diff --git a/notebooks/Multi-Parameter Distributions/MPD_7_1.ipynb b/notebooks/Multi-Parameter Distributions/MPD_7_1.ipynb
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+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
+ ]
+ }
+ ],
+ "source": [
+ "%matplotlib inline\n",
+ "import pymc as pm\n",
+ "import matplotlib.pyplot as plt\n",
+ "import scipy.stats as st\n",
+ "import arviz as az\n",
+ "import metropolis_commands as mc\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import warnings\n",
+ "warnings.filterwarnings(\"ignore\")\n",
+ "plt.rcParams['figure.figsize'] = [5, 3]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "a0b6edb3-9e0c-427c-9883-7833c9748da6",
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+ " <tbody>\n",
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+ " <th>0</th>\n",
+ " <td>51.06</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>55.12</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>53.73</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>50.24</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>52.05</td>\n",
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+ "source": [
+ "df = pd.read_csv(\"./Daten/chemical_shifts.csv\",header=None)\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "0a20eb96-0ed5-4ae1-b13a-b3f1935b3fe3",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Auto-assigning NUTS sampler...\n",
+ "Initializing NUTS using jitter+adapt_diag...\n",
+ "Multiprocess sampling (4 chains in 4 jobs)\n",
+ "NUTS: [μ, σ]\n"
+ ]
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+ "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 4 seconds.\n"
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+ "with pm.Model() as model_h:\n",
+ " μ = pm.Uniform('μ', lower=40, upper=70)\n",
+ " σ = pm.HalfNormal('σ', sigma=10)\n",
+ " y = pm.Normal('y', mu=μ, sigma=σ, observed=df)\n",
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+ "</style><pre class='xr-text-repr-fallback'><xarray.Dataset> Size: 72kB\n",
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+ " inference_library_version: 5.16.2\n",
+ " sampling_time: 3.5525271892547607\n",
+ " tuning_steps: 1000</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-590ed7b0-4f2d-4220-90ef-da39fd591442' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-590ed7b0-4f2d-4220-90ef-da39fd591442' 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'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1c4852f7-028e-4dff-9b9c-cdb1a0f53b0c' class='xr-section-summary-in' type='checkbox' checked><label for='section-1c4852f7-028e-4dff-9b9c-cdb1a0f53b0c' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-eef3078b-ea09-414f-970f-213bbcbf34e0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eef3078b-ea09-414f-970f-213bbcbf34e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64675c5e-f883-42f8-bfd9-8858af6a8305' class='xr-var-data-in' type='checkbox'><label for='data-64675c5e-f883-42f8-bfd9-8858af6a8305' 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([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-38332e67-d095-4c7d-be1b-b4eb2b8ea2f0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-38332e67-d095-4c7d-be1b-b4eb2b8ea2f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b06e73fd-5647-4cd2-85af-b803d860b637' class='xr-var-data-in' type='checkbox'><label for='data-b06e73fd-5647-4cd2-85af-b803d860b637' 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([ 0, 1, 2, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-13333c4f-8670-4334-b3de-3d84d57aea66' class='xr-section-summary-in' type='checkbox' checked><label for='section-13333c4f-8670-4334-b3de-3d84d57aea66' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>μ</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>53.23 53.18 53.06 ... 53.31 53.35</div><input id='attrs-9b3a5f82-8817-4fdb-9ea0-706c46ddc8ee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9b3a5f82-8817-4fdb-9ea0-706c46ddc8ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a917f2c0-998b-4a4e-a9d3-1c6d2637f743' class='xr-var-data-in' type='checkbox'><label for='data-a917f2c0-998b-4a4e-a9d3-1c6d2637f743' 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([[53.23261948, 53.18251245, 53.05875747, ..., 52.88685738,\n",
+ " 52.75484103, 54.17763389],\n",
+ " [53.72526718, 54.02276119, 53.15658361, ..., 52.859677 ,\n",
+ " 54.31474734, 53.91129188],\n",
+ " [53.65676929, 53.20304366, 53.25167534, ..., 53.51140207,\n",
+ " 52.58014843, 53.81416347],\n",
+ " [53.10712394, 53.52254983, 52.59475358, ..., 53.67696861,\n",
+ " 53.30503397, 53.35223167]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>σ</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.321 3.217 3.037 ... 3.526 3.233</div><input id='attrs-b8fed3fd-8105-467a-befd-f5ad7787ae57' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b8fed3fd-8105-467a-befd-f5ad7787ae57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2baf73f-4c2c-4ee8-b4f8-c7f7f66a7a45' class='xr-var-data-in' type='checkbox'><label for='data-b2baf73f-4c2c-4ee8-b4f8-c7f7f66a7a45' 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([[3.32053894, 3.21658896, 3.03730853, ..., 3.05205007, 3.54593739,\n",
+ " 3.41845893],\n",
+ " [3.78670283, 3.22199741, 3.90740653, ..., 2.85436557, 4.50023062,\n",
+ " 4.22420173],\n",
+ " [3.13618167, 3.70752269, 3.55244168, ..., 3.94020484, 3.40708183,\n",
+ " 3.79198683],\n",
+ " [3.84814112, 3.14582526, 3.92515763, ..., 3.43023885, 3.52563954,\n",
+ " 3.23332794]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-456d749d-dd73-4d60-a19f-fb77a35d8767' class='xr-section-summary-in' type='checkbox' ><label for='section-456d749d-dd73-4d60-a19f-fb77a35d8767' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9c12b7a1-47ce-4e7a-ad10-30e918812d7d' class='xr-index-data-in' type='checkbox'/><label for='index-9c12b7a1-47ce-4e7a-ad10-30e918812d7d' 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(Index([0, 1, 2, 3], dtype='int64', name='chain'))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-de53be40-7088-4b1f-841a-48faee657452' class='xr-index-data-in' type='checkbox'/><label for='index-de53be40-7088-4b1f-841a-48faee657452' 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(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " ...\n",
+ " 990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
+ " dtype='int64', name='draw', length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d97850b5-9daf-4c28-b4dd-f6cf8ac3b2ec' class='xr-section-summary-in' type='checkbox' checked><label for='section-d97850b5-9daf-4c28-b4dd-f6cf8ac3b2ec' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-10-13T16:50:52.084344+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.19.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.16.2</dd><dt><span>sampling_time :</span></dt><dd>3.5525271892547607</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div><br></div>\n",
+ " </ul>\n",
+ " </div>\n",
+ " </li>\n",
+ " \n",
+ " <li class = \"xr-section-item\">\n",
+ " <input id=\"idata_posterior_predictivec27e76f5-f947-405f-a74a-16f573525240\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+ " <label for=\"idata_posterior_predictivec27e76f5-f947-405f-a74a-16f573525240\" class = \"xr-section-summary\">posterior_predictive</label>\n",
+ " <div class=\"xr-section-inline-details\"></div>\n",
+ " <div class=\"xr-section-details\">\n",
+ " <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
+ " <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+ "<defs>\n",
+ "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+ "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+ "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
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+ "</symbol>\n",
+ "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+ "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
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+ "</symbol>\n",
+ "</defs>\n",
+ "</svg>\n",
+ "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+ " *\n",
+ " */\n",
+ "\n",
+ ":root {\n",
+ " --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+ " --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
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+ " --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+ " --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+ " --xr-background-color: var(--jp-layout-color0, white);\n",
+ " --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+ " --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+ "}\n",
+ "\n",
+ "html[theme=dark],\n",
+ "html[data-theme=dark],\n",
+ "body[data-theme=dark],\n",
+ "body.vscode-dark {\n",
+ " --xr-font-color0: rgba(255, 255, 255, 1);\n",
+ " --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+ " --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+ " --xr-border-color: #1F1F1F;\n",
+ " --xr-disabled-color: #515151;\n",
+ " --xr-background-color: #111111;\n",
+ " --xr-background-color-row-even: #111111;\n",
+ " --xr-background-color-row-odd: #313131;\n",
+ "}\n",
+ "\n",
+ ".xr-wrap {\n",
+ " display: block !important;\n",
+ " min-width: 300px;\n",
+ " max-width: 700px;\n",
+ "}\n",
+ "\n",
+ ".xr-text-repr-fallback {\n",
+ " /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ ".xr-header {\n",
+ " padding-top: 6px;\n",
+ " padding-bottom: 6px;\n",
+ " margin-bottom: 4px;\n",
+ " border-bottom: solid 1px var(--xr-border-color);\n",
+ "}\n",
+ "\n",
+ ".xr-header > div,\n",
+ ".xr-header > ul {\n",
+ " display: inline;\n",
+ " margin-top: 0;\n",
+ " margin-bottom: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-obj-type,\n",
+ ".xr-array-name {\n",
+ " margin-left: 2px;\n",
+ " margin-right: 10px;\n",
+ "}\n",
+ "\n",
+ ".xr-obj-type {\n",
+ " color: var(--xr-font-color2);\n",
+ "}\n",
+ "\n",
+ ".xr-sections {\n",
+ " padding-left: 0 !important;\n",
+ " display: grid;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+ "}\n",
+ "\n",
+ ".xr-section-item {\n",
+ " display: contents;\n",
+ "}\n",
+ "\n",
+ ".xr-section-item input {\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-section-item input + label {\n",
+ " color: var(--xr-disabled-color);\n",
+ "}\n",
+ "\n",
+ ".xr-section-item input:enabled + label {\n",
+ " cursor: pointer;\n",
+ " color: var(--xr-font-color2);\n",
+ "}\n",
+ "\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
+ ".xr-section-item input:enabled + label:hover {\n",
+ " color: var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary {\n",
+ " grid-column: 1;\n",
+ " color: var(--xr-font-color2);\n",
+ " font-weight: 500;\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary > span {\n",
+ " display: inline-block;\n",
+ " padding-left: 0.5em;\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in:disabled + label {\n",
+ " color: var(--xr-font-color2);\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in + label:before {\n",
+ " display: inline-block;\n",
+ " content: 'â–º';\n",
+ " font-size: 11px;\n",
+ " width: 15px;\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in:disabled + label:before {\n",
+ " color: var(--xr-disabled-color);\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in:checked + label:before {\n",
+ " content: 'â–¼';\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in:checked + label > span {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary,\n",
+ ".xr-section-inline-details {\n",
+ " padding-top: 4px;\n",
+ " padding-bottom: 4px;\n",
+ "}\n",
+ "\n",
+ ".xr-section-inline-details {\n",
+ " grid-column: 2 / -1;\n",
+ "}\n",
+ "\n",
+ ".xr-section-details {\n",
+ " display: none;\n",
+ " grid-column: 1 / -1;\n",
+ " margin-bottom: 5px;\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+ " display: contents;\n",
+ "}\n",
+ "\n",
+ ".xr-array-wrap {\n",
+ " grid-column: 1 / -1;\n",
+ " display: grid;\n",
+ " grid-template-columns: 20px auto;\n",
+ "}\n",
+ "\n",
+ ".xr-array-wrap > label {\n",
+ " grid-column: 1;\n",
+ " vertical-align: top;\n",
+ "}\n",
+ "\n",
+ ".xr-preview {\n",
+ " color: var(--xr-font-color3);\n",
+ "}\n",
+ "\n",
+ ".xr-array-preview,\n",
+ ".xr-array-data {\n",
+ " padding: 0 5px !important;\n",
+ " grid-column: 2;\n",
+ "}\n",
+ "\n",
+ ".xr-array-data,\n",
+ ".xr-array-in:checked ~ .xr-array-preview {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ ".xr-array-in:checked ~ .xr-array-data,\n",
+ ".xr-array-preview {\n",
+ " display: inline-block;\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list {\n",
+ " display: inline-block !important;\n",
+ " list-style: none;\n",
+ " padding: 0 !important;\n",
+ " margin: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list li {\n",
+ " display: inline-block;\n",
+ " padding: 0;\n",
+ " margin: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list:before {\n",
+ " content: '(';\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list:after {\n",
+ " content: ')';\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list li:not(:last-child):after {\n",
+ " content: ',';\n",
+ " padding-right: 5px;\n",
+ "}\n",
+ "\n",
+ ".xr-has-index {\n",
+ " font-weight: bold;\n",
+ "}\n",
+ "\n",
+ ".xr-var-list,\n",
+ ".xr-var-item {\n",
+ " display: contents;\n",
+ "}\n",
+ "\n",
+ ".xr-var-item > div,\n",
+ ".xr-var-item label,\n",
+ ".xr-var-item > .xr-var-name span {\n",
+ " background-color: var(--xr-background-color-row-even);\n",
+ " margin-bottom: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-var-item > .xr-var-name:hover span {\n",
+ " padding-right: 5px;\n",
+ "}\n",
+ "\n",
+ ".xr-var-list > li:nth-child(odd) > div,\n",
+ ".xr-var-list > li:nth-child(odd) > label,\n",
+ ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+ " background-color: var(--xr-background-color-row-odd);\n",
+ "}\n",
+ "\n",
+ ".xr-var-name {\n",
+ " grid-column: 1;\n",
+ "}\n",
+ "\n",
+ ".xr-var-dims {\n",
+ " grid-column: 2;\n",
+ "}\n",
+ "\n",
+ ".xr-var-dtype {\n",
+ " grid-column: 3;\n",
+ " text-align: right;\n",
+ " color: var(--xr-font-color2);\n",
+ "}\n",
+ "\n",
+ ".xr-var-preview {\n",
+ " grid-column: 4;\n",
+ "}\n",
+ "\n",
+ ".xr-index-preview {\n",
+ " grid-column: 2 / 5;\n",
+ " color: var(--xr-font-color2);\n",
+ "}\n",
+ "\n",
+ ".xr-var-name,\n",
+ ".xr-var-dims,\n",
+ ".xr-var-dtype,\n",
+ ".xr-preview,\n",
+ ".xr-attrs dt {\n",
+ " white-space: nowrap;\n",
+ " overflow: hidden;\n",
+ " text-overflow: ellipsis;\n",
+ " padding-right: 10px;\n",
+ "}\n",
+ "\n",
+ ".xr-var-name:hover,\n",
+ ".xr-var-dims:hover,\n",
+ ".xr-var-dtype:hover,\n",
+ ".xr-attrs dt:hover {\n",
+ " overflow: visible;\n",
+ " width: auto;\n",
+ " z-index: 1;\n",
+ "}\n",
+ "\n",
+ ".xr-var-attrs,\n",
+ ".xr-var-data,\n",
+ ".xr-index-data {\n",
+ " display: none;\n",
+ " background-color: var(--xr-background-color) !important;\n",
+ " padding-bottom: 5px !important;\n",
+ "}\n",
+ "\n",
+ ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+ ".xr-var-data-in:checked ~ .xr-var-data,\n",
+ ".xr-index-data-in:checked ~ .xr-index-data {\n",
+ " display: block;\n",
+ "}\n",
+ "\n",
+ ".xr-var-data > table {\n",
+ " float: right;\n",
+ "}\n",
+ "\n",
+ ".xr-var-name span,\n",
+ ".xr-var-data,\n",
+ ".xr-index-name div,\n",
+ ".xr-index-data,\n",
+ ".xr-attrs {\n",
+ " padding-left: 25px !important;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs,\n",
+ ".xr-var-attrs,\n",
+ ".xr-var-data,\n",
+ ".xr-index-data {\n",
+ " grid-column: 1 / -1;\n",
+ "}\n",
+ "\n",
+ "dl.xr-attrs {\n",
+ " padding: 0;\n",
+ " margin: 0;\n",
+ " display: grid;\n",
+ " grid-template-columns: 125px auto;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt,\n",
+ ".xr-attrs dd {\n",
+ " padding: 0;\n",
+ " margin: 0;\n",
+ " float: left;\n",
+ " padding-right: 10px;\n",
+ " width: auto;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt {\n",
+ " font-weight: normal;\n",
+ " grid-column: 1;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt:hover span {\n",
+ " display: inline-block;\n",
+ " background: var(--xr-background-color);\n",
+ " padding-right: 10px;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dd {\n",
+ " grid-column: 2;\n",
+ " white-space: pre-wrap;\n",
+ " word-break: break-all;\n",
+ "}\n",
+ "\n",
+ ".xr-icon-database,\n",
+ ".xr-icon-file-text2,\n",
+ ".xr-no-icon {\n",
+ " display: inline-block;\n",
+ " vertical-align: middle;\n",
+ " width: 1em;\n",
+ " height: 1.5em !important;\n",
+ " stroke-width: 0;\n",
+ " stroke: currentColor;\n",
+ " fill: currentColor;\n",
+ "}\n",
+ "</style><pre class='xr-text-repr-fallback'><xarray.Dataset> Size: 2MB\n",
+ "Dimensions: (chain: 4, draw: 1000, y_dim_2: 48, y_dim_3: 1)\n",
+ "Coordinates:\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 8kB 0 1 2 3 4 5 6 7 ... 993 994 995 996 997 998 999\n",
+ " * y_dim_2 (y_dim_2) int64 384B 0 1 2 3 4 5 6 7 8 ... 40 41 42 43 44 45 46 47\n",
+ " * y_dim_3 (y_dim_3) int64 8B 0\n",
+ "Data variables:\n",
+ " y (chain, draw, y_dim_2, y_dim_3) float64 2MB 51.74 60.0 ... 52.93\n",
+ "Attributes:\n",
+ " created_at: 2024-10-13T16:55:03.974523+00:00\n",
+ " arviz_version: 0.19.0\n",
+ " inference_library: pymc\n",
+ " inference_library_version: 5.16.2</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-ad67341b-d8a6-429d-b52b-b3007355279e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ad67341b-d8a6-429d-b52b-b3007355279e' 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'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>y_dim_2</span>: 48</li><li><span class='xr-has-index'>y_dim_3</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-89d1eed8-a40e-4994-bc34-fc0c92d31aee' class='xr-section-summary-in' type='checkbox' checked><label for='section-89d1eed8-a40e-4994-bc34-fc0c92d31aee' class='xr-section-summary' >Coordinates: <span>(4)</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'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-0138792b-626f-403f-b693-1290830590c7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0138792b-626f-403f-b693-1290830590c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e54b28b6-b85e-44e5-b2eb-ce29a26c43f0' class='xr-var-data-in' type='checkbox'><label for='data-e54b28b6-b85e-44e5-b2eb-ce29a26c43f0' 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([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-32f5107f-9a78-4bab-bdf3-191b9f4f10d8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-32f5107f-9a78-4bab-bdf3-191b9f4f10d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04f7a1f2-d6a8-47b0-9f86-2878de51a6fb' class='xr-var-data-in' type='checkbox'><label for='data-04f7a1f2-d6a8-47b0-9f86-2878de51a6fb' 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([ 0, 1, 2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y_dim_2</span></div><div class='xr-var-dims'>(y_dim_2)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 42 43 44 45 46 47</div><input id='attrs-aee1286d-8694-4cf8-b7a4-1e97a486e89a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aee1286d-8694-4cf8-b7a4-1e97a486e89a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17031d78-6e15-4733-bcfb-37313a815f22' class='xr-var-data-in' type='checkbox'><label for='data-17031d78-6e15-4733-bcfb-37313a815f22' 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([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+ " 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
+ " 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y_dim_3</span></div><div class='xr-var-dims'>(y_dim_3)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-caa7286d-5800-4886-9155-5800a3f8e652' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-caa7286d-5800-4886-9155-5800a3f8e652' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-870d12ed-b7ef-4491-9514-ae1438a39f4d' class='xr-var-data-in' type='checkbox'><label for='data-870d12ed-b7ef-4491-9514-ae1438a39f4d' 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([0])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8be9c274-49e0-4d54-a33f-3cb53cf84946' class='xr-section-summary-in' type='checkbox' checked><label for='section-8be9c274-49e0-4d54-a33f-3cb53cf84946' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(chain, draw, y_dim_2, y_dim_3)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>51.74 60.0 52.9 ... 51.38 52.93</div><input id='attrs-ca6ce269-83d7-4576-afcd-c0dfe9d81336' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ca6ce269-83d7-4576-afcd-c0dfe9d81336' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f8a129d-3da4-4179-9016-b524b4b1998c' class='xr-var-data-in' type='checkbox'><label for='data-3f8a129d-3da4-4179-9016-b524b4b1998c' 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([[[[51.73957714],\n",
+ " [60.00415395],\n",
+ " [52.89608826],\n",
+ " ...,\n",
+ " [49.67834517],\n",
+ " [49.91922993],\n",
+ " [56.71858708]],\n",
+ "\n",
+ " [[54.62664302],\n",
+ " [54.06501245],\n",
+ " [60.19421591],\n",
+ " ...,\n",
+ " [48.43345209],\n",
+ " [51.1323349 ],\n",
+ " [51.97586264]],\n",
+ "\n",
+ " [[52.41277329],\n",
+ " [51.21853464],\n",
+ " [49.87638836],\n",
+ " ...,\n",
+ "...\n",
+ " ...,\n",
+ " [49.84917853],\n",
+ " [56.86215152],\n",
+ " [53.16613865]],\n",
+ "\n",
+ " [[55.37868563],\n",
+ " [56.44263558],\n",
+ " [59.02731904],\n",
+ " ...,\n",
+ " [53.86733241],\n",
+ " [52.19221997],\n",
+ " [50.3411012 ]],\n",
+ "\n",
+ " [[53.10894819],\n",
+ " [56.99538444],\n",
+ " [48.931168 ],\n",
+ " ...,\n",
+ " [58.51995554],\n",
+ " [51.37529538],\n",
+ " [52.92865879]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d8c36f0a-fa93-450f-ad1b-fd1ae34e4c5e' class='xr-section-summary-in' type='checkbox' ><label for='section-d8c36f0a-fa93-450f-ad1b-fd1ae34e4c5e' class='xr-section-summary' >Indexes: <span>(4)</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>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6e73edab-7bdf-4b07-96a7-4462db2e6e54' class='xr-index-data-in' type='checkbox'/><label for='index-6e73edab-7bdf-4b07-96a7-4462db2e6e54' 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(Index([0, 1, 2, 3], dtype='int64', name='chain'))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7d54192e-cde6-48e5-8592-0d4917e29b37' class='xr-index-data-in' type='checkbox'/><label for='index-7d54192e-cde6-48e5-8592-0d4917e29b37' 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(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " ...\n",
+ " 990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
+ " dtype='int64', name='draw', length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y_dim_2</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fc80f8ca-c8ec-4ad0-85f0-0047721c2079' class='xr-index-data-in' type='checkbox'/><label for='index-fc80f8ca-c8ec-4ad0-85f0-0047721c2079' 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(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+ " 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
+ " 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47],\n",
+ " dtype='int64', name='y_dim_2'))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y_dim_3</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0fa4a873-9150-4d08-a02a-a0a94f1a3002' class='xr-index-data-in' type='checkbox'/><label for='index-0fa4a873-9150-4d08-a02a-a0a94f1a3002' 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(Index([0], dtype='int64', name='y_dim_3'))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3c84c22c-e635-42e5-9415-e81cecbb84a9' class='xr-section-summary-in' type='checkbox' checked><label for='section-3c84c22c-e635-42e5-9415-e81cecbb84a9' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-10-13T16:55:03.974523+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.19.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.16.2</dd></dl></div></li></ul></div></div><br></div>\n",
+ " </ul>\n",
+ " </div>\n",
+ " </li>\n",
+ " \n",
+ " <li class = \"xr-section-item\">\n",
+ " <input id=\"idata_sample_stats3094411e-f416-40de-908c-6848a554d354\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+ " <label for=\"idata_sample_stats3094411e-f416-40de-908c-6848a554d354\" class = \"xr-section-summary\">sample_stats</label>\n",
+ " <div class=\"xr-section-inline-details\"></div>\n",
+ " <div class=\"xr-section-details\">\n",
+ " <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
+ " <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+ "<defs>\n",
+ "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+ "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+ "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+ "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+ "</symbol>\n",
+ "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+ "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+ "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+ "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+ "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+ "</symbol>\n",
+ "</defs>\n",
+ "</svg>\n",
+ "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+ " *\n",
+ " */\n",
+ "\n",
+ ":root {\n",
+ " --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+ " --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+ " --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+ " --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+ " --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+ " --xr-background-color: var(--jp-layout-color0, white);\n",
+ " --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+ " --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+ "}\n",
+ "\n",
+ "html[theme=dark],\n",
+ "html[data-theme=dark],\n",
+ "body[data-theme=dark],\n",
+ "body.vscode-dark {\n",
+ " --xr-font-color0: rgba(255, 255, 255, 1);\n",
+ " --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+ " --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+ " --xr-border-color: #1F1F1F;\n",
+ " --xr-disabled-color: #515151;\n",
+ " --xr-background-color: #111111;\n",
+ " --xr-background-color-row-even: #111111;\n",
+ " --xr-background-color-row-odd: #313131;\n",
+ "}\n",
+ "\n",
+ ".xr-wrap {\n",
+ " display: block !important;\n",
+ " min-width: 300px;\n",
+ " max-width: 700px;\n",
+ "}\n",
+ "\n",
+ ".xr-text-repr-fallback {\n",
+ " /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ ".xr-header {\n",
+ " padding-top: 6px;\n",
+ " padding-bottom: 6px;\n",
+ " margin-bottom: 4px;\n",
+ " border-bottom: solid 1px var(--xr-border-color);\n",
+ "}\n",
+ "\n",
+ ".xr-header > div,\n",
+ ".xr-header > ul {\n",
+ " display: inline;\n",
+ " margin-top: 0;\n",
+ " margin-bottom: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-obj-type,\n",
+ ".xr-array-name {\n",
+ " margin-left: 2px;\n",
+ " margin-right: 10px;\n",
+ "}\n",
+ "\n",
+ ".xr-obj-type {\n",
+ " color: var(--xr-font-color2);\n",
+ "}\n",
+ "\n",
+ ".xr-sections {\n",
+ " padding-left: 0 !important;\n",
+ " display: grid;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+ "}\n",
+ "\n",
+ ".xr-section-item {\n",
+ " display: contents;\n",
+ "}\n",
+ "\n",
+ ".xr-section-item input {\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-section-item input + label {\n",
+ " color: var(--xr-disabled-color);\n",
+ "}\n",
+ "\n",
+ ".xr-section-item input:enabled + label {\n",
+ " cursor: pointer;\n",
+ " color: var(--xr-font-color2);\n",
+ "}\n",
+ "\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
+ ".xr-section-item input:enabled + label:hover {\n",
+ " color: var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary {\n",
+ " grid-column: 1;\n",
+ " color: var(--xr-font-color2);\n",
+ " font-weight: 500;\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary > span {\n",
+ " display: inline-block;\n",
+ " padding-left: 0.5em;\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in:disabled + label {\n",
+ " color: var(--xr-font-color2);\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in + label:before {\n",
+ " display: inline-block;\n",
+ " content: 'â–º';\n",
+ " font-size: 11px;\n",
+ " width: 15px;\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in:disabled + label:before {\n",
+ " color: var(--xr-disabled-color);\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in:checked + label:before {\n",
+ " content: 'â–¼';\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in:checked + label > span {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary,\n",
+ ".xr-section-inline-details {\n",
+ " padding-top: 4px;\n",
+ " padding-bottom: 4px;\n",
+ "}\n",
+ "\n",
+ ".xr-section-inline-details {\n",
+ " grid-column: 2 / -1;\n",
+ "}\n",
+ "\n",
+ ".xr-section-details {\n",
+ " display: none;\n",
+ " grid-column: 1 / -1;\n",
+ " margin-bottom: 5px;\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+ " display: contents;\n",
+ "}\n",
+ "\n",
+ ".xr-array-wrap {\n",
+ " grid-column: 1 / -1;\n",
+ " display: grid;\n",
+ " grid-template-columns: 20px auto;\n",
+ "}\n",
+ "\n",
+ ".xr-array-wrap > label {\n",
+ " grid-column: 1;\n",
+ " vertical-align: top;\n",
+ "}\n",
+ "\n",
+ ".xr-preview {\n",
+ " color: var(--xr-font-color3);\n",
+ "}\n",
+ "\n",
+ ".xr-array-preview,\n",
+ ".xr-array-data {\n",
+ " padding: 0 5px !important;\n",
+ " grid-column: 2;\n",
+ "}\n",
+ "\n",
+ ".xr-array-data,\n",
+ ".xr-array-in:checked ~ .xr-array-preview {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ ".xr-array-in:checked ~ .xr-array-data,\n",
+ ".xr-array-preview {\n",
+ " display: inline-block;\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list {\n",
+ " display: inline-block !important;\n",
+ " list-style: none;\n",
+ " padding: 0 !important;\n",
+ " margin: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list li {\n",
+ " display: inline-block;\n",
+ " padding: 0;\n",
+ " margin: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list:before {\n",
+ " content: '(';\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list:after {\n",
+ " content: ')';\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list li:not(:last-child):after {\n",
+ " content: ',';\n",
+ " padding-right: 5px;\n",
+ "}\n",
+ "\n",
+ ".xr-has-index {\n",
+ " font-weight: bold;\n",
+ "}\n",
+ "\n",
+ ".xr-var-list,\n",
+ ".xr-var-item {\n",
+ " display: contents;\n",
+ "}\n",
+ "\n",
+ ".xr-var-item > div,\n",
+ ".xr-var-item label,\n",
+ ".xr-var-item > .xr-var-name span {\n",
+ " background-color: var(--xr-background-color-row-even);\n",
+ " margin-bottom: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-var-item > .xr-var-name:hover span {\n",
+ " padding-right: 5px;\n",
+ "}\n",
+ "\n",
+ ".xr-var-list > li:nth-child(odd) > div,\n",
+ ".xr-var-list > li:nth-child(odd) > label,\n",
+ ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+ " background-color: var(--xr-background-color-row-odd);\n",
+ "}\n",
+ "\n",
+ ".xr-var-name {\n",
+ " grid-column: 1;\n",
+ "}\n",
+ "\n",
+ ".xr-var-dims {\n",
+ " grid-column: 2;\n",
+ "}\n",
+ "\n",
+ ".xr-var-dtype {\n",
+ " grid-column: 3;\n",
+ " text-align: right;\n",
+ " color: var(--xr-font-color2);\n",
+ "}\n",
+ "\n",
+ ".xr-var-preview {\n",
+ " grid-column: 4;\n",
+ "}\n",
+ "\n",
+ ".xr-index-preview {\n",
+ " grid-column: 2 / 5;\n",
+ " color: var(--xr-font-color2);\n",
+ "}\n",
+ "\n",
+ ".xr-var-name,\n",
+ ".xr-var-dims,\n",
+ ".xr-var-dtype,\n",
+ ".xr-preview,\n",
+ ".xr-attrs dt {\n",
+ " white-space: nowrap;\n",
+ " overflow: hidden;\n",
+ " text-overflow: ellipsis;\n",
+ " padding-right: 10px;\n",
+ "}\n",
+ "\n",
+ ".xr-var-name:hover,\n",
+ ".xr-var-dims:hover,\n",
+ ".xr-var-dtype:hover,\n",
+ ".xr-attrs dt:hover {\n",
+ " overflow: visible;\n",
+ " width: auto;\n",
+ " z-index: 1;\n",
+ "}\n",
+ "\n",
+ ".xr-var-attrs,\n",
+ ".xr-var-data,\n",
+ ".xr-index-data {\n",
+ " display: none;\n",
+ " background-color: var(--xr-background-color) !important;\n",
+ " padding-bottom: 5px !important;\n",
+ "}\n",
+ "\n",
+ ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+ ".xr-var-data-in:checked ~ .xr-var-data,\n",
+ ".xr-index-data-in:checked ~ .xr-index-data {\n",
+ " display: block;\n",
+ "}\n",
+ "\n",
+ ".xr-var-data > table {\n",
+ " float: right;\n",
+ "}\n",
+ "\n",
+ ".xr-var-name span,\n",
+ ".xr-var-data,\n",
+ ".xr-index-name div,\n",
+ ".xr-index-data,\n",
+ ".xr-attrs {\n",
+ " padding-left: 25px !important;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs,\n",
+ ".xr-var-attrs,\n",
+ ".xr-var-data,\n",
+ ".xr-index-data {\n",
+ " grid-column: 1 / -1;\n",
+ "}\n",
+ "\n",
+ "dl.xr-attrs {\n",
+ " padding: 0;\n",
+ " margin: 0;\n",
+ " display: grid;\n",
+ " grid-template-columns: 125px auto;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt,\n",
+ ".xr-attrs dd {\n",
+ " padding: 0;\n",
+ " margin: 0;\n",
+ " float: left;\n",
+ " padding-right: 10px;\n",
+ " width: auto;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt {\n",
+ " font-weight: normal;\n",
+ " grid-column: 1;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt:hover span {\n",
+ " display: inline-block;\n",
+ " background: var(--xr-background-color);\n",
+ " padding-right: 10px;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dd {\n",
+ " grid-column: 2;\n",
+ " white-space: pre-wrap;\n",
+ " word-break: break-all;\n",
+ "}\n",
+ "\n",
+ ".xr-icon-database,\n",
+ ".xr-icon-file-text2,\n",
+ ".xr-no-icon {\n",
+ " display: inline-block;\n",
+ " vertical-align: middle;\n",
+ " width: 1em;\n",
+ " height: 1.5em !important;\n",
+ " stroke-width: 0;\n",
+ " stroke: currentColor;\n",
+ " fill: currentColor;\n",
+ "}\n",
+ "</style><pre class='xr-text-repr-fallback'><xarray.Dataset> Size: 496kB\n",
+ "Dimensions: (chain: 4, draw: 1000)\n",
+ "Coordinates:\n",
+ " * chain (chain) int64 32B 0 1 2 3\n",
+ " * draw (draw) int64 8kB 0 1 2 3 4 5 ... 995 996 997 998 999\n",
+ "Data variables: (12/17)\n",
+ " acceptance_rate (chain, draw) float64 32kB 0.9823 0.9202 ... 0.9129\n",
+ " diverging (chain, draw) bool 4kB False False ... False False\n",
+ " energy (chain, draw) float64 32kB 131.2 130.4 ... 130.5\n",
+ " energy_error (chain, draw) float64 32kB -0.3593 ... 0.03753\n",
+ " index_in_trajectory (chain, draw) int64 32kB 1 -1 1 1 1 -3 ... 3 2 0 3 -2\n",
+ " largest_eigval (chain, draw) float64 32kB nan nan nan ... nan nan\n",
+ " ... ...\n",
+ " process_time_diff (chain, draw) float64 32kB 0.0005456 ... 0.0005263\n",
+ " reached_max_treedepth (chain, draw) bool 4kB False False ... False False\n",
+ " smallest_eigval (chain, draw) float64 32kB nan nan nan ... nan nan\n",
+ " step_size (chain, draw) float64 32kB 1.203 1.203 ... 0.9818\n",
+ " step_size_bar (chain, draw) float64 32kB 1.104 1.104 ... 1.036\n",
+ " tree_depth (chain, draw) int64 32kB 2 1 1 2 2 2 ... 2 2 2 2 2 2\n",
+ "Attributes:\n",
+ " created_at: 2024-10-13T16:50:52.100578+00:00\n",
+ " arviz_version: 0.19.0\n",
+ " inference_library: pymc\n",
+ " inference_library_version: 5.16.2\n",
+ " sampling_time: 3.5525271892547607\n",
+ " tuning_steps: 1000</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-0c489ef3-fee7-497e-a542-80545d951c2a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0c489ef3-fee7-497e-a542-80545d951c2a' 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'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2c8abe2f-0059-43d3-a6eb-e1df07b4499f' class='xr-section-summary-in' type='checkbox' checked><label for='section-2c8abe2f-0059-43d3-a6eb-e1df07b4499f' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-75d26120-4bcc-4ed3-a7a1-8c40e585e0e1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-75d26120-4bcc-4ed3-a7a1-8c40e585e0e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12bad50c-a76c-4701-a7a3-84ba96f8889f' class='xr-var-data-in' type='checkbox'><label for='data-12bad50c-a76c-4701-a7a3-84ba96f8889f' 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([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-106cfc87-0386-4f6c-a6b6-0dae4675b716' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-106cfc87-0386-4f6c-a6b6-0dae4675b716' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b7c5b6d-4944-4203-8556-7adbf66839f6' class='xr-var-data-in' type='checkbox'><label for='data-9b7c5b6d-4944-4203-8556-7adbf66839f6' 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([ 0, 1, 2, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2e4506d3-baa3-4003-afd0-5b253ba81357' class='xr-section-summary-in' type='checkbox' ><label for='section-2e4506d3-baa3-4003-afd0-5b253ba81357' class='xr-section-summary' >Data variables: <span>(17)</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>acceptance_rate</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9823 0.9202 ... 0.9831 0.9129</div><input id='attrs-756b85f8-9734-4895-a2e8-0585fe142b5f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-756b85f8-9734-4895-a2e8-0585fe142b5f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-439c1ef8-e6b7-41b3-aa8e-8814c416e763' class='xr-var-data-in' type='checkbox'><label for='data-439c1ef8-e6b7-41b3-aa8e-8814c416e763' 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([[0.98233142, 0.9201508 , 0.72828057, ..., 1. , 1. ,\n",
+ " 1. ],\n",
+ " [0.84745381, 0.87863004, 0.85667218, ..., 0.80435482, 0.98775848,\n",
+ " 1. ],\n",
+ " [1. , 1. , 1. , ..., 0.84996779, 0.84830199,\n",
+ " 1. ],\n",
+ " [0.97257294, 0.93319246, 0.32041334, ..., 0.813945 , 0.98310079,\n",
+ " 0.91287219]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverging</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-fb4690e7-dd52-4523-a8a7-0ab6c5856632' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fb4690e7-dd52-4523-a8a7-0ab6c5856632' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8993b4d3-6493-4903-a7b8-f219bd04e152' class='xr-var-data-in' type='checkbox'><label for='data-8993b4d3-6493-4903-a7b8-f219bd04e152' 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([[False, False, False, ..., False, False, False],\n",
+ " [False, False, False, ..., False, False, False],\n",
+ " [False, False, False, ..., False, False, False],\n",
+ " [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>131.2 130.4 131.3 ... 130.1 130.5</div><input id='attrs-3ad4887a-22ca-4c99-b9f2-e9926c633827' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3ad4887a-22ca-4c99-b9f2-e9926c633827' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de4654f1-8fd1-4b3e-b4b1-490c5c601a10' class='xr-var-data-in' type='checkbox'><label for='data-de4654f1-8fd1-4b3e-b4b1-490c5c601a10' 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([[131.19091503, 130.41935028, 131.33910596, ..., 132.44913327,\n",
+ " 132.25887889, 131.14988804],\n",
+ " [131.0180807 , 131.52426119, 132.36925829, ..., 135.68535857,\n",
+ " 134.09522099, 133.38022683],\n",
+ " [130.92808258, 130.48102424, 130.22242544, ..., 130.84844841,\n",
+ " 132.36011807, 131.51635626],\n",
+ " [132.19928038, 131.54915516, 135.35139367, ..., 130.94586712,\n",
+ " 130.11157009, 130.51005873]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy_error</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.3593 0.08322 ... 0.03753</div><input id='attrs-7dab5f7b-7a39-439d-aa8d-26a5dc7a53af' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7dab5f7b-7a39-439d-aa8d-26a5dc7a53af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee2c5fa6-6d53-4ff5-80bb-9e09a1fdba59' class='xr-var-data-in' type='checkbox'><label for='data-ee2c5fa6-6d53-4ff5-80bb-9e09a1fdba59' 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([[-0.35930722, 0.08321771, 0.31706891, ..., -0.25877446,\n",
+ " -0.30030873, -0.0532621 ],\n",
+ " [ 0.13235953, 0.0593723 , 0.00539839, ..., -0.47077221,\n",
+ " 0.0374159 , -0.23553976],\n",
+ " [-0.27263982, -0.05775146, -0.06321763, ..., 0.25962611,\n",
+ " 0.30333856, -0.38015259],\n",
+ " [-0.36433533, -0.2160436 , 0.60675273, ..., 0. ,\n",
+ " 0.00572578, 0.03752763]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>index_in_trajectory</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 -1 1 1 1 -3 -1 ... -2 3 2 0 3 -2</div><input id='attrs-82563cd0-eae4-49b1-a12e-52446461f243' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-82563cd0-eae4-49b1-a12e-52446461f243' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a92906a-325d-43ac-b01f-5576c3e35c11' class='xr-var-data-in' type='checkbox'><label for='data-4a92906a-325d-43ac-b01f-5576c3e35c11' 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([[ 1, -1, 1, ..., -2, 1, -3],\n",
+ " [ 1, -2, 3, ..., 1, -3, 1],\n",
+ " [-1, 2, 1, ..., -2, -2, 2],\n",
+ " [-2, 3, 2, ..., 0, 3, -2]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>largest_eigval</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-10181b9a-a094-45b4-aa4f-1f2d11aa18e8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-10181b9a-a094-45b4-aa4f-1f2d11aa18e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ba649ae-5b65-465f-9809-5c3f3d857c76' class='xr-var-data-in' type='checkbox'><label for='data-8ba649ae-5b65-465f-9809-5c3f3d857c76' 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([[nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-130.1 -130.4 ... -130.0 -130.1</div><input id='attrs-5ace2106-d6ab-43e1-b995-3505fd0134db' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5ace2106-d6ab-43e1-b995-3505fd0134db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0ce41c7-9b55-47ce-a78b-0fb6308d5f68' class='xr-var-data-in' type='checkbox'><label for='data-b0ce41c7-9b55-47ce-a78b-0fb6308d5f68' 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([[-130.10971049, -130.36148843, -131.22979234, ..., -131.626767 ,\n",
+ " -130.97736249, -130.83176282],\n",
+ " [-130.34742887, -130.75082089, -130.73795739, ..., -133.02991673,\n",
+ " -133.47566227, -131.80040698],\n",
+ " [-130.40551379, -130.26628605, -130.0372137 , ..., -130.64073597,\n",
+ " -131.64172585, -130.43851267],\n",
+ " [-130.65294074, -130.31604114, -131.88157395, ..., -129.94745787,\n",
+ " -129.97577584, -130.14226251]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>max_energy_error</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.3593 0.08322 ... 0.04354 0.1923</div><input id='attrs-b535fd4b-1faf-4fde-a6e1-e439582d54b7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b535fd4b-1faf-4fde-a6e1-e439582d54b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c3b411b-a6c7-4197-86fe-5d7ee331fe5e' class='xr-var-data-in' type='checkbox'><label for='data-9c3b411b-a6c7-4197-86fe-5d7ee331fe5e' 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([[-0.35930722, 0.08321771, 0.31706891, ..., -0.29018412,\n",
+ " -0.30030873, -0.17690912],\n",
+ " [ 0.40596205, 0.2170257 , 0.28001157, ..., -0.47077221,\n",
+ " -0.53641729, -0.23553976],\n",
+ " [-0.27263982, -0.10931441, -0.06321763, ..., 0.25962611,\n",
+ " 0.30333856, -0.38015259],\n",
+ " [-0.36433533, 0.21751894, 2.45598624, ..., 0.30450419,\n",
+ " 0.04353744, 0.19225052]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_steps</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.0 1.0 1.0 3.0 ... 3.0 3.0 3.0 3.0</div><input id='attrs-d5317dd5-f702-4181-b0f4-a4ef96654d36' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d5317dd5-f702-4181-b0f4-a4ef96654d36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b51c3ac2-8b83-42ea-946a-b3702969147b' class='xr-var-data-in' type='checkbox'><label for='data-b51c3ac2-8b83-42ea-946a-b3702969147b' 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([[3., 1., 1., ..., 3., 3., 3.],\n",
+ " [3., 3., 3., ..., 3., 3., 1.],\n",
+ " [1., 3., 1., ..., 3., 3., 3.],\n",
+ " [3., 3., 3., ..., 3., 3., 3.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>perf_counter_diff</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0005454 0.0002798 ... 0.0005262</div><input id='attrs-d6030922-4b20-454b-80ab-1d2c983bf3e8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d6030922-4b20-454b-80ab-1d2c983bf3e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-edd80a82-58a3-4e4b-bd42-7860df581f0f' class='xr-var-data-in' type='checkbox'><label for='data-edd80a82-58a3-4e4b-bd42-7860df581f0f' 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([[0.00054541, 0.00027978, 0.00027614, ..., 0.00063151, 0.00060207,\n",
+ " 0.0005959 ],\n",
+ " [0.00063947, 0.00063165, 0.00061651, ..., 0.00052794, 0.00054546,\n",
+ " 0.0003091 ],\n",
+ " [0.00091833, 0.00119509, 0.00050863, ..., 0.0005161 , 0.00050907,\n",
+ " 0.00051113],\n",
+ " [0.0014636 , 0.00146821, 0.00139667, ..., 0.00048676, 0.00049692,\n",
+ " 0.00052621]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>perf_counter_start</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.136e+06 4.136e+06 ... 4.136e+06</div><input id='attrs-039a5645-5a34-44ab-be7f-84aba2db4062' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-039a5645-5a34-44ab-be7f-84aba2db4062' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac5802b9-99e2-4df2-b4f1-0f1cb928f4c5' class='xr-var-data-in' type='checkbox'><label for='data-ac5802b9-99e2-4df2-b4f1-0f1cb928f4c5' 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([[4135721.03959939, 4135721.04030435, 4135721.04072397, ...,\n",
+ " 4135722.43649118, 4135722.4372895 , 4135722.43805224],\n",
+ " [4135721.16133643, 4135721.16217489, 4135721.1629728 , ...,\n",
+ " 4135722.53532741, 4135722.53602953, 4135722.53674517],\n",
+ " [4135721.13614271, 4135721.13956542, 4135721.14107429, ...,\n",
+ " 4135722.63322503, 4135722.6338902 , 4135722.63454816],\n",
+ " [4135721.30518586, 4135721.30703612, 4135721.3088868 , ...,\n",
+ " 4135722.77030724, 4135722.77093305, 4135722.77156876]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>process_time_diff</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0005456 0.0002807 ... 0.0005263</div><input id='attrs-0ed15fcb-3390-4b6c-978f-722e01612e3b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0ed15fcb-3390-4b6c-978f-722e01612e3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4ccfd84-d286-4817-b665-929d6630195d' class='xr-var-data-in' type='checkbox'><label for='data-e4ccfd84-d286-4817-b665-929d6630195d' 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([[0.0005456 , 0.00028066, 0.00027682, ..., 0.00063288, 0.00060338,\n",
+ " 0.00059713],\n",
+ " [0.00063986, 0.0006329 , 0.00061771, ..., 0.00052919, 0.00054699,\n",
+ " 0.00031033],\n",
+ " [0.00079344, 0.001162 , 0.00051004, ..., 0.00051735, 0.00051064,\n",
+ " 0.0005123 ],\n",
+ " [0.00146425, 0.00146951, 0.0013982 , ..., 0.00048736, 0.00049749,\n",
+ " 0.0005263 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>reached_max_treedepth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-d771608f-593e-41c5-8793-271d4dd41b02' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d771608f-593e-41c5-8793-271d4dd41b02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fda612d8-c930-4c6e-ba2b-832a9a4e9596' class='xr-var-data-in' type='checkbox'><label for='data-fda612d8-c930-4c6e-ba2b-832a9a4e9596' 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([[False, False, False, ..., False, False, False],\n",
+ " [False, False, False, ..., False, False, False],\n",
+ " [False, False, False, ..., False, False, False],\n",
+ " [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smallest_eigval</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-3550d901-ed76-4dbe-b9f1-c92d4b6bd93c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3550d901-ed76-4dbe-b9f1-c92d4b6bd93c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75e80411-db74-4a23-b7e1-89d4e4d8c8dc' class='xr-var-data-in' type='checkbox'><label for='data-75e80411-db74-4a23-b7e1-89d4e4d8c8dc' 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([[nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.203 1.203 1.203 ... 0.9818 0.9818</div><input id='attrs-6074ecaf-7fc8-41e6-8c0b-b2c7b5673062' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6074ecaf-7fc8-41e6-8c0b-b2c7b5673062' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-302bda79-340a-43ce-b421-0bf16b6b6cf4' class='xr-var-data-in' type='checkbox'><label for='data-302bda79-340a-43ce-b421-0bf16b6b6cf4' 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([[1.2029647 , 1.2029647 , 1.2029647 , ..., 1.2029647 , 1.2029647 ,\n",
+ " 1.2029647 ],\n",
+ " [1.414844 , 1.414844 , 1.414844 , ..., 1.414844 , 1.414844 ,\n",
+ " 1.414844 ],\n",
+ " [1.34163623, 1.34163623, 1.34163623, ..., 1.34163623, 1.34163623,\n",
+ " 1.34163623],\n",
+ " [0.98177644, 0.98177644, 0.98177644, ..., 0.98177644, 0.98177644,\n",
+ " 0.98177644]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size_bar</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.104 1.104 1.104 ... 1.036 1.036</div><input id='attrs-f9052a32-f9ae-48bd-951d-43cef1fe927c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f9052a32-f9ae-48bd-951d-43cef1fe927c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c5521e2-87d2-44db-bc16-f92881887bdd' class='xr-var-data-in' type='checkbox'><label for='data-5c5521e2-87d2-44db-bc16-f92881887bdd' 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([[1.10443619, 1.10443619, 1.10443619, ..., 1.10443619, 1.10443619,\n",
+ " 1.10443619],\n",
+ " [1.08544818, 1.08544818, 1.08544818, ..., 1.08544818, 1.08544818,\n",
+ " 1.08544818],\n",
+ " [1.05973092, 1.05973092, 1.05973092, ..., 1.05973092, 1.05973092,\n",
+ " 1.05973092],\n",
+ " [1.03643435, 1.03643435, 1.03643435, ..., 1.03643435, 1.03643435,\n",
+ " 1.03643435]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tree_depth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 1 1 2 2 2 2 2 ... 2 2 2 2 2 2 2 2</div><input id='attrs-7b558199-e630-40ba-a402-cd6496b28ec9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7b558199-e630-40ba-a402-cd6496b28ec9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cd51d40-65e4-4a70-8615-38abbb3b7f70' class='xr-var-data-in' type='checkbox'><label for='data-6cd51d40-65e4-4a70-8615-38abbb3b7f70' 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([[2, 1, 1, ..., 2, 2, 2],\n",
+ " [2, 2, 2, ..., 2, 2, 1],\n",
+ " [1, 2, 1, ..., 2, 2, 2],\n",
+ " [2, 2, 2, ..., 2, 2, 2]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-07a0ebd1-cdc7-4c4c-87d4-3449dffc1815' class='xr-section-summary-in' type='checkbox' ><label for='section-07a0ebd1-cdc7-4c4c-87d4-3449dffc1815' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3c26ca2f-3175-4b64-a1b4-78cb1eac8d58' class='xr-index-data-in' type='checkbox'/><label for='index-3c26ca2f-3175-4b64-a1b4-78cb1eac8d58' 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(Index([0, 1, 2, 3], dtype='int64', name='chain'))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e9292f89-ed9b-46e5-b1a5-1bf604fcbcc7' class='xr-index-data-in' type='checkbox'/><label for='index-e9292f89-ed9b-46e5-b1a5-1bf604fcbcc7' 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(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,\n",
+ " ...\n",
+ " 990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
+ " dtype='int64', name='draw', length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8358c3a9-1e6f-483e-8cbd-56211f1091a9' class='xr-section-summary-in' type='checkbox' checked><label for='section-8358c3a9-1e6f-483e-8cbd-56211f1091a9' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-10-13T16:50:52.100578+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.19.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.16.2</dd><dt><span>sampling_time :</span></dt><dd>3.5525271892547607</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div><br></div>\n",
+ " </ul>\n",
+ " </div>\n",
+ " </li>\n",
+ " \n",
+ " <li class = \"xr-section-item\">\n",
+ " <input id=\"idata_observed_data63156ff0-9b7f-4522-89d0-9bb2c93ea913\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+ " <label for=\"idata_observed_data63156ff0-9b7f-4522-89d0-9bb2c93ea913\" class = \"xr-section-summary\">observed_data</label>\n",
+ " <div class=\"xr-section-inline-details\"></div>\n",
+ " <div class=\"xr-section-details\">\n",
+ " <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
+ " <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+ "<defs>\n",
+ "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+ "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+ "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+ "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+ "</symbol>\n",
+ "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+ "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+ "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+ "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+ "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+ "</symbol>\n",
+ "</defs>\n",
+ "</svg>\n",
+ "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+ " *\n",
+ " */\n",
+ "\n",
+ ":root {\n",
+ " --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+ " --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+ " --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+ " --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+ " --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+ " --xr-background-color: var(--jp-layout-color0, white);\n",
+ " --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+ " --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+ "}\n",
+ "\n",
+ "html[theme=dark],\n",
+ "html[data-theme=dark],\n",
+ "body[data-theme=dark],\n",
+ "body.vscode-dark {\n",
+ " --xr-font-color0: rgba(255, 255, 255, 1);\n",
+ " --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+ " --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+ " --xr-border-color: #1F1F1F;\n",
+ " --xr-disabled-color: #515151;\n",
+ " --xr-background-color: #111111;\n",
+ " --xr-background-color-row-even: #111111;\n",
+ " --xr-background-color-row-odd: #313131;\n",
+ "}\n",
+ "\n",
+ ".xr-wrap {\n",
+ " display: block !important;\n",
+ " min-width: 300px;\n",
+ " max-width: 700px;\n",
+ "}\n",
+ "\n",
+ ".xr-text-repr-fallback {\n",
+ " /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ ".xr-header {\n",
+ " padding-top: 6px;\n",
+ " padding-bottom: 6px;\n",
+ " margin-bottom: 4px;\n",
+ " border-bottom: solid 1px var(--xr-border-color);\n",
+ "}\n",
+ "\n",
+ ".xr-header > div,\n",
+ ".xr-header > ul {\n",
+ " display: inline;\n",
+ " margin-top: 0;\n",
+ " margin-bottom: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-obj-type,\n",
+ ".xr-array-name {\n",
+ " margin-left: 2px;\n",
+ " margin-right: 10px;\n",
+ "}\n",
+ "\n",
+ ".xr-obj-type {\n",
+ " color: var(--xr-font-color2);\n",
+ "}\n",
+ "\n",
+ ".xr-sections {\n",
+ " padding-left: 0 !important;\n",
+ " display: grid;\n",
+ " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+ "}\n",
+ "\n",
+ ".xr-section-item {\n",
+ " display: contents;\n",
+ "}\n",
+ "\n",
+ ".xr-section-item input {\n",
+ " display: inline-block;\n",
+ " opacity: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-section-item input + label {\n",
+ " color: var(--xr-disabled-color);\n",
+ "}\n",
+ "\n",
+ ".xr-section-item input:enabled + label {\n",
+ " cursor: pointer;\n",
+ " color: var(--xr-font-color2);\n",
+ "}\n",
+ "\n",
+ ".xr-section-item input:focus + label {\n",
+ " border: 2px solid var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
+ ".xr-section-item input:enabled + label:hover {\n",
+ " color: var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary {\n",
+ " grid-column: 1;\n",
+ " color: var(--xr-font-color2);\n",
+ " font-weight: 500;\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary > span {\n",
+ " display: inline-block;\n",
+ " padding-left: 0.5em;\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in:disabled + label {\n",
+ " color: var(--xr-font-color2);\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in + label:before {\n",
+ " display: inline-block;\n",
+ " content: 'â–º';\n",
+ " font-size: 11px;\n",
+ " width: 15px;\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in:disabled + label:before {\n",
+ " color: var(--xr-disabled-color);\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in:checked + label:before {\n",
+ " content: 'â–¼';\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in:checked + label > span {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary,\n",
+ ".xr-section-inline-details {\n",
+ " padding-top: 4px;\n",
+ " padding-bottom: 4px;\n",
+ "}\n",
+ "\n",
+ ".xr-section-inline-details {\n",
+ " grid-column: 2 / -1;\n",
+ "}\n",
+ "\n",
+ ".xr-section-details {\n",
+ " display: none;\n",
+ " grid-column: 1 / -1;\n",
+ " margin-bottom: 5px;\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+ " display: contents;\n",
+ "}\n",
+ "\n",
+ ".xr-array-wrap {\n",
+ " grid-column: 1 / -1;\n",
+ " display: grid;\n",
+ " grid-template-columns: 20px auto;\n",
+ "}\n",
+ "\n",
+ ".xr-array-wrap > label {\n",
+ " grid-column: 1;\n",
+ " vertical-align: top;\n",
+ "}\n",
+ "\n",
+ ".xr-preview {\n",
+ " color: var(--xr-font-color3);\n",
+ "}\n",
+ "\n",
+ ".xr-array-preview,\n",
+ ".xr-array-data {\n",
+ " padding: 0 5px !important;\n",
+ " grid-column: 2;\n",
+ "}\n",
+ "\n",
+ ".xr-array-data,\n",
+ ".xr-array-in:checked ~ .xr-array-preview {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ ".xr-array-in:checked ~ .xr-array-data,\n",
+ ".xr-array-preview {\n",
+ " display: inline-block;\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list {\n",
+ " display: inline-block !important;\n",
+ " list-style: none;\n",
+ " padding: 0 !important;\n",
+ " margin: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list li {\n",
+ " display: inline-block;\n",
+ " padding: 0;\n",
+ " margin: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list:before {\n",
+ " content: '(';\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list:after {\n",
+ " content: ')';\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list li:not(:last-child):after {\n",
+ " content: ',';\n",
+ " padding-right: 5px;\n",
+ "}\n",
+ "\n",
+ ".xr-has-index {\n",
+ " font-weight: bold;\n",
+ "}\n",
+ "\n",
+ ".xr-var-list,\n",
+ ".xr-var-item {\n",
+ " display: contents;\n",
+ "}\n",
+ "\n",
+ ".xr-var-item > div,\n",
+ ".xr-var-item label,\n",
+ ".xr-var-item > .xr-var-name span {\n",
+ " background-color: var(--xr-background-color-row-even);\n",
+ " margin-bottom: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-var-item > .xr-var-name:hover span {\n",
+ " padding-right: 5px;\n",
+ "}\n",
+ "\n",
+ ".xr-var-list > li:nth-child(odd) > div,\n",
+ ".xr-var-list > li:nth-child(odd) > label,\n",
+ ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+ " background-color: var(--xr-background-color-row-odd);\n",
+ "}\n",
+ "\n",
+ ".xr-var-name {\n",
+ " grid-column: 1;\n",
+ "}\n",
+ "\n",
+ ".xr-var-dims {\n",
+ " grid-column: 2;\n",
+ "}\n",
+ "\n",
+ ".xr-var-dtype {\n",
+ " grid-column: 3;\n",
+ " text-align: right;\n",
+ " color: var(--xr-font-color2);\n",
+ "}\n",
+ "\n",
+ ".xr-var-preview {\n",
+ " grid-column: 4;\n",
+ "}\n",
+ "\n",
+ ".xr-index-preview {\n",
+ " grid-column: 2 / 5;\n",
+ " color: var(--xr-font-color2);\n",
+ "}\n",
+ "\n",
+ ".xr-var-name,\n",
+ ".xr-var-dims,\n",
+ ".xr-var-dtype,\n",
+ ".xr-preview,\n",
+ ".xr-attrs dt {\n",
+ " white-space: nowrap;\n",
+ " overflow: hidden;\n",
+ " text-overflow: ellipsis;\n",
+ " padding-right: 10px;\n",
+ "}\n",
+ "\n",
+ ".xr-var-name:hover,\n",
+ ".xr-var-dims:hover,\n",
+ ".xr-var-dtype:hover,\n",
+ ".xr-attrs dt:hover {\n",
+ " overflow: visible;\n",
+ " width: auto;\n",
+ " z-index: 1;\n",
+ "}\n",
+ "\n",
+ ".xr-var-attrs,\n",
+ ".xr-var-data,\n",
+ ".xr-index-data {\n",
+ " display: none;\n",
+ " background-color: var(--xr-background-color) !important;\n",
+ " padding-bottom: 5px !important;\n",
+ "}\n",
+ "\n",
+ ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+ ".xr-var-data-in:checked ~ .xr-var-data,\n",
+ ".xr-index-data-in:checked ~ .xr-index-data {\n",
+ " display: block;\n",
+ "}\n",
+ "\n",
+ ".xr-var-data > table {\n",
+ " float: right;\n",
+ "}\n",
+ "\n",
+ ".xr-var-name span,\n",
+ ".xr-var-data,\n",
+ ".xr-index-name div,\n",
+ ".xr-index-data,\n",
+ ".xr-attrs {\n",
+ " padding-left: 25px !important;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs,\n",
+ ".xr-var-attrs,\n",
+ ".xr-var-data,\n",
+ ".xr-index-data {\n",
+ " grid-column: 1 / -1;\n",
+ "}\n",
+ "\n",
+ "dl.xr-attrs {\n",
+ " padding: 0;\n",
+ " margin: 0;\n",
+ " display: grid;\n",
+ " grid-template-columns: 125px auto;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt,\n",
+ ".xr-attrs dd {\n",
+ " padding: 0;\n",
+ " margin: 0;\n",
+ " float: left;\n",
+ " padding-right: 10px;\n",
+ " width: auto;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt {\n",
+ " font-weight: normal;\n",
+ " grid-column: 1;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt:hover span {\n",
+ " display: inline-block;\n",
+ " background: var(--xr-background-color);\n",
+ " padding-right: 10px;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dd {\n",
+ " grid-column: 2;\n",
+ " white-space: pre-wrap;\n",
+ " word-break: break-all;\n",
+ "}\n",
+ "\n",
+ ".xr-icon-database,\n",
+ ".xr-icon-file-text2,\n",
+ ".xr-no-icon {\n",
+ " display: inline-block;\n",
+ " vertical-align: middle;\n",
+ " width: 1em;\n",
+ " height: 1.5em !important;\n",
+ " stroke-width: 0;\n",
+ " stroke: currentColor;\n",
+ " fill: currentColor;\n",
+ "}\n",
+ "</style><pre class='xr-text-repr-fallback'><xarray.Dataset> Size: 776B\n",
+ "Dimensions: (y_dim_0: 48, y_dim_1: 1)\n",
+ "Coordinates:\n",
+ " * y_dim_0 (y_dim_0) int64 384B 0 1 2 3 4 5 6 7 8 ... 40 41 42 43 44 45 46 47\n",
+ " * y_dim_1 (y_dim_1) int64 8B 0\n",
+ "Data variables:\n",
+ " y (y_dim_0, y_dim_1) float64 384B 51.06 55.12 53.73 ... 53.84 53.16\n",
+ "Attributes:\n",
+ " created_at: 2024-10-13T16:50:52.106181+00:00\n",
+ " arviz_version: 0.19.0\n",
+ " inference_library: pymc\n",
+ " inference_library_version: 5.16.2</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-3f8e9f69-818c-48f9-8c64-6d35d11bdb40' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3f8e9f69-818c-48f9-8c64-6d35d11bdb40' 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_dim_0</span>: 48</li><li><span class='xr-has-index'>y_dim_1</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d6ada524-a790-4fb2-917a-12fdf182e306' class='xr-section-summary-in' type='checkbox' checked><label for='section-d6ada524-a790-4fb2-917a-12fdf182e306' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y_dim_0</span></div><div class='xr-var-dims'>(y_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 42 43 44 45 46 47</div><input id='attrs-83f5440d-be07-4105-bb4c-bc3c44b65601' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-83f5440d-be07-4105-bb4c-bc3c44b65601' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24558f26-b91b-4259-b2cf-2e069423ee31' class='xr-var-data-in' type='checkbox'><label for='data-24558f26-b91b-4259-b2cf-2e069423ee31' 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([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+ " 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
+ " 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y_dim_1</span></div><div class='xr-var-dims'>(y_dim_1)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-0c72d3a4-9801-4a07-8582-03b557cfca0f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0c72d3a4-9801-4a07-8582-03b557cfca0f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8399c01-84f4-4179-ae1f-bcc13a606a62' class='xr-var-data-in' type='checkbox'><label for='data-b8399c01-84f4-4179-ae1f-bcc13a606a62' 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([0])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cc8134ff-af7b-4270-acb4-539f2e7432e3' class='xr-section-summary-in' type='checkbox' checked><label for='section-cc8134ff-af7b-4270-acb4-539f2e7432e3' class='xr-section-summary' >Data variables: <span>(1)</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>y</span></div><div class='xr-var-dims'>(y_dim_0, y_dim_1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>51.06 55.12 53.73 ... 53.84 53.16</div><input id='attrs-75599a2c-b32c-4cf1-913b-5ca238d4d314' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-75599a2c-b32c-4cf1-913b-5ca238d4d314' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8828c8bd-f823-4844-93d9-2ab24d72fa02' class='xr-var-data-in' type='checkbox'><label for='data-8828c8bd-f823-4844-93d9-2ab24d72fa02' 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([[51.06],\n",
+ " [55.12],\n",
+ " [53.73],\n",
+ " [50.24],\n",
+ " [52.05],\n",
+ " [56.4 ],\n",
+ " [48.45],\n",
+ " [52.34],\n",
+ " [55.65],\n",
+ " [51.49],\n",
+ " [51.86],\n",
+ " [63.43],\n",
+ " [53. ],\n",
+ " [56.09],\n",
+ " [51.93],\n",
+ " [52.31],\n",
+ " [52.33],\n",
+ " [57.48],\n",
+ " [57.44],\n",
+ " [55.14],\n",
+ "...\n",
+ " [54.95],\n",
+ " [50.39],\n",
+ " [52.91],\n",
+ " [51.5 ],\n",
+ " [52.68],\n",
+ " [47.72],\n",
+ " [49.73],\n",
+ " [51.82],\n",
+ " [54.99],\n",
+ " [52.84],\n",
+ " [53.19],\n",
+ " [54.52],\n",
+ " [51.46],\n",
+ " [53.73],\n",
+ " [51.61],\n",
+ " [49.81],\n",
+ " [52.42],\n",
+ " [54.3 ],\n",
+ " [53.84],\n",
+ " [53.16]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-25e6db25-d933-4319-8ac6-3f430fa49e03' class='xr-section-summary-in' type='checkbox' ><label for='section-25e6db25-d933-4319-8ac6-3f430fa49e03' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>y_dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e6e607fc-8dd8-4b02-8bb7-981eb6d8c6dd' class='xr-index-data-in' type='checkbox'/><label for='index-e6e607fc-8dd8-4b02-8bb7-981eb6d8c6dd' 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(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+ " 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
+ " 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47],\n",
+ " dtype='int64', name='y_dim_0'))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y_dim_1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5e709680-2c43-473e-914d-e675675f8027' class='xr-index-data-in' type='checkbox'/><label for='index-5e709680-2c43-473e-914d-e675675f8027' 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(Index([0], dtype='int64', name='y_dim_1'))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d410beeb-0fdd-40c8-8f8a-55ec9540c348' class='xr-section-summary-in' type='checkbox' checked><label for='section-d410beeb-0fdd-40c8-8f8a-55ec9540c348' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-10-13T16:50:52.106181+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.19.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.16.2</dd></dl></div></li></ul></div></div><br></div>\n",
+ " </ul>\n",
+ " </div>\n",
+ " </li>\n",
+ " \n",
+ " </ul>\n",
+ " </div>\n",
+ " <style> /* CSS stylesheet for displaying InferenceData objects in jupyterlab.\n",
+ " *\n",
+ " */\n",
+ "\n",
+ ":root {\n",
+ " --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+ " --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+ " --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+ " --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+ " --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+ " --xr-background-color: var(--jp-layout-color0, white);\n",
+ " --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+ " --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+ "}\n",
+ "\n",
+ "html[theme=dark],\n",
+ "body.vscode-dark {\n",
+ " --xr-font-color0: rgba(255, 255, 255, 1);\n",
+ " --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+ " --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+ " --xr-border-color: #1F1F1F;\n",
+ " --xr-disabled-color: #515151;\n",
+ " --xr-background-color: #111111;\n",
+ " --xr-background-color-row-even: #111111;\n",
+ " --xr-background-color-row-odd: #313131;\n",
+ "}\n",
+ "\n",
+ ".xr-wrap {\n",
+ " display: block;\n",
+ " min-width: 300px;\n",
+ " max-width: 700px;\n",
+ "}\n",
+ "\n",
+ ".xr-text-repr-fallback {\n",
+ " /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ ".xr-header {\n",
+ " padding-top: 6px;\n",
+ " padding-bottom: 6px;\n",
+ " margin-bottom: 4px;\n",
+ " border-bottom: solid 1px var(--xr-border-color);\n",
+ "}\n",
+ "\n",
+ ".xr-header > div,\n",
+ ".xr-header > ul {\n",
+ " display: inline;\n",
+ " margin-top: 0;\n",
+ " margin-bottom: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-obj-type,\n",
+ ".xr-array-name {\n",
+ " margin-left: 2px;\n",
+ " margin-right: 10px;\n",
+ "}\n",
+ "\n",
+ ".xr-obj-type {\n",
+ " color: var(--xr-font-color2);\n",
+ "}\n",
+ "\n",
+ ".xr-sections {\n",
+ " padding-left: 0 !important;\n",
+ " display: grid;\n",
+ " grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+ "}\n",
+ "\n",
+ ".xr-sections.group-sections {\n",
+ " grid-template-columns: auto;\n",
+ "}\n",
+ "\n",
+ ".xr-section-item {\n",
+ " display: contents;\n",
+ "}\n",
+ "\n",
+ ".xr-section-item input {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ ".xr-section-item input + label {\n",
+ " color: var(--xr-disabled-color);\n",
+ "}\n",
+ "\n",
+ ".xr-section-item input:enabled + label {\n",
+ " cursor: pointer;\n",
+ " color: var(--xr-font-color2);\n",
+ "}\n",
+ "\n",
+ ".xr-section-item input:enabled + label:hover {\n",
+ " color: var(--xr-font-color0);\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary {\n",
+ " grid-column: 1;\n",
+ " color: var(--xr-font-color2);\n",
+ " font-weight: 500;\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary > span {\n",
+ " display: inline-block;\n",
+ " padding-left: 0.5em;\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in:disabled + label {\n",
+ " color: var(--xr-font-color2);\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in + label:before {\n",
+ " display: inline-block;\n",
+ " content: 'â–º';\n",
+ " font-size: 11px;\n",
+ " width: 15px;\n",
+ " text-align: center;\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in:disabled + label:before {\n",
+ " color: var(--xr-disabled-color);\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in:checked + label:before {\n",
+ " content: 'â–¼';\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in:checked + label > span {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary,\n",
+ ".xr-section-inline-details {\n",
+ " padding-top: 4px;\n",
+ " padding-bottom: 4px;\n",
+ "}\n",
+ "\n",
+ ".xr-section-inline-details {\n",
+ " grid-column: 2 / -1;\n",
+ "}\n",
+ "\n",
+ ".xr-section-details {\n",
+ " display: none;\n",
+ " grid-column: 1 / -1;\n",
+ " margin-bottom: 5px;\n",
+ "}\n",
+ "\n",
+ ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+ " display: contents;\n",
+ "}\n",
+ "\n",
+ ".xr-array-wrap {\n",
+ " grid-column: 1 / -1;\n",
+ " display: grid;\n",
+ " grid-template-columns: 20px auto;\n",
+ "}\n",
+ "\n",
+ ".xr-array-wrap > label {\n",
+ " grid-column: 1;\n",
+ " vertical-align: top;\n",
+ "}\n",
+ "\n",
+ ".xr-preview {\n",
+ " color: var(--xr-font-color3);\n",
+ "}\n",
+ "\n",
+ ".xr-array-preview,\n",
+ ".xr-array-data {\n",
+ " padding: 0 5px !important;\n",
+ " grid-column: 2;\n",
+ "}\n",
+ "\n",
+ ".xr-array-data,\n",
+ ".xr-array-in:checked ~ .xr-array-preview {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ ".xr-array-in:checked ~ .xr-array-data,\n",
+ ".xr-array-preview {\n",
+ " display: inline-block;\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list {\n",
+ " display: inline-block !important;\n",
+ " list-style: none;\n",
+ " padding: 0 !important;\n",
+ " margin: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list li {\n",
+ " display: inline-block;\n",
+ " padding: 0;\n",
+ " margin: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list:before {\n",
+ " content: '(';\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list:after {\n",
+ " content: ')';\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list li:not(:last-child):after {\n",
+ " content: ',';\n",
+ " padding-right: 5px;\n",
+ "}\n",
+ "\n",
+ ".xr-has-index {\n",
+ " font-weight: bold;\n",
+ "}\n",
+ "\n",
+ ".xr-var-list,\n",
+ ".xr-var-item {\n",
+ " display: contents;\n",
+ "}\n",
+ "\n",
+ ".xr-var-item > div,\n",
+ ".xr-var-item label,\n",
+ ".xr-var-item > .xr-var-name span {\n",
+ " background-color: var(--xr-background-color-row-even);\n",
+ " margin-bottom: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-var-item > .xr-var-name:hover span {\n",
+ " padding-right: 5px;\n",
+ "}\n",
+ "\n",
+ ".xr-var-list > li:nth-child(odd) > div,\n",
+ ".xr-var-list > li:nth-child(odd) > label,\n",
+ ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+ " background-color: var(--xr-background-color-row-odd);\n",
+ "}\n",
+ "\n",
+ ".xr-var-name {\n",
+ " grid-column: 1;\n",
+ "}\n",
+ "\n",
+ ".xr-var-dims {\n",
+ " grid-column: 2;\n",
+ "}\n",
+ "\n",
+ ".xr-var-dtype {\n",
+ " grid-column: 3;\n",
+ " text-align: right;\n",
+ " color: var(--xr-font-color2);\n",
+ "}\n",
+ "\n",
+ ".xr-var-preview {\n",
+ " grid-column: 4;\n",
+ "}\n",
+ "\n",
+ ".xr-var-name,\n",
+ ".xr-var-dims,\n",
+ ".xr-var-dtype,\n",
+ ".xr-preview,\n",
+ ".xr-attrs dt {\n",
+ " white-space: nowrap;\n",
+ " overflow: hidden;\n",
+ " text-overflow: ellipsis;\n",
+ " padding-right: 10px;\n",
+ "}\n",
+ "\n",
+ ".xr-var-name:hover,\n",
+ ".xr-var-dims:hover,\n",
+ ".xr-var-dtype:hover,\n",
+ ".xr-attrs dt:hover {\n",
+ " overflow: visible;\n",
+ " width: auto;\n",
+ " z-index: 1;\n",
+ "}\n",
+ "\n",
+ ".xr-var-attrs,\n",
+ ".xr-var-data {\n",
+ " display: none;\n",
+ " background-color: var(--xr-background-color) !important;\n",
+ " padding-bottom: 5px !important;\n",
+ "}\n",
+ "\n",
+ ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+ ".xr-var-data-in:checked ~ .xr-var-data {\n",
+ " display: block;\n",
+ "}\n",
+ "\n",
+ ".xr-var-data > table {\n",
+ " float: right;\n",
+ "}\n",
+ "\n",
+ ".xr-var-name span,\n",
+ ".xr-var-data,\n",
+ ".xr-attrs {\n",
+ " padding-left: 25px !important;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs,\n",
+ ".xr-var-attrs,\n",
+ ".xr-var-data {\n",
+ " grid-column: 1 / -1;\n",
+ "}\n",
+ "\n",
+ "dl.xr-attrs {\n",
+ " padding: 0;\n",
+ " margin: 0;\n",
+ " display: grid;\n",
+ " grid-template-columns: 125px auto;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt,\n",
+ ".xr-attrs dd {\n",
+ " padding: 0;\n",
+ " margin: 0;\n",
+ " float: left;\n",
+ " padding-right: 10px;\n",
+ " width: auto;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt {\n",
+ " font-weight: normal;\n",
+ " grid-column: 1;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt:hover span {\n",
+ " display: inline-block;\n",
+ " background: var(--xr-background-color);\n",
+ " padding-right: 10px;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dd {\n",
+ " grid-column: 2;\n",
+ " white-space: pre-wrap;\n",
+ " word-break: break-all;\n",
+ "}\n",
+ "\n",
+ ".xr-icon-database,\n",
+ ".xr-icon-file-text2 {\n",
+ " display: inline-block;\n",
+ " vertical-align: middle;\n",
+ " width: 1em;\n",
+ " height: 1.5em !important;\n",
+ " stroke-width: 0;\n",
+ " stroke: currentColor;\n",
+ " fill: currentColor;\n",
+ "}\n",
+ ".xr-wrap{width:700px!important;} </style>"
+ ],
+ "text/plain": [
+ "Inference data with groups:\n",
+ "\t> posterior\n",
+ "\t> posterior_predictive\n",
+ "\t> sample_stats\n",
+ "\t> observed_data"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pm.sample_posterior_predictive(trace_h, model=model_h, extend_inferencedata=True, random_seed=4591)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "b8b28417-d063-4cc9-9a1e-d858c30e1bef",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: xlabel='y'>"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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jjzyCWCyGb7/9Fqeeemq3bXpal23btiXF2syZMzOuP0APQD755BOsWbMm+V4iJB0AzjnnnB7P4Ve/+lXGzzPh8XiSx5s3bx5Uqn3ve5177rkwmUwIBAJ48cUXcdttt3X5/PnnnwcA2O12nHXWWV0+G8z7mzmwYceWYRiGGThhLxAPAzoroNHv3WMl82zTOyYMszfRarUYP348fv3rX2P9+vU4+eSTk58lcmaLioqQk5OTcYzx48cnv+/sSs2bNw9HHHEEZFnGTTfdhLy8PJx44om4++678emnnyYd3YGyZcsWAMDWrVthtVp7fCUqN2eq4Dx8+PABz6NzbnHntdgdjUaTzOVM594BQF5e3qC5tQA5s5kYN25c8vvd86MT9LQuifUHgNmzZ/e4/o899hiAruvf+Zg9zbOoqAh2uz3j55nYtWtX8h7L5CbvbSwWS7IqckLEdibhYM+dOxd6fdffNYN5fzMHNuzYMgzDMAMn0CkMeW+j0QFaMxD1A9EQhSczmdGayNk8GNGa9vohli5dmgyVVavVMBozRwkkHKJEG6BMdBZinQs/abVarFixAgsXLsTTTz+N1tZWfPLJJ/jkk09wzz33oLCwELfffjtuvPHGAYWBDqSgUDgcTvu+yTTwte/spPV1rTqv02DNIx09zafzZwOZz56uv9/vT37fm5NvsVj6fbzO5zSYDwv6y7x58/Diiy9i/fr12LBhAyZOnAiAQv23bduW3GZ3BvP+Zg5sWNgyDMMwA0OWgVAHIKkoTHhfYLCRsA17WNj2hiTt9XDdgxmj0dir+EqQ2K6nENPdP99dQNhsNixcuBAPPPAAfvzxR3z55ZdYsWIFli5dipaWFtx8881oaGjAww8/3M8zSc1vxowZ+Oyzz/q9/2DReT37ulb7Smj5fL6MbmdP160vdD7vnTt39tv17ixmO4vcdPS2runI9MBlX3PKKackQ4Wff/55LFy4EEDKwS0rK8PMmTO77be/3N/M0MOhyAzDMMzACLsBIQMGO7CvcrL0yh9gIc++OR7D9IHKykoAQHNzM5xOZ8btqqqqkt93bsXTGUmSMGXKFFx77bV45ZVXUFdXhxkzZgAAHnvssWSOan8YMWIEAGDHjh393ncwSawT0HUtdicWi2Hr1q0AMq/TYJPIYU3Hpk2bkt8PZD6J9QcGdg06H7OneTY3Nw/IvaysrIRarQaAIS2qpNFocNFFFwGg0GMhBOLxOF566SUAVHAsXf7v/nJ/M0MPC1uGYRhmYCR6yu4rtxYgB1KlASI+QN6zvEOGGSx++tOfJr//73//m3G71157DQD9AX/MMcf0aezs7GzccsstAKgX6u5/vCcqwPaUh5vIB25oaMAXX3zRp+PuDSZNmpR0RV9//fWM2y1duhTBIFU/77y2e5OeeuYmrqlGo8G0adP6PfbEiRNRVFQEAHj55Zf7vX9OTk4yt/bNN9/MuF1Pn/WEzWZLntd//vMfyLLcr/21Wi2Anu/BvpKoeFxbW4vPP/8cy5cvR0tLS5fPdmd/ub+ZoYeFLcMwDNN/ZJnCgSU1Obb7Er0NgKDCVQyzH3DUUUdh0qRJAKjdT2tra7dtNmzYgH/9618AqABOVlZW8rPOxYXS0VnM5uZ2rT6e+LmpqSnj/qeccgomTJgAALj66qvhcDh6PF5v8xkoarUal19+OQDgxRdfxKpVq7pt4/V6cfvttwOg0NN0FYj3Bv/4xz+SLnFnNm/ejH/+858AqCJxdnZ2v8eWJCn5cOKZZ57BO++80+P2Ho+n2/WcP38+AGpt8/bbb3fbp62tDffff3+/55bghhtuAECtcO65556M28myDLFbu62+3IN95dhjj8XIkSMBUAhyIgx54sSJGQtb7S/3NzP0sLBlGIZh+k+oIxWGvLd612bCYKOvYQ5HZvYfHn/8cahUKjQ0NOD444/H66+/jpaWFjQ0NODf//43Zs+ejVAo1KVPa4JrrrkGkyZNwgMPPIBVq1ahqakJTqcTVVVVWLhwYbI/6bHHHovy8vIu+x5xxBEAqH/nN998A7/fj1gshlgsltxGpVJhyZIlMBgMqKqqwtSpU/HYY4+hqqoKLpcLLS0tWL16Nf73f/8Xs2fPxlFHHbXX1unOO+9EYWEhZFnGGWecgcceeww1NTVwOBx4//33MX369GSY8t///veMPUkHm/z8fMyaNQvPPvssmpqa0NTUhCVLlmDWrFkIBAIwGo3JnM+BcPPNN+O4445DPB7HOeecg2uuuQYrV65Ea2srXC4Xtm7ditdeew1XXnklhg0b1s15vOGGG5Kh3BdffDEWLVqEXbt2oa2tDe+88w6mT58Ov9/f5YFJf7j44ouTbXTuvfdezJ07F8uXL0drayva29vx3XffYdGiRRg3bly3cOfEPfjWW29h+fLl8Hg8yXtwdxHcFxLO7Kuvvpp00jO5tcD+dX8zQ4zoA263WwAQbre7L5szDMMwBzvtO4Ro+F6IYMe+P3Y8JkTDWiGa1u/7Y+8nBINBUVVVJYLB4FBP5aDimWeeEQAEALFixYp+7//ss88KnU6XHGP3V3Z2tli1alW3/WbOnJlxn8SrsrJSbNu2rdu+S5cuzbhPdXV1l21XrlwpioqKej1WdnZ2xjnOnz+/13Xobdu1a9eK4uLijMdXq9Xib3/7W9p977rrLgFAVFRU9DqP3uh8vb/55htht9vTzkev14u33357QOfaGZfLJc4444xe1x+AeOutt7rt/+OPP4r8/Py02+t0OvHmm2+KiooKAUDcdddd3fafP3++ACBmzpyZdn5+v1+cd955vc7N5XJ12W/dunVCq9Wm3bbzv6Pejp9g69atXcaQJEnU1NT0srp7dn8z+4aB/O7qjw5lx5ZhGIbpH3KcijepNEpY8D5GpaZcWzkKRIP7/vgMk4HLLrsMGzduxLXXXovRo0fDaDTCbDZj0qRJ+P3vf48tW7akzRldsmQJnnzySVx44YWYOHEicnNzodFokJOTg+nTp+ORRx7Bhg0bMGrUqG77nnbaaXj33XdxyimnIC8vL1kEKB3Tp0/H9u3b8dhjj2H27NkoKCiARqOByWTC6NGjMXfuXDzxxBPYvn37oK7L7kydOhWbNm3CPffcgyOOOAJWqxV6vR7Dhw/HL37xC6xduxY33njjXp3D7kybNg3fffcdrrzySpSXl0On06GoqAiXXnop1q5dm3Qz94SsrCy8++67+Oijj3DppZdi+PDhMBqN0Gq1KCoqwsyZM/HHP/4Ra9euxdlnn91t/0mTJmHDhg24/vrrUVFRkZzjBRdcgC+//DLZB3agmEwmvP7661i6dCkuuOAClJWVQafTIScnB5MmTcLVV1+N5cuXd6sePXnyZHz66aeYM2cOioqK9thlHz16NI4++ujkz9OnT+8WqZCO/eX+ZoYOSYjeYwQ8Hg/sdjvcbjdstiH4I4ZhGIbZfwg4gY4awJQLZPX+x8ZewdsCeBsBawlgLRyaOQwhoVAI1dXVGD58OAwGbnvEMANh8eLFuOKKKwBgQCGzDMP0j4H87uqPDmXHlmEYhukfoQ76ui+rIe9Oou0PF5BiGIZhGAYsbBmGYZj+IMskJiV1SlwOBToTzSHqB9hpYRiGYZhDHha2DMMwTN8Je5RqyLZ9Xw15d/QWmkvEP7TzYBiGYRhmyGFhyzAMw/SdkNLmYV/3rk2HTnGMI76hnQfDMAzDMEMOC1uGYRimbwih9I6VhqYa8u7oLfQ1zMKWYRiGYQ51WNgyDMMwfSPiA+QY5daqMrcU2WdojdRyKOKj3N/OCEEhyr5WwN1AL18bhy0zDJNkwYIFEEJwRWSGOUjYs0ZTDMMwzKFDMgw5a0in0QWdhao0R/0kuOMxwNcCBNoBEU+/j1oHmAsAc97Q5wkzDMMwDDMosLBlGIZh+kZS2O4HYcgJ9FYStmHFTe6oI0ErqShcWmcBNDraNhahis4RL+CpB/xtQHYFoDMP6SkwDMMwDLPnsLBlGIZheicSAOIREopq7VDPJoVOqYzcvj2Vc2vOByxFgDrNrzhrIRANAd5GEuqObYCtFLDk79t5MwzDMAwzqHCOLcMwDNM7+1M15M6o1IC7EfA0Ul/b3FGAvSy9qE2gNQA5I4CsCgpF9tTT/gzDMAzDHLCwsGUYhmF6Z38UtvEoObUqNaAzkfOqt/Z9f1MOCWFJTXm53pa9N1eGYRiGYfYqLGwZhmGYnomFgVgQ0BgBjX6oZ0PIcaB9BxALAZZCErVytP/j6MyKuFVReHLQNfhzZRiGYRhmr8PClmEYhumZ/c2tFQJwVpPY1tuA/LEUUjzQVj46E5BdSd931HJLIIZhGIY5AGFhyzAMw/RMyENf9xdh62mkysZaE5A9nISppNozQWqwA7YyKkTl2kWOMMMwDMMwBwwsbBmGYZjMyHEg4gNUWhKQQ02wA/C3AioNiVqVitxarZna/ESDAx/bkg8Ys6n6c0ftoE2ZYRiGYZi9DwtbhmEYJjNhLwDRv6JMe4tYJ8GZVZ7qTwukRHcksGfHsA8D1HrqjRtw7tlYDMMwDMPsM1jYMgzDMJkJJ8KQbUM7DyGAjhpyZS2F3cOidWb6GvHt2XFUahLNAOBpAOKxPRuPYRiGYZh9AgtbhmEYJjNhL33VDbFj63eQaNWaAWtx98+1irCN7qFjCwB6C2DKA+QYiVuGYRiGYfZ7WNgyDMMw6YkGKd9UZwHUmqGbRyxMrXggAVnDKKd2d9QaQGOg9j+D4bLaSiivOOhMiXuGYRiGYfZbWNgyDMMw6UkIuqHOr+2oo2rF1iJAa8y8XSIcOToI7XpUasBeSt+7GygUmjkkWbt2LX7zm99gypQpyMvLg16vR1lZGU488UQ8+uijcLl67n0sSRIkScLixYv3zYSZtCxYsACSJGHWrFlDPRWGYfYSLGwZhmGY9CTa/OiHML824KTWPhoj5db2RCIcebD60Bqzya2OBYFgz+KFOfgIBAJYsGABjjzySPz1r3/Fjz/+iPb2dkQiETQ0NOCTTz7Br3/9a4wePRrPPvvsUE+XYRjmkIeFLcMwDNMdWVba/GiGrs2PHKeetQBgL0sfgtwZ3SALW4BCkgGahywP3rjMfo3b7caJJ56IJUuWQAiBY445Bs8//zx27NgBh8OBdevW4YEHHkBOTg7a29sxf/58PPTQQ0M9bYZhmEMaFrYMwzBMdyL7QZsfbzMgRwFjDhV06g2tAZDUVEBqsEKHdWZybuUo9c9lDgl++ctf4uuvvwYA3HLLLfjyyy/x85//HCNGjEBubi4mT56M22+/HRs3bsSECRMAALfffjs+/PDDoZw2wzDMIQ0LW4ZhGKY7Qx2GHA0B/jYSqgnXtC9oTZSPGwsN3lysJQAkwNdKLjJzUPPhhx/ipZdeAgCceeaZ+Otf/wqVKv2fS0VFRXj//fdhsVgghMDVV1+NWIxbRDEMwwwFLGwZhmGY7iQLRw2RsPU0ABDU2ket7ft+ibDpyCC0/Umg0QGmXOqh62PX9mDn0UcfBUBFnx577LFetx82bBh+85vfAABqa2vx2muvZdw2Fovh0UcfxZFHHgmbzQa73Y7p06fj+eef7/EYO3fuxA033IAJEybAYrFAr9ejtLQURx55JG666SasWLEi475+vx9/+ctfMH369GTxq9LSUlx44YVYtWpVxv12L7b0+eef44ILLkBZWRm0Wi1mzZqFqqqqZHGsN954o8dzaGhogFqthiRJePrpp9Nu880332DBggUYMWIETCYTbDYbjjjiCNx///3wenuuTh6JRLBo0SJMnjwZJpMJeXl5OPHEE3udF8MwBxGiD7jdbgFAuN3uvmzOMAzDHMhEgkI0fC9E65ahOX7IQ8dv3iiELPdv34CL9nXVDu6comEhGtYK0bhOiHhscMceAMFgUFRVVYlgMDjUUzmo8Hg8Qq1WCwBi1qxZfd6vpqZGABAAxIUXXtjls8T7Tz75pDjhhBOSP+/+mjdvnojH493G/vjjj4XRaMy4HwAxZcqUtPNau3atKCsr63Hf3//+92n3nT9/vgAgZs6cKR5//HGhUqm67Ddz5kwhhBBTp04VAMT555/f4xotWrRIABAGg6Hb35PxeFzccMMNPc6zsrJSbN68Oe3YbrdbHHPMMT2eY+fzYRhmaBjI767+6FB2bBmGYZiuDHWbH08TfbUV914waneSLX8G0bEF2LU9RPj6668Rj1O4+cyZM/u8X3l5OcrLywEAX3zxRdptFi5ciE8//RQ33XQTNmzYAIfDgc8//xynnHIKAOA///kPFi5c2GUfWZZxxRVXIBgMYtSoUXj++eexfft2uFwu1NfX4+OPP8btt9+OsrKybserqanB7NmzUV9fj8MOOwxLlixBdXU1nE4nvv/+e1x55ZUAgAcffBBPPvlkxnPbvHkzbr75Zvz0pz/Fxx9/jNbWVlRXV+OOO+4AAFx66aUAgHfffRcejyfjOAlX+swzz4TN1jUS5Le//S0ef/xxqFQq/OpXv8LXX38Nh8OBhoYGPPfcc6ioqMCuXbtw5plnwufzdRv7yiuvxDfffAMAuOqqq7Bu3To4HA58+eWXOP300/Hggw/is88+yzg3hmEODiQheq+w4fF4YLfb4Xa7u/1nxDAMwxxktO8Awh4gd3TfijYNJkEX4NpFrXvyxwxsjOYNgBwDiiYDGXIjB0QsArRWAZIKKJxAvW6HiFAohOrqagwfPhwGgyHtNhc/9RUaOwYx13g/oiTLgJeu/smgj/vUU0/hmmuuAQC89NJLuOiii/q872mnnYYPPvgAABCNRqHRaABQSHOCP//5z0lBmCAej+NnP/sZPv74YxgMBtTX1yM3NxcA8OOPP2LKlCkAgHXr1mHy5Ml9ns+ZZ56J9957D4cddhhWr14Nq7X7g6o777wT999/P/Ly8lBbWwujMdUnesGCBViyZAkAEvnLli2DVts9LaCxsRHDhg2DLMt4+umnccUVV3TbpqqqKllk680338ScOXOSn61evRrTpk0DQOt/1VVXpT3G4YcfjtbWVjz00EO49dZbk599/fXX+MlP6F645ZZb8Ne//rXLvrIs44wzzkhem5kzZ+LTTz9Nv2gMw+xV+vK7a3f6o0M1gzFJhmEY5iAh0eZHUqfcz32FEF3d2oGiMwEhN/WfHcxzSLi2AQfgdwDWXvrqDjGNHSHUOgfZuT7IcblS/YqzsrL6tW/n7Z1OJwoKCrp8Xlpa2kWQJVCr1Xj00UcxadIkhEIhvPjii7j++usBIOkeA0BJSd+LqO3YsQNLly4FADzxxBNpRS0A3HHHHXj00UfhcDjw4Ycf4pxzzkm73aJFi9KK2sS8Zs2ahU8++QTPP/98WmGbcGtzcnJw2mmndfns8ccfB0CCM52oTRzj+uuvx5/+9Ce88MILXdYxIb6tVivuu+++bvuqVCr89a9/TQpbhmEOXjgUmWEYhkkR8VFVYb21/2HAe0qgHYiHqWDVnoRBaxP9bPeCqLMoYsXfNngthZiDjnTBcGeddVbSxd2diRMnYswYilDoHMp82GGHJV2NK664Atu3b+/T8T/55BMIIWA2mzF16lT4fL60r3g8jrFjxwIA1qxZk3asvLw8HH300T0eb968eQCAFStWoLGxsctnQgi88MILAIALLrgAOp2uy+fLly8HAMyePTvjPH0+X9LxXb9+PSKRSHL/L7/8EgBw0kknwWxO/yBr3LhxOOyww3o8B4ZhDnzYsWUYhmFShJUcOYN93x5XlqlvLdC/9j7p0CrhlFE/gPw9G2t3NHpam5CbwqZNOYM7/iBSktW3MK8Dkb11btnZ2cnvOzo6+rVv5+1zcrrfFwkBmYlx48Zh69atqKmpSb5nMplw//3347e//S3effddvPvuuxg/fjxmzJiBGTNm4OSTT0ZeXl63sbZs2QKAKiJ3PqeeaGtrS/v+8OHDe9137ty5+NWvfoVQKISXXnoJv/71r5Offfnll9i1axeAVD5uAp/PlxTCd911F+66665ejyXLMtrb21FcTFEdibF7W9+xY8cm14VhmIMTFrYMwzBMisEsHCXHqYhTLEIusCRR6x6NkcJ6OxN0AnIUMGSlhOlA0e1FxxYAzAUkbP1t+7Ww3Rs5qAc7lZWVye83b97cr303bdoEgEKO04XtWiw956snPt+9rc1vfvMblJeX4+GHH8aaNWtQVVWFqqoqPPHEE9BoNLjwwgvxl7/8BUVFRcl93G53v+YOAOFwOO37JpOp131tNhvOOussvPrqq3jhhRe6CNuEW1tRUYGf/vSnXfYbyDx3n6vf7weAjG5tgt7Wn2GYAx8ORWYYhmGIWBiIhUh49qd37O4EXVSAqnk90L4dcNcCnnrAXQc4dwKtG+kzdz0QDVJIr6+F9rUW9Tx2X1CpAY2BwprleO/b9xe9hcKdo4HUgwDmoODYY4+FWk1FwfpTRbeuri7ptB5//PFpt0lXzTfd5+nyYS+44AKsXr0aTU1NeO2113DTTTdhxIgRiMVieOGFF3D88cd3EcQJEVdeXg4hRJ9eixcv7vP5piPhxn733XdJZzQWi+GVV14BAPz85z/vUkir8zwB4Omnn+7zXDs/gEgI2oTAzURv688wzIEPC1uGYRiGSIg0wwCr34e9QOsmqmoc9pDANGYD1mLAVgZYS6j4ks5CgtPfBrRtBhrW0r4G+567tQkS40R6/mN3wFiUEGdu/XNQYbPZMHv2bAAkbKurq/u0X2dReO6556bdpjcHOOH4VlRUZNymqKgIc+fOxWOPPYbt27cnKwDv3LkT//nPf5LbjRgxAgDQ1NSEQGDfFBA7/fTTkyHYiWJRH374IRwOB4BUHm5n7HZ7cp8dO3YM6LgJkdvb+vbXgWcY5sCDhS3DMAxDJPJr+xuGLMtARx25s7EQoLMCOSOBwolAdiW5sJZ8qiKcVQ7kjabPbGWASgt0VAMdNYDA4BVk0u6lfrYJDFk097CHQq2Zg4ZbbrkFAOVyJr7viYaGBixatAgAOaTnn39+2u3effddxGKxtJ9t3LgRW7duBZDZ8d0dSZJwyy23wG6nfPjOwu2kk04CQG2H3njjjT6Nt6dotVpccMEFAFLhxwmBO3XqVIwfPz7tfieffDIA4LXXXoMsy/0+7nHHHQcA+PjjjzO6tps2beL8WoY5BGBhyzAMw5CgDHuVNj/9yEWLR0nQBhwk9LIrgbxR5Pr2VFVZrSGxaykikaizAGE34NhGIdF7ik7JC9xbwlaSALNStCfg2DvHYIaE0047LSnQ3nrrLfzud7/LKLhaW1tx+umnw+v1QpIkPPXUUxkrH9fX1+Phhx/u9n48Hk8KaIPBgEsuuST5WUNDQ48htq2trckQ5ETvW4AKUSXa6vz2t7/Fzp07ezpl7Nq1K2OObX9IuLI7duzAxx9/jLfeeqvL++lInPuWLVtw++239zh+PB7v5uzOnz8fAOUm33nnnd32kWW5S84vwzAHLyxsGYZhmE5tfix9b/MTj5IQjfpJmOYfRqHHfUUIwN9KBZgqjqMxon7AsRUI72E+nMYIQNp7BaQACquGRG2KBuA0MfsvTz31VLLFzSOPPILjjz8eL774Iqqrq9He3o7169fjwQcfxPjx4/Hjjz8CABYuXIhTTz0145iVlZX44x//iFtuuQVVVVVwOp348ssvcfrpp2PZsmUAgD/+8Y9dBOqyZctQVlaGa665Bm+++SZ27NiBjo4O1NTU4PXXX8dJJ50EWZahVqsxd+7cLsf717/+hby8PDQ3N+PII4/E/fffjx9++AFOpxNtbW1Yt24d/u///g9nn302Ro0a1a1o1UA4/vjjk6HB//M//4NAIACVStVFrO/OMcccg9/+9rcAgIcffhgnn3wy3nrrLdTX18PtdqO2thYfffQRfv/732PkyJF49NFHu+x/7LHHJl3yRx99FFdffTXWr18Pp9OJr7/+GmeddRY++OCDLnm5DMMcpIg+4Ha7BQDhdrv7sjnDMAxzoNFRL0TD90L4HH3bPhYVomUT7dO+QwhZ7v8x/e20v2M7/SzLqXk0rBUi4Oz/mJ1p3UxjRcN7Nk5POKvpGP72vXeMNASDQVFVVSWCweA+Pe6hhNfrFfPmzROSJAkogfLpXjk5OWLx4sUZx0ls9+STT4qZM2dmHGfevHkiHo932feZZ57p8dgAhFqtFk888UTaY2/cuFGMGTOmT2M4nV3/vc2fP18AEDNnzuzXut1xxx1dxj7xxBN73Scej4s//OEPQqVS9TrXW265pdv+brdbHHPMMRn3ufXWWwd8PgzDDB4D+d3VHx3Kji3DMAzTvzY/chxw7gBiQUBvA7KH993lTSBEqm9tohKyJAH2UsA+DICgIlT+9v6N2xntXg5HBgCTEo7sT98DlDlwsVgseO6557BmzRrcfPPNmDRpErKzs6HT6VBcXIwTTjgBf/nLX7B9+/ZkOGxP6HQ6LFu2DIsWLcLUqVNhsVhgtVpx/PHH4z//+Q+ee+45qFRd/yy78MIL8c477+Cmm27CMcccg7KyMuh0OpjNZowbNw7XXnst1q1bh2uuuSbtMcePH4/169fj//7v/3DGGWeguLgYOp0OBoMBlZWVOPPMM/Hoo4+ipqamz/1ue2P3XrW7/5wOlUqF+++/Hxs3bsSNN96IiRMnwmazQa1WIzs7G0cddRSuu+46fPDBB3jkkUe67W+z2bBy5Uo89NBDmDRpEgwGA3JycjBr1iy88soreOihhwbl3BiG2b+RhOi9UofH44Hdbofb7YbNNsBqmQzDMMz+SSxCLXg0BqBgXM/bCkE5tRGfUiRqBKAawDPSgJMKRultQO7I7p+H3ICzGoAA7OWAObf7Nr3hb6dWQ5YiwFbc//37StsWEs95Y1I9dPcyoVAI1dXVGD58OAwGwz45JsMwDMPsCQP53dUfHZq+wgHDMAxz6JB0a/vw4NJdT6JWa+67qI2GaJ9oEIhHqDiUcwf9bB9GolBSA2odoNGTwNYp4zt3kjgF+i9uEy1/9qZjC5Br664l13YfCVuGYRiGYbrCwpZhGOZQp69tfgLOVPXjnOGZRa0QJGSDLnJe5d1anET8JGq1ZjqmiNM28TAQ6VTARlIrLXXcJBxVasCY1ffz0ioFpKLBvu8zEIzZgKcBCHYA9jjNk2EYhmGYfQoLW4ZhhpZIgISApFa+9jNXk9kzkm1+VD0L21iE3FpI1NJHre2+jRynCsH+NnJmAdpeZ6GX1khurLuenM3sylQVZSEUNzdEQjTsowrJcoREr6uGxHLx4VS5uS9IEh0vFqQKzunmPBioVHQeAQeJf0v+3jkOwzAMwzAZYWHLMMzQIcuAY0unNxQhYrDTK9GLlNl7RPzkmOp76TvbUUvbWYq6C0tZJjHra6FtABrPmE3XsbODGQmQK6vWU//aBJKkhCHraR+rMm6oA/A7SNy6G4CACyg9inJm+/IQRGskYRsNAGp7HxdlAJhyFWHbzsKWYRiGYYYAFrYMwwwhgsSNkMnti0dIhPiCgK+ZepFaC/vXG5XpH4kwZEMPos/XRmJUY0xVME4Q7AA8jRRGDIkEnrkA0GYoCuFvpa+Wgt6FqUpFPW5NOSRkm9eTwK77GsgdBViLAXNez+NoTUDQSXm+PZ3jnqIz0bGiARLv/FCGYRiGYfYpLGwZhhk6VGrK1exMLAyEPJSfGfVTyxdfG5A1LFUMiBk8emvzEwsD3kZQCHJFSkTKccBdR9cJINFoKyXHNROxMAlhlQYw5vRvnnorUHEcOcEduyjnViihz1nDMhdt2lcFpAAS9e4AzYmFLcMwDMPsU1jYMgyzf6HRUyinJZ9El6eRBG7bFsBWQk4fMzjEoyT4NIbMgtTTQI66tTglEsM+atUTj1AlY/swwNCHisr+NgACMOcPrEUQQO2IVGoqShUL03uOreQS20q6u7fJXrZ7uYAU0KmIlItE/kDPkWEYhmGYfsO/dRmG2X/RW4H8wwBbGf3saaDeprI8tPM6WOjNrQ15SECq9YClkN7zt1Mf23iEXNf8sX0TtfEYOZmSitrjDBSVGsgeToJVowcM2VR4zN8KOLZRkasu26tIuMfD5DLvTVRqJbQ+TrnBDMMwDMPsM1jYMgyz/2PJJ4Gr1pNgaN9GQonZM0Ju+pquf60Q9CABAOyl9NXdkOopm1VBocl9bW0TcJDza8oF1HsYLKQ10LEByhHOHk5Vl6N+KkYW9u62/T4ORwZIxDMMwzAMs8/gUGSGYQ4MtEYgbwzl3Ea8JG5zR+29Fi4HO53b/OjStM/xt1HrHb2NHM/m9UrVY0HurbdZCVMWNIakIpGr1tG10pmpT61KlaqaDIlChgcDg53m4WsBvE10L3ibyLlt3wFklVPRKUApIOWicOTeevXuKXoLPYCJ+KhgVaYiWgzDMAzDDCosbBmGOXBQa4DckYCrmtxGxzb6uaeCRUxaYkEfPIEIdCYbTJC6hu/Icao+HHKTYN3+CRB200ME+zAAMoX2SioAEoXeylEgDnJFE2G4id64QlA+rykH0OgG7ySsxdSuKOIjUWsvVfrk1lEOsByjnOx96dgC5Np6G8m1TbjdDMMwDMPsVVjYMgxzYCFJFHraUUMuXPsOcnL3NLz1IEWWBTY2erB8cwu+3NGOVk8ITn8EnlAilLsFwDbYDBoMzzNjVK4OY4xeTDQ6cdQwC/TxRiAeIoGYPx7Qm8mVVeu6FmoSggRxLESvsJdeQTc9iBBxKq4Ujw3etZIkCol2bCGnVm8BzLkkwF3VqcJXiZzefVFACiAB721SikilKWjFMAzDMMygw38JMgxz4JEQNEKQO+jcSaGoXIUWACCEwPe1HXh1TR2Wb25Fmzfc6z6eUAzr6t1YV594xwSzVmBmuRanjSnBKcdMht7QQ7slSSLBqrYoAjOPro+7ltzLxLUKe0kkmwsG53pplKrMrmpymRPFrHJH0UMPbxNtp9aR4JblvX+fqLXkVIc9dL59Ka7FMAzDMMwewcKWYZgDk4S4dcYoFLWjpntP3EOMWFzGmz804pkvqrGx0bPH4/mjAkt3RLB0hwPZn63C+UeW4fKfVGJYTh97tEoS5ZnaSuhaRQOA36HkwjooTNeYvcfzhDELiORTHm9HLYWn68z0NSFuE05xLJi55+1gYsohYRt0srBlGIZhmH0AC1uGYQ5cVCoKS3ZsJTfQ2wxYi4Z6VvscIQQ+2dyKB9/fjG2tvh63LbIZkGNUIUcbhV0nEJM08EdktASAXa4oorJIu58rEMX/W1WNp7/YhTlTSvCrE0ZiVEEvhZjCXhKzWpNSyCmHnFpvEwk+1y4K17UPI5dTlqmycSRA+bGJl5DpKyTaTqWmwlR6Syp/1lpC/XXDHsDXSq5wUtxupxxhlZbCkfeFsNXbqQ1RyE0h2n2tHs0wDMMwzIBgYcswzIGNWgPkjCBx620iEXUIOWSNHUHc/t/1+GxrW9rPjVo1fjo6D7PHFuCEwwpQZNMDdd8CHbsAWylgzidHVW9F1NWAmroaVDV6sbJFi09qonAGurZVissC/13bgDd/aMAFRw7Db04ZgwJbhsq/vlb6aulUCVmjo1Y9plxyVwPtQEedcs0kAOmFdZKYkicbdNFXlYaqOhvsQNYwKijmaaRQ4ER15uzhJLLdDXS+5j3oo9tXVCpykgPtQLCDcn8ZhmEYhtlrsLBlGObAR2sgUePaRSHJeYcNbvXd/RAhBF79rh73vVMFb7h7T9+xRVZc9pMKnDO1FGa98l99NAi0bSZBKamAnFGAtZBChuMxaAOtGKVuxaiJpTj7uDLELUX4YrsDL62uxUcbWxDr5ObKAnh5TR3e+bER150wCldNHwGdplPuajRI7qlaDxiyup+ApFIqGDcAwXZ6KGEpBLIrSaiqNSRaEy9JDUCQcxuPkDsb8VFV5FAHvSQ1AInEr2sX3QcqlZJzOxpw11ObqOxKQNfHcOo9wZijCFsnC1uGYRiG2cuwsGUY5uDAmE0ix99G4jZ31EFbjTYYieP3//0Rb/3Q2O2zEflm3PazsThlfCGkzufva6MqwZEAPQjIHQ3YOoVt+9uoyJPWSG6qrRhqADPG5GPGmHy0ekL49+fV+M/XNQhE4sndApE4Fn24Be+sa8SDcydj6rAs5Xgt9NVS0PU6hH30WVjJAbYVAVllQMhDDyOETGHLmUJ3VWpq75ToRyvLJHCDLqXNkNIzN+gkUZ07grazFlJ7IF8z5d3m74OHH5172sbC3JaKYRiGYfYiXEKUYZiDB1sp5V5GfKkw2IOMOmcAc//1ZTdRa9CqcOeZ4/HRzTNw6oSilKiV4yTkPEq5Y4MVsJd1DceV44BjO62bpZgKPe1Ggc2AO04fhy9um42rpg+HVt31ocHmZi/O/ecXuPedKvj9AQq/VWnItQSAWISqV7dvSzm59nKgcCJQMA4omQpojPSZYxv1ve0LCUc2u4LGspUCtjJ6yNGwGmjbQuIXoPPW24Goj6ooJ97fm5iU80+ETjP7NYsXL4YkSd1earUaubm5OO6443DffffB4XAM9VT3a2bNmgVJkrBgwYKhnsohR2VlJSRJwt13393tswULFkCSJMyaNWufz2t/OD5z8MPClmGYgwdJIoEjqSi0NRIY6hkNKhsa3Jjzv1+gqqlrxeOjK7PxwU0zcOVPh0OjVpGo8zZT2PG2ZUBrFYXEGrIpjFcWKccToHBgXxOJvryRPRY6yjbr8IczxmP5r2fhZxO6FuoSAnj6i2qc/vgX+LElTP1jJYnc4rZNVEhJrSfhXDCOwnMTAlyjp37EehuFEju2ksvZH1RqcoiLJwPFUwBIQPOPQPN6EtpaE4U7SxoqatVR07/xB0Ki6nPAufePxew1ZFmG0+nEV199hT/96U+YMGECvv7666GeFgAkxffixYuHeirMIUpPYpph9iUsbBmGObjQ6MmxgyDhsi9cuX3At9VOXPLU13D6I13ev3bWSLx09U9QmWMkl7p5A4lCx1ag6Ucl91RFea7+FnJD3XXUbicWofVpWkchwIUT+lwxuDzXhCcuOxJPXXYkCm1dQ2xrOiI471UHnlztgty2hdxiIUhU5o8lFzNdmLhKRYXAjIoAd2yjdkH9RaUmkVx2NDn47lpyaH2tgIiR+FXraW38e9l50+gpZzgepjBs5oBh6dKl8Hq98Hq96OjowLp16/DLX/4SANDa2oqzzz4bHR0dQztJhmEYJgnn2DIMc/BhziXREvZQXmnWsKGe0R6xcmsbrn5uDULRlEg36dRYdP4UnDG5mMJc3fVKSxwAkSAQj5PAzyojQRmP0DZ6K6DWAf5WIOAAgm4KQbYWD2idTplQhGNH5uLhDzbjP1/XJt+PycDCZdX4vEqFv5ycjYLS4YpL3EvVY0mi4k6Silzm9m2UL51o69MfsirI9Q20k3uv1pKoVxuoDZBjK62Jztx9/HiU9o1H6Hu5U4EuSQK1HtLRflpjz/ncxhwlD9hJebfMAYHRaITFkrpekydPxr/+9S9Eo1H83//9H9ra2vDUU0/h1ltvHcJZ7p98+umnQz0FJg2LFy8eUmd/qI/PHPywY8swzMFJVjnleAYcVJjoAOXLHQ5c9WxXUWs3avH8/xyDMyYWAs5qqgAsx8iV1RgBnZHEfdFEWodEsSW1jlr7lB0NWIpIuDV+RwWV8sb0f3KRAOBtgc1fi/uPBV44JwcFhq4O+aoGGWe+6sJ3W3cBji0UGtyykfJtfa1UPTkdWeXUmkeOUR/aTNv1hEpFoekJ4WqwkUPdtgmIhSgfF0JZP5nOx10PtFQBLRtIVHfUUFEtf2vq5WuhNXPXps7JsZ3Cv9M5zMYsEurBjoMmguBQ5s4770x+v3z58iGcCcMwDNMZFrYMwxycqLUkjgBqbxPv3hJnf2f1LieuXLwG4VhKDOVb9Xjlmp/g8BITOY6hjlTeajxC+alqJV+1cz9fWaZerioN9Xy1FQNQUYscY44i2tp7npAQgLeFQpy3Lwd2LAfqvgZa1gPtO3CcfifeP7YKswu75ja3+mVc/Ho7ntskQ6j1NM+Qm9z0ts0UPu2qIeEnOjm69jLAXKCI2x39z7kFSNDaSshRFVAKYwkKc5ZjJPh9zcCulSRS/W0UNqwxUki0pRCwDyMXOXu48qqkcSxFlBMMCYh4Ka+7bRMVrPK1pUSsSk1rLuJK5WbmQKaioiLp5NbX13f7fMeOHbjuuuswZswYmEwmWK1WTJ48GXfccQfa2tL3m07w9ttvY86cOSgtLYVOp4PNZsPIkSNx8skn4+GHH0ZdXV1y20SBpgRXXHFFt6JXu3bt6nYMp9OJu+++G0cffTSys7NhMBhQWVmJBQsW4Mcff8w4t90LQr3zzjs4/fTTUVxcDLVa3aVQVF+KR3k8Htx777048sgjYbfbYTQaMWLECFx55ZXYsGFDxv3uvvtuSJKEyspKAMD69etxxRVXoKKiAnq9Pvl+X+hcLAwAqqurcc0116CyshIGgwElJSWYN28eNm/enHGM/qwLQK3aXn75ZZx99tkoKSmBTqdDbm4uTjzxRCxZsgRyLw+/WltbceONN2L48OEwGAwoLS3FJZdcgrVr1/Z6vn0t3rR8+XLMmzcPI0aMgMlkQlZWFiZNmoR58+bhrbfeglD+n06MV1ND9QruueeebvdgZ/c+0/GnTZsGSZIwZ86cXs/h1FNPhSRJmDFjRtrPB3p/98bu911VVRUuv/xylJWVwWg0YsyYMbjnnnsQCKR+/zmdTtx5550YN24cTCYT8vPzcemll6b9d7k7dXV1+N3vfofJkycn/32MHj0a1113HaqrqzPuFwqF8N577+Gaa67BpEmTYLVaodPpUFRUhNNPPx0vvPBCj/fY7vfzihUrcOaZZ6KwsBAGgwGjR4/GbbfdBrfb3ad12+eIPuB2uwUA4Xa7+7I5wzDM/oOrRoiG7+nrAcSGhg4x4U8fiIrb3k2+jr5/mdjZ5hMi5BGi6Uc6L8d2IUJeIZo30M9t24SIRbsPGHDR585d9LPPIcSmd4XY8IYQrVuFaFhLn3fUd91PloUIuoVoXCfExreE+OFF5fWSEBvfFmLbciF2fSnE1o+EWPlXIVY8KOQNb4ln3lspRt/xbpf5V9z2rvjtKz+IYChCc/Y0CdHW6dgN39N5ddQLEQ2l5pC4hs0bhYhFBragju00Rt1qIao/F2LnSpr7pqVCrHuFzqnuWyF8benXrzciASE8zUK0bOp6Lp4mIeIxWsPE9RoEgsGgqKqqEsFgcFDGY4hnnnlGgB6BiBUrVmTczmazCQBi3LhxXd5/7rnnhE6nS46x+ys7O1usWrUq7ZhXX311xv0Sr0cffTS5/cyZM3vdvrq6ussxPvroI5GVlZVxe5VKJf75z3+mnV/iePPnzxe/+93vuu07f/78tNum44cffhDFxcUZ56FWq8Xf/va3tPveddddAoCoqKgQr7/+utDr9V32raioSLtfOjpf72+++Sbj2uj1evH222/v8bo4HA4xY8aMHq/ZySefLLxeb9pjrV+/XuTn52ec45tvvikqKioEAHHXXXd123/+/PkCgJg5c2ba8f1+v7jgggt6va9cLleX8Xp6df53lOn4jz32mAAgdDqdcDqdmS6XaG5uFmq1WgAQTz75ZLfP9+T+7o3O990HH3wgTCZT2mOcdNJJIhKJiJ07d4pRo0al3aaoqEjU1GT+m+S5554TBoMh43kYjUbxxhtvpN335ptv7vWanH766SIUCqXdv/P9/PDDDwtJktKOMXHiROHxePq9jgP53dUfHcqOLcMwBze2UkClpTzLA6R4T2NHEL9YvBq+cMplzrPo8MJVx2K4TaIwXjlGbqKlgH6OR6j/bO5IQJ2mfEJIebpqsFHOqKuG8j5tpUDeKMpjVWnIuXXVkMPtbQFqvgS2K5WVY0FyUIunAqNPplfl8YAln8KcLflA+bGQyqdhwbRivDI3D8WWrr9mXv2uHpf837doi2gBaxGQNxoomkROqMFOrYf8rXQ8ZzWFB9uHUZh1PEzOrRzvdnq9kghND/vIGY4GgJALCLsp79VeSi6tISv9+vWG1ki9cgvGAnmHkQsux8nFba2iQl0qDbnmfW1lNFg8OXNwXx/+ofdjbvlg8I9bvWrvr1UfaGxshMdD6Q0lJSXJ9z/77DPMnz8fkUgEI0eOxCuvvILm5mbU1dXhqaeeQm5uLlwuF84444ykw5Vg2bJleOqppwAAF198MVauXIn6+no4nU5s3rwZL7zwAi666CLodKney++//z68Xm/y5yeeeCJZ7CrxqqioSH6+Zs0anHnmmejo6MC0adPw2muvob6+Hu3t7fjiiy8wZ84cyLKM6667Dh988EHG8//444+xaNEinHvuufjiiy/gcDiwZcsW/OIXv+jT+rW1teHUU09FU1MTrFYr/vKXv2Dnzp1obW3Fe++9h8mTJyMej+Omm27CG2+8kXEcl8uFBQsWYMyYMXjzzTeTa/2Pf/yjT/PYnYsuuggGgwGLFy9GQ0MDGhoasHjxYhQWFiIcDuOiiy7C9u3bM+7f27pEo1GcfvrpWLlyJex2Ox566CFs3LgRTqcT27Ztw8KFC2E0GrFs2TJcddVV3cb3+/0466yz0NbWBqPRiIceeii5bm+99VbSlRyokyaEwMUXX4xXX30VADBnzhwsW7YMTU1NaGtrw7fffosHHngAo0ePTu7z5JNPwuv1orycoqNuv/32bvfg9OnTez32xRdfDLVajUgkkjx+Ol566SXE43HodDpccMEFXT4brPu7Nzo6OnDJJZdgypQp+Pjjj9HW1oZt27YlC8t9/PHHeOqpp3DhhRciFArhueeeQ0NDA5qamvD4449Dq9WiubkZv/vd79KO/9Zbb+Gyyy5DKBTCKaecgqVLl6KpqQkOhwPLli3D9OnTEQwGcfHFF2PdunXd9rfb7bjqqqvwyiuvYM2aNcljf/vtt/jNb34Do9GIpUuXdkmpSMfKlStx22234fzzz8dXX32F9vZ2bNmyBddffz0AYMOGDfjzn/884HXcawy2UmYYhtnv8LeTW9ayiRzI/RhPMCJOffSzLi7nlHs+FJua3EJEw0I0radz8TQrTuoPys9NmQeVZcXhXUvuoXOXENuWCbHlQyHcDantoiHabtsyITb8V4j1/yUns+ptIerWCBHo6DpuPJ5yQrd8IETNV+QMJ4hFRFtLo7jw8eXdnNvjFn4sNjeledobi9C5Jc4z6Ur7UsdybB/Ydeyoo/P5bJHiVG8RokFZv50rhahfI0T7zv6Pm4loOOU2N3wvxM7PyN32tu7x0P166n2XbXBfL/6892OufX7wj1v1zh6vW2/0xbG94YYbktvcd999yfcnTZokAIjS0lLR0tLSbb9169Yl3cWLLrqoy2e33HKLACCOOOKIfs85MZdnnnkm4zayLIuJEycKAGLGjBkiGk0flXDZZZcJAGLChAlC3u3fWGeH+NJLL+1xTj05ttdff33SPVu5cmW3z91utxg7dqwAIMrKyrrNNeGcARBjx44dkGuUoPP1NpvNYsuWLd222bRpkzAajQKAOP/88zOea2/rsmjRoqTb9sMPP6TdZtmyZcmxvvnmmy6fLVy4MPnZW2+91W3f1tZWUVpamtymv47t888/n9z3jjvuyHge8Xi8273Rk0vc1+OfeuqpyfszE0cffbQAIM4555wu7w/G/d0bne+7Y445Jq3jmXDjNRqNyM7OTuvK/uEPfxAAhFar7aarAoFA0pH/+c/T/z8bjUbFrFmzBABx2mmn9eschBDi/fffT97v6f7tdL6ff/nLX6Yd45xzzhEARGFhYb+Pz44twzDMnmLKAXRWchz3dnuXPSAal/Gr57/H5uaUC6PTqPDvy4/C2AILtayRo9QfVqMjp1bIVP3YWtTDwIFUPmkslHKvTbk0VoJ4lIofObbRK+IDCicCI08Eyo4EjPZO2ypFncIe+hVoyqF8U0OnbdRa5BUU4z/XzsIvftK14nJDRwhz//k5PtvS2nWuaq3ifI4H7OWAxkDHaN8KQKKc4LCHnNC+Eo9RgSh/G/XqVeupmFP+GGpxpDVRWyBfG1UuHqyesxodOcV5h1HLH7WW8r0dW9Ell5jZ75FlGdXV1bjtttuSjqDNZsPVV18NAFi9ejXWr18PgHLxCgoKuo0xefJkXHvttQCA119/vUuroHicohCKi4v3yvxXrFiRzFtdsmQJNJr0UQkJB2bjxo1p3SAAUKvVWLRo0YDmEYvF8OyzzwIALrnkkrRuns1mw4MPPgiAcpg//PDDjOPde++9sFqtGT/vD9dffz3GjOleRG/s2LG47rrrAABvvvkmXC5X2v17W5fHH38cAHDrrbdiypQpabc56aSTMHv2bADACy+80OWzJUuWAABOOOEEnH322d32zc/Pxx/+0Idoigz8/e9/BwBMmDAB9913X8btVCpVl9zuweLSSy8FAKxatQq1tbXdPt+2bRtWr17dZdsEg3l/94WFCxdCr9d3e//CCy8EQPf5jTfemHSyO3PxxRcDIAf/hx9+6PLZiy++iLa2NpjN5mQEx+5oNBrce++9AIAPPvgg4/2YiZ/97GfIz8+H3+/Hl19+mXE7k8mU/He4O/PnzwcAtLS0pL1WQwkLW4ZhDg3spQAkEkT7OhS0Dwgh8Mc3NmDVtq7C+9ELp+KoyhyqzhsNkEDXGkmoQShFjPJ7HjwRhqy3UtXfiI8KIxmzSHzJMtBRR4WgnDup9U/OSKoobM4DdKau48UiVDE46qf5GGwUZmspSNv2RqtW4U9zJuOhuZOgUaU+90Vk/GLxajy3amv3OatUVNk5fywJXJWWwobjYRKenqa+CdBgBxV0CrooXLr8GArXjgaoirFaQ2HYegsJc08jrcVgFhvTmSjkOnc0oDVQy6GWjfvlfcikOOGEE5IFcNRqNUaMGIGHH34YQgjYbDa89tprSQH7+eefJ/c777zzMo55/vnnA6A/fL/++uvk+1OnTgVA4cV///vf4ff7B/VcEtWbx4wZg7y8PPh8vrSv7Oxs5OfT/ydr1qxJO9bhhx8+YAG+fv36ZBj33LlzM253xhlnwGikFlyrVqUPQZckCT/72c8GNI90nHvuuRk/S1zTWCyGb7/9Nu02Pa3Ltm3bkgJg5syZGdff5/Nh8uTJALqufyIkHQDOOeecAZ1DT3g8nuTx5s2bB5Vq38uDc889FyaTCUIIvPjii90+f/755wFQqO1ZZ53V5bPBvL97w2AwZAyvHjFiRPL7k08+Oe02I0eOTH7f3Nyc9jx+8pOfQAiR8TzGjh0LgP5u+O6777odo62tDX/+858xffp05OfnQ6vVdinolShit3Vrmt+9Csceeyzsdnvazzo/AGppack4xlDAfWwZhjk00BqpfYy/larxZlcO9Yy68NzXNXh5TV2X924/bSz1qfW2UDVdjYEEmLsOgNLv1ZjV++CJdkdynARdLEKi0ZyvOLRbKZ9VjlAF4bzRJILbd9CDAJWGBC5AlYnbt1NOr8EOWEuosrFKQ3mlPXDR0eUYlmPCL5/7Dp4QCce4AO58bxuqWzvwxzlHQqVRd91Jkkg4SypyO/0OWouWKjpu0SRq56PW0hx0ZnJHIQGeenKnAXKmbaUkmHNHAE3r6PyM2dQOKXckud8RL+Cpo2PmDO99bfuDOQ8oORxoWEtzi3gpZ1mSlJ65UUDEae4qDc1LZybBzf1vhxytVovRo0fjZz/7GW666aYubkwiZ7aoqAg5OZn/HYwfPz75fWenY968efjHP/6B77//HjfddBNuu+02HHfccZg+fTpmzZqF6dOnQ61WpxuyT2zZsgUA/SHbV4czUwXn4cMH/u+ic25x57XYHY1Gg9GjR+PHH3/M6Ajl5eUNmlsLICkW0jFu3Ljk97vnRyfoaV0S6w8g6cj2Ruf173zMnuZZVFQEu93e7zzbXbt2JaMGMrnJexuLxYI5c+bgxRdfxPPPP4/bbruty+cJB3vu3Lnd3NLBvL97Iy8vL6MjnHgYA9C16G2bYLBrG7vEeXz88ccDPo9Vq1bhnHPOgdPZ+4Pfnu6TzvUDdsdkSj3s7lwFen+AhS3DMIcO1iJy7oIuEjr7iVj4vtaF+96t6vLepceU4+oZI5ResU0k7LTm1PfZw7u288lELKy0ANJRT99YWBGCegACqPuW2t1ojUDeRBJ4GqVATc4IErHuehJZal1K1JpyqaiTp5HGMeWRaOyF40bm4Y3rjscvFq9GTXvqF+LTq9vQ4l6Fv1x8NAwmM4m8xLWKdvrFac6j0Oewj+blbweKpwDGzq2N4iTmNVpAn0XOc+e1MuXR/GNBKpSVN5pa8uSOpJDtts30MuX2bY37g9ZELnFHHaDvoK+2YhLjKg2g0lGYshwDIlFy130ttPaJ0PH+Frcqnjq459CXh0LGnME/riG9e7C3WLp0adKZUavVXf4g3R2fjwrTJdoAZaLzH6udCz9ptVqsWLECCxcuxNNPP43W1lZ88skn+OSTT3DPPfegsLAQt99+O2688cYBhYEOpKBQOJy+vVbnP2r7S2KdgL6vVed1Gqx5pKOn+XT+bCDz2dP17+zgm83mHvezWCz9Pl7ncxrMhwX9Zd68eXjxxRexfv16bNiwARMnTgRAof7btm1LbrM7g3l/90ZfHzD1ZTuxW0rKnp6H2+3GeeedB6fTiYKCAvzmN7/BrFmzMGzYMJjN5qQTP378eNTV1SEWyxyZ1Nfz3P0chhoWtgzDHDqo1ORIdtSQIMvvnk+1r2n3hXHd898jGk/9cjhuZC7uOXsCJCFSIccqAxBspxzTnBF9F+WJMOR4DIAMQAK0evpa8yV9bs6nXFNLYddQYr2FckQ7asjVldQ0F1MuvR+PkViWVDRGHxmZb8Gbvzoe1/znO3xbnXqq/N5WPxz//gJPnV0Eu06mYwHkVOvMJAo1BqBYT2K35hvAXUNh0dkjAFMWCV1XNTmfGqMSgg7AXkIVj1VqGsOYQ+ce9VNIsq2YXN+8MSQmXbuoX2/FcX0S7L0ScpPzHvWTM2zIoj64kJUw7mLA1ukJvxD0ACHspXDqRJ9cXwtdJ3U/BPc1n+35/PvLYT+j1wGM0WjsVXwlSGzXWbilo/PnuwsIm82GhQsX4oEHHsCPP/6IL7/8EitWrMDSpUvR0tKCm2++GQ0NDXj44Yf7eSap+c2YMQOffTYE98Nu8wD6vlb7Smj5fL6MoZc9Xbe+0Pm8d+7c2W/Xu7OY7S1Mvbd1TUemBy77mlNOOQX5+floa2vD888/j4ULFwJIhSGXlZVh5syZ3fbbX+7vPSVxHpdffnkyp7o/vPbaa3A4HFCpVFixYkXGqIhEOsDBCOfYMgxzaGHKIecz6ic3cAiRZYFfv7IOTe5Q8r0imwF/v+RwaNQqwNtIOaVyDIgHSUD2R9QCJKjkODm1QigtfRyUdxr2kJtZfiy52emcIFMOiTDXLnoZsknUAiRqhUxCt58uYrZZh+eunIY5U7uGO33TGMWFL+5Ck8NFYbr5Y4GCcXRMvQ2IBilsunUz5a6q1BRe3rYRaPiOQnz1FhKN5lwqBtW6Edj+MbDtIxKrAQcQ8ZMDKKnJsU60gtLogKLJSr5tPTnUe0JMaVHk3En3nNZEDxFyRgLZ5eQ2a02Ar4nc28TTb0kil9ycR+2YCiakHh54m1KFw5j9gsrKSgCUM9dTCGBVVSoyo3Mrns5IkoQpU6bg2muvxSuvvIK6ujrMmDEDAPDYY48N6I/SRO7fjh07+r3vYJJYJ6DrWuxOLBZL5v9lWqfBJpHDmo5NmzYlvx/IfDrnXg7kGnQ+Zk/zbG5uHpDrV1lZmXTo9qSo0p6i0Whw0UUXAaDQYyEE4vE4XnrpJQBUcCxd/u/+cn/vKXt6HolrN3ny5Iyitq6ubsAtoQ4EWNgyDHPoYVPElKdpaKrTRoNAwIkln67HZ1tT+TEaFfC/55QjT/KSu+dtJscOUETtyP6J2rgSyhr2UliuWkuiqH0HieXcMUDp0eSG9jRGNEAhshpdSvzKMlUZhkQCdADoNWo8esEUXPOTrrlIW9xqnPd2CFsbHEDIS4WWtn8CbH4H2LkCaPwecNdSH1pzAWDKp7Vy7qL8aUkN5FSS+1p6FIljg53WvW0Tuc++FhKtsTCtj2tXqj+uzkT7SSrKxR3IAxAhaE6tygMEjYEeSuQfBmRVAmo1PXTQ26iolEpLgtu5k9Z2dzQ6wF4G5I9T+v1GlAceXIBqf+CnP/1p8vv//ve/Gbd77bXXANAf8Mccc0yfxs7OzsYtt9wCgCqp7v5HbyLfL5EjmY5EIZuGhgZ88cUXfTru3mDSpElJV/T111/PuN3SpUuT+Yed13Zv0lPP3MQ11Wg0mDZtWr/HnjhxYjLn8uWXX+73/jk5Ocnc2jfffDPjdj191hM2my15Xv/5z38gp/s/qAe0Wi2Anu/BvpKoeFxbW4vPP/8cy5cvTxYo2r0acoL95f7eUxLn8c0332TM5e6JRFhyT9ch4X4frLCwZRjm0ENvIXEQDyvibC8jx6mCr2sX0LwBaNuMLTu2Y+Hy3YpFHW/DkVk+wFUL1HwBNK4lFy/kITHe35zgkJvCheNREnvt1VR0SWcGiqYAJVN7dlrjMRLB8Qi5pvbyVDucQDsJq0Rl5YEQCUDVvhW3H63Cn2bY0dkvbvLJOP/FWnz72TtA0w/kygqhOJiHASVHkLNaOIHCjM0FFF6cMxKAoHn7WkhUAlQ9Om80iUqNQck9jpBADPtI8NZ/R+8DgLWAXFIRB2q/7l+V5GiQxLO3iR4EWEtS4hqg0GaDncYOdShVk8ekWhu5qtOLW4DWOmcEhS4DNK+EG88MGUcddRQmTZoEgNr9tLa2dttmw4YN+Ne//gWACuBkZWUlP+tcXCgdncVsbm5ul88SPzc1ZW6Bdcopp2DChAkAgKuvvhoOR89tz3qbz0BRq9W4/PLLAVBrk3QVj71eL26//XYAFHp66qmn7pW57M4//vGPtFViN2/ejH/+858AqCJxdnZ2v8eWJCn5cOKZZ57BO++80+P2Ho+n2/VMtFhZsWIF3n777W77tLW14f777+/33BLccMMNAKgVzj333JNxO1mWu+VV9uUe7CvHHntssnLw888/nxRiEydOzFjYan+5v/eUyy67DHl5eYjFYliwYEGvhZl2P49EiPvmzZuxfXv3aKPNmzfjgQceGLwJ74ewsGUY5tDEWgJq/9M8uK1dOhP2Uths83rKUw26ADmGsMqEm5Z5Een0UHXGqGz84sQpJMAklRKCHE1Vx3XXA21bSFT2VcQEOyjsWG+hisJtm6jdzIhZQMFh6UOPE8gyuYexoOIqjkwVDeqoozBdgPI9+0vCzXRspdzReBS/ONyOx3+WDV2n30qeqArzvizE+/VGYNhPgAnnASNnA6WH03xsJSTqbCVUbXjkDKD0CKDsaMqZleNAyEfbBDvI/Y2FgKAHkMhhgEpLgldSAa6dQO03VFAqGgIKx5NgDrlJXPe27kKQ0962RWnNZCFBay3svtaJCtIJN1ijI+dWYyRxm8m5TWDKprlLUtdQc2bIePzxx6FSqdDQ0IDjjz8er7/+OlpaWtDQ0IB///vfmD17NkKhUJc+rQmuueYaTJo0CQ888ABWrVqFpqYmOJ1OVFVVYeHChcn+pMcee2y33phHHHEEAOrf+c0338Dv9yMWi3UpDKNSqbBkyRIYDAZUVVVh6tSpeOyxx1BVVQWXy4WWlhasXr0a//u//4vZs2fjqKOO2mvrdOedd6KwsBCyLOOMM87AY489hpqaGjgcDrz//vuYPn16Mkz573//e8YKtINNfn4+Zs2ahWeffRZNTU1oamrCkiVLMGvWLAQCARiNxmTO50C4+eabcdxxxyEej+Occ87BNddcg5UrV6K1tRUulwtbt27Fa6+9hiuvvBLDhg3r5jzecMMNyVDuiy++GIsWLcKuXbvQ1taGd955B9OnT4ff7+/ywKQ/XHzxxck2Ovfeey/mzp2L5cuXo7W1Fe3t7fjuu++waNEijBs3rlsoa+IefOutt7B8+XJ4PJ7kPTiQ4kIJZ/bVV19NOumZ3Fpg/7q/9wSz2Yynn34akiTh008/xRFHHIF///vf2L59Ozo6OtDU1IQvv/wSjzzyCKZNm9atZdbcuXOhUqkQjUZxxhln4O2330ZzczNqa2vxr3/9C9OnT4fJZOqxcvuBDhePYhjm0ERroNzQgIOcvUSRocEg2EHCLaaU8ldpyKEz2AGdFYuWbsbm1lRebY5Zh0cuPAKSwUC5n+EOQNIAZdMot1SIVHXgROErcz69MhU2isdIBEf9NA93A4Uijz4VyBrW8/yFINcw0ac2ezgJKL2Fety2baH5FE2iasr9IR4j59rXTE60zkyVh0UMZx5mRa5B4Oqlbngj9MdQRJbwq8/1uFtTj/mzslKuZzRI4i8eIeFdMJFyaZ27aE5C6c0bDyvrnwNotSRY4xEg0EYPHdx1QOmRQM4oOmdfMxXXCjqVyr6TgV2f05zN+ZST3NN5Rbwkkq2lPfcX1luV3rwk7KHWpnrqOnfQOK5qcmczPYCQJCU8XEXnGwvTQ5ABVM1l9pyZM2di8eLF+J//+R9s37492bO2M9nZ2Xj77be75Jom2LBhQ1LApqOyshLPPfdct/dvuOEGvP/++9i+fTuOPfbYLp9VV1cnj3XkkUfio48+woUXXoiGhoakg5iOgbiSfSU/Px8ffPABTj/9dDQ1NeGWW27pNhe1Wo2//vWvA+7LOhBefvllnHLKKUlntDN6vR4vv/wyRo0aNeDxdTod3nvvPcybNw/vvfcennrqKTz11FM9bt8Zs9mMt99+GyeeeCLa2tpw66234tZbb+2y/SuvvIKbbroJHR0d/Z6fJEl46aWXcNlll+G///1v8tUXfvnLX+Lf//43HA4HTjrppC6frVixArNmzerXXObNm4d77703ma8uSRJ+/vOf97jP/nJ/7ylnnXUWXnvtNSxYsABbtmzBVVddlXHbww8/vMvPo0ePxv3334877rgDW7duxZw5c7p8brfb8frrr+Pyyy/vUzugAxF2bBmGOXSxFlOIrr8tFYK6J4R9QNtWEiSxIBWpyqoACieSQDXY8fkOJ/79eXWX3R48bxIKbAYSlM0bSBRbCslJtBSQ41cwlhw9g52cXG8j0FpFVYDTPREPuUm0B12Ar43EXNlRNGZveBo65YUO7yqeLYVKGG8olZPaVyJBoGEN0LKenGRzHhWnMhcoFZmBn5Sb8OqFRSi0apO7CQB3fdqBh975ASLgJPHftpXEZyxCY23/iNaueR1Q9zXQsYvEIiQSj55aWleVlpxUcyEVxQp1kBtb/03K/WzfSQ8FvE3klBuz6X3HVjpWt/PyU3ugiJcKQeUd1rOoBehYxmw6u2BH6v2EuE04t+66TCMo46gUMauI2zg7t0PJZZddho0bN+Laa6/F6NGjYTQaYTabMWnSJPz+97/Hli1b0uaMLlmyBE8++SQuvPBCTJw4Ebm5udBoNMjJycH06dPxyCOPYMOGDWmF1WmnnYZ3330Xp5xyCvLy8nps0zF9+nRs374djz32GGbPno2CggJoNBqYTCaMHj0ac+fOxRNPPJE2jHEwmTp1KjZt2oR77rkHRxxxBKxWK/R6PYYPH45f/OIXWLt2LW688ca9OofdmTZtGr777jtceeWVKC8vh06nQ1FRES699FKsXbs26WbuCVlZWXj33Xfx0Ucf4dJLL8Xw4cNhNBqh1WpRVFSEmTNn4o9//CPWrl2Ls88+u9v+kyZNwoYNG3D99dejoqIiOccLLrgAX375ZTch019MJhNef/11LF26FBdccAHKysqg0+mQk5ODSZMm4eqrr8by5cu7VY+ePHkyPv30U8yZMwdFRUV77LKPHj0aRx99dPLn6dOnd4tUSMf+cn/vKeeddx6qq6tx//334/jjj0/+f2CxWDBu3DhceumleO6557By5cpu+95+++144403MHPmTFitVhgMBowcORLXXnst1q5dmyxEd7AiiT7ECHg8nmTDZ5ttkHv6MQzDDCXeZhIwxpzMblxvyHESg4F2+llrItG8Ww9UfziGUx5diYaOVFP2S6YNw8LzJtMPju0k/LRmYPh0yl9NRyxMc06GsRqop2znHNyWTcDWD8jZNOeTQC0Y17sz7WujMGOVhgTa7vmzITeJSn8rhQDnjOy916sQFArd+IMS2mwlh9RaAOiU6sNB5emxKQ+wlaDBE8GCp7/FttaurSsuGAUsPMIDjUoAUJHoFkrLHK2RhGvET8conEBVk0MeKjYV9tG1MthIODqU0G6AHhaojYDBkmoHpNGRgNcYyCWPBinkuWAsjQ/QNXDVINkGyVbW9/ZA0SAJYq2Jikp1JhahNkbxCFV4thV3vQyhEKqrqzF8+HAYDMpDkVhIqXytJrHLMEyPLF68GFdccQWA/a8fJ8McjHT73dUH+qNDORSZYZhDG3MBObZBJ7mG2r79R5sk2EGunhwlUWUrIRcyDY8u29pF1A7PM+POM5WS/P52cg4liZzVTKIWINGSXUlz9zaRs9e+jX62FgMQQO1XJBjzx1G7GIOdhFdPhNy0T6KtUELUyjKFJcejVKE41EHCzrmTwn2zK0lMqZRwWpVSgVmto33at6ecx6xyCmHWW5VWONtI7Kp1SksfEoylWUa8+suf4Kpn12D1rlRV4le3A05XDP+Y5oKxcJQSHl1CDqneRuvnqlHc3CCdhymXclIdyjy8zSSEZSUH0ZRDubHuGsBvoDlEgyTaLUXKWCEgEqAHD/Ew5fGGPKlcY/swcqD7g9ZIAjsaoON1DutOFIlybKPwaLW25/ET7YFiYRLv8QitKcMwDMMcIrCwZRjm0EalIvHiqSeRmDO8b/sJQS5toqqyKRewlZLAS8P6ejee/qJrCPLD50+GSachQdnwHQmtwond3LmM6ExURCngJHHtbyWRG3RTmxmdBRh2LCCi5AL3JNpjYcV5BFU/FoLaIUV85IBCkABz15Lw1dtSebwRP2ArIrGo0tIayHHA10pCMuwnl7TkcCBfqf4bctPxRJzGyqroVqE5Syfw3Nxi3PRmEB/uSOUkL2/PwaWr7Xh6bjGyyid1r+xsLyPR6dxFocNqHc1fo6MHD4nQX5WGQr1tw4AimUR7+3ZyZwPt1E6obBr1+NVZgKa1dI8IQZWrc4bTNciq6PlBRE+Ycug+Crq65ytrjSRunTtoHVWano8jqQC1HoiHlIJokhKOzTAMwzAHPyxsGYZhTLkkCkMd5MrpTD1vH4tQoaCon8RGVkWP4bixuIzf//dHyJ0i3X5+TDmOrsxJhfYGXSSM88cOYP455DJ21JKDuukdchhHnQQYrOQ49uTWyjIVUgp7KefYXUvhvUKQkxiPAhKAjnpyK835tAZ6CwlAXzONozGQc+130HmFvQAkcsJzR5HIcu0CAh0kGnUmIHsEFafqHL4rx5Pi0iDH8M9jPPhjPIoXd6X67X7vUOP8V1rx7LxmlJSWpfaNR6kYWDRABaICDjq2OY/WSK9cJ08DzcXTCCAOlBxJLYRaNlDIdNtWKrgV/AgYNo3aBBVNpnDl1ioAMjm/E89NFbQaCMbslLBN9FfujN5C95ermgqHqTQ9t31SqQAozm08SmI3w8MWhmEYhjmYYGHLMAyTcG3dteTI5Y7MvG3Er7RiiZGLl13Zqyv2zBe7sLHRk/w536rHbT8bq7iK1SQErQVU2KmnvrI9odaSeK39hlxkUy6F0gY7SNz05PS1VtE5SRKJ62iQzi8SBCBIzPpaSQjKMq2BpCIBGvHS/J01VE047CNBFfXT+hhzySn1NtG8oiHqHavWUfGmqJ+qGRuySKDHIyQo5Sg5vcF2qOUQHviJBgVF2fjb16mw5O2uGOY+sx5LLo1jTHkJCVq/g+asNQK5Y0jgG+wU5tyZrHIS4r4WcqZ1W0hAFk8m4Z5VTqHhASf1t/W3Ua9ZESN3OuSldan9ih6GJNzbgVw3vY2c9rA3lbvbGWMWIA8j19a5k+bREyo1jRuP0npKBq6UzDAMwxz0sLBlGIYBSFT5WnoWGCE3iTshk/ixlfYqGOqcAfx12dYu791z9gTYVSEStbGQ0nooj3JkB0rYB7RUUbXkrHJyfr1KdeOC8eldu5CHihc5d5JQNWRTrqkcJcEa9gOSACABATe5ueYcyuVUaUmwWgtpv0SuqbWI3FJNGYlXrZ7cSHedIojVVJgpp5LEYkcdHU/SABEPiTGtSQkflmnNDVmQCifglp/mIc+qx5+WNSNhfjf5ZFyweAOePnUHjqzMVuaYaIWkptZEYQ/lMJt3c60tBUD2SMDXRNfBsZVylG3FSri1lXoQB520Vts+AjQmul6lRyj51Q3kjvrbqACUtaj/ItKYTXMMONPfdwA5zvEouePOHYC5l5ZNai3dp4l8Wy4mxTAMwxzksLBlGIYBFLeymISrt7m7wPC3KwWQBAlaS+8iVAiBP765AcFoqi3OSeMKcNpoC4lJIZNANOrJMe5rNd3diQZJlLkVkZg7Ehh9CrW+ifhJWCZ6pQLkmnoaSMg7tgABF/V5FXInl09FIcRqDY0RC5NTWTCOwmF1ZnJF41ESw6YccjBN+anQaJ2FHhJ4m8n9DHmAUDuF/7ZtIgFrzAG0OlrfmNKqJlG0SlKRI24rprlHfLhsnAq5mmzc/KELEZk2c4eBS9/14x8nBHHSkRNpWzlKc88qp3P0NFC4+O7uusEKqCR6qBBw0IOBqJ/c20T7nNYtdL3CfkB2pdbcWkKub0c9rWVbFRWRyhlJwtpg65ugNGQBUh2FwsvDMt8HtmI6r0A7nU9vVVzVOkAobZnisYFHAzDMQcqCBQuwYMGCoZ4GwzCDBP+WYxiGSWBUHMuIT3EKldxJbzOFq8ZCinMWIbc1HiWhEY/Sdio1iT61DtDo8fZmHz7b2pYc3qxT497ThkNyKaLWlEsiRaXtvWJxJmIRoH2HInTiNOfsShJVOguFycZCVJwqezgJTb+DxGzzOjoXWwmJO6G0rDFmKwWgZMqtFbLSV/cwJeTWQg8CvE0kxrR6coXbNpFba5tEBZwCDspddlXT2JYieigQ7gAiYQrrDTqBiJ7e11oAxEgkxsJKeHIezT/oBuwlgCELpx+lR7ZxG65+xwGvsvQhWYVrPhFYGNuGC6coa6nSkHgWoJze9h3kqnZ2VLVGut46E2AaQw82gi4KvdZZyJX1NlKFZXMeoFP6y3rq6Trby+n9sFvpdbuN9rcPoxBijZG+mnIzh6yrVHTdgi5azwxVtQHQuLEIEPAAopfCUJJEc4yF6TqrVPSwgGEYhmEOQljYMgzDdMZWTO6ctxnQWSn/tKOWxJWlUKkS3LW3KlSKwBAybRcLweuRcd+HrV02++3MEpTEG2g7W6lSbRgUvrq7SyfHU4Ik8ZLjJF6T28hKXnAbCcqgk8RMPAbUfE4C1phDYjbQDtR8RfuFXFTASa2h/E61NhVSrDGQ4BQC0GrpmFoLieLCCTR+LEKFjCI+Cv015ZKINeVSbq5jK7mXAReFzspxGr9kKp23wU7rW72K1jfip/3NinNqyafqvjoznVc8Rp8H2ylkWcTxkywbXpqbhwXvutHmI3UbFxJu/SyMtngYvzomF1LUTyIUsuLGNpFgtRbSeWpNtIayTA6s3kZzc9WQwIwrDy1sxdQuyJhLebtRP1D3DT0EMdhJgGePJNEY9pAjHdsG6LNJ9AbaSDCbC2is3asfA3Sdgi569SRsJYkeXIQ2pRx29FDtWqWm6xyP0XXrbzsrhmEYhjlAYGHLMAzTGYOdRJVrF+WsxsPk/CX6lOqt5IIl+rSqNF0dQJnCVx9fuhmOgJx8e0qBGpfnbgYcKir+ozGQy6rSkkMacpO4iviV4k3RnucpBPWAdTeQcJKUkGGtMVUVWK0FsixAXAYCSg/XWAQwZ9N5aAxA7nA6h2gQiClCTmcksZZVQUJNpSZXV5Jonh21JGC1ZspbddfTuGod0LaN8nMTrm9WJbWsKZwIqJU830iAxi0cRxWL27dRqx13LQnevDHAYT+jbcMeGjvoBnwN9DUeAYzZmJBnwuuXDsflr9VhV3uqP/Ciz9vRFtXjT2dPgSpODxqgs1K+bcBB55d4EBAL04MMxzbAaKc10yp5tM5d5Lia88i9bd8GNK4hl9Y+jARs/RrFbTbQGpnzaBxfGxXfioeoinE8SveUSkMPSLKG0YOAhPutt9K9EPZ0DRtPh1oD2MroOsRj9OCgp8rHKi3dl0LmkGSGYRjmoIV/uzEMwySQZaXtj5uEjhwF8seT02jO71sOrEqFHe1xPP1NU/ItSQLu/6kRar1SGEnEgZ2fkuNpL6MCReicLymRoNLoFBGtJ6GTENFBF9C8kYSTEED2KBLgYS+JSHspiR6Vls7DuZMEoUpFYkrIlIdbOF4RcWbA30LnLkcBlZWO6dxBrqs5l4Sur5XEOEDuo85CIdmuahJkGj2FQGuNJLTyDyOh17mdT8BJAlvIgEoHaCTK27WVUihzyyYSgGueoQrFtlIqzOTaRQJXjiphvTqgdSPK9bV47bQiXPGxEeubU+J28TeNaA/E8MhFR0BvMpELqjUqwtZKOdLRIM3F30qCTyUp7YBUJLJDHrpGvkYAaqWolo/CkwsnAMOOon7B8QiFbAc7SLSWH0eh6+46IOoDJBuJXiGUKswN5B6bcmgdjVmK82tXnGlX7zncOiM9zADo2kuGzGHGktLPNhmSrOYqyQzDMMxBBwtbhmEYgASXp4GcyLCHRJvOBJRModDVPiKEwL3vVCHWqWntRRPMmFRmA0wjSJy2baaCRpIKgAow+kmQmnIVoWnsLjziMRJ4AQfNNeSi/UsOJ7cvrPSNzR1DnwU7qNpvUHGCTXmpXrdhD4knnRHIGkHbanRA3lhFgMVJ3LdspDG1emDH8lRecO4oml/DWhKjKg2JMlMBOZHNP5IwbN0MlB9LoclCpvkHnQAkOs94RHGKQ/S5MRuYcC7Q8iOFA9d8TULZWkxCLxaleUoSiTQ1OZx5nka8eIQJv1xbgc+bUuv2zvpWuALf4InLp8Gi19C5hT3kKEeV/FmVmoS3LAG5I8j9dmwl19Rop7zesA+IBQAYFNczQvdK9nAgSxHH8RiJ70iQBH7+YVSYytNEIdQ6Kx2/+HA6RkcdXTPnTgpFD7TTMf0OJR+5DxWyVSoIlZqeicSUyseZBGsi/1uOcZVkhmEYZkgQvRU93ENY2DIMc2gTDVEobcRLP8txync0KVWCA05y4frocH2yubVLwSirTsJvjzaSGxx00phRP4kiWwk5o5KKQmOjJqVCbqdjxWOUp5rozxoNUSivKU9pi6MhsdZSRft6m4H61VTcSGMg0ZQ/jkRa+w4k+9JqzEDbVsCxk8SipZDEmN5KLqtaD1icgN5OIjLgJHFksAGO7UDTOiUP2UQtgKwlNJ+2Kto+0Q7I25iaV9ithMxKFNYbDgBQwrXV2pSzO/JEynHd+RmJYTkK2KYBE06ltfE0knscj5AAjwRgCbbj6UlV+I1UjncaUxWtP9/hwsVPfonFvzgGeRY9hfA2rAbad5JglkAiL9gBeOpoLU255GhnV5KDK6nIvXVV03FblRZCYQ+541o94GunBwlOJRQ7FqZc4USF55YNQPt2Eqz2YUpxLQNtFw0q66Wna+NQ2kPZlXDlNJECKuW9uJBofkJWKkHrMt+cai09tJDjvYcvMwzDMMwgE49TnRDVQLtA9AILW4ZhDl387SQAhUyiTGNUChSpgbzRJKAS/UV374GahnAsjvverery3s1TgDzJDajy6Q2dmZzJ7CyqJAyQaPU1p8JQrcUkLoOulIssqUlkCSdgLQD8TnL51BoKb5XjJPrc9TSeKRcoO5pELWQKfdVZgMox5KI2riVxGvaTOFNrSbh56sl9FTIdU5sQrkUkemu/AqpXUkiuMZdczvyxSoi0k8SZtZjycztq6PzUSr9bUw7lAfuaSewaskhAG6zkWAuliFPYS+eU6BMcCZJwj4aA8p9QZWZbCV0fvYUcy2gZdMEO/M1Yg7wfwnimJi95DTY0ejH3n1/guQWHoxzNgM9BQlvEKJ83Gia3NOyjeeeN6V7AyWgHjFOpYJRGRw8FfG1AqSI+cwWNsesL6h/cvAEYd6bykCSX3Fx/G+VER4N0zpYiEqMJJ9uUS2vqbaaxJYnWqnNfXgWtVgutVguf3w+LpYDWJq7cJ5kEqySRgI5HaK4sbBmGYZh9iNfrTf7+2htIog+esMfjgd1uh9vths1m2ysTYRiG2WfIcQrJDXUAkEiIqXVAxy76OXcUCaZIgEKG1ToSob24tk98tgMPvr85+fMoWxzvn6eDNquUHFZzPgnVoJOcQ0t+aufOzmw8Qg6izkRC2JRHjqqrmvb1NpPTp7dSnmUi71VvoxY+Kg1QcRxQNJnEb9QPuBvJLSwYR++FPXQsnYXGsRQo4dj1JN7aqwE5TOddMJ7yQLcvp3zgiI8EW8LhTVRWFoLCm/MPo7zdgIMEdDzWKfTYT06yKY9CrnVmckcTxZI8TRSeG/HReeitJAZbq0jo24qBkiOBip+QiI74SeBGfCRwRRzC14J/rfHg4Q1df1/lGwSWnBDC+FwVrZ/WCBRNohDn5h/oPCqOo3PtibAXqP2ailFJEpA7mh5EmPPpum15n8Rq0URgyiUkhKNBJde5gx6oGKzktJoLaC6unan812iIzt1WSsfTmRVXvYiOo9yHLS0t6OjoQHl5OYx6LYUjS6D17amtT1QJ/VbruJAUwzAMs08IBoOora1FVlYWCgv7nuLVHx3KwpZhmEOLaIgERjxMbmF2Bb3v2AZAUDhsZ2Hj3EnhrrsL0d1o9YRwwiOfwh9JteN59lQtZhx9eCqcNBYhgaZSAwUT0hej8jYD9d9ROKtaR0I0bwzlnLZvJVEVj5OALJxEIa6O7bSvSkO5oPljgeEzyL0VcXIitSalB2+UxtYY6FyTvWhNJNDkKLBjBdDwPYkfCBKP/jZaO70VKDuKBKEcI5HsbVLydPVU/diUQ/t1NFBocjRIDw9MueT8GrOUqsomIGdkqh1NRw0JbkmlhOHmKE6ql65Pw3e0DmqlSnXFcSS61RrF3W6i6woA8Rhe+b4Rt3+tpnBdBatGxv+bU4xjh2eTiA+00zq7dlE4dWHiAYbU9askpdxQlYZEdP23NC+1jhz9wgm09u3bgfWv0rpXHA+MO0s5xyjdT2E/hWXrLeSg2kqo1ZJjCzm1HbuU0OxKGjvgJNGrU8LEs8sBgx3xeBx1dXUIh8Ow2WywGg1QqwBJUqVykdMhxxURLPWcl8swDMMwe4AQAvF4HF6vFx6PB3q9HsOGDYNa3feIof7oUH5UyzDMoUPYR66nHFPa2ZTTH/mOrQAEiaXd3TprMQlbX0vGfEcAePDdH7uI2pOH6zFj5uyu2/vb6DimvO7jCKH0WW2hgkUaIwmhWIicUn8LvSdpAWs+CXLHNsphDblp3loDkDeKziMRBqxReqaqNDRWNEBuau5IpXBSJeXeRry0NvEYOcBqHQk59y5yb6NBEppFk4HyY5S10ADBAto2EVqcqNocUJxlKIWePE0kzIIumoe1mJxxlZqEq6uGRLXGSHNK9FtVa+m45ccA+WOAujVA43dKESwvuaalR9B4BeOUcN9GIOzDhcOjyA3swq9+HI6woF+i3pgKl7/RjL/PaMTPshVBblLCheUIifi+5v7Yiinntn07XbuAkyo95x0GjD0b2PAaUPMlOcEjZtC55I6mdVZJtKYArXfER6LYmE3H97aQG28togcAER+dW2srFebKqoA6/zAMGzYMDocDXq8XHR0dSl9b0XNIMkD/BhLh5hySzDAMw+xFtFotsrKykJeX1y9R21/YsWUY5tAg4CQxBEFhvbYSKujUvo3EnimPKvqmw7VLCYMt7V6tNhbG95u24bzna5Jv6dQSlt0yAxV5ltR2cpyqDEOQW9s5BFSO0zHCHhIa2RWU5xqPUU5r7Ve0Xc4IKjoUj1HBouaNJOwMNnI+bcUk9sx5Sq5rCQmueJTGlURXUdv5+K2bKC+0eT0JT3OukrvqJ7GqMwPmQgqPVqmBnNFAVhm5wgCNKamAjnqar6+F9rEWK4Wz2mk8Uxa9bysBtJZUXq1KInFpK+tZWEZDJCRbN9FXlTpVqdmihDZ5W6hVUTu58Gu8+fjFutHwxFLnrILAn6e045JyD7m+5lxyRfMOU/J2BV2r5FeZxGCi8FI8Qu5wwAk0riMnPqY40yNmUVhyywZg1+ck/MedA5QdTtdFCHrwEHQpudFqCiFW60nUh71A9Wc00RGzAUsehcUHnbTenga6plojzT1nJIQkIRqNQo6GqDAWZMoX1pkzrKMS/gyVEgrOz7kZhmGYwUelUkGr1UIaYHQQO7YMwzCd8TaTwIME2MtThaDctSRqdVYSjJmwFJEI8bWk3FYhAH8bZE8j7lnW0mXz/5k+oquoBZQ2LnHav7OISIRGRwMkcGxFJHbc9eSktm0lYaSzUi6pYzOQN57Ch911VADJmA0Mm0ZtdgASMzkjaHs5TkJVZ6J82pwRXUVtPEpCsG0bhdZ6m0kgWotJ/JjzSTCXTSMh5dhCYrr+W2Dr+7RupUdS1eCQm3JqQx0kciUN5dmqtABUgFZLOaU5I5VewVvovNVa6hdsLe7dLdUayNlU62huju0kBHesSFU4Vik9f3NGAJIKR1UW49VhEi7/MIaWII0vQ8Lt6/LQri7AdbYOSAAVyUrkufaV7EoS47YSoOZzWoeG76n3cc5wemDRuhmoeoOuf9mRdA7ZlTRPfysJfpWWhHL7NnLc88dRGHfrRkCaQA8rdCZ6uBJopzE99RSe7ayGVDQZOksBoLMBUjkJ50ATYB6bXrQaDEA8j65PzA2YS/t+zgzDMAyzH8LClmGYgxchyKUNOhUntJLcTYDEXNCVcsl6epKoNVCxpKCTwkGN2SQcIj68UeXHutZYctNCmx7XnTCq+zz8Sgsgcx6FREcDJCoc2ylUV2eisFOPUjXX00Dhu0IGskcAEQ8JYEkCmtaSgIr4SFBZCkhkhjoob1ZS07j+NtrGUpDKZ00Ix3iMRJWnkQSxcxc5xiotCbv2HbSt3gZEs4DaL0n0Jfq4tm1SQrSbaY3N+XSseITWKreU+rUGXPSeHFPyX5W81WA75XlCJsHtU9oB2UqVYl49/HpSa8iNDDgofLmjhtZYAqCzAwYdOd72YZQPLMdwWFYTXj+rGZe/58VOf6qH6yPfy2jzAHdNqYUq6KJcV2TIpRZCcWxjStucWKoatSUfKJ5CojbgBPwuWsvhM5RWQg3AprcpdLz8WLpu9lIS4N5GGk9vp2vQUUMh2dYSWmN3ndLbNp+OZc4Dhv+U8nGbfiChW/sFPbTJHalUn/bR+x019F46rMWpezrh8jMMwzDMAQoLW4ZhDk7kOOCsThVhyhlBjiNAYsHbSK5izvC+hWFaFdfWsY1CVSEQEjr85euubu3tp42DWb/beJ4mEm6SmtxOCBIeCeFqzifnU62j0F+/0nLInE8ObyxMjrPeDqjVJHQSbWd0FhJNHfVKZWKZhKqvhY6hs5JYyh9PuZ0aIwlObwu937qJhHbEA6hNgCqu9NsVSnjzMKXKLgDJCEAmAZ0zkkRZyEPusmM7CaP8MSTYIooTbi6k841HlcrAO2i+2cPJOTcXkEAMukhgddTQ8WyldM0M9pSDmugF7K4nJzTkJjfalE/XUm8Hoh56SKAxUt6xq5reVxlQZo7jtVPCuOJTFda5Uq0Glmw3wBmI4i/jt0EHQWOpNbTuidY48Ygi0OOp/NREYSmVRikqpaVj+lqBpu+A2Biaf8XxwLYP6dxiAUVsjiJxm11JEQG+ZrpXLUV0faIBcqL1VjpvT70Sdt2pDZElHxg5m1ze9p0UWpx42GEtpnzhsIfms3sIPUDnaCmke8vbRHNhGIZhmAMUFrYMwxx8yHKqZYzWRAIp0U4mGqJCRQC5fgmx2xsqreKk1lNOZ/44PPNFPRp9qYJRU4ZlYc7UEvohEiABGHJT2Gg8TMdTaUgYxaIp8WYrpn066qnHathL+2kMNEZHDfVytRSQmA12AFZQsSY5BqiNQP5oEpvmPHJfZRmQEw6jABpW07kHnfRZLERiUsQAqEkkqXXk0MYCtC55YynEWW8hZzvh5GaV03Y6Mwmq5vUk+FVqEoGtm+kznZkcVXMBFU1y15N4jYfos7zRJNzlOJ1z0Enn6lOc5Lpv6Rz1Vhon7KVXxK+0ArLQGJKktAnaQQ8EYlES/K2bKMw33EFC3ZiHnMLheOEsH375vhur2lLX/p1GK1yeAjxx2ApYPM2A3qwcV3Hj1RoSvCq1khOrJkdbUtM2KhXNx1oCBN30fjQM6EEPI4bPABp/IDc35Kb1ivjoPtXb6D6NBelBQe4Ycq9VaiVCIIcc3Y5aOu9E1AFA2+SPJXHfvhXwttJ9GvHR/AVoLRPXY3fMBRQmH3TR9zpT3/49MAzDMMx+BhePYhjm4CIpar3dCyXJMlVAThT5sRb1bcxYmNzfsJfycq3FcAobZj5dC28k9V/oy1cdg2OKJAqRjQbozYiPHFhLIeVdRoNKxWKJBKLBTqKibSuNDUFhwpAoBDoWIcFnKaRXRGkVE40CISdtF4+Q+1k0ifZt20IOoDmPBFOwg9bE3w7Eg4oDGQNElMSzpZCEn1pH4qZwnCLCVcoajqLvO2opvFWr5Ov6W2lsOUaiKeSlvNmwl8SXzpoqyOVtonMRgoRdPEzOrsZIa2DKSV2nWAhw1dN6BNqVPFylxY4hC7CWAgWjlZxcDbmSzRtoW6jINQ120HUOuuj6yXFaTyEArRGRSAS//bEEb3dUdLnUk/UteGbEp8i1W+kczfnUWkmtVeaaRaHo5jxaO5UGgEznFgvSGjSvB1w7AEsxOfG2IkBrpfVz16bmqDUDWqXHbVxZGxGjhwg5I5Wc2+0kfAsnkasuqeh6pBOpkQA51H4HrbG1KFUl2ZhNAjhd/rC/neals1JVbYZhGIbZT+DiUQzDHJoIQX/YR7xKX9bdqv+6a0l86G19F7UhD4W9ijiJL3Mu0LwBj3/n7iJqTxptxzGmRsCtOLgaIzm7/rZU0abOotZWSoLNXUcFm4IuEkoGm+KyyoDBSiI1EZIc9tJ4YQ9tb7BR2Gkij7ijDogorqzeCkBF+/hayInT26kycsBJ4kdvAaxlQKQDCLgBlZKnGvbSA4Cwh8Suux6wFqZykqUIEGkmB1itIdFkLab19dlJSAacdPwWpY+tWktzkFQk4oJK/1udRXETreQam/IACLpuBhsJeTlOjrkpF8gpBwxK8a9oUHFGQySg7cOU9js7SNhF/STsNDoaLxZWxK4TOrUOj00MIXdzEM84xiav44/hQpy/83Q8O2Ujhum1NG+9jdYBgsaM+imcWmOgczLmkFC1FCoto7LpHD0N9FlHPc1Brad94mHF/RV0H5jz6RqFPXQvhDy0/lkVdK84d9I8LEV0fdp3kFO9e7SBzkTiVbOL1jTgIuc56KZ11Joo9H53TDlKPrY3Ff7MMAzDMAcY7NgyDHNwkBC1ITeJytxRFKIaC5Hw8TQobpmanEJJUlq5QBEZGnqptSQ+NDoSGEEnbZNwTB3bULNtPU56z4ioTAWn1BLw4aX5GJWjS7l5OjM5aI4tKVe0o4bEmM6s5GiChJgQJJgtxZQvGXCQeGvZAER9gNqgOJayIkrbAKOdWtMYssnBldQk2pp+JBFuG0b7JHr2WgpIoAXaKDRVb1Mq7DqpGJUcA/LGUKshOUaFiQIucmVjITq2MZvEpsFO5ySUfFRjDhB0AEEPAKUtTthHY8tR5fiFgLWAvkoaoGMXCWWVluYZ8QHxuOKoGuh4QhH3Kh1dL43S21ZnIsHqrAG8DSQiTXm0n68V8LRQT1qVknertwEaLQlLnZnc1agfkAChteJfW614eFdXwVegi2DJsc0Yl6MCCsaScDbl0jkG22nuiWJZ8WhKAOtt9MDAVau05YmQ2AXoQYa/DQj7qQKzuYDmmDOKnNKAkwSu30GOe0Lwd9SRGDbaab3M+XQt8sbQfZru34KnQRGrAbof/G207mVHU5/k3Qm66AGOTgnvZhiGYZj9AHZsGYY59OioJWEgZMCQQyGcsSB9FgspPWxBIkPEAahT4laOkQBLIEBuYthDgiVnJAAV5WzKUTy80YqonMqtvWiCCaPKikm0dRYa/lb6qtJRqGzIRYJEyCQyIn4SKhJImG56j4SaxghE15Io0ZoAozKmpFQsNmYDuSOAoikk5iU1zXPnJySKDDmArxEwKTmTIk6un6eR8mcNWYAxl0J3d31BwthaRA8FWqqUirwFSuVhJ1D/Dbl+cSUXEwKARC6mIQuIbKQ8VjlOQlRjoPPSW5SqzYUkhrUGmqsxh5xDx3Yg6qX3PA30c8BB18WQRXm5WcNpnFiYrqG3ma5LwJnKt5WklPhT6aiIldZEX015dM1tJTTHeITeVxsAEYek0eNXJUbkbgzg9tV6yNT4B60RHS78ohj/N2YNptV/S7mzhROA4kn04MJeRssgR5X5KCHTvmbACxKIYS/NKRaksHNJydP1b1QqXCvXsnUjuaXWYjrXiI/WuKOW5qqzUN9iWQbkILn4phx6SFJ6RPfwYklSipHpKSdcjtE6+JqpuvXwGd2LSRmzqaBYxMeuLcMwDHNAwo4twzAHNnIcaNtMQiEeJWdNpTyzU+tTobQqFZBVSYWaVJru7X1kmURKNEACK1EZ2JRLwsHTAIT9WNuuwrnLUvmNJq2ET3/9UxRk7/Z/YzxK7qm/lcRXLEziQWcm0RXqUHJCQa5cw/fkBOosSnVfv1Kk6jASmjoT5fl6G0jslR1JYzb/SELEWU1un1YpEiSBRJ4pl87LXU8CS1KTyIuFgdYqes+mtMXR6Cj3NhZU2tmA8jp9beSEarQ0t0A7uY4qFQk1WckLNdgp9FVjpDBZcz6dZ9RPx9WYgMSyS2ra11NPgh9qyi8VEm2faN8jSXRO8ajSJsmXaiGk1lP+aqIaNJT2ROY8QCXRWCJK5yqpySGNx2h7o51e8bgiiDX4qFbCDd/aEJZTQlGvkvGP0etwsrWa5mIrpVxma5GS5yrRNVNpqShWQHFzg66Ua5tw7K1FJHCbfqSqyUIA9gpywfUWJR9XiSaIBikc2dtCa2grBQom0gOXQBu9Z86nVlAVx9LapyPkJidWllPtmdR6oOI4Ci/vTNK15VxbhmEYZv+AHVuGYQ5+IgESjc5dqVY6OSMoT1NnIcdJrSXBZ7STqMwuzzyeSgXIEjmCag21PskZobTo2QRIKoiwHwt/yOuy29WHW1Bg262SrBDkfDauVcJtNSQiDFlUiMjXToJOyOSkuapJ5GVVAgWH0f4axQ1VKQIwEgBkxZFUa0lodtSQoA/7SdTqrUDBhFT7oliEHFo5QgJQUgGFE0k0Nawh0WsrIZETVSryymES4v52yv8NdtBYaj0JTl8bCTq9mcRS2KM44Ehtl1UOmLLpIUHMQA8JvC20v0pxLaMhJUw8SO9ZCsiF1phI2PkddHxPM+0HFbmXGj1gygJkpSWPLMjNNOcoVaMNVMAp4ieH1teqFI+SAfU22hdIHVdvpXButQanWPR4blwEV24+At44ueRhWYVfbp2KRYcPw3nZ1SQoG39IVYZO3DuJ0HKtSakunQXkqJVogRq6D9w1QOsGus6mIqpiHN1CY4V9dO6WIqBgPI1ZNAVo2UgVrf3KPVM8CXA3kmB27VIeOjRRbm3+WMr17YzBTvexcyf9O5Dj5Io3fkfj20tT2yZdW861ZRiGYQ48WNgyDHNgEexQcgeVkEl/K/1BXjSZHLHOTqy/nYSIWp/Kc8xEJEB//MtREizZw2ksZ7XidobxceQwfNvmTu6SbxC4qryJBGyi8JGvlZy25nUkOuxlgL08VazKVUMhppJE4aSJfqySkvsbU/qlxsNK7qaeRG57NfXeNeeRoAu46DjREAlIvZXcXXspuW3REOXoNu5IVSkediyJLmc1oNKT0LeUkGCKBJX8zjbFGfWTII34AY2ZBLlQkTBK9Nj1t1F4scZIc4qHyFH2NABQAxqNEhqs5J9CIqEdDZHLG48CWgu5qxBKmx5NKhQ34XCHfSSGEwFGWoPSyiauFEUykkiN+GldIn4SrtEAHUeSaBudSQnDzqKxIp5UCySldc80vQ+vltfh8rrT0RojZz4uJPz6+zx4RvuwILs5JTKtpakiTnq70hJICdFW6ehaQaWcWzhV7ErIwIiZFO7trKbrrbOS0I2H6fzzRtODh/JpdJ9sWar0uh1F7npoNIXGt1YB7gYS7q5ddL8VTiIRm0BvpXB65w7AaKN1C7spXD8eoYc4iX831kIax9vCwpZhGIY5oGBhyzDMgUHITb1KE3mzQpBAyB1Jf7Tv7lRFQyQ+IFFBpHRtThKElX6iIq7kZJbR+44tQMsmADJi9uF48IOOLrvdMrMUZmsrOYsdtSRaE4LN30y5rtZiEjghFwldfyuSrX7iYXohDtjKKPRWyCSAgiEl39JP+aSBNsrp1BgVAdqutM+RKVQ3axiFJ9vLaB5N60iwGWwkUCxF5OxVf0b7qTXkqvpbFQEYJ+GXN5ryaZ3VtGZ5h5ELGgvTsTQGEofuehpDl0dhuHIs1V4n7CGRCighyAYaK+JRWv1EFQGoByDoYUIsAlhraQ46k5Kja6V10dsAfwsJuFgQiIGcW3M+fR7yKPnSslJN2UourqogFaYcDaTyk4umUOslWSYh3r6d5iVkIOjFWG0jXrd8jnmbj0VNNCUQ795WCU+xDzfkrIYUFQCa6Lzzxyhi20JzDToBTw2dGwQVh4oFSPyGXBQOHWgDSg4nwR4LKUW0sug+kmW6F/xtJHjNucCwY4CmH0jIqlT0oKTiJ3QNndWpFlHuegpXzqqkBx2Jnrd6CxVUa9+h9BeO07XSGuj7nOGp9Um6tj7aj2EYhmEOAFjYMgyzfxP20R/qER/9rLOQk+VppDBL+7DuolYICv8UMhX9SdfzM0EiB1HIqd62QlC4qWMribeCCXh5hw47HE3J3UbmmXDhBAtQ+4NS/TZGLqfGRCGwWhOFgJpyUnOXVED+OKpmG/XTcfU2IH88kFOhVF1Wil7pcsidFjKJM3MBiaiQW+mX6lZEUTDlMJvygLo1FO4KNYlgcwEJlvbtlI8bj5HQNGZRDqrBSi6dWqnoHPHTK3ckkFNJ4ksAqPuKRH7QDdR9A0DQetnt9H3QR6It4lFCgX0kcCP+rqHKPaHSAAE1hQub8wHZRA8NEkW+zDnAzpU0njmPcpJtw0jYJ/JO1Tq6J8qOAkz5tH5hN7Xcad1EoebtO2gtRsyi8GVjDr3vrlXErRbDzDG8OuYzXL7tJ9gcyU9O8a9NE+FW2fGHvK+g8jTSfeiqA2wFdM0TDxr0NnK5NQYASvumqCJu/a2At5WOqTHQQwq9UgFZZ1bOWdDDD42BCmMlnGBZVh6gxOl6FYwj19XXSv8uTDkkjp07SBjnjKBxdCYaO3cknX80QGMGO0hUO7bStho9rUlHDc2ThS3DMAxzgMDClmGY/RM5Tu5Tot2O1kRCSmchkSZH6Q95c173fT0N9Ie7ztq9QE5ngi4SCRDk/FnySfjVfkVjqLXAsGPhNxTi0eWfdtn195O80NSuItERC5IYGHUy5YY2fAtAohDceITc0liYRHZC6Cba9pjzyfHLHQm0bQUc26iIUjxOjqUph5zNum9JiORUkKAPB4D61RTpalNCmL9bTOOaspV+sBKtlYiTiNLZFAdTEZrmXCBnNAlBlUSCqH07fVY8FSg9nIRb7VfkNgMkmCKKKHLtIlc74CShjV5rEfaARCJVkqliscaoOMQ6cn81Su6ut4HEsnNH1931NrofjNm01hE/3RvGbEW02+iBgmOLkrvbBDSsBfJGUvXlRAuhoOKsx2Mo0GrwcsmruKLpbHwfLkse6v8ahsEt2fFg6VfQuHeQO+trpGOZcgCLGtDqAI2d3NWAl4S1WkPX25ClPEDw0UsFuj7mCKCz0/VxbFPc4LHk9kb8tMb+drrXEi2fNEbl/opQq6joMKBsGuVt+1qp4nKwne5vW6kibkcpD3920VrHI3Q927aQuDVm08OkkJsiH7SGPbiuDMMwDLNvYGHLMMz+R9Cl5A1GyU2yKYIQIDEV9ZPATZc3G3LTH/0qDYUgZ8LfrvS1laitjSmHBFvtV0oIqJnaohjseGrZFjh84eSu0/JjOMnWBEAPlBylFGcqJkHQsp6EZ+5omoPWRCHRWlNK1AadJMz0NhIuWeXkJu76nAS5MZdEtrWI3L+qN0mc5IwEKn4KQAAb/ktuWv5YCnWtWUkCU60ikdO6kY6lVXJRdXYlP1fJk7WVUNhyPAp4lZDtph/I3bWVUijslg9ICAZdNGawg84hewSw/oXBudYJVGpgys8B5zZyX+U4FWCCAOy5JMwiPhJ46Qh76OWqBhq/p7U35gJZZTRfWyk54qVHAfZKoH0zVTB21tC6GbLI3baXK4WyQoCtCPZYGP8xfYprqn+KVcHK5OFeq7fBhxPx90nDoWvfTA9ChEw5wxoD5TALkOjMHw9YtlKhs3iY1tM+DNCPIpc7WpSKGlBpyNX3NStVjFuByul0Ddu2AKodtA46CxDyAwYV4Kmja9vekRKyBePpHg60k9Pua6Prl1NJ92feaBqzfTuJ2uxK+nfl3EHi1lxA962vped/RwzDMAyzn8DClmGY/Yd4lNy0kFKgyZRLgiSRH+ttJlGg1qWKO+2+f6JfbVY5uZPpSFTchfIHvTFL6de6hkSnMYfEhFqH1l2b8P8+29ll9zuOkiCVTCYhay8jwVD7NVDzFYnxnJHAyBNIuDZ+T0I5HqUiTokcWYOdBLXBSqK2ZQN9biujOenNVJRo60dUNCp7ODD+HHJx1yyhMbMrSQA3rCbBZ7DR3J07qYiSwZ7qz6vVkUNryVOqRxcCDd/ROCEf4NwOQNDahn30vqdZaf2TaL2jpoJHpnwSPok+vYOCoBBqvZnc2bCfHjzIMRJnvrbMojYdcozycv0tdJ4qLd0TuaOAvHG0nno7OdK+VuWBg4uunV6pop01DJBUMGkt+Lf+G9y8LYb3fak2OB/Ua3BDpAD/ONYMbf5h9DAm6KIwYkmi3Nmwh+7h4qnK8RrofVc1RRTYykgIq7VA+05ym8NeEpOeJppb83pg9El0vxi/pTBmIVOxLrVeCTdXAeXH0r0U9pJQzh1Ja+ltonvIWQ04Sum+yR1F+dOxIEUdqDQU1uxrpvsnq4Kud9BFD0Ey/VtiGIZhmP0EFrYMw+wfBF30B7aIKy1jhnWtyhp00R/oibY+6jT/fXUoQsicn7mvZ8Cp5DCqSNwYbCRIWjaQQLWVACVHkPBwbMWjXwoEYrrk7meONmDq6BwShsYcEgLeVhIsIRdVpB0+ncSyr5VEeVYlHddTT8dQqwHdMBI0gXYSkdGgImqHUeEjIQP139O41hJg/Bxajx2fUNhrolVN/fepXEhjHuVPSmpyctUa+l5rIjETcpNAlONA62YSio5tQP13NHc5prTeyadtBChEWaOnORlyUqG+lkEWtgLUozZLCZltXEtrotGTOGzfvmfHk6PkRjp3ANuXkRi0FNLaWoooFDlxrHiY8mNDHjrPwrHQa3R4XPs17tgSxSvucclhP2zNwk1fxfG3cTXQxpSKxiEPEPSSix8N0AOZ1ipykM25dD+4dlK+ckc1rXcsSq6rv51ygoWg+8vfSueu1tLDElspzVNvpXzsUAeJd0MWvYqnkICVI3TtS4+kc3JspX9f7jqan7uOxjJkAaoWKiyWyMF115Ew1hpp/r7Wrm2BGIZhGGY/hIUtwzBDiyyT4Au008/mAsqlValS20QCKSc2u4L+4N4dXxv9Aa8xklhJR8BJRXEgkajVWRQXayvta1eK7GxfBsTC2O5W4eWdqcJTWhVw60/zqIJs2Au0bSYB6m8DsoYrrXn05DSHPKmiUpKKWt8YspS+qrJSAKqD5tFereyXTV+jQRLMgTYaq/RIEpTNP5IjF3AA5iLKu41F6DNIgGsHgLiSy5lHczNmk8h1K6LG20KOYcBB5xDqoFY/sYCSUxkAtMdQyyCDncJ2wx7KwxSCxKcAPQBo2ZD5ukoSOdbGHCXEt5TCmFV6yj2GoPZBOgPlE/tb6fjbl6f62uptJBTjERpTyEoBpViqb+xAEDKtr6+Z3PNhxwI6Pc1VjqVC4f0uutYSgIADGo0WD1WshaleYLFzfHK4pW25kNTj8bexm6GR4zSmpx4ItgHWMnLlE85za5Rc87yxdJyIH3D5yc2VY/SAIuwBgg66lnoLPQxp20LituQIunbxGDByNt0D3kb6NxIN0hpbi+jea99K91PeGPo35WulBzbOXeSIu2ppLrZCGifgpAcx2ZV0r4S9tPaBdhqzp8riDMMwDDPEsLBlGGboiARIaCbanWSVp9qTJIhHlVY8Mjma6ZzYaIj+uE+29lF13ybYoYhjJfxYayL3zlVNobdqIzmhcSV01z4MD64BZNGRHOKyI/NQXlqqOMNRcsQgkWiIhwHIJCT8DhI3sQiJlYhPaeHjJIGgt5DjprMAu1YplWnVtG3bVtpOCHpfa6Tcx83vk3PXVkXn61Ra42gM5M4FlPxT6/9n77/DLLuu60B83fRyfpVz527kQIBgJhUpyVSWrDCSRh6NZI+t8SePPSPL45HlbEmW7fHPlmTLkj2SaeVoSaZEMYEBBAEQGZ27unJ6Od78+2PtW+dVo6obAAGQAO/+vvqquuq9+84999zbe5219trTrO8NPc5VboLzd+1hgkfN4PHsrrDMTQKpA/PZE6OhHMF74EgbnRIBqm6yB+6lP+PrEzmp300SoGXKlPRmqjIvIeepOMffQSdrvQ/+u5wPzwHcLWEJt7kmdI09YSduB858PQFzv0bnZbtNdrO3Q9A2aOBlG1hlKjQp27sIGE+Ik3JIUAoD2H6Oc6azNlkzLPzk9GPwwxC/2rh9/zB/vFWCYd6Nf3XfHgy3Q+a8uw20VwB9iZ9jZoRhrQH6Na6Dzo5sZvQJXO0W3aoDh+ssWSSTOmiyVtZIAKkyN0YCF5h/gL/vrHGcvV3VV3fY5HnpJpnY/CQ/M5HjGrXbgGZynqfvB9YeoSJg/iGuG6fPdRKCc56beHlzG0ccccQRRxyvY8TANo444vjixKDBxD0MhBlceLG8OAgIyiIH5Nz4i48z2tqnMHs4mxu19EGoQG3tkvT83CYgTGQJLPJTwNTdeOR6Gx++pBjJfNLAjz5QoFEPQHYvkWMdqGEScOUmyM6uP8a/u31xQG4QLIaQXrFnCTzXn2C/WacPTN5G9tgb8hwSWUp+rawwuJt0BO7sUq6dLvHzy/NkXM00MHEHMHM3Xz9sC1C+AGw8zs+ARvDT3+Pv3f7h12b3AjB1L9C6rljfyduB6mnWwGoGZeF3f48YeGnCrA4JlMrHgcAm+OzvUHKrW6xnzU9xA8NK83flJY7XtXnOuklQOGwQwCeyQGGK5xq4fK3TJaNpdwm4KyeB2Qc49t42QW53mxLgmwFdTSfgcx2g5/O4rRWpWU2yl+vQF+Z5ioDdbkFz+/ipqc/C9318sH3X/uH+cC0NY6jhZ49dhhE4PL47pKFYusxNBzPNc21tkBXPloFBm9datyhX1nSuyaDBaw6d8x56rLetSt/m3h43cibOEHgP6gS+3oDSZk3j2mhc5d/Lxzif42f4un6SQNicIGt7/H00T9t4HJh/kMcIpG496ht8Y117HHHEEUcccXyJRAxs44gjjtc/2puUgQLiVnsEE9RcVm17DnNABghe3L6whocA32GbgBEhDXEiUNu4zjrT0CeTlZ0Apu8CsuMIW6v4Z//j0oHD/G/3p1HW2oCWotRZN8iuhi6ZTLfLcTaWWbfquQRGkTxaC/i6dJlGTe6AwNodUqra2yHjOXaKzr07zxGYVk8SwAwbbBkTOAQ1Y6eEgcuT0cyOAxNngc1ngNoFAqUwFGMkATqDGoF0ZCh1VAQusPk4gWxuUup1DTLa9QGludB4vsOWmFaVhGHVWTc6aJD9zk8D5ZMAPAJRaJSNj5/hl5Uha37+T4Dtp4FhX9jhsmx0aGTjvQEQTvD3uUkgP0s2uL8roB1AcYYGSrkp1gR7DrD2KNsirT9OBnM0yseAubeyFZBmAFvPS49XMeJKpDmWxnUB2Rky05oJ3bDwjyf7CJ5dxq/vLe0f8vf25qCHHn569jMwoPO4dktk8Os8rm7yy2mzLZPd4ljNJNdHIk/AmqlybjrbagyZKtnTdIUMuGcTbAY+WV1PWvcMW2pzo7vFmt/uLu8BM8ENBmic466YUSULwPS9vO9qy2xDFXpA/xqwLvXGpSPuwzjiiCOOOOL4IkcMbOOII47XLwKf7OqwxYS/vPRi6XEU7Q2+zkjydYcxRU6PzJxmiIvrDa+xOwQGCKV+Nktp5saTNN5J5pioz76FIKe3A6w+iv9+oY2ntv39w8xkAvzg/A7gjJPpaiyTSbbbBGbDp8mCBiBjWF8mqEsVCBY0YdysHMGYZxPUNpYJZgIBoFYKaCTIhjZENr13mXLd5iqBWSpPUJIdA4rHKNUNA4L7z3+QrJ2VohTV97iJULugapRfavTrwOIk57W/y/G5PZELD4Q1rhBYR4z7sMWWQd6QGwXpEsdaOcFaTmfAeWsuU+J7/dN8z76plgVkKzy37DjnZtDk5/kONwaOvxeYfyvn0e5wXHvS/7cnLG7jGqBZcn0XgMW38TrsvABc/BDBbmeTgB2agEwxjLJSgNfj8XOTXDNXP0rWc/d5bjRUTwHlJehTd+GfTjjwH+nht1ZULfbv1JagF2bwL+7ahN6riWnVVY5t0CAL7w+40ZAuE6zaPTLe7XVuEDSvSZugccrNW2uAuy5Ad0PcuOuco9w4NxuyVd43YSAGYB5B8bDF8/FH5PBRDNtcJ/VlMtLpMa4xd8h1Wj3FjYOdF4Dn/xC47QMsCThM7h9HHHHEEUccX8SIgW0cccTx+oRni9R2wES9cpxJ+WHRryvAepQDchAIWAuZ5JuJg393egdrcxNZYP3zwMqnCYCSeTJS47cRiF36M8BuwfaBn/5cDkJLAgD+j/s0pIpTZK8629IiR1oSBaGACRBI2F2eX7LAutDpOyhxNdNknQOP4N7tUXaayFCWmzpJ4Ot0OM5Bi4ZG9SbrMH2b85AqsQ408IHVT6tWQoHHuchOALlpsp/XPv5ilvJWkR2nYVRpjsCruyWuyBmegzMk+LMynPfKSQL4zhbPqXKcTHFxlqCyvU4gO6yTSc2OEYyuPc55iOqLS4vsGzx9F1lM3xZX5BTXQ/0qweW1j3M+T341gXNpHpi6k7XAVz/O1keDprhGFwhEdZPzcubrgDu+jeDy8l/wdf0ax6EbvB5TdwODHaBXJ3BujWwIuH0aeO2+QMDXWoM+cQ7//I4QfjiL313N7b/0t64lYOTvxj/9ygp0fwg0t4CrHwZ2L9GoKxATLM+mFDmR5SaFK87KTpebCEvvBIoFMrjNdWDrSc51Y036E+coY89O8Dz7dd4XmkajqZ0XODeuDXgeN4rCgGDac4CsLq2JREHQWue1taOWWxXg9PvV/bv8SfbILS2o3tJxxBFHHHHE8SUQMbCNI444XvuIQGbgEWyUl452WLW7yuSpcowg6rCI+oGmii9OsN0BJaX7dbcZAteVRwhI0xVg6g4Cs94u61cBIF3Gr11MYrWr+qWeqxr4lnsnFBscyT11k+Ctt0ugGbHHqaKYYBXJ+Ll9AsPSPMF3e5eOs74YMiWzBJPVkzyPSx8joLQ7gGMRLJppson5OWDxAXHSvUY21hvy72YCSBTIXD73Oy+v56tuEuhM3EG2Tk+Q7cxOElgOmwQ6vkcQNX0XwU3oA1vPEey5AwLv6mmCNLtFQJudlBrhXcpb2+sEy26PICuSok/fRSn12GnpXSx1ps0VnpuVokR35zlex84WMPcgkC6SOe9ukbGcfZAMsDvk+Po1oCbS3PwUr0UEBu0e2d7OOjdRclUaJ2k6zbbWHwOu/MWL58t32b5n7yKwdyeM4+/Dzyw9htA+i9/bUWzorz/dhJ7I4B9/853QqyeBqbPA5Q9TMWB3uT6dHuc/DHl+UUmw0yajH/gE7tkKMHUb19Hyw1zfe5fJgusmcOyd3DjJVgEEyhhs4SFunKw/Li7bZXHfTvE+BERBIC7K3V2u2TCk1H7Y5hhOfAWgf0Jq46+ruS3OH32PxhFHHHHEEcfrGDGwjSOOOF7bGDSVuVN2nKDlKAMazxmRDt/Qx3Y0hm1Kf3WTEtgDx7AF1PpksYYd4IlfY9IehMDYCYKXwrSAv4BANF1Gq2fj335u+cDhfuJdReiJnGLDGstkE5MFAj3PFgfkPmtTKyeBmXsEhD3D40/frZjKYYegVbfITGarMs42cO0TbLvSq/FYvgNAwG8iByTTQGuTwCOqsywtsp506xngyp+/PIY2WSCLWpwlyDHTPLfMGBnC0Gdd77BJtjpdJjg0LEqua1elLUyXYCk7xtd5QzKEtshc9QSw+jBfq5sEvvkZYPEYMHMvr2HzGud36yl+L/OaYOwMZcMAAZY/YG30ymco8c3PApkSzyU/DSy+g/PSuEpzrt2LXFPNFb5m7LTIeBsANK6H8VMiee6zhdHEbcBd3wlsfl5cnY+IwONrtp6GMf8QfvZ0CB8G/nBH1Xp/8LENGHYD//A7HoJWOQbc9wOsj77+Ga7T/dpkg+dnZQGnxe/ddZ6j73K9dndpODX3AAAdqF8iiL/+aa6HqTsIfMdOc031dnjvTd1FoL/2Wc7JsE3lQWGaCgCE3EgoLvJa2R2uQSulQOz4WXG01oBkifMFjWxwboLrKJYnxxFHHHHE8UWMGNjGEUccr1309ghmAMqBD3M1jiLwFaubHSdIOix8T9WLFucPypR9V5n/hKBR0vVPcRxmCjj2EOWu6RJrVhFKDSyNoH7+s0M0bSWPfveJIt51xwmC2fYGDXwCFyjMA9XjZMX6e/zcRA448TZKj0uL4sq7TsBSnOUBWyscu2cTHCbS0r7IB/ausG3LQOqK00UxAoJyuDUSBJmBRzY4O0ZQ1FgGLv4PApBbhrREGjvH14c+gZ6VolS1OE+QGzFzvs3NiIkznKvWKnDlo/yuaQQ7M/cLu55lfWqvTpCzc4XXw+mTCTaTlPsWpoX5vZsgDwDyEwTKUf9Vu0W2N1Xg+fo+ZdpWhu/vGGRtnT7gTgPT9wDzb9vvOYsQUputA50igZhmSs/XAZCrcD6tDJDOEwh2tniNtp7h9U5X+fej3KOjCH1g5VMwVj+Nn5t5AL72/fjjbaUi+NVnBjBav4WffN8YtPIxrg9nyD/WrwLpDFBaIksLcEOgt0dpcm+XX4ZJwGm3KaGvLkov4i7bQJnSFmrrWSD5KTK8RpJrvHaF17Awy3ti/TEeY9hkP+LRDSQrRcWAabE9kdbhuhy25f7VeD0qx3nfaOBaH7aP7jEdRxxxxBFHHK9DaGEY3rLpX7vdRrFYRKvVQqFwhNFLHHHEEcdodLYJIjR9nxE9MsJQmTElC5I0H8HqNpYJujJVHjeKwGdS314j0HT7wObTZA9z0spk6nbKYiODHd9ncj+oYb2n4X1/nIMj2FAD8CffbOJcusFfOD05l0Vg4hwZrdVHKI1NloC5t1BKm5tifeTOCwQioc/PGjSAxirHFwYEFKUFguzWOrDxFEGV7xGEW0kCLw3iyJvjZ2erBLPZCQK9nfOA3aAbcuPq0XNspmiSde4DZE03nxKmtUQJs9snGE2VpD2R1AWPn6a82LQIXporBLXDDkFnYRpAVKc55Jh6NaArTs52n+8tL7GlTGuT81ic45ylijy3VIFMbncLqF1jvScCsse5calHFvOt5hqwd4HX0XcJ8PPT2G/nlCnzM1IlSqp7dcqG1z5HEGamaGh17F38fLevmFm7Q5bXHxK4BQFdrK99ggDyJYQbGvjfzf8Hf9o7deD3f2X6Ov7+PQNopTmCzM4mcPVjZPJTJa7pwOXqGzZp9NSrcd4NgxsHZoqS9fw0r1PtvKopLh0HitME8Kkc67d1i59VWqDcffVRth/SdGD8djpJV6QNUOjzPrK7POdBi3Pcr3OOsuMEz7pBc7LSAp2tA5eMse/xHNIlqfm+ITSdX4bFtW1YSkYfRxxxxBFHHIfEy8GhMbCNI444Xv3obDFpj8yfkrmbv761TtmkmSaDd1T9bb9OFtFIUBoZvW7YBtYe4zEA6RO6KuyqAKjyIt9vt0Ui60vdqAMkcvhbj5Xxu8819z/q24+5+NmHBFDmJgmgzISAr22C1JVPkRmcOCegdoL/bq7QjCc7QSCw8qi07GkBCPi6iXOU9bY2KX+tXxYQmBJzpjSZNCtN1rAsrYoAjqe7wzpPdyD1pzngkf8fxzkaVhqYvAM49l4CxN4u5zsMCUwmbgOGNfZy1S2gcYWg1UxynLoJIAT6LcBt832ZKo2xNI1g3BZptDckuBk0gNChGVZhhtcrAmyZEtcFNAHSVbYwcgecM4Cf4doAfI6pepLy2vIi53BQA9pb0p/4urQCCsly6xbrdZfeJSy0owysOtvKmMxIEDRP383jporcvLDbIp9foYS5tSbtpubIwEe1t7cINzTwN9wfxYeCBw/8/n+duoqfOL0GLQw4x77DDZjApYTaSvNcdJObC0Nhb32XgFXXOfYC+y0jCIDlT5C9NdNkfvMTfE0izXUxaJIxn7id71l/lCZWVgaYPEMp+Nipg2yr7xLI2x0yvoMG59LKcP59hxs4RoJy8VSeSgRApOZTsnZeQugmx26l1XqP63bjiCOOOOJADGzjiCOOL2ZEPWo1A6ieYJJ7s+jVCI50k+DlKKdk3yULGvp0pE3myGS211nnN2xRsmumaAblO6zjnL6TbN+gLiZIXY5t2CSjVZjHs+0kPvBfru779iT1EB/9wBAzM7MEl3sXBXTpqmfo6qMESWMnWbNbnGWSP2gQAIUak/StpwhcIrfk7Dhw/D1k0epXydRuP8uxJ5KUf2bLBHyZEvZZyLkHCbTWHqeM2u/zMwrTBPn5KeDR/0BJMkAwNnc/zZTGT3M+mquca0/kxdUTBHuBS0A4aPD80lWCI98GWtJ2yelyLJGc2BI20BtIrbJGUOgMgPIsWxFNnKEb8uazPNdUidc4mSMwH9SUoVgo9coIAQjw9YcEdnZXybfTZbKVmbIwimtqvaWlZtlIiEnZSM9VI0FA7ttkk3fPc+xGkvNXXiR4zU0QWDoC6C79Ga9zIk85cNSD9sIfkwG+STihgb/u/k38efCWA7//kYkX8OMLF6ClClwjviutnxKct8IUENicr9Y656p5nXOjm1LbPZAa6wWea+0S0K0BVoLXNl3mnAYhP8Nui/v0Ajebdi8A7VWut/IiNw+m7iLAj+T97pDsbnePa9Tu8hiJLL98W+qfDdb3Zsa4lhJ51oWXFnmtw5BjD0Pev5GLtzeUtkIDqhhGw0jw3kvmeX5HbXbFEUccccTxpo6Xg0PjGts44ojj1Yv2hmrTUz1J4HizsDtSg6uxj+xRoBYggxYZQpkpgrR+jeyw3eExUgWCWt0AciK91Ewa8LTl9+6QoMFMA76PcPci/tlfmAihPvt/uSuBmcVZYqzGNSb0gUfJayLLWtDAZZ3tvT9AkOX2CNJrlwgEU0Um/R1hoqFRpnzntxFIrD9BxnXnebJuySyQzgFWnsxqcZ4gxkjwXDMTfO2wQbDoe5yz6XsI2AyL7V1WHuH3qdsJiLOTPE5zBdBCjqEwzTrh1goZwI7Uauo6jxU6nNfA57U0LSA5o3rxOh2em90lwPVsghTdApLSQzdT4rWwOzxGfpLgur1ONi4MxCDL4+dM3s6xAQTRgyaBdntdAWtvwN+1VrgGTGk7lBkjuG1e5aaCYYpbsEawO3aSYM/p8PppGjcimqsEreuf43XLTRAsFqbJipcWgKV3U+KtW2QwI4ntHd8GnPgqcTl+TMDbwUhoPv6d9W/w19wfw18E9+3//hd3ziFXrOBH5z1+ljsEUmWuU6cFDExg8i6u1+pJfm4k07ayBONOj/PbuE5AHPpkaEOPYyxMce0Pm+CGxDhgN3kfGAmCeN/hPdS4zlrlQYttngKX1yTwOQavzzUZ1RoHLsHm0rvotLz9rOqxnJsiw+z1pX57gdf+ZhGGXENun8y92+f59Wv8gkaAmy7zcw9r/xVHHHHEEceXfcSMbRxxxPHqRGuNMlfdZP3irUCtOyQTGvpkdm7WEzMyoTLTIt9dY1Le2yMTNWwDCAmifJdgJ1Xke4dNkRvn+d1MAQj4PQzx0RUXP/hZ1aKlkjbxsR+9F4VsmkB19zyT7dIiDXla68Bzf0BwfOrrCCAQsnXMzrNMyPMzZNsa1/leDZybwizrXBtXKU/eeYGsV3aMoCrwyEZOnONgNp8Gnvtd1iqf/UsEY44wx5FDcVoYrb0rIgcWCXRunEA/AkyBx/nJTvJ9dofzuP0863mtFFA6BuTGlKtxv04wqOmc+8ARZnxI0BMGqq2OpvE8Sse4BpwOzz1quZQd4/tDcH4yZV6PuhhGBQHBZDJPls9I8hjeEIBBdnfQpNw8BK9faQ5IVXg93T77/3Y2+ZnpMkGZZihpbLokVzkkQz5s8hwHdYLDVIXgPTfF92Sl1+ugppyL68sEzKkya1wRECxe/xTrfm9kHgHYoYm/6v4YPhrce+D3//zcNXzX0pDAO/CBxgqNs3ST5zd+hhsNyRKwd55z5fTEJTvLeei3aNg1qHENGhbHkJ8CTrwPKB/ne3t1gt7urjggz3GddHe5ZgKfgDdd4HoCeN0TWelf3OMa6mwShGoG5fenvprreOd5zmt2nPJnp8c1ny7xeVBeOrpu/rAIfK7RYYtf+8ZoGo+ZqR7tmh5HHHHEEcebJmIpchxxxPH6RnNVtd+pnry1M6rvEdT6NsFMYYa/D3wmz4EnckWfCfLeBf7bSJC5C0RiOdgjMNF0MfpxCT7MlJLJporifKzxKwwJHu0ufE3H1/9BiAt15Sb8U994O37g7Us87vLDTOLLx8nebT8NXPgQE/bZtwDH3iEsk0hm3R4Zv4nbgM0nCXh8h5+bLHF8jWsEzPUrnIf8JCWg/TrHPnsvgdbnfxXYfk7NmZUBzn0LWbmKMLWezXYzvV0CNV3jfKbKBNzpquqF2tngeesWweLGEwQpuUmCnIWHyHzaLQLk7Rc412FAIOS5BBdGQjHrTk9ArUmQZJjcaGivE4B7AwL24iIw/xYC1sYqAe6oG29rg+2DfJfHCeW66xY/K5njNR00yCxqOseVyPJ7GHDOfIcAv7dNcFVckBpTqf81pRbZSHDteTZZ5+62OtdMVRyoUwTf0LgxUVrivzub3BBwB1Jv2iI7qRnSr/kSJcw3mCcNkcT/WvpPeHhLtcTREeIXzj2Nr5ns8tqEgbg4b9EhWTNZ+5qf4jg2Ps91phsH69Z1iyCwuSzzbivn4pn7gIW3itPyjjg/r3FNpAo8D7tDWXsyzZr0+YdoKgVN1nef89daY034QDY8CtPA6a/jRszuefbV1Q2O18oQkLbXOMbKScqVX4mkOAwVyB00FMg1ktwwyVRjqXIcccQRx5s0YmAbRxxxvH7RXCEQikx+bmX6EoaUVjpdspqVY+KgvAkgfPFrWytkxiIgo5tkX5vLTHKj2klvSHbNSJDRMpNkBq0UwcewQcBnWARQqTx+47yH/+vD9f2POzaWxZ/92LthDZvAtY+Lo/IEQdjWM5QPDxpkoBbfTgCv6QRUnU0m2RN3ACufVlLdpPTADQNg5wIBfW+H4yrOA8ffq8yzKidZk/vsbx/K/GHmXuDe7yNw9l2RMXtKlm0mCR6qJwnqdl4gQOruUOI82AVgyPtc9jxdeBtlr511ynGbK0C/qUyKsuNk31MFAt/MGAF7xIRHbr52m2OoX6Ek3e7wsxCQETZTwrKVKYlO5jmPmTIBSl9Y+V6d8xqBKcMEIA7HpQWRm2/z8xvXxSxLI+NqWHztoAXULxCMJwtkCwECbStDk6l0lecVeDTLql0m6HO7BLWGyb/pprSPEpZWg0ijB0AyQ9CugXJtzRRJssY5XvmUMM4A5h5E78TX4XseO42naoq5TBohfvWei3gws0nAZmU5J71dglgzzbrk0izX0+qjUq+dALJTgB5yLKmibCpsiIpB1AuVJW6czNzDa6EZnLfdC5Q2G0mpcdVU+6mZe4GTX6lUFEHA+7Untba7F/lep0s2/vT7+b23zddomvRoHifY33me85CfAebfyvXwSiMIhGmvSd03eE7ZcX7FMuU44ogjjjdVxMA2jji+3KNfV0Y8+7d4qJhN3eDPukkwpFsEMi8nwpAgaFB/6aAWYFI9qDOBr57k5/b2CCoMS8x5DH7v7om8U2r1cuMERyuPMIE3kwSH7U01jqTITnPTZOXaIlvOTjLhNhOAkUDfKOG9v3QFOx17f2i/8L334f2LIbDyWYKnVJFy0MY1ttMJfH5+ZYnj0TUygHaX4CY3xcS/vUkAnBsjUOvXyOq21qT/KwhiT30VE/6tZwgElz/BeTgqdAv41l8C5a8blNs6PUqbu3sEP+O3kc3dfUGMrLYF6DQJNupSL1peYrsb3yUz3N0mmDRMgs7MGFnKSJpamCXY2niSrG6okdXTDZ7/sCXMp0hWjYQAPZFAD5s8h0RONigsAsxyJF3uisFXh68rHyOIDcWwyjDZ+3XY4Gv7dX6O0yfAzM8Q2BTnOD+9uvQw3uU4qqe4iRK43DTIjXEN5ibECMkj67jzgrR2MnjP9HaA2jIQiCTaGwKmSdDse0AixXplXSPoCj0AJjB2gmB+7bPA9c8AX/2PgM4majbwHY+extWuAmD5pI7fem8TZ/3LZNKLi5RZt1cpvzaSwOJDwOz9HN/Gk9LPOOA5m0nOcSJDJYMWEnwORM1QmAMmTgPj53ivBS7BsN3i/BqWMl+DmDuNnwHu/MtSqzsiIXYHbBW18TRbXYUBgffcfZyzzhaPmcxTyp2dIDhffYTrMJEHFt9289KDlxrukNenX8f+8y07rtQDb6YID3uWvwxpdxxxxBHHGzRiYBtHHF/usXeZUsGXE7rJBNpMKFMeK3N4ghiGdGmNGNPqyZsbP0URtQEyEkyGDevo13Z3KQUOPB6/cpzJ3NWPiQFRGqickvrBHQKl4jxQXiCIbV6X3ycomdzvn5kEinP4fz+1jZ/784v7H/eWxRJ+61vL0DafIoNanBYgUJOenk0m5ZkiAWz5GGXETp8yXzNDFqu1RvYxVSGI7e8Atsg9o9rUTIXyzeIssPU0z2n3/K3nrzAD3PEdNJ8yMwRRuxcISKfupDx62ADWH2e9Zb/OOTAs9qq1mwRxuWnWR9Yvkw3XLTKOaalbzI1TUg2QKR02uZnQ2eD5ZioE1ZomxlgQ9+IW/12YIRtbmOZmh9Pn/NWvUHbsdLjpYGWFcZc2N96QoNKzuQ7Hz7Gesl8Xd+Y6wZemkQm3MrwmqRxBXmGK/y4v8XN9F7j0EWD5YwRk6TJrpXNjBICZqmqJk5/iuTeuA1c+IhslCY6rIyZYxTnQrdkhMGytiIRc4+cGvrSTEllzeUnY4YqAvHFg+3msdoBve/Iu7Nhq/U9kdfzOPU9i/vF/DmTGgZNfQbfmHXH81jQabJ38Kn7G9vPccAl8bsCE4PwHIc8zN0bpcnuLGympEq9H9aQYaImcOZJ9D1sKLPdrqk73zPsJjKNrBPB6bjxBgNta5XmVj1M2HRl+edIeK10k8K8cp4t0Z0Mk9/fzd69G+C5VCf09kXMLg5ub+NKUKAeRcZqjHKIPfAX8HvUV3q8vviFGNyr3NwSjzUGTCpIY/MYRRxxv8IiBbRxxfLnHsK1q/DQNzGwh9YihSpgCl4mV7/DrMPmrkRAAkSUAsNI3gNpTTGBvFVFbH814cc/M0QhDAomVRzim8TPA5J3CaD7MRDqRI5u486ywNQDmHqBUVbfYosR36M6bnwYlmBqBTGEOuz0X7/2Zj6LnqITxd/6SifvTEdM6xZpTt8/+uLXLZCytFAHCsXcz0XeHBKSaJq1n1kWWbZIZHbQJxhJZjgeQPqOTfO/m54Erf6H6fx4VVhq47VuAyduE6TJ5TlGf0el7gDu+leN97g/I/LY2APgEq5UlwA8os40AzqBJMGukCIw0g5LUZIE/WwmCk53nxaxLWilVT3L86aIyGRp2AbdDMJGfJjN6WJsnz+amwdYzNEnavQxAAGZxlu9N5nke7XWuGTOB/Z63VoYgMZFjEh/N6UDqk0NxO86OcS2ky/x7axM4/4c0fkLA65ut8jipIuc08MnA6jo/f+t5roVEhtfXHXBDYepOzuGgwc/fvcD5MRK8f7w+z6uzTqCeqoiJV4lAMj8FdDbxQsvEdz7zIDq+2jg6buzgt8y/j6omm1LjZ4Fz3yzux6scR3mJ6yCZ45gicGtYvK+bG1K7PgHoKWHuRc2QHaeT9+x9PP/uDkFqqkiWt3aV/zaSI62YisDS28ngJzKc07QYfy1/kveH3WGv5PHTlD3Xr7DOOJJhhwHPf+IcN7ca1/j76kmu3VeLXfVdqgZ6e1xXmsEa8uz4y1ekvBrhOVIiYfPeitzDb6i/vmnopgDYaPyjz3J5jt9YwhHF9D0xsI0jjjje8BED2zjiiOOVhe+q5MvpMXH2Bgdf09uT2soyMH3XQRObo2LQoIkRNPZOPcrNNAhYO7snLXOqp2im1N2hPLi5THCTmyK47mwRbJ14L0HRoMEaVoDsbbok5k0G60kF6Py933sG//WzK/sf+/XzHv79O7rSg3WRCeGgSdDZvC4s3wQBUGmRwC0MOU43YkZ3yTK7Xda8ujYZtMw4x2AkKeutnuTcPvafpDfpLWL2LcBt30xQYUqtauCRWfSGBBQnvoLS5Gd/iz1j3QE/M1Mhy6gbBN6ZMkGML+2OIsOiQY1zbySxDwiGLXEY9ggAJ27jWLIV2ejISZ3rspJF5ybJwh210eF7ZJC7O5SIbz3HDYAwYBuhwgSBtpmQDReXAHnidl4/M0XjqQgw+DbrgQd1gr9Bm3NrpcgyVpZ4vcwk8cDa56U1TUuuR4UMvCb1xLrBNWqlASMNdDd4XVMlkbmOcRy6RVY6qm+uXeEcJgsibc7T0XrjCc574AKGwXsmmWMLou4mHq2l8H2X3gE7UKzi3doVfDDxj5HVZLNDN4D5t3E99/e47lJF3hsFWfPdbW5gpHKcg62nKde2xJnc7gIIOL50metg4jaujd4upfPR5sLWM2IEl+LathI8dvk43xdtNCTznLNLH+Zmk24RNE/eRvBfv8bj+D7n225yPPlZbvbsXSGgLS2yXv1WLuovJzyHc9KvcT0bSV6vfVfsVznCUJ6ZfdWyyBsewbRqnDdDasINi3MX9UjWRtnXl8g2RwzvgS+fyos44ogjjjd4xMA2jjjiePUiCFRfydplslO6QUbMsKS1TolJ42Fy5GGb4AUh5btHJZe+y2S4t0OAWFog+9PbJkjYuyR1rNMEku01JunH3kMwUbssstuEyJY1MZzK81gCti7vdPG1//oT8AM++iw9xJ9/wMFSNUdmJ1PhmC9/mKAhP01DpnSJCePYGSbhnW3KKttbTKC7W5T1Nq+L665Lpk43Ac0CIG2N1h8DLv7prVmbZAE4+TXKpCrwCGicLj8vqvctLxJYXvkY0FmTdjpTlPE6PaB1neAlWRT2aoznZKbJrA2bYPugEv+mWzye0+X4KyeA+Qf5N4SqzU97nTXWvV3WNBamyUabiRF5pKGubQQ0fKm5dfvCCIsZUejxWlkZ9kMtznMOzAQBYWmexxt1zfbE2dh3ON7WKgG/3RH2OU/wWloQhYBGsFW7KjWlDsFHMsfPzo4TgEZSzkxVgemunGdlSdWop0oE0J111r0OWlLDbdKsSwvJULc22O/VHUjPYZPXo1/Dn21n8Vcv3IcgYuIAvEt/Gv/J+hkktBFgZGUJZiP5dyrP6zx2hpsw3R3KqjVDuXTrOu8Z3+M9EzpAoijsawoYO8vzGcrGkyWbCK3rBOuhRnCbm+AcRvOo6QTrANf81Y+IFHyWYGruQb62donXOT1iLhaGnDe3q2rLs+PA0jvJRt+sPOHlhmerPsjRPRUpL15pHAZi3T5exJpqhvRaTnLOzCSv2+imj++JSiZUahlN1tabrUY4jjjiiOMVRgxs44gjjlc/ovpY6AQxvstkNZIbAi8GuYMmjYm8gSTTIskNPGWEomnSjmaTx3S6ZHUmzvDYu5dYM6rr7JHqdskwpYrA4jsIvvbOiwNxkYZGYQBAI4DLTSg5XhDgh37lM/jwpeb+kH/wjgR+8uuO81w0g2Ne/SyT4sIsMH0HAWl7nSzl2CkmtXsXgfp1gim3A7R3gfaK9E+1hIESgxenx4T2wh/f3BwKEHZ5EVh6NzB3v3IFtjIER8O2tC6SGsneHg2F+ntAqkoAaGXIbnsDno9ucp6yE0D1NF/bXlWuwfkJnpvvkmXWTWkrdJJzMKpmDKFa3thtHjMvcs/9eRaZ+6AtLXo6B+sGQ419gKEReNptXtMwJCuYyMjrAr4P4JqauG2E2dNGzMZG5JqBR6DcWhWjLpMuvLlJflbUTmfrGc6n7/A1WkjWODdBmW5mXOp0HYK37i7Pb+Yuqb1NcSxWlgy+53CjwO6onsGDBtnC8iI3QerXOfeZca5TwwS2n8evXwR+fOOdB5bBtxsfx8+Yv/hiJWm6TMZX03n9i6JE6O1Ij9sCza00i9cG4DXtNQhgdbD1VLrAazl+Gqie4X3XvM65mDgLIOAcuQMaeo2dJMNbnOO9ly6LKVkLWH+SsnrNJLDNT9IpOV2mVDsyf+vvEQQmZBOpuwOsfIabHlbk/rzA63TURtkriaG0oPKGXDf5Ka6HW8l0IxDrDg4qWG4s2dBN8SRIK2+CCMB6Dt/jDnnuvqtKQI6SEAPA1N1fHPl0HHHEEceXWMTANo444nh1o7tLhlQzxHxmRDboDsn6DZpM4JwBk2SnT3mmlWYiGdU7QlOST00jIG2uMPl1B2Q5kjklt61dYT3n2FkmuvWrPOb82/jvxjVKUrMTwgaHPEZp8eA4h2186plL+N7fUcAyn9Txif/9fpRtaZEyaIkLc0jWt3qcx+nXCc7Kxwgcdl+gzLR2jeCqcY3gJ1UUgyHQvEnXCTjq15j43yqK8/zKjAGnvkba4hRH5tfhOTpd1R+3vkxwm66wZtbukEU2E8KCFoFMiaDNdyjZjdyjy8fIAloZso79Oo+Zn2Y9pBkxWyGvVxgCjRWC5mjOC9PCKPs8vitgetiUesKAwDiE2rjwbW5yWGkypblJ1TLJd6QvqUUZbGRmpOlk/mbvkxrem0hXw5BgqblKJnLQIBCNzHaMFBnLrmwKdLekTjXFDYDyPOc9auHj2wTLdo/ArTRHMBPNi2HSsMxM8hyTOY5h53lhPn0yoU5PjMhaZJPHTvPca5fw757y8TOrZw6cxt80fgc/Zv3Oi88vcjo201wjKTF2Cn3Oa6oCOMJS5me5GeT0uYYHwuQmCxyvN+TaqZ7k6/t7cp8f5/XdOQ/0tuhIPX0X5z4rTHblOL+3N4Dzf8z7QNNobJbKAye+kmurt8vrVT3Ja+INuAFWXuKaXvssr0MY8LwKszTksrICoktfOMgNQ46js8nPMdPcBIpqwYNAAOhghIkd4EXg82Yg9kYpsjs4Wo6sm3x2aIYC2JomztoBNxziiCOOOOKIgW0cccTxKka/LkyOTlnqYTW1ns3XdbYIZveNokwm7pO3ESyly5LMaSPHXgEQEsjYHQHKdcpFa1eY0JYWmIi2VpjoLjxEoNZeI6hIlQlcdINJenFesR2BD7TW4PVq+IYPbuNCXbEtP/61p/BXz4i7rd0nuLMyTN5zE9K3tUJ21kxR9rn9rJhYbfBc2huUa+oWe8LaHYKqqFZ55TM8/s0imQfm3spEWDOA019LIKBblGLrBucyUyVgdPusLa1fJSjTdAG1fYLZZEmYvQLBWmGKwKSzTrYoO0FGuHqCY6tdISCz0jTrylRezCgFPgF6awWAznrM0gKvTxgoZ113oFyWE2JE1d0moBi2xYArT9BiZeTaG5RvD1vSf3XIuY8YWTMtpkA7vP4Tt7FmMjdxtAkZwE2T2hXOU+2yam9VPsY1GLlU9+v82e7wvDWpsy1IOyMzSTC6/ZwyUDIz3ByIPidwhOGTNluZcYL+5jKwdxVAQHdghEDzGjcjEgJuK8cQtrfwk08k8f+tTR44hZ82fxHfaX788POzMpTmZydUPaU75PXTk4AnTHl+ioyx3QE8kXInM2RNA4+ATjM45wg5H1aS0vLyIjdwapc4VxO3ARUxadN0xX42V1gz7Q+5yWMmuSE1/1au12GLYHbsDK+lBr6vtCCtll5guUHgS0lAVVonSZhpAtxU8ebX/KgIRUbv9vls6e9wrsw0j+fLdQtD9V3TRUacku8ZrusobQo83vtOn19hKPJvUWroprS3yqmNHDP56sqt44gjjjje5BED2zjiiOPViUFTHEw1sjOpG+5/u0uwEdWwAeKo2hBWRMyOItbCTIlhT1XagqxhXxrY3mTyHTk6N5aFAZ0Xl9pnmCwW5wmu7S57l6aLqpa1MEc2aXT8rTUgcPGrT9Tw9z+p3IcXKhn8+XcVkGxeISize2Q2y4vi/pwhm9lcVq1+BnXKlOvXmBBHvVeNJHDqq8mI7V0lKxr6BPO+Czz+y0dMsAac/QbKDq99gqB17CwwfScIKkMm05pGE57OGtBck36xfSbVRoIANggJYouzPAfflXrjJTLugxqT6/ysmEnVKQU1LL6uNE9Ar5vYZ5Sir8An+9jZZHI+cY5zvc9wDZjQhxrBiJEgWGlc43s8YWhzk2TpirNqjg05fnSc3QtSizsEoCvnV10n6O3VOHWTt4lsVWqHb3Rh9j3O0yio9h2+zhkIGytgzkoSzA1avIZ2S8aX5d/NFOC0CfAGdY4lklYj4GvNFNeBponpVYomZ+kSWe7eLpk9Kyss5zqBnGGSzZ28Db7r4kf+wsWH11V9pQkPv2z9DN5tPHP0fZqfAeYf4vGDQOTOEwCkbjld5jWvXSGgdnu8VpkC11sY0OwslBZMQSD12hqv9cLb6WK9/nlej8nbOWZ/yI2SRF42H7aostAgLHmWmxiVk5SFezbnozArrbhMXsPiHIFyc1UM1cRkriT3fr9Bt+nI1d1IcM6TeT5jopY4gX9Q6utLDXZkpuY5qo+xI6ZvYcB7IDfJtRTVxZopuRduiEh1YHeltja6lTXF5BoJAbA3mKhFxlCjxlFW5rUztYojjjjieBNEDGzjiCOOLzzsLlkuhNKPs3zwb51NJniA9CKtUt7YvK7kjeVFJsnDJsFK9Pp+g0lnpkpWaO8CwaI3ZLI6qBHUJktkPxrXmIDmJphwNleV/C9TlVrYu5U8VVhaDOpAGKLVG+K9H2yiMVSPu1/4uiLeX1qTmlWTjrKlJWEJDTKXngusPkKgkCwBq5+h9Dcyzxk0yUBO3sFkfvciQQwCYZ3GCPS2nyPjNRqFOeDBH+EYt54hYNMNgr5kkWDVzBA8Rm7L/YbaRPBdfsbEOc6NbnHuSrM0ttLA33m26klaOcHz27soc+oRAJ34SgLgKOEedWMdNIFrD7Me1Ugo8yzfUQxYxHIaCQEo1wncfDG8qhznfBZmb103GPhcd05fgZb2Gtm1UGpXOztqrRTnCEryU+KcnBaH6i3paapznnyPfVej2uLqcSW3HjQ4T91tzp3TFRmqxnNNFkTevENm1soq868wEMZeAEuqQKDn2wrIezavf+RUHLg8r0GT68UfkM2tnkRfy+K7P2ziqV2lLMhhgN9N/D84ra8fPW9GAlh6F1lRz+F10S3eJ+VFGqDBkFZQsu4j2f7YKQL+qKbZSnBOWuuc79w4MH0/57x2idd67AzZ2KhVmG6qOtL+rqoxjureczNA4wqvayLD69Le4BznJ1XLqd4uN7g8V+p1p3lugSclDj3ObXQrGyOs6GFsaFR7relce1Fv4gh4Riy9YQrzPUUDsahUAuIBMGzyfNyeOraeEOOxLNdEZAI1CrTDQLkUh/6LTeOi+yOOOOKII45DIwa2ccQRxxcW7pDgJzJ9iVhQdyisqoArM02wmS4zgatdZmKbKiq5543H3X6W0lCnz2TQFwlo4NLYyOmKcRCYfHZ3Ce5yknT6Q/4tEDCli0mOmSC4sdJkjXybiXIY4Kc+0cSvPKkS0rfPmviv72pAs9uqfUluSurjZPzugHPQrwtY2mbtahgQ+LlDwNAJDDWNYKG/BYQ6JawZ6Z1pZYFcBfjcLwvYMGl6VTkD7L1Aaa87FMOpLE2bkkW6ENsdMohGism322UCHzHo42c4psGumBQtyLXRKNU2pY6vuETwMmxyUyD0hCmqioRbl96u45y3CKDULgMbn+dnZ8rAxB0iee5K/0xwzFELovaGyEw1JvyVE5TavlxGync5977DMaUrZPzsrhhitQh2By0BZB5Brm7JucyQfY82F/o1AiZvKH2PNTKAuQluMBTnVdum+lVg77L00d0DEPJcqicJtrSQTL7dpRQ5P8vz33qSn2F3CSwNk2ulvATM3s9r2d8jGPJtMV6zeT16u2RM85NAMo9dL4Vv/fQiVntqE2DebOAPjB9HJepxe1SUltiuChB5dZdzUpjhBkxxngC/cU36BIvcOzfNVk6G3A+Zqmy6PEcFQrLAZ4HTFXm8RbZ18W3Sv1dqSo0kwXV7hfW8dpfXf/wsAW3tCl+XzHPOW+vSc7ogpmi6KCjaBNnZKu9vK4l9sBnI50U9YaM6VSvDnteZktQQR+yrfBnJw92G+3Ve78Dje8pL/G53OAfDljKMMpIiiS69shZFYag2A3xXGbvFEUccccRxaMTANo444njlMQoqcpNMiMOQ9bPdbbAvZIJMSqbC9wS+gNUu2arK8cOZucZ1Soz7DSaqwybr3XyXAEg3xETKJoBzOkwq81MEN7rBZDhVlL6b8xxLd1tY4TqZqExFDIk8XK718f4P7sKTvFTXgD957wbOmrsCkn05D031vNUNjqmzJS05fOWqmqkqUJcu8/P7u8LU6qz/nbpdtUPJzxF4XvgQsPMccPJrOcatp/m+0FdmO/lpAuJhkyAIOtkzbyj1gMIylRaYEDfXOEfpCk2uLJEAmykC6sK0sKQm0LiqHI/zs8LQWmQNI0OdRJZzHYTA7nn+XtO5SVFZEuZJJOa6zmMl80C3phhgzeB1mTj7hUksXWEHA4+gOz9FgNre4JwNBRjabQLOtjD0kbSzfEyZUkXzFq3ZyNW5vSHnLL93+grMRL1pBx2CWT0hLZHGuemQLCiwXJhlj9/Ohkip9/i3qOY4NwXM3gP0RMZcPU0g1tkgC9hcoZGV25dNIh9XWgG+5emH0PYVA/lgqYtfS/5zJFpXbz53mTHgru/h9Y7qsQ1D9fc1U5wDuy33WoabNKawj5kS7+P8FK/n7nmuH13qtXu7nDvd4Bwffx9vrN4eSwrSFc7p7gsCUD2e18y9fE93m67NlUXlWByG4mAOAAbnsLPF+Ro7w/ZVqbySygMAQjErq6s66Zw4QesmnxOpEtforRyQfZfXYSDPEDOlgKtm8NqnKy+tb3ccccQRRxyvWsTANo444nhlEQQjvSfLZC6cPhM+byBmMdMHW7v4LkGANyAYrRw/KGUNQya3O+eVCVVxjkli45okyAlKPDvb0jdzmolpxKoliyIbbEoLnynKKyPZYeBL+51I6igOwFYaP/BhDR9fcfeH872z2/gnx18QwJNQ7rLDBseaHeP4+w2VlLdWhFGbUr09dZPJd/0Kj2EYNICqHGPi77tMqh1xfTZTkuxfADYeI5MVQiS6U2QEKyeY5G88wfdolrChtrixZnnO3R2CX98TB+NTZJKGLV6XdEVk2xkm/nYLNDSq8vqkSyD7JRJN3yXI8l15fQ+AgNTqKdYeRmEkBTAUFUAfNPi3wjRZwVT0+ugztFsDi8PCHbAONfSlR+oEx9helxrtbY4bkHpdS2ogO8Iqu1wLpTmgcorgNVUkwBu2gK3n2Spq2OJ1L84LQ1zm+1prytSovaFaXFWPk73UNAJSDQSE4+f4vbslNcx7Yo4Gfm62yvcni6p1ke/xd41luc+G/Gy7jU/tpPD9174CPtQm0Xee8PAvlj4P7ZF/xzV8WJz7Rq4nuyOv0fg5iRxbUyXzQACWALgDMUw7zjUSyYqTUjecn+S6a4tSI1Oi63Jrlb8LXJ7H8a9i26idF3gvlY9TPbF3SdoeWTzWsfcKI9vinIyf5Xru7fA+rBzj3wcN3tPNFd5DlePA7AOU6h+1lnxPDMiaPPdIr6wZas0mC4dvujl93rf1qxwPQj5npu6QTbVbSOjjiCOOOOJ4TSIGtnHEEcfLjzBkUme3Feva25G+qyF/V1pQta2A1A5eobQyWSBLpusEmnaHCeawTXZo0CA4nbydwHb3EkFFMk/QsvkU61d1g8l9GKjayUSWwLk4L0zchEpu3SEBctRP1+4D288A/T18tFbGDz6+tD/cgunjo29/DtWwQflfforAq7crTOYUE/DAY+LerzPhDTzpw5sjyB00KT1trfHAY2eBmfsJtu2msJ8FzpXdIUjITTFJX32EAN5IUKKarbAvaTJLQLn9jDJbivqhJgsECe5AWGSXkuWFh2ga1NmSa9chSDZSIneVtkDJHFmv8pK0KUkflGfqOufx/J+yLZE3oCHR7H2cHyOh3qfpBP3bz5ChCwMChrFTisE/NATk6qa4PBuK9dXFwMowVZ1uJBl1epREh4EAzzHZyLhMBry3R+BTPUlQ3bzO+Ri2CTizE9iXE6fLCsgHPn/u7vA6en0gOwVUj/F8olpIp8fj9es00OrtcuzlJW5cmGI8pZsEfaVFYb19Wfctgv/QJyA0E9LWKsG1byS4lnt7rDO32wSa0/cCVgq/9nQH//eVMwdm8v++u4sfemAcePhngeufOjjN+Rng3Ddx/bg9rlVH1lC6ynWfzACOsN2rj9CUy8qyR6+RAkJXbRAlUlKrm+LvEErf47zMzaYyUFt6F8Hr9nM8t+l7+Bl7l7gBkSpxE+j4ewjk3R43Egqz6tkzWnPqOSxd2Pg8nx/5aV7n4hzXwc1aAAX+QZAbSYk1XeaixHvUbilDLYDX1krz3tHA61NeerE5WRxxxBFHHK9LxMA2jjjiePnRXCUDaaYJYFtrTPY0nYlnduzg650eE/HAJQOVqfL1+26h8mjp7RFcpYrAzH1kd3YvMFk1EsDYOYLE9oYY64gpURDwEDrIsE3dTaA1mmAOmpRJ2m0mrsMe6wHdARw9ifd/dAZX+6o1yN8/u4n/ZWaZSXgyz7EM2kyQx04T/OgG0Fwnm9WvsU1OKFLKzhqwLbLM0f6UhgXc9V2qB292kuxcJFnWE5zbC39KEJ4q09U3AppOn/PWXiGISJfICLauAx2RTPu+AM5xMtpnP0AmsrtNObfTIXg+4FQsYD0/fZBxCkHg5jsENc3rYtjU4ZiTeZH/TpBRjvrZejYBSGuNx0hkRkyhDK6V/c+Q6x8GN3wd1tfzkNB05TIbBARQukVg5g0EeGpqY6KxzDU5foqtbjRdwF1fmQ91tlS9ZCQVT1d4fpH82xS5bvWEYi01k+vK7gPXPsl2VIUZji+Z4bm2t1Rv35m7COKy4/y81jo3DOwuQZnvAfDZY7i/y3EFLs9z0JC6ajHqShfxk4+l8F/WJtTUIMQvva2Or7x9lsDvk/9KNo6SNAJLFnivQOda7e3yXjEsgsZEmk7FVhKorwBXPkIW3EpSdZAscAyesLFmglJjR2paU7I+dIPnaXelt7FDVlUD11RpESgd4zVoXec9np3gJsjMvUpqXlrk82HvIjeo8tNct1E0V/m8aK1L/f4SzyGR53PpVpL3ICCAHTT5rPAd9bOZVMfJTSp2NvC5uTVocJ0VZlVLpTjiiCOOOF63iIFtHHHE8fKiI5JO3WLy1tkmAEnkVK/S0ejVCSjdngKJo+rAqB2HOxB3W4tMi5kiGN54AkAIjN9ONqt1nUDJShMgR061YUCgW1okE2WJWVUiz/Yju+fJiJkJaRPkcjxWFv/+hQR++jn1vDqeGeBDt30YVipLg53SPFu46JZIZzW+v9/gcVurYkCl8/N3L4qE+AjznsnbgXu/j4A0N0b2NWrL01wl21e7QiA0cTvZWt9mP9yu1LLCYC1rdozn5zk8Tn+PoDI7ztrVk19DsB84BLV2h0wTAtWqpDArc54kWHB6YkbVJ2iJ+hPXrohhFwgQqqf4PTLxio7lOwQmms5rO3UnZZqjsvOXEmHkHCtOsYGnQLbvituyJ27OIyC4twtsP8+fC7ME1OUlgvf6NQLH1jrB6PxDwPhpsrHNVQLx3rY404Jzlx1TZlNmitff7YupWYbrY/I2ru0ourvA+mME92HIeUrmueZckexHbsPVU6rXb7rM92w+yfWVzAlALHMc3kAYawHYa49R4m5YQHEenp7ED37+BB5uKGfyrO7hd992FWfmp3hPPPYrlIJbaaC1KZtBd5Clba7wswFhwg2OYeZeztPW88DFP+Xr9AQwdz+fA3aHANBMi9Tb4PiizY9kiZ9TWeL81S6yPrt6QhQDHd5biazIhNu8jsVpsrnFBZ4nQLCrGdxQCgPK8kdNlVpr3BDr7ah5i9aelSEQ3pfAHxF2l9ews84NHacrfYlLZKyTeQL1VFEpBno11m+HAf9WWnj5az6OOOKII45XHDGwjSOOOF56RAAHmvT4lJY8kWGPJvV5TpdfjesiwQ2VI7JuSduLvLBcSYKK9jqBR/Ukk//mMmWKvkPGaOcFoH5ZGfBkJwmcWitMInPjbCmiG0zWuxusW2xcFVfiBBkwbyguyRaQzGC1Z+Kr/zSHoa/Q9q+cfgTvy1wjOCzNE8z4NseVqRBEuA6w+zzrKoNA9dFdfljA+C3i638OmDhD8OJ0mLR3d2lAtPsCz2P2AQKm/i5ZyH6dYCSRp5mO5xAQ2G3OeyBgz8oQCOTFJTg3SZDQXKP8OVGknDlV5Fwmsy9uLeL2RRq+RRDo9vfBE2bvI4AHxBl5wGu9+wJfGwG5wgzrVRNJApFRSfOt3Gdfbng2Gc/GMjcJ+uJQmy5zoyOUNixGQsBIQtWDG8Lauj1haUMC8uwE16iVlp7KFWGuW/weSZNDX+blLZR3R9HaoHwXIfvhauBmTFbk8TvP0UDJd1Wf0vw0exPbXeD6ZwiUApdA9uRXA0vv4LUaNjkOp8d7Y/0xKgqyVbT8FL7liTtwdaAUC3OpIf7gocuo5rOyIVLl/XHpz3hts1Xex8kC2dfdZ3mP1C+Rhc6UKaM/9i6e17O/zWeBmaIUujjH+u7GKteubnJenTb/nSxwvVppYO4tvAYbj/N12UnOiy+bKoEn/WiHnLvKMWDuQT5zWqt8hoyfkWfMMo8xfvZg+57Wumxm1blWi/O8VyIZsZXl+hw1eIrajfV2Vd9Z3eTYMlVZYzLvo/dLIi/uxyJLr19Tz4vKMZ5zHHHEEUccr3nEwDaOOOJ4aWF3yNgFvrAQIRPe/KT0pewzKXX7fE13i8m5kSDDkp9RQHY0IrCs6QeZ2uYKk8jCHNmdvcs83tgZymrtHlA7T9Oj8gKZTd+lRLe9SWlwc4VjMhL8bN+T+s8MAA0wE/ihJ0/gw9sKAHxteQu/ePIRMrVWmom5MyDDNXMfAU57BVh/ivJI3yVoD1zgud8jyLxZJLLA/T9IEDRsMVneu8DPcNpkfcKQifjYCZ6H3Qe8Ls2ocuPSniTBeXZ6PJfuDsdSmAHmH+BnhQGvW32ZPVkDn0l+YYZsUm5crqUmMlqdCfmwI67C66qOMy/nX76BhQp8fvauGH51d/n68jGO30gQBEa10IfFFwp6o76xUT9aI8FzDAJg9VGCQzMlPURzPKbb4/rYu8Q5zFbZ6mjmboIYp0vwGWqiMJD//lJFaSVlc+221rl50t3l8afuJotppaTu8xl1H0Qg2EyzX2siz+vTXCHTHvq8/k5XtYpJ5KXXrLTiWXg7MPeAql/3bCoWVh8lwHX7gJHCtZ6Fb37kBFq+qnN/oNDCr931FJLZMgFXpkJ1wd4FzqGZoWIikeUa6GwAz/8+18X4WbLe+WluIAUu8MKfcGPJsKhCyE+yfdLueYLUQPqzeg4ZzkyJ18nK8BySedkY8nncpXfxffWr7D/c2xX36hRroo+9hxsR/Ro3HqonpdXSrvr3qFlUe4Pqh36DNe2Td3DcnS0FXFMl3r/DNq9BBFitDDe2UqUXm0GFIa/RUCTLkVQdEDY3J07cYqJXmFNt0OKII4444njNIga2ccQRx63DHdCpOGpJooVg/8qxFwOPMGSirZtMCsdPH81YRGAZEGYjy6S2XyPrlh4jS7t7kSB3/AyTze4OJclWhjLdqHdp1HInENYYMs7AZ4IceMoxOFPGnzWn8cNPLOwPJ6N7+PCdH8HMwklxQE4ycQ8Cynp9l6C5dZ0AJPD5GeuP09zqVjH/EHDuA6p+1MyyP+2gSSdlw2J9n5EkMIt6WBop1momCgTY6QplkU6HCfTeJb43M04XXg0Ev+6ArHf9qpJsjp0kAMhUCQbMjIC0PWE5G8roKZEj2zt1x4tllYFPQFG7QkDpS23x2Gn+fSjux+myOEGbnD/PVkB39OeXCnpHDa0AVZfq27zW+z2MbQLJ7g6BJ3TF0PUbyrk3cAlyNJNrqzjipD1ocY4BrpsQrKGGJnWWws52toCNJ0Ve6xPgTd/LDZjWOj+jtEhWu7kibF5CDMjEIMqzZXPCJgtpd7m+Q1+k9SleFyvJ6zh1J88nYil9j7Wl28/yWJqOT9cy+P6Hi/BCBcy+faaGnzn+NLF6USToW0/RNTpT4XUaNPj55/9IAUCA8zN5F99XOUEQef3TUlsa8neJLNfAsEGp9qDFe8+3uZayE9wASRXJ/todmsP5QzLC93wP39teA2rXWM/d3+O1nzgHnHif1IX3+PwpznH9uz0euzh7cP20Nzkngyav6dSdXD9RH+LuDp8VmQql2FGbppfTqscR461hS9ahhN3nuBJZKiNKC7FjchxxxBHHaxgxsI0jjjhU+B5Bhu8wOfaGTE73Lko7EFvq5fI0JTITigG1UmQd+7vK/ba0eHSN2Wh7luICa+RqV5gkdneY8G8+JWymMLX5aQLc1ir/PnE7x2h3APgADLJCnk2WyBAXXd8m86UZ0uKliz5S+OqPzWG9r8b3Ewsv4IdP9w+y0JkxJrq7F4Qd2uPvQgESm08e3UolikQOOP1+Ml5GgnOVqhBQ9PbEAdgguLXbitk2EgThrRXO8/jtQHme8zxsMvlvLBP4pgtkHBNJaUnUFhOjDse78A6pbxRjJt9lIm63hRHs8VobFoFzdpwguLRw8Fx8V8mau5uUeSdE+lycI9gJPIKd1prISXXWEidyAupDAOHIzxB2z1WS6v16WmHDNE25I2s6QabT53U3UgRN0SZKJBG22yLzTQGDPc6bYUpvWamRzFR5DrvPk3X1HQIRXSegzIwLWAl4HmaS/w4DrpHCLEFR5BS+8hkCXStN0Ch7K5i6i5/VWCZYs7si2TVUj91ElnM/cy/X3voTwl6KGVYiTxYwUyGorZ7k99wkz8tzyFDWr1FeDg3/9Tkbf++Rgy1vfuLMNn544gXFbqcKYqoWklnUk8BTHySbemPoJs9l6g7WzeoWZdAAr0tunNeqtIRIFYF+naDe6RB4ZsZ4D2cngdNfy3W89lmugen7gXu/R0ykVrkRULvCn60c1R+Tt0kLrhSfMck8nxOBR6XAjQZRnS1pNdXkOi0tcCxOl+t02OJGUG6ShllfSP9ZdyAOy23eU+6QzHfg87wn75A5jmtv44gjjjhe7YiBbRxxfLnFYeA1+vlGF9rAJ0AYtmk+lJ8G8rNsc2JlFVvrewRfwxboCjpD5uyo8ByC5cDlMdNlJvu+Q9DU2lT1h6EHFBfJpA1aTJB1nQ6qhiSHiSylu8k8a3M9h8e0suLUmmKLFnfAc0qV8M8+08MvPquGdCbdxn//il1YE6eYgHa2aT4TeDyv3i6ZPsMkGF17lODypqGRxZqTnpqJHBlXmMDmE2TzACbSnkvTIiNB9jCR45w7XQKG6bvIEA7qdD3ubHBMIVj/WD5GGaku0l+3z2s7dho49VW8JgCT+/aGMpkaNJX5V6okoGmaP4/KOp0+AUhrjUChV5Pa0zKvYeIQVj4IOIfDlrpOuSmC56hn7QEnsfAg6N0/jsf58R1ew6iXaRjweqQrHMuwTbY/8DiHxRmlKnCHfI9u8ndzD3ADIzpHp881OGwL2B+qPq3pihhwyTyZ0mYoqtdM5AjqrTQ3Ejaf5rHcyOjJ4v1QWuRrBzUyvJ0trs20GBwFnvSwHRdZfoLnuvJZaWPTkrmTuTQSvL7FWV7f7DiPsXuR1yldBNJl/INPdPCfn2iMrMoQ//H+dXxVeZPXHxrn3e1zHL4HfO4Xb760C7N0VTaEOa9dAAINyBQJQCMWNFng/HlDqhoc+QwzzWtVPkYVQ+0ycOXDImu+g8xsZAzXuCbGaVs8XmWJmw69PW4WzN0vta1XufkxfubFJQ/NNda/dzYJvqunRVY+wbF0NlQZQXacx/9C2VXfVWty7yLnRDd5vbITqlfuaFu0OOKII444XnHEwDaOON6MEfgjMk+bLI9nHw5eo9DE8dVIqO/tdWlj02PiXJx7MWDt1YSR8JjklpcIJo8alzsg++n1mdCnKkxqBw3VY7W1QiMcDWI6NcbXd7eZuFaOUcaZnWIdZ2aMY6hdBo2qpgkWQzCR9R2eX6YMFGZxoZvEN/zyRXgj2Om3730Gbzm7SLOj1hqlkUZStT/p15mcr3+O0sZbRbpC06vqSZpE5aaY1HY3gcYKHYoNS9oINYWttDnmVEF6yhbI7FROsoetJyxVb5fHcYY892SOSXimwmtsJES6eZYMV3ac4+/t8jOcnjgZiwmYpvEYqQqlru5A6qV7fF93lyA+ep+V4djyk/x8Ky3rJiGuuGmOM2Ik3YFcB5f/zk0d7C985DoODrb/6dfJ3EV9iJN5ADoB9855VSO5b/IlgCFyM46kvZpJ4FNaEIBoyWtDgv7QB6w83xfVGRsWGUq3x+uYLPAzAl/JoLPjBPlhwLW4d1FtJKTLnC9vyPPWLdax+g4ZxtwU10JXXMaLc8DEbRxnEJCJ3XhCWHBbXJoNHi9VoNw7U+EGT6oomw87QDIPz8zgr/xJF5+40tqf2qzh43feuYGzeUfutxaPZSZ4bjvPi/HVTUK3gJNfxbE6PYLHZE7a4BisGQ88UXiIbHrrWZYyZMa42eD26Xq8+CCwdZ41yak8nzcz9wMIgH6T93e/zmdZaZGbL3qCm0GJHLD4do6/u83rPHaa82x3CICHLW5M7b1AED9xO7D4toMAuF/n9Q48xQYf9Sx7uRH4rIFurRDcp6tUlwDqfkoVX73PiyOOOOL4MowY2MYRxxs1bmRefZHf+vaLHW6jOABekwd/HnUUBUQCeJX1bsUFZTYThdNX/WsBSdQqB1uxRO1YfJGWhsIAuwNxmy0wke1uMQHtbEvrHo+tQJJ59kfVdCa3VhqYPMex5CYJonp74hi8R8BRWuC89Gr8nZWR388D6TIC38df/hMfn9tVbMx3ztTw0+80AX/AhN7pURaczBD8uT1+zvpjB2sODwvd5Hwl8kCuSlCeKsq1splc92sC/Cw6yRoJXjuEBASZCsF5qkTZYjLL80gVgfp1kc3uyPVM8POyY/w5P6tYzFSRc9bZIiDt1xUDHLWvsTK8/qFPoOGJ6Y8rLX+iNeXZZMDTJaljneSxbyap1C1xx7X4s92WHqoWr21p8aXJPj2HgDZi1BI5kVu3+PuIOU8WpK2PbMr0tnlOuibtZoRVHtR5PmaSzOMomAh8AlG3z7/nJlX7I29AWXNCmFk9QcCam+TGROiLcdUs57i+zDWz8zwAjW7S2QnFuOomr013h4xwYVbMyVYpVc5N0qjJSnFse5eAnQtkMBMZbvLYAo4Dn6+zUmxxk8ioDQ4rjban41t/u4HLNVUDOpuy8Qfv2sRYPkMX8R2R/ZtSx+vbwNO/wbrvm0X5GE2tPBsY7AKpKq+D57BdTuCLydo4+1jXl3lN8hNsB6YFwOJ7yGRuPsGNlNKC1Oif5fOgtSa19i2upYUHWZ7Ql401M02m17dl88FS7asA5W4MsMRh2GbN7cx9BxlT32MNsN3mNctPv7RNmJcaPTG18/pS7y/KjCiilmKpAp8hcU1uHHHEEcdLjhjYxhHHl2IEgSSr7kGQuP+ze3Pm1UiMANYkk10z9WLwelR0tsguRK0yqidV3VokT26tEDRAE4bmZomf1Ef2dqR3J5iA7p5n3aPbV61LECoDnuISYFlkb60kzWXm38JkMAKzwyaTxVSe5kqdDYIndyB9Jgus5fN6gN3Db17R8X8+Ud4fWcl08JG3P49KQsyQult0XNZ0JuqBy6R559lbOx5nxvhZutRxpqUvp+eO9IeVczTEjEgX9s5p87oXZgmWinMy3x4wfoqgrb7MWsTmukhYSzTUqRznfKWKvCZOGxiK7DGqo9U1AsJ9F+QkgZkGMpi6wbXl9Mk4mgmpZRUX3yjRLkxLL1e5TmHI1+z3mR2pk/Vd7LsJRxG1yXF6AuIngNKs9D5N8nOj72HIaxKx7q5NkB9CWPQ9HiMyrUoVydhF18lIEswYFteY3SU4jY47aHDuC7OULRsRwLeB5jXVHzk/S/DR2+U5ueJ26zvSSsoSB+OcOv9UngxuGLD3a2uVLOTEOToIh4GoIboEcjvPq42ZqbtYMzpocF4W3y5S5YBO3M0VHi+S+fZ2Cf7CAPBD1hPrhmojlJ8FinO43jPwTb+5h+ZQSRXuL/fxwXfsIpnO8fN2nuN4oPHenzwHPP4rAs5vEkaCTt+JAoFqZpw9aq0MN6+GXekFm+NrBzWC3GSO4NxMAGe/GXDbLCcY9jiHpTnWh7sDHmfzKc6bpvPzZu6RnrNbignvbvO4xQUqOzJVrpEInPZqvI+GHYLb2ftf/Gzs7XFzJDJRKy2+epJhp89aa9/mvVWY4fNh2FZmZWHA622mlAIiuiei+22/gBsi7R/5ilou6YaSw8cRRxxxvMkjBrZxxPF6RBiBAH/kuydSS//FwPUoxjWKyCk2kg3v/3wI8/pyo19n8tjbZTI3cU7qF4Uda64SJOgGk+1Uie+L2rRYadWmxZBaRN8hiK1fUz1A25vC3hn05Ql9JnZhQPBUFhmg3SMYmbwTWHgrP7u5Km1p2gSfAI9pixlM6Csgr5vCpoVoBBl8xZ9PoOEolvGnz1zCd54QsN6vSz/RtGr1sp8UBsBnfwEvAmoAAUb1BAGsppPhSZVo6GSkFNDq10SKamIfGEInexNJNlMl6YVqUA6drjCpb6+x5ZHbV61IFt4uDGXI4wzqZBsDj3PnCitppDnPZhpI5fg5usVzNCyuQ88RBjfFzw49HjOR4VdhhsDx5UYwsr5H2d8IOPiOANzqwbped8g1GDGSiayqIbU73NxIF6V2OU+QOwpo81MHwUwUo32W2xsEGGEgctlJjiEySBvUVRulqBaztSqMbpdScH/Atez0FLAykjTLyk0B4+cISHaeJxi3UtwkGD/H+yvwpDXVKqXGdoegbPGd3LTpbHJ+pu8mixr4lDi3VlhrbaY4D+0N3nOJvBhobXNNN67zvFNFoDCPz3Sq+L4PBfBGlvG3zvfwL+/ZgZYuUBnR3iAr7A64wVKYAzY/D1z/lFJoHBX5GcqhE3luyBQXCfobVzl33V2eg5khu5sZ43r2Hc79ifdyvgcN5aydLLLmNjfFMoCrH+Xmm2YAU7fxfe0tPiesLAGx73C+jr/3cEDaq7FFkt3idZh74MUO7+6Q7K0r7Gp5SeTvr0L4rkjV67z/cpMcpzMQV+qWap0WhW6IUZ94B7ysZ73GZ2q6fOuXxhFHHHG8QSMGtnHE8WpGZ4sJ7o0g9qhWJodFxLjqlqr9i8BV9PNrJU8btplcd7ak9cm8mPVsKtAJ8G/lJSaU+/WUN4zJ7hK8dHeYXO+eB4Y1soR2R1jBNOvtorYYmsljFWeYvHkDHqt6gqzksKVkp54j7W46BK6DJv9mWALgRIZrWFIPWcWPfzrEr19VNXVvKffxm+9tQ29ckV6UGpPniGlLZGnIBA1Ye5w9PZvLIyepUf5YWmQy6gwIXHSN+DeZ53E1CwhdlaSaGbKqeoKy1mGH4yyIkVZxliB20BAWpwf0tgicI3lsbgrQAbgOAZU3lL6ZBqWoEVucyLOWL5lT8kwzpWpDnb6s0VBa2kh9dhjw/NIlzt+o2ZNuKFYoYnsjB2ojKb/X5Evne/bZpJGfoZFh74qTtmZwzJ0tynGdPseaqXB+fIfzaib4uuSIxFszFQDPiCxbkxrfyE350DXfUe1gfFu1Uor6NfsuNzw0qUdOl5SBldOTml6L4+puS/2rCWDkfLJjspYNAbK7IlmfJ8DNT3AtOF3g2ifEBEnnppKZoETfStFpuHKc13D3AtdidpwqAafHzY/ApYx97DQ3TOrLNDrrbO23Tfr1jUn8+HNzB6bhx+9x8VdnrvI6On0AuvSArXF80PgZjWVK4W8WhsjjcxMCyE+yFCCSE/d3VLssd8D7xExyjosLYk6X4eZE7Qrv80QWmLiDx2wsA9c+zo0PK02zqEReNn1SLIvo7kgLoHFg4SGR6qdkfcpa6DeAlU/zeVRcAOYfPLx9WdQr96UY40XvCbyDmzr7JSPuwXKR3h7nGODmTmaM49vfFHRVayy3L8oYXV6Tkvsgx+elph2ioPDUhml58dUD5nHEEUccX4IRA9s44ng1o35VOcACUCDAUL1LgwAvanVyACRI0rXPNGnq3wckZiNfo+D3ldaCOX0mi/UrTJjyU2TtXDFRSmQJvEpLZERujDDkuXe2yT7tvcBkqt+kTDBiGzwx2rEkyXT7TG7tDn9XPiaAUljfnEgJXQERfkBg0FoX9neoGN90mUle5QQT6fwUQZFu4fHzV/BtH1zZH66hhfjj92zibHKPIDxwWc8X+AACMRMScNZclf66e+xZ63QJJiZv47UdtniZslNi3NUXWaokr2Eg9ZnCrNttJtnJvGq9Y6XIdpUX2Zqku0c20EiR1QkhTOSkyK6zAkDA8fd3wHZHZVU3G22OWGmOU4uY6kCtOyOhTJi8gUjcQ2G+iur1kTTyMMb6xtANZW40WmMbrdEb63KdPjc/6ld5rrolPUoFpAaOAuCawfE6fZFtylrKjBF03rSNiqbAeQQCAF6n3h43Enyb82KlVash3yH4MBJcixO38xp4LmA3OXeRJNp3xUm8xfH5LtdWv0lANHMvwXz9Gtd8Kk9GND+tNgfqy2wF5TtiCjUmxmmgGVhmjOfcWCa7WpgjO9rdIeC1W1JzejvH1NsDNp8hYxp4gNPGP7xyCr+8e2ZkZkL84tta+JrcNXGPNrgB01rjvJhJjjdi+dcfk9KBm0S6Qmn1mNTFT9/N89t6gvX7EajzPW7w6El+z45xsyg7ToVBd0/qoofiwJ3lvbh7gdeqcgw49bXcgGlcxX5P5Y3P8zqkK3RVz06MrEVRc3g2sPE411NhHph/QK2h0Wdpv865CDwy7rlpAIEAyFEVjnfrjczRchEjIaZXO/y8TJXPrxsBNsD7L1Ib2J0X1/tbGdnMyonk+5BjxBFHHHG8iSMGtnHE8WpGZAAUMUX7iZt962Tn1YobGV7dOigL1kfAcxiKa+gucO1hglo9QVCYrVLuma1Ka4oCzy0ygooSdnfI8+ztKoY18EV2a4ujbIJJa1THaQqLqutMWpurBKTlJWVMFBldhZIsdralBnZHAIjDektd5NfV42Sppu58UasPt7GOD/yHp3C+oR5hP7ywgZ84vszxpUpkYiJn3xBAIkUDq94egUhni69x+/xdcZasU3db9QPVdMpDNUgddKAYFA2UBntDyoKLiwSx7U3W/xZmgPycOsfA5XXoN5SRTXFOWriUAAgL2a9xHKbUippJjjsQlsbrs74xcFUCrulkOzNlgmVvoDZJxDmaktbUwRptM6UciqMkfvTLGzEyCzzst+85rIVPqBG4OwOClkGTGxxeX0yzxtXnRZs6iZwytgJ43VNl5XYcsWQRcA8hzDBkf0gb+fdoTaL83hkQMIUB5zE7ofrWOgPWhXoO18jYaYI2K8uNls4216pmcO4i8DFsE2h2tqUf80lg6m6OYfc8N2h8m5sI6TIBdVR/u3uemxaarjYyfJvgJzcubXVaBDzjZ7le3D5Z0e4Oz7eyRODnu7zPvAFgZuA11/BDny7hY63J/UuSMQL8zju3cC5dp3N3QsoKurucP7vD+TETNIiqXTy81+1o6BbHUDrOTabyIlnnQYNtkfbbarlKMp8o0LgtmRfAbwCuK2ZLaUr1M1WOcetJfsbkbZRvJzJUblhZbgKtPkrGNVWRY+dEgTAyRqcLrD/JuctNssb5MM8Az+ZGQtRDtzD7YvAY3UfR/wGGqBgMS5WMHCYh9mxudkQbYDdzl4/C97h5Yne4Tnz74N/NNJ+rMdCNI444vkwiBrZxxPFaRmdLauTMgw7EEZtlWAB0BTr2gcCIlAyhMGUCknxXJe8H5GYjbIE2Ihfdl2OaKlkLfGnd0uMxm8sEa2FII5XyEgGtZnD8N7J0IQguhm1pGxPwXBI5MU8ymHC11qRPqrCS3W0mjsU5vr52hW1RoiRx6hyQKPJc2ut0VNU0HkMzlPNzIkOWKgJipSWgvHDQtdkVs6bdS/iPH3se/+S8SuBnUg7+/G0vIGt4BIdXPkIg8M0/r9jT3h4/t7fLn6N2MYbFZLy7TqCqaQSzRoIgw3MICDwx+HL7/HsobKcprHRxFujs0ugnU5Fa5hTnzO5KfXGXbViiGtREFvvOyZ7NdRHV3FYW+ZoIuBkjUvZI4h2tvSCgY3B/V9XxGQkyW1GdtpViYjzKgGq6qqW+lat25IS7XztuC7NcU6ZJkcw3cAhEchO8rnaT7s+By+uen6LJlDYimc5UCCDDEdn/KHP2SjeSwkCxtwCBQW6S96nTJUva3eY4knkC8Mg4athUio0IwLgDznd3m+1wrAQBbGac4NSzxfCqw82jyF3cTPDabz/D+0zXATNLCbs7lHrorNz7Dq/d/IOcw0SGa6t2ka/LTRGsaRrvK90CysfQbuzi2375KVxqK8Azn3bwh+/eQNkcsm7VSIjpWZvra9jmfWmmOVZ/CFz6s5vX3lZOqT6/yQLXS36S469d5ldnk3OhGVLXnSOonTwnrH+Szy8zSWVDforzuvEUsP4or8/Cg8Cx93Cduj2O18qwTtbpc71oIY8VmaBFG3XDFrAsNcT5aT4HI1Z/VD4fBNJySgBo9bhs0pm3UAu8hAgCab3U4GcV5w8+024VnjPC6B4GdMWIyspICUk6BrtxxBHHmypiYBtHHK9l+CJFPVRW5rOG7VZGUS839uu7XAV2I+Dr9Ag4nYFK1gZNMkrpCutFs1UFVIKA4NwSAINQXIx7ZDyMBKWyuQkm6SFoOtPbpavysMPjO10m2IUZJrSaxb/Xryr5XfkYk07o0qtzoBxwc5NikiO1o8miSrRzE/JejcmcI+yF0wW2nsPatefxNU++E/1QGcj8wntDvH+8Blz+C+DinxIYAMB9/zNw57eJOZVLsIGQLJU7ZELYWme9ayDzHCW03oCAIQKHgIDrLgFP1F928g7KSXeeo5TStIDCApNpp02G1reZ9Dt9jk2T9aKFPL6VkVYgmsgs58limUlej1RBAXErxWPpkpQP6gRS7lDVgybzqk4X4Uhdnkhx9zdHdNmUOUzuro0wUgn1cxjwuKPSycDneboDAr1IGeA5rBONarDtLtesL3XE6TIZz9y0AtejTsqjUvzROseo1VQEfvdLAUa/QrX5EIY0LeusK7CVrXKuNY0gqHaZIC/wKJ21UjyPqMVM1K/WSPOY3oAAKwi5GRPVlmYnyCQ6fc5P6BO4ZcbJwBoWsPo5Ah7DYj9Yu8XNgchYq7UmDr9zwImvlrpwcP52XuA9kSpxw8pIsTY7VQLGz2GlMcQ3/duPoTFU/72/a3yI/3zPRRjDupQQZDkXdkdYdIi50ZBSYT1F6XTj6ouXRXGe8xNqBMJWkoDKShHApsdoxLX1DNlQuyNScKkfLS4AC+8EgoHa4Eqked+Pn6OU+9KHgbXPcX3P3Qccex/nwHf4+ZEzeLLIeynafMhUuaEWAVK7S2Mqp0cgvvD2w2uzb6y7fbVNmbq7PD5CkeTPvbISk1sBXYD36I1g99Vyf44jjjjieJ0jBrZxxPHFjK1nRmpkrRvMboyD5juAqg8EsJ+A3/g9Sugj5sqzpafrLhPR0Md+H9HurgKX42eZ3ABKnhn1wZS6vP1WNcBIAqSTtenuMBnbd/UUUJvMkGkqSR2hZ1Py3FrluWTGgcKkOmbklJsqMqGLZMmBJ+yl/JybZGKaHRPQp/Pcol6pO+cRtNbx3c++BZ/tT+9P+VcuGPil4w9De+Y3KYEcDTMNvPvvkIVzuwAMGgsNG2Q93QFBqu8J0AfnO5HGvhtyJGV2ByKttYHAprwyqh9uLvNvkdtvqiSJco3tTpJjlORGTrtmigCiclzYr3l5b5J1gREDHrGoNybBgS9y8R21kZIscA5H+8gGUgvsDgiynL6Sl++/xsO+YZRuqrUSSdPDUN7bU6wsoABSxIJFpmPFea7FyNV4H5AGNNxybamldmXOfSXb1W/cMNIOgupogyZimV8uoxYEZBJ7uwCE6YvclgOfQLVfI8A1BYhpUHPiDrHvPm0m2SfWH5KVDkMy1cO22kzq15XhUKYsztoVXqf6FW6EhaGwsnkAooToSe33oEFQfPprOUcaePy9i9zMCALeL3ZPalnHgepJfHrdxff9+lX4I//D/8jpLv7uyQ2aeFlSt9nf4bUIZX1FztHRGm2uAmuPqGueyAL3/RDVAfWr3DiI1lxUY54syJwWCOC3n2M7KwQcf7JIgD9zL48bgVQjQda1coKmUsufAFY+yXOcvJ2vj9yDy4uqPrZynOOI/q1bPH5UTz4KbguzwOI7jjYe6+3JcwzSS7r68tbXzcIWdUDg8jwqxw6XML+c8L2D97c7UH1+R0M3pU4/KkGI1BkxuxtHHHF8aUcMbOOI480ankMg068pWWbU99JKA3uXaAATApi+B8gUCcI8G4ha0UTtTlybyZ0mBlARkPMlMW+tkjHyHCZOwybfn5sgcFl8GxPIfp39QXcusK40WQTGzlBul8wDO+f5/uwUMHFazJVCskWNNZH12mSdkuIGGgZM1ANPmK8BQaJn4z+uTOGfrN21PyUZI8SHpv495mufOnrepu4Czn0jQfLeFSVJtdI0wQp8kRp7ZIUic6Rkmf+ONhN0nY7FCJk0T95Ghmn7WQWIzBRBqd0C6mtAZ41z7PR4XpHL7+xbgLN/ia1NnD7ZwtCnxLQwffS5uAPVrzVaA+kywYWVfmnrKJI7R2DV7SuGe/81IhH2HenxKgAycKRWOi3tmVryXo3yXSvNzYpIxhoEZPSsLIFLKi/s5RjBU2+XADiSj0bAKAwOus8eFZGkdb/e8Qi290XzOOSaGm0nlJvkuu3XVL/TEGI65WDfDd3uSj14nvfKsEmWNpnjXBmWcqFO5ISNFbMwK8Xrq1vcxAk8MqPucOReThIAezbwzG/yWhcXgOk7CaCz4wSYexfV5kkiI5Jkg8AwN4H/9EQX/+iT7QOn/W8e6uKbCtcIaBN5pfiI2lSZYu6WKoh8VkoXlh/mM+Hu7wXGTtCEqnmdNbG+S9VFtCGVyPBa56d5X208zfXdXOVnaCDTO3YMKJ9gHfOwI31fWyIVn6DZVGOZn+M7QPUU3dQNg2OsniCY1U1u4kFk2YM6TzZd5rNKN/g8ufIRrstbgdtBk5+LUCTSk4e/7pWE77Lu1u2JfHzp4EbUqxE3bmZFG3KHmcRF908EdEe/v1LTwjjiiCOOVzFiYBtHHG+2cAdkZwZN0MhKF2ZzXElkW2vAymcIRuYfYn1qFL4LtDYIsuyOamMTiDFOKCZBoSeSxC72gXAqL5LTIV1aZ98iCeW6ALKL7JHbWmdiPnUXUJoFkiWyUcMGk8vpu5hQOj1KDOvXyXpaWSbAxRnsuw87wor2a9JPlEDifCuBb3zhvXBClZD+M+uX8N3GR24+f0YSuO8H+Nn9mhgWBUD7On9nZQBoTDRTRSbmYSCSU5/HyFQVAEtXWK83fpbmQ80V1hbXl2meBE1codsCZjKUHKcqwOy9TGbLi9L6JclE92agNggInqI6VoBrIF0RRjn5MhfUIeEOuGnS3VNMa9TKKDI6ShW55oyk2uhAwPPTLZHStpTcOJT6Yw18f6aiFARRaAbny24pyaqRoAw+Ny09eHWlVtiv8bVVD90ja28jhcKI8dqNP3sDSvMjgBsBzkSW91w036mSSMnF2CdwVc12VMOZnyYYcIS5TxY4P2ZKeuyKDDoMpaXPOMGVniTjP2yquuh0UWpXM8ALf8AxlhYIOKMa4GRBmU85XZ5ve5P38cRtCHOT+Fu/fxm/97wCt0kjxG+9p4G7rDVp9wWguyHtqRKc50SGhZQ9YQAA2fRJREFUx59/O//d2iBD214Hpu/lZk8YUILdXOZ86AmRJCeF3Tb5uoSoCLae4v3RWuFnRi2TyseA6hLnfdjh/WP3uB4TWbLcexcBe8BnUXmJteyaxmtSnOO1iPpOA2pjznd4TqUFcd0WcLvP3L79aMbf7ihGOjvBz3y1Igylx/Yer1lhluv9tYww5MZBpDiJek97w6Pvn33X8xGX/lfLsT+OOOKI4yVGDGzjiOPNEu6QbEnU51U3mdRmxg5KyHo14OrHmNDO3Md6T0AxvO1NoHWd7Umiti+6zgTTMETONgRam+Ig6zNBTZeZJBoWpYCTdzCxbq4QCPTqTDprF5k4Lr2L7T+cPrD9tOolufB2Mjc7L7Cnbq8mjM4UAV52QuS0AY877HD8To8gyR3Admx882eO44VOav+0v1J/Ar9k/ezNc6vZB4CFtxF82D3KRuvXhbUNmBCnSuLGnCCQClyRmwoYTRaUy2u6TAfc+QeZPF9/hAl/d5tj91yCNF+cdrPTBPqTdzGRtptMKLPjZFM6m5y76mn2+owiDAlW+nVe/yj5NKUNTqbyhRnb+J7U6om8+MY2I2ZaQIrI5yOg3tvhmgA4X5kSAG3EwTjkXCbyXGOpkrhvayOg1JHaWKn5DQVABz6vyz4brfG9maqSbGqGqoGOetkGAY8RHWu0Dj3wVPJ9QPav8ccoeQ98VWOqm1JbXeLPTo9rJdpQSpU5Z4M664o3nuDnjp8Tp1oL+2DdSPB3qTyv3d5llhCEAecvXaFkF5r0zO7ws5M5bnRECoeNJwjCiwvqXDNVcQyWTYhEjufUuMbjjZ3GML+I7/iVZ/HMhgK3UxngD9+zgwmtTvA4aChGVdNEDh5yE2Lx3cD8WwjiVx9RfYjz07yXwpDvRQhYeSArQHT9ceDYexUgzJSB2jUpWVjj+LKTnOPyAsFn1B86ECfuENzoiur87Tbvm8pxaQOW5bWAxp/HzymAGPhidCfsbXaCSgmnz2el0+H8Lr796PpTp8/xBp5qc/RqArl+nc+QMDjILr/e4Tmqr+4o4PVdHMryjsZom6Xo+6iR4ui/YxAcRxxxvIKIgW0ccbzRw3MIeKKkzEiQ9UhXXiyfszvA5Y8w6Z28na1xIoa3vcGkddgSiV9e6iAzBMa6qUyoulvCRHnSCigPdHYIdNPC1EXH0RMEVrXLwO4lgpvjXwlMnmEvy+42k+3iHFnJwGPSvPMC31+YpswwPyWyTZEg+h4Tes+lrDkEQVB7A//i+TJ+/qqqd6ugjf+R/L8wobUOn8PMGN1UdUOMsSwC/M4G6EIsksvyksz5gIA/AnhWFshPMPHuN8jM5qaAuQcpTVx7jCxtd4dApV/jWKPevpY4wBamOZdTd4sBVEKY3gaw8aQYZo0x8TdTChS5fVU7qwlAjOTdryTcgfSJjcybbjCdMZIiA84RlI5unAQ+z7O7LQBXGH2nxzXjdBVrFrnkRgxmMieM7hGyT4DANAK5gUdw2d1ivXjgSM2z1C3rhgLCLyVCHDz26M++/HuUsQo8taESeEDUTigEAF9MeXJAtiIGSiGBXGtNmHNNmfckCyLl7nMNWBmu+WGHaycyYDOk1tHM0lF7UJeWTBmuNSOpgLSVpRwZPtd1qBHQ+jaBUjJHtUTE4FaWsBkU8IFfvY69rrt/mvdMp/Dr76kjFYoZU2uFyovdi9wgC8BjmAn2+J2+i0B04zFVk15akPZfHW7umGnWjT/z21IzawGzDwLzb1Xy8NoloL4CdDf5u8Ismd78DGto7RYdx5O5kY24FD+zdpkMebrC50dC6m3TFa4JK80Np4g5B3ifNVfl7xkyxIEHXP04Pys3CSw8dLSM3x0S3EYeAaWlm6/llxtOn6A9ajdUWrx1S6DXMyLDucgJPRj5Ofr9S+mDDYyAXBP7/Z33AbGh/j3avi6OOOL4so8Y2MYRxxs1ovY5vT0cAF/ZscN3u90hpXV2mwnb5O1kfdprTAAjhjFZ5M9mSqTHAgwiGWZnWySYFbIhhWnKYxvLYsaSJNPoi5tsbpwJ8MYTBLkLDwLl40y8HZER5iYpDbQyTOJb1ynRLM5KTegEz9fpSu1lkSAh6p077Egbkg18esfC955/CCFUsvML1s/h/cZjL54TXdjlwjxZmTAAXZlXAbdDeXEyT3CZKvKzIuMaaALsx5kcZ8eZgA/bfG1+RkxqloFBmxsHzkCcdocEruUTrJutniYoGzTFFMkgQBw7xXOPrnGqTODTXj9YN2umOIeFWemt+TKYHHd4sLYucuYdDTOtWrAkcoezVmHIcXa3FMMbrR9PAK5hqXUV9Qw+zBDHTCun1kRG2g7dInk9zCDrQD9cXzkgByM/3+iMfJQpW5SQ+/5Iwi5g13O4fgYNJcsOfVEQ9NW5piu8B7wBAao/JGCCGMQlC7x/3R7XQCTnTpWofIhcg5M5ZdjlOdyACcG1Wpjhe7vb/HtxVgByW4EM3eQa7O1i3w1atzjHxTk8Xk/gu//MgjOC4b/1XBb/8t4aNF0nSG2vA9vngcYV1pvbspGFkGUIlZN8Rmw9xxKDwON9Zib5DOmsEXxG5ktRFOeBU1/LDZxEDth5lmUKdo/zkylzPWTKwMRt3EgbdnjugSuMtMt7rrvNuc6Ir0BypLY3cHg9pu5maUO6rOa0cV3YcJPgUTeB5U/ymZWbZGlB6oj8xnMIbr0hN30qx15dZjUyLRu2uGaKczynN0pE98x+L3T/6H+/1JZdkTJjVAodscP7pQRfoPFWHHHE8YaIGNjGEccbLYJA6hu3+R+/ZhD8ZMePTqB8j06f/ZowGPMCjppMJKw0GQ0ro4xAfIcMQRjwM+22YhlLi8DYaSZ3q58DNj/PhDI3oQDJ+DnKBi/+OfDMb/FzJs5Ij9g62RsrTeBbOc5jdtbJ6vb3mKBnJ0C2r499WWjEyg1bI6yaDzSXsbu5gq/f+CvYRWn/1L9N/zj+ZeIXXzwn1ZPA0nuYGPsOE+H+HpNGu826v3SF8+oPySB5tnLajdoXFReB4jST9dY6x2SleL5uj2OLrldPjLzyE0zej72H59O4zsSsvMTPbF0XAxedGw/Q6Sqdm1LqWN8TsBZKIifAT9MJCJJ5xboDAoKG0uZHvtz+i5NHTRc32awCs7dKzPt1bnoMmkz+PZtz5It5VKrIr2RBWjaVFcvreyPAuqfcog8OSvXgTGRVW5LDwG4Q8Dp2RQ4OSH9lqTV9rSOqcR40OB/9Oq//oCEgeMi1lhvnhoknjHOvLhsaLu+fyBQrN072s3KM89rZknXlUf3gDdlvtrVG9tRIch2lCmQfzQyBZvUEx9LeFOAQjPTQbStZs6YDyRx+e72Ev/3oQaOiH7/HxV892eBnJDJc841r4lidAlrXgEGH98j4Gd7TZpKS6tYKx50q8bX1S5QrHxZmClh6NzD3Ft6Huy8A28/zPszNkLVFwPshP0WwqVuUNocGgXRf6tmHTZ5T5TiflYHLjSgzxdcZSY4zN8E5jtr2tDeUa3p+hpsJq49yvrLjZIyP6jHrewS3+z2mT7z6jsKjLYFSJW42fDGkya9lRK269hUU3tH/vpUyQzdVycSBVmGx8VUccbyZIga2ccTxRopBgwlXBBgyY0zqbpY0BQFw/VNkVQOfcsV9ianHRC5TVv03I+RkWEzKwoDJcFQDOXUnAcKgCVx7mC6/bg8oHWMv1fIxJrW6AVz5OPD0r5O5qp4giHT7ZK0SaSaa2Qkm4etPkp0JAgWQzRRBmwYmJckcx9ffk6QkxfE9/wcI1h7DDwx/DA8Hd+2f+qK2hf+e+HvIawM1H5kx4PTXcYzZCc7FzgsEF/0avzRTMV+BR+bFbioQaSYI/sZOKbfk1gZBmZlQtY7DLqXHwyadpQ2LSe7t30yn6J3nmfSHnphizRKQhQHPL5J8pwoCVgtM5AuzB5n5SOI5bBG8+EMl/Qv8EfZCErl9EBxJoUcYUkvVJd8y+nXOXXuDgDZy9jUSvFaporjmCrB9qf0x98FuX4Heo8BuInOwB2d0bkHA+6W3o1qaGEmurcNk+q9VhCLDdnqcp84msP0CzdAKc6z3NJJk+hrLMl6pW4xaNyXybCVTmJa2O3tcT0HA8zakFdXeRbblMgXIW0nee+UlKhPGz/Ca9/eErfX5ucMWf+5ucyzpIuA5+EePBvhPF5TZmIYQv/j2Lr5mUSOoa60TUDdXeM8k0jSIGrSpLpg8Cxx7J8e//QKw+STXtJECrn1UmXAdFdWTwPGvAnJVuiU3r3P8qRzVFP6QfXCLM1zf6bJqEebaQO08JfyR0qNyUtagJmsyz7VmJZV5WXaCm3bZKu/b5grXdarIZ8fm0wSUmTJ9BEalzKMR+HzmOh0et3Li1e8P6/S5Znyb51de4sbPl2NEruz7EuhRWbRztNNz1H876vudKr68Z2AcccTxJRUxsI0jjjdCuANxFhajpFSJ4OZWiVIYAsufYlsfp0fzJUvqzMwUpYARYIh6bWo6k8fIDMcbCkCZpEzQ7jBB33kO2L3AJHz2AQLXyhJBWBAAVx8GnvxVykOLs2SJNYNgsDjPRNp16LJ67WG2HwoDAqJMVdVb5ib53swEE/hBnUl04DIhv/oJ4On/hn/nfQA/433X/qlb8PC7iZ/Enfo1/sJMA8ffC5z+eh4vkWGSuvqIGAHZZM00jQyNlRKpbEKAok/Aako9Y3aCrqzNVSa6ri2AVkyR3B5BWdQ+KTvGJHjuLcDcA5Rg7l0S19UZMkqj/VoNk58zdlrVTdrtEZmeJmNMSf2zJHEhhI3tMfF1B1A9iQWUJwR0JrIjDqaGuvaaLky9sMKR4VJU09rZoGS8t8O/mQlhYoXhLswIQ5t/9Vgk3x1hdvs8t4iRHY2I2Y36cFoZzkV3V90/ukmQkh374kgUt18A9i6o+lrfUZsZoU/w26+xbjSqn9Y03heFWUpqhzVpCxRy/nWLc9LdAJobQCjMr2YS+BTnuZbGTgswlDU1bEld9La08PKFBR2H5zn4of9ex8fW1SZAxgR++xt03LYwRRC5/gSw8ThZYzPJMfS2eN8n5bkxcycN0QBg7zyw+QzPa/VRUSTcJKwscOzdNMKqXeZmRabC8QcO5ybqhxz6/P3YKbKv2SmC2/N/IiUEaYJ7zwbgc73mp0SiPM5xOz3ZcBsHxk8ToDeXeU9FbZ72LvH+TRUIvkuLh28uBoE4WIsxWOXEqw+aAp9jGTQAaIrBjlnIgxEBX2+oDLD8ke9RlI+9PsqOOOKI4zWJGNjG8crDHUoSoMmXrmrnYmv/Vyd8T5LcPf7bTBMkvhRTILvLmtrrnyYYGDvNRD47JvVhOhkJTww9DItMiikgZyByPoAgxUhKgg1KEGtXmJTP3AdMniMYBCiVXP4UcP6PyArlp8SBdII1cb5NQNze4PqpL5MNNVJMJPPTBFzJHJNyTQMg7NtQZI5Wmslp/QoQhvjclS181/b3wocCUT9p/hf8oPkhrsmpO4Hbvg2Yvp2s56AJrHyaCarbA/yASb5nc35zE9IaxSE48IdAbg5IprBfD2kl2O5mUCcwDkW+bSZVK5owEBZInIknb1M9NDef5PyUloCpO5ig9utM+JNZjrsgcxp4wjoMpT1O5EwcPZI12bAQZjRVEgYzScDji3Os21cy5Je6/nybBl2+rYCTLfe9mQGqx5kM5iZknbzKksubju8GsHsoswsB9CkAmrrOhsl/ZyrcpHg9WZphm2vXyvLzhy2umVFGye3TkM1uk8V3BgACjrNygpsjoU8Ze2RilhkT5vY8e0IP21wzkI2JTIWvyU+J1FwMytyBuGrvyoZBwOs6eRvaNvBtv13DpZZ6ns+kXPz+V3cxcfYhrt+tZ4HNJwhmE3myiI0VmlvplqqVL8wSnIcB2eXABbaeZjnDYZsUo1E5QQDpdGnuVl7kGhw25b4TAy5dV0B+/BwN6XrbwPN/xPvLtAhufXHVDnxuyJWP0fTKbgvT2uXnpsv8bGhkwzWd66WzwXNMpAm6y0uHGzmFIYFnv8YNlcrx14ZVHXVNTuREBv4qM8Rv1gh8aW0km7hxPW4ccbxhIwa2cbzy6O292PhjNHRLSRzNFP8zfzV6aH65RK/G5CnwlDFUqqjadwSeqkEKRv5tdwiGN58Cdi4QpE7fwzq7qPWPmRL5VZqJclRbCxDMNJaVuRM0HtPrE1j298h+ZaqsMzv2Lo6vv0dGpb4MXPsYGeb0GEFvpsqx+w4Zzo7Idvt1tsnJTABz9zPpy1bFnAmKfWytiyFVQGOkfp3AQDewo0/gGx+7G1u2Ska+Wn8M/8H6OWiFGeDM13OMxQUCiOYqsHuefSfdDsHZPqid5zh7IudNl4BkBkiPCwB2Oe78DMfQXecxtQRgWYr9zE5wHnWN6784TzCRnSBwX36YNYfJEgFEUsBAv06AmMgqKXQUmn6wVyQkgXeHZK6gHdxMGn1t9HPEykJTDK/nKAmfNxw5pqs+FyBIipxv0xVulBzFVH0xw/doGLQPdiMgP/Lfl++qfsuGyU2b7DgZyHTxtR9jGAJbz3BMk3dKGyK5d52ekmAHPoFbd0fqYzekFjbBzY+Z+yiHjRzI91ndaaC9zXXW3aQ02O3zWmdKXI+ZCv+tm3wG6Am+pnGVpQe+zbU6dSdW/DF80x86aIyYY9+ba+K/PXANqVPv5lrYeZ4yY03Me3afE3DbAgF5nptBqaJ6HjRX+TntLeD6J+kmfrOwMqw3NpIEnBPnKAlurXLDJ1nkvAzaQCAgpbQAzL2V53bt4/x/K10hmA08md89OldXThAwlxY597VLwoRiROkiG7fJonJ41sBnQmnh6Lrb9oZixSvHX7lj+c3Cs2WjQ1jn4tzR44kjjjjieBNGDGzjeOXh9Pkfe+QoilAZOUSJ8o01LVGfxmSeu+tfaknx6x1BIHWQETgVJ9XWKlnAwCcDki4r06Cjwh0q05zmKmvfElng+HsIQPfb99zEYdYdkIkdNPhzuiggUyeoqV/lVxiS4SgvMYkKPALhXo09KXt7ZKMqx5lYpfJcCrvnlZQ4dLkW8jPA0jukdUVa1YEaCSbqrTVVl6vrrOldfwLwfThmHt/z1G14rK7Ytlns4o/z/xSlqeOs76ucxH67mX6DbFZ3iyA5mRMm1KY8M1UmaPe6QGaSzKdpSQ1iABrWTPP8+juUUus6k97MmLSZyXMDwBsShJcWxW06R2DbuKZqEsfPUBZdv0r2tjDDZLS0KPXFow6ft7hXRtvz7DOXt3xkHx26CcAgkHL6Io1Oc4y5yTeWUU0QCNgdKmbXGwjAbXO9+wLk99sPTamNuWgtvppRv0ZQVTlxuMNuGCoWyekRQLU3eX/Xr3BNmikC1fGzXKvR89hIENxqJrD9HF/f3gTXg0HlQW6WhmSR5DwyI9NN3h/NFX6+sLyf7Yzhf/rsPNxQzcM3la7hX59+BtrMPXQ87u0Q9JWXyIjuvQDsXeGzLHLDdnuKQS8vCavc4+bV9nN0Wb6VG24yUiaU+YwpL/DZ50fPlAllrmQkuDE1fgqARulzb5cbGZO38++tdYJOU4ykrKRq92OmgfYq/w7wmWwkeB+kS5y3fp3rKT9F0F6cO/z+6GxLCzGNUunUa7CJEoZUzXS3+O8vZs/bOOKII47XOWJgG8drF2EotX7Cnjg9JjD7oTEJSZfEKfNNDHLtrgIb+339bNWWBGBd5kDq5hCqFi5WaqSdgSU9/Ez15TtSj7dH4GZ3yQ4kC8Dxd1Ne91KiXyfLO2jwM3OTYqZREmnhdY4vMhzSNCaIEctkpqTedIN1puVjZDAK04Afst2P0wKgi/yzCkzcCZz8SibnN0YEsiOXWN+jfHf3ghjIFPB3n53Gf7uumA8LHn599rdw/5njTDJTwqoMmkxoI4bI98jshAbgdqWfbJbJoDOgdLg0S8mj3VYbD2aGLJfbVS2RsuPKqTeZV/JPK8WkOzL3srJka3aeJ7My/yAlyLsXWYeXLpNZz0++sjV2WPgiYQ5cabMhLTQiN+UIrEVlBLopddYGNyD6e+K8/RKNyt5IEYZi0jTgNe8K6LBH6nDTJZEmChCL6nej71+IZLFfpxlSZoybGy8lop7V3R0C1a2nuNYSaSo6chOqRlo3gdwYWcbGMuXCzevCUkvbpdwEMH4br6kGPq+jjcrWJlC7yPmQllu/uVrA//n09IEh/e3xz+FvVB/nPWNJzXZxlkxy4BHsdgRkaRrHV78iJRDgxlYizfu9scx7tH5ZSYGPCt3isyldAaqn+Dzpbst5TQPjJ7mG68t8PppJID9L5Um0AZubJOubrdL8zesRyGYn1P9VhsW5TeTJfvcbvH8GDW5IFBdVb+BhUxnflRbF7O6G6NWo1ngtwS3A/wea18XJ3uIae60+K4444ojjSyRiYBvH6xuR3C5ycB2tq0rkhd0rvfkarjeWlaQtCk1X0lVvSBYwEOOggvSeNBJMUA9ji5weE8bI4dRzmFg2l5nETd9NoHQrpsmzR9x5fQF1S0zKBk1g5RHVn1MzKMXLTxMMRn0Hh21g7XNkIwOHgHXqTsoGe1sEqN6A13b8NgLZyglg4uzhdWm2sMOhT7luvw6sPSry4SGg6/gvG/P4yeVzB972z85ew3ffVZT6O51gvL1BpiRidOwegagW8nO8oSTgNQAh533sFMH0sMVENrCl/jhFBjNygE3lhSGaltpaqX1NV8ii5cYUgNQtstn9Gusjx88x8axdEmOrd6tWI1+s8D2CgzczoL1VDFtUCXR3pb5Y6qZTeanTHYmohYiVGvmeemnsmO8B289wXUzd8fLG6A6UE3XtKjdtQhCIZ6ryzDA45twkQdawBVz5GNC8JkZoWd67qTwwdZdysjaSHL/d5drcOU9wlBkDzCT+6dMZ/IcL6QPD+fkTj+Lrchd5fw2bvFeqx8ji7oNlnyxnfpobRFtPU8Fhd6kMyU3KPbsMNFbZ57a7jVuqDhJ5PiNKCwJud6mUyM9KHbJL0Dps8hlrJlQ9sybM9tQdBK9tqVEtLdCx2unyGesNVe9u3SBYH7Y4doRkhMuLnEu7zQ0uw+I5HeaaHG1qvNbgNvC5lgd1/jtdOZxNjnoZR72cb+zvvN/Lef8NIz9Hm2Ma9sshonIHTT/49Wb7fz2OOOL4kosY2MbxxQ27Kz0fmyM1fYYyOXmz2O4PmkwO95vFS51W4FMuF5k0JQuUjd3M9CMCtFGvzGGH8xXVafoOj7HwtpsnEnaXiePeBYK6wOf7cuMEerVLPJ4jzIWZZb1p5TiQroqD75DOrWuPMUl3+sDYSYLXZJYgb9hkIjl2huZRVoLvr548HNQOmvxchEzELvwJa/Xq1wG/D2gJfLi3gB9efg8CqPP7vmMd/KN3pHnM7pZqlTNocU56e/x3Iksm1ulQIuk7HKdmMqE99l7OwfZzYnDjk4XJTSj3Wt9hQp0fZ0Ic+PzdsMmNgcoJjkM3OZeeRxaqvUYma+w0r1vtIhPm4+/54tbCeTY3R/q1L19Ae2N4tkjOa8oRWrfE6dlUJlyHyWYjhneU3Y1a94zG3mWuw7HTr8xQyO5Iy51VJdlP5PjcDKEcgzMVYOJ2MowX/pTrGhqBoG4Rm4yaGkWu6MkCGdSVzxDkV0/B1xL4kQ918eF1BY5SRojffuc27kjXuJlUu8x5yVSAdIFqlAjwFKZl02eazPP2U0BzjWArN82xtzd4v9g9btS9FPY2M8Y6eivJ52Pg8fkxeQeQqwBDkeh7fQK97o44kKeB/BhQPs7z9YYsPygtqjmI5OreUMpDcpyj7jbnx+lI3W+FADeRVj1TE1k+V1Ilef5bHK/dUcxteemVO/GG4YjXQvQ1+m+XYL+5QhY6BK9L1CotKrd4vSJZoIN+HHHEEcdrEDGwjeNLJ+wOk8hBE/s7wok8QcabUUI1bDPZCNyDzqFHRQRE7baqF00VxIylQuamu0UwsvTOw2WSYchkvblMMBsZjaSKrPdMZHn8zWdVX9LKcal7TVA26A0pyQsCXqud51hH59vAzP1M0rtblC3bfWVyU15QzNZRoDYyJPNFMnvlI8DH/zmTyNwkUD2BZ3Aa33nxvRgEKrF+a3WAX3tgDVY4FIdiqVWGzoS0u81kz0oxKR12gGGD7K+mMbmbfwi47S+RoVv5DLD9NOerMEOg6vaYDAc+k7OcmA1F/RLtJtmZ0nEaJ+sWzyFKYNsbZLCqx5nEt9YIkCbvZK3jFyMcOadhk/+OAe2LIwikfnxXuUlHEvR0WVogDVTZxZGu05oYtiUV6LG7XPP5Ca6lVxqDBh2+mytcb7pFtYDriKN6SDVHcZZ1pSuPsFY9al1l5XjvV5b4b0eYSIBrdNAC6pfItM6+BV09h2//5adwfk8pbqZSLv7gK9uYLOYokd56ioC2OCMKnTbv+YQ4QVdO8MvrA2tPEMh6Du+h3CTPqXGN89vdAXZfAI5ib1MlbrwZFp8/6RJLFpwO57o0z42pCMR7Lv+v6WzwGaYLuzp1m3IzT+ZZTpHIqF7b/TqvcSTpjxzQa1dpuuUOOJ5kQdjaKQJtQ0yzshMHNxudPteVbhLcZsfECR5Kth3VQEfy6RuBa+gfnIsgGOn/PGKcFoYcf7RJkMzxOpTFyOoAu6rd8D0a8yg7KxGG6rpEP7+I/Y2++1xflWMvaVnHEUcccbzciIFtHF964UdGRHsESwCTk9yEmCi9wdsIBYGwtHv891HysCgihtZui9SvJbLBgkj4pijrayxzfo69mwxRFJ7NpLK1xi+nx3kdNFWf2PHT/HnvImvxfJuJ2OQdkiDZlBhHyUng8zW1a6x7dYdkYSbO8Px0XfVwNdPS5qOsPuswdqq5QgbLbpPB+fyv0uhpJNbMeXyL/VPYdRWTfzzVwe/e/hmUUjoT26gFTCTDC3zKjs08mRS7RddUf8B2J7lJYP4BYOGtvAZrnweuP8xzKi+y72z9mrQGCUTeOc6k1x1wY8IdkomKemlGLTcGDQKIQZObNOVjQK5KQKOBIHLszOsr0QtDrqHerkpydUtaEo3FgPZmMWwd0Q93/OC8BYECuKNg98ZWRIFHeb2ZppohkTnoWP5y1kUYsv5240n1rMhOELT0drlhZEhdammR6oHaVTKqIfg8SWalNn5e1kmTzw6E4iZ+gYDo+PuwZszjm3/+09jrqnO6qzjEb3xFB+l8hTLj2lWed2Qa1d/jz/k5gujchHL/rl1hv+1Bi6/Jjonio06FhZkQKf/ewfNOlYDF9wCNi9ywMpLcSJu5lz2qm8t8f2GOzGzEUloZ3td7F6QfcMDn59gZupmnirynk0X+zcoqufGwq8BnqkTQ29sBrn0GaF3nfa8b8v/WJJ/HukEDvKyoa6K+qsMmr08Y8vNHn93ADSof+TJFLh7VxQOqt7PHUg3+zeB8RB4AZkrKXmSDTjMIMuO+t3HEEcebJGJgG8eXdgxb0sNREnAjyWQoU31j/kfs9MiS+jYTkuL80RK0UUALCIgSGZyZJPuSyEmt2kUmnIvvZFLTr/Ort8tkt35VSYp1SXhSRSAzzoRvWGctnd3h3yonyOD2azxG4FO2aGXIKPR3ga3nyNYGLuvZZu8hUDQsSbZCcSQtSzIqkrvqCSW79QRg1y4TmG+/AFz+M8qgb5B4tsIMvt35B7gUKmaragzwe2c+goVqiqyT02ctb7/O5FIL2V82XeZYorpudyg9fcelt+wZJqfXPw1c/wyTxMk7+NW4KoZQfY67MM+NCLdDAAOQZcpUOXfekG6t/Zok6k2+ZuIc32dlOE5dZy3va9HT8rCImMfuzsENo+w4z+uNeD99scLpExzsq0u0l9YPN/AF5NoK9O5dpnqgOP9iFcOoaZWZemk1vL4nMvoLAshk8woa6zo7m7w/8xMEaI1l9QzKjAOpLDeishP8rmnSTqjJFkX7LcTuwuODKXz3b6zC8VVq8A0LHv7t+4vQAw/YeIr3frJMw7W9y3ymhR4BY3lBShTO8bnR2eY92JM1qusiIe7zM4uLrINd/5w632/4VwTkm88AK48SsBsWVSEzDwCddWkNZvI+Lc7yfVaam039Gt/buMzrYaYI/HWdCpWpuwD44oweEmCnSrJ50QegS7uoKje8WuvcuGyt83ka+NIyrKqejdExUkU+T12RR2sGwXSqBCDge8NAGQZGYDUCrKFsoPiuOMlbSi6fzB/dYi8IuA56u1y/Vubw9RdHHHHE8QaLGNjG8caIURkuwP/U81NvHAY3YlOilhGpIuvBDmPHbgS00MnsRNKw3CS/fJds6cbnKeErzysWwHcUGDMsqRtLcd68ISiLTAEIpdWF1KSlypTKZipkzNsb/NzyEsfsdAUs75Dt8HpMNhceJHMVikzX6fJ7ZozsB8DkKWJxI4AeOMDeNWD5Y8DyJ8kqHVKzOAgT+D7n7+Kx8Mz+75Kah1+/4zHcO51W6yPqyxp4YthkcBwAgIBSyiixTxdpGlNeYtK5/CnW+zk9oHqW5ledNbrPhh6Z8fISz8nu8HjJPBn3RIaJ+qAm4N8Arn6cCbWm08SmcpxJdb/G+YlayrzW4btSK7qnXLiTBQLaw9rMxPHSw3NGapNFEposcPPtpfYpjUoCkjlutkTsbgR+DwvdOgh0I5Z3tPygX2dbrP4e7/vCjBjCNUSy3OXa0E0pO/B4PtlxArrKIo9bnFMlEoEPrD4KbH6e7x07g99bz+PH/qx5YHh/8x4NP/bOCc7L3kUpJZggY3n1Y8DOCwSRUSlFcQaonuacRWZ23S1uQAFUXkQeBaUl3tfXPwksvgN46K+JkZ0A+uVPsozADyjxn7yTgLm7LeB4ljWvoUdzu+w4r2H9KoF7bw9AwI0q6Hz9nd9BFruxzM0Mb8B5S+Z4jQZS/qDpQKoicmep3R80CYA1nZsFZpryZA002cqOE5g7PT6bwpAbbTP38tyj7gLRmrC7yhk78qWInnPpknhTiOQ7kb35Jog7EBWPbBxnqnwufSFu33HEEUccX8SIgW0cb6xw+vxPPQJ9ZkoB3C/VcAdMXL0BE7zi3OFGQXaHCVgkc4z6uQ6aKsHNVJlc9vfIau5eZOJTPc458GxpVSmJTqrExCw3TZa1V+PxdVOchy8B3T2R1JXJOiBk4htJlQuz0iN3hxLeQR3obrB+rjAvPWhnmehmxFQqUwagS6siSeh8h8lYawXo1ZlQbXyerG9fHIkPm77QwA+7fwsfDe7d/52GED9/zyreP9Nnva8zUH04gwAEsSnV9iRV4PfNpwic87PAzN08t0SayXB0zuNngOn7gL3nRaIYksGpLJIVtttMFvPTIg00CXo7sgmQnwae/0OeWyIHzL0VmL+f1723x9dZWbK1r+WmjCuSw6h9FIS5zo7HzMyrHYGv1A2R3NjKqDrcm13nwCeg0g0qBG6sX9yXNN8IeA+5XzRDQG5CMbvNVaC5Tul9dkLaSekEcnZLyXB9l8+oYUuUMZOU3qZLUju+QMAThsLcnudYKyfwM58b4t99tnFgKP/vuwJ84+1VMafbE0Z7kvfEymdZM+v11XMuXSJoTRcJ8pqrBKPDpurN6kg7nvIix1dZJMMZtY6z0jyfa59kezHf4WZBsiBt57o8VmaMtbiZEucpVQYQAO0tgvbmqpRUGJzn3Dhw/Cto8pau8BkYuHxNMs9r3Vnn+72hqtNFqDoB9MRlOzPGNSEtlOjOLCyrN2BdcSJHR/vcuJrQGx3Lw4BzZ2X4DHKHfP+NG4NmSkBuTrk13xg9qTeONlBzky+uB44jjjjieANEDGzjeGPGjaymlVF9S79UIgyZzLQ3AISUvZUWDjoeBwETlfaGuAwL05jISE/alqrvMhJMXLwhE5H2JhOPyTuZ6CEkI6JrytwmUyXY273Az3D7TKz6NYIeb0AWs7TEWrtujQlaa0X6uYpD8qAufVAdJm/DJhPV8TuAZEocgiekDU6OAFELyFqkypQEd2s8bnOVSW396g19jV8cQajhb7l/Db8fvPPA7//BPV38z7PrZKVHGVpHpH+6GLAki0z+DIPOzYFPs5Tj7+Hfnb5KvIct9sMsz3Mjwunz+kyeJYOlm7weyQLr0jyR8xbnudkSuATKz/4uzadSReDYVwAn3yfJel/YK43yy9fK8XvYEgdouTeiesXM2M3dtuP4wiOqS+3ujvRBlfKJdOVooFC7wutVOfHSWPT9HrxDvIjhPcyluV8XYNjh/V6YEYWFR8VGc4Vjs3KURXc2lfFTbkLYyQLlvVErrc2nafiUyCKYuB1/7Y928KHnd/Y/MmmE+I33a7hnNsOyhfYGgVp2gsfaeIKbW5rJjbDA5/qsniSwsrusj21vAIMOyyWawi4aFu/j3DjBrWaKA3NZejb7bD+28STB7d4LHNTYWT5bg4AgODsm8uQ5+b9D5/Frl/jMDD3Aljr6ZAaonOT45h8gSPdlg0G3OKeJHGXI3W0ps2gIK27I/wc73LxEKLW0Op/RpTmyv6kyQeigwdcUZvn33i43BSNWODt++P0chgLg+zwPp6/KDqIwUwrkJvOK0Q18jru7c/Cc3iiqqDjiiCMOxMA2jjd62B1J2ERKlchJgnGLusVgxAQp+r7v5hgqF8dDHR6Dw187+nvP5rjcHhORhEjsRl+330JCGB5TnDPDkMDTlSQ1mVMy4khW3N0lMMvPSDsNkUGaaSaJCZFBRlJAu6McUJ0OW/QEAjrTJbqEBraSrVpRzWqXkkCA4+xu8/Pzs0B1icmPkWRSHNW3un2eh57gOTk9AvFhgzV+zRW+5hYRhsBPed+P/+y//8Dv/+btDn7s5BbPzbfFBXQAOG2ZJ5NJuS7JrmZQkhm4wNTtwPzbmHx6Q47FswmMM1UmvZ44HJspMmhR0u32ycwWZ0WKrBHUDhpSfxcAq58DVj/Lub7ru4Bj72IrjSAggPaGfE927Jbn/7Ii8DmOUedeI0EQEfXejOP1DbtDkBBtMOiW8ge48XpEfU2jdjFfSPjuCNC11c/DlnIBdrqq7jwqT2it8B4vLREM1q8CoUu1R2lByV7NFP+dLhNodreATBn93DF8x29u4LmN9v5QxjMa/uC7pzFjtLlZ1FoVBYVsQK49xnlK5gjizDTIkE5RBaIZAurEsKuzQyXEoCaO5XN8XbJM5UV+ks843SDY3X4WeOqDI2UdEFfeE2zzo+tUqSRLrC22svJ+U5ymL/L/B9ch22skVT/jTBmonqEJlpnkMzNZEuCvccPA7fFZ6w3k+ZKTUo5tbiD6DgCN55LMcV6SOTHbGvBZliqJwkfjcyM3+fKkwr4nILcrfbsHI3/URkBujufuu7JOGup65yZjgBtHHHG8ISIGtnG8sSLq2XcjKB22hJHsiSNtVuo7TWmXMPoeAZavVQxa3JkHRCo9A8CX1j6B9E/t8Fx0k8lUbpyJUXODzETgMZHIVAmMrSxBlztkstXbISgtzvDvo7WenjhttjfEqCWQuq606u+qWeK8KkDQTMhuv9Tihh4AjQlhssAkp7VOJmffKEXaWeQmyZwO6kzY9qWSAvydDs+9s3l0zeANEYQa/oHxo/j/+g8d+P33nxjip05dheYKiA6krY+ZIHCIHJFTBSackTmP3aFp1fhpMs6OtB9J5vgeTZNkUeMcJAts/WGkhPEIgdIxMq92W9UdD9sErFHbluVPcp7v+35g4SGVCLbWmNC+2j0c3QE/N2rNBHBTIzsmxjRxIvpFD6evJLWAYtCz4wqgBD5BGEAFxmshAQ1DjqV2mWtxUOPzyDDlvhnKxojDVkGegFu7w/t9+j6ubd/msVJ5rufWGoF5YRqbiUV842/WsTvilHzbmInf/it3I+NInXevwY0jaATL1x7m8yVSmET3Yrqs3IudtmrfpWnA1YcJDD2HG3PpsqpRzs8o+e3654An/+vh85GusN92IkUWO5IsR6Ua0Pgc7mzzeeh7nC8zTTVK4PP3RoJjz03y+VCcp7N0fkqVX0QsfrpExtcXp/pBk/fuoK6kxIY4HtstKRPRKZs+9dUEuMYNLO1om559p2RDeTLcGL7La2p3+CyL6u4BvjeZ4zMkMg0bLfuJAW4cccTxJR4xsI3jjRO2SMRuFsM2kydf2IVkkQlLZNK0/5/+6H/+uvoZ2iG9/CTJPPT3Iz8HnhhxCJsXgT5NIwDpiotqGJBtTRWZXAUCeutXeAzDInuRn5I62SLHt3eJ/Se7W/z72Bm+JjumHH/7NSYi3V0yMJopdWdiUBO4Io0cV4l1ssDj9OtkMu0uYCUoRc5PMSmvXSOg3pfvzdDwxEgQsLoD1Q8y2jhwekzW6pc5lpcY7tjt+DvDH8Tv780c+P03znbxr2+7DN0b8lw1nZsVhqUArqaRwSmfokxw7wrQvMbzKMwSBIa+qusLfM6tIw7HyRznY/w05YyBw3MeO80143R4juUlSsE3n2RyO+wRmBg6cPrrgVNfNbJuOwQTusmWSF+oMUsYEghEJlQA5yJd5mZOXD/7pRmH1TyPOik3rhPglBYPr8F/NaO3J62wPH7pAm7b60BjhdL94jzviY3HycyaSQK2wjTvCxjyXNW5mWS3gUwZTw6n8Jc/ZMIecUr+2uMp/Px3noXe3yHY0wzZUDL4vL7wJ0BrQzbLJlWdarpE0GlYVHwM28oIb/cin01Oj2tf15XzcW6C4PHp31T3yGFhpth72zDFY2Cc8+90+Izx+ry3nS7oUhxQgZEeJ7C3uwKwRY0TqSQK08q8KZHjWLq70gZIJ9OcLskmmTge+474R3Q5v2aGzx6ny/VRWlQsu5lSvZAT6aPPr3L81uU5jsiW7Q6/j0rZ9ZHri0C1G8pGyoO4BjeOOOL40ooY2Mbxxgl3SAZhH5jqhwBVAaiDBs2SwlAcOScpUX6tenQOGipRjNpFmCnu1N8IQJJFVS87aCi5YiiJ7vgpgitNo6R22CKg37vI1xcXyAaW5pmERFLYwJU6rhoTo8aKMtuK6tJKC2QM0iXZfa8w6Vp7nMA5sFXfw3SZgLt2haA7qmOevJPjdPpsNdRa5fgjI5NkgQxqIid1rAPgmd/ATVlyzQCOvRvD2bfjbzy1gA/vHHSU/aqJLv79HZeQ6G1I+wuLyZaVIbB0hzSCGTvDRHX7eWDr81wv2XHOuZUUpqkkNbgax7d7SZj1SUog85MCnDUmb+PnpJdvj5+bKjKhrl9VMubIWXT+IeDcB9Q68z0yur5DUHBUa6eXEp7DtTTqbmymCGZjufEbJ3yXzGi0yQLwnklkea8m8sDYydd+HE6fLr++jX2jN7fPcW0/x02eyTu5IbTxOHD9EanRLxOYBT7XfLRB5LmU65pJ/NHeFH708YkDH/fX7/Dxd95RVs9II8FneKoE1JeB83/EZ2F+hhtLoccxmikqUsyUPC93xbwpoHzbF6dgMw0EQwCGtPLJclOtdvnm82BlxCVZ43vy0+wpbCXl2dpWNfhmUuTKSWn9NQ1kp6iOqV3ka6PNSStLpjs/zWdGIsf7t3md1z1doqO83WXrMLvL8g2nzfNO5LhJlZ0keNV0fn5Ugx2G/N34GVXiEvhSGiPqpNICr+NLjVA2JCPZstPF/nPb6Ss3eCvD1m6FKT4jD2spFEccccTxRYgY2Mbx5o3Al/qsbWFJBeBmx1+9nebAJ7CL6pGy40zUBg1+RYmrnlBgxx2AjGaXINRMSZI0qyTMvquSqmEDaAszUVliz0cI4HWlXiqSK3sDgq36Nanx6kvP2xkaI2XHVf9Du02w1trgORhS45suEZDVr9K8pbvD2rF0mQxA4PK9/V3FVJgZAZymYk6yY0yAzRTwuf/IY90YRhJYfBswfS86vo4feuosPts4WB/9TeUV/OzEh2BpwqhqGufSEvBvJdmuZ/Z+AuoL/4MGVXaHYDdVAfLjYD1ZnuPXTSag288zmS3O0qUY4JxbKRrKlI8JC97h+xI5XtPGdWmrJE6ldheYvoumVKP1s/WrvE5fSO1kpEIYtuQXGq9Tduylt5SJ40svAl96Te8oJ+X2Jq/t3AOvj9HXgeeXxnUa+nx+7DzPcU3czmdOvwasP87vmSo3Cs0UJc39hqrdBYDcFP7V5Qn8m+cPsok/d/cGvnW+z/s3mZfnVobPpt4u8NzvU4KbneDvEmk+C32b9152nPMSKVKcAXtfa6G0vKmwPjQy0Is2DOuXyEYfFbpJAJnMEdyli8DYOWD6bn5uZ4NmUr0djqcwI6qXMW6GjZ3mRmPtCrD9DNBYVayzLv/vVE8AE2f5rNz4PJ/rAfjsd7tcC5HDenaMm4qtNfl/a1wk4pM0rivM8NkfBuIK/RpFEBysz3V7fOb3G7KZaRF856bE7b8ay5TjiCOOL2rEwDaON3/4HuW7vT3suz3mp77w/4SHbambdSn5TYlB1L4xkibMoC5saiTx0pn0BL5Ie6sEtYFHBqXf4K599BnDNpOY7Ji0NQoVO6sZsoNvsm3O+mPKhVnTmSQXIsdPMVmy+2RDAlclo5okmoksj9tcY0sfp0/Amy6ytU/Ukzb0yCYbCannLInM2mVdaiQNjOZ/7wLwwh+ouUuXgePvA469F/CH2OmF+KFHxvB0+2Ai/H2lZ/FTxT+BbphMEBNZID1G8J3IipNriiYu7VW283GHwohUVS2w78jmhiYq0JBSRjNBI5mxc0B/m/OTqdL9NJEhuxIB3dwk57Qj9ZJOj0m+YREAT5wVybLMUU9coI0kWZWXw6j64kTd21OuprrFsWXH4j6Tb6aIpOXdHaC9xmtenAcmb//CGP6XE70aPzsMpMfxBHvNbj/DDZXCrOplvfkUa/fTBdaoZio8h8Y1kVM3AKeDIDOBH31qAX+8qtaqpYX45bfu4l3VJugMnOH9pYGg1RkAK5/iZ0bgNiumS/2RtmS5cW4W7jzP+2TrGd7XibR4GgD0CGgp+bLTI/N8szp/M0WQFjHKmSoB69Rd/NyNzxG8+g6fL4Upyp7z01ROjJ/ms7C9Aeye5wZBb0e8IVzVqmj8HDcUWqvy/DjBMgUtVP9/pKt8Jjeucb4HdYJ/K8cNxilRzrxWKqTDImpd5HT5vO9uiZu/lP4ksgrk5iZjqXIcccTxukcMbOP48gnPptQvYleNJHe7X24P3CDg7n9/j4mYBumZKmYjfTl+4AljG4JSvwyB6LCpWkDkJoT1aMrvh6zt1DR+zlBMm6J2G4msSN1MqX8K+Xm1y9IyqM1kIpHjsRM5BWJDDYDG+jnN4u6721O9GM00k5TGsjAdIcFdusTENlUmiPZ92bkfCrAVF+ZUUdhwk+fT2VSJDzTgykcIEGffQnY0OwaEwOPbAf7aE7PYcQ6Ctb9efRx/u/oINDMFZMZZ9xsEZGgj19NklnK+1gqviW6QoTUS/NkQQylN5yZCIHV99Wv8e/UkewA3VjiXxRmew7BN0KrpUistyW79qkoyA49zkxkjgzJ2WtW3ugNxVA0P/v5WEX3usIV9CWAiz+Q+VYrZkDd7dHeB65/iOi2fIFDLThzcKHqtwh3w3veGqo68s0FVQ2uNEmRTZLudTbLLoUfn8Ow4f+8NgJXPsXSiv4ehnsNffuwUnmqoeztrAr/+VQ7unBDXdCutWn9ZGTKXa5/jPWYkgew0kC3S+dgRlYmZFpOm40Bvk/fz7kUCzkRWbehN3g5sPUXQHoQEmdc/qYDYUZEsEJiZaVUrW1pkW57mqvIryI6xzCFT5bMjLUC4OMtz6W7zmtavcQzDDp/pdldckKvSZ7zCEpNMlfMRPV80g6A5Yo1rl/lc8Ww604+f4aZjuqy8GF7PiIyoOptcI4Mm9p9bkalW5DWRyMVAN4444njNIwa2cXz5hTsgCHwlbo9Oj7Wo/RqTk4yA4ggsO0OgdZ2ABhoT00QW0JOA02JiAxCQWVnpN9hXbK6ZJrjt7orRzJ7UrqYlMTBVfShAh9H2qkpG06WD/XyjumMrxQQsVZS63DqTkAic+o7qZ+nbzE2yY6w3nTjHz9cMnn9rXdUw754H5h4E7vs+qbnrEZQ5XbbIaK2o+l53IKYnKSCZRRiE+LWNGfzD5TNww4MJz9+dfBQ/MnMZKC4ReDp9cW7WmNj2doRJrhA82z2ONV1RTp6aQUAZMdeeSPe6uzxWTlj77jZfk6lwrqO2JskiMHmOybzdJnvb3aZTaVKS55zIF6MabkBa+1zk5xVmucFws/BdqZ2tKUmqbvJcMtXXrt9tHF+a0VgWJi+l2DjdVL1LX0uGLggIwPo1/js3JcZ323w2hAK2kgXFgtodymMjAJObIrDduQB01rEz0PGtD09hra/GPZYM8Dvv3sJiJSOS/yVxcPf5DOztAlf+guoId0iX5kRWerrWVNsxK02mMwwINpvLyuk5WaAS49g7+byoXxHp8jN0L4/KRG4WyQIBdG5aNheFkeztAr1twA+o1Emkeb8WF7kBVzlOQFoQ1/rOFsewd0U2/HYBf8AyEc8DUtLuaPyMus4alNGYIc+rRIYAcv0x6RWuA5VTfO7rOjceU6UvDsgF+BxubxCE93ZUGzpN5/XLjnOTMluV/xdjX4A44ojj1Y0Y2Mbx5Rt2V3rgCtg0kkzODgO4dpdtYxrXCPwiMGNlCaRaawcTpXSFyYWRUIlh6JFlTGTFbGWo/uNPZvk3p0t5XXuTCWMiR7lb+TiZSs1g4heG7AHZXuHPuTGgepaMQqokRiMJ6YFY5mf6HsFZewPYeErqxTzK37o7ZC1DUGKYn2GyNnaKCVeySMC+8ijlfLvnFQgDgPf+BDB9D1lnI8kEc/cS5ySRAUKdQM/pAIaFYZjAT1y/B7+7N3dgmhOaj3988iK+82RI45qob2+0UTCU2q50SVqXXBU31hKB/aApfTELwlhnOIeDBq+DpvGa6eLKOmyqGt1kjtcrkQfKC2Sxh20C3WFbbYYYac7R2GmuBSPJ5DpiI5qr3JC4WWufMJQ65dpI7SxidjYOruu9C8qErrejmDBN57MlN/HaGvb06wTXYSB16Z70qBb386i+0rXFUAgEndXjHHdxThnqtdZwteXj2/8ij7qtNrAWcwF+523LGMun+fp0ifeYbvAehcZaVG+gWg5lx6V3dBtYe0T1+y4v8RlXu0oXdM/l89bMAFPngLm3Af0dHlO3WCu8/AkC1JcSiRwwdpYKD1M2GjubUoqQ4DNZ1/gsqCxKv9sSnwvRJqPdFE+FJMspQo/PyMYVys+dPl87dlK5GRcXwJ7n8oyI/o/STWDtUQJk3eBnmUleK12UOYkcgW6y8MXZHPNd9h3ubsr5ddT/d9E1Tle4gZoR2fXrKauOI4443pQRA9s44rC7YgAkDK6RINAxEwSa3V1x33SYWJYWmEiEgbCbAQFhJmq3klWGJ601/ufudEZMi3xpsWExUdFA5jbwCHIGLb43qmsbP8NEykwSiK48Alz9KBNHTaeD59QdAqQtji3q6xhFa0Pqb4WVdXoiaU4C/SaTD92UfroZJqez9zNR1S3g6d8Crv4F68sOYzp0C3jn/8Fx1i5LIqYxYTWT/DzdABI5XG6b+JvPLuK57kHjo+nEED9/7yruOTnLel5/KGxmnS/o7RKommlhhztMBq00k+32OhNN3aLE3EzznOwOgFDknBaZ5kgenS4rd9PIYTiZFZk3+BpNA5rrZGgAMsOTd0nvWJ/JbmTiNGhy8+Oo1j7ukO/r1xUzrFscQ+wuGkcUe5f47ImctD2H67+/p9QdqeL/v73/DpPsLO+88e8JlUPnnpmeqBmFUUQiSCAThIUJa6L3NSYZk8H2b71OG+wFY7+X9/eC03oBm/XaZl9ssDHsYhZjjBFrmySBhLJQHs1opid27spVJ7x/fO/nPKd6umd6Zro16f5cV19dVX3q1DmnTj/Pc6fvzTTlU1G9PRV6bUlNbtGhZtp4+QVRAt8vrWlC6W8trcPGruB9nKvyb70G0JjGvZOLeMs3cmiF1mFz7ZiHz73wGEr5HP+PABrUQVuE8VwqGwddjr+lMWDkEjqV8sPAoXsYgW3MSpufMY4/M0/S+O3W+f87tB3YeD2zZhyf//edRbbo2n+HHftPRGWC52ZKQvwCHZDdOhWC82Weq7kOuQrHHqO4PnwpsOW5dIAZQUPXZ63w3F6moC8eZppxYVCivhtonJY3cayJQ6n/zXG8WDzEax8FPP8okDIMH1SmEryc9KeVn2dCmCxNnBJLNIrgQbM/JdzL8prlB3n++QEa6pm8OvkURVk1atgqCsAJtj5lVYCDNhdziLlYyJZp7JY30OAy4k2tORFdksWlEYnqNWlMtub5OFfmYsgISnkZJBEYx+dCI+zRQA57rLkqjDBi6npcQD71TS7Caoe4YBzaAex4MSO6+QHbUsIsAnodGuSH7pUWExGPpzErCqIlttloz3EhWhzncRQGmV6cLQL3fgZ46p+s+vKJyBSAXS+TVEVpWdKuU/EzU0I3U8Yn927AHz1eRTfqX6i8YGAWH3/W0xgdkzo110RnG9xv2OM19IvS3mPR9s01PXNrhyVqUWU/2U6TUZoo5nszRan5nWeN8eBWGg7lDUBxgNEDT5wN2SIXVY4D7P8+rzkcplxuFKO2W+9PQQ66jLRFAQWpTFuOKOSxmvcYclUuTvMDunBT+mkvMnXWL1CUzBCFNAoaU9YxkinRoFuPKH9aTyCKbPpvfoD379w+3tftBWDxCI3gbIlOndIIAEeO0wOiHv55Tw3v+XobYWyP80UTwJ//yAKy5SFgx4s4JhoF4oWDHGfrR2QsrQN+iWPe6GWs+w86NFAXD9p+1dN7WKLRWqRqM2L+r255PoDI9hrvLNKwPHI/x1Yj1LYUxwV23Srn4gOlQZ6T47A+OI4Yhc1XWG7iujyuTF5ahA0CpWE6sIYvSynbSwmLn+f4c+g+YO+36Rh0ZK4oj8t5TUhZRLrlTtk6XHstjjmm/CQrGSimVVka09LNRHVXI0aXXv6Zx45zevdcFPGYmnO8t5pzPOd0FpC57huv4zFmiuJ0fYaNckVRzivUsFUuPkKTVtfm5Npt9E+oQZuLx/pRa9yWN3BR4ktKV67KFKrZp5bs3GF68+JB7idXsS12/BwXOa5Pw81x+ffcgKh3LjCSl5VesMOX0ljbfwdw5D6JQDS5qNt2M7D1eYw8ZFJKwlHIz557mimxjaM0DsMuF21tqT3tLLB+LehyAbrhGi5Q/BwjwLXDwDd+k9dptWSKwO5XA+PXMApdP8rFXXkUd7cm8Gv3VPF47fhFyft2zuHf31SAXx7mdzL1KD/fdaX/bIHRiBhcQDam6VDwMzY6vjApvSPLvKb1IzxHNyuLPZ/GfWuOxuTEc2zbjKIYtH6e+zOLvMYUsO+7/DzXpZLp+G5es9ohnq9RQY5jRom6dUbRBjZzAdqcpSGd1FDLAtZ8pqKsxNKobZq0krL5H/VyNIIKw2sv0tOa43gSdhl1Kw5xXCtvkAyQOjNNZvfYTJLBLTTIshXpAZsFvBy+8Egb/+6fan27f8O2Nn7/hmNwKxuBS2/l/6wZR+f30znVa0qd+xHJqhll2q/r01Ca28sxLVtkCu+CjH8RbCpvtsiSidHL2eu7Lmnepk3ZQ3/LUpCo13/+lQk6+6JI5oqQzis3Q0GrqcdYv1saAbID1pjOFmmIZ/KMtjqi4u76Iji1VZybEgnPlYFMhc7I2SeZUu25/NzSKMeW6gTHRKPM7vo0anMVqevdZFvKOS6/p4I4AJL+tDXOg6bfrevL3OSmHkuU3mxzoj7kxVE6Cs+EKOL5tGZ5D3Vq/FnazsjxJIMp3/9b63UVRYEatsqFTBRK+52W7e/aax2/aAFE/bJoBZ3qx2jQmBYTjkz4lU2MlJp+iu1F2xO1doRGmUkrzlVpwLgZ62U27XnyAzTaHIdRj/Y8F39uhu93PLbbmH9ajrsDVMaZyjbxbC4a0+fZnBHRmYM81+YsW/Yg5sQfRUBtkq/3OlzUwWF7irFrgO48P7s8Bjz6FeDJb6z+OheGGKGpbKHislEuzpaxUNiOP3hiFH+xp4AY/Z79AT/Af35uE69+zqVcIM88ybYdvSaNxsIQgJCLu+YU1Zjrh6UOeFD+7jA6E7T43flZRmHCLqMnhVF+r80ZRuQrY8ClL2fP2fKG5aMNUchI9uF7xdFQYFuT0Uv5XU8/wfeNXmFr1xZFAdq05GnN2eiP4/JYC8PrlzaqXHisFLVdbrvGlE2ndX0aGmvdFiro0GHWWeA4UxhkWcbAdjp6TFR38SAdfkGX970RCgo6/L8pjuCP7u3hd79X79v9+y9v4Nd27eN+d70cGNnBc5t5ikZeHHE/9Rm5LjkRbJoAo7AtGsF16QVeGOSYXDvE/0vHCHE5VEy+4l8x6jv7FPcXiyF48F7g6AOMAgdtAI4Yk0U6zhACbh7IZDjuVbcCc08Bhx/g/FLZRMM5Z8QBxbHoiWMz7NLx52UAuFJTP8Qx2XFtX/MooKFeP8Jr2V6g4VkYZopydTOPrTFl1ZZ7Ld4vIzvpUIjFaM1XRLneZJGIc7fbpLG7kkq0yWAxjlk/y9fcrM06iiMe03r10+217XU0c3nSOi+FK85JP2vbNfly7KrGrCgXDWrYKhcGtSNchIQ9/vT1jU3jiIc3nzJmi/T2mmjh4mGmvBVHuDDLlrngCDr9qWpejvsLA/Z8rR3i4ipTkDrcIS6i3EzKmK1aYyoKaYzWJeWuPceorOMwhbbXobFWHGbqWnHYtpEIu4w0zB8AagcZLek1uRDs1LhwyeS4EGvP8zPaC5Im3aBRvfFZXIw0JDIddoB7/uLEfR7TjF4BbHs+r1FrlueOEHBzaHlV/L8zu/Hf9o5ioXe8J/3VGxfw4ZcMYWxih/SXvJfn7/ps0TF8KY97+gmeX1vqoD1fatY2scZveg+/62yJ59Sr81wGt9NwBXjejSlgdBew+7Vc9K2UPtea50J35iku1EujXPwOXcLPmHqM12lgm6RaynuOPMBISGmECyqAC8niMNMQdWGlnA4nitoupdeSCOQcpFkz77/S+NqJB8Uxx1oRhYLns9fs6OWMAs7v59jTadDAbUzxWKoTIphWA6IIcXkTfvPeHD79YP9Y88EbOnjPhie5343XAZufzTFz6jHg2EMyron43rGHaVyVNzB6mi1xDJp+TDJmOtJTVsYJx5Wxs4ckmnrJS4GrX2d7xDoOo8OH7uY8cOyHNP5yA7y2YZvv6zalNzYYlR6/HJi8B5h6WMbWa+mAHL6E41VDxt+gK05O14op+XkaupkyzxUxn5v6/mxRWqy1JQp9TLJ9Bpi9Y7JZ2rN0PJg6bNOyKA6B0gaZDwpUJa5stFoCrs/PMtoPUY9zWtiTKPMy98GJhPGeCRKHddv+DjpYMarsZjgue778NgZ6Rn77GvFVlAsENWyVC4Opx2xje8CmU3kZ8bSLIevnjjdqwpARganHpZdigd7wykZGPtKLwk5N+hjuE8GpGhd6jiwiRy4VFeGqVaRc2sM0DBhBPHyfLFSkzUtTlDH9HNPKqhNWPMMYc2FHesQe4QK2LQIpcSwLlNDWV5VGGbVd2M8apmyBLUSqW4DNz+L2xx4VgawjwONf48LmRGRLTMnd9Cwa/lHA85G+um1/EP/z4AA+9lgFx9rHK1xuyrbw289u4tYbLud3M/sUr6Xr8Xx3vAhADBx5mBFTswjtNbmg9TL8fo48KP2CIy7UcmUuFAtDPL5sWVLKZ4CgwajK9ufTSF2OoMs2J/VjwNwBGsilMUbnh3fyO5zZI4rMQ3y92+T3cPg+fn/VCXGGSKqxCkEpZ0oStc0zOriaesZEaGrGCr3lqryf82s0J3cbwMxejptRj6rtE9fbtjvdGo/DtBXrieJvpiBOvHmEfgn/5oFt+OrT/U6f/3prAa8rPkRDq7qJRv3Y5cyeOHyf9KX2AcTsD4vY9tHNVWj0ze4VUaUe2+QsTAIHvm+ji906HWeuz2O/+nW2N7hRIt5/O481W+b4euxhadszA0aI69K+LeYcUztks4GMc23sChr9I1dw/DVig906x4xeh99T0JaaWDGyTHoyQCeB6aebq1JduXaE45+focNjYBs/rzTKMWrqMWnDtNO+ryfH6xrn4AYap0a1eTniWMpYuiLg1eFjU9d9KsSxOJ67Nr05Cq0oGS8cj+t0MMcatO2xBvITdrGi0ZvgWEdJafT0jkFRlLOOGrbKhUFH0tqMJ3a1C8CFSWDqES4CHElRG71CUohlwWXq2RYPckHRk5So5qxtdZGtsF6qMCALiYqIc5S4CIgjLqRMtGPuKUYXwp5EmrvSp1bSgY2n3fQwzJRp+NaO0PjqNXhc+QGKLcUhF0LFURp6nQWqJ09L3Wd5nEbXhquZxhz0eN7zkzbt7kSUxigMNXIpPfi+1DZ1F4FeG7NREZ85NIG/eDKP6c7x0UkHMX5m/Cn86g0xytUhHm/tKA3awgjrhceuAI4+Cuz7Z7Ye8nNclEQ924vXywML+1g3lykwgt1tShR1KyM3TmzFSHJlikmNXk7l6KX3RRQxAlI/Kv2ND3Nf+QFr1Po5SWkU5ejSuLQPavK77LW4qN5wtSysVQhKWUOMQ6UycWqLflOi0JiyGgJ+Xmou16AON4romDt8P/8XyuOs/c/kOUbVDtNp2J6lgR52OR7mh+hEmt+PdhDhZ+7eie/PlpLdeq6DP371Jryiuo/7yA/QcBzcyvGxfsSKsLVrFK3yPNb9ljda3YHZvdJ7N+T/bKcuzrKQ23Vr7Dcdhzz2rTdyjPQyNBQLQ9zH4kGpkY1ZHjL9KDNrAPbV7i6Ik3Ph+GvkZrifkct5/PkBcYxdAlQ3yLywSEdBY4qZNxlp2+Pn+JmZPI1pB5yXypukfn+Kivthm+NxaZjjX2mM720v8DrkBjkvdRu8j4xT1Dgxi6PSq3fMOoHP9L4wGhbmd9ixTsoT4XgsE1kPwsAa6SarK+zy/yTsSbQ6oJK/GraKct6ihq1y8dGpS2TuKYlqgAubjdfaCS3sWWOmfpSLAoATr+fTKDbG68il4imvc8HVrXOh1xHVx9aMrbsNA/7dKFcGbRH4GGekd3gnEPe4YGzXJEW6ZXuoOh4Xp0YBOZOn8edluP9ek/Whs3tosPkFLuLKsv/yKI3amce5ID149/Lte9KMXwVc9gor2lEek3TpWTxSK+Avnh7CFw8U0QmXN+huHTqGX71yAVdefgXTjRcO8nr6OV638St5Xfb8s1UV9rJsn+FmeXz5IV7v5gwdBbkKFybzT3NhNryDC/9eU8SaQGMzDrkQHN/dv2CLIln0H7PRiNYcF7u5CqM4wzv5XbcXgCMPcTFZGGaUBJDauR6/uw1XqUGrrA9BhwaV4zAb4VRVYeOY/xONaWsQOp7Uqa9B3XdrgQJ37XkaRttu5jjTa9Ow7DWkBnRWUnELNB7iCDh8PxbmZ/BT39+BR+s2syXjOvjvr9uIl27qcuyLY1tyEAV0cGWLNAqb09wGoOFXGGVk2tTYzjzB//fiINBcYAaL49HB12vTuG1McUwd3iUiTaNsERSFzORZmLT9rsMuo9WNo+IwcNgTdyVFZV5wHntxVATx8pKVs5nj1MhlVhuhJT9hTwxcjwJUA5t5zfKDfBx2qBuweJAK/IExcMVwLgxzzsiWODaOXsbxLAw4di0coOHerUsrOsn08Qsc570sj3NgK6/rSoQ9mfcadDL2mljWgO1LAfb5HZh5ELAKy4WhU7n71p441rFcUc5j1LBVLg5M1LV2lJ7u2T1se5MrM001X5Eeo3O2fQVAL7njcTFRHhfxjxZ/Z0pMV3Z9LipaCzRiW/NMF+vUuU8TcYxCLl6i2BqhDmh4Du3gJL8waWuxTLpbFIpqb5GLtdI4F25Rj8aW8UB3G1xINqZtz8eBTcDwFRRNqmxk7eiBO3kNnr79xIuxwe3A7teIGNIMzzNTQNsp4qvTY/jM3hLumV65LunGyjT+/c5JPPeybfz8OBLRkjYXpsVhADFw4AfA5B1At83IS3kjF329NhdlG65mdGlR+jVmK/z+OotcfA5fxnqxTo3Xy6iNxhGNgOpmfnfA8mmavkSY6kd5Xceu5PujgAvmQ/dIqvFmG3Hxskw9dDzbt1NR1gsjTnamtY3dpkQGTR0uRGF46HiF9VMhDIAD37Pq5BuuEdVgj59Xk/6sZmwKe4w+l8aB+jEcPbQfb/ynIp5u2v+jnAd86hV5/MgERLwvI+rx8zRos2XW4gctUUs+yu3g8DyyZY4lcUCHWbfJc2xMi0pzjk7JbpNR8cZRjrXFERro1QlgaKsI/O3neORlaVT6BTrFph9nlsmB767+WrkZWxbj5ZiiPLSTDr7CAB2ZZt7oNZmunCsyEwcxz214F4UE/Tzvi7mnWdZROyylIVlpyzTI93g5Ohs2P4/p3cYB26nTMF48xPEz6PJ6ZcucGx1XvqcNVlzRF4Xnbl3e0z7+/Ey/8ROVACmKoqwDatgq5z+hEbxIpROZVKNey/Z9DDo0WjsNqe/pcgGTVk42glOuL3Wyg1wMxSGNu0j+bjzaYVfSZZfUpvpZ6RE4RGOpOSPKoaJc2Wvx/ZWNPIb2HFCbYj1o0JFFj6g7FgZZX1sc4YIhisSjL8ax63FBM7uPxp6X4/ZjV9Dwc1x66ifvksjjAvu4ZsvA4Xu4sFnKphuAK18NOA7CbBUPtUbx3ZkS7jgc4a7DAdorRGcB4NahKbx74mm8YEsezshOLqi8AhdgUSjnlgGa86yZW5wE4NJwHL+KtWBBS1LkNjPdbvEgvy8vS+M8SRuXRX63ye9u6BKbUtee5zmOXGpT/drzcpRSE+1lgaMPc5FaGAQ2XMfFmHFwzO/nvTS4g3V++UF+1yayvBpRH0U5U6KI91zQptPlTFMlk97KkqVg8Ati5A6eurMmjoGjP6QAXxwxKju2m/9nRvioU2eEtSdlGJmiOAxdTB6dwU/9XRMHm9ZZVvCBT7/cw40D86yrLY/SMdiaZXsfL8syAy/HMaI1yzHTdTi+xJH0Dc9yjG3OSjueaVv+UdnI7RYP8u/dOs89Cmk4X/YyOrsaR6W2dYbXyYmZ1jz1GNuxHbovNb6sEtObNT/I77S8kRHc4jCvpzFAA1EGDjqiHyFzxxWvALa9QDJo5pgmPb2HUemgK+UbOX7XYQeAQ+dcdRM/L1PiXFYYAvvqLvK7iUPOL5k8HYlRl/Nmr86x1oGoJIvxbIQWcxXtM6soyllFDVvl3CfsWQGIoMNJPgqs53+5tKdunelizWlu02uJESie7EyBi5+epAJ7WS4wikNMXS0McjHZXuCCrHaYi7KoJwsnVzzQjq05LY6wNrY6wZQ1R45j6jF5f42Gbu0IgIjbZoqS7nwMiEQFuSSG2cAWLtoqG3l8rs+oxOJBnld7kYuMIw9yn3HAetLyONU4MyVRT54DjtxPIw2xTXmGywXU4iHgh/+L+wYQbnw2ntjyBtxe34jb5wbw/SMRap3lFKYtOTfET2yYwru3TOLSqqigDu5g+l57ntFsV6LWgKTPTdoWHsO7eCzNaXr8S2NcgM3usb063TzQmeOCr7zR9k0MOnxvdRMjQMURtg6KejTwu02mQwKy71EazY1jjMa2a3IMO7jvOOJ3b/plDl3CVj8AX595gvdSaYzfkaI8E3SbjBA6Dg3GtcoSCHviyJnrF+DzC/wfzA8cL4B3IhYOsW6/NS8pvZfw/8TL0rm0eEgcTdMcI6NIMl88PD3XxRv/dh5HW9ZxVs44+MtXl3BD4Qj/P3MDPPeFg1RMjwKOB5kSDVYj3FQcAY6KyFzY4xgSBkyJdjLMqokc1qYWhzg3dGp0unWbkhGzwLF4603UXkDEv5t5I2zT4Js/QGX3p7/LceOkQkVLcXiNBnfweheH+blZcaB2WyJWtUhDszNnnZgD24CdL6EjszTKbWqHOb7OT9IYjSGpztP8uOoEx8bCIMexXMW2y0Fse3CH4vT1RWHfiXkNHZevZYsiWgh5LRWlNb+940UEVySKpHdu6scofCeXysy7jo3Qu55GhBVFAaCGrXKucJyKYce+tmzbHtim8bH8RIHUXMmE3JiykdQYQNzl4iVbocFl1CezZU7QXpYGYrfBhUtnUVrkiNJwfkDEiCrcPlsWgagiknYJgRHMaIk41X5pFyGiKUGHn5mv8j2mlioGxUTKG8TwlPYyhtYcF4wL+1kXFcVc1E0/xdToXFlqyyp83JFWEnFM1eHWLM9vbDcjnRlGlBd7wKMzwKPHmnjk6UN4xL8Cj7WH0DqJOLJhZ6GBt47txf81fgQDBRFWypWYUpcpcREUtGxqt+uxLqw1z7VKtiwtMbJAu8H3eTl+b3NPyeJXFJMbU/xeMgVg7FJeMz9PAzdTZMqc49PQb80A+WHW1QFSazxu67emn2B9cWuOi7vhXTwGx2Okvj3PmyZbYeqnWTTN7pVI8JLXFeWZwNT9Z0qsmVzr+8+UY7QX+H9r8LK2XVm2fHLhqfaCVRH2cqzxHJigERWKaF9rjs60MJDa0QqQKePJqTre9PcdTKc+vppz8Fc/uRnXVMRJFYX8X64dBeb30emYKVLMriYpyaOXA7t/nMb60R9KXekhabMjZRFGNHD0Mo49TsbW2/pZYOEIx9leizW5Q5dw34UBMfTAz+4s8vfc02z9NbOHEeIT1t0uQ3ULo8O5Esev8rgYjFnOGX6R1741T2HA6Uc55+QqjPQO7RDhQ1/qdReAoEkBqeIgr1EcSc9jl+Oq0Wdws9x3FErWTFfKO2ocV02N8OAWm66dydu62qQFUiiZTfLb8WxdredzX54PzpkBt0+M2DPAcaVu1+fnFYZOXBusKMoFiRq2yjOHkePvtWzk1fwsndRiyKQXyYQlXlqAj9OvRwEXUq15PnZcGh5hF6hPM4UsjoDyCEU4AE7IJg3MdZEYyLNP8fjaC5yUfREMqW6WhVeRkV5DJHWw3Rojf0Z1sTkjvRR7nGSzFdtbtzjEqEOnxihrYYgG58guRh6yRXutmjOygHmMUYGoC8Dj4+4iH2dKQF4WLODE/tXF7bhzNo/FVg+1wIfv+8iWBhG6Ocy2I8y2geluBtPdU/CmC1sLHdw80sLrRg/jBYPzcDo1Rpv9HI9l4gYufBpTdoHsiUpne0FSrfO8DgNb+Lewy+8taPN6zh/gtS0MMpqzcMC2LBq5XNSUByQlPCf9H40g1DS/p7ErbNsl4yRoLwB7v8PrGcc0qod2pIR0KnQEdBZ5LiO7bH/DhUmek5eT9MdTv3aKckbEMZ0yvQZTQId2rJ9zJejY/1kjOgVYZ2BO/q8yheWPoddi2UD9MI3KyiYajANbOca1REiuMW1r+N0MUBzBo4dm8aZ/iDCfsguH8g4++/pRXDUMcUo6/H9tL9D47DZpNC0epMp7Txx+49dIH11RQG/NMZJpjrkn7xu/kmO857P8IVfh+NBpArOPS+Q3oHhcHAEQoaPqJttSJ5MHggA4+ANg3+3A4gG5hvMnF+kz+Hng2jdy3HEz0qpphM6FOOJnlkZoxDWmgf13ssa4M8/rki1LGcwgx1jHocOxV6dh7DicK70Mx9hs0Rq0UcB7zAgU5qrcZ7dO52ivyb/D4XkP7+J7zfwdBXSOdOvW4O01bVufOLRZMYDtSz5yuU2bNiJTrisR2aWknNnJY9M6KLT7NtlTiqJcVKhhq6w9cSyGa8qA7YkBayafMKBRZ6KKrieGqiuCTSIWslLTdMeTfoANTpzGE5wp8vHiYS4mopALC9PDMVuSiINvFw6eLKhMqm+nbtOsBrZwgZPGRDUa0/IZUtsbBDRw5/fbFhsmxas9C8Cj8WQWOp6oAlc3iSBRzrYqCjpMq6tP0fNurlNnkYsuhLxG5U0SKW7w+MvjQK6K//TwZnz28NpM6mN+EzcXJnFz5Rhu3hBia67G7w8uF4J+3hq15XEal815fr9xyO0QSU1yh86F6gZGJDyPUYVOjdc0FnGpGKyn8/NMKW/P8bsdukTSIyup8zXfj8O05WyRxrUxZo3i8dw+4OA9vIZ+nuIrY7tp1LoeF3lze+XvBdbmGuM13e5n9HIVi1LOHmGPxm3YoQNpcNsz85ntRYlM1vqNNMez42qubKOCplxkbj/Hu+YcUKjy/7U0RkMX4P/V4iGO2QgpLJer4qGpLt78d03UetZoHswBn3nDKK4ZkMhgtkpD0xhkpmZ2/gDw6JfZkxoxjWkvw+MMe9Q7qB0TB2ZHav/zwOAlHHcAzgOuz+sbdJnG25jhmNA17dYq/A6qE7bljpfjsTVngCf/ySrcd2pA86htEbcSu18N3PDTUvs/Jc67DK+L50vkNifOwREex6H72FO4NS9ZJyWJqpbsGB20bUaUn5U5sibthUqMQOdKKWeFy/flKvxM16ex2hTxLcfhOJ0piKhUVub1Huhgde2cHoZ8PeldG9iUZtflWNuHI+ectTXSXlbmeVkrGEXl5LHcJ1EojlJP630V5SJEDVvl9IkiW4PTF4HtcIINjAiFTGSA1PGYSSrHx6ZGx2CMWjORpdsEOD4NWeM9NsJPXpaT2+EHJM3KYZR0eAejo4Vq6vMyMmlm7L5N+4OgzfdWNtLjaybLMLAGaaeGpIbKlZSq5qwo6x6RelKXSpKuz+OJIy4g6lOiPNkTD39BaoBTKcytOX5Gr8Xr60h0ArEIe4ApVvkBafXjsTZraBvPbeEgPvpADp88dvUpf6UeYuwqNrA7P4fnFI7gR/wnsSs7Cydb5KI1Cnid80M0yrMlW4fXmGILoZqoF5ua5eKo7UfrZmwkALH05G0CkHS1XsNGybs1Xu9unal0Y7u5P09qcAe38/ONuujsk1zQDGyjsWrEcToLTBGc28t7a3A7sPOl3JchDCRa3xCjdhc/B6ADY+EAF0ojl55avaGirAdBl/W2UY//C9XNz1xafBzzf7Yxw3G4Ocd0V6OoayJ+fs6K4BnHVadus1bcrK2LBzh+NI5JzXsZKAzi3mngbf97AY1UaUQ1C/zl60fwrHGPDjTXp/HWmJIetFvpfAq6wL5vs3wgaFuRtygA4LD/7czjtsVO0GZEc3A7DdRey0aqB7ZI9LQsqsMe91Of4jZejlFUN8M5L1e1c9z044wid2ocu8241pzpF+4yvOFPgatfTwfp7FMU42rXOHcVhzlfhD3pwWoylKSUJooZmc0UOO86sf3OvDznIqNN4YtBG7SsaNjAZuvsNanCAB8bY9rPUqm5cbS/H64n6dLFUX5/6fnV849v8eO6to97UoLUPbl+xkpohFZRFKhhq6wG03A9ib7K4+a01NAsWVD5WU7agajYJoaseJy9jDUuXZn0/Jw1et3MEoEIx7aKqIvHu9fgxJ2r8KdT4+KkdlSElyY4wZ5srRcFTFfuNcQbPkQPvZ9fEnUWxeQ4toIVvQaVjNtiiC4eZppyflC8/G1bUwuXi7/6URrcA1ulR61cz46kUjdmmFLWrXGRYsQzwh6AiF71wrB93c+KCFUBaNeZ9nf0QXxy4fn4aPDmZU/ZQYzBbIzhbICNuS52l1vYXWnjymIdl+VmkHMhaW7HGIV2Y+nfmuUidOQKAIH06RWF08YxG4nwsvTguz4XeZ063+vnuPDJVXhv9BrS1zLPcwsCIJvn9WrXJb2sx0X7yKVAZZwLsMFtUqOcYmaPRMFZO5w4EzoNLl6DLt+z8TqmHKYzAYIuRaqCttT87rR/N5FeODR2l0bvFeVs0WtLj9aAhtTg9vVNj49CqztghPQAydAx80Lb9nZ1PI6HcWxrWo2aeyhCedk8jcnKBhpZ7UWm/Ddn+D9YGsfdrY34ma8sot6zh1LJxPj0v96MZ49DHJ3zAFwxRCNei43Xci458D0627KS0huFNoOoswAce5xicwv7+f8e9pgJMrCd41Yc87qOXSFtbPKiWgxGmk0kNw6tQd8T8SlTUtOuc/yPQp572GWZSmeBWgIGxwVu/AAwfgUwfrW0S5rm+FqfoU5ErwNArqmpr61s4rGFHQpNdeqSIhxLZlTE78cBX/fFIWlaopk05EyJRnxxUKKv4D6NIxngfJwpisjVGK93t4mk/7jr8n4sDHEuPFk99krEse1uYAzdsJdKaY5EbEoeF0epiK8oykWNGraKpS8Cu3Shsgzzk5wovaz1zJvIaU8MQZPy65yGamEMGkv1o9ajHvZS9UwFAA5fdz2J9A3R+DLRNkAmbUmDMinQQVvqb49ZoapskcdpvMdpb7FpVm/SsboN22Ko1+RxFAc5kVc2yeKiRqM36tnegq6k7UEisJ06F2WmRjdoWSMwI3VS3YYY+DEQO7bmLQY/M+gyVXb2SS4IEeNYPID5uIyq00QZLUSOh+6uVwGVDRjMhvBd8Zxn8nQuRLKIgUOP/+Jhu5AZ3ErjeUT6xc4+xUhrt87rFbZtGvjAFi4ee21GSef3ciGWKQCbnsVISrcu7ZfaNPLrx6SVjw84WX5ursSFTXGIKXWVCS42yxv7F0phQKN05gk+HtgmNdASRa8dkojOILDlOYxupem1eD5hlwu1wR12/+1F/g2xtvVRzk2CLu/RoMX7vLp5bQVzgm7KmE1lqkCMG6Mw7+dFaV4cQqal2dJ5JAqsQJ8RjTI1kRljMDqsuV2YpGPKz+C+7hb89HeG+9KSy16I//dfFfHcyzZbp2tzltkVfoHR4OpmzgVzexldLQxzjvCNWvss/88XD/O9M49zvuk2aTAObGP01pHzKm9gBpATcywyonYLB9m2zOgC5CucX1oznAt6LT42LXRMllDQ5Xi48LSUrgyw1Vp5jPsojUu02GPUt0bles4jFSmn8WzvWy9LQ681z+8qU+A+cwM8Dz8nbfA6PH/Xk0ymPI3sxgz/ninwu8iWRMOgTKevl+VYbZzQcOgwNKU07QVxXsiawfHkegzyeq5FVkGfwSuGbrq+1hjpSV2vOKQBmeMdHn9lo4r/KcoFiBq2FyNGvdd4rU1kcVkFR5OCZNKQUj+t2X5lYgDJwmc1z03UMU5vI3Wki4dti5044gScKwO5IVtf2W3YNi6ZorTJyZ/k3GMr7NRrSEpWnkJE5piSXrYiEhKKZ71Tl8WIGL1OhkaU59tIYmUTU8gOP0Bj1fXtsRVHRCCpxYVHZ1HS+lqyEPAA3xNv9wg/u3aMnv5eg8ZjZ8EafIURRhNn93DhdjJxkmwZuPkXuFjyRYikNU9DMuyJt7/J4+q1aOhtuA4oied94RAXb2GX5xQBcGTx4Od57aOQ723O2u+lOiFqxxkKscQSsWnXpGekGNd+jmrNfgmAOEWyRaqEDm4VRwb6RW3qU5Im7ND4LI7w8+f2iuHt8v3jVx4fzerUmKYYh3zfwFa70Ok2GMWJJfqj6prKuUoU0YHTmOJzowB+qtGydOS125CxLiVN7Pq2jCBXWVn/YMXjDO1cszDJMTgMkPQeN+NXfoBGVNRlb27JpnhwsYy3PfQsLPTs5xbdHj61+x48f6uo1JsWbovSBigvjrGgTSdmV9STsyXp1VvmeGTOGQ7nnrknGCF1Yl7HTF7UfX2pRS1zbMgP0ECKekyLrh+z5SPlMTtPGaXphmQHOS6NTcQ8z6DHYzDR9+Kw7QsbtGwta5LtlOXYWBiQjJkp26Io7AKx1K7mqzzmwojUQZdsvW0UiGNVxupsmfNCY8ZGX8sybxlF5vwA05WzJRspN9+bI5Ha/IBk3cxLVwEzn4qDsTDIz1rJqEw6JIgQozFgTfq1yaJK+tb3bC/7tBG7FNfrr+XddL0atopyAaKG7YVKFKba5qTqV8yEthxe1hqtaS/8eg/+prXOooiIdBbkD6J6ObBZUq1kMVU/SuM0jjjRlzfaBvNppcSoR2+5acHTWpBo4yG77+IoFyFBk+cagRFIYzx1GpJqvCARhpAGWBimIpV5TthBix71XotGrSfGXrYMptr2uIiJQgZsw9Cm7WXEK54b4MK0PcdFUGdRBDh82VYWQo0ZLtROpUVCdTPw0l8DypspTNWat4rUXp7HP7uP16K8ARjazuPptdhSo73I83f91GJFDHGjZmnS7DJFKrZufR6vc30G2HMbU4a7TVE1HpY2RcNW6Mn1uP9Qorxbn89a3m6dhminJnXQ4HVcOMhrN3Ylr9vRB+0CvzQObL7BtvgxxDGjRvUjfF7eyOhO+n40KZ7VzTQSFOVcp9uQHrFSF5q0MitL5C1rM1lMP2mTodNripDSkrkhrY6bLa3tXNBt0AlodA1M/9iwTWM9N8CMlG6LrYMWJvHDeR9v+/5WzKWM27wb4FOXfQ83j0qtqMnkaUv9Z7Yibb8cWyJj5rZMgeOLl2Gt8Pw+cWQ2qWjclBRcryDOXRdw8zJWVzn2FEep6psvSa/hJzn+x+DfCoO2Rc/iYQp/1Y5QtMpxbDQ4EKMtCsA3eyLeVKCh6onYIWLuqzBsBbEqGznORoGd402Nqpfn+7IVZsIYJWLjGDZGvSO6FAMTnM86NY7zji/R3rz9/h2XxndpA8dss7YwGVpmfs2W5buu90f8Y9i0bsdLpWenMqWiIKXd0bHpyK6fEn80n+cuEYRMCUuZH9fjeG5SmZfOC4qiXBCoYXu+YtJxTHsZI8JgJgdT/7QUx5XUIxOBTUViT7cWZjUsHqbBEcdWJKknkcvpJ6yIhgN6hksjrN/JpTy7xrABOHmVxkQASFR3Tc2N8VoHsnCIAkn3bdh2BJUNMqH2OLkvHJC64XkeS9CV1jqOpGsVpB4sz8/zfC5qOi1+vEkp7jWswQ2ZUOMIEt5E0ms+iuS7yIhwxyC94lHMqGhHFpmFIR7X3D4x2E5BTMMwtAPY9gLWbHUatkehAy484pBGf0vq2ooj7KfouJKqtyBpXbAqk67L76i8mcJcjohkRQHPY2gHF37dBqOqxx7morJ2xNZo5cqMkpoFU1bUNRszjMqP7UaifJ0+70yR+27OiGEey+cc5Xa5CiPNg1uOX4h3G1RMDVr8/ge29Edjgw4jtWFXxUiU85NOnf8bpgf3cUgZxHKvm57eaaXb9SSK6GCqH+Mx+fLZpj2O49JBVR7n+HroPjx6aAZv/aciZjr2fzvnxfjTm2bw4s1iIMdgJLa9yP0NbuMYG8cyh7hUUw66NvJaHuf4dfSHHPc6TaYmz++X9jhZjksmGlreYK9VvsJSi/wgP39+UozkLoXvymMSoZUsmYN3A5N3SfszSds1Bq1plWOyobwCxzTPtN2RdGEvz/EpIyrG+SoNXC8vRqGIKxpBrihAopick22LYxx34XB+ac0iUZfOV+ycmjg4JJ3YZDY5rvQtH+a5Rz3rSE87mY2jHDHnV6MqDUhJTMGqHxvRKpMynBiyfv+6xZQ+JY/Xsb5cUZTzBjVsz0VMWk2SapNSCUzUEFcwXAH0pQ8nRqx5/AzL3wcderxn90lf17YVDurWkbTjMWlKxSEaLmk14taCtIKRVKNMgQugpO+eLADS5++6nMQjEc7wc5yUy2NMJQu7FAupH2W9UbfOY2rXrHqvL3W1mbIVKwq6YhBLulTtGNBdAFpi1OUk1StT5ILB8W19ruODBrgYiZkiF1PFYS7Aph6THpVNGs1engbtwtOnd+2rE8ANb6eh2lmggEnUYQp1rsKFS9CxiqVelgIr1Q08xuacrWt2XZuCV9kkBp9DA9GIenmi2lndbO+z2X08B5OaXJ0Qg1oEvzqL/O7KG/jZXVmIDmxJRXEzViQsV+HCcXYfe1XWj8n9Ai7GxnaL+NMSJ00UMrXdRHOzFaY3p9v29FqMKEe9Z66NiqKsF0a92KQVm3kEsJE/L5OqlS2dPeOg25Tobcsas3FEYaY44phb3sDo6sIkHn/sh3jL10JMt+3/edYF/uClBbx6l8expVunE7N2GEDE6GJlI8+71+a+hndaB4CpUc6W6NzqiAF2+CFgihHjpD1NpiTXrsIx0fP5e3AH57Cox3lrbi/HdseR1GcxEDNFvn7oHjr+ei1RMYaUx2TFuO2KYy/iHNNdPP7a+Tmgup3nUxjgdfKyPH+zXmiZ2tcOgJBzgBH5Kwzy+zcdDLqLViAxU7DZPKURjrHGoI8daavj2PkQUpoDn/N12BPDXb5DxwMKFZYUAaJ7IVlDxkDNlq3jM1NIRV4dcVSH1mHjZoCN16zTTakoyvmIGrZnk+asnXxM6lCShnQiHJtWlpbUN8ar6YX6TNNrS0P2lk1vMxNQKBO9Ua00x5kf4kSfLSLpbxv1uK/GlCgvh1yk5cq2fscIRcQQb65rJ04HtgYzlkipX+CE2xNRp25TxEPmxTPs2QVersxocGkDDdVei70YjRpze9EeW7cukWHP9mcsb+IioFC1IledRUZLM3keS2UjP6cxw2jm1GOMZgZNvn9oJ/DEV+nZP1W23wxc8eM03BrTdjHrF4DqRi6+/Bz7w87tZWQhCnn8Q9ulFmzGprQVR+S6VBl1iENJqZaIiOtbp0F1s9TfhsCRh1gfVz8iNV4FoLSRhvPiIRra7QVGVhDb+rLRK4DRS60ha2prw4Df2dRjwOH7+B1WJ3jNRy6lIbp0YR7HNNpNzd1KAjvdBo3aOOT+Brac+nVXFOX0iWM6yGpHAMRWm6DbSETx2Lt6A5At48knH8db/udBHGvaXTgAfuPmHN75wp0cO+YPArNPcKwzYkoAx/2oxzF45ErW9hv140yJY15TBO6cLDD/NMec6cckI0pKXfws4BWB0gDg5ljSMLab5TOmn3v9KB2LQYeGcCYn3QHyHJunHpV5LpCOADK/uaKD0F4EnvgHW4KxHI4LVLbaDgO5Kq9TeVRKPgY4z9UOicbEEenB3qXj1/Vt/1rTsaBbkxTgkOdZ3cQxGCG3KUrdbnWLzN09JL3pXV9Sn8u2zti0y+vWuU/XtVFYAElvepMZVBiQiHRRIuMi7pgWo3R9YPSytb4TFUU5j1HD9mwyv18mbIOTEocw6TemLY7fb8yuBWmxpLR64NLXgP7nifhUk8ZFT36W7sOoEZv+hqalj5eTti/V4w2RKKJnuTXPVYqZQCsb++vEHF+8ypF1CJhFRGOKRmRXanp6Pabftue5SAhEJMtxbIsYP29r0TI5ALK46NTpUe62eK5xbNObu3V6rbM5RjELI4DvS2QWUrvbklpcz553aZQLmsVDTIFuzvOzyhupGlyo8vnTdwD3fXZ136WXAcauAna+hFEIk77ruFapuTTO16YfY7pd/RivR0689tmqrbmKIr6eH6B3vVC1C65E4ToWxc0KFZOLw7wuM0+yf+TcPn73xWF+h+WNTKNrzvL+yBapdpytSNsgMVQrG2iEF4e5yAy7/J4WDzH6MruP5zt+Jb31lU3HC9kkjos5a9gbdeWl91xrngvXODq+3lZRlGeWXovlAkYYMFdlrapRMQY4H5bGsHemhbd+5lEcqvdrDfzsVV38+xeU4YxfzZrRhYPAofvYSg1iHM3uSdXjj3G8jkOrJJyv2DrVTIlj2bEHgem9QOMIDbCwB0ZAPRGvEtGozc8Btt7E8T4MWF7SmOI84rp2Ds9VOS43jkjbuSbnotpRSQuO5Fj3Ak9+/eTXbuwqHoMTS3R4kPNSJgem+IZAKNHhMASiBstpgq4IHRZsaZLnAj0pyYlDjsmR6EK4Hq9XtsgSl5GdvGaZvESdu3YOjiGGfA5ARKd1a5bZUb06r49JbwZ4nI5nHcyZHD/b9NotDEjqc5Vzd9qJnygim5/QpkQD/Y/h2Eiw69kU57OBWVqrmJWinDFq2J5NjApiYryeosrkqj+nbduWLDVYT0YU9ws7GBXlpe93ZGJwfTE0JaJnRBvcrJ2QsiXrdTW1M3FolSNNbU1xhF7npenTzVkaIoDUV9a5cKkdkfSzFpI6MpOqbGpZ/QIn41yFk2TYleixz4k+jnjszTmgdpALrMYs67FMux5Azs23xplJkQWktqtOgaagZxdLpp61vSApz2Jw5QeBoV3A8FaJ3Ms90ZwGbvvQ8t+L47GOdcNVwPAuXquwa1O6MkWgOM7FGSJek/q0RAem+F26viwah5ja3VnkPvyctNfZQMPWCHEY4z+SxR7AbSqb+D3MPQ0cuJNR5tY0r091gvuKeiKEJYuIge3Axqu5YK0ftYZ9Js/6tMZRW7dsWjW5nvSjHQQmbmBU1whNGXGRJO0ylapv6slManOa+jFGcwFGHrQPoqKcG7TmqM1g1PqLI5w/mjPWwHVcHO7k8I4v7MdjM/3lOT+xo4eP3tRGpjLGMdLLWJ2CbJVj2/SjNBrbixwzITX97UUbvXUzFJ8qb2IE9MhDnGtmnpSymjrQbfO3ETHyM1RT3/oCYPQSvtau07A26d/5QZvR5Hgc9zoLLI3xfI5p8/uRCA0+9lVgbs/Jr1tlE7NyMnmwXKTDOTQ/JI5xl8cZxTKv5bn/9qwVHku3AXJdiRZ7dOLmR3jc9SmrRAzQEDViWfkK3++K8KHZTzIHF/g9BD1x/krasqmrNkYpIh5nLA7yKOh3oHsZyVQTocYkIODzHBFLVpfMO0bMajnWMlOnT/8kSKk7BzbbLN2iCOB3NXH92ny+olzEqGF7MdBr0aBJeyjTaoGO1Cklg7EYpoEMxqbexnE5yToZqX8x6UEO39OtS3sI2dbPpeT9lzEqABH9OUZvPMD3FUdsS5o0xhs7uxd4+ns0eppHJTopeDlOqpkSj60wKC0fhqSFgijxruSZXTjE2k2jAhxFUh81xHNoznJhFYWM7FUmwMk3sMJU7TlOYJ4vyp6j9MTPPU0jKhF1cYDKOFAYlR6uYDpbYZjGZK8J/MN/sEaa4wITzwa2PR/Y8SJO3kFbDPcsJ/fCoLTyiW1dXXsROPaIOAMiOhlyZTmnsk0dz+Rtbanp2QtwGyMEZRSrM0VGnmNwgXf0h3LdDvB6VScYxXV9Rl5MqvzANmDzs2lABh0el2nBkR+QlkqL0jrjqKQCunx/0KEjYWALP3tFHH5X+UHuc7m68ihiFKU5w/0Pbtc+tYpyrhHHUsN/xDpKiyP8327PJ+r4C+0I7/3KLO481O17+y2bgT/+kTqK2az0LXU5hmdy0pv7cjowjQZArkqj2egGmBThbpPG8OAWjhXTj9K4XTxMwy0KpG1aV9SEA3HmDgGbrwcuuYXZJUGb81fY4VxVHOWY2q1zTJrbJ0a7yzmmW6PB22swQnnsUdb79mV6nYDyRmDD1aK2n+Hz0jgzi4Iezy/s8lgzeevA7jRkvu3aPu1xJH/v2myqqEtDPGxLZDZltJfHJG1cnKtRT4xdMTDLG7mNGXejgPOQKSPq1Kxj2mhlxKm5Nu1ojyIkNeNeVjQyRFPErG8clxHtwS3ixIhtBDfsibN78NTuT9PHPmmd2LFz8slwTPmUw+vrOMxCUhTljFDD9mLBpNAu7REXduygvFxtr5ezdaF+Tlo+uFb4x0w+6e0Lg1I3u4IxG0VclJiefkBK5XiERl7tKCdvY3SZWlbTA3Z2j0Q2PR5baYwprIVhGrGmlU/SUuAEKT6teWDmKeDw/Ux1jbo0jAe2AMOX0iCuH6FRGksq1fBOGtCOx+sSR4zymrTjrKTxNqZo+M0flJ6tWR5vboDXKRQF68SAy7POtj7NtN0nv8HP3XANcN1PARuv4zkiNXE6rojBQPYnypPtBS685vdx8eF6QHkCyGaBMKIX3BMjOFtiyl5pzBqSOal9bs5QOMUoEBcGuYBYPMJMgPkDTKmOQ4phbbiKtbRBSyLzoiA6ejk/J2jzmk89ZlPvcmJgB22JDJe4+DGtOOb30+jNlvia4yHJQHAz4q3PiQhO8cQK30Zlutfke4d38jwVRTk3iUJbZmLUeIsjYhhS86Dd6eDffm0W//hUp++tz9qQxZ+/IoNRryXGTpHjfNjhWDFyKeehoE3jdfxKjjmdGueomSc4xi0e4piWHwTGr6IDbm4/9xV0aBQnYl0tW8PqZqltMHIZ249Vt9jt4pCpwq5HQ7kzT0HC2iGOg0Vx4DVn6KDMV2mozjwJfPMjq7t2jgdc+0ZpB9TjuRXHOFZnCjz++jEkIl1Af+0twOvTafCcO6mMGDfDsTYSozcKrEM8hogvFqySfU7KfUykNZNPiQOWaQSbOl83AxrEor0RylogCqW8KbZ1upCa5yiVSeYA7GCQtRlxlU3AputWXpuciDDob4/Ua2FZEU/HlVTubOpHSslcP1UnrGnHirIeqGF7oRHHtqdcYsSmUolXEqZyM/29a9MtgEy6r2m300updZh+dbmKCCXlVz62Xpse4uas9Wj6BStOFHatcTMvisWA1HbGVpjKqAwXR5iOW9rA8/TFU5v2gCZ1y/7xE0mnTg/4U/9EIw0RJ+DBrRRv8jwR2jjMa5krc9FTkgVBrsIJyohiAOKR9nmec09L7Wib21Y28frv+Sfgia8DL/u/gbHLeIzFER7P0YeA6cdtbdXCIabB5YdSkW/Hio+YOuw4AnqLrJeKxZu9eIiLPtfn+6sbbc1wJPW3nseoc3EESRueTIGf3ZwVj37Pfs9enoub+f00uOtTXGgUhhnBrm4G4i4XdnHEYx7czmsHcNtOjQZxt8HPG5iQ/pN5UaCWeyk/wH3MPMl7ojBMcaszoTXPY49DLqKGdmibCEU5XwgDtq85zsAdA4ImwtoUfuO2g/jsg82+t01UPPz3Hx/GNWM+knY2iwc5vjkO04w7i/zxshSwy5WtUdRpsK/t4ftFxT/DzBY3J+r6hzkWlsY4bwYtPm/NUzW/1xZRpwGOw5UJMBW3LUrAJTr/4lgEDhetsVmd4NyxcECyhSJgaBudjXf96erKitwM8OJ/x8+a38/53PM53pY3cu5sTbPFURSIeFSLzk/40v82ayO17QVGsY3xaFrateeZbdMz7xUjLpOXzBkxNP2CiGgZ/ZC8VUHOlmQtIdsbMckotLodSRaTLwJeWVHSr/TP80YQzPV43Uz6r5+VuTqTMnwla6o0xu3DnlXG7taXEe9yxAlbtI5/35ynoihnCzVszyeSHq09W6+Rrt0wNR0rGq8i4GAGdSOv7+ePX9ybNjCdRRpD6dQaP2+N2WzlxNGxKOTk3prlpBd2mQKVKYiUv7u81zOS9No4lEmtZ72geRETMS14uk0KIp0MU3fTnAJmnwbqh3gsUUAjasPVwMguXt/aEUlFmxUP9wiVLk0Kc9DhIqNTQ2JowuViIGjIMXdpBA9s5cT/wF8DD/9vK141sAV442eYrjbzJCOY3ZpM+gWp1cryWmQrNFh7ErU2KePdBmy/XZ9ffeMIFahdn8de2cjr1pji8WZLEo0t0RjNDzCKkc0BEaRX4hw/LwzsPYKIxvLcfi5sTOpXfpDGaLYs35vU7g5Iypep/TWp5Cb9tzTKa5OTVOdsud8xEnSlp2yHxzh0yel7uaOQC0FTm2dqg9VrrijnH0sNXEi5SHkccQx8/LaH8QffPtL3lrwH/O6PlvGaK2XeKAxzvKwf4riaG2DEtCHG7sCW40tWwoDlEwv7rcMSoGZC8xjf62aB0pBkrTSZyZOo9EuKcqbAsW/4EjG2ZJytTHC8jmPOU8cesa3YBrawlrdxlGOiEd07cCeNdFOLvBKOC7zwl4AdL2F/3oVJzu++lIFUJ0Q12qFR6mQ4BndkrDct84xx2WtLu7wWDcLRKziOG4Gp1rx0D5jhnGUyflxPymVg62Qdj3WwMWQ9E1rnrlF5Lo3wnLNFMbJdOwcGkvkThnzct0bqiPEqEeL8AKzDO6VrYgzn6maJqMY24up41ug2zl+/cOK1j6IoZwU1bM8WcWwX+kkNSYh+VT8RzTGD9Go9s8ZraAxB0wZopUE4ClMKxw3pn5cyNh3XRmRzleMn++QY5biNim1jmoaEEX0wvWrz1dSE4qQ8naJKGAVSz1mzBrWXFc/8yPGq0EHXioKkm8KbVNWgw/Suhf2sM20v8Hi9LNOxRi5jGnPQsefeWeTkVRpnqmpJRIXaC1wQNKZ4jK15LoZ6LVtn7Hg0vAe2Umxk7zeB7/235RceY1cCO3+U+3AzTEszqshhyJTofImLCMTWgRGYWugQgDzuNKTmLGRE1tQMRYFtfVQckUXMZlEfFq+161HcpDnDeyDq8TzygwAiKhHXDovAh9Qz+ZLmnS/aSH9hhPvNVayIh4mWmxZOrk+jcuwKcW4sY1wGHTFqRSxqaMfpG6GteVn4dfmdD26zC1JFUc5fwoDGXWPKlsRIq5u/uX8W/+lLDyGI+pctP3dNiF99Xg5uoWqV8NuLgFF5d8T5GUU0Jr2MnYMjMbgWDjJduNuU9mYB57u5fXzdtE8rjwGFMaAzJ05JU7Ma05DLVSlsVRq3WUeZIo1bP0dH6dSjjLKGPc5XcKUGdQ6AGH6Ow5Tpoz+k83GlPveOC/zkXwCX/hjnnNm9wNxTdOSa8hNT1lEe47Xx83SUtuesQ9PN8Jp1arz+cJgVNLIT2HQtzwvgPFo7DMwdYDlPpy5pvBIZTtJywXPtNZBEeU3Zjp/lseQHpaQKQOzwseltb8SnEIvac9dqhkSBbRWYrTBiHsW2s4PJZot6vI7ZimSFFW0U2RizSWeKrBX9TKcbK4py1lHDdi1oL1pFv0TtLhIDI5QBeCUlvFViUk5dX1JrMnYwTVSVMyf3IIY9W7catKS+trVkI0lJzZZohGbLMpEuWhGHtKS+iRB3W4w4putujVFcGOLElEkZsCZ9x3Gskdia6xdLyg/wvbkq9zu3LyV+5eG4Xr7ZEl83qdO1w2KITvPYGrP8/PIG6TUYicrmnBj3dVtzmpM0rXwFjMZKSlKvzWsRi5hJEPAamuiliToGbeDgPcAjXz7xdzJ6BbDzpfSYx7E9//IYv4fWgvR2dES8y+eEHgZM1woketFt8NxLw9xnfpDn1ZrlvVHdbKPOpr66U2NUOlEfjiSlO8tzqh2mkyKOrTiI8XzD4SIhP8iFWWlI6ltT9UTG4I0CLrgQSxR788rXo9tkqnLUY1RlcNvpGbVBx0YlAN5HA1uxburjiqKcHSIZxxvHbHuvbBl3zuTws59/DDONflGpWycC/JcXtFDNZ6T0YYDjqid6BY7L8dHx6Ng0pRRp5g/QqIthW501Zzl2HbqHRqNf4P4Ht3M7k/rckUybnggNFYbpvMtXpWuAT+M2P8BxcPEgMP0k95m0uJNMnsGt4mx2bN3nI19mf/Sl5AeB576L2hGbn83Sjm6dJS+Lk7aOOFvlflxPWvMNcs7p1K2BXxiSvvIZzhFzezmf+Dk6BCoTzAgqjXHeaNeYTTT7tLQ6nLZZYUGHx+9mbAYPHDGAOzRkTYs547g2ApWmC0BBVLNLQxRkLAymuhV4LNVpLUg7wTlxRndtxN+sSzyf28Os32A/N3HWivGdOG5FYNP17Dot+buXMuBFrDN5vOR1zSBSlDNGDdu1YPoJK5N/Uhw7kKUHyXTrm2TQNAbrMvWhJ6KvzraT+mn1Cz0ZjEGYKdl6kaUG8uJhelz7Pgf8jLBDoxaxPe7CIKOWpTGJCi7Zn1HtNfWpYWrhkavaSdNEgOOYk+D8fvT31xUhiV6LC4WgI1HNnkRmZcI0Rqpp+4OIhm4oIhndBo1GUyObLfO4W/NMt2oviDNA0pqykirs5mhI5gZ47l5GDKqngT3/Auy/4+QKieUNwC2/LtHiwJ6XeV8s4hmIYdvulLgQax7jYkP6OmLiekZDm7PAlCgOBxKpLAzyPjL1yp2aTYuGLBJccQrUj4kjoycOhhHWWYVdXr/qJmD0MqtsaVo9uf7x33Vzlt8bYhrX5fGVr0VzVoSoIol6bzn1yT4MeK82pvmZniy08hfJeKQoFytxTKOlfixx2B5seHjfV2bww6P9dbc7KjH+6OYmrh6KpFYUtswmX00ZPDkxOgeO/7xkXnQkE6TKuWJ2D/vmHr6P47qX4ftL4xL1hJ0r2vPWeehlxRE8TMO2OsF0ZdNObfEgP6/X5NwTNDi+Gd0H43htz7GX+MyT/cc7tAPY8CyO0ZkSDfLRy4Etz+M5t2YYjZ7by3nPZAjFYsgObJda1iIdoLkq54BI5tvpJ/jeXkvqn4dELLHK+cmINtWn6IRYPCiiWTXO1XEkkduWdRxnS5IdlBNntszPWdM6aBmBSLOGMGVMZh4z6ceOLz3aB2Q/ebvWMGU+5nGy1oBsk4rem30nolXiuHWk7jgOkPSoTS+fTcvD43Cs4z7pROEuec1JnWtKSwRYoi+CEzxe8pnLsdptV5yfT2Xb1X6+7Dc5T1lHn0jbRbnoUMP2dGnOckB2HGkLE9iFfRJBzNhUmkyBAgneGUaL4rg/5dek4CwdjJets3VSAlFFq3a8mhSaXtum9fRaVtUx3ew8WxZF5MHl9xmFNJbaizb6a8iWbWugdKqxWTikWw2YczSR5yjgBNJps16qNcfPcn2ZvIdo7OUqNu3b9SXluMlaGnMdslVOoM1pRitbi1wsdOvcp2lt5HqcbE3fPL/Aa9GYBg7cwcn9ZFS3AGOXW/Er4wUvbxAF5byNmucqoiaZ4z1QO2TFRfwclT1L4+xFaBYNcczU49ErZNvARoRNPW7SOkEWQ615oN3gwidX5rGUxrlINGla47vp6V9NOm/tKI/VLPyKw8tvF8eMFjSOcdvq5lPvKRt0eP1NOrbj0YgujWstlKJcbLQXOBZ2a2j1Ivy7byzgK0/0ZydlPeC3XpDFm3Z14fQaUrrSY3QyP8xxxHU5Vo/sWn78aszQGYdYamQ3yOfXgGM/ZGu6Yw9zjDOih+b4cqICHATWwDWR4l4bQMh5oDQi5SObOAbnq5xHW7IOiQIR8NsoY/kiz/3Jr1OQ0HD5K9l2qD7L6Kmpey2OSu/zEY6ZBalDbS9IDWvHGlheVjoYjAHlYTqyyxv447pcG00/znG4doTH0m2yz29o0sWL4vget2JbnfrxrX6Maj8im6HlSpTd1MomRg54HSNx5JbkOkchj93oevh57ss4xZOeuUCynnHsQ2baSQbewBbOwb2GFbM0fXiDDn8PbOYO0m2KIOVBJluvuoVrh0hEt8KQv+P0T2zLfpKDQr/BlzZq00Zx2hAuDCJpeXQhkqvyf1NRBDVsT5f6FFN3TpW0+rCft8blcgvvbpM1oYuHpAZERCYAGdDMAOb1D2i+7NO0TMlJNNak/a4Wo9DYbTC6afqiJuciIkH5Kn8v9UBGoaQE12nImqh2LKlEJuUoV7ZR5r4a45BGarrva3LukFpcqctpzXICNYO5n+G+Hd8eg6lTDru8dn6G18V4hb0MP6+zyAkv6PBzjchWHEtNqKQCR7Gkk7W4GJndC0zeefI08+pm1jiVR1nXagQySuPynRXFMM/yvjBtGKIeFw2mp6OXAapbuZAyToduk2IfmRIXWaVhey5Rx6Yd95p2wu82eN5wRFyjSs9+eROAiNc/U+TnjF1FNcvV3DtGxdNxKfy0UsQ0CplibhSch3asvgY2jnl8zRmbcgyHwlTljVr3pCgXO+Lwipsz+OM75/F7d9SOc/v+xO4CfvuWARRdMTCNAxagXdGapdG36Trbtiw937UX6cw0OgYDW212TGOaht7kXSyNiMTA9XOclxDLPFii4deaoUp/0LbiR9kCkKnQSZsfkEybiPO849GhGTt0Bg5spWEahjyXR79CFX7EwGWvBMZ2c43Qa9hOBVHPOgYbR5k6vem5wOAEa2fLo5xbFg+JnoUjok/i0C6Ncx6vbOAxNWcYtV44SBX+oN3vSPUKgO/bNYQpT6ls4tidKXKOWjjIn8YRzlNmnnM9wM3TWG4vcn2S2H8xANc6nHNlG9E1bQ89n+eQLg0zxmQaU79rRDtHdp1kbnJs5pNzghTjwiCSVkonI5I0aER27ZToiJjjNNuYGza2T8evXN6wXXY5v8IS33xuUl4Xoa+kbqkx3nct08eT3r/TfwzpS2Tuq0xhmWNdcg0yhRNngSkXHWrYngnHDTKxNchM+uhyqS3teRvthfwYA9ekAmeLktJ6gNuGXRmoRQjKKNX62f5IsZtZPrPE9Do1YlKJInJKWMqkB5taUtNnz+7EGsv5Kief9IAddCUVqmkFIgxGkTdTlAGruPoomvHUGpEpgF71hUng4A+sDL+Xsb1wXZcTsUlxNrXExkEQtOgtNaneQVvSn4xR6rAVgi/1otkKvcwm8u44NAabs1wEPfVNRmpPxqbrgevexOMzfVrLExKdjFh71Z7j77DNa2ra7nQWJRU4L317N1rRLnNfhYGdFFzffg/GsHWkHsj0BkTE77QwytqkyiabMtau8Rj8HF8vb1idYyTs0cjvNXDSPrG9NmvSwg7v++FLjhcnW+kzmjP8Md+xl2UUwPRCVhRFMUia8ncfOYB/+5VDmG71OyAvG/bxx68awmVjRc45XRFHqk9LJHERQMRFtBEUzBYl+6nI/c8/TSMoJ85BY/yGMl8dvp/puiZryMuJlsFRqvlCsmLi0EZT/ZzV8DCRQNPjtdeWed23fWZdj5k/2RLrTvOD/Mwnvw7sfImcrehX5KuSBSRGymNfZfo0wM/KlWkoD+0UJ3kZ8MtMgW7O0LELmU9MWx1X0q5zFVmreNLOp8L5riJR5c6ilAvJtTBGj+cDvnHKF3gNOk3OgfXDFILs1tDXFSJJSzXGJKzAYhxxvnXk2Mzax+ho5AZpaPp5a4SazKY4ErtM7pWCXLNkP7KWMh0Mlqb7LpcmfDqP7U1s7+VlHy8T8TUZAmZd2vc7Wv3rp6oLc6ZUt5x61paiQA3bZ544pqE6t09SV9o2zZcbIJHEzxSltkSEFwxm4DQ92pLUZxlos0W+ZgZnY/gsra81Kai9thWjcH1+ntlfYcgKSKWN0bBnU3GMovJSJUYvJ5NHxUZ+l9aAmEiz+by0IIOpNzYpzJ2aqC1PMeoYivhGfkBEnqo8FtPfMC0MgYgTYa9pI7RBh/syqdHGe24EukzbnWyF7w/aTE0O2zYF/PD9wMG7lq9dXsqOFwLX/7R44rtigJZSXl0z+UpNdRTyGHsNHqOpwfKyXAT1mtKoPpJ6WTGEMznrLTaLHsej88SkM7u+rRPODwGDW3gNsyWey8IB7s/NrD7tGOBCZX4/95EtS5/YzPLbNmZYYxWHXHwNbj+xs6PXTrWgqiGZzHNVLphy1VPLSFAU5aLk6MwC/s3n7sWdBxp9rxd84LdfUsFPXFWGkykCptdsFLCcY26/zJMux5x0+xczf9WnAIScl0YuSwkYgePj0YeBg3dzfwDgZLh9rylpyD7TfItD0je+kRJtiiTKWbIKzo2j0n9WBAF7ARB3bSaX6zH7pzwKwOc2YVd86i7XGBnJSHr4fy9fRuNIOnZ5o7xgInAR1Z6NiKQxcE1ZUXkTHaZwOP8aB3VuwGo+dGpWnLI1xyysSPZpjFaItkS+yvnNdHxwc+KcFmMWjp1D8xXrJI3Aa2wc2N0W59CgYwMOJoPJGMhGayM/wPMpDkm694R8p0YZeZ3ECKOlqcnRkohp1G90JoZohOMM1ZUisavCWSKA5VlHQZIpuExN8LJGvuwP6D+m4yLlsTiNCmdw3MrFihq25wqmH5v5MTWoZgCb24dEbdb0dzVpPct9LcaoMRL1ftaq9cVRqjakaz2frmv7s2WLKU/uEs+gken3silDxOnv85Yt47QalZvWQ8Zg7rWsCFZrlpOgm+UAa2p6k9TkWCbtnlWHjAO+182KdH+RRuLskxKVbnL/pu7Yk0kzk+X+gg6902FgB20vS2/1vZ9hdPVkFIaA57wT2Hg9PepxKOnM4nF3ZJHgQM67IQrTddsnF7CKjWbCc8V5ETQlJacoBl6Zk322JH3/SuLhl+/OtIIqb+AkbQQ9ooj1sI0pPs9VaWyuJvoZRTRSm9N8XhpjyvVyhmavxeiFSU2vbKIXP41pi2EWIqYtg8H1bfun1UR4FUVRUgRhhN//+mP45DefOu5vP77DwW+/KI+hAdE3iGPJ+OlSxMn0VS+O2OhqIsTnMJ241+Y8VRqVlOFUBK5To6py/QjfP7CN9ZudBtvwmAytykaO3Y0Z2WfTRiG9HA2uXJnPA8k46jaQpP0CnO/aCwDEcDNO70DEmhCLw7MEPPZlOo9PhOnpWtogzkTuAq4Z66VsxpF0ZS8P5MtMP457NjMqknPIZAG43Inrgq18InHIB9y/iYhWNkkbnpLVL+lLWQ1tGc/gFnvMJruuT5ukTYdApy4RYLmuTgyEET83DPrXYYiZFeRnUpFSxzqPXZ8p1cZ5bBSTjYMBrqyZ0nWwqfTdoGuDEUltr0n7TW2XTktOZw1WJ/rXXX3KyykF5uMM1RO9rs5i5fxCDdtzlSiyokWmV1wygMH2WDNGSa8pEa06sHiAtZvduky2S742U4dbEA9kYZATdLbMydH0kDUKi0t76DqOTcHJlmRyrRzf583LLj8omkk1NGrNEmkM2v1R3zi2599ry8DrcqLMije9K8ZvHNNjbdKIAB6jqa+JAl4bkz48v48Dt5+jkWmUFf2MTNLGAymDu2kdYNog3PknJ2/js+l64JKXcGFjUoPyVXrjQxG+cn1OtN2GGOMRJ1kjSGbSzrIl+71lK/yuujUbta9upmJlYUDaV1QlPXlBWhjJ9+fnJV13+PgasYUDdkFV3byy0NNSug1GaYM23zu4bXkFUWM4Lx6mw8GoebqezShYqkSZJiPRfyOkpROuoihnyD89ehS/9Df3Y6HVn3E0VgB+56YOXro9wzmuspFzUGuOwoLdBsek4V2MIhonnBE5rB3hvB32bBsf4xg16bCtWba/MZ0BBrZy7GzNMUvLFauxKNHhXlP6w89JFk/I14vDLJ0xvVy7UgaSL7KPbiYH1KaBpkR3jYEMiMNQakn3fOPUUk4rm4Dhy6wxFXbpaO2aLDCTCuyLc7nIKGsyR2fZYikRdspyPvayzDDKSFq4l7ERwqDdH5k0EUXfRG/FoZ+r8vswKc+9FhJD1GTFmc/xczwugOsI15Pe9pLVZloj9tpW9yKWLDCIQJQxPKsTsDWtsf3Vt33MOdC0EzLX3DUGry+R4FSKtTGSTZTYpFZ7Eo13XApF5ipqlCoXNWrYni+EgRh5ImqRNgD9vIg4DXBAP/IAXzcDbdKwXIzHxaOMHPapAQom2poT1cHSGA0lV4QiICk/Yccao4g5sJt+uIkAVHrfsT0eAHbAdu0kEIODcRxxIjK95sy5GgGnOLTebzdj3xPHtp4nU5SaXzESOwvcZ9Szfe+MkZQftEael5FJJcfzNull7Xkb4TUR9G9+ZOXva8PVwLVvTNXEjnLRkqvw2jVmGe0NOrymYY/H2avZdLN8lY6HfNkeq5e3rXq8DJ0To5elHBxt26fPRDgdL+W8KC25r3oUBGnN8nl+kMd5sihtc5bHUT8qisugc6CyUbICUt7lKOT+G1OSAudxoVYYxHElRACS/remB675rf1nFUVZBybnmvg3f30v7t0/f9zf3nKFi/907SJKWYdz4uB2jsOzT4g4nkPjduRSjtFm/kPMkpmFSW6TH5Re5SYaKbobnTpw5EHW5yZZLBs59zRnrMPQy9B49XyOva15mSdnaUh64qA1LddM5pJf5Ou5EqOhYRuAKwKCgczjTRq8B+5Ypq/9yXB4XgPbOFabqGkUilqwZF3FAT/Xy8gaoyoGWsYatUZM0s0ku4bjWGFFM2FEvVR2VpvzclobpNe26cyxYx3VQCpVFrbsyEnVB2cK/J6rE1bzxM/ZjDfPT6XemrKwMCXSGFnBLJPqHPVSBmxqbkwirpBr4ds1kYm29qX7Akm01xjBXo7ijOlWkekobGls+T7MinKBoobt+Uq3YWtDjUhTFFrFwuIIjZ4+r538XpikYWXEiUy0LGjaOls3Y1Nm0m1n8gO2gbyXBRCLMTUDzDxhU2nCwE5wS28b00fOcWwaE2AjjO0aDXHTx84zHlU/VTPq0Otp2u2Y4wk6MknXbDQXsdTKVKRFwShrfPy8RJfFm3zgTuCBz3GR8bz3Art+1Pbp9TK8FnHIidjPALd96Pg+gXCALc8FrvlJG+UtSOTT1Iam2wNEofXym8k3W2HUszJuv0cvy/fURS0Tjgg6jYtRPC+OgJRgV9IPePD42lWj1lk7bKPCA1t4zKvh0H1UvQx7/I7K48dHaXtt27rBtGswi7tMPvV9Sqq8qVnS1jyKojzDBGGEP/6XPfjY/3kCQdQ/Z20byOA/Pz/Ei4YX+EKmAAxewnnKpCbnB1iHWlpSItFrUUwv7IgRsv14ByPA2tzD90sPXslQKgxyXDSvGRXYwhDnpG4DaIkTuDXLz8rkRODQo/hha9Gm7BaHeAyjuyjAaOpaO5L9064B04+x48PC5PG960+GaduXq/AaXPZyzkOdBpWNG8eYTRZK/awLzqemPtbMBflB6/zM5Jkp5YLH3BOj0XRq6DZEKFIc4GbOjkNr5HkZa9DG4GtwJSLuct9wkbRk8rJMKTbKxYnKcdohLw5ciLPeEcfrcn1nvRzXDG5KsDMRxJQoNQAYBeG0ivDSmlnj3E87jzMFW3u7NENv6JLVz+uKcgGghu2FQNDlBNWY5sSY9GJz6XFM6iw9TmLzBzlhOWJVplNh4lDEpNp2AjH1HsYwNmnGfo6pWBmZROMe4EnBf6Zo02XMZ/SanKCjbqofbc+KT5kaIde1qohZqZ0Ju2L0ZJi6ZGqFIknviSQqbQZ/QKLPkr5aGLKCD8k5hzRMn/wGlSDTNZw7Xwrs/vFUbWqXIhmmXVCmyGv94Oe5/fAuTuLDl/BvRgQEsJ5lk1qcKdLzDoj3XOpjyxvYSmJgS3900higpvYpKwZ62JEUq7r17mbLNtV4pRrnboOLlp6ocBqF5dVERNuLTLFrTNPxUBgEKpv7F3GmRjzscP+uL/15x1UMQlGUc5oHJxfwS5+/D08eqx/3t9dfPYQP3tDBaDTFucAvcF7p1iWFNSPR1iznxsIgDV7XZ7lGex5JhHO5FiVRSEN55nHJzApFE6HA9wVSwmLEAAtDjMYFHemnO0njMWjbqJ3J5spVmV47uI3zVJowsCm7nRoznJpzbF93318xcnyqOC7w5s9b8SzX5efUDjP9un6U+hWBrAeMWGLQSmV8xUjEFN0MWP8aAzApyCnjMT8IDExQ5ThfFfHHnM3uioGkNtWsbUKJqgZdWZf0OEfFkS3NMn93UmnFAKz4VsE6/6sb5XlBDGTfGseni4nWbrx2ddsbQ9hkS5n2Q4pykaCG7YWEaddj1IPbNSR9bwEaU0ELaM4D7Rn054GmUmKS56k6krAtgkYNK+RjlIFdU/chdTAmFct4YV2ZWIIuj8m8npaQdz1RUa4wspot2EhtDHqjjbc1EXMQb2kkaUBxL5W+Kv3rMgUxgAN7XFHACXzPP1OdcjnFwB0vBq76CaC7wH1DItmZIr3HmRwnvL//JWDzjVQ8NtcrW5J6KlEPNGnP+QGeXySOCLM4KY4B1U3LpAkHnPyf/o54Z0FHhREyMXgZSeFKqUAaIYskPUnEqpozTH1zPU7+g9v5vhNNfKZXbGPKpsq5WUYmvGxK6KvZX5+VKfZnDiiKopwHtHshfudrj+FT39173N8GChn82q3b8MZtC3Abx0Rp37dZN0mpRSqDJVPk+B/2OJfFkYjzbVteNb7bpLjU4gGmHJsUWdOqLgxB486VHuhjUtrh0vG4eIjlMqYdmsnkyRQlbfoyYGSHbZ+XVrIFxEEpmhS1I5wnTTeHqUdWdxGHdwEv/hVpo+PbqCkgGWId4Ht/TGO8vMH2Oc+URXeiaQ1LU1PruRL9LKSc67n+Vokm+8fL23aI2RKvUdRLpYqLsz4y9bqBZDnJ92bqX02E1ghJpX8SRWVJs65sSNX/RtYoNdlvrLeyqcSFIWaPGTHQdBp3IiAlAqEju/qjtCerhfZzfI+iXGSoYXsuEcf9UcOTbXvyjWy6sqnLNYNhpkgPZ766QhNseb9REowCURN+yir3tus2Hch4QFuzSFSTXQ9JI3SYCa1rBS5M3VAciyCUTFYm5ccYsZEY1lHAfZnrFHb52JXUYE8mOaNImLQ6yNj6GNcDJu9iv75eAysydAlw5Wv42LQnyFUkCiopW90af2dL3CZftdHxnKRkFYZ4fR1XPOE1WzNs2hQt7fEaBvS6m3598wfEeC3ZhYEnn5er2gXPUuPdRK5NjWtrTq6XL7XT6f9PxxrAJsoehTbFOewhEdwwUYKlOJ6krEt04HRUsRVFUc4Rbt8zjf/0tw9h7/Txc8XzdgzhQz+2DdeVF5iJ1F60WgUma6m8ienAaSLRkPB8a9yuJNTXXgAWDgGtaT52PJa2hB35vAWO82aszVVoJBZEHLA1z9Ti6SdpmLZmaSz6BaAyAYxdCQxtlXkzm9LSSB9vSCOzOU3n5uxTwGNfowDjidj+QmBst0RGe5znHCnnMdHTx/7BqvAb3Iz0lRXl5YEtPD5TtwyP0VOjrwHPOs8BrnGCjpREiSHr+SnBQRFm6ktTltrW8oREeX2b0Qak0n9DEX4K7XlEPbsecRxGfc1zRNwesRXbjCK+bpwb+QF+doz+sjGznknX4iaCU+LUN2u45cQa/TwwfuWJvyNFEcIoxtHFNmbqXSy0en0/rW6AbhgjCCP0wgg37RzBv7p209k+5BW5uA3buX3Wq5b04HJS0S7PLvaTiOQZqMxFIWttlvZwNak0vbao9RrRgJS4kpM6ltOl1xYBqrqkiQqe1N8Y2fyVTjHo0MhKCy54UmdihDBqh63YRSKuENsUYdcDYocTk4nIthdYgxMZZVwzIclEFkkqcCjPE2XAjHhnc6k+guZ6QqKUodQQ1fl9H3lgdWlV5Q3Azb/AiGOmaOtgjZiV+fziCGtxShus8mVaeKm9yAVBexHJJGkUNpem5fZavL4LB0XRusUJqzgs0eJUnfNqUnqjiBHfxYMiUhUBhREuGownOG0Im+e9lhXdMo6QbMmmc5tWT0mNkIh1ZfInPyZFUZTziHYvxB//yx588l+eRC88fgn02mdN4N/dMoGt/oLts75wiMaTacFWGLFiPsbpaNSN/TwdjSOXikG3pH1ZHFOsr3aYc0K7hqTPvetJGdIMHcxRYA0zU4aUq4oC8www8xS1MGpHWcKSyQOVrcD4bnHCSkqtl03VlQKSd8v5oFPnHDr1KMt4Ju/Esn3cn/defnbQtCm+iG3/9SgAHv/aKtoLuRRj3HkrsOFarh16RoxKalC7DVGlborhCCDpoRfxNUfWI7ExWE0ta2Sjtokta6KuZjuknOypiG8U8ncY8bjiCEnPXFPvGolRmstTYCsOuX3SssfUz6aFpcDvMVvhZ5uIbyIOJevC8kZp27chZfzKfpMOEY5d0xXHpbxryX28NFuv77V4me2WbnMa7xu9XJzygV3vpVsrHdejN0rtS861smF5o15Zll4YYd90A48dreGJo3UcmGvi4FwLB+dbOLLQPk5bYCXecfMO/OZrr17noz19Lm7D9vADNqq1WtK9ZH0xzMzi/mTplmEPOPrQif9eO8rHKxmXMfqNXNPfzTOKfSY6ucyxpI3yoGsjiEFKcMjxJW22wpSg9CB63I+Tuh5GnTjmxFc7whrUxUNisMqgnS0BhVGpgcmBk4Jp7yKGfgQg6qS8pBGNRU/qZYwhnqtIf8DkwvDaxBEN2Ie/xImzd4oqj64PXPuTwI/936I+ucDvLVEn9KR2anB59eAopGFq2jGYtkLFEao7ZgpSeyMp0o1pinU0ppA0ic8PcNvikKQZV0+uVJymNW+dDAA/u7Jp+bS3XlsEOGo810Rx26VRXRq197e2D1AU5SLkyWM1/PrfPoQ7984e97es5+Jnbt6O/9/NGzAQznJerR/lPGiMzMomGhVJBpFDp+78finp8TjWFsf61XiNMnwcM5OnfswaQ2b+DbsAYpYLNY5xDkZs04uzFWb8ZIp8vjgJHP0hMLeXTmXXp1hhZYLHkaTzpn5cH4lBgZiO5l6TRvWxH1LVuVPnfOtlgRf+kl0fOZLya4SUHB9wIuDL//bUBKoyRToBiqPWsVoa43UzhrcRp/SzqWudWrOY/rAQAzOWx4lBKllqvY6IUoneSKLhEQFxLxW5lWWxAyB2wR3FNiptIuGeT0M0FGHM0LzXRLAdztNGjMq0TTTBD0hZmHFQJ8GYGIlwVUI6yhvbY6yM0zmQXYVj/LRIBW3SytimN2/U47WPAtaZn2mJ0uD21bclvMhodAI8dHAB90/O44HJBTx+tIa9041lnXOnyltu2ob//xtWWfN9Fri4Ddtee4mXLFW/YCJZvSYNBKPilwgppTHKdxkRDUiJB2RL0qpE6ksSD2Bqgkh+y+cuHKQX1hxT1BMvYY/CEmHbeuTMxOWkDFDXt1HMrAgoZStAriCiTEbwSQahsCtN3Bdp3CSeNteqCWfL1nBderxxxMGru8j63c4i97l4iBODMYDNZ6UxCoGZPOs2jafUN2135FzyA9J3d4jnZPromoFxdi9w15+xt+z8/tO7H7beBOy8Bbjix3mNurI4iAIaitmKRGXLKQMvdR26dU7y7Xl7n2RKrNnJVpDUxvaa9MC3ZrkQSASz8hRiGtjK+tVcxRrAxnFxslrY1pyIcohBm6tK0/a8jaqbyHPi4U552x3znQ/ymp+KMa0oinIBE8cxvvCDSfzOPz6K6frxZUMDhQze9+KdePuzR1AJZpmtUxdD0/Vt33ij/O5J67nmDH96LdoGpbElWTmOdaB7WaujAVhxvkS/oSY9V5u2D3zUQ7JOcXzbN7bbAKYek3VFl+N/ZSONcD+XWh/JjyfZOiZVNyldAee2bl2yr0Rjw7QINJ0HzOeaDJ//+S6bvn0muD4VqXe8ENj8bH5m3LXRVWNgxYFohvRonAYBXws6qdY8JnJo1mpOyolv1Ix9MCXaGMzmBnFsnbXj0fDNiIMi7DEKi3RwIBWY8LIU9TIOb0BaBnVFgLJr5/DiGGB6DydaJekIs9TzxkhFPkPu+5KX2Ey7vvUo+HtgS39KNLD886TGOLWuMD2cw+7Jg0Ym885kAJpATbpVkenha8rT0pmVpnxKne0A2LLsjj0z+MG+Odw/OY/Hj9awygDsKfOTz9mC3/3JZ63PzteAi9uwXQ3dBjD9eP9rRqwoXVth/pmXEyIyMvbGSPOlntQoCiciPxL1XDzECcJEZRtTNIQAURUWT5gxuozwQEfEHhKvYsoATyYYT44nw1Rg0wTdGL+mD57xriW1l67th5ct8r3GMOo2+1ObHdfuLz9gRaVcL9XSZ4HGYq+JPu+il+X+84OirOwfn5plCEWpOAqA/XcA3/qd0/qKse35wAt/RYQfUtfMzdg61lwVy7ahCQMuMJozVqDD8ehFzFV5jJ0aDf76YWn5U7eeSy8jrQ2GJF3tJIO0cVyYZu3Gm9uaZeTX1FH7opiZKcq9sNIk41ixK/OdabsdRVGUFal3Avz3b+7Bf//2U2j3ljq6gcFiBu990U68/bkbaODOPQ00jnLMz1XovPRz/ToPkaTpmoV7psj5J476+7kn26c0ECKJVOarzIjKFGRtIrWmbZl3Aok+Jmm68qvbYsS1OcvP9jNAfphzop8Xw7BrI5TLCReZ0ilj+NKqsgaTce4n5V4u8P0/4fpquVTm0+Xqn6A4lonOpvvNhj3riDfdCoyz3UR6TT9gs34xoo8mLdtkkrmedTib8wqNkdyyLZuMIrPnydqvnIqCS7pw2JESoDbXCEGLtddJO0Zpq+fnbSTafLbp1GCiuSY7ztTymjRyk1Je3mAN8eXYeB0/y1wbU6scpI3rzvL3gMFxrRPAiHyZx33tJJXT5VitjTv2zOCOPTO4fc8M9s82T/6mZXAdYEM1j82DBWweKmBDNY+BQgbVQgYD8lPOech6HnzPQcZzMVDIYKyywrr8HEAN25MRRTb6tSImaieDWq9FgyuQPqomnSWteGcGhaReUfq3+Vl6NtO1tB0RaEor3KbrbU2KbK8t6awSWQ4kMtdr20hh2E15JsU4XopjPKxSs+r63CwMJO1FBspcxf6UxiSiKh5pP8ttokC8ju3lhbFCY4TL8RojOA440SfXIZbedeKBbi/ymoZGOTACvvdHEnFeJaUx4AU/Tw+mmWyNgZerHi/qlByzLBTa89KjNZZrKYsR1+Xfew27kAjati4qW7YtdtI9ZtNpO+k6Y6PkmE7pAXhftRd4Tcz9ZPrmJvWuxqvp2pRxE+02KW7q8VQURTllji628QdffxxfuPvAstGRgUIG733RJfiZm7ag0psCjj1qI5SlcWB4J8fh9oI1ctuLfJ7Jcx4a2CrGiGMzbYyBYaJmjWmmIJterl7GiviZ8pNEvyKw86hRs48CzqHtRaYpt+Y5B3tZID8EDG5le6KslMSkS5IAa8B5JqsorQ8i9aFxL6Us3GXdbdSz7efqx4Aj99MJYNSQT5XyBuCaNyIx6JL1i0Qx3RiAB2TzLLUy3QQKw0BxUAzZQc6nZr1h9DUMJqLrSAZbr2kj5Gnng+NJSrlEp/28NSgDUX3uzLNmOuzY7wfg+mloh43SOxKQAKxzIQplPZbSPOmbylOlYul1IyRd2qRSm7VgLJmBpovEskGa1LmljdW0EWuCOMqaMtfo4ntPzeCOp2jILteO7EQUsx4u21DB5eNlXLGxgss2VLBztISNA3lkvAvLyaCG7TOF6dEWdqzx221yMjMDSzr6GkXWqwkZnH3j6TqBt8sIX5ma1yTVI5UKbVJXgh771ZmJtDXHGsv2nHh3Re04MbZk8DMGsZPyGqZFt9I1v8nrXqomWQZ5L3f8ABi0ZSLpWCXeoMt9ZfMiOJW3KdZ+znov7/sr4L7PnPh7cDPA2BXAlucB228WQaQqVX6TFj3piKhjFxTtGhcl7QX7XZraKJNOY1QY06ljmYLUzI4tk8p8igQdetUb07bvLxwRpBqn4by0DlpRFEVZNx49sojf+8fH8Y1Hji7794FCBj/zgu14y/O2YGN8jPWtpnVaYQgYuRwoj3Keac1z/m3Pspes61KVf3gnawq9FcpR4pjvbUxJOYzM89liKuvIs/WWJsXYcSQiZ3q6tqnIP7env995YYjGeEVUl/MDUtK0RHsj3Se+TwRI1h4mkmjKu1rznNNML+BIal/bs8CRB4HJH/S3t1sJNwO87o+Bsctt2U9HDHjTMsiV9N8kJVqcu6aPbbqFoekHG0V2fnc8JLWzveaSoIcj7YIGrdCiMSp7ba6r0plqxkHtuPZYshXbRSEJmEQrRFgdGwH1fK7H3CXzfiJ4FViH+Ul1ZZyUkWqirEaoM2vPSVlXau0e7tw7m0RkHzmyuLpmKACyvourJ6p41pZBXL91ENdtGcCOkRLck2UEXiCoYXsuEHT7J5V0k3KDmRxMvUiiduegrwbG1MSkvanAksksVUds0lTNwJcoz0m6da9N4zvq2eyl2BXDuJnqNdcDIMJH8Kzn1k2nohilxdQ/l+OISEPDHjtiTgCz+4DZPfzsm38ZyGSkTscIdpnJSYzd+hTwuTf3X1s/z4jsxA2MkI5dyTRwUxe01DMZg9ffqEf3UhOjSWPyfKkRkknRTDC+pHbnB7gQyZrjK51Z65teW1LO5vsn+GyFKc/pqK+iKIpyVnjo4AL+6/95Arc9vLyB67kOXn7VBvz087fjBUOLcGae4LjuQKKz24CBCc4v7UU6MBf2cxtIqc7AVmBoO43LlSJjQZflMa1ZG200ooTGmXsip6fpdNCaZZR56lHWC3ebdj/5QTHiBmU+LJ44xdVgyqa6de7PzL/pEqbCoM3eCrvAU/8CHLqPxzHz+PLR3Jf8B+C6n+rPYEr2W7Zqz4/9A/C/f44iSkM7+PlhB/AKrHEtjkhKdc4KVgYdZogFHVnXiNGXKdlrUBjia6FEodtzQFMCBaYVkEkJ9otSF12is96FGPQduz4zKdxAyrDM9ac/J2umVdIX+PCXN2DPtPuHclq0uiF+8LQ1ZB88uIBwlUWy+YyL5+0YxvN3juAFu0ZwzcQAsv7FuyZUw/ZcJjTF+Z0lNQ7dE3veEs9oup1Lqu7WiFIlta2ptGij3mfUDJNm6L719vY1KA9SxmEskeiWRF3bttYiDIDpR1g/HMvg3mtRTXHq0ZNfi6veALzwF22kO2hab7Mx4v0C8M//mY/HrwImrgd2v4b1q0b6PpGWN3UjXZmA5mk8tudFRTlV9+uAk0+2xEkvV7GCEJkS1SaT2hH/zCcFI0SVqFanvMJ+wU6iK9UeK4qiKGeNkxm4AHDZeBk/fdM2/MRlHsq1p9iOJ+zZFkED24DyGADH9o+tHU5l6VQp9FMa7+9Hv5ROTbKx5u2awfGk+8GARBaXUctP02szRXl2H1WMOzU6pH3RiMhLh4L8IH+yIhBl5kKT8dStiXNWoopelk7g4hB/eyeZP4Mu91E7RqP76duBA9/j41f9rhUaypakc0KZRmhX9EcO3QP8/a+cuMXQxuuA53+AademF20iaORZp7qflwBA25YFtees4JPBcXkcfonXxRMn93ICS45nHfVJQCC3vMPA6G0kWWZu6nEq8ywRntS61nOJThDivv3zSWrxvfvnVq1YnPEc3LBtCC/YOYKbd43g+m2DyPkaRTeoYXs+kzYyE3W8uN+ATUip2aXV5szA6GXPbNALAxtt7jWBA3cCk3dxsF88CBz4/ipqlU/Caz/O6KuJUAetVEp3y6YbmZRrBxzMM3lwIpXrEIf9qVd9glG+1NhWOEHnBqhqnCmcfPI/XYyHt9eUtjsN9HlhM0WrUqy9YhVFUc4LHjq4gI/9nydw2yNHV0wjLGQ83Lp7DK++ooRbRhaQ70wzoun5jKxWNkoZyyDfMLeP/c47C5z7fSl1KY7Y9nDZ8vHzeRTxPc1Zqw1hyBQ552VLdNauFAmOY4obzR+gk7o9zzk+CpCU32RKNHjjlGCUL+rPfobRStOPPVtGolFhssf6etmn9CZWEizysvb4/YKkN8/xpzkn16kHLB4Gvv37PP6TURgCfuK/izp1qrOF63Hd0KnTmO41bA/dKLJaFtLbveMVMN/1MdvoYK7Vw2wrwmw7xkInRqMHNEMPjdBBM3DRCBw0e0AvihHFMcIIiOIYUQyEUQzHoXBP1nOQzXjI+q48d1HO+agWMqjkfVTz8luEf8YqOYyVcxgsZuBoJPas0e6FuHf/PL731Ay+v3cG9+6fRyc4gQBXCtcBrtsyiJt3MSL73O3DKGTVkF0JNWyV9eFPfxQ4ePfa7jNXBV71kf6G3Olb0qTymMh2t2nVhwErouRnJY1ZhJwKQ+yLVxrlhLtewgeJMS1ReOPpXap26Wb6hbnWy6BWFEVR1p0Ds0185vtP4/N3HcBcs7fidqWsh1svH8a/usTFi4bnUArrnCsyeTpZS6M0YLNlzh8LBxnp7dY5r2QrQL4s7f0qKWO12B8JjSJGPhMhxiWpvV7OCikmmhhL5sUwkPZyxxhRbi0AzaOi/9Ch3Ww0NxKtCdGzMBFJT0qUVmNvub70U8/YGlTX53m3ZqWGtbbMuWRpzH7jt069tdDOW5CoKEc99qHd/GxGyuFiMc5jsunhQN3DZCuLyQYwuRhhshZgcjFErXsKacLrTMZzMFrOYayS4+9yDhsG8tgyWMCEKOJuGsgjn1GDaS1YbPfwwIEF3Ll3Bt97ahb3HZhHN1ydIQsAV22qJobs8y4ZRjWv68DVooatcmq0F4DaUU5Sg1tX3u7+vwH+9n1r//nX/hTw3Hfa50ndipkZU7L2RpU5CkVlWUQrYqRk6JfUvpqa4CR9x7epPWlBpqRpOpBEkNM9bU0k3RjZQXt5bzMc29fPLEA0KqsoinLB0e6F+MoDh/GXd+zD/ZMLJ9w26zm4cVsZt2xx8dINLezMN+BEHTpljSBUaYzzU9i1Kv1dEX3MFGhEmgisqTM1v9OGbtARTYlUBHIpxrA0qbRRaBX7OzXpwNCgszYORaVX1HcBm0WWZIqlFJWNPoXpCNFX+5mx7+21bNsik7GViDA54hSuSmq2GOZeBtj3XeDv/s0pZ43V4gIm41FMxmM4EI8njyfjMUx6W7EYXHhG4Fglh4nBAraIsbt5sJC0gtk8VFADaxnCKMbjR2u478A87t0/h3v3z+PJqfqqxZ4A4NLxMg3ZnSO4aecIhktnoMtykaOG7cVAFIkAVMA0pqe+SbXBpe13jktTST3vLLKR++JBPr/0ZcDb/tfKnxl0gP9yDVsQnAnljcC2m1j3suV5wObnWqXilY7V/M3xlo++xnGq15wxPFMNxk9FjGG1OEYVOpuqnxFPuNa9KIqiXFTcf2Aen7trP7720JETRnENWwezeOlWHy/aGODG0TYG0ORclynYWtWMiBlGgfRGlVY0gPSsL9k5xwggpoUQk36vAdcMnbp0TpD6WNNuKFpaGyrKvkkatCeimGJ8xmJ8xpG83rXaHGHXimEaVV/ulO8z3RFMr1bXpzK0SQ32ChSUNOnCcSRil2lV5gioH+XapzXLKPXcXtSjrDVU49HjjNcFlNfgm149Wc9B1vfgOhQa81wHjuPAcxzEiNELY/SCCJ0wQneVaaxrTTXvY/NQEZsHC9gyxB8+LmLzUAFDF3jKc63dw2NHanjkSA2PHl7Eo/K70T2Z2nQ/O0dLuGmnCD7tHMF4VQMaa4UatucTYQDUDnGiSkcqyxvomV2JH3wK+Movre2x7LoV+Okvnnibf/5/gG9+5OT7GrkMuOKVwNAlViwhWwTGdrPFwTM5SCYCU92U+JZpxWTqlk0ds1FzTEeNnf4orzFgta+boiiKsoReGOGOPTP4+wcO42s/PIKF1smNXNcBrhzL4YrBCJdWAlxa7uLScgfbyjEohmr6yHpWTwOQWlUxJD2Pc60RjEx6nns2Q8n1bClMDNtH3bQRTLbLiCEZ9ndzMJjOD0YMM+hZJ7JRYA460t+2a1N/ky4NsMeStA2U1jPOUsewk/RSbURZTLZzmGz6mGy4mKzFOLDQw+RiD5MLPcx31idVuJT1sGWoiA0L92K4ewTDziKGnRqGUMOIU8MA6ig7bRTRRkl+F9GB7zrA+JVMNx+9nCrNrs/U51wVqGwCRi/lJYljBFGMThCh0Qmw2OphsR1gsd1Drc3n880upmodTNU7/C0/p2qEnQqFjIeJwTwmBgvYWM1j00AemwYL2DiQx8QAf1fz/jlt/PbCCJNzLTw908DTM03sk99PHKvhwOwqWk8tw6XjZdx0yTBu2jmC518yrIbsOqKG7bmGEXiIImD+aeDp7wJ7vw3MPAHM719+0njjXwJXvXblfU49BvzRjWt7nKsxbGtHgN+/wj7fdD1w7f8FXPU6Kyvv509slCuKoijKRUA3iHD7nmn8y2NT+KdHj2H/bPOU3p91gUsGHFxaCbCrGuDSaoTLqhEuKXWQj9o2kun60sgglnRhiWrCo8HriBGZyYvY0yDTewuDfJxZoQ89wLWLMaATh7AIW0ahGLAdiQq3rTpwGDBCa7o6JCnJxpDNiYFOJ3IYA1ONEAfrMQ7VIxyqhTi4GOLQYhcHF1o4NN/BQnuZ9dIaUEQbWwey2LJpo0Qti9gyVMDWYf4eKEjU8pMvBI4+uHYf/Nx3A6/+gxNvM/0E+w+Xx9nOKFs6bpNGJ8BUrYNDCy0cmm/j4FwLB+ebODgvz+db6xoRLmU9jFfzGC5lMVLKYqSclce55HE1n0Ep56Oc81HKeShl/dPuwxrHdADQ+O9hoUUHwHyzi2OLHRxd7OBorY1ji20cWWzj0Hx71a12liPnu7h28wCu3zqIG7YN4cZLhjFW0S4WzxSnYodqyGktWJgEDt5DleB0OnAUMlWmdvj4FOGTcTLP1+jlrMVpTJ3eMS/LKv7pS2PArz5he9iu1I5AURRFUS5ysr6LW64Yxy1XjOPDr7kKe6cb+OfHpvAvjx3D95+aPan4TDcCHpuL8dicB8DWf7pOCVsrLi4dAC4dcnBpJcSmQojhTBejmQ6GciEynis9TFOR2Ej62zeO8seQro/1MqlIbzpbKZPSpXCXiaxClJ/ZLi9wMlgMM5jteJjruphtBZhrdDHb7PJ3vYO5RgezzS6m610cWewiOAPj40QUnS62ONPY4s1gizuHLc40toZP8zVnCoOow3nVJ4HrX3PiHbneif9+qiyr07GEH/wP4Ht/xMeOyxKu8SulT/IWYPxKlEavQCnvYkc+BsYzADIARNgrW0IUxZhudMTgbfX9npTH9c7pOw0a3RB7pxvYO904pfcVsx6KWQ+e68B3Xfge07UzrgvXdRBGEYIwRjfk7yBiyna7F52ScNOp4DrAJaMlXLdlEDdsG8T1Wwexe2P1ou4jez5x4Rm2Rx46cT8zgKII9WOpXq8OH88+xQhqGADTj1kP2S+exDt372eAf/l/1uTwLScxbB0H2H4z8PD/XuPPPQmux2uiKIqiKMqqcRwHO8fK2DlWxrtfeAma3QDff2oWt++Zxu17ZvD40dqq+15GMfD0YoSnF4H/c8C86gEoyA8wkHMwUnAxnAeGckDBj5F3feS9GHk3Rt6PkXcj+E4IJ4rgIIKDEI4TwoEDV+pBu5GLbuyiFzn2d+SgEznoRC7qgYt6F6j3gHovRq0To96N0AqeOQXhQsbFlsECtgwXj4u2bhkqLl8nungIeODzwOH7+XzgBOKZBneNl82rMWyXbn/g+/xZLZkiXMfDOGKMA7jB9YD/uL9/t3GMxVaAA3OM8h48NoPJ//MnOBiP4mA8gsPRMGaw9hmbzW6I5jqmUZ+M4VIWuzdWsHtjFbs3VXDlxiou21BWJenzmAvPsP36B4Gn/nnt9heevDYGI5eu3ecZVlOrsP2Fxxu2+QF6WQEcF4FdmnXuekxrGbuSNR4jl53u0SqKoiiKcgoUsz5eunscL91NZ3EQRtg/28QTx+p48lgde47V8eQUH5/O4n+hE2OhE+KpZf8q+hFwcfpLwRhAKD/rSznrYfNgXmo9i9g6UsLWoWIidjRcyp56jWd1AnjhL57ae179ByzJOvB94InbmJUXdtkm6bR4Boz/3pL092WMc8dxMFDMYKA4gGs2DwCXZIFvfrpvm3acwdF4CIcxjMPxCA7HwzgSj+CQ/J6Oq5hFFV2cOyrLnutgrJzD1uECto+UsGOkiG3ye/twCQPFc+dYlbXhwjNs15xVDJQju87O5172MmDh37Bf6/BORnBLo+twLIqiKIqirCe+5yYR3VdcbV+PohiHF9t4UgxeY/Q+cay2KvXlc51i1sNQMYMJ6b9qfjaLYNHE4DnUkmbTs/hz+SuAW3/Dvt6cBWaeZKYfwJTvow8BT99Bo7cxtXw24WoitmstynQqPWtS5J0etjvHsB0rd8aIY6CGAmbjKmZQxUxcxWxcwQyqqA9dg8alP456J0C9HaDRDVDvhOj0QgRRjCCM5DdFtMIogue6yHoOfM9FxnOQ8VxkPBf5jIuBQgbVfAbVQgbVvI9KPoNqwcd4JY/xag7jlTxGStnTruNVzk/UsD0ZqxlQhk/TsN1wLTB2OZWDiyPo6506vnsVn7sTePlvn95nK4qiKIpyzuO6TtJ79CWXj/X9babewZPH6tg73cBMo4vpegezjS5mG13M1OV3o7PqFOfTxXGAsggDlXM+ynn+ruYzGC5lMVTKYriYwZAICg2V5PVi9sJI+ywOA8Ubga1pUc832odxTKO31wK6dWDhADMCh3Y800e6rjgOUEULVaeFHTja/8cbrgB+9Jqzc2DKRYMatmtBvgqUxtnfNVtmMb+hMMx0l8pGqgVnSyz63/wcrVVVFEVRFOW0GSnnMFLO4aadIytuE8cxmt0Q7V6IdhChJY87QYh2j1GyOI5FVDmmiHEs3YDiGFnPRdZnpCzru/LcQdbzkMu4KOd8FDKeRsZOhOMAg6ka3s3PXv17n/VmYOIGqiM/+vfAsYepMn2+sUGNWmX9UcN2KX6BxmdhENhw9erEBADgX/8pUBgCxq+yPeIURVEURVHOIo7joJTzUcrpku+8ZOM1/AGAl/4a2y/FEVOcjz1M8atuXTYW54IRRW3OLkmBdpZXs16KnwOuf2v/+5Z5mDzptSjGtWIHkBiYuP7kn6soZ8iF18d277dZzH8i/BxQ3shm4Mnpx5ROH9qx9nLuiqIoiqIoiqIoyilxcfexveRFZ/sIFEVRFEVRFEVRlGcQ7TasKIqiKIqiKIqinNeoYasoiqIoiqIoiqKc16hhqyiKoiiKoiiKopzXqGGrKIqiKIqiKIqinNeoYasoiqIoiqIoiqKc16hhqyiKoiiKoiiKopzXqGGrKIqiKIqiKIqinNeoYasoiqIoiqIoiqKc16hhqyiKoiiKoiiKopzX+KvZKI5jAMDi4uK6HoyiKIqiKIqiKIqiANb+NPboiViVYVur1QAAW7duPYPDUhRFURRFURRFUZRTo1arYWBg4ITbOPEqzN8oinDo0CFUKhU4jrNmB6hcmCwuLmLr1q04cOAAqtXq2T4c5SJC7z3lbKH3nnI20PtOOVvovac8U8RxjFqthomJCbjuiatoVxWxdV0XW7ZsWZODUy4eqtWqDnbKWUHvPeVsofeecjbQ+045W+i9pzwTnCxSa1DxKEVRFEVRFEVRFOW8Rg1bRVEURVEURVEU5bxGDVtlzcnlcvjwhz+MXC53tg9FucjQe085W+i9p5wN9L5TzhZ67ynnIqsSj1IURVEURVEURVGUcxWN2CqKoiiKoiiKoijnNWrYKoqiKIqiKIqiKOc1atgqiqIoiqIoiqIo5zVq2CqKoiiKoiiKoijnNWrYKqfEvn37UC6X4ThO8vObv/mbJ31PevvV/nzta197Zk5KOSf4l3/5l9O6Tx599NFVf8btt9+O973vfbjqqquSpvJXXXUV3ve+9+H2229fx7NTzlXW4757Ju5l5cJkamoKn/zkJ/GKV7wCu3btQrFYRKFQwNatW/Hyl78cv/3bv43bb78dYRiuan8PPvggfvmXfxnXXXcdhoeHUS6XccUVV+Ctb32rzrFKwpned7rOU84V/LN9AMr5xfvf/340Go2zfRiKcko0Gg38wi/8Aj71qU8d97dHHnkEjzzyCP70T/8U73rXu/Cxj30MpVLpLByloigXK1EU4eMf/zg++MEPol6vH/f3yclJTE5O4rbbbsOHPvQh3HXXXXjuc5+74v6CIMBv/MZv4KMf/SiiKOr72+OPP47HH38cf/VXf4VXv/rV+NSnPoWxsbE1Pyfl3Get7ztFOduoYausms985jP4+te/fsb7ecUrXrGq7XSivXjJ5/N4yUtesqpty+XyCf8ehiF+4id+ou/eLRQKuPrqq+H7Ph5++GEsLi4CAD71qU/h4MGD+Pu//3t4nnf6J6Ccl6zlfbee+1QuLIIgwE/91E/hi1/8Yt/rO3fuxObNmwEAR44cwZ49e44zUlfi/e9/f58jL5PJ4KqrrkK5XMajjz6KmZkZAMBXvvIV/NiP/Ri++93vqkPvImM97juDrvOUs0asKKtgamoqHh0djQHEV155ZTwxMREDiAHEH/7wh0/43r179ybb6i2nrMQ///M/J/fI9u3b12y/v/Zrv9Z3/733ve+NZ2Zmkr/X6/X4gx/8YN82v/7rv75mn6+c26zHfbde97JyYfKWt7wluV98349/5Vd+Jd63b99x2y0sLMRf+MIX4h//8R+P77nnnhX39yd/8id949lrX/vaeHJyMvl7t9uNP/7xj8e+7yfbvOUtb1mXc1POXdbyvtN1nnKuoHefsire9ra3JQPWN7/5zXj79u1q2CprynoYA5OTk3E+n0/2+9M//dMrbps2bguFQnzw4ME1OQbl3EYNW+Vs8tnPfrZv3PnGN75xRvtrNBrxxo0bk33ecsstcRAEy277Z3/2Z8l2juPEd9999xl9tnL+sNb3na7zlHMFFY9STsrXv/51fOYznwEAvPOd78SLX/zis3xEirI6Pvaxj6HdbgMAisUi/vAP/3DFbT/0oQ9h69atAIBWq4X/+l//6zNxiIqiXKTU63X84i/+YvL893//93Hrrbee0T4//elP48iRIwAAx3HwyU9+csWyine/+9246aabAABxHOOjH/3oGX22cn6wHvedopwrqGGrnJBms4kPfOADAIDR0VH87u/+7lk+IkVZPenaoTe+8Y0YHh5ecdtsNot3vvOdyfO//du/XddjUxTl4uav//qvMTU1BQC4/PLL8f73v/+M9/m//tf/Sh6/5CUvwe7du0+4ffozv/rVr6LT6ZzxMSjnNutx3ynKuYIatsoJ+dCHPoS9e/cCAH7v934PIyMjZ/mIFGV1PPbYY3jyySeT56985StP+p5XvepVyeMnnngCjz/++Locm6Ioyp/92Z8lj9/2trfBdc9sSVav1/Gtb30reX6qY169Xsc3v/nNMzoG5dxnre87RTmX0LtZWZG77747Sce85ZZb8DM/8zNn+YgUZfXcf//9fc9f8IIXnPQ9z372s5HNZlfch6IoylqwsLCAu+66K3n+oz/6o2e8z4cffhi9Xi95vpoxb+PGjdixY0fyXMe8C5v1uO8U5VxCDVtlWYIgwHve8x6EYYhsNotPfvKTa7bvt7/97bjssstQKpVQKpWwbds2vPKVr8Tv/M7v4NixY2v2Ocr5y/z8PN74xjdix44dKBQKqFQquOSSS/D6178en/jEJ5L2PCfikUceSR5ns9mkfvZELN0uvQ/lwmct7rtnYp/K+c9dd92FOI6T59deey0A4Pbbb8c73vEO7Nq1C/l8HsPDw7juuuvwi7/4i7jvvvtOuM+l49WuXbtWdSzp7XTMu7BZj/tuOXSdp5w1zrJ4lXKO8pGPfCRRt/vQhz503N/PRBX5RD/5fD7+4Ac/uKKKo3LhklaSPdnPwMBA/LGPfeyE+3vnO9+ZbL9z585VH8dLX/rS5H3vfOc7z/S0lHOctb7v1mufyoXF7//+7yf3QKlUijudTvz+97//hPeK4zjxe97znrjT6Sy7z9/6rd/qa98ShuGqjiU9Vr70pS9dy9NUzjHW476LY13nKecO/qmbwsqFzp49e/Bbv/VbAIDLLrsMv/7rv76m+9+0aVMSvZibm8MjjzySKNe222389m//Nu666y783d/9HTKZzJp+tnL+sGPHDmzevBm5XA7T09N4+OGHEQQBAKZT/cIv/ALuu+8+/Pmf//my709HwgYGBlb9udVqNXlcq9VO8+iV85Uzve+eqX0q5zczMzPJ40qlgne961347Gc/CwDwPA/XXnsthoaGMDk5iSeeeAIAlYv/7M/+DPv27cPXvva149SO02NepVJZde2kjnkXD+tx3y2HrvOUs8bZtqyVc49bb7018azddttty25zKhHbffv2xTfeeGP8p3/6p/GhQ4eO+3u73Y7/6q/+Kt6xY0efV+8DH/jAWpyOcp7wzW9+M37Zy14Wf/azn41nZmaO+3utVos/+clPxqOjo333yUc+8pFl9/fKV74y2eYFL3jBqo/jTW96U/K+V73qVad9Psr5wVrfd+u1T+XC4ud//ueXjWa9+c1vjg8fPty37X333Rc/97nP7dvuN37jN47b5wc+8IHk75s2bVr1sfzH//gfk/ddeeWVZ3xuyrnLetx3cazrPOXcQQ1bpY9PfepTyYDz1re+dcXtTsWwXS2zs7Pxc57znL70l/vvv39N9q1cOOzfv79vciwWi/GRI0eO2y7toHnRi1606v2/7W1vS9536623ruWhK+cxq73vzvY+lfODd7/73ccZF295y1tW3H5xcTG+6qqr+tJIlzpN0vvcunXrqo/lgx/8YPK+Xbt2nfY5Kec+63HfnQq6zlPWGxWPUhKOHTuGX/3VXwUADA0N4Q/+4A+e0c8fGhrCF7/4ReTzeQBMf/nEJz7xjB6Dcu6zdetWfO5zn0ueN5vNZVM4i8Vi8tikQK2G9LalUuk0j1K50FjtfXe296mcHywdWwqFAj72sY+tuH2lUsF/+S//JXneaDTw+c9/vm8bHfOUk7Ee992poOs8Zb1Rw1ZJ+IVf+AXMzs4CAD7ykY9gfHz8GT+Gbdu24U1velPy/LbbbnvGj0E597nppptwyy23JM+Xu0/K5XLyuNVqrXrfzWZz2X0oymruu3Nhn8q5z9Kx5VWvetVJ+8T/2I/9WN+8nO5Zu3SfOuYpy7Ee992pous8ZT1Rw1YBANxxxx34m7/5GwDsfffe9773rB1LepG3b98+dLvds3YsyrlL+j55/PHHj/v76Oho8vjw4cOr3u+RI0eSxyeb8JWLj5Pdd+fKPpVzm/T4BLCH9slwHAc33HBD8vypp55acZ/1eh31en1Vx6Jj3sXDetx3p4Ou85T1Qg1bBQBw9OjR5PEdd9wB13XhOM6KP08//XSy/W/91m/1/W3fvn1ndCybNm3qe55W8VMUQ/o+mZ6ePu7vV1xxRfJ4ZmamLypxIg4cOJA83r179xkcoXIhcrL77lzZp3Juc+WVV/Y9X61Bmd5ubm6u72/pMQ8A9u/fv6p96ph38bAe993poOs8Zb1Qw1Y551hqgKTrhhTFkL5PlrtHlk7gq2kyf/DgQUxNTa24D0U52X13ruxTObe5+uqr+553Op1VvS9dD2vqFA2nM+b1ej089NBDK+5DubBYj/vudNB1nrJeqGGrAAByuRxGRkZW/ZPuj1coFPr+tpoeZyfi4Ycf7juuU+lBqlw8pO+T5erBb7zxRuRyueT5d77znZPu89vf/nbyOJ/P48YbbzzDo1QuNE52350r+1TObbZu3YqdO3cmz/fu3buq96UzojZs2ND3t507d2LLli3J89WMeXfffXdfPe6LX/ziVR2Hcn6yHvfd6aDrPGW9UMNWAUABgenp6VX/bN26NXnvv//3/37Fv50qcRwntb4A630VZSmtVgtf/vKXk+c333zzcduUy2XceuutyXPThP5EpLe59dZbVSFU6WM19925sE/l/OANb3hD8ng1AjpHjx7FAw88kDx//vOff9w2r33ta5PHX/jCF05au5ge866++mrs2rXrpMehnN+sx313Kug6T1lP1LBVzin+6I/+qG8Aff3rX3/2DkY5Z/nQhz7UVxe+0n3yjne8I3n8wAMP4O/+7u9W3Oc999yDf/iHf1j2vYoCrP6+O9v7VM4P3vnOdybZTw8//HCfg2M5fu/3fg9BECTPX/e61x23TXrcmp6exp/8yZ+suL/JyUl8+tOfXva9yoXLetx3p4Ku85R15ey20VXOV7Zv35402P7whz+84nYPPfRQ/K53vSt+9NFHT7i/KIriP/zDP4w9z0v2OzExETebzTU+cuVc5B//8R/jX/7lX44PHDhwwu263W78H/7Df+hrLv/sZz87jqJo2e2jKIqf9axnJdtu2rQpfuSRR47b7tChQ/GVV16ZbHf99devuE/lwmE97rv1upeVC5O3v/3tyfc/NjYW33///ctu99d//dd98+MrXvGKFff52te+NtmuXC7H3/nOd47bZmFhIX7Ri17UNzbqfHvxsJb3na7zlHMJJ47j+Bmwn5ULjB07diTKyB/+8Ifxm7/5m8tud9999yUy8c95znPwoz/6o3jWs56F8fFxFAoFzM3N4d5778Vf//Vf49FHH03el8vl8I1vfAMvfOEL1/1clLPPl770JbzhDW+A67r4kR/5EbzkJS/BNddcg9HRUWSzWUxPT+POO+/EZz/72T4Fz+HhYdx+++3HqYGm+cEPfoAXv/jFSR1ZtVrFz/7sz+LFL34xfN/HnXfeiU984hNJ1KxQKOBb3/oWnvvc567vSStnnfW479bzXlYuPI4dO4bnP//5Sa1jLpfDe97zHrz85S/H0NAQDhw4gC984Qv40pe+lLxndHQUd999N7Zt27bsPvft24cbb7wxEcLL5XJ497vfjZe//OUol8t44IEH8PGPfzz5TNd18aUvfQmvec1r1vdklXOGtbzvdJ2nnFOcbctaOT9ZbcT23nvv7YtIrOZn48aN8W233fbMnYxy1vnbv/3bU75PLrvssviee+5Z1f6/+MUvxoVC4aT7LBQK8Re/+MV1PlvlXGE97rv1vpeVC49HHnkk3rZt26rulYmJifjuu+8+6T6/+93vxsPDwyfdn+d58Sc+8Yln4CyVc421uu90naecS2iNrbKubNq0CW9+85uxcePGk267YcMGfPCDH8SDDz6Il73sZc/A0SnnCrt378brX/96DA0NnXTbHTt24Hd+53dw77339jWNPxFveMMbcPfdd+PWW2+F4zjH/d1xHLzsZS/DPffc0yesoVzYrMd9t973snLhsXv3bjz44IP42Z/9WZTL5WW3yefz+Lmf+zncc889ePazn33Sfd5888144IEH8K//9b+G7/vLbnPjjTfi29/+Nn7+53/+jI5fOT9Zq/tO13nKuYSmIivPGAcOHMAPf/hD7N+/H3Nzc+j1eqhUKhgdHcUNN9yAK6+8clmjQ7m42LNnDx555BFMTk5ifn4eYRiiWq1ifHwcz3ve8/paFZwOBw4cwO23346DBw8CADZv3oybb775jNS8lfOf9bjv1vteVi48ms0mvvnNb+Lpp5/G7OwsBgcHcemll+JFL3oRCoXCae1zamoK3/rWtzA5OYlut4uJiQk873nPw+WXX77GR6+cr6zVfafrPOVso4atoiiKoiiKoiiKcl6jqciKoiiKoiiKoijKeY0atoqiKIqiKIqiKMp5jRq2iqIoiqIoiqIoynmNGraKoiiKoiiKoijKeY0atoqiKIqiKIqiKMp5jRq2iqIoiqIoiqIoynmNGraKoiiKoiiKoijKeY0atoqiKIqiKIqiKMp5jRq2iqIoiqIoiqIoynmNGraKoiiKoiiKoijKeY0atoqiKIqiKIqiKMp5jRq2iqIoiqIoiqIoynmNGraKoiiKoiiKoijKeY0atoqiKIqiKIqiKMp5jRq2iqIoiqIoiqIoynmNGraKoiiKoiiKoijKeY0atoqiKIqiKIqiKMp5jRq2iqIoiqIoiqIoynmNGraKoiiKcpq89a1vheM4cBwH27ZtQxRFp/T+l7zkJcn7b7zxxnU6SkVRFEW58FHDVlEURVFOk/e+973J4wMHDuDrX//6qt/7xBNP4Fvf+lby/D3vec+aHpuiKIqiXEyoYasoiqIop8ktt9yCyy+/PHn+53/+56t+76c+9ankcalUwpvf/OY1PTZFURRFuZhQw1ZRFEVRzoB01PbLX/4ypqenT/qeMAzx6U9/Onn+kz/5k6hUKutyfIqiKIpyMaCGraIoiqKcAe94xzuQzWYBAN1uF3/5l3950vd89atfxeHDh5PnmoasKIqiKGeGGraKoiiKcgaMjo7iDW94Q/J8NenI6TTk3bt340d+5EfW5dgURVEU5WJBDVtFURRFOUPe9773JY9/+MMf4vvf//6K2x49ehRf+cpXkufvfve71/XYFEVRFOViQA1bRVEURTlDXvrSl+LSSy9Nnp8oavsXf/EXCIIAAJDJZPD2t7993Y9PURRFUS501LBVFEVRlDPEcZy+OtnPfe5zaDQay26bTkN+zWteg/Hx8XU/PkVRFEW50FHDVlEURVHWgHe+853IZDIAgFqthi984QvHbXP77bfj0UcfTZ6raJSiKIqirA1q2CqKoijKGjA+Po7Xve51yfPl0pHTr23duhWveMUrnpFjUxRFUZQLHTVsFUVRFGWNSItIfec738Fjjz2WPK/X6/j85z+fPH/HO94B19VpWFEURVHWAp1RFUVRFGWNeNnLXoadO3cmz9P1tH/zN3+Der0OgDW573rXu57x41MURVGUCxU1bBVFURRljVgqIpVWQE4bubfeeit27NjxTB+eoiiKolywOHEcx2f7IBRFURTlQuHIkSPYunVrYtB+6UtfwhVXXIErr7wy2eZzn/scfuqnfupsHaKiKIqiXHBoxFZRFEVR1pCNGzfiNa95TfL8z//8z/tEo0ZGRvCGN7zhbByaoiiKolywqGGrKIqiKGtMWkTqq1/9Kv7H//gfyfO3ve1tyGazZ+OwFEVRFOWCRVORFUVRFGWNiaIIu3btwr59+47724MPPohrrrnmmT8oRVEURbmA0YitoiiKoqwxruvi3e9+93Gv33TTTWrUKoqiKMo6oIatoiiKoqwD73rXu+D7ft9ryxm7iqIoiqKcOWrYKoqiKMo6MDExgZe97GXJ81KphDe96U1n8YgURVEU5cJFDVtFURRFWQeiKMLDDz+cPH/jG9+ISqVyFo9IURRFUS5c1LBVFEVRlHXgtttuw/79+5Pn73nPe87i0SiKoijKhY0atoqiKIqyDvy3//bfksfXXHMNbr755rN4NIqiKIpyYaOGraIoiqKsMQ888AC+/OUvJ89/7ud+7iwejaIoiqJc+GgfW0VRFEVZQx555BG88Y1vxEMPPQQA2Lx5M5588knk8/mzfGSKoiiKcuHin3wTRVEURVFOxPXXXw8AmJ2dxeTkJNI+449+9KNq1CqKoijKOqMRW0VRFEU5QxzHWfb1D3zgA/jkJz/5DB+NoiiKolx8aMRWURRFUdaQgYEB3HDDDXj/+9+vfWsVRVEU5RlCDVtFURRFOUM0+UlRFEVRzi6qiqwoiqIoiqIoiqKc16hhqyiKoiiKoiiKopzXqGGrKIqiKIqiKIqinNeoYasoiqIoiqIoiqKc16hhqyiKoiiKoiiKopzXqGGrKIqiKIqiKIqinNeoYasoiqIoiqIoiqKc16hhqyiKoiiKoiiKopzXqGGrKIqiKIqiKIqinNf8f+NT384SawO9AAAAAElFTkSuQmCC",
+ "text/plain": [
+ "<Figure size 1200x400 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "az.plot_ppc(trace_h, num_pp_samples=100, figsize=(12, 4), colors=[\"C1\", \"C0\", \"C1\"])"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/Multi-Parameter Distributions/pymc3_nmr_smc.ipynb b/notebooks/Multi-Parameter Distributions/pymc3_nmr_smc.ipynb
index 7e033e24b03a4606e9d623151aaa5bc4689cf4d2..c31bf5e8ef5b69d2aceda82d862e22d05c466018 100644
--- a/notebooks/Multi-Parameter Distributions/pymc3_nmr_smc.ipynb
+++ b/notebooks/Multi-Parameter Distributions/pymc3_nmr_smc.ipynb
@@ -110,7 +110,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 3,
"id": "dd9e8dcc",
"metadata": {
"tags": []
@@ -162,13 +162,13 @@
},
{
"ename": "AttributeError",
- "evalue": "'InferenceData' object has no attribute 'report'",
+ "evalue": "'InferenceData' object has no attribute 'log_marginal_likelihood'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[8], line 14\u001b[0m\n\u001b[1;32m 11\u001b[0m y \u001b[38;5;241m=\u001b[39m pm\u001b[38;5;241m.\u001b[39mStudentT(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m'\u001b[39m, mu\u001b[38;5;241m=\u001b[39mμ, sigma\u001b[38;5;241m=\u001b[39mσ, nu\u001b[38;5;241m=\u001b[39mν, observed\u001b[38;5;241m=\u001b[39mdf)\n\u001b[1;32m 12\u001b[0m trace_t_smc \u001b[38;5;241m=\u001b[39m pm\u001b[38;5;241m.\u001b[39msample_smc(chains\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m---> 14\u001b[0m np\u001b[38;5;241m.\u001b[39mexp(\u001b[43mtrace_g_smc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreport\u001b[49m\u001b[38;5;241m.\u001b[39mlog_marginal_likelihood \u001b[38;5;241m-\u001b[39m trace_t_smc\u001b[38;5;241m.\u001b[39mreport\u001b[38;5;241m.\u001b[39mlog_marginal_likelihood)\n",
- "\u001b[0;31mAttributeError\u001b[0m: 'InferenceData' object has no attribute 'report'"
+ "Cell \u001b[0;32mIn[3], line 14\u001b[0m\n\u001b[1;32m 11\u001b[0m y \u001b[38;5;241m=\u001b[39m pm\u001b[38;5;241m.\u001b[39mStudentT(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m'\u001b[39m, mu\u001b[38;5;241m=\u001b[39mμ, sigma\u001b[38;5;241m=\u001b[39mσ, nu\u001b[38;5;241m=\u001b[39mν, observed\u001b[38;5;241m=\u001b[39mdf)\n\u001b[1;32m 12\u001b[0m trace_t_smc \u001b[38;5;241m=\u001b[39m pm\u001b[38;5;241m.\u001b[39msample_smc(chains\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m---> 14\u001b[0m np\u001b[38;5;241m.\u001b[39mexp(\u001b[43mtrace_g_smc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_marginal_likelihood\u001b[49m \u001b[38;5;241m-\u001b[39m trace_t_smc\u001b[38;5;241m.\u001b[39mlog_marginal_likelihood)\n",
+ "\u001b[0;31mAttributeError\u001b[0m: 'InferenceData' object has no attribute 'log_marginal_likelihood'"
]
}
],
@@ -186,7 +186,7 @@
" y = pm.StudentT('y', mu=μ, sigma=σ, nu=ν, observed=df)\n",
" trace_t_smc = pm.sample_smc(chains=2)\n",
" \n",
- "np.exp(trace_g_smc.report.log_marginal_likelihood - trace_t_smc.report.log_marginal_likelihood)\n"
+ "np.exp(trace_g_smc.log_marginal_likelihood - trace_t_smc.log_marginal_likelihood)\n"
]
},
{