{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d9de8716",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "# Gamma-gamma Model\n",
    "\n",
    "In this notebook we show how to fit a Gamma-Gamma model in PyMC-Marketing. We compare the results with the [`lifetimes`](https://github.com/CamDavidsonPilon/lifetimes) package (no longer maintained and last meaningful update was July 2020). The model is presented in the paper: Fader, P. S., & Hardie, B. G. (2013). [The Gamma-Gamma model of monetary value](http://www.brucehardie.com/notes/025/gamma_gamma.pdf). February, 2, 1-9."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a579696d",
   "metadata": {},
   "source": [
    "## Prepare Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "813aa3e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import arviz as az\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from lifetimes import GammaGammaFitter\n",
    "\n",
    "from pymc_marketing import clv\n",
    "\n",
    "# Plotting configuration\n",
    "az.style.use(\"arviz-darkgrid\")\n",
    "plt.rcParams[\"figure.figsize\"] = [10, 6]\n",
    "plt.rcParams[\"figure.dpi\"] = 100\n",
    "plt.rcParams[\"figure.facecolor\"] = \"white\"\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%config InlineBackend.figure_format = \"retina\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4e9df33",
   "metadata": {},
   "source": [
    "## Load Data\n",
    "\n",
    "We start by loading the `CDNOW` dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4039ce96",
   "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>frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>T</th>\n",
       "      <th>monetary_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>30.43</td>\n",
       "      <td>38.86</td>\n",
       "      <td>22.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.71</td>\n",
       "      <td>38.86</td>\n",
       "      <td>11.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>38.86</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>38.86</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>38.86</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   frequency  recency      T  monetary_value\n",
       "0          2    30.43  38.86           22.35\n",
       "1          1     1.71  38.86           11.77\n",
       "2          0     0.00  38.86            0.00\n",
       "3          0     0.00  38.86            0.00\n",
       "4          0     0.00  38.86            0.00"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_path = \"https://raw.githubusercontent.com/pymc-labs/pymc-marketing/main/data/clv_quickstart.csv\"\n",
    "\n",
    "summary_with_money_value = pd.read_csv(data_path)\n",
    "\n",
    "summary_with_money_value.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d244cc1a",
   "metadata": {},
   "source": [
    "For the Gamma-Gamma model, we need to filter out customers who have made only one purchase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bae5c34c",
   "metadata": {
    "id": "a374e74d"
   },
   "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>frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>T</th>\n",
       "      <th>monetary_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>30.43</td>\n",
       "      <td>38.86</td>\n",
       "      <td>22.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.71</td>\n",
       "      <td>38.86</td>\n",
       "      <td>11.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>29.43</td>\n",
       "      <td>38.86</td>\n",
       "      <td>73.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>38.86</td>\n",
       "      <td>11.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>35.71</td>\n",
       "      <td>38.86</td>\n",
       "      <td>25.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   frequency  recency      T  monetary_value\n",
       "0          2    30.43  38.86           22.35\n",
       "1          1     1.71  38.86           11.77\n",
       "5          7    29.43  38.86           73.74\n",
       "6          1     5.00  38.86           11.77\n",
       "8          2    35.71  38.86           25.55"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returning_customers_summary = summary_with_money_value.query(\"frequency > 0\")\n",
    "\n",
    "returning_customers_summary.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16374e35",
   "metadata": {},
   "source": [
    "## Model Specification\n",
    "\n",
    "Here we briefly describe the assumptions and the parametrization of the Gamma-Gamma model from the paper above.\n",
    "\n",
    "The model of spend per transaction is based on the following three general assumptions:\n",
    "\n",
    "- The monetary value of a customer’s given transaction varies randomly around their average transaction value.\n",
    "- Average transaction values vary across customers but do not vary over time for any given individual.\n",
    "- The distribution of average transaction values across customers is independent of the transaction process.\n",
    "  \n",
    "For a customer with x transactions, let $z_1, z_2, \\ldots, z_x$ denote the value of each transaction. The customer’s observed average transaction value by\n",
    "\n",
    "$$\n",
    "\\bar{z} = \\frac{1}{x} \\sum_{i=1}^{x} z_i\n",
    "$$\n",
    "\n",
    "Now let's describe the parametrization: \n",
    "\n",
    "1. We assume that $z_i \\sim \\text{Gamma}(p, ν)$, with $E(Z_i| p, ν) = \\xi = p/ν$.\n",
    "\n",
    "    – Given the convolution properties of the gamma, it follows that total spend across x transactions is distributed $\\text{Gamma}(px, ν)$.\n",
    "\n",
    "    – Given the scaling property of the gamma distribution, it follows that $\\bar{z} \\sim \\text{Gamma}(px, νx)$.\n",
    "\n",
    "2.  We assume $ν \\sim \\text{Gamma}(q, \\gamma)$.\n",
    "\n",
    "We are interested in estimating the parameters $p$, $q$ and $ν$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8696dc0a",
   "metadata": {},
   "source": [
    "```{note}\n",
    "The Gamma-Gamma model assumes that there is no relationship between the monetary value and the purchase frequency. We can check this assumption by calculating the correlation between the average spend and the frequency of purchases.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c413718e",
   "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>monetary_value</th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>monetary_value</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.113884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frequency</th>\n",
       "      <td>0.113884</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                monetary_value  frequency\n",
       "monetary_value        1.000000   0.113884\n",
       "frequency             0.113884   1.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returning_customers_summary[[\"monetary_value\", \"frequency\"]].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea4bea0a",
   "metadata": {},
   "source": [
    "The value of this correlation is close to $0.11$, which in practice is considered low enough to proceed with the model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df93d769",
   "metadata": {},
   "source": [
    "## Lifetimes Implementation\n",
    "\n",
    "First, we fit the model using the `lifetimes` package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7e511391",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a0dbb204",
    "outputId": "743601c8-48c5-46a7-993f-4bd19f083ea7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lifetimes.GammaGammaFitter: fitted with 946 subjects, p: 6.25, q: 3.74, v: 15.45>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggf = GammaGammaFitter()\n",
    "ggf.fit(\n",
    "    returning_customers_summary[\"frequency\"],\n",
    "    returning_customers_summary[\"monetary_value\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b65b7690",
   "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>coef</th>\n",
       "      <th>se(coef)</th>\n",
       "      <th>lower 95% bound</th>\n",
       "      <th>upper 95% bound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p</th>\n",
       "      <td>6.248802</td>\n",
       "      <td>1.189687</td>\n",
       "      <td>3.917016</td>\n",
       "      <td>8.580589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>q</th>\n",
       "      <td>3.744588</td>\n",
       "      <td>0.290166</td>\n",
       "      <td>3.175864</td>\n",
       "      <td>4.313313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v</th>\n",
       "      <td>15.447748</td>\n",
       "      <td>4.159994</td>\n",
       "      <td>7.294160</td>\n",
       "      <td>23.601336</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        coef  se(coef)  lower 95% bound  upper 95% bound\n",
       "p   6.248802  1.189687         3.917016         8.580589\n",
       "q   3.744588  0.290166         3.175864         4.313313\n",
       "v  15.447748  4.159994         7.294160        23.601336"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggf.summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cace007a",
   "metadata": {},
   "source": [
    "Once the model is fitted we can use the following method to compute the conditional expectation of the average profit per transaction for a group of one or more customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "12dbdd88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    24.658616\n",
       "1    18.911480\n",
       "2    35.171002\n",
       "3    35.171002\n",
       "4    35.171002\n",
       "5    71.462851\n",
       "6    18.911480\n",
       "7    35.171002\n",
       "8    27.282408\n",
       "9    35.171002\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_profit = ggf.conditional_expected_average_profit(\n",
    "    summary_with_money_value[\"frequency\"], summary_with_money_value[\"monetary_value\"]\n",
    ")\n",
    "avg_profit.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f0986b8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35.252958176049916"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_profit.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ff387fd",
   "metadata": {
    "id": "a2z_ZcC74wPI"
   },
   "source": [
    "## PyMC Marketing Implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d153908d",
   "metadata": {},
   "source": [
    "We can use the pre-built PyMC Marketing implementation of the Gamma-Gamma model, which also provides nice plotting and prediction methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9607aa4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.DataFrame(\n",
    "    {\n",
    "        \"customer_id\": returning_customers_summary.index,\n",
    "        \"mean_transaction_value\": returning_customers_summary[\"monetary_value\"],\n",
    "        \"frequency\": returning_customers_summary[\"frequency\"],\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5e27387",
   "metadata": {},
   "source": [
    "We can *build* the model so that we can see the model specification:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c6c912da",
   "metadata": {
    "id": "eoQmmIrj43NV"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gamma-Gamma Model (Mean Transactions)\n",
       "         p ~ HalfFlat()\n",
       "         q ~ HalfFlat()\n",
       "         v ~ HalfFlat()\n",
       "likelihood ~ Potential(f(q, p, v))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = clv.GammaGammaModel(data=dataset)\n",
    "model.build_model()\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1826502a",
   "metadata": {},
   "source": [
    "```{note}\n",
    "It is not necessary to build the model before fitting it. We can fit the model directly.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21608cf4",
   "metadata": {},
   "source": [
    "### Using MAP\n",
    "\n",
    "To begin with, lets use a numerical optimizer (`L-BFGS-B`) from `scipy.optimize` to find the maximum a posteriori (MAP) estimate of the parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e39da004",
   "metadata": {},
   "outputs": [],
   "source": [
    "idata_map = model.fit(fit_method=\"map\").posterior.to_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b8f11643",
   "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></th>\n",
       "      <th>p</th>\n",
       "      <th>q</th>\n",
       "      <th>v</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chain</th>\n",
       "      <th>draw</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th>0</th>\n",
       "      <td>6.248787</td>\n",
       "      <td>3.744591</td>\n",
       "      <td>15.447813</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   p         q          v\n",
       "chain draw                               \n",
       "0     0     6.248787  3.744591  15.447813"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idata_map"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b140c648",
   "metadata": {},
   "source": [
    "These values are very close to the ones obtained by the `lifetimes` package."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "756eb4d5",
   "metadata": {},
   "source": [
    "### MCMC\n",
    "\n",
    "We can also use MCMC to sample from the posterior distribution of the parameters. MCMC is a more robust method than MAP and provides uncertainty estimates for the parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed88b572",
   "metadata": {},
   "outputs": [],
   "source": [
    "sampler_kwargs = {\n",
    "    \"draws\": 2_000,\n",
    "    \"target_accept\": 0.9,\n",
    "    \"chains\": 4,\n",
    "    \"random_seed\": 42,\n",
    "}\n",
    "\n",
    "idata_mcmc = model.fit(**sampler_kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "52c3b00e",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 208kB\n",
       "Dimensions:  (chain: 4, draw: 2000)\n",
       "Coordinates:\n",
       "  * chain    (chain) int64 32B 0 1 2 3\n",
       "  * draw     (draw) int64 16kB 0 1 2 3 4 5 6 ... 1994 1995 1996 1997 1998 1999\n",
       "Data variables:\n",
       "    p        (chain, draw) float64 64kB 5.152 6.6 6.22 ... 6.451 5.998 5.995\n",
       "    q        (chain, draw) float64 64kB 3.742 3.767 3.896 ... 3.793 4.104 4.133\n",
       "    v        (chain, draw) float64 64kB 19.88 14.62 16.04 ... 15.13 17.79 18.07\n",
       "Attributes:\n",
       "    created_at:                 2024-04-05T07:27:09.778837\n",
       "    arviz_version:              0.17.1\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.11.0\n",
       "    sampling_time:              36.096750020980835\n",
       "    tuning_steps:               1000</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-31982271-ced1-4ab4-9ef4-498bc5e6274f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-31982271-ced1-4ab4-9ef4-498bc5e6274f' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 2000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-177854b2-a317-4244-8db4-6d483c1e7c9f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-177854b2-a317-4244-8db4-6d483c1e7c9f' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-b3f9ca2f-836f-4550-bc95-4eef65a14010' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b3f9ca2f-836f-4550-bc95-4eef65a14010' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6364a9c4-480d-458d-b9f4-a71c60fc7716' class='xr-var-data-in' type='checkbox'><label for='data-6364a9c4-480d-458d-b9f4-a71c60fc7716' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1996 1997 1998 1999</div><input id='attrs-f0d3db84-944a-4ebd-8383-8d335dd7bc02' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f0d3db84-944a-4ebd-8383-8d335dd7bc02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5efac5b6-91cf-4af5-ba4f-ceadd9b3d893' class='xr-var-data-in' type='checkbox'><label for='data-5efac5b6-91cf-4af5-ba4f-ceadd9b3d893' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 1997, 1998, 1999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7d2c70b8-0b2a-45f0-8b1c-1072b86c49b6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7d2c70b8-0b2a-45f0-8b1c-1072b86c49b6' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>p</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.152 6.6 6.22 ... 5.998 5.995</div><input id='attrs-2f2b0280-98ab-4aab-a1a0-3026a0b36718' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2f2b0280-98ab-4aab-a1a0-3026a0b36718' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42fc0064-bd3e-4257-8427-480ed1114bdb' class='xr-var-data-in' type='checkbox'><label for='data-42fc0064-bd3e-4257-8427-480ed1114bdb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[5.15231061, 6.60037756, 6.22022233, ..., 6.74467924, 6.42937746,\n",
       "        6.67633923],\n",
       "       [7.04724297, 7.68728873, 8.84283604, ..., 5.58978179, 5.53040301,\n",
       "        5.64323314],\n",
       "       [6.44883834, 8.60579053, 4.20296912, ..., 6.86109519, 6.55075589,\n",
       "        6.32433425],\n",
       "       [5.38492217, 6.25804466, 5.99455456, ..., 6.45075422, 5.99833806,\n",
       "        5.99523862]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.742 3.767 3.896 ... 4.104 4.133</div><input id='attrs-86d04915-110c-43e2-be34-cfe2ce508339' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-86d04915-110c-43e2-be34-cfe2ce508339' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9a65594-c3ff-4d41-889f-daf3b886c594' class='xr-var-data-in' type='checkbox'><label for='data-d9a65594-c3ff-4d41-889f-daf3b886c594' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[3.74158441, 3.76678306, 3.89648086, ..., 3.64645175, 3.6015663 ,\n",
       "        3.57853856],\n",
       "       [3.38391693, 3.44177406, 3.72263041, ..., 3.80848527, 3.82422058,\n",
       "        3.73762474],\n",
       "       [3.65857154, 3.72446917, 4.12231348, ..., 4.00532905, 3.95606033,\n",
       "        3.94859782],\n",
       "       [3.72295201, 3.76654566, 3.7896605 , ..., 3.79311068, 4.10397656,\n",
       "        4.13288142]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>19.88 14.62 16.04 ... 17.79 18.07</div><input id='attrs-ebd7c833-b8a8-489f-81cc-c15a76c1fc97' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ebd7c833-b8a8-489f-81cc-c15a76c1fc97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37537714-bf9c-4963-8b16-62e2f42e8134' class='xr-var-data-in' type='checkbox'><label for='data-37537714-bf9c-4963-8b16-62e2f42e8134' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[19.87956015, 14.61757069, 16.03596754, ..., 13.86289579,\n",
       "        14.51553309, 14.31665952],\n",
       "       [12.23643478, 11.42082917, 10.54528843, ..., 17.70833599,\n",
       "        17.33521984, 17.04616804],\n",
       "       [14.24492908, 10.86890966, 26.52694204, ..., 15.37296931,\n",
       "        15.52362981, 16.10503582],\n",
       "       [17.29281956, 16.28306576, 15.53093235, ..., 15.12576992,\n",
       "        17.78613973, 18.07186589]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d7e23520-5307-4962-957b-df2ea2b4a16e' class='xr-section-summary-in' type='checkbox'  ><label for='section-d7e23520-5307-4962-957b-df2ea2b4a16e' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0c329e6e-c9ac-4bdb-a840-1de629ca1351' class='xr-index-data-in' type='checkbox'/><label for='index-0c329e6e-c9ac-4bdb-a840-1de629ca1351' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-323fc597-73d4-471b-8f3e-d0e622d37655' class='xr-index-data-in' type='checkbox'/><label for='index-323fc597-73d4-471b-8f3e-d0e622d37655' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,\n",
       "       ...\n",
       "       1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=2000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-beaa4692-16f9-4493-92b2-f7d860567a87' class='xr-section-summary-in' type='checkbox'  checked><label for='section-beaa4692-16f9-4493-92b2-f7d860567a87' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-04-05T07:27:09.778837</dd><dt><span>arviz_version :</span></dt><dd>0.17.1</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.11.0</dd><dt><span>sampling_time :</span></dt><dd>36.096750020980835</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_sample_statsebc75352-411d-4f0f-ac8c-9f35662fec26\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_sample_statsebc75352-411d-4f0f-ac8c-9f35662fec26\" class = \"xr-section-summary\">sample_stats</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
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       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 992kB\n",
       "Dimensions:                (chain: 4, draw: 2000)\n",
       "Coordinates:\n",
       "  * chain                  (chain) int64 32B 0 1 2 3\n",
       "  * draw                   (draw) int64 16kB 0 1 2 3 4 ... 1996 1997 1998 1999\n",
       "Data variables: (12/17)\n",
       "    energy_error           (chain, draw) float64 64kB -0.004048 ... 0.01401\n",
       "    acceptance_rate        (chain, draw) float64 64kB 1.0 0.9969 ... 0.9936\n",
       "    step_size_bar          (chain, draw) float64 64kB 0.07411 ... 0.08088\n",
       "    perf_counter_start     (chain, draw) float64 64kB 1.84e+04 ... 1.841e+04\n",
       "    smallest_eigval        (chain, draw) float64 64kB nan nan nan ... nan nan\n",
       "    process_time_diff      (chain, draw) float64 64kB 0.000842 ... 0.003041\n",
       "    ...                     ...\n",
       "    n_steps                (chain, draw) float64 64kB 3.0 51.0 ... 79.0 15.0\n",
       "    reached_max_treedepth  (chain, draw) bool 8kB False False ... False False\n",
       "    tree_depth             (chain, draw) int64 64kB 2 6 5 4 5 6 ... 1 2 4 5 7 4\n",
       "    lp                     (chain, draw) float64 64kB -4.053e+03 ... -4.051e+03\n",
       "    largest_eigval         (chain, draw) float64 64kB nan nan nan ... nan nan\n",
       "    step_size              (chain, draw) float64 64kB 0.05936 0.05936 ... 0.0734\n",
       "Attributes:\n",
       "    created_at:                 2024-04-05T07:27:09.804289\n",
       "    arviz_version:              0.17.1\n",
       "    inference_library:          pymc\n",
       "    inference_library_version:  5.11.0\n",
       "    sampling_time:              36.096750020980835\n",
       "    tuning_steps:               1000</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1a2de849-e372-48d8-bd4a-10a33502e0e6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1a2de849-e372-48d8-bd4a-10a33502e0e6' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 2000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-540014f3-6779-423d-8a4d-a83729b79cc0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-540014f3-6779-423d-8a4d-a83729b79cc0' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-010c4e6f-8435-4f97-9e45-9edde08fccfc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-010c4e6f-8435-4f97-9e45-9edde08fccfc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a373552-3976-4e51-b219-03f8418fad60' class='xr-var-data-in' type='checkbox'><label for='data-4a373552-3976-4e51-b219-03f8418fad60' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1996 1997 1998 1999</div><input id='attrs-7ee10f4a-8c69-44e6-a335-896e8a9e3f57' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7ee10f4a-8c69-44e6-a335-896e8a9e3f57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cc70eb2-a272-4885-80c2-a85fe786ad33' class='xr-var-data-in' type='checkbox'><label for='data-6cc70eb2-a272-4885-80c2-a85fe786ad33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 1997, 1998, 1999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-81df96e7-52d3-4c9a-9dd0-6f5c7e851ea1' class='xr-section-summary-in' type='checkbox'  ><label for='section-81df96e7-52d3-4c9a-9dd0-6f5c7e851ea1' class='xr-section-summary' >Data variables: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>energy_error</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.004048 -0.8778 ... 0.01401</div><input id='attrs-e6396655-2b97-4bca-ba51-7c646d0d0bde' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6396655-2b97-4bca-ba51-7c646d0d0bde' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-670ebb6f-14d0-4026-9837-bec3601d2d10' class='xr-var-data-in' type='checkbox'><label for='data-670ebb6f-14d0-4026-9837-bec3601d2d10' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.00404801, -0.87778381,  0.05219859, ...,  0.00186527,\n",
       "         0.04510354,  0.84771448],\n",
       "       [-0.34150582,  0.0274966 , -0.03576437, ..., -0.72503414,\n",
       "         0.46294055, -0.42454581],\n",
       "       [ 0.14658044, -0.23656453,  0.00434448, ..., -0.08898982,\n",
       "        -0.24922014, -0.00279373],\n",
       "       [ 0.52458727,  0.23655814,  0.19394028, ..., -0.36026588,\n",
       "         0.00243672,  0.01401059]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acceptance_rate</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 0.9969 0.9618 ... 0.9747 0.9936</div><input id='attrs-52228a55-f9bf-4eaa-bcb1-570316ae87a1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-52228a55-f9bf-4eaa-bcb1-570316ae87a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac6da02d-54a1-463d-9370-1188842028fb' class='xr-var-data-in' type='checkbox'><label for='data-ac6da02d-54a1-463d-9370-1188842028fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.        , 0.99691348, 0.9617954 , ..., 0.99864335, 0.96569231,\n",
       "        0.40659205],\n",
       "       [0.99996201, 0.98102936, 0.99852296, ..., 0.99515045, 0.74418475,\n",
       "        0.99612029],\n",
       "       [0.92984316, 0.92136625, 0.99789354, ..., 0.99804996, 0.77764338,\n",
       "        1.        ],\n",
       "       [0.59179957, 0.93437965, 0.9412357 , ..., 0.88439203, 0.97470054,\n",
       "        0.99359909]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size_bar</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.07411 0.07411 ... 0.08088 0.08088</div><input id='attrs-631ddebb-0e33-4ec2-9ba4-4533be94f647' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-631ddebb-0e33-4ec2-9ba4-4533be94f647' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e10d9e91-8f12-4144-beb2-0aa18f2c9812' class='xr-var-data-in' type='checkbox'><label for='data-e10d9e91-8f12-4144-beb2-0aa18f2c9812' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.07411161, 0.07411161, 0.07411161, ..., 0.07411161, 0.07411161,\n",
       "        0.07411161],\n",
       "       [0.0654921 , 0.0654921 , 0.0654921 , ..., 0.0654921 , 0.0654921 ,\n",
       "        0.0654921 ],\n",
       "       [0.06476847, 0.06476847, 0.06476847, ..., 0.06476847, 0.06476847,\n",
       "        0.06476847],\n",
       "       [0.08087948, 0.08087948, 0.08087948, ..., 0.08087948, 0.08087948,\n",
       "        0.08087948]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>perf_counter_start</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.84e+04 1.84e+04 ... 1.841e+04</div><input id='attrs-0e923c1b-3d24-47f2-a9c2-e2b41d8b1e3c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0e923c1b-3d24-47f2-a9c2-e2b41d8b1e3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2374cfca-8625-48ff-9e5f-ec6b23d0a4f9' class='xr-var-data-in' type='checkbox'><label for='data-2374cfca-8625-48ff-9e5f-ec6b23d0a4f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[18397.75820298, 18397.75918655, 18397.77073606, ...,\n",
       "        18412.89322823, 18412.90821319, 18412.91375039],\n",
       "       [18397.65679505, 18397.6718964 , 18397.68558871, ...,\n",
       "        18414.24050779, 18414.24146749, 18414.24239101],\n",
       "       [18397.81620892, 18397.82338605, 18397.83632851, ...,\n",
       "        18413.29208824, 18413.29943945, 18413.30229464],\n",
       "       [18398.81019868, 18398.81093067, 18398.81557223, ...,\n",
       "        18414.29460919, 18414.30153075, 18414.31859446]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smallest_eigval</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-de8466e6-7005-4782-ab04-8c32d38cae82' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-de8466e6-7005-4782-ab04-8c32d38cae82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89459ccf-b807-4375-a187-49883ad69ccc' class='xr-var-data-in' type='checkbox'><label for='data-89459ccf-b807-4375-a187-49883ad69ccc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>process_time_diff</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.000842 0.01134 ... 0.003041</div><input id='attrs-d6195049-2117-4ba4-bc7b-1fc4b4c53bc1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d6195049-2117-4ba4-bc7b-1fc4b4c53bc1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a02b21f7-789b-4ae9-ab16-a2721178495d' class='xr-var-data-in' type='checkbox'><label for='data-a02b21f7-789b-4ae9-ab16-a2721178495d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.000842, 0.011339, 0.006295, ..., 0.014479, 0.005383, 0.001075],\n",
       "       [0.014922, 0.013456, 0.01465 , ..., 0.00078 , 0.000796, 0.001602],\n",
       "       [0.006979, 0.01272 , 0.013456, ..., 0.00688 , 0.002662, 0.003237],\n",
       "       [0.000536, 0.004458, 0.001238, ..., 0.00672 , 0.016697, 0.003041]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>perf_counter_diff</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.000842 0.01139 ... 0.003041</div><input id='attrs-92a8df61-4d71-4ad3-8feb-3c080f660fd0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-92a8df61-4d71-4ad3-8feb-3c080f660fd0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-557b7533-ccc4-46fa-9f79-9e8fbc93d272' class='xr-var-data-in' type='checkbox'><label for='data-557b7533-ccc4-46fa-9f79-9e8fbc93d272' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.00084198, 0.01138836, 0.00630635, ..., 0.01481564, 0.00538202,\n",
       "        0.00108758],\n",
       "       [0.01492847, 0.01351074, 0.01465594, ..., 0.0007931 , 0.00079541,\n",
       "        0.00160137],\n",
       "       [0.00701213, 0.01276968, 0.01346058, ..., 0.00700491, 0.00266883,\n",
       "        0.00325713],\n",
       "       [0.00053644, 0.0044711 , 0.00124454, ..., 0.00673478, 0.01685527,\n",
       "        0.00304052]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.054e+03 4.053e+03 ... 4.051e+03</div><input id='attrs-7cab9e72-c851-4d64-bf42-92420d5c8ecd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7cab9e72-c851-4d64-bf42-92420d5c8ecd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f3d8089-4437-48b6-8624-663666117c80' class='xr-var-data-in' type='checkbox'><label for='data-8f3d8089-4437-48b6-8624-663666117c80' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[4054.27255725, 4052.95126871, 4054.59251741, ..., 4051.77562266,\n",
       "        4050.65959922, 4054.31754102],\n",
       "       [4054.06568627, 4051.6764204 , 4053.08019966, ..., 4053.06537835,\n",
       "        4052.65477087, 4052.17163208],\n",
       "       [4052.89659786, 4053.74279133, 4054.5249474 , ..., 4053.66499396,\n",
       "        4054.65950205, 4050.78678333],\n",
       "       [4052.83228432, 4054.07190481, 4053.4656644 , ..., 4051.86194866,\n",
       "        4051.7693395 , 4051.08481066]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverging</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-bb924322-2efc-4c9c-9547-229c4e8d5724' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bb924322-2efc-4c9c-9547-229c4e8d5724' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ecea226-ca06-472c-89b1-b5b308d57e91' class='xr-var-data-in' type='checkbox'><label for='data-6ecea226-ca06-472c-89b1-b5b308d57e91' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>max_energy_error</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.4457 -0.9076 ... 0.0518 0.01401</div><input id='attrs-2d6eebc4-2a50-40ba-a2c0-c44a6c46b38d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2d6eebc4-2a50-40ba-a2c0-c44a6c46b38d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c41d61b7-1229-49e7-97a1-7abcbb3cc4b6' class='xr-var-data-in' type='checkbox'><label for='data-c41d61b7-1229-49e7-97a1-7abcbb3cc4b6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.4456585 , -0.907631  ,  0.0697719 , ...,  0.00343148,\n",
       "         0.07075855,  1.53052411],\n",
       "       [-0.38329258,  0.04768219, -0.03858784, ..., -0.72503414,\n",
       "         0.50563216, -0.44521112],\n",
       "       [ 0.16793913,  0.27911386,  0.00861644, ..., -0.35829877,\n",
       "         0.7206459 , -0.00621418],\n",
       "       [ 0.52458727, -0.78150891, -0.95930185, ...,  0.40857701,\n",
       "         0.05179735,  0.01401059]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>index_in_trajectory</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>-3 -15 29 9 -7 ... 1 -4 9 -12 -6</div><input id='attrs-a0ef1613-9c6e-497e-bfd1-a14884841894' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a0ef1613-9c6e-497e-bfd1-a14884841894' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e56efe7-b683-4749-8592-fb90ef9640c2' class='xr-var-data-in' type='checkbox'><label for='data-4e56efe7-b683-4749-8592-fb90ef9640c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ -3, -15,  29, ...,  -8,   6,  -3],\n",
       "       [-35,  14,  22, ...,   1,  -2,   7],\n",
       "       [ 16,  14,  36, ..., -16,   2,   9],\n",
       "       [  1,  -8,   2, ...,   9, -12,  -6]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_steps</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.0 51.0 31.0 ... 31.0 79.0 15.0</div><input id='attrs-8c8d85a0-7d84-4e0f-aa48-d28c7262b6e7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8c8d85a0-7d84-4e0f-aa48-d28c7262b6e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-620ccb1a-980b-49c1-854e-2952ae8319a5' class='xr-var-data-in' type='checkbox'><label for='data-620ccb1a-980b-49c1-854e-2952ae8319a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 3., 51., 31., ..., 63., 27.,  3.],\n",
       "       [71., 63., 63., ...,  3.,  3.,  7.],\n",
       "       [31., 59., 63., ..., 31., 11., 15.],\n",
       "       [ 1., 19.,  3., ..., 31., 79., 15.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>reached_max_treedepth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c55c6428-8351-4334-a24a-f1d5d3c72f10' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c55c6428-8351-4334-a24a-f1d5d3c72f10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9aceb3c4-f07b-46da-9594-4bc59ec7f8cd' class='xr-var-data-in' type='checkbox'><label for='data-9aceb3c4-f07b-46da-9594-4bc59ec7f8cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tree_depth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 6 5 4 5 6 1 7 ... 6 2 1 2 4 5 7 4</div><input id='attrs-1795bc32-3ad3-4ed4-9e49-cce0f87e2aa6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1795bc32-3ad3-4ed4-9e49-cce0f87e2aa6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b26720b-36ee-4514-8018-88cbacd6cd33' class='xr-var-data-in' type='checkbox'><label for='data-7b26720b-36ee-4514-8018-88cbacd6cd33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2, 6, 5, ..., 6, 5, 2],\n",
       "       [7, 6, 6, ..., 2, 2, 3],\n",
       "       [5, 6, 6, ..., 5, 4, 4],\n",
       "       [1, 5, 2, ..., 5, 7, 4]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-4.053e+03 -4.05e+03 ... -4.051e+03</div><input id='attrs-00c8fe97-a06e-4a5f-a708-be09f496254d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-00c8fe97-a06e-4a5f-a708-be09f496254d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-073aa8db-d5fc-47e0-a862-6c29143371b4' class='xr-var-data-in' type='checkbox'><label for='data-073aa8db-d5fc-47e0-a862-6c29143371b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-4053.28754164, -4050.14206652, -4050.31966921, ...,\n",
       "        -4050.18167269, -4050.39291422, -4052.56359631],\n",
       "       [-4051.236748  , -4051.03569017, -4052.47175598, ...,\n",
       "        -4050.21180628, -4052.07070022, -4050.43333101],\n",
       "       [-4050.72982931, -4052.13576019, -4052.78436312, ...,\n",
       "        -4052.04201476, -4050.68195262, -4050.42148968],\n",
       "       [-4052.70576257, -4052.76377875, -4053.325407  , ...,\n",
       "        -4050.09474298, -4050.83173141, -4051.01666406]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>largest_eigval</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-acb89263-b571-4f07-a2a4-3b1b4a5cc85d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-acb89263-b571-4f07-a2a4-3b1b4a5cc85d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7318485d-1b56-4baa-ab37-b3d765cad8ec' class='xr-var-data-in' type='checkbox'><label for='data-7318485d-1b56-4baa-ab37-b3d765cad8ec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.05936 0.05936 ... 0.0734 0.0734</div><input id='attrs-c77c9a48-0299-4cd9-a7c2-4cd99cf64100' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c77c9a48-0299-4cd9-a7c2-4cd99cf64100' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c67d1ad3-061b-4aac-a97a-7100deafb178' class='xr-var-data-in' type='checkbox'><label for='data-c67d1ad3-061b-4aac-a97a-7100deafb178' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.05936022, 0.05936022, 0.05936022, ..., 0.05936022, 0.05936022,\n",
       "        0.05936022],\n",
       "       [0.08515972, 0.08515972, 0.08515972, ..., 0.08515972, 0.08515972,\n",
       "        0.08515972],\n",
       "       [0.05979049, 0.05979049, 0.05979049, ..., 0.05979049, 0.05979049,\n",
       "        0.05979049],\n",
       "       [0.07339606, 0.07339606, 0.07339606, ..., 0.07339606, 0.07339606,\n",
       "        0.07339606]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6d1ef69f-1651-48c2-b840-3f6d6302227c' class='xr-section-summary-in' type='checkbox'  ><label for='section-6d1ef69f-1651-48c2-b840-3f6d6302227c' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a5389351-76e5-4fcd-9898-9ed33140b972' class='xr-index-data-in' type='checkbox'/><label for='index-a5389351-76e5-4fcd-9898-9ed33140b972' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5ef05858-3381-4d22-ab8e-69ac7da87cd7' class='xr-index-data-in' type='checkbox'/><label for='index-5ef05858-3381-4d22-ab8e-69ac7da87cd7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0,    1,    2,    3,    4,    5,    6,    7,    8,    9,\n",
       "       ...\n",
       "       1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=2000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3f4bd711-2424-483d-bc61-91a104e71542' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3f4bd711-2424-483d-bc61-91a104e71542' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2024-04-05T07:27:09.804289</dd><dt><span>arviz_version :</span></dt><dd>0.17.1</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.11.0</dd><dt><span>sampling_time :</span></dt><dd>36.096750020980835</dd><dt><span>tuning_steps :</span></dt><dd>1000</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_fit_datab6603fd4-3601-40ef-8086-7a105bb3993b\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_fit_datab6603fd4-3601-40ef-8086-7a105bb3993b\" class = \"xr-section-summary\">fit_data</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 30kB\n",
       "Dimensions:                 (index: 946)\n",
       "Coordinates:\n",
       "  * index                   (index) int64 8kB 0 1 5 6 8 ... 2348 2349 2353 2355\n",
       "Data variables:\n",
       "    customer_id             (index) int64 8kB 0 1 5 6 8 ... 2348 2349 2353 2355\n",
       "    mean_transaction_value  (index) float64 8kB 22.35 11.77 ... 44.93 33.32\n",
       "    frequency               (index) int64 8kB 2 1 7 1 2 5 10 1 ... 1 2 7 1 2 5 4</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b3028e6d-5837-4fb5-b26d-c15db94f0aeb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b3028e6d-5837-4fb5-b26d-c15db94f0aeb' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>index</span>: 946</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3472bb59-029e-44a4-888c-fffd0ea1bc3e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3472bb59-029e-44a4-888c-fffd0ea1bc3e' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>index</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 5 6 8 ... 2348 2349 2353 2355</div><input id='attrs-14cdf3a6-02df-4bba-8507-11af2ee5340d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-14cdf3a6-02df-4bba-8507-11af2ee5340d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2d97a5a-9ef1-4c04-a76c-1e91cf33aa48' class='xr-var-data-in' type='checkbox'><label for='data-c2d97a5a-9ef1-4c04-a76c-1e91cf33aa48' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    5, ..., 2349, 2353, 2355])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cf601298-8419-4e40-92f3-90ff1bb11ce2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cf601298-8419-4e40-92f3-90ff1bb11ce2' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>customer_id</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 5 6 8 ... 2348 2349 2353 2355</div><input id='attrs-c449c0b6-2176-4198-b5a8-e365da816160' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c449c0b6-2176-4198-b5a8-e365da816160' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-831c373f-7166-401b-bb63-c6ad9f3bbbf9' class='xr-var-data-in' type='checkbox'><label for='data-831c373f-7166-401b-bb63-c6ad9f3bbbf9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    5,    6,    8,   10,   16,   17,   18,   20,   25,\n",
       "         27,   29,   34,   37,   44,   45,   48,   55,   56,   58,   60,\n",
       "         62,   66,   69,   72,   74,   78,   81,   85,   87,   90,   91,\n",
       "         97,   98,   99,  100,  104,  105,  108,  110,  113,  115,  120,\n",
       "        124,  128,  129,  131,  132,  134,  135,  137,  138,  139,  141,\n",
       "        142,  143,  144,  146,  147,  149,  150,  153,  155,  156,  158,\n",
       "        161,  163,  166,  169,  177,  178,  182,  184,  186,  187,  188,\n",
       "        194,  196,  199,  201,  202,  205,  207,  210,  214,  216,  218,\n",
       "        219,  225,  227,  228,  229,  231,  241,  242,  243,  246,  248,\n",
       "        249,  252,  254,  256,  259,  260,  262,  265,  266,  267,  269,\n",
       "        271,  273,  274,  275,  281,  282,  283,  286,  288,  290,  291,\n",
       "        293,  296,  297,  301,  302,  303,  305,  309,  311,  313,  316,\n",
       "        318,  320,  322,  323,  324,  329,  331,  335,  338,  343,  347,\n",
       "        349,  350,  356,  357,  362,  363,  366,  367,  369,  375,  376,\n",
       "        377,  381,  384,  387,  388,  393,  394,  396,  397,  400,  402,\n",
       "        403,  405,  406,  409,  411,  414,  421,  422,  426,  427,  433,\n",
       "        438,  439,  440,  443,  445,  447,  449,  450,  451,  452,  453,\n",
       "        455,  456,  460,  462,  463,  465,  466,  468,  470,  473,  476,\n",
       "        479,  480,  482,  484,  486,  490,  492,  494,  495,  498,  499,\n",
       "        504,  506,  508,  510,  512,  516,  518,  520,  522,  525,  527,\n",
       "...\n",
       "       1782, 1785, 1788, 1789, 1791, 1793, 1794, 1797, 1799, 1807, 1811,\n",
       "       1819, 1820, 1821, 1824, 1825, 1826, 1828, 1830, 1833, 1835, 1839,\n",
       "       1841, 1842, 1844, 1848, 1850, 1852, 1854, 1855, 1859, 1862, 1864,\n",
       "       1871, 1872, 1875, 1877, 1881, 1885, 1886, 1888, 1890, 1893, 1898,\n",
       "       1900, 1901, 1905, 1908, 1909, 1910, 1913, 1914, 1915, 1918, 1919,\n",
       "       1920, 1921, 1924, 1928, 1931, 1933, 1934, 1938, 1942, 1945, 1949,\n",
       "       1952, 1954, 1957, 1958, 1960, 1961, 1962, 1963, 1965, 1974, 1979,\n",
       "       1980, 1981, 1982, 1984, 1986, 1991, 2004, 2006, 2009, 2011, 2016,\n",
       "       2021, 2023, 2026, 2029, 2032, 2033, 2035, 2040, 2041, 2043, 2044,\n",
       "       2047, 2048, 2051, 2054, 2056, 2057, 2067, 2068, 2070, 2072, 2073,\n",
       "       2074, 2075, 2080, 2082, 2084, 2088, 2089, 2091, 2093, 2095, 2097,\n",
       "       2099, 2100, 2103, 2105, 2109, 2110, 2116, 2118, 2120, 2122, 2127,\n",
       "       2131, 2132, 2134, 2137, 2139, 2142, 2144, 2145, 2148, 2151, 2153,\n",
       "       2154, 2160, 2162, 2165, 2167, 2169, 2171, 2174, 2176, 2178, 2182,\n",
       "       2183, 2188, 2190, 2195, 2198, 2200, 2201, 2204, 2206, 2209, 2213,\n",
       "       2217, 2219, 2220, 2227, 2230, 2232, 2233, 2234, 2238, 2239, 2249,\n",
       "       2251, 2253, 2259, 2260, 2263, 2269, 2273, 2277, 2279, 2281, 2284,\n",
       "       2289, 2292, 2293, 2295, 2300, 2302, 2304, 2305, 2306, 2307, 2308,\n",
       "       2311, 2312, 2314, 2317, 2324, 2327, 2329, 2331, 2332, 2334, 2335,\n",
       "       2338, 2339, 2341, 2343, 2345, 2346, 2347, 2348, 2349, 2353, 2355])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_transaction_value</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>22.35 11.77 73.74 ... 44.93 33.32</div><input id='attrs-2d2b2916-bcad-4493-bfb2-cd7efb49eb96' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2d2b2916-bcad-4493-bfb2-cd7efb49eb96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b63a0396-3753-4d81-8252-49c377e2e6c5' class='xr-var-data-in' type='checkbox'><label for='data-b63a0396-3753-4d81-8252-49c377e2e6c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 22.35,  11.77,  73.74,  11.77,  25.55,  29.44,  24.24,   9.99,\n",
       "        25.08,  32.3 , 227.14,  80.46,  40.47,  31.14,  13.97,  14.96,\n",
       "        29.54,  14.37,  28.03,  15.36,   6.49,  53.4 ,  43.13, 133.48,\n",
       "        42.63,  69.23,  20.77,  20.22,  42.09,  27.63,  13.66,  14.99,\n",
       "        50.87,  33.42,  20.68,  26.22,  17.47,  92.8 ,  52.3 ,  39.09,\n",
       "        12.97,  24.99,  28.31,  13.77,  36.71,  13.62,  19.36,  71.66,\n",
       "        13.87,  19.54,  23.48,  51.8 ,  29.33,  23.54,  35.58,  15.16,\n",
       "        22.92,  18.98,  29.86,  29.34,  28.53,  24.94,  16.7 ,  34.32,\n",
       "        26.26,  46.59,  46.28,  62.44,  25.46,  14.49,  35.18,  49.25,\n",
       "        15.76,  33.91,  11.87,  37.99,  28.46,  28.77,  27.13,  41.48,\n",
       "        18.88,  21.87,  14.96,  34.27,  71.81,  14.47,  37.26,  14.99,\n",
       "        21.74,  20.35,  13.97,   9.77,  28.65,  22.29,  24.09,  55.28,\n",
       "       194.86,  36.11,  30.32,  26.94,  16.36,  11.77,  32.12,  38.77,\n",
       "        52.94,  64.45,  11.77,  21.36,  90.83,  79.72,  41.82,  20.5 ,\n",
       "        25.74,  17.56,  87.43,  19.62,  36.67,  33.97,  36.98,  65.07,\n",
       "        20.75,  18.99,  76.41,  12.97,  20.08,  31.48,  69.84,  14.37,\n",
       "        48.81,  25.61,  93.4 ,  29.75,  48.94,  60.75,  41.77,  15.89,\n",
       "        37.21,  59.42,  10.88,  45.07,   9.77,  15.7 ,   4.79,  79.02,\n",
       "        12.77, 242.98,  36.22,   9.77,  41.81, 176.45,  26.42,  24.74,\n",
       "        13.77,  13.97,  16.7 ,  37.11,  22.86, 100.59,  22.08, 189.64,\n",
       "...\n",
       "        38.25,  30.04,  14.96,  13.97,  11.49,  38.97,  49.97,  25.66,\n",
       "        37.45,  22.77,  60.25,  48.23,  15.16,  35.86,  26.54,  20.65,\n",
       "        13.97,  62.95,  21.7 ,  19.99,  20.1 ,  11.77,  27.94,  16.6 ,\n",
       "        10.77,  29.2 ,  34.9 ,  26.56,  13.77,  13.07,  66.43,  37.66,\n",
       "        30.94,  14.79,  43.7 ,  12.25,  50.48,  14.96, 122.37,  39.34,\n",
       "        19.47,   8.97,  35.91,  28.74,  11.6 ,  27.09,   4.79,  44.99,\n",
       "        14.96,  12.99,  16.36,  21.66,  25.85,  45.64,  77.06,  31.76,\n",
       "        54.51,  48.91,  18.82,  15.76,  42.29,  14.37, 176.86,  33.47,\n",
       "        37.81,  53.57,  19.64,  40.54,  47.28,  16.7 ,  24.99,  41.83,\n",
       "        11.77,  13.57,  37.83,  48.68,  22.55,  15.37,  38.13,  51.97,\n",
       "        18.63,  14.37,  15.36,  19.54,  29.6 ,  12.57,  54.59,  62.23,\n",
       "        26.33,  14.79,  59.66,  12.02,   9.98,  37.95,  87.9 , 100.37,\n",
       "        20.96,  18.43,   5.96,  11.77,  15.81, 125.6 ,  28.39,  41.44,\n",
       "        38.97,  71.5 ,  39.98,  45.61,  31.1 ,  18.76,  13.77,  61.24,\n",
       "        95.95,  79.86,  77.29,  14.96,  26.99,  45.29,  29.65,  14.37,\n",
       "        41.79,  29.33,  22.15,  34.67,  25.34,  38.37,  38.57,  27.98,\n",
       "        40.41,  14.96,  28.93,  15.96,  12.49,  20.68,  50.87,  23.66,\n",
       "        22.99,  34.97,  15.36, 146.96,  68.63,  32.91,  21.85,  21.  ,\n",
       "        43.8 ,  52.69,  15.24,  11.77,  32.64,  16.36,  13.97,  18.56,\n",
       "        44.93,  33.32])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>frequency</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2 1 7 1 2 5 10 1 ... 1 2 7 1 2 5 4</div><input id='attrs-e024243c-a9b2-47df-b966-a0ba9501530c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e024243c-a9b2-47df-b966-a0ba9501530c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef1b307d-5b68-4403-9639-3d0c130ad175' class='xr-var-data-in' type='checkbox'><label for='data-ef1b307d-5b68-4403-9639-3d0c130ad175' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 2,  1,  7,  1,  2,  5, 10,  1,  3,  2,  1,  1,  1,  1,  1,  1, 12,\n",
       "        1,  2,  1,  1,  4,  1,  2,  3,  1,  1,  3,  1,  7, 10,  1,  6,  2,\n",
       "        4,  1,  5,  1,  1,  1,  1,  1,  1,  1,  1,  4,  3,  1,  2,  1,  2,\n",
       "        2,  1,  1,  5,  2,  7,  2,  4, 13,  4,  4,  1,  1, 29,  3,  1, 10,\n",
       "       13,  1,  6,  8,  1,  4,  2,  3,  3,  6,  1,  2,  2,  2,  1,  1,  7,\n",
       "        2,  4,  1,  1,  2,  1,  1,  2,  3,  4,  1,  5,  2,  1,  2,  1,  1,\n",
       "        5,  1,  2,  1,  1,  2,  1,  1,  1,  3,  1, 13,  3,  3,  1,  3,  5,\n",
       "        2,  1,  1,  1,  1,  3,  1,  1,  1,  4,  2,  3,  1,  1,  2,  1,  3,\n",
       "       25,  5,  2,  5,  1,  2,  1,  2,  1,  1,  2,  2,  4,  1,  3,  1,  1,\n",
       "        1,  1, 11,  4,  1,  2,  1,  1,  3,  1,  2,  8,  1,  2,  1,  1,  2,\n",
       "        3,  2,  2,  1,  2,  1,  1,  2,  4,  2,  6,  1,  1,  1,  1,  2,  1,\n",
       "        7,  4,  7,  1,  1,  2,  1,  4,  1,  2,  4,  2,  1,  1, 11,  4,  4,\n",
       "        3,  1,  1,  6,  3,  2,  2, 18,  1,  1,  5,  1,  6,  2,  2,  1,  1,\n",
       "        1,  1,  2,  1,  2,  2,  1, 10,  1,  3,  3,  2, 12,  1,  2,  2,  5,\n",
       "        2,  5,  1,  1,  1,  1,  2,  1,  7,  2,  2,  4,  3,  1,  2,  2,  1,\n",
       "        1,  2,  2,  1,  1,  3,  1,  1,  3,  4,  2,  1,  1,  5,  1,  1,  2,\n",
       "        1,  1,  8,  1,  6,  1,  2,  3,  6,  1,  2,  5,  1,  1,  1, 13,  1,\n",
       "        3,  5,  2,  1,  1,  1,  1,  4,  6,  1,  2,  2,  7,  3,  5,  2,  1,\n",
       "        3,  1,  1,  2,  1,  1, 10,  3,  3,  2,  5,  4,  1,  1,  1,  3,  1,\n",
       "        6,  2,  1,  6,  1,  2,  7,  2,  2,  1, 13,  7,  3,  1,  1,  1,  2,\n",
       "...\n",
       "        1,  4, 26,  1,  6,  1,  2,  2,  1,  2,  1, 14,  1,  2,  4,  1,  6,\n",
       "        1,  3,  1,  1,  1,  8,  3,  1,  3,  1,  1,  1,  1,  1,  9,  3,  1,\n",
       "        1,  2,  4,  1,  2,  1,  1,  1,  3,  2,  2,  5,  1,  1,  1,  1,  5,\n",
       "        1,  1,  4,  4,  3,  1,  1,  2,  2,  4,  2,  1,  1,  1,  3,  3,  2,\n",
       "        1,  1,  5,  1,  3,  2,  1,  1,  1,  2,  2,  3,  5,  2,  1,  1,  9,\n",
       "        2,  1,  1,  1,  1,  2,  3,  1,  2,  4,  4,  3,  3,  2,  4,  5,  3,\n",
       "        2,  2,  3,  2,  4,  7,  1,  2,  2,  2,  3,  1,  1,  3,  1,  6,  1,\n",
       "        2,  3,  3,  1,  5,  1,  9,  4,  2,  1,  4,  1,  1,  1,  1,  1,  1,\n",
       "        1,  5,  1,  1,  1,  1,  1,  1,  7,  1,  1,  1,  1,  1,  2,  1,  1,\n",
       "       15,  1,  2,  3,  1, 21,  2,  3,  3,  2,  1,  1,  1,  1,  1,  2,  2,\n",
       "        1,  4,  2,  1,  1,  1,  2,  2,  2,  1,  6, 11,  1,  1,  1,  1,  2,\n",
       "        2,  3,  1,  1, 17,  2,  2,  2,  2,  1,  3,  1,  1,  2,  1,  1,  7,\n",
       "        1,  3,  2,  4,  1,  2,  1,  4,  2,  1,  3,  1,  1,  1,  1,  2,  3,\n",
       "        1,  1,  1,  3,  3,  1,  2,  1,  1,  1,  2,  2,  3,  4,  1,  1,  1,\n",
       "        3,  1,  1,  1,  1,  3,  6,  3,  2,  5,  1,  1,  1,  2,  1,  2,  7,\n",
       "        2,  2,  2,  2,  8,  5,  1,  1,  1,  1,  3,  2,  2,  1,  1,  2,  2,\n",
       "        1,  1,  2,  1,  2,  3,  1,  1,  9,  3,  3,  2,  1,  1,  1,  2,  6,\n",
       "        1,  1,  1,  1,  4,  2,  1,  1,  1,  2,  1,  6,  1,  2,  6,  1,  2,\n",
       "        7,  1,  2,  1,  1,  1,  1,  5,  1,  2,  1,  1,  1,  5,  1,  1,  2,\n",
       "        1,  2,  1,  2,  1,  2,  7,  1,  2,  5,  4])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-057640e0-472c-449f-a2cf-e8368d080da2' class='xr-section-summary-in' type='checkbox'  ><label for='section-057640e0-472c-449f-a2cf-e8368d080da2' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>index</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5b2cad2d-5c4e-40be-83f0-f10f6d1e24dc' class='xr-index-data-in' type='checkbox'/><label for='index-5b2cad2d-5c4e-40be-83f0-f10f6d1e24dc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0,    1,    5,    6,    8,   10,   16,   17,   18,   20,\n",
       "       ...\n",
       "       2339, 2341, 2343, 2345, 2346, 2347, 2348, 2349, 2353, 2355],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;index&#x27;, length=946))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-43254a1a-f548-4130-b98a-cb645cfae180' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-43254a1a-f548-4130-b98a-cb645cfae180' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div><br></div>\n",
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      "text/plain": [
       "Inference data with groups:\n",
       "\t> posterior\n",
       "\t> sample_stats\n",
       "\t> fit_data"
      ]
     },
     "execution_count": 14,
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   "metadata": {},
   "source": [
    "We can see some statistics of the posterior distribution of the parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "58a103ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
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       "      <th>mcse_mean</th>\n",
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       "      <th>p</th>\n",
       "      <td>6.351</td>\n",
       "      <td>1.333</td>\n",
       "      <td>4.142</td>\n",
       "      <td>8.819</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.024</td>\n",
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       "      <th>q</th>\n",
       "      <td>3.796</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>v</th>\n",
       "      <td>16.311</td>\n",
       "      <td>4.428</td>\n",
       "      <td>8.575</td>\n",
       "      <td>24.606</td>\n",
       "      <td>0.111</td>\n",
       "      <td>0.079</td>\n",
       "      <td>1565.0</td>\n",
       "      <td>1947.0</td>\n",
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      "text/plain": [
       "     mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  ess_tail  \\\n",
       "p   6.351  1.333   4.142    8.819      0.034    0.024    1625.0    1973.0   \n",
       "q   3.796  0.298   3.249    4.359      0.007    0.005    1850.0    2160.0   \n",
       "v  16.311  4.428   8.575   24.606      0.111    0.079    1565.0    1947.0   \n",
       "\n",
       "   r_hat  \n",
       "p    1.0  \n",
       "q    1.0  \n",
       "v    1.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "model.fit_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee948a75",
   "metadata": {},
   "source": [
    "Let's visualize the posterior distributions and the rank plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "63137cf5",
   "metadata": {},
   "outputs": [
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Wlp544gmNGDHCp1mdvPH3339r8ODBPn0xtmrVKl166aVKSUmR5PpS77LLLvMp2CW5ZoG55557fDrGkz179mjo0KFehz0kaePGjbr00ksPhkwKCgo0duxYvfDCC14HuyRp8eLFGj16tNtQRGWoyHHiC29/TkePHn1YLf+1bds2jR071qtgm68+/vhjjRo1yutgl+QK8tx0000Hx+Off/6piy66yOtgl+SaiWn8+PF6//33fS25TCtXrtTQoUO9DnZJ0tKlS3X55ZcfDPDs2rVLQ4YM8eln3el06tFHH9Xzzz/va8kBU5HjJBCaNGmiU045xe2+/Px8zZkzp9Rjy5qxb+jQoV4vg+utb7/9Vueff365l4LMzMzU9OnTNXDgQK1Zs8bn41955RWNGzfO62CX5Lpe33333QdnsQuk3bt36/LLL9fUqVP9DnZJrtnQHn74YV1++eVuZ5WC/7766iuNGDGizGBXZSnvNennn39W//799eKLL5Yr2OWLvLw83X///bryyivLvRTz3Llz1b9/f/36668BrBAAAAAAKhbhLgAAAABBzZsv8Y0xGjFiRMUXUwV++OEH3XLLLT4FjsqSlpamK6+80qfAyAGbNm3SHXfcIafTqbFjx+qnn37yq4a5c+dqyZIlfh37X3l5ebr22mv1119/+XxsSkqKbrrpJhUWFmry5Mn65JNP/Kph2bJlPs2uUhECPU58sWfPnlKXwTxUgwYNdOaZZ1ZCRZVv+vTpeuWVVwLW36pVqzRu3Di/lrP75JNPNHPmTO3Zs8fncNgB1lpNnjy5XCGRQ23dulXXXnutMjMzfT521apVeuSRR5Sdna0rr7yy1CVkPfm///s/r8ZpRQr0OAmksmaTe+ONN9yOxVWrVmnVqlVuj4mJidGFF14YsPokaf78+Ro5cqRfY7o0Gzdu1ODBg/XNN994fczcuXP1wAMP+H3OGTNm6Omnn/b7+P/avHmzBgwY4NNj8OSbb77R4MGDA/pcH8k2bNigMWPGeL2EcGXx55r09ttva+jQodq8eXPFFOVGbm6uRowYoZkzZwasz61bt+riiy/Wd999F7A+AQAAAKAiEe4CAAAAELRSU1O9+iK/WbNmh9WsXf/1ySefeFzyx1tr167V1q1b/T7+s88+05gxY7R8+fJy1fH444+X6/gDdu/eXa4ZHFavXq2xY8fqzTffLFcdzzzzTLlmkQmEQI4TX3g7K1SPHj0CPotPMHn44Ye1adOmgPS1bNmycoUAnn76ad10003asWOH331kZGTohRde8Pv4Q/3www9KTU31+/g5c+bopptu8muGpQMKCgr05JNP+n18oARynARS9+7d1axZM7f7du/erUWLFpW4v6xZu8477zxVq1YtYPV99913uvPOOyskwJqbm6uxY8d6FRLeuHFjuYJdBzz11FPl7kOS9u3b53dg25Nt27bp6quvVlZWVsD7PtLceeedfoVbK4Mv16SPP/5Yd955pwoLCyu4qiLWWo0fP97j8q7+yM/P1/XXX+/XPxAAAAAAgMpGuAsAAABA0Przzz+9Wu7uhBNOqIRq/BMREaEGDRqoRYsWat++vTp16qT27durefPmio+P97qf559/vkK+TIuJiVGrVq3Utm1bRUdHe3XMp59+WuK+WrVqqUOHDmrSpIlXfaxfv14//vijL6V6JSIi4uBznZCQ4NUx7h5PUlKS2rVrp2OOOUbGGI99pKam6qOPPvK53gOCfZyUZe3atV61C+af09jYWDVp0kStWrXS8ccfr06dOqldu3Y66qijFBUV5VUfhYWFmjZtWoXUl5ycrHbt2qlFixZyODx/lJORkaHvv/++xP2NGzfW8ccfr1q1anl13vfee69Cgh0JCQlq06aNjjvuOEVERHhsX1BQoM8//7zE/fXr11eHDh1Uv359r867bNkybdu2zed6Dwj2cVIexhgNGzas1P3/nZ1w586dWrx4canty5oJzFdpaWkaO3as1zPZVa9eXW3atFHr1q29Dpilp6fruuuuU0FBQZnt7rzzTmVnZ3vVZ1RUlFq0aKEOHTqoTp06Xh3jqwkTJni91GlycrLatGmj448/Xo0aNfLqmN9//z1gYewjWXmCtmWpzGvSP//8owkTJvi0DHVsbKyOPvpoHX/88WrZsqVq1Kjh9bEHTJ8+vcxrzaFiYmJ07LHH6oQTTtAxxxyj8PBwj8ekp6dr3LhxVba8NgAAAAB4y/NfOAAAAABQRbydYerYY4/1ql1GRoZWr17tVy3NmjXzGIgIDw/XSSeddPDLtRYtWqh27dplhoO2bNmijz/+WC+88EKZM9ts2rRJ33//vU4++WS/6v+viIgIjRkzRsOGDVNcXJwkKTMzU3feeac+/PBDr/upW7eu7r33XvXo0ePg41yxYoWuvfZapaWllXns8uXL1alTJ/8fxH+MGDFC1157rZKSkiS5lmx89NFHfVrGJzExUZMmTVKfPn0Ofin4559/6uqrr/Y4HpcvX67zzz/f4zlCaZx4w9uwjLc/p9u2bfN7drkOHTp4/EI7Pj5ep5xyijp06KB27dqpadOmSk5OLrV9YWGh1qxZo3fffVezZ88uMzz3/vvv6+677/Y6KOlJjRo1dPfdd6t3794HZz3bsmWLRo8erXXr1nndzwknnKApU6aoefPmklwzocyZM0f33ntvmV9o79+/X6tWrQrYeIqNjdXEiRPVv39/RUZGSpL27t2rG2+80W0YrTRHH320pk6dqg4dOhy879NPP9VNN91U5oxnTqdTX3/9tVfLBYbSOAmUAQMG6PHHH1dGRkaJfb/88otWrVql9u3bS5Jmz55datiqa9euOvroowNW14svvujVzG9t27bVhAkT1LFjx4PX08LCQi1fvlz333+/xxDUhg0b9O6775Y6Pn7++Wf98MMPHuuIi4vTTTfdpEGDBik2Nvbg/b///rsefPBBn8Z6Wb7//nt98cUXHtsNGDBAI0eOVMuWLYvd/88//+ill17S66+/XuZ1YM6cORo+fLjXgTB4lpycrM6dO6tu3bqKjo5WSkqK1qxZU+bSsVV9TXrwwQe9mn3MGKOzzz5bl156qdq3b18iYLVlyxZ9/fXXevPNN/Xbb7+V2Vd6erqef/55j+c8/vjjNWbMGHXp0qXYDKGZmZl6++239eSTT5ZZ+x9//KH3339f5513nsdzAQAAAEBVIdwFAAAAIGh5u4zXgTCPJ2vXrvV7NpEHHnhAAwcOLHV/ly5d9PXXX/u8DFWjRo00atQodenSRYMGDSrzC9ZAhnYmT56sQYMGFbsvLi5OU6dO1Zdffqn9+/d77CMhIUEzZ84sMVtXx44dNX78eN15551lHv/rr7/6XHdprrvuOo0dO7bYfZGRkZo4caK++OILr5YcioiI0LRp09SxY8di9x999NGaMmWKRowYUebx3jyeUBsn3ti3b59X7bz9OV2wYIHfy+ctWbJEDRs2LHX/lVdeqTvvvPNgsMgbYWFhat26tVq3bq2GDRuWuSRbfn6+fvrpp4A8/7GxsZoxY0aJQEajRo10991365JLLvGqn9atW2vGjBnFvrQ3xmjo0KH66aef9N5775V5/K+//hqQx+NwOPTMM8+oa9euxe6vUaOGHnzwQfXu3durJfcaNGigWbNmqXr16sXu7927t0aNGqXnnnuuzON//fVXj+GuUBongRQbG6sLLrhAM2bMcLv/1Vdf1f/+9z/l5uZq7ty5pfYTyFm79u3bp9dee81juy5duujFF18s8ZqFhYWpZ8+eateunYYNG6Y///yzzH6eeeYZDRw40O0Sst7UERUVpRdffLHE7xGp6Gfx2muv1ZdffumxL0+8uU7ee++9uuiii9zuq1evnu666y41b95ckydPLrWP/Px8zZgxQ5MmTfK7VrhUq1ZN48ePL3WMpaSk6I8//ihxf1VfkzZu3KiPP/7Y4zljY2P1yCOPqHfv3qW2adSokS666CJddNFF+vbbb0vMCnioGTNmePyHAueff74eeOABt89nXFycLrvsMnXq1EkXX3xxmctnv/DCC4S7AAAAAAQ1lmUEAAAAELS8XfooMTGxgivxrEaNGj4Hdg7VunVrjzODrVy50u/+D3XSSSeVCHYdEBsb6/VsWldddVWpyzAeOvNVabxdSsqTxo0ba/To0W73GWPUvXt3r/oZOHCg2y/kJVdwoGbNmmUev2nTJo/L+oTSOPFWTk6OV+3K87gDpVGjRj59Of5fvXr18tgmUM//5ZdfXiLYdUDHjh0PzrjnyaRJk0qdjaVfv34ej9+wYYNX5/Hk/PPPLxHsOqBBgwYHZxXzZNy4cSWCXQd483i8ue6E0jgJtEsuuaTUpT8XL16s3bt36/333y811Nm4cWP17NkzYPV8+eWXHt8LxMbG6uGHHy7zNUtOTtYDDzzgcZndf/75x21Q1+l0ul0a9L9GjhxZ6u8RyTVz49SpU4vN6OWPXbt2eVza+Kyzzio12HWooUOHqnXr1mW28WaGMJQtKSlJr732mi688EK3QSTJtaRot27dStxf1dekBQsWeBW+vf/++8sMdv1Xly5d9Mwzz5S6f9GiRWUeX6dOHd13332lPp8HtG7dWkOHDi2zzfr168u1bC8AAAAAVDRm7gIAAAAQ8jx9WVsV9u/fr+XLl+v333/Xhg0btGnTJqWlpSk7O1vZ2dmlLmdVlh07dgSktosvvrjM/c2aNfP4Ra7D4dAFF1xQ6v74+HjVr19fmzdvLrWNN7ODeeOCCy4oM0jmbWikrC/BjTE65phjtGfPnlLbOJ1OZWZmKj4+3qvzSZU/TubNm6eJEyf63OcBa9eu9fvYYPw53bFjh5YtW6a1a9dq48aN2rJlizIzMw8+/2UtY1WanTt3lrsuY4zH8di0aVOPS1q1aNGi2NKF/1VaeOxQgfo59ea6s379+jLbVKtWTWeddVaZfURERJT5c+PP4wnWcVIRGjVqpFNPPVVLliwpsS8/P19z5sxxu++AssJh/vj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r6isqUk6Oqfc/NPXJZ2fW+ftLd443dM0wx0CnylhXFy429dTTZ/6O28cZuuE6grsqg8pYX1E5UVfhS6iv8BW+VFdr1KhR0UVAOdmxY0ex961WrZpCQ0Ndbjt16lSx63hoaKiqVavmclt8fLxycpy/UDQMQ5GRkZKkxMREl8cODg5W9erVXeabmJiorKzifXEYFBSkiIgIl9uSkpKUmel+VIisuHTZ4jNcfvEe4B+oyOqu801OTVZGZvFGUfHz81eNiCiX21LTUpWW4X7qLaN6oEzTlJFlOpXZkKHoqGiX+6VlpCs1LaVY5ZWkmlE1Xa7PyMxQcmqyZBgKD7fqTHJyij2wQ5KMYH/51QhWYHSI0/7R0dHy8/NzWp+ZmamkpCSn9Z6KiopSQIDz89HZ2dlKSEgodH939eL/2bvr8Diq/Y/j7xNPaqlQKE6huF3c4ccFLn6LuzsUKe6UFop7Ke7u7nIvbhd3LwVaWtrU0nj2/P4Y2m1IUklj27xfz9Onc2ZmZ7+7O3Oy2f3knC6dupCXm1dv/1QqxYTJTf98vXNRZ/LzGv52evzE8bO8fWPPcWv2EdN+xpaUlFBSUjJbx/7785yfl0/nos4N7jtpymRqapvWR+Tm5NG1c8N9z+TSKVTXzHrkmIauvfbYR8ys5hbtI7r3okuX5LWbMqV0eh8wvY+YTX8/l9trHzGjGc/j9tpHzGjG57i5+oi/vwcoKCiY4/cRs6M9vo9oTHVJBVlTUnTNbfh5aA99xIxCl1yI0LNz9waDY7PsI2byPgBm431EE4QuuRR3LSa7NtSruaq6iilTpzTpuADdOndrtI+YVDqpSccM+dkUL9KLTgt2q7ctlUpRUlLSpOMCdOnShfz8hvuIcePGNfm4nTp1Ii/mNDjd5/iSEuJfScBAmH7NT506dfr6xhQWFNKpqKjBbRMmTqA21bQR0gry8uncrQupsmri1No650Vbv49oSE52LsXzdSe7Rz65ver2xVOmTKGysml9RHZ2dqOfJ0ydOpXy8qZN8xtCoGfPhq/lioqK6ddMY78LzkyvXr0aXF9ZWcmUKU2/lmd8H1E9rpzakkpiRW277CMg/btGKMiuc1605z6isLCwwc/aWvvziNkxJ+8j+vXr16T7aIzhribKpA9yJc9XtZXS0sigIZF330uvm39+GHxOYIXl6wec5tVzdfgNKe69L1nOyoIrLk2moVRmm1fPV817PFeVSTxflSky6Vw13NVxzCufyWXS9TWjGT/kzwRZXXNJTQtrtKeaQ2gw2AHU+3IiE2TieZHVLY/c7vUDdK1p2pcls/slXCY9z+322puJVq15Jn3AHB3G/qLFtVR/MafX/5yqLatuMGDSLmWHBsMl7dlc9xfN1AfMiUzrlzOxf4O/+rhGRnSbLoTpYa2pZWWt8vo3Wkp+FqlU5pwX7eU93NzKhN8FM+nndab1F5nw+s+p5v5MzmkZJUktYuSvkVNPj4z8Nb1u/fXgjFMDXbt2rGDToQcFvvkm8tHHkErBOYMjt94IvXt3rOdBkiRJkiS1b6mKGqrHNW0khuYQQqC6Mvnaoqa0YtZf6mQHUpUZEtRQqwl5WZDt524trbn7izm+/ufUtLBUWe3MAybtxPRwSVsXIjWXmU0xChACITe5OGNFbduGu/Lqj/bY3rX1e7imyCrKIbsot63LkDKG4S5JUrN7/4PI2edGSmcYCfnA/QP77wtZWR3v19GcnMCgs+CgQyN/joOJE5OA17CrINsPmiRJkiRJUjuRqqgllta0XfAhBKorsgGoKauc5Re7hh/UmIz6kjtDQ4rN3l/M4fU/x4ef1l/MKmDSTmRiuERS22nz93Bz6q83b4a7pNlnuEuS1GxijDz0MAy7LjJtOvOCAjjr9MDGG2Xux2xvvR2ZUgoV5fCvLaCwsO5jGTcu0r37zINaPXoEhpwLA46N1NTA51/AvffDPnu1dPWSJEmSJElzoC2DD3M4aofhBzUmk77kzuiQYnP2Fy08ao/9haR5XoaEVyGZSvLvo2zGGKmpqZm+LKkuw12SpGZRWxu54qrI40+m1y0wP1xwfqDfUu37o4mbb03x3XcwajRcdXmgZ8+69V50SaRkQrK8wfqBwr9NT733/pHqalh8schFFwR69Wz48a64QuCQg+C6G5I3pbfcFllnLejXr30/P5IkSZLUmmpra4kx+oG+JGnuZMiX3IaOJM0up53VvOTvo2y2+PS8cyvDRtrMtP7CcN+sGe6SJM21iorIuUMib7yVXrfqKjDk3ED34vbxxmHc+Mg338KyS0OvXnVrevMt+OHHZHnUaOjZs+5tCwuBv8Jdf38/UVOTnn7yx5+guFvd7TFG3nva4G5EAAEAAElEQVQfVl8NcnMDu+8Kb78Dn34GNTUwZGjkpushP799PE+SJEmS1NZijNSWVVMzvh1+oN+QDPuQX5IkSZkro6adBd8rq1H1Rtls4el551YmjrSZSf3FtHBfVpERpsb4zEiS5sqkSZFTTo988WV63TZbw4kDA7m57eMtzsWXpXjyqWT5tFMC22xVd/uyy8wQ7hoFK61Yd/tGG8KkSckUkwUFdbdNnQq9e8PYsbDYYpCTU/cx/zISTjwlUlwM22wVOfzQwBmnBfY7MFJeDj/9DDffGjnqiPbxXEmSJElSe5Aqq6GmpP19oN+QTPyQX5IkSZkpk6adBd8raxZmHGWzhafnnVuZONJmRvUXIVBdmU0uhZDf1sW0T4a7JElNNnZs5LgTIiN/Ta/bf1846IBACK37Vn3s2MiHH0G3brDeunXve+GFAtPeuXz9TWSbrepu//f2gXXXhUUWgoUWqn/so45o/A1bt26BRx8MlJZGJkysv/2dd5P/J05Mgl4hBBbsA8cOgAsvSWq6/0HYYP3IKiv7640kSZIkzcjptCRJkqS/yZBpZ8H3ylKby5T+IgQoausi2jd7U0lSk4waHTnq2HSwKysLTjw+cPCBWa0e7Hr9jciOu0bOvzDy0CP14+fLLgOdOiVTI/Zdon5tyy0b2HjDQN++ocnTI3buHFhk4fq3Le4Gffoky9tund6+zdaw/nrJcoxw8aWRqqpMiM5LkiRJkiRJkiRJklqLI3dJkubYyF8jxw6M/DkuaefmwrlnBzbasOVCXRUVkc8+h+9/gL32qHs/KyyfXv7sc6isjHVCWqusDM89FcjKav2RsbbaMvCvLeCTT2HlldLrQwicfEJkx/egtjYZ1eve+5ORzyRJkiRJUl0hLwuyHfFakiRJktTxGO6SJM2Rn35KpmIsmZC08/PhwvMDa67Rch+w1tYmI3NNnpy0t9gM5psvfX89ewZWWD7StSusvlqg9m+ji2a38Ye/WVmB1f5Rf/233wVqa9Ojdd1xZ+Sfm9LgCGCSJEmSJHV0qYoaqseVt3UZsyc7kKqsaesqJEmSJEnzAMNdkqTZNuKXyDHHRyZOTNqFhXDqyZCdDS++FKmqhj4LJAGrGY38NTJ1Ksw3H3QvbjhsFWPk55/hy6/LWX+9PHp0T2/Lzg4su0zk/Q+S9ocfw5Zb1L399deGVp8Ocm79+FMynWUqlbSra+DSyyNXXkbGPRZJkiRJklpaqqKWWFoDcdb7trWQn0UqFfG3e0mSJEnS3DLcJUmaLb/+muKoY2DSpKSdlQXV1XDOuTDjp6qbbFw/3PXkU5H7H0yWD9gPDjrgb+GvkZF7H4g8/UwEyjjuGNhlp7r3v+YagQkTImusDkv2rV9fJoah9tkrsP66cM3wyIcfJSGvDz+Cl16GLTZv6+okSZIkSWqHIsSK2lnv18ZCXlZblyBJkiRJmkcY7pIkNaqmJgkdPflU5PU3Ic7wl7GpVHrEqToa+OvZyqoZlivh3fci66ydDmMNPj/yzbfpfR58uILu3SP9loSFFkpG7tp9V9hjt3nvg9G+fQNXXBq47oYU99yXrLv62sjqq0cuvRz22C2w8kqZF1yTJEmSJEmSJEmSJM09w12SpHpG/hp5/InIy69AyYSZ79u7Nyy0IPSeDwoKoF+/+kGkRRYOLLF4ZMQvcO/98M676XBXjJFfRtbdf9So1F8jgkFhASy1VGSZZWDppWDJJWHxxSA/f94KPB2wX+DV/0RG/wETJ8LJp8K338Fbb0cOOSgZ5UuSJEmSJEmSJEmS1LEY7pIkTffNN5G774u89nrdUbpmtMbqsPlmgaX7wcILQWFh/dBRdXUkNze9ftedA/23h222j5RXwM8j4I8/IgssEJg6FZZaEn76GaZOrX9/5RXw+RfJv2nDgmVnwSKLRpbsC0v2DSy2GCy2aBIym/F+M0lBQeCYAXDamclj/Pa7ZH0qBdnZbViYJEmSJEmSJEmSJKnNGO6SJDFhYmTI+ZH3P2h8n5wcuGgorL1W41MjfvRx5PkXI6+/DtddC0ssng5a5eUF1lg98sOPsO466fBY586B64YFYoyMGRMYNboT33xbw+dflPPd9zBmTP37qU3BiBHJv1deTafQcnPhxWfrBrzKyyNVVdCtW/sPfW2wPqy5Bnzwv6TdtSusvRbssVvb1iVJkiRJkiRJkiRJahuGuyRJdOkM48fXXdepU3okrexsOG9wYO21Zh6QeuqZyEsvJ8uv/idy0AF19z/rjEBhIYRQ/zghBPr0CSy3XB7/3DSPiROriDEycWLk+x/gm2/hhx8jP/4Ev45MAl5/N3/v+iN3vfsenDUoUlwc2W4bOOyQxsNpbS2EwNFHwQEHRWpTMHkybLJRw8+XJEmSJEmSJEmSJGneZ7hLkkROTuDE4+Ho4yL//D/48Sf44cdkW1YWDDorsMF66YBRSUlk7FhYdtm6oaN/bhp46eVkJK1vvq1/P0VFcx5SKi4OrLlGMqIVJLevrIz8MhJ++AFG/JIs/zISllis/u1/GZn8P3FiMsXh3w29MMU330K/frD8soFll02miczPb5tAVd8lAjv0jzz8aNK+7kZYb930NJcxRi67MrLqyoHN/mnoS5IkSZIkSZIkSZLmZYa7JKmDqamJvPpf2PyfdUeEWmnFwIP3RS6/Mh3sCgHOPC3wf5sk+/35Z+SqayJvvAWLLAJ33Vb3GGutAfvtA5tsFFhqqZZ7DPn5gaX7wdL9YFrgC5Lg09/V1sbpo5Attmj9MNQPP8JPPyf/XngxuX1ODvTtG1l+OVhhucDyyyWPNyurdcJUBx6QhOQmTYbffoOHHoE9d0+23Xp75PEn4PEnIn+Og913dWQvSZIkSZIkSZIkSZpXGe6SpA5kzNjI4PMin34GpaWBHfunt8UYufseePOt9LqTTghssXk6ONSlC/zvQ6ithREj4IsvYaUV0/vn5QUOOajtgkYNhZwOOiCLA/ePjC+Bgvy622KM/Ppr/ePU1MB33yX/Hn8iCXx17QprrB5ZZ63AWmtBr54t9zi7dgkcfBBcdkVy33feFdl2a8jPh9ffTO/322+RGcNtkiRJkiRJkiRJkqR5S1ZbFyBJaj1335sEuwCGXRv59bf0SFf3PQCPPp7ed689YLNN696+oCCw+ebJ8uqrJSN7ZYIQAr16Bjp3DvXWP/V44KbrAycMDGy9FSyxeMOPa/JkePU/MPSiSP+dIoccnuK+ByJjxtYfLaw5bL9tUgtA6VS4655Ifn7g2qsCq68G668HA48NjtolSZIkSZIkSZIkSfMwR+6SpA5kwBGBH36IfPU1HHxQYKEFk/Wv/Ccy/Pp0SGmppeDpZ5KRog7cv+4x9tojsNvOsPDC80aoqKAgsNyysNyysMO/k8dUVhb59jv4+hv48qvIl1/BuHF1b/f1N/D1N5Frr4OVVoxs+n+BHf4NOTnN87xkZwcOOxROPT15XR55FHbaMbLA/IFLL0pGT2uu+5IkSZIkSZIkSZIktU+GuySpA8nPDwwdAn+MgeWWTYJBn34WOW9oOti1+OLwww/J8kOPRHbfFYqK0iGiBeaf9wNFRUWBf6wK/1gVIEyfvvHd9+Hd9yKffAJV1en9P/8CJkyI7Lxj8z43668Lq6wMn36W3N8tt0bOOC2QmxvIza2//8efRFZZGbKy5v3XSJIkSZIkSZIkSZI6AqdllKR5VE1N5OVXIjHWnTawe/cwPdg1cmTk1DMi1X8FlZbsC9delQS8ALp1g9F/tGLR7VQIgUUXDey6c+DyS7J46vHAWacH1lsXcv6KSW/6f9SbIrGycu6mbAwhcMRh6WM+/yL8+FPDx3z8ycjRx0UuuTySSrXMVJGSJEmSJEmSJEmSpNblyF2SNA+qqYkMGhL572sw4hc4+MD6IzmNL0lx5DEwZUrSnq8XXHJhoFu3wDFHRaZMgU02TqYHVF2dOgX+tQX8a4vAlCmRN96ElVaqv9+gwZHqmsiRhwX69m3a87jiCoGNNoy8/gbECDfcGLn4wrrH+uTTyKWXJ4Gup56GRReBPXZr0t1JkiRJkiRJkiRJktoRR+6SpHlMVVXkzHOSYBfA7XfCB/+rO5LTx5+k2G1PmDgxaRcWwsUXBnr3TkJDa60Z+OemwWDXbOjSJbD1VoFFFq4fuHrjLXj3PTjw0Mi48U0fTeuwgwNZf/3EfvvdZPrFGa28Emy/XbK80oqw3TZNvitJkiRJkiRJkiRJUjtiuEuS5iFVVZHTz4q8+VZ63d57whqr193vsSegoiLdPuRg6LeUQa7m9M23kP3XT9l/bQG9ejb9+V1sscA2W6fbN91Sd7rNrKzAiQMDRx0RuPySQOfOvpaSJEmSJEmSJEmSNC9wWkZJmkfU1kaGDI28+1563YH7Bw7YD0JIh32efiby6n/S+6y/LuywffOEgVKpyLhxTB8BbJqRIyM33xaprYUQoKgIiothwT6BhRaEhRaC+XtDTs68E0rafdfAumvDzbdFDj6g/uMqK4sUFc3+4z1g38ALL0SqquGzz+G992GdtdPbs7KCUzFKkiRJkiRJkiRJ0jzGcJckzQNijFx+ZeQ//02vO/TgwL57B95+J1JcHFl+ucCnn0UuvSI94tPGG8F554Y64a85UVkZ+fwL+OJL+PyLyJdfQm0KXny2bqBs8hTqBMpmqHz6UkEBLLVkLauvNpWTT+zUpHram8UWCwwZVP+5LSmJ7HtgZOstIwcfGMjLm/Xz37t3YIf+kQceSto33RJZey1m+dp99HGkpAQ2++e8E5yTJEmSJEmSJEmSpI7CcJckzQNuvCXyxFPp9p57wL+3g8HnpXjxZVh8MbjgvMgZZ0NNTbLPcsvC2WfMebBr4sTIO+/CG29F3v+g7vSO6X2ge/d0Oyd71setqEhCYrW11Q1si+TkzDsje115TWTiRLj3fvjfR5HBZ8PCC8/6se29Z+DJpyLlFfDtd/D6G0lArzFvvR0565xkxLSCAthg/Xnj+ZMkSZIkSZIkSZKkjsJwlyRluIcfjdx1d7q97dZwxKGBcePgrXeSdSN+gQHHJqErgJ49YeiQQH7+7IV9Yoy89jo881zkvfchlWp83169YOKkuuGuBReC8wcHsrOhthbKymHcOBg1KjJqNPz6G4wdm+y7/PL1fzQ98hjce19kow0jW/4rsPJKsx6xqr2qrIxMnZpuf/cdHHho5OQTZj26VvfugV12jtz51+t9862RDdaH7Oz6t0ulIrfflUzjCHDVsGSkr9zczHzeJEmSJEmSJEmSJKkjMtwlSRnsrbcjVw+rO83iiccno3HNNx8MOAIuujSyyMJJgAogLxcuOC8w33xzFvK57Y7Ijz/VX993CVh9NVhxxcCKK8D8vesft2uX0MgIUzNM3Tg58v0PgYUWLKi313/+G5k0GZ56Bp56JtJ3Cej/b/jX5tCpU2aFlfLzA5deBE88BVcPi1RVQVkZDBoS+fTzyLEDwkxHKNt9t8Cjj0VKp8LPI+CVV2GLzevvl5UVuPgCGHBMJGTBpRcGg12SJEmSJEmSJEmSlGEMd0lShvru+8igwXH6KForrADnnFk3GLTtNjB6DNx5V/p2J50QWH65xkM+McZk5K3i9D4hBLbZCq6+NhICrLoKbLh+YP31YaEFmycw1LVrYI3VA8XFdX80lZUlwa4Z/fQzXH5l5LobYLttIrvvGujdQKisvQoh0H97WHEFOOfcyC8jk/WPPQ4jRkSGDILi4oYfT9cugT12h5tuSUJ9t9wW2fT/Gp6ysntx4PJLoagQunTJnOdHkiRJkiRJkiRJkpTIausCJElz7s8/IyefFimvSNqdOsGvI2HSpLr7/fAD3P9Aur3dtrDVlg2HfGKMvPl25KBDI8edEIkx1tm++eZw8IGBhx8IXHNlFrvuEpot2DUzRUWBB+8N3HxDYKcdksc6TXk5PPgw7Lpn5IKLU4z8NTZ+oHZoqSUDN10f+NcMI299/Akccnjkx58afyy77ATFxcny76Pg+Rcbv4/5eweDXZIkSZIkSZIkSZKUoQx3SVKGqaiInHJ6ZNy4pJ2TA1OnwuQpcN4FkdraJBRUWho585xk2j+ApfvBcUc3HPL56OPI4UdFTj098t338OOP8NHHdffpXhzYf9/Q4LSLLS2EwLLLBAYem8VjDwVOPjHQb6n09poaeOZZGHhi+vFniqKiwJmnBwYcGcj666fy6D/g8KMiH37U8GMpKgrstUf6dbjjrkhNzew/7pEjI9ffmKoX4JMkSZIkSZIkSZIktS+GuyQpg8QYuejSJIAFSbDr2AGQ/VdvXlwM1dXJfkMvivw+KlnfuRMMGRTIz68bzPrpp8jAE1McMzDy5Vfp9YWFMHp0yz+epigqCmy/beDWmwJXXBpYfbX0tt12DmRnZ94oVSEEdt81cPEFgc5/jUxWXg4nnRJ5482GA1g7/Bt6dE+WR4+G556fvfv65NPIYUdF7r4X7r63GYqXJEmSJEmSJEmSJLUYw12SlEEeeAheejndPvmEwA79szj8sMAZpwYGnRUoKAjc/yC8/kZ6vzNOCyy0UDr0NHly5IqrUhxwcOSD/6X3y8+HPfeAh+8PbLtN+w5JhRBYc43AVZdnceN1ydSG225Tf7/HnoiUlGTGCFXrrB24YXhggfmTdlU1nHl25IUX69dfUBDYa8+6o3dVV8/6cb7+RmTKlGT5zrsjEyZkxnMjSZIkSZIkSZIkSR2R4S5JyhA/j4gMvy4dxNl5R9h6qyTcs8duga22DIQQ+PSzyPU3pPfbc3fYcINkv1Qq8sRTkT32jjzyGNSmkn2ys2GH/vDAPYEjD8uiW7f2Hez6u+WXC5x1RhZFRXXr/vSzyGVXRHbbM3LLbSnKytp/kGmxxQLDrwkstmjSrk3BkKGRx56oX3v/7aFnj2T5jzHwzHOzPv6RhwfWWB1694bhVwe6d8+s11qSJEmSJEmSJEmSOhLDXZKUIUpLIwUFyfLKK8GAI+uHckpKImefG6eHtlZdBQ49OL3fqFFwxVWRSZPTt1lnbbjrtsAJx2XRq9e8E/SJMTL8+iQQVV4Bt90Bu+8Vef7FSIztO+TVu3dg2NWBpZdOr7vsisijj9etOz8/sPde6dfszrsjVVUzf2w5OYHBgwI3XRfo12/eeb0lSZIkSZIkSZIkaV5kuEuSMkBZWeSU06GsPGl3756EdGZUWxs597zI+PFJu0d3GHR2qLPfwgsH9tlr2jJcfEHg0ouyWHTReS/kE0Lg0IMDy8wQkCqZAOcNjRx1TOSHH9t3wKt7ceDqywMrr5Red/mV9QNe228LvXoly2PHzt7oXV27BHr2nPdec0mSJEmSJEmSJEma1xjukqQMUFQUOPmEJIzTozvsvmv9YM5d98CHHyXLWVlJsCum6h9rn70Cxx4duPPWwHrrztsBn9VXC9x0fWDQWYEF5k+v/+xzOOiQyJXXpCgtbb8hr86dA5deFFhl5fS6Bx+KVFama87PD+yzZ/p1vOvuuttnV0lJ5K572v+oZpIkSZIkSZIkSZLUkRjukqR27JtvI+XlSdhmk40Dp5wYuOmGwEor1g1lffpZ5Nbb06Gc/faBV/8b2W3PyIhf6oZ18vICu+wUyMubt4Nd02RlBTb7Z+DuOwL77wt5ucn62hQ8/AjsuU8yVWN7VVQUuOTCJOC10IJw9RWB/Py6r92228B800bv+hOefmbO7uP77yMHHx654abIw482U+GSJEmSJEmSJEmSpLlmuEuS2qGpUyM//5xi4ImRQ46I/PRzEj7abtvA/L3rBnsmT0mmY0z9NUrXssvAiy/D409AVTVcfGkklWq/4aXWUlAQOPjALO68LbDO2un1JRPg40/a9/MzLeA17KpA7971Q3n5+YF995lh9K5752z0rudeiIwdmyxfd31k3Lj2/XxIkiRJkiRJkiRJUkeR09YFSJLqKimJnHByZNRomDoVpkyBc86N3HFrMgrVjGKMXHxJOpiTnw/ffc/0oBdAz55QUQFFRa34INqxhRcOXHIhvPkWXHVNpHQqHH5o+x/FrKgoNPgalpdHCgsD22yVTM05diyMGwdPPg277DR7xz7y8MDX30R+/RXOPjPQq1f7fz4kSZKkTFJaWsprr73G559/zhdffMGYMWMoKSmhsrKSLl26sNRSS7HRRhux8847071797YuV5IkSZIkSe2I4S5JakfKyyNHHhP57beknZ0NIcDpp4Z6wS6AJ56C/76ebldWppc7d4YTByZTEqquEAIbbgBrrgE//gTdi+s+R+PGRz78ELbYPNm3vXr9jcgll0cuuxiW7hfYd2+49PJk1K2774lsvy31pnBsSE5OYPA5ECMNjgwmSZIkae589tlnHH/88Q1uKykp4f333+f999/nlltu4ZJLLmHDDTds5QolSZIkSZLUXhnukqR2pLAwsP22MPz6SHY2HHYw9JovsNyy9QM3P/0cueqahqfPW3MNOO3khqfwU1pBQWCF5euvv+76yAsvJaNfnXAc9O3b/p7Hl1+JDD4/mY7z5NMiNw6HbbaCu++BP8bA+BJ4/EnYbZfZO95887W/xyhJkiTNS/r06cPaa6/NCiusQJ8+fZhvvvlIpVL88ccfvPDCC7z00ktMmDCBI444gocffphll122rUuWJEmSJElSO2C4S5LamT13T0I2SywO667TcOCmsjJy5tmR6uq66/Pykin2duxffwpHzZ7PPk+CXQCffgYHHBzZdZfIAfsFioraz3Pabyno0hkmTYaCAqiqgtzcwL77wMWXJqG/e+6N/Hu7JMTWFJWVkd9+hyXbYbhNkiRJyiRrr702//3vfxvdvvXWW/Pyyy9z1FFHUV1dzbBhwxg2bFjrFShJkiRJkqR2K6utC5AkJWKMVFYmoZw9dw+NBrsArhkeGflr3XXLLA233RTYeceGp3DU7Fm6H+y/L+TmJu3aFNz3AOy9X+S/r0VibHi0tNa22GKBoecF1loTbhweWHjh5DXfekvo0yfZp2QCPPZE047/62+RQ4+MHHdCZNy49vGYJUmSpEyVnZ09y30222wzllhiCQD+97//tXRJkiRJkiRJyhCGuySpDY0dG7n73iQw9NLLsN+Bkc+/mHmQ5rU3Io/PENgJAQ7YD24YHlhsMUNdc6ugIHDwgVnceVtgzTXS68f+CWeeEznxlMhvv7WPsNMqKwcuuzjQtWv6dc/JCey3T7p9z32R8vI5qzeVipx2ZuTHH2HCBDhncKS2tn08ZkmSJGle1qlTJwAqKyvbuBJJkiRJkiS1F4a7JKmNTJ4cOf7kyPU3RgYNjlx2ZTIF3lHHRN56u36QZuLEyJixkQsvTm/b7J9w601w0AFZ5OQY7GpOiywcuPySwOBBgV690uvfex/2PSByy22p6SOttaUQ6r/ua60ZWXDBZHniRHj08Tk7ZlZW4OQTAtlZkJ8PW/0rkOU7BkmSJKlF/fTTT3zzzTcA9O3bt42rkSRJkiRJUnvhV7WS1EYeeiQyYkSy/Op/YerUZHnhhWD11dL71dZGbr8zsssekbPOiUyZkqxfcEE46fhAv6XsyltKCIFNNwnce2dgt10h+6+nuqoabr8Tfv65betryNPPRPbYGzbeML3uvvsjZWVzFkRbeaXAaacGbr4hsO02ocEQmSRJkqS5U15ezogRI7jtttvYZ599qKmpAWC//fZr48okSZIkSZLUXuS0dQGS1FHtv2+gpCTy5NMQ/8rdZGfBmacHCgqSIM348ZHB50c+/CjZ/tXX6f0GnRXo1MnATWsoKgocfWRgq39FLrsi8vkXsM1WsOyy7ev5f/yJyKVXJCfTU8/AAvPDH2Ng4iR45DHYZ685O96WW7SvxydJkiTNCx599FFOO+20RrcfeuihbLfddnN83HnlDzJmfBwBiJnwuEIgRIAImVAvtNuaZywlBIjUXdEea25UptUL7aLmmZ4DjdygrWueI5lWL7RqzXP8+s/kQBn1PGdavdAiNTfb6z+TO8io5znT6oW5rrnFz4FG7jSjnudMqxdmu+Y2ef1nUkxGPc+ZVi80WHO7OgcakmnPc4bVW/f1b//1tgXDXZLURrKzA7vuEnnu+WQkKIB99obll0t+YH3wvyTYNWFC/dvut2+Yvp9az1JLBq69Gp5/AdZdp/72jz6OLLM0bRa622hDuPveJNBVWgpFhelt9z0Q2bH/3NeWSiXhsawszz9JkiSpOS233HIMHjyYlVdeuUm3Ly4ubt6C2tC0EcyKOhVBURsXMxtiLsRaiDm1ZHfKbutyZkt7rnnauNOdunepu74d19yQTKsX2k/NjZ0DDe7bTmqeXZlWL7R+zXPy+jd6jAx7njOtXmi5mpvj9W/02Bn2PGdavdA8NbfkOdDg/WXY85xp9cKc1dzar3+jdWTY85xp9ULjNbeXc6AhmfY8Z1q9ANPyfJ07dyYnxyjT3/mMSFIbqamJDL0wHexaemnYb59ATU3kltsjd9+THtFrRssuA/vu3bq1Ki0rK7D1VvXXjxsfOeX0SFEhHH4Y/Gvz1g9A9egRuOA8OHxApLISxv4JRUVQVgaTJ8PDj8J++zT9+CUlkfMuiKy1ZmD3XZuvbkmSJKkj2WyzzVhxxRUBqKio4Ndff+W5557jpZde4oQTTuD000/n//7v/9q4yraXVZRDLoWz3rEdSIVITKUgBdnZmfGhuTW3vEyrF6y5NWRavWDNrSHT6gVrbg2ZVi9Yc2vItHrBmltDptUL1twaMq3eabKKjDA1JsTYUHSg6SY0NMTMPCiEMP0vIidOnEgzP41Ss/J8bR9efyNSWgpbb5UEfu68O3LjzclrkZcLN98Y6NQJzh0S+ezz9O2mhXMA8vLgtpsCiy02b46alMnn6pChKV54Md1efjk4/NDAav9o/dfqxZcjg8+r/9x16QIP3Rfo3HnOaxr5a2TAMZGSCZCTA9cPC+1uWsrWlsnnqzoWz1VlEs9XZYpMOle7d+/e1iVoNj3++OOceuqphBA4//zz2XHHHefo9hMnTmyZwlpZCIHOnTsTY6S0tLRdX19qftNef8DXv4PyHOjYfP07Nl9/eQ50bL7+8hzo2P7++s8LmnuE9axmPZokqUHffZ9MsTj0osj1N6b45tsUt9yWflNy6CGB0aPhgIPrBrtWWhEqytPtIw6bd4NdmW7D9QPzz59uf/U1HDMwcuzxKb74snXfgG6xWWC3XeqvnzIlGb2rKRbsAwstlCynUvDl102vT5IkSVJ9/fv3Z8sttySVSjFkyJA5DmvFGOeZf9nZ2U7B0IHl5OT4+ndwngMdm69/x+brL8+Bjs3XX54DHduMr39bfy7RHP+am+EuSWoFt94eqahIll9/A84bCrW1SXvVVWDzf0bOPjcyeXKyLjsbDj8EJk6E1F99/+qrwU47tHrpmk2bbBy4547AAfslI6xN8+FHcPhRkZNOTfHtd60X8jrisMA/Vq2//v4HI1OmzHkdOTmBc84KLNkXrro8sNMOhgwlSZKk5vbPf/4TgLKyMt544402rkaSJEmSJEntgeEuSWoF55wZ2Hgj6NYVVlkZRvySrC8shDNODfTsmcVxRydhmT4LwPBrAuPGw6+/Jft17gSnnxrIyjJQ054VFAQOOiCLB+8N7Ng/mb5wmnfehYMOjZx5doqKipYPeeXkBAYPCvTuXXd9aSk8+HDT7n+B+QO33Rz4x6qeh5IkSVJL6NGjx/TlUaNGtWElkiRJkiRJai8Md0lSKygsDAwZFBh4LDz9bHr9sUcH+vRJgjLbbgPHHRO49aZAWVnd6fMGHhuYv7eBmkzRq1fg+OOyuP/uwLZbQ/YMP20nTYb8/Napo3txYOiQQF5u3fUPPgyTmzB6F2DAUJIkSWpBY8aMmb5cVFTUhpVIkiRJkiSpvTDcJUmtpKICbrgZpk2xu8rKsM1W6e0hBHbeMQnOXHBROnizyUawxeatWamaywILBE49OYu77ghsvhmEAIccFAihbkCqurrlRvJadpnAicfXvb+pU+HBh5rnPisqIg8/GkmlWm/KSUmSJGle9fzzz09fXnrppduwEkmSJEmSJLUXhrskqQWMHBn55NO6YZdh10VGj063x46FsrL6t73y6sjYP5PlHt3hxOPrh4GUWRZdJHDOmVk8cG9g5ZXqvpaVlZE994lceXWKceNbJiC19VaB7bapu+7+B2Hy5Lm7v2++jRx0aOTKq2OdkeYkSZIk1fXoo49SWVk5031uv/12XnvtNQAWXnhh1lhjjdYoTZIkSZIkSe1cTlsXIEnzmvLyyBlnR0aOhMMOhT12g3ffgyefqrvf6D/ghZdgx/7pdf/5b+SFl9LtU08OFBcb7JpXLNin/mv52BPJufDwo/DUM5Httonstkt6us7mctwxgW++jXz/Q9KuqIA77o4cfWTT7+ftd+CXkcny9TdENtoQFpjf81WSJEn6u2HDhnHRRRexxRZbsPrqq7PIIovQqVMnSktL+e6773jqqaf46KOPAMjNzWXIkCFkZ2e3cdWSJEmSJElqDwx3SVIzu/HmyM8jkuVbb4+sshKceU7dffLzYeAxgW22Tq8bNz5y6eXpkZS22xbWW9egzLzuy6/Sr3llZRLyeuzxyKabRvbcPdBvqeY5B/LzA+cNhv0OjFRUJOseehj22j1Fjx5NG8hz373hnfdg5Eg4/tjA/L2bpVRJkiRpnjRx4kQefPBBHnzwwUb3WWCBBRg6dCjrrbdeK1YmSZIkSZKk9sxwlyQ1s912DfzwY+TjT+CIQ+GcIUloZ5pFFoHzzw307ZsO7cQYueiSyKTJSbtPH+ZqRCVljsHnBD7cDm66JfLlV8m62hS89DK89HJk7bUie+0R+MeqzPX0nAstGBh0VuTUM5J2KgXnDIZrrmza8XJyAoPOguwsWGABz1dJkiSpMTfffDOvvfYaH330Eb/88gvjx49n4sSJ5Ofn07NnT5Zbbjk22WQTttpqKwoLC9u6XEmSJEmSJLUjhrskqZktMH/gqsvh3vsjd9wFJRPS29ZcHYaeFygsrBuEeeoZeOfdZDkEOPO0QFGRYZmOIITAGqvD6qvBx5/APfdF3ns/vf299+G99yPLLQsXnA+9es7debHB+llsvFGK115P2h9/Ap98mmLVVZo2etdCC3qeSpIkSbPSt29f+vbtywEHHNDWpUiSJEmSJCnDNO2bXEnSTD31DNx0SzrYlZ0NRx8Fl19aP9j1+++Ra4alp+bbY3dYZWUDMx1NCIHV/hG47OIsbrs5sMVmyYhY00ydCj26N899DTobZhwM4L+vNc9xp4kxUl0dZ72jJEmSJEmSJEmSJGmmDHdJUjOoqqobZMnJgdraZLlnD7j6isBuu2TVm1avtjZy3gWR8oqkvWRfOPgAg10dXb+lAmefmcX99wR23gkKCmDP3QNZWXXPjR9/ikyeMuchqtycLE46Id1+/MkkZNgc/hgTOf6kyLXXG+6SJEmSJEmSJEmSpLlluEuS5tKo0ZHd9oy8+FIkxiTQssD8sM3WsOIKcMuNodGRuO57AD7/IlnOyYEzTw/k5RnuUqJPn8BxR2fxyAOBLTavuy3GyKDBkZ12jQwbnmLs2DkLU23+z8DqqyXLNTVw481zH8YaNTqy7wGRD/4HDz8Cn3xqwEuSJEmSJEmSJEmS5obhLkmaCzFGLr088uc4GHx+5KZbIr/9Fjnl9Mj7H8B++0CvXg2HtX76KXLzrenwy8EHBvotZbBL9XXrVj/098678PMIKC+H+x+EXfeMDL0wxc8jZi9QFULgiMPSx3zlP/DRxym++LLpgaw+C8AaqyfL2Vnww49NPpQkSZIkSZIkSZIkCcNdkjRXxo+PfPNtspwVYKMN4fwLIxUV8OefcNMtkErVD8vU1EQuuDhSU5O0V1oR9titFQtXxsvLg35Lpds1NfDs87DP/pFTTk/x6WezDmktu0xg883S7ZNOheNPiowc2bSAVwiBEwcG/rEq3DA8sPOOhhUlSZIkSZIkSZIkaW4Y7pKkJqqpidxyG0yenLQX6AP9lgrstWegR3fIzYUzTwtkZdUPuDz4MHz9TbKclwennxrIzjYIo9m3xuqBW28KXH5JenrFad56G446JnLEgBRvvhUbDBhOc+hBgZycZLmyEsrK4IyzIzU1TQt49egRuObKLJZd1vNZkiRJkiRJkiRJkuaW4S5JaoLS0siJp0Seeia9rlfPJBizwXqBO28LDDk30Ldv/YDLyJGRm29JB2cOOSiwyMIGYTTnQgistWbgqsuzuPn6wKb/B1kz/GT//As49YzIk083fow+fQI77zjjMWG/fSEnx3NSkiRJkiRJkiRJktqa4S5JmkN//BE5YkDkfx+m1223LVx1eaBLlyQQU1wc2GC9+uGYVCpy4SWRquqkvdxysOvOrVG15nXLLhsYfE4W994Z6P/vZEQ4gM6dYPN/zvy2++4d6Nw5WY4RJkxo3mBXWVnklf80bSQwSZIkSZIkSZIkSerIDHdJ0hz49rvIIUdEfh6RXnfk4YFjB0Bu7qwDMY89Dp99nizn5sJpJzsdo5rXwgsHThyYxSMPBPbbB/baM9CpU91z7KefIs8+l556sWvXwIH7p/e55bbIhInNE8b68KPIfgdGzjk38tHHBrwkSZIkSZIkSZIkaU4Y7pKk2fTe+5EBx0YmTEja2VlwxqmwwvKw257w5lszD66MGh25/sb0PvvvG+i7hMEutYzu3QOHHJTFPnvVP8duujUy9KLI3vtFXngxUlsb2bE/LL5Ysr20FG65NTlXJ0yIjBvf9FDWY09ERv+RLF9wUaSy0oCXJEmSJEmSJEmSJM0uw12SNBueeyFy8mmR8vL0utoUVFbBeUMj40vg1DMijz7ecHAlxshFl0TKK5J2v6Vgrz1aoXDpb779LvLGm8nyb7/DkKHJaHRffQ3HDEgHwZ58Gp5/IcWBh0TOOidSXd20UNYJxwWKu0Fxt2SUu/x8A42SJEmSJEmSJEmSNLsMd0nSbPjoo0htbbKcn5/8v/xy8NVXTB+VqLgbbLxhw7d/+hn48KNkOTsbTj8lkJNjyEWtb9FFkpBVt67pdd99B0ceHXn2+ciaayTrUikYehH8OQ4+/wKuva5p4a7u3QNDzwvcdUfg/zbxnJckSZIkSZIkSZKkOZHT1gVIUiY45aTA+JLIpElw8QXw5VeB30dFrr0uvc9JJwZ69qwfXikpiVx7fToYs/ee0K+fIRe1jcLCwJ67Q//t4cGH4Z570yPKvfxKEl7MykrCXalU+nYPPwrLLxfZYvM5P3dXXsnzXZIkSZIkSZIkSZKawpG7JGk25OQEzjs3cM2VgZ49s1hhebjn3vT2rbeCjTdsOMByzfBIaWmyvNiisN8+Bl3U9oqKAvvvG7j3rsC/tkivr6ysG+rKy0svX3Rp5IcfmzaClyRJkiRJkiRJkiRpzhnukqS/qayMPP5EJMa6IZaiokBRUSDGyEWXRCZOStb3WQCOHdBwYOu99yMvvZxun3RCIC/PcJfaj/nmC5x1ehbXXxtYbtn626uqoEuXZLmyEs44KzJ5ytwFvH79LXL8SSl++smgmCRJkiRJkiRJkiTNjOEuSZpBRUXk1DMil14RuXpYEvCaODHy+hvpsNeTT8Pb7yb7hwBnnh7o1Kl+YKuiIjnONNtsDauuYrBL7dOKKwRuGB44/ZRA9+6w0QbpbVOmQEFBsvz7KDjrnEhNTdOCWa+8GtnvgMj7HyQjgdXWGvCSJEmSJEmSJEmSpMYY7pKkv5SVRU46NfLB/5L2Q4/ARx/DnXdHTj8rcuTRkdfeSHHNtekwyp67wyorNxzYuuOuyOjRyXJxMRx1uMEutW9ZWYGttwrcc2fgzNMDm2+W3tapKAkzAnz4EVx+Vf3R7WbH0kunl7/5NvknSZIkSZIkSZIkSWqY4S5JIh3s+viT9LojDw8ssAA8+njS/vwLuOEmqKhI2kstCQcd0HBg68efIvfen24ffWSga1fDXcoMXbskU5AeMyDQrWuybnxJOtwF8ORT8MBDc37sRRYOHLB/YIXl4babAiss73UhSZIkSZIkSZIkSY0x3CWpwysvj5x8WuTTz9Lrjj06sOfuga5dYNddIC8XFuwDI0cm23Nz4ewzAnl59YMpqVTkkssitbVJe/XVYIvNW+GBSM2se3Hg6AHpczyVqrv92usib74956N37bEbDL8m0LevwS5JkiRJkiRJkiRJmhnDXZI6tIqKyCmnRz75NL3uhIGBXXZKQiddugSOPCyLQefAH2PS+xx2SOPBlCefhi++TJbzcuHE4wMhGGJRZvrX5rDWmg1vixHOGRT5/vs5C3jl5ASys70mJEmSJEmSJEmSJGlWDHdJ6rAqKyOnnhH56OP0uoHHBnb4d93QSVlZZNjw9KhFq68Gu+7c8DHHj49cf0M66LLfvoFFFjbEoswVQuDkEwNFRQ1vr6yCAcdFxo5NNbzDbJo6NVJaOuejgEmSJEmSJEmSJEnSvMxwl6QOqbo6cuY5kf99mF53zIDATjvUD2JdeXVk1KhkuUsXOPO0QFZWw4Gtq4dFSqcmy4svBnvu3tyVS61vgfkDxxxV95wvLEwvT50Ke+8Pf4xpWsDrzbcie+8XGTbccJckSZIkSZIkSZIkzchwl6QOadw4+P6HdHvAkYFdd07CK6lU5PEnImVlkVf+E3n2+fR+p5wYmG++hoNd77wXeeU/6fZJJwRycx21S/OGbbaG9dZJt3NzkwDjNGVlsNe+8P0Pcxbw+va7ZAS9P8fB08/CRx8b8JIkSZIkSZIkSZKkaQx3SeqQ+vQJXHdNYOGF4JCDArvvmg5h/ec1uPSKyM67RYZemA6abLs1bLJxw2GtiorI5Vek991uG1hlZYNdmneEEDj91EDv+ZL25MnQuTNssH56n8pKuO4GiHH2A1rLLB3YfLNkuWdPqKpqxqIlSZIkSZIkSZIkKcMZ7pLUYfXpE7jlxsC+e6fX1dZGbrs9CaZMnpKEVQAWXjiZtrExt94RGf1Hsty9OxxxmMEuzXuKiwODBwWys5P2F1/CIgvDlv9K71NTk75uZtcxAwK77wr33BFYZ22vHUmSJEmSJEmSJEmaxnCXpA5j8pT6owl16hQIoW6YZN+9kxG9unSBC8+HPgvAOWcGiooaDp388GPkgQfS7aOPCnTtakBF86YVVwgceXj6/L7vgWT0rq23hM02hUsvChQUzNn53704MODILDp39rqRJEmSJEmSJEmSpBkZ7pLUIbz5VmSX3SPvvDfz6eKyswNbbB64+47ANVcGNlg/i/vuDiy3bMOhk1QqctkVkdpU0l5zDdj8n81dvdS+7LozbLxRun3eUOj/bzj7zEBeXvpaGTU6MmhIitLS2Z+mUZIkSZIkSZIkSZKUZrhL0jzvm28jg4ZEpk6FU0+LvP7GrIMmOTmBpZYM05cb88JL8PkXyXJeLpw4sP5IYNK8JoTAaScHFlkkaVdUwGlnwNg/0/tMnhI58eTIy6/AEQMiY8bOWcDr5xGRt942FCZJkiRJkiRJkiSpYzPcJWme9scfkVNOi1RUJO2FFoJVV2l431QqMvyGFKNHz16gpLQ0ct316X332hMWWshglzqGzp0Dl14YKO6WtMeXwMmnRqZOTa6J99+PjPw12fbziCTgNfLXWV9bVVWRm29NccDBkSFDIyUlBrwkSZIkSZIkSZIkdVyGuyTNs0pLIyedGhlfkrSLu8ElFwa6dq0fwPro48h1N0TuvQ8OOCTy5luzDpTcdkekZEKy3GcB2HtPg13qWBZaKHDB+YG83KT9089w1qBITU1k7J91r4exY+GoYyI//Djra+vV/0BNDZSWwrXXGe6SJEmSJEmSJEmS1HEZ7pI0T6qpiZx9buTnEUk7Lw8uHBoaHFmrpiZy/gWR+x5I2qWl8PU3Mw+U/PRz5OFH0u0BRwXy8w13qeNZacXAGaenz/33P4Bzz4tsuUWk31LJuuzs5P8JE2DAsZEvvmz8+srLC5x0QnK8VVeBfff2upIkSZIkSZIkSZLUceW0dQGS1BKGXx95/4N0+6zTAyuu0HBI5KWXYczYZDkrC1ZaEQ7Yr/FASYyRq66J1KaS9lprwkYbNFflUub55/8FRo+G629MQlv/+S/k5sDll8Ajj8E6a8PJp8HkyUl4cuAJkYsvhH+s2vB19o9VA9deDSuvBCEY7pIkSZIkSZIkSZLUcTlyl6R5zlNPRx58ON0+5KDA/23SeEBkqSVhw/WT5QP2gyGDAjk5je//n9fgw4+S5ZwcOO7oYABFHd5ee8Cee6TbL74M518Ie+4eWHGFLIZdGejZM9lWXgEnnRL5/IvGR/BaZWWvK0mSJEmSJEmSJEly5C5J85RPPo1cdmU6MLLZP2HfvWd+m379AhecH/jxp0ifBaCoqPFASXl5ZNi16ePvugssuqgBFCmEwBGHQlVVesrSd9+DYwZGLr4A+vYNDL8ajh4YGTsWKirhhJMjN98Aiy7iNSRJkiRJmrXy6jIqasrauozZVpBTRGFuUVuXIUmSJEnKcIa7JM1TRo+G+Ff2arll4bSTGx/95+tvIvl5SegEYMm+sw6Y3HlPZOyfyXLPnrD/PoZSpGlCCBw7AIq7wc23JhfiN9/C4QMil14EiywM/1gFXngp2b9LF+jVMwIzv45ijLz6X3j5lch55ways73uJEmSJKkjqqgpY3JFCZHGR4JuLwIBCjDcJUmSJEmaa4a7JM1TttoyMP/8cNWwyAXnBfLzGw6BjB8fOf3MyOQpcPxxsM1Wsw6L/PZb5P4H0u2jjggzHeVL6ohCCOy/L/SeDy66JFKbglGj4JDDI2ecBnl56X3HjIFjj4ehQyLzzdfwtRRj5KxBkf++lrSfeAp27N/yj0OSJElS26mtrSXGSIztP8Cj1heJVNVWtHUZs5SXXdDWJUiSJEmS5hGGuyTNc1b7R+C2myArq+GwSHV1Ehb5c1zSvu6GyIbrQ9euMw9qXT0sUl2dLK+yMmz+z+asWpq3bL1VoGdPOPPsSHkFTJ0Kp5+ZTGW65+5w7/3Jfl9/AwcfHjl/MKy4Qv1rMITAMkvDf19LvtR5/IlI/+0bv74lSZIkZb4YI+XVZUwsH58RIzSB0+9JkiRJkqSWY7hLUsaLMdabenFmwY9hwyOffZ5u79h/1sGut96OvP3utGPDwGMbn+5RUmLttQLXD4fTz4z8PipZ9+BDsPhisO/eScCrpgbGj4ejj4us9o/IiQOhT5+sOsfZYzd45VVYfTU46IBgsEuSJEnqACpqpjK5soRUTLV1KbOUm5VPdsg23CVJkiRJklqE4S5JGe3rbyKXXxk550xYeOFZBz6eez7yyGN1102eMvPbVFVFrh6W/kvhHf4NSy1puESaHUv2Ddx0A1xwYeSNt5J1I36BX3+FLTaHd96DiROhuhreex/2OwjuuCVVJ+CVkxO46XrIzfW6kyRJkjqSTJl+Lzc7n8qaCiaUj2vrUmZbVsgmP7uAgtzCti6ljhgjNTU105clSZIkSZLhLkkZbPLkyJnnRMaMgYMPi5w/BFZfrfHwxzffRi65LP3BYHZ28v+eu808MPLwo0wfdai4Gxx0oAETaU507RIYeh489zxceU2krAxqU/DcC9CjO/TsmYzeBVBWBnvtC5dfmmLVVdIBL4NdkiRJktqzippyUtVTMmYaybzsfKqyKiivmdrWpdQRCNTkJIG+0vKpdZ7P7JBNVU1lW5UmSZLULpRXl1FRU9bWZcwRpzCXNCv+oc+sGe6SlJFijJx/YRLsAsjKhoUWbHz/CRMjp58VqapO2v2WgksuhO++D/Tu3XhoZMKEyB13pX+AHHxQoGsXQybSnAohsPVWsNo/YOhFkY8+TtaXTEj+LypKgl0AVdVw9HFwxGEpdt+18WkYS0oiPXp4PUqSJElqHzJlpDFIRhtrj4G0EAI1OeUATK0sq/Ohfl52PrWp9j9NpyRJUkuqqCljckVJu3oPNzOBAAUY7pLaQCaFQaf9oU9BTqe2LqXdMtwlKSM98BC89Xa6ffYZgQUWaDjkUVMTOXtQZOzYpN21KwwdEujVK9Cr18zv55bbIlP/+iPWvkvAtls3Q/FSB7bAAoErL4MXXoIbb4r8+desJWV/e28ZIwy/Hv73YeTkE2GB+dPXd3l55LY7Iw8/DMOvgWWXNeAlSZIkSU3R3gJpIQTyYy4AVbUVdcJdudn5bVWWJElSu9Le3sPNTF52QVuX0CFkUohnGkd0a3mZFAZN/tCnguJCyMF+oyGGuyRlnC++jFx3Q/qH0D57wTprNx7uuO6GyMefJMtZWXDu2YE+fWYdBvnpp8iTT6fbRx8VyMkxRCLNrayswFb/gv/bGO5/EO65N1LeyO+h738A+x4QOfJw2H7b5LZXXxt56q9r84JLIrfcgNemJEmSOrxx48Y1+badOnWisLCwwW3jx49v8pQIhYWFdOrU8F/dTpgwgdra2nrrQwiUl5czsWI8JeMnNPilVV5eHp06N/wlwJTJpdOncphTubk5dO7SucFtpVNKqa5u+LiVuSlSqRSpWE1Vqv60gTk5OXTp2vBxp5aWUVVV1aR6s7Ky6FbctcFt5WXlVFQ0PoXhzGoOBIp7dGvwdhUVlZSXlTepXoDuPYobXF9VVcXU0jICgerK5HkuLS2t8yXEzGruVtyVrKysesetrqqmtLTpU0926dqFnJzseutramqZMnnKTG+bl5VPTUGgtoHvJbp27UpeXl699alUipKSkqbX26UL+fkNh+AyrY8oKSmhpKSkScfOz8+nS5cuDW6bNGkS1dXVc3xMSPqerl0bvuYmT57c5Gs5NzeXbt0avuamTJlCZWXTpiPNzs6me/fuDW6bOnUq5eVNu5ZDCPTs2bPBbeXl5Uyd2vRrbr755mtwfWVlJVOmzPyam5kePXo02EdUVVUxefLkJh+3uLiYnJz6X7PV1NQwceLEJh+3o/YRIYTp1+ekSZMoKCiY4/cRs8M+ItEe+4i/nwN/P3d6NfLX+h2tj6jNbfhcSqVSTJrY9Ho7dS5qsO8BmFAyscnH7dalmG6N5DRm7CNm9fr/XVN+15gdmdpHNBTimVo6larKJv6ukZ1Ft+KG+57ysnIqGvtSZ1ZCoHuPYnKz8skO2XXCXWVlZdP7ntLS0jk+tH1E4u/vI6aFQdtrH1FYVEhBQT4hBDpR9/f81v48YnbMSR/R2M/ZpjLcJSmjTJ4SGTQ4Mq2/XXklOOiAxkMdL74UeeChdPugA2DNNWYdAokxMuy6yLTR/tdbZ/ZuJ2n2FRQE9t8XttsGHngo8tgT0NBnBmVlcOnlkVf/AyedAPvtE3j5lUh5OfTsAaWlUFzc6uVLkiRJ7crSSy/d5NtefPHFHHzwwQ1uW2eddRg/fnyTjnvyySdz6qmnNrht66235ttvv23ScXfa89+cNOi4hu/zyDP5+P1Pm3TcTbfcmKFXD2pw29AzLuXV519r0nH/sdYqXHf3lQ1uG37pjTxy7xNNOu4SSy3Gfc/e3uC2u29+gFuG3dGk4xZ378bz7z3e4LanH36WSwdf3aTjArz73X8aXP/GK29zxrHnNvm4z737WIPBsc8+/pKj9hnY5OPe+8yt9O23RL31I38eyZ7bHNjk4z755JNssMEG9daXlJTM1bV866230r9//wa3ZVofseGGG/LVV1816bgHHXQQl1xySYPb9t57b956660mHXf77bfn9ttvb3DbMcccw5NPPtmk466//vo89dRTDW4bPHgwt9xyS5OOu8wyy/DOO+80uO2aa67h4osvbtJxe/bsyffff9/gtnvuuYeTTz65SceF5Iu2hjz33HMceGDTr7n3P3uXnj171Fv/7tvvsdcu+zT5uM+9+gxLL9Ov3vrvvv2erTbdpsnHtY9ItNT7CPuIRCb2EY2FG+e2j/jsy0/pVFw/oNhe+4g77r+VldZcrt76SRMns9U6OzT5uOdfdQ7/3GqTBrfNzXFPGXQChx5yWIPb7CMSzdVH/H1EtysvuqZFfte49YY75vp3jdzsfCprKphQng4X33X7PQw6o+m/E/z4+3cNrn/2qec4+vBjm3zcae8jskM2qRiJJF8et9c+4p6H7mKd9dYmO2RTVZMOALfXPuLEs49h570bvn2m9xFNDaY1xnCXpIwRY+SCCyN/jEna3brCoLMaH03rs88jF1yc7jQXWQTuvQ/Ky1PsslOgZ8/Gw1rvvpeMGASQnQ1HHWGwS2opPXsGjjw8sPdekUcfgwcfijT0B9AffQyDBkeuuDQw8JhAVjb8a/PkL3okSZIkSVLmizE2efQ9qTFTKkrIqaj/5drUqqaPXgFQWjmRSRX1v3AsrZw4V8eV1LoqasuprSivN21Ze+0jqlNNG4lJam8qaspJVU+Zfu1VVDd99E+gwesNoKy66aN2Qfp9RF52PrWpFKlYQyS22z5iatVkJlWMn16v5h2GuyRljIcegTdmCMSfeXqgd++GQx2//x45/czItJEP+y4Bo0ZBRSXcfS+ssTo0MjIwNTWRYcPTb+J3+DcstpjhEamlde2SjOS1x27w3PORa4bD30cV//Y76L9TZMMNYdutAzGC2S5JkiRJklpOWXVpnVEVZhRj078wqqgpq3fcQKAmp4LaVNMDXpU15Y3WW5Nq2lRKANW1VY0et7rWL9rbs6pUZYPT/M5tQKK60eM2bZo8SW3n7yMegX2E1BpmvPZqYtPfpwENXm8wd+//IP0+Ijc7mdp4Ws3ttY+oTlXVqVfzDsNdkjLCyJGR4denA1d77g7rrtNwomPylMjJp0UmTkraPXvCUUfCsGvh5xGwdD9YfbXG7+uJJ+GXkclyly5wwH4mR6TWlJ8f6P/vwDprRw44JJKVlUzXOG3K+apqeOVVeOXVyPy94f82ieyyc2D+RsKekiRJkiS1J1MqJzUYEppY0fB0U7NratWURsNHc6O8emqjIyGkaPpUIxU15fWOG0KgJqec2ljb5ONW1lY0Wu9chbtSlY0e1y/q1ZwyrY+QJElSywuxmSd6bGxO9HlNCIHi4mIAJk6c2OzzZUrNaV44X2OMPPQwDL8hsuwyMOyqhqdjrKmJnHBy5MOPknZBQbLvsssEUqnIu+9BXh6ssXrjwbDd94pM/mskzWOOCuy6i4GR1jIvnKtqXj/9HOmzAMQIr/4Xnnk28vkXDe/bt28S/Nxis0BWVstft56vyhSeq8oknq/KFJl0rnbv3r2tS1Ar+f7775t8206dOlFYWNjgtvHjxzf5HC8sLKRTp04NbpswYQK1tfXDGyEEOnXqxMSK8Ywe/1uDf8mcl5dHp85FDR53yuTSJk/plpubQ+cunRvcVjqllOrqho9blNuVVCpFKlZT1UDAIycnhy5dGz7u1NIyqqqa9hffWVlZdCvu2uC28rJyKioaD5vMrOZAoLhHtwZvV1FRSXlZeZPqBejeo7jB9VVVVUwtLSMQ6Nw5ea5KS0vrTI80s5q7FXclKyur3nGrq6opLW369C5dunYhJye73vqamlqmTJ759C7pemvgbwGozl06k5uXW+82qVSKSRMmNbneTp07kZef1+C2CeNn/bl97vSpZuo+x4VFhRQUNPyX/xNLJtWbxmp2FRTkU1hUt+8JIdClSxcmTpjE5EmTZ3nshs6L9tZHzCgvK58uBT0oLugxw3Fz6dat4WtuypQpVP59WPPZlJ2d3ej7gKlTp1Je3rRrOYRAz0amRCgvL2fq1KZfc7169aJLly5A8tinqaysrNOeHRMrSphSUUJVqrJd9hEz+nt/0V77iLoCXQq6U1zQo9neR4QQpl8LkyZNoqCgYI7fR8yO/Pz86efZ302aNInq6qaFQPPy8ujateGfy5MnT27yz/tM6yMqayoo6lbQ4LaK8gqmlpU1ettAcp4CTC2dWu8nQM+ePerdBqCysorS0tKmlEt2yKKgcz6VqbL6I3e1kz5iRkW5XenUqYis3PqjFKVSKSZNbPo0cZ06F5GX10gfUTKxycft1qWY+YsXonthr3rbZuwj/t4HzKrvaMrvGrMjU/uICeXjmFQxvs550Va/a8zMtN81OuV1I5VKURurp9dcWVlFdkiumb//LjA7ZvW7RlNNex/x95rbYx8B0LlzJ3LzcuvV2177iGm/a4QQ6N61B8WFvcipSX6OtPbnEbNjTvqIfv36Nek+GmO4q4ky6YNcaV46X7/+JtK9GBZYoH5wI8bIRZdEnn42aYcA5w8ObLTh7Ic8rrk2xQMPJcsLLwx33RbIzTXc1VrmpXNVLWfEL5Fnnk2u9YY+11tkYdh5p8DWW0JhYctdv56vyhSeq8oknq/KFJl0rhru6jjmlc/kQkjCPRPK/+T3cSOprGl6kKi1NPTFRHvXXmueFuyB5AvrGfvX9lpzYzKtXmgfNc/sHGhIe6h5TnTK7UqX/GK6FmTWz+fy6jIqapr+pejsCgQ6d0m+hCudMrXJwcHskE15dRnlNaWZcV5k2HkMkJddQLeCng2GNZoqk95jq3ETyscxuaKkSddvEvJPwrlTp5a1yjmQ91ewOVOuv0zsL+bkZ19DAV/NnobCXe1ZQ+fynL4PbG2Zdv1lWr0NhbsyXXN/Jue0jJIyynLLNh7USKWSQNc0Rxw2Z8GuX3+LPPJYuj3gCINdUnv022+w5JKByy6OnH0u/PFH3e2//gZXXBW57XbYbVfYaQcoKvJaliRJkiRJbSwEKmsqMmpqvGlBqYqapoetZte0aTkBplY2PdgxLawhqW1EYpOCBCEE8mMyal1VbUWrBDtysxsemVLNaDZ/9gUCNTnJeVNa3vI/c2alIKeIwtyGRwKVpLZguEtSuzVpUqSwEPLyZi+UkZ0dOPlEWGQR+P33yB67JdM0NjR9Y0OGXxeZNir76qvB+us1tXJJLSGVitxxF9xyWyQ3F665MnD/3fDoY/DlV5FX/wu5uTBtlOGJk+CGmyL3PQC77gy77woFBYa8JEmSJElS26moKSdVPaXNv7SeXdOCUk0Na8yJ5gp2GNaQpPZldn72NVfAtzkEAhRguEtSu2K4S1K7FGPk3PMi40vgnDOgb9/ZC2SEENhzd4gxWT7p1BSFhZHddgmsvFKyriEffRx5461px4ABR4ZG95XUNqqr4c234vTlM86O3HJjYNddAhUVsPdeyZSML74Md98TGTU6ud3kyXDzrZGnnoFjjoKNNmy8L5AkSZIkSWpprRGUai4GpTQvaa0pRpuLIwdpXjKrn31tMXJbY/Ky540p4STNWwx3SWqXHn0M3v8gWT70yMhD90H37vXDGN//EFliceqNzhVC4PvvIx/8L2m//U7k0QcDPXrUv6/a2sjVw9JvErfdGvotZfBDam/y8wNDz4ODD4tMmADjx8MZZ0WuuTIZkavfUsl+228LP/wQefTxurcfMyYJhK25Bhx3NCy2mNe5JEmSJEmS1FFU1JQxuaIkI0bOc+QgSZI0o6y2LkCS/u7nEZFrr0//crXrzg0Hu778KnLkgMjpZ0YqKur/Mvbxp8koXAD/3BR69Gg4yPHc8/DDj8lyYSEcfKCBD6m9mr934LxzA9nZSfvLr+DyK2O9v+JZeOFAYWHDx/jgf7DfQZGbbklRWdn+P8iRJEmSJEmS1DymjR7U3v9lQgBNkiS1HsNdktqV6urIkPMjVVVJe9ll4MD964etxo+PnHRqpLwC3n4XLr60/i86u+4cuPeuwI79YfddGg5slZVFbrw5fdt99w707Gm4S2rPVlk5cNwx6ev06WepN0rXtlvDppvAIos0fIyaGrjjLjjosMgPP/pBiSRJkiRJkjS7crPyyA7ZzXrMGCM1NTXU1NS06XRskiRJ7ZHTMkpqV269PfLd98lyfj6cfUaoN+UiQI8esNsugZtuifTo3nAADGCRhQPHH9d4WOuueyMlE5LlBeZPRgmT1P713x6++x6eejppXz0s0ncJ+MeqgQkTIgcfHhkzJhm977hj4PEnYMQv9Y8zYgQcenjkyCNgpx2SKV0lSZIkSZIkzUQIVNZUMKF8XPMdkkBNTgUApeVTm33kquyQTVVNZbMesyW1RIBO0uzx+pPUHhnuktRufP1N5J770u2jjwwsumjDQYsQAvvtA8XFsPxyyRRsc+qPPyIPPJBuH3FYID/fYIeUCUIIDDwGfv458sWXUFsLZ58bufVG6NULlu4HY8ZAjDD6D7j9lsADD8Ett0aqquseq6oarrw68sH/4IzToGsX+wFJkiRJkiRpZipqyklVT2m2EFYIgZqccgCmVpY1++hdedn51KZSzXrMFtUCAbqWlmkBOqlRGXb9ee1JHYPhLkntQlVVZOiFkWm/W629Fvx7+1nf7t/bNT2Ecd2N6ZDHSivCpv/X5ENJagN5eYHzBsPBh0XGjYMJE2DQkMiwqwInHBcYOTJy8IGBTTZOPhzaaw9Yd20YfH7khx/rH++tt+HwIyOXXAgLLWTAS5IkSZIkSZqZSKSqtqJZjhVCID/mAlBVW9Hs4a7c7PxmPV5raO4AXUvLuACdNBOZdP157Ukdg+EuSe3C7XdGfh6RLBcVwcknhjrTo1VURG6/M7L/voGCgsZDF6lU5MxzIuutG9j8nzQ6EtcXX0ZeeTXdPvqo4HRsUgbq1TMwZBAMODbSrSscclByLffqBXfdDllZda/rvn0DN14HN98Wue/+ZGSvGY38FQ49InLB+bDySvYJkiRJkiRJktpOcwboWlomBuikmcmU689rT+oYstq6AEn6eUTknnvT7aOPDMzfOx2qqKmJnDUocve9MPDEyOQpjafk33sfXn8DLrw4cuAhkVSq/r6pVOTqYen1W2wGyy9niEPKVCutGDj37MCtNwVWXSV9Lf892DVNXl7gyMOyuPqKwPzz198+aTIce3zkpVfa/1/kSJIkSZIkSZIkSZq3Ge6S1OYWWxSOPSZQWABrrgHbbpPelkpFzrsg8s67SfvzL5IAV2OeeCodxthwg4bDHS+/Cl99nSzn58NhhxrskjLdJhsHevWa+bVcWxv59LN0H/GPVQO33xzYcP36+1ZXw7lDkhEDm3sIeEmSJEmSJEmSJEmaXU7LKKnNZWUFduwP66wN2dlMnx4xxsgVV0defiW97yEHBTb/Z+MBjrPPCDz9LDz2RGSnHervV1ERuf7GdFBjj92oM0qYpHnHb79FunWDLl0Cv/+eBEW//AqGXwMrrpBc9126BM4fAvfcBzfdEvn7tPQ33xr5/Xc45STIybGvkCRJygQ//vgjf/zxB1OmTKGiomlTaPTv3795i5IkSZIkSZKayHCXpHZjwT51gxO33BZ57PF0e7ddYN+9Z36MoqLArjvDLjulQ2Izuv9BGDs2We7ZE/bc3bCGNC96480kzLXqKnDBeTD8hsjnXyTbzhsaue1mKCxMrv+srMA+e8Fyy8LZ50YmT4ZevWDcuGT/516AqWWRQWclUzpKkiSp/XnjjTd48MEHefPNN5sc6JqR4S5JkiRJkiS1F07LKKlN1NREqqoan+rswYcit9+Zbm+9JRx1RGgwsNWQhvYbNy5yz73p+zzs4EBRkUENaV4zcmTk9LMiU6fCW2/DnXfDcUcHunSBnBzYdptAXl79262xeuD6YYFtt4b774Yd+qe3vf4GnHpGpLzcKRolSZLak8mTJ3PkkUdy6KGH8vLLL1NeXk6MsUn/AKfkliRJkiRJUrtjuEtSm7j7Xjjw0MhXX9f/4Py5FyJXX5tev+EGcPKJgaysuQti3XhLpPyvP+BeemnY8l9zdThJ7dSii4Y6o/zdcltkxC9w9pmBm64P7L1nIDu74f5k0UUDp56cRUFBFscfW/c4738Ax58UKS31Cz9JkqT2oLy8nIMOOoj//Oc/00NZs/sHQQ0x2CVJkiRJkqT2yGkZJbW6kSMjd9wVqa6Gw4+KDL8GVlwh+QD+jTcjF16U/kB9tX/AoLMCOTmNf0BfWRm5+97I9tsG5puv4f2+/S7y3PPp9jFHzX1YTFL7deD+ga++jnzwP4gRzh0SueWmwPy9Z/+6DyFw6MGBoqIU19+YrPv8Czjp1MhlF0OnTvYhkiRJbemGG27g888/J4RklOcYI/n5+ay//vost9xy9OjRg6KiorYuU5IkSZIkSZorhrsktapUKnLxZUmwC2CF5WH55ZLljz6OnHNupDaVtJddBi48P5CfP/MAxfMvwm13wJ13R/bcPXLYIXUHJYwxcs21kWl/hL3JRrDqKoYypHlZdnbgnDOTEQLHjoWJk+DsQZFhV0Fu7uxf/zFGRo2uu+7zL5IpGi+9yJEdJEmS2kpVVRV33HHH9FAXwK677sqJJ55I165d27g6SZIkSZIkqfk4LaOkVvXsc/DJp8lyTk56usVvvomcekak6q/Q12KLwqUXBYqKZh7CSKUiDz2cfJBfWwu9etXf//U30/eZmwtHHG6wS+oIiosDQwYFcv6Ksn/5FVx7XTqQNXlKZPB5KV58qfGQVk0NlJTUX//Rx3D6mSmqqgx4SZIktYUPP/yQ8vJyIBlxdccdd2Tw4MEGuyRJkiRJkjTPMdwlqdWUlESGzRCs2GcvWGLxwC+/RE48JVJWlqyff3644tJAcfGsQ1ghwFFHBtZYHbp0ga23rLu9qioyfIb73GUnWGhBw11SR7HC8oFjBqSv+YcfhZdeifz0U2S/AyIvvgyXXhH5fVTDIa3c3CQgtslGsPuucPSR6W3vvg8nnVJKTY0BL0mSpNY2cuRIIBlpNYTAwIED27giSZIkSZIkqWUY7pLUaq4aFiktTZYXXQT23jPwx5jIwBMjEycl64uLk2BX796zF8AKIbDu2oErL8vivrsChYV1b/fIY/D7qL+O3Q323dtgl9TR7PBv2GKzdPuiSyKVVZGioqRdVgbvvNv47XNzA4MHBQYcmcVuu2Zx6MHpfuTlV6sYMnTq9KmAJEmS1DomTpwIJL8TLrroosw333xtW5AkSZIkSZLUQgx3SWoV77wbeeXVdPukEwJlZTDwxMjYP5N1nTrB5ZcEFl2kaQGsv4/0NXFi5I4704GLgw4MdO5suEvqaEIInHRCYInFk3Z1NfwyMjDorEDv+eDiCwI77zjzviErK719370D++yd3vbwI5XcfqfhLkmSpNaUn58/fblbt25tWIkkSZIkSZLUsgx3SWpxZWWRy65IBx+22waW7gcnnBL59ddkXV4eXDQ0sHS/5gtf3Xp7pHRqsrzE4sn9SuqYCgsD5w8OLLYoXHNlYMstAv36BR64N7DeunPe7xywLyy4YLp9862Rp5814CVJktRaFltssenL00bxkiRJkiRJkuZFhrsktbhbbo/8MSZZ7tEdjjg88PsoGPV7si47C4YMCqy6yuwHLCZMjHz+RWx0KrSfR0SeeDLdHnBkICfHUbukjmzRRQN33hZYeaV0X5Cb27R+4ZLLYNSov627NPLOuwa8JEmSWsOaa65Jbm4uMUZ+++03Jk2a1NYlSZIkSZIkSS3CcJekFrfEYoHOnZLlYwYEunZJRui65qpAz55wxmmB9debs4DFY4/DEQMihx4Z+d+H9cMU114XqU0ly+usDWuvZbBLEmRnz7wvqKyMPPJoJJWaeUhr150DhYV119Wm4OxzIz/9bMBLkiSppXXu3Jltt90WgFQqxRNPPNHGFUmSJEmSJEktw3CXpBa37TaBu+8MHH5o4J+bptf3Wypw312BLTafs+BVVVXksSeS8MTXX8PUqXW3v/d+5N33kuXsLDjqCINdkho2cmRk4Ikpxo+P/D4qcsTRkSuujtx978xv169fYNBZWYS/dS/l5XDqGZFJkwx4SZIktbTjjz+ebt26ATB8+HB+++23Nq5IkiRJkiRJan6GuyS1il49A//eDsLfkhBFRXMevKqogI03gvx86NMHNlg/va2mJnLN8HSoYvvtYYnFDXdJqu/NtyMHHx754H9wzuDICy9Gvvsu2XbzrbMegWuD9QMnnVBUb/2oUckIXjU1BrwkSZJa0nzzzce1115LQUEBEydOZP/99+err75q67IkSZIkSZKkZmW4S1KreO75yB77RL74cu7DDl27Bk4cmMVjDwUGnxPqTLP21DMwYkSy3LkTHLS/wS5JDevcCSorkuVPPoXJk2H11SAvF046PrDE4rM+xr57F7Drzvn11n/4EQwbbrhLkiSppa2xxhrcfffdLLLIIvz222/suuuunHDCCbz88sv88ccfVFVVtXWJkiRJkiRJ0lzJaesCJM2brrshxXrrBlZZOfC/DyMXXhKprYVjBkauuBRWWXnuQ1dduwa6dk23S0sjt9yaDlPst2+guNhwl6SGrbpK4PDD4Nrrkn7j4UfhuKNhwBGBfv1mr+8IIXDaqZ346utKvviy7raHH4Ul+0a229Z+SJIkqSUst9xyddohBGpqanj22Wd59tlnm3zcEIIjgEmSJEmSJKndcOQuSc3unfci99wHRx0TGXphik6dIt3+CmEtsTj0W6pl7vfOuyMTJyXLCy0IO+3QMvcjad6x+66w2T/T7eHXQ23tnB0jLzcweFDW9H5uRpddGfn0M0fwkiRJagkxxun/pgkh1Fnf1H+SJEmSJElSe2G4S1KzqqyMXHlV+oPw3FxYbtksbhgeWGdtuPiCQFFR00axmTwlUlXV8Ifsv4+KPPRIun3k4YG8PEfLkTRzIQROPSlMD51WVcPpZ0VKStJ9TSoV+f33mX/BN3/vwNlnBsLfup2aGjjj7MiYsX5BKEmS1BLCX2/AmiOYFf7+Zk6SJEmSJElqB5yWUVKzuu8B+H1UstytKxx2SPLheJ8+gUsvmrsPym++JfLa67BDf+i/PXWmXLz2ukh1dbK86iqw0YZzdVeSOpCCgsDQIXDwYZFJk2Hsn3DWoMiVl8HUqTBkaOTbb+G2m2G++Rrvx9ZeK7D/vpHb7qi7fuJEOOucyLCrMHQqSZLUjNZcc822LkGSJEmSJElqcYa7JDWb0aMjd96d/ivpIw4LdO3aPEGGyVMizz4PFRVw862Rf6waKC5Otr33fuT1N5LlEODoo4J/cS1pjvTpExg8CI4/MVKbgk8/g2uujXz/A3z+RbLP2edGrrkScnIa71/23zfw2eeRDz+qu/6rr2HY8Mjxx9k3SZIkNZe77rqrrUuQJEmSJEmSWpzTMkpqNlcNi1RVJctZWdC9e/NNQzbqd+henCwvvTSsvFKyXF0dufKa9P1sty0ss7ThCUlzbvXVAkcdke4/Hn0cttgcsrOT4Oiaa9SfdvHvsrMDZ54W6NKl/rZHH4cXX3J6RkmSJEmSJEmSJEmzz5G7JDWLt96OvPlWup1KwalnwDVXRlZdZe7DVssuG7jvbnjrbSgsZPrIXA8+DL/+muzTpQscepDBLklNt8vO8O338PIrMPCYwL+3T8KqC/ZJwl2zY775AqecCGeeUz/IdfFlkaWWhL597askSZIkSZIkSZIkzZojd0maa5WVdUfPmma9dWGlFZvvfnJyAhtvFFhrzSQU8eefkdvvSN/voQcHiosNTEhquhACJ58QuG5YoP+/kyle/71dmO1g1zSbbBzYdut0O+uvd1wVFXDGOZGyMkfwkiRJkiRJkiRJkjRrhrskzbV77oPRo+uu67sEnHNmIDu75cJWw2+IlFcky/2Wgu23bbG7ktSB5OcHll9u5n1XjJGKipkHtI4ZEFh4oWQ5lUqmd4RktMErrjLcJUmSJEmSJEmSJGnWDHdJmiuVlZHHn6wbUijuBhcNDRQVzX2wK5VqOADxyaeRl15Otwce27JBMkkd29SpkcefSPqjysrIeUMjp5yeoqam8ZBWUVHgrDMCWVnQZwHYZ6/0tudegOdfNOAlSZIkSZIkSZIkaeZy2roASZktPz+w0YaRx59I2jk5cP6QQJ8+zRO0OntQJC8vsvtugaX7JcesqYl1Rr351xaw8koGuyS1jJEjI6edGfllJNTWRp57Ab75Ntl2yWVlnHZKp0Zvu8LygfMHw+qrJWGvkgkpnnwq2XbZ5ZHll4NFF7H/kiRJkiRJkiRJktQww12S5sqbb6eDXQBHHxlYZeXmCSqM/DXy2hsQI7z8auSRB2C++QKPPwk//pTsU1QERxxmMEJSy3nksSTYBXD1MNhii3S4Kzc3maJxZjbcIN1HHXNU4PPPIz+PgPIKGDQ4cv21kJdnPyZJkjTNaaedVqcdQmDo0KGz3K+5NHZ/kiRJkiRJUlsw3CWpyf4YEzn/gnSoYZONYMcdmu/4776XBLsANtwgCXZNmBC5+Zb0fR64f6BXT0MRklrOUUcEvv0u8sWXUJuC116HDdaH9dYJ7Ldv46N2NaSgIHDuOXDwYZGqKvjue7juhsixR9uPSZIkTfPYY48RQvL+KMbYaNhqxv2ay8zuT5IkSZIkSWoLWW1dgKTM9Oe4FOecG5kyJWn36QOnnhya9YP1XXcO3HZzYMt/we67Jse9/qZI6dRk++KLwc47NtvdSVKD8vICF5wf6NMnaZeVwfc/wPrrz3l/F2Pk+++hS+f0uocegTffmvnoX5IkSZIkSZIkSZI6JsNdkubYn39GdtkdvvwqaefkwJBzAp07N//IM/2WCpx5WhYrrRj46uvIM8+mtx13TCAnx9FuJLW87sWBSy4MdP4rlDVmDJxyWoqysrqhrIqKmYe0Lr4sMmRoZHwJLLBAev3QiyJjxxrwkiRJmibGOMvpr2fcr7n+SZIkSZIkSe2N0zJKmmODh0ZqatLto44ILLtsy4asamsjl12R/qD9/zaBNVY32CWp9Sy+WOD8wXD8SZHaWvjmWzjtjFKuuKwztbWRa65N8fkXcM2VUFjYcP+06SaBp55O+rIAzN8bxoyFyZPh/AsjV1wKWVn2bZIkqWN75ZVXmnW/9uLzzz/ntdde46OPPuKHH36gpKSE3NxcevfuzWqrrcZOO+3EGmus0dZlSpIkSZIkqZ0x3CVpjrz9ToqPP063V1m5daZGfOQx+Pa7ZDk/PwmUSVJrW321wMknwgUXJQGtl1+t4rIryvh5RIrXXk/2GTI0MmQQZGfX76fWXCOw9VaRHt1h/30DP/4ERw6I1Kbgw4+SKRp326UVH5AkSVI7tNBCCzXrfu3BXnvtxf/+979666urqxkxYgQjRozg0UcfpX///gwZMoS8vLw2qFKSJEmSJEntkeEuSbOttjbF4PPT7YICuPD8QAjNF7SKMTL4/Mj66wY22RhycgJjxkZuuiU9atdBBwQWmN9wl6S2sc1Wgd9+j9x1d9K+/c4Kdt4pvb1TEcxsRp/TTk73myssD/vtC7fentzghhsja64Offvax0mSJM1Lxo4dC0Dv3r3ZcsstWWONNejTpw+pVIpPPvmEW2+9lTFjxvD4449TU1PDZZdd1sYVS5IkSZIkqb0w3CVptl18GZSWpttnnAZdujRvAOHd9+Gll+GllyNL3Qu33QxXXhUpL0+2L7kk7Lpzs96lJM2xQw4MjBoFr7wa2WWnfAYcWU1hYQqAQw+aeej179v23RvefQ+++hqqquHc8yM3XQd5eQa8JEmS5hV9+/Zl4MCB/Otf/yI7O7vOtlVXXZXtt9+ePfbYgxEjRvD000+z++67s+aaa7ZRtZIkSZIkSWpPstq6AEmZ4eefUzzzbLq94orwfxs3fxfy2OPp4W7WXgtefxPeeCtphwCnnBjIyTHwIKltZWUFzjg1cMF5nTnnrE7k5AQOPShw2MFZczyaYXY29P93MuUswI8/ws23zmToL0mSJGWcG264ga233rpesGuaHj16cOqpp05vv/DCC61VmiRJkiRJkto5w12SZsvzL6aXs7Ph4qEtcz9nnh449ODAfL1g6y2TUbum2bE/LL+cwS5J7UN+fmD77fKnh7kaC3WNG9d4UOuXXyLHnRAZeiFssXl6/X0PwMefGPCSJEnqSNZee+3pyyNHjmzDSiRJkiRJktSeGO6SNEujRkceejjdPuRA6Nq1ZbqPrl0C++4dePiBwEOPwp/jkvXz9YJDDzbYJal9q6mJ3HRLipKSSIyRu+6J7LF35MuvGg5qPft85MOPkuV33oV1/vo+L0Y474JIaakBL0mSpI6iqqpq+nJWlh/ZSZIkSZIkKZHT1gVIav+GXxepqk6WV1oR9tqz5UNWn34Gjz+Rbh93bKBTJ8NdktqvysrIoMGRN96Cd96LrLcO3H5nsu3kUyM3DIeFF67bj+23T+CllyNj/4Rx42CD9aB7d5gwAcaMgSuujpx1un2fJEnSnJowYQITJkxgypQpdUJTs2vNNddsgapm7oMPPpi+vOSSS87x7ed0evD2asbHEULIiMcVgMDMR/Rtb9przX9//etso33W3JhMqxfaR80zOwca3J+2r3lOZFq90Lo1z+nr3+hxyKznOdPqhZapuble/0aPT2Y9z5lWL8x9zS19DjR4n2TW85xp9cLs19wWr39jMu15zrR6oeGa29M50JBMe54zrt52/vq3B4a7JDUqxkhNTaSwMGmHAMce3fIfrJaVRS64OD1azab/BxtvaCcuqX375lt4+91k+bvvIC8XuhfDhImwzDLQo0f92xQVBY4ZAGeek/R5Tz0NRw+AK69Otr/wImyyUWTDDewDJUmSZuWNN97g0Ucf5eOPP2bMmDFNPk4Iga+++qoZK5u1VCrFjTfeOL291VZbzfExiouLm7GitlVTUwNAUVERRRS2cTWzlk0uqZiipjaHzjmd2rqc2ZIJNRd36V6nnQk1zyjT6oX2V/Pfz4GGtLeaZyXT6oW2q3l2Xv/GZNrznGn1QsvXPDevf2My7XnOtHqheWtuiXOgIZn2PGdavdC0mlvr9W9Mpj3PmVYvzLrmtj4HGpJpz3Om1ZtIvgvr3LkzOTlGmf7OZ0RSo156BZ57Hk46IbBDf/jkU1h2meYPGIwbF3n8ychOOwa6FwduvDkyenSyrbgbDDzWUIOk9m+VlQOnnwJDhiZBrS++hDVWh4UWgoHHBHJyGu7LNt4I1loT3v8AalPwn//CtlvD088m2y+5LLLyStCtm32hJElSQ3766SdOPPFEvv76ayD5Q6VMc/vtt/PZZ58BsMUWW7Diiiu2cUVtryCnE8XtP9eViIGYiqRiiuycDJlS05pbXqbVC9bcGjKtXrDm1pBp9YI1t4ZMqxesuTVkWr1gza0h0+oFa24NmVbvXwoyJojW+gx3SWrQxImRq6+JTJwE+x4QufSiwJ67t0yw4MGHI/feD/c9EOm/feThR9PbBh6bBL4kKRP8a4vAhIkwbHjyheL/PoRFFoHs7MZvE0Jg4LFJX1tdnUxLu8Xm8P7/YOxYKJkAV14TOedM+0JJkqS/+/zzz9l3332pqKggxtjoMP5/D3z9fUTqtgyEvf/++1x22WUA9OzZk0GDBjXpOBMnTmy+otpQCIHOnTtTmFtEbWUq88J6NW1dQBO0o5qnvf4ApaWljb/+7ajm2ZJp9UKb1Tzb50BDMu15zrR6ocVrnqvXvzGZ9jxnWr3QbDW3yOvfmEx7njOtXmhSza16DjQk057nTKsXZlpzm7/+jcm05znT6oXpNbfbc6AhmfY8Z0C9f3/95wXNPcK64S5JDfroE5gyJVnu0QOWXaZl7qe0NPL4k8lyZSW88mp628YbJVMySlIm2X3XQMmEyL33Je3HHofOnSKHHZL+AvGjjyMrrgB5ecm6RRYO7Ll75I67ku033wonDoQzzk7aL70Mm2wcnaJWkiRpBpMnT2bAgAGUl5dPD2t17tyZjTbaiIUXXpgbbrgBSD4g/Pe//83888/PxIkT+eGHH/jyyy+prKycfrulllqKLbfcstUfw/fff8+AAQOoqakhPz+fq666ip49ezbpWO36g+85lD2zv47QPM/pN+Q50LH5+ndsvv7yHOjYfP3lOdCxzfj6z0ufcTQXrw5JDYopuOwSuO4GOPzQQEFBywQKiorgzNMCd98bGTUKxo1P1nftCiccF+r9NbUkZYIjDg1MnBh59rmkfdc9UFAQ2W+fwAsvRs6/MLL+ujDkXKZP17jPXoEXX4qM/gMmTEhG8Np+O3jyqeQYl14eWWUlKHY0Q0mSJADuuusuxowZM/33xo033piLL76Ybt26AXDDDTdM39a/f3/WXXfd6bctLS3l4YcfZvjw4UyePJkff/yRkSNHMnTo0Fb7MPnXX3/lwAMPZNKkSWRnZ3P55Zez5pprtsp9S5IkSZIkKXNkzuSaklrNL79EBp8fOf2sZOSs1VdrufvKygpstGFgwJEwcVJ6/XFHB3r0MMAgKTOFEDj5hMAmG6fX3XRL5NrrU5x3QSSVgjfegptvTf/lQUFB4Nij0/3eI4/BdlvD/PMn7QkT4Iqr/EsFSZKkae677z5CCMQYWXzxxbnmmmumB7tmpXPnzuy///489dRTLLvsssQYeeqppzj11FNbuOrEmDFjOOCAAxg7diwhBIYOHcpmm23WKvctSZIkSZKkzGK4S1I9114fqa2FsjL48CNo6cGzyssjF1ycbq+/HmzuZ9qSMlxOTuCcMwPrrpNe9/4HsM9eyfIyS8Meu9XtYNdfD9b6a7CG2lq46VY45cT09lf+A//5rwEvSZKkESNGMG7cOCAJ1h911FHk5eXN8XHmn39+br31Vnr16kWMkWeeeYZnn322ucuto6SkhAMPPJBff/0VgLPOOov+/fu36H1KkiRJkiQpcxnukjTdpEmRV/6T4u13knZWFgw4suWnRrxmeOSvz7Tp3BlOOt7pGCXNG3JzA+edG1jtH7DcsnDNFYFDDgqcMDBw1eWBbt3q9nUhBI4+KpD91zu09z+A6upA/+3T+1x2RWTCRANekiSpY/vqq68AiDGSlZXFpptuOtP9Y2z8/VOPHj049thjp7evu+665imyAVOmTOHggw/mhx9+AOCEE05gr732arH7kyRJkiRJUuYz3CVpuutvjAwanG5vuw30XaJlQlaffhb5+pvI629Ennwqvf7EgYFevQx2SZp35OcHLjw/cOVlga5dk/DqDv8OdO7ccF+3xOKBHXdIt6+5NnLwQdBngaQ9cRJcM8xwlyRJ6tgmTJgAJOH4BRdckKKiopnuX1FRMdPtW2+9Nbm5ucQY+eGHH/jll1+ardZpysvLOfTQQ/nyyy8BOPzwwzn00EOb/X4kSZIkSZI0bzHcJQmAb76JPPUMTPtj5rxcOGj/lglZpVKRy66IHHJ45KxB6YDCv7aAzf5psEvSvKeoKNCpU/3+7fdR6T7w8y8id96dtA/YP9Cta7L+t9/h2ecCp5yUvv2LL8M77xnwkiRJHdeUKVOmLxcXFze4T2Fh4fQRu8rKymZ6vE6dOrHwwgtPb3/++edzX+QMqqqqGDBgAB999BEA++67LwMHDmzW+5AkSZIkSdK8KaetC5DU9lKpyGVX1g0J7L1XoGfPlglavfEW/PRzslxbm/zfpw8cf6zBLkkdx+NPRK64OnL2GVBcDKeeHimvgBBgn70ChxwEl16R9M233xm5767A1lvBs88lt7/08shdtyXBMUmSpI6moKBg+nJjUy526tSJ8vJyAP74449ZHrNr167Tl8eOHTuXFdZ1wgkn8OabbwKwzjrrsPPOO/Pdd981un9ubi5LLLFEs9YgSZIkSZKkzGS4SxLPvwBff5NuFxfDHru13P0ttSQsvxx89XXSzsqCs89oeFQbSZoXvfKfOD24de55keWXg/K/Zgp65LFI/+1hu23hsSfhxx+hrAxuvCUy4IjAO+9GJkyAMWPgplsixx5t3ylJkjqeGUfrmjp1aoP79O7dm3HjxgHw448/zvKYJSUl05erq6vnrsC/efHFF6cvv/vuu2y//fYz3X+hhRbi1VdfbdYaJEmSJEmSlJmcllHq4KZOjQy/vu5fOR92cKCwsOXCAuXl8P0P6fZ++8BKKxpOkNRxrPYPWHLJZDmVSsKuffvCAvPDtVcFunQJZGcHjh2Q7huffQ5GjYbjjkmve/hR+PIrp2eUJEkdT9++fYFk1K7GRuVaZpllpu/z3nvvzfR4o0aN4tdffyWE5L3WjKN4SZIkSZIkSW3JkbukDu7u+yITJ6Xbiy0KW2/VcvdXWRk5d0hk2h9Br7A87LePwS5JHUv34sDVl8PAEyPffZ8EvH7+GY48HBZaKN0nrvaPwCYbR959D/beM7DE4rDM0vDiS/DW2xAjXHRJ5JYbITfXvlSSJHUcSy21FDk5OdTU1FBRUcGvv/7KIossUmef1VdfncceewyA0aNH88wzz7DNNts0eLyrr74aSIJgIQSWnJbEbybffvttsx5PkiRJkiRJHYcjd0kd2NixkQcerLtuwFHJaDEt5aphkZ9HJMuFhcl0jDk5BhIkdTzdugWuvDywbDKgBDHCtdfBE0/VHYnr8EPh3jsD++8byM8PhBA44bhAUVGy/aef4Z77Wrl4SZKkNlZUVMSKK644vf3uu+/W2+df//oXeXl5hBCIMXLOOefw7LPPEmP6/VZpaSnnnnsujz/++PRRu7p06cKqq67a4o9BkiRJkiRJmh2Gu6QO7OZbI1VVyfIKy8Pll8A6a7Xc/V1zbYonn0q3jz821BmhRpI6mq5dAldcGlh+ufS6Sy6LPPp48oXjyF8jxwyETz6re7vevQOHH5ruP++4K/LLL07PKEmSOpYNNthg+vKrr75ab3uXLl3Ye++9p4/GVVpaygknnMB6663H7rvvzo477sh6663H/fffD6RH7dpnn33Iy8trtcchSZIkSZIkzYzhLqmD+v6HyHMvpNtHHRFYa82s6X+p3NyefyHFAw+l21v9C7ba0mCXJHX5K+C1UnrgCS6/MnLTLSmOGRgZOxbOGxp56ZW64a3+2zP9NtXVcMnlsc4oFJIkSfO6bbfdFkhCWW+88QajR4+ut8/RRx9Nv379pge3YoxMmDCBTz/9lK+++oqqqqrp2wBWXnllDj/88FZ9HJIkSZIkSdLMGO6SOqjrbohMywBstCGsvFLLBa0qKiKXXZFud+kCxx9nsEuSpunUKXDZxYFVVk6ve/Bh6NolWS4ogAXmT5a//yFy/Ekp/hgDp5wUyMlJ1n/yKTz/YuvWLUmS1JYWX3xxrr/+eq666iouu+wyUqlUvX0KCwu58847WXPNNacH4acFuUII05djjGy88cbcfPPN5Obmtt6DkCRJkiRJkmYhp60LkNT63v8g8v4HyXJ2NnWm9moJl18VKa9ItwcPgsJCw12SNKOiosClF8Epp0e+/gYuvySw2KJw+lmRww4JrLRi4MGHI8OGR1IpGH595Lxzs9hz98iddyfHuPa6yPrrQteu9rGSJKlj2GSTTWa5T/fu3bnrrrt49dVXeeaZZ/j0008ZP348MUZ69erFP/7xD/r378/666/f8gVLkiRJkiRJc8hwl9TBxBi54ab0tF0F+VBW1nL39+TTkWefS7f33hPWXN1BAyWpIYWFgYsvgF9GwjJLJwGta65Mjy6x8kpMH3Xxrbdh9OjIfvsEXnolMno0TJwIN9wUOekEw12SJEl/t+mmm7Lpppu2dRmSJEmSJEnSHDFhIXUwb7wJ336XbkfSU301ty+/ilxxVTpIts3WcPihdjuSNDMFBWF6sAvSwa4/xkSWWRq23grWWQuOGQB9+gTy8wPHH5ve/8mnk/5XkiRJkiRJkiRJUuYzZSF1IKlU5MabU3XW7bl7oLi4+Ud4KSmJnHl2pLo6aS+7DHXCB5Kk2ffDj5EDDo5cdmXkoP1h3Hi4/Ep44cUkxLXuOoGNN0r2jREuvTxSU2PAS5IkSZIkSZIkScp0hrukDuT5F6r4eUS6XdwNdtul+e+npiZy9rmRP8el7+e8wcnoMpKkOfPHmMjAEyNTpsDjT8DRx8EPPyYhrgsviYwZm4S4jhkQKCxIbvP9D/Do421WsiRJkiRJkiRJkqRmYrhL6iBqaiLXXFtWZ91BBwYKC5s/cHXdjZFPPk23V1wR8vOa/W4kqUOYrxesu3a6/fso6NIFsgKceVpg/t6ByZMj8/cOHHhAuk+/+dbIuHGO3iVJkiRJkiRJkiRlMsNdUgcxYUKkqirdXrAPbLdN89/Py69EHniw7rr332f69IySpDmTnR049eTADv3T66ZMgeWWg3/8IzJseIoddon8PCKyy06wZN9kn7IyuOZaw12SJEmSJEmSJElSJstp6wIktY6QBRMnpaa3DzskkJPTvKN2/fhT5MJL0kGC4mKYOBH6/xt693ZKRklqqqyswPHHQn5e5P6/ArRffgX7HwQlJUn76mGRyy6GEwYGjjw66Ytf+Q9su01kzTXsgyVJ0rwtlUrx3//+l9dff52vvvqK0aNHU1paSkVFxRwfK4TAV1991QJVSpIkSZIkSXPOcJfUQVx/QxnTPtNeZmn4v02a9/hTpkROPzNOv4/FF4cbhwe+/gaWWLx570uSOqIQAkcdAYWFkdvuSNZNC3YBfPA/OPQI2GkH2HZrePrZZP1lV0TuuBXy8w14SZKkedPLL7/Meeedx5gxYwCI0dFLJUmSJEmSNO9wWkapA/h9VOShRyqntw8/NJCV1Xxf8qdSkSFDI7+PStqdOsHQIYGiosDqqwV69DBQIOn/2bvv6KiKPozj39lseqdXadJEBbsgFsReEDv2AohiF0HsigiIDRC7IvYuvPbeUewIKAqIUqQIpPdsdt4/hmSzZAMhhWTD8zlnT+6duTs72dzMnbvz2xmpDcYYhl7g4eKLQrerf/wJd06ydO0KyUkubdW/8PyL27GSIiIiItvRQw89xOWXX87atWvLgrqMMRiz7feh1XmOiIiIiIiIiIhIXdPMXSKN3JKllnZt4cax8Tz0aB4ddqr95bkeedzyzbeB/ZtvMOzUXh+Ki4jUlbPPNMTEwJRpoWelyM01XHIxTJrs8p973nLEYdCundpmERERaTw+++wzpk2bBgQCs0oDvNq0aUOTJk2IjY2tt/qJiIiIiIiIiIjUBgV3iTRihYWWa8dYvF645GJ4a1YKa9Zk1uprvPW25YVyM8IccRgc0K9WX0JEREI45SRDdBRMvtfSry/M+calR0bCsUdDaiq88y4sWAhFxXDfVMu9kzUjhYiIiDQe99xzD+D6N9ZaWrRowciRIzn66KNJTk6u59qJiIiIiIiIiIjUDi3LKNKIvfs+bEyDdf/Bo4/nEx1jaNGi9gb1f/zJcs/9gVljunWDDz+GkZdbfp0fejYZERGpPccfZ5jxmGHCHYadu7i04mJ49HGLx2O49hpDxKbe3vc/wKef11tVRURERGrVX3/9xV9//VUW2NWlSxdmz57NkCFDFNglIiIiIiIiIiKNioK7RBqxmGho2tRtD70glqjI2gvsWr7cctOtlpISt9+9O6Snue0FC+H3RbX2UiIisgVduxoiIgxXXh5o4999H76e46dLZ8OppwSOnf6gJS9PwbciIiIS/hYsWAC4ZRiNMdx22200adKknmslIiIiIiIiIiJS+xTcJdJIWWv5/Q/LddfCddcaThocXWtlZ2RYRl9vyclx+y1bwu03wxGHQ1QkNGsGJw2utZcTEZEq2KOPYcAhgf2bboV//vGTlxdIW78BnnlOwV0iIiIS/jZu3Fi23aRJE/bZZ596rI2IiIiIiIiIiEjd8dZ3BUSkbsz9HmbNdo/997Wcc3btlFtUZLn+Jsvq1W4/Lg4mTzS0a2e4ZAScdKLLi46uvVnCRESkai692PDFlxa/H3w+uGC4W6axvJdegWOOtuzUXu20iIiIhK+STdNIG2No06ZNPddGRERERERERESk7mjmLpFGyFrL408GZmbp2NFgTM0H8a21TJpsWbDQ7Xs8MO5WQ5fOgbJbtjDs0UcBAyIi9aFVK8ORRwT2i4tdWw1QukqRzwdTH7BYqxm8REREJHy1aNGibLugoKAeayIiIiIiIiIiIlK3FNwl0sj4/ZbnX7IsXuz2Y2Pg7DNrJ9hq5jPw4ceB/SsvN+y/nwK5REQakmuuNGWBXAB+P0RFwWUXQ8Smnt9338Ocb+qnfiIiIiK1oXfv3oD7EtKaNWvw+/31XCMREREREREREZG6oeAukUbm6zmWRx4N7J9yCqSm1jwA6+NPLE8+FZjl5ZST4eQTDZ9/YcnN1ewvIiINRWys4bprg9v9oiK4dyoccnAgbep0S2Gh2m8REREJT506daJXr14A5Obm8t1339VzjUREREREREREROqGgrtEGhFrLdMfDuxHRsIZp9c8sGvhb5YJkwIBAP32h8tHGpYstdx8m+X0syyvz9ISXyIiDcUB/UxQIBdAbi58+x0kJrr9NWvg6mvVdouIiEj4uvrqq8u2p06dqn6NiIiIiIiIiIg0SgruEmlEvvvesnp1YP+0UyApsWbBXWvXWW64yVJU7Pa7dIbbbjF4PPDwoxZrISMDfvnFYoyWaBQRaSiuutwQFxeclpcHhYWB/fkLYPxEi9+vgVAREREJP/379+fCCy/EWsuvv/7KTTfdRElJSX1XS0REREREREREpFYpuEukEZk6PbAdHQXnnVOzYKu8PMt1N1jS0t1+aircNcEQF+fKPWGQoW0biIiAi4YrsEtEpCFp1swwIkTbXFQEXm9g//sfQGOgIiIiEq7GjBnD8OHDsdbyxhtvcMYZZ/D111/Xd7VERERERERERERqjXfrh4hIOJj3q5+VKwP7Q06nLAirOkpKLLfdYfnrL7cfFQkTxxtatXJlGmM4+EDoux8sWAg7tVdwl4hIQzN4ELz/ISxaBPvvB38uhk4d4YLz4PKr3DHp6fDLPNh3n3qsqIiIiEg5119//TY/p127dqxatYoFCxYwfPhwEhMT2WWXXWjatClRUVHbVJYxhgkTJmxzHUREREREREREROqCgrtEGokp0wLb0dFw7tk1C7Z6+FHLN98G9q8fa9i1V8Uyo6IMe+1Zo5cSEZE6EhFhuO5aWL8B+u5n+He1pUkqxMYaBh3n58233XFTplmengGRkQrUFRERkfo3a9YsjNn2fokxBmvdctNZWVl8991321yGtVbBXSIiIiIiIiIi0qBoWUaRRmDFSstfywL7Z5wO0dHVH6B/+x3LS68E9i84Dw4fqAF/EZFwtHMXQ9/9XBveto0hNtZtjxhuSEx0x6xYCa++7ra//8GSm2vro6oiIiIiNWaMKXuIiIiIiIiIiIg0BgruEmkEXnrFsunLyey3T81m7fr5F8vd9wUG9Q8dABecFyhv9puWJUs16C8iEu6Skw0XDQu070/MsLz7vp/RYy1XXmPJzFRbLyIiIvXHWlsvDxERERERERERkYZGyzKKhLm0NMv77wf2LzjfEBVVveCulassN95iKSlx+z17wo1jDR6PK2/FCsv9Uy1+Pxx3jOXqK6v/WiIiUj+ysizTHrQceoihuDgwgFlUBBPvAmvhjz/h/mmW225WGy8iIiLb3yeffFLfVRAREREREREREWkwFNwlEuZee8NSVOy2d98Ndu1VvYH4rGzLmOst2dluv0VzmDjeBC3v+MjjgcCvtesgMrImNRcRke1t0R+W6663pKXDTz9ZJk+CJqmQlu7ySyer2Kk9XHmZArtERESkfrRt27a+qyAiIiIiIiIiItJgaFlGkTC2bp2fF18O7J85pHoD8T6f5eZbLStXuv3YGLhrgqFZ0+DyLhlhOLA/eDxw+UiDMRr4FxEJJ+3agtnU+1u/AZYsMTzykCEmJnCMMTBsKKSmqo0XERERERERERERERERqW8K7hIJYxMnQ/GmWbtSkqFf3+qVM3W65aef3bYxcMtNhq5dKw7qt29nmDjew/PPGDp31qC/iEi4SUw0jL7G0KIF3HOX4ZijDW1aG6bc69p/cLN33XEnLFhog55bfglHEREREREREREREREREdk+FNwlEqbWr/fz40+B/UMHgMez7QFXb71tmTU7sO9m59pyOe3bKbBLRCRc9T/A8OKzhv33C7Tlu/bycOaQwDHFxXDNaMvC31xA1++LLEPOtvz2uwK8RERERERERERERERERLYnBXeJhKlJdwe2vRFw6SXbXsbC3yz3TgkM1B95BJxxei1UTkREGrTo6IpBusMuNLRpHdjPz4dRYywffuxn1BjLunVw1SjLvF8V4CUiIiLbT1FREaeeeioDBw4se3z88ce1UvYXX3zBYYcdVlbuGWecQUlJSa2ULSIiIiIiIiJVZ63F5/Ph8/mwVuMQIpvz1ncFRGTbbdjg5/sfAvtHHAHR0dsWq7l+veXGmy0+n9vv0R3GjDIYEzzg//Cjftq3NxxzVPVmBhMRkYZvw0bLP//A6FGGq68N3DTl5sKjj7kgYoCYGGjSpH7qKCIiIjummTNnsmDBAgCMMZx88skcdthhtVL2wQcfzNy5c3nqqacAWL16NS+88ALnnHNOrZQvIiIiIg1Mcb57hIvIWPcQEWmMNmuTjQHrKXLb+Tk0yPgutctSjxTcJRKG7p9G2QXN44HLRm7b8wsLLTfcYtmY5vZTU2HCHabCTC6/L7K88JKLlH7zLZh6H8TGKsBLRKQx+eJLy+R7LL4SePpJw8EHwRdfuryICJg0AWJiDLfcZrnxesNO7XUdEBERke0jPz+fxx57DGMM1lo6derEbbfdVquvMWrUKObMmcOSJUuw1vLQQw8xZMgQIiMja/V1RERERKQBKM7H5KcDDTFiYHPG1VJBBLK5cAtSBAXESGibtckGMBS67bzc+qtXpdQuS/1ScJdImElLs3z1dWD/wAMgKbHqs3ZZ65ZiXLTI7Xu9MP52Q4sWFQfrX3rFlgWRtWiuwC4RkcbG57M89oQlM8vtj59oGTsGvp0LRUVQUgJLlxqOPsow43EqzO4oIiIiUpc++OADcnJyANcPueaaa/B6a/ejLK/Xy0033cS5554LQEZGBp988glHHXVUrb6OiNSScBvM1ECmiEgDZDG+wvquxFZZb3R9V0EaqrAKUgQFxMiWBdpkYyDCRrntksIGd4qrXZb6puAukTDzwksWv99tR0Rs+6xdr71hefe9wP5VVxh67x56sP7GsYbOneDV1yyXXaoBfRGRxsbrNdx8I4wYaSkpgXm/wpdfGc4+E2bMdHdODz1qObA/JCS464DPZ/F63fa/qy0//QyDjtM1QkRERGrfW2+9BbjArl69etXacoyb23fffenTpw/z5s0DYNasWQruEmmowmowUwOZIiIiUlfCI0gRFBAjjYeNiAQTUd/VkB2YgrtEwkhammXW/wL7E+6A1q2rPmvXTz8X88CDgQ+/Bh0PgwdVPiAfHW04/1wYcppbkktERBqfHt0NF54Pjz/prg+PPWF5cBq89z6sWQvp6fDU05bLLzUsWGgZd6dl4nhIToKrrrFlx5x7tmb2EhERkdrj9/v5+eefy/oXRx99dJ2+3lFHHcW8efOw1vLDDz9grVXfRqTBCo/BTH90ggZ/qsFai8/nK9sWkR2H/v+l3s6BcJoZ1ERAGPSDRBonA758yCup74pUnWYSblQU3CUSRl582VK4qc+2S0/o17fqHzSv3+Bn1JhsSjZdb3bbFa6+omrPV2CXiEjjdtYZMPc7WLAQfD4YPxGGD4Vxd7r8116HnXf2c98UyM93QV29e7vgL4Bnn7ccebihVat6+xVERESkkVm8eDH5+W6AxRjDwIED6/T1BgwYwKRJkwDIz89nyZIldOvWrU5fs7Zt2LCh2s+Nj48nNjb0B74bN26s9uBabGws8fHxIfPS09MpKan4obgxhuLiYgAyMzNDvnZ0dDSJiYkhy83MzCx7/raKiooiKSkpZF5WVhZFRUXVKjcyMpLk5OSQednZ2RQWVm+ALiIigtTU1JB5ubm5Zf9D28oYQ9OmTUPm5efnk5ubW61yAZo1axYyvbCwkOzs7K3//fPTMfkZbpmWcpqkJOPxVPwCZFFRMVmblnetjpSkxJDLwfp8PjKysrf4XH9UMUTkQsR/FfKSEhOJioqq+By/n7T09GrXNzEhgejo0LNjbNi4cesFmAjAD5u97/Fx8cTGxoR8ysa0tJq1EXFxwVUwYD1FpKWnk52dtXlVQvPGQmSgfmojnHBsI5o3bx4yvbSN2CbFBW4AFmiSmlpJG1FE1raWW05KcnLlbURm5rYVVu48TkpKqryNSEurVl0BEhMTK28jqtOP2PQe11YbYQzkx7l+Q15eLjExFduIUunpGZT4t3Fw3URAcT4xHh9JMaGHRzOyssuCi7ZVZGQkyYkJIfMys3O2ue3xR8VDvofI4ojwaiN8hTRNCt2vzM8vIDev8jZi83Ng81OnWSVtT2FhIdnVvd6bCJokRBNRUsDmM4MWFRWTlV2DfkTyFvoRmdVre2xEFEkJcUR7K47b+f1+0jK2se0pJzEhnugQbQ/AhrTq90/iklKIqSS2pPy9RlXuA8qrzr1GVagf4dRLP6KgkIJC9/7m5OZUrR9YTrMmoetbWFREdk71+ydB9xq+AkxRLmAbZBsBkJSYQFRUJDYiyl37NgV3Nbh+xCaln0eECvDd3p9HVMW2tBGV/Q9Vl4K7RMLI4YcZVq2yfDUHLjzfVPlbxD6f5dox2axf7xq/Jqlwx22GyMiKzy+/3JaIiOwYvF7DTTfABcMseXmwciV8/Q3sszf88COU+OGNWVB6n5GRCb/+Cr13h98XwcTxhlatdO0QERGR2rNs2bKy7ZiYGDp27Finr9ehQwdiY2PLPqBeunRp2AV31aS+kydPZtiwYSHz9t9/fzZWJSgkhDFjxjB27NiQeccccwx//vlntcodOnQod999d8i8s88+mzlz5lSr3EGDBjFz5syQeVdccQVvvvlmtco94IADypYZ3dy4ceN48sknq1Vu9+7d+fbbb0PmPfDAA0yePLla5TZt2pQlS5aEzHv++ecZM2ZMtcoFKh1MeO+997jwwgurXe7SOW+GHMz5bt5Cjj/vimqX++2bz9Cza6cK6Uv+XknfQedWu9y3nnuY/vvtVSE9fWM63fY/strlPjVtAoOPDr2Ebbfe+1W73Mm3jmb42aeGzNv/4CPYmJ5RrXKvu3wYY6+4KCjNAIZCDj1yEL8v/qta5YZdG7H/vrz12gsh88bdcitPPv18tcrt3m1nvv30/ZB5D9w3lcn3P1CtcuuyjUivJLixpm3Ekrkf0KxpxTbih+9+4vizL6l2ud+8+yI9u3apkL50yV/0O+aMapf75ptv0r9//wrpaWlpNbrez5gxg8GDB4fMq1E/Yju2EaWOPfF0/lj6d7XKHXrWKdx705Uh88667Abm/DCvWuWecOQhPD3ljpB5V9x8F//74PNqlRt2bUSTVJZ+9yGhllB+/rlXGXN76Pa5KtKXfB8y/f33PuaCK26odrmLv/+E5qmJFWYG/f7HX+qkH7F0yd816ke8+fxjHLhvnwrpaRmZ7HzAoGqXO/P+cQw+akDIvJqUO/nW0QwbPiJknu41HN1rOM+9/g7X3nF/tcoFyFj0Vcj09z6dw/lX31Ltcivea7iZhBtqG/HW09M4cN89XHBXuZnG0jZurNE9wYxHpjH4uGNC5tWoHzH+Voadf07ZlzyICgR2h3sbUduzUCq4SyRMFBdbPvrYcskIw0XDoWOHqj/3kcctP/7kIl0jIuD2Ww3NmoWK6rdcfpWlS2fLRcMMSUkaqBcR2VG0bWO44Tq46VbX2fz0Mzj/XPhlnpvNa9EfcMlF8OwLkJPjArzMCrjhOthnb10vREREpHZlbprtwhhT6Uw/ta158+asWLEi6PVFROpESXHIpSU3n5FsW5kSX50sWbnlcmswYOEvqVCuMRBho2pWbrjx+zD5lcyiUIO/p/GXVFqu8YXJ8mO1xJQUhj6HS6o3+0pZub5K/pd9NSs33GzPNiJQrNoIoIG2EZbKllA2JdWbFa3s+ZX8vjUtV0Sk0So305jJz6hRUaYop/LrUU0U52Hy03CjTKXtfOhZBHd0FeehFZEG6dPP4MWX4cxzLa+8Zqs8a9fnX1hefClwo3PxRYY9+oR+7ptvuSW5Zr8J5w+zFBXtQDdIIiLCIQcbTj05sP/cCzDw0MD+67Nh0p2QsGmG+fQMmPYgLPs7+HqRla1riIiIiNRMVlZW2XZtT2NfmZSUlLLtbV76SUREpLqsH+MrDP3Y1mXngsq1lZZLTcoVke1ObYSIiFTfpva+lr5IEvK6URvllhSiaQS2TMFdImHAWsvLrwUGydu3q1rTtmKFZcJdgecdflgUZ5xe+XP/XBI4dtBxhqgoNaEiIjuakRcbdunptn0+mDcPSsdT//vPLdM45R4TCPBKhyuvsWUBXrm5lmuutYy90ZKfrwAvERERqZ7yX2jaXoFWCugSERGpX9ZafD4fPp+v1pexERER2SbF+ZCXFvyoyYyPJUUVyysrt6gG5Ra7Mop3rFk5RXZExtZyD7myNdEbG2NM2Tc6MzIydKMhdSY9w/K/N2GXnpY334Yff4RXXzIkJm458Covz3LRSMs//7j9Th09vPR8Mj5f1hbP12/nWt6YbZlwhyEyUsFdsn2pbZVw0pjP17XrLBcOt5ROmNGhAyxf7rYjI+G5mYbsbLjqWktOjktPTYX77oapD8C8X11an94w7X6Dx6PrSX1qzOeqND46XyVchNO5ur1mvaptL7zwAuPGjQMgOTmZ7777rs5fc7/99iMzMxNjDDfddBNnnXVWnb9mbVqyZEm1nxsfH09sbGzIvI0bN1b7HI+NjSU+Pj5kXnp6OiUlFWeFMMaQnJwMuOUxQ712dHQ0iYmJIcvNzMykuLh6gy5RUVEkJSWFzMvKyqKoqHqDLpGRkWW/0+ays7MpLKzeN50jIiIq/R/Pzc0lP796AzzGGJo2bRoyLz8/n9zc3GqVC1S6zGphYSHZ2dlb//vnp2PyMyp867xJSjIeT8XvMRcVFZNVetNSDSlJiXi93grpPp+PjKwtB4T6oxLcrCh+X4X6JiUkEBUVWfE5fj9pGdVfFjYxIZ7oqNBLmGxI2/rn9pXVOT4ujtiY6JDP2Zhe/etgbEwM8XHBbY8xkJiQSFp6JpnZmVtdec1GRGNjUyA28L/Q4NuIzc5jr9dLSlLo+mbn5lW/jfBEkJoSur65efnkFxRUqRx/VAJEREGEO2eNMTRt0iTksfn5BeTmVb+NaN6sKfHx7ttkubk5ZX//wsJCsrflf9lEQHE+pjgPU1LYINuI8jY/j5OSkogK8b/s9/tJS6v+ckiJiYlER4f+X96wYcO2F7jpXE6IjqiVNsIYSNj098/JzSEmumIbUSo9I4uSbZxdqrSNi4n0kBRT8e8GkJGVjc9XvSX+IiMjSU5MCJmXmZ2zzW1PaX2jIiAlLnTb3hDaiPL8UQkYLM1SEkPO5JJfUEhuXl6lz9/8HNj81GnWJHS/p7CoiOyc6rU9/qgEmqQkEYG/Qp0bShtRnj8qgaSEeKK9pkJ967sfUZm4pBRiUttAXMVrR/l7jarcB5RXnXuNqijrR+SlYfLTKb+8bGZWNsXF1WwjorbSRhRVr38SGeklOSkJG5ta4T1u8PcaeWluOb5N53JBYSHGuOt1qDZga+qijYDAvYY/OtHNuOp3M001xDYCAvcam9e3obYRpfcaxkBcYhNsfFNy/O51tvfnEVWxLfcaXbt2rdZrVEbBXdUUTh/kSnh76FE/L7zolsC64lLDwQdBfPzWB8nvnOjnvQ/cdkwMvPx8Mjvv7NX5Kg2a2lYJJ439fP3ue8vosRa/3+03bQIbN31+eEA/uGuChz/+sEEBXk1SYfAJMGOm279xrOHooxTYVd8a+7kqjYvOVwkX4XSuhmtw12effcYll1wCuPf7008/pXXr1nX2emvWrGHAgAFlM4Y9+OCDHHrooVt5VsPSWD6TC6f/L6l9W/37bzYA1JBtPpgSDhpCnUuDuwCyc7K3HtzljcbGNgk5YNxghdF5DOXPixLKD3DXBQPEx7lBuNy83Gq/mo2IBlsSNv9/YXkeQ62fy9v6/7+tGkIbty3Crb5Q8zrX9TkQSri9z+FWX6h6G9fg7gPC6HrdWK4j9dEGbItw+/8Lt/qGCu4Kd7X9mVzo0HQRaRDSMyxvzHLbOTmQklK1wK73P7RlgV0AY0cbdt5Z/+4iIlJ1++1ruGQEPPiwu4PamOY619bCnG/cTI999zdMuScwg9eF5xsGn2Bokmop8aPALhEREam20m83lgZbffHFFwwZMqTOXu/LL78E3HJQxpha/3aliIjUDRsR6WZpku3A1vnAoDEQYd1gnikprHYsmY1oHAOCIiKNQVWv1aVL85ZuS9WpPySyY1C0h0gD9tLLltKZb3t0h777b/05K1Za7r0v0OkZdDwcfljFKadLLVho2aUnRERoAF5ERIINOQ2W/gUffFgxb+oDlr32hB49XIDX0r/guGPdtWTwCbqmiIiISM20a9eOli1b8t9//wHw2muv1Wlw12uvvVa23axZM9q3b19nryUiIrXJgC8f8qq3rMp2ZyIgDGZOEBERqT1Vu1YbA9bjljg2+du+JF+tCrvrdZj1hyAM32OR+qfgLpEGKiPD8vobgf0LzjNl31iuTFGR5bZxlvxNAWGdOrqlHCuzZKnlsiss3brDmGuga1cNxouISIAxhjGjYMUKy6I/3KxdpbN3rfoXXn4VzjnLBXj16LHlsoqLLeMnWE460dB7d11vREREZOsOO+wwnn/+eQB+++033n77bY477rhaf5133nmHBQsWlN1zH3HEEbX+GiIiUod8BZiiXOp6ycDaULpcoEh5mnFFRBq9KlyrDWDYtDxfXu72qVclwvJ6HUb9IQjT91iknim4S6SBeukVS8GmgGVj3IxcB7DlwfBHHrMsXuK2o6Lg9lsNMTGhn1NSYrn7Xrds1qJF8OgTlnvu0mC7iIgEi442TLgDhl9i2bCBoG9MPf2s5cjDoUWLitePrGzLuPGWkRcbOnaAOydZPvkMvppjGXcb9O+na46IiIhs2emnn87zzz+PMQZrLePGjaNr165079691l5j8eLFjBs3ruw1jDGceuqptVa+iIhsL3W/ZGBt0HKBEppmXBGRHcGWr9W1tTRvbQjf63V49IcgnN9jkfpT+VptIlJvMjIsr70e2LcWUlK2PAj+9TeWVwKrSHDl5YbOnSp/jrVw8EGG6GgXCHb1FRpkFxGR0Jo3N9w90RAbC2cOgW5dXXpBATz4SMW77Kxsy1WjLHO/gyuusvzwo2Xery6vqAiWL9+OlRcREZGw1a1bNw4//PCyoKusrCyGDh3K/Pnza6X8+fPnM3ToUDIzM8teY+DAgfTY2pSkIiIiIrXNV4DJT8fkp4XFg8IsKAmPAAIRERGRxkAzd4k0QC+/Gpi1C6BJEzjs0MqP/+8/y8RJgcH1AYfAoK2sVOH1Gs46wx3755/Qtq2Cu0REpHJduxqef9rN0rXwN8vFl7rrziefwgnHW/bcI3AdWbcWVv/rtjMy4dZxcONYePhR6NvXBYiJiIiIVMUNN9zA3LlzycnJwRjDhg0bOOOMMzjvvPO46KKLSElJ2eYyMzMzefzxx5k5cyY+n69s1q6EhARuuOGG2v8lRERERKpEM66IiIiISGiauUukgcnIsLz2RnDaSYMNkZGVL694xwRLZpbbb90KxowyGFO1YK02rQ0DDlFgl4iIbF3p8ou79jIcc1Qgfco0i88XCDLu2tUw9X5DcpLbz8mBOybA5ZfCpRdX/RolIiIi0rp1ayZPnozH4z7CMsZQUlLCU089xUEHHcSoUaN49913WbFixRbLWblyJe+++y7XXnstBx10EE8++WRQYFdERASTJk2iTZs22+PXEpFKWGvx+Xz4fD6srce1eEREREREREQaEM3cJdLAvPyqJT8/sB8VBYMHbel4+GWe246IgFtvNiQmatBcRETq1sUXGT7/0pKXB8v+hln/s5x6cuD6072b4YEpcNUoS1o65OXBzbfBpDth770C5fj9ljffgmOOhqgoXb9ERESkogEDBjBx4kRuvPHGoICsoqIi3n33Xd59910A4uLiaNKkCYmJicTFxZGXl0dOTg5paWnk5uaWlVcaMFJajtfrZfz48QwcOLBefj+pqKSkBGutgnvqWnG+ezQgxoD1FLnt/ByCTgETAWEyo42IiIiIiIhIbVJwl0gD06uXISHBkpPj9o88AlJSQg92L1lqeeyJwKdcQy8w7Nqr8oHx9ev93Hu/n4uGQ2olZYqIiFRFfDw0awor8tz+Q4/AYYdaUlMD15fOnQ3Tp8FV11j+Ww8FBTBmrGX8OOjX1x33yGOWF16Cz76AO8dBQoKuTyIiIlLRoEGD6NSpE1dffTWrVq0qmwm0fPBPbm5uWRBXaeBWKOWf27ZtW+6//3523333Ov4NZFtYa7HF+Zj8NMImvisy1j3CSXE+Jj8daDhvsgEMLoDL5OUG5dmIaLAl9VArERERERERkfql4C6RBqbrzpCfF9g/7ZTQg9yFhZY77rT4fG5/t13hrDMqL9day/gJuXz8qeWrr+GGsYGBdRERkW0VGQndusGKlW6/uBgeeNByy03B15ad2rsAryuvtqxZC0XFcMPNlttuhtRUeOEld9xPP8Obb8OZQ7bzLyIiIiJhY7fddmP27Nk88MADvPrqq+TluZvnypZ8DpVeOhtUXFwcp5xyCpdffjmJiYl1Wm+ppqI8F3gUBtFdNiLKzSoVbsFdAFhMA5oNyxiIsFFuu6QwKO7MRkTVU61ERERERERE6peCu0QamNdnWUr8bnvffaBTx9AfUj/2pGXZ3247NhZuusEQEVF5sNbvi0r4+FM3rX1mFiQk1Gq1RURkB+PxGG66HtatsyxY6NI+/BiOPsqyz97B16M2rQ0PToMrR1lWrgSfD2693TJtiuGykYbpD1kOHQBDTquHX0RERETCSkJCAtdffz2XXXYZL774Ih9//DG///47vtJvPm2B1+tll1124bDDDmPIkCEkJSVthxpLjdiGFXhUGRsRBb58yAujWaW0xKGIiIiIiIhI2FBwl0gDkpdnefOtwP7pp4YO1vrpZ8vLrwT2r7zc0LbNlmfh6rWLlxmPJ3HTrVn06wu776ZZu0REpGa8Xhe0NfEuy3sfuLR77rM88xRERwdfZ1q0MEyfAleNsvz9DxxwAPTaBXrvbujcCfr0dgFjIiIiIlWRmJjIRRddxEUXXUR+fj7z5s1j2bJlZGZmkpmZSW5uLvHx8SQnJ5OcnEznzp3p06cPsbHhOLOShAVfAaYol4a0xOGWaIlDERERERERkfCh4C6RBiAryzL3e8jKtOTkuLSOHd3MXZvLzrbcOSnwQeGB/eHYo6v2OvvtG8kzMzzYMFjSQEREwoPHY7j0EvjmW0tmFvy7Gh6fYWneDE49OThgq2lTwwNT4NkXLCOGGbxel7fvPqGDuv5ZbunYQQFfIiIismWxsbH07duXvn371ndVZIcXHjONgZY4FBEREREREQknnvqugIjAy69axo23TH84kHbaKQZjKg5o3z/V8t9/brtJKoy5NvRxlYmNNcTGaqBcRERqT0qKW16x1EsvwwMPWibdbSkpsRWOvXykh6io4GuRzxd87KuvW869wPLW2wpIFhERERERERERERERkR2XgrtE6llWluXV1922z+d+piTDkYdXPPaTTy0ffhzYHzvGkJpSeaBWbq6lqEiD4iIiUveOOhL22jM47d334NbbLYWFW74WWWu55z5bduzX31imTbf4/XDXPZbPPte1TERERERERERERERERHZMCu4SqWevvGbJywtOO3EwREcHB23995/l7vsCg9snHA/9+m55Bq6777MMvcjyx58aFBcRkbpljGHU1YbIzRb9/vxLuGa0JSu78mvRY09a3n7XHXvtdZZOHSw9e7i8Pr2hn1ZYEhERERERERERERERkR2UgrtE6lH5WbtKRUbCiScEB235/ZYJd1lyctx+u7YELX8VyudfWD7+BP7+By66xM+qVSW1WHMREZGKdmpvOO/citenX+fDyMst6/6rGOBlrYVyyb/Mg5tuhVtvgtNOgYnjTYWAZxEREREREREREREREZEdhYK7ROrRK69ZcnOD0444HJo0CR7Env0m/PiT247wwM03GmJjtzzQXVQMcXFu+7hjDe3aRdRWtUVERCp15hDo2KFi+j//wIiRFWeTNMYwYriHKy4LXNeWLIWrR8PJJxoSEyte73w+zUgpIiIiIiIiIiIiIiIiOwYFd4nUk6zsirN2AZx2SvAg9r+rLQ89EhjEPvcc6LXL1mcwOeIwwzNPGY4+Ei69RDOeiIjI9hEVZRg9Kvi6490UX7xhA1x6heWzzysGZ512iuGWmwwRm45dvRpGXGr57ffgYz/93HL+MMvatQrwEhERERERERERERERkcZPwV0i9eTVELN27bM3dOkcGBD3+y0T77IUFLj9rjvDuWdXPVCrVUvDjdd7iI9TcJeIiGw/vXc3DB4U2I+JhaQkt11YCDffZpn5jHVLMpZzxGGGyRMNsTFuPyMDLr/K8vkX7riff7Hccad1s4BdavlnuQK8REREREREREREREREpHFTcJdIPcjOtrz6WmD/vHNg333g9FODg7BmzYZ5v7rtiAi4YawhMlKBWiIi0vCNvNjQqqXbzsmBXXpCx46B/CdmWMbdaSksDA7Q2m9fw7QphtRUt19U5ILBXnjJkptnMZsug8lJ0LRJ3f8eIiIiIiIiIiIiIiIiIvVJwV0i9eCV1yw5m2bt6tIZhl5guO9uD/vvFwjc+vdfy8OPBQa8zz/X0HXnygO7rHWzoPz3n2YxERGR+hcXZ7j+usB1a+53cOYQ2H+/wDEffexm5lq32bWrZw/DYw+ZsmAwa+GDDy377m2Ycq9h5y5wz12GxEQFPIuIiIiIiIiIiIiIiEjjpuAukXqQmmpISXbb559r8HiCB6f9fsvEyYHlGLt1hXPO2nKZ77zrZkE59wLL+x8qwEtEROrfXnsaBp8Q2H/oERg7Bk47JZD2+yK4YJjlm2+Dr12tWxsefsCw157QtClMnmSIjjbsvpthxuOGFi0U2CUiIiIiIiIiIiIiIiKNn4K7ROrBSYMNr7xoGD3KcPBBFfNfnxVYjtHrhRuvN3i9lQ9i5+VZpj/sBsVzcmHpUgV3iYhIwzByhKF1K7edkQFTH4ArLvMwepTB63XpWVkw5nrLQ4/68fkC17DERMM9dxkemmZoWS6Ya/OgaIB5v1oefdyP369roIiIiIiIiIiIiIiIiDQeCu4SqSdTplmsH4qKgtNXrbI8Um45xgvOM3TpvOXZSeLiDPffY2jf3i3zOHyoZjMREZGGYfPlGT/7HD793HLC8YaHHggEfgG88CI8/GhwcFZkpKFt24rXtbfetvzxpzv2r2WWsTdYnn0exk+wFBcrwEtEREREREREREREREQaBwV3idSD3xdZ3n0f7rnfcsbZlqIiNwjt91sm3W0pLHTHde8GZ51RtTJ79jDMeMww6U63bJWIiEhDsecehhMHB/bvu9+SlmbZpafhyccNB/Z36ampcMaQrV/DvvraMvley8jLLR99YnnrbUtOrsub9ytkZtX+7yAiIiIiIiIiIiIiIiJSHxTcJbKdZGVbFv7mgrj+91ZgRpH994OoKDeQ/dY7my3HOHbLyzFuLjbW0Lq1ArtERKThueQiQ+vWbjsjE+6cZPH7LUmJhgl3GK64zHDLjYZmTbd8HSsutkx9wGKtm/3y9jssUVFw8kmQkAD3TN56GSIiIiIiIiIiIiIiIiLhQsFdItvJy69YLr7Ucs1oP8ceDaOudssonnqKG4Bev97y0COBoK9zzzZ03sJyjHl5ltxcLTslIiLhIS7OcONYg2dT7/O77+G11922MYbTTjHss3fF694LL1n+/idwvYuMNEy939Clc/lj4N9/4aEHoHMnBXaJiIiIiIiIiIiIiIhI46HgLpHtICvL8uqmAezvf4D0dMOJJxheeMbQuZPBWst9Uyy5m5aU6tQRzjlry2VOm245f5hl/gIFeImISHjo09sEXd8efsyyeEnl17FvvnWBz0Mvsrz2hsVad2zbNoaHpxsOPihw7Nzv4MZbYPny4PL+/mfLryEiIiIiIiIiIiIiIiLSkCm4S2Q7eOkVS16e2965CxzY320b42YX+ewL+GpOaRpcN9oQGVn5zCNffGV5+11YswYuuzJ4RhMREZGG7ILzDLv2ctvFxXDbOEt+fsXrmM9nuX+aSy8qgt9+t2XXTXAzgd1xm2HYhYG0lSvhopGWOd+45637zzJqtOWyKy0//axrpYiIiIiIiIiIiIiIiIQfBXeJ1LHMzMCsXQAXnG/weAID0VlZlvunBgacTzkJdu215SWlvBGQnOS2jzoCOnXUElQiIhIevF7DLTcZ4uPd/oqVMPWBioFXXq/h3rsMPbpDixZwzZUVr3Uej+H8cw0T7jDExrq03FwYe6Plmef8jBtv+W895OXBbXeEDiITERERERERERERERERacgU3CVSx156xZKf77YTEiA2JrCsFMD0hy3p6W67VUsYPnTrgVoH9DM8/ZThuGPgyssV2CUiIuGlTWvDtdcErl9vvwvvvFcx8GqnnQyPPGiYep8hMTH4epeTY8nNdc856EDDow8a2rRxedbC62/AyBHQsiVERcIdtxliY3XNFBERERERERERERERkfCi4C6ROpSRYXntjcB+Tg5cMxpuuMkNRv/wo+Xd9wL5o0cZ4uKqNvDcrKlh7BgP8fEaqBYRkfBz+EDDsccE9u+9z/Ln4tAzeLVvV/Fad+8Uy/lDLb/Od8/p3NnwxCOGvfeCCA+Mu83Qq5eHR6YbJt5p6NNb10sREREREREREREREREJPwruEqlD5WftiooMpO+5pyE/3zL53sAg9pFHwH77auBZRER2HNdcaejW1W0XFcNNt1iysra+dOLHn1g++hjWrIXLrrQ88pif4mJLUpLhnrsMU+839N7dXVObNzchr68ZGRafT8s0ioiIiIiIiIiIiIiISMOm4C6ROpKeYXm93KxdRcXuZ2IiHHs0PPGUZc0al5aSAldcWnlgV16e5Zbb/axYqUFoERFpPKKjDXfcbkhMdPtr1sK4Oy1+/5avd7GxkJLstq2F516AYSPczF9eb+hZur762jL1AT8+nyU723LF1Zabb7UUFuraKiIiIiIiIiIiIiIiIg2XgrtE6shLL1vyC9x2TEwg/aTBsHwFvPpaIO3Kyw3JyZUHd02ZZvn0Mxh6keWTTzUILSIijUfbNoZbbjSYTZfBud/BzGe2/JwD+hmenmHYf79A2l/L4KJLLE8+5WbxKm/lKsv4iZZXX3czfY0aY1n2N3w1B8Zcb7FW11YRERERERERERERERFpmBTcJVJHSkrA63XbBZuCvKIi4YTjYdJki9/v0vrtD4cdWnk5/662fPKZ2y5d4lFERKQx6bu/4fxzA/tPPW359rstB1w1bWq4e5Lh2msMsbEuraQEnnoahl9sWbIk8Pz3P7Dk5rrthb/Bsr/dtjFwwiCDMVoWWURERERERERERERERBomBXeJ1JHLRnp46TlDh50CacccDe++b/hrmduPi4NR12x5ULltG8Pjjxg6dYTjj4OBh2oAWkREGp/zzzXst6/bthbGjbf8+++WA7yMMQweZHhmhmGvPQPpS/+CYRcHZvEadqHhyssNEREuv6DABXYd2B8GHFxHv5CIiIiIiIiIiIiIiIhILVBwl0gdKiyEFSvdtjFw4AHw9LOBgeqLLzK0bLH1YK3OnVyA15WXKbBLREQap4gItzxjq5ZuPzsbrh1rycra+pKJrVsb7r/HMOpqQ+ympZDLz+K1dCmcerLhgSmGZs1cvrXw5Vdw862WnBwtyygiIiIiIiIiIiIiIiINk4K7ROrQS69Y7Kbx4oMOhKefg+Jit7/brjB4UNXLiokxxMQouEtERBqv5GTDnXcYYjYFaK1cCTfcbCkq2nrwlcdjOPEEw9NPGfbcI5BeOovXjJmWnj1gxmOGPr0D+Z9/CcNGuGUcCwstY673M3crS0KKiIiIiIiIiIiIiIiIbC8K7hKpRRs2WtassWXb738YyGvTGuYvcNuRkTB2tMHjCR2sNX+BJT1dA8siIrLj6d7NcNvNhtIVi+f9CnfdY7G2atfFNq0NU+41XHNV8CxeM2Zahl9s2bgRptxrOHNI4Dmr/oURIy2XXWX55lu47gbLBx/qOiwiIiIiIiIiIiIiIiL1T8FdIrXoiSctZ5xjuXeKn2eft2WzdPXaBWa/GTjuvHMMHTqEDuxas8Zy3Q2W84dafvxJA8siIrLj6X+A4fJLA9fJDz50SyxWlcdjOGmwYeYMwx59Aumls3g98xwMH2qYcIchId7llfhhw/pN2yWQmVXz30NERERERERERERERESkphTcJVJLVq6yvPc++Hwwaza8+14gz18C+fluu0tnOOuM0GVYa7l9vCU7GzamwT33WXw+BXiJiMiO59ST4eQTA/szZlpm/2/brolt2xim3me4+sqKs3i99AocdKDhyccN3bvBpRcbnnzM0K0rnH0mnHaKlkIWERERERERERERERGR+uet7wqINBZPPmUp8bvtnXaCFSvcdvPmsOhPt+3xwHWjDZGRoQeMjTGMGA633WHJyoLbbzV4vRpcFhGRHY8xhssvhTVr3VKJAPdOsSQmwcABVb82ejyGk0+E/feDSZMtv8yDtm3glJNcfts2hkceBK/XveaD0yBmUyDY2rWWVq10HRYREZHasXHjRubPn8/8+fNZsGABCxYsICMjA4ATTzyRSZMm1W8FRURERERERESkQVJwl0gt+GuZ5ZNPA/ubPpsFICc7sH3aKbBLzy0PEu/Rx/DU4/D7H9C9mwaURURkx+X1GsbdCldfa1mwEKyFO+60JMTDfvtu2zXSzeIFs/7nZtGMjQ08PzLSYK2bFaw0fflyy7ARlsMOs1xxKcx+0zB4UPDzRERERLZFv3796rsKIiIiIiIiIiIShrQso0gteGKGZdOYMF13hqwstx0TA/kFbrttGxh2YdUGhJs0MfTvp8FjERGRmBjD5ImGLl3cvs8HN95iWbBw25ctdrN4Gfr0Dr7GWmu5/ibLjJluOeSCAstNt1ryC+Ctt+GUIfDgw5arRlkyM7VcsoiIiNRcmzZt6N+/f31XQ0REREREREREwoCCu0RqaNEflq++DuxvTAtsFxQEtq8bbYiJqRiwVVhoycvTQLGIiEhlEhMN9042tGnj9gsK4JrRll/n187189334es5MGOmZcRIS3GxpWfPQH5mpvv52+/wznu18pIiIiKyA7r00kt55JFHmDNnDp999hm33357fVdJRERERERERETCgIK7RGro8ScDA8u9doG0TcFdnnL/XYOOhz33CD0T15RpluEXW5YtU4CXiIhIZZo1Ndx/j6FZM7efnw/XjrHM+7Xm18+v5wTK2GsvSEz0cMN1Hq6/zhAdHXzsuv9c8JeIiIjItrriiisYMGAAzUo7NCIiIiIiIiIiIlWg4C6RGvh1vuX7H9x2RATsvRfExrp9v9/9bNYMRo4IHdj13geWt96B5Stg+CWWf1drsFhERKQybdsYHphiaF4a4FUA115n+fmXml0/7xxnuPpKQ88eMPT8wDX72KMNjz1saN8+cOzrb8DIKyxr1+qaLSIiIiIiIiIiIiIiInVPwV0i1WSt5bEnAgO7xx4Dw4d6uPmG4OOuvcaQkBA6uCs2BuLi3PZhA92gtYiIiFSufTsX4NWiudsvKIDRYy3fzq1+sJXHYzj5RMOjDxmio4OvxXGxMHAADDgkkLZoEVww3PL1N37++FNBXiIiIiIiIiIiIiIiIlJ3FNwlUk35+RAT47ajIuH8cwwFBZbpDweOGXgo9O9XecDWIQcbnnjUcNhAuPoKBXaJiIhURbt2hgemGlq2dPuFhTD2RsuHH9cs0MrjCb4W+/2WiZMtM5+BVf/CuedAZKTLy86GG2+GYSMsL76sAC8RERERERERERERERGpG976roBIuIqLM9w72fDrfMvff0OLFobpD/lZvdrlJyfBVVUI2NqpveG2mxXYJSIisi3atjFMnwJXX2tZ9S+UlMC48ZasLDjlpNq5rn7xJfz8i9tesgT+/huOPxa+nQtr17nXBHjwYUuzZnD4QF3PRUREpP4Y0zj6IsG/hyEcfq2gGodBfaEh13nzv78NndOg6hxauNUXGkqdKz8Htnp0GLzP4VZf2N513ra/f5VKCYP3OdzqC3VV59r5+1ep9DB4n8OtvlAbda7bc2CrrxgG73O41Re2pc7b/+9fpZqEwfscbvWFyurccM6BUMLtfQ63+pavcWP5fKO2KbhLpIZ23w2aNYXfF1leeS2QfuUVhtSUig2PtVYNkoiISC1o3drw0AMwaoxlyVKXNmWaZWOaZfiFpsJMXNvq4INcoPbDj1oKC8Hng1n/g647w0EHwtK/XPDX3nvBIQfVwi8kIiIiUgMpKSn1XYVa4/P5AIiLi8MQV8+12Tqf8YL1g9+H1xNf39WpkoZc503foSAuMTUovSHXOZRwqy80nDpXdg6E0lDqXFXhVl/Y/nXelr9/ZcLtfQ63+kLd1bk2/v6VCbf3OdzqC7VT57o8B0IJt/c53OoL21bn7f33r0y4vc/hVl+ovM4N5RwIJdze53CrL0BpOF9CQgJer0KZNqd3RKSG5n4PY8Za4uLA73dp/faHwwdWPPb7HywzZlrG3epm+hIREZGaadLE8MAUtyzjvF9d2rPPwfLllpuudzNtVpfHYzjlJNh/X5hwl2X+Ape+ZCn8sxwuOBd67QJnn2mIjDT8tczy1ddwzlkQEaHrvIiIiEiNRLmgrob1Xe1KWOM+NMeP9UTUd22qRnWue+FWX1Cdt4dwqy+ozttDuNUXVOftIdzqC6rz9hBu9QXVeXsIt/qC6rw9hFt9S0U1/C941RcFd4lso4W/WTp2gIQEg7UuWMtayM11+XFxMOoaU2F2rtVrLLeOs2Rnw9ARlrsnQY/uGvgVERGpqYQEw72T4bY7XHAVwJdfwSWrLXfdCa1a1ex6266dCyB7/Q145HFLUREUF8NjT8IuPeGoI8AYy823WlashHm/wtVXWjrs5Kn5LyciIiJSRRkZGfVdhVphjCEhIQEiY8kpLMHasAjvCvDXdwWqoQHVuezvD+Tk5FT+929Ada6ScKsv1Fudq3wOhBJu73O41RfqvM41+vtXJtze53CrL9Ranevk71+ZcHufw62+UK06b9dzIJRwe5/Drb6wxTrX+9+/MuH2PodbfaGszg32HAgl3N7nMKjv5n//xqC2Z1jXiJPINsjNtYy90TLkLMtrb1iycyyJCcHHjLzY0DLErFx//gn5+W47OgpatdwOFRYREdlBREcb7hxnOOesQNpff8Gwiy3zF9T8JjAiwnDaqYaZTxp27RVI/30RXDDMcsvtLrAL4Mef4Ozz4OVXw+COSURERBoNa22jeURERGgJhh2Y1+vV338Hp3Ngx6a//45Nf3/RObBj099fdA7s2Mr//ev7c4naeNQ2BXeJbIPnX7RkZEBGJrz6miXSC5mZgfw+vWHQcaGfO+AQw/SphjZt4M47DCkpmrVLRESkNnk8hhHDPdxyoyEq0qVlZMAVV1vefrd2OtI7tTc8OM0w8uLAaxQVw9zvoHnzwHHWwgMPwm13+PH5GvA3jERERERERERERERERKRBU3CXSBWtX295+dXA/oiLDC+9YvhzsduPjobrRhs8nsqDtnbtZXjhGUP3bgrsEhERqStHHG6YPs3QtKnb9/lg0mTLxMl+8vNrZxavM4cYnnrC0GuXQPr69RARAeVXZv74E7j8KsvatQrwEhERERERERERERERkW2n4C6RKnriKUthodvepSe0bWOZ+UxgoPbiiwzt2209aMvrVWCXiIhIXdulp+GJRww9ugfS3nkXhl9iWfZ37QRadehgeOgBw6WXGKKiXFpJiZu1KyIicNyChXD+MMsXXyrAS0RERERERERERERERLaNgrtEquCvZZZ33wvsH3cMjBvvBnAB9ugDJ58Y/Jz16y13TvSTna2BXBERkfrQvLlbQvH4YwNp//wDwy+2zH6zdtY8j4gwnHG6YeYThl17BdJLSsDjcQ+AnBy48RbLtOn+Gr+miIiIiIiIiIiIiIiI7DgU3CVSBQ8/aikd/+1/ADzyOCxf4fZjYuCG64KXYywqstx4i+W9D9wA8rJlCvASERGpD9HRhutGe7jlJkNsrEsrLIR77rOMGmP577/auUbvtJMLJLtsZGAWL78fBh0HbdsEjvvgI1i8RP0CERERERERERERERERqRpvfVdApKH78SfL3O/cdoQHmjeHrKxA/sXDoXXr4KUWv/8Rfl/ktteug9y87VRZERERCemIw9wSjbfeblmy1KV9/wOce4HlyivgqCPAmJotnRwRYRhyGvTrCxPvshQWwVVXGAoL4fY7LN/9AJmZcMlllrsmwN57aalmERGRHcmPP/7IihUryvbT09PLtpcvX84bb7wRdPxJJ5203eomIiIiIiIiIiINl4K7RLbA77c89Ehgdo2jjoI33wrkt28HJ59UcWC2fz/DpDvhjgmWiy8y7LarBm9FRETq207tDY8+BE89bXn+RTezVk4u3DnR8sWXMPoaaNq05tfsndobpk91gVxer8HrhTOGWOYvdMszJiVBt67u2OXLLc2bQ1yc+goiIiKN3WuvvcasWbNC5v3888/8/PPPQWkK7hIREREREREREdCyjCJb9O77sHiJ246NhZWroKTE7RsDd02ofJaP/gcYnn/GMHjQdqqsiIiIbFVUlGHEcA8PTzfs1D6Q/vUcOOcCyzvvWayt+bKJERGGJk0CfYQ9+nh47CHXn0hLgzdmQ3q6n2uvs1w43PLnYi3VKCIiIiIiIiIiIiIiIhVp5i6RLWjXFjp1hL//gYP6wwcfBfIOOxR22mnL8ZHNamH2DxEREal9vXYxPPUEPPak5ZVXwVq37PLEuyzvvQ/XXgMdO9Tudfzrbwz5+S6I65nnLAt/gzVrXd6IkZaRF8OpJ9d8eUgRERFpmCZNmsSkSZPquxoiIiIiIiIiIhJmNHOXyBb06W146gnDZZfAl18H0qOiYMy1gX2fz/LCS5bCQs26ISIiEi6iow2Xj/QwfaqhXdtA+rxf4fyhlsee8Nfqtf2g/tB7d7c9fKjhuGMMCfFu3+eDadMtY2+0ZGaqPyEiIiIiIiIiIiIiIiKOgrtEtiIiAn78CfLzA2kXD4fY2MC/z0OPWB56xHLZlZZ1/2lAVkREJJz03t3w9AzDBeeBd9O8tj4fPPMcnHuB5du5tbNUY7t2hgemGG692XDaKXDIwYYp9wVeE2DONy6wbN6v6k+IiIiIiIiIiIiIiIiIgrtEtuqtd2Du94H9li3hpBMDyyX9vsjyymtue9Ef8NXXiIiISJiJjjYMvcDD008a+vQOpP+7GkaPtYwaY1n2d80Drjwew+EDDRERri+RmmrYuUvwMes3wBVXW5562lJSoiAvERERERERERERERGRHZmCu0Q2M+cby//ecoOpK1ZYpkwLHlS95kqD1xsI7tqlp2HMtQavFwYOgJNP3N41FhERkdrSoYObXevGsYbkpED69z/ABUMt907xk5FRewFXLVsYpt4HycnB6X4/PPmU5apRlg0bFOAlIiIiIiIiIiIiIiKyo/Ju/RCRHUdBgeW+qZZ162D2m+ArhqKiQH7v3aFf34rPG3ScoUtn6NwJjDEVDxAREZGwYYzh6KPggH4w42nLrFlQ4nePWbPho48sF5wPJw2GyMiaX/cLCw07tbcsyKyY98s8+Hau5fjj1L8QERERERERERERERHZEWnmLpFynn/RBXYBrFwJf/8TnH/pJabS4K1euxhiYzXwKiIi0lgkJRmuutzD008Z+u4fSM/JhQcetJxzgeWrry3W1mxmrSZNDNOnGi4baYiKrJj/0CPw1deavUtERERERERERERERGRHpOAukU3WrLE8/2Jgv6AgOH/gALcEo7WWTz93yzaKiIhI49exg+HuSR7uucvQsUMgfdUquP4myyWXWeb9WrN+QUSEYchphqeeMOzSMzgvOwfum2IpLvYDqA8iIiIiIiIiIiIiIiKyA1Fwl8gm0x+2ZUsweiOC87xeuGi4m5Xr1dfhltssY2+05ORocFVERGRHsf9+hplPGq6+0pCUFEhf+BtcdqVl9Fg/S/+qWd+gQwfDQw8YLhlhgvoj6zfANaNh8WI/5w21fPyJ+iAiIiIiIiIiIiIiIiI7AgV3iQBff2P54svAvq8kOP+kwdC2jWHxEsuDD7nB1G/nwsuvamBVRERkR+L1Gk4+0fDS84Yzz4CoqEDet3PhgmGWO+70s3pN9fsIXq/hrDMMM54wdO0aSP9lHgy/BP75B267w3LzbX4yM9UXERERERERERERERERacwU3CU7vLw8y31TKh8YjY+Hc892s3bt3AXOO9dt9949kC4iIiI7lqREw8gRHl56znD8cRCxqVdtLXzwEZx5juX+qX7Wr69+8FXnTobHHzYMu9AQsWkWr5JyAeiffQ6nn2X56msFeImIiIiIiIiIiIiIiDRWCu6SHd4TMyz//Vd5/oXnG1JSXBCXx2O48HzDpAmG8bcbIiMV3CUiIrIja9HCcN21Hp55ynDIQYF0nw9enwWnn1mzIC+v13D+uYZHHzJ02Klifk4OXH+Tmy0sK0tBXiIiIiIiIiIiIiIiIo2Ngrtkh/bHn5bX3qiYnpzsfnbqCCefWDG/fz9DaqoCu0RERMTp0MEwfpyHxx8x7LVnIL2o2AV5nXam5d4pftb9V70ArB7dDTMeN5xycuj8Dz6Ccy6wzPlGAV4iIiIiIiIiIiIiIiKNiYK7ZIf2xiyL3x+c1qolvPAM3HGbYfQowx9/grUaKBUREZGt69nDMOVe9+i9eyC9uBhmzYYhZ1nuub96QV7R0YarLvdw/z2Gpk0r5m/cCNfdYLlzop/sbPVdREREREREREREREREGgMFd8kObcy1hj69A/teL4y7zZCc7GHAIYYNG+HiSy0TJ1uKijRIKiIiIltnjGHvvQzTpxqm3R/c1yguhtn/c8s13nOfn7Xrtr1/sc/ehmefMgwcEEhLiA9sv/cB3HSrrfZSkCIiIiIiIiIiIiIiItJwKLhLdmiffgbzfg3sXz7SsEtPt9zi3/9YJkxyg6LvvgdPPa0BUhEREak6Ywx77mGYPtXDtPsNe/QJ5Pl8MPtNN5PX5Hv9rFmzbf2MpCTD7bd6uPVmw+GHwSsvwjFHlb4u/PQznHmOZe1a9V9ERERERERERERERETCmYK7ZIf1x5+WSXcHBjw7dYRjjg7st28HRx7utjt2gLPPNNu5hiIiItJY7LmH4YEpHqZPNey1ZyDd54M334IhZ7vlFP9Zvm3BWIcPNNx6k4ekJA83jPVwwvFQupp0i5bQsqXbXrzEh9+vQC8REREREREREREREZFwo+Au2eH89rtl3To/N9xkKSpyaYmJ8Pc/cPb58P0PbuDT6zVce41h1NWGuyYY4uMV3CUiIiI106e3Yep9Hh6cFhzkVVLillM853zLjbf4+eOP6gVijR7lYep9EBUFy5fD6LGWRX/4OfPsTM65IIuFv/lr6TcRERERERERERERERGR7cFb3xUQ2Z5WrbJccbXFGCgocGkJ8ZCT47bXrXNLGZUyxnDiCdu/niIiItK49d7dMPU+w/wFlpnPWL7/waVbC198CV98adlnb8s5Z7nlHI2pepD574sMRUUuOGzhb/DYE5b8Apg3z8eIkdC6leX2W2CXXfQ9DxERERERERERERERkYZOIzqyw/D7LRMnWwoLA4FdHgPjx8FtNxtSUmCfvWGfvTVDl4iIiGwfu+9muO9uD088ajjk4OAg8x9+hCuutlxymeXrbyzWVm02ryGnwTlngccD11wJu+1qiIoK5K9ZCyNGwgcf+qtcpoiIiIiIiIiIiIiIiNQPBXfJDuON2fDr/OC0yy417L2Xh4GHGi4a6gZRH33Cj9+vgU4RERHZfnp0N4y/3cNzMw3HHAUREYG8hb/B2Bss5w+1fPixxefbcj8lMtIwYriHZ54yHHG4hwvP9zD7tRTatw90/S1wxwS47nrL6jXq94iIiIiIiIiIiIiIiDRUCu6SHcKyZZbpDwYPXB5/LJx6stv+c7FlyjS3/exz8NIr27mCIiIiIkCHDoYbxnp4+QXDKScRNOPWX8tg3HjLmedaZr9pKSzcclBWxw6BacA6dIjgvbdSGLzZctPfzIVzzrc8+ZSfP//0U1KiQC8REREREREREREREZGGRMFd0ugVFlquv8niKwmk7bEHXDICzKa1j9q3g/32c3ldOsPgQfVQUREREZFNWrU0XHWFh9dfNpxzNsTHB/JWr4Z77rOcdqblhZcsublVC8jKybF8+23F9MJCeOppuPgyGHqR5YcfFeAlIiIiIiIiIiIiIiLSUCi4Sxq9e+63/Ls6sN+2Ley1B5w/DL79zg1exsUZ7hxnGHah4a4Jhrg4U0lpIiIiIttPaqphxDAX5DViuCElJZC3cSM89Ijl5NMsjz7hJy1ty0FZPh906RKcZsp1eYqLYelfcO/9W1/6UURERERERERERERERLYPBXdJo/bRx37eez+wHxsL11wJzzwL//0Ho6+zzPnGDV56PIbzzzW0aqXALhEREWlYEhIM55xleO0lw9VXGFq2DOTl5LplpU8ZYrl3ip/Va0IHZqWmepg80cPoUYaYGJdmNx2amEhZ2iUjDF6vISvbsmSJgrxERERERERERERERETqk4K7pNFatcrP+ImBfY8H7p4Ejz4ORcUubZeesN++9VM/ERERkW0VE2M4+STDy88bbr7B0KljIK+oCGbNhjPOstw+3s/SvyoGZhljOOF4w8wnDL12CaRnZ0OEBw47FA7s75734suWC4Zbbrndzxuz/RQUKNBLRERERERERERERERke1NwlzRKhYWWa8ZASUkg7fZb4PsfYPGSQNq++4DXq5m6REREJLx4vYYjjzA8PcMtKb3broG8Ej989DGcP9Qyeqyfeb9arA0OzGrXzvDgNLckdUSES8vNg48/hdvvgBUr/Lz2mkv/9DO4bwocd4LlxZf9FcoSERERERERERERERGRuqPgLml0iostt46zrF4dSDv3bGje3PDcC8HHznwG3npbA5QiIiISnjwewwH9DA9P9/DgNEO//YPzv50Ll17h5+zzs/js8yL8/kC/x+t1S1I/+qBhp/aB53zyGVx2JXTvHlxWQSE89Ajce79lTSVLP4qIiIiIiIiIiIiIiEjtUnCXNCo+nwvs+npOIG34UDj7TMP4CRa/36UlJrif3bvBYQO3fz1FREREalvv3Q2TJ3mY+aThiMPcMoul5s3zcdmV2Zx7oZ/3P7T4fIHgrB49DDMeN5x8YuD4tHSY9ysMHABdOgfSrYXZb8KQsy0TJvlZsVJBXiIiIiIiIiIiIiIiInVJwV3SaPh8ltvGWb78KpB2wXlw7tmGu++zrPrXpSUmwuOPwmmnwOSJhthYLcsoIiIijcfOXQy33OThxecNJw2GqKhA3t9/w/gJltPPsrz+hqWw0AVnxcQYrr7Sw72TDU2bBo7/5DPIL4ArLoNuXQPpJSXw7vtw9nmWa6/zk5urIC8REREREREREREREZG6oOAuaRR8Psstt1s+/zKQdu7ZcOH5hnfeg48+DqSPHmVo19bDFZd5aNpUgV0iIiLSOLVpbbjmKg9vvOLhouGxJCYG+j3r1sH901yQ16uvB4K89tvX8MwMw4BDAuWsXg3TpsNuu8K9d8O++wTy/H6Y+x2cea7lk08V4CUiIiIiIiIiIiIiIlLbFNwlYa+w0HLjLcEzdh13DAwfavj7H7h/amCgcfAJcOghCugSERGRHUdqquHKy+L4+P0ULr0keGauDRtg6gOW0860vPKaC/JKTjaMu9Vw+y2G5KTAsa/PgplPw72TDY8/YujXFyIiXN7GjfDPctfnys+3fPChpbhYwV4iIiIiIiIiIiIiIiI1peAuCWu5uZZrr7PM+SY4fY8+UFgIt9xmKSx0aa1awuUjFdglIiIiO6aEBA9nDvHw6ouGa68xtGwZyNu4EaZNt5x2hgvyKiqCgYcanp1pOPigwHFnnWEwxtCzh+GuCYabb4CWLaF1Kzj7TNfPev9DuGOC5ZQhlmeft6SnK8hLRERERERERERERESkurz1XQGR6krPsFw7xvLn4uD0886BIw43jJ9g+Wd5IH3tOliyFHbttX3rKSIiItKQREUZBg+CY4+Gd9+HZ56zrFvn8jamuSCv51+As86AEwbB+NsNn34OCxZY+h8QCJQ3xnDYQEP/Ayyr10B0tMHvt7z8qgvm2rgRHn3c8uRTcNCBluOPNey1J3g8CrYXERERERERERERERGpKs3cJWFp5SrLyMsrBnYNHABDLzC89gZ88FFw3rHHQK9dtl8dRURERBqyyEjDCccbXnrOMOZaQ6vyM3mlwbQHLacOsbzyGhzQF666ouKtw1dfWx58xJY9t6QETjje0KJF4BifDz79DK6+1jLkbDeb18aNms1LRERERERERERERESkKhTcJWHn518sF11iWbkyOH3XXnDDWMP8BTD9wcCA4XHHwDlnw+hr3DJCIiIiIhIQGWkYdJzhxecM111raN0qkJeWDg886JZrfOkVS0FBoI+VmWm5+17LrNlw3oWWf5ZbIiMNZ5xumDgeQnW7Vq92s3mddJrlhpv95OYqyEtEREQqKikpwefzYa36CiIiIjsKay0+n099AJEdlNoAEZEt07KMElbefNty7/2WkpLg9NatYeJ4Q2Ym3HybpcTv0nfbFUZdbYiMVFCXiIiIyJZERhqOPw6OPgre/wCeftayZq3LS0uH6Q9Znn8RzhwCgwfBx5+4dABrCZr5q2MHw9lnWV54Efr0hm5d4b33ISPT5ZeUwIqVEBe3fX9HERERCQ/WWvLzLekZuI5GGIiJgdhYff4kIiI7jvx8S0FBLRZoDMXF7rqfk0ud9AF0vRapH1VqL7ZDG7At1F6ISEOj4C4JCyUllgcfdssCbS4lBe6ZZIiPh+EXW9I3DTI2bQp33K7ALhEREZFt4fUajjsWjjpyU5DXc5Y1a1xeejo8+LDlhZfgzCGGm26A6Q/BVVcaYmICfa7oaMOIYYbDD7UkJECLFoaLhlm+mgNvvmX58Sc44nAqzKr69juWtHQ44jBo1Up9OBERkR1Zfj5kZtb7mE6VpQCxsfVdCxERke2noCDwJa7aYIDiYvfN/bxcqIsuQAq6XovUh6q0F9ujDdgWKai9EJGGRcFd0uBlZ1tuH2+Z+13FvIQEuP9uQ4cOhg0b/Py33qVHRMD42w3NmmpQUERERKQ6goK8PoRnnrOsXu3ySoO8UlLglJNgrz2Cn2ut5b6plkMPMXTu7PpjkZGGQw+BQw6CYSMsr7wKrVtZDjvUBXlZa3n+Jbf09mNPQJ/elqOOMBxyMCQkqE8nIiKyQ7JQWFjfldi66Oj6roGIiIS7Wp8Fq45FREBhkQVMrV2rjYGo6AgACotqP8A7HK/X4XZegGY7ki3bUntR123AtgjH9kJEGj8Fd0mD9tvvltvGBZYEAjh0AERFwRdfwD13Gbp2dZ3EOyZAdrY7JioKunSuhwqLiIiINDJer+G4Y+CoI+CDj9xyjaVBXhkZ8MQMeH2W5cwhcOIJEBNj+PQzmDUbZs22HHm45ZIRhmbNXJ/trXdg8RL3/AmTLLvtamjVEv74E1auDLzuvF9h3q+W+6ZAv76WAQMM/fbXB4QiIiIiIiLS+NT2LFh1LTISrL++a9H4hdt5AZrtSEREpK4ouEsaJL/f8tIr8OjjlpKSQPqwCw3nneOitc89C3baKTC4d+XlhlGj3VI+d44zxMVp4E9ERESktni9hmOPhiMPhw83BXn9u9lMXm65Rsvb7wSe98FH8MVXlrPPhCGnQetW7rFmLZx7tqFVS9dn69wJxt1m+OBDN2NraR+wqBg+/xI+/9ISHQ1997ccdqjhkIPV1xMREREREWlMrLX4fL6y7R1ROMxYCS64S7afcDkvNNuRiIhI3VFwlzQ46/6z3HW35fsfAmmJiXDj9Yb+/dwgnjGw004wf4Fl5y4QF2fo3Mnw8IOwZAnsu48G+0RERETqgtdrOOZoOGJTkNfMZzdfrhFSkqF7N/hzsUsvKIAnZljefAsuvsgw80l4Y7bh9FMD5UZHu2Ub994TSvzw6WfwwYeW3xcFjikshM+/gPR0q+AuERERERGR7WS7LQ1nDMXFLqgrJ5carcmlpeFEREREpDFRcJc0GNZa3nkXHnjIkpsbSI+IgD69od/+wcf/Ot9y9bWWzp3gnrsgJcXN/NCq5fatt4iIiMiOqHyQ1wcfwcxnLGvWuLyMTPdISnQfqP+33qX/tx7G3Wnp2RNGjoCoqOAP2rOyLWedZ9l/Xxh5ieHkEz38+6/l08/hsy8sizcFix16SMUP6N//0BITDfvv55aGFBER2dFs2LCh2s+Nj48ntpL1czZu3Fjt2VNiY2OJj48PmZeenk5J+enaNzHGkJ+fT3q6JT09l4LCiq8dFRVNfHxiyHKzszPx+YqrVd/IyCgSEpJC5uXkZFFcXBQyLzoaSnxQUhK6DxIZGUlycnIl9c2msJrTcURERJCamhoyLzc3l/z8/GqVa4yhadOmIfPy8/PJLf/B3TZq1qxZyPTCwkKys7MxxlBc7P5+mZmZVT73mjRpgsfjqZBeVFREVlZWteubkpKC11vxI3Sfz0dGRka1y01KSiIqKqpCut/vJy0trdrlJiYmEl3JtCnh1kakpaWRlpZWrbKjo6NJTAzdRmRmZpadY9sqKiqKpKTQbURWVhZFRaHbiK0JhzYiI8OSlV26Z0hNDd1GFBTkk59f/TaiSWpziovdWn95uVD61y8qKiQ3N7vyJ4aQlOjGDCA82ojS97iwEBISkoiMDN1GZGZWv42Ij08kKip0G5Gevm1tRFERWL+lxG+I8MQTHRO6jcjIqHob4a4BLoowJyeH6OgYYmNDtxFZmemU+Cv2I7ak9Hrt9caETRuRkWHJy48kKip0G5Gbm01RUTXbCE8EScmh24j8/FwKCra9HxEdDf4SQ2pq6Ov91voRW+sHbK0fUV3h0EaUF479CIgJmVe+jdi8Ddha2xETE1urbUSpqKhooqPDo42AQD8iVCB2Tk7N+hEpKaHbiLy8mt1rNGni+hGbB2Ln5+dX616glNoIJxzbiIbyeURVbMu9RmV98eoytpbntk1PT6/N4hosYwwpKSkAZGRk7LBTBNeWtWstk+8Nnq3LGPB4AkvynHE6XHqJazjT0/2cfLq7gQDofwBMurNioyqOzlcJFzpXJZzofJVwsb3OVZ/P8v4HbrnGNWuD8xIS3BeuN//8bt99YMRwQ/du7ib+nvv8zH7T5XXvBk88ajAmcIO/apXlsy/g6KOgWdNAut9vOeV0y3/r3YcC++8HBx9k6Lc/xMcr0CucqG2VcBFO52ptf5AkDVf5a+a2mjx5MsOGDQuZ17VrVzZu3FitcseMGcPYsWND5vXt25c///yzWuWedNKFXDv67pB5l44cxC+/zKlWuQMGDOLOCU+FzLvxhgv47LM3q1XuAQccwFtvvRUyb/To0Tz55JPVKrd79+58++23IfMmTZrE5MmTq1Vu06ZNWbJkSci8J554gjFjxlSrXKDSwYTZs2dz4YUXVrvcxYsXhxzM+frrrxk0aFC1y50zZw49e/askL5o0SIOOOCAapf75ptv0r9//wrpGzZsoFu3btUud8aMGQwePDhkXpMmTapd7vZsI0qvsb169eL333+vVrlDhw7l7rtDtxHHH388c+ZUr40YNGgQM2fODJl3/vnn8+abO0YbkZLSlHffWxwy7/XXnuDee6+rVrkA385NKxssy87OLutjffrJ/7jpph2njZj+4P/Yc8+KbUR6+gaOPaZ7tcsdP34Ghw48IWRev76hA/aqYtSouzj5lNBtxDFHdyMjo3ptxIVDxzBsWOjz6awz+/H339XrR4RbG7HXXgfwwPTQz73n7tG88caMapXbqVN3nn/hm5B5TzxxFzOerF4b0aRJU5YuVT8C1I8oNXnyZE4+eSgZmRWXGG2IbcRJJ13IjTfdTUoypKYG32M1xDaitB+Rnm7JyAzOmzRxNK++Wr02onPn7rz6Wug24tFH7uKxx6rfj/jkU9eP2Pw9Vhvh7IhtxLBhw0J+1tYQP4/Yln5EbX9eqJm7pF4VFlpefBmefd4GXdBTUiAjIxDY1SQVjjrSNe7WWp58KhDY1SQVrrpcg3YiIiIi9cnrNRx3LBx1JLz3vgvyWrvO5eXkuJ+xsVBcDD6f2//+B/j+B8uhAywXngflv/A19AJTYZC6XTvDOWdVfO3ffg/MDlZQ4JZu/PwLS2Qk7L2X5eCDDP37Bb61LSIiIiIiDZO1Fl/pDYOIiIhIPYiMdCtLhaPy4+3VnJgIcF/UrWzSL18NygVXbiUTQMkOrPx9QEP+EmV9UnCX1AtrLXO+hWnTLatXB9KNgU4dYdnfgbSOHWHyREOb1m4wbsUKeO+DQP6BB0KrVhqoExEREWkIvF7D8cdtCvLaNJPXuk1BXqXBW16v+3Ch9B7t08/giy/cEo/XX+f6gv36Vuzf+XwWr7diequWMHyo4dPPLX/9FUgvLoZv58K3cy0eD/TpbTl0gGHwIPUdRURERESqKi/Pkp4eeoClJuMuBQVULNcYiostJSU7zoCOzxfifdikMMTStFXl91de7ubLNonsSAoLK2/TahJbWlxc+f9cNVdxE5F6VFBoKdlswbKG2Eb4fJCVZSkssoA+85Rg5ZfrzMioWf86N7fyc7gmyu41Nt0HVLJCo6DgLqkHC3+zPPKYZd6vweldurj1zssHdu27D4y71ZCQYLDWYoyhQwfDfXfDtddZDjsURl2lC5WIiIhIQxMZaRh0HBx9JLzznpuptTTIq/SDEI/HDTgAlPhdMNj7H8Khh8BfyyxdOgf6eYuXWK6/yXLZJXDIwcFLTzVvbjjvHDjvHMOqVZYvv4YvvrT8Vm4VF78ffv4FoqKsgrtERERERLZBfj4VlvkpVdqfr46CgorlGqC42F+jmSbCjc9X+ftb2YwZVVFSUnm5O1pwV0aGJSKi4mBkdnbNBiizskIPcmZl1ahYqWOFhZX/b9QkcKOouPJyixTcJRJ2Cgsgd7P+SENsI3w+yMoGW4M+mTRe5fvbWdk1Kys3r/JzuCZK7zUM4Cv2Ax4iI2v/dRoDY2t5TrP09PStH9QIhFrzU7Zs2TLL409avtpsKeKUFBhwCHz0EeTkBtJPGgxXXGbweg1Lllrum2K543ZDs6alM3hZ2raFiAgNzm2NzlcJFzpXJZzofJVw0VDOVZ/P8tEn8PyLln/+2frxzZrBay+5vqDfb7n4Usvvi1ze0AsMF5y39T7g+vUu0OvLryy/zHMDT9ddazj+uODnznzGzQxw8EGGLp2psBykbD8N5XwV2ZpwOldTU1PruwqynSxZsqTaz42Pjye2kq/Hbty4sdrneGxsLPHx8SHz0tPTKQkRvWGMIT4+nvR0y9q1uRSEmLUmKiqa+PjEkOVmZ2fi81VvdCQyMoqEhKSQeTk5WRQXF4XMi46GpMTKl4COjIwkOTm5kvpmU1jN6I2IiIhK/8dzc3PJL7/m9TYwxtC0adOQefn5+eTm5obMq4pmzZqFTC8sLCQ7OxtjTNl7lZmZWeVzr0mTJng8ngrpRUVFZNUgyiIlJQWvt+L3o30+HxkZGdUuNykpiaioqArpfr+ftLS0apebmJhIdCVr3GzYsKFKZWRkWLKyg4OK4mLjiY4J3UZkZFS/jYiJiSU2NriNMMaQmJhIZkYamVlbPwfi490XOLzlPiOOjo4mISF0G5GVlUlxNafHiIqKIjExdBuRnZ1FUVHoNqK8iAjIL7AUFJiy99jrjSQxMXQbkZubTVFRNdsITwRJyaHbiPz8XAoKqtZGxMeD9VtK/K7Orh8Uuo0oLMgnL7/6bUSTJs1JTHR/u+zs7LK/f1FRIbm5VR+RLF9ngOTk0G1EcXEROTnVbyMSEytvI7KzM6pcjtcb/B4nJCQRGRm6jcjMrH4bER+fSFRU6DYiPb1qbUSgrECdIzy100YYY0hISAAgJyeH6OiYCm1EqazMdEr82xYFWlrnCG8MXm/D6UdUprS+xhNFVFTDbSPKi4+H6Cho0SL09b6gYCv9CGOIj3N/89y83ArTQzZtWnk/Iien+lELrVs3IT6+4hp86kcEVLUfEUp8fDwFBTFkZFYMWi7fRmzeBmyt7QjVjyhVnTaiVFRUNC1bJmL9Fl+JCapzQ2ojSnm9kbRunRyyvg2tjYBAPyI6GlKSITU10IcrKCgo+5/ZlnuBUlu716iucL7XSE+3Zf97DakfUV7pvYYxhtTUOFJTPERG5gDb//OIqoiOji7rr24uMzP4XqNr167Veo3KKLirmsLpg9z6tvA3y7PPW+Z8E5weGekCuLp2hfETgtOvuMxw4gmuMZ+/wM+Y6yEnxy3R+MAUQ2olH5ZJaDpfJVzoXJVwovNVwkVDO1f9fss337qZvMrPrLW5Sy+BM053N83/rrZcdoVl/QaIioJnZxrattm2/mBGhlsW/IC+wQOvPp/lhJMsmZvuw9u1hYMOgoMPNPTsAR6P+p3bU0M7X0UqE07nqoK7dhyN5TO50kGdtDTLv//mhAzuamgSElxwV1KS+g01FU7ta2NVfgCoPpQGd0FwcE9lEhIoG8wMB5GRhBx8bcjKv8d1Xedt/ftXZnvWuTaEW32hbupcW3//yoTb+xxu9YWat8kGQ1x8HAB5uXlYtk8/YPMAE6l9Velf1HUbsC3C7f8v3OoLld9DlT8HpObqu2+/LUIFd4W72v5MTssySp0oKXGDdi+/WnH5RWPgqCPcjAutWrm1U1+fZVm0yA2mjbvN0K2ra8g//8LPLbcHpvdOS4MNGyA1Zfv+PiIiIiJSOzweQ/8D4IB+8Ot8eO4Fy9zvKh73xiyw1nLs0dC2jeG5py3DLoad2lfsC/r9FmO2PONWSorh2KMrps9fQFlgF8Cqf+GFF+GFFy3Nm8FBB7oZvXbfDbxefdAnIiIioRUUWkrCLL4uJgZiY9W/kcYjHAattMSMiOwoqtMmGwNR0W4GrcKiChN31YlKJqoSke2gwj2UcXEDsGm1rwb4RQ/dQ0l9UnCX1KqMDMvb78Ks/1nWrQvO83hgwMFw3jmGzp0DjV5kpGHcLfDM85bLRxri4lzeG7Mt900JPD8iAu6ZBF13VoMpIiIiEu6MMfTpDX16G5YssTz3ouWzzwNB/WvWwkOPWJ54EgYeatm1F6xc6R5nn2959cXA8tz/ews+/sRy+aXQo/u29RV77QITxxu++Mry9Rw3W2yp9Rvg9Vnw+ixLchIcfpjlqisqTsEtIiIiUlgAudVb1aHepACVrMYpIiIiIiJSpza/hzJAcbH7cDgvl+00d9+2SUH3UFJ/FNwlNebzWX74Ed55zy29WLzZUsORkXD0UTDkVPjjT8ODj1gm3emCukq1bm247lq3X1JiefRxywsvBZczcAD06KHALhEREZHGpmtXw+23GC4ebvnfW5a334GMTJdXVAzvfeAepfbdOxDYlZ1teeJJt6zi8Istd0+C/ferep8xOtpwYH84sL/B57P8Mg+++NLy5VeQVu6bY5lZbgZZERERkS0Jh5mDIHxnqcjPtxQU1Hctqsbjce9zTIw+zxQREZHaE079IXCTdxQWWVzojkhFpfdQ9TF737YI13soaTwU3CXV4vNZfp0PX82xfP5F6IGupk3ghEGGQcfD2rVwx0TLokWuFX7+RTj/3IrPycq23H6H5bvvA2lt2sB++8A1V5ktLrUjIiIiIuGtdWvDxRcZLjzf8vmXMGu2ZcHCise9+x5sTPNzxOGGqEhLXr5Lj4uFyEiL3++Wf9xWXq9hn71hn70NV19pWfgbfPmV5YsvYe06OOigimVOnOwnK9PlHdAPkhLVXxURERGpKwUFgS8BNHSRkW4gMz+/vmtSdRp8FRERafjCqT8Erk9k/fVdCxGR8KfgLqmy7GzL9z+4gK65czetdRtC793hxBMMBx8E/66GadMtn34WfMw771nOHAJRUYEPCv5Zbhl7o2XVqsBxhxwMN99giI7WBwoiIiIiO4qoKMMRh8ERhxmWLLXM/p/lw48gf9O3Ev0Wvp0L3861xMa6/uePP0FuHlx5DbRuZTnicMuRRxiSEiEmZttnTIiIMPTeHXrvbrhspGXxEmjfLviYoiK3lGRenusjR0TAXntaDjrQcOAB0LSp+rAiIiLSMEVGukCehsZai8/nK9uuTDjMkBYZGX7LdWrwVUREdjQNtU9UFeHQHwL3HouISM0puEsqlZfnZkr46RfLL7/An4vBX8nNffNmbunFY44ytGtnWL7ccuckyyefBk+ZGB0NZw6BM4eYoMCuDz+y3HWPDeqIDLvQcO7Z1Zt1QUREREQah647G0aPMoy82M2g9cFHlp9/CfQx8/NdYFd5a9bC08/C089akpOg2AdnnWk583QTtDR4VRlj6N6tYvpvv7vArlIlJfD9D/D9D5Z774fddrUcfJDhoP5uVjIRERGRhqSg0FKSvvXjtitjKC52Hb2cXII+WAznWaU0+CoiItJwNcg+USXCuT8kEu7CORhUGgcFdwngvgm3Zi0sWgSL/rD89jv8vsgNUFWmXVvofwAc0M+w+25udoMFCy2PPenns88rroN75OFw0XBDyxaBDkduruW+KZYPPgocFxEB42+HA/urYyIiIiIiTny84Zij4ZijDevXWz76xH1BYOlfW35eZpb7+fgT8MknlsMPg4MPgp3a17yvuUcfw2svwZdfwRdfWeYvCPSBrYX5C2D+AssDD7oZvabe56nxa4qIiIjUloY4q5QBiovdt0vzcqH8x4uaVUpERETqQkPsE1VG/SGR+qVgUKlPCu7aARUXW1auhGV/w7K/3RIzf/yx9fWZIzywyy7Qr6/hwP7QYSc3i0F50x60LFoU/LwD+8MF5xm6dQ0+dv4Cyx0TLGvWBB+fmgq9dlEjIyIiIiKhNW9uymaD/We55auv4auvLb8v2vLzlv0Njz5uefRxaNfOcuAB0Hd/90UFr7d6/c9WrQynnQqnnWpIS7N8+TV8+ZXlp5+DvyjRrFnF5+bkWOLjK/apRURERLanhjSrlDEQFe2+Dl9YFPzlUc0qJSIiInWpIfWJKqP+kEj9UjCo1CcFdzVS1lrS02HVv/Dvv7DqX8uqVfD3P7BiJfh8Wy/D44FuXWHPPWCPPQy9d4O4OFNWfloaNG0a/JxTTzKMu9N96lJZUFduruWRxy2zZgc/t0tnSEqC8bcbkpM1wCUiIiIiW9exg6FjBzjnLMOGDZav58CXX1vm/QpFRZU/b9UqePFlePFlS6QXunWz9OsLxx0DTZtWb4atJk0MgwfB4EGGrGzLN9/CF19avvseDj6wYv/2zomWJUvh4IMsBx1o2G1XLUkuIiIiIiIiIiIi0pApGFTqg4K7wpC1lvx82JgG//3nHuv+g//W27L9tesgL2/byk1JgV16QM+ehh49YNddIDExeHDp338tn30BH33igrtmvRo8y8GAQ2DxEjjuWEPHDhUHpjIyLBcOt/y3PpCWlARjRxv69XX71Z01QURERER2bM2aGQafAINPMBQWuqXGf/jJ8tNPsOiPisuGlyr2wW+/u8eTT0GHnfx07AidOho6dIDWrdwjJaXqs2wlJRqOOgKOOsKQn2/xbnbnlZdn+e4HF4D28qvw8quWpk3gwP6Wgw8y7NFH/WIREREREREREREREVFw13ZnraW4GAoK3bR9+QXlfhZCbi5kZkFWFmRlWbKyISsT97M0PRuKi6tfB2OgbVs3U1bnTtC5k6FnD2jZsuJglbWWv5bBnG/crAOLlwSXNfc76H9AYD8y0nDZyMoHoaKj3fqupQ4+CK6+0tCsqQauRERERKT2REcb9twD9tzDwDDIzrYsWAiL/nA/5y8IPbOX3+9mu/37H/iM4GiwyEho09rSsqVbZrFJKjRJNTRpAk2auOXFm6RCYmLwDFyxsRX7uitWQmxMcB02psHsN2H2m5bERDign+WQgwz77O1+HxERERERERERERER2fEouKuarLU8MSOfr+cUUVhYQkmJGwgq/yjxg78EfCXBgVwl22lt05RkF8TVri20bWto1xY6dICOHbY8OJSfb/npZ/h2ruXb79xMYKHExroZw7YkJ8eSkOBey++3XHKZZc1al3fwgXDnuOoteSMiIiIisi0SE91Msf36lvZN/XzwIbzyuuu7Wz+sXw85uVuY4asYlq9wj4CKBxsDcXGW+HhIiMf9THA/4+MgPgHiYg1nDIEN62HZP7BkKWRnB8rIzob3P4D3P7C0bAHTp1miowxRUS7ILCqq6rOIiYiIiIiIiIiIiIhI+FJwVzX98QfcP3Ub1z2sRfHxkJoCLVpsejSHFi0MLTftt2xBWVDVtnrgIcubb4XOi46GvvvDIQe5wbG4uNCvsWKl5bkXLJ99Bs8/4+rm8RiOPw7un+oGwBITXZCcBqVEREREZHvzeDwcfRQcfVRwn7Sw0LJiBTz2pOXbudUr21o3I29uLlT+XYhKIshCWPcfnDqk4nO8Xkt09KZgr00BX6WBX+W3y9IiITIqsO3yXMBYdDQkJbol05OSIDkJkpPdFzrUXxcRERERERERERERqT8K7qqmdu3cEixp6VUflAH3Lf6YaIiOccuwRMe4/ZgY94iNdQMpblDFkJwEiZsGV0oHWxITweut/gBLUZHl90Uw71dIT7dcfWXw7Fn772t4863A75WSAvvvBwf0Ney/X+hlZTZ37/1u9i+AF1+2XHm5e86JJ8Avv8AhBxsOG6hBIhERERGpf+WDl6KjDZ06Wf74M5A/7ELIzITDD4P4OMPadbBmjSUn1/DMc5a8evrOh8/nHjWz5fuZ6Gho3szSrJn7Qkmz5tC8maF5M7ese9s27r5FRERERERERERERETqhoK7qikx0fDxByks+sNHbm4OHmPxRIDH4x4RHsr2IyJcIFdMzPZfPsVatwziokXw+x+WRYvgjz+hqMjlR3hgxHAbNAPX3nvBLj1h332g7/6GHt0hIqLyOvv9Fo8nOP+QgykL7lq7NjAbgsdjGD9Ogz8iIiIi0nB5vYYp98I771oWLIRzzzZB/d327S0nnwapTWyVg6u8XhcM1byZ+/JEcpKbjbekBAqLoKhw08/NHsXFUFgIxb5N+0XuuIKCuvndN1dYCKv+dY+A4ICwhARL2zaly8FDx46Gzp2gw05uZjAREREREREREREREak+BXfVQHS0oU/vSDIyDHbbJvCqc88+b5m/wAVzZWRWflyJHxYshP32DaTFxRkee3jLgzA+n2X+AvjiS8tXX8PTM1zAG8Cs/1nuvd8dt+ceMPFOzxZKEhERERFpeLp0NlxxmQm5jPiChbB+g3u0aAH33AW//25Y+Jtl4e/wzz8Vy/P54N9/3aOU1wudO0G3rtCzp+HYoyEqauvBUKvXWO6favn5Fxd8VZnUVOjaBQafACUlhuJiKCoOBI25ADJLcbELFsvKgsyscj8zISd36+9VTg78udg9HHdzFBHhAuE6d4LOnVzA1847Q+tWWupRRERERERERERERKSqFNwVpkpKLKvXwF9/QatW0KN78ODIF18GLyWzuU4doU8f2KOPoWfPqr3m2rWWefPhx58s33zrBn1KffypW3IRYPfdAukLF8LadZZWLTV4IyIiIiLhJ1QQ0vIVEBXpAqUO6AedO3no3AmOO9Yd+8GHfl5+1QVXgQvoCp75yvH5YPES9/jkM8sJxwe/1saNln9Xw85dCJppt01rw92TDIWFll/nw9zvLN997+pVXno6/LceDjrQU6HcoiJ3H2HMlr+IUVBg2bAhEMy2fj2s32DZsB7WrIV/V7vgrlBKSlyg2z//wKefBb4NEx8PXXe27LwzdNvZ0HVn6NhRs3yJiIiIiIiIiIiIiISi4K4GrqTEsnYtrFgFK1fA3/9Yli6Dv/8OLMVyyskVg7t69KAsuCslBXbp4WYD6NkDevaA5OQtL7O4fgMsWwZL/4Klf1l++w3Wrqu8nj/9ZDnxBFdml86GQw6yYGDkxUaBXSIiIiLSqAw6zjBwAMz9DnZqXzF//gIXsAUwfKjh3smGnBxbFsj152LL4sWwYiVlMwB360qFpc6/+RbuusdiDJx+quWykcGBWNHRhn33gX33cc9bvcby/ffw3Q+Web9CdjbssUfF+s36n2XmM9C0Kezay7JrL8OuvVwdoqOD6xATY2jXDtq1K58afExWlgtCW/UvrFwJy/62/P03rFwFfn/F18/NhXm/ukfpLF9eL3TqaOm6s2H33fPp2cNLq5aW+PiKzxcRERERERERERER2ZEouKsB8PksXm/wAMnc7yzTH3KDJMXFW37+X39VTDv2aMMefVxQV6tKlj35+x/Le+9bcnMhNw8yMmDtWlj339ZfE9wyKyUlcNYQuHhEcPm33GSqtKSMiIiIiEg4io83DDw0dN5PvwS299rT/UxIMOy5h1u2fMxYS0qKWxp9jz1g7VpDcnLFcv5c7AKfrIVmzSr2re+5z09WNnTraujWFbruDINPMAw+wVBSYln6F8TGVCz3503127gRvvjSzfoLEBkJ3bpaeu0C3bsZunVzwWsREVvu1yclGZKS3JdIHHd8YaFl+QpY9jcsW2b5axksXQob0yqW4fPBkqWwZKnl3ffzytJbt2bT72Y2zfQFzZtrWUcRERERERERERER2XEouKsO+f2WrCxIS4e0NPfYmAZpaZaNG91MWGvXQk4ufPBO8ABFVBT8s3zL5bdsCV06wW67VRzY6NnDlBtcCW3tOnjhpar/PgnxbsnFPn0M2VmWZ19w6T/9UnFwRYFdIiIiIrKjenCq4ed5MG+epUf34Dyfz/LLPMgvgF/nw9lnGg48INB3ttayZCl07AAtWxq6dbUs+9sFOG3uqzkuQKv8kodNm1i6dIGdd4aduxh27hL8ZRJrXWBZUlLwMuvgvuDx2+/uUTqj1t13GfruFzjGWrek4+YzfIUSHe2CzlzdA8dv3OgCz5YshSVL3O+7clVgFrPy1qxxj9IANIDkJNh5ZxsU9LVTeyp8YUZEREREREREREREpDFQcNcW/Drfsnq1+xa5r8TNUuXzuYffb8HkkZtjSUv3c/Jg6NEjeFDmiGNs2dKJW5ORCakpgf3S5V2MgZYtoH17aN8OOuzkBi86d4LExJoNXsTHVZ7XtAnExALW/d4Txxs6dw58az8nB159wxIZCb13h+JiS2SkBlNERERERJo2NRw+EA4fWLF/vGKFC+wCaNsGmjQJPmb1GrhwuMXrdbN8zXjcQ1GRxRO8IiMbNrovjGxu46YvlHz/A5QGaEVFQqdOtizg69STDWPHWDLSDQt+g4W/WRYudF8u2TzAqvtmQWVpaTD4FEvr1paOO0GHDtChg6FjB+iwU9XuUZo2NTRt6mYuKw36ys8PzOy1fEUUf/xZwp+LfRQVVXx+Zhb89LN7lP2OUW5Zx512gtatoGdPd+/UqmXgiyczn7Hk5bvgtMsuMUHBYD6f5aZbA2WNuzX4Dc/Otkx/2BId5ep/3jnBv2dengtSS0py93UpKbo3EhEREREREREREZHaoeCuLXhjluWTz7Z0RH7Z1r77GHqUmynLGENSYtWCuyI8sGFDcHBX06Yw80lD+3ZV+1Z8dfzxh2XXXcFfAgcd6JZeadXKBZNFRMCxgyw5ue7YxKTg5VgSEgxT7nVLv9RV/UREREREGpvOnQ3v/M/NjlVQWDF/6VL30+ejLKCr/Ky43861vPKapV1buPoqiI02/LnYBRb9tQxycyuWWVQMfy52j9JgKICWLS1dd4adu8BTTxjy8+H3RfDHn25GrfUbKgafLV/hAsBWr3aPb+YGl9kk1dK2LbRpDT17Gk45qWr3CrGxhl17wW67GlJSEgDYsCGdFSvd77Z0qeWHn2DlitDvW1FR+d+xlMUYaNHC0qY1LFjo3leAnbtY2raBlBT3pZeoKMvXczbVJcRSljk58M67brtdW1shuGv5Crj0Cvc+7NoLHnnQ5ft8LphsyRLLu+9DUrILguvW1VBU5OpdXOx+FhUHtouLoLDIfdHGWveIjIQBB0Pr1rr/EhEREREREREREdmRKLhrC7zb8O6EGkRp2sx9QN+0CaSmup9NmrgBkiapuECqltCsacUlRIxxS6jUpd8WwcKFbvuM0w377B1ch113tcz9btOxv0OrlsHP37WXBhVERERERLZVcrKhX9/Qeb4SF/yzYqX7IsXm/lwMP/zoHuecBSOGG445OrDk4rPPW+bNc+V4PG4msNWrQ7/WunXusXAhDL3Azbq1375uRq0VK+DnX9ysXp07QVyce40NG9wXQUpKQpeZlu4eCxZCWrqtENz1+iw/n37m7oXatzM0bw7Nm0GzZvDDjy7AKisrg4sviqVfX0OnjtCpIxxxmGHsjX6WLHHlnH+uu4dautQFf/1bye9obeD3LG/SZCgflFZefgGccrqfiAh3TxgRASX+QP76DTBshL9sZueSkuD7wUV/wMAj/RQXg99fsfxNNassY4tm/Q9eei74izciIiIiIiIiIiIi0rgpuGsL9uhjiIqyRHgDH+p7N33A7/UakpJiSUwwYPLYpUfF5z/6oMHjabgfusfHB7Zz8yrmn3yi4dBDYLfdoF3b7VYtEREREZEd1sABhoEDDPn5luLiivkrVgSCgtq3q/gFkcWLLd//6PbH32445GBDbu6mJQ//ghdftqSnu9mhSgOPdt7ZPRfgw48tERHw00+WN992+ccfB9ddaygqsjRrBhPugLX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      "text/plain": [
       "<Figure size 1200x900 with 6 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 911,
       "width": 1211
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = az.plot_trace(\n",
    "    data=model.idata,\n",
    "    compact=True,\n",
    "    kind=\"rank_bars\",\n",
    "    backend_kwargs={\"figsize\": (12, 9), \"layout\": \"constrained\"},\n",
    ")\n",
    "plt.gcf().suptitle(\"Gamma-Gamma Model Trace\", fontsize=18, fontweight=\"bold\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e0b065a",
   "metadata": {},
   "source": [
    "We can compare the results with the ones obtained by the `lifetimes` package and the MAP estimation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "21ef284f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 3 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1011,
       "width": 1211
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(\n",
    "    nrows=3, ncols=1, figsize=(12, 10), sharex=False, sharey=False, layout=\"constrained\"\n",
    ")\n",
    "\n",
    "for i, var_name in enumerate([\"p\", \"q\", \"v\"]):\n",
    "    ax = axes[i]\n",
    "    az.plot_posterior(\n",
    "        idata_mcmc.posterior[var_name].values.flatten(),\n",
    "        color=\"C0\",\n",
    "        point_estimate=\"mean\",\n",
    "        hdi_prob=0.95,\n",
    "        ref_val=ggf.summary[\"coef\"][var_name],\n",
    "        ax=ax,\n",
    "        label=\"MCMC\",\n",
    "    )\n",
    "    ax.axvline(\n",
    "        x=ggf.summary[\"lower 95% bound\"][var_name],\n",
    "        color=\"C1\",\n",
    "        linestyle=\"--\",\n",
    "        label=\"lifetimes 95% CI\",\n",
    "    )\n",
    "    ax.axvline(\n",
    "        x=ggf.summary[\"upper 95% bound\"][var_name],\n",
    "        color=\"C1\",\n",
    "        linestyle=\"--\",\n",
    "    )\n",
    "    ax.axvline(x=idata_map[var_name].item(), color=\"C2\", linestyle=\"-.\", label=\"MAP\")\n",
    "    ax.legend(loc=\"upper right\")\n",
    "\n",
    "plt.gcf().suptitle(\"Gamma-Gamma Model Parameters\", fontsize=18, fontweight=\"bold\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d6855dd",
   "metadata": {},
   "source": [
    "We see that the `lifetimes` and MAP estimates are essentially the same. Both of them are close to the mean of the posterior distribution obtained by MCMC."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "772fe770",
   "metadata": {},
   "source": [
    "## Expected Customer Spend\n",
    "\n",
    "Once we have the posterior distribution of the parameters, we can use the `expected_average_profit` method to compute the conditional expectation of the average profit per transaction for a group of one or more customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e6c61b7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "expected_spend = model.expected_customer_spend(\n",
    "    customer_id=summary_with_money_value.index,\n",
    "    mean_transaction_value=summary_with_money_value[\"monetary_value\"],\n",
    "    frequency=summary_with_money_value[\"frequency\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9a2805e",
   "metadata": {},
   "source": [
    "Let's see how it looks for a subset of customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "57706dfe",
   "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>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>x[0]</th>\n",
       "      <td>24.733</td>\n",
       "      <td>0.528</td>\n",
       "      <td>23.779</td>\n",
       "      <td>25.706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[1]</th>\n",
       "      <td>19.064</td>\n",
       "      <td>1.351</td>\n",
       "      <td>16.490</td>\n",
       "      <td>21.478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[2]</th>\n",
       "      <td>35.188</td>\n",
       "      <td>0.931</td>\n",
       "      <td>33.546</td>\n",
       "      <td>37.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[3]</th>\n",
       "      <td>35.188</td>\n",
       "      <td>0.931</td>\n",
       "      <td>33.546</td>\n",
       "      <td>37.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[4]</th>\n",
       "      <td>35.188</td>\n",
       "      <td>0.931</td>\n",
       "      <td>33.546</td>\n",
       "      <td>37.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[5]</th>\n",
       "      <td>71.350</td>\n",
       "      <td>0.628</td>\n",
       "      <td>70.233</td>\n",
       "      <td>72.528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[6]</th>\n",
       "      <td>19.064</td>\n",
       "      <td>1.351</td>\n",
       "      <td>16.490</td>\n",
       "      <td>21.478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[7]</th>\n",
       "      <td>35.188</td>\n",
       "      <td>0.931</td>\n",
       "      <td>33.546</td>\n",
       "      <td>37.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[8]</th>\n",
       "      <td>27.337</td>\n",
       "      <td>0.405</td>\n",
       "      <td>26.606</td>\n",
       "      <td>28.091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x[9]</th>\n",
       "      <td>35.188</td>\n",
       "      <td>0.931</td>\n",
       "      <td>33.546</td>\n",
       "      <td>37.002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        mean     sd  hdi_3%  hdi_97%\n",
       "x[0]  24.733  0.528  23.779   25.706\n",
       "x[1]  19.064  1.351  16.490   21.478\n",
       "x[2]  35.188  0.931  33.546   37.002\n",
       "x[3]  35.188  0.931  33.546   37.002\n",
       "x[4]  35.188  0.931  33.546   37.002\n",
       "x[5]  71.350  0.628  70.233   72.528\n",
       "x[6]  19.064  1.351  16.490   21.478\n",
       "x[7]  35.188  0.931  33.546   37.002\n",
       "x[8]  27.337  0.405  26.606   28.091\n",
       "x[9]  35.188  0.931  33.546   37.002"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(expected_spend.isel(customer_id=range(10)), kind=\"stats\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "17e3de45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x700 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 711,
       "width": 811
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax, *_ = az.plot_forest(\n",
    "    data=expected_spend.isel(customer_id=(range(10))), combined=True, figsize=(8, 7)\n",
    ")\n",
    "ax.set(xlabel=\"Expected Spend (10 Customers)\", ylabel=\"Customer ID\")\n",
    "ax.set_title(\"Expected Spend\", fontsize=18, fontweight=\"bold\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75d29614",
   "metadata": {},
   "source": [
    "Finally, lets look at some statistics and the distribution for the whole dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f700b627",
   "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>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>35.263</td>\n",
       "      <td>0.634</td>\n",
       "      <td>34.145</td>\n",
       "      <td>36.489</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean     sd  hdi_3%  hdi_97%\n",
       "x  35.263  0.634  34.145   36.489"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az.summary(expected_spend.mean(\"customer_id\"), kind=\"stats\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bb63ea2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 611,
       "width": 1011
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "az.plot_dist(expected_spend.mean(\"customer_id\"), label=\"Mean over Customer ID\", ax=ax)\n",
    "ax.axvline(x=expected_spend.mean(), color=\"black\", ls=\"--\", label=\"Overall Mean\")\n",
    "ax.legend(loc=\"upper right\")\n",
    "ax.set(xlabel=\"Expected Spend\", ylabel=\"Density\")\n",
    "ax.set_title(\"Expected Spend\", fontsize=18, fontweight=\"bold\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b72a8110",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: Fri Apr 05 2024\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.11.8\n",
      "IPython version      : 8.22.2\n",
      "\n",
      "pymc    : 5.11.0\n",
      "pytensor: 2.18.6\n",
      "\n",
      "pandas        : 2.2.1\n",
      "pymc_marketing: 0.4.2\n",
      "matplotlib    : 3.8.3\n",
      "arviz         : 0.17.1\n",
      "\n",
      "Watermark: 2.4.3\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -n -u -v -iv -w -p pymc,pytensor"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "include_colab_link": true,
   "name": "gamma-gamma model",
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  },
  "hide_input": false,
  "kernelspec": {
   "display_name": "pymc-marketing-env",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
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   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
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 "nbformat": 4,
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