diff --git a/RELEASE-NOTES.md b/RELEASE-NOTES.md
index e783f76dc5..c09edb7b9f 100644
--- a/RELEASE-NOTES.md
+++ b/RELEASE-NOTES.md
@@ -5,9 +5,12 @@
 ### Maintenance
 - Mentioned the way to do any random walk with `theano.tensor.cumsum()` in `GaussianRandomWalk` docstrings (see [#4048](https://github.com/pymc-devs/pymc3/pull/4048)).
 
+
 ### Documentation
 
 ### New features
+- `sample_posterior_predictive_w` can now feed on `xarray.Dataset` - e.g. from `InferenceData.posterior`. (see [#4042](https://github.com/pymc-devs/pymc3/pull/4042))
+
 
 ## PyMC3 3.9.3 (11 August 2020)
 
@@ -27,6 +30,7 @@
 
 _NB: The `docs/*` folder is still removed from the tarball due to an upload size limit on PyPi._
 
+
 ## PyMC3 3.9.2 (24 June 2020)
 ### Maintenance
 - Warning added in GP module when `input_dim` is lower than the number of columns in `X` to compute the covariance function (see [#3974](https://github.com/pymc-devs/pymc3/pull/3974)).
diff --git a/docs/source/notebooks/model_averaging.ipynb b/docs/source/notebooks/model_averaging.ipynb
index 0f7334dfc9..89b9507cfb 100644
--- a/docs/source/notebooks/model_averaging.ipynb
+++ b/docs/source/notebooks/model_averaging.ipynb
@@ -2,16 +2,25 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
+    "import arviz as az\n",
     "import matplotlib.pyplot as plt\n",
-    "plt.style.use(['seaborn-darkgrid'])\n",
-    "import pymc3 as pm\n",
     "import numpy as np\n",
-    "import  pandas as pd"
+    "import pandas as pd\n",
+    "import pymc3 as pm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext watermark\n",
+    "az.style.use('arviz-darkgrid')"
    ]
   },
   {
@@ -67,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -140,7 +149,7 @@
        "4        0.92   0.012294 -1.885353"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -160,7 +169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -170,8 +179,43 @@
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
       "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [sigma, beta, alpha]\n",
-      "Sampling 2 chains: 100%|██████████| 5000/5000 [00:04<00:00, 1028.80draws/s]\n"
+      "NUTS: [sigma, beta, alpha]\n"
+     ]
+    },
+    {
+     "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",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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 4 seconds.\n"
      ]
     }
    ],
@@ -184,7 +228,7 @@
     "    mu = alpha + beta * d['neocortex']\n",
     "    \n",
     "    kcal = pm.Normal('kcal', mu=mu, sigma=sigma, observed=d['kcal.per.g'])\n",
-    "    trace_0 = pm.sample(2000)"
+    "    trace_0 = pm.sample(2000, return_inferencedata=True)"
    ]
   },
   {
@@ -196,7 +240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -206,8 +250,43 @@
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
       "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [sigma, beta, alpha]\n",
-      "Sampling 2 chains: 100%|██████████| 5000/5000 [00:04<00:00, 1163.58draws/s]\n"
+      "NUTS: [sigma, beta, alpha]\n"
+     ]
+    },
+    {
+     "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",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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 3 seconds.\n"
      ]
     }
    ],
@@ -221,7 +300,7 @@
     "    \n",
     "    kcal = pm.Normal('kcal', mu=mu, sigma=sigma, observed=d['kcal.per.g'])\n",
     "    \n",
-    "    trace_1 = pm.sample(2000)"
+    "    trace_1 = pm.sample(2000, return_inferencedata=True)"
    ]
   },
   {
@@ -233,7 +312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -243,8 +322,43 @@
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
       "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [sigma, beta, alpha]\n",
-      "Sampling 2 chains: 100%|██████████| 5000/5000 [00:05<00:00, 962.26draws/s] \n"
+      "NUTS: [sigma, beta, alpha]\n"
+     ]
+    },
+    {
+     "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",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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 4 seconds.\n"
      ]
     }
    ],
@@ -258,7 +372,7 @@
     "\n",
     "    kcal = pm.Normal('kcal', mu=mu, sigma=sigma, observed=d['kcal.per.g'])\n",
     "\n",
-    "    trace_2 = pm.sample(2000)"
+    "    trace_2 = pm.sample(2000, return_inferencedata=True)"
    ]
   },
   {
@@ -270,14 +384,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
+       "<Figure size 1000x500 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -286,7 +400,7 @@
    ],
    "source": [
     "traces = [trace_0, trace_1, trace_2]\n",
-    "pm.forestplot(traces, figsize=(10, 5));"
+    "az.plot_forest(traces, figsize=(10, 5));"
    ]
   },
   {
@@ -298,14 +412,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 993.6x331.2 with 2 Axes>"
+       "<Figure size 1656x552 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -313,7 +427,7 @@
     }
    ],
    "source": [
-    "pm.densityplot(traces, var_names=['alpha', 'sigma']);"
+    "az.plot_density(traces, var_names=['alpha', 'sigma']);"
    ]
   },
   {
@@ -325,18 +439,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/junpenglao/Documents/pymc3/pymc3/stats.py:219: UserWarning: For one or more samples the posterior variance of the\n",
-      "        log predictive densities exceeds 0.4. This could be indication of\n",
-      "        WAIC starting to fail see http://arxiv.org/abs/1507.04544 for details\n",
-      "        \n",
-      "  \"\"\")\n"
+      "/home/osvaldo/proyectos/00_BM/arviz/arviz/stats/stats.py:150: UserWarning: \n",
+      "The scale is now log by default. Use 'scale' argument or 'stats.ic_scale' rcParam if you rely on a specific value.\n",
+      "A higher log-score (or a lower deviance) indicates a model with better predictive accuracy.\n",
+      "  \"\\nThe scale is now log by default. Use 'scale' argument or \"\n"
      ]
     },
     {
@@ -360,65 +473,78 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>WAIC</th>\n",
-       "      <th>pWAIC</th>\n",
-       "      <th>dWAIC</th>\n",
+       "      <th>rank</th>\n",
+       "      <th>loo</th>\n",
+       "      <th>p_loo</th>\n",
+       "      <th>d_loo</th>\n",
        "      <th>weight</th>\n",
-       "      <th>SE</th>\n",
-       "      <th>dSE</th>\n",
-       "      <th>var_warn</th>\n",
+       "      <th>se</th>\n",
+       "      <th>dse</th>\n",
+       "      <th>warning</th>\n",
+       "      <th>loo_scale</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>-15.44</td>\n",
-       "      <td>2.58</td>\n",
+       "      <th>model_2</th>\n",
        "      <td>0</td>\n",
-       "      <td>0.89</td>\n",
-       "      <td>4.76</td>\n",
+       "      <td>7.54742</td>\n",
+       "      <td>2.69847</td>\n",
        "      <td>0</td>\n",
-       "      <td>1</td>\n",
+       "      <td>0.881161</td>\n",
+       "      <td>1.56652</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>log</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>-8.88</td>\n",
-       "      <td>2.05</td>\n",
-       "      <td>6.56</td>\n",
-       "      <td>0.04</td>\n",
-       "      <td>3.96</td>\n",
-       "      <td>2.1</td>\n",
+       "      <th>model_1</th>\n",
        "      <td>1</td>\n",
+       "      <td>4.41805</td>\n",
+       "      <td>2.07408</td>\n",
+       "      <td>3.12936</td>\n",
+       "      <td>0.0513577</td>\n",
+       "      <td>2.04406</td>\n",
+       "      <td>1.05462</td>\n",
+       "      <td>False</td>\n",
+       "      <td>log</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>-7.13</td>\n",
-       "      <td>1.94</td>\n",
-       "      <td>8.31</td>\n",
-       "      <td>0.07</td>\n",
-       "      <td>2.95</td>\n",
-       "      <td>4.13</td>\n",
-       "      <td>0</td>\n",
+       "      <th>model_0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3.5033</td>\n",
+       "      <td>1.98011</td>\n",
+       "      <td>4.04412</td>\n",
+       "      <td>0.0674812</td>\n",
+       "      <td>2.41377</td>\n",
+       "      <td>2.05646</td>\n",
+       "      <td>False</td>\n",
+       "      <td>log</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "    WAIC pWAIC dWAIC weight    SE   dSE var_warn\n",
-       "2 -15.44  2.58     0   0.89  4.76     0        1\n",
-       "1  -8.88  2.05  6.56   0.04  3.96   2.1        1\n",
-       "0  -7.13  1.94  8.31   0.07  2.95  4.13        0"
+       "        rank      loo    p_loo    d_loo     weight       se      dse warning  \\\n",
+       "model_2    0  7.54742  2.69847        0   0.881161  1.56652        0   False   \n",
+       "model_1    1  4.41805  2.07408  3.12936  0.0513577  2.04406  1.05462   False   \n",
+       "model_0    2   3.5033  1.98011  4.04412  0.0674812  2.41377  2.05646   False   \n",
+       "\n",
+       "        loo_scale  \n",
+       "model_2       log  \n",
+       "model_1       log  \n",
+       "model_0       log  "
       ]
      },
-     "execution_count": 8,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "model_dict = dict(zip([model_0, model_1, model_2], traces))\n",
-    "comp = pm.compare(model_dict, method='BB-pseudo-BMA')\n",
+    "model_dict = dict(zip([\"model_0\", \"model_1\", \"model_2\"], traces))\n",
+    "comp = az.compare(model_dict)\n",
     "comp"
    ]
   },
@@ -435,7 +561,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -455,12 +581,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/osvaldo/proyectos/00_BM/pymc3/pymc3/sampling.py:1679: UserWarning: samples parameter is smaller than nchains times ndraws, some draws and/or chains may not be represented in the returned posterior predictive sample\n",
+      "  \"samples parameter is smaller than nchains times ndraws, some draws \"\n"
+     ]
+    }
+   ],
    "source": [
-    "ppc_2 = pm.sample_posterior_predictive(trace_2, 1000, model_2,\n",
-    "                     progressbar=False)"
+    "ppc_2 = pm.sample_posterior_predictive(trace_2, 1000, model_2, progressbar=False)"
    ]
   },
   {
@@ -472,14 +606,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/osvaldo/proyectos/00_BM/arviz/arviz/stats/stats.py:485: FutureWarning: hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions\n",
+      "  FutureWarning,\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 720x480 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -488,13 +630,15 @@
    ],
    "source": [
     "mean_w = ppc_w['kcal'].mean()\n",
-    "hpd_w = pm.hpd(ppc_w['kcal']).mean(0)\n",
+    "hpd_w = az.hdi(ppc_w['kcal']).mean(0)\n",
     "\n",
     "mean = ppc_2['kcal'].mean()\n",
-    "hpd = pm.hpd(ppc_2['kcal']).mean(0)\n",
+    "hpd = az.hdi(ppc_2['kcal']).mean(0)\n",
     "\n",
-    "plt.errorbar(mean, 1, xerr=[[mean - hpd]], fmt='o', label='model 2')\n",
-    "plt.errorbar(mean_w, 0, xerr=[[mean_w - hpd_w]], fmt='o', label='weighted models')\n",
+    "plt.plot(mean_w, 1, 'C0o', label='weighted models')\n",
+    "plt.hlines(1, *hpd_w, \"C0\")\n",
+    "plt.plot(mean, 0, 'C1o', label='model 2')\n",
+    "plt.hlines(0, *hpd, \"C1\")\n",
     "\n",
     "plt.yticks([])\n",
     "plt.ylim(-1, 2)\n",
@@ -517,26 +661,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "pymc3 3.8\n",
-      "arviz 0.8.3\n",
-      "numpy 1.17.5\n",
-      "last updated: Thu Jun 11 2020 \n",
+      "json     2.0.9\n",
+      "pymc3    3.9.3\n",
+      "numpy    1.18.5\n",
+      "autopep8 1.5\n",
+      "pandas   1.1.0\n",
+      "arviz    0.9.0\n",
+      "last updated: Thu Aug 13 2020 \n",
       "\n",
-      "CPython 3.8.2\n",
-      "IPython 7.11.0\n",
+      "CPython 3.7.6\n",
+      "IPython 7.17.0\n",
       "watermark 2.0.2\n"
      ]
     }
    ],
    "source": [
-    "%load_ext watermark\n",
     "%watermark -n -u -v -iv -w"
    ]
   }
@@ -557,7 +703,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.2"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,
diff --git a/docs/source/notebooks/model_comparison.ipynb b/docs/source/notebooks/model_comparison.ipynb
index 7e519fb738..1c5a983602 100644
--- a/docs/source/notebooks/model_comparison.ipynb
+++ b/docs/source/notebooks/model_comparison.ipynb
@@ -1,25 +1,34 @@
 {
  "cells": [
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 2,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "# Model comparison\n",
-    "\n",
-    "To demonstrate the use of model comparison criteria in PyMC3, we implement the **8 schools** example from Section 5.5 of Gelman et al (2003), which attempts to infer the effects of coaching on SAT scores of students from 8 schools. Below, we fit a **pooled model**, which assumes a single fixed effect across all schools, and a **hierarchical model** that allows for a random effect that partially pools the data."
+    "import arviz as az\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pymc3 as pm"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
-    "import pymc3 as pm\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "plt.style.use('seaborn-darkgrid')"
+    "%load_ext watermark\n",
+    "az.style.use('arviz-darkgrid')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Model comparison\n",
+    "\n",
+    "To demonstrate the use of model comparison criteria in PyMC3, we implement the **8 schools** example from Section 5.5 of Gelman et al (2003), which attempts to infer the effects of coaching on SAT scores of students from 8 schools. Below, we fit a **pooled model**, which assumes a single fixed effect across all schools, and a **hierarchical model** that allows for a random effect that partially pools the data."
    ]
   },
   {
@@ -31,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -49,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -59,8 +68,44 @@
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
       "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [mu]\n",
-      "Sampling 2 chains: 100%|██████████| 3000/3000 [00:01<00:00, 1622.25draws/s]\n"
+      "NUTS: [mu]\n"
+     ]
+    },
+    {
+     "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",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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 4 seconds.\n",
+      "The acceptance probability does not match the target. It is 0.8935826700064077, but should be close to 0.8. Try to increase the number of tuning steps.\n"
      ]
     }
    ],
@@ -70,19 +115,19 @@
     "    \n",
     "    obs = pm.Normal('obs', mu, sigma=sigma, observed=y)\n",
     "    \n",
-    "    trace_p = pm.sample(1000)"
+    "    trace_p = pm.sample(2000, return_inferencedata=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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9gOSOJ8CsSuS+dm4meNt8WDOXgR95NnjbAtht871/kW4J71SvgI3sgNm9AuaOx5F44XYkn/81rNnHovqy/4B11GsU0et1r2VoawOu+ALHhy/h+NY3gDmzSfAiCGL6uPbaa7F+/Xr88pe/DK0LfnRoNNHG0NDQlNo2WRIvPAQAqM+YvD2FgjjWoaFigy2jMcbGYBQKAIBWHDjtcyDS2dnZdPuMjXEUCsDwCNA2dGA8Q4eHhU3JBDC0D2yaSPscjlD7RDOtbVMeRaJQALeSsKbYBlYcgFkogPMBWG2TL5uunXiofeJpRggksYsgDjCSf/8e0o9+G7Vlb0T2/7sVGCvsk3pWrea47AoObgM338TwkhOn7gWR52eiftzbUD/ubc4CDtSKYMUBsMqYEKGYCZ5IixxfiUl8vU+kwWccjfqMo1F/8btQqZWQWP83pJ74PrJ//CjqC09H5XVXg3ct9XY5/TSGm78BfPqzHB/9GMe3vgnQBxOCIKaDL33pS7j33nvx85//HHPnzvWWz5o1CwDQ39+P2bNne8sHBgYwc+bM/W4nQWg5gGIHKUE9QcRBN4hCrQzUS/tkkq9DkT09HKvXAOecDRgHUvx6k9BsjARxAJF88r+F0HX8O1A57+tgiX3jjn/v/Rwf+yRHSwvw/e9OrdClhTEglQfvWAx7zvEitHHWcvDOJZMTunQks6gffz6KF96O8uuvhdm/Drmfno/kU7cpb8LHH8fw7W8yFIvARz/GsXXrgTWTJEEQhzacc1x77bW488478ZOf/ASLFi1S1i9cuBCzZs3CI4884i2rVqt48skncdJJJ+1XW6tVjvvuFxN8EISMfQBdEgeQKQRxyLJqNcdd9wTvtoNP/DC33I/Etken24yDho2bANsGqtXptkRleLi5np/ELoI4QEg892ukH7oJtWPfisq5XwKMqc+FwTnHz3/JcdUXOY59EfD97zAsXHjwPahiMUzUX/xuFN7/F1hHnYP0g19F5k8fA8qj3ibLjmG45WaGeh248IMj2LyFXpUJgtg/XHPNNfjjH/+Im266Cfl8Hn19fejr60O5XAYgwhf/5V/+Bd///vdx1113Yf369bj88suRyWTwlre8Zb/aOjgE1C1g2/b9Wi1xEHBAeVNN82yMO3YKEaBRqDFxYDA6yjHU5ECZ8Nm9Z7otmBqYTR+5D3bqdY6nnm5uWxK7COIAILHuL0jf/UXUl56DyrnXA2zqb816neOrX+f4r1s5zn0d8M2vM7S3H2JCl0y2E+W33IzKqz8Pc/P9yP3iXWADm7zVS5cy/Oe3GEyD4WOf4NiwgV58CILY9/zqV7/C2NgYLrjgApx11lnef3/+85+9bT70oQ/hwgsvxDXXXIN3vvOd6OnpwY9+9CO0tGjyFx6o0MD/kCZ4em2bY2xses75dF9qm5xXC+twGENXC0D9AHPxmCCPP4mmB8oE4WL0rUfihTum2wwCoq9tttunnF0EMc2YWx5C+i+fg7XwNJTf8k3ATE55HePjHF/4IseTTwHvvxD4wL+y5mdcPJhhDLWTL4A198XI/OljyP2/f0bp/O/Bni9CgRYvZvjJbW14/weHccknOb7xNeC4Yw+DdiEIYtpYt25dw20YY7jkkktwySWX7AeL9hUcB2OIC9EkgZHGho3A9h3AK87kyOX273mfbrHrcCKx+X6AMdSXnzfdphDEfsUY2DDdJkwPB3n/Sp5dBDGNGL1rkPnTx2HPWoby278zdfmrJLp3c3z4oxzPrgCu/DzDB99vHB5Cl4Q9/6UovfdX4NlOZH/zAZjbH/PWLVpo4j+/zdDRDnzi0xwrnzvIe3WCIIgpJje6BkbP6ontFKVA1EpgIzv33ihiWnBPa/DsjjqZAvZVXpdymaN/QH9NuUsPs1eb6YPUReJw5jC9/g/W/pXELoKYJlihD5nf/wd4tgPl8/8LSE99eMrK5zguuphjYBD4xtcY3njuQdpTTQG8fQGK7/kF7M4lyPz+IzB2POGtmzOb4T+/zTB7lpip8elnDs8HGUEQhI5ccQuMoa0T3Evfj7LRXTB3rzxsBwwHO+5ps211uWGo66eavz8OPLsi3qbpJtgmxOGF0f3sgRXm5qoTE71BKuNAvRK/TVSZtoXEur+AjfVMrE7igMcY2gxUxqbbjAlDYhdBTAe1MjJ/+ChYZRTl878Hnp/66eTv+AvHxz/F0d4O3PpdhpNPOnyFLo9cF0rv/CHs9oXI/v7DMHY9462aOUMkrV+wAPjM5zgee/wAeXsmCII4GIkYDDHuKAKclIGDEe+0Bk7vZMfVzVKrx6w8QB7XB4roRkwPxmj3dJswJSS2PABz8/3NbRx096kWAG7D6F8/5XYdOOy/G71WEx/gS6Xp61zcfi3RtxbmtkfiNz4AIbGLIPY3nCN955Uw9jyP8nlfhz1r+ZQWb1kc3/mejRtu5Dj5JOD73z0EZ1zcG3JdKL/rNvDWucj+7iLw7ue8VZ2dDLd8k2HpUuDyK0jwIgiCACY7iI/YiTdYTxwUBM+eO+a1p+G0utfnwRpms69gI7tgSl7sBNEszI5Tl5sp4NC5GdngFrDhHfte0dewp8fG4BCwZet+q1KPc8wHykyWEzkFJHYRxH4m+fh/IbnuDlRfdSmso86Z0rLHxzkuv5LjV/8PePc7ga/ewNDScug8cKYKnp+J0rtuA890oP6z9yn5Y9raGG6+ieHoo4ArvsDx1NM0ICMI4tCmWuXgU/0C36g48uw6KPEcuwLn1wtj1JxWo3fNPg3vOlCe0geaZ5e5ewVYoW+6zWhIvc6xYSOHPR1KKTHFeBn0ptWKqcTsXQNzz3PSErpODyZI7CKI/Yi5/k6kH/02ase/A7VT3j+lZa/fwPGBizgefwK49FMMH7/EQCJx6DxsphreMhuld/wAsGrI/vYioDTkrWtpYfjG1xgWLwIuu4KS1hMEcehSq3E88BCwceP+qjEiwzlxcOCevgmEMRqDW/atSRHXEhvbvW9EVc7BBrcAAS8Hytk1OTZuBLZuA3p7p9uSybNjJ8fAIHVqvpvl9Jqxb9j/nl0HDvygfWST2EUQ+wk2sBGZv10Oa8EpqLz26ilz8eWc4w9/4rj4Ixz1OvCdbzOc/7ZD8ikz5fCuI2G+72dgo93I/v4jSkLOtjaGb97EMHcucOnnOFatPli7eYIgiGhqNfFvb4wDyOQeV1F9ZkTSJ+KgoFHOrn0t+Gg9EN2cMrVh7znOxnth7noGxsDUq7hstBtm75pQ2QODU17VtMCGdwhPPKet+/s5env33f1quWn8DuIu4YV1QPcBl7LL7bgP4oYlDhAOrGuIwhgJ4kCjWkD2T58AT+VQfss3gURqSort7+e47AqOr93EcfLJwG0/YDjheBK6JoKx+FSU3/x1GLtXIn3XVUoP2tnB8K2bGGbOBC79LMe69QdWZ08QBLG37LPUKo3eRg+GMMZaGUbfuoN7FD7FeLMxRohdVtxpnYJ21GpdzrLOwUdgbnnQMaQq/q2V97rOEG4+I+df99hfWDf1VU0HRt9a8cM5vmdXAiuf33f1HXI51/ZB37ZrF4dlTe7+6d7FsWIlR+KFO2D0rpkiixp9zDgATmat7N+rzVCvNrnh/n8eTPe9wcDFYU+3IZOAxC6C2NdwjvRdV4MNbUX5vJvA87OmoEiOv/yV433/yvHU08DHPsrw1RsY2tsPvk7oQMA6+nWovuLjSK79I5JP/1hZN2OGELxa24BPXsqxaTMNegiCOHTwws+mvOSoBPXNz8Y45XnEnDLvuodjy9bGZZt7nhPeO+XhKbfjYCUqZ1dog31Vf7RjFwCAWc0OWKeOhpdprXhwzU7n6RUH4TtlvRoKL93vTLHY1dfHseYFYNPmye3f0wf09Yvf+zqk2OMAuHYSm+5BefXDGBmJiL2W/66VkNh4F9jApsYFH5bDgKk96EpFPIeHhydXLnl2EcQBRGLlr0RC+rM+AXvR6Xtd3u7dHJ+5jOP6r3AcfRTw0x8x/OO7GAxj+h8sBzO10y9CbfmbkHro6zC3Pqysmz2b4dvfYMhkgI9/imP7jsPySUcQBDEBDsx+0n1J3hg3pnG/8PNDIL5Kw3PPczz08CSPqYlx496wYyfHM89GFxbn2cUabThNmDufhtG/AagWJrbjvvBKa4r9653Dp7C6xMa7YG7/+94XtDdMsdjltk95ui6HAPJHiNBdNsX33ZatHA88NPkyt6wbxxNPAUb/eiTW/TlaCK2VAABGzIQO/AAQ8KYDHtHn7w1Dw+LfnbumrswoSOwiiH2Isfs5pO//CupHvQa1Uz+wV2VVKhw/vM3GP1/IsXIl8KlPMHz7mwwLFhyene+Uwxgq514Pe9aLkLnjU2BD6tevefOE4GUw4NOf4egfOHBepAmCIPaaqe7Sot6M98Wb8wRolFOKjfeKL/yFfrijb3aACneTpacXKFcab6dj705r43Z8Yd0kcl9NQixhQ1tEXqqJhDlFldWoXh6uY3SswYez4iASm+4BG90Po8FI9s91P5kwxmKRY3RUbx+bbk/MKe7b0sWdmD98J+q1iZY7DeF2UyyUbtwEVKfAWZMNbxc/lPvdb5+J9fEH9vOgXhdpVyYb9qpFvqanUMzdH68BJHYRxL6iNITM7Z8Ab52D8hu+DLDJ3W6WxXHnXRzvu5Djtp8AZ78K+OXPGN5xPnlzTTnJLMpv/09wM4Xs7/8DqIwpqxcuZPj6VxlGRkUOr/HxA/uBRxAE0Yhpc4BpKoxxP9gRgJWGxY/ysBTjeRDkF2uEbcHoWb3X4o4iapVHgVpRWb6vifPsUmAMhQJHX7/eMGNoq/gxqbDHvT/Yx58A1sVENjLn/YMVh0LrBgb4Pgnx9dm/N95kJvB75DHg8Sf3hS0c9fpeHv8U9xfpoVUweA323sz+EGPTho0cTzzZ/DE3d+k5Z9PtT/eSNWvjK61UOB59jKNYjNpOc3UpAs4ERLp9ce/VK2AxXmUTYdt2YPsOYNcU6uReq3COxLq/KDPYT6q8/Th8JbGLIPYF3EbmL58DKw6g/JabgUz7hIuwbY6/3VXBhR/guPZ6jtY2MdPi1VcamDWLRK59BW+dh/Jbvw02shOZP18acnledgzDDdcxbNsOXH4lR6VCghdBEAcv+2zM3LBgDlTGRULx/ZhnqaFZzuOVce6HrRxAIXGThQ1thTG0FcbgJBP/OMg5uxJbH0Ji032oVjlGR/1tLIvDDmawnyIa5eySN9y8BdiydZ+Y4WHbHNXaPig4YjQ4OMjxzApg8z5MvcScRn76aRtPPrX/rv0DIUrsueeB+x7Yy0L2Quy6735NbljnfIyMTNogMETbtHUbMDIauTpcGkd0nygtZ2N7kNj2yJR4J+5qMNNlbx9QHR3G7vV74jds1JfHXoT77gI1tz8Kc8cT6O3d+8iRfZOyTrVp2r0nJwCJXQSxD0g+cSsSWx9C5ZwrYM85fkL7cs7x8CMcH/gQx6cuHYdhANd/ieGH32d4yYkHwJvAYYC94GRUXnsVElseROrhb4bWn3Iyw5WfZ1ixEvjS9VPsKkwQBDENTIWew4Z3yCXGV8Q5jIENYJUxJ2Rw/9C8cwTH/pxRbPv26LCsqYA5A/Boz4eJIV8vTz7lh0VyAPfeDzzyaIOdGpbPI37vVbFTjpz7LVUbACsORG+sGUhHi4Lutup6N6SrVGrexglRHvXEmqERYHjSAsv+Yarvmd6pcK7ZiwuybmmETK/L3Itj3VfeqaFr2o9J9e6F/fQxY87YI2gdfFqEJ5cDF65WxOIRvxsx9R0OqwoP2ZXPA8+u2LuyvC5lCh9fBt8Xij6FMRLEQYm5/TGkHr0FtePejvqL3930fpxz/P1xjos+zHHZFRzVKvC1r7Tgxz9kOPuVDOxA+OR1GFF/8btQfen7kHrqh+LBGeC15zB8/BKG+x8Ebr5lX4cUEARBTB+WBYyNNe7jjJHt/h8Np+uT1kc83ybVrdaKgFXD5N/0pekp96Nn17oN+yYsK8hzz+/lu4QmN1dRI7xMNieYiytKbnxiM+65O36wrM35tJ/emeSk4bPH/65Pju61lWpTproHGNwaX0GT116xOMkPb4HZCxNbH5p4GXvJ3txe++OeaRbu9R2TE5YihU+u6TNlKmNhcSfAVN4NzZwvDgB1pxNIZKaw9ga4nrmVcf0KuQ01B9Lcpdh4q2KRo69v758bk7k33H0mkunGtsXsiDt26iucOxp0d5zeMSnNxkgQ0wQb60H6jkthdx2Fymuvbupli3OOxx7nuOgjHJd+jmN4BLjiMoaf3sZw3pvSlJdrGqme/VnUF52B9J1XwuhdE1r/rncwXPA+4He/B375P/vfPoIgiL2lmZfGsTER6lKbcILkmAo53yciUmLTfTC36dyK1OqD7NzJUatxZcatShnYvoNj6zYLz6/aPx806nWO7t1TkDfIJRDT0lR0YWnIm53MxbKEmLK/5hfgXNhR274GnaVV6vLQxlEFTA6jd21okpopQRJBDAOYWXgabPdq/bZenlf1OPSebRyPPAasDrym2DZHb290SOnQEEf1uTth7ng8tK5Q2Df3p45pnrOiMdyGufXheK89dYdJVVOPSKfHpQbavp3j2RVq+YktDyIRmEXc3d5nP+UdVJTnvT+hHU4WmBldE9zRMJvYSCN8NSOSN3GhPvIYsOK5JkyYRNnNFjERvb/mOG5t0XR7cui6z96NTffnyJbELoKYKqyamMXPqqD8tm8ByWzs5pyLhJAXfYTjM5/jGBkBLv8cw69+xvCmNzIkEiRyTTtmEuU3fwM814XMHz4KFMNTRF30QYZzXwf81617MZU7QRDENDGRgebE8iPHF8gU74f45934OEe53Hz/yqrBr/o+umMoFjnWrgNWrAT6+zm2buMAODZvZRgZAXbs4NjToy+vXudY+dzU5W/s6QW2P7MJe9Zui9zGsnhTnnawakis/yuMXU/D6I/Jhh4gse1RJDbdqyx74CERntjoepnsbIxsvFeZFMa2AeYIdXIITbD8HTu5F9o3Ojo1gqwxuBlmT/gD196S2Hy/l7Db2EunQXkg6x7/0LC6Tf+AuJ527tSX8dQzwKbNgD3uv9tYlhB992VOsCDeNbX/qpwYtRJYeQTGnlWNt90LXLHLiBidMwgP0P5mNTd534C3Wfdujmp1X7Y4A/bS0w0AWlvEv/n8RPds5FnsMzrKsXFD3fGMnJ6cXT5TKHZNQuVpWiCbIs/ZyfR/Dz3M8cQEPDpJ7CKIKSL18Ddgdj+D8rnXgXceGbvtuvUcn/g0x6c+44tcv/wpw5vfRCLXAUeuC+W3/SdYaQjZ2z/hhMf4MMbwuc8wHHcscO11HBs2HrCvawRBECHGCzEr96Y7ayqMMT65iFvEY48DDz3i5ASz9k3uEEDkJ1q/0cDYmGNfhHeNTF+fyPMj527inGNwcHKNZ9tAe+kFpPrVgfXmLRxbtooy174A/P2JJoQdW7SVMdYgaXMTWI6D2L56wpk7n0Riy4Pe37YN7YBKvqwKBY4X1gE7pPzXffsv/dukcBM7u4JG6DaxqmKhF3XVWChwvTJSSXW5K+xaDYrok/JUbdwEvLBOv11UugZu1ZB44Q6wsQhFuEkOWM8uj6k10LI4Nm/xZxCsO/eYGRid++0y+fqZtG+pxLF6DbAqwqmwEbHnSV7phYFPrp7IOt3rTUp8H7IpuCAmZdfGTUB10zNKSPL0wcH2Mj/WZGY3dYnTsKI/cOzfybrKFf9eaQYSuwhiCjA33InU0z9G9aQLYC17Y+R2u3dzXHOdjQ9exLFpM/DJjzH84ickch3o2LOPReUN18Pc+SRSD9wYWp9OixkaW9uAz32eY2AvZ1IhCILYXzy/F84KrNAHpggp8nOsgevPBMMYk/URmHueg7HneW2ZbGhrU8KAzrMr0gxpZsYo3GgZudzt24Gnn8WkZtXyp3gHzC0PeeLeps2+oDbuOK6VGg3OdHZP5lWDcywcugNtpXWNPQGbGAgb/evBBjbGVhnlRWgObxF5puCLPDIHZnpTcdxjYxx/f1yIHK7YpRxnvYrEhrtg9G/ARE6U2w7JpH693Ca6kEbZhmpMejQ7KieYkx/JGNykX98sB/yrUwNvVV1CuxhGx8R9vX6D+NuNOA565EyF2AXYeH4VR6HAvfNdcVJqMV5Da3nT1KqN8kU31cnx66LjMwYmNrPs0BDHyLBsizheV8iPEthD7ENVtrO4CguG7wzVMZFubTKeXV51TVfkb7hlC/Dgw5iY9/V+7KdJ7CKIvYQNbUXmb1fAmvdSVF91qXabep3jl//D8b5/5XjoYeBf/wX4f79geOc7GJLJA/LNjAhQX34eqqf9G1IrfoHEqv8Lre/qYrjxywxjo8Dnv7B/v3IQBEHsLZNJe2TueALmrqcnV5POAyAGBjEiYVY48zkb3QWzZzWMgcaDbd0xyYN9d8C6apWFqBnxZNxwNLkMN2F7ZS88BTgAVhkFK/SG1mWcfM9VuSlqmsqmbFAmymmtbPaaYmIhrSpG/waYfWH3Ifm5qTM9WR9Bom8NjD0rwzY4O0z4jWov26ip3Z2N9vQAddvA+LifwL9UFqGznHPAubbZ+B51cgR5NkrnX/mWccPfTClNkbnlQW9jd9MXXuC4577mj41BJNE3raJ8GDFMri3dcpvKJzcduKqBzj7b8sS+ZqlUnPyAzvUb8iiKaAc2BZ5dY+PKQgBAR3EN2ksvgBWam4pSm7upXkXihTtgDG9Tl4s9JmFxYE9tEbHKuvcrXesDOLBzl/gIEcS9b4QHZEwPIn+JiGHjpvj1iRfugKHp/wAgW3M+HtUn//Bw+8WJCEp74w024ERBW31btfmNpxsSuwhib6iVkPnTx0Vup7d8AzBToU3Wref40MUc3/0vjjNOB375U4Z/+4CBfJ5EroON6is+gfqSVyJ9zzUwuleE1h9zNMPVX2BYsxa48Ws0QyNBENPLuvVihqUogmFw9TrH08/4+bH2qgdr2P/Ze1uDjxva2MQU91pRT/lLjBSE95R4TrOY5M6uh45lidxfTz3N/ZC/SRyeF+Tp/tCIWO7gzAvlqIwhsekeTVL1qX8GRYlQC4fuQNIa3auyXQ8XQC+mMVjgHDDGhQCoE0f6+tVwu3xlx6TtMXY97Q3e7rufY9PmBp49zbzWMQNr1vp/7toFPLsS2LYd2LjRPWjmNYAxthvmpntjLya3HeRcT0zKf+ba5YZ76ry7TCmE1CVhFzCz8DQ6S6435b55p4nyFly9hmPbtubrbDSpg21rkvVXC/6sgZFE554ydq+Aufn+CXkvPfiwCMuWj7dej578wRd89qL9Xfsk7TRX2gaUhmHw+oTK125WFwq/J5hN0LNr+/b4STkm7Ock7TBr/AmwWjG8p3MgpsY7d7LYNseWrXF2Od6tAxth7njC81J1sQznS0ZNM8Vtk0xmNkZu1WHYFRRLiH1n8NB0dsm+1TAGg88glXJZvF/sz+ERiV0EMVk4F6JH/waUz/saeOs8ZbVtc/zq/3Fc9GGO4WHgy19i+PKXDMyeTSLXQYtheuc686ePiYS6Ac56BcPFFzHceTfw819Og40EQRAO3VsLMOxKpPC+O5DKqacHGByC97K+Ny+kDb0QpnA2RnMCX5N7w922MjBSQhadF3rTLqO9uEZrrxvGyDmweYtIEu5+6Z6Mp4pXhful3a7B6F8P04pOrsaqzkAuOIkKd8N0OIaH/TgVwy6jvbS2+faXMoiHxDgAmZpo1Ey1Z9IJ6oNoB55cHSRZFtBa3oB8ZZtS9kCoGWLqddbVakIY7uvztzXG9niDt7qFhknbddUMDYkJDHwBmWnDTzdsFDOCAgAYg7l7hbeO1cueYKCdjTFikO5t6rx2uoNfS5PvRhbHvGVcbJiuDzr17FuxK3h5dO8G1sdHu3rs3Mlx3/3xXvUPPgTcd7+6LLH5fq3QF2Gp+mdlHMbYHifFmhW9nQbLUm4r3PcA8JyjKYbzTzVfbpS9cn/slt82vgqJbY9MsMyoqnS2NfaMBUS497oN4h6YSPG1qu0kldfuEVunvI0SUryvwxhtf8pNVuiTPOEESUu43jE7YmrOJpjMbIypXX/H/JG7tesMK9xhyTOHjoyo9cbx0COO0Nu8aXsNiV0EMUkSz/8vkmv+gOrLL4F1xCuUdUNDHJ+9nOM73+N45VnAT29jeNUrSeQ6JMi0o/T274BVC8j86WOhrzIA8E/vBc59HfCDH4oZNwmCIKaDuaP3Y/7I3drcRiFkjUezbMI0nK6P+/UwJrwrAi/4zbw8r1svzUzYYIehIf1Xd3W3sHLQVt6I1soWbZiPewyyOGMExnhG3wswN9yptYmN7QaXw6C48g9QK8Ho34CZhac0hod+BNYLo3btAnbsFF/VGURemNbyZhR6+5vKK6bzBEJxGKtWixxA7qhqb8KsgpQ0jg3B8m0LaC+tR2dxFeQ2sANiTjPXkTtRw/bJO4Jp6XHE1ULRXcIi7fFCdcsj4ZUBRUvJwxX07HIq8G81dVCvE7v0NHk+mxlVV8ZEUnHNR0KvtlhNMl7IKm7bgJmFJ/2cYxoFsFZXBWjueE2yJjxCAYAFPMASWx6QDYzeb3QXzM0PAKUh5Z3RE7scU6tOPx3SulzxoikrI5Daw5O999GwhHMuPCydv43RbpF/MAL3etQ9p/QamjB81RoxQ6zcP3nHNIGkgp7YNdX3hbc5h7H7OaBWDFXCoxJr7YWg1lxotQ1z490w+jeAjfX4fQ5X7WMjuzB36B5lWbXG8dgTwhsPiGgNuy68JvfGximCxC6CmATGnlVI33cd6ke+CrUz/l1Zt34Dxwcv4njmWeAzn2b40hcZWltJ6DqU4DOORvlNX4W5eyXS91wT6rUZY/jspQxHLQWuvpZjVzcJXgRB7H+8cLcmPxJPPEltbGnxyzlHqcyxfoPIX5PYeDfM7Y9PuJYd2+vY6n0cj+9rg4Mp1+NHHgAzZ1DIwMOjwZjQEllAYIGZ9oyBTWARs0iau55RvCuiQpmYNFiV0zk5v9w1gdJdryV9mc88a+HZFVqzFPzwIr+AzOBz4FzMRClJfvEFTeBR+PxqKcw2wvVHDgeUCXqFcQ6gMhYxmm1slOwZ9tDDHOXS5D1s4tqI8ZjRdkwomLvKH+jrhTHXC3Hlc45A0LRnn1ts/Pndvl1M0FOp8JDHDSsNiX/Ho2dsjLPGTYJdqXDtjKy5sfXI1Hwx2tjjz7xRrXI88GC4dL72Hjy/imPdeo41a5ttC005PL7LNAY2gVXHkdj2KBIb7woVFQpbjDRlcu+SDFwNxeZAIsZTNEi9Hj6fcdfO0BCwZStDT68jgpeGwSqjDa+3pmcC5EJkT1rjMOxKyEPZ2SiqpNBvt97GjouqgfU6x8iIulP4XHL07xiAMbID5u7nQx90orPIBwTrCdDUPrUSWL0ivIZ3+R9SErb6jGOlweCeqDl6bW9Mijdj1zNIbL4/xshoW8dH6xNKdt8IErsIYqKUhpC5/ePg+dkov/FGpaN6+BGOj1zCwQzg+99lePtbGdiBOTUQsZdYR78WlTM/iuTq3yK5MhyvmMkwXP8lBsaAK77Ap7TjJgiCaAZ3CvtmPLs4j/YoaBolT4vYuVzm2NPDQ8vBOXbs4KhUgKFBZ9BRHp5wrkNZIDCGtzdtHiCJIvpP06ElWzZEJw22bf/QcuUdALeaDmPkdf8EBZxxYmNS3PIrZY7nV3EMDjVZHxOqB2syz1C9Fj4Q1y+PMYA7wwkGPqXhKToPJFlvUURK2bMrOOCsV5HY8iCMnlWo1Tiq1fD1uHt3tB3yJVmuiETzLq5XyNw5jfcVdkbTlNilaWA7dIkE3QMFrsfh6JibFD1cmJczCHKuusZntVTi6B8QoWgPPgw882zDXTyiRB+Z/gHxb7VQgrn+b2L2QIdHHuVOcnEfVhzwfg8OCa8pxmuYPfqQE/bqT4hQrQK7uoM2hUUR8VN/z8wdvT/a+AhsLmya238HMlWtYhOo3f+1fQfH+Pjk7jbbFvY2Ozq57wHgiSclewLVjo5y2NJC9+NKPXQ56+1tJuxOrvOZZ20vpHjO2EMoFnU7NNG3xYZexu1nw+jfgOefHsMTT0V4vjrs2AGsXsswPMIBcI3YFVWfX+aM8afQ1X1HY7vcPZu4n4L4/Wq0qKmep2gPVUASySLCMSP3rRWx486/4pl7pfDOyvheuYKR2EUQE4HbyPzlc2CFfpTf+i0g2+GtuvMujs9/geOopcAPvsdwzNEkch3q1F72YdSPei1S938Fxo4nQuvnz2O45iqGzVuAG79OCesJgtg3jIzUwXVJlpv+Yi1wPTc8z6EpsO2JJ4HnV4WXj49bGPXSBIVfqIO/o5m4labz9TpuUKALyevp1Yk+AneA11l8Dp2F59BeWtfYfo2n0YQeE8624wXxYzDwEV4rZjEmiVPNiV1BISFQnM7VDACwezfH3x/fu6tIHgvK9tZrQaOka8hW/3bzUbFaEfc/CDwQiKjinIfEDpmg6KaEENpASwuQz/tlyezcFTCR25GXrJr3KUDQW0uzKhjGGNxWTmBvB9rIpWa2SgU7IrQvwUaaV6uJ7d3Z/oZDkZjR78Raxz3OwbgQgS2Le22e3novOAcyNV9xLJYQFjw0kwGadhkpaxTtpXVqvQAMu1GSercs/c1g8PAXBXPzfbB3xqh+3PGk4SJUOmhv8G+5T1q3Hli1OrBdhPeo2FcKY+TewnikYx2PcASrVIDHnwQ2aSbCDX3sjwxtjzFBs2501F9oRp43faG6fl25/pqZGbg8CqN/Pdju50PrgvaKWVeZ04dwjaAdUZ9UULYW7Q0Zsm1gIzoGH21O7NPt38Tz1BW7tB603kbuFJd6sct9Jwl9DHDyT3rHXC0gseUBGP3rG9oVBYldBDEBkn//HhJbH0LlNVfCnnO8t/z2Ozi+9GWOk08CvvUNhq4uEroOC5iB8ptuhN15BLK3fwJsdFdok9NOZfj3DzHcdTfw699Mg40EQRzyrPnz/Rh69C4Ye8Iv3wCa0oOylV1o2/4X5CvbxFdkTciebuAxOMjx5FOaFc7GlVA6HLG8WOTeNhMRuGQvWdvmTeeJKhbFdplaL+aN3ItZY49qxS6vPM41ntnRHy1qzjt9vrIDYM7gt5FpXvyZEVoUDFHkmkFR2BSxzcZNXAwII2z1PbvEwCsuFxLghzHq2lp4dvk5u+QqV60BxsZlAWhiwle4+f39oxNTO+KctG1Pt7gIuZnWbM3VMF/NINHzaHBmrQuKXabReIzc3S0GvnHXK+O2cn2NjnL09bvXY/Tg1RUjfccusY+bJ4yxcJ02jxI7nXPNwraOjka3+7Y4p8ryCMw9z4lyK2OhXD66+9AY2IAFw3eC2VXs6lb11OYEYf+EGMV+zBx/QhJ/w8n+Z43/vZlC9ddHaIHThtUiMLgVUYIG5+J/IW0r6FwTsaIQEKCM4EQd0vbusXM0P+sgG9N7m8lmuPfOuJR20HZEo9C1PhEBJiDcBcMYFTsZNAJhhNhlhwXBiX+HFseRCHw0GRvjIWFcrJMv3qALYnwY40Qx+9YhWRuCwWtTnher+XBbAEZC/Ct5din5IRva5txDdSevnhMGPRlI7CKIJjG3PoLUY99B7fh3oH7Cu7zlv/09x1e+xnHmGcCNX2bIZEjoOqxI5VF+23cA20Lmj5doB4j/9F7gnFcD3/0exzPPkncXQRBTS6o2iPGCJoxvAt1NtrITHEBbeQMYxMtlMy/Lu7p1XhzhynlgRMttfzDNJAEsrs6REY7Va+DNmFetwhs81DShdi59fTYeeUzkGHE9MNL1IZEUP2ypZFPQQ4aHB4rOJnIYo+F4OzX0qPMGoP57Aw/+aOIkuDNIcsbAuUjC/+RTSmkBk8XrvzvL3t33xifoDg7g5Cnt3a/8Av1gdrKDLs55yLPLbf9QaKUcbhPwWtq6LWaQzYMDVI0wZAPpWh8WDP8N2eoupMq9yjpN9G4MMTm7AmLhtu3Anj0iIXRTszFKYYyVCndCFaXtZCusqMJ8sSto68rnVFGrXufo7298cmWvDFYaUnP5cI58YT2YXVXDUh2hxeRVeOnz3Osc/jUcjX9S0n1PI1Pri/Wcc2fBk8yS/5J+as5foyaIUEJ13i2M17Fg6A6woW2aPdSKQv1LbIb1cIL6RoyOcu8jgWJFYFFHcTU6h6XoBi76NCMkskb0R66Y7DZTtYDEhjvBIsPSo49gYAB4fhXHY4/p77PZsqg54TBGrvzjfYjg4sPL35/QezFz+VfIlWlfJahvsH/gmpQF/dbyBqn+6A8cseJXQOzq6+NKfsjIw3PsytT6A+GLkx9bk9hFEE3ARruR+fOlsGctR+U1X/Buxr/dyfGNmzle9Urg+i8xpNMkdB2O8M4jUH7zTTD61iH9t8+HXoYYY7j8swyLFwNXXcPRqwmFIQiC2BuC0QKWxb3ZvSIJjX4BmyWddTz0nrt7DyIn3GBDW2LDaFx6e0V+KTnEzrZt9PZybNoMLxcLEA43KzmD9/ECsHUbx0OP+K/A5bLwUKpqRK/de8Sx8fIY2pwQJsAXmdQuO/olHzxejPOIGAyEBspuSIs04HHFnEbVmHYJqJcBzmGUhWg1NATw/i2YM/ogTF5SxAHfNgbbEOfYsH01JG7m4OAEB3I4nHCq8HN26WjYZhHXTXg/v/39pPm+HTJmjx/jZcaGa3Ilp50u9NO2gaQt3GhS9REkK33KOtNsKvrJKT+uMfTrtmxGrFdMSPQICnga22zbXzwwKG8qeQNp7JPDBddvAJ5dCYyPN/LYiW4cVuhDS2EDOkprA+dbmtKUifZtqW5zvKHQuMGl9cz1Lgm0fdSZMOOStzfhnWTuXgnUZKUxKlQtbJYrxnc/t9ET8D0holHFMW0iT1LoamKNmnDl8wyPPNaoUqClstULOQV0OeSkyjW4S7t3Azt2cm9mQFYciPC+jcbfrnkvMjX8rpkbWS+IA8DQsG77mLwAEdVZ9cmFIfrFNuh0g/eC82d7aR3aS+vBxvwkhpruBWDxObvc2TDce68UI7yry6X7ttjvb+mcFyXfYpOQ2EUQjahXkbn9kwC3UH7LzUBSJO98/AmOL9/IceopwDVXMSSTJHQdzlhLzkL17M8iuf6vSD10U2h9LicS1lerwBe+yGO9EAiCICaKmwzYFQFe8DUdfU6qQh+S49KXcye8gzmDS3ArlKdl4yb9DHjJ+gjMnjVebiSnVmUb1wY3N5LtjVqBnTs4ehxnmbExRMOBVH0IbXvux+7dlr8QYrCxfgOwbh2U5fLPzuHHldmm4nJ2ab/CI2Kmv8Bv8V5uaMQuZ4FdFyNOtzBJ7KoEU9CE3XYAAPNG7kXLrntQ37ka3au3exWzPWuQtMawYPReIYZpYbKhAICRUc1mjudb0GHEndXPNZ1LMWa6tvQnAvBX1i2ODRs50LceiQ136s0MeXZJTRjjNhdcY7BojxdzzyrU6xzMriJX3aXZW3MZuDYxkbfGMCbidxAnmOpXiOTqNlAtINf7BAy7AsZrMLc8CFTG/EtEGuir7Rb0SHEFCbU+c+M9ntgiPKmi86IBvgja329p1zcLB2DwgHehc0+44qNhAIZdlcIYG7W47G7nLlGPp7cXITK1HswbvR8Yk/IkKTd5dJirV3OtiMSme7Sm6PZThEln4WC/hR07Vfsbtq9kW73O8cI6ZxZFruaKihYagmKgE55sV9FRXN2U0OdXwMLHHSV2SYvXb4AvfjuifHCbGYWnY8twbWicJ1cvfloWRyluxlV3xl7Zm7RaQqo+pN/euRk5t1EtBTr4iGpqkxB11HLD4bGrVjt9LkT/qW8f96KM/nC10cnPZgfaeOdO0a8D8J9prkdqxHMzpC0qHZcU3g9gYICHPoA1A4ldBNGA1ANfgbnnOZTfcAN45xEAgLUvcFx5FcdRRwFf/hIJXYSgdvKFqJ50AVJP/QjJFb8IrV+8iOGKyxlWrwFu+Q6JXQRBTB22LWZEu+8BYPt2juGh+IGJueMJZAf9HF+MW14OGXegG5e0uyERX469gbeU9bxclr284otl4DDqBS+RrSx2NSLqo73yYd8NY5TEOGXbWMccP8F/0hoBDw4YnPWJ9X+DueUBfyAAjWcXF15wlXI41FE2vH+jGuqj6Ge6AYuS4IaJ823XtAeW2Hg3AMAKeFEZAQHK/3+854ZMbw+wdRswtG599MCSB8bL3A9jtAO5o7JZ/+/gtWDEzCrIKqNAaQQziivQVViBRH08tE1QWFNydnFNGGMwp5CcOyk2jDEG2wIr9MIs96GtvBGJ6iBYZQxG37rwrHfB+jWLbSdhtkKthKQ15u3TyDvETco/3B0xyG8CbiTAOWDweuBG9D27GHP/FGHE4s5sMISVTop/tavHExKWITz3AOe60FAt1/HgQ/rwvhhjtEt1JcwZe8TZo45UMrhdfJ2GlGNr23Yxa6jvtSeVEnEJcg5lFku3DdvLG9BS2Yp8dae3XZwt7hoj7BMUa7+Hm1fLTGrWWTAlj1QOMet5UPtmsGMn1hA7c/kfj2eeBR5+NHo3/37mSNUGYPStQ2LTvZg9Ft5JOEiLduzezbHxgZXqx+6Ik6HzUJ4I3nNsbA+MXc+g1NuL3XtEn1suc9z/oOpF7X34gSpSyY0zMiJCRL0abLXt6hbQ1+fmx2TK/qFz0czhMUNRxXaG0yI3BYldBBFDYs0fkVr5K1RP+xCso18LANizh+Mzl3F0dQFf/wpDLkdCF+FTPftzqB/9OqTu+zLMTfeG1p/9Sob3/RPw298Df72TBC+CIKYG2/Y9LQYGobxENxN6Z3AL4EDCLrrBfWjJN1m5VkWKF7uU/DwT9AhhAOqWGjqnC+VqFmXTmFwlAPDsCmDNWh7ePLBPuj6EloGn1J2r40is+7Owu1aCLozRLa9WBXp6gc2bo0dsHICcFodLM2RFCXtMNRMLRv6KOQN/xsxCeEZhl3pAWFLSzARzdkmbtpY2YOHQHdrxHHM8dfbs8T0FgnCuql1M8j0Jnl5Ttim0Li6XEcDlAXRotjTdQFpaZzmeXaoqF1db9Nq4MFnOATOFoSGA8boyKB0aCtTKOUpS+tD8rvtg7HxSFbvs8H03Kus7gQslU+sBg+ppl0wAueou5EZWRh2RU1ZceB13RE07FG7rrpdlK87h5PuLvi+2b+fYuk0nDu9daBgA9PZwVKrAriYG3mNjHKWSHXn8unPtergxbvvOMVEXfQPkUyif66gwQ2NoG8ztcqJ+KZQ0shJNsKt/IQZWRKls4p9stRu56i4v9I27eZ9kgt6qntdUuNCIiQBj7BV/6HNQagwGR3t5HYyBjU09wUZHdB9m9HtqPbtKw2AjMReer1gB4OCWBXPX0zDGdqPnWf8edXP5ySHJfmos57nqJZb37ZDDnZV9JPr7IUJfmXrtpEc2YMa4/zzc89xaJKw4N27A/RgDCGG6t6/B5hGQ2EUQERj965G++2rUF52B6is+BkCo4Z//gpi55+tfpVkXCQ2GifKbvgp77ouRuePT2tnR/u0DDKecDHztJt+lmCAIYm/hHGgvrgHngNXws7a6oxxGVKkC3LKR1HxYb77M8KKhIe7N5sZtWxqARXt2ybmZvF9MCv3hugGET7XGPc+XqIS6+jFYeKZHBo6xcUR7vDkFuu/5ZnVUmdGMVYuKEf4Maf67hOe55Nnmf0mPqFSx0N1f5NWKFxwBP3daptYfUX44H5wsLCmzMQYauL28XqlXtsc0AZNHhVm69vKQvTzULuGyg2uMRvl7pItHJ7zaVqBM2VXKqio5zLzlETTK2VUoiNxzoXcDbgFgKJWE0MMMAyMjHMWCFcqpBnDlGjXsCozxXhEuaFcAboVmrAR095C/vqvwbGh7zoGkpfeAaoh089kc/kyXhT6Ymx+QtvPDGJUJImKEq5FRYGAoXFUjUV0OyYo6TZbTmZiSDhNV6tZtwAvrooXWDRtjzZGOVfwzkY8Ccm+RsItoqWz1yopqOlYQasLgIEehwBE+MqfvbmSGc12Ypeg+RdncKW9G4Vl0FVY4yzgGBqXLxK2zrrrjeX1nUJDmdiDEXGe0079YFamOJtrYOx82bJbCps0xlyP3z5vbZyn6YMAuty+puo9jaXVi2yMwd69oyryWylYkCr4wZtjBSUiivA2dGPUmPpZVqvp1yfqwdy255aSH1yNb64EIqa2htbwZs8d0M6DKXzd8L2TLnvx4m8QugtBRGUfmTx8HT7ehct7XASMBzjlu+CrHxk0iR9eihSR0EREksyi9/bvg+dnI/P7DYMM7lNWJBMMXr2JobwOuuIpjdIwEL4Ig9h7OgdbKFgBArdpsfhXuDVjdnmh4GNi40WrekSBmIJGvbINhV8A58NQz/lpbJPwAoAoAQQ3jPnncq6zjyr+6L/uAyOG1e49oi9C08E5lqlwkCSwxDRBMkC7jDmbKZY7y+qd8z6gmEpcEHcvihBrOgwNgpggWwWTr/uFEl2lZYhIVObQwGDKYkAb66puQvk10zWiawW38jXKVHegqrBCCaMBjKipnl+K5Eji8lny8Z1d6tzyTnD5BvWyGa5MxtBWzB+4Ss87JY+7yCMztjwWSlHulRdrBwLFtu/B0k2dSzGbhNKI4xoRdRMvg09i+A3jhBTssCISSSPk/54/cjVnjT4RmrNTao4RfhsUP7tbVoJ8oFjnGgu85bjs7cYmuOGHsWQVWHUdxrOLVyxiE8M65JwJYZi6+0pChQKOk5WqzMTz6GMf27ard7n0v3wON9RH5/o64TjUeUm6uvGgZV/QxDaoEACSkpPvuvTMeiNh1TdvVLULcdJM1BDHGe7TL0/UhsKDAMgHPuoEBYN16oKdHtS3KJl0KP1Xskn+rN0y2uFVfZmReQPm+sHxhKnZL6Y8IL9DhEV/MbfT8HhoWuX9ZcQDmtkdC4lRLZRvSg2v8HXRecrIVbpMwp2O266jVGk/IolvvhuI6W4jibPlv12uz3kTOLm2jTQgSuwgiCOfI3HkF2MhOlN/yTfD8TADAL/8HuOde4OKLGM44nYQuogG5LpTe8X0w20L2d/8OlIaU1Z0dDF+6hqG3F7juyzw22S5BEEQzyL1IttarLq+VYPSuCb2dMlj+l2dp1ejwRLwINMluOQeqBXQWV2FG4Rl5hfjHtiWL/X3dARjjdaTqgbgJtz4pU/lEnsajgaiJkEgg28KDQotq55NPBZeEPS+KRY7NW4Bt29zd9YlL3DxPCasQmo2RwQLnHFyKHQxEiESjGYkwxqSE5eH1z68S4ZNyaGFwK9MELEOIDcwAUimnbF7XmqTzupPPYXCbruJzIll8vayOfXi0Z1cwbxY3TLS0iL8zqYl4Oarbjo1xMZCWvMyCY9XgeU/seBysOCglKQ+KRpGVA4iYBIHbntCSqg+LPGsQg/84jyzd4nR9MCTgBXFzZMUhxFYRfphMho+tMl4Edq3AulVFbN0W3NkVIIVnV8Iuesc5OMjxwnpXDQ2UWXX1sYBaGixeGuJ691Lgeq+ZLaHjcSkWgUIRWLdBLXfAcVaSxa5GgoCcB2sikYiucy6PjoSMFVoA/RnMd/tfEMS7Z5RR+vtGDfvTJOwPrKrWnOeD5uDt536HTP+zoeXuTMJh/bY5QZ1BvS/k9atWCy9jv0RJqY4SI5XK/E7aTfzfTM5IX2RTlnq/dkjfxYN5EmVsm+Opp4GVzwHGnufBSsNArRQsGEyZWSQomQaEa0/sEveNbVm4/0GExN5geVHXX1BQVPdz+tLQxxiOwQFpe+mZZxgkdhHElJF85idIbLgT1VddCnvByQCAvz/O8V+3crzutcD/955pNpA4aOCdS1A6/7tgo93I/vGSkPv18ccxfOIShkcfA37682kykiCIQwf5PdEZqNTMVrFg57PgfZuB8giMPau87bLV3f6O0v61euOvuj62Zlt/gcGrofU2923U5d/pLD6P2WOPibArhwEpd3Ioxif45b4p453Xfp0Yo/FkUUQ5aaZKMTteXSP4iX9KJeGBsWmTrW4j/W4rr8fc0fuRLIvwDy/3VsCGTZujzwuHPx18uQxYtYikNTFto7Sxu3lAWEomActIAwCsTBdMQ6w3uL4+7UCVq39rBbGaSDyVrg+ivbgGAPfPVVV1S2FS8jJRnwHLzLqla+2SSXr5Y/xyhoY4tm4Dtm+3ELi5AoYGBUhXtNK0s6bt/XFczDXLLaxYIVXJ3KptP+TVEwLV49UNEy2NZ5ci3DKgvbxOWckgzpObPJs7g/0oh52nH+zB9ud2IqULdbR9Jce1O1EZAMBRLvuDbq9vCImKjWayCHtLyv0Ms6tIWuOomm0AgHJyphLGtkpyipHPmRuG7Z4zy+LYrjrvTxmuJ6o74ycgPLTStX5kq92YNfYoeL2qVcJcsVJ3q7Oa5OUVvCXl7TS//O0a969j46J91q0DundD39ECSBQmMBNKdOcXuyC42+CQvw3nciK6bGif7t1cSszu78dgR/Z5Shncrce/byINc7AC3rTyKXCvi0IBgOeJFe+92shLz/K8CJ0caM79qXseyESLXcEfgoXDf0W+qs87tn078NyqwEKN9/NEIbGLICSMnU8h9eDXUVv2RtRO+hcAwO7dHF/8EscxRwOXfYaB7cUNRxx+2PNPQvlNX4Wx6xlk7vi0mHJe4u1vA970BuCHt3E8/sQEPvkRBEEEUD6mOi+3NhMuCGvXcqxZK9Ybw76bRabeD/3AhWOkyXQ82hfpQAhUoej/BoDx7duQtMZD27q4oZUGr4HZVXQW1fyHUqpyvVFGvOeHWpbuL46RkcCXcl1dXISGzZZCN4LiDmPAlq1AT4+t5leSkqGnHS+29uEnlDKEZ5e/y+YtQG7gWY3dwbqBSsVWlnGuikLa47HCU9QFRTwRgsi937YFdHUBJtdPV68bzwUXySGmNhOuYkzy7GqtbFGSmGd7noAphWYZsthlux4X7kAw/tkqr03sXuGd95pzOMWinL9N/Pv8Ko7ubnEV5sbXw5QEhDgxUdfmXs6vqHG8M2DWhazJHizdu51BcqB+3bizry+mQmfblpzeE7F7l/+nO2mA/pANMSAPUKtxrF1bF3mhZG89Ls2+Js3mxrl7CsXvvn4gU+sT3n8x3HUPR6XCpcP0j6etLBJmpaxR1MwW2CzVlD4e9IKtVNSQ00Y0+wFBnmyCQ/QhDDbmjt6PWeOPo7O4Gun6EFAravePG6rINgwM6D82iA0DHxQalBVkfNzXNAcHY8qRdWTH7rEIL9xghQa3wOxqeBKJgMdjcL3cD9uyF6D03HD3dwWf3l4uhF752eb04U2dV50g1azYJRfjHhcDxoruLAZW+L5X9HnZzS1cZjHwfPY8WVn8sYXzBQbQvBu0lV7Q2lgsBYyTZw4msYsg9h5W6EPmjk+CdyxG5dzrAMZQr3Ncc514UF5/LUMmQ0IXMXGsZeei8porkdh0D9J3Xa0+KBnDpZ9iOPoo4JrrOHbvJsGLIA5lnnzySVx88cU466yzsHz5ctx9993Kes45brnlFpx11lk48cQTccEFF2DDhg0RpekxqyOhl9tKxBfYmuPZEJy+fWIzJEoDVkNktd/dbaM25IRSchF24f4GVIEsONhqa4UUhsTRWtnsC2MQw2D3aezOoqd4KHAoIRBRb+vcFiHk3ZJjgRrqFN0GbaX1QLUAw0nEK9vnvZ9LYWn1uhMyqiQndo67MoakNaI1VZfQPFXsjjssaV91cMW5m38qYnBr1zF/RL0e2egupEuq54XB1OvD5iKvksFrWpt8J7ygeBjeJlUb8CdLsGohnxIvgojHiGuA8BjyprNr4AUUsDnoqWNLXlCM+9nq3QTa2fFNyPWpIqX6W65AnxMMiL7nhNilepf5g39bST5dqSDk2eUyb+AO7/fIaPw9LoTR0FLNX3bEcfpeIi5yrqg9e7gI25JzDTmDWy7t6wqcntcVB0ZGgIRd8pKZ6xH7D4/4VinWKAfHQgnNGwVIu/1NMA9gIybSq3qTVbieXZKB3j0SUWCUs2Ctri7r7ZU2CvY9kSKY+GdgkGPL1ijrw0Wa3SviN5aIDs8M28RqxVD+seC1HewC6nVIXxT0ckiwL9uzB9ixPVhPQOziHPnKViwcugPgln/dAuCS2FVuOxqccxhDW8FGw55tcV2We81Vq8C6DQbGxrgasqi7Jljsn17IrOeFJnlOa58z3H+26VD6hKqqeDNdP4Zwe4P7QrU51q183JgIJHYRBADYdWTu+DRYtYjS274NpMR86z/+Kceq1cBnLmWYN4+ELmLy1F/6T6i8/GNIrv4tUg9+VenV02mG664VX/GuvNr5EkkQxCFJsVjE8uXLcdVVV2nX/+AHP8Btt92Gq666Cr/5zW8wc+ZMvP/978d48G1eg9tzdA4+jFzVfYFmgZfIYP9i+94Vis7TfD+kiCqMoVLh2LwN2P3M6iZL8OuaM/ogMhlhtyjbDnuOMcduiBm83CLc0DoAIS/aqFqHh4O5vIQt2vAUp01aSxvQVt4Ac+dTSPc9HVyNsYhTJeecEjb6x2VEeAgYTPqM7+7mDawCg0Kmnmud6MGVEx0YEFph8cjsXoHs2HplWS4vGco5bMsVwPQjf61nlzQIBPwB3Oxxf4YubteVUVl7aX0oZM/1imIAFi1093MGbJqZxZqxLyjyyLbK3mXe9ggLi/4egfA5XQL8CLFIqSNisoGEXcL8kbuQd2fbk1cG6wk5asmDzmivkKBt0rgejNuolLlW9AkKDrYkVLaX1sGoF8ClHbkzuFU9UoTAybn4HXVvedt76ZcMry7fQUn8MLiltKcQ1jSDesdrKNAxev/U6xy792BiNNmtMvhtym051HWSBTqMjgLlgPOmNytsSOzSyoR+eF9E9KHiibpZKi/CCy18qYWPyetuNH3a9p0JJyxRLVXx7IrpAoKibNw+Nge2bbOxY6crfvOAyTZay+KgzUD4vitIrd8APLB6Cfr6hPeq2f1sKOQ5OClIlF2cJVCtAbt21fHMszbqFg+dXwBeVFJnYSUyOx4IrQ/NnWK7KQb0pKxhYUujy49zGP0xH+usqufxphfV/HDnOWMPN6hMD4ldBAEg9fDNMHc+icrrrwWfcTQAYMVKjp/+HHjzecBrzyGhi9h7amdcjOrJFyL19I+RfOJWZd2C+QxXX8mwbj1w87dJ7CKIQ5Wzzz4bn/zkJ3HuueeG1nHO8dOf/hQXX3wxzj33XCxbtgw33ngjyuUybr/99oZl+1+XfY+nIO4Lt22LgSPjfoiWFa8JRKIIAI7gwuALGKqHQLh/kweeSWsMSV6QErLzkIeBSEcTGBxwb43YxrakAUR0nxrlmZG0xiLzsbSXhfizYqWN7fKX/oYCYXBEEZpuDZXQQMUZCMgzuknFjCkOZQFhUxM2Izy79HbWa40vgJNfKkSlo46SyuS+6BQtbAUXev/z7Apilocj9vPbwBUBWsdXo6ODiWT5zqDa9+wKKGT6Iv16pQhYmyWUuRQ8jxp337hTzpvZSMxcB85D11vdzCOfhxfGGDfjpDIhRVC0jKxe72EB+PfFsmOA+fPFb7l+NrILuf5nvFL0h6gulHNQZWt70Dr4uCavmR0S3Tn322CilMsi7FHY7zdaRmqvqCH9vNH7sGDkLtSqYVGQc2BoWHe/xjORQ7Dk7oFF3LcRBfqiX5igzan6sN6AKNE0IDTH7VaTLsVmJmJyz0QiNFur+6/zwUGaW8DmAAvcO4yrorQ2J6AXkqo//5yriewB0a57ejiGh72SFfsWDv8VCbvk2BDsf1XBuKcXqFtixs96YFOvrTRh5arYJRpqy8YqBoe4E56sPRoAQL66E6w6juCV4VbntUmDZ2dbeRNMuxT5HUE50pCS5v+c2XcXzB2Pi8U83p+yUX60yP0mtRdBHEKYG+5C6qkfovrS96H+ojcDAEZHOa69jmPBAuATl5DQRUwRjKF69mdRO+58pB+5GYmV/6OsPvNlDO+/EPjTHcCfbifBiyAON3bu3Im+vj6cddZZ3rJUKoXTTjsNzz77bNPluPltdFhOko09e9yZD/0XUfkl2k8MHd0XuV6oyiDM9aiKGoTpxK7AspZMFe5rb8IuQiuQBZdxXxByw76iwqt8W3mswCeHhwlbAmEUnKtfthuMZENePSGvAN0+YY8Tz7tJs73i2eWMlt2cQq53TKToE9EY8uaZPY+DMYb2VrGtyMnEwQzXC8fCvJG7ka71eQNubZ72wN/Kteenawp5WSkDPcAbHaVrfd6+ZnUYtRpH2XaSTWtyD0VMVAZAHcRzZsK2faHJtCPUDY244EVJySKRJgxr1tijmFl4Ai2ydxaA4eyxqMx8CWwkEApjDDo7ygPrqhhs53J6U13mSHnmVq8REymIY3BDCIXnecIMXzfm7hVIlXYr5SXkHGp2BcGGlcUuADBhKaFdsLm43pW9OCpVoGdPDdla825Urgiwc6e8UDr/LCFtawjBnvu5AgH/XD/8mF+EF5rMAVMzit68ZaLvbo2FeNtmzjUZ3rZWi7ifY2bTDAr8UeGKUZ6atgVPhNQRtXz1GmDzJjt08eq8CEM5uOwaYFvgjsdTvkXd3gjdl1ypJi4Uz/MChBo2btsiVFmxg+lD8HXlG7weLeAwBg4DgwMipDiYCN52Pauq4dA9uZu2WQq1qpjB1rCrSg6tCTyapH7Vub6b+PLFuBXt2eVpZeHzHRTZmTJbvbzORtz90SyJxpsQxKELG9qKzN8+D2veS1A9+zMAxFeDG7/OMTgEfP+7DNksiV3EFMIMVM79ElhlDOl7rgXSrZ7ICgD/+i8Ma9ZyfPNbYlKEF72Irj+COFzocz7LzpgxQ1k+c+ZMdEfFjDik02lkMybSaQuZDIMNA4ybYMkM2trbMZDJIFVLoyXfjmQ+j0SijnSaI5dJwUxlka6lkUoxpNPi5dLKZFBz3ErSJREe2JoxkKnsQmfn8QCAsXQO6bSFXDaDfC6PRIIBXUcg0b0F2WwGGTsDzjksI418XqQHyFTTSNRVESlpDMBI+yGIXe15FLNZpKppzK+vQSmzGGn467M5E7laBolUHumiWJ7OMGQyWZiWjXw+ATOXRZ1XkU7XwcCRz+eRLmdgSu/dbe3tMCo55PP+CCFbTyPthEOaiRQSdb/eNIoo5v06E2YGhs08r4Jka4tyHEHq2QxyuTxSKUdMaM2DF/JIJDjSad+GZJKJQSyAbJYhnzeRyWa9NkxZol3HxgC5umw2i2xWnBMAyGWzyCdakE5XvXbLZjNIWHWkWRosKa4J9/ppzbciLRWYz4v8a5lM3UuO3sqKaO/sRG2wBel0GsVSBrlsDi35HEYyNrIZhkISSPJtyGTSsGwgmzGRzxtgHR1g6RbY+TzGCxbydhbpatqpw/TszucZCgWObM5EJmMrHiHpDEM+n0A6Y6HFyCPLM6jWOLLO8kymjlQujy2b06i2zEd7ug/5nNN2LS1eGw4O57BkrjgPpZLt1Q0AmTRDfv4RGN6wFclcJ8ocyGYzSPM00qgik/bbmDEgmzOQymWRTgP5XMI7v6WSLa67ot+mPO3fC+5yBiCNwMkEsHx5O2rp2agPbkZrSx65bBZpy20vhrKU8oAlRbkdHSkk6ymk02nM6DJgWTayWdH+6bTvZhO0CxAeP+m0I/5wG9mcaNNqTbRPLp9HuppGOu22tYXWYgWw4F03+XwemcoudIw+hWqyCynpmFIpx46MhXTaRiabQWu+xds319KCfCqHTKaGrJVBqppGLpcF562Ybz+OTMqEN+mAexy5LMAM71hyOXEd1XJdyOfzmNFlIDnorHPOIQCwVCtS1YrTdllwZqBjbBM6jBEgnUY2m0XaTnt15HI5MMaQzeVgJ/Joa0sgk8kp1w0gckHl80nYXL6ns8jnDaeNuXf+AaDVrCNdTcNIZpRzks0yGPlWdHYm0ZvOAODIsSzSdfWcbd2aRUd7DjO6xDVndHYCAEZHLeRyZWSSWaTTqthgGEAyIUSTtlaG0THutF3CeQaI6ySXycDO55HhWaSRRjabA7J5tLYm0J4uIpXybck49x8AZNJ1VKt6kcLc9TzaU0MwTvwHAIC9Q/Rb+aQ49kQCaE0bSKXEOeA50VaLxv+I9p4WWDOPRTqdRj4n9Re5HMBtpCtS26TSyOb88yP3L4Dw3mzv6ABr6US+ZQg8nUYmY6C1rc07P+0dSQyPWF6fzlkC2ayJbD2DNHPqcjK4Z3Nm6FrIZ5NobW1FOVdBLqdeK7l8K9LlDJIpjnTaRi5rKOepzjNYv6EFM9L94tmeNZDPi2u/3NqBfF6cowzPYnQsjXQaWFJ5DJnOjPcMz+VM77rLZOrIp3JIF9NieTmLdCXt9fGpVB22zcHTaaRZGvlcDvl8HlmWhWnrn2m5fB6tre3e81PuT3K5BJJJBtbaijqvYryQ9rs3Keu9e90YnZ1oaamhlKt4z4S21jYYdhV8LA/TFH2QfO80C4ldxOFLrYTMnz4OmEmU33IzYIrZf/50B/DAg8BHP8KwfBkJDcQ+wEig/OabkPndvyP918vA062wjnwVAMA0Ga6+EvjgRRxXXM3xw+8DHR10HRLE4URw1t9gPh0dlUoFxaIYsDIAlYrwVqhYJQwNjaBcLoPXKxgaGka+UEC5zMVMYiiiVC8iX6nANP0QlzIvoGAUAG6j4izM9d6PpDWG0cefh7X01agWi6IMYwTj4wUkkwx2poJasYhyqYiyXUalAlgGUHCmZWspl5EMhGZYhuoxMzo6glK5Cl6rOLYUYVT99cUiUCoWUa4VPNsKBaBcMZGwKigUKjBswCoWxPFwjkKxgLZKWalnZHgYo5UaCuMc2UQRJSuPbLkEOPXW6iXP1hctB15YJ47DrZMzMeBwv/RndtyPckxcUxkFFAoF1Gri/NrDwzAKBVQqXAkt4tzPxZVMOsdWzqGQKDjHX9bOglVGAYVM0StrfHQERqaMSkXYWSwApVIRSUu0Z9UqgdcrSKfTqFQqGBoc8I4NALq7K2hvZyiVfPtKJcAeGgIfH0WlUkHFKqPEiyiliyiXgdE9Y0hWKqjWy2CowLKAQhHIFRjqw8NAqoZEoYBikaNQFNcdAIwX/GvPvQ6LzjK5bUpFoFBgKBY5SvUCKpUyqjWgnBTLKxUOs2clKpUKSmYJmUoFhcI4CukCUNiMwvhygDGM2EUUWkW4Y7FsolJRG3QkPRcj9iBYuQKbO+0mXWuyTeUyUGfiPikUKqjVGAaHOHbtAgqdBaVNy3YRBVOcx0rMtQIAbTkDY8Oj4BWOsaEBVCud3j7uve5SrRdQSBYwPFxAangIlUoF5bLTjkVxHcnby9exi3vd2cyEwS2USqJNS0XunI9xVCoVJBLOOShx1IslmM7145abLO4Sf1d2Q65hdBRIJBgK40554zZGhga9fe2xURTS4yiVOMolcW2WzAKqw2PIFAbBNCFdxfERcCOl9AOVCmBZu1Gy56CQziDrrCuxgncOS3bd618qVhlJawyF7p1eGxWNMlJeWxcxNlaAaTKUCgUUEgWMDAN2QdxruZw/m52woQrb5o54mMbYWBGuf0+5zL2+EACS6XEkKhVUrbJ3TpYvE/mwRkbGMDTEUCqVYRgcJV70jsU7/mIBPZUiMo6QUB8SXjIjoxzFYhGlZMk7prqRczxlgURChApWq/51USiKa9f12C2zMRRQQLJUQqJSQcksomAXMDgEpLMjyvVjmuKaAIBSWe3PXGyWQsv4DhQKDPWhIVSrHHtW1ZAwKyiY4tgtCxjv3ohyGSixEgpctNXgUBlbNpaRh7i2S2Xf7mKxAMatwH1WQqHg94WDg+r1bxjAyPAQeMmC1bcGtUoFpRIwPDzinZ9dq7ahMJ4Cd69PZiFp1lGulZTnEeD3VTLlwghGR/PiPJjFwP1XRLlSwdiY5fVxSj+HEvZsH8NYoopcpYLRUaDgjAUGB4a9WU6TpSIS0o5yv1AsCuEeAEoljkJt3OmjgEKxiNZKBYVC1VtfrYp2Y7UKnnxqDIXWAloqfr8XpFAoYmSEebbI7b/yuQqWHgkkMyPYtm4k1Ne4uNdNffc2jI62olgsoMXZdnh4BCavYHjHODJpcVyF0PSu0R+XXCiMkTg84Rzpe66B0b8B5fO+Dt46FwCwdRvHt27hOP004B/fNc02Eoc2iTTKb/8O7FkvQuZPH4ex80lvVVubSFg/NAhcez2PnYKYIIhDh1mzZgEA+vv7leUDAwOYOXNmw/29cDWooSlqOIcTeugl9raVWQNdGDhMu4QjWrZ5y9x8RcyuA7WiF2LWUVoLeU/XCrfn0s0oKLZg+vW2FUgaHA5ZDIZGjIxIYYwAeL0uhetExduIY85Xt+NY435kq93KtozbqCSEp4R7rF1uQnyEwxIT9RF9PdL2yqFqkpoDahidNPm6v1tUVKaRDoQx2rClV30ON/TKuQaCec8CiWO274D++WP709x74X2GaCM5UbQXjqibjZEDUTmo5DDGICOjIp+NSNotxQgxf1+jNu5UIY49UR329jd4JVQfD8zGVqkAzz3vFsudmTR9Y8qBdHjy7KCci5kGd+0K2+5sEbUiTCIFxoC6kQEr9MGQnJqCpXjhVLYNo9DrG+YQDDeSw/Vc/OvOuS8bzOAWdSjBcEzPBnd2QTcMl3MldNbrMZRyeWACiYDNEaF2mfoA5o7er5xnOeePG+YoamAwAjN7qn2asL231096zuFfn7rcVfK1q4TFBmLLWms7PRukuFdYmY5wGGNkaHizy/y2MpyLVjEnmAeuuhvZqhqq6m5nxYS5RbcL9/Lh7d7NsXWb8IQbCVyKwfBf08mB1dMDrFqti4nmCD8j1L62pxdhOIe54wkka0PyIo/h1avQOvSMsos4J83dw6ZdiQkfFCfA+2gRutcs5d/RUUeQqnElD1rkjJmSmZUKd1IWyOHUgb4/EMYYmcctWEVE9fW6SMQ/OGjDrjcOiTQGNobainOO8XExW/Kubv/5PlFI7CIOSxLP/y+Sa/6A6is+BuuIlwMQncEXr+XI5YArL2cwoqc+IYipIZVH6R23wm5fgOzvLobR7Q+ili9j+PSnGJ54Evjv20jsIojDgYULF2LWrFl45BE/l061WsWTTz6Jk046qeH+Q8PiX/elsb1d/KvOSiVWegMRaaCgJvG1MXP8CbSMrvGWsJgkKMprNFeXRL1k+wNOf9kRiwHYVuxLPEf4ZT3Ipk0c6zb4wpu2HC5e1hO2EO5Mu6IclzKLnlOUP8ulVEiThI4pYqQgv354Odbl2RgjkwIHZ2Ose4KPZaQBDnS0C5GLyQqNW24wSzLCh8cYgEpYKEnExIpE56X2V4xKOmFczi5AeKNwOGJXYB8pQgbcSxpmo1AQH47c5NGW7QijibR2ENU/yNTlUkMEB+eA2pR9/eH1/nYTuV7E8VjcBLPrSEE/mx3g35tsaDtQF2qc3HbBS23O6EOhMvzE/FHvv9K9m2jsURFk9x5gzVpJMOII5OwK1iIsyVV2wEBEcmp3qkYJ2+lXDG55fSIA5KqyAqm5yaLgHAODQjBxa7Jt8Z9O6BofV/OORc0KmK9uR76yDTpMxj0hxPbug/C1w8DBmHzTOyK05hQqOQiZeq/oSNglzCjIYo//nKgF5kBA2ARtLq5UUogYq9YA27YH1wvk/qK1vBHzRu71/q56eSIDlTuVvWg5sHy5kz+wiVuNSX1ZvabObtvZobEtQuwKLsnnIeWY0xjCGADDF7vMFErJOd5qV5iVc/FVKsC6dcCaF+SK1bJHNX2T4+wX3/cE+3lwMF5DPTxBr0fKGpVmBtaXXSyFJ5jRorlgX1gH9PaIcq26fptmILGLOOww9qxC+r7rUD/ybNROv8hb/l+3cmzcBFxxGUNXFwldxH4i24nyu24Db5mD7G8/BGP3Sm/Vm9/E8Pa3Aj/7OfDXO0nwIohDgUKhgLVr12LtWuE5sHPnTqxduxbd3d1gjOFf/uVf8P3vfx933XUX1q9fj8svvxyZTAZvectbmq7Dfe9MinQcSkJbd5YlTxyQvvaXjBnIZJzl3BbeDvKYUNpWDLI0lTsDKDWxelT/ZYTKTacBo14IeE0FhjZ2XJliVbHIPY8d/yU/HB4aP7NYWEwJooh3DbrpcIJ6fXJjeew9OgpnwCHNxhhXT2CUzbmB3taXo6f1VZIIyf1k9RI6bw2bq/UxBpi7nvZm9XSnyjPN0K6eLTp7g4vKUpSLe6Tbd2jKhD9I132TNA05+bzhHcPmLWJw7SayHq+KC92e91JwaIxnBryLuRkivPTC29lgdhULh+7wFs2bq9+UOYJEIbUAAGBGCT6Q7iFJ0IiXesO47Rn0dAte+56YoSukQXtZFiSxiwdc7MJtmKoPo6v4nJL8HgBqpshQnrJGMWv8795yO5FBlFjHpaT0CxdKy5vwGPEG/dLB8wixa8vWaI8X1dOsGtjfF3JSKSFu2DZ3hF0xk16YeOs5JPFYOn4WIXY1EzJvhfXFQBnqvy4Jw4ZtO4JSTAF+2/HQJCF6wc/vy5JJhmQCSNWHIs9B2FDxz9g4sGKlv87UCPiJpKYf15uFpDUKs15Attajr55Jnl3gyn3nehrKYpfuo0Gjj0IAUKurS+L7Y/+X+2EgihmFZ7zrmUX0TUnThtGE95n3kUwybmiIY0+PbJmTl86uYNbYo0hY0lTEMZDYRRxelIaQuf3j4PnZKL/pRu+N8rG/c/zv/wHveTfwsjNI6CL2Lzw/C6V3/xg8N1MIXntWees++XGGU08BvvJVjmdXkOBFEAc7q1atwvnnn4/zzz8fAHDDDTfg/PPPx7e//W0AwIc+9CFceOGFuOaaa/DOd74TPT09+NGPfoSWlpaYUlWkaBhnhjx/nSsEuANbJnlGDKSOQyIBLF4EeAMvWexSPLsCM2opYWHB4DvNZ39vO4QGCunRDUqYVXA4t3uPZidhofdLDBbMUJ2KzRCDTw7mtAdXBCBxvPqQrsnBnRm+RHhGpNgVqGvDxuZKZ1BnMeNOOGg10QnbSIUKDkePigVK2JVmQixWr4Aps59xrWeXu5tVDy5poIk00dacA4bBJdFWYCYA10FNmZ0TIt+YPzhk4NkO8PxMz/tNtT0g+DSQjBiPFqJkMvUBdJZWKctyOWDRIk2ZjiABLmwxmBTyFzDHtCtOuBfz70XZs6sJ26QrP7h7uJRgSO4EapLFLtmza2RnDyoVVyR3+p+Idh3JLAcAdJRWI12Xp8xTrTYC16k7C2MiySL30WFJAoz7f4szreAaRBa43HKyzkShOrELANIpIXI9u0J4ITIDgeN0d+EB86XOn4v+xj1Pw9ljva1cr0H5HK7f0Jy2azcpdgVJJIRgU62qHzhkVJvCXkFeuK5ao7KEMSaee669TKfEA9CW5ZTBa66Or6D/KKLvwxlsdPbfj9by5oj6/Wtj924O7+MP86/7uH6F8Roytf6GYq0nqHFJ7IoMifWXm7Yar10zW0PbeyGrtt4FbHCQR/adra2y1mVoTVL3FceZskaQrg+ho7gqvIMGEruIwwduI/OXz4EV+lF+67eATDsAoH+A4/qviJnv/v1DJHQR0wNvmS0Er0wHsv/3bzB6RehQIsFw3TUMCxcCn/8Cx/btJHgRxMHMGWecgXXr1oX++8pXvgJAvKhfcsklePjhh/H888/j5z//OZYtWzaxSgKOTOslsaRW43h+FZemVPdfRm2I3DDt7QxzZjTwVOExYSI8EAIYIXy5HjXBcEHO1fw6updlN98Uh4FZM8VAyn3pd0NRXDGNeQNn8UI+e7Z/DLbtNJP3+Fdco7wyg5MG6OCRgypg7lxxnBs3ieTTW7YCdmkc3bt5KCwxysssU+v1Rhe5HNDWpt1MMt8K5OwSjeu3t9qulZIdql+IgRokkaKzQzyrFJgfUllwou84577niMazTK6zEdwO7O9Ub5rwch2558Ot0pb34TbADNTrHKPj4Qa3DTdMjwf+3XtygTxIkV6DjthlwRW74sXCXGWnNvfZRHGFPm4kYLcv9MtxKq3XgXqNa5uktbK5Yd2eGGfbYFbV82jJV3eitw/NNXVE6CHnanxupi5iSmfNAhbOt0P3aCXRhVIywrXOL9XL09ZeXo/W8iZfMGB6qUw+P9t3Gtgz1gnAv7aTCQBgTg69MK2tooDBIedSjfEsZQyo1538roEwxuFhf1ubJf1QuYjymhG7LAsRClH0SV+0SFy/ti2uH613lFy+b1HQQo2d0n2RSIMnc+I54lUR0ch2+GOD96zQxdR6u4QPXq/7NgqllNuL+55LBpCuDwGcw5Q9GgNltZU3wuDVWCGep3IYzR0buV4yNVSJeF76z9nelpeF9nOv52DOO5fRURuM+c9RGUXYZMwRCOX6LU+sds+Ka4uos7kPDCR2EYcNyb9/D4mtD6Hymi/AniOmTbdtjutvEA+xL36BedNFE8R0wFvnovTu28DTeWR/80EYfesBAC0tDF+7QbhmX3oZx9AwCV4EQUTjevcw5+u+TNkRNPzwAzVc0B18qR4RGuLCUHjAZUAzmAAQCpdyNtCMHMIDI9du20iiq0veGajW3GqlnGDchuGEhLhijmU7IUcIDjscgStwjA3Fg+jkVEglgUytT1m2Z/MQBgZE+EyjerK1XswcfxIZJySmXgfSqeBWqmcXuNwGzt+S10ywobduUwfKQFh48tZ5YhfH0iPldX6Z7r7VqhBZH34EeORRuWZ9e7mhtKnQ8Tn7OjoLM/whlGuXLHb5OwBFJxTQEwNgA8zEmrUIiV39Lacqf6cruz3RJIqAHjRhoi4t97gsiyNh6j27Fi5wy7DkEb5X5q5dsnddNH6uM3f2ClU4cqlWRe4t3aG2l9YpduuoSxGwRnkINkt664LiqusBFbI14h4NVuwK3F1dQDoleWo6tQzlXuwdb9y5cwV00y6jvfQCuBXl2eYfm3dMMLCjW63DDfuVQ+XkvjEpeZ7ZdpwHmQ0GYO0LwjMrlmDb6MIYtTsKQS1b60GqPohazc/9qGzlConOqqHc8RjNHA0AaG0RnpicAwOD7rWqr831REomwqKYd76Vfs7/YGMtOBUwEuLYPLui3LGiBTfh8RxuDyGYNw5j9IQbabk72Ym8k+cZxS3YLIm6kQM3hdCesAtI1kdEvkUNUQJTYCvUbTecXzbVFQ2DF4D/d1t5Y+CDgsbX033XiBCeDG4hYRWjz4GmXpeu4nNorWyRlkw0MNuxYUJbE8RBirn1EaQe+w5qx78D9Rf70yz+z6+BJ58CPv5RhiOOIKGLmH542wKU3v0T8GQWmd+8H0afyEQ5bx7DjTcwDAwAn7+Se1NDEwRBBJHDGIPYdnDwoMapuYMvsz4Oxu1QGf77bozYBWdA4NlhB9Z6pYX2lZfMmS0EjzgvACZcfMTgxhls+Z49XhZ+7QClVBIv6W3lTZ6dDNw7SCGIMCw7xqmr4WtCuE2yWXj7B6m4A+iAQ1hcaFS+KhJZ2bbwVokzgQOwYSp/M1/x0ngEiDZKSgPwYCiPFzYY4XkXZfqOnUKwKbkzhsY8wgxDCF6u6OUya6Zkkw2YsEMVKknZXQ8lACPZ5Y59TiJ3boEzA4WCv51LWUoUDQCdo08oOaPKSbXhm30aj2aOhs1SofoiQ6MAtLcJq8fHRTtns6pIcfRRQGenK/zYKI5ZkreEX1YlmPqoGYLiiHKe493v4q5hV8xwry1tzjSHSjmqdSMGvlxd5uU+ghBK3D6CSY2kC2NtBHfEkAg9MCAS+3VwribK1+bscj5SHLnEXcoic+hn6gPeb9GuOs8ntxwpJ5TjkRbSOjSnlXELXV1AptaP2WOPKUnc1Q1DTwsADLkcYJrMu25FmJ7ec7hWg9dHpJK6LTg4jMBThMN/2DCAGbBtwJ3UOOr66u+ro1DQX1+xXqeaxg0ucZtC/pAxkn0Rdna8Ed3tr9WWzZmJPe3nYKTlRFEGt2DaRVTNDrWOqKRoAUZGxAcd21bvlarmO5ZblHw9Ja3xQDoBjQes9qOZT8oaRsB12kP5NmKEJ6uRsSxIntpupc2N20nsIg552Gg3Mn++FPas5ai85gve8hfWcdz63xyvPht4y5un0UCCCMDbF6L07h8DiTSyv77QS1p/7IsYrrqSYdVq4PqvcO2XNYIgCHWwqwob4fdjaaAgh9Ww6C/HY2Mc1YqtDHqUcpkpxIRGdjb5GqrP7+J7J3nORo6HiOvF4pUf5aFlc7SWN2FGl+QFx3nALoZ0mqn7RaEZfCSTQDrNmgvrcogSPwDh4QWIRP6myZAOfPQPihryQM/mDFzygkgl1Irb8hyZjOptEhlSaEeFuerPeqGg2azBYC0otCm5xKDmsAp6eIk/HIHBlq812bPLAFiM5weYdtBbTs7WbOvbrFswlj4So9nlqCQ6Y4Uiy5AUPi7EUg6G4RFgZCTs2aXk1IONbds5uJFCOTmr6TBG15PNd5qJag9VSZ2sF5syGyNHKLRQLnfWzAhfI6aeT98s1TVUziXElLBk378lGO4aRNuM3A55hEYdg1IKB7oKK2DUxgDA62/T9SEk6+pUerLoHHUuDbsa51CqgXn/MKO5c5iv7lSEp2oV3kQnatG+uOxWIoevBfNxRfUVI8MiLD2T0eTsckTqUqv8BUG+GFmoA9Xd34wB69fb2LxFNcMX8fX5FJVQaK921rAdvdXMhO3c42bIe9ppK8MN06vBsKuomu3aMhlscGZgd9urleWDuRNRM1tQKAA7d4bFrp5eSSpuYLd7/yxfBuSymotQ+0HLJ2EVwBgwnj5Cu96tvlzmsbPYysR9/NJBYhdxaFOvInP7JwFuiTxdSdHBFAocV1/DMWMG8NlLWVO5OAhif8I7FqP0np+DZzuR/c0HYO54AgBw9isZLvkIw733Ad/4lpT/hCAIIoDu0cbr4cGD8io8dzms2cf6M3cFyuAc2LoNePxJG9t2MGW599tINhViETUI8QcGvo2avQP/wrO5WHL/dnOCqQMrt11qdfGS3tIi6vIHMYE8V6o50ccTs26qXzMWOwnNj1oKLF/uWzAueRJ0dwPDY6pwpwwyA4KVZYdn2AyOaXlGHXjJbetmVImFc6xazTE0HO094SdmDyyXtEuRoF5OUKX841jiW8TVq9zxCHQH59GeRdpDCFwJrrcKANS7gvn13HBboUpGXfPu/q4Xh0siAYAZGB0F5KTwUWIXYIMzITI0e8lZLKOU6YtP0SUw2BgZiVjH/FDFWNzLUJol0CvDGdB2tEUNbN3QrMBEGVCvK7kfMgzpXEsdVpS4FzcniDuZA5j+3o56NfN6GGeWQXnfGcVnvTIB+V6MFssNXlUF6diceMyfFCRoUAO75cs2M7oerNin2UgVDX1hMVAfB5LWGPLV7dq6bFuI+br+xOA12CyFctsy9La+AgCQrg+go7QWfmXB86lvPPcjilxDa2UL0rV+yAnuZW9mXc6uUmpeuK+KqDEOr19x7gf32uUsITxCA5MkMG6jbuRhmfnIJPzVGoNlux5RspEBwS5S6PUvrpefGbbf5kBraQO6Cs9GHhdDQMSXVzj0bemN3N/FtCvoKK6GwRukWAhAYhdxSJN64Csw9zyH8hu/At6xGADAOcdXb+LYswe45iqGtlYSuogDE942H6X3/Ax22wJkfnsRzM0PAAD+8d0MF7wP+P0fgFv/m8QugiD0iCAStY8IhTEGcnZ1LTsGvGspeELzchooO/jyXrc4+vvF4NXgVf3sSqoLmLZgeSAvQl50Obvcf/1BoTto9gUfX+CYZ2wIDRi3STmqxCoR0qkOCJt/R5i0p0t4TNoQNyG8aTKkkgytrUB7aX1ou53d/iCoezdHtrBFJD+GGCjLcMsOtZE0Waf4OyrPmrdBTCNwjq7iSnAenejZK5KF18reCJwDBmw/jIYFNpL+4NzfwN/MwuNPG84AMq7BdTZGb28bqnDj5vryhbfwoFQJvZT+4NwNcZXCd1yBSBtuZoNxGzY34Ki38YcRMMA/dRN/Lx7N+J42zd4H7nUve3b53nzuvakXuzxhQCfWSMvcJNbexK86j7AIz76k580UPiCr3nzOLiGqqG3cOfx3mHZJqdoMzBwpn4a28ER4ABzxR2oiHuP1wp0nAuD3rcFDiBQ9JFvyhQ1IjO/WbKQRmWQxlolzzgHMKDyLzuJqbV22Ix4bBg/lZDN4FbaRgmn4treVNyFhF706g4Je1MQh7nOlEtBOsrUeyDPlypMIBCeAqCS6YLF0uB0n83XD3cXx7PK8zBiDZaTkzzpgvAaDVzyhtphaFCjIPc8MNW06rYk/rJjG5di2xaQNBrcwZ45mJ8ccm4XzjsmefrzeTP4xoKWyNXZ2Sx0kdhGHLIk1f0Rq5a9QPf0iWEe9xlt++x3APfcCH/o3hhOOJ6GLOLDh+Vko/eNPYc9chswfL4G5/m8AgIs+yPAP5wM/+wXwy/8hwYsgCA06j4OAsiLncJJHNJ5XVNRjUjOo2rRJzPxYrSfCnl2cw7SKUD2xNAN/IPQeHjVNvboTUDNb3KowY4ZffsfoU8iXtgQ3F9tKXhMiRxmP9vSZ4leGojs+Cx7vJOrp6NAvl9vYCHrIBUN0LK54GQHAzl2SnYDnvSF7Oxh1fwPT+eqeqg9r7clWu6MNcKvQLlVXcC7CGDs7Apuw8B8iL5TrFubm7LIBGKjVIgQ8iAFr0hLKaS4nWx3YXvLsAlNDS2eOP6UYr6srkZDtDopdkmehGzonCwhKceL63bDJgMFrzWuQXtu4/6g+Nd79EfQGcSqvJrv0nhsS2vXcPTeq2AUAKSdXkBkldsn5+KQD7e2VLQfS9UF5N+9YmTezpZ9PKxhqG3sfVsbV8x60Txa7uPC2q1S4NzsiY0DCLmqvdelSEmXFGGLwGkbH/PWWlPw/XHAgVxwLd+OlUkQ9QRE8KDI65duumgVoG6dcjq7DxXZmn8yOb0S2tkdZ15qpYt7CFBYtAuR7ZfEix8PVydml2qa/v6Pyhhm8Ip4DznEYgceBN2Oj87DizAi1h7699OfRvzeYUmFH6QXHfiiTOADAguE7ka4P+ZMrBFRF95gtW2oPycjZY48FF8USPJVuHkr5XM6c4f9OmCLHImOAlZkV8oaVsTuO0IfFRuAK2HFlKts3XTJBHEQY/euRvvtq1Be9DNWXX+It37yZ45vf5jj9NOCf3juNBhLERMh2oPSuH8Ge9xJkbv8kks/+HIwxfPJjDK9/HfDd/+L49W9I8CIIQiU4Ox4QnkUrYRWQtMc0O8eHdulC0KpVAAyo2klPJHBpK2/AvNH7FBHMfSFPmPEvrtqQSM4xZ/QBL4k+50ApORfDOTHbcjotlW+N6xx+ACfoSRV41BHs0Ufve8+uQrHxNo2I1CRhRCYLD4aa1WrhMMZwRerQIWmNIzG02TNiRkw4izjLfiMlrWAiL6kaFm5Pb/DPxaDeNHhoHKczXx5oegNViNw/YOpgOOo6XLos4080ENFIHAbAI8IHI/YR+eL0noRtbeJfw/SFHfc4/cTQft3Cs8sChzGhnF3uMXtdg3eOgwUExC6Eww+j6Gk9K7SsXAFGRuGHTGuqic5v5p9P+Tqp1dS+yfNeZI7DDFNFTzDmCcK7VV0ldgbL1OjmkPAoo/O8W79B9SJi3IZhQO8Rw6PvaRnTrqhGDG6Hue0R7bavOot54ilz2sMK2LljZ0RFAS9BXV83VjSweo0/G25wx6AHVRSu2KU7foPVMXdeAoahqvKpFJBKRtyXGoHZsvTPMMAVU/x1wQlE3GvH71eN4K2B0L0S82xwc3J5kxg416PveSuuUbeM1ors2eQq0UxNVcaY+q/GJt+2yXh5iX+HR/xlRuBB4+aEY6qa7693TTJMcOeGiTonnq0wGn/8CkBiF3HoURlH5k8fB0+3ofLmrwOOS3m5zHHVtRytLcAXPs9CNyVBHNCkW1B6xw9gHfN6pO+7Hqn7vwIDNq64jOGcVwPf/k+O//0/ErwIgvDRPeUyo+H56d2wAGWgb7j5rqJQZ8LrlVK4bNudC22dsoYjLVy6FDhyCcO8ucKThUtrGXSJfIW1QUENAMoJMWWfaQQGOUz7M2CNE7oibZEPH8qkiBMeigGxq9HbyXh6cdP1cmYqg7XguCaVkv8Kz74ZZCpznLZUtjbeSLLXTZLNuRA10ik/35xy0XiLxB9iljp3hZ8dncMUA/fIBPWC8dwyWEe/DqnOWUq5IVOZAXemOFeo8rwNnW1KybnKPvPnq2W5ZR+xGEg6g3dPoJCEn6AzJmems15MsGCzlHLNxTpOeAKQ+0/U9JBhz658Hpg/z0DUQBoAqqmZsI00qmZbaN3gIJR8Q5yrJUUlo446B+mMWN7WJkR0ZV2aIZ12c30pQWGRtkdRH+73vKh0e0flLLOMNIZyYra9oNdZEMWDL8ZEy8j62/WuAysN64NvDTVHsWH6nmBBgm0SJ5m4CdrHx/2tWlvd7eIn54iiVoO2YQ3pY8TLXyZ5PXrucAZYRU30H+nZFSGacDAwbmNoSPTNrojkC+XcrcrxBNZlR9OV66MPS3Xuc024IJcENTlcXfbsMqTb2MuXJtWrsylqZk233/IsC+zczCn1T0lEf8Ic2wwThXH3Pm8gdjGDEtQThzmcI3PnFWAjO1F+683guRnOYo6vfp1j2zbgqiuZN00zQRxUJDMov+WbqJ7yr0g98xNkbv8EEryMq69kePXZwLdu4fjNb0nwIgjCgYmv1AuG/+KFlcW/J4bFria2BACRQNuhmugQPxp0R+6LeiIBtLYyzJzZ/Bu1PPCXQ8Dcl3zTVMOj9GWI/xuG6gUnD46YoRlQRbA384Wkko23mQwchpoTKmDjrFn+b8btqPRF0kbOP6z5dnFJWkEPwvhBiyt8tLYAJxzvJGuHmCCBcyCbDturG6KLgbOrComCWUT4UbC0VBI49RRHEZQSgw3lXoyR7DKvSIEB2JaTPF8s8RPOi31DXkxRNUsH4t4XIuTYOYyQ2GUA3IZpl2AZ6ZBYER/G6Hh2eaeDBf5VqRs5p04TiQTQklfXLz1S/Xuo/QxtOeXkLJQTM5TZLTkPeD8514g8Xl4wH/AnFwiErDnHsHCBar770w0xleUJywwcQAB/hj6Be6/WqvJlpbZVdzcCiPXj6SUoJ7q8pTodwO2LzHIfugrPirxVMfeYK/ADQtit1zleWCfKCBfu1xHyWAobof3Tz7knFRoIR00mhVhr2mVpx5j6ApRK4VBAUZMF1+s4ldZ1Vgz27OOaqiNaNJElIt05ksIYnXPTH5xJMGB78P5bHpzHwisQmmev+9knjH/9s8D5dIUz2ZjwCdi4Sd839LSdDcsI59kKGxBzUp1qLa633/XetT0P7DFkapqJD+TqGnic6yCxizikSD7zEyQ23Inqqz4De/5J3vL//T/gzruBi/6N4ZSTSegiDmKYgerZn0PlnCthbroX2f/9VyRLPfjiFxhe/Srg5m9z/PTnNEsjQRBALTcfpl1WXupdD4/e1leE8oDIL6TyoHzhAk3hMX2M+wLeaEY2eaBq52eCp3LgZlI701yQtpLqoVZ3x6POIFiELOkLkEMWldkWvXaS92OaX3qivpKrdepRkgg3fE3RDxyiNo2LZElLnl2tlS2T8HFpnq7ic8rfweTTyrrAKdB5lLXna6HE9I3aWeRJcmdhM2HzoLeV2ljtHUAu78wy5y5lBgrpxaGZEzljITXZn0DBEbsatbAu3MfN28VtL3l3cHMOE5n6AFLWKCwjB9mjppLobCDqufl9/ONzrQckYc0Vg91Z92B4AmScOBD0jHEZySxDf+vLlHYsFgNJwB2xq8vXh9DVxXDqKfrZGO1GfYebs80TtvUbyt5mwet2rnO5FIxZrpENw68UE6SQtOiNbOR6n0Cu2g00CNuS+9GRUWDtC4jon9VlRtD7NaJMoMF9xcQ527XHkBfBYKpoNBVvpQwcOkVezpnHW+ei87Sz1H20RIvt2o8pgWIM7wMJC4WtBttL5LaSvOo0ze55YIVyjjHHa1RvqfuvXyaTrmtZaNKXMDSsXax5P1BZeiSQska06+QQX84N7X3mil3utTZ39EG0VLbF1tmcT5kKiV3EIYOx8ymkHvw6asveiNpJF3jLn13B8Z3vcpz9KuB9/zSNBhLEFFI76Z9Rfvt/whjchOwv3oXUnqfwxasY3vQGMUPjt/+Th3LzEARxaNLRoX8B1IWouEmYG/YO/ggVGe2s4TFeOQ3dgxwbmJssGoCZgrX0HCCRkRJiI/Ruu3hRuH7dQMwwYjy7WHhbkSPGRsIqBMIf988HsiZSqzS/v7pGGVg1eiw0OnXa8z7JJjKMRq5/juNEhJCVK231TdBuEzYsX92JdNIJV2Ui/MpmqdB2gBDjGIPfKKYY/Bm2mkPOtbFmtADcgppvyXZmvXMNjAipkjyEEqZ6zxnM9exS276lxc+TU0z5inTdyAGMSWXGe7B5Il6jMEZnS3fWUw4DCVMM+uWWDoldUc4YkbnBfFJJjmOOBuYG81pFpCKxOXNskIR7SfjzE9OrDTKaOUa5DmpSyKXinQTfw9CeZIheM0ISs/wJBgxuxd5i+v5ZL5DV6qKksTHhoRZ9rqNrrFaBqu1/DOEwAnm63CKE2OVJLRPt13Ttwu3oDkHaiafaQyHD2rKiCBi7fDmQzQTySjkip64Ng5bJns9AVH8dd1z6e9gLY4zI2WU0yIcJ6LwQ3bL9c6wzKZOB6O90+0p9oB1xjZlOGG2p3LwcFX29RkNiF3FIwAp9yNzxSfDOI1A59zrvruzp5fjCFzkWLQKuuEyNVSeIgx1r6Tko/tOvgUw7sr/5ALLP/RyXfxZ47z8Kb8brvsxRq5HgRRCHOsmIxLyWHf85vq9FDS/igfXx+AOFYNiS7uVaH56gvJ0DAKw5J2DW4k7MmqmfYVAf9uN/xXZfhoXYxQLbyTUJS+XxRa7aDQaOlFGT9pHdaPyfbW3RMyBOhmZfT+rBkCvNji1SupVgGGPIO8EAjj7KRNL5iB8sjcNAMTUfADBnNpDSROFJThUTwmTRzychEIWXhfDHnU2zcL7v2cXtuNAYOfMNYM9cBivVjkrSnXbMr5UxoJhaKMIYpVWM24pgFjXwlENxjz2Wqfe0I9wwJ67TE0Gke6GQXuT9rhtZcHm9rjH1lSt/R80CKItCzIAwyBmJj6eXoD5jGUrJ2UilxID4uBfpqw3O+qg1za4hkxHv70cslu45Qx8Oamu0ECV01DvXaiaj0ewydHe8Xq450ibZgwYI5waLwvXwk683uYkriU5p6yYmi3Do7PLL8/KINZHrrLWVYdasCM+u4MyNgc1WjL1C+osF/hU/vRDwSQ699MdvxYfYS+denrFTv2l0GGNwXSrJ/GvPudZFn8nR9AHK+dK0zR5VjuOpJR1Gezj9XeC6FGUps7lO8CuKaUdPmynCYBnmzlbLXHKE+JdLDniuAB08n654t2rNBOSoSYzjSewiDn7sOjJ3fBqsWkTprd8CUuJFsFLhuPIqjloN+PJ1DLkcCV3EoQfvWoriP/0a1tJzkL7/y8jeeRk++qEKPvzvDHfeDXzyUo6RERK8COJwI5EAZnTGbMAYTn1Fe2iZ/zP8gppKAYkjXiL+iPUUadKzSxkcOb9zXcDSV2DuXP1EMlG5bl1sJ88IY4ABPzYwOtlzcDZGwDaS0kBJPyBKpxoPcRhTp2NvtG0z9OdPgVqz68kSWXLDROWtrYaXqD7oeDSWWeoNzmfPZuAJP4/LZBJ7K5bFnEs3xCUK18MvThCL2j1pup5dIjRIEbuUQ3JKcBs3lccxbzjLSwjuHb+zmW2kvJxdcqisqjU1mRRNwr8PbOWY1GP1/7CNjGI2h+Gd94ULwtfasqPl+17NA6TiClpHYDy9BKXUXOEVKVpRbMEMWF3HYKDlNCQSwDFHM3R1MWV/FzmxeiPsjsVoa2NYtNDtl/ShUbVa/HVTajla7N8gyXV02Jh6rzGI2RQbC15M8vBTr4HOTn8bwHWIis/TJZNJ+xv63rzxs1i6RH0oaVS53Payd5GuhKgwwJKUq61ZC8RZcb2OdVv4ApsrHtaNiJxsUYJgIPwyKOUx2MikgfnzxEQSQTuKqXkodJ6or9MtQ2N6ZH/KRBijPHNm3vmgUUiJyUrS9UHlGvRmduTSw22CrnX6iWFcm1zT1DJdL0O5KpsLRVwndlXNNlStiXh2SX1Vk4nqSewiDnpSD98Mc+eTqLz+WvAZ4iHmJqRf+wLwhSsYFi8ioYs4hEnlUX7rt1A565NIrL0d2V/9f3jfuZtxzdUMa9YC//4Rju3bSfAiiMOJxYsAMxH97JNnb3K3P/N0aQPN27hpuoNApnwlDm0aUDEW6HJ+Ieg9sPfPadeisfSR4stzUp9zxLVXzAYYFrvATO9F2hiNiPFAaOwW4oTjGfJ5dwAbf3zBHFVRBPOouAPMqOI5Y0o4azCvWNALJlwMw2jmGLlEqeyEsm8wb00U7nXXSLgMWSIZ196uP+BGYYwAYDLXUNfbSq9UMM+Nyi9HuaQCjc7BwJN5peZsrVexRb5mdJMSaHPbGH5+Ki45kkRNKs5ZcHDJvMFnMhlu9zlz/ILELKZR7SGoGzkM545H3ciKEEbme41wiI/LxxztC5I66kbO97hqQum12xdqDGquz3AT4PPcDFgJJ7k+59oE7f0tp6K39eVawT5hCs+yYLdlmgwzZoY2D+GdL+WjApAwGWZ0hb2pZHSHahlpJwTbF/V9zy5deFm4kFRWf64nEy4mly9C04O5p1QqCbXR5L4tHZEbfWxMVpID9Uk/GAMK6SXY3fZqVM3ARx1vb32HZfAaDF6TNxT/eIfDceRSIJVyBUyVwfzJsDLxXzm0fV9k/jQAMDAoTd5pMCGqVZKiDQ276gtNqu+ydwAJOzDtr0NKH8WN8fQRvmkRt5oZWK7LABCVg5PbQF/LGdHpBrRI57zBBCcuJHYRBzXmhruQeuqHqJ50AeoverO3/Ec/5vjbXcC/f4jhrJeT0EUcBjCG2ukXofyOH4AV+5H7xbvxhpn/h1u+KRK+XvQRjsf+ToIXQRwuNBYSVLGrvZ0h3+IvkHsLV8QwDIgQEsYgvEz0QktwwJ6OeJlWXkMj3qYjIqwA6HPVAMBo9hjU55+Kl567HPPm6coUheSqu7V1yCJCtEfYBImd+kxfTyhkUazV/h1tG4vN0xUSu4xwDZaRxe62VwMAeIs0c15AFGlW7HKJu0YZgPHs0aibwoXBmrUctSNfHdrO8yDQCW5uOF74iMRyx347KowxEIIobI6/CGqtR2hDRYMj5hkzgMWLY4vy93fMY9xWPCaYNAnDwvmS2RBil+fZxQyUnbRTOo85ecZRwwBsqCJmGF9skD27Mmng5JeKdUuOYCGvIVdAGcy/FD1tr9SUF0MyfC+4gjTXhEPLv+pGRjoW55qw3VtSrbucnINqolNr08xZwKKFaiLw2PRRIYP9+nLtovMyE/469zg8r9rAvsH+ruPoo3HUUl9oBPyJOrK1HqSsYW95NdEBZDtCx9XWZnihZ6qt6nbuMbveUnJoqCIM+eaG/QID153sCbZw6A4wbnnlH7EYKOi1mdAECupPte3ETJsRz5aIzxWZWj86i6v97QIfAky7EvBa05Sv6dwUryTNLjFB3eGQfLlPgZh1WdXKnOuc+/tmPOFdJarfHskuj7HI+ZepOxuaU8Jt7nwgU3H7Nf1zTo/aDs2NaUjsIg5ajP4NyPz1MljzXorqqy71lv/5Lxy3/QR461soIT1x+GEteQVKF/wO1oKTkLnrCzh566X472+PY/584DOXcfz3j2xYFoleBHFYIL0XtmlyfIQHavLLuDoABoBcFoDGsytccHy+F5eonEBKURE2AyKM5MUnhMvgLAm0zQEzTP1gNJC8GiwQmiRva/reBpV0MEt2mCgBDg2mTNcNEiqJrtB2s2cBJ75YNlD8jgtjbIYozy53cGGZedSXnwfe5rvp2UzNm9RslExnh1qnDivZgrHcct+mbCfMrGZQFOE1oOKvnD0bMBAQSaRzowxIGxYeFnN4xJ3h56cSdc6cAWSz/v5255LougLLhlpOCi1evoyhtUWtS76vx8cjDgGae10WE5RYSVuxh8OAYbgepBzZLJBOxymYYl2dZRSxRPVE0dzPqRYgkYK18DRvmez5M3+BPuF7e4dbpukco+ztxrHkCNGH6OAsnBDc3TcRdGdpAg6m3OOzXrQUy5eLXFB+2fHlLjlC7ccXLTZFPjMpnMv17OoorUV7ab23fDR9lGeHAjOQzYXrspjetWo4dwJ62l7phYsDiJjFFp4QqjsqcR8Ezhts7/6I/Tag6ez8S73x/eMbMTF1XldMOqX3gjObcFvtaT0rkDfTFajC24ZyqAWuF0UwYsx7tg4OS/cW0+e5q9e1i2Ga8QnqAX+SEbfvkT8G+B6/jjdeoIzZs8RGdbN1AiHx/nZJK6ZTk21ssmSCOLAoDSPzh4+Cp1pQfuu3AFN8Nn7qaY4bv85x+mnApz9BCemJwxOen4XyO36AyisvRWLDXVjyt3fgB1c8h7e+BfjxT4FLP8cxPEyCF0EcalQSXRjLLAUQfjk9YnHgyzBsGIYYQC1zo9QU9yL/FTGbZVh6pMhNI4QBI/RGLocsNT9tvTTQi9oo5jEeGk+w8Iu2bnf53YAhPNMZM4De1jOdv/R95UR7UN5o8KMxVDfgnDUTyMqpjuKSNcM/F/Pmysuk43f1nsDfehvD4o5mcSwzuvzZ7GL1Uidkz58hkCGhCcttbRX/djiRSvJlqRtAdbTDC/HSh42pngNiTBnhGeKW4y2QBqvyrSRd3zpvSLttPtKtuUib65YvuHIOlNPCjSs44C06+aQ5M5UwZd1gfDy9xLdP9uwyYzzdgjBRRzbLcOxyYP58xRzYbfPBk76SEj0jpUZglNdWxaCW5/2wNzeEDJC8oxzcPHlKgn4Xw81lZCOdZnhRRPJ80ccF7JCKyeeFWM8066KQPfGYYXpCl7RFZH0MIr+Wm5i8u+vNXjvK4Vy6GXgBoCXKeYYZ2j6ykpiBodwJIVtsmMpMlTJqDkagnupUbkjlGcEMfaik1CFFi+eGuq1agMYuPQw2jlwSVYe0v1tAMvwlI5X26ywl56C7XUxywLQpBNRltUS7E4bItevV/cIfkOT2HsoeH5QapX3FmqbvawddGoTu9tcpf3d2CNtnzdJY7ezObf2RqV6yzT1ElBmcm4TELuLgw64jc8enwMb3oPy2WzyX+s1bREL6JUcAX/qi/qWIIA4bmIHaaR9E6T2/AJiBtt9dgCtf9UN8/rMcK58DPvAhjpXPkeBFEIcSNktiJHssCqmFqM9c7r0RjmSWhTfmQuxqbWVIpbwhm78+8DaZz4sPSNwwATCkrGEYUl6YnDOmrRst4ZCLffQ4jis3mNZFmQFN/vrMLfHRWXojNgyRU0isD3/9H0sfiXLL0onZOolX7mB+Lrck1VhxXJFampNUxhWDAGBX53m+6CIVCwB2slVbzAnHhZe5OWB0HxaHs8cin3cEkAiKgcm+SkmhyAmtyHaSxyec8Zr+ZKfTDC8+gXleUuoAWVU8R7LLnTBNV6Rq8pxEKIBuOdmM75njDVsD5vpeZJrjMBLSrGnh9bUIzwtmALaTqxZmUhI6DIBJXkxBkZIDw7nj0dP2SvS2vlw1xfBzmHEw2DOO8uuT8nK5x+SGaLYfuQRon+97qAGw558E66hzAIgZW702CN1TDHPic5VrcVNCJQz9u4zrHSTnBPJEcOdCifoorvPQU4bmrtDI5L/jUbZJqe5UlhUWX7Vlho1AMEl4kGXHACe+WLT5CceHjYrqO9z7UbUlbJQ3E2jwY4M7Y6azuLXN8JLxVxJdWs9eT0BHjNjltlMihYH8yaHjEeUI2lr1NoulE3wH1ng6GYbvoWyzpJikAoBpxvct+jBGXR/mXIuBHUL9C0sq3d3eTh4CqN9RotIJdLQDxx0LLzclID6KLT1SbFNKzkYps1B7n9mt84TdaM7LWybffOQjiV3EwUfqwa8jsf0xVF5/Dex5YraL7t0cn/oMRyYLfPUrTLnpCOJwxp53Iorv+y3qx7wB6Ye/gXcU/g0//EYf0hngkk9wfP+/bdRqJHoRxEENV38M5V8Cq/NoKQwrvEvdyDXImaR/jlqJNnDGkK4PqdszoL/lNJRS80LiQEIfPaEaFutSJF5uZ80KeDuETJQ8VEIeS/JARcrBxC0Yhnq0BpMHXeFZuaqJdpRbjkIjZK8W2dh2fb5kaVtnf12bMATELjW/UkjAcYQFmwNDuRM8zz+3ErkNK4kuWLmZ2utFF55p8AgVxil/yRFO6GsAXUgtIGbmGskuc/bmABcCVS07x5tpG9CX6dWqaTJZnGEAmC1yDOnDevwCGHdmP4wSu3gd8+YCXV3yTHuInUjPu67kIg0TvGMRxtOLpfPjExVmxADYnUeInLWG6SWaDg4cowa+NbMN1URnyPFJycWWzKHWfqQTshr0QJGEEjMFe8HJXqRFkHlzoXgitcmaKmNoXXas/iAV1ONwZ/MzTTmRmb+NOwGAdx8x38tFVhTa9Ppuo+qV6iYq6PO02glwLq6NRZo8/NrypX6gkWiTTjNvpr6WlsC1zIyYKBiNGKW5Fypmp35vBsXrjCcyjgAyB4P5k7yJA3Tlx7anZIPrZRa1vfgIEyF2aZP4a/C8S8OeUQxAKTkP4+kjMJL1r2FjEmJX/EFrUgNI26tXgP7+Dz6zG6E/hKDKZktCvWD2LOZ8HAMGWk5DzWwTqQ8Cu8rCeLDcpUeGE+ePZo72tps7Bzi68SMYAIldxEFGYvXvkXrmJ6ie8q+oH3c+AKB/gOOTn+aoVYFvfI1hzmwSughCId2CynlfQ/kNN8Dc8xxOePB8/Pzz9+OtbwZ+9nPg4v/g2LaNBC+COOTwBnTq617NbAU3UuFBjiYMsJaZA9vJ02S3L3S8osJfmRnzp3iXBRfDQOhl2DPPE1zC661FZ4gcPc4hzJwBzJ3DQgPzKIJl8qCa5W7nCDbKLFLBAX+49JCgMJY+MnwMjleLsMdfrptpTD/OMTCcDbhUscDGzud3d1HdzKF/znkYTy92LRUDaRsoBAZk4Xp59MB3oq9WMQM3JdeUlAOZgaOQWuyJRZwDZXMmijNP9USUE44Hlmqc6upLzgIAL8xLNVr9102oLeccc8UA9bxyoRVGiF01syMgXDL090V5pLjlGspf4o8EYCQwnHux5+kgExRAvIT1gSTVS490PaiE96V/KJKApzkt8n0kxK7ALImMQZ35zBUPWXwIcgC5X1gYnKHV0M+cGkc10QEgalAuvFYFcr/m3igVb53ufOlmepOP0vXsCtegYga8YziAlhaE2kyEvrKJ3WeusJiImLowVIFfr3/OozpRrtwL/geLsIFBjz93q5AQaKbA4Hp1JcBZEsXMkkCt4X63arajp/UV/gLpgo16c20ufyBv6tJ1i4qcLZMZGM6d4Hl1AeKZV0gv0ubkisYXy9XFTOuFqoaN+teiyEPpX5nuJB8Ju9CgfpXIPJYydmPB0LahP3Sp0OCHk0QinLdtNLtcCQOXcx7GQWIXcdBgdK9A+u6rUT/i5ai+8tMAgJERIXQNDQM3fZVh6ZEkdBGEFsZQP/58FN/3G9jtC9D+l//AFSd9GV+9roqeXuADF3H87g8cvNkMwwRBHNCI90h3EGKIJM8xyMmfAWnQzzl4Wt1Xl5OLMT/sThERIr26gMDwUa0jPxO8ZXZkWJhumc6DxRtwKcvkQRkPrRdamCYJsreRKvgN5U7wBt7gQFdXhCeGQ2T+nEA9HAbGM0JEkz2rlEEXU8UuBo6OTtk+Z+L64BhS45FSTsxSQjzd8gLmN4U7dg9i2ap2pOYtErN2gQuxgXMxUFIHXX4+Vp7MgrsTCHgeIWJdJdEZGghzJ7QvNbLZ+Vs0SjoVzvsEiGMX16/+6M85t0WdcZAx7Njl7azWHWhwuW14g7xrbW3Mz6sHfxAYtCqZFB4ViYSBzg69zfr7yF9oWeH7m3v5q5xryRtHG7GCs4xpAmOZo2GzJKpme2g/ZhhYtEi/b5TxFhM3BUtIOc203khuWCbAjLBwoBWfAzNfBquXtASdaR4d7f4GnAPtrcBppyAkoArPLhbopzR2Set4bgasBSej1hk9a55fgS/eLTkCOMaJfo3LJSi3ky7M1rdPNFTQ2UB+Bqk7+NuFBCTp/uBK56/00GFjozR6Fu3ZqAtR1yPqs3m8aKzsYQBDuROdnFx6u7zfGrEwsHVEmJ9+ew7AsKvOFhZsI426mYdhh2fNBAI5ICVME9jTdrbeIu/xEt2GXs5ANz/9VAzRKWcXcajChrcj84f/AG+di/J5NwFGAqOjHJ/8DMeubuCrNzC86EUkdBFEI3jnkSi995eonvIBpFb8Aq/d/B788hubcfJJwE3f5Pjs5RwDAyR4EcShgPcSzRisBad4y7UDwtyMwM4RA4SIdfVlb/S/bEteUe4L7+JFfvhaKamb1TD+Ga4bkzXzwlucdyZGWl4S2FEOVbNCZcmJpHWoX87FoHDxYr/MBfMZTjg+Wu3SHouuHsfOGTOEIOPZKdnPnTBGN9lvV4eNJUfIJXIwA1gQkTvLH7QAtUSb1n8gyuZGMBYOXcnlos90awuHHJrFIcSxyLrNFKThv7d4d/trIjwqhOeMYQuvHg41BBQQ3oOAyEWz7GiOjg40PbJy76ve1legMOsMdWVcGY7Y1dkR3TZ215Heb9cLLeTl5/Dqs4ETTggt9u2Mjm5GrSr3D/6/DDbaSxsAALO925d5ObuaoZroQHfHueBGCjODGoDB0NHO0NaZQn3eSU2VV0wtxGDuRNidR3qzwelQPI4YUE7OUiYVOOF4XWisJlG+RsBsFMY4c5aBTMZJaM+BZMrJKazxEion5wjPutxMr0w7IGZ7trg2tM5DNq+KpcHwL7Ghczy2peZpjA0f1/QGsjdOYHVnp6LgiNkYecDfi6llyB60M7p8zy7GGGY7UY7B/lbX2KEl0ulzn0vh3YRQU0l0hspTinLFdS5fR/HXvG42ycm+WYsW1J0n9RkU9NIFgGLK8cpmSa23IiAm+Qh5EEOcP8tQleCQ05kdF8ousCzANnKomznUDfVGe1GMTqtr4Uytr2F9QUjsIg58SkPI/vYiMHCU/uFWINuBkRGOj3+KY+tW4CvXM7z0JSR0EUTTmClUz/4MSu/4AVhxAPPueA++8a9/xac/yfDMs8AF7+e4514SvAjiYEPO3SJetqUcOzwchqTuHM7lIpD7AuelX7O/EeHC5Q6I2tuZCN9BMIdOtEk64sMYNSJetgul7BJl3/rMY6U9wmKX67kylHsx6ke+Kly342ngzRTIDLS2CmMicyo3Oj5NU7ieFXNmC8ELcDxF5MGiNEPdi09gWLxIhCLKCehNU4hhL5YFEC+qyS/LZsmQZ5e/YQP7Q4S/wLe0ADO6wgKYe+l0dXLhhSisATSeXZ5VqRzs2cfKB+Kts1hKCK7BULGgTZIXibsomfYry+cdETFQzuxZUeKh2Lea6EA15NHhl9Hd/lrwWctC6049heF1r9U3tD3THxUmXWc2BkVY9kpzvN/kczuWPhIjWXXqwdNPDw82E0o0oSp6Gbzqle9ihK4XPcFzmAzMROjm0Vu8CDjuZfOxs+NN0oyoejhjKKYXgRsmliyJs8NXpBYvEjM5yuJYMskwd666h2VkMaNLXVaVPKhCcqBUvRK6CCCZEN1vtSp5kQWvTRuoGXkUl74ZPNshHaO+X5V3z+cZli+Dd0zVqm4PJ3df67xAQbGx4MqfxqITlTBb2bPruGOFB6JunVxXcAZR+fjmz2fKs8UPSQ544yk2N77+umbrXZcY58jnGTpeekZolsHAlgCAltbmZRPXWTOZiPc41tWje4bovKlD5SrPENVbbMnSZOgal3dzRTGZZtIEGAMbY7ZxbOdCcOzvPAd72l+j1Cx7lYUE3Zj6LSMNu0X30SwMiV3EgU2tjOwf/gNsbA9Kb/8ueOcRGBrm+NinOLbvAG78MsPpp5HQRRCTwVpyFkrv+y3s2cci++dP4z1tN+C2W2tYtBC4+lqOq66xMTxMohdBHCy4uYhe/jKRlNhVYzgMZfavscyR4Z0j4+44EFJiJvfcZYFfcUl1XVxBydDpY0143CSdsZRlS9um/Dds37NLGsA7A5VCejGQ1mWvZgF7mUYk0u0jqBz1Rj/sMXYXX5zJ58Wsg+m0/5Xf7lwCHrTPabCXvkTkbwLnXk6jRia6YldcAvg4+ltOBSASBy9aHBZhvPC7gB2LTzkaS44ADEN4dnEOVFqOQN1JB6MbdFlLzxHeiJo41xNcLZO7OXlUL5ZQeYqAKv7IZiG5QKk7vOREhuOODYo16vUcnEFRHpjaRgZISO43TVzHTDJaCc2VxUrJ+0v1+mAYyR2HscxRKC95Lfa0vRqACKddtFCte4E05nVnzguHg+pDz2LtlzY5SpNzTXExBMR10GCY6onN7ULw6uxQ11vzT0IxNV/xHsrnGU58iSmJbXrbRzPHoHXhPLz4BL2w516b6UzAfAgxVD02MfMo50BLa0RbOddp8Np0xSC3f8pkxH+LF6vbpVL+p455kqjhJfF2GyvXBWuOPCVj1LkLv/stP2OxIhK614cQ1MN9oDerrbfY7fclz9SgmCeHmLseVYH+lsdNE+huI2ngpi5OFfA8nZYsScA2ovOeuWVlmswR5bLsaODUU0RNbo1e3XFFaV67G89WqD6P3EkXPIE/Hd2pR6Yia+ZZHxPG6Jps24gMY5QXHbFYpAAAxLMiofGO88pMtcFeeGq8bQ4kdhEHLtxG5q+XwehegfJ5X4M9/6UYGuL4+Cc5du4UoYunnUpCF0HsDbxlNkrvug3VU96P1LM/w/LH3o/v3dCLiy9ieOhh4eX10MMkeBHEwcD/396Zx8lRlQv7OdXrTM/WmX1JJttMQjayhywkJBBAFhdAxYuiXEWJ+qGooF65yCaL4sKFC7IJeBVEEVFQEbhcFxRlERCIaAAhBBJCkpnJZCYzvdT5/qheqrurl9lnet7n90u6p+rUqbdOn3OqzlvvEld2ZWQkVgYYbnYEj2dH8HgOOrzFzUZK3BqlmDE9V2wRawF91Kbkgutg2WynWjO3FOgqlm4Rko94rKusGe0cMnLlO4Mm1QJhznw/yuUQeDxLpYbDU3xvzPoMkmnVHdvZvmrQZiIbY1I4q239/ng8JZ1YQKcupDXTbDGS+n31RIwylKGoqVGJmD4DidkVd0EqKVFMa80MOG6ayUuw4y/zUV6ejFe0r/EdhKbMS5TPndgsPUMgVFbZFRTJvjVrJrgdkiUokhZNJSWKQxfGLZ6clV3ZSdZtV7x0l7YnssY5lS2kdY00uTtLDiFUmZqOzKybZ2VmhKyraeXxE3EFshbxehTTp1sxwirj8abSXHAthSu4XYU/G9j7nlN83Xj2OqVNm1yFjXXlcmHWzc/IGKoDNewLLMk8wCE2aWua8sg0vJg2129LSJuyKwIolcj4mM2yC6C3vD0xlivTu0GM5iaYNlVZbrNxq0wFEZV6UT6fom22c4zi+GXZrWUSQbztSgm7NWCevr0zphhNnMP2PRF7UGeOQRWTv6tkbnK6SphQ2hRXacou0yFBQ49vatpc6DCG0zZVV1uuqaku3Q4UMLZz90fnMWBGobVVUVamCu3GyRozqky+4MiUKVnGvqk9ftuNXV/UP4WqyvRjknU5XYVzmLACLibdajHT+DaxwT4vaG09N7S3gdutHN1xVWmV9TkAp1BRdgnjE63xPnIp7m2/IbTxP4i2bWbXW5pPf0bz5k646krFsqWi6BKEYcHlIbThPA6ecDXGnn9SdsfJnL7+SW65QVFbA18+X3Pp5Sbd3aL0EoTxTHoA2vib68GM3LiCQKFTnlJ9PoXb4/zgvWql9SZbKUV3y5G8WbkZ011qK2h9dPtmETX89Lvtrl657+n2B+WFC6w4Ow6lMraUlMD0VleKYsduxeWNdmUc4/Va1hnzDsnY5Yjpn4KKLSCzxRpPCYqvyFjRHCifz4zplkKmpCR+fVmC8cYXq07Krvhv7g3gdkPU8Cdi32Rbp6g5m3HNXA5KJd6m+/3xFarOeaydZBB2w7EhuruzyZG+ITXweWHxwjIXw1p56PPUszewFJ8XGptcWeubOtXKEOjzG9TVxeIqOSzis55dJS2epk2FxsbkMT2lbZmL6jwxiJzqt3PAP5PwlLnOhQskm2Vk67SYBWFsv8sdix03BebPUxixBkxmVCxAWZfDrHDtajASGiJbhrx87ZKmiM/YnKHIiM1pvXsyjnO7k4qrbNjbKxFQPt7ls4iqm5dxsCyZXSBbO3g8itmzY66ntjqjRuFmlulWsKnC2zbaszGktdFBT6qvW9SVPZtGhqtiWiNYbrap7ofWOLEru1LniZArqZGJTFudfDmTMl5slmFZ+p7Xq1i7xkrYYD823ZW3kJcsyf5YuNokanuHkp6cIu0r6XfodGVX+suV3EKm1plwZze8eDwqpvzLc2gMw8j9Yisb2u1PHA9Jy644YVfSGtm+XWtrfMSV6bXpFpJA48IZtDRD6QCs7ETZJYw/tMb7+2/gffZOQis/QXjJB3nlX5otn9Ls3Qff+oZiyWJRdAnCcBNtP5re034CJUFK7v4o7b0/48brFf/+EcVDD8PpZ2j+8rgovARhvBEflR2l85jTZt8R35P5uFeWOzkjzgvYuAJMORatKFcJyyvl9mMaXgyl04sRdleys/JITJXflavPW08owyom+0Ldqdz8eW5KSq02yBcHCKw1zaGLFM1NDou3GCVpViRxN0nDsDJb2mN92eXtKml3dMkEKCtTlJaqZHnHxZWCeAZCl4f0mE3xDGO6ahrGzMNYeWQTM2LxjFK8f2JKBe2rQPnKmDZVsXpVMolAxlkLUXappMIiHjjf0XUli8tsMs5MqgtL7jVmpvWVXVmyr3w5IfeUnPZpSlkWX1aAbfvqK26KNjDLrrg+o7aGjLhPuY7LRWpGytRPJ3ItUhsdYvf0uaszN8aoqDRoaSYj5o82rH6oSrKYjBRISYlN3kFkhM7qbuUYdzBXRXl2Oyiq8nuXpRYwjFyyxJW0yc6fHiA8G9obSFhCpmOW1aGrbFoOu4Lc7garvPS7C+qwKSStbGx12d6RJA2jVGKk288JVpD4SOsa2g6tpbR2CjpQC6VJWbLH7MqPvUtljotCLLuymSZlx67samhw6jfZ68oYAgM4LyRfckGy3XJlfC1P84SPJ/dwCrJfCGZd6pso0wS7J2o09psrM5Jq2ZVWj8ulMqw13W6VmgihAETZJYw7vI9di/epWwktPZ3Q2s/w3POaT51tDYHrrlEsWiiKLkEYKXRwBr0fuJPotMPwP/gVSv94Ff9+usmN11vBpT9/nubr3zTp7RWllyCMJ0zlZvrCZqZNs98j49kYUx/3Nh+pWL0q971Um3YFRFpZI66QyX68Uwz63DgX7Kxczu6KwwurJ26JkvchPbOyaOPiROazbEYoPT1WewZKLUsg+9N5xLQu2GVY7uEZsb5ispnKlxK4N47WEK2bhxmcntg2q82IuQOlCqTL6og2LLKClqctYqJ1SXM0HahJZl3Lcl3R1qTyr6xMZZiFJNwYbcdGpx2GWdNGOgmXJK3BUxKvID+JVbFOHJ5i2ZWrnzmlFnSwoHC7Mi0OHIqn/TFIN8bYR0ODoilNYWoYcMT6NIELaCPHxXGu43LEMpo/DzZuSN2+p2yVQ+nk9QSDKsMqyfSUEWldAw05Uj8WQLpiNzVWUybaa2nqM9yjMooPVEkwgP3a2uLsImw/JlW4gpT0tjGdbvkEyeu3YwZbExlbXWnGnunzRFxJaT9XfV2qlVN83M+dA1Nb7Ce3HWqkb0wef+CAw7iNZ5a0uY5HXdY84TP6oSRIY6OidcMaolNXph/sdOLk3hzNGrdAtF7wpL3AcHBjH0jd2Yhb0wLU1RqxTJMxi6X03BUx4vud9L2FuO0lp9EoGdZ2sRckTv3U61EctclI3Df7Y8k1BpJpNQUjNYOomTZFx+NVZozvgpYVA5dHlF3CuMLz+E14/3wd4UXvJ7ThS/zxMfjs5zXBKrj+v5191AVBGGZ85fS9+zpCSz6E96nv4f/F/2PO9F5uuVHxwX+D+38JH/53zV+fFoWXIIwHDjQdxa6KjRkP5elv3H1OKemzYMZd6Awyny/TT+QwFRhZF6ADYyAyxznEMZ150qrAafGiyxvYV7o4Z73xh3aPO3PRWllpLXydXC8g2WRx16eD3mRWtB1Vx1r7pszArEim+WtudnHUkYrI7KMyKtJVU61FheFKVXAFZ2SVX6W8RY+lqs9wg8yPLq3GrGnP2O71uxPxxnD7LEVIjNwWKmmWXToto13OPuQQL8hWPhCwXFLntJNQQGYxLMs82CH4fUGS5Cnu8ah0DVvKfrOiiWjT4rznG8wiXGP1Xbc7fbLIVHRAAYO4JJgSPH/QOCgUvQ7x+SLT1yUUtM7trWzep2la5eQgTClfsIi24HHWK4CkBU2oeTV7AssyDxpI08Tls1lFxuPgxYnWLyA6fW3msS4vzc1WjCqndkvBnTSXiSe4SLfuiVNfB+Xt8xIZIktLnUplKpx9PjKaVgdqY8WSP0DcTXP24jpyET+iooK0OSu/ErGy0kru4fGQaOPEfJQjwHqchHVygUpvt8uySkqKmPw+by4sPjTPiybHmF35SVrSmZku2A7zvNudjA/pROpLo4Hf0BPDzSQRww1gv38Oe8pWWMkSbM1fkFFnoi0lZpcwAfE8fhO+R79F+JB30n/kBfzyAfiP8zWzZloWXQ31ougShFHDcBPa+B/0HXUhrlf/QMmPTsPXt5OzPm5w3TUKtwfOPkdzzX+bhEKi9BKEscRXXoZpeK2H+RRS43asWglLFhdWZyL+S4pll7UxHi/DyUIpjpOrVaS8hQO+6WnSxQ9wlmPhAlgwLxkUOx9rV6fGS0qQsGxQ4PQ23yZorgWAk6wK8PoM5s9TyYDQMaLNS4k2Lra5QFlKsW6/Lbh4ivWGfZEUe0x353Zl0lOc0ttlYre02F2xltAUJ61getsVHrNr7Tpv6kvJ2KJao9hZsSnLUcnK7YvgHF43aeLldjFyuy2X1JoaBYaLyMwjHItGGxbFhbFXnrNuSLOSUanjwvmAROGsRcymJeiKzCQSIVcl+/1tOcedI04KvFxk+lEVeKL8ZLW6TGu0eXNh6RKH8/orwZWqAbdiQw1dtnx9PD1ml9Jm0lImMIV+T7WtLCllnZSyWUmx7IqbyMbc0crqHRUXuqIZl0tRHsv2mM0dGQCPP2kdlhL/yvbj2PqAnjKTaKulYJs/zyZm7NB47EUzUEtnqeXCVl+faXGnK5s5ULOCHq89gKKLNys345nqGITRVs46WWMjGb//QIhf1UBioSWbyOktSeamzGyvqf0mH56My1MZJ8ppVa3t2VhiL0dcvoRsb1Yexa6KI5i1anZyvnaoMD2hSTbMyqmpG9Lqsru0xvf3eSzlZrBKJQLnZ0mamVa1WHYJExGt8f7hmwlF18HNl/Ldm+DyKzXLl8PV31JUVYmiSxDGgsii99N30o0Y3TspufNUjLdeYMF8xa03KU45Ge76CXx8i+bV10ThJQhjxayZLg5dCHV1aS4acbctHQIshVFNdWH3U1PH3whD+kN+6yxfzIIgfz0pgWnLpiYWQw4lHbd6PMpZeZWFeMyrDOzaEzO5GJhpN4SKCZvvodvxeTub21h5I7rSFmBZOVuWDeBEBR7sIIttmg67KghXOWTKzCJcQTKna6jibkvoLJY7iYLJcmRadhXmCum8pMmQ2xtwsIAE7bdWXNqTNF1RBcTsis48InGeeHycnLouJ51HgR1id8U69pdkWtQ5k6yzsoLEgnJAIbGyBQcbZBdcsQzWHJbtXKlt3NysKMk2lp0riH3mj4mVukk5fXU+OqOASaRhEdHGQ7Meq5RhueXGFew5tbgOll2x7IS6pMrKtOnxZzs4hWlTcyfY0L5MV8g9ZSszGiH9ujIsAoGQO2jJVhJMvMxwGUnlesoLj5K6TGWI4c3b+NnG1pQpg+uMUVW4wiyRxbDAcZpZLM9xaYNSYSVhibs7atv/9kJOLq4A2vBlHBW3vlQKTMNHxBUgWmN/2ZEpoxU3MXW7o2txlj6dKyeHnRXLFXPnON+7AxmWhKLsEiYaZhTf/16E94mbCS0+jY71l/GVC1384A44+T1w5WWZb0kFQRhdotNW03vqHeDyUvLj03G98n/4/YrP/j+Db1yh2LMXPvpxzc/v0+hBBJcVBGFoKKUyFF1gxXEBS7GRjcisjURaM91i4g+1lq4r1ZRENRxCebmiKmhtTo8RA0mXv5S4LTkXCyN8r4/Fk1I6gnaX0DbbikcTCKSZHwCuXEGibHRXLGB3ebztcj9SJ4OvGymxjzIWD/Y2yrKYGS6yqLXS/i7csgvArJpGtGWF9Yfhyqp7aG+DWXFdW7oFiIZSm+FFYed2Vlo4hrpyanJ/BdGmJZgp8acKsMZxezGrZ8WKqLzFnd1cB9b3BxK/bu4cmDNHJaw+C2vLtOX1AAOCZ6OqSiWzfKYzhHNYMb+yX1gh8Y5iBXPvts8LMcsuqqaiK1uyxoFThitmBRZTsNussqJNi1NjU8X7vz1AfVwpM8BnK6VUqitd5sUk6jWD09H+CiIOmRdzWhDl+MmUUsm4jQXImx/nWhYtsOaSgdeTGfzciYULlG0uSsoQbTw0+xnSRXWwXM1FvFQ8sL0Cwq5KDjkktW6dds+J7+vzNTjOu5EZ6wlPXZ1RPv2POW2W67c31vWqqsAM5HAzTbM0TGSATKs+V18ypju4AAM1a45gd/kadlZstOrJiBOXH1F2CWNH/wH8P/8Unr/dRWjVJ9g+7z/45NmKP/0JPvdZxTmfMRzfIAiCMPro6lkc/MCPMKfMwv/zT+N+5g4AVh+muP0WxaGL4Bvf1Jz/Vc3+/aLwEoTxgC6tZkfweCKuLAFZADylEIvHknJszLLLykSVdi+OrexqaxTz51mZ7DKOjxnFpGRjTFci5dNKFIhZ3khvWe4VTzwblSIK3gDuBUfjLk1f3DnLEJl5hKNCsK+0NRFsN5/8CWWXbSG7p/ZIdlYelVEy+XV4n4HS3VwLMRwrWFEQw2xYaAXoB4hZtcSxW9H5fMrWbzIXg1kt9LKRpa2cwklla1Zd0ZS6cEu4SOZeLpm1cwnNPi5RLhGYuWpaSgy25UthQcKwMWUpmLP+rBTiEuWxAsvPOwTaZ0NFRQHnyh/5vdDT52Ttali2JP5XAVrJNFItSw3H4t4BeLzFD/d6k7ECI9NWZxYgqQ5Mzawat8yyBduPzzsJZZfNRbGiORHHKgV/8uWEOUhlVwqOGt+YHNrErJ9PpPXwxC6fL3OucKy20NPa222wl5GlL7jcqmAX93RhZkwfvAy6JJi1WFY3xiwXb8bih8XvDe6MdxyaiKsM5h2HNmKZDNPkSfnTKT6AwopZaMtwmSZk4ltVleX6XVpqVRqecQRmkzVQE1ZudumyWXYNYIrT5ckYlvYDDX8ZIXeQqMsy8cqVZTYbouwSxgTV+TolPzoV12t/pO+oi/hz6Wc4cwvseguu+rripHeLkksQxhs6UMPB991OdNYm/I9cgvd3V4I2qa5WXHWl4uxPKf70mASvF4TxwmDiW8QxE8ouMhcaNoVNeoa25PHxsrbDYp8V5TBrRvoRhcuaHiNE+ytYdGQ769flOMhnLSBN3NbluNNcZ3K1lTcAJVU4ZSYs6Hjb7vjb+EMXwvKVfsuFhywWP05KliH8pn6/YtVKCFYN/NhBnValLkSdLCkiMzYkY5LFY3zF+k7pABI62nHb9FVOchuG4qCnPmeZFAqwOrIrcuNfzYaFiUUiWEqEhLXNIBS9SxdbCqLCSK3T61W0tuY+j8ZAOyUscNZ1Y+aP7Z2T0lKVdENzbOPc8q5YbsXzs4omy3bWbiAScy9dcxgsX5ZZJme9y2D94bGypVMIx14W2C2ltLaUs86enjZXZSPuxmgpuxzb13acdYzdjTFugTXExk4nrqAwLffKlIQQkFCY5FJO5RsWCV2XrW2G2meGqv9PKksckjRkOyb+m7mcNYAN9al/ZyrYk1ooe3OujiVA7ZuygI7ShYTdVcyaacWpTBXA2Z06PXmB/bw6YPlApicGyfp7psxH1kdFhXW/mDozkOgvS5dYLoepJy5A2eXwdzaitTn8byVmlzARcL3+OKV3vBejZy8HT76F255/L5/9vKayEm68TrFiuSi6BGHc4imh74TvEFr6YbxP3Yb/vs9C+CCGoXjfexU3XGe9DfrM5zQ33GwSiYjSSxAmIgnLrpTH87hvYn73ukSAe9uTZlwx5nbDzJmDv9drXzkolbJw9Hhyv+E3a9rYW76SkC2IdPpiWtsWJTnPnx4+xW7VkfWg2EdswVJXlxqmYcZ0JyXI8D+mV5SrREwy5yR6adehcyj4bERr52bdd8A/M/v53D50aTXR5uWEg+32UxZEdOoqzMqWlG0ul2LD4Va8l+mtmcdE5h7P4hOX0x5zo8x7vgEusAbqdlko1dWqcIu3gZhVAIsWwrRjjiHattnhuNTjh0vZlcIg4tOVljrH8zM9ZZaCGmte8FdY8al0aXXs09kqJ6vFX9zy0FbAshhLWnYZRqrVSaLlYm6MPb6Ygt5TWGB0s6YNs6yexFJ9uJVdCe27VW/8OuJDoaqKlO2OVeQ7hcOx6WNtTpsrexw3B8yMxA0DtQAd+JyqK1uI1s/HrHaIcYjl6phyijSRzGrL6thMUzwlFaIeenzTAJg5I9PVN5t1rc5i2aU1mDXt7KzchGn4Y3VYuNN0rdGWFQnFcPJ8SSrKVUq/d7mUlU025YC0SlXmeLHLNzRyW8k5MfB8w4IwWLSJ58nv4f3j1ZjBGezb/N9cfF0zf3hUc9SRcN7nB3ATFwRh7DBchI74ErpyKt7fXkbJTz5C37v+Gx2oob1NcfMNcM11mv/5ATz1lObbV0XzZzcTBGFcYeZyYyyA+EN1iqtRTNmVfE4tXLGjDTcqHljeGyAy5ziMt/+J2rutMIGUot9XC6HUbUMh7urkclHAIiru+JRPUTiyyi6AQ+Za1l3OyX+c28RpbdHlb6ey759ow4OunpVZAEuxNDWoibyaxdIwtk2X11Md0ExthhkzHIs4yxWoSVgx2PF6FWtyWEEZhkIZyYD4uRiwheRAu9Ug++Fwhsisr1MQ75t5KnaNhP5lqFaMueIi+SuIzN5sWXMCZv1CjAP/O+DTxfsLWErUAyQV+PZjfX6V7AOGC4jQ42ulx9fKITnjaCX3mTXtRMs1+qXe+Jbcwg0UmxsjZCq1ZsyAxipyxv0q9Ofpqt9I9UzrBOkK0ppaA21mr6imGsrK4NXXYEfVscxuMIbFUiddUZSzrKcEHZyeutHZZxDIbEtd2UxHSxP6decqcikUM86hkv0t/V6SHoPLKeOk16tS6ku4nGerpwB0SRVmVSsohdHxasJC1040LQGyP1cSmJznF8suYZyievfiv+cT+P7wTSJtR/PXJXfwkS8089if4ZzPKL56vii6BGGiEV5yGn3vvBZjzzZKfvQB1L5XACgpUZz3eYOvXazY/jqccmoX//dbsfAShIHwwx/+kE2bNrFw4UJOOukknnzyyVE9f9xKqpDYLU40NljZwBqTIYuG5FYZX6jGKkrZpQYcvDnxbUDHhQ3LQiQeR6etDebPi2UEy3dtOh6zK8+jdyzbWq6YMEPF7Va0tBR27dGYZYDTgqy7pI19gcVEp2fGM7PTUK9YvUoRnbqSaNPitL1JOQxDMXdupoXeMIcuS54vbiSQZb/OGt8mE7uMkXDqvnisstQsnwOzvMp2rhylBlRnzhOlnTDeF6IjrewaWAW5d9vnj2zahaxGI9YOl+0wj0elxC+yV2m32FQuV6I+T14Tk8xr0LZA8sNKPL5cLEukXX5TW2MjazKBGNlc2NMx3aVJizZHq9jsLFmsaJsd74eu3HEfC8AWQTJv2bCrjGjzUnTV9MS2ZBD/1OPtfSGncl47fM+nZHWQ2iyrp9fblFouzTovZV/29wwFb88uoIHZsMCK/dZ2NHhKiNYvSJnro9FkvfMOgcNWFV59ilunuDEK4xHXtoco+f67cb3xJL1HXsJ33/4GZ322FFPDtVcrTn6PGtoDsCAIY0Z01kYOvv9/INJH6Z0fwHj98cS+DesVt96kmDXTxX9eqLnqWyb9/aL0EoR8/OpXv+Lyyy9ny5Yt3HvvvSxbtowzzzyTN998c9RkmDZNMW2qFV9rMCilaG5SqJQseamWXSnBZvO5yTWvsNde0DF5cUojmYOukrm8XbaKsNta2bhciqa4G1WBll15hTbcROYeT7R1zYBkGzZsz2N7ypaz39/GyuVkjW/T621OuIzlQwdq0SXV+QuOEomfLMttycnFpxDSq2toUGw+Mi073hCee6dNtRQnU3Lp4ob0XJ3FbcpTQnTaYYl2G2s3xvTjqyqhokwXEHh8oJZ6zm5ZKaoTpRytyuwZ8w7JEYoo22lNw0+3b0Yyw+mgcFCiGamWXclrG9qkunY1zI4ZeTr9pGZaE41+Au8BWHRhWIHTbReyq/xw3qjcnFHPsqWwZLH1Pb+lloWZSOJixXCsdhjPYXcV/e7MHdGGRaRn603XA9qtpwZhJJltr8Mm2wXH4prpYCva5nJqt+wq8ZPpCpnjHA31ijLrPZOtv0g2RmEcoHr34rv/HEruOxtdXs+rR93FmTecxC23wTFHw203KxbMFyWXIEx0zPr5VqbGsjpKfvox3Ft/kdjX2Ki47ZYKPvhvcO8v4OOf1Lz6mii8BCEXt956KyeffDLvfe97mTVrFl/5yldoaGjgzjvvHDUZlGFQWZmmVBrUysR2TE5LgDzPA76yWAwb0O50H4iByZVQtqW5WyxdrFIyBmaKaNDvqRniAm3iPPf0eepB2frBSFDIKsxWxKxswazJnXmzUFz5jGYMd8GKPPtlDLh/DFAxVV6uOGLDQLLQDfT3c1bMan8VurQ60W7DquxyYgBiayxl4uzZBbRLlvbONs1ljeWXVjDuVqaD022ZCJOWXXkNobLEOOoqnWdl0htOYvVpb1nmviHMb6WlqbEI04n3mZqYztvnHapV44DNkGKfg7xI5UpkRLRjGCrx+xY6nOOWnk1NVgzHpUsyD9xbtR5tj4lVmBcsACvz6EeHz86ksIryJKUs6AyDOVaUXcLwozXuv99H6W0n4H75EfrWfo4f+u/k3z43m+2vwyUXKv7jSwaBwMR54BMEITe6opmDp95BtGUZ/ge+iOex/07clTwexVkfN/jWNxT79sHHPqH55a81evRf6QnCuCcUCvHCCy+wbl1qasG1a9fy9NNPj54geZ5Mtbd0EFVm5GTPPF8OzJblRGYfZVv4De05QqctIMvL1Yg9m8SDY5tqkH6hhZzDlbkIGzjj79nMLpHZeChmTfvw1DtCHmIF1Wfr73octjmQdUyOSIB6ZwEGUHQgK2nnehsarE9vwcMo0ydvR/B4zNo5CUWGcrkTIg3UU3OwyghdgIJWB2qJzFiPTkvuAJnWV0PF/pPE23bqVDhyIzkVY04MRUFTV0sy+2ceLPf9AsraLi7ebk6WXU5ye72WxedUB5dys2pa9gNjrFwOh9gyI6afN0XpOwJTTLRhIWZZXTKbbh6SGYnz4FBf/NjB9E0JUC8MK8burfj+7zJcbzxFtGkJryy4lIu/O53nnrfS/577eUVt7Ti9qQuCMDR85fS95wZ8D1+E77FrMTq307/54sTulSsUt90Ml1ymufxKzVNPwRc+h8TrEwQbHR0dRKNRqqtT3b1qamp4++23sx4XDAYdt9fXhZg+3UUwmD+Doh3t0+g9AfCXoSor0V0BKC/HiJ1HL30PdG5Hv/5XgMT2jHrMDvSBAPjLidY1EgiECQQUwaCXQKAP30EfgYAHVVmFqnSuI6uMoQr0wQCqvAKV5fxxgsEgZYF++j0QDHrxehW6cim6d0dCfu2JoPcFoKzc8XoCgX7AmrOCwcwVsRnLxOF0rK5cw/6X5lHiChAMJh/m43XatzmRq24AvfwUMFwo1+CUafH+o9UBdHcgJlsgp2yFyp4iZySE3pVcjKuqICqL309ZWQhlaCqrPASD+TUF+doonVAoSiAQobTE+fe0k218xQmHNYFAKCa3i2Aw9xJLu8NWXwNUVRUqMLC+nw8d8qJj7aEq8o+PlGNVD7ozOd61cTDl76oqzcG+KLNnuRLKinztUwjpv5+ORhLXkO831T6s+aqkLH9ZM+pYbzAIixbqjKDsfn8J3nAvlRUV1jwYpzT1XPbxEJgboOeAxuszCJSVETU1VVn6cfy6VXBKiqtkJJLsU4WMsUQ9bRvRu/8BB/ZANGT1L7+TZVhqOx292eSvj+yhpsJPRXl51j7T2Bhi/35NIBDA5wsTCKTOaXE5KssrCQQiVFQkx8PyZZrmJpOWluT9qJC+k2xbb0qsMG2aid8SsveTuExrFgf5670+tM+fmN+MYJATjtP0P1XKK/+y/O0WLXRjBMpxpdU375AI+/aZVAVLrPN6ShLnjM8nFRUGwWDqPFxRHiEQiFJZ4S7oXqyrDgczCg9GU+Zhv9+HaUBVVRV1JZZl8vYdVtuUlZXh84WZNrU8cf54u1VVeaisMHK2pc/vIxqBKVOCua3z4u0249C81xE/F0BFhUHvQZPKitzjwJg2D7PzH9b3WNvW1ISJmibBKk2gLwClzvdoJ0TZJQwPBzvw/fFq3H/7Mbp0CvuPuJSbnnoXPzrHoLQELjhfsflIJ393QRCKCpeX/qMvxayaiu+PV2Psexn9wdsB6yZWXa341jfgh3fCzbdotr6oufir0N4mc4Mg2Em/X2qtc95DOzo6HLcvWhjfP0AB+vbj7ulBh8H07cfV04Pp2o+ZUlEF7p4eACJZTqC6uqxjvbV0dXXS02O5j3V0KHp6Nf39/fT0hIh27UebmdmjcmHs34/R04O5fz+mL/sFBoNBOjo66OnR9Iego6MnlpWKFPlVryWr1n6iDtczt13z1NOWO1JHR+Zvka8t9vcB9NDR0ZvY1tNjvaq2b3MiX91DId4+AOgAhq8eMziDnn/25JStUNkzqF6Ee/tjAEQ6O7NaLxw4oOk9CPu7wJ0ri12MgbZRd7emp8daUzr9nnFS2icLkYhVF0BXV+76AFSP1dcAIl1dEBpmZ5vwwUR7mN25x0eGbPtjY9bVjdnRgeqO/W10J8Z/SzP09Vn/CmmfQsj4/cxo4b9pX5c1X0VcjmM3BW0OqK/09fWhI/10dSV/MwAddaecyz4e3H29iYy03d0H6OnJ3o8TsnR2pmyPRpN9qpAxlqinT0NFO67d21GRfqteXyTv8QBz23sJdPaxf/9+zFLntpnbrgmHoee1Hvr7NT09qXNaXI79Zpd13ftTx0MgkLwfFdp34m3b2dmTeh+0/Zba7cv62yfapjdCf38/na5SetL6gGn20h/TzfT1hdDKm1Ffc5P1r7Oz3+pv7miiTF+f9Xsph/vD/thcU8jcYGf50gp+94cuwOoD3jIXXTv72b+/E1efpWCcOUPj90HPrh6mTVMwsztxjni7dXWCGU3dlt6W/X39RKPQ2dlBX192GQcyduLnAujuhp4e6Ox0jtlleKrRvgp0R0fGOaZNta4xEtlPT08POuoh2tFRkKJUlF3C0Ij04/nbj/A+dh2Eewkv+zC/6d/Cf11Wxtt74LhjYctZiqBjemtBEIoSpQivOguzdg7+X3+RyPVH4zruKqLTrDzwhqH40Glw6CK48BLNJz6p+eRZcMpJohAXhGAwiMvlYs+ePSnb9+7dS01NzShKMvxjMWfcjUGM/YGkjx8O4q5JRe2ArRRmXTyS9ghd6QCyHI4kxhi6MWq7BZ4amNVlQQyhzngsO+2rGC5pRoGBxGIa2LxRX6+IdDnsKLTjjLIb41AoLVUY+xW5PFTdbpVQ5FVPgfrFoyGZReYzYvLv6KxN+SvwlfFm5VGYhg/Ymla39bk3sBR4moH2k4oKxWErdSKY+nBQX586jhtXLqfuwD5c3qQlXUO9JWcksAm3ckGWZCKFMtL9LtuwMRsWZj3G41E0NwM5lHDZkJhdwqDQ0TDuv/2Y0u8di++3VxBtPJS/b7iXM396Lv95WRk1NXDDdVZsLlF0CcLkJDpzI73/9hMoq8N/90fxPvodiCZzsi9aqLjtZsVhq+DqazSfO1fz9ttFvYwUhLx4vV7mz5/PH//4x5Ttf/rTn1iyZMnoCaIGsngcYJUD3pmPwmScPt36dA/yVW/8OHua+YESGHios+Imx+8e3zVSi6+hBEzOVhcUGMvKX0mkdQ3R+gXgHwGlkjEEBVrpFCKta9BTZg6fPAUQbVxMxJ6FdPhSyA2JlmbFjOkqb0dZuRwOX5u5PT5vDFTCYbukEWybpiaVPw7WaD3W5dIm2trAUnRlP1zHFcW5b1jWh5F6MykvV44vbYftp/T4cAcbnXd6SsDt7I49Ht4jx++b/oEZcGeh8E4lll3CwDCjuP/xKyJ/uQ7/vleJNi1h9+FXcP1vVvDza6GiAr50nuK4Y0nxAxYEYXKig624P/ErDt77JbyP34Br+2P0HXcVumoqYL0Ju+wSuP+X8F/Xaj50hubz58DmI2X+ECYvZ5xxBueddx4LFixgyZIl3HXXXezcuZNTTz11FKWwB7cdHkVU7gC1Iz/mp01VTJuauk0bbpRZmIuPz6dYc5imJMvDulk1DV2S3Wpp44bC09ILNt1CoV1D5VdI2Inrg4ZjLW4fItn6RwYlQXTJ8MbqSpCi7BrE2HKUa2S1FrqyOW3LQOQeOcVYtHYOrjefQccsErW/EtXXRXp7ZMtaOns2vPEGA7b4sZQmQ2nzwcypw/eSYzwoWOJE2o7JWyZ55fFvOSZrt5do3Tx0eX1B56+phVe3Q9UIDffBkM2TYqR+t9ZWRU2NHvUEdaLsEgpDm7i2PYT3sWtx7X0JGhfS+Y7r+f6f13Pn5yDUb7kgnfFhRXn5OJrdBEEYc5S3lP7NFxGZvhb/gxdQ+oP30L/xfCLz3gXKegt24gmwdAlcernmoks0jz6q+fw5iooKmU+Eycdxxx1HR0cH1113Hbt376a9vZ0bb7yR5ub0xeAIUnDqpIFX6VSnHqiPD0DMzUoPwTImOmM9hA8WXD7Xg3ouNwyw3H+EkSMy60goUHEJVuw1YFj6uH2BOGP60OsTGNyqeySyPJcEic7aCECkdS0oA/erfyj4XMEqRbBq+MUSKLyP2JS/y5dC136IVswHT9LUNmFJGnPi1HmsI/WUGQWLGayyYlePFePESLJwRZcyQDuZyKrk/gIRZZeQm2gY94v34338JoyOf2FOmUn3sd/mV/96J9d/uY/OTti0Ec78qHPqVEEQhDjRtqPpbViI/9dfxP+bLxP5x6/oP+pCdEUTAM3Nimuvhjt+BLfcqnnmb5ovnAOHr5O5RZh8nHbaaZx22mljLUYqI7GQjDOYmF1ldURmHgHeQN6yWfGUWP+wv9EXxhMF/ypuH1B4ZsjhjNllt8IpvtiTxXY9Q6CkCkLxIPXDMx9qbxkqdGBY6kphnPTD8RacIhhUBIOgmZ6y3fJI0igdU5gPxRV4HFNSAgdzvN/J123Msnp05dTchWIYhuXW3dgwAAGBSNtm54nZX4FZ3YZZVdj5QZRdQjbCfXie/ymeJ2/B6N5JtG4ePcd9m1+/dhS3XGywc1cfK1fAxz+mmDtnfEymgiCMf3R5IwffexueZ+/E+4dvUXr7ifSv/wKRRe8HZeByWcHrD1sJl12p+fL5miM2aM45W1FdLXONIIwaiTenw7RUsT1BOysXBjm+h6LoGgesWgHhcP5ywvAznDG7xjNmafXQKogv+l3O8YBGGl3IeUdTsVNAPMPIjA1ZLFMyiU5bDeGe/AXzYFZOhWj/0CoZJwqyscRNXNnlyV1wAmH/WVcsszJkDhazZXnBZTcdET//APuVkV1FZda2D6gqUXYJqfTtx/O3H+H56/cxevcSbV5Oz5EX8eAra7ntMnhtOxwyFy69uII57SPwFkIQhOJHGYQXn0Zk5hH4HroQ//9eTHTrz+k/4suYjYcC0NamuOm7cNdPLCuvJ5/SfHoLHH9cMb41F4TxiF0TMDxjbuQC1E9cxrOrtscN4cK9AoedkQ5QX+zx08yKJstNa4jZL3WghmjDwoQV9mgSbVqM9lcN4IjR0FyqtE8HfAMI0OX2Zg0sPhDMxkVpW4YwcIbF3HHoVYw2M2eAWR+FA4Br4qtJnKK++XyK2tpROv84uK9P/F9RGBZUx2t4nv4BnhfuQYV7iczYQO/yM3nwH0u5/ULNa9th1iz42sWK9YfDlCkeOjrGWmpBECYyuqKZvpNuxP33+/D+4SpK7zyV8CEnElr3OXR5A2634rQPwIbD4evf1FzxDc2DD8PnPgvTW8f+BioIRY39ITVu5TWYuFq2J+3cljQypscbqw8bH1ZnI63sMovUsstsGr7srbpq2rDVNaDzVoxinMJC8ZRgVs+yLKmEUeGQObDjjZE/TyCgiDS0oF9/E7OqdeRPOMJkDX01iRBl12RGa1w7nsDz19txvfx/4HITmXsifYd+iIeem8Nt/6HZ/rpm9iz42iWKw9dKhkVBEIYZpYjMeyeR2UfifeIWPE9+D/e2hwgv/TChZR+BkipaWhRXfwt+9QBce53mw/+uOeUkzRkfVpSVyZwkCCND0lVHB2oxa9owg9OHVGNcueCYrW4wijRhRPH5FL7CQ2CNHCM0zRfgjSZMOEbnxzRr547KeUaf4RtsjQ2wezcZGXAHQ0uLoqVl6PXkQ7v94PYRnXH4yJ9sFHDFYmYVQuv8evZ2qKJLpCLKrslIpB/3P3+N56nv43r775il1YRWf4qeue/jl7+v4Uef07zxpqZtNlx2iWKdKLkEQRhpvAFCa88mvPAUvI9+B8/jN+J55geEl5xOaNmHUf5Kjn8HrFsDN31P85Ofwm8e0nziY3DcO8DlkjlKEEYErUEpzBrnOBnR+gWJAO+Oh5dYudZ1oBavV7F4kaaqyqHgOHB3ECYXwxmgXhhjPKWYFU2YU2aOtSTjhyFNqUMfFB6PYvmyIVczakTaji66ly4DcdX2ta9g9B2VRx5Rdk0i1N6X8Dz3Ezxbf47q6yJaM4e+Yy5jX+M7uOd+H3dfqens1CyYD5/+pKXkGg++toIgTB50RRP9x32d0KqP4/3zdXj+8l1L6bXoVMJLPkhlZR1fOEfxrhM1V1+jufIqzY/vho/9O6w/XOYsQRg2ChxKOpjH1aOkisicdyQWEbW1yYrXrgZj62AFFCYLIzWrx7OvNRXjCm+yodSwum0KkxBX8QSkjxPPLTGZH41F2VXshPtwb3sAz3N343rjKbTLQ6TtGCIL38sO13J+/FO4/5dwsE+zdg2c9gHFooWTeEQIgjAu0NWz6T/+W4RXnWUpvJ78Hp6nbiMy5x2El32EttmHcM134Pd/sCy9vnKBpr0dzvyolclRlF6CMFwMR6Bi59fLpaUKd9wVucjeqAsTg40bwOUaaykEITul2Q1nhx95dCoqXGK9KsquokRrjJ3P4Nn6c9z/+DWqfz/mlJn0b/giobnv5M/PB/nZdZrH/mzd4I/eDB94v2LGdJnhBEEYX5g17fQf/y1C697A88wPLOvUv/+CaNMSwgtOYcPqY1m3toSH/xduuU1z7hc1ixbCR06HFctF6SUIgyaW+tusaRulE8pYnWgYBgSrRv48IzmNF1t8GqG4WLcGPIM2OBp439becuuztGawJxXGEfG5MxodWznGElF2FRGqeyfurb/As/VejI5X0Z5SIm2bCS84hY6ypfzyAcW939S8+aampgbO+LDinSdATY3c6AVBGN/oymZCG75I6LBP4nnhZ7if+wn+B7+C/u1lROaewDsWncKRt8/jlw8obv++5nPnWsk1/u0DsOkIWdAIwoBRBpG5x4/i+WSMTjSO3Dg6v5l0DWGyUlIyyp3fX0GkbTO4vKN7XmFEqK2F7gNDUZhOfETZNdHp3Yf7pYdw/+MBXK//BYUmMnUVoZWfoG/GUTz1fIDf3Kb57e8gFNYsWwqf/IQVj0sWf4IgTDh85YSXnk54yYcwdj6L57mfWEr+v92Fv2oa7207hhO/czS/eW4ed/wILr5Uc8NN8P73wgnHWW5TgiCMQ0SjIQiCMIwMck4VRVfRMHMGtDRbmXUnK6LsmoCo3r24tj2E+5+/wbXjcZQ2MafMIrTm/xE+5ET+vrOZhx7WPHw+7OvQVFTAO0+Ed79LMb118nZ2QRCKCKUwmxbT37SY/iO+jHvbg7i3/QbPU7fhfeImTq5o5l1nbOa5g4fz3QeW8l/Xern5e3D0Zs2736mYPUvmQkEYX8iYFJwRPaggjD+ijYvB7RtrMYQcKKXwTfKfSJRdEwTV8zaulx6OKbieQGmTaPUswqu2cHDm0fx1Rxu/f1Tz6FXw9h6N1wNr18IxmxWrVlrpXwVBEIoSXxmRBScRWXAS9O3H/cojuP/5IJ5n7mBZ9DZunFlC17KV/GHXWv7nD6u59+czWDBfccJxig3robxc5kdBGHMkQL0gCMLwM0LRyXVls+P2aPNycImKQRgfSE8cr2gTY9fzuP/1e1z/+h2ut54HIFo9m9Bhn2RX9dE89tJsnnhY85e/wIEeTYkfVq2C9esUa1ZDWZks4ARBmGT4K4jMezeRee+G8EFcO57A9eqjVLz6KCe6fseJ66DPqOK5zkP5y72Hcsn3DyUwZyFHHFXGYasmt6m3IIwp40rZNYlTVwmCUByMkUmkLq8f1fOZlVPRZbWjek5h4iDKrvFEXxeu7Y/hfuV3uF79A0bvXrQyMJuW0LHkHJ7p28jvXpzFk/8NO3YAWIHmN260FFxLl8hCTRAEIYGnhOiM9URnrCcEqK4duF5/HNfOZ1j65jMsr/g9Ck1UG7zyyCyevK+d/qp2qtrnMHtNOxVNDeI/IwijhYy1nERa16IiB8daDEEQhHGF2bhorEUQxjGi7BpDVO9ejB1PWZYHO57A2PNPFBrTX8Xe4OE8X7KBh19dw5N/ruStt6xjAgFYuhjee5Ji2TJonWb54wqCIAi50ZUtRCpbLHdHgP5uXLv+hnrjGYLbtrJy77ME+SW8DtwFvdEA+z3TYUorZdNacddNxwxOxwy2gr9yLC9FEIqGaN08lBkZazHSGIfPVSVVaKrGWgohB0esH2sJBMEJsVQVJi+i7BoNtEb17sHY+xLGWy9gvPUCrre2YnRtByDqKuUt31L+ro7l99tX8cDzC+gPuwBobISFC+AD71csmAezZ0sWRUEQhGHBV060dS20rsW/xtrU3dfNzr9t442n/0F45yuU9m1n2v7n8e/+DW4jmjg07Amiq1pQU1rQFc2YlS3oihbMyiZ0eTO4JZuRIBSCnjJjHC7Fxp9EwvhH4uMK4wvpj4Igyq7hQGsI96IOvIXRvRPVvQvVvQtj/5sY+17B2PcKqn9/oniXayr/6pvP02+/j0deWcbWjnlEtZuyMpg9C045BebPU8yfB9XVMlEJgiCMFspfTtPKpTStXApAf79m69/hvmfCvPXPHUTffpVg9DWmlb1Gc+kOpgb+TmPJw/iNcKIOjaLfW0c40GwFcK1qxgg2Y1RbijFdVi/KMEEQBEEQRp4RClAvCBOBcansspRFO22DM/aZ5e+EOij2t9lRhqu7O8dxJP+27zMjqEgfRPoh0odKfFrbVKQPwn2o/m5UXyf0daH6ulB9nahocqFj1aY4oOp4MzSDl7qO5/m3ZvJS10xe7DqE7nAljY3QNhtWvkvxb7Os7/X14pIoCIIwnvD5FEsWw5LFXmAmMJOODs1LL8M//gX/t0uz802T3rf34D2wg2rXmzSV7qCp9E2aS3fQVPoMDSW/xmOkuml1hKvpiNTTadaxn3oOuBo46K6j39tAqKQes7QOd2mA0lJFSQmUlkJJieXKHghAWcDaZhhyzxAEQRCEgRBtWYF2+8daDEEQRphxqewq+eF7MXr3DPr4KFAyfOKgXR60y49p+IgoP/2qnF5dSXdkNvv6Knn7QCW7uip5ZU8dO3sb2HWwgT19teDy0NJixdWathqOmaY4c6r1dyAgCxRBEISJSDCoWLEcViwH63WLATQADfT1aTo7oaMTOjtheyf0HYxg9OzG17sDf/8b+EO7CahdlLneotq1m1nGc1S691qVm0CP9S9suukIBekMBensr6IrXMWOUJDucDkHI6X0RAJEXKVodynaE8BVWo7p8uEp8eIt9eAv8eALeCkp9eAr81Aa8FBa5qasXFlKs1IoKxPXeEFIYFghJLTLN8aCCIIwkuiyurEWYeQRAwpBGF/Krgcf1tzyPU1Tyfdp9O/EMKxxargsiyfDsN5iG67Yp2H7dClcLqus3+claoaT223Haa2IRiFqKswoRE0wTYhEFL0hN90H/ew/6KOr109Xj4/OA346u11EHGKnugyoqYWGeutf/QJY2KBoqLdibTU2yCJCEARhMuH3KxoaoKHBvtUDNMf+OXMgEkL17I65w+9Cd79NpLsTX08H9T2dNPR1YvT/C3f/U7ijB3Dr/vzC9MX+7U3dHIp6iGg3pjbQKEwMa4cyAIVWzn8rpaxtSoFSiedo50/Fthnn0VG3CZcruc/Jm8K+zem71qnfW5qhpUXurcII4K8k2rAIXd6Qv6wgCMKEQNwYJzqB0rGWYOIyrpRdTY2waCFEo630mK2WUipqKaMS3yMQ7be+R2LbopHY35F4eYNw2EwpE99nGOByWf/c7uR3lwv8PsstpLQUSquhusX6Xl4BwSpFMAhVlVifVdZ3UWYJgiAIQ8btRVe2oCtbMGObFOBKKxaJ/cOMQPggKtRjxYwM9VDuMziw7y2Ihi3XejOMGQ4ROhgm1Bch3Bcm3BcmEgoT7Q8TDmsiIdP6DJuEI5poWGOaGh01iZoabWrMqEabJtrE2meaqDwPzxrFHb9p4B/7h/8hu7ERfnKn3HuFkUFXTR1rESYFkemHo8IHc5YRwxRBGDza7UfRBSr9SUKYSKxfZ+kphMExrpRdC+YrFswf+p0tGAzS0dExDBIJgiAIwjjEcIOvHO0rB6z3tkYwSDSYee9zM7w3e6014TD092e+UIpGYtsicG7shVO6ZbTTAta+zel7zJgMsCypBUGY4Pgr0P6KnEVmzoTnngOfeJUKwoAxGxehD7wNvrKxFkUYAj6faP2HwrhSdgmCIAiCML5RSuH1glcSSgqCMILU1ynqjxxrKQRhguLyWhmhBWESY4y1AIIgCIIgCIIgCIIgCIIwXIiySxAEQRAEQRAEQRAEQSgaRNklCIIgCIIgCIIgCIIgFA2i7BIEQRAEQRAEQRAEQRCKBlF2CYIgCIIgCIIgCIIgCEWD0lrrsRZCEARBEARBEARBEARBEIYDsewSBEEQBEEQBEEQBEEQigZRdgmCIAiCIAiCIAiCIAhFgyi7BEEQBEEQBEEQBEEQhKJBlF2CIAiCIAiCIAiCIAhC0SDKLkEQBEEQBEEQBEEQBKFoEGWXIAiCIAiCIAiCIAiCUDQUtbJr06ZNzJkzJ+XfVVddNdZiTXh++MMfsmnTJhYuXMhJJ53Ek08+OdYiFQ3XXHNNRp9du3btWIs14XniiSc466yzWLduHXPmzOHhhx9O2a+15pprrmHdunUsWrSID33oQ2zbtm2MpJ2Y5GvjL33pSxl9+33ve98YSTsxueGGGzj55JNZsmQJq1ev5pOf/CSvvPJKShnpy5OPyXhPLmQsFDLnhEIhLrnkElatWsXixYs566yz2LVr12heyoiQ71mikHmiWNsGnNcHc+bM4aKLLgImV98ZjuejQtqiq6uLc889l2XLlrFs2TLOPfdc9u/fP+LXN1RytU84HOYb3/gGJ554IosXL2bdunWcd955vPXWWyl1fOhDH8roT+ecc05KmWJsHxi+sVSs7eM0D82ZM4ebb745UaaY+89oUNTKLoCzzz6bRx99NPFvy5YtYy3ShOZXv/oVl19+OVu2bOHee+9l2bJlnHnmmbz55ptjLVrR0NbWltJn77vvvrEWacLT29vLnDlzuOCCCxz333TTTdx6661ccMEF3H333dTU1HDGGWdw4MCBUZZ04pKvjQEOP/zwlL594403jqKEE5/HH3+c0047jR//+MfceuutRKNRPvrRj9Lb25soI315cjFZ78mFjAXIP+d87Wtf46GHHuLb3/42d9xxB729vXziE58gGo2O5uWMCLmeJQqZJ4q5be6+++6Utrn11lsBOPbYYxNlJkvfGY7no0La4vOf/zwvvvgiN998MzfffDMvvvgi55133ohf31DJ1T59fX1s3bqVLVu2cM8993Dttdfy6quvOq413/e+96X0p4svvjhlfzG2T5zhGEvF2j72dnn00Ue57LLLUEpxzDHHpJQr1v4zKugiZuPGjfrWW28dazGKilNOOUVfcMEFKduOPfZYfdVVV42RRMXFf/3Xf+l3vvOdYy1GUdPe3q4feuihxN+maeq1a9fqG264IbGtv79fL1u2TN95551jIeKEJ72Ntdb6i1/8ot6yZcsYSVSc7N27V7e3t+vHH39cay19eTIi92SL9LGgdf45Z//+/Xr+/Pn6l7/8ZWLbrl279Ny5c/Xvf//7EZV3pMn1LFHIPFHMbePEpZdeqo866ihtmqbWevL2ncE8HxXSFi+99JJub2/XzzzzTKLM008/rdvb2/XLL7880pc1bDg926Tz7LPP6vb2dv3GG28ktn3wgx/Ul156adZjirl9hmMsFXP7pLNlyxZ9+umnp2ybLP1npCh6y66bb76ZVatW8a53vYvrr7+eUCg01iJNWEKhEC+88ALr1q1L2b527VqefvrpMZKq+HjttddYt24dmzZt4pxzzuH1118fa5GKmh07dvD222+n9Guv18uKFSukXw8zjz/+OKtXr+aYY47h/PPPZ+/evWMt0oSmu7sbgMrKSkD68mRD7slJ0sdCnFxzzvPPP084HE5x76uvr6etra0o2i/bs0Qh80Sxt42dUCjEL37xC04++WSUUontk7nvxBmuvvL0009TXl7OoYcemiizePFiysvLi6q9AA4cOIBSioqKipTt9913H6tWreL444/nyiuvTLGMK/b2GepYKvb2ibNnzx5+97vfccopp2Tsm8z9Z6i4x1qAkeT0009n3rx5VFRU8Nxzz/HNb36THTt28LWvfW2sRZuQdHR0EI1Gqa6uTtleU1PD22+/PUZSFReLFi3iyiuvZPr06ezdu5frr7+eU089lfvvv59gMDjW4hUl8b7r1K+L3RVoNFm/fj3HHnssTU1N7Nixg6uvvpoPf/jD3HPPPXi93rEWb8Khtebyyy9n2bJltLe3A9KXJxtyT7ZwGguQf87Zs2cPHo8nQ0FWU1PDnj17RvsyhpVczxKFzBPF3DbpPPzww3R3d/Oe97wnsW0y9x07w9VX9uzZk1FHvN5iaq/+/n6uuuoqTjjhBMrKyhLbTzzxRFpaWqipqWHbtm1885vf5MUXX0y4zxZz+wzHWCrm9rHzs5/9jEAgwNFHH52yfTL3n+Fgwim7rrnmGq699tqcZe6++24WLlzIRz7ykcS2uXPnUlFRwdlnn80XvvAFURwMAfubL7AeNNO3CYNjw4YNKX8vXryYzZs3c++993LGGWeMkVSTA6d+LQwfxx13XOJ7e3s7CxYsYNOmTfz2t7/NuLEL+bn44ov55z//yR133JGxT/ry5GKy35OzjYXBzjnFMF5yPUvE3/4PZp4ohrZJ56c//Snr16+nvr4+sW0y9x0nRqqvFNNcFQ6HOeecc9Bac+GFF6bsswdkb29vp7W1lZNPPpkXXniB+fPnZ62zGNpnJMdSMbSPnZ/+9KeceOKJ+Hy+lO2Tuf8MBxNO2XXaaaelDBwnWlpaHLcvXrwYgO3bt4uyaxAEg0FcLleGlnjv3r3U1NSMkVTFTWlpKe3t7bz66qtjLUrRUltbC1hvRurq6hLbpV+PLHV1dTQ1NUnfHgSXXHIJjzzyCD/4wQ9oaGhIbJe+PLmQe3L2seBE+pxTU1NDOBymq6srxapg7969LFmyZCTFHnXszxJHHXUUkHuemCxt88Ybb/CnP/2Ja665Jme5ydp3CrmnFNIWNTU1jmEL9u3b52iRMtEIh8N89rOfZceOHdx+++0pVl1OzJ8/H4/Hw2uvvcb8+fOLvn3sDGYsTYb2efLJJ/nXv/7Fd77znbxlJ3P/GQwTLmbXlClTmDVrVs5/6RrROFu3bgWSk7cwMLxeL/Pnz+ePf/xjyvY//elPRXVzH0+EQiFefvll6bMjSEtLC7W1tSn9OhQK8cQTT0i/HkE6OjrYuXNnygO0kButNRdffDEPPvggt99+O1OnTk3ZL315cjGZ78n5xoIT6XPOggUL8Hg8Ke23e/dutm3bVnTtZ3+WKGSemCxtc88991BdXc0RRxyRs9xk7TvD1VeWLFlCd3c3f/vb3xJlnn32Wbq7uyd8e8UVXa+99hq33XZbQcYU27ZtIxwOJ57ti7l90hnMWJoM7XP33Xczf/585s6dm7fsZO4/g2HCWXYVytNPP82zzz7LqlWrKCsr47nnnuPyyy9n06ZNNDU1jbV4E5YzzjiD8847jwULFrBkyRLuuusudu7cyamnnjrWohUFV155JRs3bqSxsZF9+/Zx/fXXc+DAgZRYEsLA6enpYfv27Ym/d+zYwd///ncqKytpamri9NNP54YbbmD69Om0trZyww034Pf7OeGEE8ZQ6olFrjaurKzk2muv5eijj6a2tpY33niDb3/72wSDwYSVgZCfiy66iPvvv5/rrruOQCCQiKdSXl6O3+9HKSV9eZIxWe/J+cZCT09P3jmnvLyck08+mSuvvJJgMEhlZSVXXnkl7e3trFmzZiwvb8jkepYoZJ4o5raJY5om99xzD+9+97txu5PLocnWd4b6fFRIW8yaNYvDDz+c888/n4svvhiA//zP/2Tjxo3MnDlz9C96AORqn7q6Os4++2y2bt3KDTfcQDQaTcxFlZWVeL1etm/fzi9+8Qs2bNhAMBjk5Zdf5oorrmDevHksXboUKN72KeTZbzL3n7g+4sCBAzzwwAN88YtfzDi+2PvPaKB0kTqYv/DCC1x00UW88sorhEIhmpqaOP744/nYxz5GSUnJWIs3ofnhD3/ILbfcwu7du2lvb+fLX/4yK1asGGuxioJzzjmHJ554gs7OToLBIIsXL+Yzn/kMs2fPHmvRJjR/+ctfOP300zO2v+c97+GKK65Aa821117LXXfdRVdXF4ceeigXXHBBSrBjITe52vjCCy/kU5/6FFu3bqW7u5va2lpWrVrFZz7zGRobG8dA2onJnDlzHLdffvnlnHTSSQDSlychk/GenG8s9PX1FTTn9Pf38/Wvf53777+fvr4+Vq9ezVe/+tUJPy/le5YoZJ4o1raJ8+ijj/LRj36UBx54gBkzZiS2T7a+MxzPR4W0RWdnJ5deeimPPPIIAJs2beKCCy7IyFo43sjVPp/+9Kc58sgjHY/7/ve/z6pVq9i5cyfnnnsu27Zto6enh8bGRjZs2MCnP/1pqqqqEuWLsX0KffabrP3niiuuAOCuu+7isssu49FHH6W8vDylXLH3n9GgaJVdgiAIgiAIgiAIgiAIwuRjwsXsEgRBEARBEARBEARBEIRsiLJLEARBEARBEARBEARBKBpE2SUIgiAIgiAIgiAIgiAUDaLsEgRBEARBEARBEARBEIoGUXYJgiAIgiAIgiAIgiAIRYMouwRBEARBEARBEARBEISiQZRdgiAIgiAIgiAIgiAIQtEgyi5BEARBEARBEARBEAShaBBllyAIgiAIgiAIgiAIglA0iLJLEARBEARBEARBEARBKBpE2SUIgiAIgiAIgiAIgiAUDf8fPcQUceQy0QUAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<Figure size 864x144 with 2 Axes>"
+       "<Figure size 1200x200 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -90,7 +135,7 @@
     }
    ],
    "source": [
-    "pm.traceplot(trace_p);"
+    "az.plot_trace(trace_p);"
    ]
   },
   {
@@ -102,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -112,9 +157,44 @@
       "Auto-assigning NUTS sampler...\n",
       "Initializing NUTS using jitter+adapt_diag...\n",
       "Multiprocess sampling (2 chains in 2 jobs)\n",
-      "NUTS: [tau, mu, eta]\n",
-      "Sampling 2 chains: 100%|██████████| 3000/3000 [00:02<00:00, 1359.34draws/s]\n",
-      "There were 4 divergences after tuning. Increase `target_accept` or reparameterize.\n"
+      "NUTS: [tau, mu, eta]\n"
+     ]
+    },
+    {
+     "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 00:08<00:00 Sampling 2 chains, 2 divergences]\n",
+       "    </div>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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 9 seconds.\n",
+      "There were 2 divergences after tuning. Increase `target_accept` or reparameterize.\n"
      ]
     }
    ],
@@ -122,34 +202,26 @@
     "with pm.Model() as hierarchical:\n",
     "    \n",
     "    eta = pm.Normal('eta', 0, 1, shape=J)\n",
-    "    mu = pm.Normal('mu', 0, sigma=1e6)\n",
-    "    tau = pm.HalfCauchy('tau', 5)\n",
+    "    mu = pm.Normal('mu', 0, sigma=10)\n",
+    "    tau = pm.HalfNormal('tau', 10)\n",
     "    \n",
     "    theta = pm.Deterministic('theta', mu + tau*eta)\n",
     "    \n",
     "    obs = pm.Normal('obs', theta, sigma=sigma, observed=y)\n",
     "    \n",
-    "    trace_h = pm.sample(1000)"
+    "    trace_h = pm.sample(2000, target_accept=0.9, return_inferencedata=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/junpenglao/Documents/pymc3/pymc3/plots/__init__.py:40: UserWarning: Keyword argument `varnames` renamed to `var_names`, and will be removed in pymc3 3.8\n",
-      "  warnings.warn('Keyword argument `{old}` renamed to `{new}`, and will be removed in pymc3 3.8'.format(old=old, new=new))\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 864x144 with 2 Axes>"
+       "<Figure size 1200x200 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -157,27 +229,19 @@
     }
    ],
    "source": [
-    "pm.traceplot(trace_h, varnames=['mu']);"
+    "az.plot_trace(trace_h, var_names='mu');"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/junpenglao/Documents/pymc3/pymc3/plots/__init__.py:40: UserWarning: Keyword argument `varnames` renamed to `var_names`, and will be removed in pymc3 3.8\n",
-      "  warnings.warn('Keyword argument `{old}` renamed to `{new}`, and will be removed in pymc3 3.8'.format(old=old, new=new))\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 432x403.2 with 1 Axes>"
+       "<Figure size 600x560 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -185,84 +249,100 @@
     }
    ],
    "source": [
-    "pm.forestplot(trace_h, varnames=['theta']);"
+    "az.plot_forest(trace_h, var_names='theta');"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "### Leave-one-out Cross-validation (LOO)\n",
+    "\n",
+    "LOO cross-validation is an estimate of the out-of-sample predictive fit. In cross-validation, the data are repeatedly partitioned into training and holdout sets, iteratively fitting the model with the former and evaluating the fit with the holdout data. Vehtari et al. (2016) introduced an efficient computation of LOO from MCMC samples (without the need for re-fitting the data). This approximation is based on importance sampling. The importance weights are stabilized using a method known as Pareto-smoothed importance sampling (PSIS).\n",
+    "\n",
     "### Widely-applicable Information Criterion (WAIC)\n",
     "\n",
-    "WAIC (Watanabe 2010) is a fully Bayesian criterion for estimating out-of-sample expectation, using the computed log pointwise posterior predictive density (LPPD) and correcting for the effective number of parameters to adjust for overfitting."
+    "WAIC (Watanabe 2010) is a fully Bayesian criterion for estimating out-of-sample expectation, using the computed log pointwise posterior predictive density (LPPD) and correcting for the effective number of parameters to adjust for overfitting.\n",
+    "\n",
+    "By default ArviZ uses LOO, but WAIC is also available."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "61.173190685882204"
+       "-30.541155330185944"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pooled_waic = pm.waic(trace_p, pooled)\n",
+    "pooled_loo = az.loo(trace_p, pooled)\n",
     "    \n",
-    "pooled_waic.WAIC"
+    "pooled_loo.loo"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/osvaldo/proyectos/00_BM/arviz/arviz/stats/stats.py:682: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n",
+      "  \"Estimated shape parameter of Pareto distribution is greater than 0.7 for \"\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "61.38571263305805"
+       "-30.79701778020852"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "hierarchical_waic = pm.waic(trace_h, hierarchical)\n",
+    "hierarchical_loo  = az.loo(trace_h, hierarchical)\n",
     "    \n",
-    "hierarchical_waic.WAIC"
+    "hierarchical_loo.loo"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "PyMC3 includes two convenience functions to help compare WAIC for different models. The first of this functions is `compare`, this one computes WAIC (or LOO) from a set of traces and models and returns a DataFrame."
+    "ArviZ includes two convenience functions to help compare LOO for different models. The first of these functions is `compare`, which computes LOO (or WAIC) from a set of traces and models and returns a DataFrame."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hierarchical.name = 'hierarchical'\n",
-    "pooled.name = 'pooled'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/osvaldo/proyectos/00_BM/arviz/arviz/stats/stats.py:150: UserWarning: \n",
+      "The scale is now log by default. Use 'scale' argument or 'stats.ic_scale' rcParam if you rely on a specific value.\n",
+      "A higher log-score (or a lower deviance) indicates a model with better predictive accuracy.\n",
+      "  \"\\nThe scale is now log by default. Use 'scale' argument or \"\n",
+      "/home/osvaldo/proyectos/00_BM/arviz/arviz/stats/stats.py:682: UserWarning: Estimated shape parameter of Pareto distribution is greater than 0.7 for one or more samples. You should consider using a more robust model, this is because importance sampling is less likely to work well if the marginal posterior and LOO posterior are very different. This is more likely to happen with a non-robust model and highly influential observations.\n",
+      "  \"Estimated shape parameter of Pareto distribution is greater than 0.7 for \"\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -284,125 +364,99 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>WAIC</th>\n",
-       "      <th>pWAIC</th>\n",
-       "      <th>dWAIC</th>\n",
+       "      <th>rank</th>\n",
+       "      <th>loo</th>\n",
+       "      <th>p_loo</th>\n",
+       "      <th>d_loo</th>\n",
        "      <th>weight</th>\n",
-       "      <th>SE</th>\n",
-       "      <th>dSE</th>\n",
-       "      <th>var_warn</th>\n",
+       "      <th>se</th>\n",
+       "      <th>dse</th>\n",
+       "      <th>warning</th>\n",
+       "      <th>loo_scale</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>pooled</th>\n",
-       "      <td>61.17</td>\n",
-       "      <td>0.69</td>\n",
        "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2.2</td>\n",
+       "      <td>-30.5412</td>\n",
+       "      <td>0.661857</td>\n",
        "      <td>0</td>\n",
+       "      <td>0.56286</td>\n",
+       "      <td>0.986818</td>\n",
        "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>log</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>hierarchical</th>\n",
-       "      <td>61.39</td>\n",
-       "      <td>0.99</td>\n",
-       "      <td>0.21</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.01</td>\n",
-       "      <td>0.31</td>\n",
-       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-30.797</td>\n",
+       "      <td>1.14051</td>\n",
+       "      <td>0.255862</td>\n",
+       "      <td>0.43714</td>\n",
+       "      <td>1.02204</td>\n",
+       "      <td>0.256169</td>\n",
+       "      <td>True</td>\n",
+       "      <td>log</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "               WAIC pWAIC dWAIC weight    SE   dSE var_warn\n",
-       "pooled        61.17  0.69     0      1   2.2     0        0\n",
-       "hierarchical  61.39  0.99  0.21      0  2.01  0.31        0"
+       "             rank      loo     p_loo     d_loo   weight        se       dse  \\\n",
+       "pooled          0 -30.5412  0.661857         0  0.56286  0.986818         0   \n",
+       "hierarchical    1  -30.797   1.14051  0.255862  0.43714   1.02204  0.256169   \n",
+       "\n",
+       "             warning loo_scale  \n",
+       "pooled         False       log  \n",
+       "hierarchical    True       log  "
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df_comp_WAIC = pm.compare({hierarchical: trace_h, pooled: trace_p})\n",
-    "df_comp_WAIC"
+    "df_comp_loo = az.compare({\"hierarchical\": trace_h, \"pooled\": trace_p})\n",
+    "df_comp_loo"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We have many columns so let check one by one the meaning of them:\n",
+    "We have many columns, so let's check out their meaning one by one:\n",
     "\n",
     "\n",
-    "1. The first column clearly contains the values of WAIC. The DataFrame is always sorted from lowest to highest WAIC. The index reflects the order in which the models are passed to this function.\n",
+    "0. The index is the names of the models taken from the keys of the dictionary passed to `compare(.)`.\n",
     "\n",
-    "2. The second column is the estimated effective number of parameters. In general, models with more parameters will be more flexible to fit data and at the same time could also lead to overfitting. Thus we can interpret pWAIC as a penalization term, intuitively we can also interpret it as measure of how flexible each model is in fitting the data. \n",
+    "1. **rank**, the ranking of the models starting from 0 (best model) to the number of models.\n",
     "\n",
-    "3. The third column is the relative difference between the value of WAIC for the top-ranked model and the value of WAIC for each model. For this reason we will always get a value of 0 for the first model.\n",
+    "2. **loo**, the values of LOO (or WAIC). The DataFrame is always sorted from best LOO/WAIC to worst. \n",
     "\n",
-    "4. Sometimes when comparing models, we do not want to select the \"best\" model, instead we want to perform predictions by averaging along all the models (or at least several models). Ideally we would like to perform a weighted average, giving more weight to the model that seems to explain/predict the data better. There are many approaches to perform this task, one of them is to use Akaike weights based on the values of WAIC for each model. These weights can be loosely interpreted as the probability of each model (among the compared models) given the data. One caveat of this approach is that the weights are based on point estimates of WAIC (i.e. the uncertainty is ignored).\n",
+    "3. **p_loo**, the value of the penalization term. We can roughly think of this value as the estimated effective number of parameters (but do not take that too seriously).\n",
     "\n",
-    "5. The fifth column records the standard error for the WAIC computations. The standard error can be useful to assess the uncertainty of the WAIC estimates. Nevertheless, caution need to be taken because the estimation of the standard error assumes normality and hence could be problematic when the sample size is low.\n",
+    "4. **d_loo**, the relative difference between the value of LOO/WAIC for the top-ranked model and the value of LOO/WAIC for each model. For this reason we will always get a value of 0 for the first model.\n",
     "\n",
-    "6. In the same way that we can compute the standard error for each value of WAIC, we can compute the standard error of the differences between two values of WAIC. Notice that both quantities are not necessarily the same, the reason is that the uncertainty about WAIC is correlated between models. This quantity is always 0 for the top-ranked model.\n",
+    "5. **weight**, the weights assigned to each model. These weights can be loosely interpreted as the probability of each model being true (among the compared models) given the data.\n",
     "\n",
-    "7. Finally we have the last column named \"warning\". A value of 1 indicates that the computation of WAIC may not be reliable, this warning is based on an empirical determined cutoff value and need to be interpreted with caution. For more details you can read this [paper](https://arxiv.org/abs/1507.04544)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The second convenience function takes the output of `compare` and produces a summary plot in the style of the one used in the book [Statistical Rethinking](http://xcelab.net/rm/statistical-rethinking/) by Richard McElreath (check also [this port](https://github.com/aloctavodia/Statistical-Rethinking-with-Python-and-PyMC3) of the examples in the book to PyMC3)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x144 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pm.compareplot(df_comp_WAIC);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The empty circle represents the values of WAIC and the black error bars associated with them are the values of the standard deviation of WAIC. \n",
+    "6. **se**, the standard error for the LOO/WAIC computations. The standard error can be useful to assess the uncertainty of the LOO/WAIC estimates. By default these errors are computed using stacking.\n",
     "\n",
-    "The value of the lowest WAIC is also indicated with a vertical dashed grey line to ease comparison with other WAIC values.\n",
+    "7. **dse**, the standard errors of the difference between two values of LOO/WAIC. The same way that we can compute the standard error for each value of LOO/WAIC, we can compute the standard error of the differences between two values of LOO/WAIC. Notice that both quantities are not necessarily the same, the reason is that the uncertainty about LOO/WAIC is correlated between models. This quantity is always 0 for the top-ranked model.\n",
     "\n",
-    "The filled black dots are the in-sample deviance of each model, which for WAIC is  2 pWAIC from the corresponding WAIC value.\n",
+    "8. **warning**, If `True` the computation of LOO/WAIC may not be reliable.\n",
     "\n",
-    "For all models except the top-ranked one we also get a triangle indicating the value of the difference of WAIC between that model and the top model and a grey errobar indicating the standard error of the differences between the top-ranked WAIC and WAIC for each model."
+    "9. **loo_scale**, the scale of the reported values. The default is the log scale as previously mentioned. Other options are deviance -- this is the log-score multiplied by -2 (this reverts the order: a lower LOO/WAIC will be better) -- and negative-log -- this is the log-score multiplied by -1 (as with the deviance scale, a lower value is better)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Leave-one-out Cross-validation (LOO)\n",
-    "\n",
-    "LOO cross-validation is an estimate of the out-of-sample predictive fit. In cross-validation, the data are repeatedly partitioned into training and holdout sets, iteratively fitting the model with the former and evaluating the fit with the holdout data. Vehtari et al. (2016) introduced an efficient computation of LOO from MCMC samples, which are corrected using Pareto-smoothed importance sampling (PSIS) to provide an estimate of point-wise out-of-sample prediction accuracy."
+    "The second convenience function takes the output of `compare` and produces a summary plot in the style of the one used in the book [Statistical Rethinking](http://xcelab.net/rm/statistical-rethinking/) by Richard McElreath (check also [this port](https://github.com/aloctavodia/Statistical-Rethinking-with-Python-and-PyMC3) of the examples in the book to PyMC3)."
    ]
   },
   {
@@ -412,153 +466,28 @@
    "outputs": [
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "61.203032352985645"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pooled_loo = pm.loo(trace_p, pooled)\n",
-    "    \n",
-    "pooled_loo.LOO"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "61.47557753820364"
+       "<Figure size 600x200 with 1 Axes>"
       ]
      },
-     "execution_count": 14,
      "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hierarchical_loo  = pm.loo(trace_h, hierarchical)\n",
-    "    \n",
-    "hierarchical_loo.LOO"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also use `compare` with LOO."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>LOO</th>\n",
-       "      <th>pLOO</th>\n",
-       "      <th>dLOO</th>\n",
-       "      <th>weight</th>\n",
-       "      <th>SE</th>\n",
-       "      <th>dSE</th>\n",
-       "      <th>shape_warn</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>pooled</th>\n",
-       "      <td>61.2</td>\n",
-       "      <td>0.7</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2.21</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>hierarchical</th>\n",
-       "      <td>61.48</td>\n",
-       "      <td>1.03</td>\n",
-       "      <td>0.27</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.01</td>\n",
-       "      <td>0.31</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                LOO  pLOO  dLOO weight    SE   dSE shape_warn\n",
-       "pooled         61.2   0.7     0      1  2.21     0          0\n",
-       "hierarchical  61.48  1.03  0.27      0  2.01  0.31          0"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "df_comp_LOO = pm.compare({hierarchical: trace_h, pooled: trace_p}, ic='LOO')\n",
-    "df_comp_LOO"
+    "az.plot_compare(df_comp_loo, insample_dev=False);"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The columns return the equivalent values for LOO, notice that in this example we get two warnings. Also notice that the order of the models is not the same as the one for WAIC.\n",
+    "The empty circle represents the values of LOO and the black error bars associated with them are the values of the standard deviation of LOO. \n",
     "\n",
-    "We can also plot the results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x144 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pm.compareplot(df_comp_LOO);"
+    "The value of the highest LOO, i.e the best estimated model, is also indicated with a vertical dashed grey line to ease comparison with other LOO values.\n",
+    "\n",
+    "For all models except the top-ranked one we also get a triangle indicating the value of the difference of WAIC between that model and the top model and a grey errobar indicating the standard error of the differences between the top-ranked WAIC and WAIC for each model."
    ]
   },
   {
@@ -567,7 +496,7 @@
    "source": [
     "### Interpretation\n",
     "\n",
-    "Though we might expect the hierarchical model to outperform a complete pooling model, there is little to choose between the models in this case, giving that both models gives very similar values of the information criteria. This is more clearly appreciated when we take into account the uncertainty (in terms of standard errors) of WAIC and LOO.\n",
+    "Though we might expect the hierarchical model to outperform a complete pooling model, there is little to choose between the models in this case, given that both models gives very similar values of the information criteria. This is more clearly appreciated when we take into account the uncertainty (in terms of standard errors) of LOO and WAIC.\n",
     "\n",
     "## Reference\n",
     "\n",
@@ -578,26 +507,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "pymc3 3.8\n",
-      "arviz 0.8.3\n",
-      "numpy 1.17.5\n",
-      "last updated: Thu Jun 11 2020 \n",
+      "autopep8 1.5\n",
+      "pymc3    3.9.3\n",
+      "arviz    0.9.0\n",
+      "numpy    1.18.5\n",
+      "json     2.0.9\n",
+      "last updated: Thu Aug 13 2020 \n",
       "\n",
-      "CPython 3.8.2\n",
-      "IPython 7.11.0\n",
+      "CPython 3.7.6\n",
+      "IPython 7.17.0\n",
       "watermark 2.0.2\n"
      ]
     }
    ],
    "source": [
-    "%load_ext watermark\n",
     "%watermark -n -u -v -iv -w"
    ]
   }
@@ -618,7 +548,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.2"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,
diff --git a/pymc3/sampling.py b/pymc3/sampling.py
index 8f0fecb3c0..240ebbf6d7 100644
--- a/pymc3/sampling.py
+++ b/pymc3/sampling.py
@@ -226,9 +226,7 @@ def _print_step_hierarchy(s, level=0):
     else:
         varnames = ", ".join(
             [
-                get_untransformed_name(v.name)
-                if is_transformed_name(v.name)
-                else v.name
+                get_untransformed_name(v.name) if is_transformed_name(v.name) else v.name
                 for v in s.vars
             ]
         )
@@ -491,10 +489,7 @@ def sample(
                 start = start_
         except (AttributeError, NotImplementedError, tg.NullTypeGradError):
             # gradient computation failed
-            _log.info(
-                "Initializing NUTS failed. "
-                "Falling back to elementwise auto-assignment."
-            )
+            _log.info("Initializing NUTS failed. " "Falling back to elementwise auto-assignment.")
             _log.debug("Exception in init nuts", exec_info=True)
             step = assign_step_methods(model, step, step_kwargs=kwargs)
     else:
@@ -559,9 +554,7 @@ def sample(
             has_demcmc = np.any(
                 [
                     isinstance(m, DEMetropolis)
-                    for m in (
-                        step.methods if isinstance(step, CompoundStep) else [step]
-                    )
+                    for m in (step.methods if isinstance(step, CompoundStep) else [step])
                 ]
             )
             _log.info("Population sampling ({} chains)".format(chains))
@@ -625,9 +618,7 @@ def sample(
 
     if compute_convergence_checks:
         if draws - tune < 100:
-            warnings.warn(
-                "The number of samples is too small to check convergence reliably."
-            )
+            warnings.warn("The number of samples is too small to check convergence reliably.")
         else:
             trace.report._run_convergence_checks(idata, model)
     trace.report._log_summary()
@@ -664,14 +655,7 @@ def _check_start_shape(model, start):
 
 
 def _sample_many(
-    draws,
-    chain: int,
-    chains: int,
-    start: list,
-    random_seed: list,
-    step,
-    callback=None,
-    **kwargs,
+    draws, chain: int, chains: int, start: list, random_seed: list, step, callback=None, **kwargs,
 ):
     """Samples all chains sequentially.
 
@@ -833,9 +817,7 @@ def _sample(
     """
     skip_first = kwargs.get("skip_first", 0)
 
-    sampling = _iter_sample(
-        draws, step, start, trace, chain, tune, model, random_seed, callback
-    )
+    sampling = _iter_sample(draws, step, start, trace, chain, tune, model, random_seed, callback)
     _pbar_data = {"chain": chain, "divergences": 0}
     _desc = "Sampling chain {chain:d}, {divergences:,d} divergences"
     if progressbar:
@@ -909,9 +891,7 @@ def iter_sample(
         for trace in iter_sample(500, step):
             ...
     """
-    sampling = _iter_sample(
-        draws, step, start, trace, chain, tune, model, random_seed, callback
-    )
+    sampling = _iter_sample(draws, step, start, trace, chain, tune, model, random_seed, callback)
     for i, (strace, _) in enumerate(sampling):
         yield MultiTrace([strace[: i + 1]])
 
@@ -1012,8 +992,7 @@ def _iter_sample(
             if callback is not None:
                 warns = getattr(step, "warnings", None)
                 callback(
-                    trace=strace,
-                    draw=Draw(chain, i == draws, i, i < tune, stats, point, warns),
+                    trace=strace, draw=Draw(chain, i == draws, i, i < tune, stats, point, warns),
                 )
 
             yield strace, diverging
@@ -1067,9 +1046,7 @@ def __init__(self, steppers, parallelize, progressbar=True):
                 import multiprocessing
 
                 for c, stepper in (
-                    enumerate(progress_bar(steppers))
-                    if progressbar
-                    else enumerate(steppers)
+                    enumerate(progress_bar(steppers)) if progressbar else enumerate(steppers)
                 ):
                     secondary_end, primary_end = multiprocessing.Pipe()
                     stepper_dumps = pickle.dumps(stepper, protocol=4)
@@ -1136,9 +1113,7 @@ def _run_secondary(c, stepper_dumps, secondary_end):
             # but rather a CompoundStep. PopulationArrayStepShared.population
             # has to be updated, therefore we identify the substeppers first.
             population_steppers = []
-            for sm in (
-                stepper.methods if isinstance(stepper, CompoundStep) else [stepper]
-            ):
+            for sm in stepper.methods if isinstance(stepper, CompoundStep) else [stepper]:
                 if isinstance(sm, arraystep.PopulationArrayStepShared):
                     population_steppers.append(sm)
             while True:
@@ -1682,13 +1657,9 @@ def sample_posterior_predictive(
             nchain = 1
 
     if keep_size and samples is not None:
-        raise IncorrectArgumentsError(
-            "Should not specify both keep_size and samples arguments"
-        )
+        raise IncorrectArgumentsError("Should not specify both keep_size and samples arguments")
     if keep_size and size is not None:
-        raise IncorrectArgumentsError(
-            "Should not specify both keep_size and size arguments"
-        )
+        raise IncorrectArgumentsError("Should not specify both keep_size and size arguments")
 
     if samples is None:
         if isinstance(_trace, MultiTrace):
@@ -1714,15 +1685,11 @@ def sample_posterior_predictive(
 
     if var_names is not None:
         if vars is not None:
-            raise IncorrectArgumentsError(
-                "Should not specify both vars and var_names arguments."
-            )
+            raise IncorrectArgumentsError("Should not specify both vars and var_names arguments.")
         else:
             vars = [model[x] for x in var_names]
     elif vars is not None:  # var_names is None, and vars is not.
-        warnings.warn(
-            "vars argument is deprecated in favor of var_names.", DeprecationWarning
-        )
+        warnings.warn("vars argument is deprecated in favor of var_names.", DeprecationWarning)
     if vars is None:
         vars = model.observed_RVs
 
@@ -1741,11 +1708,7 @@ def sample_posterior_predictive(
                 # the trace object will either be a MultiTrace (and have _straces)...
                 if hasattr(_trace, "_straces"):
                     chain_idx, point_idx = np.divmod(idx, len_trace)
-                    param = (
-                        cast(MultiTrace, _trace)
-                        ._straces[chain_idx % nchain]
-                        .point(point_idx)
-                    )
+                    param = cast(MultiTrace, _trace)._straces[chain_idx % nchain].point(point_idx)
                 # ... or a PointList
                 else:
                     param = cast(PointList, _trace)[idx % len_trace]
@@ -1783,9 +1746,9 @@ def sample_posterior_predictive_w(
     Parameters
     ----------
     traces : list or list of lists
-        List of traces generated from MCMC sampling, or a list of list
-        containing dicts from find_MAP() or points. The number of traces should
-        be equal to the number of weights.
+        List of traces generated from MCMC sampling (xarray.Dataset, arviz.InferenceData, or 
+        MultiTrace), or a list of list containing dicts from find_MAP() or points. The number of
+        traces should be equal to the number of weights.
     samples : int, optional
         Number of posterior predictive samples to generate. Defaults to the
         length of the shorter trace in traces.
@@ -1811,6 +1774,17 @@ def sample_posterior_predictive_w(
     """
     np.random.seed(random_seed)
 
+    if isinstance(traces[0], InferenceData):
+        n_samples = [
+            trace.posterior.sizes["chain"] * trace.posterior.sizes["draw"] for trace in traces
+        ]
+        traces = [dataset_to_point_dict(trace.posterior) for trace in traces]
+    elif isinstance(traces[0], xarray.Dataset):
+        n_samples = [trace.sizes["chain"] * trace.sizes["draw"] for trace in traces]
+        traces = [dataset_to_point_dict(trace) for trace in traces]
+    else:
+        n_samples = [len(i) * i.nchains for i in traces]
+
     if models is None:
         models = [modelcontext(models)] * len(traces)
 
@@ -1830,7 +1804,7 @@ def sample_posterior_predictive_w(
     weights = np.asarray(weights)
     p = weights / np.sum(weights)
 
-    min_tr = min([len(i) * i.nchains for i in traces])
+    min_tr = min(n_samples)
 
     n = (min_tr * p).astype("int")
     # ensure n sum up to min_tr
@@ -1933,8 +1907,7 @@ def sample_prior_predictive(
     if vars is None and var_names is None:
         prior_pred_vars = model.observed_RVs
         prior_vars = (
-            get_default_varnames(model.unobserved_RVs, include_transformed=True)
-            + model.potentials
+            get_default_varnames(model.unobserved_RVs, include_transformed=True) + model.potentials
         )
         vars_ = [var.name for var in prior_vars + prior_pred_vars]
         vars = set(vars_)
@@ -1942,9 +1915,7 @@ def sample_prior_predictive(
         vars = var_names
         vars_ = vars
     elif vars is not None:
-        warnings.warn(
-            "vars argument is deprecated in favor of var_names.", DeprecationWarning
-        )
+        warnings.warn("vars argument is deprecated in favor of var_names.", DeprecationWarning)
         vars_ = vars
     else:
         raise ValueError("Cannot supply both vars and var_names arguments.")
@@ -1974,13 +1945,7 @@ def sample_prior_predictive(
 
 
 def init_nuts(
-    init="auto",
-    chains=1,
-    n_init=500000,
-    model=None,
-    random_seed=None,
-    progressbar=True,
-    **kwargs,
+    init="auto", chains=1, n_init=500000, model=None, random_seed=None, progressbar=True, **kwargs,
 ):
     """Set up the mass matrix initialization for NUTS.
 
@@ -2036,9 +2001,7 @@ def init_nuts(
     if set(vars) != set(model.vars):
         raise ValueError("Must use init_nuts on all variables of a model.")
     if not all_continuous(vars):
-        raise ValueError(
-            "init_nuts can only be used for models with only " "continuous variables."
-        )
+        raise ValueError("init_nuts can only be used for models with only " "continuous variables.")
 
     if not isinstance(init, str):
         raise TypeError("init must be a string.")
@@ -2092,9 +2055,7 @@ def init_nuts(
         mean = approx.bij.rmap(approx.mean.get_value())
         mean = model.dict_to_array(mean)
         weight = 50
-        potential = quadpotential.QuadPotentialDiagAdaptGrad(
-            model.ndim, mean, cov, weight
-        )
+        potential = quadpotential.QuadPotentialDiagAdaptGrad(model.ndim, mean, cov, weight)
     elif init == "advi+adapt_diag":
         approx = pm.fit(
             random_seed=random_seed,
diff --git a/pymc3/tests/test_sampling.py b/pymc3/tests/test_sampling.py
index 91ce994862..6c6baf5779 100644
--- a/pymc3/tests/test_sampling.py
+++ b/pymc3/tests/test_sampling.py
@@ -718,22 +718,24 @@ def test_sample_posterior_predictive_w(self):
             mu = pm.Normal("mu", mu=0, sigma=1)
             y = pm.Normal("y", mu=mu, sigma=1, observed=data0)
             trace_0 = pm.sample()
+            idata_0 = az.from_pymc3(trace_0)
 
         with pm.Model() as model_1:
             mu = pm.Normal("mu", mu=0, sigma=1, shape=len(data0))
             y = pm.Normal("y", mu=mu, sigma=1, observed=data0)
             trace_1 = pm.sample()
-
-        traces = [trace_0, trace_0]
-        models = [model_0, model_0]
-        ppc = pm.sample_posterior_predictive_w(traces, 100, models)
-        assert ppc["y"].shape == (100, 500)
+            idata_1 = az.from_pymc3(trace_1)
 
         traces = [trace_0, trace_1]
+        idatas = [idata_0, idata_1]
         models = [model_0, model_1]
+
         ppc = pm.sample_posterior_predictive_w(traces, 100, models)
         assert ppc["y"].shape == (100, 500)
 
+        ppc = pm.sample_posterior_predictive_w(idatas, 100, models)
+        assert ppc["y"].shape == (100, 500)
+
 
 @pytest.mark.parametrize(
     "method",