From 0d4ab2097d24ab919f6dc21d1422693888784cc6 Mon Sep 17 00:00:00 2001
From: Matt McCormick <matt.mccormick@kitware.com>
Date: Fri, 6 May 2022 08:38:23 -0400
Subject: [PATCH 1/3] BUG: .gitignore example output data

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index 445c130..e277d92 100644
--- a/.gitignore
+++ b/.gitignore
@@ -6,3 +6,4 @@ examples/cthead1.zarr/
 examples/chelsea.zarr/
 .ipynb_checkpoints
 examples/sub-I46_ses-SPIM_sample-BrocaAreaS01_stain-GAD67_chunk-00_SPIM*
+examples/dask-worker-space/

From dc40d45cabca8c03b855ad6dab996274b4d6b961 Mon Sep 17 00:00:00 2001
From: Matt McCormick <matt.mccormick@kitware.com>
Date: Wed, 4 May 2022 12:53:31 -0400
Subject: [PATCH 2/3] ENH: And Convert Tiff File with OME-NGFF Zarr output

---
 examples/ConvertTiffFile.ipynb | 1276 ++++++++++++++++++++++++++++++++
 1 file changed, 1276 insertions(+)
 create mode 100644 examples/ConvertTiffFile.ipynb

diff --git a/examples/ConvertTiffFile.ipynb b/examples/ConvertTiffFile.ipynb
new file mode 100644
index 0000000..8d03873
--- /dev/null
+++ b/examples/ConvertTiffFile.ipynb
@@ -0,0 +1,1276 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c82b644e",
+   "metadata": {},
+   "source": [
+    "## Package install and setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cffe4de0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "!{sys.executable} -m pip install --upgrade spatial-image-multiscale matplotlib tifffile zarr ome-types distributed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "a3a9d3cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from spatial_image_multiscale import to_multiscale\n",
+    "from spatial_image import is_spatial_image, to_spatial_image\n",
+    "import tifffile\n",
+    "import os\n",
+    "from urllib.request import urlretrieve\n",
+    "import zarr\n",
+    "import ome_types\n",
+    "import dask.array as da"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b3f65ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Faster encoding\n",
+    "from dask.distributed import Client, LocalCluster\n",
+    "cluster = LocalCluster()  # Launches a scheduler and workers locally\n",
+    "client = Client(cluster)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa57d874",
+   "metadata": {},
+   "source": [
+    "## Download example image\n",
+    "\n",
+    "Lightsheet volume [from the human cortex](https://dandiarchive.org/dandiset/000026/draft/files?location=rawdata%2Fsub-I46%2Fses-SPIM%2F).\n",
+    "\n",
+    "Mazzamuto, Giacomo; Costantini, Irene; Gavryusev, Vladislav; Castelli, Filippo Maria; Pesce, Luca; Scardigli, Marina; Pavone, Francesco Saverio; Roffilli, Matteo; Silvestri, Ludovico; Brady, Niamh; Ramazzotti, Josephine; Hof, Patrick R.; Boas, David A.; Fischl, Bruce; Morgan, Leah; Yang, Jiarui; Chang, Shuaibin; Laffey, Jessie; Magnain, Caroline; Varadarajan, Divya; Wang, Hui; Frost, Robert; Kouwe, Andre van der; Player, Allison Stevens; Atzeni, Alessia; Gonzalez, Juan Eugenio Iglesias; Balbastre, Yael; Vera, Matthew; Cordero, Devani; Nestor, Kimberly; Ammon, William; Nolan, Jackson; Mora, Jocelyn; Pallares, Erendira Garcia; Augustinack, Jean; Diamond, Bram; Fogarty, Morgan; Boyd, Emma; Varghese, Merina; Dalca, Adrian V.; Edlow, Brian; Frosche, Matthew; Wicinski, Bridget; Chen, I-Chun Anderson (2022) Human brain cell census for BA 44/45 (Version draft) [Data set]. DANDI archive. https://dandiarchive.org/dandiset/000026/draft"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "865974c8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image_name = 'sub-I46_ses-SPIM_sample-BrocaAreaS01_stain-GAD67_chunk-00_SPIM'\n",
+    "filename = f'{image_name}.ome.tif'\n",
+    "if not os.path.exists(filename):\n",
+    "    url = f'https://api.dandiarchive.org/api/assets/fcb6c425-ee14-4247-b2e4-d2550273f1f3/download'\n",
+    "    urlretrieve(url, filename)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d34a205",
+   "metadata": {},
+   "source": [
+    "## Inspect metadata, data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "9d11d71c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tiff = tifffile.TiffFile(filename)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18739faa",
+   "metadata": {},
+   "source": [
+    "[OME-TIFF](https://docs.openmicroscopy.org/ome-model/6.2.2/)'s contain useful, standardized metadata and have broad community adoption"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "432abb91",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tiff.is_ome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "eef1e9e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'<ome:OME xmlns:ome=\"http://www.openmicroscopy.org/Schemas/OME/2016-06\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xsi:schemaLocation=\"http://www.openmicroscopy.org/Schemas/OME/2016-06 http://www.openmicroscopy.org/Schemas/OME/2016-06/ome.xsd\">\\n    <ome:Image ID=\"Image:0\" Name=\"sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM\">\\n        <ome:AcquisitionDate>2019-10-01T00:00:00</ome:AcquisitionDate>\\n        <ome:Pixels BigEndian=\"false\" DimensionOrder=\"XYZCT\" ID=\"Pixels:0\" Interleaved=\"false\" SizeC=\"1\" SizeT=\"1\" SizeX=\"3882\" SizeY=\"342\" SizeZ=\"242\" Type=\"uint16\" PhysicalSizeX=\"3.3\" PhysicalSizeXUnit=\"µm\" PhysicalSizeY=\"3.3\" PhysicalSizeYUnit=\"µm\" PhysicalSizeZ=\"3.3\" PhysicalSizeZUnit=\"µm\">\\n            <ome:Channel ID=\"Channel:00\" SamplesPerPixel=\"1\" Name=\"GAD67\" ExcitationWavelength=\"638\" EmissionWavelength=\"690\" ExcitationWavelengthUnit=\"nm\" EmissionWavelengthUnit=\"nm\" Fluor=\"Alexa Fluor 647\">\\n                <ome:LightPath />\\n            </ome:Channel>\\n        <ome:TiffData IFD=\"0\" PlaneCount=\"242\" /></ome:Pixels>\\n    </ome:Image>\\n</ome:OME>'"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tiff.ome_metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "59062f84",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "binary_only=None creator=None datasets=[] experimenter_groups=[] experimenters=[] experiments=[] folders=[] images=[Image(\n",
+      "   id='Image:0',\n",
+      "   name='sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM',\n",
+      "   pixels=Pixels(\n",
+      "      id='Pixels:0',\n",
+      "      dimension_order='XYZCT',\n",
+      "      size_c=1,\n",
+      "      size_t=1,\n",
+      "      size_x=3882,\n",
+      "      size_y=342,\n",
+      "      size_z=242,\n",
+      "      type='uint16',\n",
+      "      big_endian=False,\n",
+      "      channels=[<1 Channels>],\n",
+      "      interleaved=False,\n",
+      "      physical_size_x=3.3,\n",
+      "      physical_size_y=3.3,\n",
+      "      physical_size_z=3.3,\n",
+      "      tiff_data_blocks=[<1 Tiff_Data_Blocks>],\n",
+      "   ),\n",
+      "   acquisition_date=datetime.fromisoformat('2019-10-01T00:00:00'),\n",
+      ")] instruments=[] plates=[] projects=[] rights=None rois=[] screens=[] structured_annotations=[] uuid=None\n"
+     ]
+    }
+   ],
+   "source": [
+    "ome = ome_types.from_xml(tiff.ome_metadata)\n",
+    "print(ome)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "290db649",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['z', 'y', 'x']"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dims = [d for d in tiff.series[0].axes.lower().replace('i', 'z').replace('s', 'c')]\n",
+    "dims"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "5679e320",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'z': 3.3, 'y': 3.3, 'x': 3.3}"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scale = { d:getattr(ome.images[0].pixels, f\"physical_size_{d}\") for d in dims}\n",
+    "scale"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "59e371b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'z': 'micrometer', 'y': 'micrometer', 'x': 'micrometer'}"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "axis_units = { d:str(getattr(ome.images[0].pixels, f\"physical_size_{d}_quantity\").units) for d in dims}\n",
+    "axis_units"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "33d1afd0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 612.81 MiB </td>\n",
+       "                        <td> 126.61 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (242, 342, 3882) </td>\n",
+       "                        <td> (50, 342, 3882) </td>\n",
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+       "                        <td> 5 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> uint16 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"201\" height=\"108\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "</svg>\n",
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+       "</table>"
+      ],
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+       "dask.array<array, shape=(242, 342, 3882), dtype=uint16, chunksize=(50, 342, 3882), chunktype=numpy.ndarray>"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_store = tiff.series[0].aszarr()\n",
+    "zarr_data = zarr.open(data_store, 'r')\n",
+    "data = da.array(zarr_data)\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "4650f030",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM'"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "name = ome.images[0].name\n",
+    "name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "18ed5e3a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
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+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\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-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\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 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\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: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",
+       "  padding-bottom: 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-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\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",
+       "  margin-bottom: 0;\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",
+       "}\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-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",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-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",
+       "  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",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.SpatialImage &#x27;sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM&#x27; (z: 242,\n",
+       "                                                                     y: 342,\n",
+       "                                                                     x: 3882)&gt;\n",
+       "dask.array&lt;array, shape=(242, 342, 3882), dtype=uint16, chunksize=(50, 342, 3882), chunktype=numpy.ndarray&gt;\n",
+       "Coordinates:\n",
+       "  * z        (z) float64 0.0 3.3 6.6 9.9 13.2 ... 782.1 785.4 788.7 792.0 795.3\n",
+       "  * y        (y) float64 0.0 3.3 6.6 9.9 ... 1.119e+03 1.122e+03 1.125e+03\n",
+       "  * x        (x) float64 0.0 3.3 6.6 9.9 ... 1.28e+04 1.28e+04 1.281e+04</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.SpatialImage</div><div class='xr-array-name'>'sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>z</span>: 242</li><li><span class='xr-has-index'>y</span>: 342</li><li><span class='xr-has-index'>x</span>: 3882</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5c9697bf-7ae9-4960-af64-8636df67b890' class='xr-array-in' type='checkbox' checked><label for='section-5c9697bf-7ae9-4960-af64-8636df67b890' 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>dask.array&lt;chunksize=(50, 342, 3882), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table>\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 612.81 MiB </td>\n",
+       "                        <td> 126.61 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (242, 342, 3882) </td>\n",
+       "                        <td> (50, 342, 3882) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Count </th>\n",
+       "                        <td> 6 Tasks </td>\n",
+       "                        <td> 5 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> uint16 </td>\n",
+       "                    <td> numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"201\" height=\"108\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"37\" x2=\"31\" y2=\"58\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"37\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"42\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"18\" y2=\"46\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"23\" y2=\"50\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"27\" y2=\"55\" />\n",
+       "  <line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"58\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 31.08469041486957,21.08469041486957 31.08469041486957,58.96802878764755 10.0,37.88333837277798\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"14\" y1=\"4\" x2=\"134\" y2=\"4\" />\n",
+       "  <line x1=\"18\" y1=\"8\" x2=\"138\" y2=\"8\" />\n",
+       "  <line x1=\"23\" y1=\"13\" x2=\"143\" y2=\"13\" />\n",
+       "  <line x1=\"27\" y1=\"17\" x2=\"147\" y2=\"17\" />\n",
+       "  <line x1=\"31\" y1=\"21\" x2=\"151\" y2=\"21\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"130\" y1=\"0\" x2=\"151\" y2=\"21\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 130.0,0.0 151.08469041486956,21.08469041486957 31.08469041486957,21.08469041486957\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"31\" y1=\"21\" x2=\"151\" y2=\"21\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"31\" y1=\"58\" x2=\"151\" y2=\"58\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"58\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"151\" y1=\"21\" x2=\"151\" y2=\"58\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"31.08469041486957,21.08469041486957 151.08469041486956,21.08469041486957 151.08469041486956,58.96802878764755 31.08469041486957,58.96802878764755\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"91.084690\" y=\"78.968029\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3882</text>\n",
+       "  <text x=\"171.084690\" y=\"40.026360\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,171.084690,40.026360)\">342</text>\n",
+       "  <text x=\"10.542345\" y=\"68.425684\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,10.542345,68.425684)\">242</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-8dab55ed-4866-477e-b01b-4a5fc40ec381' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8dab55ed-4866-477e-b01b-4a5fc40ec381' class='xr-section-summary' >Coordinates: <span>(3)</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'>z</span></div><div class='xr-var-dims'>(z)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 3.3 6.6 ... 788.7 792.0 795.3</div><input id='attrs-4cd7fd6b-ca0e-49ea-ba35-4bf47512151d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4cd7fd6b-ca0e-49ea-ba35-4bf47512151d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8fd2f7e-0ac1-4087-8d13-64f8b81d2aa1' class='xr-var-data-in' type='checkbox'><label for='data-f8fd2f7e-0ac1-4087-8d13-64f8b81d2aa1' 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'><dt><span>long_name :</span></dt><dd>z</dd><dt><span>units :</span></dt><dd>micrometer</dd></dl></div><div class='xr-var-data'><pre>array([  0. ,   3.3,   6.6, ..., 788.7, 792. , 795.3])</pre></div></li><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'>0.0 3.3 6.6 ... 1.122e+03 1.125e+03</div><input id='attrs-b72ea656-3845-498a-a42e-e26ab76a53f0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b72ea656-3845-498a-a42e-e26ab76a53f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eda15115-682c-4ffa-978e-6f9f9a5e3119' class='xr-var-data-in' type='checkbox'><label for='data-eda15115-682c-4ffa-978e-6f9f9a5e3119' 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'><dt><span>long_name :</span></dt><dd>y</dd><dt><span>units :</span></dt><dd>micrometer</dd></dl></div><div class='xr-var-data'><pre>array([   0. ,    3.3,    6.6, ..., 1118.7, 1122. , 1125.3])</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'>0.0 3.3 6.6 ... 1.28e+04 1.281e+04</div><input id='attrs-65c412c3-9a61-4c4d-9b28-78ade69285b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-65c412c3-9a61-4c4d-9b28-78ade69285b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7adeadb6-d9eb-4409-b069-cbcbea4d0442' class='xr-var-data-in' type='checkbox'><label for='data-7adeadb6-d9eb-4409-b069-cbcbea4d0442' 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'><dt><span>long_name :</span></dt><dd>x</dd><dt><span>units :</span></dt><dd>micrometer</dd></dl></div><div class='xr-var-data'><pre>array([0.00000e+00, 3.30000e+00, 6.60000e+00, ..., 1.28007e+04, 1.28040e+04,\n",
+       "       1.28073e+04])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2b2bbee2-6818-4d6a-a379-534dc81309cd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2b2bbee2-6818-4d6a-a379-534dc81309cd' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.SpatialImage 'sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM' (z: 242,\n",
+       "                                                                     y: 342,\n",
+       "                                                                     x: 3882)>\n",
+       "dask.array<array, shape=(242, 342, 3882), dtype=uint16, chunksize=(50, 342, 3882), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * z        (z) float64 0.0 3.3 6.6 9.9 13.2 ... 782.1 785.4 788.7 792.0 795.3\n",
+       "  * y        (y) float64 0.0 3.3 6.6 9.9 ... 1.119e+03 1.122e+03 1.125e+03\n",
+       "  * x        (x) float64 0.0 3.3 6.6 9.9 ... 1.28e+04 1.28e+04 1.281e+04"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "image = to_spatial_image(data, dims=dims, name=name, scale=scale, axis_units=axis_units)\n",
+    "image"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd03e2b4",
+   "metadata": {},
+   "source": [
+    "## Generate isotropically chunked, multiscale spatial image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "a0eba925",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataTree('multiscales', parent=None)\n",
+      "├── DataTree('scale0')\n",
+      "│   Dimensions:                                       (z: 242, y: 342, x: 3882)\n",
+      "│   Coordinates:\n",
+      "│     * z                                             (z) float64 0.0 3.3 ... 795.3\n",
+      "│     * y                                             (y) float64 0.0 ... 1.125e+03\n",
+      "│     * x                                             (x) float64 0.0 ... 1.281e+04\n",
+      "│   Data variables:\n",
+      "│       sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM  (z, y, x) uint16 dask.array<chunksize=(64, 64, 64), meta=np.ndarray>\n",
+      "├── DataTree('scale1')\n",
+      "│   Dimensions:                                       (z: 242, y: 342, x: 1941)\n",
+      "│   Coordinates:\n",
+      "│     * z                                             (z) float64 0.0 3.3 ... 795.3\n",
+      "│     * y                                             (y) float64 0.0 ... 1.125e+03\n",
+      "│     * x                                             (x) float64 1.65 ... 1.281e+04\n",
+      "│   Data variables:\n",
+      "│       sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM  (z, y, x) uint16 dask.array<chunksize=(64, 64, 64), meta=np.ndarray>\n",
+      "├── DataTree('scale2')\n",
+      "│   Dimensions:                                       (z: 121, y: 171, x: 485)\n",
+      "│   Coordinates:\n",
+      "│     * z                                             (z) float64 1.65 ... 793.6\n",
+      "│     * y                                             (y) float64 1.65 ... 1.124e+03\n",
+      "│     * x                                             (x) float64 18.15 ... 1.28e+04\n",
+      "│   Data variables:\n",
+      "│       sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM  (z, y, x) uint16 dask.array<chunksize=(64, 64, 64), meta=np.ndarray>\n",
+      "└── DataTree('scale3')\n",
+      "    Dimensions:                                       (z: 30, y: 42, x: 60)\n",
+      "    Coordinates:\n",
+      "      * z                                             (z) float64 18.15 ... 783.7\n",
+      "      * y                                             (y) float64 31.35 ... 1.114...\n",
+      "      * x                                             (x) float64 242.6 ... 1.27e+04\n",
+      "    Data variables:\n",
+      "        sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM  (z, y, x) uint16 dask.array<chunksize=(30, 42, 60), meta=np.ndarray>\n"
+     ]
+    }
+   ],
+   "source": [
+    "multiscale = to_multiscale(image, [{'x':2,'y':1,'z':1},{'x':4,'y':2,'z':2},{'x':8,'y':4,'z':4}])\n",
+    "print(multiscale)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "f7108355",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "            <table>\n",
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+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 612.81 MiB </td>\n",
+       "                        <td> 512.00 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (242, 342, 3882) </td>\n",
+       "                        <td> (64, 64, 64) </td>\n",
+       "                    </tr>\n",
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+       "                        <th> Count </th>\n",
+       "                        <td> 4398 Tasks </td>\n",
+       "                        <td> 1464 Chunks </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                    <th> Type </th>\n",
+       "                    <td> uint16 </td>\n",
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+       "</table>"
+      ],
+      "text/plain": [
+       "dask.array<rechunk-merge, shape=(242, 342, 3882), dtype=uint16, chunksize=(64, 64, 64), chunktype=numpy.ndarray>"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Now the data has isotropic chunking\n",
+    "multiscale['scale0'].ds[name].data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "2342795c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f770c18ff10>"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multiscale['scale2'].ds[name].isel(x=250).plot.imshow()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "998fe844",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f7707b0c160>"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multiscale['scale2'].ds[name].isel(y=100).plot.imshow()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "c0382d35",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f770781ea60>"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "multiscale['scale2'].ds[name].isel(z=100).plot.imshow()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd7eec14",
+   "metadata": {},
+   "source": [
+    "## Serialize as OME-NGFF Zarr \n",
+    "\n",
+    "Generate an [OME-NGFF](https://ngff.openmicroscopy.org/latest/) dataset.\n",
+    "\n",
+    "This also uses the [*bioformats2raw* transitional layout](https://github.com/ome/ngff/pull/112) to preserve the OME-XML metadata."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "id": "554a5c53",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "store = zarr.storage.DirectoryStore(f'{image_name}.zarr', dimension_separator='/')\n",
+    "group = zarr.group(store, overwrite=True)\n",
+    "group.attrs['bioformats2raw.layout'] = 3\n",
+    "store['OME/METADATA.ome.xml'] = tiff.ome_metadata.encode('utf-8')\n",
+    "\n",
+    "store = zarr.storage.DirectoryStore(f'{image_name}.zarr/0', dimension_separator='/')\n",
+    "multiscale.to_zarr(store)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "52283bcb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9eb9655",
+   "metadata": {},
+   "source": [
+    "## WIP: Serialize as OME-TIFF with sharded Zarr / FSSpec view"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b8408c72",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with tifffile.TiffWriter('{image_name}_isotropic_multiscale.ome.tif', ome=True, bigtiff=True) as tif:\n",
+    "...     options = dict(photometric='minisblack', tile=(64, 64, 64), compression='zlib')\n",
+    "...     metadata={'axes': tiff.axes}\n",
+    "...     tif.write(data, subifds=2, **options)\n",
+    "...     # save pyramid levels to the two subifds\n",
+    "...     # in production use resampling to generate sub-resolutions\n",
+    "...     tif.write(data[:, ::2, ::2], subfiletype=1, **options)\n",
+    "...     tif.write(data[:, ::4, ::4], subfiletype=1, **options)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6fbc64ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tiff.close()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 834893fe567885c794a976ad23e9379a3346c7fa Mon Sep 17 00:00:00 2001
From: Matt McCormick <matt.mccormick@kitware.com>
Date: Thu, 5 May 2022 17:30:25 -0400
Subject: [PATCH 3/3] ENH: OME-TIFF to OME-NGFF with OME-XML

---
 examples/ConvertTiffFile.ipynb | 343 ++++++++++++++++++---------------
 1 file changed, 191 insertions(+), 152 deletions(-)

diff --git a/examples/ConvertTiffFile.ipynb b/examples/ConvertTiffFile.ipynb
index 8d03873..0186e51 100644
--- a/examples/ConvertTiffFile.ipynb
+++ b/examples/ConvertTiffFile.ipynb
@@ -16,37 +16,26 @@
    "outputs": [],
    "source": [
     "import sys\n",
-    "!{sys.executable} -m pip install --upgrade spatial-image-multiscale matplotlib tifffile zarr ome-types distributed"
+    "!{sys.executable} -m pip install --upgrade multiscale-spatial-image matplotlib tifffile zarr ome-types dask_image"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 25,
    "id": "a3a9d3cf",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from spatial_image_multiscale import to_multiscale\n",
+    "from multiscale_spatial_image import to_multiscale, Methods\n",
     "from spatial_image import is_spatial_image, to_spatial_image\n",
     "import tifffile\n",
     "import os\n",
     "from urllib.request import urlretrieve\n",
     "import zarr\n",
     "import ome_types\n",
-    "import dask.array as da"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7b3f65ec",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Faster encoding\n",
-    "from dask.distributed import Client, LocalCluster\n",
-    "cluster = LocalCluster()  # Launches a scheduler and workers locally\n",
-    "client = Client(cluster)"
+    "import dask.array as da\n",
+    "import datatree\n",
+    "from numcodecs import Blosc"
    ]
   },
   {
@@ -63,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 6,
    "id": "865974c8",
    "metadata": {},
    "outputs": [],
@@ -85,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 7,
    "id": "9d11d71c",
    "metadata": {},
    "outputs": [],
@@ -98,12 +87,12 @@
    "id": "18739faa",
    "metadata": {},
    "source": [
-    "[OME-TIFF](https://docs.openmicroscopy.org/ome-model/6.2.2/)'s contain useful, standardized metadata and have broad community adoption"
+    "[OME-TIFF](https://docs.openmicroscopy.org/ome-model/6.2.2/)'s contain useful, standardized metadata and have broad community adoption."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 8,
    "id": "432abb91",
    "metadata": {},
    "outputs": [
@@ -113,7 +102,7 @@
        "True"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -124,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 9,
    "id": "eef1e9e7",
    "metadata": {},
    "outputs": [
@@ -134,7 +123,7 @@
        "'<ome:OME xmlns:ome=\"http://www.openmicroscopy.org/Schemas/OME/2016-06\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xsi:schemaLocation=\"http://www.openmicroscopy.org/Schemas/OME/2016-06 http://www.openmicroscopy.org/Schemas/OME/2016-06/ome.xsd\">\\n    <ome:Image ID=\"Image:0\" Name=\"sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM\">\\n        <ome:AcquisitionDate>2019-10-01T00:00:00</ome:AcquisitionDate>\\n        <ome:Pixels BigEndian=\"false\" DimensionOrder=\"XYZCT\" ID=\"Pixels:0\" Interleaved=\"false\" SizeC=\"1\" SizeT=\"1\" SizeX=\"3882\" SizeY=\"342\" SizeZ=\"242\" Type=\"uint16\" PhysicalSizeX=\"3.3\" PhysicalSizeXUnit=\"µm\" PhysicalSizeY=\"3.3\" PhysicalSizeYUnit=\"µm\" PhysicalSizeZ=\"3.3\" PhysicalSizeZUnit=\"µm\">\\n            <ome:Channel ID=\"Channel:00\" SamplesPerPixel=\"1\" Name=\"GAD67\" ExcitationWavelength=\"638\" EmissionWavelength=\"690\" ExcitationWavelengthUnit=\"nm\" EmissionWavelengthUnit=\"nm\" Fluor=\"Alexa Fluor 647\">\\n                <ome:LightPath />\\n            </ome:Channel>\\n        <ome:TiffData IFD=\"0\" PlaneCount=\"242\" /></ome:Pixels>\\n    </ome:Image>\\n</ome:OME>'"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -145,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 10,
    "id": "59062f84",
    "metadata": {},
    "outputs": [
@@ -185,7 +174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 11,
    "id": "290db649",
    "metadata": {},
    "outputs": [
@@ -195,7 +184,7 @@
        "['z', 'y', 'x']"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -207,7 +196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 12,
    "id": "5679e320",
    "metadata": {},
    "outputs": [
@@ -217,7 +206,7 @@
        "{'z': 3.3, 'y': 3.3, 'x': 3.3}"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -229,7 +218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 13,
    "id": "59e371b9",
    "metadata": {},
    "outputs": [
@@ -239,7 +228,7 @@
        "{'z': 'micrometer', 'y': 'micrometer', 'x': 'micrometer'}"
       ]
      },
-     "execution_count": 97,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -251,7 +240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 14,
    "id": "33d1afd0",
    "metadata": {},
    "outputs": [
@@ -352,7 +341,7 @@
        "dask.array<array, shape=(242, 342, 3882), dtype=uint16, chunksize=(50, 342, 3882), chunktype=numpy.ndarray>"
       ]
      },
-     "execution_count": 98,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -366,7 +355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 15,
    "id": "4650f030",
    "metadata": {},
    "outputs": [
@@ -376,7 +365,7 @@
        "'sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM'"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -388,7 +377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 16,
    "id": "18ed5e3a",
    "metadata": {},
    "outputs": [
@@ -753,7 +742,7 @@
        "Coordinates:\n",
        "  * z        (z) float64 0.0 3.3 6.6 9.9 13.2 ... 782.1 785.4 788.7 792.0 795.3\n",
        "  * y        (y) float64 0.0 3.3 6.6 9.9 ... 1.119e+03 1.122e+03 1.125e+03\n",
-       "  * x        (x) float64 0.0 3.3 6.6 9.9 ... 1.28e+04 1.28e+04 1.281e+04</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.SpatialImage</div><div class='xr-array-name'>'sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>z</span>: 242</li><li><span class='xr-has-index'>y</span>: 342</li><li><span class='xr-has-index'>x</span>: 3882</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5c9697bf-7ae9-4960-af64-8636df67b890' class='xr-array-in' type='checkbox' checked><label for='section-5c9697bf-7ae9-4960-af64-8636df67b890' 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>dask.array&lt;chunksize=(50, 342, 3882), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "  * x        (x) float64 0.0 3.3 6.6 9.9 ... 1.28e+04 1.28e+04 1.281e+04</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.SpatialImage</div><div class='xr-array-name'>'sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>z</span>: 242</li><li><span class='xr-has-index'>y</span>: 342</li><li><span class='xr-has-index'>x</span>: 3882</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e8b423c7-771f-4b73-874c-c526e9075b7a' class='xr-array-in' type='checkbox' checked><label for='section-e8b423c7-771f-4b73-874c-c526e9075b7a' 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>dask.array&lt;chunksize=(50, 342, 3882), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table>\n",
@@ -841,8 +830,8 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-8dab55ed-4866-477e-b01b-4a5fc40ec381' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8dab55ed-4866-477e-b01b-4a5fc40ec381' class='xr-section-summary' >Coordinates: <span>(3)</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'>z</span></div><div class='xr-var-dims'>(z)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 3.3 6.6 ... 788.7 792.0 795.3</div><input id='attrs-4cd7fd6b-ca0e-49ea-ba35-4bf47512151d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4cd7fd6b-ca0e-49ea-ba35-4bf47512151d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8fd2f7e-0ac1-4087-8d13-64f8b81d2aa1' class='xr-var-data-in' type='checkbox'><label for='data-f8fd2f7e-0ac1-4087-8d13-64f8b81d2aa1' 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'><dt><span>long_name :</span></dt><dd>z</dd><dt><span>units :</span></dt><dd>micrometer</dd></dl></div><div class='xr-var-data'><pre>array([  0. ,   3.3,   6.6, ..., 788.7, 792. , 795.3])</pre></div></li><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'>0.0 3.3 6.6 ... 1.122e+03 1.125e+03</div><input id='attrs-b72ea656-3845-498a-a42e-e26ab76a53f0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b72ea656-3845-498a-a42e-e26ab76a53f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eda15115-682c-4ffa-978e-6f9f9a5e3119' class='xr-var-data-in' type='checkbox'><label for='data-eda15115-682c-4ffa-978e-6f9f9a5e3119' 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'><dt><span>long_name :</span></dt><dd>y</dd><dt><span>units :</span></dt><dd>micrometer</dd></dl></div><div class='xr-var-data'><pre>array([   0. ,    3.3,    6.6, ..., 1118.7, 1122. , 1125.3])</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'>0.0 3.3 6.6 ... 1.28e+04 1.281e+04</div><input id='attrs-65c412c3-9a61-4c4d-9b28-78ade69285b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-65c412c3-9a61-4c4d-9b28-78ade69285b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7adeadb6-d9eb-4409-b069-cbcbea4d0442' class='xr-var-data-in' type='checkbox'><label for='data-7adeadb6-d9eb-4409-b069-cbcbea4d0442' 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'><dt><span>long_name :</span></dt><dd>x</dd><dt><span>units :</span></dt><dd>micrometer</dd></dl></div><div class='xr-var-data'><pre>array([0.00000e+00, 3.30000e+00, 6.60000e+00, ..., 1.28007e+04, 1.28040e+04,\n",
-       "       1.28073e+04])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2b2bbee2-6818-4d6a-a379-534dc81309cd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2b2bbee2-6818-4d6a-a379-534dc81309cd' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "</table></div></div></li><li class='xr-section-item'><input id='section-5f448cbf-6bf5-4fcc-a8ba-61e6abc42aab' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5f448cbf-6bf5-4fcc-a8ba-61e6abc42aab' class='xr-section-summary' >Coordinates: <span>(3)</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'>z</span></div><div class='xr-var-dims'>(z)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 3.3 6.6 ... 788.7 792.0 795.3</div><input id='attrs-d1e7b367-49c6-4c52-8fbb-2ae53388fbfc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1e7b367-49c6-4c52-8fbb-2ae53388fbfc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d67f089b-695e-48aa-8cdf-c2be1f22905f' class='xr-var-data-in' type='checkbox'><label for='data-d67f089b-695e-48aa-8cdf-c2be1f22905f' 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'><dt><span>long_name :</span></dt><dd>z</dd><dt><span>units :</span></dt><dd>micrometer</dd></dl></div><div class='xr-var-data'><pre>array([  0. ,   3.3,   6.6, ..., 788.7, 792. , 795.3])</pre></div></li><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'>0.0 3.3 6.6 ... 1.122e+03 1.125e+03</div><input id='attrs-510b79bf-fa24-463c-ae37-6dd716fe7d8e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-510b79bf-fa24-463c-ae37-6dd716fe7d8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50e0796a-c43c-4a35-aec2-528f022c000f' class='xr-var-data-in' type='checkbox'><label for='data-50e0796a-c43c-4a35-aec2-528f022c000f' 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'><dt><span>long_name :</span></dt><dd>y</dd><dt><span>units :</span></dt><dd>micrometer</dd></dl></div><div class='xr-var-data'><pre>array([   0. ,    3.3,    6.6, ..., 1118.7, 1122. , 1125.3])</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'>0.0 3.3 6.6 ... 1.28e+04 1.281e+04</div><input id='attrs-c7acd22a-5b4f-49df-825e-16b9a0e6d958' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7acd22a-5b4f-49df-825e-16b9a0e6d958' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc6cf74b-0020-462c-bdcb-ef7d17df2e07' class='xr-var-data-in' type='checkbox'><label for='data-bc6cf74b-0020-462c-bdcb-ef7d17df2e07' 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'><dt><span>long_name :</span></dt><dd>x</dd><dt><span>units :</span></dt><dd>micrometer</dd></dl></div><div class='xr-var-data'><pre>array([0.00000e+00, 3.30000e+00, 6.60000e+00, ..., 1.28007e+04, 1.28040e+04,\n",
+       "       1.28073e+04])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3eea9676-0401-4075-bd22-a18ccd2c1896' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3eea9676-0401-4075-bd22-a18ccd2c1896' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.SpatialImage 'sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM' (z: 242,\n",
@@ -855,7 +844,7 @@
        "  * x        (x) float64 0.0 3.3 6.6 9.9 ... 1.28e+04 1.28e+04 1.281e+04"
       ]
      },
-     "execution_count": 113,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -875,7 +864,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 17,
+   "id": "dff01f94-1ad6-4d3d-92fe-2cb358444508",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function to_multiscale in module multiscale_spatial_image:\n",
+      "\n",
+      "to_multiscale(image: spatial_image.SpatialImage, scale_factors: Sequence[Union[Dict[str, int], int]], method: Optional[multiscale_spatial_image.Methods] = None, chunks: Union[int, Tuple[int, ...], Tuple[Tuple[int, ...], ...], Mapping[Any, Union[NoneType, int, Tuple[int, ...]]], NoneType] = None) -> multiscale_spatial_image.MultiscaleSpatialImage\n",
+      "    Generate a multiscale representation of a spatial image.\n",
+      "    \n",
+      "    Parameters\n",
+      "    ----------\n",
+      "    \n",
+      "    image : SpatialImage\n",
+      "        The spatial image from which we generate a multi-scale representation.\n",
+      "    \n",
+      "    scale_factors : int per scale or dict of spatial dimension int's per scale\n",
+      "        Integer scale factors to apply uniformly across all spatial dimension or\n",
+      "        along individual spatial dimensions.\n",
+      "        Examples: [2, 2] or [{'x': 2, 'y': 4 }, {'x': 5, 'y': 10}]\n",
+      "    \n",
+      "    method : multiscale_spatial_image.Methods, optional\n",
+      "        Method to reduce the input image.\n",
+      "    \n",
+      "    chunks : xarray Dask array chunking specification, optional\n",
+      "        Specify the chunking used in each output scale.\n",
+      "    \n",
+      "    Returns\n",
+      "    -------\n",
+      "    \n",
+      "    result : MultiscaleSpatialImage\n",
+      "        Multiscale representation. An xarray DataTree where each node is a SpatialImage Dataset\n",
+      "        named by the integer scale.  Increasing scales are downscaled versions of the input image.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(to_multiscale)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
    "id": "a0eba925",
    "metadata": {},
    "outputs": [
@@ -883,50 +918,56 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "DataTree('multiscales', parent=None)\n",
+      "DataTree('None', parent=None)\n",
       "├── DataTree('scale0')\n",
-      "│   Dimensions:                                       (z: 242, y: 342, x: 3882)\n",
-      "│   Coordinates:\n",
-      "│     * z                                             (z) float64 0.0 3.3 ... 795.3\n",
-      "│     * y                                             (y) float64 0.0 ... 1.125e+03\n",
-      "│     * x                                             (x) float64 0.0 ... 1.281e+04\n",
-      "│   Data variables:\n",
-      "│       sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM  (z, y, x) uint16 dask.array<chunksize=(64, 64, 64), meta=np.ndarray>\n",
+      "│       Dimensions:                                       (z: 242, y: 342, x: 3882)\n",
+      "│       Coordinates:\n",
+      "│         * z                                             (z) float64 0.0 3.3 ... 795.3\n",
+      "│         * y                                             (y) float64 0.0 ... 1.125e+03\n",
+      "│         * x                                             (x) float64 0.0 ... 1.281e+04\n",
+      "│       Data variables:\n",
+      "│           sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM  (z, y, x) uint16 dask.array<chunksize=(128, 128, 128), meta=np.ndarray>\n",
       "├── DataTree('scale1')\n",
-      "│   Dimensions:                                       (z: 242, y: 342, x: 1941)\n",
-      "│   Coordinates:\n",
-      "│     * z                                             (z) float64 0.0 3.3 ... 795.3\n",
-      "│     * y                                             (y) float64 0.0 ... 1.125e+03\n",
-      "│     * x                                             (x) float64 1.65 ... 1.281e+04\n",
-      "│   Data variables:\n",
-      "│       sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM  (z, y, x) uint16 dask.array<chunksize=(64, 64, 64), meta=np.ndarray>\n",
+      "│       Dimensions:                                       (z: 242, y: 342, x: 1941)\n",
+      "│       Coordinates:\n",
+      "│         * z                                             (z) float64 0.0 3.3 ... 795.3\n",
+      "│         * y                                             (y) float64 0.0 ... 1.125e+03\n",
+      "│         * x                                             (x) float64 1.65 ... 1.281e+04\n",
+      "│       Data variables:\n",
+      "│           sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM  (z, y, x) uint16 dask.array<chunksize=(128, 128, 128), meta=np.ndarray>\n",
       "├── DataTree('scale2')\n",
-      "│   Dimensions:                                       (z: 121, y: 171, x: 485)\n",
-      "│   Coordinates:\n",
-      "│     * z                                             (z) float64 1.65 ... 793.6\n",
-      "│     * y                                             (y) float64 1.65 ... 1.124e+03\n",
-      "│     * x                                             (x) float64 18.15 ... 1.28e+04\n",
-      "│   Data variables:\n",
-      "│       sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM  (z, y, x) uint16 dask.array<chunksize=(64, 64, 64), meta=np.ndarray>\n",
+      "│       Dimensions:                                       (z: 121, y: 171, x: 485)\n",
+      "│       Coordinates:\n",
+      "│         * z                                             (z) float64 1.65 ... 793.6\n",
+      "│         * y                                             (y) float64 1.65 ... 1.124e+03\n",
+      "│         * x                                             (x) float64 11.55 ... 1.279...\n",
+      "│       Data variables:\n",
+      "│           sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM  (z, y, x) uint16 dask.array<chunksize=(121, 128, 128), meta=np.ndarray>\n",
       "└── DataTree('scale3')\n",
-      "    Dimensions:                                       (z: 30, y: 42, x: 60)\n",
-      "    Coordinates:\n",
-      "      * z                                             (z) float64 18.15 ... 783.7\n",
-      "      * y                                             (y) float64 31.35 ... 1.114...\n",
-      "      * x                                             (x) float64 242.6 ... 1.27e+04\n",
-      "    Data variables:\n",
-      "        sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM  (z, y, x) uint16 dask.array<chunksize=(30, 42, 60), meta=np.ndarray>\n"
+      "        Dimensions:                                       (z: 30, y: 42, x: 60)\n",
+      "        Coordinates:\n",
+      "          * z                                             (z) float64 11.55 ... 777.1\n",
+      "          * y                                             (y) float64 11.55 ... 1.094...\n",
+      "          * x                                             (x) float64 103.9 ... 1.256...\n",
+      "        Data variables:\n",
+      "            sub-I46_sample-BrocaAreaS01_stain-GAD67_SPIM  (z, y, x) uint16 dask.array<chunksize=(30, 42, 60), meta=np.ndarray>\n"
      ]
     }
    ],
    "source": [
-    "multiscale = to_multiscale(image, [{'x':2,'y':1,'z':1},{'x':4,'y':2,'z':2},{'x':8,'y':4,'z':4}])\n",
+    "# Isotropic 128x128x128 chunks\n",
+    "chunks = 128\n",
+    "\n",
+    "multiscale = to_multiscale(image,\n",
+    "                           [{'x':2,'y':1,'z':1},{'x':4,'y':2,'z':2},{'x':8,'y':4,'z':4}],\n",
+    "                           method=Methods.DASK_IMAGE_GAUSSIAN,\n",
+    "                           chunks=chunks)\n",
     "print(multiscale)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 20,
    "id": "f7108355",
    "metadata": {},
    "outputs": [
@@ -949,18 +990,18 @@
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
        "                        <td> 612.81 MiB </td>\n",
-       "                        <td> 512.00 kiB </td>\n",
+       "                        <td> 4.00 MiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
        "                        <td> (242, 342, 3882) </td>\n",
-       "                        <td> (64, 64, 64) </td>\n",
+       "                        <td> (128, 128, 128) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Count </th>\n",
-       "                        <td> 4398 Tasks </td>\n",
-       "                        <td> 1464 Chunks </td>\n",
+       "                        <td> 750 Tasks </td>\n",
+       "                        <td> 186 Chunks </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                    <th> Type </th>\n",
@@ -975,18 +1016,13 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"10\" y1=\"7\" x2=\"31\" y2=\"28\" />\n",
        "  <line x1=\"10\" y1=\"14\" x2=\"31\" y2=\"35\" />\n",
-       "  <line x1=\"10\" y1=\"21\" x2=\"31\" y2=\"42\" />\n",
        "  <line x1=\"10\" y1=\"28\" x2=\"31\" y2=\"49\" />\n",
-       "  <line x1=\"10\" y1=\"35\" x2=\"31\" y2=\"56\" />\n",
        "  <line x1=\"10\" y1=\"37\" x2=\"31\" y2=\"58\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"37\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"15\" y1=\"5\" x2=\"15\" y2=\"43\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"49\" />\n",
-       "  <line x1=\"26\" y1=\"16\" x2=\"26\" y2=\"54\" />\n",
        "  <line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"58\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -994,31 +1030,29 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"15\" y1=\"5\" x2=\"135\" y2=\"5\" />\n",
        "  <line x1=\"21\" y1=\"11\" x2=\"141\" y2=\"11\" />\n",
-       "  <line x1=\"26\" y1=\"16\" x2=\"146\" y2=\"16\" />\n",
        "  <line x1=\"31\" y1=\"21\" x2=\"151\" y2=\"21\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"10\" y1=\"0\" x2=\"31\" y2=\"21\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"15\" y1=\"0\" x2=\"37\" y2=\"21\" />\n",
+       "  <line x1=\"13\" y1=\"0\" x2=\"35\" y2=\"21\" />\n",
        "  <line x1=\"21\" y1=\"0\" x2=\"42\" y2=\"21\" />\n",
-       "  <line x1=\"27\" y1=\"0\" x2=\"48\" y2=\"21\" />\n",
+       "  <line x1=\"25\" y1=\"0\" x2=\"46\" y2=\"21\" />\n",
        "  <line x1=\"33\" y1=\"0\" x2=\"54\" y2=\"21\" />\n",
        "  <line x1=\"41\" y1=\"0\" x2=\"62\" y2=\"21\" />\n",
-       "  <line x1=\"47\" y1=\"0\" x2=\"68\" y2=\"21\" />\n",
+       "  <line x1=\"45\" y1=\"0\" x2=\"66\" y2=\"21\" />\n",
        "  <line x1=\"53\" y1=\"0\" x2=\"74\" y2=\"21\" />\n",
-       "  <line x1=\"59\" y1=\"0\" x2=\"80\" y2=\"21\" />\n",
+       "  <line x1=\"61\" y1=\"0\" x2=\"82\" y2=\"21\" />\n",
        "  <line x1=\"65\" y1=\"0\" x2=\"86\" y2=\"21\" />\n",
        "  <line x1=\"73\" y1=\"0\" x2=\"94\" y2=\"21\" />\n",
-       "  <line x1=\"79\" y1=\"0\" x2=\"100\" y2=\"21\" />\n",
+       "  <line x1=\"77\" y1=\"0\" x2=\"98\" y2=\"21\" />\n",
        "  <line x1=\"85\" y1=\"0\" x2=\"106\" y2=\"21\" />\n",
-       "  <line x1=\"91\" y1=\"0\" x2=\"112\" y2=\"21\" />\n",
+       "  <line x1=\"93\" y1=\"0\" x2=\"114\" y2=\"21\" />\n",
        "  <line x1=\"97\" y1=\"0\" x2=\"118\" y2=\"21\" />\n",
        "  <line x1=\"104\" y1=\"0\" x2=\"126\" y2=\"21\" />\n",
-       "  <line x1=\"110\" y1=\"0\" x2=\"131\" y2=\"21\" />\n",
+       "  <line x1=\"112\" y1=\"0\" x2=\"133\" y2=\"21\" />\n",
        "  <line x1=\"116\" y1=\"0\" x2=\"137\" y2=\"21\" />\n",
-       "  <line x1=\"122\" y1=\"0\" x2=\"143\" y2=\"21\" />\n",
+       "  <line x1=\"124\" y1=\"0\" x2=\"145\" y2=\"21\" />\n",
        "  <line x1=\"130\" y1=\"0\" x2=\"151\" y2=\"21\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -1026,33 +1060,30 @@
        "\n",
        "  <!-- Horizontal lines -->\n",
        "  <line x1=\"31\" y1=\"21\" x2=\"151\" y2=\"21\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"31\" y1=\"28\" x2=\"151\" y2=\"28\" />\n",
        "  <line x1=\"31\" y1=\"35\" x2=\"151\" y2=\"35\" />\n",
-       "  <line x1=\"31\" y1=\"42\" x2=\"151\" y2=\"42\" />\n",
        "  <line x1=\"31\" y1=\"49\" x2=\"151\" y2=\"49\" />\n",
-       "  <line x1=\"31\" y1=\"56\" x2=\"151\" y2=\"56\" />\n",
        "  <line x1=\"31\" y1=\"58\" x2=\"151\" y2=\"58\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"31\" y1=\"21\" x2=\"31\" y2=\"58\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"37\" y1=\"21\" x2=\"37\" y2=\"58\" />\n",
+       "  <line x1=\"35\" y1=\"21\" x2=\"35\" y2=\"58\" />\n",
        "  <line x1=\"42\" y1=\"21\" x2=\"42\" y2=\"58\" />\n",
-       "  <line x1=\"48\" y1=\"21\" x2=\"48\" y2=\"58\" />\n",
+       "  <line x1=\"46\" y1=\"21\" x2=\"46\" y2=\"58\" />\n",
        "  <line x1=\"54\" y1=\"21\" x2=\"54\" y2=\"58\" />\n",
        "  <line x1=\"62\" y1=\"21\" x2=\"62\" y2=\"58\" />\n",
-       "  <line x1=\"68\" y1=\"21\" x2=\"68\" y2=\"58\" />\n",
+       "  <line x1=\"66\" y1=\"21\" x2=\"66\" y2=\"58\" />\n",
        "  <line x1=\"74\" y1=\"21\" x2=\"74\" y2=\"58\" />\n",
-       "  <line x1=\"80\" y1=\"21\" x2=\"80\" y2=\"58\" />\n",
+       "  <line x1=\"82\" y1=\"21\" x2=\"82\" y2=\"58\" />\n",
        "  <line x1=\"86\" y1=\"21\" x2=\"86\" y2=\"58\" />\n",
        "  <line x1=\"94\" y1=\"21\" x2=\"94\" y2=\"58\" />\n",
-       "  <line x1=\"100\" y1=\"21\" x2=\"100\" y2=\"58\" />\n",
+       "  <line x1=\"98\" y1=\"21\" x2=\"98\" y2=\"58\" />\n",
        "  <line x1=\"106\" y1=\"21\" x2=\"106\" y2=\"58\" />\n",
-       "  <line x1=\"112\" y1=\"21\" x2=\"112\" y2=\"58\" />\n",
+       "  <line x1=\"114\" y1=\"21\" x2=\"114\" y2=\"58\" />\n",
        "  <line x1=\"118\" y1=\"21\" x2=\"118\" y2=\"58\" />\n",
        "  <line x1=\"126\" y1=\"21\" x2=\"126\" y2=\"58\" />\n",
-       "  <line x1=\"131\" y1=\"21\" x2=\"131\" y2=\"58\" />\n",
+       "  <line x1=\"133\" y1=\"21\" x2=\"133\" y2=\"58\" />\n",
        "  <line x1=\"137\" y1=\"21\" x2=\"137\" y2=\"58\" />\n",
-       "  <line x1=\"143\" y1=\"21\" x2=\"143\" y2=\"58\" />\n",
+       "  <line x1=\"145\" y1=\"21\" x2=\"145\" y2=\"58\" />\n",
        "  <line x1=\"151\" y1=\"21\" x2=\"151\" y2=\"58\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
@@ -1068,10 +1099,10 @@
        "</table>"
       ],
       "text/plain": [
-       "dask.array<rechunk-merge, shape=(242, 342, 3882), dtype=uint16, chunksize=(64, 64, 64), chunktype=numpy.ndarray>"
+       "dask.array<rechunk-merge, shape=(242, 342, 3882), dtype=uint16, chunksize=(128, 128, 128), chunktype=numpy.ndarray>"
       ]
      },
-     "execution_count": 119,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1083,23 +1114,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 21,
    "id": "2342795c",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f770c18ff10>"
+       "<matplotlib.image.AxesImage at 0x7f48ea350ac0>"
       ]
      },
-     "execution_count": 121,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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wy3P1CstpgQ3WdNZhk1kVM7sIV3sp5Ws477DhtosM1v1J970UlztvU5KFTCaTyYyJAdUWji08GmQhk5kIarXQNmcz6O2cpuj5WadXJ6rpVlxen4oaSbUzzkrDuj4G/e3eBiCa9fy6VGMbsWLQEO0+0NgqVEUbjyqog1fttmhPCbaRoktjgG8tGfO7vV3nzjBe93JjiKerO6IfbCohdKZDc211G3q+bqfqpI+gQZVgLW+H2Vc0ffe2QxG8nRfA2q7N4onEfrebv2eiXPLXNAv9baHvuL/2Zef6J/RZPMHdt9426O0IzhX+nhn03FdIa5HmXi+eKAqvbPb9uGRqbExLO0VrwWsyMwWLe4qB69xxQ/wu+jMFrQW3f1u9g1bb281mO1TTXgPyDhda6mFT3lGjqpxrN2Dd5W7vZtbYAa1iQ7WZo2cR2ppkIZPJZDJjYthEbDLHE1nIZA6P4G7abmPb3TS4ni5RWFcPdoaZMmodtTU2h6oD/dmgnfhD5qvGdmKlGu2k6BllL2z3XXUKim78Tx9sL62lmt5O9/PuzYqqPejiakViTylo9ldTcRz96eh9FsZbdo32gjuwqEqWdnuvKT+57uxNzqGovbTmozZVLPnrKUF1OF5YiJksjfbcoG0lRRVU29yO/lzZ9Fu3RX/Ga45+/9QdJb1tXlva0aXrvfqsBFWD96To0YzBetDd6e/JdLz+7q44BrxtpL8NyiXz90z0vTYUPOeWdhSNptfdIdjjvrz2fLtxB+/PltQdf80nukG091fNcR07hWLfnPuwsBgDYj22tBQDexeX2CjTtxn0soxZF1nIZDKZzNiI6ijlutuqZCGTOSxCAKZarcY7yiQsBGM2HlMFqspmW9BqTErsM76vOtph+h1R+KljCGyEOLNeZpMJ2Wx6orvdtQ92Btd+hIZgMXZFBq2FwWusW6Lo0xwXPKXqFk0e884+HwPUFgunuHN0T6jp7I1th8NAOvvVaGRFD0IykUKi9GOwEtQbGnMBNutjVGaKxiMMizYe7fbxJ3dNQ+G2bd+2yMGds6Fp46EWju+n9qYWVLuS++bHXs0EOw4U/aj9BRtS3aLRZMK19XYI+WvrbYd6ytuY7igpdsTvvLvd3zfvyTdze0E15d5vb4n2NmfkKQ8uUYcA2ZD5Zn4pxt8cOIAt+kDaNA/cBLQbY+3UdplBspDJHDpSDJbrtJvIeNXWLHM0EfgttyQSj41vg8G7eZBNq4lot4ImAh+Ly1ptHx2PxeW2om+0/LKNFTRG+XIpni8VLkXiOR0Ei2qaJaci+ECjZjmobov5k73gnAIrrDkO3DmDoCsX1LgPu2N9W/+wK5bULGXVZXzYt/erEV4GjYBrnAyA1nRIb9WmdM9WFk6GouuF8z73QO7tMOopN7h9+2dH1vao/BJZf7s1gqeaivehPadl0fJWOgcE19YaIY1BNRucGeJyWzjeWjSu490dikGnrdhff7v7W3bVLEfW7Tbquwbbb+o09ztMUMruTJMxoX37dop9rrK4LS5FJ4DbRlz8IZA1mfWRhUwmk8mMiQvGzEJmPWQhkzl0VMTlsk4nBkUWGnyPcz8uq2i0D5pKeyHNvktzTJEY+MMyWdGzZUtOELWIomtUwXhs0Z25s79ulm7KRtNRY6zvT6vRlnrblj9ArAWt/VHjqKZDX9DZ66/TD0wWnQCYj2ln2nNQT4V7EoIr4zJTb2d0hmijxmGg6DmNyF2nN64nQyyXRO2XyBbu2aXY57NMe+3FSjXLZfVSSTukI+vE+xY0qLoVAzerTnSdrttxaaxc9EtaO+tGI1HttE/XGOqWTz1Tx9Q4AfVjv1YWjfaycIpFB4Zpd/z0raXTQoGDZ0FrPmjFLVrzcZzu/rXo7HX9bttWIq8OtQ/0XEAvTESTMaBno/TBzEpkIZPJZDJjYogqFxReF1nIZNZPSBlTltGVtN1qgumsJaw3qBHIUiO9mgSYViapTBrbg5z7qt+/cJJrMNWzqO2Usf/Sr8XLaFycgcYorCSRc9Cs2vN10ldMm1L0abaHFDWqkm3taMRWZZTeHbnl7SJ1B6Z9mpe504rG1tM+aDFIs0mNY41m0NqhZlYf+gIoluL7ftD++mJxl2vcWYo2G4B6h7tvxYyv07Kvg3ywppXW2Gyq6Wg3C04G2hHtYmUXusGuVsRgyjCezt6iadufMcqFEHQbtbdg86qmosNA+4Aa92rqovmOWvPRPhU0neAuDdA9rUe1t+X7K5i+3W1f2uPHXiVac7vVOAy059qNhsinmAi1Ldd2MyuThUwmk8mMSbbJrJ8sZDKHjNqtJoGhTXeiJ1mhqGkks+GgDai2Zn81FT23Qs2Vut1qPLv6naKx26i2Zo0+aD9F1xp7ihUa0DiCVtObjQkng7ZQVKI176bRrcWo/bQXDFWDXmBVp4zXpqg5lIvR8y3UxCl6iTaw5DQYcPaOxo7gbTpFPwZ+hkSa4bztea8ZdKAKaV9C8clF6Nzstbu9xtQB71V1W5veLn8P51pNX527QtoZo7PP9dHdHcsJtL3mpDpxDe/TuF9P355eX9AQ4r2sW1ELU+W8woCmdMFSqcZ7uH3QUD/amILWVvTV9B0SgYb75AYf3b3rKaPn3ZyXTnI9T90RE55WJymmsdkWNc/JIKpsk1kXWchkMpnMmBhQZ5vMushCJrNuokdZ21W8BKrZzkARsDBLjtpLnPWbwNrBllFHr6p9Tk0J8Q7huM4+X/mxJfCz2coHe5ZVUkWziBpLIWvW+6sOjQYUtJuqo0azKHo24M0WCpsFm0zZLeNMvoa6G8cW4nKCB1UayNnZH7WsakqNVtOk4U+CA9sHrUkoWXUSb7ftBR3v2bbYCTN9mLlNzbUFZm4R8lpA3yfQVE+0fDaWckm0vFbY3l/Ee+K3lQtRq1SlRstoz8WxBTtLuWj0Z/z9WUqupYjaWZNGpxdT6WAW3yvag2zWGo/DJqB2uo72mypqJ+rDwhneZjfrK2veVtKfjd5wISjV3dvJCQUz0bVy7YaZhixkMuMTStkGITMz4ypeAnW7HHA1DgKnXHIPg9ZcNdhPMCQv1pS+sqO67oFRVkY94zPyzvWbjM697e1mDGU3PAytySqAWXRnXoTpfT6/15TobQ8u075pEV2ci240ylfToliq/ftYtjjsb3XrJiC0tWTNgy8suZU9iwGj83Ui1MpGIIVraC3E8cpizKAztPvrXLDGJToEXbYPGKV/2C/tUuNQse1bNbM3u+0Hz/S1YHYa03d4AdqKS33bvhVdhsPY2wfUnKPa5QQJQGeupl8FoRaWy6y5p9O32ICxP7h1N44eAy7XUAZXZIm6E4RpXA7r73bfd+eOknLJL51NRaFR9IW1ovABaM3FYNV6yqhn3P7OnkXaV25nktTZJrMuNkzvk/Ttkj6bvPZL+hVJJ0i6TNJX/N89vr0kvVzSdZKukfTgjRpbJpPJHArO8F+M9co4NkyTMbNrgQcCSCqBG4F3Ac8HLjezl0l6vv/8PODxuPrU9wEeCrzS/81sMsJyGZ029ayvXDgVlxBUR6NyMyNfqunPhERdcZarGupy8D9ksdjF2iEzswaSRcm7PpcjjLlWxDQl5VJF+0AIeiyoOoM16kOG5Wa8ZZypN/VrOsGAXTepa8olY2YxuFcP5lMLBK2nc7Cm9FpRa6ZoluSaVGNF6jJsA2luQqbn1pKaJbGpve5vayEGnbYWrMlCvfvaO6DnbkxRnQrAXfctmqqf/ek4A6/boj0fzw1+ea8KThjRaaHoGWqHYMwYdNqes+b4MJ6Z2/v0tnmng5CLrUcznZ06UDN9Z9ncv/DVlktJFu79bv/07aKfGv+D53NpqO+/jx1Lflwd2gdjCqPaLxf2FtqcdMMkK8Bkw/96OVJ36zHAV83sG8D5wCV++yXAj/j35wNvMMcngd2STjtC48tkMpk1CYb/cV4Zx5GyyTwdeJN/f6qZ3eTf3wyc6t+fAXwzOeYGv+2mZBuSLgQuBJhmlswRxNeOUXBb7rSpQwBmqaYGTN2Odo1gMJdZ1F561iTWlFmT1ThUQbSypPaah7ULtOArIraKWD2zMQIbeJtO1S6oWt6O0q0bO9BSMdW4QYdZf4vocOAM/8HuE4MM05i7xr6QxHoWXYsVKP31WiGqTtQAgt2nbquxyaTZlNtzwSgDLa+9tBfSNX+j8lpdsJ0UlaHFmJqlcaPeewA6bX8v3NHtORpX7f50mmY5STzqKbvx+solp8GFsYd7EtLgFP1oI1raKdpzyX0aSkZaLlqjZbQWatoHfHbsbdEGVC5Zk9Im1g8C+fQ6VkJrMW4PtpoFdXy/0b61dKKhtv8w12L2liSidQJUEwjGlPTtwFuSTd8G/B7wBr/9bOB64KlmdpckAX8FPAGYB55pZp/2fV0A/F/fz++b2SVsIjZc3ErqAE8C3ja8z8yMZfldV8fMLjazc8zsnDZTExplJpPJrI0hetYa67VqP2bXmtkDzeyBwHfjBEdqTrgPcLn/DIPmhAtx5gQknQBchDMtPAS4KNi5NwtHQpN5PPBpM7vFf75F0mlmdpNfDrvVb78ROCs57ky/LbMZkKLr8qxz46mnOlQzUZNpUuNX0N7v65n49P91O864i6Wq8dwyxVly0F7oRE2mqGooQmr96ILbTE3MGq3JJGo/hrpTUM67MbQWKnq+tkxwj7ayTGq51I2m0losKXohSDNqIY0torJmalZUhryW0PRbQPAqLro1B890n+ZOF8V/B7ftGFzauE5Ljc2hXKixVpI2J7gNL0XNInit1UtG19fhmTp5d3O/g/2lNR/tLAPBn/OxwmdjO6lqet4tefrOivac29Hd2Wq80kKAJcBU8z75bnvWuHGH66xL4vdVw/RdXvPslI320Vq0RhtqUCxd0Nkbgypb8zEQtDtX+ntkBBWqc1fBgi8rMX1bSTk/uWjMYPifMI05QdL5wKP89kuAK3A268acAHxSUjAnPAq4zMzuBJB0GfA44srRYSHp0tX2m9mT1urjSAiZZzB4wZcCFwAv83/fnWz/JUlvxknlfcmyWiaTyRx1DK1nuewkSVclny82s4tHtDscc8JK2yfFw3z/bwKuhPX7b2+okJG0DfhB4OeTzS8D3irpWcA3gKf67e/DrTdeh1Mdf2Yjx5ZZPwpeYFNudm7TrYEZd6Cc71MedOvg9TYfrDldNl5XRVeNbcVaBWpiXoKWEWeKVbsA77lmhZZ5opXdfkwD06+bhJVWimqm7fu1qAF5UgchVXWjvRRL0XbUVF1citoAdWJP6luTRj/gtBt3beVin7rlqzkuJMXKwvWaUS4st3uob1SNzSraToLNpq9iYJF5aae7mM4p26i9/SbM9DsHrdGyyq41NrJwXQDtgz6ZZkuAT7y5t9vEH6WBtGly0HCflnYWTcyM3QbTd/kknU0garzZRWWNHWp6bwyenbm9l6QE8oO3GNQ7YC/qRm2p6209/alYNqKzT1jhHm3tOahbk9U81mHUv93MzlmtQWJOeMHwPjMzaVRxiyPK3XDP8GcAPw68F3iTmX1h3A42VMiY2Rxw4tC2O3Dq4XBbA569kePJHAYqXM0YoN7uAzCnWs1DQHUMTKQU/V1uSS19qDVdJS7JA8b1pRD6bxQhYzFF87Brz1WNUThE5Rf9ukmxrNooF9xTsL+9HZfculUj1MLfulNQBTdmA3nDf/tgv1lm688EV9x6IEdZMMRbK0ahh+dOLTF1Z0iwZuz+ymJzvuDC3YylF11r63bZCANV1lQRVa9uhFJc9on33dVKWV5xtMkJ1nPLkwBTdyWZGJTW6fH3xAqmb+v6MfbpnhD9h1sHY1bs8Dcs3xU9a5Yj66noqt0shc1V1GUIqiwofY669nwUMu29i81kIRVKYalwcbcaF++i8lm0gf4doaxqElR60JrlvboleruStAiHiRmTdmE+XHPCjcTltbD9ikkNzswq4APAByRN4YTNFZJeZGavGKeP7GeXyWQyY+IM/+VYrzFZyZwAy80JP+2D1s8lmhM+CJwnaY83+J/nt00MSVOSfhT4B5wi8HKck8JY5LQymbFQoZhxObgat2JKk7pUs+QENA4BrXk3/SwXK8okzUhYElNtjeuz6pAqBORn9a0qGm1bc1BNBW3AbzdrHANcUGVQKRJtqZUYpn2VxPYBNamcrYwBmOrXzYy6uR6LQaBNah2cVhDS5oRroCAWr0nGWSyBlVPNvQBoHVhqDPUmNRqHqprWgtcMWjGPWRhX0AQCQSMre3WzlDflU+q056qmdo8syQ82Nag5uQG5MQFUs+1mebNcqBqtq669E0ah5tpai23KW6p4r8LyZbglvZr2nOurt72MWuiSRW3o4BKUwVU7ZOMuaB/wruUzrWYJsFwwpu7yOcv819KbUXPe9rzR8U4E3W1MnEkZ/idhTjCzOyW9hFgt58XBCWBCY3wD8AB//heZ2efX20cWMplMJjMmhiZWtGxS5gQzex3wuokMajk/CcwBzwWeozjJkh/WzrU6yEImszpJUkx5g3816342aXCftdQkK4RoiwkzZ/UqiiU3+6y2dRrNoJ4qKXrBmhyyZhZx9r3UizNtAPPG/G48pglJKGOeknKpjovBlUWjeh20lKpJqyIjakOpYTyMoVfHOvES8imX6060ATWz917VjK2eaSM/21cpil57sN/FXnPNxVJFGYJOE63GVMbraIIcrblOVTWdfX7s3ZqyCvfd35KFqtGsrCia+1701ATXhv3lXA+aiqVFo+m0FqrGZkVwWU9S4Ezf2Ysabatg+BmsvlH6dD7VVNHcq9Zi5c4JUIjaO2o0wblF1I7bC0aP4AJeN/e95e1jqtRoNe25OvmeiwEb4CQ4nvKSmR2+ASoLmUwmkxkTwzlIHC/4YM8VGWdpLguZzFio1cKmvXeZ9/aqO0Wzpq46podpzfWX/7IKRU3F4my+TrWhUsvfd1rQTbQFG7RPmIgz/doguFSbxSDDpT61t300WsbUNApuy90KgiZCMTizb25A7De4ZzPbofDaRzOufg39YKcpGzuTUTT9BVdtyoL+jqnmXrb2hxKVaqpDspicO1AbRaLJtQ52m36Djad9YPnY1a+ba04vSY3GFrWe1oElysWQyFLNmIOG5bYXcVtwL+7VjT0u2HRU1bQXo5t0sOWoskZzqqc71NOhvGZ0327Nh+OKxkOt6MXvICRmNZWNpld068bl3FX7nKQmo+Ot/PLVq+wzXDqcVclCJpPJZMbEYD2eY1seM7vn4faRhUxmVZpUMtNTTRBmU+2ysuiJRQwABBrNoJn0VdYk05QZ1HFmHItb+cb9GlnUHML2gcDP4IlWlhA8w3p1TG5ZDpYIaK4neImZDaSmaWwxvajV+LhEqun436R1sOtsKUBpFm0V4VxVNaD1DJw7FGdLbCRLJ3obUx+mvG1K7Va0ndQWNZ8ieM7Vg9pJehIfHFoEzaFU9OSr6kYDsjrGH1mj0ijeh7qOEas1MTA1aCH9utEOG61qiHpnp2nbfN/dOsZDJatO9Wy7CSRtNCujseNN3VFTt13sVbFUowWnvXW+5TXTs3Y1famO3n6tfn+iNhkzHW/LZacAvw3cG7gGeJmZ7V9PH1nIZFZG0TisTodq1guZ8FRL7fGFovDpVY07aszCTLM0gqK7aTnXbR7qYalHVRUfcMMP6iAAGrflGprll6pxr6avpiaN+jUKUd+Nob0fH9rJeawoGnt4I0CmW42r8gA1UNUDxw/0WSfbSyUu2v6eLHXZ9vUDrulMOwrOuo4OA624fNc4ONSg4CxRMCB8muwJ4UHdNawT6hlH4a5+fIAHUqcH9esmw3TR7TdCphEMFgVSsdiPy5s1jfNBk0VhbqkZQznfi/ehTgWKoTAJCcKrVzXLkdYqaM377AmLFeoHR4y62dYE3/bqZknOFF2pJ8VxVk/mDbgls78GnoiLkXnmejrIQiaTyWTGxDjuyi+fZma/499/UNKn19tBFjKZVWnylU1PDSwbgV+KSYzHZRKEl7oYh/3pcU3/vWR5qQpLT80/A324Ja6haWk19F8+zNQhmXXHZb0mH1e/BkvON5DLzLf1S++tg92ohYVxMLjsN5AqJ6Ta6SbGd2iW2eJ5arRvzredihqJ5LS55HqaMeOWtwa0oiK6i8vqweOqGsI1V9H1mcIHQALWHvEYqOpGk9NSN34HrVDnp4zLbNB8Xer2oAou1f430K+aaysY/C00969Ho20WdaL9eqwsaR3wDg6J80KaXid+b2q+LxUM/JQOn+OvMqbPJBCTRiWfs3dZJpPJTBDnwnxcaTK7cMtl6UUHbSZ7l2UOExXQDilkokdNk46kU8R0LAbFXEz10tgzUmN1kdpFhoz9EGfDdd28t1aBpYGSjWE6sZE0DgMVSrWexD6ipeA+nBi2m1m9IHSX2FFC8kaqCoINpFUOaBeBNFFo0GqMIS2kGrLr1DUs+dk56T1OvJd6id0k3Ici0QSXetGw327F+xPuQ11HzaGfBGaq1WhLCuc1izamQo3dp7F/ABbsMIldjaXu4H1v3kd7VWNDSpW5sojXN9VJ3Mj9JXT7ia0LCq8Jqp84PiRaT3EgqYDZaLHRwWEShNxlxwtmdvbh9pGFTCaTyayDdaT63/JIugew18z2+c+PBn4EVxr6b8ysu1YfWchklhNmiUkqGZJZcjN7rOOsVf06pkUpFTWRoOlAdGNNEzwOeDQldohW8DQzGjUjtZ003kUa1CyqdPYca9Y0hOMGtIoyesPV9aDWMkwVU5aYJbVnGg+wMt6nXvRgs14/zuZT7S1oW+lsv181Ws2AFhHGUyU2oKpKAj6TMVfRVmThfb+KGlevH+v3hO+gSmwdnRaaTzSDRrur4/X4Xaqii7J12k2SzujGruZ+q4paiFkZ+6tTG9CQxx7OdqfgDVck33n4/XWT+5toptZR1KongEv1f1wtl70VeDKwT9IDgbcBfwg8EPhb4GfX6iALmUwmk1kHx5lNZsbMvuXf/yTwOjP7M0kF8NlxOshCJrMiKgs07QuUtYom6LFJepnUuy/nk1iJooBWjA8B3MwyTJRTO8OwJoKb7Vpjy6ma9DADtpPUyyzM9s2cxgVoqR/HWddYWQ4cp6qOtoxeH6sT+00YX5hlJzYQpHjubi/xiPP9pvsTzUC9frQBBVtFWSS2E4saQD+JAzGLWl2qNaX3LLXDDGlW1IktyIxg8VC3t3ycaZBnkmJm0M4StbTGK00aig9Kzu33Kz1HuCd1Hc+deAkG+9mAVpgGVFb1ctsT8XuzsojnkwZ/b4eJy8J8/CyXMWjw/358BU8zq6XxhG0WMpnlJAGYNuNryLTLplJk66A3wNZghMC7inoqVpJc9vMrEieBuh4UDM15V/jRNg9MVhBI0QhsyYOmOa5fN4bpdElOyUN0INJ96EGtWiPHpn61PKtAKgAGnBrqwWuFwWOLof5HVBRNheLo6P/Ry5BxwFq2vAeJq3U63l5/4EHeLNUpEVLp+YIrcqVm+a0RFlNpoGnql1wPCsOhviBZTlvptxHGs9gbdF8Puxe7Ix01DhUDeseXkPmIpLcCNwF7gI8A+Kqda9pjYIMrY0raLentkr4s6UuSHibpBEmXSfqK/7vHt5Wkl0u6TtI1kh68kWPLZDKZ9eM0mXFexwi/ArwTZ+h/pFlT5OFuwO+scMwAG63J/BXwATP7MUkdYBaXB+dyM3uZpOcDzweeh6t1fR//eijwSv83cySRmnxlTE1BJ2bGbWqU+BQrdadssjC7ZST/1pbPPF0m3xFuy1quJQwsTxWKBmRL8nilM/EwUa2qxu3YpjqDyz0jjltxDCEAtVnusuX7BvYTNRFbQTtZYYlnUNtJj/UOA93oHqxq1GyfwWsbzpuWXluqOazUttH+qjj+IlnWS93F04ls0Iam4vdsXe+eXRaDWk8SfGshY3W7hbVHaHOpE0U63qF8bko1r1RrrG20A8dhcDxF/PtiaW8esf0z6WdJnzCzh43qY8PEraRdwPcBr/WD6prZXuB84BLf7BKcOxx++xvM8Ulgt1fJMplMZlMQvMvGeR1nTK+0YyN1unsCtwGvl/QZSa/xNa1PNbObfJubgVP9+zOAbybH3+C3DSDpQklXSbqqx9Lw7swEULvlXrPTmIRJ1O2SsltTdmuf2TdWLQR8IkJn21CvQlXlE1262aiWKm/vqJ1L8agUMX5/4xIbDMPh/Yi2TT9hdjtCM9KoPlJDdZjBh9l9eJWFew2Ps1+516i1fjM3q+/23Cw6vEZpUAPXk+wPBm/zx1aVe6X3JB1Lem1VuGfxvAqOBOHYamjczf2rkvMmffT6aKk78HJjquM9H/4e0jH0q5Hns7p2QbX1Ci7GaZ/pdQ73B/7+JOdLviMri8aeNQkmtVw2KXOCpAt8+69IumBiF7o+VvyBb6SQaQEPBl5pZg/C1Yl+/sConCq2xv++QczsYjM7x8zOaTM1scFmMpnMWjjvsvFeYxDMCfcDvgv4Eu4ZebmZ3Qe4nPjMTM0JF+LMCaFy5UU408JDgIuCYNosbKRN5gbgBjO70n9+O+6G3SLpNDO7yS+H3er33wiclRx/pt+WOZKoQN4OY9NT0W05SYYZKmMO1HpplzHoLdmupkLhoBtx427abi333PKzb3dg3DcwOQx9hdn3UNtlpLYIcJpMql2M8ggLM/5WGTWfYXfYpo/oimxN0swhG0pRLD82zLC7vXhNA8GaGmzf9Je69FZxnMPX0a+bBJoD2mNRDqSQcW2raINLvNacZsPKmEWPspXsVGFciX1HEtZKyhAMaXuqh2xpNuI6UhtSCAita9SK7tWaqAsz9Cdg1E/MCc8EZ04AupLOBx7lm10CXIGzWTfmBOCTXgs6zbe9LCSqlHQZ8DjgTYc9yPWx4n++DdNkzOxm4JuSvt1vegzwReBSIKh0FwDv9u8vBX7aq4XnAvuSZbVMJpPZFKxjueyksLTvXxcm3UzKnDCWmeEI8FMr7dho77JfBv7Re5Z9DfgZnGB7q6RnAd8Anurbvg94AnAdMO/bZo4wKgRtl0rGZjoxJUupWLkwTIYHUrYPBQsmCRqXnaOf2AVacfY6MAsepZWYxZiYYbtC0gZcHIcFjSwNzhvlgZQGMWLLY1ZSz7Beb0jTGCyGtuK0rSgGZ/NDY7C6RiO2D7RPr3lUfMnAmBO7RXN8TLk/YNcoEu+r/nJtlP4KmsCAppZ8901KoBHXM3RtTUBfVaNi6P6kGksVC+E19qOkrXV7jQaussRmnR1acwvLbVCHw/hLYQC3m9k5K+wL5oRfNrMrJf0VI8wJ0qRLrq0PSXfiXJjfBHzEzEaOx8w+v1IfGypkzOyzwKib/JgRbQ149kaOJ7MK4T97WbpSy0B/pj0U2Ybf7h4inQNJ9Hb6IK6JSzShiUYv+6iqR9czSRleGknfp8tGaQR9aphPnQGSeioDFMn+UYbx9DrCuctycKkqHJ9GvA8LIcD8Q1tJTq8B6hEPYVj2YF3GGoJ34OGcRumnx4UHeY+4/LRCv83YpOT+1RCScSeCLM30bMPLdOCcRBpBvfyaLTiEhHOMIlzb7Myg2/JaThfrwJiYC/OkzAk3EpfXwvYrJjFAz2249DEvBt4g6e3Am7wH8FgcMxFDmUwmcySYhOF/guaEDwLnSdrjDf7n+W2TYs7MXmFmjwAehhNqfyvpa5L+YJwOclqZzABqtZqlhv6O9kDNlaLvl3bCKkhZxIzMVTJbLIg1TAYC6MLSRx1nmqlReJSmkrzXqOWiYYa0pGZzSMliQxqW25iMXbEK5ghjNWUZl4/qermx3obGmPYxPOZEK1Kaw60oRqaNaahraCX/dcMMPl0OGz7P0PkGjguUMVeYmcXqnKlmMZxGJ1xn8t6WlocWWJ1qsuH+pm7kFoM3k/FaL6S8KeJ9L8uoDdaJVhSO6/VHVhadBMZEE2QetjnBzO6U9BLgU77di8epVrkOmos1s/8G/hj4Y0n3A542TgdZyGQymcyYGKJfT2YBaFLmBDN7HfC6iQxqOR9d4ZxfBl40TgdZyGQcSVLMeruzyXR3lk0UU2u+boIv04y4A5Um0xnzsOG/toGElI1mUdUjU8WMdCkm0Ujq5ev6wGjjuCXnTvtvZuqdZPvaGlKjvaSz5KROjVZKY1IGg/jySpPpOdTvR1fixO7TuEYPp6VJ3bmHSTWb1NU4tRGFbXUN3RGOGtJyu0yhAaP7iq7W4BwL0iqkaR9hu9RoKuZ/i8Pf7YAtK6mn0/xNf3P9xJ15JRvWIXKcpZX5tcPtI9tkMplMZlxsMjaZrYKkJ/uATySdLOkNkj4n6S2Szhynj6zJZACXSgZA01P0dzpNpi7VmCtkxESUQWGxmLBS/RXWvofrgMCAppPOeQe8h8JxZdI2sQHFg5TUQ6kGtal09p0GkOJmxSvWw2i8nBLPsFFaltng7DoQNIuBGisjbCvDwZVp8OJSN2k25ElXJvab1A3aLGp4qevvKFfjYkiLCudK24b73q+iHSVovKkNyWqkpLbM8HUWGrSzJLV3QqkI5zI9QoNp3Mzje+v1Ytt07MFmE9Lx+LGtGqS7TiZsk9kKvNTM7u/fvwL4JC7J8Q8Arwd+cK0OspDJZDKZdXCcCZl07ffeZhaM/X8v6VfG6SALmeOdkOLDeyvZ9tkmDkYGpV+jN0HtU7GH9P5WqFFF0jrqlhYos0SDGPY4g8EgxXRMYaJqFtO5p5pJqhWlwYJDFTCbNsMpS1ItI6k7Pyq9ycDxQ5pDc82pV1t67Aibiw1rMM1pFPcPzMQHZ+1mNnDNMaCxipphE6tTRU+02hqNxPoMxgaB+8IHyg2EEy7XUleKnQHidzA8Pjf4pL8ijqdKzzPCpmVD2m16XNp32h6gqjGtMtZ1YohqQob/LcIVkl4M/KF//2Qze5ekRwP7xukgC5mMIzx0O+1GmKiyWPqYaORPK1GGJ6oVxaBBuzGOh2OrwYDFtRhlKIbBZaAw7jQ4cpSwGBYSw9dcFkkZ4BFuwKlbcxpMONymGXviipyUPrbhsSs+ZIcfzIOu0INLVamwQEXM6TYsnFbD6uhCPOpBr+S+mg1mBRgxRkvbDjdLt/VjGeqBh38iTAeEbbPMVtD8mAaEyYg6NWnW67XuwyFwPBn+gV/CFSe71n/+VUlzwD+zSiqZlCxkMplMZkzMjq/lMl8J84XAC31Sz5aZ3bGePrKQOd4Js0fvGmwS7QOh8mWczabBlkHTKfpE43C7bDI2u89DrsZVbLvMp9FPxcMZVNUxvcmQC24zQ023h+WgtGZITdI2MUYPG+PdgSNn4KQBfYlW1NyrRHMYmH03mYFjtcbBJbBwrkGDfDSCrzH7LgT91AlgSFOA5P6VqNNpztsEN/b7LJuQp5UzlRjSV2LUPUvHkmppo7TXAUeNRKvzPxC1BjN0Wzqc4aqeELXRkoHjNMFaMu6Ux4+QAZDz6ng8cD//+YvAB81srPTWx9XiYiaTyRweE60nc9SR9MQ19p8BfAH4deB0XIbn3wK+IOn0cc6RNZnjGalxXQ7agHp9ygUfFNeK9hkrY1qZgHMG8POUmVbjxqxeHdPN9BP7xLAxHlwQ47CWMTAbttVTg2hISxkI0hzSgtLzDs+Ax00/MpRBOGgfqb1lIJhwwJAebAYj+h2wPZWjAytHkbZLbTXptafZi5dGaBQatM0se18bThVNj0murdDgOJScb3iMEo3OWlWJLWqEfSfJrOySjY54cCdZnAdI3L4b7W1CHGOazIuB96yy/6W4wpN/mW6U9BycM8AFa50gC5lMJpMZEzOo6mNKyKx1Meea2TOHN5rZyyVdO6L9MlYVMiHScw1qM9s7zskyYzBqpr1h5yoopnwJ6z27AFxKmTDhLkXdCvaXxP4QDjcaV2VrF9HrrCyaWjOW2mHq4NZszXalHkbD6VJg+Yy+HtJIVighMBDcmB6XepmtlG5kWKtJvZxSL7F66HxN+yQBZKhfX4ywS6iI9gJpMBAyadP0QdLvKMpimcIBDAR2xntSLt9Wr+DBVYz4XoYZKBcw1Hbo/jWkGpKKQZsaQL+Pjbp/ozSaVGtMvOHMegNej5PgGPMu+/k19i+ssm9+nBOspcl8y79Wu6slcPdxTpbJZDJbGWPrL5dJmsXZWO5uZj8n6T7At5vZqGWzXZJ+dFQ3wM5xzreWkPmSmT1ojQF/ZpwTZcZkVGzHBqFCaHYGgN4p7vfS3dWOS+bTMQZDNcuC96qpIqaaMSiaWTDUPo1LMZWkWAmeVlWSsLKKM9CmFvtAfIQG7wkj7kmajqWx61SjPZpS+81hphtRWcSYmYH4khEaiY3Q2JIZuaWpdFayjYxIpTKg6RQlyzQREW0SaZG1sowBuKkXWXOOevXfn4okVU896EK0LBmpxf3D30nzuVoeHDrQLqmMWdXRkzEdb/rbSO/bcAzSYbF1jPqr8Hrgalx9GHA1Yt7GaNvMx4AfXqGfj49zsrWEzMPW2D9um8yhsNFLZ2UJM652TG+7+yksnNRqjPamYWO/+w9ddcJ/cOJ+i4Z2FUax5DMHt8umr0awFIk7b13GbAHJcpGlLrgDyyBDxvPUPTlxIrCpNmoyIycu0f0RSycDS0OJ88BK9z9kRS7LxJ15lQckjBQQKoqB6pHLgi7DtmGhNGpprhn/iGXENIgzuKy3WhCcPrq95cf31/jNDWciSO/VasG2K7hnu7xrQTCOWjZcISh31PIeNMt3K+anOwyOxEr2BnMvM3uapGcAmNm8VrhRZvYzK3Ui6dRxTraqC7OZLUoqJX15tTarDOJ6n7Hzs5Ku8ttOkHSZpK/4v3v8dkl6uaTrJF0j6cHjXEAmk8kcScw01msT05U0g1f9JN0LWF5pbgSSdkt6lqTLgbFWsdb0LjOzStK1ku7uK6Otl0eb2e3J5+cDl5vZyyQ9339+Hi7Y5z7+9VDglf7v5malmdLhTneGl4wm0WcgGN07HeqdswBUM26+0ZsFNQZ6qPvBhRn604PXWiSrLEUp6tBvbc3KRRrEGQzmRb+Oy2npdQZ6/YEMyE36EbNoHAzpoVPX6FYSVDmQH03Lz5EeN5CleIxZdKgLk7hPhxZWEA3zhbyGN0S6vLdS4OWo7aOW0wbSrayQcXiUBjSqUmehJqebpf2k2klYpqrqxMCfal6JZhbOW5SDgZkN1WitrEo/J+3TpbGQ5mbUfSrKpJNJLpUF77ItH154EfAB4CxJ/wg8AnjmSo29QDof+HHgQcAO4EeY0HJZYA8u+OY/gLmw0cyeNObxKecDj/LvLwGuwAmZ84E3+Apwn/QS8zRfxzqTyWQ2BVt9uczMLpP0aeBc3PzouUOKQIOkfwK+F/gQ8NfAR4DrzOyKcc83rpD53XE7HMKAD0ky4NVmdjFwaiI4bgbCut4ZwDeTY2/w2waEjKQLgQsBppk9xGGtg5W0iLXWekftX++vc9SsfBKEdfnpaXq7vU1m1m3rz4oyWQAtgsaAmum6wiRRhvxMve7EMarSYBJNwFXG9A3KuB+pscnI22/ULaMGNBA0WWGtUF0yyQRNci5vL2mcCALj3vvUJpMek1SfXJZVmCSAEAY1h7QeSiDYvIadGEalm1lxnGPO0NNaL0naH3q9JKmlt18U7cFjRxnVR2SVxtJMz6PH0Ny/YSeDwp8bxfMNuFQn52rUxTQw0/eb2pUG7FhM/P/PpJbCJF0PHMCpXX0zO8eHjbwFOBu4Hniqmd3lbSZ/BTwB5zr8TDP7tO/nAuD/+m5/38wuWeF8wyaI8Gy9u1+p+vSIw+4P3AV8CecIVvnn+diMJWTM7GOS7gHcx8w+7F3gxvmVP9LMbpR0CnDZsG3HzGy9A/aC6mKAnTphi88pMpnMVsKYuL3lsMwJXihdBJyDmwlcLelSM7trxLn+bJVxGPD9yzaaPVDS/YBnAB+WdDuwQ9KpZnbLOBc4lpCR9HM47eEE4F44DeNVwGNWO87MbvR/b5X0LuAhwC1hGUzSacCtvvmNwFnJ4Wf6bZuDScyGDtcteUJuzQpaRqdNf8b9BHrbvEbSjrYWU/K+hNLH9NX+V1MVcTx1K7o7F30ou4PjLJfqZpZbTRWNplJ1ClqLfjzeo0mFKLpeXerHoEkl1SUbTaiMdhiTja7lklZxTIIfBzzN0jo0aUVMWJ4WZY3voEmWSRqEOMIVedieMOo3tpZLdFnGGX+acDIl0cKa+5dWu0zS9A9oZOl5l3mtDdrS4jUnbZt+kj7TINmVgkdHJVJN3aBVrOHBNuThNmETygbPbNdlTvBtLzOzOwEkXQY8DnjTsnGbPfpQBmRmX8YJsoskfTfONvMpSTeY2cPXOn7c2/9snHFovz/pV4BTVjtA0jZJO8J74Dzg88ClxHw3FwDv9u8vBX7ae5mdC+zL9phMJrOpMLBaY72AkyRdlbwuXN4bH5J0dbJvveaElbaviKRpSb8m6Z2S3iHpVyRNj3X5Zleb2a8D98BpWWsyrk1mycy6YbbiUz+vJdBPBd7lj2kB/2RmH5D0KeCtkp4FfAN4qm//Ptx643W4NccV/bOPGBvgY7+yv/8qbJSlsa4pem62WoR4vSQDiZVDIQsWt4PXaBqPsqTb0r0gOoFVnZKiF7QeNbaaIpnJthZDga8iXnIaX1MlRcnS1do0GWJaCiBNZhlMAen9LxM7w6iUNoGhwM1wjpViMOIYFMc5KrXJsJaSajipfWLA22qIgQJmGtRaQp9JGYJmxGm/oypyDjNsAyoGbTMDqXaGNZk0mWZyPrVauHIlqzDK+6xQDEAdqf0J0rIJE/YGW8dy2e1mds4q+yduThiTN+BsQX/tP/848EbgKcMNJX0HLq7mUv/5L4BdfvcrxjnZuELmY5J+G5iR9IPAL+Iqo62ImX0N+K4R2+9gxDKbVwOfPeZ4NpaNEC7jnOsIua2YfwDV+w8wddMBAPozu922sqSacmMq0wS/rShIguHfpkTdcmMuu3F/fwaqoUxEVkDRD0tHUPTc+/aCubo1QO2FUAFUfllLVRSEAySuxo2TQJ1UtSyTJRWzxmFg4H4HJ4LUF3ulPFtD2ZeTD+5P08xiaefkGHMnGryGQgMPwCaws18P1qwZFenfdLx6ZLtaUTBLGlwOGza0Dxw4JACXtUmFfJVkEiiWLwem4yqigX+gwug4S8FpdoVm7Mn+URmoB7ZPhkn9N52QOeFG4vJa2H7FGqd+gJndP/n8UV8jZhQvw2VbDjwW5wg2C/wezpV5VcYV8c8HbgM+h0uo9j4z+50xj81kMpljAoOJBGNO0JzwQeA8SXt8YPt5fttqfNr3EcbyUOCqFdqeZmb/nnzeb2bvMLM3AietcR5gfE3ml83sr4C/Swb2XL/t2OFIajArnf9IaDPBWD8/T/EN51uxzRvBZSewuNvNPIsqLnv1Z9SsQNTB07VOls5Kml9T3YptrdkWl84waM3H5bKWz/NaTfu0NRbnPkWvdk4DgNpFXGZbjNpHk1/NLBrzpZhbC5ankykL6il3IUW37wJAw70Jy2FpxudwfOriHD5Ds23gFzSQd61otIFGE0rHOFB9My5xCQZdj8M190fkeUv6tqA9pJmmy3IwI3HIoJ0s/zX3sihiJcqVHA+CNqHE/RhGuiA342q1YsBntzu4rDdKo0oJbUdm165Hj6FidU1wvRiMDLBdPxMxJ5jZnZJeAnzKt3txcAJYhe8G/l1SCK6/O3CtpM+5Lu07k7Y70gPN7Nzk46p2+cC4QuYCnI92yjNHbMtkMpljmknMAydpTjCz1wGvW8fpH7eOtt+S9FAzuzLd6DWhb43TwVr1ZJ6BMwrdU9Klya4dwFrSMnMoHMl6MmbU864khL5xAwCzZrTv5ux61hLdne4nUvSLRmsJRZvKrtHdIb/fGltNuWSNthNS0dQtkiDOaJ/pV9CaCm7JQ8F3QGtRTfXNsls39pemHk1VNxqSKaZFoVUM1LJR39t4enEmrzrWvBmws/jEkY3NoEosTH1G16FJ7TDpdxiCQ8vEeaAZY2pDKWNSR/qjzxGoUy2CqCGNShUzkF5ndJ9BuxmwJ3XaUWOri2VODlYm9V1STaGuRsz043Wq1XJ94zTPZrRrrSIUwoKLew20h9rXiSakAqqxys8fAo3n2JbFzL7hl9bOIpEBKwRjPg94i6S/B8L+78YpHk8b53xraTL/josKPYnBQJ4DwDXjnCCTyWSOKbZ4CLhfXnsm8FXi1awUjPkf3mbzS8T8Zl/AVcw8/GBMM/sGbl3wYUMR/zPADE7YHBtslD1mpWC6zUIIZFxySVjr62+gfedeANTp0Nnj6sxUO6fp7nZVNKtGO4laBiReZwWNh1rVCeeJ9pnU3bmaFn0fjFm3ozt0cKnuzSpqKgui6AYXN/+nXzQpZlQKaweNpcamYpmBohgMPFRlA1pNOguuZ92gi4XEvbZJEFk0GoEV0RU7tktSmoxKtgnLNZrQtqkM2RrUhoZ/m1LiijyUbj9oTuGYVIMyi1qGWTO+wZQ4RfN3IKh0XGqjeW4FD8EkkSjp+zJqbwOXFzzrqprGz7ooUfBDTxOepl5mo7zhUg1nEti6XJg3K0/FuSV312yJ837DeZIdEmNZxHzE/9uBV/tNZwL/71BPmslkMlsWG/O1efk8sPtQD5Y0qrjZioxr+H82zof7SgAz+4oPIMqsxEBBp9TWsIm1GsB6Xao773IfVCD/vpyaYvaE3QDUu7a5tp0WrTkXKNyfLTDvrdQt1GgibZ+zu+owUEWzKVff8Z5rRK+1ok8z4y67Ri/sLwum9rv/vf0ieMBZE3OjfvQMK4C6HVPF1KF9uNBeFe0Xw9pCut3/bRJz9sFCsa9C2JAmo24vxuRYgeoQ5TqkcUAszAYx4DS0TT3bQrsYoZpoQ0M2l3Jo3liWMVlpqjVUiTdbeg3Bg6uX9hvPZ8GLTsLStP7pb7w1+FhJg0Ct14taVuLpprKI8TSN5tUbLE6XXuvIGJ/ExpUeN3G2vCbzh8BnJH2epI7MOrLqr5pRYJiNjPjfWoxaltgIht0pN6PQSbLrmq9wWXW7KDgJ3OqWk1SWbNvuBc72WWzGbe/vnKaa9TnRtrv/+L0ZNRU164Fkv9FtNtYwMYqkDk1vuz9uEVpLIY+Z218VBUXPb+sbZVhOKwW+DxnNg7aa9o4MpWI25FZ0jdZSFR9QI5acDBqDvXM0GPpv0O3FipNS859EVR2FSrpcVsR+GyEgJf7eDDoVNNuSc6aVPMM4pzpx7CFjNdHZwW0f+r1bDUoyBqSZk4ccCpYFdYaHeas1tNw16JAgCXw5aKvqIcGhwb+pEJKiW3aaVSB8b6kThdnyejiTZBP+l10nlwB/hIt7PJSr+cx6Gm9YxH8mk8kcc0wuTuZoMm9mLz/Ug83sf6+n/bhC5vnAs0gi/oHXrG9oW4D11oyZBJvdMSBg1gQANoGAEvWc125uk3NPBdqdDp1trtbPjJ9R27YZeif5KpztgnrKzXaXdpXRDTpoHv2YFRpi1me1YWlHMdC2LqNjQNk1Ot4VpaqLJhea+XYQl+ysFWvamKDsBeeBOOtOnQjCcpbMmmVBiiKq80FDaJWYX6arZju09vvz9fqDGaBxGlSTYRowDQWMhrbBgL7kNYB0mS1dAkszSKfVN8MSV22DS2NNDZjoqjyQAqZJ7VONztfWihpbHJDFyqFh27CDQ5LbbSAFT1UNtk+qfto6tBFLAmpdzrXNmVbmKPIvkv4Ql0UgXS4b5cIMgKRHAC/EJcZs4X+yZvZta51s3HoyNS7a/+/WapvJZDLHNFtfyDzI/02j90e6MCe8FvhV4GpGF2hYkXHryTwReAnLpdjO9Zxsy3E4dpomM+6gDSbUchk2GC/jSKWYORzMCDXdraZJvMnSEhx0Fv9Yu6ZD+2bnJNDZvg2b9e9P2MbSHqft9La7e9WfUpORpD0Xgzy7O0XfF0MtF2OgZbnk7TMdEYy/qqyxW9RtuVQ30GSCHqYOwZr9WMEzuFSXizEItJwvnQbiSSt/grNT1d7u09/RbjSK8sDiyN9S3fEOCXUdZ/UDdXOKGDSaakJhvxKDuBQDPMO5+kM1ZkIfSXocTU/Fa0j7Cr+/VmvQMWGYtP+BtDXl8jZpWp6qHkpwaQNt1Wk3WrOkaGcpk+DZoFUO18JJs0ZPMq0MbPnlskOsK7PPzN5/KOcbd7nsL4EfBT5n69FbM5lM5hhjQ5LvH2Ek/RDwHUBTR8bMXrzKIR+V9CfAOxlziS0wrpD5JvD541LADHjRHMIMZihxX9BgBjSalSolbjUGUuNHDcd9rGDR/TZ1cA51nPbSuq1D68Q9ANTb3Yy6v2OK3naf2qWkWdyvpsomENSSX27deAyL3ox7316AxT3RC6wJ3gweZYVi9U6j8XBDMGwasZ0lLa8tlbPlwHJJsA3ZKFdZsyZYtZhKXOqCBpF40FlZJjVaypg2xwyC9pWWMUiSaVrh+x6RSDPVQMwsajp1jfyM3DrJ2AZq8CTJRoe0jGVadhOsOqIqe+qSXQ0FwA73C3GMiRa3rO/hujkM2YuGkn9ODBNs8bQykl6FS9X/aJxt/ceA/1jjsIf6v2l9nLWW2IDxhcxvAe+T9DEGpdifj3l8JpPJHBts/an2w83sOyVdY2YvkvRnwKpLYYdauhnGFzIvBQ7iVKvOGm2PXQ5Vqxlhn0k1GksTDQ5USlxh1rgVSe03S5VL8w4wV1AE+03bzag7szN0dm4HoN41S3+7+8lNzRR0vRVwIObGwqwf+rPufX9WA/E4dfjVJrPQEMRprWSZvYDSlx5IE3qWiz7Is180Wo072P0J3nDdXWLqLrdx280xSWOIGwLnrebOH79rmypdgOgQpgL6vl2Iv0ligGgVjV2nnmpR+D4aLSnRllwq/+Cp1m2CSm26HY9JA1DDcf1YDM4aj7N68HcZftqp5pEGlDKkjeHjZ0bFswQNqky95QrUS9L8pKlpwnnD+9Sjjo3QZibb3VHA/8KZl3Q6cAdw2qiGkn7SzP5B0q+N2j+OojGukDndzB4wZtvjg0MROMM1L8Lh4zoDHEskAZ/1YuIwADA3j/buA6CYmWZquxM4rYN7WDjNu0F7IdObLejuDO7JFoXPFAMPg8rbtvszfnmr9JkFwnDCimUJRcjoFJ6RBkXX9dtagHIhnq9xZ/Z/erNRSC2cWDbBo1aIwmcRbs37B2C/Hoy5DIGmlTXCop5qUfjfV+0dDjRUJjm4TFPELAdFcABqlxTe9RmpEQY204nHNXKhaoSwqioGdNJdtqxlSfCj0lo9RREFUbrcluwfCKRM34c+g1t2+v+hLKCf9DPcNnWTbpWxPpCE+hPOyLz1/5u+R9Ju4E9wmZWNlUNStvm/O1bYvybjCpn3STrPzD603hNIKnFV1240sydKuifwZuBEnDvcT/lsAlO42tPfjZOsTzOz69d7vkwmk9kwjoFgTDN7iX/7Dp+HbNrM9q3Q9tX+74sO9XzjCplfAH5DUhcIOuu4LszPBb4EhLZ/BPyFmb3ZG6CeBbzS/73LzO4t6em+3Vj1Co466wniHFo6s9qim28hrB7hBHAsLZutRprOxqeHqbpd2H8QgHL/Abbf7CZUttNNsHonb2PhZDfjrjpi8QR3r9p3GFMHXH/dbWq0nSa/2s66cR4wgU15DWe6gm5Sq8VTzrlt3V2isz/M9tVoSCFXW9mF2mtTCycpCf6k0Rg6+0NAaXREKJcM+QGpIqbHMZBf9wtu2K6NX77r1c05yqWqcTQwP+tXqehyXcSlNYBq1lcG9amDim7s12DQvTh1OgifQ9DqsBu112BCFmyS3GWYNct0qQZk7WKZMV9V3WhW1ipRWKlPQwvSdDJpep00KHU4fc5hstW9yyQ9BfiAmR0AfhN4sKSXmNmK6WIkTeOe0cMeaWtG/4/lQG5mO8ysMLNp/37HOAJG0pnAD+FVMTn3j+/HZXQGl0PnR/z78/1n/P7HaLhSUiaTyRxtbMzX5uV3zeyApEcCP4ALtHzVGse8Ebgb8FjgY7hM/GOVehlXk0HSk4Dv8x+vMLNx0j3/Jc4zLaznnQjsNWuqh99AzOh5Bs5VGjPrS9rn298+NI4LgQsBppkdd/hHlnHsNYlGkzoBRPvMFkk3s1GMcIeuDhyISTrvdLPa9rfadE7wLtA7Z+OsvbLGFtE9dTtLe9ysfeGg29/ZWbC02zXtz8ZAvqJTUQdXYj+LntqxRHfGG8dLY2GH9ygwp80AWOmPXyhiMGeL6JZsUC74YNNtIZAS5M/RORCdC+o2qHJtWwtR2wlVRouKaEfvG2XIeH0gnjtoJLXAfHCojKjpFKK/zWkBpdeQyk6BusEpoUKL/r9pK1bBtI53FmhFo7369UCqHXVH2EDSrNHBiaBVxu0Ste87fG/WTzJlt8pYObWum3Q6A7ah4OptNpiNerUqo4fAJDWZSZgTJL0Ap2VUwHPM7INrnDaokD8EXGxm75X0+2scc28ze4qk883sEkn/BPzLONc4bj2Zl+GWvb7oX8/1uW9WO+aJwK1mdvU45xgXM7vYzM4xs3PaTE2y60wmk1kb03iv8QjmhEAwJ9wbuAsnPCAxJwB/4dsh6f7A03HLWI8D/tYLrtW4UdKrceaI93kBtpYsCGaSvZIeAOwCxir3Mq4m8wTggT6HGZIuwaV7fsEqxzwCeJKkJ+DW8HYCfwXsltTy2syZwI2+/Y24mtM3+FICu3ASe2uzllYz7HHm36uol9tntkKqmY1kVJLOBaHgAj0zE91cy7JJc9O5dRtTO5yH2raT3Cpvb2eHpT2+HMFMwcLJ7v/lwbM7tLzGUW1z970/3WrsM2WrwrybdNGqYzqaMMbtRmvKjW3H7BLdvuv3wP4Z+k2dFbetv6NCvkxBNaPGa61ux9ly+2D0VuvuCl5mXpvBuWG35oKm02Jqv7eHFMG+Y1QzwdsrpspBLn0PQMtrMvViTdny11y2Ked9SpfaKBb8ffd2lrpVxODPygbehyecJcGhTcqc9Kfeq6i2uYlikWhDFly5Z8qoFaXuzlZgA+UigF5/tMfbpFfcJ7gUlpgTXgr8WmJO+HHf5BJcUspX4swJL/Tb3w68wrc/H3izmS0BX5d0Ha721ydWOfVTcQLpT81sr6TTcLaZMK49ZnbX0DEXS9oD/F9cYs3twO+Oc53r0SN3J+93rdXYzF5gZmea2dk4SfsRM/sJ4KO4CFOAC4B3+/eX+s/4/R85LjMMZDKZzc3kbDJ/iTMnhHXxsc0JQDAnNNtHHDN6+GbzZvZOM/uK/3zTkOfw5SMOu9zM7jKzj5vZt5nZKcBY3sbjajJ/gKuk9lHcpO37cOn/D4XnAW/2a4CfwRmd8H/f6CXxnTjBdGyxklbjNRWrC1REW014nxYgzAyRajcHEjtkqvUtLTVxN7rVmfimypKpGecko1aL6m7OrrPwX9tQ7fqbP9n991g4dYbujuDNNY1tc++raYNdwX7gvs/p3YvIqyEnzMyzVHkbBtD1tpGlwuW+2XbKHJW3vdR3EwuLvm2/oDjgk2xuE3XLn2+7/yF0auT3d/YW9HyphKUll3rHdej+lEkV97Jr9EKw6jY1KzodX220nBaF18yWdhYUfW+HMpi5zd2T4DlnpQbsQk3RtzTBaCdo4lB4G0ndLhLPuIp62ic0XYiFxhqvNCA8oqwomimxkqDVJui03YoxM2UxULRs1eSeh4DGN5WeJOmq5PPFZnYxDJoTJD1qogM8fEapf+8AHjy07e04G9GqrClkpKaow7nA9/jNzzOzm9c6NmBmVwBX+Pdfw6lzw20WgaeM22cmk8kcFcaXWbeb2Tkr7JuUOSFsD6THHCrNFUq6H87es0vSjyZtdpK4Mq/GmkLGzGpJv2Vmb8UtaWUmwajEgBbtMCqJtpowNbQ6WZfOK4mrMuCdlmg7aYLGoPmoQLc789/sl1vIx2vMhsSRe3ZR7fFlplsFSyf6RJ4zBT1fcjqklZk7KxoL/uvkmeb9thMWqL3W0tmzCMD26SYNICfMzHOw6/pd6LW5o3QOmTO755npeC85b9/ZOb3EbQfceBa0HZvxGQQWCxZPDtqAu/7O7SWzN3tNqCt6Pl5oaY81MT4L3ozVmi+abAe9ncbUHfG32Z8JGoW/ZVXUalrz1pRbQNEWE/6qssZbrm4XTeaDoltQBC8xqdFqYlxPjOGxIhaZK8qCYrE3cA7KEvP2HSqL2ktSNmESyCbjXWZmL8DbtL0m8xtm9hOS3oYzF7yZ0eaET5CYEyRdCvyTpD8HTgfuw9rJLtfDtwNPxJlLfjjZfgD4uXE6GHe57MOSfgN4CzAXNprZnWMen1mJlWrWJA4B2a15AxhykbbwMOsuT6HCnXspgsApS7b5DNK0Wshvt+3Onb538nYq/7CsZsrG7fjg6bvo+Hnf4kmu/1tnZpqFiVumd9OacYKwP9cGn1ett72kDtmS/d92Oc/JO9x/w2+dUrJ91gmrPbML/Mhp/+mG3HdC6B+/+D3M+8wg5SIsnO6vs1NTbnfnm9rmhN6BW7c3btvFth5Lp3rngaWClg9GHVwq8stsi6I1H50SQmqfsBzXXog1gfozorXgj+tFk3C5UDe53EovhKqpsln+q1sxLQ8SRddfRysIr5LeTvddtBYrar88Vy72WcvVat1sbMT/uswJZvYFSW/Fef32gWebHfYCexI7a+8G3i3pYWa2mjPBiowrZELk/bOTbQasWXozk8lkjikmvIhwuOYEM3spzkNtXUi6N/BdwJfM7IvJrseMaP5kSV/AJdf8APCdwK+a2T+sdZ5xyy/fc5x2mQmQOgGEKVjj4pw9AA6bpnbPCvdyaJkttG2SeAL4gNC0v6BtlteXtFKjs686uXvHDqqTnFOm+YDIeqpFf9a7M8+WdLe7mXh7oab2y0Bzp+9k/lQfWOmXwK6b2RYfdFM1d807zerEe8zzy3u+AcCH5l1fHz/53nztoNe8eqJzotNaWq2KbdNubex+J9wCwNemT6JbufGcvetOnnDS5wB43Tcewc13ueW7xsW5V1J5J4HugTbFYkjeCdWUT5vjfaSW+tFJoOpA54C/V0vWuFQX3YKZO1zf7Tn3t7uzpApxr4WYOuCDcjvFwFIcuHMGV+1qOi7JlUuinh72dz48tmpaGe+49RQzu13ST+FckD8OvFDSxWb217DiCtV5ZvZbkp4MXI8rYvlxYE0hM24w5rN91s7weY+kXxzn2EwmkzlmMLdkOM5rE3KymYUMKs8BHmZmP4srSLaWfSVI6h8C3rZSQs1RjLtc9nNm9jfhg5ndJenngL8d90SZVRjlBADR/uIN0TLFCfjxHpi5XkIKlMa+dRh9rVIBlH5/cDUllC/Ytx/dfOvAGEqgFZJCliUKwZoWa6PsPmE3vdOdBhTsDHUnJqdcPDH+F77txLO459d/1n3wST5bB0pmDsQAzMW+c0bo7+myuOA0nG92nEbTrwvapbuem+d2cv8znJPSbLvLg8+6AYBTppyzxPVzJ7J/yRmZvtXa1Uzv+0stFncEt2RvyJ+qUUjTb9DfFg37oWpNuSR6/rht33JtF08o6G2Pt7K6w9u6OqI95953DnqnB4sJRE3QWnTb63YBnbHm0uOzdf/b9SSdYWY34uqDBfv6EqxpuvpnSV/GLZf9gqSTgcVxTjqukCklKQRH+rQFx2/xskwmc/yydYXMrwIfkvQO4AvARyR9EHgk8PrVDjSz50v6Y2CfmVWS5nHZBgCQ9INmdtmoY8cVMh8A3uLz3QD8vN+WmSSpp1nqztxkPSwG3Zkz42PJPTwa5wWs112+ew33Wu3bT+ub/r9pO9oWgtYz04r/hU8oC874Z2cDslnvypb0X0+3WDrJaTIHz5jG/Nz15tNdgHhn3+Ayz88v/hQAvapk+kRnYPnS4t0AOHPb3sZ+s2vHAqdudxrON+7aQ+091HpdN7YzTtrLqbNu/xdvO5WlJW97alfMH/Tu4EslS2e6e7W0OwSBGpW/jP7OisWTfEG2vjXebC2v0bTmrbEBWQFVKGkgZ8OZJFvVJmNmV0h6OC5tzQ5cAs5F4JfN7MtjHH9n8n6OxNMYl0vtsITM83CC5Rf858tYuZJaJpPJZDYh3pbyyvBZ0ilmdusEul5xtjSud1ntB/bKtdpmDpMVgjQBt24fYiayo9mhUayuORxx1rCrDQSPNqWGBWm9e09t1hT8il50SbxVWTDttaHZnTuagl9B69Fd+zHfr9ptbplzEQpFC750d18+yg/h+nudSOXjXKxX0DnTqREL81OcsNsVmfPKBidOz7Gr7bJ87p5Z5JQTbwNgsWpzU9t5rS0sdZj2Qac6xd2TO2/c3Zzv7HvdwlzXrdDvOzjDwpx7r4PuGtr7CwofW1T0aLS07p0tV3JhkmxRTUbSCSM2/4ekBwE6zLjHFe/Kqrdf0lvN7KmSPjeqEzP7zsMYVGYtmiWxkI/KhrIAhOqJW/RXn1mZps7K2ks9TR7ZqmpqE2GpEPLOCX0aR4T64MHlfQ9UYy045d0+C/POHdiMW9bq7XHLbUsndlja5YVUAQf3uGW0mTbceapr25pz13D1Sdtgxo1henuXu+ZdHwvzsVRHq91nqu3Od9aOvYCTpbff4oTbabP7edxZzqX6lV///6i2O6HV8cGldx6cpd9z41ncN4VmXV+L+9uoN8GJhW1az7FxuB34xtC2M4BPs4Fxj2vJ+Of6v0/ciJNnMpnMlmPrzul+E/hB4DfN7HMAkr4+oTjI61fasaqQMbOb/N9v+AHtXOuYzIRInACaypkTz4+R2exoheW9gSoYdfJ+2CFkNS13tTVXq6jucKsn2ruv0XpaPo1OuyzZPuU0kRBwCmAzU9is/1x7N+LpNtWsO27+1G0s7fZG+1L47DfULbhr2i2d3XKP3a6vXtGk1/nW3C6+PHu6vySxzbtd/8893wLg1h07+OpdJwHQnenyP0+5CYAb53Zxx9zkKuiKLW34/zNJbwH+QtI3gYs4DJGZepSZ2Y+u1G4sgSHp54EX4TwRwqByWplMJnP8sUWFDICZ3QA8RdKTcA5chyOBXwvcfa1G42olvwE8IIkWzRxJwuzUNHqNPgdmjk+9jvt0tDJeK/mex3G5blIRjUiJczj4Pqzfj1p1P7H1JFmsU0IW6zAuqaDlNbLdU1Nop9NYKAtX8RKwsoApZ8yfP9vZYdS3JtPzXfc+nXftOt1vh33+FAsPdRrS3oMz2LUucrN7Ro/bdzoV6cTpucZhYCJMKAvz0cbMLpV0GXCv1dr5LM8jd+GKpq3JuELmq8D8mq0ymUzmWGeLGv4l/doK288DMLM/H7H7e4GfxGUIGDiMEYk8RzGukHkB8O+SrsSlIMAP6jljHp85FFZKN5M5dNYTxHq0tEMVURtYi/VoZofDOmw7Vo+w9fjfcNXtrqwBeW1n5qv+sVQUzXHbP7W90XSorXG/3n+N82o7bb5i+haXTuvAvXfw399+D9dFHxbuNlmpsIU1mR2HcMwngXkz+9jwDknXjtPBuELm1cBHgM+xZeV4JpPJTIAtKmTM7EWHcMzjV9n3feP0Ma6QaZvZSFUrc+Sw2lCRZfzhYEdq5n8ohCSeZTkYNBrGvJKnWRMbU29e29yocS3TgPxfX8V0ICXO/PxI+9S2byVV4L32t/P6Nrs+sa3ZXJ3uTAfXH8Kwl2FsWSEzCkmfNrMHj9HuVFxMDcCNZnbLuOcYV8i8X9KFwD8zuFy2YoSopGlcvYEpf563m9lFku6JKy16Ii53zk+ZWVfSFPAG4LtxtaufZmbXj3shxzTNstnRHUZmA5FQy+fsasf/lgMR/6mwSQXLscqwYBrhch0qmiLFZ//CAuyPJoRQWntSbOHlslGs+lTx2QBeCewCbvSbz5S0F/hFM/v0WicYV8g8w/99QbJtLRfmJeD7zeygpDbwr5LeD/wa8Bdm9mZJrwKe5S/iWcBdZnZvSU/HJVx72kqdZzKZzFHh2BIy711j/+uBnzezK9ONks71+75rrRNsWGVMXxYgTCfa/mXA9+OygAJcArwQJ2TO9+8B3g68Ii0vkMHPWn2tjkI5f9mhsFln/ioaDUatVlwySpf3Vsq7Zjm90GpaT6PtTIgtnFamQdLdcN5hV0q6m5ndvELTbcMCBsDMPilp26gDhlnVCV/SOGt1K7aRVEr6LHArLvDnq8BeM/P/g7iBuM53BvBNAL9/HyP8sCVdKOkqSVf14spdJpPJbDy2jtcqSJqW9B+S/lPSFyS9yG+/p6QrJV0n6S2SOn77lP98nd9/dtLXC/z2ayU9dq1LkPSzwH/gSij/GPBJSf97hebvl/ReSU+T9HD/epqk9zJmuZe1NJnXS3oUq6/bvRZ40KgdZlYBD/Slm98F3G+cQa2GmV0MXAywUycct1O3ZQbsoxU4uNXYpPdHZYk6Y9aiT777Te3IcAwiJmYanYg5QdL9gacD3wGcDnxY0n39s3clfhN4kJndASDpRODfgdcNNzSz50h6PG6lqTH8A39jZu8b50LXEjK7cMb51e7rbWudxMz2Svoo8DBgt6SW11bOJBqTbgTOAm6Q1PLnnqzFLpPJZA6XiSRTmIw5wW9/s5ktAV+XdB1uGewTq5z+DuBA8vkAqzxrzez9wPvDZ0l7gL1rXGLDWgkyzx63o2F8DeieFzAzuOyffwR8FKeivRm4AHi3P+RS//kTfv9Hsj1mOXnmeozRuC0XjQsutTXJJVdks9qWjgPW4V12kqSrks8X+5UY148rY381cG/gb1iHOUFSMCecgQuYZMQxg+OOEf/X4Wwx78YJtvOBa1Y45veAt5rZl70H8PuBBwJ9ST9uZh9e6yZsZEbl04BL/I0s/EDfI+mLwJsl/T7wGdxyG/7vG70kvhOnAmYymczmYnwhc7uZnbNiNxtgTliDEPH/Vf8KvHtE28DTgJf49xfgnuUnA/fFaVpHT8iY2TWMsNWY2dcYkfPGzBaBp2zUeI4JzHKszLFGCDBMUqhgFSOV+FFa7GYOwDwW2YCiZYdpTgjbA+kxw+dZd8Q/0E1WlB4LvMkLxy/5cazJGCleM1sKKec624okucqsWvspZrXlpdOjxWS8y072GgyJOeFLRHMCjDYnwKA54VLg6d777J7AfXCeY6ud+6OSPjL8WqH5kqQHePPHo4EPJfvGKhMwbj2Zy4E/S70JJF1sZheOc3wmk8kcK0wo4n8i5gQz+4KktwJfBPrAs9fwLANXuiUwDfwvf+wonotzNDgZ5/X2dQBJT/DjW5Nxl8vuCTxP0vckKteKa42ZDSQYfFeqM7JZl05yzZuRhGzLKot4f+p69ezK2eh/dJmMd9nEzAlm9lLgpes499VDm/5N0kjtxwdiLrMVeYVjIi7Mgb3AY4CXS/pnXH2BTCaTOe7Y6rnLJJ2QfCxwCsOuMY77IVw8znTYZmYvXuu4cYWMvCHqFyU9E/hXYM+Yx2YyTvPKeXAGkWKqGBVRe6nGuE9Zmzk6GMdCsZOrcVcioIdLUP2s1Q7wgaGzOLvMa3B2oVVtP4FxDf+vCm/M7O+BZzJoAMpkMpljHuE0mXFem5jnAQ/0OSnfCMyxduXjh5vZT+OyDrwI5wl333FONpaQMbNXD32+2sxWynWTORIMz2TNNrfNI8+8R6KyjPVjrAarR7svQ9yf2ms283d+rDIB77KjzP81s/2SHonLMPAaXFaB1Vjwf+clnY7TgE4b52TZhTmTyWTWgczGem1iwnrsDwF/Z2bvBTprHPMe73L9J8CncUtsbxrnZBsZ8Z/ZCJoCZlssFmZz/6c7OqgY+B6b+Jja1vYizBwdNr+WMg43Sno1PtWXTxez6g/NzELU/zskvQeYNrN945ws/4IzmaOECrlI/8Ib/atqtNHfL5MNft7ky6PHMMeATeapwAeBx5rZXuAEXGbmFZE0K+l3Jf2dT8Z5iqQnjnOyrMlkMpnMOtjqRcvMbB54Z/L5JuCmNQ57Pc4r7WH+843A24D3rHW+rMkcC+QZ7dZEBZKQvNG/9q8VnCRyKplNwtY3/B8K9zKzP8YZ/IOgGmvNPmsymUwmMy6bfylso+j6HGsGIOleMF5p4ixktipmW8/4n3GE760YNPqPdF3Ort+bj+NTyFyEK7d8lqR/BB6Bi5dckyxkMplMZkxCMObxhKQCl+HlR4FzcbfhuWZ2+zjHZyGzlcm2mC2NUk10laSYjR0maDX5ez+q6Dizi5lZLem3zOytwHvXe3w2/Gcymcy4jGv0P/bk0Icl/YaksySdEF7jHJg1mUxmM2CW7S9bhK3uwnyIPM3/fXayzYBvW+vADdNkvMT7qKQvSvqCpOf67SdIukzSV/zfPX67JL1c0nWSrpH04I0aWyZzVFHhXkWBmTmDf318Prm2JMehJmNm9xx+MaLOzCg2crmsD/y6md0fZyx6tqT7A88HLjez+wCX+88Aj8eVDr0PcCFrJ2zLZDKZI84xEPF/yHhl4DGSXgt8c5xjNkzImNlNZvZp//4Arn71GcD5wCW+2SXAj/j35wNvMMcngd2SxsrymclsWXwA5kqZly3kMcupZDYHRvwe1nodQ0g6V9LLgW8A7wY+zibQZBoknY0rNXolcKpPYwBwM3Cqf38Gg5LxBr9tuK8LJV0l6areeLFAmUwmMzFUj/datY8JmhMkXeDbf0XSBRO9VukPJH0FV945lIy+zcwuMbO7xuljw4WMpO3AO4BfMbP96T5z07d1iXwzu9jMzjGzc9pMTXCkmcyRQYVcckyIs16fMianjdncTLBo2UTMCd7D6yLgocBDgIuCYJoQPwvc4s/3RjO7g3U+szdUyEhq4wTMP5pZSMh2S1gG839v9dtvBM5KDj/Tb8tkMpnNwbhLZWssl03QnPBY4DIzu9NrFpcBj5vgFZ8G/D7ww8BXJb0RmJE0tmfyRnqXCXgt8CUz+/Nk16VAUOkuwK3vhe0/7dXCc4F9ybJaJrP1kfyriHVi1kiKmdl8rEOTOSks7fvXhSP7OzxzwlhmhkPFzCoz+4CZXQDcC/h/wL/hatL80zh9bGSczCOAnwI+J+mzfttvAy8D3irpWTgj0lP9vvcBTwCuw9Wb/pkNHFsmk8kcGuMvFt1uZues1mDYnJBmgTAzkzaPn5qvI/MOXOGyHcCTxzluw4SMmf0rK6eCfsyI9sZgoE8mc8wTvMqGbTEDqWSOMU+lrc6kHvurmRPM7KYxzQk3Ao8a2n7FZEa4bLw/BHwHML2e43JamUzmaBHyla0kYDKbDwMqG++1ChM0J3wQOE/SHm/wP89vmyiSXoWL+v9lnPLwFOAe4xyb08pkMpnMOpiQJjMRc4KZ3SnpJcCnfLsXm9mdExnhIA83s++UdI2ZvUjSnwHvH+fALGQymaOE5XxlW5MJLF9O0pxgZq8DXnfYg1qdBf93XtLpwB04z7M1yUImk8lk1sHmMcUfUd4jaTfwJ8CncQuHrxnnwCxkMpmjRQ6+3Hocg8kvx8HMXuLfvkPSe4BpM9s3zrHZ8J/JZDJjIkCVjfU6lpD0bK/JBFfmQtIvjnNsFjKZzGYkTYqZ2VTIbKzXMcbPmdne8MFnF/i5cQ7My2WZTCYzLsfpchlQSpJ3QEBSCXTGOTALmUzmSJM9yrYwx14a/zH5APAWSa/2n3/eb1uTLGQymaNEUysGYi6zzKbnOPUuex5OsPyC/3wZ2bssk8lkNoDjUJMxs9pXw/xX3ILhtWZWjXNsFjKZzBFmpNuy1VGbyctpmxfjmPMcGwdJj8KVHrge52R3lqQLzOzjax2bhUwmk8msh+NPxgD8GXCemV0LIOm+wJuA717rwCxkMpmjxWoay3G4JLNVOAbdk8ehHQQMgJn9l88ivSZZyGQymcx6OD6FzNWSXgP8g//8E8BV4xyYhUwmc6RZSYPJtpjNjwHH59f0f3AJOp/jP/8L8LfjHJiFTCaTyYyJOCaj+VfFB17+p5ndD/jztdoPk53zM5kjhR23gXzHFnU93usYwbsqXyvp7ody/IYJGUmvk3SrpM8n206QdJmkr/i/e/x2SXq5pOskXSPpwRs1rkzmqJOFzdYlLJeN8zq22AN8QdLlki4Nr3EO3Mjlsr8HXgG8Idn2fOByM3uZpOf7z88DHg/cx78eCrzS/81kMplNxfG2XOb53UM9cMOEjJl9XNLZQ5vPBx7l318CXIETMucDb/DJ1z4pabek03wN60zm2MQMNKI44vH5ENs6HIffj5l9LLyXdBJwR0iWuRZH2iZzaiI4bgZO9e/PAL6ZtLvBb1uGpAslXSXpqh5LGzfSTCaTWYbF5c61XmswKZOCpAt8+69IumCSVyvpXElXSHqnpAf5sX4euEXS48bp46gZ/r0UXPeUwMwuNrNzzOycNlMbMLJMJrNhjNLcthIGVDbea23+Hhh+UAeTwn2Ay/1nGDQpXIgzKSDpBOAinHnhIcBFQTBNiFcAf4CL7v8I8LNmdjfg+4A/HKeDIy1kbpF0GoD/e6vffiNwVtLuTL8tk8lkNhWTKlrm837dObT5fJwpAf/3R5LtbzDHJ4Hd/hn6WOAyM7vTFxK7jOWC63BomdmHzOxtwM3+3JjZl8ft4EgLmUuBoM5dALw72f7TXiU8F9iX7TGZzDHIsWDPGH+57KSwtO9fF47R+3pNCmObGg6R1E9uYWjfWF/mhhn+Jb0JZ+Q/SdINOJXuZcBbJT0L+AbwVN/8fcATgOuAeeBnNmpcmUwmc8gYMCqL9mhuN7NzDvlUZiYd9eo13yVpPy7z8ox/j/88PU4HG+ld9owVdj1mRFvDpSzIZDKZTcyGxzjdEjxrxzQp3Ej02A3br5jUYMysPNw+csR/JrNZyEGaW4MJeZetwHpNCh8EzpO0xxv8z/PbNg05d1kmk8mMiwHVZML5J2FSMLM7Jb0E+JRv92IzG3YmOKpkIZPJZDJjYxPLlj0pk4KZvQ543UQGtQFkIZPJZDLrIS9prossZDKZTGZc1uddliELmUwmk1kfWZNZF1nIZDKbgfzg2jrk72pdZCGTyWQy42IGVXW0R7GlyEImk8lk1kPWZNZFFjKZzNEkP7C2Hvk7WxdZyGQymczYWPYuWydZyGQymcy4GNiEgjGPF7KQyWQymfUwobQyxwtZyGQymcy4mEGdhcx6yEImk8lk1kM2/K+LLGQymUxmHVjWZNZFFjKZTCYzNrnmz3rJQiaTyWTGJSfIXDebqjKmpMdJulbSdZKef7THk8lkMikGWFWN9co4No2QkVQCfwM8Hrg/8AxJ9z+6o8pkMpkE80XLxnllgE0kZICHANeZ2dfMrAu8GTj/KI8pk8lkBrDaxnplHJvJJnMG8M3k8w3AQ4cbSboQuBBgmtkjM7JMJpMJZC1lXWwmITMWZnYxcDGApAMftrdfe5SHdDicBNx+tAdxiGzlscPWHv9WHjscvfHf43A7OMBdH/ywvf2kMZtv5e9oYmwmIXMjcFby+Uy/bTWuNbNzNm5IG4ukq7bq+Lfy2GFrj38rjx229vjN7HFHewxbjc1kk/kUcB9J95TUAZ4OXHqUx5TJZDKZw2DTaDJm1pf0S8AHgRJ4nZl94SgPK5PJZDKHwaYRMgBm9j7gfes45OKNGssRYiuPfyuPHbb2+Lfy2GHrjz+zDmQ5RUImk8lkNojNZJPJZDKZzDFGFjKZTCaT2TC2rJDZ7HnOJJ0l6aOSvijpC5Ke67efIOkySV/xf/f47ZL0cn8910h68NG9ApfqR9JnJL3Hf76npCv9GN/ivQCRNOU/X+f3n31UB+7GtFvS2yV9WdKXJD1si937X/W/m89LepOk6c16/yW9TtKtkj6fbFv3vZZ0gW//FUkXHMlryGwcW1LIbJE8Z33g183s/sC5wLP9GJ8PXG5m9wEu95/BXct9/OtC4JVHfsjLeC7wpeTzHwF/YWb3Bu4CnuW3Pwu4y2//C9/uaPNXwAfM7H7Ad+GuY0vce0lnAM8BzjGzB+C8LZ/O5r3/fw8Mx4+s615LOgG4CJfl4yHARUEwZbY4ZrblXsDDgA8mn18AvOBoj2uNMb8b+EHgWuA0v+00XEApwKuBZyTtm3ZHabxn4h4O3w+8BxAugrk1/B3g3M4f5t+3fDsdxbHvAr4+PIYtdO9DiqUT/P18D/DYzXz/gbOBzx/qvQaeAbw62T7QLr+27mtLajKMznN2xlEay5r45YsHAVcCp5rZTX7XzcCp/v1mu6a/BH4LCImaTgT2mlnff07H14zd79/n2x8t7gncBrzeL/e9RtI2tsi9N7MbgT8F/hu4CXc/r2br3H9Y/73eVN9BZnJsVSGzZZC0HXgH8Ctmtj/dZ27Ktul8yCU9EbjVzK4+2mM5RFrAg4FXmtmDgDnicg2wee89gF8mOh8nLE8HtrF8OWrLsJnvdWbj2apC5lDynB1xJLVxAuYfzeydfvMtkk7z+08DbvXbN9M1PQJ4kqTrcSUXvh9n49gtKQTwpuNrxu737wLuOJIDHuIG4AYzu9J/fjtO6GyFew/wA8DXzew2M+sB78R9J1vl/sP67/Vm+w4yE2KrCplNn+dMkoDXAl8ysz9Pdl0KBM+ZC3C2mrD9p733zbnAvmS54YhiZi8wszPN7Gzcvf2Imf0E8FHgx3yz4bGHa/ox3/6ozVzN7Gbgm5K+3W96DPBFtsC99/w3cK6kWf87CuPfEvffs957/UHgPEl7vCZ3nt+W2eocbaPQob6AJwD/BXwV+J2jPZ4R43skbongGuCz/vUE3Fr55cBXgA8DJ/j2wnnMfRX4HM6zaDNcx6OA9/j33wb8B3Ad8DZgym+f9p+v8/u/bROM+4HAVf7+/z9gz1a698CLgC8DnwfeCExt1vsPvAlnO+rhtMhnHcq9Bv63v4brgJ852t9Bfk3mldPKZDKZTGbD2KrLZZlMJpPZAmQhk8lkMpkNIwuZTCaTyWwYWchkMplMZsPIQiaTyWQyG0YWMpkNR9LZkhYkfXadxz1JmyDDtqRHSXr4IRz3vXJZuD+/dutM5tgkC5nMkeKrZvbA9RxgZpea2cvGaeuD+zbq9/woYF1CRlLLzP4FFxuVyRy3ZCGTOSwkvVjSrySfXypfO2eVY872dV7+XtJ/SfpHST8g6d98LZGH+HbPlPQK//5USe+S9J/+9XDfz7WS3oALWjxL0p/4Giyfk/Q0f+yjJH1M0rslfU3SyyT9hKT/8O3u5dudLOkdkj7lX4/wyU3/D/Crkj7rtZNl7fzxL5T0Rkn/hgugzGSOe1prN8lkVuV1uNxaf+k1iafj6oGsxb2Bp+CivD8F/DguS8KTgN8GfmSo/cuBj5nZk309oe24KP77ABeY2Scl/S9cpP93AScBn5L0cX/8dwH/A7gT+BrwGjN7iBeIvwz8Ci4/21+Y2b9Kujsulf7/kPQq4KCZ/SmApH8abuf7Blff6JFmtjDGPchkjnmykMkcFmZ2vaQ7JD0Il879M2Y2TnLGr5vZ5wAkfQFX4MokfQ5Xm2SY7wd+2p+zAvb5HFffMLNP+jaPBN7k998i6WPA9wD7gU+Zz0cm6avAh/wxnwMe7d//AHB/ly4MgJ1yWbSHWa3dpVnAZDKRLGQyk+A1wDOBu+E0m3FYSt7Xyeea9f0u5yZ4vgI418wW0wMTYcIY7cYdTyZzXJBtMplJ8C5cvZPvYeMy514O/AK48tuSdo1o8y/A0/z+k4HvwyWMHJcP4ZbO8Od5oH97ANgxRrtMJjNEFjKZw8bMurg09G/1S1UbwXOBR/vltKtxto9h3oXLuvyfwEeA3zKX9n9cngOcI+kaSV/EGfwB/hl4cjD8r9Iuk8kMkbMwZw4bb/D/NPAUM/vKiP1n48oFPOBIj+1oczxfeyYDWZPJHCaS7o+r/3H5KAHjqYBd6w3G3Op4reefgduP9lgymaNF1mQymUwms2FkTSaTyWQyG0YWMplMJpPZMLKQyWQymcyGkYVMJpPJZDaMLGQymUwms2H8/2f6SWypR+ZHAAAAAElFTkSuQmCC\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -1116,23 +1147,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 22,
    "id": "998fe844",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f7707b0c160>"
+       "<matplotlib.image.AxesImage at 0x7f489e93c430>"
       ]
      },
-     "execution_count": 122,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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Ltjx3WOZ2qbRoApKSq3YN8iFhtQTnfEbqGJQ5TzgtGIt7bv1ZLpN0MPBi4APAW+Uz/D4X+J3Q5WzgPcAngBPCPsD5wMdD/xOAc82sDtwp6Xbg6cB/92WSfSBqMpFIJFKRwiZTZavAPwFvo5NEYC9gi5kVboz3AgeF/YOAewDC+a2hf7u9xzULgiWlyZhZTPc/HYV244IGI9fzeakoeZt7u4RXeiwoPV4zUBb64e0ZRS2XPAO1hGu2Yx3bXlt5AlYzzBmWGmqp7WFG6iaVUZaBawF1QR7mLbCasNRoDNXYETST+sQQedNhTd8vcyk4m+wMJvMeZSbIBS3vBafca18YHY82Fe7M6tTPCd5vFmvLLHM6rvIV2FvStaXjM8zsDABJLwEeMrPrJB3b3zkuLJaUkIlEIpFB4itjVhYyG83sqCnOPQt4qaQXASPAGuCjwDpJadBWDgbuC/3vAw4B7pWUAmvxDgBFe0H5mgXB0hAyMd3/7NAUfyRmPjamHBMDbQ2mMGIUMTHKg/0k95qKhZjOvKhQiYJXWbBz1Iy85Que5bVOrEyeirzmUPDgKqpfupaPr5GpbT/JMx+82ZpImQBvYxlPIRNqBNWlsBMVtp/i65UKnikTagZNrVUYfKxTWbNUG82cprZxReYUOc1run8z0bA9/60xs3cA7wAImsxfmNnvSvoK8HK8h9nJwNfDJReG4/8O5y83M5N0IfBFSR8GDgSOAH6wxxPsI4tfyEQBM3ume2a5BcHhje8AhCUzlwVBE5wAlPvIfpl1lsYEzqksk9pGdLWEWsHtOe+UZPZZnZ1fMjPvKg2gFjiEtTo/+oVjQavmaOVBuEwkuFZHWJjDB4EWTg2FDCkO/esoruGX9fzSXxAmKgWeBqFjRdBqzI83Lyy0TMz5YONk3g6cK+lvgB8BZ4b2M4HPB8P+I3iPMszsJknnATcDLeANZguh6k6HgQkZSb8EfLnU9Bjgr/G+3l8GDgXuAl5pZpuDp8RHgRcBY8BrzeyHg5pfJBKJzBZv+O+vVmtmVwJXhv2f4b3DuvtMAK+Y4voP4D3UFiQDEzJmditwJLQDju4DvgacBlxmZqdLOi0cvx14IV7VOwJ4Bt5t7xmDmt+SZybjdG6g3AcplpplhYbil8EKzcW1jBxwRcriSbdQl9F88luna0GeiTyDLO/kB/M3s6DpOKyWQJ5jKW2NglaImSzuZX6Jzk04LBNqiWTCa0iupU5WaKe272Svd2DhNSW15B0MDN/fec2G0nJZJ+VOXDKbV5wWQFaZWRn+I8ydC/PzgDvM7G68X/fZof1s4LfC/gnAOea5Cm8AO2CO5heJRCIzUhj+q2wRz1zZZF4FfCns72dm94f9B4D9wv5U/t73l9qQdCpwKsAIKzonQk2Z6MY8DXnu06Pk5k0yvda5zYoas752S0i3YkGb6WQqDloNNtn9l45tox1AmYdx8467c7v2DPjqlDWRpw6ZyBO1jf/KCvtIeY4iScGCjScdD5pMFpwMiutdZw5tSgMp81pM2yYTtKzCpZlgp/GuzK6j0UTml3nOxJz1KRhzuTBwISNpCHgpwZOiTPCOmJVFL/iZnwGwRhsWjjUwEokseQzRtMXvLzWXzMXTeiHwQzN7MBw/KOkAM7s/LIc9FNp32987pvufBWZe28syIJmcqiPYPZTl/m0xM2+jaBkO71Ujs7Z9xIKbssw6GoOEqaTxiFK5AB+8aQm4oIkUHmkWAkSNEACpjgajVtAuinsYWF1YYrimSCYI3mXgEu/JVta4Ji+hB8+3wlU5o22TkTqaTFt7EliqTt0dCH7aMSBzXpjndP+DMPwvZIKL9JSY2UtnGmMuhMxJdJbKoOPvfTq7+oG/UdK5eIP/1tKyWiQSicw7hpbbctkxeDPGl4CrYfb+2wMVMpJWAs8H/rDUfDpwnqRTgLuBV4b2i/Huy7fjXZh/f5BzW9bkuU9IaXkwRFiXncbbTpQbhk+RT8t7iZhTJ95E5tPHlLSMQjPxSS8L24wFDcF7hlka7DApnXCdsg0nOJ4VGoZrlrzLclD4V2uJt6kk9aDFtCyUHijZZEoaVYEVGkth88k645dtQRQFPp1Cypvl8wa74JnHdP/LzKi/P/43/CR84s5vAF8ys5uqDjBQIWNmO/FJ3Mptm/DeZt19DXjDIOcTKZHnU/6hygjLaqUfd/ChXs4gaUuZ4OpcXNgRMirKGZeWwyzzffNM5Am44bBCVxjay3PIOwLANc1ngsYfW8tH6lvihUtSt0mGf2UWnAs6GQfKvwuFkEH+mrKQKQJQXVZaFkx8zjKXxEzMyx0zlpULcwjs/BbwrVDT5iTgSknvNbOPVxkjWrAikUikIt7wv7wMwEG4vBgvYA4FPoaPeazE4hcyRb6pSDVKtWYspI/pueyQm88Mo5BexYQSkeNKhn7fh0lLaLSN9m3De1LSHkwo9wGYWVNBmyjq14SqnLmhzHDIp39phpo2Bkp8sKcy2hqTaxQaifmlsraLtJ+nCq0G2tpWsZzWWS6zjnOBdYz/hGdAEt2XI55lZvg/B3gS3pzxXjO7cbZjLH4hE4lEInOEob4VLVsk/B6wE1/B802lGEThrRxrZhpg6QiZmChz9vRyww3/iHxwoiEMS5w/zgxH7itgAlb8g8s7tVtM5jPl5l1ajYINxYIy1ezUnGm7C5eSYBbBkC4zXDDq+zl4rWSSJtPsVPFEFvrQDuq0DG9LouyYENLO5GHskuZSnkORONOcgyTxyRqjRjOvzHcm5uWkyZjtuQFq6QiZSCQSGTAG5MvI8C9pw3TnzeyRmcZYGkImajGzo2SX8c8uUP7bMaOdOjPLMVx4gwy2CwcquYQVb/eSH1IE24mTr0ZZpKjBp+93LW8/UXAT9pqFvFZU0koK+4hrFVqFH7cwAhWaTtvLrW1zkfcyc2VX6s55pdapW9MsaVPtL1TYovw1bZvMgkjSuPxYOOn+K5dWXipcN805w2fXn5alIWQikUhkDjBYVt5lZnbYno6xtIRMSJIZqUCRLLOb8ttisa+QOsbw2o15laRs/1SRlwVQSKhZJJ2kpNE4QsmAzHuZmRPZsMhGHFk9Ial7NaHQSpQFbSVoUIVm0vZEy6zjeWaF3cVrTkXfcumBIjgzt04STq8lWdt21PYws/aXmxyQGWNl5o951iTNtNyWy/YF/go4HLgeON3Mts1mjCX5tBaGWr0AqFIyWK6zDNRNeI6+kmRwMS5cnq3jdtzestx/tgy1cp/zLMu9gMiK64OhPQvLZym0VkBjlaO5OiEf8nMugjHbwZJtF2V/vWsZrmkkTf/Zc2tNda4YwwuYYslt0v1KAaLmhKVBuBTPKZZjnl/mMftCZq7SNh2SRiT9QNL/SLpJ0ntD++ck3Snpx2E7MrRL0sck3S7peklPLY11sqTbwnZyn7/uOXjvsv8HrMbHyMyKpaXJRCKRyADx9WT6slpSB55rZjsk1YDvS/pmOPeXZnZ+V/+eRR2DYf7dwFFhetdJutDMNvdjksABZvbOsH+JpFlXK14yQiZmYp6CKhmDpckbdJbKCq3FOf+MXTsE01/apTVOCsoMCXND3KZfmsIvObisE7CZjRr19cJcgmtZ2zVZeUlraRVOANZ2e+7Um7HJ7sfYLq7T7fkVy2fqLKPt4jhQDi5tOxuEmjKSN0JX/h8Q6Tvzmom5P5UxQxqtHeGwFrbp/lm1izoCV0kqijoeC1xaeHlJuhQ4nslJifcISevphLwn5eMq3mVR549EIpGK+JVjVdqAvSVdW9pOLY8lKZH0Y3y5k0vN7Opw6gNhSewjIaULTF3Ucar2frEW72FWbGuAH4b9a6sMsGQ0mUgXZZtBL22mOB/quOyixZS7e/UDoXaAZjcdI7m168IoDxpD1gnWLF5AC/sKgtYo1Nd5baE2nnTO0bGPFPacdvLNwskg79h3lJVdqn1iy8Klut2eBM2mMOYXcwlaEuyaUBPwLsyuMPxH55L5YBc35nnIxDzL3GUbzeyoKcfyySePlLQO+JqkJ+GLOz4ADOGLM74deN8eTXoPMLND93SMqMlEIpHILMhxlbaqmNkW4ArgeDO73zx14LPA00O3qYo67naxxypIerSktaXj50j6qKQ/C1WPZ2TpCZk8rpZXxhWeZS5Uo5wiCWROxzYTXHsnbWWPs8ITrQhkzK1dOkCFR1rQVAotxVIjW2G0VkA2FJShlgXX5Y4W07a9tPdD4GbJe63Y9x5j3sutsOm0zzVtkq2n7f1WVNcM7tLF92trPWmCYl2ZZY1P9a9K23RI2idoMEgaxdds+UmwsyCfJOy3gCIh5YXAa4KX2dF0ijpeAhwnaX2wlRwX2vrFecDKMKcjga8APweOBP6lygBxuSwSiURmQZ8SZB4AnC2pWJw9z8wuknS5pH3whvUfA38U+vcs6mhmj0h6P3BN6Pe+Ksb4WTBqZr8I+78HnGVmH5LkwvxmZGkImZjuf2am8jIr4mSkjl6rEI1IKcAxD21FYOM06+Ft240LthMLgZqTUukXcSzCNdXxqwm2laTZCbpse5BlFtLZMMn7q7DZqGxLKipclm0ype/nsHZgprIQjJkV46rjHSf/Hbx3mfcsa9uwIssOn4W5L95l1wNP6dH+3Cn6G1MUdTSzs4Cz9nhSvSn/Q38u3maEmeWq+DewNIRMgeVxKWO2OHWyDDvXLn0Mk92TfQ0ZlRwCrPdiaxHA2SVoihxlxVhFPrKkDskEKJPfbxhJPQi2hElLYH6pLIxf/Ps2cFneXtIr5l7INaCdgaAt4ZyfYdttuVgaa+XIibx4BqZO1uZUWJL4LAlRwMw785WJ2aeVWVa/MZdLOg+4H1gPXA4QlvUaVQYY6NMKvtznS/qJpFskHSNpg6RLQ3TqpWEdcdqI1kgkElkYeE2myrZEeAvwVeAu4Nlm1gzt+wPvnOKaSQxak/ko8C0ze3nwRFiBz4NzmZmdLuk04DS8m17PiNbKd4qZmGeNisBC58AFTcOFpStnWK5dtJmCSVrNTH9PFv4TDOmFe7Nrea0lGfe1ZdKdRjpuJPUMU4KltJfWJi2JdZN1nAs6c6Wt6Je1Gq+WhGW/IrOylTQl83nWiuBRCMGYiSB1Pn9ZksT8ZfPJPOcv61PE/6IgLNOd26P9R+VjSf9tZsf0GmNg4ja4vf06cGaYVCO46p0AnB26nY33oIBSRKuZXQUUEa2RSCSyIOiXd9kSZGSqE4PUZA4DHgY+K+nJ+AjRNwP7Bdc78EFH+4X9qSJX7y+1EaJmTwUYYcXAJr8kqZJiZjpKWZk7Y87Qr5SKpqiuKXnjvtdkoLYTXMOo7YR0PMM1chKndgXCsnty2e7SfT/lXZpWcAiRTzTTDvAMk6Ntn8lLwZgiaDYhk3N4azUn8pojSRKfYmc3H2Fk8bOElsL6yZR/EoN8WinwVOATZvYUfCbP0ybNyqtis/p7NbMzzOwoMzuqxvDMF0QikUif8N5lldPKRBisJnMvcG8pH8/5eCHzoKQDzOz+sBz2UDjf18hVM6Oqi92ypjsx5mzo9YqSd9rbnl6FNpOH25Q1mYaR7oR0wqiNGcl4C9fIOkkqnUJgpJWCPnt8DbNJgbgd92kIelRHm3FhnkVtHAvjm9dclJmvnGn+em+TAUscliYoSTq2rD3RDCN7hhxz7WJmQCtqMr2Y8gdkYE/LzB4A7pH0S6HpecDN+MjVk0PbycDXw/5UEa2RSCSyYFhm3mVVefVUJwbtXfanwBeCZ9nP8FGqDjhP0inA3cArQ9+eEa2zwb8xRy+zGQtq9TpvwR2r0Bamolvj6S4N0D1mu0JmuUyAtYMfXdN8nvOdOenODDfeQlmGS32qm+COBplN0jp2oZS+BnUHg0JZmyEXcjYpONSP4WNqZN5zThkoCY5mIRjTe5fFJJnzznyl+19mS2GSHsG7MH8JuNymqAhpZjf2aocBCxkz+zG+mE43z+vRd8qI1siAcJpU7XGSQb3XKlAQKDbd0lq3/Cr7D5d/+MM51/IG93Q890b/etMb3usZLnGdTMnBetcWMOVS22UBE8ZVyBXdDiKdStCUv7OF9knjFYZ/78ZswfAfg37nh/nOxGwsLxdmvPPWj/GZoM+RdD7wpeABXIn4lxKJRCKzYJkZ/nea2cfN7FnAMXg7+b9I+pmkv60yQBQyy42qtelDt2m1liqoh5ZUnArZln0qmQzXyKDZgqZfMlOW+y0vXJeDcT8vaTN5l0tz+a22SEHT1nDouEAXec5KWyejNB0HA/PfwRzkNQepN/7H/GXLE2PZCZn2FzGzn5vZ35vZU/GmjXqVAZZW7rJIJBIZIIZo5cvq3fyKXo1m9hPgvVUGWDpCppyJubxevxzJ8+oaS8mFuZzZGGi7+u6izUzK1ty1PzmHyy7Hk8YKNWHIQc0cNb32AvhUMVmR2LK323J7riVbzKRPqf1voZ1NunA8KGwzPcZR0HasbQPySUTzVORDCUk9AbfMnUuWMcvJJmNmb93TMZaVSI5EIpE9wpbXcpmkl0naEPb3kXSOpBskfVnSwVXGWHJCxmJlzGpITKolU9RJcepqY9etuL78Cb2PYXrvn9xXonTNHFq518JsBjdq6FStLPpnu/Zva2SFHadcsbOryuekIM9QUqDwaAOCTUZYLcFqoUJmVW0xMhA0D6sVy9Am84FSEbSPAz/CJzP+Jr489IzEv5JIJBKZBf0QMpJGJP1A0v9IuknSe0P7YZKuDiVPvhxiDJE0HI5vD+cPLY31jtB+q6QX9PnrlteFDzezj5jZvWb2OWCfKgMsLSFjMcXHrHHylR+LCpDyQYdWaDSuS9vp3mBq7WUGjaStSRSJL3toMerhLTbpHnlIoFmuilk6X8S8dDSfydpMZ9t1buV5mIM8hXzIYTVfvCymLZpHylrMHP5/MESWu0rbDNSB55rZk4EjgeNDppMPAh8xs8OBzcApof8pwObQ/pHQD0lPAF4FPBE4Hu9e3E+D4ZWS3idpNOy/LNz3OcDWKgMsLSETmR3FH2r5X0EhbBIXgg+LypmTt12Ybf6znM4PfXmMgmkEVHsZrHBrtrKLc9f1ZpOW4MqCpuOuXBJmYYmw7QRRjCcfjJnXgpAp3JjjktmyI0eVtukIJU12hMNa2Axf4vj80N5dCqUokXI+8Dz5t5wTgHPNrG5md+Izpjy9T18V4I34v6pbgVcAF0jaDryeaVLJlFk63mWRSCQyYCwY/iuyt6RrS8dnmNkZxUHQOK4DDgf+GbgD2GJmrdClKHcCpVIoZtaStBXYK7SXo+/L1+wxoRLme4D3hBphqZltms0YS1rIxEzMFSiej5N/W3fqZE+2Tp/uZatef2cKgYu7uDFP6mOdWi8BC/c1yb//dbsi09E0egV17kKvVVPXmVORS63s2tw9l14u8OYgT0SeBqN/TC0z/xTpvecQqy5kNppZr7RaYRzLgCMlrQO+Bjx+z2fXfySleGP/48PxzcAlJWE4LfGvJBKJRCrT/3oyoWLwFfi0LevCjzpMLnfSLoUSzq8FNrGHJVIkvWSG8wcBNwF/DhyI15LeBtwk6cAq91iaQiY6AExPD7djU7C1JMLSYHspElSW7THBXtN2dy5t3nmAng4Cxd/cLoGdZddpV5pbOXVML8oaSAWXZ28D6upXcm1u30fq1LLpyjBtDv9saqXUMpH5Y540STNV2qYjxJysC/ujwPOBW/DC5uWh28lMLoVycth/OZ2MyBcCrwreZ4cBRwA/mMXXed8M5z+ALzx5rJn9mZm9xcz+N3557++q3GBJL5dFIpFIPzGDLO/Ly8UBwNnBLuOA88zsorAUda6kv8HHpJwZ+p8JfF7S7cAjeI8yzOwmSefha3W1gDeEZbiqzPRljjaz13Y3mtnHJN1a5QbTCpki0nMG8qDuLQws1pMBZk4t0+2aHFyW27YR1zbIeBNL4Wa8q7kkNACJQnXJ2dGxg7hpU7d3uxW3tZP2QCFpzCzscO3vU1ziOtpYd5odcyKveQ8zaqlPLRNtfnPOLun+55h+pJUxs+uBp/Ro/xk9vMPMbALv3dVrrA/gNY7d4Q9nOD8+zbmxKjeYSZP5Rdime6oJ8KgqN4tEIpHFjH/PWdwvFpJW4G0sjzKz10s6AvglM7uoR/e1kn671zDAmir3m0nI3GJmu0jbrgn/qMqNIguUUvClpSJPvFaTl1P054ac2in3vfdWV0xL6GulVCxVadt/Uoclzo/cjuGZ4g96pjfZQonLu44D03qptW0y3fPEJ8kM6WXmI61JZL5ZEiljPot3nT4mHN8HfAXoJWS+C/zmFON8r8rNZhIyx8xwvmqfuaGrCuOyN8zOtGQm147st8QLmLzmwpJR59kVpZKVW1g264rExwsgCwGWu/yA9wq4bFfZDEGOQw7lKW6ohjVbnaWzmejKBO1LNpfaktL9K4xXZD+gLGTK+cvSkMMsdTjnMOcgm4cywJEOcjArM8SeMY8rdf3isWZ2oqSTAMxsTFPEepjZ7081iKT9qtxsWvcMM5uQlEj6yXR9ppnEXSFj54+LoCRJGyRdKum28Lk+tEvSx0IOnuslPbXKF4hEIpG5pB/eZfNMI3i0GYCkx1KxAJmkdZJOkXQZ3jFhRmb0LjOzLCRee5SZ/bzKoF08x8w2lo5PAy4zs9MlnRaO344P9jkibM8APhE+Z43lRl+z9yxlVKqVUnPkQ37fwuuHrxYplBmuhTfu5wZ0gjbJfdCrnKG8hwd595tfe4kOcJAPiRYJJnATNRxMSmdTGOFFl0aUhBfYdkAp4NykQNFJWtWkuja9+7RryfR4VKYiGFOT8pct/hfbxYuc5jTzuvcuW/SRH+8GvgUcIukLwLOA107VOQikE4DfwTsrrManu+nLclnBenzwzQ+AnUWjmb204vVlTgCODftnA1fihcwJwDnB9/uqIDEPMLP7d+MekUgkMhAW+3KZmV0q6YfA0fhF4Td3KQJtJH0R+DXg28D/Ay4HbjezK6ver6qQeVfVAbsw4NuSDPhUyNuzX0lwPAAU63rt3DyBIgfPJCEj6VTgVIARVuzmtJYR09ll2vaYYHRPRTbsyGpekwC8jSU3XEu+7kvLUKb2q75PUKmQCTl85pNVgW4bjcmnsCFoUFlN5AlYkpDUh7yx3uGDPttaDOGzY8uRMdlbvdCQ2k4L5rVaemswPdtCLZneeXPAEq/NWOKgHZTq/HOOzC1OUJhipnF97zcLfClsSnqYIIrf1keFlaof9rjsCfhs0LfgHcGy8HtemUpCxsy+K+nRwBFm9p3gAldlQerZZnafpH2BS7ttO2Zms51wEFRnAKzRhkX+ThGJRBYTxoK3t0zHh6Y5V2SAntxodqSkxwMnAd+RtBFYLWk/M3uwyk0rCRlJr8drDxuAx+I1jE8Cz5vuOjO7L3w+JOlr+CCjB4tlMEkHAA+F7nuUgycyDT20GZW9vAqNYki0hkU27L2ooGSTaeG1mZbhMnzgYw4yr9W4Zkifr9BWqixpvcKsiqSYTuQpmHNYYiQrEtRKUZZP9hQL7yKdoMnCFqK2J2FbQwoY8o5HsIu3oXUn/WyfMFxmWGK7ljRQSC2T+PQyJNHwtxxZrG+2Zvac3bzuJ3g7zrslPQ1vm7lG0r1m9syZrq9qwXoD3ji0Ldz0NmDf6S6QtFLS6mIfOA64kck5eLpz87wmeJkdDWyN9phIJLKgMLBclbaFinxVzrdK+qqkCyS9RdJIlWvN7Doz+3Pg0XinrRmpapOpm1mjePsNWUBnEuj7AV8L16TAF83sW5KuAc6TdApwN/DK0P9i4EX4ojtjwJT+2dNi1hUnGNP9A1PbZgptoua3bBhaoyKvdbooB2VeW3FN4bKg3eTWPmfOtzmFapT5rqk/ygujFrSCQqPJa/4zHXGkY97WYakDeZsQGW1NpFtjKWxLxbidexVlBYCkdE1Yv2+fa0+wmFzXM1IpfiYJAZkhcDSm+19+LOLlsoJzgO14Qz54zeTz9EhbI+mJ+LiaC8PxR/AZoAE+XuVmVYXMdyX9FTAq6fnAnwD/Pt0FIQfPk3u0b6LHMlvwKntDxfnMTMxhtitdgqZwv/UVMP0SWTYUlsuGaLsxQxAymXANcM0iMFOhHZKG74MIgZtMMp7LbJdsAH65rLMEhdHOPGCEwFDANXMvSDrp1DpLWeUXiFKUv4lOYGjhw6By/6LRdnVzVuez/Ay8sIE8wc8tTbzxPzJ/yCGXz2Us5qL3LgOeZGZPKB1fERJz9uJ0JmdbfgHeEWwF8Nd0KndOSdW/kNOAh4Eb8AnVLjazd1a8NhKJRJYERe6yRR6M+cNgkgBA0jOAa6foe4CZ/VfpeJuZXWBmnwf2rnKzqprMn5rZR4FPlyb25tC28IhaTHXK6V1c0GiGoDVqWIp/ew9dXQauoaDJFM4AXrPJE79vzn+qpLX4fbXT0/gbBgN69xJVCHgkLOEBkIukpcmvRFIn/U3337OjrcWUXzrbNXOKIaxH8Kgra1il2jLFGEHraucvU0h/E92Y55R5y8Qc/i0vcp4G/JekIrj+UcCtkm7ALyr9Sqnv6vKFZnZ06XBau3xBVSFzMtAtUF7boy0SiUSWNEtguez4WfT9haRnmNnV5cagCf2iygAz1ZM5CW8UOkzShaVTq/GFcxY2MUnm7Aj2i0KbyYcNc4bVgjOFgZrCNYRaClqMt9OkNf/pNZmOUwAEx4Gg2SgpaTmFbUVdcwhaTDbkgvbjsFZI8VxoXona11oI7GwT3KcnLVmEfu0gU4LtqMguHe5tIUloYadq22eK6UmYM6/JJAkucYvWpTWyOyxsz7EqmNndIWfkIZRkwBTBmG8Hvizpc0Bx/ml4xePEKvebSZP5L3xU6N5MDuTZDlxf5QaRSCSypOjDW4WkQ/BeXvuFEc8ws49Keg/werwNHOCvzOzicM07gFPwvpZvMrNLQvvx+FWlBPiMmZ0+w73fj1+JuqP0baYKxvxBsNm8kU5+s5vwFTP3PBjTzO7Guxkf0xXxPwqM4oVNZBFiRb37LEet3GslWSlljMBqhqU5GslIajlyOXnuyJsOazloOtQQyYQjr4mkATjhGsHzLC+COfEBm1lnX7mFNDDentNxL6adsNMSr404+cBIFSlj2vYjte0nk79ckVamq7lwmS5c8ZPggp1bW+vNE/kkobWOLWZSbZngcZYnQOp8QGZM9z//zFW6/24NefdpAX9uZj8M8YTXSbo0nPuImf1jubOkJ+BLLj8ROBAfff+4cPqfgefjU3FdI+lCM5vKWwx82MhjzaxRZaJm9hDek2y3qORdFiL+zwc+FZoOBv5td28aiUQiixaruE03hNn9xfKUmW3H5wY7aJpLTgDONbO6md2Jjyd8ethuN7OfBaFxbug7HTcC62boMyWSehU3m5Kqhv834L/M1eAj/kM+sgVLTPc/BXneSYeSG2QZrpGRTPislOmI0VohWi3aGk1SyxldUWfFUJPE5TSzhFbmmGjUaDUTmhMp+VCNZMJrGkld7ViZQlNQBsrU9jwrvMwKjcUnzPRtbU83F5IehliaXK5tf/EeaOH6Kexu3WljvD2FtlZSlDBQ5j3fTEVAqk/Y6fuUBygGDrab1JE45z2dYsDv/CFHJ1PmnNywase9izpagTNC7sXJo0mH4lPoX43PrPJGSa/BuxX/uZltxgugq0qXFQmEYdfEwjOVSPk74EeSbqRUR2YWWfWnE4a7MMiI/8hCxcwLmzxDzRZuosnQtoSskWAu9VmZQ6bhLId8VCQyVg41WDs0TupyWrljIqtRz1K214d5ZHglzbGUPKmRjoNaKi2Nebfn9pZZ+7yFCHqAJLMgRGgvo8n80pkyFwJHJy+n5clkwdH+iqLdWCybFcKrQGbtfGxFqucif5s5f51rhkDSshtzMP5bcLWOzCNO/mUJ2pkcBk51T/WNZnbUdB0krQIuAN5iZtskfQJ4P/739f14W/gf7P5ke3I28EF83OPu+N3/aDadBxbxH4lEIkuOPsbJSKrhBcwXzOyrAGVjuqRPA8XS1HQJhGebWHjMzD62u/M2s1kJvapC5jS8V0M74h/4zOymFllQmEGzhU3Ucc6hzEhqCa4xjGvVUCtBmWitSKgnQ4wNZaweqTOUZKytjZPIyMxrODtaQ/y8toHNY6NsT1dQH0vaDgTtoM2mX0Zzde/inNRBLXzwZeqdAggGdpPaFSiVW/gMS2apN/hnNR8Q6TM4F9eVvl6XxlLo3WWXaZ+PDZJmJ+I0r0E2RNtl2+TT6RTpb9rjC58PzbnJdU0iS55+KEvyy0Jn4mu0fLjUXi7U+DK8/QR8AuEvSvow3vB/BPAD/L/UIyQdhhcur8KHnUzHf0j6uzBmebmslwtzMa9nAe/BJ8YMYdqYmT1mpu9atZ5Mjo/2//RMfSORSGRJ058VuWcBrwZukPTj0PZXwEmSjgx3uQv/Uo+Z3STpPOBmvGfaG8y8O52kNwKX4F2YzzKzm2a491PCZzl6v6cLc4kzgT8DrmOWr1RV68m8BL8+2C3F1szmZnNGzMQ8PSFRpmUZqte9k0SjiZyjNjZMMj5KunOYdKJGc6VQnjCuETYmOcNpi1Q5w67FaNJkZVpnXTpGTTmbR0b5xdBato8Pk4c66GbQannngNZ4QrLD4VqQjImk6adjAkKCTQvBoEXCTuVq17YxhzfKp6I1EvaHQu2boMm07S8lzcNX7Oy0FyjzGlVSp13N0xv0w/kcvO91GC/Yf7xWJfKklIk5ujEvH/qwXGZm36e3B8HF01zzAeADPdovnu66Hv2fU7Vvia1m9s3duK7yctk/Ab8N3GDzkjAoEolEFgazq+W7MJH0YnzMTbuOjJm9b5pLrpD0D8BXqbjEVlBVyNwD3LjoBExMlDk1hTbTbEGWQ7Ph38gn6iTjdUZ2jpKOr6S5KvU2mixlvLWSuxopW9aMMpy2WDcyzj4jO9hQ28mGoZ2srk2wdmiCna0h8hBEmZuYyGpsmRhl885RxraNQD0hGXG4enBpDmk61CpSxVinAqWjXWsmGxKtUciHRHMlZKMWyhL4/rjSP08HlnSO1Spl+hSQ+2SfSV0k4/Lu1WVFpAgilbC8o2EV10+qjpk4lCm6W84jcpqbdP8mWORpZSR9Ep+q/zl42/rL8fad6SjcosvecjMtsQHVhczbgIslfZfJUuzDU18SiUQiS5DF/zbxTDP7FUnXm9l7JX0ImHYpbHdLN0N1IfMBYAdetRra3ZvNKVGLmZkiNb0ZlmU+zqDV8lujQa3eJB0dJmmsJKkPkY4lTGwfZdPaYWwo54GVLe5bNc5Ba7dy+OqHWZuOc9DwZmrKGFGTmjIyHPW8xoPNNdw1thd3rtzA9olhdm4foTmWoobDNXyuFhe8zQoK+0k25FO8ZKOisRqyEWiuyclXZrjRFkktI0mMJMmRDMmoJRlDaYaTkeWORishy127HkiWORr1Go2dKRpPQgxPKGMQirF1Kn5O1mSUQ2sEWqMOG05REuocxHT/c0o73f9ce/ctfiEzHj7HJB0IbAIO6NVR0u+Z2b9Kemuv81UUjapC5kAze1LFvpHFRvmH0QzLcy9osgyNjTPUbJGMr2R42zDjm1Lq6xKy4YTWyhrb1g2zc/9hVqQNaisyHjfyAIfUNrHOTTCijASjYY6t+TB3jO7Lj0cexcbGKu5auYFHdq5gbGyY1s5aO7uzhVowvkpmEXgJ+TA01kB9Q46NZoysn2Cv1TtZNzLOqlqdVDmjSZNh12LYNRlNmqxwDRLlTOQ1dmTDjGdDNM2Rm2N7a5hNEyvZuGMlO3aOkLUc2ViKm3C4iVATJxPWDPNpB4j60tJINLY7RlcMkaYp1moBzXn53xeZYxa/kLlI0jrgH/CZlY2pQ1JWhs/VU5yfkapC5mJJx5nZt2d7A0kJPj3CfWb2kuDPfS6wF94d7tUhm8AwPivp0/CS9UQzu2u294tEIpGBsQSKlpnZ+8PuBSEP2YiZbZ2i76fC53t3935VhcwfA38hqUHnda2qC/Ob8cnfir4fxGcZPTcYoE4BPhE+N5vZ4ZJeFfpVqlcwLSG7bnRjrkh5Ca3egEYTspy03iDZNsrQI6O0VtfIhhyN1Y6J9Y4djRXctmIfchNPXHEfK9Vgg2ux2iUkiJoSmtZkn+RuVrtxHm6t4aCRfbhr5V7ct3MtD9TWUB9fQT6RkCdq5wvztV18bZvGaqOxT8bKfXeybsU4v7z+QQ5f8RB7p9tZ6erUlLHS1Vnh6qxUgxFljCgjQ4zlKZvyFWzPR5nIa0xYjYdbq7lneAM/Tzfw8PBKxupD7EhHaKkGJNDw6UqscJ9OIBvq1NbJRhzpmKO1okZaS6GRTKo7E5kH5igT82L3LpP0CuBbITHnXwJPlfR+M5syXYykEfxvdLdH2ozR/5WyMJvZajNzZjYS9ldXETCSDgZeTFDFQpTrc/EZncHn0PmtsH9COCacf56iVIhEIguNPmRhnmfeZWbbJT0b+A18oOUnZ7jm88D+wAuA7+LT11Qq9VJVk0HSS4FfD4dXmlmVdM//hPdMK9bz9gK2mFkrHJcziR5EyCZqZi1JW0P/jV3zOBU4FWCEFdPePGZi3gOCRmMQUtA0YKJObfsY6egwpAmjK4cZ2WsEqLFlzRpubqbsP7IdR8722iOscHVGlLFadVY6MSx4VLqZvZId7JXuYL/aNm6t7U+WO+7ZPEI25CbVbcmHIBuG5mqjsVfGhoO2cNR+9/DokUd4xsrbOTTdygpBIuGAESUMq0ZN3jcls5wWGVvzBsPZdh5Rk02sghySrl+BwmGguL9PGwNZ4l2hbciwkQw3knn/iFpKc3ONbMR1UstE5o85zMS82DUZOg/qxfjM0N+Q9DczXHO4mb1C0glmdrakLwL/UeVmVevJnI5f9ro5bG8OuW+mu+YlwENmdl2Ve1TFzM4ws6PM7Kgaw/0cOhKJRGbGVG1buNwn6VN4c8TFwR4+kywozCRbJD0JWAtUKvdSVZN5EXBkyGGGpLPx6Z7fMc01zwJeKulF+DW8NfgSoeskpUGbKWcMLbKM3htKCazFOwBE5ouS15nluQ/arNdhfBzkSLYNMTq2CkvW0FpZY2Lbai7nCO7aewP7jOxgyLUYdi32qu3kgKEt7JtuY4XqDCljQ7KD2nCLDPFQfRX3Dq/HksIA4pNkZkPQGoXG+ow1+2/nmP3v5rh1N/CodDOH14xVbhVNy6hbkwwjwxizBvU8Z0sOD2ej7MxX8Ei2L5uyVWxsrubBxhq2NUd4cHw1G3esZOfYMM2JFDKhiYR0pyMJkWDeDoOvEFrLccMZaS3zBUWtFtycQ4XRfPG/3i4JBp3uf+EvhVXhlcDxwD+a2RZJB+BtMwBIWh9q2JQ5Q9J64P/iE2uuAt5V5WaVNJnAutL+2pk6m9k7zOxgMzsUnxn0cjP7XeAKfIQpwMnA18P+heGYcP7yRZdhIBKJLH0WuU3GzMbM7Ktmdls4vr/Lc/iyHpddZmabzex7ZvYYM9sXqORtXFWT+Vt8JbUr8KvWv45P/787vB04N6wB/ghvdCJ8fl7S7cAjeMEUWQh0B202Q+Bhs4GyjNHUsW5oDRObHDvG1nDzhlXYcB6qfoEbbbFmtQ/a/KXVD7K+NsbBQ4XNpsnKtIESbw9Rbj5ZZbDJtFbm1DZM8L/2vZ9nr/kpR9Q2stp5TeXu1jib8lEeaG1gIq+xNVvJ1myUra1R7htfxy92rmWsWWOsPkSjkdCqp9hYipoi3elIx8RwHUYyfBqZDJKGodyXEciGoLkGWubIDPK8RkM11BIjGxNWPGAMba7DRN3HFcV3omWBln68ba+1vguAp3a1nY8POZmWGYWMJIevnnY08Kuh+e1m9sBM1xaY2ZXAlWH/Z/hSzt19JoBXVB0zEolE5oWl/y7R/oaSHo93W14r6bdLfdZQcmWejhmFjJnlkt5mZufhl7QWB13p/iN9oDszQEht7zYlrJIYfWiIkc3DNFY78tS79fl0MDXqa0e5ZZ91/GTf/Vi5os6j12/m4BVbGHItNtdXkDcS0lCeuShQ1ho1slU5B67fzuNXPcC6ZIy7W+vZkq3glokDuW3Hvtw/toZHdq6g0Uypj9WwiQTVHelOR22bSBpQm4DhluEakNYN14J0PMM1clzTQnlltQuVIWitSGiNOibWieYqkdf890ka4Oowuiln1b0TpA9vwybqPuI/ppRZ8siWhHfZbPgl4CV4c8lvltq3A6+vMkDV5bLvSPoL4MvAzqLRzB6peP38EXOYDYbCxbnZgh07cFmGG6qRbh4lH675qpFmIJEPpzTW1BjfO2V83xU0Vq7gxn3WcPNe+7NyRZ16IyV5JKW2XaT1LFTD9FmW3aom+67w7vhX73wsN2w7kPt2rOWB+9ZT21ijtk2kYzDSgNVjRlI3koaRjreo7WyhZo5rZJAZyjLUzCDLUSvz9V8Kg73zqWKQsDRhaMUwNpQyvHaI1srEL+UVwqmekW6bwG3ege3YiTUacalsnmjnL2OOMzEvbdpf0My+Dnxd0jFm9t+7M1hVw/+JwBuA7+FTwVyHTxUTiUQiy4s+GP4lHSLpCkk3S7pJ0ptD+wZJl0q6LXyuD+2S9DFJt0u6XtJTS2OdHPrfJunkqe7ZYw6HS/o/kp7Qdep5Pbq/TNIaSTVJl0l6WNLvVblP1fLLh1Xpt+CIWszgyTLMDMtylDjYOYZLJj/zJE1JR4cZeWAFqzaM0BpNGNsnYWLvFUysHQVg5SYxtMVId/hXUZ8UM2d4qEUrT/jhlkO4bdM+jN25huHNjn3uMUY3ZtR2tkjGW6iVo4kWaobEnq3goJBlWJb7fwslV+NiqW9SuqEioDJJcNtrkKaMbBmGWvgzaWWo0YRWhtUbWD0uky0Y5jATc5+Wy1rAn5vZDyWtBq6TdCnwWrwn1+mSTsM7WL0deCFwRNiegU/F9QxJG4B34+u8WBjnwh4uyATHrVeY2UZJr8a7IH8PeI+kM8zs/8GUK1THmdnbJL0MXxb6t8O1/zrTF60ajPmGkLWzOF4v6U+qXBuJRCJLhlDMrso27TDebfiHYX87Pr/jQUxOr9Wddusc81yFjzc8AJ/m5VIzeyQIlkvxMTC92MfMigwqbwKOMbPX4YXWTPaVWvh8MfCVqRJq9qKqTeb1ZvbPxYGZbZb0euBfqt5oXglJMiMDorDPFO7NXZhzaGwct30nI5tHsKEaow+uoL5hmObqhDwV6XhGujNjaGuD1kr/71kmWq2EOzdvYPuWFQz/bJh9b88Z3txk9P6duG1jPoFnq+W1lTzYWCzHcvNr9V3OCr3YpVUtrNH0Gs74eCfxZZZjQXMjLzSjqMUsO6prMntLKpsVzjCzM7o7SToUeApwNbCfmd0fTj0A7Bf222m3AkVKrqnae9GUdJCZ3YevD1bY1+vMvOTz75J+gq9F88eS9gEmZrgGqC5kEkkqgiND+v7FUbwsEolE+kl1IbPRzI6aroOkVfgYlLeY2bZyTmAzM6mvvmx/Bnxb0gXATcDlki4Bng18droLzew0SX8PbDWzTNIYXrsqvsfzzezSXtdWFTLfAr4c8t0A/GFoW1TEdP8DZLo3+sLdudXyaWmShGT7TlZsGsFGh7FaAnmOWj51jRtKcC1Idjgyhtm+cZgV9yWsvy1j1c+2o50TsG2Ht4lk+aRg0cpzmgkz/1vSanU0mfL4UYNZmMxBuv9+/exLquEFzBfM7Kuh+UFJB5jZ/WE57KHQXqTdKihSct0HHNvVfmWv+5nZlZKeCfwOPmnxdXht5E/N7CczzbdsqzGznZQ8jfGlWfZIyLwdL1j+OBxfytSV1CKRSCQyDaGMyZnALV0ljIv0Wqeza9qtN0o6F29D2RoE0SXA3xZeaMBxTJNTMthSPlGax75m9tBU/WfzlaY6UdW7LA8T+8RMfRcaMd3/AqCcaDPLOjaPiTpscyhJOhpmrYabGGZoxwgjDzvyLQlD22H1PS1W3rkN9/AWrNHAJuodzWIQWkXUVBYfc5Xuvz+azLOAVwM3SPpxaPsrvHA5T9IpwN34ZJYAF+MTFd8OjAG/D167kPR+4JrQ731TxS8GT7RufiDpKYD2MO5xyqcyrZCRdJ6ZvVLSDb0GMbNf2YNJRZYbvTIGSD4PmhTcUHPcWIORjU2UeweA4S0thn+xA7dxMzY27q+LRvdIoByQGRoGFxxr/cldZmbfZ+q3/13iVII9/A1TjHUWcFaF227EC64yBwE/xP++P6bCGLNmJk3mzeHzJYO4eSQSiSw6Fm9yh78Eng/8pZndACDpzj7FQd411YlphUzhSmdmd4cJrZnpmkikEt1aDYCEAG0fY/iBhKGtNTDDbRtH23Z4LabV2vX6SGSOEIs3d5mZfUjSl4GPSLoHH8S529+m7FFmZr89Vb9KAkPSHwLvxXsiFJMamHoViUQiC5ZFKmQAzOxe4BWSXop34Jq+hv30nAk8aqZOVbWSvwCeVIoWXRyUMzHHgMyFS6GVOOcTbu4cw2UZJInXcup18nojJLSMGkxkHlkiWZjN7MKQxuax0/WTNFXmfQF7VblXVSFzB96jIRKJRJY3i/Q9R9Jbp2g/DqDLlbrg14Dfw2cImHQZPeqC9aKqkHkH8F+SrsanICBM6k0Vr59fLA/ujZEFTZ57baYRtJZA9CaLVGUu0v0vYk1m9W5ccxUwZmbf7T4h6dYqA1QVMp8CLgduYNHK8UgkEukDi1TImNl7d+OaF05z7terjFFVyNTMrKeqteCJ6f4XF0WyzZjCJTJbyun+BxUrU6FWzGJC0g/N7KkV+u1HJ/HmfWb2YNV7VBUy35R0KvDvTF4umzJCVNIIvt7AcLjP+Wb2bkmHAefijUbXAa82s4akYeAc4GnAJuBEM7ur6heJLDGiYIksUBbxclkvpvWGCtkAPgGsxedJAzhY0hbgT4pyBdNRVcicFD7LOXFmcmGuA881sx0hEdz3JX0TeCvwETM7V9IngVPClzgF2Gxmh0t6FT7h2okV5xeJRCJzw9ISMt+Y4fxngT80s6vLjZKODueePNMNKlnDzeywHtu0MTKhuE7hkVALmwHPBc4P7d1FeYpiPecDz9MAUiZbrMUeiSxtBuzk04+iZfONpP1DrMzVkvafpuvKbgEDEAqnraxyr2n/b5TrSO9OH0lJSP72ED7w5w5gi5kVla3KBXbaxXfC+a308MOWdKqkayVd2+ys3EUikcjgsVlsCxRJrwN+gC+h/HLgKkl/MEX3b0r6hqQTJT0zbCdK+gYVy73MtFz2WUnHMv263Zn4qm67YGYZcGQo3fw14PFVJjUdobLcGQBrtKHS/0rLDbnoABCJLHkGnIlZzGDEWBz8JfAUM9sEIGkv4L/okWTTzN4k6YX4laa24R/4ZzO7uMrNZhIya/HG+eme68Mz3cTMtki6AjgGX5s6DdpKUXgHOkV57pWUhntvmmnsSCQSmVMWsJZSkU3A9tLxdqb5rTWzbwLfLI5D7ZotVW82U4LMQ6sO1E2oAd0MAmYUn/3zg8AVeBXtXHYtynMy8N/h/OUWDSiRSKQCu6T7H+S9FumvUini/3a8LebreJF5AnD9FNf8NXCemf0keAB/EzgSaEn6HTP7zkz3HaSF7ADgCknX4wvqXGpmF+GrbL5V0u14m8uZof+ZwF6h/a3AaQOcWyQSiewefbLJSDpL0kOSbiy1vUfSfZJ+HLYXlc69Q9Ltkm6V9IJS+/Gh7XZJ0/1urg7bHcC/lWb5deDOKa45ESgi+0/Gy4x9gP8N/O3M33KAafvN7Hp62GrM7Gf0yHljZhPAKwY1n0gkEtlj+lS0LPA54OP4+MAyHzGzfyw3SHoC8CrgicCBwHckPS6c/mf8StG9wDWSLjSzm3eZ+m5E/AON0orSC4AvBVv7LcGsMSNLvzZMzMQciSw/5BhYErM+LZeZ2fckHVqx+wnAuWZWB+4MKz7Fy/rt4eUdSeeGvrsImYJgH+9V6fi5PbrXJT0JeBB4Dj4jf0GlMgFV68lcBnyo7E0g6QwzO7XK9ZFIJLJUmIVNZm9J15aOzwjesTPxRkmvAa4F/tzMNuM9u64q9SmHf9zT1f6MGcYvC4oR4P8ArSn6vhkft7gPXsO6EyAs4/1o5q9SXZM5DHi7pF8tqVxHVbx2QWC5oejBHIkseeSE5QO0zlcfeqOZzfZ38hPA+8Nd3g98CJgqhmW3MLPrupr+U9IPpuh7NT1CT4LC0RcX5oItwPOAj0n6d3x9gUgkEll2DNK7rJx4UtKngYvCYRHiUVAO/5iqvSeSNpQOHV5hWDvT3CS9GG8TGinN930zXVdVyCjEtfyJpNcC3wfWV7x2/on1ZCKR5cGgMzEbAy12IukAM7s/HL4MKDzPLgS+KOnDeMP/EfiofQFHhMTD9+GdA35nhttch/8mAprAXfjckdPN65N4G8xzgM/gw0x6aj/dVBUynyx2zOxzkm4A3lDx2kgkElkSiP5pMpK+BByLt93cC7wbOFbSkXghcBfwhwBmdpOk8/AG/RbwhuDlhaQ3ApfgU5qcZWY3zXDrtwPfMrNtkt4FPJWZKx8/08x+RdL1ZvZeSR+iFKA5HZWEjJl9quv4Ovq8ThiJRCKLgv55l53Uo/nMHm1F/w8AH+jRXtk+Evi/ZnaepGfjExb/I94WNJ3DwHj4HJN0ID5DwAFVbhbXkCKRSGQWyKzStoApFhRfDHzazL4BDM1wzUUhB+U/AD/Ea1lfqnKzpR8n0wMzYwBVBCKRyFJngWdYrsh9kj5FSPUV0sVMq3CY2fvD7gWSLgJGzGxrlZstL03GFniRh0gksufIoQEGXcuqbQuYV+JtOC8wsy3ABnxm5imRtELSuyR9OgSE7ivpJVVutiw1mUgkEtldFnpBspkwszHgq6Xj+4H7p74C8FUwr8Nn0gfvyfYVOi7WU7J8NJmoxUQiS5o5WwJf5EXLdpPHmtnf412eC0FV6YFHTSYSiUSqsvCXwgZFI5RsMQBJj4VqpYmjkIlEIpHZsDyFzLvx5ZYPkfQF4FnAa6tcGIVMJBKJVKSfwZiLBUkOn+Hlt4Gj8Y/hzWa2scr1y0PImPkUExDT/Uciy4UBpfvXIJNvLkDMLJf0NjM7D/jGbK9fPob/SCQS2VOqGv2Xnhz6jqS/kHSIpA3FVuXC5aHJBGK6/0hkmSBHJ7C9z0MvT0fVE8NnOWelAY+Z6cKBaTJB4l0h6WZJN0l6c2jfIOlSSbeFz/WhXZI+FupUXy/pqYOaWyQSWSYMwq15GWoyZnZY90aPOjO9GORyWQtf1e0JeGPRG0Kd6tOAy8zsCOCycAzwQnz66iOAU/EJ2yKRSGRBsQQi/neboAw8T9KZTK7IOSUDEzJmdr+Z/TDsbwduwZcLPQE4O3Q7G/itsH8CcI55rgLWSaqU5TMSiUTaDNKxx/CORFW2JYSkoyV9DLgb+DrwPRaAJtNG0qHAU4Crgf1KRXkeAPYL+wexa63qg+hC0qmSrpV0bbNaLFAkEon0DeXVtqWApL+VdBu+xMD1+N/xh83sbDPbXGWMgRv+Ja0CLgDeEorktM+ZmUmzUyzN7AzgDIA12lD9WrNJSRBiJuZIJDJblmGczOuAn+LNF/9uZvXZ/mYPVJORVMMLmC+YWZGQ7cFiGSx8PhTap6thHYlEIvNP1aWyCstlks6S9JCkG0tts3aMknRy6H+bpJP7/I0PAP4G+E3gDkmfB0YlVVZQBuldJnyVt1vM7MOlUxcCxYM4Gb++V7S/JjzMo4GtpWW1/hETZUYiy4JBpfvvo+H/c8DxXW2zcowKsSrvxle1fDrw7kIw9QMzy8zsW2Z2MvBY4N+A/8TXpPlilTEGqck8C3g18FxJPw7bi4DTgeeHdb7fCMfgy4f+DLgd+DTwJwOcWyQSiewefXJhNrPvAY90Nc/WMeoFwKVm9kiwkVzKroKrL5hZ3cwuMLOXA4fjc5nNyMBsMmb2faZOBf28Hv2NyYE+A5hUDsRozEhkqSIJs5A6ajCxmLOxyewt6drS8RnBpjwds3WMquQw1Q8kvRh4IjAym+uWVcR/m5i/LBKJ7A4GZJWlzEYzO2q3b7UbjlGDQtIngRXAc4DPAC8HflDl2pi7LBKJRGbBgIMxZ+sYNVcOU880s9cAm83svfgKmY+rcuGyFjK2xAKmIpFIFxrAT9xggzFn6xh1CXCcpPXB4H9caOs34+FzTNKB+AqZlYLll+dyWSQSiewm/VrAkvQl4Fi87eZevJfY6cB5kk7BR9e/MnS/GHgR3jFqDPh9ADN7RNL7gWtCv/eZWbczQT+4SNI64B+AH+IXDj9T5cJlJ2RiJuZIZJkwiEzMfUx+aWYnTXFqVo5RZnYWcFZ/ZtUbM3t/2L1A0kXAiJltrXLtsl4ui0QikdkgQJlV2pYSkt4QNBnMrA44SZXCTKKQiUQiS5s+p4+SWaVtifF6M9tSHISYnNdXuXDZLZdFIpHIbrMEa8VUJJGksGyHpAQYqnJhFDKRSCRSmaWXxr8i3wK+LOlT4fgPme+I/wVJVybmSCQSmS0LIzxyznk7XrD8cTi+lOhdFolEIgNgGWoyZpaHapjfxy8Y3mpmlVz3lqeQiTnMIpFlgZyo9lNYEWPJeY5VQdKx+ISdd+HXgw6RdHJI8jkty1PIRCKRyO6y/GQMwIeA48zsVgBJjwO+BDxtpguXn5CJWkwksqRpZ2Ie1PjLcLkMqBUCBsDMfhqKUs7I8hMykUgksicsTyFznaTPAP8ajn8XuHaa/m2Wr5CJ6f4jkaXNIGrKGLA8i+v+ET6tzZvC8X8A/1LlwuUrZCKRSGSWiCUZzT8tIfDyf8zs8cCHZ3v9sk8rs2TT/btl/782EvH0O91/nlfblgjBVflWSY/anesH9ksk6SxJD0m6sdS2QdKlkm4Ln+tDuyR9TNLtkq6X9NRBzQt8JuYlTRQwkYin3wKmWC6rsi0t1gM3SbpM0oXFVuXCQS6XfQ74OHBOqe004DIzO13SaeH47cALgSPC9gzgE+EzEolEFhTLbbks8K7dvXBgQsbMvifp0K7mE/BFesAH9lyJFzInAOeE5GtXSVon6YBQ+S0SiURmxS5uzFL/4luWoZAxs+8W+5L2BjZZRVvDXK+r7FcSHA8A+4X9g4B7Sv3uDW27IOlUSddKurZJfXAzjUQikV2oWHq5wu+vpLsk3SDpx5KuDW0LwqRQmuPRkq6U9FVJTwnmjxuBByUdX2WMeVu8D1Jw1q8EZnaGmR1lZkfVGB7AzBY50R4TiQwOAzKrtlXjOWZ2pJkdFY4Lk8IRwGXhGCabFE7FmxTmgo8Df4uP7r8ceJ2Z7Q/8OvB3VQaY61+kByUdABA+Hwrt9wGHlPodHNoikUhkQTHgomUn4E0JhM/fKrWfY56rgHXFb+mASc3s22b2FeCBcG/M7CdVB5hrIXMhcHLYPxn4eqn9NUElPBrYOjB7TPl//lLzMitrMUvIhTISWVBUXy7bu1jaD9up3SMB35Z0XencHpsU+kz5h2S861ylH9CBGf4lfQlv5N9b0r3Au4HTgfMknQLcDbwydL8YeBFwOzAG/P6g5hWJRCK7jTGbl9ONpWWwXjzbzO6TtC9wqaRJ2oGZmTTv1WueLGkbPvPyaNgnHI9UGWCQ3mUnTXHqeT36Gj5lwdxhef996OeTaIuJRHrS33T//auMaWb3hc+HJH0NeDrBpGBm9y8Ek4KZ7XE24eX5y2RLbCmpW8DEpbJIZHD0wbtM0kpJq4t94Di819b8mxT6TMxdFolEIlUxIOvLS9x+wNckgf8d/qKZfUvSNSwxk0IUMpFIZOnS90zM1peVEDP7GfDkHu2bWCgmhT4RhUwkEonMhmUY8b8nRCGDz8Qc1NbFR7THRCIz0y8nn9l5l0WIQiYSiURmR9RkZsWyFTKWG3I5sMceevNHdFuORGZGjr4aZqKQmRXLVshEIpHIrDGDrN81nZc2UcgsVnppMdEeE4m02SXdf7+ImsysiEImN+/muJiIAiYSmT+ikJkVUchEIpFIZSx6l82S5SlkzHx6t8VI1GIikVmjfgVlGthSS0s1YJankIlEIpHdpT9pZZYNy1rIWG5oEXswRyKR6ZGE9dOD2SyuHMySZS1kIpFIZNZEw/+sWL5CZjHWk4n2mEhk3rH4Nzcrlq+QiUQikVnTv6Jly4UoZBYLUYuJRHafmCBz3lhQ60WSjpd0q6TbJZ02l/ceSGRwv+iVaTkKmMhSY5C5+PoUcG2AZVmlLeJZMEJGUgL8M/BC4AnASZKeML+zikQikRIWipZV2WZgPl+q55IFI2SApwO3m9nPzKwBnAucMPC7LvTAqlgvJrKnONd7m8959Do3IPpdK8pyq7TNMKdl81K9kITMQcA9peN7Q9skJJ0q6VpJ1zapz9nkIpFIBOiXJjM/L9XzwKIz/JvZGcAZAJK2f8fOv3X3BgqfGdDsz9ymYW9g48DvsufEefaPxTBHWF7zfPSeTmI7my/5jp2/d8XuI5KuLR2fEX6/oPdL9TP2dH4LkYUkZO4DDikdHxzapuNWMztqcFPqD5KujfPsH4thnothjhDnOVvM7Pj5nsNiYyEtl10DHCHpMElDwKuAC+d5TpFIJDIIduelelGyYISMmbWANwKXALcA55nZTfM7q0gkEhkIy+aleiEtl2FmFwMXz+KSM2busiCI8+wvi2Gei2GOEOc5L5hZS1LxUp0AZy3Vl2ot6CDESCQSiSxqFsxyWSQSiUSWHlHIRCKRSGRgLFohM58pGSQdIukKSTdLuknSm0P7BkmXSrotfK4P7ZL0sTDX6yU9tTTWyaH/bZJOHtB8E0k/knRROD5M0tVhPl8OhkckDYfj28P5Q0tjvCO03yrpBQOY4zpJ50v6iaRbJB2zEJ+npD8L/89vlPQlSSML4XlKOkvSQ5JuLLX17flJepqkG8I1H5N2L4x+inn+Q/j/fr2kr0laVzrX8zlN9fc/1f+LyDxiZotuwxvK7gAeAwwB/wM8YQ7vfwDw1LC/GvgpPjXE3wOnhfbTgA+G/RcB3wQEHA1cHdo3AD8Ln+vD/voBzPetwBeBi8LxecCrwv4ngT8O+38CfDLsvwr4cth/QnjGw8Bh4dknfZ7j2cDrwv4QsG6hPU98AN2dwGjpOb52ITxP4NeBpwI3ltr69vyAH4S+Cte+sI/zPA5Iw/4HS/Ps+ZyY5u9/qv8XcZu/bd4nsFuThmOAS0rH7wDeMY/z+TrwfOBW4IDQdgA+WBTgU8BJpf63hvMnAZ8qtU/q16e5HQxcBjwXuCj8SGws/VG3nyXe0+WYsJ+Gfup+vuV+fZrjWvyPt7raF9TzpBOlvSE8n4uAFyyU5wkc2vXj3ZfnF879pNQ+qd+ezrPr3MuAL4T9ns+JKf7+p/u3Hbf52xbrclmlPGdzQVgCeQpwNbCfmd0fTj0A7Bf2p5rvXHyPfwLeBhTJlPYCtpiPS+q+Z3s+4fzW0H/Q8zwMeBj4bFjW+4yklSyw52lm9wH/CPwcuB//fK5j4T3Pgn49v4PC/qDnC/AHeE1pd+Y53b/tyDyxWIXMgkDSKuAC4C1mtq18zvyr1Lz6h0t6CfCQmV03n/OoQIpfQvmEmT0F2Ilf3mmzQJ7nenwSw8OAA4GVwKJIM7IQnt9MSHon0AK+MN9zifSPxSpk5j0lg6QaXsB8wcy+GpoflHRAOH8A8FBon2q+g/4ezwJeKukufJbX5wIfBdZJKgJxy/dszyecXwtsmoN53gvca2ZXh+Pz8UJnoT3P3wDuNLOHzawJfBX/jBfa8yzo1/O7L+wPbL6SXgu8BPjdIBB3Z56bmPr/RWSeWKxCZl5TMgTPmjOBW8zsw6VTFwKFR87JeFtN0f6a4NVzNLA1LGNcAhwnaX14Sz4utPUFM3uHmR1sZofin9HlZva7wBXAy6eYZzH/l4f+FtpfFbylDgOOwBuC+zXPB4B7JP1SaHoecDML7Hnil8mOlrQi/Bso5rmgnmeJvjy/cG6bpKPD935Naaw9RtLx+CXdl5rZWNf8ez2nnn//4dlO9f8iMl/Mt1Fodze8h8xP8V4m75zjez8bv/RwPfDjsL0IvyZ8GXAb8B1gQ+gvfIGiO4AbgKNKY/0BcHvYfn+Acz6WjnfZY/B/rLcDXwGGQ/tIOL49nH9M6fp3hvnfym56Fs0wvyOBa8Mz/Te8d9OCe57Ae4GfADcCn8d7Ps378wS+hLcTNfGa4Sn9fH7AUeE73wF8nC4njT2c5+14G0vxt/TJmZ4TU/z9T/X/Im7zt8W0MpFIJBIZGIt1uSwSiUQii4AoZCKRSCQyMKKQiUQikcjAiEImEolEIgMjCplIJBKJDIwoZCKRSCQyMKKQiQwcSYdKGpf041le91LNcRmHKeZxrKRn7sZ1vyZfDuLGmXtHIkuTKGQic8UdZnbkbC4wswvN7PQqfUP0+qD+PR8LzErISErN7D/wQYORyLIlCpnIHiHpV0OxqRFJK+ULej1phmsODUWqPifpp5K+IOk3JP1nKJb19NDvtZI+Hvb3CwWt/idszwzj3CrpHHw0+iHyBbBulC+wdWK49lhJ35X0dUk/k3S6pN+V9IPQ77Gh3z6SLpB0TdieJZ9l+4+AP5P046Cd7NIvXP8eSZ+X9J/4bACRyLInnblLJDI1ZnaNpAuBvwFGgX81syrLQ4cDr8CnMbkG+B18up6XAn8F/FZX/48B3zWzl0lKgFX41DNHACeb2VWS/g8+Pc2Tgb2BayR9L1z/ZOCXgUfwxbg+Y2ZPl69q+qfAW/DJQz9iZt+X9Ch83q5flvRJYIeZ/SOApC929wtjgy+09WwzG6/wDCKRJU8UMpF+8D68oJgA3lTxmjvN7AYASTcBl5mZSboBX9Sqm+fiEzNiZhmwNSRxvNvMrgp9ng18KZx/UNJ3gV8FtgHXWKitIukO4NvhmhuA54T93wCeoE5l4TXy5Ry6ma7fhVHARCIdopCJ9IO98JpFDZ8UcmeFa+ql/bx0nDO7f5dV7lX1fg442swmyhdq13L20/WrOp9IZFkQbTKRfvAp4F34YlMfHNA9LgP+GEBSImltjz7/AZwYzu+Dryc/mxT638YvnRHuc2TY3Q6srtAvEol0EYVMZI+Q9BqgaWZfBE4HflXScwdwqzcDzwnLadfhbR/dfA1fKuB/gMuBt5mvVVOVNwFHBUeGm/EGf4B/B15WGP6n6ReJRLqIqf4jAyd4aF1kZtN6nS1FlvN3j0QgajKRuSED1s42GHOxE7Sefwc2zvdcIpH5ImoykUgkEhkYUZOJRCKRyMCIQiYSiUQiAyMKmUgkEokMjChkIpFIJDIw/j/3Xm+xgM6xeAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -1149,23 +1180,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 23,
    "id": "c0382d35",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f770781ea60>"
+       "<matplotlib.image.AxesImage at 0x7f489e87bbb0>"
       ]
      },
-     "execution_count": 123,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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XW58GwDUdxaid9YiK8xPqNc/kRIFrOpvnaQOxXxq54NyW3bhHLbHnU6nPCATGTHP4ccvg3k3rQa1P8JNA9GL9p7mKY+zb/nF1QHu8b4Fo+p5aa+a7SaDcaK2k1UTKrZh6Okr/fKR/trPZoi0lFoLrLJj4RukGjv75gCgs3TOhXG+o1jvUGQst9GBy3NGseKqtyPYjHBItoIZKmNw2pNyOqIPqvDHbtHSzIVNEZkSCYrvFTVrickUcljbzM7b/Jr1zXQqiBaJqwR1oVvI/xcOOuOCSYcjf+OuJaQC6kPE2jzj3dY1qmcG0N9OFHeIBoM5ZhtS3X+Nu0hL6BcX5sQWiwkGMhGE52yeWftZPUe9w4xYVoyxr4eif6xg+0Bq5oI0711MkIkThcJOW6tS2BYzUU4mFJOUBpVsbIFFxo4kRExJDrtqMhMqhzm7a0gaa4z1EleZEn+aEZRr+/jP4cYdWjvpkBV6MxjxwFKNAu+QpRhEVy26KcWTpwUAsSUHIsiMi9M+0tGsl4zv6jG+rkACxEpbuC7gWfKPGajsVExsukQaKnT5Qt2SfjTTRmG7RMjk/6iwIe5mxBiVE6uMlYVBQbrT2PpzYgGpjvSEwJQbaPX3/Mm5w6IL9ntzz2UEOPjcC9iIdTEtvc1mStq0FjinpAKwsN537iQpFgfYLtCoozo0h2iR+7BWod5RnjTUnTcR1kbBczWZ5NGUsopi6AdbQRwQZ283R2FzQ3X7MbsKjGloLHiiEnrMy3dgCo04b9cM+3VJJLKcyMykzmXRInUgVUXF1mDXx6ZnPnGtjauYDQWlWPeqgG1rgcYlZ1vWFes3hWsV10CwJXX+OOl4IzbKnHQiTY0I5ipSbgf7pCcffvsn4pKdZ8fTPdqhAO3D42jIpBDbvdBTbAdfGFGS8BRQvhKFlMbGwMqffbnCTDukgeitJGnMRiq2GWDnKrdbUF2q1LDfjUEIV2gWXDEPu+WRkZGRcMYRwYwpJ3rDImc+NgD1IB7NBU3Ez8VARMTp1m5rzhbdSmCpapQn5wuPGLTLpCGt9wkqfMCitxOMEtzWxvsm4taZ4bf2caU8o9rz1dkphfEtBs2bNfy2tL+NHjc2wTOwcMmns2iYtYVDg60i3UhKWSrrjA4rztc21rFg50NcBCUo3EFyrdMsleKFbqWYzOeX5iREYlodQlTRrFeqFcrujXTVyhURwrc3e+Ikdc3g6UI6Urm9Eg8HZONuud8YIEsUk4hulTOoFAONH9G0OqTRyQn2soF2SVHazmSEVYfhAKpPNSm3tjGwgIWm7JQFSrQrCsKQ631jpzglu1FoWNyhoVguatYpyo5vRuDMOJ5Q0IbHAkmHIwed6YxHiQUgkgum2dTMLOHRWrpLOZmToOrRfEasCqRtUjLXmx+1OeS9GtCqI/TQTVEx7PSYXU53aNjUDrBfRO9+ZKnMiFWhlzXIwVWaq0iR/zm9QnRnZQOioswBVByQEC3ptoNhO5IZOrbTlZm8UV4dZYGpODKzZv1Sh/R7FeFqCtJ6SC2rECQflyGZ5RI2pNi2R+VapNjqKcUS6pD4dbUbHBeifDcQC1h9fsX2b59QnrrJ8r73XWGAK14XQDoVmyYgHg1MdsedmpcNpL604b2QK9UL0NkwaesYkdE3YUf0uHOWWkQ+sfKc0ayW+jruXXzMODULKfvZbMgy57HajQNzDA9HUfkAVgiKFsd+kMJkbZMcuQXuVZUPOod7UAnDO+iltIA6MoRaGJTroWXCJmiR1TB3Bb9ZQOMKwwHWR6myDa0u6gac8n6jTVYE0dkztlYTjQ9QJ5b1n7XomLXG1P1PSng6fQmKOLZUU52va433aFW+WB20k9i1rC32jVPs6WB+qbmnuWKV6YBN0SOgXTI4bNdkFUwkYPFgzelSfYhqEUla0fbtj47E9+qeV3mZgPOxRrXeEvknrxNJ6PmBEgnYoxEronevgeEExsQDbDh3VtqbrczP16lh5U4PAWZbTN8miWNrn77daJrcPKSY7gTNWntC3wVvndKb7FirJP4sPMWzINAeWS0HOfDIyMjKuEAq06hZa9oOIfKiI/PXcsiEi/1pETojIa0TkXenv8bS9iMiPi8i7ReStIvLxc8d6dtr+XSLy7Kv3CVw6cvC5kZEyIZlKy6givcrYbWWxw3brAlI3M3VrabvUnzG6b1juJeHLVEob10gb7Ve7E4r1mrhsFOep1M3ULC70rJ/iEtNt+jf2K7rUw7HhUhM3ldbmWNQJsUrMuakcT1TcJBAHhc0TdTsma2YMp0kh2/pQdJHmliXrVXWWCQEs32s9FFcrfhIZ39bDdUq50TK6vaR3vqMdOHrnddajAaz8FsE1ip9YdtQNHMVEaZesVDdZSxTuiWm8jU86irEyeNDed+98i2uVYrulOJdYg8E+23bZm4YdEPpW1vTNzvljIbTLSeXaS3qfwaR+fP7VfJihCAG30LLvsVTfqaofq6ofC3wCMAJ+C/gu4LWq+mTgtek5wOcCT07Lc4GfAhCRE8ALgKcCnwS8YBqwbgTk4HMIoCFYAHL+IQEHMNKBTyW4ws9sF6wsZmUhaQMEtZJb6dClAZp6Fm7cmpFbZ7pt6p3ZBGxMaNd6bD7KW+mvcMRhZQKmAIWbERaKzZq4nGjZhafYamfKC66161Tvkiq2qWSHfkExCjtN+mTPMEWsPBSO6swIXwd0qW+zRiHOZnbqYzZT0w2E/oPmADo40xF6NvxZbUaGD7QsPdDSP9VQnW8QtVJd6Am90zXVRqBZcfTPBIYPdqy9Z0w7dLPh0mJi/almzUzw1Fl5LBYOnJ0nVt706rYCsV/MqOXtsilsT05UNtOjJkYaBjszQq7pWLqvxTe55HbYEVUWWi4RTwfeo6p3Ac8CXpbWvwz4ovT4WcAvqOENwDERuQN4JvAaVT2rqueA1wA3jINqDj43OtKUv6pCDEYWmM76hGABh3RzH5iwqM37RNykS5lHJKxWFNtGKpiqYKt3qHO4zggIWnrioCAOSlMriEpvYzrQGvHnRiY6WndGeAhqvkFBkRCItx6zIJisG4rz9dwQ5Y6dg0vDDjPSQhdnDXyzwZbZOmk6a9y3geL8GFcHQuWIPZPriaXQWze1geZYZW6laztBzHx7AuPbe2bZEJVyO1BtBvzICBnldqQcdUin1CcrNu/0xNKYeL31QKgswPnGvIhiOldMDEKzhTB1bxW7fhs+tfftJ5HJrZUN0gaTAALAC9uPXWZyophlhxmHE9Oez4KEg1tE5E1zy3MvcuivAF6eHt+uqvelx/cDt6fHjwLuntvnnrRur/U3BDLh4EaAxkSn3oV0AHajF1O31qYxG+3pkGkaTJQu4LZqy4jKIpXggqkaOMGPOrol857BO7QQJNgv767fxzUdEsQIAEHR0jKYwWlnJnOA9kvLopwg9VxmFc2CAVVkNEEGFe1KyeDsNrFfmQ6dtwDixi2lgOui0acLmQUm1wT8dkN3rI/bDsi4Iy736ZYKylOB7uSSaaZFBWxIdHAWXGsMMz8JdAPP4FRLGFgAaY4VVOsdfmJkBE2fBSJ0Kz1jmYnQ9T3lZktzrKAYK/1TjSkgRHABqnXz9Ql9Cy6D+ycz+3BXd7RrVvrz27VliZWjG9o1TG3KZ+ceFPYZdmoZW7KVyDjMEMIC/ZyE06r6lH2PKFIBXwh894WvqaqKyKH+xZIzn4yMjIwrhAIRt9ByCfhc4C2q+kB6/kAqp5H+PpjW3wvcObffo9O6vdbfELhqwUdEXioiD4rI386tO1JsjauC3ZxNYafXw5zZXFlaVuTdjuZbZ5I2MmnR0uPGHW7UGN26ENNnE/sFPm2KTxmiEmwKznTLrLTmx2buFlb7tCcG1vOJVmYDmxHqjg+JwwrZ2Da31TaYY2fPBk2152dGajJprfRUh1kzPiyVs77PtD+lTqhvHzK5xfTd2luXZz2hZtWz/riC4WkTGDXSghBLh69N9sa1ZoWNgquDee1Ujm7gTR9O03ArqSx3i0kP9c62LD0QmdxSUW6FJDyqTE7YEGnXd6iD+mQPt2Wf6/TzFFW61b6JqKah11ia/lwximhyd51q3qlYD6gdFjaIm43GDi1UhUb9Qssl4CvZKbkBvBKY3gOfDfz23PqvTffRpwHrqTz3auAZInI83WufkdbdELiamc/P8/Dm1pFia1wrWL8n7ihcR0XrBm2aWflN+9V0Y7QsCGsD1Hsr6fTK5FgKk1srszlIAp6I4Lcbmw8SjFGmVnYLg5KNx/dt3zrQLiVx0sLPekauTeoKTkyHLUTk7DrlmW2zcXhw00znxkZC0F5B7Hu6tR7tUgFJ0cDVnSlB90p8PXVJjbigFshKZyZzpaMdmsV19EKoHGGQ+kWF0K54C1xrnuEDHdVGR3OsouvbP3o/iRTbgdC3ntboERX18ZJiovhxy+RkSblhhm/mSiqU22FWFvONEipzf+1ODmybZLM9dYOdotzumBz31CdK2iXTgIulldu0NJKIBOs3+ftOX9PvVMbBIyILLYtARJaAzwZ+c271i4HPFpF3AZ+VngP8HvBe4N3Afwe+CUBVzwIvAv4yLS9M624IXLWej6r+iYg87oLVzwI+PT1+GfB64PnMsTWAN4jIlK3x6SS2BoCITNkaL+eoYdr32Q1xR7Jltq0q4ku0bRHKJH9TQFGYvM6ohcIh2w2xX5kzaSH0ziXKdJt6RW0KNsnmYMaOE0F6Bb31QHm+xjUdg/u2U3YUEUyKR+oOlxSp4+oQ2RrDeGxZ0MAyMzdqaG5foVivEWfWCFol++tNY31p4XBNoFuu8NstEiK901uMH3sMUSgf3KY9MbRgMFa6ntAsCyv3tHRDs6KWTolVooYHNfFPMTVqCelzTLbavg5s3dmnd64zcdJxRCYdvrbMz9eB4AQJyvhkwdJ9TfI/UqrGKN5d3+M3wIVAfayHdDp7D8BMMHSqsNANTckg9D1u28RetfSUD24Rz69fzW9XxlWGEQ4O7re8qm4DJy9YdwZjv124rQLfvMdxXgq89MAu7ABxrXs+V42tISLPnbJHWuqDveobADOZnfnSjPdmtja1RJj6+zQt0namNi1i/5XVDOKMMhxnjCz79W1lsGKrwY0aM6brjMEVC0FSdiPBmHdEk/VRScGrcOAFGTdGBji2Cl1nc0AiqLfggIMwKIkpC+md7wiVo+v7mZSORKU71sM1gfbkEsW2sd1QpV0pzKpBoF02vbUwsCBmJbdAudEQeo52yRsrrhSzC2+iWSF4Kz2GnqdaD4SBo3fO9Na09PTO1jMPoSlJQBSqcxNcE838rlPKjYbqXJ1KaaUx3QohDEu6oQexGaLhfTWh51JZ0DIp10S6aRYZFR48kxWtDz2McLDIkmG4bmy3g2ZrqOpLgJcArMqJQ80CycjIOFyYEg4yFse1/rSOFFvjqmE30sFU9yvN/VAURjZwySMmmMqB1klhOs36mEq1ze60S556xYzQuuFO49P6QQpdNF24NHhKFxl+cIKbmAsqYLbcabbIj9sZOUHGjQ2jOmHyhJPgPcV6nYZNI367mem3lecmALgmUm519E9P8NttEgqVmQYa3noiCEb9HplAZ+gJzaqgBYTKSAWxEOrjVdKqs/KYbxIhIRqZotg2xYZYWSbi2ogf2/FdUMaPXkpDozYQW4zNatvXShhWiIIfJTFUNd09aW3/bslbD6pf4DolVA7X2LHMNE5nGm+hb6VB1Nxj49Z2JhscAQSVhZYMw7UOPkeKrXFNoeYiqtOyV5MGTZ0x3bQsTF06OWsiQlwZolVh5bPS4yeRchRpbl0yBWaRJDDKjhtq6dGeT+QAE8jsjg2NvDANZv2CuNK3cl0bCMNqp/TX79G/dxMKjz+/hfZL1CV1A91RPHDjjvpkORuudE03s/r2E5vDKdZruuUSP+poTwzwI5O2mZwQfA2jR5jraP+ckQOaVUe7XFjgmdgwqorJ5iAQe87cSGvrY8Weo9xsZ06uEpTJrX26gaNZMxmc3jkjH9g1RpPFSSy92PO4ukVLR9eXGaPO19Zz8o3ZZ5tNt2P7Eb2ZjE51atuOuTnZMQ3MOLRQhFaLhZYMw1X7JETk5Rhh4BYRuQdjrb0YeIWIPAe4C/iytPnvAZ+HsTVGwNeDsTVEZMrWgBuMrXHguBjpAOv7SIyz7TRExEeoEuEgRqQwPTcd9nBNh6qx1lzdwUrB5Jhj+a6a7TuXKMZxZoVNZ4FFVCGpTLux2WcLFmSk7aAv0CZFg74NYfrtmrg6MEq3Jqp2iMZ866LRsNs4s25obxkml1OIlaNYb+lW+7hJZ1I9VWE22b3BjCQQKkexEamPF4weoaiHclPwdaRe81QbgcGDAYnMbBrUWZZUpiFZP474MTOmGxhlOlSmXtD1hWKiVBvm6lqs17SPWkIH0C17emeSk2vhqE/26Z2ZUD9iBT/p6J81RpyfBPykoxv0qLY7XBuZ3NbDNeYj5JpIsW1SRt1KSfm+rav7ncq4JjhowsGNDhF55cVeV9Uv3O8YV5Pt9pV7vHRk2BoZGRkZkIRFb66S2idjZLCXA2+EBTnkc7h5QvVhwbT2f7EMqE3GcomtRheM4TaaoF2X1Kqt5CRzwp7dwFGOLTMhGbFJl4ZTC4fEaL0e5yzjaU2BOlbesh5NvY9ovSFXt7NvkNSpfNV2VmY7sQKFZ3znqnnYLFf4rZo4TFRqhd6ZBtdEwlJS1E5DpKKKC5FYCr41GR7X2bnVQbUhFGMIA6UdOhPsDEp1vk5lr0gx6igmgd6Z5IjqjG0W+g7fRhtYVSg3O5buHVOMAtWGDZWGntAumc7dtHxXbgTalcJYcJ0Nsk5VwmPP45pIs2ZZZ7tqrqvtckG7UiHBxEnLrbCjAVdYLyhu5sznqOAqKBzcyHgE8G+BjwJ+DJtJOq2qf6yqf7zIAY7MJ3FTIJENJClX03UzGwWSpQEhoIOe0awLN7NG0MLRLDt66ykYOWiXrbdDEsMMS5XZb29P8FsNsVfSDq1nROGh8HQrJjpKstyeKRYERfsFOqiQYEZzcW2JwXvOGI25MLFQqdudv0nzzHV2Q5/aEagTJJEF1FkvJVSOyS0VLpjxmx8L7fFpwLAfXd2yBacw8Li6I3qhPdYzGwcvdEOjWksTcZNg1OlJRxgU1gfaaM3ltDFX1NjziJoVg12YWSWADataWS8FkdKGUdtV+2/jWp0FPF9HU5eog4m5lo5uuaT3vlNo017DL1DG1YL5Pd48VGtVDar6KlV9NvA0rGXyehH5V4se42h8EjcTEulAQ0SbFp3UFnzA3EwlqVsX3rKR0pudwsAjEQb3bNksjzCTf9GU9RTnxzZwOuzZN8OZ/EvsJe+gLtC7fyv1f4JlTd4RK5tZkTaYCvVSD2K07KkLuA88QPXgdrL7NuJEHFYze4J2pUImXcqwQiJMQHX/pqlIV/Y1LUaWmdTHlNd903/Gr7R0S2kGaNWCgs3i2PUVSam6OVZRbjQUIws4o0f2Z3M2U7HVMCxM9bqLVOcb60d5Y95JZ/I9oe9SVqbmhFp5yq2O+rgNlfpRZz5ByeLbJxsF+4/DTI3CJdabZpbbkYERDvxCy1GBiPRE5IuBX8LaJj+O+Q4thEy9uBGxF/FAzbKZGHeynxgsjelSdqIpg0jSOa7pzNRs1NGsVLj1bWg7uv5xfKvp5hpnhm06NN0zqQM6KFl/XMHyfUL5QLCspt2R30EVwVmmU/qZMoLrorG4ysJYeOdrKwGqopVRuIkmMePrFrdRzwKd9iwY+I2asNK3gcykYBAGjskxRzESfursJxJqb54744hr1YgEXSR6R1jqmb33+gRZtaFVSQFlOuxZbHeEgWnVFVstKBRntgjHhpSbFgTbZfsn0vaTblxiz4WBpzpvjMNq3VS08ZLYc+yQKcYd3aDAtdECZLRh1HKrQ7dH1+wrlXH1cZMRDn4BK7n9HvB9qvq3++zyMOTgk5GRkXGFUC7LKO4w46uBbeBbgW+ZuS0b8UBVdXW/A9w8ofowYpfsR1VnA46zdZ0pUMtc/0C9T8rJgpt0tCsl1aaiW9vEjU2ziQ6pr9GaSV1cMgVpCUpIFtnFGPOcKc00bqZq3ZgxGq31nWazPk2L266RLpjRXOGh1zM6d8qYRE1I1I9tPscke9J1C7i6Myp3ZWKc1bqJknY9I0qEnvKVx/6SYye3GN1qn1G77E3DLR3Hj9vZHJKbBMKwtPkcB4NTLa7bKdXF0nTlio0JOqiob+3PuDuus/5PudnhkgmcSioBFua+WmzWuNa03rqlwnTh+jZTFEp73y6YHly1biSL8twkS+ocMRyUjfZhgKo6VV1Jy+rcsrJI4IEcfA4f0nyKtt3OwGlSvQYsYLXdDgttdsOH1Q9MrM/Qtgzvb4illZBivyAu9azfMpkOVJrlwvB0Z06nIsiknrHjtFfOFK6lTa6gpUc6K711t62aGKoI3ZMeORtQdclsTZ3j/JOXzI46XbobNcYwO7Np/SGFbqlIwqfGQusGQnMy8L33fAHDXkO1odRrnnI7QITQc5QbNdKk6+6V5uhazvWNBBMfrS0A+8ZERbVwbD5pFaIFpGK9ptjemUcCU+AOA28kiTYmtp5p0sXSzOWm+m0SmNk7TGeA1Am9szXu1Pnc7zlCUCCqW2g5Ckj2OHsuixwjl91uVOzlbpqUDvAeEbEMyHm0C0i/lyRfupkEThxWECOhJ/RONbNf2+V95yluvQ3prEEuTWv9mBgJgxIEth7dwwXoT89dFtbzqEytemb9HMwjyI1b4toSYbmHm7Q7qgcpi5j2idQ5cFAfF4q/aezm3Tr8Vk1xboz2KgtUdUc4Xs1sFOx8gmsc7/vJD6VZEfqjHf+e6Y1f0sDs9LztyT7FqIPSQafgPVpYMJCoqHdsP3GV3vmW5Q+MaNZMSqe+bWBSO0kN3OjVJlba9c3fp1pvqE/2CT3BNUo38CkLE6rzyeNnYOoSU7sFGbfEjc2r+OXJuPaYWWTfLHjzRV5T4An7HSAHn4yMjIwrhMKRYrLtB1V9/JUe42jkgDcj3FToMyTG21x2NC3BOUfoF4Tlimqjw3/wTPICisjWiOp8Z32LNCypXsA5/MjESY0NZ4KjkkptEkxmR703SnfSmHNj6zfJpMVvN8aWK1waaJUdOwYRc0EV4fa/2IJo9gIz7bf1bdON62LSowv0ztWzAVnXKnGt5cSf38sjXvNB2qF9DrE0YzmiEoYlsWfOpFNBVHU2ZyMzgVajSk+ZfuVmoFkpZ59duV4jQSm3TNtNgqbyIxQjY9dV5yZ0SyXdwJmsT9Jtc52JmLoQ6Qbe+kVtpFtKGWOMZgSYcWSgKjdb2e02EflREfkdEfkBEVmozzOPo/FJHFXs0RPQEG2YNIRZD0jKnRun9irr+8RoytNiN+b5Uk/c2LQ+hRcrUblkcz1OvZuoFNuR3nog9q3c5ta3if3KKNfJJI6ymJELjIwQUxBL6gkwIz5IF4mDktgvrb9UJ0HNaR+rKqwk5owKHQYF1XpDGBRsP1LYusPTrAm9D/TQc+vUjz3Jmc+eEAtwtQ2lFmNTiwZwbaBdreidHhOGBbH0aGmBIlaO0E99HFV6923QO2O6cr6Os2tXkTQPVBC9BVJfJyWEYcX41tJKfjH1jlSRzh6H0hErMYfV5WLWf6Nps5joEcTNNGQK/ALGdvsJYAWb8bkkHJlP4khjH7FRQkCj9V3EpTmgLpjUTeHMObPTh0zTawgzN9NZAGsDeBP/nAYGUy+IxGFJXBnQnugjdWvMN+fQqiAu9+3G6oVwcjkpGJi76awvVDgIauKjMUn8eCEs99L1RTueS5mYCMVWQ7tS0awVbD+hZfNxSv3RI574Mx9Am4bP/ok/xd/TJ5SSGGvRBFYLh7SWOZUbzWymR+KcSZw35QEtPaFfpKzOJIt8Ih+45Ewq0VSypyoN5anR9MNncKqlWm93VBb6ZustrTmWVuvJMG/o6AYF/uw2nDl/sN+PjOsO8/M5OBvtQ4A7VPX/UdVXq+rzgI++1APk4HNYMB+ApqSDKby3X9JJ083KYvYr3U06/CRQnb5goDHaUGZ1vjY22MDYazKxm3V924DB3ZYpqXe4icniVKdHiYlmFGSStE6sPP7BdfymucjGlb5lVF7Mq6dwyKSeyfmQAo10kcEDY9zEmvOSCBHSdDM7a1crJ95U8LRPfbv9Iz+/jqry/JPvotgUfGsuraFnZcapVt1Utsd07kjMwCTfE5Ty/MT065IDqYRIs1rSrFVW8hsluna346XkU8B2qQwnQQk9hx8H6mOFKVe3SrdcWsB3YrYPZ1ujgm9uE0d5uPTo4eZzMk1WN1N2m7/g+b44Op9ERkZGxnWCUa1loWURiMgxEfl1EXmHiLxdRD453dhfIyLvSn+Pp21FRH5cRN4tIm8VkY+fO86z0/bvEpFn733GS8YaxnibLqvAW9LjNy1ygBx8bnRcrO+janTrECwzmgqNOmeN9bIwrbW+x22MHn4sBbc+sv5M3Zr0i3fIuKF3eoxMamKRXE4jZlqXBEVns0XJ1dPVnVGxvVjvJg3DmvBo0jIb9mb6ZnRWIpNgPaKp+nZcGuDHRtP2oxY3aemdmdA/r/zdL3w4YaOC1sqHP3HusfgWiokmkVLMnbROQ7OVKVOrmHRPGBSzspqfmCRQu2qK2d2yuawO332GYtSZLFHlZ+SLYrPG1xGC0h0fJJme5JraWRmv2kjupK3OynztsselEmZ1tiGub+R+zxHEVdB2+zHgVar6YcDHAG8Hvgt4rao+GXhteg7wucCT0/Jc4KfAZnEwH7WnAp8EvGAasK74/ao+TlWfoKqP32XZl2YNOfgcXmhEQ7CezxTeg58qCRRm5rY9tkb/g6cfdojevetQmM32tJSGs3Kdlh4JMVlXJ/22KVEgabhp6VP5zERF43LflBVg1jPSqjTLhMmcAnfQVGKz86l3UDcpWCXr7XENXaRb7dEc7zE5Ltzxu3dz25/72Xv+B/27QWFyzBF6iTwQog2uillw+1FnmnGFEQVQnZXm1FkvyDU2/FnfMkC6gJYmltoc23Fo7VZ6s+FSExn1sxKhlftcGoK1uSAjKAi9Mw2IMDlZUpzaMDuMjCOJg7JUEJE14B8BPwugqo2qngeeBbwsbfYy4IvS42cBv6CGNwDHROQO4JnAa1T1rKqeA14DfM5BvFcReWy6zunzzxCRHxORbxORapFj5OBzmHAh8SDuKBtICjo0rVkqtB3aM2+ear3ZVbpftsfEfjVTm1YRC1pTnaYuzLIa9Tu05KmHkNQmYDodUJW6w02SYvZ0CNUlqZsYdwgIzvxsdKryPGpnWZp0FrTaRxxLg6aByYmCjSdar8e1iRwhwrf++DcSnrpB6EtSHkiBzMksuJDo0a4OJtsTlWLU4hpjwkkakC22WopRIK4O6RIBQqLSJZkh9abAHXueUDmziSgdKDSrRiyo1jtisWOhoIWpNKgXU7veHmdVgyMKa3/KQgvm7vymueW5Fxzu8cAp4OdE5K9E5GdEZAm4XVXvS9vcD9yeHj8KM3ab4p60bq/1B4FXAEsAIvKxwK8BHwA+FvivixwgD5keBuxlMKfJCA4rw0lRmKX2aGJ062iZh1sfER9WcovQdWjPfsHPVKlTuaxbKin8dJYoSfSMaii8PW5aU1EIqbzXBrRXWINeseAEFgQq2Zm3KVPZIVmCx7LANR3tbctIG/GlN2kcEcKymbLFUnjMqxsIgSc+7x2c/f0+3H4rd/yvDR74zI5QWfbh6ziTxaFIgagwYzw/lQ3aauhODhBvZIZupcI1EdcG/LkRcdij3Gyhi8mnx8327ZZNcbvcbM0gr+fxo47hg5GQjPCqrUAsTYNuqnodK2feRFnF+kjjEoRFT6vqUy7yegF8PPA8VX2jiPwYOyU2wJQ7RUR33fvaYKCqH0yPvxp4qar+kIg44K8XOUDOfDIyMjKuEKZqfWBDpvcA96jqG9PzX8eC0QOpnEb6+2B6/V7gzrn9H53W7bX+IDAfaT8T60Ghunhqn4PPYcMF2c9s0DRlMjPlgym8g7Pndz2UhjhTtZamMz+f0iyzm7UCXRrYzE3hzDLbG9lAC0dcG87UCtSLDaJ6bzbcXTQVbLFSlVaWYKu3HpEEIzBo6U0DzjlzF22TJltVEAtHGNjAqa+V3t/ejbYdb/zTD+cD3/CRvP07j+HGLdtvPw5igp/Mfe3NU0eJhbOy38z8LjmKFmKZ1ninBxPWBlYmHLVWUnOCawL1iV5SQ7A5oVh5iq3GekhizqixmArYgXrwdaQ+WdIcq4heKDca4nhypf/1M25QmLyOW2jZ91iq9wN3i8iHplVPB/4OeCUwZaw9G/jt9PiVwNcm1tvTgPVUnns18IxEgT4OPCOtOwj8kYi8ImVlx4E/gllQXEi+Iwefw46oZqkQdnoxRLXeibO+jtZ7fBfq2iT/nSP2ShMl7SJxdcj4uKM7NrA5m5GRAbRwVhJLN3MtfVI1sAFV13TWFwrBpHfAekhgQTGx2rrlKikBWA9JvVCc3sSfWp+5omrhZjf0cjtCunFX54Vf+sYfoVxq6I4NePJL7rOyW2n9lWlPCUiDopFupaJbroj9grBUEQYW3LRwxJ6nXS1szigmEkQSJnVtpFsuKbeNMagOEBMXNWVuU3LQysps0QvlZmdGcWIDp82Kpxx1FA9u5H7PkcaBy+s8D/hlEXkr1kf5AeDFwGeLyLuAz0rPwQzd3otZWf934JsAVPUs8CLgL9PywrTuIPCvgd8E3g98mqpOm8qPAP6fRQ6Qez6HCbspXSfWmwDattZrWR4mi2rQ9Y09dcSmrLFYmKOoVsWM8gwQ+p5iuzWlhH45UyuIVWGZUIhoWaRhU1svbQp6TUe32jfadCIzuKntQlBiP21bejte4aE1irMbNcRblgnJPbQYB/MEGk8YnFL+2Z99I9V7Bmw9OnL8PWOTuqmMaeZrmZ1DNNlZb7eod/Y+J91MtbocN6h3lFsd3VoPaSLiBD+q0WJgltzjzgZOCWar7YCoNGtmA24EhoAfRwtOzj7PcqM1mZ6gbN3Z49hbzlzFL0bGjYCDVC9Q1b8GdusLPX2XbRWzsd7tOC8FXnpgF/bQc/7KLuv/av65iPxvVf3k3Y6Rg09GRkbGFWLKdst4GPp7vZDLbkcBUS27mTpjTmdl6sbmSuIepJgYzedGMG2zqRCoCP3zVg4L/QIdVLMZHXUO16Q+SZKx0cJBk+jUutPjKTZMvsbVLS7J5ZA8iKZaaFp6e23aD6oD3fEhqFKd76jOjE0A9bHGKr399+9i7c/73PFnNetPcIw/+k5O/l0gVHa8qT/PTNYnsetcGywLU6U63+AngTAoOPdh/SR6qvjt2kpvTYvfbo21hhnLRS+EgSeU6dhJxHSa/bgQccHo3aHnCMMC1wTCwLN0X5NVrG8C3Eyq1peAPRl5N90nceixG+1aI9p1doOra7RtbUjz3Lr1g/bqNQQTzXStUYptyLOBGCm3I6c/qppZIciotl5SEhBFxKjWSRB0OmyqyS7beiNJ6VqEbq0/s/Z2k3ammzajipceHfSsbFX5mYV2GJZ0A4+88y7bbtBj9QPWg+mdU9YfV7LyjrMgzBxDXW29p9Av7LFilHCYzQHFytENrI8ztfLujg9oTvSJq8NZbyf0zRbbREaj2WqHRDwoLAhZf8fbnNQ0qAq0KyXtkqd319msanDEYWy3g5PXuRmQg89RQQho1xHrGh2Niesb6Hiy702vWypBFb9VI5sjIxZ4TzHu6J1X/MS8bCgLtF8mWR2SioK3bCcNurpJM7Nm0EQwIMaZncJMHcEZay5WabYompV3t2Y+PuXpkQ2CtoHmeEX/1MRmmJxwz7PuYHLc06w6hqci3ZKRLNolIfTFBlfBRFWbkIRDXerVkDx+zOa6GAeOvasBxXyPSkex2bL1pDUjJIjND5nnkfWG1O/MLE2HSUXBN0nWBywQBpPY6Z1r0fWNq/QfPeNGgQKduoWWmwx7Rtub7pM4Ethr6DQme4W2g7a1rOdihwn2qx0RZNKiq0szdlpxZky5nTLmdqpkYH+1XxAHqUwXIrI9MevuqEZEAHDgNsZoVaCD0rTfkg4cYNpuMcn5TN+GYhlbatojQrPiGT9iYKoFwOZHNjv6aeNI76zSPmKNZhW6ng2kSlI1kHanTGgZD/ZYpvI4SrlRU56bJGkhwY9bhveMEFXKzXam50ZUuqWCUFlw9ZOObmhWFbF0dH3P5BYbWI2F0A09seetpJkp1jcFctltV3zNXi/cdJ9ERkZGxoFjwZLbUSm7icjZJPvzdBHZ802p6t/u9VoOPkcJD6Ffx8X6DE6QcTsTJDVCADZYOmd9bVlVNE+bs1s2+9OZ+RxVSVwdmv31uEbGzcyYzoZX46znAkbDlmA22+Z86vCbtWUl/YputY/rTDrIN8rGnQU6qRHvWX57RSyF3ro1+4enOtaf0CcMFMTKX5AERvvFrNw2zbJCmj+KpVgvqTHTu/LchHKjoVvrUZzeJBYOV3f4UUux1SARGzKNahTqJJQqUWc6bq5R2tWUOY4CxWZDsVFnMdGbADehmdwpTEbnhcA9SVT0aZdygOsSfJLy6dtE5G9F5OUi0heRx4vIG5Mnxa9OlVFFpJeevzu9/rjrcc03HPYqvV34+j4otloblPQ2Z4MTwrAiDM0MjWDlKJygVbLqjhH1Hi0LUyPolUYsCEpcWzIn1boj9itkYkw3SesAYq+gPTFMA6behEWdKWFrOXU1teb96l8/wO3/e93KfoXd2MuRUh9LOnIRNh8rxALUWbkrDEu6lZ6Jg7amUj0rxSXzt+pck+Z7BrO5JH96E1cHutvMjj4MSroV05dr1lJQmQS6pQLtebqBldu0MNWGqTPs4AErs7m6w73/vjxcepPgZsp8gG1V/UlV/VTgkzHZnv8qIu8VkR9Y5ADXPPiIyKOAbwGeoqofBXjgK4AfBH5EVZ8EnAOek3Z5DnAurf+RtF0GXPlNTe0m3x0fmM21c9a3aQN+o6Z3prVeyBSFQ4c94uowWSJMrbbDbDhVC4f2e5ZRaSIqlJ44KNGe2VXbPlMX1J3sLA5L4nDKsMOCUt0wuWO4o7Qt0Dvb0j/TUm20SFQGDyrlptD1LQCFfmEMvvSe/PYOzXlqiUAaGI2Vo7llibDcmzm/Sm0swKligpaecrOjXTXadiyE2C8pRmHmH1RutEgEP4m0qxWTkyVuY5TFRG8SHLSZ3CHA7I2o6gdU9T+p6scDnwfUixzgepXdCmAgIgUwBO7DxOl+Pb1+oVfF1MPi14GL1hhvSuyV/Syya1SkiVaOSlYERPPVcbU156dlN6nbHbaal5mpnHQRGTe4STObFZJxjWyNZ+wwtz5KtgstfqumPLOdpGkKwkoPrQpj3HUWNFwX0UEJZcHWHZZ1yNoqS/caSy1UjtC3YLB8X6DchvGtxmIrN5tEsTZaeLfaJ1YmBRR63tQIEi3aNRaMth8zpLt1xbKYZESnhdltt8vlbNanXSootzqa1XKWeYEFXT/pKLbbmbeQnjtv5c+MIw9F6KJbaDkieN1uK1X1Har6fYsc4Jp/Eqp6L/BfMO+H+4B1zHr1vKpOi+PzvhMzT4r0+jpw8sLjishzp/4Y7WKBNyMjI+PAcDP1fFT126/0GNej7HYcy2YeDzwSMyS6Ync9VX2Jqj5FVZ9S0rvSwx0eXGHpzW/VhEFBsT5GJo05eRaO7rZV/FYK4lPLbBFk0uwoFUxN6KqCuNxDe6XN9ISQhEST6OgkqW1PBz1TGY40E+S3ayMmhIjbtnOqF2Llae48SX3crMK1XxF6wuSWyszjKsf4lgIJsHRfRCI2k1Oa6KeKmJfPZo2EaHM/aVDWTgLNWkl1rqZ31myzZ5/LpCN6x/i2imK7oxx1DB5srAznxfTmUo+pXS5oVwpCv6A+3iP0hMEDScU693tuDujNVXYTkX+abLoRkVtF5BdE5G9Sf/7RixzjeuSAnwW8T1VPJSXU3wQ+FbN+nWrNzftOzDwp0utrQFZpvBCXW3qTZC8djHWmTsw2e8puS2oG0nbJOrvAjW2WR8YN6gWpWwsyUxmdKRmhZ415HZTGhAvBnFNhJvRJIgUAppiAMdXcpLN+zLGS+oSiqshoQjc0i4Vyo6UYB4qxUq23IBD6aSjUOyQ1/10TCCs9pA0meTPu0pxQpFvy+MZKbK6N5nY6bmlOmjCrayP9M/ZeY2GlRgnGbouFSfKUWx3FdqA61xAGjmISkAjl/etZ1eAmwk3Y8/n+OYXsnwT+Cvhc4PeBn1vkANcj+HwAeJqIDFPvZupV8TrgS9I2F3pVTD0svgT4I9X5LnjGlcCdSdP304/UOVwbKO560FhrU70354xa7QSa5NC5YooERDVWmU+ZQ+FMDSFB2mBN/C7aNy7J09hxIS5ZphqWKtN1S9fj6oB6KLes57T9MY9i7b0t5XpDGNjvlKV7RqgXXKuUW0YGkBCJgwKXnFndJNmAtxZM/aid6d1Jp0nhoUErl1QbTKZHUr8mDIuZQpULSu/BceoNKe1KQbHV4JpAudWxfUdFuRXg/ObB/8fKuKFxkwUfP/f4Sar6I6p6j6r+PHDrIge4Hj2fN2LEgbcAf5Ou4SXA84FvF5F3Yz2dn027/CxwMq3/di6wk83IyMi43lCEEN1CyxHB60XkhSIySI//KYCIfAbWl98X18VSQVVfALzggtXvBT5pl20nwJdei+s6tJj3+Zk+XxQhmDnasGflsxDphiXu3HniY2/DTYwDot4hTYtOS2N1h9uaoP3KsiaHmdIlwzntlbNyHGCvNwG3Fa0/VHqKjcmMTSfBzOr8qDWvn84oy+3A4ZJiz11fKDzqDwXX2NfWjwPNWkUx6miWe4QelJsB14SZIZ2K4Ddr4rDEtTYc2jxiCV9H/NjcU2NljqnRO3vsBVebnI4LSrtiDLliFImVg8JRbLXEnjcx09L6PohQjE14VDdz5nOz4aiQCRbEv8JM496Znn+biGwD/5OLSOrMI/v5HBVMA9AlIm5t48edNfxh1nfREIml3+nvpJ6QzQJFtFcgtTmc4tN5uzgzmZNRIisMjYggY4tCWhazEtxUBDQMC2SclK7bgHihPT4gFsLkpENTgn/r//b0zjb4ifVtwqCgPlFSrdscT+8cFuSmwcynWSOXFA+qAu/FnEvPthQjM5lzrbm/tislUIGCH7ezclu13pkCdiGEyqFrlQ3gAuW6levCwKjY/bMtbhL21dXLOFrQRDi4WZD69d8LfK+IrAGFql5SLz4Hn5sc2pgUjlZFEvuMMzaYhDhTcJZRRHCoSxbddWeKBxh7TeoO2RqhSwPbvk5Cpd5sF/BCXOrjavPYUedwXTfz3WE6N9RZkKhPVmw8tqBZg2LbrjVWJAsEY8mJKsP7asKwQD2Mb1fW3seMWKAiM3+hWe8KGDxQE3re+j7eBlNdUKoN6wVJiIRhOSMXFKOOQpnNFfk2pnmhwOiRffpnWnqnG2LPUz2wBafOkTluNx/0Jgo+MCOAfS7wYen53wGvnhuZuSiOTAEyYw6XkgEl8zViNEZbiGYXjdGwJZi2m1bljvfOdPsuJAM4k+XRtWUoPDqorBzXhZn6gbT2WJPHj5ZWElPv0/Htd1B7y5DY97jGLLD7pxU/N7YV+lbiEtXZgKirA4MzgZN/kwgCpZXcXLtz7ti3408HTV1rpAQw353QS/YOYvI/sTLVa5feP2IDucU4EHqeWFoGNTjdzK5LvbD5ocfR7e0r+s+XcRhxtIRFReQL9nn9UcDbgP8bG5l5FPCdwNtE5JGLnCNnPhkZGRkHgCOW+bwQ+J2LvP79wE+p6o/OrxSRbwH+IzsM5T2RM5+jBI2XRjYQBxrxZ7eQcZMM4oqZiKbbHJukTnotppIavcoynLKAprWZHu/NBVXEhklFiEPTS9OeSdtIoj7LpDPac/r2xTKRFNIcjbSR0W0FYQASoD5h27XLVkbrnxpbVtIkbbkuUoxtyLRe82byVnlCvyD2ChM8LT1aOWKvsEHTEHHjzrKYyogGsVek+Z8CdWK9JUz/rdgy59VYTS8aiu1AKB3d0NMteULPsfye9axifRNCFUKUhZZFICLvT0Obfy0ib0rrTojIa0TkXenv8bReROTHk/jyW0Xk4+eO8+y0/btEZN+AMH8J+7z+tAsDD4Cq/jiwkLr1npmPiLx1gf1PqerTFzlRxjXEogEobSeTZqdXo8ry+7fs5UQw0J5HRjVuNDG2W9Ma8QCgtBKYpgFU9TIbGnUTK0lJK6nU5pPKdGIQRIjLhbHTqsLmcRIJYfm+ltOfoYzWS7Rnx9v4iJa19xo7bvSoQWK2RcKS9Z4mxxwr93amTlB5uoGnOjcxpezpIGtUKG0Q1W/V+MKcSjWREkK/oBh3uDoQS0eoHK5TmuM9pLNSYCwdfhIQ1ZkqQrkZaFc98sBZNKsa3JS4Cmy3z1DV03PPvwt4raq+WES+Kz1/PtZ3eXJangr8FPDUpEDwAuAp2KTam0Xklap6boFzf8M+r48v8tpCaroXK7t5TKF0Lwg2AJpxyKHjMXpiBalT9tFFu4E6R+wXuJHJ7jwMPsnsxGhSOoURD+KwZ0oH02DjZcfPR9WyEWeq0n672VGsnoqSqtINHaoBWWuR9TSw6pXQ89Qnegw+OLYsJkRi4fGjDigpN1va1Qo/CXTLnmJkZAPpIt1KRbFREwvBCXRrAyRa78g1Ab/d0B3rI22y/Bah2LZg59pAt1RSbHdo6ew6lo2CLUHphp5iK2TL7JsUyjUpuz0L+PT0+GXA67Hg8yzgF9Lw/RtE5JiI3JG2fc1UiUBEXoNJmb18t4OLyBDr4TxGVf+liDwZ+FBV3a38tiYiX7zbYYDVRd7MxYLPN6jqXRfbWUS+aZGTZFwjpDLaw9bBRbMhndS49e2kxRbRoSkOSBesDJassbUsTDIm2SRIF6FuiCtDK6F1EUqP2zY/m9k/RRFwapTrrYCbtGatIKZ4HQYl5Tn7IeWmAaiDst/RjktufYsdafjuit75hmKrNcVt78y6IdGeEaiPV5RbHeqE3unGzN4qT7FZ48cdYVBSjOz1brmkd2qEa4xAEXsF0sSkhu2oT/YpN1vqEyUSS6oNm+2Z3mPKDdOKm9xSIgH6p9usYn3T4pLIBLdMS2kJL1HVl1ywjQJ/ICIK/Lf0+u2qel96/X7g9vR4Jr6cMBVm3mv9Xvg5TOT5k9Pze4FfY/fezx8D/2SP4/zJRc4xw57BR1X/TEQ8FlG/aq9tFjlJRkZGxlHHJYh+nVbVp+yzzaep6r0ichvwGhF5x0PPpZoC00Hiiar65SLylekco73sa1T16/c6iIjcvtdr87go4UBVA/DYqatoxg2O3bKbBfoP2hnFWp19zyRlLrSdldRU0cFONkSIyMT03SgLJATCcmW9ni7aoKoqsr416+Fo6ZFxa1mUiKlXB02q1i2xKmxbtdKaRKV42xLilFtffw9gcz7RW8bS3DI0KnWIVjrrIoMzkfEtRnkOPTdT3fZ16kFtWYkv9Au6pYLemQmxX9KuVrPSXxgUptIQlXLU0S2ZarZ0RoSQOFW1tt7S1NNn6f1bFO9/IKtY38RQlYWWxY6l96a/DwK/ham/PJDKaaS/D6bNZ+LLCVNh5r3W74UmyeVoOscTWdAYLpX6niMir8VERvfFIlTr9wL/S0ReCcwGGFT1hxc5QcYNgP1uiFHRyQTWlm1+Z5SCT7JRUO+QcT0TFBXnoO3QYT8pHHiKM2O72Tct2i+RFsLtx3AbYyhNekbUSm/qHJTgtsZQleiwQJqOsNJLataear1l/Bjov783cwN1NcRKcGMLNu1aDxSKrYZYmUI16lCBYhxoV0qkM4VsrQozhKtcKu25FFjTc1W08DNpntBPQUih3DKF7elskq/N8E6c0A08fhLx69vEjSypc7PC2G4HQx4WkSXAqepmevwMjPo8FVl+MQ8XX/5XIvIrGOFgXVXvE5FXAz8wZcWl43z3RU79AuBVwJ0i8suY28DXXeQ6B1i/6Z8DHwesYCagV1Z2m8N70uLSwTOOGjRC15lqtar9BQgBaTobPvV+x9cnBPtbeAtKMZETlvo4VSMuDHu4rXpWi5gOehqVOsysGtDkkDppcaUnLJUUo5ZuqWT17SVLH4wW9IByC/oP1qYBN25xhbHW1Dsbho0wPN0xLUYU48701xJlWqLSLnuW7honvyBj2oVEnJhmfq6NqECzWtA/ZQoKANE7U1hwyc8H6E6WDO6v0fWNTLG+yXGAWvu3A7+VKl4F8D9U9VUi8pfAK0TkOcBdwJel7X8PI4e9G2Oafb1dj54VkRcBf5m2e+GcDcIu16+vEZG3YFRpAb71ArbdDCLyP4B/CPwB8BPAHwHvVtXXL/om9w0+U0tUERmqajakz8jIyNgFB8V2U9X3Ah+zy/ozmAXNhesV+OY9jvVS4KUXO9/8XFDClNTwGBF5jKq+ZZfdPgI4B7wdeLuqhkvtQe0bfETkkzFbg+V0MR+DMeEy0+0wYTcm3By0aXGtMci0Tm6idWPzPM5ZtlOVKfNJIqEixmibKlOrKVNLZxkTqpYBbYzQYd9ESlO2o95keCBlRasDM7Yb2wxRu+JZfX9gfIubiXSOHqm0a0ajDkslfqNBCvu+x7l5nWrc4dpIEI9vjBotrWnT9c63ph837OFHpjPnJx0hZWXqLQNydUe1ngz0xLKdarujOWbMNi3MfK4YRVwXiVvbud9zE0NZvJ9zA+KHLvKaAp/5sJWqHysiHwZ8JfCHInIaWBGR21X1gUVOukjZ7UeBZ5JmelT1/4jIP1rk4Bk3EPa5MWoIVmZTtRvpdJ9UatNehWxswaBvJThAUg/IqNQyIxPooMJtjtHC76hbhzg7TnvbkikGNN2O/UKXXi89zfEe6oSl922x/D47v3hHMUqEiM4UDKZuVt1SSbFZAz2aZUe17s0ptbSejas7M5erO9y4Iw5Ls1Co/MwSXKIS+0Z0IGLDowLdMCkmJHtuFPwkEAtHNygoxgF/dpsYs7/hzY7D+g1Q1c+4zP3egfWJXiAin4D1fv5SRO5R1U/Zb/+FtN1U9e4LGHfZH/iwYZ/Mh6jWW6lKZkax03UixnIripS1TCVmos3+iFhwKQtoO9y4nr2ONwVrYiQuD/Dr27hJz1hxYgEr9quZ8kGsCoqtls1HlayeXke3tlHA3XKSySPCjNnm62AePU2gG3jKc5HqXANUxEIoRy24wmZyZirdO/I4rrNAZ46mShgUFJuNBa2+RyL4UUc4VlnWs97QrFWUm5bRuZY0WQicOZeznpsdCrqgdM6NChHpA98EfBr2zf5T4KeTp9pFoapvxhQU/g3WC9oXi9Az7haRTwFURMp08LcvcvCMQwSNxioLcWYxrcFu9lMPH0r7rSJRdxStU5NdmtaUDAqP9ivz7ZkfuKxK3KhG+xWu6WbfPC28yfAUzhSwQ5xZZAPE0QgNEa0bVJR2yQKGCqZoPWooN1vCco/JbT2jaG+n8mHSZ4u9RBhIUjiiZpOthcwmYf24mxnQhSqZ4qnSOzPBNZbpmJWCseNCypK6gZ+x8TJubhwk1fo64ReAj8QIBD+ZHv/ibhuKyEeKyBfOPf8REXkp1qLZWuRki2Q+3wj8GDYZey/Gbsj9noyMjIw5HCDb7Xrho1T1I+aevy559OyGF2Pq1VM8E/geYAj8e4xyfVEskvl8qKp+larerqq3qepXAx++wH4ZhwyxrokXapOlLIEQU2/HvjJaFrOsJw05mK+PWqYkXZj5/uDdzPF06ucjQe3Y/dIM3LbGM18gSKZxTbOThW1uwiBQjE2jLSyVFOtG5XaNZUz9Mw39D47MXK6y/gxBjXAQlfZYb5b1tKsV3cBUrCW5mKoTwsDPZoHa1YrmRN8+m56HoLSrxYyc4JrA0jsezJI6GTNtt0Oe+bxFRGaK1CLyVOBNe2x7h6r++dzzDVX9DVX9ReCWRU62SObzE8CFVLzd1mXcyJi32d6rPxHVbvjzu1WlEQsKn+Z7LIhImu3Rqe1232wWTBVBUxDyFnjSzTku9XGbI8QPAXastAs/C1Ld0EREY8VsvgeNaBeRjRI/aWYq2rHySFLLVjHGm/QLXGN2DVP30ma1pHdWkyq1Mdkklrg2MrmtTzEKFKOAn3Q0xyok6sypVEJMnWQLUoMH6mQ9YWU9PXc+93syUvS5oQPLIvgE4M9F5APp+WOAd4rI32CM7o+e2/YhM5+qOm+jcNsiJ7uYpcInA58C3Coi3z730irMiEYZhxF7BSGNRiWZd0KN0YZOXRoK7Vrw1awPJF2yXhAx6rWIZThlMesRTdWt3ai23k8a+lQxZWsVB0VvlsUg8hD30imWP+BQL7hRQJyJkrpRi4uRMCitD1QZrTpUBS5GJAT82IZni63G+jqlp9hOvarKUWw2hEG50xsqBAlQrJtvULfWR0sHbaTYrAlLFX67NYr4eN9ebMZNgiNQdvucS9j2gyLyVFV94/zKlDl9cJEDXCzzqbDZnoKHRrkN4Esu4SIzMjIyjjjk0LPdVPWuJMVzJ3OxYY8h0+cDvyoiPw9MX/8ETPbnyxc538VUrf8Y+GMR+fl0UVnh4DBinmI9X3q78LV5OLEMKPVvZj2bKd1+6uHTBbTvd/o80/Jc29lAatPafFCyZYhD65/IVKx0qWeDpzEQl3qmw9YEJrcNGN8qVuabu77h/ZHR7SXLH0hCoW0q51XWgynGwbIfb/0e1wZk0tI7NSIsVSbD05l1AqqJtq10y5VRtpdLinEg9JxRufsl40cObNhU7X13Kyaw2hzr0b/rnM1HZWTA4R30SUhSPF+HyalN381eQ6Z/kXpC/4od/be3YQ6nBzZk+kgR+X2ywsHhxDTg7NWX2O21+YFJ1Z16wnTWB6zfMygs6ExqczGtSrupq2m+ab9CglGytSwecsyZ0ZwIeI8bNcRhhRu1SOxTbrIzb5QwfCB566iV1+OgsOHRcTfr+0jUpL8G7XKJP2PmdbHy+FFrx1dMODQRC2Lh6IbWK5rOATVrBbJU0H+gJvY9sRB8F63UVwd8G+HUmSv5L5NxlKAHJ69zHfFlmK1Cs++WMFXc/veXe7JF2G4/itHozqQT/h8gKxwcJkyDy15BSHb5Gsz6Qin7ETHx0TYZy3mXZn0seBCS+2cwYVItbB3BbtjaK4wEMLXgnpfk6Xm0KmZBzteR/rmHX2csHdVGQFojB7g64EfdLBjhjUzgt1twgm+jOZ1WZnwnajM+7WoFqtbbaSJ+3OFrk8uJheAnZi/RPz2hOD8i9KzFGXue+nhJu1qaCkLu92TMQxdcblz8LXDscncWkd1M5/ZEVji4mbCX38+FwUeTMkHbpQHQOX5JmbKbNqlgFyZlwzTYeLczjLo5QleXbD9ndgTiA7QdkuTnNZXMpplQWOsTS0e7LIh3D/m3Wh/zDB9oUe8IQwsovm2J/dLKZJWfkQaKrcSUa1rLhkqXBlMt25GglBsNsVeghbmhqhckgmsC/VPJHvv4kFgK1XakWypYunubdq1HeWabmEtuGQ/Boc98/iPwVyLyt8z5+KjqF+69y0NwMZfUh2GR4PMQhQPgW8kKBxkZGRkPxeFn3L8M+EHgb7i8d7OQidwUl6twsKt8d8YNjt3KbnuV4qZ9n2S4Nk8ioOzv6KW1SfkZ0KV+cjM1vx9dHljm0Sax0aTfJlGTf44RGWTcEE4u4+sWCRH10PVlJmA6vc4HPkV59B96RBVXBythOIebpFKgeHwdCIOSbqkwOvXUDK9NWm6TFhUhLBX4OiZyQSQM7Fy+DjTHKsrtzvpQxwYUo5AcTKFd7eHHHdxz/wH/x8k41Dgacz4jVf3xy91ZVf+vS9l+ET+f08BXXe4FZRxiaNyxUpgGninhoAuzAVLtlTMmnHpB1IJK7Fe4zZH1eFQtgEmZ1K3TDFBlwqSuDWgiHrjVimKs6HTIdHY9gJgOG4DUweaEvCkYFJtmzU2/oNxo6JZL8OZeGsuC3ukx2rPhUpcO7RNjzo86is2a9sSAMs0AxUFJLB0+mdL1ztSMHjlg6e6GWC/kLpxxE+EIzPn8qYj8R8zBYL7sthvVGgAR+VTge4HHYvHEpHxVn7DfyRbx83k88DzgcTyU+71oHTDjRsE85Rp2JxpcuEvdIIUFCO06pKqSikEEX5gjKVjPp4tGo05kArdl1tp4Nye9w6yHpD0bDDWlBAcFuI3WqNmBGblhirV3etQF2tUK10SKNpgb6cRsElxQtGd210SlGyS7hKD4EIil2SS4JhB6fubzgxeksTtHN/SzxrBKj3KzIfQ8zWpBBSzdtYW//xwhWyhkXIjD/5X4uPR3Xq1gV6r1HH4W+DbgzVwiF2CRstv/l07wPzmgqqaIHAN+Bvgo7M39X8A7gV/Fgtz7gS9T1XNiTIcfw2xiR8DXXSwSZ+yD/awVLthWmwaqEinLnfVdt2MyB0YwCBE3moAIcWUwy35m8jqJpKDDXpoTAhm3UFogcqPGymLJwjtWD78c3yihFIbna6SNxH5hszwxzmy6jTRgUjrF2M7rR+3MKsGPO1NJ6CLtsMB3KegsFabflm4gM2bcSoWoUm10jG8pWbt3nbixmSV1Mh6OQ152u0xfn3VV/f3LOd8iwWdyJXXAPfBjwKtU9UtEpMKUUP8t8FpVfbGIfBfwXdgU7ecCT07LU4GfSn8zMjIybhhcmon0jQkR+XzMSqE/XaeqL7zILq8Tkf8M/CYLluqmWCT4/JiIvAAjGlzSwXeDiKxhc0Jfl47TAI2IPAv49LTZy4DXY8HnWcAvJJ/yN4jIMRG5Q1XvI+PSMf+LfTea9QWZkXYd0nVQFJb9xGhupiHsFLlTZqPDPjKubXjUexh4czSdKlUPeumgRq12kwbtmYGca8XKYkt9UChG7GRWAE5oVoSl+wLNiT7leoNrA91yZZptpalR+3FHTONrvg6Q5nym2nMq5uHjWjOk64Ye15k6QrNW4jo1le30EYS+o9zsiKWjGCsymhCbhWbwMm4mqMAByuuIiMcUpe9V1S9I7Y9fAU5iJa6vUdVGRHqYD88nYLOYX66q70/H+G7gOVg57FtU9dX7nPOnsUTgM7DK1JcAf7HPpU4TgafMrduvVAcsFnz+AfA16WDTu9JCB98DjwdOAT+X1BLejNG3b58LKPcDt6fHjwLuntv/nrTuIcFHRJ4LPBegz/AyL+0mwYWSO7thGpiiopPaej0+qU9PpXbSnA8iRjAYVrip82mMEFPASYw4O67tK20wskEEv10TlnuQSl1aCOUowoc8Dv727+2Si8KCRqcUqXRGC8V2S5wKl6o5kqJGRijWG+i6mSpBtVkTkvtpGJRIG/EixniLai6lDupjJf3TDaHvKbc7Uz1QqM43ZjmR+z0Zu+FgvxbTkZbV9PwHgR9R1V9JQeI5WBXoOcA5VX2SiHxF2u7LReQjgK/AsphHAn8oIh+iqhfry3yKqn60iLxVVb9PRH4IuGhJ7XItuGGx4POlwBMWlVxY8JwfDzxPVd8oIj+GldhmUFUVubQkVlVfArwEYFVO5LvDpWK3XlB6rl2HdsECgCpMagtEKfBI26GDHrFX4KbMNrDA1SvAlxaYxq1t06S+Tr9IjDWzJyDZJMRCaJecBYrppTQtKtANHH4ilJsTtDCtNtdFmlsHFNsdbmKqC7HnzTF1qpTNjpNpGJS0KwV+Eo223aoRHhx0fU+13pnLqWKZEhYUy3Nj4qTO/Z6M3XFAdx0ReTTw+cD3A9+e+t6fCfzztMnLMIbZT2GVoe9N638d+Mm0/bOAX1HVGnifiLwb+CTgf1/k1OP0dyQij8QyqTv2uMavVtVfusDxYAZV/eH93uci8jpXJLmwC+4B7pmT4v51LBg9ICJ3AKS/D6bX78VUVqd4dFqXkZGRceNgcXmdW0TkTXPLcy840o8C38lOpekkcF5Vk3vjrPoDc5Wh9Pp62n6vitHF8DuJDPafMaXq9wMv32PbJF3Cyh7Lvlgk8zkGvENE/pLLk1x4CFT1fhG5W0Q+VFXfCTwd+Lu0PBuzZ3028Ntpl1cC/0pEfgWrL67nfs8VYj+x0QsRdWfbsjQ9t7rZ8etJ3joSosntFM7YbJgOG2C6b13E0eHObKCrS0jKNtrVPn67pVupQD3FKKCuoLzvPGHuOmPFbDDULfeQEJGQZn6C2vkBaSKu7gjLFV4EdWL6b87Phlh9nY6ZJHXsiZX1YiG49M88Vi5to8iZ8znrydgdlzZkelpVn7LbCyLyBcCDqvpmEfn0g7m4xaCqL0oPfyPptPVVdX2Pbf9b+vt9l3u+RYLPCy734BfB84BfTky39wJfj2VhrxCR5wB3YQqrAL+H0azfjVGtv/4qXM/NiYvZLcxD40PmfXAOelWa9bEbOl00k7gQofRozyGbrTmhhoDUwUpv2zXxxAo4Ryw96oViqyX0PK42JYF24GlWk23DFE5oVpVmxdM/086CWuwXZou97KlOj1L5zNEtV/hJh7YteCF4IyOokxkdO/okPtom8VFVU7n2Dj/piIUjFAUuKK6NRrHOyNgDB8R2+1TgC0Xk8zDG2SrGDj4mIkXKbuarP9PK0D0iUgBrWLnskitGIvKlGAt5E/gO4ONF5EWquqdsjoj0sb7ThQy5fdUO9i27JV+fd7CTTr09rbtsqOpfq+pTVPWjVfWLVPWcqp5R1aer6pNV9bNU9WzaVlX1m1X1iar6D1R1L0/xjEvBpf6Cb1u062b9HJ3URjhQRcuCOCiTPYKDoObTM7XbBlO0HrcWnJyb3exnitRY9hL69nvINfpQthvQOycUdSSWJqmjpZ+5qJZbYRZ4pirWMzvvCC5EupUS9Q7Xmn23KKZknfpMMWVsiFkuSDTxURWoHtxG246MjD1xAKrWqvrdqvpoVX0cRhj4I1X9KuB17Jh4XlgZenZ6/CVpe03rv0JEeokp92T2Z659j6puisinAZ+FzXf+9D77/CLwCMz54I+xILfQr7R9g4+IfBl20V+KZSNvFJHsZHoUcAkBSKf2CF26AXdzN+IYkSaVvyaJlyJidGvYsVAAdFAh48aypcQaiz270ce+N4XpqAzOKnF1jrUYFd+AH0dcUGPHxZ0BUT/qiFUaMk02DmFYGiUckDZSbLW4pLRgRnIR9W5WlvOTkGR6Glwb6ZZLYmllQB48m0tuGReFDSbvv1wmno+RD96N9XR+Nq3/WeBkWv/tJPKWqr4NeAXWzngV8M37MN1gR6Hg84GXqOrvYo7WF8OTVPV7gG1VfVnad6E5zEXKbv8P8InJOAgRuRX4Q4wokJGRkZEBB65woKqvx+YdUdX3Ymy1C7eZYInBbvt/P8aYWxT3ish/Az4b+ME0Q7RfgjLVwDovIh+FjcnctsjJFmG7uWngSTiz4H4ZhwEXM5K7ANo0ySBuztF0aqHdRftWJP02GU2QSZ3KXhF1Dk0lNe2bVI85jJooaOx5YumIpWmxVRuB7lj/IeePHhDB18HKZ5POSAedJvJBRLpINyzNtbQJRvcuxLx/kovptPRGNIM615gJnt+2EltYKiGV3HAgXUQ3c78n4yJYtOR2Yw+BfBnwauCZqnoeOIH1fgAQkeO77POStP7fYaW+v8NmjfbFIpnPq0Tk1exQ7r4cIwFkHAXsp3gw99pMWLQsdwREUw8oDkqb4anM5XNqqU3Und6PGivOjRrC0LJ5P+4I/cLcQtUERbVwhJ4w+MD2jpigE1yAMx9Rcuv/ifaPeKlM5TIr+al3xIGRBSSq9aDKIum5ObRMrLsqnatTtNgRGDVFbmiHnqqJdvne0X/gHCHkklvGPrixA8u+UNURJpMzfX4fDx3mfy02FjOP16rqOeBPgCfATIx6X1w0+KRhpR8HPhH4tLT6Jar6W4scPOOQYDfW225U7BRIZkOkU9WCGG1YtFc8lKFWJKHQRDIw6YGkhjC1pnaedqUgFjIzghw8UFMf66N3P5RRX24p1aadW6KCF6KIefs4s+SOThAR6yO1AUKwAVbFyBFeZj0fCYq2NpAqYD2jQuidqVERYt9IEXpuPfd7MvaFHP2vyG51xd/g4QHp1zG5n4viosEnKQ38nqr+A+YiYsYRxF7ltwtvuk1rsz4ws0aw2RmZmcQByKRGlwZzdOxAWBtYAKvTNsECSbEdCD2HFoJ6aFZL1IEkkzoAqSpCT5DzET/u6JZL+vdu0q31cW0wdYTCSnjSGoMNb/JAoV9Qbppltt9uLAZ2U1sJwTVGtS62GmghlhYQq/MNfqsmjkYH+lFnHFEc8sxnAczeoYh8GEavXhORL57bZpU5yvXFsEjZ7S0i8omq+peXdJkZGRkZNwmukMl2GPGhwBdgIgT/ZG79JvAvFznAIsHnqcBXichdwDY7TnUffUmXmnFjw+3B1IkPzYi0aWDQR+ZmcLQsUOeIhcMn1YPpjM7UfhrATTrrwzStEQOWCqpzNbGy/o9rIvXxguH5hm7ooNeD8cTOManphuBbEw/106HVNpqwqCquDbSDAj9/EygLm+uZUrDTYKsG6z9pMsNTZwQIV3eMH9E33bcuIqMazUKiGYvgkPv5LIDZG1TV3wZ+W0Q+WVUvphe3JxYJPs+8nANnHD7IdBZH1UpeqjtBKd2AVRWpa7SsLABNhUQdNttT2EyP9s3xVPuV9X0itl0aBMUL5UZLGJaEvqMduiSBY5I2rtOHlfzW3hNoh47+AzVhUODExEIlaOrRFNb3KdzMDI4iGc4l11NRcy7VwuHGHSiEQWEDp22gvsUsHWIlFKc20DPncr8nYzEckd8oIvIk4GMwQYG/m3vp6bts/k9F5G2YKOmrgI8Gvk1Vf2m/8yxCmb4DOKuqd6nqXcA5bKI14yghKqq2TDENRg/bru2gbWYDnNMhUunizDeHzvowMmlM721GADDqNcFIB5OTJe3QUY4iEi34dENnrLeU9QCgkWKctvFC74EtYuWNdp105bRwuElSOsDcUVldRr1Rwc2uwSFNtP4UmLtpYsvFwlGdb2iWHeVmQM+dJ9Y1GRmL4CoPmV41iMjrROSW9PhrMDbz5wK/KiLPm243VZ25AM9Q1Q2sBPd+4EnM0bMvhkWCz08BW3PPt9K6jIyMjAwwEktcbLkBcauqnk6PvwX4ZFX9F1jLZb/+TWIf8fnAr+0lRLobFgk+onM/h1U1sli5LuOwYJGy0lxPSLvO/HVCNJmdEJBJhx81yKi2clfq+ah30JmitRZWbpPOMqZu6ClGkVgKzbInlEJ9zOEapT7mkLLYuTZxDD64jToIwwJZ30ISk81YciaT45pkNBfNLRWwx5V9Za0kl2aAvMzRsAEvFOfHhL7NH+mkzsZxGYvj8A6ZtiIytVvYwnr7YC4Gfp99/6eIvAOjVr82KeBM9tkHWCz4vFdEvkVEyrR8K6ZEnXGUoHHXG+1epTdihLpGY0TaDjeapNLbVOBTLjyQWSqMW7SwPk0shWbV4zoo6ohvlFhgtGsRzvyTj9iZOQLc6XXaJaHY7tA1K6ehijph/Ii+tUOTMZ3rImFQQlnMqOAArovWIyocoV/gmh2auLSRbm2Aa6E4tWnBNfd7MhbF4Q0+3wb8gYi8EHgb8Eci8gKsh/NzF9tRVb8L+BTgKaraYs4Dz5q+LiKfvde+iwSfb0wHvxczJHoqya464wjiYgFoPvtRNeZb06KjCbo9QrYnMBoTy7kfS3POplo4wlIFIjTHKiRCs5JmfjroBpLKF4o6KMc7N3836BNPn6F3Ps6yGj82WSn1Qu98i6sDoV9QjMxyQYISe+XM2VS9oxuWqDDr+cx6RW0SGa0cgzMBzp7PgSfjknBYez5JQ+5TMDWDFngzlr08T1X/ywL7n52KlqrqtqreP/fynlI7+5bPkq7bV+y3XcYRwMXkdS5EChCMxzZE6n0iIzRmX92ZPTbBlKK1bpBeZSSBdIjxSY9EGN3ikCgUycQ39BxLD3QM3/ngTGZXH/8o9O/eMyMchKHZI0wzGomKrwOTW/uUSYYt9j1+u4bgiN7hQsCFiChmGDfujG7thHbZvIr8JDC4f0zc2iYj42ZB6tXMevkictsFmp6Xiz3553sGHxH5TlX9TyLyE+ySLKrqtxzAhWVkZGQcDdyAWc0iEJETu6z+CxH5OKznvxvLbVHs+alcLPN5e/qbzdtuJmi0wVL/0B8sD5n7mZbmUvYjIaAhGPnAeyt5td2s/0LbQZr38bVRsH0TWbqvxTeR7TsqVECdsHJPQCL0zkzQrR1ZG7n/DFQlEqFdKZGo9E6Pib2CbuCh5/Gj6dyOKWQXIzOci4WJjYZB8g0qzPAuVpaDGWFhx07b3/0gIRMNMi4FesMy2RbBacw9eh6PAt6CBY8nXI2T7hl8VPV/pr8vuxonzrixMR003RdR0emPG1UkThv6Hmk7BG9BqRxCa9bUDujdt0l76xJ+o+H43eeIa0PGj1yiHTqqrWA2DJMd0kw8v44b9GmHQu+csefcqKFdMxkpCUq3VFKOOqRTQs/TrFUU252V2Uqz0Zao1pNyEHoePw6JoKD4ccDVHXFzK/d7Mi4dh/f3yndgHj7foap/AyAi71PVhdSp98H793phESfTp4jIb4nIW0TkrdPlAC4q40ZFYr7ND5zC7sSDh2C6z9RuAYxs4M3CgMqa/7KxBU1LcWZMHBgbLQwr1EO1ESi3Otyke9ixAVxqAmnpCCt9XBspN1skKKKK32rMmTQqvjEygQrm36OWXcXKBk2nTLfQE0Jl/xT8uW20acnIuBQIh5pw8EPAvwD+vYj8sIiscAWhdJ7hpqpfvNd2i8zr/DIWGf8GyD8HMwzz5bd5RLW5nlHKWnpJXboL6MDjRi2qSjy+jLSB4vQWeu485bCPa3uoE1Oeblpj0wFoRAYDAJploV8YMaBbrnBNMEuEqChi9GoHiM3qxMIZ7bpfWOmtZ/TqKULPmap239OslZTv2MxZT8bl4QYMLItCVe8BvlREvhB4DTDcZ5eL4WeBx+y30SLB55SqvvIKLiQjIyPjaOMGzWouFar6ShF5DfDEi20nInvFBAFOLnKuRYLPC0TkZzAXu5nQlapmf5+jjP2IBxeBTBp00EO2RjM/H7MFBSHA2gphWODG4O86S9we40cTvJofkKSB1fnMyp08DiESKiH2HMWoI5aCC2L9nEkwQVKs/2Mq1kqxNTGlg4F91aWLhL5Hh57e2ZrQ6+GCEoMaySHbZWdcLg4oYRaRPuYM2sPu0b+uqi9IDqG/gt3c3wx8jao2ItIDfgFTGTgDfLmqvj8d67uB5wAB+BZVffUu5/v2Pa7jGQCq+sO7vPwPga/modJrYMHnkxZ5n4sEn68HPgzT8Jl+vEo2l7spsDDxYB4xmqCo9+ZiCuALtOdxG+OZ8Ztvoxm1aUTPnUcmA5uvKQpb5tA8+gR4wdeKn0S0NOXr6B2uibgmmKK1t95NsdUQhgVxtcJ1kWKrNRJDv8C3kc57Qs9Ycd3QU210FPdlu+yMy8cBZj418JmquiUiJfBnIvL7wLcDP6KqvyIiP40FlZ9Kf8+p6pNE5Cuwwc4vF5GPwGY0PxJ4JPCHIvIh04HQOaxcxjW+ARip6h9f+IKIvHORAywSfD5RVT/0Uq8s4whAI+AfFoB2pV3P71bXyDR4TBUORHCjBh1USBtQAX/q/IzSHLfH5t0TFXWNDa3O6br5OrD+pCWqbaUbenqnO7S09ZKIBNIpjogkZ9L6eImfKK6L1vtp1YZLO2ZkBAmKE2Pf6fpG7vdkXD4OKPgkLc1pRlGmRYHPBP55Wv8y4Hux4POs9BjMwvonxf7BPgv4FVWtgfeJyLuxrOQh/juq+n2XcY2fe5HX/tEix1hknP3PUwTNyMjIyNgNi+q6LRigRMSLyF8DD2IEgPcA51V1SgO9B5vFIf29GyC9vo6V5mbrd9lnv/O/ZcHtbheRj0/L7YvsM8Uimc/TgL8Wkfdh6WB2Mr2ZEHVXavW+vR/voSyS0dzOb5w4rHCjhmLUoRtzzDKNzLR0okPjHNVaI9IGynGkHTqbzSms7Kalg05BFdcEurIkDgtCzzG4f4J6h9+q6Y710SSto96ER7u+x9eR8WoJD5zO3j0ZV4RLKLvdIiLzw/svUdWXzG+QSmMfKyLHgN/CWh/XEhettSf1g58C1jDdT4BHi8h54JtUdd/gtUjw+ZwFtsk4qkjEA3V7KFzvVnprWuvZODdTPaAsjG4tgtQdzvu9b/a7lb5iRAJ0PXMhlXEwZQIn+LYzwdDCEatUiotmVqciMKzwo45YeVwyn5u6nSIwON2i26Nsn5BxZVj863NaVZ+y0CFVz4vI64BPBo6JSJGym0ezc9O/F7gTuEdECiwgnJlbP8X8Pvvhd/d5/eeAb1DVN86vFJGnpdc+Zr8T7Ft2mzqYXrjst1/GEcIl9kG063a8fsCUD0YTKDyuNfUCf37r0m72zqEeYpVUr6czrALSBlxjS7HVIp0dt1k1nysJ1vNpV0pizxN6jm7gCZXD1YHeB85l+4SMK8ZBmcmJyK0p40FEBpj6wNuB1wFfkjZ7NvDb6fEr03PS63+U+kavBL5CRHqJKfdk4C/2Ofcj0qzPG0XkYo7VSxcGHgBVfQOwtP+7vLiw6FtU9eP3udB9t8k4Ioi6MO1a2w7RCL5CW2OZSb9nxxBB2s4ssi/hZq8iFFuBfmUkAS2dBY9WzdcHZtnOlPHWO2OZVRwUSBvNSjsq6hy+iTSrnmKrQe8/dbmfSkaG4WC9eu4AXiYiHksQXqGqvyMifwf8ioj8B+CvsGFO0t9fTISCsyQXAlV9m4i8Avg7oAO+eRem2wwi8i+Afw/8EVZ2+wkReaGqvnSXzX9fRH4Xo3hP+0p3Al+L+QDti4uV3T58HxkdwdK7y0L6YN8E3KuqX3A5HPaMjIyMGwHCPk2SS4CqvhX4uF3Wv5ddZmhUdQJ86R7H+n7g+xc89XcAH6eqZwBE5CTw58DDgo+qfouIfC7GqJuSGO4F/l9V/b1FTnax4LNIg2vPKLoAvhVLJVfT8x/kEjjsV3DejMuBRlRl8b6PKnTdDuW6bqDfs5c2t0zp+hLg7nkQve2xtEOh3Hb4STKZC5bNSDBr7Jjo07FyaGmq2rF0FE3Et5F2WNAuO4qJ4mtFtsbEpsklt4wrx+FvGZ4B5qesN9O6XaGqvw/8/vS5iBwHzi96sj17Pnv1ei5Y7ln0RPMQkUcDnw/8THouGIf919MmLwO+KD1+VnpOev3pcslTjxkHgmmAcfsz9HWSyAQhQAhWfis8btSYunVdL25cB8SNDdoVTywwZYMmooXQDs0ETtSERc0yQfDjQDssCD3/EMZeGFjg0empN7cz0SDjQHBYhUVF5NuTysG7sV7P9yYb7TcAf7/HPv9eRD4sPe6JyB9hdPAHROSzFjnv4v/6DxY/CnwnO4oJJ7l0DvtDICLPFZE3icibWjJl9mph1uO5MABdQMfWrrOAM90nKrQdsrGNdt2l3/Cj0g4dw1MBP7avTdd3uCSlE/rFjN0WezsOp66NpqTdBghJGbuJVOudUbbzYGnGQeEA53yuMVbS8h7g/2PnKn8beN8e+3w5MFUyeDYWS24F/jHwA4ucdBGq9YFCRL4AeFBV3ywin35Qx008+ZcArMqJG/M/cUZGxtHEITaTuxyFA6DRHbbRM4GXJzLD2xPde1/su5GIPA/4JVU9dxkXuBs+FfhCEfk8oI/1fH6MS+ewZ1xrTMVGJc4ynz2HTaOaL47bkcmRtoO2Raf9nkvJODQyONXROzOhPtmnWyrwjSKd9XpcG1EnFHVL6PXMKmEckrGdoN7RrpaEUmhvKVi+t2Hw3jNZyy3j4HDIf/KmeaKHvQtV/cxdNq9F5KOAB4DPAP7N3GsL2TEsEqFuB/4yyS28FHi17nq3WQyq+t3AdwOkzOffqOpXicivYRz1X2F3Dvv/5qEc9ozrBFXdndlzIfEgBAsw3uyqadqHuJNeKopxwL/rHrj1SShWPis36mQQV1AfL+mfUlxQfB2I3kRDXRMJaxWh7/Ctmg5cUOKpM7nklnFguBH7OZeI+QDSB/4ZRtHeDd+K9eBvxYhi7wNIScVfLXKyfYOPqv47Efke4BmYwvVPJu74z6rqexY5yYJ4PpfAYc+4TpgqHhBM/DNecPOeC0AaTGmarpupVF+JS2h1yvpFfmIOpNXZCd1KRbFRo2JqB93AI6m3o4XYYJ9CKIToIRZCOYqUp7eIk9wbzDhAHPLgo6pvvmDV/xKRXYdS04DpwxjRiWZ9xVTr+QOqiNwP3I9FwuPAr4vIa1T1Oxc5xh7HfT3w+vT4kjnsGdcRUcHvv402LeKd+fOIoGGOnS/u0jKPB07jTh4n9B3SKf7sFu3qSRChenAL9SvEQiwQeQuC3bKnHXqKcVLW7pRiHJHz2bcn42Bx2DMfETkx99QBT2GBWU4R+XzMtqE/XaeqL9xvv0V6Pt+KTa2exqjR36GqrYg44F0Yay0jIyPj5oVyYGZy1xFvxt6JAC3wfmzOck+kmcwh1vf5Gaw1clEJnykWoVqfAL5YVZ+pqr+mqi2AqkbgCxY5ScYRwzRbubDkNsU87bptTTctxCS1c/k/D+P6JnSBcr0lVo64MsCPOxtoxXpArlPTgCsEX0dcq5TbCgLtUPCtMvj7B4nnzud+T8aBQTi8cz5zeD7wsar6eOAXgW1gtM8+n6KqX4sJAXwfJoD6IYucbBFh0RfsJSSqqm9f5CQZRxAak6Hcxb9CqrpDPNhN1eDCQdN9Bk/jyVV8HSi2A9JFXNMRhhXtiSGxdOZaOgq4JjK+rUI9dAOxYNQo/VMN8cHTFhAzMg4Sh3fOZ4p/p6obIvJp2ND/z2AqMxfDOP0dicgjsYzpjkVOdr2GTDOOAlIWs6fkTtpGQ0DbDm2a3Y8j7qFBZ68A5ASZtKhA7/5NYmW2DRKSK2k0G2111vORCJNjnmbFej3tQCgeWN+hemdkHCBmKhv7LDcwpg3Zzwf+u6r+LlDts8/vJAXu/wy8BSvVvXyRk13zIdOMIwSNxmjbT+0omtnbvtnGfnI7Uc2OYbtB1reQk6vE0hEGBa4ONvtTR9rlgmq9RZ1QFhC9Y3SLY/XuDh48M7v2jIwDw42f1SyCe0Xkv2EWDj+YRJ0v+o9SVV+UHv6GiPwO0FfV9UVOljOfjIyMjAPAEej5fBnwauCZqnoe6/d/x8V2EJGhiHyPiPx3Va2B25KKzb7IwSfjyrBI6Q0eSrFeBLtlQRqJa0Nke0w4fQb1HmkjrrOl3OqMiFAJ3dBTbqXymgPXQe/UxARNc9aTcRVwUGZy1wuqOlLV31TVd6Xn96nqH+yz288BNUY0AFOk+Q+LnC8Hn4xrgwNSjnbj1ozoouJPncc1HX6rSSoHDp8kddplT32iBBEmJ4VqU/Hv/WBWsM64ejj8hIPLwRNV9T9hRANUdcSC1ka555NxZbiYzw88hHhwydhjCFU3t2z9bJh0aq0t4ME1ikQIlRBKWLpXWb57TJzul5Fx0LjxS2pXC02y+lYAEXkiLGYrkINPRkZGxkHg5gw+L8Bss+8UkV/GhKO/bpEdc/DJuHJEBS97K1xPsZvj6X6Y9n6mytjbY2KiSmu/Iiz1cF1E2kB5LtDcOiBWQr3qGZzpaFYKqq1IedcpQi65ZVwlTIdMbyYklZvjwBcDT8M+hm9V1dOL7J+DT8aVY670tmcAcguVgfc/1XaaaROHLvVxkw7teQQHYhYLXd/RrAmu87hWWb5rRDxzNpfcMq4q5Cb7caOqUUS+U1VfAfzupe6fCQcZB4OpkvXVGqJLGZBub89WhUFpg3ttQAVir8CFSLUZKEZ2HcVE8fefy4oGGVcXi5INFvjnISJ3isjrROTvRORtSV8TETkhIq8RkXelv8fTehGRHxeRd4vIW0Xk4+eO9ey0/btE5NkH+6YB+EMR+Tfpmk9Ml0V2zJlPxsEgZT9T7FuCuxyIM3UCjSCOYn2MVgWxV1Cc3qK9fTXZKjjUMVOwzllPxrXAAdKoO+D/VtW3iMgK8GYReQ3WS3mtqr5YRL4L+C5Mj+1zgSen5amYJM5TUxB4AaZOrek4rzxAY1AwO22Ab55bp8AT9tsxB5+MjIyMg8AB/dZS1fuA+9LjTRF5O/Ao4FnAp6fNXobZ0Tw/rf+FZLL5BhE5JiJ3pG1fo6pnAVIA+xwWlL9Z8Foff+E6EdlPkgfIZbeMg8QiNe8r6f1Ms5f5AVRVXG3K1tKZtI4olCOl3IoM33Uma7llXBNcgsLBLSLyprnluXseU+RxwMcBbwRuT4EJzFvt9vT4UcDdc7vdk9bttf7AkUp/TxeRn73gnHsiZz4ZB4uo4K5CyW03NC0UjtgvcaOGMCzo+oJLYgrDu9bR+09d/evIyFBm1h4L4LSqPmW/jURkGfgN4F8ntemd05nB53VnOIjI04B/DnwRJsfzzTzUjntP5Mwn4+CwaF/lgJhvM0QlrPTpBh7fKq5Rqs2I3n0fOslyOhnXBgcpryMiJRZ4fllVfzOtfiCV00h/H0zr7wXunNv90WndXuuvGCLyAyLyLuD7gbdi2dkpVX3Zoj2lHHwyDhZX80Z/od6b92jpiX1PHBQz357QF5b+/qwFnoyMa4CDNJMTS3F+Fni7qv7w3EuvBKaMtWcDvz23/mtT6etpwHoqz70aeIaIHE/MuGekdQeBfwE8gJEbflFVz3CJXa9cdsvIyMi4UqheStltP3wq8DXA34jIX6d1/xZ4MfAKEXkOcBemQg3we8DnAe/GnEe/3i5Jz4rIi4C/TNu9cEo+OADcgVkvfCXwoyLyOmAgIoWqLtRkzcEn4+CR+j6wD+X6chQP5lF41DtiaYKivlHqVU9vI8B9D+6/f0bGAeKgOjCq+mfsLc759F22Vx5KdZ5/7aXASw/myh5y3IDJ6rwq+f58ATDAPIFeq6r/fL9j5OCTcfDQCHh7eLHAc6XoAq7ukK4yF9NS6J8LDO7dNuuEjIxrieve/r8+SD4+v4EZyq0A/3SR/XLwybg6mMt+LrrNFUDLAukiLkRi6eid7SjPTXDvuZs4PXYmG2RcI1x/7tn1gYh8PvCRQP9S9svBJyMjI+NKoUC4+aKPiPw0MAQ+A/gZ4EuAv1hk38x2y7g6uAYZh7QdtF2S1PGUmw3ug6d2Sm4568m4hjgCNtqXg09R1a8Fzqnq92GOph+yyI45+GRcPSyqeHC5/Z/CQxq865Yc/sHzZjR3k6kLZ9wgmDLe9luOFpLMPCMReSTmaHrHIjvmslvG1cW097NbD2j++eUw39I/5NDzDD84Qc+toyFc4QVnZFwejmBWswh+R0SOAf8ZeAtWgPyZRXbMwSfj6iGpTy8cVC4xAMm4Jq4t4ZpI8Z770KbZ2T+X3DKuJRa0SzhqUNUXpYe/ISK/A/RVdX2RfXPZLSMjI+MKIYAEXWg5ShCRb06Zz5Ry7UTkmxbZNwefjKuLq5iBqHfQRcrNBt3cyoZxGdcVorrQcsTwL1X1/PRJ0nX7l4vseM2Dz0G69GUcElylACRdwJ0+h3vfB7NtQsb1xQE6mR4yeJmT2xYRD9ywfj5Tl76PAJ4GfLOIfATmyvdaVX0y8Nr0HB7q0vdcTMgu4zDioP1+6gZVRUfj/bfNyLiqWJDpdvQyn1cBv5q8fJ6OGdW9apEdrznh4KBc+uZMlTIOA+bJBwdkqaDjMdp2D896Mtkg4zrgJmW7PR/4BuD/l56/hsPAdrtCl76HBJ/kBvhcgD7Dq3fRGRkZGbvh6GU1+0JVY3Iv/TOsqPjOJDq6L65b8Dlolz5VfQnwEoBVOXHzfQsOEw4o+9GmzXM9GTcGlCPHZFsEIvLpWKXq/Rjp704Rebaq/sl++16X4HMxlz5VvW9Bl76Mw4Zp6Q12+j9XEIQyySDjhsLNF3sAfgh4hqq+E0BEPgTr+3zCfjteD7bbQbn0ZWTsjgsdTzMyrgFuUqp1OQ08AKr690C5yI7XI/M5EJe+jIyMjBsKRy+wLII3i8jPAL+Unn8V8KZFdrwebLcDc+nLOISYL73BgbLfMjKuGxS4OUmW34jdn78lPf9T4L8usmPWdsu4MXGlFtsZGdcQwpEsqV0UaaD0/6jqhwE/vN/2FyIXxzOuPRadw5lmRDkzyjgMiHGxZR+IyEtF5EER+du5dZesACMiz07bv0tEnr3bua4EiVL9ThF5zOXsn4NPxvXBfACKupPlXJjt5MCTcRgwLbstsuyPnwc+54J1l6QAIyIngBcATwU+CXjBNGAdMI4DbxOR14rIK6fLIjvmsltGRkbGAeCgym6q+idpAH8el6QAk7Z9jaqeBRCR12AB7eUHcpE7+J7L3TEHn4wbBxdmPfNkhNwDyrjRsXjwuUVE5hlhL0lD8hfDpSrA7LX+QKGqfzx9LCK3AGdSINwXOfhkXD9cyHybRw40GYcKlyQaelpVn3LZZ7oMBZiDRpq5fDFwFngR8IvALZifz9eq6r7iornnk3F9kUVAM44CFAi62HJ5eCCV01hQAeZqK8P8JPADWBnvj4B/oaqPAP4R8B8XOUAOPhkZGRkHgKuscHCpCjCvBp4hIscT0eAZad1BoVDVP1DVXwPuV9U3AKjqOxY+wAFeTEbG1cWl9H3E5awq49rigAgHIvJyjDBwi4jcg7HWLkkBRlXPisiLgL9M271wSj44IMz/47rQUCv3fDJucuQAlHGtoBxYn1JVv3KPly5JAUZVXwq89EAu6uH4GBHZwNRqBukx6Xl/kQPk4JNx/XEx4kFGxqHAkXQp3ROq6q/0GDn4ZNwYmAaggw5EOfvJuFa4iYLPQSAHn4yMjIwrhQIh/8i5FOTgk3HjYJqhXGg6Ny+xk4dNM25IaM6wLxE5+GTcmMh9oIzDhlx2uyTk4JNx4yN7/mTc6DhAttvNghx8Mg4HriQAZdJBxrVAznwuCTn4ZGRkZBwEcvC5JOTgk3HjIvd9Mg4LVCGE630Vhwo5+GQcHuTeT8aNjJz5XBJy8Mm4sXFh9pObuhk3KnLwuSTk4JORkZFxxdD8w+gSkYNPxo2PKVMt938yblQoaGZUXhJy8MnIyMg4CGR5nUtCDj4ZhweZ/ZZxo0IV4v+/vXONteKq4vjvL7RQaeVRCMGCclFiSkxKERBabCgipU2DEmsKkgBWY6zRPvzQgMREjR9AGx+kRiC1VStgK20tJTVUaIO1SSkQeZcLF7AW0haoCrW2lcfyw14Xpsf7OBfmnDnn3vVLdu6evdee+d915pw1s2fP3hF8OkIEn6C+iAAU1Cox4KBDRPAJgiDIAYs7nw4RwSfo/MSD4KDidK3F5PIggk9Qf8Tot6DWiIlFO0zdfHslTZXUKKlJ0ryi9QQ1QNzRBDWCAXb6dFkpSNRF8JHUDfg5cCMwApgpaUSxqoKaIAJQUAuYLyZXTmqHrnKhXRfBBxgLNJnZATP7L/A74LMFawqCIDiLnbGyUlt0pQvtegk+VwCvZLYPedlZJH1V0mZJm0/yblXFBQXT3tVkPBsKqkE+dz5d5kK70ww4MLNlwDIASUfX2aqXAThZpKp26Q8cK1pEGYTOfKkHnfWgEfLR+eELFfEm/1y7zlb1L9O8p6TNme1l/vsFLV9of/JC9dUi9RJ8DgNDMtuDvaxFzGxAxRXlgKTNZja6aB3tETrzpR501oNGqB2dZja1aA31Rr30R2wChktqkHQxMANYXbCmIAiCvOnQhXY9UxfBx8xOAd8A1gIvAY+Y2a5iVQVBEOROl7nQrpduN8zsKeCponXkzLL2TWqC0Jkv9aCzHjRC/egsCzM7Jan5Qrsb8EBnvdCWxZQQQRAEQZWpi263IAiCoHMRwScIgiCoOhF8ckTSEEnPStotaZekO728n6Q/Sdrnf/t6uSQt9mk0tksaldnXHLffJ2lOhfR2k/RXSWt8u0HSRtfzsD/wRFIP327y+qGZfcz38kZJN1RAYx9JqyTtkfSSpPG16E9Jd/tnvlPSSkk9a8Gfkh6QdETSzkxZbv6T9AlJO7zNYknKUeeP/HPfLulxSX0ydS36Sa1MTdPaZxEUiJlFyikBg4BRnr8M2EuaIuOHwDwvnwcs8vxNwB8BAeOAjV7eDzjgf/t6vm8F9H4LWAGs8e1HgBmeXwLc7vmvA0s8PwN42PMjgG1AD6AB2A90y1njr4GveP5ioE+t+ZP0YuBB4JKMH+fWgj+B64BRwM5MWW7+A150W3nbG3PUOQXo7vlFGZ0t+snTfmCYnyvbgBFtnduRikuFC+jMCXgC+AzQCAzyskFAo+eXAjMz9o1ePxNYmil/j11O2gYD64FJwBr/8TiW+bKPB9Z6fi0w3vPd3U7AfGB+Zp9n7XLS2Nt/1FVSXlP+5Nxb6f3cP2uAG2rFn8DQkh/1XPzndXsy5e+xu1CdJXXTgeWeb9FPWR9n7do6tyMVl6LbrUJ4V8rVwEZgoJm96lWvAQM939qcde3OZZcDPwXuAZonm7oc+Jeld6pKj3lWj9cfd/tK62wAjgIPevfg/ZJ6UWP+NLPDwL3A34FXSf7ZQu35s5m8/HeF5yutF+A20p3V+ehs69wOCiKCTwWQdCnwKHCXmZ3I1lm69Cp0fLukm4EjZralSB1l0J3UFfMLM7saeIvUTXSWGvFnX9Lkjw3AB4FeQF1Mt1IL/msPSQuAU8DyorUE+RHBJ2ckXUQKPMvN7DEvfl3SIK8fBBzx8tam0qj0FBvXAtMk/Y00a+4k4GdAH0nNLx5nj3lWj9f3Bt6ogs5DwCEz2+jbq0jBqNb8ORk4aGZHzewk8BjJx7Xmz2by8t9hz1dMr6S5wM3ALA+U56PzDVr/LIKCiOCTIz7S55fAS2b240zVaqB5hNAc0rOg5vLZPspoHHDcu0PWAlMk9fWr6ilelgtmNt/MBpvZUNID72fMbBbwLHBLKzqb9d/i9ublM3z0VgMwnPQAOi+drwGvSPqYF30a2E2N+ZPU3TZO0vv9HGjWWVP+zJCL/7zuhKRx/n/PzuzrgpE0ldQ1PM3M/lOivyU/tTg1jfu2tc8iKIqiHzp1pgRMIHVhbAe2erqJ1Oe8HtgHrAP6ub1IC0ftB3YAozP7ug1o8vSlCmqeyLnRbsNIX+Im4PdADy/v6dtNXj8s036B62/kPEc6taNvJLDZffoH0mirmvMn8D1gD7ATeIg0EqtwfwIrSc+hTpLuJL+cp/+A0f4/7wfuo2RwyAXqbCI9w2n+Li1pz0+k79ter1uQKW/xs4hUXIrpdYIgCIKqE91uQRAEQdWJ4BMEQRBUnQg+QRAEQdWJ4BMEQRBUnQg+QRAEQdWJ4BMEQRBUnQg+QWFIGirpbUlbO9huWna6/KKQNFHSNefR7lNKy27sbN86CDonEXyCotlvZiM70sDMVpvZwnJs/W39Sp3nE4EOBR9J3c3sOdLLkEHQZYngE1QESWN8EbCeknopLbT28XbaDPXFw34laa+k5ZImS3reFzEb63ZzJd3n+YG+0Ng2T9f4fhol/Yb09v0QX5hsp9LCZ7d624mSNkh6QtIBSQslzZL0ott9xO0GSHpU0iZP1/qs5V8D7pa01e9m/s/O239X0kOSnifNfhAEXZ7u7ZsEQccxs02SVgM/AC4Bfmtm5XQzfRT4Amk6l03AF0nTFk0Dvg18rsR+MbDBzKZL6gZcSpqCZzgwx8xekPR50jQ9VwH9gU2S/uztrwKuBP5BWiTtfjMbq7QK7TeBu0iTrv7EzP4i6UOkec2ulLQE+LeZ3QsgaUWpne8b0gJoE8zs7TJ8EASdngg+QSX5PimAvAPcUWabg2a2A0DSLmC9mZmkHaTFxkqZRJrQEjM7DRz3yS9fNrMX3GYCsNLrX5e0ARgDnAA2ma9tI2k/8LS32QFc7/nJwAidWyH6A0rLZpTSlt3qCDxBcI4IPkEluZx0J3IRaTLNt8po824mfyazfYaOna/lHKvc470PGGdm72QbZoIMZdiVqycIugTxzCeoJEuB75AWAVtUoWOsB24HkNRNUu8WbJ4DbvX6AcB1dGypgqdJXXD4cUZ69k3gsjLsgiAoIYJPUBEkzQZOmtkKYCEwRtKkChzqTuB675bbQnq2UsrjpCUZtgHPAPdYWiuoXO4ARvsAit2kgQYATwLTmwcctGEXBEEJsaRCUBg+YmyNmbU5Cq4z0pX/9yCAuPMJiuU00LujL5nWO36X9CRwrGgtQVAUcecTBEEQVJ248wmCIAiqTgSfIAiCoOpE8AmCIAiqTgSfIAiCoOr8D42mQuBRwKCpAAAAAElFTkSuQmCC\n",
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t+xc0oDU01cTWx2gKU6y1rYVvg36Cf70X+AngK96PgyQBPw180Z9yIfAkH/V2DrDfzL6Nazf/UEkn+nb3D2VjWtBPRTK7bSVCJcvg9wn5Pj6/h2D2qkAZhICDFb/P8UM8rgbdCkLohpLRCBSiB3lUbbqbswON+cuZtYwcURagnjdThXHR9eN5ujk3Idu/fh0fr5qHd4hyq+cL58T+I/9hWta9sH4fjODMbOZMcPG9KOUDB6wuOhqSo1oRf9H1LKuHtD7PuErWwWTX+r+Lx2WZ8/cltg3r1WpW4TTgDVKdvv02M7tI0ocknYL7Tfkc8Ot+/HtwreavBBaBXwIws5skhZb3AC8MwQdbgSR8NptVQq7r41UFlSsBsCLiDXztt/hB7/0X8bd/a3w58QRdzQb/wwUJNJF0rQdtpK1UvXAtqzWxrJSLGB6jV8caVN3mgLbQaQY37hZVPgkzCN+87Ztv3ROjOXEcho9+M3/dEK1HHZxgsbYV/D6mtuAJwQPRZ6trt9H2U9X3IiprVPuMYo1pq7f9SIzFmJ3wMbPLgHuM2f/gCeMNeNqEYxcAF8xkYTMmCZ9EIpE4QgwYrVZhN7GCJHy2GsH0Bi7iLfLzhP219lOZL4bZ/salqMq1G6tG6+mGOTNZQWgG+p8d31Lcv6fKVddha0xT1jqvHek25trd413/UmkuJLoS5hNrQzmflo8pJIH6vJ3QWycr/DzBjTNytd4sx0UTaqVJsb5G6UKxW03kxpn3Ik2uVVIn5PlAS5sMCbyN78hPmqLdthWGKJMLfV0k4bNVCQ8hq+qwa1VRAmJsrquYWOG6Rvgq0CLDWg/PbljwROtBLRCiE8cILpd3pCa0ud4f+UWCQIrWF4cxj/scLdNbWHfwfXlzmUqRyVx7Bm/WyoZyW9HMEypjk6lVHTvkD9ULiwMPfDuF+J62P2BjXqsrRAQhFD6bmvtIcCVVwczpkkwT25NqbJvaxCSS8NnKhKCDytrJppV7eso0Piex+60c/IPRPWgriSwEL0T+m7E5KaEbZxk9JOu5Xe+deg4vUDKvZbS+u/v5g/aSgasiHbtmOppO/TDvaCT1sRyn+VVN5YEQLCBZk/8T+WdqP4vcJE0ZIScJqtA3CCfI6uuHaLmw1Fir6WqDUYTeinFdjc4arSwbhv/bpPVsN2bp89ktJOGzFVgr6KAqXZmZqmrXeAsld4J5zrepdmVgrA45boUzh2/55hzuWdxiIAzzD8Ws9JHG5oVGJCTqn16ohYMyl3Ca5W4d7XYHwUroNZNe87Fb7bb9vE0wA3VwQpVD1TOqHli/cp+xe+sqJyyQrdSios/X7HD/WGYoA/Namxku2i4SMLW2Na56BNEYonsdkmHj0HJPVuLC05eht1yiwciF1ye2GaJMPp91kYRPIpFIHCHu+1ESPuvhqN0tSRdIuk7SF6N9J0m62Jf3vtgnPm3bkuBHjUgLspBcWpZO66kq5DdKd8yZwqxtEvPO69i8Y0Fz6LvNemqHQ1fR1jURrVgjLbNY8OWodPXdcl/nrfbRdE1NXW2rjDd30NVfa9Ybb9Y3lxQa8nOC2dDPraD9dEyLFm9xK27C+r3ZcCSyEWgUzRGZ0cK5rWTXMEdXQwxv1X4tAxWQL0N/qSJfKmBUJLPbNsRMDC2faks4jqaofj2ugmrMc4APmtmdgQ/699AuCf5UXElwopLg98GVknheEFi7gtq/U7nXZekqG5RO8DghZOFr10RCgED8MLeeK/FvsUmomzwZzzEhe78t7HxQwNDoLTsBpHJlUdFuodBwftNGmhXCqn7w507wWN+gb5BZp/6cX0PRCJKWXyv8DEKkZ3WOD+aj2UY4wVOIzEe4EZkwaxkSRbh12yrUn2kVAYQ3UfaWjd5iRbY4hJE3u6UCo9uOCk21JRxHTfiY2ceAbjbtucAb/Os34EpEhP1v9HWLPgGEkuAPw5cE9z0oQknwncdqD5tQl6NOOI00nwmVsVd0EA2CpxdpEz3qgIH6G3+UyBn8PkEjyWJBUgubRsDEBH+Sm8/aAqDzkG6V88nkfEiE+ZtrWebXPW/YfEk+X6K5yvU7CvNV1BWqs5H/WcYXo/W61kBqbQkncArV96IJCHDaUDYEjaiDMYJQrIMV4oKn4YtBx8dUC8kh9BcreoslWhpio1EKs96GuICDbKot4dhon8+pvuYQuOqsp/rXR1wS3JclfyrAHo6Z4ZI3iW6+j1VYWfmabhOEDjTfzst2G23LnLCp5qwOOMigcYZ7QQJAJh+15ipExyHOQci02iOEb/ghAs0a4RFXR6hXbM0D34y2thULp1hIBeE5b9jekt7egr3HDFhemmM0yLBcTWVrogc/ME5jC/trwRSe91HUXxAc8g3psgLygQtGcJqRH5a7tcUh1a3/jyjEPL4vWQG9gdFbLOktjNDyEEsldbYpKeBgvWxawIGZmTRtOcyp5jsfV2acW+mkZLNIJBIbRgo4WD8bLXy+K+k0M/u2N6td5/evVhL8gZ39H9mAdW49QnM5X9Hact9hMx5jIMyFXOfuK7gLMhDWc22uqx4uD8WbgqrcD+1qCkEjoXkN1JoOdLUL8058IREFQdAEUNS+HF+LrvIRy1F77rDmVsWAOsQayj3G/HEDjjtmwK32LHMDMFrq1x1UrfI+rjjMPGhU0X0KCZ6tBFvhioxG9e/i+mvOHBf5lCKTZp3XI9pmSWu0nrpVOH6Oka8CvlSgpVHy92xzypRkui42WlRfCJznX58HvDvav+1Kgm8I3s9TN5IrSx+EEHxA3fH+Z+yg9w9uCz6J8JANNXNjU1N3rljABDoRXm5f1Dl13PGuYAsCLcrvqSt2dz5D5c1a1ZxR7ak4Yd8Spx13gNOOOcC+PQOUN9mf7sGvFe0OVvhdiK5VX8eaIIzwl9HxO9UfJ76/3b+icW64Tr6QSid0e8tGtlSgwRAriuTv2aYYYmS9qbaE46jdCUlvxmktJ0u6Bhe19hLgbZKeAlwN/Jwfvi1Lgs+cNZNNfUKpD7mmDMmkYT8+O1+N8Ikjy7LouRhFaU0q+d99hgZNpPbNdB7GIiSV2thJ45pmwZek6OEeKmMbalp4542/qpwD7S04ae8ip+3dz616y1y/tM+1PLAoMMJHunWd/BYJlPozdoRKXYMu3r9CO/KCnM7xSe87giwEceQjyIYV2WCEhiMnfBLbkhBwsFuQdOFqx83s0WvNcdSEj5k9YcKhh4wZa2zDkuCJRCIB7gvTLjO73RcXDPZm4BLG205WJemAW41Y+4ki3up8n7KEXs/5fSQXdl16/08GVO3fgdhvUlei6ZrmssgfEftkjKYaczjX725pP2r2Q/D7tBNeXSSd98MIV9zUa2Wx1uO0I3f9OBG06juzW2++4NS9Bzl9fj8n9Ra4ev4klFtdqFTeLxPn98SJpJZF96CldeHuZ0bTzRT3QUOR8GBq6zbBa5ndYl9V6z8iiuirXH5P7iPdtDyE4agJp09sS3ZZwMH34jqsPgH4eeCfgTeb2eXTTrCr7ta2J871qdtph/d+TPDpyEuT8NAUdV5JNnDJk4SHaPdhOc5vFJuOrDOOZnxzjsvXCbXe8qFrr50VFuUONflIcXBAXeMt8tNYbticsXfPiLP23sgP7r2Wu+y5htP37qffL2szXW3WKhrzW10vLv5McaXpyo8fQTYI9yfyH0WfNZzXum9jzG5xJYVwjZZ5r4TeoCJfdILHRqOmYnli22EGpWVTbTsBMyvN7H1mdh5wDs5l8hFJT592jqT5bCNCAVGrXL6PJKwoUZnXfXtWrXTgy8aobB6e9Tf+SPMI39yrHlR91XlD4eHeCB+nxnRbRcfHs8IiAQL05fvnULc2UFwGCPczKw0KNQEAAusZx+0ZcKc93+Wuc9/hpKzk8/M3sWduxCC3uoNrUy2h+YyVcEVDIy0IAXUCLXWQQtC0QhvvFTKhI6hV0VTCDp9pXOBFpH2qhHzZyJZG2GAIKdhgW+MCDnZX6RxJ88CjcNrPWcArgXdNe34SPluRcaa3+sFarah2QFWhokLejKU63DjYxNwDOBu5OmL50Kj67sGOf/iHMQCWydeAE1XfaxOtrP3GpGbB7hY/aIMJLDj2o5DkVhttb46Lgw7cCf5lJOhMQG7smxtwSn6AU/KKE7O9nNw7wLHzQ27pmy+Vo5bwUXQ7lXvhkPtEW9QkzUaVHEKEYCMco/+bjqbTquwQAjrGRfv596HqQz6E3lLhEkurMpncdgC7LODgjcDdcMFiLzCzL65xygqS8EkkEokjxNBuayb3C8AC8CzgmWqidN33PbNbrTVBEj7biaqCPKMuNFo1fh8rDRUV9LLaeQ80WkgBvRHMHXCFLEd7oZyHai5EEESmM9HUKIv9GlGI8lisramYT3Qlp062rB3/3Xm7ZXuiatnB76S5kmN6QwCWzThUDajI6GWVq3AdFSp1fqNGlYprt0lqItpjrcuaa7aOxX2JaExnrY8efDxRsmn9efyWlc7nli8b/cWKfLFwgQZlmfw9O4DdpPmYHbnzKgmf7UKwHcUmN6nxAVXBWWErTV/mTG35AOb3V/SWKrIiY1RmDCNh4AIErO1Qr68/ZkmxMz0mCI1YAFlHANWCRXVNuXFVDupCqHNG3ncHvzE6hWXrM7Iely+ewcJwrk4ijbuWrkhyDUVCG2tkIzBDZB2NAJE5OU/XhxM+oxfS9bk+mTc4zlq168pg9jR6S9BfcFWsLVQ1SP6ebY0B1YyCCSTtAT4GzOOe0W83s+dJuj3wFuDWwKXAL5rZ0Pte3gjcC7gReJyZfcPP9VzgKbi6J880s5kk6fuOAxOZJh8zCZ+tyoSQ67prqUVb/D74eyJcKRfoLxjzt4zIlktMc5ignM9WZugTaSawQvCsCC4IyaG+GnUTTaZIQFnsgqrnrQWPFwJZ2QQO1NWi54xqb8We+RGF5Vx26Ewu40xuGh7DtxdvxcHFeZdwG0fN1QvsaCFxmaDw2bxQjMPG4xI8dVRg5x4o0syqPk2LBvnPVdEq7qrg6xkYvYUCUqDBDkKzbKM9AB5sZock9YGPS3ov8Gzg5Wb2FkmvwQmVV/ufN5vZnSQ9Hngp8DhJdwEeD9wVOB34gKTvM7NZVK+9dJVjBtxhrQmS8EkkEokjxGBm0W4+6f6Qf9v3mwEPxuXUgGtJ83yc8DnXvwZ4O/AqOSfMucBbzGwAfF3Slbi+aP8xgzXe/kjnSMJnuxCX3bHKa0BrtFeoxzvNp7dckS+MyJZH9OZzrCeGZfOtPk70bDVy6/hyVvh8Yo1GY/bhtCC16vE4H0i3uZrTNpzWFzSKct7o3WrI6Scc4IS5Ra4b7OPQaJ4Dgz0My9zdmq6Z0Gswps630WD+C8M62pKFfzrPkdY5cQReMLn55NQ4mTXWpMIqssIVFM0GBRqMmhYKyeezrTHTesxuJ0v6dPT+fF+Vv0ZSjtMu7gT8FfA14BYzCzWY4vYydesZMysk7ceZ5s4APhFNO7ElzXqR9D3AH/j1XQa8xMwOrGeOJHy2GyHooCzdsy1UQV7DcpOVRr5ckS2P0KAgG1WosHaIc3gwx76T2CEfHrZZE75twTYVJbQ2RT69OU5qgiCC8PL+lJYvpWu1kDO5lcdU3O7kW7jfyVdxTDbkPxdOpagy5va6AIT/6h/P1QfnqQZZaw1B8NSCw+K1RZ95TDHT1jkWLa9q1r8aiiVV1hRdVelbjA8Kb3Kb4stDYluwjgTSG8zs7NUGeNPY3SWdgMud+YEjW93MeSNOOP4l8JO4HJ8nr2eCJHy2MpMKjdZBB42vJxQXxSeaxnk0GXK+huUSLY9Q4dpxuy6kjabT5OB4h7lPnmy+6QuTdZSbyD9TP/RpCZJYo4of6CveRwK0rnRtbg375gbcdu5G+iq4rn8cmYxT5w9wm7mbuCS/A9fMn0i1lLfWIH9tM7XmbrmEOs/9WjB2NLM6f6pVjqfRcOLzwkVWaIhV6IhaoWGBFSnKbafg3ImzD7U2s1skfRhXS+0EST2v/YS2M9C0pLlGUg84Hhd4MKlVzSw4zcz+0L9+v6TPrHeC3RMbuIOwWvA4rSfWfpoAAItaQBv5oHKl+0dFXQ/OlbqxlRWgu6/HPR8j01NdBTrgtaMVJi+CkLNIOPoQ5II64bTWRgrIl0W+kPFfB27F5xfO5AuLZ3L14kncODiGXBXH5cvszUdIhslaAQbxWmOh0a2m3fpMIRw7bFW0RftbrR+i+9E2WcoFHPjz86HRW66aem4p2GAHoZmV15F0itd4kLQXV0Pty8CHgcf6YefRbklznn/9WOBD3m90IfB4SfM+Uu7OwCdn83nBt7o5yUe+5Z33a5I0n0QikThCDGaZZHoa8Abv98mAt5nZRZK+BLxF0h8DnwVe58e/Dvg7H1BwEy7CDTO7XNLbgC8BBfC0GUW6gdOuLqVtLA/aj5Gi3XYA40xvocK1qV1ktDa9OWRWF8jsLTl/DyPnr9SoJCuqpvjmBO1G/iddjaUzPgQirGZE6gYX1LXXKq/1BG0iCxqRkY1EviRuvu44PlR8HwDDQY+8V9HLKo7JhiyV/dZ1GjOirbieAfKmslZSbdf0Z9QldsLxlq9nXBLumDUETcqV04H+wbIuJko5q+dAYrOZZW03M7sMuMeY/VfhotW6+5eBn50w14uBF89kYe15zzrSOZLw2a5UBpQuwTSY4cA7/pvEzqzwZrfFAi25pEZJaFiQDXrkw4qynzUPYBj7QG21ho5ycyD4V6IqAGHMFE75FUPCnJlr/x3WlR3qsbi8z7VMKKGYN76+99Z8754DjKqcLK9aTdtaQRS4oqtBuJl1BEcYFwsf1Zazxu8T31pFW4h0a0X3NVOHPKvektFbHLliosNR0502sSPYTS0VJN0OF323379/EPDTwDeAvzKz4Vpz7J67tZ0Z84Cq/T5VJHg6OD9DRT6oyJdct0yKwmXUF6XTfka2wvdhmSs6GrbwW9IqFrqKY91VwY58JWs8X+sWDL5CQJX768/55M0cNBL5oYx8SagUlGJ51ONgsQeAXq+EXtXuVhpdVyE5t1tKp5Ns2tqgFlBdi8rKcHOtGgWnwrVQyJaL5O/ZgbiWCppq2yG8DTgWQNLdgX8EvgncHfh/00yQNJ/tQtf8VlWQ51CV1OV2wjjCt3gjGxn5oCRb9hn1Zenm8UEHwXFuUD9kqxyqnqjmqEO463I5wbwWQqZjDWCctkT9nI/qurXDsuuaaLl81WnfPK7fnJP57IZyr1EdP6I3X7Jvz4CFYo5h2XPN7+Q0kBU2sFgIeS0o2tFoPZFWR9aUx2mNDfOZk8lV2B/CzbH2PfKv85GRLxsaeNOnJcGz09hlhUX3mtl/+de/AFxgZn8uKQM+N80ESfNJJBKJI8RVtc6m2nYIsaR9MPBBALPpv1UlzWcbU9d5K13ejlU+CMGb5FTgtJ5BCYMhNi6psfZZqN5vOa6YZz7h20movVbHF4uueapVBaD1UysUk9iE1VSxVp3s6cKwRdV3Ws8dzrye4+eWyGQcGO7h4HCeYpSvaCEef8Zmfn/9OGBgzFoszucJ5/rz6hpxUVBBXQ07mN/CXN4EmQ+a/j2EYqLJ37NjMGC0cwTLNHzIR9J9GzgR+BCApNOANf09kITP9sZHvblupqVrqSC5HJ7SoDSyQUm2NPKlXHxZnngONQ/8ICGCia2O1go5Ll3/TRBCY0xtwWE/Ke+uFZwQ+UokKPPIhOeLc2YjN1nWqzjtmAOcOn+A64f7uObgCRxcCsJnteup+ax59JmhEUQtP5a3m4W1RWVzrPno9fhukmw9j2+j0Ft2QR+MCv8lIJnddhbrKq+zE/hN4HG4sPAHmNnI7/9e4A8nnRSThM92YkzYtZUVyksoShiO3Jf6UQlek8mWRmjJfduOQ3tdlemOBhRehoAB33WzTso0Vn5bl2rtIHT+bEXOeRdMLWCyqN01zZja1yIiNcn7egx6y1AVMNg/xzWHTmC57LFYzLEwmGM46Dutp1a7uvctinjza221jRCYT7S1aK3xzzhCLrSGaN2G0PWUZo5wD3vLRn+hREsjbOSCPhI7j6NR4WCr4pNY3zJm/2fj95L+w8zuO26OJHwSiUTiCAnRbokV7Jl0IAmfnUBZYsMRyjIoKzLJmeDM0OIADd03bgtRcXFodiscmcZUVPgINN+YrVvTrDm/ia6zTI3S09V+wjU6GkKXuBhoVnhlr4R8yegZFDfkfOv4E7lx3zHM9QrKsq2C1DXmwueJri/5nkK9RkMKeYH1z57A9xQa+ywJEX/d0jzjhtb5PdBbKNHyIBUT3cHsMrPbtEz8RU/CZ7sxJuTaWbaG7mcvd/6KXu5MWSHQYLSKn8H7K0JiqCrnN1IZEjOtMUOxMpigfshX1jj0YaL/ReZNYDTjQhfVMF+4flz/jQqyAVQLfRYF5R73x57lFWWWQW7t1godx38sOFotI+o1uDwjhaCI2C/UvWWi7f8Kc4TzvMDOClddorcwQstDbFQ0bRQSOwYX7ZY0n/WQhM9OIBZAReYSSHP31DTv61lRPbnuhNr4NILDPCSHhge+K7/TjA8IWkKpqcWzMo8H2g/8Ot8mElQmRYLQL7PnLlvscQOLYwx67mBZZmSZua1XUZai8u2sayE05ntXKITa+ozdtgqdnJ66anVX4IZhnYoJMlAB2dDoLVnt70lVrHcmBhRJ8xnHRImchM92ZEK9N/dYq5BVTgiZM7HV5rb4vFAFO9RUi56mjcPc2lWdI6Ew6cEeznMaQBOCHY+Pk1rrDNR6gvbrKpTL2uOESrnXUM9NVBY5pYw8r8hkVLlhufmK2s216pI9XsOqBUQZmrut/CzKfPBc5i1kkQmvVcmgK2SDYCtCeDX0F4rI5JZCrHcqyew2ll+cdCDdrUQikThSzJndptl2ApJukvRaSQ/xLbvHYmZfnHQsCZ+dRFU5DcjnklhZOf9C99t26APkS+zEJqd2cdBI22kFJlgdGh1ClquQO9MyadlKjSm+Rr2j2UJ5H5PXTEZNuHUwqVklqlFGuZRTLvcoC6ceSYb1zJndunk49eKjjeCXcd1F84HfRubuS2xyqz97ey7LcD6eFXNCvmz0l4x8weVZpSrWOxfDhVpPs+0QrseV0XkhrondKySds54JNkX4SPotSZdL+qKkN0vaI+n2ki6RdKWkt0qa82Pn/fsr/fGzNmPNW46JPhy/VZV72E2qnFxWUFZesNjK2mxRnorCM7M2L0WN4rwpy5mzwtbMQWjDEHZ1q0lDu/VB/BD3pqtsSNPaAGAkGORoKYfljHKYuW+UAoKA6kS9tapVR0LDFU9VS3DQEbZ15erwMySqdq7jn0B1x9LestFbdK0sbDhMVQ12OLtJ8wEWzOxVZnZ/XJfVa4H/J+kqSf9nmgk2XPhIOgN4JnC2md0NyHHNj14KvNzM7gTcDDzFn/IU4Ga//+V+XAJWf5CtdqxqBFStwcS+EPPdRb3WETQY6DyA64eurYz8GseEv7txHU/D+PCQr6tbV6BhhgYZ2XJGvpTBMMN8ZJxl5oqLTtB8YgFrmZu3nIdy3hVSXaE1xeuOPns3kCKu4B0STPOhkfvW5amK9c7G2HXCp/4gZvZNM/sTM7sn8EhgMM0Em2V26wF7fb/xY3D1gR4MvN0ffwOuNwTAuf49/viqNsZdy7S3JAglb4oL5qVa+zAfhFD6Zm6F+dI67XDrifPSeSh77acbNdasO4xtrg9OGFZzUOx1WzXnHvoqRTb02wg0EpRyVa39fHEPnvgaK5asSADNQTmnWsjVQQsdIbbCPLliUrdlI3MldZZL18oimdx2NIYoqmyqbYfw4XE7zewrZvaCaSbY8DthZtcCf4br/fBtYD+uHestZhbqjlwDnOFfnwF8y59b+PG37s4r6amSPi3p06PpBG8ikUjMjN3k8zGzZx/pHJthdjsRp83cHjgd15Do4Uc6r5mdb2Znm9nZfeaPdLrtQ8tfMt0vduigqbKqtZx6C5UMqk6YtUXHWnk9zbUbv5EaE1V4zxpaT3TNUMiznHd5PcWxRjnnTlQFKuQ7tPracyNndrNQ1TpKNO1W1R5bJDUOHMjUrvkWnzcucGEMWahivViSL41cH6VU1WBnY7vL7CbpMZJO8q9PkfRGSV/w/vnbTDPHZuiAPw583cyu95VQ3wncHzjBm+EAboNzYOF/ngngjx8P3LixS95GrEcAVRUqK2dai/0VsXksvK9zgiLz24RnacinCUEILYf8qmtvtlBtoJqDas4w/5tRC8nCC6GRM8ExylxuU3e+8LL+HOOFai30FAmgFR+M9mcPr+PzfY5Pb9nIlwrXMrsYk+Sb2FHsQp/Pi83sJv/6VcBngUcA7wX+dpoJNkP4fBM4R9Ix3nfzEOBLOBviY/2Y84B3+9cX+vf44x+y9Jfc5nBuR1m6aLcihFtHfplx7a+j6DfnB+qEUZs1QovG79L9W4vHNOHVqoVVFbU7sB516LQTaJH2U6kWQlnhtB8KNZ1Xx/2NW2erWKHxWSQAu8KwToqNP3en7p1rXR5aKIxSy+xdxC4TPnn0+k5m9nIzu8bMXg+cMs0Em+HzuQQXOPAZ4At+DecDvw88W9KVOJ/O6/wprwNu7fc/G3jORq85kUgkVsMQZZVNta2FpDMlfVjSl3xKyrP8/udLulbS5/z2yOic5/p0lCskPSza/3C/70pJs3x2fkTSCyXt9a8f46/3IJxffk02pbyOmT0PeF5n91XAvceMXQZ+diPWtWOQ1gi19vkmZYlGpdNkvOJTl4extvYgs9o3Y5l87o8Paa5zfrxm4s8J/p44+s3CaRXgNZm60VvQMvIQgWZU3tdjhaAMEW+RDwqn/VAIMqFKTcSc5GrIqaPFhc8U1bKbKpJtEpFZMSSX9hZK10cpRbrtGmYYTFAAv21mn5F0HHCppIv9sZeb2Z/FgyXdBZeuclecH/0Dkr7PH/4r4CdwQVyfknShmX1pBmt8Oq5p3BX+/W9JWgD+iVVK6sSk2m67lcqwqnLCZ1ShMneKdOQbCf6PLnVYdOVbXWNNC+tgmgLvwHf1q7u11oIQ6tZyc6HPciHWx1XYMaUbXwgbqRFcUWXqrHRVuCkbwRPPCWMESsf8t6KagRe+gtr/EypWx/N1i7LmA5g7ZPQPjtDyoCkmmizFOxrzAQezmcu+jYsExswOSvoyTfTvOM4F3mJmA+Dr3koUvshfaWZXAUh6ix97xMLH++ufDzxf0vFAz8zW5YvfMUHnu54xHUZXpSzdNirIhhXZyOqAgibPxwsh63QftfbPcccU+4CmePAGP0/Vh3IPFPsMbjVibt+QfG+J9ZwwdAmcakfoFZAVQoUaIRgngnb8Nq2AClgZtdZdbqwV+eoG9fyRL8hFuRn9hYp8Yeii3Iaj5O/ZJZhpqg04OaSF+O2pk+b0FV3uAVzidz1d0mWSLvCRwxClo3hCqsqk/TNBUk/STwFPBX5Z0qOioLE1ScJnNxKEQVmhUUE2dKa3rKAORc5G5rY6LDl6Iiv6OdFB3wm9nrQUqVWypuqJsg/l3oo9xw7Zd8wy83uGLvBgTMmcul11ARqpLgVksWCIt/rC7XFx1YYVVQw6y++WIoqj+bIh5EvO5GZFkUxuu4Z1FRa9IaSF+O38sTNK+4B3AL9pZgeAVwN3BO6O04z+/Kh+IuknVzl2BnA58Ns4U98ZwO8Bl0s6fZr5k9ktkUgkZoDNyOwGIKmPEzxvMrN3uvntu9HxvwEu8m/rdBRPnKoyaf80vDC6RpcXA682s7/orPuZwP+liVCeSBI+O4lxfX4mDjVklWs8N/Smt75c/bTChVObQJmfsmOKCqa25mdzbOo6b7GmFC87A5urOHHfIsfNDTjYn2dxzx7KYeZK6pSg4G/y2k9W+HkqObNcZBI0hcCGjomwux4Yn98DLfPdxI/k67n1llxJHSuK5O/ZJZhBWc1G+PgUlNcBXzazl0X7T/P+IIDHAKFdwYXAP0h6GU4LuTPwSdxv650l3R4ndB4P/Px6lrLKsXPM7MndnWb2SklXjBm/gonCR9JlU5x/vZk9ZJoLJbYYVeWkSlG4oINhieaz2pmfjSrvYM9c0EHsI/G0HPGR6SnGRbg1TbPj3B/rRsJ5U5oJNF9xyt4FTt17gOuX93H93n2MhhlVnpEVoqogj66pUigzQj5SHcwQtgnP/27O0bjj9e6Q4xN/Xrk1ZyOX39NfMrLFqKpB8vfsGmYY7XZ/XMTYFyR9zu/7A+AJku6O+038BvBrAGZ2uaS34QIJCuBpZlYCSHo68H5cONEFZnb5Otbxa6scW1rl2OI0k6+m+eS4CqWTEE7iJrYS69R+KAoYjsiGJfkwp+q5yDGV5jqRxv6eltsnaoMdCRSFcSFcOwvPfreu1sOc5uFetzwIAQt5xb7+gL35iLk8duQ0c3d7DtWh0xOESIiya/mDaOZZcX86vp16TNy/x4LGA/mSC7HOFn2UW/L37BpcXM3Mot0+znit4z2rnPNinCmsu/89q53XRdIxOD/Obc3sVyXdGfh+M+ua346X9DPjpgBuNc21VhM+v2ZmV6+x0N+Y5iKJDWZaM09VNUEHg4JsvlfnyWSjCuvJ1UzzwQPqyCBVjZO+m1sUcoIgqpXmjzcmM9fyulZMDB8lJ6zMWCz63MA+rl/ax2hxjmw582V1/FbXocNXGpBvqcD4CLY4uk2d/dGxON+oDj6IPnM3RDxuHNc/VKCBDzZYz/9FYpuzY6oX/C2u0PN9/ftrgX9kpe/no8BPTZjjY9NcaKLwMbOPS8qBN5rZEyeNmeYiiUQisdPZId8z7mhmj5P0BAAzWxzXwsbMfmnSBJJOneZCqwYcmFkp6XaS5sxsOM2EiW1CMM9ZBcMRGpWocA4XF77sX+cZZIYyOR8GoKjXjQt5dhrHOP+JzFoJqLUGVTXHXUVptXwrVogblvYBcOOhY8gO9MiX5H0sGu9birUXr5HVfzaraEKtLqdxmHWoKTfm3Ni0l42gt4TL7zk0gOVBqmK9C5lltNsmMvQlc5zdQrojUzSHk3QC8D9wAQ0/iAt8WJVpot2uAv5N0oXAQtgZR2EktjG+0oFLNi2wfg6Va7dAnpGVlU+Oo+l6WjvzhQQZtvIPr/vWVj7E40TPJnjBDyrFweV5lod9lg/OM3fIVbCu+k0F7m6hUJV4IckK4RSvSdUYWaT2OIv9OquQlS63p79Y0T/U5Pckf8/uwkW77Yi0yecB7wPOlPQmXPDDk8cN9ELqXJzAuQdwHK4J6JGZ3SK+5rfMT57YKZi5kOuyRCMX9VZrJP7hqcKa35JaU3DBCGa+/04hyG1lKZ5I+6kj5iYkbtbUjnyxuDzHcGEOHfRaT0bj44mCDcAJgQpcaHhclbsTxSYzF45tIfy6CZqor1+Pbdw7477Uut5C3t+zUJEvdvw9iV3FTlB0zexiSZ8BzsH96j/LzG7ojpP0D8CPAv8C/CXwIVwpn49Me601hU9oiSrpGDObKoQukUgkdhvb2ewm6Z6dXSGf6LaSbmtmn+kcvwtwM/BlXD5SKU2V4VezpvCRdF9cwtM+v5AfxkXCpUi3nYCPeKMo0aBAPWv2A8z1oGRl+Hbwe1T+TR13TEs7aZ0QRb7FocorMNBIDA/NkR3o0VsQqqCcA+uDFU1It6r2PK3GcGPCst2gMLhzWbV/rkYo75ONXO+e3kJBvujquVlZ7YyvwYmpMbSthQ+rl+ox4MGtHWZ3l/QDwBNwVbRvAI6TdGpciWE1pjG7/QXwMHxOj5l9XtKPTTN5YosQBMekB6JVLtl06HNTfNi0JOf7UUZcXLQ2VVVW26RagQXQNKcLuyoXaSDTWHnTrSidDYWVPeZuyciX3YN+1DeqvpENaQvDyARn3jQ3kWhNdSuHMW23688RPkZkMoyFWTZyIdb50qip55YSS3cl2/nrhpk96DDO+QrOR/Q8SffC+X4+JekaM7vfWudPVV7HzL7VibZL3tTtwhQJp1Y2wke9vD7P8tzl4ngNR5kTQLKoZoHhghYkN3bC9ZwDPzzhG2Wlebj7pFVwEW2FyJdg7hanWZTziuaJhERHs4nbgdfXVuS3CTk8LlJi9cCCMC/Nea2ghcK1UOgtVmRLo+Tv2c0Y2IzK62wmkvYAvwE8APcX8K/Aa3xftYmY2aW4vkO/g/MFrck0wudbku4HmC929yycnS+x3RjXZM7MfVMvCkyZCzSQIMugl0NpqGeTv9VVVkeYhd4+tZCJNYzo/YplhajkOorOvc4Hon/IaRbFEHrHC+s1Ji+3GaG8DriiDLUg6gggF0atukKBC/FujoMLr260tShoIdKQxpnctDTw7RO28/ffxJGwzc1ugTcCB3FBBOC0mb+j09BT0l1xOUEX+vcvB473h181zYWmET6/DrwCVzL7Wlx0Q/L3JBKJRMQOcfPdzczuEr3/sKRxzedegqteHXgY8EfAMcD/xoVcr8o0wuf7uxUOJN0f+Lcpzk1sNSa02LayQgyxIoM8Q72eaypnho35Nq9ug7nufME81w1lbs0x6Vx/vHCFOvsLFflQDA/llHONllPPYU2odbdsTl2XLja5BVNbVDrH4mNZo93E1zBR9wtS4SpY9xcrF2gwHKWupbsYY8doPp+RdI6ZfQJA0n2AT48Zd5qZ/Xv0/oCZvcOfs1pB0ppphM9fAt0wvHH7EluRcYVGuwEI3vRmgGRglTPBTQxQoCVMugU4m/2RQz++vPfztOqoZWre+6lkzqGfD4xsaPQWM/Jj1VxnjO+nXvKYHJ96PV0hVF+wET519erI7BbnDgWTW/9QSbY4xAZDV6Q1BRvsTozpwiS3PvcC/l3SN/372wJXSPoCYGb2Q35/K+fTzM6J3n7PNBdaraXCfYH7AadIenZ06Fa4iteJ7U5XCAUBRO4i4Py3+KYVdqTNtHw04aG+0rezonKBeT9NR+BYBlUeCarQUbV0RU6dD8jtC5rJinBt6+ynCT6ogxp8+aAVQicSPmQd4RgLz+DvKZxQzJecv8dVNUgh1ruZHfJf//Apx/2XpPuY2SXxTknnAP81zQSraT5zuNyeHm0pdwB47JQLTCQSiV2AdkS0m5ldLelEXAfUXrS/m2T6+8BbJb0eCMfuhetg+rhprrVaVeuPAh+V9Hq/oFThYLsyTY+faIyZD3oO/guv/VjwgWSr+Htin4s6+6CtdcTnaOU4VdS9hWRGVphrJNen1nZMopU51DEJhn2COiE2lAGySOupNa7g88FpYt20obB+Vc7nky0VMCqgLJPJbbezAzQfSS/C1XL7Gi2bwook0096f9DTaWq/XY7rcDqzJNPTJb2XVOFg5zIpBBt8kc4ghOoTmnGhntu4oILWU9vvWk0GxiHUZfTazIV8l35/3pjT3JxqzzHpIWDN8XWbSPznq4DM3Dry5YpsMMKGPtAgsXuxHRNw8HO4EOo1uxiY2XW4yLbDYpoyrH+BC6O70V/w80CqcLDdmObhGI+pzDWaq6q6SnT9UI+7m4ZIuFWmD+2yravZrCIkguPfNXQT5GpVIugGE7QEX/cYKwMi6utbO7KtFoClaxKnkqa6Qd0UD/KR0Vsu0XIUaJAE0O7Gpty2Nl8ETjicEyV1G86tSqpwkHDE2k8wH5kXQGWJysxFwKEmIi2U1KkiU1YWPciJxplakWlhfvl2DS280Kl6UPVEOZeBRLFXlPNOkGQhAi1Up7am4oILLgjXbaZdEZEXPwx88igVdUh1VlCb8Sx3r7Mi9O8x8oWRSyxN7RMSwErVf1vyf4HPSvoiUR8fM3v0FOeesZ4LpQoHiUQiMQt2hsvvDcBLgS+w/k/02fUMnsbs9uvA02gqHNzdv0/sNFq+k6p2ojuHP15T8A4TX9rGaT9t1aU2T61I+Fzd1FYfDz6foFBIFHszRvtEcYxRzXlNJJwXyux0zYNE6/Ch3ZY1JsCu6a1VBZuOfyqEWA+d1hPn96TE0kSd5zPNtgaSzpT0YUlfknS5pGf5/SdJuljSV/3PE/1+SXqlpCslXRa3R5B0nh//VUnnTfFJFs3slWb2YTP7aNimugVmvzzNuMA0/XxuAJ641rjENmCaqLdAZa7qQVFiWeaFEFie+bwZb3Lr5vQoeh1RFw5tRZipqbcW5qm8uWsgessuyVSVa7Vd9ZzQqYVSEDy16cx9vqZtQ5AwkcltzMcPAQh1TqnP7WnVeTOXc5QPzCeXNvk9KcotATP9/lEAv21mn5F0HK5g58W4qLIPmtlLJD0HeA4u5PkRwJ39dh/g1cB9JJ2Eqzp9Nu5X+1JJF5rZzatc+18l/V9cF4PY7NYNtQYI1W6eD9wOJ0/khtsd1vqQ0/TzuT3wDOAs2nHf09gAE9sUM0NW+X4/JSoqV2zUzD+o1TzQvb+nbmnQTf6ExndSCwI1QQWdStWhYnQ2Mid8SosKidJ0Mo0DQa1zTe/zmaRp1VUWaARTXGzUCMEO0fUKX9VgsSJbHMHyIPl7Eg0zEj5m9m18MzczOyjpyzjL07nAA/2wNwAfwQmfc4E3mgu5/ISkEySd5sdebGY3AXgB9nDgzatc/h7+Z1yxwOiEWke8Dvgt4FLWGQswjc/n//MX+CdmZNWUdALwWuBuuA/2y8AVwFtxQu4bwM+Z2c1ykQ6vAB4JLAJPniSFE1OwHu0nNJmToKiwnu/tU9edaefLtC4T5990/yil6IGvdiRZd644V2jFNcYc00qhNLacT0sQtj+DidDCqDEH+qoGvWWjt1SRLQ+xokwmt0TD9KHWJ0uK66Wdb2bnjxso6SycQLgEONULJoDvAKf612cA34pOu8bvm7R/IofR12e/mb13necA0wmfZTN75eFMvgqvAN5nZo+VNIerhPoHrEOlnPF6EolE4ohYRxPpG8zs7DXnk/YB7wB+08wOxBHHZmbrbVs9LZIeBdwV2BNd74UThn9Y0p8C72QKM13MNMLnFZKeh2ulsK7JxyHpeFye0JP9PENgKGldKmX0DSBxNKgqsAorCpRlTgMq88bR74m7m7Z8LdGxWgPqaBmtn1FeT5X7nz1R9YT6GVVPtRmsvk54bWO0r9hEN4lVfEBdbU1lCDaoyBcLtJy6liYiTK1OvkeKjyx+B/AmM3un3/3d8OzzZrXr/P5rceVwArfx+66leaaG/R9Z47qvwSkDD8JZpx4LfHKVU4IiEAvT1cx0NdMIn/8G/KKfLErFW3vyCdweuB74W18t4VJc+PZ6VcqW8JH0VOCpAHs45jCXtkuY1vRWmfNpREmUsa+kJpjO8vFz1kmmUXBBq7K0FypVHgmYPGzCMmsVHrXcmnkyNXlGkTCRWe2raRZC00ahE3gU/FWqolNi/1HpG8ctlim/JzGeGekh3tXwOuDLZvay6NCFuNppL/E/3x3tf7qkt+CEwX4voN4P/J8QFQc8FHjuGpe/n5n9kKTLzOwFkv4cmGhWO5z224FphM/PAneYptzCOq55T+AZZnaJpFfgTGw1h6NSepvp+QC30knJCH8keOHkgg4Mq3zUW2leAPnItcjXsaIddfC11EJGK1oYNNFuTsuJI9pawQiZ2sIq2uLq2EGgxL853QrWFs1RJxpYMxYf6t0l+HvyhZHz9wxT755Eh9n9Ktwf94X/C5I+5/f9AU7ovE3SU4CrcaVwAN6D84lfifOL/xKAmd3ka7V9yo97YQg+WIUl/3NR0um4yjandQdJ+gUz+/tOx4OajtAcyzTCJ5RbuG6NcdNyDXBNVIr77Tjhs16VMpFIJLYOs4t2+zhjDcIAPGTMeGNC7qWZXQBcsI7LX+QDwv4UV63acOa3Lsf6n8eNOTYV0wifE4CvSPoU6y+3sAIz+46kb0n6fjO7Anczv+S3qVXKw7l2ImIt05tvMEdZunFFicoSK3NX28ab2LqdQuNmcBP/FuNos3zl5kxj1oqKa3xDVpv5giYUJ4XGyaLjrhmb5poyQeEzu617V1SFkjouxDrl9yRWUCeIbW/M7EX+5Tt8rbY9ZrZ/zLi/9j9fcLjXmkb4PO9wJ1+FZwBv8pFuV+HUxIx1qJSJGTBJALXqvJkPuS5qAeQEjwu7jq1OIX8HwLDOQ93bv2rTV+zfiXJ+BFVukW/G+3wm/V2r49bphlqv+Mx+65jnatNbxQrpo8I1sustlWh5AINh8vckVnB0Ys82Fkk/i4tEPgj8LnBPSS8ys89OGL8HeAoro+PWrHYwTYWDj0o6FfgRv+uTvpT2YWNmn6MdHRFYl0qZOPqYmdN4ihKNCuj3IKtclem8abXdRLixehkdaGkydYJpFO1GlHQaJ55mJajQykCCMCfUgqXle/Lnh9I9K9o7dBJWu4EI+dDoL1XkCyM0GKX8nsR4dsavwx+Z2T9KegDw4zjz22uYnN7yd8BXcJ0PXoirhjNV7c81a7tJ+jlcqN3P4rSRSySlTqY7hUkP0KARVZV70FYlFKVrnObbLKgM7RasfU6YoiuEaqGjKLzaa0C9KNhARL18aFc36JrUulpOJ4oNonOLqCdQ2OJrlO3rZL6ydb4M/UMV+cIQG0YtFBKJiPD7vta2xQkq/aNwya//jOtqPYk7mdkfAQtm9gZ/3lR5mNOY3f4Q+JGg7Ug6BfgALlAgkUgkErAjfD7AtZL+GvgJ4KWS5lldSRn5n7dIuhsuTeZ7prnQNFWts46Z7cYpz0vsFELgQVHU2o7GaEzdUOtulegab2arQs+evtd6elD13GCVIhu5EGdX283qa8SmOWIzX8v/FAIi/BpKb7arVtaJU0VdEbub25ONjP6i0TvkAg0ofMvsZHJLxNg6tq3NzwHvBx5mZrcAJ+F8PwBEOUOB8/2+/4ULDvsSriXDmkyj+bzPJyuFYnSPwwUBJHYTocp1Vbk8n+7DtxNFJmiauQkUKiDQCA3LVZvbqj4uedRXkg4P/mxoZMOKbE/mqln7+SyzpoV3l3i3N6mFa9cVrEO/vGg/YfpgRSxcVYP+ovP3MBjCqMBSsEFiHFtfsKyJmS3iSuWE93WRU88HcXma9XtfJftjwB2AUIx6TVYVPj7T9pW4YIMH+N3nm9m7ppk8scOwykW+Va7d5zj79dhos8jebdG4upJBD6q+NaHWwedSQDaqyAovhEZOe3E+IbXmDxpQ3auHyfb1kCQ7roxO/Rkq3yp74KoaZIuDJrE0kRjDqqWcdg7db3zvoC2MwLlk7rXWRKsKH19p4D1m9t+IpGFiBxGCBLIpLKl17k+FsqwV8RVHrrUqDMQahoVQtOZHnevT8+Vzei4oILTlVmmo8AJoZKiQy/UZYyablJYnW+Ugkak+irqT+RDrIa6W2yDVckuswe74XmIAkn4AF159vKSfiY7fiijkejWmMbt9RtKPmNmn1h6aSCQSu49tEsk2S74f+ElcEYKfivYfBH51mgmmET73AZ4o6WpgAepOdT+0rqUmtjx1yfbgS+m0xw6FRjUqnEO/7IUTgciXE3U0beqmBWdNc6yp62ZUfaPqAT2DkVq+GFXmzG8jZ4qreh2TX6xFqb3P7VfreN1DqK6aEPuh/PpLZ3bLBxX50qidWJpMb4lx7Ixot7UQgJm9G3i3pPua2X8czkTTCJ+HHc7EiW1K7MTvCqHQYsE3mFPZQ1Wv9TAOravX/Dv0D/pqzm/zhvXMmdS85HGRaN7sNqzIh84UplBgIZJncWuGuoed/Ng4Ck8rt7iDaRVaRlTO5NZbqtDSEBslf09iDXbQr4ekOwE/jKus/aXoULcQwGMkXY4rSPo+4IeA3zKzv1/rGtOETJ8G3GRmV5vZ1cDNwPdO8wES2xhlboO2QDKD0QhGBRoUXiMxstJcUmY5uXV1PUV44PegnDfKvRU2X8F8ieYqrGcu8i24hwxUVuQDIx867SeuchBXqbZxgiX26cQNubqCKCLUcgv+nibQYgc9YRIzZTsnmUr6sKST/etfxEU0PwJ4q6RnhHFjqmI/1MwO4Exw3wDuRBSavRrTCJ9XA4ei94f8vkQikUhAHdI/zbZFOcXMbvCvnwnc18x+Bed2Wc2H0/c/HwX847gipJOYxuwmi+wNZlZJmua8xHagW1y0mpA/45u2QYlJTlkY9dBgRD6XUw4y8sx8w7fxYdhAo5HkUPa9uW2+IttTOEVLRlmKqpdT9aGcy8j6GZirsZYvU3dTlUVaT6ThqOxcK4yJ1tDqogqNb8qX2ckHRr5UrvT3JBKT2KJazZSMJJ1hZtfiFIwFv38A5JNP458kfQVndvufvgLO8jQXnEaIXCXpmTTazm/gKlEndhJVhWVZE3RgVWN6s87XtbLCKNBwhJYysjynnwnokfVE1V8xe4u6RfYcVHsM7SnJ+yVZ5v56q36G9Y1qLqPck5EVPbJhhSojHxjVXNSYLlQ8CEIkWmrsA1ohpDqmOSLBlQ9wVQ0WC7KlqJBoIrEa2/tX5LeAf5H0DuBy4EO+uMADgL+ddJKZPUfSn+Ba3ZSSFoFzw3FJP2FmF487dxqz268D98M1cLsGp4Y9dcoPlNgOxAEDZiuj3GLfT2V1sqkNhmhpQHZoid7BAf2DI3oLJfmgcvk5pYtyC5UO6iTQOrnUsF5F1qvIcyPPq1oAhT/kqi/KPRnFsT2nUUUFQePIulbkWlQhu95CEdOOllQLKC+AshJ6S8b8gZL80KApqZP8PYk12M4+HzP7CO45/21cvbZLcRrMM8zsz9Y49yYzK/3rBTP7TnR4YqmdaVoqXAc8fs3VJ7Y/VdVONg3az4px5toqFIUvR2OorOiZUc33gDmY9xFrXuCoBOWR7buEbCS0nFP23KDQOd2GGfmy6C25xNKgucQ9eOoItc7rWmgZqJKvVmCtnkGxOS6OcMtKF2jQXzD6BwqyQ8uwPEgmt8SuwPtran++pO850vY5rAjlaZgofCT9npn9iaS/ZIxCaWbPPMJFJRKJxM5hi2o10yDppDG7PynpHji/fzfKbVom3pXVNJ/QEOjTh3nRxHYiBB4E309FO/Ag+H6C6c0HH2AVVpWu4Ggm8mIO62WgHpZHpXQEmMi8TwUzbL9QkVEs9Cn39lx9t56RLWX0D4q5gxVzBwo08s3rsn7bfzPuY4RreY3G5fmo7eMJGlOsCflSPfnAmDtU0TvoOpbW+T3J5JZYDdvSkWzTcAOug3TMGcBncH8dd5j1BScKHzP7J//zDbO+aGIHESLgRiM06GES2bCP9TMqy6IHvpx5zidvZoUzvfUWoJoTo2NdAEKx18gHYu6AMXegpHfLAI1KbD4n29trio9GZra6Q6mntqu7ILna9DeO4CvK6ig36B8q0MIyNhikxnGJ6dne309+F9fD53fN7AsAkr5uZrc/wnm/MenANJ1Mz5b0LkmfkXRZ2I5wQYmtSPh2X3cvtXakW8f/Y761gpW+2nVRoFGBRiUaVWSli1BrVbk26iKh/UVjfr+x94aKvdcZe6439l4v5m+C+VuM/sGCbHGABkNUVC48ex7KOR+mHUW51Q5dn3w6qWJ1GLtiXxklli6M0LLrWGplEjyJtRGzCziQdIGk6yR9Mdr3fEnXSvqc3x4ZHXuupCslXSHpYdH+h/t9V0p6zmrXNLM/B34F+N+SXibpOA5TnEr6iWjen5k0bppQ6zfhpOIXaH2/TOxIunk/sDLwwJveJDUhyFZhVeVK74xKJywyV98mFkB1AIK5fj0uMg76C6KcE8UeZ5qb21+SLzb5NdV8j+G+nNE+UfqaudmQVm+eccR/7K0It2hzFRTcenpLRrY4clUcKktRbonpmd2vyeuBVwFv7Ox/eTfyTNJdcAFhdwVOBz4g6fv84b/CaTPXAJ+SdGGnVE4LM7sG+FlJjwYuBo45zPW/DrjtWoOmET7Xm9mFh7mIRCKR2PnMMIzazD4m6awph58LvMXMBsDXJV0J3Nsfu9LMrgKQ9BY/dqLwia5/oaSLgTtOGiNpkkwQcOtpFj6N8HmepNfiOtgNogWm/j47mRB40I2U7AYe1OPNaSlZBkWJygqVrjGO5QIzVKquTgCg0jVqyxdGkImql1HN5ciMfKkgWxy6gXlOsW+O4a1EcQxUc853RCHoRkH74IFQVHRFZYOoDlzl839UOZNbPoD+QoGWB6mQaGL9TG8XOllSHMh1vpmdP8V5T5f0JFwQ2G/7DqJnAJ+Ixlzj9wF8q7P/PpMmlvTsCfsfCmBmL+sc+lHgF2iXXgP3F3hvpmAa4fNLwA/gaviE22uk5nI7l9j0NqncjqdtejOscgU4VVQor7Bckb078vwbZEMjXxiR71+CsiTPMuh56VSUaDiCLMPmc8q9GaNjRbnH2gKlE2wQV68O+Tuqom+lnUTUIKzygdFfstrfY2WZAg0S62Idms8NZnb2Oqd/NfAi3G/8i4A/B355nXOsxnHrHP8JYNHMPto9IOmKaSaYRvj8iJl9/zoXltgJtMKuI7/PatpPZagooajQnPfxVP6YlwwuydToLZdkiyO0sISNRu5YXHXaDO11Dp6q78r21J1Go8CCrl8nFk6hdXeo99b09KEWPNnIldPpH6rIFkdO6wmBBkn7SUzLUfxVMbPvhteS/ga4yL+9FjgzGnobv49V9o+b/wXrXM8jVjn2Y9PMMU15nX/3Tq1EIpFIjMPWsR0Gkk6L3j4GCJFwFwKPlzQv6fbAnYFPAp8C7izp9pLmcEEJ6/LdS/rMFGNOlXRPv526nvmn0XzOAT4n6es4n48gdTLd9YwrOAqEum9Uof9N1nLGygwVPtJtyftXlpd9Po25v81g5lMGc3ON9lHXcrM6cdR8/mjcITWMbYVd02g7cYmduJBof6EgW1zGUi23xGEwq4ADSW8GHojzDV0DPA94oKS7437TvwH8GoCZXS7pbbhAggJ4WqizJunpwPtxVakvMLPL17uUVdZ4D5wp8Hgajeo2km4BfsPM1hRc0wifh08xJrHTGFvxYHytt1YlbHPnUPriot4sp8pc99HC5fnkg5Jscej8KyGfJvhYvIlMfd8l1Rcmrb81yr+NioSu+MO3Ma87AQchubS36Gq59Q4OU/uExOEzu2i3J4zZ/bpVxr8YePGY/e/BNYU7XP55lWN/C/yamV0S75R0jj/2w2tNPk1h0W7JhcRuo6ogH9PSQ5l7eo+Leqsq5/cpKuhlLtenMhBO8CyXaNn5V+p8mhXX9YKnrFxiqq+KEOfpmFzn7ZBUOk4QWaetQl0hoXJaz9who39gSHZwGRuOUmJp4rDY5uV1aiR9Ly5i7RJJ39upUh04tit4AMzsE5KOneY6E30+U9r71hyT2Mas1Wqhi6+IYOaCDlRVUBoqrf6ZFUY2rMiGrh8QRdEOaY7qqJk1giwbGdnQVZ4OEXOWxed1lh4CC7qtFeoWC77Uz5Ir45MfGqDlAYyGyeSWWD9H2eezUUj6FZzP6GeAxwKfkDQuqu69kv5Z0uMk3c9vj5P0z8D7prnWaprPD65RRkc4e99hISnHxatfa2Y/6Z1lb8ElKF0K/KKZDSXN4zJ97wXcCDzOzL5xuNdNJBKJWRP1I9zu/C5wDzO7EUDSrYF/By6IB5nZMyU9Ape4GvKKrgX+ypv71mQ14fMDU5x/JMbxZ+EqZ9/Kv38prnzEWyS9BngKzqH1FOBmM7uTpMf7cY87gusmjoRxPX66IddlCYXb1M/dKVVG5jWgbFiiQQmjojHRBTqlfax0JXuyYUU+NFQI9a3lvwkJpe78Zl9rHvz+HljufEhZAf1F6B8cocVlbHmwtnaXSExiZ/zq3AgcjN4f9PtWYGbvBd4b3ks6Ebhl2gutVtX6qPl6JN0GeBTOSfZsOY/1g4Gf90PeADwfJ3zO9a8B3g68SpIspZ9vDN3Ag0kVDwKV89FQlnW+D0CWVb7kgKHBCA2GPqpsjf9Gq6AoyYYl+cD5aKo5Wsmk3dbYNmYf4Nt3Wx0J5yoauEKiDEIh0RRskDg8tmqX0mmIKhxcifP1vBsnTs8FVljAJP1v4G1m9hVvnXovcHegkPTzZvaBta45TbTb0eAvgN+jyaq9NXCLmRX+fVwi4gx8mQgzKyTt9+NviCeU9FR8e+89h10PLzGWFRUPJnQ4rcdXWFGiUQG9HFmOZZlzMBYVWh6h4Qgry9VL2ATfS1mSDQr6ixW9hdx1Jc29cPEh1nXkW9wmOwqtdr4fw3r4VtxyUW6HSrKFZRf4EARP+l6TOBy2969NeBZ/zW+Bd08Y/zhcpQWA83DxA6cA34dTHrae8JH0k8B1ZnappAfOal5fG+l8gFvppO39a5BIJLYXtr2j3dZb4QAYRtanhwFv9vlFX5Y0lVxZc5CkZwB/74vYzYL7A4/2/Sj24Hw+rwBOkNTz2k9cCiKUj7jGf6jjmWCDTBxlYtPbhDYLzdjS9fcpek45ybKm5ttw5HxC04Q0lyVWlGRLI/oHS+YOZFjuS+3k/g8+TBOXzonf5+ZNbk4DygtfUmfB6B8YoaVBk2uUSBwuO+Arr6QPM+aTmNmDO7sGku4GfBd4EPA70bGpTE/TSKhTcb0gPoOLeHj/kfhbzOy5wHMBvObzO2b2REn/iAvtewtOjQvq3oX+/X/44x9K/p5NZo1io1Qu8caqCpWlc7nkGVRyQQajAhuOVgYbTJqrKtHSgP7BEfMHc6q5jHJelHM+9LrygQTQ9vHI+3n6XvD0XaJrVmTe32PkBwfYcNgkuaZfrcRhsp19PhGxENkD/A9c5YQuz8L54E/BBYp9HcArFZ+d5kLTJJn+L0l/BDwUV+H6Vb6cw+vM7Gurn70ufh94i6Q/xi0+ZPS+Dvg736fiJlyNosRGs46KB2581US9ARRunIYjH2iwhr+nvqyhsoThiHxhSG9xjnxfhsk5e0IJnSrIwpAL6wWP9Yxqzm2WG9lyRjZwVQ3mDvhyOsNRqmCdOHJ2gPAxs0s7u/5N0ifHjLuEMRHR66mqMJVtzsxM0neA7+Ck4InA2yVdbGa/N80cE+b9CPAR//oqxvSBMLNl4GcP9xqJo0wc6bYi5LrCisIpIyFgoXDmuKlL2FSV+5sejtDyiHzgE05zNSWrAWUiDsRzYdVG1XP9f2zOlf5xgQYwd9BWltNJWk/iCNgJmo+kk6K3GXA2a+RzSnoUrpPqnrDPzF641rWm8fk8C3gSLrrstcDvmtlIUgZ8FRe1lkgkErsXYz3N5LYyl1LHkDLCFTF9yqTBPifzGJzf57U418gKTWkc02g+JwE/0837MbPKR64ldhst01vH9xO0nxCSXZatgtN1bs+kem4Tr1m6kOuiIisgH1EXBx3Xv8d8p9Kqb87X0zMYiXwZ5g4Y8/sr8kMDbDBIHUsTR4zYGZoPzv3xPjM74N0t9wQWVxl/PzP7IUmXmdkLJP05UeLpaqzZz8fMnjcp4dTMvjzNRRI7hPghPa3gKCuXvBl8Pd7kdjgPfJUVKox8ZGQj15pBBpb5vJ9Wy2yrfT70KsgMjUT/kNhzS8XczUO0sORbOaRAg8QM2AG13YD/5QXPA3CJ/6/FJftPYsn/XJR0Ok5bOm2V8TXTNJNLJMYysdho0IZ8odEQsbYuX08XX+FaZeXab49cles4yTQuq2PClbvOgNytMRvIaT23jOgdHGDLgxRenZgZMptq2+KEP9BHAX9jZv8MzK0y/iJJJwB/CnwGZ6Z78zQX2qwKB4ntSqvawYRWCzG1+a15v26tJ2hZZmhUkg8rIKPKzef8RIY9H+UWTHL4um82yugtivn9Ru/AgOzgUhPltvUfCImtzvbQaqbhWkl/DfwE8FJfOmeikmJmocrBOyRdBOwxs/3TXChpPolEIjEDZNNtW5yfw3U/fZiZ3YLz+f/upMGSjpH0R5L+xswGwPdMGwuQNJ/E+om0HzNbPfAAWqa5Wus5nLyasnKaz2KBityb2zKqvlBJVEDUqLy/x3KXBKTljLn9Yv7mguzAEiwtp46liZmyncvrBMxsEXhn9P7bwLdXOeVvcRFy9/XvrwX+EbhorWslzSdxeGy0qconrWpQkC8X5EsF+VJJ7gMPstL98ddVDeZ8lJsMhhn9Axl7bjTmbx649gmjkROEyeSWmBU7I+BgvdzRzP4EF2gQhNdUrY2S5pM4MqYJu/a0fD2+1tv01/HleoYjsqUcFRU2l7kK1aG+W3ANhSg3ASbyhYz5G8Ux14/Ib17EBoMmyi2RmAXbw6R2NBhK2kuobiXdERhMc2LSfBKJRGIWzEjzkXSBpOskfTHad5KkiyV91f880e+XpFdKulLSZZLuGZ1znh//VUnnzfCTxjwP1zb7TElvAj7IlIUHkvBJHD7rNFlJU2njEy5lTlsZjlypnWEBZQhfpQlhDWHXPZdtqpHo78/Ye70xf+Ogzu1JIdaJWRKSTGcUcPB64OGdfc8BPmhmd8Y94J/j9z8CuLPfnorPyfFlcp4H3AdXtux5QWDNCl/l5kTgZ4An40Ksz/Zl09YkCZ/EkVNVa+f8jCNb569fWWLDUd2SQZWt/CYpsMyZ3FSIfClj/hbYe0PhTG7Ly6mCdeKooMqm2tbCzD6GK6Iccy6uSRv+509H+99ojk/gWtOchuuxc7GZ3eTb4VzMSoF2RJhZBfyemd1oZv9sZheZ2Q1rnuhJwidxZHQf4GsIoMPWfqrKCY2icIKnKBvBE35GHUxVimw5o7cg5m8x5m7pVDRIJGbJtCa3w/++c6qPPANX4PlU/7ru9OwJXaAn7Z81H5D0O5LO9KbBkzrFSSeSAg4SsyEEHkwSLt2K14dLaNXgqx2E8FaTr2odkktL0VsU/UMwv7+kt38JGwwbc1vSehIzZh2h1idL+nT0/nzfiXkqfJeBrfIL/Dj/82nRPgPusNaJSfgkEonELJheHNxgZmevc/bvSjrNzL7tzWrX+f2h03MgdIG+FnhgZ/9H1nnNNTGz23f3SVqtHE9NMrslZspE309ESztaj9+nqpq5Y83FazxVDpWv45YPRP8gzN1izN80QovLMBomX0/iqHGUKxyEjs6wstPzk3zU2znAfm+eez/wUEkn+kCDh/p9RwV//YdIeh1tc99EkuaTOHKiLqerCpPI9CbpsFsZmDVRbk3rBLdZD98wTszfbOy9uXImt6XlFOGWOHoYM/tSI+nNOK3lZEnX4KLWXgK8TdJTgKtxZXDAdQ19JHAlrvXBLwGY2U2SXgR8yo97oZl1gxhmsdZzgJ/HBUCchDO//c5q5wSS8EnMltWSTiex3oRTAMn38hFVH8o5L3wE2Qj6B2DvTRV7bhqmIqKJDWFW5XXM7AkTDj1kzFij7W+Jj10AXDCbVbWR9H9wHaa/iQuxfgHwaTN7w6onRiThk5gNcbXrwyFoTFMIoWC2szxzpXR6vp9P7swaQevZc9OQ3s2pjlvi6LODmslNy68A/4nLK/onMxusNwgiCZ9EIpE4UnZfncDTcG0XngD8haQPA3sl9cysmGaCJHwSs2fKem9j/T7TmuB8aHXI6wFn9siXxNwB2HtzSe/mJWdyGwwxH56dSBwtdpPmY2YlrqzO+3zPn58E9uL6AX3QzH5+rTmS8EnMjiM1vQXW6wMyyApQCfky7L2+Ys/1Q7IDi7A8SCa3xMawi4RPjO/j8w5cQ7njgMdMc14SPonZMk2vn4gjiXoDH75aGdlAZCXM31JxzHdH9G5awJaWYThyWk8icZTZTZpPjKRHAXcF9qznvCR8EolE4kgxoNx90kfSa4BjgAcBrwUeC3xymnNTkmni6BCbzbpJpx1NaOp6b1k2VovKSugtG70FY89NJXM3LqIDCzAYYIX3fSZ/T+Ios0PaaK+X+5nZk4CbzewFuI6m3zfNiUn4JI4qTdvsGQmgeB5fqFEl9JZgbsGY2z8iO9CpXp1IbAQh4m2tbWex5H8uSjod19H0tGlOTGa3xOzpVDwwMydcKpud/8dXOchKIx8YvWWjv1CRH1xuggyqVEQ0sXHsQK1mGi6SdALwp8BncF8JXzvNiUn4JI4uq5XcOcxK13V5nVFJvmzMWUX/UElvYeSrGQxTKZ3ExnJk7RK2LWb2Iv/yHZIuAvaY2f5pzk1mt0QikThCBKi0qbadhKSnec0nhFxnkn5jmnOT8EkcHWJTV+h0CrPx/VQGZYUGJf2DI+ZvGtG/aZl8f1RKJ/l6EhuM6oK3q287jF81s1vCG9819VenOXHDhY/vePdhSV+SdLmkZ/n9J0m6WNJX/c8T/X5JeqWkKyVdJumeG73mxGHS+UNrCaBYCI3zA61WHdtcR1MtD8j3L9O/aZH8lkPo4GK7YVwisVEc/U6mW5Vc0TdGSTmwZfv5FMBvm9ldgHOAp0m6C/Ac4INmdmfgg/49wCOAO/vtqbhCdontxjhNZC0BNInStdTW8pDskAurtsUlbGkpaT2JTWLKSLedp/m8D3ir7+XzEFyF6/dNc+KGBxz4Rkff9q8PSvoyrrf4uTSd996A67r3+37/G33p8E9IOiF09NvotScOg7jkznpbbcdldrwm5IINnOZjACM5gVMUUNkRVUtIJI6EXRrt9vvArwH/07+/mO0Q7SbpLOAewCXAqZFA+Q5wqn99Bu3OeNf4fS3hI+mpOM2IPRxz9BadSCQS49iFX3zMrPLdSz+OMype4YuOrsmmCR9J+3DF6H7TzA7E34bNzNbbG8LMzgfOB7iVTtp9vwVbmU7B0TrvB8bm/rTyfcb5fioDot9vqxo/TzK5JTYDY8dFsk2DpAfiLFXfwAX9nSnpPDP72FrnborwkdTHCZ43mdk7/e7vBnOapNOA6/z+a4Ezo9Nv4/clthOTEk8PayrfksEXDG0CGSYIHmlXfitNbDC781fsz4GHmtkVAJK+D+f3uddaJ25GtJuA1wFfNrOXRYcuBM7zr88D3h3tf5KPejsH2J/8PTuDieHXTBdybWZrC55EYoPYpaHW/SB4AMzsP4H+NCduhuZzf+AXgS9I+pzf9wfAS4C3SXoKcDXwc/7Ye4BHAlcCi8AvbehqE4lEYhp2nmCZhkslvRb4e//+icCnpzlxM6LdPk7de3IFDxkz3oCnHdVFJTaG2PQGbX/OWr6fRGIrY8DuVL5/Hfd8fqZ//6/A/5vmxFTbLbGxrBV80EFRY7qJJJNbYpMRszWpSfoGcBAXWVOY2dmSTgLeCpyFc/D/nJnd7F0Zr8BZiBaBJ5vZZ2a2mMlrzIHPm9kPAC9ba3yXVF4nsXl4oTGNdnO4wQmJxIZRVdNt0/MgM7u7mZ3t32+pRHwfUn2FpNsezvlJ80lsPB3tZ1qOyAyXIt4SR5ONMbttxUT8E4HLJX0SWAg7zezRa52YhE8ikUjMgHWY3U6WFDvlz/d5ijEG/IvPd/xrf/yIEvGPEn90uCcm4ZPYHMbk/cRM39k0+XsSW4Tphc8NkSltEg8ws2slfQ9wsaSvtC+1/kT8o4GZfTS8lnQycKNNaZ5IPp/EluSoRLklv1HiqDHbwqJmdq3/eR3wLuDe+ER8gM1OxJd0jqSPSHqnpHtI+iLwRb/Gh08zRxI+ic1jjQTRFGad2DYYUNp02xpIOlbSceE18FDcg30rJeK/Cvg/uGoGHwJ+xcy+F/gx4P9OM0EyuyUSicQMmGGo9anAu7zpuQf8g5m9T9Kn2DqJ+D0z+xcASS80s08AmNlXpjWZJ+GT2BrE2s8qjeRWRLzFbRcSic1kRsLHzK4CfnjM/hvZOon48R/dUufYVDciCZ/E5jIu7NoHIRwVUsh14mhgjE2S3sH8sKQDuGo1e/1r/Ps900yQhE9ia7Ke6tfr1X6SAErMnB3ZpXQiZpYf6RxJ+CQ2n3Umnc6k5lsSQIlZk36f1kUSPolEInGkGFAm3+N6SMInsTUI3xpjDehomt4SiZliYOn3bz0k4ZPYWkxpgkvtFhJbjvT7uC6S8ElsPcYIoJlrP+lBkZgluy/a7YhJwiextTmaYdeJxCxJX2jWRRI+iUQiMQuS8FkXSfgktiax6W09gQeJxGZgBmW52avYViThk9gedFovJCGU2HIkzWddJOGT2LqsEvmWIt0SW470O7kukvBJJBKJI8ZStNs6ScInsbUZ4/tZlZRsmtgMDCwlma6LJHwSW5/1CqBEYjNI5XXWRRI+ie3BtAJoGq0n2eYTs8YsadzrJAmfxPYk/KEnLSixVUhfatZFEj6JRCIxAyxpPusiCZ/E9mGju54mElOzu5rJzYIkfBLbi0kCKJHYTFJh0XWzbb4ySnq4pCskXSnpOZu9nsQmkr5hJrYYBlhZTrUlHNtC+EjKgb8CHgHcBXiCpLts7qoSm0oSQImthPlmctNsa7BbvmhvC+ED3Bu40syuMrMh8Bbg3E1eUyKRSNRYZVNtq7GbvmhvF+FzBvCt6P01fl+NpKdK+rSkT48YbOjiEptE0n4SsHUCTmaj+eyaL9o7JuDAzM4HzgeQdPAD9vYrKIGtbWI9GbhhsxcxBWmdsyWtc3bMYo23O9JFHOTm93/A3n7ylMP3SPp09P58//yC8V+073Ok69uKbBfhcy1wZvT+Nn7fJK4ws7OP7pKOHEmfTuucHWmds2U7rHOrrNHMHr7Za9hubBF9dU0+BdxZ0u0lzQGPBy7c5DUlEonErFnvF+1ty7YQPmZWAE8H3g98GXibmV2+uatKJBKJmbNrvmhvF7MbZvYe4D1TDj9/7SFbgrTO2ZLWOVu2wzq3wxqnxswKSeGLdg5csFO/aCt1hEwkEonERrMtzG6JRCKR2Fkk4ZNIJBKJDWfHCZ/NLE0h6UxJH5b0JUmXS3qW33+SpIslfdX/PNHvl6RX+rVeJume0Vzn+fFflXTeUVpvLumzki7y728v6RK/nrd6hyeS5v37K/3xs6I5nuv3XyHpYUdhjSdIerukr0j6sqT7bsX7Kem3/P/5FyW9WdKerXA/JV0g6TpJX4z2zez+SbqXpC/4c14pdau+HtE6/9T/v18m6V2SToiOjb1Pk/7+J/1fJDYRM9sxG85B9zXgDsAc8HngLht4/dOAe/rXxwH/iSuR8SfAc/z+5wAv9a8fCbwXEHAOcInffxJwlf95on994lFY77OBfwAu8u/fBjzev34N8D/9698AXuNfPx54q399F3+P54Hb+3ufz3iNbwB+xb+eA07YavcTlxj4dWBvdB+fvBXuJ/BjwD2BL0b7Znb/gE/6sfLnPmKG63wo0POvXxqtc+x9YpW//0n/F2nbvG3TFzDTDwP3Bd4fvX8u8NxNXM+7gZ8ArgBO8/tOwyXBAvw18IRo/BX++BOAv472t8bNaG23AT4IPBi4yD88boj+2Ot7iYu8ua9/3fPj1L2/8bgZrfF43ENdnf1b6n7SZKWf5O/PRcDDtsr9BM7qPNRncv/8sa9E+1vjjnSdnWOPAd7kX4+9T0z4+1/tdzttm7ftNLPbmjXgNgpvSrkHcAlwqpl92x/6DnCqfz1pvRvxOf4C+D0gFJu6NXCLuZyq7jXr9fjj+/34o73O2wPXA3/rzYOvlXQsW+x+mtm1wJ8B3wS+jbs/l7L17mdgVvfvDP/6aK8X4JdxmtXhrHO13+3EJrHThM+WQNI+4B3Ab5rZgfiYua9emxrfLukngevM7NLNXMcU9HCmmFeb2T2ABZyZqGaL3M8TccUfbw+cDhwLbItyK1vh/q2FpD8ECuBNm72WxOzYacJn00tTSOrjBM+bzOydfvd3JZ3mj58GXOf3T1rv0f4c9wceLekbuKq5DwZeAZwgKSQex9es1+OPHw/cuAHrvAa4xswu8e/fjhNGW+1+/jjwdTO73sxGwDtx93ir3c/ArO7ftf71UVuvpCcDPwk80QvKw1nnjUz+v0hsEjtN+GxqaQof6fM64Mtm9rLo0IVAiBA6D+cLCvuf5KOMzgH2e3PI+4GHSjrRf6t+qN83E8zsuWZ2GzM7C3ePPmRmTwQ+DDx2wjrD+h/rx5vf/3gfvXV74M44B/Ss1vkd4FuSvt/vegjwJbbY/cSZ286RdIz/HQjr3FL3M2Im988fOyDpHP+5nxTNdcRIejjONPxoM1vsrH/cfRr79+/v7aT/i8RmsdlOp1lvuIid/8RFvfzhBl/7ATgTxmXA5/z2SJzN+YPAV4EPACf58cI1jvoa8AXg7GiuXwau9NsvHcU1P5Am2u0OuD/iK4F/BOb9/j3+/ZX++B2i8//Qr/8KDjPSaY313R34tL+n/x8u2mrL3U/gBcBXgC8Cf4eLxNr0+wm8GeeHGuE0yafM8v4BZ/vP/DXgVXSCQ45wnVfifDjhb+k1a90nJvz9T/q/SNvmbam8TiKRSCQ2nJ1mdkskEonENiAJn0QikUhsOEn4JBKJRGLDScInkUgkEhtOEj6JRCKR2HCS8EkkEonEhpOET2LTkHSWpCVJn1vneY/WBrfLmLCOB0q632Gc96NybTe+uPboRGJnkoRPYrP5mpndfT0nmNmFZvaSacb6bP2j9Xv+QGBdwkdSz8z+FZcMmUjsWpLwSRwVJP2IbwK2R9Kxco3W7rbGOWf55mGvl/Sfkt4k6ccl/ZtvYnZvP+7Jkl7lX5/qG4193m/38/NcIemNuOz7M+Uak31RrvHZ4/y5D5T0UUnvlnSVpJdIeqKkT/pxd/TjTpH0Dkmf8tv95aqW/zrwW5I+57WZFeP8+c+X9HeS/g1X/SCR2PX01h6SSKwfM/uUpAuBPwb2An9vZtOYme4E/CyunMungJ/HlS16NPAHwE93xr8S+KiZPUZSDuzDleC5M3CemX1C0v/Alen5YeBk4FOSPubP/2HgB4GbcE3SXmtm95brQvsM4DdxRVdfbmYfl3RbXF2zH5T0GuCQmf0ZgKR/6I7zc4NrgPYAM1ua4h4kEjueJHwSR5MX4gTIMvDMKc/5upl9AUDS5cAHzcwkfQHXbKzLg3EFLTGzEtjvi19ebWaf8GMeALzZH/+upI8CPwIcAD5lvreNpK8B/+LP+QLwIP/6x4G7qOkQfSu5thldVht3YRI8iURDEj6Jo8mtcZpIH1dMc2GKcwbR6yp6X7G+39dprjXt9TLgHDNbjk+MhAxTjJt2PYnEriD5fBJHk78G/gjXBOylR+kaHwT+J4CkXNLxY8b8K/A4f/wU4MdYX6uCf8GZ4PDXubt/eRA4bopxiUSiQxI+iaOCpCcBIzP7B+AlwI9IevBRuNSzgAd5s9ylON9Kl3fhWjJ8HvgQ8HvmegVNyzOBs30AxZdwgQYA/wQ8JgQcrDIukUh0SC0VEpuGjxi7yMxWjYLbiezmz55IQNJ8EptLCRy/3iTT7Y7Xkv4JuGGz15JIbBZJ80kkEonEhpM0n0QikUhsOEn4JBKJRGLDScInkUgkEhtOEj6JRCKR2HD+f35j8H0OTyeUAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -1187,14 +1218,14 @@
    "source": [
     "## Serialize as OME-NGFF Zarr \n",
     "\n",
-    "Generate an [OME-NGFF](https://ngff.openmicroscopy.org/latest/) dataset.\n",
+    "Generate an Open Microscopy Environment-Next Generation File Format [OME-NGFF](https://ngff.openmicroscopy.org/latest/) dataset.\n",
     "\n",
-    "This also uses the [*bioformats2raw* transitional layout](https://github.com/ome/ngff/pull/112) to preserve the OME-XML metadata."
+    "Here we also uses the [*bioformats2raw* transitional layout](https://github.com/ome/ngff/pull/112) to preserve the OME-XML metadata."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 26,
    "id": "554a5c53",
    "metadata": {},
    "outputs": [],
@@ -1204,55 +1235,63 @@
     "group.attrs['bioformats2raw.layout'] = 3\n",
     "store['OME/METADATA.ome.xml'] = tiff.ome_metadata.encode('utf-8')\n",
     "\n",
+    "# Compression options\n",
+    "compressor = Blosc(cname='zstd', clevel=5, shuffle=Blosc.SHUFFLE)\n",
+    "\n",
+    "# For OME-NGFF, large datasets, use dimension_separator='/'\n",
     "store = zarr.storage.DirectoryStore(f'{image_name}.zarr/0', dimension_separator='/')\n",
-    "multiscale.to_zarr(store)"
+    "multiscale.to_zarr(store, encoding={ name: { 'compressor': compressor }})"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "52283bcb",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
-   "id": "c9eb9655",
+   "id": "f2dc8ff6",
    "metadata": {},
    "source": [
-    "## WIP: Serialize as OME-TIFF with sharded Zarr / FSSpec view"
+    "### Deserialize"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "b8408c72",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with tifffile.TiffWriter('{image_name}_isotropic_multiscale.ome.tif', ome=True, bigtiff=True) as tif:\n",
-    "...     options = dict(photometric='minisblack', tile=(64, 64, 64), compression='zlib')\n",
-    "...     metadata={'axes': tiff.axes}\n",
-    "...     tif.write(data, subifds=2, **options)\n",
-    "...     # save pyramid levels to the two subifds\n",
-    "...     # in production use resampling to generate sub-resolutions\n",
-    "...     tif.write(data[:, ::2, ::2], subfiletype=1, **options)\n",
-    "...     tif.write(data[:, ::4, ::4], subfiletype=1, **options)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6fbc64ca",
+   "execution_count": 27,
+   "id": "1ac7998c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f48884e1430>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "tiff.close()"
+    "store = zarr.storage.DirectoryStore(f'{image_name}.zarr/0', dimension_separator='/')\n",
+    "\n",
+    "new_multiscale = datatree.open_datatree(store, engine='zarr')\n",
+    "new_multiscale['scale2'].ds[name].isel(x=250).plot.imshow()"
    ]
   }
  ],
  "metadata": {
+  "interpreter": {
+   "hash": "5108159e926a957efbf78924adfcf51570e7c16c3091fc603e070659d85c3c31"
+  },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
@@ -1268,7 +1307,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.10"
+   "version": "3.9.12"
   }
  },
  "nbformat": 4,