notebooks/1_Exploring_Mesogeos.ipynb

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        "# Exploring the Mesogeos Cube\n",
        "\n",
        "## Steps needed to access the dataset from google drive\n",
        "\n",
        "\n",
        "\n",
        "1. Add the dataset's [Drive link](https://drive.google.com/drive/folders/1aRXQXVvw6hz0eYgtJDoixjPQO-_bRKz9) to your shortcuts in Google Drive.\n",
        "\n",
        "2. Then you can use the shortcut path to open the dataset.\n",
        "\n",
        "NOTE: For reference, our path in this notebook is `orionlab_datasets/mesogeos`, so after we mount `/content/drive` the dataset will be in `/content/drive/MyDrive/orionlab_datasets/mesogeos/mesogeos.zarr`.\n",
        "\n"
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        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
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          "text": [
            "Mounted at /content/drive\n"
          ]
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        "!pip install zarr"
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              "  grid-column: 2;\n",
              "  white-space: pre-wrap;\n",
              "  word-break: break-all;\n",
              "}\n",
              "\n",
              ".xr-icon-database,\n",
              ".xr-icon-file-text2,\n",
              ".xr-no-icon {\n",
              "  display: inline-block;\n",
              "  vertical-align: middle;\n",
              "  width: 1em;\n",
              "  height: 1.5em !important;\n",
              "  stroke-width: 0;\n",
              "  stroke: currentColor;\n",
              "  fill: currentColor;\n",
              "}\n",
              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
              "Dimensions:               (y: 1752, x: 4714, time: 6026)\n",
              "Coordinates:\n",
              "  * time                  (time) datetime64[ns] 2006-04-01 ... 2022-09-29\n",
              "  * x                     (x) float64 -10.72 -10.71 -10.7 ... 36.72 36.73 36.74\n",
              "  * y                     (y) float64 47.7 47.69 47.68 ... 30.09 30.08 30.07\n",
              "Data variables: (12/30)\n",
              "    aspect                (y, x) float32 dask.array&lt;chunksize=(1752, 4714), meta=np.ndarray&gt;\n",
              "    burned_areas          (time, y, x) float32 dask.array&lt;chunksize=(1, 1752, 4714), meta=np.ndarray&gt;\n",
              "    curvature             (y, x) float32 dask.array&lt;chunksize=(1752, 4714), meta=np.ndarray&gt;\n",
              "    d2m                   (time, y, x) float32 dask.array&lt;chunksize=(1, 1752, 4714), meta=np.ndarray&gt;\n",
              "    dem                   (y, x) float32 dask.array&lt;chunksize=(1752, 4714), meta=np.ndarray&gt;\n",
              "    ignition_points       (time, y, x) float32 dask.array&lt;chunksize=(1, 1752, 4714), meta=np.ndarray&gt;\n",
              "    ...                    ...\n",
              "    spatial_ref           int64 ...\n",
              "    ssrd                  (time, y, x) float32 dask.array&lt;chunksize=(1, 1752, 4714), meta=np.ndarray&gt;\n",
              "    t2m                   (time, y, x) float32 dask.array&lt;chunksize=(1, 1752, 4714), meta=np.ndarray&gt;\n",
              "    tp                    (time, y, x) float32 dask.array&lt;chunksize=(1, 1752, 4714), meta=np.ndarray&gt;\n",
              "    wind_direction        (time, y, x) float32 dask.array&lt;chunksize=(1, 1752, 4714), meta=np.ndarray&gt;\n",
              "    wind_speed            (time, y, x) float32 dask.array&lt;chunksize=(1, 1752, 4714), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e9b48553-2643-42c9-816e-7230f0d579ab' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e9b48553-2643-42c9-816e-7230f0d579ab' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 1752</li><li><span class='xr-has-index'>x</span>: 4714</li><li><span class='xr-has-index'>time</span>: 6026</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6728c7c0-c532-49d8-99b9-d1b7774bf8c5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6728c7c0-c532-49d8-99b9-d1b7774bf8c5' 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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2006-04-01 ... 2022-09-29</div><input id='attrs-44708a08-f390-4851-aa7e-84afff96129a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-44708a08-f390-4851-aa7e-84afff96129a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-adb4f26d-54a4-4ab5-b1df-d002e6853269' class='xr-var-data-in' type='checkbox'><label for='data-adb4f26d-54a4-4ab5-b1df-d002e6853269' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2006-04-01T00:00:00.000000000&#x27;, &#x27;2006-04-02T00:00:00.000000000&#x27;,\n",
              "       &#x27;2006-04-03T00:00:00.000000000&#x27;, ..., &#x27;2022-09-27T00:00:00.000000000&#x27;,\n",
              "       &#x27;2022-09-28T00:00:00.000000000&#x27;, &#x27;2022-09-29T00:00:00.000000000&#x27;],\n",
              "      dtype=&#x27;datetime64[ns]&#x27;)</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'>-10.72 -10.71 -10.7 ... 36.73 36.74</div><input id='attrs-e0d4c485-763e-4965-a609-7cf9992d6366' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0d4c485-763e-4965-a609-7cf9992d6366' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f985ea48-b5d2-425c-ba83-c1b81e59c032' class='xr-var-data-in' type='checkbox'><label for='data-f985ea48-b5d2-425c-ba83-c1b81e59c032' 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>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-10.719097, -10.709028, -10.698959, ...,  36.715829,  36.725898,\n",
              "        36.735967])</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'>47.7 47.69 47.68 ... 30.08 30.07</div><input id='attrs-14ed42af-6ae4-4da3-b8ba-820a20b6a659' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-14ed42af-6ae4-4da3-b8ba-820a20b6a659' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d2b5253-2f1f-4f17-aa51-5d38bae22bb6' class='xr-var-data-in' type='checkbox'><label for='data-1d2b5253-2f1f-4f17-aa51-5d38bae22bb6' 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>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([47.69924 , 47.689171, 47.679102, ..., 30.088608, 30.078539, 30.06847 ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fd8bc11e-c7d3-4e12-9274-1d0a4c286d2f' class='xr-section-summary-in' type='checkbox'  ><label for='section-fd8bc11e-c7d3-4e12-9274-1d0a4c286d2f' class='xr-section-summary' >Data variables: <span>(30)</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>aspect</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1752, 4714), meta=np.ndarray&gt;</div><input id='attrs-ed46125f-cc92-4692-8a58-3710707f5995' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed46125f-cc92-4692-8a58-3710707f5995' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3989ed6-e4b6-43e1-bf80-f35d108ab08c' class='xr-var-data-in' type='checkbox'><label for='data-c3989ed6-e4b6-43e1-bf80-f35d108ab08c' 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>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
              "    <tr>\n",
              "        <td>\n",
              "            <table style=\"border-collapse: collapse;\">\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> 31.51 MiB </td>\n",
              "                        <td> 31.51 MiB </td>\n",
              "                    </tr>\n",
              "                    \n",
              "                    <tr>\n",
              "                        <th> Shape </th>\n",
              "                        <td> (1752, 4714) </td>\n",
              "                        <td> (1752, 4714) </td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th> Dask graph </th>\n",
              "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th> Data type </th>\n",
              "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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              "    </tr>\n",
              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>burned_areas</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1752, 4714), meta=np.ndarray&gt;</div><input id='attrs-8c057c5d-ee00-4535-92d6-530c71560865' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c057c5d-ee00-4535-92d6-530c71560865' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af5c527b-5393-4085-ae82-3f1523c266cb' class='xr-var-data-in' type='checkbox'><label for='data-af5c527b-5393-4085-ae82-3f1523c266cb' 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>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
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              "                <tbody>\n",
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              "                        <td> 31.51 MiB </td>\n",
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              "                    <tr>\n",
              "                        <th> Shape </th>\n",
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              "                        <td> (1, 1752, 4714) </td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th> Dask graph </th>\n",
              "                        <td colspan=\"2\"> 6026 chunks in 2 graph layers </td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th> Data type </th>\n",
              "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>curvature</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1752, 4714), meta=np.ndarray&gt;</div><input id='attrs-ae0c6d26-ba1a-432b-afae-8574478894f5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae0c6d26-ba1a-432b-afae-8574478894f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b328198-fc3b-4a70-b866-39b9a8ccc14e' class='xr-var-data-in' type='checkbox'><label for='data-7b328198-fc3b-4a70-b866-39b9a8ccc14e' 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>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
              "    <tr>\n",
              "        <td>\n",
              "            <table style=\"border-collapse: collapse;\">\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> 31.51 MiB </td>\n",
              "                        <td> 31.51 MiB </td>\n",
              "                    </tr>\n",
              "                    \n",
              "                    <tr>\n",
              "                        <th> Shape </th>\n",
              "                        <td> (1752, 4714) </td>\n",
              "                        <td> (1752, 4714) </td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th> Dask graph </th>\n",
              "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th> Data type </th>\n",
              "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>d2m</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1752, 4714), meta=np.ndarray&gt;</div><input id='attrs-0e10e74b-27fc-4df0-8c11-5ad80eeef70e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e10e74b-27fc-4df0-8c11-5ad80eeef70e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-165a940b-ded9-4265-83a0-a6a50fb92e4a' class='xr-var-data-in' type='checkbox'><label for='data-165a940b-ded9-4265-83a0-a6a50fb92e4a' 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>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
              "    <tr>\n",
              "        <td>\n",
              "            <table style=\"border-collapse: collapse;\">\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> 185.40 GiB </td>\n",
              "                        <td> 31.51 MiB </td>\n",
              "                    </tr>\n",
              "                    \n",
              "                    <tr>\n",
              "                        <th> Shape </th>\n",
              "                        <td> (6026, 1752, 4714) </td>\n",
              "                        <td> (1, 1752, 4714) </td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th> Dask graph </th>\n",
              "                        <td colspan=\"2\"> 6026 chunks in 2 graph layers </td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th> Data type </th>\n",
              "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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              "    </tr>\n",
              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dem</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1752, 4714), meta=np.ndarray&gt;</div><input id='attrs-aa05021d-f4c2-4ed1-9786-730bc81d64d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa05021d-f4c2-4ed1-9786-730bc81d64d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10424ea7-823d-4932-81cd-01a0dc0c2b74' class='xr-var-data-in' type='checkbox'><label for='data-10424ea7-823d-4932-81cd-01a0dc0c2b74' 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>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
              "    <tr>\n",
              "        <td>\n",
              "            <table style=\"border-collapse: collapse;\">\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> 31.51 MiB </td>\n",
              "                        <td> 31.51 MiB </td>\n",
              "                    </tr>\n",
              "                    \n",
              "                    <tr>\n",
              "                        <th> Shape </th>\n",
              "                        <td> (1752, 4714) </td>\n",
              "                        <td> (1752, 4714) </td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th> Dask graph </th>\n",
              "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th> Data type </th>\n",
              "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ignition_points</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1752, 4714), meta=np.ndarray&gt;</div><input id='attrs-6ebb8357-b6d2-4ca0-98bc-4c8b82f0530b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ebb8357-b6d2-4ca0-98bc-4c8b82f0530b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7d23437-b971-46a3-bc8f-b52cf7e7c68b' class='xr-var-data-in' type='checkbox'><label for='data-a7d23437-b971-46a3-bc8f-b52cf7e7c68b' 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>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
              "    <tr>\n",
              "        <td>\n",
              "            <table style=\"border-collapse: collapse;\">\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> 185.40 GiB </td>\n",
              "                        <td> 31.51 MiB </td>\n",
              "                    </tr>\n",
              "                    \n",
              "                    <tr>\n",
              "                        <th> Shape </th>\n",
              "                        <td> (6026, 1752, 4714) </td>\n",
              "                        <td> (1, 1752, 4714) </td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th> Dask graph </th>\n",
              "                        <td colspan=\"2\"> 6026 chunks in 2 graph layers </td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th> Data type </th>\n",
              "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lai</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1752, 4714), meta=np.ndarray&gt;</div><input id='attrs-cabb0bd0-b835-4156-ae73-047579673607' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cabb0bd0-b835-4156-ae73-047579673607' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-daa4c49b-8b54-4d12-91ca-18fef48aa5eb' class='xr-var-data-in' type='checkbox'><label for='data-daa4c49b-8b54-4d12-91ca-18fef48aa5eb' 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>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
              "    <tr>\n",
              "        <td>\n",
              "            <table style=\"border-collapse: collapse;\">\n",
              "                <thead>\n",
              "                    <tr>\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> 185.40 GiB </td>\n",
              "                        <td> 31.51 MiB </td>\n",
              "                    </tr>\n",
              "                    \n",
              "                    <tr>\n",
              "                        <th> Shape </th>\n",
              "                        <td> (6026, 1752, 4714) </td>\n",
              "                        <td> (1, 1752, 4714) </td>\n",
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              "                    <tr>\n",
              "                        <th> Dask graph </th>\n",
              "                        <td colspan=\"2\"> 6026 chunks in 2 graph layers </td>\n",
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              "                    <tr>\n",
              "                        <th> Data type </th>\n",
              "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lc_agriculture</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1752, 4714), meta=np.ndarray&gt;</div><input id='attrs-fa41d051-10bc-42c1-8482-db04ff4282f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa41d051-10bc-42c1-8482-db04ff4282f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a931aba5-caf8-45fd-8f37-84205f027f24' class='xr-var-data-in' type='checkbox'><label for='data-a931aba5-caf8-45fd-8f37-84205f027f24' 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>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
              "    <tr>\n",
              "        <td>\n",
              "            <table style=\"border-collapse: collapse;\">\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> 185.40 GiB </td>\n",
              "                        <td> 31.51 MiB </td>\n",
              "                    </tr>\n",
              "                    \n",
              "                    <tr>\n",
              "                        <th> Shape </th>\n",
              "                        <td> (6026, 1752, 4714) </td>\n",
              "                        <td> (1, 1752, 4714) </td>\n",
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              "                    <tr>\n",
              "                        <th> Dask graph </th>\n",
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              "                    <tr>\n",
              "                        <th> Data type </th>\n",
              "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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              "    <tr>\n",
              "        <td>\n",
              "            <table style=\"border-collapse: collapse;\">\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> 185.40 GiB </td>\n",
              "                        <td> 31.51 MiB </td>\n",
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              "                    \n",
              "                    <tr>\n",
              "                        <th> Shape </th>\n",
              "                        <td> (6026, 1752, 4714) </td>\n",
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              "                    <tr>\n",
              "                        <th> Data type </th>\n",
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              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lc_grassland</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1752, 4714), meta=np.ndarray&gt;</div><input id='attrs-26a85b4f-b9c9-46fa-ac83-035373c06d43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-26a85b4f-b9c9-46fa-ac83-035373c06d43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8856b11-7e42-46ec-a46e-ee3a21d7251a' class='xr-var-data-in' type='checkbox'><label for='data-a8856b11-7e42-46ec-a46e-ee3a21d7251a' 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>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
              "    <tr>\n",
              "        <td>\n",
              "            <table style=\"border-collapse: collapse;\">\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> 185.40 GiB </td>\n",
              "                        <td> 31.51 MiB </td>\n",
              "                    </tr>\n",
              "                    \n",
              "                    <tr>\n",
              "                        <th> Shape </th>\n",
              "                        <td> (6026, 1752, 4714) </td>\n",
              "                        <td> (1, 1752, 4714) </td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th> Dask graph </th>\n",
              "                        <td colspan=\"2\"> 6026 chunks in 2 graph layers </td>\n",
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              "                    <tr>\n",
              "                        <th> Data type </th>\n",
              "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lc_settlement</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1752, 4714), meta=np.ndarray&gt;</div><input id='attrs-3086b1a1-19a6-4e28-84a6-d30e0c4f21cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3086b1a1-19a6-4e28-84a6-d30e0c4f21cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9af92cf6-c6f6-4710-95e1-0739109eab36' class='xr-var-data-in' type='checkbox'><label for='data-9af92cf6-c6f6-4710-95e1-0739109eab36' 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>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
              "    <tr>\n",
              "        <td>\n",
              "            <table style=\"border-collapse: collapse;\">\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> 185.40 GiB </td>\n",
              "                        <td> 31.51 MiB </td>\n",
              "                    </tr>\n",
              "                    \n",
              "                    <tr>\n",
              "                        <th> Shape </th>\n",
              "                        <td> (6026, 1752, 4714) </td>\n",
              "                        <td> (1, 1752, 4714) </td>\n",
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              "                    <tr>\n",
              "                        <th> Dask graph </th>\n",
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              "                        <th> Data type </th>\n",
              "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lc_shrubland</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1752, 4714), meta=np.ndarray&gt;</div><input id='attrs-e337568b-0091-4da4-9e78-e2c32a37ab62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e337568b-0091-4da4-9e78-e2c32a37ab62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12231b27-7ed8-4b5d-bfe6-a7578207f3a5' class='xr-var-data-in' type='checkbox'><label for='data-12231b27-7ed8-4b5d-bfe6-a7578207f3a5' 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>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
              "    <tr>\n",
              "        <td>\n",
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              "                <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> 185.40 GiB </td>\n",
              "                        <td> 31.51 MiB </td>\n",
              "                    </tr>\n",
              "                    \n",
              "                    <tr>\n",
              "                        <th> Shape </th>\n",
              "                        <td> (6026, 1752, 4714) </td>\n",
              "                        <td> (1, 1752, 4714) </td>\n",
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              "                    <tr>\n",
              "                        <th> Dask graph </th>\n",
              "                        <td colspan=\"2\"> 6026 chunks in 2 graph layers </td>\n",
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              "                    <tr>\n",
              "                        <th> Data type </th>\n",
              "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lc_sparse_vegetation</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1752, 4714), meta=np.ndarray&gt;</div><input id='attrs-41eba9ee-106e-44d8-8521-46edff5cea3c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41eba9ee-106e-44d8-8521-46edff5cea3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc2dc366-b6ca-4860-ac67-4f56ff91bfaa' class='xr-var-data-in' type='checkbox'><label for='data-bc2dc366-b6ca-4860-ac67-4f56ff91bfaa' 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>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
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              "        <td>\n",
              "            <table style=\"border-collapse: collapse;\">\n",
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              "                    </tr>\n",
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              "                <tbody>\n",
              "                    \n",
              "                    <tr>\n",
              "                        <th> Bytes </th>\n",
              "                        <td> 185.40 GiB </td>\n",
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              "                    <tr>\n",
              "                        <th> Shape </th>\n",
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              "        <td>\n",
              "            <table style=\"border-collapse: collapse;\">\n",
              "                <thead>\n",
              "                    <tr>\n",
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              "                        <th> Array </th>\n",
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              "                <tbody>\n",
              "                    \n",
              "                    <tr>\n",
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              "                        <td> 185.40 GiB </td>\n",
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              "                    <tr>\n",
              "                        <th> Shape </th>\n",
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              "        <td>\n",
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              "                <tbody>\n",
              "                    \n",
              "                    <tr>\n",
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              "                        <th> Dask graph </th>\n",
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              "    <tr>\n",
              "        <td>\n",
              "            <table style=\"border-collapse: collapse;\">\n",
              "                <thead>\n",
              "                    <tr>\n",
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              "                </thead>\n",
              "                <tbody>\n",
              "                    \n",
              "                    <tr>\n",
              "                        <th> Bytes </th>\n",
              "                        <td> 185.40 GiB </td>\n",
              "                        <td> 31.51 MiB </td>\n",
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              "                    <tr>\n",
              "                        <th> Shape </th>\n",
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              "                        <th> Data type </th>\n",
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              "    <tr>\n",
              "        <td>\n",
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              "                </thead>\n",
              "                <tbody>\n",
              "                    \n",
              "                    <tr>\n",
              "                        <th> Bytes </th>\n",
              "                        <td> 185.40 GiB </td>\n",
              "                        <td> 31.51 MiB </td>\n",
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              "                    \n",
              "                    <tr>\n",
              "                        <th> Shape </th>\n",
              "                        <td> (6026, 1752, 4714) </td>\n",
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              "                        <th> Dask graph </th>\n",
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              "                        <th> Data type </th>\n",
              "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ndvi</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1752, 4714), meta=np.ndarray&gt;</div><input id='attrs-a6a1e47e-8565-452f-856c-a3fb83b9e65a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6a1e47e-8565-452f-856c-a3fb83b9e65a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f93602c1-9ae5-48e0-a0ce-cf12164b0081' class='xr-var-data-in' type='checkbox'><label for='data-f93602c1-9ae5-48e0-a0ce-cf12164b0081' 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>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
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              "        <td>\n",
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              "                <tbody>\n",
              "                    \n",
              "                    <tr>\n",
              "                        <th> Bytes </th>\n",
              "                        <td> 185.40 GiB </td>\n",
              "                        <td> 31.51 MiB </td>\n",
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              "                    <tr>\n",
              "                        <th> Shape </th>\n",
              "                        <td> (6026, 1752, 4714) </td>\n",
              "                        <td> (1, 1752, 4714) </td>\n",
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              "                        <th> Dask graph </th>\n",
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              "                        <th> Data type </th>\n",
              "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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              "    <tr>\n",
              "        <td>\n",
              "            <table style=\"border-collapse: collapse;\">\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> 185.40 GiB </td>\n",
              "                        <td> 31.51 MiB </td>\n",
              "                    </tr>\n",
              "                    \n",
              "                    <tr>\n",
              "                        <th> Shape </th>\n",
              "                        <td> (6026, 1752, 4714) </td>\n",
              "                        <td> (1, 1752, 4714) </td>\n",
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              "                    <tr>\n",
              "                        <th> Dask graph </th>\n",
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              "                        <th> Data type </th>\n",
              "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rh</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1752, 4714), meta=np.ndarray&gt;</div><input id='attrs-9d17398a-c7c5-49f7-8356-8928a81f047f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d17398a-c7c5-49f7-8356-8928a81f047f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-906cd0ba-846f-4760-97fb-7b78df8aee51' class='xr-var-data-in' type='checkbox'><label for='data-906cd0ba-846f-4760-97fb-7b78df8aee51' 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>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
              "    <tr>\n",
              "        <td>\n",
              "            <table style=\"border-collapse: collapse;\">\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> 185.40 GiB </td>\n",
              "                        <td> 31.51 MiB </td>\n",
              "                    </tr>\n",
              "                    \n",
              "                    <tr>\n",
              "                        <th> Shape </th>\n",
              "                        <td> (6026, 1752, 4714) </td>\n",
              "                        <td> (1, 1752, 4714) </td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th> Dask graph </th>\n",
              "                        <td colspan=\"2\"> 6026 chunks in 2 graph layers </td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th> Data type </th>\n",
              "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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              "    </tr>\n",
              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roads_distance</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1752, 4714), meta=np.ndarray&gt;</div><input id='attrs-20dab6ec-73dd-4f4b-a213-2f6efb576861' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20dab6ec-73dd-4f4b-a213-2f6efb576861' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2bf5d530-b4ce-4814-8029-e06cf5abc75f' class='xr-var-data-in' type='checkbox'><label for='data-2bf5d530-b4ce-4814-8029-e06cf5abc75f' 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>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
              "    <tr>\n",
              "        <td>\n",
              "            <table style=\"border-collapse: collapse;\">\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> 31.51 MiB </td>\n",
              "                        <td> 31.51 MiB </td>\n",
              "                    </tr>\n",
              "                    \n",
              "                    <tr>\n",
              "                        <th> Shape </th>\n",
              "                        <td> (1752, 4714) </td>\n",
              "                        <td> (1752, 4714) </td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th> Dask graph </th>\n",
              "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th> Data type </th>\n",
              "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
              "                    </tr>\n",
              "                </tbody>\n",
              "            </table>\n",
              "        </td>\n",
              "        <td>\n",
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              "        </td>\n",
              "    </tr>\n",
              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>slope</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1752, 4714), meta=np.ndarray&gt;</div><input id='attrs-ed8fe10f-e700-4e38-891b-f6475aef7f77' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed8fe10f-e700-4e38-891b-f6475aef7f77' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-110409e5-eb76-4a5a-9c57-eae178f63f17' class='xr-var-data-in' type='checkbox'><label for='data-110409e5-eb76-4a5a-9c57-eae178f63f17' 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>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
              "    <tr>\n",
              "        <td>\n",
              "            <table style=\"border-collapse: collapse;\">\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> 31.51 MiB </td>\n",
              "                        <td> 31.51 MiB </td>\n",
              "                    </tr>\n",
              "                    \n",
              "                    <tr>\n",
              "                        <th> Shape </th>\n",
              "                        <td> (1752, 4714) </td>\n",
              "                        <td> (1752, 4714) </td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th> Dask graph </th>\n",
              "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th> Data type </th>\n",
              "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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              "            </table>\n",
              "        </td>\n",
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              "        </td>\n",
              "    </tr>\n",
              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smi</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1752, 4714), meta=np.ndarray&gt;</div><input id='attrs-1f13bc3e-4e61-40d4-8930-291b5c2bec94' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1f13bc3e-4e61-40d4-8930-291b5c2bec94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c393188-89f9-47e8-a8c3-2112da18b13f' class='xr-var-data-in' type='checkbox'><label for='data-3c393188-89f9-47e8-a8c3-2112da18b13f' 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>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
              "    <tr>\n",
              "        <td>\n",
              "            <table style=\"border-collapse: collapse;\">\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> 185.40 GiB </td>\n",
              "                        <td> 31.51 MiB </td>\n",
              "                    </tr>\n",
              "                    \n",
              "                    <tr>\n",
              "                        <th> Shape </th>\n",
              "                        <td> (6026, 1752, 4714) </td>\n",
              "                        <td> (1, 1752, 4714) </td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th> Dask graph </th>\n",
              "                        <td colspan=\"2\"> 6026 chunks in 2 graph layers </td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th> Data type </th>\n",
              "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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              "        </td>\n",
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              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sp</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1752, 4714), meta=np.ndarray&gt;</div><input id='attrs-279626fe-02ad-423c-969e-72e102ab3ee3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-279626fe-02ad-423c-969e-72e102ab3ee3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34c22f9f-dcf9-4080-b6fe-fffc4d4de888' class='xr-var-data-in' type='checkbox'><label for='data-34c22f9f-dcf9-4080-b6fe-fffc4d4de888' 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>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
              "    <tr>\n",
              "        <td>\n",
              "            <table style=\"border-collapse: collapse;\">\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> 185.40 GiB </td>\n",
              "                        <td> 31.51 MiB </td>\n",
              "                    </tr>\n",
              "                    \n",
              "                    <tr>\n",
              "                        <th> Shape </th>\n",
              "                        <td> (6026, 1752, 4714) </td>\n",
              "                        <td> (1, 1752, 4714) </td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th> Dask graph </th>\n",
              "                        <td colspan=\"2\"> 6026 chunks in 2 graph layers </td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th> Data type </th>\n",
              "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-728aba85-c25b-43bf-b683-43eb60cabaef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-728aba85-c25b-43bf-b683-43eb60cabaef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-268418c9-193b-4831-81f4-91096d287c81' class='xr-var-data-in' type='checkbox'><label for='data-268418c9-193b-4831-81f4-91096d287c81' 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>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssrd</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1752, 4714), meta=np.ndarray&gt;</div><input id='attrs-e931f8e2-a719-4af6-a12c-55e5ec9ccf0a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e931f8e2-a719-4af6-a12c-55e5ec9ccf0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-809e2122-d0ff-4cd9-921a-19a16398c086' class='xr-var-data-in' type='checkbox'><label for='data-809e2122-d0ff-4cd9-921a-19a16398c086' 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>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
              "    <tr>\n",
              "        <td>\n",
              "            <table style=\"border-collapse: collapse;\">\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> 185.40 GiB </td>\n",
              "                        <td> 31.51 MiB </td>\n",
              "                    </tr>\n",
              "                    \n",
              "                    <tr>\n",
              "                        <th> Shape </th>\n",
              "                        <td> (6026, 1752, 4714) </td>\n",
              "                        <td> (1, 1752, 4714) </td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th> Dask graph </th>\n",
              "                        <td colspan=\"2\"> 6026 chunks in 2 graph layers </td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th> Data type </th>\n",
              "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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              "    <tr>\n",
              "        <td>\n",
              "            <table style=\"border-collapse: collapse;\">\n",
              "                <thead>\n",
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              "                        <th> Array </th>\n",
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              "                    </tr>\n",
              "                </thead>\n",
              "                <tbody>\n",
              "                    \n",
              "                    <tr>\n",
              "                        <th> Bytes </th>\n",
              "                        <td> 185.40 GiB </td>\n",
              "                        <td> 31.51 MiB </td>\n",
              "                    </tr>\n",
              "                    \n",
              "                    <tr>\n",
              "                        <th> Shape </th>\n",
              "                        <td> (6026, 1752, 4714) </td>\n",
              "                        <td> (1, 1752, 4714) </td>\n",
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              "                    <tr>\n",
              "                        <th> Dask graph </th>\n",
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              "                    <tr>\n",
              "                        <th> Data type </th>\n",
              "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tp</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1752, 4714), meta=np.ndarray&gt;</div><input id='attrs-6d49cc59-027e-4541-8773-ae9e8f50ee6c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d49cc59-027e-4541-8773-ae9e8f50ee6c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b603c0c-23b7-4f23-b967-2761e6a9c17f' class='xr-var-data-in' type='checkbox'><label for='data-7b603c0c-23b7-4f23-b967-2761e6a9c17f' 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>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
              "    <tr>\n",
              "        <td>\n",
              "            <table style=\"border-collapse: collapse;\">\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> 185.40 GiB </td>\n",
              "                        <td> 31.51 MiB </td>\n",
              "                    </tr>\n",
              "                    \n",
              "                    <tr>\n",
              "                        <th> Shape </th>\n",
              "                        <td> (6026, 1752, 4714) </td>\n",
              "                        <td> (1, 1752, 4714) </td>\n",
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              "                    <tr>\n",
              "                        <th> Dask graph </th>\n",
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              "                    <tr>\n",
              "                        <th> Data type </th>\n",
              "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_direction</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1752, 4714), meta=np.ndarray&gt;</div><input id='attrs-4d6e5300-c187-4984-ad95-bed17e26321a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d6e5300-c187-4984-ad95-bed17e26321a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1cb674a0-46f8-4a14-91b2-e2627bb49814' class='xr-var-data-in' type='checkbox'><label for='data-1cb674a0-46f8-4a14-91b2-e2627bb49814' 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>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
              "    <tr>\n",
              "        <td>\n",
              "            <table style=\"border-collapse: collapse;\">\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> 185.40 GiB </td>\n",
              "                        <td> 31.51 MiB </td>\n",
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              "                    \n",
              "                    <tr>\n",
              "                        <th> Shape </th>\n",
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              "                    <tr>\n",
              "                        <th> Data type </th>\n",
              "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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              "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wind_speed</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1752, 4714), meta=np.ndarray&gt;</div><input id='attrs-2b9d57c5-6d2a-4a44-8586-88c4c8be6ff7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b9d57c5-6d2a-4a44-8586-88c4c8be6ff7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fa1d068-d082-454c-b013-9226b400f474' class='xr-var-data-in' type='checkbox'><label for='data-7fa1d068-d082-454c-b013-9226b400f474' 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>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
              "    <tr>\n",
              "        <td>\n",
              "            <table style=\"border-collapse: collapse;\">\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> 185.40 GiB </td>\n",
              "                        <td> 31.51 MiB </td>\n",
              "                    </tr>\n",
              "                    \n",
              "                    <tr>\n",
              "                        <th> Shape </th>\n",
              "                        <td> (6026, 1752, 4714) </td>\n",
              "                        <td> (1, 1752, 4714) </td>\n",
              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th> Dask graph </th>\n",
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              "                    </tr>\n",
              "                    <tr>\n",
              "                        <th> Data type </th>\n",
              "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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              "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-cae6cd38-a253-4582-adda-0a4af7f09b98' class='xr-section-summary-in' type='checkbox'  ><label for='section-cae6cd38-a253-4582-adda-0a4af7f09b98' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-57814801-3771-4b8f-a653-3b524380a00a' class='xr-index-data-in' type='checkbox'/><label for='index-57814801-3771-4b8f-a653-3b524380a00a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2006-04-01&#x27;, &#x27;2006-04-02&#x27;, &#x27;2006-04-03&#x27;, &#x27;2006-04-04&#x27;,\n",
              "               &#x27;2006-04-05&#x27;, &#x27;2006-04-06&#x27;, &#x27;2006-04-07&#x27;, &#x27;2006-04-08&#x27;,\n",
              "               &#x27;2006-04-09&#x27;, &#x27;2006-04-10&#x27;,\n",
              "               ...\n",
              "               &#x27;2022-09-20&#x27;, &#x27;2022-09-21&#x27;, &#x27;2022-09-22&#x27;, &#x27;2022-09-23&#x27;,\n",
              "               &#x27;2022-09-24&#x27;, &#x27;2022-09-25&#x27;, &#x27;2022-09-26&#x27;, &#x27;2022-09-27&#x27;,\n",
              "               &#x27;2022-09-28&#x27;, &#x27;2022-09-29&#x27;],\n",
              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=6026, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-585c7912-5e57-406f-b683-b0b0f6416c92' class='xr-index-data-in' type='checkbox'/><label for='index-585c7912-5e57-406f-b683-b0b0f6416c92' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-10.719097328696062,  -10.70902835692278, -10.698959385149498,\n",
              "              -10.688890413376217, -10.678821441602935, -10.668752469829654,\n",
              "              -10.658683498056373,  -10.64861452628309, -10.638545554509811,\n",
              "              -10.628476582736528,\n",
              "              ...\n",
              "                36.64534589281951,    36.6554148645928,   36.66548383636608,\n",
              "                36.67555280813936,   36.68562177991264,   36.69569075168592,\n",
              "                 36.7057597234592,   36.71582869523248,   36.72589766700577,\n",
              "                36.73596663877905],\n",
              "             dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=4714))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f934e060-825e-4445-92c9-a535afcb7fbd' class='xr-index-data-in' type='checkbox'/><label for='index-f934e060-825e-4445-92c9-a535afcb7fbd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([ 47.69923994531206,  47.68917097353878,   47.6791020017655,\n",
              "              47.669033029992214, 47.658964058218935, 47.648895086445656,\n",
              "               47.63882611467237,  47.62875714289909,  47.61868817112581,\n",
              "               47.60861919935253,\n",
              "              ...\n",
              "               30.15909111625591,  30.14902214448263, 30.138953172709346,\n",
              "              30.128884200936067, 30.118815229162784,   30.1087462573895,\n",
              "              30.098677285616223,  30.08860831384294,  30.07853934206966,\n",
              "              30.068470370296378],\n",
              "             dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=1752))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-672ad51c-4222-4c2d-98f6-f0787f8cbc6d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-672ad51c-4222-4c2d-98f6-f0787f8cbc6d' 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>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "# plot the ndvi for the 100th day in the dataset for the whole Mediterranean\n",
        "ds.isel(time=100).ndvi.plot(cmap=\"RdYlGn\", vmax=1, vmin=-1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "K9i9vLkgxEE9",
        "outputId": "7ff76fee-2cda-4efa-f804-54a560ee43dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 1.12 s, sys: 165 ms, total: 1.29 s\n",
            "Wall time: 53.8 s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.QuadMesh at 0x7faea1af5360>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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pT/oe/lj4mxdU9DyPiYkJotHon9WKoYUWWmihhb8++L5PqVSip6cHWf7TFU1qtRqWZf3B+/F/wxcQIBAINP0D/xBs3rz5WWrt559/Pu94xzsAYT2zbds23ve+9zX/Lssy55xzDps3b/6Dj//nwt98IDQxMfFbpfpbaKGFFlpo4XdhbGyMvr6+P8m+a7UaRlsMqvYfvK9IJEK5XH7Gtg9+8IN86EMf+oP3PTU19Sxj5c7OTorFIqZpksvlcF33t47Zu3fvH3z8Pxf+5gOho15BY2NjzzB2fL7gnO+/GkOTSQZVFiR02gIyO+YsFElCkcH1oD+msi9XZ35Mp2r71F2fJUmViCYzU3UxXVE9NRSJuZpHb1jh0SmT6bJNzfVY1x3hgbECly9N8dBYGdeH0+cJewvXh86QQjIgU7Y9OowAqixjqAHev2mY2arF/LjBwUwFVZfpDOm4QF9UZ02HwdOzNWK6yp1DGfSgyor2MCPFGq9e2YYqS3z8rlHeeFofqgT/ce8o56/u4hdbJ+joizF9pMDqZWkWJINUbBdNlvnlzmnsqsO1G/tZmFAJKhJPzIj38bMd05yyuA3X89EVmWLdZcdEEUmSUHWZZFDD9X0imkLZdsmWLJZ0hHF90BWJXM1hIlvF93xUXcWu2ciqTCSss7ozQrHuYjoes6ZFu6EzNJZn5O9/AMDCL11FMKhiuz59sQCH56rYFZt0V5jZmSpGIoBddzmxP86jB7MAeJZHIh1idryIHtIIx4NUKxahsI7r+7iOj2M5qLqKYzl4locSVPA9H0VViIY0FiSC7JipYFUtXr2+h/sPlxjL1ziuK8Ku2QoXL27jkfESYxMlJBna2kMA5Ip1VF1laXuI161s4933jBIJquSyVSRVxvd+VXGXZIlQWKfd0Dg8VWJ5f4L9s1VW90Qo1j2KdYez5sd4OmOiSBKrOw325+p0h3UOF2u8/JgUN+/LU7E8tuyaZt0KMel2R3TWdurcfbjCaX0hvrcnz/ChHBeu60VXJHRZwlAlsjUPXZFIBmUOFy0UxGd1Uk+IbdMmiixRrLsokoTlevTFAtiex707M/R0RRifKNHWEaZiuVRnq1x98jwqtsehfI35cfHUPVe1MR2P7XszyLLM+uVpyrbHwUwFx3Ko5WtoQU18bo6HHtMB8D2fWDxIUJXJlqzm071dtYm3GeSmK0iyhKzL6CGdtpCG5Xn0RAJYrk8yqLBpxwxLFiTZP5Inlg7RHQlQtBzypsOJvVGmyzajhRp2zSYaDdBuaMxPBJgo2bSHVPbOVYnrKpoiY6gy+7NVkoZGTFc4UqqL77Hno8gSZt2lJxZguioyGcmgRs3xWN0VZn+2xoJEkIrlkqs5vHF1itv2l3jBYISPbprkHes7aQsqbJqoocsSuiKxd65GUBXXf99MhQuXtVOxXLZNlXnhwgQFy+PEzgCHSw43PJVB12SuXdnOd3dnWZAIsqrTYLLs0h1RuP9wiSPFOi9YlML1fQ7latx+4lpeuetptk2WmB8PAhALKMxWHZKGQs3xWZAIsitT5dR5EXbMmCwJK1x36feaa8efApZliSDoFWtAV37/C37njlzK337yWevbHyMb9HzC33wgdDRlGIvFnpeB0LnHpOgMKbx91RcBeO09r0EP6wzNVVmUNLhqaZKv75iljETQlRhI6syaHjlJpuxBWZboj4svajwgE6y6aLLESYMaM1UPu7HgbVjYhhtQ6W4P0xPRMIIyddfnhqczvP2ETgJhBVyfJ7I2AVWiM+QyWbZ4/Uk9PDhWRgnr2HUHN6BieR5+QGPClVnTH6Vk+ZxyTAdL24J8c8sE0ViAfabET7ZPcv66XgqSguuDGgvQnjIwuiIMdEUIRwMs7o5SqDs8MllkcUcY2/WRgiplRSUvqUyWXHJInNwfZrTezrY5E6tiEUsEqVRsFsyLk2k8ta3ujXHnrhnMmMy6eQnGS3UkWaIjqGK5PgdNm3AqhO369ER1TpsX45sPjTKvI8IRy2WqapEO6awbTJGp2gTawlx4xxt56MrvcvKyNNuny0i2x1jFRg5qaJrCC5d38N3aJAQ08OBQzUGPG/iej6zKFEt17n3HWbzopscp5GpoIQ0/qCIDdsXmHWcP8NCRCtvHCqhhiXBYIxVUOTxToeR4bJ+pEI0FkAIq3z1QwHM8alWbgzUH2dD40aE8vucTTIvANlu2SLWFUMNwXE+UJw/nqahtSAGVouWixoKsnhfnqSNFnLpDqi1ELmdiITGar/HaswbZPWuyKhogFlDZO1GkNx6kIxVkZVBnz1yVPWWXeDTAuv4Q9oTKl/cVGM7XOHVenPUn9PLUSJ7jB5MUJPj3HVnetKaDRFCmLEtcvHGAqYpFRJGJ6ypTpo0iS7ieT6Huk3HhhK4QpbkaXlCjPSUxXqpz5pIYj01U6TZUds9WsVyPhYtSrO6KsC2sY7keYUli3PEoyhKjNYcFXRF0RcZyPcYKNSYOF1i8opP+WIBszcGTJYyEWHwdxz/q5YEaCuA4Ht3zYsQDKlNli5zp4KsysiJz0kCCxydKmJaLEtEJxoP4nk+9VCcjS8SjAYqSRCqqkbc9Tjy+myf2zbFiRQfxgIrpuEiOiqlY7C3bdEV01reH2JGpsDQlAtmtsxU2zk/gej5pDyzXY6JqUy9b9LWFiAfFeQ10ivc4XqpTqDvgQUGWiMWDWK6PoimEAUdTGeyMENYVZMfjJWvCfObxDJctTeIHFZbMizPnK0R1lYEOg+mqR0dYYdTyOLU3TDIo8Z3dGkMVm+XpEC9oCzEvqiCXXSRDRXZkFvbGKNQdJEMjGNHxgiqH6zCYNtg+XWXrS17Gmx+7nc3jJZa2GfzDaX3EOxbzo75B1t/+Qxa0hZgqW7RHdBZ0KYwV61RrDuO2x6zv86PREucOJnlitADw56FS6AqS/j9fho8+bvyp1reuri6mp6efsW16eppYLIZhGCiKgqIov3VMV1fXH/18/lRodY39jSOuy7x91Rf54o43cfJNL6EvouJ6Pid2R1mY0Pj8kzOMl+p0hXV+8ugYhbqPIsFw3qIzpFC0PAACqoTr+0R1cctsOlIlHhATRbbmsK4rQH9UwXRcnpqp0BdRsF2f165O0x9VeGzKYl/eYa7m8fODBf5zW4YzlrRz/Y4M6ZCGEVBY1hvjimVJsThULOZFFaq2z5GSzbxYgKG5Gn3pMJ84vYehuSqvObmPEzuDmI5PpmpzwcpOAorE8p4oazpCWJ5H1rR59EgRM2eyZ6xAKBXi2KXtnD/fYKbq8oMnJ8nWbD6zZZpVHWGuWt6OZmgokkRltkI6pBHRFFzPp2y5nLggRc10AFiUMtAViaFslQsXRtjYn8B2fV64OEU8qLJr1kTRFRQJxgt1+mNiUbxvJE+manPq/ARZ0wb/Pn587mUcft1tIhAp1FB0BddysT0fNaBimzYdKYOZ6Qq+5+PaLmbORA2qvPuBvcSjAVYdk8aIB1jaFsKuuTg1h//cPMHu2QpqQCUZ0/nGBcsYL9T59AX9rO1PcMJAkttevBZFlQjrCoGAwlvPGcCsu9g1F9d2sU0RCLq2i121KVVtXnV8J/3xAH3pMB+4d5xAQKEjZbCoM8K2g1kkWULRFfKFGuGYeDpVdIVNYyWmyhZPHs4zWqxhVm364wF+OVJCkWDXZBldkbj/YI6fD1e4dySHIoEiScQDKuOlOsf2xylbLrtnqwDcMVzC9uD1q9IMzVV5elQETjtmKvTHAqQMjXRIJx5Q6QrrKBJEdIVczWve+09Om6QMlY+fciIAhirz5jVt/HRXBkOV0RWZwUSQ1QuS6IpMfzzAaLHOtskSWdMhV7QIxAKMz1XJ1hz2TZUxbU8ESrZHe59YpDzHQ9EVPNdjerJM2XKp113a4wHUgIqsyjxyMItTF/dYd1eEeqmO53h0dIZJJ4K4jf6WobkqFy+KsydTxW4EfOOlOl0RHUOVUWSJjfPjrO8Os3u2ylkDCYYLJjsyZc5dkGR3pkLWdHB9n/GCyPz0JYMYmkI8oJKt2IyXLMqWS6HukApqhMOauB6yTERTiAdVXruqDYBtUyV0RWZRMsBD4+J+35ezKFkeK9Mhai6MFBxGig6m4/HYRJXFySAPjFV4dNLi+M4wru9juT5Fy0WRoN2Q+frTWYbzFhFdYWlbiLtHSnz4lG7uOfdiPn36Oj659iJ+ed6l4Dpct/5Cnnzx5dx0ygu4pOt0iFyGXy+Rqdq4ns+bV6eJaDIV22N5e5BVHWGypo0iSazribL5SJEjxdqfajp+FiRZ+oN//pTYsGED99xzzzO23XXXXWzYsAEAXddZu3btM8Z4nsc999zTHPPXgFYg9DeOkaKYUN+08os8cvX3+ND6L3PjBd9kpFBjrOTyxHCOU/pifOLU9Vx80jw0GUKqxGBCZ7LiMi+qEFAlJisuVccnpEqkgjKXLYmyL2cR0WUUSeJbO7LUHZ/13SFWdYR578+G6Y0qTFc9pk2XM/oMCnWPtqBIv7/i2DbKlsu6nhjbJktENIUDs1X+c/MEhbrDyo4whbqPpogS3rKUWATP6I+xO2tx2ZI4SxIqH71zhB9tHSeiKxiaKH0sShn8crhAOqSzI1PhlHlxQm0hrLKFmTN5wYIY39lTQJMl3nByL2fMi1CarvCN+4Y5lK9TnatiOi7RrigHciazVRtdkUmHNTJVi4UdYR48mOXnO6ZRJInBuMEH7zzMXUOzuJbL7kyVqbKFrkik2kIYqsKi9hDpkIauSHiOR6HusOlwnrLlMv9r/8k1D/yI197zGiRZIhAL4FougWiA23ZlMHMmAJl8jRWDSSRZQg/rXLi2B9/zOaErTC5nsn+mgqEqZE0HWZX5h3PmI0kSSuPJ1nJ9XnfnEL7nszTZQX88wJYDc7z4h1uxay4Ah1/9JnI1j65YAEVXUAPiabVeqhOPBjCSBoGAwvf3ZtnWWMjDYa2xABqMFmtohoZTdzBCGpIsNQPHdCJIfzzAYMLg7GXtWK6PbqiULZd4QGGibLOiO0I6rDG/zcBQZS5a3MZ4yaJedxmaqzJ2IEvWdDi+K0I8oGKoCsvbQ8R1iZLtMVGyeOUJ3QwmghiqzFTFpi0oE9ZkEgGZnohG3vK4Zf3p9EdV+mNBsqZDOqxhaDJX/uxBCnWHbM1hx6yNFlQYydeYLv+K1DpVtrBcn/XdYc4eiBPRFXzfZ3F3FDWgokjQkzIo1B1Mx8WqWLieT3tfDEVTcGoOgai4vtOzVayKxWyhjpkziQRVQhGdSERHCypMTpVFaUyVmZmukDI0KhW7uXjfdbgksoOKCEz64wFGC3UUWSId0vjR9imu3zrJWQMJsqbD0lQIx/LIVGwKdZfxUp1M1WZdX4x6XdwDi5IGWdOmNx5geXuI8UZ57Ch0RSYeVOmK6IyX6ty0J89Qtko6pPPAaJ6pikPZchlMBLh33xyjJZf1XTr9UYWaCxFNzBm6ItEXUUgZKj0RhZFCneXtYc7oC6JIErM1j0fGq2zojbIjUyYd0rhvxzS90QCqrODnxuivyCDJvPOJO/jG2H38bGYTY9IcSDJ4Hv7Ul5CMJKs7I2RNh5uGcmRrDsN5k6FsnfsO54noCq7vMzRnEg+qLG5kzf4ckKQ/MBD6b2atyuUy27dvZ/v27YBoj9++fTujo6MAvO997+MVr3hFc/wb3/hGDh06xHvf+1727t3Lddddxy233MI73/nO5ph3vetdfPWrX+X6669nz549vOlNb6JSqfCqV73qD79Afya0AqG/cVx35td+6/Z0SGeqbHHK4jZsz+dfH99KMqgS0iRqLigSlCyfxQnBa1iSUJsLquuLgGhFW4C4LqErMhMli89smWa66pEMyrx14zyezticPc8AYChXpz+qsGu2xsWL4gQUia6ITqHukA5prO6KoGsyS7ujpIIa26fKdIdFDV+RJD764ATXrkyxoTtIuyFzsOAwWXE5fkkbfiNbc/NDhxnO1/jhE5Os7AgTDyj0RkU24sKlbbT3xYh0RgA4vS9MPCCxL2uRDMqsX9HBG84eZNtUibYecQ62aaPLMgtTBqbjsmlMpMwVWWJhR5hgVGfbwSzZmi0yP5qCJEvsnyzh+j5dEZ11PVE2zgsznDcZL9Wbi0pEU1BUiWrZomY63HMgS9lyRSZF+xVnwKk7fPDCQV6wooPV8+JkqhavWNPJ+QtT3LFjGkVTOL5D5yuXLKY7EURXJDIN/sYNu+YIhzUUWWR7dEVwYGzT5qW37+Dc/hCLGpkKLSiO2fPV6/jJ/jlGJ0u4lsgIJdpDBKIBSlUb3/epli1KjSfscs2hUrFZlDTYNDSL1Qh69LCOZXti4a87SLJE2XI5kDWZqlgYqkJXWMcyHQYa3I14UCEV1BgvCg5bpJFN64rodCeC7J4oEU6HMTSZ6YrN0jaRkfvRzhk0WWIwpvKylWkem6ywptNgsFGWSgZlJsoWJdvD9WE4X+OyRx9gd9biruEs6bCGKxKfWK7Py1a0kQ5paIpENVfD93zmxQNMVUR2ZFEyyKNHivzsYI6bdmYwHRdFUxjOmpzUF2PfnMlkvka97lLOVAlEA7i+j66ILFkgKgJdQAROuoJt2qhBlXLNoWY6VE0Hz4O3n9FPe5uBrslIjYzPFcd1EtEVKsU6rufTGw8gqzKFusNU2UKRJVJBjYiuMK8zwuLuKFnT4UDWZDhf44Mbe3h8QpSPXN+nZnsoskR3Ikg6pLNtskTZcjE0he3TZVzfJxUU91F/I7tn2i67Zyu8cGGK5ekQy9vDnNIXYzARZHemwnipTndY4Y3ruzEdD9uDguUR0iQKdZeeiMJ4qc7dhyucN99gSUJlaZu4fwuWx7yowu7ZGguT4vt+zkASRZZINh5CliS6QJKR2ubzmV0/5UjJ5ps78/xgf553PfCoCIR8jxdufpiz77yN+TGdeFBhvGSRqdqNz1HwrIbzNWq2x7qeKKOFGvuz1f/hbPuXj61bt7JmzRrWrFkDiCBmzZo1fOADHwBgcnKyGRQBDA4Ocvvtt3PXXXexatUqPvOZz/C1r32N888/vznmqquu4tOf/jQf+MAHWL16Ndu3b+eOO+54FoH6Lxl/8zpCxWKReDxOoVB4XnKEfhfefN9rWZbS+NgD48xrM+iNBliQCOD5UHE8UkEFTYZUQCYekLEbHIuOkMxkxaVg+UxVHDZ0ByjUfaarLvm6xx3750hHdNb1RukMKaxqDzBXc9gyZfHzPRleeEyatZ06e7MOS5IqH73/CBsXtTE0JyafTNli42CCoTmT0ckS7zpjHqMllzP7goyWHJYkDe4bK7NnzuT8gQi2B5+85zBqUGV5TxRdkdk5U+H0+fEGIVxi85Eiy9tDPLBjmo0rOzl7fph7Dld4aE+GExa3sXX/HGsWpXhi3xwnLUtTqDscajypB+NBqnNVNq7sZPdslXyhxnH9cZ4ayRNLBJvE6ZrtkWxkN5a3h3lirEB7g0j71uPb2TXn8KN9s3ieCG6OBju+5+PUHfSwTk9UZ3S2ivtrAZFTd7h8bQ+m41G2XB4ZyeO5Ht++5Bi6wgmeyhxhtORySk8bb7tnP0vbQmSqNpmqyFoUsiZGPCAyWiEN1/MZna2K7I4qN7kgc5lKM9PkOR5aUKEyZ9LTE2VR0mD3bJVyTQQ5/ckgozlRPnBtl6XdUcZLdfKzVaIpg0qxju/5BKIBjkmH2DleEu/V94lERIkqZYgyVaHuYKgK9+6eaWTPxLPZopRBpmqzd7zIecekGS/V2bZrhuVL27l4UZzJikdQgZoLCxMKmiwxU3WZNT0sz2fLeImIrtAV0SlbLilDpTusMVdz6QqraDJMlF3CmszBXI3BRADL8xkt1NEViR0zFZa3h+mPiyBmtCAyJ+cOxvnmlgkWd0cZydcoT5cJtYVY1hnmyb2zrFjcxsm9EW7dO0dvNMCB6XIzQK6X6izqizFVtihlTTRDw6pYnLI8zZYRwcXyPR9JllADKq7tIisyt122mhd9ewtOzUENqmiGxiXHdrBtssTeoVmuPWsBd4/kmJ0z6WwP0R8LYnkeB7Imi1IGrufj+oIHNJqroWuyKH3V7MZnoTGcN9nYn2C0WGf34TxrF6aYKlvMVm1cy0VWZRa1hyjUHEFEV2RWd4a593CeVFCjKyLI35brsTJtsG2qyqWLo+yYtWk3ZPbMWfz98f388vAUC+Mqj0/b5GoOy1I6Vcfn+3uzpEMa/3BiJ49OVTmQq9MT0bE9H9f3SegyT85UcT2flKERD6hsmyyRDmsclw5RdcS4nrDCzw8WWNcT5UCuRqZqE9EV+mMi6HnRghCPT9vceSjHS5aneHzSFI0Fvs/SlMGOTJWo5/GLl9z8J10zjq5LypvWIwX+AI5Q3cH94mOt9e0PRCsj9DeMk296CVfcfu1v/dtwvs6Xt89ybG+UdT1iIds7V2Nftkah5qLJMGt67MnZ2J5PzfVJBiXqrk93WDyp90ZUjpRdkkGJIyWbB0bzABiaTF9E5WDeYnfWIqLJ6IrE+cvaWRBX2Z6xmDVttk7XScUDxIOKKDuVLXrjIp2+oTfKB8+bz3jZZTCmsjdn0xdROFKucd/hAo8PzZKti8f47q4IkYhOoe7y1JEinuPhejBVsbh3JIeuSDxyMMuigQSn94XYm7XZkalw9bpenjpS5LRj0sQDKi9c003ZdlmUMuhLBjl5WTtBTUYLaWwezlGuOXiOx1MjeRRNIaKJtH5+VnA0pmYqnLsgSbZms7AjzPiRIut6Ynx/X5FHx4ucOi8OgGu5+J6PmRPEbEmWiAUV0iEdSRLltKMcGz0sFvLtU2Ue2j/HK9Z0ohsqiUCIV93xFNdtz1KoexQtk0LdwfV8hvMmZculmK8x2BPFMh0s1yNrOkxVLFLxAKbjkilbDOdNKpaLoiuk2kJ4joesylimWHTLliu6qhqlocGUQdZ0MAJKk5+wZ6zA3EQJRVdEmahB5LZNm0zFZklXhGRMp16si1JiIshx6RC6IlOou6zuCLCoL8by9hCXLElw0eIEazuD7Ng/x8vWdhPRFQ7kTDau6UaRJDZPVLlx8xgl26die5QtH9vzCSgS26crjBctkcUwxAKTDmkcyJqsaNMYL9XJ1kTAlA4pHGwEdOMli9FCnZShciBnsrozgusL7tmmsQJly2VlR5gfDmU5eWEKgEqmwsKFKV6zVjz5dvTGsFyRdaqZDqPFGk7NIRXWMHMm9VKd4ZkKhiaj6ErzM378sCCpu5aLGlCRZIkN/XEUTcGqWFzwlUfQwzqyKqMGVWqFGpuPFBlMGLTNi7MzUyEV1Ig1MmDpsNbM4GRNEWiWLZerj0mwujtKpVjnyGSJ0nSFbEUEzamgxo6ZCsd3hlnUFxOBQVuIJW0G4bBGJKiSDomAJ6IrnNgd4UCuxurOCP3xAJmqjeV64nodKWO5Hp9/YpZNY0W+s2uOyxZH+MXIlLj/fZiuiADlUMEhoEi8cGGSM+dHecudI4yX7Gb2WZFg+1SZiC7TGw0QD6hEdMFFvGRJgtP6IhzKiyA1bSiEVJl0SCeiy7i+z1SxTta0sVyfbVMlvvxUjumKzbmDCe4eKbEjUyZr2vTHgoyXLAYTQZ6YfGYr+p8SkiT9wT8t/OFoBUJ/w3jk6u+JTo/fwIceewO/uPQGLl6c5D0n9PK5jVewPB0mHhQLTqHu0BlSSAVlgopMyfIJKhK5mkgezpoeHSGZ0aLNg2Nlds+Jp8T3rOvg5as6GEwEeWyiyiULIzx0pNwsW1y8IMJATCVtKDx4uMALB0KsTEeYKluYticWDMdDDaisbNcYLjh895ExbM/n6UyNguWxPJXkVcelGJgXp1D3ydY9DFXwU0bGClgVwQMamquK9L6qYKgya+YniOgKj0/X2NAtJtQbN43iuUcJsxpZ0yEeUPjptgkyVZvHDxfITolJMR4NNEnDkixhmzYTmQp7pyvN63rq0nZGC2KxK9Qd1h2TZvN4gR1HipzSF2PbZJklbQaSLGFVLPSITjQlfl+UNBjKVulJiXKF53j4ns+G/jj3jeTRFYkTBpKMFi10ReZd9w8xVaxzYneUH+/P8ff3HGZjf4JNQ7OYhTq1ksWS3hgrOyIs6ozgOqKMZZmCv+FYHunGU3x7SJQ/5zIVtKDCuYtSpOIB2pKCmKtIYsF2Gpky03Eply1s0/5VBiOoYldtCnXBT0rEg2iG4N2Ml0QAtGAwSX88wNrOIIfydbrCojzy+cen6QqLcxkruYwUbD55xzBG0uCR8RL3juQxC/Umr0RXZF64phuAzeMF7h8r84tDZdoNmeGsycm9oWZJdH5MJ2s6nDUQp1D3uHRxjJ6wQlCRuHekwJJUkJShYmiyWGBtj3hARZElFiWDzVJmPKCSDAguSW9Ux9BkzljVRX8swM5MjX1zJpbrYToeN++e5ZQFSYqTZXzPZ2wkj6IryIpMKh4gV7RY1hvDtUVJrVaoIcmCOxYyVBRN4eFDOXRN5oTFbU1yNYjgC6BQd9gyUWxkc2rsnxRZt95ogLLlMlUR5UXTcTE0mXhA4cf7i4wWa9QKgmsWTocpHCkQD6joisRs1ebRiVKDUC5xIGeyYyRPOqQxmAiK76kjymh3j+RRJInxkkWh5pJu3ENvWJUiFVTZ0BuhUBfzwmuOa+fOwya5mkfV9kkFAxyXDrCxV5xrKigzUqgzUXb5zNm9RHSFK5aI1vWQKvHixQnO6e9EkSQeGxXzhml7FOo+T04LXs+iZIAHRktMV0X2b2iuhml7IsBsFD3SITG/uL7PVEV8lzf0xrFcny0TRbZPl3ngcJ7wH9LO3sJfJVqB0N84fvl333nWtg+t/zIA42WHmmvzkS0/oDciOEAb+mIsSQliczIgMxhTuG+0QtUWk8mRsktIEwTcxUmdDb0R9mVrrGwPsDfr0GnIbJkoEQ8q7M3ZXHNMnK3TdUKaxIG8xQPjNb7++CSvXZ1m63Sd1R0BTu0Nc95gjNWdEeZKIvPwgbvHmKzY/PMFg2gN4ue+nMPOuRwfvWuU8bmqeFqcrlK2XbYdzBJscE30iE62Jia6jfMFmXXHVJnl7YK4PFtz+eDJPSxekETRRCfKj4bmRDkhZ2IkDVKGhqzKpLujaIZGxXI5aVGqSSBWdIVgVMe1RXbn7GXt7MhU2D1bob89xMREiRO7Q0Q0ha6kwXTVJh0SwVYwKp7uPcfDrNp0JQ22jhUp5muULRez7qKHdT50ejcHsiadEZ1rV6b4/FlryZoOg4kg8YBKPKTyg92CowKwebxIPGWwblEb4ViAQ7NV7hrOMpw1SYU16qU6akDFMh2hJSRLeB6kDJU3bRDEa0NVuHhBhC+du5RzBpKA6AbyGgHq0WBQ0RTakkEWd0ebJR1ZlTEL9Sb/ZUmbIbI+WZOIrjBVrFO2RDbG9X22TFYEh2owgSJLGJpMV1jh3kM5fM8nElSbZbTVC5JNgu4jI3m6wyoPjOa59th2cayUwXd2F/jQ6d08Ml6lNxpgZdrA9uCJsQJRTcL24GObJhkpOkxXbZanwwzERTDWboiyoeX69EYDWK7HVMVmMBEUhOqazVMzov38J/vnmu3kw/kaD+6awbVcUobQmTqxJ8rQXBUjKYJcI2kgqzInrewkHlC5bGUH+6bKyIqMU3ea7fFqUKVqOsRDKlpQQZElnp4oEW4zUDTBLeocTOJ7PoWZCrbrEw8o1EyH4/rjpEOauH9sr6H7A6btMZyvCb6VLHh5y5a2kwqqvHRFO6G2EIokMTJV5pwFSfpjQeJBERhdsCDOYE+0+TA1WqwJXpHnN7N0S9sM8f+syVTZ4rEp8f1dnFD52vmLWdoWYqbqcVpvgIGYyuGizSOTFSK6xB2HTeJBhYgmsaojSKHuMFsT98YvR6ui89SDgCLxiS3DJAIy6/vjfG9vkRsueDHL21SWtwcxbY8dMyYb+6PsnRPfofFSnd5oAKfu8IIFSXbPVpoPTMP5GsP5GqbjMZw30RWJpW2hZsNFUP3zLYt/6V1jzxe0AqHnMb593je4ZMFnKVk+JcsnawrOz+WLO6i5PnuyNrM1j1P7QtgeRHSJ7rBoOz7aXRZSJVZ2GJRsj+mqTdUR5MtCzSUeEN1mmiKxKB7B9nzWduhsGEzi+rCyXSOkSTwwVmGm6jIQ1+iIBTBzJmcsaWd9d5D+qMqCuMapPQE0RYjjKY3yS7sh0uWKJOFabrPTSTM0VndGWN4eJms6dEV0bNPmruE8WdPmk49McdNQjqmKharL/Ozxcc5fmGJNh+gWWZgyGJ0sUclUml0/Tt1h084ZJEnCtcVCX5kzcS2XS07oZdNYQbTa64IzFIwH+fq26abY4paJEv3xAPddeSmGKojVyaQgkmdrNm1RnWTSoNTQLLrzqvX8YF+Jq45J8oWzl3FSVw/veuAJDuRMdk2WWdlhYKiivOJ50B8PcstFq3nVynaGslWMBrlWV2SuXd1BtmKzbH6C/a+5llMXprhsZYfI3shQqLsU6mIhvvKYFF95Osvb79nPT/fPPeN+iYdU4imDswaSrO2NMpcTi8lRHC31nH1sJ+Waw2ixTjygsGGJaK9e2RVplmALdbeRiZDpDGlkTZtCzeUrDx+h3tBnKtdEoOF6PouSBoWaw8KEznlL2rA9+MCGTqarLmu7wni+zytXJPj5oQqW55GpWmyZrJCrOZy/rJ1HxqvcvDfHovZQs+NqOF9j15xNbzTAwoRCtuZQqIsyouX6HMgKgcepssXqzgin9EV4YqxAV1hvtuMvShnoYZ2FHUJnaVHS4P6npuiPBYlFdBRd4aIVacrTZZ6eKLFn3xybxorMazPwXA89rCPJEuFYoNkd5nrgOj6u5+O5HsXJMooqsohzEyLz0z8vzkAiiK7IxCI6uizjeqKcla3ZnDMgCP+uL66d0DsSTQUTJYttUyWenKmysCfKaLGGoilsmyyJkmnFZmEiyAOjJcq2S38syHC+hiJJ7M5UWZQySIc0eqM6hZrIOJ3eH6dsizb75e1ByrbPBx8ZRVdkFidVEgGdEzpTPDScw3Z92oMKC+IanSGlEZh6zI/pbJ0Wmb+0obA9U6M/qlB1fOIBlRM7RdZwIB4goiX46cEygUYDwFTFEiU5TwRSq7si9EU1+rujVB2fcwYS7J0WmdpM1aI3KvaVDum4PmRNm5zpYLk+lufx50IrEPrLQCsQep7jQ4+9AU2R8HyfwUSA7Rmbn49kmCi7WK4IeAqWT9XxCKkSticI00e/f4W6R6chs7YjxMr2ANmax6tWpXnlijh7sw5BRaI3ovD0bInP3DXCZMXl7Plh1qSDPDJpEVQkLlksSma5mseRyRKfungBFy8UC8toySGiBUkGg2weL3O46DDYEUYzNLZOmWyZKJKt2CxdkCRTtUgmDWJBhS0jeX645Qg9EZ3hfA01IBZ5y/WxKqJz5KyBJBcsTHHGqi7Klsu3n5hqtnK7lsslG+bRHwty+tJ2+tt/xduRFaGcfMryNGes6GA4bzZLfIWqgy7LrOuLETJUDFXmn0/qxqy73LN3lpXfuoW5TIVaoUa+UOPSlZ1YpsMlS0TZ6GjZ7Ss7DvF/ThpgsuLxoc0Hed0vd7J7VpBF33FSNxfMb+djp/YT1hWO746QqVq86Jat/OJQUZBjPdj56qv4ynlLeMtxJ/DUq/6O8wZjhOUQx7YHuP1AVhBITbEwVByPtmSQh4+UOZAzmSmKklA6pOM2RDPzZcEDuXckx4GciRpQGW2UZI5yXiIRneG8yaL2kODLeJBtdJLtnBFlHdP5Vdv2aKHGwXwNQ1V4YqxAb2eEM1Z00NYTxcyZnDWQYGlbCNPxSIc1QqrEaKGGJsMdh03aDZmpikOm6jBreuzIVBq6NyLQMjSZqQa/aXVnhFRQkIMNTeaCwV8pB99zWPBjBhNBuiJ6M6gdmquSMkRL/FcenaAzEWRkqszuw3kUWWJBXCcZ01naIHoP52u86MRexkt1Kg3Oz4+2T/HmFywSGbekaKs/kquxoi+G53h4rkdhRpDVg5qMIkMqrDXPLZwONzNtR0uREV0hW7ObUhOGJtMV0blrOIuhytw9Ump0eQU5pk1vXG+L/pgg/8cDKkNzVSzXZ11PjGN7owwmghTqLpbnUXNF8GG5PoYq+DYRXSEd0rh3JI+uyGSqNilDxbQ99mVNLl2SolBzyZgu2ZrHOQMxzp9vsG3aomRZbJ6c41Pn9HHRAqG6fVy7Qd3xuXO4SNly6YsoFGoOK9o0bNenUHM5UnbpDMmcOc9guCg0jz538t9x7m3fQFdkHpuokjI0kZlq6H0dlZB4OlNlfK7KcL7GUzMVTh5IsLYrxNquaFM00/I8FEnwCY9Jh+iPBVjTEfkTzrgt/CWiFQg9z/Gh9V8mW3PJ1hw6QmLi7w7L9ESUZttrd1imP6piez65mofnQ1AR2Z52Q6ZgeWRrFl/YNkNfRKHdkBkvOyxJqFQdn7LlM1x0+OhFC+gOi1T4gbwlnvZsn8enLZF56gnwojXdokPN8lAkiSdmahwulXl4QgjxJQKiTThpqGw5UqQ/FuTCpW0cmipTnCwzO90QsjNUVi5uY6IsiKDRkIbtwfoekfUxbY/tU2V6wgr3bB3nl09MsLRbEItHC3Vc22W0UGO0WGPLRJHxQh01qDa7vEBkUo4KLe6eFaUKRZUo1B0eHclTs0Wn108PlajOiW4wz/GIpgz6+uNohsZtT00hKzJffmRccFNUkRV4cLTAIxOz3Hkox/87YyWT+Rqlqs2/nt7N8R1x3vfQIYbyJfKFGgsTQVJBUcq7eFEc1/d530kdfGv3AyxNzqdP6SQZ6KQvooBV5eTuBFcsa2PXkSLxaIBvv/AYPn7yRgrVX2Vgjr7Ho4J/kiwyYemQyIYYquBRKLqCrMoomsIlq7uoNgjZortIZvdshT3DORRJdE0dJbzGAyq7D+exXLHAPjlepJqt8ndLkxyXDpKfraLo4l4yNJmBmMrmI0XuHS2TMoQW0N2HctgejJfqmI5YNDf2J3jRwjiLkgZbJoq4nhBHHEwIPspoozyXNhTuGhFiiL1RIbi4c7babEEfL9VZ3xPhjP4YvdEA9x7OY/1aF1WtUOPRI0Wezpj0RgMM500OzlTY0BcjazpMZCpYFYtKpoIe1vnurlkkWUILKhRnq7i2y3jJYl6bgRHSSHVFkBWZUrGO64HpeE39paPl15NWdqKFNNSgys6hWUzb4/Cc0JjqaTQLLG0L8cTuDDv2Zhgt1HB9n1v2ZjFtj4gmJBSMRiayOFlm/75Z7nhEtEtnTYesaTM0V2XHjMiG6opE2XZZ3h4SXYh1h/5YgNGCKLeJEpNLV0RnuGCxJBVk85EiigQH8xYHCzYxXebxaZs16RibJur8+GAG14cvbM+gKRIndkdZltIpWB7XHBNnuODQH1M5uTfEspTKaElomNme+M694aEfoMsyazqCDc6YxJKksJUpWy7DecFzHC/VueCYNK4vst2PHikylK2ze7Yquj0tl0JNaEYNxg3xvffhyZk/I1m6lRH6i0ArEGqBr539dXRF5nDRIVO1eXLGolD3OanbIFvzmKl6TFdd2oIa3WFhZzFreixLqSxNBtBkic9uzfBPG7rI1j3uH63SbsiMllwCiviiZqo2e7MO2ZpH1REZhqrj828PjfOiwRjLkiqyJHFKj+Aa9YQDfPiXhzm5x6A7HGRVu87sdJmq47O2K8olS5JcszLN+p4Qdx3Ksbg72pzgq2WLU+fFUSRxXEMTreOFukfZ8li9IEnKUJnIVJg1vWZ7c6oR6Bia+FrsnigJhWbLwwiIkpve+Nvsa19GOqQxVbFwfdEpJ8tgmQ6266MFFYKaaE+/azhLOB0WXT+6EAmM6AqxoOAbtUV19LDQdbp4cRuSJGF5Ht/dneVjp3Xzoh9u4fUndLKsM8ynHp/lnx8aAeDvV13B4u4oyaB4Ou+MiMXkJ5eu5RfDVS5ffBz/tOkxlt18A57v8dWnsxx2xvn6zhnylseXL17EYCLIWelTOOV7P2HTNWc0eS6aITIiR9vZjYA41zP6Y5Qtl0zVIhIUxF7daLwHWWJ9fxy75jZ5JZbtccyg4Bqde2wH/bEgj40WMB1XtMtrMr98epplnWEuXN9H3fXZOiWCoNULkuyZq/OiwRg/PVBolkF1ReKuQznOWZDkUEGUtnqjASbKFqPFGtNVUdowC/UmcTikCuJwfyxAV1hDluCErjBLUwEKje7DTKOT6Sgh+MnpCk/OVFmVDhDRFF68vo+T+mJ8+fLFxPviOHWHJ4ZzjBbqPLE7w+kLU2yfKjNarNGTDotuwO6I4E/JCGFFxyfcZlAviuDNcn3Mqk2lIsqjgbDozFJ+rRtIkiQ0QyMd0gTHqyIyXIos1M/Llst9h/OYtljYBwaTrFvZSXa8SDqkMTNdab4v0xFimRMTJYykQcf8BINL2ylbLq7vs6EvxtI28bAwWqyzujOC5frsnq1y7mAC14c1nWH640GWtoU4rS9Moe4ynK/RHda4dc8s7z+pQ/DAPBjK2cgSBBV4aKJAPCCzLKUSUkW2akfGpC+iENElFic0ds4J/aFdc6Jb9b4xk5Ll88iESd31eegFlwqVa0Nl1vQ4pVfIRRydZ46WAPtj4uHgjj0ZUkHhz5Y0VLrCGorMrwQhC3UGE0FGizXiAYWsaXNqX/yPPcX+TrQCob8MtAKhFgBY06ET02U29MVQJInOkPAKSwVl2oMymiwxVxOTWtUWT2eTFY+hnCih9EaFuGJfRKFQd9iVFYTU2ZpYZK5cEuX4Do3hoihhzJoudcfnfRt78XyfRYkUqaBQk77pqWkqts1/XjwoiNaqzmTV4ZjBJN1hMaF7PsyYLkfKDqfOi4tW5cbi25Ey2DFTEWTToVl0RWJlR5jVaY3JisOipEE6pPP3p/WRqTqcsaqLi0/s5dGRPK4lFic1oKIbYtHpiIkFuFaoUcqaXHl8N6947C4eGckzXqozNl0m2+hqci1hfVHN1Sjma9zyonO47tzBJlF5UdLgtr2zHJoSqsxXH5smk69hVSz2T5a441COV5/QhSJJvOOEdr62I8dNL1rFWElkEBRJPBV3hXVk12EkX+NnB0RX2TvWtrEwruI0eBJn3nIPb1s9n3RIp+8/viCyHNffSURXuOtQjnWdy9m6f46OL32Jz541n7FyBhAdUu8/tVs8SddsAgGFVFBkq+4ZEUGMXXOb3W2poFikl6VEB5Osig6squmgqBIj+RqKLDFaqNMT0XjN2s5GR5/oKFvQJ7IuhqqwZbLC9ukyHSmD/liQhw/luCR9Mq85LkU6pLFzpoLl+lyxrI15UZWBmNpUen7hYBhFkpraM3pYR5FhbVcEWZbojQq7DcvzeXyyiixLaLLIhAznTaFm3lDYPsq36Y0GGC25xINqU0PnDd/fz/HdQrLBczysRkfXvbtnGJ2tMjVTYTovurM8TwSRgwmDaEoII/bHgiR7ovTHRFdeb1sIM2cyfqRIZc5k58EsM+NFIa1wVMTUdrn3UE6UThvCll1hnd6BBMP5GjnTIR3SRSlLk9m2dxYjafDg9ikkWWK0WMNyfXoiwjdt1aIUA+kQqaAqlLRNp8mH6goL/tPqzggPHi6gSPCyFW3snhUNCofydUzb44SGGepRfap2Q+b0+XG+u7fISKHOWf2G6AYNyszVRGldzA8+M1WhMn/2fOEzNl5yieo6tgcHCw7thshCR3SFkUKNqbJFd1jBdwWxPaIr3D2S54kZkZnqDiuMFmsMZ0VZd6pska3ZGEcfVjxY3h7m9gPCsHiqbPHqlUl0TWZdd5gz5yfE9yqis22qlRF6vqEVCD3fUb9d/OMIMnRYlWkLyhwuOcQDMh0hIahYd31yNSGqeFS8LBWUefGCQdZ2xPjAhgXcuKfI9ozNVcvitAfFk1lIlRjK2Uw2OBwr2jR+OVzE9uDU3ggL4yEqts2e7BxFy2TnnMWrTxBmfbIkcfWSNmRJZutUneXpEEfKToPjkudnT06S0GW2TZVE10ciiBbSyORrLG8P8+SBLCsWtzUn0artMxDX0GRBsDxUcNiRKTNarHPHngzt8QDBeJCx6TLJjjBru6KCtGu7VBqdUNl//AUHcjXGS3USEY1ayeLY/riwOGjwZNSgihpU6UmHOf2mn/P62w8iqzKyKrN7osTWl1+IU3Oo5mrcuneW1fPivHBlZ9PjbEfDhf2RyTrvWtvDNbc/3dS20RvHAfjQtts4/Po3MlURbc3vf2iSgVice8cmePRIkbVdUd7/8H7Onh9lWa8w+PzUOX38xxkvol53uehH97BsfgK9obfz6jv2NjMDH9s0Sb5s4zo+tuv/Wju28M8KGqLDqT0eYHKuys7RAqNFp3lucyWLBQ2n+rCuMFW2Glkbm1nTawQl4ri90QCGJjfVty1TBKvbJks8+ZqLIXoFH31wAl2RWdUZYctEkccmK4wUbPbMWWycF26UX6ymQe54qc7GwQRbJkpsGitStjziAZmlSa3pnVW2PGZNjyUpnUWN4HlFu2iZLzc+b4CnZirsnq7w2GiBu3bOoOgKD+/OiDZzXcGyPXzfR9EV1vYnUAMqvu/TP5hElkVpbqqhIG7ZHkNjBWqmw+6DWWayJuNzVTrnxdEjopsw3GYQjAcFiTqsNQU4FVVkgLSQRqhhAxHRlGYgtrrToCuic2iqjOd4rOyLsXBhCqsirv1gIsh0RYgogigPKw2+Ub5Qa2bxFFlqksZ7ojoXLYzxvd1Zzh2IUqi76IrMaLHGaNFBl0UH6cWL4uyYrWO5ogw1lK0ya3r0RTV+cahIpmpxuOSgybA7a1F1PHbN1pipijb4dkPhP5+codaw8Tmzr43RkiM4R1WbdEjnYN7hPTse4PgO0X25tjvKwoTOgrjGbftL6LLMlSvSrOiOMFoUvLNFKYOhBo/Ncj0iusKVy1KUqjZ3HzZZ3h5m21SVu0dypEMa67rDrGwYDLfw/EErEHq+I3Ahn93+RrJ1ofHh+kKttzOkcKTsMlJwKdkemiw1uTudjeAorkvcsv8QT2aK3D06zerOEAvjKtszNvvyNgviOkfKR7kmPqMll1zN4+JFcSK6xK378liuQ931SQZUIppohb3jUIGv78ihKypFy2R3Nt+cpIOKzMPjFVZ3RZBkiR/vz9EfC7L9UI563cUqW6wbSHDPzmna0uEmyXOy4vLhnx5k1vQYK9ZpN1Rue3SMmekKi5IGK/tiWK7gxvheww6h8bTlej62aTO/Vyi3KpLUIJEqHDMvjutDdyLIXMlqKkLrjdKb5Xr0JYMk4kGcukNvW4g1X/8J7ztvAM3QeOXKNFv2ZLBcj1hQlKJ2zFT4f2ct4l1rlvDIZJ61DTL0cN6kK6Lz5OUvY7RYp92QuXX/Tzh3MIWuSPzy8pN4yfd3cMOuOWJBhWPadO689FpuG8oR0RTW9cT4x/snmKoOo+qilfjgTIVayaLmiAAiX6ixrifGWQuSnDaYbAr9uY5PcbZKxXLpjQao112cutPUaIklgty6d46y5aJroi18tFjDrgmeyex0mU2H88yPaaKDLhZs2BwYjJfq/PCJSXaM5FndKTItpuMxW7XpLlpsmvwAr1jTybbJEuOlOut6YsLItlRnfkxj27QQAj2qAzVVtjhjXoQXDIS4fFkbp86LUbY9+iJKw+pCZGV2z1bJVB1sDxK6zNkDcY6Ubdb1xMjWHHbPVmkLKmSq4nNd3BHGtVzqpTrhNuGr1iQwSxK6IdrO66U6a/sTTE6VBe9LkgThWlfojQeIpwxiEZ2Vi9voS4ep5YWoo1W2UAMqtZKF3yhPKpJEPGVgVSyqOeHj1tWfwDZtFqVEKTg/VWYgEeRAzuKxXTM4NYdYd4SlKYN4QOHcE3qb2Z6huWozWO2PB3A9n2LN5Ywl7cKUNqKLLI4hOvquXZniB/sKbJwfJ1fzeMWxoqwsSqjCvmR+TGO05DTVy9+0OsVZ8xPYns9MVWSM0iGdtR06u+YcXrq0naotmjPag2IJOrY9zLHtoju16vjcdiBDRBP7ft1xKRYldUYKdRYnVW7aW2zYtMjcd7hEria0n9Z2RxkriYB9ZUe4adZ74fIO5jIVHm3cXz/al2dxR5jBhN7oMLSbApTDBYui9WzttT8VWhmhvwy0AqEWsF3oi4j08kTZQpOFouvuWdGVM2t6DBcdSrbPrOkxXfWa//ZHVQZjKid3J1jXGSTaaLEv1ITe0O1Dc5zUrZMMyigSxHWZAwWbVe0BFiY0QqoQvvvFSJU9uWJD0dbg8qVx7h4tc7Bgk62JJ7nbtk6Qqzmc0hvGcj3iKYMXLUqyJGWg6AoL2kOkuiJEdIW+7ij5Qo10SGNxMshgTCXUFiIekDEdj+8+MckLTughEA0wXqo3bAh8UnHRgq/LMvfvmgGglDUxcyZPvuxm3nzfaxkv1Rkt1LBcoUOyf6ZCtiEueLSjTFdk4ZKer/HAlS8lO1dtZo1uvfw43rvqUg686VWMlRzOWtXFaKFOsRE0pAyNV/x8D8tDy7ly8QIOZAUh9ieXnMtt51/N1uJ2nrziFdywK8e9R2psHi9QrLncsPcgpy1uY3VnBEWS6I+qbJnexIa+GFcsjbK20X58+o0PokjCk+zY3ig96TAffHgSQ5UJRQRXaft0mW1TJZIxnaAhymLHLkyhNbzMAg1l6aNclnJZmN3qisTqzgjtbQau41Mr1DgyWsBIGqQjOj/enyMeEMHFafNiotwnC2uPro4whqqwtivKP5zQy8RrXs8xd97LO+4d4vYDOcq2KFE9fChHf0MzasZ0G4J/ATrDGsemw0R0hWRQ5tu7C2SqgiCdqdjsmrPZMimybSd2BTk2HWb3bIUdMyaHChZ1VyhJ/3LfHGcNJDBUmcmKzdquKLGgUFM3kkLTp5qrUW2oUjt1h855cVzHZ9PQLKuXtDFeqrNsfoJsxcb1fYZnKriWOP+j95ppiw7FQCxApUGk932hx3TUsLZUtTHrbjNL1NEZZmo0j20Kv6yy7dLXLzgtmarFisVtDb6bRqbqcGw6jOv59McDRHSFww2j30UpA8v1OZKrkTRU7nj8CP3xgPAky5mMFupEdIX3//ggrz0uSWdIIaLL3LgrhyZLLG0zOJSv88SU2SgxCu7WYEzlYMFpSjIsTuoMxAOc0RdgZXua4zs0qo5Ff0zF8nx6IypPzNR4cLzEhu4ARctFkyVcH+4+XCakSTwxYzFatPneC6/g4Yk68YDK2fOjDZVwm1zdbRqn9kR0rl4Woz+m0xsNYDpCSuGfzplPLKKzY6ZCOqyxqiPMtinRxag3fA9TQZVUUMVQWjpCzze0AqEW6AzJ1ByRsVkQ/5XC78JEkIAiUXeErUbd8emLKJQtj7rr8/NDRWzPZ9p08XyfkBpodnh0hjWqts/7T+2m7sJ42WVDd4DHp2uMFy3GKzbruqLIkoTr++yerfDRu0ZZEBcT0mTFJVN1mKx4zFRdXA+WzRfCe09Mmzx4uMCp8+LcM1LgiakyK/tiIogIqmRNh7HxIt1tIVwPfnEox3DR4eNn9XIgV6crovOCFR3oioyiitJApmpTLltkC3X620NMzlV58fE9qEHxJP/Gcxfwqrtew6axIooscdZAEtPxcCyP7kQQyxblCc8VOkelYp19c8JPari4h/7uKN940RIeuvTlDMb6WHPTN3jjPT/hzr2zlC1XGLVaLqf0xeiPBbh4cRsLbvwWPz40TLbmENEUPvzYo1DNszC+hL3V3Xz53OO4dyRPPKAS1GTuGi7gen7zqf/u0Sr/8sgB4VNm+3z8kSnaQxrpiI7t+qzrEURYgOnZKis7ws3PAsTTtCJJVBrWGOOlOvW6iy6LgK4jZZCt2Dg1h3QiyDFtIZanwzw2WiCiKazsinDC8jRtPVFO6ouJzEzNYfdEiacOZKk3upD2jeTpigWYmqmwY0a075/X/2+8c/Nt9EYDjBfqZBsByXDeZElXBNMWliHzoqLFO1N1aPjGNstjRzk4K9MGC5NBUkEZXREdkY9P1ajYXvM9H9sewGyQ+M9YmBRO5AHBP9o0VsD1RMno3EWp5vfmqJCkGlAplOooqkR/d5R9c2bD/NZG12TKlktX0mAgHWK0UMd2RcYxWxPNCX3pMPVSHT2iY1dtzJxJpSFf4NQdYcViOlQyFd68Jo2iCR2tu7dNcOq8eFMAMh0SitfL5icwNJkdmTL7siZl22W0IAx/589PCOuQio3peEiyJK7tig6myhbzY7oglDdkJ6KdYbZOW+yZq9MZklmUMghpwoutN6ozmAjwkZMuJaBKzXLi/pyFock83rCqOL5DZI+GC3M8MWPzdwv/X1Ogdet0ndXpIHVHSHQMxFS6wzIvXpDkyqUxBmIqZdvjLav6ee+mn7C9wa07VLAp1F1evbIdyxVCqMe26bQbMr8YrjI0V2NNp8HSNgPT8Xh4vMrly1LEgyp7MlX2ZU0KNafJb+uJ6A2PPofBxK+kC1p4fqAVCLXAK4+5DkWCVFCi3VDI1wWRMaAKb7HeqAh+2g2ZQl04SGdMl4sWxijUfeK6uI2ezBQJKBKbxkp4vs+hgsWWKeFp1h6U2TpdZ2FCp2y7fPj2Yb63N8snthzh54cqvHNtO2sXpqg6Pj/YMcNYyWF+TOOJqTI7MlUuWxzhwHSZZEBh8745ZFmURs4eiJMyxIKVDul0RXRWtBu87vR+VnaEMR2XiCbasGeqwogyGVCINdRwV3dGmrwXuyoW9ULdxbVc7h3JYVdtTlvcxq7ZGtuny8SDgl+xaayA5YqFJB5Q2DiYAEAzhGu5ZojJNBHRODa+hq+edyyv+skQea/MV3c+ga5IbJ8uC3d4SUJuCOONFOqkwxqFush03LwnxyuObWddT5Q3HbcAwimmq6MsCy3nwfExIppCPKBy+bK2ZlBz1GPqgeE8ezJVFEniE5un8D0hNHf2QJywrvCtrZMcmK0ylTOxqzZ37Zzh8+cM0BsNNLlIs4U6Pekwlu2JriVdZnk6hOsLFWYjoKAGBan18ckSowXh1j46W20Sm3ujAQ5kTW7eOsEFja64gXlxhvN1NvTGWNgfFy3YHWG2XnMzT5xzJm+497V8+7FxsqbT1NTZPF6gXrEp1AQXaWlbiAfHysR0mUVJnf25Ok9MlYVp6qzNtskSMV2hZInrosmgyDTvrbAms62xWD86UWWqbHHu/CgRXZDDDU1GkYXq8IsXJ8lUbHZnKqw7Jt0siWkhjUBAIdLo9JrIms2M4FHV5aOqzllTkK2PKnjrjU6n1Z0RFvXF6EmHsU0bI2kQjgXwXOH7pkd0rLKFrMp8YvOUCJrCOnpEZ/ORYlOTabxUZ2iuylTFYvfQLDPTwgVeCEKGKcxU6Gos+NmaUChf3hmmOFGkKyIkEQ4XhXTA0QxJPKA2hDFVDuYdOkMa7UG5SVAOqRIfeuyHnNErslLbM0LZWZEkXro8wf6cRcn2GYglmB9L8sIB8T3pjyookkQyKBMPSLQbwsx5uuqxobuN+8cL3DJU5NXLr+Pb532DW/dP8PODWdJhDb0hori+O8xTmTqdIeGtNlJ0OK7dYEemjOuL7sPRQp10SMNyfaYqYi7ojOgclzZ42/FtDa6Yy86MuDarOwK8/8T/+BPPuL+CxB/mMybRygj9MdAKhFoAYF5UZHB2Z4VWTzwgU7aEsNpM1WNhQiUekInoolR2YqeOpkjIEsJDyLGaqtMn90Y4oVNn+3SZpUmNy5YmmTY92g2FnZkavVGdl548j96oxmixzv1PTTFeFhPzcL7OSY3J8nBR2FJctSzOnYdNzl3azuOTZS5c1dVs7b5npCAmsM4IhiYzWqjj+VBvPN0DdEV0Zk2PTeMVzh6I89RMhZFCjeO7hAhiKqhy8GC2SXJWZAjGgxRnq/ieEJaLN/g7B2aFh1nFcqlXbFzbZedogdXpIIoufKMKM0Iw0aqIDpydhSd5yc+e4r2n9nDRj3/I9TtmG3wW4cCeMoSlwo0XHsejI3mimsxrju3mX09bwM//7hw8X7Qv37xvhJJf5ZxbHyFHmcmKKAmMl+pUbZFlGC3WyZqCl9EVC9AZ0dkxIzzOrIrFjpkK335sHEUWNhm6JsowRtKgqyPMBzaJ4GO6bOF6Pq9c2yXKlWnRpuw6PpuPFAGIB8RirgaEcKQiSUw1Wrv720PUCrWmSGE6rBFJBMk2LDqOZg+G8zWyNQfT9njiZa/Gn/0GAz/9JT/dJ1StFyUNkZlSRXYqEQ8yOlYga9rsyJQbdiMSu2drnNIrbBIuGIwwnK+xrjdKZ0jm8ckSFdtjX85ivGQR0xXmah7Fxjm4vsiimY7LF5+cecY5DSaCLEkZ3Lgjw+quCKlGx1JbjxBjVAMqZtXGbdwn9VK9Kdj5wJOTTE6ViejiOhVKdWHz0CCR54oW6ZDGj5+YENcuZxKMB3EtF7sR1L5kdVdTRFGSJfKjefSwjlWxqBVqZCs2L1iQaN7ry9sF0berP8FxC5L0RgMsbQvh+rB6SRuW67G2K0pEU3jgsMgmvuqchViuzyM7p3n4UI4z+mOYtsex6SBnzk+wLCU+t7maeBg6WHA4Y16EdkPME8uSKodLZRYmdA5kzaaC9WjJFQKlusysWeLLT49z71ier+16M9tnbUYLdSYrHsmA4PCFNEHSvmHPNLcfyJE1bb68803csv9teD4UG+rWA/EAo4U6R8o2/TGNibLQPnJ9+PTj0wC8/rg0iiRxTJtQYH/NyiTHpUVA/Ypjk1Qdn5v2lhgt1ljTKUqyt73oet5/4pf/iLPq70erNPaXgVYg1AIAVy7+T+RGnT+oShwqOPRGFKarLiXLZ3tGcB3aDZkf7Jhhf87hSNnF9nySQZmJSp12I8i582NcMNDOSNHhggVCP6ZQ9/nhUJbRkiAhDsZ1ju/QOT6tc/myNj5w4QIKltfIuCjEgyrD+RqrOwKkwxrv//FBbnt0jE1jBRalDAxVZnbOZFWHwbreKOFf8wZa1xtlomxTc33u3jdHytBYmTaIB4Qr9UzVYV2PWNAePlLE0BQe2CEmz6NP+XO5GrZpc+X6PvobjvGWK3gWRkCh1BDVUzQFIySEDL8/lGP8jW9nfm+M6y5f1DQhvf+il3BsYi2DiSDX78wyNFkiHdJIGSoHZxoaMCN5eqMBrrn9aTa98gxKtsfnt4/z9Z2T1N0q71xzdkPUr8g/brqdfz29h403/4Drn84wVbFY2x2laLmYjseipEF/PMBUxeLDp/TywZN7WNkRwfXhNSf3UW6oHZctl8uWp7FdQYJui+rNxXh5e4iT+mKcOT/O1zaNoUhSs2U/1dC5yZdFaSVXtNA1oagMUKg6LOmKUKi7nH1sJyOFOh2GwrHtIcyqzbxYgIF0SHBwNJnBRJCIJhSLB7/6FV7y+IMsbw9zzoIkrz+lr0l2v2xpu8jUGMKdXVdkZqYrDM1VeXBMZOpsz+eYNoMtUyJDsmW8RDwgs7Ij3HBYV4loCo+OFzmQE1yhrohO2XIbWjJ1FqUMdo4L49H5MY1CzWXWtDlrQZI79mTYPlbg2PZA09Q1rCvYVRu7Yc1wtDuwWLYIt4eZ3xtrtqb3t4eEDUXDyiPcMBluS4c5NFtt8su6OsLUCjVkRebhI0Wqc1WC8SCe4xHpjCA37ndFUzh1foLrt05y4aIUKzvCHMiZzUzT04dy7J4VcgRbJkrNcx5M6GyfLLGhT5D/f7A7w3C+xorFbdRLIsAwNJkHx8rUXA/XF9lXYcgs2uEHYio/3F/C9nyiuk7N9XnJkgXNbsBV6QAvGuzg3Sf0sz9vsztrMRDXOLknzKJEkmzN5eplUV44kGhwfGTiusxNL/gWZ80LkjIEwTqkyhwq2GzsC3PNCT30RgNMV22O74qwJKmzebxMOqRyyZIEluvTFdG5dEmKd9x7GEOT2ZetcfvuGR6eMEVGOGfy5IyF3CCwL02FOFy0+MWlN/xJ59cW/rLRCoRaeAbCqswDoyXWdIgsStpQ6I8KJ/pcTcjRv/qELgKqRFQTE+6s6RFUJMbLJveMFtk6neO6h8fpjSiENEF8vHRpipAq0RvVGS3a3Ddm8vFHZ7Bdn5rrM1J0eOuaNjaNFcg0yh/bZ+psmywjqzJXnDyPcxckcT2fOw5mOWd5mvsOlziQNXF9aDdUliY1RgtCqO7ekTzHdItW6wdGS1Rtn56IQruhctdwARAZjf5YAD2iMzCYbHZ81Yt1rLLFbU9NsborQlgT2Y4dMxWK+RrRkIaiK8xvM6jXXYyQCA56v/QfTBXrvOOOUWbe8lbOXtMNWhAkmZ0zFVZ2hFEDKg9e9TIyVRstqHDLRSfSGdE5a36MK49J8ZKfPcbXTrmEsuXyiVPXc/2e3bzrwV+yMKFh2h4/2T/HXaOC99EZ0VneHmJoTpS/4gGVHZky5w9EcT2fyYrJz4aLnNStY9ou39mRITtXRZIl3r+hi1uemqJWqBFuE23XiiwW68dGC+iKxMF8jQUN7tXlSxOkDJVT58V559p2VnRHKFtu0xw0HdKEy7wqkTVtuiI6UxWh7zNX86g4Hl1Jg1zN4fCcyY92CiL69ulywx/Loafh//SCBRHu3DvL5vEyZ8wTWj6PTpS4ZEkSXZE5aVkaQxNBwzXLkyxK/Uqv5uiife6gUNjem7VxPZF5Gi9ZLGsTJOuVaWEBYqgyqzsjrOoIc+q8OGXL5ZQFSYbzJjOmIK8vTOgMzVVxaoKvc/2OWUzbRdEVFBnCbQa2aVOsiUDaczycutO8RkeJvK4vymCWJ8qMhipj2h7lmsPijjCe6zVKsw6BqLBbGZ4oEWoL4Tb863RDZclAArtqE+0Mc/fTU9hVm2/fNywytppCoepQsVxCSWEY2xXWOaUvhukIT67dsyaRoEq7oQndoFigYS2jcum6PnZnhJXL+u4wuiwJr6+gSqZqkbc8MqZL2fY4f0BYUfxgf4E3HHsh3z8wzJpOg6mKIJ7/YmSGI6Ucr15xHEFF4mMbvoIqy5zR+zF6wgo11+fp2RJbpizuGi6QCga4+hfXMlJ0+NfTFvHuE/q4a9SkYHkMF2rUGpwy0/Y4mPuVcGbZ9vjBUI6eyK8EQJemhBL2aLGG5wjx19Gi3RSr/PwZX+XGC77Jjy/+Nre96Po/3wT7G2hlhP4y0AqEWmgipIqOr5N7I0xXXeIBGU2Gh8erlCzBBaq5PsuSKlpjghwuOlQdH00GTRZqvEuSQY6ZF6fq+Py/x2c4tSdAKiCzKq03LQ4ATp0X4+lMlZv35Fie0pmtubx7XScrOwzOGxSZmIiucM2GeQ1HbcHNOXdBkssXx1iUMlBkibFiHVmCA3mHY9oMsqbD8naxgJ41kOTE7gg/2jfLRNllrGSxridKpmIzlKly+7YJzJyJoclohsb8NgM1qJLoEpO85Yrg76HhHLoinvZzORPPERYgvuezsiPM44cL+J7Px87o4SdXrWXWyfDZjev5xtAPWX/zV/n8ufP5/JkX8O51nRzzzW/ylfOWsLYrypd37BP+TYeLfGfnLABX3PMD0iGN/3zqCd6++nQKdYeHx4V9w8Z+0c103TkLcT2f0YLgbxzImViuR1dY5+7DZaGmbQR4++pFrGhLN7lNlx7fTb1U5yMPT2GVreb72D1dIVO1WN0VYX6bwdCcyXC+Rtl2GZsz+ff7x3hissx4qc71uwqYtmhZPlqiPJKrCU6NJMobwvNJJh5UiOnCSLQ/HmDLRIkF7SHmtYnW76VtIe7bm2FtV5SuiM6WiRK/HCk3sx6ffGyKh8cKjBXqXL8jg65IbBvNMzRX5fQVHQwXhBigocncvLdAVJd40cIomarQQ1rZLgxd3762neXtQfbnaixPhwkogiR/TJvBVNniYMNaxLQ9psqik+2oQ3muJvR2jnYFur6PoSl4jke+bPOmNR1NAnNPTxQ9rKMGRFazN9oIQhqO8ENzJq4n7utsxWZdb5R4SHiZ6YZKW0+UcqZKrSAybJqh4TkeS7tFKW4wYdAV1gmnwxQmSqhBlXhHGEVTsF2fdEN3SFNE51i2ZjM0VuDB0QKLkgHGR/JCbV2VOdj4zEA8FCgyhDWZ/rjopKy5IsgQ/DuNRUmDnrCwppisuMgy2J54CPnktp8xa4rGhsFEkH9Z92X+9eSv8NKlX2Ag+lb+6YQvAXDx4L8D8H9P+gpBReKfTvgSU2WL4cN5NvaexMr2ABFdIhUIE9GEY70swYWDC9k0VuBA1mT3bJUV7UEO5EyWtoXYNlkiHlDZM1fn9L4gtuvz8uVCGHZ1Z4S/P62PRyctzpwnrt03z7zyTzuR/jfQCoT+MtAKhFpo4kPrRX28ZAtPsbIlWuQVWSjC1lyfG3fl2JV1UCSQJRrESDhSdmk3Aly1LM6XnpplaZtBKiDzjhM7sD2fAwWbsu01OjNsFiY0ZEkSGj4dYYZywtrj6YzNXcMFHhkXJNalbQZjxTr9MY2huSrL28OEVZnv7y/y8x3TdIV15sUCeD4M502emqlgOh6jxTpTFaGb0hGSGUwYmI5QM753JE/KUFneGWbRQIL3vnAhwzMV6qU6+0cLKLpCtWwJETtJIqRJLOsU3Iul3VEUTeGqlR0MZ02SMZ2tY0XCYQ3btPno5mlOSp5Iu2MwP3IMH9g0yWN/93Lefs9hzv3+bfxgn7CKWN+1jh2ZCo9NlMlUbVZ2hFnbHeXfz1yM5Xqc3W+we9bk7++/h0+fvpbT+iJ86KSTGS/VuWxRG+976DDpsCZayhuK1YYmvJ9+cNFlLEoavP2ew9y8b5g337WX0UKdzojOef0h6qU61bkqZ63qIhgPUq+7vHyV6KL7yc4Z4bdVFJ1a8YDKqr4YRjxA0lB5YrLMo3sz7D2cJ6ILona2YQ9RbnBbPnRKNyCEEk3bI6KLydp0vEaZymbfgSyPHikyL6rT3mY0MxLlssVAPMjrju/AdASfpTOiE9RkqrlaswPLcn0UWeKxiTIndRt0GAqXL42TMV0em6xhqBIXL06xebLOIwez3DdmUnN8FiaCWK7HZEUYlu6cFcajQ3MmuiILby1LeHZlquL+ueNQjoMTJQJRIa1QzdXQFbEI2abNZ+4dFSWtWEA4mDf4O9PDOR5/aoqHjxQbPB0f03HpjYqOrMuWp7njYJaIpjBarLMyLWQPtJBGT08URRXXTQ2o7B0vNvzJ6mRrDov746gBFc3QWNsdRQ2qbJ2qcExbSNy3ssToWIFC1cGIC8HKB0aLdPUnWN4eZmlbCMsTpdSUoXLavBjL2w32zFVZ2HC1tz2fY9t0eqM6S5I667tDwth2pkLJ8pmsCLXoWVOYK0+ULa4782t87eyvP2tuuWX/23jFL1/N+ze/vrntlcdcB8BoscaFa3t4072/4Ox53YwUXJ6eHeOnwxOULJ+xksPXdu7jvMEEZdvl1Hlxns6YnDEvwvbpMhv6YgwmgjwykueWoSI/3p/ju3uLzWzcL0dKfPPcr/P+e46QDqmgnv8nmkFb+GtFKxBq4Rk4ULDpDMlkaw4BVeiCzIvqNB4ceePqNvoiCk/O1Jg1PQbjooTWF1GYNet0hgIclzaYH1WZNkXq+vv7SqSCClumLJa2hTi5N8JI0eHxyZJYdCtWI2vgN93f++MBVncE2DFTYSAeYKriosiC09HgkdKTDpM1HTzfb9gqQMrQhI+VoXIgK5RjD+Sd5qR4IGeycX6cAzmTA1mTQ0eKfHvnHIomeq9lVea4/jiu5bKsM0wyqPKxDVexoTfCdNli73gR3/e5eccMvfEAuaIQ2/s/Gzp5+o2Xcuiaa8G1QNV53+bvsbozQtdXv8K6HuHhNFWxKFsuUUm4ladDGq9fleLTp16AIknsy2WJB1XajSCfOHUFluvzrd07+dC6q+nWe7n/kpfzD/cP88bVKQ5kTWq2x4r2AENz1caipvHGe37C0JzJup4Ydx4qEA+q5As1sqbNm350ED2s4zfa7DtiAZZ1hvnGo+NC36bR8ZOONPhAYZ2nG55r8YDwYpNVmY7OMKPFmihJhbWG0riG7/k8PCH0XY4u2o9OVNg2WRJmuIrElcekWLJIKB4/OFYkoimYjvh87arNdMVmX05cp+3TZcbnquRnq5y8rL3puj5dtsiaNoOJIONll73ZGjtmbVJBRbiP1xwKdZGFOXtZOw8eLlCyPQKN4GIwrqMr4p7L1mw2zo8T1uSmcenQXJWV6QiGKoKjK4/v5rieKFbFEqTzBmFcb6hLgyB2+55QmFZ0pZkZslxhw1G2hPTAcL6Gocrkag4r0xHSIaF5s3Ukx+x0GUmWOHwwK2xK5qr4vk8oohOK6FRNh6HJEkOHcsiqzJkDCR4+lCOdDuF6Prc8NYWqy5h1UUoDEZCOZKoNHy6hfZUyhGXItkmhyHy4aFF3fE7rizDb4DTFAzLjZZeQKvHoRJVHxqtMVWzmShaaInFWn/AeE3INEpcu+t2u7Vcu/k9eOBDiYxu+wi9H/5G/+9kr+fsHXgfArlfeypnzDK5d3oHlCW7iY1N1Zk3RoWqowqLjlB6D5e0hliYF6flwyWF5exjXE4T3kKGSqdi84tg2EaDXbO4aForRAJet7OBf1p/8R5op/zhoZYT+MtAKhFp4Br529teJaDLxgEpnSEGWoOJ4PDxhsnvOot0IEFIlrlwSpT8qDFgDqtTMCFVs0RW0MK6yebzMbfuKvHhRhLojODoAsixUbnVFZmjOxFBlnmoQhxUZXF9o2GyfEYaI+7LCBb4/FiBTtZiu2PTHdFZ3iU6xTWNFxktWk3MRD6ikQxoXLW4jHVLoiTS4QLIwepwqi66eeEAlEA1gqDKnzk80r8GejFh0XR8+f8ZXkaWz+e6uWcbf+HZeu74HgHdt6OZIrib8msIaD43Xef3dD3DGT78HWpD3P/lTfrgvy2fPWMP915zKaKHGVUuT1GyhmHzCzd+gUBfqxaMll1fe+VMAPvTwJJ/beD6zZo3FiVUMJnRu2Ztltj4Odo19lV189LQ+vrEjx2AiyLx4gBt3zeH6Pg+M5umN6gzERTB4IGty2rwYB2areI6Hocl86sIB3Aa/JZMXtgqKLJFMGlyypI1zj+3gUxsHeOnyFLOFOvc9OcnrT+jk7Ru6yVRtqnNV4imDYs2lMie4SgCuBzNFwREyVImN/YJTZNrCu+3M+XHROVVzeXCsjK7InL60nZUdYXRFbmr2tHdG2DZVarbvl7ImZy1pA+Dxw4LbNZgwOGVenN0TJRRZ4vHJMq4nspPjJZv5MZX+mN6UEzBUhRcuTlGouRTqHu2GRsZ0aQsqrOuJEA+ojBctJhpmvboiiS6woMLydJh1PTGG83X64wEWL2lH0RQGO0SGMBJU8Rp6PHc/LVrb60XR3m4kDbSQ1liUnSZXaDARZG13lB0zFYYLJk9Nl3Eb3CItpCFJEouXtJMt1AkmgqQTQg/Jsj2+ddFSoUAdVOntjPCLp6YIGmrjvYtj90YDKKpEtNE5OFUWwpkRTaErIgJAodAdJR3SRYNCQOXRCUF+LlviQaJq+/RGFOIBmWPaDFa0B4kHVM5b0sYPh7L8+7YJVqdDvHddB4oE947V/su55eolnwfgvP5/47YXXc/yNpWPbHkDN+17K4mAiiorPDo522iJV1iSUIk2dJh2zJR5dNLkisa8s6Ld4EDW5MLBMIW6w8KELoyWZdGK326onNQTZWU63OQAXXfm1+gOvfoPmyD/yJDkPzQY+t9+B38baF3GFp6F9679EivaNB6brBHVJU7qCrAoGWAgrvHLwyUimkzBcrljWHj41B2fwZhK1bYYL7tCz2XG4ryBKOcNRNFkiYojLDwMVeZAri5UaBNBMlUhiT+YMBqWBza6IrFlooTr+yxJ6piOyEYYmtyYyCW2TJRpa0zg6bCGoQkHdtMRGY27d2fwfJ+gAnvm6gxlq8QDMilD8FoWJYXQWjqiMzZe5L69GcJtBpqhUS/V6Y8FeOjK7wJw9g+uoVq2SH7sE9xxqMCizgiPTppIssRZ82PsuuoV/GznNENzJvdffA1X/PJmHhwrc85AkgeOHGC0NMvKjghvu3uEU+bFkWU4oz/G29d28rHTunnjytV87JTlHMiZdIV1klo7qWCAjDnGjhmTV69s57NPPkhd13liZoR33zcqBCAbnlmW62HaHp8+Yx4HcibffCqD6/h84OQubhuaIxXWeOOpfVyzvI2fHaogSRKe4+E5Hhv6YuwcL2E6Lo9PltnYZ/Dqn+3jkoULiTVI5F99YoYvbJuhWLZ40Ym9gBATjDV4WIokociwoPH7VEUEw0c1hCKawqMTJQYToqOtbLkYmsz8mE6s4YpuaDLbxwoYqszy9hAPHBZclpOXtTM0Z/J3J/SgBZVmcANw6cpOTNtjY3+UoWyVrOk0Wrc9ds+axHQhoKjI0BdR6WxkrvZlTTpDCiXbw3Z9djRMNo9LBzEdcU17owGG8zUyFZu7DmRRZPF+xqbLIpt2KIdruZRrTpPPpAZVnJpDuKEHVG5kd+KNrFBEV5qB59BcVdyLmkIsqLD7YLZp1dGTMgSRO6Th1BzGjxTF/R9QeOWP94pyZqlOpiykClzfx/OgWrb42IsXCr84xydlqIxNl0kZgphdqAsbjBXtBsvbDQp1l6emyxRqLvNjasMOBAbiATpCKu2G3CSgT1dtNEUirMlkTZuUobJ1/xw3DeX48cEy+3IOp/QEnvskU7+dsiW6UEdLTkOMtcCxbVE29AQJqKJE3x8VZb7T5sVIBmV2zNp88akcJdsTBqkztUY5UwRwVy1L0hZUWZxUWZUWBP6rf3HtHzQf/knxh2aDWhmhPwpagVALvxVvX/VFju8IUHNF63zZ8jiYt8hUHe48bNIW1HjbmjY8z+fxyTKyJDFaEr5kA/EAtgcdIZnpqsf+vOjceXK6QqZqNy0jxkt1NvYnyFRtNo8XmhL/ILhBridMXo8uJGlDcGDaDZXT50Uo2R5ZUxA/y5bL6s5IQwxQYqBLtIyPlVzu3jbBlctS9EcVRgtC1PEoXyUeVGnvjGCVLWolCzNn8pqT+7A8n/RnX8T6713NjiNFZEUm3R1lUdIQ5NOhWW699Dg+ctIVzL/hm0RjAeIBhXc+9iO2T5e5/oKT+OSpF1Ooe1iew50HsyxvD3P/vlkcy+NlxwzynV1ZPrM1w7pv3sGb7nmaH714Iw9e9TLGqvu5cU+OV//yQUaLNb761Cz/fMKFLPnGV/nQI1OUqjYRTQj1lS0Xq6GQ/JY7R4SzuaHi1B0WJbqwXZ9Mvsb6rgife+gIIJzMbdNGbmj/DKRDlDNVdh/M8pWnsrx4eZoX3vao4NLMVdEbdVFJlrhz1wyW6wn9IU2mUHMo226zPAEIjZ+ZCqMFQTYeLpj0x4SZ6ZaJkrjuAZXHJsrszFQEH8l0ePNJPaztjtIfC3DR4jZWpsNsHSuyoTeK68GG3jjpkMby9hCbDuexXJ8tE0WmKi5zEyUszyMeVNg6VSEd0snVRYaxMyQEQSu2R19EZXVniLLlE1ZlDuXrDKZEgPbL4SKm7WE64h4+GnyftSDJcL5GoeZwyuI24RnWFyPUFiI/mseu2tRLQglaj+iYORM1oCKrMiv6Yjy4bZyRhrClZTrsmiyTCgoNLdcXkgNaSEMzNFb1xRg+nEdXZAozFWzTFqarkkS5bGE3rFwAUmGN9UvaMVSFnqiO53h8d7fQBhps+JAdP5hkImsyXrIYTATpjeocKdmc2hNhaK7Ky1aKDrwjZYfVnSFGig4r2zWCqmiGeHiiTtnyOLEziCbT5BA99pKbyP7jLzBUhWRQfJavXXHdc5pbvjv0Fr524PZmidv2fO4fL3Db/hKHS+WmR9jWqSrZuiDzd4cVCpZPyfbpCguOlevB2o4gg3Gdg/kab13dy1DO5qnZOkFFzEcfP22Am17wrT98QmzhbxqtQKiF34l3H/8lBmMGK9pUTujUGYzrbJsqcddwlocnTBzPY3mbxsb+KLM1V6TRdZnjOzQenSgxa3qkghLZmt8kw06VLbG4KnLDD8kmW7PpCuuCo+IJ9+rxkkWh7lC0PMq2yPJcd88IqaDGdNVm52wd0/aYFws0LQHGG4vRUT7QjpkKigxLFqUYK7nsyzk8OV5kX1aU0bKmCCiyc1X0iE4wqpPqitAfVfjCGZdz1Zpu0o1SRayRiRqaqxKIBjhlcRvf2j3OS35xI5csaSMVVBmbMynZoiX723t28Zb7fsS/PTTOO+89jJkTJZA3behBkiUO5KeYadgoAFy1LE5XaJAPPfZTXvrzB5kf07h6WYzLliY5f0GSjbd8l3ev66RYc0lENDKN8l5EV4gEVRRZYuNggqG5Kv+yoRNJljj7m5v4xgsWY1dt3n3fKG8+pZf7dkyjBlRh7pkX5OPhxoIbiAYYPpynO6xQNR0qRVFqsV3hGabqomXdMoX4ZVdY57zBeMPrK9jsBtw3Z5Kp2hzJ1ZgqWwzGDebHNI5LC8uDiCZ4N7oisShlsLo7ytBclb3ZOooMmarDg6MFHtgxTb1U57uPjDE0VwXEApkKyqTCGncNZ9EVEQSH0+Gm71mh7mKoYv99EZVycwFV2J+zGC/ZBFSJibKN5fqkQ6KN/OTeCIW6ELp0fTiQNZsGpbOFOq4v2siXDCSYmCihqMIjDWhmhY5yhOqlOoqusHXHNAuXtLOkKyI+r4jO+v44lifuk8l8jVRYayqLZ02bdHeUgwezzf15DQPaozw2SZaI90SZnCrz8NNT6I0OuFSbyMgdmq0yXqo3Va0H0oKPdve+OU7sDLIkpXPHSLGRzYOUoXJcu04qIJOp2Dw4XqNq+9iuz3DeJN8QVgU4Y57gEL31fsHvObc/SCoocfUxiec0p3xkyxt46dIvIEvg+fCtnTkAoprEeQMRkgGF6arI1J3eF8bz4dg2jacydU7tiQibj6TOmo4QG+cZPDppMlwQmbHPPXGEobkq58/v4LNbM2Kf/f/23532/qxocYT+MtAKhFr4L3HJgs+iyRKTFZc1aVHm+PQZvbx4YQdd4TghVeZAzqJq+8zWXOqu6CZ50cJ4o6tEpt0Q5QlDkzlvMMZpi9uYH9PZMiE6O4o1l6wpCM1HF4ij5bDN46JVu1B3ePlp89k2VRIdQ5LEQFxrmMRKzZbkqYpF2XYZTBi4vi8yE7NVXN9nsmLTnQhiNBbirohwnz7qGVXOVCmXLWZNj7c/8APGinW2jglSbHauSqHuMPn/2Xvv8Dju81z7nroz2wsWHSBBggQ7KVKiRPVuyZJL3GM7xXGV7ePEyUlxEif+0hznOHGKe4vtxI7c5G5LliVLsiSKlEhRAgk2kCDRgV3sYuvsTv3++C1W0Ym7ZEsn4Xtde1ECQGCwu5x5532f57mX6sQNhS/d/Cr2TpcxNZlbH1vgw9dt4Wsv28lYrs7LN6Z4147nc2ihiqIp3DycoqsniqYI0amiSrz5WxP4nsiT2dgT5cq+VTzntn/jd3bs5qY1cR6Zr/HBR5f49Gie750pYnsBtx5d5tFffwEAm7qEtmZlLbV3WkwzFFni7d+b5gsv2UZ3Z4S/3jeNHtGZO1PkvimxAnKbbltIW2q6+K4Qtds1GyNh8JnDec7ri2NXbXpXJaguVMlkI/g+dHdGkCSJ0TPLTCw30BSJ37+gkxvXrAiLZVKmSrkqwKxTM2WGkiGO5Bt881SRvdNlqi0d11DSxFDEurNeFanPvRGF+04JwbhqqEQyJoMDAqNyaKHK94/l+JfbTxHVFS4dSFCzPe48NMfOnmgb5RHVFQ4t1Fr6EnERVySoOwGHFqqkDBVDgVVxjSsGxFRwXSrEsUIDLxCRCflWFtI3DszyvRNL2DWbsXydb7fCNyVZJF3Lqty+IDl1Ma3xbA/VEMGPF+/oEaw3W7jFsmGNB08VKDVclio2zYpAQFy0OklfTOfEeAHL9egaSLCjpY1aSSw3Q8KibxUtaksWzUqTC7Z0Yqoyu3ujFIsW95zIM/fi55ANa3h+wHAqxKaOCKYmc9W6NN87W+NEQfwuA/EQPRGFpC7QOY2W7VxXhBPsnsky2zuFSzPdosTPtUwL4VaG2CtHPsCWTIjusPpTnU/euVs4U39r0wdx/IBrV8dZrPs4fsBk2SWum8Q0ie9P1Tix7IiGzIcLewwey9VIGTJRTaA5Pnpoifde9jH+4fJLubQvgiJLPH84wZGlJe5+6U383s4PP+Xz3y+6zjVCz4461widqx9Z3zzze4DIFxqMqSw1BCtpru5yulTk8FKR0SVbnDgbgkh/24llKo5PypDpCsuENYnpqoupKgwldBbrYmrweK7OYEKIQFOmSl9MTHX6YiEu6IliqsLhBeLC1B3VuW+yxJ6+ONuywgL90GyNS3pNaq5YkXmtlcFQQljlT80KDVM0qhPVZOarNoNxIY7e0xum1PA4PFMhkxVBh1pY41d3dBPTJNYmxIn9/IE4nuPR1yWo9nIr0Xjggx9kJBNm/2yZN+7u5o6z02TMOFetivGO81/Ga+67jelcDatosdTwWFyoUa/a7D+ao5qr49kegx1hHj5b4ppVMWZrRY7n6uz+7Ld4/8EFJstNlNZJrtzw+PaLrqXQcPjA4w+yXHVIGxqD8RD5UrPtxhI6KBXP8XjFVw6zkK8znq/z4q2drBnOCIed67NzWzdu0yWcCTOYMOhqIR88W6zMRtJhHjy8gCRL5HJ1Vg+leO7aFF1RnWtXJzFMlWjSIKorfPjeSd7y7TO85/457BZMNBvWiEcFAFTRFb5wcI69Y4vkcnUalsu3RxfI1R3iusxs1SZjyLx6l5i+HVywWNsSIq9MWQoNIWy3XA+7apMZSHB6vkq6JVS+aVcvg/EQiiyE1NmwxrWrY9TdoB3DUGp6NLyAXT0xzpTExCNlyBxYaLC9Q6festZv74yQbnHicnWxlqouVvFsj9xUiWalydklwRMbyYTRwhp6REdWZVRDxWtR5D3bw22INW8ipDCzJDRM81WbbYNC1NuTNOjoivLI6AL3H88zXrQYWJ2kO6JTbbg8eiyP0sorsms2iZDK6oFEm3EmyRL7HptnvmZzaKHOBWvSxOIhLv/B/XiBuDG4d7IsdEmSxHjBwnI9hpK64LRNlnh00eb8rhCnSy4niza3n7UwFJm1SY3nDSfoiShc1meyKhYl1dILHZqvEtMkPjR6C58cezOnSw4vW/cvP/P55Z27P4IiCXNET0RMu247VaAvqnJhjwDUAlzUk0GWRE6ZIkn8yQUf4U8u+Agv3ZDiUP5vOLE8wUTZ5a8u3oQswf0zDbrCv/kzH8+5+p9bz5pG6G//9m+RJInf+Z3fedLH9+7dy9VXX00kEiEej3P55ZdjWdYzc5D/w+rm1X8PQFjTiemimXjttjSlpoAo/v3+BfwANmV0+qMKigTXrIqLO84lIZidLLt0hRUimkzO8tBkqb0mE2GF4uT26ExZMJ4ske2yqyfKrp4YVwwmma/Z3DuxzFDSoCciGpR7Rxfoi4U4VXK592yJsXy9TdlOmypjOZEEPFNp8qL1aaK6OIknDJWxfJ1/2TdP1fHaKy9FF3fbJwoN6m7A390zRXdUZ9/pInpEp9R0KZSafOimNZw3nOaa4XR7otUZVviXffMcL87w1xf9KpIkc3ypznmrkuhRnbGcoLnbVZvx3/s1hlcnkWTB5fqNXd08OFPnw4/Pi/yZio3nBlhNj1zdQZElIrpCd3iID183wicfz3HmljdwfKnOnt4wa1sX7kLNQZEkcnUbvaURchsuZkhh/2yFXNVmaqbM669cxfndEV5zUR+v3dWFrkjcNJwi2hlt277Hi5a4wEd1AfdUJD4/ukgiJJLCBf5AgCxftWeA3rRJf8qg1HQZL4ggxk0dYUbSYS4aTnPRcBozZRJNCpHv9tVJDi1UcVoBhvvmajw4I0LxPF+ELBYssQry3IBmzeH7Z5apFxu86doh+mIhLl6bxgsE1FaRBWMNxIQrbap870yFva38Hk2W2JQROUdiJSeAwp8/WmQ4pfP9qRpfHl3kMw9OUWyIpmFzJsT4+BJSa90WtPRPkiRRy9eQZIn9R3P4rk88quM2XDzbw0yZyKqM23TZsT7DfNWm0HDJJg3hqpNFeKXnB8yXmyzlasR742SzYXRFZr6VgzWUFt8nko1gJAzUkMp0rtZm4tVyNXxXTDPLc1X2HVnkoWM5hpJG2/K/gkXJhoVIPBvWSZsaTTdgQ1rl+qEEhxaqfOBQgd3dwmV3XqfOQk2sntcmVCqOz3zd4Y6zy9x6dJnBmMJfXdbfCl81WKz77bDEn6d6IgqGIqZ1iZBMf1Sl1FqxDqcMHD/gSycXmat57Z/zb8feAohGakfHH9NhxPjI1R9nU/oP+YNdH+azN/xr+/u/6vbX/NzH9suopwRcbT3O1VOvZ0Uj9PDDD/ORj3yEbdu2Penje/fu5YYbbuD6669n//79PPzww7z1rW9Flp8Vh/0/pg7l6pSaTRbrHpkW02m+VuKvLuthe4dogkKKsMz3xRS2ZUN4ATQ8QaCfqrgcXapjOT63PrZA1fbYP1turcDENMNzvDacEqBgueRqAsw4GDcwQwoPniqw1PCZq3lcsbWLjCHyYhIhhVKl2QJMmi33mE9ffxzPFflCvh/QFxNQSFOVaVaaHJ0SKInZ2QqBH7C2N8ZYvsaxgk1vNsJ81W5rNKrLDXavTuIHPkfmqtw/VeKdF3fj+/ChR/NIssQNgzdAo8zx4iPs7I6y78gizXKT09Nl1g0m+LOb1rDjM1/m7GKN7auTrUToJgcml3nwzDLRpNFOKAbw/ICXbMjwgnUpJqtH+fTYPNmwzu/e9xUUWWJ9ysByfXJ1oZEYTpk4DQ+n4SHJEi+5qJ8rBpMcP7mE7/r09sZ4cKbKPZNlOsMCgFuwXLpMmddd0M3qARHS5wUB8aRgW6VboY2SLLGnL8qBeQEnna/ZeEHAfzw8w+RchdmWGDdtatSrtnBc1R3Gi0IvFG0R6i/d0snodBmr6fGFg3MMJcWEbqrUFA7CVrpx1fa4ek0KuwVxtYoWl23M8pXjBUYyYR46s8xkqUmxbDNftbm8P0JME5Z7RZLY2R1lUzbMqrjKbNVmribWfyK2QQSE7umPM5ZvsC5lcO36DM/Z0d1umG47scymkQ7CmTBbNncyOJSiezCJrMqYKVNMfJouTt2hWLTo7IqI94rnE8mYhGKhNncrEVJZqtgko4KcPtwh8A++66OZGo2S0Got5+uYLVfd0Yki61cnuaA3RjRp4FgOVlE4FVfeHytrEVkV2i3Hcnjo0TkBdk0J51mu7hDTpNbaVExXz7QmLRUn4CUjSa5fHaPUDNjRKRyAl/WbnNdpsFAX+WCOH9ATUbioN8bahMbf7pvjsbyNoWhP+dxyy9YP0RkW06DHc03edeFHGEmleXCmzgev+jjv3P0RYrpEqSl+57Aq8cK117b//t8ffBNbMn/EW+95fbtBWqn3HXrTk5qiZ2NJ0lNcjZ1rhJ6WesY7imq1yqte9So+9rGPkUqlnvS5t7/97bztbW/jj/7oj9i8eTMjIyO87GUvIxT6GWya5+op17su/AhHCi6yjEAuVFwmKx6fOrLMLV8eJ6brzFQ9JsoeH39sibwlYvnzlk+6lXHi+QFeELClL4auCPfM46eL5Oo208UGgR9wdsliptJkvmpzoBW2+NhClTPLDeqWy+b+OBlDBNIdmK/gB3BgrooXgKzIbEqL9FtFknjBuhSmKtMoiQnPdNUhbaiUmi6mKqOFNQa6oszOVtg4lBJAS12hVm62BdzjRQvN1HjL7m7chsv9hxfZkOqlp7UW+sDBJW59/mb+7bnbmX3zb3P75O0AnFie43tniniOR89AnIs3dHDizDKfHF2imquTSRmMTpeZm68yX7VxGy6hkACPvnF3N39+hcgqeuH6DJ98aIYvHlviCyfGGMvXmFmqE2qtn971wCxpU8VUFX7rvE4enq3gOeICvW0wwehijTtOFRhZl0EzFF48kuLC3iiX9Mf56slSy3LusLUjgR8IS/8lAwkq5SbFxRpqSCVfEkiURFhlNGdx45qoAK0qMrWagyQLwXBEVzieE9/vsnVC22J7PiNpIeCt2R66IrMuZXDFcIY9gwlSKZNc3SVXd9jSGaHaAsceXaozlBQTpktHOtjQF2fjUIruqE53VMQpPG9zFoC37elhvmrz8HyDphcwX7XpiSg0WtPGqu23NC9OC3kh3GALdYFOWZcKMV1xWuGKMFfz6YpoXD4Qx/MDOuMiHbtqe+TmKkIIXW4SzoQJZ8JiahMPUSg1hQtPU6jm6gR+ILhesxVMVSYW1ig3hB5ustRgcqpEb1owyhRdoTuiYySEAN12fBRd4cjRHAfmKzSbHvHeOKFYiA3DaSIZk1h3rD0RciwHsxXmqJkiSuLlIylGMmG6ozoni01OzJTZd1oIk6O6wscfW2Km0mSmKqa0907V0GSJiZJLb0TjWMFmruaRt3wenGlQskUDtWh5ZMMa53eFmChXqDj+Uz6//MGuD/N7Oz/MF2/6FABX9v31kyCo7774oxiiV2I07/C549/l7uk/5rfvfX07IPP9V36M+2dFg/eX+98IwNt3PPs1Qufq2VHPeCP0lre8hZtuuolrr732SR9fXFxk3759dHZ2cvHFF9PV1cUVV1zB/fff/2O/X7PZpFwuP+lxrn6++oMHnojDv6g7TEyTSYZ0ruw3GIwpvH5rik+8bAN/uXeOdUmNwZjC89elOLXcwPaCdppw1RahepYj8m6OLYixvqzKNCo2PUkx+g+bKq7tM9fKdcnVHVKmStwQgNNcfWXd1mBXd4yy7dMXE2srt+ny0cdylJoik2bZ9rG9gLWDCawWT8v2gxZewMAMa2ztjLB+dZLxhaoIGZQkerMCAaFIotkI/IB/+sE0523ooLsvxrX/sZdHX/Jqlio2n3nuDi7P7GFHxx4OLN7PH983BYHP6niW5wylUDQFy/HZe2IJ1VD50HUjRLNhfnNLB3bVJhQLceTkEk5LmDu1ZHHfVJUPPbqEokp87uAcz9ncSbPp8f6DC1zQE8OpO/xgqiwmH60142AixKllG8/20EyNX9nZg6nKnJ6v4jZdSk2Paq5O3vJZbgonX7R18a3aHl8/XWD/XA3HF0y1FdFvo9QQ+qhYiJF0mNHFGl87KZK165aL53jIqszW7igjGZO+RIi0qTHaWu/s6omxb7JE2lTpigpX4IMzFXJ1m/2zZbJhje8fy3HloACCWq5Igj6aq7cTuOdrNiMZk0JDOLwuH4hyQZcQepuazNiSzeWDArD6pccXmCw3+M7pUgvGG7BoiTVsT0Tj0gHB6ypYLmfLLiFFpCcPp3QGYgoJQ2lbxG99bIGTp4soskS+7lCYKbNjfYYbtnbRMxBvC8xXUqWb5Sa+61NfqqOFNTFZ8wM29cZaK05Bqm9WmizPVzFTJgXLaU91qo5HRBcBpfWlOgNdIqV5MC6mQZX5ClbRomC5uLaPVbTaQm0jYaDoCus3ZHFbUNK/fmiWmUoTUxUxB+v74rxyZ08LMRJw03CKwbjIfJquemztNHl4vkFYk5ivO3SGVXoiCh85tMhQUmcwpvC8tVE0Gc7rMtk310CW4K/3fPSXcCaC9172MQC2ZzX6omHOlJf5p0tewkuHL2Sh/ikA3rJ9LfCEIPv/hTonln521DPaCN16660cPHiQd7/73f/lc6dPnwbgXe96F69//eu5/fbb2blzJ9dccw0nT578kd/z3e9+N4lEov0YGBj4hR3/f/eKaRIHc3/Ft8/8Pqoss1D3OLJk8ciCjReAoWrYnsuVg3FOlVzumrQYL9qsS4mJyUgmjKnJ9MVCDCUNoQFqOHiOx0XDaYxYy7Juqtg1m2rLaaSZGrWysFKvNDErkMq6G7CjJVw+MFfh6FyV8nKDga4oaUMVAti5CmM5oRkaTpusS2qsS+n0R1WGkjrHlxrs6BJW3fmqzdb+OM/b1s1wyiBtqixUbbqjOsdzdWJhja++cldbQCvJEuhh3nRBNzuSu1gKykiSzAceO8Njv34LSDLPu+1hvnw8z5uvWU13VOdPrxskFg/xov94lEbFbmegPfRr13Pj+b0YCQPPD9AMhWMLNXJVm6GkSU8LyXHNcJrf293Ffzw8g6IrnJos8f3RBRQJLMfnaK7OeMFqZ8x86aFpTFVMLqJRnT39cbauy2D7Abu7dRqeyPzpi4kV5n2TJV67JUnN8fndPT309cfbacnXbhKTlwPzFRqWS3dUZ6gzwjXDaXqzEaJRncOLNY63UqYtVzRmigxfG8vhNl0+/4OzmKpwhw0lDbqjIgV4MBFibW+MH0yV6Y6IxOMt2QiaIjGUMInqCn2xEIfmqywuiCgEP4DvT9U4MFdlstRgvGjRFRaojF/Z2kUuV2dHV5RNHWHShkrGUDAUicW6S1iV2oLuqu1hqBIVJ6DU9DlTdpkp2/gBmKpMEAT0DMQB2ius0TPLHJivkF+yGMiYJGIhnretG10RE8aVxtCu2ly1TmiYVlKaFUkSIYmJkPiamk3a1EikRYhnsdwKR/QDzJTJ5Jxgm+XqDo1Sg/41Ylpebbioujhty6pMOBPGqYvwxpMn8vSvSaHIEh+7fju6LLct9KfzdfKWy31TQjw9WbYpNT2GUyEcX1jlL+oxmK161J2APT0Gf3fPFNcNpemJyExXPaKa3IYtrzgWf9lVbPgcL9Q4uOjwyRNfozO8io8e3suXxt/Gtsw7funH81TrXCP07KhnrBGamprit3/7t/nsZz+LYRj/5fO+L+643vjGN/Ka17yG8847j/e9732MjIzwyU9+8kd+33e84x2USqX2Y2pq6hf2O/x3r3fu/gj75md47ur/Q9UWIWV1N+D8Ll2M0csWYU3nxtVpDEViT6/BjUNh7pksM7EsiO5iGiSmNLt6YuzoirJtMMHBuSq25eK5gnnVXissN9qcpqim0GgF3J3X2XI2BTAY11mb1BhMhIhExNRmbrkhsAX5Jq/e0kFfLITt+fRHNX4wXWOx7nG27HL3mRJ9MRGo+OXjeWzHb7t5ZirCmfPyTR1MlQQuwvZ8XvqVx/H8gPySxTsu6YFmld1dcZa9Ml7g8IcP/AdV20OyxPRxqAWtvP10ifHpMvvmGnzvpVfgNlxSKZP33D2JrMrseP/XefXGDJ7tMTFbobZkYddsdE0mV7eZWagynDLZP1vhO6erZLNhjJjOtdsELLXQcMm1QvZOTZYIpwx8TyRGd0U0ZFVui9LThorl+Nw/06DDkDFVWQAr+2JsyoYpNH0GYgrDSYM9/XGhg3E87jg0T9rQqCzUCAJBu1ckQWbXFcGB0hSJREikdQ8lTGYqTbJhnW29MepLdfpWJxk7VeD4Ur1FPhc8sJmKzfh0mVOTJboiotE8UbDoi4VYnzY4MCPWSrYX8Hc3D2F7ARMlG88XazzBE3PZN1dnXSqEoUi86dJ+LNcnEZIJKRKGKrFQd9jZGeJs2RXuwpbwuTMs4/sBMxWbVEhMhPKWy/ElC7fhsrggRO6d8RA3bu9m6+oki61p5snTRbqjOntnStw8nKI3KzRCO9dnuH5nL4cWqhQsh6rjtVd+dis2wLEcstkwigTl5QayLDRQlivE3LGw1ga3Wq5ghkU1ReiTQmKSF4qHUEMqlbkK4UyYIAiE4L3VnHzi8Gls32diWWQ53TjSga7IbQv/YFynYLkcX2qwPqmyNqkSauFRHss1GSvYXLUhy3mdOmsT4ljHCmLd3XTF83ffzI9Havwi6vd2fpjpqkex4XJZ3zBfHv8KsgQvGf5nPnf8LT/5G5yrc/VD6hlrhA4cOMDi4iI7d+5EVVVUVeXee+/ln//5n1FVla6uLgA2bdr0pL+3ceNGJicnf+T3DYVCxOPxJz3O1c9fczWPr0/8LgCTFY+c5fHF4xUO5mximsxovkbVaRLVJd5/MMe+eRFWOJIJYygyHaZGh6kSaQUYmqrClQNR1qbFHXUirLY1FW7TRQ2pRHSFiK4wmRcheg3H59OPzFFfqrN/tsL+2SoPzdaFJdrxUUPCSWYoEmlDJW+JddxwymSuJnAJk+Um3RGFS/rjjObqWK6P7yNQBXWbbERj70SRqK5wbKlBo9SgUbEZTIjVxIH5CrIq87b1N3Bf8WE+czTHi7/5VV5/5/e4eaiTsXyNfzv7XfaXH2vDPGeWhE3+zsOLvOuhQ0iyxHKpwUt2iVBF3/X5ra+doL9HwDxTnRFuPq+HXT0xlqtCO/KlA7Ps6BJBf8tVh6Gkyf86rx9FlWg4oulx6g4bh1I0aw7NcpNQPMRCzaG7L8amjjAb0qEWz0tha4eYAGzvNBnJhCk1PTpMjcmKx5myyz8fXORbx5d4zoYOhtakUXSFyXKjjX+YLDc4MVNGVyRyddFUNJsem7IRDswILIrnw86uCAcnigyNdKDLMlvXZbhpOE02rJGrC4Fzd0Tnui2dbFmb5rZjec7vjrQdhYfzdfa0dE4vGknxNw8toCsSk6UmfXGdTz0wxY6uKINxg8lSk7ma2+JkiYnP7acFZHUs3yBtqDy80GBjWqMvpnPvxDIA90zWW+8PkUJdsASC4rlrU9y4vZvOrginJ8R6bG1So2p7bGtNZmJdEY7PVVhcqPHpvdMMJQ1WrU3THdEZL1rMni2xWLCYnxHctLrlIsmSsLJrCqW6SOTWTI16sSEmOw2xClyBhAa+SFV36g5pU2Pd+g66WwGObkOsg3fu7MWu2QwOpajlxUpyTUJHliARUumO6mTDOrefXCJjyKRNjVJTNEDXD4k18cHFJvdOWRxYsInqwjo/XXW5rN9kOBHlZLGOJks0PNE8vmBtln+97hN85voffUP6i6h3PPgGXvTN36ArrLApo/Pg7DhhNURYk/jI4VtYn+qm4nwFgLun//iXemw/b8nyU3+cq6dez9jTeM011zA6OsqhQ4faj/PPP59XvepVHDp0iDVr1tDb28vx48ef9PdOnDjBqlWrnqGj/p9XIUVismIxVxPJ0Zbjs6PLxPECvnyiwmTFY6LUoDus8du7slwzYPK6bSm2dojUWq0FUT26ZIkGIQhaUFXa/Kbh1UkAkq2Qv3LVplJ3kBWZiK7QFdWRFbm9QhJMKZ9ESKAFHMthQ1eEzz4yC8BkqclQMsT2TkGnzxgyO7tMruhL8vCcyJ+p2h47e6KEUwIkee/ZZWRF2N4PzVVYN5hAVsUdtNLCWSy+5a18/PQdxPVwi8sk8asb4vz23ScZjBv821iJqtNgLF+jtCgmB6GYWIV84tqXY6ZM/uLaASaWG7x6dx+KrvDrO7vZ3Rcj24J9fnt0gUt6Ta5fn2lrfu47VSBXd1B1gbR45TePYKqKsMg3Bevq5FylPSrfNphg70yJcsPj7tNFjuSF4Hlnp7hA9rcSwJ87FOGaQZO0IbE2oXB+V4hNHWG2dovJwp7+OG7DZXqyxAVbOulKGm0d1fFcHbul/7p5YwcTyw3cpsvDx/NU6g5nSk3+9oZV/P2VQ2zKhtmaDfPV40scWqiKaaDvU2gILVa0lZVz6+EcWzsj7Ow2BbOsarOjO8qtR5fY1HJaDadNLMfnxbv7KDVdHm8x6QqWS9XxBVZDkrikP05ElVvcLIWusMapkosXwNVrUmzM6Izl63zvxJLIbJIkwTxTJQ4u1BhdrLGrO8aqVUm2dka4b6rK7t4YVkugXM3VaZabuA0XRVcYiIewXJ+7xgviva0rbR3c3LJI5XYshxvWpom1HGZLRdFw9/fEWNcZYUtfrN3oBH7ATZesIhFS0aN6q8EMOLPcYGJWNOXhdJhcXaBScssNJEkiN1Wi4cGRvMVYvkbBcrikN8TVa1I8nrPakRW7e0y+earCWN7iwh6DVXGNy/pC9EcV1iU1IqpMQpfwEZlgjh9wcU+Ex3IOV/f/zS/9PPT2+15Pww14yboosgRrExpd4SjLzTprE2EmKx7nd76T28bvBHhGjvHnqZX4kKfyOFdPvZ6xRigWi7Fly5YnPSKRCJlMhi1btiBJEr//+7/PP//zP/OlL32J8fFx3vnOd3Ls2DFe+9rXPlOH/T+uxpZsdnf1ENUlEiGRwdJwAxbrLrbvc+eEcKIsNRy8QIzPNVlisuLy0GyFk0UxPh9OGfTHxJ3uvlkR/JcIqVSrwjWmR3RqtsfqbBjP8QiFhAi11lqtSbJEo9Tgjy/q5NKBeJuivZJAPHpmma6OMF4AGzIGHabMiUIDLwj414dmKDUDorrB67alWsGNOps6TH5vdxfZsMYNa9Ns6xViWqtocWq2wq7BJHcdE9b4t5+fZax8iMFYjH84OE5YlShYDl84USERUlpoCIeybWFbrkBRVG3cpstl6zJc/aVPsz5jMpoXsNTnrEpy/bYuTpdsxgsWDcdnqdggnjT4j6NFJksN0pkwtXwNuyao6E7DI1e1adaEZRvEBdNIGPzFtQOoIZWbdvXSHRHBlC/d2ME7LumhP6bRHdWZq3nUW3ECaxI6BxdtTi67PLbYoNQM+OxYkQ9e9XHmqzZ7Z0rsnS6ze2OWa87rYf/oAtVWxMFgIiTE5arEjq4oo4s1jk6VRNqyriDJEqO5GieWXR6aLwl6eK7O2rRJVFPY3hXl0OmiaLZ6DdanRQPsWA6fvWeCO06XGEmJ1aft+fzK+jSJkMr9U4I+rysyMU1M3TIxnVduSnPtqkjbnThetEgbEiFVwvED5moiTHElw6ovpnE432RTR5gXbulkT2+4tc7TSBmi+b1qVYIOU2VrZ4RUSOHQiSXunFhmfgV06oisG0mWCEd17mg5sjb3RBk/s4xne239hu/51JYsNvfHOTRfpVkTWi5Jloi0hOCTpQaTpSZVx+Pw8TyRjgi9UTGFisRD2F7AYrmJXRORDs1KE8dyWC41sIoWjeUGvudjpkxOFYUdfzAu4hUeXliJVzBEfIQmU3cDdvdEeOWGOFFNrLz9APqjIfwgINz6WDIU4Z7pBru7Y+StJrdsW/8LPNv88Prc8bewIa2SNmQmyi7DSYMOM8ahXJl7Z5qokkLVDnjpt36TibL3Sz++p1KKLD3lx7l66vWsHqz9zu/8Du94xzt4+9vfzvbt27nrrru48847Wbt27TN9aP9janSxxgNzs2TNEKdKHjOVJh2mzKaM3s656TBlHF+s0bZ1mNw/2yQRErTr8aLFQs1hNFfHUIWmBGC+JtYjIz0xJFnkCBmazJlcnY6MSa3cRNWFqLTYArFqYY0PP1agLyqyYjw/INriL8mqTNFyOTBX4WzZZrbmsbYlfn7Jrl4emq0gI+EHcFW/wYa0wcSyzXcnapRa4YX7j+aYrwk3lxHT2T++BAgkxf93/zyv+vZBvjVR4FuPzfN4rk5fLMRYax0xulijL6bzwUM5+jJhLtzc2Q4o3D9b5i07xMV8dVwlV3d4aL7E/tkK3RGNkUyY12zPCpF4TehKCpbLcqlBpCOCJItJUTyq8x8v2EyopadRWroRTZG4faLGJQMJbC9oM8jOlJos1D06TIEsma56+IHATUxVbRIhmXdf/FG+9vzP8Ae7PszVq8QaORvR8Hxxkt77+Dyji4JY7/kBaVNjYlk0t1Fd4XiLK7ay6vNs0SABlJo+paaYGg0lDRIhhbOLNUZzVbo7IxQsh6+PV/j86CKxsNA0aabGifECVSfg3oll7jy8SEiRmKk0uXQgwXjBQpHhm+NF0qZGVFdwfPjGKeFCK1guR3N1Ti07aDJUW3gWr3VxX5vUUCQYSYsIjoLlMppvkgipba6YIknUXJ+8JUjxC62py3KpQTlfb2dLrTShpcWaYNfNVxmdFjqxUDyEogmt2wrCZWxW5C2pIZXnbc6STRpkwzpRXcHUZGzPb/ParKLFF4/m2xyxQksIH4mH8GwPWRETJs/2SA0m2qnWK1qjFVG65Ygoi8lyk8lyk929MRIhVej9nIBjRYdiw2cwphJSJCq2zWTFY11SpeEFLNbLXNIb4rmrnosXBGxI/f4v9oTzf9Vtp34HWZKIaULz1RNR8AMf1/cIayIParxURZNBVyROFm3eePfrfqnHeK7+369nVSN0zz338I//+I9P+tgf/dEfMTU1Ra1W48EHH+TSSy99Zg7uf2j9zvkdHCu4PJ636DBkLuuPtFKkJQYTBooEt50UxOiusELDdTAUiYQus6cvwWDc4O6xRaq2x+2nS/REFIaSobarzPMDEmEVuaWrCYKAQqmJGlKxSk0kWaJZaeK1Qv2qtsdM1WP/bIXJchPL9ZEkCVmR8V2fS/oFKyltKJRb8NWYJvGykST3zSzQH41ypuzyndPLgABORnWFB88sI8kStSWLIAjw3CcC74yYzis3d1C1Pd554RUcuuX5jE6XeXi2wsefs4mrV8VxvICxfB1TlTkzVeLAqQIXbciyqTfGnr4E7zuQ51c3xNEU+F+7smiyxOdu2krKkLlmwMRQJL70gm287zkDLFcd3rBDrMaaFfEcpFoBeUcLIlBwbW+MwZRBPGnQFwvxG5vTdEU0Lug2GEoavGpjgnWpEI8tisnR+pSwQJeaAR8fXWax7jMYU570Wr/7YmGFvuvFn0VXJC4fiDOyLsOOriiqoVKcrXBysSYYWeUmc1NllksNajWH81YliScNunqiXL06xe7eGFXb419/MElfLMRMRbxWN27upC8WYrFgMVNqYns+N450sKkjzA0bs6JpCAI+vm+WN5/fxRsu6eef7xHfY0V7dmi+ytWrkyiSxI1rEvgt8bPl+ti+z59f2o0XBExVhDh6QzpEb0RhvuZSdwLmax57Z6oossSq+ApOw6Y7ojJVEQJsz4cOU21Z0MXz5DbcdgOkmVrb/aXoCoWGgKU6dQGvXZkGrTQrkizRLDepL9XZ2h3ltkdmmZ4scfJEnvmZCrNnS5QWaxQnS2hhDSNpcO2aFH0x0bB5QYDdCrFcseyvNENLp4vtnxH4AZbrM1+zOb5U53S+zvndJhPLFutSBjMVEUtQavp0mDKpFkNsdVylJxKn7gr0SLHh8+iizfiyyOGSpWtI6E9+v/wi6/65P+Nzx99CzXE4VXKoOOJ4m15AzXEZL1VwPOH4C6sSl/ZFuWrAZFNGZ7LU5DV3vraNCHo21zO1GvvABz7A6tWrMQyDCy+8kP379//Ir73yyit/aKL1TTfd1P6a3/zN3/wvn7/hhht+rmN7JupZ1Qidq2df/damD/KRqz+OJkPdFTH4x4ouPRGZ3d0h+qIKv7ohTqkZ0HAD7p4W+SL/+NAcigx9sRDD/XGyYY09fTEW615bkzFftZkoWBRKTXxPBBpGo+IO2q7ZDPfHCUW09rolmRAZKJYbMJQ0WGitKTRDIRXX22yqDlNDkyGuC02TF4gU3ZAKDy+UGCvY7OiKMpwSWIFESEAtVzQdvuvTrDR585WDDCRCDKdMzpYd+mI6133pGyRDXVw30sHv7e4ib1XoiSikTCHY3jtRZPVAAs3UODhVQpEFBd3zA0q20K+kQjLndUY5uZwjocsUmy6dYZnpqiCOv2V3N4t1Ae4EsX7Z3Rvjj/d08YnHC+iyaBrThgBrFhoOOavJQs1BkYTu6jsTdRoeXNgTbgtdV9Zi53dH6AzLTFZ+9BphR1eUo0sWJ8YL3Dsukqn1qE7YVAmFFGJdERRdwa7apOJ6m9q+1FrL3DuxzF3jBRKdESaWG0wsN0iEVL53WgjS3YYQD2/JRuiOKDx0ZlnotFSZSEeEWDzE2YqL4wX80wvWsDapMRRXUWTojuoYisTNa8LkLcG4u6Q/SqnhEtUUzpRFoxzVxPvh0QWLU8tCj1RvOZ4GEwbzVZu7zpZbrDaZybLN6GKt3fgcXbLIhjVmKk3OG06jR3ThyvN8sRrUn2iQqrn6E4GLlSbplk1+xR4f7wi3E6At18dtCG1XvFc49FRD5ZUXDxDOhHEbLolYiDuO5VvJ6SrzM+J7u7Z4b7pNl1A8hCRJnL+zF0VX2u//TR0RFElMX1dlTL5zuswL13dwstigLyb0Rj0RhVLTx1AkhhIGjyw0mauV2dqRZTBm0hdVuWGViaZAawuIoT71JOmfth5eWKRk+5Rsn6G4SsbQaHoCwBrTdcKqhCzDpozGRNnlyFKd0bxDWJPY2mnyvDUxFuvPfhSTLAut5M/7+HnE0p///Of53d/9Xf78z/+cgwcPsn37dp7znOewuLj4Q7/+tttuY25urv04fPgwiqLw0pe+9Elfd8MNNzzp6/7jP/7j53lKnpE61widq5+qYrpMf1Sh7gRU7ICFuo8fiFyP+2YaTFZc/vnAAofmq3zqgSmuGU4zlhPE9pXQv68czXNoQaySHp+tENUVNnVF6E6ZmGENy/WpWy7JhIFqqJycLLVBp4omMBorSdVDSQNNkXj07DK+D4sLNRzL4UShgeMHhFWJsi1WTBd2hzhdcshbPtNVj3VJwae6c0LYtjekDYZWJcn0xvjT6wbxXZ9VfXE+cPdZTs9XOTJdZjRXpdT02NQR5sXf+DK3bB/glRt2M5zo4uKeXv7p6mF0RUymFlp2Zc8RQZL3Ti4zmDD4xqkqczWPyYqL7Xns6hxkd3cGQ5GYq/mcKTs4fsC+uTqX92V42fZuentj7BxK0WGqvHHji0gYKqYm3D+6IjGSCbOnL8EdZ+qsimvcN10XU5ahMJoMhYaIoVifVOkwZXoiMvtmq7x9x4fbE6AfVvM1m0dOLhGKh0jEQugtnlbD8WlYLoNxo90cbeqIMJwyOdPS8Iwu1mhWmqzrjFCv2owvCIH6WL6GLMOWjnA7fPBEwWKm6rK9P47tCLhoZb5CYb7KnaeLdJgKp0oujg93T1a5+8wy16+OslB3+OzRMn4QkKu7LNQ9EobKpg6DQsPnuWvTHJiv4vlimrK1Q0ylvEDwyCzHZzhtkggpWK7P5g4Tz6eNeFnBU6zwydKGRm/aFEGJtofSWsl6tse6NSl2behooy9AgHIDP6Dh+HQlBVxYNQQDbuzsMvEeMWUL/IDX7+zkN/b088VD822d1aZW4/To4QWOThTbTecKZkOSJXYNJlF0hcG4mECFWtMjyxXOOy+Ay/pjXNAT5YHpMhvSBhvSGmuTOoWmz+qEylzNI21ECKkSiVAI23MpNZvkG15LX+VjtFadL1zzvqf/xPJDKpj/MANRnbojdEsVx+d4sUlCl3G8ANf3SBsh+iIaq+NJmm5Af1ThxtUpNFmiy5S5e6pGyX7qqdf/Hesf/uEfeP3rX89rXvMaNm3axIc//GHC4fCPjKVJp9N0d3e3H3feeSfhcPi/NEKhUOhJX/d/kyKezXWuETpXP7H+ZO8beNv2D1F3AxpeQIcp8+nDS3iBEKBqssTquMqOrigv25DCSBiMFy0myw0KltO2A1+6KompyUyWmmzrjRHVFXI1gbOwHR/b81F1uZ3zokd05mcEB8wqWuimykylxZ5yfQYTBoEf4NkeoZjIVVFkSBsyiZCw66dNlbGCTVSTaboBXz+W55F5i05TYVePEEc3POiO6JQKFscK4k49qivoUeHgicUFg0lXJO6dXOavLlnP+Z0X0hMaYCR1Hs+/7SDfnVwiEVKJhTU82+O8Tr0dRri7Ny50GqUmnWGhp7I9l0WrzNFCgYcXHJpeQL7hs1AX04m/f2SaREhiOGWyMWO2Vxi7eyJEW5Z2EWAonrdVcZ0NadHgmZpYbQCsT4kG6FDOaSMHHnzFj75Te+s9r+fvD76JsYUaRsKgJxPGcr0nmGsLVVJxnclyg1AsRDJhtLVgekTn+o1ZbM9nw6okUU0h2XqNLumPY3sBru2zUHfoGYhz065ehlMGX9s3zXjRorpYbV/kAz+gtFjjUK5B3vIJKRKX9Ue4YjAJQH9U46LeMMdbmqFSQ4jq752sMFlqtoGuYhon9Gsra6a0qXJBt0Gu5rAlG8ELAh6Zr4m8q9ZrP1Npckl/nB88vsDxJYtS0207usyUKQIUW3qetCmmRpFspD31aVYE7sGzPQoNodkK/IB4r3Di1ZYswlGdRFjl+5NVjuStNmdu25oU+6fLVBerhDOiaVwh2a+Uoin84NFZPNvjjiOL2DWbZqVJ/5oUMxWbXN2mYDmcLtkcXbJ43nCCuhtQsX2+cXKZDlOmO6wRUiRkSWJDSmPvXJW8VSERCnHz0Ga8ABwvIKpJlO0v/1znjp+1vjT+Nu5onmb/QoOQKrEhFacrbFB3A6J6iIYX0PR8Hm5lPN0znSesSeQtnxPLFfqjRhux0hf55U2wft56ulZj/zdNodls/tCfZ9s2Bw4ceBLJQZZlrr32Wvbu3ftTHfMnPvEJXvGKVxCJRJ708XvuuYfOzk5GRka45ZZbWFpa+jmflV9+nWuEztVPrJWL6tu2f4i0IaNI8Px1KQxFuHIGYwqTFY9sWEdThD15JBPG90XD4rWYYIcWquiy3BK1CjBqNqKRCIk7Y1NVGIwblAoW0aiOXbPRwprIFzJUnIZHd1QnYSh0R1RMVcaI6QxkTHRNhM2VmiLr6O7JOlFd4aoBs6V/ULjtRIEdPTG2ZA0xYtckpspNDudqlJou0aRBru4w0BXl2ExZgDUbIofoisEE+172WnZ1x+iP9vL5E99itnGWvfM/wG26/OVFv8LZJYuXbUxjmCp5y2uxrfS20+otOzOkQzLnd0Vpej6dphAnT5WbVGzR/FUdMYk4XqjjB3B5f5h1KZVS0+eTx79CoeEzEFO5elWSXd0RPD9gstzk1sM5/vLBeXqjOookMV310FrTjbob8P4rP8ZY4T38yQU/Gj/wjgffQFdY5gvHl/Fdn46wxsxClfJcFVmRUQ1h5S6Umri2TxAELJcaHJ6pMLFYo1ESENfBhMFMpclDx3KAcLY9PCd0Pdt6Y0yWmty8NsXdp4vYfkBnX5y0obYnGisicLfp8u39M3z7VAE/COgMyyiyuDg7Ppwpu4ykTdYmdbJhVdjtuyKkTRXL9blqVYLxooXt+5Rt0TAWLBddljhTdhlKhpgqN9srxlzdaaVtB5zXFeHzj86R6RVZTDOVJgenSuxcnxGZV63GJAhEIKguy/TGdDzbw/dE41JfqiPJgqtXqrvYVRu7ZiPJEo7l4HgBC62E7PsfmWFzTxS34XLgyGIb4dEsN7n5gr72axT4AaGYcO1FO6PoUZ140iDWHSMUC1FueKQNleG0SdFy2xDahC4T1WX+7cgSn7pxG+mQzGzNaTnpmmTNCNs7RKM3WbF474HHOFZ0SYRkvnm6Rlx/8dN2PvlR9fEjbyZnOeSsGoMxkQheti0KjSZ5y+dksc72bJShRIbBmMJnj5ZYHVcZjIlE+a+Nl7E9F1mSWJ/UaHrP/onQ09UIDQwMPImo8MNoDQD5fB7P89o5fSvV1dXF/Pz8Tzze/fv3c/jwYV73uicL0m+44QY+85nPcNddd/Ge97yHe++9lxtvvBHP+3/DxXeuETpXP7EKDZePHL4FEHfWHzq42LrrCjiy1CTf8NmcEdOYb54SAXK253PJQKLNtLphTYLhlEk2IlZgfTGRM7QSoCerMqVKk/GFqgiZs1zWDSawq0KMqmgKQRBwcrFGV1hhLG+1yPUyw2mTZlMEKJ4qWORqDr1Rnd/a3MV79i3yd/sX6TBlXrutg/Vpg/d/7wyzVQdZXkEuyK0ViMw9RxY5PVEUTjbb43m7ejm5WOP200W6P/R+fu/8QUw1xtpkB2/43r389t0nsYoW6z/5MRzLoWoHRHWF2ZrHtavjdJgapYbL+pTOZMXjHffO8ekjS0Q0lUWrTKHh4wUBB+erHJiv8f3JqmieNIXRnEVnWOErJ8ss1l1OLrv0RxUu603ysvVdzNdcTE00lmZI4YXrM631l0JYk3C8gELD563bPsTr7notm9J/+GNf53df/FHylt9mumUjGqHWWmxFrB5PGkKY7vkomtIWBUuSRH+L+WU5HqVFMVHSFYm1vQK0m6sJplqu7nDbiQI3jWQYXawR1RWyYYFbkRWZzr44Lzy/j/O2dInYBMfnX74/ieND1hTvu2LTw1SFkyimyVQdMSHM1d021+7UcoPhlEnBcsnVbQbjGtmwSq7uMrHc5MB8te0oG06bbT2Q7flMVxxeubOHpdkKeyeKWK5PPCpSmVealHR3FD2iU5gpC0jvmeX252JdEULxEI7l4DZcKvMVACF4djw6VyUpzwqH2e5egTT5xPV7hNZHESLsP3juWraNdHDv5HI7M2pjqxFTW/9mfNcnN1VqN0gpU4CF989U6AhrZAzhSJureSgSvOPCTs4LOqk4Af3REHU3YLxUxVA1kqEwuqIyWfFIGxLnd4q07plKk54PvpBVH3vR039yadUnx97MXM1jruYRUmT8AGQJlpsuTS9gR1ajw5Q5U64zX1vmVMkjbWqsSaQoNX1OLru8ZUeW+bpLWBMQ6F/b8IEf+fM+fuTNALz0W7/JNV9+Fa/4zm/ygq//Opd94ZW/sN/xF1lTU1NPIiq84x2/GNzIJz7xCbZu3cru3buf9PFXvOIVPP/5z2fr1q288IUv5Jvf/CYPP/ww99xzzy/kOJ7uOtcInaufWPdOLPPwgs109aOclw3xys0d1B2xFuuPahwvNPnW6SqTpSbdEZ2JZYuLe03uO1VgV3eE40t1ZqpifSIaH6WtvwDRDHm2hxpSaZYFxVtWhZW+sy+OrMhtBMeqjHBYXTUQYb4q+GT3T5XwPYEvGEiIFOUNaZXX33GS5VKDQqnJdNVjbMmm4QW86KIB1qd1FAku7A2LxGtNZilXIxQLkemN8arzugnFQtzfusjs6Iry4K9fw+VdV3Dn5P2sTw6LsLuWLihtaowMJHD8gKWKjS5LPLZocaYkALSPLTY4nGuwsydKb1TjoTmLL51cZHxZiGEHEyHShkY2rGE5wv3UHdX5pwN5NmbCeEFA3RGZOJ8/scRsdZlSw2VjJsxIWgQhypKY/vREZJ431MtiXWhrAD5+zSd+qtd6qmKzNRsRrKwWeHVFSN7dGWG5lfb9su3dKKr43VdWWvlSk8lyg8l8axJSs5lfrHFqtsKxhRoPnFxi73SZREghGxYZOVFd4cxUiYNTJcpzVRzLYU9/nKrtsacvyuuvFOGpvivQDhVb6FY6TaHRuWeyzFxNxDp0mgrZsHhN0qbK6oSBIkvs6YuxNWsylm9g+wGW69EX19nVHWVLNsJIOsRkSWROjWTCDCXEFK9s+2itxsdqem39kNrKriou1ti1Ns3QmjQ3rE23PxftjGKVmiiawsiAsLarIZUgCFA0BTWkUipYmCmTyzeLVPK/fsVGdn7wW/iuEGPLqsxHDi1yaGyRHV1RAj8gnAlzcrKEZ3s4lkMtV0PRFa47vw8zpNAR1tjUIdYVm7JhBhMh/AAKlsPqhErDDTi46PDH449QbPj8SmwTWzIhfjBtcbRQoOE5RDXhBK07AWMFm387Ila+2ajO2dff9vScUH5IOb5wgA3GVFzfZ00izIZUloW6OEfcebbOwwsOZ0ouszWH9UmV3d06d0/liOoSpabPvdM1dnZm2TtXZW1S/bE/b31KvF66IokpdEO8F7dmw7+w3/GH1dOVI/R/0xRCodAP/XkdHR0oisLCwsKTPr6wsEB3d/ePPdZarcatt976U+X4rVmzho6ODsbHx3/KZ+KZrXON0Ln6iRU1VPqjKv/7Bw/ysnX/QliTqDhCLL0upbKlQzQfg60m5LyuCA/OWnQkQowuWuTrDmO5GtcNJRldrLUnMCvNkOX6rO0UsM1wJtwOjRvImOiKuKCu2JZtL+DBmTpeAFs7I+imSrPmMNgRptBwWpwqnY8eEidwz/b4X3t6eWC6yrqUztqEiqlKrE2opEIyp0sOWzujZMMaRsJgV1+MUsHipqEODFPlvVcO8N7rB3jrjj6iWpLTtaN88NASncYAf37RZo6fLranIiOZMLcdydGfMtg7U8FyhRNuLF/D9gK8IGAwHuLoksXpkk0iJLWzYwTtXm1T5Xf3xhmKq2TDOgfnq4wXLKYqIqwyGRKOr+2dBlNloTu6aU2Uk8UGXzle4KE5m88cncbUZN514c9G4h4vWOydKHL3iSUKlst5w2lkRabwh9/BVGXCKQNJlrjtWL4dHBnOhMnEdOJRnWJZ5OQM9MXp64oSTRp0ZEx6koaIRHA9Tk+LzJ/7zpYYTplAK4nZ88kOpZiv2gyndMbyFkfyDTZmwxgJg6NFh/Uplf6owqOLoiHrjohYgL5YiO+cXma+Jij1K0225weM5upUbJGBtLKivX+qxEzFpmx7jOUbLXefzHjRYqIkLOaji1XchktvNkJ3PMSZk0vcO7Hc1gGFYiFG56vs6omJXCvH45oNHdSX6viuz59e3ssFPYIiL8ktW7EsEe8Q73HP9rj/eJ6pM8u880vHsYoCnAui8fN98ecK8DeeNNqgVUVT0MJaCztTbqde64pEX0z8O7ywJ8x9U2WG0yLIc7HuclmfyUTJJqpLXPuDexlftrl2lcndkxZfO1Vm33yBA/N1UobMXWcrZMMaF/SE2dEd5eJbf/Wpn0x+TG3t0Hjd5g/yypEPcPPqv+e7k/NosoQXwEvWRUmEJMKaxGTZ5avjFQoNkXq9OZPlFSPdrEup+IHPVf0pThRdPjR6C8G80MXN1j7e/jm3nngrd0/l+IMH3oAXBEyWGy0XlsQHr/o4b7jrzb/Q3/M/l0IrZf/nffyMP0/XdXbt2sVdd93V/pjv+9x1113s2bPnx/7dL37xizSbTV796lf/xJ8zPT3N0tISPT09P+MRPjN1rhE6Vz+xLNfjnskKXS3RsyxJNN2AB2ct5mo+UxWP/bPl9r56uSkagLSp8ehMGbfpMpmvc+v+GcpVEaTYFwvRFwuJ0DfXY7LcoF4Va4OBrijNSrPdPKxgKsKmiucHXNBjcseZClcOhLEtF1mRMTWFo7k681UbRYLLBxMkDJU1/XEezzV57po4igTfO1tlstzktpMVvj9ZI2OIsLr7Dy8ykAgxWRIiw1d/bQxTk/nk4SVGlxw+cXiOF37tO7z9nkf49c1JvjrxdW4bH2frugxGwmgzpi5enWR3b4zpXI3HJ0tYjs8Na9OYmsz2TpNS02N92sTzAw7nRDDhSkq25YqVTq7uYDk+XzmxzIvXx1BkiWxYb1/cQdxBa7Ik9EFHlzmy5LKpw8RyPTamNQoNl/7oj78r/mH10g0pbtiYZc9QioShMtnKcgJ45FWf5+rVqXZA4ErmTziqU7RcylWbeCv+YHquwmJZPJdbs9H2dMm2XDLZCPtOF3npxg6+Npbj2m3ddLQaUdvx2X80xyceniMb1nnw8AKPT5a4YFWCmCaTtzz2z1sMJYUOaWunyeElh/mqoLlnwxoJQ6EzrLK1Q2VL1uDS/igNT+AzhlMmV69Oce1qQWlfsdj3xUS21UpjVrWFHk1WZfJ1h7Mz5TbTbuV3v359Bl2T+fbRHHuny5gpk3tOFdu6tr+8Z4bPPDiFU3fo6omyeiiF23QpzJTbOqOVyIYV19emkQ76VidZ/MO3szwpVm1ff3iGv335BvLTZepLddymSxAEyKrMVRf04ftwz6kiIxmxBgQx6Xik9TxZjt8OOFVlMY1VJNEwjRVsvnyiwhdv+hTbshp7Z+vMVJoUG6KpOrFkMRhTuH+q9GNF9k+13rjlQ/zGxg/+l4/PVD1imsSC5ZEOyfRFxdr3dVs72Dtbp+lCoVFlsV6mP2rwvclFRvMlbh5KUWoG/MCf5o13v47eiNC0/N2BN/FY3hYA3tYUaFd3hA1po81O++g1//U4/jvV7/7u7/Kxj32MT3/60xw9epRbbrmFWq3Ga17zGgB+/dd//Yeu1j7xiU/wwhe+kEwm86SPV6tVfv/3f5+HHnqIM2fOcNddd/GCF7yA4eFhnvOc5/xSfqenWucaoXP1E8u1fUpNj3LLjuoHARVH3HU3vYCxfI1sWGdiucGquEp3RGG+KuCrsioTjersGEiwqi9O0HKE9cWFMNNyfNKGhiJJYn3gi6lJZ1eExXKTpWIDSZYwQ0rb0XPfVJWb18YoNAR0NB7V6Y7oXNsCYlbsgLQhGFZn5qtcu0pc3CpOgKnK7GvhHbwg4JvjRfZNljASBs8ZimNqMv/6kg3cvLGDcsPjNzenqNo+Nw2FGUoaZCMaxYbPwcUaf3fXWcbzdV60OSvE1prCK0bijBcbYp3XQoDENImHpsv0RBSOL9V5bLHG9k6DgXiIrR2hVhCjxHhR4A/6YmK96AXw5RMVwUZzhCbmscUaczWHtQmVqC5x3ZAIrUwbElXb59KBBImQhKkq+EHAPz92y8/0Wp9cdphYFuu887sj6IrEhZs7259/xfoMnfEQ9aU6siKjmyr1FkpE0ZW2w0xWZTzHo1Zucv/ZZWYWqsiqzHBXlHLVpicT5t/3z9CTNLjvVIHhtMnztnWzoydGtidGNmmwMS2ao8APuP/wIls7NErNgLsfm0eXJTZ1mNw5UcJyhTB9sIWi2DtdJqaLzKANKY35miDAV1suMsvxcfyA7ojGfM3huWuecL+Mt9AlByaXeWAsRygWaocidvbFhYDf1HDqDnccy9MXC2EVLabnKjSWG1TmK9j/CcPhWA52zWbmzDJnJortKIiLd/QQioeIZEzsmi0yhzojnF2ymJsqk/nL96KGVGI9MXzX54+/fIKugQTda9P4rs+vXbGal23v5p4Ds9g1m3WdERIhlbQpkss3psRNS7qVb6VIsDphsCndx0ja5F8O5Pjavmku6Q1xWX+E8/795dx1tkZfLMSe/jjLto/nB6xOGrx3fw6r+csXvXaYMoMxBU2WCKtSW/y/rcPg1uNFnrc2ynjJIW81cH2f+2eqdJgiabzuivDJr4wvcGG3zq9/97d41e2v4asnS+QsD1OV0GWJguUyU3HaK+Rfdj0TiI2Xv/zlvPe97+XP/uzP2LFjB4cOHeL2229vC6gnJyeZm5t70t85fvw4999//w9diymKwuOPP87zn/981q9fz2tf+1p27drFD37wgx+5onu21blG6Fz9xLpxpAPPD1Ba75a6E3BoQWS0fOlYgYv7Ylw+EGd92uSusxXuPlsWdmpFYmM2zA1rUsxUmuzoijLSEyMbESuKUtMVd6f/6R90OKpzvFAnbYjk3hUava4IrlXaFPkod52toUjQkRFZMIKoXeeN21OcLducKQu6dyQe4gvHyxxeshkvNkmbGj2ZMBPLDeEo8nzWdITZ1hsTmTWzFf7p4AIRVSZuKPzDIzluO5LjUN7h7tNFfve89fzVHWf4wrECt716J7omc+9kiecPJ1rTCcGs2t4fZ3t/nIyhsNTw6Y3pfG5smWorna7hwVBcodD0GYyHkFsIi9HFGuMFi6iutMMSFVnCcjw2dRjM12yiusKC5fHoos3WjNDa5C2fmYrN+pTOfdNC71JqBjyy0GSs8J6f+rUey9XwWjqauhtw7eokk+VG+/Pv3j9D2lQxW5MTrxVQeN1IhxD02j5hU0WP6Jy3KklXh9BcrAQIFhoOHYkQuiJxw9YubliTaH/vrz4yg6nJvGpzhqLl8u9HCii6wiXrMrx8zwDfPF0jpEqku8W6yVDEtHK8YDGSMfGCgEt6Q+3f/a6zNT50qMBwUsX2AlbHVdalhP7n0EK1/ZyfLLpENbm9Vrqgy2CoM8LqgQSBH3D+6pSYDC1U2zECINAro8dyBK3UZ6AtIJcVuR0auWKL9z2/zRhbeU4aFYF0SffFGUoa7cDGSDZCEDwhijYSBoszZYaSBnpE59/vO8vn9k2LrKJSg2xYYzAuxN4rGUITyw3GcnWqtsd8TeQC3XLXo2zt0LhiVQLP8fjyiQohRWIkY7I2KQJLSw2vvbouNByqDZe5N3/1ZzpnPB311m0fIqQKe/8bt3wIgKYHqqxgqoIvNrHcoNjw2zdnJ4ou++ctVFlGkSSWmz4HFx0yhoKuyLx4fYL1SY3zu3R6IgrndQr0y6bME9PT3773rb+031F+io4x+edMln7rW9/K2bNnaTab7Nu3jwsvvLD9uXvuuYdPfepTT/r6kZERgiDguuuu+y/fyzRN7rjjDhYXF7FtmzNnzvDRj370vzjTns11rhE6Vz+xPrlpO/NFi/GCxd8+8iY6TIW0IU66uiLxaCvT4+B8lZlKk4LlMpwSBPHBuMHdZ5fJhjXuPlNEkeBork53ROdPLurECwIWqjZ1S7hiHC9oCz41ReTJLJcaKJLEhT2CzWR7PrYXkLd8rl2dwgsgG9a4eW2Cb56ut3NgJksNHC+gLxbiyn6DbFjj20dzXLUqwVDSYFd3jMJMmVzd4Q8uGOAjh/J86sUbODJX5YHpMju6oliuz+9f3EOHIXPqDa+l0KwR7wjTHdF5ya2HsOqOAKK2Qhy/cKzAxHKDmUqzdbKC/S130G9sEdk6r9yYYL7mUmiKC2pnWOVEweKN21PMl5uUmk/ATXN1h+tWhdmUjXD76RI7uqK8aG2aRxdt4rrCsaLDpf1hxvJ1JstNCo2AvpjGZKlBzfW541iescLMT/1aDyVNoVmSJb5+LM+p5QZ9sRBrPvGEffrEksWVa1PtqY/bcNk/W2lf+KOawpYWS2xHVxTHcgjFWzlPkqC8l5oeY/k6H31gmiAQ1vXLN3eSqzvsm62zvSvKSCbMLTs72d1jYrmCml5q+uzpi/Pl0UUeW2xw7eoUu3pimKqC7QX86fdnKJSafPaRWfaeWGJ8ocrXx0skQiqH802aXsDEsiUCIEtCG7TU8OkMK8yUbdYkNB6aE++3REjollZiAFbWWI4l2GMrOUIrQYpBIPRSobhw2mV7Yuza3EkkG8FIGEQ7o20jQLTFoVtxfgE8frqIVbRI98VFczOQwCpa9PXHsatCN3d4poJjOfSuShBOCVitoikcmKtwaEFMs0xNJqxJ7OmP8zu7OsjVHXRFotR0uaxfNJFdYdGwPXBwlvumawwlRGOwLasxWW6wI2uwtTPCP1w5hO/6/PXDb/w5zhxPvd667UO8Yv37AVioe6xJhCk1m+zq0lkV13nZiMCC7Ju3MRSJvCXWfn/+wAxzNY/eqELakBhKqKyKqyRCMo4f8MiCzeq4iKX48uhiO3EcIB765V0Wz0FXnx11rhE6Vz+xphM62VbGTlSXqLs+GzIGe/rj7fBDTW5lBvkBwymhhfnK6AKjiyI7aAUEansBhibyYL41IVKQB1o4gmTCIKIr7VRfU5NplMSaqdR0SehinZUNCy3IVMXmGyeXSJsqfTENL4DjS3XGCxa6LNOwXFKmyvGlOs0Wffy5G7OEVImJ5Qb3ji8R645RqTucKZdYyNf5m4dm6U8Z6IrMR695Hm/YnubVGy7igZk6Y4VDvOqbh6kULB4/XeS5W7sY7oqyOaOxOWOyJRNq3Y0LYe7xQh3Hh0sHEmzKRnhgtsmmbITpqriAKpKYajwyX2MwEeKuqQaDKYOXbUyzNmkwWWqyqzvGPzycw/b8NlT0jrPLzFSafP/sMsWGz0zV4/KBKC8ZSTJVbrJ/VqRg67KEYaq8ZPiff+rX+tpVJi9cn2Q4ZeI2XUpNj7te/FmuGEzyueNvYTxfZ/ZNX+HWGz+F3+K8BX5AuWq3L+rzRYuDE0V0RWZiuYHbcHHqDr7nU6g5bfBuqeky3B9n9+ok16yKYWoy16yKMVGyODC5jO35PJ5rcueZCqOLNWYqTQ4tVClYLpevTZONaHzp8CJWq5GI6gov2pzl+Vs6uXykg+u2dHLdSAdDSYOxvMiK2j9XI22K53Ft0mg1Zh4ni3abyp6r25zO158QKXeE203Pyp+qoYpk7Yguco8UmVh3jP6U0Z4KVRsuE8sNFFV6EqTVqTusiuuoIRWnBXStV0UgopEwcLyASMYkP10W9vya+Brf89nYE+U5mzvxfGhUbLZv6eKybV0Mp01ydbsVSyHx0UNLlJout41XKDVdeiIK/VENxxfOu60ZnRsu6Cc1mOC+Q/NENYlG69/I67elmat5vO+Ky7nlzlP4ns9HD+WenpPJU6h0SMb1fU4su0xWPA4vNXF88AKxRivbwjkKMJIxyVsuh3MNCo2AkALvuvAjvG7zB9mYNonqMnvnmrz3so/xa+d1MfOfcDN/edFP/+/lXP33qHON0Ln6iXX+J7+NIkkUag7HCi5/cdFHMVSJQwtVbl6bIqorHCs0SJsqaVOIVSdLDdIJodkwNZEWrSsy8zUbRZKYLDU5MF8R1tWmEI16gWCIlZpeW9uQ6BRMq5FMmIYX0BdVeWAsR9UWAu2Xb+pgJG0S0yQ0GXJVW5DtazYDGTFJ2NEVJW/5XNlvsiahYihg+744vppN2FR53yM5Lh/pAIRwdihp8MrvfJWMoXK0eBLbC3jvgVOYmsyGVUlu/809fPfEEt1RnULDpz+a5h33zXJRb6zNL7thTYqDCzUu7RWroILlENWE7qEvKmjfmizxuq1JsqbCNQNCdH1q2Wa26rSf/6GkwfElq31XvzapMpwyeemGNCFFYixfZ6Hu87mxAn0xXYROhhResq6Tm4bTfHLsp3fBvHHLh/jqiWVKTZcNffF2Kvi/XvcJ3vbNM5ghhT/Z+wYALl+bFm4vV4Qrru2NtfU0l6zLULW9dgK1kTDQTRXHcig1XXRFYjlf5+RkicmSmNQMJXTOll22ZqP4rbDNQwtVsmGd6VytnUll+z79MY20IVZO3ztdpCuicf9UCdsPiGlyK9pAxtRktnaEuHZ1nOGUya7uCGsSQp92cEGkSYOYyq2K63SZMpf0Rzl/IE6h5eCqtUTfkiwhqzKhWEgAecNCK7RqVZKB1UkGEiHmazaDKYEfGUqb1MoiUmIFr7Hi+vrayQKe47WDI2VF5oLt3Th1h+pCldqShazKWK3E7eHVSSRZYmy2wrcfnSNtqrz4vB6yYY2J5QbdEZ2+WIhS00WR4fqhhIi2WLLY1BGh6a3wxQJmqh7fPlPn4j6DP7qom1+/YjWj+SaGItFhKHSYIaK6xHmf/BpDSYP+bISG5f7fb5Vfet00tJa6Y3NBV4StGfG+XKx7TFdd6m5AxpAJqxL3n11mvmrz3aM5VidC+EFAWH3iUveitf+IoUCiNflJGTLvvexjz8jv9JQcY63HuXrqda4ROlc/sZSWRkfXZG47lucPHngD7774o1wxGOeeyTJbsyYb0gZbOsLs7o2Rqzts7YxQLIs71K2dT4hRbc+n1rLMXzGY5IrBGJs6ImRiIhF5Za3ktRAJ5XydlCk0HqWmyBr57atXcWihSqNiM19zGS82CCkSxYbPb+4Qwt6orrApKwSk40Wrzdz62sllDi5Y5HJ1CqUnYuhXbO6XDojjf8+le/jmTa8mrOk8ni/wT1dtZ7Ik9BcTBYvnff4RfM/nvuN5dmTDPDA3y8s2pjlTajKcMkmbAl/wovVxSrbPxT0hdnaZDMZEg5cKycxUBQUc4HihSUzX2dMbpiusocjCwRXVFdanTbZ2RtjUYWJ7AfdM1dmR1ThZtInpAlyaNmS2dkawWwndfbEQH3pslvmaw7Giy98deNNP/XrnWmLfREhpNwoA+d/7Fqdf+2WM1tl3KKlTX6rTNSB0PvOtv2cmQhyYr5A2hf0/HtVpVproLZHZru4YZmt65Ls++brDaM6iLyocfGP5GoEf8O2TBaK6wmhO5BRNlhuc12XSHdFZbvr4viCxd0V1bjuW57mtLJ/FustkuUHGUDAUmbzlU7WDVlyDT1SX6Y6K0MqortBhaiw1PKYrDt84Veb7Zysio6qVbbUytQHaf/quz661aaJdURbLwuE4GDcYTglH4HO3djGYeEIoKisygz0xdvZE2dYbY2m2gqzI7XBEq2iRqzttJ57bdBlalWzr5K5eneD3blzL1v44gR/QHdHRZIlsRGMkE26lYotmaGNK43TJpuHBDWsS7J0pMZYXFPmusExIlbhu0OTBmQZfOl5iQ1plZ6chUBUNj9F8nfVJlYtXJ9s6ustbz+0zWRtSv8+RgkNcNwVTLCy3dF7i348sSyzWXbZ2R5ksN/Fdn1sfW6Dq+PzFg0/OzemJKG3W3o9LW/9F17nV2LOjzjVC5+on1p9f2o3tibC5uKFw95kSN3/t13C8gMsG4uydqbJoeRzO1xldrDGx3CBtamzsiTJTafKNIzkKpSYzS3WchkekRe22XJ/xoo2uSBQtF1OVSZsqjRYxfihpsH04TSKkokgCqVBouJwuOTQscXdtez7jRQs/gM6wQk9EYXdvjMmS4JxFdaW9ionrJrt7Y1yzKkI0KVYYq7uFtfuy/hjZsM58zWUoafDg3CgEPnu6z+dE0eVzx0+yKRum1BToBMdy8F2fazdl2Tdf42snyyzUPQoNlwPzFY4v1TFUiW+eqpDQxcql1BSctlJTcMWiusxcSyt0UY/J43mLuuvTGZa5uNdkruaxKi5yczw/YGLZpjuioysSd09abO0Ikbd8yrbHgXmhjRovWOyfLbcAowE3r4kIx1D8p+cuzb35q1w1IKzY33zBv7U//o4HxSTonbs/wj0zf0Ku7qGFNSqtJOqG5YokakWmXmwwkgljez6KDJqp0XB8IvEQD89W2NUT41d29xPOhGlWmq2wTYELMVWZ3cMZBhIhTFXGcny6UyY3rknxtZPLVG2PZEjmZLGJLMMl/SKZ+YuPLzBTtkkZKs8dilF3A6bKYtpUtn0BAHZ8wi2IaFRXSIQUHF9gMuZrNluyES4diLGjO0ra1NrIDEVXMBIGnu1htCz3ByeKggPWdMkvVFtTP5fuqNCx7Z+t0JuNcPJ0EcdyODNVomCJAM11a1JtrZEkS+hRnbn5ajujyG9lAkmyRKp1IzFX8zivM8zujdlW4KPHWK7eTgIXaAmV70/V2N1tsjap4HgBf31ZD30xva2JKTV9HsvZJAyF3b1R9s83OVN2kSWhyXnn7o/wV3sXGcvXcBoe2bDGey7b+VROIU9bXdGX4p7pPPfNNAirEmfLwiTxg5NLfPz+KSaWGzx6dpmFiSKe7dGVFLb4k7/1pSd9n1u2fugZ+g3O1bOxzjVC5+on1nv2LVJqusxXba4YTPK3l6/hlRvirE+p1N2A562N0xdVW9yrhviz1ODIdBnL9dneH+dXtnZhhoUTTFckcnMVMoawW6cNFU2RhEPF9lifMYVrKhAhhZbrY7k+o7k6950tYXs+t1zYw69s7WKmImz6nz9W5EzZ5ciSw6GFKoos7OibOsKsTRp89tF56q5NZ1jh1qPLVJcbrYuNIFr/+2OLzFSafOuxeX5lWMBYb5/9PnvnH0GTxUXI82FTR6QNwgz8gLSh8q3xZeZrNhFVZms23E6H3t6h47XyfvKWz4XdOnlL0LwnyzYzFYcOU20BIx1immBCNb2AvOWRCMltYnyHqdIfE82M5fhc1GNQskXDuCmt4wUBl/WGGEqaXDqQEBMPXWHfvE2h4bPU+NlWGx84uNS+wAJcf9urGS/aT/qaiCbj1B229cbarihJligu1lg3mODeyWUuHUhgOSLiAMSaSWvBa70gYGM2zNbVSZaKDRbqHnPLDRKGiqnJbOqIUGqK406bKp99dJ5dPVGyETHBSRgqru3TE5G5eLUgsadNlZgu8fBCg/mqzaYOg6guM7FssS0bZiCmUnV8JpYtLNfj3slSO4BxYtliqtxkpiIEt1XbayMvRgYSJKMaTgsi7Dke8aRBs9Jsa4K+/egcuiLxg8cXhJtpscZiuUn/YIJE2uSCkQ7GThVIGApnF4XBQGs1W3pE5C91dEXRTI0XXrqKV2xMcu22bkbSYQ7M19g/W0FTJExVrJirttcKTxTHenC+yn3TdXRF5C39n4cWWLZ99s3bDMZUxgoODQ/WJ1XWJlVWxVQ+dOdpxgsWaxMq6daqaM0nXszuXhESuXMgweHJEmvib/s5zhxPf6myQkKXWR1XKTR9DsxVeHxskfpSHT2iM3osR6MkXI6qoVJoOD/hOz6z9XSxxs7VU6tzjdC5+okV1RS8QDC0xosWeatCvuFzKOeQNmROLLsUGkKo+Gtbsrxue5ZsWOeydRl0Raw0xvJ1NnVE8Fp5OH39cR6cqbA2aXC8YPHqLR1YpWZbI1S1BU18BckgJgMeL9rQQSIkiOrfGl9ie2eErZ0RdvVECaviglAs2wzGQ5TqLl86MMvDc1WuGBYhYGFVWKQTaZPV2TAjmTAdYY0NXRGGkgbpTJjPHStxw6oruWHwRnojKa7oj9ITEU603d0miiq1HT9j+Tqlpsvu3jgdpsx8TWhgRjJhvnyyypWDMRYtIcRMhsJs6YhwatnFcsTE4IHpMt8+VWIorvLtiQqTFY9CIyDV0js4fiDycNIqy00hUt/UYfDQnDjZTyyLP3f3RFib7GBVXLCmXjQco2A59ESEQP1nvQP+0z1d1P5TgGMipPK/zutv//+VfX/NbNUm0SlQJxv64vRkwkR0kXg8WRRZRLc+MEnDcqnUnXbzWClYIsfJD5iv2ozNVujPRji93OS8vjiW47MmIaYqaVPD8QJOLFmYLbG47QUiYkCTuXAwwWi+ycZMiJdu7MDUZDQZLugyRCCiBIWGx/bOCI4PfgALdZ8rBwV249c2Z7Bcj6GkQSIkVrCKLDG6WOPETBm7ZqMaKomQyuKCWNkdGy+I6VDLLRZvBS++7MJ+Tk2WuHBzJ6dmK0iyxIauCIsFC88PeGy6TLYnht5aZ8TSJmZIQTVUXrm5g1RnhHJVQFm/sW+a707UGE7pPPD4PLosVp91J2AgLtyYL1gbaXHaNKKtKetIxsD2fOZqLletTmI5PrIkcSjXoDMsmuoVm7kmC6H7Vati/NUdZ3j7HVMAnH7tlylYYjJqaiL36NlShUaVibLHTNVj31yDs2eX20w6VZcZHErx8stW8a+/sZk3X9LHe6/s+8nf9BksofN5Ko3QM/0b/Peoc43QufqJVXU8dnXHmChYWI7I7Hg8L6YDhxYstNaJPW1qzFRdTpdceqOCMJ8ICWFvIqTw2EKVvraeQbC1AGwv4I7TJbLZMMeXrDadPhESFPGjc1WiutC9CCxFwLGCy/PXieYmbahs69AJqRLzNZu1nREOT5ZIhAXRPNqaFNw/U6U/Kk7qfTEh5E6GZBaWG0L706LFb+kI8eHRO7lv7i4enJvh+1NV6m7Ax667mOGkzl9cKmLjY3HhEqtXbQ7MVThdEmP6qKawMaNzWb/JZ48sMVFy6YnInK1USYYilG0x4Xrzzg5OTZbojuj825ElAB5brPHwXIWTRZeFuk/Tha5wiLmaCIFbrLuEVQlTE3DVHV1hNFk0eB96bIq85WOqgszdFxOrs3+97qfjjP3netm6f+Hlmzra///Fmz5F3qo+6WsSIVVAVKs2nh8wm6thuV6bvP6HFwq9VrMitFie47FpVZJwyiC33OC7R3MsFiw29cZ4wbokiiy1c5Iez1nMVJr0xXRSpsrOnmh79Xf/VAlFlphYbqLIIjwShHNopmxTagYsWH67SVyf1AipEoYiyPU7shp5y8dpTd5KDQ9TFQ3F8YJwMo4vCF3SCmPtoWM5kYWkiFVW4Ac0LBczZZJfqLJ9dZLbTxXYPpxmqIXC0EyNtKmxrieGFwT0pk2GUyYb0hqdaTG5UySJaNLgwZkq33vpFSI12g/oXZXgwt4wIUXikm3d3P/YHF/dO4XTar5mKk2+dqrGmoQIUDRVmeuH4owuWhxaEK+TFwSkDJWGJ6IHRN4OVG2f9SmViuPz2mvW8P7vncFIGLhNtx3AOV+zGS9ajC7WWHjb13/m988vov7uwJtYbrrEdInZqpj8Bn7A1haCBeCKwQSbMxqnSw5/cdFH29b7c3Wuflyda4TO1Y+tN979Op43LBAR1w2nGUyEKDR9sqbCV0YX6Ivrwq1Vdzi+VEdXhMW9bPu8YF2ctKkylq+z73SRSAthMJgwMFVhif/emWX29EXJhnVG0gIUWbU9dvXESJsqNw+nWJ0N4/miITuca5Cr23SYMhPLTWqOT4cp88XjJWarnki01oTOoi8WIvADHp+tiImK7TNZ8djdLcIAX7NVCECDIKAvpvOne7owVYWFulhV5a0qq+MCZvqK9YN84PFDjC/bfPF4BVmVKS4KhlhPJsxwymQsX6NqCyp2X1RBluAdF3XyuUMLxDSZqCazWC/z9UNzWK7Pxx9bYm2L2F4si2bCC8TqC2BtUmF3d5oDi3UantCADMa1tsZl31wDTYZGi7/W0VoLmaokUneLVrvx+3nqfZc/2UljKKJxzb7vZu6b/VNMVWhizJDCqcUaa3tjWKUmnV0R9KjOn9wxiWqoSLKEVbTQTI0jE0Vsy21ztzb0CcBqV1hm72SJU6cK7bDLmYrNTMXmhetTIjSy1MTUZNKGSlRT2qT4lQDFqC6xucMgqkuMF5usTwtRbSIkMbpo0RkWqcNfH6/iBwHTVYeTRdGkKTIMJgxevTnDVQMmY298OcmEgWqoLFVEho+iP/Fcru6OCv2OqRLJCAG3qcntqalne0iyxH3H85zO12nWxEotV7d5dNFmZrqMIktPuCNDKn+57zGu3tTJc8/rYanYYLoq9HA3romydUOWC7Z0UnV8ThSstp5uuurh+QHjRYvpqstQMsQ7L+4mERJNY9qQuPN0ka0dIfqiCo/MW9RdcSPhB/C5g3O848Y1/O+LuvmdKwZ423YxOXz01Z/n2G9+6b9oa57JGk6KZidneRxaqJLL1dHCGp9/ZBbf9bluKM0Hr/o4b9/xYf7o/A8/w0f705Us0+Kc/XwP+dwV/Gmpc0/jufqxNbpY45+u+Bh3n1jijmN5Sg2P/fNNRnMWgx1igmOokuA0pUWDoSsSF/XoLNbFuuy6oRSb++NUGy4zlSYnF2tUHY9LB4RjbO9MVUwW6gKYmQ1rpE2VA3NVvnO6KLhkcR3bE9DVvlgITRbBcYcWqpwo2uzsijC6WGV+scbhmQp6RLiCbliXYUNXhEfmm3z98CJhTWK6KkjiJ4ouj8zXyCYNbjuSQ5Hgf53XzXjR4k1bVzNdbfLIQpVjSw1G87PM10Rq7TsuXIUkS3i2x0TJwvZ9jhfqjGTCvHJzBxf0hJmpeowtOXzxeIXf2NWNoWrMVD0+faTAnvUZThUs5hdrTObrjOVrDGRMRs8sc6pgMTVT5nhBTNoeXSwQ02TSIZmusEzFDig0fbojKqsTGk034NBik+6ozgPTZRpuQFSXOVZwuKw/2qZ3Px11YFEEQ+be/k2+Mr5AX1Sh2nCplJtohsJUqYkaUlmuOm23lWd7TwoelFWxarlgVYLulMnRqRKmpvBn35vihnUZOvrjAFTKTRQZJksNvnqiiKkqbOkTbrNCw+WRM0XyLQ2T5wfEdDHxSBnilLYqrtMVlik2fE6VPK4YiHB4yaZs+/TFdQ7MVzm+ZOEFAX1xnfdcuptOU2Gq4vHvYyXe9dC9PGdNCqfuEAs/ITSXVRk9qrOjO8rmnii6IqMrMneNFxhJh3lkqsyROTGp8Byh/emMh7hwTYrjS3Wmliy6IwrZnhh3jhfaq61S02VNQkVXRCzFhWtS3PbILKWGy7sfmKM7qjOcMrn7TBEQ7/2J5QaJkMxz18RZqth0hRUGYyqP5cRKdGUiaDs+01WPudpKVIBCypDpDMu8YFsXIRUKTZ937n7m3FM/TU1Xm7z34UVWxcQkslFq4NkezUqTC9d3/FyTz2e6zmmEnh11rhE6Vz+2VkCLsiKTiutC75OrsTphMJw2GYyHKDR85qs2uVqLJu/4fPhQoaUbUkgbMlbLXXNmucHFq5N0R3Tun6pwxYDZ1gOJWH+7HbxYrtrs6UuwLWsIi+xcBRCrtA5TZlVcp2A5dJgad0wUyYb19p37UNpkptIk2RKA3jVewKk7dIdVbjuWp2p7zFZtLh+IUmq6vGRLJ985U+dfHp1HkSTefs9RPnIoz1Bc4fnDIoeo1BCC8XfvO0s60UoPDutcvSpJIqQyEFPZP1cjocucKNocmK/iBQEbUipfOLHMgQWhm3n4bImO1sVVkiTqxQalplgb2TWbyzd3Mhg3GM07LFhP2NcbXsBCzeGxRYvJso0fiCwURRbN52UDoomo2j4hVWKu5vGrIwNP23shqj9x0n3f5R9r244lWcJpeLxwJIPv+Zw/EH8STPSirV30DAhOV+AH1HI15qs280WLVMpkYtkiFNG441SB5XydtKmihlTShsbVq1Ps6IpycEoAbEtNoecJfBHM+fDZEls7QsxUXRwfJiseTTegtUEib7liXZRrCKG07TFZajJTaZI2RdbQDasi/Mbt97NsC6dWrm7j+AHv3/NC9IjIZTJTZhuN8Y4r+rjtkVlyNREToSsSjiUYbaoukqev2dLFeauSXLU6Sb7uoCuyCDvUhZNLkWFXX4xsWNjfvSDgyFKTvpjGKzam6YuFuHhDB4WGy00jQmsHcNNwBi8IuHdimcv6o4wuWvzD3jkuXp2k2PAp2T47O3WOLDVb0QN1dvXFuON0kZgm7NaGItH0AooNn7QhsoUeW7SetvfJL6ruOltjJB0mZQjxO9B2b37nV/7tJ/ztc3WufnSda4TO1U+s6297NTeOdGA5Am1huT5fHsthe0JAXbXFhCUb0RiM60yWmzx3rVj5HJivsnemysRijVBEo1lpkqs7zFSaVG2PTzxeAMTkKaorDCVM4fgqWFy7PoPt+XSFZUxVepJo8+EFG02RGE6baDIsV4VVvrMrQkdYYzhtcvXqRDs07eBrnseHXjzMXVMWz1m7YsmXeGzRYms2St5yMFXR9FiuT9pQ+dj1m8iaEQ7lHEq2T8JQubA3ynzN5qsvEGyequ1x/1SZtKHh+DCcMpmsiIvtCmbki8cr7XXX/vElNENhbqnevrB6tsdyqcGpgiWShBsutudz7+QydSeg7vqMFcQ60HI9Xj4SZ2tHCFmCuisEx6Wm24JNBnSGFfxArCt3d/3Z0/Y++OyRYvu/V6z0kiwhSRKrs2EOzAttiiJJ7fDASMZkstzAcoQFPRQLEYqHmC4K/U7aVGnWHKxSE7cpEB0Fq8XokkXqc8FyMUy1xdFSGJ0uE08aVKs2XUmDfzuyxHhBQGq/MrrAp/bPMlWxKTXF2uyqwQhrkzonCjbHlwRdfSQTbsN+bz9bYyhpYjmiGR+Mi1Tvm2+/FcdyGDu7zG/v7gZAj+q86xunCcVC/M3lA3Sa4v2/QqkfjBtopsaquMbmDhFBEDdEPtJMSyvV8GBhrtrOhdIVmecPJzBVheNLDU6XXLwgwPNFhITni38fuboQ4vfFQrzr8h7SrenXS7Z0okgSq+MqY/kGsgRDCb3tuBvL1/mVkTQvWbeNXN1hsuISUiTxPmmJ8S/vj/C+Qz991tQzUaYmc/fBWf70G6exWtNHPaITzoSf6UP7uetcjtCzo841Qufqx9anj76ZieUGd04UaDY9BhMiJ2YwZXDf8XzL4q2yOmFwfKneYoWFeWzRomC5XNQbYzhtIklifTbYE+PS/mh7xWV7AssR1RW6ozoTJYtSw2VTNkzKEGLpr5yskDJk0hEN2/cZXaxy+6kCx5caHF+qs1h38RyPe07kyS9ZbRt+oeHRH1XI1R3+dexh9s03WZtQiesyVdtjKKmjK6K5yNUduiMKp5ZF6vFMpcmZcp75ukA7HM43+d2dw7x+yzBzyw0u/8x9eLbHqVOFdnJ23RE2bMcPmKmI5OyIJvPImSIPnipwxyMzvOnSfjZ1RNpcLs14gjdVma9QXahiOUIDsVRssFBz+P5klclSgwdnGrztvF4USbifThSENX53b5RSw2N9SuA97jpbocOQuajHfFrfC4+86vPt/373xR/l7uk/JqIL6/hUK2zSsz1mKk2u3tQp0BHFBvMzFYaSBt0pE02RcOoO6zojmK2pmF21UVtohE2rkizOlHEswce661ie+ZqN3Wokx4vCPVarOUSjOrO5GksVYSUvNX1u2tRJR8YkG9ba/KgzZZftHSG6Ilo7NRxEuOdMpcl4wWoz8v7zanbviaU2EuNv75ho/+6SLDGUNvmbh2ZxfLhiMEkyYYim6VSB8/rifPLBaWK6mC4VyyLp/IpVSaoLVRZqDm+6YpCq7XNep4Ei00Z7JAyVOycKKJLE8UK9fWw7uoRYfG1SJ6rJfO+sxWfHiqRNlaNLdY4v1ck3xI3Kd8/WCSkS16wSjr4XrEtTtX0+f+JxXrc12XYj7p1t8OhinS5T8Le2ZlJP6/vl6SyhSVNI98WJZsNcvUkw3OIdYdb2xp7pw/u569xq7NlR5xqhc/Vj6/97cKGNWfA9n7ShYXtC+yPWGjaaDKeKDX5tc4q6G5A2BOJgTUKn4QWkDYWBjMnuXrG6uXOixHDaFFOksIYiS+zoipANa5iqsOpPLDco2x7bsgYX9YY5nGtwxaBw2eiKzG9ty9IX09nRFeXu00VkReaydRk2DiTojggg43BC48SyS9X22JwJ4/jwWK7JVMUmbapsSKnYns9IJsyrNia4f7rK104WxXFoCqNLDv9ycIm1SYNXbkjw5w8e4y/2HeUFm7LIikxvbwxZFSsPy/XJW0IwfudEEavuUG54nCo2CEU0rt+YZfVQiu+dEWGHqqHSLDfbKAXf9QWUVBcZM5s6Irz8vB5mKmKyNF8TAXjvOzBLo4VL6Ipo7ZyhDRmDhbpH2lBEBIHtc6r0syVK/6R6wdd//Un/L0sSJ3/rS4RNFUOTmSw36OmOMl1sENUVNFMj1FoHPX66SNXxWJ6vougKoyeXKLWmHFpYw226ZJMGoyeXUA2VWDzE8aU6PZkwXitg0KqLfJ/XbMvy0m1dpA3ReDmWw45useLsjihszUYZy9eZWG622VPfOVOn2MpSWolnGC9a7TvqzR0mlutxfKmOIkvk6g6JtImhye006ZUU6MAXDe+JmTIhVcBZBxMhNvTFMRIGP3h0lnWDCWZrHjOlJq/c0SUAvo7Pqy9fxaq4xli+wYH5Kg0vIKLKNFuTPdsTSJgDcxXetCPbZusBfOXBSbrCCp1hhUJDrOVShtp6v8ocmK9x1WCEHVmDtCFzrCAs8F87WSCsSgzG1PZ75+C8xeqExnNWxwipQl917cC7n7b3ytNdnz02jyJDd2sdfccjMxTPFFFkiX2/euszfXg/dz0VofTK41w99Tr3NJ6rH1uFFlE+bWhcvT5DwlBYqtjk6g6VusMDUyUenKkyXrQ4WXSJaRJfOlagLyZi/oX7x+G5axOMLgpX1aZshL6o2rZK255PSJFIhGT29MUYbOWkjBcs1idVNqV1NnUYzFRstmVNdnZHKTQCCq0L26auCM/dmGX/bBnb8xlMGDxnVYrxksOB+RpeEHBwscY3Ti7x7Ufn2gyp756tkw3rHF+q81jOIRFSGMmY9Ec1tndGWB1X+NWNKYpNj7/dt8BIxmBDOiQanaLF3HyVdz9/DVFd4aY1ce6dWOZlG1Js6ggz1Bkhois8cqbIYNzg9tEF5otWm75ut3AUK2wuu2azcSjF83b1Ul+q84OJIp/dO8VMpcn+2TIj6TC2F5A2VR6cazJZcekwZc6UHEpNcXFzfKg7Ak1SaIjsoZnq0yeW/trzP/Ok/z+wsMSZyvt5wbo0lXITzxeWbVkVEzeraNEoNejriqJHdMrLDUJxgZ2QVZl0dxRTlcUEJyvWG919MTRTo265dEeENX8wESIS0ZAVGUWWOFZoMlu1ma3Y6BGdvq4opYbLWK7Ogfl6Cz6qM5gI0R9V2jqqvpjG1s4VQb/MSCbcZrN9d2KZwZYFe75qY7k+Ziv8c8Uu/48vXIPbdLlwcyd/c/kAr9/ThyI9kU01GA+xrjPCxTt6GE6ZjBcsrl6T4q4zJXRFFkDgokBdeEHABT0xHlu02tqrrZ1RFElqr8y+P1nj5rUJFFliU0bnVy4eJKFL/NqGjeJ95PhMlZuYqsIFPbF2rlHDE9//7jPLgMgBe3Cmylzd5RunqtxxpsrObpOP7Z9jrubxl/fM4Pjwurte+7S9V57u+sjVH+fj13yC8ekyVdvDczw2bOni9Gu//Ewf2rn6b1DnGqFz9WMr0RnB8QKqjsd4weL2UwVUXWZXT5TLhlL0xvRWCnCYvOXy2dEcL1iXomr7dEe09h2548Nz1yYYTpmMLtao2EFbN6NIEo8u1JivPXHHHtdlsmGN+2ebHMzZRFtCz6WGyDEaiitc3GuSCKmYqsz+2TKJkNCRRHWFz59YYluHcJr90UXdPH/NAC/dkOEfX7iGXN1hT2+YK/rFFKA7quMFQoexvdNkruYS1iQ+OVpkse5xeZ/Bf9x8IW/eto2huIgACMVDeLbHH39zAsvxGc03iRoqXzhW5JGpstCbNFwUTeHY2WXMRIir12dQZInBeAjVUAlnwoQzYYFYiIh06K/tmybWHcNzvDZaIRvW8AKxblshuhcaAadLYgVYtX0SIYmYJjFVbpIwBH7hTMkjEZL4s4fe8At5b9w3XeeRhRO87/KPIckSs6cLlBsCQfG9x+fp7Y2J1GRFoidptKcpKyJqRRIQ2fySxXJV6F+Wig18z8eu2Uzm67hNl8F4qM0Rq1ZtOkyxunr1VjEZKjQcvCDA9n2RbB7A908W2Dtd5nTJxfECsqbCfM3ldZvX0xcTid9tMGuLzTax3KA7KgwB2bAmGv5cvf37/u/bJ2ksi8Tq4UQ3xwtN7p2s8Kn9s4DQsGzvjLTzr7qjosne3Rcj1ZoaPT5bIarLvHJDnLLtkQipTFVcUoZMrm4zU2mSq3uMZMJkIxr/sneWmUqTsSWbdUmNhbrPwwvj7DuyiBcEjGQMIi2Q73Da5N+PFDiSb/KPDy+2Ix1yVZsrBmMUGz6rE0YrNTtgqDOCpkj81vndLNQdPn7Ns991ZRUtqg2R3/TfoRSe4mqMc6uxp6PUZ/oAztWzuy4dSDBTaZIN6+TqNiNGmInlBgfmqnh+gK5IjC3U6I6HGE6ZbOoSCb5VxyeqyXRHNHJ1l6gm4QeQjWh0R3VOFCy2dkYYL1oi1ddQWR1XOVN26YmoOD782qYkZdvlXw4u8Ye7szS9gDNllw5T4WjRYVVMpTMskpSHUyYPte+AZTRZ4sFZi1dvSqDJEg8vzBJSJUIt946hSOyft7lxdZi7phoUGy5bOyMs1D36YhpV2+ei3hgVJ+DbEzVG80fJWy6lpsuu7jCHpkroUZ2eTJhsWGXvjHC0pU0Ny/WZzdXQIwJKGoqHePFIB/90xRO5PNff9mqOL9Wpld02WPPoiSWCICAUUvB0mfJyQwA2++NtEe/dZ4rccl4nh3KNtj5gMK4ytuSyOqHQF9NJhGRWxxVmqh5rEyr3TDf/y+v6dNTKhGis8B7+7Kp+/vyOs9RyNbqGUpR1hZFMmF09Me49u4zt+Fw2lOLe8aV2Q7RcajwhGHc8YmENWxbNo6IJqv1Qd4QvHJxDj+i4TZeBrijFhsvoYo3BRIhS0yOqKxycq2JoonnWFYm1nZH2+suOh1BkCdvzecOdB6naXjvqoWp7dEdEMvPGjMmp5QabOiLtSZDv+jiWg6KJVZ/Wen3/+IHD9EZ1hlNCc2SqMoYiEVJFNtBkqUna1FFa4u9YXCdtapzXF0eT4f88nOfygTh5y6U7IiZ3W7MmozmrtTJW+eKheW7Z08sdE2UKssuRpYC1SZ2q4/PWa1czVXHZO1Pl+tUxzpRdusIKL1yf5MvHi/zxni6+fbrGFYNJbC+g4QZ0RBUOLojnzfEDhpIGMxWHX92Qxgsqv5D3yNNd9b/5/jN9CE9ryU9R8OydE0s/LXVuInSufmxVbQ8voHWn6pCrOdiez3SuhqkJW3wirLbvZksNl4W6Q8FyKDRc1qVUhpKCLJ8y5DYM9DmrowzGNS7rj1FouNx9pshUxWsDUjUZ7p6q4we0mFlC6HzNgMG6pEZMk5ElmKs57D+zzMOzFTRDYWNPlHUplc6wzP5ZkdA8tuRQdwK2ZnROlRyePxxjsuKxJSM0RPNVm7F8nT29YZK6yOzpiyo8tlhjR1bjlRsSXNJrsCGt87u7BvjckTw7BhJkUgZzS3UGYyqbOsJUGy6HpkoAuA2X6kKVTG+MbNJ4UhME8N0X/TtDSYNIPES0xaySVZnsQIJESDC0FE2huzPC3SeWqNre/8/ee4e5dZZ5/59TpaMujTR9xjP2uNtxS+w4vSdAKKFDIPQSygLLCy8s78IusMv+lmVh2VBCTZbeAgQCISGNFCdOnNhxt8cee3rRqEtHOvX3x6OIZQlLilPYne916brskXR0pHN0dD/3/S3snq2wqSvKraOVVpJ7xXb50cEix4oNbh+tsTSpUm8a5rnNOIlHUuGfKgwXZ3jXSV/ivWf3taInOtIhcqbNzqaSzMyLMFg1oNIoC4WYGlAZbA+3vIUs1yOTCKJrMpmITtAQrsmKpmBVLcIxEcL6m72zjE+Vuf94kUPTFSr/KQj12FwN0/ZEoGpQ+PIossS+uSquB6OlBqsyYaFikwVhuthwSRkqWdNhOG+23Ktr8zX85rjpkS5VvVgnZYjiu2p75Oouq9p0eiNKy+FbkWB9h0GwOX57/eo20oZMMqAQDwrl49vXp4joYuS1L2uyY7pCru6J4N+KheX5nLYkRdYUnKH+eICN7UEajs+BnM1MzaXdUHjd6kQzRsRhrOzQHlJYlQ7z5Z054sGm0i4oxqZ75m3O6AkT1SRydaep9pTZPl3mwyef/5SeIwtYwLMZC4XQAv5bmI5HfywgZMiLEswU6ngerOiJYbk+m7tjGKrC5u4Y01WL5y6JM1ezOas3zLKkzs8Pl/A8n9GyQ77u0R8ThcyDs6JLcd2hHLoi8caTMliuR1STuGmkSMESKeFZ02NrT4SQJnGkKCTiD85aRHWJJXGNM3oMfM8nFlRQJIlD8yb/311TpA2ZV60SERwRXWZ1W4zOcJxUUKbdUOmJKGSbkQMvWRYloivsm7dYm9bImi4jJYf+eJAdsxZf353ncMHizJ40H7vnGEMpg+G8ydxcjZMHklyzJ8uvd03j2sI8cL5s4TlCLl4q1Nn7uh896md7+8u+y/G3XMfzhlLIqsyqJSmGkgbjU2UCAQXf9xk7VuCytR2MlhpMFIWT8pm9YYZzJu0hlf5YEF2RSBka69qDNByRX1RoeDQcH0USI5unEi8Y/FdApNJfvCLN8rYQ07NVwYXyPPLN3K266eBaLmcsT7cMB4sNhzed0iXcp2t2s/D2GUoZVEuC7Oy5HsmkgWV7QjLc7M40yg1836eWr2M2XGpN2b3leiL1XoLlKYPFcaEWy9Vt1mbC7JurUmw4uB4t7s7BefF5uh5s7om2wnVlVSbWHSOcDqMZGot7RRbaRLmBoQqbh6mqR80RMvSfHy4QD8gUGx71piLySzvnGC27ZE2HjpCG58OGTBeeL0bGq9IG79nYhta0Cyg2RHfHcgUPLhUU+1i0PEKaRESX0WWJo0WL3xwTi4W0obGmTWeq6tId0VAkWi7nc1WbB6crnNMbYHe2Qd0F16MV2Fq2fJKBVzyl58gCHh0LqrFnBxYKoQX8txjOi9Wxrsj89liedb0x0iGNYt0hZajsnq00XaDLGKrM9/blsFxfqJYKNv3xIHVXeJlkTQ/Tcfn10RI506Fs+azNhFEkicmqIPUGVIn1nRGG4hp7s3VGyw7FhseKpEZXWOELD80TVCVkSaLmeExVXVb0xNBlmZetaGNjVwTXdjmQc/jh/hz3TVVJGzJ3ThbI1askAwrTNcEBSgZkliZE+vsZvcIluGz7LEmoLEtoDMRUErrMkmSQkZLDbK1EzrR5+fIY/29rB0O9sRbHRZIljJDWytk6ZU17y1Twz+GL536Nrs4IR2arDOeFl9D6jgj9acEf+vGOScyGSyqskQnpfH9/jolyg9maw75slYimENNlHpwRxoHzdY9FMRVNkcg1PM7sPfF8ijN/+OrWvz98z1v50u4r+ei9b+WKlR2c2hXkBeu7cD2fYs0h1BZi2UCCUwcSgBiPXry6XZhfSoJcvzwTwghpNBqCH7MvKyI7KpYr3KqLdWrzNUYKJuGwhud6aCGNTCKIJEv0xAN4rkdXIkjFFnYIADtnqhwt2mTCGlaTRBwPquRMh92zFZHcbrtENIWy7QmFXsXiwZE8tmmTSIcw8ya+5xMPqRw4mEVXJLLzJtNVm/MXRQkqMF11kSWJsWbOl9z8gbphxyS6IqHJEj1RjbGyxbf2zPFvD40wZ7pMVV0Oztf57oESR/LCb0kkyws5fc50mDNdNnSE2TNXx/bg9lExxtrcabBtokiu7gln7YbHkYLFuozw0ZquWPQ9wuHLhPj1MREQPBhTWBTTWJZQ6QorbGzXT/j5sYDHhgXV2LMDz6qP8Z/+6Z+QJIn3vve9f3Sf7/s85znPQZIkfvaznz3t+/a/FZs6hfvtSMGk0iR3zjUN/OIBlWLDZaRQ57yBZMsNulh3+MnBXJNr4+L5PpmQTtYU7fhTuiIsSQbpCsssTYrg0rAqc3JngG/umqMzrBIPCIPGeECM32pNP6F3bmhrhmV67M7afOl3Y63Iju/vz2KoCi9b38ntoyVc3xfdpGY2l+OJYqtsedw4UsH2fA4XBEF7ICZGFseKDv+2Y44jRYffjJRIBmWCili53TRa4wVLk9w2ZjZdeTUGE0GcusNQb4xMSCPUFiIQC7DjQJZFixIMdUQe0+e893U/wmk4rEqHiIY0to+XmKvZLB5MMtQbozZfIx5Q2T5ZEiq1oRTJoOgIjRTqfOvecR4YKzGcMxkpmIRUifaQTG9EIR1UwL35hJ4Xd778u61/3zBcoNjw2T9vcXbPOSSDMt0RQd7tiQe4aFlbywzQtV1uPVZgX7ZGIKwxV6hTqTtC0l4QnKE9E+Umodfl3Zsy6JqMrMioQRWz2MD1/BZ/J1tsoId1pqsWiqY0+ToK07NVRgpCxt9uiNyt4WyN0WKD/XM15ioWPVGhYCvVXfZlxeN1RWK6ItRods2mWrWRZIlGuSGI4PEgPdEApyxOsbItwNGiwzfvnSBn2mgyvOP0HtJBmcmKxY/vHUeSRbduWVJtktpVzhtIsnOmguX6BJuFGMCylEh7v3hQKBXXtkfoiQYo1l2+89A0Z/WG6AornNUXwXR8RkoOZ/cnuHOsRECReP5gFxFd4UcHy3Q1PZM+N7SWnOmQa373BuJB7p+p0x9ThQu36/Ph302e0HNjAQv4S8OzphC6//77ufrqqznppJMe9f7Pfe5zSAttwKcdubqNIksMJgwaTX5Qo6leUiSJoaSB6/nkTOHs/KI17azvjLC1J85IweL0boOQKjGYEKvOeEClZHloMhQtn+/uzTKcN9Fk+Pcdc3zjOUuJahI3HK2wNKExEFOa0QjCgHGk5HAgV+esniDf3jGF7/l0JYIiiFNXuHusiNXkdeiKRM0WcQvLEio3jdaQJSHTr1gud43XWJpQuWOsymcfmOPWY3kiusylS5L84tA8q9LhllPzcN5k53SFpQmVYt3lN8cqfOTUQYbzJh+4aKAli1dUkUHW2RNlbXu4FVHyWKAZGsWGyxVr2jACCilDY3yuSs4UxdrBsSLFhsOB4RzbJ8scydfpjQofpmjKoC0qDCIzIZ27J2p4zXQO2/O5ev91J/bE+E94+IofIktw2VCYj2z7CfvmbX52KM9gXGcoZZAMKKSCQpfx3LUdVKs2MxWLN56UYVN/gkxEnBtqQEXXZDxXjFAvGEjwD3dMUDcFoTzVFkLRFcyajSQJpR0If5+IruA0HLZPluiPBQhEA5RzJjvHinzjnnH2jpfwHI/pvIlt2ph5k+mq6LiszIRaSfCKLKTwtmnjez61XA2n7uA5noj1iOr0xQIoMnxz1xyKDK/Z3MPytiCKJDhZvz1eZU1aBP4m28NkQgqH8g6m4xPTZZYmNM7oizOcM0kbguDdHwsQUCW6whp3j5dQJImwJmT/5/UbvGBNO7uyFr8+WuGqO8YYzpv86P4JliQ01ndGCCoSn7j3KPvmavTEdLZ0Brjq3HP42Ngezu4LE9EVdEVmz1yV1W0BRkuCQ9YfVXjZivRTdm4s4L/Hwmjs2YFnRSFUqVS4/PLL+epXv0oy+cfupjt37uQzn/kM3/jGN56BvfvfjbmqTSqose94AatiMVIQpnmW67F9skSubhMPqMSDKlMF4fTsej5hTWZZSmdX1hImiwERTllsOIwUTKaqYnTREdFZlQ5xy/ESL16W4v6ZIvGAzMmdIfbON9iddcQPjOmxqUNnQybIkkSQh+Ya4ocpHmSmYomoAllmTXuYjKEQ0RVeubKNjpBMqKkWe8HiOEeLNgOxEC9eFmdJMsiuOZvLlkZZ3xGhOFtlSVzl+/uz/NXJHXRHFE7pCJM2ZN60JsFEscFH75pCkeG1q+Jc8au9/O3WHr6ycw7T8ZgvW9TLFoou5NgndwYe12c9/a6fs+94gTvHq6zNhPnEGb04dQdDlQnEAtSLdSzTwfd8xuZN7nxoku89PMvm7ijVUoPppk/T7rkK01WLgCpx66jJRMUl+hTwhM7/yeUARD56PooEe3M2K5IqSxIqb1zbxq3HhaXBsWKDoZTBoXmTdkMhHReFxr2TNUzHpWK7jE6VCYfFSCcaC3AwV+MHu2cJGiK9fnNvDMttmk5qCkFDbfGwfM8nVxScs1q+zr3HCi2ZvmuJLs45y9IousL6vnjLxfrodAVFkogHVG47ViAeUBkp1HF9n0BYI5gI4jkeru2ihTQUVcKxPH51JMdosUFQk5koWciSKCimqk5z9KaQNT3edt4AW3tibMzofPvuUWK6cHB+YNpkuilp/9GBPMuSOlu7AgQVkR138eI4hiqxa7ZKWJW5Y9zkl/uzuJ7PirYgruVyyaDwW9qTbWDaHu/feBkHczWOZmvkTIddWYvvHrqXIwWbnbMN2oIKdx/Noysy39ydpd60l5qpeQzGF8TDzxQU6cnfFvDk8awohN75znfyvOc9jwsuuOCP7qvVarz61a/mC1/4Ap2dnc/A3v3vxcPznyIeVDmYq3HeqnYSnREsT7T3bVf4sLieT7Zms2+uiqqLYicZVDlWrLdGUsWGy76cRc60GYgHWd4WEkGZZZsLB+PcdqzAq1Ym2dwZpD0kkwzI7M02OLvXoCssU3dhruaSbQaQXrgoxk8P5ehIh3Btl2VtRjPYUuLylTGSAZlXrIijySBLglwtSxIx3SCgQr5eJ65LdIVlNrZrjJRcXrOyjWtetQqAKze0i+dNm9w2VuEz989x37TF353VxV9t6mBJIsj1R6rEAyo/PJRneVuIVekQJ3VH8RyPrrYQr1oR58zunsf9mc9/4Ffc9OJvkzI03nfrccJtBtN5k1Q8QHpRArtmk+yOYja5RJ7rcd2uaUEc9nym8yabOqPNAsRhdTrA4YLNjOny3YPvPHEnB3DLS74DQOXjt/D+jV9m/3yDouVhe8LU75UrE9wxWuCc/hBdYY03rMsQ0cWoU1Zk+uNC1h4PqPR3RdEViYsHolQqFnXbY1GbgVmzcW2XHdNlMiENz/EwAgq1ioXnetSLdbSgKIZCER01qGKbNpmIjh7WCUQDJA2hHuuI6AznTRF22hXltCUpig0xMpJl+N2RnDBxrNnU8nUapQaaoRFpj9DZHuaVK9MsbQ8LR2vfp5gzuWfPDJ4Pd4yZotCvC9dq0/EZzovi7G/unOLVp/XxqyN5Ak0VWyasUXd8nr80QVdY4f/dOUUyKJOve3zt/mkCisTGjjDtIZnnDYZZ2nSRnq+7bFndzl3jNd58SievXhHHdDzec8ePsFyPxekQuiJxw3CBvfO2IID7gjS/sivCdMUS41ddxGzM1lwemn1qVYULWMCzHc94IfT973+fBx98kE996tHt3d/3vvdx2mmn8cIXvvAxba/RaFAqlf7gtoAnhv9zx15M26U/FmD3XIVIs9Ph+n5Lrmw6Ht1Rncmyxdn9IoU9X3dYkgjSHxXeQEuTOm1BhXP6QyxLqliuz3DBoT2k8oN9WWrzNRQJJqo2bUGNiu1xWk+QW0dNbjhSIqoJ473jJQfLdanZgt+Rq9rEowEmyiKAdTBhoMkSvxuvYns+vRGFG0fK7M3WSQRClCyTJfEQ2brLr4/VuGOsSlhTGSlYbJsqM1quU7Q8hgsOx0oO6zsMBuIiA+0Hu2fZN+8wUXHpjyqYjsumrgi7Z0VBtCShsWe2iiRLzJYaXLu3wMntf/uEP/tvXvh1zhtIYNddnLowGixlhblfcbaKrMr4ns/a3hgvXteJ2sz5sms2OdOhM6JTtn0qlggSbTgwXnF5YPYTJ+TceATvuO3NALz4l68ThYonJOQ7ZoS68LJlKTRZIqRJxHURB6IrEhv74txwcJ6JcoPOsE4mpJErNvjM3ZP0NEM0j05XcC1Bhn/zunZeuDSBa7tUqzaaIZymE2kRnup7PrbrI0nC/6dii+IGoNhwuPlgtrV63jlToVSxRCeqVOeVK2JYpkM6HmBtu8H6jgihZLBl2tcoN+iJBrjleKkpEhCRF3pY56KN3Rwp1PnKBc8hE1IYSuqsSAklX7EhOkRvWJvmJ7tnuXhxkofnapzebbBjqsJ83eWq3x4D4L2ntKPJEvmGy/tP76YjpHAoV2e25rFtqsGWrjBdYbkZPKuyvsPgUM7iW/sKTFcsbhjOMZgwiOgK28ZLdEZ07jhe5PZR4bbeE1XY2hPhjL4YK1JBJiqi09oTUVpO6wt4+iFL0pO+LeDJ4xkthMbGxnjPe97Dd77zHYLB4B/df/3113Prrbfyuc997jFv81Of+hTxeLx16+vrO4F7/L8LuiJhNBPfI5pI2Z6uiHiN/piQI+dNh8myxcauSIts2h/TCKgS2bpLseETUiXm6y4TZZeRovDtWZnSiOpNTkZvjJojiKNHixZ112ei7GI6wpX4cMHmzF6DtmwzLwABAABJREFUREAm1/BQZYWXLovRGQtw2fIUru/z6XN6eNWKBDeM1FiSCNIXETlnr1ud4C0ntZE1K+yaq3IgV2O05HLRohAXLIrw8XumMR2XXbMmAQW+tTfPypTGRNliouJwz0SdNekQJ/fFGCtbuL5wVH4kEbxiiRnDL4aLVOeqBKIBXrgqw8aO8JP+/K8+72v0tRl4jsc5y9Ktcc/mlRm2rmpHC2nCuC8o89wNXQTjQXzPZ7RUb0nED+cbrGwLUHM8ipbHjtnJE5o/9sVzvwbAdZdey9XnfY2aIwwol6cCpA2ZOdMl37Qp+N7+PPm6UJKlmqOtUt3F8jyKDQfdEFL5nqhOd1TH93xWLUqgKxI/OpDjvqnaH7x2Oh7A9XxMxyMcC9AR0YlFdAa7RUcsHlCJBRXsukt/OsREsUF/LMDyppt3fyzIps4o/+cXIwy2h1nbHubmkSJBRRKS/LzZitfImYLsrSsSI4U682UL13b5zQMTRHSFq3ffxlzNpWwJftposcGadIj1mSAPTJtcsKyNXbNVAHINj7XtYYp1hw0r0kQ0mSMFcR69enmaFakQARXWthvYns95faIw3DEjTDQjukI6KNMbFQXZy1bEKTfDWU/KiEJuWcogHlDY1BWhPxbgM9tnmDNddkxXWJpU6QyrWJ7P7myDc/r/ctPb/9IhP8mx2IKf4onBM1oI7dixg9nZWTZu3Iiqqqiqyh133MHnP/95VFXl5ptv5siRIyQSidb9AC95yUs455xzHnWbH/7whykWi63b2NjY0/iO/mdhOG8yWqxjOh7rOyOkDI1ssUHNdLhkMIbr+Pie3/yhEIRe1xcutvuyJsWGz7qMxqGCzQsWR+iPqZRtjxVtQTRF8BPedFKaiuXSFVbIGBoDMQ3bFVwJ1xNmiookEVIl9mZNbh2tUbVFvlZ/LMBpXSLS4LrhMvtzJhvbA4yVLe6cEP4uo2WHqm1zqOAwEFOJ6oIs3RuJEtUlXrYiyaq0wSuWx/B86AzreD6c3GnQFlRY3x4goktCeu35PDhdIR5U6YnpjObrbOqKoMhgqELVFNaF/8t/NVB8onjLupQILpVBC2lsWpJiWcrgDWsSvHtrtwidPV4SqildJNlnQhqn9sQYzpmYjicyyUoO++cbVCyffMPjPXe85YTsH8BHtokIj/f97i1saI8wV7NZEleZqXmsz+gMxFSRgea4bJus8Z7NnZiOx7lLU4JnU260Ii8kWWJftobZJCcfnCqze7zEfNni/uNFQhGdZEynLy7MFTsjOroignhzpk2l7jBRbFBsOOSqwpfI939Ppl7XHkaXZXqTQXKmEAIozXNsomxxbKxI3fX50AWLAFqk6UeOvyJLuL7PS9e0ozf5S11hjaIlrCGyps2BnDBdHCtbDMRU1rUHiehC7TWUNDhSsDBUmXP6Q5zeG+Fb+4ooEmybrPPdg1ne/psRDuSEbYQmS9x4vEpEl1mTDnBqdwhFhnun6tw5VuL5SyI8NGvx6i29nDeQ4IHpKosTAb5153GGUsLUcV+2xvqOCJbrc9nSBL86WkWRIKzKRHSFh+cWOkLPFGTpyd8W8OTxjBZC559/Prt372bnzp2t28knn8zll1/Ozp07+chHPsLDDz/8B/cDfPazn+Wb3/zmo24zEAgQi8X+4LaAJ4ZizWF5W4iK5fKrwzks10MLKqzuinDjSAkjoCDJEp2xAKsyIbZ0R3A9qDk+W7tDpIIyszUPRZLYM99gtOQwGFNJBWU8T4RbjpQc3nBShkMFh8mqTcP1mKi4LE/qzaDMAJevaCdjBDitJ8x0xeK7+0vYns+b1qb52u4s5/THuKA/xI6ZOmXb5+KBEEsSKv1RhY3tYc7pXcuSuErN8dg7L3xUDhVKyBIUGx6DMYVbx+rUXZ9V6SBDCZ2a45M2ZG4cKXOoyW9SZImNTaL4aLHBWza2s7otwI6pCqd0RVi1KMGHT23nmxeeuMymnohQSm1sD3Ll1m5O7Rar94brsS4T5qSMiDbRZZklKQPXctkzWuSHu6aJ6GLscftojWRAYWVbgNvGqmzIPD4S95/DP2z9CgCfPeurXDrwGc7qi2B7PkeKDp4PfdEE7SGZN679vTrJcj2R+dYT44w+YcLZEw2QigcoFeoArXGfoilEQxqv3dDBm9dlWhl14/k6w+MlclUb0xHWDpGgKE42dUaJh1Tetr6dM5akmChbRGMB7h4vsSQZZChl8Lu9syiS6PjsH8ljuR6e45EKyvzqaAlJFmO2QCxAJizUeamgRmdY51ixTk80QFdfjNtHSxzMNRgp1OmP6eyerfKK5UmeOxhma9cqJqsuP7p/goolFgwRXXRXjxVFt2x12qDqeKQNFdP2ePGKNFs6dRQJbjteZCiu8fCc+ExcXyiNhvMmQymDuycb9EVVrr19hO/fcYyz+iKMl20uPaVHFHbN/bx3OEdMVxgti9FeXJeZqdlYrk9fVDmh58MCFvCXhmdULhCNRlmzZs0f/C0cDtPW1tb6+6MRpPv7+xkcHHxa9vF/MwZTBpmQ1kriLjZE+174kohVZHczvXs4Z5IJ6SyKabg+zNY8frA/z4uWJViWUMk1POquaO8/dzDM7qzN85dE+OHBElFNpmx7pIMyeUQHY9uUGAOMFOr84miWB2eqHJwqc96yNjpCGrccL+P6sCQRRFMk7p+xyIQ00obM3ZMNFEniwv4gjudy+/huGq7Prjmb/qjKz49USRkqc1UbXZEJaeKxZdujPSRz+7jJpYNJfnM8z8aOMAFVoiOscaxYx/Z8liV15FSAYsPjnokSr1qVwvbgnetTvHLZVSf0GFw68BlO7X0dmgLtmsKRgsPatMq2qQYb231O727jpmPH2NgR5tq7RjnzpA7uHc6haELynwmJ49MfFU7aZ/SEuWXMZDCmct2R9/LiJZ87ofsLsKUzwoOzVa4+72t8ZNtb2TOf467xGuf2h5uhuKKLV7EarVy3TV1RQDgeB6OCM2S5Pm5Yo5gzyZs2//Fgg6GOCNVSg+MNkUCeyYTIhHQRAVOxiIdU+uIBig2HfMniukMFIroIyq3YLoemK608uHRHhIguMs2C8SDFhtMcLdq4PjgNh3A6TNBQ2TNRpjtlMFFuMFOok4vpvH5Nmh0zde4cyXPhoJDf3zVW5g0nZfjBwTz/ctaZdBmDvHe9MBBNBmUius6euTrr2oO4PoRUmW/tneeyZSnuGC1zRl+Uk9sDjJVt5moO5w/EWdUWItfwGGuOi03b45LFceK6zHDR5ndjFf7hsqWMV1yOFISNxc8emABg3VCK+48XWdkXBxDu1fjUXXEer0rpTFScE34OLOCx4ckqvxZUYycGzzhZ+n8KFn/9Jc/0LpxwGJrMcN5s8iNEHtNQ0miSLwPoikzFFrwhYa7ocLRoEQ/ILE2qvG5NiiVxlborVCuHciaXLhbePF1hhSNFh8uWRjmcr7MyqREPSEJebQgjwA0dBqOlOlnTIaIpZBJBbjuc41dH8lw4EOWXwwWqjsfOmRqW67f4OqtSOs8ZCDFjuoxVLAKKzIGc3eoKndplcEqHzouXRnjF8hiyJDFjuvzmWI2+iM6lg0ku6X8ODVd0hTQZJisWS5NBnjcYoyeiEA9IrEipXLY0wcdP/QqfOu0rJ7wIegQpQ+UfbxtntOxytNDA8yEZlJmqujwwk+PVK+MsTaqoQWFwedbyNEpTsTc+V+UXh+b52XCJ6arLeMVFa453LNfhx8N/dcL39wWD/0rakPn8rivpjyookuC7RHWJK9ak2D7dYGlSqLN+ejBHru40w3lFce06PhNli4iuUC4J5VV3JkwqHmC6ahFLBHFtF0mWyBUbTJQbmA0XRRUjzFRQcGc29sUZTATFeG2uQrHucPriJLvGS3SGdda2h9k2UUSSJZymQWj3ojjJoMpgIiiCX10P1xd+QpbrYToeyZjO8lSIrz+cZfdchRUdYW4YzlOxXDIhjYYrRmgX//hGPvPQD3jXrQ+LuIugzD+sOpfOiM6Rgs0HNl7IcMFhVTrMcN6iM6KTq7vsyjYYLbsMJnRSQRnH89gxXaUvqnBat8GGDoO4LjNScvjF3jmevyTGP9wxwV1jZUzHQ1ckgvEgL9/SS388yLlLU/THAmRNm3hAnBe3HC9Ttnx+dqTC+zd++YSfAwt4bJBl6UnfFvDk8awrhG6//fb/lhzt+z4vetGLnrb9+XPovOqFvOHmN5GbrtD7lcue6d05oRDkZ7lVYDzSHeqJBpir2ZQqFq9fkyYT0pur7iDn9IXoCAkZcNn2OVQQ+WCrUjqbOkPcMmoiS5AMSmgy3DFuMhAPElAlpqoeRctl+7TFbWNVvnTfFJmQzqo2nfUdBnMFIZVOGSo7ZxvNHz2RGr65U+eSgRDpoMLJHcKLqiuk0hfRaQ/F6IkqLE2G2NgexvZ8+iIR2gwD0xEqsPumBNH73x7MUnMszr/ua6xIaTw0WydtyLxyeYzD+ToVq8G9UxazNY8l8QQfOvmp/xG5ZFGYf7mkn6ACp/eEsD3Y2hVlquoSVCXqrs8PDhSJJYIcmChx6eIwZrHBGX1xejNhLlicJBVUMR2XmapN1rRZmTKI6QZHi0+NdHq25qLJiG6f5dMVlrlros5U1SWmK1Qsj5csT3Lx4iSjxQa3Hi/QGdZ5y7o08ZBK1RKZW+0pkSV30WAcQ1UoZGvUbSG/d+qig1Mq1PEcj0ZVWCjk6jajpTrFhsPB+RqyImOZDqbjsS9bxW4Sn1OGKpzPHY9oLNDkAcH9UyLC4hEfouVtIVI9sea5r5OdN7l91zQA2XmTfZNlig2HnphOru6wLyuiMnRFYlFMx/WFuvK+qTqHmeLTZzyH0VKD5//8p7i+T19UZ2WbzidPW8Wtx4RL95FCHU0Wo7DvHRAeR9/bl+OeSZNUUIgGKpaL03D4121TrOmJclpPhOGciev7fPj0LlzfZzhn0hHSGCnUOThv8oU7x9k9WyETEq7tSxI6n9154sjzC1jAXyKedYXQXxK2fO+VNMoNfrlnhkA0wHkDyf9RxZChyhyerRLRFYrlBsN5k/5YEEOVWd5mEDRUPv/ADKd0GczVRNr8fVN19s47zNQ8OkIyni94OLOmS09EYWlSx/Z8eiIhcnWf1W0B1mc00kGFoCIR1xUuXmSw7dA8fW0Guw/P8883jnDj0SKyItMTDXBmbxTX9/nQlgyDMYWZmo0mC9PEiB4gV6/geOI1H87WeTibY6LssjtbZbRcJxWUmTOrHC5UmaiImIGOkMaypE5PVCdXtxhKGjw0a7E4ruN58MuRKlu6Qlx/tIrpuAzEFC7oe3TLhxONoKphe3Aob1G0PMYrLvl6na1dwiH4oVmLFw3FSAVVfM/nk9vE+Xhw3uToSJ5f751leVsQXZFZFNNYktCZrDY4WqqgKfCNfe844fv88VO/wgd/cpii5TFadpmqemxsD7A0oVK1xZj03skaP9w1zVDKYHkqxMo2nX/dNsXWnjgDiSDxgEpPNEBbJkzB8pipWHR1RjDzJq7tougKbckg3Zkw4bBGNBbAUBU6wzpDSYOUoWI6HomIkNpXLZd8SWzj4ckyv9g7R080wAUndVK3vVbHMxPS+MWOSRRdIRANtDo9uiKRMx0UTRCvOyM6iqbw7q3dRHQF14OhpEGx4YjQV1ni/901iaHKwoHd9/nOgYO8+tc/4zWr4hycElEid4+XuHeyxqd3HGJtJkzWtDm5M0Sx4TdHwD4pQ6UnqvPbfXMcyjtcfesxAM5b1c7JfTE6w8IuwfXF4uDXRytsGy+xsTNC3fVbIbSbh9o4sy/GwfkaB3INbjteZktHxwk//gt4bFgwVHx2YKEQeoI4UvwcB4/mWzb/TsPhxv1zmMUGV9z0xse1rRdef8VTsYtPClc9fCVzhTqyKpM3nZbU+Y7hee6fLDOcN1neFqIjorN/3moluD9i3rY2LVRDubrHKR06yYBIfU8bMsWGT75ex/N9AoowdhstO9ieT9FymTVd4imDoZTB4KIEW1a3M1Woc3JfjLXtYebrHqbt0RmOo8kSr1yeJKYr6IrKzcdLhDSdxfEM6aDMiqSG6/uc15dg+5RJ1nTpCgfRZImK5ZMMSuTqHls6dX43VqHYtNxdm9bImTazpsuM6fGCxRGKDY/+mIixeNuaLz1tx8LxPBIBlVcuT/KrI0WKDY8jRYfDBZujRYs7jhcAGEoJqf1b12WwTZvOsI6iK5w8kOSO0RLTFYvD+QYNR8SOpIMKUU24Hf94+K+YNf/jhO53+e9+iyxBSJPwPJ8HZxtUbJ8N7TpdYVmY+3VHyZk2ixMBypawByg2HIoNh5mKxc6xIgC/3J9FliHbdJD2HA9FEwq9iu1SqVhUmx2hiabvz0TZwmy4FGtOSwYfNFTmCnW6EkG6UwYHj+ZFTMbaDD3RAPuyVe7aM0t3d5SXbOjCqlocOlbg0HAOQ1WYKdSxTZtQW4j9zX07WnRIBTXCmlCurWsPkzI0hpIGQ0nh7TNRbrBzpsJZPSJ37bYxk0+c38tczWIoJfyKcqZNT1SnI6SxJK5iez6DCcFdmq5YvHR5nHed0cv++RorhlLcsGOSYsNBkSTShjBOPKPJBeqJ6TxvKEnakFEkob48tSfGlu4Q90yUyZtiFPjmk5Jsm5o5ocd9AY8dC6qxZwcWCqEniI9u20UgGkALKmiGhhEXShw9rPObA9nW48r2T7ln6mN/9PyT/uPlAKy+9mXk6g5XPXzl07PjjxHbpxvohugwdER0NEMjqMlIkkQ6pAnOTkgjExLBql/dlROOuk1Fz5GiS7HhcUZ3gLsmG9Qc4f9zOO8QUiW6wjGWxFXyDVEspYIigymkyqxIxtAVqSn/dtlxJIfneixJBBktNrhrrMhw3iRrlpsEUZOG66HLKpcNtVOzLbZNTxMLBDlUEK/3m+MFPrZ1GV1hhalqnc5wnP6oQjIgxmxXPZTj5M4wmbDGzjkbz4c1mSA9ERXP8/nBwRLxgMxczWUg9vRqDH45UuKqh7LcMV7h/EVCBZk2ZJJBmb6ozitWpUkGZQxVYcNQiqmqy0n9cS5YFOZ95/YzWhKKo+mKxVm9IebrHgdytnDabnbhHpqr83B2/wnf92RApTeisCKlYWgyNdtnpOiwfcqkPx5gtNTAdDwO5+s8NFPFqlp0hjV0RRYjvXgAXZHY0BPDsTzS8QCxdIhANEAgoFAq1CnmTNSAihZUGM3XOTZd4Y7RAvGAUEPpTfPPerEunKotl+lSg5xp4zkev35gku/uncO0PV62oo2hgQSbuqIM501cS8RraCGN0akyK7siJNIhnLqDXbOxTZsb9s0yWqpTb/KJJis2czWL6arg/YhMviCKJPGt/TOc0hVFkSS+sTtPytDojWgMJYPsnquyc6bK0qTKt/YV2TZeQldEnlTOdMiaHvGAxOauMC9fnuANZ/aLcXR/iP3zJj/ZPsH1B7Jsm6iQMx1s1+emkRLtIWGEetGiKN/fP89Q0qBeFHE4ACMl94Qf9wUs4C8JCyEzTwCnff9V4gLV9B/RNQnX8+lpCzFXs7BNl/Z/ez6ZRBDL9SnXbBa1vRJFkjhWqKMpErbr0/bp5xKIBljbHuaf7p1mWeL/8nf3jLZImXtf96Nn7D1WLBdFkljfI9yTz1iUEK61E0UMTca0PW7cPUMkIUYuZy+Ks3O6wqpMmP3zJj3RAGvaNO6btgipEjXH59v782xoD9Jwfe6bzhFUJJIBmaFEkJlaQ3SCdInRcpmIpnBsugKAJEus6o4yWbHZl63ywS2d9EYCOJ7LYEzB9mC07LAh08aatg/hz34FaelbuerhK/m7za/kHbd/h/6oii6rrG5rw/E8PN8nquvM1Bq8bnUbv5soN72PXAYTQaaqEq4Pa9s0DuRtFsUEX2hRTOfjp37laT0WEyWL5W0h1mc0fn2sJgwtowq7s41W2Ov2yQqvWBEnE1JY26bx22MFZkyPFUmVzoiOaYtz6ss7s3RGdDZ2hJkzqwQUmd5oiqnaLA/M5Llr8m383ZarT9i+lyxB0F7bpvG187/O7yb/Hz8dnsFyfbYPz3P2ykxzlOSzrj3M6b0RfjdWwXI9to2X+OhpHfz1r48zV6ijBsR7AWFYWGlyhEB0ZGVFxrVd8biwzmipQX8yyGhepNprIQ27JtLka/M1pEwYWZXRQhqVuRrFdJh7JhrM1YT/kOv5bF3VTq5uMzxeYrBbFEdK073acz02LE6zY+8snckUO6YqjM1UWDeQYDARxPXE96gjrLFnrkpPc8wW02UOzteEO7ppM1qs8471bbx0aTs3Hs+ybbLeNEYMMVG2Wdlm8JODWW4ageVtoZa/VzygclZ/nMN5h7mazcZlbcKh23S4+YEJ3nzeIHM1m6gmcfPBLBFdIRXU2D1bJZoyuGQwxu6sTW9kQT7/TGFBNfbswEJH6AlAVySRqm2oNBouddujPy4M2uIBlTX98VYRVKk7eK4n5L01uxUF4Frigp00VA7OC+7NJ+4d4/BUmdFsjaGkwUXXveYx79PX9r6DT2x/G/9w/9tOyHssNhx6ogGG82ZTzSKKkOWpELoik62JmAPb9cmENKYrwnXZUH8frLpn3qbuepzWLVyWL10cJmt6BBQRwZCtC/5I3REcn5AqUbZ9PnTjcUzH4yPn93HBqgySLDFaarCpI8j7N3fQHdaQJYl9OQvbg4gm0x9VWdP2IQB+Yw4DMFFxOV7Zz0WLorSHZH56ZARZkjlX7afmNNAVhZWpFBWrwTk9cQbiQc7qi7A0oRHRBJn7wVmLdekAZ/VGefmyKDXHPyGf7+OB6bi8Ylk7n7pvlhctifCxrYN8d58I/iw2hJXBRNmibPmkgzINV4xUJso2D87a9MeC9EQD9ER14gGFYt1BluBQweEXR6uMluYJKhI9EYUTnc365tVf5J9P/wqvXfEFAM7q/iRRXeKULoMrtvSwuVOMhR45f67dPYehyrxuTRulisXXd+c5c2kbutH0FGpaKuTzJooqEYiKTqwR0miL6sJ7yHbZN1nGUGVG8/VWR8i1XPTmuPD8DV2saRfnZVsySKYrSk80gKHJFKYrjBbrHJo3WZ0OYtoemUyIiu3iWF5rW4qm8PDRPIqmcGwkj+m4vH5zN4Yqs3O6Qs60RSGiSxyYqWI6HhFdYf+8yTn9MZa3BRkp1JmuWvzoUJmjxQIjBYuc6dAeUtg+KWJtSpZHfyzI4akyP90+zvI2Q6jaZImxUoPpqs3yNqNVBJ2UMVA0hV8O5wW5u+6xeSAhlGuGyvOGEvzVpnZuOFqiN6KQDC78mj5TeKYiNr7whS8wMDBAMBhky5YtbN++/U8+9pprrhHRNf/p9l+TIHzf56Mf/ShdXV0YhsEFF1zA4cOHn9C+PRNYKISeAG5/2Xe5q8kPaIvqxIIKczXRBjdUGcv1ec7iBMVyg85YgL8+vQfHEooT13apzdfQgiKF23Q8zuqLsSojuklqQEWSJO6fLDNXs3nf797CR7a9tZX0/V+x9BsvZfW1L2Ok5PCjA3l+eqjAm2950x89ru3Tz+Wj9771Mb2/839yORPlBsMzFVJBIWseKdSpWC6jpQZ7juRacQ9JQ8VyPfFD5vncPJLntJ4oFcvFcoWy5VC+zj0TdQ7kHbZ0Cufmt67tIKRKpA0ZVRYJ8fdON/j4beP4ns/UdIW/v2GEfXNC9fOGtWm6wjJxXSaoahQbDU5KG4I7s+yq1g8twCWL/hmAT532FRZF38GLFn+Whuvzt5uvZqQ4j9T5dobiHVSsBg9nc/x2zOS6Izm8Jr8pV/daBc/qNo1d2QY12yKmG1x93tce17nyRPBfOWO3v+y7eL6wB7htvM4n7z2G5fq8bEWct56UwVAFN+sXR0r86miJuiuM+27cPUNQgd8cyZGrix/luZqN6wtbgLGyQ09UFJXFhk+27jL4FIz9/mtx/vFTv8JATOVwvs6+nMiN29ob4+7xUssh+v7pOkvaw4yWGty6a5r1HRH8Jmm41iQsK5JE0lBbxZDpeCiqRFdbCEmSmMnWyER0VqXDaIqEGlAJGipdbSHuOpJjtFQnGBeFTtVyOThf44F9cyi6wshkGU2R+PnhPBMzFfIli4imkIho1CrCxVsLadim6DDpEZ2IpnDTiLguKLJEPKhyXn+EiYrDlv54K6ZmtFRv8aZetCzJ8lSIiC7sJOZqFq7v87PDxVakjQhx9XAtYRnw43vHmShbbOqKMF0VkTddYY2D8yYvGIqyLqPz/gsH2NQV4Zz+KMmATDygsnOmwnTF4tbjJQbjQXqiOpoiPa18twX8IWSeZMTGE3jNH/zgB/z1X/81H/vYx3jwwQdZt24dF198MbOzs3/yObFYjKmpqdbt+PHjf3D/P//zP/P5z3+eL3/5y9x3332Ew2Euvvhi6vX6E9jDpx+S7/tP/xL3aUSpVCIej1MsFk+Iy/S1+9/BO793kJNWtTM8U8H3fBZ3RjilK8rvRovEgyrD2RquLebukWYEgGl7pAyNqSZRc3yuihpQ2dwbY7Qkgidd32e6YpGt2SQNlYiutFa0r16d5sajRfpjAe49VsBpOASiIl9pqlCnLaozmAgynDcpVIQfiyRLTL3jZ2z49iuYLjVolBvUi3XOOLmHm1787T96b9G/u4D+wSRu85RIBQX/p2IL196ZiiWCPjsjxAMqO6bLmA0XXZNZ3hYiHlCF423SQJFFuOWlS5LIsoQiwb5snTWZIA3Hpz8myJ13jpu8fFmU0bLDL46UGM7WuGRpG9smipi2h+36nLUozouHoszXHeK6jK4ojFUsPI8n5IHy/UPvouF6DMZTPJydJxWQ2ZuzuXeyyv65Gi9bmWZpUqU/ajBbE5JvgL/dfOJGRo8XV++5kr+/a5r3ntLB4rhOoSFM8H42XGZdu0HN9pmqio5kKihz13iFiKbwgqEIWdPjcMGmJ6JStnx+dSTHa1a38ZUHZnjDxg6WJlQUCdoMg33zVT646amzBLjipjdyTm+QN676IgDfPfhObh0X5pkVy2U4b3JaT5SOkMKtoxUMVWbbSJ7LN3RyYL7O/UdzxBJBys1keFmGeECcS4K3pjNSNHE9KNdsQoZKRFMoNhzqpoOsip+OSFAUfNmZCukOEZNSsVwK4yUSvTFyxwpohkZ7T4x4QGG0GXgbiehUqzae61GbrxHtjNIoN+jriZGt2fzt6Z384IAohiK6gqEqRHSF7ZMllreF2D1b5Yo1bRzIiYJn+2SZV61qo2b7FBoeJ2U0jhTEyPfB2QYVy6UzojNabLBzqkxnTESLrO8Is3uuRiqooisyHWGNI/k6/fEA/VGFf719jL+/eBGzNRfbgx3TNUZLdZ6zOMnXbztG6WM3P2XH+C8dJ/o34797jb+55fUEm+PeJ4J6xeIfz7/mce3rli1bOOWUU7jqKuF75nkefX19vPvd7+ZDH/rQHz3+mmuu4b3vfS+FQuFRt+f7Pt3d3bz//e/n//yf/wNAsViko6ODa665hle+8pVP7M09jVjoCD1OZE2P52zpw3I9OpMGoSb/4v6pMhFdYbpiMZgy6EwayIpMzXTIhITSaKoguAqWK3xLbNPm3mMFFEnkeg3nTbI1m7CuMDlZZqLcQJIFn2jHtLgQj5YaKLpCZ3sYWYb1HRHaojrxgMru2Sr5ksWiNjFuWJ4JseHbr+AnLzgdgEA0QOeSFCOFP67SO696If/y0qWtbK94QGW0VEeRJSKaiGrwPR/Xdtk9XWFftsraTBhdk2k03Oboz2qFU+6erXLZshTzdY99WZN0UGZde7CZQK4wUnSo2T5r0gEOF2y6wooISN3YzuK4yqbOKP3xIB/a2sn6TBDTcam7Prbno8oKgzGDgCrx3YPvfNzHMKYb2B7IksxALIQsSRwpWCiSRFhX2NiuMVFxsVyHsu2xJK4+o0UQQF8kwjs3tfO9/Xn25xr8470zlG0RXwKwLKkyUqhzx2iB/qjCW09KMpw3uXXUxPaEn0wyILNnrkrNdOgKi6iUmi0ct3MNj2OlGrO1p4442/XFF/HKZW3Yns/nd13JN/a9g1cv/wI7pyvNIF+N/liQB6aryBJctjTKXM0WxORm7pxruTx/KIXneNimjWWKTDGA0WyNFwxFmt838bk8UgS5vjh3XdvFzJsAlCoWsXRIuIzP1TCLDRK9MRRJwkga9PbHWdse5vTeGJlEkMHmKM1zhWJND+vYpo2RNBifKlOdq7JzTnTdNndHWJU2mK5a/HLPDJu7Y7hNFdgvjxSZq9lsnyzzyTM6+eauOaK6xO7ZisjQiyrcPSly8xYnAtx6rIDpuKzqCJMJaVw0GMP1YTAhRmsXLDK4e7zUUmyOlBzeeWYvh/MOAUVi+5QIB548XuR4yVoogp5FOFGqsVKp9Ae3RqPxqK9nWRY7duzgggsu+P0+yDIXXHAB27Zt+5P7WalUWLRoEX19fbzwhS9k7969rftGRkaYnp7+g23G43G2bNny327z2YSFQujP4PyfXE7nVS/kzbe8iRXXvJRDBUcQJmWpxaOxPK+1ol3eLEJmCnUSEY2lzYun6bioutz8t0cxZwr5b9VibF4oaDZ3R1uvayQN7LqLLMMp3WLUtCodwvV8gpqMrsg0qjbDzYv6sUKdiK6QjIm4gZAhujMVy+Uf79/FJ87uRpIlqlWbquWy9BsvpfvLl3HFTW9k8ddfwuquCB+9dYJjc6LgOjxapF62sFyPeFD9fZcoHsBzPCxX5IllmuOBifES8YCK7fookkRnROeO0TJ9UYW1GYOy7XOkIIiZdddnSUJlaUKnJ6IQUiUyRphEQKUrrBBQJLZ2B3nRUIzOkMqqthBhTaWnyQ1yPBddVumNBHn18t+PxB4rnjvwad68+oukg1FCaoChRAepoMp7N3axviPC/TMiYuHdN41yIOdw1+SjX1SeThwtVVjZlFkPxFReuqKNAzmH0VKDwZjKomiEU7ujrM1EKFs+RUv44uiKxK5ZUdB6Plw4EKUtqnPNnjzPHYySb7ikDTGa7I0EObXL4JfH3v+UvIepd/yMyWqVI0WXZEAEwYIIlu2L6syZLroiFgpf3SWUl0Mpg3ed3cd1B7IcnK9x7toOvrdzGlmVec7qdlLxAPGQSJqPRwN86aF5AObLFp7rMVMQSrFMSCccC6BoCsG48MIKhzUaDbd1zltVi1XpMBFdYXlfnPUdERbHdQ7l6qQMjePzprAk0BR8z0fRFRpl0WlVdLHdXxyax3J9vn7PODceKbCuPcxpS1Jcv3OKuw/PM1ezSQW1ltfQ1/cUeOemdu6fMvnoaT0czlvsmbcwbWGRoEhQtVymqxYTZeGiPV5xuP5Alht2TbO1N8b9MxZn9cV46HiB4Xyd3bNVds3WqToeXWGhGHvZijjvvHCwpepcwLMDiiQ96RtAX18f8Xi8dfvUpx7d4yybzeK6Lh3/xTuqo6OD6enpR33O8uXL+cY3vsHPf/5zvv3tb+N5Hqeddhrj4+MArec9nm0+27CgGvszODhfoy2qc+uxAqmgyq3HCly8OMm9EyWAprqEZhEUarkwy6pMoWKjyzLxoEompIv2vOdRa7boYxGdclN98vCosPrXDZVCsU5bMkjB8dAVmbN6Q/zmWIXhvNlq8ReyNVxLZCdJskRbVGcmW8P3fK7Y0sM9E6Kj5Da5JVNVl6Amk5uvEYwHKVku4bDGDU2H3N0VC8/xUAPCZ0UzNBRdwXQ8RgomruPjOR75koXTcKioMv+2fZp4SBW8JlliTSbIXM3G0GQmcg2Wt4XYk22QCWkokvgs04YodhqOz7GSjaZA2fbZMVukNyIS6COaKyI5TAdZksiadWqOTyogCsmD+QYfOeXzT/rYzppFjpUKKFKBtWmNHx3OsW2iyDcuWcahQonLVqbZPlnm9N5nPrj3XSd9ifN/cjlThTq3jgkX7N1Ns8uBWJwjxQLn9kX494dmSQVDyJI4J+dqNstSBmf1hrh70iQeUPn02QNc/fAMIU0U7/1R4as0Va3j+tAffeouC29e/cU/+tvb1nyJ5/z0tSjNZdnFg+LzvnXUZCips2u2zlmL4owWG0R0hbW9MXYdK/BX6wd53oH7iTW5eRFdqJ/MZuenNxNuEYQtVyTQm7LodlquRyqosrk7ys17ZrFrNp39CXKmzWAiyPZjws15aVJnumpxdCRPIBbg8FSZkweSItbD8XATQapVm3qxLl5zcZLZmSqKrtAfD3Dt7SPIisyW1e0cnK8RDyjsnqswlDSEQ3vVZrTssqU7xHtvPU4mpNHT5DzderzA2f0JPrS1k889MMvWnhi7Z6vcNVakPxlkcDDBKR06hwoOO6arOHVh8jicNxkrNljTHqZYd1FkWuM240Sz4RfwrMDY2NgfjMYCgRMXrLx161a2bt3a+v9pp53GypUrufrqq/nEJz5xwl7nmcTCt+JPYMU1L+Wk/3g5jSbvYjARxHQ88nmT3xzNM121WgaCj2RvZUIqczWLVZkQkaCK54rVVyqoYqiCsJgOaai6jCRLlGs2bVG9ZfFv12zMYqPVcfFcj02dUe6fqfOPZyzHUGU6Izqe66GHdbSQyFRybZe5Qh3f8xnsjvKDfVlGZquUpoT8/Lf75rhzvIrZcLny3EW0xwLEmlwHSZYYbHai1ICKrIroAi2o0BcPUDMdvOYi0rWFpf8L1ne1lDj5kiUe2yFkz48Qq1OGyq37ZrFcj33ZGtsmyvREA2RNh4N5Mco4UhScn1UpvRmv4eM0X8z1YVlCbZoJCoJ12fYYLTu8avnqE3KMz+r+JG9c9UX6ogkWRSNs7tTZ3B3jUKHEaNklV3dwfbEvzwac3hPm3Vs6ufHwPD+8b5y17WEOTVe44oYD2B7osuDDzNRcQqrMNy9ZS080QFCBqarb6hBN16rcN1rkSEFkfNkeFC2PqapLwxEZZE8Vfnb0ffzdfX+sbDylyyBliCKgPaSQNhQyIVX4DhXqwtFZFsWf6/ks64nx2QeP0t3skhUbLjMVERdiuT6aoTGZM6laLv2xYGs8VsyJxcQjReKth+ZxLRdZlZmdKDFZtrhrzyz1Qp17D8yRr3ts7o5y/oYuEdVRsdg+PM/4VJn+eID8bLXlch2IBhg/mmdxbwyrYnHf0Tyrlqd54ZZepisiFFhpFmKH5k1GZqsARDWJL9wzQbnJFTRtj+mqcDefq9lkTTEO/MV944xPlbFcn7lmcvz7fnSIfVlhV/GqU3sZLdZxPZ83rMuwLCXMHC3XJ2va/PBggesuvfYpO7YLePw4UaOxWCz2B7c/VQil02kURWFm5g9NNGdmZh414PzRoGkaGzZsYHhYqHMfed6T2eYzjYVC6E8gFdSIB4XCpGK5LV5NX0eEquXiNlVFEV2sskZLYjWdCenccbxIbr5GKh4gVxcXrCOzVaZLDSq2eO4jNv3ZYqOVlyTJEkY8QCAawHI9fM9n91yF3bNV3nLTHgxVwbQ9zlzahud6NJocIlmRRUCk45GrOwQ1GSOk0b1IKFUkWWL7/jkAbj1eYjpvYqgytmnTnQljOqL48D0fWYZgPIhjCSVYe5PL9Aivwqk73DFaQJEldE1GVmXsustUoc5oURRAQvkCQ70xig0xLuyJ6txzJMfu2SqJgMzdkyZBReKm4zX25Syypk0yIJOtuzw4a/GvD2RRZZl/vHeWsmXRHdboChvEAzKLYyc2KPS83n/kzskisgSndhmsSCaoWB4vWhLhodf8gKvO+eoJfb0niqu3T3PjSJnnLW9j5WCS3ohQHvqeT832eWiuxIYOg96IQs3x2NzxUQxVIhmUKds+gzGV6YrFkaLDio4wsixxMGdypCi4JGNll76o9pTljwG8aPFn6Y8qvOv2t/CZB9/OG24WCsePn/oV2oLiR/unh0vIkihAA6pEfzzQUi12hnWKDZf1HWFuGc5huV5rHD2QEFwy1/eJBRVs08ZpOOTqNvXmSEjRFfpjASK6QrUkFh3LF4tsOs/1OL0vTrw5zlZ0hfunKri+KMZChkp7T4zO9jCnrUhz+co4WkjDqTsk0iFAjLSHjxXwPZ9GqcHweIm7xork6jYHDmbZP1fDUGViQQUjpDFTqHPneBlJlkjEg01ln0VPVGekOe7ek21w93iJCzZ143s+Q0lDWAMU6/zLS5ey7dA8P/ndMQZiKjnTYXN3TKgwJ0ps7hSd6N/snG51mhbw7MHTHbGh6zqbNm3illtuaf3N8zxuueWWP+j6/HdwXZfdu3fT1dUFwODgIJ2dnX+wzVKpxH333feYt/lMY6EQehQUGj8SMtdinWwzEfsRh9eUIbx/euIB+mNBcqaDaXtkiw1GCnXmahb98QCRRJBizWkltMuqjOd41G2vZQKXbjMYyIgLqBoQPim1fJ1YUMEyHRa1h1mVDnNGX7ylxDI0mdv3ziIrsiiYqhbJmE48pJJsXsDjAZWhlEEqKPKR1IBKpiuKVbU4PFpEN1Sm5msouvA/yRYbSJIY0XmeMKezTRvb9ZmcqyIrMp09UfSwjpE0KJca5Ger1E0RXeDaLl2JIGbNZmS2Sn8syFxNrIC39kRIBcWITdEUZksNrjs4T8oQ45fnDIS4Z6LK6rYAB/K2yBxTJTIhjfm6w0WDcWRJYrpmM1urs7mj6yk55p867Sv0RSJ8cNOX8XyPL577tacsTf6JYiAT4hUr4mwbL3E0W+Nru+bImw7v29JJV1hhQybGqpTOTE3I/7978J08dzBOf1S4O6/LRFiTCVKxRDp6XJc4KRNi20SF+6YtTu3SeWC2wW+PVzhaevKjxz+FN676IrcdL/D+jV/mmxd+vfX3z571VaYrgrQeVIRf0ETZRpEkrljTRrHhYGgyhipzx2iRV6xtb46Owy2rhoolDDGLNQfN0JAk4VDeF/+939C+yTIz2RrhWIBgVOfIZBk9IjyIfvvwNKVsDVmV6euIYLkeP394RvjxjJfI503ypsM9B7J8c0+BzqTB+mVtuJ6PGlBb3+1YV0Q4UhsarudjFhucvLaDNe1hMiGdfMnCdn18X4zsIhGRj3Y0KwwzXQ8uGkwAsKMpxBgp1HEtl33ZKsWGw3i+zuG8w1tP72Xlygy/OVbmrP442ydL/Pt902RCOj8+VML1fHzPJ2faT9kxXcBfDv76r/+ar371q1x77bXs37+fK6+8kmq1yhve8AYArrjiCj784Q+3Hv/xj3+cm266iaNHj/Lggw/ymte8huPHj/PmN78ZAEmSeO9738snP/lJrr/+enbv3s0VV1xBd3f3syog/b/DQiH0KDj3Rz/G9YXCozuqY3leU/4ufrxd32e6arVm/Yos0ddmcEpXiGJDeH+0VpupEMvbDKyq8B7xHI8zB5NEgiovWJoioiuct6odp+GwdHESzdB4x4YMsiKTMjR2TJXRZIn17SJQcrpisWEoxQtXZVjTH+fFJ3ejSBKW69MTDbR4D2vSIWGIeKzA+q4osxMl9LBOMC6coBVNoastxLqVGVzbJWioeI6QBJ+xJAUI8qhruXiux3xeXIStqoVds9HDOuGwhpk3kWSJifkads3Gqlgcma1i2h4PTlX48YF57hgtUWl6oDwyLvzJ9gnuHi+xNydGFg/PNfjKvZMcLtisSKocnK/xgwNFbA8emLFaCpjNHR99yo77cwc+DcBF/f/fU/YaTwYvWZbgu/sKvGtjGkmWMGui43Htnnm+8nCe647kCKoaFVsYVQZVjV+NFMnWXZYndfL1Or8bq3AoJ8Yn39o7T9kWY9jRYoMjRZeoJrEsZXDr2IETtt8vu+H1f+Rh9adc099yUhur0wa/m6izOm2QNlS2dof49dGSsJjwxHfveUNJ7p0skzJUds0Jvs7a9jCpoNYSEDgNMeIdTARb/CHL9ohEdHzPp1Ko43nwkg1dGCGt5Y010Mzrsprn6ovXddIZ0Yl1RVADKrZpE00ZrUBi0/HIhDS6U4YwWtQVSlOCu9co/55ov/tYgZ1jRUaKJsFmfI2syIwWG7iez4NjRU7qjqIrMjtGC3x35wy/PZoXn9d4iZHjBRRdLJLiAZVzliSxPag7Pp0RnXvunyBr2syPCb7haT0h+mNBVqVD+J7P1p7ICTumCzgxkOUnf3u8eMUrXsG//Mu/8NGPfpT169ezc+dObrzxxhbZeXR0lKmpqdbj8/k8b3nLW1i5ciXPfe5zKZVK3HPPPaxatar1mA9+8IO8+93v5q1vfSunnHIKlUqFG2+88Y+MF5+tWCiEHgUVy6XYcPjVZa8iZWjEm9b+ri8Iv64HhqrgeUINpisSKUMjFZDpjwdEaz4RJGiojBRNrt8zy6pFCTzHozcZZPdchUxIY1lCxW2uIPs6ImzqitLXZnDnuMmGnhibOkO8Zk2aPXNVDuZtVrbpZEJif24eyeP6sHO6wqfO6iMeEPwPwWnx0WQ4rSfIO89bxGAigOd6uLZQoVUqFkvbw0zOVTk8W+UlG7owazapthBG0uC23TPIqkxHOkQsHcK1BPHU94VSRo/o1HI18k2Og6yIbpcW0gi1hcTYruGKUd10hQPDOQ4ezeN7Pqu6o2Sa2U3j+Tr/8dAMD48WWZbSWd0b44Fpk+sOV9AVmemqxbGiGAvEAxLxE0gA/HM450evftpe62U3vP7PPuZre9/BxvYkR7M1vrIrx1A6xNbBJF++dAkvWZ5kY0eYk9I6luu0glk/u2OWixZFGYwZqLLCsZLDB07pxPV91mTC9MeCDMU1Dhwv0B8P0BtReGDaZDCmUGycGHXRe+54Cz963jV/FEvywOzvSZa9X7mMk7/zCjqveiEvXvK5Vrv/gekq+YbLDUdLjJbqItfOFu7SP9iXRZEkHjw03yQhq8QDCmf1x4WaRpWQVbllCGqoMtl5k3hIxWy4+J4g/y9rM7j+QJahlMHWVe34ns9kzsRzRCTJeL7eVDWKVPt6sY5VsWg0hDHqkdkqE+UGuiIzVxOCA8/x6F4UFwaO8SC55iIknhKWGtl5E9v1iYdUrKrF5NEcru/T12YQD6hULVcUZr5QiCrNSBDbtLGqFkZI4+j+OW47nCMekEgFZQbiQRRdYcdUhSvOHaQnHuA7++b58d3H+c5do7ztvAEi2oKD9LMNJ0o19njxrne9i+PHj9NoNLjvvvvYsmVL677bb7+da665pvX/z372s63HTk9Pc8MNN7Bhw4Y/2J4kSXz84x9nenqaer3Ob3/7W5YtW/aE9u2ZwEIh9CfQEw3w/J9/XxAsm+RK03aJ6Ao9UZ2tvTHCutIKGs2ZNr89Xm215zd1RjlvIEnFctGbvJlAQGFs3iRXbKArEj88WOLkzjCndAqX1+G8yZl9MU7KBPjwlkUUGx6pgMyhecHp+cVwkbXtEbaNFlmeClGsCwLpvz04w2TOZEN7iJ6ozmBMRVMk9s7bKBL8+OEZXnr6IoY6IrxtvbjYT5QbKJpCvVjnxzsmcS2XQlHwoGRVRg2oTIwLE7hQMsjYTIXOpMH/d3E/Tt2hfzCJFtJItofRgmK1LUkSHREd13JbhpLLFyfRIzqSLBEyBEn0yGgRqyL8jmzTJhEP8pV7J8mZDncfnsfQZN50Upp4QG0q3+Ck9NO7srj9Zd992l7LfQyepl99OIfjuYQMlS9dsJI3r03wno29/M2d43SFFc7ti3AgJ0Yf/29rN+f3GSgS/PtDs1iuw47ZGkeLDnXHxnQ8juTrrO8IcfPxMq7lkjMd7pk02dxlENEl4oETc2n4t7MfnV9169gEl9/4Bs7/yeVs7YnzwOU/4NKVaZ7z09e2Xvv8RVF0RSjbeqIBto+XKNYdShWLlKHh+j56WCdlaMxULIbzdWxPdEf0ZnE+W2qQLTbYPVtFD+sUKjbVuSqSLBGMB9k7XhIRLs1U+0eI02pQKMsSEY2gKnHNzlkx3tUVwpmwyDRrCiLMYoP9I3kcyyPUFhLjYg8qs6IrdOHJPX/QGbJrNpoiMXm86UKtKdh1F0NT+O3D00KB5vvohkp+tsrs8QLBeFBE8vTHqVUsEv0JtKDCZ288yj//6gjfuvM4vuczPVrge/eOM121GBvOtTq6V996jC899KedgxewgP/NWCiEHgWH3/hjzuyNYtoiIwxEhtblq5PNH2eLnc1A0IotLtKP5HGdt0ikZG+bKLJ9ssRgwsCxPCKaQsoQqzwQRNBTukLcM1Fh11yDzojOmnSIZUmVm46V+eXILDtnqnz6nim8Jpn5uUviWK6HrsnsnqswnTe5bGmCPRNlOhJBDuREEGNIk9g2UaFYd9k/b3H2UBu6IhHRhWfPOcvS/M2pHazpiTI0kOA1m3sIRAO0pwwuHEqJzlBAYXBRggdH8ngexBJB5ioWhwuCGPqyFSn0ZtyBY3nIqkyw6YbdKDdY3BnBbpor+p4vVHIl4Wz9CI9irKmQm21aEUzOVelIh7igP8SNIyWOTldQZIl3bmhjzrQJqf8zyZ47Zyp/9jH3ver7XLLonzn6pp9w5W/3c8tYnUKjxqVLkoxXXO6aqPCzg/N8fXeeuyYqPDTXIB4Qo9zvHywJ9VOnznzdYXmbQcV2uf5wDtP2eP1Zi9iXrbJvrtaKFkkbT+1n/cFNXyaiK9zyku/wo+ddA8DXzv86mbDGTM2j3VCoWB4JXebFyxLM1WwyEZ1Te2Kk4wH6YwEmyg1kVWbXeAmr6bMzXbHoDOvkZ6vEm4qyjkQQy/bQFDGa1ZtOvrYpkuidpqJsrmahhTRqTf5cvSziLa59eI5MRJCOXcttxWo4dQc1oLJqUYKzVreztD2M1JTxZEIaelinOF6kM6xx5soMxZzZKoiyx4U7PED3orjIRzteaI2vl7eFMIuNVj7aIxlpmZDO0q4opckSruPj1EXYrO/5RDujeI5HuiNCZ1i8x03ru1i7Qoy/12YWRmPPNkhPUjH2BBtCC/gvWCiE/gRcXxCTI5rS8gJ6aNai2HDQFYlMWGN9RwRFEsaKj5A87xgt43s+hqo0Za4WvUnBURhKGrzj1G6GOiLoisTPDuW5YCDK9skyxbrLr47k+e7+IqbtoslwwYCQtfuez8H5GrYH/TGN5w2l+LvTu3j7li4+fdsoyZjO5p4ohqrQFVbZOy8M5FzfZyipM5TUWZZQeeOaBKvbYqxOB0gFdXaPC7nwj/ZnObU3xhvXtvHKZW1cOpRkbSbMi5clCMcChJtu0x88rZPhvMiGum9SjCM+dnY3J/fFUDSFd25s52XL42iGhqHK6GGdXLGBXbPxHI9QszOkBoXvkNNwCLcZKLqCU3fozoSZydb4v786xnTVQlZk1raHsT0f24OfHck+w2fFU4P6nzG5+8yDb/+D/ysSTJQbOJ7L9/dn8XyYqjo4DQdFhqVJlTtGyxiazFDSoC+qc3KHGO22BVX2zdUYLdZZngqxsTPCg81AUDFSlWi4ULaeOuXYI3i03Lb/uOgblCyXqapDT1ThgWkxfk0FVfrjAequ8NZKGRq6ImwoYhGdUxaneM7iBPuyNX57SJgq6oo4x6abPDZDk3Gt3ztnP5JEv7Y3RiFbo9hwWdkXJ9EpCgZFU6jl65w7kGB0rIihCgWmVbFolBqia1S12DuSp2K7rM2EWNMeJhgPsvvAHH6TpPyDBya55YEJOtvD9PXEMJKiQAvGg3R1RijWHJy6WDA8EoMzV7WFSWMiiBpUaZTFouHQgTn2HckBUBwvougK0Y4w527oQtVl3n/pEMVyg2PTFc49pYfdh+fpjOiE0+HWom4Bzx483aqxBTw6FgqhP4FlSbGanqvZWJ5HznQYzpmtldYj4YmPuCiDWAVarsfKrkhr3DGfrzNXsxlMGFQslwM5Qew0bY9c1WZPtsFQ0mCi3ODSoSQ5026aL/rUXfi/Z3SRjIn2f1CBixdl6IkovGn1y1jTFhCKsJDOSWmdjrBGV1ghFVQ4u8/gvP4Q33xwhg2ZADOmR9Fy2TlXIqhI/ORwEakZKRHWhSz/trEq3ZEkp3TovGdjL/+xR1xEBxNBqpbLf+zJ0xsV5oi6InHlhjQzNY+3r2vnfVs6uWhRPx//7aho0Vet1sVbDapEMiK13vd87JrNkv44mYRY+b721F5OWZ7GbPIr4imDy1e18dmL+3juQJSIJnNSOnXCk9GfLXikQ/an8J/rpKsevpLJskWx7pA2olx1wQAX9qfJmQ5re2OYjkdcV5goN0gbGjceyTFTEyPSG0cqNFyPT54xiKHJbOgwGCs1eN+mdtFpVGRuGinRHdYEH8v5zVP8zh8d/3HRN/jOJd8ExILknokaa9sjTJQtJkoWikRTYh6gLx7AdASnDyA3X8OqWqQ7IsznhSWFa7k0yg0hKOiItBSciweTyKqMLsuctDiJIsN0Rai5rIqFVbXQDI0bdwt/lGqT8B+IBQi1hVjcGyPVFkIzNEaLDeZqYh/OHUiwvimAGFyepjZfI9IeETYcR3NEm8RsM28yOpKnNCk6ok7daUWATMxUSMUDNEoNqnNVrOb36ZGOlKIJ36L2RQk8D+LN7LR//dURLlvbwRtP7eHBqQqBaIDhnEktV+P48cLTfCQX8OdwonyEFvDk8JgsZDdu3Pi4NipJEtdffz09PT1PaKeeDbhy7Ze4cq0gcpq2RzygCqJyUGQ6ZUIaKUPj4HxNxGvY4mK8KhNmOGfy0hVt/PRgjsXNgFJFhteuivHp+7MYqoyhyazqCLNjqswbTsqQCso8NGthNd37Hpyu0BMN0BsxeMeGDDXbpyss85vjc2zIBPjczh+wNBHmI2f3kAzI3DpWpzOsEA/ITFVd/u7mUZ5/UieBgMJE1SaogCZLfGXXLIVsjdNWpIlFdF6/to2fHirw4S2LuPrhCY6V5rA9n7pjc+1zVnPd8CR20zkb4NdH82zqFJEf2ybrvH/TEsYrOX54IM+RosMFqzKszwS5Y6xKseZgVS02D7Vx0WCYq3dmhe+RKkJab9o/x9S0II6PFOoUinVkVSY/W+UzN1d46zmL6I96KBLUHZstnf8zR2Pv2/LopmNLv/FSUobKps7fjzTeddKXeNdJcM/Ux/DwydUb1GyLJckgk82u5KwpgjrP6TXoCmeYrXlkjDBbu112z9tEdZP+WJAv3z/Nx87u5su7sgwmDI40ZeZHixYDMZ97s9s4tfPip+x9b/j2K3joNT/4g78t+uqLqRTqzH/gV7xv/ZeZKL+Fo0Wr+T0TnbCeaEB0YusOlyxJcP3hnHCcTmuoARXLscjN11pjKkmW0MM6luthKRI9bSGMDhnX9zltSYptI3k2DyQwCx6F6Qqe6yFJEpIstUbZj4S1PjIS8xyPI8U6iq6wrCcmkuKrlvD3KTXoiepkTu7hloemkFWZkKGSCqrkEBlnii5iOvSw3uoGPXdzDzfcN45miPHceLPwUXQFWZGRgsIzzGk4+L6PbdpMHZ4nEA2wdGM7fWszfK3u8q3fHiHSEcGpO5yzup3z+kP8zUiepU2/pAUsYAF/iMdUCO3cuZP3v//9RCJ/fsbs+z7/9E//9CdD3/6ScNF1ryETEhfQVWlh179zutLKIxpM6OzLirFCTzRAsS48hXqiAfZlTco1G12RWNseJqjIfPr+LOs7ImybKNKp6CxvC6FIElNVl46QzKKYysR/kvqOFExMRxQg71jXyZwpxgRpI8qxUo6xcg1NFgq2g/M1wpo4PrYH6wYSbJ8sEdEVlsRDpDoiXLVzlP+7pZ2/+90UOybK9CeD/GB/noiuMFkpcNEig+uGi5zTG+Q9t47wmXP7WZJQ2T4tJPFL2sMsT4XYMV1mU2eUkUKdi799L2cubWMwYfCKZe3cPJrlokW9nNfn8fF7DzNRtrj/aI54UOFfzxkkpOmULJM3/ewQakBF0RTuGiti113UgJDwK5qCrMqMFCwUSefM7jhBVUO1/2e1hH54+N28fOm/c/dEDTb98f2H3/jjP/nc0fI8+YZLri48g5YkFPqjBvdP18nVPTZ1Rvj67jwvWx7lhiMlfjdW4u3rUxzMN3j7r47y9lM6OaUrxExNjMRGi3Vq8zWOOx41J850zaFiz3LqU2gM+1+LIIDjb7kOgDN/+GrufPl3+fnhPOcNJFAkidFSnU3totu6Y6qM6/v8YF+WwYQg0n/w5vFWwaIZGl7TlDTU7Ng+klCvKzLDMxU2DyTYPl5iVXeU0WKDFy1r45rpSqs48Ryv1b08Ol7i9L44t+yZ4SMXLuLvf3GEUFuIelNgsLY9zI7pMoaq8KaTUkxUXPZl66xe2iYKMNfHcn2uvHAxPz4wT6MssslWd0XYcSCLFtLYPVtl0+p2tj80xYvPGeTn94wS6YhgVazWOLnR5EXJiozrCcL2K7b2cd2hApu7o1ywrA19ZYYXLomxO1ujK6ywc87mvRcP8o2H5566g7mAJ4Qno/x65PkLePJ4zKFCH/jAB2hvb39Mj/3MZz7zhHfo2YR4QKUnGmDHVJnhvMnh2SqL2gxMR6StpwyVVFCMih6xxh/Om0wV6oKvoSnMFepsA16yPMna9rAodsrBptTd5+y+ML0RBduDtCGzqTPEkrjK53dkefO6Nh6atRgt1Xk4WyYeEPL4bVPznN/XxZ75WWzPZ8dsHUOTuX+qzNp0kq1dAZYkVGQJjhUdVElhspInHlD5xu48gymDiwZjfOm+Kb7w3AEKzbHCkkSaTe0hPrn9AO85OUNEC9IXlfiHbdNcsKwNRRYmd8tTIbZPlnnLujS/1RVR7KUDvPpne1nREabuHOcjm88mHhhha3eIHYkgE2WLz+yYZHNXmGsfnuOeN57H9UcOMWN6TJRtDs7XOG8gLmTRssTm7ii9EQ1Zgh2zJdamQ+jK/5xovC/tvpKyJVLYN3cZj/v5r1x2FV/afSUbMjGuO5Lj9K44NafBbM3leMmhLShzdl+Y0bLLGX1RFAlqjs9ATOUVa9v5/v55XrA0xa7ZGgfna5zdn2AiEWTfZJm0IVOxfLLmU5dE/+dw58uFau+RYvCfd7wd0xGKzOG8yWDznNJlmaGUwXDObD1XVmUa5QYbl7UxnBNS9dP7hNHgxYvj/G6sQk+b8Nk6GDSZKFu8alWKje1hrgE8R5D/JVmiLRNmNCtCWX93JIfv+Xxm+wxaSCMQUPAiOoosMVEW8RgPHchyR9Lg4oEQO2d8DE0mV7eZGiuxZXU73949R3m6TKonRt102D9XY/2yNg7MVBkfLWIsTvKO5wyxN1vn0q19/HLbGMF4kPed1YvtwWzN5frD87x5XTtL4iof+O04+7I1xmYqvHVditGyzNKEyt55QXx3fZ/1GY2BWPyPLAwW8MzjyY63FkZjJwaPaYk9MjJCJpN5zBvdt28fixYtesI79WyBItNys52rWURDGmNNmW1nRGe6YmFoIkPs4HytmTmms6U/zulL23BtF0mS0BWZkCrz9rV9HM5bpAwVRZKIajKr20KMlt2WWmdrV4QHZy3eubGNO8dNXrI0wabOMAEV9s47HJyvc0pHgmOlLCtSaXoiBq4Pg4kgl69KkgyodIbj2K5Pu6FyVm+Ug4Uy//7QPBPlBsfmaiiyhCLBR87uYb5u85NDZVakukkFwsyaJV6wOMw1u3M8MJPjdxNlXrQsTTygcGG/wap0CMv1iQcUvr03x8H5Gtsny+yYqbOozWCibHFgvs6Vt/yWg/M1nje4iA+ePMgZvRFM26PY8HjxijQPzY5w7Z4cp3aGWJsO8Pb1bYwUhBt1vTmKnKo67JmrszYdwvFciv8DuowA1+5/BzM1j4mKS0CBqP7EOl3X7Mnj+C5ndgeYrFaZM216ogo50+Zfzvwq/VGFA7kGxYZHT0ThQM7hUN5isiLy8248UmBpMshgQmRauT6s7Y1Rs33qrk9XWDnB7/yJ44Obvkw8oDbDjQ12TJQBwVXbMVXBcn06EkF0TfDQ1g2leOFQjJ6m+7oiQ8rQuHeyiqHKpAyVfdkaL1ya5O3r09w7WePB2SqeKzhtsiqzYSiF6bhYFYu2po+W7/k4DaEWK85W8T2fIzkT14flbSEWLUpw1/ECd0+Kc7U/FiSiKYTaQuwcK1JpKgR1RcaqWsSCCoMJg6XtYV64pZdNXVGWJlVShorrgR7Wef3mbs7t7eQ7e+dJGzKXLEkxVRVByu/c3ElPVOfcFRm2TTXY1B6kYotrSc32CSoSubrHBX2Pnka+gAUs4DF2hB5vUdPX1/eEdubZhorloisi1VpX5BZPpmK5WK7TSo9WIqJTs74j0kylFu7SwaiOoSqc1RfjYN4mHphFVwQ3QVdkZmsO/dE2UsEw4+U8qWCYVDDCW9emee9te3jXhjQAJ7cHaA/FWBStsj8nM1IqsS7dgSzJ9EfTXNCnMVUtc9Nxk5zp8LZ1QkIfVDV0WWVpIsxFAz6b2mNUViWE+7DVIKhqDCU62JCx8XwPz5cpNGrkGh7/dNZiEgER//GFXcfYOVPhipW9DMY9jhSEeutQweF7++bJhLSWBLwnGmDXTAXf8/ngaZ1cu2+EeEBm54zJq1cJZ+TOiM4Xd1ZZ3hbib++aYDAR5KzeMPGgQmdEjAxNx+OusSKdEZ3PPDDD35828JSGgT5d+Ma+dzBREarAFSmVb+8rsrYZs/J4cd+rvs9No/+XqO6hKwq75irk6x7L237vudQb0ZiqOhzISZzRHSAZjPONPVnmajZvXZdh22SNiuUSDyhMVxzRaWnuX0iV+MT2t/G3m68+UW//SSGiK0xXLXbOmOiaKGbmqjZzFYvlzc9wbXuY/qEU01Wbf7hjgoFMCEVT2D1bZWq6Aj0xDFV8l6fzJntH8nzw/EWkDJUHZ0zWrhALvmLDwXJ9lreFeLhqs7zNYHdz1ObUhYw+0RlphajqisT1B7J0xgJEgip3jRUpVSxiEb1F4A5EA8KUVFN4+/o0/3Kvw9iwUIBt6ozy62Z0zk9sF7tmc+kpPZy2Is2d42W+9rt7cW2XY70x+qIqqYDMz4ZLDCWFCeOyhEoyKHPvlMmqNo2HZh02d+qsTXdTm554xo7ZAv57PFnl14Jq7MTgCc0aCoUC27dvZ3Z2Fs/7Q+nvFVdccUJ27NmAX77wWwBccdMbGS3WKTYczIaLocpYnsfBXI0LB5PM1Wz2ZautFPqcKZxsXd9HkSRKlsdczeJIQaYzrLBjusaZvWF6IgpZs0yuXmVZspOKXadkmfxuYp43rU2yON6OKisMF2ZwPI/7Z2pcvChD3bF5z20H+PvT+onoQRKBMJ7vc24fjJQcbhursCL1++T2yUqBeECm7to0XA9V9kgbEdpDcWp2g5AmSMiyJDEQSxPTq+yayzMYNxmIpXnJ0gQb2nV2ZWe4d8oiZ9oM500+uDnDUNJgX7ZKTzTAps4I1x+e545Xncnrfn0/d46bpAyVw/k6nz5rNcfKc3RGamyfLHHlhnZmax5XrltK3bH55UieYt1lomwxnDdZnhKji1zdJqIp7MrmOJBzsLy/5gWD//pMnhZPGM/56Wt53eo47SGfiuWTqwt/qYP/aazzeHHPVIHbR8u8fV2Kf7tnkn+6sI9lyTS/PPZ+ipbwztnaHWx2HT12jZUwVJlCtsaNIyVMx2txbCzPY/t4CUWOs6E9SCookwg8e8aRV5/3NS6/8Q2MFOqsbQ83u1hNh2ZXjKEiusK+rCjuZFVmuGmYuLY9TCakYagKI0UTRZL48Nk91Gyf8YpLse7SE9N5YKxEW1RnrlBnKGmwfbzERSsz3Ho0j2u7LYJzKK1SacrozbYQxYbD2YMJbjucIxnTyc6bOHWHou+3wpFdy6VnIEGx5vDpe6YoTZYYWpGhPx5gXSbAz+8xcS0XzdBI98a47VihZf44NJDgzL4YqaAQQ9Rsn40dYX4zkueDm9t5cNbmUMGhP6pwuODQF1VwfdiY+X9sfOzN/AU8zZAlCflJ8HyezHMX8Hs87p78L37xC/r7+7nkkkt417vexXve857W7b3vfe9TsIvPLF54/RXccbyArshkQhrLMyEiuvAIimgK28ZLTJQbrM2I1WHOFGnzmZBGTzTQ6iLlTIcDuTo/2D+P6XhkTY/ZmkdEC/KTwxV+OTKOLqv84miWk9JBeiJxPN+n0KiyKtVDIhDiZUv7CSoaneEEl69K0h1JEtGClCwTXVGJaDKeD3XXJ2No6IpCRAuyLNnJGd09yNLvYyraQ3HGK/OEtACFRhXHc5k1S9wxMU5MN6g5Prm6xae2D3PLaIk7x01+cUQkcRuaTH8syM45m9Vpg8GE0SSLVnjJ8jR/c9fDLQKuLgsjx+lakYdmKwwlAyxvCzFb89jSqZMIhBguVugKK6xs04noCmf0xbn3WKGVHQVw3aESNx7JUXf+coMjX7kiSrbuctuYSdES54DpuDzU7Ao8EXSEZE7vjSBLEt9/yVpiusqRQpagotMTMUgGZI4VHYoNj9vGzFay/PlrOliVDmHaHnM1m6FkgJcsTxEJquyerXK06GB7PoWGw+His6fw/M4l3ySiKczVbO571ffRFRnTFoTvU7qEmnGkUMdyfXRN5kevWs/Kvjg375lloix8wJanQjxvKMknbx6l5vj8ZN8cu+cqLTdogPV9cSbKDd5xsshf8j0f1xKEfqfhUDMdetpCdPXFcG2XVFO+HjRUdFlGMzTi7WHsms2WdZ0E40GMpMEZfXGMgIJt2nz0xctZlQnxwFiJnx0uIisy8V6Rc5abrrAyE2qRv0/vjbG1K8jDcw1uHilQtj1sz+e1q9vYnbVJGzLrMxrbJmtMVBxkSfofazexgAWcaDzur8r73/9+3vjGN1KpVCgUCuTz+dYtl3viF/RnK56/RKhUXrIsKowTq1Yr9yhlqCiyMFQcKZqtlGhDkxnOm0Q0IRMvNhz+6awlWK7Pc5ek2NgRJh6QOKe3k2OlLCdlAhzINag5Fu0hmVWpHmK6QUw3CKkBJqt5PN/nnqkJVFmhZNU4p7cfrynt9XyPsXKBoKpxbm+S3ojSUptNV4uMluep2HW6w0lGy6L7kKtXSAej7JobJx2MosoKqWCEC/sGCKk6W7tSnNrZz2VDYWxP+LbEAyrDOZPeiMZgIsDzBtMcmK+zrj1Mse7ylpNSJIPCGuDU7iiGprB9skxXWONIsUDZ9vnxgXkUSSLfcLlrssHfbzvE3nmb42WHXN3n1SvjnNkd4LkrM2yfLHN4qszIZJnRYp3N3TE+88BfrvJlvOJy62iNtekAmiyRNmRMx6OrM8I/PfD2P7+B/4J33f4Wrlz7JS7sF0v+XL1CTDewPVBlmYgW5O6JGt/fnyWqSSxJ6GzuFEGmKUNl20SZ5y9NcEZvBM/zSRsyL12R4hNndNIZFh2Fsu3xbw/tP9EfxZPGfa/6Plfc9EYqlkt/M0h4MKawNmPQGREF9f2veRGO53JousKqRQlMx+VYoc7JnQajJYtIIojnC4fpCwdTGKqI3QAwHY/JssV9UzUmyg26EkFkVSYa0lqqxke8jBolkTVmuT5DKYO5ioVruxiakLorkoSZF92e6/fMsqkzyqtO6aFoiW76C1dlUGSJdG9M8I+aHKXd4yU2LmvjzWf1s3/e5GBeFLFbe2MEFZmRQoPRskvD9fnKQ7NNw1Uxrp+tuTRcoXy9dv87nrHjtID/HvKTNFNcIEufGDzuQmhiYoK/+qu/IhR6YryGvzS8efUXGX/rT/nNsVrrb54n/ExypiOM6GThQJ03He4aKzJaFIXS9skyIwWTTR1BvrhrnJxpM1mxCGkSlyzq4tsHJkgEQmRNj/6YTtYsszvbwPN9VFmhYteZrZXojaSIaAEmKi6O55IKCJm8KsmUrBqHCiUG4ynSRhTP9wkoEu//zSiff3CG1//8AG/75RHuncry0NwUxYbH/pxJSNWpORaD8Tb2zI9Tseqt95etlxktFxmv5Bgtu4Q0iQ0dYQA2dkawPeFefNPoPGf0hpipii6N7flULI+BmEpEl3A9n0xIZ2/W5HDeYTjf4Io1gve0pk04HSuyxMZ2jWUJjZmqzZbOHu6bttg+WWJ8roqsyEQSQc5elOC1q1JcuiT+dB36E4qLrnsNRcsnoiu8e91FdIWFUvARd/IPnfzlx73Nq84ROV7n9PwDR4s2qWAEz/fpDBsczOfQFUEIXpUO4/pQbPjIEhwp1LljtMAli+P87FCesuXz8JzJfVN1JioO2bqQfrcbossR0iQuv/ENJ/TzeDJ4+IofsuirL8b1BGn/kZT45akwu+dM1rYLe4tTvv0zOsMJ3n96N4oEL1jaRjqksak9RkRXeN5QiusP5wnHAvzi4WlO7tB57toOzu6PM1ezkGVaYcuKBKGI3uL9NMoNXMdHl2UCsQCKLDGcM9k/V6M4XmTrYBLLFenyB+drBKIBFF1h80CCeFChKyyzbaLC2nSAjpDMkZzJ2f0J0m0Gr9zcwzmr25Ekic6wTk9UYTARZKrqCgFH3aE3orRy2GRJ4tyBBEcKDhFdZm27QX9U5YGZBpevjPHBm8efVcdvAb/HI6OxJ3NbwJPH4y6ELr74Yh544IGnYl+etbj056/lukuvxfWECiTdTIDf1BUlHlA5oy9Gru7QFw8w2HRL7m9eHM/pjxLSpJaPiKHKRDSJw4U5LloUZbJapT+qYLs+u7IW71jXS8kyKVk1PN/naKlCzbGo2A0uX76YiCZWrKPlLKqsoEoKK5IJHM/D8z2Gi2X6oyqu5XJwvkYmEeS1Gzr48s45DuQcfrA/Tyoo88DsDIcLeUZL83RHkhwpZnE8l9HyPDXbYn2mG8d3ee7gIlanVPpjCiOFOqd0aIQ0iZtGquyYrrJtskZEVyg2HLZPW7SHFLKmx/75BhcNxsRFOR5gb7bOopjOw3Mi7PM3x8rsy9YwVJnfHKuJffZ9PnTnATRZoj8m3qfnemRCGjFdYbRscnZvhqv3XMlM7Zpn8Ix4/Ljpxd+mYnkoksTH7r2RvfM2tudz/YEscxWLW8f/5klt/0Mnf5mJSlGMSWWVjBFg7/wcPVGdy1fGWJFSCSowUXEpNlxmpircNV7hOYsT3D1e4rWr4jxnIMSqlPDcMZpzFduFuZqL6ZyYNPoTheNvuY6JcoOxeZOIrpAJaXzvQI5MSOx/seHw4Gtfxu3jo/x/t47SGdEpNlwUWeJ1P93P6jaNiu1xwUCCM/riWBWL+6YtXM9numqTL1nYdZeOkNIyS1TkZlaZIkxBXdvF8jxeelJHy2393IEEZ5/Sy92H5xlKGsJJ3fVZ3hfnlO4opuMxXbHI1z0GEwZdYYVDeYtzBxJs6tDZ3B1jpNDgloemeO7KDK7vczjvENUkLM/HbbpIFy3hBN4eUukIiYT6fEOQ3G8eKfL5B2YYzpl8bsc8Hzyjm23NPL8FLGABf4zHxIS8/vrrW/9+3vOexwc+8AH27dvH2rVr0TTtDx77ghe84MTu4bMArgcnf+cV9ER1Ds7XWN4mumHTFYvpqkWxESQeUHE9v+W1M5QysFyf+yZr9McDnJQR3kHn9xtUbJ9cw2OmVmMgpnFyRy9Hi7OMVxr88NAMr17eTUgNULHrnJRuw/M9gqr4nGVJou7adIYSWJ5D3bVRZRnLFbyA3Vmb3oiHZmhYtkfWdPj54TyZkMbRQoO17WFuOFphY4dBQJE4VnJZlmzGZsgqi+PteL5HQAmQqwvuUJthcKhQpicaoOb4vGSoj1z9ONMVi6XJQLPQ03jeYJj5uk1PRMiuNRmWJVRCqkbdDbA7a9MX1bFdnzN7RVfrkkVdfOjOQ1y7t0g8qLTykBRJItT0aMnVHdKGjCbDeDlPXyRCR+j1T/+J8CRRbLic22fwiyMVFicCTFZcFFWiUbW5fTzLeb1PbvuvXfEFvnXgneTqVRquMFqcKFscKTq4PnSFFUbLLjnTZuOyNkYKdR5WZXKmzVTV5cHZOmf3GpQtUbTnGw7HSg6GKmE6oiv0SPTFswG3v+y7vOv2t3D3eIlNnWFuGM5z0WCCB6ardIZ1Xn7DLxkp1OlsD5M2NH708AzJmI5maDw8Z7M0obF3vsGND06hhTSu2yWS36OdUSqzFV5y1gA12+f8RTHuHC9TsVwqskSuahMIazSqNmf2RhivCDVjJqQzXbXYM1FGkiX2zFZbDtLxgMrBXI21mUjTckNmvu6xb95mfSaI6wsCfTKgENFkrFUZ5moWlwxG+NbePENJgxcuibB9uk7d/X0WYlSXuOV4mT2jRQY6I9x6zGJzd4yjI3mGGw4vX9fJ/TN1ik+CkL+Apw4LZOlnBx5TIfSiF73oj/728Y9//I/+JkkSrvvMmbA9Vfj1Zd+i+8uXAaApEpbrtYjRW3ti7Jgut4jRruc3nWQ94gGFHaMFKl1RolqYVFDlvmmRaN0T1Wg4PqmgjOd71B2brOlyxcq+luzd8Vx0RSWkBqi7NpbncP3RMfqjKkOJJA9P5djYnqHUMJElCVVWeN5gDMfzaIvqxAMqczUb03EZmW0wYTRYlQ4zUqizNBnEVmE4b5GrV0gEQvx4eIRlCZVlyU7CWpREIIQqK3SHkwSVCpbr0RcVxobv27Ccq3YdYr7uoSmi8Ll1rMbSpMrShE5vxCUeCBBSdcYrZWK6ymDc51DeIaRJVJqKpptHp+iPB+kMq0yUf6+22zVeIh0PcMFAgj3ZGr0RhfWZLu4YnwBq/83RenbiH+5/GytSOl94aJ5LFsfRZPj54QI90QBEA8zUTkzHpeH6BBSZ+2dsorrEqnSI/qjK9w4U0WWZiwbDLZf09R0R7jqSY6gjwrV75tncHWPvvMMpHRp3HcmxPBVg50y1KQ4QishnG64656usuOal/M3JF3Lp4C52zs2zIhXnyztzwkTSdCiaNvsjOmt6ovREA9y4f46fPTDB5Vv7mChb9PTGsFyf2YmScGy2XALRAHeNFdncHaUjpNH5/7P333Fy3dX9P/68de7c6bOzs321q94lS7ZsufcGmI4Bxw4EA6GE0AkklED4Agkl4QemNxPANIPBYOPecJEtWbJ63dX2Mjt97szc+vvjvV7i0CwsS+ITv/yYx8O7mp15z7137j33nFeJ6LS36WzVFfJ1l95EiMcLeb744CgruuNzWYKbxoT7uxbWMDSZpipTnaryaF2Mjzv7U3REFOTZDtKefIPzeiI0HKh6PiFVQvaEL9j2qRqKBO9Yn+HukQZf317gnJ4IubrPtOWxaaxMeyTNkhaTsCq+g2d0x/nvTaOsXpLhQM7i23cdQg2p6LPu2s/hxILMMyyEeK4QOhp4WqMx3/ef1uOZFkGf/OQnkSRpTn2Wz+f5h3/4B5YsWUI4HKa3t5e3ve1tlEqlZ/Q+R4pF33wZa9qinNIZoz2qiztDW4wLhspNrlreQliVWZ4RPBpFlgirCl2xEFed1M55vVEOlx02doRmfYbERe/JLsfOmXEALp7XjixJjFTyWG6TfKNG1WnMevz4yJJEri7u9E01xOpMGl1WMVRtzvNnsFzH9lx646IDVXc98f9hlfP7UuyYqrGkRajCZAnO7w1juTY+Pmd2pvECcH0PRVK5Ye8Q39p5CFMNcV5PG+87pR9TDaHLKrbv8orFbbx2RQfL0xpZUyWkSDge6IpKVzSB5QhyZ67uszrTy0DJZVVGpWKLQNkXL+gmV/cJqzJbJy2mLYdSU4z0nLrDBze28aOd07x4UZysKdRxUV3i1Uu+eEz3/9HAk2o+RYJN4zUqdoA+awJySkdsLmPumeLaFdeRDJn8cv8Me/NN7ts5halKLGsx6U2ESOgyVyyICIJ/sYE3exy/6aQMddej0HD58b4KiqZw1+EyuwcK6IrEu0/pIGuqXHPb33G4ct1RWevRQn8izHk//gkPj+d425ovsWvGnRM2tMR0ls9LsntaOGjvnRFF9ItO7mKy5rB3XNzETA6X6O5NEGmNUJkQZo3tEZ21rQZl2yffcDEUiVZTo2I5bD1UINsWwXd9dgyVeGi0hBcEdMVCfOS8Lq67uI9q1eb85Vnee/kC6oW6GKcFkG/6/GawQqnpsiYbpuEFxDSJvfkmi5MqIxWHNlOhN27MRqj4VG2P9e0mmbBM2hCn7TN7EnREZM7sDHFKR5hzeiIUmh7RpMG23cLBWpKlucy19z/4huOzg57DH4XoCMnP4PFcIXQ0cMQcoeuvv/4P5ojZts3111//Fy/k0Ucf5Stf+QqrV6+e+93Y2BhjY2N8+tOfZseOHXz729/m1ltv5XWve91f/D5/CVpNjborrP0VSZgntkdEKvu0ZXPD7jyeD/m6I+7A4wabxsrsylk0vYDdMza7cjW+v1vM6Ve3GixIKFzU20FEUxmveWTNOEndnFVvRbA9F1PTSc66PeuyiqFovGHVfNZlW9mdn+CekRx7CyIZW1dUVEnwj/xZiXu+7rK2TbTiF6bCdEVVkXhdanLXYJFMWKbUDNies/jAfcPIksRJrT1sy01S96r0JVQcHwbL02TDcdJGlEw4hiIrRLUIO2dmUCWFZel2XrWkhysXd3FyWxqA63dPsqfgUHWadEcVik2Lly/q5Ed7yzh+wIoWlR/sHSakShizXbb+pCjeFElEG/zjrUMA/OJAhardpOo0SOgnjtvxkeCDG76C5QS0R3UGig3ShszGrgS2F2Ao8K2LvnHU3sv2XZZnhG/O4r4klhuwOKWiyRKWG5BriIiYUtPlzCUZLp8loHdHNda0hlieCXP+/BT5uousyoRVBdf3uaxP7Ntv7Nx61NZ6NHDLi79LVFcYLIsRlalJvHZVhq5YiERIxfZ8lrWK7+VE1WZhW5SfbRphtNKkWW5StT1eenoviiQhy5DoTqBHdF6+NEHWlFnZovE3y0UX78E9OXzPJxQLkS+JUNR40mBVqwhXBviXWw/zn5snScRCzItrXPfYJG39KRrFBt9/ZARNhpOyJt1RMZIuNHxGqh4LUyGGKi7dMY2ELrMiE6LpBTwyYbN9qoapilH2/SM1Hhgu8ev9eW7YXWRX3iHfEG7gfXGVarHB4oVpjISBrMjoER1rxuJrDz1nrPgcnsMfwhEXQq997Wv/YEemUqnw2tf+ZcqEarXKVVddxde+9jVSqd8lJK9cuZKf/vSnvOAFL2DBggWcf/75fPzjH+eXv/wlrntsXIYvvvFvmKjZLGkx2dAZR5+9K5yo2eydqaPLMi9anCIdVqk6HpossTitc2ZPgrVtUTZPVFmbDVGou+zK1ViTDZOr+xQaAQ9NTNAdTZMISbzhtn00PIf7RodRZQVdUcmG44xU8mTDcWzfZaxW5FBpCttzSYRCLE6qLEhkiOthGq6DG/ic2TmfXMNnQ3uY9R0xRis2aUOjN2GQ0GXiGZOpcpNz5iX4+rYZtueahBSJr168nIbrIEsS/fE4iqSiSHBmZ4i+eCt3Dh9EV0JE1DAyMg23zpJUShC2ZYWGJwjdDdch32hyQY/BcMVj50ydybrHzQMjNDyHhakwp3fo5Bs+4zUHxxMn8LCqMFRqctl8QV5VZIjPtvMPFOroioLl2Owv/vX6CH3u7K/N5WRdv2OGFkMmEVJYmtb+/B8fASZqdRakDPbOWLz1pBa8IGCg5HJqu06pKfyrEiGVhuNz/+5pHh6rcXpHF71xlYVJg4QusSCpcX5fkt9eex6tpsoDo1WqTpOXLExgqieeQc3yjMkv9s9w+g2v4vs7czh+wPp2wUPzfNGpGa006U0Y/NuZ/Vx7di9eEHDxuk6mLYdf7Zqi6ni8dV0bz1vSwvyMOef11RMT++eJ6TqxdBjHcmiUGizvjNHeFUORJH47XGJli8ZQuUFXW5Td41U+cXYn33hgmMAPqJSbQh7fcLnuniGiuugOPdnh7Ygo5BsembDC4pRKoekzZXmUmgHTlkNuNgIlqkn0JwWn6IyeBG9em0aT4e7DRQD+e+cMnu1xVk+c5izfrlFqICsybZn/G0rfJ/HCX5z45r7PqcZODBzxGS0IAqQ/sPFHRkZIJP4yafNb3vIWnve853HhhRf+2eeWSiXi8Tiq+ofpTc1mk3K5/JTHM0G+7mDVXYZKTSaq9twdZntEpz2qY/s+26bqdERUwqpMoemxY7qBJkskQzLtEZ0HRizihsJ7Tm3jvuEqi1MqiZDEE9MOI9U8reEQ16zMsHlqjM6Ihqnqc3ESpqbjBwGGIk7Gk1aTYtNifiJLb7wFHzFSsX0X1/f4z8f3sK8gHGZP69DZ0Blj74xF3fH57s4ZauUmJ/cITsTLl4o7/OUtwpk6a8YZqszQ8Bxc36bNDFF1xBjvnK4+ABRZ5ecH92BqEX64bwpVloloBrqszpG6HV/wVDqjClFd4pHxBlFN5vGpPGlDpieWZEkqRNpQ8QO4rC/Gy5fEOLPbpCOi8OKFMV68OC3yoSIa5/QmKdsu22dsEiH5r9oXpTduoEgSC1NhbtidY7Ri4wVHZyz2JK5dcR2aDNOWw88OVGkNhzirK4Iiwd6CQ0iRsH2f8/qSXLamndM6I/x4/9Csy3KTzZMNHB8SIZmyXWdXzsLx4UCxQcW2SRkSPz/0jqO65meKr5z/daK6kJm/dnUrwxWhjlvbFkFXZEoNl3RYoyum89D4DI4vCqSBYp1SpcmGviQbu+LMT2isbdV50aIEmbBMb0zBDwLuHhbqNNsRMRuqITzEumIh1ndEMTR5dpupjE5WeeWaNt577yihWAhZlZmXjXBOb5K+/hR2zeZbT0yTCMn8Yv/MnLP4QLHBpOUxaYlMPlmSWJpWaTU1zutLMl7zGCi7TFsOvfEQp3UYvPf2EX55oDT3XV6SNjl/TTt7Zhr4ro8/ay0gqzLDg0Xmf+Olx20fHWtsGjnxlXLPFUInBp52IXTSSSexbt06JEniggsuYN26dXOPNWvWcNZZZz2tQuZ/44YbbmDLli184hN/PhQwl8vxsY99jDe84Y/Puj/xiU+QSCTmHs8092xDZ4yTuuIi18iyqbs+C9NhFqUEOVqXhZFaR0ShNx7C84NZPgFMWS498RBRXeEdJ7dSaPgsTIV5bLJJR8Tg5YvStEeS3D8qJPSKJLE83YU9S5J2Z3lBT6rDemMtrMlk6YwkKTZrlJt1DEXD9lxUWUGWJHpjKjXHZ1/RJRVSydddqo4n/I4Umddv7GLacuiKquzMNTlQqPPYbE7Y7vwEhqoR1QxuPHCA9dnFDFWEGm3r9BDX797JvSO7WNMqunaX9cWIagYV2yIZitAeSWIoGuuznRSaHivSGpYT8DfLWgCRfp7QZXbMFHB9nxctaOG8nihDlQZVJ6A/LsaEmXCM6bpHVFPY0BlnoiqUT0tTGqmQTK5+Ykm5ny5+tP8fOKXDZLTSZOt4hcZsEfTwuH3U3+tzZ3+NVy5L88olce4dqbFzpo6hapyU1bHcgPXtMQ4U6thewI7pBgsSKhXHpzMirCEqdkBHROZffruP0YrNpOXQF9e4ddCiagdMWg2+uevEKkjbIzr5usvOXJNXLclQagq1nIhqcRmerDJtOXxx8xR3Hy5xeKrG+X0JgtmOUcpQuWekjqlKVB2fpisuVD/dL1ynEyGV5y1pQVZl0okQGzoi9CfFuO2i/hQpQ+SOaWGNH+/OUcyJjLEF6TCjpSa7cjUmiw0654ui5bO3HsKuu2zek+PzmydZ3WqiSHC47GKqEjXH52DRZfN4hQMFUZA+MFwiX3fYum+Gj90ywCVLM6TDKl/ZPMlnbjnI0haD1a0Gjx7KI6syakidK4YkWaKY++sTGjz/pquJ/+tFR/Q30Q9dQMI8ceJhnsOJjad9pDypHNu6dSuXXHIJ0Wh07t90Xaevr4+XvvTI7jaGh4f5x3/8R26//XYMw/iTzy2Xyzzvec9j+fLlfOQjH/mjz3v/+9/PO9/5zqf83TMphobKTXRFYqImiqCwKiz958XE6Ghhi8m0ZfObwSqrW8P0xkJ4QcDOXIP+ZIjfjpTZ2BXj7uE6i1M6C5MqhYbPeK2BqUpsmixzVleErdMWr1g0n5ieYKA8IkZdvs+SVBeHK1MkQyZlu86vBsc5oyNBMmTSGU1RdRrE9TC5uiB4Tloe69t0HD/glkGLUtOlNx6iPaqTb7jcPlBiSYvJxo4o+UaZC+elWNHSKi6SrT0MVWboi2ew3Bx3Du/g4nnt6Iowc3vJwj5+sn+A0ztbOVAcpWLbNDyHb+48zD+etIzfjhwipEgsT2dZk8nyw31jXNAb47HJKt2zkvrFqTQjlQK6opJv1Bitujg+LE2HKTWbTNU90kaUXdOCPH37QJ4XLc5wVmeSzVMllqRCyPJfpzLxzuE6aUOmP2nQamrsnbYoFxt863VHjx/0P/HBDV/hS9vfRCYssyQl4kziepiEXuIX+/PUHZ/RShOn4XHfzin+8fx5PDxeZ15c5c7DFe4YtClWHZy6w15d4VsNj22TVRQ5xWuWt5NvVPn8tjfxtjVf+qNr+MmBt/GyhZ9/Vj7f/8ZQucnyjImuSNwxXOC8ngg7ZpxZ3hms7BWxGYYmsyoboT2qc6AgcsBu3DYBCLWXLGXoiMiYmvDmef58k/vHmiR1IXt/9andFBoujhcwWrZZ2RphX77OgYLo7DQrTdo6oixIp0iEFEYrNlFDZf9QCdUQAa1dMYNyzkKShCdRe0Rnz0yD0UqTdFiltTOK7fmc1hHhx3tmeNeGNkaqHlXb54HhMssXpFnbFiFrKmzsCJFYmaRk+/zqUJWzu0W0hxpSCfyAUCyEXbUJgoCQceIp//4YWv7jchLpMMWcRTgVJvqhC1ixrHUuPy4SD1G3HL5weR+v+95u9IjOzHt+Tfa/XsDJq9qI/hXwCZ+Tz58YeNqF0Ic//GE8z6Ovr4+LL76Yjo6OZ/zmmzdvZmpqinXr1s39zvM87rvvPr7whS/QbDZRFIVKpcKll15KLBbjZz/72e95F/1PhEIhQqGj92UfKjX5wGlZPvbQJImQSldMZ9pyGa95c0aCTxwq8PJTusiaCk0voD+h48WFEd3fLE9xsOSyKhNivOajSAGTlsNtAyUuXZDk7K4YwxWLTFjBDXxKzSJdkQyjtRy251F3G2TCMWzPpdi0eF6f2O4j1TxL053YnjuXv6XKMi9d1EHVbnDzQIElKY3RSnNW1luh1dR40cI4P9pbRJdbef3KeVSdBqoki5GY6xDXwwxVZjizM8UdwwUu6GlntDrJSdk0YTVEvhGwvzDOipZ+Rqs7OFSaYnFKRUZmUTJJ1WkgSzLJUJhMWGbKamAoQu32ZH5ndyzFvSPTrMqYLEuHuX+sRCYc45GJGqOVJhO14twYojWq0xtTMFSN7bkm3905w57X/OSo7d9jia+c/3Xecd/rAdg9XmVBNsKew78vPDiaSIWEwWUmLLp3VaeBIjFnozA6YxGN6ly+rJUvPzohAkrbo6xqNbH9MLumRQehantUHVGA6orEpok88xMak5bHj/b/A1HNYMKq8fJFF/Hzg3dw9dIv8spbXsM9BwuUz38zf7f82VeaHXrdT2n7/BW8aGWWactjoGhjez5LWgwyYZUtkzWimsJYw54LZk2EFP7+1A6+9NAYgR8QNxR+fTDPmT0JqrbopK7Jhrl0XoS7hi2ypkR3VEGTIaSKEWNtNq9t2rJRJAlFF91dgF05i6im4AUBJy1MU58dNT9xqEAqG2FhKszW4RKlpsfBoWlOW9rKo4fynNQmLA4++9g0L1jUwr6Cy8/35Xjvqe2ElAQ7c3W6osIXqjuqzHZSZdGRUuDcNe1snqjQrDnUC3UUTUE3dRrFxp/ahCcMnn/T1fR3xvACsCI63SmDfYU6B0bKuA0RQ1K3HBzL4S03D7B2eZYdB/OkP3UZbsNlS7HBggXp4/0x/izk2f+eyd8/h2eOI9qKiqLwxje+kUbj6HyZLrjgArZv387WrVvnHieffDJXXXUVW7duRVEUyuUyF198Mbqu84tf/OLPdo6ONp645kcY6u8CVHflLOquIEW3mjqjlSbdHTGh/mj6s/wYiVLTp9VU0BRJOCXHVNZlNcq2T93xedv6DA8MV7Acm4odcG5375wX0LiVJ6oZ6IrC5qkhynadsVqBuB7GUDVkSaI7mp4dicmYqk5cDxPVDNRZSeX5PRF25W0Wp8OsbY/SnzQYKDb46b4SF/bF8YOAA8VJfrJ/ClML0R1Ns33GnusytUeSvGbZAr66YysNzyETjgHw7vVrWNHST4DPsrSQ+5/fPQ9JktlTKCJLEjcPjHCgNEFHRCGuKyxvMbl4XjeX9PYT1Qxc32dRUnyOCatGR0TB9T26ouICoisqW4dLeI4nSK5Vj8PlAt1RjS9csOiY7v+jjTsHS+ydqaMZIpPusjXtz+r7vXLxF/ACGCwXKTZr+EHAwZJLvu7QHtFRQyqrWiPcun+GpW0RFmZMbE90HX6+dYKBfJ394xWqjkdXTOfseQk2jVY4uS2FHwR0R0WRarlNTFXmPx+/lU8+MgWIoOF6oc7B0rERNgDUC/XZzq0I+627PgNFm0JTKOUUWZp1eH6yqJMZLLus7k1wyYosE6MVFEnitn0zhFWZXTmLFbNk9nO7wyRCErIEhiqxe6bJ4wfybJ2sMlm18X1Y0mLiNlwWpsKkDZVazcH2fepNj6GSKHonajYd7VGsusuOqRp6WKViOaiGym+fmMCzPRxfGCYubxUE50RI4pxewRMqNoXbeqHhU2q6yBIMVTy8AC6eF6ZqB8xPhrh0QZpYPIQW1lBnQ2HXLDvxo+jbv/BC7ts6wWhJ3MQ1K00GJ6p0zkughlTCqTCe7eHP2pDIiszjOybRIzqKrtA5L0E4FeaM7vhx/iTP4a8FR1xOrly5kkOHDh2VN4/FYqxcufIpj0gkQktLCytXrpwrgmq1Gt/4xjcol8tMTEwwMTFxTI0bv749hyJJs1b+Op4Py1tULukzSYSEWsxUxQkypAizwI6IQltY5vodeZamVSKaRq7usySl8dqVLUxaHm9b18KE5fKKxSsZLE/jBwG2784Ro001xJJUlrgeZk9e3JnvyQvPIdF5kYhqBlP1MrbnCj+gICCuh9k6bXHtSpGx1BpWWJIOcWZPgmtXp7CcgGTI5GDJYXFK5cYDgwxVZvjSfcNsmRohbURRJZmIFuNg0abdTKDLKpqs4wfi5HOgOEqxWcNybO4aOYzr2yxNJdFllUxYYddMjaGKy+pML199YppcvTLnfZE2ouiKSjJksmXKYX7CxHJsYprEB06dx+58nmvWtRON6igyXDIvycGSy7qszoU9f55LdiIj33BIhBTetaGNi+aJIN9nG5f3iYDPhufw2GSBUlPI+C+cF+G1a1rJN1zO6U9iewH7JqpsO5Dn5UtTvPfcHnzXR5IlzuxJsGva4mWL4jxvYZKyXWew7NIa1mi4DjXHpTfWMtcpfeNd1zJtOXiOx29Hqs/6Z3wS5Q/fzvUXf5Pze0zydYcVmTDr24zZcVSIJS0mUV0hbWhEdYWhcoMWQ6E9IvzBNi7PMjFUxPd8bt+b45zeJL3xFmYaDjMNEaC6O+/w1d+OcO/uaWLpMKMzFp7tYWhi7Jluj/LwYBFFlljcHiWqKSzMmNRdj4XpMNWqTTqskY2HaFaa1EvNOb+fnr4k8YyJocDWySp7Z+o4fkCh4ePN5ggqMsK3S5U4tcPAUCQu6I2hycLaoyuqYKgSm8erVKs2btOlWWliV212Dx9bD7ani8xnnkfb568g/q8X0Sg10EyNwA9YmArjuz6O5VCyXF66KgtA/7wkWlhDj+j0tITRIzpu0yWZMJietpBVmYbnc81tf3ecP9mfhvQMidJ/SLj0HI4cR1wI/du//Rvvfve7ufnmmxkfHz+qCq3/jS1btvDII4+wfft2Fi5cSEdHx9xjeHj4qL7Xn8Kq1jC275MOq+QbztyIID97R3blshZCqkgTNzWJpWmNTFjm0ckGV61I8bHfTjBWa5I2ZFKGRNqIsKLFpOn5jNc87h3Zxfx4FlWSabgOB4qTyJLMwxNjqLLCRK3I2V3twh+mpWvO7dkPAiZq4sRWbFqUm3W2TE+xtzDF5X2djFWLvHxxB6Yq0RVVuGRemN5YghfMb6XhObxwfj+LkzFGqi4PT1j895UrcfwAXVGRJXFofOS0FfzH5n1UHdEFtBwLRVLZlS+yINHBLw8JJ92hygzjtTJZM87KlhRtpsy53RkenRxkVWuYO4YLQlWmaOiyQruZwPY8XrQgQzIUoTOaIlf3GasWMVWJQyUbXRHk04bn8PCYxQNjz+4Y6Vjg/L4UpabH17fN8P1deRalnv0O54HiJI+Mi8J5oOyyL99gcUqnJ6YxXHG5YF6MrZNVDuUsXroqyxnLW/n3Xx9kX9Hhrad1YFdtbtk5xVvWtTBdd8gYMjtn6jQ8EQ6cDJmEFDFeNVWJAyNlHhguMVm1OXVNOxu7on9+kUcZL8ueweaJCj/ek+PWgbIwQHWE83tfQmNJiyiUPDfgu4+O8fMHDpMOa3hBQKw9RrPcRFZkTsrqTFllOiJhWsMaWVNI1z3bEzL6usvG/hSKrpCfqDJRtbHqLiu7Yty7e5qBYp2RQoNSw8VzA/J1Bz2sMlAUKrQnOTxPjq9mKjbnzEsyVRfO32FVZvN4hR8+Nsa05fCtJ6bZNW3NCQ/25F2GKh625zJleVSdAFOVWZ81uHJpioVtUWLtMSRZIggCHOvEtJ/ItIRplBp4jsfCviR2zaY8Vua+zaMouoI5G2u0dbJGzNREnJEqcf81ZzM8U8dtuLx4XQe2J4omXZPZNFrhV7PcrxMVz6nGTgwccSF0+eWXs23bNq644gq6u7tJpVKkUimSyeRTPID+Utxzzz3853/+JwDnnnsuQRD8wUdfX98zfq+nC8cLRBhjVah7bM/nlwer7M4LX5yJmkdClxkqezRd4SBszrbmDxbFnWI6JJMMqYxWPcp2nX2FOh2RGK9a0o/lCvm0T0BUC9EZTeIHPsvTKSasIpZroysquXqFsWqBqtOYdZwOaI8kZkdqMmO1GlU7oCeWpOE5LE6103Ad1rRGMFWJ3ljLnCw/3xB36cmQyZtX92M5AU/kKoxWPW47PDHX+THUMFctzfKj/cOMVadJhtIE+Gxoa6Pm1nnX+gUsTsbojbVwUmsPO2fGufHAtDCdNBNYro8XwKaxKrcMDgDQ8Bx8AtJGhIfG84xU8vxq4DCTdZ8vbSuwPecwUGywqjVKvu7y6KTFKR0m15339WO2z58tnJTV0RWJQl24Hz+5759NWG7A2lZhcbAgobJ9ukrWVBitOfTEVPwgYEnaZGlbhMv7E5zcbnLlWfO46YlJbjlUnlMfbZt2uHWgxpapJilDRpEgE45huTZDFXFcXTwvzfnLs1y9ooW3rM+yvj3CJ07/6rP+Gf83rnrwVrpiIcKqwoFCHc+HsYpNb1x0Zm8fyFOd5cvoEZ1wKsxAsU6rqfHp8zqJtEaY1xImpAjbgFxdPPfOoQpDsx0LRVfwPXEzZNfEueHcXhO36fL6VSkS6TDtEZ2z+lP0Jw0qE0L9FVYV7LrLwakaqqHORXpEDZWYqXEgX6fuiHDWhSmD/qToiuyarFHJ11neapIIyezOOyRCMuM1D9vzyDV8cnWPPQWHsi32x4V9Ma5cnkELa0iSRLL92Belfw6pT1zK8IE83uwN5u7d06ghlUR3QnCbIjq6JiPJEnsOF2mP6vQmQrx0SYbn3fgwkYiG2WJy42NjFMYqnLFajJvzDQctrLH6+lf8wfc90btFz+HY4YgLobvvvnvucdddd809nvz5/0UUbVEULG8Vbr1PnqS2TlbZm7dQZBgsu/gBVBwfTYa+eJL1WZ20IXH5gjSZcJS4HiZlyFiOzWBZkJ/9IOCsznnoirggAXRHs9ieS9qI0m4m55ymXd+jM5pCl1VMTeR46bKY/e8rlpElYd+fDJnIkjTn7fPYZBXHF6/tBh4/2T9FXA/TEs4iSzLbc2O8YdV8lreYrM4YZMIK+4oTeIFL2a6wKNlDb0yhN9aFF7g4vs28eC8jlTzFpsWCZPuchD+sKixNC0Xd5qkhtk07lJo+6bCGLIMf+Kiygu255OoVFiRUZEliQUIj3/BY0hKmL6HylpNasD2fXbkay9MaH9947C+mzwbesfbLAKxpi7KxO05//NmX+F674jo+c/8I47UKpibx1nVtlJo+N+4rs3tGFDUJQ+VVyxL8/GARxw+IqDLPX9lGq6kxf5ZrMVRucsG8CF4QsCfvElLEaFaWRDe06vjYvsurliaZn9BYkgpxYc8zvzn6S5AyVNa3R/GCgHN6k9ieT1/S4Gf78myfqlEerxJOhKhMVLh8UZpmpcnu8Srbp2pM1j0apQb9SQM/gHXZFHFd5Yd7yzS8gC9feBKLOmIouoIaUsWxrcpEZ/k88aTB++4dY317jDO645SaLr/dP0MkE+HqFS0oMiRmc9v0sCpGQYbIcysU6qzKRkiHVcKqwu0DRVE8pcJcvSaL23BpDSsoEsxPqOzK1VmV0RiqeKRCMpYT4AdilCZL0BdX2TPT4PkndaCZGrVyk9hHjtzm5NlC+lOXccqSDJHWCFpYIzMvie/6nLeqDUmW5kZk5/elOK8vydWndbMmG6ErFmL3jOiqWXWX165rI9sWQZIl+pMh1ncITqNTd2iNaJz6g1c+5X2/suNNnJQ9/vlrzyxeQ57r3D+HZ4YjPgufc845z8Y6TmgokhjRlBouUV0hqguia9oQrfShUpOrlsUZKHu0mTJtpjE7XgLHh1UZlV8N5Hnxwiwdpsr+ok1HREg7f37wMBf3ts/K4y2imkG+WRN5XrPdG0PRiWohlqY65yT0S1MaEU0j37Bpj4RZnk6RCcfoi1ewXJtsOMGh0hSW28QL4NFJm2Vpj9sOV8g3XJKhCOO1MVRZZl48xUStiOuLIuX5/QvJ1Yt4gUtCT1BzKrxw/hqGKqN8evMgnz3nFDzfZWm6B1mS0WUDmSqqrPOrgRr/sLafu4dH6IqqnNymM1QReVZLkuLkZDlNoprIDsuEo/z0wAwX9ZqsbdVZmIjNkcbrbplXr8hw9dK/vmyxP4bX3v46dEXmQKHOhs4Y8jHqbE+/42Z+cuBtaLKHJvsMVXwxGvIDBks6UU3GcgMGig1imsmk5TBasUmEFM7ojrNNr7EgaTBUdgmrCmlDIm3IHCpN0R1NM14bx1Akpqw667LdANiey4a2Dx2bD/i/cGq7zpYpmxcsTJOru+iKTNX2KDc8Wk0d3/N50eIMk10JfvTAEOev6+SBvTk2dsf54I37uea8fnblLA4WNXpiNnXXY32bwUjV48f7D3D1iiQfLzdwGh4PHMzj2R7RRIgpy2djV5wHhkvcsy9HR4vJxu44oxnBTfrGEzkAarXfjajCqTBhVUFXJKIZk7sGi5wzL8HatjAAp3eZfHHTBL86UEA1VB4YqaJIsCYbYWnaoOEFTFoeMV0iaypMWR6yJHLtqk7AiozB9VsmSLcIInxP24nTFVrUmyAdVmmWm3NKt1AsxN3bJ+nrSTAJQsm3e5p1PQkUWWK65tCfDKFIEmFV5g0nt1FqBlw2P8V4W5RS06MnprP+pCxDFY9f7Mmxqj3KP94rFJv78g0WpAz8IOC7e95yXM8vz8nnTwz8ReVksVjkM5/5DNdeey3XXnstn/vc5455EOqxREdEQVdkWiMiXPTCvhhVW7Rxe2I6q1vDeAGMVF1aDJWKbWOqOl1Rk+VpDU2WePHCLBO10myeV5xT25OUbQ/LDciEY1SdJrbnocsqbWYSWZIo23Vc38NQNW4ZHOCmQwNUnQYX9bbSYpi0mwk2tPdgqiE6IynKdp2+eDsyEvlGld+Ol2b9gQwu7AkjSzKGIpQypqrTcB0Rj+E6pI0oI9Um7WaCsl0lqoU4WJxAVwSHpWKXaI+0cM3yNGW7ghvY6LJBSDGx/QaSJKNIKh8+7STuGBphQTLMd3aW2F90KTR8+uIqvbEW3MBnrCYUTKaq0xvL4AUBT+QaXDZvCZNWld5Yhu/unuTt6zqOy1jl2UR7ROHc3vicPHuocmw9kbwAErpCJizTFQuhyBKOH/BkoG/d8ZlpeHizzsu7chZRXZrzyPn+zhwnZXXuG67SHY3ygz1FbN/llLYulqSyLE9nKdsWDddmwioe08/2JM798at5aLzJ/kKDsaooOHblarRGNF60pIXlGRNJlvjeQ8MiqmJ1O7Yn+DOaLKFHdLZPW/QnRTjxlFWnIxInE5b59cE8HRGFj9w9yrVrsqzsihH4AfJs7MhP9uTpiKh4QYDbcBmdrNIRUeacn5+U1QdBgNt0aTV1zl2QwgsCIbywPbwg4EC+zg93zxDVFe4fETy86WKD0GwnqT8ZRpZgqu5RtQMMVaJiB/TFtTm+Ym9M4cGxOilD5nWndHDOvATVYoOh8cqx3iW/h9NveBUvuflv6Y0b9MZFYarIEroi47s+4VSYyapNW1Sn3PA4ZV6CbZNV7jhU4JyeCD/fO0NrRON5C+L8984Zfrw7xxPTFqsyIV6/qpNESOLe4ZowvdVkwqrgb+6eqbMqG6YjIrMgofJfm3PHdTs8xxE6MXDEhdBjjz3GggUL+NznPkc+nyefz/PZz36WBQsWsGXLlmdjjScETmk3qNreXFDpqmwEXZEYqzo8MV1nT8GlO6pSaLosSbXRcB1m6nVShkFUE92fXMOjN9aCqemU7TrJkMr53UnuHD5IVBMnuF8PDlKbVYTtLxYB8IOAlCGzJBWi6ojw0awZx1A1ynaduB6mONtF2jp9CFML4RNwVmeSmXqdsi0CXO8YmmRBUqXFEOuJ6gYPjQ/hBh4PT4yxPJ3kPx8fZKJWxA/ExdH2GuQaFapOE8uxSBsRvrjtIIokmokyMj87uAOAuid4R70xlVy9ybtO7ubqpQs4qyvMq5csEnENM6PE9fCsjDvHgdIEb1g5j/N7Wrl7ZB9LUlm+vmMfVy5u4fK+/ziWu/iY4BOnfxVTk6i7HvOTIb5w7teO2Xu/bOHniek6vzwkuGS9iZBwY264xHRx8bx6RRJFkljfZpAIqSiSxKPjFq2mTtn2efWKDCNVjzXZCPePFfntcIlcvULDdSjbdRqeQ7EpFI7tZvKYfbb/iXte/n0Gik1aTZ2BYp2tk1XWtkU5oyvCQ6MVvCDgitOEyeqmsTIP7ZriwR2T6FGdHzw6yj9dOI/RSpO66zFQbLK/KMbSS9MZVrVG2Dnj4Hs+h8su1yxPkE2HicVDzBQabOyOs6HdYGEqTKYtSjgR4lDJ4ab9Rda3R6lYjghutj082yNfd7hzTw7b8xkqN6iVm4RVmXN7Y6xvj2F7Pr2JEL7nE43qQmXmiGJJkQRf8Wf7i4QUCUOV2F+06Y4K76iDJZc2UyOmi9P8woQ2F8Ca+sSlx2XfAND8FYdyFpvHK/x2uMSuXJ1EZ4y7t09SLjbIdMepF+r4rrAJ0BSJgWKD165p5by+JJmwzGfP7+IVi1OsaY3SHtFZmY3QHtHZWxDH4eqMQd31eWC4xEXzU4xWmnx/Z44tAwVimoTjCy6ncqxass/hhMYRF0LveMc7uOKKKxgcHOTGG2/kxhtvZGBggOc///m8/e1vfxaWePwxUnX57aiF7QUMVTxqrrhzfmC4RH9S55QOk01jgocT02QmrCLFpkWu7vOpTePUXQ9VFicn23epOg0MVSOuh8mEYyR0GT8IGK8J1d2UVaLhOiR0mVsPjzNllVicypAJx9AVhb35GrbnznGAXN/D1EJMWEXielh0kRSNtBFlQTLDhrZekiGT09oTRDWZq5b2YvsultNkY0fvbLK9hKnqXLOslWTIxPZdxmsVvMBlfrwXQ9VwA5+uSIYPn3oa//X4ZgJ8Cs1prlx0MlWnxh1D+5GQGaq4/GbQojfWQsNz6Iu3znkhtUcS6IrC4UqFnliSHbkamybHUWWF3liMYtPizK4ol8779+O81589fHzjV+lP6OwvHHtzu4PFOmd2GXRFFdZndU7vEtlnAyWbqh0gS3BRr4EsCePEVy1Ps3VWAXbXYIGi7VO1fSYtcXy+ZX0WUxVcCzEmVXF9H3f2uDxeqDrCYX1Ji4kiSaQMlVIzYGi8wq/35wHItEWpzdQxEgavPKMXu2rjuz6mJoKVd00Lz7C+uMJ0vUa+USWsyfx4Tw4zqtM+O96+YlEKRZJIJgy6osJOQ5ElehMhqtMWE1Wbacum6viYYZXp8QqBH6DoCuWchRbW6I0bNGsO8aRBOqwxXvPYO2PRFdOZF1M5Z2ELa9uiyKpMb8KgPaLiBXBZn8ll8xOMVF2imujq3X5YqMo0WSJryjwy3mBpSiVX97h6TRZZFV2XB8c/fFz2Tet1X0JWZZZnIrxoSQsHCnWaNYeerjiKplAuNnjNGT10pwymLRHM/LyFKUxV4rUr2mmPhKk6Pl97Yppys8Fl8+PsytUYKDa4vM/kq09M8/h0g30TVZZnIuyatuiKCXd9I6aTCMlzJq8vXpQ8LtvgSTzXETox8Bd1hN73vvc9JfRUVVXe+9738thjjx3VxZ0oiKgyVcdjfkLHVCX2zpL01rZFWdUiPH/O6I5ySluEnXmHdjOJqel4gZjjz4unuHdkkkvndVBsWmSMGKqk0PAcDpWm6I2LgqHNjGJqgh9z98gE7ZEEK1tEp+iGvePE9TD7izXO6upksJyjbNc5XC5guTa5eoW4HsZUdcZqIsJipJpHlWQOlaewHBvLtdFkQXA1VdFdimoRFqd6WNmSxdRC9MZa2DQ5ScN1WJRsxXIsAnzCaogpq4SPjySJqAGAqCaCduN6jJNa2wjw0RT48GlLcX1RAFpuE9f3kCVR8P3m8BTjNZ/Hp2dYl02wOBnn0ckpsmaCe0eLvGj+547Pjj6G+MTpX6UrduzjDt617suM1zymLJ+HxpvcsFu4eOfrLpOWz7Zph4GykGKvyRqUmgHLO2PUpmvUyk1+uXMa2w9QJInRqih0nizu280EcT1MdzRNVDPmAoGPNZ5/09WkDZXbB2tsGqvwgkVJfrRtgrLtc86yVv52dSubxips7I4jqzJO3eEnm8cIp8Kct6oNQ5F460kt5CyH/mSYXTMufhAwWq3zisVJ+pNh6pbDvUMVBsrCQ6xme/QnDUpNn0LDZ317hLrjY7aYbD6YR5Ekbj+QR1ckwqkwbsOdK0h812eiZhOKaFh1F9vzSRtiFD9UbvLjPQV25WpsnazSljGZF9dRJFAk2DrtMFJ1WZbScHwwNYkXLohQtYU1R9MTdgE78y5dMYXemMLlG3tx6g6XfOW3vPxXrzmm+yb7Xy/AjOpcOD/F0haD2weK5GbqeLbH4cNFTpmX4AWr29EUifUdMdpnO1jffHiUgbLLwVKRWwfLDJQFf/JnB2pc99gkTsMjEVL56f6qcPeW4K2ndbA3b4ntGdZY2xZlbVuUD918iM8/NsnNBwpUnOObW/gcWfrEwBFvxXg8ztDQ0O/9fnh4mFgsdlQWdaLhxQuzJELiDmxxSuXUjghpQ2ao3OT2oTo7phusyYTmujj5RpVi02JlS4yL54l4jJNahfJGl1Uen55EV1RM9XcXQtcXsvrTO7pwfY8XzO8jbURZ2dJNX7yVc7vDlO0653X3UraFW++23BRd0QQPjc/MdYGqToPeWAaAvniGYlMQsAE6I0mank/ZFnwP4VItc6g0hizJPDY5wuapIXpjBlump9g+M0WuUaHhWTRdm2XpfgzFxPVtLuhpxw98vMCl7lYZq05juTabJvbSHw8TVsV75hvVuSKs5jgYisYl87JoMnMKl4bnsL/gsic/xt+vWnsM9+zxxZN2DMcaHz3tq/zTyV/mjM4Q/UlDFMe6UCINFJvk6j6Hyw5bJut0RBQSs8GdzUoTI6yya9pieYuOJosx6LvvOYzri27nWK1A2a5jOU1sz+XWw+8lyF9/TD/f2d0R0mFhmLg8Y3LHYJkzF6S5ecckd22bYG++yfhggVt3T8+Z9Z25JENb0iCsynMXx7esz7IsrXHHYBHHh3tH6vzmcJXRSpOuFpO66/OlR8bZOdOkLaozUGxww6ZRfrC7wPapOl2xEL7ro4ZUNnTGceoOU5M13KZLKB5iaVccSZZw6g7lYoN6qckbTm4TnR8J0obGUKlJwlCZqdhENeGU/d1Hx2i4AeM1F0OV6I+rWG7AeE3whbbnBC/KVCUWJTWqthijOR5smmjy0GiJV18wHzWkcueOyT8qLz/ayP7XC1A0hVq5ya5pwXsKqzJ6RMepi3HjPZvHiOsyWyctbtw2Ifzb2qN0tka48bExfnmwyuKUymDJYfNEhbrrcea8JKm4zkCpjiLDho4wvTGVnTNN/mF9K1FdmY0/EVEx56xqw9BkCoU639g8yee3vemYfP7ncOLiiAuhK6+8kte97nX88Ic/ZHh4mOHhYW644QauvfZaXvWqVz0bazzuOLfr47RHNAxV4q6hOjMN4Y2zoTOGIkls6AizLddk3HLpjakcKFXE2AohZVdlRURhaCEankNrWGO0WpobKTw52uqLt+L6/lycRdm2sGZJp72xDLvzeYrNGlNWmawp02aGKDYtEUQqyTQ8MR9/7327eGxymC1TY5TtOsWmeJ2G57AkJUYZtu8S14UqpS/ezqHSFMvTrSxKtmKqOouTcb6zo0g2HCcIhCvxpDVN3RU8oJ6YyDyruzV8fNrMFOtaTxIjNN9j3BLSelUWY4XtMwU+/vAkg+UcqqRgzs7pdUV0Fk9t1ynZ3hw5+/8Cbrri2BYI/xtdUeGMHtMl5id0EiGJacsWZn2Ggu0F7C04KLLE357bjxbWsB2fUzqE6igRkinZPhu7EqiyjKFoJEMRDFVjW26KqtNg81SJVzx0FzcPvuuYfa42UyYZkvGCgI+dsYy9hwoArOiOE06F6YnpnL62Y86VNwgC7tk2Qc5yuHX7JJosUbJ9fravyA92F1AkiT15hzO7wtw+IH5+1bIUYVVGMxTu2DXNZNVmalR4Lu0/VOCRfTlevjiG23TJJEJPUWuFIhp21eZgvk5rR4xGSYxIAz/gy4+Ms3WyymDJ4a5dU/QnDQ7kLJy6w0ShTntE5/JVbTg+bJ6ocOuhEjFdFiT4kIzji8DnUtOjL6Hi+AFl26cvrpBr+CxP60R1hR8/OIxds0lmTB696rXPuqdO5jPPQw+rohO1IouuyDwyVmVouERf0kCSJIyEwcmr2nD8gNFKU2SG7ZthVauwE8i2RSg1PSq2j+0FbOwSgoP7DuYJqwpV2+OufTOYmthfD42UWZ/N4AVw5ZI4qzIa5/bGmKgJ00u34VKZqNAZeWp25YpvX/Wsbov/CRnpGT+ewzPHERdCn/70p3nJS17CNddcQ19fH319fbzmNa/hZS97GZ/61KeejTWeEJAliZAiSKRndIpOzkCxQTIkM1Tx8AMYr/lM1T3aTY0Jq4aMTL5RY9NEfjZtvUrFFl2ApheQa1TojbWI10eMxJ50X7Y90Y4XpOoIewuTbOzoYcoqU7J9uqImpqozXW8yYTkMln+n2nv+AlFIDZRddEWd68TYnouh6liuzZRVJqyG8HyXXfnDnNK2kHyzihuIcZbr+7z7lDZyjQpf3LaXqGaQNhJIkowf+PiBz32jOwEoNWvU3QaDlf3Mj4tIkO25aTEysRtENA3LCbikP8V4zaPhOZzSluXC3jZ2zBSo2sIxemNHLx3mcyZnxwrP7/sMi5Mqq1o01mV1Gh6c1iXUURFVZtpymKjac0qnV27oYmNvgknLIW3I/HhPgd5YmJ6YwtbpEg3PQVdUVElmRUsLo9U6TS+gM6pRaDT4+KNvPCaf62+XXYemSJzZFeYd9+xg/dIME1WbsCqiLr778AhbBgoYYZU1C9NcdHIXqqFy4fyUGJX5Ae2mxuULEpzdE2dVNsIj4zW+v6uIVXcpVZo0vYCF6TBRXaGzNcIZPQkUXcGzPT7+wgX87cZu3nLzAGcvyVB3fe7fPU2+7nDWslaaNYdYW4TFLeE5J+tIPEQsHWZDXxLPD9g7Y+E2XO7fPU1vyuAfzu7hnIUtJEIqB/J1NBmWpE02dsWYtDy02TP5qozGqqxY12BJKDbP6AyxPefSHVVYnYkR1RSMhIEe0emNh+j5wnUsSWn8YuCdz8r+eP5NV8+p6i5f1YYXBPQnDda2RQj8gO17puc8mVa2RkgZMhs641yytp1TlmToiumsbI1wZk+CiarNrhmbzqgoXuquTyquz3Y0JZZ3xvj6thkOlx3CmsxrbtkNwEPjTdrMEIfLLkOFBid1xVENlWsvWoAfBOwvfRaAV97yGgzt2I2bjhdH6Itf/CJ9fX0YhsGpp57Kpk2b/uhzv/a1r3HWWWfNmSdfeOGFv/f817zmNUizkR9PPi699DgS8o8QR7zHdV3nv/7rvygUCnNBqfl8ns997nNHNfX9RMPz+1P0xxXyTZ+dMw6jFYfF6TCPTdTI112m6yIFOmPIuL5orZuaCEPti6uMVYvcNTxNTNdJhiK8aMHpczlhvx3LMViZngvFtGfHXIaikatXsD2hRJMlicWpDkKKhOXYZM04LYaGIsHKliwZI0YmHOPj947ymUenUSS4d6RKi2Fy+1CRZCgiRlVuk7QRmVN+zY9nGa3lkJExFKFo0xWVXx6qcMvgFKe2iw7S/4SuGJzesYiolqBs15mwhDWA5dokQxFWtLSQDEWoOg2KTRtTk6i5PlFduGBP1IoYikZCl8iacf522XWsbnn/Md+vxxNPXng+9PAbjtsa3rXuy7x6yRd59ZIvokjwhXO/xsc3CmVbIqTQnwyxaazMHYMFOiIK7RGN83rCOD588ux5uL7ojj460WCsVsBymriBT65epSsqbCXWZcVFa01rlHtG//mYfK6HRqs8NNag7vi0R3TaozpeENAV02lpjWBXbT5zXjfTlsNDQyUCP+CmR0YA6DBVpusOjg+aDL/ZOUXd8di5f4bqVJVELDQ7hvJ49ylZVmUjKDK8/JQu3n9xH5smmvTGFRzL4eERIYBY1JtgbVuUiZogZCdCKrvHqyxJm/ieL3zJwhrbp2q8eHGS/YcKZLvizO+Ok6+7PDhaQ5GhK66zvNXk5gMFvFllZzoks3WqieMF3H7Y4kChSSIkkzJkHF+YvJ7SpjE0a+Ia1RXk2ZHU9sEi7zyjiy9unuIn+4tk/+sFnH7D0e3sd8VCJGIhorrC5vEKWyerDBQb/Pd9hwmnRE6YJEt87fIFnNImuE4vXBDnzK4w5/SIwgig7viiCy9L/Gjn9JwT+JK0ybRl05802D1eZWSoxLIWnelpiyuXtfCG1SkWpVR+c7jKrlyN9V0idHdZT4KNHSFGaw6f3ryL7+55C7YXEFWVo/r5TzT88Ic/5J3vfCcf/vCH2bJlC2vWrOGSSy5hamrqDz7/nnvu4VWvehV33303Dz30ED09PVx88cWMjo4+5XmXXnop4+Pjc48f/OAHx+LjHBX8xba2pmmyatWqo7mWExpxPTzLrXFxfOiNa5SaProi0WqqLE1r9MXDlJpN9hXFHbMfBGyayLO2NY7l2mxoN5iy6jRch00T29jYcTI78zs5ozPM3kKerih0R1vmEuRVWUFXVKEyUzQsp4nl2vTGEhSbNSZqJeYnsmRNEbcxWJnmnuEya7rj4qQwYzNaaaIpEi9f1Mn1u4e5tC+OLou4jkOlKUxVjOtWZXpphmzuHztMmykK2quXtfGT/VP8asDitI4IttckrEaQJZmZ+hSmJjyE+uLtWI6FTzA3GhvI52kJ13F9QR4dqXqc322gyvJcEdfwHP522XXHd8ceR+wvWPzj0Ov5r3OOnYT+T+F/ejYJ1ZjMud1hvCBN3fXJ1X1+tGUc76QOLugJo8oydw3WOLvL4GDRnnM8b7g23dE0ZbuOH1SZtHy6o6LLOGWV+f7et/DqJc+eid3ntv49a7IR2kyZBUmNbVMN5ifFMW1qElsn4QWndrM732R0pEx7V4xc3cGpO8xvj1KyPfrjMR4YnebUDoPFXXH2jZZRdAVpVm69dNYf7JvbC/ztyiT/cvswZlTngVddzDd2Pk6h4fO3Z/bym4ECuZk669tjnNUdZqgcYrTS5JzeBD98bIwHd0yS7YpTslzKskcqrHL7YIV3XNzPlCUS5bdP1Xj8cJFQRPCeoprCVSta5mTg4zWPaUt0SdJhlf64SqHhIyyUAhpWwLjis6Hd4OaBGsszJpfPj5M2ZCYtIcX//IU9/MNtQzTLTS48tYP2L7wQu2ajGipfv2IRV/R/9ve28w373sqVkcVIXW/7k/vjpl3TzGsJM1RoUJmo8LoL5vO9x8YIgoCYqTE5YxFOhemNZXjHXZvpiun0x0UxctdQlb0zFq9e0cq2qSa25zNcarIwYxJWZTwfRmtNoQqLiM5QVyzLf++cQVZlNk9YZIwoK1ri3DYwTqnpsjAVZmnaQJYl3nbLYfqzEQ7P1MnXU2ydrLIoqv3Jz3M0IUJX//IO1F8SuvrZz36W17/+9bz2ta8F4Mtf/jK/+tWv+OY3v8k//dM//d7zv/e97z3l569//ev89Kc/5c477+Saa66Z+30oFKK9vf2I13Mi4Ij3QK1W44Mf/CCnn346CxcuZP78+U95/L+KhucwWHZQJEiEJEarLkPlJomQKrgSTZ9Ss0mLYbIuG6HdVIlqBkvTYdzAw/U9Hp+qkgzpmJrgBj0+vY2RSh5VVkjoCqqkYDlN8s2qkCLPJrRnDDHqEgRrnWKzxqGSTTI0a+mvm0xYJWxPGDQeKNSxvYCzusMsTIdRJLjxwBhXzBembveOFrB9l85IimLTQlcU7hjaT9mus6Klhb54hscmC3RH0/z96gVs7DRouHUUWeHWw9tpeBayJKHJuuAmuXVCqo7tiVGYLMl0RGKMVi1UWaY/YdAdVTA1nfZIYm7cd2bHR4/PzjxBULJFDMIN+956vJfye3B8QWS96WCVREiEjyZCEm0Zk83jFQpNlzuGC5zdZTDTcLl4nkmxac1l2MmSxMFSEYAzO9sA5uJgTE3nm7ve/KytfeeM+J6OVD2GKx5nd5soEjw4WmHHdIPz5yVRZImrlq7g/DXt5EtNtLCGGlK5YmGCVS1ZbN9lVUZ0fkpNV4SW+gGXruugYjk8kbOpuT5n9sTI1QUhujdhcNFP7uDOwxXWZSPIEqxvj5FKhUkYCtc9Ps2qjManz+3i1kMFPNsj0hohN1nlzSe3ce3aVq5d08JFfTESIYkFCZVL5kV5+dIEsiLjNDwKZZuq41Fq+uwrukR1iZAqUbU9Ji2HuuMzUHYZr4kuteMFdEcVmm6Aqemc0RnCC8RovuL43Dtco2QLU9dPntfJxes6MVWZmKkRS4e5Zl07f3fjXq657e9o+Y/L57qYi775Ml53/S4y37+Vceubf3J/uE2X/UMlatM1Vixr5ef7ZnAbLpIkMTVbYPa1muTqFf7x5FZWtkZI6DL/8stDVG2P3rjBXYMlEiGFoVnLiWnLJl936IrrbOiMcc3yBBfPC3NxX5TTO3QUSZorWnMNn/vHiixOG1zYl6I3rvH1+4ZI6BIb+1MMTlssbYswWmmiSBKHS8cu3PlYj8Zs22bz5s1ceOHvYlZkWebCCy/koYceelqvYVkWjuOQTqef8vt77rmHbDbLkiVLeNOb3sTMzMwRre144og7Qtdeey333nsvV199NR0dHX9RRfrXiCmrTNXxiWrCsbTV1EiHNUoNl764guUGWK5Pz6xZ4pNEZFPV8YOAuB6mN+bN/fxkp8cPAsaqBfriQuklSzJyIJFrVMgYMeJ6GF1R0X3RxWmPJOmNZTi5LU2+UWRPfpzOaBLX95iwXPbm65zZk+D8HoPHJm26oiqLkiqmKjpU5WaD7qgoukaqwk/FDwJydUGIHq2WUCS4eF43B4qTzE9kWZyMU3WaJGWFU9t7uH/0AO1mhNWZFPlGEVMNzZlAPskTsX2XjkgYUw3x2GQBWYJ8o8nSVCdnd/7bcduPJwpuPPh2oppMyoChisu1d76Or1/wjeO9rDn8+xlf5bW3v46TsjohBR4et0mGhEOvIktMWT6ntZs8OmmxIKFSsn26ozEMRWPCKhHVDBYkkviBcJ42NZ1i02JPwWFdVqU7GuO+sX/BDwLO7fr4UV277QW0hiX6EgpTls/dwzVesCDK/SOwJmtwx2CFFy0Stg8f3biM9zg7Wdce5jvbp/nh7gLL0mFGqk0Gyi6ntOnYnkibf9HJXRwo1PE9n+maw6X9othZkEjinww37S9SslxsLyBXb3JOdxTX98jXXaFW6k0yafnE9IBazWH5gjQD+Tp9PQlShsymiSbDFYn1bTrz41EanoMqy6xIJ3jRSpcbt03wkjXtTFRt2kyFkarHaNVjoGSjyBJbJ6tcvaKFgbJLylBJhIRXTqjpsyNncWq7uAG7oCeMoQrO4CuXxjlYEmPvi3rXUbI3E1JgecbkwYN5vnbPYYyEwU2PjLB0YZqPPjjKP9zxEs6Zl6RraZrvPDHNSd+8iU9d8Bhf2VbgwVf+bhzyycf+nv+4e4jWVpPRwSKSJOH5AVbVRjM1oZizHHzXR5EkHhqfYdN4naFyk7IdIT0bENtqaiQMlbiuMFRqoivCgbo/aXBOl0HTEzYNj046nNkZotD0WJ4xiXbG0BWZTeM1dFkmrMmc1R1mf0EUtj/ZW6TUdNEMhf5kmLes6eP1t+1gVSbM3qN6RD77KJfLT/k5FAr9QapKLpfD8zza2tqe8vu2tjb27NnztN7rfe97H52dnU8ppi699FJe8pKX0N/fz8GDB/nABz7AZZddxkMPPYSinPijxiMuhG655RZ+9atfccYZZzwb6zlh0fQE76fQ8DmnJ8KXHp/i1Sta2Ttj8fC4xupWIVN9fHqG/rgoXlzfY7wm7Oz7E2law5G5Cl546whVWdqIsGNmioXJFFHNYMfMFCe39bBlapD5iVZcRfi1GKqGjIShaoxVpzG1EI4fMFEr8b57x1jSYrKhI8LmCYsf7nXojYeQJQlDETEKTzpSt0ekOY+f3lgLI9W8kMMj5Ozrs/PYlR/lQFF0nZ4MePWDgIhmkDXDTNdrVJ0SreEMNx3axoa2DqFG89y5rkCpKe6svCCgI6L8nx6D/U+8/FevYVmLTqkZ0JcQJ4kTqQh6Emd1hRiqeExZHidldW4brHJRf4IHRqp84eFxPndJD5oseDS+D49MFFmS0umLt5KrVzA1nVPaOrE9l3YzQb5R4/QOadacsEnWDDNl1Sk233FUvaNGK02qtocmh9BkWJQy+NHeMl2xEI+O19k7XMJZEGfL1AFsz2O00qQ9qlObqXPphjT5hk0qJLMqo7En76DIIMkSyZDMRNXGsz2mLQdZgjbTwPZcemMKGzpj/HSmzvvO7uDzm3Nc3J/g/J4kl82PUrHFePBgycXxJV66KsuunMVVq1qpOAGWE9AVVfnE6V/lu3vewo6ZCh0RhfGax+pMnJt2TeM2XG4fKLC+PYYmCyuPScujzdQ4syvMUMWjZAc8MFxibVsUxxfd6qodcNWyBIaqMVRp8MaV/7+5bfWLgXeyMGmiSgpDlREu7p3Pg+MD9MRDXHlSBz94dBRFV4i1RUiEVB7bP8PpSzPoisREzePVKzI8PmUxafnsOpjn9BtehSJL7JuoEo/q2DWb4VJDJMjPupibUZ3iRHVuDb7nc3CqxvZEiLAms7ErxtK0RlxP0xGRGa161N2A3xwqcH5fkgeGS5zRHWdpWqUrmuD7eyfnCtOpuiCOv++UfiasEj8/WMbzA7ZOVnj9uiy3HKpyVrcIZ02EhNLsnze2k9CFEe6VS1P85/0jR+1Y/HM4WlljPT09T/n9hz/8YT7ykY88k6X9QXzyk5/khhtu4J577sEwfqfwfeUrfxdqu2rVKlavXs2CBQu45557uOCCC476Oo42jng0lkqlfq8l9n8BJTtg0vLZk29w+2CF3rjBQNEmbWic3yOM57qiKpossXXaouE6WK7NvHiKkCKTq1cp23WqTpOBcoWq3ZwtMnRyjQoLkymSoQif23KI/UWXhuuwMtNN2hB3hhO1ElNWGdsXarLBco6Ga7Mo2Uqh6XHRfOFuuyChsjBlsKo1zPJZs0dZkliUbEVXVIpN4Uq9p1DGD3wmrBKmGkKVFZK6iReA5doYqsb8hMbBUo49+RybJoeQJYnHpwbojrawIJlBV0L4gc/iZJyoFqLqNGbVcTXKzQZNL+CRiSpVJ2BRsvU478Fjj1sPv/cP/r4torG21aTm+hwsehSaPl944k20f+GFx3iFfxqGImI3zuwUIyJdkfnS3YfZcTCPJEt8e6cgyJdscUd+88ESj083GanmMVQhpR+qzGCoGiPV/GzRHyURCtFuRpio1XH8gIPFOg9PfOSorXtDh5BbHyw6VOyANlNhWYuwCtiVq5FIh9k61eSUtqX0xluYKosuQ1tHlK0TVeK6Qn9CqDlv2l8gbWgs6UmwZ6ZBsdQQ6ejzYlhuMNcFrTgBth+wsivGf+8qsbY9yk37Czw8UcJUJRYkNGRJoi+u8oVHJ+mIKJzRHWVBUmXTWIWDJZeNs7YEmizRHVVIhEIsTZsiRDkeIpWN4PkBUV0hZYhx/JqM6IDsybvENIkVaZUXL07Tn9A5r9sgoUtsmazxd8uvI9+wmZ94Kv/liv7Pcnbnv83F3uQaFdZlu7lmWQ+ntGm8/ZyeOXL3k5L2RSkDXZbojCo8PiVcm+84XEXRFfYOl+bk/pPDJXzXR4/oZNqiVKeq5CeqfOqcToyEge/5BH7AqqWtnNaX5MJek99snWDrZI3v7Srwy/0zfHHzFJmwzK5cjcsXpIhpwmixK6qwIBFn00SeoVKT1ojGt3cUaDdVUiGFtBEVitqqTanp0RrV+cGuPBfOi+IF8M8XzWPHUInJ4RLv/8Uhbh9qcM0Pd/GZ+0fojh870c/RMlQcHh6mVCrNPd7//j8sPMlkMiiKwuTk5FN+Pzk5+Wf5PZ/+9Kf55Cc/yW233cbq1av/5HPnz59PJpPhwIEDR7A1jh+OuBD62Mc+xoc+9CEsy3o21nPCouEGXNRrUrVFevW69ihRXeHUTpMHxposSKjkGqJzY6oSVUfMsmVJmhuNPckNmheL0htvYbRawnJsVEmZywsbKjeRJeZGa09GabiBRyYcI1evoEoyKzPdc6ozgNGyzTk9EZIhleUtGn1xhYyh8OIFPXPGiX3xVhYmRdL92tYWkVemaHxr57R4XVlhbWunUJWFopyU7afFMPnR3jK9sQS5eoWlaXGHD1C2q9ScCp2RFEOVGfxAXBBLtkfF8dkyZZMJy/TFFU7v+Ndjur9OBBwo/eFwy8FSg5rjkNRl0oawZYhpEhNvvekYr/BP49VLvshgyWWo4pE1ZVpNjd6eBKFYiGRUw/Z8LDfA8QK8AF68KElfXOHNtwwgSxITtSJZM44fBCxMts85n4Nwo3b8gNGqyO+bqpdx/N8clXV/4vSvUnd8OiIqp7TpDJRdBme9enRFZlVrhP6kzu1DO7jq5h3823ldfOCUdUxPW3gBPD7dpGo3MBSJQqHOwaka5/TGaI1ozG+Popkak5bHzhlhS1GxbaYsj86IwhldEUoNlw3t4mK6ZbIuOESyzPYZh2/tKHJOf5L9RYcXzu/mZ/uFKWDakOe+V53R5Ky7dwPLsck3bLzZ79b6jhhLUtqcgWKh6XJGZ4jJmsNjE3WumH8OlhOIaI2JJktSIe5/xfcBuHbFdbxi0f/vD2wxWNnyT5ze8a+c2fFR1mY+wKHSFKe0zeOsrgzZeIhcqcmFfUmWLW4hposuxuKkyuMH8ixJaezK1eacspuVJk7dwWwxCafCLO5LUq7a6BGdwA94+23DBEHAhlVt9PWn2D1Q4G9XpBkou7z/4j4WpsM8f0GC9R0x3n5KG1sm67x+dZqUIZM1FeqOT9XxmbSq/Gx/kd5EiJgm88plSW4ZtNAVlbFagftGGnTFQrzz5AyKLHHJ/BRbphrENIktU02uPqWTS07u4pxVbezK1bBrNif3pTgwdOwCxI8WRygejz/l8ccU3Lqus379eu6888653/m+z5133snGjRv/6Dr//d//nY997GPceuutnHzyyX/2c42MjDAzM0NHR8cRbpHjgyMuhD7zmc/wm9/8hra2NlatWsW6deue8vh/Fd1RhbuGLUpNj98Ol1iX1fCCgCnL48zOEFumbDRZ3EWnDZnuaBpdEcnu4zWPXL0yJ6sv23UmakX6EyKANd+oiRwuM8l/nL2Yly7sZaiSQ0aaiyl40p8nE47NXWRyjQqu7/GTfWW64sLp98lipM005t7rE4+Mz6nERqozFJsWD47l5swWnz/f5NeDRabqgtsR1QyiukHFtjBUjX87YzGj1RJT9TJTVpnHp8dRZ+9Eik2LTZNDVJ0GB0siyXmo4tERCZM1FTRZYkEieYz31omBlS1P7ZzeePDtPDD+Ida1hdkyZVNxAhYlf9e1O1GxtjWOJkv0xhSiuoI1Y7GhM87eaYtpy2Oy7pMJy3zp8Sm+ub3Aly+fzw/2TnLLYIGG6yBL0lzumO27ZMPCZf2Utk4WJEQX1XJsHp188KiteV02RCIkk2v4/HLfDGFVQVcklrSEeXSswvapOmlDR1dktkzZDJRHOX95ltEZi1PbI4xUq/xm0GJZT4IgCNiVa9BiKLx0cZJELMTqjM7FvSayJJMIhViQULGcgHwjYGS8wqaJJv+yMctrVqRIGSLW5vaBEtt2T3MgX+f6i7/JhrYPUXd9euMGlhPwsoWfB+Dszn/jn0/5CgCjVZe7h+u8dEmK6y5cQCKkzgY/+/TGVH66r8KnN00zL66xKBXimzvvJhGSuG5Ljo6IMveaR4reWAu27zFcKXJxf4K+VpPemMIl/XG2TTXQZNgy5fC5K/qJ6TLtEZ1GqYGsypgpg3RXnLP6U9hVm/1DJeqFOqFYiEhrhJN74rQmDaZrDkPDJV5xajcHig3644Kp0RMTTtlndIZpegHr2sJUHJ82U6bpBcyLa6zLRgQHcjampiOiULUDSk2XfEN4tQ0UGyLMtuJybm+cK+aneV5/BFkWDtMgxvYntYXx/IBkd5zRSpP+7vgzOvZOdLzzne/ka1/7Gt/5znfYvXs3b3rTm6jVanMqsmuuueYpHaVPfepTfPCDH+Sb3/wmfX19TExMMDExQbUqxpvVapX3vOc9PPzwwwwODnLnnXfywhe+kIULF3LJJZccl894pDhijtCLXvSiZ2EZJz6GKh4HCg3O7Y3TZircsEd0YqYth7oboMiipd0XT845OVedBrqssr6thbFqEQBVlqk5AcmQQblZJx4KE9VD+IHg8DzZCWqPJGl4wirf9lxMLSQyxGQVWZIZKJdZk2njiVyDZS0mhYa4c58X1zmzs4eq0yBjxCjbdT519gKGK0WWpNqYsIqoisLZXW1zI4yHxqs8vz/FzQNT5BvjnNKmc0pbF67v0xlJMWEVRe6Ya3Pb0AxndkZFaGvDpieWIaob3Dc6RK7uU3UK5Oo+siTGKouTcS7s+cRx2WfHG/+7uEmGTPzAZ3XG4Cf7y5zXE2a06tEbU2gzo3zhiTfx1tVfOk6r/cPwAtg5U2ZjRxt/f/tuJgp19KjOXYMF6gUR1aJIsGtGOFAnQiq6rLJ1ssrHzphHsVkjqoVmQ4F/56ZuKBoHiqI9nzYiWK7NWK3AXSMf4Pzu/+8ZrflDD78BxxdrDykBnTGdZS06y9M6D41LbJ+qsW2yyg/2KLx7Qyuf3jTNtSv7iOqCrzVSbZAOCWLtTQccejMmWyer9MRDjFY9rlrRQrupUnc9bM8lqhmUmk3WtOrcPlTHqTv8/KFhtk6kuHJZiv64yu5Cmaiu8NLTe1H+x+3nk92aP4RrVwhO3dd3vplkSEWWJC7rM3l8usntAyX+6dQ2+uJJ2iMJPrd5mNaIRsqQSegyXTGd6jPI0Tq941/ZX/osp7b3s216L7v3zTDeHeO7j0/yghWt3Lh3hnPmJblnxOc3B/M0Sg3chuhovf2cHnJ1nzGh38dtuKiGiGlZ2hYhX3cZG6tgJAzWLm5h01iFta1p7h4WgcDrsi2M18osSbXR8IQv2U/2D3CwKF4va6pcvytPvu5Qd32qtsfqjFA1jlaaZM0Wqk6DVy9PMl7zMDWJl3Z3zIZUBwxVSqxsNbigJ8NVv9jF9qka1YZLwlT59Usu5JLv3fIXb7cjxTN1h/5L/vbKK69kenqaD33oQ0xMTLB27VpuvfXWOQL10NAQsvy7g/RLX/oStm3zspe97Cmv8yQPSVEUnnjiCb7zne9QLBbp7Ozk4osv5mMf+9hfjbfgERdCH/7w00ss/sEPfsAVV1xBJBI54kWdiLBcn1ZT44Yd0zxvSQvhWafUhakw9w4VOb8vybZph9HqDImQTG9MIt9osjydYLCcY6DssqJFR5WUuVFZsWkxVi3Q9HzCqlBx3TtS5eWLOnEDj7guLpxR3UBGIhmKsHV6mtM7uohqISxX3Pn0xxVydYdVGY2xmkNUK7Ig2c5odQZdVumMpHh4fIYpa4wzOtvZX5ym0PRpN1X2FeosTQuZ/isXx6k6Da7fPc45XSGKzRq3DR1idSaNKitkzTjrWivsyVuYWp11rVn2F8bpjQlPkykr4Gf7K3MXFE2GtBE9bvvseMP1f5fwnWtUyNUbZMKGKHptjy1TDkvTKn4AD44X2dB24t2J7plpAAand/wrF/e/nt41LXxla46RoRJGwuBnm0ZIZCP0Jw26YiG2DBTYOz/KSxcn+cD9g1x34QpUWabYrKErCj4BUc1gqDJDeyQx9z67C2VMVSJ9FBJWxmribr81rDBYdmmP6uyesemNqfz6YIGuWIh9o2Vsz2ew7PG+U7O87jfb+ZvlKX5uOdw6UOW8ngirMgkeizU5qzvKzlyTU9o07hxuMFhq0h1V0GRhBSBc58X54KSszvLnLyRX9/j+zhyf/NVB2nuT/OsZbbx2ZZLHJm1M9cguXk8WRAA3D76LiZpFq6mzt9BkvOYDU5Saws5jWSrFw+MNbC/g4bFnRl9YlHgnH9v0Rk5uC9HxggU4PvS0hDEUmTN7EmyfqvHaVWlajAyPjNcYzQg7j/+8d5gzZx21ZVVGVmXsms3SeUl6Ewa37ZvhH87r5dcHS7xwUZwOU+Wh8SZ116diBzQ8m0QoxPW7hzm/J8KUVaYjonCwKNyi20wFQw1RamqYqsRtAyWeyNlUbY/L5ifYX7BYlUmwNBViypphVUuKhutQ9Gr8/GCRS+dFuH3I4gtbR9nYm2B9m0F/XOWukQY/PrCFzqjOrme05Z4+ZJ4hWfovLKLe+ta38ta3/mHbjnvuuecpPw8ODv7J1wqHw/zmN0dnrH288Kx5ib/xjW/8PULWXzO8QMhy13bEeGikzK6cxWilydpsCEWSmBWVYaoSlhNgOTbJkM5UvUwyZHJOdxuTVoNi0yKkyCJOA5/OaIqUYVBsCoL01cvmocoKUc3gUGmKQ6UpXN/DcoVh3fx4lNsOjzBSzVO26wyWXUaqHuf1hMmEQ3RHQ6xomY83l/xuk29UObMry7J0THRyXB/HC3hsqknakDlYdBmp5rE9F1VSmBcTd58t4TirM2lMNURUC5FvVFFlmULTJ2Mo5JtVZhoWvx0/TEzXqdgBp3WatEc0TDXEmkwbz//RYxyu/N9Ui+XqFdzAI9eosGWqRtoIkTFizDRc/mlDPy+cn+KJaYc9eYcnphvHe7l/ELe8+Lt87mxh+NiXUIlqEhf2JVk0PwUINVXucJHH9+TYdbjIK9d38tFbBtky1aA3YfDrwVGGKjNM1EqzBos+tidy7qKagSxJ6LIYjy1JpdkxU+DmwXc9o2yy9VmdmCYjS5AJyyxNGygy/GbQ4sVL0pzaGeXsJRk2dIR5YKRKwxMjlcm6jyRL2F5AJiwzUq2woV0ozx4aLXGw5BLXFdZkZwOFmz4N16FqN2nOjmD2F1w2TzaYqnu8fJmwxNj/dz/h1Uu+yK4ZB02W+PRZvzPQfPf9r+ek/77yaX+2iVqdc7vFXXah8aSS1aU/GebwaJmHxxtMWzarshFue8l//8Xb8Ek4fsArF3+Bd637Mmd3tfGRM7o5tzssuii2R2dEo+IIx+6wKjM9XOJ5a9rpiulsPpgnFAuh6ArXnNfPZfPjnNEZ4iUrWsmaCi9bkqItrDBV99iXb1BqiqxGGZmhSp1zu8NkzTi6otIRMXjpogTndxtkTcE79AKI6jK279MdVSk1hL9SR0Th04+N8tB4noYnrE9sz+XXAyVawwoTlkMmLNMT0+mJ6cR0eVbhqHPbQI1Ne3LPeLs9h78uPGuFUDDLVfl/BSe1GlRtj4Qh0rh7E8Iy/kDRZVU2wmkdOpmwjKYILoWhasT1MIYi1CIyEjFNRlfEvw2Wp8mGE8KfyG4S1xVkSSLfqFFs1sjVK0S1EO2RJDtnxvnt+BhT9RK6orI6E+NA0ebekSphVcZQJTRZIq6H2VdoYHsNduVHaTOTzIu34gcBe/I5onpo9u4qjCzDmZ1RhiouRdvniVyDXw+OsacwDQjlWHM2LkOozSzSRhRD1dBkQYh2fR9NlmgNh3h00mK8JmIJ1rRqc15K7zuzgxfddO9x3nvHB6qsiO0nq/TGRPDunsI0p7Z3YTlNNk2WaY8oNDzwZu0NTmS8dfWX+Myj0/zygPCfesEKoQQMp8K88oxe2rMRFiQVzlvVRkdE49VLE2xoi7MnL46FdCg6ZxRqzvptFZsWu/LTGIoQGHRHf9dK/0uzyQ4WXWYaHk03oNQMqDgBng+tpsqmMWEQOVRqcO9wjTXZCLm6z6XzUzw0WuWClW2Umi7vuW2YB8ebLE0lMTWJeqlJviEKJMcXpo2aLPHoZIm0EaE1HOGOw1V6YyqtpkpXVOWGbZNEMr/riH/6rK/9nou4F4CuPP27+mtXXEfFCVicNsiaCsPlJpmwykTV5tMv6CdhKJSaIu7naOCjp32Vd9z3egA+9vAhDpUsfrK/zMJ0mEevupJDJZsDhTr3HRYE48AX8v3v3HkIx3JwZ80ol6Y1ZAm+uGWGG3dO4/gBC5M6qixz5+EavYkQy1pMHptscs+oUNrF9TADpRnuHq4gSxLd0TS98RZ6YwksV1hyrMtGePcpWda2mrxpbSuKBI9O2iRCIn2+av+Ol3l2l8Gp7RGWppKkQzKTlkNhNsR1qNIgV/d5/eoW5vcm/uj2ONo4Wqqx5/DM8NxWfJp42cLP0xXX50iXE1WbhekwZ3WFWNYSYsryOae7ld6YwcGSy4Fig7JdnztQ9xfFl3/Ccmi4DmkjOpfYvafgMFoT828/8NEVdTbtvcq+Qo6QIrMgYbIk1YXtubiBR1QXJ8/L+mL0xRXRbQjHeMnChXiBy9rW+ZSaNQAy4RjL060cLheoOS6mGmJJMsatg6LlvDChcbDosDpjsG3a4bK+bgDcwMdymhiKRiYcw/ZcZup1VmcMOiKiyGt6PjXHIWvKnN4ZJt/w6Y/HUWWZ+8cGuX5Hgcf/5ofHYY8df+iKwli1gKFqOH5Aw3XIhA1c38f1fR4ZE2aE+/J1lmcMhivO8V7yn8Q/P/QGqrZHvenxzpMz6LKEoitcsiJLR0ThpUtS+D5zhN6a42L7Lpsm6qIbRMDOmWksp0lUM3B9wbEZrwlH9G3TIpS40GhQtZts7GjhxoNvP6I1vvv+15M1FbqiKqYm0RVVmLYcbM/n53tn2NgVpdDwWdJickqHSa7ukKsL4vH+qRoPjZbYcyCPYwlV3/f2TNF0wXd9Cg2X+0eqDJWd2VGNWPtgOUdnNEWp6TI/oXFqu87fr1rLrje8CjP8p9kH67I6a9uObHxsqhIdEYWf7y9xRpeJJsO69jBTls/ChMbfLE/xwQ1fmXv+X5pl953db+Yjj7yROwdLdFz3In77xATX78jzm60T3Pn4OD2f+AoHSy6btk+yMhtheLBIOBUmP1FFkiV8z8dtuMiKzGc2TfLvtw6gyBLvOb2DjCEoAncO19EVmXVZjRv35HhwtDo7Ug9h+y4xXafNVEiGTGHJMEsX6I+H6Y8rlJpNQopEZyRFbyzD8haRvfiqpUnWZQ26ogp98Qx3jzQwNX3uuMs1fJ7XH2Gg2OAzdw1x22CVjR0t1ByH8/qO3Yj6eIWuPoen4rlC6GniKzvehD3bQq+7PlcsSs3KOAMUSWQzPTiWY09eeKN0RxUMRSiCXN8nookT4tldvfTGWuiOpvn6jkGGq1VimoSpivDRsl1nuFIkV28QUmQ0WcLUdLLhOMOVHFWnwWOTVRw/oD+hMl5roEgSFdvG9lxuPHAAVRaE1LSRYKZe5rHJAcZqBZak2nhkwmawXOKu4SIpQ2brtEOu7pE1Vb67q8hF8+LkG1UemxxBl5U5gm++USXfqM75Ee3JW0xaVfJNn4imkQ6JEduZnYIYC9BiaP9niyCAbdNV3MBjQaKHFkMja8bJhuNzJ6/nLYiiyXDZ/CiOF2C5J24X9Uvb38Q3Nk+ypMVk/M0/59VLvsj5PSZffuECdEWoeRwfJus+PTGFtCHz3ttHuHu4yrIWYew5UJphUTKFrqhsnhpj63QJVVZYktJZnIyhydLcMa/KMvlGlUw4SjDx5ae9zuUtGn4g7C5KzYAdMzbzE8Id+m9WtbImozNcsemNa1hOwCuXpDA1iY6IzFn9KbpiIZLtUV5zRg+vWNxDiyFjzXJdduUs2iM6paaLoUosTassSKh84P5x1mY+wMJUmMenm9x8yOIHe5/gwp/8nGLO4o13XftH1xtWFRKhIzsNj9eEncHCdJjdeYdPnyWCcoWqT+KiefOe8vzvbD+yUU/kX85n/jdeyk0HazS9gFcvT7OqPUoQBOzePY3bdHGbLoEf8N93HSLwA7YOi45QvVCfS5KXZAlZlXnZyiyOF3DBSR10xXR+sLvAvaMNCk2X0UqTzRMV/AAypsZQWXRmSs0mO3I1kiGTrCnyCYvNGg3PoTeWod1MsCSVpSuaYFm6Hctt8sDYMAuTQmYf1UIsTBoYisRUvcyVi7NYjo0f+GTCMTZ2RDlYckmEFJ5/UgcP7Jjil4em2ZV3OK39/y6v8f8qniuEnga+u+ctFBoBJ7eF0BVhE795wqIvofHgWB1ZkjA1iZWZCPmmj6nKcxe70WpptpgI6IklGankGanm+f7e/bPmhQFDFQ9Tlee8fBKhEE0vYLDs0BNL0hlJMVUvs79YmM0bk7GcgM6IOJlrssgO2p2f4OWLlmI5giSpSCpT9TKd0SRLU538enCUS/vibJ125pKpFUmswfHhVUvjPDZZxfV9lqdbUWUFQw2Ta1Tmipt8s0rVbuIHgiPheFBsCnLj8hYVRUKcsFznL5bu/r+CA0UX2/PYWzhMJhxjd34Gd5Yj4wYeigQHSy4LEiZL0xrjNY/PbPn7473sP4iuaJiJt970FN7Jzw5U8IOA1a0atx4qEdUk7hosUbHFMd2sNOmIKDw6bpEJxwgpMo9N5vGDgI0dvQBMWg32F0VI67psClWWZ31gHAxVE/llyac/qvi75deRCEnEdImDxQbtEZUHR6ssTgtzwabnMy+u86sDRZamhVhg+1QdTZZYnNZ56eIkb1uf5Z6hMg9PjHNmV5Rc3aerLUrd9UiEhIWAM1v47S44vHaV4AKlDJVvb53i9C6DlCHNJd3/4P7Df3S9L1v4ef79jK/+0X//Q/jnU77C3y2/jusv/ibfuuh3juT5ps/ydIrVLU810zv8+huf9mubHzgPSZaYOlzkt4cK/GRPnpAKQ+UG6a44sfYYkiShaAqvv3A+WlhEZbgNF83UeN0F82ltNdGjOq09Cf7lkj5+fTDPuo4o6bBKbzxEq6kzUGxwx+E6ddfnymUtbJoQvKaL+tMoEjS8gL64iqGI74WharSbyTmemSzJJEORuePlQLHA6kwLru/j+MLWY+u0RcUR5PxsOM7SdCe6onKoNEWu3uD0jiQX90XZO2ORyEa4Ycc0X753iGtvOFZUaZAk+Rk/nsMzx3Nb8WlgT8FhqOLyyHiDtKFyRnecREjFUCTaTKFcKDUDds3UUCQIqbArb8/9/YRVoup47MoXmK6LcdXKlhC9MYXxmo/jB+QanuBPyCrjtTodEYOYLjFRK3GgOEnDFZyEoUqDlZnIXLp9V1QlZRhoMrSZUb65axchVccLhJTVUDQ6Iyks1+b8nlaimsG1K/vZ2NFCR0SmK6qQNRUWp9RZh9ombuAxViugSCpe4NJuJvGDgLQRpd1MsjPvULJFPtDStEnVCdies2Y9lCJYjo3tecdlX51I+NZF35g12ytzoFhgSSpNvlHlvtFJvvrENCNVj96Y6K5tmbLpix+xiPOY4Q+lj3/v0m+xt2AT02QODRT41cEyvQmDxydrfPneId503jz2Fhx2TNU4VCowWHY4u6t9dlw2TG9MZcryKdkBWbMTU9VnDRgFmX+wbGE5Nr85/MARrfXWgRpRTebivii7cnXO6Y0RUiXuGqoyUvW4Z0hI2UerHt/ZMUM6rPLxh6dYkdY4uS2N48OSljAJXSJXb3Jym05vIkTa0Mg3RJZYIiSjSNAZUcQYyr+T83sivGJFKw+ONmh6ItrDCwLaep59zsnNg+/irau/9EetB/7x3tf/2df40MNvINIaIRQL4bs+PS1hXrc6w/0jdbpiId5zahtL2yK87/L5/MdLF7I0rfKui/qIZqNzRoo/3zfDmT0J3nxaJ3+/tpV5cZ2FqTCbBoskdZnemMrSFoOVGZO9MxYvXhTn1PYIG9p1lqZ15sVFLMh/7yphuT6Wa9MdVfjZgSlsz6Uv3o6maCIrzRdj/juHD3NO98K5bvqGtjh98VbO7EyRCElYbpObDg2Qb1T50b5JCk0XRYJJq8qevMMnzppPb8KgXqgjqzKKfuyysSTkZ/Sf9Nwl/KjgWduK8+bNQ9O0P//EvwKsSAt/jr6EhhfAcMUmqiuUmj6LUiqGKvK8tk032ZVr4PuwttWk6jRIGWImnTXDJHR5brR0z0idmC7TdAO6ogr98fCcEVhrOMQtgxW8AApNj9GagyorZMIG3dEQtucRUsTIzNR05sezLEhmaHgOL+jvBKDQqDJcncD2XQbL01SdBrvzM+iKypRVIq6HObWtjaGKx3hNuOSGFIm0oXL3cAVTDVFoCmfkhudwqDRFw3N4IjeOIsHSlMrBosuevMXydJIVLWFKdsBItUomHKMzmjxeu+uEwt8uu242kDSKHwRkjBimJrE0bbAgobI0nWFp2kSWJBIhaY779deCrqhCxfF55Rm9bNsxyQsXRJi2HBRd4UDBJqxKvGuD8Cc5t7uTx6cnKTZraLK4wfjghq/w72d8lWz4GnINcbzJksyilEk6JFNoCv7Qd3Y//bT695zSx2DZ5fEpG9sLqNgBhiKsLnbN2JzdE2fP4SKTls/CVJiBYoNzehNM1gVnqSuqsHm8SsUJyJoiW+9AoU4ipNI966idqwuhwEdPE92cO0bvYPOURVdU4ZI+k5sPVrj+kVFBgp/11zlSPDj+YW48+HbuGf3nP/qcl//qNXxn95t5ft9n/uhzfnLgbYK4/S/nc81tf/dHn/fR077KlSd1MPOeX7NwaSsX9Sf4p5O/zI3Pew1ndEX4/q4iZ/dEGa8JR3E/EKqyf9zQzvPWd9I9P8XfrBTcsTsPi3HXWM1hWUuYb79wKUvTGttzTQ4Vm2ybqvH2kzNoMuTqTSYt8Zr9cYXlaY0L5olRKcCKlg5Shoztu3iBiyKpyEjkG1UansN53b0MV3KoklCSZc04hqpRdZo8Me2wbbrGGZ0ZGq7DpX1xUiGVPQXB7+qIKHx39ySndkRo7YgRioV469ndf9H+eg5/vTjiQmh4eJiRkd+F0m3atIm3v/3tfPWrT23v7tix4/eC4P5ace9ok+GKOJl5QTDnk+P4zKY/uwyUXTJhlaiuIMswUhWBo5Yjcrt2zdTINTy2TE8xUm2QCMnsyTt0RYX78nVbp7lvtIHlNtlXEAnxpabw1Sg1BdFWkG2jZMJRMdJyROE0Us2zr5Cj4TokQxE83yMTTgo+CqJz5PoefgC6rJIJxwDRPl6cVKm7QnJquQFZU2FdVkdXRFtanj1Enuxs5Oo+D49ZbJ0Wa8+EBZdj50ydbFhh0vKelUTxv2aYqsijetJdORWSuby/FccPGKsWOFiy6I2JY+pE5gn9Mfx/d4+webzCKWvambR80mGNeNKg7nr86kCeH+wu0B2NYblNTm3v4lBJhHSe1v7UTkl3VFg1+IGPMXvX3xrWWJhM0RV9el2VO4bfz5kdH6XUFNv69K4Iv9w/w3DFY9NYhXuHioxVHbo7YmTCMnXX51DO4tZDBUYrHluny/hBIOJDnFmCuxEjEVK5bH6U/YUmQ6Umk5ZH/H90DlRZZlWLTl9c4+GJJtOWQ2urSbPpUZ0SDrwrvvPyI9qup3f8K1kzzj0jf5zjc8WCyO8dM6+9/XV8buvvRqwvW/h5Jt/2C047qYOT2/60wd0XzhWqtm9ftpqGGzBufROk8/jomudx2fw4CxIqp7Rp3HywQkfEoGIHJEISZ3cbvHVdK34gjvdL+2MMzBajpiYKmu5olFctTfOyxWJ73nyoRptp0B2N8vhknY6IKGQeGhdqry1TNqaqYyga53ZnUGUFx3PQZB1VVnB9n6hmUHWEMOWTm0YYr3lsnhrjvtEhemMttJoKfXFVOOuXq3RGUsw0XNZkdOYndDZ2pLE9n5sPFihXbSK6Qt07dt/B50ZjJwaOuBf/6le/mje84Q1cffXVTExMcNFFF7FixQq+973vMTExwYc+9KFnY53HFeuyGqVmwL6iQ9pQOFBoMi+uY6gSo1VB+DujK8JgWRRL6ZBMSJHZX7RRJIlEyKNk+7SZCt/fVeRFixIMlGx0RUQAdEREqvW1q1IMV2125W1ObgvRcAMcX3wpH5mwGas6TFszvHBRnBXpxFxQYlwPMz+RpWzX0ZUQXuDy2OR+FibbREGjapiqUEykjCjTVknI+0NhHL+CMpuHNlgS64/pOqPVEg3PZnGyg6rdwAsg37BxZkMfY7oIhizZPpbrElIkhioumvyckuF/o2QHtJki7+2BsUmaXkCuISToju+yJBljx0yFVEh9SjL4XwPShriwRnWFA4U6o9UIdcfjPRuyfOTuUc6Yn2JZi3BO94OAkWoeU5XQZCFp/p8QocQNsmacKas8p7jM1UWe3c2D7/qTnQ+Af3v4MBf2wAW9YW4ZsIhpGi9Y1MKKFpXRikbd9Ti9y+BAoY4fwFXL4gyVGxSrDjftL/CKZWka5YArl2dIG8Jf5saDY7z/1A5uHijTGtHmRmNPZgcCnNv1cR4Y/xA37B3n+f1xSg0PLwhYv8LgoTGLjz/6RiZzR25weGbHRznzj8Q1ffKxvyekwjvWfpk3330ttx4qkAiptJoar1vZ9XvPv/Ol3wPg/Q++ga89NEq9UOe8U7p4w+pWXpBYy7/sfYCPbxQ3tD/eP8RXbzvI/kIDXbmXeXGdm/bn+ZsVLZzWnuCieT43HijRF1e5daDGmqxBJixMJhO6QkiF3pjCihYTXVaJh4TKdMIqElJkpi2HJS0mA+U667MRSk2XjCHTG29hy9Q45/ckcQOPYlNssyeNWTVFo9QsMlYrsDjVw5SVxw98Ji2RKzZa9YjpMrcNVkmHJri4V2Q6ur7P2tZWbhkcIaqLbvpD41WiWpPeuEYilGSi5vDihVFqleYR76e/FEL59ZcXM8+da48OjngP7Nixgw0bNgDwox/9iJUrV/Lggw/yve99j29/+9tHe30nBLZMibyw6drv5M0Vx2eo7AgTsZqDF8Dfr+ohZagkQuLEf1lfD5brs2nCZstEndsGq/QmQliucBZek9GZtjy+9UQe2/eZabjcNlBjSUqj4Qb8dtRiyvJpegFndYWpux5dsRAJXebRySKWa2P7Lm7gC1J0pANdNmi6Nhval2CqIdJGhKRu4vo+yZCJIqlz+WXJ/z977x0nx13f/z+nz872vb29qpNO3ZIly5a7DTa4UU0LX4pDCIGYXkJLCCWk8KV9CYQAtumBH46BQMAYMNgGG4N7kS1ZvZ6u3972Mn1+f3xWaxRs8MmyXOLX47EP6eZmZz87czv7+bzfr6JbnD7Qw2BCYaYVMpJSGUmp/HaiwUA8yY0Ha9Tcdud5kDGEJPnU/hj5mIIdRGiyRN5UWJLSMFVBGr91av7xulRPSGwvebR8t0uqt4OIFZk4XgAtL8KPAkxVYm/1iS2ffyj8eE8NSZbIxTRGMzF+N17jL9fl+PJ9JV69oY9T+2Oc2Guyr1bHDXyWpgrYQUTWUP4gAPTcoY/xgiWfYbpZZbrldUmxlmoQEtFw//QX1I0vv5LP3/dm7pvzGExoaIqEqcCmOY/1vTGWZ2P8fG+D5dkYLS/iq5srlGsuPUmdZy/JsL/qoUhwcsFgphWgSgonFwxumWqiSBI/uGOCcxbFGW/47K8dPrEp2Q2esySFLqtcvCzOYEInbchc86Jv883N85ywJMPg5S85Kuf929vfykwrYLYlfHLcMKI03eB163Isz5rsqRS5et+7D3vO5+97M9ceeD/PH+0nlTGRVZlbd84jSxJfm7qNPkvhs5vexAdvvZQTew3efNEydpfbPGM4xlTT41PnDHHPTJttpToJXUJXJH430eSCJXGeOdTD0rTFmQMZgihie8mn4UVUHXHN/DCg4dmYipg8ntSfoMeUWZTQmW3V+LtTh9lT9Sm265gKNDxbtJJjScIowlQ0Zls17p7Zy++mDpAx4rS8JmEUUnPbDCUsXrYizbK0St0NWZE1uXNGRAKtyw+yed7lyu2T/HxvDafTqXz+aD/PGBJKwvNHEpzaH6PlhySOIaVD6oZsHPnjaTx6LLgi5HleNz/k+uuv5+KLLwZg9erVTE1NHd3RPUFgKkKFcsGSONftF6uf/Z1W2J6KzSeeuZgD9Qa3Ts1w4eIkxbbNVDPgyu07OHsoTlyLGErp6LLEoarrsozJC5cux1D3cMFIL3Uv5LYpl5P6Y6QNmTW5LKtzFv90ywRv2tDDzRNt1uRjrM2pFNshJ/amyBgWqqwQUw0sVaftN/BCrxO90cCPQnRZxVB1Sk6DbaVpzhhYyoCVwws93NCnZDexVIlzhgxKTsj2kt/hb4gstV8cGOdly5czlEgz1ayxOGmyrdRmXzVgNK1SskOSWsiyTIzVWYuW75LUjt2K6smALz3rq9w48cHOTV7irIE0FafFmp44qqSws1IniCL+7uRHLhN/omCi7rBuOMWND8yix3WSlsZsK+DP12TZWfHZXHTYXfXY0Ktz/ViZU/vbQlzgPjyZ/vcnSL8c+1tmWg1kSaJoB1y2+c28ed0fz2Mbq/ksy4i/TbmjihyrOrx4RYKEbnHvTJu0qVBzQ3ZONzBjKusKcWGYaigULBlT1XACh5rbxlQ11uYCbplykVWZ6w7U+et1WYr24e/h4tF/5Xu73s6WeYdlaZWkLtFwxQd+11/9V3e/xV956YKUXA+FpK5zSh+8etUXufCHf86GPotcfwJZgtP6dT5++yyfeOYiPnLbpZTsiJcu78VSJVRZwQ183npiL9apBQBsX3gpVd2Qc4YykIUrdxQ5saDzmrU9DFgqpw2Y5GNJnrVItOOXp5OU7SovGC2wudjCDwNkZDbN1cjHZOG2H0XYQcR0q8ktUyVeubLAXTNlgijieUsyNDybhGaSMSx+MzHN2p4YlqrzgqUF3MDHDrxObqPTyS5MsjjVy0RjvltJOWRaetWOKVZmVWQZzurLs61UwgshrUu4gc+2eYfj80bHCFfqhlP/y237ecZwnCWpCFkSi17FefItSJ7Go8OCp5Nr167l8ssv5+abb+a6667jOc95DgCTk5P09PQc9QE+3njdda/H6MZmRCiyxK/HGjTcAEOV6E/obJmvU3cj9lRFTIaQRGvoiszWkstp/TorMxpresRKY7zh88KleSw1xZpclv/e3eRHuxosTQsl2upshpyZYH+tzWgmxvaSx2n9OmEYsbPiM5QQTs+6rOIGPm3fQZV1DKWTTdaRuguZdogiqRRiaU4sLEKSZJzAoeI0kZEYjGdwgojtZY/fTrQxFRhOKGQN4ZJ9Qm8CO/CwVJ2BuDAaG02pjKQUiu2AnClzSn+equMw02rgBj6vPe5/Z6TGw+Fjd76RitPi2gNN1uf7SRkxcmacmiNWvZpMd5X6ZMIrf/6X3L2nxPF5i9NW5rGrNusKcT577V72VH3GqiI/qjemUIip/Hxvhf01T9g/PEIulB+G9FkJVFlhOGF2CbR/ClUnRJGg4UY0vJCq4/P/ba3ywx1l0qZKUpMZq9kcN5DggqVZNs82CSLhVnzfnODjzbRElTahmbx2+Qu5dbyGrMqs77UoOwG3Tv6hE/j/WfHvWKrMa4/7Eh8/88vUHyL4tN06si/aaw+8v/v/72wrc9OEWHBctCRByQ5Yno2xveTz/Z11PnpmP5vmWtwz0+b8EdFWWpJKE0ZiPKNpkx5TJaHJHYd4OK0/TsawuHu2Sn9cKPpmWyGb50U+2q8OzpGPyQwlRDbh3qpPzoyzLK1Sc9vsrTU4d7ifrKFiBxG7Kz5DCZE/ljMVKk6T0wd66LMUBhNZLE0nH0tStOusz6dYkspTsITbfkqPkdJjbC9XsFSjwwkK2FYap+W5tHwHXTHIGklSeow3HD/K6myKlhcx26pxWv8Qg3GN0XSOb20b55WrUowkVd60fojje3IMxrOM10ts7LdYklL59tYq1x9oiBilXp1jhaedpZ8YWPBZ/OQnP8kVV1zBueeey6te9SpOOOEEAK6++upuy+xI8YlPfAJJknjXu97V3WbbNm9961vp6ekhkUjwspe97JhmmPWYQiZ783gdJ4h43mick/rixFSZHfM2EzWXshNSdkJO6NWYbfsU2yGWptMf13jD8YNoskS1Y/W+Lq+xMiPKvNce2ETLd1ic0hhK6azIaJzSl+a3kyWu3LGfH++ucf7iGKf2i2yjgbjCyozK8kwfqizT8t2uaaMbOEw2p7srHVmSsFSdjJEiih68GUdRiKZo3bT7HeVZlqSEN9EpfSYndG4CDS9kdVbF9j12lqe44eA0GcPiQN3GVDXMTixAsR0wXi/TE4sx1QxoeE/L5v8nTEXi1wcbPGPQEPljnew4L4y4bbrKZffOYx9DgubRwlXP/SZnr8qzpkflzr0lJFni5l3znHPiANfuLXeclnW+cc8Mt07Z/MOZ/ZxUyNLwQhYlHtmXzfOWfBqg49Iu8UjmQf/vGV/BDoQa8+rdVU7uM3j1mgwn98f56FmD6IrEVNNjX8Wm4QbcMVHn+cuzjKZ1vr+9zJKUyl2zDposkdZlam6bz2z+MRMzDQI34PvbiyxOJvjOc77xkK//+5W9j53xZV7w49cc9vv+bIwLf/jnj+j9H8LXt77lsAiWi5fFGU2pXLHlzdxXdDlnyOSS48RCJaYq/M3143xrSxE3iHj9D3Z0KiwuF9CHLElUHJddFVHdeeXKL3Bf0aXYdrhjZoY+S+mavBqqxHOX9FN1IixV5jvbauzptHAvWhyj5rbJxxIsz/ShSPCpu/YRRhG7yi5nDRrsrbYoWDHOX5QloZlYqsFIMoksSaiSQsVpkTfFz7YvBAXDiRwZI46pCKPWlB7rkqaXpgrkY0mu3jODGziUnTqqJHzb9tVqWJqErqiMN0osTYuK1xuOX4KuqCxNZwBhKbK7MsNgIkPVCfnCvfO8bGWSlbkY/XGl+/6OBZ5ujT0xsOCzeO6551IsFikWi3z961/vbr/00ku5/PIjL+3feeedXHHFFaxfv/6w7X/zN3/DT37yE77//e9z0003MTk5yUtf+tIjfp2F4v894yvIEpw+mGSmFbKv5rOl2GLTTIO2H/Kek4cZSSpYqkRaVxirB5y3KAbAi5f1oEoitb3hRixJWSxPJzm+J0nJCcnHjC7PQJEkfj1uc9NEBUuVOHMgzhvX56g6ETvK4ktTliFtGGwrTVOwUliq3rmhyGiyRo+Zwg193DDAj0JiqknDbaArJi3f6ZabD9TmCImouW1uOGgz0QiQJShYQgUTRKDJEj2xGCPJPE4QYigSD8xPMWCp3DbdIoiEdNpSZewgwlINppoBf7Xm6WrQ/8TGvh6CSPg8JXQDN/CZaTl4ofCTevnqLIrEkzKc9scXfwuAbDZGvCfGaUuz3Hmgiu2F7JltcuUDRZ67Kk/dC/ndpM2/3HYAS5UWZLY53qjT9LyOouyRPedjZ3yZ/TWfk/oTbC95XLm1wv1zLX51sMnaTmX22Usy/NNZI1w4mmbzbJvdZZcLR9NYmkRfTGZlVjAH1udH+cr9RV53+hBLh1PkTI1/v3f6j77+j/b+Tff/pbbPS695bffn+//ie1QXUAK8aqdICVflB1Vqe6o+Zw8VOFRwmm0F7KkKfxw3CHnnqf00mx5tP+Qbf7aakt2kbNs887a7qLltNhc9zhrMU3VDLtv8ZhRJ4hf7W3zspgne88Pd/NvN41x2zyzL0qKSA/DbiSa6IrGxkKblu4RRhCop7KvV+OqW/Vy7r8n6XpMlKWEwmTN11uQyLE0VOC43yorMID1misF4FlPRWJEZZCTZQ8lusiw90CXP24HXbYvVvZAt89PYgUfDs5luVSnZDV66fBA78AijqLOvUKCZiphQ3TrVQJZkdpZL7CxP88B8i/uKJVRZ4cd7JxlvBPxg1wzr8hoXjaaoOhHPHBJ+bE/CNcnTeJQ4oulkFEXcfffdXHHFFdTrwvtD13UsyzqiQTQaDS655BK+8pWvkM1mu9ur1Spf+9rX+Nd//Vee/exns3HjRr7xjW9wyy23cNtttx3Ray0UV2x5M/urHpoMO0ttynZIEEbd4NVvbZvhuFwOWYKxus9AXGYwkUWWJKaaIn3c9j1esrzAF++dJaGbmKpGUpO4c6ZJzpDZXrK7ZfnVnZvvYDwrHJ574liqWAafkM+hyjKn9C0hZya6fXJTjSFJMoYiFBpJTVwHLxQrm7JT7vCGRLvMUnVs3+PX42XesWG0e3xDkUkbBsMJQYQ2FR039NlT9bsRChlDZBttnfdo+REJXSKlq9wxXcJ5+g7ykDh36GP0WTJz7SYVp8U9s1WK7ZCdFZ+9VY91eYtVWZ0bDm55vId6RHjj8ZfxklU5RjMxcjGNl6/v47heC9/xOaEvwS/2lOizFAxV4jmjKXRlYYZ1lip3JO3+guwF/un0L7M6p7J5rsWZQwlOHYizs9Qmqcm4QcTuUptr9pVZllHpjWts7DOZbQfsr4ok+rQusano8e+bNvNPZw1w5mCct5zYw49efDY7Si22zH/iYV97MP7gfeyWV/4nm2ebh/3+9ldd9YjfxytXfoG/WvMlXrz0s91txbaIvQG4YCTGz/bV+dHOCmUnIKbJXHbvLFEUsafU5vbpBv+9u4kmS5zX4TCaqkTNbVNshwQR7Ku0uXlfmVRCJ9kX57PPX8I7Tu7jtimX/2/bPL8ea3LGoMVtu0tsmquhyjKDiQx24FF1ImRJYlWPyfkjfYw3Srx8ZYam55HQDCwtjh20KDt1QsLuBEaRVXTFxNJ0dlUmOS63hJrb7i7aLE1nV1lM7vww5N65Ge6ZLXPljmKX6+SGgmBdbDfIGBYJTUaVZc5b1MPm4gQjySQfvWWarfMua3NpWp6DpUrcOW0jS1B1Ig7WA47vSXZNcE9dYPbbo8HTrbEnBhZ8Fg8cOMC6det40YtexFvf+lbm5kRa+Sc/+Une+973HtEg3vrWt/L85z+f888//7Dtd999N57nHbZ99erVjIyMcOuttz7ksRzHoVarHfZ4NDgu10NMk8ma4lRtLbbIxTQSusLm2SavXNnDNXtnUSQJJ4joiynUnDaWamAoMj/YNdNpQZV47do0NafNp+88yO3TLifkDbww4tWrU/RZCn2WzExLTH62zE+jyjJuELAqJxKsVVmmEEvjhwFqhyjY8GyiKMRULJygxfd27eUHu7czVi/ihj4hEWk9TUYXpn2mIpLhd1UqXLS4l/21OcHxcUPafkDGsBhvBLS8CEvV2VedZ2VGxVIl+uMxYdBY9ViSUmm4EUlN4qzBDSxNa3zpWV99VOf60eLrW9/Cf+1+x2EeKk8EfOS2SzmtP0PLD7F9oUDcXnLZV3Fp+SJy44r7S9w14/7pgz1BocnCjflbF36dLXNNJuoOpy7v4Y7d86RMhd+NizBNTYbpBXJkWr7I6rt5skp6gYaTb1t/Gc8aSTCUVEjrMv0Jnd9M2Nw6VsUNIuZaPpuLHj2mwuaiQ0KTqbghd0wKgnbLi1iZVXnfjRMcqNuEEVyzbxu2F/Kr8YePzhirH+7982ercwsa95/CF879CnuqLbaXfLaWPEZSJu86Oc//PfN0+iyNjQNJXrKuj+Zck3xMZjAh2uFrcjqD8QTrejS2zrcotkP+9c5Zju+Ns34wyYa+BIEf8YV759nQm6I/rjCa1jlnUZxP/3I/xy1KY6jQcB2mm1VyZpwzBwv8xXGLOKlX55tbJ7BUwVE0FGGCWHFqaLLOktQoveYi3MAnjELcwMYLXRKaiSortP1mlxt0yHMrbcgcl+snayY4Lpfj7ME+3nXiUraVpml4Ng3X7kSxuJTsJvtrwm361+PzLEsLk9m/ObmXk/sN7MBjc7HKSFLh5SuTjKZVErrEucMGW+brnDmQEWaaHd+nY4GnfYSeGFjwWXznO9/JySefTLlcJhaLdbe/5CUv4YYbbljwAK666iruuecePv7xj//B76anp9F1nUwmc9j2vr4+pqcfujT98Y9/nHQ63X08WlPHu2eL9MZEFEYQRZzcH2esapPQFVb1WFy1c555W9SnR5IKyzN9zLWbnYT5OOcMx1FlhUWJBPcVXa4bq/D8ZQkG4oow/IrFsTSdE3tN7p51ObVftNOWpgWZsO66qJJCn5UgpVtkDAtdUTEUA0mS6Y31YCiiAqTKOn+55nhesnwlKzMDJLQ4qiTT9puoskh7jqkmuqJyQr7AtlKJkWSe26aKVB0R+nn1nhmSukQ+JlO061RdwXdKGzI3jNXIGBZnD5nMtoTjb9Y0uXNmCwXr2CU2PxxGkmmank/Le2JVpp63ZJCK02JFJtuRBMu8bEWavrjGQFyh5gaMZkxW5568TuwfO+PLTDfERO7Gl1/J+Uuy5GIqo4NJUX0pt/nRzjL7agEjydifONrh0GSh8ska8iM2Vvx9DCUUJuoBmgzPHDIZq9qs60+wptfiGcMxNs+JFozeCXw9ZJPx3R01FAn+9c45Th1MMRTX6I/H+M7WMk7d4Yc7qw/7mr+bbB8Wtnqo6vqncP3BD/Cr8b//o/tsmf8Evxr/e+6adqi5AbvL4ryHEdxwcBOyBC9aFmckqfCaZyzmik1Fvr+tyJXba9w169DwbPZUfX411sDSJF61poffjdc4Z1Gc0wdM3nt6PzFV5kO/PcA37pmh6oTcOtlCkiXOHk6gyRIf/O0Y1x9s8/P9s2wuzvG6azczVve5eGmaK+6f4VtbS/hhyHSzym8nDwIQhD6Tzb3oikqpE6Da9h32VmfRFbGoW5RYxHCi0JkQWTx3yXB3UiRLEg3PZrZVozcWJ6GZDCdyTDerFKwYO8s2Zw320PIdRlMqKT3GbMvGC2E0FWN9filnDfYjSxLLM32szCQxFYnemIgsume2yqJEglXZY0eWfhpPDCx4InTzzTfzoQ99CF0//I9lyZIlTExMLOhYBw8e5J3vfCff+c53ME1zoUN5SHzgAx+gWq12HwcPHnxUx9tV9vGCiKQucVyPRdaUiWkKbS/EDUISmszGPp20IZE1TVRZJq6pzLWbhFHUIf5Z2IHHmpzGOcMJdpV9TuvPMN4okTEs/v3eWXJmggtHLGzfw9IEMbDfymAoMiPJHsIoouU52IGHrpioso6pWJSdCkHk0/Rr3Xwx4bwqrs+h0mnLFwoTL/QYTuRI6TF6TJVtpWlWZmNcuDjJaf1DFCyFqWZAQlMYq7dRJEEuPLlvmGcMxfnezhnW9vRgaRL7qi5j9TZ5M8mBWvlRneeF4Hu73v6Q2w/dMAfixy4r6JHg9P6PosoK9xVL/GRvk7MGhbrSDkJ6TBUvjBiMK5y/aMnjO9BHiWte9O3u/5teyB2TdaabLm0noD7TRFdkkZCuLeyz3mNatH0RUnv7dGnB4+qNGZxU0EnoEhlDZaLuMpI22DrXwlJFiPKO+TZVx+fXB6qkTZWGG3De4jhbii3+cl0Pr1mTwwlC7ppp8LKVGV5y0gBjNedhk+U/+8yvEEQRX98qokE+fOoVj2isbuDT8OzDFGL/ExW3RUIzWZHVSekKfXER+zHTCthe9rlqyxx3znhct7/OwZrDRaNZ/uWZA9y2u8Sv9lf5r101vr+9zNZik82zbU7sNWm4AXfP2BQshZl2yGgmhhtEFFIG375pPztKLXzH50s37OfKrRVety5Hn6V0A3YTusKd0zZ5M8m5IxZBGHHLlDCHXZ/vodguU/NKFG3hJ5U3k933mzMTJDSTlB6j6hZpeE0ano0uK+RjWYYSfTi+WBBamsHq3AgjyR5MVWN3dQYnCMmbSdb0WEw0quytuixNZ2j5LvmYQc6Q2Vdrs782xWy7xqpsH7qsdgw+A1JGjAfmPfIxmb21Rldwcizw6JLG5K7z/9N4dFjwWQzDkOAhAjXHx8dJJpMLOtbdd9/N7OwsJ510EqqqoqoqN910E5///OdRVZW+vj5c16VSqRz2vJmZGfr7+x/ymIZhkEqlDns8GixJqdQ9oUDZUxal8RMKcZZlTXbMt0noMlUnpOpEFNs2Y/V5pltehxhrYgcuqixzf7FNbyze8eJI4IZ+J2W7zD+euYya2yalx0joJrqs4kchfhhwav9KJpsVdEUloZt88s6deKFLhCgtxzUTN7TRZB1dNpElmTAK8UMXPxQrRT8MUGWh0Agj0aM/REwcqwdsLraoOg5hFLE6Z5GPKZQdn/X5Hvosg4xhdfhIEgNxmWK7wZKUykBcQ5HgvuIse6qPvf77mv3vAfgDI75DeM7iT/Ha476Epcp84JZLH/PxLATDiRzL0sL07XeT88y0HNwgouYGPDAvHLmv2Lz78R7mUcM3LvgaQ0mdnKmxqtci8AJ6LY2GFz5sMOjD4XlLPs14I2AoofK1+x8+buKPYaoZsHXeR5Zk3CDkpgNVei2NnRUfNwjZMVUnCCOGkgZjVbsrGNBlmeGEwl0zDcY6MTtTzYDBuMK63jhj1Yf3zPrqeV9bsHhAKDI1rt778BO+hmtTbNdJahLLMiIw+eR+g//aUWGi4fP8VT1cdfckrzs+wytWJdlZsvnSvfO88xnDDCUNphsuvXGNyniN390/zZZ5h9J8ixMLJlPNgOGEwvs2ruTlq7O8Y2MeSZI4uLuErMi89PRFvGZtBoDnLxng/6zs4zWrl/GB0wYYzQg5/AtGV/L6dXlWZlWu2V3hO9un+eJ9+7lm3050WSVnJtAVg4Zr02f1sjwzREIzkSSZ6VYFN/BRZYWtpQl2VybYV5ug5rZRZYWW5zBWn0aVlW6g9Am9w7ihsDnwwogNvYI+cN3YBC3PJWNYjNUCdpRL5MwEFafJr8YPcONElReMDtNwbVbnVHKmThhBRl9YxfLR4OnW2BMDCz6LF154IZ/73Oe6P0uSRKPR4B/+4R943vOet6BjnXfeeWzevJlNmzZ1HyeffDKXXHJJ9/+aph3WctuxYwdjY2OcccYZCx36EcEJIkxF4tZJm7SpcM2eKqYCkw2Xs4ZTOL5odciSCCBUZZmkJnX5OLqsktBMXjA6jK6o+GHQbXfdOtVgJJlnvC5uemEkPDCmW9WuLN7226iy3LVSf9/JK2h6Ng23wVU7t7GtNM5Us0TQCSRsuEIt4YcBUkdWqisqMdWg30p3bya6otIfT/OsRX20/IhdFY/JZpnZliARDsSFNPb+YhtT1ZlslNlXazOaUrlzRiTNL8uo3DHtsiyd6oZPPla4et+7mW62u5OhP4ayE3DW4MIm5Y81lqaXkjMTPDDf5oyBHMW2qCYKd24wFInnjx5dHsnjja2Tdcan6gwlDf7xxSuIqTInHyER9YOnXIEqy+x9/Q8W/NyXLvscU82AJWmFWyabKLLE8qwgdt8x2WCs2CJmabx0RYKJusOOuRZnDaf4t7vmaPsB39hS4Uc7K6zKGpzen+64qyfRFZkdpYXHZvwx6IrKhSOf5Nzhh/8yzpkJDjYazLRDVmWSrM/nhNTfULjm3il+tGmaj5w/giZL7Kv5DKV0Xr8+xwPzDsf3mrx+XZYTCxapwRSBF7C36vHsNQU2FlKUnZCf721w08R+BuIKY3WfZctyWD0WS0ezbOzT+e72KsvTwtspbyY7Hk85nrM4z5b5cfZUpsmZCZakYnzhvON40dIsFy2OMdUMGU4MdnmN/fEevrdzE25gU7IbXZuPhmejKyoHai79VhpdFjFBuyszmKpGy3MJI0EWT+kxQaL3XBYl85zctwhZktlXq/Gy5csJIhhJ9nDJ6hFOyPdRshuYqsaeis/KjMp3to8RRhF9lknJdkloT0dW/G/Egp2lP/OZz3DRRRexZs0abNvm1a9+Nbt27SKfz/Of//mfCzpWMpnk+OOPP2xbPB6np6enu/31r3897373u8nlcqRSKd7+9rdzxhlncPrppy906AvGZze9CUsV3iX5mErBknn+aJyf7mviBiFeGGFqEr0xA012GYwnuhWdQ6qGitNCleUOIVCm4TqMpnsIo5AXLR2k2K6jKyq6rLJlvs5JhSwpPUbFaXVVYYc+7LbvYQcu/VaGitPihUuX0PIcemKi6hUhJPNuYGNpcRpug8lmmZFknr2VKQYTWXTEjfRQ+dcPA160dJDfTEyzq9xiRdYijCKmmm3G6gGjKZX9tTnG6jajqRg7y20SmkTLj7BUidMH9Me8lPzt7W+lNxanZIcMJ/502+tPOQ8/Hrh67y1snfc4td9kolHlosVD/Nu9+xiry8y2fEZSyjEtyR8LPHtlD9dtmeX6nfO8YtVyFiW1blDwkWAhkvtDuOTa1/Gd53yDoaTCgCVc2Tf2J7lxZxEjrjGSMtFjKnbb58d7mqzqsThjOIUmC4LzrRMNXrQixV3TDvO2hyorPGu4h2K7zvpeg764RuHfXsjsO39yxO/r93H2wD/xy7G/pT/+0Fyoa/a/h5QeY11PgUWJFm7o02jZKBLsq9jIqswpS3PU3RAviNhR9hhKqPx0b4M3nVBgtiUqwXUvoi+hY45mufrOCV5y6jAH6g3W5DRWZ1UeKPn0xWTyMYW3nthDEOXYXBQKrhMKgtfY8Owub6fitPjh7ipZQ2FZxuH5Swa7pOfBRJalSgFTnWS2PYsfBkw0RWXvgsWLCaOInJlgti3y5VRJYrxe4pzhIYp2vXs/XJntZ6w+31UdJjSTmtumYOVIaCa6bLKvOYMsyfSYGjsrUxTtgJ2VaSqOy0gyTcawyBhxXrg0z55qpXte91Ta2B1l7AmP0OPqaODRKr+eVo0dHSx4IjQ8PMx9993HVVddxf3330+j0eD1r389l1xyyWHk6aOFz372s8iyzMte9jIcx+Giiy7iS186Nn4rt0/ZXLjYYqwekI/JVN2IXx20Gas6XLw8jR1E5EyZ8YZDWpcYbzTIGGI1k5CVTmtMuDIfsurPx5Kossxks0wYCf+dvbUGpuKyoTeNHbi0Wg6qpOCGPvlYUkg+NZFfNtuukjeT6IpwlRYp8YIsXXUqZIxct20WU01GknnCKGRVdghJkgkiHy8QN/SW75DSLW6dOshIUnBVxM0rRE3ILE4qWJrOeL3Mht4eam6bZw71M9uusbnYZFlao+WHPGfxpx7T67C15LEs3WBDb7rLL/nerrc/bIvsiYZ33vTXjCQVvFAQPscbAb2xOueNxMjHTH59sE7ZDvnFgTLPWfx4j/booc/SCP2QwYEYe6otBiyV4eSxq3o9979f0/XrGbBUxuo+X793luV5i9edOogXRDhBRK+lEUQRi5I6eyo21+2rc+pgkrmWx3mLk1y1rcK6QpzFySQJ3WRnuUjY8dG6f87hjNHsnxjJwnDhyCcf9ncvWPIZrj/4AXbXS/Rb8c6iS+arm8vENJm2KnPX/jIjaYO1PRqn9htsL/ms642JyUJCRZZkrtu3k1ccJ7hqm3IWMVXqtPd9ErrE+YuyfG9XkY0FneWZbEchJvzGzho0uXtmnouXrWSuVaXitLh9usk7NoySNuJ4gcfBxpy4T8ki23C8UWJD7zK80MULPAzFYKpVwvY9ZFXqcocG41lCIizNEBV1RWW8XqLmtkloJgnNIGPE8TvVo5Qew/bF5M4NbQpWWgT3KinG62WG4hozLWGOub1cEerGrLBi2FUWC5CvbpllY5/J2h6Lku3g+MfaUPHIOY1PGyoeHSx4IgSgqip//ucLc0Z9pLjxxhsP+9k0Tb74xS/yxS9+8TF5vT+GZRkdL4xYnVOZaYXMtQQ3KqErWJpI0LZUiYQms6vicdHiIe6enSJrhJiKxmRD+PfYvoeuCJfm5ekllOwiKT1Gw3WYaFRZk8vyu8l5NhdLnDFgMFb3WZ2zyJlxKo7wnslHyW57zFRjmAhSdNkpYwctFEklpppEhByoTzIYz9PyRNm+5btoika5XUOWJBKaiRsc4kv4LE4maPkOuyoOXlhmJJlGRvje9AKDiUzX9v5QhSpnttlV8Thv0UNztY4WPnr7G7H9iOWZB79s7pj5JyxN58odb+XVq47938VCccHiFG4QsLFPKF3W5HQankPOjHPtgSmSunAwTj/FxCrb5tvEsjH+4vgsOUPkd905M/uwaepHGyXb48wh0SLdVfFExpQfMt1w+eZdU/zZ+j6evchiW0kIBGQJ7plqkLc00obKhYstynbIa9ZmuXvG7XqCWarMRMPnhN4sx/X4fG/7wgncjwamqpHSVbaV6qQNma/cXyIII9b1JrjVDRjNxNhdbtP2Q1ZkNO6cqvOxs0dpeS7TrSbDiSTvO7WAoUh8Y0uFl65MkTNksqaJLEnsr7UYb9R58bIMB+stJhuVrqXG1nmfZRlYl08z16p2KQAXjAgTRV02afsO9xdLnD0Y4/qxcV4wOkrBSuEGthB7yCpO8CC3arJZFhUhr9Z1mE4bccp2g4RmkNAN1vQspuLUOtWhJqkohq6ozLZqFNt1zh5cz9bSHoYTOSpOk4RmsjLbz97qLGcMLKLlOZScBtPNNg3PxvY96l7EVdsq9Md1vBDGGzaDcYOMfmR+eEcC6VFWhJ7mCB0dHNFZ/Pa3v83ZZ5/N4OAgBw4IL43Pfvaz/PjHPz6qg3u8sTqrcf2BNvtqQccoUaHU9ui1NKpOSMuPqDohuqKSNWW2laaRJdHnn2k1mGs3yccSpIxOH9t3KdrCd8kPQ1JGjCASVYLnjy5ClmB/Taxgr95bxVR0iu0G98w2afkufhSiSgo1t4EbOFSdCrqs4gUeQeRT91pIyCxJDgvSNBGWZmGpOvNtYYJWcVroiiFKzLKKLEkdMrTMcTmDhhd1zNKyHJfr5+7ZKndMz7Nl3iFjxBlviFVcWhfeSgn96Kj9HgofuOVShhMKz12SQ5UVEU8RBYw3Ssg88ryqxxu3TtVZksoTRhHTrYoIw1UEIX59PkZal5FleM9JT77Q1T+GHfMt7KpN2Q7RZDEBrzrH7prd/qqrGK+L1f2HT70Cx4dsSudFK7KsGUwy1/KpOD7v3/hi2n7EaFpFU0SqetsPGUmmWZWLU2yHZAyZmmPjhwFOEDIQF3+PI0mhMDuWmG5WmGt7rMrF2VfzabgBuiLTa6m8bFWel65McfpgnKrts7YnxvtOKRBGIQUrxVhdjHVZOsVsK+SFy1LcONai6kbcN9dElZSO35NEzbFZnEwwVg+6dhlrelQKMYXBeJY+K8t0q0p/PIMfBsJc0RWWAucOj5DULV6yfCU/37+P8XoJPwxE8LAqOgcpPdYZV5qW72BpOi3fJWsk8QKv42lW6tzz6piKRk8sRc5MkDHifHXLfraXK2zoXYwb2qiyws6KiBkqtuuMN0oUrBQlu0HNbeMGASPJJDcerNH2A0ptn5gqvKVypszJhT76rTR7q3PH9Ho+jccfC54IXXbZZbz73e/muc99LuVyuasgy2azh5GonwpIGwbH9ehsmmmxq+yLaICVSZ4/ajHTCkkbMpYmkdAMFAl6YjGOy/Vg+x4D8RQrMr0UYmlSegxVlum30jRc8YGtuW32VefZ0DtIzW1Tsps8YyjO0rRQ1jxncRxT1TrPCXnfTXu5Z3aOW6YqXLljP7PtKkVbfNgbnkMQBpiKRoQoGauS4O5UnJpQoEVhJ+enhyDyWZouoCsqo6nl5MwE+ViC/niGZw4tYrpZxQ18dpaneeHSJTS8kKQmccfMFJYqjmtpOsMJk1unxh6Tc//Lsb/lhLzOymyO/nhG5A51uE2C9C3zhrVPjkiKE3tNim3hwF6IpbuZSjISk02HmXbIA/NPvcTrhK6g6ApDHV6XG/iMJBW+cP+b+fb2tx6TMeRiDxa9s6bE2YsE2XlfxUbpGDxeseVqMobcHfO6QoJS22OiUeVgXQgDLhjJ0B9PYwceWdMkH0vgBGIx1Cr/YfjqY4mlaRGtUXNs1vXobBxI4gYhK7IqJ/SKfK6ZVsAJBZPdFZsv3y9aS3urs2gyXWfn1bkYs62A16zJ0G+pyBJdNemuisfVe5tsK9XZNGdjqBLr8hp9lknRDthbm+W+4n7cwKfmtmj5LrOtGvfOHmRLcRzb95hrVWn7Ds8YGiRlxAijqCsCAfH3IBZX87Q8UTmfbJYJIp8wihirzzMYz5LQza51SLuTRF+06/z56iGWpCy2lyex/Tb5mGjFWaqBqWosT/eR0mNMN6uU7CaqLBSvU02fL99fJm0qvGJ1mtU5lQ29PWwtzTLZLLM6d4xKljydNfZEwYLP4r//+7/zla98hQ9+8IOo6oM3mZNPPpnNmzcf1cE93vja5iI/21NlJG0w2w5YlRVtqwdKHssyCjOtAKtjVZ81FGREQGNCN9heriBLcjenBxAVHVlh09wBUnqM0XQPuqxSiKUwVY2ClWK65bMmp7O74vKzfQcIiTh7KMGGvgT3F12ypsymmRZuEPDjPbPcN1dmW2mauJZEkRXh2Bra+JGLpeqosoKl6iQ0UfZ2A597Z/ehyUKmu6uyAwBLNSjZDXZXpgU5O/RZn19ETDXY0GsJg8W2GH9CN1iTG6Llubzx+MeGmNzwHIaTOVqeCJadblXIGBamopExLCzNeExe97HA3bN2pxX2oGJKliS2l+ewVIkwjFCkp55a5ZyRNG7DZU81oOqGfH9XiR/vbvDd7VVunXLYWnp4LszRwhXPftDtvOVHNNyAr24SBok37ikz1QzZXvKYbfnsqfisK8RZnlHZXRY5fIYixBIHGw0hbJBV8TepasiSaI+baZP+L7zoMX8vh2D7HrYvYi3umXM5Ia9z1yXf5W82XE5KV+mJxRiMKxzfI+JyTh+02Ft1SekxlqU1CrEUu6t17pltcvpAjHvnbGbbohJtaTqKJLE6q7EsozOUUNnQa7IsrbIqm6XfSlOIicnGXNtha8lld6VEzoyLyb0kcVJhCZaqixggJNzQ71aMFiUHaLiNjh+aShiFjCTz6IpKwUqxMjMgJjaBhyxJxFSTmCr4kTW3hS6rTDerXaf8vJkU2WWqIGbnY0lqbpt1PctoeMLOZGW2n4xhkTNFOGwQRbS9gImay/aST19MIaVbLE0LoUaKS7MAAQAASURBVErZbv6JK3D08HTExhMDCz6L+/bt48QTT/yD7YZh0Gweuz+gxxrvvfmvWZQy+LNVWZ4xaHDGgEHbD6i6EWtzGmldZmPBImuaND2fjGHR8GxyRoKMYXHu0GJUWe76Ac20GshItHyHNT1DFGIpJhtlQiL8KCSlxxirzTOSNNlTFXyGhC51nmvzjCGD0ZTKzeNtNvRZfOOBaQbiCi9cupwTC4sIoxBFEhPTKArxQhGKKCNRcVpkjBSTTWF6uD4/QtsXig9VVrh9eh8JzeT4niUsTYt055LdoOHZVJ0mI8ke6p6wEWh4Nm4g2lMLzYxaCF667HPMtmo8Z/GnWJl5L3kzyaa5ScIoYrJZ4eyBf3rMXvtoYySpdGNRhB+KUBEuTWepuqLNkjaeeje0Lz3rq0RhxKYZEdK7u9zm7uk6bhAyUXf55rY9x3Q8f7NBtB5fsSbPaEYnk9CouiEJXSahiyy01TmDL949SxDC0rTGnqpPwwtRJElUJAnpj6dRJYXemIi+ueZlG1k/mKT3sy94VOP72f73PaL9/ChgeUZUZj511pcPa6nWXJ/pZpuWJzyqSh2PM/E+RBzFbLtG3hSRIxXHZ3VWE5E6msTW+Sb5mPAOW5vTSOmxTuVIuESHUcTK7AApPcYv9rf44Y4yYQQtz2U4kWNjYQQ78DoKMGEjMtkokzHiIuMwaGFpFmk9QUKL0/Ac/FBE+9w+PYGpxhhO5LqB0k2/Td1tkTEsam5bTH5iyW52oq6oVJwmDU9wKcfqRXRF4bqxLewozwIwVp9HlWVMReOOmSnW9hgkdIXeuMZzl2QZbwSU7Aa277GzUkNXjog6+zSexFjw3Xd0dJRNmzb9wfZrr72W44477miM6QkBWZaouWIlG0SwLt9Lyw9Zm1O5bszmvjmPubbDVLNN3Yu65eax+jy7K6WupHS6WaFkNxlN95DQzW7lxQ19NvQuxlQ0/DBgslGmaIfsqbRp+cJbpt/S+O/ds6zK5hhOJDlrMM/GPpNXrhzhg6euZkNvmi9v3sHnN+3m9ultXWfpQwQ6RVaEqkzVaHnNDtlZVB6EasxFV1RWZQsU7TolW7TETFXr5AFFqLKCKiusz/dgqEKyagcuy9LDnNa/5jG9BofK6NceeH9HHSfhRwEN79i2Ih4txuoBvxxr8d979pEzReSKHwbcXyxx/5xH0Q4XnKH1ZIGe0EnoivjiNRTec2of0w2XO8ZrmIpQ1D3WeOXP//LB8SjCrmCi7vHsxZlu267th6zOqtw+2eL04RQN2ycfk5lsiCrHTCtgT7WCH4ZigdGZGA3EzQfd2+cfnafQ85Z8+hHt96uDc7x02ed42/rDq7HX7H8P/Vac8UbAQFwhHzNIahJ2EHFKn87eqsvOcrsb05M1hf/ZdWPCKqNsh0w0Au6cadIfT1O0Qw42GoLg32MRRHQ4OzIlu8GLlomJYLEdYqoaJaeBKivENbMTGi2MXE8sjAIPtt1kSWauXcEPXbJmQijHJJGzOFafphArdGKJejEVDUs12F8rkjMS3cmQG/rdYw4ncuiyiq6otP2gk/MocVyun4RmsrGwEkszcAOfu2dcZlrCOXuu6XGgXqfsCE+iyWaDMIKqc+wW9E8bKj4xsOCz+O53v5u3vvWtfPe73yWKIu644w4+9rGP8YEPfID3v//hbeGfTPjqA29hZUYV1vslh/vmPGpOm9lWyEwrJKaKLK4wAkWSMBWp04aSmW0HDCeS2IHgGLgdDtVP902zdX5ClIh9D1PRuj5DKT2GpekUYgr3FV1yhsyiRIJ5W9i+19w2FadFsV2n4UV8f9cYs+0aGSPO6QMx1vaIlRtA0xNclCAMiKspJEnG9r2Oq7SoPh26kciS1A1vTWhml4fjhwEHam5Hsh9i+y43T8wRduTfpqJzsDHdnXg9Vjj0xfCcxZ+iZDdZme3HD0Ms9cklrzIUiaQmE0SCFwHCPLNkh7xsRQZLlZ6SrTGA4nt+yh2TNSxN584DVT592wxBFBH6IT/aWWFdXuNHe//mMR3DVc/9Jp+4SwTxxlSZgbhKwVI5dzjGrZM2JTug7YWkdJV1hRgNN+CMkTS7yuJarcyopA0ZpSMssFQdXVH51UFRofzRngM8eySBJB+ba/jR0x46rkOX1Y7lhsxP9tS4fqzBLw+0OXNAp9gOWZ9Psb/mo8sq/VaaMBLijOmGS80NWJHRuGBxiqoT8fE7dndVYhONKhONNoYi87af7afh2fTHM1xxX5mXr87S8kXLXJdV7i+O4QUel92/syMKEP4+h7iFURQSRiEZI44kyZTtBpZmoMsKKzNqZ5IpM94okdAyAGSNXpamC4AwWzzk0RZGEQ3PpuQ0GW8I5d4JvcNUnBbH5YSaNaXH2FU5yHCiwK7KHG9ct4i1PSpxTWZDX4yBeIyNBZN752xO7Rvh+J4sI8n8Y38RO3g6YuOJgQXXAN/whjcQi8X40Ic+RKvV4tWvfjWDg4P827/9G6985SsfizEec9w27bA+r6PIEucusii2AyabTX64s8IFo2kuXprjtukqdhBRbIecWEh0LeDBY7xRx1JFQKTte9w3V+acoQw7K3UGEwFu6DPeEIGnbuhjqjFs3+M3EzYXjMTojcW7BOuBuEfVcRiIp9AVhazhcue0zXRzmqVplZIdkjNlVmQGxY3Fa3QrDhVnvhO2KgjUpqKxszLNcCLXrQy1Or17PwzJmXEszcJUAjb29TDbqna3a7LEoqTWzUIbr5d45U82M37p247ZdQmjSJhPPslK12lD4ubxFn+2MoWp6h0uhcwzh3r48Z45ErrEcbknD+dpobDrLrIkkU3pbOhLsLvUZk7ySJsqe6o+wTEwkvztRBNOFnYXS9IqW+c9yo7PurzBTCtgd6nNt7dW2FFqsTwbI22oXL+/QsMNOL6nwGhaqCPvL5YoxES48sqsyMc6vV9nOJHj3/qnyX/m+RTf89PH/P289+a/5v894ysA3DX7z7R8B1mS2VluM5JUmW66PGc0xT3TDXpW5yi2m/x8f5kzBgx2lGdZlMzwxXvned6yNMuzJm0/YqIRMNtqUrZ9XrDU4oyBpVx7YA/byz4n5HW+saXC/z1/Ed/aNs4zh0xeszbDVDNgWVpYg4zV66zM5pAlmfNGkvzXrn38+eqV6IqIzxCqrimOyy7CDwPiWhI7KNFjpqh7wjDxgfkp3MBnZXYRVVeYLpadOfwwYLZdoxBLdReTKT3GYDzL/lqR43uGafkubuCzJJVne2mKZw6dgBOIhebP9z/AxsIAuqJy9e4Gbz1RmEuqkkLZs1mb0/jd1AEWJ5NUm0fXLfxpHB18/vOf59JLL8U0TT7/+T9urvqOd7xjQcde0DeK7/tceeWVXHTRRVxyySW0Wi0ajQaFQmFBL/pExtX73s3ytMb2kseSlMqeis9FizPMtZucNSz8Nv57T4llaZW0LhOGIbqsMuu20WRBnjYVCVMVk4bpVpOhhFjpLEnFUCWFkWSeYrtOyW6Q0S3G6vNYms7/WdmH7QvZ6NbSLP1WnKlmm4F4jBsOzvOSZcNU3ZDhpMbr1i5l6/wEq3OD3DM7Ttu3mW3XuGO6xAtGlxBTRTDrgfpkVyZfchr0d6SqAP1WhpLdEGVlWbTLDik28maS8UaJlBHDDjxW5yx2lVtois/6fI6iHXDfX/yfY3Zdzl/0cUDc9E8ufPiYve7RwO1TNv985nIszWBneYqV2QH8UPAS0obEtnmXMwaeWJEgRxOqIZzTKw2PHWqbth/gOAFr8ha/OlDjfac89vePQ4GwP9wxz6vX5pnoTM6KbY9nLYqxq6wQRBFr8nGO6zEo2yGvXpNjX82n7kXMtR0G4jEKMQUvjHhgfoo+K/HgZyk+zAXLc/xi+5FloS0EV+54KycVdH645128dNnnmGyWUSWR3h5GsKfq8YXzVtDwbFpLQmqOza8PNpmouxSsDBsLacIoYnk2xrZ5hxMLJldtKzFRN3ju0gQxTWZP1afu7cFQIG/K7Kl6vP1Ekdze8uuEUcTmoseFi5O4gc9vJ0u0/Iic2aAQS7MyM8BxOQVFUpGQiSkJAl0sAr3QY3d1htVZEdHhBA5u4DMQL5A1kjT9NiVbiE0sVUeRhbns8nQfs+0aY/V5ZEliUM6iywoJzehklAXoikLLczlz8DiKdhHbF5XtZw0vFfdAu8FoxuDGgzUKlsLyjMF0q0lMljkh3yviNlKZx/waHsKjbW/9b2qNffazn+WSSy7BNE0++9nPPux+kiQteCK0oLOoqipvetObsG3B0bAs6yk1CQK4ePRfafkhI0kVO0CYsBEy1QxZnVMJIhEtIT70BkMJUaEYTiQxFJk1uV7G6gFjdZuEbrA629shTYsW2eWbD+IGPiW7QUjEzZMHur87hOlWlaXpLDkzzsbCIL8+WGdZWhVfnLrMJatH0GWVkwpL2Fme4tS+ESpuSyg6LHFJDzbmqLs1+q1Mx68jYiCew48CcmYCSzWIqXEszeCTd+7r+gF5gZBx64pKSo8JIzK7yf5am+NySdK6IF+PJGM8UHrgEWV/HU082SZBAGt7jA5nrMnSdKFrQnd8zyI29ArztkNp3E9F+I7P1x+YIWlpHJxpoMsy6weTjKZUXrAs3amkHhsc+OsfosngBhHDCYWcqWKpMg03IAhBV8RCZiihkI/JtL2Q1VkNLxSEYF1RSeo6KzK97KzUu8npefPP+daFX+c1J/Y9ZmO/et+7+c3kh9he9ihYKV667HMAjNXbXWJy3RM8n0Pk34IlVJ6rcyZnDCUJI3BDcf85c8jkGcMxqk7IqYMpNk83WJGJ0/ZCnjnUQ7+lceeMywm9WYYTCoOJLA3PZnU2xfH5YZ69yKLuuqzODfLMoR4abtglOYdRyCfveoBfHdxO228SRKIVn9EtwihkeVqcpzCKCKOIgpVjqjmL0kmFT+oWeTOPoVjde9KhicySVJ77ig4tz2G6VWVVdjF5M8niVG93QgiwuzJNxojTcG0UWUzKZts11vRonD+S5aRClpLdZCSZZmV2gF2VuU4rbfIxu4b/E0+rxh459u3bR09PT/f/D/fYu3fvgo+94LN46qmncu+99y74hZ5MEGZpAWldYl2Pju173DrZYtOci6VJDCcU1uREi+NQ5k7LFyvM+4qzrOmxeObQIg7UOqqwUBgh2r7HcxbHmG1XWZLq5Z7ZuU7CtcqiRC+FWApZkijEUh3fC4WS3eCS1SOsz/ezr1ZnfT7FnTPig/ovd2wlpccoOQ3yZpLZdo1nDC0npoqS/XSrynSrQqnjTh1FonrVcG0SmsGO8gFSeoJ/OGMlqiQLaWqrSkiEKusMJnofNEvzI1q+Q9YUE6Zi2+ZXB+f45B1Tj+elelLgvEX9/GD3PJ+8Y4wH5qfYOj/B8T1/J9qiioYi01XBPBWhx3XunmpQqdqcsjTHdLnNXEsoI88YyDEYP7oRFX8K/3T6lynZHmlDYiSpkjYMYqrCaMbgWYti3DIhvIN+trfJTWMVfnGgzfJ0knvmXMYbDtfsq3HDwWmKbWEQ+N2ds91j/79nfIW1//HyP3jNM6961aMed8awmGxUGIgrVJwmV+97NwBvW38ZM60GFcen6kTsqzj4YUg+lmS2FZDSY1y8rI9laRVDkbhs0xwJzWRx0iRnyJxU0HnuEosNA8luFtnm+TIVx+f/rMhjqhqrsgUans1gPEvBSjPbqmGpBi0/5JN37iRjWJzcJ7h7Qvou8/6Nx/HMoaUAtP0mqizsPDRFw9LEhEiVFYwO56/mtmm4Qq0ahAHtoMG8XcJQDNTOl/7KrPD4eeHooJDnSxJ3z+4kpWdp+w4hEevzI7S8Jif2LiWuJTufM4vbp3dhqQan9w+SjyX59fg8y9N96IqYPK3LD1FxW7SOYcTG0zgy/Pa3vz2qx1vwROgtb3kL73nPe/jCF77Arbfeyv3333/Y48mOz9zzJjYXPWRZYqIRoMoybhDwvKVxNvTqmIrUdYN2g4BdZdFPLsRS1FyfHlOl6jhsnZ8grgnV0yFuy9J0gYRuMN9ud1ZFCZak0gwmspSdOtOtKgnNpOQ0GG84XLVjjIwR55apSf5r9zib5kRG2J6KIHG+d+MKVmaHuy2uvCkcWeftGiBunHkzSUa3WJoudGSqcfKxZMcU0cAPhU/P1tIEW0sTLErmiasxWl6Lltdke2mS4USO9fkUTc9HlZROeVzwOv5m41OrIvhYoGjXMRWJvz11hKu217lrRrRT1vd8gOct+TTr8gbXHnjsWyqPF6Iwot7y0GJa9+eZhktSkyi264d5bR0rJHSFnWUfO4j46b4aJ/XHcMOIlh+hyDDVDGj7Ql1091Sdt98gVplZQ8ZUZE7pS3eUY3Fesuzw/LTl2T/MXFzT++hjG1K6xUTD55S+Qf5s+eeZara7vyu2Q1K6wsGaw0jaYCTVQ8t3WJ83OxMGmXOGh2h4IWvzJj/cIxziNxc9TFVjf83jrScWSOkqMU2m5UUULJOvPzBDy3MYq88zVpun5bvsrc7Sb6WpOC0WJRKMpDSu2jnWVY8BXTuPMIrQZI3JZpma28BUY5TtBg1PXHNTjdH0bBpug4KVYm9tlpLdENUtt0VSt/BCj3xsgNXZ9xFXRav+UKBrIZbqeASJCZLaqZCYaoyyU2dzcQ9u4HNfcRd2IMjV060KKT3GX65ZLRZ+UUQYheyuTLOvOo95DDmITxsqHhme/exnMzo6yt///d/zwAMPPOrjLfgsvvKVr2Tfvn284x3v4KyzzmLDhg2ceOKJ3X+f7NhT9VmRVdlddrhwcZKMEafYiQhouBEtL2IgLkr5XhgRRHQ5OMOJJKaq0WNaFKwUlqpjqTors/1UnCZ7q7NYqsFoOkfDc0joBrdOlWi4NtePTYjsnHaNludyev8AWVNmd2Wmc/OFFy3N8suxebKmzO3T+wDYWxUVGRGTIeFHIQlNyE3dwO+2HdzQx1AMNFncqFRZ9OcPrdJGknlSegxN1lFlnYSeQJUVNhaWUbTr7KzUeaD0oOKp4YqS9uX3Hbsv8Bf8+DXH7LWOBv5j21sA0fa6YLGo8lUdH005XF00nFCesvJ5EBMfw1Bwmy637yyiGiqmJpPUH6wgHGvc8LLvYGkSU82AhhswklRoeyE/39vg7KE4LT9iKGnQcANKtseavIWlSuyqeFy8NMcvDpSZagb8YNcMk81G97jn/eASfnzxt/jgrZcC8LYbhT3AV8/72qMe8z2z45w12M/JhQ/z+fveTMuP+OwmoYZ7z0mXM5jIcnJ/jHxMZnupyK5yi4rjkzN62FedB8ALRcX7nCGT3046tPyIr2+Zp+GFNFyHG8fb/MVxAwzEFW6fbvD80TgZI87SdIGSE6IrwqB1tl2jPy4mgysyKklNos+SmWxU2FudRZWVrldZSMhgPEtKT3DL5DZMVUOXVTKGaHPoHQ6Q7XuMJPOszg52ic9ex1ix7pbYW/s8E80i/VaGuJbEUHUmm2JSs7W0CQBT1ZEkGV02mW3VWNOzGDf0sVSDJakMbuB3U+snGvOM1as0PJvdlRJzbY9T+pbQ8I5ddVa0NB9Na+ype9/4Y5icnOQ973kPN910E+vWrWPDhg18+tOfZnx8/IiOd0SGig/VkzvS3twTDWt6NB6Y93jeaJypps3WkggcHKsH2EGEHYhKSNsPiKkKI0kVVVYEsViPde3d87Fk1/JduCHHWZ0bZDiRo+E6XU+NA3Wf30zMcOtki5vGZ8jHkgwnxArzGUO95Mw4PabF+SMFQkJeMFpgXT5O3RMTkZwZZ2dlCk3RcMOAmGrgRyHLM33kzERXraHLKmWnzr7aDDW3jR8GtHyH6eY8vx7fI/LF4hmCjj+H7bcZq89z5Y5tosxuygzEZSxNZ3OxRUKX0GSJcxbFj/hcf+zONy5o/0OE1ycLXnuciAA5M3cqqqTQb6Vp+yFpXf6D/T54ykNLop8KOHEoRUJX0GIai4dSAFRLbUxFYzCRQZcfHxWgFwrn6SVpjfvmPIaSGmvzMbKmhBtGTNQddEWiVbYJoogf76qQ1mVavstZgzGmGy4jSYV7Zj3e+Ks3AGKCBfCxM74MwBfO/cpRG+9frfkSp/d/lO/tentnAheh/Z5kv+a0WZXVOWMgycpsjrMG+8nHDDYVd9BnJXhgfop1PVonMFpirOYwklT5y7VZzh3Oc1/R5aq7J7m/OM/t0y5fu3uGoUSa2XaVO2cmsVSJneUS+ViSHeUyhZi4lsszOQxF4sZxMbHqt9LoiklSTxFGEYqkUrTrBJFQdInAZwk3tGl5TWRJmB02PJuULu5ZSc2iJ5YS7TB1lCDyqbkNhuJC2u4ELWREbFFCE1QANxCmkVEUUnPLnNp/ApONOdbkFndJ7ZYmHK/twGO2VWMwnmC21eb0/sWkdZlNcwdYnjl2Ve6nK0JHhnw+z9ve9jZ+97vfsWfPHl7+8pfzH//xHyxZsoRnP/vZCz7egs/i4sWL/+jjyY6kJrE+r1P3QkZTSbwgIm/K9FkymixuntvLHooENfdBS/eCJW4KLd/pZuqossLu6kyn5SX63C3fQVdUQaAOQ4IQNhcd1uRNzhnuo+a06Y/3kI9lqDgtErrZDUXVZZVfHBBVpTMHhroJ8ik9RtVpYqk6QRiQ0pOk9CQxNU4QBqL37tmU7AY5M46paBTtOmk9wa7KHM9etKLrM6TIKnW3xnhD5BOd0BtHlYUHTtWJmGxUWJXVKcRUlqVVlqW1P3FG/xDXH/wAvxz7WzYW0kf78j0xYTyfMIqYbdfY0Jeg6oaP94iOKV68IkHDDVBUiamKzcahJLIqc7AhFEZu6HPVzmNnw3AInzpLTFY+dsaXRZvLkDlQcwkj2DHfZiRtcPpgEiNpMFZ1CMKIqhsy1bT52uYya/KCLxdGESszKuf94BLg2JhELkureGHEVPNBoUXLdxhMZDlYb1FxWt3P9JZikztnKgA8UPI6JpASf3V8D2t6LNwgoOK0GEoofOT8EcbqAectivH5i0ZwA+FU/e0HKt1J182TE2QNhRDR8hd8JJlnLzL5wc46pqrTcBs4QYuKI4jSk40yju8y2Sx3PNd0Kk4NNww6izIRARJFIaZiYSgWE415UcHuSOZVSWaiWUSTNXTFpOm30RUVpVP19sOAMAqRJJkwiig7s1iawf7atOBpyjI5I0HFabIkOUzBOtRWM9hZmWYwkWEk2YMXHNsQ3ccDX/ziF1myZAmmaXLaaadxxx13/NH9v//977N69WpM02TdunX87Gc/O+z3URTxkY98hIGBAWKxGOeffz67du16LN9CF6Ojo/zd3/0dn/jEJ1i3bh033XTTgo+x4InQ1Vdf/ZCPn/zkJ1x33XXs27dvwYN4IuHn+1ukdYn91YCS3WRb2evmbC1LawzEZQ7W/Y7jdETNbdNvpZlsVDqVlhA7cKk4TdzApxBLdZQUMpPNCkCn+pLlE3eMcdagQdpQSGgStu+xKjuE7be5Z3Z3V/E1Vp+n4dpkjDiXrFrW4S35FNt14prJcKJAumNQpskaEjINt4Eb2tRccbPYX5sjH0tiKiIjqeW5SJLMVPNBjyFLszo3J5/BeEZ4pWQGWJkZoOqGOEFEyRH7t/2go47zCaMbFnSO3cCn5bvkY09dpdT/hBuKkvxgQvmDitBTHW88/jLShooiSSzNW+RiGueuzLMqm0NXRJzDK1d+4XEZ2yHX6S+c+xX+7uTLmWt57Cz7rMlb7JhvMxBXiMKIquPz6jU5SnZE1YlY3WOS1mWmmgEl22db2eOGl32Hz256EzOtx7bV939W/DtvWPslCpZQth3C3qpLy3PoMUWFbdNcrfO5lliR0VmcynJC3mB7qU0YRaT0mMg6tIRI4/ieHGtyGUZTCtOtB1tIGUNlTa/F5nmPPVWfobiGrgjRhRv4WKrOcCKJE0S8eEUCVZI73mQBpqojIxZ9ACuzA4RRhBs4qJLczSc7FEQddkKja26ZZelFSJKMJ4VonYWlLqvM2zVBtJZkFEkVJOko6uQpytTdFiERU80Scc3EDwOGEzkG4jl+MzEDiIrSYDyLHXiossySVG/X4Lbn9zIBH2s8Hqqx7373u7z73e/mH/7hH7jnnns44YQTuOiii5idnX3I/W+55RZe9apX8frXv557772XF7/4xbz4xS9my5Yt3X0+9alP8fnPf57LL7+c22+/nXg8zkUXXdRVmD9W+N3vfsdb3vIWBgYGePWrX83xxx/PT3+6cB8vKYoW5mYmyzKSJPE/n3ZomyRJnH322fzoRz8imz22apCHQq1WI51OU61WSaVSf3L/7+16O1vmHYYSCi0/4ud7G7xgWZIbDjS5YEkcx4/QFImlaQtVUroOzYJvozDbqpEzRYaOHbg0XKe7crtw8Qh6J7LCDjz+/Kf38o6NeX59sM3/PesUml4dWZKFO6zZy3Rrmiu3H+DVqxezaW6S9fmB7sToUAWq4dr0x3u6Ls9t3yGhxYmikKrb6MZU7K3NYio6KT1GxrDYWZkibyYxVR2909pbmRWZZUHko8l6ZzLlkzFS/OrgduwgImfI7Kv5nDHQQ8Vpsa9qsyip8X9W/Psjvia3TX8UWZI5te8jR3BFn7y4bPObKdkhx+ViXenz/xZcseXNTDQCbpts0fZDXrs2w5pcL3bg0fBsLh7918d7iLzhhtcznFDZXnJIGyo5U2bTTBtFhiCEZ43ECSNYklK5Z85lIC7jBRB2boUXLx3le7v28kDRoddS+dKzvvpHX+/GiQ9y7tDHjtr4v/rAW1ieyVJz29w316RgyYymUp2AWDHpuHtmnrQhkzN1TEVjrt1kbc8A39t1kGcOJZGR2TJfJ2eKfXRF4YaxGpoisbvssDStc0KvTsMLWZS0uveUli+Uaj/dV+Svj1+OGwbsrkx321amKqIytpYmcAOf5Zk+TEXD6FSxdcWg5bWEa3ToE1MNdNlkplXkhPwpPFC6G1mSsX2XhucIZ2wiYqpBFIXYgUfLc7A0g4Zrd1/PD0V1Z600yOZoAlWSMRSDuXYFXVEptuv4oaAS3DO7n5FkD9unpnj9xq884u+MI8Gh76VK9SekUkdOL6jVmmTSL1zQWE877TROOeUUvvAFsfgIw5BFixbx9re/nb/7u7/7g/1f8YpX0Gw2ueaaa7rbTj/9dDZs2MDll19OFEUMDg7ynve8h/e+970AVKtV+vr6+OY3v/mYGC1/4AMf4KqrrmJycpILLriASy65hBe96EVY1pGJEhY8nbzuuus45ZRTuO6666hWq1SrVa677jpOO+00rrnmGn7zm98wPz/fPSFPNuyquEx2ys1vWHsRFy9PYqkSz1saZ6we4IUgS7Cj1BTKK9Wg5tgivqKTfuyGPi3fYaLRYqzuc+uUw+8mWnzhvj184q4d+B0uz0fO7Gd5JsuHT1tDREjBGgLohHOWkSWZZy/K8PP9B8kYwp/jh7t3M5zIEUYRFadJn5XFD10USUWXRal+Z3mckJCkbmGqMUpOU5ixSxI1t40iqazKDmF2ZKtuGLBprkoQilZb23doec2Oy7RGEPkULJPl6SQJ3WBFRvTZhxM59tUC5u2FRW3sKM92I0H+N+HN6y5DkaT/dZMggP21gCUplZG0wVs35PDCiDMH/pFnD//fx3US9Jl73sTn73szL//pX/KW9aNMNQPyMY1LVvfT8iLafsBE3eWk/hg/21tDlqDqBjxvSS+LEjqmKrG76nHdgTq/GNtH2Q5puAFt/0+vL++Ynj+q7+UNa7/EuUMfI6GZPGMoj6XKVJwWk40Kfij8w5ZlHvzc7a01GEqkaXg25y/KIiMWYcszJqtzgouzdb6JqUosSyv8xZoct0w0KNoBp/UPUYilmWrWsAMXSzVI6TFetmKA2XaN/bU59lZdTFUTLbRO/MfKTD9reoa6Y/ACj5gap+W10BWVmtsmphrIyDhBi/X5jeiKSUIzu4vIsDPxiasPvpeUnuxEdQhfNzfw0RUDN/RJ6il2K0VMRWN3dYaDjTkmmxXcwO9WzN3Ap2iL+36x/eRrjdVqtcMejuM85H6u63L33Xdz/vnnd7fJssz555/Prbfe+pDPufXWWw/bH+Ciiy7q7r9v3z6mp6cP2yedTnPaaac97DEfLX7zm9/wvve9j4mJCa655hpe9apXHfEkCI5gIvTOd76Tf/3Xf+W8884jmUySTCY577zz+PSnP8373vc+zjrrLD73uc9x3XXXHfGgHk+cNZgnZyqEESS1l7C56FH3QpwANuQ1gkhY0Q8lRPm55TuiPO40uonLk40KLc9le8nn1P4e/mrtYlb1mLxrwwqet8Si4rQIwoDlmX4KsRRxLcne6iRVp0gYiTKwIguZ+mBCmJnNtR2h9IrJHflogpwh0rDFaqiFE7RoeQ45M4Hjuzi+S8WpkdDMbmtGrKRC6m4L23dJ6QkyRkqEDboNGm5DcJzUGG7gYChixbU2N0LOTDBWFyGHtu+yozzDcELBUCQ+c8+b+M3khx7ROX7tcV9idfaRJW0/1fB3J1/+p3d6iuI726rMNT3ysSRvXnfZn37CMUDLF9XaXEyl4ohq1f6qzVU7plmWURnNxAjCiPtm2zxvaYqdFY//3FZly3yJP1v+eU4q9PLV877G8XmLv9lwOfurgmDda/1pk8j3b7yc6w9+4Ki+n1+N/z01t83uSpmGFzLb9jsGhxFbiuNsL4lQ57m2w9KUaAG1PLerQMqZcXJmnDAS5ohJXWKuHZDWFXaW27z2+AymInXdnZel89wyWcQPA3pMQQNIaGJBNtsSEwpdVvE7MT+yJDHdrNBn9SJLEpqi0fTqJPREJ7g6JOpUpd3Ax5RMJN/FUlMYiphs9cczHfPIFm3fQVfE68VUg5rbJqGJiVMQ+fTG8kw1Z2n7DtOtCr8+KEww59oOuypzrMwO4AY+28uTnNRboOQ0WJfPPcSZfWwgRY/+AbBo0SLS6XT38fGPf/whX69YLBIEAX19hxt/9vX1MT09/ZDPmZ6e/qP7H/p3Icd8tDjUEsvnj04u3ILlGnv27HnIElwqleqqxlasWEGx+OTzRfnnO97Iaf0Z8qbMWYMjAJw9aHDXjMuyjMrmeY+hhMJIUmaq5QNt8rEkx+UUQsJur3tltp+a22ZtTxlZktAVlfMXZfGjkPX5RfhRyO7qNCPJPKWOh8reaoW1uZWElJCR0RUTL/CwQ49V2QJ+FDLZKLOz4rMy22KyUWY4metMdkR1alGit5MwL3e8flySuoWMTEIzCaMQN/SptFrd3n7LEz5I548MUWzXGU33EUPG9ttYWlxMjIjwQ2HCuCaXZbpVBWAokabqlhlOJMmZLrOt2uNy3Z7GEx95U2ZfxeZDZ/R141Ieb/zzHW8U2WMpla0lj7tmSpzcZ9Bvxblj2uaGA00aXsBQ0mB3uc1Q0mA0pTJRU3D8iK9vfQv5mPgS/rdzhDrsxxd/i4/d+UZafsQHbrmUj5/55T86hkO+O0cLGcNClYWAQldUEppJxWl2U9sB1uQyqLLc5Qoe2v/QvUqENDdp+S5BBBsLOr8etzlzQGdfLSAfk9lcbHW9hNbnkyR0k5AQSzWYbJZZnx8lY8zQb2W6SfG6rOKHgcgRcyok9AQNt9GpyNik9TQytW6war81BG4LFJVheiB9KfvrX+jmOsY1E7mjnHJDG03Wu+fz0HttenUhTumET7/thLXcPbuHZw0v68ZTVJwWI6keNs/Psq6nwFyrflSvyR9FFIrHo3k+cPDgwcO+lw3jqZddePXVVz/ifS+++OIFHXvBE6GNGzfyvve9j29961v09vYCMDc3x/vf/35OOeUUAHbt2sWiRYsWeujHHR8+9Qp+Ofa3nD8yjN1xF91a8siaMv2WSt4U5MhZX1SEJho+OTNkstmk7oXsKvu8aX2eneUZ/DBkMB7HUg3cwCfXIeC5YUDLc3jBks9wx8w/UXPaqJLM0nQGEGnEhwiDOyvTnfabaGGpssLFS/vxw4DBRBYZqavaGE7kUGSVllMnH8siIWMoFk7QIpRgOJGj5rZJ6hYt36XYFiujitOiPy5eO2PE8QKPhueQNRMEofAhmm5Vuu6/e6sV1uR6uWduljAKMRWJO2cqHJeLsbvi8r1db18QX+hp/O/ATDukWmrznW1V/mrN4z0age0ll+885xt8fetbSOsy20oeL1uR4dr9NYptD12RWZ8xuHemyctW5brk6IYXcNuUjRuEvHAZ/M1v/pqTCjqvWf1FAD54yhV84q43kdAlFn/lpRz46x8+7BgOcfiOFnZXZkRWYCxJw7XZXirSHxfhpPcXp1idiwnuYttBliSWpHq7qlLRflJpRQ6b5qrkTJl+S1h/eGGFIIKyHVJsh9wz3cBQJGZbAWcPJURqPDJO6DIYzxJEPv2WqNzIiEmJG/qk9CRB6JM2Msy1i12fs5rb5s6ZPTxrWDhRJ/UUxfYUhXhP50QlwP8FEnI3db7h2WR0C13xu1WhhCb4Nn7oEkVhV1E22ayws1JjJJknH0tiBx7TzQolu0nGsDqmkQErMh4J7ck3iUilUo+II5TP51EUhZmZmcO2z8zM0N/f/5DP6e/v/6P7H/p3ZmaGgYGBw/bZsGHDQt7GH8WLX/ziw37+n1xl6fc8lYIFKv8W3Br72te+xr59+xgeHmb58uUsX76c4eFh9u/fz1e/KsiBjUaDD33okbVJnmjIx0SI4Kl9H+FX439PwZLZ0GtxsO5RckKKdsjilM5Uy+eEXqE68MKIvpjCsxdZjNXFh3tpupfhZI6EZtLwbCxVxw3Eyiihm2wtfZIeM0XKiDEQL9BvpTnYOEjTbxOEAV7oMt6wWZLKo8oKGcMiY1jcMjnL/cUS+2tztHwXO/A4WK90TA4blGxh7tb06kw1hQpAVJhEv9wLPExF69jv16i5baabFUxFw+1MfOxAcI5AOMSOpoYIo4ix+jzr8/1Mt6qszKQYSeapOiFJXajYNvSmHvEk6MaJDz4GV+9pPNHwsTvfyEduu5RbJxoM9SU4Pv/oHZaPFk7uF194mixx5mCejCFTc2zsQNhaAFiaREJXOFj3SeoSU02xQHrFqhQn98e4fqxF2pCpuiHf2/X27rG9MOK6/U3WFeLcMvUPDz+Go5Sdd+WOt3LVzrcxkuxhouHzk71FxhsNPvq7KW6dqvPtbWOMJNPcNN7gYL1F0Q66VaBDrbPxRom91Vlm2zVOH8izJteLKiuC+2jKFKwY540kWZ1T6bU0Gl6IpUl8adMcO8tT7KvNsLMyjSIr1N2WMD1ULBqejR+FwivIbRBEPm2/QVpPkDXEPdRUNM4aHOm6jJedCnEtCaEPsgpRiC35OEGLfEx4T4VRSMsX6lfbF/Ectt/uEsMB5ts1JptlRlN9nNo3gCIr6B3n6JXZRazMii/x43tWsDYnvIxmOhXvY4JDFaFH81gAdF1n48aN3HDDg0rfMAy54YYbOOOMMx7yOWecccZh+4PgCh/af3R0lP7+/sP2qdVq3H777Q97zCNBGIbdxy9/+Us2bNjAz3/+cyqVCpVKhZ/97GecdNJJXHvttQs+9oIrQqtWrWLr1q388pe/ZOfOnd1tF1xwAbIs5lX/c+b2ZMJJvR/il2N/S937b5F8LElUHYe6F5E2ZM4aTLK/1iKpCU+gnJnADXzysSR+GOKHAS4Be6tzWJrolwt5qkhSNlSdst0goRkoskpGt2h6ohSbM9O0vFZXiXb+IrE6KtHEDQWx78zBAnkzyWSzjKlqpPU0Gwsi5FCWJI7LjaJIKq5sk9JjGIpF22+Ksrescn/xIMPJHH4YULRD1uRSVByRa+YGoi+fMeIEkY8iid5+06vjRyGD8Qx3z47hhRG273HPbJWRpMJMO8ALI56z+FOP+DwfMpR8Gk9dfOzON7J13qVgqSgSuB2/mScKbpmw2VV+A2M1h+cvDUnqErdPu6zP6/xwZ5VzR5I03JCYqrC71OaZQybnDMcYq+tsmXd43pIhNhbq3D1bYyQpJhXjjS/zphtu5poXfZs3/uoNnFTQuGt2lh/tvZS5VsC+SpsbX37lURn/VTvfxsa+paxIv5t8LMH2co2GN8vanhRre8Rn7JXH5dFkCU2OmGrWOKVPtI/2VW3c0O9WggD6LUGc1hXBlzrQKLMsne+2tqabbQqWialIuEHEZDPgnCETWYp3PHniTDSqeIFHyRbtuMF4lp5YCkVSCSJhm6ErKg3P7kj7U+iyih146LLCQFyYGdbdGpqs01QhrlxANPMFlP4V6IpJw210zGCrLE0XOFgviiqP73Xa/cKkseW79MYyWJrBLVO7MBTxt5fRLXZXZ9hZPsCFi0dQZZmGVyFjWFiaTn/6GPqbHaXW2ELw7ne/m9e+9rWcfPLJnHrqqXzuc5+j2Wzyute9DoC/+Iu/YGhoqMszeuc738k555zDZz7zGZ7//Odz1VVXcdddd/HlL4u2ryRJvOtd7+Jf/uVfWLFiBaOjo3z4wx9mcHDwMZsLvOtd7+Lyyy/n7LPP7m676KKLsCyLSy+9lG3bti3oeEdk6SrLMs95znM499xzMQzjsJLUUwEXjnySrz7wFgqWyUjSpGQ7JDWJhCbzu8k6aV1m1g9IaC6q1KY/nul4UDRFEnUsQckWKxs38PHDgIZrk48l0WQdO3DJxzLsLB8kY8QpWDmmm0V2V2dYmip0FWgt3yVrJEloBvcVZ0hqMrlOqyxjWNTcNhWnyQPz85y3aBkNz8YIhG+D6H8HTDRmGEr04YfiBrSmZ4hr9x+g5UeMJJWOkszHDX0O1ivkzARJzSJEkBZlJDJGD22/Qdu32VgYwVAMbp/exdK08BPZmIx3W4mPFE8EufTTeGwx2wpZlxfBnIosUZxv852t83z41Md7ZPCiq/+CH1/8re7PV2x5M8vSKvtqQVc15IURWVNGlnXGajY7Kz4XjvRgKFV+eaDFgdoBXrI8zcZCinvnagwnFH45tok3nSC+zE8qaFSdiCUpje/vqHJyf3zBk6Cf7X8fz1vy6Yf9/Wfv2cqXniXuWQ3vXSQ0QRjeVnIwFInVOY2SHTKc0NhV8fizFaP8bN8BTh/owfbFhCWfEUpXPwrRFRVLNSi266QNA1mSRLCz2qTuOUw12xyX6+E1ayRShokfhqzJyd3K97r8INvLUzww36ZgySxK9CJJMm7gEEZioeiHAaaiEdeS1N0SYRRSiBWwgxZ+KCo8060qhqoTRiFxDaTCCiRkCrHFNNwHMBWNgpXCDX2WpPqZbApVmP17SfWzrRpmSiOuxtha8liT09hbnWVltp+1PYsYjDfww4CEZnLXjPC/G07muun1T1W84hWvYG5ujo985CNMT0+zYcMGrr322i7ZeWxsrFvUADjzzDO58sor+dCHPsTf//3fs2LFCn70ox9x/PHHd/d5//vfT7PZ5NJLL6VSqXD22Wdz7bXXYnZCuo829uzZQyaT+YPt6XSa/fv3L/h4C16ehWHIP//zPzM0NEQikegaKH74wx/ma1979Hk6TxQEUSQCSw0LJ4go2iETDR9LlciaEiU7YlfFI2fGaXmOWE2ohvhwBgFu4Hd72bdPT3D7zEy3NZYzEjS8pjDxCn0aboOcmSBjWKI91THKSukxQkRC8xkDI2QMi5snK1w/JvJUhBmZzEwrZLxRQpUVyk4d6ZAE3ncfDEEkFJJSWaXPklEkaHgRs60a+ZhJyW6wOJVFlxUkScbplJwtzWLL/C5mWmX8KOyk04eszg1gaTqD8Sy6rHLhyCcft2v1NI4dPn/fmwF46TWv/ZP7/ts5X2FH2WN/zSehKSiaQqXxxEj2/v1JEEDZjqi6ETePN7hnRlRtZloBaUNma7FN2lCxVIkf7p6jbIsq0QuXCkNQPwoYSarYQUTBivGCJZ8BhJHkSFLkEL52bYa0IXezxx4p7p17UIDw0dvfyG+nHvTeypnxw7Km+q00D5Sq3DtnU3VCNvSmmWoG/HqsyWA8zsVLBW8zoUv4YYil6cKKA1HhFR49NpvmDlBsN3ADvzuxqDgu1x9o02eJyc9gIkPLc7lntkl/PM31YxOM1eYxFY2RZA92EGEqEqqssyS5FkVW8Dtp8y3fxY9Cqm4VXTG7mWMVp4Ykyfihy+JUL73mIvzIxQt/gSOFuKFN238w1+0QpynqKNwALFUnjCKSutWN0mj7NifkdZK6TssX98EoelDcMt4o0R9P0x9Pd9ypF2St9+gQRY+yNXZkY33b297GgQMHcByH22+/ndNOO637uxtvvJFvfvObh+3/8pe/nB07duA4Dlu2bOF5z3veYb+XJIl/+qd/Ynp6Gtu2uf7661m5cuURje2R4JRTTuHd7373YdylmZkZ3ve+93HqqQtfaS14IvQv//IvfPOb3+RTn/oUeic0EeD444/vcoSeCthfC/jt5AznDn2MN6+7jKoTMt4Q4YxVJ2I0pZA1ZRGeqpmM1edJGSLvJqXHUGUheW/7AT2mStkW/eyW1ySmmsTVGMV2XWgeJAlFEuXo/TWhthNeRG5X8eUGPikjxroenbQhsb00xX/tmsX2PU7vT/O7yXlsX6Qyx9REN3BVlRVundrKnsp0lwOwrxawLK0SdD5EGcPC9kWyvdlJnk/oic7NQsbSDO6Zm6XHFJyn++b2YyoafihWkKf3f/RxvFJP41jiHScI2fsPX/AfvOXXb/iT+/daCjFV5ne75gm8gA89c/CxHuIRwQ4iBuMa5y5KoEgSL/jxa5iouaR1mZP6YqzNx/jlvhrnjyTYMu+xNC0UUDeM1Wm4Dj2mhRMI35233fjXlJ3vAuAEIW0/oOqEaDJs6F0YOfr3M+g29FpdDs3bbvxrfr6/hKnAnurnADhz4B85pa/AaErlzME4m4tVCpbM+Yvj/HBPiTtnJrl39iBeGLG1VGEgnqPlO5TsBkW7TtGukzeTrM4NkjEsVmYHuhlgS1IZXrDUouo4+KFw3R9J9pCPKfhhiCzBmp4h7MBjrD7Ps4Z7uOGgzefuvRdZOk+YJspql4gsIzHZKFNsl2l4FcJI+J4dqgh5gcecfZCSXeHmyZu6Dsp1b56dlWl6Y5kuv8kLva6opOQ0aXg2E415plsVeswcLd/FCyMG41kSmpiIlewGaSNOxWl2sx0zhuAzHVqwHhOE4aN//C/E17/+daamphgZGelylRctWsTExMQRzUMWPBH61re+xZe//GUuueQSFOVBr4wTTjiB7du3L3gAT1Sszqpcf+DB1cdIUqXPUvBCWJ2zOLGQw1KljquqS0IzsX2huFJlmcmmQ59lMhBPdm6EEffOTVG063ihhxM4hETkzETng+qxMjPAcCKHKivkjB6yRgYvFM67h3J0+uNpekyN26ddLE3i9ukSD8zXWJZW+cWBCX60ZzcH6+Ok9Bh7q7PcMX2Q/nia8UajWzZPahLbyx6aLDGYyHSSmXuRkRirzxBTTdzAZqZVBoTs9bmLV3VK1wHH9ywirolw2A35v3+8LtHTeJxx9uCfVtfsLjtcvb2IoinocR1LfWK00S/84Z8fVtX66GliwjHVDHjt2hxjVYehlN5Vjc7bASNpA1PReNmKDEtSKvO2T9aUGasHTLeajDeCTqApfOG+X/HDPe8irqnENY18TGZtj9VVhx4JXrz0s4wkhW/KF879CgNxhYG4wrL0u7r7mIrG80dPZmNhJc9aNExvzMDSJF68LMOeiviCd3wYThh8/YHt5MwECc0kZyZQJYWG59DynG51xNIMTEXHDwNmWiFeSJdPWLTrZA2ZiUaV1TmLneUpfnlgnIxhsadaYUVGE+Ty6n+iyApb5g92oz38KOxWXg79W3eFrcfm4hi7qzPoskkYiRDXmdYB2n6Dkl1hpuXQ9sU9sTfW0zGW7e16CAnOj81wIocXuixKDmMo8oPKOEWlYOWoOk1mW0KuL/hdJdFOO8qWBn8Ux5gs/VTB8uXLuf/++7nmmmt4xzvewTve8Q5++tOfsnnzZlasWLHg4y14IjQxMcHy5cv/YHsYhnjeE6PsfTTw2uO+xC9f+v8BInZjvBEwlFCYaor+9tZSmTW5AjvKM7R8p+PBoWCpOrqssjKTouW57KvV2Fz0sDSJE/J93ZWNLMkMxvOYqvD70GStY5QYE5wir4ofut3yraUZuEHQkeVnSRsSwwmFqWZI2pDZVPRIGzJlJ2R3ZaabgZbQhLvsyX3DqLJCQjdYnNI5vseg6oSkdIux+jy7q9NMNstdro/UqQQFofAbqnstynaDjGGhyRpO0Dpqipen8eTEq1d98Y/+/gO3XIoiSUiyROAFPHNxGkt94pClX7L88EwpIS6QmGk5rCvEOb3fYGvRZqwWsCqr8dwllsimkhSWpfMEEazJiXYLgKUKOflZg7FOa0jrtrj7rARjdZv9tcqjGnOx/aDHzfs3Xk7hfxg3VpwWdbfE5vk9XLF5PyXbFZYfrTan9pt8Z1uNqWbAXNvhlD6L/2/7Pq4fm6DmtJlpNbqTiDCKkCW506aXkCWZ5RkTS5X4zcQ8w8kcKT1GUtdJGwaqpLA0XeDuGZtCLEWfZbIsrZI2VH5YvJ25VpXV2UFkScIPAzJGiooj3KTtwKPkiLZaQk9wev/xpPQYE40ZemM9pPQsbmhTcqbww5DemMbvpg6QMxO0/SYZI07ZKWNpRtcbaVV2CFVW+Ohtm4gpSZZn+roquUOWBS3fYTiRI2cm2FEuYakG20rOgvmOT+Pxwa9+9St+/etfc//997Np0yb+8z//k9e//vX81V/91YKPteC70po1a7j55pv/YPt//dd/ceKJJy54AE8GJDSTc4dj9Mbi5GNCjbA6m6Hmtik7IdtLrY5pl7gp2YHXkbZHWKrM6pzGxoIgjWV0q1vd0WTRWtRlFS8UqokwCgmJhGN1q0TJFnlhMhJuIFQee6pFNvRaeEFEUpeYaQUoEuyrCg5T1Q25c2aS307O4IVRp/X1YNXqv3fXSWgma3uEf0ZKj2GpBoPxLJamo8gqt03t6UpQDVXvKMtkNEVDkoRH0dN4Gn8MEw2fti/+hs5f08u+it2VLj/eOGXA4vZp97Btv50ssy6v8eZ1l3HGoEnVCZlreewq23zwlCswFLnb9j5z4B9Znc1QdnxWZDS2l31GU8JlPaGZnNhrcrDR4KXLPsf5iz7OcxZ/ineccBnKoyyI/f5ECASh+5A7dRjdwK/H50nqOebbbaqOT8kO+fVYg3+/Z57fTNiC+xiT0WQR8nz+oix2ELEkJdRlh1LghT9Z0InyaQlCs5XqOnHvLBe5es8Mtu+xq9wiY4g4npcsF7wl4ewMA3GFnBkXx+tUgdyOPcjSdC8Vp4ntu5iKRslpYvttam6ZwXiWfCyJrogg1ZbXwlSsTiB0lqlmSMOziWtJElqauBojocXZOj/BSLKHKAqJqQb/fMbJTLf2d4nUKT3G/toc0815TEVH7Tj4nzWwGF1ROGe4t8s3OiZ4uiJ0RPjHf/xHLrzwQm644QaKxSLlcvmwx0Kx4LvSRz7yEV772tcyMTFBGIb88Ic/ZMeOHXzrW986LJTtqQQ3FDf0itNgZ9nHDtqsyggS5CGX1prTxlSFiqrlO52E+QQJ3WTT3FzXp6NgCblobyzTDUqNqXHavnCHNlQdAg9ZljAN8eEt2nVKdqM70QqjCFWWMVSJjQWTHWWHdT0aY3WR51R2ArKGQsvzOS7Xjxv4lOwmc21Rvge4ebLKmpxGQnO5b67MKf3CrygMIypOjbU9AzQ8m95YD22/iaUanQmZIHIvTr7lcbseT+PJgartE4QQuAE5U+XPzxjqEokfT/xPA8RDMBSJv1rzJQCK7ZCPnnYF7zlJ5JFNtb7OK1d+4bD9VVmmN2Zw/ViDfEym7kUsS4uU9HvnbM5blOeXY39LzW3zZ8s/D0CfdfSSzW+Z+gd+eaDFpesy3DjxQWpumxPyBlVnHk2WGE3r7Kx45EyVthdQtn1OLFhYqkRCU2j7AXfOVBhNKXxz2x5O6tU7OYfC2uJQgrwsSViqiaUZbOgd4Ee7d7J0VR/9lphIrh7JUnGazLZr5GPCXDGMwg5vqslko8LKbL9wjJbkTiXHYbxRYiSZ78r1C7EUOytTZIw4Y7X5DhF8P0tSeSzNEFEcmkmxXeeS1asEj6hdJGOk2FY+SM5IsL/ms6YnxJRjaLLOwfoUa3LHc+PELeTMOEtSI1iaQRiFXVNZXVaZbJbJx5K0fLd7nz0meBzk808FXH755Xzzm9/kNa95zVE53oIrQi960Yv4yU9+wvXXX088HucjH/kI27Zt4yc/+QkXXHDBURnUEw3FtkPaMMiZOooEy9JWd9JzqMcMYPsew4kClmqQN5OEUUTJbpDUpE7qe5yMEcfqEAYbbqNrD3/IO6jp2d0EekuLc9PEOBWniS4L742S7RBEkDeT5AwZVVbQZImEbjCSVFjTMyRSoVWNrPmgsVjDsxlvBMy2QkZSGllDZlk6j64I0vfds0VKdoOEZjLemKfitDAVnbtnd2IHHj1mipbvUvdaNNzGw56rp/G/G+//3aXd/+diGlXH5/ihJM9eZD0hJkEA5wwnWJcf+oPtv09MPsQZuv7gB7A0icvuv+0P9j936GO8dNnnWNOjUbAUBuNCNeqHARctLrC/JhLO522Pf77jjd3nvfOmhSnHDuGqnW+j5orP8y1T/4AsSbx0eZLv7pzlv3fPsrnY5Of7W8y2q2QMi9P64wwnVFb1mDxjUYqzh0xOH4gx3gjYMu/wnW011vZYrMj0Mt30GUn2iImArHRFGxWnhaWKwOf9tTl+sPsgb9swgC6rqLKMHwWMN4RQww8Dxht1So5QwQpVaZySE3Lb9GTnODL3zO5nulnpptZXnCb5WKYrZS/EUthBhBOIClLNbXdtSO6aGWdFZhExJcHe2mxXNWb7HvfMzVKwZEp2o2sIa2kG28sPkI8lqTgtWn6NjCF83TJGnJLdEIa3mkFaTwgfouChA0ufxhMHruty5plnHrXjHVHD/hnPeAbXXXcds7OztFotfvvb33LhhRcetUE90WAHEftrbWzf46SCTrEtlAWyJNEfT4sYjdAnY1hsmd+HKivcMTNDfzxN2bZJ6jo1t03JbjBeL6HLatcrY/P8HqIoZKxeRFM0wihkb3WWitNiy/w+8qbC1vkmu6v1Tn5YRLEtZOyHqjt9loypaOiKgu173aT5kwrD1Nw2s+0aZcfHCQQpsWSHjCRV9teKTDYqLM/kyBky/fEM+2tzqJLgOo3Vi6zLj1CIFZAkMekqtuvYgYcT/PzxvCRP4wmIj97+RpalFa7c8VbW/sfL2TTTYPdMg79cm2Gy+cThXewstym264dNTh4OpqoxlIgx2Qy4YsubH3Kft62/jA29vews22wtlfGjgF8dnMNUJH43WeQ/tlQ4LmfwsTvfyIF6nfdu3HhE4xYWHaK3dubAPxJGEffOOSgSrMtrLEtrLE2LfLH+eJqRZA9eGHHmQJy1PRqaLBZkJxV0VmQ0XrMmzUzLZrxR4tSOy3bLd2j5YiJgqQamqlG0612C9MfOeAUVp9VNbc8YFik9RkI3GYxneV3/6ZiKznSr0p0sbS56tLyI6WaVYrtOwUrx28lyp3IUCZm+3+64WIcYikW/FWdZRizU/FAES8dUk4QmvrIiQo7LLmJ7eZK5doV522NJyuLMgVEWJXr58pZN7Kke5Kf7DqDKCpuLcyQ0k9l2jTAKhVKuXSelxyjaddzAZ091iu2lKcZq80d0fY4IT6vGjghveMMbuPLKo2NMCkc4ETpauOyyy1i/fn03J+WMM87g5z9/8At2enqa17zmNfT39xOPxznppJP4wQ9+cMzHmdQktpc8dEX0uzVZ6hALlY4BmU4hlhJuz4rg05w5MMh0s0raMJhsOliacHSdazdp+Q5+KMrKGSPe7cVPNUsU23UqTgvb95httRlKpBlOKIzVA9p+wEQjIIyEr0fakKi7Lkldxw48BuNZJpvlbvttullBlRXqrkvWEK28hC7x3CVJ8rEkOTPOymw/xXaDB0o+Sc1ifX6UFZlB3NBnOJGjbAtL/L3VKWZbVcIo5OTChzGU5x7z6/A0jj6uP/gBrtn/niN67ntvfrCy8fWtb6HlR/TGxJdnww0Ym6qTThoYityV3T8R0GfJFNt16t5De7D8/vkYr5cE3yUUlhpvuOH1D/mcitNkaVpjf9XnYL1FPiZjaTrFdshQ0mC65TGSVHnj8Zdx8Y9+fUTjfs7iTx1GUD9z4B8ZSSp86Vlf5Q1rv4QXCv6OH4b8dN9BdFnlWcM9XLOvRsESk4ixep3RdA9OEDGUSGMqwlMobypMNss0XKdjpSFjB+7/z957x0tW1/f/z9PPnOlz584te/fu3b4sC7v0okGkKEaNxkS//mLsIqJo7IomBsWW2JKIARNL0GiIBWMHEUFEisDSFthe7t5eps+ZM6f+/vjMHVhYYJdddhe4Lx7zYO+UM5+ZuffM+/N+v4oIOJVEzMZgsovvbvwx/7Oxiu27jNXLIqfMbdFjZRipF5H0OCP1IgOJro5K6xVLsliaxIQteIa3js9yfOFhDyQR6+OhSCoFK0XFrbA820dKj5GPJSlYKUxFoxW0OKFnCZqsE0Q+aptjafsuXaaGpRqiAFNM1uYNhlJ97Kj6/Gz7bl44sARL1RlIFJiyi3z3oUkm7AZ+FDKQ6EJtGwhmDIu+dvbiIcE8R+gpwXEcvvzlL/OCF7yAd7/73bz//e/f47K/2CeOUDab3Wf36GKxuM9PPjAwwOc//3mWL19OFEVceeWVvOIVr+Duu+/m6KOP5g1veAPlcpmf/exn5PN5vv/97/Oa17yGO++885ASs++f8UjoMrvrLnkzIKkLOamMREgkuDVR1G6LhxRiKSbsCm7o4/geKzIpdlSrrMzmqLstyi27bSwmdl8jtSIpPUa5ZeOHISnDbLdxI3bXynTH4hzf3aTUCuiLK1iqRCuImLRDNDlkaVpia9nnjAUpcmacKbsqjBjbnZ2+eIr1UyWOzceouS6255IxLGRJYqZZI2NYLE238EKPe6Z3cXLvCizVYKRe7GT6iG7Tc/OP7tmG3wx/pGOAKYr7LOP2t+iz9l1t8Xe/Px8niPjBlnejKyrDtYBNsw5vO3oFVbfJ6vwsd9ZdXrE8u4fp35GANx717/xo63swFeEofcGaPYu0W8druMF7mbRbWKrEr3ZMA3Bct8Gmkstl913IRcfu+Zi5sd+EfSGGIrULjIDBlMJgSqHX0jrBy+9Yl+ODfzifjUWHX7ziuwf0WuYk8QClVtj2NnOw/YjN5Ql+vr1GSpd5cNbG9kNO7k2yfmqagYRBuWVz/4zHiT0GA8kcMhJVt8l0s4EXRgwkVFK68Earuk1SeoyFyQzvOyHLlF2lN57u+KY9VByh7rb4U+MB/DBgyq7gh2EnnmPKDig6ESm9SVKX255CJuunpzi1t5+0nkCSZLJGFjdwkCSZkdo4S9IFnMAjqVmoso6umLQCG1XSKbZmkSVR1E41fcKoRn8iy47qCCf2LGe4NkHOVFjXbXV82TaXdlN0Grx8iTDL/fGWcU7rM1mWEa7KDxVnWaIfwoiNeTwl3HfffZ1A1w0bNuxx21NJutinQuhf/uVfOv+enZ3l05/+NC9+8Ys7gWq33nor1157Lf/wD/snp375y1++x8+f+cxnuPzyy7nttts4+uijueWWW7j88ss7TpF///d/z1e+8hXuuuuuQ1oIDaVVWn5EUpOJqUqnkNAVtcMTMlUhldUVma2VSXqtDFvKJVZmc9w9PcviVIzdtTJbyh41T7DaVUnBjwKxg/Yc6l6ApcoUHZcdVZ8VGWH5P2nXqHshQylhgiis8/M8T4/x4OwoACf3dmGqOhkljqU6nWyxOY+MwaTK5pLDsoyOH4YUnQZu4GNpOkviWcIowgk8wRPwbFK6SKwuWDm2V8baoaxNcsbBI3vO49Bjw+zn6Y9nOz/bnsvvdk+zKjtBf2I7Z/R/+kmP8Y0H3smk7TFac8mZMmnd44GZFgldYUXmg/xu5GMszhgwJDoBc5YRRxL+etm/8dfL2Ot4bFVWww+F4EBw7QJmnbCdyeWys+Lz+TvfwZK09piQ4fesvZx/u/dCUrrKsflBVucCtlYm2Vax+etlnwWE4/RFN55PQj9wUu4X/+w/O//OGuJ9vnrrNMfkVYqOy18uSzPTdBhMphmuVdgw0yBrSjQ8jxknoOyG3D7R4v7ZCf6sP0O5ZVNxQ1ZlM8LQVZLaTtJZwigkZybEuJ0qG2ZLLEsnuHdmipN6+gmtiN/tHsMLIxYkAgaTae6bKdIfF3L1pRmF3+22+ZtV/Xz3oTH+aplMzhCGsSEhnt9C03VUWSciZF33SoLIJ4wqIvuw/XVlKBZRFBJXY6iyzF1TNsd1m/TGM8KNP4qwPRuA164Y7MQfzdkAhFFEPiZ8k47uEgWqKis8VJzFC2FX7RCOxubJ0k8JN9zw1Lqqj4d9OkO98Y1v7Fz++Mc/8qlPfYr/+Z//6RgZ/c///A+f+tSn+P3vf/+UFxIEAVdddRWNRqNTYJ1++un87//+L8VikTAMueqqq3AchzPPPPNxj9NqtahWq3tcDhR5U8H2RRs9oZnChFATvJ8witAVIXuf657kzARu6LMgYfGjLVNYqkTREYTrIBIt9ut3zzDdFGosgJ1VD0USnARNhrV5MVJY02WgKbA8o3HnpMtAwmRpWpiqFZ06rSAkH0siS5JwRW0HKTq+hxv6mKroXo3bPjUvxFINxm2/7RzbiyqJtnSvJXZBlmawtTLZblPn2DC7g5yZaEtoow7Rex7PPDxY/KcOZwzg/7a/j4RukjYk8rEkdffJSaLfeOCd1LyQB6dtKo6PLMFMM+D+6TqKLHZibuATRPDSpQlqbtRxEz4S8Q8nf/0x12myxA831yi1gvbvvEQQRWyt1Ll7ysFrf/dMNX2u2nwRIIjMF9/ydi6//0LWdXeRMeJU3SYj9SIrMn0c1921x3O8dmUf5wweXAuK16/6Gu9ZezlpQ8JSZR6Y9ai0WpSckHunS9w+4bKp5PHgrM/Oqs8Dsx4rMhq/2Fqi5Uf8drhExrDImwq6onQ8hXRFnNfmNktu4LMq18+kHeAEHkvTqY4M/XWr1uGFEQlNFJEvHVqJLMmc3JvkzokWBUthyq6yKqcSRnOyfY2oHb8h8shcgtDHCexOwnwYReiyidz+ygoin5CQNV1D/OXShZ3Hzvkeba9OkdKt9mawRdVtYioaqiyjyjKWarCtMoOhSB1LAtuPGG3zLg8Z5kdjRwT2e6t27bXXct555z3m+vPOO4/f/va3+72A+++/n0QigWEYvOMd7+AnP/kJq1evBuAHP/gBnufR1dWFYRhccMEF/OQnP9mroeMcPve5z5FOpzuXhQsX7veaHg1dUZElQTLeXK51fDISmokqiR1N3kx2ujDlVoO66+CHIYNJhZITkY+Z3Dvd4AULshiKxNK02N1sq5TZUW2yJC3m3UWnhRNENH1RvMw6HuONkNF6wHlDKbaVm7ihz5fvEiTAldkCBStFQjMxFY0witruqgpVt0m8vc4tJZ8/68/gBF6nbW8qGvlYshOYeO/0CBuLYxzTtQhFVpGQ6Y+L0UY+lmRFpo+1buqA3895HBpsr/7bHj8vSa9BV1TcwOdnO97fsX0IQzpuuv+3/X2d+//d78/n0j9dwGfuEJe/+/353LC7ydWbRSE1mDb4v80lvBBW5+OcOxgDBM8iH5MZSJhM2Q/nVT0TcNPY36PKMj3tLsYPNpdZlDSpuwGFmMJxBZMTe3SGaz5bSj6VtorrtSsuo2AJf56MESelxzix8A/0tvkmfhjyuxHhwn7ZfRdiKhoDiaenuzpn7Hhan8nOqo+mSCxIqLxr7VKWpFWypsgnXJHRqHsh5y4WOWhLMyq98QyDya5OF89SDSxVZ8oWn7mMREI32Vwa5+WL+7ltQhCn75wcaXNwVvLJwWOFY77X4u7p7SQ04VG2IKlxbF6YGo7WAjaXa1TdJl7oYakioX5LeTde6CFJMkHkM1qfJGeKTZobOjiBTcmZRpFUNFknikIemB2nEBPnpTmaglCb2WwuT5CPJckYcXZWZ7BUA1VW+OJd2/DCiG0VH9sPuXFkmJwh8/9WDD0tn8k8jmzsdyHU1dXFT3/608dc/9Of/pSurq69POKJsXLlSu655x5uv/12LrzwQt74xjfy4IMPAiLItVwu89vf/pY777yT97///bzmNa/h/vvvf9zjXXzxxVQqlc5l9+7d+72mR2PSblFxIzQFCjGhzBJ27obID/NdxhplbL9FGEXkDBH2t63cZCilUXFDbp8QXiOTdp3zFvVRcUPimoomS/TEFNwgYFvFZ7gmTqzDtYC8qXDTiMODM00qbojtuWiKONF+7ORl9Fk5TFVv55XJHZmrKitCyaEJE8eEbpLQ5XZnx2WmGbYl+gEpPYYiK7ihzwsGVrOx5BFEIgi25lbptXoZq5eouk2GazNssZ7dyczPJixJvQeA2yYuAeAXO64F6PxuhFHErGNzUm8ePwxIGTEc3+PrGy7ktolLOHdRisGkwkwzpO5FNHxhMPjQ7gq6IjFteyxIGmwrO5zQY3Z8eU7tvYSXDvXRH89y9mCMv1j85cPy+vcXt01cwm+Hp9hUavGiRUmKTkjOFIXjyb3iC3xBQuH+WRG7Iboa8JV73sHu+hWszed429H/zrFdF3NyzyfYXb+CXmsBI/UisiRxdE6EUF507OWcWPgHzlv0zwBcs+vDB/V1fPykr/OB469gSbpAX1whrYtMrv+4fzs1TxQJi1MKQymNfEzhxB6drCmzICFieaaaVbaVZ/jD6HSHRLyrVmOmWSOhJ5CRaPoBbuhz5oI0vfEMBcvkrqlhJF7I/9Y34gaCk+OHIf90xwj3z4yxLi8S44tOizMGkhRiCvlYElONocgqMTXBsswChmtCuq/JOgldnMPSRhcSojjKGF2oso6hWCS0DC8YWE2PlWVBPM/O6jRVV0j+TUWnP55BleTOps8PRSD2Xy8XhdOcueS67i6ypvAoCg9p5mpIFAUHcJnvCB0M7Leh4ic/+Une9ra3ceONN3YSa2+//XauueYa/vM///NJHv1Y6Lre6fCccMIJ3HHHHfzrv/4rH/7wh7nsssvYsGEDRx99NCDyzP7whz/wta99jSuuuGKvxzMMA8M4uK34LlNleSbDDSOz9FlC6SXaxq22uiJkuFZhMJlGVoWPxWCyiwdmd/OLHQ2GUioPzbrcOxXw8qUJtpSnWZjQmXU8puyQpWm1TYwOOHPAYNIOKVgyrSDimLxBsaXxV8sWMtYocXyy7XXhu5RaQtbqt1vClhZnyhZkdZEcLVxiNxbHyJuiJbwsk2N1TpAgJ+xKxyl3QTyPGzi8aLCXm0a3c2y+l6Ij/IKWZnpp+S5u6Hf8OebxzMFcKG7OTLS9XoqdcerCZIaUbvF/23bxF0sWMpDMsUzRcAOfG3dXmWoGnLHA5Ofb6uwoOxQdj7VDGYIw4g1HZ5iyQypuSD62557q9L5PAnByz6F+tfuOd97wNv79hQ8HNP50+xhe2w1+LoZhaVrlzskWfXGF7RUPWYJ1ebG5CSJo+RF9cYWHijs6BHTYk4RdsD7HlF3l/tlN9OxlGpbQTS6+5e187vT/OKiv747JMRanhH/OaMPjpB4NJ4hI66IAmWnWsL2IZekEfhSwrWLz0qFlDNdmKcRSLEr5TNlV4QmkCkf93+1+kDVdvTxY9IASR3f1MVybZSjVzfZwCrxfdziPIhA14tq/fBM3jd1Azky0M8CSrJ+qcHp/nnLLJmemqblFknqOolMmoZnE1ARNv05cjVFuVTEVC03WMSULJ7BRZR1FUvFDF0OxKHnTbC5PcHTXQjaVRnGDgP54Frt93pozhnRDH1WW2VV1KbZCTEUibwp+0NJ2Dpx8KLn9ByqBf47K5w829rsj9KY3vYk//vGPpFIprr76aq6++mpSqRQ333wzb3rTmw54QWEY0mq1sG1BdpPlPZeoKArhIf7wE5rJlF2lxxLy+bFGCTfw2VqebMvcq2QMXVjCyyqFWIrh2iyj9YAuU2F5RuOoLp2/WZWiFUDdC9lVdWkFsDKro8oyI/WAU/t0al5E2pDosUSuz6l9Xfz5UHcnIyehmR0SoyxJFJ06uqxQ9xyC0CeMQpxA3L4gLrhEdS8gZ8oM12apuk3KLRtDMRhI5NryWJn7Z3cJQzPdxPaFEeRIvc5IvcgDs7tJ6im6Y3mSeu6QvvfzOHiY801pBYLHVoil0WUVXVZYlRVfXiO1IlW3iaUZnNoXo+L4DKXSrOwyedvaLl57VJ6/XpHiVSvS1L0IQ4UggkXJ5OF+efuNVbk9wzULlkzZDUnoEvfPVEgbMrYfYagSsgyWJrG6K87GkocTRAzXRMhqxrAeM/57pBKt7jpYqt4xXn00nt/3qYP+2u6Z+SxpXRizDiRznNbby9JMHkWSGK4J8nDOjBO0XepTeozxhvDrWZIWrvK9VoaTelZRdOrENeFx9ubsGuqe0yFmu4HPTLOFpepYqs7PRq5juOZw70yLjBFnxglQ5XPb5xqbuisMY1flYrhBQEIzmGjMMtYoMd4Yww/bEUOeKH4AuswckiRTbs3S8KvMOkVagfh+kCQZTdbbBOgkumwylOxmTdcAAH3xAoVYAYDtlXJHmbut4lN3IxK6RKklbElMVWNRqpu++KFUjR0oP2i+EDoYeEpyjlNOOYXvfe97rF+/nvXr1/O9732v0x3aH1x88cXcdNNN7Ny5k/vvv5+LL76YG2+8kde97nWsWrWKZcuWccEFF/CnP/2Jbdu28aUvfYnrrruOV77ylU9l2U8ZCd1AVxQSmsR4o8ZgsgtVVgTZWFZY2z3EioywkHcCl5F6EVPRWNut8f9WLGBjyWNNlziGF0R0xzQMVWIwaTLa8Nhe8eixZLFTMxUWJMQJYFmmRxiQxVJYqsHK7CIc38UPA2yv1Z53i51axojTCkRumCopgi9EiCrrbKv45GMGRafFikxf2xXbRFdMlqT7cQKvbYyWJaYanNrbhxN4nNy7kKNyQ5xYOIpWYFNplSnE3nBI3/t5HFzcMz2MochM2RWqblN0BpwavfE0GSPOUCrP3VNFdlanGUjkeP3qDGEUctZAhlN7+zhrYYbT+haiyfCNe2fZUvJRJDpjnmcSam7IP9/1js7P71t3BcWmz0wzZLwRsKUk/r2t7HLnRIvbxx2m7CbH5k36LJW+uEyXqTJhN7h9os6VD+09dub0vk/iR8ET2giszR9YvtUjPZ0A1uU/xs1jLWzP5dc7J/jj2AQjtSKTdkBSl5h1PNZPVah7EUWngR+GDFddbh0fZsoudgq763dvwFINVmX7eOWSl0PfUcw0a/RYMsfkRbCppUpM2MKzbFvFZiBhoMkihDrXLphWpI5rRwMJ7uL6KeGnltDMTtahpYroiwdmp9EVkyDyCSJfjPEDhy6zBz90GWuUMBQLWZJRJJUg9EkbGXRZJYh8/ChkZ3Wa4dosumzihqL4Gkql2Vxq4oURZywwOXdRiqOyKSqtiLMWduMGPg/M7mb6UXlu83j2Y58Kof1VXtVq+/aLNDU1xRve8AZWrlzJ2WefzR133MG1117Lueeei6Zp/OpXv6K7u5uXv/zlHHvssXznO9/hyiuv5M///M/3az0HirF6mRtHmhSsGFlThACqsig2LFVnvFHEj0IsVafcstvW7Q0WJCwsTURfrMr1M+t4vGBAnDy8MCKlx1iatliW0UU2mB/yYNFlZ9Xm5tE6m0qTbRt6F9tv0Qps5LYqAoRqLGPEsVSdB4ujuGHA9uoUvxmeYGtlkiAMcPwmf7l0ISk9xqm9i1BlIZ8PIl/M3EO/rRrLYPtVFElI7ldl+8kZXbQCm2ZQx/aFAeSM89+H9L2fx8HDmq4BUoYg0te9FvlYss1fkfHDkLrroMoK5y5aJL6cNKOTzv2/m2cpOnX6E0J6b6kSHzq5m3MGhTLsmYiPn/R1lqR1vrvxXZ3rrn7ZlQCkDRlZgtvGaiR0hbShsLZbjNwTmsmME9ATU5h1fJaksyQ1me5YvHOcm8b+nm89+HBhdEb/p1ElhR9sefde1/LaFZftsY79wUU3ns94w+czd1ywx/FP7BHd5uO6TRanxZqXZ3QsVRIGhJpEWpfRFTEqf8mQxU0jDn4U4gY+X7l7Uye02dLijNvb+O+NPyIfS9JjJZholDuqWTcIWJLu4xv3ihT5kbrwUCu2Qt530/kgn82UXeXe6RI5M87z+7Ok9Bh+WxWWNROYaoxNpSm6YwabSrvwQhdV1gmjEEVS0ZUYhmKxpmsxAF7oCoVZ5JPQsvTFC9Q9EeIqzsMWVU/8flfdJnXPQVNgW8Xn/lm3o5hdkTW54r5RRusVis4hjteYV40dEdhnQ8Xx8XEKhcI+HXTBggXcc889LFmy5Anv981vfvMJb1++fPlhcZJ+NFRZ5pi8ypQtdhN5s8WMU2Mo1S24OaqB0y4U5gL7ClYKVZaZsqssTWeYadb4q2XP44db/sjCpMXyTLxTyIRRxHTTo8tUObHH5JqdVd62ZoiiI5xY/TDoZADNEaKTukXLd5lqVkloZlvpISEjYyoSA4kcM04NPww4KrucijuDKutM2UVWZRcz48zQZeYotWpt4myIImvE1RQDiXbXya8iSTIJLcN4Yzvlls2aro8ezo9iHgeAjaVxVFlmSbrAvdMjjNRnsT3BI9tcrpHWJY7u6gNgtF5BleVHhFwW0BWFKbvCQCJHj5Xggdkq/3nfJBcd332YX9lTx4TtPcYc0VQkZpoBmiLx4sUp7hhvktBFsvzmsscJhYiBhMmOapOemPh7z8ceDisFUfic0b/ncwnp9uN7Bz06BHZfkTMlcqaI2JjzNXrfTedz7mCG3fU6liqzpeywpksUcoYiU275LElbJDSTj/1hO58/I4EsSfx/q3L8ZtcEIBz1627E9bsnOK5QZ6LRJKUrFJ06fxVfxR3yDGEU8q/rZ3jXupAbRmY5oS/JH8YqrOsWm8Jv31ckoSvcOXUpr1lwFvfYD7G9MkUYhZiqRsYQyfTbyhMcm19BsW0K6QQuiqSyqzbGSK1EfyLD4tQCgshHk3XqXhldNtla2SWu90q4gdPmShoMJruYsCvoisrvdm+m1ArJmzJOEHFiT4IfbC5xy3iZwaTKPdMep/SZ1L0QTZaotvzHfa8POuZ9hI4I7FMhFEUR3/jGN0jso9zT8545ctl9wdayy3DNp9IKOLFXKMV6rQyO73VMFYE2cbmKJesdf6CxRqmTp3PV5j/SHzeouy0SusHuuktCM5m0HSbtEEMRviVnLDDbJMK44HAoKqYv7O51WURlhF6LMBImb34YMNYosanU4s8WdLMqJ7o8Gd0ioSeYbk5Q9xzBC7FyuKHDzup0p6jSZYUNsyMUrBQkQJV13MBGV0zcwKHutXd+8jxR+pkMU9FI6AZFp07WNHF8j5yZYGe1QiGmULBSPFSc4Jj8AtZ2D+D4HmEU0R/PsmF2hIRucFLhGGadSSxNJ4hAkaRDqrI52Hh0EQRgqGCoEooEu2sBJ/XFsFTRPL9vl81JPToJzSSttxipB5hqA02mI6V/PLihz6uW/gsA39/0rj3iMvYXL7r6b/nNq0R39lOn7kmyvuy+C1maUdlerZM3FSpuQEKT2Vn1GK4FnNSjcc+0x9FhxDH5OB89pY+xehlNlvj59go5UyaI4JzBLHdNVciaMhtmGhxfSHPPdIXBpMp13i6Wpvv4zfAm/uaoNKN1n9P6kvRYNpVWyEDCZKbZ4qOniCL5xMI/QHAdfj2g10qzsTTNH8eavPnoPLqsMpjs4uc77uKlQ8u4c3IHOTPOA7M7SBmxNheywYqMjoYgSW+r7GZpeiFFp8GytErVLWEoBoZiYLdFJLIktbPEQuquEKWkEZvVQkzc3h+PY/t1HD/C9kK8ELqOMCf0eTz92KdvtsHBwf1ShPX29qJp2pPf8RmCIBLKkIKlsK0csDDRIGMIHo8sSdRdp9PmLcRSzDg18maSclvG6YchYRRScjySmktXLEbJcVieEaZrQpERsLPqc+ZAnpwpdme212KiUXnEOOJhwqWp6JiqjirJbCyN0fQDBpNqu0VdQJdVNFnD8ZtibKcZ6IpJUuvCD1sc3bWQIAzE/RSNEwpLAWj4TWKqKOoUSWWmWSNnJliaXkjNrbBh9vPzXaFnKP586AtctfkiwigiZZgUrBQTjQoDCWEAur1SJqHJbTfhoK06tCi7Nqty/W1SvuCC/GF0msGkypfOXExKjx3ul3ZQ8b51V/CVe97BlB2yLKMy3gjx9JDRuk+3peGF4IcBOdOg0k5G747FKbds/jT5KU7u+cRejzsXwwEwkHzY3Xtj6Qusyn4IEJltyzK5x3X4/sGWd7O75vGXy5OPeewcLjr2cq586J1U3Eh0jzWZrCG3R6I2ffEkYTTb9hsSIaStIKTHSvDq5UJiXvccErqJJlfZUvI5Jq+1x6kyyzI99Fjd7KqNEUTQHzeQJYmJRpMdFZ+TegR5OWcKAvVdUxVe/cs38f+tEs7PvVaaghXjxYvEeG0wmcdUY3THDIZrs4w3AmpejbMWLme0PsuargESegJdMZlpjpOP9bEg0cWu2hhL0gWCyMfS4gSh384gUyi3GhRiKdZP7WRxSiGIFFZkc2wtCyuDW0brLMvFCKMKu2sByzIq90y1ePWKJIPa/tvAPGXMd4SOCOxTIbRz586neRlHNsJItL6DCFZkVExVGBfOSZEHEjmmmlVUWcYNAupuq13g6GwrzxBTFZp+wJayx1E5A8f3SOo6U3aT/niCHdUaLxrsQlcUUroosOquQ8YQrWtZEhlAgh8k4/gejt+gYKXxI+Esvb1SomDFOmubalYYSBTQFYOUniWIfJp+HVuq0PTr7KxOty3pkwShT8Nv0h0TWucd1UkGk11UWmVO7/sku2r/zsLEO57oLZrHMwSvXXFZ59+/2PkB+hNZ6q4Ix7RSBgnNEGagkQjvNBWt8/tXiKWQJJkNMyO8evlytpYnqbrNZyRR+snwvnVXcMntFzBlP2wNsCStMd4IWJZOktBNfru1xKqchqXpIiIijBiuzeyTZcAjC51HFjJvWf3v/Hb3xY/7uFLLJx+TeeNR//6Yxz4SQQRpXXREHpitsjitMWkLZ2dVVjilV2fSDgkil14rzpJ0QUjNAx839FmR7aPuOpzeX+CG3ZNoMuRjCVK66FrfNbWJ9VMNTu1NExIiI3PD7jKV9vrSRkjJCRlMupzSm+OYfIvRerPdDRrrGMWeOZAXthxt77OZZo1zFy3iVzt2ocsmGSNOxsihyTpu4NAdG6AVNJi2K4JaoFsie0wWn4Htu0zZFZzAI5WIMVr3qXkRg0mFuttiMJnmOw9N8upVabaUfP442mBN3hJ+SwtjTNg+hnbgaQT7jHn5/BGBIy8E6AhEQpcYqQdoMuyo+rhBgO230BUhlXcCDz8MsFSxM9IVFdt3uX9mDBCuspN2yJounbGGR0qPcVrvWtZ09bK5XMNSxQkrbyaxvRaO75IzE0zYFWacdhp94HWk7rqiMN6oMVyb6RiErc4VmG0KG3lB4jbaJ48WDb+KF7pIbbfYqWa1HZQ5w5RdpOE3kZGouUWavoMbiGTnuc7PouTe1TDzeGYjZyQII5E7l48l0RWlM+adG/2CkEgLt3KhbFqV7WP91E7GGg2OKyw+bOt/uqFIsLno0BeXGW+IAmDTrM2kXafaajKYUhmpB/zF4i/zsqEvYSji7+szd1xwQKKCcxZ+7nFvu2DN5Z0i6ImwJJ1lcTqHG/ocV8ixq+bQZarU3Yid1TILEmlWZGPkYyZO4HH/zBg3j07h+B4JzWS4NkNIhIzEST3pdue7Rd0TVgDHdC1iXbfFbRMVVElhU7nGUFrjhQvj2H5EIaYylFKJqQpbyuVOfEvGsMgYcTRZ4uSeVNuhP04QBhSsFAPJHOVWg9esEOkCWSMjNn+BTdGZoRU0uG9ms5D5pwoYqsgm214Zp+W72J4wta20WtywewRDlTixJ4EmS0w3GziBy3EFnZYP07bPi4eSGKrEb3bZ3DXpUnPDedXYcxDzhdA+4qbddUqOIENMN1sUHSH/VGVBHixY6U4uz0AiR6+VZmEyQ1csRhCBqUqYqpg9D9dq7KoNt4NNRatdkAdj5GNJutp28XPuvxnD6jijuqGPpRrUvIiBRK5tFCaCBBelRLtdk4WaLYxC4loSRVKJopCG5wg5aSDIgMvSvXSZKRJanJhqMmGXsX2XZZmejivxPJ69OL3vk5zR/2lShvjiEzJl8bshRj9CBaXKCptL4zi+IAObaoxSK2C45nPL2NbDtv6nG5N2yGDaYLQesCqnsrnk8o61OSpuxFijwcaix2DyYfKz7YfoikrOlPnoHw8sFPJPk59iw+znn/Ljq26TiUaFSkuM13OGOEfIkshSe7BYZrjmoMtCMfbTbQ0Gkwph25fmptEam0sz1D2HMIrwwojpZgNT0QTvsX2/Px/qx48CVmaSmIpEsRWS1GSqbkBMVciZCWFBUPFJaHInE2z9lMcvdpQYa5Soew1AcNgKsRTllo3t2R2VbNUtteM1PMIoJGNYFGIpxholmn4L27Pxw4CdtWlun5yk6DTwwoiaJ87XdbeFF0YcletlZ9VmcSpGEEUcnTdoBRETDZ8VWZ2tpSa/391gsnEI88bmVWNHBOYLoX3ALaMOZy9KMpRS8UKYskPumLRxA0FSFoaLFWzPZaZZo9xqsLUyie238MOQBYk0J/f0cGpvH/1xjf54HFPVmHEE/+b5/YtxQ5+xxgzDtVmxs/GFSVnOFAT1uVFcQjPbKfEx4f8RhR01iqloVN1mx3E6iHxKrRJ+6HLH5FZxUkOcrC3VYGtlghARctj0HZalh9oF1DOY/TqP/cbLhr5EIZam2nqYTwZgtotpPwxo+gEbZkeounXWT21leSbD8/uznNSz6DCv/unDZWf+J6YiMWmLL5vnL4hx81iLZekEQRSxIKGQ0B4uhN6yWnRq+uImL1hgPuGxfzP8kce97Z6Zz2Kq2gFx8XRZZVWun+uGmwzXAoJIkLmPL2RJGwbH5nP0WiqbyzWcwOUVS+PoisLumk3dczhjQZKKK4JQLU1nSfrhzMHBZB4vEJ3tm0Yn6I9n2VWr4wQRr1u5lGPzSYIo4qFii8FkF6f0Zji6K4bthwzXZtlcrvKyxVletjhLf1yMZkHYgUw1qxyVG2BrZZJbx7cD4AQeMSVB2ogjSzJ98QJJPcVAIsdEo4wbijgP23N5/arnc+bAcSgSHF+IM5SyyJlx7p/x+NPkOM/vX8jvR+oclYuhSMI5/OyFJif2JHj+QIKXLEmQMQ/h1+J8IXREYL4Q2gesyOnsqvr8dneTdd0WNU+Qp7eUhR38REPINAtWSvxhey1un2hgKqKFPGVX8aOQmWYNU9XYVBaydl1W6bXS5GN99FoZAPrjGUIi/DDsyOWdwGubIKrCw6jNE5pL9XYDkThfdZuCr2RX2Vmdxg1amIqGJAnn2OHqLKP1UQqxFD1WnmXpXpq+aFkXW3V2VnfjR+Hj8g7m8exFGIX0J7LkjDgzzRoj9SJ+KHbGsiTRFYvhhyGyJLEq289vhmcpWGk2lsYP88qfXjhBhCZLjDdClqWThFFEuWWzIGFxdJfF3dN7+s7kzASvXPIVXr/qa9wz89nHPe4j4zgejXX5j3Fs1+PzhB6JS/90wV6vz5kJju26mL9ZmccJIkxFwgvhzskSKT3G7lqZnVWPjUWP2WaTuKZSbvnsqAbkzSRTtuj+zWWNFZ2G8A6yy8S1JH8Y28kPt0yzuiuOH4ZYqoSlSpRbNlsrdbpMjaVplVvHt1P3Wvx+pEF3zMBUNUxFwvZdErrRiTIJo5Byyxb5iW1H6aUZEfsz06wht208wvYXvx8Ku5KCJVygTyis4MyBk6i6Re6Z3sT9Mx4zTYeS47B+qoKmSDy/fyGqJPP6oxbhBj6yJMxyDUVmY9EmoUl8+74iI4c6gX4ehx3zhdA+4KHZFneN1yg2fe6YbNAXlzEUcZK8dbxIsRUyVi9TdUUyfMFKcUpvHD8MmG6WWZHtJYxCbL+FGwgjts2lCWy/Rbllc9PonUw1BUGv7rWQkTpp4E7gdbo9uqyitosaIQ8VowxxchGjs7r3MMka4M7JHSiSIHjXvJCt5UkeLI6ypbybuJak3GpQbtlt59WF6E/gczKPpxefueOCAxqHHAhO7b0Ex3cpthodpeDDCkUR7bIq14cb+Nw9vZuzBjLIksQ90/ZhWe+hQtqQWZFVafkR39wwRdOPuOzuIpuKDW4Zb5DQpD2ME+fy+a7e9l7W5T/2uMe9eXzvyrL9xUuGBvZ6/Zxy7bxF/0xSk8iZcQoxlawpdaTySV1iMKnyQFG4NwuPNBk/CsjHhHv2r3dOAaArCqaqsSRV4Hsb70GTJf7figKrc4twQ5+krnNKb47rd09Qc0MGEjnysQRBBLtqDsuzKg3Pw/FFXltCMzAVnZCIkIiq26Q/kaXhN6m0yu3QVI2KW2emWaPhV3ED4RANYCjC5LE/PkDOzOCGwubDDVpcN1xmdZfGaD1g/ZSLocJgUnhg2b7Lr3fupj+RYW13gv54llYQsqYryQsGevjimYs5ofsQxsVE0QF2hOa79wcDT6kQ+sMf/sDf/u3fctpppzE6OgrAd7/7XW6++eaDurgjBc/rj3FMIc5RXQb5mMzRubTg9YRwVM5gyg6YcYTCZqZZ6+xy7piscGrvJe0CRgQdllsuvfE0liak8P+7eQxdUZCRUCWZMAoF10hWRSp84GMqGk7giV16FLalqXGcwO34+8iSRBhFOL7H1VvHKLbqOIHHuu5BFEkloZmMN0IqbtCW9Ac8VNwBQLklZvTDtRFW5x6/ZT+Ppw+X3Xch5w4uOKxrOLX3krYhnU7RqXeK7WJLRDD4ofCtWpQS/jJ3Tu4mocn8YucHDuu6n07kDBnbi6i6IU0/4KicRtMPWZTSuWZbmXxMZqb58HjiLxZ/GaDjF/R4OFj5YicW/uEJb3/ZT1/PB46/AlPVuHu6he1HVNyw/RmHneJnR8XBUiWGUhp/GJ1mtN5k3PapuRG7qiVG68IxX5ZkTu7NsSyTw1Q0tlfGcXyvzZusU/MiSk7IrRMT/Gx7kQdnPcIQVueyDNdEiKvtR7ihUKdNNMqoktwWhTSEWCTwyMeSVN1mm6QvukGKpOIENn7o0gpsdNmk6dcxFIusUcBUrHZsjMy2is/a7jin9Zm0fDix0IOuqPhhwGl9XfhhyGyz2baJEDzMsXqJm8dKFFvNJ3xPDyrmVGMHcpnHAWO/C6Ef//jHvPjFLyYWi3H33XfTaonWcKVS4bOfffxW8DMV3934LrwQzhgwWZxSWRDXGK7V0NohjBtmW8iSRFoXarEl6QJVV8hEd9V8fjP8EUIiJuwKU3aVghVjw2yJSqvFlnKD5/WbFGJp/CggY8TbgaoyTuBRcurkY9mOPfxAItdxsJ7r+AhTRPExjtSLWJrBOYNZpuymUH5EESm9i4FEP8cX4ixNZ7hlXJCiTVXDDQTZOm8mGUzufXc5j6cPF9/ydq7e9l5G6wHZtn/ULeP/yG93X/y4cQxPJ07tvaTTUZwz3MzoFr3tEcSDxVGqbpOXLl7ElrJPjyV+d56N+O7GdzHTDPjl9iovXhSjN6EzZQectyTNL3c0cMOQm0YcKu6R+2X0i1d8l8/ccQG6rHJCwaTejkNxA5/lGZ213XGGUiKVfqYpCl1TkdBkMeo6byjF8kw3YQSO73HfzDB+GJDSLVRZYXGqh5wZZ7gWUHUDLFWi5kVUWhFhFHF6f5yHSh4TjQpeCF4oiNe3jovCx1Q13NAnY8QxFZ2iIzrUYRQx06yxszrNqb1raAU2fuSiyTqSJKPKOlW3hCbraLLOL3fcwlRzip3VaUxFYlVWKGfzsSTLMg9HH82FVU/ZVYZrAaaqs7EovIduGnWQJQnvUDqEznOEjgjsdyH06U9/miuuuIL//M//3MM08XnPex7r168/qIs7EnDreIuaF7IgYZHSFXbXPAqWOFnsqAbYfsTqnE7aMHADscM5a+CznNp7Cd84+5us6z5aBKC2XaCFrB1O6hkirQvzOmH+lcb2W+3OjiCo5mMZ3MBpJzsbVN1mx7xuTi0meEOS8M3QY6iS3E6OjnPTSI0Ju0LDqwBwXPcKClaKE3sSDKXy9FoZlqT7cAJRXPXH33bY3ufnIv7t3gtx/Ihbxhosz6gsC/LUvRan930SVZbpjaf5t3svPOTrEsWxUDhW3SYhUcepV5YkMkacndVpTiiY4guzzVV7tqHUCrH9iON74ozbPqNVl19sK2F7EUvSKut6Erxw4VM3k9yX8Vi064tP+fhzePGiAe6fnUKVFRK6hKFIbCqJDexM06HuiQyyhC7xULFGsRUiS8I13FIN3FB0hqaaFeE+D+ysTpMxLFpBi+2VKU7qieOFEQMJhcGkwuKUwssWZxlK5Tm6S8NUNZKaxB2THn/Wv4Cjcxo7q0JwEkYRddfBDX10RaU/niFrJFnTNcCyzAJagU0Q+theAzdwsD27HaQq4YUuX7//D6zMFvjXu7cx1fTpiyssSKQ7ETG98QzTzUabo6lz2/gsDxZd7psWdgDrujWuGx7lNSt6MBWJrHFgAbjzeOZhvwuhTZs2ccYZZzzm+nQ6TblcPhhrOqIwmFRZldW4c7LOmq4B1nZn6bUyLEikOXNBmuO79bYsVeL0vk8+hgRZiL2BFZkPMmF7HXn9aX0L0RWDMBIeQ3dMltlZncbxPWzP7SjDNszuxAk8QZZWNBKaIcZfj7huLtF5QaKLpekBbN+lYKW4a6rKRevWcudkic/e8Sf+Z9Od3DuzhZzRxVHZhWSMVEcddlR2ISP1Ijtrl+3tLZjH04Czf/w6AF63ahBLk3h+/xK+vPMWsmaCjaUvYCoaffEc71n72AiIpxvnLPycKLLbI1e1HfRbdYVPldruQI41PGxfmAg+GxFEEU1fmPGNN4SUfl1PAi+MqLRCcqbKDbubmMpTi2SQ2fvjbhz9OCBcpP+7uWOfj3fn1KV7/HzV5osA+MWOEVZmc20Bh+h4LM/oqLLCnZMuv9jeoOoGJDXxueZjMhlDp+aFbU7YLAMJpRMbZGkGuizGTJqi0RvPsCrbT1/cZFkmi6lIGIrMrlqdD930ELYXMVJvUXFDFEkUUQndwPYipuxq27dMYaJRptdK47ZJ+m4YdBSto40ZIZ9vy/aFstZld32Cly8Z4taJCU7o0QkjEapqKiLH7O7pcR6YnaYvniJnxrG9FucMLuDEngRNP+CB2WmOyvVyXHeKbz8wzqaSx1G5Q9gZn+8IHRHY70Kot7eXrVsf6x1y8803P2nI6jMRx+aTxDWNS075OqtzH+GchZ/DCVxkSRAQB5I5Unqswwt6PFx07OX0xtMdzk/VrZOPGZ0dVj6WRJYkErqBKivMNGuUHIe663Qyvtzw4Z26qWgUHUEkBIRXEIJfNNGo8NLFC7HUFCf2ZKl7IUflkvhhyFRzCl0xiakJNFnDDVpM2iW2VcoMJS96Gt/JZyZ21Z7YvG5z+ant2E/ojXNyTw87q9O8bPEA5ZbNK5euwgs8NFlDluSOou9w4OSeT7Cm66Mc3/33yJKMJMnsronMOTec86FK4AQRO6v2U34fjmSUnJCULlNsCVXURN0lCBHFUUplQUJhQVLjvukW77xh/7upp/d9cq/XJzSTzeUvktBM+uKpfT7eo/lC2yoel913IS8a7GV3rYzdHlltq4hC4z/vm2UwqbIkrfPArEcQiXG/7UXcNmGzMKEz1qjTZ7ULYlmIM8IoJCTEDQO8wOOWsSkm7DKyJGEqGsfk09w52eJHm6ucuyjJKb05ZAkqLVFUDqW6SWgmyzImlqYzadeRJYneeIatlUl0WaHi1hmrlzpFkqWK4ssLxIbSDX0cXwQGy0hCieZFWKpE1pTbDvshWUNFaUfGjNSLVN0m5ZbNb3bVOL4nRt2NsP0W983UWJRSWZRSafjOfn+WTxnzHKEjAvtdCJ1//vn83d/9HbfffjuSJDE2Nsb3vvc9PvjBD3LhhYe+jf9048+HvvAY4uMZ/Z/ujL/qrkNCN55QDjuHoiNIyTur0wzXZnjlkq8wlOplMKm2xw4yKd1iZ3UaXVZZmMygK2pnLAFQbtk8VJzAVHVSeozBZBdhFBJFITur49h+i9GGRyFW4AdbbmW8UeVVywQHKWfGSesJVFknCH0kSSTbZwyLk3oOL1H3SMSrf/kmvnrP3Y97+5fWv4N/vushXvGzN+zXca/Z9WGKTsDn/rSd3rj4jE1Vo+E3CaOILeUxTFUjpj7+yOlgqY72BauyH+Le6Z0sSKSFilFSBF8t8FAkKMTUjhHjswlOAA0/JGfIrMiovO+EhSgy9CcUam6IpYlg1hN6THaUD17RavstVmQ+iKlqnY3S/uJbD76TcwcXMJiMcfPYJMM1n4QucWJPlrwp85rlX+WcRXHGGwH5mMLSjErFFaGjSV1mXV7rKMk2ljy8MEKWZDaWqp0YoJ3Vae6ZHiaMaG/eHLaUp9EVlbXdOs9bEGd1l8X9syXumXZZnlVZnrU6G8eEZlBzxVg+pcfaBH2ZqtsUxHzdoNiqM+PUsFTRwRLcRp2kbtEdy6DLKlPNanuUBzlTZyhlkWyvcTDZxRkLltCfyDKQEJtWu73BOG9RHyf15lFlhZ9sKbOx2GJpWu3YRszjuYP9jhP/6Ec/ShiGnH322di2zRlnnIFhGHzwgx/k3e8+9OTOw439yVl65ZKv8IMt7yYfS5IzE3x347vQZImYqpDULRqew+bSOLOOz6JkgplmjXwsuYefi6lqLE53sbU8wWAyT91z6I5lUNpjjGXpXmSmuPKhu9BkiTVdmbZl/iyO7zHrVFFlndH6JE4g0seTqtUhXM/jYdwzWWdRKstl912415TyhC4x3fBIm/v3Z/Sz7UUuWru0ozC0PRHXYioafiSkx5ZmocuPb8o30Sjv78s5IPz1sn/jweI/UfcEN6PuiSiWpekUbug/K4N4//l5/8Fn7riAh0oeHzj+CgD+6U+v55TeBMO1JpYqsTRtsX6qQUJ/6rYTvxn+yB4bqUfmkD2en9At4/+4147SPTOfZV3+Yx1zR4C/eFQKyjkLxf8XJFQRh2HJrM4V+O+NowwkFIIoYqYZsDQdMVwLWJxSsf2QiUaZJWkLWZIYSnbjBB7DtVkMNaDuOqzKdrOtMoPje/TFU4RRhZtGauRjCucsFLlf440mhVid3ngXE41ZsITT/oRd5obdNV66OI8fhbjtmKHtlSmWZXrYXJ5gSbpA2ojT8BxkZLzQY8apUXTqmIrEoq5kO5NRYrQ+y3XD05xQsLA0Az8MRBGlaDi+x0k9GqqskNMTvOeGexmttXjZUiEIqLmHsiMUicuBPH4eB4z9/vaTJImPf/zjFItFNmzYwG233cb09DSXXnrpkz94Hrxm+VeZaFQ4vvs0vDBiRbaXVdl+gjBAlWQyhkUQIcZuiVxH2j7VrIowzHYQpqUaDNdm+OPYBLeMb+HB2V2okoj7CAnpsRTShmhVl1s2k3arzS9yaXg1FqcXMZTqpcfqRZLkzi5pHg9jQVJnS6mFqUj83e/P57j//n973H50VzdDaYMuU+HMH/4N39/0rj1uf/N1b+38+1W/eCN/9oO/4RU/ewObik3WT42QM+OdkaoqP5zzJUsSjt/E9vce/njj6Mfpj2e58qFDmwHntp3NhbJII2cKM705871nI0pOyHTD6/y8NCsEDzPNEE2WcAOfNV0GLx6ynvJz2G0l6KMx5yq/NzzeWG1d/mMdjtHj4Z/vegcf/uPb2VjyqLRCKm7Iqb2XsK5bwwuFWeyxebOTnabJYCoSCc3sjKL8KGwn0DuMN0Ku3VUCYFEq2/FAm2mGBJHIZyy1hLDEUmUszUCRVME1UlSOzfeyqdjgJUO5tiWIGPHPdavnYoZiqkGl1SCumZRaghLg+MJnbXPZ547JCkOpbmRJeCWtn2jyrQ2z/O0v7+br9+/kN7smuG9mnLunW2iyxFSzwi3jO/j081bgOQFFJ6TLtIgfSvJ/dIBjsXmO0EHBU24D6LrO6tWrOfnkk0kkHv8Pdh6PxfP6j2bC3sGSdBa/PeMNoxA3FOGWM03hFWT7Lo7vUXZtdEWl20qTMSzKLfHzimwfa7uzLM90Y6oaw7VZdlZnSOkxlmWyLE6lqHstEprB0V1dHc+hcssmikKRZo9M3WtwfPffH+Z35cBw+f0XcuVD7+TN172Vi295O++76Xxum7ikc/urfvHG/TreS37yemKqQt0TJ8iGH/LXKzOAGEu99tdvwvE9/nJZgbQhszpvcWrfys7j33zdW1FkoQx77a/fxKl9MY7ptvi74wc4oVfIjW3fRVdUMf5s58XVPYe612LGqSFJMlPN7+yxLi+8VhTCmkGPdej/7nJmHFVWBEejTdp/ZPfh2YahtMrZi+Kdn50g5DsPTXLjcI1WEOEGQedv+KnilUu+stfr92bI+ETJ9HM4c8Fn+NmO9z/hfdblNR6YEWn0442QD/7hfIpOyPKMxngjYFOp1R6Zi4DUvrjottTdFlW3SVwzuWNyI2EoRlIvXJgkY1gdLo8b+ByTT7M0o6LJEjU3IqFLTNkBX1m/lVJL+AdZqujWHJVLUm7ZWKrBWL2MKik4gcfCZF50TNv8IFmSCNoFebklQlTvmGxgqRJpQ+auqXFWZftZP+WSi6nkYhrLcjHShiD8t4KIFyzI0BtP87KhL5GPmfxxfIxWrcUDM01642kGEwv37YObx7MG+1QIvepVr9rnyzyeHDuqo0SE9FqZzky86jZR267O5wz24AReh3cxUitSbTW5f2a4fT+ZcqvBz7fvxFJ1hmuzbK+U2x2fiIwRZ6xeRm5zgFRZQZXEseV210mSZCRkmkGdqvvM3tH/dvfFLE4l6YsnWZhUWJ5R6bEUrt89zuX3X8grfvYGTu2L8Wc/+Jt9Ot6br3sraVPh9AUWR3VZpA2Jdd06JSfklvF/5At37OT4gokTuNQ9h9P7MgwmVdygRRhdz6V/uoD/b2WeP+s3KDohp/SZbC573D9t84U7dmN7EecM9qDLaqeVD1D3HAqxFH4YUIilCcJgj67QzeOf4IdbfgaAKsmsyB5a36d1+Y9h+y7DtRkSmkneTD6pSOCZDtuLuHPy4W7pN87+JiuzGqvzFjlT57phBy+MnpZYhpvG9tyc3Dz+iSdMpp/DnVOXdv7e9wbbj9hc9nnxkMWHT7iCSitkbbfOmQP9bKt4VNwIRZL4/qYZ1k81sDSdOybLHSNYSzMIwoBlmR4KlkLelOm1MqzOfYQTC//A6X2f5M+HvoAsyazKZjhjgYmpSsw0Q0bqASld5je7hvmfzdtRZZkt5WkGkjlyZgI/DDg2v5ANszVsr8Wm0igZw0JXVOpeq0PWnts0pvQYi1MqS9MaaV3imh3Ch6gvrrC222BtXueEHpObdwvvt0XJBD/aOtuxIfmLxV9myg7RLK3tE1VlzB49sA9uf3AEk6WLxSKve93rSKVSZDIZ3vrWt1Kv15/w/u9+97tZuXIlsViMwcFB3vOe91CpVPa4nyRJj7lcddVVT9vr2BfsUyGUTqc7l1QqxfXXX8+dd97Zuf2uu+7i+uuvJ51OP20LfTbhtvFZppszlFsNLNXoOPg6vke5ZfOFO3bihwGzjt0pXGy/xZquAfww6Mjej8nHcQLRti9YJjUvZHWugON7FKwUqiwzlOqm6NTJGBYvWPB8VmSOYnXuI/RZb2FR8p1IyCxK9h/Ot2OfcfW293b+vbsuOBvrpz+NHwbUPQdT1VjXbZEzDZZldOptlUwuptFjybx6Zfpx1T1Xbb6IX+38EP+3/X1kDJmErmD7ETlTIt3mf5zYIzxV1nSbnNzbRc5IkDeT3DNdYUXWpO45/GnyD5y9sJeClabYClmd01EkiYwus7YQZ6Lh8udDmc7JXJbEyCEkQldUNpbGOrlxcS3ZSXwHOnEqlmYgSxJLUu95wvfr6RidiVT6hMi8k5VOQv2zFR8+4QpO7NlzVJI2ZCqtgB0Vh2+f+01iqsKpvWk+f+c79umYY41vdP49Uv+Px73fI7lCV22+aJ/dqMstmz8f+sJjrr/yoXfyz3e9g764TKUV8rajRSfv62d9g51Vv1McJDWJmhdy9kKThCaUYvmY+KqY68TMKQe7Y3FsP+pEejwS5yz8HOcs/BwZw6I/rjGQEH9TVVc4Wq/NG0zZVU7rW4Ib+Fiqju27jNSLnDnQ3zFBrHutzuh+brNYdx1CIjaXimgyjNs+SzN5fvoX3+Hknk+0lWMSI/WAuyYdXro0xQkFUci9fc2iPRR2E42AKIx41bI0qqzQ8A4hTWCOI3Qgl6cJr3vd63jggQe47rrr+MUvfsFNN93E29/+9se9/9jYGGNjY3zxi19kw4YN/Nd//RfXXHMNb33rWx9z329/+9uMj493Lq985SufttexL9inQujb3/5259LT08NrXvMaduzYwdVXX83VV1/N9u3bee1rX0s+n3+61/uswPMXFPDDkKrbZP3UTm6bGGemWWNTaRJdUVmdN7ljssyCRJqiU2esUWd1Tqi6LM1o8zMSpPRYRwmR0mOszOZwQ789GluAH4bEtSTHF45hYXJFx4PjkZi0Z1iY2LcT+OHEVZsvYkWml5r3E+6b/RzbKru5aezv+eWO3ZiqxkAihyoppPQY+ViSoVQ3082AhC7xZwsMhmsBNTdkMPlYYvNLfvJ6ZpyAhC58mmpeyOl9BjU3YiilcvtEC9sTPAXhpSPiTMYaJZzA42WLF7Oma6ATimv7LtcPDzNlh8w6PpVWyPKsym2jVbotjYFkrsOzCaOoY6mgSjJLUgUKVpqUHiOtd7OxNNZZpxv4LEkXUCW5k033RHjjUQd/ZHXmgs/ghwFFp97JInu2Y6a5Z7dnSSrBqpwwIATRZS23bNbk960onDMu3VL5MvfNbNnrfR4d2PraFfvu8fXIrtGJ3/t/vO+m89vrlPjwCVfw8211tj9K5eaFERtmd2MqEvmYTKUlussVN2R3TWy25nyEbM9Fl1V0WeWhUrXzPjweXjT4T/TGM9w15eIGEboiUXEjvr2hjCorjDUEv8hUNUbqDgUrjR8GWJpOudXYo2M99zeW0E3qrkPNC5lphvTEFPrjD3//DNcC1nRl2V5uEVNlhmsB398k/K4eXbS9dHEcRVOwPcFPmm0+ftfjuYKHHnqIa665hm984xuccsopPP/5z+erX/0qV111FWNjY3t9zJo1a/jxj3/My1/+cpYuXcpZZ53FZz7zGX7+85/j+3uqSjOZDL29vZ2LaT6+MORQYL9VY9/61re4+eabUZSHW6+KovD+97+f008/nS984bE7kXnsid8OT3LmQB5LM8gDq3JJdFklZcTYVp5hbV5HlYVvy8psgR9tGWVZRiQwT9lVXjhwGlnjYeLuisyex//BlnfzmuVfZehRyQe2/3NG7a1kHrHB3dtO7kiE6Iq5TNg7SGgi0HZreZKVWREDcf3ucVZmDY7NDzJhl/HDgL9Ykua7DxZp+gGr8zGh8sk+9lc+iCLun/G4ZscuzlucYHFK7eRH3T7hclKPThBFmKpGQjZ5Xr8oYkxV476ZIqf3mTxUnCCp623lV8BpfT18f9MYvxsWJ/GPnLSU8WUhL1s8wHBthoFEF3XPxlQelgXPKWTmDAxH6ltxfI/bJi7B9l3+MFbmdSu7mLArnDVw+OJsTu29pPPvJ0pYf7bgU6fu2bWxNB0niLhgjVASCq8bmZcNfWmfj3nNrg8zkMjttXMDMFKfZd0B7isvu+9CPnhSd2f0+vpVXwPgpUsSWOqexcvOqs85gyn+etmn+dL6d3B6n876KZeCpXBSbx43CHACj7F6iYFEDgA39OmJKfs0Fnx+36f47fAF9MXF3tv2I04+JsuWcpm1+QK23yKlW9ie2BjMFT+iYy6zq1piaTqPGwiC+nBtFlWWO95EXhjt0SG95JSvAzBafxeGIjPr+PxZ/yIs7bGk9qrrE3gBuiL+Dpdlevb3rX7qONDxVvux1eqeGyPDMDCMp076vvXWW8lkMpx44omd68455xxkWeb222/nL//yL/fpOJVKhVQqharued5917vexdve9jaWLFnCO97xDt785jcjSU/NmPRgYL/J0r7vs3Hjxsdcv3HjRsJ5c6d9wuqc3onLSOkxtpYnOTrMU241+Om2Bq1ABDwuz3Rz3fA4Xih2YW4gku0fWQTtDa9Z/tW9Xm+pL2dV9kNPx0t6WnHNrg/jhr6QbiOzvTKOH4aUWj6jDY+EZtJjKeyquvxmeBt3TMxw9/QkbuCTNhVevSLJ4pTCiT0J7pxs8bKfvp7N5S+ysfQFLrrxfNb1WORMmVU5kcWUj8ltRZ/Kum6NZZksp/UNkjMT3DMtHMBBuILbXsR9M+NsLHlYqs61u0pcP1xtj40kYprMR05aiu23OG9RH1bb/0mVZPriufaoMySumSxJF4irsbZ3lI6maOiKILcXYimO6zYpt+wjqhPzRAnrz1acueAzHJ17mBv12hWXMfYIZdm+wI+CPSwH/jQpxl5f3yC82PanqHo0Zpz/BoSJ62tXXNY5H8yN7i485vLHdAu/86JvdUZxHzj+Ck7sWcyZC1Os6UryUHFWcA2ltlEhwknfDXyCCFrBvo1nTu1Nc++0x2g94M4JoWJ9+eLj2FmdIWckmGiUGUoJfyG5He8xlOql18qwNJ3HVLUOdzJjWIw3mgwmFQaT6cftfh6V6yWhmZwzuByAxY3HboRKTkg2GyNjCKXuSK24T6/noCCMDpAjJN77hQsX7kFh+dznnpxL9kSYmJigUCjscZ2qquRyOSYmJvbpGDMzM1x66aWPGad96lOf4gc/+AHXXXcdf/VXf8U73/lOvvrVvX9nHSrsdyH05je/mbe+9a18+ctf5uabb+bmm2/mS1/6Em9729t485vf/HSs8VmH1yz/KmEUsbM6jSormKrGN6fWc+t4jW5L445Jj1Ir5N/u2YUTROyuufx2uNThkzzb8d2N7+Ly+8UXws3jn0BXVFJ6jHLLbpsOhkzYZepuRM6Q+eWO3ViqhNaOD6h5EQ/M+siSzOl9BlnTZFEyQc5IcHKvyTHdMX64ZQu/3LGNtCHRF5cZTCoULAVNhpXZHOcOLuDYfI6l6RS25wpOQhRxfHeBcsvm3pkpZpotnCBiXXc//9+KJdwyXkZThHrlvx4cJh9TeMNRPWSNJFdtEiaYIhpFp+zaeIFHxrDQFI0dbUfdht/EUCyiKCQIA5akxcmo2KpTdX3KLZstJftwfjzzAPoTmT1+XpFJPobc/ER4dKEz15md6zLtC360de8csdsm9m4C+ueLF3HZfRfyq51PvBl69S/fxA0j2yg5DlsrdbKGyorsANfvHmcgkWOiUSap5/jt8CgzTsCkvW8b4PMW/TMvW5zl+EKcuyZqfHtDmW8/eBdeGPG7kV2MNeoMJrs64hHhBj0FQH+iu5PZ6IY+W8olNhZ9puyQldlFj/ucJxb+gWt3lXnP7+5mTddHkfoey5vbMOvx/63O8WCxjBv4DKW69+n1HBQcJI7Q7t27qVQqncvFF+9dXfjRj350r2TlR1721ujYX1SrVV760peyevVqLrnkkj1u+4d/+Aee97zncdxxx/GRj3yED3/4w4d9krTfo7EvfvGL9Pb28qUvfYnx8XEA+vr6+NCHPsQHPvCBg77AZyteueQrXPnQO+m1IgqxlBiBjFWYqLsszpgk9Ri6IuOF8LIlcUbrAaaiPet24FPN71Bzi9w9vR1LNdhUrrEyk6THkrlx9OM4voel6SQ0s9Myn2nWGakHIv9I07GaTZakuwmiacIootIKsDSJ6WaLHstEliRsv0VCF74lg0mf7pjG1rJLWpdJ6zJL0lnKLZucGcfxRY6bLEn8ckeRVyztxo8Cqq5N3W2xreJjKBJDKZVVWUEmrXsOq7Iav93dpKXKnLHAZLTuc9XmKf56WcDbj1mCLitoiihkvcDDDQN0WeHuqR2szi1AlmRMWaHmVjst/DkDQzcIWJUVJ+gXLpwXJRxubC2XOPMRZuzTzQbJYP+6QgeCjaUv8NfL/m2vtz1eN8kN/L0agz4aP3zpf/G269/K6X0GCU1iRbaPicYsK7MGY40St403WdtdZrwR4AQR7j52hIDOKHC4JgKHp+wAkgrbyj6n9RnsrM7QG0+TjyXZVS2RNU16rQxJrYspu8iUXSWhmWQNFU0JyJrSk3Ic//UF/7mHlcYj8dvdF7OmS2NpRrj0y5KM3XrmeaqlUilSqSd3If/ABz7Am970pie8z5IlS+jt7WVqamqP633fp1gs0tvb+4SPr9VqnHfeeSSTSX7yk5/sEc6+N5xyyilceumltFqtAxrnHQj2uxCSZZkPf/jDfPjDH+7MJfflA5jHY7EwmWmTAg0Gk10MpmyO7bbYOOtw/7SYkS/PaGQMnZLj4D8LzLNuHP04i1MLuH3iITKG1SaaNhhreAwmFRYmdExV63Btyi3R/RhM9vLA7DheWOPXO2x64ho5UyZlyBxXyDHWKNNjJZhp1hlMKqyf8qi7Ea3A4cRCAVtxO6GhC5MyU3aTFVmTa3fVOb2/H9trkY8JrpalGvTH+2j4VS44JtmxIRhrlKh7Dqf0ZtpyXof+eJb7Z8bYUQ1YkVEZrbq88eg0q3L9/M/GjZzcF2NJuoAb+NwzPczxhSHc0KfcapDQTO6YHOGEwiC6Iqz9FVk43vqhi+23mLKr5GMJ7pqa4ZQewV14pns+PdPxq50f4vjCnkrL16/6Gv+3/X2HbA1PZcT96CyyJ8JJPTq9VpxZx0aXFbY2SiQ0k/5EFkOtccuYyJt89fJuvrdx6kmO9li8Z+3lvPqXb+LYboOKG/FXy4WdxIbZKW4bn+W8oUFIiUxFSZLZUd1KudVAlYW/0KaSy0BC2efA20fy2h6JolNnZS5Or5XhlvFRzlm4BF86hBEbB4kjtK/o7u6mu/vJO16nnXYa5XKZu+66ixNOOAGA3/3ud4RhyCmnnPK4j6tWq7z4xS/GMAx+9rOf7RMJ+p577iGbzR62IggOwFAR9r0Kncfe4YcBG0vTVF2bnVWhaPjhxhn+YlmCtKHy6hVJ1nbHedXSf+EDx1+xz/LZIxWX338hM80as06RnJkQhYcicqsGkyYzzRaWajBarzBhV7BUg954Gl1R+cHmDWL3CPzlsjivWtbdTrrWCaOIwWQX2ypVClaKlK6yKqeSNWVO7+tHlRUyhkVCM9EVlSWpAi8dOpnBZBdvPXqQhGbSG8+QM+LoikrWSNL068jIuGGAIivUPYdtlSqDyS5uGS/j+B625zLTrLG2e4ATexKsyPayOGNw+0SL3+waFuaJBRG0GxJxYs9i/DDADXxSuoWpaJxQGKTqNtlZncZQhOOuJouTv+N7NDyPh4qzvHBgkKlmlR4re5g/xXn8+dAXOhYWj8Qrl3yFy+47cvIW109/+snv9DhIajK6ouKFEb/euYOmL2wT7pzczTFdOiP1gKG0wi93zHDu4FMb1796RRJZgvFGQEoXDtJDqTRnDvQyZQvvmbrXYkdlElVWKFiiE6rKYpRtKAemjLx5/BNctanKpmKDu6bGGEpZjNQPIT8Ijlj5/FFHHcV5553H+eefz5/+9Cf++Mc/ctFFF/Ha176W/n6xCRgdHWXVqlX86U9/AkQR9KIXvYhGo8E3v/lNqtUqExMTTExMEATi3P3zn/+cb3zjG2zYsIGtW7dy+eWX89nPfvawx3Ptd0do8eLFT8ju3r59+wEt6LmEhGby4GwRTS6xMJnh5UtURmstsobCaf3m0yJ/Ply4beISLFVibfcQCS1DubUVN/CZaFTavjQSg8kkv90tpLSW6jNpj+EEEQviGpYmMZiMccPuGmcPpsiZCVRJptiqs7VcJB9LckrvAh6YHSemKhRiqpDUy0pHnl506iK7LQoptaZJ6THcwO8QMFN6jIyRQpFU3MDBCwUBeld1GlmSWJRMoMoK5y3qw4/CTlfI9lrU3Rbf3TVMwVKYaYbkTJm/v3kHf3/qALIk5PZSWw2W0mOMNUpkjDiWqtNjZSmXhIlbQstQcWeIolDwIlQNPwxRJZmcGWdp+r2H8VOcxxwer7uyL6OnfcWvdn7ocVVl+4K9FWsAvxv5GLbfelJCdrnV4M5Jl7oXclqfGIu1gohTexdy7a7NnNCjY2kilf6p4DXLv8onbns7XhCJDULbmyiMInrjmc7f5li91JbNC4K240fENZUFiQPzsKq7DitzBpoisTbfg6nqwtndPnLECIcT3/ve97jooos4++yzkWWZv/qrv+Lf/u3hcazneWzatAnbFl379evXc/vttwOwbNmyPY61Y8cOhoaG0DSNr33ta7zvfe8jiiKWLVvGl7/8Zc4///xD98L2gv0uhN773vfu8bPnedx9991cc801fOhDzzxF0uHEtsoMr1052Oa91NhUalFp+QzXfHLmsycEdWPpC/zPplEKltwpMnJmgrhmsmF2iqpbYVkm23ZpjvPArM0JhTQ5M8G1u0YpqT5JTcLxPf5sQRw3EEorS09gqDq9Voa657TVYwn8MKRgpXDalvxZM0HLd7F9t2PI5oY+YRR1pOpzYzrba2AoBl7oUW416IqlOsnXlqp3COsieFQ43SZ0k5wZ58JjF/FPd27kdasKWKrOJacnsX2Xa4d38LLFK/ACD63txJwx4iJTLPCQ8Vme7SMkJIh8TMWi4dX43e4H+bMFy/jD6FbOHFj1rAw2fbbg6xsu3C+i875gIJnr/Pvm8U/sd0f48Yq1olN/3CJpDnO0HzeM6I2rDNcCsobgQH3q9ofosTR6LQ1NhvffMMJrlu/X0jqwVBnbe3gE7ocBW8rTnFAQndq651CwUliq0Y6fCWgFEbIU7GEDsr+4aezv+dn2IqtyGif39LTd90UX9vbRyad+4P3FIR6N7Q9yuRzf//73H/f2oaEhokf8Hp155pl7/Lw3nHfeeZx33nkHbY0HC/tdCP3d3/3dXq//2te+tofb9DyeHHVPqJ9USSEfS3LNLhGwWnFDeqxnRyG0fvrTqLLCiT06p/YtBUCTdQoxoYY6Z3A1hmJRc4tIkswdE9vosRQGk10AvHzJIu6dHqHYCjkmkcYJXFJ6jKQuChdZ0qm6NdK6yN1anO4hikJ0xcQLXabtSmccFUYRm0vjDCbznWiLRckCod9s83ICNEWj6QvX2pQeY7xR7ASNCuKyiEQRYZAiE85vZx/VPYdXL8+R0IRbeEaOI0syR+d0mn4L22uR0oUB373TI5zWtwRV1lEklYgQN3CwvSpe6BFGIWcOrMINWpzcM0i59eQGivM4fKi7B39E8cjk+YM1Fv/Wg+/ECyN6rCceZ81xnjK6TBhG9MTFBsL2IvIxjeMLGndNOQylFC48rvCEx3oiaDIszajcOVnijAU9JHQTN/T5ybYd/OXSxdieS86ME0Yhju+xIptnc2mGgUQSP3zqXJ4dlSKWKlFphZ2u8T3T4yzLZDm17xD6CEUHWAg9C3ijRwIO2rftS17yEn784x8frMM9J3DhMZejy6Lle9fkLHU34JhCnJlmyC+2P/Ml0jePfwJT0ai7Dkflemn5Lrps4gQ2RWeGcmsWN3BoBeK1KpLKqlwfZyxYghsKAzdVkjmusJCjsil0RWFldgE5I4EiiRo+jMTYSJV1QsJ2iKxD1a3R8l3c0GekXmw7Tic4sWcFbugzkMiRjyVpBS1SerLj1OwFHpZmoUoyhmIwkBBEZz8MkCUZXVZI6CZ+FNIdyzCQzOH4XqdtPzd688OArJkgZ8bpjWf28CaRJYm13QNIkkwQ+USE7bR5l5pnt19XRN1roMlCvTZPkD6y4YXwm+GPHO5lPC5uGf9Hrtp8EeONgFbw+EGvj8Qrl3yFE3sM3rB6CYYqMW77WJrEmQMxggg+d/p/cMGay/n4SV9/yuv6wPFXcNeUy2BSaXdHJR6ctTmtL8fm8gTTzQYTdoWd1RlkSaLo1FmSzrKu+2jOW/TPT/l5X7fyL6l5wjPMCTzqnsO67j4mGpVDmz4/jyMCB60Q+tGPfkQul3vyO87jMcgYcY7Jp5m2PXosheMLGj986X8d7mUdEO6cuhQZiZSeJaELJ+aMkSMi7BQxhmIQUxMEoY8iqdie3enc6LKCpRpU3SZXb92JpemAKFQMVfw7jEKiKOyMu0pOXXRzooi664hCSlY4Nr8ES4uzKNlPEPptUrRBrJ3zFoQi5wgezjKKa0kkSSaKQhYkerG0OCk9QRhF4nGSTNN30GUVPwrQFZWQCLd9rJyZIIpCkrpFXI1hqhrdsQyGYuAEHrqsICOjy2JnrisGO6vTbQVdiYSeQJdVap7dyZObx5GLdd0pNpaOzK7d+ulPc3rfJ7F9wV2T98PA97UrLuOm0WGm7IDrdzXQZAlZknnL6oPHX/yLJWnumHT5+fZpvrR+C+ONgC3lMptLDpvLPjPNFqoss7VSYyjVzZkLPkMh9oYDes5/v/9qdpQd0rpM3XUot2wm7DKruxZgKPpBemVPjiiKDvgyjwPHfhdCxx13HMcff3znctxxx9HX18fHPvYxPvax/fO4ufzyyzn22GM76rPTTjuNX//613vc59Zbb+Wss84iHo+TSqU444wzaDaf2Wnpj8SLBv+JlB5DlmS6LY2xesDfrHzR4V7WAWGk/h8d7owsycRU4d/jRy5B5FNxK8RUk+lmGQBV1pEkGUuz6IsXOiMoWZLQFZXFKRFKa6kGmqIhI7NhdiebSyNsKo1Sbtn4oYsfBntYDOiK2vYfcvBCF03WiakJUnoWx28KVVjgExKK7C81hq6YRFHIaH0SWZKRJBkvdJGQ8UMXWZKQkdEUrTNOE2uVyZtJwigUuWGyjq6YjDeKjNtFZElCkVV0xSShxdEUDUUWuUkAY42ZTp6c7bcI2sRRU9E6GU/zOHJRdBq8Z+3B5QgdLMx1E9d197Mym+VFg0v36/EXHXs5x+ZNXrokwQ27m7xq6b8c1PXdP9PgvmkbL4SMIfP6owbxQpF/FkYiUFqWZIZSFnnz4Jgdvmft5YzWWgzXAhK6yWCyi/54Vjhoy0/se3NQcQSnzz+XsN8coVe84hV7qMZkWaa7u5szzzyTVatW7dexBgYG+PznP8/y5cuJoogrr7ySV7ziFdx9990cffTR3HrrrZx33nlcfPHFfPWrX0VVVe69917kp6hSOFKxKvshLr/vfJZmdH68qUhS27cclyMVm8s7CaOI4dosZy0UX+6GIjg9TmCT0kXBULBEB1FXTILQxw0dFEklH8siIeOGDlvLkxyT70eXVWKqybhdpM/KsSRVEOOoMCCpWcw6Veqegxv6LE4tICLEUCxKzjRJPUcQ+gSRuEjIWFocx2/iRyEJWScIfaIoBElGkVX6E92EUdgpVKJ2YK2maASRT0xNUAtEQWeoOi1fFGIZI47f7lTpisWS1CAAbuiI1xiJ13jb+DZO7VuKquiEUUh/PE/VrXfMExt+ExmJmGrihfMdoSMdf7Pya4d7CU+Iy+67kKQm0tg/ftL+58PNmTdeeMzBXhl8/KSvc//Mm2h4Ilpoa3mSZekECd3oxH48HbhgXR7bi7C9VtvhX4ghmt4zn5Ywj/2DFB1hvbVcLscXvvAF3vrWt3Lqqady7rnncumllz7l41WrVdLpdCf87UjGZ+64gPtnWlz1kv863Et5yvjVzg9RsFL4YcBgUvBgKq1ZdMXAD4WpYY/18K6u6dfbZoVlVmYX4YUuM80yPVaeVmAz1iiJk5SikdBMhmszHJNfie1VafhNyq0GfhjSH8+2M7tE0qwsyYRRyHRzlrQRJ2sUGKkPk9ITndvcoIUmi+6NGwrJrCaLwgSg7jVIaHGCyO+M84AOuTmMQhJahqIziSaLDk8YhWws7uaYvNh1z3WivFB0w2TkzsgNQJJkNFkniHy2lkc7cnoRJRK1w11jDCUvOiSf396wufxFVmQ+eNiefx57x1TzO/s1Irpq80XYfsjJPYNHpALxS+vfwW3jDm9dUzgg/s++4usbLqTuRhyVS2Jpugi+1mMETZl1Cz/ytH5nzH0vla55F6n4U+/4Vhstsud97Rnx/XYkY787QoqiMD4+/phAttnZWQqFQsc4aX8RBAE//OEPaTQanHbaaUxNTXH77bfzute9jtNPP51t27axatUqPvOZz/D85z//cY/TarVoPcIi/dGpvEcyDoR0eKTAVDXSRpyEliGKQlqBvUcRlI8JQ8AwCtldn6DcarAkVWBntYIsDbOzWsZUhJz9N8PbODbfhS6LLpEum6zMxvFDF10xMRSLXmsBda+MG7Qw1Rhu6PDA7G5OKKzEDUU8RVxN0fTr5E1RXEmSCu0OTxD5NNwmCS2OIqm0Aruj5IqpBlEUYioW080ZkrqFqQi1mBcK4rftV5EliVbQwpJVdNnk2PxyIkKkNgdIPKdMEAYoiuBCGaouRmyyTsOrIUsSfhiwINGDH7qk9CTlVlV4HIU+G0tfOGyBuTPNGlPN/Zdvz+Ppxf7yZExV47UrnpwkfbjwgeOvOKTPN94IKTkhi9PtEGUlJGNYOMEhjNg4UFPEp8lQ8bmG/Z4xPV4DqdVqoev7TzK7//77SSQSGIbBO97xDn7yk5+wevXqjjHjJZdcwvnnn88111zD8ccfz9lnn82WLVse93if+9zn9kjhXbhw4X6vaR5PHQlN8GI2lbYxYU9gew2htlJMbL+FhMx0cwZFUjsxEzNOraPYOHNgFTkzzs1j2ynEVHJGAlMVM/u5zkylVe50beYKlZgaJ66mMBWLZekewihktlklqVsokkrDq9EM6qIrIwm+T0yNI0kyCS1OFIX4oYsb+EzZRfzQZbZZpdSqUWqVCKMIRVI747W5f4dtqf7cCE1uK8EkZCLEaE2V9U6hZns2CT1BTEl0XhOITLGhVHfnZy902/wgHRmJldEhlPQ+Cqf3fXK+CHoWYE4QMA+BVVmNE3t0UnoMJ/DImXFUWUF+AsPgg455jtARgX3uCM05SkqSxDe+8Q0SiUTntiAIuOmmm/abIwSwcuVK7rnnHiqVCj/60Y944xvfyO9//3vC9gd8wQUXdFLtjzvuOK6//nq+9a1v8bnPfW6vx7v44ot5//vf3/m5Wq3OF0OHCDtrl1F1m6iyTEqPsSA+AMB0cwJVVhhM9vKjLfdx7qJFbC7vxA0CZvwaBStF2ojjhRHbK+PIksSqbDcJ3WRjaYzjC8vQZZOmXwcgJKLpN8gaYsSmSCqqKsZLqqzTFSvghy6DyQHc0CEkRFcMgsjH9l2gjqFYne7PXGGT0DJIUhlLE8VTVyyFJuudosYLXeBhgvPc9YqkEteSqLIuUuPbxRaIzpfoLsWJCEkbGRRJxQlsUTBFQr5789goZw0sYqQ+Tj6WJGtkiGTxnLIksUWeYsUh/0T3xJ1Tl+5XXtU8jhz8aOt7SGhPLQrj2YqEbrCzaqPJNZZleqi6TRKaiSLPF4zPNexzIfSVr4iWahRFXHHFFSiK0rlN13WGhoa44or9b23qut6x4z7hhBO44447+Nd//Vc++lExw169evUe9z/qqKMYHh5+3OMZhnFYw9sOBj78x7fzz8/7j8O9jCfFNbs+vMcsf3tlDF1RGKmVSOVieKGLH7nkzDxhFLK9OsxLFwvujO23yMcSjNXLAFRaDfriyTbhWBCr03qak3rS6IoYLymyitBsOZ3CJoh8dNnsdFK0NvF5jtOjyyZ+6DJSn+q4OYdRxHhjiq5YqsPbEUVStTN2c0Onw+NZFGbZKc9Sc21yZkYUJ22uTxD5IItiTEYmlGhzj8TJtBXYtIKWyBGTVbzQJZRCbM/G0iyCyGdjcYxzFg6hKVon5kOWZCRUDM1i1p/CDfxD9bHuFb8b+Rj98fmcs33BL3Z+4EnjKw41Hi+p/rmM63ZVOXtQcArnTFGrbhPHO4TihDA6QGfp+dHYwcA+F0I7duwA4IUvfCFXX3012ezTc1IMw5BWq8XQ0BD9/f1s2rRpj9s3b97MS17ykqfluY8EXL3tvVy3owLPO9wreXIszzycwD3W+AaFWAo/ChlMyWQMQdybU3/pssnKzDJ213eLFHe3hS6rrMr1o8sKqqxg+y364jlMxep0VxRJRZEf/jWNopCYana4Oo8mMc8VKWEUdooRL3SZaFRIaGYn3HZ1boG4HyGSJEMUUnVr6LJKED5cXEnI7JRnCaOQBfEBnMDGVCy80O2szwvdzpA5ikIhz293fOZ8guaKuCBy24oyFTdo4QQea7oGkCWZ2WaVrlhKxIeoFlEU4oYOaSND068z1vgG/fG3Pa2f6ePhrIHPsqXy5fmu0D7gSCuC5vFYfPzWt1N1Q362vcKpvQYrMn3MODUAdEl5kkcfRMxzhI4I7DdZ+oYbbjhoT37xxRfzkpe8hMHBQWq1Gt///ve58cYbufbaa5EkiQ996EP84z/+I2vXrmXdunVceeWVbNy4kR/96EcHbQ1HAs7+8eu4/q++x3c3vouKG/K3q4/8nfedU5eSNZJsq/wLCS3DTaP3sCwjMntSegwQfJlmIDorhmLhBLbwEwpDdEVBV1ThPO059Fo9JDSnU+DIyEiyiiIJfpAiqfihi9TmG80dX5FUJElGQW6Po9TObapsEBGgRTqn9R1Nw6uRjyXIGHEUWaHhN4mrMaECi0JSepK6K1RsSV3vqLnmjBsVWcXE6ijMlHbRNGmXWZTs7yjEZp1J4Ukkm0SENH2HrlgBue1FpMgqnu+hyArVpijQdEVFV1Q0WUeV9E4RNFfoOYGHrhzeMMjl6fc/+Z3mMY9nANbmDSzVRZEkUrrScbEPo6iTBziP5w72qRB6//vfz6WXXko8Ht+Df7M3fPnLX97nJ5+amuINb3gD4+PjpNNpjj32WK699lrOPfdcQAS8Oo7D+973PorFImvXruW6665j6dL9MwQ70vGBE/pZP/1pdlZ9lmc0Sq2nnqHzdGPS/i96rDeRNuK0ghaaolH1ihybX0hcS7KlvJt8LEkQ+TT9eme81ApsIW9XY51CaVl6EX4kSMuarNMdy6PLJmH7PzkCP2p1RlBCeeV3ukyqpKNIdMZbzcAmoWVxgyaqrOOHQv0hSTK6ZIIGS7Q0O2sjzDRr7K6V6YsnWZZZQNN3SOgJZpwahViKVmBjKBa21yDbNnFr+nViagJCUWjNFT7D1VkWJnrRZJWQkIzRRcMXakU3aKErbTWapGMoFn7koisGsiQzkCgI/lNQJ6EJPyVVEXEhc68TQJdVbK9xWNVj85jHswU3jTrkYzLpmMSNI01WZHlEnuB+9weeOo7g0NXnEvbpE7/77rvx2nPT9evX72GoeCD45je/+aT3+ehHP9rhCz1bUW7Z9CeynNaXY2e1gqEcQtXCfmDD7OcZqRdZ2/0t0e2IQoIwIK4l0WTBYVmU6qbk1Enpakc6/lBpN6tzizrKKV1R6IvnqLolrLZPjxs6SIguThB5HbLxnBGi3J49BdCRps8RmGVJFkaIktpWccXwQlFAyZIMoXCPVtv8of54ns2lEZZnulFlhYnGLIPJBbihQyGW4o7JXZzWtwQ/dDEUg1ZgU25VsVRdGEJqOTJGD6ONLYRRyJK0UHvJkYyCWMOu6jSLUt1t36Q8Da+GpYlRoYSIOVAlnYCHVWhO0ERRVMYaE3SZKXTFFMVeuwgKo2i/4hHm8dzESP0/GEi8/XAv49DD+zVo+0abOKVX5/YJl6VplaGUykSjTMawSOkxpPAQckznC6EjAvtUCD1yHHbjjTc+XWt5zmJNfgDba1GwRLDo+2/YghdeyAVrjhzL/pvHP8Ht41MsTovIirmxTbklHJ3n/IFqro2l6h15uaFYJDRBWE5oaWRJJqVbyMhkjC7c0OlweQDcsIkux/DDVidE1VTiBJFHEPqYquDn+KErRlb4qOiEkSiIWkGjY1LoteXwpmoxN/Wf4/8clRvaQ9kVIThFsiYTUxVuGt3OiT3ic1mQ6CFjpFAlnapbQtJlbL+K006t745lADG+214dZmGil6orYmBSeowIYfQ4J6cfb0zRFy8gSzIRMjW3ghsGqJKMF3p0mSmktukjhMiRiPRo+aKTtLn8RQzFYlHynYfq45/HMwj/du+d/PPznluF0O9GPkYYRZyzcN8KobRhcOaAwqzjMZTS6E9kkZHQFA0ncp/m1c7jSMN++wi95S1voVarPeb6RqPBW97yloOyqOcatlemGGuUkZGwPZd3H9/F4iPIJXTD7OdJaCYn9HQxkBBkZhAjqYKV68hyp5uzdJk5DFXv3EdkBPURUxOi6yPJpPQEMTVB1S2JUVh7fDbH+QkiT/B+2i1qL2whoaDIKo4vcsXmMHefuQiMOddmCaXD8QE6JogguD1a2zRRSN9T1NwKD8xuZ6ZZYrThUbBMNpfGGWuUxHHbHShVVqi5RSruNE7g4YdijGm3bfkHEgU0WWd1bgEt36XcahC2PYrm3rOZZg0JmaIj/JQMxcANfCHblxURtOo3O+vvqwju1Jy/iSTJ80XQMwgbZj9/SJ/vlUv6DunzHQkot2zOWbh3S5W9QZZEeGylFXH97iZ11+kELoeHMmwhih4mTD+Vy5EVDPGMxX4XQldeeeVeQ0+bzSbf+c53DsqinmvImXGmmy3Kro2paixO5yg6jcO9rA7mSM3HdK1hVfZoVNmg0mp0CgldMQijkJyZJoh8ZptVQoTR4RzHpSNzb0vbq26JqtsUXZu2qiyIfNzAwW8rsuaIxSJQtYUbOJ14jLkCJ4j8ThE0R2oW13sokkZcS3Uyw+bGazIPF1mKpFJ0hM/RQCLHQ8UJXrZ4BWu6hjihsJQ1XQuJmDNbFF2wkBA3cDohsE3fIaaaDNdGUCWdhl8lpsbRFZWClaPuNTrrs70GyzI92H6VjJEjjEJaQYt8LEPdaxBXU6iS6ACJojEH/UcLryRZEcGvyLSCPcOJ53Hkouw+nF11za4PP+3Pd+fU1NP+HEcK7py6lK9vuHC/g2CrLYea67IkrbNp1iahm+iKgSKpaPOhq8857HMhVK1WqVQqRFFErVajWq12LqVSiV/96lePid2Yx5PjlvF/RJUULFVmuDrLdcPT7KqWuHfG5Uvr33G4lwfAVLNKPtaHrpg0/AqtoEF3rAuAmlsBYHtlnO2VcfzQfXhn1Q4+VSUdXTY7Y7C5PK+ClevwY9zQ6Yyo5h4L4Ph2p5szVwDNmRgKrx254xw9VyTNFT5zxU5a78ZQ4lhauvOa5tRgAEk9x1ijhBv6dMfimIrFcG2yrVAzcPzmwzL7NlRZBDR6gUdMNWkFLbpjGWRJJq6mOq8rikLSepq7prbhtv2EVFknpibw2y14U421R4ZJGn6VhJ6g5bu0ApuqW8QNRDEZUxOdkeOu2gNP2+c9j4OI4Dp+vXOCd97wNm4e/wSTdv1pf8qBxDPbR21/cN/MOMsz+6+ynXFCvBD8MMQNIuqu03GcnzsvzOO5g30uhDKZDLlcDkmSWLFiBdlstnPJ5/O85S1v4V3vetfTudZnJUIi/CjgX++aQVcUTulN0GVa1NyItLHfDbuDjt/uvpicEafoTGB7FaJ2UTLrFJEkWeR7BS1WZhexLCOS5i1VyL81WRddFFntcIq80KXqlkSKe5sIDexRBM1h7jagw+eZu+9cUTRXGJhqrHP/ufBTAF2OCe7P3H+PUKBFiGO0ApucmUCVFVZkB9hW2c2G2SKO3xTPISvIyLhBi3tndjJan8X27E6hJQo+UeB0xm+Siq6YnecZSOSYcWodAncQ+QShT7E1iy6bbf5RTXR+JJ2YKrpnsiQzYe+gP7aYMArb3KGo7ZA9jyMd/7bhalp+hCZL1F2HmKpw+f0XPuFj7pvd9xHP3rAk/dzYkP5gy7spWDHOGvjsfj/2PWsvR5ZgY8ljccak3LLZXhnDUCyC6BCqduc7QkcE9lkneMMNNxBFEWeddRY//vGPyeVyndt0XWfRokX09/c/wRHmsTcUnTq9VobnDaT4301V/t/KFLqm0heXCaKInbXLDmvyeD6WJIyiTkK7LpuMNccB8WVfapWZaJQ5KhfHDRxUWcdQVOpemQQZITd/BILIR5PFOCltZHACMTaICAnCh12aH2lWKDo9dAqXOSK0JuvtoiLsqLHm4IcuriSjSkaHmxRFIZaaxvYrHRm+2iZqZw2100lK6TEKMZWNpXEyhkWvlWZndYZdtTqGIjFpt3jFksXUvTKaonUKoiDy8UOXbZURjs6t6KwRoMfKoshqx7RRV8SuPWd00QzqyMjE295Dc9Effuh2XlMtEK7Xc5YFKeW5s+t/puLOqUsZrorivNQKeGC2iqZInPiIzvmu2r+zozrKplIRTZZ4y+p/59iuiw/oedflP3ZAjz/ScePox7lvpsiZA4MH9F6VnJA1XQa/2VHFDX2WJXrYVhkhFx1Cfua8oeIRgX0uhF7wghcAwmF64cKFyPLh71Y8G5DQTFRZZlVO5Zi8xo0jTV622GRxSmVbxecPow8xtP8RbnvgtolLOLX3kqf02DAKkWVFGPwFDkHkU3WbFGIpIkRnJmNYovsTOhSdCqaiYaji56ZfJ5xTZEkyD8xuR5YkBhK5DudmblSmKyZO229Il809xltz47BHEqvnCq9H3s8NHeJyioAQx7eJqQpe6FJpTRNTk8TUBMXWeGd8tUdXSZlTpIWk9BiWpmN7LhtmRyi1fI7r7mHD7BQLEjGcwO7whWRJbpObRVE0kMixo7oTPwrJm0liapyYmqDhVwmjiAm7zOLUAiJJPDamiDHZgvhydtc3CQ5S6HQ6WDE1wawzCkBST9P06/N/f88A3DE5RkKXUSRY261zSu9iik6dGafGLeP/iCxJjDXKTNotjsrluHt69oCeb8Ps51nT9eywGpnrij260Pm/7e8D4LxFS5mwywf0HE4Q4QYBuZhKxrDaGz4Zxz2ExqXz8vkjAvvtHLVo0SIAbNtmeHgY192zRX/ssccenJU9RzCnOhpvBKzpMphsCMJ0yjAxlAZ3TrpsLL2dz5z21LPH3HD/c6qc4JfsrD7Y/rfwkNIUDSkKKcRSVN0mOTMvCjlDdD06ZGRJIggD7prcyMm9K6i16sw0J7E0g8FkF24YYLQ7GnO8HjEWk0lomc74TZb0PcZlc5Eac2O2IPKJyQnBXfKqHXl6SEjdK2OpKZp+jVYgyMpB5FH3SuSMPvyoRdOro8smXuRTcyukdDHGslSdbKaXHZVJLE1HV1SafpntlWkSmsxRuYFO52YuzkOWJFpBC0uLU3HqdMcyzDpV/DCg5lbxoxBdVthemeL4wjL80KXhN8mbJn7kokgqk/Yu8T63O16KrGIoFk2/LjpYbRNHANs7csj089g7FiRiZA2X0bpPj5XA9l2qbhPbc3lwdpLVXUIk0WMZ3DdTZGFC57e7L0ZXFKpuk5cNfYnvb3oXBSv1GEXU3ow1ny1FEMBIrUjKiPGtB9/JQCKJpRn8fmSSVyxZjK6ohFHEGf2fPqDnGEiIpPn7pxqYikYYhRSdBgX5yFHszuPQYL+3ldPT07zsZS8jmUxy9NFHc9xxx+1xmce+Y2PpCx3p+VkLu8nHkvQnNDbMFrE9l/FGwGBSwQvBC6990uP9buSxLfE5Mvb+Yv3UnzpFWloX/Bkv8AThV9XpT3TjhS63T+zAD12mmlOCM6RohFGEE3ic3LuCicYsU3aVgpVmrF7C9l30Nplakh4eZ3U6P49Kb5+7fa5Ysv2qUIBJMpaaoj/KokiaGJGFfieeAqDcKnZMF7X2qMkP3Y46TZdN6l4ZPxIEb0UWqfAJPYGMTMFKk9BMLFUQo4/NL+T4wlBH4VZq1fBC8Z5IkowfifFexhBmiCk91u6M+aT0BIZisDq3gCAUj03r6Y5fkNqWysuS3HkNQSjcuTvdMr/Zea9MNcZtE5fs9+c6j0OH47tX0RtPI8uIL9hYAdt3MVWNFdkYju9x27iLqWgMJAwyRpyd1QoTDSFAuHrbe9lYetii4ZF4truLW5pOn5XjrIWrGUp1k9Et/nbVWgxVR5X1g/L6k7pOGEWcNZTFCTy2ViY5pmsR6v5/LT5lREF0wJd5HDj2+xN/73vfS7lc5vbbbycWi3HNNddw5ZVXsnz5cn72s589HWt81qLo1LE0A1VWmGnWRCclgh9tqpCPJRmpeRRbIectynP97uv3eox7Zj7L+umHd0a761fscXvOTFBu2Y9+2BPiweI/tUdDliiAQuHrYygGsiSR0DIAPFTcyXHdC4mpCVK64AK5oc9wbRZdVoirKfoT3Qwmu4hrJmu6BrBUHU3RCNuS9DkV2NwXfvgIvs+jJfISImfMa3NnIkIahoobNB8TvprQMqSNzB5dJKA9+no4tkNXTCRkDMVitjmFFwjjxl21KeKaiRv4pPQYMVUhJCKmJjqdp7QRb8eDaG0qtugKPTymE1YCliqUZzePbSZrdCNJMgnN7KhU5nLU5oq/qK22m3uP5mBpceGR1OY7FayHeXrzOPIwkHg7Zy74DO9bdwWvXXEZN47cjS6LMUzOjBNGEc/rN3BDn6FUvvO40YaHjMwDs03OGSx0zDlBiBdAbKKezZiyRUTN3N9vTBWmrDElgRu0DspzrMj0sqPqE9dkpuwqS1KFPTZhhwQH4iF0oPyieXSw36Ox3/3ud/z0pz/lxBNPRJZlFi1axLnnnksqleJzn/scL33pS5+OdT4rIUsSY/USg8kubhtvsrrL55RenaobYPstjs4baLIw+PKjgN/uvpgwClnXfTSF2Bv4v+3vYyjVjSzJbJj9PLoiPs5bxv8RgG4rzVhDHD/iBiRe+KRr+sGWdzOQyJHQzY7/TdVtktJjtEIXS7NoBTZe6LI004sm651EdjdwSGgm/QlxIvFCt+0GLbLFxp0iOSNBEAYo7a7QXCfHCTwMhQ5R+JEE5LmiyI1EMTNHQO6zljJub+usfY5b80jV2JzMPmgXWXME7LlE+zkvpCDySRsZ3MCh6TuYioYXeOTMBPdM70KWJIpOnZhqkDUK1L1yZy1zXCmgo15reE1SehLH97D9FmONEs/vX0HdK2M8wpDSC90OX2lufXOveS60FdiDCC5LMl7ks8zL7Nsv2jyOCPzNyq/t8fPPdryfdd2LGKkXGakXsVSDgUQCJ/CQJYmNRZcXDNAx0vz6hgsJIxhvvIsTCoOH4yU8rbh5/BP0x/MsSb2HgUSOUqtG1khSceuk9YTYZDxCaHCgcEOfwaTKaX1pJhoVqm6TfCzJ7vqBcbXm8czDfpe+jUaj4xeUzWaZnp4G4JhjjmH9+vUHd3XPcvxpUnBQ3NBneVbl6/cWmYsZ+++HZsmZ4uNxApdrdlbF6CyeZawxwobZz4s2bnscZWkWOSNOza1gqhr5WJKEluGo3DIMVeeHW67e47n35nb7290Xs6nksrE0je21iKkGpqLRY+U7JmMdTk8UMm0L9ZWpWKT17k7naK7rA8Jvp9yqinGOIvg2c0WQoVgUnTIVt47alpnritkpgpq+8FyZKwbmTBbnioJZZ7RTJIVRSExJEEUhdpsvNLfeOZfquZ3eXCdmrvM095xzx/KjkN648EkKo0jY70syfhh2xmlhO2et6dc7xVRSTxFFIUWn0hlxWaqOG/isy68UnkqyznBtFE3WkZA7BO05xRo87GItnl9cvyCxvDNCm/NOkrredCC/fvM4zPiLxV9mrFHC9lvkzAS23yKhC/HEdLPBhWsXUndb7Kq6jNvf4uiubhanksQ1jZF68XAv/6Di6m3v5Z7paYpOhXtmPktvvKsTzlyIFdpxPUbHUuKemf2XzD8aI/UiO6s+tt/CVDX8tmz+7IWfOuBj7zOC6MAv8zhg7HchtHLlSjZt2gTA2rVr+frXv87o6ChXXHEFfX3PPWv3A0F//GEH0/FGyD+etpD1Uy6vWZHlL5bEuW9GjEX+f/bePM6Sqj4bf2q9dffb6/T09PQMwzLDsCoEMkriApHF16DxEzXRAAmvvCYQF0iMvK+iURG3GNQYl48iooBGDYn6UxBFwCiCoqMIwz779DK93r5L3VrO+f1x6pw6Vbdudw89M93D1DOf/kx33VpO1b23zlPf7/N9vk/N1PGi1SZ+vGsXbN8VabTt1TE0PCdIqXkglCJn5NFtsbJ1nzCvGtbXK4d79/4//Hr/B/H7yQ9jvFnF7yc/jIfG2Jf+95MfhqUbMFQFf7TmOOiqBktjlRQ0aFjKn0xZBZmHNYVV0FUTc84spltjrKM6VMy0GkLrQ0FQNgtBr7FAIB1M5BQkqJrj1vahYzQQCsllkiDDpx6ere4S7tQNrwpFUTHdmhPRFtltmkdtfOqJsnkeXdIUHR514BEflmbA0nKs71AgFM/pJoaLPWJ93juNmyzy6jNdNdGb7UJWz7Nzz1SwOh+G3DVFx2ChL2i8qorlPKrESRDXRPFKuT019p2jIMLpOsWRj5euuR7/a/2/AAAGchUAgKnqyOoaKmYOM60GTuvrwoOjj8AjPmZaDfRmC3jF8EeWcdSHBrvmfPx03144vhe9D0i9v3zqoebUhM5wacdrIqMp2D3XQCWTQ8M9/N8pSikoWcJP2mLjoOCAU2Nve9vbMDLCfGTe+9734oILLsCtt94K0zRx8803H+zxPa9RMCz8dO8EXjLUh3MGu/Hb/dM4b7gLqqJgQ7kPpcwcKpk83v/As/jfp3ThDwa6hHDXIT5URUGPVYJLXGgKC/U23DqKZjlo3VATE/7GrnWYdWZRc2zYcKGrKnbM7cdAroLfTd7A0m/Ex8m9eRiqia6Miro7h6yeF/oaNaha2t+YhanpyFEPDbeBrG6JdUbqU+i1imh4jhAAU0ow3qxiMN8rzAxF9ENh/jkcnCAQSlAyu+BR1m6DAJhzGygaOXGTrLl1rM53Q5X8gHzqoebacPz98KiP4eIAdJiCbDBiAREREikyVYdGdFQyPVAUFWONUeiKin21aZQyWfRmi8gELUNc4ojjAYDtN+ATH5aWA4UKCH1TNJ3FtD2MMLX8RlAWP8X6HkFBJdPNyuaD66NAFalHinAZBREELcXzA3vmplAysyIqoasa7t69A2vyBtaV+jBSn8IfD34QT81+Ak3v4GhkVgp+tPta2B67h7U84Df7R9DwKI4t57Ch1I9CoD/UVB2EkKCwYelEyNIUzLYIGi5rI6QqSsQ9PsXRgwMmQm9605vE72eccQZ27tyJxx9/HMPDw+jt7Z1nyxQy7tnzf1EwLbxqQwUe8aEqLl48OIAJew45LQOPEtz++BTefIqFq17Qj289NYMey8a5w038eFcVF67vxvpSH1p+CzW3hcH8AGZpHXmjGKScxpDV85h1ZjHTYoTh6ZkxbOpajYyWwe7afjw1U0fNaaHbymN9qQ8e8VFzWTUVVGCuOQ5TY0TG9l1k9Qxc32W+QYGRIAFlZIJ42FufEE+1GS2D8cYU+nPd4oluyp5FwbBg6GakWkxOt1GFRYXmnFnkjSIARpbW5I9HXh9D1Z1CQS+xqi8tA8dvQVXDCJNPgTWFHkzbNXSbBUGs6l5VpKM0VReiYwVaEJlxYarMCbrhVaEHN8SNXWugB21BMloec+4kLC0HBSocwvqiZbQcqBqN6ChQI1Ed2SBRVVThtm2qGgzNiDSIldN0cQNJBSo86izZdC/FysJxlVVwfE+Uhp+16rrI68eX+f9XL8PoDi1yuolN3avx4OhObJ+1cUzZhO1RrC14eGhsF16y5gQA7CGp7rLvzKbyP+J/Rq7DOaufexrLCDhPzlBQMrOwNIMVghy428hzh4+lpbcOown28xkHTITiyOVyeOELX3gwxnJUwfZcPDQ6hZevHYSp6Tit62T8bN8DqDktlMwsdlUncenmfmHud9lmZgP/2/01/J9T2I3B1CwANnqzFXjUCVIyBdbvyijCJY4Q7E7bNZzRfwJ86mG8MYV790zgTzesFVVRHvHhER+n9R4PTWFl5HnDwkyrGuyPlflP2HMglGK4uAoj9XF0W+xpbXt1DB7xmdeN78KmLiqZPGyviYyWwdrCgChlB8KWGapiBiJhFT48QQJKZhfzCdIKTB9Qr2LSG0PeKMLxbYzUx7E634+sziqu+L5ZNMVEj1Vi6UFJkGz7DfjwAMJE2TmjBJ+68CmgKQYUJRQm54xckBokImrEyZJPPTiEdYR3g1YaPvGgqTor4Q9Ij0xenpndgQ2lYVatFpT5A0AuyI7y6I8Bdj26Mqsw6+wXnkmcLBqqDqTR8CMSv97/Qbyw791ty5diePp8QdVp4uwBEz8fAV7Ql8ET08zSYrzhYOfcOAqGhdX5QsQ1eykkCADmXNb6pOFSPDs7jhf0HY+xxig2mAtve9DgE/azlO1TLBmLIkJXX734p5BPfOITz3kwRxMeHq/iBf0FjDdnMZCr4Kd7f46cYeL+Z22c1jeJsqkxEpTJ4JmZCQDAmkIZx5Y97JqbQH+ujCm7hlW5LlSdGroyFRiqiTlniqV5guojSlnLCE3V0PIbmGk14BAPrz52HaoO+7vbKgSOyF0iUkFBoIIJhPtzFUzZM8jpGeT0DExNF7obTdHh+DYG8xWMN6twfRd7alMYLvZCVRR4hAoiQSgRposcTPirBiXppoiYOMRmURUQ9FhrMOFOQida4AHUgu27QlAsV4nx7RWVRYEAwFStgEQExo+BW7NPPFhGHi5pieWOzxrDmooFGvj3yCXrvHyfa3cM1RQCThUqaCDE5sflpfYbSqzKR4UqHuI4yRGtQwISBACzzn5RPSY3geRRpxRHHpJIEICjngRVMjnsq8+gN1vAsZVZ/Haihd4s66nXZSmwPRf92RIm7SmsLSy8v8WCUGC4qGPXnIc9NRsn9dg4vff/olqtHryDpDgisCgi9Jvf/GZRO1MCMW2KhWGoCnJ6Br3ZIhyflY8XzAxevjaHnJ7BpN2AqrBGjXlDR9PzoasqRhoeds3NwtKrOLO/HwQk1M0QVqVlapbQzTRc1kenK0gzPTs7jim7jllnEqvzzCyQT+4tvwGNsgl+plVlQmwoGK1PwNR0PD69D5u6BpHV86LLPAEBCQR7VaeJ4eIqDBd7QSiB7fsoGjlBGphOJuwaz8XCvGzcow70IEKkB67SHnGwv7kbTT+szpppTcH2ohoZj4SaJE2xgKCth66Y8Cg7N1OzgmOhbVtDz4BQCO0PpQSaZgpHZ6ZTItChwScsXaUoRJAgIDSIVGhYncasPojQCMk+Jby1iEsclm4LNEQKQgdpvh+uO/Jo2h07xfMLv5/cg6FCN3JGBiUzi331GQDMNuCknj70Z0vQFB1z7oH5oS2EoUIGddfFeMPH+rKOunsYW2sE4KLnpWyfYulYFBH6yU9+cqjHcdTBJRRVpwmP+BjIV2BqTPy3s+pgdd5H3jAwZdexvtQLXdUwmDdBQPGr0RZev7FLGLO5vgtCKWqujW6rgEqmH6qioupMoWR2I6PlkDOYZfxkcxxDhW7sb9axKpfB+lIfSmaBtZkgDpqejYxuYs5p4Jdje9HygRetHkQ5k8dks4qTetYKh2auB5pt1THamMFwsRcn96wHDUTOM61JkU5zfDvSdZ23zpC/w1xLw0XEfLJnWiC2Hz3w7OnPdWMg3ysIFtfW8H3IHe1bpBE0gs1BVVSYatgxXgVg+3WMN8fRn2VEhqWyNFDFhwINWT3LUm6qCpe0QOEjqxfhEWacSBUiiA53g+ZIqnYTVWbEExqigsH8i3ziSb3VVKEHUqAL3ZGm6BgqXiH2N2nfjh7rL5b+gUyRYpnwuuM/jV/v/yAcn313uq08dFWDR3xUzBxs34Wu+Ae9maztueixcujP+dhQOoihpgPBUkvg0/L5g4Ila4RSHDienPk4+nMq+rMlOMRDzbExkKtgojmHM1Yx/5qaa2MgV0bNtTHRrGHrhItXDPfg9Ru78MxsA2f094q01lRrDqtyFUFSMlpeGBzyru4eZWX2e2pTOKV3DYpGThj7cS+cgsmiIQ1vBuuKRXRbBeyo7sdg3sNgoQ9VZw6u4sIhPizNxERzDqamY6jQg9H6DDZ2ldEiDeyp7UMlw/bNG67ylI4REJF4ekd2kpajITyKY0hVWmw9HQrC9Jym6pFoi7yNEDDzJqlgOp0JewK9Vi8G80PQFCPYtwtVQaD5QUTATBQCCh8eaUFXM4IMKQoR/dGSDBBlcKLGxsnTlySIMLHUnEyeZGNJIPRU4mh6c50+ZilSHDEomwXsrU9AVzQUDAtdmSKano2ckYPjtw5JSxHbp8gZJs5c1S2qZg870u7zKwKp2GAZ8OXHnsTqfBYEVJiGWZqBkpmFrmqwPSY0rjpN5IwMhopdeNHqPGzfAaFM4MddUMebs5hozsEnPhpuHaZmSRN0aCTIKpc0IW7mkQmul8nqBbjEwZQ9g4bbwkC+Aod4OK1vPSydkQRTZfuoOg14xEfOyMD2XTTcFoYK3Wj5DWGoyN2hZZ8engZLgkediKGibCgY346ZM+bE+Pn2POUGhI7UnGBxQiHGo+rYV5tGzZ2Fphig8APhtBcYF2Yix+PH0hRDkKCMlmfLEaa6dNWEqVpMp6Wy94D/qMFYwua0zFCRj48GKUJ+DlwAzqNBClQMFTZGrttQ4QqkSHGk49jy21HJ5JEzMiCUBu1v2HfiUPVV21vz4RGCHdUZ9OdKws8sxdGHlAgtA7oyKk7uWY+CwSrBVEWBqijotgqwNOYKrULB+lIffrBjFB4hgVO0hQ3lfhQNBRtK/ZiyazBVHUOFbti+i/FmFT5hFU0k6N8FQJCLnF5CV6aIXqsXpmqJaAsX7ZqqhYnmHAbyFTw5PYLuTB675yZQNBnpcIiH8WYV/dkyBvI9Qd8xC73ZInRVE1qajB6SIJ7OUSVSJkd/OAglsL0ma8tBWVWV7ATNyVHDrUNTdCGEjhsPytVj3HwwFI6z1/i2L+g/EQ2PpbtY5+kxNL1asK0vep/xMnb+N4BAVN5kuiE1g4yWF6k1HnXqBJmY8d8dYovUn0zo+Hnx6yC3SYn3lUuR4kjGqT3X4vTe/4uzVl2H/3rmaZxQ+QccW377ITvee876PCaac1hfqgBgZfyHGyu56erU1BTe+MY3olQqoVKp4PLLL0etVpt3m5e+9KVQFCXy85a3vCWyzq5du/DKV74SuVwO/f39+Md//Ed43uH0LGhHSoSWAZoCNNwG7t+7G6amw9R08TTC01e6qmFT1z8ipzNl7zMzE+i2CvCIj3WlLuypTcEhHm5/Yi9+NzECU9WwvrQamqpj0p5iAuAg3cKEy43ACyfHyIEaNvrUlbAze81tBeXxPbD0LNYV+4WGSLgu61lGaqDAIz5MjZEqTq4M1YykdxSoyOnlgIwxQxQ+BhK0zeAl//J2PIXEyRrT1bAvPg30N5zAcRLE/YjikRUeIQOAvM40Uy2/gaHCsNDydFurUDAqqLtVZLQ8DDUDI4hsxdNzfBvWa4xFkwCIijKAibBpUCnHnba5+JobS8o90XifNfY5UAWJNDXWoHVtIXpDif+dIsXzBX+64djDcpySmUXBYA+AJ1T+4bAcMwJKALKEn0NYOPHGN74Rjz76KO6++25873vfw/33348rrlg4Av3mN78ZIyMj4uejH/2oeM33fbzyla+E4zj4+c9/jq985Su4+eabcd11182zx0OPlAgdZkzat2NNgU2U561dD0IJLM2AoqgwtQyKZg7DxR4QSnD/vnfjDSeciG6rgBf0r4VHfIw3qrh71340PAeVTB4XH9uHl6/dDFVRUTR6MN0ahx6kuzIaq9jaU9uFglEBkfQrTa/GNClBqfyEvR/7mzMsraWojKCpVtBag6V1+rK9WJXrE9Eeh3joyhQjUR/eLFROZVEQOKSJrF5Ey6+z6EdAvngKjPvqyI1HeQRmtD4Jn/ioe03kDBadygSmhnLDU06GuLhaFhgz4qaKUngVKrIaSxPK5IuTG08iNEBY4cUJTyfwY5CgLQkJjgewdCQFEWPgZfbcrdr2G2I9eTw0LZlPcZThUKXD4iAg+MXoiGgflIJh27ZtuPPOO/HFL34RZ599Ns455xx8+tOfxte//nXs27dv3m1zuRwGBgbET6lUEq/98Ic/xGOPPYavfe1rOP3003HhhRfiAx/4AD7zmc/AcZavbVB6dz3M+Pivf4LNPWsw4zSQM/LCidn1Xdhek2l9PFaa3p0pwCUufj2+B47vIadnoCoK/miwgkomh7t37kQlk4tMkpQS9GT7I5VMvBqDe/80vKrw0eGpqLyehe27OLYygKlWDUUzF+lpxb2JuOiZdZw3oKkhAeLHlIkJgIAchW00uL8Oj5AQhOknXkYPhLqivlwZmqqh4bZE+wkgmjKTz4/raTiJE+NQwlYcMuK6JU6kkuz2+fGNQEOkBOX0POITITGBRQCVyBAnd45vixQhtxKwtJzYl7wfn3qpFihFioOMf936FkzbNo4rF/Cb/cvUcf4gNV2tVquRn1ZraW1YHnjgAVQqFZx55pli2XnnnQdVVfHggw/Ou+2tt96K3t5enHzyybj22mvRaIS2Bw888ABOOeUUrFq1Siw7//zzUa1W8eijjy5pzEtBSoQOM142FJS+m2zSe3pmDE/OjCITtNWYsmtCQE1A4RAPfziwDpZmoOba6M0W0ZstIqdnsDqvIadn4BEH5UwPKHwUjAosLQ9TzUKBBk3VsS4w85PTMF5AgHgaiYBgIFeBqVqoZJgIWIGKDHdXVnSR9uGpJkvPQlfMyDIg1L1wwsJf80gr4tQsC7pls0Ne7cb/NlWmcSqZWTEWnvbjRILDVK2QKAXkjB+PE6U5Z0oImDlC0qZBgRZZH4Ao/1egwacuau40K7MP7BH5vuLbZLQ8snpRaIZ4StAKyvl5Cw2e4uO/yxojOTKVIkWKpeN7O67B8ZU8Hp92QUBwtjQxH04sqeGq5EG0du1alMtl8XPDDTcsaVyjo6Po7++PLNN1Hd3d3RgdHe243V/+5V/ia1/7Gn7yk5/g2muvxVe/+tVIW67R0dEICQIg/p5vv4ca6R32MOOY8irsb8xCVzVkdSY07rFKuG/vk2j5FC9Zsx66qkGFgpyRg+01oataEBEy4QWptJlWA6f2rsV4cxbDxQEAQM2dAQBovi4qm3gayFAzcHybmRYGKRtVCYlOVisAGjBhT6DH6g7TTKoFlaoRcuMLfUxOnBdPU8WR1QvQFANNfw6myjx5ZA8dnoJygz5cJHBv5uRGV0xBFnzNi6TdNCVsQsr7dwEBaUHgFRScP18OgPUTkiBHjSh8aIohSBrXIkUIDjTk9DJmnf0om33QVETIE48M+dQLCCdLp/F0HAFB06+xcw1IHt+mbPZhpjUWKdtflbvsOX3WUqRIkYzRehMlU8NpvRlsKPXD0IzlHtKSsHv37kgKKpPJJK73rne9Cx/5yEfm3de2bdue8zhkDdEpp5yC1atX49xzz8UzzzyDY489PLqv54KUCB1GPD79MUzZTHVv6QYICCaacyiZWWwod2FtoU9MgFWHESDbd6ESHzndBKEUpsKs5wklaHgOSmYODbeOnF5CTi9JupMmLC0flIQTURru+DYIpSiZXSCUYNJm7TsszUAl04OuTAVAqJuRS9d96kFTLYCGvj8i1USZJqblN2CqlqhE44JnS8tHqp/8wHE5HpmRiVboPs1ey2qFiJ6HkxSXeKAgQmfESYsKFURhaT2XOFAp+x1B1CmeJuMRHwWqIEGyKzQ/Hl9WNvvEtuxcXRbhQmjayEgdM2gEgNnWhNA3mTx1h7CFR5xMjjVHsCqHFClSHCR86+m3otsy8ehkExeuH4KuapFCiMOKg2SoWCqVIkSoE6655hpcdtll866zYcMGDAwMYHx8PLLc8zxMTU1hYGBg0cM7++yzAQBPP/00jj32WAwMDOChhx6KrDM2NgYAB7Tfg42UCB1G/M++7Wh4FCdUihhtzAIAhos98IiPXquI/c0ZdFsFKIoqDAlNlXV4t30XBSOPqlND3rBg6SZURUHJZK0zuECX6WAyUBUVE/ZelM1eAIGWhbLy94bbwrFl9gRUMCwUzW5BoNi6atgSIiAZLD3jifVoIAaWyQINWmNwMqerpojGuKSFlt9gZAosNRdPiwEQLSd8MJLliBYWXBjN1uPd2nl5uxHTA8ll7HKPM161pSdUgLG0W3hTJEqYO+bnKeuffOoKI0aHNIVHkK5m2GtQoSsZMU5KCYpmNwBWUSZcsSlL6SmKitnWfqGZWlt4C4aWyfA2RYrnKzxC4BIfG7tMlMwsTM3C+uJVyzOYw+ws3dfXh76+vgXX27JlC2ZmZvDwww/jjDPOAADcc889IIQIcrMYbN26FQCwevVqsd/rr78e4+PjIvV29913o1QqYfPmzQd0LgcTqUboMOJlQyeg11Ix02KGhNtnbRQMC3tqU3hsai96siV4xMfDY8+i6jQBIEihZVAw8oGOhKVgcrqJrM7SXbrCJn1TzcLS8tjf3IOmN4ey2StVKLVgqha6M3n058qCDOSCUnIeBeF6FEKJIEGyqJeXpSsI00VctBy2qAgrsgCg7lbhEYeV16ss7WVqltDJyOSCR5J43zD5daFFUnVRAcfFy3G/HRmcmMWjT3Fw/yA7MIYUztQ8IoQwfUbht23Lxp2JEDuPtgRJk7VKSvA3vyZ8OQmcwNMqsRQpDg1aPkHRNPHQqI2CUcFEc3q5h7TicOKJJ+KCCy7Am9/8Zjz00EP42c9+hquuugpveMMbMDg4CADYu3cvNm3aJCI8zzzzDD7wgQ/g4Ycfxo4dO/Cd73wHl1xyCf74j/8Yp556KgDgFa94BTZv3oy/+qu/wm9/+1vcddddePe7340rr7yyYzrvcCC92x5G7K1PoJSx0PIJCoaFckZFw2thQ7kfG8r90BQdT8+OYVM3+6A1PKb85y0YAIBQGhHSNjxHkAdVUfHE9Db0ZleL12XjwpbfgKEZ6MpUhDDXCKI2/H9OPnzqoeFVI2XxctpGJhtCXxNM5JaeY9VkYOX5AIQgm//Nx8YnfJnsAIiUr/NoDNc2yURDCcTUcYhoEMKKsYUQd6/mfcPk683dpRVo0BQjbLga/M3PS2714ZBmSCyDaxTfLx8zP0aczKVIkeLgwPYpJpotvGgwj6ZXW57WGgEOllj6UODWW2/Fpk2bcO655+Kiiy7COeecgy984Qviddd18cQTT4iqMNM08aMf/QiveMUrsGnTJlxzzTV47Wtfi+9+97tiG03T8L3vfQ+apmHLli1405vehEsuuQTvf//7D9l5LAbLmhr77Gc/i89+9rPYsWMHAOCkk07CddddhwsvvDCyHqUUF110Ee68807ccccdePWrX334B3sQYHsuSmYWOT0DSzMwWOhCzbWFczMz9SugkilByahoenU0PGZwmNFy8IiDnJFDza2jYOQxZc/C8T2Yqoay2QcFKjaUNwAAZlqT6LL6Ik1J9YD4cL8aVQkrr4Cw3JtXZuX0khBJC8FyQF54usfULBhSVRMnEHw7FkEion8YJ1aORHT4vm2/ARVh1MbSciJNBrAGrHLkxw1MIznkLvAyRMVWrCw9TubksnxeVacEGh45SiNHqnzqiuoxLphmETMNkNqEqIoqtEniOgUaK1mIzav4plv7MZhf6BOVIkWKA0VXhjWs7rZY4QKvkl0W+IT9LGX7Q4Tu7m7cdtttHV9fv349KA2J2Nq1a3HfffctuN9169bh+9///kEZ48HCsj52Dg0N4cMf/jAefvhh/OpXv8LLX/5yXHzxxW1+AjfeeCOUI9zw6v5978bW/bOBcNiAR9lTCNeveMSHTzxUzBxqTo2ljvQsCKXQVQ0j9XFsqLG3y1R17Jobw57aFH69fxzPVsfhkZZkvqehP7cmEukBICI9ckqK0sDZOdC28I7nHIoUJeIeQnw537em6jC1rHBA5p5AcipKXs6JVqQUHmEvLkM1RRrJUE3h68PB3Zp5Cb1MbpIiP4oUYaHBOcc1PwDTDxWMLihBJItI68pGjeJ9C8TVjPAZsLQcTM0SVWxytRmvlJPTfry/WLyMn4CgK7NwHj9FihQHjiemHdi+C0szMNmsisj7coDSJUaE6KGLCB1NWFYi9KpXvQoXXXQRjj/+eJxwwgm4/vrrUSgU8Itf/EKss3XrVvzLv/wLbrrppmUc6dJRdZo4c1U3RuszsH0XT89M4cHR3dgzN42W30LOyMAlrF/YVKsmRL6mpuPpmVEM5HuxvQhM2bMglODZ2Wlc9z97cc7gWgzkKmj6NeypPd12XF6q7REHLnFEM1TeDgOASC3xMm+5zxfQ3kmdkxrW/iEb6RYvT/g8+sGF15zwyPCoEyFfvKJKpMwCjRInb1wfFDcvBCCIBRBGgUTUiYTrk0BnFE9B6WpG+A7J0R9e2s9bgfCUIhCKtgtGRYilOcnj+5LTYXyfekD2IsaJgTDcJx6q7lTbe5kiRYqlwyUUI3VWlTthz+GFfe9e7iGlWGasmKox3/fxzW9+E/V6HVu2bAEANBoN/OVf/iU+85nPLLq0rtVqRVw1q9XqIRnvgWBf/YsYbzQxbddRMBUMFUxsKHdBVzWUzCxW5Yaxt74DDa+F3XMzOKV3EDvndqHm2rA9F6f1rWddyAMPIYf4GG8QbO7LYUd1PzaU+1EwKiibfag6kyhpWRAK+NQOWlRMwNTCNFTJ6BZREidIAXERtCxOjguVeRUZjxAxYpWJRD14w1MAIQmgrCcOrz7jAmyKoOoLwfHVsJN8Ti+Ffbqk9B2PoPB98mNwDRIH/10TFXCGVN4edcFm2h7297Ozj2K4eDyAkORoQTBS7mcmp/UA5uHEDChN2G4jOE+WLstoTATokCZz6AZEOk0QsSAFx1KDBBbSmvkUKQ4FTu21sL7UB03V2nR6hx2HuWosRTKWXZH5yCOPoFAoIJPJ4C1veQvuuOMOUUb3jne8Ay960Ytw8cUXL3p/N9xwQ8Rhc+3atYdq6IvG/+z7LQZyeZzW1wVLU1BzW/AIga6ocHwPs85+mKoOSzOwOl/E1v37oCqsoWm3lUfdtZHVWMNV3pz1hf1lnLMmC4/4mGnVRQSiZPYEHkMsclRzZ1F1mqg6TeT0TFu7Bx4himtnkrQ0XDzMSRPrN9YMmrrWhYEh1x1RKSoCIJJW4/vhmiFOoCL+RQgtAQCIqjYa7FM2UOwkiuYkTSYd8UgQj4QRSlDOVESkjKfy+O/cYJL/Hb8+FAStQH/lUUbGs3pB/FhaHrqSgalmQ0dqyclaU3RktDxUqJhtzXT6OKVIkeI54qnZT6DuetBVDWONGawv9S7vgAhd+k+KJWPZI0IbN27E1q1bMTs7i29961u49NJLcd999+Hpp5/GPffcg9/85jcHtL9rr70WV199tfi7Wq0uOxl6bMrBq49llVw5nXnjFAxLVCuYqoWG24BDPPRmi8xZWlEwXOwFoYS13mhNwvE9WJqBKbuGx6er8ClFTs+g6jTR9GrwqSeqwThRqTpNHFteDZ96yATpJQ7RmysQ8ZqaFU3nUAKPOjCVaBNVICRLNEjn8H3xSI28HoBI6bhLHNZXi3ogQWqMg6fFuJgYYGkuP2hW6gYaIh49ARCJBMXhUw8KDYmPphjwSItFsgJyJLtKT9pT0BQdBaNLaKL4OGThuWysyMHIqIO99R1Yk18vNFM8isQNJ5WA8HB7AnbeOlqkLlJx5cDYMkWKFAcPT02P4MkZD+cM5pA3LMy26ss9pBQrAMtOhEzTxHHHHQcAOOOMM/DLX/4Sn/zkJ5HNZvHMM8+gUqlE1n/ta1+LP/qjP8K9996buL9MJrOsfgRx/Hr/B3FyTwYndZ+Bh8cfgKoo6LYKmLJryOkZ5HQ2OXqUwPZYZKLmMm2NpRksreK3sK8+g8F8RYj8Tu/rQcnMYbQ+g2qrhVlnFmbgS8OJwawzgd4s066YmtXWs0pTdTS9mhAsUzl6o6gAJZGWFnzy5wRACJ8lkhOWjUejM7wcXi4tFxEeEOT1UkRrBETLy7nzsqbq4nw4MeEkTE6P8cgXj06p3HUaPnQ1+fOhQMPawjGi5QffJ7cO4NofFs2JRpR0JQM1SKEN5tfCIy1xXePj5GMzVFNUvnHN01MzT2B1vr9NIC7DJXfBUM/v+HqKFCna8dTsJ7CvXscJFXYPeXpmDEPF7mUdE/UBuoT0Fl2+yv/nFZY9NRYHIQStVgvvete78Lvf/Q5bt24VPwDwr//6r/jyl7+8vINcJH4+8l4WxdENfPvp72K0MQuHeMwtmlJYOusZVveYeWIlk4Ouaqz5qaZjX30Gc24DM60GE0hXx6EqKnJGBv3ZstimP1eC7bkwtQymWpNMqExsOL6HmluHoZqsOWqQTuLl4Rxyd3ZZcMwJTtzTRq7A4uJheV+y0zT/4X3EIi07SDTdJKeY4um5ePpOhjBkBBECbT4+/j9P6/E0lE/dSNUY9wAaa+xqa3PBfZRkyP5HfJ/cHNFUsygYXYIEiW2UaKSLky3bb8D266CUYF1pWBDWx6aSewKNNXbimdkbE19LkSJFMkzVwjOzHmYdCsf3YPsuyHJXXaWpsRWBZY0IXXvttbjwwgsxPDyMubk53Hbbbbj33ntx1113YWBgIFEgPTw8jGOOOWYZRntg+N6Oa1Aw2ATfny3D0pqwdAMFwwq0PKw03tR0mKoOh3jYNTeJ/lwJ440qNpT7sa8+DVVRkDMyIKDoN8owVQ0Nz4FhMKH0/mYdL1p9PFp+S5TmA0ElmBE6HsdTODziILfB8KknqrfkCRsIU2EqwkgPNyxserW2vl3tXj7BcqqKCIjcsd4ljvibp+KECJt60BVTdKXPaLlIFRhP5QFg5es07N0lgxG0kAzJaTHeLmNVbli8BiDS1oOnuJjQmUS255EqTtg0VRdVYABAKdNBzbmTKBo9rMVIYD3gBe+dG1xvfn1KZlfiZyteyp8iRYqF8d3tj+CUHgMn9w7hZyM78eLV63Byz7uWe1gpVgCWNSI0Pj6OSy65BBs3bsS5556LX/7yl7jrrrvwJ3/yJ8s5rCVj68SH0J8tI6dnMNGcg6UbrHM88aEHE2Mu6BVWMCz8fnI3bM+Fqij4+cheWLqBieYcpuw6vr99J0xVQ82xMVqfgaEZeHJ6FHWviaFCN/5ozYkgIOjNrmbakqCtBhcE5/QSFIS9tTjpqbvVSCSFSDoW2eSPRzFkwbK8HxpsA4RVVZrKeonxKAyPPvEWGryfGCu/ZxM6JxIRXx+pNQbXMGW0nCBgvKqMr0Ml8hQvo+fr+8SDR1rheKW0n2gVgpDg8LRYfGxcP8S3G21sj5T0c6LmBj3FdMkPSe6RVnOnWR844gVOSlGR+s65f498tnbXPic0VClSpFg8dsx60FUVO6r7cUpPP7L6CniY4IaKS/lJsWQs6930S1/60gGtf6SYRz0+tQ+bugdx/97d2NRVgqkyJ1NCWfPUbquMhtuACgUNr4VTe4eDijAV/bkyZlp1UVoP2Pjq48/gb07ahGdmRjHnNLCpezV+M74bJ/WshurWsSo3jFlnP3zi47cT23Ba74kA2CTu+LYgGQDzyvFIC3mjJIgK16pwcN8buUUFT5nFS9Rd4kTIjAoVtt8QkRmAVU4x4qOibtvI6yXWTDWIkvjEg6bqyGoFQWjkai2up5EF0vLY4uOUI1nA/O01uI0AK4kPzl8q8+dIcqEOhc4qujL9ES2QpoXtQEJNFdCdWc2qBINrxsw0WxHjSUKJ6GC/q74H64rheDNaDv3ZS7CnFlrdp0iRYmHMOARNz8ek7aGSyYnU/3JiqW0yDmWLjaMJy/9JeJ7h1/s/iPWlXow3ZuH6FHtqcxgqdIuy94zOoi2272JffRoFw0LDc4IIkQpT1ZDTMxhvzmK42IuGN4myqcL1XZiajrJZwKxTwzHlbkw057Ch3I+R+k4AQDlTwVAhGsmJ6HfA+l6pUAUJAiCiETQgIXHCEYkG8ShKsG5cP8RL6DkoJbC9BjRVR8tvoNfqbUvVaaoe0dPw6I1MajjRkccmH1P+PW5kKCraAqIj66CEJ1EMwvlZ3rfwDwp1UBwKNGGGKJfpMy0RI4zccDGnl4PUoM8MIoN9MTIa/UoeU1qPSft2PDL5e2zsOharc38DABgqXNE25hQpUnTGcWUDRdPEqpyJkplrK85IcfRixYmlj2Tsq38RlmbA0k2UzCxO6S1jMF9ARsuAUBpJM5XMLDaU+gEANcfGj3btQdHIoeo0oasqdEUT+y2YCgilyOkmJu0qCKU4rrweJ3QNBSkXHaqiQIGKHmtVW1sLnvpSocJUs1GPICmakkQI+H7kVhw8LcNJhJySAiBSZbxqS1VUtPwG9KByi7tFA2hLZ4mISlABRmL75uRGJj/x1+Vyfnkc/HxlEiQj3lg21ATJFWKeaGciryf/LlJfJKqx4tVoLb8eHE8LyV+Q5pOry2y/EXgZuTix6/jIOe2rfxEpUqRYPKZaBNummhgu9kBVFDSXsbWGAKGhqeJz+UkjQgcFKRE6SNgx929QFBU1twVT1dBtFTCQr2DbdBW/ndgBNeiV5lMPjt+C7bv42chOPDAyCUs3cOaqbtS9JqbsOmzPRW+2iPf8bBv6cxpe2NcPh3gomVnUXBse8XH3rl/DIw5qbh0e8WEEImmeWuJRE05KQr1OoI9BlFDIXec5OeL/uJs0Ny6UiZWl5QShiIuTeaRJUVToiikExj5h/c445Ootvr7ciT1J+CyDv84JT7z/GAc/ryTTxdBnKFopxzVAPnXhUzex/J5rhWR4QTsTkeLj15+2QruBmMkjE6Ozvx0SNmftdO4jjSO77UyKFIcL39l+NVbnVfzRmj44xGcyhcCuZDmxkrvPH01IidBBwFOzn0DNqWHa3i9aWRBKkdUzcH3g5J4hYZ4IAFN2DY7vIaerGCpoIJQGXeR1rC/1otsqYKbVwF+f3IPT+/rwq/Fx5PQMam4L/dkSPEpwzuAJyOoFzLTqyGgZ1N1QCyQTiXgpvNwSQxY8R4TAAcnh0Zq45obDDfqXyRGWSIRHIlI8uiILoLlZYVI/Lnnssht2nBSJ8vyE5fH/OxkvyumpJAIlC6V5xRlH3C1bLqvnLtPc/4iLxqfsUWT1QqR6TewvSJEpUJlXkpQS5CSNE6BUMJ0ixeIx26IwVR0TzTmoioKzVl233EMC9emSf1IsHSkRWiJ+vf+DmHMaUBUFTkB2dFWDGZCT4aKOhsfaR2yfHcO0XUPVacLUdEy3CLas3oB1xX7oqobRxozYR2+2iNlWC998cgTnrV2PmVYdJTOLjG6i1ypi1qkBALozBWiqjh6LGYNxXU08ihCWyLuRcnO+DYC2qEy8qzuvuuLbyl3i5ZYaYptAi6Spuqia4vogToDknl38uEn9f+aLCPExx6Mn8jnEI2WdBNQzrbE2z6CwHxlEawwxLkTPV/5fHrfj24ENQAYNz4Hj21L3erZvntLjfk+cSMlkqOp8W4zfSzUOKVIsCpZm4OSeDBzioeG1YK4AoXSKlYOUCC0BPx95b5CqYhMV9/AhlAaTnorxpg9LM4T4WVdVHF8ZhEd8nN7Xgym7ht21/ei28ui1WHnQlF3D0zOj+Om+Fgw1jBjsqU3B0nLI6nn0WN1oejVkdFOUU3MdD4DE9BDv2u4TL9FlWjhHg3QkHpHqqSByJJfPyzBUU6TC+E9cnwSgbXm8bxg/l6SID38t3pE+Hp2S9xc/Hy50VhS1rQ2JLIqmgYeQcMZOKGOnIIIc8QgYHxdPla0PmrrKBFSONPFoHNdHycL1ujeLrFbE6tzfIKsXkSJFivnxgYf+Dwil6M0Wsa82A1VRYGoro/tAmhpbGUhp8XPA93Zcg16rCALW64uJlVXYvgtT1VAwymj5DRTMAgbzjBxxc8WcnsGsU8Ozs+PotgpQFYInpqfwyvUvwHhzHFN2HetKfRhvVvG643sxVOiGpWehuzZyOitzz+sl1L0qaq6NSqYkdDpxXQoAkZpC8ATEI0ai3QWiDUtVaTve4wsIhcxcDyO6wIOAEg+6ygwPZWLACYAq9s3W5z5FcW8efhzZJ0hGkrZHJkjxFhvx9cR5Se0/eBd4BRooJbC0fLBcFZqmTscHpChah+ozDt5ixJBIIydZ0bRb2NhWrBtcC6IwMsTF1mlqLEWKhTHRZFW6BdPCCV0Dovn0SgDxKcgS0ltL2TZFiDQi9BwwkKsEbSrCbvDyJMgaa6rYUR3BhnI/qk4TRTMX+MoYQcNVIvREawo5VJ1plM0CNpRXo+m1YGkGSmYWqsK8gFgn+gIsLYemX4OhmhjIMedtHg2Je+jIZoEcnUiQDG7sF+/8LlI/UrUYn6x5pVckeqKEnkC8Wk5EO6QSdO4/NB8Wc+OSU4JxA8ik7XklV/waJREati5r2Jr0umzAON0ai7zGqwUJbRdqy/vi5fSCtEnra4oeEbGPNW5GzZ3GePOW+S5JihRHPf5oTRYFw8JMqw5VUfHLsR1o+SugYizFikH6SHkAGGnchF+O/R69VhEe9WGqOmzPEYRFVyhmWg30ZjXoionBfBdMzcKT07vRm63ANCzU3Tl4lKCSycHSDDS8FjaU+tHwHFg6289Mq86qx3wXtu+iZGaRMzLIG8XIxCiLj+OQJ36mcYmSIZ94UCUSsxAc32Zd7ZWoVw4AYXhoe43IMk3R4VEnrFyTusmrUIFAq8SjHfw1mZzFCQ3/fb4xUynaEl8/4osELYhyaZH1HNKEqWYj+5QJS6fSeU4UuzOrQSWLAH694mX9fF9yRMinriCKhmoKjVW8mayuZmAqWXRlXt/xOqRIkQJoej7Wl3IAcmi4LZQzGVFlu9xIDRVXBlIidADY3xwTjVFNRWdtMygRX6qG10IlE6apZloNFIywRJqCYKbVwEC+B6bGKhd481RLM4IJELhr5zhevLqM3mwRqqIIHVJfdpUkemZpqqxWCLbPw/GbbWPmhCO+TA2cmvkkLYuW5fV4VIgbHmqKGiEYXPsDILLcCFJlfD88AuVTD1Ci6Tj5eIqiQg2+253IUVLUh8aiLfH4kvAlklJjPCUW3y/vg5YEUwsJkkx2kv4GogRKToXF98/Hoioqe4Wfj6QR4qlETVHhkPb3OkWKFO1YnS+h4bVgey4enZpFzaE4rrz8pfMAQAkBJc89TbeUbVOESFNjBwDbc6ErWtC0NCaSpQRFMydIhaboWJXrhaVnsa7UJ0TDawqr4PgtDBW6kdNZFcNoYwYFs4C614RHHLxsqAfbpuZYdYOmI6dncGx5CC2/IcgL15Dwqi03aNOQ5LrMnaRl/Q1PI3HyInsKOcQWf3MRtksccQ4yXOJErOr5xB93beXjMjWrzdpeRIU6iLST0ndylKdTOX0nL6HIsWNVYGG0KFlYHa+K44QmiQQlkSnZQFH2T4qIpSViyluLcEIajyqNNG7CvvoXMd36RtuxUqRIAegq+14O5CsYqROcPVDB5u5/WuZRpVhJSInQAcKjPkxNh0f8QL8jt6pgk6rtNcFbTZiqJfQzelBFBbCeUbbPnkoaLuu3xUs6/2ffFNYUdJTMrCi1b/kNZAITQj1IUXHik6QN4n/z33lVFF/ONTs8hcW1O4oSrYTygqahPC3G/YBIUKGWVGEVb+Ghq5mwBJyTKkRTXRG3aoTi5yR0KrGfD3JrjkiajafFEFamZbWiIDE+Ze+RbHzIz4OjPboT6oX433x97jwdB4XfRsrkccuO2ELXFPzLaHm4pIU5947FX5AUKY4C/Nvv/hYzrYbQdNYcgv5cebmHFWKpHkKpWPqgICVCi8TWiQ8BACyNV/2wD2BftifSnZ07SANhiwRN0WH7DUYmoKJglGGoJiaac6g6TQzky0JH4/ge9tQ89OdK2Lp/H1QoGK3PtFVjRXqFBWknABESIUdF4t5C/G9RvZVgZijrafgy3n+LR0B4awh27uEYuGcQI2muOKZHnLYGqvJ4+LnIXkdJBpByqTyQbLQYR1JkSSYffJvRxg6xjHv8iH1IAnSZ4CRBmEcGkaNO6Ta2jpa4PN5QVv4c8EgRhY+8XkZeL2GscTNmWt/seJwUKY4mdGdU/M/eGkpmFg3PwZbVmcg9erlB6RLL54+QRuQrHSkRWiRYxYECU9NBKA3+j7oH8y9YXEyrqTpM1YLtN8TfPvWwsWsdujN5WJqBWWcWtu9itDGLq19wAkbrswCAnJHBcLEXOaMkjiM3A01KM3F3YtlHxw0iOzIx4oiTnfj+eEqIR5QUhBVkclsOOcIkp6fkSAYfm0yCknp/Ob7dRlxkXdFC4ulOabGk5ZwM8f0O5jckpsb4+QJRQsSvR9xDSNaGxcHJoTyGMDoVJUVytZi8jMMlDqruJMaaO5HR8vBoCxP21xLHnyLF0YQ5l+CF/Sbu37sL1VYTD4yk1WIp2pESoUVg0r4dBcPCk9OjIhWmKyoMzYi0zgCAqtOEqihwfVdoawCWYqKUBKXwYSXVhM2qyLoyXSiZBRSMDJ6cGYWlG1hfquDJ6RGUMyzqxLVBNPDi4WkrHiWQIyhhascLyQmIGIccWehEIniJu6xNUaAKo0QOPYjwxPfFozxx8iGMBEEiBIpvG+8HlkheEvRBndZNSqO1+/cgYkjZCbL/jxhLnLDFrkHSa7JTNSdiPnUDIbUvxs1ThaLSLjhH2XKARwc1RUfLr8MnXtpZO0UKALvmfKwv9aLhUnRbebzl1GPRcFcOGUpbbKwMpERoEdg59wwAYLjUI/RBDY9FNSw9G4lodGWKQXkz6yHmE19MVHogbnYJa7HgEger893I6szllFKC0cYsdFXFnOPA9lyc3rcuSIkYyBtlOMSGTzwmlA6Wy+aGfBw8+kIowZwzBVOzwjHIjsZK6AnECQkfr088eJSdpyzsJSBwiN2mO+Iu06q0LO4YzSfveCQrvm4nxHUy8rI44ulCsTym95GjPJyMxJuoxhEXO4tziH2l5H3z/crpuE4pMQ6eWgVC/RR3uObgpEi08wgato41bp533ykWxkNj7xe//27yhmUcSYrngtefcAxyRgan95XRbRVYwYs6/3fucCJ1ll4ZSInQIsD1QDndhKoo0FUNuqrh5yNPAWATlKlZCR4xzMpdRDyCii+ATXC210ReL8FULSHWfWSige9vn4XtU+yr15HVC2h6NaiKCjcw88toObjEiZCNeEm7puiin1VOZ2k1XXIqlid7ucM7j4jw1hDCfFFRBcGRIxBcaC1K4wERpUoSOwuXZIlUdUJSmk6OVnXaJl5OL0dTBGmViIRwywbrKbaQS3TicQPNFHej7kSk2py0eVpOGg8npnFwIToAeCR8qo2kJYM0LNdRTdq3zztuj9yNnXP/jqdmP7G4Ez2K8F/PvgO75ibwi9H3sXY6jr3cQ0pxgHjT//cb7KlNoua2MNGcw89G9uHM/vcs97AECKFL/kmxdKREaAE8NvUR8XtIHJgWaGPXKjH5y/27+MTEtTKdJkZd1eAQlipziI2qU8OrNqzDBetLIBQ4c9WQ6GdVd2fhUw9W0AvL8W1BbGQxMZ8AOXkhlLQ1WZWF1pww+MQLel6FEQxDNSMVajz6w6M5XOvEITdUlRFPxQGAoWbaS9lpcm+weLVXfL0kx2b5usgQ+5CiMvH3Rjhex8bcyXFaHE+qqOORJXm9+aJMFH7EdDF+7vJ5yj/y/pPScD51O4qnn5z5OKZaI/CpB9d38czsjR3Hd7Th8emPIaczU9SSybyjbN/F/fvejR/u+ifQ3Tcu7wBTLArve/Egak4LA7kyds1N4h2nf265h5RiBSI1VFwAXiCCNlUdM04DNcdGJZNDJZMXhIgGZENuHqpJ5ehyGTUQ6lMaXtgbTIOOklmAqqjYNTeBdcUC9GBi5OXiiqKi6dVgaTnoOuvXRRU2obvEgUectkagpha6EwNoMz/k4BNpRssJF2kgFOryyFBeL8GnHrJagXkIScJon3rwpX5jcRG0nKpy/Gab+NdHlADIqT4aXNskYXXcW0cmXvJ+1CDyI0fDNMUI9yU9XHHTQx7lifcFk1NeamwscRNFtmtf9DUT40wql5f270uEKE70DDUTcRY31WyoeVJUaNABeJhzppDRcpi0bxTpUZc4qDq1QKQ/gTmngUqmBNtr4tnqp4Sn0/Hlq9vGdzRg59y/49nZceQME+PNKgqmhUomh331GazJ92LWqeERtYmu2uewtvCW5R5uig7YOvEhVDI5/GpsCiWzgaFi93IPqQ3UB6i6BGfp9ltIiueANCI0D34/+WEQSqGrGn4/uQdTdg0AYOkmaq4NM9BmKEGEJsn9OK5DAcLJruo0sKM6CkVR0fJb+PX406g6zHE6Z5jI6IyMPDLxBHzqCadm22/A8e0wkiOl3ERUAe2RIE7Y4iQoTBep4hhAVOgrl7Xz8XOylNHygWdSti11xcmLLITmUKV/86XIuOA7ToKSSJGMeKUV0E5cACZk59cv7vosk1f5f3EOsfeVi53j2p8kLVC8SkwmMnw8nUThPnVFOw5FYU7TmmKI1B7fH4/a+dTDbGsG26a2Y6ZVDcauwCc+uq2KSIk23AZmnRp84uPX+z/YNubnK364659w58534t69/w875/YBYFYW/dmSuAcM5JjNRbdVgQoFTa+GPbUvLPPIU3TCN5/agarTRLelwiHsvXx8+mPLPawIUo3QykBKhBaAR3x4xEfJzKJkZuEQD47vCWdpYZyoWSLiQCmr6tID3yAeJXCJA5e4YqJak2fd5fustfjk1qfQmy2i4bawsWsN1hb6ADASUMnkQyfp4H8jqBqL+wPZfiNMzxBPpLLijtAyFCU50sFTb0AYzeFO0rwc36ceWn5d9MniY45rcuL74deOIyl9ljROeX2SQBJ4yohHsWR9UNJ45NL3TiaJSRGeTmDl9AcmxuSRIU4Yk7RQSQQWCPVhPNrE3wO+XFfMoDUHRdVpYjDfBUszUMmUkNNLqLlMtG9qFiw9i6xuoWBYQgv3+PTHMNK46YDO50iER4j4jledJioZZl66rz6NieYcdNVEIYjYaoqOjM6+i02/lqYUVygMVYGuqhguFmF7Lhzfw9effHq5h5ViBSJNjXUAN1B0fA8Nz8FwsQcAMGXXg5J5g7XaUDKYaVWRN6w2R2ZOJNhyVullqnwyI3CJC4/4+O72e7Eqx1yqG66DnmyJiV4VHQpUrC0OCZGxpurwiIOmV0Mm0AvxKIcSCJRd4iCrF0SKQ452yFOpTz2YqsVIgxqms+L6mEiFFglF1Jy8MNfWVhCBUkWPMBlyeo0fW1fNSFSnrboLYen4fJGdeLsNnkrk5x8nQzLp82hL9PhKAktrtledxYkjX8+nbiIR6uQTFE+X8fPh/2uxZxVedu9TNyDdgQ4saP0inwerHCQwYcGnHrot1peu6jThUYK8zrpy8/GrCuAEkSFN0ZHV2TVsuNXEa/N8wZ0734nhYg+mWjVM2Q5Lg7dYO5tNXYPI6qE5qKbq4nf+IOBTD3tqX0BWL6LH+ovlPp0UAVxCMW3bWJUrYML2oasaerMr69k/bbq6MrAzDKYZAABmV0lEQVSyPhUrCKqiwPYcFEwLvdkiHOJhxmlguNiD3mwRhFKRhurKVESljlw+zdMNDrGjLTACUqCpGh6b2ounZlz86Ya12DU3h4KZCQiTJSbbhlvFnDMlUmI+9VAwKnCIzTRDek7oh+peFWWzD6aaFTdtIBQ2y72ueCPSJMiRCO4IzVNUXEDNrlMYueBpOv63/DuQlN4J15eJSvx3mdhwxIXaHLJhJI+WxX2JqHQNOFzSEu+nfI14FZgYj0Q85XVFG5MEUtXW3DXwDoq31ZAtBTqJ7OVtCA37s3GxtQItJFxKSABZJNGAqijosgooGHkQEFh6VpyXTzxhr8DfF/5e/2r8A23X+kjHntoXcM+e/4tKJgdT09FwHWGaqqsqTJVFfgiYcWrk+yRFZrM6ixSljXBXFlbnNRxf6cOe2hw2dVVgahlcdepnl3tYEaQ+QisDKRFKwP373g2AuTpPNOeC0LgGSzNQMCyMNmYCIXMY+sgbLIrDzQ45YeDOwzWnFomsPDWzGzur+/HQqI2L1g9CVRSc3NOP1fluKEEpuqqoaPo1zDo1FM1ukYLLaDloqo6y2YeMlmPCa7cKS8sJs0Mm9NVAg+ovAOI1PbiBy73K4i015FSYvNwLBNJyWxGSsG2SJiipjQcnKhzxSrJIdCSWOuNP5fJ6nQwWk6rR+Ngp/I6tNJKQJIgOXaT9iEBanGs86iMRFvl4EWNJGoq05WhSpBpNul4yCZOjZTV3FpbGyDLvVxd3yOYGjDzqyNOwPDrYba2g/kwHCbvm9mIgV0ZftoIpuwZVUTBU6IalGTA11utPU3RYWg4FoyK+P5piQFN18Znk36GFUrspDh9+tPtauIQiZ2SwvlTBTKve0W8sRYqUCCWApwsAlkawdANZPYOCYUFTNRQMCwWzAFOzwsgKJTBVKYQe3BT31kYBADkjh5/ufQQ3/uY3uGf3Y9BVFQ2vhVN6DUzZdVSdJmpuCz7xsb26F7OtGZiqhYJRQY/VDY84yGg5mGpWOFRT+MgbZXjEQdFkFRGsr1kdtl9nN20pcsLJixojO7JpHxB1lhYCa4Ql+xEtS0C2gLA/WRLk1+KkZKG+YPGqsPg+4vubTxOUhPlSY3J0iP8/H0maL+0VL6mXEe8fx8cuEyz5f/46EGq8eF83Cj8iti5neiLGlzylKTyiEFYnmprFqh+lSJKmMG+iH+76p+dNY9cdc/+G3mwRXnANK5k8BvIV1Fwb/blu9FpFFAwLlpaDqVpSmtsQxNRQQ4+wFCsLj03N4JSeLuyrTWMgV8aGcj9mndpyD6sNlBJQsoSflNwdFKTf4AS8sO/dLOIDBSdUBjCY74WpWjBUA02vhaKZg+01I+0mVEVFJbMKe2ujkUqjocIgxhoTeGxqJ3RVxdtecBpevnYzLM3AIxN1nNg9gFImizWFHnRbefx8ZDuOKa1B1WlCU3U03CpKZg+KRg8M1QSFLwgY9/3hBnw5vYycXhYVXFyvIhspcpdooL2sHYB4wpWXN71aVBsBAj2YBCj8tnYYcipKnqyTUllySX2n6rE2E0IadXWOGygmHYcfg5M7vh53embXSkWnHmN8HPy9TiJO8xGkuKYobqTIr+tClXAy4uuwPnBa2zo8QpfXS/AC/ZgCFZaWg6bo0INULCf0PEXHjT41lWljLN3AnDO54ipvDhTTrW/A9lgaK69n0fJbGG/OIqtn0GN1w1BNlDMVVDLdbQ8P8RYrppoVFgwLuZGnOHz44zVr0fBa6M0WYfsuPEJEe6SVhJWcGpuamsIb3/hGlEolVCoVXH755ajVOpPJHTt2QFGUxJ9vfjP0Mkt6/etf//ohO4/FII3lJoDrBnJ6BpqqBWJgJ5hUFCF6ZhOuDjOoyppzJrGmMBDxnHFIAz0Wq845uecYfHXbI8jpCv5goBeaoiCnm/j92ChUKBgs9GFzN9Pj9GaLAICMlkPVmYQapDVYibQOgHW2pyDIG2UoCIW/AES6xyWtSP8sogCghEWW4IMg1IcQECBIpfGJW1dMEJ9EvIiYi3RQIUZCd+vFRG2SdEIyZE1LYpqL8v+laAkNK864KFvWJcmia3k8mqJHojbcNyhyDvBBaHsqSYxXUReMEiW9xgXOoq+YosKnISGM92hLElXzayKIHdxISkwGj/pxDRAv0edpU4fYonoOgBAG8/d3VXadIKC27+J3kzegZBahKyaGCld0PPeViP3N3Wh4LdRcG2vyvai5LZiBI7ch6d8AnpI12q592DOOE+NMWyPdFMuHPbVJ9FpFqIoCx/egqxrGm3PLPaw2rGSx9Bvf+EaMjIzg7rvvhuu6+Ou//mtcccUVuO222xLXX7t2LUZGRiLLvvCFL+BjH/sYLrzwwsjyL3/5y7jgggvE35VK5aCP/0CQEqEEWJoBj4QTAnf09WlYNt/yG1AUVWgvNMVAy6+LbRgIWp6DglnAhvIg7tvzGE7ry2NDqR83P/YMzhvuAgFFwdCwKteF0foEHOKh6dnIGTn4xEPLbyCj5ZA3ypGIRMHoAiBHNUhER8Juyq4gcAASCQEgmSwGr0eMIRUDmuq1VTDFdTIyaeHtNg4kZSC7RzODyWStED8W90gKJyQknptcjRZ/nZsScrDfFy6Pl6M7Sb8vtG1SRIlrscRYQGAktSiJEUo5NdOJrAEsugMALhzYfoNFgIIKRG7CKTuLc/C06GhjuyCtJbMAQzUx25pBt9WLPbUvHDFk6Nnqp6AoKkbrsxgsdIGAoGBkMN6sCqIog2vHOBGVhe/8e+dTl0WFDtA2IcWhg+P78KgP2wNmWg2sL/Xi9N6Nyz2sIwbbtm3DnXfeiV/+8pc488wzAQCf/vSncdFFF+HjH/84BgcH27bRNA0DAwORZXfccQde97rXoVAoRJZXKpW2dZcTaSw3AZbOqmv4hMLD6HLZLBBO+Oxp3hW+QQAw05oSfiNVZw6/m3gWx5S78auxGnbNTeKSzRvgEA9Tdg0n9wxhzm0gZ2RQyeRRc21k9QJ86qGc6RURH64xkauZ8kYJAMRkxsfCdUQ81WVpeehSWwuHNEMSEWuAyr2RWLsON6I3aYtUKNF0VqecdRIp4tqW+D6SdEZxnRAfU8tvwAjcufnY5aiKbOYYT70lEYYkUXM8LRIf12L31anPGBCaU8qkNWx7Eo0cif3FKtoi+47ppri4VxbUA4gI+jlB45+jTmaXpmrBJQ6KZglT9kTi8VcyXN8NKj9ZVNDQDPRnS5G0Z2hMGlb38fcCiL4PcaF9iuVHt5XH6jzTTQ7ky8gbxRVJ1g9Wr7FqtRr5abVaCxx5fjzwwAOoVCqCBAHAeeedB1VV8eCDDy5qHw8//DC2bt2Kyy+/vO21K6+8Er29vTjrrLNw0003gdLlrX5LiVACKpkSVEWBoRnwqYeckQ8IRig6JpSK9hm21xDOz+FNVAEFwbRdw9MzY9AVDU9OT+H4ioHdtRp0RcWGUj8aroMJm7lJ64oK23PRbRWEGaL8lGlpecw6+zHe3Ck8TOpuVaQ0Gt6siDiYWhZZvSgmQE6kdDUj0kcAmzCFz1BAILgXEU+7cKLEJ1QvMFOMi645OpXkx8mQLAaWt4v7AsX3wXUshBJBGCMtNxDqlJSE/c+H+FP9QqmvhaJAcXQiLnIz3jh4FILEyA0nS3IpP5GJnxKSQXk5T4/5JGrGyTVS/DMjk0pFUcNiACmNBDASN2F/7YCuw3Jg59y/swcEwkgN+56rcH0XeaMYPMiEUdWkz0IcXKPH35vp1jcO/YmkWBCEUrQ8h/VzXIHaII6DpRFau3YtyuWy+LnhhhuWNK7R0VH09/dHlum6ju7uboyOji5qH1/60pdw4okn4kUvelFk+fvf/378x3/8B+6++2689rWvxd/93d/h05/+9JLGu1SkqbEEjDemoKsafOKLyJBsMkhBkNXzoCCoubPIG0UxqYftM5oommXMtFi6rNvKY7jYgz21KfRmizA0A5PNKoaK3VChwCEeylYZls5KdrlvSc2dRleGNXd1CfMP4hOf7ddhaXk0/TlYWl70kvKJB0XV0PLryGh58HSPoYY6BkpJ26QrtDUxnRCPEAAQLThkXVE85cb7hsWjR/zvpKgRn2wXqijj11kWPMf7isUJT5wkid87pLPk9MeBan/mg5wai4MTO5W2p/DYNrEGr7I4PNaHjW8v67c4FEWFEUQ1VYkoarH0qRiz9LeiMKdqANA0XaSKNVUPPmcrHyyKpjDTUoXZXfDvXCdoitFR/8N1eUlkNcXyYUd1Bt1WAVWniaFCN5pefbmHdEixe/dulEol8Xcmk0lc713vehc+8pGPJL7GsW3btiWPp9ls4rbbbsN73vOettfkZS94wQtQr9fxsY99DG9961uXfNznimWNCH32s5/FqaeeilKphFKphC1btuAHP/gBAKZY//u//3ts3LgR2WwWw8PDeOtb34rZ2dlDPi5LN6GrGlRFiZAAHhXyJXdlU8tEOrS3/EZESzRc7MXm7jWouS08NrUXvxidhaoo2FubxJ7aFFQoaHgOLM2ApeXgEQeWlkM5wyrVSmYPbL8ubsRhvzHmi+GSFlSocAkLhbIeVEEjTs2Cqqgw1Ix4jWtR5KgQf6J1JVdhHi2I9BuTJlZC2zu+LxRxSeoXlmR0KINHo+ITDBeK89fk9FcnoiV7FgkxtVS9Fa/keq6Ip8RUibDMpyVSYynCtus7jxg9sh9Jb8XTZzx94/g2E/FLvep41EgmPWEK1RYpsqSKubLZy6KT87RwWSmgAUHM61n4xA/E8uxcQv+taAoyHiHir7PvUkv4UHGoiopJ+3ZM2rdjvHkLplvfeN5YDhxJGMgxYp4L+kJq6srUbx2sXmN8DuU/nYjQNddcg23bts37s2HDBgwMDGB8fDyyred5mJqaWpS251vf+hYajQYuueSSBdc9++yzsWfPniWn85aCZY0IDQ0N4cMf/jCOP/54UErxla98BRdffDF+85vfgFKKffv24eMf/zg2b96MnTt34i1veQv27duHb33rW4d0XHzC8YgPTdPbelHxqInvs6qbkfo+DBfXg1ASmB/m0J/th0cd8bS5oZzBUKGFgjGO1flB1NxnceaqEzDTqqLLKsDScnCIjYHcMUEn+Rbr6+U3oasZNLxZIXitu1UR/eEGh8xJmY2fN+FUoQIqn+RZKb148g2IjKlaIrIj0mQqm7QtPQcQWQPhzfvkDKAtTcWrs+SoAl/O0zvxFEz8vWC92zJwaDPy/gAQ2hYx6SMa/eHHlQkdrzoL99G5JYY8ycXdoTsRprAk3oh4BykJJIgfY75UYxyqogIJ2iTZRoCTm1DTRgQp4q7bvu+J1+NRKtlZOm67wGGoJlziIK8V5x3vSsCE/TX2PQH7HBQMZhApevjx9G/wHWFEu5PRJhEPGPFIUdRck/3f8utoeregP7vwpJDi4KDqNNHrF0FAQSgVadyVBupTUGUp3ecPbNu+vj709fUtuN6WLVswMzODhx9+GGeccQYA4J577gEhBGefffaC23/pS1/Cn/7pny7qWFu3bkVXV1dH8nY4sKxE6FWvelXk7+uvvx6f/exn8Ytf/AKXX345vv3tb4vXjj32WFx//fV405veBM/zoOuHZ+jyEzqPDvHJF2BP2Kvzg7C9BhpeFRPNOfRnWW5VgYq6OweH+NAVFVOtGnKGidnWJNbke9Fw66hkSvCJh6ZXE3oXHhnI6gU0vRryqo6cXsass1/Y+supEd4INZ4u8kFASEgwaDABymRDdhQG2FMz1wVxLY4gVQibeXaKAMiRniR3aT5uMWmruiAlBCw1xI8R8QmCH05klLl3y7YA8vsl+wklRZ4YQQnbjSzkAM2XyxGjhaJGzJsojC5wQidfP+ZEHo1AGKoZEacrMcKTVKUW91ES1zJGqMLqQEagTc0STug+YVFMHRo80opcOzltKVJ4IqWmd4xwrSRwHZ9cGCALxgH5vQ6jPPFzk0vpmdg67P3GCRR/cJIrzjxiH8KzSyHjyZmPY6jYDScoNmC9IVMcCE488URccMEFePOb34zPfe5zcF0XV111Fd7whjeIirG9e/fi3HPPxS233IKzzjpLbPv000/j/vvvx/e///22/X73u9/F2NgY/vAP/xCWZeHuu+/Ghz70IfzDP/zDYTu3JKwYsbTv+/j617+Oer2OLVu2JK4zOzuLUqk0LwlqtVptCvoDwVcfv1L8TiiNuPBaWk6QIDcoO87qrCzQ1CxUMv04ve+kSCrN0rPoylRw796dsD0XOT2DZ2fHMdKYwq65SZHqMjULtt8Q1V8tvwFNMYJWB4YgQXHnauEOHaTriDReRdzoM5G0EI8WxDVCpmZFtuP7k/t18agRj8DEW2TI6FQpJv8ed6MWqSva7prKe4fFK8iSWk4kRZfC/YRP8fGJTha+JvUDk5d3QqfXZFLDBOwLEyog6sEUr1Ljy2QyxPVs/P1xfDtwImfVhvI4uFO4aLobGGTGzTb5vnkqTSZko40dC57DcsMhNmyvKSopgfB9AOKO4O3LAERE6TKYyFwTqVpOhvh+VEVFTi93FJTvqX3hIJxhCg7bdzFl19CdyaPhOjA1HeuLVy33sJJBl5gWO4TVVrfeeis2bdqEc889FxdddBHOOeccfOEL4WfVdV088cQTaDQake1uuukmDA0N4RWveEXbPg3DwGc+8xls2bIFp59+Oj7/+c/jE5/4BN773vcesvNYDJZdLP3II49gy5YtsG0bhUIBd9xxBzZv3ty23sTEBD7wgQ/giivmL4G84YYb8M///M/PeTyn9K4BwJqusuaLpjCW47oaVVGhqGEFlamELTnCdIQTVJK1YOkqTqiUMFToBqEU/bkSqk4TU3YdgwUXBYM9kWe1AigIXOJBV0zxlEkoEb4vSeXMfBIzVBO214ikiZgvj8ue5CVPIe48LSB1jJePoSkGqJJcwaUiudO8/LrfgSTFoxXyMduOw0XGwfH4RBw+eett+5S1LnHXar5/j7QiEQLZCHMxiKfPOpkeJp6/0h6t4R5KPOUEAGpwn+NjdoNUqBz9U2gYrfGJByjMANQhNjJarm2CppTA1Kxwwld4rywewQhJcrzCUFNZRaGGUPjdlx1a1DkvF8YaN8NULZiqhZo7G4noCqsAiRyKViRS+pNbNDA3+YzoB8g9hDjCz0Q0fcuOZWDC/lokmkrAChNGGjfBUE30Wm86tBfjKICp6tAVZnvRbRUiLZNWGihZYmrsEBoqdnd3dzRPBID169cnlr1/6EMfwoc+9KHEbS644IKIkeJKwbJHhDZu3IitW7fiwQcfxN/+7d/i0ksvxWOPPRZZp1qt4pWvfCU2b96M973vffPu79prr8Xs7Kz42b179wGNR1UUqIoCS2MOzjziw0kIr5Dh0ZmsXhBP4RyzzoTQFDnEw0RzGgWDCZc9SkAoxbpiP4ZLPcjqGTTcBlp+Cx51UHOn4VOX9XyiHkwti4Y3K44PsG70amwSA9gkyXPhggAEY1OgCdErpQQeaUV9eQIRuKznUdHeQqNT9CdJ16Ioqog4yIjrjGTixslb5JhS1IOX98v76jS+JGLEYagmHGJHo0lytEoSyc4HebKcjwTJUcKO++LkhnjiWvCxy9oqWXDOSZGisF5wBEz0ziODYl1EG4MK3yBJCM1IQOhOLX9e5NSbfF1Zzy1zxWowAKCSWRW0p2HVnsJSQgkJiowkMixSyGj/vHDBtBxJZEUL/CeM1irQYGpZMQb5++ompHpTHDjGm7OwfTdwQXfS1FiKBbHsESHTNHHccccBAM444wz88pe/xCc/+Ul8/vOfBwDMzc3hggsuQLFYxB133AHDmN+8LJPJPGfR1UNj70dOZzcm+Yk5LsAFWLoJCJ8U+Q3P8W00PAc1dwLdViWoBjPw2NReEFAMFfrDp30ocH0XpsYIVd2dQ8nsQs2dgU89WFoOE+4MK5mXdBoFo4KaO4OcURIprngaiD/VMu1HNpIG0BVTpLQ0hFEIlk93BOGQ9xVveSHK1nnqTAmvT1ygnKRh4VqTuNkhPw5rFhtNf8nnKqJ0lAnXTc2KpJDmc7UmIADxUDR64NGwUkF2fU5qctoJia0vkkTQCWMS0SQpTch/l3U+EQ8rEFhaHo7fhBJEf4Q7tKiGI0Gaz5EiHjKJDIXvPE3HNC5h6lAmxCItGrvG8vhXKmZaYyAgyGg5oc/qRIK4tkteLns0iQpA/tmW3mfESBL/zGpquJ4spGeRNzcUah8BWqsjAaP1WQzky/AoganqyBm55R5SR1CfguLwiaVTJGPF3b0IIaKMrlqt4hWveAVM08R3vvMdWNahDXGqCitlzxk56IoZ0aIw0bDN9Dbwg6oRUzxFO74Nn3gYqY+jK1OB7bG01r76NBzfw/pSH7bu3w/Ht+H6Llp+C6amw6PsBr23Noq8UUTdqyKvl4Tdf8GosOsCRlL40yWPVAHJzVPlJ16XtJiuhYZ+QvL6XKArEwi+PY9ORDQ5IJFlizUtlKMG3LE5ScwcPy8AbceK+wHJndUXMxY2iROh04m32uDL59PxzBctSnKCliGEztL+eXNT+bqIc0RYYcd0P03xOyePsuZLD6I0su5LaIPQ7u8kg38WePSNa7P4dYuDv277/1/H67GciH9e+TWOWwHw951X+wlCOA9BWfD9lSJtsuCdSN9F1j/QmDdamGJx2Ff/IrqtAnJ6Bjuq+2HpJixt5RKhg+UsnWJpWNaI0LXXXosLL7wQw8PDmJubw2233YZ7770Xd911lyBBjUYDX/va1yLC576+PmjawfWF4I6wPCrCO2/HwdJMvDqEoOmHjfx2zO1Bf7aEscYECCgm7SmUzGzQ/djHy4fWw/E9NDwHuqqiZGZRNLvh+DaOr2xEy68jqxVg+w1WTu/bMDULWa0YOQ4fpwpENDryxAgEYmcFLOoTVCnxPmQ8okIpAWRxLJUiAYoaicoA0YgPP17csFBeriLsOyb30+JPx6oi9XGiUcKVNPny/XdKI3QybIyfgyz4jV/XODpFhRaKFsXL0pPK1BNbZ1AuCE/4/AU6qbgfknzeIqUWfDZk4XRc00UQNuD1KdNN8cgcjxhqio5ZZwJls5dVNfL3kLKjE9pCRsvB9hqwVqBdi5xK5enjuGEmF5jLqS9eITafvcJ8ruNJ4OSKp6uj40xbdSwVM61J/Hb/NMqZWfTnLGaBsoDlx3KCEMyrs1zM9imWjmX9hIyPj+OSSy7ByMgIyuUyTj31VNx11134kz/5E9x7772ipwlPnXFs374d69evP6hj2TbFdEmmxtpncBEunxB0aNBVE1P2CCqZVUxjo/pShZeOoUI/JprTqGRymGjOoWjksHOOmVIN5MowNAN110Ylk5NC83qQXnNFuXrLDYgQWIVL058TE7cspGUi6lCDI9JhsdJnjziiRJj3IZOjA6ZiAHBF+kzjRAGS8WAHjUj8tbhfEP9dJmhiu3ipuqSDie9DhgoVHsIGoQumwRKWsUkvaoQHSCQkwUdoPiStu5gn/CR9kZiAJXIqn0tS6k8WrsfXC4XveuR6cD8lj7SCFJvNNF3Egw+WbuRppKLZLY4Tf5+AqMHlSoNsjsgjL1zXI5MRYV1Bo6XvsmYodJk3xD4WSsXK1XxRGwQCIBoJGm+mfkNLwUyrgYKpoJLRMdtqodty4aTWBSkWwLISoS996UsdX3vpS1+aqEg/VGBd4C3YHiMZs60ZVDLdUIKKKYc0WWWXXsCcO4mGW4dDfHRlKjA1C3tre9GX7UFftgcecTCQ74GqqBjMd2FffRo7qhM4vms1xhuzOKFrLWyviZLZhYbHul5rNKzM4aTFVC14xMGe2j6sKw5DUUhEH6OpuogacaLE/YS4izEAoSPhAmxKCXTFFBOhIGGKAY+EmhnZbZhHoGTCI1dxARBplOh1jZbB8yjDfIinbeKTDNO0SAJf4rVXfyHsxxX3gmKaKGaGF9fyxJ/UF0uGFrOOrBWSHYn58TjpUJWoizfXUyFGEOVz415S8evUNgaEwnQgaLUCAi5T8KTKQl6hJvyaJAIUF7Qzse/yOcPOB/4dARC81xBmiZ0qBbloXG6y6pJWsC6kfRGYWlac+3xVh0nO4D5cyDKPND22NFi6gZcNHYuZVkP8vbbwlmUeVWekEaGVgfRbF8NoYxaaqqGcqUQmde7X4lMPH3zoN8gbRQzmh9D06phsjkNXNcw5VRH1aLgN3PHM7/Hzke3wiI/hYg+aHruR1pyaaPKY0XJCw9HwWOrP1CxBPhRFxaauk2AHUScAospn29TTYny8QkwmRPLYOYygHQif9LlPEY9CEOlfEmTfoPg6SQ1D46+LtI2cApMqboB2A8IkY0a5F5p8DWSvHXkbkTYKUiA8mpSUmgqdgQ9Oyw0xlth44/oj2aeJQ472yRVjcgoyPv444tVzPF0Z1yJxAi57O4lqp5h2LMmJeqWmdgbz/1v8LhsiAjz9G/Tfk6q92rVDYasavh5HfP249qgT5M+28PY6AlqVrFTsqX0BOT2DH+9+BrvmJpDTTZjqyk2LAQERWuJPiqUjJUIBCoYFQgk2lPvR8sLu6j5hlWAucYTh4V9uXIV79/w+iKD4IKAwVQ092X7hCr1jbj9O7Crh9L5BnNh9DGyf3TgrmRzKmQoIJai5M2j5DWS0PBzfRlYvgFCClt8Qtv8+8eCQJspmnwjr87LpkpkFwJ7iaYxI8JYIvIQdCMWg/DVedcX8eOaPaMg39iQdz3y6nHj0QAZZYCLXFKNtWy4MZs7MkhlgwhhkmwF+PBYNCqt+4uCT3GLK4pPG2wmd9EEiDRecl0xECUgkOtQpBZmUroocO0aGZM0W35b3IQOipJZfX1k0HSeuTW8OLb++4ruvy0L4uHkmB6voCnuLUfjwaKtNPC3/H0+hyYh/PuX3KCnamYTp1jcwad+OscbNz+W0DzuenPn4YT/mTGsKppbBhetPwoZyP1h7jZQppFgYKREC8K2n3woAyOkZmKqOrB6tTtte3QtDNZHVCzBUEwO5MjZ29aPmTqOcqcBUdVQyPWh6Ndh+Ay3PQU7PYCBfQdHMBc1ZdZTMIrqtMnTVRCsQRDONEeuMrAeNH3n/r3gJOyD52ygq1peOiUyuSaJQHsVSFBW91hpxLE4wmLdRS5Ai2Zk5rjWJT7py+XUS5PEnTdBJ4mE5fcTNIOMiYLlCTFfN6CTfIUokj8On7ry9wmQsdj2OTl3K45CjTiI9h6gOS3buTvJpSkLSexH3VpK9gWTCEzcWlNNnAIS5qLxM3tbUrCNm4uGVn/FoJE9ZymRYJkb8b4BVfPFrFhdBy2XybZHABfREkb93fQIT9teELxTA+qatVMI53rwFk/bt+NP/+gW+t+MaeOTuw3bsXXOT2DU3BpewJta6oiKjLV//qsWA0KX/pFg6Vnbc8DChP1eCqigglGBffQbHlFZFJujh4iqhO4HC2mYAjAiMNsagqxoMr4ZHp3bhsckG/nB1D9YV+7G/OYOZVh3rSyaGC8dh0t6L/tw6zLTGhBEjd6XWgkog5l8UPl1yJ19e2cNuwob4XVdD7x85SqCC63FCXcxoYzuyutwg0xBCWV3NRPRBkLaTxc5cExT3BlpoAo5Xc8VbRfBlSZqN+LZxAsW1TvLrbPzzlObHyI3s88KjAHGtEHBg0SF5W3mijGO+fUau7zwkI66Fii/jAvFIpEfopcxIN3rx3kg6MF6iT9Ae2SBifyu7BFzWBcnvCYA2MiRXjLVpewKBtEtaYr0kd2lOtMK0c+dCADmiN2nfHn4m+lnfQk7egUBLpmoYb94Cn3oYqHlQ+ud33D8cmG59A311D2/+7X344vmb8Z1nR/HCvp0YzIfrjDRuwurc3xz0Y39n+9UYzHehZGajNh0r+PMIpBqhlYKV/Sk5TKhk8sjqGeiqhqECq44RbQVA0HAbmGlNiU7cZbMPq3LD8KiDciaPmmuj6bHKhN6siuPK6zDr1FDJ5JHTM5hoTmO/vRstv4WZ1li031FMb6NAQ9ObE94jmmJAhSrE2hwKNEzZI4G+RxdEJa47YJ3snSAqZYn98h9FURHX9iRFcuJESx5z0nYyZG2LOF8pLcERTzkkjSHiWZSQakjyhwHQdr3l9WVBdFK7jE49xxaLTp5CEbIXpCzl84lfz/nKgDtVx8lI0nBpii5IkKyv4qJ7HpXiKTv+mYqcn3QePnVXZLRC/my19Y4LPp9xfZlMhOO6IEFc4DMbCk5SgsKDuNM0T33OB/66R8IILU9h87Frqh7xMjPVLCZLOYw3bwHs7z6na7MUjDRuwkzrm3hm9kYQSvBbjOIfXrgJvxwbx6YuHVVnGjvn/h3jzVsw0rgJtz3+0CEZR8Gw0PBasH2XecAFUU9TXbntNVKsHBz1RKjqfBu69NRtqAZcwm5mXFtTc21kdQtNr9YeuqYE+2rTonXGqb0DUu8vA+tL67Eq1wdDNZEz8hGBtFxSrik6sloRmqKjYHQFQmYW1tXUsDUCL6FnfXRWQw2iORktj04QT/M0WqnEu7Az8awrrgFHXEybVDYfXyan1uTjJxn4HWj0IL5dvK1DEkmKbyv+ljqI84kqPvEtJiW2kI/MfPtIjIpBFYRbdnaWySRflrjPRX6l48RQ9lVKilbILT+M2Pj49gDaIi0rBV6cvMW6y/PvbNQ92o1EB0VkJxY94pE0vk9DzbSR+vg16USK5jMR5VEnfpy4+/UYmWSE6DDi0cmn8JGH78be+gT+/Xf3YlVuAL3Z1cjpCn415uAHO58BBcGld96Nmx59EOevW3/QxzDd+gYG813ozhRQMNh9sJzpZY78Kz0iRJcolk5TYwcFK/tTchhQMl8Lh7BQ92yrDgLWH4yCCPHomsKAcOjVVRNNb471/wr+3tzNGrUSSuEQDyP1fSiazM101plATi9DV1j6Ia+XRVsO3odLVzMw1Sxafh0OaQodj0tacEmr7WbNWnk0BVEDICrG+IQWTy21CY7FzbSz0Ji3XQCQGK3gE7Y8ScrrciRFkuSJV1OMtok+PiZ5wk6K7nCyIDQ28egLYpPXIvVBC2GhCqH5HKbjYl3ef4qfozB+RBiRAaJ+QPHrPp+eKK7/aosEKmE/N36cTuvHj8H1XD514RNvxUWFwghvlADFxd/8cxkXRid9FhJ9uxQVtl8P3kMpXUajNhCLTdnw7wUnYT7x4BIHLnHg+E2RetMUQ0Tx9tW/eNgI0S3bxrC/6WP77BT6cypG6vvg+Dbu2tHA/oaH3+13ccXdv0DN9fH0jIer7vndQR/DrLMftu9CVRSR6t1R3Y5dc3siFYMrEXQpJIjMmy1PcQA46okQAJiqBkIp+rI9LDKjZ0TvJkvLQYEqQqz8yVIPyrd/N7Edv5vYjUcm9qI3W8DawgBURUVeLwnvEoCRmEqmH3VvFgQk0oyUhd096EE1k6KocPymaKnBb3Ty5G1qWTh+aBQW19oIMa5EMOKiT94fTTxxqrpo4cHHJetFgCiZEf3GJHDS1cl8Mb6MUtLWBV7eV1KaiI9Zbjsh91brVEEltugwCc2r1TlIJfQcsm9RxzYNCN255fPl1VtJxEcmO50IaaeKs/h6fD/8GPPpwOT0EidFE/bXDuiaHEqsyl3Wtkz+zshEUz4Xbh8gR1NVpd0glPV700SRg6bqkcbGcfsBvp8kxN8X2a4gQuiDikL5YYlHh1VFPSxk9MxVGQzmNTw65eGmR6bxj/dvwwcf+gXecuoATE1ByVRRMDWc1p+HqSkYLlsYa9yM/3PPwSEo061voObUoCoK9tVn8KNdT4GAYE1hDY4prT8ox0jx/EdKhAB4lKDhtdDyG3D8lrDa19SgNQEImn4NDrHR9GqiPN0jDh6dbGCqRZA3DJTMHHbXRtFjdYuJuWz2gsKHqWZhqlnxpA8AI4190EWvIXbjFLltLSuEzdz7RvbAUcBKlrkfTtw1GIjeSONhdZnkRMhQEOWSy7Y7IU40OHFJiu4A4SQtRz74dpzAJO0z/iStB73R5KiITOaAMGWRVHkXGVOCJiQeMUrSlfDlnXROi0Fb5C4Q2PJxyUQPQCQdJb/fnd73JN2QKv2Lb8M/m/Jrsiml8ICK7Tf+txxlmbRvx57aFxZzOQ455OvdVq3Y4dwEsUwSuUceMnQ4pClec4kj9hX/Hi1EgGSNHydBLDrsRN4/OUrLCx3kz+mhMrictG/Hn33vUvxw1z9hyib47biNmkPQlzPQlzPwP7ur+OXYDGqOj1+P1WFqCjQFmG152F938Zv9j+HzL/8i3vmzK5ZsBzDdGkPBZCkxSzdw/roT0fIb7F67wj2EgCWmxUgqlj5YWPmflMMASzNgg5VfOr6HTd1rhasuAEE29tTGMVqfxfpSX9CKg+CVx6xD3rAwbdfQY3UjbzRY+XvwJAnwKhQNHmVVJj5Y2H1HdT+GCsPwqS9ulj71RCSJmbgFJnfgfj9E3HTLmT52E6Q+VCWql5HBq1c45PRAp9JzWZMUf/rliBMXuZpM7FOauNlKRERD5E7mnbaPkyAx8ah62zJ5H5HzR9jZPq6BAtqjPfHr1QlJE9p8abKF2nCIdCV3kkZIUnmFV7w6TEyWCc80kVQhkqvKZLF8XGgPJUzNAWEVWZIOLB6ZklNKpmaJCidDNaErGdTcaQwVDm+lE/cFI5Dee4l8JF0jvp1MoIQuSCLWcpSNX0Nu+imLsBdDlOOEiN8b5Kq/pPebPxzwiBSlBBQ/gYKXAQDGGjcnRsYOFI9M/h7dWR0ndK3F9moVT5kaRusuTE3Fnx1XRG/WwKcfHMVQlwVTU3FsxcQzMw6emGyiYGr42K/24uHx/4OPvnhpBHmkcRNz36cOnp7Zi+Ei0wXN1yNupYEQJHziDmz7FEvHUR8R+tHua2H7LvbMTWEgV8ZJPceIyItDbNh+AxQEo/UJHFdej7MGNkJXVfx41y78cOcemKqGvF7ChvIGaIqOstnbVlVjqBmYmgVTzUaW/dHgWfCJh6ZXE/1w4uJKXWGCadFXK0iNaIohbpA8RTavxka6qco3dTlMHy+L7lTqK0OeAJLWi/vW8NRUpAJOSqfxyTtR10R5TCSckDu5WYvIEJgGS6SXpH3KHcbFWGLNOPm1STz3A0zQdyJXQqQsCbbVhGvAiVwnDRC/Nvw6JRFCIBoplAXvPBXEX+OVgvLr8n6J9E8Gv86yaJgfxyUO09epOkYaN2Ff/YuLuXQHBa7UPuS5Im7dEP9sy2TelYxOkyJRfF9tKdzY+8OR9IBBYv9Ew+Ugwjplj2LSvh2T9u3Q1aV76ow0bsJEcw6DeQ337H4Mz8z68INWSE2XoOr4+PVoDT1FE1O2h2MqFlblNGyfsXHBhjI+8OJjUHN83Le7jtueuHLeaOF8Oqfx5i0iSm6qFk7p3YiuTB+AsBL0SEAaEVoZOOqJEHdnPr5rNXRVw6Q9Bd5sVVdMzLSqmHNm0QjcphtuHd1WBa885li8YePJmLDnUHWmAQC230DNnQEQVIEFqS09Vt3UChyquTaIp7r4EzP3MfFoK/hSRydwTop4tICni3gkKT5Bc9Ix3tgbko3AH0VOi8k3SlVhT5UyeZtvEp4Pncre+WvxiJCMpOiQXIqflOKJI8kvhotMk6JB8v/zoZOL9GIIUsS4kJ9XAlGKE0mZDALt5ytrqlSE6S4gmvaJk8I4SeL74tEF+bW4biueTotP/kCowWnTLVGCkcZNIPTHHa/VoUDkus/zfhGQxHWTtomLp3mEqS1KGvu9TaOV8KAiI64Jk6O1ceLEq95URW3zCTtQjDVuxrS9H7trDk7uyeDxaQ+zLYKmS6ApCrbPNvHxX47jxpediM29eXzwnAG85rgiRuo+fEIxWvfxma174ROK47os/OXGz2DH3B6mJYuX/tOfoGT2AADm3DtQdb4Nl9yFSft2PDN7o1iNF7HI18TxmyuycjHFysVRT4TOWnUddJ4CoASWxkrKW34DDa+K1flBZLQM1hX7oam6IESWngWhBKf2nAZDNbCntgsAa5SaN8qBtojfgNiT4VhzpyiP51VpumrC0nOw9JzoMcZhqtnITThpcvaJJ0LmXGsUNxvkUQJPSitxEqZCRctnDQplF2dOlFp+PZH8xCuL5PHJ6RhZN8SPvViNhLwsCUkC3iSBtFx5JROQJMLSicTEJy8AopRaToEeaJQojvlScvP1couTHP4Tv9Z8GY9YEDA9WhzxNNd8E38nTVK0bUVoAcEJu0yyxpo7D3l0SI5sxcl3UhWe/De/Blw7l2QxAEBEgWTtEK8MTfpszEvCgu9+UvFBnHgm6b4Adn/wicc8yVRduFJP2F/DSOMmTNq3dzx+HJXMKtTcFtYVLTQ9HydUdBxb1rGxx8J567I4b30XNEXBtqlRnLHKwp07m9hdc+BT4MVDJdyzYxr7Gw5et6kL5YyKZ2ZvRHcmD00xMImaOM548xZAeRmqzqQQfLvEQc2dAYXPHM55pVzgkE8CTSeLvmdhqBn0WH+x6HNbLqQRoZWBo54IAUBGy6DhOcgZeWS0jIiUAMAT088A4B5DJsqZPKpODYQSTNmzmHMnAQBdmQoACLt9TqxkX5Veaw0oon4tlEYN09gTHiM0/EYYn2C5Xogfz1SzbdoZGfw1SzMCwTaLZPAn1oyWE+sqQSRI+A0t8EQqn0eSwaG8DgehYUSj07qdnrzjuhRZwxI/X75O2/47RIGAqKFifJ0kYTSv+uokwp0P8ZYWcZG23HcsKdUFJJtfypEJedKW05LydXH8Zht5lSdfOWoIhORaLvGPI8nCQY4UJU3oiqJipHHTwhfuOYKTQEW65klkMQ45ZQjEeq51eF/48fjnkwupkxrqLkSG5P3J70u8Em2+1zghIpQ5YivQYKgmGt7soqr7Rho3Yc6dxBPTY3B8Dz4FDBUwNODPjh3E6nwWb3vBicjqKn6wo4Gt+23sr7vYO+djtO4hZyjoyxl46XAR332mii2rC1hXPAk1l/Vwc0gT481bMN68BaqiYsL+mriXcjsRfi3yRinx+sjLlvpAcriQEqGVgZQIgd0sJppzGK1PYM5toOrUUHNtmJqFkpnFrFODpugYb+yFqVpQFQWj9UmUzCxc4qDlt0BBWKl9kJ/WVVO0xVCgwZa+zLI/i6bq4omcPzmytA2rXPOpF1awxVINAIQeSRZn83OKkA8QVDL98KmHujsbEVbKE8GBdBBvqy5KmFTkaEz8HORtOy1LevKWzy2R6MRSEXK5OdDu/SNH3eZrpSG3AOF/y68dSFpNPra8D/558YkXCuU7ELs4CZSvFT9n/r/Q/CRNHCCRdWRwoTYHj3C0pbhiaa9OerW4DgyIptYoJYdFNxTvmRYX0nPIkSJ+nQCIaCrfh4roZ4NQ1vpG3l7ukScTqzYtV+xzlYT49yP+mZa1SvFrL4+RmbsaC5Khlt/AeGMvHp92kTNMGKqCPTUf3RkVd+4cwQMjNXz+d9tw1mARrzuhghf2W/jAi0/Aiwbz+JuTVmO07mPK9vDjnXNYUzTh+Czq/sT0GEbre8R9qOU3IkRGbn4rzl2qrgQQeXDkFZ7zubCnSBFHSoTAbmoDuTIKhgVLM5A3LBQMCxktB0Ipeqxu7G9OglAKCoK8nsVUqwZdNWGoJgzNgO2FT9X8CcanHiaaI+jRu0Wai3v3uEGPL+H/oWQipd9cOwRIk4/CqsY45DYgQnMUC9vLT518ouXl6/zm7BMv0hspHg3h6wPJVVzxSS/+NApATBRiLDFNStJ+ks5BXjbfxBsfD7MFiIpF5ZvrfMZ5MpIiQ4tFPL0mp9SSxsxJc5yMAItzJubb8WXcSTyJPPJ9xidpjk6pF/l1+bid3tek91ImC3y8B5q2WQxkks97qhG0twyRr2FcXxUn4nwCdokT+Z4I0oyw4i+OOKkVyzv8vljE06Lye8GjQ7JeiEeIOvkO7al9AVmtgAl7Dn+wKg9CKcqZDI4t8+azwHBRQ8FU8fBoDd9+ahbPzHq4a+cePDzeQNVp4unpJo6pWKg5Phyf4hcjTdh+A3+05kSoiiI+7+xBU20TPMtkKKKJklrf8PdXAavQPRJAKV3yT4qlIyVCYDe7gllAOUhvjdSn0PActPwGBnKrsLs2Csf3kNUt/H5yO56ZHcEpPceCUiIqtkypy7GiBKZ38LEqN4wZvypeE20zggaVob9P6ERraXmp23xyZCQefWh4s7C9RscICZ94ebUZz60D7VEVuUGoHAURNygl1P60HSvoqZT0dB2fFOfT/8THnxSt6LSu/L9MuHgfqHgKKj7+hTCfS/R8E1e8hDriGaSEXcvjhphAVB80X3Vg/DUCIkxAZZ8bvi7X7sjXjTufA+HkIqcy5/MR4lETOX3UCXHRdfz9Xaq4VwZPtXDIRDMparIYyOajXHclNEfS++ZJ1Wpy1SMQaubmiwrFl8ejP/NFkOIPJW1i6uDHC1zsG15UtExHP8cqXrUsVue7Ucmwh0Nd0dDw2L5W5TS4BFidV3Ht2auxOq/DUBVsHW9hU5eB7zw7i1P6cui2DJy+qoCsrmKk7mHW2Y8np3ejP7dGnJOc2o/39+PfTTn6zb8zsk/ac+0JuBxIU2MrAykRCqArJmy/AVPLYKJZQyXD8tAT9gSOLa9HX66MvfUJDOQqyBkZ7Jzbh7rXxHRrTugk2JOWI25yLnFEdIciNOWTGzjyUn1uZCg6viNaGu4F+worxlyoiioatALt+qB4NAiIRj6SbvgucSK6pKSJPT6hxCE/FSct57/HSZEiEcM4kiJGndJC8XQH110lpe6SMB8Zmu+aLHb/8r5k8Ju4/Nng449HXCLj7TRpRkhgNCIhfzbkCZyPS25JIVI/McIj/98J85HXTuRWjsIs1XBPHCtmiyATzE7pV46kyE0kLRaLEsb1evOl3Lh2R96OjzOebo1/tuQHFTm6qHb4HnWqQuNjpJSg5k6LZePNWzBSZJWjs85+7J6bAKEUBSMDh3iYaBL4FBjM5/HktAdDVfC97VXkDAXTtoemx+53U00PDqEYKhowNQWPjNfx5ydUkNUL2DU3FznnyJgkgsMh9I38QSFIg/EiEUIJi64fQIo/RYo0kQogo+XgEBt118a+2jRKJnOAdokDU9Xw6ORT6M+V0J8tAwAmmnOoZJjAeMquiRJ8h9gomd0sNBs8zfrUFX4qmmJI0YiwhF0Bc68mhAS6kGgUhhOg0EyxFXSwN0RlF9v//Dc5Sgmowm5O3DBSAU+RuUL34BIn0tBUvtEuKCyV2hAIgiY9dcfD9HF0Knvlk7l6gJHgcMILycWC2yzg99NG8BKMEuPL5rt+kXJpabu4ezZPZfKIg5pwLWUNTicdSjwt1UlPIcizFMGIEx95edLvLP2UnMqbL7IlkzjmGWMsqQpIGFLy75OithG8+OezEyglIArEPSIueue/8/RbfN/yOcrLuD6QI/z+GOLzG+lfltDHLm7FkESeEFyD+Jj471ywzKPdO+aeQiVTQs21MVzsxWh9BkOFbqjKNExVR821cUqvjoFcHv/rmAw84mPKplhTNPBvv5nEy9eVMOdQEAo8Ml7Hq46vYEO5H3POFNaXyhHzWjGWGHGVCR+r1oRI43O5gEtagcDaO2LK51NDxZWBoz4itGPu3zBhTwj/nm4rj+Mqa1B357CnNg5DMzBc7IEKBZaeRcnswgldQ6hk8hhvVKGrWuQJ0iUOS8EE2oMwuuPC8ZvC98JQM21ffp+6wvSN57jlXD9bh01uvNs81yVoatRLQ0Y8vWJwIbd0M4zrQzhhk0uneWWYfIOXkRQtkVMOMpSEm/d8YwcSxNnovE8xXp6KiL/2HPqKJYms4/tKElsnlt7HrlN8ApNbNPBJnGtZ5Agff12u3tJVM0Ik5OjbQoSE75t7LHVqsyJf26SokBzxiFyz2LoLpTvDz+XSJjauf5Ovvzx2/rsceYyfW/zaycQq6XjzkaD4fnh6Sq4g5dvKKUJuycF9sIAwPSQbci4U1eRpKJGSCkrR5WhS3igjb5SxtnAMymYf/nhwC3RFRW+2iF1zTDPpUR8Fw0LDo6i5NmzPhUcITuph97Y3bu5Cw6XY1K3jxYMWvnbRFtQciscm9wIAxhtV4VWWdB8QDwXUFdeBkyH5s+lTN5Ie689e0vH8VxLS7vMrA0c9EXJ8G8/OjqPuzmHKrqPqNOESBxP2HCqZPAzVhEd8FMwCNEXHTGsShmpi19wETE1Db7YI3pyVT0iawsrUC0ZXUDlGhDaIUhb9cQmrNONffpe0YKpZ8TebiKQJLNiG61zY2Jvixi2Tgk6aCw45hcYnSXnSBaI2AHzcfDmASAn8YsGbui5W3yCfQ2S9eT62kRRPEO3gJDeOTnqeJGKTtH680iy+bXw9GUqEyBjt+5bOURj0Sdc+vi85ssOb9XK/nvmiG0nXkrco6EQW45GM+H549EqJjTfp87JYPQ6l5DmLp2da3xQkMv456kS0k/6OI5I+k6N6Hb53SandpMIB/vDRiSz71BUPKWLfsfdqPqIf/xzFRcnyOfEUvB7oxlRFwURzDrqqwtIMFAwLpqajbKqoZHJ4asbFAyMtRn66dPiUwqdAzaEglOLX40/jJUN5DOTLKBgVPDnjRZpTA9GoF7cfka+vbIRqaTnx0CavlyLFgeCoJ0KEUnRbefxo97OwfQeWbmDOYQaDWT2DhluP3DynWnWYqoXN3cfC0kw0XPkphX0JwwaI0bSMqWVFo1RDzQhioyoqLC2Pph82UfWpm1j5wFNsLb8uRIN8IoyLn5MmGce3hcEa0G6MyCuNfJIsduXpmoUmMB5xklMNEeuAhJB9XJQ9H+Jajfj5yhGuSDuPIPUkpzDmPY40oXRaX660S3oi7xQl4/uUG+ByRESjNPSa4oRInmx5REEugwdCcz++XdJ7xj838nqO30wkL5zgxNN18fV4JIUTOLk/VlxrFMd8GpalRIXixEBYOkhWFp1E4PGoZrxaUv68Jl3nuIA/6dyA0DeqE5IirgtFgOTX5YgRH3cS5IotTrh4mmxVbhiVTA66ouGkng04rrwOM60GcrqKhudgokmwpqDB0BS4hGJtwUTV8WGowIOjLeyruzij/wTUXBuKouJ/n3QGxhs7284zqgEyAjKWEZ8Bn7rMpDJ4mPSCtBilnQshViLoUqJBhG2fYuk46onQpq5/xLHltRguZgMhoIWq00R3EA3KaBnkjSJM1cL3d/wWJ3adKG7s/bluEErFDVUmEwCL8nBS5JIWdNVEVi/CIU2hHQKi1ScOaYqbTkbLi5sf7yRNKUms/uHEJR4d4uj0pCRK6ZVoyTN/TYYoy+X6j3kq2preXGKqSh5Pon4hYbzzEZZOE4dcNSZHUxbz5Bx/Ql7MjZXfoGVB/EJVZEC02kw+lk+8yPvIPZjkCF0nPUtcMyTOJSn10OlzEUSp5DF0SoHNV1qftP9ItAjtY49DjgrO138qCXX3vwFES6vl/fLvLn8ASBpX0jUMxf2h5YWIsMwTFUo6x3hENi5sj5DR4IFhvsijTGKSmi3LlVb8usQd6eUHkvjDiUcJBgtd+M3+J/HkzA6sL/WiksnB8T3YPsVI3ceT0x6mbYpJ28UF67I4viuHN24axlmrVmGkPo6CYeErjz3Eji1HqCVtkPxdks9FfnBgD4Su+H4oyuIaJq8UpFVjKwNHPRECmFi6YFjQFQ0TzTmsL61GyeyCpuho+S00PeYkfdH609D05oT2R1N0rC+tRV4vibQPz7szYmTA1LJBBMZDw52FTz1YWh4KNLikJVJgAJDVimG5Mic1wc2aaQWcdrdgKZIiR4WAzjoc2WQv7mot36R94kW8UeLotJzQdk2EIFGxVNd8ehx5HPFjylGJJBiqCV01w9RfcI3iuqf48ZOIUtKNNSlNthjik6TV4ZNMp2MJYa8SRu+SSFA8ciFHLZJSLRyepD2iscmEE524YaL8mjxOeRyChCJsgRBHJxG2DKHZWWQUT4btN2Ku0OEDCBAK+S0tH1m+GI8qHnHkn2t5bPw6LiS+lqNOnd4jmaTI1WFirHJKLXjfWOWUHkkj8f/ldLFc5r+Y66spOk7qPglDhWGsL/VhMN+FvmwvdFXDyT1DyOkKyhkVlgaoCjDeIDA1DYRSPDk9ij21KdyybQ9+uncCOUPBBx56CDm9NO8xgeSoK1+mKQZ0JdOxWm4lIyVCKwMpEQLQn70E/dkScoaJwXwXRusTaHhVOL6NvFFE1WlKEYWQkHACwwXEpmrB8e2I86lLWshoLLpkatmwOkv60soThEyiZLIj38x5CJgfm7ftmC/1EJ+geUhZJifyjTuu71gIbeme4KYuN22Ng2tRksYqHzc+JnlyBdpF1Byyl02otQpbh/CJQ/5JcrFNGp8skpX1C4slRHEiy4/VMcWhdPZSShJjc7KcdO3mgxAPx1KPna53vIw+Hu2RPxdcp3OgYwKiZOhAokJG0IxYTqu5pCXOQVczIjKkxG6HC2nt5LHF3xuZAPFrEic6siibb8/TmfwBhEeL5/s8ye12eBopbrwql5gDUVIZ/z++X94UGgjT/VVnCgUjD4/4yOoFHFNaBUIpNAXYWfXQ8Ch6syqOrxgglMLSDLz/gTFYuoF3nnk6Llw/hNccexpO6TVYD7GkSGDw0BI/d1btqgldkAJWXHKkkaAUKwcpEQqgKgoG810wNR192QoangNdNTHdmoFH2MTIny65o7Sm6nCILaJBvLLHUE04pCk96RNhA2+q2Yg2SL5Bc0LDtUEy4epUdcWJEydLkSiQLGCVJ1xB0pzI8vi+Oz3NxiNNcbIhR3DiaTwZEbIjhcTjT9ZJvydFKOLiVT4B8aq60Lpgnqf8RaTO4tdMjm51KpPvOAlKkapOkKMFPArRKRKmqSzCUTC6ojoWRKvH+LHbtg8iJHK6tlNlmIx4NFEmAPLrMiGQq6rm80mKRzpVRQX8u+cdDwBM2rfDIU3h9M7Oz4hcEwo/GgmKEc6O11lJdluPfxe4fk9eL641in9PBUnvEM2T07z8/iLbXQCIkDoae+946574Z7hNy8ajVSCR/oYKVGT1AgpGBU7gSl/JrELDc9Cb1dBjqTBUBYaqQFUUTLc8jDVqeN2mbkw2m8jpJUzZNShQ8brjt+DuXduShfTB+fE0rWwJYAX9EVVFDfu4UfeIE0ynEaGVgZQIBdBV1h+MUIpZp4aSWWCVN6qG4eKQ8LqIPF1BhamyigdWkm6hYHSJCdcNymG5xsf261AUVfgKceEzjybxNhlAqDFh6bl6ou6D/6/HboIcchSJxCYpPmYgSmaSkBSJ4JEkEhtX0tNxYtVUgn1+Utg/jjgZSpqk5UlXPj5LL4a2BPxY8z9tR6M0fP2k6I/sVUQ6TGLx8SZpl+Kpxfj15BV7cspPPieAOY3LSJpo5PdPthmQozsLRd2S3m8q7TOptQTfR1LUj28/H3ziYdwZmXedmdY32zQxACtkICCi3Qonr1zrw1Ms8pjimjj5f1lsLf8ta+nmO68D0W1xJH1PIholqG3kJ/4+yLofuYAhHhWLjEsiRjyVuCY/JPbTY5VQMBXMOARVh2DXnIeWT2BpCnbN+bjy1BfhRYPHQVVUvLD/FFh6Dk/OPI3z153Ufq14ajYgQPyzqiuZ8AFNumew82aR+COldB4ACF36T4qlIyVCAUYbM6i5ddg+88G4d8/jeHJ6D0pmF+peVZS61905kcqKEw0+aXi0Jb68ppYVTyyqogZ9vaR0VPAWxEPgsp28XN0CtN8oPalXUlI5rpz+ikzcvBIJWttElKRlkSGTC/kY84lz5ciHrIeRn/SSSJMa26f8VJ903kCYluFC8qQIzmIq1ORxJqWg5BSnvK+k/c4nLJZTcoaaCdOWiF7n8JjtDWwT9yu9T/FoECdRSemrpMhG0liA8HvAyU88KslJlqKoot3EQpP9fIJjvn3V+Xbi61Xn28F3Jtp2QU73+dRtI8rxlJJ8PJn8xD/jcZIYifDEXks6d/mzyve9UAUZB08NAdHeg/L9QpDyYH9u0E4j/OEO+G7k/PmyTg8LbkAqAeCZ2ScBADtmPfgE2Nhl4DXHbgAJemFdcuJp0NUMujL9kTGtLw5hXSsHU8u2p+8TIsYAu9/ZfgNZrQggfP880jqiokEpVg5SZ+kAOT2DgpHHaH0SALCpqw/HlNeJCZa7MOcNFgnypZsNF4LS4MYRVoMxE0UrMD/0qRtJFRmq1ARSqjzzaCtCXISGISHykoR4xUvc1I5Pfoqqiwk4UrG1iOsVb+7KxypDjozIJCjJiZlvr0oTAU8/CC2A9CTYKQJBKQGkyYjEJwUQaME23GE3cu4JhCkJUfF5clm3nAqKnCdCYTqJ7UuuGlIVFa5EMuL7TBpfEkHk5xX/DHAkEReZ1IRjbferIkr0XBnJkMTtqg4E3yFO9OMki4BApZ2jQp0InkOamG59A12Z14cv+Hd3NDpMIl/scyg9OFAeWYhG5pLSxPHvo/x623VCSHQ0RU+MukXee4Tf+XjKUYl8Xgxxrjy1LrfpYQ9dbF05usJc87PwSAsebYXnIn37w6h0NG0rR0RnWlOoKTPI6SZmnRp6sxp+t7+J2ZaHhvcM/nLj8ei2VkFVWCWpTz3kjZL4/BWMLiiFPwekhq9tVgeUiHOQtUAOYdW1sh7uQMX0yw1CIL5Dz2n7NCJ0UHBkfWoOIaygg7yp6ViV6xItNBxiSxMwidzEgOjNm6eo5Fy1pupBd2cVuhJtuMpD2KHfUCzCFKRyxJNe7MYsl/3yPmXcYVp+epdL5Pm++WtJk3iUFCXTok4kbD4BJn8yl6MoYSWLEY2mIPxdTjklTTRJmhJ5uVwlJ5sryqLneMoraSLtRJjm62PGCYQsKpYjdnJqjEfoOGlUoEXOM0mrJZ97Ut+spLHzbePp0iSNDx8z0E5I5Ion+fzktKNMdvg45Osh1gXpeH7xqFachE63voFJ+3aMN2/BuDMivn+RscZSeJ20UrpqzjuZKtLnQ/6Rx9rJj6gtQoqofkgm7XEPI/n6UEoi95AkbQ+/t/jUg+3XYUs6KQUaTJUVbmT1ImvtIV1XOQrKomRhDzxOpvj17bYGIsd+3Qmb8doTihjIG+jPafjOs8+IY2b1IgpGl/ibt8cYadyEjJaLFE/w76JcRcv/11VTjEFTDFGpC6CjE/pKxUrWCF1//fV40YtehFwuh0qlsqhtKKW47rrrsHr1amSzWZx33nl46qmnIutMTU3hjW98I0qlEiqVCi6//HLUarVDcAaLR0qEAmR1C5aeRVemCE3VYenMEFBX2I1RD8qxgeiXjael+FMzpaEgl09o3F1anix1JSMIEH9yk630uQBaJj78d7mlAiNBmci6HHENQxy6JBKNQ5WOxf9O1PokRBki0awE19qkULsbhLX5NvxJmFBm5kYpge01wu1jk82CugpJUyOPOymULpOipHRYfN0kgSZNGKM8lkh6hfu6qLrwmOITgqaGejR+vPi+k8hPPJIhkz2gfcKQJ/F4+oePmf+fdF7cx0om4byIoO16BR5JcrQjXlmVFAmR98H/l4W08ueOfb/aU5pJmqZOSCI8cV1Y4nbS9ZPToZ3SovJygnDf/EEn1KB54rMT+laRNvEzjw7xzxG/3uIzpeisnQf10PTmIvcdICr653/HxfwKNNTdKgAEomkf/dl+WFoOL197PFbnNQwXsxgqaOJhRtZsyTBUE1N2VPMlpzXl98NQM8zWQzGEbtNUWVrNmOd+luLA4TgO/vzP/xx/+7d/u+htPvrRj+JTn/oUPve5z+HBBx9EPp/H+eefD9u2xTpvfOMb8eijj+Luu+/G9773Pdx///244oorDsUpLBopEQpgahZM1WLeMwGBcIgNjzKiM+dMAWAaCo+GXeWFeFrVYWpZyUcoTHs1falDfPAkx0PXvNUGG0MWHm2JfcvCRVmjoMo3WUWNVMUA4eQn+pBJkSMgTFnx7ea7sS+koZHXi+8nfGqN+n7ISEqRxZfxKIIR+ALJmqi4HooTBa6t4oQxsv95JrH5okBxTZFsk5AUQQKiJfzyeyiPwdSseUv2xecliGjJIt74vuIpmvn0UfI28k9SRd58hFren0ySOFGXf48fV953HPOlOmRSYqhmGEEL1vdoK/JAImud5OhHvLkn1/Bx/x35R07zLkSI5FShjPksCPgyI+gVF48CyUQxsk/abrDIyTmvDJUJlUdbbcRWJmDczTnUKboRawl+nXlhiKGaGMwPwdJZFL1kdAMAds018eLBk8T6PCLOrx+XG7BIUDyqFSVNnOS4QSqPE0GPtoJK3Uxide1Kx0qOCP3zP/8z3vGOd+CUU05Z1PqUUtx4441497vfjYsvvhinnnoqbrnlFuzbtw//9V//BQDYtm0b7rzzTnzxi1/E2WefjXPOOQef/vSn8fWvfx379u07dCezAI6sT80hhkPsYFIiaHpyKFkV/XA48eHNUzn4k6ncN4qTIUvLC3Gr7PnB892c+PBtwqeh9tQDv6HJN9lOVWP8OHwfEbNHVW+bIOM39oUIkJzO4sZmMnj4eiGn1/gTe5JoslMqIyk6IT/xyzoXvv+k9MhC4+J/R8baobGq/H7N51Ysh/NlwTUQkixWfRgVMEeiBwnnL0cGZMjmmDzi1WnikMvnZbI1n+A7iUDJ5wuEqSc5UhSPAM0nkubkINpwMzkdIqfH4gJnmdTI44hD9phK+j50+o7EI3JA8mc16VouVO0pvzeaore5R3NtD4/Q8QbNYv+xaGAklZ7gRC1HZ+J99eIPAg6xoasZPDjSwHHlYpg6hBbq/oJ0nk89oWvaWxsV4056GOHbqIoKU81CUwxBgA0pWn/EaYSeR01Xt2/fjtHRUZx33nliWblcxtlnn40HHngAAPDAAw+gUqngzDPPFOucd955UFUVDz744GEfM8fzXixNg6qFarU673qtBsWUPY2iSdHym9BUDS6aYH3BWMPBJuZgqCaLCulyzx4dVX8KZuBtIefoxeQVPA2zL7oX3Iw9mFouEuEg1IMa3CwAwCW2mFgZ8QpC4sEyU7WEOJRtH6Z0fFoXwkkVzEuH3QTdYN8ONNUVoXy+XfxmwgWY8lMbO7+WdLxm4k1INmKLl+by/XUSWVNKYPsNSfvEBNOOb4tJXNZZ8OuiKb64XhEjyoQnRpkYxaM6cU1Vp3WSSFB8XPz44rrwqJ2igwafM3lyU5RQQ+YRVyIlbuL+44L4TteVEw6mdQs/S/KYkgTB3F8oSeTLj8+/J3FhdxyyELiTEJp/3mQ9Wxy8epN/Pvl3ii9L+jy2kUPFTfxMiN8TPqNJWrFOEcFOY4+MSbqO/Hr4tCn0iJy48IIMNm4diuLCUH2QgFDEvYO4pxn/vvM+gfx7IN8X4teLf/aSlsuQhf0MGcw1G7hsQxfW6H3YPzUBJ6NEHtaSCG83+lCrtiLH5N8Jdt9jqTxDtdBADT51gvukDwpbPGj61IHpzH+vXwh8ruBzx6FEs0P6/EC3j89vmUwGmczhTRWOjo4CAFatWhVZvmrVKvHa6Ogo+vv7I6/ruo7u7m6xznLgeU+E5ubmAABr165d5pGkSJEiRYpDj4OjN5mbm0O5XD4o+4rDNE0MDAzgraPbl7yvQqHQNr+9973vxfve9762dd/1rnfhIx/5yLz727ZtGzZt2rTkcR1JeN4TocHBQezevRvFYhGKEtYpVqtVrF27Frt370apVFrGET4/kF7Pg4f0Wh5cpNfz4OFouJaUUszNzWFwcPCQHcOyLGzfvh2O4yy88gKglEbmNgAdo0HXXHMNLrvssnn3t2HDhuc0joEBVkE4NjaG1atXi+VjY2M4/fTTxTrj4+OR7TzPw9TUlNh+OfC8J0KqqmJoaKjj66VS6Xn7hV4OpNfz4CG9lgcX6fU8eHi+X8tDFQmSYVkWLMs65MeR0dfXh76+vkOy72OOOQYDAwP48Y9/LIhPtVrFgw8+KCrPtmzZgpmZGTz88MM444wzAAD33HMPCCE4++yzD8m4FoMjS1mWIkWKFClSpDjk2LVrF7Zu3Ypdu3bB931s3boVW7dujXj+bNq0CXfccQcAQFEUvP3tb8cHP/hBfOc738EjjzyCSy65BIODg3j1q18NADjxxBNxwQUX4M1vfjMeeugh/OxnP8NVV12FN7zhDYc0ArcQnvcRoRQpUqRIkSLFgeG6667DV77yFfH3C17wAgDAT37yE7z0pS8FADzxxBOYnQ37Gr7zne9EvV7HFVdcgZmZGZxzzjm48847I5GvW2+9FVdddRXOPfdcqKqK1772tfjUpz51eE6qA45aIpTJZPDe9773sCvrn69Ir+fBQ3otDy7S63nwkF7Lowc333wzbr755nnXiVfWKYqC97///Xj/+9/fcZvu7m7cdtttB2OIBw0KPRw1gilSpEiRIkWKFCsQqUYoRYoUKVKkSHHUIiVCKVKkSJEiRYqjFikRSpEiRYoUKVIctUiJUIoUKVKkSJHiqMVRSYSuv/56vOhFL0Iul0OlUklcZ9euXXjlK1+JXC6H/v5+/OM//iM8L7m549GOz3zmM1i/fj0sy8LZZ5+Nhx56aLmHdETg/vvvx6te9SoMDg5CURTRoZmDUorrrrsOq1evRjabxXnnnYennnpqeQa7wnHDDTfgD/7gD1AsFtHf349Xv/rVeOKJJyLr2LaNK6+8Ej09PSgUCnjta1+LsbGxZRrxysZnP/tZnHrqqcI4ccuWLfjBD34gXk+vZYrnE45KIuQ4Dv78z/9cuF3G4fs+XvnKV8JxHPz85z/HV77yFdx888247rrrDvNIVz6+8Y1v4Oqrr8Z73/te/PrXv8Zpp52G888/v81GPUU76vU6TjvtNHzmM59JfP2jH/0oPvWpT+Fzn/scHnzwQeTzeZx//vmwbfswj3Tl47777sOVV16JX/ziF7j77rvhui5e8YpXoF6vi3Xe8Y534Lvf/S6++c1v4r777sO+ffvwZ3/2Z8s46pWLoaEhfPjDH8bDDz+MX/3qV3j5y1+Oiy++GI8++iiA9FqmeJ6BHsX48pe/TMvlctvy73//+1RVVTo6OiqWffazn6WlUom2Wq3DOMKVj7POOoteeeWV4m/f9+ng4CC94YYblnFURx4A0DvuuEP8TQihAwMD9GMf+5hYNjMzQzOZDL399tuXYYRHFsbHxykAet9991FK2bUzDIN+85vfFOts27aNAqAPPPDAcg3ziEJXVxf94he/mF7LFM87HJURoYXwwAMP4JRTTsGqVavEsvPPPx/ValU8EaVgkbWHH34Y5513nlimqirOO+88PPDAA8s4siMf27dvx+joaOTalstlnH322em1XQS42213dzcA4OGHH4brupHruWnTJgwPD6fXcwH4vo+vf/3rqNfr2LJlS3otUzzvcNQ6S8+H0dHRCAkCIP4eHR1djiGtSExMTMD3/cRr9fjjjy/TqJ4f4J+zpGubfgbnByEEb3/72/HiF78YJ598MgB2PU3TbNMEptezMx555BFs2bIFtm2jUCjgjjvuwObNm7F169b0WqZ4XuF5ExF617veBUVR5v1JJ+cUKZ7/uPLKK/H73/8eX//615d7KEc0Nm7ciK1bt4ru4Zdeeikee+yx5R5WihQHHc+biNA111yDyy67bN51NmzYsKh9DQwMtFU+8YqIgYGB5zS+5yN6e3uhaVpbtcjY2Fh6nZYIfv3GxsawevVqsXxsbAynn376Mo1q5eOqq67C9773Pdx///0YGhoSywcGBuA4DmZmZiKRjPSz2hmmaeK4444DAJxxxhn45S9/iU9+8pN4/etfn17LFM8rPG8iQn19fdi0adO8P6ZpLmpfW7ZswSOPPBKpfLr77rtRKpWwefPmQ3UKRxxM08QZZ5yBH//4x2IZIQQ//vGPsWXLlmUc2ZGPY445BgMDA5FrW61W8eCDD6bXNgGUUlx11VW44447cM899+CYY46JvH7GGWfAMIzI9XziiSewa9eu9HouEoQQtFqt9FqmeN7heRMROhDs2rULU1NT2LVrF3zfx9atWwEAxx13HAqFAl7xildg8+bN+Ku/+it89KMfxejoKN797nfjyiuvTLsux3D11Vfj0ksvxZlnnomzzjoLN954I+r1Ov76r/96uYe24lGr1fD000+Lv7dv346tW7eiu7sbw8PDePvb344PfvCDOP7443HMMcfgPe95DwYHB/HqV796+Qa9QnHllVfitttuw3//93+jWCwKrUq5XEY2m0W5XMbll1+Oq6++Gt3d3SiVSvj7v/97bNmyBX/4h3+4zKNfebj22mtx4YUXYnh4GHNzc7jttttw77334q677kqvZYrnH5a7bG05cOmll1IAbT8/+clPxDo7duygF154Ic1ms7S3t5dec8011HXd5Rv0CsanP/1pOjw8TE3TpGeddRb9xS9+sdxDOiLwk5/8JPFzeOmll1JKWQn9e97zHrpq1SqayWToueeeS5944onlHfQKRdJ1BEC//OUvi3WazSb9u7/7O9rV1UVzuRx9zWteQ0dGRpZv0CsYf/M3f0PXrVtHTdOkfX199Nxzz6U//OEPxevptUzxfIJCKaWHn36lSJEiRYoUKVIsP543GqEUKVKkSJEiRYoDRUqEUqRIkSJFihRHLVIilCJFihQpUqQ4apESoRQpUqRIkSLFUYuUCKVIkSJFihQpjlqkRChFihQpUqRIcdQiJUIpUqRIkSJFiqMWKRFKcUTgpS99Kd7+9rc/r4572WWXLdklev369aKp8MzMTMf1br755rZu4SkOHi677DLxPvzXf/3Xcg8nRYoUB4CUCKVIMQ/+8z//Ex/4wAfE3+vXr8eNN964fANKwPvf/36MjIygXC4v91Ce97j33nsTSecnP/lJjIyMLM+gUqRIsSQclb3GUqRYLLq7u5d7CAuiWCyumK7fruvCMIzlHsZhR7lcToloihRHKNKIUIojEtPT07jkkkvQ1dWFXC6HCy+8EE899ZR4naeC7rrrLpx44okoFAq44IILIk/tnufhrW99KyqVCnp6evBP//RPuPTSSyPpKjk19tKXvhQ7d+7EO97xDpEGAYD3ve99OP300yPju/HGG7F+/Xrxt+/7uPrqq8Wx3vnOdyLe3YYQghtuuAHHHHMMstksTjvtNHzrW996Ttfn5ptvxvDwMHK5HF7zmtdgcnKybZ3//u//xgtf+EJYloUNGzbgn//5n+F5nnj98ccfxznnnAPLsrB582b86Ec/iqR+duzYAUVR8I1vfAMveclLYFkWbr31VgDAF7/4RZx44omwLAubNm3Cv//7v0eOvXv3brzuda9DpVJBd3c3Lr74YuzYsUO8fu+99+Kss85CPp9HpVLBi1/8YuzcuXNR577QeX3iE5/AKaecgnw+j7Vr1+Lv/u7vUKvVxOs7d+7Eq171KnR1dSGfz+Okk07C97//fezYsQMve9nLAABdXV1QFAWXXXbZosaUIkWKlYuUCKU4InHZZZfhV7/6Fb7zne/ggQceAKUUF110EVzXFes0Gg18/OMfx1e/+lXcf//92LVrF/7hH/5BvP6Rj3wEt956K7785S/jZz/7GarV6rz6jv/8z//E0NCQSEUdSCrkX/7lX3DzzTfjpptuwv/8z/9gamoKd9xxR2SdG264Abfccgs+97nP4dFHH8U73vEOvOlNb8J99923+AsD4MEHH8Tll1+Oq666Clu3bsXLXvYyfPCDH4ys89Of/hSXXHIJ3va2t+Gxxx7D5z//edx88824/vrrATDi9upXvxq5XA4PPvggvvCFL+D//b//l3i8d73rXXjb296Gbdu24fzzz8ett96K6667Dtdffz22bduGD33oQ3jPe96Dr3zlKwBY1Oj8889HsVjET3/6U/zsZz8TRNVxHHieh1e/+tV4yUtegt/97nd44IEHcMUVVwjiOR8WOi8AUFUVn/rUp/Doo4/iK1/5Cu655x68853vFK9feeWVaLVauP/++/HII4/gIx/5CAqFAtauXYtvf/vbAIAnnngCIyMj+OQnP3lA702KFClWIJa352uKFIvDS17yEvq2t72NUkrpk08+SQHQn/3sZ+L1iYkJms1m6X/8x39QSin98pe/TAHQp59+Wqzzmc98hq5atUr8vWrVKvqxj31M/O15Hh0eHqYXX3xx4nEppXTdunX0X//1XyNje+9730tPO+20yLJ//dd/pevWrRN/r169mn70ox8Vf7uuS4eGhsSxbNumuVyO/vznP4/s5/LLL6d/8Rd/0fG6JI3nL/7iL+hFF10UWfb617+elstl8fe5555LP/ShD0XW+epXv0pXr15NKaX0Bz/4AdV1PdJR/O6776YA6B133EEppXT79u0UAL3xxhsj+zn22GPpbbfdFln2gQ98gG7ZskUcZ+PGjZQQIl5vtVo0m83Su+66i05OTlIA9N577+143p2w0Hkl4Zvf/Cbt6ekRf59yyin0fe97X+K6P/nJTygAOj09nfi6fH1SpEhxZCDVCKU44rBt2zbouo6zzz5bLOvp6cHGjRuxbds2sSyXy+HYY48Vf69evRrj4+MAgNnZWYyNjeGss84Sr2uahjPOOAOEkIM63tnZWYyMjETGq+s6zjzzTJEee/rpp9FoNPAnf/InkW0dx8ELXvCCAzretm3b8JrXvCaybMuWLbjzzv+/vbsLaXqN4wD+ndDWfJlZrjVfUnxJZyFpEaViXWir0BZlIokzMENBp0lkggiKophK4EWphCAU2IVelLRUiKJRw6wkoWZavlxkr6JRWHv5nYs4O/4zz5Q657Sz3+fK/Z+/z7Pn8Q/++P+/e6a3vx4aGoLBYBDcKbFarZifn8fnz59hMpkQGBgoyB4tXKuFtm/fbv/506dPGBsbQ05ODnJzc+3HLRaLPUMzNDSE0dFReHl5CfqZn5/H2NgY9u7di+PHj0OtViM5ORlJSUlIT0+HUql0OHdH83J3d0d/fz9qa2vx7NkzzM3NwWKxCNp1Oh3y8/PR29uLpKQkHDlyBNHR0Q7HZow5Jy6E2P/W96FdkUi0KJfzK7i5uS3qd+EjuuX4M6PS09MDf39/QZtEIvm5N7jEeJWVlTh8+PCittWrV6+oLw8PD0G/ANDW1iYo/IBvheaf52zbts2eJ1pILpcDANrb26HT6aDX69HZ2Yny8nL09fVh586dPzWv8fFxpKSkID8/HzU1NVi7di3u3r2LnJwcfP36Fe7u7jhx4gTUajV6enrQ29uL2tpaNDY2orCwcEXrwhhzDlwIMaejUqlgsVhgNBoRFxcHAHj//j1MJhOioqKW1Ye3tzcUCgUGBgaQmJgI4Nudg4cPHy4KPi8kFothtVoFx+RyOaanp0FE9hzL48ePBWMplUoYjUb7WBaLBYODg4iNjQUAREVFQSKRYHJyErt3717WHJaiUqlgNBoFx+7fvy94HRsbC5PJhLCwsB/2ERERgampKbx+/RoKhQIAMDAw4HBshUIBPz8/vHjxApmZmT88JzY2Fp2dnVi/fj1kMtmSfcXExCAmJgZlZWXYtWsXrly54rAQcjSvwcFB2Gw2NDY2ws3tW0Ty6tWri84LDAxEXl4e8vLyUFZWhra2NhQWFkIsFgPAomuAMea8uBBiTic8PBwajQa5ubloaWmBl5cXzp49C39/f2g0mmX3U1hYiNraWoSFhSEyMhLNzc2YmZn521BucHAw7ty5g4yMDEgkEvj6+mLPnj14+/Yt6uvrkZaWBr1ejxs3bgj+yRcVFaGurg7h4eGIjIxEU1OTYC8aLy8vnD59GqdOnYLNZkNCQgJmZ2dhMBggk8mQnZ297HnpdDrEx8ejoaEBGo0GN2/eFDwWA4CKigqkpKRg48aNSEtLg5ubG4aGhjA8PIzq6mokJycjNDQU2dnZqK+vx8ePH1FeXg4ADkPLlZWV0Ol08Pb2xr59+/Dlyxc8ePAAMzMzKCkpQWZmJs6dOweNRoOqqioEBARgYmICXV1dOHPmDMxmM1pbW3Hw4EH4+fnBZDLh+fPn0Gq1DufuaF5hYWEwm81obm5GamoqDAYDLl68KOijuLgY+/fvx6ZNmzAzM4Nbt25BpVIBAIKCgiASiXD9+nUcOHAAUqkUnp6ey/7bMMZ+Q/9tRImx5fk+tPzhwwfKysoib29vkkqlpFaraWRkxN7e3t4uCAcTEXV3d9PCS95sNlNBQQHJZDLy8fGh0tJSOnr0KGVkZCw57r179yg6OpokEomgrwsXLlBgYCB5eHiQVqulmpoaQVjabDZTUVERyWQyWrNmDZWUlJBWqxUEs202G50/f54iIiJo1apVJJfLSa1W0+3bt5dclx+FpYmILl26RAEBASSVSik1NZUaGhoWrYder6e4uDiSSqUkk8lox44d1Nraam9/+vQpxcfHk1gspsjISLp27RoBIL1eT0R/haUfPXq0aPzLly/T1q1bSSwWk4+PDyUmJlJXV5e9/dWrV6TVasnX15ckEgmFhIRQbm4uzc7O0vT0NB06dIiUSiWJxWIKCgqiiooKslqtS67DSubV1NRESqXSft10dHQIAtAFBQUUGhpKEomE5HI5ZWVl0bt37+y/X1VVRRs2bCCRSETZ2dmCscFhacacjojoHwhNMOaEbDYbVCoV0tPTBbtJ/86Cg4NRXFz8r3z9iMFgQEJCAkZHRwUhdPYXkUiE7u7un/7qFMbYv4f3EWIua2JiAm1tbRgZGcGTJ0+Qn5+Ply9f4tixY//1W1uR0tJSeHp6YnZ29pf2293djb6+PoyPj6O/vx8nT55EfHw8F0E/kJeXx4/IGHNSfEeIuaypqSlkZGRgeHgYRIQtW7agrq7OHmh2BhMTE/ZPqIWEhNgDwL9CR0cHqqurMTk5CV9fXyQlJaGxsRHr1q37ZWOs1ObNm5fcYbqlpWXJgPY/7c2bN5ibmwPwbZuGhZ+kY4z93rgQYow5jYWF3/cUCsWivYkYY8wRLoQYY4wx5rI4I8QYY4wxl8WFEGOMMcZcFhdCjDHGGHNZXAgxxhhjzGVxIcQYY4wxl8WFEGOMMcZcFhdCjDHGGHNZXAgxxhhjzGX9AcGqDqiBu1ODAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "# plot the time-series of the average land surface temperature of the year 2022\n",
        "ds.sel(time=slice('2022-01-01', None)).lst_day.mean(dim=('x', 'y')).plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "YvsIRDcnWA8I",
        "outputId": "95299939-f7a6-4e91-e9cc-d30dfb6098a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 14.6 s, sys: 1.87 s, total: 16.5 s\n",
            "Wall time: 2min 4s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fae98fa3fd0>]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jtaGhfvuYilw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "Am0gMAp5aFKW"
      }
    }
  ]
}