diff --git a/examples/diagnostics_and_criticism/sampler-stats.ipynb b/examples/diagnostics_and_criticism/sampler-stats.ipynb
index a79389edf..ae6bd214a 100644
--- a/examples/diagnostics_and_criticism/sampler-stats.ipynb
+++ b/examples/diagnostics_and_criticism/sampler-stats.ipynb
@@ -4,12 +4,19 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Sampler statistics\n",
+    "(sampler_stats)=\n",
+    "# Sampler Statistics\n",
     "\n",
     "When checking for convergence or when debugging a badly behaving\n",
     "sampler, it is often helpful to take a closer look at what the\n",
     "sampler is doing. For this purpose some samplers export\n",
-    "statistics for each generated sample."
+    "statistics for each generated sample.\n",
+    "\n",
+    ":::{post} May 31, 2022\n",
+    ":tags: diagnostics\n",
+    ":category: beginner\n",
+    ":author: Meenal Jhajharia, Christian Luhmann\n",
+    ":::"
    ]
   },
   {
@@ -17,11 +24,18 @@
    "execution_count": 1,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING (aesara.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running on PyMC3 v3.11.2\n"
+      "Running on PyMC v4.0.0b6\n"
      ]
     }
    ],
@@ -30,12 +44,11 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
-    "import pymc3 as pm\n",
-    "import seaborn as sns\n",
+    "import pymc as pm\n",
     "\n",
     "%matplotlib inline\n",
     "\n",
-    "print(f\"Running on PyMC3 v{pm.__version__}\")"
+    "print(f\"Running on PyMC v{pm.__version__}\")"
    ]
   },
   {
@@ -75,29 +88,41 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
+      "Multiprocess sampling (4 chains in 2 jobs)\n",
       "NUTS: [mu1]\n"
      ]
     },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "data": {
       "text/html": [
        "\n",
        "    <div>\n",
-       "        <style>\n",
-       "            /* Turns off some styling */\n",
-       "            progress {\n",
-       "                /* gets rid of default border in Firefox and Opera. */\n",
-       "                border: none;\n",
-       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
-       "                background-size: auto;\n",
-       "            }\n",
-       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
-       "                background: #F44336;\n",
-       "            }\n",
-       "        </style>\n",
-       "      <progress value='6000' class='' max='6000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [6000/6000 00:03<00:00 Sampling 2 chains, 0 divergences]\n",
+       "      <progress value='12000' class='' max='12000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [12000/12000 00:06<00:00 Sampling 4 chains, 0 divergences]\n",
        "    </div>\n",
        "    "
       ],
@@ -112,14 +137,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 2 chains for 1_000 tune and 2_000 draw iterations (2_000 + 4_000 draws total) took 11 seconds.\n"
+      "Sampling 4 chains for 1_000 tune and 2_000 draw iterations (4_000 + 8_000 draws total) took 7 seconds.\n"
      ]
     }
    ],
    "source": [
     "with model:\n",
     "    step = pm.NUTS()\n",
-    "    trace = pm.sample(2000, tune=1000, init=None, step=step, cores=2, return_inferencedata=True)"
+    "    idata = pm.sample(2000, tune=1000, init=None, step=step, chains=4)"
    ]
   },
   {
@@ -180,7 +205,7 @@
        "}\n",
        "\n",
        ".xr-wrap {\n",
-       "  display: block;\n",
+       "  display: block !important;\n",
        "  min-width: 300px;\n",
        "  max-width: 700px;\n",
        "}\n",
@@ -489,89 +514,137 @@
        "  fill: currentColor;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:             (chain: 2, draw: 2000)\n",
+       "Dimensions:             (chain: 4, draw: 2000)\n",
        "Coordinates:\n",
-       "  * chain               (chain) int64 0 1\n",
+       "  * chain               (chain) int64 0 1 2 3\n",
        "  * draw                (draw) int64 0 1 2 3 4 5 ... 1995 1996 1997 1998 1999\n",
        "Data variables: (12/13)\n",
-       "    n_steps             (chain, draw) float64 3.0 3.0 3.0 3.0 ... 3.0 3.0 3.0\n",
-       "    perf_counter_start  (chain, draw) float64 7.234 7.235 7.235 ... 9.779 9.78\n",
-       "    perf_counter_diff   (chain, draw) float64 0.0002823 0.000284 ... 0.0002742\n",
-       "    energy_error        (chain, draw) float64 -0.5087 -0.4354 ... 0.04225 0.1197\n",
-       "    tree_depth          (chain, draw) int64 2 2 2 2 2 2 2 2 ... 2 2 2 3 3 2 2 2\n",
-       "    max_energy_error    (chain, draw) float64 -0.6245 0.5775 ... 0.3449 -0.1726\n",
+       "    lp                  (chain, draw) float64 -17.41 -11.12 ... -13.76 -12.35\n",
+       "    perf_counter_diff   (chain, draw) float64 0.0009173 0.0009097 ... 0.0006041\n",
+       "    acceptance_rate     (chain, draw) float64 0.8478 1.0 ... 0.8888 0.8954\n",
+       "    energy_error        (chain, draw) float64 0.3484 -1.357 ... -0.2306 -0.2559\n",
+       "    energy              (chain, draw) float64 21.75 18.45 16.03 ... 19.25 16.51\n",
+       "    tree_depth          (chain, draw) int64 2 2 2 2 2 2 2 2 ... 2 2 3 2 2 2 2 2\n",
        "    ...                  ...\n",
-       "    energy              (chain, draw) float64 20.89 20.49 18.01 ... 17.55 16.76\n",
        "    diverging           (chain, draw) bool False False False ... False False\n",
-       "    step_size_bar       (chain, draw) float64 0.9604 0.9604 ... 0.9419 0.9419\n",
-       "    lp                  (chain, draw) float64 -15.3 -13.86 ... -13.54 -13.95\n",
-       "    acceptance_rate     (chain, draw) float64 1.0 0.8879 0.927 ... 0.9095 0.9688\n",
-       "    step_size           (chain, draw) float64 0.9213 0.9213 ... 0.9988 0.9988\n",
+       "    step_size           (chain, draw) float64 0.8831 0.8831 ... 0.848 0.848\n",
+       "    n_steps             (chain, draw) float64 3.0 3.0 3.0 3.0 ... 3.0 3.0 3.0\n",
+       "    perf_counter_start  (chain, draw) float64 2.591e+05 2.591e+05 ... 2.591e+05\n",
+       "    process_time_diff   (chain, draw) float64 0.0009183 0.0009112 ... 0.0006032\n",
+       "    max_energy_error    (chain, draw) float64 0.3896 -1.357 ... 0.2427 0.303\n",
        "Attributes:\n",
-       "    created_at:                 2021-04-06T16:24:24.465349\n",
-       "    arviz_version:              0.11.2\n",
-       "    inference_library:          pymc3\n",
-       "    inference_library_version:  3.11.2\n",
-       "    sampling_time:              10.891047954559326\n",
-       "    tuning_steps:               1000</pre><div class='xr-wrap' hidden><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-cc8d41f3-8ee0-4e2a-9f9f-0e268925301a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cc8d41f3-8ee0-4e2a-9f9f-0e268925301a' 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>: 2</li><li><span class='xr-has-index'>draw</span>: 2000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-45607efa-b00c-42c7-b425-6c29f6380d25' class='xr-section-summary-in' type='checkbox'  checked><label for='section-45607efa-b00c-42c7-b425-6c29f6380d25' 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</div><input id='attrs-e2ed037a-78a5-4626-b47b-64a416959c7b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e2ed037a-78a5-4626-b47b-64a416959c7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8733f32e-7818-4da4-bc14-a1f706406f81' class='xr-var-data-in' type='checkbox'><label for='data-8733f32e-7818-4da4-bc14-a1f706406f81' 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])</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 ... 1996 1997 1998 1999</div><input id='attrs-bacf9693-9004-4439-b601-31ac943df3e0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bacf9693-9004-4439-b601-31ac943df3e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d46070b6-25cc-4c97-8bba-89222051f484' class='xr-var-data-in' type='checkbox'><label for='data-d46070b6-25cc-4c97-8bba-89222051f484' 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, ..., 1997, 1998, 1999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d94dd07c-403e-45c6-852a-875f83235e89' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d94dd07c-403e-45c6-852a-875f83235e89' class='xr-section-summary' >Data variables: <span>(13)</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>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 3.0 3.0 3.0 ... 7.0 3.0 3.0 3.0</div><input id='attrs-b326da47-ccf6-45b7-8fcd-d488399f6d65' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b326da47-ccf6-45b7-8fcd-d488399f6d65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26c94d22-4fbc-4542-9e86-b809c6ec77ea' class='xr-var-data-in' type='checkbox'><label for='data-26c94d22-4fbc-4542-9e86-b809c6ec77ea' 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., 3., 3., ..., 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_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'>7.234 7.235 7.235 ... 9.779 9.78</div><input id='attrs-7e903123-3321-455b-b56e-0c3a1387fec1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7e903123-3321-455b-b56e-0c3a1387fec1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-577d2682-d5ac-49b9-b624-4e847f61cbff' class='xr-var-data-in' type='checkbox'><label for='data-577d2682-d5ac-49b9-b624-4e847f61cbff' 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([[7.23425966, 7.23463133, 7.23499786, ..., 8.21941184, 8.21982745,\n",
-       "        8.2202315 ],\n",
-       "       [8.8954175 , 8.89596285, 8.89643539, ..., 9.77893421, 9.77928411,\n",
-       "        9.7796441 ]])</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'>0.0002823 0.000284 ... 0.0002742</div><input id='attrs-5c151dda-6364-4dec-bf8a-2a2a0a781adb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5c151dda-6364-4dec-bf8a-2a2a0a781adb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb90fb64-6519-41a2-923b-a83c2b24b424' class='xr-var-data-in' type='checkbox'><label for='data-fb90fb64-6519-41a2-923b-a83c2b24b424' 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([[0.00028233, 0.00028403, 0.00028609, ..., 0.00033475, 0.00032492,\n",
-       "        0.00031708],\n",
-       "       [0.00042187, 0.00031782, 0.00044144, ..., 0.00027279, 0.00027981,\n",
-       "        0.00027424]])</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'>-0.5087 -0.4354 ... 0.04225 0.1197</div><input id='attrs-16455eb6-f992-4b93-99cc-e291520356d9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-16455eb6-f992-4b93-99cc-e291520356d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e5ff7b6-e0cc-4082-9b54-4c7112096ddb' class='xr-var-data-in' type='checkbox'><label for='data-2e5ff7b6-e0cc-4082-9b54-4c7112096ddb' 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([[-0.50873119, -0.43537361, -0.2754945 , ..., -0.41472269,\n",
-       "        -0.04624652,  0.58648128],\n",
-       "       [ 0.        ,  0.05162033, -0.51012677, ..., -0.10103067,\n",
-       "         0.04225451,  0.11974125]])</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 2 2 2 2 2 2 2 ... 2 2 2 3 3 2 2 2</div><input id='attrs-9a229c16-906b-4ace-a394-ce07c4e27445' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9a229c16-906b-4ace-a394-ce07c4e27445' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a43147a-c497-4f0e-ba24-27d67a9b47e6' class='xr-var-data-in' type='checkbox'><label for='data-4a43147a-c497-4f0e-ba24-27d67a9b47e6' 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, 2, 2, ..., 2, 2, 2],\n",
-       "       [2, 2, 2, ..., 2, 2, 2]])</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'>-0.6245 0.5775 ... 0.3449 -0.1726</div><input id='attrs-fe3f7720-a9af-4a28-b2ed-a070a51b2414' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fe3f7720-a9af-4a28-b2ed-a070a51b2414' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfae479a-ddf6-451f-93a1-964d83a90dc1' class='xr-var-data-in' type='checkbox'><label for='data-dfae479a-ddf6-451f-93a1-964d83a90dc1' 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([[-0.62454923,  0.57749804,  0.68574548, ...,  0.52434551,\n",
-       "         0.23932216,  0.74183316],\n",
-       "       [ 0.61023739, -0.29264149, -0.51012677, ...,  0.78759243,\n",
-       "         0.34488576, -0.17257857]])</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'>0.000282 0.000284 ... 0.000275</div><input id='attrs-6a25c484-f35c-40fa-a546-0551dc660ce4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6a25c484-f35c-40fa-a546-0551dc660ce4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f675bf56-337d-4fc5-b944-b4826a8c4cd9' class='xr-var-data-in' type='checkbox'><label for='data-f675bf56-337d-4fc5-b944-b4826a8c4cd9' 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([[0.000282, 0.000284, 0.000287, ..., 0.000335, 0.000326, 0.000317],\n",
-       "       [0.000422, 0.000319, 0.000432, ..., 0.000273, 0.000279, 0.000275]])</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'>20.89 20.49 18.01 ... 17.55 16.76</div><input id='attrs-165f2820-a189-44dd-a1cb-a53e1f97acc9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-165f2820-a189-44dd-a1cb-a53e1f97acc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6062b5b-9650-420e-835d-10b4d77da559' class='xr-var-data-in' type='checkbox'><label for='data-e6062b5b-9650-420e-835d-10b4d77da559' 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([[20.88764382, 20.4921193 , 18.01116499, ..., 17.49176409,\n",
-       "        14.92873488, 17.43965595],\n",
-       "       [20.20316506, 18.13457635, 17.89032511, ..., 19.00599324,\n",
-       "        17.55328583, 16.76147247]])</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-33b88b64-bdb2-4928-a594-3e54ac5c5e21' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-33b88b64-bdb2-4928-a594-3e54ac5c5e21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a672e103-8edc-418e-ace9-97244b43bf67' class='xr-var-data-in' type='checkbox'><label for='data-a672e103-8edc-418e-ace9-97244b43bf67' 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]])</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'>0.9604 0.9604 ... 0.9419 0.9419</div><input id='attrs-e6b1d819-c13e-47e9-b953-0e13c4d48c80' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6b1d819-c13e-47e9-b953-0e13c4d48c80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa047794-eaed-4254-8c4f-ef02d2b6bb6c' class='xr-var-data-in' type='checkbox'><label for='data-fa047794-eaed-4254-8c4f-ef02d2b6bb6c' 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([[0.96041447, 0.96041447, 0.96041447, ..., 0.96041447, 0.96041447,\n",
-       "        0.96041447],\n",
-       "       [0.94194673, 0.94194673, 0.94194673, ..., 0.94194673, 0.94194673,\n",
-       "        0.94194673]])</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'>-15.3 -13.86 ... -13.54 -13.95</div><input id='attrs-c465f465-d0f6-4584-81cb-a7cdfe6d51ad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c465f465-d0f6-4584-81cb-a7cdfe6d51ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-517c3d76-ec67-4532-ac0f-fd8d4a2cca65' class='xr-var-data-in' type='checkbox'><label for='data-517c3d76-ec67-4532-ac0f-fd8d4a2cca65' 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([[-15.3048286 , -13.86066125, -12.92070308, ..., -12.06651172,\n",
-       "        -11.88730918, -14.62170062],\n",
-       "       [-14.30776839, -14.61483991, -12.18578006, ..., -13.37718754,\n",
-       "        -13.53911458, -13.95227994]])</pre></div></li><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'>1.0 0.8879 0.927 ... 0.9095 0.9688</div><input id='attrs-2d2ffbde-d619-4a52-8348-4fd8f7782045' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2d2ffbde-d619-4a52-8348-4fd8f7782045' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b3bb626-37a9-486a-a67f-da18003d4b4e' class='xr-var-data-in' type='checkbox'><label for='data-7b3bb626-37a9-486a-a67f-da18003d4b4e' 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.        , 0.88790791, 0.9269517 , ..., 0.92792804, 0.91796823,\n",
-       "        0.53323407],\n",
-       "       [0.69160202, 0.96691222, 0.93334257, ..., 0.91483728, 0.90947644,\n",
-       "        0.96880681]])</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'>0.9213 0.9213 ... 0.9988 0.9988</div><input id='attrs-ece8bd7f-873d-44da-b1c3-2fb6a7cba486' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ece8bd7f-873d-44da-b1c3-2fb6a7cba486' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7a20e30-1d16-4c1d-bc62-e13cab832dab' class='xr-var-data-in' type='checkbox'><label for='data-c7a20e30-1d16-4c1d-bc62-e13cab832dab' 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([[0.92128394, 0.92128394, 0.92128394, ..., 0.92128394, 0.92128394,\n",
-       "        0.92128394],\n",
-       "       [0.99880883, 0.99880883, 0.99880883, ..., 0.99880883, 0.99880883,\n",
-       "        0.99880883]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0672ec37-2b1a-4491-b8df-89953eb44f18' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0672ec37-2b1a-4491-b8df-89953eb44f18' 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>2021-04-06T16:24:24.465349</dd><dt><span>arviz_version :</span></dt><dd>0.11.2</dd><dt><span>inference_library :</span></dt><dd>pymc3</dd><dt><span>inference_library_version :</span></dt><dd>3.11.2</dd><dt><span>sampling_time :</span></dt><dd>10.891047954559326</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div>"
+       "    created_at:                 2022-05-31T19:50:21.571347\n",
+       "    arviz_version:              0.12.1\n",
+       "    inference_library:          pymc\n",
+       "    inference_library_version:  4.0.0b6\n",
+       "    sampling_time:              6.993547439575195\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-2f2aea16-aa4d-4a21-ab5a-b638d371f757' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2f2aea16-aa4d-4a21-ab5a-b638d371f757' 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>: 2000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1d827cef-81ba-4ad0-ad63-b4776d87a8b0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1d827cef-81ba-4ad0-ad63-b4776d87a8b0' 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-4980e32d-8aa1-4ce3-bcfc-8bd90ffb04b2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4980e32d-8aa1-4ce3-bcfc-8bd90ffb04b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-140b454c-a28e-4e80-9ea4-214f88ad2749' class='xr-var-data-in' type='checkbox'><label for='data-140b454c-a28e-4e80-9ea4-214f88ad2749' 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 ... 1996 1997 1998 1999</div><input id='attrs-6cd5342b-f60c-415c-b1e6-580793193c4b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6cd5342b-f60c-415c-b1e6-580793193c4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48cb7af1-5062-44e9-a9e9-af2c6e910390' class='xr-var-data-in' type='checkbox'><label for='data-48cb7af1-5062-44e9-a9e9-af2c6e910390' 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, ..., 1997, 1998, 1999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-787549c1-b7e8-4a2d-9b8f-1c44e2f74eba' class='xr-section-summary-in' type='checkbox'  checked><label for='section-787549c1-b7e8-4a2d-9b8f-1c44e2f74eba' class='xr-section-summary' >Data variables: <span>(13)</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>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'>-17.41 -11.12 ... -13.76 -12.35</div><input id='attrs-499f12fb-2b6b-4b29-a291-7a2da8a98a53' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-499f12fb-2b6b-4b29-a291-7a2da8a98a53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d37f9144-2a78-4a17-821d-6f3b7b90b223' class='xr-var-data-in' type='checkbox'><label for='data-d37f9144-2a78-4a17-821d-6f3b7b90b223' 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([[-17.41481817, -11.11746073, -14.00889121, ..., -14.62579449,\n",
+       "        -15.12640488, -14.92067376],\n",
+       "       [-11.79754517, -11.37321931, -12.41967116, ..., -15.0553557 ,\n",
+       "        -15.03960873, -11.84774166],\n",
+       "       [-14.40032205, -20.68972653, -13.74758915, ..., -15.20451325,\n",
+       "        -13.46400824, -14.82413174],\n",
+       "       [-13.44881972, -15.9252134 , -11.73013359, ..., -14.77629777,\n",
+       "        -13.75935262, -12.34852981]])</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'>0.0009173 0.0009097 ... 0.0006041</div><input id='attrs-e53b0ec6-fef0-4ae5-9965-2dcf329cfbab' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e53b0ec6-fef0-4ae5-9965-2dcf329cfbab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52963fb3-6ab2-4617-962b-185f5a1df4ba' class='xr-var-data-in' type='checkbox'><label for='data-52963fb3-6ab2-4617-962b-185f5a1df4ba' 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([[0.00091726, 0.00090966, 0.00090066, ..., 0.00086925, 0.00092221,\n",
+       "        0.00046417],\n",
+       "       [0.00092182, 0.00084332, 0.00081795, ..., 0.00048199, 0.00048006,\n",
+       "        0.00048616],\n",
+       "       [0.00050058, 0.00061117, 0.0005809 , ..., 0.00170264, 0.00124334,\n",
+       "        0.00069834],\n",
+       "       [0.00050844, 0.00052396, 0.00054123, ..., 0.00051385, 0.0005149 ,\n",
+       "        0.00060409]])</pre></div></li><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'>0.8478 1.0 0.5188 ... 0.8888 0.8954</div><input id='attrs-fc8c61cb-a161-4056-aead-f020b2bd6ca8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fc8c61cb-a161-4056-aead-f020b2bd6ca8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ab56336-01dd-4ffb-8124-ccf4bbdf5a5e' class='xr-var-data-in' type='checkbox'><label for='data-9ab56336-01dd-4ffb-8124-ccf4bbdf5a5e' 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([[0.84781696, 1.        , 0.5188267 , ..., 0.91934539, 0.85438787,\n",
+       "        0.76057673],\n",
+       "       [1.        , 0.81181648, 0.93142852, ..., 0.94123492, 0.75399724,\n",
+       "        0.93529202],\n",
+       "       [1.        , 0.29414685, 1.        , ..., 0.46697774, 0.83249289,\n",
+       "        0.65167911],\n",
+       "       [0.79107091, 0.56355851, 1.        , ..., 0.92367151, 0.8888214 ,\n",
+       "        0.89536079]])</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'>0.3484 -1.357 ... -0.2306 -0.2559</div><input id='attrs-1d5b7d3c-bf6c-41f4-a06c-ac4d19505c83' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1d5b7d3c-bf6c-41f4-a06c-ac4d19505c83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf106364-eb0d-4055-a594-cd917c76c217' class='xr-var-data-in' type='checkbox'><label for='data-bf106364-eb0d-4055-a594-cd917c76c217' 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([[ 0.34837379, -1.3573709 ,  0.72431555, ...,  0.2857954 ,\n",
+       "         0.1180722 ,  0.03878686],\n",
+       "       [-0.15890021, -0.10739294,  0.2229767 , ...,  0.01717362,\n",
+       "        -0.13001742, -0.69487895],\n",
+       "       [-0.21978325,  1.47962416, -1.65668177, ...,  0.50839378,\n",
+       "        -0.41858308,  0.3440592 ],\n",
+       "       [-0.14333517,  0.51606888, -0.88905807, ...,  0.0211668 ,\n",
+       "        -0.23064281, -0.25594826]])</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'>21.75 18.45 16.03 ... 19.25 16.51</div><input id='attrs-8c090a3d-9fd0-4025-8b4a-9b907e6c97af' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8c090a3d-9fd0-4025-8b4a-9b907e6c97af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d2c4d0f-f1b5-47a0-a080-60f092555848' class='xr-var-data-in' type='checkbox'><label for='data-0d2c4d0f-f1b5-47a0-a080-60f092555848' 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([[21.75076832, 18.45266859, 16.03241029, ..., 17.16983192,\n",
+       "        20.13659553, 23.01097321],\n",
+       "       [14.09025318, 15.15479489, 13.75490571, ..., 18.64000448,\n",
+       "        21.55766921, 19.64569656],\n",
+       "       [17.32485111, 25.19494315, 24.5830488 , ..., 21.14581229,\n",
+       "        20.19801996, 19.52502693],\n",
+       "       [22.87267199, 20.94006237, 16.61780155, ..., 19.3110406 ,\n",
+       "        19.24706179, 16.51436075]])</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 2 2 2 2 2 2 2 ... 2 2 3 2 2 2 2 2</div><input id='attrs-62a59235-b160-4fd0-9712-4fff12aa234f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-62a59235-b160-4fd0-9712-4fff12aa234f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac579db0-bac0-4cef-9461-7a77fd847dc9' class='xr-var-data-in' type='checkbox'><label for='data-ac579db0-bac0-4cef-9461-7a77fd847dc9' 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, 2, 2, ..., 3, 3, 2],\n",
+       "       [2, 2, 2, ..., 2, 2, 2],\n",
+       "       [2, 2, 2, ..., 3, 2, 2],\n",
+       "       [2, 2, 2, ..., 2, 2, 2]])</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'>0.9358 0.9358 ... 0.9276 0.9276</div><input id='attrs-1d86a912-d656-45e7-8455-cbc7cd7de6da' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1d86a912-d656-45e7-8455-cbc7cd7de6da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-056ea606-d48e-4266-a472-45b83d72203c' class='xr-var-data-in' type='checkbox'><label for='data-056ea606-d48e-4266-a472-45b83d72203c' 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([[0.93583778, 0.93583778, 0.93583778, ..., 0.93583778, 0.93583778,\n",
+       "        0.93583778],\n",
+       "       [0.96472688, 0.96472688, 0.96472688, ..., 0.96472688, 0.96472688,\n",
+       "        0.96472688],\n",
+       "       [0.97413339, 0.97413339, 0.97413339, ..., 0.97413339, 0.97413339,\n",
+       "        0.97413339],\n",
+       "       [0.92757304, 0.92757304, 0.92757304, ..., 0.92757304, 0.92757304,\n",
+       "        0.92757304]])</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-cdc177dd-9191-4f92-8f85-ce9f1f3e76cd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cdc177dd-9191-4f92-8f85-ce9f1f3e76cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7287367-b59a-4d75-9834-649815578bf1' class='xr-var-data-in' type='checkbox'><label for='data-f7287367-b59a-4d75-9834-649815578bf1' 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>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'>0.8831 0.8831 ... 0.848 0.848</div><input id='attrs-5b20ee53-11a9-4778-aea7-7046f55407af' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5b20ee53-11a9-4778-aea7-7046f55407af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-006ebe06-87e8-4e2b-b1bc-e7be065e96b8' class='xr-var-data-in' type='checkbox'><label for='data-006ebe06-87e8-4e2b-b1bc-e7be065e96b8' 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([[0.88314133, 0.88314133, 0.88314133, ..., 0.88314133, 0.88314133,\n",
+       "        0.88314133],\n",
+       "       [0.98491061, 0.98491061, 0.98491061, ..., 0.98491061, 0.98491061,\n",
+       "        0.98491061],\n",
+       "       [0.79717973, 0.79717973, 0.79717973, ..., 0.79717973, 0.79717973,\n",
+       "        0.79717973],\n",
+       "       [0.84803742, 0.84803742, 0.84803742, ..., 0.84803742, 0.84803742,\n",
+       "        0.84803742]])</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 3.0 3.0 3.0 ... 3.0 3.0 3.0 3.0</div><input id='attrs-95c8dd92-df9e-4777-8355-fab750b6a460' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-95c8dd92-df9e-4777-8355-fab750b6a460' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ecb329b2-3812-4bde-b765-859d43bd1d5b' class='xr-var-data-in' type='checkbox'><label for='data-ecb329b2-3812-4bde-b765-859d43bd1d5b' 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., 3., 3., ..., 7., 7., 3.],\n",
+       "       [3., 3., 3., ..., 3., 3., 3.],\n",
+       "       [3., 3., 3., ..., 7., 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_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'>2.591e+05 2.591e+05 ... 2.591e+05</div><input id='attrs-4016ed88-4db6-4334-ad47-9ea1f72ab4dc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4016ed88-4db6-4334-ad47-9ea1f72ab4dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39d16478-4754-4ebc-95ed-489f5029a657' class='xr-var-data-in' type='checkbox'><label for='data-39d16478-4754-4ebc-95ed-489f5029a657' 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([[259080.94794072, 259080.9491462 , 259080.95033726, ...,\n",
+       "        259082.86608642, 259082.86711713, 259082.86821631],\n",
+       "       [259080.90127503, 259080.90247671, 259080.90355453, ...,\n",
+       "        259082.70122436, 259082.70185994, 259082.70249527],\n",
+       "       [259083.94110883, 259083.94177995, 259083.94255876, ...,\n",
+       "        259085.68260603, 259085.68468606, 259085.68622814],\n",
+       "       [259084.07774306, 259084.07843519, 259084.07920939, ...,\n",
+       "        259085.84236159, 259085.8430168 , 259085.84369495]])</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'>0.0009183 0.0009112 ... 0.0006032</div><input id='attrs-48953b79-f512-4253-b58d-37c4967d2616' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-48953b79-f512-4253-b58d-37c4967d2616' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ecffd8f6-d179-45d3-a542-bfe93b48fdad' class='xr-var-data-in' type='checkbox'><label for='data-ecffd8f6-d179-45d3-a542-bfe93b48fdad' 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([[0.00091832, 0.00091119, 0.00090133, ..., 0.00086943, 0.00092247,\n",
+       "        0.00046421],\n",
+       "       [0.00092232, 0.00084386, 0.00081837, ..., 0.00048233, 0.0004802 ,\n",
+       "        0.00048617],\n",
+       "       [0.00050046, 0.00052388, 0.0005813 , ..., 0.00170327, 0.001244  ,\n",
+       "        0.00069857],\n",
+       "       [0.00050863, 0.00052436, 0.00054133, ..., 0.0005133 , 0.00051457,\n",
+       "        0.00060323]])</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'>0.3896 -1.357 ... 0.2427 0.303</div><input id='attrs-5ca37b8a-0510-4335-bdb8-d9bcf8d1bef9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5ca37b8a-0510-4335-bdb8-d9bcf8d1bef9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01c3a78f-cd90-4458-aa08-596877449d0f' class='xr-var-data-in' type='checkbox'><label for='data-01c3a78f-cd90-4458-aa08-596877449d0f' 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([[ 0.3896375 , -1.3573709 ,  0.72431555, ...,  0.34345   ,\n",
+       "         0.42071847,  1.28485975],\n",
+       "       [-0.21887002,  0.50399625,  0.2229767 , ..., -0.78518544,\n",
+       "         1.03001369, -0.69487895],\n",
+       "       [-1.00930297,  1.47962416, -1.65668177, ...,  1.11431688,\n",
+       "         0.47088174,  0.88911589],\n",
+       "       [ 1.87193377,  1.14125086, -0.88905807, ...,  0.31397184,\n",
+       "         0.24269705,  0.30297649]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-330600d7-fabe-4df8-94fa-dd5cb311b25b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-330600d7-fabe-4df8-94fa-dd5cb311b25b' 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>2022-05-31T19:50:21.571347</dd><dt><span>arviz_version :</span></dt><dd>0.12.1</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>4.0.0b6</dd><dt><span>sampling_time :</span></dt><dd>6.993547439575195</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset>\n",
-       "Dimensions:             (chain: 2, draw: 2000)\n",
+       "Dimensions:             (chain: 4, draw: 2000)\n",
        "Coordinates:\n",
-       "  * chain               (chain) int64 0 1\n",
+       "  * chain               (chain) int64 0 1 2 3\n",
        "  * draw                (draw) int64 0 1 2 3 4 5 ... 1995 1996 1997 1998 1999\n",
        "Data variables: (12/13)\n",
-       "    n_steps             (chain, draw) float64 3.0 3.0 3.0 3.0 ... 3.0 3.0 3.0\n",
-       "    perf_counter_start  (chain, draw) float64 7.234 7.235 7.235 ... 9.779 9.78\n",
-       "    perf_counter_diff   (chain, draw) float64 0.0002823 0.000284 ... 0.0002742\n",
-       "    energy_error        (chain, draw) float64 -0.5087 -0.4354 ... 0.04225 0.1197\n",
-       "    tree_depth          (chain, draw) int64 2 2 2 2 2 2 2 2 ... 2 2 2 3 3 2 2 2\n",
-       "    max_energy_error    (chain, draw) float64 -0.6245 0.5775 ... 0.3449 -0.1726\n",
+       "    lp                  (chain, draw) float64 -17.41 -11.12 ... -13.76 -12.35\n",
+       "    perf_counter_diff   (chain, draw) float64 0.0009173 0.0009097 ... 0.0006041\n",
+       "    acceptance_rate     (chain, draw) float64 0.8478 1.0 ... 0.8888 0.8954\n",
+       "    energy_error        (chain, draw) float64 0.3484 -1.357 ... -0.2306 -0.2559\n",
+       "    energy              (chain, draw) float64 21.75 18.45 16.03 ... 19.25 16.51\n",
+       "    tree_depth          (chain, draw) int64 2 2 2 2 2 2 2 2 ... 2 2 3 2 2 2 2 2\n",
        "    ...                  ...\n",
-       "    energy              (chain, draw) float64 20.89 20.49 18.01 ... 17.55 16.76\n",
        "    diverging           (chain, draw) bool False False False ... False False\n",
-       "    step_size_bar       (chain, draw) float64 0.9604 0.9604 ... 0.9419 0.9419\n",
-       "    lp                  (chain, draw) float64 -15.3 -13.86 ... -13.54 -13.95\n",
-       "    acceptance_rate     (chain, draw) float64 1.0 0.8879 0.927 ... 0.9095 0.9688\n",
-       "    step_size           (chain, draw) float64 0.9213 0.9213 ... 0.9988 0.9988\n",
+       "    step_size           (chain, draw) float64 0.8831 0.8831 ... 0.848 0.848\n",
+       "    n_steps             (chain, draw) float64 3.0 3.0 3.0 3.0 ... 3.0 3.0 3.0\n",
+       "    perf_counter_start  (chain, draw) float64 2.591e+05 2.591e+05 ... 2.591e+05\n",
+       "    process_time_diff   (chain, draw) float64 0.0009183 0.0009112 ... 0.0006032\n",
+       "    max_energy_error    (chain, draw) float64 0.3896 -1.357 ... 0.2427 0.303\n",
        "Attributes:\n",
-       "    created_at:                 2021-04-06T16:24:24.465349\n",
-       "    arviz_version:              0.11.2\n",
-       "    inference_library:          pymc3\n",
-       "    inference_library_version:  3.11.2\n",
-       "    sampling_time:              10.891047954559326\n",
+       "    created_at:                 2022-05-31T19:50:21.571347\n",
+       "    arviz_version:              0.12.1\n",
+       "    inference_library:          pymc\n",
+       "    inference_library_version:  4.0.0b6\n",
+       "    sampling_time:              6.993547439575195\n",
        "    tuning_steps:               1000"
       ]
      },
@@ -581,7 +654,7 @@
     }
    ],
    "source": [
-    "trace.sample_stats"
+    "idata.sample_stats"
    ]
   },
   {
@@ -633,9 +706,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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       "text/plain": [
-       "<Figure size 700x300 with 2 Axes>"
+       "<Figure size 1300x300 with 4 Axes>"
       ]
      },
      "metadata": {},
@@ -643,7 +716,7 @@
     }
    ],
    "source": [
-    "trace.sample_stats[\"tree_depth\"].plot(col=\"chain\", ls=\"none\", marker=\".\", alpha=0.3);"
+    "idata.sample_stats[\"tree_depth\"].plot(col=\"chain\", ls=\"none\", marker=\".\", alpha=0.3);"
    ]
   },
   {
@@ -653,7 +726,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x480 with 1 Axes>"
       ]
@@ -664,7 +737,7 @@
    ],
    "source": [
     "az.plot_posterior(\n",
-    "    trace, group=\"sample_stats\", var_names=\"acceptance_rate\", hdi_prob=\"hide\", kind=\"hist\"\n",
+    "    idata, group=\"sample_stats\", var_names=\"acceptance_rate\", hdi_prob=\"hide\", kind=\"hist\"\n",
     ");"
    ]
   },
@@ -726,7 +799,7 @@
        "}\n",
        "\n",
        ".xr-wrap {\n",
-       "  display: block;\n",
+       "  display: block !important;\n",
        "  min-width: 300px;\n",
        "  max-width: 700px;\n",
        "}\n",
@@ -1035,7 +1108,7 @@
        "  fill: currentColor;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;diverging&#x27; ()&gt;\n",
-       "array(0)</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'diverging'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-164703c4-22f6-4458-bb49-3ff536f525e3' class='xr-array-in' type='checkbox' checked><label for='section-164703c4-22f6-4458-bb49-3ff536f525e3' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0</span></div><div class='xr-array-data'><pre>array(0)</pre></div></div></li><li class='xr-section-item'><input id='section-e1494d06-c4b0-4518-80f5-42456b3473cb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e1494d06-c4b0-4518-80f5-42456b3473cb' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-ae119cae-24ce-4829-8773-1c3c20e6d91f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ae119cae-24ce-4829-8773-1c3c20e6d91f' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "array(0)</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'diverging'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-47aa5a24-473e-4def-b6ab-a657a70a43b3' class='xr-array-in' type='checkbox' checked><label for='section-47aa5a24-473e-4def-b6ab-a657a70a43b3' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0</span></div><div class='xr-array-data'><pre>array(0)</pre></div></div></li><li class='xr-section-item'><input id='section-67cd149c-22f5-4420-b05b-7b0987ff081b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-67cd149c-22f5-4420-b05b-7b0987ff081b' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-0d9138e0-fa99-4138-8331-97793dc4d913' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0d9138e0-fa99-4138-8331-97793dc4d913' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.DataArray 'diverging' ()>\n",
@@ -1048,7 +1121,7 @@
     }
    ],
    "source": [
-    "trace.sample_stats[\"diverging\"].sum()"
+    "idata.sample_stats[\"diverging\"].sum()"
    ]
   },
   {
@@ -1074,7 +1147,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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eVHBroWyD5cCHfsizvPeORsdEJDo0MiYiETH7I6iu7vjn7T3O4cSbPCRkgrcE3OrmPztaH/iOBP54UiFrhKHb2QkULnB5e47L9/6t465bRCQcCmMi0iKfz/L3N22HtrIwHjjpNg99rnCwfqjJC+94f1XgT0IG9L3Sw/JDhjHVLmektM/1ioi0he6mFJEWLVwEdT0ZO4RJhFPv8tD32w7+SvAWtf5cvjKoybck9HX4W5mH5V4Ts/3RRKTrUhgTkRa99Y6lo3pVmgQ47S4PvS518JaCvyICJ3Wh5pClpAzm+R2+9Dm4CmQiEkMUxkSkWXv2WNasDSzC3e5MYGqy52gnUB8W4ZsFqvLBVwnL/A6feh38CmQiEiMUxkSkWR/O7rhRsX5XO/S5wsFXBm573Cxg4MghSMGS4zr8S4FMRGKEwpiIBOX1WmZ/1DGjYpnnGE643oNbA/7KdnoSC0eOgMcLqVjWuA6fa8pSRGKAwpiIBPX1Qigvb//nSeoNp/yfBJzkQFuKdmWgsAhSTCCQ5fgdFvocFfWLSFQpjIlIULM/6oApSgdOuSOB5H5QW9DOzwVg6+4MtYFAloRlid9hrV8NYUUkehTGRKSJwkLL8uXtP0XZ51sOmeeZQPuKDhqd8nqhrG7EL80AWL7wOexWIAvqpZdeYvTo0axcubJdzr9y5UpGjx7NSy+91C7nF+kM1PRVRJr47Iv2z0bJ2dD/Bg/WR6PFvdudgcOFkJER+N8MoMQaPvY5fMfxk6VM1mUsWLCApUuXsmXLFnJzc6murmbatGncc889YZ1n9uzZPP744422GWNIT09n4MCBTJo0ieuuu67JcaNHj27xvK+99hpnnXUWAPfddx85OTkA/P73v2fMmDFBj7nrrrvYuHEjAM899xyjRo1q9Hynnnoqb775Zugvrh1UV1fzz3/+ky1btrBlyxb27NmDtZZ3332X/v37h3WurVu38vnnn7N582a2bNlCSUkJI0aM4IUXXgi6/+7du/nb3/7G5s2byc/Pp6Kigl69ejFw4EBuu+02Lrjggki8xLApjIlIEx9/0s4d9w2cfGsCCVlQc7AdnycYC0dKwO8Hjyew3ngWlmLX8InXw82JfjwKZB1m6NCh/OMf/6B79+4d/txvvPEGOTk5pKen07t3b/bt29em851//vmcd955APj9fvLz8/nqq694/PHH2blzJw8++GCTY7Kysrj55puDnq9Xr15Ntnk8HmbNmhU0jO3YsYONGzfi8Xjw+/1tei3tqaioiGeffRaA7OxsMjIyKC0tbdW55s+fz6uvvkpiYiKnnHIKJSUlLe6/fft2vvzyS8455xzOOecc0tPTKSgo4KuvvmLhwoVMnz6dO++8s1XX0hYKYyLSyJ49lm3b2/c5eo52yDzXtKm7fltYCyUlUP9e5xjIwLLTNSz1OVyS2BGN1QQgJSWF0047LSrPPX36dHr27MnJJ5/MZ599xv/9v/+3Tee78MILmTp1aqNtBw4c4I477uDtt9/mnnvuISWl8Zpc3bt3D2sk7uKLL2bhwoUUFxc3WYD6ww8/xHEcRo8ezcKFC1v/QtpZ9+7d+cMf/sDZZ59NVlYWM2bMYMmSJa061/jx47nssss444wzOHLkCNdee22L+48ZM4a5c+diTONPXAUFBUydOpWZM2dy8803k1E/dN5BFMZEpJFPPw8U7rdXvVhCN+g/2QN08PTk0QwUFX0TxgASDSRay1K/4VQPnNgFKmpzcnL4+9//zrp16ygvL6dHjx4MHjyY73znOwwfPrzJ/nPnzuX1119nz549dOvWjfHjx3P//fc3Chher5f33nuPhQsXsnPnToqLi+nWrRvnnnsud911F4MGDWp0zpUrV/LAAw80mR6cPHkyEBi9+tOf/sQXX3zBkSNHOOWUU5g2bRpXXHFFm19/sNcYaf379+eUU05hy5YtVFZWNglj4Zo0aRJff/01H3/8MbfffnvDdp/Px9y5c7nwwgvp27dvWy+7XaWlpXHRRRdF5FwDBgwIa/+kpKSg2/v06cO5557L/PnzycvL6/Aw1gV+3YhIqKy1zP1X+xbu95voIbmfobaw/Z7juGygbYe3tvHmdKDaGj71eqiN83YXb775Jvfffz/Lli3jwgsv5Pbbb+f8889n27ZtzJs3r8n+77zzDr/5zW84/fTTmTJlChkZGbz11ls88cQTjfYrLS3l6aefpra2lksuuYTbbruNkSNHsnjxYu69996GeqZQ+P1+HnroIZYuXcrll1/O1Vdfzf79+3n00UdZunRpm78GHeHgwYPs2bOHvn370rNnzzafb9iwYZx++unMmTOn0fYFCxZQXFzMpEmT2vwcXdGRI0fYuHEjKSkpYdetRYJGxkSkQe42ONCONVypJxr6fMvBX2khBmYCi0vg6EEEYyATy0HXsMTnMDZOpytzc3P5wx/+QO/evXnxxRcbvflYazkcZGX45cuX88orr3DqqacCgSLsqVOn8tlnn/Hggw/Sp08fADIyMnj//febjM7s2LGDu+++mxdeeKGhXuh4CgoKGDx4MM8//zyJiYkAXHnllTz44IP8/e9/j9joSqQsW7aMmprAcG99zdjXX39NSkpKs1OgJSUlQe8kHTZsGBdffHHQY6677jqeffZZNm7cyJAhQwCYNWsWWVlZjB07lhUrVkTk9fzjH/+grKws5P3HjRvXcMNBrNuzZw9z587FdV0OHz7MggULKCsr45FHHiE9Pb3Dr0dhTEQafDHP4nHA304ZpP8UD540qMlrn/OHq6i4cRgDSDCQjGWF33CmB06Iw/mD9957D9d1mT59epNRAGNMQ7A62q233toQxCBQ6zVhwgRmzpzJ5s2bG45JSkoKOk02YMAARo4cydKlS/H5fCQkhPb2M2PGjIYgBnDBBReQnZ0d1ghbR1mxYkWTIOTxeJgyZQoDBw4MesyRI0eYOXNmk+233nprs2Fs4sSJvPDCC8yaNYshQ4ZQUFDA0qVLufnmmxt9rdrqH//4B3l5of+wnnDCCZ0qjB39dU9LS+PnP/85EydOjMr1KIyJCBAYEfn0s/YLYpnnmEBPsfbush+GqkqorYGk5Mbb04Bia5jn9XBrUvzdXVkfZC688MKQjzm21gtoCF3lxyzVsHXrVl5//XXWrFlDYWEhPp+v0eMlJSX07t37uM+ZkZERdMqob9++rF+/PuRr7yj3339/QwF//YjLV199xTPPPMPixYt59dVX6datW6NjWtNqomfPnlx66aV89tlnzJgxg48++gi/3x+0fUZbvP/++xE9XywZM2YMS5Yswev1cvDgQT744AMee+wxNm7cyMMPP9zh16MwJiIAbNkKh/Lb6eQOnHC9ByfB4K2KrWKskiNNR8dM3d2Ve61hvd9wXkJsXXNblZeXY4wJKRDVCzZ14/EEbsQ4uo3C2rVr+f73vw8Ewt4VV1xBWloaAF999RW5ubnU1tY2OVeoz1n/vG5HLJraBo7j0LdvX26++WYOHz7MK6+8wttvvx2xtgnXXXcd8+fPZ968ecyePZuzzz6bM888MyLn7krqW2I8+OCDVFdX8/bbb3PxxRdzySWXdOh1KIyJCABfzm+/KcqeFzqkDzDUFsdeqCkOMlUJgbsrjbUs8juc6fHXdeuPDxkZGQ21YZG+8+6VV16htraWP/3pT03uVtywYQO5ubkRfb7OYOjQoQBs2rQpYue85JJL6N27N8899xwFBQX85Cc/idi568VzzVgwF110Ef/85z9ZtWqVwpiIdDxrLZ990T5BzCRCv2sChVc2tAGRDlVZGVgiKVipTQZQ4hqW+Rwuj6Ni/iFDhrBp0yaWLVsW8amt/fv3k5mZ2SSIVVdXs2XLlog+V2dRH2giOZrn8XiYOHEif/3rX0lOTubKK6+M2LnrxXPNWDAFBYEFckOtZ4wkhTERYcdOCON3blh6jnZIO8lQG6UGr6E4cgSCzdg5dcX8q/2GczzQK06K+adMmcL777/Piy++yKhRozjhhBMaHqsfMQtWxB+K7Oxs9uzZw44dOxp6QPn9fp555hmKi4sjcv2dSU1NDf/85z8BGDFiRETP/Z3vfIdhw4aRmZnZLn2xYqlmLC8vj+rqarKzs9vUq23z5s0MGjSoSdPXgwcP8tprrwE0e+NEe1IYExHmf0W7NHo1idDvagdrwfqOv3+0FJcED2MQKOYvsoZFPodJSfExOnbGGWcwY8YMnnrqKW6//XbGjh1LdnY2RUVF5OTkcOmll/LDH/6wVee+5ZZbWLp0KdOnT2f8+PEkJSWxatUqCgoKGDlyJKtWrYrwq2m9+fPnM3/+fCDwZgyBurb6v5922mlNOuq35OjWFtZaCgsLWbx4Mfn5+Zx11lncdNNNEb3+nj17Mm7cuIies6M888wzDUsXbd8eWPLj2WefJTU1FYDrr7++0ejqf//3f5OTk9Nkvc1du3Y1hKj6r/3u3bt57LHHGvb5z//8z4a//+EPf2Dfvn0MHTqU7OxsjDHs37+fxYsX4/V6ueOOOxqWtOpICmMiwhfzbLs0eu11iUPqCbE9KgaBBrD1a1UeyxhIw7LVNex346cz/y233MKAAQN44403WLx4MVVVVfTo0YOhQ4cyfvz4Vp93zJgxPPHEE7z66qt88sknpKSkMGrUKH77298GbeEQTVu3buWjjz5qtC03N7ehrm3EiBFhhbFjW1ukpqZy8sknM2XKFL7zne+0uft+PPniiy+aTIEe3Wx45MiRIa2QUFhY2OR7WFRU1Gjb0WHslltuaVhYfOnSpXi9Xnr06MGll17K5MmTj7t4e3sx1oa2HHBXHF4W6Qr2H7DcenvkC+tNIgz+rwRSsg21BRE/fcSddhocs9RfA2sDrS7O8rjcmOhi4qiYX0Ta17FriAYTJ5/xRKS1FnxNu4SLnhcGRsW8neFznAksHN7swwbSTWAh8T1WSUxEIkthTKSLm/9VO7SbcKDvhNivFWtgobQ0MALWnCTAaw1LfU6L+4mIhEthTKQLKymxrN/Qcghpje4jDKknG7wlkT1ve3LdQO1Yc+pHx/a4sMfV6JiIRI7CmEgXtnhp5IMYQN9vezCOwXojf+52YwItLlpSPzq23G80OiYiEaMwJtKFLfja4kT4t0C3QYb0MwzeI50srdiW68bgm9GxXa5hv2rHRCRCFMZEuqjaWsvSZZHvLdbnWw5OIrjVkT1vR/B6ofo4190wOuZTGBORyFAYE+miVuVAXY/EiEnpb8g6z8EX+nJ2Med4U5XGQJqx7HANefHRA1ZEokxhTKSLWrTYBm1y2ha9xzp4UsFfEdnzdqTjhTGAZKDWGnJ8+hUqIm2n3yQiXZC1lq++DnSdj5SEDOh1sYO/KnLnjIaKCvAdpx2HMZBiLJtdQ4lGx0SkjRTGRLqgnTvh8OHInrPnaIeELDr1FGW90hBeQypQbQ1r/Po1KiJto98iIl3QoiVE9C5K44He45xAg9fOPlJkoDSEqUpjINFY1vkNVZ3sxlERiS0KYyJd0IKvI7sweOY5hpQTDN4QQkzMs3DkON3466UD5Rg2+XVnpYi0nsKYSBdTWmbZuCmy5+w9thM2eW2B64fKEG5CcAwYLDl+B79Gx0SklRKifQEi0rGWLYts1/2U/oaMwQZfeRylEQOHdlqyUo6/q8/CNuCLBDjF0/5fg6wsyO6nkbjZs2fz+OOP8/Of/5zrrruuw573vvvuIycnhyVLlnTYc0r8UxgT6WIWLwm0tIjUnZQ9Rzt4Ug01efETxmqLLYt+7wtrkfMF7Xc5jSQlwRt/bVsgO3DgADfeeCMAPXv25MMPPyQhoenbwc6dO/nOd74DQHZ2Nu+//36rn7OrGj169HH3UbAThTGRLsR1LQsXRy6IOcnQ6xIHtyZ+ghgE+qSFE8Q6Um1toBdadr+2n8vj8VBUVMSiRYsYO3Zsk8dnzZqFE+n1siLk8ssvZ9iwYfTu3Tval3JcWVlZ3HzzzdG+DIlhCmMiXcjmLVBeHrnzdR/ukNQLaiPcJkM6xrnnnktubi6zZ89uEsZ8Ph+ffPIJF1xwATk5OVG6wuZ169aNbt26RfsyQtK9e3fuueeeaF+GxLDY/MgjIu1i6bLItrToeakDGGwEm8dKx0lOTmbChAksXLiQoqKiRo/Vb2uuHqugoICXXnqJadOmMXHiRC677DImT57M7373uybnAnjssccYPXo0+/fv529/+xu33XYbl112GY899ljDPqtWreI//uM/uPzyy7nyyit59NFHOXToEPfdd1+T6b7Zs2czevRoZs+e3Wj76NGjue+++ygsLOSxxx7jqquuYty4cUybNo2VK1c2ua7Nmzfz+9//nttvv53x48czbtw47rjjDl577TV8x+v+2w4mT57M5MmTqays5KmnnuK6667jsssu44477uCLL74IeozX6+WNN95g6tSpXH755VxxxRVMnz6dr776qsm+kfw+/OlPf2L06NF89tlnQa9r1qxZjB49mldffbWNX5X4pzAm0oUsXBS5lhapJxoyzjL4yuJrirKrue666/D7/Xz88ceNts+aNYvMzEzGjRsX9LjVq1fzxhtv0LNnTyZMmMAtt9zCSSedxLvvvss999xDeTNDsE8++SSvvvoqgwcP5tZbb2XgwIEALF26lAcffJCNGzdyxRVXMHnyZPLy8pg+fXqz52pOeXk506dPZ8eOHUycOJFx48axefNmZsyYwfbt2xvt+8EHHzB//nwGDhzI5MmTmTRpEtZann/+eX7+85+H9byR4vf7eeihh1i6dCmXX345V199Nfv37+fRRx9l6dKljfatra3loYce4plnngFg0qRJXHXVVeTl5fHTn/6Ut99+O+hzROL7cMMNN+A4Dh9++GHQ5/jggw/weDwdeoNFZ6VpSpEuorTUsmVr5M7X4yIHJwW8JZE7p3S8oUOHMnDgQObMmcMdd9wBQGFhIYsXL+bGG28kKSkp6HGjRo1izpw5pKWlNdr+0Ucf8dhjj/H2229z5513Njlu27ZtvPbaa2RnZzds8/v9/PrXv8Z1XZ577jmGDx/e8Nhjjz3GRx99FNZrys3N5aabbuLhhx9uqHk7//zzeeKJJ3jnnXd45JFHGvb97ne/y49//GM8Ry3Uaq3liSeeYNasWaxZs4bzzjsvrOc/VklJCS+99FLQx0477TQmTJjQaFtBQQGDBw/m+eefJzExEYArr7ySBx98kL///e9cdNFFDfvOnDmTVatWcdddd3HPPfdgTODGjoqKCr7//e/zzDPPcPnll9OnT59GzxGJ78MJJ5zARRddxJIlSzhw4AD9+/dveGzHjh2sX7+ecePG0atXrzC+Wl2TRsZEuojlKyLX0sJJgp4XO7g1kTmfRNd1113X8OYJMGfOHPx+P5MmTWr2mJ49ezYJYgATJ04kPT2d5cuXBz3ujjvuaBQAANasWUNeXh5jxoxpFAAApk+f3igohSI1NZUHHnig0c0H11xzDR6Ph40bNzbaNzs7u8n5jTHcdNNNAM2+jnAcOXKEmTNnBv3z6aefBj1mxowZDUEM4IILLiA7O7vR9buuy3vvvcdJJ53UKIgBpKenc9ddd+H1evnyyy+bnD9S34cpU6ZgrWXWrFmNttePlt1www3BvyjSiEbGRLqIJcsi19Ii8xxDUi/wNi0Nkk7o6quv5rnnnmP27NkMGzaMOXPmcNZZZ3HWWWe1eNy8efN4//332bJlC2VlZfiP+sd1uJnFT4cOHdpkW25uLkDQEah+/frRr18/Dhw4EPLrOfnkk5sExYSEBHr27Nlkqs3r9fL222/z2WefsWvXLqqqqrBHfWpp7nWE49RTT+XNN98Mef+MjIxGo0z1+vbt2xCYAXbv3k1paSm9e/fm5ZdfbrJ/SUlJw37HitT34dJLL6VPnz7Mnj2bu+++G4/Hg9fr5eOPP6Zfv34htfYQhTGRLsFay+IItrToeZGDMQbrU71YPOjRowdjxozh008/5YorrmD37t08/PDDLR7zt7/9jWeffZYePXpw4YUX0rdvX5KTkwF48803qa2tDXpcz549m2yrqKhouI7mjgknjKWnpwfd7vF4GgVGgJ/97Gd8/fXXnHLKKXz729+mR48eJCQkUF5e3uLraE8tXb97VNFnaWkpEJgS3LFjR7Pnq6qqarItUt8Hj8fD9ddfz8yZM1m8eDFjxozhyy+/5MiRI9x8880x2xol1iiMiXQB23dASYTWjUzqBZnDHHwVCmLx5Prrr+fLL7/kl7/8JcnJyVx11VXN7uvz+fjLX/5C7969ee211xq9sVtref3118N67vrwUVxcHPTxYHdnRsLGjRv5+uuvGT16NE8++WSjabj169eHNZoVDfVft29961v8+te/jtj5wv0+3HDDDbzyyit88MEHjBkzhg8//BDHcVqc5pbGFFlFuoBlyyPX0qL7SAdPmsEfwtqN0nlcdNFF9OnTh4KCAsaOHUtmZmaz+x45coTy8nKGDRvWZIRl06ZN1NSEV0x45plnArB27domj+Xn53Po0KGwzheq/fv3A3DJJZc0qYdavXp1uzxnJJ122mmkp6ezadOmiLThaO33oW/fvlxyySUsWrSItWvXsmLFCi666KImNWnSPIUxkS5g8RIbseL9nhc7WL8FDYzFFY/Hw+9+9zt++9vfct9997W4b48ePUhOTmbLli1UV1c3bC8tLeXJJ58M+7nPO+88srOz+frrr1m3bl2jx1588cUmU4uRUh8W1qxZ02j7jh07eO2119rlOSMpISGBG2+8kby8PJ555pmggWz79u0hjyy25fswZcoU/H4/jz76KNZaFe6HSdOUInGuqsqydl1k7qRMO92QepLBV9b2c0nsGTx4MIMHDz7ufo7jcNNNN/HGG2/w7//+74wZM4aKigqWLFlCdnZ2kzYKx+PxePjpT3/KT37yE77//e8zfvx4evfuTU5ODgUFBZx55pls27attS+rWUOGDGHIkCF8/vnnFBYWMnToUA4dOsSCBQu49NJLm22yGq6WWlsA3Hjjja1u/3DPPfewZcsW3nrrLRYtWsTw4cPp0aMHBQUFbN++ndzcXF5++eWgNWLHasv3YfTo0WRnZ5OXl0evXr0YM2ZMq15PV6UwJhLnclZHrnC/xygHJyn+76L0pINJiM31KZOSICsr2lcB999/P5mZmcyZM4d33323ofnr3Xffze233x72+S655BKeeeYZ/vznP/P555+TnJzMBRdcwOOPP86PfvSjZova28Lj8fDkk0/y/PPPs2TJEjZt2sRJJ53Egw8+yCWXXBKxMFbf2qI5benFlZSUxP/8z/8wa9YsPvroI+bNm4fX66Vnz56cdtppTJkypaGhayha+31wHIeJEyfyl7/8hWuvvTbowvPSPGNtaJ+XmyvoE5HY9vQzLu990PZAZhJg6BOJJGaBtwv8OqgtsY3q4oyBM88M/DcU1kI5cEGCy1BP5OZ0s7Igu1+IFxEHKioquOaaaxg4cCD/+7//G+3L6bJC+T48/PDDLFq0iLfeeouTTz65g68wdjV3d+rRFF1F4tyiJZEZGcsYbEjqCd4I3ZUZ65K6G+jeeFtiv/BGpRItlBgPZyb5cbpOfmqVqqoqXNdtNPLi9/v54x//SE1NTbPLMklktfb7sHPnThYtWsSFF16oINYKCmMicSzvkCWM9kwt6j7CCUzdeSNzvk7HQElJeGEsDSi0hr3WcKrRHQ8t2bt3L9OnT+eiiy7ixBNPpLKyktWrV7Nz504GDBjAv/3bv0X7EruEcL8Pc+fOZc+ePQ1LJU2bNi0al93pKYyJxLEVKyJzHk9qoKWFv2nvyK7DwpEjgenHUKcqEwCfhc0+w6lJCmMt6dOnD1dccQU5OTksWbIEv99Pv379uOOOO/je975HampqtC+xSwj3+/D++++zZs0asrOzefTRRzn33HOjdOWdm2rGROLYf/6Xy5dfwVFNu1ulxwUOA+7zUFsItn26DHQaZ54J3bqFvn+5hSTg7mQ/qZqqFOlyQqkZU58xkTjlupZlK9oexAC6jzJgTJcPYpjA6Fg40oAKDNtcJTERCU5hTCRO5W6DY9ZEbpWEjMDyR/5KTbNhobgkvEMcEzhuvd+JWONdEYkvCmMicWr5isgsgZR1nkNCOvi0/BEA3lqoqgzvmFRjOeDCYYUxEQlCYUwkTi1dFpklkLqPcgADEZjujAsm/EXXk4Faa9ji169cEWlKvxlE4lBNjWXd+rYvgZTYAzIGGXwVGtJp0IqpSmMgwVg2uAa/vpQicgyFMZE4tGYtBFkzOGxZ5zp4UmnUiV6gpjrwJxypwBFr2K1CfhE5hsKYSBxavsLi8bT9PA1TlBrNacyEPzqWaMBvYYtfYUxEGlMYE4lDi5e2fQmkpJ7Q7QyDr1xJrAkLRa1ovZhsLFtdg25MFZGjqQO/SJwpLrbs2tX282Se6+BJg5pDbT9XZ2QSwLTwcdXrQkU1JCWHfs4EC2UYVvkNZzutS2QpBrppcE0kriiMicSZFasic57uIx2sS5ecojQJkHaawRxnqjffCQSscPiBf3k9LGnlWpVpwNRkvwKZhOW+++5rWOIoVI899hgfffQR7777Lv3792/HqxOFMZE4s6KuXqwt05SJPQJTlF21cN84BIKYJRBIm+GzkBjmb1EPgUDmsYE6snB4gUqg2rZtdOzAgQPceOONLe6TnZ3N+++/3/oniROTJ08GiMuvxezZs3n88cf5+c9/znXXXRfty+nSFMZE4oi1liXL2l4vVn8XZU1+ZK6rszreyKD1A274zXV9gM9AWriBygaCXKScdNJJXHXVVUEfy8jIiOAzSbT94he/oLo6vFuA77//fqZOnUrfvn3b6aqknsKYSBzZvx8KC9t+nu4j6u+i7IJzlGHyecOrG6tXaQ2ZrZyqjJSTTjqJe+65J6rXIB0jOzs77GN69+5N79692+Fq5FgKYyJxZMXKtp8jMQu6nalGr6Hy+sIPYx4CU461FpI6Se3X6NGjGTFiBI8//jjPPfccCxcupLq6mjPOOIP777+fUaNGNTmmoqKCN954gy+++IIDBw6QmJjI0KFDufPOOxk+fHijfetrmubPn89f/vIXPv30U/Ly8vje977XEBjnzZvHq6++ys6dO0lLS+Oyyy7j+9//PlOnTgW+mUr8xS9+wdy5c5k5cyZDhw5tcl1//vOf+d///V8ee+wxrrzyyqCv99ip3NGjRzf8fdq0adxzzz2sXLmSBx54gGnTpnHRRRfx8ssvs3HjRsrLyxtqs2bNmsVXX31Fbm4uRUVFpKSkMHjwYKZOndrka3b0+caMGcPzzz/Phg0bMMZw/vnn89BDDzWp3dq8eTOvvvoqGzdupKioiPT0dE444QTGjh3LnXfe2eTrW39d9fVgAI8//jiPP/54w77H7hOsZmz27Nm8++677Ny5E4DTTz+dG2+8scl0Z2teU1ekMCYSR5atsDgOuG1YuihjaN1dlF18ijJU1gamhcPp61a3djiVGJI60R0S5eXlTJ8+nW7dujFx4kSKior4/PPPmTFjBq+88goDBw5s2PfIkSPcd9997Nixg3PPPZcpU6ZQUVHBV199xQMPPMATTzzBuHHjmjzHz372M3Jzcxk9ejQZGRkNb9SzZs3iV7/6Fenp6UycOJH09HQWL17MD37wA/x+P56jvgFTpkxh7ty5fPjhh03CmN/vZ/bs2WRlZXH55Zc3+1ozMjKYNm0ab775JgC33nprw2MjR45stO+6det49dVXGTVqFDfccAOHDn1zC/Lvf/97zjjjDC644AJ69OhBQUEB8+fP58EHH+Q3v/kNY8eObfLcmzZt4vXXX2fUqFFMnjyZrVu3Mn/+fLZv387f/vY3kpMD6X/r1q3ce++9OI7D2LFjyc7OpqysjF27dvHBBx80CmPHGjduHOXl5Xz11VeMHTuWM888s9l9j/Xkk0/y9ttv06dPHyZNmgQEgvLjjz/O1q1b+dGPftTq19RVKYyJxAnXtaxc1bYgBtB9uEFTlOHxecMLYxAIZJUWskzg79Gwb98+XnrppaCPDRs2jIsvvrjRttzcXG666SYefvhhnLpCufPPP58nnniCd955h0ceeaRh3yeffJIdO3bws5/9jBtuuKFh+3333cedd97Jb37zG0aPHt3kTfjw4cO8/vrrZGVlNWwrKyvjf/7nf0hNTeUvf/kLp5xySsO5ZsyYwebNmxtNww0fPpzTTz+dTz/9lBkzZpCamtrw2JIlS8jPz+e2224jKSmp2a9NRkYG99xzD3PmzAFocTp32bJlzRbB//3vf28y8nP48GHuvPNOnn322aBhbNGiRfzyl79kwoQJDdv++7//m48//pivvvqqYfvHH39MbW0tv/vd75qc58iRlhdQHTduHGVlZQ1hLNQC/pycHN5++21OO+00Xn75Zbp16wbA3Xffzd13381bb73FFVdc0WTkM9TX1FWp6atInMjdBhVtvPvRkw4ZQxz86koaFm8rlp5yCBTy10TxS71v3z5mzpwZ9E+wFgipqak88MADDUEM4JprrsHj8bBx48aGbSUlJXz++eecf/75jYIYQM+ePbnjjjsoLi5m+fLlTZ7j7rvvbhTEAL766isqKyuZNGlSQxADSEhIYPr06UFf2+TJk6msrOTTTz9ttP3DDz8EaHJdbTFo0KBmw0ywKbjevXtz+eWXs3fvXg4ePNjk8REjRjQJJ/XnP/rrXC/YqNKxX8NIqZ/avPvuuxuCGEBmZibTpk0DaAiwRwv3NXU1GhkTiRMrVwUWpG7LgFbmEIeEdKgpiNx1dRU+HySE8Ru1fjSsCkNKlKYqR48ezdNPPx3y/ieffDJpaWmNtiUkJNCzZ0/Ky8sbtm3cuBG/309tbW3Qkbe9e/cCsHv3bsaMGdPosWA1Xrm5uQCcd955TR4bOnRooynKetdccw3PP/88H3zwAddffz0AhYWFfP3115xzzjmcfvrpx3u5IRsyZEizj+3fv59XX32VlStXUlBQQG1tbaPHDx8+zAknnNBo26BBg5qcp/6OxqO/zuPHj+fNN9/kkUce4dvf/jYXXnghw4cPb9e7H7ds2QI0naoFGmrgtm7d2uSxUF9TV6UwJhInlq9o+xt65jl1c2ZtnOrsirze8MIYfDNV2T2KU5XhSE9PD7rd4/HgP6qfSmlpKQBr165l7dq1zZ6vqqqqybaePXs22VZRN+Tbo0ePJo85jkP37t2bbM/IyGD8+PF89NFHbN++nYEDBzJnzhz8fn9ER8Wau2YIhM5p06ZRUVHByJEjGTNmDOnp6RhjWLVqFTk5OU3CGQT/OtcHzqO/zsOGDeP555/nlVde4V//+hezZ88GAuHwgQceCHpTRVtVVlbiOE7Q70XPnj0xxjR8v44W6mvqqhTGROKA12tZs7Zto2JOcqC/mL/p+6OEwO8P9CVraQmlYzkE+oZVW0jtDGksRPVvvLfffjs/+MEPwjrWmKZfiPrzFRc3XRDUdV1KSkro06dPk8duvPFGPvroIz744AN+9KMfMWvWLNLT0/n2t78d1jW11j/+8Q9KS0v5xS9+wcSJExs99tvf/pacnJw2P8fw4cN5+umnqa6uZsOGDXz99de8++67/OhHP+KNN97gxBNPbPNzHC0tLQ3XdSkuLm4SQouLi7HWNhvapXmqGROJAxs3QZAP2GHpdpYhIYMu23U/EsKtHWuYqrRxlMQIjMwYY1i3bl1Ezld/p1+wUbb6KdFghg0bxhlnnMEnn3zC0qVL2bt3L1dddRUpKSkhP7fH48Ft5V0x+/fvB2hSXG+tbXHEsDVSUlIYNWoUDz30EN/97nepqalh2bJlLR5TPzIVzuurn25ctarpumv1284666yQzycBCmMicWBVTvhd4I+VOczBeMC2ohhdArze8I8xQBXxNTPcq1cvxo8fz7p163j99dexQYZs169fH3JH+LFjx5KWlsasWbPYt29fw3afz8eLL77Y4rGTJ0+mtLS0oY9WuFOUmZmZHDlyhJqamrCOg28ara5Zs6bR9tdee43t27eHfb5jrVu3Luh1FRUVAbR4tygEXhvQqBXH8VxzzTUAzJw5s9F0ZHl5OS+//HKjfSR0mqYUiQPLV9i2tbRwoPtwBzf895u4ZpyW16Y8liVQyO+E+ZvVB5S7x5+qbEXWa1FLrS0Apk6d2ur+Tz/5yU/Ys2cPf/zjH/n4448555xz6NatG/n5+WzatIm9e/cyZ86ckEapMjIy+MEPfsBvfvMbvve97zFhwoSGPmOJiYn06dOn0R2eR5s4cSLPPfccBQUFnH322UELyVsyatQoNm3axA9/+EOGDx9OQkICI0aMYMSIEcc9dsqUKcyePZuf/exnjB8/nqysLNavX8/WrVu59NJLWbhwYVjXcqy//vWvrFy5kuHDh9O/f3+SkpLYsmULK1as4MQTT2yxjxoERg6Tk5N58803KSsra6i9u+uuu5o9ZsSIEdxyyy28/fbb3H777XzrW9/CWsu8efPIz8/n3/7t30L62khjCmMinVxNjWVDG+8M7zbQkNQTfGWRuabOzrqBdSeNp27B8DB4XWh5PCLI8wEVGEwId1WmASkRmtWsb23RnNtuu63VYSwrK4s///nPvPPOO3z22WfMnTsX13Xp1asXZ555JnfddVdY7RcmT55MZmYmr7zyCnPmzCE9PZ3LLruMBx54gClTpjRbG5Wens64ceP45JNPWlW4f9ddd1FWVsbChQtZs2YNfr+fadOmhRQ4Bg0axB/+8AdefPFFvvzySzweD+eccw4vvvgiCxYsaHMYu/HGG+nWrRsbNmxoqD/r168f3/3ud/nOd75z3NqtrKwsfv3rX/Pyyy/zwQcfNIyytRTGAB5++GEGDRrEu+++27DqwYABA7j33nu14HgrGRts/DiIYIWTIhJ9K1ZaZjzctjspT7jBQ/8pDjV5EbqoOGASwivGP9oZZ0JSYuj7V9rAEkm3JvmPG7RSDHSLrxKzNtm7dy+33HIL48eP51e/+lXQfe644w4OHDjA7NmzVVwuHS7YnafH0siYSCe3Msfi8QTu5mut7iOMasWOYX20rvuXgdJCOKZ1VIu6AaUYyjCc5KjhbjClpaWkpKQ0qoOqrq5u6JMWbGklCHR+3759O5MnT1YQk5ilMCbSyS1f3rYgltLfkJJt8OkuysiwcPgwZGcHmvCGwlPX2y3XbxjsURgLJicnh1/96ldcdNFF9OvXjyNHjrBixQoOHjzI+eef36RdxT//+U/y8/P58MMPSU5OblhMXCQWKYyJdGKVlZatuW07R+YQg5Ns8JYoBESKzwclR6BH99CPSTKWna6hKs56jkXK6aefzoUXXsjatWspKSkB4KSTTuLee+/ljjvuaFLA/9e//pWCggJOOeUUHn300aDLEonECtWMiXRii5dafvJI20LUGQ8nkDnUUJsfoYsSMJCeDmedGfohfhuYqrwh0a/RMZE4EkrNmPqMiXRiOXX1Yq2VmBW4k9JfGblrEsBCRTkEWe2nWZ66dUW3+TUsJtLVKIyJdGLLV7atXixjiIMnFYWx9mCgIMwF15ONZYdrqNbAmEiXojAm0kmVl1u2bWvbOTKHGcDEV/v3WGGhqChQPxaqFKAKwy5Xo2MiXYnCmEgntXZd2xcGzxzq4NcwTLuxFgoLQ9/fYwBNVYp0OQpjIp3UqjbWi2lh8I6RXxBeaK6/q7JGGVmky1AYE+mkVrS1XmywE1h7Uc1e25XPC3WdGEKSAlRi2K2pSpEuQ2FMpBMqL7ds39GGEzjQfYSDG+mVpyWoQ4dC39djwLWwQ1OVIl2GwphIJ9TWerG0UwxJvTRF2VGqqqC8PPT9k4xlu2vwaqpSpEtQGBPphHJWt61eLGOwwUkyuDWRuyZpgYFDYTTVTQHKMezTVKVIl6AwJtIJtbW/WPfzHKxfwy4dxkLpEaipDm33hLqpyu0KYyJdgsKYSCdTXm7Zvr31xyf1gtRT1HW/w5nAnZWhSjSWba5BmVkk/imMiXQyba0XyxisrvtRUddzLNQmsClAmTUcsBodE4l3CmMinczqNW2rF2vouq8Rlw5nLRw+HNq+CYAP2Km7KkXinsKYSCfTlnoxTypkDnHwVymJRUtBiE1gjQEPllzXtGkkVERin8KYSCdSWdm2erFugwyebpqijCafD4pLQts3FSi2cEhhTCSuKYyJdCJr1oHbhkW91XU/NuSH2OYiEfBawy5Xv6pF4pl+wkU6kdVt6S/mQPfhjnqLxYCqSqgIoeGuMeAYS65fU5Ui8UxhTKQTWbmq9fVi6acZEntoijImhNHmIgUosFCiMCYStxTGRDqJykrL1tzWH6+u+zHEBhYP94awNmgyUGsNu9QAViRuKYyJdBLrN7StXixLXfdjTihtLkxdBtumMCYStxTGRDqJnDb0F0vqDaknGy0MHkssFBwOrc1FsrHscw0VytIicUlhTKSTWNWGerGMwQ6eFPBXRfaapG38vsB05fGkADUYdmt0TCQuKYyJdAI1NZbNW1p/fNY56rofk0ygCezxOCYwgrZD3fhF4pLCmEgnsGFj27ruZ5ytrvsxyQZaXFSFMGKZbCw7XUOtvo0icUdhTKQTWLMWnFb+tDZ03Ve9WGwyoRXyJwOVGPZqqlIk7iiMiXQCK1fZVt9JmTHYwRiwrRxZk3Zmoajo+HfKJhhwLexUGBOJOwpjIjHO67Vs2NjKg+u77tdG9JIkwlwXiouPv1+isWxzDepQIhJfFMZEYtzmLaE1Bw0m7VR13e8sCkKYqkwByizkKYyJxBWFMZEYt3pN6+vFMoeo635nUVUZ+NOSBMCHFg4XiTf6iRaJcatybKsXic4arq77nYaBw4XH2cWABy0cLhJvFMZEYpjPZ1m3LrQu7cdK6gOpJ6nrfqcRYiF/ClBooUhhTCRuKIyJxLBt26G6lVOMmUPUdb+zcd3jd+RPQguHi8QbhTGRGLZm7TcLRYcrc5i67ndGoUxVGmPZproxkbihn2aRGLZ6jaU1WcyTVtd1v1JJrLOpKIea44yGpgAHXCjXt1ckLiiMicQoay05qwONPsOVcbYhIR18amnR+ZhA7VhLkgksHK6pSpH4oDAmEqN27Yby8tYdmznMAQOo637nY6GwkBanl7VwuEh8URgTiVFr1rTuOJMAWec66i3WiXm9UHacIJ5sLLtcQ42mKkU6PYUxkRi1eo1tVbPX9IGGxO7gU0uLziuEqcoUoArDbk1VinR6CmMiMchay8qc4/ecCiZzsINJANvKJZQkBlgoLgG3hWlmT93C4bs0VSnS6SmMicSgg3mhLRwdTNYIoyAWB6wLJUda3ifRWLZZLRwu0tkpjInEoNbWi6WcaEjJNrqLMh6YukL+FqQAFdaw32p0TKQzUxgTiUGr11o8nvCPyxxicJINrrrud342cDdtbW3zuwQWDoedmqoU6dQUxkRi0Koc8LeiLUXWeU7rGpNJzGppurph4XBXC4eLdGYKYyIxpqjIcvBg+Mcldof0AUZ3UcaZ401VpgIlFg4pjIl0WgpjIjFm7brWHZcxxMGTCn7Vi8WVmhqobOF7mgh4rWGX1qoU6bT00ysSY9a0tl5MC4PHp+P0HDMGHGPZ6tdUpUhnpTAmEmNWrgq/XsxJhsyhDv4qvRvHHRsIYy0FrRSgwEKRvv0inZLCmEgMqaiw7NwV/nEZgwwJ3TRFGa/8figra/7xZKDWGnaqG79Ip6QwJhJD1q1veQSkORlDHYwHrC/y1yQxIMSpylzVjYl0SvrJFYkha9a1ol7Mge7DtTB4XLOBbvwtLY+UDOS5UKqpSpFOR2FMJIbktKK/WPrphsSe4FdLi7h2vOWRkoEaDDvUAFak01EYE4kRNTWWzVvCPy5zqMFJBLeFTu0SBwwUtdAA1jGAhe2qGxPpdBTGRGLE5i3ga0XNV9ZwR7ViXYGFslLwtbAIfLKx7HUNFZqqFOlUFMZEYsSateCE+ROZnA2p/Y2mKLuQ4pLmH0sBqjHs0uiYSKeiMCYSI1avsWHfSZk5xMFJBr8WBu8yWrqr0jGBu3G3qW5MpFNRGBOJAX6/Ze268NtaZJ3rAHrj7UoqKwNLJDUn2Vh2uQb1/xXpPBTGRGLAjh1QXR3eMQkZ0O1Mg18FQl3LcXqOpQBVmqoU6VQUxkRiwNp1gcad4cgY4uBJA5+67nctFgoLm3/Yo6lKkU5HYUwkBqxea8MOYw0Lg7vtckkSw7xeKC9v/vFkY9mhqUqRTkNhTCTKrLXkrAY3jFBlEiFrmIO/Wu+2XZKmKkXiisKYSJQdOAglJeEd0+0sQ0KGuu53WRaKiwNd+YPRVKVI56IwJhJla9eFf0zmYAeToIXBuzLXhSOlzT+uqUqRzkNhTCTK1qwNc3FwA1kjHC1/1NVpqlIkbiiMiURZuIuDp51qSO6jKcouzwZGxppbQstTt1ZlrqYqRWKewphIFBWXWPYfCO+YzKEGJ8ngttD4U7qIutqx5iQZy05NVYrEPIUxkShavz78Y7KGO1i/3l0loKWeY6kEpip3aKpSJKYpjIlE0dp14dWLJfeF1JO0MLh8o6oKqptZm9Spm6rcqqlKkZimMCYSRTmrw6sXyxjs4EnVwuByFAOFLRTyJxvLbtegVbNEYpfCmEiUVFdbcreFd0zWuQ5YA3pjlXo2EMaaW2Q+BajGsF1TlSIxS2FMJEo2bQ5vVMyTDt0GGfyVSmLSmN8Hpc30HKufqtyiqUqRmKUwJhIla9eBE8ZPYMZgh4Q08KleTI5lWi7kTzGWfa6hVDleJCYpjIlEyeo1ttmppWAyhxgwaGFwaaq+55g3+MPJQLU1bNfomEhMUhgTiQK/37JuffN1PscyCZB1nqPeYtI823whv2PAMZbNrn7li8Qi/WSKRMGOHVBdHfr+6QMNiVmaopSWtThVCRx0oVAjqyIxR2FMJArWrgMTxoxRw8LgzUxDiQDU1EBFM4E9Gaixhm26q1Ik5iiMiUTB6rU2rDCWNcIoiMnxGTh8uJmH6qYqN/mdsGoVRaT9KYyJdDBrLTmrwQ1xuiilvyEl2+CrbNfLknhQt1alv5nFw9OAwxbyFMZEYorCmEgHO5gHJSWh7585pG5hcHXdlxBYC0XNLB6eCHgxbPXrV79ILNFPpEgHW7cuvP0zz3WwmleSMBS0MFWZgGWTa/Dpn5RIzFAYE+lg4SwOnpAF3QYa/JqilDDUVDdfyJ8GlFrDHhXyi8QMhTGRDrZqdejLIGWe7eBJQ2FMwtNCIX+CAb+WRxKJKQpjIh2otNSyd2/o+2cMM4BR130Jz3EK+ZONJdc1VGmqUiQmKIyJdKB160Pf10mCrGEO/mq9Y0r4bAsd+VOASgzbNVUpEhMUxkQ60Jow6sW6nWlIyAS/uu5LKzVXyO8xgIVNmqoUiQkKYyIdKGd16PViGUMcjAdsM1NNIsdTWwPlZcEfSzGWva6hWFPgIlGnMCbSQWpqLFu3hrizge4jHNzadr0kiXem+dGxFALLI+VqqlIk6hTGRDrIps2hj4qlnWJI6q0pSmkjCyVHwBtkKa365ZE2ankkkahTGBPpIGvXgRPiT1zGEIOTaHBr2veapGsoLAy+vX55pAMKYyJRpTAm0kFWr7Ehj0BknedgXb1DSgRYKCgg6L+9+uWRtmh5JJGo0k+gSAfw+y1r1wV/QzxWUu/ANKWmKCVSfL7g66EaA4lYNvkNtcr+IlGjMCbSAXbshOrq0PbNqO+6r4XBJYLyC4JvTwPKMexQIb9I1CiMiXSAtesCoxChyDrXgDWgkQqJoMoKqAoS8D0mMGKrnmMi0aMwJtIB1q61IYUxTxpkDHbwVyqJSYSZQO1YMCnGsss1HNE/O5GoUBgTaWfWWlatBjeE5poZZxsS0rUwuLQDC0VFwderTAGqrdHi4SJRojAm0s7y8gKLNocic5gDDtgQ+5GJhMNaOBykzUV9z7ENfgfdxCvS8RTGRNrZmnWh7WcSIetcBzfEQn+R1miuzcU3Pcc0OibS0RTGRNrZmjWhLQ7ebaAhsTv41NJC2pHXC6WlTbcnAj5r2KypSpEOpzAm0s5CXRw8Y6iDSQAbZOkakYgxkJ8fZLOBJGPZ7DfUaKpSpEMpjIm0o+ISy779oe3bfYTBamFwaW8WysuD971LJdBzbLt6jol0KIUxkXa0NsR6sdRTDMl9DD7dRSkdoZk2Fx4DWNigqUqRDqUwJtKO1q4NrV4sc6jBSTIq3peOYaGwuTYXxrLXNRSG0IpFRCJDYUykHa3MCa1erPtwLQwuHcu6gUB2rBSgxho2a/FwkQ6jnzaRdlJZadmx4/j7JfUOTFNqYXDpaPkFNFl2yxhIMJYNrsGvzwciHUJhTKSdbNgYWtf9zKEOnhQtDC4dz1sLpWVNt6cCJdawW4X8Ih1CYUyknawJsV4s6zwH0MLgEgWmbnTsGIkG/BY2qpBfpEMojIm0k1D6iyVkQMYgg79CSUyiwEJZKdTUNH0oxVi2uYZy/dMUaXcKYyLtwOu1bNx4/P0yhzh40lBLC4keA4cPN92cAlRp8XCRDqEwJtIOtmwFb5C2AcfKPNcABtRGQKLFBsLYsfWNTt3i4ev8TtC1LEUkchTGRNrBmrXgHOeny5MKWcMc/FV6p5Pocl0oCtLmIg0osLBfi4eLtCuFMZF2sHqNPe5oQsZggycDtbSQmBC0kJ/A4uEbfApjIu1JYUwkwlzXsnoNxw1jmec4GAdsCE1hRdpbTTWUH9Pmon7x8C2uQQO4Iu1HYUwkwnbshKrj9AxzkqD7eY6WP5LYYaAgSCF/GlCJYasK+UXajcKYSIStWRMYUWhJt0GGhCzwlXfMNYkcl4WSkkAj2KM5JvDgehXyi7QbhTGRCMtZY48bxrLOdTAesCHccSnSYQwcLmy6OQ3Is3BIYUykXSiMiUSQtZZVOS0vg2QSoftITVFKDLKBqUp7zL/fJKAWLR4u0l70kyUSQXv3QWlpy/tkDDIkdgef7qKUGOT3QXFJ423GQCKWDX5DrUbHRCJOYUwkgtasOf4+WefV3UXpbf/rEWmNYG0u0oAyDNu1eLhIxCmMiURQzpqWFwdvmKIMshagSKyoqoTKY0ZuPSbQrmW97qoUiTiFMZEIsdayclXLi4NnnF03Ram7KCWWGcgP0uYi1Vj2uoZCLd8lElEKYyIRcjAPCoPciXa07iN0F6V0AhaKi8F3zFR6ClBjDZtUyC8SUfqJEomQ1atbftxJhu7DdReldBK2aZsLYyDBWNa7Bp8K+UUiRmFMJEJWrW65XixzqCGhu6YopfMoKGi6rFcacMQadqiQXyRiFMZEImTlypbrxbqPdDBGU5TSefh8ga78R0uoK+TfoEJ+kYhRGBOJgLw8G3Rdv3qe9EDXff9x1qwUiSkG8vObbk4xlp2uoViF/CIRoTAmEgE5x+kvlnWuQ0KGpiilk7FQGaTNhQr5RSJLP0kiEbD6OPViPc53AAMaSZDOxjRtAqtCfpHIUhgTiYAVLdSLJfaAjMEGX4XetaQTsoHlkbzHtLlIA4qtYZcK+UXaTGFMpI3y8y2HgtTV1Os+0sGTCn6tRSmdWMExo2MJBlwL61TIL9JmCmMibbT6OPViPS92sC6ggTHprCwcPgzuMdPsqSrkF4kIhTGRNmqpv1jaaYa0k40K96XT8/sDXfmPVl/Iv0GF/CJtop8gkTZavrz5erEe5zs4yeCqpYXEgWOn41XILxIZCmMibXCohXoxkwg9LnJwazr2mkTaS001lJU13lbfkX+bCvlFWk1hTKQNVuU0/1jWeQ5JvcBX1vw+Ip2KgUOHGm+q78ivQn6R1lMYE2mDVTnN14v1utjBGKPljyR+2MDIWPUxi92nGste11CgQn6RVlEYE2mD5SuC14sl94WMIQZfuQppJM4EWSIpGahBhfwiraWfHJFWOnDQcriZ9Sh7jnbwpBr1FpP4Y6GoCHxHNYE1BhKxbPAbavT5QyRsCmMirbRqVfDtJhF6XerBrdW7ksQnCxQc80EkDSjDsEWF/CJhUxgTaaWVzdSLdR/ukNQHfKUdf00iHcIGOvIf3QTWYwIPrPU5WH0OEQmLwphIK1hrm60X63WZCvcl/vn9UFjYeFsakGfhgMKYSFgUxkRaYc8eKClpuj3tVEPGIIOvTO9GEv8O5dNoFCwJ8FrDWp/eWkTCoZ8YkVZYmRMoWj5WrzGBjvv+yo6/JpGO5q1t/KHEGEg2li2uQZ9HREKnMCbSCitW2CZhLLFHYFFwBTHpSvKOaQKbClRZw3qNjomETD8tImHy+y0rVjUuXgbodalDQjejjvvSpVRXQelRN6s4detVrnENXo2OiYREYUwkTNu2Q+Uxo1+eVOhzeV07C70BSVdi4ODBxpvSgRJr2Ko2FyIhURgTCdPKVU3rxXqODqxD6S2JyiWJRI8NfDg5egHx+jYXOWpzIRIShTGRMC1b3vjdxSRCn287WD+gtfmkKzJwMK/xpnTgoDXssRodEzkehTGRMNTWWtasbXw7f48LHFJPMBoVk67LQkU5lJd/sykR8FlY7VMYEzkehTGRMKzfAN6j1uTDgX4THCyoyat0bceMjhkDqcay3TUc1oixSIsUxkTCsHxl4yWQelzgkHqKwVscvWsSiQkWysugouKbTSlADYbVfr3ViLREPyEiYViy9JslkIwHsq92wID1tnycSJdg4MBRd1YaA8lYNvgN5SrkF2mWwphIiEpLLdtyv/n/Hhc5pJ2qUTGRBnWjY0fXjqUCldawRk1gRZqlnw6REK1c9U0LMScJsicGbtvXqJjIUQwcOPDN/zoGEo1ltd9QrdExkaAUxkRCtGz5N/VivS5zSD1Jo2IiTdhA3VjZUV3504EyDBv8urNSJBiFMZEQWGsb6sU83aDf1R6sT3dQigRlYP8xo2MOlpV+R0skiQShMCYSgn37oeBw4O/9JnhI7mOo1aiYSHAWqqqgpOSbTd2AYmvYqNExkSYUxkRCsGxZ3Z1h2dBnvIO/yqrbvshx7D/wTYPk+iWSlvsdfBodE2lEYUwkBEuXWYyB/pM9JGSA70i0r0gk9tXWQGHhN//fDThsVTsmciyFMZHjqK21rFgF3YYauo90FMREwnDgILh1vfkSDJi60THVjol8Q2FM5DjWrgMfcOJNHpxEg78y2lck0nn4fXAo/5v/1+iYSFMKYyLHsWSpJfuqQIPX2kJ9nBcJ16FDUFsb+Hv96Ngyv0OtfpxEAIUxkeNast3Q5yoPbo1aWYi0hqVxI9gMoMga1mh0TARQGBNp0YFDlsTLPSR0A29JtK9GpJOyUFz8zSLiHgOeutqxKo2OiSiMibRkzh6HjKEGb1G0r0SkkzOwdx8Na4p1A464hpVas1JEYUykOUUu5PZwsH5wa6N9NSKdnIWqSiis+2BTv2blKr+hVKNj0sUpjIkE4bcwt8ahNhG8hcffX0RCs38/+OpqL7sBFdawVKNj0sXpJ0AkiBy/YWutoVZBTCSi/O43xfzGQIqxrPMb8rWihXRhCmMix8h3YaHPoaYCXG+0r0YkzthAV/7y8sD/pgI11vC1z2lYOkmkq1EYEzmK18K/vB4qraHsIA3FxiISQQZ27wbXDYyOpRvLdtdhu6tWF9I1KYyJHGWRz2Gva0iutXg1KibSPmygCWxeXuB/k02gTnOBT8skSdekMCZSZ5ffsMJvSDaWcq0/KdLuDuUH7rAEyDCWQ9awWo1gpQtSGBMBKiz8y+fgtYY0oERhTKRD7NoD1gaWSfJgWeJz1OpCuhyFMenyXAufeR0KXUOWsfj9UFkR7asS6QIsVFd9M12ZAZRjWOBVMb90LQpj0uWt9hs2uw7pxuIYOKJRMZEOlXcIKisDxfypWDa5hl0q5pcuRGFMurSDLnzlc/BgSa773X/kCKD3AZEOtavu7soUwGsN81XML12Iwph0WdUWPvF6qLKGbnXbXBdKS1FLC5GOZKGmOtAM1hjINJY8a1iuzvzSRehfunRJ1sLnXoe8ujoxUzcSVlqKalVEoqSgAMrKAsX8iViW+w2H1ZlfugCFMemS1voNG1yHNGPxHDUlWVKCpihFomjXrsDalelApTV86XNw9QFJ4pzCmHQ5h1yY73MwWFKOCl7WrasX0y9+kajx+WHPnsB0ZTdj2eE6bFAxv8Q5hTHpUmrq6sQqrCHjmMfKygM1YyISRTbwoaigAJJMYMMCr0OZPiRJHFMYky7DWpjndTjgGjKPqhOrV1yMpihFYsS+/VBVFeg9dgTDfPUekzimMCZdxnrXsK6uTizhmNBlbV3Xff2yF4kZO3YCLqTV9R7L1XSlxCmFMekSCtzAqJg9pk6sXlkZuP6Ovy4RaUbdYuJ79kKKARf40udQqQ9MEocUxiTu1Vr4uK5OLLOZfTRFKRKDbOBns6gIMoFC1/CVpislDimMSVyzNvBpen8zdWIQuIuypARNUYrEqD17wVsDqcay3jXs0HSlxBmFMYlrm1zDGr9DapA6sXqlZbqLUiSWWRuoH0u24LOGL3wOVfrwJHFEYUziVqELX3gdXCypLXyQ1hSlSIyzUF0N+/cHlko67BoWaKkkiSP61yxxyVtXJ1ZmDVkt7Of6dRelSGdx+DCUHQlMV67zG3b69SlK4oPCmMSlr30Oe1uoE6tXciRQMyYincPuPeB4wWsNn2u6UuKEwpjEna1+w0q/IaWFOrF6RZqiFOlUXBd274YMLAWu4WtNV0oc0L9iiSslFj7zOfiBtOOELK8XykrRFKVIZ2KhohwO50OKsazRdKXEAYUxiRt+C3O9Hkrc5vuJHa2oqN0vSUTaycGD4FYG7q7UdKV0dgpjEjcW+xx21tWJOSF8UC5UGBPpvAzs2gXdbGC6UndXSmemf70SF3b5Dcv8hiQsiSEEscpKqKlu/+sSkXZiA6UGB/YF7q5c6zds13SldFIKY9LplVv4l8+h1hrSQjymsBAV7ot0djZQblBV8k0zWK1dKZ2Rwph0aoE6MYdCa8g6ThuLeq5bVy+mX9oicWHvHkj1BprBfqm1K6UTUhiTTm2pz2Gb69CN0OrEILAOpZY/EokfroXduyAVywbXYYvWrpRORmFMOq2dfsMSvyEBS1IYv3sLDrffNYlIFNhAHWhxHrhY5vkcSjU6Jp2Iwph0SiUW5tbViaWHcVxVFVRWtNtliUgUHToETjmUWMPnXgdXgUw6CYUx6XR8Fj7xeihyQ68Tq3f4MCrcF4lju3ZBss+y1XVYq7srpZNQGJNOxVqY73PY6Q+9n1g9v7+ut5g+LYvELb8f9u8CYy1f+RwOqz5UOgGFMelU1rmGVX6HVBNaP7GjFRVpUXCRuFe3XFJ5HlRYw1yvB58+gEmMUxiTTmO/C/O8DmBJDXf2wUJ+fntclYjEovxDYMote61hkbrzS4zTv1DpFEoszPF6qLSGjFYcX1oKtbURvywRiWF7doLxWpb7DTtUPyYxTGFMYl6Nhdm1HgpdQ/cwC/brHcpHhfsiXYxr4cB2qHUDq3So3YXEKoUxiWl+Cx97HfaGsQD4saqqoLwcFe6LdDUWamqgeDcUuYG7sFU/JrFIYUxilrUwz+ew2XXoZiwJrRzZOnQIjYqJdFUWSo9A5SHY4ap+TGKT/lVKTLIWFvocVvodUsLssH+02looLkajYiJd3OE8qD4SqB/bovoxiTEKYxKTVvgMi/0Oia25c/Io+aoVE5E6eTuhrBI+9ToUqM2NxBCFMYk5q3yG+X4HB0t6G4KU11vXcV+jYiJSJ28bHK4xzPZ6qNTvBokRCmMSM6yF5V7DF3U1Hd3aOKKVn68cJiLHcCE/17Kn2vCx11FBv8QEhTGJCdbCYp/Dl34PBshoYxDzeaFAo2IiEoTrg/ydlvVVDvN8Dla/JyTKFMYk6vwWvvA5fO138GDbPCIGgb5i+gUrIs3xV0PBXsuicsMynwpLJboUxiSqaizM9jos9zsktbFGrJ7XCwUFaFRMRFrkK4fDh+DjEof1usNSokhhTKKmxMLbtR42+h3S23jX5NHy8pTDRCQ03hIoKoF/HHLY6ov21UhXpTAmUbHbNfyj1tPQWb+1fcSOVVsDhwtRGhORkNUWQmkVzNzjYUtltK9GuiKFMelQroWlXsO7tQ4ldWtNtrazfjAHDkbuXCLSddQWQLUxvLDbw7ID0b4a6WoUxqTDVFj40Bu4Y9IFurdyrcnmVFaq276ItF7NIYtNMbx22MM7K6J9NdKVKIxJh9jnGv5e62GT3yG17o5JE+F62b37ULd9EWmTmkMWpxvMS/Hwq7csNTX6dCftT2FM2pW/blry7VqHw64hy1iS2yEwFRdDZQUaFRORNqvNB08qHDwngftfNGzN1S8WaV/G2tC6MRUXF7f3tUicKbPwL69DrhtYYzKdyI+GAbh+2LAp0OhVRCRSEnuAceDQRy7X9na5/VZIiGSRq3QJPXr0OO4+GhmTdrHTb/hbrYetbqBtRXtMS9Y7mKcgJiKR5y0GtxayJzn8yzrc+5Blxw6NkknkKYxJRPksLPA6vOutu1uSyLWtCKa6CvIL2u/8ItK1+coCf3qN8eDckMD0/59l5l9camsVyiRyNE0pEVNi4ROvh12uIQlLGu03GgaAhS1bobIK1YqJSLsyCZDUG2ryYe9ffWQUW/6/nxiGn6dpS2lZKNOUCmMSEdv8hn95HY5YQ4axJHbA76eCAti3r/2fR0QEAANJfcDWwqG5Lgdn+/n2OHjgPwy9eyuUSXAKY9Lu/BYW+RyW+Q0+IBMi2jusOTU1sGmTFgMXkY6XkBm427Jsk2Xv6z78h2HqvxtuvQVSI7Wum8QNhTFpV6V105I7Ompaso61sFXTkyISRSYJknqC9wgceM9P4QKXzG5w1/cMk66FpPYslpVORWFM2s12v+FTn0ORNWTSMdOS9fLy4KCWPRKRGJDUE0iA0jWWvX/3UZMPPXvCd//dcN21kNwejRWlU1EYk4jzWVjoc1jhN3iBLDpmWrJeRTlsze245xMROR4nOdCTzHsE8ub4KZjnYr2QlQm33Wq44XrIzFAo66oUxiSiCl2Y6/Wwx3bstGQ9nw82bQ78V9OTIhJTDCR2BycJKnZaDvzTT+kGizGQmAgTr4YpNxjOGKhQ1tUojElEWAvrXMN8r0N5B94teew1bNsO5eUoiIlIzDKJkNTT4HotR1a7HJzlUrXX4vGA3w9nnQU3TDJccTlkaLSsS1AYkzYrtTDP67DZdTBYMunY0bB6+/dDfn7HP6+ISGt40gJ3XfqroHChy6G5fmoLAr8/rYWEBLhsDFxzteGC87XMUjxTGJNWcy2sdw0LvA6lGNJpnwW+Q3H4MOzdG53nFhFpi4QM8KSDrxwKv3Yp+MJPTd0Hy/rRsswMuHICXH2lYdAgMNH4xCvtRmFMWuWQC1/6Ap30DZYMOrZI/2jFJbBrZ3SeW0QkUhIyA6Nl/kooWupyeL5L5a5v3n7rg9lJJ8Kk6wxXTUCNZOOEwpiEpczCUp/DWr+hxhq6mfZdV/J4Skpg5y5UIyYicaN+pMytgbLNlsPz/RxZZ7Heb/apHxi78AK49hrDZZdCYkcX6krEKIxJSCos5PgcVvkNFdaQYiypRKc2rF5REezeHb3nFxFpT540SMgwWGupLQjUlRUt81OT980+jgOuC926wbUT4frrDKeeqlDW2SiMSYtKLazxOaz2G8qtIdFY0onelGS9Q4fgwIHoXoOISEcwCYEpTCfJ4K+ylG+zFC91ObLWxVf6zX4eB/wunHsO3DjZMPYydfnvLBTGpAlr4YCFdT6Hza6hKoZCmN8Pe/YEpidFRLqa+tEysPgqoGyjS8lqS+n6b4JZ/WhZRgbcMClQX3Zif4WyWKYwJg0qLeT6DRv8DgcseK0h2XR849bmlJfBrt3gVUNXEenqHEhIB09aIJj5K6E813JknUvZRpfquuXg6oPZqJEw+XrDGNWWxSSFsS7OZ2GPa9jiN+S6hkoCP9hpQBKxEcJqa+DAQSguhrrLExGReg4kpIGTFvid7dZAdb6ldK2lfKtL+TaLrf6mtmzi1TDxKsOZZ6hFRqxQGOuCfBb2uYYdrmGLayizBr+F5Lqi/GhPRUJgqrSiHAoKoUQhTEQkZE5qIJyZRLBuoFVGxU5L+WaXiu2W6j0Wb2WgRcbVVxnGjYXTTlUwiyaFsS6i1sJe17DTDYyA1QewxLoAFs3Gzt5a8PkD60lWV0NFBZSWBurDFMJERFrPeAJ1Zp6629+t3+KvgoodgVGzyl2Wih2WPlkwdgxceIHhvHMhNVXBrCMpjMWx0ropyJ1+w666KUi3LoClQIevHRlMZSVs2XLMRgUwEZF2YRICwSwQzsD6wV8NlXsslbmWsm0u1XssA0+AEcNh8NmBjv8nZGvkrD0pjMWRGgsHXMMBa9jhNxRYqMVgLSTVBbBYW9qsrAy2bYv2VYiIdE314cxJAeMEpjXdaqg6YKnItZTvsFTucvFUwOmnwemnw0knGk48MRDQ+vWFHj0U1NpKYayTshbKgEOuIc8a9ruGPBdqMLiAgyUZSCY2asCaozAmIhI7jCcQzDypgb9DYOTMewTKNrvU7LOBUbS9gelOCCxo3rMH9OsXCGh9+kDfvoZ+fQNhrV82ZGbE8BtRDFAY6wR8FkosFFtDkYV813DQGsrrRr4ADJYkAndAxtroV0sUxkREYpgDnuRAQHOSDVgCdWfVUJMfqDerzrPUHLLUHrb4SsDWBu7cPDo5pKZC//4w4DQ47TTD6afBwAFwwgngxPKIQQdRGIsya6GWQI+vSqDCGioslFlDiYXD1lBmwQd464KXrav7SiQQvjrzv2OFMRGRzsUkgpMcCGkmsS6g2cDama4X/BVQW2ipLbL4ysFfaXFrAo/ZGihZ7uKrDZwrJRkGDIDBZ8NZZxnOOjMwHZrQmUYVIiCUMJbQAdcRNq9tXONtjvrj0D79sWzdc7qAv+6PSyAo+eoCU+DvBh+BkOWtC1u1GGptoK6rCqjEUGWh2oK34ZwGP2DqXpgFEowlgUDoSsMGglfX+jcqIiIxxHrB7wV/OTS8EzvgJAaCWmIWJPUyGI8TeLzuvdNJMvjKLJUHLL6dgeOqa2DjJtiyFfz+wLaEBDjtVMvgs+GMgYYBAwKtN7p3j0xtmtdryS8ILKuXXwAFBVBYZCkuDtzJX1YWuLns9NPhV485bX6+SIm5MJbvwpu1HvxBHjNH/bdRQDON/59j/g6Bfyz2mL9bwD0qhDW3zzd/TKP/N/aoJ6k7jzEWBxr9SSJQ5+UhUEQpIiLSabiBZrPUcNR7c+NJNZNgSewe/D3Of9Qbus8H27bDzl3gurZhujM1FU7sbznpJOjbB3r0MGRlQVoqJCWBxxMYNPH5oKoKKirhyBFLyRE4XACH8gPh68iRxlOoHk9ds1w38Kfenr1t/qpEVMyFsUobaNOQSCDU1Ds2KB29/egNocy5Hpu9TZD/Okf9/Zs/tvH/O0FOIiIiIi3yHzPiUlUVCGnbdwQClOvaRuHpWMYEloMyBHpZhvo89RJiLP3E2OUERrkSsdR0hnSjflktqk2C5Gx9kUREugJ/ZaC3WVvUj36Fsl9zQSsULQW9aIi5MNbfWK5LdINOU0rn4ncsaw+D1xvtKxERkfbmuHDlLWBs7A+m9O8f7StoTHdTioiIiLSTUO6mVDm5iIiISBQpjImIiIhEkcKYiIiISBQpjImIiIhEkcKYiIiISBQpjImIiIhEkcKYiIiISBQpjImIiIhEkcKYiIiISBQpjImIiIhEkcKYiIiISBQpjImIiIhEkcKYiIiISBQpjImIiIhEkcKYiIiISBQZa62N9kWIiIiIdFUaGRMRERGJIoUxERERkShSGBMRERGJIoUxERERkShSGBMRERGJIoUxERERkShSGBMRERGJIoUxERERkShSGBMRERGJov8/NWAEvp1echoAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 600x400 with 1 Axes>"
       ]
@@ -1084,7 +1157,7 @@
     }
    ],
    "source": [
-    "az.plot_energy(trace, figsize=(6, 4));"
+    "az.plot_energy(idata, figsize=(6, 4));"
    ]
   },
   {
@@ -1126,31 +1199,43 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Multiprocess sampling (2 chains in 2 jobs)\n",
+      "Multiprocess sampling (4 chains in 2 jobs)\n",
       "CompoundStep\n",
       ">BinaryMetropolis: [mu1]\n",
       ">Metropolis: [mu2]\n"
      ]
     },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "    /* Turns off some styling */\n",
+       "    progress {\n",
+       "        /* gets rid of default border in Firefox and Opera. */\n",
+       "        border: none;\n",
+       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+       "        background-size: auto;\n",
+       "    }\n",
+       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+       "        background: #F44336;\n",
+       "    }\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "data": {
       "text/html": [
        "\n",
        "    <div>\n",
-       "        <style>\n",
-       "            /* Turns off some styling */\n",
-       "            progress {\n",
-       "                /* gets rid of default border in Firefox and Opera. */\n",
-       "                border: none;\n",
-       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
-       "                background-size: auto;\n",
-       "            }\n",
-       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
-       "                background: #F44336;\n",
-       "            }\n",
-       "        </style>\n",
-       "      <progress value='22000' class='' max='22000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      100.00% [22000/22000 00:04<00:00 Sampling 2 chains, 0 divergences]\n",
+       "      <progress value='44000' class='' max='44000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      100.00% [44000/44000 00:14<00:00 Sampling 4 chains, 0 divergences]\n",
        "    </div>\n",
        "    "
       ],
@@ -1165,8 +1250,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Sampling 2 chains for 1_000 tune and 10_000 draw iterations (2_000 + 20_000 draws total) took 11 seconds.\n",
-      "The number of effective samples is smaller than 25% for some parameters.\n"
+      "Sampling 4 chains for 1_000 tune and 10_000 draw iterations (4_000 + 40_000 draws total) took 15 seconds.\n"
      ]
     }
    ],
@@ -1174,13 +1258,12 @@
     "with model:\n",
     "    step1 = pm.BinaryMetropolis([mu1])\n",
     "    step2 = pm.Metropolis([mu2])\n",
-    "    trace = pm.sample(\n",
+    "    idata = pm.sample(\n",
     "        10000,\n",
     "        init=None,\n",
     "        step=[step1, step2],\n",
-    "        cores=2,\n",
+    "        chains=4,\n",
     "        tune=1000,\n",
-    "        return_inferencedata=True,\n",
     "        idata_kwargs={\"dims\": dims, \"coords\": coords},\n",
     "    )"
    ]
@@ -1193,7 +1276,7 @@
     {
      "data": {
       "text/plain": [
-       "['accept', 'accepted', 'p_jump', 'scaling']"
+       "['p_jump', 'scaling', 'accepted', 'accept']"
       ]
      },
      "execution_count": 12,
@@ -1202,7 +1285,7 @@
     }
    ],
    "source": [
-    "list(trace.sample_stats.data_vars)"
+    "list(idata.sample_stats.data_vars)"
    ]
   },
   {
@@ -1219,7 +1302,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1656x552 with 2 Axes>"
       ]
@@ -1230,7 +1313,7 @@
    ],
    "source": [
     "az.plot_posterior(\n",
-    "    trace,\n",
+    "    idata,\n",
     "    group=\"sample_stats\",\n",
     "    var_names=\"accept\",\n",
     "    hdi_prob=\"hide\",\n",
@@ -1296,7 +1379,7 @@
        "}\n",
        "\n",
        ".xr-wrap {\n",
-       "  display: block;\n",
+       "  display: block !important;\n",
        "  min-width: 300px;\n",
        "  max-width: 700px;\n",
        "}\n",
@@ -1604,20 +1687,26 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;accept&#x27; (chain: 2, accept_dim_0: 2)&gt;\n",
-       "array([[  3.75      , 269.57489704],\n",
-       "       [  3.75      , 147.77852694]])\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;accept&#x27; (chain: 4, accept_dim_0: 2)&gt;\n",
+       "array([[  3.75      , 573.3089824 ],\n",
+       "       [  3.75      , 184.17692429],\n",
+       "       [  3.75      , 194.61242919],\n",
+       "       [  3.75      ,  88.51883672]])\n",
        "Coordinates:\n",
-       "  * chain         (chain) int64 0 1\n",
-       "  * accept_dim_0  (accept_dim_0) int64 0 1</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'accept'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 2</li><li><span class='xr-has-index'>accept_dim_0</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-1a56706e-3074-43a7-aad8-07dd5ba50c2e' class='xr-array-in' type='checkbox' checked><label for='section-1a56706e-3074-43a7-aad8-07dd5ba50c2e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>3.75 269.6 3.75 147.8</span></div><div class='xr-array-data'><pre>array([[  3.75      , 269.57489704],\n",
-       "       [  3.75      , 147.77852694]])</pre></div></div></li><li class='xr-section-item'><input id='section-46d29b11-57be-4243-843a-2021b92c0355' class='xr-section-summary-in' type='checkbox'  checked><label for='section-46d29b11-57be-4243-843a-2021b92c0355' 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</div><input id='attrs-f95cef0c-4213-48a4-8169-e144d3cea038' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f95cef0c-4213-48a4-8169-e144d3cea038' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c4a1c11-ba15-4a35-b767-98ada6236869' class='xr-var-data-in' type='checkbox'><label for='data-6c4a1c11-ba15-4a35-b767-98ada6236869' 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])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>accept_dim_0</span></div><div class='xr-var-dims'>(accept_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-b16fc994-e1e4-49fd-a6ef-ebc95abeecb2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b16fc994-e1e4-49fd-a6ef-ebc95abeecb2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ab4f398-d064-4e55-8743-40cdc6284d12' class='xr-var-data-in' type='checkbox'><label for='data-5ab4f398-d064-4e55-8743-40cdc6284d12' 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])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7f78ebcc-bad2-4881-9300-e1f341f4e01a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7f78ebcc-bad2-4881-9300-e1f341f4e01a' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "  * chain         (chain) int64 0 1 2 3\n",
+       "  * accept_dim_0  (accept_dim_0) int64 0 1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'accept'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>accept_dim_0</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a685c51e-1795-4ac9-a743-109935a04949' class='xr-array-in' type='checkbox' checked><label for='section-a685c51e-1795-4ac9-a743-109935a04949' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>3.75 573.3 3.75 184.2 3.75 194.6 3.75 88.52</span></div><div class='xr-array-data'><pre>array([[  3.75      , 573.3089824 ],\n",
+       "       [  3.75      , 184.17692429],\n",
+       "       [  3.75      , 194.61242919],\n",
+       "       [  3.75      ,  88.51883672]])</pre></div></div></li><li class='xr-section-item'><input id='section-d644dff3-1273-44b8-951e-992f447e513f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d644dff3-1273-44b8-951e-992f447e513f' 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-2e13b43d-ddd7-4e87-8c7f-b27ee4536c9c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2e13b43d-ddd7-4e87-8c7f-b27ee4536c9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-651271fa-b825-4960-bafd-a8c18d311eaf' class='xr-var-data-in' type='checkbox'><label for='data-651271fa-b825-4960-bafd-a8c18d311eaf' 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'>accept_dim_0</span></div><div class='xr-var-dims'>(accept_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-31b98dbe-115b-444f-97c9-31208261cd8f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-31b98dbe-115b-444f-97c9-31208261cd8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-905289ae-f52b-45fc-8f61-b49536d480a8' class='xr-var-data-in' type='checkbox'><label for='data-905289ae-f52b-45fc-8f61-b49536d480a8' 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])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9dc78cbd-392f-4691-b992-6a8152a8fd7c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9dc78cbd-392f-4691-b992-6a8152a8fd7c' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'accept' (chain: 2, accept_dim_0: 2)>\n",
-       "array([[  3.75      , 269.57489704],\n",
-       "       [  3.75      , 147.77852694]])\n",
+       "<xarray.DataArray 'accept' (chain: 4, accept_dim_0: 2)>\n",
+       "array([[  3.75      , 573.3089824 ],\n",
+       "       [  3.75      , 184.17692429],\n",
+       "       [  3.75      , 194.61242919],\n",
+       "       [  3.75      ,  88.51883672]])\n",
        "Coordinates:\n",
-       "  * chain         (chain) int64 0 1\n",
+       "  * chain         (chain) int64 0 1 2 3\n",
        "  * accept_dim_0  (accept_dim_0) int64 0 1"
       ]
      },
@@ -1628,7 +1717,7 @@
    ],
    "source": [
     "# Range of accept values\n",
-    "trace.sample_stats[\"accept\"].max(\"draw\") - trace.sample_stats[\"accept\"].min(\"draw\")"
+    "idata.sample_stats[\"accept\"].max(\"draw\") - idata.sample_stats[\"accept\"].min(\"draw\")"
    ]
   },
   {
@@ -1638,7 +1727,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1656x552 with 2 Axes>"
       ]
@@ -1650,7 +1739,7 @@
    "source": [
     "# We can try plotting the density and view the high density intervals to understand the variable better\n",
     "az.plot_density(\n",
-    "    trace,\n",
+    "    idata,\n",
     "    group=\"sample_stats\",\n",
     "    var_names=\"accept\",\n",
     "    point_estimate=\"mean\",\n",
@@ -1666,20 +1755,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Last updated: Tue Apr 06 2021\n",
+      "Last updated: Tue May 31 2022\n",
       "\n",
       "Python implementation: CPython\n",
-      "Python version       : 3.9.2\n",
-      "IPython version      : 7.21.0\n",
+      "Python version       : 3.10.4\n",
+      "IPython version      : 8.4.0\n",
       "\n",
-      "seaborn   : 0.11.1\n",
-      "pandas    : 1.2.3\n",
-      "arviz     : 0.11.2\n",
-      "matplotlib: 3.3.4\n",
-      "numpy     : 1.20.1\n",
-      "pymc3     : 3.11.2\n",
+      "arviz     : 0.12.1\n",
+      "numpy     : 1.23.0rc2\n",
+      "pymc      : 4.0.0b6\n",
+      "matplotlib: 3.5.2\n",
+      "pandas    : 1.4.2\n",
       "\n",
-      "Watermark: 2.2.0\n",
+      "Watermark: 2.3.1\n",
       "\n"
      ]
     }
@@ -1693,13 +1781,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Updated by Meenal Jhajharia"
+    "* Updated by Meenal Jhajharia\n",
+    "* Updated by Christian Luhmann"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    ":::{include} ../page_footer.md\n",
+    ":::"
    ]
   }
  ],
  "metadata": {
+  "jupytext": {
+   "notebook_metadata_filter": "substitutions"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1713,7 +1813,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.2"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,
diff --git a/myst_nbs/diagnostics_and_criticism/sampler-stats.myst.md b/myst_nbs/diagnostics_and_criticism/sampler-stats.myst.md
index e0325c4a6..ef18c9d38 100644
--- a/myst_nbs/diagnostics_and_criticism/sampler-stats.myst.md
+++ b/myst_nbs/diagnostics_and_criticism/sampler-stats.myst.md
@@ -1,34 +1,41 @@
 ---
 jupytext:
+  notebook_metadata_filter: substitutions
   text_representation:
     extension: .md
     format_name: myst
     format_version: 0.13
     jupytext_version: 1.13.7
 kernelspec:
-  display_name: Python 3
+  display_name: Python 3 (ipykernel)
   language: python
   name: python3
 ---
 
-# Sampler statistics
+(sampler_stats)=
+# Sampler Statistics
 
 When checking for convergence or when debugging a badly behaving
 sampler, it is often helpful to take a closer look at what the
 sampler is doing. For this purpose some samplers export
 statistics for each generated sample.
 
+:::{post} May 31, 2022
+:tags: diagnostics
+:category: beginner
+:author: Meenal Jhajharia, Christian Luhmann
+:::
+
 ```{code-cell} ipython3
 import arviz as az
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-import pymc3 as pm
-import seaborn as sns
+import pymc as pm
 
 %matplotlib inline
 
-print(f"Running on PyMC3 v{pm.__version__}")
+print(f"Running on PyMC v{pm.__version__}")
 ```
 
 ```{code-cell} ipython3
@@ -47,13 +54,13 @@ with model:
 ```{code-cell} ipython3
 with model:
     step = pm.NUTS()
-    trace = pm.sample(2000, tune=1000, init=None, step=step, cores=2, return_inferencedata=True)
+    idata = pm.sample(2000, tune=1000, init=None, step=step, chains=4)
 ```
 
 - `Note`: NUTS provides the following statistics( these are internal statistics that the sampler uses, you don't need to do anything with them when using PyMC3, to learn more about them, [check this page](https://docs.pymc.io/api/inference.html#module-pymc3.step_methods.hmc.nuts).
 
 ```{code-cell} ipython3
-trace.sample_stats
+idata.sample_stats
 ```
 
 The sample statistics variables are defined as follows:
@@ -91,19 +98,19 @@ Some points to `Note`:
 - `InferenceData` also stores additional info like the date, versions used, sampling time and tuning steps as attributes.
 
 ```{code-cell} ipython3
-trace.sample_stats["tree_depth"].plot(col="chain", ls="none", marker=".", alpha=0.3);
+idata.sample_stats["tree_depth"].plot(col="chain", ls="none", marker=".", alpha=0.3);
 ```
 
 ```{code-cell} ipython3
 az.plot_posterior(
-    trace, group="sample_stats", var_names="acceptance_rate", hdi_prob="hide", kind="hist"
+    idata, group="sample_stats", var_names="acceptance_rate", hdi_prob="hide", kind="hist"
 );
 ```
 
 We check if there are any divergences, if yes, how many?
 
 ```{code-cell} ipython3
-trace.sample_stats["diverging"].sum()
+idata.sample_stats["diverging"].sum()
 ```
 
 In this case no divergences are found. If there are any, check [this notebook](https://github.com/pymc-devs/pymc-examples/blob/main/examples/diagnostics_and_criticism/Diagnosing_biased_Inference_with_Divergences.ipynb) for  information on handling divergences.
@@ -115,7 +122,7 @@ energy levels with the change of energy between successive samples.
 Ideally, they should be very similar:
 
 ```{code-cell} ipython3
-az.plot_energy(trace, figsize=(6, 4));
+az.plot_energy(idata, figsize=(6, 4));
 ```
 
 If the overall distribution of energy levels has longer tails, the efficiency of the sampler will deteriorate quickly.
@@ -139,26 +146,25 @@ with pm.Model(coords=coords) as model:
 with model:
     step1 = pm.BinaryMetropolis([mu1])
     step2 = pm.Metropolis([mu2])
-    trace = pm.sample(
+    idata = pm.sample(
         10000,
         init=None,
         step=[step1, step2],
-        cores=2,
+        chains=4,
         tune=1000,
-        return_inferencedata=True,
         idata_kwargs={"dims": dims, "coords": coords},
     )
 ```
 
 ```{code-cell} ipython3
-list(trace.sample_stats.data_vars)
+list(idata.sample_stats.data_vars)
 ```
 
 Both samplers export `accept`, so we get one acceptance probability for each sampler:
 
 ```{code-cell} ipython3
 az.plot_posterior(
-    trace,
+    idata,
     group="sample_stats",
     var_names="accept",
     hdi_prob="hide",
@@ -170,13 +176,13 @@ We notice that `accept` sometimes takes really high values (jumps from regions o
 
 ```{code-cell} ipython3
 # Range of accept values
-trace.sample_stats["accept"].max("draw") - trace.sample_stats["accept"].min("draw")
+idata.sample_stats["accept"].max("draw") - idata.sample_stats["accept"].min("draw")
 ```
 
 ```{code-cell} ipython3
 # We can try plotting the density and view the high density intervals to understand the variable better
 az.plot_density(
-    trace,
+    idata,
     group="sample_stats",
     var_names="accept",
     point_estimate="mean",
@@ -188,4 +194,10 @@ az.plot_density(
 %watermark -n -u -v -iv -w
 ```
 
-Updated by Meenal Jhajharia
+* Updated by Meenal Jhajharia
+* Updated by Christian Luhmann
+
++++
+
+:::{include} ../page_footer.md
+:::