2R4WEuiQOAi1GbqkyM7ilkMmA79al03z7R7OJ7cfWFxfx9Sxg8v5xVWhT0TKBf6Srh9C/aFj3qY6XpJTdPgWtIUnJOFKx2VFl43b8u+IiX3DwFYyU0b2V7PYycmrO0jKhaCibKZo0PhZhLJ1XE2P5+F3OXQv4YFl8hyiil0wgK7lXIH+f7TF7JPfg6Q0Ka8nZ87ytBmkYWvZBBmpBBmjKRiUxkIhOZ+MZGBmn6auOUH5qkbPDjV4+f5H/hGqQn+4qpWfxEnV1nYWVPw4J8tDroCb2+n97rUiQQi9LxipUqVhJd9N16KGWUObr1SjiqCGGSCoUalHJAFFbd91k/Ds8RKo1oCoWdAuVSyzFWQ5nAn5xOXNInXKRr6Bg9sgSqBLKy0evFo5jgMQzMogxwJOjnIq86kY0k4ilEFTRxfjjSj9U5xLEIjcdQ3UN7+3dluxlx+aCqilUskjLmZrsdDwvvpl8PD7NK7hZtDkYEojUQjzNv6HsCtbojNxePCk7HsUiEs1mFbMJraJXNhqBQKmp1KmzwTdRrAoAlRiNsMfr8Lz2jXO/rAfcobtAQ10Anl3Mmemc/oSH98Thu2UVjtfdSM2ytlEGNFRrweJQ4GY8XFWH3JOflq53Ubgk9a9TpkB+h13VzbKgTbVPKZLhauDfLqjSoE0iZSqBjW4Y9mBoQTs+KIM7qof43lJvZ0ykVSCItuEJtoRAightmEty4XIcRHweJxzBfo2SOW9qsYnXjJ6oihFrpM1bBz9FG5RgRqFWWSo6QqE1ojygZkdQtzEZHFZ2nZqcfW0+jY1+hpnnUUSSHSfSB0aLm+m8ltVbOfC0FenS29HNbAfKsqVxAc981FII1TnP3ULMLC84jPsxnb3Ww75lSJef1KR2jvFHLyqX8EhM+FPW+Vnx/Kqv0dGYVK1o114hMPz6B7LZZgKNPEiIhW5CFqYLXsueTPkwVyIAhDnacPu1C4pZ0tbi53twrPT3sMi0DYBJK2eVmM3YKpLLKTGssvtcNv/B3aliQjzcihBQ0ymSscpMNRvDmFDrGw/k6FIlzJwtpHBa3tzPC05aKMULlUKsxJNbYJr+fHcbt/4snGkCcIWmdZh8MYLiCxv7hnBw0iTYXqFRYJioELAOhVSGjgnmId/f1sZfbsUiEuYwOtZpRkrYYzb/2hgY+t1Eux+4EnWMoHGeU2qpQMO9xgcGAvVZCpo6I63/b42GEakVXF9bl0lg8lBjB78z0/kd+H3OB3nfRfe+4I4FusR7Xlpej96gXAJA3zca1JbUWNVrFPadBp8NwLs0Dr5hft9tNeNVDvJ3zzBM13ZpCISTEdTVotTAIzo/kht+wIA/rX6b5VQ7wLkJLmQahdjqfdkiFhFiTe0vkmCvWqVYoNdNaBYr+hRTcIXec3en9ijTzXwPjUWhFPbizBBq079N+9jHbO10Pq4Lma4lfznykkniKi8XbC/VcW1Uqjl29opTb3FasxNAJmq/5pSZEBUIruYT/MyOZTn/ph54va1nwTYqMei4TmchEJjKRiUxk4hTilDlN29f/KwBgxqJ8foI/0eSCU3A6zlpZiaTIxpUyGcZ76Qk812Fkt97ZrfTUrjepWQkViyYnVAfJNGchPVVazookXlJ3q4cz2EAyyYiMzpuAwU7Z+Bq3GwaRnV1htnL7k+LxsD0aRY6bskxzlvYkZ2jJWfZwnYZrO0lQnK0/BqeDMozuaJS5EBaFgjk1XfaJmmje5ISLcfwj4mDMvbyCM+ZjkciEik+h4grg/mSSUSfJWXu2Xs/vpfd7WcXxRmScuQtz9Ho0C8VPg07Hjt/TJnGZJNXacDzOfKSbFGYEzaKSdyDAvCjJ3ymZBp60UtatVMvxbpiy0iUBFXMahhMJ5IkOVmkV3G4ps9fJZOzKvcBoxPQT1OZDNWpW+f3WkAuVyFClPu+Px2FxT1Qn900hFGVKTAGvk5CawSIVDIfpGCarBp/niKrwLfT3E9N0qBZjWeyZyBxLaq04Hqc+cqjVnFVHOmjeaiuNjKDsCgaZh1IkV2JHOMhjuLibPhOeauK+022mjDS/1MS8o6w8PXMszFkadkVuDocZ9ZivpLE6ko4xApKXlGOv4P+5T7fiLFEnrEkZZ2XSwAEXq86q1VLl9gjcIzSGllozurbSHKw+s4A5KbkOI/NPJA+sM6+rgSpOtwS3PMV9EEmlUK8R5xZzAwC+1dWFv40TaiGhXRVTs3DQkOQxlPrlJoWZOSfFU20YF68lXuTGSACzxyecoD/W0PhcLJvw1ImkUrw2EwCSQqX7TorG5HpbFn4zSteyxGRiXk5tXIkeNbXJlUigeoyusclG/ybSaeYx3ZWby+q5Rt2Eu/6asTEel32hEKNK0hq8oDPNSJ9SJcejCbrvXWSxsCLuJreWawI+NkJjckN2Nn4p+FHXZ2Wz+nSxwch92iaPM3p/5tF+dFuIr7Yrlz4712CAcwtd95YZaqwU6F6VRgPPMF3LeJYShhFqa0cW9ct8rZ7vgX2JOHMk3/V6uZ+3+P08/y29UfY/kr73e48Td1kIIUxq5IgLj6XeE15Y5hB/SSOT8dqKltFglstU+OUYtfnevDx0fUGvy+ptPE9SyTSjsvVLClnl2bKTPnvRd+vxeZw+O0On43vxZvMEH9KhUnH9w13CY7Bquh2uCrq+HT89hlt+Sf5N7yUDaBDXDdC9DQDqh1P4mdoLAPjRMP1do1WiaDqNZc9+J9+fbAoFDAPUB4XlZgwlhPpbfQe+zpA4TcZtN0H2JTlN6UAMgcUvZThNyHCaMpGJTGQiE5n4xkYinYYssz33lcUpI02p9PMASP0kuaA6qixIZ9ETbHIkgk4rvW8+GoBacEq6W92wXUR8HEklV+uXQZVLT+vOo15GnQLjMd5nHuz0IV5KGUlRipAQmUbOe+QHPuxF50LKeFbZbMwTGHeGsVlFT/lzDAbOclePUGby85AF7+eIyubJJG6MElrizz/5SVzKFHryhNoEGuZY3JCdzdmb1xXBX7Lps1dYrVAdoYwyv9SEIRO1ySgQMbtSicOS0k6j40ztTy7nBLIlk/HnJaQqnEoxN2O+W46ogz6r6Apxez/ImnDuVoprBwBNjIZ3TJFidKm6K46+CrreRSo9emQJPo7kxyIhEDlTrNgn+ErlfQmUTiEVmTORYPWfLk+HnYIzU72iFJph6g8p6wZORidmqKmd7/nHOXOv1Wo5EzVVTdSQk7hZU49EWE2ViKfwjp2ytx9Y7fgoTH2+KKxi/pvky3JQMaEqdKjVmCqj6/Yq08hKEaqxMRLAUqGsOi6UZVXT7ewCDgBOPY2Vc8sIpsyT1KBKVt2E/HE0FdJnLjUR6vPhX47BLurDzV9egqFOuhZdqQEKL7VfQvkAsEpxcOMQKs4l3tH4QQ+yGimbjabTsPioTZ8qIlg4Tt/NLjEyuiehViPv9WOpcOtPyyZ8q4aL1bAep/GsnpmDNcJjTMrKj0UiOF9wdfaEQ4iKNTtfqWMFZMfBMb4HlDdkISj8et5I0DhcntCfVNdLmq+TI3rIy6paCRn6F40VbUKllTcU574b7vGzck+pknPleb1RxXXo+ic5XZ8np+9d4+zFMyXE+3IlEqyw8+erJxyuR+hYwSINu2F7EwleP5IyFgCK+uOszuyORpkTJyle94dCJ7mYS0jUSy4X7hL+SFv8fpyjpmNL/dkfjyNbnGa41888mJc1QUYTtTIZFmvoe9uiQb4PSvfUpWYzFFF6/bzfzf5Ua9xu9pf7qKIKMjFu0rUOHHDh1cIE9+FS0eZNPh9zsmYk1dymwS4fr+u2FPV5JJ1GfUrwSINurmRwpLyOFdOHdgyxk7a0RscUKUbpKjUaXv+T65U6qiyM7Oa1hSEX/Ssp3CSvKACIZqv43tkSDvNuwMhhD7dDug6fOwpbPbVnVzDI618FGe92qDUKRmtfzY0xB1XqW89wCHs2EvfTsLLoJLWkNL8CqRTma0UtSuWt+DpDQpq0W7/7lSBNkTP+nEGakEGaMpGJTGQiE5n4xkYGafpq45SRpoEOcjMtqDDz/nyDVssutfPdcnhE1v1BGbgi/bZggLlJUnazzuPBOcOUMRjrzBgTT+VVGg22CESlQatlRUv7JLdsyXfoXa+X3XwTmODrqPwJnGiife+Ghfn4IEJtPSdJ2c1wrx/7y+hZUQFgRJx7vsGAWUr6TPshF2e5Et8irAR0Ir1LpdLMT7HVW2CTCw+iSJjRnuF4nPk6F0fpPddgkFGDXIWSK4MHs1Xs8Nsfj8Mp2tQon3A3lvb1FXlaVs+dN67CkZwJTpakfHvb4+EsMU/43hxXJWDpoPGRy2WsAPm35BjuctE1qqZa4N9DvI7gTMomHGo1c8c2+Xycee8LhXDNP+j6WlflcRt+as7Bn4KEXkioxzqvFwfr6gEAb3g9uFhGmVfIqIBJHLs9EjkpSwTIdVnK2KafiEE+gzLDR4aG8FoZqYc2BvxcU8ybTCLURIpF6bM57iR7Db2BAPfLFr+f0RkAOD9Cfb31XfIrsl1firOEV1UinkJrkuag6oifPWSMFxZCvpOuVXFaNqMd88V8x1AEqkLq27QzepKXkqTi6271YKiBPi9xKaxKJftoRVIpnKehzD7kj8EiHJQH2sbxhZ3W00y9njk4q4TzdNgXZxf8/WVKXK6j87UddKGzTsv9JfFkviOnaz2oSWCuTNR0VKcxsJVQoMk8pTKNhrlmmzRRzCM6DhTV1E7DWBzRbMrohxMJngeGveOwLaL+3/a7w7jm/ll0DLFOmkMhXOPW8LEMYzQ+B7YMsFJQb1Jz3UFzlgZvK+m6J99PnPtpDlfPsLMDdLJcj7UCVbvbZOf1JHF78lUqPCh8jh53ONg/7DtGG/vzeFJJeFrp/fKGrIlaaUIRuDMURKWb3tthSuBiPfX5YJcPOwT3yJVITKCrzWL9z7XwPe471iwsbDsOgHzMpvVQ3x0uVTByIvHWAEIDAWB6Wo3eEzT395cpGbnOVyphEOikzKbmsZD4Ww61GtcJJ/TmcJiRMotCgea/kao5Z2UxDMdEBQe5HFl51I79Yj4sMRq5/WOfDGPeubRbMKJIYUTMH8vpOSgSxE1JOW20aOCW0xyWj0Z5pyK/LwZZBa2JcIuXlbLWHC3iJuo7fzuNa1aeDlP7TtA1lZWxF1c6mWYUuL99nH3W7g8RCvZHRwk6j7h5LGXiV/BwJIzsIZp3UYcW2X5qn1qrhEJP8yDup/brTSpW1NkL9cyX7CpQ8Nw43WiEX6BV2QV34+sMCWnClhu+EqQJS17JIE3IqOcykYlMZCITmchEJk4pThlpSiaeAwB8EPCxgmenIobFcpFJ6xW8d+5KJDgriqfT+EBkbd8yWAEQz0FvpExUl6Vh75NqjYYzX7tSifgoZYNBkbU+MTyMX+opUzVaNaw4W+t2c/amlcs5W9LK5awoq9xGbch1GJlL4Ukl0bGF0mS1RoGxGZQpn2U0seJB4g5oZDJW4kjIEgAc2TMC91TKiipHU9idRZnJpSYLo1FWoewbNytYZXZoxxA0p5GypDgAzlLSZXoMCcfjwDj1Z1l9Fp9vXU4cxQJVW2G1sltx0XgaK32ksnrH7GDOmKTmG/poAHUXUbb+pseDG1WULcjlclY9fv7KcSy/rhYAuO35pSaunWSZYWNe1OxxOdeN8jrDGLHTMcqjcrwQo+9KXlCeZBJTYvT3Pm2aUZQFBgNKQ9SnpuwJl/JaUaR9uMeP3GIak3tSLkaXRpMJPDxEXkkzdXpcniAEx5Sn4/GSeFiVoyl8YKa+zVepmCc2WR20cFyBvVYaN8mNXK1VYkhF773l8XBNsTt6exmtmmMwICq4F06znJEmiZtkVSgwW0HnmKwqzEvKMSbqV73qdrPz+3eMlA3/zuvEOSKby1EqGbE8Fokwono4EmavMJVWgfVijbWK+fDtiB75JaImWsCPqU5qk67UwOtjrcfDa1bqt/MPxuFcSON2utEIU5LGZ7KSDgCKxc7+W8Fx7sdHRC3FMrWa+/nqpAHjWfTZbH+KuU5KlRzv+6jNEo9GBxk+W0dO1+WXljCaJcvRsLN0pTuNNpHdz19WzI7fDQmVGDcFH2PWkiI0majNpe0RRo1jkSTX15M4K1l5ugkvLpmC7y2yUJL5VEOJOKOhRrkcjw/TOpXQsVyHkRV6ed4UxmwT9wkJZWk/5EKdpJxMUd8Xq1RI91E7rHYdu93bCwx8Dzi6ZwR9Z9H8sL07wjXWJDWY4dulOEsh+E96JfNyzDYtlGo6t88dgSKPrluaq9UpFdrkYl6qVHhTeMvdbLefVL9yo+AWLjObER+ktkoonSuRwOIYjWsinuJ+PpyMnuSHJSHWqwXnabZejwWjdIycGgvG2n08htL9BAAKPCm+LklNGM+h82nlE4plo0LBKHWdVstojyuRwLQ4jeHTEbq+e/PyuP6gAhNVGy42W3BpJ/EzXykrY87YJr+fOU0SSrfCamXUf35ThFXgqiwNv9+g1TLfdoruTnydwUjT5u8BXxJpQiAGnJVxBAcynKZMZCITmchEJr65kVIBqS/50JTKcJqkOGWkqTf2DADKsCRukkYmY/VDmXrCc2cgHsf5lAChaJYdQ6Ka9FgNZULJT50oWk6ZksWd4IzMOInDMpxIYH6MsscxE2UBeREZK5q8rgiKhYLi4aEhzgLsSiU/CUbbAqyaSBZT9tP6VheKLyPERSuXMyerLxZDgfAoOcer5OxS2rdfajLB2UdIyK9lXlzZQn2QX2LicwTGo3gpl/rjwYIC9O4lTtZU4bA82OVjjkV+qQm2ckIC/P1BvKejjHllTMd1u6Tq8Z1HxtjpVqlSYNfH5Lhtu6gIyS+ob+VyGSM/OVOsjNJJfWtRKGAM0VB3qCaQwO2BAMqH6Bq7Wz3Ms8q5tpTHZKpQu/Ue9zI34Nj+UT7f+4YortdR+7alwszNkXyogp0BFFZQduJKJ3HgDcrksi4vZoTA55lQx+1PUtsjqRRXuve5oygSbvBrPR7m7rTuGEbPNPpeg07HLuRS1up0qFnZ03vCg3cLadxMcjnPGZUzhmzBYZPG+HfycTwuXNjrjhzBRiW9VmsV6M2hNj00MIAnHOS2XOBJca0qSYEXmmGGcjdltr8ui+Fycb6OaBT3WCkrXT3uwq1KQnYkFMYpS/I62BII4Ad2+uzGgB9nqugcwz1+DDjouspdKciLaCzsMmrbo6PDJ9UbnOsUXjyFSsxV0ftd6Tg8AgmQ0OD+WAx35BISosQEcnW+xYLWTcT5seZoYZ1G/a+SyaAJCvd8M7Un6I3CaBHKT6QZ7Z2r0uE7AzR3Xy0pwx2DpDz6g3Cy3hgJoLKDkJWaxhzEQfM1Oh7DYRX1R057hB2ng3lq5tJIc86uVJ7Ei5RQP8l/CKA6YhIaInGb1FoF12AcaBvHnQpaV2vKy+EVY2EJA2OiS1WDkQmF5qSaibvVtHZneGXMZ3MlEnxvvMmWjc9D1D6Ju3iBTM88OXuBAZuEP9VVVhu/X1hu5rZGLEpGvaV5YhmNIyV+2MIFGmj66bOfWhP/H3t/Hh5Vla4N43fNc6Uq8zwnBBIgCEgQsBkFFVpRnFpt9aitttriUVtt7W7sVlFbj3rUVlttscWpQQVBRUGMDDKTAAECGcmcVFKpSs3z98ez9lPhvN97Dv3qq7+fXz3X5UVZ2cPaa6+1az/3uu/7wTIxV9YODzPawxUbdDoUUXOgSNEw8pihUjGa+5QpA7cM0+db09IwRuLehUVtOq2Cr8VxZJjRu+L52YxOhmMx/q2QlGq9rSO4LETo5OsFBdC10n4ZYy38DH+guxtPptNvhdcVZLWp9NxelJTEHnDuSAT3JafztutlNO66gnHfM6lu5OqhIdwcpXbsMoSZh/jFyAg/W5QyGW4SVRnmm80oEeip5Jm3UGNkzuxFpiRGJxv9foxX0Lb3D/TgJoFS/2hI01e/Boya/3mH/y7cAWDeXxNIExJIUyISkYhEJCIRP92Iqb870pRQz3GcMdL0H/1/BiBqD4k37VKNhteNpbV+AMhRqfCkWO+/PzOTq29LlcX3xXI44y/TaHiN+PqUFPYlUcpkWCUyCMkzpcZg4DXmMMD8oBaEYOqkDKknR8Wqs3AwikdctH6+MpOylW+8bnZ5rdJqmY/x7MAA7hQZtiMcRqhV1FgTtedMJ72seNKZVTi2h9CE/g4Xpot6Ru2BADIcYg1cE2BlWMaBOLrkzKbB+/l9ezH2sWoAQGVvlNUix9e0YdZV5K8jLGvwjnMYhvcp01tw6zgMN1H2aUnVQiey7mcHBtiVFyClHgDs2NAOgPgdkjt1xeR07AtRVjdVpYNL1NRTucLQiPqBH4s1+cJ9bkyYQY7gWr2S/ZYqtFrmRQyGw6j0Cy8tqxpDjYRaZAhkKOYKs6+P3xtCm7Bvsrb5uS7hQoMJ6120n5QNDjWPMKrgGPRzH6l6/GhKoeNZFApWLPr6fThhomuZGKHrkJniyOPHDgfzwVKVSkalNjqdzA3bJTLYjUUljCRc0tuOZwSi1B4MYkaIjnFAHXflrnW5OOu/PoW4arFAFA8OUib9mDUTfxkh5HG13Y5VhYXUv2o1HhDzQ8rEb0pN5XF+hcyIQxqhptTr40jAYARvqOLcMCnblo4bGcXF+drt4jqI16ekMD/oWpOVPa6kyDvkQVUNuU17XUF8oxT10QIB3CCjzHzEHsA7SX4+t8TVutZK3LuBLje6R3FSXhEq2EVmM8Y2C2R6bDIcgzSWfFl0j7UyGSMCEW8EzYfpmmLVSXyPG/1+bBRj89phLSvGJN5fcoYOuzcRzD1hRhbcwjE8PdfA99OcrEXrUaESraDBmNEbOs3/bNs6Uo7VLCrgdmr1SnxcSOPrHkMqc4/WJ9P135aUwnOsSRNlVKd7Uw9uL6NjfKHPZb6gxIGp39aLGT+n+9YeDCJNIMIGS9w7ShcGemXU/nAsxshO4ZDwW0qNewMZ5XLm8IzYA4hGYqJv9PC6qT8kZFVyygaAFn0sznVqCrBr/Wil5nidjtWOEi+yThlXoer2OHj8yBUyvOoiPmS914tnjYS4X2YnhHF5Rga32R+NsjK60e9nJOym1FT2sLp5qAsrsuk5Lo0TfzSKBjGeu4JBVgJ2hUK4VaBEyzMy+F5I3L8ag4Hn7nt2O84X6Mlah4ORxYeysmAWNlAPDffhmRx6Bmz30Lxb3tnJv01P5OTgjg4ad/PNZub0jdFqWR3+oyFNX/4WMHxHpMkTAM57KoE0IaGeS0QiEpGIRCQiEYk4ozhjpMk1/J8AgJXeIfbCyFQqYRFv/JlKJWe7iy0WdoWdaTQymsO1z8Jhzpj9sRj/3XNoGCUi++oKhfAXUZtprJa4AaUaDRZoKTN82TmEO4SKqT0Y5EyiKRDgNefBcBjXmmh9ulPog2pdLs5G3nhoDy58ZDIAYGiXDfdnUea4qrAQkXZaD88pI9Sj0e/n2lutR+04LurUlVQms/dSmlLJfh9NwQC7a0soy/6Aj+ukJSVrkVJKb+yeXi98Qg2Sp1ThqKgDpjPQ/oVjrZydGpPUCKfSturhEAZ7qJ1KlRxteXQvyvqirJiRVGGLzGa0HKCMv6vZCc1iQo803w6j+2y6n6ZNNvbDkRCn7uMOaIT/jufYCNJFvbmpfS3YXVEBAAj1+Lh92cVm5m0l1RDyFTspSBMA9uTIML5R1HwrTeKagWHEFW+Ses6YpDmtdpvE2eiOhiET98eSpuW29oXD7PwuZbC3p6cjt48+S8pGgFSFEuKYqVRiseAbSW3wx2KM9tQYDMzPuvXUKUbCqnQ6HtOjFaNShvtwVhbzNJry4ivhuSoVO52PdmF+WrT96dxcaAVnZXlnJx7IpMy9KxTCpUq6V2qtghHCJMi5vqM0l9qDQeYSLTKbmdvjj0ZPU4BJHmoS2lvv9eIJgarpIGMV6euDg1gorjtPpWLkKlOlYj5RxiChA6psHZ87V61mBC1VqUSJUEv+3mvDnXbhri3UmfOvKMMuL/V/ykkv8suF+7w6joBUaLVQCZ+cvlNupGbTMSRVVUaBCSf209wsnZCKL94jD59zry2Hs4XG5TfrWzFG1G/UCRVvUrIWWgPdI7VGAaPgN+3xe2FuoPE7UmWE6TAdo3CslT2D9n1AHL20pXmYJJ5V4VCU50F2sRlfvkvtyFmWj4PPUt+ddx+14a3bd2DWRcUAgMqz01lJm55n5GNYy8zoPkjP1+QMPTwZwpfulPBeU8igzKe+sCoUsLXRfoM9HlRNp/HTfHiQ0TapfmjhWCuO6+lzTlcI1jIai7HhIFr08Z8GaS6MdtqW7uulOjPe8TgAiPEg/j5y0I6TY6gf60c5q0v8MqNCgbKDdL8nzMjCV0Ex/zvC+CZLoG1yOQJi7F6dZMVXgg821U99bzPLGaFa1NTE8wYAc7k2Op34uJD694MRaucUvZ7n4BVWKxY0NQEAVmRlMUqklctZ0bvabsfG0lL6LOaKVi7H9E5qW2exmhHcMMAegquGhnCNQJ7zf6Tac9j0EGD4X135/6Xw+IFFj50x0vTyyy/j5ZdfRnt7OwCgsrISf/jDH3D++ecDILPMRx55BH/7298wPDyMadOm4aWXXkJlZSUfIxAI4N5778V7770Hn8+HefPm4a9//StyxfPpx4ozfmnqD70EAFjvcPCPxpaRER4oW0ZG8KggziplMkwUEycQi0HVQ9u8pqYBON9s5gG7PD2dpcorsrP5ZevWtDQYBOp7bS/BnhaFgl+UwojL6f3RKH++1GrFh+KBfk1KCg966aWqoD++bDF6CW2aV4kPFDRpZzVHME6Qt1vCwuZfqcbhndTONaXAtT0CHvaGMfFcgowDnjD/yA90uZEmijnK7HQMuVyGTvFy8dUHzbjtmXMAALY2F7IEUTrgCcOnFQWKxUNpeGM3Lw1q9SomY/e0jcBUSC80w00jcImSAgVjLFzGxu+h9hzZ3Y808QMTqDDCt50ewGqtgkm79dt7uIyNdKxYNMZLHOXVqVwCYf/WLi4D43UFkT+G2tRSoMJ4+g1kU8CT9YP82ZKmw/p0+oG72GLhB2gYtIwHAMf20ctDZr4JbVnxItDSg7tar0ekXzygQlHY0ulaU5VK2A/Syf+ZT9v+PiUDCvECucPt5nGQq1azIWRfKMTHXjBAf9+VFX8BKdVo8LmA9a1KJbd5td2OOonkbDDgbqUFAPByhLat0uk4kVhfXIIXBuml1R2J8PLCnG4Zl7SR5tLtR6yIzKW/39vVBV+M2qaCDM9m0wPjHecwH/tKq5X3lcjt9T4fL0XclJoKabFsvcPByxmv5OczkVojfmwWmM1Y3EyS/adzc7kP9nu9+NhB82qq3sDt/3zEiceyad5LL6qNfj9L0Ou0YeS10vgJjTFwO5UyGRaJh6/UzotMSeiO0jGUALKU1M8n620wVdF1mTxRHo9tqXLk9YhyFUKaH/RHoJpCY1Erl8PqjJfCkV4YjEkaXlqTlhnPPxnlF//5V5QxAftgbTfPCa8riP2ieHIwEMFVv58CAGw70bCrn01jg/4IdlbTvKrW6dDzKvVpSVUKTFb6Pm0y/ZhqnWE2WkyqSWU7h9ajQ9zm8klpOFhLy7hyuQz904RlyDp6JllSdSipShbXp+Zr0ZvUXL5k27pW7JpBL8nSD7nOFmQBRt8pF76x0vkWmc0YanAAADZnR3lc9YXDXAZJCifi5VAylUoeB0qZDEk99P0bOg8ToqUX7gu7Zbw8umpwkJOH9+121IiyPhdbLEgL0tjUGJRYI/aVCpEbFQpEmugZ/7bVj/uttKR4c18nXikgMcujvb1MI5FesJr9fr6mKQYDCo8coW2zs/k349GcHBSpaPuXB218jdLf15tyeZmztKGBX6qe7u9nsvwhr5ctdY5XPo8fMvil6bNHvp+Xpgv+eMYvTRs2bIBCoUCp6JO33noLf/nLX1BXV4fKyko8+eSTeOyxx7Bq1SqUl5fj0UcfxbZt23DixAmYBChz2223YcOGDVi1ahVSUlJwzz33wG6348CBA1CMogP90JFYnktEIhKRiEQkIhHfWyxZsgQXXHABysvLUV5ejsceewxGoxG7d+9GLBbDc889h4ceegiXXHIJqqqq8NZbb8Hr9eLdd98FADidTrzxxht45plnMH/+fEyaNAmrV6/GkSNHsGXLlh/12v5lIvhthmRc3U/Iz8NZWWyUN9pArFMbQ1GEPl/S247HBAIlQde/7urAKzuvAQAsmfomZ7NXJidz+YIKrRZNIguWlkB0cvlpZNmQnf7+ZMDO5NsXBwY403nVmo3WBsrgpLIghroR9AsTy0K1Gr599PfuCQZMGqaMJpSt5WNIGfWLAwO4uo2utXCsNV6QVB4FeinTHOhyo1zA/g27+riwpVQksvnwEJOqbeoYFMLQLhyKMvpVv72H9xtdoJatFmx+NuZTa5RIqaAsMjYc5GxVLpchbzplXBIiY0zSYOObxwEQYiQVPbWk6niJI+iPcKFeCdnqaYuTsUPBKGJiiaxwrJWz4OMHbOgcRcj9eDxlAY8K0uZQh5vJvv5olJc5+zpc8HtE4dRxVia9Sksxo0ugjNgDnDG/4xzmArOmwRD+EqM235GezvfreC0R57vPMnJ2aW3zYxd1P5aqTbCp6VoafD4eg5I5XllTAJfoCcm5LyNeJkYpk3GG+rLNhlmiHYPhMMP6ElG2cNTS1A63Gx8Ic84bO04x4tUXCvEywugMV5Ko7/Z4GJ2ZrNdzht0cCMAn+muPx4OXVXS/V6kJQfnUGS+GPEWvx20K6rv34IZL7HeZ1crLGaP7fo1AlLKUKkbjrk9J4SW+cCzGy3o73G7uXwmtK9VoUCj64rn+fjyYQctDIcQgC9ExYioZYqLAbLecxlGaN8YIj6nKAoVoW7jDC12Bgc8hGUFmF5nZ3FFCgzyyGAJ99HeHzc9L415XEKUTCOn49rN2LiIuScM7TjrYDHRk2M9zsKd1hI0iiyuTed6P2P1crFWKsxfk8RxMztChYZdU/DmFEdodG9pw+V0TAZBhJQC0NtiRUUDnnjQrm5fWhxRR7HqNSqosuHUcWz5Yf5aOT+/fCwC48MmzAVCB8ZN1hGT+7NISNInP+eVWFoUMdLkxaQ5d985P2gEAFVPSGD3LWZbPy9CZKhW2iNWAaXY5I9obnU4eu9L9nhPScHmfBp+Pn51auRwZbaLweYGKyxJ97HQAIFPZP8mFJUdOLi+tXz2kgayczndNWxsm6unYd6alM2LkE/+2BwI8x65NTsbDPTTvawwGnsv+WIzbLLVtmdXKAowVWVm88lGl07Eh52A4zIWWX7HZGBmVzCq3jIywvUWuSsXFofd7PEzUP+Tz4SEj/SYYkn6k5bmNK78fpGnxg+js7DwNadJoNNBo/nuSeSQSwZo1a3Ddddehrq4OWq0WJSUlOHjwICZNmsTbXXTRRbBYLHjrrbewdetWzJs3D3a7HVbxfASAiRMn4uKLL8Yjjzzy3a7nO0QCaUpEIhKRiEQk4qcakrnld/qPEv+8vDwkJSXxfytXrvzfnvbIkSMwGo3QaDS49dZb8fHHH2PcuHHoE8r6jFHJqPT/0t/6+vqgVqtPe2H6r9v8WHHGPk0SAhSKxTjTvrqtDWNHmcpJpmBrbMOoddEbeCAaxdPiIqW13TcKCnD9zzcBAA54zCwvBeJy8xcHBhjFWiqtp4dC/Lb/aG8v//2m1FRunzsa5bXxOmUIOYLQeUigVjVTk5H0DfGmdkzW4zLBA3DKQ1zuZMvbTThrNmVkyRk0WC61WjFBIDLD0Qg2C9lpo9+Pq7PoGLlaBfaJrO2cCwvQKQqLSjL4afNy8cYwkQiDf27CPGFVAIAJn/u+6sKNf5gKIF4s+PUVe3HHU8R/+ud/HkZlEg22gU4nCsZRnz9x/z5csZxIdMYkNeo+osKzUsmY/fu7cPGv6O/123sYSVJrFcxN2re1C5VT6diSwWY4FEXjfspaz/tFOWezaq0SDpsoc+MMIHY98QdqLFaUCr6IRDzvaRuBbBEdN/JpH1RCHp5bmoRdycLQLhbDqT1xAi8AaLO0TAwucMnZPG6h2czmfo1JIcyOUAb7+uAgIyBriynTe9Fs5pIGxhw5CgX68rLHjq5hylCvSE5mM8DZhXQswyQzxnfQucOxGGe4t6WmoSkoivfKZJhxnEjAC5J0TNiW+HXuSIRlzfNNJlzaRjL2a5KTeYw6IhEuFCu1vdblYkK6OxLhse2KRHBAID+Nfj+jq8vT07FimBCOV9rouh8pMDHaE47FcI+b+rYrGGRDzgafjxEtCVF6IDOTC9DON5uZWzKmJ4JDmXQDxndH0VsQ5mPvFtm2hGzNNpk4o28OBNgw1d7vwwvJ9Pn+UBKP+U8r6LjXp6bCMYb6QH5sBBnCQBOFRng6qT+O7+1nVCkajWHCOQQdSqiNYpIFVkFyLpiQzOadp3w+5HrjY1pCSfZ/Q/O1YnIam9R2NTu5cLPfG+I51HvKBYfgL239Zwuu+ndCjOq39/Bxy6tp7L733CHMWlwojhGGvZ/uz+J/G3uawS1ACK8kcnA7A/jnfx4GAJz3izKMEcKYza8cw+J/o6LX3pEgFlxVDiCOeihVcrQfJ9Sm79l6FFfS2Og71YHsInqmTpiRhc2CkF62jNrWd8COGVeTxQkA+Lrp/uzafgolAnlLnpTGqN/CgIbNK18WXgBVaTq8J2wlJhsMjJg2+P3ItdDzszqkx0AkznkDgHp1DDcLk9cGnw93qel+e8YoGDGaaTSycOfp/n5edZCEC5kqFdYWE8lb4sYBQJ3Pi+dzaZxc395+mngJoJWMHMFNfKC7m5GoxUlJuNBIc/MLjwv3dtH4eDgzkwvIj7bcKRyFskpjfp3DwcjVi/n5uGGQjvHPJPz/ffy/IU3/uxgzZgzq6+vhcDjw4Ycf4rrrrsM333zDf5eNIu0DRA7/r9/91ziTbf5vR8LcMhGJSEQiEpGIn2pIaNF3OobwODObz9inSa1WMxF8ypQp2LdvH55//nncf//9AAhNysrK4u0HBgYYfcrMzEQwGMTw8PBpaNPAwADOOeec73Yt3zHO+KVJQoOyVCp+yx//XyTXkprnvaaxuHMsSTgfysribFaSOjcHAvxmv8XlwnN5lBFscjrZKO+N/AKkHD4EAPzdw1lZnPHfr0nG2B46xx3p6TgiTBcfzspiTswMnQE7C6n9S0W5jg1eL2pm0425UqVC005ha1BshjqD2lSzKJ+zY4mPBAOY75OaZcBCA2UuNQYDbIft3E8Tzs/j/pIM8iTjvdYCE8b30nGLbx/PKiCfJwSbQGd++/ocBMT3Eh+jcKyVlXtzLyvhTDV/jAWRIGXMWYU6LrNQ+1ELrvntWQCArWuaxX6l+I+7iAdx91+n4YXfbAcAnHtxMSvbFHIZq38kg8BoJIZ8Ua7m8M5e/t7e70W1UA2qNQos8NF+blmAM97J8wjR0OpVsO+nzOt4l5sz7DoE2LJi6z9O4NxrKXveLMZLrl+NrFNCrl5sxojoO2uWns03D3i9zG9anp7OY/P5JEJ9Xh8cZNTm9vR05gSN9cpxXYCy2aqwCnvSaHzvELyKZVYr3i8iDlL18eOMrt7T3cWFiG9KTeVMvysY5HEqoRgrW5V4Yyx9p5XJsFyYp97S0cHzaabRiBdM1Na3onGjSQkdc0ejeF9k0hfWR/F7YZK4IiuLZdxdoRBMooj0DUKqfb81HSdi1Hd9o/hWQByd2O3xMJdLur7eUIgVeovbWrhQsVwu437enDWC80U/+qNRLpicnEr/vjxo4+O9mJ/PvClThg5Peqnv5AoZTon5do3I+C2KeEFr2QQdl6NRTLLgiFkosmx+lvrv2tSBJMG3kwwV+zpczIM7sbufUcuSnggG/dQfQX+EVXfJAqlyDPpwdC+hcWULc9hYs6omk80tg/4Iz4U7n56OEcGplBDcbetbmYd422M1PA86TjqYV3T2gnysemwnAODXT0wHAIxfkIs3frsLADBuajo/c8zJWrYfuOS2Kqx9iRReao0C515MY9Mu7Dxau9y4+BZqx44N7dxOS6oOdaB2hqJBVt7qeum7gCrO0NjodGJeFs3NaCQGZzV9VugVuEZIxx/IzIQyia7xaR2hWfu8XuYVpSiVjOpo5HJGhK9OsuIlO/XpaIWntApRYzTCIcbonKCex6jElwOIIykhUJL6b5nFEi+bo9Ox2WSDz8cFlYcjEf4NEo9UXJucgqli26kGA883i0KBOS30u1Kq0eAe8SPeHgwyQibN3Ry1irmAO9xuLO8kjtuGo/PwzdwGAITEviB+3360+B5fmr5LxGIxBAIBFBUVITMzE5s3b2ZOUzAYxDfffIMnn3wSADB58mSoVCps3rwZl19+OQCgt7cXDQ0NeOqpp75zW75LJJCmRCQiEYlIRCIS8b3F7373O5x//vnIy8uDy+XC+++/j9raWmzatAkymQzLly/H448/jrKyMpSVleHxxx+HXq/HL37xCwBAUlISbrzxRtxzzz1ISUlBcnIy7r33XowfPx7z58//Ua/tjF+aJA7G6qEh3CKyz2f6+5lvcbHFwqZmU/PrsWmE3ky7QiHOUKW3/AuDWjwWJnTm+pQUzD5BCpGnc3NR5yM04VnbAIYmEGfgj32EsjzQ1cVo1fUpKXhFqBV2uN0ICE5A7KQblYKbdFThx3uC61HgpfYvTNIgYKSGDLe54D+LMt9doRCWCG7PGpUXpaV0DLOdMtJyKLEvm86db1Rhr2jnWUotkkQ2u2tTB/OQAGDRtWMAALZk2k/e7OW/dTU7Gc0Kh6Ko30YZ1M8uKkL9tl7xPfXhaLXens2dUIh2Vp9r4Kx6yrxc5mRYUnWMRklFdeVyGe54kow8Tx2249yLCb1IzTKwueB5vyjndkicp6rpmdi9icwas87PwSXi+/rtPThpiHH7peu293tZsVg6gbLB3V+cwtxlBNNmFpjYQ8bc4IZVtG/iNWPwleCJSVlmaPsg2l1B3u/vCvr7LC8ZiQLENesWWW4S5MzzGTxByFZproYLhBZqNNAJvs5yn41LMnwT8kErvpfKdRRFlLheZNcVWi2XWclVx4vEvj88zHyLvlCIDR2lckFD08dhxShuxq2izMKDmZmMiM03m/GNkvYzhulePtzdjXUC1jbK5bi6jfg1dxZpUKqh7LjyWyU+nRJXMUnn3tw2nq5D087n6AwG8aTgMd2bkcGI8PUpKYyOSVnyNKOBM/DxOh0jUe+anVga1nI/Spn5QrMZc7ooM784ZgFAvkQ6B82bFcEB9ucpVKuh0FObhyMRaGN0vRLqbDvmQKSMzr26z45bMuhz86YeTDqfOIbdGTpGdovPy0ZajPaVzBpH7H7ojTRXTp108DyYfFkxcyA7Tg4zh0gqp9LTNsKozkR3BNNuH0f3cPsAo1UdJxys7pPLk/D2k/UAgFk/p+dQRr4J/UJp98nrx7hMStMhH275Mz3LotEoHvjbHADA3i+pz53Dfpx7ESFH3352ilWiDpuPz+f3hphnqVTJ0Xx4iNsEEBI10EXzo3RCCra8T/ek+txsTBconA8xHLHFVXUA0JuuRMsOQmQWlCbhuIrGeejnmXyPU5VKvJ1F12jv97Ky8DnhqTfbZMKdXvquzgjMMtBnic8HAC2hIK4Qyyw3C/PXDzIL8GcfPeuuSUlhw+P7/U7mBxWq1fx5rcPB91Ayj3y6v5+5dLUuF561EHJ468AAF57+Y6cTFUa6txIH9xWbjVEio1zOaKg/FjdSvdJqxR6BUr84MMBzoVJH/96dnsHI16fDKTieS8+yGdP2wBGhbZa1trIJcEa8ZvQPG1EVEPmuSFP4X9q8v78f1157LXp7e5GUlIQJEyZg06ZNWLBgAQDgt7/9LXw+H37961+zueWXX37JHk0A8Oyzz0KpVOLyyy9nc8tVq1b9qB5NQAJpSkQiEpGIRCTipxsxNf33nY7xr700vfHGG//t32UyGVasWIEVK1b8b7fRarV44YUX8MILL/xL5/6/HWfs0yTbdzsA4PmCLM5U3ZEI1paQAuzR3l58TIkH3ihL4sx1XUkJVtspM5Qyl+fy8nhd+K4jVtxdTpnO6PXrvlCI0a3wKE8OyWOmUK3GUZH9S2oGgNQPkj/HFIOB7faP6ARqo9Fw5pLSEWAeUKw6CVnDdOyDtd2QLaGMJe8EHWtfsRIXxShzkitk2C6nrMhUa2f+QN4YCxfytXW5kXEhZYaSA+7lHXJk5FEWtuntEzj7PFrr7jvlYh+XqppM7BH+L5VnU6Ykl8uY/yRXyNAikByNVsGZ9LkXFeGw4GdpDUr+XirWqVDLMaZalJ05bmfuxbb1rex+LpfL2EdKylp72kZgEI7hCoUM00SZle4mJ3M9Du3sxdy7qqg/PFH0CrDNuUOo51pHuI/CoSgmzqV+aTlg46Kgtk43X2N+OW3benSIs3y3I8COyGfNzuEitkaFAkWiSks0EsOQlbIQyVm+UBNHmkYX6a08dgxflZFqKEOuhOpQHQAwejlRr8dtIiOeZjRgtvCY+dTpZC+bQrWa0RyFTMYol8TzO+H3M8pa63YzKvVEXx80Ai3UyeTs6yT5w/hjMVb5rCst5QKiy7u6mC+168RCpBV+AoCQmv/qlVRjMHBmvGpoiD1kNo+McLb9vt3OalUpFprNOCTmT5JCgQ/E3H04K4uRtyl6PfNWVtvt3KdSBnZ9aipn6xMjakZzSmoyGBGbqtLh6wBl8bNFH61/qQGX3kFI2XCfl0uZjFaGBUr1XBTbmaxE3eukBpO8y2oWFqBFRf01snWAFbHr/nYUOQIlzSww8bjyCiRzoMvN6FNxZQp7l71w7y7MWEx9N2IPYJyYk5+91cjor8QTCvojPN/UWsWoQr8qJGfo+Ngn62le9Io5qlDIuBxSNBJDtqgOkJyh4+MVjk1Gw26a3w6bj/tGOm5+uZWR5nAoyuWO2o8PYwOBWLhLn8Lo9vo3jgEArlo+kbmTLosSTesEqrwkF50fETKatjQPGb30jNUVxF3dx7ikItxhdGXS3VfJZIwYlY4qxv7cwACqpTJDoqpDrcvF86M9GOTnMgDcIrbZ4/EwqvT64CD+KMrGVJipDUuSkhiZ3uF28++KUR4vr6IE4iVTkuleRWLx341Gv5/nZpVOx1wofyzGau0XBgYgsXqkskGzTSZW7M00GvFmE3X08xP7+bpuam/H9QJp/U36w/ghg32a1rwNjEL9/o/C6wUuuzZRsBcJpCkRiUhEIhKRiJ9ufC9E8H8Nafopxxm/NE03CbfjkRFeV9bKZJh/kjI9RySCl0oIObmxLg1Li+kN/EWbjTOB+4WPzeqhIc5wh34mw71d9A7/bKMatxaN8DmljOWWdlo3fqs4l1UaC0ZlxA9kZnKRVOB0d2PNTsrqDpxDb/7hWIxrb83ON8JSbOBzhU5QmwvHWuEQ66ZSDbcrrVber0KrQSk1A0NqOSMjG/9+nHk8FVPSgX7KZHJep4wttCCPeRN6kxpNgpcweXYOmg/RZ7lChojgMu3Y0A6AMlgJAfK6grAJFKi1YYgdyI/tG2C/mNzSJM5QpRpb+eUWrosXjcawbT0p+yJhLcoFAvWPJw5AJ7Jnj6jltXdzJxZdQ9yscCjKxVAHutzsCD7jgkJs+NNBAMDiG8aiW3g5SWhWMBDhfmnY3c/KvaXP1DCKuGn1Cfz8JuKRSBl66YRUViB2zrFikqjz9EHMDbeX7nGmSgVLUlwx4/aKLFdkg+FYjLPBeWYz3hbISaNbHlfdaLXsXi6hoqUaDbsOqyDjsXZ9SgpzmrpCIeZFLe/sZPRl5uu3AgA+veElRmcutlg4Q09VKjkjTlUpcaSfPK6gpPZclePhcX5xczNn5sssFnYxvnXSNrzSQrwnGwBHMqkkpQz385ERRsRKNRqeSwDYe+bP2dl4XiByUqZ9X1cXX1NzIMDtfLq/H79tpLnwVIWX3cFzVSou6isp+Jr9flQKVOHe3h4sraLsvskZ56oUDUZhTKX2SUjsrLm5OCQ4dfnlFnYKz/LHUCT81GydbrQKFWt6rpEVnJJr/f6tnTxn88/L5mfVpYoqRmL7TrkY1ZT4Qw27+3icD/Z6GLmae3kJo1XffnaKfcrGnZ0Bp0CpJT5f2bxstG2jcxys7YZOqOCSM3Q4WUfovEKlYDWtxEcaOyMLDhuhy796YhorZU/WDTJ6ZO/3wigKUydn6HDOBYXU10LlN9jr4TqTablG9lkrmJaOWRvp+XPM0M8O/Jc9Sl5w/g4vrEWCRzLoxzGhIMwvtyLvEkLYMlUquEXBaaNczkq0KUJZdkNrKzIH6VrvHtACpdTOAq+M+WqFajVznM5pofu62hrCjSbqiw99Izw2al0uPCjG+TXJyVxYOwxaxQCANWJV4OGsLPY5W11UhCnHqerBTKOREeZal4tR4JcFp6tMq+U5McVgYCRqtd3O6tjnBgZw5MgNtH3lG9gkkGkJxV5tt/O8aQ4EcHcF9bMSGqwSqzEzjUaekz9aSOaW3+kYof95m/+PRMIRPBGJSEQiEpGIRCTiDOKMOU2/bHsQAGALh+ERSE6j3w+bg7KRO4tcp1VElzK8VUNDzHlo8lDWMceqYPfwF202XusezcOY39TENbkknkYoFmM+xuuDg1wXqFSj4eynapTix6JQsMJjdWEhAEARBdaOOAAAlSeDGCOQGiCOcBwxRdkJ2XaSUApVsYGVOs2HBzmbVWsUXMdNqZJzJtqwu4+zQem7mkUFrHaTK2TsxN3X4WJH46A/wtmzlBW6nQHOHEfsfuZQ+L1h7N9KqMGUubns7+Kw+blNknty8+FBzmx9HiMqp1HmcWR3PzKEGqb9uB0zl9C6vKSY83vjsKxcLkPldEILPY4go0dZM9NxXHAhLKk6rr8l+T/Nv6IMz95N6NLCX5SjUnCaAGCD4LkVH/SgehZdt0tBQ1Ipk8Eko/7qaRvBp2aBDqjV+BlozBxSBDFZQfd7Z8iLok5qb6yU0Kc0bwzDBjpGOBZjjsUDmZlcW+t9ux1rJb6dUJldarXiKqFa80ej7HN0ZXIyLjxMCMPUVDurz25KTWW+zujaW1KWmapUcq249mAAU/XUvptOneJxLqFdYz6djLRSKlxp8xqxJI3642KLhd2W671e9qTZ7fFw+yQ+1cUWC6v43NEoHhYmciVbU3HVGPI/a/T7ea5I7SzUaLBa9NEyqxWviYzZGYlwtu6ORnFXCs2bntYRvCv4JV+I/izVaFhJuzgpibkjuz0ezGmkvimuTEajcIyvnUhtXtQURZUYXzKFDE4bzUdDqhYu4UJ9/IAN3nOFi/9b7SiupM8SJ7DxwAAjP9nFZh6D6984hht+NwUAzc3yScQz2bqGHN0taXHE1THoYw6V1xXCBdcRAnVs7wB7tTUfHoRRcP0kntPB2m72iyq4rAAf/Xo3ACCn2AitgZ4XWr0KHsGjMon99SYVt3+gy40icU0Gk5pRp4EuN3szRSMxRpglTpZcLsdn/2gEAMy7bRyOfkrIVW5pErqFAs8x6MN40b7WBrrHbmeQEaxplxQzQnJzUgojyU96BrFCjJ9FTU143klIjOSU/vawnd3sATDC2ez38xi96dQpRn6uVifxcaXx+r7djvXZdH0ve+yM5rxSUMCrCMszMlgpJ43XR3t7GQ1dc2ghxo9ZD4DmoHTs0fUdJSR5vsnEyO/oOo6zTSYsEUjTPV1dmC7Ok6tSMfq7/eRs6qMJu/FaM81ZyIOAVqgQUyLsoWbzq3FfDl33U7l/wg8ZzGl6ZyOgN/zPO/x34fUAVy9OcJqQ4DQlIhGJSEQiEvHTje+F05RYnpPijJGmCceWAyDlzJs2Ql/GG1ScYbzWWA5tGrna3p2RwRmxPxrlzENClzY4ndDJ4yuDH3dRZvX7Mh8r7PpCIdj8dKNNasoIXI5yzMomZKVap2Mfjh1uN2cgi5OSoPiWtjk0KYX5VGM1lMld3d7GbrKOSISVSSWbh3kNX9fqY0XJ0Sxq53yTCU7hSO2w+REVmcm+r7pYlaNUKeAcFq6xNh9+dhF5IUm8iebDQ8gsMPIxGg9QNpWabUDDLspS9CY1e85MEY7ao9GltS8eQShI5zOn+BAR22YXmZmncXSPDYXjBGdMqHI8I1FEIvT30gkpiAjOU0qGnrPnwrFWVt1J6rlfPjAZWj317faN7bB10veWNB2mzKX2bfmgiY9x/UNTmE8lHUOrVzKqUFyVzD4v5mQNbGbq35Pvt6FAcMOk/gqHopzNv+1zIEnwI85PSuLMUCWToXsvHds70YwTInuU0MsXbTZGSLaMjDCC2B4IMC9HUsMAwAwV8S4+9buY59AeCHBmO99sZnXmswMDOF9kXYPhcBz5EWM/DGBNr+B65APPNtL4mpXfyKhUKBbj2opS5ntk80rkzf0tAEJ1JCfxhxqysX7qMF+LxNXyx2KsvJMUp587ney3tH3Ej9/npPG1SOdJUSrRL1AgiWO4amgILwoH40a/n/lbi5OSEBL99cHwMFYLt3TDgSPwTCbFm4QaXm6xskprpy7EqHON0Yj3RaY/02jkuSn15zSDAe5G6vNoJMZeYW5ngMfgUW2Enzlfvd/EY372JTTXtnzQjAt+Sb44PW0j3A65QsaISsco/yaJx7R7UweP4aqaDJ7/epMaB7ZS+xyDNlROo3l4sLYbNUJJKvEU88dYuM3H9vYjINDe7mYnozbZRWZWkkoo0e4vBmBJpfP1d3igF4jMrJ9nMk9xxuJCbF/fJtqkwtL7yUl529vEKR1fk8lKYHOylv3WahblI00odn0jIUbTpX4Z6HLzfJPL5fxck8vl7BPXsLsfM5cUAiAe2f4AHUNy1/f1+/B8jMbiPWor1svo2EqZjOfWOoeD59BfhOv+iuxsHBA+SIPhMHvwdQWDyBCrE4e8XuaobujMwvpJNF4vbiGEcFNZGXuhXZOczKiTPxbj1YftPbmoSG8HAHjE3w0KBRp7SPGbl3EIrvfuAQDornoGueLc+0aAPB2dr1Cj4fZL47be62VUOVetZj+yjx3D8ETiKlaJO/nP4ifwQwYjTW9/+f0gTdeel0CakECaEpGIRCQiEYn46cb3QgQPfj9t+QnEmfs0fUM1YeZk2Rid6QqFMFkoIjbt/g1UlY8BAEK+NEBNGWWFXolGN2WaH5bTevo6h4MRJVffuajIp7X/NKWSM+Y9JWOgrqcs6u7suPJNihdtNqzMIW5MlkrFHAp/NMqZaPXx47wWn7NXcJ5qMvGIixCeezIy8JXIRqp1OvR/SiqM/DFW7M+hbGKWyC50jjDUGnrH7OtwMfJj7/eyA7G938vokc6kZuRk69pmbre0n1qrxOb36PtYLIpf/XkaAGD1UwcRjRK6YhZ1CksnpHDW2nokhICP+larVyGrsBoAkFloY2dvnyvEHAmJ0/T2k3WYfn68BpKUoYdDUUaGmg4PMnIl8YvMyRqusZVbmsQOxYVjrfj6Q4EejVcx2tZ4YADzryCVibRfa8MQt6er2QnnFXRPCrYOs8txLFnNiMMMHd2/LR80IVNUWrf3ezH7EvIEi0ZjeH6Izu2PRpmv8P4ozyAJ9RkTUmKnjDLw1wcHGWV5IDOTEaEKrZa5SVJcZLFgSIzz/R4PcyFSlUpcJJChQrUaV54g5OfDimRWyl2dQsjpM7l5MAiTnKVVW7FbZNUTdTrm/2i2PAH/zP8AAMxKpQx9tsnEWfIis5ldh2NRJWaZqR/rvV5W1f2lbipunUhzKEtJ17/f62W+0uuDg4zMXWa1xo+dlMROyOzArNFwpt3o9/P316emstooValkZLc3FEK/2EY6x3idjpE+o0LBx8hUqRhpmqLXI8dJ465ZJK5VYdVpSEfHSepbvUkNlVC59u+ysVpVl6HDzg+EalBw8TpODqOnlfpWa1Cx8m00l0hnUDGnR1KL5hSZ2T9Jq1cypykciqK/g8aMzgjmKZZOSGGfpbajcQd8ab+BLjdShNqzu9nJbVZrFThxkPrDmk7HDQejjMoCYF7X4Z29jOYe3tnH6tKu5rhHmhTjzs7gc2xd28yIl96k5vqT5/2inNE2qc/HarU48TWNr8q5OegWbuudxWq+x75YDOOCwsndIGcu4NUGOp9MI+d77I5G+R4/mJKBNR5CoCyjXJwln78PcguxcohQpzDArvvXp6bys36D04FXuunYi1KjfAyJX3TC70dsYDYA4I2pbdjnpTl2wONlXtFso5HRqtEefhJiYFQoeLVjTlacY7vb42El6fXt7eg9Sl6FyCe+oUw7wM+eTKWS1dUWhYLR4cXNzfx79GX50/ghg5GmN3cC+u+o4PO6gRtmJJAmJNRziUhEIhKRiEQkIhFnFGe+PCeUAV8fXwRFJ9VO+s1Ff8azrfQGa6p6HNVi3XR7bzVgJsdZ6Adh1VCGeul+epu/ttTBmceM0oMAKPtZYDajKUBv6yUnjuLDckJGRit4pAwk5piAo8nEXSpUq9mB/IucYqQeInXQHenprCB6ZwFloiHEcLHCAgB41WZjZGKMSoPwBZRVqJRKXCKUcgqhdlun8qBU1A4ylhuZd2Tv93IF82N7B3DJbbROLvkgAeDsc+6yUhgtgldxwoHx55D6LzlDh8/eIuVLKCCHRkdZVHou9VfToQiiMTrG1Pk5aNxP/ak1KKFStwMA6moduOA64nJ89U8HWo+S4qT6XEJ1NDo5K3F0xnRo9RIPSw2NjrI9g0nFTskSl+KOp87hWnE9bSN83cf2DsAzQtl6Rm4ZUrPo3hdXpjBfRKpB13HSwZwUc7IWC53Ukd5yK29TNT0T09X60/azXJyL8B667hmXFHM2bknT4cJuQkges/qhE0jALVoTIqnCvVwgKE5lDLU2as81ycmMSM4/eRLPCe5OezCIKQIxlXyQdns82C+QIX8sxqq6VwYH8arwelmekYHxSXQv6n0+uLzUd58qqM2vdMqxtKqNjydxIhr9fsQOvggAwII7gRN3AQDc1lcBkK+MhAA93d+P2DFyEq4462lWkiplMkZrMTAXta4tAICA+HtbzxRoy8izBgB7SAFx5dFrXXJMTaY+XSec/Rv8fnbztiiVzE3c5HRytl6oVvO8WltSghLh3dV3NiEkOrkcbd/SmGqYaOQ2a2UyRiccyhi2aOi+zHUI5+ZkOU6Icg3tQT96BYfnrNk5UAoOTnKGnr2Ezj4vj2srHhBu8WMmpcJho/EgeRwBNE8lteeqxw7A5SBk8fxrCRV9/7l6Hvv2fi8jraNr0lWfm8UIz7b1bZj8b+UAgFaBdjXs7uNx7hmJsGO4zlAIuYLmUO8pF1IyqV0jdjquOVnLLuDpuUb4hGL11yvPwesr9gIgzpaEvKXnGnFKfB4rFGyqMSauN1c+KY2fSa0Ndlz+mwl8LZKDuFuo64YqU5jT1B4IQCZ4TFknfFCMp+/HxJTwhqitTes6cYOoI9kmvrP5AoiI53K1Xg9Jb+tSxBhVWpmTA5PgtkkozGvOIUazdrjdjMTmjqo3N0Wvx6JU6juTQsE/WHliDC+1WLAyWC+OoYh7SBkMuFSgQA9+9DssX/IIADBH7+vOovhv1OBMTC/aR9+3TMWYygYA5Ih/l/hd6dvxHJBFCuA8E/W9Uqbm37HVL16P4cueBQDcmmFh1NmiUGBzC9X8RDl+nIiov3vtue+6/08oEpymRCQiEYlIRCJ+qhFTfQ+1536sasP/vxdnzGm6vPUBAMCabgOuyqGMIFOlgi9GWeQH9mEM++LZ7FtifX15ZyfX+2kPUGayx+PhN/Rb0tKY39EVDLL64VKrBQc8XvGZ9r9Dl4y3Ag4AVBVbepu/NS0NEwWCYFUoWN2w0elkNZFUT+hmtx4bLNSOi1waVsmEg1HmI+WPseBUKXEhyoTvz64sYIGPBp5ao2A1WFVNJqtM6rf3oFOgMmUTUlH7MWW/8y8nRONkvQ1afdwl+NvPCA0676py9o358t0mbpOEDC2+YSwjV3K5nP/uGQFSRM2ncCjK6pnkDD3GinpyEs8p5I9wvSq/JwTHoORmrEXbMQfft9ufrAEAVhKVTkjBl+9SBqtUyfkcg70edjZOyzVyBp6WZeCM/pPXKZNLyzOiU3hE/fKByZCZqM0RZ5D70esKIrOAxo/UR6Mz/sM7e5lPpbg4m1U5ax1x1HKZ1cqeLhKn6c7UNGz3EFqSq1bj9g5y7b3MamXU44beDh4zEgrzis3GapjbjxqAnp/TtjPeOI3fJDkC94VCeLuZFFxLiqi/drjdjGy1BwLolDgR2Z/AWPtL6ptLHmfV0JFeQj201iPwd18IQHjB7L6c9iv+G9D6KwDkUCxxJd775A+Yf8EfABBaC5AK7oioDVZhjKJboEdrS0qYk6KVy7n9dULN947dztn/a7n5mNNC11Kp0+KQl+5xg8/HajujXM5orXSs9mAQ55uoHUf8Pr4+WzgM+S7qO8OMVJg6Kes/kkHtXGQ2Y8NrNGbKJ6XCV0njoVSjwRd/PQoAmDAjk8eHMUnNjvfb1hEqNfeyUgT9NGcbdvczZy7oD6NZOPCHQ1Hm/EhzTK1V4LxfEBTwj5UH2I+sflsPyiaSe7Z35AS73He3ypFPwC7cw4TmxmJtzHs0Wafi0I5NAIC0nDIEfIRYKFVyXP8Q+UX95dekNtYZYowADXS5kSq4UCUTUnFc+Lel5xrR2SI9W1KYRymhaZY0LXu5dTU72S8qOUOHbQKxK52QwnNI8qSypGlRp4wrxDRiHqDXz9e6K+jFil7qp0+zi7iygOQz91bUhVuEs/eQIsqq1PdHjaWr1UnsI7eyX9TIlMtxzzGab99MiZ7mmySN0VKtllXX6xwORoclxdwL3SFYdYQuDQc0uDY97tFXKLzL7u3qYiR5soH+tSqUWPntYgDA9PHvYdeRq+i6U3cAA3Ppc/pWnvfQd9B/AMbnUZ1KRziMziN3AwBOXPopqo7R2A31LsBlYwm5ag8EmFsVPOsV/JDBnKbX6gG96X/c/r8Nrwu4uTrBaUICaUpEIhKRiEQk4qcbse/Bp+m7IlU/oThjpKn4yJ0AiCshrQXfmqPEB3Za3y3VaLBvkLKNvKR+dNrGxk9iOQwAiPlpLfvOvBirlT7e8gTev4yUebVuF8ZqKeNa53Dgaxt9lmkJAWoYNw6VDaSoS1ODawTt8njwjOCcGOVy5j3t9ngwT7wVZ3fT+R7XOvG4lrIstUaJIToFclUq+EZom0OKIKszxjdSNqyYZEGKyJr07gjzlNJzjezyvW19Gyom07H1JjWOiKrkOn0cDZL8USypOs58L7iuAu/8pR4A1boa7dANAI5BP9ex0mgUOPgN7VdZY2GH39qPWmBNJ+8WlbqJfWEkBEgmy0UkTNluZoGJM2y5Qhb3hZqby4q3tmO0n0Lph0dksIVjrWgRbc4vt7DfzPEDNsy4gOqnffL6MfZhkpzNAXD2b07W8rnnX1EGXQFl1b5THka3wkFqT+nEFCTl07FirjDfq5V9vbjMQtnndLkOTXJqnzsaxfgQ9fULfqF+isUYbVzncDC/5rm8PMxvIhTFolCwW7GU7b42OIgDY2kML25u5orpFVot7hCqtcXNzej0UPuXpMVYzSbxopr8fvayqf/8zwhNfonuxdF7EJv0kOiYZKR9SKgSbnwZAI3FOoESoeFRXDv3UT635Gje5NJx345PCsT9YgTqo5TJsMtm5W3SzDQWawwGbDg+i/Yr+eo0FRwAPJadzX5MR/1+Rtv2ezysPGoPBhndm2k0cp9JfJJlVituTqH9chqO4IC5EACQmW/CccFZHKPSsMpN4vM0Hx5kJWdyhh6BTEIKVD1+HKFHC6x7nayolCtkPJYkH6GDtd3MJfK6grALJ/GT9S5kF9L3bcccUIp+0ujofJf/ZiIjv9lFZsjkZwMAAr5vWfk5bmoGdmwk1KZxvw0yObW7bKJ03BhzD5OsGubgJaUWwOemMVExOY1d/CWkJj3XiP5Oet7c8PAE/PM/6Xnpdgaw5EZSzKnUcqx7ldC2UCiKWJTaLflMSX0JAIuuHYNvP20HQN5x0jOgelY2dn9BaElVDSHRB2q7uSpCapYBvmIaV4PhMGJb4nXo9OXU50PhMCM4Twv0forBcFp9NYnvdoXKDKcYpit6e3n83CsqOaxzOGAT43aSXs+IZTgWY37Tml0347G5q/kY88Q8lZ7Pr+bn89hf1tqKWPsvuR1lYz4AADQNp8XRo9QddB9Se9jTLTZSwXUfoe1Dmng/UMpk6N31AgAgreYO2LziGqVjZW7CDQIhdEQifH1NtiLATbwvWOoxNZWe43vHPosfMhhpeqUZ0H1HpMnnAm4tTSBN+BdemuQHbgEAxPrOw/SSnQBo4MZevhEA4PnV3+JLEcEgP7z3e738gJVMLlOVShwRxSXvy0rhH6RHe3thc4unY1SNWwtoUKcp45NJMjisdbmwaZCOd0OWAq8X0I/2muFhpIkfggqtFq8IIuLFo2TikiT2ptRUdB93AABCJXo2C7xFa4VbTw80+QC9RGgydXC20I8ClWGgB41qjAkdXxJUrNMr4RaFbjtOOLjIrkvI+zNyjfyQj0Zj/CLUcmiIjfKO7e3nJQeJmJpdZGaCae1HrUjJomUEhbKbX7yu/90UrHp8PwB6OEoPbIcoRXHOhYVIy14GANj95atoP0bXml8+Fr3tRBieMi+XSbZzlhExuH57D/weuieT56j4pa/liBwpmfQ5tzSJlzaLK1OQs4helnS91BdvPX4EE2fSkl378WFcefdEALSMMHq5UmpzxiTa9siGU8gvpx/+7GIzv0wFDHK8IsjYJRoNv5gMhsO8ZDUslnk/HB7mH/Vb09IYKq91ufghl6lS8UNfGicPdHczEbx2zBhc3Exk3+ZAgF803NEov0D98ZsrsGjK3wEAR8VxO11Wtt4AAKuKptqwswAyIy23xnoW46VpJFyQiN3NgQDaBmjtZ2p2M5NXfdEo/ENEKk1Lr2fTzldsNriC9MOepo37qfBDPmzkJfVCjea0gqSSfYh0/U/3959WnkharplqMLDYwqJQ8Jyt83rxoCjELfULAF5mn6rSIaSiY2wZGeFjbCor49IvEXFf5QoZAkLP649G+eXNdmAI3WJpas5VZWgRS7otR+3IK6FrkUr32Pu9LErILDCh9iMyQTxrdg4nBx0nHfziJZUbkstl8IllJ8egHwuvojlW+1EL23bUb+/h5a30XCNsIhELh2l8XXpbFbZ/QmN4qO8UckviJq3S/P7k9WMQ/GmMOYvu32iLg0hYj4AYr0G/B0XjiMTd3niEC82XVSfz0pn0PDn/lxU4vo/mYHl1Ki/l6fRKaMQcM5hUvLwuRdmFeYi0x0n20gtWdpEZBwW5/qzZOZzQHMqU8X2TxuVMoxFPC8PKOq+XRQcLzGa8Nkj3apbRhE3i+bpGvFR9UFx8WokdKbqCQWwXAoSX8vN5rmeqVPxiLs3zKp0On4ulPJ1Mxi90jkiEf3f2jzq2tHRYodViexctpxelN+IBMYYf7umB7TBRUVC4ChXJ9PvR2FEDeOke8gvRuD/hwWx6Rq9sVWJpNrX5c6cTEXG+eSYTNjVcAACILZ2DHzISL03/dyKxPJeIRCQiEYlIxE81IqrvQT2XIIJLccZI020dtJzwqcPJb/Pb+9KRZ6XMsVCjwXaHMB/rWxTfMXkv8pIoC5GgWH9IhyUpdBOkUhUAcEdaGkOtb/fE3+e0Oso0llosuFKQTXPVajSJjOxyixWvDVFGMN9sZpnrbo+HyakS9GvePozkeZRJ73C7sTRIKMVxc4wl7x0nHQiU0ufeDQSla/QqpGXTd/u+6sK8yyjbaNjVxwTr7CIzL4u99x+HkFV4uqFY9bnZnL0VjrXi+D7K8NxOByCLG7dJRpES4mRNL0RfB2XMlWdnwNZNbTZZR1jeb07WMlT/jycOYNp5FwEAtn/yEQDgirvuwxfvkMzd5QxwsdBwKAKvm7K9gjFVMFmp76Qin8P9XiSl0LLqpb8u4PM17O7DqUbKHKctzEWTQLwQTUJBBSELkpGevd8HczKdT66QMRKg1ioYSarb3oNzLiS0UCZGZFsoiJwoZYtBfxhHVDQ2qnQ6GGJ0jidt/YxwljUFsKsoTgoHgNVDQygVpPGNDgcX5r3SakW1IIdqZDIcEuiQlA3fkZ7O4oFwLMbLAVIRUIDG/NcnfkZtzt5IMD+ASRm0zOOORtHUQktvFaWf8XLAFL0e+1pnUpsLvuGMeJ+bzvFYbjpeFNn1MoslXvT0rTsxT0inj/p8bAEw3DsLCAqENkxj7q3Z2/CclP0fvRowCoPVjivx4OIVfK2SAaa0XL7IbGbEa7JBD50omLx2eJj7cYxWwwWHL7ZYGKENj8riJQQhValEyQC1sz9Lxeer93phPU7je2ScWM6Va9Ao7Avypqej6StCcPunmRkdazk0hHefISLuomvG8HyTjCkXXTMGJ9JobHx71wHkFNOx88dYMChKCjUdHkSZQHElZNhgLkduGV339vWtqFlIqEJnk5GR2NFlUhoP2Bh1TcshxCIt2wOvuIdeVwgagQ4HvGEuInx0zwDyyugY0lK41x3EzMVkiVL7cQvMVuojuULG5pxqrZKXuIH40venqwixnHNpHmziedHWYGdky2HzoeQSmldKmQzO/TRPpWXQnrYRtm2Qn5sK/5c05k0Ls2BoFBYf463Y+gLJ8ItvKUeWtAQs2tIcCDACdEWfEuuy6X47IxFecdDK46aY0pJvrdvNSGemSsXPbY1cjjyxX43RyMiUJxJB5x6xxFX1sDj5HYzmjh//JmrLCSGsOHqUbTs6O+cwOvTYuesAAA/tPhemOhJehC5+FH7bdABAUfZ+RsrWORxs2Fqo0TAFRLLkuKUxikWZdE2zjEb+LWvw+TAcoOddhTGKxq6zAACxhdfhhwxGml7oBXTfER3yjQB3ZiWQJiTMLRORiEQkIhGJSEQizij+5YK9hWo1NrRU0pdRNTBIGXPahCe4wC4OP4WdVxGB7mdv3Iawhd7Al8x4BgBl4F/3EhcE8iAWZFCmqpDJuKzJw5mZjDrNEHyLDx0OzBafd7jdnBGEYzFGG9zRKMvOr09NZa6GtA5fqFZzRrzR6cTPRJsPGSJIP0aZY8AbRoUwjZPk8VqZjHkaSZCjQ0jotQYlk5W7D9sZidGbVEwgzRhPqMdrd+1gntLoMggj9gBLh+VyGfMmtAY6rjUtHWk5gtBaZ8MMUTLixIEBlhlHwnpEwrSfTG6EWie4WBrBJ9MrMdRH78ganZ9LnXz0ShOKKwk1aD48iPGCh9VyiDLSm1acjX88cYD6bqyVs9Kv1jSjpCpZHFvF6NH+rZ0s896+oR0AYErScHmM1CwDzrmwEADxTyQEquOEA8VVxPGQvtuxoZ0NOwNqGdsCdNYPcj+GQ1EE1PT9spYWHhMSopGjVmOVQCE9kSiPg5tSU5Gzj5AYDMzFqQuJZFtwiFCFOUk6fN1P6MbUVDuf+470dM6Y3zw+niXJs2b+AdvtIvcW3Idri7vw7qeEDEXGP4uKVEJOGg/ei6XnEOr38e5fAYWruB0AUPhPoP2XlPGX5e1CU9199HfzMSasZuV9jV5JbGE+Bpmc5krMQRyYBysGsHL/OXQtYzezRcP7w8MsaCjTaLBfWA30nqAseFLlO3hCZNS1LhcjRjUGA2+bqVLx94vMZuajSGju1cnJ2Ck4KTOMRlgFktY8qkjyfo+Ht5/qoHEZDkbhy6d2qlroXACVRjm8k/qjelY223Ps3dzJiMobjxAv7LrfjccOMe50JhUMRjpHNBrDni+I/2OyKpAqUGPPCM1NhcLGvJ25y0rZRmTPl/3MWao5bwn6OolI3NcehkYUc5XJLOIcEfjcNG8yC0zMZcwpMuPoHkLCbn5kChvZuhw0FqORCKYtpLm0+b1mzFhM19R7ysXCi3MvKsKhHS7R/hDzsyTOot6k5mfI2QvyeD72dbjY6HL2JSX49jPqG6kQdmaBiVEnpUrOHK+gP4yv1hK6fcPvpqBFoLyrhoa4mK6Eul5mtTIaNF6nw/vi83yTiXluq4aG+BktzZ9al4v5c4PhMM8xi0KB1ULwAMT5caUaDfMTDWJMNQ5mAyNEloc8iLKCbwDQWJPmQkXmSUwVqPLbjfQdBmdi0mT6PapzagA/cZqmZnZgXw+hUqbkBrjCNDYPVZZhYh21aXoK9ecumxVFFkJG27xKzLFSm3a53ZgkzrerPwOQ0ziIzfo9fshgpOk/h74fpOk3KQmkCQlOUyISkYhEJCIRP92IKOi/73qMRAD4F5CmpIdJquq75HGE2q4FADwyfTP+WDceALCgbBfb53/dmxZ/+0/fiqWplJFIPJOrv5qHtPJVAEjho9UQ0vRSfj5ubCPOD/oWYek4yuqkbKXB52PJ+GyTiU0GH83OZiXUiuxsLv3QFwqxYk9SYDyanc3f5Y5Cnbx1w8ieQhlsVzDIxRevMFsAAP3RMF+fpi+AvZuJyyVTyLBAoDYHa7s583M7gzhRR+e86u5qAMCI3c8lGU7W23DDH8g0b83zhxmdqf2oBSlZpC5rOkTcjSlzUxnZMidr4XJQVjplngI7N7YDIMNNCQVSKMdCZ2jhdgCATFaOEwdJ9ahUyXnbwR4PfzYkqZElDCYlVU5mgQkRkYm2HB7COGGa6XUF4XNTxhGLDbGh5WgTvl4hgz5rdg6rdkYfr6QqBQ3ClkGrV+Jk3aC4LkI68sZY8M2HdB3jzs5gczyD4JABxDuQMti+UIhVXQUK2tYji/HYuL69nbkxL9ts+KKM7tt7w3Z4IjSuJHXdQ5mZ+EBkzKPHUTgWY07DMquVx+NahwO9vXQ/87LI2K6zdyquLSdUQSmT8bhs2vcIGecBVJ5I4gAm7+V/TWrqr2q9Htv7CPWENx+ytsvoeDV3INS7gL7Xd8SVPSKrnVSwh69leXo6nt1PKNatk7fglV5CcYr0Ye476V+tTAaNuNZPnU42E9w8MoIHPifbkfsWPMvZv0WhwCKReUocq/1eL+7T0Rj9raufeVG5ajXuTqNriUZj2OmLl80AgKyQHG0KmmWh/cOM/EyZl4uBTuo7uUKG+m2E2PV1uJjHJ/GL2o8PM4pSPimVOUgNu/tYaZqea4RVqM+GBArjcqQhq5DGbccJB1QCtTFbJ8JkaePzSfMwFIoiu5DGT1igbiq1Hd2t1E6dQc7WBwplOnrbiXtUOe0cnKwj5NacTHPmsjvzsfqpgwDA/EIAyCkuR0872WLMvqQE+zbTMzjoj/AckRCnpsNDXCC8/fgwz8ezripB0xf0TE3L1nP7JR7mlLm5aD06xMeV2ux2BBCZTMer1ulwqzCFfchnhqmUnhES2uiIRBh1Gs1NKtVoeExcdKIXl6XROaXVAmnMAcDCpibmDPljMdQLVPPtb2+CadzTfDyJW+je+hRd/8/uQazvPADApKLtfL5wLMYKVBibmes3yUpoXV3LPEaXxle+w/O4LxRi49ZSjYZR1K8PXYHxle8AACu/MTiTj5uWv4nR7UqdFkd9cZWi9PyxV/8VP2Qw0vSs4/tBmu62JJAmJJCmRCQiEYlIRCJ+siGLRiGLRv/nDf+biEWjOCN05f8DccZIk2wP2cVrtj0C/QLysfBFo7hZKCFeOKXBHMFN+rplMm4VRQ89kShnpZLCwhWWxx1KHdWYU3QEAL2RTxZrwYFYDF8P037PF1HWtNvtZo7Sit5efrMvVKvZt8MVjeJOURDSFYngJYEwzfmWMhfFwgyMHaG181aLDBNiksLLiy+SKIMoqnWg5OeUuWdEKEtWquSs1PF7Q0gS6Mzmd0+yMkapkjNy0nHSgV+vJEXGS/fvBgBMnJmJiinE5dq2ro05TyfrbWwI6XYEkCHQnhSRDR/aMYSsQsrSTjU6UT6JvHp62w9DJqNtfZ5hLq1QMSUN+74i1Z9KGG9mFpiYp+V2BhDwUYZ+8JvNmCqy1p7WEVbs+dx0rRn5BmQVEoISjUTQdOhbAGR4J/kqzVxSiE8FT6NwrBWTBWomlWppaRjC0l9RIeOBLjc8LsoWj+8bYL7FWbNz8GEltfXhLOJV/f2ew7hiOflFrZQ7MOMjQn7wyzycF6a+26DwoXgXjatPqpWsiJMQwueHbMyJM8nlnDmuGhqCQSAqVoUCNWIbKYuo9/mYc/PnI0VYUkKcp/ZgkLNqrVyONScIFTRlbsPVKYSufOqgedAZiAGNNFfyJj6JTmGUh4qnWM2jzdoM/6kr6HtRFBuDM+NIlKWeeYOQBxmNmmSK8bxq2/0fqDjnXvpeZO+Nfj/aV90BADBe8xz7T73Q68GpSdSnxX/7Fc6adz8AcL91BoPQiX753OnEWDHHrkhOxgNH6L6snzrM/VhjMDBS1P1eOwBSsN3eR+Pv7gEt5mmIO7aqsJDnaV8ohIIY9baE6hz4qguTxVh02nxcvic918Ale7rbRnBAjO1bH69hZFcai6dOOrhkz5R5uWwgqdEreTx6nAH2UPv2U+I/mpM17JVkso5hJd3hHT5kFhCHcKCzA4O9hNoUV07AQBehL8kZdKy2Y0eYbxUJ56B6FvnyfPXPV+ESKr1YNMa+TzYx9lsOD/EzJBqNQi76X2tQsp9U8+FBqLXUX1U1GVx+SDLTbTxg48K7XlcIBlEY3JSkhl8UAHY7gmxIqxZcx9RsA7pEKabs7hBcefScGY3qAGAkpiyqgsZA20uKS5MjjN2bqC9KLi9kL70DXi+M4lquaWtD56mFdDBhjjwny4aHhT/SGscwl82qMRjQJ/isa5rG8tyzKBQ8jm8SvNXl6en4YxvNg7cq9LhuP6kKK3IPcumgxUlJjEBt2HkPtSF9Kx6cQM+sle1RFJkdAIC2vU8AuWtpG0c1KqrIe80TibB69OtdxDHMmvRn9Abo5zPt7dtgvJkKbs80GnlOvGKzMc/yo5In8UOGhDTJn7JB9h2RpphvBNHfpiWQJiTUc4lIRCISkYhEJCIRZxRnjDTpDt4KAPCfuCvuCZP/bvyzsZl5TLL0WsSOkY/GDbNexrtrKNsOzqU39FhUCa1CoDZDkwE7lSyYOmEVK+aq9Xp83EjffzGLOAXP9vdjy2d/AgDcecnj7EjbHgzi/SLKBv2jOCe+aJQzZang5IqsLOQ46ZLlChk2KChTvdaajM9dxMH5GXRs/68Qig534wjyBWcgHIoyojRxbg47FJdOSGXfl/efPcReKlJW9+1npxDwEXqRkiljzsZZs3NYdReNxpj/o1BShjJ5jgUDXZSVOmw+RqgaDwxAJvgiMnmM/V0yCyeg5XC9+J76M+iPIC2Hsnh7fx/yyujYaTln48gu4jqVV5u4aO7OjYQOJGdkorv1pLhvMsjk1HeZ+SYuXdFx0sEcKGOSBnkim5Wy56EeD9LzCMkJB6OspCudkIq67cRPGVOdxopCSflDzuuUueuNamhFhrv/qy787BryY1Ei7v59W0cHVgiUarwox3M84EcJqM9PxIKcHRvlcuYajHYjfnv/JQCArxZ+w2Nm+4gfRVrq50dzcti/SQngXpEpvz00hE126mvZgb8AAKpm34sjLfMAAGVFm9HkJCTApB9EjshEu9Y+APd8wXWQ5pK+A1oVtc3vS4sXDXVUQ2cjFMs35y/xQqX907iI6JFh4fyrdAMNVH4FpS9iSQ7xVko1GuacXGyxoFIgUw+0UR8sTeeuwHyTia/1yuRknlc1RiPzUnLVakYhpPIxlTod7hZo73Akwg7p7cEgbqwjZOSxqh7cKFBqk0AmXF0e5uBtW9fKhW2/drswQ0VI2D9WHmA+T+OBAS7oLKHApRNSsX8r8Q0dNj/zdVRaJfQG2k9nHMKQKK+iGYW4SK72qVkGmFNImRiNtPC2BeUWHPmW+q5yWgqXSZLKkAT8YQQEqhPwmeAc6uW+1BmIz2kwBxnl6hLFvXvaRtiDKeCTwZwqyi4FdAgFaZtzLyrGuLOpT+u39aL5MLVD4jSlZhtYBdd8eJCfPUF/hLe95dmZ+PuDhHpnP0DXt1BuYKVgoy6Kco+ohCCXYVBw39QaBT8XetpGGBWvAz3rLAoF8/X2e718X+/r6sKFgh/0QEsIjxWLqgZixWGxxcKfw7EYq+f2ezyssvRHo8xpbfNHMd1ESNiu7QK1UdvxxEJCeB44boqXOClchQoj9Y0nEkGnUIeyQ7/aziVV4M0H2q+nz+ZjjPguKj2ITe30nNl9jhs1O2h+6g6Q91ps0cOYKpDT9kCAkV+XO5vPY9z0Wyy45jkAPx7SpHiiDzLtd0Sa/COIPJCZQJqQ4DQlIhGJSEQiEvGTDfn3xGmK/M+b/X8izpzTtP4LAMCkss9Rd5wUPLDUY1YuqUK0Mhmvgfd++CDcoiZPxTn3orF1Pm2T8ykAyiylte629vNww/hdAIhnIins6r1evJJPvCJpfbvGYGBX8ef7+xlhaA8GcaeoAeaPxdgJttHvZwdxCZVSymTMqwjHYvhYuB9PNxhQ6aY2mZO1eM1JGeqcU/ECoqZc2i/iDKK1gTIJ/1lJ8H5OGWVfh4uVYS57gJGYrWtIAZaSrUeJ8CKq39aDiCi6KZdZ4RdqkaJxGpwUqrtoNH5rJNQmp+Rs2LpJfZOWMxlyJaFALYeGoNFRJmq0uuET/k0+D+03dooW/Z0W+nuShY97bO9uzmyBuJpI8nxJzTLAYCb+jWekGbGYxNkY4G1G7H6oBN8ip8jMPCUpetvdmLaQsvziyhT+fs0Lx3DNyzW0n1zJGXFuCbXv2L5+rrk3piYDez8j3oT33GTmTRiOuPApdTNuTk1FoIv6sdJB4/Lp3FxWd71tt8MkqeBGte99u53ruEno0yuDgygRnk6NfeVYkEdop1GhYMTFolDwuCrVaLBFZM1TBD9o394H4uhR9XJGq9r6JiAtvR4AYAsCqe8S9+jO+6jA6J8++h0iBurDSdMeQV0/OTqj5+dxZ++RcezZVDb+ZTSdImdyOKrpX2NznBflqGYuVIUxika3VOAtk7PqZwqpnbNNJjzQRZyhGqORVaelo2rWPVRfjvXTCc1Z53AwOnyHQJfmaAx4z+3gvpC4YaFYDF8Ij55rkpO55twVXe0AgJVOE06VCgWSLYb9grt09nl5jG7s2NDGc+/iWyphM9O1fPUIIW1qrYLVc/OvKMXal4gvaevxoHgc9Y1C2cVqO8kHbM0LTZCLMeUZGUZWoeCcGewYEk7i4VAEKVn0vVyuQE8bzWsp9CYTQgHiRRVUTEF3C7UpFFTDYKZnlUqThaFeGkvTFi4FABzYuh46I/WdyeJlDpLfG4ZX8P8qJqfz88De72VkTapBp9UrUTWdUM9vP20/raZe4Vh6puaPsTKqJKl4jRY1z8l9IR+mafXiWqNxxDtDy9dolMvRVi98zyronrxis6FaIJZLvVpuU78iynPFHY2y277ES/XHYnCLZ/iLNhs/t7tCIXYN3y0QLIBQ/031l9N5ziLe0cddyfEb4KiGLJcqIMSCFsjqHqP7YrfDM30NAKozJ51beoY0dp0Vn6faPmRlkOdXjcGAjxsEcqW2x+eeUN3Bmx9XvA7OxKQx6wEA04wGVqg+kpeE5wYIwfyx1HOqx3u+F6Qp9LvsBNKEBNKUiEQkIhGJSMRPNmSRCGSR74gTfdf9f0Jx5i9NwgemPRhk7pK15B1sF662GJgbV8SVOzB9EvE6dvVnw5r3OQDAIVAYv18Ni4FuwvSyr7HDTZlVqlKJNW2kLDElN+B2URFdqkN09b4sPDKesrRCjQaDgotiVShQKzKS9kCAM+IKrZYVdtK6eJYP6BXg2m6PB4uaKNu1TtPgQz9lQld5FZwdZxfFaz+dFK7Eg70edqSOtgfQIHgY51xQiNYGQqgGtXFH46rp5G1k7/fhq38SAnLh9eXMoejv8MMvrmXEHmPOkqRki0VlMJiFgqerEznFZtGmVpyscwAAqs/NQl8HoW2GpG70Clfx1CyBrITUkMsps+pq3Y9xU0jJUj5pMpoO7QEAnHdVOXZ9QWjOhOmLAQC1H61BWg6hWZY0LU41Uh+o1GGEQ9SnPjcwYqe2ajQK9s6ROEp+b5iz1k9eP8q8lZlLCtG0jlQwlkuLT1MQAeT+LHFSlDIZDp5NGexFPREE/XSM0rMzIA/QsdcOD8Ovp3tryyXn38cG+qDoIzTosgwr9kmqrwEZnPnCedztZu6OVFdxtNM19B3SrUSGSslo6IqeHt5+y5YnMH8+cfc2HyEEIa36T3GXfHcpOr8lXiCyG2CTeErGZhT96m8AgD8eEFltSgusY0lpV+fS4M4S6oPN6f8J25u3U5N++SSjZTUGE5pEG+eUEWr7dXcO0vQ0J2w9+cwbLJv2KpwR4QLeV41rJ9QCAP7w2i0AAO/5v2ffmyunnWAH9Sqdjnkm7087hbCo/XdNcjLzWSRV4Z+H+rnm2KO9vVgZpP76NDmE821izChDjFS+YRLqsyxAtp7QpeQlRYxY6I0qOCF8tLb14pLbSIm5d3MnPBcQQiPxfTqbnYxOfvvZKfZpGrH7MXYqjZPGA1FEwvS9pLozWaPIL6fvDtQ64BykcZmUYoXfS6htKBRFKEDt83vDsKbHHbgBoO6bCPQmek5a03sQDlE7u5pP8rV0nGxHajYh1o37CaXILjYjI4/uT/PhehRU0J0d6k2Fc+gwAHoW9AreYyQUZRRbqmVZPSsbn68+AQAom5DC6NHB2m72YetpHWGkSeJTnvcL4uwAQO9bbRgUx03LMzJHSiOTsZ/d568fh2cZ3a8G4WP2pC4NXyqpb3vTlRgG9delTa3shfZoby+vHMw+Sc+TF/PymMf0+uAgjAL5SY3FeNzVulxcm3G/14vp498DAPRJdlZRNX4/hp7bfz61lZVqX2//AwpnkeI7Vankuo4W4d9Wd+QGDJa9DIA4Sr5Z9Bnt16NXeJ05tG24avzXAIB/fvYnnLPgQXEMug+bRg7wb83H/mbUdVOljDp5ENNzCIVc2dcHv3MMfsxIvDR9v5FQzyUiEYlIRCISkYhEnEGcOafpA1rnRfpWoGsZAMA6fiUjOe5oFI9mk2LjmrY25hKtGhpCyEUoidZEKJFJoWClQaj7QlYa3D2umf00FpnNuFdwK6RspDkQwKrCQgCUucwzU1ZRptHiaVHR/f2iIvbIaPT74RLnsQqkabJHgX6L8EGRy5mTsqKnB2/5CT3KH2NBt1zUnnqJPEXOmp3DSMjhnb2cvYVDUc5mD9Z2w+2g402YkYVyoap5fQVllGqtgjkKablGtB2l6540O4e5RNlFZpw4SGlULOagvnUGEBJ+INZ0Lde6yh9TgYEuQuOKxoWwcyOhDXKFHKUTJgEABvuo/ZZULbwjlBX1d7ad5gs1Zgpl6XW13XyNHgKqYLTKEBLKntQsA6t1CsdasetzUr7llRkw/0rKKP2eMNa+RNmx5AulVMmhFH5RbmeQXYnHnZ0OnXAgLpuQgkAmZZfDewbFdcgYlRqx+6H7OY2vXLUaSZJSLU0D5wlq7EChBuZjAl2poMx+vE+BjC7iMWwqLWWn97vahvBgLt231UNDPI4ll+Ljfj9Xc+8Nhdit+I7OTubwWBQKroVlO/gopk//M4B4nUODQo5aF7WncaAQqiRCAq5PScFrJwupg1tvwoOLVwAAVm76Le3X64FnwUq6PqWXx3+uSoW2PlE7Sx5HfJH9CbJEHTQJ4dl3bBmjw3OmPR+v9eguZTXeIxc+xjwsCVkFwKiCLxplz6b9Hg86XYQYBadkY+UQzbfecIjd1J/IIX+uep+PUbpqnQ5TBIew2e/H1Ajd74O13Si6kBAaCQXuC4WQ4adr3RhxI30rzY9xFxXA6I0rw+qF4nL+FWVcp7FfIEZ5JUno66BrmjAjC8f2Ujt3buyARkfXUj4pDZZUut8S4lI+KY0VoNJYBWjcBQPUN8WVBjiHBMqr0aJ8ElWvr/2I+DIVk6fCMUgcsJ7WFpRVWwAAziEzBjppbp41ex6ahbJVL1CRWKyPr+OCX1bg87fJnyo1K4d5Vnqzk/3UGvfb4HVL84KeN5f/ZgLX3CudkIJPXqd5f+0LM7D5aZqPVTWZMJ1Dz7hvHqfnuSVNB51w2h9fk8kKYXu/l+taKgr1zFedBA1Coh9lIyFuQ5ZAv2/v7MBsI12XVanke+uPRrFJ8NmkOXOl1cr1DIcjYfY321haiod76B63B4PMf20PBtHbcin1dfk6OtbBe2GqehwA4N71Akxiv5Qr/oK2g6TgNk1YAVdQ8LKk1ZCuZeSBBgD2s1FW+QYAoKlpKfP8snJ2ck1G05EsuIpoHE+tIQX3Poeaq1moZDLcKni1f111B7znU505i0LB83dg4kv4IUPiNOn+0AaZ1vSdjhXzu+D7U1GC04QEpykRiUhEIhKRiJ9sJJbnvt84Y6Qp+zApfJSgmloAYNx9Hh67jd7Q7zoRYzXPgpQINp+iddylxc2scPh9GXFc1gwPo7GP1tLfn+TCG8I3ZrPTx3W4bp6wm1VIUtaqlMlwjUCw9nu92CjUGEa5HFeL71Uy2WmZrcQ5kVQaleq4EuRQ0M/r5Xmj3h8P7+xlxckJE3VPrloNnY2yrRN1g6gUHIqDtd1cj6350BCcw5RFTTwnC7sFP+jEQeKkTJ6TxZmh1xWEXEEZSDQSY2VM9axsfPneSf4MAPu3dkFnoLd7v9eDkvEWAEDLEQd0BsrCjBYNxk65AADQduxLDPXRsUsnVAMATtYdYAUPAHYPlrJ2ADBbtUjLIf+W3nZydD97QT7ajhH6klU4gj2i5l4sGkNuCR2j46SD+ysl24CfXSQ8s4QK6Kt/hhHwkfLE5QwiKuo1zb0sHTFx3elTUxFsO125p9UrcWwf8b4kZAAA2sq1rIiLBOOeWSnT09AukMPpaho7azxOVsF5IhEeM4PhMCOSzYEA1gp+xtMCURoMh7FKVFofDoexVDgRL7ZYWNGjlMnwolCXVWi1nEGH+knJZsrcBlfrNdRoeTDu+D3qc8qGpRgaT/24dOZ/AAA+7siEVqBSgVgMMTv5FaFvESt47pz8FV6omwUAuLl6G15rFNwUMQdllsOIBanNGJgLU/466v9j92LqWS8CIERJQtg2jVK1SWjvaPR4td3OfLDn8/J4Tl5rTUZOAynU7hLqOZNczn33SkEB1yh7fXCQVYpjWoPsYO/LpzZo5XI499N+Ryo0qDwqHLwnmdHyIvVHdrEZYy8mNaHzULw+3QLBzfnq/SZGnS69bTyjvNlFZhwXHMKyCanILKAxKEX78WEer6GACQol9ce4s9PZfbu4Mhn7ttB905tMUIu+87npuOZkLR/DafcjSXATi8bNQMsR8gTye0OM8krzPyXbgJBQwymUY1G/rRYAkFdmZn5jd2sMY86icVw41orNH9A4kNz3LalaVu4e+baXz1FcGa/veNbsHFiLCHHQQSiMDwygWCh6+0650Cm8o866IB/vrKAaipf/ZgJkVrpX79vt/HwNiZ+Os3V6vDZEz/CZRiPPgykGA6OWO9xuftaO5ihJYyMci3HNtxcHBpgf98rgIM/JdQ4H8+ck9MkRiUAjkBydXI5hD12Lcv/vcNtSUs9JcxQAYu5iAEBZSjeahqjv4C7lmo3Ti/Zh17BAXf2ZcTRXbcfUQhpL+w7Sb2HRhOcYReoMBuEPUPtx7A+YPoOQpl27fo+yqX8EAJys+k/8kCEhTfqHmr8XpMn7WGkCaUICaUpEIhKRiEQk4icbCaTp+40zfmnqdRKakpfUz66r7kI5Vog1ZAQrYfmMsurNP38Fd1cQyrLO4QW6fgUAeD/leQAiK2+l76613onlIkPdfGQp8spIHbHdrWC32BUi2wWIywQQOrBauID3hULsr1NjNLI3yBrHMArVlJ1NEqjVvV3N7OI8HA5jiUAQPnI4sChKiEr5pFQoVZQNZQnFyojLDZ1wtc4qiL+1V9VkYt3fqDaSpKIBgNajQ1z3at7llN34PSHmWCz+t7HMQcgvtzD/acsHTXwciVux4MpfYssH74jv1OhppSx4/PR09jbye8KQK6lSekbedPS0ka/WUK+onu5yYcxZpHpx2v0IBalN6bkjXL/L6w7C1n0cQNz/JRScAJ2RHMP9XiUUIjuefVkpKwErJqezo/FQj4frUJ0Qfi6mJDVXT7/ktir22QkFIrztdTUZCCXTvdq/lbhs05eVYPJC6ouv32vCWSKr1veGMOQmpMOQpUf2OTR+zAFgnRgHJQOElgyawoxK1Xu9nPlemJSEFoFKrRly4YY0ylAfEq7Wd6anMwoz02jEc4Iz917TWEZz3p/kwr0ZNC82Op24ZeOvQTeJ/u5yl2KSqIy+zGplNKvOEwJO/jv114IdjB5JYxiOagTMxMOKhfVx9V7yXvaFeaElk71ldns8uGEMITGr7aJvI0pMt1IW36z/BItELb7a8c/CotBym6RMv06cO1OlwkNdhJzMSdIxMndhUhK2CDTqFZuN0YKV/X2oEv00XSAQL9ps2DOG/I9G7H40yGhsTNHrucbdYIUaaQJNSBdKqJZIiLk9V04vx7dOUrBN1+phFXyeoX4furfRvdDqlcgRaGfd13Tf0nONXGPupfvrkJZDY6q3w4WI4ONp9UpkF9F+tR+RysnWHcC4s8kzTKFsRmcT9df2De1IE2q3EXuAj+dyRGFOpjFz/rU0Bj5+tQ06A50jKVkLr/DD0ur1MIpacH5viOu/7fki7hiuNRBSdnL318gqpOdLNBpj9ajfG4YhiTyiDtZ2M/Iqebq1Hx9mB/Whfh+0ekJLHIM+5iaN2P1Q5lP/PyzUok9PyYVriPorGonxcRWBKHLuoXsoM6p53kw+EkDldEKV7xukZ7/TEmEO626Ph5GdV3Q6HtPtgQB2CXTybjFn7mkO8lypMEbj9eG6U5BlJUQ7U6nERuGlV+/1spfTsJPQxqvyhpmX6o/FMNxJKsRwxT+xVuwX61kcr1ZR8RQAoHnbS0DZmwCAsoJv2K1/184/QzuJ6tP5e36OsjHkndYXCmFfR424WdR3bY2/xDvzvgIA3PHEMoQFp2/J4hXwRWl+jJ/2CBpqCUEGiSl/8JDHvgdzy9h32/+nFAn1XCISkYhEJCIRiUjEGcQZc5qS6ymLHj70R6o5B0CbdILVNcNtl2FR1WcAgO5QCEeOXi123MsOqqqszQAo49z1zTPUgEgEsUr6/Fi5n9U8N6Wm4kXhpCqtkQ8PVaIsgzLqTJUK20/OBgDMGfMNr3HPN5s5GynUaPC0yKgkVdRfsnKw00cZz+SoBkOixlyeUsUuwYEUFdeck+pieXq9jPBkF5u5Onpfh4t9Yz57qxElwr9puN8LY1JcKQQAGQUmzlqjkRjzDno7XGjcJzxYIhGUjqfvpez06B43XMPCBflXlVBqyGPps1UvYMxZxGup+2Yrq9VOHBzE1PmU9UiKoOKqFHScIKTDMxKGXEH9NXV+Nl/XhBlZ2Pw+XZdMRmhd9bnZ6Gyi7NrW3cpttqTquNq5wajCtvWkjCyuTOa6V5+9RWjJWbNzmDfxzl/qcdvKaQBIeTjQSZlmMBDBuKkCtYnQdzOHFYjk6fj+NAUpo+wKBhkFag4E0CvQksUKI170UT+VCR6TUiZjD6ZctZrdiN2RCLYLfsRYrZY9YgxiHEViMVaUrXM4sOEUZbZF6Y1o66GM/tryRuZFrRoaYkRLyph3uN08LjNVKmzf9gQAQDX5TlYVDWz7D/z656TGGV0Dr15k6KGjDwFeQllQ/hzyUggZ8cdiXPH9teY4EitVkEfzHbhzBqFcL/QPo0xH11el0+HjHuqD6WnD3FbJb2aH243dnz4CADDO/S2GfYR6dE/OY2f+yXtimJVNaKAjEsEDArmVkKgag4FR4idyc/GwQO9uOBBBbim1+US9DdMXkrrvpEAkkzN0kI2jPswLK9B+nO5lx0kH+w71nXJxHbRoNIr8ckI9tnxAirPsIjOP0a4WJ1qEb5pj0I+sfNrv6J4BJGdQmy3pYkw1+VA20cztkLiHwUCEkdi8spnobCJuUiAQwbB4Xkjcw4O13bCm0xjOLS1HUDy3WhsOQycoVNFIDAEfPVs0ungNTOk6ThwchkJJSyHlk9Jg66bxHwz2Y8YFNAY3vnkc54jPkjN4JBpD+3Ga32UTUvhZ9vMbK1lp19pgZ5RLcgTvaXPinAsKuR2Sb9UavR9zmuP1/CSe4aGgH2OEP1uLisZDnhu40UXPmTvS0nhMaeVynm8zGxv5e+m7Nzt1iMykZ9brg4OMGGnlcmSK+fFAdzfPzVKNBttHqE/Z+V7fgQoLjbs8lQqb99xF3xf/jXlKOPwErNPI30xCqNC1jOeKNe9znscNPh/uECq4/V4vt8nW/AvMqiK38e0NVJ+youKfGHr1Zvr7Rd+wqhwVTwCN5NmWNenPrKbdNoZ+536okDhN5nsbINN8R05TwIWRp6sSnCYkOE2JSEQiEpGIRPxkI8Fp+n7jzH2aPiZnVOg7IEv9FgAQ86djSQa9iW84Og95RRsAUPbp3kWOxpqaOxAQp4iJjPmJshj6BTrwbLsaS7Mpuxldy2v7oB4qHa2NS2/+2lGV6e/NyOBs3ahQsILig+FhrBNr2RdbLMgQ2/SLLDlXpWLk6h5LGpoOUZbb3jiMectEvSm9knkAUsX0cCjKGeXezZ2sPssuMrNT71cfNPP3pRNSmJsjcZvyx1iY26M3qlgZc7Lexplhb3sUbtH+pBTKUOZeVsKozbkXF2PPF5SByxUK+NyUSeuMKdDqCcUa6G6HTqrZJjyY2hrsp6l2/B66vnFnZ2DPl9R+51AvrGl0j5x2Om7Q74FCQW02WRWIhIkTUVChYk8qo0WDoT469phJMezYSKjTNfeRj03D7j6YJCXRWCtnx61Hh1A4Npn7cfemU6LvCK0LBiJcx87rCuLzGjrGEzk5+IvgGJV+aMOcW0jxp3KF8XFUOJOL8XC5xYo6P42ZsVEVbrHRPSnUaDgDBMBolcTRsyqVuMhC9/KVpmyorKQQm6LXY5dLKPRMGubPdQ6VsAfMzaV0jFqXi/3IHJEIQm3X0sn0Hcj4lNyD+xfEeS1phZ8AAGztP0dZCdVpbOodH0ePwkYUmR103RoNNh+4EQBw98x38ex+4SaeuYn+9Wfi7lIaJ88enIkFYwnlrd24AssufhQAoUqSq7ikLq3W6/lz3VAKKpJpftydns4osCMcZuflCq2WETkJSZhvMjHS5I/FsEBLY+Mjj5PVrF+NjODcA9SPLuEWf94vypmjE4nEYBBzpfHAAH52DXF+eo8Ow1Mi6jA6Y+yXJs2x+u09zJnLH2OB3yP8vBQytAnUKT3PyMq9rz8kXt60hbn83Tfr2rHwanoW1H0TREYete9kvY0Rr9ajQ+xpJs3j1Gw9n9s+EIVGoKHW9PgPjsPmRzAQPq3N0jVIIdXD+2pNM1oOD4ljZMAr+jSrUIMJM7LE8Whs601q9otzOwOspMsrTcJn/6BnhyVVx9tIKj+tXokK8YxY9+pRLPnNeADAJqcT8yLa+LZZwo+vcQRp4ywAwHw3BYAyca2OcBhzhE9TWyiIZS2EjI7m9K0tKQEAXNzSwujsppERHGkj1BypOzBJqJar9XoeV2uOT8X0EuJXSnO3ORDALqfwsNMEuA8vtliYd7fR6USTT9wDodyFPAiZkp65sZ7FuG8SKQX/svNSLDprNQBg/1//DYOLRf3GqBqqFKr5GRqmPrqqqBPv9QqlXd8i5mchdQcWpNF9SVUq8d5xUpvHLrwUP2RISFPS3Ye+F6TJ+ezEM0aaVq5ciY8++giNjY3Q6XQ455xz8OSTT2LMmLg7eiwWwyOPPIK//e1vGB4exrRp0/DSSy+hsrKStwkEArj33nvx3nvvwefzYd68efjrX/+KXKGo/DHizF+aPqTBCm8+kEvFEqdndbI5Wa5KxYO0rnMSMDiTts/cxMVOpcHfFQzyQ7fe52Myqq3953imhoiua4eHuSCvJD+9LyODlwjWORyYLUG/RiMvZ4RjMTbFHJw4EctFKRbJ1NCiUPBEvSk1Fb1iaSo918hy+gnn58EgykS846SJfnWSFetd9JCoMRjQ+iX9MBaflw2LeDELx2LY/Ar9wLUeHcKUuXRjpeWHuctK2fhx1kXFqBPLdqnZesxcXCS2teFkHW0/+xIia3/2ViOmLSJY+asPmnCukPRvW98Gk1VYLaj9XCIhq3ACwmH6MQiIh+PwANkVAEBSihpFVbRf+9EoyqqV4hhTEAnTg0GyQDi8s5cNLafMzcWpE3QvQoG4rNne72VDQZVax8scEqFdrpBBLwz0Bnu8/IIYjcZ4aTDruiJktMXtGADgzbww7hyhF8FNq09weZbUbD1chviyV8sh+mHpLdFgYoCuxWkWy21DIegy6Edpk9PJY7DR7+clqaf7+/khLL0EvT88zPL4N9vSUJZGY3ie2YQTfkE8jUaxy0bLKjfn+9mwckEBLSEXajR4beu9AACUvsjEbWvKUQw33UDf+zPpbwCXOoH5GCal0DU1bHoUoanP0vcj44DUHfFtxRzLmvgkVgvT1/lNtEwVs0/h5YnLijq5PNGDFQNYuYOWF2ZV/4OvW5q7SpmMzTvnm81cvLdQo+GXoydycngpMVOp5Lkn9ecdHR1MDM5UqZhEnKpUYqqK7oVSJUezeCF4M43684K6ECacL0qSfNKOKXPpc9txO8ZNo2UvWSxO3nbNT8XPwtT+L9+j6y6uTOZl7X/+52EYzHR/isZpcORbetHWm0zILKRxYhLFfQe63Lys/MW7J5Enkh+HTSwHgWw6Ok7S86CqJpPtDlKzaIz6vWFMmEHLftvWt2Gol/o/GgshPYfuT0qml0sKOYeoDUqNDwGRxJRVz8TwQJ1opxqpWXrRPg+XXUrO0MM1THM9p4T+fs1vz8Lqp0j0kZ5r5FIyzYeHeB72dbj4ORMQ80dmD/L87jvlgmEy3bdTGzqRcgH1x3itDuJxiFgk/nOxS4g4us8xs9CmORDg5bcGnw+dYj491teHGUbqp6M+uv5MlQrN4rksLcEBNK+kMVPv83GB9V3DKsjU1OeSiatj7QNYd+/7AGgpTxq7bYPFbNvhP34f8sbTHOrbQkvkIWuIRRXXTqjF2zuIfmKqehyyD6lcysiSZ3GnuLfrHA441tKSm/siMrGNjVTgqgIaU2sdDlpKB6CtfAx+YeisrP8N0ubcBwDomfAifsiQXpqsd9V9Ly9Nw89POuOXpkWLFuHKK6/E1KlTEQ6H8dBDD+HIkSM4duwYDOJ+Pvnkk3jsscewatUqlJeX49FHH8W2bdtw4sQJmMT7wW233YYNGzZg1apVSElJwT333AO73Y4DBw5AMWrM/JCRWJ5LRCISkYhEJOKnGpHod16ei0X+NfXcpk2bTvv/N998E+np6Thw4ADOPfdcxGIxPPfcc3jooYdwySWUxL311lvIyMjAu+++i1tuuQVOpxNvvPEG3n77bcyfPx8AsHr1auTl5WHLli1YuHDhd7qm/9M4c6RpHxHp0tRxUvXX/UlMeEP6VqjyPgYALLNYGLr1jzq8tJz2aHY2L4OEASb9Hff74RfGgIiqkZdHS4JPCChOK5MxcrQoKYmzjXqfDy/mUTbVGQrBKQaIOxJhk713UugYMXO8XMSWkRHOsKfFtCx37mlzcomHFKEC/yA0wkstcxrDbGh5NBZEpYwy292bTvHS0+xLSthYTyquebJukG0EotEYL+W1NtjhdoqlriQNZ6J2IZsvrNBzlplbmoRYjOD0sokRNB2i9qvUg8jImw4A2PPlZ0hKoSUuhYCgJfkzAOiNUf7/+16uwQfP1gMgFMgpzq0S5pCZ+SqWLzceGIBKWDioNFFeqojFihGLtfDxj++ziW0EIX+slcmmHSccTEw9trcfMhllFNfeX45jewdO6y97vxfhVOrbw2vbRlkxKKAT9zEWiUEmbBDWDA/jUzHuDILo3h0M4Q5habHR4cByIXfe4XbjllO0HHhVcjLebKMlYIloXTrKrHJjaSkvLXzscOBucbxbTvqRZqTlmEVmM95upBIni0op428PBmEV42txUhLeFnYA3cEgXPvJ6M405TdwDdIypmQ9AHcp8jKozEXn8ZthKn8FAOA6eSs0HQIpOPtlFCUTCtTWMRtpubSMYPOK5YfBmZhVXguAlrqnpxAytOv4EkytoGXACq2WkTdpTvujUR7nVTodPhf9ucBsZkT40exsnler7Xa2DJEMMh/OymIC/DqHAzaR/V+bnMzke6tSyQiyqY/Gw66kCGYIlGLj43WMTqZmGVhI0HFimD8fyAAmdlH7fQKpGer1YLCHxnzD7j62qSgca2Wi9KmTDmQIlFRCcuq39zCy5XUHWUBRXp3GhaclcjhANiFHhGnkWCHACIcLMDJElh1DfWEoxfMpKSXGRXpP1rtQs1BsLxCeHRva+VmQmmVAMED9b0xSY2SY0J7G/XsYeR6xB/hZVSPI9F53COEgHS89z4Bt6wgZnXtZKS/hRaMxvq5zL6JjffHeSVx9TzVfl7S82NXsxM9vGsf9KFmQnEiTnVY6C6Dlr/kCGXBEItDIpbkXZDPKu30mfC4sMCQkqtbl4s/Xt7ejVBy3PRDgMVit1/M8PHJqGrJyaMVDWkK+2GLB2/vpRxe5a+Olg/La8HW3sADI6+XfI2nMjdFqsWn3b2i/wlXISiLEaDgchv8QlTAaP+0RpoNoZDI2ryx5Zh4AQP+nz1h0tM+hBoSNDor/hhuyaN6/Z7fz0uXhcc/hhwxGmu44ALnG+D/v8N9ENODG8IuT/4+J4M3NzSgrK8ORI0dQVVWF1tZWlJSU4ODBg5g0aRJvd9FFF8FiseCtt97C1q1bMW/ePNjtdlhFkXQAmDhxIi6++GI88sgj3+ma/k8jgTQlIhGJSEQiEvETje+DCC7tPyISIyk0Gg00o7ih/28Ri8Xw7//+75g5cyaqqkhp3icUzRkiiZUiIyMDp0Qy29fXB7VafdoLk7SNtP+PEWf+0iRsA5SafnwtyTrL/wNL51JRxI+PzUQoJIrYArwO7Rh1syTL+Qe6u5kv8vrgIOqcotO9ZZxJ0Bs8bX+r6MTlGRnMH5qi1zNydXPQyMTgXLWaeSmD4TDzLF4LivIZfWEmlg+GwzhfRW/gI3Y/bKWUERjtfmj6KKuMStnuqMy4teEUF50dsfvx7TBta0pSM/qyafUJzL2MCI9NgrvhdgY4u8wuMqNiMrUjNcvApVPMyRp0NdOxZy8lVGHPlw6WGLcdU2DSzyiL+eytJkwWpn/tx4NwDhEfyWBOQl4Z9U0wQBli4dhk5jm0Hx9mEuqeTR1MeldrFWyJkCMy30gwyqRRtVaBKXNpHJROSMXRfdTnGk03qqYXAiCzvbFTKZO2Cfmy2xlknpLbGeCSK8WVKSxldjuDKDifMsPNbuJH6Q/YUSK4V9MuL0GHQLD2be6E7xbqj0utVpiFUn+n280I04vZhBp84XFx2ZP9Xi9nji8ODGCWyHLbg0HcUETH3iEQuK5gkEuIvDgwwFyJWUYjbmklztnSTDXCMeqvtzssWFJWJ/al/lTJZLhI2AK8NDCAzs45AECo0BTKcl3N/wZdkyg7s5A4EfMLTgAQJNyKv+FKK42p1WNexXAhzZU0bRBtPYID5S6F4LczT2PpuB34+OCV/F2phjZoLl4LR0QhrtXNBbAlJM0djfI87QuF8EYB9fPyri5UCzTq6f5+zBPZ5o3WFDzYR6ixNKeb/X5Got7IzkefEEpYjTKkiTnb6PdjskfI2OkWo1CpgXyA5tK0hfkoEPJ4vzfMhO5zLizk4803m/HNXuKtdDZRmyunqVk2P3dZKYrF+NnwRj+mzqc2n6yzsRBi+yf0bCkeb4VSTfc+FIpi7JTZAICmQ9tQMn4GAEAur8cpURzanKzhMikGMyGFnpGDTAq3D3ihUBJCkpJ1NgZ7CDnU6iLYK7iTkrBk7rJSngeFc7Ow6S+0bXFlMiJR4j3KFTJGfI/s7sfSXxFZtkuUPfnqn61YcCU9b758twkhUaDW5wkxyjWUr4Eo98xodvWsbOz8jPpg6tWlzNNafMNYDPbSxKqqycReH6F3XX4/asS82SSQ0xqD4TSDSckqZlfbVJyYQ/MqJpfjlu10j96qpj50RaNY1Ezo6tO5uYzauKNR5trt9niwQQhjZhXvh0VBzzOJl/poXx9b4KD5Dlw2/TUAQL03yBxCd7SbSwq1NRIaNHvGG9B1Cn5dVQ96A4Ljte9FZM1cTv18agYenEAk+gafD/sFJ9ReTChdiy0LUtxaNIJas0CP5XJo5NTO5/PyTltt+TFCHo1C/h3NLSH2z8vLO+3rP/7xj1ixYsV/u+sdd9yBw4cPY8eOHf/L32TinksRi8X+l+/+a5zJNv83I4E0JSIRiUhEIhKRiP8xOjs7T1ue+59QpjvvvBOffPIJtm3bdpriLVN4u/X19SErK/7yOTAwwOhTZmYmgsEghoeHT0ObBgYGcM4553wv1/N/Emf80iSpeepcKlSc9TQAWrN2i6wVlnosFfyTHW438432Nc+FNe9zAOACvEa5HE9/8gcAQGzsC7hMKBAutbjw/IAoNxCNMhdFWhfPVak4293hduMakdnWWWMweuLZs4Q0pSiV/4t536O9vfhzH63rP5+bhxZRjFNvUmOLklCIO6uS49lxLqFB81xy/i5tUiqiQkXiGPRxdpmea2TUpnCslbkQAZEln70gD1+tbeG/b1pN6FLfKRfzN6rPzeZMWeI5zbgwOZ6dnpvNpUcy8grQVEcwpSVVh2iUskefJ8L7KgSHakgo1gDgxEEnCiroXtR+1MLy5GkL8hgJk4p8WlJ1fH2mZC0rDB2DfqTnUgY72OPlvhmxB5jXJdkJeF1B/i4cip5mfSD1V1ezExDHKBGZsTdZw4heuV4J+US6h9dNy2AkIzkqx64YXdu9mZlsHSApHXPUaowRnIIw4mVGHsjMxHMiI64tL2cE6j3x92dyc5nv1uDzYbKB+qvFH8Dd2dT+dQ4Hj8Gm1B7s99I1SjwNo1yOFW/dSX3X3Y1Jt74OAKiz5XNhahSuwu4LCc2ZuH453bMpf8emVlFzYWAuXhBqVXTdwpJpW/67jP6ayl9hZZtUhNQXHeQMvEgfxttbqJTD9Fn345V84n2sttvxsOAWSnOstryc+6XB52OV3FelZbi/h1CPar0eXwiOyAGlh/tJUsZ9HfAwWtUri6A3h9qWolMg1U+PnBqjEf5euleynQLlO9eM64TKZ9y0DEapN8e8mNBJc1prUDJi+sVfjzJnqWIyjeHMAiOPu7UvHsYpoXZzOUYgV5QBoHkqcYLScoQ6rTMGl536wpikxuJ/ozHVckSO4/u20X1RGlD9M+rzk/WDjP4CxB/a//UAktMJPTJZ1HAOxe0kdGJ+27oDOHdpIQAqigsAn798lOfrwdpu5g/t39qFUID6w2zVsqL1Vytr+LklKQkXXl3KJZUyC0zMkQp4w4yIfXHXbix/lmT9X60lhCfJqkWOsCFQDgaRcjVd05FIAJZUarPa7ke2ne7VlGIrpLWDi8TKwjXDPRgSSGypRoMt5WQPUR3ehd0e+kHcMjKCVyfQ2G0O0LFSlUpWQPujUZSIZ3t/KIQHhCFqo9/Ppa6Wp6ezMlriOS1OSoLfRM+s3Nx/YOV+QnOh70BeFtkIjNEmoVT4v76vpd8uR8QM38+orBf6zmWzTGXNHej1iALa5fXYMkLjrl38pgDA8KUv0YeGR4F04hIOhXcyl7ax/VxcOplKaxkVCgyP2vfHCFn0e1iei9L+ZrP5jDhNsVgMd955Jz7++GPU1taiSJQ8k6KoqAiZmZnYvHkzc5qCwSC++eYbPPnkkwCAyZMnQ6VSYfPmzbj88ssBAL29vWhoaMBTTz31na7nu0QCaUpEIhKRiEQk4ica3yen6Uzj9ttvx7vvvov169fDZDIxBykpKQk6nQ4ymQzLly/H448/jrKyMpSVleHxxx+HXq/HL37xC972xhtvxD333IOUlBQkJyfj3nvvxfjx41lN92PEGavnzjq2HADgi8U4qzDK5bz2rJDJ4BGoTtsTy3DZn6ikyg63G/tayU9mVimZYhrkcmw6ScUP03K3wuYndEmr8uHOUUociRcloUtbXC5cIWC6SXo9I0r+WIyRrb5wmNUdK3p6sExsL31nC4fh/5JuYNqibLw+SNnZEzk5sLUR0tGfpWJFnKRICQbCjLhsW9eKRpHpXf/QFCj0lGn3nnRi6xrK4KbMy2X0RSrG+48nDmCOMNAc6vEwtwcAWo8SwhH0R5jTtOwOYiB0NjvhE5ygjpMOVu41HxpkYzx7vxcpAqHR6ZVo2EXoXShI1+F1uaAXChe5QoGUTLrtMlkuOpooK5pxQQHzl6RMfKDLzeVZyICSUK65l5UwGmVM0nDG/8Yj+zFjMWXQVTUiK68bxLQllMHuWt8Gl5Ou5fB2G5beSt5L+eVW9nqSDA5LJ6QiIAoB6wwqLkvzhSHI6hlHJMJIRxjAQsHt+SaPkI4qnY6zxFSlEsNi7L4xOMiFm1cNDjLvQOLJzTaZ8IooPFqh1fI5HJEIn/u9TisWZdP42eTwAh002a0lVL7EF42iewLdwwe6uxk5WedwsLpsY2kpZtQRmiMZSd6Wloa7jtK9LEptZbTUNlCNJUXkR7ThxDQsrSD+0mA4zHw7yYCySqtlRdyzDeO4yOjNxYN8jY1+P6NikiHsvRkZzG9q9PvxuuA0tQeDrEDSymRcwuX1wUFcn0pKTUldV6rRMCKwzGrFlWIOaoZCcCbTua3OCN4DoVvSsXSOMPN22o/bGS0Jh6Ks6iquSuZtDtR2Y3wNzYW1L5H56GUPT0bLDrrWLR80ISS4KtMWZvGc0BqUzCeUjjvu7Azm/ny++gQiYt6fc2EBTojxWFKVwr5i0WiMFa1S+Z+MAhOaD0mIUhMCPhpTCmUMkTCNR4M57i1jFB5RqdkGHBWFvMdOTmf+nzQXAXqeSP5tlTUZbHopeZ51nHTwfAsGwjjvKkJ79m/tYm+mz/7RyCWfpNi7ufO08lCSt1xTNMilRTTBGCIa6sdNTicmU1NZCTxi92Nlqigpo9ezArI/FMJxgQi1BALMC3w7lXgxkzpP8nepSiU/qxv9fn5erx0e5iLP/miUeXNaMX8e6O5mtGq/1xv3WwuFeJwrEUeKpPPVGAw85l3eVFa+TZr8DO+3eedD7KG2JMvFaKz0LJhpNLKJa6lWy2q9Rbk92NReLjrJzYrcjh/Jpyn937ZBrv6O6rmgGwN/P/eM1XP/O87Rm2++ieuvvx5A3Nzy1VdfPc3cUiKLA4Df78d9992Hd9999zRzy//KrfohI4E0JSIRiUhEIhLxE43vkwh+pnEmWIxMJsOKFSv+WyK5VqvFCy+8gBdeeOFfOv//zThjpOm8k+Rs3B4MommYsrRrc+MFDafo9XjhFL3lP1EWY3+aBp+PXbylNzR/LIaPOygryktp4axit8fDFvu7R3GWpDf8e7u6sKWMeAkv2mxYIQhkqUoll4NwRSKcVQyFw6jspZsdKKSsuzkQYDdyV5cn7pVijWBKN3WFVq9EWhFtI7W95ZMOzvyC/gimXk/tiHX6WImy/6suzlbzyi1wDNL3koos6I+g9ShliEqVnDO1uctK2ZtFq1dygV8p0z5ZNwi/l7J4S6qOEZmzZudwWYSethHmPeWVjeVSLFJB0tYjw7jwekJ19nzpQsBH2bPfG+ZyJ+v+dhQaHd0XhZLaaU7WMofEmqFHb5tU3DcT+74iNGGox4uzzxOuw6dc7JAs8TRG7AGkZlO26HYEYUmLl2fIzJfQLxn3b365RewfhGoM/X3LyAiu0lJ/HJeH4PqC+CKReWmYGqGMeDN8p2WoACFD0jja7/Hwvc9Vq1HTSMqY94uK2P+rREtjeL3DCb94UAwPl2FBDt2TTJWKx9f21inMK1pQ+SlnxEcEP+q2tDT2I0tVKtEpkJjGES3yDIQuSe0BwHPpIouF/ab80SgjMQ+1hvFIEc2rP55IQloyoZq2ww9g1tnPAQAuFNy9B9o8KDNRO7RyOc+9Ok8IS6x0f+5IT+diuhKqq5TJTpvT0ufZJhMju+scDs7Y3dEoHhbz8GrhAr5qaAhjpbIakQi7O4/d54FtOrVvdNHlWRo6t00WgcpG51BkxNE9WQw85tuPD7PybaDLg8t/Q0ieNNb83jA7jRdXJjOv8GBtN7t7601x7zEJ7e1pG2HkyJSsZWQ3HIoykjS67E/7cTsCPmqfMYnuT8A3CLWWejoS1mP8OZTdH/nWDZPwKALAnD6p8PA3H/fCZKWxllOaBJsoYp1dbD6tGLCEJO3f2sXI1EXCS+nQzl5MFUpah83P13V4Zy/6xfNnfE0mzzHp2ZM/xoL6bTRG515Wii88AjnRaGDopj5V5uuRrqDr6gzHERxPr/CAc4XwoZX66+60dFbavWqz4RahVK7wyXGfR3jNiVWB1wcHeW4a5XL2UKtpbOQi0Ps9Hh5fyzs72fNrqhivXcEg8/m6gkG8fYyKaS+t2MvjbsNQCNOTCPnYted+2v/sJxhJsygUjAJ/3JHJTvs3TP04XkR4FLp6+zdkqvjOvK9wY3s7AOD61BQc8NB1D4bDXNQ7LXMvLrXSfi/nP4YfMiSkKevar74XpKn37XmJgr0A5P/zJolIRCISkYhEJCIRifiXkaYKrRYv9FMWf0NaEupF7bkKrZbRnlqXC5NF1m1VKDgTmCKygwe6utAZoNO+U5KHq1soy3+rOBeP9hKCkKlSoVJHmegH9mE+h6SaWF1UxJ8lfghAagWJW3FHWhouEgUjCzWUVaxAMlTZhDrd0dGBdwppvf+pgX78vJOyEa1exXydxTdQMdg6vw+TtLRfMBBBqyj+OTzWwDXT8sstrHzbuqYZWoNQn2jiPAYJPfK6Qqg+lzKoEXuA0aWKyenYtp7qxkkZqVqrOK2WlKQ4mzIvl1EutyPI/AYgXkRU4kflj7Gyf9LGv7cgFqM2L7lxHNfAazvmwFlzKbNtE1wPryuIfOGXMzyghc5A15eabWCOkfT/0r8SD0zqi65m52l8K+n6UrMM+MW9pJw4WNuNjHMp04y1EgrTnatiXo6zxQV1ER3jgNeLFJHtdgeDWCoy/YEuN1qS6R5OAiFG7/mdPP6muhXYoKH+ujApCSt6KcMeq9UxN2GaGKMqmQw5Yr9VQ0PsL7TMasU1baSWch27F2UTSYFzscXC41Fyxp5tNDIq1XTqZ+xwvygpCZsEktTZtgRvzSQH8esaaBwvyLJxYdvOYBBHBXLVeeRu/H7O2wCAPx9Pw93lglOmUODPJ5IwOopSWzmLXzU4yHOvORBgROgdoRQEqNAqQEjTK4LH9L7dzpl2o9+PmwR3qSsY5Cw+U6XCfJF5ShyReSYTLrcQ0vFPxzC6xXy8LmqEJ4X6VAnAMELZvSmFxmrIH8E6H/XddKMRB/5O6tJp/1aOSDuN7R0b2xjt0RtVjKh43XSOEbufOTqtR+0onUB9kJJlwJDwHRro8rBPWbKoS5hdbMbGv5Obd9X0TNhEoeiC8jjv8Ni+flbMhUNRfPVPmjcxod4cze07trefi3MbLGp4HDQOJszI5Hn9yetUp1KlVXBNN+dwAAYj9VHHSQfPsTFT0k+r+ybVn5SOYU7WsoP3Kw/txnm/IE6NzxOCRqBfSpUcH71M3K9rfkvoslqrYGWrKluHQBddiy9LA0M/tfmoJcboUnMgwPPpbVHU+4DHi0dz6Lrft9vxGOj+fG6M1xhtDgSYPyrxjhaZzczXU8pk7O+0OCkJ7wuk+JbUVC74fl1rF25OtwAAq2cfzszEHztpLl2WpuHqEvU+H6u1j/v9WCpQolsa6Lu05GbYDlM1i6yJT6K3fyJ1rD+TFaqmwn8yemxRKBgZlcb+s61GqEyiJqXJBJXg8SyzWk+7ll1Cgfp52V/wQ4aENGX/YjPkasN3OlY06EHPuwsSSBMSnKZEJCIRiUhEIn6y8WNwmn7KccZI0yUt9/NniduwbwRYIjLHTJUK7SIr3bb+D5iw+I8AiAvxgnjrfkaYW71gsyFLZCtjtBp84aTMcEV2Nq9339rRgXlmylKezxWVz30+XCaQo9oxY1Cy9gIAwM6L1nPWc31KKiKjLqlEZDUSkgAAF8goe3AZ5IwEFKrVUIhMQSWTIdJPqMFwikA0NnahYgohNfssUVR2UnZWVJWMU8coK5IrZGg/Ttl7OBRldcyODZSNXP6bifjsH8SjiUZinCUH/RF23x0zOR0qVdyZGAB620aY+6PWKtjTKRyKwi0y2Gg0ir5ThOzklCbhuOA8RMJ0jv7OU5yJ9p1y8bnrt/dwv8jlclQKNZJHKNz2bu7kzLiyJgNWodY7WWeDUkUI2riz09lJWKtXMq9D8rIa7PVAL7Ln5sNDKJ9EiIXD5keJcAEPNLn5PG2iD1My9WjcT2Mn5YJs9v4pCCq4Snuty8Vcg1qXi92KJRf5ZVYrMsSYCkSjzGPY5HRy5tjo9zOidZPYr6lzOg79jPpwRW8vPrZT396QlsRjpsZg4DZtGRnBlQLBueuU8OdpfABLzyHFTHsggLrjl1Hbpn6GV+rOBQBMLd/EfKImMX8isRgaBGpVodVy9rzR6YTLTUjg3YVBLBL8pcFwGL98998BAH+9grLZR3t78b5wLp53khAbAFhisTAfSSOT4VqBRknzbnlnJzaWksJzUXMzK1dXRpOxJznK/SyhSxVaLR9vsoL68xmHjdEIfzTKFexnm0w8D69OsjJK+rWKrrtar2cekykiYwSk8cAAc/5qFuYzd6+nbYQRIwkNVWkUqBTq0pP1NnboX/3UQVRNJ56M3xNmXp3EK/rsrUZ25XbY/LjybkIeXnpgF6rPpT5vPz6MXz4+DQDw4eMHWbkq8YPCoSgsqXTc3FILwiFqc8PufhwT81GrV3JbpTl9fH8IKvUIH2vuZdT/B2u7Wc32wfMnseTGQgBAKBiFSriXS07p7ceHkV1M90Qul/HzZPqVpTjwcRtfq4TSScpepUqOlwW/buFeP6Njw2fHa3uG9g8zQm60aPh6IwLgf2VwEBlijF6sM8Mjynu2B4OMqN6alsaKSkmReWtHB4+daQYDIzXuSASvCFTqrvR0VoZ+6nTi7T4ag4tS42PR330hAKCsaDOeE6qqp/v7GT2dbTIxovVaIyFwX0y34Wkhg6/W6xkFGz5+JyZNfBUAUDeUgifK6Bn2wJEszMqnZ7f03PBHo8y3beuYDehJWZyV0oJeFz1fl2YGWUF4vFL4Qv1AISFNuVd+8b0gTV3vL0wgTUggTYlIRCISkYhE/GTj+zS3TMS/gDTJtv+ZPoSNXNNnTtERdlKerNczwuOPxdj/ZWWrEo+UxD11AFrflrLqWUYjZxJP9/fjCbE2vsxqxdOinpzkzVHn9fL69g63m/drDgSwUuz3ks2G28OUwXky1IwESGvMT2bnoM5PbXaEw/z39mCQ/WTCIOQAACYP0d93b+pA2i8LAQDjfQquIv7zm8Yx/ykaibEH0fG9/Zj3UDUAYGg7nTs1y8C8o2AgwuhLZoGJVXxvP9mAmx+h/d59ph4AcMF1FTgulD2N+1xISonx95KnkccdQsshQtsKx1o5az60g74Lh4LIyCOErWp6JornU/bsPRl3Iz9Y282V4EfoH6g1Icy/gpSCp0462L9m/hVlnM26HAF8KHxyzr2oCOdcSP0kcUu620bYdVitVaBNnGOox8PnPntB3HdD4oId29uPylnE+3rRZsN10biTuM1Maa5zhw2+aRYAQEF3GG+Y6d5K/Jt1DgeqRGZYrdczF8IRiTCCk7dhEK6lhEJIHkxJCgVu66DMcW1xMat2rmzphHcsIXb/bu/Begdl0tckJ7Nir81BiOSCDCdf0+buHGQJv5bBcJjbNNtkYuRKmh9fH1+AOWM302enDxiYCwB4ZNIR/LGF7uuSLBfPoe5gEC8Kl++VInvWyWScXc82meJzz++HT8yh2SYjWvx0jLeFY+/dnZ08dwfDYfavWma1ct8oZTL2ybm3qwvPCQRZ4j+5o1Go3qK+27UsBbcKBRUARp32ezy4yETohaTYmhWNl2TQm9TsV/TtZ6dYUak3qeFy0HkMpnidOYm79Pc/78Nc4YW2/RMbXA6631cur2Z+XzAQwcAohRpACtXrHyLFU+OBAThsNI4saToUV9Lz7t1n6rme5GgeorTtjCVFjDQFvGF2645GY4zUKFUKHuvbBQId8IYZZQXAPmyfrz6Bq5YT4tWwuw8jdrruwR4PVGJ+SzynKfNy8c//PAyA/NGmCCWdXC7jOV01PQM+i1D3CY7YBksQF9ppvgX9Ea7V17Crj3mRbXlK5ibV+3xcIy7FJjyWsg14x0nnqDEYmNNnlMtxKQjh6Djp4Hu4UxUf7xLvaMvICF4QvmhHx41D9XHil71WUIAWMa7mmUz4QhxbQoZuT0vDq+Jzg88HWx/VY1xU3MBeSR9OsePSg9S/47PI50wJMG/wc6cTT4oxvGVkhFWzDX4/LrLQfZtlNPHvg6W+nq91c9N0AGJuCm7VnVkGRmJfHBjg+X2yimrT/VAhIU35l26AXPUdkaaQBx0fLkkgTUio5xKRiEQkIhGJSEQizijOGGn6wP4EAOCE30+VpQFcabWyA/Gj2dm8vnt9SgorjC62WDjjlRQY+71efN0yGQDwxtQ2Vh3t93o5+2/0+3kteE9FBQByeX1CnPvNYCoeNDj53BLitdHpxHLhKt4VCrGSbkmAsqbULAPzI1oQwhgVZRseWQyyEdq2ThnCe8PEq/m3k/ReOW5qBme1GWMtiAhE6Zv1bSgTWe7WNS0Ydzadu3pWNruGS0jUhBlZnJV2NTsx7t8JsRh4/1S8cvsVZawuk/gYZ5+XhzUvUhaZVWBiHkd32wimLSCE4dvP2jmDXfe3o8yncgreSNmEVOZS6I0qyBV0DHu/l2tdtTbYUTGFUAGJTxL0RzjjdDsDXAtuyrxcVva0Hx/mDHXdq0dZuSP1l9cVwvgFlMkpAlFW1zXs7uMsWK6QMcfDsIAybYtCAVstoY3RaAy5JSJbV8vhyqRze/YMobeasshalwt/TiHE6FM/9edujwdPZhMKubC5iTkPtS4XrtNQfxxSBBm1vEP4NdV7vexQPNNo5LE422RiNdtzAwOMaPWFQpgvsvFbBUJlUSg445xtMjHaOUWvfGUPegAAKUBJREFU5zHvj8WYkyXxP5oDAVbojFaD5qrVPD+SlAp8OEz3okKrjdd6E9taFQpuW6lGgxdFFt8XCjFPyaJUolIgXl+JeRyOxbBSTuNuqbeb69RV6XRck67GYODaYA9kZmKhga57f4D6BQDS2wkdkBAWANjgHWGX9U8LShBTUZuHOmicSOMQICf+EypCMhr9fq5q749GIeuhvutNV6KIdsXaFwnpnHtZKXzCxykWjbEvUXqukTlUepOaUY/VTx3k/daKOfbzmypZVffy7/bi9ieJx6RI0SDQR9fYsKuPOY5dzXTfzpqdg5cf2g0AyMg1omYh9V1SiQndBwkNOVDbDZdAjCTfJb1JxTyhzhYnhvsIBSqdmMpo9Mn6QfZb+tlFRafVuASI3yWhbRvfPM5eaX2nXMxlXPvSEcy+hJAyCRFr2N2HvAtobqa4osxpcgz6EE0Xz5BIhMcXEH+OO1tozNwuH8SHeYRUbvCOMC9ti8vFc6I5EOBjSIo0AIy43tvVxfPAH4ud5nV2vfBCqtbp+BjSOFpVWMjocalGg4WC5/fO0BCjq0Bc0SrxkQxyOXsJLrNa0SXmzcs2G3uMXZ+SgosOUD8uyGvDZie1KTKFvMGeGxhgdLlUo8E+oSSvLS9H5ia6949MPoDesDj2j+TTVLh0/feCNLV/fFECaUKC05SIRCQiEYlIxE82ZJEIZPIftvbcTznOGGnKPnwHAMqSN9gEz2dCJt4Q68kzjUaslrwzsrIYqTnfnMRqOym7XpSUBNl6UhjdPyXKWeSmkRHcK1xh/dEortVZAAB/GaGs4srkZHZxfr2ggJU4t6alYayGsoMXBm1YJjKW3R4Pe4JIqqn37XbmYyyN6lGnpUw0rzWI99KpndeN6E7LkAHA7QjwWnzavhF26vUpSUkDAJfcVsWozM7PTnE2191CmWhOSRLaGykzaaqzsVeK2xFEzlhq886PWqES6rMTUm2rszMYXXI7A3yO/i43K+28rhArgXpaR7giupSJ+j1hyAS6VDYhhZGtY/v6ERG8iJQMPSbMIA7R14KP0d3sZI5Feq6R0aqe1hFGBvzeEKvwLKk6zqAll2a1Vsl94UlRoffrPtHmIM69mBReETlwfCd9nzedMvj9Xi8ry1p29CGphpAT96jMd7/XyxnlbKMRa4VXkMTLkTJLgMbfRvH3Kp0OE0XGa5LLeSxJvJzq/6e9L42Oqzqz3VX31jxLVZplSZYsybONjbFjHJwYYze2GYLdCQnEZCDAgxB4oR+kCY3zSB7QJOtBIA1poHESMzWTwSHMo3E8YGN5lAdZgyVrqlKppJpuzf3jO+creb21Xmut8NKv6bv/2Esq3Xvumep+++xvfxPcsCeyTgOZDF4V15jI/GwJh1mfIbPotkYizBL9tLISe0UkWqGqnB1UbzZz5C4j9Puqq5nxWuFyscdMRyrFEfaFbjfr7mL5PGtOZL8MZjK83kLZLDOx15SW8vyfZbNxlqF0Y45ks6w7+lZJCWzis7FcDruE30wsn8fVPnpGqWsDgEyA1thgNstsVi6dx840PbdfVfHZI6RVOeem6fANUd9I1rDebOasQRWA53SKry1dq7+0po4zxlSzEaVThOv2h/T7shon+3nt+f1JZl+720c5G9VkMnIG58uPHgYAXHRlM/qrhT/Sv5zi+351fSOO7qF12HEwhJhgmKsa3EgKl35FrKvaZh8axHrr6xjjNWE0Gnjd+CsdePtZymaUTFR4KInlX6N1cKIthI4DNDfW3zSHWepYJMX1KUvK7cziSsbMqBiYRTIaDXj+1wcAAGs2tsJsofn1dHoMM3bTeFWtpHXuVRR0fUz3eL3ViFWHRK3HZX5eO/uTSWZX681mPjmQWp01QwreKqc1+G40yutxc309rhPz61q/H+eFaS4dCVB/qQA+FKcT9WYz6wbrzWZsEOvtlt5eZoFm2WxcZ26L8BhrsVrhEnP0Bn8A/xqh/fX+wUHs7yGG8NZZR/kURM7tDk1jTRNQzB6VawYgBkruARMhfZeerKvjrNu98ThWtBP7utKncD3GzaEQr7fXmv7x/7jW/0tIpqnhkpc/F6ap67Wv6UwTdKZJhw4dOnTo+MLCkMt/DkyT7tMkMemXJplRYDUasUTUGVr8fhPeu4AGoy2Z5CgxlM0ioNKbu19VizWAhGZjeT4P9RJRYd5cdJW+y+FHl4Xu8+DwMEcF8roVJhOuElH8/P4CIoLp6EilOAoGipXeFzsc3G4Z5V9VWoq94rNJqwrfTopMouf68H2V3qD32hNwgf7OEKZnTftMKBVanJomD/pFDTYtkcF5t1FV5j1be3H+OjrbP+fiKdj9MmmTpD6otMKOxlmkOwhUOuCppPY/+pPd+PHDywAApgmszGU/mMnPJKPTiikuzrTx+m3sFL7rrdOsN3J4zewqLDPYLvn+DLz/ItUqe/uZE8yk1U8vYS1RfoIGRPq5LFldB2cr9cunT3dwRpPVYSrWkxtL8+dLym3sgSMj7Ys3tuLvBigKu7XbxfXyzFaF21w3w8fsmByrC0x2DImo1emxFOtO2e08p1qtVh7vWTYbvIK1kS7x0Y4o4nXUnr3xOH40RhFXQ20JR7CfxGLsebT8+HEAwL3+SvQ46H5eRWHmpMZkYlZqudPJc9SpKDzXJKu51OnED4W+7opTp/A34h6bR0ZYd3ff4CDPacmO3XHmDM/n1W43Z5H2ZtLYEaOfPxYKIT6TMquu6O3Cg+Iz0gX8uXCYs9bOdzpZFxLKZtkdfFN/P+tLZH/eaivBj8aIeXg6HMa6AXo+ZbqLNU3XBwLoFToNj0XhcVNEn8+z2bBJOPvPtFqZkfhuzokrb50HAEgZgZO9tPam2L0AgK6+EAJO6rt0KoeRRupzU9sYeyWVlNvRLry7gn0xzBJMq/x9NpPHUIHGYd6yKmZnju0dxvqbZgMAju4ZZh2SZKLKm9xwC63Rrs5xzqo7vGuQ129JuR2LVxM7lMsVYBNZnjIrb/qCQLHe5ECc3cNnLqnA0d00Pt3HRrHsUmKVFl1M13rpwQO8n6S1LGePdhwMoXSZYF03fYa5d1L7q/sy7FUl7wFQVp3Et348n/txz9v02e+snoLuGlH1QDCSR17vhU/ot273+TEsRGL920P4ZAmNi1YocN3HPySL7KnMrHS2+vCu0PFdHwhg1X6RZVlPbAxAa1ruW6qRxueh4WFeMxe63ezZ9M74OJoOEwO4tbGRWaJoPo8VH1AfPLdsP7Vf03BA7BdbIxFeQ9/3+7HLRhq1VqsTfxTfPZLN3VRVxTqnX9bUYLNgfnfF47wv3GaxnHWaIbVTVwoW7LFgEDPFvlBvNuOuWuqjmVYrrhUM2yNTprDO8D8Khs/B3LKgm1sy9Ow5HTp06NChQ4eOSWDSmqabT/8UAPDw8Tq4/KTh2VRVxRGL1Wg8qwq6jPg/GM3hoQaKfm/w0L8PhIf57Hixw8ERy2PBIPs0yagDAC5NigwfZwYrkxSJPmwYZ53GQrsdt7roDPnxxCjahQ/TXRWVzFKtdBKbsm18jM/k55xI4/R04eprMGBpge6zbqgbT5uJCZM+SDVNXoSm0u9rTSaumxUbS5/l0yQjP6vDxDXnJOMSHkowI2M0GvC6cAef+6VKFL5K7S8/meTMGK+f2rbnnV6OiP1Vdv592/Z+9lA6fTzC7bh4YytiwsvG4KP+ygxrrMdIRNMc9UVCGg7uIFYgUOPEzj91AwC+fQdlN050K6+oc3EGUrzVicphkd20d/gsvYXURUlWymxV+LnjJiDeSZGju8TCTuIv1BdwVynpauRzlNU40VNOfdibTrO2Z7QvztmBBpeKQjTL/zdlhFeY0JvsVNLoEHPg+kCAa1o9EQrhlRLq02O2PFJiTsgoc7nTybqi9T4fR6hOoxHXiaj6N7W1zHKFslmOSneXEZPwcDbCzthbwmF2zH+ivh43iWtsbWxkbcUVwu0+oKpcrd1a+Q4219cDAKZbrbhdZK25FIXvV282s/u31Dnd1teHLcJ7aUlcxQMF6udILsf6rHqzmSP9N8Tf5QqFYkaQ1cqM2K54nPVlvek0O/qrBgNruRQRjB5JF72gfIrC63RpVOXagPVmMyoFGy2zWfO5AhQ79fPubT3sNWS1q3hzCzGA51zZiD2/J6+d0cvK8XU7Rfc7MkIvZjIhf4BYBZPZiJpGLwDgtSeP8Hpqnh9AXLBYA6/QGM9ZWsnsU3gogdYF9Nx//lM3Fq+iNd3dHmYmNq3l0Cu0il+/mbKp7E4z+k5RP3/wYgQ2p6hB+LVGZqtiYym+hlyjAJih6u8cx+qrKftUNSm8h+TzBWaSkoksukTty/U30b1PHR5hJ/Rj+4bxp9/R3nLNnQvxovBQ27BpIXzG4vUA8pgq1qkcw4+tNPa3lJWh8C4xeqeWeVgzF0gUkHDSNXYIBuhyj5f3pPcKCWZ7XDkD+88NmPKwDdDzttNWgDfHx5npvNLrw4/6aCy2hMPY2kj6rA+jUVybput97M5yO+4XWdQXut1naRpl/cO9iQSuE0zr/YODSIp5fq6Yw8Fshk9DDieTuL2igsdiRHz3aPk81nfSacHPq6qYoZU62ceCQWZz3x0fx3VtNGd+MasfbWLOexWFdbrN1pvx14TUNDVd9CwUk/3f/4P/C3KZBDrevlLXNEHXNOnQoUOHDh1fWOiO4J8vJv3SJN+yr2/pRipPkWq1yYTjIoo/3+nkKHe1x4P94pz51ionrszQW+6Ng1R7yK+qnFGQRfGcuW3GDM4OarJYsKmfGJoO8WZ7n68amzRiRW4pKyv63jgcHIXdNC2Ay0TEvv2f2jH0TWI9Kg/T772zi5Xn47NdcIoIxKkonLX2an4KOg8Tc7JI+CAZFQNyBopAKkwmxHzU5uPmDEeoF29sZTbn4I5BvHMuRSR1wjn46rsWcn9molnMFnXejn46BK/QNMz+5jR0CXNge4gm6uqrmjHiov468M4AAkJL5C6x4uOtnXxNmRH0+3v3YfXVLQAAVUTx4+EUBkTGXEbLMlOjmowcDZ5z8RR8+BL5a8nIWEakAEWirQsosupuG0WFyJJz+6zwXEyaEl+XBncJXXtvNbEKG3w+9p7p6xiDdg6xAxWqGYV59P+Np5II+umeMgvQUevAXqEjuCkQ4ChyfrTA/k4zFpVhv0pRbkvOAJeohyfb7/eoWCx0Pt/v6cFPK2k+DGYyOGaj5341EmHXeRlFfhiLcfbQPJsNdWm6bmI8jd8K76JYPs/z/KXGRjj3k84i7RNO7xETrjtB6+O56QGed2+OjXGGndRVAMVspGtKS+Gf0wYA+Hn1PLSJe2QBvDaFGKWd6QTrrO485sYnZaI2npuuu7jFwRlGIasVEElBaz0e1oisKKvAq1Gau/MnODNLf6e2ZBJzc8RUHjYmuTbjq5Eig7ba4+EMKNlfhX0R3F9FkfaTrkpcWkJj/JESg1cwHbnuBKKNNM4RQVEdTidRmqQ1GF1ewjqf3DQHHJcTA20ezWDB1eT43b61B7FldM+WBM3TfC6FmMhmS0QziISoHWt+NAfxXtoDZPYdAOTW0hx2OB3I+GmNnTPmg11oq+Ytq8LpEzTXvAEbz6vwUALrvjMdAPCOTWg8rSYE+2mszlvlQCRI13vuwWO46EqaM06PhdebZIk/fPkU15j7ONoJ00zqr8KJGOsDLZ6i+3nLfD8/l6zvWNfiZc+puTdPxw2/Jo3kNd3deHYTuWR3HAwhKda9dOIf8SkoEbqw0lYPXih4AZA2bGQNMbFLVJXZ+UMmDbOMQsslWNloIY8tqQg9k9vNe/vj4RB7oRl64thWRn1XD2rDz7zlWNNP+41fVZl12lRVxWt9lceDB8Q8vs1Zjh8K9vfnVbTf7IrHsbGTvleebKjmOfhiJIKHhM7vQpeLTzbk71UNrHN63V2LBwS7Wm8247oSmv+7tQQzXk0WC69Pea0L3W68IP5ug8+HHUtof13aPowjs4gtfDhYPFVpLiby6vhPjEm/NEkx6mq3G7Xii6nSZMI+saFfduoUpy1bDQb8QhyzdadSuDdGE0seC5yrqXBU0gxaeOwYnhmnjd62yIDvKfSC9J2RftwkjgakQeBtfX1Mdab6EpjnpUXW0p9DtUjp/UZXF9YKwe1F36zCs6CNRn4RN6tWnB+gt5Jd8TiWOej/bdv70X2eEGln8lzuRGJIycMpJGCbBgY49XV21IgykTpcXufCoU/opc4bsOKWMnrB8P4DfVG3JZNQ9kW4PdJI7rIfzISrhl5Kj7x/BgMLqa0NVvq902vB+08cBUDHTi5hKjlrcTleFT+/4obZGDWLDbTJilgnLfBkM/XRqW0D+Op62gByXhM+fZ5eLK12lQuLHtM0fOkhOpbb8wcSMh65vBTXNdI4aKdiCNrpC8npsbBpW/tbPWymZwzY+LmWmGlzjUdS6PHQ3/W2j2KxGIve8ixG36L+alldh4Mf0/+lvcJUu3pWKvCpOymNuumXi1A5TRT3zWYRSomCsYkCHk7Si3S9hdrwNxYPG1NuqqriIp6xXA71I/RFcKcngIdG6eVMCqm1fB73lNO4xQ0FiPqo+Fk0gu4g9W2HpuHJIM21aH0O2XPIQmL1STo++mVNDeb7KAioNHm5BJDJYODj52tKS3ltHZhOX8KbR0Z4zu+Nx1mkPZjJoDpKY9VSacUFh+lZH5qp4Fse2uhv7acvkMVOJxaIL6FLzC7cKF5s3h0f53To3VqCv0Si4ktxscPBAdIChwNvpelFo8li4QBpucvFL333DQ7iF5X0BbYzQZ91LvBiE7wAAK/Vho6D1Le+sRSvQ0+TG/EBWmMP5ml/uFMt4fI/z2YTaG2mPiiP5lGaoQEY7Iui/0N6mTp/XQOOFMQRcI8ojRTSuLRI1VQ3PtnWDQC4eGML9r5HfTP36ibsEl+Ycq8wJfOo7acxKZ3mQZcImmJjaZSKue0L2Pgo+6sbmniPWJMXc/TDIUwXQUVfxxgXtP7B/5yLAZdYN0MZth+QR1onL/Cg1EHj0Dw/wEarH7SH2TKkfc8wm1eePh7BsrX1AMBt6DwywuWLKq1WPmJ9qrSGk0HKapxcfkhUG4LttIbP+kgEPX15FV7+JxJgN80p5WP2fKaAITGGoWkWfumW6EunUSrkGBNlFX2ZDMYTNGe8ASv8Kh3PzY1QXwQrCnijloKAHw6f4aM3LZ/nQLo7lcJ58lg4k+HgpjpFz/FJLIaXmmj/bbVa8VMRaK/1eLgfL3V58OJ4hJ5xQiB093Ea+zfKNDhTRdsCWWZrYhJTWzLJQYpM0vhGSQkLyK8pLeWfH5nVzAa3myor+eXyPwpGXQj+uUI/ntOhQ4cOHTq+oDDkcjAYdHPLzwuTfmm6UwjlfjU0xEccsXyexajrfT4WAPpVlUWvVqORo2ZJU46qKl7tJiHf9/1+9NfQ239jIssGkvdHPTAmRGkIG72pn+90YmgnRe5zv1yF88VA/jEwhooJaeCrRkSpiRYbFgjavl0asiXGcKFCEc08ux1f7yZ6+Kq5JfCLKMkUTKN8DTFl/zsS4s+6twuR5OoaFre+Yk5i41yK3OORFMpqqQ9ee+IoZs+h+yyxU6RUOZxFhYjeHg0FsUpYJvhqHByNNC6vROgZsgZoE3YCjZdMgeObFE3V7BnjaHfXW6ex5O9JCPrxK50cGca8edTNISZAmh2aFgRwRvibjb1zhk3xRoaS+NubKXW983AY1eJoYFBEtReafciPUtuMLhM+FWPo7hpHi5ciOYvdxOJWa6MTap6itrYEjUnlvijfr+GyBngCFLGdSRZFqO+/0MFWBNOX0lxLjaUxt4/GbepMK0aEINd2IoGd4shzw4/msDh6eYMLK010DRll9uwextgsevDjmsZWBGuO5bFvAV17lg3Y0CWO1Mqpzx8ZHuZjhlgux1T+ep+PU+/3trZisIae++2REbwuPvNWNUXPj0bD2CvYoxdGR7HBQm3rsmeZ6r9vcBD3VhBT87RgBzpSKb531GDgCHzlyZN4QRNzza/gpVYa43qLBWu7iDm8VTCxTZYiI9Cr5vBLUZD08VAIMw3UNx/lE6juo7GtFWnnAbudWYqFdjvWWOneLyXHsUqsY5ei8Fpuslg4CWCxKKXzaSKBM+L5amOAZQb9nXswhR0u6q9L80CHj57xyj8DAGBfbMb2PK2rVUMqoqfEsdhCP19vSy6Db+eoTb+LjWKjk5iY56fR+AVOROAV88vuMjE7YzQasejbdBy2L5HAkpOCEREn5pt/sRdrfkYs6+43T0MTpVjK11SjIUdzQktk0L6X2ITFq9MoVNHc7f2Mnn/aiipk+qn9TXP86GoPi3aY0SKOwP58YBBVUzPcPoCOkA1txVIsvcKGY2yln4/+fQEbm1RWLA7g6LvEqLSspH2qJJTkJA1TNoslvYLVnKMiP0D/73UCM1Vh6WCivbNqqpuP049pGuZeS8dKnuEMenLCWiCfR1+LSK1XVWZuNxppHI7l84iL/dWrKHhOHKe1Wq3Ia3TvpwpRLDXT3tjXQePqKw9ga5JYsO5UCrOEqPrFSARfF6cSz4+OMqtflzBA89Ce+UOx5q8qKcFi8ftvdHbiKQftgftsOd4Dnhwd4eNkaRtwj7scoVZqcyxoxCMFYYfjcsEi1t6WcJiNl58Lh3F5Be0/P55QOkWuq80jI7jZSXNtRc8p/o68b3CQ16+OLwZ0pkmHDh06dOj4gkJnmj5fTNpy4F9C/wsAmeDJ8ia3hwcwKNiZtR7PWSVLynP0tr6k6wS2WSgaet5ZjE5l6YhbenvxYxEdX2ZzI2ehv9sWiXB6qDzfPpxMst7ixdFRTpeeZbPhtj7SK3xQ14QRISwNZbNoEGfVUvg83BdDUIjGZy4qxwcxYkPmayoO2YoTQz5L/ChFQu4SC4tAK+pcZwkxx8ooinENplnbk1SLLE9UTLiAqiIlRKiDPVGOIqe0eDnaexYxOJ6g8/B1/2MeALrOkbfp+TzLy+DqLRZDlQxPIprGGaEbakgZcWI/MWQy0n707z/D9+4mRmnAZ2T7/y3hMKbvJ11EeCjBbE/pQvq7LIDk4Qhdd56fx1vL53HiOWLpLvp2C7ra6H52pxnJKcKaIUvj1m7MILOdfm9a5uciq0mvyoxLoEODMp0iMhmp/tDqw1Ez9d0imx2Dp2msSsrtXPj4VZvGzGK8zsoGqm8JgfWDNTVnlUKQJpa9bSHsmEJzY7nLxcyhbE+2UMAVoxSpjk2xcFpzRyrFgtW2RALLaFiQaSlaZ0hMSxlZpN6dTqNF6Clm22xQUjRHpSYHAOZbaUwsbfvxYXMz30OOlV9VmfW4MXSGf67l82cVwwaAW4etaBEFZR8JBtkWoDedZl3HJ7EYp0Yzm5U0454c9f8vXGWwCP3ch7EYvmIR6dqGHL4nzPt+UlHB87yyh55luN7CpS0+icVYN/Tm+DhWjdFaGasyc1uXEmmCjyoLuNpFDMNT42HeZ/q7xpFqsnM/yvutsDvx/gvEykqz1rSWQ3y+YMQORmEQ+rjyWicXoB3+NISyc0VJnmM0T6bOLMHJNM2TaWYLtv7zEQBA47enIvcpMQuqychFtG1uE/dd/B3SpHlWluPREDHhP8140VdBYzIjrfA8nm+zITpEfyetFjoOjGCOYFezmTycXmrnif1BZESixDybDXFhUTBgA4bfJaa+RSR/tLsLaKFmoq9jDL6lxNqM7ghygojRaGT2S5rsprUs3++XoWHcZCO20GxV0Ata6zeePo3nfMSo7DClWbc00Tj40WoSjX+8tROFi2jenZszY4CmKPyqyqaYjaA58KtIkLVxpdE8s84D2Qyvt+50mnVFWyMR3FtdTAgAgKDbyO1ptFiwroPmw/WBojbsweFh3CmYn58IhurFqVN5rfel0zz/DyeTzEpNZJivzbv4O2TARM8dzGZ5LSmxHAv1PyjP8zzemU5wctMzU6no/V8L0nJg5pf+GYr6F1oOZBM48ucf6JYD0M0tdejQoUOHDh06JoVJM0257GMAiKmRJnGfFjSOngOqypF2KqTBaqe39dfSUX4bl2/2s2w23CHe+P9h2I7PphVLrshsns0jI7hQvP1PZH0cQh/RkUoxOzB/XxJ1KyiSUAwGPrf+774AFwstP0mfnTqzFBkTRZ+bQyFcIc7OU4UCF1+948wZZiRk9latZsC9CWGj32OEKjQKvS02REWm2cUbW9mcb2wgwZ+R2WQ7Mgm23TePZhAJirIzA3E0Lqf227QCgiILblxoJZoXlWFUlhNJFPBSlhiXFREVlaLcw5N378Eld1H2Vpmi4sgein5lqYdkPIMZ5xGjtyseR/uvKOtu9VUtrK0IDyVxqo7+L40M2zUNM0S6/QcGDfNJzsNMGyAyAUWKdsphRCFIEbGvgqKb+wYHOXPScSaFAxT4IlsocIR3+5k+bILQYU0oQizLWXyUiLHu63Kjg9O231A1xETfrPf5WAskM7PsTjNGRLRrC6bR6yvGCdMECymvBZDuAwC8qsp6vStUF94r0PU2j4ywoV1fOs22GA/W1mKdndoaVYpLKtVHf/exO8vZZ7eUl3ObHwkGWTchGaxILsfzbks4zGV/mqxWNohNazkcUGg91Q3l2AR0cEJpGJkJOC1lZEbSbFFgEqzmUHsEbwTo5xuELYhSbkVGrOPfBIPFbNXBJK7TaE79wV+L9cM055c5ncwWpA5GAACWOV7UipP/bYmigaHDaMTgLoq6R+Y6eaxk1ppSb+coPzmewXET/XzLyAhujNA16qf7OBPq/FEFR8U8l0zT6RMRpNZQm1cY7JwZ6i6xIirKq6w/dQp3HqH+6vmyFwCwpLfAWW2ynwBgPKxhXJRXiY2l0DSHJu/OdIIZkLKjNMYzF5WflXkl2fYdmQSWCnPBwzsHWXv41Dit76+cMbAZaz5XQPN8ml9/fKqdTW23PdWOJSJL12xV4BFMsCzRMc9ogUGw9IPZLO9lWYD1paXBLM746blmiX32lUgEf+ultZnPF/BWnOb8hS4X2wwMTsj+aoQJKTPtn8kz9NzvOTO8X/Sl08yAOo1GPCGyy671lGLv+0TLSh3Wm+PjaBR7e65Q4Puss7vxvX5i2x+oqQEGaN0YFQOXjfqTWI/njypQp1Df5s8k4RTMaNRhZIbZ8udRPDSN5pJcu1sjEbbW8CoKm9RaDAbOCL+jooL3gKVOJ5t5ytOOWtXEcyqUzXKZp+lWK94QDFWt2czfaSXqf8NfE5JpmnXeY1BU2190rVw2icO7r9eZJuiaJh06dOjQoeMLC13T9Pli0kzTqUP3AgCOT1H5vHmtx8OGcW6flSN6U7+Guw10wH6v2c8ePbMViirGwxoMAfq/ls+jV0QYjkNRNC8SnkCFAoYO0TWCTfSm7lUUlEdEuYuOMVQspqihTFFx9yBllH1n1IrtFfRITRYLazmkh8azo2HMt9HPriotReceok5M5qJeQTEbMSJ0T9IHZZqI/qjTwHqkN3xF75BcIod7xuh6N2pO1kA97qXoU0Y5AEV/MkvDpygoHaXPDvfFuMTJESc9a+Nwnv1rLo324ckoRYZNc/xsPvmb8REsbaPIasaiMo6OP/HRdS/KWtkIz1ZuwzZhHrfB5+MsK6+iwHIsxtcGgJyxaMC41uNhNnFXLIa6HpFVZzRguJ7Gc1bWhPvjFF3+xE3POx7WOGtqrccDE2g+bPnHz5iFW/vd6WjLU5sXWmginT4ewR98FGX2ZTLMSq1KWfi53SVWDPdSmwv1xUK+dYookbBzELOWkF5kKJ9FuZHihM7DYfawSk5YAq1JGpOP1BRWqhRRPjAexGWihItXVTG2lxjHqgY3OkTQFc/luG9kZL+iDzjSQO1oPp5CyTnEyMTyec7iM/Rr+MBNfXOliPi7Mumz/KlkxL/E7sA9Yp5vHLfBNJXad1jTOMNIjpWc9wBQ6E3C10B9tzkUwrWCrcpm8rg7QkyN1H+cezAF5Uv0exVgrdQxTWN9xyybjT/vUBQuOyFNOJe7XKgbonl3qszIZWKShyMYmkZj2/Xwcay+igxYpUdZKJtl/dxIs53vHd89As9imo9aPs+eUi+NjuLGGP1toJmYpqH2CDwtNCiRXI73qtrRPE7SRzA3Z2Z29R2haWy1WlErymoM57I49kdiOhatnIKMjcYqmc/DJsZtMJNh9ndqRIx7+yjKv0wsl2c8x9mq04xmvBwvMg8S8yYYir4xTr9f5nTxXDuUTMIn9Jy1WQX7IdaH3Y4PniUvsOkbqFSO02iEJU79ckBJ8/yqt1jgKNB6ey8Rw3lpE7cVAJ5uyONnXmpz0mrguVZZUNB5hOZ587wA6wktNXa4cnS9z7K0NqdbrcwS1ZjNuE+UOGmyWPCVEWE2m8rhtz7aB6XGLZbP82nCYDaLQJqu++vYCP5OMJxtySSX51rn9uCQYPIkS2k7rUGpp34c2hnE7ZW0bz9RX48/ij3OqSg8l5ZmqP+PWHOsqbvBU4pHx+hZV/UBW6uoD1a63TAIPVt6oRfn2osF4mUbdoq9c4PPhzMnaQzvNI3ySUWFqrIO9Jsld+CvCck0zV74m8+FaTq090adaYLONOnQoUOHDh1fWBgLn4O5ZUE3t5SYNNMUHf01AOCQKcsRUueeYTxTS9HWPeWVCBWKFN5uqcOwWFAjoiup43C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+      "text/plain": [
+       "<Figure size 750x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rng = np.random.default_rng(42)\n",
+    "H, W = 200, 300\n",
+    "\n",
+    "# Two overlapping sine waves + noise for terrain-like structure\n",
+    "yy, xx = np.meshgrid(np.linspace(0, 4 * np.pi, H),\n",
+    "                      np.linspace(0, 6 * np.pi, W), indexing='ij')\n",
+    "elevation = 500 + 200 * np.sin(yy) * np.cos(xx * 0.7) + 50 * rng.standard_normal((H, W))\n",
+    "elevation = elevation.astype(np.float32)\n",
+    "\n",
+    "# Geographic coordinates near Portland, OR\n",
+    "y = np.linspace(45.6, 45.4, H)   # north to south\n",
+    "x = np.linspace(-122.8, -122.5, W)\n",
+    "\n",
+    "da = xr.DataArray(\n",
+    "    elevation, dims=['y', 'x'],\n",
+    "    coords={'y': y, 'x': x},\n",
+    "    name='elevation',\n",
+    "    attrs={'crs': 4326},\n",
+    ")\n",
+    "\n",
+    "da.plot.imshow(size=5, aspect=W / H, cmap='terrain')\n",
+    "plt.title('Synthetic elevation (m)')\n",
+    "plt.gca().set_axis_off()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Covers a small patch near Portland, OR. Values are meters, from valley floor to ridgeline. The CRS lives in the DataArray's attrs, so `to_geotiff` embeds it automatically."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-22T15:14:47.984103Z",
+     "iopub.status.busy": "2026-03-22T15:14:47.983946Z",
+     "iopub.status.idle": "2026-03-22T15:14:47.995377Z",
+     "shell.execute_reply": "2026-03-22T15:14:47.994452Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-obj-name,\n",
+       ".xr-group-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name::before {\n",
+       "  content: \"📁\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-name,\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 20px auto;\n",
+       "  width: 100%;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 20px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;elevation&#x27; (y: 200, x: 300)&gt; Size: 240kB\n",
+       "array([[515.23584, 448.0008 , 537.5226 , ..., 481.40427, 412.1639 ,\n",
+       "        516.3998 ],\n",
+       "       [598.98865, 435.91577, 555.7634 , ..., 551.45496, 522.611  ,\n",
+       "        421.46262],\n",
+       "       [550.96246, 496.29047, 588.8162 , ..., 478.49066, 544.9579 ,\n",
+       "        443.5986 ],\n",
+       "       ...,\n",
+       "       [516.7113 , 548.19977, 523.5575 , ..., 448.2582 , 469.20718,\n",
+       "        496.94867],\n",
+       "       [564.23895, 456.79202, 443.16757, ..., 442.958  , 461.8408 ,\n",
+       "        445.42407],\n",
+       "       [532.1338 , 469.7007 , 504.0479 , ..., 464.74902, 489.26068,\n",
+       "        558.9692 ]], shape=(200, 300), dtype=float32)\n",
+       "Coordinates:\n",
+       "  * y        (y) float64 2kB 45.6 45.6 45.6 45.6 45.6 ... 45.4 45.4 45.4 45.4\n",
+       "  * x        (x) float64 2kB -122.8 -122.8 -122.8 ... -122.5 -122.5 -122.5\n",
+       "Attributes:\n",
+       "    crs:      4326</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-obj-name'>&#x27;elevation&#x27;</div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 200</li><li><span class='xr-has-index'>x</span>: 300</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-47ba4bc8-92b5-4f9d-89b0-58906f56dac0' class='xr-array-in' type='checkbox' checked><label for='section-47ba4bc8-92b5-4f9d-89b0-58906f56dac0' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>515.2 448.0 537.5 547.0 402.4 434.9 ... 502.9 515.6 464.7 489.3 559.0</span></div><div class='xr-array-data'><pre>array([[515.23584, 448.0008 , 537.5226 , ..., 481.40427, 412.1639 ,\n",
+       "        516.3998 ],\n",
+       "       [598.98865, 435.91577, 555.7634 , ..., 551.45496, 522.611  ,\n",
+       "        421.46262],\n",
+       "       [550.96246, 496.29047, 588.8162 , ..., 478.49066, 544.9579 ,\n",
+       "        443.5986 ],\n",
+       "       ...,\n",
+       "       [516.7113 , 548.19977, 523.5575 , ..., 448.2582 , 469.20718,\n",
+       "        496.94867],\n",
+       "       [564.23895, 456.79202, 443.16757, ..., 442.958  , 461.8408 ,\n",
+       "        445.42407],\n",
+       "       [532.1338 , 469.7007 , 504.0479 , ..., 464.74902, 489.26068,\n",
+       "        558.9692 ]], shape=(200, 300), dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-0b018fbc-11db-4a33-921e-ea2413811764' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0b018fbc-11db-4a33-921e-ea2413811764' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>45.6 45.6 45.6 ... 45.4 45.4 45.4</div><input id='attrs-636566fd-a25a-4fd4-bf70-3afba90377ed' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-636566fd-a25a-4fd4-bf70-3afba90377ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9bc36c25-732b-4784-8022-e558ad1a062d' class='xr-var-data-in' type='checkbox'><label for='data-9bc36c25-732b-4784-8022-e558ad1a062d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([45.6     , 45.598995, 45.59799 , 45.596985, 45.59598 , 45.594975,\n",
+       "       45.59397 , 45.592965, 45.59196 , 45.590955, 45.58995 , 45.588945,\n",
+       "       45.58794 , 45.586935, 45.58593 , 45.584925, 45.58392 , 45.582915,\n",
+       "       45.58191 , 45.580905, 45.579899, 45.578894, 45.577889, 45.576884,\n",
+       "       45.575879, 45.574874, 45.573869, 45.572864, 45.571859, 45.570854,\n",
+       "       45.569849, 45.568844, 45.567839, 45.566834, 45.565829, 45.564824,\n",
+       "       45.563819, 45.562814, 45.561809, 45.560804, 45.559799, 45.558794,\n",
+       "       45.557789, 45.556784, 45.555779, 45.554774, 45.553769, 45.552764,\n",
+       "       45.551759, 45.550754, 45.549749, 45.548744, 45.547739, 45.546734,\n",
+       "       45.545729, 45.544724, 45.543719, 45.542714, 45.541709, 45.540704,\n",
+       "       45.539698, 45.538693, 45.537688, 45.536683, 45.535678, 45.534673,\n",
+       "       45.533668, 45.532663, 45.531658, 45.530653, 45.529648, 45.528643,\n",
+       "       45.527638, 45.526633, 45.525628, 45.524623, 45.523618, 45.522613,\n",
+       "       45.521608, 45.520603, 45.519598, 45.518593, 45.517588, 45.516583,\n",
+       "       45.515578, 45.514573, 45.513568, 45.512563, 45.511558, 45.510553,\n",
+       "       45.509548, 45.508543, 45.507538, 45.506533, 45.505528, 45.504523,\n",
+       "       45.503518, 45.502513, 45.501508, 45.500503, 45.499497, 45.498492,\n",
+       "       45.497487, 45.496482, 45.495477, 45.494472, 45.493467, 45.492462,\n",
+       "       45.491457, 45.490452, 45.489447, 45.488442, 45.487437, 45.486432,\n",
+       "       45.485427, 45.484422, 45.483417, 45.482412, 45.481407, 45.480402,\n",
+       "       45.479397, 45.478392, 45.477387, 45.476382, 45.475377, 45.474372,\n",
+       "       45.473367, 45.472362, 45.471357, 45.470352, 45.469347, 45.468342,\n",
+       "       45.467337, 45.466332, 45.465327, 45.464322, 45.463317, 45.462312,\n",
+       "       45.461307, 45.460302, 45.459296, 45.458291, 45.457286, 45.456281,\n",
+       "       45.455276, 45.454271, 45.453266, 45.452261, 45.451256, 45.450251,\n",
+       "       45.449246, 45.448241, 45.447236, 45.446231, 45.445226, 45.444221,\n",
+       "       45.443216, 45.442211, 45.441206, 45.440201, 45.439196, 45.438191,\n",
+       "       45.437186, 45.436181, 45.435176, 45.434171, 45.433166, 45.432161,\n",
+       "       45.431156, 45.430151, 45.429146, 45.428141, 45.427136, 45.426131,\n",
+       "       45.425126, 45.424121, 45.423116, 45.422111, 45.421106, 45.420101,\n",
+       "       45.419095, 45.41809 , 45.417085, 45.41608 , 45.415075, 45.41407 ,\n",
+       "       45.413065, 45.41206 , 45.411055, 45.41005 , 45.409045, 45.40804 ,\n",
+       "       45.407035, 45.40603 , 45.405025, 45.40402 , 45.403015, 45.40201 ,\n",
+       "       45.401005, 45.4     ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-122.8 -122.8 ... -122.5 -122.5</div><input id='attrs-8d165eb0-04b8-43ec-99d7-e9b71dfd6ae0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8d165eb0-04b8-43ec-99d7-e9b71dfd6ae0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e512f4fd-c644-4502-b297-a7ca8ad6d7da' class='xr-var-data-in' type='checkbox'><label for='data-e512f4fd-c644-4502-b297-a7ca8ad6d7da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-122.8     , -122.798997, -122.797993, ..., -122.502007, -122.501003,\n",
+       "       -122.5     ], shape=(300,))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b8e9552e-650e-42b5-8f11-6ad7696d9335' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b8e9552e-650e-42b5-8f11-6ad7696d9335' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>crs :</span></dt><dd>4326</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'elevation' (y: 200, x: 300)> Size: 240kB\n",
+       "array([[515.23584, 448.0008 , 537.5226 , ..., 481.40427, 412.1639 ,\n",
+       "        516.3998 ],\n",
+       "       [598.98865, 435.91577, 555.7634 , ..., 551.45496, 522.611  ,\n",
+       "        421.46262],\n",
+       "       [550.96246, 496.29047, 588.8162 , ..., 478.49066, 544.9579 ,\n",
+       "        443.5986 ],\n",
+       "       ...,\n",
+       "       [516.7113 , 548.19977, 523.5575 , ..., 448.2582 , 469.20718,\n",
+       "        496.94867],\n",
+       "       [564.23895, 456.79202, 443.16757, ..., 442.958  , 461.8408 ,\n",
+       "        445.42407],\n",
+       "       [532.1338 , 469.7007 , 504.0479 , ..., 464.74902, 489.26068,\n",
+       "        558.9692 ]], shape=(200, 300), dtype=float32)\n",
+       "Coordinates:\n",
+       "  * y        (y) float64 2kB 45.6 45.6 45.6 45.6 45.6 ... 45.4 45.4 45.4 45.4\n",
+       "  * x        (x) float64 2kB -122.8 -122.8 -122.8 ... -122.5 -122.5 -122.5\n",
+       "Attributes:\n",
+       "    crs:      4326"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "da"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Write and read back\n",
+    "\n",
+    "`to_geotiff(data, path)` writes a DataArray or numpy array as a GeoTIFF. `open_geotiff(path)` reads it back. Coordinates, CRS, and nodata survive the round trip via the file's GeoKeys."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-22T15:14:47.996789Z",
+     "iopub.status.busy": "2026-03-22T15:14:47.996675Z",
+     "iopub.status.idle": "2026-03-22T15:14:48.023597Z",
+     "shell.execute_reply": "2026-03-22T15:14:48.022645Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Wrote 212,414 bytes\n",
+      "Shape: (200, 300)\n",
+      "CRS:   4326\n",
+      "Name:  elevation\n",
+      "Match: True\n"
+     ]
+    }
+   ],
+   "source": [
+    "tmpdir = tempfile.mkdtemp(prefix='xrs_geotiff_nb_')\n",
+    "path = os.path.join(tmpdir, 'elevation.tif')\n",
+    "\n",
+    "# Write\n",
+    "to_geotiff(da, path, compression='deflate')\n",
+    "print(f'Wrote {os.path.getsize(path):,} bytes')\n",
+    "\n",
+    "# Read back\n",
+    "loaded = open_geotiff(path)\n",
+    "\n",
+    "# Verify round-trip\n",
+    "print(f'Shape: {loaded.shape}')\n",
+    "print(f'CRS:   {loaded.attrs.get(\"crs\")}')\n",
+    "print(f'Name:  {loaded.name}')\n",
+    "print(f'Match: {np.allclose(loaded.values, da.values)}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Round-tripped data matches the original. `open_geotiff` derived the DataArray name from the filename."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Accessor write\n",
+    "\n",
+    "The `.xrs.to_geotiff()` accessor does the same thing if you prefer chaining. Same idea as `da.to_netcdf()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-22T15:14:48.025685Z",
+     "iopub.status.busy": "2026-03-22T15:14:48.025571Z",
+     "iopub.status.idle": "2026-03-22T15:14:48.792327Z",
+     "shell.execute_reply": "2026-03-22T15:14:48.791848Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape: (200, 300)\n",
+      "Match: True\n"
+     ]
+    }
+   ],
+   "source": [
+    "accessor_path = os.path.join(tmpdir, 'via_accessor.tif')\n",
+    "\n",
+    "# Write using the accessor\n",
+    "da.xrs.to_geotiff(accessor_path, compression='lzw')\n",
+    "\n",
+    "# Read back and verify\n",
+    "loaded2 = open_geotiff(accessor_path)\n",
+    "print(f'Shape: {loaded2.shape}')\n",
+    "print(f'Match: {np.allclose(loaded2.values, da.values)}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-block alert-info\">\n",
+    "<b>Dataset write.</b> The Dataset accessor also has <code>.xrs.to_geotiff()</code>. It picks the first 2D variable with y/x dims, or you can specify <code>var='elevation'</code> explicitly.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Windowed read via Dataset\n",
+    "\n",
+    "`ds.xrs.open_geotiff(path)` reads only the pixels that overlap the Dataset's y/x coordinates, so you skip loading the full file when you only need a subregion.\n",
+    "\n",
+    "We'll make a small template Dataset covering the southeast quadrant, then window-read the full raster through it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-22T15:14:48.793651Z",
+     "iopub.status.busy": "2026-03-22T15:14:48.793539Z",
+     "iopub.status.idle": "2026-03-22T15:14:48.899922Z",
+     "shell.execute_reply": "2026-03-22T15:14:48.899327Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Full raster:  (200, 300)\n",
+      "Cropped:      (81, 121)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Template covering the SE quadrant\n",
+    "y_sub = da.coords['y'].values[100:180]\n",
+    "x_sub = da.coords['x'].values[150:270]\n",
+    "\n",
+    "template = xr.Dataset({\n",
+    "    'placeholder': xr.DataArray(\n",
+    "        np.zeros((len(y_sub), len(x_sub)), dtype=np.float32),\n",
+    "        dims=['y', 'x'],\n",
+    "        coords={'y': y_sub, 'x': x_sub},\n",
+    "    )\n",
+    "})\n",
+    "\n",
+    "# Windowed read: only loads the overlapping region\n",
+    "cropped = template.xrs.open_geotiff(path)\n",
+    "\n",
+    "print(f'Full raster:  {da.shape}')\n",
+    "print(f'Cropped:      {cropped.shape}')\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
+    "da.plot.imshow(ax=axes[0], cmap='terrain', add_colorbar=False)\n",
+    "axes[0].set_title('Full raster')\n",
+    "axes[0].set_axis_off()\n",
+    "\n",
+    "cropped.plot.imshow(ax=axes[1], cmap='terrain', add_colorbar=False)\n",
+    "axes[1].set_title('Windowed read (SE quadrant)')\n",
+    "axes[1].set_axis_off()\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is smaller than the full 200x300 raster. Only the overlapping region was read from disk."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## VRT mosaic\n",
+    "\n",
+    "`write_vrt` writes a lightweight XML file that stitches multiple GeoTIFFs into one virtual raster. The tiles aren't copied, just referenced."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-22T15:14:48.901194Z",
+     "iopub.status.busy": "2026-03-22T15:14:48.901093Z",
+     "iopub.status.idle": "2026-03-22T15:14:48.917702Z",
+     "shell.execute_reply": "2026-03-22T15:14:48.917219Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "nw: (100, 150) -> 54,041 bytes\n",
+      "ne: (100, 150) -> 54,052 bytes\n",
+      "sw: (100, 150) -> 54,090 bytes\n",
+      "se: (100, 150) -> 54,051 bytes\n",
+      "\n",
+      "VRT: 2,178 bytes\n",
+      "Mosaic shape: (200, 300)\n",
+      "Matches original: True\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Split into 4 tiles and write each\n",
+    "tiles = [\n",
+    "    ('nw', da[:100, :150]),\n",
+    "    ('ne', da[:100, 150:]),\n",
+    "    ('sw', da[100:, :150]),\n",
+    "    ('se', da[100:, 150:]),\n",
+    "]\n",
+    "tile_paths = []\n",
+    "for name, tile in tiles:\n",
+    "    p = os.path.join(tmpdir, f'tile_{name}.tif')\n",
+    "    to_geotiff(tile, p, compression='deflate')\n",
+    "    tile_paths.append(p)\n",
+    "    print(f'{name}: {tile.shape} -> {os.path.getsize(p):,} bytes')\n",
+    "\n",
+    "# Stitch into a VRT\n",
+    "vrt_path = os.path.join(tmpdir, 'mosaic.vrt')\n",
+    "write_vrt(vrt_path, tile_paths)\n",
+    "print(f'\\nVRT: {os.path.getsize(vrt_path):,} bytes')\n",
+    "\n",
+    "# Read the mosaic back\n",
+    "mosaic = open_geotiff(vrt_path)\n",
+    "print(f'Mosaic shape: {mosaic.shape}')\n",
+    "print(f'Matches original: {np.allclose(mosaic.values, da.values)}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The VRT is a few hundred bytes of XML. `open_geotiff` assembles the tiles when you read it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-03-22T15:14:48.918929Z",
+     "iopub.status.busy": "2026-03-22T15:14:48.918822Z",
+     "iopub.status.idle": "2026-03-22T15:14:48.921386Z",
+     "shell.execute_reply": "2026-03-22T15:14:48.920798Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Clean up temp files\n",
+    "import shutil\n",
+    "shutil.rmtree(tmpdir, ignore_errors=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### References\n",
+    "\n",
+    "- [GeoTIFF specification (OGC)](https://www.ogc.org/standard/geotiff/)\n",
+    "- [Cloud Optimized GeoTIFF](https://www.cogeo.org/)\n",
+    "- [xarray I/O naming conventions](https://docs.xarray.dev/en/stable/user-guide/io.html)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.14.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/user_guide/images/geotiff_io_preview.png b/examples/user_guide/images/geotiff_io_preview.png
new file mode 100644
index 00000000..6eaa779f
Binary files /dev/null and b/examples/user_guide/images/geotiff_io_preview.png differ
diff --git a/examples/viewshed_gpu.ipynb b/examples/viewshed_gpu.ipynb
index 61f1ffa8..28b71eb6 100644
--- a/examples/viewshed_gpu.ipynb
+++ b/examples/viewshed_gpu.ipynb
@@ -35,7 +35,7 @@
    },
    "outputs": [],
    "source": [
-    "import pandas\nimport matplotlib.pyplot as plt\nimport geopandas as gpd\n\nimport xarray as xr\nimport numpy as np\nimport cupy\nfrom xrspatial.geotiff import read_geotiff\n\nimport xrspatial"
+    "import pandas\nimport matplotlib.pyplot as plt\nimport geopandas as gpd\n\nimport xarray as xr\nimport numpy as np\nimport cupy\nfrom xrspatial.geotiff import open_geotiff\n\nimport xrspatial"
    ]
   },
   {
@@ -66,7 +66,7 @@
    },
    "outputs": [],
    "source": [
-    "file_name = '../xrspatial-examples/data/colorado_merge_3arc_resamp.tif'\n\nraster = read_geotiff(file_name, band=0)\nraster.name = 'Colorado Elevation Raster'\n\nxmin, xmax = raster.x.data.min(), raster.x.data.max()\nymin, ymax = raster.y.data.min(), raster.y.data.max()\n\nxmin, xmax, ymin, ymax"
+    "file_name = '../xrspatial-examples/data/colorado_merge_3arc_resamp.tif'\n\nraster = open_geotiff(file_name, band=0)\nraster.name = 'Colorado Elevation Raster'\n\nxmin, xmax = raster.x.data.min(), raster.x.data.max()\nymin, ymax = raster.y.data.min(), raster.y.data.max()\n\nxmin, xmax, ymin, ymax"
    ]
   },
   {
diff --git a/examples/xarray-spatial_classification-methods.ipynb b/examples/xarray-spatial_classification-methods.ipynb
index ab56f074..26cc4519 100644
--- a/examples/xarray-spatial_classification-methods.ipynb
+++ b/examples/xarray-spatial_classification-methods.ipynb
@@ -47,7 +47,7 @@
    },
    "outputs": [],
    "source": [
-    "import xarray as xr\nfrom xrspatial.geotiff import read_geotiff\nimport xrspatial\n\nfile_name = '../xrspatial-examples/data/colorado_merge_3arc_resamp.tif'\nraster = read_geotiff(file_name, band=0)\nraster.name = 'Colorado Elevation Raster'\n\nxmin, xmax = raster.x.data.min(), raster.x.data.max()\nymin, ymax = raster.y.data.min(), raster.y.data.max()\n\nxmin, xmax, ymin, ymax"
+    "import xarray as xr\nfrom xrspatial.geotiff import open_geotiff\nimport xrspatial\n\nfile_name = '../xrspatial-examples/data/colorado_merge_3arc_resamp.tif'\nraster = open_geotiff(file_name, band=0)\nraster.name = 'Colorado Elevation Raster'\n\nxmin, xmax = raster.x.data.min(), raster.x.data.max()\nymin, ymax = raster.y.data.min(), raster.y.data.max()\n\nxmin, xmax, ymin, ymax"
    ]
   },
   {
diff --git a/xrspatial/accessor.py b/xrspatial/accessor.py
index 388f2875..1632432a 100644
--- a/xrspatial/accessor.py
+++ b/xrspatial/accessor.py
@@ -26,14 +26,14 @@ def __init__(self, obj):
     def plot(self, **kwargs):
         """Plot the DataArray, using an embedded TIFF colormap if present.
 
-        For palette/indexed-color GeoTIFFs (read via ``read_geotiff``),
+        For palette/indexed-color GeoTIFFs (read via ``open_geotiff``),
         the TIFF's color table is applied automatically with correct
         normalization.  For all other DataArrays, falls through to the
         standard ``da.plot()``.
 
         Usage::
 
-            da = read_geotiff('landcover.tif')
+            da = open_geotiff('landcover.tif')
             da.xrs.plot()  # palette colors used automatically
         """
         import numpy as np
@@ -482,6 +482,18 @@ def rasterize(self, geometries, **kwargs):
         from .rasterize import rasterize
         return rasterize(geometries, like=self._obj, **kwargs)
 
+    # ---- GeoTIFF I/O ----
+
+    def to_geotiff(self, path, **kwargs):
+        """Write this DataArray as a GeoTIFF.
+
+        Equivalent to ``to_geotiff(da, path, **kwargs)``.
+
+        See :func:`xrspatial.geotiff.to_geotiff` for full parameter docs.
+        """
+        from .geotiff import to_geotiff
+        return to_geotiff(self._obj, path, **kwargs)
+
 
 @xr.register_dataset_accessor("xrs")
 class XrsSpatialDatasetAccessor:
@@ -820,3 +832,75 @@ def rasterize(self, geometries, **kwargs):
             "Dataset has no 2D variable with 'y' and 'x' dimensions "
             "to use as rasterize template"
         )
+
+    # ---- GeoTIFF I/O ----
+
+    def to_geotiff(self, path, var=None, **kwargs):
+        """Write a Dataset variable as a GeoTIFF.
+
+        Parameters
+        ----------
+        path : str
+            Output file path.
+        var : str or None
+            Variable name to write.  If None, uses the first 2D variable
+            with y/x dimensions.
+        **kwargs
+            Passed to :func:`xrspatial.geotiff.to_geotiff`.
+        """
+        from .geotiff import to_geotiff
+        ds = self._obj
+        if var is not None:
+            return to_geotiff(ds[var], path, **kwargs)
+        for v in ds.data_vars:
+            da = ds[v]
+            if da.ndim >= 2 and 'y' in da.dims and 'x' in da.dims:
+                return to_geotiff(da, path, **kwargs)
+        raise ValueError(
+            "Dataset has no variable with 'y' and 'x' dimensions to write"
+        )
+
+    def open_geotiff(self, source, **kwargs):
+        """Read a GeoTIFF windowed to this Dataset's spatial extent.
+
+        Uses the Dataset's y/x coordinates to compute a pixel window,
+        then reads only that region from the file.
+
+        Parameters
+        ----------
+        source : str
+            File path to the GeoTIFF.
+        **kwargs
+            Passed to :func:`xrspatial.geotiff.open_geotiff` (except
+            ``window``, which is computed automatically).
+
+        Returns
+        -------
+        xr.DataArray
+            The windowed portion of the GeoTIFF.
+        """
+        from .geotiff import open_geotiff, _read_geo_info, _extent_to_window
+        ds = self._obj
+        if 'y' not in ds.coords or 'x' not in ds.coords:
+            raise ValueError(
+                "Dataset must have 'y' and 'x' coordinates to compute "
+                "a spatial window"
+            )
+        y = ds.coords['y'].values
+        x = ds.coords['x'].values
+        y_min, y_max = float(y.min()), float(y.max())
+        x_min, x_max = float(x.min()), float(x.max())
+
+        geo_info, file_h, file_w = _read_geo_info(source)
+        t = geo_info.transform
+
+        # Expand extent by half a pixel so we capture edge pixels
+        y_min -= abs(t.pixel_height) * 0.5
+        y_max += abs(t.pixel_height) * 0.5
+        x_min -= abs(t.pixel_width) * 0.5
+        x_max += abs(t.pixel_width) * 0.5
+
+        window = _extent_to_window(t, file_h, file_w,
+                                   y_min, y_max, x_min, x_max)
+        kwargs.pop('window', None)
+        return open_geotiff(source, window=window, **kwargs)
diff --git a/xrspatial/geotiff/__init__.py b/xrspatial/geotiff/__init__.py
index 57001366..be0a1d3f 100644
--- a/xrspatial/geotiff/__init__.py
+++ b/xrspatial/geotiff/__init__.py
@@ -4,12 +4,10 @@
 
 Public API
 ----------
-read_geotiff(source, ...)
+open_geotiff(source, ...)
     Read a GeoTIFF file to an xarray.DataArray.
-write_geotiff(data, path, ...)
+to_geotiff(data, path, ...)
     Write an xarray.DataArray as a GeoTIFF or COG.
-open_cog(url, ...)
-    Read a Cloud Optimized GeoTIFF from an HTTP URL.
 """
 from __future__ import annotations
 
@@ -20,7 +18,7 @@
 from ._reader import read_to_array
 from ._writer import write
 
-__all__ = ['read_geotiff', 'write_geotiff', 'write_vrt']
+__all__ = ['open_geotiff', 'to_geotiff', 'write_vrt']
 
 
 def _wkt_to_epsg(wkt_or_proj: str) -> int | None:
@@ -98,7 +96,53 @@ def _coords_to_transform(da: xr.DataArray) -> GeoTransform | None:
     )
 
 
-def read_geotiff(source: str, *, window=None,
+def _read_geo_info(source: str):
+    """Read only the geographic metadata and image dimensions from a GeoTIFF.
+
+    Returns (geo_info, height, width) without reading pixel data.
+    """
+    from ._geotags import extract_geo_info
+    from ._header import parse_all_ifds, parse_header
+
+    with open(source, 'rb') as f:
+        import mmap
+        data = mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ)
+    try:
+        header = parse_header(data)
+        ifds = parse_all_ifds(data, header)
+        ifd = ifds[0]
+        geo_info = extract_geo_info(ifd, data, header.byte_order)
+        return geo_info, ifd.height, ifd.width
+    finally:
+        data.close()
+
+
+def _extent_to_window(transform, file_height, file_width,
+                      y_min, y_max, x_min, x_max):
+    """Convert geographic extent to pixel window (row_start, col_start, row_stop, col_stop).
+
+    Clamps to file bounds.
+    """
+    col_start = (x_min - transform.origin_x) / transform.pixel_width
+    col_stop = (x_max - transform.origin_x) / transform.pixel_width
+
+    row_start = (y_max - transform.origin_y) / transform.pixel_height
+    row_stop = (y_min - transform.origin_y) / transform.pixel_height
+
+    if row_start > row_stop:
+        row_start, row_stop = row_stop, row_start
+    if col_start > col_stop:
+        col_start, col_stop = col_stop, col_start
+
+    row_start = max(0, int(np.floor(row_start)))
+    col_start = max(0, int(np.floor(col_start)))
+    row_stop = min(file_height, int(np.ceil(row_stop)))
+    col_stop = min(file_width, int(np.ceil(col_stop)))
+
+    return (row_start, col_start, row_stop, col_stop)
+
+
+def open_geotiff(source: str, *, window=None,
                  overview_level: int | None = None,
                  band: int | None = None,
                  name: str | None = None,
@@ -285,7 +329,7 @@ def _is_gpu_data(data) -> bool:
     return isinstance(data, _cupy_type)
 
 
-def write_geotiff(data: xr.DataArray | np.ndarray, path: str, *,
+def to_geotiff(data: xr.DataArray | np.ndarray, path: str, *,
                   crs: int | str | None = None,
                   nodata=None,
                   compression: str = 'deflate',
@@ -445,14 +489,6 @@ def write_geotiff(data: xr.DataArray | np.ndarray, path: str, *,
     )
 
 
-def open_cog(url: str, **kwargs) -> xr.DataArray:
-    """Deprecated: use ``read_geotiff(url, ...)`` instead.
-
-    read_geotiff handles HTTP URLs, cloud URIs, and local files.
-    """
-    return read_geotiff(url, **kwargs)
-
-
 def read_geotiff_dask(source: str, *, chunks: int | tuple = 512,
                       overview_level: int | None = None,
                       name: str | None = None) -> xr.DataArray:
diff --git a/xrspatial/geotiff/tests/bench_vs_rioxarray.py b/xrspatial/geotiff/tests/bench_vs_rioxarray.py
index 82abe85b..3ecd6734 100644
--- a/xrspatial/geotiff/tests/bench_vs_rioxarray.py
+++ b/xrspatial/geotiff/tests/bench_vs_rioxarray.py
@@ -36,12 +36,12 @@ def _fmt_ms(seconds):
 def check_consistency(path):
     """Compare pixel values and geo metadata between the two readers."""
     import rioxarray  # noqa: F401
-    from xrspatial.geotiff import read_geotiff
+    from xrspatial.geotiff import open_geotiff
 
     rio_da = xr.open_dataarray(path, engine='rasterio')
     rio_arr = rio_da.squeeze('band').values.astype(np.float64)
 
-    our_da = read_geotiff(path)
+    our_da = open_geotiff(path)
     our_arr = our_da.values.astype(np.float64)
 
     # Shape
@@ -96,7 +96,7 @@ def check_consistency(path):
 def bench_read(path, runs=10):
     """Benchmark read performance."""
     import rioxarray  # noqa: F401
-    from xrspatial.geotiff import read_geotiff
+    from xrspatial.geotiff import open_geotiff
 
     def rio_read():
         da = xr.open_dataarray(path, engine='rasterio')
@@ -105,7 +105,7 @@ def rio_read():
         return da
 
     def our_read():
-        return read_geotiff(path)
+        return open_geotiff(path)
 
     rio_time, _ = _timer(rio_read, warmup=2, runs=runs)
     our_time, _ = _timer(our_read, warmup=2, runs=runs)
@@ -120,7 +120,7 @@ def our_read():
 def bench_write(shape=(512, 512), compression='deflate', runs=5):
     """Benchmark write performance."""
     import rioxarray  # noqa: F401
-    from xrspatial.geotiff import write_geotiff
+    from xrspatial.geotiff import to_geotiff
     from xrspatial.geotiff._geotags import GeoTransform
 
     rng = np.random.RandomState(42)
@@ -150,7 +150,7 @@ def rio_write():
 
     def our_write():
         p = os.path.join(tmpdir, 'our_out.tif')
-        write_geotiff(da_ours, p, compression=compression, tiled=False)
+        to_geotiff(da_ours, p, compression=compression, tiled=False)
         return os.path.getsize(p)
 
     rio_time, rio_size = _timer(rio_write, warmup=1, runs=runs)
@@ -166,7 +166,7 @@ def our_write():
 def bench_round_trip(shape=(256, 256), compression='deflate'):
     """Write with our module, read back with rioxarray, and vice versa."""
     import rioxarray  # noqa: F401
-    from xrspatial.geotiff import read_geotiff, write_geotiff
+    from xrspatial.geotiff import open_geotiff, to_geotiff
 
     rng = np.random.RandomState(99)
     arr = rng.rand(*shape).astype(np.float32)
@@ -179,7 +179,7 @@ def bench_round_trip(shape=(256, 256), compression='deflate'):
     our_path = os.path.join(tmpdir, 'ours.tif')
     da_ours = xr.DataArray(arr, dims=['y', 'x'], coords={'y': y, 'x': x},
                            attrs={'crs': 4326})
-    write_geotiff(da_ours, our_path, compression=compression, tiled=False)
+    to_geotiff(da_ours, our_path, compression=compression, tiled=False)
 
     rio_da = xr.open_dataarray(our_path, engine='rasterio')
     rio_arr = rio_da.squeeze('band').values if 'band' in rio_da.dims else rio_da.values
@@ -198,7 +198,7 @@ def bench_round_trip(shape=(256, 256), compression='deflate'):
     else:
         da_rio.rio.to_raster(rio_path)
 
-    our_da = read_geotiff(rio_path)
+    our_da = open_geotiff(rio_path)
     our_arr = our_da.values
 
     diff2 = float(np.nanmax(np.abs(arr - our_arr)))
diff --git a/xrspatial/geotiff/tests/test_accessor_io.py b/xrspatial/geotiff/tests/test_accessor_io.py
new file mode 100644
index 00000000..8380b111
--- /dev/null
+++ b/xrspatial/geotiff/tests/test_accessor_io.py
@@ -0,0 +1,128 @@
+"""Tests for .xrs.to_geotiff() and .xrs.open_geotiff() accessor methods."""
+from __future__ import annotations
+
+import numpy as np
+import pytest
+import xarray as xr
+
+import xrspatial  # noqa: F401 -- registers .xrs accessor
+from xrspatial.geotiff import open_geotiff, to_geotiff
+
+
+def _make_da(height=8, width=10, crs=4326, name='elevation'):
+    arr = np.arange(height * width, dtype=np.float32).reshape(height, width)
+    y = np.linspace(45.0, 44.0, height)
+    x = np.linspace(-120.0, -119.0, width)
+    return xr.DataArray(
+        arr, dims=['y', 'x'],
+        coords={'y': y, 'x': x},
+        name=name,
+        attrs={'crs': crs},
+    )
+
+
+def _make_ds(height=8, width=10, crs=4326):
+    da = _make_da(height, width, crs, name='elevation')
+    return xr.Dataset({'elevation': da})
+
+
+class TestDataArrayToGeotiff:
+    def test_round_trip(self, tmp_path):
+        da = _make_da()
+        path = str(tmp_path / 'test_1047_da_roundtrip.tif')
+        da.xrs.to_geotiff(path, compression='none')
+        result = open_geotiff(path)
+        np.testing.assert_array_equal(result.values, da.values)
+
+    def test_with_kwargs(self, tmp_path):
+        da = _make_da()
+        path = str(tmp_path / 'test_1047_da_kwargs.tif')
+        da.xrs.to_geotiff(path, compression='deflate', tiled=True, tile_size=256)
+        result = open_geotiff(path)
+        np.testing.assert_array_equal(result.values, da.values)
+
+    def test_preserves_crs(self, tmp_path):
+        da = _make_da(crs=32610)
+        path = str(tmp_path / 'test_1047_da_crs.tif')
+        da.xrs.to_geotiff(path, compression='none')
+        result = open_geotiff(path)
+        assert result.attrs.get('crs') == 32610
+
+
+class TestDatasetToGeotiff:
+    def test_round_trip(self, tmp_path):
+        ds = _make_ds()
+        path = str(tmp_path / 'test_1047_ds_roundtrip.tif')
+        ds.xrs.to_geotiff(path, compression='none')
+        result = open_geotiff(path)
+        np.testing.assert_array_equal(result.values, ds['elevation'].values)
+
+    def test_explicit_var(self, tmp_path):
+        ds = _make_ds()
+        ds['slope'] = ds['elevation'] * 2
+        path = str(tmp_path / 'test_1047_ds_var.tif')
+        ds.xrs.to_geotiff(path, var='slope', compression='none')
+        result = open_geotiff(path)
+        np.testing.assert_array_equal(result.values, ds['slope'].values)
+
+    def test_no_yx_raises(self, tmp_path):
+        ds = xr.Dataset({'vals': xr.DataArray(np.zeros(5), dims=['z'])})
+        with pytest.raises(ValueError, match="no variable with 'y' and 'x'"):
+            ds.xrs.to_geotiff(str(tmp_path / 'bad.tif'))
+
+
+class TestDatasetOpenGeotiff:
+    def test_windowed_read(self, tmp_path):
+        big = _make_da(height=20, width=20)
+        big_path = str(tmp_path / 'test_1047_big.tif')
+        to_geotiff(big, big_path, compression='none')
+        y_sub = big.coords['y'].values[5:15]
+        x_sub = big.coords['x'].values[5:15]
+        template = xr.Dataset({
+            'dummy': xr.DataArray(
+                np.zeros((len(y_sub), len(x_sub))),
+                dims=['y', 'x'],
+                coords={'y': y_sub, 'x': x_sub},
+            )
+        })
+        result = template.xrs.open_geotiff(big_path)
+        assert result.shape[0] <= 20
+        assert result.shape[1] <= 20
+        assert result.shape[0] >= len(y_sub)
+        assert result.shape[1] >= len(x_sub)
+
+    def test_full_extent_returns_all(self, tmp_path):
+        da = _make_da(height=8, width=10)
+        path = str(tmp_path / 'test_1047_full.tif')
+        to_geotiff(da, path, compression='none')
+        template = xr.Dataset({
+            'dummy': xr.DataArray(
+                np.zeros_like(da.values),
+                dims=['y', 'x'],
+                coords={'y': da.coords['y'].values, 'x': da.coords['x'].values},
+            )
+        })
+        result = template.xrs.open_geotiff(path)
+        np.testing.assert_array_equal(result.values, da.values)
+
+    def test_no_coords_raises(self, tmp_path):
+        da = _make_da()
+        path = str(tmp_path / 'test_1047_nocoords.tif')
+        to_geotiff(da, path, compression='none')
+        ds = xr.Dataset({'vals': xr.DataArray(np.zeros(5), dims=['z'])})
+        with pytest.raises(ValueError, match="'y' and 'x' coordinates"):
+            ds.xrs.open_geotiff(path)
+
+    def test_kwargs_forwarded(self, tmp_path):
+        da = _make_da(height=8, width=10)
+        path = str(tmp_path / 'test_1047_kwargs.tif')
+        to_geotiff(da, path, compression='none')
+        template = xr.Dataset({
+            'dummy': xr.DataArray(
+                np.zeros_like(da.values),
+                dims=['y', 'x'],
+                coords={'y': da.coords['y'].values, 'x': da.coords['x'].values},
+            )
+        })
+        result = template.xrs.open_geotiff(path, name='myname')
+        assert result.name == 'myname'
diff --git a/xrspatial/geotiff/tests/test_cog.py b/xrspatial/geotiff/tests/test_cog.py
index 40b24808..4fdea88f 100644
--- a/xrspatial/geotiff/tests/test_cog.py
+++ b/xrspatial/geotiff/tests/test_cog.py
@@ -5,7 +5,7 @@
 import pytest
 import xarray as xr
 
-from xrspatial.geotiff import read_geotiff, write_geotiff
+from xrspatial.geotiff import open_geotiff, to_geotiff
 from xrspatial.geotiff._header import parse_header, parse_all_ifds
 from xrspatial.geotiff._writer import write
 from xrspatial.geotiff._geotags import GeoTransform, extract_geo_info
@@ -83,36 +83,36 @@ def test_read_write_round_trip(self, tmp_path):
         )
 
         path = str(tmp_path / 'round_trip.tif')
-        write_geotiff(da, path, compression='deflate', tiled=False)
+        to_geotiff(da, path, compression='deflate', tiled=False)
 
-        result = read_geotiff(path)
+        result = open_geotiff(path)
         np.testing.assert_array_almost_equal(result.values, data, decimal=5)
         assert result.attrs.get('crs') == 4326
 
-    def test_read_geotiff_name(self, tmp_path):
+    def test_open_geotiff_name(self, tmp_path):
         """DataArray name defaults to filename stem."""
         arr = np.zeros((4, 4), dtype=np.float32)
         path = str(tmp_path / 'myfile.tif')
         write(arr, path, compression='none', tiled=False)
 
-        da = read_geotiff(path)
+        da = open_geotiff(path)
         assert da.name == 'myfile'
 
-    def test_read_geotiff_custom_name(self, tmp_path):
+    def test_open_geotiff_custom_name(self, tmp_path):
         arr = np.zeros((4, 4), dtype=np.float32)
         path = str(tmp_path / 'test.tif')
         write(arr, path, compression='none', tiled=False)
 
-        da = read_geotiff(path, name='custom')
+        da = open_geotiff(path, name='custom')
         assert da.name == 'custom'
 
     def test_write_numpy_array(self, tmp_path):
-        """write_geotiff should accept raw numpy arrays too."""
+        """to_geotiff should accept raw numpy arrays too."""
         arr = np.arange(16, dtype=np.float32).reshape(4, 4)
         path = str(tmp_path / 'numpy.tif')
-        write_geotiff(arr, path, compression='none')
+        to_geotiff(arr, path, compression='none')
 
-        result = read_geotiff(path)
+        result = open_geotiff(path)
         np.testing.assert_array_equal(result.values, arr)
 
     def test_write_3d_rgb(self, tmp_path):
@@ -120,15 +120,15 @@ def test_write_3d_rgb(self, tmp_path):
         arr = np.zeros((4, 4, 3), dtype=np.uint8)
         arr[:, :, 0] = 255  # red channel
         path = str(tmp_path / 'rgb.tif')
-        write_geotiff(arr, path, compression='none')
+        to_geotiff(arr, path, compression='none')
 
-        result = read_geotiff(path)
+        result = open_geotiff(path)
         np.testing.assert_array_equal(result.values, arr)
 
     def test_write_rejects_4d(self, tmp_path):
         arr = np.zeros((2, 3, 4, 4), dtype=np.float32)
         with pytest.raises(ValueError, match="Expected 2D or 3D"):
-            write_geotiff(arr, str(tmp_path / 'bad.tif'))
+            to_geotiff(arr, str(tmp_path / 'bad.tif'))
 
 
 def read_to_array_local(path):
diff --git a/xrspatial/geotiff/tests/test_edge_cases.py b/xrspatial/geotiff/tests/test_edge_cases.py
index 1a8a8680..834ff0bd 100644
--- a/xrspatial/geotiff/tests/test_edge_cases.py
+++ b/xrspatial/geotiff/tests/test_edge_cases.py
@@ -8,7 +8,7 @@
 import pytest
 import xarray as xr
 
-from xrspatial.geotiff import read_geotiff, write_geotiff
+from xrspatial.geotiff import open_geotiff, to_geotiff
 from xrspatial.geotiff._compression import (
     COMPRESSION_DEFLATE,
     COMPRESSION_LZW,
@@ -35,35 +35,35 @@ class TestWriteInvalidInputs:
     def test_4d_array(self, tmp_path):
         arr = np.zeros((2, 3, 4, 4), dtype=np.float32)
         with pytest.raises(ValueError, match="Expected 2D or 3D"):
-            write_geotiff(arr, str(tmp_path / 'bad.tif'))
+            to_geotiff(arr, str(tmp_path / 'bad.tif'))
 
     def test_1d_array(self, tmp_path):
         arr = np.zeros(10, dtype=np.float32)
         with pytest.raises(ValueError, match="Expected 2D"):
-            write_geotiff(arr, str(tmp_path / 'bad.tif'))
+            to_geotiff(arr, str(tmp_path / 'bad.tif'))
 
     def test_0d_scalar(self, tmp_path):
         arr = np.float32(42.0)
         with pytest.raises(ValueError, match="Expected 2D"):
-            write_geotiff(arr, str(tmp_path / 'bad.tif'))
+            to_geotiff(arr, str(tmp_path / 'bad.tif'))
 
     def test_unsupported_compression(self, tmp_path):
         arr = np.zeros((4, 4), dtype=np.float32)
         with pytest.raises(ValueError, match="Unsupported compression"):
-            write_geotiff(arr, str(tmp_path / 'bad.tif'), compression='jpeg2000')
+            to_geotiff(arr, str(tmp_path / 'bad.tif'), compression='jpeg2000')
 
     def test_complex_dtype(self, tmp_path):
         arr = np.zeros((4, 4), dtype=np.complex64)
         with pytest.raises(ValueError, match="Unsupported numpy dtype"):
-            write_geotiff(arr, str(tmp_path / 'bad.tif'))
+            to_geotiff(arr, str(tmp_path / 'bad.tif'))
 
     def test_bool_dtype_auto_promoted(self, tmp_path):
         """Bool arrays are auto-promoted to uint8."""
         arr = np.array([[True, False], [False, True]])
         path = str(tmp_path / 'bool.tif')
-        write_geotiff(arr, path, compression='none')
+        to_geotiff(arr, path, compression='none')
 
-        result = read_geotiff(path)
+        result = open_geotiff(path)
         np.testing.assert_array_equal(result.values, arr.astype(np.uint8))
 
 
@@ -411,9 +411,9 @@ def test_write_list_input(self, tmp_path):
         """Python list should be converted via np.asarray."""
         data = [[1.0, 2.0], [3.0, 4.0]]
         path = str(tmp_path / 'from_list.tif')
-        write_geotiff(data, path, compression='none')
+        to_geotiff(data, path, compression='none')
 
-        result = read_geotiff(path)
+        result = open_geotiff(path)
         np.testing.assert_array_almost_equal(
             result.values, np.array(data, dtype=np.float64))
 
@@ -421,22 +421,22 @@ def test_write_integer_list(self, tmp_path):
         """Integer Python list."""
         data = [[1, 2], [3, 4]]
         path = str(tmp_path / 'int_list.tif')
-        write_geotiff(data, path, compression='none')
+        to_geotiff(data, path, compression='none')
 
-        result = read_geotiff(path)
+        result = open_geotiff(path)
         np.testing.assert_array_equal(result.values, np.array(data))
 
     def test_read_nonexistent(self):
         with pytest.raises((FileNotFoundError, OSError)):
-            read_geotiff('/tmp/nonexistent_file_abc123.tif')
+            open_geotiff('/tmp/nonexistent_file_abc123.tif')
 
     def test_write_dataarray_no_coords(self, tmp_path):
         """DataArray with dimension coords but no explicit y/x."""
         da = xr.DataArray(np.ones((3, 3), dtype=np.float32), dims=['y', 'x'])
         path = str(tmp_path / 'no_coords.tif')
-        write_geotiff(da, path, compression='none')
+        to_geotiff(da, path, compression='none')
 
-        result = read_geotiff(path)
+        result = open_geotiff(path)
         np.testing.assert_array_equal(result.values, da.values)
 
     def test_write_f_contiguous_array(self, tmp_path):
@@ -444,9 +444,9 @@ def test_write_f_contiguous_array(self, tmp_path):
         arr = np.asfortranarray(np.arange(12, dtype=np.float32).reshape(3, 4))
         assert arr.flags['F_CONTIGUOUS']
         path = str(tmp_path / 'f_order.tif')
-        write_geotiff(arr, path, compression='deflate')
+        to_geotiff(arr, path, compression='deflate')
 
-        result = read_geotiff(path)
+        result = open_geotiff(path)
         np.testing.assert_array_equal(result.values, arr)
 
     def test_write_non_contiguous_slice(self, tmp_path):
@@ -455,9 +455,9 @@ def test_write_non_contiguous_slice(self, tmp_path):
         sliced = base[::2, ::2]  # 4x4, non-contiguous
         assert not sliced.flags['C_CONTIGUOUS']
         path = str(tmp_path / 'strided.tif')
-        write_geotiff(sliced, path, compression='none')
+        to_geotiff(sliced, path, compression='none')
 
-        result = read_geotiff(path)
+        result = open_geotiff(path)
         np.testing.assert_array_equal(result.values, sliced)
 
     def test_crs_in_attrs_with_coords(self, tmp_path):
@@ -468,27 +468,27 @@ def test_crs_in_attrs_with_coords(self, tmp_path):
                           dims=['y', 'x'], coords={'y': y, 'x': x},
                           attrs={'crs': 4326})
         path = str(tmp_path / 'crs_coords.tif')
-        write_geotiff(da, path, compression='none')
+        to_geotiff(da, path, compression='none')
 
-        result = read_geotiff(path)
+        result = open_geotiff(path)
         assert result.attrs['crs'] == 4326
 
     def test_crs_kwarg_no_coords(self, tmp_path):
         """Explicit crs= kwarg preserved even without coordinates."""
         arr = np.ones((4, 4), dtype=np.float32)
         path = str(tmp_path / 'crs_kwarg.tif')
-        write_geotiff(arr, path, crs=32610, compression='none')
+        to_geotiff(arr, path, crs=32610, compression='none')
 
-        result = read_geotiff(path)
+        result = open_geotiff(path)
         assert result.attrs['crs'] == 32610
 
     def test_crs_and_nodata_no_coords(self, tmp_path):
         """Both CRS and nodata preserved without coordinates."""
         arr = np.array([[1.0, -9999.0], [-9999.0, 2.0]], dtype=np.float32)
         path = str(tmp_path / 'both.tif')
-        write_geotiff(arr, path, crs=4326, nodata=-9999.0, compression='none')
+        to_geotiff(arr, path, crs=4326, nodata=-9999.0, compression='none')
 
-        result = read_geotiff(path)
+        result = open_geotiff(path)
         assert result.attrs['crs'] == 4326
         assert np.isnan(result.values[0, 1])
         assert result.values[1, 1] == 2.0
@@ -501,9 +501,9 @@ def test_crs_projected(self, tmp_path):
                           dims=['y', 'x'], coords={'y': y, 'x': x},
                           attrs={'crs': 32610})
         path = str(tmp_path / 'utm.tif')
-        write_geotiff(da, path, compression='none')
+        to_geotiff(da, path, compression='none')
 
-        result = read_geotiff(path)
+        result = open_geotiff(path)
         assert result.attrs['crs'] == 32610
 
     def test_pixel_is_point_round_trip(self, tmp_path):
@@ -516,9 +516,9 @@ def test_pixel_is_point_round_trip(self, tmp_path):
             attrs={'crs': 4326, 'raster_type': 'point'},
         )
         path = str(tmp_path / 'point.tif')
-        write_geotiff(da, path, compression='none')
+        to_geotiff(da, path, compression='none')
 
-        result = read_geotiff(path)
+        result = open_geotiff(path)
         assert result.attrs.get('raster_type') == 'point'
         assert result.attrs['crs'] == 4326
         # Coordinates should match exactly -- origin IS the pixel center
@@ -533,9 +533,9 @@ def test_pixel_is_area_default(self, tmp_path):
                           dims=['y', 'x'], coords={'y': y, 'x': x},
                           attrs={'crs': 4326})
         path = str(tmp_path / 'area.tif')
-        write_geotiff(da, path, compression='none')
+        to_geotiff(da, path, compression='none')
 
-        result = read_geotiff(path)
+        result = open_geotiff(path)
         assert 'raster_type' not in result.attrs  # default, not stored
         np.testing.assert_array_almost_equal(result.coords['x'].values, x, decimal=6)
         np.testing.assert_array_almost_equal(result.coords['y'].values, y, decimal=6)
@@ -544,9 +544,9 @@ def test_no_crs_no_nodata(self, tmp_path):
         """No CRS or nodata -- attrs should be empty."""
         arr = np.ones((4, 4), dtype=np.float32)
         path = str(tmp_path / 'bare.tif')
-        write_geotiff(arr, path, compression='none')
+        to_geotiff(arr, path, compression='none')
 
-        result = read_geotiff(path)
+        result = open_geotiff(path)
         assert 'crs' not in result.attrs
         assert 'nodata' not in result.attrs
 
@@ -555,9 +555,9 @@ def test_nodata_nan_in_attrs(self, tmp_path):
         arr = np.array([[1.0, np.nan], [np.nan, 2.0]], dtype=np.float32)
         da = xr.DataArray(arr, dims=['y', 'x'], attrs={'nodata': np.nan})
         path = str(tmp_path / 'nodata_nan.tif')
-        write_geotiff(da, path, compression='none')
+        to_geotiff(da, path, compression='none')
 
-        result = read_geotiff(path)
+        result = open_geotiff(path)
         assert np.isnan(result.values[0, 1])
         assert result.values[0, 0] == 1.0
 
@@ -567,9 +567,9 @@ def test_nodata_string_numeric(self, tmp_path):
         da = xr.DataArray(arr, dims=['y', 'x'],
                           attrs={'nodata': -9999.0})
         path = str(tmp_path / 'nodata_str.tif')
-        write_geotiff(da, path, compression='none')
+        to_geotiff(da, path, compression='none')
 
-        result = read_geotiff(path)
+        result = open_geotiff(path)
         # The nodata sentinel should be masked to NaN on read
         assert np.isnan(result.values[0, 1])
         assert np.isnan(result.values[1, 0])
diff --git a/xrspatial/geotiff/tests/test_features.py b/xrspatial/geotiff/tests/test_features.py
index 65b12024..48868b19 100644
--- a/xrspatial/geotiff/tests/test_features.py
+++ b/xrspatial/geotiff/tests/test_features.py
@@ -5,7 +5,7 @@
 import pytest
 import xarray as xr
 
-from xrspatial.geotiff import read_geotiff, write_geotiff
+from xrspatial.geotiff import open_geotiff, to_geotiff
 from xrspatial.geotiff._compression import (
     COMPRESSION_PACKBITS,
     packbits_compress,
@@ -76,12 +76,12 @@ def test_single_band_selection(self, tmp_path):
         np.testing.assert_array_equal(result, 42)
 
     def test_rgb_write_geotiff_api(self, tmp_path):
-        """write_geotiff accepts 3D arrays."""
+        """to_geotiff accepts 3D arrays."""
         arr = np.arange(48, dtype=np.uint8).reshape(4, 4, 3)
         path = str(tmp_path / 'rgb_api.tif')
-        write_geotiff(arr, path, compression='none')
+        to_geotiff(arr, path, compression='none')
 
-        result = read_geotiff(path)
+        result = open_geotiff(path)
         assert 'band' in result.dims
         assert result.shape == (4, 4, 3)
         np.testing.assert_array_equal(result.values, arr)
@@ -109,7 +109,7 @@ def test_uint8_nodata_masked(self, tmp_path):
         path = str(tmp_path / 'uint8_nodata.tif')
         write(arr, path, compression='none', tiled=False, nodata=255)
 
-        da = read_geotiff(path)
+        da = open_geotiff(path)
         assert np.isnan(da.values[1, 1])
         assert da.values[0, 1] == 1.0
         assert da.dtype == np.float64  # promoted from uint8
@@ -119,7 +119,7 @@ def test_uint16_nodata_masked(self, tmp_path):
         path = str(tmp_path / 'uint16_nodata.tif')
         write(arr, path, compression='none', tiled=False, nodata=0)
 
-        da = read_geotiff(path)
+        da = open_geotiff(path)
         assert np.isnan(da.values[0, 1])
         assert np.isnan(da.values[1, 1])
         assert da.values[0, 0] == 100.0
@@ -129,7 +129,7 @@ def test_int16_nodata_negative(self, tmp_path):
         path = str(tmp_path / 'int16_nodata.tif')
         write(arr, path, compression='none', tiled=False, nodata=-9999)
 
-        da = read_geotiff(path)
+        da = open_geotiff(path)
         assert np.isnan(da.values[0, 0])
         assert np.isnan(da.values[1, 1])
         assert da.values[0, 1] == 10.0
@@ -140,7 +140,7 @@ def test_integer_no_nodata_stays_integer(self, tmp_path):
         path = str(tmp_path / 'no_nodata.tif')
         write(arr, path, compression='none', tiled=False)
 
-        da = read_geotiff(path)
+        da = open_geotiff(path)
         assert da.dtype == np.uint16
 
 
@@ -250,9 +250,9 @@ def test_zstd_multiband(self, tmp_path):
     def test_zstd_public_api(self, tmp_path):
         arr = np.ones((4, 4), dtype=np.float32)
         path = str(tmp_path / 'zstd_api.tif')
-        write_geotiff(arr, path, compression='zstd')
+        to_geotiff(arr, path, compression='zstd')
 
-        result = read_geotiff(path)
+        result = open_geotiff(path)
         np.testing.assert_array_equal(result.values, arr)
 
 
@@ -272,7 +272,7 @@ def test_geographic_crs_attrs(self, tmp_path):
         write(arr, path, compression='none', tiled=False,
               geo_transform=gt, crs_epsg=4326)
 
-        da = read_geotiff(path)
+        da = open_geotiff(path)
         assert da.attrs['crs'] == 4326
         assert da.attrs.get('geog_citation') is not None or da.attrs['crs'] == 4326
 
@@ -286,7 +286,7 @@ def test_projected_crs_attrs(self, tmp_path):
         write(arr, path, compression='none', tiled=False,
               geo_transform=gt, crs_epsg=32610)
 
-        da = read_geotiff(path)
+        da = open_geotiff(path)
         assert da.attrs['crs'] == 32610
 
     def test_geoinfo_fields_from_real_file(self):
@@ -296,7 +296,7 @@ def test_geoinfo_fields_from_real_file(self):
         if not os.path.exists(path):
             pytest.skip("Real test files not available")
 
-        da = read_geotiff(path)
+        da = open_geotiff(path)
         assert da.attrs['crs'] == 4269
         assert da.attrs['geog_citation'] == 'NAD83'
         assert da.attrs['angular_units'] == 'degree'
@@ -310,7 +310,7 @@ def test_geoinfo_fields_from_projected_file(self):
         if not os.path.exists(path):
             pytest.skip("Real test files not available")
 
-        da = read_geotiff(path)
+        da = open_geotiff(path)
         assert da.attrs['crs'] == 26918
         assert da.attrs['crs_name'] == 'NAD83 / UTM zone 18N'
         assert da.attrs['geog_citation'] == 'NAD83'
@@ -322,7 +322,7 @@ def test_no_crs_no_geokey_attrs(self, tmp_path):
         path = str(tmp_path / 'bare.tif')
         write(arr, path, compression='none', tiled=False)
 
-        da = read_geotiff(path)
+        da = open_geotiff(path)
         assert 'crs_name' not in da.attrs
         assert 'geog_citation' not in da.attrs
         assert 'angular_units' not in da.attrs
@@ -346,7 +346,7 @@ def test_crs_wkt_from_epsg(self, tmp_path):
         write(arr, path, compression='none', tiled=False,
               geo_transform=gt, crs_epsg=4326)
 
-        da = read_geotiff(path)
+        da = open_geotiff(path)
         assert 'crs_wkt' in da.attrs
         wkt = da.attrs['crs_wkt']
         assert 'WGS 84' in wkt or '4326' in wkt
@@ -362,19 +362,19 @@ def test_write_with_wkt_string(self, tmp_path):
                'UNIT["degree",0.0174532925199433],'
                'ID["EPSG",4326]]')
         path = str(tmp_path / 'wkt_in.tif')
-        write_geotiff(arr, path, crs=wkt, compression='none')
+        to_geotiff(arr, path, crs=wkt, compression='none')
 
-        da = read_geotiff(path)
+        da = open_geotiff(path)
         assert da.attrs['crs'] == 4326
 
     def test_write_with_proj_string(self, tmp_path):
         """crs= accepts a PROJ string."""
         arr = np.ones((4, 4), dtype=np.float32)
         path = str(tmp_path / 'proj_in.tif')
-        write_geotiff(arr, path, crs='+proj=utm +zone=18 +datum=NAD83',
+        to_geotiff(arr, path, crs='+proj=utm +zone=18 +datum=NAD83',
                       compression='none')
 
-        da = read_geotiff(path)
+        da = open_geotiff(path)
         # pyproj should resolve this to EPSG:26918
         assert da.attrs.get('crs') is not None
 
@@ -393,9 +393,9 @@ def test_crs_wkt_attr_round_trip(self, tmp_path):
                           dims=['y', 'x'], coords={'y': y, 'x': x},
                           attrs={'crs_wkt': wkt})
         path = str(tmp_path / 'wkt_rt.tif')
-        write_geotiff(da, path, compression='none')
+        to_geotiff(da, path, compression='none')
 
-        result = read_geotiff(path)
+        result = open_geotiff(path)
         assert result.attrs['crs'] == 4326
         assert 'crs_wkt' in result.attrs
 
@@ -405,7 +405,7 @@ def test_no_crs_no_wkt(self, tmp_path):
         path = str(tmp_path / 'no_wkt.tif')
         write(arr, path, compression='none', tiled=False)
 
-        da = read_geotiff(path)
+        da = open_geotiff(path)
         assert 'crs_wkt' not in da.attrs
 
 
@@ -448,9 +448,9 @@ def test_band_first_dataarray(self, tmp_path):
 
         da = xr.DataArray(arr, dims=['band', 'y', 'x'])
         path = str(tmp_path / 'band_first.tif')
-        write_geotiff(da, path, compression='none')
+        to_geotiff(da, path, compression='none')
 
-        result = read_geotiff(path)
+        result = open_geotiff(path)
         assert result.shape == (8, 8, 3)
         assert result.values[0, 0, 0] == 200  # red channel
         assert result.values[0, 0, 1] == 100  # green channel
@@ -461,9 +461,9 @@ def test_band_last_dataarray_unchanged(self, tmp_path):
         arr[:, :, 0] = 200
         da = xr.DataArray(arr, dims=['y', 'x', 'band'])
         path = str(tmp_path / 'band_last.tif')
-        write_geotiff(da, path, compression='none')
+        to_geotiff(da, path, compression='none')
 
-        result = read_geotiff(path)
+        result = open_geotiff(path)
         assert result.shape == (8, 8, 3)
         assert result.values[0, 0, 0] == 200
 
@@ -550,9 +550,9 @@ def test_float16_auto_promotion(self, tmp_path):
         """Float16 arrays are auto-promoted to float32."""
         arr = np.ones((4, 4), dtype=np.float16) * 3.14
         path = str(tmp_path / 'f16.tif')
-        write_geotiff(arr, path, compression='none')
+        to_geotiff(arr, path, compression='none')
 
-        result = read_geotiff(path)
+        result = open_geotiff(path)
         assert result.dtype == np.float32
         np.testing.assert_array_almost_equal(result.values, 3.14, decimal=2)
 
@@ -578,7 +578,7 @@ def test_vrt_write_and_read_back(self, tmp_path):
         vrt_path = str(tmp_path / 'mosaic.vrt')
         write_vrt(vrt_path, [lpath, rpath])
 
-        da = read_geotiff(vrt_path)
+        da = open_geotiff(vrt_path)
         assert da.shape == (4, 8)
         np.testing.assert_array_equal(da.values[:, :4], left)
         np.testing.assert_array_equal(da.values[:, 4:], right)
@@ -657,7 +657,7 @@ def test_single_tile_vrt(self, tmp_path):
             width=4, height=4,
         )
 
-        da = read_geotiff(vrt_path)
+        da = open_geotiff(vrt_path)
         np.testing.assert_array_equal(da.values, arr)
 
     def test_2x1_mosaic(self, tmp_path):
@@ -673,7 +673,7 @@ def test_2x1_mosaic(self, tmp_path):
             width=8, height=4,
         )
 
-        da = read_geotiff(vrt_path)
+        da = open_geotiff(vrt_path)
         assert da.shape == (4, 8)
         np.testing.assert_array_equal(da.values[:, :4], left)
         np.testing.assert_array_equal(da.values[:, 4:], right)
@@ -698,7 +698,7 @@ def test_2x2_mosaic(self, tmp_path):
             width=8, height=8,
         )
 
-        da = read_geotiff(vrt_path)
+        da = open_geotiff(vrt_path)
         assert da.shape == (8, 8)
         # Check each quadrant
         np.testing.assert_array_equal(da.values[0:4, 0:4], tiles[0])
@@ -720,7 +720,7 @@ def test_windowed_vrt_read(self, tmp_path):
         )
 
         # Window spanning both tiles
-        da = read_geotiff(vrt_path, window=(1, 2, 3, 6))
+        da = open_geotiff(vrt_path, window=(1, 2, 3, 6))
         assert da.shape == (2, 4)
         expected = np.hstack([left, right])[1:3, 2:6]
         np.testing.assert_array_equal(da.values, expected)
@@ -748,7 +748,7 @@ def test_vrt_with_crs(self, tmp_path):
         with open(vrt_path, 'w') as f:
             f.write(vrt_xml)
 
-        da = read_geotiff(vrt_path)
+        da = open_geotiff(vrt_path)
         assert da.attrs.get('crs_wkt') == 'EPSG:4326'
         assert len(da.coords['x']) == 4
         assert len(da.coords['y']) == 4
@@ -775,7 +775,7 @@ def test_vrt_nodata(self, tmp_path):
         with open(vrt_path, 'w') as f:
             f.write(vrt_xml)
 
-        da = read_geotiff(vrt_path)
+        da = open_geotiff(vrt_path)
         assert da.attrs.get('nodata') == -9999.0
 
     def test_read_vrt_function(self, tmp_path):
@@ -880,14 +880,14 @@ def test_memory_filesystem_read_write(self, tmp_path):
         fs.rm('/test.tif')
 
     def test_memory_filesystem_full_roundtrip(self, tmp_path):
-        """write_geotiff + read_geotiff through memory:// filesystem."""
+        """to_geotiff + open_geotiff through memory:// filesystem."""
         import fsspec
 
         arr = np.arange(16, dtype=np.float32).reshape(4, 4)
 
         # Write locally first, then copy to memory fs
         local_path = str(tmp_path / 'local.tif')
-        write_geotiff(arr, local_path, compression='deflate')
+        to_geotiff(arr, local_path, compression='deflate')
         with open(local_path, 'rb') as f:
             tiff_bytes = f.read()
 
@@ -1016,7 +1016,7 @@ def test_big_endian_windowed(self, tmp_path):
         np.testing.assert_array_equal(result, expected[2:6, 3:7])
 
     def test_big_endian_via_public_api(self, tmp_path):
-        """read_geotiff handles big-endian files."""
+        """open_geotiff handles big-endian files."""
         from .conftest import make_minimal_tiff
         expected = np.arange(16, dtype=np.float32).reshape(4, 4)
         tiff_data = make_minimal_tiff(
@@ -1027,7 +1027,7 @@ def test_big_endian_via_public_api(self, tmp_path):
         with open(path, 'wb') as f:
             f.write(tiff_data)
 
-        da = read_geotiff(path)
+        da = open_geotiff(path)
         assert da.attrs['crs'] == 4326
         np.testing.assert_array_equal(da.values, expected)
 
@@ -1126,7 +1126,7 @@ def add_ascii(tag, text):
     def test_extra_tags_read(self, tmp_path):
         """Extra tags are collected in attrs['extra_tags']."""
         path, _ = self._make_tiff_with_extra_tags(tmp_path)
-        da = read_geotiff(path)
+        da = open_geotiff(path)
 
         extra = da.attrs.get('extra_tags')
         assert extra is not None
@@ -1137,12 +1137,12 @@ def test_extra_tags_read(self, tmp_path):
     def test_extra_tags_round_trip(self, tmp_path):
         """Extra tags survive read -> write -> read."""
         path, pixels = self._make_tiff_with_extra_tags(tmp_path)
-        da = read_geotiff(path)
+        da = open_geotiff(path)
 
         out_path = str(tmp_path / 'roundtrip.tif')
-        write_geotiff(da, out_path, compression='none')
+        to_geotiff(da, out_path, compression='none')
 
-        da2 = read_geotiff(out_path)
+        da2 = open_geotiff(out_path)
 
         # Pixels should match
         np.testing.assert_array_equal(da2.values, pixels)
@@ -1162,7 +1162,7 @@ def test_no_extra_tags(self, tmp_path):
         path = str(tmp_path / 'no_extra.tif')
         write(arr, path, compression='none', tiled=False)
 
-        da = read_geotiff(path)
+        da = open_geotiff(path)
         assert 'extra_tags' not in da.attrs
 
 
@@ -1218,7 +1218,7 @@ def test_round_trip_via_file(self, tmp_path):
         write(arr, path, compression='none', tiled=False,
               gdal_metadata_xml=xml)
 
-        da = read_geotiff(path)
+        da = open_geotiff(path)
         assert 'gdal_metadata' in da.attrs
         assert 'gdal_metadata_xml' in da.attrs
         result_meta = da.attrs['gdal_metadata']
@@ -1235,9 +1235,9 @@ def test_dataarray_attrs_round_trip(self, tmp_path):
             attrs={'gdal_metadata': meta},
         )
         path = str(tmp_path / 'da_meta.tif')
-        write_geotiff(da, path, compression='none')
+        to_geotiff(da, path, compression='none')
 
-        result = read_geotiff(path)
+        result = open_geotiff(path)
         assert result.attrs['gdal_metadata']['Source'] == 'test'
         assert result.attrs['gdal_metadata'][('BAND', 0)] == 'dem'
 
@@ -1247,7 +1247,7 @@ def test_no_metadata_no_attrs(self, tmp_path):
         path = str(tmp_path / 'no_meta.tif')
         write(arr, path, compression='none', tiled=False)
 
-        da = read_geotiff(path)
+        da = open_geotiff(path)
         assert 'gdal_metadata' not in da.attrs
         assert 'gdal_metadata_xml' not in da.attrs
 
@@ -1258,7 +1258,7 @@ def test_real_file_metadata(self):
         if not os.path.exists(path):
             pytest.skip("Real test files not available")
 
-        da = read_geotiff(path)
+        da = open_geotiff(path)
         meta = da.attrs.get('gdal_metadata')
         assert meta is not None
         assert 'DataType' in meta
@@ -1271,13 +1271,13 @@ def test_real_file_round_trip(self):
         if not os.path.exists(path):
             pytest.skip("Real test files not available")
 
-        da = read_geotiff(path)
+        da = open_geotiff(path)
         orig_meta = da.attrs['gdal_metadata']
 
         out = os.path.join(tempfile.mkdtemp(), 'rt.tif')
-        write_geotiff(da, out, compression='deflate', tiled=False)
+        to_geotiff(da, out, compression='deflate', tiled=False)
 
-        da2 = read_geotiff(out)
+        da2 = open_geotiff(out)
         for k, v in orig_meta.items():
             assert da2.attrs['gdal_metadata'].get(k) == v, f"Mismatch on {k}"
 
@@ -1291,7 +1291,7 @@ def test_write_read_dpi(self, tmp_path):
         write(arr, path, compression='none', tiled=False,
               x_resolution=300.0, y_resolution=300.0, resolution_unit=2)
 
-        da = read_geotiff(path)
+        da = open_geotiff(path)
         assert da.attrs['x_resolution'] == pytest.approx(300.0, rel=0.01)
         assert da.attrs['y_resolution'] == pytest.approx(300.0, rel=0.01)
         assert da.attrs['resolution_unit'] == 'inch'
@@ -1303,7 +1303,7 @@ def test_write_read_cm(self, tmp_path):
         write(arr, path, compression='none', tiled=False,
               x_resolution=118.0, y_resolution=118.0, resolution_unit=3)
 
-        da = read_geotiff(path)
+        da = open_geotiff(path)
         assert da.attrs['x_resolution'] == pytest.approx(118.0, rel=0.01)
         assert da.attrs['resolution_unit'] == 'centimeter'
 
@@ -1313,7 +1313,7 @@ def test_no_resolution_no_attrs(self, tmp_path):
         path = str(tmp_path / 'no_dpi.tif')
         write(arr, path, compression='none', tiled=False)
 
-        da = read_geotiff(path)
+        da = open_geotiff(path)
         assert 'x_resolution' not in da.attrs
         assert 'y_resolution' not in da.attrs
         assert 'resolution_unit' not in da.attrs
@@ -1327,9 +1327,9 @@ def test_dataarray_attrs_round_trip(self, tmp_path):
                    'resolution_unit': 'inch'},
         )
         path = str(tmp_path / 'da_dpi.tif')
-        write_geotiff(da, path, compression='none')
+        to_geotiff(da, path, compression='none')
 
-        result = read_geotiff(path)
+        result = open_geotiff(path)
         assert result.attrs['x_resolution'] == pytest.approx(72.0, rel=0.01)
         assert result.attrs['y_resolution'] == pytest.approx(72.0, rel=0.01)
         assert result.attrs['resolution_unit'] == 'inch'
@@ -1341,7 +1341,7 @@ def test_unit_none(self, tmp_path):
         write(arr, path, compression='none', tiled=False,
               x_resolution=1.0, y_resolution=1.0, resolution_unit=1)
 
-        da = read_geotiff(path)
+        da = open_geotiff(path)
         assert da.attrs['resolution_unit'] == 'none'
 
 
@@ -1479,14 +1479,14 @@ def test_cog_round_trip_mode(self, tmp_path):
         assert ov[0, 0] == 0
         assert ov[0, 1] == 1
 
-    def test_write_geotiff_api(self, tmp_path):
+    def test_to_geotiff_api(self, tmp_path):
         """overview_resampling kwarg works through the public API."""
         arr = np.arange(64, dtype=np.float32).reshape(8, 8)
         path = str(tmp_path / 'api_nearest.tif')
-        write_geotiff(arr, path, compression='deflate',
+        to_geotiff(arr, path, compression='deflate',
                       cog=True, overview_resampling='nearest')
 
-        result = read_geotiff(path)
+        result = open_geotiff(path)
         np.testing.assert_array_equal(result.values, arr)
 
     def test_invalid_method(self):
@@ -1569,23 +1569,23 @@ def test_bigtiff_read_write_round_trip(self, tmp_path):
         np.testing.assert_array_equal(result, arr)
 
     def test_force_bigtiff_via_public_api(self, tmp_path):
-        """bigtiff=True on write_geotiff forces BigTIFF even for small files."""
+        """bigtiff=True on to_geotiff forces BigTIFF even for small files."""
         arr = np.arange(16, dtype=np.float32).reshape(4, 4)
         path = str(tmp_path / 'forced_bigtiff.tif')
-        write_geotiff(arr, path, compression='none', bigtiff=True)
+        to_geotiff(arr, path, compression='none', bigtiff=True)
 
         with open(path, 'rb') as f:
             header = parse_header(f.read(16))
         assert header.is_bigtiff
 
-        result = read_geotiff(path)
+        result = open_geotiff(path)
         np.testing.assert_array_equal(result.values, arr)
 
     def test_small_file_stays_classic(self, tmp_path):
         """Small files default to classic TIFF (bigtiff=None auto-detects)."""
         arr = np.arange(16, dtype=np.float32).reshape(4, 4)
         path = str(tmp_path / 'classic.tif')
-        write_geotiff(arr, path, compression='none')
+        to_geotiff(arr, path, compression='none')
 
         with open(path, 'rb') as f:
             header = parse_header(f.read(16))
@@ -1595,7 +1595,7 @@ def test_force_bigtiff_false_stays_classic(self, tmp_path):
         """bigtiff=False forces classic TIFF."""
         arr = np.arange(16, dtype=np.float32).reshape(4, 4)
         path = str(tmp_path / 'forced_classic.tif')
-        write_geotiff(arr, path, compression='none', bigtiff=False)
+        to_geotiff(arr, path, compression='none', bigtiff=False)
 
         with open(path, 'rb') as f:
             header = parse_header(f.read(16))
@@ -2135,7 +2135,7 @@ def test_palette_8bit_read(self, tmp_path):
         with open(path, 'wb') as f:
             f.write(tiff_data)
 
-        da = read_geotiff(path)
+        da = open_geotiff(path)
         # Should return raw index values
         assert da.dtype == np.uint8
         np.testing.assert_array_equal(da.values, pixels)
@@ -2162,7 +2162,7 @@ def test_palette_4bit(self, tmp_path):
         with open(path, 'wb') as f:
             f.write(tiff_data)
 
-        da = read_geotiff(path)
+        da = open_geotiff(path)
         assert da.dtype == np.uint8
         np.testing.assert_array_equal(da.values, pixels)
         assert 'cmap' in da.attrs
@@ -2185,7 +2185,7 @@ def test_palette_cmap_works_with_plot(self, tmp_path):
         with open(path, 'wb') as f:
             f.write(tiff_data)
 
-        da = read_geotiff(path)
+        da = open_geotiff(path)
         cmap = da.attrs['cmap']
         assert isinstance(cmap, ListedColormap)
 
@@ -2213,7 +2213,7 @@ def test_xrs_plot_with_palette(self, tmp_path):
         with open(path, 'wb') as f:
             f.write(tiff_data)
 
-        da = read_geotiff(path)
+        da = open_geotiff(path)
         artist = da.xrs.plot()
         assert artist is not None
         import matplotlib.pyplot as plt
@@ -2229,7 +2229,7 @@ def test_xrs_plot_no_palette(self, tmp_path):
         path = str(tmp_path / 'no_palette.tif')
         write(arr, path, compression='none', tiled=False)
 
-        da = read_geotiff(path)
+        da = open_geotiff(path)
         artist = da.xrs.plot()
         assert artist is not None
         import matplotlib.pyplot as plt
@@ -2249,7 +2249,7 @@ def test_plot_geotiff_deprecated(self, tmp_path):
         with open(path, 'wb') as f:
             f.write(tiff_data)
 
-        da = read_geotiff(path)
+        da = open_geotiff(path)
         artist = plot_geotiff(da)
         assert artist is not None
         import matplotlib.pyplot as plt
@@ -2261,7 +2261,7 @@ def test_non_palette_no_cmap(self, tmp_path):
         path = str(tmp_path / 'no_palette.tif')
         write(arr, path, compression='none', tiled=False)
 
-        da = read_geotiff(path)
+        da = open_geotiff(path)
         assert 'cmap' not in da.attrs
         assert 'colormap_rgba' not in da.attrs
 
@@ -2324,14 +2324,14 @@ def test_planar_band_selection(self, tmp_path):
         np.testing.assert_array_equal(result, expected[:, :, 1])
 
     def test_planar_via_public_api(self, tmp_path):
-        """read_geotiff on a planar file returns correct DataArray."""
-        from xrspatial.geotiff import read_geotiff
+        """open_geotiff on a planar file returns correct DataArray."""
+        from xrspatial.geotiff import open_geotiff
         tiff_data, expected = _make_planar_tiff(4, 4, 3, np.uint8)
         path = str(tmp_path / 'planar_api.tif')
         with open(path, 'wb') as f:
             f.write(tiff_data)
 
-        da = read_geotiff(path)
+        da = open_geotiff(path)
         assert 'band' in da.dims
         assert da.shape == (4, 4, 3)
         np.testing.assert_array_equal(da.values, expected)
diff --git a/xrspatial/geotiff/tests/test_jpeg.py b/xrspatial/geotiff/tests/test_jpeg.py
index 535be202..8edc89aa 100644
--- a/xrspatial/geotiff/tests/test_jpeg.py
+++ b/xrspatial/geotiff/tests/test_jpeg.py
@@ -131,10 +131,10 @@ def test_4band_rejected(self, tmp_path):
 
 
 class TestWriteGeotiffJpeg:
-    """Test the public write_geotiff API with compression='jpeg'."""
+    """Test the public to_geotiff API with compression='jpeg'."""
 
-    def test_write_geotiff_jpeg(self, tmp_path):
-        from xrspatial.geotiff import write_geotiff, read_geotiff
+    def test_to_geotiff_jpeg(self, tmp_path):
+        from xrspatial.geotiff import open_geotiff, to_geotiff
 
         rng = np.random.RandomState(1050)
         data = rng.randint(50, 200, (32, 32), dtype=np.uint8)
@@ -144,8 +144,8 @@ def test_write_geotiff_jpeg(self, tmp_path):
                     'x': np.arange(32, dtype=float)},
         )
         path = str(tmp_path / 'api_1050.tif')
-        write_geotiff(da, path, compression='jpeg', tile_size=16)
+        to_geotiff(da, path, compression='jpeg', tile_size=16)
 
-        result = read_geotiff(path)
+        result = open_geotiff(path)
         assert result.shape == (32, 32)
         assert np.abs(result.values.astype(int) - data.astype(int)).mean() < 10