diff --git a/.gitignore b/.gitignore
index 44504a49..ac4503fd 100644
--- a/.gitignore
+++ b/.gitignore
@@ -146,3 +146,4 @@ dmypy.json
 
 # Gallery images
 docs/source/gallery/images/
+docs/gettext/
diff --git a/docs/source/gallery/gallery.md b/docs/source/gallery/gallery.md
index d72d4873..b7632a96 100644
--- a/docs/source/gallery/gallery.md
+++ b/docs/source/gallery/gallery.md
@@ -72,6 +72,11 @@ Welcome to the PyMC-Marketing example gallery! This gallery provides visual navi
 :img-top: ../gallery/images/mmm_allocation_assessment.png
 :link: ../notebooks/mmm/mmm_allocation_assessment.html
 :::
+
+:::{grid-item-card} Multi-Objective Optimization
+:img-top: ../gallery/images/mmm_allocation_assessment.png
+:link: ../notebooks/mmm/mmm_multi_objective_optimization.html
+:::
 ::::
 
 ### Lift Test Calibration
diff --git a/docs/source/notebooks/mmm/mmm_multi_objective_optimization.ipynb b/docs/source/notebooks/mmm/mmm_multi_objective_optimization.ipynb
new file mode 100644
index 00000000..42503244
--- /dev/null
+++ b/docs/source/notebooks/mmm/mmm_multi_objective_optimization.ipynb
@@ -0,0 +1,7719 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "(mmm_multi_objective_optimization)=\n",
+    "# Multi-Objective optimization with PyMC-Marketing\n",
+    "\n",
+    "Have you ever faced the challenge of deciding whether to focus on bringing in new users or keeping the ones you already have? It’s a common dilemma in any growing company. On one hand, acquisition drives visibility, expansion, and excitement. On the other, retention builds loyalty, stability, and long-term value. When resources are limited, choosing where to invest—more ads to attract newcomers or more experiences to keep your current audience engaged—feels like walking a tightrope between short-term gains and lasting growth.\n",
+    "\n",
+    "This kind of trade-off isn’t unique to marketing. Product teams constantly balance innovation and reliability. Operations managers weigh efficiency against flexibility. Data scientists tune models to maximize accuracy while minimizing bias or computational cost. These are all examples of competing objectives that must coexist within finite resources. Multi-objective optimization provides a systematic framework for navigating these tensions. It allows decision-makers to explore how improving one goal may compromise another, and to find balanced solutions along the Pareto frontier—the set of decisions where no objective can be improved without sacrificing another. In essence, it transforms difficult trade-offs into informed, strategic choices.\n",
+    "\n",
+    "This notebook explores multi-objective optimization using PyMC and PyMC-Marketing, focusing on the trade-off between acquiring new users and retaining existing ones. It demonstrates how to balance competing objectives and includes practical examples of building and optimizing models (both custom and default) to achieve balanced solutions in marketing scenarios.\n",
+    "\n",
+    "## Import dependencies\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n",
+      "/Users/carlostrujillo/Documents/GitHub/pymc-marketing/pymc_marketing/mmm/multidimensional.py:216: FutureWarning: This functionality is experimental and subject to change. If you encounter any issues or have suggestions, please raise them at: https://github.com/pymc-labs/pymc-marketing/issues/new\n",
+      "  warnings.warn(warning_msg, FutureWarning, stacklevel=1)\n",
+      "/var/folders/f0/rbz8xs8s17n3k3f_ccp31bvh0000gn/T/ipykernel_46807/1835901904.py:18: UserWarning: The pymc_marketing.mmm.builders module is experimental and its API may change without warning.\n",
+      "  from pymc_marketing.mmm.builders.yaml import build_mmm_from_yaml\n"
+     ]
+    }
+   ],
+   "source": [
+    "import warnings\n",
+    "\n",
+    "import arviz as az\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pymc as pm\n",
+    "import pytensor.tensor as pt\n",
+    "from pymc_extras.prior import Censored, Prior\n",
+    "from pytensor import function\n",
+    "\n",
+    "from pymc_marketing import mmm\n",
+    "from pymc_marketing.mmm.budget_optimizer import (\n",
+    "    BudgetOptimizer,\n",
+    "    BuildMergedModel,\n",
+    "    CustomModelWrapper,\n",
+    ")\n",
+    "from pymc_marketing.mmm.builders.yaml import build_mmm_from_yaml\n",
+    "from pymc_marketing.mmm.constraints import Constraint\n",
+    "from pymc_marketing.mmm.multidimensional import MultiDimensionalBudgetOptimizerWrapper\n",
+    "from pymc_marketing.mmm.utility import _check_samples_dimensionality\n",
+    "from pymc_marketing.paths import data_dir\n",
+    "from pymc_marketing.pytensor_utils import merge_models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Notebook settings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "warnings.filterwarnings(\"ignore\")\n",
+    "\n",
+    "az.style.use(\"arviz-darkgrid\")\n",
+    "plt.rcParams[\"figure.figsize\"] = [12, 7]\n",
+    "plt.rcParams[\"figure.dpi\"] = 100\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "%config InlineBackend.figure_format = \"retina\"\n",
+    "\n",
+    "seed: int = sum(map(ord, \"mmm_multi_objective_optimization\"))\n",
+    "rng: np.random.Generator = np.random.default_rng(seed=seed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Optimizing Custom PyMC Models\n",
+    "First, we'll show some of the under-the-hood functions and how you can apply them to your custom PyMC model.\n",
+    "\n",
+    "To do so, we first need to generate a simple model and some data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_observations = 100\n",
+    "date_range = pd.date_range(start=\"2020-01-01\", periods=n_observations, freq=\"D\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def random_walk(mu, sigma, steps, lower=None, upper=None, seed=None):\n",
+    "    \"\"\"\n",
+    "    Generate a bounded random walk with specified mean and standard deviation.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    mu : float\n",
+    "        Target mean of the random walk\n",
+    "    sigma : float\n",
+    "        Target standard deviation of the random walk\n",
+    "    steps : int\n",
+    "        Number of steps in the random walk\n",
+    "    lower : float, optional\n",
+    "        Lower bound for the random walk values\n",
+    "    upper : float, optional\n",
+    "        Upper bound for the random walk values\n",
+    "    seed : int, optional\n",
+    "        Random seed for reproducibility\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    np.ndarray\n",
+    "        Random walk array with specified mean, std, and bounds\n",
+    "    \"\"\"\n",
+    "    # if seed none then set 123\n",
+    "    if seed is None:\n",
+    "        seed = 123\n",
+    "    # Create a random number generator with the given seed\n",
+    "    rng = np.random.RandomState(seed)\n",
+    "\n",
+    "    # Start from the target mean\n",
+    "    walk = np.zeros(steps)\n",
+    "    walk[0] = mu\n",
+    "\n",
+    "    # Generate the walk step by step with bounds checking\n",
+    "    for i in range(1, steps):\n",
+    "        # Generate a random increment using the seeded RNG\n",
+    "        increment = rng.normal(0, sigma * 0.1)  # Scale increment size\n",
+    "\n",
+    "        # Propose next value\n",
+    "        next_val = walk[i - 1] + increment\n",
+    "\n",
+    "        # Apply bounds if specified\n",
+    "        if lower is not None and next_val < lower:\n",
+    "            # Reflect off lower bound\n",
+    "            next_val = lower + (lower - next_val)\n",
+    "        if upper is not None and next_val > upper:\n",
+    "            # Reflect off upper bound\n",
+    "            next_val = upper - (next_val - upper)\n",
+    "\n",
+    "        # Final bounds check (hard clipping as backup)\n",
+    "        if lower is not None:\n",
+    "            next_val = max(next_val, lower)\n",
+    "        if upper is not None:\n",
+    "            next_val = min(next_val, upper)\n",
+    "\n",
+    "        walk[i] = next_val\n",
+    "\n",
+    "    # Adjust to match target mean and std while respecting bounds\n",
+    "    current_mean = np.mean(walk)\n",
+    "    current_std = np.std(walk)\n",
+    "\n",
+    "    if current_std > 0:\n",
+    "        # Center around zero, scale to target std, then shift to target mean\n",
+    "        walk_centered = (walk - current_mean) / current_std * sigma + mu\n",
+    "\n",
+    "        # Apply bounds again after scaling\n",
+    "        if lower is not None:\n",
+    "            walk_centered = np.maximum(walk_centered, lower)\n",
+    "        if upper is not None:\n",
+    "            walk_centered = np.minimum(walk_centered, upper)\n",
+    "\n",
+    "        walk = walk_centered\n",
+    "\n",
+    "    return walk"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We set the `impressions` variable as a set of independent random walks, each with a specific mean and standard deviation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 811,
+       "width": 1011
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "impressions_x1_var = random_walk(\n",
+    "    mu=0.5, sigma=0.5, steps=n_observations, lower=0, upper=2, seed=seed + 1\n",
+    ")\n",
+    "impressions_x2_var = random_walk(\n",
+    "    mu=1, sigma=0.05, steps=n_observations, lower=0, upper=2, seed=seed + 2\n",
+    ")\n",
+    "impressions_x3_var = random_walk(\n",
+    "    mu=1, sigma=0.5, steps=n_observations, lower=0, upper=2, seed=seed - 3\n",
+    ")\n",
+    "impressions_x4_var = random_walk(\n",
+    "    mu=1.2, sigma=0.5, steps=n_observations, lower=0, upper=2, seed=seed - 1\n",
+    ")\n",
+    "\n",
+    "fig, axs = plt.subplots(2, 2, figsize=(10, 8))\n",
+    "axs[0, 0].plot(impressions_x1_var, color=\"blue\")\n",
+    "axs[0, 0].set_title(\"impressions_x1_var\")\n",
+    "axs[0, 1].plot(impressions_x2_var, color=\"red\")\n",
+    "axs[0, 1].set_title(\"impressions_x2_var\")\n",
+    "axs[1, 0].plot(impressions_x3_var, color=\"green\")\n",
+    "axs[1, 0].set_title(\"impressions_x3_var\")\n",
+    "axs[1, 1].plot(impressions_x4_var, color=\"orange\")\n",
+    "axs[1, 1].set_title(\"impressions_x4_var\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once we have all the data in place, we can consolidate it into a dataframe. This will help us define our model structure, which in this case will depend only on impressions from channels $x1$, $x2$, $x3$, and $x4$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>date</th>\n",
+       "      <th>impressions_x1_var</th>\n",
+       "      <th>impressions_x2_var</th>\n",
+       "      <th>impressions_x3_var</th>\n",
+       "      <th>impressions_x4_var</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2020-01-01</td>\n",
+       "      <td>0.866098</td>\n",
+       "      <td>0.948983</td>\n",
+       "      <td>1.686121</td>\n",
+       "      <td>1.372259</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2020-01-02</td>\n",
+       "      <td>1.208886</td>\n",
+       "      <td>0.929276</td>\n",
+       "      <td>1.724699</td>\n",
+       "      <td>1.247626</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2020-01-03</td>\n",
+       "      <td>1.571934</td>\n",
+       "      <td>0.934704</td>\n",
+       "      <td>1.561938</td>\n",
+       "      <td>1.325415</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2020-01-04</td>\n",
+       "      <td>1.316408</td>\n",
+       "      <td>0.942521</td>\n",
+       "      <td>1.697812</td>\n",
+       "      <td>1.473127</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2020-01-05</td>\n",
+       "      <td>1.468910</td>\n",
+       "      <td>0.899849</td>\n",
+       "      <td>1.712968</td>\n",
+       "      <td>1.365724</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        date  impressions_x1_var  impressions_x2_var  impressions_x3_var  \\\n",
+       "0 2020-01-01            0.866098            0.948983            1.686121   \n",
+       "1 2020-01-02            1.208886            0.929276            1.724699   \n",
+       "2 2020-01-03            1.571934            0.934704            1.561938   \n",
+       "3 2020-01-04            1.316408            0.942521            1.697812   \n",
+       "4 2020-01-05            1.468910            0.899849            1.712968   \n",
+       "\n",
+       "   impressions_x4_var  y  \n",
+       "0            1.372259  0  \n",
+       "1            1.247626  0  \n",
+       "2            1.325415  0  \n",
+       "3            1.473127  0  \n",
+       "4            1.365724  0  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset = pd.DataFrame(\n",
+    "    {\n",
+    "        \"date\": date_range,\n",
+    "        \"impressions_x1_var\": impressions_x1_var,\n",
+    "        \"impressions_x2_var\": impressions_x2_var,\n",
+    "        \"impressions_x3_var\": impressions_x3_var,\n",
+    "        \"impressions_x4_var\": impressions_x4_var,\n",
+    "    }\n",
+    ")\n",
+    "dataset[\"y\"] = 0\n",
+    "dataset.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The initial model constructed in this notebook is a straightforward Bayesian model designed to capture the relationship between input features and a target variable. The model is defined as:\n",
+    "\n",
+    "$$\n",
+    "Y \\sim f(X, \\theta) + \\epsilon\n",
+    "$$\n",
+    "\n",
+    "Where:\n",
+    "- $Y$ is the target variable.\n",
+    "- $X$ represents the input features.\n",
+    "- $\\theta$ denotes the model parameters.\n",
+    "- $\\epsilon$ denotes the base sales, which are not dependent on marketing.\n",
+    "\n",
+    "The function $f(X, \\theta)$ is a deterministic transformation of the input features $X$ using the parameters $\\theta$, which in this context, is modeled using a Michaelis-Menten saturation function. This function is particularly useful for capturing diminishing returns in marketing scenarios, where the effect of increasing input (e.g., advertising spend) on the output (e.g., sales) eventually plateaus.\n",
+    "\n",
+    "Since the model is a simple saturation function, we only need to define priors for its parameters, the intercept, and the aleatoric uncertainty."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "alpha_prior = Prior(\"Beta\", alpha=2, beta=1, dims=\"channel\")\n",
+    "lam_prior = Prior(\"Gamma\", mu=1, sigma=0.5, dims=\"channel\")\n",
+    "priors = {\n",
+    "    \"lam\": lam_prior,\n",
+    "    \"alpha\": alpha_prior,\n",
+    "}\n",
+    "\n",
+    "saturation = mmm.MichaelisMentenSaturation(priors=priors)\n",
+    "\n",
+    "coordinates = {\n",
+    "    \"date\": date_range,\n",
+    "    \"channel\": dataset.drop(columns=[\"date\", \"y\"]).columns.tolist(),\n",
+    "}\n",
+    "\n",
+    "intercept = Prior(\"Normal\", mu=0, sigma=1)\n",
+    "noise = Prior(\"HalfNormal\", sigma=1)\n",
+    "likelihood = Censored(\n",
+    "    Prior(\n",
+    "        \"Normal\",\n",
+    "        sigma=noise,\n",
+    "        dims=\"date\",\n",
+    "    ),\n",
+    "    lower=0,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With all our priors defined, we can create the model structure. Both models will share the same structure but will have different target variables. (We can create a function to generate the same base model based on certain inputs)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def build_simple_mmm(dataset, coordinates, target_column):\n",
+    "    with pm.Model(coords=coordinates) as base_model:\n",
+    "        data = pm.Data(\n",
+    "            \"channel_data\", dataset[coordinates[\"channel\"]], dims=(\"date\", \"channel\")\n",
+    "        )\n",
+    "        target = pm.Data(\"target\", dataset[target_column], dims=\"date\")\n",
+    "        contribution = pm.Deterministic(\n",
+    "            \"contribution\",\n",
+    "            var=saturation.apply(data),\n",
+    "            dims=(\"date\", \"channel\"),\n",
+    "        )\n",
+    "\n",
+    "        _intercept = intercept.create_variable(\"intercept\")\n",
+    "        likelihood.create_likelihood_variable(\n",
+    "            \"likelihood\", mu=(_intercept + contribution.sum(axis=-1)), observed=target\n",
+    "        )\n",
+    "    return base_model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's take a look at our base model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
+       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
+       "<!-- Generated by graphviz version 13.1.1 (0)\n",
+       " -->\n",
+       "<!-- Pages: 1 -->\n",
+       "<svg width=\"499pt\" height=\"479pt\"\n",
+       " viewBox=\"0.00 0.00 499.00 479.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 475.04)\">\n",
+       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-475.04 495,-475.04 495,4 -4,4\"/>\n",
+       "<g id=\"clust1\" class=\"cluster\">\n",
+       "<title>clusterdate (100) x channel (4)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M20,-231.32C20,-231.32 143,-231.32 143,-231.32 149,-231.32 155,-237.32 155,-243.32 155,-243.32 155,-439.13 155,-439.13 155,-445.13 149,-451.13 143,-451.13 143,-451.13 20,-451.13 20,-451.13 14,-451.13 8,-445.13 8,-439.13 8,-439.13 8,-243.32 8,-243.32 8,-237.32 14,-231.32 20,-231.32\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"81.38\" y=\"-238.52\" font-family=\"Times,serif\" font-size=\"14.00\">date (100) x channel (4)</text>\n",
+       "</g>\n",
+       "<g id=\"clust2\" class=\"cluster\">\n",
+       "<title>clusterdate (100)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M164,-8C164,-8 256,-8 256,-8 262,-8 268,-14 268,-20 268,-20 268,-211.32 268,-211.32 268,-217.32 262,-223.32 256,-223.32 256,-223.32 164,-223.32 164,-223.32 158,-223.32 152,-217.32 152,-211.32 152,-211.32 152,-20 152,-20 152,-14 158,-8 164,-8\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"232.25\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">date (100)</text>\n",
+       "</g>\n",
+       "<g id=\"clust3\" class=\"cluster\">\n",
+       "<title>clusterchannel (4)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M175,-341.23C175,-341.23 471,-341.23 471,-341.23 477,-341.23 483,-347.23 483,-353.23 483,-353.23 483,-451.04 483,-451.04 483,-457.04 477,-463.04 471,-463.04 471,-463.04 175,-463.04 175,-463.04 169,-463.04 163,-457.04 163,-451.04 163,-451.04 163,-353.23 163,-353.23 163,-347.23 169,-341.23 175,-341.23\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"444.25\" y=\"-348.43\" font-family=\"Times,serif\" font-size=\"14.00\">channel (4)</text>\n",
+       "</g>\n",
+       "<!-- channel_data -->\n",
+       "<g id=\"node1\" class=\"node\">\n",
+       "<title>channel_data</title>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M133.62,-443.13C133.62,-443.13 70.38,-443.13 70.38,-443.13 64.38,-443.13 58.38,-437.13 58.38,-431.13 58.38,-431.13 58.38,-397.63 58.38,-397.63 58.38,-391.63 64.38,-385.63 70.38,-385.63 70.38,-385.63 133.62,-385.63 133.62,-385.63 139.62,-385.63 145.62,-391.63 145.62,-397.63 145.62,-397.63 145.62,-431.13 145.62,-431.13 145.62,-437.13 139.62,-443.13 133.62,-443.13\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"102\" y=\"-425.83\" font-family=\"Times,serif\" font-size=\"14.00\">channel_data</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"102\" y=\"-409.33\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"102\" y=\"-392.83\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "</g>\n",
+       "<!-- contribution -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>contribution</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"147.12,-321.32 56.88,-321.32 56.88,-263.82 147.12,-263.82 147.12,-321.32\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"102\" y=\"-304.02\" font-family=\"Times,serif\" font-size=\"14.00\">contribution</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"102\" y=\"-287.52\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"102\" y=\"-271.02\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- channel_data&#45;&gt;contribution -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>channel_data&#45;&gt;contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M102,-385.29C102,-369.67 102,-349.98 102,-332.85\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"105.5,-333 102,-323 98.5,-333 105.5,-333\"/>\n",
+       "</g>\n",
+       "<!-- likelihood -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>likelihood</title>\n",
+       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"210\" cy=\"-174.66\" rx=\"50.03\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"210\" y=\"-186.11\" font-family=\"Times,serif\" font-size=\"14.00\">likelihood</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"210\" y=\"-169.61\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"210\" y=\"-153.11\" font-family=\"Times,serif\" font-size=\"14.00\">Censored</text>\n",
+       "</g>\n",
+       "<!-- contribution&#45;&gt;likelihood -->\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
+       "<title>contribution&#45;&gt;likelihood</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M126.78,-263.46C135.88,-253.26 146.32,-241.69 156,-231.32 161,-225.96 166.31,-220.36 171.58,-214.87\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"173.75,-217.66 178.17,-208.04 168.71,-212.8 173.75,-217.66\"/>\n",
+       "</g>\n",
+       "<!-- target -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>target</title>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M225,-98C225,-98 195,-98 195,-98 189,-98 183,-92 183,-86 183,-86 183,-52.5 183,-52.5 183,-46.5 189,-40.5 195,-40.5 195,-40.5 225,-40.5 225,-40.5 231,-40.5 237,-46.5 237,-52.5 237,-52.5 237,-86 237,-86 237,-92 231,-98 225,-98\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"210\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">target</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"210\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"210\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "</g>\n",
+       "<!-- likelihood&#45;&gt;target -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>likelihood&#45;&gt;target</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M210,-133.68C210,-125.83 210,-117.6 210,-109.76\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"213.5,-109.79 210,-99.79 206.5,-109.79 213.5,-109.79\"/>\n",
+       "</g>\n",
+       "<!-- saturation_alpha -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>saturation_alpha</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"245\" cy=\"-414.38\" rx=\"74.42\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"245\" y=\"-425.83\" font-family=\"Times,serif\" font-size=\"14.00\">saturation_alpha</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"245\" y=\"-409.33\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"245\" y=\"-392.83\" font-family=\"Times,serif\" font-size=\"14.00\">Beta</text>\n",
+       "</g>\n",
+       "<!-- saturation_alpha&#45;&gt;contribution -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>saturation_alpha&#45;&gt;contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M205.19,-379.96C190.66,-367.76 174.09,-353.87 159,-341.23 154.3,-337.29 149.39,-333.18 144.49,-329.08\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"146.84,-326.48 136.93,-322.75 142.35,-331.85 146.84,-326.48\"/>\n",
+       "</g>\n",
+       "<!-- saturation_lam -->\n",
+       "<g id=\"node6\" class=\"node\">\n",
+       "<title>saturation_lam</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"406\" cy=\"-414.38\" rx=\"68.59\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"406\" y=\"-425.83\" font-family=\"Times,serif\" font-size=\"14.00\">saturation_lam</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"406\" y=\"-409.33\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"406\" y=\"-392.83\" font-family=\"Times,serif\" font-size=\"14.00\">Gamma</text>\n",
+       "</g>\n",
+       "<!-- saturation_lam&#45;&gt;contribution -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>saturation_lam&#45;&gt;contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M377.09,-377.39C363.75,-363.49 346.72,-349.06 328,-341.23 292.7,-326.45 192.07,-345.99 156,-333.23 151.8,-331.74 147.65,-329.81 143.63,-327.61\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"145.58,-324.7 135.23,-322.44 141.91,-330.66 145.58,-324.7\"/>\n",
+       "</g>\n",
+       "<!-- intercept -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>intercept</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"210\" cy=\"-292.57\" rx=\"44.72\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"210\" y=\"-304.02\" font-family=\"Times,serif\" font-size=\"14.00\">intercept</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"210\" y=\"-287.52\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"210\" y=\"-271.02\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- intercept&#45;&gt;likelihood -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>intercept&#45;&gt;likelihood</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M210,-251.68C210,-243.76 210,-235.33 210,-227.09\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"213.5,-227.13 210,-217.13 206.5,-227.13 213.5,-227.13\"/>\n",
+       "</g>\n",
+       "<!-- likelihood_sigma -->\n",
+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>likelihood_sigma</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"351\" cy=\"-292.57\" rx=\"78.14\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"351\" y=\"-304.02\" font-family=\"Times,serif\" font-size=\"14.00\">likelihood_sigma</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"351\" y=\"-287.52\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"351\" y=\"-271.02\" font-family=\"Times,serif\" font-size=\"14.00\">Halfnormal</text>\n",
+       "</g>\n",
+       "<!-- likelihood_sigma&#45;&gt;likelihood -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>likelihood_sigma&#45;&gt;likelihood</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M309.84,-257.73C292.09,-243.14 271.33,-226.08 253.35,-211.3\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"255.89,-208.85 245.95,-205.21 251.45,-214.26 255.89,-208.85\"/>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.graphs.Digraph at 0x3285ed0d0>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "base_model = build_simple_mmm(dataset, coordinates, \"y\")\n",
+    "base_model.to_graphviz()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using this simple structure, we can now generate the target variable based on fixed parameters. To do so, we'll use the `pm.do` function from PyMC. You can [read more about it here](https://www.pymc.io/projects/examples/en/latest/causal_inference/interventional_distribution.html).\n",
+    "\n",
+    "We'll set a grid of true parameters (the ones the model should be able to recover) for our model's internal functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "first_model_grid = {\n",
+    "    \"intercept\": 0.5,\n",
+    "    \"saturation_lam\": np.array([0.1, 0.3, 1, 0.8]),\n",
+    "    \"saturation_alpha\": np.array([0.3, 0.5, 4, 3]),\n",
+    "    \"likelihood_sigma\": 0.05,\n",
+    "}\n",
+    "first_model = pm.do(base_model, first_model_grid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "second_model_grid = {\n",
+    "    \"intercept\": 0.5,\n",
+    "    \"saturation_lam\": np.array([1, 0.8, 0.6, 0.2]),\n",
+    "    \"saturation_alpha\": np.array([4, 3, 0.4, 0.5]),\n",
+    "    \"likelihood_sigma\": 0.09,\n",
+    "}\n",
+    "second_model = pm.do(base_model, second_model_grid)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that our **true models** are defined, we can draw from the fixed parameters to generate data. This will help us create a dataset with two target variables. Both are dependent on the same inputs, but while they are generated similarly, their weights are different.\n",
+    "\n",
+    ":::{note}\n",
+    "This will position us in the situation we want: *two different outcomes, driven by the same drivers*.\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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>date</th>\n",
+       "      <th>impressions_x1_var</th>\n",
+       "      <th>impressions_x2_var</th>\n",
+       "      <th>impressions_x3_var</th>\n",
+       "      <th>impressions_x4_var</th>\n",
+       "      <th>y</th>\n",
+       "      <th>y_first_model</th>\n",
+       "      <th>y_second_model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2020-01-01</td>\n",
+       "      <td>0.866098</td>\n",
+       "      <td>0.948983</td>\n",
+       "      <td>1.686121</td>\n",
+       "      <td>1.372259</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5.506957</td>\n",
+       "      <td>4.623229</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2020-01-02</td>\n",
+       "      <td>1.208886</td>\n",
+       "      <td>0.929276</td>\n",
+       "      <td>1.724699</td>\n",
+       "      <td>1.247626</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5.486536</td>\n",
+       "      <td>4.903350</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2020-01-03</td>\n",
+       "      <td>1.571934</td>\n",
+       "      <td>0.934704</td>\n",
+       "      <td>1.561938</td>\n",
+       "      <td>1.325415</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5.580586</td>\n",
+       "      <td>5.349827</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2020-01-04</td>\n",
+       "      <td>1.316408</td>\n",
+       "      <td>0.942521</td>\n",
+       "      <td>1.697812</td>\n",
+       "      <td>1.473127</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5.585453</td>\n",
+       "      <td>5.061801</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2020-01-05</td>\n",
+       "      <td>1.468910</td>\n",
+       "      <td>0.899849</td>\n",
+       "      <td>1.712968</td>\n",
+       "      <td>1.365724</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5.504953</td>\n",
+       "      <td>5.157330</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        date  impressions_x1_var  impressions_x2_var  impressions_x3_var  \\\n",
+       "0 2020-01-01            0.866098            0.948983            1.686121   \n",
+       "1 2020-01-02            1.208886            0.929276            1.724699   \n",
+       "2 2020-01-03            1.571934            0.934704            1.561938   \n",
+       "3 2020-01-04            1.316408            0.942521            1.697812   \n",
+       "4 2020-01-05            1.468910            0.899849            1.712968   \n",
+       "\n",
+       "   impressions_x4_var  y  y_first_model  y_second_model  \n",
+       "0            1.372259  0       5.506957        4.623229  \n",
+       "1            1.247626  0       5.486536        4.903350  \n",
+       "2            1.325415  0       5.580586        5.349827  \n",
+       "3            1.473127  0       5.585453        5.061801  \n",
+       "4            1.365724  0       5.504953        5.157330  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset[\"y_first_model\"] = pm.draw(first_model[\"likelihood\"])\n",
+    "dataset[\"y_second_model\"] = pm.draw(second_model[\"likelihood\"])\n",
+    "dataset.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 511,
+       "width": 1011
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dataset.set_index(\"date\")[[\"y_first_model\", \"y_second_model\"]].plot(figsize=(10, 5))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This looks fantastic! Our outcomes are quite different, even though the internal variables are the same. This is where the real challenge begins.\n",
+    "\n",
+    "Because we have two different target variables, we can say, \"*Let's build two different models, one for each target variable.*\" This is a promising idea, and we can simulate this situation using our `build_simple_mmm` function again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_users_model = build_simple_mmm(dataset, coordinates, \"y_first_model\")\n",
+    "reengage_users_model = build_simple_mmm(dataset, coordinates, \"y_second_model\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With our two models built, we can now train them!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_grid = {\n",
+    "    \"draws\": 200,\n",
+    "    \"tune\": 800,\n",
+    "    \"chains\": 4,\n",
+    "    \"target_accept\": 0.84,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [saturation_alpha, saturation_lam, intercept, likelihood_sigma]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a54384a3b452402abc2974de8c95be7c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 800 tune and 200 draw iterations (3_200 + 800 draws total) took 1 seconds.\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [saturation_alpha, saturation_lam, intercept, likelihood_sigma]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f8489dd3f0c245238026ed09ac0db2ff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 800 tune and 200 draw iterations (3_200 + 800 draws total) took 1 seconds.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n"
+     ]
+    }
+   ],
+   "source": [
+    "with new_users_model:\n",
+    "    new_users_idata = pm.sample(**sample_grid)\n",
+    "with reengage_users_model:\n",
+    "    reengage_users_idata = pm.sample(**sample_grid)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that both of our models are trained on our data, we can proceed with optimization.\n",
+    "\n",
+    "Before optimizing, we need to wrap our models in a protocol that allows the optimizer to use them. We have built a small wrapper to make any PyMC model compatible quickly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[31mInit signature:\u001b[39m\n",
+      "CustomModelWrapper(\n",
+      "    base_model: pymc.model.core.Model,\n",
+      "    idata: arviz.data.inference_data.InferenceData,\n",
+      "    channels: collections.abc.Sequence[str],\n",
+      ") -> \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "\u001b[31mDocstring:\u001b[39m      Wrapper for the BudgetOptimizer to handle custom PyMC models.\n",
+      "\u001b[31mInit docstring:\u001b[39m\n",
+      "Create a new model by parsing and validating input data from keyword arguments.\n",
+      "\n",
+      "Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be\n",
+      "validated to form a valid model.\n",
+      "\n",
+      "`self` is explicitly positional-only to allow `self` as a field name.\n",
+      "\u001b[31mFile:\u001b[39m           ~/Documents/GitHub/pymc-marketing/pymc_marketing/mmm/budget_optimizer.py\n",
+      "\u001b[31mType:\u001b[39m           ModelMetaclass\n",
+      "\u001b[31mSubclasses:\u001b[39m     "
+     ]
+    }
+   ],
+   "source": [
+    "CustomModelWrapper?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You only need to provide your channel list (you can use the model coordinates for this), the `idata`, and the PyMC model object.\n",
+    "\n",
+    "::: {tip}\n",
+    "Building a custom class wrapper is quite easy. Your model only needs to follow the protocol in `OptimizerCompatibleModelWrapper`. Internally, you can customize it as much as you want.\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_users_optimizable_model = CustomModelWrapper(\n",
+    "    base_model=new_users_model,\n",
+    "    idata=new_users_idata,\n",
+    "    channels=coordinates[\"channel\"],\n",
+    ")\n",
+    "\n",
+    "reengage_optimizable_model = CustomModelWrapper(\n",
+    "    base_model=reengage_users_model,\n",
+    "    idata=reengage_users_idata,\n",
+    "    channels=coordinates[\"channel\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once your model is wrapped, we must create a custom objective function that allows you to work with the variable to be optimized. Our model has the deterministic variable `contribution`, which has the dimensions `sample, date, channel`. The optimizer works with the posterior over all dimensions, meaning we need to collapse the `date` and `channel` dimensions.\n",
+    "\n",
+    "Take a look at how it works!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def average_response(\n",
+    "    samples: pt.TensorVariable, budgets: pt.TensorVariable\n",
+    ") -> pt.TensorVariable:\n",
+    "    \"\"\"Compute the average response of the posterior predictive distribution.\"\"\"\n",
+    "    return pt.mean(_check_samples_dimensionality(samples.sum(axis=(1, 2))))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With our function in place, all we need to do is define our optimization horizon, and we are set. We pass our variable to be optimized and the utility function that gets this variable and performs an operation. In this case, it takes the mean value of the posterior from the total contribution over channels and days.\n",
+    "\n",
+    "Because we have two models, we will run two optimizations. Take a moment to look in detail at how the optimizer must be called 🙌🏻"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimization_horizon = 10\n",
+    "budget_per_time_unit_in_horizon = 2\n",
+    "optimizer_new_users = BudgetOptimizer(\n",
+    "    model=new_users_optimizable_model,\n",
+    "    num_periods=optimization_horizon,\n",
+    "    response_variable=\"contribution\",\n",
+    "    utility_function=average_response,\n",
+    ")\n",
+    "allocation_new_users, result_new_users = optimizer_new_users.allocate_budget(\n",
+    "    total_budget=budget_per_time_unit_in_horizon,\n",
+    ")\n",
+    "\n",
+    "optimizer_reengage = BudgetOptimizer(\n",
+    "    model=reengage_optimizable_model,\n",
+    "    num_periods=optimization_horizon,\n",
+    "    response_variable=\"contribution\",\n",
+    "    utility_function=average_response,\n",
+    ")\n",
+    "allocation_reengage, result_reengage = optimizer_reengage.allocate_budget(\n",
+    "    total_budget=budget_per_time_unit_in_horizon,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once both optimizations are complete, we can observe the results!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAACXcAAAP/CAYAAACBf20nAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjMsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvZiW1igAAAAlwSFlzAAAewgAAHsIBbtB1PgABAABJREFUeJzs3XdUFOf7NvBr6b0pVhAVFRSx9957w26ssUfs7auJGjUaS9Qo9o5dY0+MXezGgiKIYkFFAQsqvcOy7x+8zI/ZAruwFPX6nOM5zrMzzzzL7sxOuee+JTKZTAYiIiIiIiIiIiIiIiIiIiIiIiIqVHQKegBERERERERERERERERERERERESkiMFdREREREREREREREREREREREREhRCDu4iIiIiIiIiIiIiIiIiIiIiIiAohBncREREREREREREREREREREREREVQgzuIiIiIiIiIiIiIiIiIiIiIiIiKoQY3EVERERERERERERERERERERERFQIMbiLiIiIiIiIiIiIiIiIiIiIiIioEGJwFxERERERERERERERERERERERUSHE4C4iIiIiIiIiIiIiIiIiIiIiIqJCiMFdREREREREREREREREREREREREhRCDu4iIiIiIiIiIiIiIiIiIiIiIiAohBncREREREREREREREREREREREREVQgzuIiIiIiIiIiIiIiIiIiIiIiIiKoQY3EVERERERERERERERERERERERFQIMbiLiIiIiIiIiIiIiIiIiIiIiIioEGJwFxERERERERERERERERERERERUSHE4C4iIiIiIiIiIiIiIiIiIiIiIqJCiMFdREREREREREREREREREREREREhRCDu4iIiIiIiIiIiIiIiIiIiIiIiAohvYIeABERERHRtyAkJAStW7cWpkuXLg0vL68CHBEVhGPHjmH27NnCtJubG5YuXapyfn5vvj5r167FunXrhOnx48djwoQJBTiib9uHDx/w/PlzvH//HjExMUhOToaZmRksLCxQvHhxuLi4wMLCoqCHSd+RwYMH4+7du8L07t27Ub9+/QIcERERERERERERfesY3EVEREREWZo1axaOHz+u1rwSiQSmpqYwNzeHvb09qlSpgmbNmqFhw4bQ0WHSWCo8Tp06hWnTponaTExMcPPmTZiYmBTQqIgIAAICAnD06FF4eXkhNDQ0y3klEgkcHBzQokULdO/eHVWqVMmnURLRt0g+gDdD9+7dsXz5co37e/ToEXr37i1qe/bsWY7HR6q1atVK9JuR2+Br+e8CA/DpWyD/IIoqOjo6MDExgbm5OUqUKAEXFxfUqVMHrVu3hoGBQT6MlIiIiIiI5DG4i4iIiIi0RiaTITY2FrGxsXj//j3u3r0LT09PlC5dGjNmzEDHjh0LeoiUT0JCQkRBgaVLl0bPnj0LcERiygIW4+Pjce7cObi5uRXAiCg/eHp6IiYmRpgeOnQosz4VIq9evcKSJUtw7do1tZeRyWQICgqCp6cnPD094eLigmnTpqFx48Z5OFIi+t78888/GD16NCpUqFDQQyEiynNpaWmi83ofHx/s3bsXVlZWGDZsGEaNGgU9Pd5aIiIiIiLKT0yfQERERER5LjQ0FJMnT8acOXMgk8kKejiUD0JDQ7Fu3Trhn7rZ3/LDx48fcevWLaWvHTt2LJ9HQ/lp9+7dou9ldHR0QQ+J/r99+/ahW7duWQZ2SSQSWFpawtTUFBKJROk8jx8/xvDhwzFv3ry8GioRfYfS0tKwevXqgh4GEVGBioyMxOrVqzFgwAAeRxMRERER5TM+XkFEREREGilbtiyGDRum9DWpVIrY2Fi8fv0at2/fxocPH0SvHz58GFZWVpg+fXo+jJRIuRMnTiAtLU3pa/fu3UNISAjs7OzyeVRE368lS5bA09NTob1o0aJo164dmjVrBldXV1hbW0NXVxcAkJCQgFevXuHevXs4c+YMHj58KFr29evX+TByIvqeXLhwAY8ePYKrq2tBD4WISGvmz5+v0JaRuSsoKAj//fcf3r9/L3rdz88P48aNw65du4RjMyIiIiIiylsM7iIiIiIijRQrVgwDBgzIdr60tDScPHkSCxcuRHx8vNC+c+dO9OjRg2VtqMDIZxHT1dWFVCoFkF7i7cSJExg/fnxBDI2+AhMmTMCECRMKehjfDA8PD4XALiMjIwwfPhwjR46Eqamp0uWMjY3h4uICFxcXDBs2DI8fP4aHhweuXLmS94Om79qePXsKeghUgFavXo3t27cX9DCIiLQmu3N7qVSKY8eO4ffffxed19+7dw/Hjx9H796983qIREREREQElmUkIiIiojyio6MDNzc3hRI2qampLH1HBcbHx0eU0UdfXx+jRo0SzXPixAmWDyXKB5cvX8b69etFbebm5tixYwcmTZqkMrBLGRcXF2zevBkeHh6wtLTU9lCJ6Dtla2srmr5x4wbu3r1bQKMhIsp/urq66NOnD1avXq1QFnvr1q0FNCoiIiIiou8Pg7uIiIiIKE81b94cdevWFbXdvHmzgEZD3zv5rF1NmzbF0KFDoaf3f0mNg4OD4e3tnd9DI/quxMbGYu7cuaI2AwMDeHp6onbt2jnut3379jh27BgcHR1zO0QiIjg5OaFFixaitj///LNgBkNEVICaN2+Oli1bitqCgoIQEhJSQCMiIiIiIvq+MLiLiIiIiPJcgwYNRNMfPnwooJHQ9ywpKQlnzpwRtfXo0QM2NjZo2rSpqJ3Z5YjylqenJz59+iRqGz9+PKpWrZrrvu3s7DBv3rxc90NEBABTpkwRZat58OABrl69WoAjIiIqGO3bt1do8/PzK4CREBERERF9f/Syn4WIiIiIKHfkS9rExcUV0Eg0FxoaCj8/P3z8+BFJSUmwtLRExYoVUb16dVG2J22JiIjA/fv38fHjR0RHR8PMzAwODg6oVasWzMzMtL6+DC9fvsSLFy8QHh6O6OhomJubo0iRIqhWrRpKlSqVZ+vNTxcuXEB0dLQwbWlpKTx93qNHD1y+fFl47dy5c5g3bx6MjY3zfZw5FRYWBj8/P3z58gWRkZEwMTGBjY0NypcvD2dnZ4UyKrn15csXYX0RERGQyWQwNTWFnZ0dnJycUKJEiRz1GxkZicDAQLx58wbR0dFITEyEubk5LC0tUalSJVSqVEnr76UgyGQyPHnyBK9fv0Z4eDgSEhJgbW0NGxsb1KxZE0WKFMmT9aakpODhw4d48eIFoqKiYGxsjKJFi6JmzZooXbp0nqxTXnJyMvbt2ydqK1u2LEaOHKm1dejoaP4sW2RkJB4+fIhPnz4hIiICBgYGsLGxgYODA6pWrQpdXV2tjS+zly9fwt/fH2FhYZBKpShSpAhcXV3h7Oyc7bIymQz+/v54+vQpIiIioK+vD1tbW9StWxfFixfX+lg/fPiAhw8f4t27d0hOTkbRokVhZ2eH2rVrQ19fP9f9F+T2n5SUBF9fX7x69QpRUVEAgCJFiqB79+5aeW8ZoqOjERAQgDdv3iAmJgZJSUkwMjKCmZkZSpYsCQcHB5QpUyZX60hNTYWfnx9CQkIQHh6O5ORkWFtbo1ixYqhZsyYsLCy09G7EEhIS8ODBA7x+/RoxMTEwMzODra0t6tSpg6JFi+bJOvODs7MzOnToIAoQX716NZo1a5bvv0efPn2Cn58fwsPDhf1UkSJFUKFChTz5raecS0lJEY6vIyIiEBcXB11dXZiYmMDW1hZ2dnaoUKECDA0Nc7UeqVSKR48eISQkBF++fEFiYqKwvdeqVSvPtvfAwEAEBAQgLCwMSUlJMDc3R5MmTVCuXDmVy8TFxeHZs2d49eoVYmJikJCQAENDQ5iYmKBkyZKwt7dHuXLlcvQb/jUIDg7G06dPERYWhtjYWMhkMhgbG8Pa2hqlS5eGo6MjrKysCnqYWapYsaJCW3h4eI77y89z0KioKDx8+BCfP39GeHg49PT0YG1tjbJly8LV1TVPjvPy47g7NjYW3t7e+PDhAyIiImBsbIwyZcqgZs2asLa21so6MkilUgQFBeHVq1cICwtDTEwM9PT0YGlpieLFi6N69epaLY8uk8nw6NEjYX0SiQS2trZwcXFR+l3Ulri4ODx8+BBhYWEIDw+HTCaDjY0N7OzsUKNGDRgYGOTZuomIiIiywuAuIiIiIspzCQkJomlTU9Nslxk8eDDu3r0rTO/evRv169dXe53Hjh3D7NmzhWk3NzcsXbpU7eVv3rwJDw8PPHz4UOnrVlZWGDBgAMaMGaOVAKCAgACsWLECt2/fRmpqqsLrBgYG6NSpEyZPnoySJUsCAFq1aoXQ0FBhnkuXLsHOzk7tdX7+/Blbt27F+fPn8e7dO5XzVahQAUOGDEHv3r2zvOh9584dDBkyROlrd+/ehZOTk8plS5cuDS8vL7XHnhPyJRk7duwoXJht1aoVLC0thRv6cXFxOHfuHHr06JGnY8otqVSKo0ePYv/+/QgICFA5n62tLTp27IiffvoJNjY2OV5ffHw89u3bh3/++QfPnz+HTCZTOa+DgwNatmyJPn36oEKFCirnS0tLw927d3HhwgXcvn0bgYGBWY7BwsICnTt3xogRI2Bvb5/tmOW3k8xat26d5bLK9jtr167FunXrhOnx48djwoQJ2Y4jQ1hYGDZu3Ihz587hy5cvSueRSCSoUqUKBg8ejO7du6t9kzMkJET0njJvV7Gxsdi8eTMOHjwoCnLMzMXFBdOmTUPjxo3Vfj85ceXKFYUbgX379s2z4KnsnD17Fp6envD19UVaWprSeaysrNCqVSu4u7trtJ+dNWuWaN+zZMkS9OzZEwBw+vRpbNiwAS9evFC6rLOzM2bNmoWGDRsqvJaSkoJ9+/Zhx44d+Pjxo8LrEokETZo0wc8//4zy5curNdasfje9vb2xZs0a0e9yZlZWVujevTsmTpyoUSByXm//GbLaNt6/f49169bh33//VTheAYB27dqJgrtyenzi5eWFPXv24Pbt2yq/ZxmsrKxQp04dtG/fHl26dFF7HxAUFIT169fjypUrKrdzXV1d1KxZE8OHD892H5iZ/G98vXr1sGfPHgDpxxNr167FiRMnkJiYqLCsRCJBvXr1MH36dFSrVk3tdRYmkyZNwvnz5yGVSgEAT548wZkzZ9CpU6c8X3dycjL279+P48eP4+nTpyrns7W1hZubG0aNGpVtQM/du3cxePBgYbpBgwbYtWtXtmNZsmQJPD09RW2//vorfvjhh2yXbdq0KcLCwoTpa9eu5UkQakELDQ3F5s2bcfbsWeG4UhV9fX1UrlwZrVq1Qs+ePTX6ezx9+hSbN2/G9evXERMTo3QeXV1d1K5dG+PGjVP6W6KKqv1camoqDhw4gF27diE4OFhhudmzZysN7vL29saOHTtw7do1pKSkZLluU1NT1KxZE+3atUP37t1hZGSk9rizMmzYMPz333/C9IQJEzB+/Pgc9fXTTz+JzltGjBiBmTNnKp03Li4Onp6eOH78uNK/WWYSiQQODg5o2rQp3Nzc4OLikqPx5SVlv/Gqvn+qaPscNCtSqRR///03Dh48iEePHgn7cHlWVlbo0KED3N3dUaxYMbX6Lujj7uDgYKxYsQKXL19GUlKSwuu6urpo0aIFpkyZIgRC5eQY5tOnTzh37hyuXbuG+/fvIzY2VuW8EokELi4uGDZsGDp16pTjzy05ORk7duzAgQMHVGZ9L1euHMaOHSucr+f2+g8AXLx4Ebt378aDBw9U7qtMTEzQqlUrTJgwAWXLltWofyIiIqLc+jYfgyEiIiKiQuXZs2eiaUdHxwIaSfakUinmzZuH4cOHqwzsAtKzi2zcuBE9evTI9kJ9dtavX49evXrhxo0bSgO7gPQLnCdOnEDnzp1x7dq1XK0PALZt24a2bdvC09Mzy4vqQPqT+fPmzUP37t1z/V4LysePH3Hr1i1RW+bALQMDA3Ts2FH0unwwWGHz4sULdOnSBXPnzs0ysAtIvyi/e/dutGnTBocPH87R+k6cOIFWrVphxYoVePbsWZaBXQDw5s0beHp6onPnzlnO179/fwwdOhR79+7NNrADSM96c+DAAXTs2BH79+/X6D0UtD179qBdu3bYv3+/ysAuIP0p9cePH2PWrFno0aMHgoKCcrXep0+folu3btiyZYvKG0wA8PjxYwwfPhzr16/P1fqykzlLHpB+88nNzS1P16nMhw8f0K9fP0yaNAk+Pj5ZBtxERkbi2LFj6NChAzZs2JCr9SYnJ2P69OmYMmWKysAuIP1zGz58uBBAk+Hjx4/o378/lixZojSwC0j/Dl2/fh29evWCt7d3rsa7bt06DBo0SGVgF5D+99m1axc6deoEHx8ftfsu6O3/0qVL6NSpE44cOaI0sEsbEhIS4O7ujp9++gm3bt3KNrALSP97Xrx4ETNmzMjyJmoGmUyGVatWoXPnzvj777+z3M6lUim8vb0xbtw4DB48OMt9kTr+++8/dO7cGQcPHlQa2JUxvjt37qB///4alz1u1aoVnJychH+ZA5LyU7ly5RQCvj08PFQGCmjLtWvX0K5dOyxZsiTLwC4g/bd+y5YtaNu2bbZlI2vUqAETExNh+sGDByo/v8zkj6VUtckLDAwUBXY5Ojp+k4Fdhw8fRqdOnXDo0KFsA7uA9EBdPz8/rF69WqF0uCoJCQnC8cHp06ezDKyRSqW4e/cuhg0bhokTJ+ZqP/fp0ycMGDAAixYtUvt8QCqV4tdff8XAgQNx6dKlbAO7gPRgqBs3bmDevHl4/fp1jscrr1evXqLpY8eOZXssq8yXL19w/fp1UVtG0LY8Pz8/dOzYER4eHmr9zWQyGYKCgrBnz548PxbLKWXfN00edMrPc9BHjx6hW7dumDVrFh4+fJjl/joyMhIHDx5Eu3btcPToUY3XlVl+HHcfPnwYXbp0wdmzZ5UGdgHp29+lS5fg5uaW4/d05coVNGvWDL/99huuXr2a7TFJRkbZ6dOnY8CAASqPU7Py9u1bdO/eHX/++afKwC4AeP36Nf73v/9h3LhxuT6Ge/v2Lfr16wd3d3fcuXMny31VfHw8Tp06hc6dO2Pjxo25Wi8RERGRphjcRURERER5KiIiAhcuXBC15XVWmJySSqWYOXMmDh06pPR1IyMj0Y0wID1LxrBhwxAREZGjda5atUrlzUF9fX2FzAtxcXFwd3fH/fv3c7S+lJQUzJ49G3/88Qfi4+OVrtPKykppCaoXL16gX79+2d5cLIxOnDghuqHu4OCAmjVriuaRv2l7586dbG86FBRvb2/88MMPePXqldLXLSwslJYNjYuLw5w5c7B27Vq11yWTybBs2TL873//U/k9NzIygqWlZY7K6Ki66aGjowNzc3NYWFgo7TclJQULFizApk2bNF5nQVi2bBkWLVqk9OaDsm09w7NnzzBgwAD4+fnlaL3Pnz/HkCFDFLKXmZubq8yG4eHhkesbW1mRDxJydHTMVUa5nHj58iX69++vMojXzMxM6X4wJSUFa9aswc8//6xWkI48mUyGmTNn4p9//hG1GxsbK81qmZaWhsWLFwuBGhERERg8eDD8/f1F86n6POPj4zF27Ngc3VwDgO3bt2Pt2rWiG+C6urqwsLBQWv7t48ePGDlyJHx9fdXqvyC3/5s3b2LixIkKv4Xm5uZaLbczfvx4XLx4UelrhoaGsLa2hqmpaY7L6aWmpmLatGnYvHmzysyfqrKp3b17F/369cPbt29ztO7//vsPo0ePRmRkpNAmkUhgaWmp9G8olUrx888/i7LnfE3Gjx8v2i+8fv0aJ06cyLP17d+/H2PHjsX79+8VXtPR0YGlpaXS7T4yMhLjxo3DyZMnVfZtYGCA2rVrC9PJycnZBoJ++fJFaUDqnTt3st0fygeANWrUKMv5v0b//PMP5syZozRILmO/qeoYW12fPn3CwIEDcfz4caWBSYaGhiqPx86dO4chQ4aoFTAqLy4uDsOHD1c4Fslq/wIACxYswMGDB5W+lnHOYWZmli9lGNu2bQtzc3NhOjQ0FHfu3NG4n7///lsU+FG9enWlGWpfvXqFYcOGqfz9NTU1hbW19VdX3u358+cKbepkNM3vc9CLFy9i8ODBSgPHJRIJzM3NFc7rgfTgyZ9//hlbtmxRe12Z5cdx98GDBzF37lyV+xpLS0vRMUVKSgp++eUXtQNIM4uLi1O5fzc0NISVlZXK0rK+vr7o27evRkHkb9++xeDBg1We45qbmyt8Ry5duoQpU6bkKFgTSA9u7tu3r8pzAhMTE6X7udTUVKxevRrz5s3L0XqJiIiIcoJlGYmIiIgoz8TGxmLy5MmiJ3xNTU3Rt2/fAhyVatu3b8epU6dEbUWLFsVPP/2Edu3aCSUawsPD4eXlhY0bNyIkJAQhISH47bffNF7fxYsXsXnzZlGbiYkJRowYgc6dO6Ns2bKQSCRITk7G3bt3sW/fPnh5eSE5ORkzZsxQK8OCvN9//10ha4aTkxMGDRqEhg0bispcvXz5EmfOnIGnp6fwGX758gUTJ07EsWPHFC5yli1bFvPnzweQHvSWuWxP2bJlMWzYMJXjUqdUZ27IZ+Hq1q2bwjw1a9ZE2bJlhSxJMpkMJ06cwLhx4/J0bJr6+PEj3N3dFYIimjdvjkGDBqFBgwYwMDBAWloaXr58iRMnTmDXrl2iG1Hr1q1DuXLl0KVLl2zXt27dOuzYsUPUpquri+7du6NDhw6oXbu28F2QyWR4+/Yt/Pz8cOnSJVy9elXpDRxlXFxc0KJFC9SsWROVKlVCsWLFhBsTqampCAwMxOXLl7Fv3z58+vRJWM7DwwN16tRBnTp1lPY7efJkxMXFAQBWr14tCkKYPHkyrKysVI5JW6U2Dh06pPA3NDIywo8//oiuXbuifPnykEgkSExMxO3bt+Hp6SkKfggPD4e7uztOnjypURBUYmIixo8fj6ioKEgkEnTq1Al9+vRBrVq1hBsxwcHBOH78OLZt2yZ68n/p0qVo06YNLC0tc/nuxWJiYhASEiJqq1q1qlbXkZ34+HiMGzdOIWAio1RdkyZNYGJiAplMhpCQEPz777/YsmWL8D0CgKNHj6JMmTIYO3asRus+ePCgcHO8bNmyGDt2LJo3by58rp8/f8aJEyewfv16YduRyWRYuHAhzp49i+nTp+PNmzcAgCZNmmDIkCGoV6+ekDXj1atX2LVrl+hmekxMDJYvX46VK1dqNNbnz58LQWg6Ojro168f+vTpA2dnZ+jq6iI5ORn37t3Drl27RFmCYmNj4e7ujtOnT2dbGi5DXm3/qsTFxWHmzJlCMFT79u3Rr18/1KlTB4aGhpDJZAgLC8OZM2dyFYhx6tQp3LhxQ9RWr149DBw4EHXq1EHRokWFdqlUirdv3+LZs2e4ceMGLl++jM+fP2e7Dg8PD/z777+iNktLS4wePRodOnQQbrrHxsbi2rVr2LZtGx4/fizMGxwcDHd3dxw9elSjIINPnz5h8uTJSE5Ohr6+Pnr16oXu3bvD1dVV+JsFBgbi4MGD2Ldvn3BzWCaTYe7cuTh37lyBlWLNqVKlSqF///6ibHrr169H165dtR6gcfbsWSxYsEDUZmtriwEDBqBly5ZwcnIS/n5hYWG4evUqtmzZIgTqpaamYs6cOahUqRIqV66sdB0NGzYUZSC6desWmjRponJMt27dUnrzPDo6Gv7+/lmW3JQP7tKkRODXIC4uDosWLRK12draYtiwYWjRogUcHBxE+5Lw8HC8ePEC9+7dw5UrV/Do0aNs15GcnIwxY8aItl8AaNGiBXr37o3atWsLvyWpqanw8/PD4cOHcfLkSeEhEj8/P8ydOxd//vmnRu9v5cqVQpCMg4MDRo0ahebNmwvnRvHx8bh9+7boeN7Hx0fhgZkqVapg6NChqF+/PkqUKCHs52UyGUJDQ/H8+XPcunULXl5eKktq55SRkZGQVS3D8ePH0aBBA436kT+nkM8IlmHx4sWi4wYDAwMMGDAAHTt2hLOzsyjbVXx8PAIDA+Hn54crV67g9u3bGo0pP509e1Y0raOjgxo1amS7XF6eg8rz8fHBpEmTRAHP5ubm6NOnD9q3b48qVaoI++zIyEjcvHkTW7duFWVCXrVqFZycnNC8efNs31uG/DjufvToERYuXCjaF+vr62PgwIFwc3NDpUqVoKOjg9TUVPj6+uLw4cM4ceKE8Nub+bhDExYWFmjatCmaNGmCypUro3z58qKgrvDwcPj4+ODo0aO4dOmS0P7hwwfMnDkT27dvz3YdqampmDx5skK2rrp16+LHH39Eo0aNhO0mNDQU58+fx6ZNmxAZGYnLly8rlHxXx9u3bzFq1ChR0KuhoSF69OiBLl26wNXVVVhnXFwc7ty5g507d4oeVDl06BAqV66MAQMGaLx+IiIiIk0xuIuIiIiItEYqlSI2NhZBQUG4efMmDhw4ICrBIpFIsHDhQtja2hbgKJULCgrCunXrRG3VqlXD1q1bFYI/bGxs0Lt3b3Tq1AmTJk3CtWvX1M5SkiE2NlYIhMpQunRp7Nq1S3RxG0i/GdCkSRM0adIEBw8exPz583N0w+Ps2bOiMlYSiQSTJk3CmDFjlD4x7+joiPHjx6N79+4YPXq08ATtmzdvsGrVKoWnVIsXLy5c1Lxz544ouKtYsWIFdsHTx8dHVNZFIpGge/fuSuft3r071qxZI0wXxuCun3/+WSFLyty5czFw4EDRfDo6OqhYsSJmzJiBrl27Yvjw4aInp3/99VfUrVs3y7JIN27cUCgVYm9vjw0bNqBSpUoK80skEjg4OMDBwQFdu3ZFVFQU9u7dm+X76dGjB1q3bo2KFSuqnEdPTw/Ozs5wdnbG4MGDMX36dKG0n1QqxZo1axRK12XIHMi3detW0d+ua9euamUbyI23b99iyZIloraSJUvC09NTIXjMyMgILVq0QIsWLbBlyxZRME5YWBh+/fVXjbKuffnyBV++fIGJiQlWr16t9AaVvb09Jk6ciDp16mDkyJHCDeDo6GicPHkSQ4YM0eDdZk9ZhiAnJyetriM7S5cuVSh1OWbMGEyePFm0L5RIJLC3t8fYsWPRvXt3DBs2TLTc2rVr0axZM1SpUkXtdWcEdnXp0gVLly5VCBwqWrQoRo4ciWrVqmHYsGHC5xESEoIpU6bgxo0b0NHRwZw5cxS2eQAoX748FixYgBIlSmD16tVC+7lz5/DLL79oFByYEUBgbGyMTZs2KdwANzAwQOPGjdG4cWN4enqKvuefPn3CsmXLsHjx4izXkdfbvyoZ+wF9fX2sXLkS7du3F70ukUhQvHjxLIOS1SGf1Wnw4MGYM2eO0nl1dXVRrlw5lCtXDh06dEBKSgrOnz+fZdDQgwcPsHXrVlGbk5MTduzYoXAD18zMDJ06dUL79u2xePFi7Nu3T3jt+fPnWLVqFWbNmqX2e8v4XS1atCg2btyoNLCnQoUKmDNnDqpUqYLZs2cL7cHBwbhy5Qpat26t9voKi7Fjx+Lo0aNC8GVoaCgOHTqk1XKRwcHB+OWXX0RtGZ9b5sxDGYoVK4Y+ffqgS5cumDFjhpAxNzk5GTNnzsTff/+tNDOcfPas7MorZn7d1tYWkZGRQuD4rVu3VAZ3paam4t69e8K0np4e6tevn+W6vjZXr14VHV/Y29vjr7/+UrnPtbGxQf369VG/fn2MHz8eT58+RXJycpbrWLp0qSiwy9zcHH/88QdatmypMK+enh5q1aqFWrVqoUuXLpgwYYIQaHT69Gm0b98eHTp0UPv9ZQR2de7cGUuXLlXYL5mYmKBVq1aiNvn9X9u2bbF69WqlmWUlEgns7OxgZ2eHVq1a4ZdffsG1a9dQpEgRtceojl69eomCu86dO4e5c+dmGyyUwd/fH8+ePROmMwLG5IWFheHmzZvCtL6+Pnbv3q2QNTiDiYkJqlWrhmrVqmHQoEEICwtTCOIrDC5fvqxQ8rVp06bZBgzl9TloZlFRUZgyZYoosKtu3bpYuXKl0nMeKysrdO7cGR06dBD9NspkMsyePRuXLl1Su+xkXh93Z2S/zJzx29LSEjt27FB4UEJPTw+1a9dG7dq10a5dO0ycOBExMTFZlnFVpkSJEli8eDG6du2qMkMXkL5Pa926NVq3bo0rV65g8uTJQrbiGzdu4O7du6hXr16W69q5c6fC997d3R0TJkxQ+A0rXbo0fvzxR3Tv3h0//vgjnj59qvH1mOTkZEyePFkU2FWxYkWsWbMGjo6OCvObmpqiVatWaNWqlcJ52pIlS9CiRQuULFlSozEQERERaYplGYmIiIhII3fv3oWTk5PSf1WqVEG9evXQt29frFmzRhTYZWdnh82bN6uVKaggbN++XfT0rK2trdLArsxMTEywdu1apaU4snPs2DFR9hEDAwNs3bpVIbBLXv/+/TFmzBiN1yeVSrF8+XJR29SpU/HTTz9lWwrF3t4eW7ZsEd1QPHLkSI6eji0I8k/Y165dW+XfuXv37qKLx2/evMm2TFF+8vPzU8gC4+7urjTIIzNnZ2ds2bJFdEMtNjZWFICnzIoVK0RPhhcvXhwHDx5UGtiljKWlJdzd3bOcZ+zYsVkGdsgzMzODh4cHypUrJ7TdvXtXadmVwmDHjh2iUoyGhobYvn17tlnBRo8ejaFDh4razp8/r7QkVnaWLVuWbeaBRo0aoX///qI2+ewM2pD5dyFDVvtZbfv48aNC5gg3NzdMnTo1y31hyZIlsXPnTtEN4NTU1ByVBaxZsyaWL1+eZUaoevXqKQShZgRsjB49OtttfvTo0XBwcBCmU1JSRJkUNPH7779nm9lk2LBhCoFQx48fV8i+IK+gt/9ffvlFIbBLm548eSL8X19fH5MnT1Z7WX19fXTu3FllGScA2Lhxo6hcUpEiRbBz584sb7Tr6upi7ty5aNu2rah9//79Gv+u6+vrY8OGDVlmbAKAnj17okWLFqK2vNi/5IeiRYsqBHJt2rRJacndnPLw8BDdbG7RogX+/PNPpYFdmRkbG2PVqlWim/zPnz/HlStXlM7v7OwsCj56+vRplt+BzNmEmjZtKsrWk1VgmJ+fn+j9uLq6qh1M87WQD0gYOXKkRsG0zs7OWW5HQUFBOHDggDCtp6eHTZs2KQ3skte4cWMsXbpU1JaTknM1a9bEihUr1M5Sl3n/B6SfdygL7FJGIpGIMoNpi3wJxYSEBI32RfLnFPKlHjMEBASIjp/btWunMrBLmWLFiqn12eYXqVSKv/76C1OmTBG16+vrY+rUqdkum5/noDt37hRlZnVxccGWLVuyfJgF+L/fxsxBil++fMlRmfK8Ou6+cuWKQllMDw+PbDPgtmrVCr/++muW86hSu3Zt9O7dO8vALnktWrRQCMDLHFCuTEpKisJ5qZubGyZOnJhl2WobGxts3749R+cSJ0+eFO27S5cuDU9PT6WBXfJGjx4tOhZISkrC7t27NR4DERERkaYY3EVEREREecrU1BQTJ07E2bNnNSprkJ9iY2MVyjFOnTpVrYuERkZGKrNwZOWvv/4STQ8ZMkStC4kAMG7cOJQoUUKj9Z05c0aU7cvFxQUjR45Ue3l7e3tRoElSUhJOnjyp0RgKQlJSEs6cOSNq69Gjh8r5S5cujbp164ra5G/kFCT57DRly5ZVO9ivatWqCgEhR44cUXlD+urVq6LyJED6U8k5LeehTQYGBgpPthfGEjaxsbEK28moUaPU3tYnT56scDNK0wxFzZo1Q7t27dSat1+/fqLpgIAAUeCINigr05ldwII2HThwQFSi1NLSUu1sRaVKlcLEiRNFbRcvXlQo75iduXPnqlWOTln52KJFi2YbMAmk36Ts3LmzqM3f31/9Qf5/DRo0UJqVRJmJEycqlBmU/63TBm1t/5UrV1a4saptmcvnWllZaTWg5c2bN6KSegAwffp0tTLdZGR8zJyNJCkpCYcPH9ZoDL1790b16tXVmld+/5KT72NhMWLECFHJ0c+fP2u8b1bl3bt3OH36tDBtbGyMhQsXql3C0sDAADNnzhS1yZfGyyCRSEQZtGQymcpt6fXr13j37p0w3bBhQ1HmLx8fH5Xlwr/1kowAFEplazsr6I4dO0S/xz/88ING5WjbtWsnyprz+PFjjTNDzZs3L9tgnMzy+m+SU25ubqJp+YBvVZKTkxXOF1WVZCys712VAwcOKPzbt28ftmzZgtmzZ6NVq1aYO3eu6JxBV1cXv/32G5ydnbPsOz/PQePj40VBkBKJBIsWLYKJiYla65JIJJg1a5Zof6tq/6lKXh53yx9TdezYUe2yor17987XMug9evQQZWu/c+dOlvNfunRJVIraxMRE4bdMlaJFi2LChAkajU8mk2HHjh2itp9//lmj89yJEycqBB5mzqpGRERElBcY3EVEREREeSouLg4eHh7o0qUL/vnnn4IejlK3b98WBRxYWlpqlGGsYcOGoiwi2fn48aNC9h35i7tZMTQ0zDJASRn5mxE//PCDRjdoACjc4L97965GyxeECxcuiG6wGBoaZluGRv5ve/bsWZU3LPPbtWvXRNP9+vVTO4MCkF4SLPPTz9HR0Xj48KHSec+dOyearlatGho3bqz+YPNY5owhADQuxZEf7t69K9q36OnpaVSe1MTEBH369BG1yZfDyY4m+5ZKlSqJgk/i4+M1DlzKTubAqgzq3nTTBvltqGvXrho97d+7d2/ReKVSqUI2vay4uLjAxcVFrXldXV0V2rp166b2Ni+/fEZZI01o8n01NTVVyDamKmNQbmlj++/du3eW2SC0IfNNvy9fvmSbyUwT165dE2WGsbKy0ujYpXjx4gq/h5ruXzQJjqtVq5ZoOigoSK3gUS8vLzx79kz4p60gqtywtLTE8OHDRW3btm3TuNyVMmfPnhWVE2vXrl22GWfk1a9fX3RT/f79+yr/1vKlGTOXkstMWYBW5mWTk5NVZjr977//FJb91sgHKWszeDEtLU0U8AcgR2VA5Y/jM5fKzI6Li4tGJYiBvP2b5EaPHj1EGcTu37+PN2/eZLucl5eXqPRm6dKlVQbWZA7+BArPe1dl/vz5Cv8WLlyIlStX4tixYwq/XWXKlMG2bdsUAuWUyc9z0OvXr4s+o9q1a2v8vXVwcBAFQb148QIRERFqL59Xx92pqakKwbearEsikWg0f27p6OiIjkMjIiKUlmbPIH8s3aZNG42yH/bo0SPLTKfyHj9+LDouLlWqlMaloi0sLNC0aVNhOjo6WlS2lYiIiCgvqJcLmYiIiIjo/ytbtqxC6aXMEhMTERERgWfPnuHOnTvCE75BQUGYPn06rl27hiVLlqhdliM/+Pn5iaabNm2qUcAMkH4BcuvWrWrNKx9MU7ZsWZQpU0aj9TVt2lTtcmBpaWm4f/++wvKacnR0hJGRkRDopCooqDCRz7rVunXrbLMEtW/fHr/99pvw3Y2NjcX58+eVZtHJT69evRLdsACgUFYrO/b29qhSpYooW8ODBw+U3miVv3GSn+8/JiYGL168QHh4OGJjY5GYmCgKYgCg8LfQdhCSNvj4+Iima9asqXHms/bt22PdunXC9IcPH/D+/XuULFky22UlEolCJrrs5rezs8PTp0+FNm0EK2SmrBShsmxeeSE+Pl7hpoum25CpqSmaNm0qCn708fFRCMJTRZNMK2ZmZrCwsBAFqGqyfKlSpUTT8plEsqOnp6dQSi87rVu3xvbt24XpZ8+eITExUaMbbvm1/WfOWJRXqlWrJgS4paWlYeLEiVi5cmW2JZjVIb9/adGihcbHLu3btxf9Tvr7+yM5OVmtfiwsLODk5KT2uqysrGBubi7sU9LS0hAXF5evmfu0aejQodizZw++fPkCAIiKisK2bdsUypZpSv63t0mTJjnqp3LlykL57+joaLx69UppGXH533/5QCxl7ZUqVYKtrS1sbGxEn+mtW7cUxhsfHy8KvjQxMVEIzvwWyJdU3Lx5M8qWLZvtAwXqCAgIEP0WOzg4aHzeAEAhsNjHxyfLc7rMcrK/rFatGh49eiRMz549G2vWrMk201NeK1q0KJo2bYrLly8LbceOHct221VW0llVgLCrqyskEonw23Xz5k2sXr0a48aN03g/XdiMGjUKkyZNyrK0dIb8PgeVD1jM6f6zSpUqwn5LJpPBz89PrQzkeXnc/fTpU9HDRkZGRqJsfOpo1qyZRvOrkpaWhuDgYLx58waxsbGIi4sTBSVnkC+f+e7dO5X7LvkgfU3HamZmhpo1a6r8DZMn/11p3LhxjgL+q1SpIgq+9fHx0TigkIiIiEgTheeOGhERERF9FYoVK6Z2Jo+YmBhs3boVW7duFTIG/P3339DX18fvv/+el8PUSOYLqgBydEFOk2Xks6fk5CZH5cqVRTcNsvL69WvRTX09PT14eXlpvE4gPTAj48JyeHg4UlNTC1WgXmYfP35UyDShTsYzMzMztGnTRpRp7vjx4wUe3CUflGJlZZWjAIGqVauKgruUPWEcExOD4OBgUVvNmjU1XpcmXr9+jWPHjuHcuXNqZVCQp2ngSn6Q/9vmpByKo6MjjI2NRaVwnj17plZwl5mZGSwtLTVan6mpqWg6NjZWo+WzoyxLl7YDyFQJDAwU3XySSCRqZ9HKrGrVqqLgLk2e0pcPuMqOiYmJ6LtdunRpjZbNLC4uTqN1ly9fXqOgLCD9t0lHR0f4zU9NTUVgYGC23/383v719fVRvnx5jdejqf79+4uyl/n6+qJDhw5o1qwZ2rRpgwYNGmj0mWamjf2L/DJJSUl48+YNKlasmO2ypUqV0vhGqKmpqWh7j42N/WqDu0xMTDBmzBjR8ezu3bsxdOhQjbKNyJN/4ODZs2eiMmPqks808+nTJ6XBXfb29rCzs0NISAgAIDQ0FG/evIGDg4MwT1pamijoLCNjl66uLurVq4dLly4BUB4Ydu/ePVHGxtq1a3/1wS3KtGzZEsWLF8fHjx8BpAe1TZo0CRUqVEDHjh3RrFkzVKlSJUfHzPJBD/r6+jn6TmQEImbICP5ThyaBnBn69u2L/fv3C+cqQUFB6NGjB+rXr4927dqhYcOG+bIfVqZXr16i4K6TJ09i0qRJKjNKhYWFiTILSSSSLLNW2djYoF27dqJjhY0bN+Kvv/5Cp06d0Lx5c9SqVUvhmOtrsHXrViQlJWH27NnZZuDK73NQ+W0lJCQkR9tKxv4wQ1hYmFrL5eVxt/z1g4oVK6pdrjdDiRIlYG1trVEmsgypqak4c+YM/v33X9y+fVt0XqKurI73X79+LZquXLmyxv1XrlxZ7eAu+e9KREREjr4r8sdimuxXiYiIiHKicN6FISIiIqJvgrm5OaZOnQpbW1ssWrRIaD969Cjat2+v1hOw+UE+A4imN98BwM7OTu155W9CZy6doy5TU1MYGxurlfFG/iJjamoq5s+fr/E6lYmOjs7VTcy8dOLECVEZoqJFi6pdVrBHjx6i4K7bt2+rnS0pr0RFRYmmcxoQIP9dle8XUHzSGkCOskSoIzk5GStXrsTevXuVPvWtLk0DV/KDNj4zXV1dlCxZUnRTR36fpUpOgibkbxRJpVKN+8iKsv2dsu9gXpBfj6WlZY7+RupsQ6pouj75z0OTG8HyN13VKYGXWU5+C01MTGBlZSXah2T1fS2o7d/U1FTjm6I50bJlS/Tr1w+HDh0S2lJTU+Hl5SXc4C5RogRq1KiBevXqoUGDBnB0dFSrb23sX2xtbWFoaIikpCSV/apSGPcv+W3AgAHYuXOnkDkuPj4emzZtws8//5yj/tLS0hR+f7dt25brcQJZb4eNGjXCX3/9JUzfvHlTFNzl7+8v+l5kLsfYqFEjIbgrICAA4eHhouNC+Rvt8mUgvxWGhoZYvnw5Ro0aheTkZKE9MDAQa9euxdq1a2FiYoKqVauidu3aqF+/PurUqaNW9qPPnz+LpgMDA7VyHK/Jb5emAStA+sMrU6ZMwapVq4Q2mUyG27dvC+XlbGxsUKNGDdStWxcNGjTIt4w3LVq0gI2NjbC9vX//Hv/995/K84STJ0+K9lf169fP9txv7ty58Pf3R2hoqND25csX7NmzB3v27IGenh4qVaqEWrVqoV69emjYsKFCOcf8oixIPT4+Hu/evcPt27exe/duUeD17t27kZKSku33ML/PQeW3lSNHjuDIkSO5Xldh+F3UxvWDjOU0De568OAB5s6di8DAwBytM4OqY7W4uDiFsu05eX+aLCP/Xbl48SIuXryo8Trl5dc5DREREX2/NCtwTkRERESUA4MGDVJ44lvdEob5Qf5iqZmZmcZ9aLKM/FOrOX1qW9115uVFxpw8tZtf5EsydunSRe2MCY0aNULx4sWF6bS0NJw8eVKr49OU/Pc0p98b+RsPyr4f8jeBJRJJjraL7CQnJ8Pd3R2enp65CuwAoFYWu/wm/7fN6d9Qnc9MmZyUF8lryoIENcl8lRva2obkP0dN9rG5/Uzy8zPV1vdVVVatgtz+8zNbyoIFCzB79myVf88PHz7g7NmzWLhwITp16oSuXbti3759ogARZfJq/6Ju8Ghh3L/kNwMDA7i7u4vaDhw4gA8fPuSov6ioqDz7LctczktedqUZM0/r6+uLyo5lXjYjcCcz+QyqeRncJf+dzO1+RX757LIUNWjQAPv371eZcSY+Ph53797Fxo0bMWzYMDRp0gTz589XyJQqL6+O4zU5hleWdVMdY8aMwR9//KGyJHV4eDi8vLywbNkyuLm5oW3btti0aZPWs4bK09fXR9euXUVt8mUXMztx4oRoumfPntmuw9bWFocPH1ZZ/jk1NRVPnjzB3r17MXHiRDRq1Aju7u7w9vbO/g3kAxMTE1SoUAGDBg3CqVOnFEqMHjhwQOHvIi+/z0HV/f3SVFb7z8zy8ndRW9cPNF3u5s2b+PHHH3Md2AWofshA2faek/enyXFQXn031f2uEBEREeUUM3cRERERUZ6TSCRo166d6Aa+t7c3IiMjYWVlVXADUyEnF2Y1uRknX45G/klVdam7XE77V0dhDKgBAB8fH4XyDp6envD09Mxxn8ePH8fYsWNzOTLt0dYNBHX6yaubFVu2bMG1a9dEbdbW1ujcuTNq164NBwcHFC9eHCYmJjA0NBQ93R4SEoLWrVvnybjySn5+ZoWVhYUFSpcuLcpk4e/vXyBj4eeRN9T9Xfhetn+JRIJhw4bBzc0N//zzD86ePQtfX1+VwVvPnz/HwoULsXPnTqxatQrVqlVTez3aGi+pz83NDdu2bUNQUBCA9KDF9evX47ffftO4r4I6XmvQoIGo1PedO3eQlpYmBDNlDtCqUaOGKNDH0dERJUqUEALabt26hU6dOgFIz1L0/PlzYV4bG5sclfdTl/yNfXWy22ZFPsuMOoEDrq6uOH78OK5fv46///4bt27dUiiHmCEyMhIHDhzAkSNHMHHiRIwePVrpfHn5vcgP3bp1Q9u2bXH69GmcPn0aDx48UPnZvH37Fn/++Sd27dqFpUuX5mmW5549e2LXrl3C9MWLFxETE6MQ8Orr6ysKbDEzM0P79u3VWkeRIkWwbt06PH36FMeOHcPVq1eFfYW8lJQUIXtQly5dsGDBgjx5sCInDAwM8Mcff+Djx4/w8fER2hcvXowmTZqoDN7L731aXq2vMJzv5vf1AyB9HzVt2jSFgKX69eujefPmcHFxQcmSJWFjYwNDQ0OFMc6aNUvhYStllGUwTElJ0biMrSbvLbsA+pwqDN8VIiIi+rYxuIuIiIiI8kXFihVF0zKZDAEBAQrZArRFk/JT8iUw5J+MVYcmJaHkbxrk9Ol0dZeTL6VSpkwZXLhwIUfr/FqocyFZU0FBQXjw4AFq1aql9b7VIf89zen3Rv77rawEjHzQZVpaGmJjY7VaLiYuLk4hg1+XLl2waNEiGBsbZ7t8bm/a5gf5bS8n+xZlyxVU2R5tqVu3rii4KzAwUKGUV17Q1jYkv9zX/nmokpd/n+9h+5dnaWmJQYMGYdCgQUhKSoKvry/u378v/JN/T8HBwRg6dCgOHDgAZ2dnpf1lLnmlrf1LTsqvfc/09PQwceJETJ06VWg7duwYRo4cKSptqA5lDzxcvXoVJUqUyO0ws2RjYwNnZ2cEBAQASM9o4u/vj2rVqiEpKUkUzKHsuL1hw4bCcVfmQLD//vtPdKM7I4gsr2hrH58hp7+9EokEzZo1Q7NmzQAAL1++FLZzb29vhISEiOZPSUnBypUrkZCQgEmTJin0J79N9u7dG4sXL9bkrRQ4Y2Nj9OrVC7169UJKSgqePHkCb29v4e8in3EpPDwc48aNw+bNm9GkSZM8GZOzszNcXFzw+PFjAOkZd06fPo1+/fqJ5pPP6NW5c2cYGRlpvK6ff/4ZP//8M8LCwuDt7Y0HDx7g/v37CAgIUAgIOXXqFD59+oSdO3fmSwlhdRgYGOD3339H9+7dhcCY6OhorFmzRmUwa36fg1pZWYl+F/ft24c6derk2fryU14dw2Zl165dohKOFhYWWLt2LRo0aKDW8uoeqynbt8bExKh1PCi/jLrkf2+XL1+O7t27a7Q+IiIiooLAsoxERERElC+UPXkcHh6ucn75mz+aBGsBubu49+7dO43WBUDhRk1WbG1tRdPyGabUERwcrPbTqfIBE+/fv891qZrCLCkpCWfOnMmTvvMiaExd8t/TzMExmpD/riq7ka8syObNmzc5Wp8q169fFz0JXrZsWSxZskTtC/mZbzYUVtr4zKRSKd6/f59lv1+bli1biqalUmm2pX20Qf7vFhUVlaOAGHW2oW9BTn4L4+PjFW7SK/u+fg/bf1YMDQ1Rr149/PTTT9i2bRvu3LmDLVu2KGSpiY+Px8KFC5X2oY39y6dPn5CUlCRq+1a/z3mpU6dOogC81NRUeHh4aNyPgYGBQimq7Er2aYt80FZGkJa3t7foO9K4ceMslw0NDcXbt29FfWTIy5KMgOKxS06OrzOTz7CU0wBkR0dH9O3bF8uWLcOlS5dw+vRpjBo1SqHU4ebNm/Hq1SuF5eXXm1/fibyir6+P6tWrY8SIEdiwYQNu3bqFvXv3okuXLqLzv9TUVPz66695es7Sq1cv0bR8IFdSUhJOnz4talOnJGNWihUrhk6dOmHOnDk4fvw4bty4gTlz5qB06dKi+e7cuZNlqciCUL58eQwcOFDUduzYMZXnCPl9DmptbS2a1uT8vLCTz46Wk/1bcnKyRscK8oF4s2fPVjuwC1D/WE1PT0/hmCYn70/Z/lMV+e/K175fJSIiou8Hg7uIiIiIKF8oe0pUWQr+DPI3tzTJjAVodpNTvkTMkydPNFqXpstUrVpVYVlNSyv4+vqqPa+jo6OoTEJKSgoePnyo0fq+JhcuXEB0dLQwbWRkhPnz5+fo3/Dhw0V9nzlzRuFGeH6pVKmSaDoyMjJHF6LlS+ApK5Fkbm6OMmXKiNoyZ+7QhsxlWgGgQ4cOCuU8svLo0SOtjicvyH9mOSk/+PLlSyQkJIja8rKsVX5o1aqVwk2cQ4cOQSqV5ul6HR0dRSVeZDKZkLFDE+psQ9+CV69eKZTiyU5AQIAoGFtPTw8VKlRQmO972P41YWBggObNm2PLli2YMWOG6LX79+8rPabRxv5FfhlDQ0OULVtW436+dxKJRCHj0r///qvwPVdH5cqVRdP37t3L1djUpSq4K3OAlrm5OVxdXRWWlQ/ayljm9u3bWa5D21xcXETTL1680PjhkAxpaWmiUnzK+s8pR0dHTJ8+HceOHRNlrJFKpQqBRAAUMvc9evSowI5F84Kuri7q1q2LlStX4s8//xS9FhISggcPHuTZurt06SL67Xn48CFevnwpTF+8eFF0TuHo6IgaNWpodQxFixbF4MGDcerUKYUyvH///bdW16UNo0ePFgUmpqamYuPGjUrnze9z0ILaf+YH+X1vaGioKEuZOh4/fqz2NYfU1FTRPlBPT08ouasOqVSaq+sjmlzryMky8t8Vb29vjddHREREVBAY3EVERERE+eL58+cKbfJPoGYmn55f0ydvNbmYW716ddH09evXhXIT6rp06ZLa81apUgWGhobCdGxsLK5evarR+k6dOqX2vEZGRgqlBM+ePavR+jQlX0Ikr4M2MpPPrtW8eXMMGDAgR/+mTJkiKqMZExNTYCUty5cvr/CU8cWLFzXqIyQkROFCu6oyk/Xq1RNN//PPPxqtKztfvnwRTZcqVUqj5S9fvqzxOuW/lzm96auumjVriqZ9fHwU3nd2zp8/L5ouUaIESpYsmeuxFSQDAwMMGjRI1BYUFIRt27ZpbR3KPlsTExOFQCxNt6H4+Hhcv35d1Cb/OX8rUlNTceXKFY2Wkf8trFSpktLyVQWx/X8tRowYoZDhU1mQkPz37sqVKxofu8jvX6pWrZpl4D2p1qpVK1HQh0wmw+rVqzXuRz5QKq+P1zLUrVtX9Nn7+PggISEB//33n9BWr149pSXibG1tRcGGt27dQlBQkCgosUyZMrCzs8uj0aeT3yZiY2Nx9+7dHPV1584dhQdTtL2vL1eunEIJQGXbeu3atUVZDePj4zU+b/hadOzYUeG8LCdBkuqytLRE69atRW2ZzyPkM2flNmtXVkxMTDBhwgRRm7Lz54JmY2OD/v37i9r++ecfpQ+c5Pc5qPz+08vL65sJhCxatKjCPvTff//VqA9Nrh9ERESIyoVaW1trVI707t27GpWAlN+/avre/P39FbItZkU+C6W3tzc+f/6s0TqJiIiICgKDu4iIiIgoz8lkMpw7d07Upq+vr5B1IrPy5cuLpjV5atvX1xdPnz5Ve/4GDRqInkCOiorS6ILif//9p1EZAAMDA7Rr107UtmnTJrUDTfz9/TW+qdO+fXvR9F9//aVQ6k2b5MtwanJxNzc+fvyoUAaoc+fOOe7PwMAAbdq0EbUVZGnGZs2aiaYPHjyoUda3vXv3ii7UW1hYqLxZ2bFjR9G0n58fbt68qcFosyafpSdzZoTs+Pr65ugJa/mMgHn9vaxXr55CdoMDBw6ovXxCQgIOHz4sapMv2/a1+vHHHxUCfNetW5ej7EPyQkNDVZayk//7/fPPP4iKilK776NHjyI+Pl6Y1tXVRdOmTXM20K/AwYMH1Z43Li5OIctIixYtlM5bENv/10IikSiU55LP3gek/x5kLmEWGRmp0bFLWFiYwk12VZ8XqWfKlCmiaS8vL/j5+WnUR/v27UWf67Nnz/IlwMvY2Fh0PJCcnIxLly4hICBAaFNWkjFD5qxcd+7cUTheyOusXUD6wxryZeDkf0PVJb+cjY2NQlYlbZAP1sj8+5LBwMBAYdtct25dngeoFxT5v4my/Z82yQdsnTx5ElKpFB8+fBCdU+jp6aF79+55OhZ1vg+FwYgRI0QPKqWmpmLTpk1K583Pc9BmzZqJAiHDw8Oxd+/ePFlXQejSpYto2tPTU+3vyPv37zUq8ykf6B0XF6fRPmf79u1qzwsAXbt2Ff32PX36FF5eXmovv379eo3WV716dRQvXlyYTklJwYYNGzTqg4iIiKggMLiLiIiIiPLcrl278OLFC1Fbw4YNFQKAMpO/gXL16lW1nqZMTk7GggULNBqfmZmZQpmBlStXIjIyMttlExMTsWjRIo3WBwA//PCDaPrRo0dYu3ZttstFRkZi9uzZGt/Q6d27N0qUKCFMJyUlYcqUKbl6mjlzkJC8IkWKiKaDg4M1Lj2ZEydOnBD9bUxNTXN9s1r+u3Hr1i18/PgxV33mlLJsR1u2bFFr2SdPnmDPnj2itj59+qh8CrtJkyaoUqWKqG327Nlae6o58wV1AGoHLMbHx2PWrFk5Wqf89zJz6Z28YGZmhh49eojatm7ditevX6u1vIeHBz58+CBqGzx4sLaGV6DMzMwU9tXJyckYNmwY7t+/n+N+L168iJ49e6r8bPv37y+6YRUZGYlly5ap1feHDx/g4eEhamvbtq1o3/qt+e+//5SWCVPGw8NDVCJIV1cXffv2VTpvQWz/+S2nGStTU1Px9u1bUZuyTKcODg4KAb8rVqxAeHi4WutZtGiR6KawkZER+vTpk4MR561WrVrByclJ+FeY94ENGjRQCGJSVa5MlQoVKig8ADB//nyF74Qmsjpey6xBgwaiaQ8PD9ExVVYBWpkz5kRGRmLXrl0qX88rBgYGCpmw/v33X4XykNm5ffu2QqBk//79sywdm5qaqtE6Msg/HCKftS/DuHHjFIL+li5dmqN1Aup/J3LTf06Dz+T/JllletaGJk2aiH7Hw8LCcOPGDYVziqZNm6r8fOTl9fehoBUtWlTh9/3kyZNKSwjn5zmojY0NBgwYIGpbs2ZNrkpB5vW2ool+/fqJsie+f/8eCxcuzHaMSUlJ+N///qdRsKClpaVCxkB1MyEeOXJEIcttdhwcHBR+JxYsWIB3795lu+yhQ4c0CgQD0oPXxowZI2o7cOBArjJ0F6bvChEREX27GNxFRERERHkmJiYGK1asULhxLpFIFMpOyKtTp44oECMxMRFz5szJ8mJ5fHw8Jk6ciMePH2s81hEjRohu2nz69AmjR4/OMqNLQkICJkyYgMDAQI3XV6tWLYWgoQ0bNuC3335TmU3o8ePHGDx4MJ4/fw6JRKJR6SQDAwNMnz5d1Obj44OhQ4dqVPJSKpXi0qVLGDx4cJZ/Z1tbW9ENifj4eJw4cULt9eSUfFat1q1bi54sz4lGjRqJyiGmpaXh5MmTueozp6pVq4YmTZqI2tauXYu//vory+WeP3+OUaNGibYfc3NzDB06NMvlZsyYAR2d/ztt/PjxIwYMGKAQrKlKVFSUyhvb9evXF03fu3cPhw4dyrK/8PBwDB8+XKNMeZnJB6sdPnw4xzfg1DV8+HDRzZHExESMGDEi2xv1O3bswI4dO0Rt7du3R8WKFfNknAWhTZs2GDt2rKgtJiYGw4cPh4eHh0Y3oZ4+fYpx48bB3d09y8Dc4sWLK2TpOHr0KNasWZPlTZkPHz5g2LBhogxTenp6CjeGvkU///wz7ty5k+U8u3btgqenp6ite/fuKkuIFsT2n9+eP3+O7t274/jx40hMTFR7uTVr1ogCtExMTODq6qp03rFjx4r20Z8/f8aIESOyDPCSSqVYtGiRQkbVH374QaH0L2lu6tSpounMAY/qmjJliijrY0REBAYOHKhxkNKjR48wY8YMhcBuVeRvrL9580b4f4kSJRSy6mYmX9Yx87I6OjoK23xeGTRoEKysrIRpmUwGd3d3UXnJrPz3339wd3cXtVlZWWHgwIFZLvfjjz9i+fLlSoNbVPH391fIEKbq71SpUiWF4Mtdu3bhl19+0SizVVxcHPbt25en5QWB9N/ydu3aYffu3RplST148KAoW5xEIkHdunXzYogCHR0dhUD8Y8eOKZxT9OrVS+0+V65ciWnTpsHX11ftZSIiIhRKuebXdpMTI0eOFG3zKSkp2Lx5s8J8+X0OOmrUKBQrVkyYTkpKwsiRI9UOVM/w6tUrLFiwACtWrNBoubxUqlQpDB8+XNR2/PhxTJ48WWXZ97dv32LkyJHCcZy658XKtr1FixZlm+32+PHj+PXXX9Vah7yZM2eKvlMfPnzAoEGDVB6DJicnY926dZg/fz4A9d9bhj59+ogyyaelpWHq1KkKma6z8+HDB6xcuRL/+9//NFo/ERERUU7oFfQAiIiIiOjrEhYWlmVZsaSkJERERODp06e4ffu20huaY8aMyba0ib6+Pvr27SsKDLl8+TIGDRqE8ePHo169ekIwVmhoKC5fvoxt27YJZR5q1qwJHx8ftd9X+fLl4e7ujj///FNo8/X1RefOnfHTTz+hXbt2QrBSREQEvLy8sGHDBuGidI0aNTR+Knju3Lm4f/++KAvU3r17cfLkSTRv3hwVKlSAqakpvnz5grt37+Lhw4fCE+QDBgzA1atXRTeRMj/Rr0zXrl3x+PFj7Ny5U2jz8fFBx44d0bVrV7Rv3x41atSApaWl8HpycjJev34tfJ5eXl5C4ER2Fz1btmwpCjqaO3cuzp8/j+rVq8Pa2lp0Q9rU1BTdunXLsr/s+Pj4KGREyk1Jxgx6enpo166dKPDg+PHjGD16dK77zonff/8d3bp1E30Oc+fOhZeXFwYPHoy6devCwMAAMpkML1++xMmTJ+Hp6Ynk5GRRP/Pnz1fIniOvUaNGGDduHNatWye0vX37Ft27d0f37t3RqVMn1KpVSyh3KJPJEBwcDD8/P1y6dAlXrlxBfHw8fvrpJ4W+XV1d4eLiIrpBM2/ePDx8+BADBw5E5cqVoaurC5lMhlevXuH8+fPYuXOncFOhXr16aj9BnqFly5aiTGd37txB586d0bJlS5QsWVIhK0erVq2y/Rtlx97eHrNnz8a8efOEttDQUHTr1g3Dhw9H165dUbZsWUgkEiQlJeH27dvYtWuXQkmrYsWKaZyV8GswefJkxMXFiYIPEhMTsX79ehw6dAjt27dHs2bNULVqVVhbWwsZCxITE/H69Wvcu3cP586dw/3799W+ETNr1izcuXMHQUFBQtuGDRtw+/ZtjBgxAo0bNxYC8oKDg3H69Gls2bJF4Qb1xIkTFQIGvyUuLi549uwZEhISMGzYMPTv3x99+vSBs7MzdHR0kJycDG9vb+zatQtXrlwRLWtra5tlhq2C2P4LwtOnTzFr1iwsXLgQzZs3R+PGjeHi4gJHR0fRTcjw8HDcv38f+/btUwhC6d27tyjQJ7NatWph1KhRohvqT548QceOHTFmzBh06NABpUqVApAe1HH9+nVs3bpVofxppUqVFEoKUs5Uq1YNrVu3xqVLl3LcR7ly5fDHH39g/Pjxwn4tLCwMQ4cORZMmTeDm5obatWujRIkSwrGfVCrFu3fv8OzZM9y/fx+XLl0SAqycnZ3VHruZmZnSYJzsMm+ZmpqiWrVqSjMvVq5cOd8CB4sWLYolS5aIjjtiY2MxbNgwtGnTBt26dUPdunVF5RvDw8Ph7e2NkydP4uLFiwp9LlmyJNvsUdHR0di+fTt27NgBV1dXtG7dGtWqVYOzs7NoXUlJSXj27BlOnz6NAwcOiM6TihcvrpC1LbO5c+fixYsXovObI0eOwMvLC/3790fTpk3h4uIi2rfExsbixYsXCAgIwLVr13Dr1i0kJSWp3KdoU3BwMBYvXozly5ejcePGwm95xvlNhpiYGDx8+BCHDx9WCDpt1aoV7O3t83ysbm5uorKC586dEx1T2NjYaJQJODU1FadOncKpU6dgb2+Ptm3bonbt2qhcuTJKliwpnANJpVK8efMGV65cwc6dOxEWFib0oaurq5DpuTApUaIEevbsKTo/OnbsGMaNG6eQ0TQ/z0FtbGywfv16DBo0SMgOFhMTgylTpmDXrl3o06cP6tatizJlygj7z7S0NISFheHZs2fw9fXFpUuX8PTpUwDINrAzv02cOBHXrl3Ds2fPhLazZ8/iypUraNq0KZydnWFpaYmIiAj4+vrizp07QvbsZs2aITExUXT8lNX1g/79++PatWvC9IsXL+Dm5obx48ejZcuWwn49Li4Od+7cwb59+3Djxg0A6YFWTk5OGpUmdnZ2xoQJE7Bq1SqhLTQ0FEOGDIGLiwsaNmyIYsWKISkpCa9fv8aVK1eEYHYTExMMGzZMVFoxu2sjBgYG2LBhA/r06YOIiAgA6d+73377DQcOHMCAAQNQv359lC9fXjj/kMlk+PLlC54/f45Hjx7h8uXLePjwIWQyGUtbExERUb5gcBcRERERaSQoKEh4OlJTEokEo0aNUvsG4tixY3H27FlRwI6Pjw9GjBgBHR0dWFhYIC4uTqHcX69evVCnTh2NgruA9Cd9nz59ijNnzghtnz59wsKFC7Fw4UIYGxtDIpEoZJOxs7PDnDlz0Lt3b43WZ2Njg127dmHIkCGii/kxMTE4deqUyuUaNmyI2bNnK9xMV+dp1RkzZiA1NVUUSJGcnIyjR4/i6NGjANIvdJqamiI+Pj5XJTOGDRuGv//+W7hxJZPJcO3aNdFF4gylS5fOdXCX/BP2VlZWaNy4ca76zNC5c2fRzYtXr17h4cOHqFGjhlb610Tx4sWxfv16/PTTT6IsQpcvX8bly5chkUiEbUNVVip3d3d06dJFrfW5u7sjJiZGVGJJKpXi2LFjOHbsGADA2NgYBgYGiImJUbsMj0QiwZw5czBkyBDRNpzRr76+PkxNTREbG6vwPipVqoRffvkF3bt3V2tdGWrVqqUQFBIUFCS62ZRZ+fLlcx3cBaSXUQkKChJl4kpISMD69euxfv166Ovrw8TEROXT8NbW1li/fv03mVUn43tQpkwZLF++XPRd+Pz5M/bt24d9+/YJ81paWkIqlSI2NjbLm3tZBTOYmJhgw4YNGD58uKjs5YMHD/DgwQMA6ZntkpKSFIIiM/Tq1QujRo3S6L1+bSpVqoQuXbpg2bJlSEtLw/79+7F//37o6enB1NQU0dHRSj8DU1NTeHh4iG7SyiuI7b8gxcfH48yZM6LjCyMjIxgbGyMxMVFl5p3KlSsrZIKSN3HiRCEIMUNGudFly5bBwMAAhoaGiImJUbq8vb091q9fn2XJOdLM5MmTcfny5RyXpQPSMxsuXboU8+bNEx2L3bhxQ7h5rqurC3NzcyQnJ2uU6VAVXV1d1K1bF5cvX1Z4TZ2yio0aNVIa3JVVOce80KpVK8yZMwdLliwRlUe9ePGiELyVsY9Rdh6RQVdXF7Nnz0arVq3UXrdMJoOfn58ooEFPTw9mZmZIS0tDbGys0u+FoaEhli1bJgp6kpcRiDBp0iTRcUx4eDg2bNggBDWYmJjAwMAgy/eWn1JSUnDlyhXReUvGuUZSUpLK727p0qXzLai9bNmyqF27tvD9lf9t69atm0YZkzMLDg4WZWOVSCQwMzODrq5ulp/RpEmTVGZtLCxGjx6No0ePCr/TKSkp2LJli+iBhgz5eQ5arVo1bNiwAVOnThUdWz98+FB4GCvjWkJqairi4uK+mpJ6BgYG2LlzJ4YOHSrKpJyYmIgLFy6oLCtYqVIlrFixQiEzYVbXD1q3bo2WLVuKfhNCQ0Mxe/ZsAOkl1gEoDQj+9ddfce/ePY2Cu4D0hwCjoqKwfft2Ufvjx49VZmwzMDDA6tWr8fnzZ4X27Njb22P79u0YP368qARkYGAgfvvtNwDp26y5uTkAqNyHExEREeUXlmUkIiIionxRo0YN7Nu3D9OmTVN7GSMjI2zbtg0ODg4Kr6WlpSEyMlLhgvjAgQOFC3Ga0tXVxYoVK1SW3UhISFC4AVGuXDl4enrmOOiiXLlyOHz4MNq0aZPtvDo6Ohg4cCC2bNkCAwMDhQupFhYW2fahq6uLOXPmYMWKFaKyl5klJycjIiIiy4vqpUqVEpW9UcbR0RF//vlnljf4tSUpKUl00xwA2rZtm+MbMfLq1q0rKvEBQAhsKgh16tTBvn37lJZIkslkiIqKUhrYZWpqikWLFmHixIlqr0tHRwc///wzFi1apPKzTEhIQFRUlNKL3Vk9NV2rVi388ccfMDIyUngtJSUFkZGRCu+jZs2a8PT0FG4oaGrVqlWoVatWjpbNjf/973+YM2eOqERjhpSUFJWBXU5OTjh48GC22Q6/dkOGDMGJEycUyo5mJpPJEBkZiZiYGJU34erUqYMDBw7gl19+yXJ9jo6OOHTokMoAzZiYGKWBXfr6+pg4cSJ+//13UfbBb9Xw4cMxadIk0XacmpqKqKgopZ+Bra0ttm7dqtY2VhDbf37KLmNEYmIiIiIiVAZ2tWnTBnv37lW6z8hMT08Pq1atwpgxY6Cnp/gMZ3JyssrArnr16uHQoUMoU6ZMlusgzVSqVEmh9HZO9OjRAwcPHoSTk5PS16VSKSIjI7MM7DIzM9Mo85GyIC6JRKJWgJaqedQJDNO2wYMHY+vWrULmOnkZ+xhVgTUlS5bE1q1bMXjwYLXWl9X2npqaisjISERHRys9TipVqhR27Nih1t/YxsYGO3fuxOjRo1UGLsTHx2f53oD0wNG8lN3+L+NcQ9V3t3bt2jhw4ICoxHtey6pUpaZlLLN6/zKZDDExMSo/IxMTE/z6669fRdlnOzs7hYdzDh8+LMpKnSE/z0EBoEmTJjh69KjKsp4Z1xKyeljA0NAwy3K0BaVIkSLYt28fevfune22BgAdO3bE/v37YWlpqXD9ICNoSZUVK1aoLA8aGxur0J++vj4WLVqkURlTeTNnzsTSpUvVur5ib28PT09PNG/ePEfXRoD0TLVHjx5VeT1GJpMhOjpa5T4cSD8Wq1ixolrrIyIiIsoNZu4iIiIiIq3S19eHmZkZLC0tUalSJVStWhVt2rSBo6Njjvqzs7PDkSNHsGnTJhw4cEDlTYCqVati4sSJaN68eW6GDz09Pfz+++/o1KkTPDw84Ovrq3Q+KysrDBgwAGPGjIGxsbFQnjEnSpQogfXr18PHxwf//vsv7t69i7CwMERHR8PU1BRly5ZF/fr10bNnT+ECs/zNWiMjI42ybnTt2hVt27bF4cOHcfz4cQQEBGT7FGq5cuXQqFEjtG3bFg0aNFDrYnKrVq1w/vx5/Pvvv7h16xZevHiB8PBwxMfHi7Ip5NbFixdFWawA7ZRkzKCjo4MOHTpg9+7dQtuZM2fwyy+/qJUxLS9UqlQJp06dwpEjR7B//36hfIgytra26NixI8aOHavyhkp2+vTpgw4dOmDnzp34999/RSXtlClfvjzatGmDvn37Zjlfx44dUbFiRaxevRqXLl1S+T10cHDA0KFD0b9/f+jq6uZ4m7O1tRXKhpw/fx5PnjzBu3fvEBcXpzJLk7YMHjwY7dq1w6ZNm3Du3Dl8+fJF6XwSiQRVqlTBoEGD0KNHj+8iiAgAKlSogO3bt+PJkyc4evQoLl26JJTaVUUikaBcuXJo1aoV3NzcUKFCBbXXV6JECRw6dAhnzpzBrl274Ovrq/L7Z2lpidatW2PcuHH5UiKqMBk3bhzq1auHNWvWqCyFaGlpiW7dumHSpEnZ3ijMLL+3//zk7OyM06dP4/Lly7h16xZ8fX2VZrfIzMDAAM2bN8fAgQM1ynYkkUgwdepUuLm5YcOGDbh8+bLKgC5dXV3UqFEDw4cPVyuwnHJm0qRJOHv2rMoMmuqqUqUKTp48CS8vL+zfvx/e3t5KS55nVrRoUTRo0AAtW7ZEmzZtlAZQqqIsEKtixYrZliUEgOrVqyuUdTQwMEDt2rXVXr82NW7cGOfPn8fff/+Nw4cPw9/fP8uAJ319fVStWhW9evVCjx49NHpAYPfu3UJ2Wm9vb1HZdFWcnZ3Ro0cPDBgwQKPPSE9PD9OmTcPgwYPh6emJCxcu4O3bt1kuo6urC1dXVzRq1AidOnXK8yAEc3NzeHl54fLly7hx4wZ8fHyEknpZjbFhw4bC8WZ+69ixIxYvXqxwruni4qIywFKV6dOno1mzZrhy5Qru3buHFy9eZHveU7x4cXTu3BnDhg3TStbY/DJmzBicPHlSeH/JycnYtm2byiD7/DoHBdIDf/bu3Yu7d+9i165duH37dra/w5aWlqhXrx6aN2+ODh06aHRMk58sLS2xePFiDBkyBP/++y9u3LiBjx8/IjIyEkZGRrC3t0edOnXQo0cPVK1aVVhO/twju4ewzMzMsHPnTuzduxc7duwQZbzNTF9fH+3atcP48eO1EhDn5uaG1q1b4/Tp07hw4QJev34tZOaytbVFlSpV0KZNG3Ts2FG4BpJRojGDusFdwP+V8wwICMCOHTtw/fp1oVSjKiYmJqhTpw6aNm2KTp06qfU7SURERJRbEtnXknOWiIiIiL57ycnJ8PHxQVBQECIjIyGRSFCyZElUr149z7JOhISEwM/PD2FhYUhMTISVlRUqVKiAGjVqKM2QkV8ePHiAAQMGCNMuLi65yiQVExODhw8f4vPnz4iMjERiYiJMTExgYWEBe3t7ODo6fpMl4b4lYWFh8PPzEz5DExMT2NjYwNHREc7OzmrfCFFXaGgoHj9+jPDwcERGRkJXV1fIEFKxYsUc3ZiKjIyEt7c33r17h9jYWBgaGqJ48eKoXLlyjgNECyuZTIbHjx/j9evXCA8PF/YvRYoUQY0aNXiD4P979+4dXrx4gXfv3iEmJgapqakwMzODhYUFihcvjqpVq2rtxltkZCR8fHzw+fNnREREwMDAANbW1nBwcICrqyt0dXW1sp7C6tixY0KpHSD9xtrSpUtF83z48AE+Pj549+4dUlJSUKRIEdjZ2aFOnTq5zpT4rW//aWlpCAoKwtu3b/H+/XvExsYiJSUFJiYmsLS0hKOjIypVqqRRkIcqqamp8PPzQ3BwMMLDw5GSkgJra2sUK1YMNWvW1OiGJxUuycnJePToEd69eydkMjQyMoKZmRlKlSqF8uXLq8xW9b1LSEiAn58fPnz4gKioKMTFxcHExARWVlYoXrw4qlWrBhMTE62s68uXL3j16hVCQkIQGRmJhIQEGBgYwMzMDKVLl4azs7NWs1K9f/8ejx8/RkREBCIiIiCTyWBqagpra2uULVsW5cuXzzYLYF4LDg5GUFCQ8HuenJwMY2NjmJubo1y5cnBycvoqsjLmRFxcHF6+fIng4GB8/vwZCQkJQnlGW1tbODk5oUyZMlo/Vv8a5Oc5qFQqxZMnT/D27Vshm15GKcgSJUqgfPnysLOz+2YfqggLC0PTpk2FaSsrK9y5c0ft5dPS0vD06VNhX5OWliZsvzVq1NDa/jOnhg8fjps3bwrTGzdu1KisbmYymQzPnz/Hq1evEBERgejoaOjq6sLU1BTFihVDuXLl4ODgUKDXg4iIiOj7xOAuIiIiIqKv0B9//IFt27YJ0/3798eCBQsKcERERPQ1Uye4i4iIiIi+Pvv37xddL2jatKnoesLXLDIyEs2bNxdltrx58yYfliEiIqJvzrf5GAIRERER0Tfsy5cv+Ouvv0RtysroEBEREREREdH3KzExEZ6enqK2b+n6wc6dO0WBXZUqVWJgFxEREX2TmDc0DyxatAh79uwRtanzxGtISAhat26do3UWL14c165dy9GyRERERFRwkpOTYWBgoPb8CQkJmDJlCqKjo4U2W1vbHB9HEhEREREREVHhp+n1A6lUijlz5uDNmzdCm6GhIdzc3PJieLmSkpICPT09jUqUXrlyBVu3bhW19e/fX9tDIyIiIioUmLlLyx4+fIh9+/YV9DCIiIiI6Csxc+ZMzJ49Gw8ePEBaWlqW896+fRv9+vXDnTt3RO2jRo2Cnh6f2yAiIiIiIiL6Vq1atQoTJkzAzZs3kZKSkuW8/v7+GDZsGP755x9Re//+/WFtbZ2Xw8yRwMBAdO7cGQcOHEB4eHiW80ZGRuLPP//EuHHjIJVKhfaSJUuiR48eeTxSIiIiooLBO0BalJKSgrlz52Z7U05dOjo6aj+lwJt5RERERF+n5ORknDlzBseOHUORIkVQrVo1VKxYEdbW1jAwMEB0dDRCQkLg7e0teto2Q6NGjTBkyJACGDkRERERERER5RepVIrz58/j/PnzsLCwQPXq1VGxYkUUKVIExsbGiI2Nxbt37/DgwQM8f/5cYfmKFSti2rRpBTBy9bx8+RLz58/HwoUL4eLigsqVK6NUqVIwNzdHcnIyIiIi4O/vjwcPHohKMQLp99OWL18OU1PTAho9ERERUd5iRJAWbdmyRThgtrW1xadPn3LV3+LFi9GzZ09tDI2IiIiIvgJfvnzB5cuXcfnyZbXmb9SoEdasWaNR2QIiIiIiIiIi+rpFR0fj+vXruH79ulrzV65cGRs3boShoWEejyz30tLS8OjRIzx69Eit+Y2MjLBkyRLUq1cvj0dGREREVHBYllFLXr16hU2bNgEAjI2NMXXq1AIeERERERF9DcqWLQtdXV2NlrGxscG0adOwdetWWFhY5NHIiIiIiIiIiKiwsLe3h4GBgUbLmJqaYtSoUdi/fz9KliyZRyPLPTMzMxQrVkzj5Zo2bYpDhw6hU6dOeTAqIiIiosKDmbu0QCaTYe7cuUhOTgYAjBs3DqVLly7gURERERHR12DmzJkYOXIkrl+/Dh8fH7x48QLv379HREQEkpKSoK+vD0tLSxQpUgSurq6oX78+WrVqBWNj44IeOhERERERERHlkyFDhqBnz564fv26UHoxNDQUERERSExMhK6uLiwsLGBtbY2qVauibt26aN26NSwtLQt66Nmyt7fH1atX4ePjg3v37uHRo0cIDg7Gx48fER8fD6lUCnNzc1haWsLBwQF16tRB8+bN4ezsXNBDJyIiIsoXDO7SgoMHD8Lb2xsAUKlSJfz444948OBBAY+KiIiIiL4WNjY26N69O7p3717QQyEiou9Uz5490bNnz4IeBhERERFlwczMDB07dkTHjh0Leihap6Ojg9q1a6N27doFPRQiIiKiQodlGXPp48ePWLlyJQBAIpFgwYIF0NfXL+BRERERERERERERERERERERERHR147BXbn022+/ISYmBgDQt29f1KpVq4BHRERERERERERERERERERERERE3wIGd+XC+fPnceHCBQBAkSJFMG3atAIeERERERERERERERERERERERERfSv0CnoAX6uYmBj89ttvwvSsWbNgaWmp1XWcO3cOp0+fxsuXLxEREQFDQ0NYWVmhYsWKqFu3Ljp27IhixYppdZ1ERERERERERERERERERERERFQ4MLgrh5YvX46wsDAAQKNGjdCtWzetr+PKlSui6YSEBERGRiIoKAgXLlzAihUr0Lt3b0yfPh2mpqZaXz8RERERERERERERERERERERERUclmXMgXv37uHw4cMAAAMDA/z6668FMo7k5GTs378fvXv3xps3bwpkDERERERERERERERERERERERElDeYuUtDycnJmDt3LmQyGQBg7NixKFu2rFbX4eTkhDZt2qB27dqoUKECrKysIJPJ8PnzZ/j6+uLEiRO4du2aMP+rV68watQoHDp0CNbW1jlaZ2xsrLaGT0SUJ3R1dQEAUqm0gEdCRERElD0euxAREdHXgsctRERE9DXhsQsRfQ3MzMy02h+DuzS0fv16vH79GgBQrlw5jBo1Smt9W1lZ4eDBg6hZs6bS1+3s7GBnZ4fOnTvj6tWrmDFjBqKiogAAb968waJFi7By5cocrTslJSXH4yYiyk/cXxEREdHXhMcuRERE9LXgcQsRERF9TXjsQkTfE5Zl1MCzZ8+wfft2YXrBggUwMDDQWv9mZmYqA7vkNW/eHBs2bICe3v/F5/377794/vy51sZDREREREREREREREREREREREQFh8FdakpLS8PcuXOFCGA3NzfUr1+/QMdUp04d9OrVS5iWyWQ4c+ZMAY6IiIiIiIiIiIiIiIiIiIiIiIi0hcFdatqzZw98fX0BpJdPnDlzZgGPKF3v3r1F0//9918BjYSIiIiIiIiIiIiIiIiIiIiIiLSJwV1qSExMxOrVq4XpmTNnwsbGpuAGlEnVqlWhr68vTL97964AR0NERERERERERERERERERERERNoikclksoIeRGEXHR2NunXrCtO6urrZLiOTyZCWliZMSyQS6Oj8Xyxdjx498Pvvv2tlfE2aNMGnT58AAAYGBnj06JHGfURERGhlLEREeSUjkDWjPC4RERFRYcZjFyIiIvpa8LiFiIiIviY8diGir4G1tbVW+9PTam/fCalUqvEyMplMtFzmwK/cSkxMFP5vaGiotX6JiIiIiIiIiIiIiIiIiIiIiKjgsCzjVy4iIgIxMTHCdJEiRQpwNEREREREREREREREREREREREpC3M3KUGCwsLPHv2TKNl7ty5gyFDhgjTbm5uWLp0qbaHhmvXrommnZ2dtb4OIiIiIiIiIiIiIiIiIiIiIiLKf8zc9RVLSUnB1q1bRW1NmjQpoNEQEREREREREREREREREREREZE2MbirkPj48SMiIyPVnj8tLQ3z5s3DixcvhDZbW1t07tw5D0ZHRERERERERERERERERERERET5jcFdhcSjR4/QqlUrLFu2DE+ePMly3qdPn2LYsGE4duyYqH3q1KkwMTHJy2ESEREREREREREREREREREREVE+0SvoAdD/iYuLw44dO7Bjxw7Y2tqiSpUqsLe3h7m5OWQyGcLDw+Hr64tnz54pLDtixAj07NmzAEZNRERERERERERERERERERERER5gcFdhdSnT59w9erVbOczMjLCrFmzMGDAgHwYFRERERERERERERERERERERER5RcGdxUSTk5O+OGHH3D37l28evUKaWlpWc5va2sLNzc3DBw4ECVKlMinURIRERERERERERERERERERERUX6RyGQyWUEPgsTi4+MRGBiIkJAQfP78GfHx8ZBIJDA3N4eNjQ1cXFxgb2+v1XVGRERotT8iIm3T19cHAKSkpBTwSIiIiIiyx2MXIiIi+lrwuIWIiIi+Jjx2IaKvgbW1tVb7Y3AXAWBwFxEVfjxYJyIioq8Jj12IiIjoa8HjFiIiIvqa8NiFiL4G2g7u0tFqb0RERERERERERERERERERERERKQVDO4iIiIiIiIiIiIiIiIiIiIiIiIqhBjcRUREREREREREREREREREREREVAgxuIuIiIiIiIiIiIiIiIiIiIiIiKgQ0ivoARARUcGIi4vD/fv38eTJEwQEBCAsLAxRUVGIjo6Grq4uTExMULp0aTg5OaFFixaoW7dutn36+/vjwoULePz4MUJDQxEXF4e0tDSYmJigZMmScHZ2RqtWrVC/fv1cj/+nn36Cj49PjpYdMWIERo0aJWpLSEjAoUOH4OXlheDgYKSlpaFkyZJo2rQpBg0aBEtLS5X9paWlYcSIEQgICICOjg527twJJyenHI2NiIiIiIiIiIiIiIiIiIgoA4O7iIi+U97e3vjf//6n9LXU1FQkJSUhIiIC/v7+OHr0KKpXr47FixejaNGiCvMnJCRg0aJFuHTpktL+oqOjER0djWfPnuHkyZOoXr06fv/9dxQpUkSr7ymnoqKiMG7cOLx8+VLUHhQUhKCgIJw/fx4bNmxA6dKllS5/4sQJBAQEAADc3NwY2EVERERERERERERERERERFrBsoxERKQWX19fTJ48GSkpKQqv/frrryoDu7LqKzU1VZtDVJuurq5o+o8//hACu4oUKYJly5bBw8MDDg4OAICPHz9i/vz5SvuKjIzEpk2bAADW1tYYM2ZM3g2ciIiIiIiIiIiIiIiIiIi+K8zcRUT0ndPV1YWTkxPs7OxgYWGB+Ph4vH79WshElVlgYCCuXbuG1q1bC21Pnz7FtWvXFOa1trZGvXr1oK+vj/v37+P9+/ei11+8eIGrV6+K+tJEq1atUKlSpSznSUxMxMmTJxXaW7RoIfw/PDwcXl5ewvTEiRPRvHlzAMDs2bMxduxYAMCjR4/w9OlTODs7i/pav349oqOjAQDu7u6wsLDI0fshIiIiIiIiIiIiIiIiIiKSx+AuIqLvVJkyZfDbb7+hQYMGMDc3V3jd29sb06ZNQ1JSkqjd399fFJDl5+ensGypUqWwa9cuod+UlBT89NNP8Pf3F8336NGjHAd39enTJ9t5jh8/rhDcVadOHZQvX16YDggIQFpamuj1DDVq1ICBgQGSk5MBpL/3zMFd/v7+OHXqFADA1dUVnTt3ztF7ISIiIiIiIiIiIiIiIiIiUoZlGYmIvlPlypVD27ZtlQZ2AelBTpkDnTLIl1JUVqaxWbNmon719fXRtm1bhfmkUqmmw9bIkSNHFNr69esnmo6JiRFNW1paiqYzZ+LKPG9aWhr++OMPyGQy6OrqYvr06ZBIJNoYNhEREREREREREREREREREQAGdxERkQoymQwfPnxQaLe3t89yGkgvdahOW7ly5XIxwqx5e3vj5cuXorbSpUujcePGojb54LaoqCjRdEbJRfl5jx07hmfPngEA3Nzc4OTkpJVxExERERERERERERERERERZWBwFxERCWQyGeLi4uDv74+ff/5ZITjKzMwM7du3F7U1aNAAJUqUELVdunQJR44cQVRUFOLi4nDx4kX89ddfonlsbGwU+tIm+fUB6aUcdXTEP32VK1cWtXl7ewv/9/X1FUoyAkDVqlUBpAeqbd68GQBgbW2NMWPGaHXsREREREREREREREREREREAKBX0AMgIqKCN3nyZNy+fTvLeczNzbFo0SKFsoUGBgZYvHgxpk+fjoiICADp5RZXrFiBFStWKO2raNGi+OOPP2BqaqqdNyDn3bt3uHHjhqjNxMQEXbt2VZjXxsYGrVu3xoULFwAAHh4eMDY2hrGxMVauXCnM5+rqCmdnZwDAunXrhBKN7u7uKktbEhERERERERERERERERER5QaDu4iIKFv9+vXDkCFDUKRIEaWvu7i4YO/evTh48CD2798PqVSqsq8ffvgBI0aMyLPALgA4evQo0tLSRG2dOnVSuc7p06fj1atXePnyJb58+YKZM2eKXi9evDjmz58PAPDz88OZM2cApAd8de7cWZjvy5cvePHiBRITE2FrawsnJyfo6fGnloiIiIiIiIiIiIiIiIiIcoZ3nImIKFtHjhzBly9fMGPGDIXMXQCQlpaGCxcu4PTp01kGdgHAgQMHEBYWhqlTp8LGxkbrY01MTMQ///wjapNIJOjbt6/KZSwtLbFt2zYcPHgQXl5eCA4OhkwmQ4kSJdCsWTMMHDgQVlZWkEql+OOPPyCTyaCrq4sZM2ZAIpHgy5cvWLZsGa5duybq19raGu7u7ujSpYvW3ycREREREREREREREREREX37GNxFRERo3rw5HBwckJaWhri4OAQGBuL58+fC61KpFBcvXsTTp0+xefNmhQxe8+bNw8WLF0Vt1tbWqFWrFoyNjfHkyRO8evUKACCTyYS+tm3bBisrK62+lzNnziA6OlrU1qBBA5QpUybL5YyNjfHjjz/ixx9/VDnPkSNH8OLFCwCAm5sbKlWqhJiYGIwePRqhoaEK80dERGDRokWIi4tDv379cvBuiIiIiIiIiIiIiIiIiIjoe8bgLiIigpubm0LbkydP8L///Q+fPn0S2kJCQrBhwwbMnTtXaLtw4YJCYFeVKlXg4eEBMzMzAOmZvVavXo2//vpLoa+ff/5Zq+/lyJEjCm3aCKz68uULtm7dCiA9cG3MmDEAgB07dgiBXaampli6dCkqVqyINWvWCOUbN2zYgLZt2+ZJpjIiIiIiIiIiIiIiIiIiIvp26RT0AIiIqHCqUqUKpkyZotB++fJlUenFs2fPKswzYsQIIbALAHR0dDB27FhIJBLRfBcvXsy2jKMmvL298fLlS1Gbg4MD6tevn+u+165di9jYWACAu7s7zM3NAUAU2NajRw/UrVsXVlZWmDx5MvT19QEASUlJCiUbiYiIiIiIiIiIiIiIiIiIssPgLiIiUsnR0VGhLT4+HpGRkcJ0cHCwwjylSpVSaDMxMVEowSjfV25lzgyWoU+fPgpBZZry8fERgthcXV3RuXNnAEB0dLQos5mLi4vwf0tLS9jZ2QnTr1+/ztUYiIiIiIiIiIiIiIiIiIjo+8OyjERE3yGpVApdXd1s53v16pXSdkNDQ+H/GdmpMnv//j3KlSsnalMVyGVgYJDtONTx/v173LhxQ9RmZmYmBGLlVGpqKlasWAEA0NXVxYwZM4RgscTERNG8pqamCuvPID8vEREREREVvHfv3qFnz55ZziORSGBqagozMzM4ODjA2dkZbdu2RYUKFfJplERERERERERE9D1jcBcR0Xfo5cuXWLp0KXr37o3mzZsrBCUBgL+/P9asWaPQbmtrKwpasrOzUyiFuH37dlSvXl3oVyaTYdOmTZDJZKL5rK2thfKGGU6dOoVFixaJ2kaMGIFx48Zl+Z6OHDmCtLQ0UVvXrl1hbGyc5XLZ+euvv4T35+bmhkqVKgmvWVpaQkdHR1hvdHS0aNmoqCjh//JZy4iIiIjo63c5jAH8OdGymFFBD0EjMpkMsbGxiI2NxYcPH3Dnzh3s2rULdevWxezZs5VmLiYiIiIiIiIiItIWBncREX2nnjx5goULF0JPTw8VK1aEg4MDTE1NERUVhbdv3+L58+dKl+vatatoukWLFrh69aqo7fHjx+jTpw9q164NQ0NDPHnyRGkWsJYtW2rlvSQmJuKff/4Rteno6KBPnz656vfz58/Ytm0bgPRAtDFjxoheNzQ0hJOTEwICAgAA3t7eaNu2LQAgLCwMISEhwrzVq1fP1ViIiIiIiCh/yGc5lslkCg+SAMC9e/cwdOhQbNq0SWlJeyIiIiIiIiIiIm1gcBcR0XcuNTUVAQEBQoBSVqpUqYKhQ4eK2tq3b48TJ07A19dX1B4eHo4LFy6o7Kto0aIYOXJkzgYt5+zZswpZs5o0aZLrJ+jXrFmD+Ph4AIC7u7tCljEA6Nu3LxYsWAAgPetYxYoVUaFCBWzZskW4AeTg4ID69evnaixERERERJT3atasiY0bNyq0x8XF4dWrVzh37hxOnDiB1NRUAEBMTAxmzJiBQ4cOKS1ZT0RERERERERElFs6BT0AIiIq/HR1ddGjRw+sX78ehoaGotd0dHSwatUqtGjRQu3+KleujA0bNsDGxkYr4zt8+LBCW79+/XLV5/3794XgNFdXV3Tu3FnpfB07dkT37t0BAFKpFCtWrMDYsWPx4MEDAOnlGBctWqTw9D8REREREX09TE1N4erqiunTp8PDwwMGBgbCa+/evcPJkycLcHRERERERERERPQtY+YuIqLvUMWKFbF//374+PjA19cXb9++RWRkJCIjI5GamgpjY2PY2NigbNmyqF69Olq1aoUSJUqo7M/U1BRLly7FkydPcOHCBfj7+yMkJARxcXGQSqUwNTVF8eLF4eTkhJYtW6Jhw4bQ0dFOfPH9+/fx8uVLUZujoyNq166d4z5TU1OxYsUKAOmBbTNnzoREIlE5/+zZs1GnTh0cP34cz58/R2JiImxtbdGoUSMMHToUxYoVy/FYiIiIiIiocKlVqxYGDx6M7du3C21XrlxB7969C3BURERERERERET0rZLIZDJZQQ+CCl5ERERBD4GIKEsZJU5SUlIKeCRERERE2eOxy/fhclhiQQ/hq9SymFFBD0Hw7t079OzZU5hWVZYxu+UMDQ1x9erVPBkjEVFe43ELERERfU147EJEXwNra2ut9sfMXURERERERERERBooVaoUTExMEB8fDwBISkpCXFwcTE1N1Vo+MTERfn5++PjxIyIiIqCvrw8rKys4OzujXLlyuR5fXvefISoqCg8fPkRYWBgSEhJgZWWFypUro0KFCllmP87O06dPERgYiPDwcJiamqJYsWKoUaMGzM3Ncz1mqVSK169f482bN/j8+TPi4+NhZGQECwsLODo6omLFitDV1c3VOmJiYnD//n18/PgRycnJsLW1RdmyZeHs7Jzr8WcWERGBR48e4fPnz4iOjoapqSmKFCmC6tWro0iRIlpdFxEREREREREVHAZ3ERERERERERERacjU1FQI7gKgVnDXixcvsG3bNty+fRtJSUlK5ylZsiQGDx6Mbt26QU9Ps0t32u7//v37cHd3F6bXr1+P2rVrIywsDGvWrMG1a9eUPi1vZ2eH8ePHo0WLFhqN/9y5c9i0aRPev3+v8JqhoSFatmyJSZMmwdraGlu3bhWVxrx9+7bKfmNiYnD16lVcuXIFPj4+iIuLUzmvmZkZunXrhoEDB2ocIPXp0yesXbsWXl5eSE1NVXi9bNmyGDFiBNq2bQsAaNCggfDaiBEjMGrUKLXWc+3aNezatQtPnjyBqqIMVatWxahRo1C/fn2N3gMRERERERERFT46BT0AIiIiIiIiIiKir418gJCZmZnKeWUyGdatW4ehQ4fi6tWrKgOvAOD9+/dYvnw5fvrpJ0RERKg1lrzuP7N79+5h8ODBuHTpksoyKCEhIZg1axZ2796tVp9SqRTz5s3Dr7/+qjSwC0jPjnb27FkMHToUL1680GjM27Ztw6JFi3Djxo0sA7sAIDY2Fvv378fgwYPh6+ur9joePXqEAQMG4Pz580oDuwAgKCgIc+fOxcqVKzUaf4aYmBhMmjQJM2fOxOPHj1UGdgGAv78/Jk2ahGXLlqkcDxERERERERF9HZi5i4iIiIiIiIiISANv374VZe0qUaIETExMlM4rk8kwZ84cXLp0SdReqVIlVKlSBdbW1khNTUVISAju3buH2NhYAOnBQuPGjcOOHTtgbGyscix53X9mr169wsaNGxEfHw9dXV1Uq1YNTk5OMDExwefPn3Hnzh18/PhRmH/jxo1wdXVFzZo1s+x3yZIlOH/+vKitdOnSqFu3LmxsbBAdHQ0/Pz88f/4cYWFhmDVrFpo2barWmOVZWlqiQoUKKFOmDMzNzWFgYIC4uDgEBweLsnqFh4dj2rRp2L17N0qVKpXt32XSpEmi74S+vj7q1q2LcuXKQU9PDyEhIbh9+zbi4uJw+PBhODg4aDTuiIgI/PTTTwgKChKto1q1anB0dIS5uTni4+Px4sUL+Pj4QCqVAgCOHz+OpKQkzJs3T6P1EREREREREVHhweAuIiIiIiIiIiIiDZw6dUo03aRJE5Xzenp6igKvatWqhWnTpsHR0VFh3ri4OGzevBl//fUXAOD169dYuXIl5syZU2D9Z+bh4YGUlBTUqVMHs2fPRunSpUWvp6SkYP369Th48CCA9MCzzZs3Y9OmTSr7vHbtmujvaWhoiBkzZqBLly4K8965cwcLFy5EaGgojh07ptaYAcDGxgaDBg1CmzZt4OTkBIlEonS+5ORkHDlyBBs3bkRKSgpiY2OxfPlyrF69WmXfUqkUv/32myiwq06dOpg3bx6KFSsmmjcuLg5//vknTp06BQ8PD7XHL5PJsGDBAiGwSyKRwM3NDSNHjoSNjY3C/O/evcPixYtx//59AMDp06dRp04ddOrUSe11EhEREREREVHhwbKMREREREREREREarpz5w72798vTBsYGGDAgAFK5w0ODsa2bduE6datW2Pt2rVKA68AwNTUFFOnTsXQoUOFttOnT+Pt27cF0r+8lJQU1K9fH6tXr1YI7ALSM0lNnjwZderUEdoePnyId+/eqexz/fr1ounffvtNaWAXANSvXx8eHh4wMjJCcnKyWmMGgKFDh2L8+PFwdnZWGdgFpH+WP/zwA+bPny+03blzR5QtS56XlxcCAgKEaVdXV6xatUohsAtI//vPmTMHnTp10mj8//77L27fvi1MT5kyBTNnzlQa2AUApUqVwpo1a0QZ07Zt2yZk8yIiIiIiIiKirwuDu4iIiIiIiIiIiFSQyWSIiYnBw4cPsWzZMkydOhWpqakA0jMozZw5U2mgEwDs379fCKixsbHBzz//DF1d3WzXOXLkSJQoUQIAkJaWhpMnTxZI//IMDAwwd+5c6OllXQxAPtjN399f6Xz379/HmzdvhOnWrVujWbNmWfbt6OiIIUOGqDXenGrdujXKly8PIP3zv3nzpsp5M2cQk0gkmD17NgwMDLLsf8qUKbC0tFRrLDKZDHv37hWmGzZsiL59+2a7nJ6eHmbOnCkEs717904UIEZEREREREREXw+WZSQiIiIiIiIiIgLg4+ODBg0aqDVv8eLFMW3aNJXBSFKpFOfOnROmO3fuDFNTU7X61tfXR7NmzYTyiRnl9fKzf2VatGiBokWLZjtfjRo1IJFIIJPJAEBl5qtbt26Jpnv16qXWOHr06IHt27fnaSYqR0dHvHr1CgDw5MkTpfPEx8fD19dXmK5Zs6YQFJYVc3NztG/fXvj7Z+XJkyeiv586gV0ZypUrB0dHRwQGBgJI/5wbN26s9vJEREREREREVDgwuIuIiIiIiIiIiEgDNWvWxIoVK7IMpnr27Bni4+OF6Ro1ami0Dnt7e+H/L168gEwmE5UUzOv+lalVq5ZafZuamsLCwgJRUVEAgOjoaKXzZQ6aMjQ0RPXq1dXq38bGBpUqVRKVQ1SXn58frl69iufPnyMkJARxcXGIj48XAtEypKWlCf8PCwtT2ldAQIBovvr166s9jvr166sV3OXj4yP8XyKRqP03ymBvby8Edz1//lyjZYmIiIiIiIiocGBwFxERERERERER0f8nX9ZQWXYoHx8fjBkzBmvXroW1tbXSfuQDaWbOnKnRODIHG0mlUsTFxcHMzCzf+lfG1tZW7f6NjY2F4K6EhASl84SGhgr/L1eunFolJTM4OjpqFNzl5+eH5cuXC4FOmlAVnJZ5/BljUpe682b+nGUyGdq2bav2OgBxkJqq90FEREREREREhRuDu4iIiIiIiIiIiJCekWvjxo2itri4OHz8+BFXr17FX3/9hYiICABAYGAgJk6ciK1bt8LIyEihr8jISNF0bksIxsbGioKv8rp/ZZS9T3XIZ8XKEBMTI/zfwsJCoz4tLS3VntfLywtz587N8d8oOTlZaXvm8QOavQd1x6/Nzzk2NjbHyxIRERERERFRwWFwFxERERERERERkQqmpqYoX748ypcvj+7du8Pd3R2vX78GkF7OcP369Zg2bZrCcvKBP7mVOQNTfvSfHzIHTenr62u0rLrzv3//HgsWLBAFRbm4uKBNmzaoUqUKSpQoAXNzcxgaGooyhy1cuBCnT58GoDo4TT7oS5P3oO682gzIKojPmIiIiIiIiIhyj8FdREREREREREREarCxscHy5csxePBgJCYmAgCOHj2Krl27olKlSqJ55bNcHT58GPb29lobS173nx/MzMyE0o3x8fEaLRsXF6fWfHv27EFSUpIwPXHiRPzwww/ZLqeqlGRm8pnONHkP6o7f0NBQ+L+trS3++ecftddBRERERERERN8GnYIeABERERERERER0dfC3t4eP/74ozCdlpaGDRs2KMxnZWUlmg4NDdXqOPK6//yQ+T18+PBBo2Xfv3+v1nw3btwQ/l+rVi21ArsAIDw8PNt55D8DTd6DuuPPvI7Pnz+LAtWIiIiIiIiI6PvA4C4iIiIiIiIiIiIN9OvXD7a2tsL07du34e/vL5qnXLlyomkfHx+tjiGv+88PmbOdvX//Xq2AKiC9TOLTp0+znS8xMRFhYWHCdMOGDdXqXyqV4vnz59nOJ5+t7cmTJ2r1DwABAQFqzZf5c5bJZHj48KHa6yAiIiIiIiKibwODu4iIiIiIiIiIiDRgZGSEQYMGidq2b98umq5WrZqopN6lS5cglUq1Noa87j8/VKtWTTR98eJFtZZ78OABPn/+nO18MTExomlzc3O1+r9165ZaZRnLlCkDa2trYfry5ctITU1Vax3nz59Xa746deqIps+dO6fWckRERERERET07WBwFxERERERERERkYZ69OiBokWLCtP//fefKBuTgYEBmjdvLkyHhITg5MmTWlt/XvefH9q2bQt9fX1hes+ePYiPj89yGZlMhi1btqjVv4mJiWhanVKIaWlp2LVrl1r9SyQStG/fXpgODw/HkSNHsl3u7t27amdaq1atGkqUKCFMnzt3Di9fvlRrWSIiIiIiIiL6NjC4i4iIiIiIiIiISEOGhoYYOHCgqE0+e9fw4cOho/N/l9/WrFmjcfnEd+/eITQ0VOlred1/XrOyskKHDh2E6U+fPmHu3LlITk5WOr9MJsOaNWvg6+urVv+mpqYoXry4MH3u3DkkJiZmucyWLVsUSmxmpXfv3qIAtY0bN+Lu3bsq53/z5g3mz5+vdv96enoYNmyYMC2VSvG///0Pnz59UrsPAHj48KHKvysRERERERERFW4M7iIiIiIiIiIiIsoBNzc32NjYCNM3btzA8+fPhemyZcti5MiRwnRSUhImTJiA7du3IzY2VmW/KSkpuHHjBubMmYM+ffogMDBQ6Xx53X9+GD9+vOhvePPmTQwePBhnzpzBly9fkJaWhqioKFy/fh3jxo3DwYMHIZFIULVqVbX6b9asmfD/Dx8+YPbs2YiKilKYLzY2FsuWLYOnpycAwNjYWK3+7ezsMHz4cGE6KSkJU6ZMwYoVK/D06VMkJiYiJSUFQUFB2L59O4YNG4bw8HC4urqq1T8AdO3aFfXq1ROmQ0JCMHToUJw9ezbLUpzR0dH4559/MGbMGIwdOxZJSUlqr5OIiIiIiIiICg+9gh4AERERERERERHR18jIyAgDBw7E2rVrhbYdO3Zg6dKlwvSPP/6IkJAQnD59GgCQmpqKrVu3Yvfu3XB1dUX58uVhbm6OpKQkREdH4/Xr13jx4kW2Gabyq/+8ZmlpiWXLlmHSpElCScY3b95gwYIFKpcZMGAAjI2NhQxburq6KucdPHgw/v33X6Hv//77D25ubmjUqBHs7e2RkpKC4OBg3L17FwkJCQCAmjVronjx4jh79qxa72HIkCF4+fIlLl68CCA9u9aRI0dUlmi0sLDAwoUL4ebmJrRl9R50dXWxaNEijBs3TgjECw8Px/z587F69WrUrFkTJUuWhLGxMeLj4xEREYEXL14gKCgoy+AvIiIiIiIiIvo6MLiLiIiIiIiICrXo6Gg8efIEjx8/Fv7JZ12pWbMmNm7cmGU/qampCAwMFPXz9u1byGQy0XzHjh1DqVKltP4+QkNDceLECdy/fx+hoaGIi4uDubk5SpYsiYYNG6Jr164oUaJEln0kJCTg0KFD8PLyQnBwMNLS0lCyZEk0bdoUgwYNgqWlpcpl09LSMGLECAQEBEBHRwc7d+6Ek5OTtt8m0XenZ8+e2Lt3LyIiIgAAV69excuXL+Ho6AgAkEgkmDdvHsqXL49NmzYhNTUVQHqGJ29vb3h7e2e7jsxl/+Tldf/5wdXVFevXr8fChQvx+vVrlfPp6upi1KhRGDp0KNatWye0m5qaqlymWLFiWLRoEWbPni1kroqPjxcCsZSNZdmyZVi9erXa49fV1cWCBQtQvHhxHDx4MMuAqgoVKuD333+HtbW1qD2r9wCkB4Rt3boVS5Yswfnz54X2yMhIXL58Wa0xZi7hSURERERERERfDwZ3ERERERERUaE2dOhQvH//Ptf9eHp6Ytu2bVoYkWakUim2bNmCPXv2IC0tTfRaREQEIiIi8OTJE+zduxdjx47FgAEDlPYTFRWFcePG4eXLl6L2oKAgBAUF4fz589iwYQNKly6tdPkTJ04gICAAQHopOQZ25V7LYkYFPQQqBIyNjTFgwABs2LABACCTybBjxw4sXrxYNN+gQYPQpk0b7N27FxcvXkRkZKTKPiUSCRwdHdGwYUN06tQJ5cqVy3Yced1/XqtcuTL27NmDc+fOwcvLC4GBgYiIiICpqSlsbW1Rv359dO3aFWXKlAEAUZBvdoFRjRo1wpYtW7B69Wr4+PgonadUqVLo0aMHBgwYkKNgN11dXUyYMAFdunTBqVOncPv2bYSFhSE5ORm2trZwcHBAhw4d0Lx5cxgYGODDhw+i5c3MzLJdh7GxMRYuXIi+fftiz549uH37dpalFvX19VG1alU0bdoU7du3z/bvRERERERERESFk0Qm/4gyfZcyni4lIiqsMi6up6SkFPBIiIiIKL/16NFD4Sa4PHUyd23duhXbt2/Pdn3ayNyV+dhl0aJFOHXqlNrLjho1CiNGjFBonzNnjpBppkiRIpg5cyaMjY2xcuVKvHnzBkB6xpmtW7cqLBsZGYm+ffsiOjoa1tbWOHToECwsLHLy1ohIC2QyGQIDA/Hy5UtERUUhLi4OhoaGsLCwgJ2dHcqXL59lJr6C7r8wGDJkCJ4/fw4AqF+/PtasWaPWciEhIfDz88OXL1+go6ODIkWKoEyZMqhSpUpeDlfBtWvXMHPmTGF669atcHV11aiP5ORkPH78GKGhoYiKikJycjJMTExgZWWFMmXKoFy5cjAyYhAqZY/XXIiIiOhrwmMXIvoayGfszi1m7iIiIiIiIqKvhq6uLuzt7REUFJTrvooXL474+HjExMTkfmAqXLx4UWlgl6urK8qVK4fg4GCFLDLbtm1D3bp1Ua1aNaEtPDwcXl5ewvTEiRPRvHlzAMDs2bMxduxYAMCjR4/w9OlTODs7i/pcv349oqOjAQDu7u4M7CIqYBKJBBUrVkTFihW/yv4LWkhICAIDA4XpypUrq72snZ0d7Ozs8mJYGrly5Yrwf11d3Rx9VgYGBqhZsyZq1qypxZERERERERERUWHD4C4iIiIiIiIq1Jo0aYKSJUvCxcUFzs7OCA8PR8+ePTXux8HBAYMGDULVqlVRtWpVFC1aFD/99JPKEl3asGfPHoW2MWPG4McffxSmDx06hD///FOYlslk8PDwEJWQDAgIEJV0rFOnjvD/GjVqwMDAAMnJyQAAf39/UXCXv7+/EGDm6uqKzp07a+GdEREVnC1btoj2iU2aNCnA0WguMDAQFy5cEKbr1q3LDFtEREREVGBev34Nb29vPH78GG/fvsX79+8RHx8PqVQKMzMz2Nvbo3r16ujSpQvKli2rtI/cXF8ZMWIERo0alePxL1y4EKdPn9ZomZkzZyq9tpSQkIBDhw7By8sLwcHBSEtLQ8mSJdG0aVMMGjQoywzIaWlpGDFiBAICAqCjo4OdO3fCyclJ4/dDRKQMg7uIiIiIiIioUJs+fbpW+mnXrh3atWunlb7UERERgWfPnonaDA0NMXDgQFFb7969sWPHDkRFRQlt/v7+ePnyJRwdHQFAIbuY/MVECwsLfP78WWHetLQ0/PHHH5DJZNDV1cX06dMhkUhy/+aIiLQoMDAQurq6KPf/2Lvv8Kiq9f/7n8mk94QSeg0J0qsoCqiISBUElCMqeABB8WdBQPmqB1DEAoiiB0FQVNSDDRGlCRawIYQu3dBLQklCQpJJ3c8fPGwzaUzCJDPB9+u6uNxr7bXudW/wwu3knrXq1y92nGEYmjt3rr777juzLyoqSs2aNSvrFC9r3bp16tixo3lETFEOHTqkp556yu4ImdIULAMAAADOcLnCqKSkJCUlJWnnzp365JNPdNddd+mRRx6Rp+fVV2Zw/vx5Pfzww4qNjbXrP3z4sA4fPqzvvvtOc+bMUc2aNQudv3TpUu3Zs0eS1L9/fwq7ADjV1fe3LgAAAAAAbiAuLq5AX9WqVeXt7W3XZ7VaVbNmTbviLkn6448/zOKuoKAgu3vnz59XpUqVzPalIxfzj12yZIlZYMYHiwDc1d69e/Xiiy+qbdu2uummm9SsWTPVrl1b/v7+ysjIUHx8vLZu3aovv/xSBw4cMOdZrVaNHTvWhZn/bdasWXr11VfVtWtXdejQQZGRkQoPD5fValVKSor279+vn376Sd9++60yMjLMedddd506derkwswBAADwT3bhwgWHx+bm5mrx4sVKTk7Wf/7zH6flYLVanRbrSkyfPt0s7KpUqZImTJggPz8/zZw5U0eOHFF8fLwmT56s+fPnF5iblJSkuXPnSpLCwsI0atSocs0dwNWP4i4AAAAAAMpAdnZ2gT6bzVbo2NTU1AJ9e/fuNa+vueYaeXh4mMeQxcTEqHv37pKk7du3m0cySjJ3sElISNC8efMk8cEiAPdnGIZiYmIUExPj0HiLxaJHH31UrVq1KtvESuDcuXP67LPP9Nlnn5l9FotFhmEUOr5+/fp67rnn2FERAAAAbsFisSg6OloNGjSQJO3bt6/ALlaStGLFCvXs2VPt2rUz+2655RZFRUUVG99ms+nrr78u0H/TTTddWeL5NG3a9LK7+0ZGRtq1ExIS9MMPP5jtRx99VF26dJEkTZw4UaNHj5Yk7dy5U3v37lXjxo3t5v/3v/81v3g3ZswYBQcHX/FzAEBeFHcBAAAAAFAGqlWrVqDv7NmzOnr0qOrUqWP2nT59WidOnCgw9tSpU+Z1eHi4unbtqjVr1kiSZs+eLT8/P/MbpJc0b97c/IDxrbfeMo9oHDNmTIHdvwDAXfj4+JRofPXq1fXEE0+oc+fOZZRRyfn6+hbaX1hhl9VqVbdu3TR+/HgFBASUdWoAAABAsQICAjRgwAANHDhQVatWNfsNw9AXX3xh97nDJStXrrQr7ho0aNBl1/nqq68KFHe1a9fOLCZzluuuu04jR44s0Zw9e/aYX6i7lNclrVq1kre3t/nFuj///NOuuOvPP//Ut99+K+ni5zK9evW6kvQBoFAUdwEAAAAAUAaqVKmiBg0a6ODBg2afYRiaPHmyJkyYoHr16unYsWOaMWNGobt85d/Na9y4cTp48KBiY2N17tw5TZgwwe5+RESEJk+eLEnasWOHVq5cKangB4vnzp3TgQMHZLPZVKVKFUVHR8vTk48HALhOt27d1LRpU/3666/asWOHDh8+rNOnTystLU2GYSgoKEjh4eFq0qSJrr32Wt18881u9/fWokWLtGnTJm3evFl79+7VyZMnlZSUpMzMTPn4+Cg4OFi1a9dW69atdeutt9oV+QIAAACucvPNN+upp55SpUqVCtyzWCwaNGiQNm7cqJ9//tnu3l9//VXitb744osCfXfffXeJ45SFS1+OuyQkJMSuHRwcrLNnzxYYm5ubq+nTp8swDFmtVo0bN46deQGUCff6FAQAAAAAgKvI0KFDNWnSJLu+3bt3a9iwYZedm5WVZdcOCQnRggULtHjxYv3www86duyYDMNQtWrV1LlzZw0ZMkShoaHKycmx+2Bx/PjxslgsOnfunF555RWtX7/eLm5YWJjGjBmj3r17X/HzAkBp1ahRQ4MGDXLoG//uyMvLSx07dlTHjh1dnQoAAADgsB49elx2TOvWrQsUd2VkZJRonZiYmAJHPNasWVM33HBDieI44uTJk/riiy905swZSRcLs6KiotS8efMid9zNv9v5+fPn7QreLh25mH/skiVLtG/fPklS//79FR0d7bTnAIC8KO4CAAAAAKCMdO/eXdu2bdNXX31V4rmFHaPo5+enBx54QA888ECR87744gsdOHBA0sUPFqOiopSSkqIHH3yw0OMfExMTNXXqVKWmprrNN2YBAAAAAIB7yHtc4SXVqlUrUYzPPvusQN+gQYPk4eFR6ryKsnLlSnM387wCAwM1YMAADR8+XN7e3nb3rrnmGnl4eJjPGhMTo+7du0uStm/fbh7JKEnNmjWTJCUkJGjevHmSLn5xbtSoUU5/FgC4xPl/WwIAAAAAANNTTz2lcePGKSwsrND7YWFhGjx4cIH+0NDQEq917tw5zZ8/34x76YPF9957zyzsCggI0JtvvqlVq1bZfUN3zpw5SkhIKPGaAAAAAADg6pV/B3BJ6tChg8PzT548qV9++cWuz9/fX3369Lni3EriwoUL+uCDDzRy5MgCxzCGh4era9euZnv27Nlav369Nm3apJdeesnsb968uRo3bixJeuutt8w4Y8aMKfRLegDgLOzcBQAAAABAGRs4cKB69+6tLVu26MCBA0pOTpa/v7+ioqLUoUMHrV27tsCc0mzl/+abb+rChQuS7D9YzBu/X79+at++vSTp8ccf19q1a5WVlaWMjAytX79e/fr1K8UTAgAAAACAq83y5cu1Y8cOu77g4GD16tXL4Rhffvllgd2/evbsqYCAAKfkWFL79u3TpEmT9Nprr9n1jxs3TgcPHlRsbKzOnTunCRMm2N2PiIjQ5MmTJUk7duwwdwdr3ry53e/HuXPndODAAdlsNlWpUkXR0dHy9KQsA8CV4W+RCuDo0aPavXu34uLilJubq4iICDVq1EhRUVGuTg0AAAAA4CBfX1917NhRHTt2LHBv3bp1BfpatmxZovhbt27VqlWrJNl/sJicnKwzZ86Y45o2bWpeh4SEqFatWjp06JAkmf8EAAAAAAD/bBs2bNDLL79s12exWPT0008rODjYoRg2m03ffPNNgRh33XWX0/KUpAYNGujmm29Wu3btVL9+fQUEBOjUqVPauHGjFixYoKSkJLvxv/32mzZv3qy2bduafSEhIVqwYIEWL16sH374QceOHZNhGKpWrZo6d+6sIUOGKDQ0VDk5OZo+fboMw5DVatX48eNlsVh07tw5vfLKKwV2OgsLC9OYMWPUu3dvpz4zgH8WirvKwNSpU7Vo0SK7vv79+xf4j9/lrFu3Tm+//ba2bt1a6P3o6GiNGDFCffv2LXWuAAAAAADX2rlzp37++We7vmrVqpm7azkiOztbM2bMkCS7Dxalix+k5pX/m7GBgYHmdf6xAAAAAADgn+f777/X5MmTlZWVZdf/yCOP6JZbbnE4zsqVK5WcnGzXd91116lOnTpOyVOSHn74YVWuXLlAf506dVSnTh117NhR999/v7nT+SU//vijXXGXJPn5+emBBx7QAw88UOR6X3zxhQ4cOCDpYg1AVFSUUlJS9OCDD+rEiRMFxicmJmrq1KlKTU3V3XffXZpHBAB5uDqBq822bdv08ccfX1EMwzD04osv6sEHHyyysEu6uGXk+PHjNXbsWGVmZl7RmgAAAAAAx3377be67rrr7H7Nnz+/0LHvvvuuYmNjC733xx9/aMKECQWOJ/jXv/4lq9XqcD6fffaZucalDxYvCQkJkYfH3//7n/9D1fPnz5vXoaGhDq8JAAAAAACuPkuWLNFzzz1XoLDr4Ycf1pAhQ0oU64svvijQ5+wCp8IKu/KqUaNGobtm7du3r8RrnTt3zvz8JywsTKNGjZIkvffee2ZhV0BAgN58802tWrVKPXr0MOfOmTNHCQkJJV4TACR27nKqrKwsPffccwU+lC+pmTNn6sMPP7Tra9OmjZo3by6r1ap9+/bpt99+k2EYki6edWy1WjV9+vQrWhcAAAAA3NHnn3+u48ePm+3U1NQCY44fP65Zs2bZ9d122212RxDu2rVL3333XYF5+b333nt2u1vVqlVLgwYNKnX+S5cu1fz581WzZk01btxYoaGhstls2rt3b6FFX61atSrRemfPntWCBQsk2X+weImPj4+io6O1Z88eSVJMTIy6desmSTp9+rTd70FJj4IEAAAAAABXj4ULF2revHl2fR4eHnryySc1YMCAEsWKiYkp8LlH3bp11aFDhyvOs6Tq1atXoC/vl90c9eabb5o7gI0ZM0ZBQUGSpLVr15pj+vXrZ+7G/vjjj2vt2rXKyspSRkaG1q9fr379+pX8AQD841Hc5UTvvPOO9u/fL0mqUqWKzpw5U+IYP/74o923vYODgzV79mxdf/31duN2796thx56SHFxcZKkZcuWqW3btho8ePAVPAEAAAAAuJ8ffvih2F2NJenMmTP69NNP7foaNWpkV9x16NChAmMK8+2339q1W7dufUXFXZecOHGi0O3584qMjNSLL75ot9PW5bzxxhtKS0uTZP/BYl533XWXpkyZIuni8zVq1EiRkZF65513zC8oueoDVgAAAAAA4FqGYeiNN97Q4sWL7fq9vLw0efJkde3atcQxP/vsswJ9gwYNksViKXWepZV/F3Pp4hGMJbF161atWrVKktS8eXP16tXLjJ23LiDvZ1EhISGqVauWDh06JEnmPwGgpDiW0UkOHjyouXPnSrr4H4KxY8eWOIZhGHrttdfMtsVi0Zw5cwoUdklSkyZN9P7778vHx8fse+utt2Sz2UqRPQAAAADAlTw8PNSzZ0/NnTtXlSpVcnje5s2btWbNGkn2Hyzm16NHD91xxx2SpJycHM2YMUOjR4/Wli1bJF08jnHq1KklOgoSAAAAAABUfNnZ2Xr++ecLFHb5+/tr1qxZpSrsOnXqlH755Re7vsDAwCI/tyitXbt2mZuhFMUwDP30008F+mvWrOnwOtnZ2ZoxY4YkyWq1avz48WaRWv6fz+fdDV66+NyX8LN8AKXFzl1OYBiGnnvuOWVmZkq6eN5wSf5jcMnatWvNnb8k6Y477jC3bCxM/fr1NXz4cM2ZM0fSxW+qf/7557rvvvtKvDYAAAAAoGy89NJL2rhxo7Zu3aq4uDidP39eaWlpCg4OVrVq1dS+fXt169ZNkZGRJYqb/4PFCRMmFPvt14kTJ6pdu3b66quvtH//ftlsNlWpUkUdO3bU0KFDVbVq1St6TgAAAAAAULHYbDY9++yzBQqxwsLCNGvWLDVu3LhUcb/44gtzp/BL+vTp4/BuWd9++62mTp1q1zd8+HCNHDnSrm/nzp16++231bdvXw0cOFB169a1u5+amqrZs2dr9+7dBdbo2LGjQ7lIF3chu3TEZP/+/RUVFWXeCwkJkYeHh/m8+XcJy3v8Y2hoqMNrAkBeFHc5weLFixUTEyNJioqK0gMPPGB++7kkLm3jeMmQIUMuO2fw4MGaN2+ecnJyzBgUdwEAAAC4mrz99ttOidO7d2/17t273GM1a9ZMzZo1c8q6eXl6eup///tfieZ069ZN3bp1c3ouAAAAAACg4pk2bVqBwi7p4ilSK1eu1MqVK4uc+8QTTxTab7PZ9M0339j1eXh4aNCgQVeWbBEyMjL0+eef6/PPP1etWrXUuHFjBQUF6fTp0/rzzz/tiqsuqVWrlrp37+5Q/LNnz2rBggWSLha9jRo1yu6+j4+PoqOjtWfPHklSTEyM+dnL6dOndfz4cXNsy5YtS/WMAEBx1xWKj4/XzJkzJV08RnHKlCny8vIqcZzs7GytX7/ebFevXl0tWrS47LyIiAi1atVKmzdvlnTxrN+EhASFh4eXOAe4rx9Ps0UnINl0W80gVycBAAAAAAAAAABwVThz5kyh/b/++utl5xZV3LVq1aoCu1fdeOONqlGjRskTLKHjx4/bFVMVJjAwUC+++KLDP9N/4403lJaWJkkaM2aMgoIK/qzqrrvu0pQpUyRd3HWsUaNGioyM1DvvvGPu6FW3bl116NChJI8DACYPVydQ0b3wwgtKSUmRdPEv7TZt2pQqzv79++3+I9e6dWuH5+Ydm5OTU6pdwwAAAAAAAAAAAAAAuBKff/55gb677767TNaKiIiQv7+/w+Ojo6P1zjvvKDo62qHxmzdv1po1ayRJzZs3V69evQod16NHD91xxx2SLv68fsaMGRo9erT5c/vQ0FBNnTpVVqvV4VwBIC927roC3333nfmXeaVKlfTkk0+WOtalM3ovueaaaxye26RJE7v2wYMHS50HAAAAAAAAAAAAAAAltXnz5gI/927YsKHatm1bJuvdfPPNuv7667Vx40Zt3rxZ+/fv1/Hjx5WcnKzs7GwFBASoSpUqatq0qW655RZ16NBBFovFodjZ2dmaMWOGJMlqtWrChAnFzp04caLatWunr776Svv375fNZlOVKlXUsWNHDR06VFWrVnXKMwP4Z6K4q5RSUlL0wgsvmO2nn35aISEhpY6XvyCrJNtSVq9evdhYAAAAAAAAAAAAAADk9fbbbzs1Xtu2bbVhw4YrjtO7d2/17t3bobG+vr7q3LmzOnfufMXr5uXp6an//e9/JZrTrVs3devWzal5AIDEsYyl9uqrr+r06dOSpI4dO6pv375XFC8+Pt6uXa1aNYfn5h8bFxd3RbkAAAAAAAAAAAAAAAAAcD2Ku0ph06ZN5lnB3t7emjRp0hXHTEtLs2sHBAQ4PDf/2PyxAAAAAAAAAAAAAAAAAFQ8HMtYQpmZmXruuedkGIYkafTo0apXr94Vx81fkOXt7e3wXB8fn2JjOcLLy6vEc1CebK5OAHALVqvV1SkAAAA4hPcWAABQUfDeAgAAKhLeXQD8E7FzVwn997//1aFDhyRJ9evX18iRI50SNyMjw65dkuKu/GNtNgqBAAAAAAAAAAAAAAAAgIqOnbtKYN++fXr33XfN9pQpU0pUhFWc/LtvZWZmOjw3/1hfX98Sr5+VlVXiOQBQ3nJycvj7CgAAVCi8uwAAgIqC9xYAAFCR8O4C4J+EnbsclJubq+eee878j0T//v3VoUMHp8X39/e3a5ekuCv/rl/5YwEAAAAAAMDeyZMndd111xX49ccff5Qozrfffms3f926dWWU8dUn/+/d/PnzSxVn/vz5dnG+/fZbJ2cKAAAAAADgOuzc5aBFixZp+/btkqTQ0FBNmDDBqfHzF2SlpqY6PDf/WIq7AAAAAAAoe2Erw12dQoWU2CPB1SkUa968eU79Qh8AAAAAAABwJdi5ywE2m02vv/662Z4wYYLCw537AW5ERIRdOy4uzuG5p06dsmtXq1bNKTkBAAAAAAD80+zevZvdtwAAAAAAAOA22LnLAZmZmUpLSzPbzz33nJ577rli5xiGYddeunSpli1bZrb79eunadOmme2GDRvajT958qTD+eUvBGvQoIHDcwEAAAAAAGDvnXfeUadOneThwfciAQAAAAAA4Fp8QlUKOTk5l/2Vm5trN8cwjGLv5y/u2r17t8P57Nq1y65NcRcAAAAAAEDJWK1W8zo2NlZr1qxxYTYAAAAAAADARRR3uYlGjRopODjYbG/bts3huVu3bjWvrVar2rRp48zUAAAAAAAArnpdu3aVn5+f2Z4/f76ys7NdmBEAAAAAAADAsYwOCQ4O1r59+0o0548//tD9999vtvv376+XX365yPGenp7q3Lmzvv32W0nSqVOntH37drVs2bLYdeLj47V9+3az3bp1a4WHh5coVwAAAAAAgH+6sLAw3XXXXfrggw8kScePH9fy5ct1xx13uDgzAAAAAAAA/JNR3OVGevToYRZ3SdInn3xy2eKuxYsXKycnx2zffvvtZZYfAAAAAADA1ezee+/VkiVLlJKSIkl677331KNHD3l7e5fpuqmpqdq+fbtOnz6t8+fPy9fXV+Hh4WratKlq1KhRpmv/k6WlpWnfvn06cuSIUlJSlJWVJV9fX4WGhqpGjRqKjIxUYGBgqWInJiZq586dOnv2rJKTkxUQEKBKlSqpZcuWqlSpktOe4ejRozpw4IDOnj2r9PR0Va9eXd27dy90bHZ2tv766y8dPHhQSUlJstls8vb2VmBgoKpXr6769euratWqTssNAAAAAAA4B8VdbqRr166KiorS/v37JUlff/21Bg4cqPbt2xc6/tChQ3r33XfNdpUqVTRo0KByyRUAAAAAAOBqExQUpHvuuUfz5s2TdHHH9K+++kp33313may3bds2LViwQFu3brX78l5eDRo00AMPPKBbb71VFoul0DGnT59W3759zfazzz6r3r17F7nuoUOH9K9//cuub/78+WrevHmRc3744Qf93//9n9n+3//+p/r16xc53p0dO3ZM8+fP17p165SRkVHkOIvFogYNGqhr167697//7VDs9evX64MPPtDu3btlGEahY5o1a6aRI0eqQ4cOl403f/58u8//NmzYYK7z3nvvae/evXbjAwMDCxR3paamauHChVq+fLkSExOLXa9q1arq1KmTRowYobCwsMvmBwAAAAAAyp6HqxPA3ywWi8aOHWu2DcPQww8/rN9//73A2N27d2vYsGF2H0A98sgj8vX1LZdcAQAAAAAArkZ33323XVHLBx98IJvN5tQ1MjMzNWXKFI0ePVoxMTFFFnZJ0sGDB/Xcc8/pqaeeKjKPqlWrqm7dumZ748aNxa5f2P3Lzdm0aZN5XaVKlQpb2LVu3ToNGTJE3333XbGFXdLFz+ZiY2P1/vvvXzZuSkqKHnvsMU2YMEG7du0qsrBLkv7880899thjeuWVV5SdnV3SR9DMmTM1YcKEAoVdhTl69KiGDBmijz766LKFXdLFQsEvv/xSJ06cKHFeAAAAAACgbLBzl5u5+eabNXLkSM2fP1+SlJycrGHDhqlNmzZq0aKFPDw8tG/fPv322292HxL17dtXgwcPdlXaAAAAAAAAVwV/f3/df//9euONNyRJCQkJ+uyzz3T//fc7JX5GRoYee+wxbdu2zezz8PBQ06ZNFRUVpZCQEGVkZOjw4cOKiYkxC5DWr1+vJ598UrNnz5bVai0Qt3379jpy5Iiki4VYhmEUudNX3kKtvH3Dhw8vMu+8c9q1a+fQs7qbI0eO6LnnnlNmZqbZV7lyZbVs2VLVq1eXr6+vbDabEhMTdfDgQR04cMCh4qvExEQ99NBDOnz4sNnn5eWlFi1aqGHDhgoKClJaWpoOHDhgt0vbV199pYyMDP3nP/9x+BkWLVqkzz//XNLFf1fbt2+vWrVqyWq16tSpU9q5c6c5NjMzU+PGjVNcXJzZ5+/vr1atWqlOnToKCAhQdna2kpOTdfjwYe3fv1+pqakO5wIAAAAAAMoHxV1uaOzYsbLZbFq0aJHZt2XLFm3ZsqXQ8T179tTUqVPLKz0AAAAAAICr2p133qlPPvlEZ86ckSR99NFHuvPOOxUYGHjFsWfOnGlX2HXLLbfokUceUY0aNQqMTUhI0Guvvaa1a9dKkjZv3qz333+/0CKsa6+9Vl988YWki8VGf/31lxo1alRgXHZ2trZu3Wq2PTw8lJubqz///FPp6eny8/MrMCcuLk7Hjx832+3bt3f8gd3IokWLzMIuDw8PjR07Vv379y+0WE66eJzhr7/+qmXLlhUZ0zAMTZkyxSzsslgs6t+/v0aMGKHw8PAC40+ePKkXX3xRmzdvliStWLFC7dq1U8+ePR16hrlz50qSBg4cqIceekgBAQF297OysszrtWvX6ujRo2a7T58+evzxxwvMuSQ7O1vbtm3T0qVL5enJx8YAAAAAALgLjmV0Qx4eHnr22Wf1zjvvqFWrVkWOi4qK0quvvqpZs2bJx8en/BIEAAAAAAC4ivn4+Ojf//632U5OTtYnn3xyxXE3b95sVyj0r3/9S9OmTSu0sEuSwsPDNXXqVN12221m38cff6yUlJQCY9u2bWtXpFTUMYu7d+82d2eqV6+errnmGkkFi77yyh+rohZ35d19rFevXho4cGCRhV2SFBAQoNtuu01vvfVWkWOWL1+uDRs2mO0nnnhCEyZMKLSwS5Jq1KihN954Q61btzb7FixYUOzRnHnl5OTonnvu0bhx4wot0vLy8jKv8z5vnTp1NHHixCILuyTJ09NT7dq109SpU9W4cWOH8gEAAAAAAGWPr2CVkQ4dOmjfvn1XFKNLly7q0qWLjhw5ol27dun06dPKyclRRESEGjVqpOjoaCdlCwAAAAAAgLz69Omjjz76SCdOnJAkLV68WHfddZdCQ0NLHTPvLu0NGzbUmDFjHJo3duxY/fzzz0pPT1daWppWr16tgQMH2o0JCAjQNddcoz///FPSxcKeIUOGFIiVt+Cnffv2CgwM1K5duyRdLOLq2LFjsXPq1aunKlWqOJS3u0lISDCvLxW1XQnDMPTRRx+Z7euvv1533XXXZed5enpqwoQJuueee2QYhk6ePKkNGzbohhtuuOzc6tWra/To0Q7ll/d5o6Ki5OHB93wBAAAAAKiI+D/6CqBu3brq2bOnhg0bpuHDh6t3794UdgEAAAAAAJQhT09PjRgxwmynpaXpww8/LHW8c+fO2e3wNGDAAIePvgsNDVW7du3M9qUj/fLLO2bbtm12R/RdEhMTY163b9/ebheuvEVclxiGYTcn7xoVTd4jJ/fv33/F8Xbv3m0exyjJocKuS+rXr6+GDRua7aL+TPPr06ePvL29HRqb93kPHDig3Nxch/MDAAAAAADug+IuAAAAAAAAoBDdu3dX/fr1zfaXX36pM2fOlCrWtm3b7NqtWrUq0fzatWub1wcOHCh0TN5CLZvNph07dtjdT09PN3f2slqtatu2rZo3b24WAR08eFDnzp2zm/PXX38pMTHRbF977bUlytudNGnSxLxetmyZPvvss0IL4ByV9xhLi8Wili1blmh+3j9TR4vN2rRp43D8vM975MgRvfjiizp//rzjCQIAAAAAALfAsYwAAAAAAABAITw8PDRy5Ej93//9nyQpIyNDCxcu1IQJE0ocK3/xzn333Vei+Xl3XSqqQKdFixby9fWVzWaTdHEnrrZt25r3t27dahYzNWnSRAEBAZKkli1basOGDeYuXd27dzfnbNy40by2Wq0lKi5yN4MHDzZ3T8vNzdVrr72m9957TzfccIPatWunFi1aqGbNmg7Hy/tnahiGunXrVqJ88v6ZJicnOzSnbt26Dsfv06ePPvzwQ124cEGStHz5cq1du1bt27fXtddeq5YtWyoyMlJWq7VEeQMAAAAAgPLFzl0AAAAAAABAEW6++WZFR0eb7WXLlunkyZMljpOUlGTXzsnJKdEvwzDMuZeKdfLz8vKy2xEs/zGLedt5d+Aq7mjGvO1rrrlGgYGBl39YN3XddddpzJgx8vD4+yPRpKQkLV++XFOmTNGAAQPUq1cvPffcc1q9erXS09OLjVcef6b5BQUFOfy8YWFheumll+z+zDIyMvTLL7/otdde09ChQ9WtWzc9/vjj+vTTT3X27FmHYwMAAAAAgPJDcRcAAAAAAABQBIvFolGjRpnt7OxsLViwoMRxHC3ecUTeoqD88hZq7d27VykpKWY7b6FW3nF5C73yjsnKytL27dsLneMMPj4+du2MjIxSxbm0U1lRcfO677779M477+iGG24odMeqc+fOac2aNZo0aZLuuOMOffDBB8rOzi40ljP/TPPu4lUcT8+SHcTQvn17ffLJJ+rfv7/8/f0L3E9LS9OGDRs0a9Ys3XHHHRzdCAAAAACAG+JYRgAAAAAAAKAYHTt2VIsWLbRjxw5J0urVq3X//ferXr16DsfIW3Dk4eGhdevWycvLy9mpql27duZ1Tk6ONm/erJtuukmJiYmKjY2VJPn7+6tZs2bmuMjISIWFhSkxMVHx8fE6evSo6tSpo507d9rtXpU3tjPk3wXscjtlFSX/vMvtbtWsWTPNnDlTiYmJiomJ0fbt27Vjxw799ddfBY5KfPvtt7V582bNnDmzwJ9X3j/TKlWq6JtvvilV/mWtatWqeuqpp/TYY49p69at2rZtm3bu3Kldu3bZFdTl5OTom2++0caNG/XOO+8oIiLChVkDAAAAAIBL2LkLAAAAAAAAuIzRo0eb1zk5OZo/f36J5oeGhprXubm5pTra0RFRUVF2a13aiSsmJsbc8at169Z2O0BZLBa7wq2NGzfa/VOSfH191aJFC6fmGhwcbNeOi4srVZz88/LHLUpYWJi6deumcePG6cMPP9SaNWs0bdo0denSxe7oxo0bN2rRokUF5uf9fT579mypdx4rL76+vrr++uv10EMPac6cOfr+++/13//+VwMGDLDb1Ss+Pl4vvPCCCzMFAAAAAAB5UdwFAAAAAAAAXEabNm3sji/84YcftH//fofn169f3669detWp+WWl8ViUdu2bc12YYVahR2vmLfvUkFY3iMaW7Vq5fSdxho0aGB3NOKlncVK6uDBg+a11Wot8HvtqICAAN1yyy165ZVX9Nprr9nltmzZsgLj865jGIa2bdtWqnVdxdPTU23bttX48eP1ySefqEqVKua9mJiYMitABAAAAAAAJUNxFwAAAAAAAOCAvLt3GYahefPmOTw3f0HV6tWrnZZXcWsdO3ZMcXFxiomJMfvyFqkV1rdlyxadP39ee/fuLTSms/j5+alhw4ZmOy4uzq5QyxF//fWX3c5dkZGR8vPzu+LcrrvuOnXu3Nkut5SUFLsx+Y+pLMs/07JWrVo13X///XZ9Bw4ccFE2AAAAAAAgL4q7AAAAAAAAAAc0adLEruDn119/1Z9//unQ3GrVqql58+Zme+vWrfr999+dnqNUsHjrq6++0qlTpyRJlStXVoMGDQrNr1atWpKklJQUffzxx8rJyTHvl0VxlyTdfPPNdu0vv/yyRPOXLFli177pppuuNCVTzZo17dpZWVl27RYtWqhatWpme/Xq1aXefcwdXO55AQAAAACAa1DcBQAAAAAAADjowQcflIfH3x+pffPNNw7PHTFihF17ypQpOnToUInW/+uvv5SYmFjsmBo1aqhGjRpm+9NPPzWv8+82lVfeAq7PPvvMvA4NDVWjRo1KlKej+vfvLx8fH7O9dOlSu+Mgi7Np0yYtXbrUbPv4+Kh///6Fjk1NTS3x7/X27dvNa19fX4WFhdnd9/T01LBhw8x2Tk6OnnrqKZ05c6ZE62zbtk2ZmZklmuOIXbt2lTiPvKpXr+7EbAAAAAAAQGlR3AUAAAAAAAA4KDIyUrfeeqvZzru71eV06NBBffr0MdtJSUkaPny4Pv/8c2VkZBQ5Lz09XWvWrNETTzyhe++916HiobyFWjabzbwu7EjGy81p27atLBbLZdcsjdDQUD344INmOycnR+PGjdPixYuLLHjKysrSp59+qnHjxik3N9fsf/DBBxUaGlronPPnz+uee+7R448/rlWrVik1NbXInFJTU/XKK69o586dZl+XLl0K/T3o06eP3e/p8ePHNXToUK1atarYfzeSk5P1zTffaNSoURo9enSxf/6l9cwzz2jIkCH69NNPFR8fX+S43NxcLV26VB9//LHZFxERoWuuucbpOQEAAAAAgJLzdHUCAAAAAAAAQEUyYsQIff/99yUq7LpkwoQJiouLM3enSktL08yZMzV37ly1atVKtWvXVkBAgGw2m5KSkhQbG6vY2NgSH5HXvn17ff3114X2F6Vdu3by8PCwK5i63BxnuOeee7R79259//33kqSMjAy9/vrrWrBggVq3bq2aNWsqICBAqampOnnypLZu3aqUlBS7GF27dtU999xT7DqGYWjDhg3asGGDPD091aBBA0VGRiosLEx+fn5KT0/X0aNHtXnzZqWlpZnzAgICNHr06EJjWq1WTZ06VQ8//LD++usvSVJCQoImT56s119/Xa1bt1b16tXl5+entLQ0JSYm6sCBAzp8+HCp/v0pqdjYWM2aNUuvv/66atasqejoaFWpUkWBgYHKyspSXFyctmzZUqBg8IknnrDboQ4AAAAAALgOxV0AAAAAAABACdSpU0c9e/Ys0ZGMl3h5eWnWrFl666239Omnn8owDEkXd4v69ddfLzvfYrHI0/PyH+m1b99eFovFjC9J9erVU5UqVYqcExwcrOjoaO3Zs8euv7jdvpzBYrHohRdeUI0aNfTRRx+ZOV+4cEE///zzZefee++9euihh0q0u1h2drb279+v/fv3FzsuPDxcM2bMKPaIwuDgYM2fP18vvfSSvvvuO7M/KSlJP/7442VzsVqtZV5IZRiGjh8/ruPHjxc7zsvLS+PHj9dNN91UpvkAAAAAAADHUdwFAAAAAABQCok9ElydAlxo+PDhWr16dZFHBxbH09NTjz/+uO644w59+OGHWr9+fbHHBFqtVkVHR+vGG2/U7bffrho1alx2jZCQEDVq1MiueMmRHbiuvfZau+KumjVrOrTelfLw8NCYMWPUs2dPh35PAgIC1KlTJw0dOlT169e/bPyqVatq2rRp+uWXXxQTE6PTp08XO75KlSrq2bOn7rvvPgUGBl42vp+fn55//nndddddWrRokTZs2FDsUYteXl5q1qyZOnXqpO7duysgIOCya5TU5MmT9dNPP2njxo06dOiQXaFffv7+/urSpYv+/e9/q3bt2k7PBQAAAAAAlJ7FKO7/6vGPkZiY6OoUUIwfT9tcnQLgFm6rGVTio0gAAABcwcvLS5J4dwHgkJycHO3du1dHjx7V+fPnlZaWJj8/P4WEhKh27dpq0KBBmRT/uLOcnBwdOHBAhw4dUnJystLS0uTv76/g4GDVq1dPUVFRslqtpY5/+vRpHTp0SKdOnVJKSoqysrLk7++v8PBwNWzYUPXr17+i3bQyMzO1a9cunThxQufPn1dmZqb8/f0VGhqqOnXqqH79+vL19S11/JJKSUlRbGysTp48qcTERGVkZMjHx0chISGqV6+eGjVqJB8fn3LLB+6F9xYAAFCR8O4CoCIICwtzajyKuyCJ4i53R3EXcBHFXQAAoKLgg0YAAFBR8N4CAAAqEt5dAFQEzi7uKv3XzwAAAAAAAAAAAAAAAAAAZcbT1QkAAAAAuHqw4yhwiU231QxydRIAAAAAAAAAgAqOnbsAAAAAAAAAAAAAAAAAwA1R3AUAAAAAAAAAAAAAAAAAbojiLgAAAAAAAAAAAAAAAABwQxR3AQAAAAAAAAAAAAAAAIAb8nR1AgAAAAAAAAAAAAAAAI4IXBbk6hQAt5DYI8HVKaCcsHMXAAAAAAAAAAAAAAAAALghirsAAAAAAAAAAAAAAAAAwA1R3AUAAAAAAAAAAAAAAAAAbojiLgAAAAAAAAAAAAAAAABwQxR3AQAAAAAAAAAAAAAAAIAb8nR1AgAAAAAAAAAAXC0OHTqkmJgY7dq1S0ePHtWpU6eUlpamnJwcBQYGqnbt2mrZsqV69+6tevXqFRln586d2rVrl3bv3q3Dhw8rOTlZycnJysjIkJ+fnypXrqz69eurQ4cO6tatmwICApz+LBs3btSaNWu0bds2nTt3Trm5uapUqZKaNGmirl276qabbip2fnp6uj799FP98MMPOnbsmHJzc1W9enV16tRJ9957r0JCQoqcm5ubq+HDh2vPnj3y8PDQwoULFR0d7eQnBAAAAAD3ZzEMw3B1EnC9xMREV6eAYvx42ubqFAC3cFvNIGVlZbk6DQBAMXhvAf7GuwsAAP88zz//vFasWOHQWA8PD91111165JFH5OlZ8HvYt956qy5cuOBQrJCQED3zzDPq3LlzifKVJC8vL0mye29JSUnR888/r59//rnYuS1bttQLL7ygqlWrFrh3/vx5Pfzww4qNjS10bkREhObMmaOaNWsWen/JkiV69dVXJUkDBgzQ+PHjHXoeAABwdfPy8lLgsiBXpwG4hcQeCa5OAUUICwtzajyOZQQAAAAAAAAAwAkcLcaSLu5MtXjxYk2bNu2K1z1//rwmTpyoHTt2XHGs1NRUPfroo5ct7JKk7du36+GHH9a5c+cK3Js+fbpZ2FWpUiW98sormj17turWrStJio+P1+TJkwuNm5SUpLlz50q6+EORUaNGlfJpAAAAAKDio7gLAAAAAAAAAAAns1gsaty4sXr27KmePXuqYcOGhY5bsWKFYmJiioxTo0YNderUSf369VP//v11ww03yMfHp8C4nJwcvf/++1ec99tvv609e/bY9fn5+em2225Tr169ChylePz4cXOHrUsSEhL0ww8/mO1HH31UXbp00bXXXquJEyea/Tt37tTevXsL5PDf//5XycnJkqQxY8YoODj4ip8LAAAAACqqgns9AwAAAAAAAACAUgkICNCAAQM0cOBAu+MKDcPQF198oZkzZxaYs3LlSrVr186u76GHHlLbtm1Vr169AuOTk5M1duxY/fnnn3b9+dsldfz4cS1ZssSuz8/PT++9957q168vSTp79qweeOABnTlzxhyzbt06bdu2Ta1atZIk7dmzR7m5ueb9vM/WqlUreXt7KzMz08y5cePGds/w7bffSpKaN2+uXr16XdEzAQAAAEBFx85dAAAAAAAAAAA4wc0336zPPvtMDz/8sF1hl3RxJ69BgwapU6dOBeb99ddfBfoGDBhQaGGXJAUHB+uee+4p0J+dnV26xP9/K1assCvKkqTevXubhV2SVLlyZQ0ePLjA3GXLlpnXKSkpdvfy7/aVdyeuvGNzc3M1ffp0GYYhq9WqcePGyWKxlO5hAAAAAOAqQXEXAAAAAAAAAABO0KNHD1WqVKnYMa1bty7Ql5GRUeK1Tp06VaCvdu3aJY6T12+//Vagr3379gX6rr322mLnBgUF2d07f/68XfvSkYv5xy5ZskT79u2TJPXv31/R0dEOZg4AAAAAVy+KuwAAAAAAAAAAKCf5d8aSpGrVqjk0NysrS8ePH9fixYs1b968AvfvvPPOUueVnZ2t2NjYAv1169Z1qC8pKUnx8fGSpGuuuUYeHn//+CEmJsa83r59u3kkoyQ1a9ZMkpSQkGA+U1hYmEaNGlXKJwEAAACAq4unqxMAAAAAAAAAAOCfYv369QX6OnToUOT4NWvW6Lnnnrts3IEDB6p3796lzisuLk5ZWVkF+sPDwwv0eXt7KyAgQKmpqXb9x48fV0REhMLDw9W1a1etWbNGkjR79mz5+fnJz89PM2fONMc3b95cjRs3liS99dZb5hGNY8aMKbD7FwAAAAD8U1HcBQAAAAAAAABAOVi+fLl27Nhh1xccHKxevXqVOmZUVJQmTJhg7oBVWhcuXCi038/Pr9B+X1/fAsVdeWOMGzdOBw8eVGxsrM6dO6cJEybYjY2IiNDkyZMlSTt27NDKlSslXSz4yvv7ce7cOR04cEA2m01VqlRRdHS0PD350QYAAACAfw7+DwgAAAAAAAAAgDK2YcMGvfzyy3Z9FotFTz/9tIKDg0sdd//+/XrppZf0+OOPq3379qWOY7PZCu23Wq2F9hdWYJWWlmZeh4SEaMGCBVq8eLF++OEHHTt2TIZhqFq1aurcubOGDBmi0NBQ5eTkaPr06TIMQ1arVePHj5fFYtG5c+f0yiuvFNjpLCwsTGPGjLmiXcoAAAAAoCKhuAsAAAAAAAAAgDL0/fffa/LkyQWOPXzkkUd0yy23FDu3Tp06uvvuuyVJGRkZOnXqlLZv325XjBUbG6vHH39ckydPVrdu3UqVo6+vb6H92dnZ8vLyKrQ/P39/f7u2n5+fHnjgAT3wwANFrvvFF1/owIEDkqT+/fsrKipKKSkpevDBB3XixIkC4xMTEzV16lSlpqaavy8AAAAAcDWjuAsAAAAAAAAAgDKyZMkSzZgxQ7m5uXb9Dz/8sIYMGXLZ+dHR0YqOjrbrS0lJ0csvv6zvv//e7MvJydHLL7+sjh07KiAgoMR5BgYGFtqfnp5eaHFXenq6wzGKcu7cOc2fP1/SxR25Ro0aJUl67733zMKugIAAvfzyy2rUqJHeeOMN8/jGOXPmqFu3bgoPDy/RmgAAAABQ0Xi4OgEAAAAAAAAAAK5GCxcu1KuvvmpX2OXh4aHx48fr/vvvL3XcoKAgTZo0qcBxjqmpqfr9999LFbNatWqFFnElJCQU6LPZbHZHMF5Sq1atEq355ptv6sKFC5KkMWPGKCgoSJK0du1ac0y/fv3Uvn17hYaG6vHHHzdzzMjIKHBkIwAAAABcjSjuAgAAAAAAAADAiQzD0Ouvv6558+bZ9Xt5eemFF17QgAEDrngNb29v1alTp0D/yZMnSxXP09NTDRs2LNB/5MiRAn2HDx8u0BcaGqqIiAiH19u6datWrVolSWrevLl69eolSUpOTtaZM2fMcU2bNjWvQ0JC7ArIDh065PB6AAAAAFBRUdwFAAAAAAAAAICTZGdn6/nnn9fixYvt+v39/TVr1ix17dr1sjFycnIuOyYzM1PHjx8v0O/j4+N4svl07NixQN+mTZsK9G3cuNGhuUXJzs7WjBkzJElWq1Xjx4+XxWKRdHFXsLzyHzGZ9+jH/GMBAAAA4GpEcRcAAAAAAAAAAE5gs9n09NNPa+XKlXb9YWFhmjNnjtq1a+dQnMcee0zz5s0rdNcsSTp//rxefPFFJSUlFbhXv379An3ffvutrrvuOrtf8+fPLzCuZ8+e8vDwKDD34MGDZvv06dMFCtckqW/fvpd7LNNnn32m2NhYSVL//v0VFRVl3gsJCbHLITk52W7u+fPnzevQ0FCH1wQAAACAisrT1QkAAAAAAAAAAHA1mDZtmn755ZcC/U2aNNHKlSsLFH3l9cQTT5jX58+f18KFC7Vw4UJVr15dUVFRCg8PV1ZWlk6fPq3t27crIyOjQIzq1aurbdu2pc6/Vq1auvPOO/XFF1+YfTabTcOHD1enTp3k5eWln3/+uUDBVZcuXdSqVSuH1jh79qwWLFgg6WLR26hRo+zu+/j4KDo6Wnv27JEkxcTEqFu3bpIuFpbl3a2sZcuWJX5GAAAAAKhoKO4CAAAAAAAAAMAJzpw5U2j/r7/+etm5eYu78jp16pROnTp12fm+vr6aNGmSrFbrZccW56GHHtKff/6pvXv3mn3p6en67rvvCh1fq1YtTZgwweH4b7zxhtLS0iRJY8aMUVBQUIExd911l6ZMmSLp4s5hjRo1UmRkpN555x3l5uZKkurWrasOHTo4vC4AAAAAVFQcywgAAAAAAAAAQAXWuHFjzZ071+Hds4oTEBCgN998UzfeeONlx7Zo0UJz5sxRpUqVHIq9efNmrVmzRpLUvHlz9erVq9BxPXr00B133CFJysnJ0YwZMzR69Ght2bJF0sXjGKdOnXrFhWwAAAAAUBGwcxcAAAAAAAAAAG7kzTff1LZt27Rt2zbt3btXCQkJSkpKUmpqqry9vRUYGKjatWsrOjpanTp1UuvWrWWxWJy2flBQkGbMmKGNGzdq9erV2r59uxISEpSTk6Pw8HA1adJEt956q2666SaH183OztaMGTMkSVarVRMmTCh27sSJE9WuXTt99dVX2r9/v2w2m6pUqaKOHTtq6NChqlq1qlOeFQAAAADcncUwDMPVScD1EhMTXZ0CivHjaZurUwDcwm01g5SVleXqNAAAxeC9Bfgb7y4AAKAi8PLykiTeWwAAQIXg5eWlwGUFj3UG/okSeyS4OgUUISwszKnxOJYRAAAAAAAAAAAAAAAAANwQxV0AAAAAAAAAAAAAAAAA4IYo7gIAAAAAAAAAAAAAAAAAN0RxFwAAAAAAAAAAAAAAAAC4IYq7AAAAAAAAAAAAAAAAAMANUdwFAAAAAAAAAAAAAAAAAG6I4i4AAAAAAAAAAAAAAAAAcEMUdwEAAAAAAAAAAAAAAACAG6K4CwAAAAAAAAAAAAAAAADcEMVdAAAAAAAAAAAAAAAAAOCGPF2dQEWVm5uro0eP6siRI4qPj1dycrIyMzPl7++v0NBQNW7cWI0aNZLVanV1qgAAAAAAAAAAAAAAAAAqIIq7SiAhIUHvvvuutmzZoj179ig9Pb3Y8SEhIerbt6+GDx+u6tWrXzb+8ePH1bVr11LlFhERofXr15dqLgAAAAAAAAAAAAAAAAD3w7GMJXDixAktWLBAW7ZsuWxhlySdP39eixYtUs+ePbVkyZJyyBAAAAAAAAAAAAAAAADA1YKdu65A5cqVFRUVpbp16yokJERWq1VJSUnas2ePtm3bptzcXElSWlqaJk6cqMzMTA0ePNjh+B4eHrJYLA6N9fTkjxIAAAAAAAAAAAAAAAC4mlARVAJWq1Xt27dX9+7ddcMNN6hBgwZFjj1x4oSef/55/fTTT2bftGnT1LFjR9WpU8eh9V588UXdeeedV5o2AAAAAAAAAAAAAAAAgAqIYxlLoEmTJvroo4903333FVvYJUk1a9bUnDlzdMMNN5h9GRkZ+vjjj8s6TQAAAAAAAAAAAAAAAABXAYq7ypDVatWTTz5p1/fzzz+7KBsAAAAAAAAAAAAAAAAAFQnFXWWsadOm8vf3N9unTp1yYTYAAAAAAAAAAAAAAAAAKgqKu8pBQECAeW0YhgszAQAAAAAAAAAAAAAAAFBRUNxVxtLT05WUlGS2a9eu7bpkAAAAAAAAAAAAAAAAAFQYFHeVsVWrVikrK8ts33TTTa5LBgAAAAAAAAAAAAAAAECF4enqBK5me/fu1SuvvGK2Q0NDNXToUIfnr169WitWrFBsbKwSExPl4+Oj0NBQNWrUSO3bt1ePHj1UtWrVskgdAAAAAAAAAAAAAAAAgItR3OVEhmHowoUL2r9/v1atWqXFixcrMzNTkuTv76/Zs2ercuXKDsf76aef7NqXjng8fPiw1qxZoxkzZmjgwIEaN26cAgICnPkoAAAAAAAAAAAAAAAAAFyM4q4rcPDgQfXu3dts5+bmyjCMAuO6dOmiiRMnqn79+k5dPzMzU5988ok2bNiguXPnqm7duk6NDwAAAAAAAAAAAAAAAMB1KO66AoZhKCcnp8j7Hh4eGjJkiEaMGKFq1ao5HDc6Olq33nqr2rZtq8jISIWGhsowDJ09e1bbt2/X0qVLtX79enP8wYMHNXLkSH366acKCwsr1bN4eXmVah7Ki83VCQBuwWq1ujoFAMBl8d4CXMK7CwAAqAh4ZwEAABUJ7y7A36jz+OewGIVtNQWHxMbGqmfPnpcd5+XlpXvvvVdjx46Vt7d3keMuXLigAwcOqHXr1peNuW7dOo0fP17nz583+3r37q2ZM2c6lnwha8N9fXcixdUpAG6hR53QYotqAQCux3sL8DfeXQAAQEVw6QekvLcAAICKwGq1yu8rf1enAbiFC335PN5dBQYGOjUexV1OlJmZqaSkJO3Zs0erVq3SN998o6ysLPP+DTfcoLlz5xZb4FUSMTExGjp0qLKzsyVJFotFy5YtU1RUVIljJSYmOiUnlI0fT7MDBiBJt9UMsvt7FQDgfnhvAf7GuwsAAKgILn3bn/cWAABQEXh5eSlwWZCr0wDcQmKPBFengCKU9tS9ong4Ndo/nLe3t6pWraouXbropZde0hdffKHq1aub93/99Ve99dZbTluvXbt2GjBggNk2DEMrV650WnwAAAAAAAAAAAAAAAAArkNxVxlq3Lix5s+fb3fO6QcffODUXbIGDhxo1/7999+dFhsAAAAAAAAAAAAAAACA61DcVcYaNWqknj17mm2bzaZ169Y5LX6zZs3sisdOnjzptNgAAAAAAAAAAAAAAAAAXIfirnLQsWNHu/a+ffucFtvDw0OhoaFm25m7ggEAAAAAAAAAAAAAAABwHYq7ykHlypXt2hcuXHBqfJvNZl77+Pg4NTYAAAAAAAAAAAAAAAAA16C4qxzkL+YKDg52WuzExESlpKSY7UqVKjktNgAAAAAAAAAAAAAAAADXobirHOzevduuXb16dafFXr9+vV27cePGTosNAAAAAAAAAAAAAAAAwHUo7ipjNptN33zzjV1fx44dnRI7KytL8+fPt+u78cYbnRIbAAAAAAAAAAAAAAAAgGt5unLxCxcuaOfOnTp8+LBOnDihc+fOKT09XdnZ2fLz81NgYKCqV6+uWrVqqXHjxmrQoIHLcs3MzNTBgwdLtDNWbm6uJk2apJMnT5p9LVu2LPQ54uPj5ePjo9DQUIdj/+c//9GBAwfMvipVqqhXr14O5wcAAAAAAAAAAAAAAADAfZV7cdfGjRv1448/6pdfflFsbKwMw3B4bmBgoNq1a6ebbrpJN998s6pWrVqGmdqz2Wzq16+fbrvtNvXv31833HCDvL29ixy/fft2TZ8+XZs2bTL7PDw89MwzzxQ6fufOnZowYYLuvvtu9enTR02aNCky9t69ezVt2jT98ccfdv1jx46Vv79/CZ8MAAAAAAAAAAAAAAAAgDuyGCWpriqlEydO6LPPPtPXX3+t+Ph4SSpQ1GWxWAqdW9Q4Dw8PXX/99Ro4cKBuu+02eXiU7QmTycnJat++vdn28/NT48aNFRkZqZCQEPn5+Sk1NVVxcXHauXOnjh07ViDvF198UQMGDCg0/tq1azVmzBizXaVKFTVp0kS1a9dWUFCQDMNQQkKCtm/frn379hWYP3z4cE2YMKHUz5eYmFjquSh7P562uToFwC3cVjNIWVlZrk4DAFAM3luAv/HuAgAAKgIvLy9J4r0FAABUCF5eXgpcFuTqNAC3kNgjwdUpoAhhYWFOjVemO3ft3r1b8+fP15o1a5STk2MWalksFrNI61KfIzVmeefk5OTo119/1a+//qrq1atr6NChGjx4sHx8fMroaeylp6dr69at2rp162XHRkREaMqUKbr55psdjn/mzBmtW7fusuN8fX319NNP61//+pfDsQEAAAAAAAAAAAAAAAC4vzLZuevw4cN6/fXX9d1338kwDBmGIYvFYlfA5evrq8jISEVHR6tGjRqqVq2aQkJC5OvrK6vVqoyMDKWlpen06dOKj4/XwYMHtW/fPsXFxf2dfJ6YFotFlStX1iOPPKKBAwfKarU69ZlycnL0zTff6Oeff9amTZvMHciK06RJE/Xv31933nmnAgMDix177Ngxvffee9q4caMOHjyo3NzcYsdXqVJF/fv315AhQ1StWrUSPUth2LnLvbEDBnARu18AgPvjvQX4G+8uAACgImDnLgAAUJGwcxfwN3bucl/O3rnLqcVd6enpeuutt/TBBx/Y7dR1ScuWLXXLLbeoQ4cOat68eakKsM6cOaM//vhDv/32m77//nudP39e0t+FXhaLRZGRkZo0aZLatWvnlOcqzOnTpxUbG6vjx4/r/PnzysjIkL+/vwIDA1WrVi01bdpUwcHBpYqdlpamv/76S8ePH9fZs2eVlpYmi8WioKAghYeHq2nTpqpdu7ZTn4fiLvfGD0mBi/gBKQC4P95bgL/x7gIAACoCirsAAEBFQnEX8DeKu9yXWxd3denSRadPn7Yr6qpVq5buuusu9enTR9WrV3fWUpIu7qb122+/6fPPP9cPP/yg7OxsuyKv559/XoMGDXLqmlcrirvcGz8kBS7iB6QA4P54bwH+xrsLAACoCCjuAgAAFQnFXcDfKO5yX84u7vJ0ZrD4+HhZLBZJUuvWrTVq1Ch16dLF7HM2q9WqTp06qVOnTjp79qw+/PBDffLJJ7pw4YKZDwAAAAAAAAAAKBo/IAX+xg9JAQAA4G48nB2wWbNmev/99/W///1PN910U5kVduVXuXJljR07Vj/++KNGjx4tX1/fclkXAAAAAAAAAAAAAAAAAMqCU3fumjFjhnr37u3MkCUWFBSkxx9/XP/617907Ngxl+YCAAAAAAAAAAAAAAAAAKXl1OIuVxd25RUREaGIiAhXpwEAAAAAAAAAAAAAAAAApeL0YxkBAAAAAAAAAAAAAAAAAFeO4i4AAAAAAAAAAAAAAAAAcEMUdwEAAAAAAAAAAAAAAACAG6K4CwAAAAAAAAAAAAAAAADckKerEyjKiRMntH79eu3atUsJCQnKyMhQcHCw6tSpozZt2ujGG2+U1Wp1dZoAAAAAAAAAAAAAAAAAUCbcrrjrzJkzeuGFF/T9998rNze3yHFVq1bVE088oX79+pVfcgAAAAAAAAAAAAAAAABQTtzqWMb9+/erb9++WrNmjXJycmQYRpG/4uPjNXHiRE2ZMsXVaQMAAAAAAAAAAAAAAACA07nNzl3p6el68MEHlZiYaPY1atRILVq0UOXKlWUYhs6cOaOtW7fq8OHDslgsMgxDixcvVmRkpIYMGeLC7AEAAAAAAAAAAAAAAADAudymuGvRokWKi4uTxWJRnTp19PLLL6t169aFjv3999/1f//3f4qLi5NhGJo9e7YGDRokb2/vcs4aAAAAAAAAAAAAAAAAAMqG2xzL+P3330uSAgIC9OGHHxZZ2CVJ119/vd599115el6sTUtOTtYff/xRLnkCAAAAAAAAAAAAAAAAQHlwanHXyZMnSz330lGLXbp0UURExGXHN2jQQNdee63dfAAAAAAAAAAAAAAAAAC4Wji1uGvJkiUaPHiw9u3bV+K56enpkqTQ0FCH54SEhJjXNputxGsCAAAAAAAAAAAAAAAAgLty+rGM27Zt04ABA/TKK68oLS3N4XmVK1eWJG3fvt2h8YZhaMeOHQXmAwAAAAAAAAAAAAAAAMDVwKnFXV5eXpKknJwcvf/+++rZs6fWrFnj0Nxrr71WhmFo165dev/99y87ftasWTp+/LjZbteuXalyBgAAAAAAAAAAAAAAAAB35NTirhEjRmjcuHHy9fWVYRiKi4vTo48+qlGjRtkVYhVm8ODBslgskqRXXnlFo0aN0tq1axUfH6+cnBxlZ2fr1KlTWrFihe677z7Nnz9fFotFFotFHTt2VO3atZ35KAAAAAAAAAAAAAAAAADgUhbDMAxnBz116pReeOEF/fDDDxcXsVjk4+Ojhx56SMOHD5enp2eh81566SV98MEHZpHX5RiGobCwMC1evFh169Z1Wv7/RImJia5OAcX48bTN1SkAbuG2mkHKyspydRoAgGLw3gL8jXcXAABQEXh5eSlwWZCr0wDcRmKPBFenAAAoBu8uwN94b3FfYWFhTo3n1J27LqlevbrmzJmjOXPmqGbNmjIMQzabTa+//rruuOMObdy4sdB5EydO1IgRI2SxWGQYRqG/JJnXdevW1cKFCynsAgAAAAAAAAAAAAAAAHDVKZPirktuueUWLV++XCNGjJCnp6cMw1BsbKyGDh2qp556SgkJBasIx40bpy+//FL9+vUrtJLNw8NDTZo00bPPPquvv/5ajRs3LstHAAAAAAAAAAAAAAAAAACXKJNjGQvz119/afLkyYqJibm4sMWi4OBgjR07VnfffXeR806dOqWEhARlZGQoKChINWvWlL+/f3mk/I/CsYzujeONgIs42ggA3B/vLcDfeHcBAAAVAUcbAfY43ggA3BvvLsDfeG9xXxXiWMbCREZG6qOPPtK0adMUHh4uwzB0/vx5TZ48WYMHD9bevXsLnVe9enU1bdpUbdq0UaNGjSjsAgAAAAAAAAAAAAAAAPCPUG7FXZfceeedWrlypQYNGiSLxSLDMLR9+3YNHDhQL7/8slJTU8s7JQAAAAAAAAAAAAAAAABwO+Ve3CVJISEheuGFF/TJJ58oOjpahmEoOztbH3zwgXr27KnVq1e7Ii0AAAAAAAAAAAAAAAAAcBsuKe66pHXr1lqyZImeeuop+fv7yzAMxcfH6/HHH9eDDz6oY8eOuTI9AAAAAAAAAAAAAAAAAHAZlxZ3SZLVatUDDzyglStXqlu3bpIkwzD0888/q0+fPpo7d66ysrJcnCUAAAAAAAAAAAAAAAAAlC+XF3ddEhERoTfffFPz5s1TrVq1ZBiGbDab3njjDd1xxx36448/XJ0iAAAAAAAAAAAAAAAAAJQbz7IMnpubq59//llbtmzR0aNHlZycLB8fH4WHh6tJkybq1KmTateubTenS5cuuu666zRnzhy99957ysrK0sGDBzVs2DD16dNHTz31lCpVqlSWaQMAAAAAAAAAAAAAAACAy1kMwzDKIvDXX3+tWbNmKT4+vujFLRZ17dpVzz77rCIiIgrcj42N1ZQpU7Rx40ZzfFBQkMaOHavBgweXRdr/WImJia5OAcX48bTN1SkAbuG2mkEc1QsAbo73FuBvvLsAAICKwMvLS4HLglydBuA2EnskuDoFAEAxeHcB/sZ7i/sKCwtzarwyOZZx6tSpevrppxUXFyfDMIr8lZubq7Vr16pfv37av39/gTgNGzbUhx9+qJdfflmVKlWSYRhKTk7WlClTdPfdd2vv3r1lkT4AAAAAAAAAAAAAAAAAuJzTi7s++eQTffTRR8q7IVjjxo111113adSoURo+fLhuu+02BQcHm/cTExM1evRo2WyFf8u/X79+WrVqle6++255eHjIMAzt2LFDAwYM0LRp05SamursxwAAAAAAAAAAAAAAAAAAl3LqsYxZWVm68cYblZycLEmqUaOGZsyYodatWxcYm5mZqdmzZ2vBggUXE7FYNG7cOA0fPrzYNXbs2KFJkyZpz5495ryqVatq5syZateunbMe5R+HYxndG8cbARdxtBEAuD/eW4C/8e4CAAAqAo42AuxxvBEAuDfeXYC/8d7ivtz6WMaNGzfq/PnzMgxDXl5eWrhwYaGFXZLk7e2tcePGaeDAgWbfmjVrLrtGixYt9OWXX2rixIkKDAyUYRg6ffq0NmzY4LTnAAAAAAAAAAAAAAAAAABXc2px16FDhyRd3E2rffv2qlOnzmXnDBo0SJJkGIY5/3I8PDw0dOhQrVixQt27d5cTNx8DAAAAAAAAAAAAAAAAALfg6cxgaWlp5nVoaKhDc4KDg83r9PT0Eq1XtWpVvfHGG/rll1909uzZEs0FAAAAAAAAAAAAAAAAAHfm1OKuypUrm9d//vmnQ3N27dplXleqVKlU6954442lmgcAAAAAAAAAAAAAAAAA7sqpxzK2b9/evD569Kjmzp1b7PizZ8/q9ddfl3TxKMe2bds6Mx0AAAAAAAAAAAAAAAAAqLCcWtxVu3Zt3XDDDTIMQ4Zh6I033tDo0aP1008/KSEhQbm5ucrMzNRff/2ld999V/369dOJEyfM+YMHD3ZmOgAAAAAAAAAAAAAAAABQYTn1WEZJmjRpku6++24lJSVJktatW6d169YVOtYwDFksFkkXC7vatWvn7HQAAAAAAAAAAAAAAAAAoEJy6s5dklSnTh0tXLhQdevWNXfwkmRe5+27ZNiwYfrPf/7j7FQAAAAAAAAAAAAAAAAAoMJy+s5dktS4cWN9/fXX+vzzz/XVV19p7969ysnJsRsTEhKiLl266L777lOzZs3KIg0AAAAAAAAAAAAAAAAAqLDKpLhLknx8fHTvvffq3nvvVXp6uk6cOKHk5GR5e3srPDxcNWrUKKulAQAAAAAAAAAAAAAAAKDCK7Pirrz8/PwUGRlZHksBAAAAAAAAAAAAAAAAwFXBw9UJAAAAAAAAAAAAAAAAAAAKorgLAAAAAAAAAAAAAAAAANwQxV0AAAAAAAAAAAAAAAAA4IacWtyVlZXlzHBXLDMz09UpAAAAAAAAAAAAAAAAAECpOLW46/bbb9eKFSucGbJU4uPjNXHiRC1YsMDVqQAAAAAAAAAAAAAAAABAqTi1uOvEiRN68skndeedd2rNmjXODO2Q+Ph4vfjii+revbuWLl2q3Nzccs8BAAAAAAAAAAAAAAAAAJzBsyyC7tmzR48++qjq1aune+65R/369VNQUFBZLCVJiomJ0eLFi7V69WplZ2fLMAxZLJYyWw8AAAAAAAAAAAAAAAAAyppTd+566aWXFB4eLsMwJEmHDh3StGnT1KlTJz322GNauXKlUlNTnbLWnj179Prrr+v222/Xfffdp+XLlysrK8tcu1u3brrrrrucshYAAAAAAAAAAAAAAAAAlDen7tzVv39/3XrrrXrjjTe0ePFiZWdnS5JsNpu+++47fffdd7JarWratKnatm2ra665RlFRUapZs6YCAwMLjZmTk6PTp0/r4MGD2rt3r3bs2KGNGzcqKSlJksxirksiIyM1YcIEde7c2ZmPBgAAAAAAAAAAAAAAAADlyunHMgYFBenZZ5/V0KFDNXv2bK1YsUI5OTmSLhZiZWdna8eOHdqxY4fdPD8/P4WEhMjHx0eenp6y2WxKT09XYmJigQKuS+28Ry/WqlVLDz30kPr37y8PD6duSAYAAAAAAAAAAAAAAAAA5c7pxV2X1K5dW9OnT9fjjz+u999/X0uXLlVKSordmLxFW2lpaUpLS7tsXIvFIovFIsMwZBiGWrZsqaFDh+r222+nqAsAAAAAAAAAAAAAAADAVaPMirsuqVmzpp555hmNHz9ea9as0bfffqsNGzYoPT3dHJN3B67C5C0CMwxD9erVU7du3dSvXz81bNiwzHIHAAAAAAAAAAAAAAAAAFcp8+KuS7y9vdWrVy/16tVLmZmZiomJ0bZt2/Tnn3/q0KFDOnXqlGw2m90ci8WiSpUqqVatWmrcuLGaN2+ua6+9VrVr1y6vtAEAAAAAAAAAAAAAAADAJcqtuCsvb29vdezYUR07drTrv3Dhgmw2m3JycuTj46OAgAB5eXm5IkUAAAAAAAAAAAAAAAAAcCmXFHcVJTAwUIGBga5OAwAAAAAAAAAAAAAAAABczsPVCQAAAAAAAAAAAAAAAAAACnKrnbsqktzcXB09elRHjhxRfHy8kpOTlZmZKX9/f4WGhqpx48Zq1KiRrFbrFa919OhR7d69W3FxccrNzVVERIQaNWqkqKgoJzwJAAAAAAAAAAAAAAAAAHdEcVcJJCQk6N1339WWLVu0Z88epaenFzs+JCREffv21fDhw1W9evUSr7du3Tq9/fbb2rp1a6H3o6OjNWLECPXt27fEsQEAAAAAAAAAAAAAAAC4N45lLIETJ05owYIF2rJly2ULuyTp/PnzWrRokXr27KklS5Y4vI5hGHrxxRf14IMPFlnYJUn79u3T+PHjNXbsWGVmZjocHwAAAAAAAAAAAAAAAID7Y+euK1C5cmVFRUWpbt26CgkJkdVqVVJSkvbs2aNt27YpNzdXkpSWlqaJEycqMzNTgwcPvmzcmTNn6sMPP7Tra9OmjZo3by6r1ap9+/bpt99+k2EYkqTly5fLarVq+vTpzn9IAAAAAAAAAAAAAAAAAC5BcVcJWK1WtW/fXt27d9cNN9ygBg0aFDn2xIkTev755/XTTz+ZfdOmTVPHjh1Vp06dIuf9+OOPmj9/vtkODg7W7Nmzdf3119uN2717tx566CHFxcVJkpYtW6a2bds6VDwGAAAAAAAAAAAAAAAAwP1xLGMJNGnSRB999JHuu+++Ygu7JKlmzZqaM2eObrjhBrMvIyNDH3/8cZFzDMPQa6+9ZrYtFovmzJlToLDrUi7vv/++fHx8zL633npLNputJI8EAAAAAAAAAAAAAAAAwE1R3FWGrFarnnzySbu+n3/+ucjxa9eu1f79+832HXfcofbt2xc5vn79+ho+fLjZPnPmjD7//PMryBgAAAAAAAAAAAAAAACAu6C4q4w1bdpU/v7+ZvvUqVNFjl21apVde8iQIZeNP3jwYFmt1iJjAAAAAAAAAAAAAAAAAKiYKO4qBwEBAea1YRiFjsnOztb69evNdvXq1dWiRYvLxo6IiFCrVq3M9tatW5WQkFD6ZAEAAAAAAAAAAAAAAAC4BYq7ylh6erqSkpLMdu3atQsdt3//fiUnJ5vt1q1bO7xG3rE5OTnasmVLyRMFAAAAAAAAAAAAAAAA4FYo7ipjq1atUlZWltm+6aabCh0XGxtr177mmmscXqNJkyZ27YMHDzqeIAAAAAAAAAAAAAAAAAC3RHFXGdq7d69eeeUVsx0aGqqhQ4cWOjZ/QVaNGjUcXqd69erFxgIAAAAAAAAAAAAAAABQ8Xi6OoGriWEYunDhgvbv369Vq1Zp8eLFyszMlCT5+/tr9uzZqly5cqFz4+Pj7drVqlVzeN38Y+Pi4kqYOQAAAAAAAAAAAAAAAAB3Q3HXFTh48KB69+5ttnNzc2UYRoFxXbp00cSJE1W/fv0iY6Wlpdm1AwICHM4j/9j8sQAAAAAAAAAAAAAAAABUPBR3XQHDMJSTk1PkfQ8PDw0ZMkQjRoy47E5c+QuyvL29Hc7Dx8en2FiO8PLyKvEclCebqxMA3ILVanV1CgCAy+K9BbiEdxcAAFAR8M4C2OPnJQDg3nh3Af7Ge8s/h4erE7ia5ebmatGiRbr11lv18ssvm0c0FiYjI8OuXZLirvxjbTZ+oAYAAAAAAAAAAAAAAABUdOzcdQUaNmyoffv2me3MzEwlJSVpz549WrVqlb755htlZWUpKytLCxcu1P79+zV37txCC7fy775VXCFYfvnH+vr6lvBJpKysrBLPAYDylpOTw99XAACgwuDdBQAAAKh4eIcHAAAVBe8t/xxuUdx19OhR7d69W+fOndOFCxdK9S/gI488UgaZlYy3t7eqVq2qqlWrqkuXLho6dKhGjx6tU6dOSZJ+/fVXvfXWWxo7dmyBuf7+/nbtkhR35d/1K38sAAAAAAAAAAAAAAAAABWPy4q7bDabFi5cqM8//9wsfroS7lDclV/jxo01f/589e/f3yxY++CDD/TAAw8oLCzMbmz+gqzU1FSH18k/luIuAAAAAAAAAAAAAAAAoOLzcMWi+/fvV9++fTV79mydPHlShmGYv0qqNHPKU6NGjdSzZ0+zbbPZtG7dugLjIiIi7NpxcXEOr5G/OK5atWolzBIAAAAAAAAAAAAAAACAuyn34q74+HgNHz5cR48elWEYslgsslgs5v28hV6O/KoIOnbsaNfet29fgTENGza0a588edLh+PkLwRo0aFCC7AAAAAAAAAAAAAAAAAC4o3I/lnHWrFk6c+aMWdBlGIaaNWumm266SQ0bNlRISIg8PV12WmSZqFy5sl37woULBcbkL+7avXu3w/F37dpl16a4CwAAAAAAAAAAAAAAAKj4yrWK6sKFC/r2229lsVhkGIb8/Pw0ffp03XrrreWZRrnLX8wVHBxcYEyjRo0UHBys5ORkSdK2bdscjr9161bz2mq1qk2bNqVLFAAAAAAAAAAAAAAAAIDbKNdjGWNiYpSdnW0ex/jCCy9c9YVdUsFduKpXr15gjKenpzp37my2T506pe3bt182dnx8vN241q1bKzw8/AqyBQAAAAAAAAAAAAAAAOAOyrW468SJE+Z11apV1bt37/Jc3iVsNpu++eYbu76OHTsWOrZHjx527U8++eSy8RcvXqycnByzffvtt5ciSwAAAAAAAAAAAAAAAADuplyLu1JTUyVJFotFzZo1K8+lr1hmZqb27t1bojm5ubmaNGmSTp48afa1bNlSDRo0KHR8165dFRUVZba//vprbdq0qcj4hw4d0rvvvmu2q1SpokGDBpUoRwAAAAAAAAAAAAAAAADuqVyLu/IeF+jn51eeS18xm82mfv366dFHH9WPP/6ozMzMYsdv375d999/v5YuXWr2eXh46JlnnilyjsVi0dixY822YRh6+OGH9fvvvxcYu3v3bg0bNkwZGRlm3yOPPCJfX98SPBUAAAAAAAAAAAAAAAAAd+VZnovVrFnTvE5MTCzPpZ3CMAytXr1aq1evlp+fnxo3bqzIyEiFhITIz89PqampiouL086dO3Xs2DG7uRaLRVOnTlXLli2LXePmm2/WyJEjNX/+fElScnKyhg0bpjZt2qhFixby8PDQvn379Ntvv8kwDHNe3759NXjwYOc/NAAAAAAAAAAAAAAAAACXKNfirrZt2yo4OFjJycn6888/ZRiGLBZLeabgNOnp6dq6dau2bt162bERERGaMmWKbr75Zodijx07VjabTYsWLTL7tmzZoi1bthQ6vmfPnpo6dapjiQMAAAAAAAAAAAAAAACoEMr1WEZvb2/16dNH0sUdqdauXVuey1+RgIAAvfLKK+rdu7ciIiIcmtOkSRM988wzWrFihcOFXdLF4xufffZZvfPOO2rVqlWR46KiovTqq69q1qxZ8vHxcTg+AAAAAAAAAAAAAAAAAPdnMfKe7VcOUlJSdMcdd+jUqVOqUaOGvvzyS4WGhpZnCk5x+vRpxcbG6vjx4zp//rwyMjLk7++vwMBA1apVS02bNlVwcLBT1jpy5Ih27dql06dPKycnRxEREWrUqJGio6OdEl+qmMdk/pP8eNrm6hQAt3BbzSBlZWW5Og0AQDF4bwH+xrsLAACoCLy8vBS4LMjVaQBuI7FHgqtTAAAUg3cX4G+8t7ivsLAwp8Yr12MZJSkoKEivv/66Ro4cqRMnTuj+++/XG2+8ofr165d3KlekatWqqlq1armsVbduXdWtW7dc1gIAAAAAAAAAAAAAAADgHsq9uOvkyZOqXLmyXnvtNY0bN0779+9Xnz591L17d3Xp0kWRkZEKDg6Wh0fJToysUaNGGWUMAAAAAAAAAAAAAAAAAOWv3Iu7brnlFlksFru+7OxsrVixQitWrChVTIvFot27dzsjPQAAAAAAAAAAAAAAAABwC+Ve3HWJYRiyWCx2hV6GYbgqHQAAAAAAAAAAAAAAAABwKy4r7pIo5gIAAAAAAAAAAAAAAACAopR7cVf//v3Le0kAAAAAAAAAAAAAAAAAqHDKvbjrpZdeKu8lAQAAAAAAAAAAAAAAAKDC8XB1AgAAAAAAAAAAAAAAAACAgijuAgAAAAAAAAAAAAAAAAA3RHEXAAAAAAAAAAAAAAAAALghirsAAAAAAAAAAAAAAAAAwA15ujqBvHJzc3XgwAElJCQoKSlJFotFISEhCg8PV2RkpKxWq6tTBAAAAAAAAAAAAAAAAIBy4fLiruzsbC1fvlxfffWVtm/fLpvNVug4X19ftWzZUv3791evXr3k6eny1AEAAAAAAAAAAAAAAACgzLi0QurXX3/VM888o/j4eEmSYRhFjk1PT9cff/yhP/74Q7NmzdLUqVN14403lleqAAAAAAAAAAAAAAAAAFCuPFy18Lx58zRy5EjFxcWZRV0Wi0UWi6XA2Lz9hmEoLi5ODz74oObMmVOuOQMAAAAAAAAAAAAAAABAeXHJzl2fffaZZs2aJUl2RVtWq1UNGjRQgwYNFBQUJElKSUnRoUOHFBsbq5ycHHN8bm6u3nzzTVWqVEl33323Kx4DAAAAAAAAAAAAAAAAAMpMuRd3xcXF6cUXX7Qr6oqIiNCDDz6ovn37mkVd+aWkpOibb77RO++8o7i4OFksFhmGoWnTpqlz586qXr16eT4GAAAAAAAAAAAAAAAAAJSpcj+W8a233lJGRobZ7tq1q5YvX64hQ4YUWdglSUFBQbrnnnu0YsUK3XbbbTIMQxaLRZmZmRzPCAAAAAAAAAAAAAAAAOCqU67FXTk5OVq9erW5a1e7du305ptvKjAw0OEY/v7+ev3119W+fXsZhiHDMLRq1Srl5uaWVdoAAAAAAAAAAAAAAAAAUO7Ktbhrx44dSklJkWEYkqRJkybJw6PkKXh4eGjSpElm+8KFC9q2bZuz0gQAAAAAAAAAAAAAAAAAlyvX4q4jR45IkiwWiyIjIxUZGVnqWJGRkWrUqJHZPnr06BXnBwAAAAAAAAAAAAAAAADuolyLuxITE83revXqXXG8+vXrFxobAAAAAAAAAAAAAAAAACq6ci3uysnJMa+tVusVx8t7pGPe2AAAAAAAAAAAAAAAAABQ0ZVrcVd4eLh5fezYsSuOd/z48UJjAwAAAAAAAAAAAAAAAEBFV67FXTVq1JAkGYahPXv26NSpU6WOFRcXp127dhWIDQAAAAAAAAAAAAAAAABXg3It7mrTpo18fX1lsVhkGIZeffXVUseaPn26DMOQJPn6+qpNmzbOShMAAAAAAAAAAAAAAAAAXK5ci7u8vb3VuXNnGYYhwzC0atWqUhV4zZw5U8uXL5fFYpHFYlGnTp3k7e1dBhkDAAAAAAAAAAAAAAAAgGuUa3GXJD366KPy8PAwd+9auHChBg8erN9///2yc3///XcNHjxYCxYsMOd7eHjo//2//1cOmQMAAAAAAAAAAAAAAABA+fEs7wUjIyM1cuRIzZs3zyzQ2rZtm/7973+rcuXKatmyperVq6egoCBZLBalpKTo8OHD2rZtm86ePStJMgzD3LVr+PDhatSoUXk/BgAAAAAAAAAAAAAAAACUqXIv7pKkJ554QqdPn9ZXX30li8Ui6WLB1pkzZ/T9998XOscwDEkyi7oMw1C/fv00duzYcssbAAAAAAAAAAAAAAAAAMqLS4q7JOmll15Ss2bNNH36dNlsNrPI65K8xVx5/2kYhnx8fDRhwgQNGTKkfJMGAAAAAAAAAAAAAAAAgHLisuIuSRoyZIhuv/12ffzxx1q6dKlOnjxZYMylIi9JqlGjhvr166d77rlHlStXLs9UAQAAAAAAAAAAAAAAAKBcubS4S5IqVaqkRx99VI8++qji4+O1c+dOJSQk6Pz58zIMQ6GhoQoPD1fz5s0VERHh6nQBAAAAAAAAAAAAAAAAoFy4vLgrr4iICAq4AAAAAAAAAAAAAAAAAECSh6sTAAAAAAAAAAAAAAAAAAAURHEXAAAAAAAAAAAAAAAAALghirsAAAAAAAAAAAAAAAAAwA1R3AUAAAAAAAAAAAAAAAAAbsjTmcGWLl1aoK9fv36XHeMM+dcBAAAAAAAAAAAAAAAAgIrMqcVdTz/9tCwWi11f/qKrwsY4A8VdAAAAAAAAAAAAAAAAAK4mTi3uysswjGKLuAzDuOI1LBbLZdcBAAAAAAAAAAAAAAAAgIrI6cVdjhRtOaOwy5lxAAAAAAAAAAAAAAAAAMDdOLW466WXXnLKGAAAAAAAAAAAAAAAAAD4p3NqcVf//v2dMgYAAAAAAAAAAAAAAAAA/uk8XJ0AAAAAAAAAAAAAAAAAAKAgirsAAAAAAAAAAAAAAAAAwA1R3AUAAAAAAAAAAAAAAAAAbsizvBe8//77JUkWi0WvvfaaKlWqVKo4Z8+e1dixY81YH3zwgdNyBAAAAAAAAAAAAAAAAABXK/firo0bN8pisUiSMjIySh0nIyNDGzdulCQzHgAAAAAAAAAAAAAAAABcLVxyLKNhGK5YFgAAAAAAAAAAAAAAAAAqDJcUdwEAAAAAAAAAAAAAAAAAildhi7tycnLMa6vV6sJMAAAAAAAAAAAAAAAAAMD5KmxxV2Jionnt7+/vwkwAAAAAAAAAAAAAAAAAwPk8XZ1AaW3atEmSZLFYVLVqVRdnAwAAAACAvcBlQa5OAXALiT0SXJ0CAAAAAAAAUGG5tLjLYrGUaHxWVpbOnDmjX3/9VXPnzjX7o6KinJ0aAAAAAAAAAAAAAAAAALhUmRR3XXPNNZcdYxiGbrnlllKvYRiGeX0lcQAAAAAAAAAAAAAAAADAHZVJcVfewitnjCuMxWKRYRhq0KCBunfvXuo4AAAAAAAAAAAAAAAAAOCOPMoqcEmPXCwpwzDUrFkzzZ07V15eXmW6FgAAAAAAAAAAAAAAAACUtzLZuat9+/ZF3tu0aZOki8VfLVq0kLe3t0MxLRaLvL29FRwcrAYNGui6665Tu3btnJIvAAAAAAAAAAAAAAAAALibMinuWrRoUZH3GjdubO7qNWvWLNWoUaMsUgAAAAAAAAAAAAAAAACACq3MjmUsjmEYrlgWAAAAAAAAAAAAAAAAACqMMtm5qziPPPKIeR0cHFzeyztVUlKS9u/fryNHjigpKUmGYSgkJEQ1atRQq1atFBQU5OoUAQAAAAAAAAAAAAAAAFRQLi3uqmhyc3MVExOjNWvWaMOGDdq/f3+RYy0Wi66//noNGzZMXbp0cSj+8ePH1bVr11LlFhERofXr15dqLgAAAAAAAAAAAAAAAAD3U+7FXRXZ7bffriNHjjg01jAM/fbbb/rtt9/Uq1cvPf/88woMDCzjDAEAAAAAAAAAAAAAAABcLSjuKoGEhIQCffXq1VOLFi1UuXJl+fj4KC4uTr///rvi4uLMMcuXL9fp06f17rvvysfHx+H1PDw8ZLFYHBrr6ckfJQAAAAAAAAAAAAAAAHA1oSKoFGrWrKlBgwapf//+qlatWoH7OTk5+uyzz/TSSy8pIyNDkrRp0ya9/vrreuqppxxe58UXX9Sdd97ptLwBAAAAAAAAAAAAAAAAVBxuUdx1+vRpbd26VbGxsUpOTlZKSopyc3Mdnm+xWDRt2rQyzPCiGjVqaOjQoerXr5+sVmuR46xWq/71r3+pRo0aGj16tPksixYt0rBhwxQREVHmuQIAAAAAAAAAAAAAAACo2Fxa3PXLL7/onXfe0aZNm0odwzCMcivuWrJkSYmOP+zSpYt69eqlb775RpKUlZWl77//Xvfcc09ZpQgAAAAAAAAAAAAAAADgKuHhikVzcnI0depUjRw5Ups2bZJhGDIMQ5LM60vtvPLeK+x+WStJYdclvXr1smvv3LnTWekAAAAAAAAAAAAAAAAAuIq5ZOeu6dOn66OPPpJ08UjFSy7twpW3nVf+e64o8CqpOnXq2LXPnj3rokwAAAAAAAAAAAAAAAAAVCTlXtwVExOj999/3yzUslqtGjx4sG677TZJ0v333y/pYiHX999/rwsXLuj06dPaunWrli1bpuPHj8tisahSpUp6/vnn1bhx4/J+hBJJTU21a5dm9y8AAAAAAAAAAAAAAAAA/zzlXmm0YMECSRd33vL29ta8efN0/fXXS5JOnDhhN7ZmzZqSpOjoaHXq1EmPPPKIFi1apJkzZyohIUETJkzQO++8o7Zt25bvQ5TAvn377NrVqlVzUSYAAAAAAAAAAAAAAAAAKpJyLe6y2Wz65ZdfzF27/v3vf5uFXY7w8PDQ0KFDVa9ePY0ZM0apqal65JFHtHz5coWHh5dV2ldk2bJldu3rrrvO4bmrV6/WihUrFBsbq8TERPn4+Cg0NFSNGjVS+/bt1aNHD1WtWtXZKQMAAAAAAAAAAAAAAABwAx7ludiOHTuUnZ0twzBktVp17733lipOly5dNGzYMElSUlKS3n77bSdm6TwbN27Uxo0bzXZQUJBuvPFGh+f/9NNP+vnnn3Xy5Emlp6crKSlJhw8f1po1azRt2jR17dpVU6ZMKXD0IwAAAAAAAAAAAAAAAICKr1yLu44dOyZJslgsql+/vipXrlzs+JycnCLvjRgxQlarVZK0YsUK5ebmOi9RJ0hPT9dzzz1n1/fAAw8oICDAaWtkZmbqk08+0cCBA3XkyBGnxQUAAAAAAAAAAAAAAADgeuV6LGNycrJ5Xbdu3QL3LxVrXZKZmSk/P79CY4WGhqpZs2bavn27EhIStHXrVrVt29a5CV+BKVOm6PDhw2a7QYMGGjFihENzo6Ojdeutt6pt27aKjIxUaGioDMPQ2bNntX37di1dulTr1683xx88eFAjR47Up59+qrCwsFLl6+XlVap5KC82VycAuIX8/50AALgj3lsAAPb4zAEA3BuftwD2eHcBAPfGuwvwN95b/jnKtbgrIyPDvC5sB6v8fUlJSUUWd0lStWrVtH37dknSyZMn3aa464MPPtBXX31ltr29vTV9+nT5+PgUOy80NFSLFy9W69atC71fq1Yt1apVS7169dK6des0fvx4nT9/XpJ05MgRTZ06VTNnznTegwAAAAAAAAAAAAAAAABwmXIt7spbvJWenl7gvr+/v107Li5O1atXLzKep+ff6Z89e9YJGV65VatW6eWXX7bre/7559WsWbPLzg0MDCyysCu/Ll26aM6cORo6dKiys7MlScuXL9eoUaMUFRVV4ryzsrJKPAcAyltOTg5/XwEAAAAVDO/wAACgIuHdBQAAVBS8t/xzeJTnYlWqVDGvL+04lZfValWNGjXM9q5du4qNd+zYMfM6JyfHCRlemd9//13jx49Xbm6u2ffkk0+qf//+ZbJeu3btNGDAALNtGIZWrlxZJmsBAAAAAAAAAAAAAAAAKF/lWtxVv3598zo2NrbQMXl3nfrxxx+LjBUXF6ddu3bJYrFIksLDw52UZens2LFDDz/8sDIzM82+4cOH68EHHyzTdQcOHGjX/v3338t0PQAAAAAAAAAAAAAAAADlo1yLuxo2bCg/Pz8ZhqFz584VepTijTfeKOniLlS///57ocVKhmFoypQpysnJkWEYkuTQsYdlZf/+/Ro5cqTS0tLMvkGDBmnChAllvnazZs3k5eVltk+ePFnmawIAAAAAAAAAAAAAAAAoe+Va3OXp6ak2bdqY7fXr1xcYc/vtt8vLy0sWi0W5ubl66KGH9NZbb+nPP//UkSNH9OOPP+r+++/XTz/9ZO7aVbduXbsdv8rT0aNH9e9//1tJSUl2z/D888+Xy/oeHh4KDQ0124mJieWyLgAAAAAAAAAAAAAAAICyVa7FXZJ08803m9erV68ucL9y5cq67777ZBiGLBaLbDab/vvf/2rQoEG6/fbb9fDDDysmJkaGYZhjHnnkkfJ8BFN8fLyGDRumM2fOmH1dunTRjBkz5OFRfr+1NpvNvPbx8Sm3dQEAAAAAAAAAAAAAAACUnXIv7urevbv8/f3l6+urzZs3F3qM4KOPPqq2bduaxVuSzGKuS8cwXuofNGiQevfuXX4P8P9LSEjQsGHDdOLECbPv2muv1ezZs+2OSSxriYmJSklJMduVKlUqt7UBAAAAAAAAAAAAAAAAlB3P8l6wSpUq2rJlS7FjfH19NX/+fE2fPl2ffvqpcnNz7e4bhqGAgAA99NBDGjFiRFmmW6gLFy5oxIgROnjwoNnXokULvf322/L19S3XXPIfbdm4ceNyXR8AAAAAAAAAAAAAAABA2Sj34i5H+fv7a9KkSXr44Yf1008/6fDhw0pJSVFwcLAaN26szp07Kzg4uNzzstlsGj16tHbt2mX2RUVFacGCBQoMDCzXXLKysjR//ny7vhtvvLFccwAAAAAAAAAAAAAAAABQNty2uIgVelgAANCrSURBVOuSKlWqaNCgQa5OQ5KUnZ2txx57TJs2bTL76tWrp4ULFyokJOSKYsfHx8vHx0ehoaEOjc/NzdV//vMfHThwwOyrUqWKevXqdUV5AAAAAAAAAAAAAAAAAHAPHq5OoKIwDENPP/20fvrpJ7OvZs2aev/991W5cuUrjr9z507dcssteuWVV7R79+5ix+7du1fDhg3TkiVL7PrHjh0rf3//K84FAAAAAAAAAAAAAAAAgOu5/c5d/x979x0nVXX/f/x9p21he2HpiFQpK1WFqIigX4gVy1cj0WAsCco3xcRoEo3G2KJGE1ssiV1jIhZswZ8KiIgK0qUjvS3be5lyf39cdnZnZ/vOzszC6/l47INzzj333M/dR8JjWN97TrQ4cOCA3nvvvaCxqVOntmmd3r176+OPP270Wnl5uZ577jk999xzyszM1PDhw9W3b18lJibKNE0VFBRo7dq12rJlS9C911xzjS666KI21QIAAAAAAAAAAAAAAAAgehHuaiXTNBsd83q9bVqntfNzc3P12WeftTgvNjZWt956q37wgx+0qQ4AAAAAAAAAAAAAAAAA0S3s4a6amhqtXbvW3z/hhBOUkJDQpjXKysq0adMmf3/06NFyOp0hqzEShg4dqiuuuELLly/Xjh075PP5mp2fmZmpmTNnatasWerRo0eYqgQAAAAAAAAAAAAAAAAQLmEPd7333nu67bbbJElpaWlatGhRm9dwOp36+c9/rsLCQknSgw8+qHPPPTekdTbUp0+fRo9DDJW+ffvqjjvukCRVVFRo+/bt2rdvn/Ly8lRRUSHDMJSYmKi0tDSNGDFCffv27bRaAAAAAAAAAAAAAAAAAERe2MNd8+fPl2maMgxDl156qVwuV5vXiImJ0aWXXqqnn35akvT22293ergrnOLj45Wdna3s7OxIlwIAAAAAAAAAAAAAAAAgQmzhfFhlZaVWr17t78+YMaPda9W/95tvvlFNTU2HagMAAAAAAAAAAAAAAACAaBLWcNemTZvkdrslSUlJSRo6dGi71xo2bJiSk5MlSTU1Ndq0aVNIagQAAAAAAAAAAAAAAACAaBDWcNfOnTslSYZhdCjYVav+GrVrAwAAAAAAAAAAAAAAAMDRIKzhruLiYn87LS2tw+vVX6OoqKjD6wEAAAAAAAAAAAAAAABAtAhruKv2SEZJstvtHV6v/hpVVVUdXg8AAAAAAAAAAAAAAAAAokVYw13Jycn+dkFBQYfXKyws9LcTExM7vB4AAAAAAAAAAAAAAAAARIuwhrtqj1E0TVObNm3q0FqmaWrjxo3+fmpqaofWAwAAAAAAAAAAAAAAAIBoEtZw1wknnOBvFxcXa/ny5e1ea/ny5SoqKvL3hwwZ0pHSAAAAAAAAAAAAAAAAACCqhDXc1bdvX/Xq1UuGYUiS/vrXv7ZrHdM09eijj/r7mZmZGjRoUChKBAAAAAAAAAAAAAAAAICoENZwlySdd955Mk1TkrR69WrdfffdbV7jvvvu08qVKyVJhmHo3HPPDWmNAAAAAAAAAAAAAAAAABBpYQ93XX311YqPj5dk7cD16quv6qc//an27dvX4r379+/XnDlz9PLLL/t3/4qNjdW1117bqTUDAAAAAAAAAAAAAAAAQLg5wv3AlJQU/fa3v9Xtt98uwzBkmqY+++wzff7555o0aZJOOukkDR48WElJSTIMQ8XFxdq+fbuWL1+uL774Qj6fz7/zl2EY+t3vfqe0tLRwvwYAAAAAAAAAAAAAAAAAdKqwh7sk6dJLL9XWrVsDduDyer1aunSpli5d2uR9pmnKMAx/KGz27Nm69NJLw1U2AAAAAAAAAAAAAAAAAIRNRMJdkvT73/9evXv31kMPPSSPx+MPedXuytVQ/VCXw+HQb3/7W82aNSucJQMAAAAAAAAAAAAAAABA2Ngi+fDZs2dr/vz5Ouecc2Sz2ZoMdklW6Mtut2vmzJl67733CHYBAAAAAAAAAAAAAAAAOKpFbOeuWgMHDtRf/vIX/eEPf9CKFSu0atUq5ebmqqioSJKUmpqqzMxMjR07VieddJISExMjWzAAAAAAAAAAAAAAAAAAhEHEw121kpOTNW3aNE2bNi3SpQAAAAAAAAAAAAAAAABAxEX0WEYAAAAAAAAAAAAAAAAAQOMIdwEAAAAAAAAAAAAAAABAFCLcBQAAAAAAAAAAAAAAAABRyBHpAmodPHhQq1at0vr165Wfn6/i4mIZhqGkpCSlp6dr1KhRGjt2rHr27BnpUgEAAAAAAAAAAAAAAACg00U83PXZZ5/p+eef19dff92q+SeffLKuvvpqTZ48uZMrAwAAAAAAAAAAAAAAAIDIiVi4q7y8XHfeeafef/99SZJpmpIkwzAanV97/auvvtLXX3+t73//+/rjH/+ohISE8BQMAAAAAAAAAAAAAAAAAGEUkXBXRUWFrrrqKm3cuFGmacowDBmGIdM0/SGuxtQGv0zT1IcffqidO3fqlVdeUXx8fLhKBwAAAAAAAAAAAAAAAICwiEi469e//rU2bNgQEOpyuVw644wzNGbMGB1//PH+HbnKysq0c+dOrVq1SosXL1ZNTY3/nk2bNulXv/qV/v73v0fiNQAAAAAAAAAAAAAAAACg04Q93PX5559r4cKF/l24DMPQrFmz9H//939KTk5u9J7Jkydr9uzZKikp0aOPPqrXXntNkrWD1+LFi7VkyRKdfvrpYXsHAAAAAAAAAAAAAAAAAOhstnA/8KmnnpJkBbPsdrseeugh3XbbbU0Gu+pLSkrSbbfdpocfflh2u92/g9fTTz/d2WUDAAAAAAAAAAAAAAAAQFiFNdxVXFys1atX+49jvO666/T973+/zetMnz5d119/vUzTlCStWbNGxcXFoS4XAAAAAAAAAAAAAAAAACImrOGuVatWyefzyTRNuVwuXXvtte1e65prrlFMTIwkyefzadWqVaEqEwAAAAAAAAAAAAAAAAAiLqzhrtzcXEmSYRg68cQT1a1bt3av1a1bN5144olBawMAAAAAAAAAAAAAAADA0SCs4a6ioiJ/OzMzs8Pr1V+j/toAAAAAAAAAAAAAAAAA0NWFNdwVFxfnb5eXl3d4vfpr1F8bAAAAAAAAAAAAAAAAALq6sIa70tLSJEmmaWrz5s0dXq/+GqmpqR1eDwAAAAAAAAAAAAAAAACiRVjDXUOHDvW3c3Jy9Pnnn7d7raVLl+rQoUP+/rBhwzpUGwAAAAAAAAAAAAAAAABEk7CGuwYNGqSePXvKMAyZpqm77rpLhYWFbV6nsLBQf/rTn2QYhiQpKytLgwYNCnW5AAAAAAAAAAAAAAAAABAxYQ13SdLFF18s0zRlGIb27t2rK6+8Ulu2bGn1/du2bdNVV12l3bt3+9e55JJLOrFiAAAAAAAAAAAAAAAAAAi/sIe7fvzjHys9PV2SZBiGtm/frosvvlg333yzlixZotLS0qB7ysrK9Pnnn+s3v/mNZs6cqe3bt/t37UpPT9ePf/zjsL4DAAAAAAAAAAAAAAAAAHQ2R7gfGB8fr8cee0xXX321ampqZBiGPB6P3n//fb3//vuSpLS0NCUkJMgwDJWVlSk/P99/f+1uXaZpKiYmRn/7298UHx8f7tcAAAAAAAAAAAAAAAAAgE4V9p27JGns2LF67LHHlJSU5A9rSVZwyzRN5efna/fu3dq1a5fy8vL845L8wa7k5GQ9+uijGjduXCReAQAAAAAAAAAAAAAAAAA6VUTCXZJ0+umn691339W0adP8gS3JCm819iXV7do1bdo0zZ8/X5MnT45U+QAAAAAAAAAAAAAAAADQqcJ+LGN9WVlZevzxx7V3717Nnz9fK1eu1LfffqvS0tKAeYmJiRo5cqTGjRun888/X/369YtQxQAAAAAAAAAAAAAAAAAQHhENd9Xq27ev5s6d6++73W6VlJT4j190Op0RrA4AAAAAAAAAAAAAAAAAwi8qwl0NOZ1OpaenR7oMAAAAAAAAAAAAAAAAAIgYW6QLAAAAAAAAAAAAAAAAAAAEI9wFAAAAAAAAAAAAAAAAAFGIcBcAAAAAAAAAAAAAAAAARCHCXQAAAAAAAAAAAAAAAAAQhRyhXOyqq64K5XKtZhiGXnzxxYg8GwAAAAAAAAAAAAAAAAA6Q0jDXcuXL5dhGKFcskWmaYb9mQAAAAAAAAAAAAAAAADQ2TiWEQAAAAAAAAAAAAAAAACiUEh37pKsnbQAAAAAAAAAAAAAAAAAAB0T0nDX5s2bQ7kcAAAAAAAAAAAAAAAAAByzOJYRAAAAAAAAAAAAAAAAAKIQ4S4AAAAAAAAAAAAAAAAAiEKEuwAAAAAAAAAAAAAAAAAgChHuAgAAAAAAAAAAAAAAAIAoRLgLAAAAAAAAAAAAAAAAAKIQ4S4AAAAAAAAAAAAAAAAAiEKOUC62YsWKUC7XJhMmTIjYswEAAAAAAAAAAAAAAAAg1EIa7rryyitlGEYol2wVwzC0cePGsD8XAAAAAAAAAAAAAAAAADpLSMNdtUzT7IxlARzFHIahaQMyFe+0B11bsidfeZU1zd6fGe9S38Q4pce7FGu3yTCkKo9PhVVu7S+t1IGy6s4q3S+7e5IGpXYLGt9dXKGVh4obvcduGBqUGq/eiXFKcNklGapwe3SwrFrbCspU42v+79Mp/dKVGueSaZpauDtPxdWeULwKAAAAAAAAAAAAAACIAiEPdx1Lwa6ioiJt3bpVu3fvVlFRkUzTVHJysnr16qXRo0crMTExJM/Zs2ePNm7cqEOHDsnn8ykrK0uDBw/WkCFDQrI+EA1GZiY2GuxqidNmaFyPFPVKjA26luCyKcHlUN+kOOVV1Gj5wUJVeXyhKDdIaqxTA1Pi23SP02bo9H7pSo5xBownxTiVFONU36Q4Ldmbrwq3t9H7ByTHKzXOJUnaUVRBsAsAAAAAAAAAAAAAgKNMSMNdc+fODeVyUcfn8+mbb77Rxx9/rK+++kpbt25tcq5hGJo4caJmz56tyZMnt+t5n332mf7+979r9erVjV4fOnSorr32Wp1//vntWh+IFmlxTg1oYzBKsnb7OrVPmj/g1JyMeJdO75uuz/bkq9ob2oCXIWlsj+Q2H0s7OivZH+yq8ni1OqdYHp+p0d2TlRjjULzTrgk9U/TZnvyge112Q8MzE/33bswr7fB7AAAAAAAAAAAAAACA6EK4qw2mT5+u3bt3t2quaZpatmyZli1bpnPOOUd33XWXEhISWn3vvffeq5deeqnZeVu2bNHNN9+sxYsX6/7775fL1XLABYg2hqSxWW0PRknSiMzEoGCXx+fTwbJq+UxTPbrFKsZh819LcDk0OitZXx8o7GjZAYamJwTtvtWSGLtNfertNrbucIkOHjk6clVOkSb3y5Akpce5lBLjUFGDXblGZiQpxm6924bcUrlbOL4RAAAAAAAAAAAAAAB0PSE/lvFoVlBQEDR23HHHKTs7WxkZGYqJidGhQ4f05Zdf6tChQ/45H3zwgQ4fPqx//vOfiomJafE5f/nLX4KCXWPHjtWoUaNkt9u1ZcsWLVu2zH8E5gcffCC73a4HH3ywg28IhN+w9AQl1QtGVXt9/tBSc7o57Tq+wW5fHp9Pi3bnq7TGCkLF2ks1pX+G4uod99g7MVbpcU7lV7pDUn+Cy66haXXBzWqPLyBQ1pTUWGdAoC23osbfzq90y+szZbdZ19PiXAHhrtRYp/onxx2ZW6PdJZUdfg8AAAAAAAAAAAAAABB9CHe1Q+/evXXppZdq5syZ6tGjR9B1r9er//znP7rvvvtUXW3txLNixQr99a9/1S233NLs2osWLdKzzz7r7yclJenRRx/VxIkTA+Zt3LhRc+bM8YfI3n33XY0bN06XX355R18PCJtEl0ND6gWjdhVVqJvLrsz4lkOQ/ZLignb72lVc6Q92SVKV16dtheXK7p4UMO+45HjlVxZ3sHrL2KwUfwiryuPV1oLg5zXGaQusvabBUZE1Pp/ibPYjcwPDYqOP7HTmM02tyQnNewAAAAAAAAAAAAAAgOjT8vYyXUBubm5YntOrVy/de++9+vjjjzVnzpxGg12SZLfb9YMf/ECPPfaYbPVCGS+//LJycnKaXN80TT388MP+vmEYevLJJ4OCXZI0fPhwvfDCCwE7gT3++OOqqqpqz6sBETG2R3JAMGp9bkmr7+2REBwAyy2vDh6rCB7r0a3l8FhrDEiJV0Z83bGQ6w6XBIW0mtLwGEVXg93KXPX+7nD76tY8PiVeqbHWTmc7iypU3OC4RgAAAAAAAAAAAAAAcPTosuEun8+nTz/9VHPmzNGUKVPC8sy33npLF198sex2e8uTJU2ePFnnnHOOv+92u/Xpp582Of+TTz7R1q1b/f0LLrhAEyZMaHL+gAEDdM011/j7ubm5euONN1pVGxBpx6fEKz2uLhi1JqckKPDUFENSkssZNF5/167mxmIcdsW14ujE5sTabRqZkejvHyyr0r7S1ocrC6vc/qNVJSmzXkgsLc7pD71JUkGldWRjjN2m4UeeWeXxamNeabvrBwAAAAAAAAAAAAAA0a/Lhbt27typhx56SKeffrrmzp2rRYsWyev1huXZDkfbT7GsH+6SpPXr1zc5d8GCBQH9WbNmtbj+5ZdfHhA2a7gGEI3iHDaNqBeMOlBapQNlrQ9GxTvtAeGnWtWN7JrlMyV3I+PdnB07lXZ0VrKcR3bbcnt9bT4esdrrCwiDZXdPUs+EGGXGuzQ2K9k/nl9Zo6Iju3ONzEz07/C1Ibe01WE4AAAAAAAAAAAAAADQNXUs3RAmlZWV+u9//6t58+Zp9erVkhSw400069evX0A/Ly+v0Xkej0dLlizx93v27Kns7OwW18/KytLo0aO1cuVKSdLq1atVUFCgtLS0DlQNdK76waiadgSjnI0EuyTJ00TYyWuaarjPl9Pe+Bqt0SshVr0SY/39DXmlqvS07jjG+tbmFCspxqHkGKdiHXZN7B34/9sKt1crDhZJktJineqXFCfJCnztLqn0z4ux25Qc45DDZqjS41NRlVtd429IAAAAAAAAAAAAAADQnKgOd61Zs0ZvvvmmPvzwQ1VUVEiqC3UZhhHQj1bl5eUB/aZ2/9q6datKSkr8/TFjxrT6GWPGjPGHu7xer1atWqVp06a1o1qg8/VJjFXPhHrBqNxSVTWys1ZzGtu1S1KTgabGMl8OW/s2LnTaDI3OSvL38ytrtKOool1r1fhMLd6dr0Gp3dQ7MVYJLrsMGSp3e3WwrErbCstU47WKH52VLMMw5DNNfxguxm7TmKzkgKCZZB3ZuCG3NCAABgAAAAAAAAAAAAAAup6oC3cVFBRo/vz5mjdvnnbs2CEpMNBlGIZM05RpmurXr5/OOeccnXvuuZEsuVlbtmwJ6Pfo0aPRed99911A/4QTTmj1M4YPHx7Qr/2+AdHGaTOU3b0uGJVbUa2dxW0PRnmb2KHLUOMBr8ayYB5f23fakqSRmUmKddj9daw61LZdxxrymqa2FJRpS0FZk3MGpsQrJdbae2xnUYWKqz1y2gxN7peuBFfwX+OxDrvG9UyRw2bou3YGzwAAAAAAAAAAAAAAQORFRbjLNE0tWbJEb775phYtWiSPxxOwI1f9QFdGRoZmzJih8847r1XHFkbau+++G9A/5ZRTGp3XMJDVq1evVj+jZ8+eza4FRIsRmYkBwajV7QxGuZsIdzlsRqPX7EZwusvtbfuuf6mxTh2XHOfvbykoU2mNp83rtEWM3aYTMhIlWTtybcwrlSQNS0/wB7vcXp++OlCo4mq3RmUmqX9yvCRpRGaS9pVWqbqNO6MBAAAAAAAAAAAAAIDoENFw1969ezVv3jy98847Onz4sKSmj10899xzddFFF+mUU06RrZ3HqYXb8uXLtXz5cn8/MTFRp556aqNzc3JyAvpN7fDVmIZzDx061IYqgfBJrLfLVIXHq+NTugXNSXAG/7U0MDVevY4c5fhtXokq3F55fWbQ8YwxDpvcNd6AMZshOe3Bf2eUu9seykp0OQL+boqx25SdmRQ4Jya4/tRYp3/egbIq5VXWtPqZozIT5TpS/7e5pf7wWp/EupDZzuIK5VZYa647XKI+iXGy2ww5bIZ6JsRoVzHHMwIAAAAAAAAAAAAA0BWFPdxVXV2tBQsW6M0339SKFSskNX3solFvt52bbrqpTbtZRVplZaVuv/32gLGrr75a3boFh1kkqaIi8Oi0puY1puHchmsB0SjR5VBiWuv+CupdL8i0Kb9UbtNUSY1bqbGuoDXLGoS7kho5trDa41Wlp2O7WRmGoYGprfv/aVKMU0kx1rGK5R5Pq8Nd6XEu9TuyC1d+ZY32lFghLafNUJzT7p9XUOn2t90+U+Vuj/95SS6nJMJdAAAAAAAAAAAAAAB0RWELd3377beaN2+ePvjgA5WVlUmSP8BVP9CVmJioGTNm6OKLL9Zll10WrvJC7o9//KN27drl7x9//PG69tprm5zfMJDlcrmamBksJiam2bVaw+l0tvkehFNVpAuIOofKqoPCXd3jY3SwrDpwrFvg/z8k6VB5ddBYtDEkjc6ydvvymabW5NQdYdlwxzKPLzCoVv9oyoZzuzq73d7yJABAhPG5BQAQiJ85AEB04+ctQCA+uwBAdOOzC1CHzy3Hjk4NdxUVFendd9/Vm2++qa1bt0pqfJcum82mSZMm6aKLLtJZZ53VpmBTNHrxxRf19ttv+/sul0sPPvhgUAirvurqwLBJW74HDedWVfEf1HD021NSqWHpCQE7/PVPjtOOogqV1lhHLsY6bBrUyO5au4qDA5D9kuI0vmdKwNimvFJtyi8LbeGtNDC1m5KP7L61s6hCxdV1x0jWeH0Buxu6Ghw7Wb9f7e3YDmUAAAAAAAAAAAAAACByOiXc9cUXX2jevHn69NNP5Xa7mzx28bjjjtPMmTN14YUXKisrqzNKCbsFCxbo/vvvDxi76667NHLkyGbvaxj8qqlp3bFtjc2NjY1t9b213G53y5OADvp8b0GLc07rm6bM+MD/PyzZkx90lGG526sdRRUBRyM6bDZN6Z+uA2XVMk1TPRJiFdMg+LS/tEr5le373/uekkr/0YhNaSwktru4QisPFTd+QyNi7TadkJ4gSaryeLUxrzTgus+UiqrcSo2zgp2Z8THaV2qFOuMcNiXUO7Ixv5VHQHYVXq+Xv68AAACALobP8AAAoCvhswsAAOgq+Nxy7Ah5uOvMM8/UwYMHJQXu0tXw2MWZM2dqzJgxoX58RH355Ze6+eab5at3RNqvfvUrzZw5s8V74+PjA/ptCXc13PWr4VrA0WpDbqnS4pwBxzM6bDb1S4prdH5ZjSfgeMNoNap7kpxHQmnf5pYGHLNYa3tRhSYcCXf1T45TcbVbxdVuDc9I9O/oVVrtUU4XOIISAAAAAAAAAAAAAAA0LuThrgMHDvjDXPX/nDhxov/YxeaOJ+yq1q1bpxtuuCEglHXNNdfo+uuvb9X9DQNZ5eXlrX52w7mEu3Cs8Jimlu4t0PieKeqZ0PyOdXkVNVpxsDDqjynMiHOp75FwWn5lTZM7he0tqVRGnEsDUuJlMwyNzkoOuF7t8Wr5wcJOrxcAAAAAAAAAAAAAAHSeTjmWsb4pU6bozjvvPGqOXWzM1q1bdd1116miosI/dumll+o3v/lNq9do+P05dOhQq++t3SmtVo8ePVp9L9DVuX2mvtxfqMx4l/olxSk9zqUYh02GDFV7vCqscmtfaZUOlFVFutQWGZJGZyVJknym2eIuY6tzipVbUa0BKfFKjnHKYTNU6fbqUHm1thaUqdIT3UE2AAAAAAAAAAAAAADQvE4Ld9Xu2rV48WLNnj1bM2fO1AUXXHDUhbz27NmjH//4xyoqKvKPTZ8+XXfddVeb1hk4cGBA/8CBA62+t2EQ7Pjjj2/Ts4Fo8vnegnbdl1tRo9yK1h9n2tCeksomd8kK1zqmpE925bXpnn2lVdpXGv3BNQAAuiqHYSizm0upsdZR0HEOm1x268s0JbfPp3K3V0VVbh0oq2rV55HUWKf6JsYpNc6pbk67nDabDMMKrVccWWt/aaUOd+CzTX2GpOQYh1LjXEo78h6JLrv/KOdaC747rAqPt9m17IahQanx6p0YpwSXXZKhCrdHB8uqta2gTDWNHCdd35R+6UqNc8k0TS3cnafiak8H3w4AAAAAAAAAgKNbp4S7ao9irA147dy5U4888oj+9re/6ZRTTtHFF1+sadOmyeVydcbjwyYnJ0ezZ89Wbm6uf2zy5Ml66KGHZLPZ2rRWw3DXxo0bW33vhg0bAvqEuwAAAIDQyOzm0sTeaY1fNCS7za5Yh13pcS4NTO2mvIoaLT9QqKpGjoK2G4bG9UhWnyNHMDcUYzcUY7cpNdapASnxyquo0dcHOn6s9ND0BA3PSOzQGpLktBk6vV+6kmOcAeNJMU4lxTjVNylOS/bmq8LdeEBsQHK8UuOsfwPuKKog2AUAAAAAAAAAQCuEPNy1YMECzZs3T++8847y8qwdaGpDXl6vV8uWLdOyZcuUkJCgGTNmaObMmRozZkyoy+h0BQUFmj17tvbv3+8fO+mkk/Too4/K6XQ2c2fjBg8erKSkJJWUlEiS1qxZ0+p7V69e7W/b7XaNHTu2zc8HAAAA0HEZ8S5N6pOmRbvz1HAPqwk9U9QrMbZNa32vibXawmh5SquMzkr2B7uqPF6tzimWx2dqdPdkJcY4FO+0a0LPFH22Jz/oXpfd0PDMRP+9G/NKQ1QVAAAAAAAA0MU5EqSsM6X0CdZXXB8pJl1ypUmmV3KXSOU7pIJV0t63pJyFja8zdZGUdUb7alh/p7T+j+19gzrdBkiDrpeypkgJAyVnklRTKFXslg78V/run1LF3ubXsMdLQ38u9btUShwsGTapfJe0711p04NSTXMnQRnS/3xtfR99Xumj8VLhmo6/FxBhbdteqhWOO+44/frXv9Znn32mJ554Qmeeeabsdrsk+Y/9ME1TpaWleuONN3TFFVdo+vTpeuaZZ5STkxPqcjpFWVmZrr32Wu3YscM/lp2drb///e+KjW39f6ypz+Fw6PTTT/f3Dx48qLVr17Z4X05OTsC8MWPGKC2tiZ0FAAAAALSLzzRVUFmjPSWV+q6wXLuLK1RY2fixiSmxTvVKCPx3QUqMo9FgV5XHqz0lldpVXKHymuCdrBpbq6Mq3F7VtHE3sBi7TX3q1b/ucIkOllUrt6JGq3KK/OPpcS6lxAT/DtHIjCTF2K1/fm7ILZW7heMbAQAAAAAAgGNGj6nS5PnSyNuknv8jpYyQ4npIdpfkiJPisqSMidKQG6Wpn0rTlkixPSJddSDDJmXfLZ23TRpxq5RxshSbYb1DXJaUfpI06g7p3M3S0F80vY4rVTr7S2n0vVLaGMmZIDnipeTh1rozVlsBsqYMut4KdknS9qcIduGo0SnHMkrWDlJTp07V1KlTlZeXp7feektvvfWWdu3aJSkw6LVr166AYxsvuuginXXWWZ1VWodUVVXppz/9acBRiEOGDNE//vEPJSQkdGjtGTNm6P333/f3X3vtNZ144onN3vP666/L66079mT69OkdqgEAAABAnbIar74+UKjD5dWNBpIy461jGx22wP2x0uKc2l9WVa8ffCR9eY1HC3fn+dc1JJ3eL13pDeY2XKutSms82pJfpsKqGhVUulXl9em0vmnKjI9p9RqpsU7/v+EkKbeiLtiWX+mW12fKfuR7kBbnUlG9IxdTY53qnxx3ZG6NdpdUtvtdAAAAAAAAgGNe99OkKQukjyZIPndo1vQF/+Jpm5z0rDTwxy3Pc8RL4x6RnInSt38Kvj7hSSk122pXHpRWzJHcZdL4x6XkYVK3ftKkV6SPvxd8b0y6dOI9VrvqsLT2tva/DxBlOi3cVV9GRoauv/56XX/99frmm280b948ffTRR6qstH6o39SxjdHG4/Ho5z//uVasWOEfO+644/T8888rOTm5w+tPnTpVQ4YM0datWyVJ8+fP1yWXXKIJEyY0On/nzp365z//6e9nZmbq0ksv7XAdAAAAACylNR6VNrKjVq3cihrlVlSrZ4PdteoHoSTJbgQfjnigrCogMGZK2ldSGRTuarhWW+0rrdK+0vaHwyTJ2SC81nDnrxqfT3E2+5G5gRtEj85KlmEY8pmm1uQUd6gOAAAAAAAA4Kjlc0uFq6XSbdZRho5Ea8eq9EbyAqknSr0vkPbOqxvbO6/lnaoc8dbuVg3te7v9dfe7tPFgV+4XUvFG62jFhsdFjrpTOvSJlPdl3VhMptS3Xt5h1a+kffOt9vLrpLM+t9qZk6TUsVLhqsA1T7zfCnhJ0ppbJHdR+98JiDJhCXfVN378eI0fP16333673n//fb355ptat26dpOBjG2tDX5L03//+VxdffLFSUlLCXbK/pltvvVWLFy/2j/Xu3VsvvPCCMjIyQvIMwzB000036ac//an/mTfccIMeffRRTZw4MWDuxo0bNWfOHFVXV/vH5s6d2+5jIQEAAAC0T7zTHjRW1iAQVuYODojFOILvi21krLS6g781FwINdy1z2W2qrhfwctULdLl9dePHp8QrNdYpSdpZVKHiKHgXAAAAAAAAIKqUbJWWXiYd/EhyN/LLkVlTpMnvW8Gs+jJOCQx3bX2i5Wc1Fuw69IkVwmqv4bcEj639vbTh3rr+0J9J4/5W1zds0ti/SP9vUt1Y+gTJVu/nozmf1rVzl0reKsl+JA+RcXJguCv95LqAWe4yaccL7X4dIBqFPdxVq1u3brrssst02WWXadu2bXrjjTf03nvvqbCwUFJd0Kv2z4ceekiPPPKIvve97+ncc8/VtGnTFBcXF7Z6Dxw4oPfeey9obOrUqW1ap3fv3vr444+bvD5lyhRdd911evbZZyVJJSUlmj17tsaOHavs7GzZbDZt2bJFy5Yt8wffJOn888/X5Zdf3qZaAAAAALSPwzCUGOPQkLRuSo5xBlyr8fq0r8HRgznl1Sp3e9TNWfdPsD6JsSqojNe+kkr5JGV1i9HA1MAf0FR5vNobBccYFla5ZZqm/99nmfEu/25gaXFO/5GMklRQaR3ZGGO3aXhGoiTrPTbmlYa5agAAAAAAAKALKNlkfTUlZ5GUs1DqfW7guM3Z+PzmDJkbPLblb8FjrRWTIaWNCxzzVEibHgoc2/q4NOJ2KbbexjkZE6XkEVLxBqvvSgm8p7oguB/f68jc1HoXDGnCE1ZgzOeRvrmxvW8DRK2IhbvqGzx4sH73u9/p5ptv1qeffqo333xTX3zxhXw+X8BuXh6PR0uWLNGSJUsUGxurM888U+eee65OO+00ORyd+yr1g1T1x7xeb5vWac38m266SVVVVXr55Zf9Y6tWrdKqVasanf/9739fd999d5vqAAAAANA23+uTqqxuze+UW+P1afmBQtU02OnKZ0rLDxRpYu9U/+5cNsPQ6Kxkjc5q/Ij3So9XX+4rlKeRf4uEW7XXp32lVeqbZP2CTXb3JHlNUx6fqRO7J/nn5VfWqOjI7lwjMxPlsls7em3ILQ3a/QsAAAAAAABAK8X3Cx4r3da2NbKmSCmjGqzxnbT/g9DWVbFP8tUEjpk+qXxHYLhLknqeXRfuqikKvBaTLlXl1Oun1bVrCuvag+fUBcy2P9Xy0ZRAFxQV4a5aTqdT06dP1/Tp05WTk6N58+bp7bff1r59+yQFHttYWVmpDz/8UB9++KGSk5P11VdfRbL0kLLZbLrtttt02mmn6cknn9SaNWsanTdkyBBde+21uuCCC8JbIAAAAIAg2wvKtaWgLOC4wvoKq9z6dFeeBqV20+C0brIZRqPzJGlrQZk255fJE0WBqLU5xUqKcSg5xqlYh10Te6cFXK9we7XiYJEkKS3WqX5HgmD5lTXaXW/3sRi7TckxDjlshio9PhVVuRU9bwkAAAAAAABECUeClDxcOuFmKTU78FpNkbTrtbatN+RnwWNbH5M68tM5WyORE0e3xuc6EoPH0sbXtfNXSD5v3dGMWWdKu/9ltTMm1R3JKEl5X1t/xmRKJx7ZCKfqsLT2trbVD3QRURXuqi8rK0s33nijbrzxRn355ZeaN2+ePvnkE1VXV/tDXpIV9CoubuTc2RDr06ePtmzZ0unPqW/y5MmaPHmydu/erQ0bNujw4cPyer3KysrS4MGDNXTo0LDWAwAAAKBpx6fGK8Zh05qc4iZ3qeqTFKv+yXHNBrskaXBqN8U57Fp3uKTJsFi41fhMLd6dr0Gp3dQ7MVYJLrsMGSp3e3WwrErbCstU47Xee3RWsgzDkM80tSbH+vdajN2mMVnJ6pUYuPtZlcerDbmlAQEwAAAAAAAA4Jh0xn+lXtObn1NTKC29TKopaH5efd36S73PCxxzl0rfPdf2Guur2Bs8FtdTShwklW6vN9ZbShjYeF21qnOlPf+RjvuB1R/7F8lTLnnKpPGP1c3LXSYVHjn1bMwDdUc0rrlFcnd+dgSIhKgNd9U3ceJETZw4USUlJXr33Xf15ptvatOmZs6cPcr0799f/fv3b3kiAAAAgE5zoLRapdVeyZCcNkPJMU6lxDr9122Gob5JcUqNdeqzPflBoawJPVP8xxrWqvJ4lVdRI49pKjXWqeQYaz2j3lqL9+T5Q1OR5jVNbSko05aCsibnDEyJ939fdhZVqLjaI6fN0OR+6UpwBf8TNNZh17ieKXLYDH1XVNFptQMAAAAAAABd3ua/Shvvs3apaovBN9btiFVrxwuSp7Rj9VQelIq+lVJG1o0ZNmnSq9LyOVLJJivoNf4Jye4Kvt+ZHNj/Zq61VsooKyQ2eX7g9fI90rIfWu2MidKAq6x27jLrfWrFZkmpJ0r2eKlyv1SwSjK9HXtXIIK6RLirVlJSkn74wx/qhz/8oTZu3Kg33nhDH3zwgUpLO/gXDgAAAAC0YGdxcPAoNdapU3qlKs5Z94ORBJdDIzMTtfJQ3W+J9U6MDQp2FVTWaOm+goCjF7O7J2lQareAtUZkJGl1Ttf4jbMYu00nZFjbq1d5vNqYZ/1bbVh6gj/Y5fb69NWBQhVXuzUqM0n9k+MlSSMyk7SvtCpqdioDAAAAAAAAos6QG6W4HtKKG6wdvFrDHicNvCZwzPQdOZIxBDbcK32vwRGR6SdJM1a2fK+tQeCrpkD66BRp2C+kfpdKiYMl2aTyXdK++dLmh6TqfCtANuFJ60+fx/p+SFaoa8JTUt8LA9etzJHW3hoYAAO6kC4V7qpv+PDhuuOOO/Tb3/5WH330UaTLAQAAAHAMKqxya+3hEp3SOzVgvFdibEC4q1+DYJckbc4vCwh2SdKG3FINTIkPOIq+T2Jslwl3jcpMlMtukyR9m1vqP56yT2Ld++8srlBuRY0kad3hEvVJjJPdZshhM9QzIUa7ijmeEQAAAAAAAMeofW9LJZut0JIzWUrJltLG1F23OaX+l0tp46SPT5Oqclpec8CVUkxa4NjBBVLpttDUvPtfUvfTpMFz2n5vYwE1b4UVGNtwb9P3DZkrpY622tufkorWWt+vs76QEhs5/jEuSzrlecmRGLpQGxBGtkgX0FEul0vnnXdeyxMBAAAAoBOUVLuDxpw2m2Lsdf/cauw4wnJ38DbgXtMM2rnKaQ9cK1qlx7nU78guXPmVNdpTYoW0nDYjYGezgsq675fbZ6rc7fH3k1x1x1wCAAAAAAAAx5ztz0irfimt/Ln01WxpwVhpwQSpYl/gvMTB0on3tW7NIXODx7b8rcOlBlhxg7TixqaPi6zMkTY/Ejxendf2Z8V2l0b9sW7dtbdZ7ZG31wW7aoqlT6dKb2ZIO1+qu3f0/VJMZtufCURY9P8XAgAAAACIYkkxjQeSvGbdrlw+0wy6Hl8v8FTLbhiNBrkauz+aGJJGZyVJsmpdU2+nMbvNCJjr8QWG19z1di9rOBcAAAAAAAA45hV8I638RfB4v0usHb6akzVFShkVOFa8WTr4/0JWnt+2J6X5/aVFM6Q1v5U2PiCt+4P02XnS/H5S4ergewpXtf05Yx6SXClWe80tkvvIzyL7X143Z/szUs5C6wjHlb+QvNXWuCNe6nNB258JRFiXPZYRAAAAADpTcoxDY7KStaOoQgfKqoKOUJSk1FinsrsnBo1Xur0B88trvEpuEAI7IT1B+RU18tQLbo3ITAw4klGSqjzegACUZB3zOL5nSsDYprxSbcova/X7hdLA1G7+99tZVKHi6rrduGq8Ppmm6X8vV4PwWv1+w13LAAAAAAAAAEgqWh885ky0dqFq7mjGIT8LHtv6aOjqashbZR35eHBB8LU+FwaP5S5t2/qZp1nHTEpS7hfSzhettjNFiu9dNy//67p2TaFUul1KGWH1k0e07ZlAFCDcBQAAAABNSItzKS3OJZ9pqrjKrdIaj9w+Uy67TYkuh1JiG9+1a1dxRUB/f1mVeiXGBq199vGZyq2okdc0lRrrDAqASdKB0qoOvUNqrFN9E+MCxhKcwf8UHJaRII+3LkRW5vZoR1FF0LyGYu02nZCeIMkKom3MKw247jOloiq3UuNckqTM+BjtO/JOcQ6bEurtYJZfWdPKtwIAAAAAAACOAoZNMlvxC49NBZK8lU3fE99P6n1e4FhNkbTjxVaXFzIZp0i9G+yYVb5bOvRJ69cw7NKEJ6y2z2MdA1nLER84113cdL/hXKALINwFAAAAAC2wGYZS41z+gFJzCiprtKUgcAetvSWVGpAcr4z4wPtjHXb1TQoMXtVX6fF2eDeuRJdDg9K6tTjvuOTAH2rkVlS3Ktw1qnuSnEd23/o2tzRolzFJ2l5UoQlHvnf9k+NUXO1WcbVbwzPqdiorrfYop7y6xecBAAAAAAAAR43kUdLJz0hbH5f2viN5SoPnpJ8kjXskeLxin+QuaXrtIXMlmz1w7Lt/St6Wf+YnSRrwI2niC4Fj6++U1v8xeO7I26W9b0nFG4Kv9ThLmvRKcC2bH25dsK3W0J/VHTG57e9S0dq6a9X5ks9b94yY9MB76/erclv/TCBKEO4CAAAAgBDwmaZ2F1do3eFSNZJv0rL9BRrXI1m9E5sOc9VXWFmj5QeLovqowow4lz+cll9Zoz0ljf+m4N6SSmXEuTQgJV42w9DorOSA69Uer5YfLOz0egEAAAAAAICok36SNPEl6aQaK7BUstkKbcWkS4lDpbQxjd/33XNNr2mPkwZeEzjm81ohss4w6Hop+y6p9DupYKVUnSvZ46X08XWBrPoOL5G2PNb69WN7SKPutNqVOdK62wOv+6qlwlVS+gSrn3WmtPvfVjuut5QwqG5u3hetfy4QJQh3AQAAAEAjiqs9+nhnrjLiXcqIcynBZZfLblOM3SabYcjjM1Xt8am0xqO8yhrtL61UpafpIJbHZ+rrA0VKjS1Xn8RYpcW51M1pl9Nmk2FIbp+pSrdXRdVuHSit0qEo38XKkDQ6K0mSFWxbk1Pc7PzVOcXKrajWgJR4Jcc45bAZqnR7dai8WlsLypr93gEAAAAAAABHPbvLCifVBpSak79c2nBv09eP+6EUkxY4tv89qXxXh0psUeJA66s5hWulpf8rqZHfkG3K2Iclp/WzSK25JfjYRUna8jdrhzBJOv5qqXCNVLROyv5T3Y5exZulgx+1/rlAlCDcBQAAAABNKK3xqLTGo52tOJ6wtQqr3CqscndojT0llU3uktWRuW1hSvpkV16b7tlXWqV9pVUhrwUAAAAAAAA4Jvjc1o5dq26ydqtqytD/Cx7b8rfOq6s1fF5p18vSyp83f5xkQ93PkI77gdXO/ULa+WLj83a9KnU/3dpFzOaUJjwZeL0qV/risrYdBQlECcJdAAAAAAAAAAAAAAAA4Va0VvpghJR5utT9NClxiBSTIcVmSoZT8pZbxxCWbJJyP5f2zJMq9ja/Zvczgo9CLFwnHV7cWW8hfX6x1PNs6z269bfewZEg1RRIFXukQ59Iu/4lFX/btnUNhzT+yFGSPo+04obm5y//iZSzUBr0Eyl1jHU0ZOV+6cCH0ob7rDbQBRmmabZhrzscrQoLCyNdApqx6DC7GwCSdHbvRLndHdvpBADQufjcAtS5aGWvSJcARIXCGQWRLgEA0Ayn06mEdxMjXQYQNfjsAgDRjc8uQB0+t0Sv1NTUkK5nC+lqAAAAAAAAAAAAAAAAAICQINwFAAAAAAAAAAAAAAAAAFGIcBcAAAAAAAAAAAAAAAAARCHCXQAAAAAAAAAAAAAAAAAQhRzhfuDUqVMlSYZh6NVXX1VWVla71snJydEVV1zhX+uTTz4JWY0AAAAAAAAAAAAAAAAAEGlhD3ft379fkhXI8nq97V7H4/EErAUAAAAAAAAAAAAAAAAARxOOZQQAAAAAAAAAAAAAAACAKES4CwAAAAAAAAAAAAAAAACiUJcNd3k8Hn/b4Qj76ZIAAAAAAAAAAAAAAAAA0Km6bLgrLy/P3+7WrVsEKwEAAAAAAAAAAAAAAACA0Ouy4a6lS5dKkgzDUI8ePSJcDQAAAAAAAAAAAAAAAACEVqecZ3jgwIFWzcvJyWnTujU1NcrNzdXSpUv1/PPP+8dPOOGENq0DAAAAAAAAAAAAAAAAANGuU8JdZ555pgzDaHaOaZq64oor2v0M0zT97bPOOqvd6wAAAAAAAAAAAAAAAABANOqUcFet+gGs9lxvimEYMgxDpmlq9OjRmjJlSrvWAQAAAAAAAAAARxlHgpR1ppQ+wfqK6yPFpEuuNMn0Su4SqXyHVLBK2vuWlLOw7c8Y9zdp6M+Cx3e8IH11dYdfQYZdSsmW0k+WMk62/kwaKhm2wHnzj5PKdze/lj1eGvpzqd+lUuJga43yXdK+d6VND0o1Bc0VIv3P19b30eeVPhovFa7p2LsBAAAAaJNODXd1FtM0ZRiGzj77bP3pT39qcZcwAAAAAAAAAABwjOgxVTr9naavO+KkuCwpY6I05Ebp8OfS0v+Vqg61bv30k6XBN4ak1CaN+L2U/ceOr+NKlaYullKzA8eTh1tfx10hfXKGVL6z8fsHXW8FuyRp+1MEuwAAAIAI6JRw18yZM5u89vbbb0uydt8666yz1K1bt1av63K5lJSUpIEDB2rChAnq3bt3h2sFAAAAAAAAAADHsO6nSVMWSB9NkHzu5ucaDunkZyWbvXNrCtUvtU94si7YVXlQWjFHcpdJ4x+XkodJ3fpJk16RPv5e8L0x6dKJ91jtqsPS2ttCUxMAAACANumUcNd9993X5LW3337bv9PWrbfeql69enVGCQAAAAAAAAAA4Fjmc0uFq6XSbVJNoeRItHarqt2Jqr7UE6XeF0h75zW/5ohbpZRRnVNvc8r3SM5Eayeu1orJlPpeWtdf9Stp33yrvfw66azPrXbmJCl1rFS4KvD+E++3Al6StOYWyV3U7vIBAAAAtF9EjmWsPVYRAAAAAAAAAAAgpEq2Sksvkw5+JLmLg69nTZEmvy854gPHM05pPtyVNNQ6LrFWVZ4UmxGamhsq2SJt/LOU95X1VXVImrpIyjqj9WukTwjcYSzn07p27lLJWyXZY61+xsmB4a70k6WBPz4yd5m044X2vgkAAACADgp7uKv+rl6pqW34DRMAAAAAAAAAAICWlGyyvpqSs0jKWSj1Pjdw3OZsft2Tnq0LQ1UdtsJXY//SsVqbsvt166sjXCmB/eqC4H78kdNVAnYEM6QJT0iGTfJ5pG9u7FgdAAAAADok7OGumTNnhvuRAAAAAAAAAAAAdeL7BY+Vbmt6/qCfSt1Pq+uv/Llkiwl9XaFUUxTYj0mXqnLq9dPqzS2saw+eI6WNs9rbn5IK13RWhQAAAABawRbpAgAAAAAAAAAAADqdI0FKP0k69Q0pNTvwWk2RtOu1xu+L6ymNvr+uv//9ju+qFQ75KySft66fdWZdO2NS3S5kkpT3tfVnTKZ04t1Wu+qwtPa2zq8TAAAAQLPCvnNXczwejw4fPqzi4mKVl5fLNE1NmDAh0mUBAAAAAAAAAICu6Iz/Sr2mNz+nplBaeplUU9D49fFPSK5kq+0ukVbMCW2NnaU6V9rzH+m4H1j9sX+RPOWSp0wa/1jdvNxlUuEqqz3mgbojGtfcIrmLw1szAAAAgCARD3dVVFToP//5jxYuXKh169apurraf80wDG3cuDHonk2bNmnLli2SpISEBE2bNi1s9QIAAAAAAAAAgKPE5r9KG++zdqlqTN+Lpb4z6/prfitV7AtLaSHxzVwpZaSUMsragWzy/MDr5XukZT+02hkTpQFXWe3cZdKOF+rmxWZJqSdK9nipcr9UsEoyvQIAAADQ+SIa7vrPf/6jhx56SKWlpZIk0zRbdZ/b7datt94qwzBks9m0aNEide/evTNLBQAAAAAAAAAAR5shN0pxPaQVN1g7eNXnTG6ww9UX0rYnw1tfR9UUSB+dIg37hdTvUilxsCSbVL5L2jdf2vyQVJ0vGTZpwpPWnz6P9f2QrFDXhKekvhcGrluZI629NTAABgAAAKBTRCzc9dvf/lbvvPOOTNOUYRgB1wzDaDbolZ2drVGjRmn9+vXy+Xz64IMPdPXVV3d2yQAAAAAAAAAAoCvZ97ZUstkKLTmTpZRsKW1M3XWbU+p/uZQ2Tvr4NKkqp+7amAet3a4kyVstfX1teGsPFW+FtOFe66spQ+ZKqaOt9vanpKK11vfrrC+kxIHB8+OypFOelxyJ0tbHgq8DAAAACBlbJB764IMP6u233/YHu0zTVM+ePXXhhRfq8ssvb9UOXtOnT/e3lyxZ0pnlAgAAAAAAAACArmj7M9KqX0orfy59NVtaMFZaMCH4aMXEwdKJ99X100+WBl5T199wtxUSOxrFdpdG/dFqV+ZIa2+z2iNvrwt21RRLn06V3syQdr5Ud+/o+6WYzPDWCwAAABxjwh7u+vbbb/Xcc8/JMAwZhqHk5GT99a9/1cKFC3X//ffruuuua9U6U6ZMkWQd5bh69Wp5PJ7OLBsAAAAAAAAAABwNCr6RVv4ieLzfJdYOX5KUNKyu7fNKsT2ksY8Efh13RfAa6SfVXe9+eqe9QkiNeUhypVjtNbdI7mKr3f/yujnbn5FyFlpHOK78hbWTmSQ54qU+F4SzWgAAAOCYE/ZjGR999FH/zlzJycl6/fXXddxxx7V5neOPP17x8fGqqKhQdXW1du7cqcGDB4e4WgAAAAAAAAAAcNQpWh885ky0dqGqfzSjJNns0pAbW7du8nDrS5LKd0mHo/zkkczTpAFXWu3cL6SdL1ptZ4oU37tuXv7Xde2aQql0u5QywuonjwhLqQAAAMCxKqw7d5WXl2vZsmX+XbtuvfXWdgW7ag0aNMjf3rlzZwgqBAAAAAAAAAAAXZbRyv/s0VQgyVsZulqinWGXJjxhtX0eaUW9AJsjPnBu7W5ejfUbzgUAAAAQUmENd61cuVIej0emaSoxMVEXXNCxrXrT0tL87dzc3I6WBwAAAAAAAAAAurLkUdL/fG3tRuVIbHxO+knSuEeCxyv2Se6SzqttwI+kK8zAr1F3dN7zWjL0Z1LKKKu97e9S0dq6a9X51nGUtWLSA++t36/iv88AAAAAnSmsxzLm5FhbGRuGoVGjRslm61i2LCEhwd8uLy/v0FoAAAAAAAAAAOAokH6SNPEl6aQaK7BUstkKbcWkS4lDpbQxjd/33XN17Z0v1h1R2JQBP5ImvhA4tuMF6aurO1K9JX2C1P+KwLHEQcHzRv4hMJBWtl3a+kTL68f2kEbdabUrc6R1twde91VLhausOiQp60xp97+tdlxvKaFeLXlftPw8AAAAAO0W1nBXYWGhv11/16328ng8/nZHg2IAAAAAAAAAAOAoYndZ4aTagFJz8pdLG+7t/JpaK2m4NOwXLc8b+OPAfs7i1oW7xj4sOZOs9ppbgo9dlKQtf5MmvWK1j79aKlwjFa2Tsv8k2ezWePFm6eBHLT8PAAAAQLuFNdwVFxfnb1dWdvzc+oKCAn87OTm5w+sBAAAAAAAAAIBjiM9t7di16iZrt6pjQfczpON+YLVzv2h6h7Jdr0rdT5cGXS/ZnNKEJwOvV+VKX1wmmb5OLRcAAAA41oU13FV/t679+/d3aC3TNLVp0yZ/Pz09vZnZAAAAAAAAAADgqFe0VvpghJR5utT9NClxiBSTIcVmSoZT8pZbxxCWbJJyP5f2zJMq9ka66vAxHNL4x622zyOtuKH5+ct/IuUslAb9REodI9njpcr90oEPpQ33WW0AAAAAnSqs4a4BAwZIsoJZW7duVWFhoVJTU9u11ooVK1RaWurvZ2dnh6RGAAAAAAAAAADQhRVvtL62P9W5z9n5YtO7XnV0flvXbi3TI304sm337P639QUAAAAgImzhfNjw4cOVmpoqwzDk8/n02muvtXutZ5991t8eMGCAMjIyQlEiAAAAAAAAAAAAAAAAAESFsIa7JOnss8+WaZoyTVPPPPOMNm/e3OY1Xn31VX3++eeSJMMwdOGFF4a4SgAAAAAAAAAAAAAAAACIrLCHu+bMmSOXyyXDMFRdXa0f/ehHWrp0aavuramp0WOPPaa7775bhmFIkhISEnTFFVd0ZskAAAAAAAAAAAAAAAAAEHaOcD+wR48emjt3rh5++GEZhqHi4mJdd911mjBhgr7//e8rPT09YH5+fr527typpUuX6t1339XBgwdlmqYka9eu2267TQkJCeF+DQAAAAAAAAAAAAAAAADoVGEPd0nS9ddfrx07duidd96RYRgyTVMrVqzQihUrAuaZpqlTTz01oC/Jf8+sWbN0wQUXhLV2AAAAAAAAAAAAAAAAAAiHsB/LWOu+++7T3Llz/ccrSlZ4yzRNGYbh/6odkxQw9//+7/902223hb1uAAAAAAAAAAAAAAAAAAiHiIW7DMPQ3Llz9a9//UuTJ0/2B7gkBQS6Go5NmDBBr776qm688cZwlwwAAAAAAAAAAAAAAAAAYRORYxnrO/HEE/X0009r7969WrZsmVauXKmDBw+qqKhIbrdbqampysjI0JgxY3TqqadqyJAhkS4ZAAAAAAAAAAAAAAAAADpdxMNdtfr27avLLrtMl112WaRLAQAAAAAAAAAAAAAAAICIi9ixjAAAAAAAAAAAAAAAAACAphHuAgAAAAAAAAAAAAAAAIAoRLgLAAAAAAAAAAAAAAAAAKIQ4S4AAAAAAAAAAAAAAAAAiEKOcD/wwIEDIVvLMAx169ZNiYmJMgwjZOsCAAAAAAAAAAAAAAAAQKSFPdx15plndkoQKzU1VSNGjNDo0aN1/vnnq2/fviF/BgAAAAAAAAAAAAAAAACES9jDXbVM0wzpegUFBVq6dKmWLl2qJ554Qmeeeab+8Ic/qHv37iF9jiSVl5dr48aNWrdundatW6f169dr//79/uu9e/fWwoUL27Tmvn37NHXq1HbVk5WVpSVLlrTrXgAAAAAAAAAAAAAAAADRKSLhrvrBrsZ28WoY/GrLHNM0ZZqmPv30U33zzTd68sknNXbs2FCUreeff15vvfWWtm/fLp/PF5I1AQAAAAAAAAAAAAAAAKAxYQ93XXjhhf4g1uLFi1VUVCTJCmU5HA4NHDhQPXv2VEJCgtxut4qLi7Vt2zbl5+dLqgtxTZo0SVlZWaqqqlJJSYm+++47HTp0yD/HNE0VFRXphhtu0BtvvBGSYxpXrFihrVu3dnid1rDZbK0+vtLhiNgGbAAAAAAAAAAAAAAAAAA6SdhTQffff78qKyt1++23q7CwUJI0ePBgXXfddTr77LMVGxvb6H3bt2/X66+/rtdff11er1ebN2/Wtddeq0mTJvnn7Nu3T6+99ppefvlleTwef8Drnnvu0VNPPdUp7xMfH68RI0Zow4YNqqioCNm699xzjy666KKQrQcAAAAAAAAAAAAAAACga7FF4qG//vWv9cEHH8gwDF1xxRV6++23df755zcZ7JKkQYMG6bbbbtO8efOUnp6ugoIC/eQnP9H69ev9c/r06aPf/OY3+s9//qOUlBT/zlefffZZSHbciomJUXZ2tmbNmqX77rtP77//vlauXKlXXnlFqampHV4fAAAAAAAAAAAAAAAAAGqFfeeuf/3rX/r0009lGIbOOuss/eEPf2jT/cOGDdM//vEPXXLJJXK73frlL3+pDz/8UC6Xyz/nhBNO0MMPP6yrr77aH/D69NNPNWTIkA7V/sgjj3TofgAAAAAAAAAAAAAAAABorbDv3PXiiy9KkgzD0G9/+9t2rTFs2DBdcMEFkqT9+/frk08+CZozceJEnXzyyTJNU5L0zTfftLNiAAAAAAAAAAAAAAAAAAi/sIa7NmzYoF27dskwDA0bNkw9e/Zs91pnnXWWv/3ee++1OGf37t3tfhYAAAAAAAAAAAAAAAAAhFtYw127du3yt/v27duhtfr06eNv79y5s9E5w4cPlySZpqni4uIOPQ8AAAAAAAAAAAAAAAAAwims4a6cnBx/u/a4xI4yTVOHDx9u9FpKSoq/XVFREZLnAQAAAAAAAAAAAAAAAEA4OML5MJutLku2b9++Dq1V//766zbF5XJ16Hnh9tFHH+nDDz/Ud999p8LCQsXExCglJUWDBw/WhAkTNGPGDHXv3j3SZQIAAAAAAAAAAAAAAADoJGENd9WGkUzT1ObNm3Xw4EH17NmzXWt98skn/nZmZmajcwoLCyVJhmEoKSmpXc+JlMWLFwf0KysrVVRUpF27dunjjz/WQw89pEsuuUS//vWv1a1bt8gUCQAAAAAAAAAAAAAAAKDThPVYxtGjR0uywlamaeqBBx5o1zpbtmzRO++8I8MwZBiGf92Gtm7d6m+3N0QWrWpqavTaa6/pkksu0e7duyNdDgAAAAAAAAAAAAAAAIAQC+vOXb169dKoUaP07bffyjRNLViwQN27d9ctt9zSqqMVJWn79u26/vrr5fV6ZZqmDMPQjBkzGp37zTff+NuDBg0KyTt0tqFDh2ratGkaN26cBg0apJSUFJmmqby8PK1du1bvvPOOlixZ4p+/Y8cOXXfddfr3v/+t1NTUdj/X6XSGonx0mqpIFwBEBbvdHukSAAAt4nMLACAQP3MAgOjGz1uAQHx2AYDoxmcXoA6fW44dYd25S5J+9atf+UNZpmnqpZde0kUXXaQPP/xQNTU1Td63Y8cO/fnPf9ZFF12knJwcSdYOYOPHj9fpp58eNL+8vFwLFy6UYRiSpDFjxnTOC4VISkqKXn/9db377rv62c9+pu9973vKyspSTEyMYmNj1adPH51zzjl69tln9cwzzyg5Odl/7+7du3X33XdHsHoAAAAAAAAAAAAAAAAAoRbWnbsk6ZRTTtFVV12ll156yR/w2rx5s371q1/J4XBo0KBB6tWrl7p16ya3262SkhJt2bJF+fn5khQQDEtPT9c999zT6HP+9a9/qbKyUpJks9l02mmnhe0d2yMhIaHVAbTJkyfrySef1I9+9CN5PB5J0gcffKCf/OQnGjJkSLue73a723UfAIST1+vl7ysAAACgi+EzPAAA6Er47AIAALoKPrccO8Ie7pKk3/3ud/J6vXr11Vf9O2uZpim3261NmzZp8+bNAfNN05Rk7dRVG+zKzMzUP/7xD/Xr16/RZ8TFxWnu3LmSpOTkZHXv3r0T3yj8xo8fr4svvlj//ve/JVnfo//+97/tDncBAAAAAAAAAAAAAAAAiC4RCXdJ0u23365JkybpT3/6kw4dOiRJ/qBXQ/UDYDabTeedd55+//vfBxxN2NCsWbNCX3SUueSSS/zhLkn68ssv9fOf/zyCFQEAAAAAAAAAAAAAAAAIlYiFuyRp6tSpOuOMM/T//t//04cffqjVq1crLy8vaJ7D4dDQoUM1adIk/e///q/69u0bgWqjz8iRI+V0Ov1b7R04cCDCFQEAAAAAAAAAAAAAAAAIlYiGuyTJbrdrxowZmjFjhiQpPz9fhYWFKikpkcvl8h+pGBMTE+FKo4/NZlNKSopyc3MlSYWFhRGuCAAAAAAAAAAAAAAAAECoRDzc1VB6errS09MjXUaXUVVV5W8TgAMAAAAAAAAAAAAAAACOHrZIF4D2KywsVGlpqb9PKA4AAAAAAAAAAAAAAAA4ehDu6sKWLFkS0B82bFiEKgEAAAAAAAAAAAAAAAAQaoS7uii3261nn302YOzUU0+NUDUAAAAAAAAAAAAAAAAAQo1wVxTIyclRUVFRq+f7fD794Q9/0LZt2/xjmZmZOuecczqhOgAAAAAAAAAAAAAAAACR4AjlYldddVUol2s1wzD04osvRuTZobB+/Xr95je/0WWXXabzzjtPw4cPb3Lu5s2bde+99+rrr78OGL/pppsUHx/f2aUCAAAAAAAAAAAAAAAACJOQhruWL18uwzBCuWSLTNMM2zP379+vs846q9FrXq83YF5TAa0XXnhBJ510UtB4eXm5nnvuOT333HPKzMzU8OHD1bdvXyUmJso0TRUUFGjt2rXasmVL0L3XXHONLrroona+FQAAAAAAAAAAAAAAAIBoFNJwV1uZpulvtzag1Z57QsU0zYAQV3Oamle//qbk5ubqs88+a3FebGysbr31Vv3gBz9oVU0AAAAAAAAAAAAAAAAAuo6Qh7taE16qrzag1dr76s9v67Oi1dChQ3XFFVdo+fLl2rFjh3w+X7PzMzMzNXPmTM2aNUs9evQIU5UAAAAAAAAAAAAAAAAAwimk4a7Nmze3eu6CBQt0xx13qKSkRKZp6oQTTtB5552n7OxsDRgwQImJiZKk0tJS7dy5U+vWrdN7772nTZs2yTAMJScn684779SMGTNC+QrN6tOnT6PHInZU3759dccdd0iSKioqtH37du3bt095eXmqqKiQYRhKTExUWlqaRowYob59+4a8BgAAAAAAAAAAAAAAAADRJSLHMr722mv605/+JElKS0vTHXfcobPPPrvRuenp6UpPT9f48eP14x//WB999JHuuusuFRQU6KabblJ+fr5++MMfhrP8ThUfH6/s7GxlZ2dHuhQAAAAAAAAAAAAAAAAAEWQL9wPXrVunu+++W6ZpKj09Xa+88kqTwa7G/M///I9eeeUVpaWlyTRN3XfffVqzZk3nFQwAAAAAAAAAAAAAAAAAERD2cNcTTzwhn88nwzD0u9/9TgMGDGjzGgMGDNDvf/97SZLP59MTTzwR6jIBAAAAAAAAAAAAAAAAIKLCGu7Ky8vT0qVLZRiG0tPTNWPGjHavNX36dKWnp8s0TX355ZfKy8sLYaUAAAAAAAAAAAAAAAAAEFlhDXetXbtWXq9XkjR8+HAZhtHutWw2m0aOHClJ8nq9HM0IAAAAAAAAAAAAAAAA4KjiCOfDcnJy/O2kpKQOr5eQkOBvHz58uMPrAQAAAACAJiQNk3pMldJPlpKGSvH9JWeSZNgld7FUulXKXSrteF4q2dL6dVOypf6XST2mSfF9JVe6tV7VYal8l3R4sXToE6lwTcfqzzjFqj39JOtdYtIlV5pkj5U85VLlAal4o3TwI2n365KnrOm17PHS0J9L/S6VEgdLhs2qdd+70qYHpZqCZgoxpP/5WkqfIPm80kfjO/5uAAAAAAAAAI5aYQ13lZeX+9uhOEYxPz+/0bUBAAAAAEAInfK8dPzspq/bM6XYTCnze9KwX0tbH5VW3yyZ3qbvcSZL4x+TjptlhaMaWy9lhNT7HKkqT3ors2PvcMZ/JVdK49dcKdZX8nCp3yXSifdJX18j7X+3kbmp0tTFUmp24HjycOvruCukT86Qync2/qxB11vBLkna/hTBLgAAAAAAAADNCuuxjBkZGZIk0zS1du1aVVVVtXutqqoqrV271t9PT0/vcH0AAAAAAKARzpTWz7XZpWG/lE7+R9Nz4npKZ30uDbgyONgVDWIzpNPelDImBl+b8GRdsKvyoLTkQunTaVLxZmusWz9p0iuNrxuTLp14j9WuOiytvS3kpQMAAAAAAAA4uoT1J6gDBw6UJBmGoaqqKr300kvtXuull15SZWWlvz9o0KAO1wcAAAAAAFpg+qT8b6QdL0o7XpCK1jc+7/jZUtaU4HHDJp36hpQyKvhawSrpu+elrU9Iu/9dF5gKpbId0r750ranpW1PSfvflzwVwfNsDmnE7wPHYjKlvpfW9Vf9ylor51Np+XV145mTpNSxwWueeL8V8JKkNbdI7qIOvw4AAAAAAACAo1tYj2XMzs5Wz549dejQIZmmqSeeeEJDhw7V5MmT27TO4sWL9fjjj8swDJmmqR49eig7O7vlGwEAAAAAQPvUFEvbnpS2Pi5VHgi8NuRGafzjwfccd6WUsyhwbOjPreMb6yvdLi27QspfEbxGfF+pzwUdq12S1vxWOrxIKtkSfM2ZIk35MHinroxTAvvpE6ydyWrlfFrXzl0qeaske+yRe0+WClfVu/dkaeCPj8xdZgXjAAAAAAAAAKAFYT/74JprrpFpmjIMQ9XV1brxxhv14IMPqqSkpMV7S0pK9MADD2ju3Llyu93+da699towVA4AAAAAwDFq75vS+0Oktb8LDnZJ1k5b++YHj6c2+EUswy4N/WXgmKdcWnhW48EuSarYawXKOmr7U40HuyRrB61NfwketzkD+66UwH51QdN9V2q9C4Y04Qlr1zKfR/rmxlYWDQAAAAAAAOBYF9aduyRp1qxZ+uCDD7R69WoZhiGPx6PnnntOr7zyik499VSdeOKJ6t+/vxISEmQYhkpLS7V7926tWbNGS5cuDQh1SdKYMWM0a9ascL8GAAAAAADHjl2vtDzn8GfBO2zZ4wL7PaZJ3foGjn33T6l8V4fKC4luxwWPlW4L7NcUBfZj0qWqnHr9tHpzC+vag+dIaeOs9vanpMI1HSgUAAAAAAAAwLEk7OEuwzD0zDPPaPbs2dqwYYP/aMXq6motXLhQCxcubPJe0zT9a5imqREjRuiZZ54JV+kAAAAAAKAphj14rHxPYD/ztOA5hz6Rjv+xNOCHUsqJkiNBqsmXCr6Rdv1L2vNvyfR1Ts02p3XsY+/zpBPvDr6+7e+B/fwVks9bdzRj1pnS7n9Z7YxJdUcySlLe19afMZl1a1cdltbeFtp3AAAAAAAAAHBUC3u4S5ISExP1yiuv6IEHHtDrr78uSf6duGoDXA0ZhuEPdUnSFVdcoZtvvllxcXGNzgcAAAAAAGHU+4LgsYMfBfbTxgTPGf+41K1f4FhcTytw1fs8aej/SZ9fLFUeDE2d/S+Tvvd6y/O2PCbteD5wrDpX2vMf6bgfWP2xf7GOlfSUSeMfq5uXu0wqXGW1xzxQd0Tjmlskd3HH3wEAAAAAAADAMSMi4S5JiouL0x133KGLL75YL730kj766CNVV1c3Od80TcXExGj69Om68sorNXLkyDBWCwAAAAAAmjTgKqn7qYFj1QXSjhcCx2Iyg+9tGOxqKGOiNOUj6f9NtIJUna1glbTiBin/68avfzNXShkppYyyQmiT5wdeL98jLfuh1c6YaH1vJCvwVf/7EZslpZ4o2eOlyv3Wc01vyF8HAAAAAAAAQNcWsXBXrZEjR+qBBx7Q3XffrfXr1+vbb79Vfn6+iout32RNTk5Wenq6Ro4cqVGjRsnlckW4YgAAAAAA4NfzbOmkZwLHTJ+0/DrJXRQ47kppep3DS6SSLVZoKuOUwGspo6Tht0jr/hCKipuXNlY6+Vlp5S+knIXB12sKpI9OkYb9Qup3qZQ4WJJNKt8l7ZsvbX5Iqs6XDJs04UnrT5/HCoxJVqhrwlNS3wsD163MkdbeGhyIAwAAAAAAAHBMi3i4q5bL5dK4ceM0bty4SJcCAAAAAABao+8l0qRXJHtM4Pjq30h73wqe761pfJ3VN0ubHqrrj77fCnPVN/C60IS7SrZIm/9qte1xUsJxUuapkqNb3ZyUUdKUBdKXV0q7/x28hrdC2nCv9dWUIXOl1NFWe/tTUtFayZksnfWFlDgweH5clnTK85IjUdr6WPB1AAAAAAAAAMckW6QLAAAAAAAAXdCgn0jfez042LXmVmnzXxq/x1MSPFZTKG35W+DYt3cHB8HiekgJx7e/3lqFa6RVv7S+VvxUWjRdert3cIjL5pQmPG2Frdoqtrs06o9WuzJHWnub1R55e12wq6ZY+nSq9GaGtPOluntH39/48ZUAAAAAAAAAjkmEuwAAAAAAQNuM+J100lOSzV435vNKy+dIG//c9H3lu4PHynZKPnfgmKdMqtwfPDe2e/vqbYm7WPryKus4xfpcyVKv6W1fb8xDdUdQrrnFWl+S+l9eN2f7M9axj9X51hGQ3mpr3BEv9bmg7c8EAAAAAAAAcFQi3AUAAAAAAFpv7MPSifcEjnmrpS8ut44fbE7BykYGzdY/21Pe+rlt5auRSrcGj7d1t7DM06QBV1rt3C+knS9abWeKFN+7bl7+13XtmkKpdHtdP3lE254JAAAAAAAA4KhFuAsAAAAAALTMsEsTX5SG/TJw3F0iLZ4h7Z3X8hoHPwoeSzjeOgKxPkeCFNc7cMznkcp2talkP6MVP/6wuaSEQcHj3so2PMcuTXjCavs80oob66454gPn1u7m1Vi/4VwAAAAAAAAAxyxHuB/4zjvvdMq6F154YaesCwAAAADAMc8eK33v31Kf8wPHK3OsYFfh6tatU7Reyv9GSh9fN+ZKlYb+XNr0UN3YiN9LdlfgvblLJU9p4NiAH0kTXwgcW3+ntP6PgWNTPpLyvpR2vtL47lyuNGnc36TYzEZq3tDSW9UZ+jMpZZTV3vZ3qWht3bXqfOvoytqjLGPSA++t36/Kbf0zAQAAAAAAABzVwh7uuvXWW2UYRsjXJdwFAAAAAEAnOfkfwcEuSSpYLg24yvpqyqoGO32tvlmatihwbMyDUu/zpJItVjgq45TgdTbc2/a6a8VkSCNvt77KdlphtKocyRYjxfexjlJ0xAXfV7ZTOrwoeLwxsT2kUXda7cocad3tgdd91VLhKil9gtXPOlPa/W+rHdc7cNewvC/a9HoAAAAAAAAAjl5hD3fVMk2zw2sYhiHTNDslLAYAAAAAAI5oeERird7ntXxvw3DX4cVWUGvE7wLHu59ufTVm01+kQx+3/KzWSBhgfbXEUy59eZVk+lq37tiHJWeS1V5zS/Cxi5K05W/SpFes9vFXS4VrpKJ1Uvaf6nb0Kt7c+PGVAAAAAAAAAI5JEQl3tTfYVT/EZZpmSAJiAAAAAAAgzNb+XnKXSCPvaHzHrFreGuuYxY33ha00SdbRkct/Yu201Rrdz5CO+4HVzv1C2vli4/N2vWoF2AZdL9mc0oQnA69X5UpfXNb6QBkAAAAAAACAo17Yw1333de2H8h6vV6VlJRo27ZtWrZsmXJycmQYhpKTkzV37lwlJCR0UqUAAAAAAKDTbPyzdSzhwGukXjOk+P6SK1mqKZbKtkmHFkrbn5Yq9nb8WQunWUcvdj9dShsnxWZZRzU6kyVvleQukkq3SQWrpP3vSoeXtH5twyGNf9xq+zzSihuan7/8J1LOQmnQT6TUMZI9XqrcLx34UNpwn9UGAAAAAAAAgCMMswttf+XxeDRv3jz9+c9/VlVVlQYNGqTnnntOmZmZkS6tyyssLIx0CWjGosNVkS4BiApn906U2+2OdBkAgGbwuQWoc9HKXpEuAYgKhTMKIl0CAKAZTqdTCe8mRroMIGrw2QUAohufXYA6fG6JXqmpqSFdzxbS1TqZw+HQ5Zdfrueff14ul0vbt2/XnDlz+A/9AAAAAAAAAAAAAAAAAI46XSrcVWv06NGaO3euTNPUhg0b9Pzzz0e6JAAAAAAAAAAAAAAAAAAIqS4Z7pKkWbNmyeVySZJef/31CFcDAAAAAAAAAAAAAAAAAKHVZcNd8fHxGjVqlEzT1MGDB7VmzZpIlwQAAAAAAAAAAAAAAAAAIdNlw12SlJWV5W/v3r07gpUAAAAAAAAAAAAAAAAAQGh16XCX1+v1t3NzcyNYCQAAAAAAAAAAAAAAAACEVpcOd+3YscPfdjqdEawEAAAAAAAAAAAAAAAAAEKry4a71q9fr23btvn7mZmZEawGAAAAAAAAAAAAAAAAAEKrS4a7cnJy9Otf/1qGYfjHxo8fH8GKAAAAAAAAAAAAAAAAACC0HJEuoDV8Pp9KSkq0fft2LVq0SP/+979VXl4u0zRlGIYmTJig7t27R7pMAAAAAAAAAAAAAAAAAAiZsIe7TjjhhA6vURvqkiSHw6Gbb765w2sCAAAAAAAAAAAAAAAAQDQJe7jLNM0O3W8YhgzDkGmacjgcuu+++zRq1KgQVQcAAAAAAAAAAAAAAAAA0cEWiYfW7rrVHqZpyjRNnXzyyZo3b57OPffcEFYGAAAAAAAAAAAAAAAAANEh7Dt3TZgwoc33OBwOJSQkKC0tTSNGjNApp5yifv36dUJ1AAAAAAAAAAAAAAAAABAdwh7uevnll8P9SAAAAAAAAAAAAAAAAADociJyLCMAAAAAAAAAAAAAAAAAoHmEuwAAAAAAAAAAAAAAAAAgChHuAgAAAAAAAAAAAAAAAIAoRLgLAAAAAAAAAAAAAAAAAKIQ4S4AAAAAAAAAAAAAAAAAiEKOSBfQlMLCQi1YsEDr1q1TXl6eYmNjlZWVpUmTJunUU0+Vy+WKdIkAAAAAAAAAAAAAAAAA0GnCEu4qKCjQli1bVFhYKJfLpT59+mjIkCGy2YI3DjNNU08//bSefvppVVVVBV1/9dVX1atXL91xxx06/fTTw1E+AAAAAAAAAAAAAAAAAIRdp4a7Nm3apAceeEDLly+Xz+cLuJaenq6rrrpK11xzjex2uyQr2HXLLbfovffek2ma/rmGYfivS9L+/fs1Z84c3XfffTr//PM78xUAAAAAAAAAAAAAAAAAICKCt84KkQ8++ECXXnqpvvrqK3m9XpmmGfCVl5enRx55RHPmzPEHv1588UW9++67kqxAV/1Ql2ma/jHDMOT1evW73/1O27dv76xXAAAAAAAAAAAAAAAAAICI6ZRw17p16/Sb3/xGHo8nIJRVq7ZvmqY+//xzPffccyorK9Pjjz8eEOjKzMzUGWecoXPPPVennXaakpKSAnb08ng8uueeezrjFQAAAAAAAAAAAAAAAAAgojrlWMY777xTXq83IKiVnJysfv36yTRN7d27V8XFxf6A1wsvvKDExESVlZXJMAylpKTonnvu0Zlnnhmwrs/n07x583Tvvfequrpapmnqq6++0p49e9SvX7/OeBUAAAAAAAAAAAAAAAAAiIiQh7vWrl2rjRs3+oNbaWlpuuuuuzR16tSAsNenn36qO++8U/n5+crPz9fzzz9vFeRw6Pnnn9ewYcOC1rbZbPrf//1fpaen68Ybb/Svt2DBAl1//fWhfhUAAAAAAAAAAAAAAAAAiJiQH8v40UcfSbICXA6HQ88995ymTZsWdCzjtGnT9I9//EN2u12StGvXLhmGofPPP7/RYFd9U6dO1cknn+w/ovHbb78N9WsAAAAAAAAAAAAAAAAAQESFPNy1ceNGSVaAa8aMGc0GtYYNG6ZzzjnHH9KSpLPPPrtVz6k/b9u2be2sFgAAAAAAAAAAAAAAAACiU8iPZdy1a5e/PWXKlBbnT548WfPnz/f3W9q1q+E80zRVVFTUphoBAAAAAAAAYNHhqkiXAESBKl0U6RIAAAAAAE0K+c5dpaWl/vaAAQNanN9wTkpKSquek5qa6m+XlZW1rjgAAAAAAAAAAAAAAAAA6CJCHu6qqKjwtxMSElqc33BOTExMq55Tf57H42lldQAAAAAAAAAAAAAAAADQNYQ83GWapr9tt9tbLsAW8hIAAAAAAAAAAAAAAAAAoMsjWQUAAAAAAAAAAAAAAAAAUcgR6QLQvD179mjjxo06dOiQfD6fsrKyNHjwYA0ZMiTSpQEAAAAAAAAAAAAAAADoRIS72qG8vFwbN27UunXrtG7dOq1fv1779+/3X+/du7cWLlzYoWd89tln+vvf/67Vq1c3en3o0KG69tprdf7553foOQAAAAAAAAAAAAAAAACiE+GuNnj++ef11ltvafv27fL5fJ3yDNM0de+99+qll15qdt6WLVt08803a/Hixbr//vvlcrk6pR4AAAAAAAAAAAAAAAAAkdEp4S7DMCRJa9asCdjRqjG5ubkB/W+++Uamabb4jIb3hcOKFSu0devWTn3GX/7yl6Bg19ixYzVq1CjZ7XZt2bJFy5Yt83+PPvjgA9ntdj344IOdWhcAAAAAAAAAAAAAAACA8Oq0nbtM09SvfvWrNt9z5ZVXtnq+YRitCoJ1pvj4eI0YMUIbNmxQRUVFh9ZatGiRnn32WX8/KSlJjz76qCZOnBgwb+PGjZozZ44OHTokSXr33Xc1btw4XX755R16PgAAAAAAAAAAAAAAAIDo0WnhrrYEr2p3+pLUprBW/fvCISYmRtnZ2Ro1apRGjhypUaNGaeDAgbLZbDrzzDM7FO4yTVMPP/ywv28Yhp588klNmDAhaO7w4cP1wgsv6IILLlB1dbUk6fHHH9eFF16o2NjYdtcAAAAAAAAAAAAAAAAAIHp0WrhLal/4KtyBrbZ45JFHOm3tTz75JODIxwsuuKDRYFetAQMG6JprrtGTTz4pyTqm8o033mjTzmcAAAAAAAAAAAAAAAAAolfIw129evUK9ZLHhAULFgT0Z82a1eI9l19+uZ5++ml5vV7/GoS7AAAAAAAAAAAAAAAAgKNDyMNdCxcuDPWSRz2Px6MlS5b4+z179lR2dnaL92VlZWn06NFauXKlJGn16tUqKChQWlpap9UKAAAAAAAAAAAAILwWHa6KdAlAlKjSRZEuAQDCzBbpAiBt3bpVJSUl/v6YMWNafW/9uV6vV6tWrQppbQAAAAAAAAAAAAAAAAAig3BXFPjuu+8C+ieccEKr7x0+fHhAf8eOHSGpCQAAAAAAAAAAAAAAAEBkEe6KAg0DWb169Wr1vT179mx2LQAAAAAAAAAAAAAAAABdE+GuKJCTkxPQ79GjR6vvbTj30KFDIakJAAAAAAAAAAAAAAAAQGQR7ooCFRUVAf1u3bq1+t6GcxuuBQAAAAAAAAAAAAAAAKBrckS6AAQHslwuV6vvjYmJaXat1nI6ne26D+FSFekCgKhgt9sjXQIAoEV8bgEABOJnDohufHYBAATiswuiF59bAACB+Nxy7GDnrihQXV0d0G9LuKvh3KoqPtgBAAAAAAAAAAAAAAAARwN27ooCDXffqqmpafW9DefGxsa2qwa3292u+wAgnLxeL39fAQAAAF0Mn+EBAEBXwmcXAADQVfC55djBzl1RID4+PqDflnBXw12/Gq4FAAAAAAAAAAAAAAAAoGsi3BUFGgayysvLW31vw7mEuwAAAAAAAAAAAAAAAICjA+GuKJCVlRXQP3ToUKvvPXjwYEC/R48eIakJAAAAAAAAAAAAAAAAQGQR7ooCAwcODOgfOHCg1fc2DIIdf/zxIakJAAAAAAAAAAAAAAAAQGQR7ooCDcNdGzdubPW9GzZsCOgT7gIAAAAAAAAAAAAAAACODoS7osDgwYOVlJTk769Zs6bV965evdrfttvtGjt2bChLAwAAAAAAAAAAAAAAABAhhLuigMPh0Omnn+7vHzx4UGvXrm3xvpycnIB5Y8aMUVpaWqfUCAAAAAAAAAAAAAAAACC8CHdFiRkzZgT0X3vttRbvef311+X1ev396dOnh7wuAAAAAAAAAAAAAAAAAJFBuCtKTJ06VUOGDPH358+frxUrVjQ5f+fOnfrnP//p72dmZurSSy/t1BoBAAAAAAAAAAAAAAAAhA/hrihhGIZuuukmf980Td1www368ssvg+Zu3LhRs2fPVnV1tX9s7ty5io2NDUutAAAAAAAAAAAAAAAAADqfI9IFdCX79+/XWWed1ei1+scj7t+/X8OHD2903gsvvKCTTjqp0WtTpkzRddddp2effVaSVFJSotmzZ2vs2LHKzs6WzWbTli1btGzZMpmm6b/v/PPP1+WXX97e1wIAAAAAAAAAAAAAAAAQhQh3tYFpmgEhruY0Na9+KKsxN910k6qqqvTyyy/7x1atWqVVq1Y1Ov/73/++7r777lbVBAAAAAAAAAAAAAAAAKDr4FjGKGOz2XTbbbfpmWee0ejRo5ucN2TIED3wwAN65JFHFBMTE74CAQAAAAAAAAAAAAAAAIQFO3e1QZ8+fbRly5awPGvy5MmaPHmydu/erQ0bNujw4cPyer3KysrS4MGDNXTo0LDUAQAAAAAAAAAAAAAAACAyCHdFuf79+6t///6RLgMAAAAAAAAAAAAAAABAmHEsIwAAAAAAAAAAAAAAAABEIcJdAAAAAAAAAAAAAAAAABCFCHcBAAAAAAAAAAAAAAAAQBQi3AUAAAAAAAAAAAAAAAAAUYhwFwAAAAAAAAAAAAAAAABEIcJdAAAAAAAAAAAAAAAAABCFCHcBAAAAAAAAAAAAAAAAQBQi3AUAAAAAAAAAAAAAAAAAUYhwFwAAAAAAAAAAAAAAAABEIcJdAAAAAAAAAAAAAAAAABCFCHcBAAAAAAAAAAAAAAAAQBQi3AUAAAAAAAAAAAAAAAAAUYhwFwAAAAAAAAAAAAAAAABEIcJdAAAAAAAAAAAAAAAAABCFCHcBAAAAAAAAAAAAAAAAQBQi3AUAAAAAAAAAAAAAAAAAUYhwFwAAAAAAAAAAAAAAAABEIcJdAAAAAAAAAAAAAAAAABCFCHcBAAAAAAAAAAAAAAAAQBQi3AUAAAAAAAAAAAAAAAAAUYhwFwAAAAAAAAAAAAAAAABEIcJdAAAAAAAAAAAAAAAAABCFCHcBAAAAAAAAAAAAAAAAQBQi3AUAAAAAAAAAAAAAAAAAUYhwFwAAAAAAAAAAAAAAAABEIcJdAAAAAAAAAAAAAAAAABCFCHcBAAAAAAAAAAAAAAAAQBQi3AUAAAAAAAAAAAAAAAAAUYhwFwAAAAAAAAAAAAAAAABEIcJdAAAAAAAAAAAAAAAAABCFCHcBAAAAAAAAAAAAAAAAQBQi3AUAAAAAAAAAAAAAAAAAUYhwFwAAAAAAAAAAAAAAAABEIcJdAAAAAAAAAAAAAAAAABCFCHcBAAAAAAAAAAAAAAAAQBQi3AUAAAAAAAAAAAAAAAAAUYhwFwAAAAAAAAAAAAAAAABEIcJdAAAAAAAAAAAAAAAAABCFCHcBAAAAAAAAAAAAAAAAQBQi3AUAAAAAAAAAAAAAAAAAUYhwFwAAAAAAAAAAAAAAAABEIcJdAAAAAAAAAAAAAAAAABCFCHcBAAAAAAAAAAAAAAAAQBQi3AUAAAAAAAAAAAAAAAAAUYhwFwAAAAAAAAAAAAAAAABEIcJdAAAAAAAAAAAAAAAAABCFCHcBAAAAAAAAAAAAAAAAQBQi3AUAAAAAAAAAAAAAAAAAUYhwFwAAAAAAAAAAAAAAAABEIcJdAAAAAAAAAAAAAAAAABCFCHcBAAAAAAAAAAAAAAAAQBQi3AUAAAAAAAAAAAAAAAAAUYhwFwAAAAAAAAAAAAAAAABEIUekCwAAAAAAAAAAdJzTZig11qm0OJfSYp1KjXUpxhH4+725FdX6fG9Bs+sYkpJjHEqtt06iyy7DMALmLfjusCo83pDVH8rn2g1Dg1Lj1TsxTgkuuyRDFW6PDpZVa1tBmWp8ZrP3T+mXrtQ4l0zT1MLdeSqu9nTw7QAAAAAAaB/CXQAAAAAAAABwFDizf4a6uTr+I9+h6QkanpEYgooi81ynzdDp/dKVHOMMGE+KcSopxqm+SXFasjdfFe7GA2IDkuOVGueSJO0oqiDYBQAAAACIKI5lBAAAAAAAAICjgdHylDAuE7Hnjs5K9ge7qjxefbm/QJ/vzVfpkZBWvNOuCT1TGr3XZTc0PDPRf+/GvNIQVQUAAAAAQPsQ7vr/7d13fFRV/v/x96RMek8o0kNvUgQUdUFsWFhXULGwFDsConxxXXQVEUVxRcAfRezYdVlAVwFRBAtSpIPSa2iBhNRJnSTz+2Pkkps6IWWG5PV8PPLwnjPnnvuZSTCfyXzuOQAAAAAAAABQyxQ4HErLsVfJXJn2fOXmF1TJXNV9XT9vLzUO8Tfa20+n6aQtRwmZudp8KsXojwqwKtyv+CpnnaJD5eft/LP5HwnpspezfSMAAAAAANWNbRkBAAAAAAAAoBaIt+Uo056ppOxcJWfb5e/trRta1qvwPOm5edpzxqbk7FwlZdmVnV+gvzSJVEygXzVEXbXXjfD3lcVybg2whMxc4/hMll35BQ55ezkfjwywKqXQlosR/r5qFhbw59hcHUnLquxTAgAAAACg0ijuAgAAAAAAAIBaYNvptCqZ51h6to6lZ1fJXDV9XV8v8+aORVf+yi0oUICX959jzRtbdK0fJovFogKHQ1tPpVYqDgAAAAAAqgrFXQAAAAAAAACAWqHoNopWby/lFCrwshYq6LIXnOuPDQ9UhL+vJOlQSqZSC63oBQAAqoevl0UR/r6KDLAq0t9XEf5W+fmYi68TMnP0y9Ekl+eMCbSqSUiAogKt8vf2ksUiZecVKDnbruPpWTphy6nqp1HMxfVC1SoiqFj/kdRMbYovuYDc22JRq4hANQoJULDVW5JFmfY8nbTlaF+STbnlbBXdr2mUIgKscjgcWnkkkVwGAGoZirsAAAAAAAAAALVCcrZdDofD2JoxJtBqrAYWGeBrbMkoSUlZzi0b/by91CE6RJKUnZevnYnpNRw1AAB109XNohVkrZqPq329LLqkQbguCvEv9liw1UvBVh81CQ1QYmaufjuZrOy8ghJmqbwIf1+1DA+s0Dm+Xhb1aRqlMD9fU3+on69C/XzVJDRAPx89o0x7fonntwgLVESAVZJ0kCJ1AKiVvMofAgAAAAAAAACA58vJLzBt7XhxvVA1DPZTTKBV3euHGf1nsnKV8ucHn51iQmT1dv6p/I+E9GKrfwEAgGpiKX+IK3wsFl3ZOLLEwq6iogOt6tMkSn7eVf8xuUVS9wZhRpG5q7rWDzMKu7Lz8rX2eJJ+OXpG6X/mKoG+3urZMLzEc63eFnWIoUgdAGo7Vu7yQFdffbWOHz9+Xud+9913atasWRVHBAAAAAAAAAAXhm2nUhXq56MwP1/5+3ird6NI0+OZ9nxtOJkiSYr091XT0ABJzoKvI2lZxjg/by+F+fnIx8uirLwCpWTbRdkXAADVo8DhkC03T6FFVq9yRceYEGPlqrPyCgp00pajAodDDYL8Tds9Blt91LV+mNafSK503IW1jQoutvpWefy8vdS4UFHa9tNpOvnn1pGbT6Wob9NoSVJUgFXhfj5GcfpZnaJDjUI1itQBoPaiuAsAAAAAAAAAUGvkFjj045EzahURpEYh/gq2essiizLs+Tppy9a+ZJty850ffHat71xdo8Dh0NZTqZKcH7J2qx9WbPWP7Lx8/ZGQbioAAwAA5y/elqNMe6aSsnOVnG2Xv7e3bmhZr0JzBPl6K7bINoh5BQVadeSM0nOdhVD+3unq1yxaAb7exphGIf6KCvDVmSx75Z+IpGCrt9pGBhvtnLwCU0FZaSL8fU0rfSVk5hrHZ7Lsyi9wGNtKRwZYTcVdEf6+ahZWcpE6AKB2objLw1ksFnl5ub4saEWX+QQAAAAAAACA2ibf4dCeJJv2JNlKHdMyPFDh/s7VNQ6lZCo1J0++Xhb1bRqlYGvxP537+3jrkobh8vGy6EBKZrXFDgBAXbHtdFql52gaGlDs89HDqVlGYZckZecXaF9yhi6uF2oa1zwsUGeyUisdgyR1rx9uFGFl5+Vrb1Lx65XE18sce25+gbldUKAAL+8/x5o/My6pSB0AUDtR3OXhbr31Vk2dOtXdYQAAAAAAAABAreHn7aX20SGSnB/A7kxMlyS1iwo2Crvs+QVadyJZqTl2dY4JVbMw56ogHWNCdSw9WzlFPnwFAAA1r0GwX7G+hIyc4n2ZxfsaBBU/93y0CA9UdOC5bSG3n06Tl4sLchTdRtHq7WXKMayFCrrsBef6Y8MDFVGkSB0AUHu5viQUAAAAAAAAAAC1QOeYEFm9nX8e/z0h3fhgtXFIgDHmUGqmEjJzlZvv0PbTacr/c4yPl0UNS/ggGQAA1CyLpFCrb7H+wqt2ldXn5+OtABe2TiyLv7eXOv1ZMC5JJ23ZOpae7fL5ydl2ORznCrxiChWJRQb4GquBSVJSlnPLRj9vL3UooUgdAFB7UdwFAAAAAAAAAKgzogKsavrnKlxnsnIVl5YlybktUoCvtzEuKctuHNsLHMqwn/tQuKQPkgEAQM0K9PU2FT+dVdLqmgUO56qcRQX5Vm6jq671w+T7Z8G4Pb+gwtsj5uQXmIrBLq4XqobBfooJtKp7/TCj/0xWrlL+XJ2rU6Ei9T8KFakDAGovtmUEAAAAAAAAANQJFkld64dKkgocDtMHsEU/HM4rMH8AXPiD05I+SAYAADXLt5Tfx3mlFDvlOxwqWp7t633+v9MvCvbXRSH+RvuPxHRl5VV82+Ztp1IV6uejMD9f+ft4q3ejSNPjmfZ8bTiZIkmK9PdV01DnSqNnsnJ15M8idcm5oleYn498vCzKyitQSrZdlH0BQO1AcRcAAAAAAAAAoFo0DQ1Qj4bhpr5dienadcbmlnhaRgQpzM/5se6hlEyl5pxbjSs3v0AOh0MWi/ND3rMrYpxVuF3SiiAAAKBmlVZsXVpBU0k1Xz5e57fRla+XxSgYl5yFVgdTMs9rrtwCh348ckatIoLUKMRfwVZvWWRRhj1fJ23Z2pdsU26+M/iu9cNksVhMRep+3l7qVj/MVGgmObds/CMh3VQABgC4MFHcBQAAAAAAAAC1QGx4oIILbS3kU8JKFMG+Pro4JtTUdzQ9S8nZ57YgjPD3VZOQgGLnFdUuOlh5+ec+JbXZ8877Q82auK6/t5faRwVLcn7YuTMx3fR4gUNKybYrIsAqSYoJ9DO2SQrw8VJwoS0bz2TluvisAABAdckvZYUui0ou8CqpFqzoSp2u6hQTKn8fbyOOzfEV246xqHyHQ3uSbNqTVHoBfMvwQIX7m4vUfb0s6ts0SsHW4jmTv4+3LmkYLh8viw5UIkcDALgfxV0AAAAAAAAAUAs0CvFXTKBfmWMCfL3VKjLI1JeSYzcVd4VYfYqNKUnzsEBTOyEzp1LFXdV93c71QuX75+pbvyekm7ZZPGt/SqZ6/lnc1SwsQKk5dqXm2NUhOsRY0Ss9J0+nMnLKvR4AAKheJf0ulyQfL0uJj3lbild32fMrvnFhhL+vmoedK0jfk2RTem5eGWdUnp+3l9pHh0gyF6m3iwo2Crvs+QVadyJZqTl2dY4JVbM/c6aOMaE6lp7NyqMAcAGjuMvD7d69W+PGjdPOnTuVmJgoSQoPD1fjxo3Vs2dPXXvttWrXrp2bowQAAAAAAAAAzxUdYFWTUOeHsGeychVXyvZER9OyFB1gVYvwQHlZLOpaP8z0eE5evn47mVzt8QIAgPJl2vOVX+Aotj2jn4+X7Ln5pj4vi4wi78Iy7BUvygqx+hhF3w6HQ37eXsVWRg3xK/4xfIS/rzHuhC1biRVYCbRzTIixRXThIvXGhVY9PZSaqYRM55zbT6epcUiAvL0s8vGyqGGwnw6nsj0jAFyoKO7ycLt27dKuXbtMfTabTceOHdO6des0a9Ys9enTR88884yaNWvmpigBAAAAAAAAwDNZJHWt7/wgtcDh0NZTZW+btOVUqhIyc9QiPFBhfr7y8bIoy56v+Iwc7U2yKSuPVS8AAPAEDklpuXZF+FtN/SFWH9mKFHeFlrBtYU5efqV/r1ssFrWMKH/lUUkK9fNVqJ9zW8WMvDyXi7uiAqxq+ucqXIWL1H29LAootG10Uta5lVjtBQ5l2POM64VafSVR3AUAFyqKu2qBn3/+WbfddpumTZumq666yt3hAAAAAAAAAHCDX44mVck8cWlZpa5sVZ1zVeV1C3NIWnE4sULnHEvP1rH07CqPBQAAVK14W06x4q56gX46aTNvoVwvqPjW1fEXwDbLZRWpF12xLK/AXKhWeGvKomMBABcWirs8VP369XXNNdfo8ssvV9u2bRUVFSWr1aqUlBTt2rVLK1as0OLFi5Wb66zoTk9P19ixY/XRRx+pS5cuFb6er69vVT8FVCn+kARIkre3d/mDAABuRt4CADDjbw7wbOQuAAAzchd4LvKWksSlZaldVLCxTaIkNQsL0MGUTKXnOrdc9PfxUqsSVtc6nJpZrK9paIB6NAw39e1KTNeuM7aqDdxFLSOCFPbn6luHUjKVmnNuG8nc/AI5HA7juVuLbDtZuJ2Tz8qjQG1E3lJ3UNzlgaZMmaKePXvKx6f4tycmJkYxMTHq06eP7r//fo0ZM0Z79+6VJOXk5GjcuHH69ttvZbVai50LAAAAAAAAAAAAAJ4gNjxQwb7nPg/18S6+ulSwr48ujgk19R1Nz1JytnMLwgx7vg6mZJq2RvTx8lK/ZlE6YcuRw+FQg2B/+RUpfDqenq0zhbYxrAhXVhstqUjsSGqmNsWXvT10Yf7eXmofFSxJys7L187EdNPjBQ4pJduuiADn58IxgX7GyqMBPl4KLrRl4xkXt4AEAHgmirs8UO/evV0a16xZM82fP1+33367Tpw4IUk6fvy4FixYoCFDhlTomnb7+SUvAFCT8vPz+f8VAAAAcIEhhwcAABcScheg5jQK8VdMYPHtEgsL8PVWq0jzqlspOXajuEuS/khIV2SAr2l7Rh8vLzUNDShxTltunml7Q0/VuV6ofP8sSvs9Id20zeJZ+1My1fPP4q5mYQFKzbErNceuDtEhxope6Tl5OnUBbEEJoOLIW+oOr/KHwJNFRUXpiSeeMPUtXbrUTdEAAAAAAAAAAAAAQM3Jczi0+miSTtrK37oyMTNXvxw94/HbFEYHWNXkz+K0M1m5pa4UdjQtS4dSnNtLelks6lo/TH2bRhtFczl5+frtZHLNBA0AqDas3FUL9O/fX8HBwbLZnHs9b926VVlZWQoIKLkaHQAAAAAAAAAAAABqC3uBQ2uPJysm0KqmoQGKCrDKz8dLFlmUk5ev5Gy7jqVn64QLBWDuZpHUtb5zK8oCh6PcVca2nEpVQmaOWoQHKszPVz5eFmXZ8xWfkaO9STZl5Xl2IRsAoHwUd9UCPj4+6ty5s9auXStJysvL0+nTp9WsWTM3RwYAAAAAAAAAAAAAxf1yNKnK50zIzFVCZu55nx+XllXqKlk1NY9D0orDiRU651h6to6le37hGgDg/LAtYy0RFRVlaicns7wmAAAAAAAAAAAAAAAAcCGjuKuWyMoyV377+fm5KRIAAAAAAAAAAAAAAAAAVYHirlri6NGjpnZkZKSbIgEAAAAAAAAAAAAAAABQFSjuqgVOnjypffv2Ge2oqCjVq1fPjREBAAAAAAAAAAAAAAAAqCyKu2qBuXPnyuFwGO0rrrhCFovFjREBAAAAAAAAAAAAAAAAqCyKuzxIbm6uDh48WKFzFi5cqP/85z9G22KxaNiwYVUdGgAAAAAAAAAAAAAAAIAa5uPuAHBOdna2BgwYoBtuuEGDBg3SZZddJh+fkr9FCQkJmjt3rj799FNT/6233qrOnTvXRLgAAAAAAAAAAAAAAAAAqhHFXR4mPz9fS5Ys0ZIlSxQcHKz27dsrNjZWYWFh8vX1VVpamnbv3q2tW7fKbrebzu3Ro4cmT57spsgBAAAAAAAAAAAAAAAAVCWKuzyYzWbThg0btGHDhnLH3n333ZowYYKsVmsNRAYAAAAAAAAAAAAAAACgulHc5UH8/f01cuRIrV+/Xn/88Ydyc3PLHB8YGKhrr71Ww4YNYytGAAAAAAAAAAAAAAAAoJahuMuDWK1WjRs3TpKUl5enQ4cOKS4uTvHx8crIyFBeXp5CQkIUGhqq1q1bq23btvL29nZz1AAAAAAAAAAAAAAAAACqA8VdHsrHx0etW7dW69at3R0KAAAAAAAAAAAAAAAAADfwcncAAAAAAAAAAAAAAAAAAIDiKO4CAAAAAAAAAAAAAAAAAA9EcRcAAAAAAAAAAAAAAAAAeCCKuwAAAAAAAAAAAAAAAADAA1HcBQAAAAAAAAAAAAAAAAAeiOIuAAAAAAAAAAAAAAAAAPBAFHcBAAAAAAAAAAAAAAAAgAeiuAsAAAAAAAAAAAAAAAAAPBDFXQAAAAAAAAAAAAAAAADggSjuAgAAAAAAAAAAAAAAAAAPRHEXAAAAAAAAAAAAAAAAAHggirsAAAAAAAAAAAAAAAAAwANR3AUAAAAAAAAAAAAAAAAAHojiLgAAAAAAAAAAAAAAAADwQBR3AQAAAAAAAAAAAAAAAIAHorgLAAAAAAAAAAAAAAAAADwQxV0AAAAAAAAAAAAAAAAA4IEo7gIAAAAAAAAAAAAAAAAAD0RxFwAAAAAAAAAAAAAAAAB4IIq7AAAAAAAAAAAAAAAAAMADUdwFAAAAAAAAAAAAAAAAAB6I4i4AAAAAAAAAAAAAAAAA8EAUdwEAAAAAAAAAAAAAAACAB6K4CwAAAAAAAAAAAAAAAAA8EMVdAAAAAAAAAAAAAAAAAOCBKO4CAAAAAAAAAAAAAAAAAA9EcRcAAAAAAAAAAAAAAAAAeCCKuwAAAAAAAAAAAAAAAADAA1HcBQAAAAAAAAAAAAAAAAAeiOIuAAAAAAAAAAAAAAAAAPBAFHcBAAAAAAAAAAAAAAAAgAeiuAsAAAAAAAAAAAAAAAAAPBDFXQAAAAAAAAAAAAAAAADggSjuAgAAAAAAAAAAAAAAAAAPRHEXAAAAAAAAAAAAAAAAAHggirsAAAAAAAAAAAAAAAAAwANR3AUAAAAAAAAAAAAAAAAAHojiLgAAAAAAAAAAAAAAAADwQBR3AQAAAAAAAAAAAAAAAIAHorgLAAAAAAAAAAAAAAAAADwQxV0AAAAAAAAAAAAAAAAA4IEo7gIAAAAAAAAAAAAAAAAAD0RxFwAAAAAAAAAAAAAAAAB4IIq7AAAAAAAAAAAAAAAAAMAD+bg7AAAAXBX8vxB3hwB4jOQbk9wdAgAAAAAAAAAAAIBqxspdAAAAAAAAAAAAAAAAAOCBKO4CAAAAAAAAAAAAAAAAAA/EtowAAHg633ApupcUdem5L/9o85hTP0o/9Kv8tYKaSX87fP7nf9Vcyjhi7gttL3V4Uqp/teTfQMrPkJK3Svvflo58VvZ84V2kGzZKXj7SmY3S8l6SHOcfHwAAAAAAAAAAAABcQCjuAgDA0924WQpu4e4ozk+jv0pXfCH5BJzr87ZK9fs5vy66UVo7rPTze851FnY5CqSNo0VhFwAAAAAAAAAAAIC6hG0ZAQDweBZ3B+C6grxzx37R0uUfnyvsOrpY+u5yadNj58a1GCq1eqjkuWLvlWIudx4feFc681v1xQ0AAAAAAAAAAAAAHoiVuwAAuJAU2KX0fVJYh+qZ354m7Z5Z/rjIHlK9K819ZzZIWcfPtVsMl3xDnce5KdKae6T8bClxrRR+sdTyfudjbcZI+98yz+UbLnWd6jzOOSNte+o8ngwAAAAAAAAAAAAAXNgo7gIAwNMd/1rKOCKdWSclbZL860t/O1w918pNljaPK3/c9WuK9+153dyOvvTcceJaZ2HXWfErzhV3hXeWvAOl/Mxzj3d9SfKv5zze9i9ngRcAAAAAAAAAAAAA1DEUdwEA4Ok2jXV3BGaRl0jRvc19WSeluP+Y+3zDzx3nJpkfK1qsZQ2Xsv4s7oroLrX8c6vGMxuKr+oFAAAAAAAAAAAAAHWEl7sDAAAAF5i2JRSb7Zvn3DKyMHvKuWNrlPkxv0hzO7fQ2J5zJC9vyVEgbRwtyVGJYAEAAAAAAAAAAADgwkVxFwAAcJ1fjNR0sLkvP0faP6/42MT1546jL5O8/c+1619z7jhlx7ktGVs+6BwrSQfeca7cBQAAAAAAAAAAAAB1FMVdAADAda1Hmou0JCnuCyn7dPGxhz6Q7GnOY2u4dPmnzu0c24yWYkecG7dn1p9jIqUuLzmPc85IW5+q6ugBAAAAAAAAAAAA4ILi4+4AAADABcLiI7UaWbx/9+slj89JlNb8XbriC8knQGoy0PlV2KGPpQNvO4+7viz5RzuPtz0t5SadGxfeWQpqITnsUuouKeNwpZ8OAAAAAAAAAAAAAHg6irsAAIBrmt4uBV5k7ju9WkreXPo5x7+WlveQ2j8p1b9a8q/v3IIxeatz28XDnzjHRfWUYu93Hp/ZIO3/s+DropukS/6fFNKyyHV/ltY/KKXvrZKnBgAAAAAAAAAAAACeiOIuAADgmrZji/ft/X/ln5e6U1o3oowBFqnHXMnLW3IUSBtHS3JIjf8mXbnQ2V9UvT7S9Wukb3uwihcAAAAAAAAAAACAWsvL3QEAAIALQOQlUnRvc1/GUenoosrP3ephKaqH8/jAO86Vu7x8zxV8SVL8CmnxRdLSrs7rSpJflNRtWuWvDwAAAAAAAAAAAAAeipW7LgApKSnavHmz4uPjZbPZVK9ePTVu3FjdunWTt3cJq5kAAFDVSlq1a99cyZFfuXn9oqQuU5zHOWekrU85j+tdZd4CctNjUtZJ59euV6Qes539jf4qeQdI+VmViwMAAAAAAAAAAAAAPBDFXR7s0KFDeu211/Tjjz/KbrcXezwmJkZ33nmnHn74YVmtVjdECACoE/xipKaDzX15WdL+tyo/d9d/S36RzuOtT0m5Sc7jsI7nxthtzq0dz0pcf+7Y2yqFtJFStlU+FgAAAAAAAAAAAADwMGzL6KG++uorDRo0SN9//32JhV2SlJCQoNmzZ+vOO+/UsWPHajhCAECd0fphydvf3Hf443OFWOcr6lIpdoTz+MwG55aMZ/kEnju2p5rPK9ouPBYAAAAAAAAAAAAAahFW7vJAP/30kyZMmKCCggKjr3nz5rr00ksVHh6uuLg4rVq1StnZ2ZKknTt36uGHH9YXX3yh4OBgd4UNAPB016yS6l9l7vuquZRxpPRzLD5Sq5HF+/f8v8rFYvGSes51/rcgX9owSpLj3OPZCeeO/aLM5xZt5yQIAAAAAAAAAAAAAGojirs8zOnTpzVu3DijsMtiseif//ynhg8fLi+vcwutJSUl6bHHHtNvv/0mSdq/f78mTpyo6dOnuyVuAEA1ajNaCm51ru0bWnxMSCup+wxz35FPnStiVUbT26XARua++JVS6u+Vm7f1I1Jkd+fxgXekpI3mxxN/PXfs7S9F95YS1zrb9fqdeyzrlJS+v3KxAAAAAAAAAAAAAICHorjLw7zxxhvKyMgw2o8++qjuvffeYuMiIyP1zjvvaODAgTpw4IAkaenSpXrggQfUoUOHGosXAFADmtxefMWtogIbS+0eN/clb618cVebR4v37a3kql1+MdLFLziPsxOlbU8XH5O6U4pfITW41tnu9Za0aazz3I4Tqi4WAAAAAAAAAAAAAPBgXuUPQU1JTEzUggULjHbTpk310EMPlTrez89Pzz77rNF2OBx64403qjVGAEAdEtFdirnc3Gc7KB37X+Xm7faqZI1wHm97WspNKnncunuldGcBs8I7SdeslK784tzKZSeWSjtfqVwsAAAAAAAAAAAAAODBKO7yID/88IPsdrvRHjx4sHx9fcs8p3fv3oqNjTXaP/30kzIzM6stRgBAHdJ2bPG+vXMkOc5/zujLpRZDncdnfnNuyViazGPStz2kP16SUndJ+dmS3SYlrpc2jJJ++qvkyD//WAAAAAAAAAAAAADAw7EtowdZuXKlqX3DDTe4dF7//v2NFbtycnL066+/6rrrrqvy+AAAbvJDP/fMs26E86sqJa6RPvN2fbw9Rdr2L+cXAAAAAAAAAAAAANQxrNzlQTZt2mQcR0dHq0mTJi6d161bN1N7w4YNVRoXAAAAAAAAAAAAAAAAgJpHcZeHOHXqlNLT0412+/btXT63Q4cOpvaBAweqLC4AAAAAAAAAAAAAAAAA7kFxl4c4ePCgqX3RRRe5fG50dLR8fX2N9qFDh6osLgAAAAAAAAAAAAAAAADuQXGXhzh16pSp3aBBA5fPtVgsql+/fqlzAQAAAAAAAAAAAAAAALjwUNzlITIzM03twMDACp0fFBRkHOfl5SknJ6dK4gIAAAAAAAAAAAAAAADgHj7uDgBORYu7/Pz8KnR+0fEZGRkVmqPwto7wRNnuDgAA4GH43Q3PRd4CADAjb4FnI3cBAJiRu8BzkbcAAMzIW+oOirs8RNGVtir6j9BqtZY5X3mCg4MrNB41a1Bbvj+AJKmtw90RAB6D3wzwVOQtQCHkLoAk8hZ4NnIX4E/kLYCB3wzwVOQtQCHkLoAk8pa6hG0ZPUTRVbbsdnuFzs/NzS1zPgAAAAAAAAAAAAAAAAAXFoq7PERgYKCpXdGVt4qODwoKqnRMAAAAAAAAAAAAAAAAANyH4i4PUbS4KyMjo0LnFx7v4+PDyl0AAAAAAAAAAAAAAADABY7iLg9Rv359U/vUqVMun+twOEzji84FAAAAAAAAAAAAAAAA4MJDcZeHaNmypal9/Phxl89NTEyU3W432rGxsVUWFwAAAAAAAAAAAAAAAAD3oLjLQ9SrV08hISFGe9euXS6fu3PnTlOb4i4AAAAAAAAAAAAAAADgwkdxlwe55JJLjOMzZ84oLi7OpfM2b95savfs2bNK4wIAAAAAAAAAAAAAAABQ8yju8iDXXHONqf3tt9+6dN7y5cuNYz8/P11xxRVVGhcAAAAAAAAAAAAAAACAmkdxlwe5+uqr5evra7QXLFggu91e5jlr167VoUOHjHbfvn0VGBhYbTECAAAAAAAAAAAAAAAAqBk+7g4A50RHR2vw4MH65JNPJElxcXF66623NHr06BLH5+Tk6MUXXzTaFotFjzzySI3ECs+yaNEiPfXUU0b7hx9+UOPGjd0YEWqTCRMmaPHixZKkRo0aaeXKlW6OCABwISNvQXUib6l7HA6H4uLitG/fPsXHx8tms8nPz0/h4eFq06aN2rVrJ29vb3eHCeACRu6C6kTuUvdkZmZq//79OnHihBISEpSZmSlJCg4OVoMGDdSpUyfVr1/fzVECuFCRt6A6kbcAgHtR3OVhHn74YX355ZfKyMiQJM2aNUuBgYEaPny4vLzOLbSWlJSkxx57TPv37zf6brrpJnXo0KHGYwYA1C4nT57U9u3btWPHDm3fvl2///678XtJksaMGaNHH33UjRECAIC6LD09XStWrNCPP/6o9evXKzk5udSxwcHBGjhwoEaMGMGHGgAAwC3WrVunr776Sps3b9aRI0fkcDjKHN+uXTvddddduuOOO+Tjw0c4AADAs9hsNt18882Kj4839X/44Ye69NJL3RQVUPvxzsDD1K9fX9OnT9cjjzyigoICORwOTZ06VZ9//rkuu+wyhYeH68iRI1q1apWys7ON81q1aqXJkye7MXIAwIUsOztbjz/+uH7//XclJCS4OxwAAIASrVu3Tg888IDsdrtL4202mz766CMtXLhQzzzzjG677bZqjhAAAMDsu+++06JFi1wev3v3bk2aNElffPGFXn/9dTVr1qwaowMAAKiYadOmFSvsAlD9KO7yQFdddZVefvllTZo0SVlZWZKkw4cP6/DhwyWOb9++vWbPnq3g4OAajBIAUJvk5uZq1apV7g4DAACgTDabrVhhl9VqVZcuXRQbG6vIyEjl5uZq3759Wr9+vXJyciQ5t0B6+umnlZ2drSFDhrgjdAAAAHl5eSk2NlYtW7ZUw4YNFRwcrNzcXMXHx2vz5s06duyYMXbXrl36+9//rs8++4wVSAEAgEfYvHmzPv/8c3eHAdRJFkd5awDDbQ4ePKjXXntNP/30U4l3JcfExGjw4MEaOXKkrFarGyIEANQWaWlp6tmzZ7H+hg0bqn79+tq6davRx7aMAADAXVasWKHRo0fLYrGod+/euvPOO9WvXz/5+fkVG5uQkKAXXnhBy5cvN/q8vLy0ePFitWvXribDBgAAddjMmTN14sQJXX/99erZs6fCwsJKHOdwOLRq1SpNnDjRtKr6FVdcoffee6+mwgUAAChRbm6uBg4cqP3790ty1ioUzlnYlhGoXqzc5cFiY2M1Z84cJScna/PmzYqPj1dGRoaio6PVpEkTde/eXd7e3u4OEwBQS0RGRqpTp07q3Lmz8RUdHa3169dr2LBh7g4PAABAktSnTx+NHz++3AKtmJgY/b//9/80fvx4ffPNN5KkgoICTZ8+XW+99VZNhAoAAKDHH3/cpXEWi0VXX321mjdvroEDByo7O1uS9Ouvv2r37t0UpwMAALd68803jcKuPn36KCoqSosXL3ZzVEDdQXHXBSAiIkLXXHONu8MAANRioaGhWrt2rbvDAAAAKNNVV12la6+9tkLnPPvss1q1apUyMjIkSWvWrFF6erpCQkKqI0QAAIBKiY2N1R133KGPPvrI6Pvll18o7gIAAG6zf/9+vfnmm5Ikf39/TZw4UXPmzHFzVEDd4uXuAAAAAAAAAFzh41Pxe9TCw8N15ZVXGm273a5du3ZVZVgAAABVqkePHqZ2fHy8myIBAAB1ncPh0DPPPCO73S5JGj16tJo0aeLmqIC6h5W7AEiS8vPztWnTJsXFxenMmTMKCAhQhw4d1L17d3l5lV4Hmp6ert9++01xcXHKzc1VZGSkunfvrpYtW1ZJXDabTRs2bNDJkydls9kUHR2t9u3bq3379lUyvySdPHlSO3bsUEJCgtLT0xUVFaVbb71Vvr6+pZ6TnZ2tzZs368SJE0pKSpLValVERIQ6depUqed++PBh7dq1S6dPn1ZmZqa8vb0VFBSkBg0aqHnz5oqNjZXFYnF5vsTERP3xxx86duyYbDabHA6HAgICFBMTo6ZNm6pNmzayWq3nHa8rTp48qS1btigpKUkZGRkKCwtTgwYN1KNHDwUHB1fZdZKTk7Vp0ybFx8crMzNTERER6ty5s9q2bVuh1+ysnJwc7dq1S/v371dqaqpycnLk7++vsLAwNWrUSG3atFFkZGSVxQ/PderUKe3bt09Hjx5Venq6JCksLEwNGzZUt27dqnTVD4fDoe3bt+vIkSNKSEhQQUGBunTpol69elXZNYDagLyFvKW6kLfUXk2bNjW1ExMT3RRJ9SJvATwTuQu5S3Uhd6m9in7/CgoK3BRJ9SJ3ATwPeQt5S3Uhb7lwffrpp9qyZYskqU2bNrr33nvdHJF7kLfA3SjuAmqBRYsW6amnnjLaP/zwgxo3blxs3NChQ/Xbb79Jknr16qWPPvpIubm5euedd/Tpp58qISGh2DmNGjXSs88+q379+pn6k5OTNX36dH355ZfKzc0tdl737t01efJktW7dutz427ZtaxyPGTNGjz76qJKTk/Xvf/9bS5cuVXZ2drFzmjdvrieeeELXXXddufOX9vps2rRJM2bM0MaNG+VwOEzn9O/fv8SEfffu3Zo1a5Z++eUX5eTklHi9Ro0a6cEHH9Ttt99eZtJ/VkFBgb744gt9+OGHOnjwYJljQ0JCdMUVV+ihhx5Sx44dSx23du1azZ07Vxs2bCj23Arz9fVVt27ddM899+jGG28sccyECROMPbMbNWqklStXlvucJGnZsmWaN2+edu/eXeq1L7/8co0bN86lN2Dr16/XsGHDjPaHH36oSy+9VKdOndLLL7+sFStWGHcNFNasWTP94x//cOlnRZISEhI0a9YsLVmyRDabrcyxzZs31zXXXKPHHntMfn5+Ls1/Pn7//Xfdfffdxr+1Ro0a6csvv1RoaGi557766qt65513jPZdd92l559/vtpirW6DBw/Wtm3bJEkxMTH68ccfK7SCx4EDB3TTTTcZ7fvvv19PPvmkaUxBQYE2btyopUuX6tdff1VcXFyp83l5eenyyy/XyJEj1bNnT5diKOn/xfn5+Xrvvff06aef6sSJE6bx11xzDQk7ahXylrKRt5C3kLdUT95ydkvGs1z5ea8s8hagdiB3KRu5C7kLuUv15C5Ff8fWxOoY5C7AhY+8pWzkLeQt5C0Vz1vi4+P12muvSZIsFosmTZpUI39TKQ95C+oitmUE6jCbzabhw4fr9ddfLzFZl6Tjx4/rkUce0cKFC42+AwcO6NZbb9V//vOfEpN1Sdq8ebPuuece/fHHHxWOa8+ePRo0aJAWLVpUYrIuOe9aGDNmjJ577rkyE9LSvP322xo6dGi5Ce1ZDodDr776qgYOHKgVK1aUmqxLztds0qRJGjp0qJKSksqcNzMzU/fdd58mTZpUbrIuOe9++fbbb/Xrr7+WOmbatGkaMWKEfvvtt3Kfm91u12+//ab//ve/5V7bVRkZGXrggQf0+OOPl5qsn732Tz/9pEGDBmnu3Lnnda21a9fqlltu0bJly0pM1iXpyJEjGjNmjN56661y59uyZYtuvvlmffHFF+Um65Lz5/Ddd99VWlpahWOviE6dOmnChAlG+/jx46Y3oaX56aef9O677xrtDh066F//+le1xFhTbrvtNuM4ISFBv/zyS4XOX7Rokak9aNCgYmP27NmjoUOH6rPPPiszWZecyf3q1as1dOhQzZw5s0KxnJWWlqahQ4dq2rRpxZJ1AOeQt5C3kLeYkbdUzJ49e0zt+vXrV9ncpSFvAeo2chdyF3IXM3KXivnmm29M7b59+1bZ3KUhdwHqLvIW8hbyFjPylnMmTZpk3DB3xx136JJLLqlg1NWDvAV1ESt3AXWUw+HQuHHjtHnzZklSbGysevXqpcjISKWmpmr16tU6cuSIMfa5555Tt27dFBoaqhEjRuj06dPy8vJSly5d1LlzZwUHBys+Pl4rV65USkqKJOcvofHjx+t///ufy8u52mw2Pfroo8YvrVatWqlXr14KCwvTmTNn9Ouvv+r48ePG+M8//1z+/v4uJS9nLVu2TNOmTZMk+fn56dJLL1XLli3l5+en+Pj4YgnA2ddq2bJlpv4OHTqoc+fOioyMVF5enuLi4rRmzRpjKc4tW7Zo6NChWrBggQIDA0uM5fnnn9fatWuNtre3t7p06aK2bdsqIiJCFotFaWlpiouLM5bgLcvChQv19ttvm/patmypLl26qF69evL19ZXNZlN8fLz27Nnj0puEisjNzdWIESO0fft2U3/79u11ySWXKCQkRAkJCVq9erXi4+MlOROe119/XZmZmXriiSdcvta+ffs0ffp0ZWRkyMfHR927d1f79u0VFBRkXOPkyZPG+OnTp6tbt26lVswnJSVp5MiRSk1NNfrCwsLUs2dPNW3aVEFBQcrOzlZKSor279+vXbt2lfqGsjoMGTJEGzZsMH4OV6xYofnz52vEiBEljj958qSefPJJ401bcHCwZs6cWe1LK1e3m2++WS+99JLx2i9atKjY3WKlyc/P1//+9z+j3aVLF7Vq1arMc3x9fdWmTRvFxsYqJiZGgYGBys3N1enTp7V161YdPnxYkvP/E2+88YbCw8NL/Z6UxOFw6B//+Ic2bdokyflBc+/evdWgQQNlZ2fr4MGDFbrbBKityFvIW8hbzMhbKubYsWPG/z8kKTQ0VB06dKiSuctC3gLUXeQu5C7kLmbkLhXz5ptvav369Ub7r3/9a5Vta1YWchegbiJvIW8hbzEjbzln6dKlWrVqlSQpMjKyQt+X6kbegrqInwCgjtq6davsdrtCQkL00ksv6frrrzc9np+frxkzZhjJn91u19y5c2Wz2XT69Gm1adNG//73v4stkZqWlqbHHntMa9askSQdOnRIX331le644w6X4vr000+Vm5urkJAQTZkyRf379zc97nA49Omnn+rll182qu/nz5+vfv366bLLLnPpGjNmzJDkXE732WefVUxMjOnxvLw8097x8+bNMyXrvXr10rPPPqs2bdoUm9tms2nmzJn66KOPJEn79+/XCy+8oJdffrnY2OPHj+vLL7802p07d9aMGTPKXGZ99+7d+uqrr0rdt/mNN94wjiMjIzVjxowyX5eTJ09q+fLlxpuzypo2bZopWa9fv75eeeUV9e7d2zSuoKBAH374oV599VXl5eVJct4h07NnT5fvRJw6darsdrt69+6tF154odjrlpubq9dee03z58+X5PzZmTlzpj755JMS5/v000+NN5uSNHLkSI0aNarUJXSzs7O1Zs0affbZZ6afl+r04osvaufOncb3a9q0aeratau6du1qGpeXl6dx48aZns8LL7ygZs2a1Uic1Sk4OFjXXXedvv76a0nSqlWrlJSU5NKe9qtXrza96S18Z0dh3t7euvbaazVw4ED17t1bQUFBpc65ceNGTZw4UQcOHJDk/J7ccMMNatCggUvPZ/PmzcrPz5fVatXTTz+tO++8s9jPU2l3vQF1CXkLeYtE3lIYeUvFzJkzx3SX8nXXXVcjfxAjbwHqLnIXcheJ3KUwcpey5eXlKTExUVu2bNFnn31mKuzq2rWrJk2adN5zVwS5C1A3kbeQt0jkLYWRtzilpqZqypQpRnvChAkKCwurkvirAnkL6iK2ZQTqKLvdLl9fX7333nvFknXJ+QvriSeeULdu3Yy+JUuWaNWqVWrcuLE+/vjjEve+Dg0N1Wuvvabg4GCjr3D1c3lyc3Pl4+OjefPmFUvWJed+zkOGDNFLL71k6i+cYJQnPz9fN9xwg2bOnFksWZckHx8f4xfmkSNHNGvWLOOxG264QfPnzy8xWZecycQzzzyjhx9+2Oj78ssvjYrtwgrfheHl5aXXX3+9zGRdktq1a6d//vOfuvvuu4s9dvToUR09etRoT5gwodw3MQ0bNtSIESP03HPPlTnOFXFxccYbFcl5J8MHH3xQLFmXnM93xIgRxb5vU6ZMcXnpZLvdriuvvFJvv/12ia+b1WrVU089Zbr+xo0bTa9RYYW/H71799a4cePK3Bvd399fV199td5++21FRUW5FHNlBQcH6/XXXzfistvtxRJzSXrttde0ZcsWo33PPfeY9g6/0BVOtO12e7GtCkpTeJldf3//Ul+TNm3aaM6cObr22mvLTNYlqUePHvr000+NBN1ut5f6prAk+fn5kqTXX39dd999d4lv/i701daAqkDeQt4ikbcURt7iujVr1mjx4sVG29fX1/QzX93IW4C6idyF3EUidymM3MVsxowZ6tChg/HVsWNH9e3bV48//rhR2BUUFKRRo0bpo48+Mv2br27kLkDdQ95C3iKRtxRG3uL0yiuvKDExUZLzdfjb3/5WNcFXIfIW1DUUdwF12LBhw3TxxReXOebOO+80jgsKCiRJEydOLLM6OzIyUtddd53R3rFjh/FLyRVDhgxRjx49yhxzyy23mJbX3Lt3r7FUZXkCAwM1adIklyro3333XSP26OhoTZkyRd7e3uWeN2bMGF100UWSnK/bF198UWzMmTNnjOPIyEg1atTIpfhLczbJOqtTp06Vmq+iPv74Y+NnRJLGjRunFi1alHnOrbfeqquvvtpoHzlyRD/++KNL17NarXr55Zfl6+tb5riiy55u27atxHGFX7+afu0qon379nr66aeN9okTJzRhwgTjjc4PP/yg9957z3i8Y8eOFVqK+kJw2WWXmf69FN0bvSSpqalauXKl0b7uuutKvauposLDwzVs2DCj7erP8FkDBgww/TsAUDLyFvKWqkTeUjPcnbecPn3atPWAJD388MM1upopeQtQd5G7kLtUJXKXmlFTuUt+fr7pq6iYmBg9++yzGjVqVI1/iEfuAtRN5C3kLVWJvKVmVGfesm7dOi1cuFCS8/WtqVVEK4q8BXUNxV1AHVZSRX9RRRP6Ro0aqU+fPuWe16VLF+M4KyvLtPd5eYYOHerSuMK/ICXp+++/d+m8G2+8UREREeWOy8/PN1V5Dxw40OU75axWq6655hqjXXg59bMK761+5swZnTp1yqW5S1N0r/Zdu3ZVar6K+uGHH4zj0NDQUpcxLer+++8vdZ6yXH/99apXr16543r06CGLxWK0zy6JWlThqvuafu0q6q677tKAAQOM9qpVq/Tuu+/q+PHjpuQ8JCTE5b3TLyQWi0UDBw402rt27Sr3e/bNN9+Ylqx19efTVYXv0Nq/f78yMjJcPteV/xcDIG8pD3lLxZC31Bx35S05OTkaO3asEhISjL5u3bpp5MiRVTK/q8hbgLqL3KVs5C4VQ+5Sczzhby4JCQmaMGGCrrvuugp/KFhZ5C5A3UTeUjbyloohb6k51ZG35OTkaOLEiUb74YcfVvPmzas07qpC3oK6huIuoI6qV69eucu6Siq2F3C3bt1MyY+r56WlpbkUV+vWrV2KS3JWZBdOUkursi+qV69eLo37448/TL90y7tDpKjCyc6ePXuKLSFb+M2Qw+HQ6NGjdfDgwQpdo7DY2FhT0vniiy/q119/Pe/5KiIxMVHHjh0z2n/5y19c/uNWjx49THtgF14itiyufh+Dg4NNdw+V9rPYuXNn43j16tWaPn26MjMzXbqGO0yePNl0t8uMGTP0wAMPKDU11eibMmWKmjZt6o7wqt2gQYNM/y8q746Ms3eZSM4/PJS3DPVZp06d0vz58/X444/r5ptv1uWXX64uXbqYtk/o0KGDHnroIeOcgoIC037tZbFaraY/cAAoGXlL+chbXEfeUvNqOm/Jz8/X+PHjTd+fhg0b6vXXXy/3Tt7qQN4C1D3kLuUjd3EduUvNq+7c5YknntCePXuMr02bNmnp0qV68cUXTT+7J0+e1MiRI7VgwYLzfzLngdwFqFvIW8pH3uI68paaV9V5y6xZs3TkyBFJUosWLUy/hz0ReQvqEh93BwDAPerXr+/SuKIV/q6eFxAQYGq7mvi0bdvWpXGScy/utm3bGgleaVX2RcXGxro0rmh196hRo1yOTZIpQc/Ly5PNZjMt7dmlSxd169bNiH/Hjh266aab1LVrV/3lL39Rz5491blz52KvZWl8fX3197//XW+++aYkKTk5Wffdd5+aN2+ufv36qWfPnurWrZspOa4qhw4dMrXbt29fofM7dOig1atXG3M5HI5y3xi6+rMoOX+Oz+41XtrP4t///nctXLhQdrtdkvTmm2/q448/Vt++fdW7d29dcsklio2NdekNa00ICgrS66+/rsGDBys7O1t5eXmmN3xDhw5V//793Rhh9WrUqJEuvfRSrVu3TpL09ddf68knnyzxA9u9e/fqjz/+MNoDBw4s9/uYkpKiadOmaeHChaYlpF3l6h8pmjZt6pYPmYELDXlL+chbXEfeUvNqOm957rnnTHdqh4eH65133qnQ96EqkbcAdQ+5S/nIXVxH7lLzajp3CQ4OVnBwsFq2bKnbb79dH3zwgaZOnSqHwyGHw6Hnn39eXbp0Ma0EUZ3IXYC6hbylfOQtriNvqXlVmbfs3r1b77//vtGeNGmSx+8MQ96CuoTiLqCOcjUJLPpL7XzPK3onQmmio6NdGndWVFSUcZyenu5SohcaGurS3MnJyaZ2RfaCL0l6enqxfZtnzJih++67z0i0HA6HtmzZYiTxPj4+6tSpk3r37q0bb7yx3Dc0jz76qA4ePGj6MOvw4cN6//33jYQsNjZWl156qfr3769LL73Upf3ky1P4DgBJiomJqdD5hcfn5+crIyOj3GWNXf1ZlMw/j6X9LLZu3VovvfSS/vWvfxlLsmZkZGjp0qVaunSpJOcHgz169FDfvn3Vv39/010e7tC2bVs988wzeuaZZ0z9nTp10pNPPummqGrOoEGDjIQ9OTlZq1at0vXXX19sXOE7NYou01uSxMREDRs2zOU/ApQkJyfHpXFVtZc7UNuRt5SPvMV15C3uUVN5y7///W/T6haBgYF666231KpVqyq7xvkgbwHqFnKX8pG7uI7cxT3c9TcXi8WiESNGKCkpyfhQ3m63a86cOXr99der7bpFkbsAdQd5S/nIW1xH3uIeVZG35Ofn61//+pfy8vIkOQufXF3Vyt3IW1BXsC0jAI9SkSSs6PiCggJlZWWVe46Pj2t1ra5WU7uqpIruhg0batGiRfq///u/YssTS867OLZu3ao33nhDt9xyi4YPH67du3eXeg1fX1/NmjVL06dPV8eOHUscc/DgQX322WcaMWKEbrzxRpf3ny9L0T2jK/p9LHrXT0X2oK5Kt9xyi7788ksNGDCgxLsRUlJStGLFCj377LPq06ePXnnlFZd+5qpTSZX8l19+ucffTVEV+vfvb0p4S1puNy8vT19//bXRvvTSS9W4ceMy53366adNyXpkZKTuu+8+zZs3T0uXLtXGjRu1Y8cO0xYKH374oWkOV/9IwZ0YwIWNvMWMvKVmkbcU98Ybb+jdd9812larVXPmzPGIZe3JWwB4AnIXM3KXmkXuUjEjR46Uv7+/0V61apXxAXNNIHcB4G7kLWbkLTWrLuYt33zzjX7//XdJzuK1C2kBAfIW1BWs3AXAo1Q0+Sk83svLq8KJYlmKzvXdd9+pWbNmVTZ/4es8/PDDevDBB7Vjxw6tX79eW7du1ebNm4vdEbJu3ToNHjxYs2fPVp8+fUqcz2Kx6Oabb9bNN9+sw4cPa926ddq0aZO2bNmio0ePmsYePnxYY8aM0ZgxY/Too4+e93MovH+7VPHvY9Hlb4vOV5Natmyp1157Tenp6Vq/fr02btyorVu36vfffzeW4ZWk7Oxsvffee1qzZo0+/vhjt1TVHzhwQM8//3yx/nfffVdXXnmlLr300hqPqSb5+/vrpptu0hdffCFJ+uWXX5SYmGi6q+vnn39WYmKi0R40aFCZc27fvl0//fST0e7Ro4fmzZtX7vfX1eXEAdQu5C3kLSXNV5PIW8755JNPNHPmTKPt7e2tGTNm6PLLL6/UvFWFvAWAJyB3IXcpab6aRO7iusDAQHXt2tVYhSInJ0dHjhxR69atq/W6Z5G7AHA38hbylpLmq0l1LW8p/DOWmpqqK6+8stxzihYqjhgxwrQ62vz589WrV69y56ks8hbUFRR3AfAoZ86cOe/xISEhVbrHdUREhKkdFxdXLQn7WV5eXurSpYuxsoDD4dCePXv0ww8/aMGCBTp58qQk5x9z/vGPf2jFihXlJhHNmzdX8+bNddddd0mS4uPjtWrVKi1cuFA7duwwxs2ePVu9e/dWjx49ziv2okvOJiQkVOj8wuO9vb3dmrCfFRISomuvvVbXXnutJGeCvn79ei1dulRLliwxkvfdu3dr6tSpmjJlSo3Gl52drccff9xIFL28vGS1WpWdna38/HyNHz9eX331lWk56tpo0KBBRsKel5enr776Svfff7/xeOE7NIKDg8vdW37lypXGscVi0dSpU116M1b4TQGAuoO8hbyFvMU11Z23fPXVV3rhhReMtsVi0UsvvWS8Hp6CvAWAu5G7kLuQu7jGU/7mUnRLsvT09Gq9XlHkLgDcibyFvIW8xTXVkbc4HI7z2n60aLGXq6tWVQXyFtQFbMsIwKOUtYxsUQUFBdqzZ4/RbtmyZZXGUnS+DRs2VOn85bFYLGrXrp1Gjx6t5cuX64orrjAeS0lJ0XfffVfhORs0aKC7775b//3vfzVq1CjTYwsWLDjvWFu0aGFq79q1q0LnFx7fokWLKn3jVVX8/f3Vt29fvfLKK1qwYIFpeeCvv/5a2dnZNRrP888/r7179xrt0aNH67nnnjPaCQkJeuKJJ0pc4rk26dq1q+nf6uLFi43jpKQk/fjjj0b75ptvNm1pUJJDhw4Zx7GxsWrSpIlLcZxdrhhA3ULecg55i2epS3nLihUr9PTTT5v+YDhx4kTdeuutlYq5OpC3AHA3cpdzyF08S13KXSrCZrOZ2kU/MK9u5C4A3Im85RzyFs9C3uKZyFtQF1DcBcCj7N+/v9hysKVZv369aXnLs3cxVJVLLrnE9Mt92bJl51WpXhX8/Pw0YcIEU19F3tyUZOzYsYqMjKyS+aKjo02JzerVq5Wbm+vSuZs2bTLdVdOtW7fzjqOmtG/f3rjDRXLeIVM40atuX375pekug969e2vUqFEaNGiQBg4caPSvWbNGc+fOrbG43KXw8rn79u3T9u3bJTnfSBVeHrm8ZXYl812woaGhLl3fbrfrhx9+cDVcALUIeUvJyFs8S23OW9auXatx48YpLy/P6Bs/frzuueeeygdeTchbALgTuUvJyF08S23OXSqq8AfcXl5eql+/frVeryTkLgDchbylZOQtnqU25S0jRozQnj17KvRV+BqS9OGHH5oer+5trIsib0FtR3EXAI/icDj08ccfuzT2gw8+MLWvu+66Ko3FarWatnKJi4vTf/7znyq9RkU0bdrU1C6ciJwPi8Wixo0bV9l811xzjXGcmpqqhQsXunTe+++/b2pX9fexuhSt0q/s6+eqonunx8TEaNq0afLycv5Kf+6559SqVSvj8Tlz5mjdunU1Epu7/O1vf5OPz7mdps++mSl8Z0bLli3VtWvXcucqvMzz8ePHXbr+ggULKry8NIDagbyldOQtnqU25i3bt2/XqFGjTH8kfuSRR/TQQw9VUfTVg7wFgDuRu5SO3MWz1MbcpaJ+/PFHnTp1ymhffPHFCg4OrpZrlYXcBYC7kLeUjrzFs5C3eA7yFtR2FHcB8DiffPKJtmzZUuaYJUuWaNWqVUa7TZs2uuSSS6o8llGjRhmJkCS9/PLLFV5y9+jRoyXeYbJ7926X71iQii/126hRo2LXSUpKcnm+lJQU7d+/v9T5KmrIkCGm12rGjBk6fPhwmef873//0/fff2+0mzdvrj59+lQqjvO1bdu2Co3fuHGjqX3RRRdVZTglysrK0mOPPWbcheTt7a1p06YpOjraGBMQEKCZM2cqICBAknNJ6ieeeKJW7/MdExOjv/zlL0Z76dKl2rZtm+kOV1fuxJBkerNz+vRp/fTTT2WO37Vrl1599dUKRgygNiFvKRl5S/Wq63nLvn379OCDD5ruzB42bJgef/zxqn8iVYy8BYC7kbuUjNyletXl3OXgwYPFtlcsz7FjxzRp0iRTn7u2nCZ3AeBO5C0lI2+pXnU5b7nQkbegtqO4C4BHsVqtstvtevjhh02J3FkOh0Off/65/vnPf5r6n3766WqJp2XLlhozZozRzsnJ0b333qvZs2ebluQsKjc3V6tWrdK4cePUv39/037vZ82fP1/9+vXT9OnTtXPnzjLj2LBhg5566imjbbFYTHeKnB1z1VVX6amnntLq1atN29MUdezYMT3yyCOmD8Suv/76MmMoT9OmTTV06FCjnZqaquHDh5d4J0BBQYE++uijYt+3p59+2m17qA8ePFh33XWX/vOf/5T5xic3N1dz5szRkiVLjL7u3bubkubqMnnyZO3bt89ojx49Wpdddlmxca1bt9bEiRONdl3YU/22224zjlNTU/Xkk08abR8fH/3tb39zaZ6i/67++c9/FntzdtaSJUs0fPhwZWZmKjAw8DyiBnChI28pGXlL9avLecuxY8d03333KSUlxei7/fbbq+3fVXUgbwHgLuQuJSN3qX51OXf5+eefdc0112jGjBmm+UuSnZ2tBQsW6Pbbb9fJkyeN/rZt22rw4MEVfUpVhtwFgDuQt5SMvKX61eW8pTYgb0Ft5lP+EACoOffcc49WrlypuLg4jRkzRq1bt1avXr0UFhamM2fOaPXq1cWWvxwxYoR69+5dbTGNGjVKcXFx+vLLLyU5l1SdNWuW3n77bXXr1k2tWrVSWFiYsrOzlZqaqv3792v37t3Kysoqd+7ExES9+eabevPNNxUdHa0OHTqoadOmxv7NiYmJ2rp1q/bu3Ws6b+jQoWrRokWx+XJycrRo0SItWrRIQUFBat++vVq2bKnw8HD5+voqNTVVe/bs0ebNm00JfceOHYvtjX0+nnjiCW3ZssXYxzo+Pl7Dhw9Xx44d1b17d4WEhCgxMVG//PKL6Y9UkvTAAw+ob9++lY6hMrZs2aItW7boueeeU2xsrNq1a6d69eopKChI2dnZOnr0qNavX6/k5GTjHB8fH9ObqeqyePFi097pV1xxhR555JFSxw8aNEi//fabsdzs2rVrNXfuXNMb0KJmz55d4r7rDofD1J4zZ47eeOONYuMaNWpU4hvtmnDVVVcpMjLSeLNV+E6gv/zlL4qJiXFpno4dO+raa6/VihUrJEnJyckaMmSILrnkEnXp0kX+/v5KTEzUmjVrdOzYMUnOO2DGjRunKVOmVO2TAuDxyFvIW9ypruYtX375pU6fPl3seoWX2HfFqFGjysyLqhN5CwB3IXchd3Gnupq7SM4VUebNm6d58+apXr166tixoxo1aqSQkBBJks1m08GDB7V161ZlZGSYzm3UqJHeeOMNeXt7V/ZpnjdyFwDuQN5C3uJOdTlvudCRt6A2o7gLgEcJDg7W7Nmz9dBDDyk+Pl779u0r8662O++8UxMmTKjWmCwWi1555RW1bt1aM2fONPbLzs7O1tq1a7V27dpy5/D19S13TGJion7++edyxw0ePNil55yRkaGNGzeWWkl+VpcuXTRv3jzTPtTny2q1av78+Ro7dqxWr15t9P/xxx/6448/SjzHYrHo0Ucf1ejRoyt9/apSUFCg/fv3m5YiLklQUJBmzJihiy++uFrj2b9/f7G901999VXT0sYlee6557Rjxw7jecyZM0c9evQo8Q4OyVnElZ+fX248pY1z5dzq4uvrq1tuuUXz588v9ljhOzVc8fLLL+vo0aOmu6g2bdqkTZs2FRsbFBSkWbNmVcm/HwAXHvIW8hZPUNfylqJF59L55SAlzVNTyFsAuAu5C7mLJ6hruUtRp0+fLlaoXpq+fftq8uTJatCggUvjqwu5CwB3IG8hb/EEdT1vuRCRt6A2Y1tGAB6nbdu2Wrx4sW655Rb5+fmVOKZ58+aaNWuWJk+eXGNLsz7wwAP67rvvNGTIEEVERJQ51mKxqG3btnrwwQe1ZMmSEu8yGDlypB5//HF1795dVqu1zPm8vb11+eWXa/78+XrhhRdKvFvv6quv1qRJk9SvXz/jbo6ytG3bVpMnT9Znn32myMjIcse7KigoSO+8845mzJihtm3bljrOx8dHffv21aJFizwiWX/zzTd15513qmnTpuWODQ8P15AhQ7R8+fJqv4Pk7N7pZ+/u8fb21vTp0xUVFVXuuXVtT/WS9kqPiIjQVVddVaF5QkND9fnnn2vYsGHy9/cvcUxAQIAGDBigr776SldcccX5hAugliBvKY68pfqRt1z4yFsAuAu5S3HkLtWvLucu/fv31/jx49WrVy+Xtunx8/NT//799e677+qtt95ye2HXWeQuANyBvKU48pbqV5fzltqCvAW1lcXhzttVAUAyJXVjxozRo48+arTT0tK0YcMGxcfHKyMjQ5GRkWrfvr06duzojlANDodDe/bs0d69e5WSkiKbzSZ/f3+FhoaqWbNmatWqVblJfWG5ubnau3ev4uLilJCQoMzMTHl7eyskJETNmzdX+/btFR4eXqH4Dh48qMOHDys+Pl42m00Oh0NBQUFq2LChOnTooIsuuug8nnnFnThxQlu3blViYqIyMzMVFhamBg0aqGfPngoODq6RGCoqKSlJe/fu1bFjx5SSkqLc3FwFBAQoIiJCrVu3Vps2bVy6wwYXPpvNpo0bN+rIkSPKyspSVFSU6tWrpx49eigoKMjd4QFwA/IW8hZPQ96Cs8hbAJSE3IXcxdPU5dwlPz9fhw4d0uHDh3Xq1CnjZyc4OFihoaFq3bq1WrduXWdWfCB3AVAUeQt5i6epy3kLzMhb4Ako7gLgdmUl7AAAAJ6EvAUAAFxIyF0AAMCFgrwFAIDSsS0jAAAAAAAAAAAAAAAAAHggirsAAAAAAAAAAAAAAAAAwANR3AUAAAAAAAAAAAAAAAAAHojiLgAAAAAAAAAAAAAAAADwQD7uDgAAgNrit99+04gRI6p0zp07d1bpfDWpQ4cOVTrf/Pnz1atXryqdEwCAuoq8xYy8BQAAz0buYkbuAgCA5yJvMSNvAaoGxV0AAFQRh8Oh/Px8d4fhMar6tXA4HFU6HwAAdRl5ixl5CwAAno3cxYzcBQAAz0XeYkbeAlQNirsAuN2ePXvcHQIAAIBLyFsAAMCFhNwFAABcKMhbAAAoncVBaSMAAAAAAAAAAAAAAAAAeBwvdwcAAAAAAAAAAAAAAAAAACiO4i4AAAAAAAAAAAAAAAAA8EAUdwEAAAAAAAAAAAAAAACAB6K4CwAAAAAAAAAAAAAAAAA8EMVdAAAAAAAAAAAAAAAAAOCBKO4CAAAAAAAAAAAAAAAAAA9EcRcAAAAAAAAAAAAAAAAAeCCKuwAAAAAAAAAAAAAAAADAA1HcBQAAAAAAAAAAAAAAAAAeiOIuAAAAAAAAAAAAAAAAAPBAFHcBAAAAAAAAAAAAAAAAgAfycXcAAAAAAAAAqN3i4uK0b98+xcfHKyMjQwUFBQoJCVFoaKhatGihNm3ayGq1ljvPsWPHdM011xjtgQMHaurUqdUZOjzU0KFD9dtvvxntPXv2uDEaAAAAAACA6kNxFwAAAAAAAKrctm3btGDBAq1cuVJnzpwpc6yvr68uvvhi3XzzzbrpppsUERFRQ1ECAAAAAAAAno3iLgAAAAAAAFSZ/fv3a8qUKVqzZo3L59jtdm3atEmbNm3S1KlTdffdd2vkyJGKjIysxkgBAAAAAAAAz0dxFwAAAAAAAKrEggUL9MILLygnJ6fYY0FBQWrZsqUiIyMVHByslJQUJSQk6MiRI8rOzjbG5ebm6oMPPtDPP/+sb7/9tibDBwAAAAAAADwOxV0AAAAAAACotLfeekuvvfaaqc/Ly0t//etfddttt6l79+7y9fUtdl52drbWrl2r5cuX6+uvv1ZeXp4kZ5EXAAAAAAAAUNdR3AUAAAAAAIBK+eGHHzR9+nRTX2xsrGbMmKF27dqVea6/v7/69eunfv36aeTIkZo5c6aWLVtWneECAAAAAAAAFwwvdwcAAAAAAACAC9fRo0c1YcIEORwOo69z58769NNPyy3sKqp58+aaOXOmXnrpJfn5+VV1qAAAAAAAAMAFh5W7AAAAAAAAcN5effVVpaWlGe3Q0FDNnj1bERER5z3nbbfdpp49e1ZFeAAAAAAAAMAFjeIuAAAAAAAAnJfDhw/r+++/N/VNmDBBDRo0qPTcTZs2Pa/zDh06pN27d+vUqVPKy8tTZGSkOnbsqLZt21YqnpSUFO3fv19HjhxRamqqcnJyFBwcrPDwcLVv314tW7aUxWKp1DVKsnv3bu3fv1+nTp2SxWJRZGSkunXrpmbNmlXpdZKSkrRlyxbFx8fLZrMpPDxcsbGx6tq1q3x9fSs9f15enrZv366jR48qOTlZubm5ioiIUOPGjdW9e/caXaktMTFRu3bt0tGjR2Wz2ZSfny9/f3+Fh4froosuUuvWrRUZGVlj8QAAAAAAAJSF4i4AAAAAAACclw8++EAFBQVGOzo6WrfccotbYlm1apXmzZunrVu3lvh4kyZNNHbs2ArFt23bNi1btky//vqr9u3bZ9p6sqjw8HDdcccdGj58uGJiYlyaf/369Ro2bJjRHjNmjB599FFJ0uLFi/Xee+9p7969JZ7brl07jR8/Xn369HHpWldffbWOHz8uSWrUqJFWrlwpSTpw4ICmT5+un376SXa7vdh5wcHBuvfee/XAAw/I39/fpWsVduDAAc2dO1c//vijbDZbiWP8/f119dVXa+zYsWrRokWFr+Gq5cuXa/78+dqyZUuZ30vJWVzYt29fDR8+XE2aNKm2mAAAAAAAAMrj5e4AAAAAAAAAcGH65ZdfTO1BgwZVySpPFZGfn68XXnhBI0eOLLWwS5KOHj2qf/zjH5o8eXK5hT2S9N1332nw4MF6//33tXfv3nLPSUlJ0dtvv60BAwbo119/rejTMGRmZmrs2LGaMGFCqYVdknNFrwcffFBvv/32eV9r4cKFuu2227RixYoSC7skyWazadasWbr33ntN22+WJz8/Xy+99JIGDBigb775ptTCLknKzs7W0qVLNWDAAH3wwQcVfh7lyc3N1dixYzV27Fht3rzZpe9/XFycPvroI61bt67K4wEAAAAAAKgIVu4CAAAAAABAhcXHx+vo0aOmvssuu6zG45gyZYo++eQTSZLFYlHbtm3VpEkTWa1WnThxQjt27FBeXp4x/pNPPlGrVq10zz33lDlv4RXJJMnb21vNmjVT48aNFRwcLIvFopSUFO3du1cJCQnGuJSUFD388MP66KOP1K1btwo9l4KCAo0fP95YVcvb21sdO3ZUw4YN5eXlpbi4OO3cudNUnDRt2jS1adNGffv2rdC1li1bpn/961/GXI0aNVLr1q0VHBys5ORkbd26VRkZGcb4zZs3a+LEiZo5c2a5c2dnZ2v06NFavXq1qd/X11ft2rVTgwYNZLValZCQoB07digrK0uSc+vGl156SWlpacYKZlVh8uTJWr58uanPz8/PiMXPz09ZWVlKSUnRgQMHlJSUVGXXBgAAAAAAqCyKuwAAAAAAAFBhmzdvNrUtFos6depUozH8+OOPSk5OliTdcccdGjNmjBo0aGAac+rUKT377LP66aefjL7XXntNt956qwIDA8ucPzQ0VLfccov69eunnj17ys/Pr8RxW7Zs0YwZM7R+/XpJkt1u1//93/9p+fLlslqtLj+fzz77TMnJyfL29tb999+v++67TxEREaYxBw8e1D//+U9t377d6JsyZYr69Okji8Xi0nWSk5M1YcIEORwO9ezZU08++aQuvvhi05js7GzNmTNHb731ltG3bNky/f3vf1ePHj3KnP/55583FXaFhIRozJgxuv322xUcHFzsOp9++qlmzpypnJwcSdKcOXN08cUXV7hgrSQHDhzQggULjHZAQIDGjx+v22+/XQEBASWec/ToUf38889auHBhpa8PAAAAAABQWWzLCAAAAAAAgAo7deqUqR0dHa2wsLAajeFsYddzzz2nF198sVhhlyTVr19fc+fOVceOHY0+m82mJUuWlDl3r1699PPPP+vZZ5/VlVdeWWphlyR169ZNH3zwgQYNGmT0nThxQt98802Fn4+3t7fmzJmj8ePHFyvskqTY2Fi9//77atiwodF35MiRCm0fmJmZqezsbA0YMEDz588vVtglSf7+/ho/fryGDRtm6v/iiy/KnHvZsmVatGiR0W7UqJG+/PJLjRgxolhh19nr3HfffZo/f77xGjscDk2ZMqXY6mnn4+wqaGdNnDhRQ4cOLbWwS5KaNGmiIUOGaNGiRbrlllsqHQMAAAAAAEBlUNwFAAAAAACACktJSTG1Q0JC3BLHgAEDyt1i0cfHR2PHjjX1lVcMFRkZWWYBUFEWi0UTJ05UVFSU0ff111+7fP5ZDz74oPr161fmmODgYD344IOmvooUd0lSs2bNNGXKFPn4lL2w/+jRo+Xr6+vSdRwOh+bOnWu0fXx8NHfuXDVu3LjceLp37276Hh05ckQrVqwo97zynDhxwtTu379/hc4vq6gPAAAAAACgJlDcBQAAAAAAgAorWtwVGhrqljhGjRrl0rgrrrjCVKS0a9euKo8lICBAffr0Mdrbtm2r0OpT/v7+uvfee10ae9VVV5naO3fudPk6knTvvffK39+/3HHh4eHq1q2b0T59+rTOnDlT4th169Zp7969Rvuvf/2r2rVr53JMQ4YMMRVTFV11qyokJSVV+ZwAAAAAAADVieIuAAAAAAAAXJCaNGmili1bujTW19dXTZs2NdqlFSi5IicnR0lJSTp+/LiOHTtm+goKCjLGZWRkKD4+3uV5u3btqvDwcJfGNmrUyLSyWEWfT9HisLIUfY1Lu9aaNWtM7RtvvLFCMQUEBJi2iNy4cWOFzi9JbGysqf3KK6/IbrdXel4AAAAAAICaUva66wAAAAAAAEAJihYhpaen13gMrVq1qtD4wquL2Ww2l8/btm2bvv32W23ZskX79++v0HNNS0vTRRdd5NLYij6fkJAQZWVlSarY8wkMDFTDhg1dHh8WFmZql/b8N23aZGqHh4fr2LFjLl9Hcm45edbx48dVUFAgL6/zvz/1+uuv17///W/l5uZKkr7//nvdeOONuuOOO3TNNddU+DUHAAAAAACoaRR3AQAAAAAAoMKKFnelpaXVeAwV3Qqy8LaMeXl55Y7fu3evJk+erA0bNlQ4trMqUghW3c/nrKLFWuXx8TH/CbG0axVdpWzw4MEVuk5RBQUFSktLc3k1s5LUr19fTzzxhF566SWj7+jRo5o+fbqmT5+uqKgodevWTd26dVOPHj3UqVOnYs8XAAAAAADAnfhLBQAAAAAAACqsXr16pnZiYqLS0tIqXKBUGZVZ0ak8Gzdu1EMPPaSMjIxKzVNQUODy2Op8PjVxneTk5CqfMyMjo1LFXZI0fPhwRUVFaerUqUpISDA9dubMGa1YsUIrVqyQ5Cxa7N+/v0aMGFFsS0cAAAAAAAB3oLgLAAAAAAAAFda9e3dT2+FwaMeOHbriiivcFFHVsdlsevzxx02FXSEhIbrpppt06aWXKjY2VvXq1VNgYKD8/PxMxVKzZs3S7Nmz3RG229nt9iqf0+FwVMk8AwYM0LXXXqtvv/1Wy5cv16ZNm5SamlpsXEpKir744gstWLBA999/v/7v//6vxoruAAAAAAAASkJxFwAAAAAAACqsYcOGaty4sY4dO2b0rV+/vlYUd3322WemFZ66dOmiefPmKTIystxzbTZbdYbm0SIiInT69GlJkp+fn7Zt2yaLxeLmqM7x9/fXrbfeqltvvVUFBQU6cOCAtm7dqk2bNmn16tWm73lBQYHefvtt2e12PfXUU26MGgAAAAAA1HXcdgYAAAAAAIDz8pe//MXUXrRoUbWs3lTTfvjhB+PYYrHotddec6mwS5JR3FQXRUdHG8c5OTk6ceKEG6Mpm5eXl1q3bq077rhDU6dO1S+//KJPPvlEV155pWnchx9+qIMHD7opSgAAAAAAAIq7AAAAAAAAcJ6GDx9u2rIuISFBX3/9tRsjqhpHjhwxjlu2bKkmTZq4fO7WrVurIaILQ7du3UztX3/91U2RVJzFYlGPHj30zjvvqF+/fkZ/QUGBVq5c6cbIAAAAAABAXUdxFwAAAAAAAM5LixYtdN1115n6pk6dqlOnTlV67ri4uErPcb7S09ON45CQEJfPW7t2rUevVlXdiq56tWDBAjdFcv4sFosGDRpk6iu89SgAAAAAAEBNo7gLAAAAAAAA5238+PEKDg422qmpqRo7dqxSU1PPe86FCxfq4YcfrorwzktoaKhxfPjwYRUUFJR7jt1u1/Tp06szLI/Xp08fNW3a1Ghv375d//3vf90Y0fnx9vY2ta1Wq5siAQAAAAAAoLgLAAAAAAAAldCsWTO9/PLLpr6tW7dqyJAh2rt3b4XmOnLkiB5//HE9/fTTysnJqcowK6Rdu3bGcXJycrkrUOXn52vixInavn17dYfm0Xx8fDR27FhT36RJk/Tdd99VeK41a9bo6NGjlY7p888/r/Bqal999ZWp3bJly0rHAQAAAAAAcL4o7gIAAAAAAEClXH/99XrsscdMffv27dPf/vY3TZgwQRs2bJDdbi/x3OzsbP3444+aMGGCbrrpJi1btqwmQi7TjTfeaGq/8MIL+vDDD5Wbm1ts7Pbt2zV8+HAtWrRIkhQZGVkjMXqqv/71r7rtttuMtt1u16OPPqrx48fr999/L/W8/Px87dy5U7Nnz9ZNN92ke++9t0q2uPzss8907bXXauTIkVq4cKFOnz5d6tgTJ07oySef1PLly42+wMBAXX/99ZWOAwAAAAAA4Hz5uDsAAAAAAAAAXPhGjRqliIgIvfTSS0YRVEFBgRYvXqzFixcrKChIrVq1UmRkpIKCgpSSkqLExEQdPnxY2dnZxebz9/ev6adgGDhwoD766CPt2bNHkrNAacqUKZo9e7YuvvhihYeHy2azae/evTp+/LhxXq9evdS9e3fNmzfPXaF7hOeff15paWn6/vvvjb5vvvlG33zzjSIjI9WuXTuFh4fLy8tLNptNp0+f1oEDB6pttbb8/HytWrVKq1atkiTFxMQoNjZW4eHh8vPzU2Zmpg4fPqwDBw7I4XCYzn366acVERFRLXEBAAAAAAC4guIuAAAAAAAAVIm7775bl1xyiV588UWtX7/e9FhGRoa2bdtW7hz+/v4aOnSoHnrooeoKs1w+Pj6aN2+ehg8frri4OKM/NTVVv/zyS4nnXH755Zo1a5bef//9mgrTY/n6+mrWrFl65513NGvWLFPRVlJSktasWePSHAEBAdUSX0JCghISEsoc4+/vr2eeeUZ33HFHtcQAAAAAAADgKoq7AAAAAAAAUGXatGmjDz/8UFu2bNF///tfrVy5UklJSWWeY7Va1bVrVw0YMEA33nijQkNDayja0l100UVavHixZs6cqf/+97/KysoqcVyHDh1011136Y477pCXl1cNR+m5LBaLHnzwQd1666368MMPtWTJEtMqZyUJCgpSjx491LdvX910001VsmLW9OnTtXLlSq1evVrbt29XZmZmmeOjoqJ0ww036MEHH1TDhg0rfX0AAAAAAIDKsjiKrjUOAAAAAAAAVKHDhw9r//79io+PV0ZGhgoKChQaGqqwsDC1aNFCbdq0ka+vr7vDLFVmZqa2bNmiQ4cOyWazKSQkRNHR0WrXrp2aNWvm7vAuGEePHtXOnTuVlJSktLQ0WSwWBQUFqX79+mrRooWaN28ub2/vart+fn6+Dh06pMOHD+vUqVPKyMhQfn6+AgMDFR0drdatW6tly5bVGgMAAAAAAEBFUdwFAAAAAAAAAAAAAAAAAB6IteIBAAAAAAAAAAAAAAAAwANR3AUAAAAAAAAAAAAAAAAAHojiLgAAAAAAAAAAAAAAAADwQBR3AQAAAAAAAAAAAAAAAIAHorgLAAAAAAAAAAAAAAAAADwQxV0AAAAAAAAAAAAAAAAA4IEo7gIAAAAAAAAAAAAAAAAAD0RxFwAAAAAAAAAAAAAAAAB4IIq7AAAAAAAAAAAAAAAAAMADUdwFAAAAAAAAAAAAAAAAAB6I4i4AAAAAAAAAAAAAAAAA8EAUdwEAAAAAAAAAAAAAAACAB6K4CwAAAAAAAAAAAAAAAAA8EMVdAAAAAAAAAAAAAAAAAOCBKO4CAAAAAAAAAAAAAAAAAA9EcRcAAAAAAAAAAAAAAAAAeCCKuwAAAAAAAAAAAAAAAADAA1HcBQAAAAAAAAAAAAAAAAAeiOIuAAAAAAAAAAAAAAAAAPBAFHcBAAAAAAAAAAAAAAAAgAeiuAsAAAAAAAAAAAAAAAAAPBDFXQAAAAAAAAAAAAAAAADggSjuAgAAAAAAAAAAAAAAAAAPRHEXAAAAAAAAAAAAAAAAAHggirsAAAAAAAAAAAAAAAAAwANR3AUAAAAAAAAAAAAAAAAAHuj/A8NIeTwhyOX0AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 511,
+       "width": 1211
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Normalize allocations by budget per time unit and convert to percentages\n",
+    "_normalize_factor = 100\n",
+    "allocation_reengage_norm = (\n",
+    "    allocation_reengage.values / budget_per_time_unit_in_horizon\n",
+    ") * _normalize_factor\n",
+    "allocation_new_users_norm = (\n",
+    "    allocation_new_users.values / budget_per_time_unit_in_horizon\n",
+    ") * _normalize_factor\n",
+    "\n",
+    "# Get channel names from coordinates\n",
+    "channel_names = allocation_reengage.coords[\"channel\"].values\n",
+    "\n",
+    "# Calculate absolute differences\n",
+    "abs_diff = np.abs(allocation_new_users_norm - allocation_reengage_norm)\n",
+    "\n",
+    "# Create bar plot\n",
+    "fig, ax = plt.subplots(figsize=(12, 5))\n",
+    "\n",
+    "x = np.arange(len(channel_names))\n",
+    "width = 0.35\n",
+    "\n",
+    "# Create bars\n",
+    "bars1 = ax.bar(\n",
+    "    x - width / 2, allocation_reengage_norm, width, label=\"Reengage\", color=\"lightblue\"\n",
+    ")\n",
+    "bars2 = ax.bar(\n",
+    "    x + width / 2, allocation_new_users_norm, width, label=\"New Users\", color=\"orange\"\n",
+    ")\n",
+    "\n",
+    "# Add value labels inside bars (white text)\n",
+    "for _i, (bar1, bar2) in enumerate(zip(bars1, bars2, strict=False)):\n",
+    "    # Reengage values\n",
+    "    height1 = bar1.get_height()\n",
+    "    if height1 > 0:\n",
+    "        ax.text(\n",
+    "            bar1.get_x() + bar1.get_width() / 2.0,\n",
+    "            height1 / 2,\n",
+    "            f\"{height1:.1f}%\",\n",
+    "            ha=\"center\",\n",
+    "            va=\"center\",\n",
+    "            color=\"white\",\n",
+    "            fontweight=\"bold\",\n",
+    "        )\n",
+    "\n",
+    "    # New Users values\n",
+    "    height2 = bar2.get_height()\n",
+    "    if height2 > 0:\n",
+    "        ax.text(\n",
+    "            bar2.get_x() + bar2.get_width() / 2.0,\n",
+    "            height2 / 2,\n",
+    "            f\"{height2:.1f}%\",\n",
+    "            ha=\"center\",\n",
+    "            va=\"center\",\n",
+    "            color=\"white\",\n",
+    "            fontweight=\"bold\",\n",
+    "        )\n",
+    "\n",
+    "    # Add absolute difference on top\n",
+    "    max_height = max(height1, height2)\n",
+    "    ax.text(\n",
+    "        x[_i],\n",
+    "        max_height + 2,\n",
+    "        f\"{abs_diff[_i]:.1f}%\",\n",
+    "        ha=\"center\",\n",
+    "        va=\"bottom\",\n",
+    "        fontweight=\"bold\",\n",
+    "    )\n",
+    "\n",
+    "# Customize plot\n",
+    "ax.set_xlabel(\"Channels\")\n",
+    "ax.set_ylabel(\"Budget Allocation (%)\")\n",
+    "ax.set_title(\"Budget Allocation Comparison: New Users vs Reengage\")\n",
+    "ax.set_xticks(x)\n",
+    "ax.set_xticklabels(channel_names)\n",
+    "ax.set_ylim(0, 45)\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "ax.legend()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "New Users given marketing budget: 14.81347876559035\n",
+      "Reengage given marketing budget: 12.280383035889008\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"New Users given marketing budget: {abs(result_new_users['fun'])}\")\n",
+    "print(f\"Reengage given marketing budget: {abs(result_reengage['fun'])}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The picture makes everything crystal clear: the channels that drive new users are not the same as those that drive re-engaged users. This is problematic because if we want to optimize for new users, our allocation is directionally different from the one for re-engaged users.\n",
+    "\n",
+    "We can see that both responses are similar in terms of the budget, but the budget allocations for those responses change radically. To visualize this phenomenon better, we can compute the response distributions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 510,
+       "width": 1193
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
+    "\n",
+    "_distribution = optimizer_reengage.extract_response_distribution(\"contribution\").sum(\n",
+    "    axis=(1, 2)\n",
+    ")\n",
+    "_distribution_func = function([optimizer_reengage._budgets_flat], _distribution)\n",
+    "posterior_reengage_given_new_users_budget_allocation = _distribution_func(\n",
+    "    allocation_new_users.values\n",
+    ")\n",
+    "az.plot_posterior(\n",
+    "    posterior_reengage_given_new_users_budget_allocation,\n",
+    "    color=\"lightblue\",\n",
+    "    # label=f\"Total response of reengage given new users budget allocation\",\n",
+    "    ref_val=round(abs(result_reengage[\"fun\"]), 4),\n",
+    "    ax=axes[0],\n",
+    ")\n",
+    "axes[0].set_title(\"Reengage Response Given New Users Budget Allocation\")\n",
+    "\n",
+    "_distribution = optimizer_new_users.extract_response_distribution(\"contribution\").sum(\n",
+    "    axis=(1, 2)\n",
+    ")\n",
+    "_distribution_func = function([optimizer_new_users._budgets_flat], _distribution)\n",
+    "posterior_new_users_given_reengage_budget_allocation = _distribution_func(\n",
+    "    allocation_reengage.values\n",
+    ")\n",
+    "az.plot_posterior(\n",
+    "    posterior_new_users_given_reengage_budget_allocation,\n",
+    "    color=\"purple\",\n",
+    "    # label=f\"Total response of new users given reengage budget allocation\",\n",
+    "    ref_val=round(abs(result_new_users[\"fun\"]), 4),\n",
+    "    ax=axes[1],\n",
+    ")\n",
+    "axes[1].set_title(\"New Users Response Given Reengage Budget Allocation\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This makes everything clear: if we use the budget recommendation from the new users model, we will get a good number of new users, but we'll penalize re-engaged users, getting 40% fewer than we could with the same budget distributed differently. A similar story unfolds if we use the recommendation for re-engaged users; we then penalize new users.\n",
+    "\n",
+    "How can we create an optimization problem that considers both responses and finds a solution that doesn't penalize any target metric? The answer is the `BuildMergedModel` class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[31mInit signature:\u001b[39m\n",
+      "BuildMergedModel(\n",
+      "    models: list[pymc_marketing.mmm.budget_optimizer.OptimizerCompatibleModelWrapper],\n",
+      "    prefixes: list[str] | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n",
+      "    merge_on: str | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[33m'channel_data'\u001b[39m,\n",
+      "    use_every_n_draw: int = \u001b[32m1\u001b[39m,\n",
+      ") -> \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "\u001b[31mDocstring:\u001b[39m     \n",
+      "Merge multiple optimizer-compatible models into a single model.\n",
+      "\n",
+      "This wrapper combines several optimizer-compatible MMM wrappers by:\n",
+      "- Merging their posterior `InferenceData` with per-model prefixes\n",
+      "- Optionally thinning posterior draws via ``use_every_n_draw``\n",
+      "- Exposing a persistent merged PyMC ``Model`` for optimization through\n",
+      "  ``_set_predictors_for_optimization`` and a dynamic ``model`` property for\n",
+      "  inspection when needed\n",
+      "\n",
+      "Parameters\n",
+      "----------\n",
+      "models : list[OptimizerCompatibleModelWrapper]\n",
+      "    A list of wrappers that each expose ``idata`` and\n",
+      "    ``_set_predictors_for_optimization(num_periods: int) -> Model``.\n",
+      "prefixes : list[str] | None, optional\n",
+      "    Per-model prefixes used when merging. If ``None``, defaults to\n",
+      "    ``[\"model1\", \"model2\", ...]`` with one prefix per model.\n",
+      "merge_on : str | None, optional, default \"channel_data\"\n",
+      "    Name of a variable expected to be present in all models and that should\n",
+      "    remain unprefixed and be used for aligning/merging dims (e.g.,\n",
+      "    ``\"channel_data\"``). If ``None``, no variable is treated as shared and\n",
+      "    all variables/dims are prefixed.\n",
+      "use_every_n_draw : int, optional, default 1\n",
+      "    Thinning factor applied when merging idatas. Keeps every n-th draw.\n",
+      "\n",
+      "Attributes\n",
+      "----------\n",
+      "prefixes : list[str]\n",
+      "    The final list of prefixes used for each model.\n",
+      "models : list[OptimizerCompatibleModelWrapper]\n",
+      "    The provided list of wrappers.\n",
+      "num_models : int\n",
+      "    Number of models being merged.\n",
+      "num_periods : int | None\n",
+      "    Number of forecast periods inferred from the primary model (if available).\n",
+      "idata : arviz.InferenceData\n",
+      "    The merged and prefixed posterior (and data) container.\n",
+      "adstock : Any\n",
+      "    Carried over from the primary model when available.\n",
+      "model : pymc.Model\n",
+      "    Property returning a merged PyMC model; see Notes.\n",
+      "\n",
+      "Examples\n",
+      "--------\n",
+      "Merge three multidimensional MMMs into a single optimizer model:\n",
+      "\n",
+      ".. code-block:: python\n",
+      "\n",
+      "    from pymc_marketing.mmm.multidimensional import (\n",
+      "        MMM,\n",
+      "        MultiDimensionalBudgetOptimizerWrapper,\n",
+      "    )\n",
+      "    from pymc_marketing.mmm.budget_optimizer import (\n",
+      "        BuildMergedModel,\n",
+      "        BudgetOptimizer,\n",
+      "    )\n",
+      "\n",
+      "    # Assume m1, m2, m3 are already fitted MMM instances\n",
+      "    w1 = MultiDimensionalBudgetOptimizerWrapper(\n",
+      "        model=m1, start_date=start, end_date=end\n",
+      "    )\n",
+      "    w2 = MultiDimensionalBudgetOptimizerWrapper(\n",
+      "        model=m2, start_date=start, end_date=end\n",
+      "    )\n",
+      "    w3 = MultiDimensionalBudgetOptimizerWrapper(\n",
+      "        model=m3, start_date=start, end_date=end\n",
+      "    )\n",
+      "\n",
+      "    merged = BuildMergedModel(\n",
+      "        models=[w1, w2, w3],\n",
+      "        prefixes=[\"north\", \"south\", \"west\"],\n",
+      "        merge_on=\"channel_data\",\n",
+      "        use_every_n_draw=2,\n",
+      "    )\n",
+      "\n",
+      "    optimizer = BudgetOptimizer(\n",
+      "        model=merged,\n",
+      "        num_periods=merged.num_periods,\n",
+      "        response_variable=\"north_total_media_contribution_original_scale\",\n",
+      "    )\n",
+      "\n",
+      "Single model: auto-prefix and thin draws:\n",
+      "\n",
+      ".. code-block:: python\n",
+      "\n",
+      "    merged_single = BuildMergedModel(\n",
+      "        models=[w1],\n",
+      "        prefixes=None,  # auto -> [\"model1\"]\n",
+      "        merge_on=\"channel_data\",\n",
+      "        use_every_n_draw=5,\n",
+      "    )\n",
+      "    m_opt = merged_single._set_predictors_for_optimization(\n",
+      "        num_periods=merged_single.num_periods\n",
+      "    )\n",
+      "\n",
+      "Merge everything with prefixes (no shared variable retained):\n",
+      "\n",
+      ".. code-block:: python\n",
+      "\n",
+      "    merged_all_prefixed = BuildMergedModel(\n",
+      "        models=[w1, w2],\n",
+      "        prefixes=[\"a\", \"b\"],\n",
+      "        merge_on=None,  # do not keep any unprefixed variable\n",
+      "    )\n",
+      "\u001b[31mFile:\u001b[39m           ~/Documents/GitHub/pymc-marketing/pymc_marketing/mmm/budget_optimizer.py\n",
+      "\u001b[31mType:\u001b[39m           _ProtocolMeta\n",
+      "\u001b[31mSubclasses:\u001b[39m     "
+     ]
+    }
+   ],
+   "source": [
+    "BuildMergedModel?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `BuildMergedModel` class is designed to merge multiple optimizer-compatible models into a single unified graphical representation. This is achieved by combining the posterior `InferenceData` from each model with unique prefixes, allowing for the integration of multiple models' outputs into a cohesive framework. The class operates in a symbolic graphical space, where models are treated as generative processes that can be combined similarly to SQL queries. This approach enables the seamless merging of models by aligning and merging dimensions based on a shared variable, typically referred to as `merge_on`. The merged model is then exposed as a persistent PyMC model, which can be used for further optimization and analysis.\n",
+    "\n",
+    "Let's take a look at how it works!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged_model = BuildMergedModel(\n",
+    "    models=[new_users_optimizable_model, reengage_optimizable_model],\n",
+    "    prefixes=[\"new_users\", \"reengage\"],\n",
+    "    merge_on=\"channel_data\",\n",
+    "    use_every_n_draw=1,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that the model is merged, we can reformulate the optimization process. Maximizing two variables, such as new user acquisition and re-engagement, is inherently challenging because they represent different units and objectives. Attempting to maximize both simultaneously leads to an infinite solution space, as improving one objective often comes at the expense of the other.\n",
+    "\n",
+    "However, we can reformulate the problem by maximizing one variable while holding the other as a constraint. This approach allows the optimizer to focus on maximizing a single objective while ensuring that the other variable remains above a specified threshold $Z$, which represents our minimum goal for that \"*secondary objective*\".\n",
+    "\n",
+    "Here, we are saying we want a budget that brings in at least 10 (thousand or million) re-engaged users, and that new users should be maximized as long as that constraint is met."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "min_total_response = 10\n",
+    "\n",
+    "\n",
+    "def mean_response_eq_constraint_fun(budgets_sym, total_budget_sym, optimizer):\n",
+    "    \"\"\"Enforces mean_response(budgets_sym) = target_response, i.e. returns (mean_resp - target_response).\"\"\"\n",
+    "    resp_dist = optimizer.extract_response_distribution(\"reengage_contribution\")\n",
+    "    mean_resp = pt.mean(_check_samples_dimensionality(resp_dist.sum(axis=(1, 2))))\n",
+    "    return mean_resp - min_total_response\n",
+    "\n",
+    "\n",
+    "optimizer = BudgetOptimizer(\n",
+    "    model=merged_model,\n",
+    "    num_periods=optimization_horizon,\n",
+    "    response_variable=\"new_users_contribution\",\n",
+    "    utility_function=average_response,\n",
+    "    custom_constraints=[\n",
+    "        Constraint(\n",
+    "            key=\"min_response_constraint\",\n",
+    "            constraint_fun=mean_response_eq_constraint_fun,\n",
+    "            constraint_type=\"ineq\",\n",
+    "        )\n",
+    "    ],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "allocation, result, callback_results = optimizer.allocate_budget(\n",
+    "    total_budget=budget_per_time_unit_in_horizon,\n",
+    "    callback=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The optimization is now performed using a merged model that combines multiple deterministic variables (objective variables). As a consequence, we can use any of them in any part of the optimization.\n",
+    "\n",
+    ":::{tip}\n",
+    "You can create any type of objective function. This is just an example, so be as creative as you want. As long as your objective is feasible, the optimizer can handle the rest.\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Constraint satisfied: value 0.0 is close to zero\n"
+     ]
+    }
+   ],
+   "source": [
+    "constraint_value = round(callback_results[-1][\"constraint_info\"][0][\"value\"], 4)\n",
+    "# Check if constraint is satisfied (should be close to zero for equality, >= 0 for inequality)\n",
+    "if np.isclose(constraint_value, 0, atol=1e-6):\n",
+    "    print(f\"Constraint satisfied: value {constraint_value} is close to zero\")\n",
+    "elif constraint_value > 0:\n",
+    "    print(f\"Constraint satisfied: value {constraint_value} is greater than zero\")\n",
+    "else:\n",
+    "    raise ValueError(f\"Constraint violated: value {constraint_value} should be >= 0\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAACXcAAAP/CAYAAACBf20nAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjMsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvZiW1igAAAAlwSFlzAAAewgAAHsIBbtB1PgABAABJREFUeJzs3XVYVOn7P/D3SHcoNhaKKBZ2B3Yi1uoq6trd+tFddc01VnftDmxd12DXDuxGUWwsVAxQAeme3x/+OF/OFDPDDIPs+3VdXpfncOKZOGfOeZ773LdEKpVKQURERERERERERERERERERERERLlKPkM3gIiIiIiIiIiIiIiIiIiIiIiIiOQxuIuIiIiIiIiIiIiIiIiIiIiIiCgXYnAXERERERERERERERERERERERFRLsTgLiIiIiIiIiIiIiIiIiIiIiIiolyIwV1ERERERERERERERERERERERES5EIO7iIiIiIiIiIiIiIiIiIiIiIiIciEGdxEREREREREREREREREREREREeVCDO4iIiIiIiIiIiIiIiIiIiIiIiLKhRjcRURERERERERERERERERERERElAsxuIuIiIiIiIiIiIiIiIiIiIiIiCgXYnAXERERERERERERERERERERERFRLsTgLiIiIiIiIiIiIiIiIiIiIiIiolyIwV1ERERERERERERERERERERERES5EIO7iIiIiIiIiIiIiIiIiIiIiIiIciEGdxEREREREREREREREREREREREeVCDO4iIiIiIiIiIiIiIiIiIiIiIiLKhRjcRURERERERERERERERERERERElAsxuIuIiIiIiIiIiIiIiIiIiIiIiCgXYnAXERERERERERERERERERERERFRLmRs6AYQERER5WWhoaFo3ry5MF2sWDH4+/sbsEVkCAcPHsS0adOEaW9vbyxcuFDp8vzefH9WrlyJVatWCdOjRo3C6NGjDdiivO3jx48IDg7Ghw8fEBMTg+TkZFhbW8PW1haFChWCu7s7bG1tDd1M+g/x8fHBzZs3hent27ejTp06BmwRERERERERaePGjRvo27evMF27dm3s2LHDgC0iIiJicBcRERFpaerUqTh06JBay0okElhZWcHGxgbOzs6oWLEiGjdujHr16iFfPiYSpdzjyJEjmDhxomiepaUlrly5AktLSwO1iogA4PHjxzhw4AD8/f3x7t07lctKJBKULFkSTZs2hZeXFypWrJhDrSSivEg2gDeDl5cXFi9erPH27t+/j27duonmPX36VOv2kXKenp6i34zsBl/LfhcYgE95geyDKMrky5cPlpaWsLGxQeHCheHu7o6aNWuiefPmMDU1zYGWEhEB5cuXVzi/ZcuWCq/X1BUdHY2GDRsiKSlJ7m9ZPaBHRERElBMY3EVERER6J5VKERsbi9jYWHz48AE3b96Er68vihUrhsmTJ6Nt27aGbiLlkNDQUFFQYLFixdClSxcDtkhMUcBifHw8Tp48CW9vbwO0iHKCr68vYmJihOl+/fox61Mu8vLlSyxYsAAXL15Uex2pVIqQkBD4+vrC19cX7u7umDhxIho0aKDHlhLRf82///6LIUOGoGzZsoZuChGR3qWnp4vu6wMDA7Fz507Y29ujf//+GDx4MIyNOdxARIZx/vx5REZGwsHBQav1jx07pjCwi4iIiCi3YKoMIiIiMph3795h3LhxmD59OqRSqaGbQzng3bt3WLVqlfBP3exvOSEsLAxXr15V+LeDBw/mcGsoJ23fvl30vYyOjjZ0k+j/27VrFzp16qQysEsikcDOzg5WVlaQSCQKl3n48CEGDBiAmTNn6qupRPQflJ6ejmXLlhm6GUREBhUVFYVly5ahV69evI4mIoNJSUnB0aNHtV7/8OHDumsMERERkR7wURoiIiLSiVKlSqF///4K/5aWlobY2Fi8evUK169fx8ePH0V/379/P+zt7TFp0qQcaCmRYocPH0Z6errCv926dQuhoaEoXrx4DreK6L9rwYIF8PX1lZtfoEABtGrVCo0bN0blypXh4OAAIyMjAEBCQgJevnyJW7du4fjx47h7965o3VevXuVAy4nov+T06dO4f/8+KleubOimEBHpzKxZs+TmZWTuCgkJwbVr1/DhwwfR34OCgjBixAhs27ZNuDYjIspJhw8fRp8+fTRe79WrVwgMDNRDi4iIiIh0h8FdREREpBMFCxZEr169slwuPT0dfn5+mDNnDuLj44X5W7duRefOnVnWhgxGNouYkZER0tLSAHwr8Xb48GGMGjXKEE2j78Do0aMxevRoQzcjz1ixYoVcYJe5uTkGDBiAQYMGwcrKSuF6FhYWcHd3h7u7O/r374+HDx9ixYoVOH/+vP4bTf9pO3bsMHQTyICWLVuGzZs3G7oZREQ6k9W9fVpaGg4ePIjffvtNdF9/69YtHDp0CN26ddN3E4mIAACurq4IDg4GANy/fx8vXryAi4uLRtuQzdpVvnx5PH36VFdNJCIiItIJlmUkIiKiHJUvXz54e3vLlbBJTU1l6TsymMDAQFFGHxMTEwwePFi0zOHDh1k+lCgHnDt3DqtXrxbNs7GxwZYtWzB27FilgV2KuLu7Y/369VixYgXs7Ox03VQi+o9ycnISTV++fBk3b940UGuIiHKekZERunfvjmXLlsmVxd64caOBWkVE/0Xe3t6iaU3LK2Y8hJrB2dkZNWrU0EXTiIiIiHSKwV1ERERkEE2aNEGtWrVE865cuWKg1tB/nWzWrkaNGqFfv34wNv6/RLdv375FQEBATjeN6D8lNjYWM2bMEM0zNTWFr69vtjrYW7dujYMHD2r8BDcRkSLly5dH06ZNRfP+/PNPwzSGiMiAmjRpgmbNmonmhYSEIDQ01EAtIqL/murVq6NkyZLCtJ+fH9LT09Ve//r166Iys507d5YLWiUiIiLKDRjcRURERAZTt25d0fTHjx8N1BL6L0tKSsLx48dF8zp37gxHR0c0atRINJ/Z5Yj0y9fXF58+fRLNGzVqFCpVqpTtbRcvXhwzZ87M9naIiABg/PjxooG/O3fu4MKFCwZsERGRYbRu3VpuXlBQkAFaQkT/VV5eXsL/w8LCcO3aNbXXzfywn0QiEW2LiIiIKDcxznoRIiIiIv2QLWkTFxdnoJZo7t27dwgKCkJYWBiSkpJgZ2eHcuXKoWrVqqJsT7oSGRmJ27dvIywsDNHR0bC2tkbJkiVRvXp1WFtb63x/GV68eIFnz54hIiIC0dHRsLGxQf78+VGlShUULVpUb/vNSadPn0Z0dLQwbWdnJzx93rlzZ5w7d07428mTJzFz5kxYWFjkeDu1FR4ejqCgIHz58gVRUVGwtLSEo6MjypQpAzc3N50/kfrlyxdhf5GRkZBKpbCyskLx4sVRvnx5FC5cWKvtRkVF4fnz53j9+jWio6ORmJgIGxsb2NnZwdXVFa6urnni6VqpVIpHjx7h1atXiIiIQEJCAhwcHODo6AgPDw/kz59fL/tNSUnB3bt38ezZM3z9+hUWFhYoUKAAPDw8UKxYMb3sU1ZycjJ27dolmleqVCkMGjRIZ/vIl0/z55uioqJw9+5dfPr0CZGRkTA1NYWjoyNKliyJSpUqwcjISGfty+zFixd48OABwsPDkZaWhvz586Ny5cpwc3PLcl2pVIoHDx7gyZMniIyMhImJCZycnFCrVi0UKlRI5239+PEj7t69i/fv3yM5ORkFChRA8eLFUaNGDZiYmGR7+4Y8/pOSknDv3j28fPkSX79+BQDkz58fXl5eOnltGaKjo/H48WO8fv0aMTExSEpKgrm5OaytrVGkSBGULFkSJUqUyNY+UlNTERQUhNDQUERERCA5ORkODg4oWLAgPDw8YGtrq6NXI5aQkIA7d+7g1atXiImJgbW1NZycnFCzZk0UKFBAL/vMCW5ubmjTpo0oQHzZsmVo3Lhxjv8effr0CUFBQYiIiBDOU/nz50fZsmX18ltP2ktJSRGuryMjIxEXFwcjIyNYWlrCyckJxYsXR9myZWFmZpat/aSlpeH+/fsIDQ3Fly9fkJiYKBzv1atX19vx/vz5czx+/Bjh4eFISkqCjY0NGjZsiNKlSytdJy4uDk+fPsXLly8RExODhIQEmJmZwdLSEkWKFIGzszNKly6t1W/49+Dt27d48uQJwsPDERsbC6lUCgsLCzg4OKBYsWJwcXGBvb29oZupUrly5eTmRUREaL29nLwH/fr1K+7evYvPnz8jIiICxsbGcHBwQKlSpVC5cmW9XOflxHV3bGwsAgIC8PHjR0RGRsLCwgIlSpSAh4cHHBwcdLKPDGlpaQgJCcHLly8RHh6OmJgYGBsbw87ODoUKFULVqlV1Wh5dKpXi/v37wv4kEgmcnJzg7u6u8LuoK3Fxcbh79y7Cw8MREREBqVQKR0dHFC9eHNWqVYOpqane9p1b5MT1qra8vLywcuVKSKVSAN9KMzZo0CDL9eLi4nD69GlhukaNGnB2dtZp26RSKR4/foyQkBBEREQgNjYWdnZ2cHJy0us9/tu3b/HgwQN8/PgRCQkJsLS0RK1ateDu7q5yvbze//jixQs8efIEYWFhSE1NhYODA9zc3FCpUiVeMxMRUa7H4C4iIiIymISEBNG0lZVVluv4+Pjg5s2bwvT27dtRp04dtfd58OBBTJs2TZj29vbGwoUL1V7/ypUrWLFiBe7evavw7/b29ujVqxeGDh2qkwCgx48fY8mSJbh+/TpSU1Pl/m5qaop27dph3LhxKFKkCADA09MT7969E5Y5e/YsihcvrvY+P3/+jI0bN+LUqVN4//690uXKli2Lvn37olu3bio7vW/cuIG+ffsq/NvNmzdRvnx5pesWK1YM/v7+arddG7IlGdu2bSt0zHp6esLOzk4Y0I+Li8PJkyfRuXNnvbYpu9LS0nDgwAHs3r0bjx8/Vrqck5MT2rZti+HDh8PR0VHr/cXHx2PXrl34999/ERwcLHSoKlKyZEk0a9YM3bt3R9myZZUul56ejps3b+L06dO4fv06nj9/rrINtra2aN++PQYOHKhWZ6zscZJZ8+bNVa6r6LyzcuVKrFq1SpgeNWoURo8enWU7MoSHh2Pt2rU4efIkvnz5onAZiUSCihUrwsfHB15eXmoPcoaGhopeU+bjKjY2FuvXr8fevXtFQY6Zubu7Y+LEiWp1jmfH+fPn5QYCe/ToobfgqaycOHECvr6+uHfvntKyHvb29vD09MTIkSM1Os9OnTpVdO5ZsGABunTpAgA4duwY1qxZg2fPnilc183NDVOnTkW9evXk/paSkoJdu3Zhy5YtCAsLk/u7RCJBw4YN8fPPP6NMmTJqtVXV72ZAQACWL18u+l3OzN7eHl5eXhgzZoxGAwH6Pv4zqDo2Pnz4gFWrVuHo0aNy1ysA0KpVK1Fwl7bXJ/7+/tixYweuX7+eZfkYe3t71KxZE61bt0aHDh3UPgeEhIRg9erVOH/+vNLj3MjICB4eHhgwYECW58DMZH/ja9eujR07dgD4dj2xcuVKHD58GImJiXLrSiQS1K5dG5MmTUKVKlXU3mduMnbsWJw6dQppaWkAgEePHuH48eNo166d3vednJyM3bt349ChQ3jy5InS5ZycnODt7Y3BgwdnGdBz8+ZN+Pj4CNN169bFtm3bsmzLggUL4OvrK5r366+/4scff8xy3UaNGiE8PFyYvnjxol6CUA3t3bt3WL9+PU6cOCFcVypjYmKCChUqwNPTE126dNHo/Xjy5AnWr1+PS5cuISYmRuEyRkZGqFGjBkaMGKHwt0QZZee51NRU7NmzB9u2bcPbt2/l1ps2bZrC4K6AgABs2bIFFy9eREpKisp9W1lZwcPDA61atYKXlxfMzc3Vbrcq/fv3F2V3GT16NEaNGqXVtoYPHy66bxk4cCCmTJmicNm4uDj4+vri0KFDCt+zzCQSCUqWLIlGjRrB29s7y0F5Q1D0G6/s+6eMru9BVUlLS8M///yDvXv34v79+8I5XJa9vT3atGmDkSNHomDBgmpt29DX3W/fvsWSJUtw7tw5JCUlyf3dyMgITZs2xfjx44VAKG2uYT59+oSTJ0/i4sWLuH37NmJjY5UuK5FI4O7ujv79+6Ndu3Zaf27JycnYsmUL9uzZozTre+nSpTFs2DDhfj27/T8AcObMGWzfvh137txReq6ytLSEp6cnRo8ejVKlSmm0fXX5+fmJzinFixfHmTNntApGOX36tOhc5+TkhAsXLij9bHLiejW7ihcvjlq1agnf5dOnTyMuLi7Lfsbjx4+LrvV12dfz9u1brF+/Hv7+/irv8d3d3TF48GC0adNG7W2rup/08/PD5s2b8fTpU7n1+vbtq/R3JC/0P6py9OhRrFu3DsHBwQr/XqhQIfz000/w8fHRy0O7REREupA3H3kiIiKi74JsR4OLi4uBWpK1tLQ0zJw5EwMGDFAa2AV8yy6ydu1adO7cOcuO+qysXr0aXbt2xeXLlxV2rADfOjgPHz6M9u3b4+LFi9naHwBs2rQJLVu2hK+vr8qOFeDbk/kzZ86El5dXtl+roYSFheHq1auieZk780xNTdG2bVvR32WDwXKbZ8+eoUOHDpgxY4bKwC7gW6f89u3b0aJFC+zfv1+r/R0+fBienp5YsmQJnj59qjKwCwBev34NX19ftG/fXuVyPXv2RL9+/bBz584sAzuAb08R79mzB23btsXu3bs1eg2GtmPHDrRq1Qq7d+9W2ukLfHvi9+HDh5g6dSo6d+6MkJCQbO33yZMn6NSpEzZs2KB0gAkAHj58iAEDBmD16tXZ2l9WMmfJA74NPnl7e+t1n4p8/PgRP/zwA8aOHYvAwECVAxhRUVE4ePAg2rRpgzVr1mRrv8nJyZg0aRLGjx+vNLAL+Pa5DRgwQAigyRAWFoaePXtiwYIFCgO7gG/foUuXLqFr164ICAjIVntXrVqFPn36KA3sAr69P9u2bUO7du0QGBio9rYNffyfPXsW7dq1w99//60wsEsXEhISMHLkSAwfPhxXr17NcqAM+PZ+njlzBpMnT1Y5iJpBKpXijz/+QPv27fHPP/+oPM7T0tIQEBCAESNGwMfHR+W5SB3Xrl1D+/btsXfvXoWBXRntu3HjBnr27Klx2WNPT0+UL19e+Jc5ICknlS5dWm4QcMWKFUoDBXTl4sWLaNWqFRYsWKAysAv49lu/YcMGtGzZMsuykdWqVYOlpaUwfefOHaWfX2ay11LK5sl6/vy5KLDLxcUlTwZ27d+/H+3atcO+ffuyDOwCvgXqBgUFYdmyZXKlw5VJSEgQrg+OHTumMrAmLS0NN2/eRP/+/TFmzJhsnec+ffqEXr16Yd68eWrfD6SlpeHXX39F7969cfbs2SwDu4BvwVCXL1/GzJkz8erVK63bK6tr166i6YMHD2Z5LavIly9fcOnSJdG8jEF2WUFBQWjbti1WrFih1nsmlUoREhKCHTt26P1aTFuKvm+aPOiUk/eg9+/fR6dOnTB16lTcvXtX5fk6KioKe/fuRatWrXDgwAGN95VZTlx379+/Hx06dMCJEycUBnYB346/s2fPwtvbW+vXdP78eTRu3Bhz587FhQsXsrwmycgoO2nSJPTq1Uvpdaoqb968gZeXF/7880+lgV0A8OrVK/zvf//DiBEjsn0N9+bNG/zwww8YOXIkbty4ofJcFR8fjyNHjqB9+/ZYu3ZttvarTOvWrWFjYyNMh4aGqrwOV0X2usvLy0thwExOXK/qUuZrsoSEBJw8eTLLdQ4fPiz839zcXK7/RxtpaWlYtGgR2rZti/3792d5j//gwQOMHTsWvXv3zlbWw9jYWAwdOhRTpkxRGNilSl7uf0xISMCwYcMwYcIEpYFdwLf76YULF2b7cyAiItInBncRERGRQURGRopSnwPQe1YYbaWlpWHKlCnYt2+fwr+bm5uLBsKAb1ky+vfvj8jISK32+ccffygdHDQxMZHLvBAXF4eRI0fi9u3bWu0vJSUF06ZNw++//474+HiF+7S3t1dYgurZs2f44YcfshxczI0OHz4s6qAsWbIkPDw8RMvIDtreuHEjy44nQwkICMCPP/6Ily9fKvy7ra2twicQ4+LiMH36dKxcuVLtfUmlUixatAj/+9//lH7Pzc3NYWdnp9XTusoGPfLlywcbGxvY2toq3G5KSgpmz56NdevWabxPQ1i0aBHmzZuncPBB0bGe4enTp+jVqxeCgoK02m9wcDD69u0rl73MxsZGaTaMFStWZHtgSxXZwQkXF5dsZZTTxosXL9CzZ0+lQbzW1tYKz4MpKSlYvnw5fv75Z7UGPWRJpVJMmTIF//77r2i+hYWFwqfN09PTMX/+fCFQIzIyEj4+Pnjw4IFoOWWfZ3x8PIYNG6bV4BoAbN68WVT2BPgWjGdra6swe0BYWBgGDRqEe/fuqbV9Qx7/V65cwZgxY+R+C21sbHRabmfUqFE4c+aMwr+ZmZnBwcEBVlZWWpcGSU1NxcSJE7F+/XqlT94ry6Z28+ZN/PDDD3jz5o1W+7527RqGDBmCqKgoYZ5EIoGdnZ3C9zAtLQ0///yzKHvO92TUqFGi88KrV69EA4W6tnv3bgwbNgwfPnyQ+1u+fPlgZ2en8LiPiorCiBEj4Ofnp3TbpqamqFGjhjCdnJycZSDoly9fFAak3rhxI8vzoWwAWP369VUu/z36999/MX36dIVBchnnTWXX2Or69OkTevfujUOHDikMTDIzM1N6PXby5En07dtXqwH4uLg4DBgwQO5aRNX5BQBmz56NvXv3Kvxbxj2HtbV1jmR7admypShY4t27d7hx44bG2/nnn39EgR9Vq1ZVmKH25cuX6N+/v9LfXysrKzg4OHx35d0UDZark7klp+9Bz5w5Ax8fH4WB4xKJBDY2NnL39cC3wICff/4ZGzZsUHtfmeXEdffevXsxY8YMpecaOzs70TVFSkoKfvnlF7UDSDOLi4tTen43MzODvb290tKy9+7dQ48ePTQKIn/z5g18fHyU3uPa2NjIfUfOnj2L8ePHaxWsCXwLbu7Ro4fSewJLS0uF57nU1FQsW7YMM2fO1Gq/qpibm8tlBtU0OB749rstG5ijLBhV39eruta6dWtRYGlWD+a9fftWdJ3TokWLbJcdzAiw2rJli8KAwIzzmqJguoCAAPTs2VNlAKMyaWlpGDVqFM6fPy+3v6wyt+bl/seEhAQMHTpU7kGyDJaWlnLnq7t372LIkCFKg2SJiIgMibkliYiIKMfFxsZi3Lhxoid8rays0KNHDwO2SrnNmzfjyJEjonkFChTA8OHD0apVK6FEQ0REBPz9/bF27VqEhoYiNDQUc+fO1Xh/Z86cwfr160XzLC0tMXDgQLRv3x6lSpWCRCJBcnIybt68iV27dsHf3x/JycmYPHmyWhkWZP32229yHYPly5dHnz59UK9ePVGZqxcvXuD48ePw9fUVPsMvX75gzJgxOHjwoFxnWKlSpTBr1iwA34LeMpftKVWqFPr376+0XeqU6swO2c6+Tp06yS3j4eGBUqVKCVmSpFIpDh8+jBEjRui1bZoKCwvDyJEj5YIimjRpgj59+qBu3bowNTVFeno6Xrx4gcOHD2Pbtm2iDsdVq1ahdOnS6NChQ5b7W7VqFbZs2SKaZ2RkBC8vL7Rp0wY1atQQvgtSqRRv3rxBUFAQzp49iwsXLijsxFPE3d0dTZs2hYeHB1xdXVGwYEGh8zg1NRXPnz/HuXPnsGvXLnz69ElYb8WKFahZsyZq1qypcLvjxo1DXFwcAGDZsmWiIIRx48bB3t5eaZt0VWpj3759cu+hubk5fvrpJ3Ts2BFlypSBRCJBYmIirl+/Dl9fX1HwQ0REBEaOHAk/Pz+NgqASExMxatQofP36FRKJBO3atUP37t1RvXp1oWPz7du3OHToEDZt2iTq1Fy4cCFatGgBOzu7bL56sZiYGISGhormVapUSaf7yEp8fDxGjBghFzCRUaquYcOGsLS0hFQqRWhoKI4ePYoNGzYI3yMAOHDgAEqUKIFhw4ZptO+9e/cKg+OlSpXCsGHD0KRJE+Fz/fz5Mw4fPozVq1cLx45UKsWcOXNw4sQJTJo0Ca9fvwYANGzYEH379kXt2rWFwY2XL19i27ZtosH0mJgYLF68GEuXLtWorcHBwUIQWr58+fDDDz+ge/fucHNzg5GREZKTk3Hr1i1s27ZNlCUoNjYWI0eOxLFjx7IcYMigr+Nfmbi4OEyZMkUIhmrdujV++OEH1KxZE2ZmZpBKpQgPD8fx48ezFYhx5MgRXL58WTSvdu3a6N27N2rWrIkCBQoI89PS0vDmzRs8ffoUly9fxrlz5/D58+cs97FixQocPXpUNM/Ozg5DhgxBmzZthEH32NhYXLx4EZs2bcLDhw+FZd++fYuRI0fiwIEDGgUZfPr0CePGjUNycjJMTEzQtWtXeHl5oXLlysJ79vz5c+zduxe7du0SBoelUilmzJiBkydPGqwUq7aKFi2Knj17irLprV69Gh07dtR5gMaJEycwe/Zs0TwnJyf06tULzZo1Q/ny5YX3Lzw8HBcuXMCGDRuEQL3U1FRMnz4drq6uqFChgsJ91KtXT5SB6OrVq2jYsKHSNl29elXh4Hl0dDQePHigsuSmbHCXJiUCvwdxcXGYN2+eaJ6TkxP69++Ppk2bomTJkqJzSUREBJ49e4Zbt27h/PnzuH//fpb7SE5OxtChQ0XHLwA0bdoU3bp1Q40aNYTfktTUVAQFBWH//v3w8/MTBnGDgoIwY8YM/Pnnnxq9vqVLlwpBMiVLlsTgwYPRpEkT4d4oPj4e169fF13PBwYGyj0wU7FiRfTr1w916tRB4cKFhfO8VCrFu3fvEBwcjKtXr8Lf319pSW1tZQRLZG7ToUOHULduXY22I3tPIZsRLMP8+fNF1w2mpqbo1asX2rZtCzc3N1FQQnx8PJ4/f46goCCcP38e169f16hNOenEiROi6Xz58qFatWpZrqfPe1BZgYGBGDt2rCjg2cbGBt27d0fr1q1RsWJF4ZwdFRWFK1euYOPGjaJMyH/88QfKly+PJk2aZPnaMuTEdff9+/cxZ84c0bnYxMQEvXv3hre3N1xdXZEvXz6kpqbi3r172L9/Pw4fPiz89ma+7tCEra0tGjVqhIYNG6JChQooU6aMKEgiIiICgYGBOHDgAM6ePSvM//jxI6ZMmYLNmzdnuY/U1FSMGzdOLtilVq1a+Omnn1C/fn3huHn37h1OnTqFdevWISoqCufOndMq+86bN28wePBgUdCrmZkZOnfujA4dOqBy5crCPuPi4nDjxg1s3bpV9KDKvn37UKFCBfTq1Uvj/avSpUsX0fnq1KlTmDlzpkb9Jv/884/oOPDw8FCYRT8nrld1zdraGi1btsQ///wDALh16xbevXuHYsWKKVw+4zjIkN2s0VKpFJMmTZLL5FijRg306tULtWvXFjKUpqen4/Hjx/Dz88PevXuFY//169cYP348duzYoVFpQF9fX+E32cnJCYMHD0bz5s2Fa/6kpCTcvXtXLtNiXut/lLVw4UK5oO2SJUti+PDhaNasmdDvExYWhhMnTmDdunWIiIjA/fv3sWrVKo1fGxERkb4xuIuIiIj0Li0tDbGxsQgJCcGVK1ewZ88eUQkWiUSCOXPmwMnJyYCtVCwkJETuhr5KlSrYuHGjXPCHo6MjunXrhnbt2mHs2LG4ePGi2llKMsTGxgqBUBmKFSuGbdu2iTo4gG+DAQ0bNkTDhg2xd+9ezJo1S6sBjxMnTojKWEkkEowdOxZDhw5V+MS8i4sLRo0aBS8vLwwZMkR4gvb169f4448/5J5SLVSokNCpeePGDVFwV8GCBXXe4amuwMBAUVkXiUQCLy8vhct6eXlh+fLlwnRuDO76+eef5bKkzJgxA7179xYtly9fPpQrVw6TJ09Gx44dMWDAANGT07/++itq1aqlsizS5cuX5UqFODs7Y82aNXB1dZVbXiKRoGTJkihZsiQ6duyIr1+/YufOnSpfT+fOndG8eXOUK1dO6TLGxsZwc3ODm5sbfHx8MGnSJOGJzLS0NCxfvlyudF2GzIF8GzduFL13HTt2VCvbQHa8efMGCxYsEM0rUqQIfH195YLHzM3N0bRpUzRt2hQbNmwQBeOEh4fj119/1Sjr2pcvX/DlyxdYWlpi2bJlCgeonJ2dMWbMGNSsWRODBg0SBoCjo6Ph5+eHvn37avBqs6YoQ1D58uV1uo+sLFy4UK7U5dChQzFu3DjRuVAikcDZ2RnDhg2Dl5cX+vfvL1pv5cqVaNy4MSpWrKj2vjMCuzp06ICFCxfKBQ4VKFAAgwYNQpUqVdC/f3/h8wgNDcX48eNx+fJl5MuXD9OnT5c75gGgTJkymD17NgoXLoxly5YJ80+ePIlffvlFo+DAjAACCwsLrFu3Tm4A3NTUFA0aNECDBg3g6+sr+p5/+vQJixYtwvz581XuQ9/HvzIZ5wETExMsXboUrVu3Fv1dIpGgUKFCKoOS1SGb1cnHxwfTp09XuKyRkRFKly6N0qVLo02bNkhJScGpU6dUBg3duXMHGzduFM0rX748tmzZIjeAa21tjXbt2qF169aYP38+du3aJfwtODgYf/zxB6ZOnar2a8v4XS1QoADWrl2rMLCnbNmymD59OipWrIhp06YJ89++fYvz58+jefPmau8vtxg2bBgOHDggBF++e/cO+/bt02m5yLdv3+KXX34Rzcv43DJnHspQsGBBdO/eHR06dMDkyZOFjLnJycmYMmUK/vnnH4WZNmSzZ2VVXjHz352cnBAVFSUEjl+9elVpcFdqaipu3bolTBsbG6NOnToq9/W9uXDhguj6wtnZGX/99ZfSc66joyPq1KmDOnXqYNSoUXjy5AmSk5NV7mPhwoWiwC4bGxv8/vvvaNasmdyyxsbGqF69OqpXr44OHTpg9OjRQqDRsWPH0Lp1a7Rp00bt15cxiNy+fXssXLhQ7rxkaWkJT09P0TzZ81/Lli2xbNkyhQPYEokExYsXR/HixeHp6YlffvkFFy9eRP78+dVuozq6du0qCpY4efIkZsyYoXb2lgcPHojKXynKrgN8u2a7cuWKMG1iYoLt27fLZQ3OYGlpiSpVqqBKlSro06cPwsPD5YL4coNz587JlXxt1KhRlgFD+r4Hzezr168YP368KKClVq1aWLp0qcJ7Hnt7e7Rv3x5t2rQR/TZKpVJMmzYNZ8+eVbvspL6vuzOyX2bOuGNnZ4ctW7bIPShhbGyMGjVqoEaNGmjVqhXGjBmDmJgYlWVcFSlcuDDmz5+Pjh07Ks3QBXw7pzVv3hzNmzfH+fPnMW7cOCFb8eXLl3Hz5k3Url1b5b62bt0q970fOXIkRo8eLfcbVqxYMfz000/w8vLCTz/9hCdPnmjcH5OcnIxx48aJArvKlSuH5cuXKwyAsrKygqenJzw9PeXu0xYsWICmTZuiSJEiGrVBlWrVqsHFxQUvXrwA8C0I9Pjx4+jWrZva25ANqlGWtUvf16v64u3tLQR3SaVS+Pn5Key7yfhbhoIFC2Y7g+iWLVtEGaJMTEwwa9YshZ9Pvnz54O7uDnd3d3h5eWHo0KHCwyp37tzBtm3bMHDgQLX3nfGbXKdOHaxZs0buN8zMzEzuOisv9j9mduvWLbmAck9PT/z5559yWRMLFSqEfv36oWPHjhg0aBAePnyo8fmDiIgoJ7AsIxEREenEzZs3Ub58eYX/KlasiNq1a6NHjx5Yvny5KLCrePHiWL9+vVqZggxh8+bNoqdnnZycFAZ2ZWZpaYmVK1cqLMWRlYMHD4qyj5iammLjxo1yHSuyevbsiaFDh2q8v7S0NCxevFg0b8KECRg+fHiWpVCcnZ2xYcMG0YDi33//rdXTsYYg+4R9jRo1lL7PXl5eos7j169fZ1mmKCcFBQXJPVU7cuRIhUEembm5uWHDhg2iAbXY2FhRAJ4iS5YsET3hWqhQIezdu1dhYJcidnZ2GDlypMplhg0bpjKwQ5a1tTVWrFiB0qVLC/Nu3rypsOxKbrBlyxZRKUYzMzNs3rw5y6xgQ4YMQb9+/UTzTp06pbAkVlYWLVqUZeaB+vXro2fPnqJ5stkZdCHz70IGVedZXQsLC5Mb6PD29saECRNUnguLFCmCrVu3ijrPU1NTtSoL6OHhgcWLF6vMCFW7dm25INSMgI0hQ4ZkecwPGTIEJUuWFKZTUlJEmRQ08dtvv2WZ2aR///5ygVCHDh3KstSIoY//X375RS6wS5cePXok/N/ExATjxo1Te10TExO0b99eaRknAFi7dq2oXFL+/PmxdetWlQPtRkZGmDFjBlq2bCmav3v3bo1/101MTLBmzRqVGZuAb4OJTZs2Fc3Tx/klJxQoUEAukGvdunUKS+5qa8WKFaLB5qZNm+LPP/9UGNiVmYWFBf744w/RIH9wcLBc2Z4Mbm5uouCjJ0+eqPwOZM4m1KhRI1G2HlWBYUFBQaLXU7ly5WyXQsptZAMSBg0apFEwrZubm8rjKCQkBHv27BGmjY2NsW7dOoWBXbIaNGiAhQsXiuZpU3LOw8MDS5YsUXsAP/P5D/h236FuZhKJRCLKDKYrsiUUExISNDoXyd5TyJZ6zPD48WPR9XOrVq2UBnYpUrBgQbU+25ySlpaGv/76C+PHjxfNNzExwYQJE7JcNyfvQbdu3SrKzOru7o4NGzaofJgF+L/fxsxBil++fNGqTLm+rrvPnz8vVxZzxYoVWWbA9fT0xK+//qpyGWVq1KiBbt26qQzsktW0aVO5IIzMAeWKpKSkyN2Xent7Y8yYMSrLADo6OmLz5s1a3Uv4+fmJzt3FihWDr6+vwsAuWUOGDBFdCyQlJWH79u0atyErstmlsio9mNmDBw9E3xcLCwuFwaiA/q9X9aVu3booXLiwMK2sHHVAQADevn0rTHt5eWWrHHB0dLTcA3CLFi1SK/DO3d0dq1evFv0e+vr6ZhngLcvZ2Rnr1q1T+3oqr/c/rl27VvS76+rqimXLlqn8Xjo6OmLTpk06DyQnIiLSFQZ3ERERkUFYWVlhzJgxOHHihEZlDXJSbGysXDnGCRMmqNVJaG5urvSpRlX++usv0XTfvn3V6kgEgBEjRog6sdRx/Phx0dN27u7uGDRokNrrOzs7iwJNkpKSlHae5SZJSUk4fvy4aF7nzp2VLl+sWDHUqlVLNE+TTlR9k81OU6pUKbU72ypVqiQXEPL3338rHZC+cOGCqDwJ8O2pZG3LeeiSqamp3JPtubGETWxsrNxxMnjwYLWP9XHjxskNRmmaoahx48Zo1aqVWsv+8MMPounHjx+LAkd0QVGZzqwCFnRpz549ohKldnZ2amcrKlq0KMaMGSOad+bMGbnyjlmZMWOGWuXoFJWPLVCgQJYBk8C3Qcr27duL5j148ED9Rv5/devWVToQJGvMmDFyZVtkf+t0QVfHf4UKFeQGVnUtc/lce3t7nQa0vH79Wq4UzKRJk9QaoMjI+Jg5G0lSUhL279+vURu6deuGqlWrqrWs7PlFm+9jbjFw4EBRydHPnz9rfG5W5v379zh27JgwbWFhgTlz5qhdwtLU1BRTpkwRzZPNZJBBIpGIMjtIpVKlx9KrV6/w/v17YbpevXqirBeBgYFKy/Xk9ZKMAORKZes6K+iWLVtEv8c//vijRuVoW7VqJcqa8/DhQ40zQ82cOVOjwXB9vyfakg2WkA34ViY5OVnuflFZScbc+tqV2bNnj9y/Xbt2YcOGDZg2bRo8PT0xY8YM0T2DkZER5s6dCzc3N5Xbzsl70Pj4eFEQpEQiwbx582BpaanWviQSCaZOnSo63yo7fyqjz+tu2Wuqtm3bql1WtFu3bjlaBr1z586ibO2ypdJknT17VlTaz9LSUu63TJkCBQpg9OjRGrVPKpViy5Ytonk///yzRve5Y8aMkQs+yZxVTRc6d+4sCgIKCAhQmAVZEdlzW6tWrZReh+rzelWf8uXLJ3oYJiQkBIGBgXLLyfbnqOoPUseePXtEZXebN28ud9+lStWqVdGxY0dhOjw8XGkgvjL/+9//1D63AXm7//HNmzdy15rTp09XKyjV0dFRLnCZiIgot2BwFxERERlEXFwcVqxYgQ4dOuDff/81dHMUun79uijgwM7OTqMMY/Xq1RNlEclKWFiYXPYd2c5dVczMzDTukJIdjPjxxx81flpRdoD/5s2bGq1vCKdPnxZ1VpqZmWVZhkb2vT1x4oTSAcucdvHiRdH0Dz/8oFEJBB8fH9HTz9HR0bh7967CZU+ePCmarlKlCho0aKB+Y/Usc8YQALkylf7NmzdF5xZjY2ONypNaWlqie/fuonmy5XCyosm5xdXVVdSZHx8fr3HgUlYyB1Zl0KRjOrtkj6GOHTtq9LR/t27dRO1NS0uTy6anSkZJDnVUrlxZbl6nTp3UPuZl188obaEJTb6vVlZWctnGNB2oUJcujv9u3bqpzAahC5kH/b58+ZJlJjNNXLx4UfSEur29vUbXLoUKFZL7PdT0/KJJcFz16tVF0yEhIWoFj/r7++Pp06fCP10FUWWHnZ0dBgwYIJq3adMmjctdKXLixAlRObFWrVplmXFGVp06dUSD6rdv31b6XsuWJcpcSi4zRQFamddNTk5Wmun02rVrcuvmNbJByroMXkxPTxcF/AHQqgyo7HV85lKZWXF3d9eoBDGg3/ckO2SDJW7fvo3Xr19nuZ6/v7+o9GaxYsWUBtZkDv4Ecs9rV2bWrFly/+bMmYOlS5fi4MGDcr9dJUqUwKZNm+QC5RTJyXvQS5cuiT6jGjVqaPy9LVmypCgI6tmzZ4iMjFR7fX1dd6empsoF32qyL4lEotHy2ZUvXz7RdWhkZKTKoCTZa+kWLVpolP2wc+fOGmWOevjwoei6uGjRohqXira1tUWjRo2E6ejoaFHZVl1wcnIS7QNQLyA1OTkZR48eFc1TVpIR0O/1qr7J9t3IBnLJZmisVKmSVpn3M5M9r+niN1mTvrUCBQpolN0xr/c/njt3TnRPVLZsWY3Kf3fq1Enud5uIiCg3YHAXERER6USpUqUUdgBn/Js6dSqGDh2Kpk2bijJChISEYNKkSZg8ebJo0Co3CAoKEk03atRIo4AZ4FsHpLpkg2lKlSqFEiVKaLQ/2U4+VdLT03H79m2t18/g4uIi6jRVFhSUm8h27jVv3jzLLEGtW7cWfXdjY2Nx6tQpvbRPEy9fvhQNWACQK6uVFWdnZ7lBjjt37ihcVrbzTFEWIX2JiYnBnTt3cObMGRw+fBh79+6VyyggG4Sg6yAkXZB9ctjDw0PjzGeyJeM+fvyo9muVSCRymeiyWl42u4QughUyU1SKUFE2L32Ij4+XG3TR9BiysrKSO38qekJcGU0yrVhbW8t1NGuyftGiRUXTsplEsmJsbCxXSi8rsgNjT58+1Tg4NqeOf006/bWVucxaeno6xowZIyoLkx2y37umTZtqfO0ie3558OCB2mVhbG1tUb58ebX3ZW9vL/r9TU9PF2U9+N7069dPlCXt69ev2LRpU7a3K/vb27BhQ622U6FCBeH/0dHRSoM7ZQOtZAOxFM13dXWFk5MTKleuLPpMFZVmjI+PFwVfWlpaygVn5gWyJRXXr1+vs9Kjjx8/Fv0WlyxZUuP7BgBygcWa/HZpc76UfU+mTZuGJ0+eaLwdXStQoIBWwRKKSjorCxCuXLmy6G9XrlzBsmXLNC67lRsNHjwYx44dkwsMVSSn70FlAxa1PX9mvleSSqVyfQXK6PO6+8mTJ6LrKXNzc1E2PnU0btxYo+WVSU9Px+vXr3Hx4kUcO3YM+/fvV5j9TbaEWubsj7Jkg/Q1bau1tbVGpU9lvysNGjTQKuBf9r5ak/OqumSDsg4fPpxlcPzZs2dF/QbFixdXeR7X5/WqvpUpU0bU/uPHj4vOtadOnRJdb6oTlKpKZGSkKFDKzMxM42MRkP9N1qRvrUaNGmqXOVa07bzW/yh7jtY0UNPMzEzr3wsiIiJ9Uv/XnoiIiEiFggULqp3JIyYmBhs3bsTGjRuFDqh//vkHJiYm+O233/TZTI3IDjRo+oSvpuvIDrBlVc5CkQoVKkAikYieUFPm1atXokF9Y2Nj+Pv7a7xP4FtgRkbHckREBFJTUzXqWMpJYWFhcgON6jxxaG1tjRYtWogyzR06dChHg5sUkQ1Ksbe3h7Ozs8bbqVSpkqgUj6InjGNiYuQ6dDXpMNfGq1evcPDgQZw8eVKtDAqyNA1cyQmy76025VBcXFxgYWEhKoXz9OlTFClSJMt1ra2tYWdnp9H+rKysRNOxsbEarZ8VRVm6dB1Apszz589FwcUSiUTtLFqZVapUSZTZTpOn9GUDrrJiaWkp+m4XK1ZMo3Uz0zSQpkyZMhplQQC+/Tbly5dP+M1PTU3F8+fPs/zu5/Txb2JigjJlymi8H0317NlTlL3s3r17aNOmDRo3bowWLVqgbt26Gn2mmeni/CK7TlJSEl6/fo1y5cpluW7RokU1Hgi1srISHe+xsbE5WpZVlywtLTF06FDR9ez27dvRr18/jbKNyJIdoHr69KmozJi6ZDPNfPr0SWGmCmdnZxQvXhyhoaEAgHfv3uH169coWbKksEx6eroo6CwjqMPIyAi1a9fG2bNnASgODLt165YoY2ONGjU0DkL8HjRr1gyFChVCWFgYgG9BbWPHjkXZsmXRtm1bNG7cGBUrVtTqmlk26MHExESr78SXL19E058+fVJ7XU0COTP06NEDu3fvFu5VQkJC0LlzZ9SpUwetWrVCvXr1cuQ8rEjXrl1x7tw5YdrPzw9jx45VmlUkPDxclFlIIpGoDBBwdHREq1atRNcKa9euxV9//YV27dqhSZMmqF69utw11/dg48aNSEpKwrRp07LMwpLT96Cyx0poaKhWx0rG+TBDeHi4Wuvp87pbtv+gXLlyapfrzVC4cGE4ODholIksQ2pqKo4fP46jR4/i+vXrovsSdam63n/16pVoOnOAsroqVKigNEBZlux3JTIyUqvviuy1mCbnVXU1a9ZM9Ll9+PAB169fVxlgKfuAm6pgVEC/16s5oXPnzsL1U3R0NM6ePYu2bdsC+BYMl8HExESj8omKBAUFifrgbGxstCpDLxugp8l3x9XVVaN95fX+R13158pmSSUiIjK03DniRkRERHmajY0NJkyYACcnJ8ybN0+Yf+DAAbRu3RpNmjQxYOv+j2w2JE0H3wHIPXWriuwgdObSOeqysrKChYWFWhlvZDuKUlNTMWvWLI33qUh0dHS2BjH1Sfap1gIFCqhdVrBz586i4K7r16/jw4cPagXU6MvXr19F09p2sMp+V2W3C0DuSWsAWmWJUEdycjKWLl2KnTt3ZiurX27MAKOLz8zIyAhFihQRdcrKnrOU0SZoQnagKC0tTeNtqKLofKfoO6gPsvuxs7PT6j1S5xhSRtP9yX4emgwEyw66qlMCLzNtfgstLS1hb28vOoeo+r4a6vi3srLSeFBUG82aNcMPP/yAffv2CfNSU1Ph7+8vDHIULlwY1apVQ+3atVG3bl24uLiotW1dnF+cnJxgZmaGpKQkpdtVJjeeX3Jar169sHXrViFzXHx8PNatW4eff/5Zq+2lp6fL/f7qIhsYoPo4rF+/vmhg8sqVK6LgrgcPHoi+F5kHlOvXry8Edz1+/BgRERGi60LZgXZ1sv18j8zMzLB48WIMHjxYlDHk+fPnWLlyJVauXAlLS0tUqlQJNWrUQJ06dVCzZk2F2Sxlff78WTT9/PlznVzHa/LbpWnACvBt8Hj8+PH4448/hHlSqRTXr18Xyss5OjqiWrVqqFWrFurWravVgKw2mjZtCkdHR+F4+/DhA65du6b0PsHPz090vqpTp06W934zZszAgwcP8O7dO2Hely9fsGPHDuzYsQPGxsZwdXVF9erVUbt2bdSrV89gZaEUBanHx8fj/fv3uH79OrZv3y4KvN6+fTtSUlKy/B7m9D2o7LHy999/4++//872vnLD76Iu+g8y1tM0uOvOnTuYMWMGnj9/rtU+Myi7VouLi5Mr267N69NkHdnvypkzZ3DmzBmN9ylLH/c0JiYm6NixI7Zv3y7MO3DggNLfU02DUQH9Xq/mhPbt22PBggXC9+jw4cNo27atEAiXoWnTpnBwcMjWvmS/O58/f87x32R7e3uNtp3X+x910Z+bm4MXiYjov4tlGYmIiMhg+vTpI/fE98aNGw3UGnmynR3W1tYab0OTdWSfWtX2qW1196nPwAltntrNKbJPrHbo0EHtjAn169dHoUKFhOn09HT4+fnptH2akv2eavu9kR14UPT9kO0gk0gkWh0XWUlOTsbIkSPh6+ub7XKt6jxFmtNk31tt30N1PjNFtCkvom+KggQ1yXyVHbo6hmQ/R03Osdn9THLyM9XV91VZVi1DHv85mS1l9uzZmDZtmtL38+PHjzhx4gTmzJmDdu3aoWPHjti1a1eW5bv0dX5RN3g0N55fcpqpqSlGjhwpmrdnzx58/PhRq+19/fpVb79lqsqjZlWaMfO0iYmJqOxY5nUzAncyk82gqs/gLtnvZHbPK7LrZ5WlqG7duti9e7fSjDPx8fG4efMm1q5di/79+6Nhw4aYNWtWlqWv9HUdr8k1vKKsm+oYOnQofv/9d6UlqSMiIuDv749FixbB29sbLVu2xLp163SeNVRWRrBEZqpKM2bO/ALIl0lTxMnJCfv371da/jk1NRWPHj3Czp07MWbMGNSvXx8jR45EQEBA1i8gB1haWqJs2bLo06cPjhw5gjZt2oj+vmfPHrn3RVZO34Oq+/ulKXXLS+vzd1FX/QearnflyhX89NNP2Q7sApQ/ZKDoeNfm9WlyHaSv76ampcjV1bVrV9H0mTNnlGZCkw1GVTfrlr6uV3OCvb09PD09henLly/j8+fP8PPzE33v1MninpXv8Tc5r/c/6qI/93vNJExERHkbg7uIiIjIYCQSCVq1aiWaFxAQoLcO2OzSpmNWk8E42XI0sk+qqkvd9bTdvjpyY0ANAAQGBsqVd/D19UX58uXV+lehQgWhtE4G2WAxQ9PVAII629HXYMWGDRtw8eJF0TwHBwf06dMHf/75Jw4ePIgrV64gMDAQjx49wtOnT4V/GZlCvic5+ZnlVra2tnIDDA8ePDBIW/h56Ie6vwv/leNfIpGgf//+8Pf3x4wZM1CrVi2VZemCg4OFgTPZEn1Z7UcX+H3WjLe3N0qVKiVMJycnY/Xq1Vpty1DXa3Xr1hV97jdu3BANhmYO0KpWrZpoUNHFxQWFCxdWuOyXL18QHBwsTDs6OmpV3k9dsoN56mSXUEU2y4w6g4WVK1fGoUOHsHHjRnTs2BH58+dXumxUVBT27NmDtm3bYsOGDUqX0+f3Iid06tQJZ86cwW+//YaGDRuqHJR+8+YN/vzzT7Rs2RIXLlzQa7tkA7SUBUvcu3dPFNhibW2N1q1bq7WP/PnzY9WqVfDz80O/fv1E5wpZKSkpOHPmDHr37o2JEyfqPcBNE6ampvj999/lSrTPnz9fLotNZjl9TtPX/nLD/W5O9x8A385REydOlAtYqlOnDqZMmYJt27bh1KlTCAgIwP3790XXaU+fPs0yW1QGRRkMtXl9mqyjr4AkfX1X3NzcRJkNExMTlZaQk+2zkA0MUyanrlf1JXPgVmpqKo4cOSIKQHVwcNBJ5YDv8Tf5v9b/qO/+XCIiopzCsoxERERkUOXKlRNNS6VSPH78WC5bgK5oUn5KtgSGsqcgVdGkJJTsU2Hadt6ru55sKZUSJUrg9OnTWu3ze6GPQKyQkBDcuXMH1atX1/m21SH7PdX2eyP7/VZUAkY21X96ejpiY2N1Wi4mLi5OLoNfhw4dMG/ePFhYWGS5fnYHbXOC7LGnzblF0XqGKtujK7Vq1RKVKXr+/LlcKS990NUxJLve9/55KKPP9+e/cPzLsrOzQ58+fdCnTx8kJSXh3r17uH37tvBP9jW9ffsW/fr1w549e+Dm5qZwe5nLnujq/KJN+bX/MmNjY4wZMwYTJkwQ5h08eBCDBg0SlTZUh6IyOxcuXBAFT+mDo6Mj3Nzc8PjxYwDfMi48ePAAVapUQVJSEgIDA4VlFV2316tXT7juyhzcde3aNdFgmWwQma7p6hyfQdvfXolEgsaNG6Nx48YAgBcvXgjHeUBAAEJDQ0XLp6SkYOnSpUhISMDYsWPltid7THbr1g3z58/X5KUYnIWFBbp27YquXbsiJSUFjx49QkBAgPC+yD7wExERgREjRmD9+vVo2LChXtrk5uYGd3d3PHz4EMD/BUv88MMPouVkM3q1b98e5ubmGu/r559/xs8//4zw8HAEBATgzp07uH37Nh4/fiw3qHzkyBF8+vQJW7duzZESwuowNTXFb7/9Bi8vLyEwJjo6GsuXL8fcuXMVrpPT96D29vai38Vdu3ahZs2aettfTtLXNawq27ZtE5VwtLW1xcqVK1G3bl211lf3Wk3RuTUmJkat60HZddQl+3u7ePFieHl5abS/nNa1a1c8evRImD506JDc+erevXt48eKFMG1jY6M0e6Ayur5ezSmNGzdG/vz58eXLFwDA2rVrRb8tHTp0UKsUclZkz2v16tWDr69vtrerT3m9/9HW1lYUaKzNPVFuCqgmIiLKwMxdREREZFCKnnaPiIhQurzs4I8mwVpA9jr33r9/r9G+AMgN1Kji5OQkmpbNMKWOt2/fqv1EnGzAxIcPH7JdqiY3S0pKwvHjx/WybUNm75L9nmYOjtGE7HdV0UC+oiCb169fa7U/ZS5duiR6ErxUqVJYsGCB2h35mQcbcitdfGZpaWn48OGDyu1+b5o1ayaaTktLy7K0jy7Ivm9fv37VqvNXnWMoL9DmtzA+Pl5ukF7R9/W/cPyrYmZmhtq1a2P48OHYtGkTbty4gQ0bNshlFIiPj8ecOXMUbkMX55dPnz4hKSlJNC+vfp/1qV27dqIBzdTUVKxYsULj7ZiamsqVysmqZJ+uyAZtZQRpBQQEiL4jDRo0ULnuu3fv8ObNG9E2MuizJCMgf+2izfV1ZiEhISq3ry4XFxf06NEDixYtwtmzZ3Hs2DEMHjxYLoPV+vXr8fLlS7n1ZfebU98JfTExMUHVqlUxcOBArFmzBlevXsXOnTvRoUMH0f1famoqfv31V73es8hmtJEN5EpKSpLLjqNOSUZVChYsiHbt2mH69Ok4dOgQLl++jOnTp8tlNb1x44bKUpGGUKZMGfTu3Vs07+DBg0rvEXL6HtTBwUE0rcn9eW4nW9pUm/NbcnKyRtcKssEY06ZNUzuwC1D/Ws3Y2Fjumkab16fo/KmM7HflezivdujQQZSBKTAwUO41HzhwQDTdrl07jYNRM9PF9WpOMTY2Rvv27YVp2fsRXZRkBL7P3+S83v+oi/5cbfvWiIiI9InBXURERGRQip6EUvXknOzgliaZsQDNbs5lS8RkfiJSXZqsU6lSJbl1NU1dfu/ePbWXdXFxEXUEpqSk4O7duxrt73ty+vRpREdHC9Pm5uaYNWuWVv8GDBgg2vbx48flBsJziqurq2g6KipKq85E2RJ4ikok2djYoESJEqJ5mTN36MLTp09F023atFFZ+kHW/fv3ddoefZD9zLQpP/jixQskJCSI5umzrFVO8PT0lOuE3bdvH9LS0vS6XxcXFxgb/19Sa6lUKmTs0IQ6x1Be8PLlS7lSPFl5/PixKBjb2NgYZcuWlVvuv3D8a8LU1BRNmjTBhg0bMHnyZNHfbt++rfCaRhfnF9l1zMzMVJYNI8UkEolcxqWjR4/Kfc/VUaFCBdH0rVu3stU2dSkL7socoGVjY4PKlSvLrSsbtJWxzvXr11XuQ9fc3d1F08+ePdP44ZAM6enpolJ8iravLRcXF0yaNAkHDx4UZaxJS0tTWGZLNhPK/fv3DXYtqg9GRkaoVasWli5dij///FP0t9DQUNy5c0dv+5YNlrh7964o682ZM2dE9xQuLi6oVq2aTttQoEAB+Pj44MiRI6hSpYrob//8849O96ULQ4YMEQUmpqamYu3atQqXzel7UEOdP3OC7Ln33bt3oixl6nj48KHafQ6pqamic6CxsTHatWun9r7S0tKy1T+iSV+HNuvIflcCAgI03l9Os7e3h6enp2he5gfPFD3g1q1bN522QZvr1ZykrBRouXLl5L5j2pL97oSGhso9iJXb5PX+x5zuzyUiIsopDO4iIiIigwoODpabJ/sEamay6fk1ffJWk87cqlWriqYvXboklJtQ19mzZ9VetmLFijAzMxOmY2NjceHCBY32d+TIEbWXNTc3lysleOLECY32pynZEiL6DtrITDa7VpMmTdCrVy+t/o0fP16Uxj4mJsZgJS3LlCkj95TxmTNnNNpGaGioXMeVsjKTtWvXFk3/+++/Gu0rKxklEzIULVpUo/XPnTun8T5lv5faDvqqy8PDQzQdGBgo97qzcurUKdF04cKFUaRIkWy3zZBMTU3Rp08f0byQkBBs2rRJZ/tQ9NlaWlrKdf5qegzFx8fj0qVLonmyn3NekZqaivPnz2u0juxvoaurq8KMAYY4/r8XAwcOlHvCXlGQkOz37vz58xpfu8ieXypVqqSTkjX/RZ6enqKgD6lUimXLlmm8HdlAKX1fr2WoVauW6LMPDAxEQkICrl27JsyrXbu2whJxTk5OomDDq1evIiQkRDTIW6JECRQvXlxPrf9G9piIjY3FzZs3tdrWjRs35B5M0fW5vnTp0nIltRQd6zVq1BBlNYyPj9f4vuF70bZtW7n7Mm2CJNVlZ2eH5s2bi+Zlvo+QzZyV3axdqlhaWmL06NGieYrunw3N0dERPXv2FM37999/FT5wktP3oLLnT39//zwTCFmgQAG5c+jRo0c12oYm/QeRkZGicqEODg4aZYC6efOmRmXOZM+vmr62Bw8eyGVbVEU2C2VAQICorFtuJXsOOnz4sNDPIvuAW9myZeUCRnVJ3evVnFSxYkW5hx8A3WXtAgBnZ2c4OzuL5uXUtZq28nr/o+x1gyZ9s8C3wMjLly/rsklEREQ6weAuIiIiMhipVIqTJ0+K5pmYmCjseMlQpkwZ0bQmT23fu3cPT548UXv5unXrip5A/vr1q0YditeuXdOoDICpqSlatWolmrdu3Tq1A00ePHigcWdM69atRdN//fWXXp8wlC3DqUnnbnaEhYXJlQHKnJ5fU6ampmjRooVoniFLMzZu3Fg0vXfvXo2euty5c6eoo97W1lbpYGXbtm1F00FBQbhy5YoGrVVNNktP5s7orNy7d0+rJ6xlMwLq+3tZu3ZtuewGe/bsUXv9hIQE7N+/XzRPtgzG9+qnn36SC/BdtWqVVtmHZL17905paRDZ9+/ff//F169f1d72gQMHEB8fL0wbGRmhUaNG2jX0O7B37161l42Li5PLMtK0aVOFyxri+P9eSCQSufJcstn7gG+/B5lLmEVFRWl07RIeHi430KLs8yL1jB8/XjTt7++PoKAgjbbRunVr0ef69OnTHBk0tLCwEF0PJCcn4+zZs3j8+LEwT1FJxgyZs3LduHFD7npB31m7gG+De7KlgGR/Q9Ulu56jo6NeBsllgzUy/75kMDU1lTs2V61apfcAdUORfU8Unf90STZYws/PD2lpafj48aPonsLY2BheXl56bYs634fcYODAgaJAgdTUVKxbt07hsjl5D9q4cWNRIGRERAR27typl30ZQocOHUTTvr6+an9HPnz4oFGZT9lA77i4OI3OOZs3b1Z7WQDo2LGj6LfvyZMn8Pf3V3v91atXa7S/qlWrolChQsJ0SkoK1qxZo9E2DKFhw4YoWLCgMB0eHi783sr2UegzGBVQ/3o1pw0fPhzt2rUT/evUqZNO9yF7Xtu0aZPGlQZyUl7vf2zatKno/PH8+XONgvv/+ecfje5FiYiIcgqDu4iIiMhgtm3bhmfPnonm1atXTy4AKDPZAZQLFy6o9TRlcnIyZs+erVH7rK2t5coMLF26FFFRUVmum5iYiHnz5mm0PwD48ccfRdP379/HypUrs1wvKioK06ZN03hAp1u3bihcuLAwnZSUhPHjx2fraebMQUKy8ufPL5p++/atxqnftXH48GHRe2NlZZXtwWrZ78bVq1cRFhaWrW1qS1G2ow0bNqi17qNHj7Bjxw7RvO7duyt9Crthw4aoWLGiaN60adN09lRz5g51AGp3GMbHx2Pq1Kla7VP2e5m59I4+WFtbyz0pvHHjRrx69Uqt9VesWIGPHz+K5vn4+OiqeQZlbW0td65OTk5G//79cfv2ba23e+bMGXTp0kXpZ9uzZ0/RgFVUVBQWLVqk1rY/fvyIFStWiOa1bNlSdG7Na65du6awTJgiK1asEJUIMjIyQo8ePRQua4jjP6dpm7EyNTUVb968Ec1TlOm0ZMmScgG/S5YsQUREhFr7mTdvnmhQ2NzcHN27d9eixfrl6emJ8uXLC/9y8zmwbt26ckFMysqVKVO2bFm5AbhZs2bJfSc0oep6LbO6deuKplesWCG6plIVoJU5Y05UVBS2bdum9O/6YmpqKpcJ6+jRo3LlIbNy/fp1uUDJnj17qiwdm5qaqtE+Msg+HCKbBSXDiBEj5IL+Fi5cqNU+AfW/E9nZvrbBZ7LviapMz7rQsGFD0e94eHg4Ll++LHdP0ahRI6Wfjyx9fx8MrUCBAnK/735+fgpLsuXkPaijoyN69eolmrd8+fJslQPT97GiiR9++EGUPfHDhw+YM2dOlm1MSkrC//73P42CBe3s7OQyBqobLPH333/LZbnNSsmSJeV+J2bPno33799nue6+ffs0CgQDvgWvDR06VDRvz5492crQnRPfFSMjI7l7y4MHD2YrGFXf16s5rV27dvjzzz9F/zIHxOnCgAEDRA9wff78GVOnTs1V5wtZebn/sWTJknLXsHPnzlUro3FERIRcSWgiIqLcgsFdRERElONiYmKwZMkSuYFziUQiV3ZCVs2aNUWBGImJiZg+fbrKzvL4+HiMGTMGDx8+1LitAwcOFA3afPr0CUOGDFGZ0SUhIQGjR4/G8+fPNd5f9erV5YKG1qxZg7lz5yrNJvTw4UP4+PggODgYEolEo9JJpqammDRpkmheYGAg+vXrp1HJy7S0NJw9exY+Pj4q32cnJyfRgER8fDwOHz6s9n60JfvEavPmzUVPlmujfv36onKI6enp8PPzy9Y2tVWlShU0bNhQNG/lypX466+/VK4XHByMwYMHi44fGxsb9OvXT+V6kydPRr58/3crERYWhl69eskFayrz9etXpQPbderUEU3funUL+/btU7m9iIgIDBgwQKNMeZnJBqvt379f6wE4dQ0YMEA0OJKYmIiBAwdmOVC/ZcsWbNmyRTSvdevWKFeunF7aaQgtWrTAsGHDRPNiYmIwYMAArFixQqNBqCdPnmDEiBEYOXKkysDcQoUKyT3JfuDAASxfvlxlh/zHjx/Rv39/0VO9xsbGcgNDedHPP/+MGzduqFxm27Zt8PX1Fc3z8vJSWkLUEMd/TgsODoaXlxcOHTqExMREtddbvny5KEDL0tISlStXVrjssGHDROfoz58/Y+DAgSoDvNLS0jBv3jy5jKo//vijXOlf0tyECRNE05kDHtU1fvx40aBhZGQkevfurXGQ0v379zF58mS5wG5lZAfWX79+Lfy/cOHCcll1M5Mt65h53Xz58skd8/rSp08f2NvbC9NSqRQjR44UlZdU5dq1axg5cqRonr29PXr37q1yvZ9++gmLFy9WGNyizIMHD+QyhCl7n1xdXeWCL7dt24ZffvlFo0wpcXFx2LVrl94zusTExKBVq1bYvn27RllS9+7dK8oWJ5FIUKtWLX00UZAvXz6FwRKy9xRdu3ZVe5tLly7FxIkTce/ePbXXiYyMlCvlmlPHjTYGDRokOuZTUlKwfv16ueVy+h508ODBokCOpKQkDBo0SO1A9QwvX77E7NmzsWTJEo3W06eiRYtiwIABonmHDh3CuHHjlJZ9f/PmDQYNGiRcx6l7X6zo2Js3b16W2W4PHTqEX3/9Va19yJoyZYroO/Xx40f06dNH6TVocnIyVq1ahVmzZgFQ/7Vl6N69uyiTfHp6OiZMmCCX6TorHz9+xNKlS/G///1Po/1rS/b8ffbsWWzbtk0UfNO4cWO1A61y4no1r8mfP7/cPeCpU6cwfPhwREZGqr2d5ORk+Pn5wdvbW+2HM7SV1/sfhw8fLpoODg7G2LFjVX6nIyIiMGjQIKXnTyIiIkMzNnQDiIiIKG8IDw9XWVYsKSkJkZGRePLkCa5fv67wZnro0KFZljYxMTFBjx49RIEh586dQ58+fTBq1CjUrl1bCMZ69+4dzp07h02bNgmpvj08PBAYGKj26ypTpgxGjhwpemrr3r17aN++PYYPH45WrVoJwUqRkZHw9/fHmjVrhI6JatWqafxU8IwZM3D79m1RFqidO3fCz88PTZo0QdmyZWFlZYUvX77g5s2buHv3rtBp16tXL1y4cEE0iJT5iX5FOnbsiIcPH2Lr1q3CvMDAQLRt2xYdO3ZE69atUa1aNdjZ2Ql/T05OxqtXr4TP09/fXwicyKrTs1mzZqKgoxkzZuDUqVOoWrUqHBwcRAPSVlZW2U6XHxgYKJcRKTslGTMYGxujVatWosCDQ4cOYciQIdnetjZ+++03dOrUSfQ5zJgxA/7+/vDx8UGtWrVgamoKqVSKFy9ewM/PD76+vnJPLs6aNUsue46s+vXrY8SIEVi1apUw782bN/Dy8oKXlxfatWuH6tWrC+UOpVIp3r59i6CgIJw9exbnz59HfHy8XGcbAFSuXBnu7u6iTrqZM2fi7t276N27NypUqAAjIyNIpVK8fPkSp06dwtatW4VBhdq1a2uUbh/49p3MnOnsxo0baN++PZo1a4YiRYrIZeXw9PTM8j3KirOzM6ZNm4aZM2cK8969e4dOnTphwIAB6NixI0qVKgWJRIKkpCRcv34d27ZtkytpVbBgQY2zEn4Pxo0bh7i4OFHwQWJiIlavXo19+/ahdevWaNy4MSpVqgQHBwchY0FiYiJevXqFW7du4eTJk7h9+7baAzFTp07FjRs3EBISIsxbs2YNrl+/joEDB6JBgwZCQN7bt29x7NgxbNiwQa7je8yYMXIBg3mJu7s7nj59ioSEBPTv3x89e/ZE9+7d4ebmhnz58iE5ORkBAQHYtm0bzp8/L1rXyclJZYYtQxz/hvDkyRNMnToVc+bMQZMmTdCgQQO4u7vDxcVFNAgZERGB27dvY9euXXJBKN26dRMF+mRWvXp1DB48WDSg/ujRI7Rt2xZDhw5FmzZtULRoUQDfgjouXbqEjRs3ypU/dXV1lSspSNqpUqUKmjdvjrNnz2q9jdKlS+P333/HqFGjhPNaeHg4+vXrh4YNG8Lb2xs1atRA4cKFhWu/tLQ0vH//Hk+fPsXt27dx9uxZIcDKzc1N7bZbW1srHOTLKvOWlZUVqlSpojDzYoUKFXIscLBAgQJYsGCB6LojNjYW/fv3R4sWLdCpUyfUqlVLVL4xIiICAQEB8PPzw5kzZ+S2uWDBgiwHyaOjo7F582Zs2bIFlStXRvPmzVGlShW4ubmJ9pWUlISnT5/i2LFj2LNnj+g+qVChQnJZ2zKbMWMGnj17Jrq/+fvvv+Hv74+ePXuiUaNGcHd3F51bYmNj8ezZMzx+/BgXL17E1atXkZSUpPScoktv377F/PnzsXjxYjRo0ED4Lc+4v8kQExODu3fvYv/+/XJBp56ennB2dtZ7W729vUVlBU+ePCm6pnB0dNQoE3BqaiqOHDmCI0eOwNnZGS1btkSNGjVQoUIFFClSRLgHSktLw+vXr3H+/Hls3boV4eHhwjaMjIzkMq3kJoULF0aXLl1E90cHDx7EiBEj5DKa5uQ9qKOjI1avXo0+ffoIGWJiYmIwfvx4bNu2Dd27d0etWrVQokQJ4fyZnp6O8PBwPH36FPfu3cPZs2fx5MkTAMgysDOnjRkzBhcvXsTTp0+FeSdOnMD58+fRqFEjuLm5wc7ODpGRkbh37x5u3LghZM9u3LgxEhMTRddPqvoPevbsiYsXLwrTz549g7e3N0aNGoVmzZoJ5/W4uDjcuHEDu3btwuXLlwF8C7QqX768RqWJ3dzcMHr0aPzxxx/CvHfv3qFv375wd3dHvXr1ULBgQSQlJeHVq1c4f/68EBBjaWmJ/v37i0orZtU3YmpqijVr1qB79+5CQE5ycjLmzp2LPXv2oFevXqhTpw7KlCkj3H9IpVJ8+fIFwcHBuH//Ps6dO4e7d+9CKpXmWGnr0qVLo3r16rhz547Q5szHFqBZMCqg/+vVvGjo0KF49OiR6Hfr3LlzaN68Obp164ZmzZqhSpUqot+7hIQEvHjxAk+ePMGVK1dw4cKFHC3nmJf7H+vUqYMePXqI+h/9/f3h5eWF4cOHo1mzZsJ+wsLCcPLkSaxdu1Y4h2jTn0tERKRvDO4iIiIinQgJCRGejtSURCLB4MGD1R5AHDZsGE6cOCEK2AkMDMTAgQORL18+2NraIi4uTq7cX9euXVGzZk2NgruAb0/6PnnyBMePHxfmffr0CXPmzMGcOXNgYWEBiUQil02mePHimD59Orp166bR/hwdHbFt2zb07dtX1JkfExODI0eOKF2vXr16mDZtmtxgujpPq06ePBmpqamiQIrk5GQcOHAABw4cAPCto9PKygrx8fHZSpvev39//PPPP8LAlVQqxcWLF0WdxBmKFSuW7eAu2Sfs7e3t0aBBg2xtM0P79u1FgxcvX77E3bt3Ua1aNZ1sXxOFChXC6tWrMXz4cFEWoXPnzuHcuXOQSCTCsaEsK9XIkSPRoUMHtfY3cuRIxMTEiEospaWl4eDBgzh48CAAwMLCAqampoiJiVE7Zb9EIsH06dPRt29f0TGcsV0TExNYWVkhNjZW7nW4urril19+UbvcRIbq1avLBYWEhITIdYhnKFOmTLaDu4BvZVRCQkJEmbgSEhKwevVqrF69GiYmJrC0tFT6NLyDgwNWr16dJ7PqZHwPSpQogcWLF4u+C58/f8auXbuwa9cuYVk7OzukpaUhNjZWZQevqmAGS0tLrFmzBgMGDBCVvbxz544wUGJjY4OkpCSl5Ry6du2KwYMHa/Ravzeurq7o0KEDFi1ahPT0dOzevRu7d++GsbExrKysEB0drfAzsLKywooVK0Qd9bIMcfwbUnx8PI4fPy66vjA3N4eFhQUSExOVZt6pUKGCXCYoWWPGjBGCEDNklBtdtGgRTE1NYWZmhpiYGIXrOzs7Y/Xq1SpLzpFmxo0bh3Pnzmldlg74ltlw4cKFmDlzpuha7PLly8LguZGREWxsbJCcnKxRpkNljIyMUKtWLZw7d07ub+qUVaxfv77C4C5V5Rz1wdPTE9OnT8eCBQtE5abOnDkjBG9lnGMU3UdkMDIywrRp0+Dp6an2vqVSKYKCgkQBDcbGxrC2tkZ6ejpiY2MVfi/MzMywaNEi0SCwrIxAhLFjx4quYyIiIrBmzRohqMHS0hKmpqYqX1tOSklJwfnz50X3LRn3GklJSUq/u8WKFcuxoPZSpUqhRo0awvdX9retU6dOGmUsyezt27eibKwSiQTW1tYwMjJS+RmNHTs212fBGTJkCA4cOCD8TqekpGDDhg2iBxoy5OQ9aJUqVbBmzRpMmDBBdG199+5dYfA+oy8hNTUVcXFxubqcWmampqbYunUr+vXrJ8qknJiYiNOnTystK+jq6oolS5bIZSZU1X/QvHlzNGvWTPSb8O7dO0ybNg3AtxLrABQGBP/666+4deuWRsFdwLeAma9fv2Lz5s2i+Q8fPlSatcfU1BTLli3D58+f5eZnxdnZGZs3b8aoUaNEJSCfP3+OuXPnAvh2zNrY2ACA0nN4TuvSpYtwzwKIz1n58+dHkyZNtNquPq9X8xqJRIJFixZBIpHgxIkTwvy4uDhs27ZN6DsxNzeHubl5rvhNzuv9j9OmTRMe/soQEhIiZNWzsrJCWlqa3APIlStXxsiRI/P8vT0REX1/WJaRiIiIDKpatWrYtWsXJk6cqPY65ubm2LRpE0qWLCn3t/T0dERFRcl1kPTu3VvoiNOUkZERlixZovRJx4SEBLkBiNKlS8PX11froIvSpUtj//79aNGiRZbL5suXD71798aGDRtgamoq15Fqa2ub5TaMjIwwffp0LFmyRFT2MrPk5GRERkaq7FgpWrSoqOyNIi4uLvjzzz9VDvDrSlJSkqgTEgBatmyp9UCMrFq1aolKfAAQApsMoWbNmti1a5fCEklSqRRfv35VGNhlZWWFefPmYcyYMWrvK1++fPj5558xb948pZ9lQkICvn79qrCzW9UTndWrV8fvv/8Oc3Nzub+lpKQgKipK7nV4eHjA19dXGFDQ1B9//IHq1atrtW52/O9//8P06dNFJRozpKSkKA3sKl++PPbu3ZtltsPvXd++fXH48GG5sqOZSaVSREVFISYmRukgXM2aNbFnzx788ssvKvfn4uKCffv2KQ3QjImJURjYZWJigjFjxuC3334TZR/MqwYMGICxY8eKjuPU1FR8/fpV4Wfg5OSEjRs3qnWMGeL4z0lZPc2emJiIyMhIpQNlLVq0wM6dOxWeMzIzNjbGH3/8gaFDh8LYWP65vuTkZKWBXbVr18a+fftQokQJlfsgzbi6usqVvtFG586dsXfvXpQvX17h39PS0hAVFaUysMva2lqjzEeKgrgkEolaAVrKllEnMEzXfHx8sHHjRiFznayMc4yygdYiRYpg48aN8PHxUWt/qo731NRUREVFITo6WuF1UtGiRbFlyxa13mNHR0ds3boVQ4YMURq4EB8fr/K1Ad8G4vUpq/Nfxr2Gsu9ujRo1sGfPHlGJd31TVapS0zKWql6/VCpFTEyM0s/I0tISv/7663dR9rl48eJyD+fs379flBUmQ07egwJAw4YNceDAAaVlPTP6ElQ9LGBmZqayHK2h5M+fH7t27UK3bt2yPNYAoG3btti9ezfs7Ozk+g8ygpaUWbJkidLyoLGxsXLbMzExwbx58zTOHJXZlClTsHDhQrX6V5ydneHr64smTZpo1TcCfMtUe+DAAaX9MVKpFNHR0UrP4cC3a7Fy5cqptT9daNu2rdIsWZoGo+bU9WpeZGFhgeXLl2PatGlK700SExOz/E0uU6aMxmVFtZWX+x8tLS2xfv16pVn04uLi5AK7PDw8sGHDhhx7/4mIiDTBzF1ERESUI0xMTGBtbQ07Ozu4urqiUqVKaNGiBVxcXLTaXvHixfH3339j3bp12LNnj9JBgEqVKmHMmDFaP6WYwdjYGL/99hvatWuHFStW4N69ewqXs7e3R69evTB06FBYWFgI5Rm1UbhwYaxevRqBgYE4evQobt68ifDwcERHR8PKygqlSpVCnTp10KVLF6GDWXaw1tzcXKOsGx07dkTLli2xf/9+HDp0CI8fP87yKdTSpUujfv36aNmyJerWratWZ7KnpydOnTqFo0eP4urVq3j27BkiIiIQHx8vyqaQXWfOnBFlsQJ0U5IxQ758+dCmTRts375dmHf8+HH88ssvBusIcnV1xZEjR/D3339j9+7dQvkQRZycnNC2bVsMGzZMaadaVrp37442bdpg69atOHr0qKiknSJlypRBixYt0KNHD5XLtW3bFuXKlcOyZctw9uxZpd/DkiVLol+/fujZsyeMjIy0PuacnJyEsiGnTp3Co0eP8P79e8TFxSnN0qQrPj4+aNWqFdatW4eTJ0/iy5cvCpeTSCSoWLEi+vTpg86dO/8ngogAoGzZsti8eTMePXqEAwcO4OzZs0KpXWUkEglKly4NT09PeHt7o2zZsmrvr3Dhwti3bx+OHz+Obdu24d69e0q/f3Z2dmjevDlGjBiRIyWicpMRI0agdu3aWL58udJSiHZ2dujUqRPGjh2b5UBhZjl9/OckNzc3HDt2DOfOncPVq1dx7949hdktMjM1NUWTJk3Qu3dvjbIdSSQSTJgwAd7e3lizZg3OnTunNKDLyMgI1apVw4ABA9Qa2CHtjB07FidOnFCaQVNdFStWhJ+fH/z9/bF7924EBAQoLHmeWYECBVC3bl00a9YMLVq0UBhAqYyiQKxy5cplWZYQAKpWrSpX1tHU1BQ1atRQe/+61KBBA5w6dQr//PMP9u/fjwcPHqgcXDUxMUGlSpXQtWtXdO7cWaPB8e3btwvZaQMCAkRli5Rxc3ND586d0atXL40+I2NjY0ycOBE+Pj7w9fXF6dOn8ebNG5XrGBkZoXLlyqhfvz7atWun9yAEGxsb+Pv749y5c7h8+TICAwOFskqq2livXj3hejOntW3bFvPnz5e713R3d1caYKnMpEmT0LhxY5w/fx63bt3Cs2fPsrzvKVSoENq3b4/+/fvrJGtsThk6dCj8/PyE15ecnIxNmzYpDbLPqXtQ4Fvgz86dO3Hz5k1s27YN169fz/J32M7ODrVr10aTJk3Qpk0bja5pcpKdnR3mz5+Pvn374ujRo7h8+TLCwsIQFRUFc3NzODs7o2bNmujcuTMqVaokrCd775HVQ1jW1tbYunUrdu7ciS1btogy3mZmYmKCVq1aYdSoUToJiPP29kbz5s1x7NgxnD59Gq9evRIyczk5OaFixYpo0aIF2rZtK/SBZJRXy6BucBfwf+U8Hz9+jC1btuDSpUtCqUZlLC0tUbNmTTRq1Ajt2rVT63dSV6ytrdGqVSscPnxY7m+aBqPm5PVqXtW/f3906dIFO3fuxLFjx0RZ9RSRSCQoX7486tevjzZt2qBq1ao51NJv8nL/o5WVFdavX49///0X69evV/pZFCxYEAMGDICPj4/Ch2OIiIhyA4n0e8kvTERERKREcnIyAgMDERISgqioKEgkEhQpUgRVq1bVW9aJ0NBQBAUFITw8HImJibC3t0fZsmVRrVo1g3YC3LlzB7169RKm3d3ds5VJKiYmBnfv3sXnz58RFRWFxMREWFpawtbWFs7OznBxccmTJeHykvDwcAQFBQmfoaWlJRwdHeHi4gI3Nze1B0LU9e7dOzx8+BARERGIioqCkZGRkCGkXLlyWg1MRUVFISAgAO/fv0dsbCzMzMxQqFAhVKhQQesA0dxKKpXi4cOHePXqFSIiIoTzS/78+VGtWrUcHSDIzd6/f49nz57h/fv3iImJQWpqKqytrWFra4tChQqhUqVKOht4i4qKQmBgID5//ozIyEiYmprCwcEBJUuWROXKlWFkZKST/eRWBw8eFErtAN8G1hYuXCha5uPHjwgMDMT79++RkpKC/Pnzo3jx4qhZs2a2MyXm9eM/PT0dISEhePPmDT58+IDY2FikpKTA0tISdnZ2cHFxgaurq0ZBHsqkpqYiKCgIb9++RUREBFJSUuDg4ICCBQvCw8NDowFPyl2Sk5Nx//59vH//XshkaG5uDmtraxQtWhRlypRRmq3qvy4hIQFBQUH4+PEjvn79iri4OFhaWsLe3h6FChVClSpVlGZC0dSXL1/w8uVLhIaGIioqCgkJCTA1NYW1tTWKFSsGNzc3nWal+vDhAx4+fIjIyEhERkZCKpXCysoKDg4OKFWqFMqUKWPwrCpv375FSEiI8HuenJwMCwsL2NjYoHTp0ihfvvx3kZVRG3FxcXjx4gXevn2Lz58/IyEhQSjP6OTkhPLly6NEiRI6v1b/HuTkPWhaWhoePXqEN2/eCNn0MsqBFS5cGGXKlEHx4sXz7EMV4eHhaNSokTBtb2+PGzduqL1+eno6njx5Ipxr0tPTheO3WrVqOjt/amvAgAG4cuWKML127VqNyupmJpVKERwcjJcvXyIyMhLR0dEwMjKClZUVChYsiNKlS6NkyZJ5MigkJ69X86ovX77g/v37+PLlCyIjI5Gamiq8f6VKlYKLi8t393v3vfY/vnjxAo8ePUJ4eDhSU1Ph6OgINzc3uLu759lzPRER5R0M7iIiIiLKQ37//Xds2rRJmO7Zsydmz55twBYREdH3TJ3gLiIiIiL6/uzevVvUX9CoUSNRf8L3LCoqCk2aNBFltrxy5QofliHSEfY/EhER5TyGIRMRERHlEV++fMFff/0lmqeojA4RERERERER/XclJibC19dXNC8v9R9s3bpVFNjl6urKwC4iHWH/IxERkWHkvRyxucC8efOwY8cO0Tx1nm4ODQ1F8+bNtdpnoUKFcPHiRa3WJSIiotwnOTkZpqamai+fkJCA8ePHIzo6Wpjn5OSk9bUFEREREREREeV+mvYfpKWlYfr06Xj9+rUwz8zMDN7e3vpoXrakpKTA2NhYoxKl58+fx8aNG0XzevbsqeumEeUJ7H8kIiL6fjBzl47dvXsXu3btMnQziIiI6Ds3ZcoUTJs2DXfu3EF6errKZa9fv44ffvgBN27cEM0fPHgwjI0Zy09ERERERESUV/3xxx8YPXo0rly5gpSUFJXLPnjwAP3798e///4rmt+zZ084ODjos5laef78Odq3b489e/YgIiJC5bJRUVH4888/MWLECKSlpQnzixQpgs6dO+u5pUTfJ/Y/EhERfT/4a6tDKSkpmDFjRpYXQOrKly+f2k+k8MKJiIgob0lOTsbx48dx8OBB5M+fH1WqVEG5cuXg4OAAU1NTREdHIzQ0FAEBAaKnbTPUr18fffv2NUDLiYiIiIiIiCinpKWl4dSpUzh16hRsbW1RtWpVlCtXDvnz54eFhQViY2Px/v173LlzB8HBwXLrlytXDhMnTjRAy9Xz4sULzJo1C3PmzIG7uzsqVKiAokWLwsbGBsnJyYiMjMSDBw9w584dUSlG4NsYy+LFi2FlZWWg1hPlbux/JCIi+n4wIkiHNmzYINwcOTk54dOnT9na3vz589GlSxddNI2IiIi+Y1++fMG5c+dw7tw5tZavX78+li9frlHZAiIiIiIiIiL6vkVHR+PSpUu4dOmSWstXqFABa9euhZmZmZ5bln3p6em4f/8+7t+/r9by5ubmWLBgAWrXrq3nlhHlDex/JCIiyt1YllFHXr58iXXr1gEALCwsMGHCBAO3iIiIiL5npUqVgpGRkUbrODo6YuLEidi4cSNsbW311DIiIiIiIiIiyi2cnZ1hamqq0TpWVlYYPHgwdu/ejSJFiuipZdlnbW2NggULarxeo0aNsG/fPrRr104PrSLKO9j/SERE9P1g5i4dkEqlmDFjBpKTkwEAI0aMQLFixQzcKiIiIvqeTZkyBYMGDcKlS5cQGBiIZ8+e4cOHD4iMjERSUhJMTExgZ2eH/Pnzo3LlyqhTpw48PT1hYWFh6KYTERERERERUQ7p27cvunTpgkuXLgmlF9+9e4fIyEgkJibCyMgItra2cHBwQKVKlVCrVi00b94cdnZ2hm56lpydnXHhwgUEBgbi1q1buH//Pt6+fYuwsDDEx8cjLS0NNjY2sLOzQ8mSJVGzZk00adIEbm5uhm460XeB/Y9ERETfD4lUKpUauhHfuz179mDWrFkAAFdXVxw8eBB37twR1Zn29vbGwoULVW4nNDQUzZs3F6YXLFjAsoxERERERERERERERERERERERP9RLMuYTWFhYVi6dCkAQCKRYPbs2TAxMTFwq4iIiIiIiIiIiIiIiIiIiIiI6HvH4K5smjt3LmJiYgAAPXr0QPXq1Q3cIiIiIiIiIiIiIiIiIiIiIiIiygsY3JUNp06dwunTpwEA+fPnx8SJEw3cIiIiIiIiIiIiIiIiIiIiIiIiyiuMDd2A71VMTAzmzp0rTE+dOhV2dnY63cfJkydx7NgxvHjxApGRkTAzM4O9vT3KlSuHWrVqoW3btihYsKBO90lERERERERERERERERERERERLkDg7u0tHjxYoSHhwMA6tevj06dOul8H+fPnxdNJyQkICoqCiEhITh9+jSWLFmCbt26YdKkSbCystL5/omIiIiIiIiIiIiIiIiIiIiIyHBYllELt27dwv79+wEApqam+PXXXw3SjuTkZOzevRvdunXD69evDdIGIiIiIiIiIiIiIiIiIiIiIiLSD2bu0lBycjJmzJgBqVQKABg2bBhKlSql032UL18eLVq0QI0aNVC2bFnY29tDKpXi8+fPuHfvHg4fPoyLFy8Ky798+RKDBw/Gvn374ODgoNU+Y2NjddV8IiKDMzIyAgCkpaUZuCVEREREvDYhIiKi3IXXJkRERJSb8NqEiPIia2trnW6PwV0aWr16NV69egUAKF26NAYPHqyzbdvb22Pv3r3w8PBQ+PfixYujePHiaN++PS5cuIDJkyfj69evAIDXr19j3rx5WLp0qVb7TklJ0brdRES5Fc9tRERElJvw2oSIiIhyE16bEBERUW7CaxMiIuVYllEDT58+xebNm4Xp2bNnw9TUVGfbt7a2VhrYJatJkyZYs2YNjI3/Lz7v6NGjCA4O1ll7iIiIiIiIiIiIiIiIiIiIiIjIcBjcpab09HTMmDFDiBj29vZGnTp1DNqmmjVromvXrsK0VCrF8ePHDdgiIiIiIiIiIiIiIiIiIiIiIiLSFQZ3qWnHjh24d+8egG/lE6dMmWLgFn3TrVs30fS1a9cM1BIiIiIiIiIiIiIiIiIiIiIiItIlBnepITExEcuWLROmp0yZAkdHR8M1KJNKlSrBxMREmH7//r0BW0NERERERERERERERERERERERLoikUqlUkM3IreLjo5GrVq1hGkjI6Ms15FKpUhPTxemJRIJ8uX7v1i6zp0747ffftNJ+xo2bIhPnz4BAExNTXH//n2NtxEZGamTthAR5QYZQa8ZpXSJiIiIDInXJkRERJSb8NqEiIiIchNemxBRXuTg4KDT7RnrdGv/EWlpaRqvI5VKRetlDvzKrsTEROH/ZmZmOtsuEREREREREREREREREREREREZDssyfuciIyMRExMjTOfPn9+ArSEiIiIiIiIiIiIiIiIiIiIiIl1h5i412Nra4unTpxqtc+PGDfTt21eY9vb2xsKFC3XdNFy8eFE07ebmpvN9EBERERERERERERERERERERFRzmPmru9YSkoKNm7cKJrXsGFDA7WGiIiIiIiIiIiIiIiIiIiIiIh0icFduURYWBiioqLUXj49PR0zZ87Es2fPhHlOTk5o3769HlpHREREREREREREREREREREREQ5jcFducT9+/fh6emJRYsW4dGjRyqXffLkCfr374+DBw+K5k+YMAGWlpb6bCYREREREREREREREREREREREeUQY0M3gP5PXFwctmzZgi1btsDJyQkVK1aEs7MzbGxsIJVKERERgXv37uHp06dy6w4cOBBdunQxQKuJiIiIiIiIiIiIiIiIiIiIiEgfGNyVS3369AkXLlzIcjlzc3NMnToVvXr1yoFWERERERERERERERERERERERFRTmFwVy5Rvnx5/Pjjj7h58yZevnyJ9PR0lcs7OTnB29sbvXv3RuHChXOolURERERERERERERERERERERElFMkUqlUauhGkFh8fDyeP3+O0NBQfP78GfHx8ZBIJLCxsYGjoyPc3d3h7Oys031GRkbqdHtERIZkYmICAEhJSTFwS4iIiIh4bUJERES5C69NiIiIKDfhtQkR5UUODg463R6DuwgAg7uIKG/hjQARERHlJrw2ISIiotyE1yZERESUm/DahIjyIl0Hd+XT6daIiIiIiIiIiIiIiIiIiIiIiIhIJxjcRURERERERERERERERERERERElAsxuIuIiIiIiIiIiIiIiIiIiIiIiCgXYnAXERERERERERERERERERERERFRLsTgLiIiIiIiIiIiIiIiIiIiIiIiolyIwV1ERERERERERERERERERERERES5EIO7iIiIiIiIiIiIiIiIiIiIiIiIciEGdxEREREREREREREREREREREREeVCDO4iIiIiIiIiIiIiIiIiIiIiIiLKhRjcRURERERERERERERERERERERElAsxuIuIiIiIiIiIiIiIiIiIiIiIiCgXYnAXERERERERERERERERERERERFRLsTgLiIiIiIiIiIiIiIiIiIiIiIiolyIwV1ERERERERERERERERERERERES5EIO7iIiIiIiIiIiIiIiIiIiIiIiIciEGdxEREREREREREREREREREREREeVCDO4iIiIiIiIiIiIiIiIiIiIiIiLKhRjcRURERERERERERERERERERERElAsxuIuIiIiIiIiIiIiIiIiIiIiIiCgXMjZ0A4iIiIiIiIiIiAzh/fv36NKli8plJBIJrKysYG1tjZIlS8LNzQ0tW7ZE2bJlc6iVRERERERERET0XyaRSqVSQzeCDC8yMtLQTSAi0hkTExMAQEpKioFbQkRERMRrk7zsXHiioZvwXWpW0NzQTRCoE9ylTK1atTBt2jQULVpUx60iItIvXpsQERFRbsJrEyLKixwcHHS6PZZlJCIiIiIiIiIi+v+MjIxE//LlU9x9duvWLfTr1w8vXrzI4RYSEREREREREdF/CcsyEhERERERERERAfDw8MDatWvl5sfFxeHly5c4efIkDh8+jNTUVABATEwMJk+ejH379glPmxMREREREREREekSM3cRERERERERERGpYGVlhcqVK2PSpElYsWIFTE1Nhb+9f/8efn5+BmwdERERERERERHlZQzuIiIiIiIiIiIiUlP16tXh4+Mjmnf+/HnDNIaIiIiIiIiIiPI8BncRERERERERERFpoH379qLp+/fvG6glRERERERERESU1xkbugFERERERERERETfk6JFi8LS0hLx8fEAgKSkJMTFxcHKykqt9RMTExEUFISwsDBERkbCxMQE9vb2cHNzQ+nSpbPdPn1vP8PXr19x9+5dhIeHIyEhAfb29qhQoQLKli0LiUSi9XafPHmC58+fIyIiAlZWVihYsCCqVasGGxubbLc5LS0Nr169wuvXr/H582fEx8fD3Nwctra2cHFxQbly5WBkZJStfcTExOD27dsICwtDcnIynJycUKpUKbi5uWW7/ZlFRkbi/v37+Pz5M6Kjo2FlZYX8+fOjatWqyJ8/v073RURERERERESGw+AuIiIiIiIiIiIiDVlZWQnBXQDUCu569uwZNm3ahOvXryMpKUnhMkWKFIGPjw86deoEY2PNuu50vf3bt29j5MiRwvTq1atRo0YNhIeHY/ny5bh48SJSUlLk1itevDhGjRqFpk2batT+kydPYt26dfjw4YPc38zMzNCsWTOMHTsWDg4O2LhxIzZv3iz8/fr160q3GxMTgwsXLuD8+fMIDAxEXFyc0mWtra3RqVMn9O7dW+MAqU+fPmHlypXw9/dHamqq3N9LlSqFgQMHomXLlgCAunXrCn8bOHAgBg8erNZ+Ll68iG3btuHRo0eQSqUKl6lUqRIGDx6MOnXqaPQaiIiIiIiIiCj3YVlGIiIiIiIiIiIiDckGCFlbWytdViqVYtWqVejXrx8uXLigNPAKAD58+IDFixdj+PDhiIyMVKst+t5+Zrdu3YKPjw/Onj2rMLALAEJDQzF16lRs375drW2mpaVh5syZ+PXXXxUGdgHfsqOdOHEC/fr1w7NnzzRq86ZNmzBv3jxcvnxZZWAXAMTGxmL37t3w8fHBvXv31N7H/fv30atXL5w6dUphYBcAhISEYMaMGVi6dKlG7c8QExODsWPHYsqUKXj48KHSwC4AePDgAcaOHYtFixYpbQ8RERERERERfR+YuYuIiIiIiIiIiEgDb968EWXtKly4MCwtLRUuK5VKMX36dJw9e1Y039XVFRUrVoSDgwNSU1MRGhqKW7duITY2FsC3YKERI0Zgy5YtsLCwUNoWfW8/s5cvX2Lt2rWIj4+HkZERqlSpgvLly8PS0hKfP3/GjRs3EBYWJiy/du1aVK5cGR4eHiq3u2DBApw6dUo0r1ixYqhVqxYcHR0RHR2NoKAgBAcHIzw8HFOnTkWjRo3UarMsOzs7lC1bFiVKlICNjQ1MTU0RFxeHt2/firJ6RUREYOLEidi+fTuKFi2a5fsyduxY0XfCxMQEtWrVQunSpWFsbIzQ0FBcv34dcXFx2L9/P0qWLKlRuyMjIzF8+HCEhISI9lGlShW4uLjAxsYG8fHxePbsGQIDA5GWlgYAOHToEJKSkjBz5kyN9kdEREREREREuQeDu4iIiIiIiIiIiDRw5MgR0XTDhg2VLuvr6ysKvKpevTomTpwIFxcXuWXj4uKwfv16/PXXXwCAV69eYenSpZg+fbrBtp/ZihUrkJKSgpo1a2LatGkoVqyY6O8pKSlYvXo19u7dC+Bb4Nn69euxbt06pdu8ePGi6P00MzPD5MmT0aFDB7llb9y4gTlz5uDdu3c4ePCgWm0GAEdHR/Tp0wctWrRA+fLlIZFIFC6XnJyMv//+G2vXrkVKSgpiY2OxePFiLFu2TOm209LSMHfuXFFgV82aNTFz5kwULFhQtGxcXBz+/PNPHDlyBCtWrFC7/VKpFLNnzxYCuyQSCby9vTFo0CA4OjrKLf/+/XvMnz8ft2/fBgAcO3YMNWvWRLt27dTeJxERERERERHlHizLSEREREREREREpKYbN25g9+7dwrSpqSl69eqlcNm3b99i06ZNwnTz5s2xcuVKhYFXAGBlZYUJEyagX79+wrxjx47hzZs3Btm+rJSUFNSpUwfLli2TC+wCvmWSGjduHGrWrCnMu3v3Lt6/f690m6tXrxZNz507V2FgFwDUqVMHK1asgLm5OZKTk9VqMwD069cPo0aNgpubm9LALuDbZ/njjz9i1qxZwrwbN26IsmXJ8vf3x+PHj4XpypUr448//pAL7AK+vf/Tp09Hu3btNGr/0aNHcf36dWF6/PjxmDJlisLALgAoWrQoli9fLsqYtmnTJiGbFxERERERERF9XxjcRUREREREREREpIRUKkVMTAzu3r2LRYsWYcKECUhNTQXwLYPSlClTFAY6AcDu3buFgBpHR0f8/PPPMDIyynKfgwYNQuHChQEA6enp8PPzM8j2ZZmammLGjBkwNlZdDEA22O3BgwcKl7t9+zZev34tTDdv3hyNGzdWuW0XFxf07dtXrfZqq3nz5ihTpgyAb5//lStXlC6bOYOYRCLBtGnTYGpqqnL748ePh52dnVptkUql2LlzpzBdr1499OjRI8v1jI2NMWXKFCGY7f3796IAMSIiIiIiIiL6frAsIxEREREREREREYDAwEDUrVtXrWULFSqEiRMnKg1GSktLw8mTJ4Xp9u3bw8rKSq1tm5iYoHHjxkL5xIzyejm5fUWaNm2KAgUKZLlctWrVIJFIIJVKAUBp5qurV6+Kprt27apWOzp37ozNmzfrNROVi4sLXr58CQB49OiRwmXi4+Nx7949YdrDw0MIClPFxsYGrVu3Ft5/VR49eiR6/9QJ7MpQunRpuLi44Pnz5wC+fc4NGjRQe30iIiIiIiIiyh0Y3EVERERERERERKQBDw8PLFmyRGUw1dOnTxEfHy9MV6tWTaN9ODs7C/9/9uwZpFKpqKSgvrevSPXq1dXatpWVFWxtbfH161cAQHR0tMLlMgdNmZmZoWrVqmpt39HREa6urqJyiOoKCgrChQsXEBwcjNDQUMTFxSE+Pl4IRMuQnp4u/D88PFzhth4/fixark6dOmq3o06dOmoFdwUGBgr/l0gkar9HGZydnYXgruDgYI3WJSIiIiIiIqLcgcFdRERERERERERE/59sWUNF2aECAwMxdOhQrFy5Eg4ODgq3IxtIM2XKFI3akTnYKC0tDXFxcbC2ts6x7Svi5OSk9vYtLCyE4K6EhASFy7x79074f+nSpdUqKZnBxcVFo+CuoKAgLF68WAh00oSy4LTM7c9ok7rUXTbz5yyVStGyZUu19wGIg9SUvQ4iIiIiIiIiyt0Y3EVERERERERERIRvGbnWrl0rmhcXF4ewsDBcuHABf/31FyIjIwEAz58/x5gxY7Bx40aYm5vLbSsqKko0nd0SgrGxsaLgK31vXxFFr1MdslmxMsTExAj/t7W11WibdnZ2ai/r7++PGTNmaP0eJScnK5yfuf2AZq9B3fbr8nOOjY3Vel0iIiIiIiIiMhwGdxERERERERERESlhZWWFMmXKoEyZMvDy8sLIkSPx6tUrAN/KGa5evRoTJ06UW0828Ce7Mmdgyont54TMQVMmJiYaravu8h8+fMDs2bNFQVHu7u5o0aIFKlasiMKFC8PGxgZmZmaizGFz5szBsWPHACgPTpMN+tLkNai7rC4DsgzxGRMRERERERFR9jG4i4iIiIiIiIiISA2Ojo5YvHgxfHx8kJiYCAA4cOAAOnbsCFdXV9Gyslmu9u/fD2dnZ521Rd/bzwnW1tZC6cb4+HiN1o2Li1NruR07diApKUmYHjNmDH788ccs11NWSjIz2UxnmrwGddtvZmYm/N/JyQn//vuv2vsgIiIiIiIiorwhn6EbQERERERERERE9L1wdnbGTz/9JEynp6djzZo1csvZ29uLpt+9e6fTduh7+zkh82v4+PGjRut++PBBreUuX74s/L969epqBXYBQERERJbLyH4GmrwGddufeR+fP38WBaoRERERERER0X8Dg7uIiIiIiIiIiIg08MMPP8DJyUmYvn79Oh48eCBapnTp0qLpwMBAnbZB39vPCZmznX348EGtgCrgW5nEJ0+eZLlcYmIiwsPDhel69eqptf20tDQEBwdnuZxstrZHjx6ptX0AePz4sVrLZf6cpVIp7t69q/Y+iIiIiIiIiChvYHAXERERERERERGRBszNzdGnTx/RvM2bN4umq1SpIiqpd/bsWaSlpemsDfrefk6oUqWKaPrMmTNqrXfnzh18/vw5y+ViYmJE0zY2Nmpt/+rVq2qVZSxRogQcHByE6XPnziE1NVWtfZw6dUqt5WrWrCmaPnnypFrrEREREREREVHeweAuIiIiIiIiIiIiDXXu3BkFChQQpq9duybKxmRqaoomTZoI06GhofDz89PZ/vW9/ZzQsmVLmJiYCNM7duxAfHy8ynWkUik2bNig1vYtLS1F0+qUQkxPT8e2bdvU2r5EIkHr1q2F6YiICPz9999Zrnfz5k21M61VqVIFhQsXFqZPnjyJFy9eqLUuEREREREREeUNDO4iIiIiIiIiIiLSkJmZGXr37i2aJ5u9a8CAAciX7/+635YvX65x+cT379/j3bt3Cv+m7+3rm729Pdq0aSNMf/r0CTNmzEBycrLC5aVSKZYvX4579+6ptX0rKysUKlRImD558iQSExNVrrNhwwa5EpuqdOvWTRSgtnbtWty8eVPp8q9fv8asWbPU3r6xsTH69+8vTKelpeF///sfPn36pPY2AODu3btK31ciIiIiIiIiyt0Y3EVERERERERERKQFb29vODo6CtOXL19GcHCwMF2qVCkMGjRImE5KSsLo0aOxefNmxMbGKt1uSkoKLl++jOnTp6N79+54/vy5wuX0vf2cMGrUKNF7eOXKFfj4+OD48eP48uUL0tPT8fXrV1y6dAkjRozA3r17IZFIUKlSJbW237hxY+H/Hz9+xLRp0/D161e55WJjY7Fo0SL4+voCACwsLNTafvHixTFgwABhOikpCePHj8eSJUvw5MkTJCYmIiUlBSEhIdi8eTP69++PiIgIVK5cWa3tA0DHjh1Ru3ZtYTo0NBT9+vXDiRMnVJbijI6Oxr///ouhQ4di2LBhSEpKUnufRERERERERJR7GBu6AURERERERERERN8jc3Nz9O7dGytXrhTmbdmyBQsXLhSmf/rpJ4SGhuLYsWMAgNTUVGzcuBHbt29H5cqVUaZMGdjY2CApKQnR0dF49eoVnj17lmWGqZzavr7Z2dlh0aJFGDt2rFCS8fXr15g9e7bSdXr16gULCwshw5aRkZHSZX18fHD06FFh29euXYO3tzfq168PZ2dnpKSk4O3bt7h58yYSEhIAAB4eHihUqBBOnDih1mvo27cvXrx4gTNnzgD4ll3r77//Vlqi0dbWFnPmzIG3t7cwT9VrMDIywrx58zBixAghEC8iIgKzZs3CsmXL4OHhgSJFisDCwgLx8fGIjIzEs2fPEBISojL4i4iIiIiIiIi+DwzuIiIiIiIiIiIi0lKXLl2wc+dOREZGAgAuXLiAFy9ewMXFBQAgkUgwc+ZMlClTBuvWrUNqaiqAbxmeAgICEBAQkOU+Mpf9k6Xv7eeEypUrY/Xq1ZgzZw5evXqldDkjIyMMHjwY/fr1w6pVq4T5VlZWStcpWLAg5s2bh2nTpgmZq+Lj44VALEVtWbRoEZYtW6Z2+42MjDB79mwUKlQIe/fuVRlQVbZsWfz2229wcHAQzVf1GoBvAWEbN27EggULcOrUKWF+VFQUzp07p1YbM5fwJCIiIiIiIqLvB4O7iIiIiIiIiIi00KyguaGbQLmAhYUFevXqhTVr1gAApFIptmzZgvnz54uW69OnD1q0aIGdO3fizJkziIqKUrpNiUQCFxcX1KtXD+3atUPp0qWzbIe+t69vFSpUwI4dO3Dy5En4+/vj+fPniIyMhJWVFZycnFCnTh107NgRJUqUAABRacWsAqPq16+PDRs2YNmyZQgMDFS4TNGiRdG5c2f06tVLq2A3IyMjjB49Gh06dMCRI0dw/fp1hIeHIzk5GU5OTihZsiTatGmDJk2awNTUFB8/fhStb21tneU+LCwsMGfOHPTo0QM7duzA9evXVZZaNDExQaVKldCoUSO0bt06y/eJiIiIiIiIiHIniVQqlRq6EWR4GU+XEhHlBRkd8SkpKQZuCRERERGvTYhInlQqxfPnz/HixQt8/foVcXFxMDMzg62tLYoXL44yZcrAzs4u124/N+jbty+Cg4MBAHXq1MHy5cvVWi80NBRBQUH48uUL8uXLh/z586NEiRKoWLGiPpsr5+LFi5gyZYowvXHjRlSuXFmjbSQnJ+Phw4d49+4dvn79iuTkZFhaWsLe3h4lSpRA6dKlYW7OIFSSx2sTIiIiyk14bUJEeZFsxu7sYuYuIiIiIiIiIiKiHCSRSFCuXDmUK1fuu9y+oYWGhuL58+fCdIUKFdRet3jx4ihevLg+mqWR8+fPC/83MjLS6rMyNTWFh4cHPDw8dNgyIiIiIiIiIspt8hm6AURERERERERERETq2rBhA9LT04Xphg0bGrA1mnv+/DlOnz4tTNeqVYsZtoiIiIiIiIhIKQZ3ERERERERERERkcE8f/4cr169ynI5qVSKtWvX4tSpU8I8V1dXVKpUSZ/NU8uFCxfUKiPz6tUr/O9//xMt26VLF302jYiIiIiIiIi+cyzLSERERERERERERAbz5MkTzJ8/HzVq1EDTpk1RqVIlODs7w9LSEklJSQgLC0NgYCAOHDiAZ8+eCesZGRlhwoQJBmz5//nzzz+xePFiNG/eHHXq1EHZsmXh6OgIIyMjxMTEIDg4GOfPn8eRI0eQlJQkrFe3bl00atTIgC0nIiIiIiIiotyOwV1ERERERERERERkUFKpFAEBAQgICFBreYlEgjFjxqBatWr6bZgGvnz5gr/++gt//fWXME8ikUAqlSpcvnTp0pgxYwYkEklONZGIiIiIiIiIvkMM7iIiIiIiIiIiIiKDMTMz02j5IkWKYPz48WjcuLGeWqQ5c3NzhfMVBXYZGRmhZcuWmDx5MqysrPTdNCIiIiIiIiL6zkmkyh4do/+UyMhIQzeBiEhnTExMAAApKSkGbgkRERERr02IiNTx/v17XLlyBUFBQQgJCUF4eDji4+MhlUphY2MDR0dHVKxYEbVr10azZs1gbJy7nllNSUnBrVu3cPv2bTx58gTv379HVFQUkpOTYWZmBltbWzg7O8PDwwMtWrRAiRIlDN1k+g/jtQkRERHlJrw2IaK8yMHBQafbY3AXAWBwFxHlLbwRICIiotyE1yZERESUm/DahIiIiHITXpsQUV6k6+CufDrdGhEREREREREREREREREREREREekEg7uIiIiIiIiIiIiIiIiIiIiIiIhyIQZ3ERERERERERERERERERERERER5ULGhm4AZe3Nmzd49OgRPn78iPT0dBQqVAjlypWDq6uroZtGRERERERERERERERERERERER6wuAuPZg3bx527Nghmuft7Y2FCxdqtJ0LFy5g7dq1CAwMVPj38uXLY9CgQejUqZPWbSUiIiIiIiIiIiIiIiIiIiIiotyJZRl17O7du9i1a1e2tiGVSjF//nwMGTJEaWAXADx9+hSTJ0/GhAkTkJycnK19EhERERERERERERERERERERFR7sLMXTqUkpKCGTNmID09PVvbWbp0KbZv3y6aV716dVSuXBlGRkZ4+vQprl69CqlUCgA4evQojIyM8Pvvv2drv0RERERERERERERERERERERElHswuEuHNmzYgODgYACAk5MTPn36pPE2zp07h40bNwrTtra2WLFiBerVqyda7tGjRxg+fDg+fvwIAPjnn39Qo0YN9OzZMxuvgIiIiIiIiIiIiIiIiIiIiIiIcguWZdSRly9fYt26dQAACwsLTJgwQeNtSKVS/PHHH8K0RCLBmjVr5AK7AKBixYrw9fWFmZmZMG/VqlVITEzUovVERERERERERERERERERERERJTbMLhLB6RSKWbMmIHk5GQAwIgRI1CsWDGNt3PmzBkh8xcAeHl5oVatWkqXL126NAYOHChMf/r0Cfv379d4v0RERERERERERERERERERERElPswuEsH9u7di4CAAACAq6srfvrpJ622c+LECdF07969s1ynZ8+eMDIyUroNIiIiIiIiIiIiIiIiIiIiIiL6PhkbugHfu7CwMCxduhTAtzKKs2fPhomJicbbSU1NxcWLF4XpIkWKoEqVKlmuV6hQIVSrVg23b98GAAQGBiIiIgKOjo4at4Fyr3PhLLdJqjUraG7oJhAREREREREREREREREREZGOMXNXNs2dOxcxMTEAgB49eqB69epabSc4OBjR0dHCtIeHh9rrZl42LS0Nd+7c0aoNRERERERERERERERERERERESUezC4KxtOnTqF06dPAwDy58+PiRMnar2tFy9eiKYrVKig9roVK1YUTb98+VLrdhARERERERERERERERERERERUe7A4C4txcTEYO7cucL01KlTYWdnp/X2ZAOyihYtqva6RYoUUbktIiIiIiIiIiIiIiIiIiIiIiL6/jC4S0uLFy9GeHg4AKB+/fro1KlTtrYXFhYmmi5cuLDa68ou+/Hjx2y1hYiIiIiIiP4fe3cep2VZ7w/888yw76sIiCsgbrhipSmalruJS5nmUmppecrMLNtMM61Tptnmmmseq1NuuWXmlqmoLC6gKLiyCTLsDAMzz+8PfzyHYZ2B2dD3+/Xi1X3dz7V8b5C4GT5zXQAAAAAA0PyEu9bBM888k7/85S9JkjZt2uT8889f7zkXLlxYq92xY8c6j12x74pzAQAAAAAAAAAAG55WzV3Ahqaqqio/+MEPUiwWkySnn356Nt988/Wed8VAVps2beo8tm3btmucqy5at25d7zE0pcrmLoAWzu/h2srLy5u7BACAEu8mAEBL4t0EAGhJvJsArJ2du+rpt7/9bV5//fUkyRZbbJHTTjutQeZdvHhxrXZ9wl0r9q2sFAQCAAAAAAAAAIANnZ276uGVV17JddddV2pfcMEF9QphrcmKu29VVVXVeeyKfdu1a1fv9ZcsWVLvMUDL4ffwqvl5AQBaEu8mAEBL4t0EAGhJvJsArJ6du+qopqYmP/jBD0p/qIwYMSIf+chHGmz+Dh061GrXJ9y14q5fK84FAAAAQG1TpkzJRz/60ZV+PP300/Wa5+9//3ut8Y8++mgjVfzBs+LP3TXXXLNO81xzzTW15vn73//ewJUCAAAANB87d9XRzTffnLFjxyZJunXrlnPPPbdB518xkLVgwYI6j12xr3AXAAAANL7u9/Vo7hI2SBUHzWruEtboqquuatBv6AMAAACA9WHnrjqorKzM5ZdfXmqfe+656dGjYb+A26dPn1rtadOm1Xns1KlTa7U33njjBqkJAAAA4MNm3Lhxdt8CAAAAoMWwc1cdVFVVZeHChaX2D37wg/zgBz9Y45hisVirfccdd+Suu+4qtY844ohcfPHFpfZWW21Vq/+UKVPqXN+KQbAtt9yyzmMBAAAAqO3qq6/OXnvtlbIy3xcJAAAAQPPyFap1UF1dvdYfNTU1tcYUi8U1fr5iuGvcuHF1ruell16q1RbuAgAAAKif8vLy0vXEiRPz4IMPNmM1AAAAAPA+4a4WYtCgQenSpUupPWbMmDqPHT16dOm6vLw8u+yyS0OWBgAAAPCBt99++6V9+/al9jXXXJOlS5c2Y0UAAAAA4FjGOunSpUteeeWVeo15+umnc+KJJ5baI0aMyE9/+tPV9m/VqlX23nvv/P3vf0+STJ06NWPHjs2OO+64xnWmT5+esWPHlto777xzevToUa9aAQAAAD7sunfvns985jO58cYbkyTvvPNO7rnnnnz6059u5soAAAAA+DAT7mpBDjrooFK4K0luvfXWtYa7brvttlRXV5faBx54YKPVBwAAAPBB9vnPfz5/+9vfMm/evCTJH/7whxx00EFp06ZNo667YMGCjB07Nu+++27mzJmTdu3apUePHtluu+3Sr1+/Rl37w2zhwoV55ZVX8uabb2bevHlZsmRJ2rVrl27duqVfv34ZOHBgOnXqtE5zV1RU5IUXXsjMmTMzd+7cdOzYMT179syOO+6Ynj17NtgzvPXWW3n11Vczc+bMLFq0KH379s0BBxywyr5Lly7Na6+9lkmTJmX27NmprKxMmzZt0qlTp/Tt2zdbbLFFNtpoowarDQAAAGgYwl0tyH777ZfBgwdnwoQJSZI777wzRx99dIYNG7bK/q+//nquu+66Urt379455phjmqRWAAAAgA+azp0757jjjstVV12V5P0d02+//fZ89rOfbZT1xowZk2uvvTajR4+u9c17y9tyyy3zhS98Ifvvv38KhcIq+7z77rs5/PDDS+3vf//7OfTQQ1e77uuvv57Pfe5zte5dc8012WGHHVY75l//+le++93vltr/8z//ky222GK1/Vuyt99+O9dcc00effTRLF68eLX9CoVCttxyy+y333754he/WKe5H3vssdx4440ZN25cisXiKvtsv/32Oe200/KRj3xkrfNdc801tb7+99RTT5XW+cMf/pCXX365Vv9OnTqtFO5asGBBrr/++txzzz2pqKhY43obbbRR9tprr5x66qnp3r37WusDAAAAGl9ZcxfA/ykUCjn77LNL7WKxmK985St58sknV+o7bty4nHzyybW+AHXmmWemXbt2TVIrAAAAwAfRZz/72VqhlhtvvDGVlZUNukZVVVUuuOCCnH766Xn22WdXG+xKkkmTJuUHP/hBvv3tb6+2jo022iibbbZZqT1y5Mg1rr+qz9c25plnnild9+7de4MNdj366KM5/vjj849//GONwa7k/a/NTZw4MTfccMNa5503b16+/vWv59xzz81LL7202mBXkrz44ov5+te/np/97GdZunRpfR8hl156ac4999yVgl2r8tZbb+X444/PLbfcstZgV/J+UPCvf/1rJk+eXO+6AAAAgMZh564WZt99981pp52Wa665Jkkyd+7cnHzyydlll10ydOjQlJWV5ZVXXsl//vOfWl8kOvzww3Psscc2V9kAAAAAHwgdOnTIiSeemF/96ldJklmzZuXPf/5zTjzxxAaZf/Hixfn617+eMWPGlO6VlZVlu+22y+DBg9O1a9csXrw4b7zxRp599tlSAOmxxx7LN7/5zVxxxRUpLy9fad5hw4blzTffTPJ+EKtYLK52p6/lg1rL3zvllFNWW/fyY3bbbbc6PWtL8+abb+YHP/hBqqqqSvd69eqVHXfcMX379k27du1SWVmZioqKTJo0Ka+++mqdwlcVFRU544wz8sYbb5TutW7dOkOHDs1WW22Vzp07Z+HChXn11Vdr7dJ2++23Z/HixfnhD39Y52e4+eab85e//CXJ+/+tDhs2LJtssknKy8szderUvPDCC6W+VVVVOeecczJt2rTSvQ4dOmSnnXbKpptumo4dO2bp0qWZO3du3njjjUyYMCELFiyocy0AAABA0xDuaoHOPvvsVFZW5uabby7dGzVqVEaNGrXK/gcffHAuuuiipioPAAAA4APtyCOPzK233poZM2YkSW655ZYceeSR6dSp03rPfemll9YKdn3iE5/ImWeemX79+q3Ud9asWfnlL3+Zf/7zn0mS5557LjfccMMqQ1i77757/vd//zfJ+2Gj1157LYMGDVqp39KlSzN69OhSu6ysLDU1NXnxxRezaNGitG/ffqUx06ZNyzvvvFNqDxs2rO4P3ILcfPPNpWBXWVlZzj777IwYMWKVYbnk/eMMn3jiidx1112rnbNYLOaCCy4oBbsKhUJGjBiRU089NT169Fip/5QpU/KTn/wkzz33XJLk3nvvzW677ZaDDz64Ts9w5ZVXJkmOPvronHHGGenYsWOtz5csWVK6/uc//5m33nqr1D7ssMNy1llnrTRmmaVLl2bMmDG544470qqVLxsDAABAS+FYxhaorKws3//+93P11Vdnp512Wm2/wYMH57//+79z2WWXpW3btk1XIAAAAMAHWNu2bfPFL36x1J47d25uvfXW9Z73ueeeqxUU+tznPpeLL754lcGuJOnRo0cuuuiifOpTnyrd++Mf/5h58+at1HfXXXetFVJa3TGL48aNK+3OtPnmm2ebbbZJsnLoa3krzrWhhruW333skEMOydFHH73aYFeSdOzYMZ/61Kfym9/8ZrV97rnnnjz11FOl9je+8Y2ce+65qwx2JUm/fv3yq1/9KjvvvHPp3rXXXrvGozmXV11dneOOOy7nnHPOKkNarVu3Ll0v/7ybbrppzjvvvNUGu5KkVatW2W233XLRRRdlyJAhdaoHAAAAaHy+BauRfOQjH8krr7yyXnMMHz48w4cPz5tvvpmXXnop7777bqqrq9OnT58MGjQoW2+9dQNVCwAAAMDyDjvssNxyyy2ZPHlykuS2227LZz7zmXTr1m2d51x+l/atttoqX/3qV+s07uyzz87jjz+eRYsWZeHChXnggQdy9NFH1+rTsWPHbLPNNnnxxReTvB/sOf7441eaa/nAz7Bhw9KpU6e89NJLSd4Pce2xxx5rHLP55pund+/edaq7pZk1a1bpelmobX0Ui8XccsstpfbHPvaxfOYzn1nruFatWuXcc8/Ncccdl2KxmClTpuSpp57Knnvuudaxffv2zemnn16n+pZ/3sGDB6eszPf5AgAAwIbI3+g3AJtttlkOPvjgnHzyyTnllFNy6KGHCnYBAAAANKJWrVrl1FNPLbUXLlyYm266aZ3ne++992rt8HTUUUfV+ei7bt26Zbfddiu1lx3pt6Ll+4wZM6bWEX3LPPvss6XrYcOG1dqFa/kQ1zLFYrHWmOXX2NAsf+TkhAkT1nu+cePGlY5jTFKnYNcyW2yxRbbaaqtSe3W/pis67LDD0qZNmzr1Xf55X3311dTU1NS5PgAAAKDlEO4CAAAAgFU44IADssUWW5Taf/3rXzNjxox1mmvMmDG12jvttFO9xg8YMKB0/eqrr66yz/JBrcrKyjz//PO1Pl+0aFFpZ6/y8vLsuuuu2WGHHUohoEmTJuW9996rNea1115LRUVFqb377rvXq+6WZNttty1d33XXXfnzn/+8ygBcXS1/jGWhUMiOO+5Yr/HL/5rWNWy2yy671Hn+5Z/3zTffzE9+8pPMmTOn7gUCAAAALYJjGQEAAABgFcrKynLaaaflu9/9bpJk8eLFuf7663PuuefWe64VwzsnnHBCvcYvv+vS6gI6Q4cOTbt27VJZWZnk/Z24dt1119Lno0ePLoWZtt1223Ts2DFJsuOOO+app54q7dJ1wAEHlMaMHDmydF1eXl6vcFFLc+yxx5Z2T6upqckvf/nL/OEPf8iee+6Z3XbbLUOHDk3//v3rPN/yv6bFYjGf/OQn61XP8r+mc+fOrdOYzTbbrM7zH3bYYbnpppsyf/78JMk999yTf/7znxk2bFh233337Ljjjhk4cGDKy8vrVTcAAADQtOzcBQAAAACrse+++2brrbcute+6665MmTKl3vPMnj27Vru6urpeP4rFYmnssrDOilq3bl1rR7AVj1lcvr38DlxrOppx+fY222yTTp06rf1hW6iPfvSj+epXv5qysv/7kujs2bNzzz335IILLshRRx2VQw45JD/4wQ/ywAMPZNGiRWucryl+TVfUuXPnOj9v9+7dc8kll9T6NVu8eHH+/e9/55e//GVOOumkfPKTn8xZZ52VP/3pT5k5c2ad5wYAAACajnAXAAAAAKxGoVDIl7/85VJ76dKlufbaa+s9T13DO3WxfChoRcsHtV5++eXMmzev1F4+qLV8v+WDXsv3WbJkScaOHbvKMQ2hbdu2tdqLFy9ep3mW7VS2unmXd8IJJ+Tqq6/Onnvuucodq9577708+OCDOf/88/PpT386N954Y5YuXbrKuRry13T5XbzWpFWr+h3EMGzYsNx6660ZMWJEOnTosNLnCxcuzFNPPZXLLrssn/70px3dCAAAAC2QYxkBAAAAYA322GOPDB06NM8//3yS5IEHHsiJJ56YzTffvM5zLB84Kisry6OPPprWrVs3dKnZbbfdStfV1dV57rnnss8++6SioiITJ05MknTo0CHbb799qd/AgQPTvXv3VFRUZPr06Xnrrbey6aab5oUXXqi1e9XyczeEFXcBW9tOWauz4ri17W61/fbb59JLL01FRUWeffbZjB07Ns8//3xee+21lY5K/P3vf5/nnnsul1566Uq/Xsv/mvbu3Tt33333OtXf2DbaaKN8+9vfzte//vWMHj06Y8aMyQsvvJCXXnqpVqCuuro6d999d0aOHJmrr746ffr0acaqAQAAgGXs3AUAAAAAa3H66aeXrqurq3PNNdfUa3y3bt1K1zU1Net0tGNdDB48uNZay3bievbZZ0s7fu288861doAqFAq1glsjR46s9b9J0q5duwwdOrRBa+3SpUut9rRp09ZpnhXHrTjv6nTv3j2f/OQnc8455+Smm27Kgw8+mIsvvjjDhw+vdXTjyJEjc/PNN680fvmf55kzZ67zzmNNpV27dvnYxz6WM844I7/73e/y0EMP5be//W2OOuqoWrt6TZ8+PT/+8Y+bsVIAAABgecJdAAAAALAWu+yyS63jC//1r39lwoQJdR6/xRZb1GqPHj26wWpbXqFQyK677lpqryqotarjFZe/tywQtvwRjTvttFOD7zS25ZZb1joacdnOYvU1adKk0nV5eflKP9d11bFjx3ziE5/Iz372s/zyl7+sVdtdd921Uv/l1ykWixkzZsw6rdtcWrVqlV133TXf+ta3cuutt6Z3796lz5599tlGCyACAAAA9SPcBQAAAAB1sPzuXcViMVdddVWdx64YqHrggQcarK41rfX2229n2rRpefbZZ0v3lg+prereqFGjMmfOnLz88surnLOhtG/fPltttVWpPW3atFpBrbp47bXXau3cNXDgwLRv3369a/voRz+avffeu1Zt8+bNq9VnxWMqG/PXtLFtvPHGOfHEE2vde/XVV5upGgAAAGB5wl0AAAAAUAfbbrttrcDPE088kRdffLFOYzfeeOPssMMOpfbo0aPz5JNPNniNycrhrdtvvz1Tp05NkvTq1StbbrnlKuvbZJNNkiTz5s3LH//4x1RXV5c+b4xwV5Lsu+++tdp//etf6zX+b3/7W632Pvvss74llfTv379We8mSJbXaQ4cOzcYbb1xqP/DAA+u8+1hLsLbnBQAAAJqHcBcAAAAA1NGXvvSllJX935fU7r777jqPPfXUU2u1L7jggrz++uv1Wv+1115LRUXFGvv069cv/fr1K7X/9Kc/la5X3G1qecsHuP785z+Xrrt165ZBgwbVq866GjFiRNq2bVtq33HHHbWOg1yTZ555JnfccUep3bZt24wYMWKVfRcsWFDvn+uxY8eWrtu1a5fu3bvX+rxVq1Y5+eSTS+3q6up8+9vfzowZM+q1zpgxY1JVVVWvMXXx0ksv1buO5fXt27cBqwEAAADWlXAXAAAAANTRwIEDs//++5fay+9utTYf+chHcthhh5Xas2fPzimnnJK//OUvWbx48WrHLVq0KA8++GC+8Y1v5POf/3ydwkPLB7UqKytL16s6knFtY3bdddcUCoW1rrkuunXrli996UuldnV1dc4555zcdtttqw08LVmyJH/6059yzjnnpKampnT/S1/6Urp167bKMXPmzMlxxx2Xs846K/fff38WLFiw2poWLFiQn/3sZ3nhhRdK94YPH77Kn4PDDjus1s/pO++8k5NOOin333//Gv/bmDt3bu6+++58+ctfzumnn77GX/919b3vfS/HH398/vSnP2X69Omr7VdTU5M77rgjf/zjH0v3+vTpk2222abBawIAAADqr1VzFwAAAAAAG5JTTz01Dz30UL2CXcuce+65mTZtWml3qoULF+bSSy/NlVdemZ122ikDBgxIx44dU1lZmdmzZ2fixImZOHFivY/IGzZsWO68885V3l+d3XbbLWVlZbUCU2sb0xCOO+64jBs3Lg899FCSZPHixbn88stz7bXXZuedd07//v3TsWPHLFiwIFOmTMno0aMzb968WnPst99+Oe6449a4TrFYzFNPPZWnnnoqrVq1ypZbbpmBAweme/fuad++fRYtWpS33norzz33XBYuXFga17Fjx5x++umrnLO8vDwXXXRRvvKVr+S1115LksyaNSs/+tGPcvnll2fnnXdO37590759+yxcuDAVFRV59dVX88Ybb6zTfz/1NXHixFx22WW5/PLL079//2y99dbp3bt3OnXqlCVLlmTatGkZNWrUSoHBb3zjG7V2qAMAAACaj3AXAAAAANTDpptumoMPPrheRzIu07p161x22WX5zW9+kz/96U8pFotJ3t8t6oknnljr+EKhkFat1v4lvWHDhqVQKJTmT5LNN988vXv3Xu2YLl26ZOutt8748eNr3V/Tbl8NoVAo5Mc//nH69euXW265pVTz/Pnz8/jjj6917Oc///mcccYZ9dpdbOnSpZkwYUImTJiwxn49evTIL37xizUeUdilS5dcc801ueSSS/KPf/yjdH/27Nl5+OGH11pLeXl5owepisVi3nnnnbzzzjtr7Ne6det861vfyj777NOo9QAAAAB1J9wFAAAAsA4qDprV3CXQjE455ZQ88MADqz06cE1atWqVs846K5/+9Kdz00035bHHHlvjMYHl5eXZeuut8/GPfzwHHnhg+vXrt9Y1unbtmkGDBtUKL9VlB67dd9+9Vrirf//+dVpvfZWVleWrX/1qDj744Dr9nHTs2DF77bVXTjrppGyxxRZrnX+jjTbKxRdfnH//+9959tln8+67766xf+/evXPwwQfnhBNOSKdOndY6f/v27XPhhRfmM5/5TG6++eY89dRTazxqsXXr1tl+++2z11575YADDkjHjh3XukZ9/ehHP8ojjzySkSNH5vXXX68V9FtRhw4dMnz48Hzxi1/MgAEDGrwWAAAAYN0Vimv6Wz0fGhUVFc1dAmvw8LuVzV0CLdy+G7Vr7hJalNatWydJvY8tAQBoDN5NgLWprq7Oyy+/nLfeeitz5szJwoUL0759+3Tt2jUDBgzIlltu2Sjhn5asuro6r776al5//fXMnTs3CxcuTIcOHdKlS5dsvvnmGTx4cMrLy9d5/nfffTevv/56pk6dmnnz5mXJkiXp0KFDevToka222ipbbLHFeu2mVVVVlZdeeimTJ0/OnDlzUlVVlQ4dOqRbt27ZdNNNs8UWW6Rdu6b7u/y8efMyceLETJkyJRUVFVm8eHHatm2brl27ZvPNN8+gQYPStm3bJquH5uXdBABoSbybAB9E3bt3b9D5hLtIItzV0gl3sTbCXbX5iwAA0JJ4NwEAWhLvJgBAS+LdBPggauhw17p/+xkAAAAAAAAAAACNplVzFwAAAGxY7CrK2thVFAAAAAAAGoaduwAAAAAAAAAAAFog4S4AAAAAAAAAAIAWSLgLAAAAAAAAAACgBRLuAgAAAAAAAAAAaIFaNXcBAAAAAAAAAPBh0P2+Hs1dAhuAioNmNXcJQAti5y4AAAAAAAAAAIAWSLgLAAAAAAAAAACgBRLuAgAAAAAAAAAAaIGEuwAAAAAAAAAAAFog4S4AAAAAAAAAAIAWSLgLAAAAAAAAAACgBRLuAgAAAAAAAAAAaIGEuwAAAAAAAAAAAFog4S4AAAAAAAAAAIAWSLgLAAAAAAAAAACgBRLuAgAAAAAAAAAAaIGEuwAAAAAAAAAAAFog4S4AAAAAAAAAAIAWSLgLAAAAAAAAAACgBRLuAgAAAAAAAAAAaIGEuwAAAAAAAAAAAFog4S4AAAAAAAAAAIAWSLgLAAAAAAAAAACgBWrV3AUAAAAAALQkZ5xxRkaPHp0k2XnnnfP73/++mSsCAAAAPqyEuwAAAAD4UJoyZUqOPPLIle7vuuuu+e1vf1vv+ebMmZNDDz00S5YsqXV/0KBBufnmm9e5TgAAAAA+vIS7AAAAANbBTbmpuUvYIJ2YE5u7hLUaNWpUpk2blo033rhe4x588MGVgl0AAAAAsD7KmrsAAAAAAGhJisVi7rvvvnqPu/feexuhGgAAAAA+zIS7AAAAACBJu3btStf3339/vca+8cYbGTduXKndtm3bBqsLAAAAgA8v4S4AAAAASPLxj3885eXlSZI333wzL774Yp3HLr9r19ChQ9O9e/cGrw8AAACADx/hLgAAAABI0rNnz+y+++6ldl2PZqypqckDDzxQah988MENXhsAAAAAH07CXQAAAADw/y0fzHrwwQezZMmStY559tlnM3369CTvH8e4//77N1p9AAAAAHy4tGruAgAAAACgpdh7773TqVOnzJ8/P3Pnzs2///3v7Lvvvmscs/yRjHvttVc6deq03nW89tprmThxYioqKlJVVZWuXbtmk002yQ477JA2bdqs9/xJsnTp0rzwwguZOnVqZs6cmfLy8uy6664ZMmTIase8/vrrGT9+fN577720a9cuvXv3zg477JCePXs2SE3LNNbzV1dXZ8yYMXn77bczd+7cdO/ePX379s1OO+2UVq18qRQAAABoeXzFAgAAAAD+v7Zt2+YTn/hE7rrrriTvH824pnDXwoUL88gjj5Ta63Mk44IFC3LrrbfmrrvuyowZM1bZp127djnggANyyimnZKONNlrrnEcccUSmTZtWqu2HP/xhFi9enGuvvTZ///vfU1FRUav/Zz/72VWGu5566qn8+te/zsSJE1f6rLy8PHvssUfOOuus9O/fP3//+99z0UUXlT7/29/+ln79+q211sZ4/mWqq6tz22235ZZbblnpmZOke/fuOfroo3PSSScJeQEAAAAtimMZAQAAAGA5ywe0/vOf/2TOnDmr7fuvf/0rlZWVSZIePXrkIx/5yDqtOWrUqBx99NG57rrrVhtsSpLKysrceeedOfbYY/Of//yn3utMnTo1X/jCF3LzzTevMuS0Kr/5zW9y1llnrTLYlbwfnHr88cdz8skn55lnnql3TUnjPv/ChQtz5pln5te//vVqn7mioiLXXHNNzjzzzMyfP3+dngEAAACgMfg2NAAAAABYzk477ZT+/ftn8uTJWbp0af7xj3/kmGOOWWXf5Y9kPOCAA1JeXl7v9R555JH84Ac/yJIlS0r3evbsmZ122ikbb7xx2rZtm1mzZmXUqFF56623krwfWPrWt76Vyy67LLvvvnud1qmqqsp3vvOdTJo0KUmyySabZJdddknPnj0zf/78vPbaaykUCrXGXHfddbnllltq3evVq1c++tGPpnfv3lm4cGHGjx+fF154IfPmzcv3vve9HHvssS3m+aurq3POOedk9OjRte4PHjw4O+64Yzp16pTp06fnqaeeyqxZszJmzJhccskl9aofAAAAoDEJdwEAAADACg488MBcd911Sd4/mnFV4a6pU6fWCg2ty5GMb731Vi688MJSsKl79+752te+lk9+8pOrPB7wkUceySWXXJI5c+akuro6559/fv7nf/4n3bp1W+taDz/8cKqrq9O5c+ecd955+cQnPrFSn+UDVi+//HL+8Ic/lNplZWU5/fTTc/zxx68UYnv55Zfzwx/+MG+99VZuuOGGOj594z////zP/2TUqFGldo8ePXL++eevtMPa0qVLc/311+e6667LQw89lDZt2tT5GQAAAAAak2MZAQAAAGAFBx98cGkXq3HjxuX1119fqc99992XYrGYJBk0aFAGDRpU73UuueSSLFy4MMn7waOrr746Bx100CqDTUmyzz775Ne//nXatm2b5P3jBP/85z/Xaa3q6uq0bt06v/71r1cZ7EqS1q1bl66vvPLKVFdXl9pnnXVWTjzxxFXuTjZkyJD89re/Ta9evVJVVVWnepLGff558+bl2muvLbXbtWuXK664YpVHZ7Zq1SqnnXZavvzlLydJvZ4BAAAAoDEJdwEAAADACvr375+hQ4eW2vfdd99KfZY/kvGggw6q9xovvvhirZ2/zj777AwYMGCt4wYPHpzPfOYzpfbtt99eCpmtzXHHHZchQ4astd/kyZPz9NNPl9rbbbfdao+mXKZ379756le/Wqc6ksZ//vvuuy+VlZWl9oknnpiBAweuce4TTzxxnUJ6AAAAAI1FuAsAAAAAVmH5Yxbvv//+1NTUlNrPP/983nnnnSRJeXl5DjjggHrPv3w4rHfv3qvdTWtV9t9//9J1RUVFJk2aVKdxI0aMqFO/J598slZg6sgjjyztZLYmn/zkJ+t0RGTS+M//6KOPlq7Ly8tz5JFHrnXe8vLyHH300XWuAwAAAKCxCXcBAAAAwCrsv//+peP/3n333Tz33HOlz5YPJn3kIx9Jz5496z3/8rtW7bDDDikrq/uX6lbc4WrChAlrHdO/f/9svPHGdZp/3LhxtdqrOspwVVq1apVddtmlTn0b8/lramoyfvz4Unv77bevc+hsr732qnMdAAAAAI2tVXMXAAAAAAAtUceOHbP33nvnwQcfTPL+MX/Dhg3L4sWL89BDD5X6Lb/DV11VVVXljTfeKLUffvjh7Lnnnutc69y5c9faZ7PNNqvzfJMnTy5dd+3aNb169arz2K222ir/+te/1tinsZ9/+vTpWbhwYaldn6MWe/TokV69emXmzJnrXA8AAABAQ7FzFwAAAACsxkEHHVS6fvjhh7No0aI89thjmTdvXpKkU6dO67TT05w5c2ode1gsFlNdXV2vH8ubP3/+Wtfs1KlTnetb9nxJ0qVLlzqPS94Pg61NYz//8vUnSffu3ev1DD169KhXfwAAAIDGYueudVRTU5O33norb775ZqZPn565c+emqqoqHTp0SLdu3TJkyJAMGjQo5eXlzV0qAAAAAOto2ZGL7733XhYtWpSHH364tJNXkuy3336loxvroy5hrPqoqalZa59Wrer+pcCqqqrSdevWretVS136N/bzL79rV5K0a9euXvPVtz8AAABAYxHuqodZs2bluuuuy6hRozJ+/PgsWrRojf27du2aww8/PKecckr69u271vnfeeed7LfffutUW58+ffLYY4+t01gAAAAAVq28vDwHHHBAbr311iTJbbfdlokTJ5Y+X5cjGZOsFAg7+eSTc/rpp697oQ1s+V2+VgxKrc2CBQvW2qexn79Dhw612pWVlfUaX9/+AAAAAI3FsYz1MHny5Fx77bUZNWrUWoNdyfvby9988805+OCD87e//a0JKgQAAACgoS0f4JowYULpSMBNNtkkO+644zrN2a1bt1rtyZMnr3N9jWH5+t57770sWbKkzmOnTp1ar/mThn/+zp0712pXVFTUa/ysWbMashwAAACAdWbnrvXQq1evDB48OJtttlm6du2a8vLyzJ49O+PHj8+YMWNK28EvXLgw5513XqqqqnLsscfWef6ysrIUCoU69a3PtvoAAAAA1N3AgQMzePDgTJgwodb9gw46aJ3n7NChQ/r06ZPp06cnScaMGbM+JTa4wYMH56mnnkqSLFmyJK+++mq23XbbOo0dP378Wvs09vP36dMnHTp0KO069uqrr9Z5bEVFRWbOnNmg9QAAAACsK4mgeigvL8+wYcNywAEHZM8998yWW2652r6TJ0/OhRdemEceeaR07+KLL84ee+yRTTfdtE7r/eQnP8mRRx65vmUDAAAAsJ4OOuigWuGuQqGwXuGuJNltt91yzz33JElmzJiR5557Lrvuuut6zdlQhg4dWqv94IMP1incNWXKlLz00kt1WqMxn7+srCzbbLNNnnvuuSTJSy+9lNmzZ6+0Y9iqPP744w1SAwAAAEBDcCxjPWy77ba55ZZbcsIJJ6wx2JUk/fv3z+9+97vsueeepXuLFy/OH//4x8YuEwAAAIAGdvDBB+ess84q/fje976Xfv36rdecn/rUp2q1r7zyytKRj83tox/9aHr06FFq33HHHXn33XfXOu6qq65KsVis0xqN/fx777136Xrp0qW5/fbb1zqmpqYm//u//9tgNQAAAACsL+GuRlReXp5vfvObte75zj8AAACADU/Xrl1z7LHHln4ceuih6z3nRz7ykeywww6l9gsvvJDLLruszuGo5P1vJhw7dux617KiVq1a5eijjy61Fy1alG9/+9uZN2/easfcdttteeCBB+q8RmM//8EHH5x27dqV2jfddFMmTZq0xvluueWWlY7fBAAAAGhOwl2NbLvttkuHDh1K7alTpzZjNQAAAAC0JN/97ndrfe3of//3f/ONb3wjr7766hrHTZw4MVdddVVGjBjRaDvFr7h7/fjx43Pcccflb3/7W6ZPn57q6urMnz8/zzzzTL797W/n8ssvT5Jaga21aczn79y5c0455ZRSe9GiRfna176WZ555ZqW+S5cuzXXXXZff//73SZI2bdrU+RkAAAAAGlOr5i7gw6Bjx45ZuHBhktTrOw8BAAAA+GDbYost8uMf/zjf/e53s3jx4iTJU089laeeeipbbrlltttuu/To0SNlZWWZN29epk6dmgkTJmTGjBmNXlvr1q3z05/+NF/96ldL682YMSP//d//vdox++67b/bcc8+88MILpXutWq3+S5CN/fzHHXdcnnzyyYwaNSpJMnPmzPzXf/1XhgwZkqFDh6Zjx46ZMWNGnnzyybz33ntJkk984hOpqKjI6NGj67QGAAAAQGMS7mpkixYtyuzZs0vtAQMGNF8xAAAAALQ4e+65Z6666qp897vfzZQpU0r3J02atNZjBJP3Q1iNZdNNN83vfve7XHjhhbUCW6tyzDHH5Otf/3ruuOOOWvc7duy4xnGN+fzl5eX5xS9+kbPPPjtjxowp3X/55Zfz8ssvr9R/6NCh+e53v5tvfetba10XAAAAoCkIdzWy+++/P0uWLCm199lnn+YrBgAAAGgwJ+bE5i6BD5AhQ4bkT3/6U+6555785S9/ycSJE9fYv3v37hk2bFj233//7LHHHo1a24ABA3L11Vfn4YcfzkMPPZRx48Zl1qxZadu2bXr37p2dd945hx56aIYMGZIkmTNnTmlsWVlZrWMXV6cxn79Dhw757W9/m9tuuy233HJLKioqVurTrVu3jBgxIqeccsoadxoDAAAAaGqFonMCG83LL7+ck08+ufQFo27duuWee+5Jr169Vtn/nXfeyX777Vdq77PPPqmurs7EiRNTUVGRtm3bplu3bhk0aFCGDRuWgw46KBtttFGD1LqqL2rRcjz8bmVzl0ALt+9G7Zq7hBZl2XdtLx+uBaDheDdhbbyb1ObdBFgXs2bNyosvvpj33nsvc+fOTaFQSIcOHbLxxhtns802yyabbJJCodDcZa7St7/97Tz66KNJ3t/5689//nO952is51+6dGnGjBmTt956K/PmzUv37t3Tt2/f7LzzzkJdfGh4NwGA5tX9vh7NXQIbgIqDZjV3CcB66N69e4PO5ysWDahYLGb+/PmZMGFC7r///tx2222pqqpK8v53CF5xxRWrDXatyiOPPFKrveyIxzfeeCMPPvhgfvGLX+Too4/OOeecs9bt7QEAAADYcPTo0SN77713c5dRbwsWLMgzzzxTam+zzTbrNE9jPX+rVq2y2267ZbfddmvwuQEAAAAag3DXepg0aVIOPfTQUrumpiar2ght+PDhOe+887LFFls06PpVVVW59dZb89RTT+XKK6/MZptt1qDzAwAAAEB93HjjjVm4cGGp/fGPf7wZqwEAAADY8Al3rYdisZjq6urVfl5WVpbjjz8+p556ajbeeOM6z7v11ltn//33z6677pqBAwemW7duKRaLmTlzZsaOHZs77rgjjz32WKn/pEmTctppp+VPf/rTOm/ttmwrbloqRx+xZn4P11ZeXt7cJQB8wHk3Yc28m9Tm3QTYkE2ZMiWzZs3K9ttvv9a+f/nLX3LLLbeU2j169MgnP/lJfy5AC+PdBACg5fP3KGB5wl2NqKamJjfffHNuu+22fP7zn8/ZZ5+dNm3arLZ/t27dctttt2XnnXde5eebbLJJNtlkkxxyyCF59NFH861vfStz5sxJkrz55pu56KKLcumllzbKswAAAADw4TNlypR8+ctfzvbbb5/9998/O+20UzbbbLN06tQpS5cuzYwZMzJ27NjcfvvtGTVqVK2x3/zmN/2DBAAAAMB6KhRXdY4g66SqqiqzZ8/O+PHjc//99+fuu+/OkiVLSp/vueeeufLKK9cY8KqPZ599NieddFKWLl2aJCkUCrnrrrsyePDges9VUVHRIDXROB5+1+4YrNm+G7Vr7hJalGX/eLD8/wcD0HC8m7A23k1q824CbMiee+65fPWrX633uM997nP5+te/3ggVAevLuwkANK/u9/Vo7hLYAFQcNKu5SwDWw7qeurc6ZQ0624dcmzZtstFGG2X48OG55JJL8r//+7/p27dv6fMnnngiv/nNbxpsvd122y1HHXVUqV0sFnPfffc12PwAAAAAfLi1bds2ZWV1/xJi9+7dc9555wl2AQAAADQQO3c1sldffTUjRowofRdUu3bt8sgjjzRYSu/555/PMcccU2rvvPPOue222+o9j527Wja7Y7A2dseozXegAjQu7yasjXeT2rybABu6mTNn5oknnsjYsWMzadKkTJ8+PQsWLEh1dXU6deqUbt26ZZtttsluu+2W/fffP+3a+XMAWjLvJgDQvOzcRV3YuQs2bA29c1erBp2NlQwaNCgHH3xw7rzzziRJZWVlHn300RxxxBENMv/222+f1q1bl/4iPmXKlAaZFwAAAACSpFevXvn0pz+dT3/6081dCgAAAMCHjmMZm8Aee+xRq/3KK6802NxlZWXp1q1bqW0HLgAAAAAAAAAA+GAQ7moCvXr1qtWeP39+g85fWfl/x+K0bdu2QecGAAAAAAAAAACah3BXE1gxzNWlS5cGm7uioiLz5s0rtXv27NlgcwMAAAAAAAAAAM1HuKsJjBs3rla7b9++DTb3Y489Vqs9ZMiQBpsbAAAAAAAAAABoPsJdjayysjJ33313rXt77LFHg8y9ZMmSXHPNNbXuffzjH2+QuQEAAAAAAAAAgObVqjkXnz9/fl544YW88cYbmTx5ct57770sWrQoS5cuTfv27dOpU6f07ds3m2yySYYMGZItt9yy2WqtqqrKpEmT6rUzVk1NTc4///xMmTKldG/HHXdc5XNMnz49bdu2Tbdu3eo89w9/+MO8+uqrpXu9e/fOIYccUuf6AAAAAAAAAACAlqvJw10jR47Mww8/nH//+9+ZOHFiisVincd26tQpu+22W/bZZ5/su+++2WijjRqx0toqKytzxBFH5FOf+lRGjBiRPffcM23atFlt/7Fjx+bnP/95nnnmmdK9srKyfO9731tl/xdeeCHnnntuPvvZz+awww7Ltttuu9q5X3755Vx88cV5+umna90/++yz06FDh3o+GQAAAAAAAAAA0BIVivVJV62jyZMn589//nPuvPPOTJ8+PUlWCnUVCoVVjl1dv7KysnzsYx/L0UcfnU996lMpK2vcEybnzp2bYcOGldrt27fPkCFDMnDgwHTt2jXt27fPggULMm3atLzwwgt5++23V6r7Jz/5SY466qhVzv/Pf/4zX/3qV0vt3r17Z9ttt82AAQPSuXPnFIvFzJo1K2PHjs0rr7yy0vhTTjkl55577jo/X0VFxTqPpfE9/G5lc5dAC7fvRu2au4QWpXXr1kneP74WgIbn3YS18W5Sm3cTAKAl8W4CAM2r+309mrsENgAVB81q7hKA9dC9e/cGna9Rd+4aN25crrnmmjz44IOprq4uBbUKhUIppLXsXl0yZsuPqa6uzhNPPJEnnngiffv2zUknnZRjjz02bdu2baSnqW3RokUZPXp0Ro8evda+ffr0yQUXXJB99923zvPPmDEjjz766Fr7tWvXLt/5znfyuc99rs5zAwAAAAAAAAAALV+j7Nz1xhtv5PLLL88//vGPFIvFFIvFFAqFWgGudu3aZeDAgdl6663Tr1+/bLzxxunatWvatWuX8vLyLF68OAsXLsy7776b6dOnZ9KkSXnllVcybdq0/yt+uTkLhUJ69eqVM888M0cffXTKy8sb9Jmqq6tz99135/HHH88zzzxT2oFsTbbddtuMGDEiRx55ZDp16rTGvm+//Xb+8Ic/ZOTIkZk0aVJqamrW2L93794ZMWJEjj/++Gy88cb1epZVsXNXy2Z3DNbG7hi1+Q5UgMbl3YS18W5Sm3cTAKAl8W4CAM3Lzl3UhZ27YMPW0Dt3NWi4a9GiRfnNb36TG2+8sdZOXcvsuOOO+cQnPpGPfOQj2WGHHdYpgDVjxow8/fTT+c9//pOHHnooc+bMSfJ/Qa9CoZCBAwfm/PPPz2677dYgz7Uq7777biZOnJh33nknc+bMyeLFi9OhQ4d06tQpm2yySbbbbrt06dJlneZeuHBhXnvttbzzzjuZOXNmFi5cmEKhkM6dO6dHjx7ZbrvtMmDAgAZ9HuGuls0/oLI2/gG1Nl+kBGhc3k1YG+8mtXk3AQBaEu8mANC8hLuoC+Eu2LC16HDX8OHD8+6779YKdW2yySb5zGc+k8MOOyx9+/ZtqKWSvL+b1n/+85/85S9/yb/+9a8sXbq0VsjrwgsvzDHHHNOga35QCXe1bP4BlbXxD6i1+SIlQOPybsLaeDepzbsJANCSeDcBgOYl3EVdCHfBhq2hw12tGnKy6dOnp1AoJEl23nnnfPnLX87w4cNL9xpaeXl59tprr+y1116ZOXNmbrrpptx6662ZP39+qR4AAAAAAD68/AMqdeEfUAEAgJaqrKEn3H777XPDDTfkf/7nf7LPPvs0WrBrRb169crZZ5+dhx9+OKeffnratfOd4gAAAAAAAAAAwIarQXfu+sUvfpFDDz20Iaest86dO+ess87K5z73ubz99tvNWgsAAAAAAAAAAMC6atBwV3MHu5bXp0+f9OnTp7nLAAAAAAAAAAAAWCcNfiwjAAAAAAAAAAAA60+4CwAAAAAAAAAAoAUS7gIAAAAAAAAAAGiBhLsAAAAAAAAAAABaoFbNXcDqTJ48OY899lheeumlzJo1K4sXL06XLl2y6aabZpdddsnHP/7xlJeXN3eZAAAAAAAAAAAAjaLFhbtmzJiRH//4x3nooYdSU1Oz2n4bbbRRvvGNb+SII45ouuIAAAAAAAAAAACaSIs6lnHChAk5/PDD8+CDD6a6ujrFYnG1P6ZPn57zzjsvF1xwQXOXDQAAAAAAAAAA0OBazM5dixYtype+9KVUVFSU7g0aNChDhw5Nr169UiwWM2PGjIwePTpvvPFGCoVCisVibrvttgwcODDHH398M1YPAAAAAAAAAADQsFpMuOvmm2/OtGnTUigUsummm+anP/1pdt5551X2ffLJJ/Pd734306ZNS7FYzBVXXJFjjjkmbdq0aeKqAQAAAAAAAAAAGkeLOZbxoYceSpJ07NgxN91002qDXUnysY99LNddd11atXo/mzZ37tw8/fTTTVInAAAAAAAAAABAU2jQcNeUKVPWeeyyoxaHDx+ePn36rLX/lltumd13373WeAAAAAAAAAAAgA+KBg13/e1vf8uxxx6bV155pd5jFy1alCTp1q1bncd07dq1dF1ZWVnvNQEAAAAAAAAAAFqqBj+WccyYMTnqqKPys5/9LAsXLqzzuF69eiVJxo4dW6f+xWIxzz///ErjAQAAAAAAAAAAPggaNNzVunXrJEl1dXVuuOGGHHzwwXnwwQfrNHb33XdPsVjMSy+9lBtuuGGt/S+77LK88847pfZuu+22TjUDAAAAAAAAAAC0RA0a7jr11FNzzjnnpF27dikWi5k2bVq+9rWv5ctf/nKtINaqHHvssSkUCkmSn/3sZ/nyl7+cf/7zn5k+fXqqq6uzdOnSTJ06Nffee29OOOGEXHPNNSkUCikUCtljjz0yYMCAhnwUAAAAAAAAAACAZlUoFovFhp506tSp+fGPf5x//etf7y9SKKRt27Y544wzcsopp6RVq1arHHfJJZfkxhtvLIW81qZYLKZ79+657bbbstlmmzVY/R9GFRUVzV0Ca/Dwu5XNXQIt3L4btWvuElqUZTtJLlmypJkrAfhg8m7C2ng3qc27CQA0r+739WjuEtgAVBw0q7lLAOBDwrsJdeHdBDZs3bt3b9D5GnTnrmX69u2b3/3ud/nd736X/v37p1gsprKyMpdffnk+/elPZ+TIkascd9555+XUU09NoVBIsVhc5Y8kpevNNtss119/vWAXAAAAAAAAAADwgdMo4a5lPvGJT+See+7JqaeemlatWqVYLGbixIk56aST8u1vfzuzZq2cNj3nnHPy17/+NUccccQqk2xlZWXZdttt8/3vfz933nlnhgwZ0piPAAAAAAAAAAAA0Cwa5VjGVXnttdfyox/9KM8+++z7CxcK6dKlS84+++x89rOfXe24qVOnZtasWVm8eHE6d+6c/v37p0OHDk1R8oeKYxlbNkcfsTaOPqrN0UcAjcu7CWvj3aQ27yYA0LwcfURdOPoIgKbi3YS68G4CG7YN4ljGVRk4cGBuueWWXHzxxenRo0eKxWLmzJmTH/3oRzn22GPz8ssvr3Jc3759s91222WXXXbJoEGDBLsAAAAAAAAAAIAPhSYLdy1z5JFH5r777ssxxxyTQqGQYrGYsWPH5uijj85Pf/rTLFiwoKlLAgAAAAAAAAAAaHFaNceiXbt2zY9//OMceeSR+dGPfpRXXnklS5cuzY033pj77rsv3/3ud3PAAQc0R2kAAAAAAAAAAM3mptzU3CWwATgxJzZ3CTSRJt+5a3k777xz/va3v+Xb3/52OnTokGKxmOnTp+ess87Kl770pbz99tvNWR4AAAAAAAAAAECzadZwV5KUl5fnC1/4Qu6777588pOfTJIUi8U8/vjjOeyww3LllVdmyZIlzVwlAAAAAAAAAABA02r2cNcyffr0ya9//etcddVV2WSTTVIsFlNZWZlf/epX+fSnP52nn366uUsEAAAAAAAAAABoMq0ac/Kampo8/vjjGTVqVN56663MnTs3bdu2TY8ePbLttttmr732yoABA2qNGT58eD760Y/md7/7Xf7whz9kyZIlmTRpUk4++eQcdthh+fa3v52ePXs2ZtkAAAAAAAAAAADNrtHCXXfeeWcuu+yyTJ8+fZWf//Wvf02hUMh+++2X73//++nTp0/ps7Zt2+Yb3/hGDj/88FxwwQUZOXJkisVi7r777jzyyCM5++yzc+yxxzZW6QAAAAAAAAAAAM2uUY5lvOiii/Kd73wn06ZNS7FYXO2Pmpqa/POf/8wRRxyRCRMmrDTPVlttlZtuuik//elP07NnzxSLxcydOzcXXHBBPvvZz+bll19ujPIBAAAAAAAAAACaXYOHu2699dbccsstKRaLpXtDhgzJZz7zmXz5y1/OKaeckk996lPp0qVL6fOKioqcfvrpqaysXOWcRxxxRO6///589rOfTVlZWYrFYp5//vkcddRRufjii7NgwYKGfgwAAAAAAAAAAIBm1aDHMi5ZsiS/+tWvUigUkiT9+vXLL37xi+y8884r9a2qqsoVV1yRa6+9NkkyderU/PGPf8wpp5yyyrk7d+6cCy64IEcddVTOP//8jB8/PtXV1bn55pvzwAMP5NJLL81uu+3WkI8DAAAAAAAAAADQbBp0566RI0dmzpw5KRaLad26da6//vpVBruSpE2bNjnnnHNy9NFHl+49+OCDa11j6NCh+etf/5rzzjsvnTp1SrFYzLvvvpunnnqqwZ4DAAAAAAAAAACguTVouOv1119PkhQKhQwbNiybbrrpWsccc8wxSZJisVgavzZlZWU56aSTcu+99+aAAw6odQQkAAAAAAAAAADAB0GDHsu4cOHC0nW3bt3qNKZLly6l60WLFtVrvY022ii/+tWv8u9//zszZ86s11gAAAAAAAAAAICWrEHDXb169Spdv/jii3Ua89JLL5Wue/bsuU7rfvzjH1+ncQAAAAAAAAAAAC1Vgx7LOGzYsNL1W2+9lSuvvHKN/WfOnJnLL788yftHOe66664NWQ4AAAAAAAAAAMAGq0HDXQMGDMiee+6ZYrGYYrGYX/3qVzn99NPzyCOPZNasWampqUlVVVVee+21XHfddTniiCMyefLk0vhjjz22IcsBAAAAAAAAAADYYDXosYxJcv755+ezn/1sZs+enSR59NFH8+ijj66yb7FYTKFQSPJ+sGu33XZr6HIAAAAAAAAAAAA2SA26c1eSbLrpprn++uuz2WablXbwSlK6Xv7eMieffHJ++MMfNnQpAAAAAAAAAAAAG6wG37krSYYMGZI777wzf/nLX3L77bfn5ZdfTnV1da0+Xbt2zfDhw3PCCSdk++23b4wyAAAAAAAAAAAANliNEu5KkrZt2+bzn/98Pv/5z2fRokWZPHly5s6dmzZt2qRHjx7p169fYy0NAAAAAAAAAACwwWu0cNfy2rdvn4EDBzbFUgAAAAAAAAAAAB8IZc1dAAAAAAAAAAAAACsT7gIAAAAAAAAAAGiBhLsAAAAAAAAAAABaoAYNdy1ZsqQhp1tvVVVVzV0CAAAAAAAAAADAOmnQcNeBBx6Ye++9tyGnXCfTp0/Peeedl2uvvba5SwEAAAAAAAAAAFgnDRrumjx5cr75zW/myCOPzIMPPtiQU9fJ9OnT85Of/CQHHHBA7rjjjtTU1DR5DQAAAAAAAAAAAA2hVWNMOn78+Hzta1/L5ptvnuOOOy5HHHFEOnfu3BhLJUmeffbZ3HbbbXnggQeydOnSFIvFFAqFRlsPAAAAAAAAAACgsTXozl2XXHJJevTokWKxmCR5/fXXc/HFF2evvfbK17/+9dx3331ZsGBBg6w1fvz4XH755TnwwANzwgkn5J577smSJUtKa3/yk5/MZz7zmQZZCwAAAAAAAAAAoKk16M5dI0aMyP77759f/epXue2227J06dIkSWVlZf7xj3/kH//4R8rLy7Pddttl1113zTbbbJPBgwenf//+6dSp0yrnrK6uzrvvvptJkybl5ZdfzvPPP5+RI0dm9uzZSVIKcy0zcODAnHvuudl7770b8tEAAAAAAAAAAACaVIMfy9i5c+d8//vfz0knnZQrrrgi9957b6qrq5O8H8RaunRpnn/++Tz//PO1xrVv3z5du3ZN27Zt06pVq1RWVmbRokWpqKhYKcC1rL380YubbLJJzjjjjIwYMSJlZQ26IRkAAAAAAAAAAECTa/Bw1zIDBgzIz3/+85x11lm54YYbcscdd2TevHm1+iwf2lq4cGEWLly41nkLhUIKhUKKxWKKxWJ23HHHnHTSSTnwwAOFugAAAAAAAAAAgA+MRgt3LdO/f/9873vfy7e+9a08+OCD+fvf/56nnnoqixYtKvVZfgeuVVk+BFYsFrP55pvnk5/8ZI444ohstdVWjVY7AAAAAAAAAABAc2n0cNcybdq0ySGHHJJDDjkkVVVVefbZZzNmzJi8+OKLef311zN16tRUVlbWGlMoFNKzZ89ssskmGTJkSHbYYYfsvvvuGTBgQFOVDQAAAAAAAAAA0CyaLNy1vDZt2mSPPfbIHnvsUev+/PnzU1lZmerq6rRt2zYdO3ZM69atm6NEAAAAAAAAAACAZtUs4a7V6dSpUzp16tTcZQAAAAAAAAAAADS7suYuAAAAAAAAAAAAgJW1qJ27NiQ1NTV566238uabb2b69OmZO3duqqqq0qFDh3Tr1i1DhgzJoEGDUl5evt5rvfXWWxk3blymTZuWmpqa9OnTJ4MGDcrgwYMb4EkAAAAAAODD7abc1NwlsAE4MSc2dwkAAHwICXfVw6xZs3Lddddl1KhRGT9+fBYtWrTG/l27ds3hhx+eU045JX379q33eo8++mh+//vfZ/To0av8fOutt86pp56aww8/vN5zAwAAAAAAAAAALZtjGeth8uTJufbaazNq1Ki1BruSZM6cObn55ptz8MEH529/+1ud1ykWi/nJT36SL33pS6sNdiXJK6+8km9961s5++yzU1VVVef5AQAAAAAAAACAls/OXeuhV69eGTx4cDbbbLN07do15eXlmT17dsaPH58xY8akpqYmSbJw4cKcd955qaqqyrHHHrvWeS+99NLcdFPtLaB32WWX7LDDDikvL88rr7yS//znPykWi0mSe+65J+Xl5fn5z3/e8A8JAAAAAAAAAAA0C+GueigvL8+wYcNywAEHZM8998yWW2652r6TJ0/OhRdemEceeaR07+KLL84ee+yRTTfddLXjHn744VxzzTWldpcuXXLFFVfkYx/7WK1+48aNyxlnnJFp06YlSe66667suuuudQqPAQAAAAAAAAAALZ9jGeth2223zS233JITTjhhjcGuJOnfv39+97vfZc899yzdW7x4cf74xz+udkyxWMwvf/nLUrtQKOR3v/vdSsGuZbXccMMNadu2beneb37zm1RWVtbnkQAAAAAAAAAAgBZKuKsRlZeX55vf/Gate48//vhq+//zn//MhAkTSu1Pf/rTGTZs2Gr7b7HFFjnllFNK7RkzZuQvf/nLelQMAAAAAAAAAAC0FMJdjWy77bZLhw4dSu2pU6eutu/9999fq3388cevdf5jjz025eXlq50DAAAAAAAAAADYMAl3NYGOHTuWrovF4ir7LF26NI899lip3bdv3wwdOnStc/fp0yc77bRTqT169OjMmjVr3YsFAAAAAAAAAABaBOGuRrZo0aLMnj271B4wYMAq+02YMCFz584ttXfeeec6r7F83+rq6owaNar+hQIAAAAAAAAAAC2KcFcju//++7NkyZJSe5999lllv4kTJ9Zqb7PNNnVeY9ttt63VnjRpUt0LBAAAAAAAAAAAWiThrkb08ssv52c/+1mp3a1bt5x00kmr7LtiIKtfv351Xqdv375rnAsAAAAAAAAAANjwtGruAj5IisVi5s+fnwkTJuT+++/PbbfdlqqqqiRJhw4dcsUVV6RXr16rHDt9+vRa7Y033rjO667Yd9q0afWsHAAAAAAAAAAAaGmEu9bDpEmTcuihh5baNTU1KRaLK/UbPnx4zjvvvGyxxRarnWvhwoW12h07dqxzHSv2XXEuAAAAAAAAAABgwyPctR6KxWKqq6tX+3lZWVmOP/74nHrqqWvdiWvFQFabNm3qXEfbtm3XOFddtG7dut5jaEqVzV0ALZzfw7WVl5c3dwkAH3DeTVgz7ya1eTcBAIAPBn/XAQBaEu8mHx5lzV3AB1lNTU1uvvnm7L///vnpT39aOqJxVRYvXlyrXZ9w14p9Kyv9YxsAAAAAAAAAAGzo7Ny1Hrbaaqu88sorpXZVVVVmz56d8ePH5/7778/dd9+dJUuWZMmSJbn++uszYcKEXHnllasMbq24+9aagmArWrFvu3bt6vkkyZIlS+o9Bmg5/B5eNT8vANA8/Bm8an5eAABgw+adHgBoSbybfHi0iHDXW2+9lXHjxuW9997L/Pnz1+k/wDPPPLMRKqufNm3aZKONNspGG22U4cOH56STTsrpp5+eqVOnJkmeeOKJ/OY3v8nZZ5+90tgOHTrUatcn3LXirl8rzgUAAAAAAAAAAGx4mi3cVVlZmeuvvz5/+ctfSuGn9dESwl0rGjJkSK655pqMGDGiFFi78cYb84UvfCHdu3ev1XfFQNaCBQvqvM6KfYW7AAAAAAAAAABgw1fWHItOmDAhhx9+eK644opMmTIlxWKx9KO+1mVMUxo0aFAOPvjgUruysjKPPvroSv369OlTqz1t2rQ6r7FiOG7jjTeuZ5UAAAAAAAAAAEBL0+ThrunTp+eUU07JW2+9lWKxmEKhkEKhUPp8+aBXXX5sCPbYY49a7VdeeWWlPltttVWt9pQpU+o8/4pBsC233LIe1QEAAAAAAAAAAC1Rkx/LeNlll2XGjBmlQFexWMz222+fffbZJ1tttVW6du2aVq2a7bTIRtGrV69a7fnz56/UZ8Vw17hx4+o8/0svvVSrLdwFAAAAAAAAAAAbviZNUc2fPz9///vfUygUUiwW0759+/z85z/P/vvv35RlNLkVw1xdunRZqc+gQYPSpUuXzJ07N0kyZsyYOs8/evTo0nV5eXl22WWXdSsUAAAAAAAAAABoMZr0WMZnn302S5cuLR3H+OMf//gDH+xKVt6Fq2/fviv1adWqVfbee+9Se+rUqRk7duxa554+fXqtfjvvvHN69OixHtUCAAAAAAAAAAAtQZOGuyZPnly63mijjXLooYc25fLNorKyMnfffXete3vssccq+x500EG12rfeeuta57/ttttSXV1dah944IHrUCUAAAAAAAAAANDSNGm4a8GCBUmSQqGQ7bffvimXXm9VVVV5+eWX6zWmpqYm559/fqZMmVK6t+OOO2bLLbdcZf/99tsvgwcPLrXvvPPOPPPMM6ud//XXX891111Xavfu3TvHHHNMvWoEAAAAAAAAAABapiYNdy1/XGD79u2bcun1VllZmSOOOCJf+9rX8vDDD6eqqmqN/ceOHZsTTzwxd9xxR+leWVlZvve97612TKFQyNlnn11qF4vFfOUrX8mTTz65Ut9x48bl5JNPzuLFi0v3zjzzzLRr164eTwUAAAAAAAAAALRUrZpysf79+5euKyoqmnLpBlEsFvPAAw/kgQceSPv27TNkyJAMHDgwXbt2Tfv27bNgwYJMmzYtL7zwQt5+++1aYwuFQi666KLsuOOOa1xj3333zWmnnZZrrrkmSTJ37tycfPLJ2WWXXTJ06NCUlZXllVdeyX/+858Ui8XSuMMPPzzHHntswz80AAAAAAAAAADQLJo03LXrrrumS5cumTt3bl588cUUi8UUCoWmLKHBLFq0KKNHj87o0aPX2rdPnz654IILsu+++9Zp7rPPPjuVlZW5+eabS/dGjRqVUaNGrbL/wQcfnIsuuqhuhQMAAAAAAAAAABuEJj2WsU2bNjnssMOSvL8j1T//+c+mXH69dOzYMT/72c9y6KGHpk+fPnUas+222+Z73/te7r333joHu5L3j2/8/ve/n6uvvjo77bTTavsNHjw4//3f/53LLrssbdu2rfP8AAAAAAAAAABAy9ekO3clyVlnnZWHH344U6dOzU9/+tMMGzYs3bp1a+oy6q28vDxHHHFEjjjiiCTJu+++m4kTJ+add97JnDlzsnjx4nTo0CGdOnXKJptsku222y5dunRZrzWHDx+e4cOH580338xLL72Ud999N9XV1enTp08GDRqUrbfeugGeDAAAAAAAAAAAaImaPNzVuXPnXH755TnttNMyefLknHjiifnVr36VLbbYoqlLWS8bbbRRNtpooyZZa7PNNstmm23WJGsBAAAAAAAAAAAtQ5OHu6ZMmZJevXrll7/8Zc4555xMmDAhhx12WA444IAMHz48AwcOTJcuXVJWVr8TI/v169dIFQMAAAAAAAAAADS9Jg93feITn0ihUKh1b+nSpbn33ntz7733rtOchUIh48aNa4jyAAAAAAAAAAAAWoQmD3ctUywWUygUagW9isVic5UDAAAAAAAAAADQojRbuCsR5gIAAAAAAAAAAFidJg93jRgxoqmXBAAAAAAAAAAA2OA0ebjrkksuaeolAQAAAAAAAAAANjhlzV0AAAAAAAAAAAAAKxPuAgAAAAAAAAAAaIGEuwAAAAAAAAAAAFog4S4AAAAAAAAAAIAWqFVzF7C8mpqavPrqq5k1a1Zmz56dQqGQrl27pkePHhk4cGDKy8ubu0QAAAAAAAAAAIAm0ezhrqVLl+aee+7J7bffnrFjx6aysnKV/dq1a5cdd9wxI0aMyCGHHJJWrZq9dAAAAAAAAAAAgEbTrAmpJ554It/73vcyffr0JEmxWFxt30WLFuXpp5/O008/ncsuuywXXXRRPv7xjzdVqQAAAAAAAAAAAE2qrLkWvuqqq3Laaadl2rRppVBXoVBIoVBYqe/y94vFYqZNm5YvfelL+d3vftekNQMAAAAAAAAAADSVZtm5689//nMuu+yyJKkV2iovL8+WW26ZLbfcMp07d06SzJs3L6+//nomTpyY6urqUv+ampr8+te/Ts+ePfPZz362OR4DAAAAAAAAAACg0TR5uGvatGn5yU9+UivU1adPn3zpS1/K4YcfXgp1rWjevHm5++67c/XVV2fatGkpFAopFou5+OKLs/fee6dv375N+RgAAAAAAAAAAACNqsmPZfzNb36TxYsXl9r77bdf7rnnnhx//PGrDXYlSefOnXPcccfl3nvvzac+9akUi8UUCoVUVVU5nhEAAAAAAAAAAPjAadJwV3V1dR544IHSrl277bZbfv3rX6dTp051nqNDhw65/PLLM2zYsBSLxRSLxdx///2pqalprLIBAAAAAAAAAACaXJOGu55//vnMmzcvxWIxSXL++eenrKz+JZSVleX8888vtefPn58xY8Y0VJkAAAAAAAAAAADNrknDXW+++WaSpFAoZODAgRk4cOA6zzVw4MAMGjSo1H7rrbfWuz4AAAAAAAAAAICWoknDXRUVFaXrzTfffL3n22KLLVY5NwAAAAAAAAAAwIauScNd1dXVpevy8vL1nm/5Ix2XnxsAAAAAAAAAAGBD16Thrh49epSu33777fWe75133lnl3AAAAAAAAAAAABu6Jg139evXL0lSLBYzfvz4TJ06dZ3nmjZtWl566aWV5gYAAAAAAAAAAPggaNJw1y677JJ27dqlUCikWCzmv//7v9d5rp///OcpFotJknbt2mWXXXZpqDIBAAAAAAAAAACaXZOGu9q0aZO99947xWIxxWIx999//zoFvC699NLcc889KRQKKRQK2WuvvdKmTZtGqBgAAAAAAAAAAKB5NGm4K0m+9rWvpaysrLR71/XXX59jjz02Tz755FrHPvnkkzn22GNz7bXXlsaXlZXlv/7rv5qgcgAAAAAAAAAAgKbTqqkXHDhwYE477bRcddVVpYDWmDFj8sUvfjG9evXKjjvumM033zydO3dOoVDIvHnz8sYbb2TMmDGZOXNmkqRYLJZ27TrllFMyaNCgpn4MAAAAAAAAAACARtXk4a4k+cY3vpF33303t99+ewqFQpL3A1szZszIQw89tMoxxWIxSUqhrmKxmCOOOCJnn312k9UNAAAAAAAAAADQVJol3JUkl1xySbbffvv8/Oc/T2VlZSnktczyYa7l/7dYLKZt27Y599xzc/zxxzdt0QAAAAAAAAAAAE2k2cJdSXL88cfnwAMPzB//+MfccccdmTJlykp9loW8kqRfv3454ogjctxxx6VXr15NWSoAAAAAAAAAAECTatZwV5L07NkzX/va1/K1r30t06dPzwsvvJBZs2Zlzpw5KRaL6datW3r06JEddtghffr0ae5yAQAAAAAAAAAAmkSzh7uW16dPHwEuAAAAAAAAAACAJGXNXQAAAAAAAAAAAAArE+4CAAAAAAAAAABogYS7AAAAAAAAAAAAWiDhLgAAAAAAAAAAgBaoVUNOdscdd6x074gjjlhrn4aw4joAAAAAAAAAAAAbsgYNd33nO99JoVCodW/F0NWq+jQE4S4AAAAAAAAAAOCDpEHDXcsrFotrDHEVi8X1XqNQKKx1HQAAAAAAAAAAgA1Rg4e76hLaaohgV0POAwAAAAAAAAAA0NI0aLjrkksuaZA+AAAAAAAAAAAAH3YNGu4aMWJEg/QBAAAAAAAAAAD4sCtr7gIAAAAAAAAAAABYmXAXAAAAAAAAAABACyTcBQAAAAAAAAAA0AK1auoFTzzxxCRJoVDIL3/5y/Ts2XOd5pk5c2bOPvvs0lw33nhjg9UIAAAAAAAAAADQ3Jo83DVy5MgUCoUkyeLFi9d5nsWLF2fkyJFJUpoPAAAAAAAAAADgg6JZjmUsFovNsSwAAAAAAAAAAMAGo1nCXQAAAAAAAAAAAKzZBhvuqq6uLl2Xl5c3YyUAAAAAAAAAAAANb4MNd1VUVJSuO3To0IyVAAAAAAAAAAAANLxWzV3AunrmmWeSJIVCIRtttFEzVwMAAMAy3e/r0dwlsAGoOGhWc5cAAAAAANDiNWu4q1Ao1Kv/kiVLMmPGjDzxxBO58sorS/cHDx7c0KUBAAAAAAAAAAA0q0YJd22zzTZr7VMsFvOJT3xindcoFoul6/WZBwAAAAAAAAAAoCVqlHDX8sGrhui3KoVCIcViMVtuuWUOOOCAdZ4HAAAAAAAAAACgJSprrInre+RifRWLxWy//fa58sor07p160ZdCwAAAAAAAAAAoKk1ys5dw4YNW+1nzzzzTJL3w19Dhw5NmzZt6jRnoVBImzZt0qVLl2y55Zb56Ec/mt12261B6gUAAAAAAAAAAGhpGiXcdfPNN6/2syFDhpR29brsssvSr1+/xigBAAAAAAAAAABgg9ZoxzKuSbFYbI5lAQAAAAAAAAAANhiNsnPXmpx55pml6y5dujT18g1q9uzZmTBhQt58883Mnj07xWIxXbt2Tb9+/bLTTjulc+fOzV0iAAAAAAAAAACwgWrWcNeGpqamJs8++2wefPDBPPXUU5kwYcJq+xYKhXzsYx/LySefnOHDh9dp/nfeeSf77bffOtXWp0+fPPbYY+s0FgAAAAAAAAAAaHmaPNy1ITvwwAPz5ptv1qlvsVjMf/7zn/znP//JIYcckgsvvDCdOnVq5AoBAAAAAAAAAIAPCuGuepg1a9ZK9zbffPMMHTo0vXr1Stu2bTNt2rQ8+eSTmTZtWqnPPffck3fffTfXXXdd2rZtW+f1ysrKUigU6tS3VSu/lAAAAAAAAAAA8EEiEbQO+vfvn2OOOSYjRozIxhtvvNLn1dXV+fOf/5xLLrkkixcvTpI888wzufzyy/Ptb3+7zuv85Cc/yZFHHtlgdQMAAAAAAAAAABuOFhHuevfddzN69OhMnDgxc+fOzbx581JTU1Pn8YVCIRdffHEjVvi+fv365aSTTsoRRxyR8vLy1fYrLy/P5z73ufTr1y+nn3566VluvvnmnHzyyenTp0+j1woAAAAAAAAAAGzYmjXc9e9//ztXX311nnnmmXWeo1gsNlm4629/+1u9jj8cPnx4DjnkkNx9991JkiVLluShhx7Kcccd11glAgAAAAAAAAAAHxBlzbFodXV1Lrroopx22ml55plnUiwWUywWk6R0vay9vOU/W9Xnja0+wa5lDjnkkFrtF154oaHKAQAAAAAAAAAAPsCaZeeun//857nllluSvH+k4jLLduFavr28FT9rjoBXfW266aa12jNnzmymSgAAAKDluCk3NXcJbABOzInNXQIAAAAANKsmD3c9++yzueGGG0pBrfLy8hx77LH51Kc+lSQ58cT3v2hXKBTy0EMPZf78+Xn33XczevTo3HXXXXnnnXdSKBTSs2fPXHjhhRkyZEhTP0K9LFiwoFZ7XXb/AgAAAAAAAAAAPnyaPGl07bXXJnl/5602bdrkqquuysc+9rEkyeTJk2v17d+/f5Jk6623zl577ZUzzzwzN998cy699NLMmjUr5557bq6++ursuuuuTfsQ9fDKK6/Uam+88cbNVAkAAAAAAAAAALAhadJwV2VlZf7973+Xdu364he/WAp21UVZWVlOOumkbL755vnqV7+aBQsW5Mwzz8w999yTHj16NFbZ6+Wuu+6q1f7oRz9a57EPPPBA7r333kycODEVFRVp27ZtunXrlkGDBmXYsGE56KCDstFGGzV0yQAAAAAAAAAAQAtQ1pSLPf/881m6dGmKxWLKy8vz+c9/fp3mGT58eE4++eQkyezZs/P73/++AatsOCNHjszIkSNL7c6dO+fjH/94ncc/8sgjefzxxzNlypQsWrQos2fPzhtvvJEHH3wwF198cfbbb79ccMEFKx39CAAAAAAAAAAAbPiaNNz19ttvJ0kKhUK22GKL9OrVa439q6urV/vZqaeemvLy8iTJvffem5qamoYrtAEsWrQoP/jBD2rd+8IXvpCOHTs22BpVVVW59dZbc/TRR+fNN99ssHkBAAAAAAAAAIDm16THMs6dO7d0vdlmm630+bKw1jJVVVVp3779Kufq1q1btt9++4wdOzazZs3K6NGjs+uuuzZswevhggsuyBtvvFFqb7nlljn11FPrNHbrrbfO/vvvn1133TUDBw5Mt27dUiwWM3PmzIwdOzZ33HFHHnvssVL/SZMm5bTTTsuf/vSndO/efZ3qbd269TqNo6lUNncBtHB+D9e24p8nADQ07yYANA1/1wEAWhLvJgBAS+Ld5MOjScNdixcvLl2vagerFe/Nnj17teGuJNl4440zduzYJMmUKVNaTLjrxhtvzO23315qt2nTJj//+c/Ttm3bNY7r1q1bbrvttuy8886r/HyTTTbJJptskkMOOSSPPvpovvWtb2XOnDlJkjfffDMXXXRRLr300oZ7EAAAAAAAAAAAoNk0abhr+fDWokWLVvq8Q4cOtdrTpk1L3759Vztfq1b/V/7MmTMboML1d//99+enP/1prXsXXnhhtt9++7WO7dSp02qDXSsaPnx4fve73+Wkk07K0qVLkyT33HNPvvzlL2fw4MH1rnvJkiX1HgO0HH4Pr5qfFwAA2LB5pwcAWhLvJgBAS+Ld5MOjrCkX6927d+l62Y5TyysvL0+/fv1K7ZdeemmN87399tul6+rq6gaocP08+eST+da3vpWamprSvW9+85sZMWJEo6y322675aijjiq1i8Vi7rvvvkZZCwAAAAAAAAAAaFpNGu7aYostStcTJ05cZZ/ld516+OGHVzvXtGnT8tJLL6VQKCRJevTo0UBVrpvnn38+X/nKV1JVVVW6d8opp+RLX/pSo6579NFH12o/+eSTjboeAAAAAAAAAADQNJo03LXVVlulffv2KRaLee+991Z5lOLHP/7xJO/vQvXkk0+uMqxULBZzwQUXpLq6OsViMUnqdOxhY5kwYUJOO+20LFy4sHTvmGOOybnnntvoa2+//fZp3bp1qT1lypRGXxMAAAAAAAAAAGh8TRruatWqVXbZZZdS+7HHHlupz4EHHpjWrVunUCikpqYmZ5xxRn7zm9/kxRdfzJtvvpmHH344J554Yh555JHSrl2bbbZZrR2/mtJbb72VL37xi5k9e3atZ7jwwgubZP2ysrJ069at1K6oqGiSdQEAAAAAAAAAgMbVpOGuJNl3331L1w888MBKn/fq1SsnnHBCisViCoVCKisr89vf/jbHHHNMDjzwwHzlK1/Js88+m2KxWOpz5plnNuUjlEyfPj0nn3xyZsyYUbo3fPjw/OIXv0hZWdP91FZWVpau27Zt22TrAgAAAAAAAAAAjafJw10HHHBAOnTokHbt2uW5555b5TGCX/va17LrrruWwltJSmGuZccwLrt/zDHH5NBDD226B/j/Zs2alZNPPjmTJ08u3dt9991zxRVX1DomsbFVVFRk3rx5pXbPnj2bbG0AAAAAAAAAAKDxtGrqBXv37p1Ro0atsU+7du1yzTXX5Oc//3n+9Kc/paamptbnxWIxHTt2zBlnnJFTTz21Mctdpfnz5+fUU0/NpEmTSveGDh2a3//+92nXrl2T1rLi0ZZDhgxp0vUBAAAAAAAAAIDG0eThrrrq0KFDzj///HzlK1/JI488kjfeeCPz5s1Lly5dMmTIkOy9997p0qVLk9dVWVmZ008/PS+99FLp3uDBg3PttdemU6dOTVrLkiVLcs0119S69/GPf7xJawAAAAAAAAAAABpHiw13LdO7d+8cc8wxzV1GkmTp0qX5+te/nmeeeaZ0b/PNN8/111+frl27rtfc06dPT9u2bdOtW7c69a+pqckPf/jDvPrqq6V7vXv3ziGHHLJedQAAAAAAAAAAAC1DWXMXsKEoFov5zne+k0ceeaR0r3///rnhhhvSq1ev9Z7/hRdeyCc+8Yn87Gc/y7hx49bY9+WXX87JJ5+cv/3tb7Xun3322enQocN61wIAAAAAAAAAADS/Fr9zV0sxZcqU3H333Svd22+//eo1T//+/fPggw+u8rMFCxbkD3/4Q/7whz+kd+/e2XbbbTNgwIB07tw5xWIxs2bNytixY/PKK6+sNPaUU07JkUceWa9aAAAAAAAAAACAlku4q46KxeIq71VXV9drnrr2nzFjRh599NG19mvXrl2+853v5HOf+1y96gAAAAAAAAAAAFq2Jg93VVVVZezYsaX2Nttsk06dOtVrjvnz52f8+PGl9k477ZTWrVs3WI3NYeutt85xxx2XkSNHZtKkSampqVlj/969e2fEiBE5/vjjs/HGGzdRlQAAAAAAAAAAQFNp8nDX3Xffne9///tJkh49euThhx+u9xytW7fO17/+9VRUVCRJfv7zn+fQQw9t0DpXtMkmm6zyOMSGMmDAgJx//vlJkoULF+a1117LO++8k5kzZ2bhwoUpFArp3LlzevToke222y4DBgxotFoAAAAAAAAAAIDm1+ThrjvvvDPFYjGFQiHHHHNM2rRpU+852rZtm2OOOSZXXXVVkuT2229v9HBXU+rQoUOGDh2aoUOHNncpAAAAAAAAAABAMylrysUWLVqU0aNHl9oHHXTQOs+1/Nhnn302VVVV61UbAAAAAAAAAABAS9Kk4a7x48dnyZIlSZIuXbpk6623Xue5hgwZkq5duyZJqqqqMn78+AapEQAAAAAAAAAAoCVo0nDX66+/niQpFArrFexaZvk5ls0NAAAAAAAAAADwQdCk4a45c+aUrnv06LHe8y0/x+zZs9d7PgAAAAAAAAAAgJaiScNdy45kTJLy8vL1nm/5OSorK9d7PgAAAAAAAAAAgJaiScNdXbt2LV3PmjVrveerqKgoXXfu3Hm95wMAAAAAAAAAAGgpmjTctewYxWKxmPHjx6/XXMViMePGjSu1u3fvvl7zAQAAAAAAAAAAtCRNGu7aZpttStdz5szJyJEj13mukSNHZvbs2aX24MGD16c0AAAAAAAAAACAFqVJw10DBgxIv379UigUkiSXX375Os1TLBZzxRVXlNq9e/fOwIEDG6JEAAAAAAAAAACAFqFJw11Jcthhh6VYLCZJRo8enYsuuqjec1xyySV57rnnkiSFQiGHHnpog9YIAAAAAAAAAADQ3Jo83PWFL3whHTp0SPL+Dlx//OMfc/rpp+edd95Z69jJkyfnjDPOyM0331za/atdu3Y59dRTG7VmAAAAAAAAAACAptaqqRfs1q1bzjvvvPzgBz9IoVBIsVjMo48+mscffzx77LFHdt999wwaNChdunRJoVDInDlz8tprr2XkyJF54oknUlNTU9r5q1Ao5Lvf/W569OjR1I8BAAAAAAAAAADQqJo83JUkxxxzTCZMmFBrB67q6ur8+9//zr///e/VjisWiykUCqVQ2Mknn5xjjjmmqcoGAAAAAAAAAABoMs0S7kqS733ve+nfv39+8YtfZOnSpaWQ17JduVa0fKirVatWOe+883L88cc3ZckAAAAAAAAAAABNpqw5Fz/55JNz55135pBDDklZWdlqg13J+6Gv8vLyjBgxInfffbdgFwAAAAAAAAAA8IHWbDt3LbPVVlvl0ksvzQ9/+MM888wzGTVqVGbMmJHZs2cnSbp3757evXtnl112ye67757OnTs3b8EAAAAAAAAAAABNoNnDXct07do1+++/f/bff//mLgUAAAAAAAAAAKDZNeuxjAAAAAAAAAAAAKyacBcAAAAAAAAAAEALJNwFAAAAAAAAAADQArVq7gKWmTp1akaNGpUXXngh7733XubMmZNCoZAuXbqkZ8+e2WGHHbLLLrukb9++zV0qAAAAAAAAAABAo2v2cNejjz6a66+/Pk8//XSd+n/kIx/JF77whQwfPryRKwMAAAAAAAAAAGg+zRbuWrBgQX70ox/l73//e5KkWCwmSQqFwir7L/v8qaeeytNPP52DDz44F1xwQTp16tQ0BQMAAAAAAAAAADShZgl3LVy4MCeeeGLGjRuXYrGYQqGQQqGQYrFYCnGtyrLgV7FYzL333pvXX389t9xySzp06NBUpQMAAAAAAAAAADSJZgl3nXPOOXnppZdqhbratGmTffbZJzvvvHO23HLL0o5c8+fPz+uvv55Ro0blkUceSVVVVWnM+PHj881vfjO///3vm+MxAAAAAAAAAAAAGk2Th7sef/zx/Otf/yrtwlUoFHL88cfnv/7rv9K1a9dVjhk+fHhOPvnkzJ07N1dccUVuvfXWJO/v4PXII4/ksccey957791kzwAAAAAAAAAAANDYypp6wSuvvDLJ+8Gs8vLy/OIXv8j3v//91Qa7ltelS5d8//vfzy9/+cuUl5eXdvC66qqrGrtsAAAAAAAAAACAJtWk4a45c+Zk9OjRpeMYTzvttBx88MH1nufAAw/Ml770pRSLxSTJmDFjMmfOnIYuFwAAAAAAAAAAoNk0abhr1KhRqampSbFYTJs2bXLqqaeu81ynnHJK2rZtmySpqanJqFGjGqpMAAAAAAAAAACAZtek4a4ZM2YkSQqFQnbcccd07Nhxnefq2LFjdtxxx5XmBgAAAAAAAAAA+CBo0nDX7NmzS9e9e/de7/mWn2P5uQEAAAAAAAAAADZ0TRruat++fel6wYIF6z3f8nMsPzcAAAAAAAAAAMCGrknDXT169EiSFIvFvPzyy+s93/JzdO/efb3nAwAAAAAAAAAAaCmaNNy19dZbl66nT5+exx9/fJ3n+ve//51p06aV2kOGDFmv2gAAAAAAAAAAAFqSJg13DRw4MH379k2hUEixWMyFF16YioqKes9TUVGRH//4xykUCkmSPn36ZODAgQ1dLgAAAAAAAAAAQLNp0nBXkhx11FEpFospFAp5++23c8IJJ+SVV16p8/hXX301J554Yt58883SPEcffXQjVgwAAAAAAAAAAND0mjzc9cUvfjE9e/ZMkhQKhbz22ms56qij8q1vfSuPPfZY5s2bt9KY+fPn5/HHH8+5556bESNG5LXXXivt2tWzZ8988YtfbNJnAAAAAAAAAAAAaGytmnrBDh065Ne//nW+8IUvpKqqKoVCIUuXLs3f//73/P3vf0+S9OjRI506dUqhUMj8+fPz3nvvlcYv262rWCymbdu2+dWvfpUOHTo09WMAAAAAAAAAAAA0qibfuStJdtlll/z6179Oly5dSmGt5P3gVrFYzHvvvZc333wzb7zxRmbOnFm6n6QU7OratWuuuOKK7Lrrrs3xCAAAAAAAAAAAAI2qWcJdSbL33nvnrrvuyv77718KbCXvh7dW9SP5v1279t9//9x5550ZPnx4c5UPAAAAAAAAAADQqJr8WMbl9enTJ7/5zW/y9ttv584778xzzz2XF198MfPmzavVr3Pnztl+++2z66675vDDD8+mm27aTBUDAAAAAAAAAAA0jWYNdy0zYMCAnHnmmaX2kiVLMnfu3NLxi61bt27G6gAAAAAAAAAAAJpeiwh3rah169bp2bNnc5cBAAAAAAAAAADQbMqauwAAAAAAAAAAAABWJtwFAAAAAAAAAADQAgl3AQAAAAAAAAAAtEDCXQAAAAAAAAAAAC1Qq4ac7MQTT2zI6eqsUCjkxhtvbJa1AQAAAAAAAAAAGkODhrtGjhyZQqHQkFOuVbFYbPI1AQAAAAAAAAAAGptjGQEAAAAAAAAAAFqgBt25K3l/Jy0AAAAAAAAAAADWT4OGu15++eWGnA4AAAAAAAAAAOBDy7GMAAAAAAAAAAAALZBwFwAAAAAAAAAAQAsk3AUAAAAAAAAAANACCXcBAAAAAAAAAAC0QMJdAAAAAAAAAAAALZBwFwAAAAAAAAAAQAvUqiEne+aZZxpyunoZNmxYs60NAAAAAAAAAADQ0Bo03HXCCSekUCg05JR1UigUMm7cuCZfFwAAAAAAAAAAoLE0aLhrmWKx2BjTAk2oVaGQ/bfonQ6ty1f67LG33svMRVVrHN+7Q5sM6Nw+PTu0SbvyshQKSeXSmlRULsnkeYsyZf7ixiq9ZOhGXTKwe8eV7r85Z2GemzZnlWPKC4UM7N4h/Tu3T6c25UkKWbhkaabOX5xXZ81PVc2a//9t3//H3n2Hx1We6R+/z/QZ9WbJHePeCxgwxWCMiVl6gIVACgmELIHfphJIFhLSIBtIskmAJCRLCxATTA9twQVjm2KMG5ZtuXfLktU1mn5+fwwe6WhGvdp8P9ely+e85z3veY4WZ2Xp1vMOy1OO1yXTNLV4d7mqg5HueBUAAAAAAAAAAAAAAPAZ1O3hrs9SsKuqqkolJSXavXu3qqqqZJqmsrKyNGjQIE2bNk0ZGRnd8pw9e/aouLhYhw4dUiwWU2FhoUaPHq0xY8Z0y/pAKpMKMlIGu9ritBk6qShbgzI8SdfSXTaluxwamulVuT+kDw9WKhCJdUe5SXI8To3M9nXoHqfN0OxhecpyOy3jmW6nMt1ODc30atneI/KHoynvH5HlU47XJUnaUeUn2AUAAAAAAAAAAAAAALqkW8Ndt956a3cu1+/EYjF99NFHeuutt/T++++rpKSkxbmGYWjWrFm6/vrrdfbZZ3fqee+8847+9Kc/ac2aNSmvjx07VjfeeKMuueSSTq0PtCTX69SIDgajpHi3rzOH5CYCTq3J97k0e2ie3tlzRMFo9wa8DEkzirI6vE3stMKsRLArEIlqTWm1IjFT0wZkKcPtkM9p18yB2Xpnz5Gke112QxMKMhL3FpfXdvk9AAAAAAAAAAAAAADAZxvhrg6YP3++du/e3a65pmlq5cqVWrlypS688EL97Gc/U3p6ervvveeee/TEE0+0Om/Lli267bbbtHTpUv3qV7+Sy9V2oAZoiyFpRmHHg1GSNLEgIynYFYnFdLAuqJhpqijNI7fDlriW7nJoWmGWPjhQ2dWyLcbmpSd132qL227TkCbdxtYfrtHBT7eO/Li0SmcPy5ck5XldynY7VNWsK9ek/Ey57fF321hWq3Ab2zcCAAAAAAAAAAAAAAC0pdu3ZTyeVRj9vy0AAORQSURBVFRUJI2dcMIJmjJlivLz8+V2u3Xo0CG99957OnToUGLOq6++qsOHD+t///d/5Xa723zOb37zm6Rg14wZMzR58mTZ7XZt2bJFK1euTGyB+eqrr8put+u+++7r4hsC0ri8dGU2CUYFo7FEaKk1aU67TmzW7SsSi2nJ7iOqDcWDUB57reYMz5e3yXaPgzM8yvM6daQh3C31p7vsGpvbGKQMRmKWQFlLcjxOS6CtzB9KHB9pCCsaM2W3xa/nel2WcFeOx6nhWd5P54a0u6ahy+8BAAAAAAAAAAAAAABAuKsTBg8erKuuukqXX365ioqKkq5Ho1H985//1L333qtgMN75Z9WqVfqf//kf3X777a2uvWTJEv31r39NnGdmZuoPf/iDZs2aZZlXXFysm2++OREie/nll3XSSSfpmmuu6err4TMsw+XQmCbBqF1VfqW57CrwtR1KHJbpTer2tau6IRHskqRANKatlfWaMiDTMu+ELJ+ONFR3sfq4GYXZiRBWIBJVSUXy81Jx2qy1h5ptFRmKxeS12T+daw2LTfu001nMNLW2tHveAwAAAAAAAAAAAAAAoO12NseAsrKyXnnOoEGDdM899+itt97SzTffnDLYJUl2u11f+MIX9Mc//lG2JiGQv//97yotLW1xfdM09dvf/jZxbhiGHnrooaRglyRNmDBBjz32mKUT2AMPPKBAINCZVwMkSTOKsizBqA1lNe2+tyg9OQBWVh9MHvMnjxWltR0ea48R2T7l+xq3hVx/uCYppNWS5tsoupp1K3M1+bscjjWueWK2TzmeeKeznVV+VTfbrhEAAAAAAAAAAAAAAKCzjtlwVywW06JFi3TzzTdrzpw5vfLM559/XldccYXsdnvbkyWdffbZuvDCCxPn4XBYixYtanH+22+/rZKSksT5pZdeqpkzZ7Y4f8SIEbrhhhsS52VlZXr22WfbVRvQ3InZPuV5G4NRa0trkgJPLTEkZbqcSeNNu3a1NuZ22OVtx9aJrfHYbZqUn5E4P1gX0L7a9ocdKwPhxFanklTQJCSW63UmQm+SVNEQ37LRbbdpwqfPDESiKi6v7XT9AAAAAAAAAAAAAAAAzR1z4a6dO3fq/vvv1+zZs3XrrbdqyZIlikajvfJsh6Pju1g2DXdJ0oYNG1qc+8Ybb1jOr7vuujbXv+aaayxhs+ZrAO3hddg0sUkw6kBtQAfq2h+M8jntlvDTUcEUXbNiphROMZ7m7NousdMKs+T8tNtWOBrr8PaIwWjMEgabMiBTA9PdKvC5NKMwKzF+pCGkqk+7c00qyEh0+NpYVtvuMBwAAAAAAAAAAAAAAEB7dC1N0UsaGhr0+uuva+HChVqzZo0kWTrs9GfDhg2znJeXl6ecF4lEtGzZssT5wIEDNWXKlDbXLyws1LRp07R69WpJ0po1a1RRUaHc3NwuVI3PmqbBqFAnglHOFMEuSYq0EHaKmqaa9/ly2lOv0R6D0j0alOFJnG8sr1VDpH3bMTa1rrRamW6HstxOeRx2zRps/XvkD0e16mCVJCnX49SwTK+keOBrd01DYp7bblOW2yGHzVBDJKaqQFjHxv9iAQAAAAAAAAAAAACA/qRfh7vWrl2r5557Tq+99pr8fr+kxlCXYRiW8/6qvr7ect5S96+SkhLV1NQkzqdPn97uZ0yfPj0R7opGo/r444913nnndaJafBYNyfBoYHqTYFRZrQIpOmu1JlXXLkktBppSZb4cts41EnTaDE0rzEycH2kIaUeVv1NrhWKmlu4+olE5aRqc4VG6yy5DhurDUR2sC2hrZZ1C0Xjx0wqzZBiGYqaZCMO57TZNL8yyBM2k+JaNG8tqLQEwAAAAAAAAAAAAAACAtvS7cFdFRYVeeuklLVy4UDt27JBkDXQZhiHTNGWapoYNG6YLL7xQF110UV+W3KotW7ZYzouKilLO2759u+V8/Pjx7X7GhAkTLOdHP29AW5w2Q1MGNAajyvxB7azueDAq2kKHLkOpA16psmCRWMc7bUnSpIJMeRz2RB0fH+pY17HmoqapLRV12lJR1+Kckdk+ZXvivcd2VvlVHYzIaTN09rA8pbuS/2fV47DrpIHZctgMbe9k8AwAAAAAAAAAAAAAAHz29Itwl2maWrZsmZ577jktWbJEkUjE0pGraaArPz9fF1xwgS6++OJ2bVvY115++WXL+WmnnZZyXvNA1qBBg9r9jIEDB7a6FtCSiQUZlmDUmk4Go8IthLscNiPlNbuRnO4KRzvehS/H49QJWd7E+ZaKOtWGIh1epyPcdpvG52dIinfkKi6vlSSNy0tPBLvC0ZjeP1Cp6mBYkwsyNTzLJ0maWJCpfbUBBTvYGQ0AAAAAAAAAAAAAAHw29Wm4a+/evVq4cKFefPFFHT58WFLL2y5edNFF+vznP6/TTjtNtk5u39bbPvzwQ3344YeJ84yMDJ155pkp55aWllrOW+rwlUrzuYcOHepAlfgsy2jSZcofierE7LSkOenO5P+ZGJnj06BPt3L8pLxG/nBU0ZiZtD2j22FTOBS1jNkMyWlP/jtcH+54KCvD5bD8b4XbbtOUgkzrHHdy/TkeZ2LegbqAyhtC7X7m5IIMuT6t/5Oy2kR4bUhGY8hsZ7VfZf74musP12hIhld2myGHzdDAdLd2VbM9IwAAAAAAAAAAAAAAaFuvh7uCwaDeeOMNPffcc1q1apWklrddNJp09/nud7/boW5Wfa2hoUF33XWXZeyrX/2q0tKSwzOS5Pdbt2praV4qzec2XwtojwyXQxm57fufhMFNgkybjtQqbJqqCYWV43ElrVnXLNyVmWLbwmAkqoZI17pZGYahkTnt+3uT6XYq0x3fVrE+Eml3uCvP69KwT7twHWkIaU9NPKTltBnyOu2JeRUN4cRxOGaqPhxJPC/T5ZREuAsAAAAAAAAAAAAAALSt18Jdn3zyiRYuXKhXX31VdXV1kpQIcDUNdGVkZOiCCy7QFVdcoauvvrq3yut2P/3pT7Vr167E+Yknnqgbb7yxxfnNA1kul6uFmcncbnera7WH0+ns8D3oTYG+LqBNh+qCSeGuAT63DtYFrWNp1v9eJelQfTBprL8xJE0rjHf7ipmm1pY2bmHZvGNZJGYNqjXdmrL53O7C32Eru93e9iQAQBf0/69NAADHB/6tAwAA+hO+NgEAAP0JX5t8dvRouKuqqkovv/yynnvuOZWUlEhK3aXLZrPp9NNP1+c//3nNmzevQ8Gm/ujxxx/XCy+8kDh3uVy67777kkJYTQWD1nBLRz4HzecGAvywDb1vT02DxuWlWzruDc/yakeVX7Wh+JaLHodNo1J019pVnRxIHJbp1ckDsy1jm8prtelIXfcW3k4jc9KU9Wn3rZ1VflUHG7eRDEVjlm6DrmbbTjY9D0a71qEMAAAAAAAAAAAAAAB8dvRIuGvFihVauHChFi1apHA43OK2iyeccIIuv/xyXXbZZSosLOyJUnrdG2+8oV/96leWsZ/97GeaNGlSq/c1D36FQu3bJi7VXI/H0+57jwqHw21PwnHn3b0Vbc45a2iuCnzW/z6X7TmStJVhfTiqHVV+y9aIDptNc4bn6UBdUKZpqijdI3ez4NP+2oCONHTuv789NQ2JrRFbkioktrvar9WHqlPfkILHbtP4vHRJUiASVXF5reV6zJSqAmHleONBywKfW/tq4yFLr8Om9CZbNh5p5xaQHcXf4dT4vAAAAADHNr6mBwAA/QlfmwAAgP6Er00+O7o93HXuuefq4MGDkqxduppvu3j55Zdr+vTp3f34PvXee+/ptttuU6zJlmzf+973dPnll7d5r8/ns5x3JNzVvOtX87WA3rKxrFa5Xqdle0aHzaZhmd6U8+tCEcv2hv3V5AGZcn4aSvukrNayzeJR26r8mvlpuGt4llfVwbCqg2FNyM9IdPSqDUZUegxsQQkAAAAAAAAAAAAAAPqHbg93HThwIBHmavrnrFmzEtsutrY94bFq/fr1+uY3v2kJZd1www266aab2nV/80BWfX19u5/dfC7hLvSViGlq+d4KnTwwWwPTW+8gV+4PadXByn6/TWG+16Whn4bTjjSEWuwUtremQflel0Zk+2QzDE0rzLJcD0ai+vBgZY/XCwAAAAAAAAAAAAAAjh89si1jU3PmzNHdd9993Gy7mEpJSYm+/vWvy+/3J8auuuoq/eAHP2j3Gs0/P4cOHWr3vUc7pR1VVFTU7nuB7haOmXpvf6UKfC4Ny/Qqz+uS22GTIUPBSFSVgbD21QZ0oC7Q16W2yZA0rTBTkhQzzTa7jK0prVaZP6gR2T5luZ1y2Aw1hKM6VB9USUWdGiL9O8gGAAAAAAAAAAAAAAD6lx4Ldx3t2rV06VJdf/31uvzyy3XppZcedyGvPXv26Gtf+5qqqqoSY/Pnz9fPfvazDq0zcuRIy/mBAwfafW/zINiJJ57YoWcDrXl3b0Wn7ivzh1Tmb//2os3tqWlosUtWb61jSnp7V3mH7tlXG9C+2v4fXAMAoL9yGIYK0lzK8cS3evY6bHLZ4x+mKYVjMdWHo6oKhHWgLtCurzdyPE4NzfAqx+tUmtMup80mw4iH0v2frrW/tkGHu/C1S1OGpCy3Qzlel3I/fY8Mlz2xVfNRb2w/LH8k2upadsPQqByfBmd4le6ySzLkD0d0sC6orRV1CqXYLrqpOcPylON1yTRNLd5drupgpItvBwAAAAAAAAAAelOPhLuObsV4NOC1c+dO/e53v9Pvf/97nXbaabriiit03nnnyeVy9cTje01paamuv/56lZWVJcbOPvts3X///bLZbB1aq3m4q7i4uN33bty40XJOuAsAAADHqoI0l2YNzk190ZDsNrs8DrvyvC6NzElTuT+kDw9UKpBiq2e7YeikoiwN+XSL5ebcdkNuu005HqdGZPtU7g/pgwNd3zZ6bF66JuRndGkNSXLaDM0elqcst9Mynul2KtPt1NBMr5btPSJ/OHVAbESWTzne+L+5dlT5CXYBAAAAAAAAAHAM6vZw1xtvvKGFCxfqxRdfVHl5vOPN0ZBXNBrVypUrtXLlSqWnp+uCCy7Q5ZdfrunTp3d3GT2uoqJC119/vfbv358YO+WUU/SHP/xBTqezlTtTGz16tDIzM1VTUyNJWrt2bbvvXbNmTeLYbrdrxowZHX4+AAAAcCzK97l0+pBcLdldruY9rGYOzNagDE+H1jqjhbU6wmh7SrtMK8xKBLsCkajWlFYrEjM1bUCWMtwO+Zx2zRyYrXf2HEm612U3NKEgI3FvcXltN1UFAAAAAACAfs2RLhWeK+XNjH94h0juPMmVK5lRKVwj1e+QKj6W9j4vlS5Ovc7cJVLhOZ2rYcPd0oafdvYNGqWNkEbdJBXOkdJHSs5MKVQp+XdLB16Xtv+v5N/b+hp2nzT2W9Kwq6SM0ZJhk+p3SfteljbdJ4Va28XIkD73QfzzGItKb54sVa7t+nsBQAd1rL1UO5xwwgn6/ve/r3feeUcPPvigzj33XNntdklKbENimqZqa2v17LPP6tprr9X8+fP18MMPq7S0tLvL6RF1dXW68cYbtWPHjsTYlClT9Kc//UkeT/t/eNSUw+HQ7NmzE+cHDx7UunXr2ryvtLTUMm/69OnKzW2h0wEAAABwjIiZpioaQtpT06DtlfXaXe1XZUPqbROzPU4NSrd+HZ7tdqQMdgUiUe2padCuar/qQ8mdrFKt1VX+cFShDnYDc9ttGtKk/vWHa3SwLqgyf0gfl1YlxvO8LmW7k39nZ1J+ptz2+D/3NpbVKtzG9o0AAAAAAAA4ThTNlc5+SZp0pzTwc1L2RMlbJNldksMreQul/FnSmFukuYuk85ZJnqK+rtrKsElTfiFdvFWaeIeUf6rkyY+/g7dQyjtFmvwT6aLN0thvt7yOK0c6/z1p2j1S7nTJmS45fFLWhPi6F6yJB8haMuqmeLBLkrb9mWAXgD7TI9sySvEOUnPnztXcuXNVXl6u559/Xs8//7x27dolyRr02rVrl2Xbxs9//vOaN29eT5XWJYFAQP/xH/9h2QpxzJgx+tvf/qb09PQurX3BBRfoX//6V+L86aef1tSpU1u9Z8GCBYpGG7dhmT9/fpdqAAAAAPpSXSiqDw5U6nB9MGUgqcAX37bRYbP2x8r1OrW/LtDkPHkL+PpQRIt3lyfWNSTNHpanvGZzm6/VUbWhiLYcqVNlIKSKhrAC0ZjOGpqrAp+73WvkeJyJfzNJUpm/Mdh2pCGsaMyU/dPPQa7XpaomWy7meJwanuX9dG5Iu2saOv0uAAAAAAAAOM4NOEua84b05kwpFu6eNWPJv1TZIaf8VRr5tbbnOXzSSb+TnBnSJz9Pvj7zISlnSvy44aC06mYpXCed/ICUNU5KGyad/qT01hnJ97rzpKm/jB8HDkvr7uz8+wBAF/VYuKup/Px83XTTTbrpppv00UcfaeHChXrzzTfV0BD/IUNL2zb2N5FIRN/61re0atWqxNgJJ5ygRx99VFlZWV1ef+7cuRozZoxKSkokSS+99JKuvPJKzZw5M+X8nTt36n//938T5wUFBbrqqqu6XAcAAADQV2pDEdWm6Kh1VJk/pDJ/UAObdddqGoSSJLuRvDnigbqAJTBmStpX05AU7mq+Vkftqw1oX23nw2GS5GwWXmve+SsUi8lrs38619qQeVphlgzDUMw0tba0ukt1AAAAAAAA4BgVC0uVa6TarfGtDB0Z8Y5VeSl+9pwzVRp8qbR3YePY3oVtd6py+OLdrZrb90Ln6x52VepgV9kKqbo4vrVi8+0iJ98tHXpbKn+vccxdIA1t8rPzj78n7Xspfvzh16V578aPC06XcmZIlR9b15z6q3jAS5LW3i6Fqzr/TgDQRb0S7mrq5JNP1sknn6y77rpL//rXv/Tcc89p/fr1kpK3bTwa+pKk119/XVdccYWys7N7u+RETXfccYeWLl2aGBs8eLAee+wx5efnd8szDMPQd7/7Xf3Hf/xH4pnf/OY39Yc//EGzZs2yzC0uLtbNN9+sYDCYGLv11ls7vS0kAAAAcKzwOe1JY3XNAmF14eSAmNuRfJ8nxVhtsIu/WdgNmnctc9ltCjYJeLmaBLrCscbxE7N9yvE4JUk7q/yq7gfvAgAAAAAAgF5UUyItv1o6+KYUTvGLf4VzpLP/FQ9mNZV/mjXcVfJg289KFew69HY8hNVZE25PHlv3X9LGexrPx/6ndNLvG88NmzTjN9L/nd44ljdTsjX53l/posbjsuVSNCDZP/3Zev6p1nBX3qmNAbOyldKOxzr9OgDQHXo93HVUWlqarr76al199dXaunWrnn32Wb3yyiuqrKyU1Bj0Ovrn/fffr9/97nc644wzdNFFF+m8886T1+vttXoPHDigV155JWls7ty5HVpn8ODBeuutt1q8PmfOHH3961/XX//6V0lSTU2Nrr/+es2YMUNTpkyRzWbTli1btHLlykTwTZIuueQSXXPNNR2qBQAAADhWOAxDGW6HxuSmKcvttFwLRWPa12zrwdL6oOrDEaU5G//JMyTDo4oGn/bVNCgmqTDNrZE51m9iBSJR7e0H2xhWBsIyTTPx76ECnyvRDSzX60xsyShJFQ3xLRvddpsm5GdIir9HcXltL1cNAAAAAACAPlezKf7RktIlUuliafBF1nGbM/X81oy5NXlsy++Tx9rLnS/lnmQdi/ilTfdbx0oekCbeJXmaNGHJnyVlTZSqN8bPXdnWe4IVyee+QZ/OzWlywZBmPhgPjMUi0ke3dPZtAKDb9Fm4q6nRo0frRz/6kW677TYtWrRIzz33nFasWKFYLGbp5hWJRLRs2TItW7ZMHo9H5557ri666CKdddZZcjh69lWaBqmajkWj0Q6t05753/3udxUIBPT3v/89Mfbxxx/r448/Tjn/3/7t3/SLX/yiQ3UAAAAA/d0ZQ3JUmNZ6Z9pQNKYPD1Qq1KzTVcyUPjxQpVmDcxLduWyGoWmFWZpWmHpL9YZIVO/tq1Qkxdf+vS0YjWlfbUBDM+O/0DJlQKaipqlIzNTUAZmJeUcaQqr6tDvXpIIMuezxjl4by2qTun8BAAAAAAAAkiTfsOSx2q0dW6NwjpQ9udka26X9r3ZvXf59UixkHTNjUv0Oa7hLkgae3xjuClVZr7nzpEBpk/PcxuNQZePx6JsbA2bb/tz21pQA0Av6RbjrKKfTqfnz52v+/PkqLS3VwoUL9cILL2jfvn2SrNs2NjQ06LXXXtNrr72mrKwsvf/++31Zerey2Wy68847ddZZZ+mhhx7S2rVrU84bM2aMbrzxRl166aW9WyAAAADQD2yrqNeWijrLdoVNVQbCWrSrXKNy0jQ6N002w0g5T5JKKuq0+UidIv0oELWutFqZboey3E55HHbNGpxrue4PR7XqYJUkKdfj1LBPg2BHGkLa3aT7mNtuU5bbIYfNUEMkpqpAWP3nLQEAAAAAANArHOlS1gRp/G1SzhTrtVCVtOvpjq035j+Tx0r+KHXlO0+2FPEFR1rquY6M5LHckxuPj6ySYtHGrRkLz5V2/yN+nH9645aMklT+QfxPd4E09dOmKoHD0ro7O1Y/APSQfhXuaqqwsFC33HKLbrnlFr333ntauHCh3n77bQWDwUTIS4oHvaqrU+wV3M2GDBmiLVu29Phzmjr77LN19tlna/fu3dq4caMOHz6saDSqwsJCjR49WmPHju3VegAAAID+5MQcn9wOm9aWVrfYpWpIpkfDs7ytBrskaXROmrwOu9YfrmkxLNbbQjFTS3cf0aicNA3O8CjdZZchQ/XhqA7WBbS1sk6haPy9pxVmyTAMxUxTa0vj/z5y222aXpilQRnW7meBSFQby2otATAAAAAAAAAch855XRo0v/U5oUpp+dVSqKL1eU2lDZcGX2wdC9dK2x/peI1N+fcmj3kHShmjpNptTcYGS+kjU9d1VLBM2vNP6YQvxM9n/EaK1EuROunkPzbOK1spVX66g9b0Xzdu0bj2dinc8zkEAGiPfhvuamrWrFmaNWuWampq9PLLL+u5557Tpk2t7BN8nBk+fLiGDx/e9kQAAADgOHKgNqjaYFQyJKfNUJbbqWyPM3HdZhgamulVjsepd/YcSQplzRyYndjW8KhAJKpyf0gR01SOx6ksd3w9o8laS/eUJ0JTfS1qmtpSUactFXUtzhmZ7Ut8XnZW+VUdjMhpM3T2sDylu5L/yedx2HXSwGw5bIa2V/l7rHYAAAAAAAD0c5v/Ryq+N96lqiNG39LYEeuoHY9Jkdqu1dNwUKr6RMqe1Dhm2KTTn5I+vFmq2RQPep38oGR3Jd/vzLKef3RrfK3syfGQ2NkvWa/X75FWfjF+nD9LGvHl+HHZyvj7HOUplHKmSnaf1LBfqvhYMqNde1cA6IBjItx1VGZmpr74xS/qi1/8ooqLi/Xss8/q1VdfVW1tF/+fBAAAAIB+Z2d1cvAox+PUaYNy5HU2fvMo3eXQpIIMrT7U+Jt0gzM8ScGuioaQlu+rsGy9OGVApkblpFnWmpifqTWlx8Zv5bntNo3Pj7egD0SiKi6P/9toXF56ItgVjsb0/oFKVQfDmlyQqeFZPknSxIJM7asN9JtOZQAAAAAAAOhlY26RvEXSqm/GO3i1h90rjbzBOmbGPt2SsRtsvEc6o9kWkXmnSBesbvteW7PAV6hCevM0ady3pWFXSRmjJdmk+l3SvpekzfdLwSPxANnMh+J/xiLxz4cUD3XN/LM09DLrug2l0ro7rAEwAOhBx1S4q6kJEyboJz/5iX74wx/qzTff7OtyAAAAAPSCykBY6w7X6LTBOZbxQRkeS7hrWLNglyRtPlJnCXZJ0sayWo3M9lm2fh+S4Tlmwl2TCzLkstskSZ+U1Sa2pxyS0fj+O6v9KvOHJEnrD9doSIZXdpshh83QwHS3dlWzPSMAAAAAAMBxad8LUs3meGjJmSVlT5Fypzdetzml4ddIuSdJb50lBUrbXnPElyR3rnXs4BtS7dbuqXn3P6QBZ0mjb+74vakCalF/PDC28Z6W7xtzq5QzLX687c9S1br452veCikjxfaP3kLptEclR0b3hdoAoBW2vi6gq1wuly6++OK2JwIAAAA4LtQEw0ljTptNbnvjP29SbUdYH05ulR41zaTOVU67da3+Ks/r0rBPu3AdaQhpT008pOW0GZbOZhUNjZ+vcMxUfTiSOM90NW5zCQAAAAAAgOPMtoelj78jrf6W9P710hszpDdmSv591nkZo6Wp97ZvzTG3Jo9t+X2XS7VY9U1p1S0tbxfZUCpt/l3yeLC848/yDJAm/7Rx3XV3xo8n3dUY7ApVS4vmSs/lSzufaLx32q8kd0HHnwkAHdT/f2IBAAAAAE1kulMHkqJmY1eumGkmXfc1CTwdZTeMlEGuVPf3J4akaYWZkuK1rm3SacxuMyxzIzFreC3cpHtZ87kAAAAAAAA4zlV8JK3+dvL4sCvjHb5aUzhHyp5sHaveLB38v24rL2HrQ9JLw6UlF0hrfygV/1pa/2PpnYull4ZJlWuS76n8uOPPmX6/5MqOH6+9XQp/+n224dc0ztn2sFS6OL6F4+pvS9FgfNzhk4Zc2vFnAkAHHbPbMgIAAAA4vmS5HZpemKUdVX4dqAskbaEoSTkep6YMyEgabwhHLfPrQ1FlNQuBjc9L1xF/SJEmwa2JBRmWLRklKRCJWgJQUnybx5MHZlvGNpXXatORuna/X3camZOWeL+dVX5VBxu7cYWiMZmmmXgvV7PwWtPz5l3LAAAAAAAA8BlQtSF5zJkR70LV2taMY/4zeazkD91XV3PRQHzLx4NvJF8bclnyWNnyjq1fcFZ8m0lJKlsh7Xw8fuzMlnyDG+cd+aDxOFQp1W6TsifGz7MmduyZANAJhLsAAAAA9Bu5XpdyvS7FTFPVgbBqQxGFY6ZcdpsyXA5le1J37dpV7bec768LaFCGJ2nt808sUJk/pKhpKsfjTAqASdKB2kCX3iHH49TQDK9lLN2Z/E+vcfnpikQbQ2R14Yh2VPmT5jXnsds0Pi9dUjyIVlxea7keM6WqQFg5XpckqcDn1r5P38nrsCm9SQezIw2hdr4VAAAAAAAA+j3DJpnt+GW+lgJJ0YaW7/ENkwZfbB0LVUk7Hm93ed0m/zRpcLOOWfW7pUNvt38Nwy7NfDB+HIvEt4E8yuGzzg1Xt3zefC4A9ADCXQAAAAD6HZthKMfrSgSUWlPRENKWCmsHrb01DRqR5VO+z3q/x2HX0Exr8Kqphki0y924MlwOjcpNa3PeCVnWb/yU+YPtCndNHpAp56fdtz4pq03qMiZJ26r8mvnp5254llfVwbCqg2FNyG/sVFYbjKi0Ptjm8wAAAAAAAHCMyJosnfqwVPKAtPdFKVKbPCfvFOmk3yWP+/dJ4ZqW1x5zq2SzW8e2/68Ubfv7WZKkEV+RZj1mHdtwt7Thp8lzJ90l7X1eqt6YfK1onnT6k8m1bP5t+4JtR439z8YtJrf+Sapa13gteESKRRuf4c6z3tv0PFDW/mcCQCcR7gIAAABwTIqZpnZX+7X+cK1S5Ju0cn+FTirK0uCMlsNcTVU2hPThwap+vVVhvteVCKcdaQhpT03q36bcW9OgfK9LI7J9shmGphVmWa4HI1F9eLCyx+sFAAAAAABAL8s7RZr1hHRKKB5YqtkcD22586SMsVLu9NT3bX+k5TXtXmnkDdaxWDQeIusJo26SpvxMqt0uVayWgmWS3SflndwYyGrq8DJpyx/bv76nSJp8d/y4oVRaf5f1eiwoVX4s5c2MnxeeK+1+Jn7sHSylj2qcW76i/c8FgE4i3AUAAACgX6gORvTWzjLl+1zK97qU7rLLZbfJbbfJZhiKxEwFIzHVhiIqbwhpf22DGiItB7EiMVMfHKhSjqdeQzI8yvW6lOa0y2mzyTCkcMxUQziqqmBYB2oDOtTPu1gZkqYVZkqKB9vWlla3On9NabXK/EGNyPYpy+2Uw2aoIRzVofqgSirqWv3cAQAAAAAA4Bhnd8XDSUcDSq058qG08Z6Wr5/wRcmdax3b/4pUv6tLJbYpY2T8ozWV66Tl/y4pxW9/tmTGbyVn/PtsWnt78raLkrTl9/EOYZJ04lelyrVS1Xppys8bO3pVb5YOvtn+5wJAJxHuAgAAANBv1IYiqg1FtLMd2xO2V2UgrMpAuEtr7KlpaLFLVlfmdoQp6e1d5R26Z19tQPtqA91eCwAAAAAAAI4DsXC8Y9fH3413q2rJ2P+XPLbl9z1XV3vEotKuv0urv9X6dpLNDThHOuEL8eOyFdLOx1PP2/WUNGB2vIuYzSnNfMh6PVAmrbi6Y1tBAkAnEe4CAAAAAAAAAAAAAOBYV7VOenWiVDBbGnCWlDFGcudLngLJcErR+vg2hDWbpLJ3pT0LJf/e1tcccE7yVoiV66XDS3vqLaR3r5AGnh9/j7Th8XdwpEuhCsm/Rzr0trTrH1L1Jx1b13BIJ3+6lWQsIq36ZuvzP/yGVLpYGvUNKWd6fGvIhv3SgdekjffGjwGgFxDuAgAAAAAAAAAAAADgeFBdHP/Y9ufuWe/wUulpo+vr7Hy85S5ZzR35MP7R3cyI9Nqkjt2z+5n4BwD0IVtfFwAAAAAAAAAAAAAAAAAASEa4CwAAAAAAAAAAAAAAAAD6IcJdAAAAAAAAAAAAAAAAANAPEe4CAAAAAAAAAAAAAAAAgH7I0dsPnDt3riTJMAw99dRTKiws7NQ6paWluvbaaxNrvf32291WIwAAAAAAAAAAAAAAAAD0tV4Pd+3fv19SPJAVjUY7vU4kErGsBQAAAAAAAAAAAAAAAADHE7ZlBAAAAAAAAAAAAAAAAIB+iHAXAAAAAAAAAAAAAAAAAPRDx2y4KxKJJI4djl7fXRIAAAAAAAAAAAAAAAAAetQxG+4qLy9PHKelpfVhJQAAAAAAAAAAAAAAAADQ/Y7ZcNfy5cslSYZhqKioqI+rAQAAAAAAAAAAAAAAAIDu1SP7GR44cKBd80pLSzu0bigUUllZmZYvX65HH300MT5+/PgOrQMAAAAAAAAAAAAAAAAA/V2PhLvOPfdcGYbR6hzTNHXttdd2+hmmaSaO582b1+l1AAAAAAAAAAAAAAAAAKA/6pFw11FNA1idud4SwzBkGIZM09S0adM0Z86cTq0DAAAAAAAAAECXONKlwnOlvJnxD+8QyZ0nuXIlMyqFa6T6HVLFx9Le56XSxR1/xkm/l8b+Z/L4jsek97/a5VeQYZeyp0h5p0r5p8b/zBwrGTbrvJdOkOp3t76W3SeN/ZY07CopY3R8jfpd0r6XpU33SaGK1gqRPvdB/PMYi0pvnixVru3auwEAAADAMa5Hw109xTRNGYah888/Xz//+c/b7BIGAAAAAAAAAECPKJorzX6x5esOr+QtlPJnSWNukQ6/Ky3/dylwqH3r550qjb6lW0pt0cT/kqb8tOvruHKkuUulnCnW8awJ8Y8TrpXePkeq35n6/lE3xYNdkrTtzwS7AAAAAEA9FO66/PLLW7z2wgsvSIp335o3b57S0tLava7L5VJmZqZGjhypmTNnavDgwV2uFQAAAAAAAACAXjPgLGnOG9KbM6VYuPW5hkM69a+Szd6zNXXXL1DPfKgx2NVwUFp1sxSuk05+QMoaJ6UNk05/UnrrjOR73XnS1F/GjwOHpXV3dk9NAAAAAHCM65Fw17333tvitRdeeCHRaeuOO+7QoEGDeqIEAAAAAAAAAAB6TywsVa6RardKoUrJkRHvVnW0E1VTOVOlwZdKexe2vubEO6TsyT1Tb2vq90jOjHgnrvZyF0hDr2o8//h70r6X4scffl2a9278uOB0KWeGVPmx9f6pv4oHvCRp7e1SuKrT5QMAAADA8aRPtmU8uq0iAAAAAAAAAADHtJoSafnV0sE3pXB18vXCOdLZ/5IcPut4/mmth7syx8a3SzwqUC558run5uZqtkjF/y2Vvx//CByS5i6RCs9p/xp5M60dxkoXNR6XLZeiAcnuiZ/nn2oNd+WdKo382qdzV0o7HuvsmwAAAADAcafXw11Nu3rl5HTgt34AAAAAAAAAAOhvajbFP1pSukQqXSwNvsg6bnO2vu4pf20MQwUOx8NXM37TtVpbsntB/KMrXNnW82BF8rnv0508LB3BDGnmg5Jhk2IR6aNbulYHAAAAABxnej3cdfnll/f2IwEAAAAAAAAA6Du+YcljtVtbnj/qP6QBZzWer/6WZHN3f13dKVRlPXfnSYHSJue5TeZWNh6PvlnKPSl+vO3PUuXanqoQAAAAAI5Jtr4uAAAAAAAAAACA444jXco7RTrzWSlnivVaqEra9XTq+7wDpWm/ajzf/6+ud9XqDUdWSbFo43nhuY3H+ac3diGTpPIP4n+6C6Spv4gfBw5L6+7s+ToBAAAA4BjT6527WhOJRHT48GFVV1ervr5epmlq5syZfV0WAAAAAAAAAABtO+d1adD81ueEKqXlV0uhitTXT35QcmXFj8M10qqbu7fGnhIsk/b8UzrhC/HzGb+RIvVSpE46+Y+N88pWSpUfx4+n/7pxi8a1t0vh6t6tGQAAAACOAX0e7vL7/frnP/+pxYsXa/369QoGg4lrhmGouLg46Z5NmzZpy5YtkqT09HSdd955vVYvAAAAAAAAAACdsvl/pOJ7412qUhl6hTT08sbztT+U/Pt6pbRu8dGtUvYkKXtyvAPZ2S9Zr9fvkVZ+MX6cP0sa8eX4cdlKacdjjfM8hVLOVMnukxr2SxUfS2ZUAAAAAPBZ1Kfhrn/+85+6//77VVtbK0kyTbNd94XDYd1xxx0yDEM2m01LlizRgAEDerJUAAAAAAAAAAC6ZswtkrdIWvXNeAevppxZzTpcrZC2PtS79XVVqEJ68zRp3LelYVdJGaMl2aT6XdK+l6TN90vBI5Jhk2Y+FP8zFol/PqR4qGvmn6Whl1nXbSiV1t1hDYABAAAAwGdEn4W7fvjDH+rFF1+UaZoyDMNyzTCMVoNeU6ZM0eTJk7VhwwbFYjG9+uqr+upXv9rTJQMAAAAAAAAA0LJ9L0g1m+OhJWeWlD1Fyp3eeN3mlIZfI+WeJL11lhQobbw2/b54tytJigalD27s3dq7S9Qvbbwn/tGSMbdKOdPix9v+LFWti3++5q2QMkYmz/cWSqc9KjkypJI/Jl8HAAAAgONYn4S77rvvPr3wwguSGoNcgwYN0imnnCKPx6MFCxa0ucb8+fO1YcMGSdKyZcsIdwEAAAAAAAAA+ta2h5PHck+WZr8g+YY0jmWMlqbeK33wtfh53qnSyBsar2/8RTwkdjzyDJAm/zR+3FAqrbszfjzprsZgV6haevfz8dDXjN82bt847VfS7gVSsKz36wYAADiOOOXUUOdQFdoLVegoVLotXR7DI4/hkSlTITOk6li1DkcOa3t4u/ZG9ra4lttwa6hjqAY6BqrAXqBMW6a8Nq8cciissOpj9TocOayScIl2hXfJVPt2dGuvQnuhxrrGqshRpCxbllyGS4YMhcyQamI1Ohw9rG2hbdoT2dPiGg45NN0zXaOco5Rtz5YhQzWxGu0M79TqwGoFzECrNVyTcY0KHYWKmTEtqF2gsihfr6J79Xq465NPPtEjjzyS6NaVlZWlu+++W/Pnz5ck7d+/v13hrjlz5ui+++6TaZpas2aNIpGIHI4+3WUSAAAAAAAAAACrio+k1d+WzlpoHR92pfThjZIZkzLHxbt9SVIsKnmKpBm/s87PmpC8dt4pjfP2vSAdXtbt5Xe76fdLruz48drbpXB1/Hj4NY1ztj0slS6OH6/+tjTsasnulhw+acil0va/9WbFAAAAx52hzqG6OP3iFq87DId8Np8GOgZqqmeq9of367X61+Q3/dZ1HEN1afqlshv2lOu45Zbb7lauPVfj3ONUGinVG/VvqCpW1eV3cMiheWnzNMY1JuV1r+GV1+ZVoaNQk92TW3wHt+HWlelXKt+RbxnPs+cpz56nsa6xWli7UDWxmpTPmeyarEJHoSRpQ3ADwS70iF5PQ/3hD39IbLmYlZWlBQsW6IQTTujwOieeeKJ8Pp/8fr+CwaB27typ0aNHd3O1AAAAAAAAAAB0UdWG5DFnhuQusG7NKEk2uzTmlvatmzWhMfRVv6v/h7sKzpJGfCl+XLZC2vl4/NiZLfkGN8478kHjcahSqt0mZU+Mn2dN7JVSAQAA0Giwc7AuS79MC2oXKKZYYtxpOFsMdqVS6CjUFRlXaEHNAtWb9V2qaX7afI10pdjSuwUtvcMc35xEsKs+Vq/F/sUKm2Gd4ztHufZcZdgy9Lm0z+nZ2meT1vQYHs3yzpIk+WN+vRd4r0vvBLTE1psPq6+v18qVK2UYhgzD0B133NGpYNdRo0aNShzv3LmzGyoEAAAAAAAAAKCdjHZ+i72lQFK0oftq6e8MuzTzwfhxLCKtahJgc/isc49280p13nwuAAAAOi1qRnUockibg5u1LrBOxcFilUZKU84tcBToROeJra5XF6vT1tBWbQhu0O7wbkXNaNKcdFu6zvCe0aW6C+wFKYNd/phfm4ObtTG4UdXR6uT7mr2D1/BqtLOxidC7/ne1I7xDeyN7tci/KDE+yDFIA+wDktY7w3uGvDavJGlFwwoFzWCX3gtoSa927lq9erUikYgkKTMzU5deemmX1svNzU0cl5XR2g4AAAAAAAAA0IuyJkunPiyVPCDtfVGK1CbPyTtFOul3yeP+fVI49dYu3WLEV6RZj1nHNtwtbfhpzz2zNWP/U8qeHD/e+iepal3jteCR+HaUtk+7PrjzrPc2PQ/wswAAAICuqopW6bW617Q7slshM5R0fYhjiC5Jv0ROw2kZH+gYqG3hbUnzd4R26OPgx9of2W8Zz7Xl6rKMy5Rhy7CMj3KN0iL/IkWVHP5qj0GOQUlj1dFqPV37dOJ9bLLpyowrNdAxsMV3KLQXytbkFzb2RvYmjg9EDihiRuQw4rGaIkeRDkcPJ64X2Ys00TUxMbc4VNypdwHao1fDXaWl8YSnYRiaPHmybLauNQ5LT09PHNfXd61lHwAAAAAAAAAAHZZ3ijTrCemUUDywVLM5Htpy50kZY6Xc6anv2/5I4/HOxxu3KGxJqrDWjsek97/alerj8mZKw6+1jmWMSp436cfWQFrdNqnkwbbX9xRJk++OHzeUSuvvsl6PBaXKj+N1SFLhudLuZ+LH3sFSepNayle0/TwAAAC0qiJWoYpYRYvX90X2aV94n0a4RljGbc02h6uL1em52ue0L7Kvxee8439HF6VfZBl3Gk5l27J1JHakU/XblbwV5PbwdktQLaaYSkIlSeGupu/gtrkt1wJmIOk83YjnUtyGde45vnNkGIZiZkxL/Us79R5Ae/VquKuysjJx3LTrVmcd7QImqctBMQAAAAAAAAAAOs3uioeTjgaUWnPkQ2njPT1fU3tlTpDGfbvteSO/Zj0vXdq+cNeM30rOzPjx2tuTt12UpC2/l05/Mn584lelyrVS1Xppys8bO3pVb5YOvtn28wAAANBlzbttSVJVrMpy3rSTVUtaCn4d7YjVGc3rkCSfkbx9d6qximhjqC0Ys26j6DE88pt+y3libpMtF6e4p6jQUShJ2hDcoLIo3WXRs3o13OX1ehPHDQ0NXV6voqLxL11WVlaX1wMAAAAAAAAAoMfEwvGOXR9/N96t6rNgwDnSCV+IH5etaLlD2a6npAGzpVE3STanNPMh6/VAmbTiasmM9Wi5AAAAn2VOOZVrz9VJnpOU78i3XAvGgtoS2tLhNZt3+zqqNpZiS/N22h3erZpojTLtmYmxMa4xOhg9qJJQiWJmTMOdwzXVM9VyX32sXptDmxPnpdFSxcxYYmvGoY6h2hKOv+NA+0BLAO1Q5JAkyWt4NcszS5Lkj/m1MrCy0+8BtFevhruaduvav39/KzPbZpqmNm3alDjPy8vr0noAAAAAAAAAAHRI1Trp1YlSwWxpwFlSxhjJnS95CiTDKUXr49sQ1mySyt6V9iyU/Hv7uureYzikkx+IH8ci0qpvtj7/w29IpYulUd+QcqZLdp/UsF868Jq08d74MQAAALrVpemX6gTnCa3OCcQCer3+9aRtC9vjROeJSWNlkTJLh6yOiiqq1+pf0yXpl8hni3fnshk2zfHN0RzfnJT31MXq9ErdKwornBhrMBu0NbxVY11jJUln+c5S2B9W2AzrbN/ZiXkHIgcSXcrO9J4pjy3e0WtFwwrLVpBAT+nVcNeIEfH9WE3TVElJiSorK5WTk9OptVatWqXa2sYk55QpU7qlRgAAAAAAAAAA2q26OP6x7c89+5ydj7fc9aqr8zu6dnuZEem1SR27Z/cz8Q8AAAD0C2sCa/RR4KNOhbF8hk+zvLOS1wyu6XJdpdFSPVXzlKZ7pmu6e7rshr3FuasDq/Vhw4cKKTmItdS/VHn2POXb85VmS9PF6RdbrtfGavVmfXxr8IH2gRrvGi8pHvgqDhUn5vkMn/Lt+XIaTtXF6nQ4elimzC6/JyD1crhrwoQJysnJUVVVlWKxmJ5++mndcsstnVrrr3/9a+J4xIgRys/Pb2U2AAAAAAAAAAAAAAAAOmKKe4p8Np+W+JcoaLZ/a3G34dal6ZcqzZZmGd8e2q5NoU0t3NUxY1xjNN41vtVglyTNcM9Qui1d7/jfUYPZYLkWMAN6puYZTfdM12jnaGXbs2XIUE2sRjvCO7Q6sFoBMyBDhub45sgwDMXMmJb4l0iKh7rO9Z2rka6RlnX9Mb9WNKywBMCAzurVcJcknX/++Xrmmfhv3Tz88MOaO3euxo0b16E1nnrqKb377ruSJMMwdNlll3V3mQAAAAAAAAAAAAAAAMe17aHtqoxWypAhl+FSvj1fAxwDEtfthl1jXWNVaC/Us7XPtquDV5qRpssyLlO+3dqk52DkYKILVlfNT5uf2E7xKH/Mr32RfQqbYRU6ChPPNwwj8Q7P1D6TtL1kRBGtCqzSqsCqFp831T1VBY4CSdKG4AaVR8vlMly6KuMqZduzk+b7bD7NS5snp+HUuuC6Lr4tPut6Pdx1880364UXXlA4HFYwGNRXvvIV/eY3v9GZZ57Z5r2hUEh/+ctf9NBDD8kwDJmmqfT0dF177bW9UDkAAAAAAAAAAAAAAMDx45PQJ0ljhfZCXZh+oTJsGYmxbHu2zvCeobf8b7W6XrYtW5elX6Yse5Zl/GDkoF6se1Fhhbtc82jn6KRg16HIIb1Q+4Jl68XZ3tma7pme9A6L/Is69Dyf4dNpntMkxQNkKwMrJUmnek5NBLuCZlCv1r2qsmiZZntna7w7vn3jGd4zVBIqSeoYBnSErbcfWFRUpFtvvVWmacowDFVXV+vrX/+6vvzlL2vBggUqLra2pDty5Ig++ugj/c///I/mz5+vhx56SKZpJu6/8847lZ6e3tuvAQAAAAAAAAAAAAAAcNwpjZZqmX9Z0vgo1ygZMlq8r8BeoKsyrkoKdu0O79bztc8rZIZauLNjxrmSd4f7oOEDS7BLkt5reE+maVrGRrtGt/oOqZzpPVNum1uStLxheeI9xrjGJOZ8EvxEeyN7FTADeqfhHUXMiCTJaTh1ovPEDj0PaK7XO3dJ0k033aQdO3boxRdfTHTgWrVqlVatsra4M03T0tHr6F+6o/dcd911uvTSS3u1dgAAAAAAAAAAAAAAgONZebQ8acxluOQ1vCm3ZhzsGKyL0y+W23BbxreEtuj/6v9PMcW6rbYce07SWHWsOmksrLAazAb5DF9izG24W3yHVAY5BiW6cB2IHNCm0KbEOum2xkZEhyKHEsdBM6jqWLXy7HmSlPgT6Kxe79x11L333qtbb71VhtGYiGzakevox9ExSZa5/+///T/deeedvV43AAAAAAAAAAAAAADAsai9XataCiQd7UjV1AjnCF2WfllSsGttYK3eqH+jW4NdkhRVNGksy5aVNOaUU17DmzQeUfI7pGLI0BzfHElSzIxpiX9J4pqjWS+l5l3Jgmawca7RJ32XcBzps/+CDMPQrbfeqrPOOksPPfSQ3nnnncS1VGGuo2MzZ87Ud77zHc2YMaN3CwYAAAAAAAAAAAAAADiG5dnzNNc3V+uC67QjtCNpK0NJKrQXarZ3dtJ4baw2af5413jN9c2V3bBbxlc2rNSqgHX3traMd43X+WnnW8beb3hfHwQ+sIxVRauUb8+3jJ3iPUX7a/crrHBibJZ3liV3Ikn+mL/d20NOc09LPGd9cL2lm1nADChmxmQz4j2VPIbHcm/TUFlDrKFdzwNa0ufxwKlTp+ovf/mL9u7dq5UrV2r16tU6ePCgqqqqFA6HlZOTo/z8fE2fPl1nnnmmxowZ0/aiAAAAAAAAAAAAAAAASFLkKFKRo0hRX1Tl0XJVRCsUMkPy2DzKseVogGNAyvuKg8WW82GOYZrnm5cUoKqKVslreFMGxI7aEtqi0mhpp+rfFt6mUa5RlrGBjoH6StZXtC+yTxEzokJHYVIATJK2hba16xk+w6dTvadKigfC3gu8Z7keVVRl0TIVOgolSUOcQ1QSLpEkpRvplk5iByMH2/9yQAp9Hu46aujQobr66qt19dVX93UpAAAAAAAAAAAAAAAAxzW7YVehozARUGrNocihpE5caba0pGCXJGXbszXdPr3V9cqiZZ0Od20JbdFk12QNdg5Oqmesa2yL99XF6vR+4P12PWO2b3Zim8nlDctTdvtaE1yj+Y75kqQJrgkqj5arPFKu07ynJTp6VUQrtDuyu13PBFrSb8JdAAAAAAAAAAAAxyOnnBrqHKpCe/yHp+m2dHkMjzyGR6ZMhcyQqmPVOhw5rO3h7dob2duudYc6hmqsa6wGOQbJZ/PJJpvqY/UqjZZqa2irtoe3d/u7FNoLNdY1VkWOImXZsuQyXDJkKGSGVBOr0eHoYW0LbdOeyJ4W13DIoeme6RrlHKVse7YMGaqJ1WhneKdWB1YrYAZareGajGtU6ChUzIxpQe0ClUXLuvs1AQDAp6JmVMWhYi3zL1NU0b4uJ+Hlupc1L21eUgevlpRGSvVG/RtqMNveInGIY0giJHYgckCbQptSztsS2qLBjsGa7J4su2HXHN8cy3V/zK/X61+XKbNdNQItIdwFAAAAAAAAAADQg4Y6h+ri9ItbvO4wHPLZfBroGKipnqnaH96v1+pfk9/0p5zvMlw633e+RrpGJl3Ltmcr256tsa6x2h/er9frX1e9Wd/ld3DIoXlp8zTGNSblda/hldfmVaGjUJPdk1t8B7fh1pXpVyrfYd0mKc+epzx7nsa6xmph7ULVxGpSPmeya3Kiu8iG4AaCXQAAdFB5tFx/r/67BjsGa7BzsLJt2Yn/P26TTWEzLL/pV0W0QgciB7QttE21Zm1fl50kpJBerX9VhYFCjXGNUZGjSNm2bEvwvC5Wlwie74rsate6Ntl0ju8cSVLMjGmJf0mr8xf7F2tfeJ8muSdpgH2AHIZDdbE67Qrv0keBj1Rn1nXxTQHCXQAAAAAAAAAAAP3KYOdgXZZ+mRbULlBMMcs1p5z6fPrn27V90mDnYF2ZcaWerX22xaBYe81Pm58yTNbas1O9wxzfnESwqz5Wr8X+xQqbYZ3jO0e59lxl2DL0ubTP6dnaZ5PW9BgezfLOkhTvhPFe4L0uvRMAAJ9VFbEKVYQqtCG0oUvrbAptarGrVW+tVxotVWlD57Z3TCWmmJ6sebJD95SES1QSLum2GoDmbH1dAAAAAAAAAAAAwGdB1IzqUOSQNgc3a11gnYqDxSqNpP5hZIGjQCc6T0waP917elKwK2SGtCW0RcXBYjXErFsNZduzk7YI6qgCe0HKYJc/5tfm4GZtDG5UdbS6zXfwGl6Ndo5OnL/rf1c7wju0N7JXi/yLEuODHIM0wD4gab0zvGfIa/NKklY0rFDQDHbpvQAAAIBjQa937jpw4EC3rWUYhtLS0pSRkSHDMLptXQAAAAAAAAAAgO5SFa3Sa3WvaXdkt0JmKOn6EMcQXZJ+iZyG0zI+0DFQ28LbEudZtixNcU+xzAmZIT1T84wqYhWSJJ/h0xcyv6B0W3pizijXKA0KDtKBSOd+RjPIMShprDparadrn068j002XZlxpQY6Brb4DoX2QtmMxr4DeyN7E8cHIgcUMSNyGPEfXRU5inQ4ejhxvchepImuiYm5xaHiTr0LAAAAcKzp9XDXueee2yNBrJycHE2cOFHTpk3TJZdcoqFDh3b7MwAAAAAAAAAAADqqIlaRCF+lsi+yT/vC+zTCNcIybmu2Act413hLOEqSioPFlrX9pl8fBz7WbN9sy7yJromdDnfZZU8a2x7ebgmqxRRTSagkKdzV9B3cNrflWsAMJJ2nG/FQmtuwzj3Hd44Mw1DMjGmpf2mn3gMAAAA4FvV6uOso0zS7db2KigotX75cy5cv14MPPqhzzz1XP/7xjzVgQHLb3q6qr69XcXGx1q9fr/Xr12vDhg3av39/4vrgwYO1ePHiDq25b98+zZ07t1P1FBYWatmyZZ26FwAAAAAAAAAA9L0MW0bSWFWsynJ+gvOEpDl7InuSxpp2xGrt3vZqXocU7xDWnrGKaGPwLBizbqPoMTzym37LeWJuky0Xp7inJLai3BDcoLJoWfuLBwAAAI5xfRLuahrsStXFq3nwqyNzTNOUaZpatGiRPvroIz300EOaMWNGd5StRx99VM8//7y2bdumWCzWLWsCAAAAAAAAAIDPJqecyrXn6iTPScp35FuuBWNBbQltSZzbZFOePS9pjcpoZbvGfDaf0o101Zl1Ha5zd3i3aqI1yrRnJsbGuMboYPSgSkIlipkxDXcO11TPVMt99bF6bQ5tTpyXRksVM2OJ7mNDHUO1JRx/x4H2gYktGSXpUOSQJMlreDXLM0uS5I/5tTKwssP1AwAAAMeyXg93XXbZZYkg1tKlS1VVVSUpHspyOBwaOXKkBg4cqPT0dIXDYVVXV2vr1q06cuSIpMYQ1+mnn67CwkIFAgHV1NRo+/btOnToUGKOaZqqqqrSN7/5TT377LPdsk3jqlWrVFJS0uV12sNms7V7+0qHo88asAEAAAAAAAAAgA64NP3SNrtoBWIBvV7/umXbwgxbhiX8dFTTzldHRRVV0AwmbW2Ybc9WXaTj4a6oonqt/jVdkn6JfLZ4dy6bYdMc3xzN8c1JeU9drE6v1L2isMKJsQazQVvDWzXWNVaSdJbvLIX9YYXNsM72nZ2YdyByQIejhyVJZ3rPlMcW7+i1omGFZStIAAAA4LOg11NBv/rVr9TQ0KC77rpLlZXx3xwZPXq0vv71r+v888+Xx+NJed+2bdu0YMECLViwQNFoVJs3b9aNN96o008/PTFn3759evrpp/X3v/9dkUgkEfD65S9/qT//+c898j4+n08TJ07Uxo0b5fcn/wOqs375y1/q85//fLetBwAAAAAAAAAA+r81gTX6KPBRUmjLZbhSzo+YkRbHm4e7WlqjPUqjpXqq5ilN90zXdPd02Q17i3NXB1brw4YPFVJyEGupf6ny7HnKt+crzZami9MvtlyvjdXqzfo3JcW7eY13jZcUD3wVh4oT83yGT/n2fDkNp+pidTocPSxT1l1fAAAAgONBn7R8+v73v6/FixfLMAx94Qtf0I9+9KM2u0+NGjVKd955p6688krdeOONKi8v1ze+8Q09/fTTmjx5siRpyJAh+sEPfqCLL75YX/va11RVVSXTNPXOO++opKREY8aM6VLdbrdbU6ZM0eTJkzVp0iRNnjxZI0eOlM1m07nnntut4S4AAAAAAAAAAPDZM8U9RT6bT0v8SxQ0g4lxp5wp58cUa/e400i9RnuNcY3ReNf4VoNdkjTDPUPptnS9439HDWaD5VrADOiZmmc03TNdo52jlW3PliFDNbEa7Qjv0OrAagXMgAwZmuObI8MwFDNjWuJfIike6jrXd65GukZa1vXH/FrRsMISAAMAAACOB70e7vrHP/6hRYsWyTAMzZs3Tz/+8Y87dP+4ceP0t7/9TVdeeaXC4bC+853v6LXXXpPL1fjbJuPHj9dvf/tbffWrX01sbbho0aIuh7t+97vfdel+AAAAAAAAAADw2bY9tF2V0UoZMuQyXMq352uAY0Diut2wa6xrrArthXq29tlEB6+m2xs2ZZMtZZDLJlvSWNhMvUZ7zE+bn9hO8Sh/zK99kX0Km2EVOgqVb8+XJBmGkXiHZ2qfsWwvKUkRRbQqsEqrAqtafN5U91QVOAokSRuCG1QeLZfLcOmqjKuUbc9Omu+z+TQvbZ6chlPrgus6/Z4AAABAf9Pr4a7HH39cUvwL+x/+8IedWmPcuHG69NJL9dxzz2n//v16++239W//9m+WObNmzdKpp56qDz74QIZh6KOPPupy7QAAAAAAAAAAAF3xSeiTpLFCe6EuTL9QGbaMxFi2PVtneM/QW/63JEkhM3mLQynejatph6+jHEbyj4BaWqMto52jk4JdhyKH9ELtC5atF2d7Z2u6Z3rSOyzyL+rQ83yGT6d5TpMUD5CtDKyUJJ3qOTUR7AqaQb1a96rKomWa7Z2t8e749o1neM9QSagkqWMYAAAAcKxK/rWNHrRx40bt2rVLhmFo3LhxGjhwYKfXmjdvXuL4lVdeaXPO7t27O/0sAAAAAAAAAACAnlIaLdUy/7Kk8VGuUTIU36GkNlariBlJmuMzfEljdtnlNtxJ41XRqk7VN841Lmnsg4YPLMEuSXqv4T2ZpmkZG+0anXiH9jrTe6bctnj9yxuWJ0JpY1yNO7R8EvxEeyN7FTADeqfhncTnxmk4daLzxA49DwAAAOjPejXctWvXrsTx0KFDu7TWkCFDEsc7d+5MOWfChAmSJNM0VV1d3aXnAQAAAAAAAAAA9JTyaHnSmMtwyWt4JUkxxXQkeiRpTo49J2ks156bNOaP+VVn1nWqtlTPqI4l/9wlrHBSxyy34U68Q3sMcgxKdOE6EDmgTaFNiXXSbemJeYcihxLHQTNoqSfPntfu5wEAAAD9Xa+Gu0pLSxPHzX9zo7NM09Thw4dTXsvOzk4c+/3+bnkeAAAAAAAAAABAe7W3a1VLgaSm3bp2hXclXR/mGJY0NtwxPGks1b3tFVU0aSzLlpU05pQzZZArouSOY6kYMjTHN0eSFDNjWuJfkrjmkHWbyeZbTDbdmjLVlpQAAADAsapXv7q12RqzZPv27evSWk3vb7puS1wuV5ee19vefPNNvfbaa9q+fbsqKyvldruVnZ2t0aNHa+bMmbrgggs0YMCAvi4TAAAAAAAAAAC0Is+ep7m+uVoXXKcdoR1JWxlKUqG9ULO9s5PGa2O1lvmbQps00zNTNqPx5yIT3BO0PrheFbEKSVKakaZpnmlJa20MbUwaG+8ar/PTzreMvd/wvj4IfGAZq4pWKd+ebxk7xXuK9tfuV1jhxNgs7ywZhjXM5o/5k4JYLZnmnpZ4zvrgeks3s4AZUMyMJd7dY3gs9zYNlTXErN3DAAAAgGNZr4a7joaRTNPU5s2bdfDgQQ0cOLBTa7399tuJ44KCgpRzKisrJUmGYSgzM7NTz+krS5cutZw3NDSoqqpKu3bt0ltvvaX7779fV155pb7//e8rLS2tb4oEAAAAAAAAAABtKnIUqchRpKgvqvJouSqiFQqZIXlsHuXYcjTAkfqXuYuDxZbz6li11gfXW8JbTsOpqzOv1o7QDsUU0wjnCHlt1u5Z20LbdCByoNP1bwtv0yjXKMvYQMdAfSXrK9oX2aeIGVGhozApAHb02e3hM3w61XuqpHgg7L3Ae5brUUVVFi1ToaNQkjTEOUQl4RJJUrqRbukkdjBysP0vBwAAAPRzvbot47Rp0yTFw1amaerXv/51p9bZsmWLXnzxRRmGIcMwEus2V1JSkjjubIisvwqFQnr66ad15ZVXavfu3X1dDgAAAAAAAAAAaIPdsKvQUajx7vGa6pmqsa6xLQa7DkUOaVVgVdL4yoaVKo2UWsZchkvj3OM0wT0hKdhVFa2ybG/YGVtCW7Q/vD9pPM2WprGusZronpgy2FUXq9P7gffb9YzZvtlyG25J0vKG5Sm7fa0JrkkcT3BN0BT3FA2yD9L5aecnOnpVRCu0O8LPTQAAAHD86NXOXYMGDdLkyZP1ySefyDRNvfHGGxowYIBuv/32dm2tKEnbtm3TTTfdpGg0KtM0ZRiGLrjggpRzP/roo8TxqFGjUs7pb8aOHavzzjtPJ510kkaNGqXs7GyZpqny8nKtW7dOL774opYtW5aYv2PHDn3961/XM888o5ycnE4/1+l0dkf56DGBvi4A/Rx/h63sdntflwAAxzm+NgEA9A7+rQMA+CyKmlEVh4q1zL9MUUWTrocV1vN1z+tzvs/pRNeJra51IHJAr9e9Lr/p73JdL9e9rHlp85I6eLWkNFKqN+rfUIPZ9haJQxxDNNY1VlK85k2hTSnnbQlt0WDHYE12T5bdsGuOb47luj/m1+v1r8uU2a4aO4qvTQAAQH/C1yafHb0a7pKk733ve7r++usT3bueeOIJffDBB7rpppt03nnnyeVypbxvx44devbZZ/XUU08pFAolunadfPLJmj07eR/6+vp6LV68OLG3+/Tp03v0vboqOztbCxYsaLHOIUOGaMiQIbrwwgv1zjvv6LbbblN1dbUkaffu3frFL36h3/zmN71ZMgAAAAAAAAAAaEN5tFx/r/67BjsGa7BzsLJt2fIaXnltXtlkU9gMy2/6VRGt0IHIAW0LbVOtWdvqmiEzpFfqX9HQ4FCNc43TIMcg+Ww+GTLkj/lVGi1VSahE28Pbu+09Qgrp1fpXVRgo1BjXGBU5ipRty5bLcMmQoZAZUl2sToejh7UttE27Irvata5NNp3jO0eSFDNjbXYZW+xfrH3hfZrknqQB9gFyGA7Vxeq0K7xLHwU+Up1Z18U3BQAAAPqXXg93nXbaafryl7+sJ554IhHw2rx5s773ve/J4XBo1KhRGjRokNLS0hQOh1VTU6MtW7boyJEjkpTo1mWapvLy8vTLX/4y5XP+8Y9/qKEh/tsgNptNZ511Vq+9Y2ekp6e3O4B29tln66GHHtJXvvIVRSIRSdKrr76qb3zjGxozZkynnh8Ohzt1H4D+gb/DqfF5AQAAAI5tfE0PADheVMQqVBGq0IbQhm5dd29kr/ZG9nb6/k2hTS12yWpJabRUpQ2lbU9sp5hierLmyQ7dUxIuUUm4pNtqaC++NgEAAP0JX5t8dvR6uEuSfvSjHykajeqpp55KdNYyTVPhcFibNm3S5s2bLfNNM94+92i3LtM0VVBQoL/97W8aNmxYymd4vV7deuutkqSsrCwNGJB6z/pj1cknn6wrrrhCzzzzjKT45+j111/vdLgLAAAAAAAAAAAAAAAAQP/SJ+EuSbrrrrt0+umn6+c//7kOHTokSYmgV3NNA2A2m00XX3yx/uu//ktZWVktrn/dddd1f9H9zJVXXpkId0nSe++9p29961t9WBEAAAAAAAAAAAAAAACA7tJn4S5Jmjt3rs455xz93//9n1577TWtWbNG5eXlSfMcDofGjh2r008/Xf/+7/+uoUOH9kG1/c+kSZPkdDoTrfYOHDjQxxUBAAAAAAAAAAAAAAAA6C59Gu6SJLvdrgsuuEAXXHCBJOnIkSOqrKxUTU2NXC5XYktFt9vdx5X2PzabTdnZ2SorK5MkVVZW9nFFAAAAAAAAAAAAAAAAALpLn4e7msvLy1NeXl5fl3HMCAQCiWMCcAAAAAAAAAAAAAAAAMDxw9bXBaDzKisrVVtbmzgnFAcAAAAAAAAAAAAAAAAcPwh3HcOWLVtmOR83blwfVQIAAAAAAAAAAAAAAACguxHuOkaFw2H99a9/tYydeeaZfVQNAAAAAAAAAAAAAAAAgO5GuKsfKC0tVVVVVbvnx2Ix/fjHP9bWrVsTYwUFBbrwwgt7oDoAAAAAAAAAAAAAAAAAfcHRnYt9+ctf7s7l2s0wDD3++ON98uzusGHDBv3gBz/Q1VdfrYsvvlgTJkxoce7mzZt1zz336IMPPrCMf/e735XP5+vpUgEAAAAAAAAAAAAAAAD0km4Nd3344YcyDKM7l2yTaZq99sz9+/dr3rx5Ka9Fo1HLvJYCWo899phOOeWUpPH6+no98sgjeuSRR1RQUKAJEyZo6NChysjIkGmaqqio0Lp167Rly5ake2+44QZ9/vOf7+RbAQAAAAAAAAAAAAAAAOiPujXc1VGmaSaO2xvQ6sw93cU0TUuIqzUtzWtaf0vKysr0zjvvtDnP4/Hojjvu0Be+8IV21QQAAAAAAAAAAAAAAADg2NHt4a72hJeaOhrQau99Ted39Fn91dixY3Xttdfqww8/1I4dOxSLxVqdX1BQoMsvv1zXXXedioqKeqlKAAAAAAAAAAAAAAAAAL2pW8NdmzdvbvfcN954Qz/5yU9UU1Mj0zQ1fvx4XXzxxZoyZYpGjBihjIwMSVJtba127typ9evX65VXXtGmTZtkGIaysrJ0991364ILLujOV2jVkCFDUm6L2FVDhw7VT37yE0mS3+/Xtm3btG/fPpWXl8vv98swDGVkZCg3N1cTJ07U0KFDu70GAAAAAAAAAAAAAAAAAP1Ln2zL+PTTT+vnP/+5JCk3N1c/+clPdP7556ecm5eXp7y8PJ188sn62te+pjfffFM/+9nPVFFRoe9+97s6cuSIvvjFL/Zm+T3K5/NpypQpmjJlSl+XAgAAAAAAAAAAAAAAAKAP2Xr7gevXr9cvfvELmaapvLw8Pfnkky0Gu1L53Oc+pyeffFK5ubkyTVP33nuv1q5d23MFAwAAAAAAAAAAAAAAAEAf6PVw14MPPqhYLCbDMPSjH/1II0aM6PAaI0aM0H/9139JkmKxmB588MHuLhMAAAAAAAAAAAAAAAAA+lSvhrvKy8u1fPlyGYahvLw8XXDBBZ1ea/78+crLy5NpmnrvvfdUXl7ejZUCAAAAAAAAAAAAAAAAQN/q1XDXunXrFI1GJUkTJkyQYRidXstms2nSpEmSpGg0ytaMAAAAAAAAAAAAAAAAAI4rjt58WGlpaeI4MzOzy+ulp6cnjg8fPtzl9QAAAAAAx4DMcVLRXCnvVClzrOQbLjkzJcMuhaul2hKpbLm041GpZkv7182eIg2/Wio6T/INlVx58fUCh6X6XdLhpdKht6XKtV2rP/+0eO15p8TfxZ0nuXIlu0eK1EsNB6TqYungm9LuBVKkruW17D5p7LekYVdJGaMlwxavdd/L0qb7pFBFK4UY0uc+kPJmSrGo9ObJXX83AAAAAAAAAEC36tVwV319feK4O7ZRPHLkSMq1AQAAAADHqdMelU68vuXr9gLJUyAVnCGN+75U8gdpzW2SGW35HmeWdPIfpROui4ejUq2XPVEafKEUKJeeL+jaO5zzuuTKTn3NlR3/yJogDbtSmnqv9MEN0v6XU8zNkeYulXKmWMezJsQ/TrhWevscqX5n6meNuike7JKkbX8m2AUAAAAAAAAA/VCvbsuYn58vSTJNU+vWrVMgEOj0WoFAQOvWrUuc5+Xldbk+AAAAAEA/58xu/1ybXRr3HenUv7U8xztQmveuNOJLycGu/sCTL531nJQ/K/nazIcag10NB6Vll0mLzpOqN8fH0oZJpz+Zel13njT1l/HjwGFp3Z3dXjoAAAAAAAAAoOt69TvXI0eOlCQZhqFAIKAnnnii02s98cQTamhoSJyPGjWqy/UBAAAAAI4hZkw68pG043Fpx2NS1YbU8068Xiqckzxu2KQzn5WyJydfq/hY2v6oVPKgtPuZxsBUd6rbIe17Sdr6F2nrn6X9/5Ii/uR5Noc08b+sY+4CaehVjecffy++Vuki6cOvN44XnC7lzEhec+qv4gEvSVp7uxSu6vLrAAAAAAAAAAC6X69uyzhlyhQNHDhQhw4dkmmaevDBBzV27FidffbZHVpn6dKleuCBB2QYhkzTVFFRkaZMmdL2jQAAAACAY1+oWtr6kFTygNRwwHptzC3SyQ8k33PCl6TSJdaxsd+Kb9/YVO02aeW10pFVyWv4hkpDLu1a7ZK09ofS4SVSzZbka85sac5ryZ268k+znufNjHcmO6p0UeNx2XIpGpDsnk/vPVWq/LjJvadKI7/26dyV8WAcAAAAAAAAAKBf6vU9J2644QaZpinDMBQMBnXLLbfovvvuU01NTZv31tTU6Ne//rVuvfVWhcPhxDo33nhjL1QOAAAAAOhze5+T/jVGWvej5GCXFO+0te+l5PGcZr8QZNilsd+xjkXqpcXzUge7JMm/Nx4o66ptf04d7JLiHbQ2/SZ53Oa0nruyrefBipbPXTlNLhjSzAfjXctiEemjW9pZNAAAAAAAAACgL/Rq5y5Juu666/Tqq69qzZo1MgxDkUhEjzzyiJ588kmdeeaZmjp1qoYPH6709HQZhqHa2lrt3r1ba9eu1fLlyy2hLkmaPn26rrvuut5+DQAAAABAX9j1ZNtzDr+T3GHL7rWeF50npQ21jm3/X6l+V5fK6xZpJySP1W61noeqrOfuPClQ2uQ8t8ncysbj0TdLuSfFj7f9Wapc24VCAQAAAAAAAAA9rdfDXYZh6OGHH9b111+vjRs3JrZWDAaDWrx4sRYvXtzivaZpJtYwTVMTJ07Uww8/3FulAwAAAACOBYY9eax+j/W84KzkOYfelk78mjTii1L2VMmRLoWOSBUfSbv+Ie15RjJjPVOzzRnf9nHwxdLUXyRf3/on6/mRVVIs2rg1Y+G50u5/xI/zT2/cklGSyj+I/+kuaFw7cFhad2f3vgMAAAAAAAAAoNv1erhLkjIyMvTkk0/q17/+tRYsWCBJiU5cRwNczRmGkQh1SdK1116r2267TV6vN+V8AAAAAMBn1OBLk8cOvmk9z52ePOfkB6S0YdYx78B44GrwxdLY/ye9e4XUcLB76hx+tXTGgrbnbfmjtONR61iwTNrzT+mEL8TPZ/wmvq1kpE46+Y+N88pWSpUfx4+n/7pxi8a1t0vh6q6/AwAAAAAAAACgR/VJuEuSvF6vfvKTn+iKK67QE088oTfffFPBYLDF+aZpyu12a/78+frSl76kSZMm9WK1AAAAAIBjwogvSwPOtI4FK6Qdj1nH3AXJ9zYPdjWXP0ua86b0f7PiQaqeVvGxtOqb0pEPUl//6FYpe5KUPTkeQjv7Jev1+j3Syi/Gj/NnxT83Ujzw1fTz4SmUcqZKdp/UsD/+XDPa7a/T23JsORrqHKoie5Fy7DnKtGXKZbhkyFDQDKoqVqUDkQMqDharMlbZ4jrzfPM0wT2hQ89eXL9YG0IbuvoKKZ3tPVvTPNOSxouDxXrL/1bKexxyaLpnukY5Rynbni1DhmpiNdoZ3qnVgdUKmIFWn3lNxjUqdBQqZsa0oHaByqJl3fEqAAAAAAAAANqhz8JdR02aNEm//vWv9Ytf/EIbNmzQJ598oiNHjqi6Ov4bxFlZWcrLy9OkSZM0efJkuVyuPq4YAAAAANAvDTxfOuVh65gZkz78uhSuso67slte5/AyqWZLPDSVf5r1WvZkacLt0vofd0fFrcudIZ36V2n1t6XSxcnXQxXSm6dJ474tDbtKyhgtySbV75L2vSRtvl8KHpEMmzTzofifsUg8MCbFQ10z/ywNvcy6bkOptO6O5EDcMaStQJbP8Mln82mQY5BmuGdoXXCd3m14V6ZSdxPvL4rsRZrintKhe9yGW1emX6l8R75lPM+epzx7nsa6xmph7ULVxGpS3j/ZNVmFjkJJ0obgBoJdAAAAAAAAQC/r83DXUS6XSyeddJJOOumkvi4FAAAAAHCsGXqldPqTkt1tHV/zA2nv88nzo6HU66y5Tdp0f+P5tF/Fw1xNjfx694S7arZIm/8nfmz3SuknSAVnSo60xjnZk6U5b0jvfUna/UzyGlG/tPGe+EdLxtwq5UyLH2/7s1S1TnJmSfNWSBkjk+d7C6XTHpUcGVLJH5OvHwPchrvtSZ+yGTZN90yX23C32PmqP7DJprlpc2UzbB26b45vTiLYVR+r12L/YoXNsM7xnaNce64ybBn6XNrn9Gzts0n3egyPZnlnSZL8Mb/eC7zX9RcBAAAAAAAA0CH9JtwFAAAAAECnjPqGdPKDks1uHV97h7T5N6nviaToUhSqlLb83jr2yS+ksd+R7E26SHuLpPQTpbodXau7cm38oylnlnTKX6ThVzeO2ZzSzL9I+1+TIrUde4ZngDT5p/HjhlJp3Z3x40l3NQa7QtXSu5+Ph75m/LZx+8Zpv5J2L5CCx3anJtM0dTh6WEeiRyRJAxwDlG/PT5o3wT1Bm0KbtC+yr9X1DkYO6lDkUKtzyqPlnS+4BSd7Tk5Zd2u8hlejnaMT5+/639WOcPy/20X+Rboq4ypJ0iDHIA2wD9Dh6GHL/Wd4z5DX5pUkrWhYoaAZ7MorAAAAAAAAAOgEwl0AAAAAgGPXxB9JU39pHYtFpY9ujXepakn9bil/lnWsbqcUC1vHInVSw34pfYR13DOg6+GuVMLV0ntflorOk9x5jeOuLGnQfGlPcnelVk2/v3ELyrW3x9eXpOHXNM7Z9nDjto+rvy0NuzreAc3hk4ZcKm3/W2ffpk8FzaDWB9ZrXXCd6s16y7Up7ima45uTdM941/g2w127w7v1QeCDbq21LTm2HM30zEycN8QaEqGr1hTaCy2dvvZG9iaOD0QOKGJG5DDi3xoqchRZwl1F9iJNdE1MzC0OFXf5PQAAAAAAAAB0XMd6+QMAAAAA0F/M+G1ysCsalFZc03qwS5IqVqcYNNv/7Eh923M6KxaSakuSx9NP7Ng6BWdJI74UPy5bIe18PH7szJZ8gxvnHWkSVApVSrXbGs+zJnbsmf3EtvA2PVH9hFYGViYFuyRpfXC9toe2J43n2fOSxvqDub65iRCWP+bXqsCqdt3ntlm3pwyYgRbPm29leY7vHBmGoZgZ01L/0k5UDQAAAAAAAKA70LkLAAAAAHBsMezSaY80bh94VLhGWnaZVLqk7TUOvilNv886ln5ifAvEpt27HOmSd7B1Xiwi1e3qTOWSYZPMWOtzbC4pfVTyeLShA8+xSzMfjB/HItKqWxqvOXzWuUe7eaU6bz73GLE5tLnNOfsj+zXSNdIydjRA1ZosW5amuKco3UiXJDWYDSqPlutg5KAiinSu4FZMdk3WYGfjf4Pv+N+R3bC3ckejYMy6jaLH8Mhv+i3niblNtlyc4p6iQkehJGlDcIPKosf21pwAAAAAAADAsazXw10vvvhij6x72WWX9ci6AAAAAIB+xO6RznhGGnKJdbyhVFp6gVS5pn3rVG2Qjnwk5Z3cOObKkcZ+S9p0f+PYxP+S7C7rvWXLpUitdWzEV6RZj1nHNtwtbfipdWzOm1L5e9LOJ1N353LlSif9XvIUpKh5Y1tv1Wjsf0rZk+PHW/8kVa1rvBY8Et+60vZpQMjdrFtV0/PA8RvqMWQkjdXGalPMtBrvHq/xGp80HowFtS64Th8GPlRU0W6pMc1I0xm+MxLnO0M7VRIu0XhX8vNTKY2WKmbGElszDnUM1ZbwFknSQPtAS5jtUOSQJMlreDXLE9+y1B/za2VgZbe8CwAAAAAAAIDO6fVw1x133CHDSP4GalcR7gIAAACAz4BT/5Yc7JKkig/jnbyad/Nq6uPvWM/X3Cad16zL1/T7pMEXSzVb4uGo/NOS19l4T8frPsqdL026K/5RtzMeRguUSja35BsS30rR4U2+r26ndLgdHckkyVMkTb47ftxQKq2/y3o9FpQqP5byZsbPC8+Vdj8TP/YOtnYNK1/Rodc7ljTv2iVJe8J7Or2e2+bWKd5TNNw5XM/XPa+QGepKeZLiWyMe3S4xaAa12L+4Q/c3mA3aGt6qsa6xkqSzfGcp7A8rbIZ1tu/sxLwDkQM6HD0sSTrTe6Y8tnhHrxUNK7rlPQAAAAAAAAB0Xp9ty2iaZpfXMAxDpmn2SFgMAAAAANAPNd8i8ajBF7d9b/Nw1+Gl8aDWxB9ZxwfMjn+ksuk30qG32n5We6SPiH+0JVIvvffltrdzPGrGbyVnZvx47e3J2y5K0pbfS6c/GT8+8atS5Vqpar005eeNHb2qN8e3rzwOjXeN1yDHIMtYIBZQcai4y2sXOgo1P22+Xq57uUvrjHKO0ihXY9BuZcNK1Zl1HV5nqX+p8ux5yrfnK82WpovTrX9XamO1erM+/n/ngfaBia5gByIHLJ8Pn+FTvj1fTsOpulidDkcPy1TXv7cDAAAAAAAAoHV9Eu7qbLCraYjLNM1uCYgBAAAAAD7D1v2XFK6RJv0kdceso6Kh+DaLxff2WmmS4ltHfviNeKet9hhwjnTCF+LHZSuknY+nnrfrqXiAbdRNks0pzXzIej1QJq24uv2BsmPIMMcwnes71zJmmqYW+RcpaAZT3lMeLde20DbtjexVRbRCITOkTFumhjmH6VTPqfLZfJb5I5wjNNgxWPsj+ztVo8tw6RzfOYnzA5EDWh9c36m1AmZAz9Q8o+me6RrtHK1se7YMGaqJ1WhHeIdWB1YrYAZkyNAc3xwZhqGYGdMSf7xTnM/w6VzfuUmdzvwxv1Y0rOiWQBwAAAAAAACAlvV6uOveezv2jfBoNKqamhpt3bpVK1euVGlpqQzDUFZWlm699Valp6f3UKUAAAAAgM+E4v+Ob0s48gZp0AWSb7jkypJC1VLdVunQYmnbXyT/3q4/a/F58a0XB8yWck+SPIXxrRqdWVI0IIWrpNqtUsXH0v6XpcPL2r+24ZBOfiB+HItIq77Z+vwPvyGVLpZGfUPKmS7ZfVLDfunAa9LGe+PHx5lRzlH6XNrn5DCs3w5Z3rBc28LbUt6zomGF/KY/abwqVqWqYJV2hnfquozr5La5k57V2XDXWd6zlGZLkyRFzIjern+7U+scFVFEqwKrtCqwqsU5U91TVeAokCRtCG5QebRcLsOlqzKuUrY9O2m+z+bTvLR5chpOrQuu61J9AAAAAAAAAFrW6+Guyy+/vNP3RiIRLVy4UP/93/+tmpoa/fOf/9QjjzyigoKCbqwQAAAAANBvLZrTM+vW75LW3xX/6Iydj7fcJaup4BFp34vxj+5mRqTXJnXsnt3PxD8+Aya7Jusc3zmyGTbL+Ar/Cn0cbLkzWqpgV1O1sVoVh4o13TPdMj7AMaBTdRbZizTRNTFxviqwSpWxyk6t1V4+w6fTPKdJinfkWhlYKUk61XNqItgVNIN6te5VlUXLNNs7W+Pd8e0bz/CeoZJQiRrMhh6tEQAAAAAAAPis6pNtGTvL4XDommuu0bhx4/SVr3xF27Zt080336x//OMfcjqdfV0eAAAAAADoh2Z6Zup07+mWsZgZ01L/Um0Ibejy+hXRiqQxr9HKNp+tyLHnyDAMSfEafYZPs72zLXNy7blJ9xU6ChPztoe3d6hr2JneMxOdx5Y3LFfIDEmSxrjGJOZ8EvxEeyPx7nXvNLyj0a7RchgOOQ2nTnSeqI2hjR14SwAAAAAAAADtZWt7Sv8zbdo03XrrrTJNUxs3btSjjz7a1yUBAAAAAIB+aLZ3dlKwK2JG9Hr9690S7JIkt+FOGjsakOoKm2HTVM9UTfdMt3wMdw5Pmptnz0tcL7C3v8P5IMegRBeuA5ED2hTaJCn+Tum29MS8Q5FDieOgGVR1rNrybAAAAAAAAAA945gMd0nSddddJ5fLJUlasGBBH1cDAAAAAAD6E0OGzvedn7RdYtAM6qW6l7QtvK3NNQrthcowMtqcN8o1KmmsJlbT/mL7iCFDc3zxrU5jZkxL/EsS1xzNmr03D6sFzWDjXOOYagwPAAAAAAAAHFOO2e+++Xw+TZ48WatXr9bBgwe1du1aTZs2ra/LAgAAAAAAfcwuu/4t7d90outEy7g/5teLdS+qLFrWrnUGOgbqDO8Z+iT4idYF16kqVmW57pJLZ/rOVJGjKOneneGdSWPjXeN1ftr5lrH3G97XB4EP2lVPd5vmnqZ8e74kaX1wvcqj5YlrATOgmBmTzYj/XqDH8FjubbrtZEOsoReqBQAAAAAAAD6bjtlwlyQVFhYmjnfv3k24CwAAAAAA6DzfeUnBLim+teB413iN1/gW713WsMxy7jAcmuaZpmmeaaqKVqk0WqqgGVSGkaEiR5G8Nm/SGlXRKm0JbelU7ZtCmxJbI7YkVUisOFist/xvtfs5PsOnU72nSoqH3t4LvGe5HlVUZdEyFTri33sZ4hyiknCJJCndSFeWLSsx92DkYLufCwAAAAAAAKBjjulwVzQaTRyXlbXvt24BAAAAAMDxLd2WnnI8VeCruebhrqay7dnKtme3en8wFtRr9a8pplibz+pLs32z5TbckqTlDcuTtl2UpDXBNZrvmC9JmuCaoPJoucoj5TrNe1qio1dFtEK7I7t7r3AAAAAAAADgM+aYDnft2LEjcex0OvuwEgAAAAAAcLypjdUqZIbkMlztml8aKdX/1f+fKmIVPVxZ1wxxDNFY11hJ0oHIgRY7hW0JbdFgx2BNdk+W3bBrjm+O5bo/5tfr9a/LlNnjNQMAAAAAAACfVcdsuGvDhg3aunVr4rygoKAPqwEAAAAAAMeb7eHterjqYQ13DtcQxxAV2AuUZc+Sx/DILrtCZkh1sTodih7S1tBW7Yns6euS22STTef4zpEkxcyYlviXtDp/sX+x9oX3aZJ7kgbYB8hhOFQXq9Ou8C59FPhIdWZdL1QNAAAAAAAAfHYdk+Gu0tJSff/735dhGDLN+G+HnnzyyX1cFQAAAAAA6A+eq3uu29aKKqod4R3aEd7R9uQ2bAptarFLVm+tE1NMT9Y82aF7SsIlKgmXdOp5AAAAAAAAALrmmAh3xWIx1dTUaNu2bVqyZImeeeYZ1dfXyzRNGYahmTNnasCAAX1dJgAAAAAAAAAAAAAAAAB0m14Pd40fP77LaxwNdUmSw+HQbbfd1uU1AQAAAAAAAAAAAAAAAKA/6fVw19FtFDvLMIzEdowOh0P33nuvJk+e3E3VAQAAAAAAAAAAAAAAAED/YOuLhx7tutUZpmnKNE2deuqpWrhwoS666KJurAwAAAAAAAAAAAAAAAAA+ode79w1c+bMDt/jcDiUnp6u3NxcTZw4UaeddpqGDRvWA9UBAAAAAAAAAAAAAAAAQP/Q6+Guv//97739SAAAAAAAAAAAAAAAAAA45vTJtowAAAAAAAAAAAAAAAAAgNYR7gIAAAAAAAAAAAAAAACAfohwFwAAAAAAAAAAAAAAAAD0Q4S7AAAAAAAAAAAAAAAAAKAfItwFAAAAAAAAAAAAAAAAAP2Qo68LaEllZaXeeOMNrV+/XuXl5fJ4PCosLNTpp5+uM888Uy6Xq69LBAAAAAAAAAAAAAAAAIAe0yvhroqKCm3ZskWVlZVyuVwaMmSIxowZI5stuXGYaZr6y1/+or/85S8KBAJJ15966ikNGjRIP/nJTzR79uzeKB8AAAAAAAAAAAAAAAAAel2Phrs2bdqkX//61/rwww8Vi8Us1/Ly8vTlL39ZN9xwg+x2u6R4sOv222/XK6+8ItM0E3MNw0hcl6T9+/fr5ptv1r333qtLLrmkJ18BAAAAAAAAAAAAAAAAAPpEcuusbvLqq6/qqquu0vvvv69oNCrTNC0f5eXl+t3vfqebb745Efx6/PHH9fLLL0uKB7qahrpM00yMGYahaDSqH/3oR9q2bVtPvQIAAAAAAAAAAAAAAAAA9JkeCXetX79eP/jBDxSJRCyhrKOOnpumqXfffVePPPKI6urq9MADD1gCXQUFBTrnnHN00UUX6ayzzlJmZqalo1ckEtEvf/nLnngFAAAAAAAAAAAAAAAAAOhTPbIt4913361oNGoJamVlZWnYsGEyTVN79+5VdXV1IuD12GOPKSMjQ3V1dTIMQ9nZ2frlL3+pc88917JuLBbTwoULdc899ygYDMo0Tb3//vvas2ePhg0b1hOvAgAAAAAAAAAAAAAAAAB9otvDXevWrVNxcXEiuJWbm6uf/exnmjt3riXstWjRIt199906cuSIjhw5okcffTRekMOhRx99VOPGjUta22az6d///d+Vl5enW265JbHeG2+8oZtuuqm7XwUAAAAAAAAAAAAAAAAA+ky3b8v45ptvSooHuBwOhx555BGdd955Sdsynnfeefrb3/4mu90uSdq1a5cMw9All1ySMtjV1Ny5c3Xqqacmtmj85JNPuvs1AAAAAAAAAAAAAAAAAKBPdXu4q7i4WFI8wHXBBRe0GtQaN26cLrzwwkRIS5LOP//8dj2n6bytW7d2sloAAAAAAAAAAAAAAAAA6J+6fVvGXbt2JY7nzJnT5vyzzz5bL730UuK8ra5dzeeZpqmqqqoO1QgAAAAAAADg+LDkcKCvS0A/9/m+LgAAAAAAgC7o9nBXbW1t4njEiBFtzm8+Jzs7u13PycnJSRzX1dW1rzgAAD7DntATfV0CjgFf1pf7ugQAAAAAAAAAAAAAn+r2bRn9fn/iOD09vc35zee43e52PafpvEgk0s7qAAAAAAAAAAAAAAAAAODY0O3hLtM0E8d2u73tAmzdXgIAAAAAAAAAAAAAAAAAHPNIVgEAAAAAAAAAAAAAAABAP+To6wLQuj179qi4uFiHDh1SLBZTYWGhRo8erTFjxvR1aQAAAAAAAAAAAAAAAAB6EOGuTqivr1dxcbHWr1+v9evXa8OGDdq/f3/i+uDBg7V48eIuPeOdd97Rn/70J61Zsybl9bFjx+rGG2/UJZdc0qXnAAAAAAAAAAAAAAAAAOifCHd1wKOPPqrnn39e27ZtUywW65FnmKape+65R0888USr87Zs2aLbbrtNS5cu1a9+9Su5XK4eqQcAAAAAAAAAAAAAAABA3+iRcJdhGJKktWvXWjpapVJWVmY5/+ijj2SaZpvPaH5fb1i1apVKSkp69Bm/+c1vkoJdM2bM0OTJk2W327VlyxatXLky8Tl69dVXZbfbdd999/VoXQAAAAAAAAAAAAAAAAB6V4917jJNU9/73vc6fM+XvvSlds83DKNdQbCe5PP5NHHiRG3cuFF+v79Lay1ZskR//etfE+eZmZn6wx/+oFmzZlnmFRcX6+abb9ahQ4ckSS+//LJOOukkXXPNNV16PgAAAAAAAAAAAAAAAID+o8fCXR0JXh3t9CWpQ2Gtpvf1BrfbrSlTpmjy5MmaNGmSJk+erJEjR8pms+ncc8/tUrjLNE399re/TZwbhqGHHnpIM2fOTJo7YcIEPfbYY7r00ksVDAYlSQ888IAuu+wyeTyeTtcAAAAAAAAAAAAAAAAAoP/osXCX1LnwVW8Htjrid7/7XY+t/fbbb1u2fLz00ktTBruOGjFihG644QY99NBDkuLbVD777LMd6nwGAAAAAAAAAAAAAAAAoP/q9nDXoEGDunvJz4Q33njDcn7ddde1ec8111yjv/zlL4pGo4k1CHcBAAAAAAAAAAAAAAAAx4duD3ctXry4u5c87kUiES1btixxPnDgQE2ZMqXN+woLCzVt2jStXr1akrRmzRpVVFQoNze3x2oFAAAAAAAAAAAAkNqSw4G+LgH93Of7ugAAwDHH1tcFQCopKVFNTU3ifPr06e2+t+ncaDSqjz/+uFtrAwAAAAAAAAAAAAAAANA3CHf1A9u3b7ecjx8/vt33TpgwwXK+Y8eObqkJAAAAAAAAAAAAAAAAQN8i3NUPNA9kDRo0qN33Dhw4sNW1AAAAAAAAAAAAAAAAABybCHf1A6WlpZbzoqKidt/bfO6hQ4e6pSYAAAAAAAAAAAAAAAAAfYtwVz/g9/st52lpae2+t/nc5msBAAAAAAAAAAAAAAAAODY5+roAJAeyXC5Xu+91u92trtVeTqezU/ehtwT6ugD0c/wdtrLb7X1dQv8U7usCcCzgf0/QPnxtAgDoHXxtgvbhaxMAQO/gaxO0D1+bAAB6B1+bfHbQuasfCAaDlvOOhLuazw0E+IIRAAAAAAAAAAAAAAAAOB7QuasfaN59KxQKtfve5nM9Hk+nagiHaecCHMv4O5wanxeg4/h7AwAA+hO+NgEAAP0JX5sAAID+hK9NPjvo3NUP+Hw+y3lHwl3Nu341XwsAAAAAAAAAAAAAAADAsYlwVz/QPJBVX1/f7nubzyXcBQAAAAAAAAAAAAAAABwfCHf1A4WFhZbzQ4cOtfvegwcPWs6Lioq6pSYAAAAAAAAAAAAAAAAAfYtwVz8wcuRIy/mBAwfafW/zINiJJ57YLTUBAAAAAAAAAAAAAAAA6FuEu/qB5uGu4uLidt+7ceNGyznhLgAAAAAAAAAAAAAAAOD4QLirHxg9erQyMzMT52vXrm33vWvWrEkc2+12zZgxoztLAwAAAAAAAAAAAAAAANBHCHf1Aw6HQ7Nnz06cHzx4UOvWrWvzvtLSUsu86dOnKzc3t0dqBAAAAAAAAAAAAAAAANC7CHf1ExdccIHl/Omnn27zngULFuj/t3ff8VFV+f/H35PJTHpPCEgPHUSKFFEXRFRs6wp2XQQ7IqIurouuXbGsCvhDEOti12UB/aooLoIFKdJB6b0GEtLLJJOZ+f0xcslNnZBJMpDX8/Hg4T1nzj33M0mQz8187jkul8toX3zxxX6PCwAAAAAAAAAAAAAAAEDDoLgrQAwZMkQdO3Y02l988YVWrFhR6fhdu3bpnXfeMdpJSUm65ppr6jRGAAAAAAAAAAAAAAAAAPWH4q4AYbFY9Le//c1oezwejRkzRkuXLi03duPGjRo1apSKioqMvrFjxyo0NLReYgUAAAAAAAAAAAAAAABQ94IbOoCTyYEDB3ThhRdW+Frp7REPHDigrl27Vjhu5syZ6tevX4WvDR48WHfccYfeeustSVJOTo5GjRql3r1764wzzlBQUJC2bNmiJUuWyOPxGOddccUVuv7660/0bQEAAAAAAAAAAAAAAAAIQBR31YDH4zEVcVWlsnGli7Iq8re//U0Oh0MffPCB0bd69WqtXr26wvGXXnqpnn32WZ9iAgAAAAAAAAAAAAAAAHDyYFvGABMUFKRHH31Ub775pnr27FnpuI4dO+pf//qXJk+erJCQkPoLEAAAAAAAAAAAAAAAAEC9YOWuGmjRooW2bNlSL9caNGiQBg0apD179uj333/XkSNH5HK5lJycrA4dOqhTp071EgcAAAAAAAAAAAAAAACAhkFxV4Br3bq1Wrdu3dBhAAAAAAAAAAAAAAAAAKhnbMsIAAAAAAAAAAAAAAAAAAGI4i4AAAAAAAAAAAAAAAAACEAUdwEAAAAAAAAAAAAAAABAAKK4CwAAAAAAAAAAAAAAAAACEMVdAAAAAAAAAAAAAAAAABCAKO4CAAAAAAAAAAAAAAAAgABEcRcAAAAAAAAAAAAAAAAABCCKuwAAAAAAAAAAAAAAAAAgAFHcBQAAAAAAAAAAAAAAAAABiOIuAAAAAAAAAAAAAAAAAAhAFHcBAAAAAAAAAAAAAAAAQACiuAsAAAAAAAAAAAAAAAAAAhDFXQAAAAAAAAAAAAAAAAAQgCjuAgAAAAAAAAAAAAAAAIAARHEXAAAAAAAAAAAAAAAAAAQgirsAAAAAAAAAAAAAAAAAIABR3AUAAAAAAAAAAAAAAAAAAYjiLgAAAAAAAAAAAAAAAAAIQBR3AQAAAAAAAAAAAAAAAEAAorgLAAAAAAAAAAAAAAAAAAIQxV0AAAAAAAAAAAAAAAAAEIAo7gIAAAAAAAAAAAAAAACAAERxFwAAAAAAAAAAAAAAAAAEIIq7AAAAAAAAAAAAAAAAACAAUdwFAAAAAAAAAAAAAAAAAAGI4i4AAAAAAAAAAAAAAAAACEAUdwEAAAAAAAAAAAAAAABAAKK4CwAAAAAAAAAAAAAAAAACEMVdAAAAAAAAAAAAAAAAABCAKO4CAAAAAAAAAAAAAAAAgABEcRcAAAAAAAAAAAAAAAAABCCKuwAAAAAAAAAAAAAAAAAgAFHcBQAAAAAAAAAAAAAAAAABiOIuAAAAAAAAAAAAAAAAAAhAFHcBAAAAAAAAAAAAAAAAQACiuAsAAAAAAAAAAAAAAAAAAhDFXQAAAAAAAAAAAAAAAAAQgCjuAgAAAAAAAAAAAAAAAIAARHEXAAAAAAAAAAAAAAAAAAQgirsAAAAAAAAAAAAAAAAAIABR3AUAAAAAAAAAAAAAAAAAAYjiLgAAAAAAAAAAAAAAAAAIQBR3AQAAAAAAAAAAAAAAAEAAorgLAAAAAAAAAAAAAAAAAAIQxV0AAAAAAAAAAAAAAAAAEIAo7gIAAAAAAAAAAAAAAACAAERxFwAAAAAAAAAAAAAAAAAEIIq7AAAAAAAAAAAAAAAAACAAUdwFAAAAAAAAAAAAAAAAAAGI4i4AAAAAAAAAAAAAAAAACEAUdwEAAAAAAAAAAAAAAABAAKK4CwAAAAAAAAAAAAAAAAACEMVdAAAAAAAAAAAAAAAAABCAghs6AAAAcGoIsYQo2ZqspsFNvX+sTRUWFGYas9+5X7PzZlc5j0UWJVoT1dTa1JgrLihOFovFNO7d7HeV6871W/z+vG6wgtUrtJfa29or1horiyzKcedol3OXVjlWyeFxVHn+9VHXKzk4WW6PW5/mfqo0V1qt3x8AAACAmrMFWRQXalN8mF3xoTbFhdoVEmx+XjatoEg/78uoch6LpJiQYMWVmifKbi13v/HtjiMqKHH5LX5/Xtdqsah9XLiaR4Up0m6VZFGBs0SH8oq0LSNPxW5PlecPbpWguDC7PB6PFu5JV3ZRSS3fHQAAAAAAjQPFXQAAwC9uiLpBMdaYWs/TL7Sfzgo7yw8RNcx1QywhujryaiUGJ5r6E6wJSrAmqJO9k/6b+1/luHMqPL+7vbuSg5MlSRuKNlDYBQAAADSg81snKsJe+1+hdkqIVNfEKD9E1DDXtQVZNLBVgmJCbKb+6BCbokNsahkdpp/2HVWBs+ICsbYx4YoLs0uSdmYVUNgFAAAAAEANsC0jAADwC4ss1Q9qBAaHDzYKu/Ld+foy70vNyZ2jDJf3Sf6ooCgNjRha4bmhllANCBsgSSpwF2ipY2n9BA0AAACgYn66zWmouyV/XbdncoxR2OUocWnpgQz9vO+ocv8o0gq3WdW3WWyF59qtFnVNijLO3ZjuvxWYAQAAAABoDCjuAgAAfufyuHTUddQvc+W6c+VwV72NYV04keuGWcLUwdbBaP9c8LN2OndqX8k+fV/wvdF/WvBpamJtUu78c8LOMbay/KXwFxV5ik4wegAAAAD+5vZ4lFPk9MtcBU6Xil1uv8xV19cNsQapRVSo0V5/JEeH8oqUVlCs1YezjP6EMLtiQ8qvcnZ6YrRCrN5fQ/+elitnNds3AgAAAAAAM7ZlBAAAfrHTuVO5RblKLUnVYddhhQeF69aYW2s8T6YrUysdK5VakqpDJYdU4CnQVZFXqUVQizqI2r/XTbYmK8hyvHZ+X8k+4/hgyUGVeEoUbPGmX02Dm+qI64jxelNrU3WzdzPGbizeWNu3BAAAAKCWUvOKVOAsUIajWJkOp0KtVl3crvyDGtXJLS7RlqN5ynQUK6PQKYfLrT+1jFdSeEgdRO3f68aF2mSxHF8DLK2g2Dg+WuiUy+2RNcj7enyYXVmltlyMC7WpdUzYH2OLtSensLZvCQAAAACARofiLgAA4Bc/Fv7ol3m2Ordqq3OrX+aq7+uGBJk/IHF4HOXakZZI71iLeex54efJYrHI7XHrh4IfahUHAAAAAP9YdyTHL/Psz3Vof279r0jsj+vagsybO5Zd+avY7VZYkPWPseaNInomx/xxn+PR2sPZtYoDAAAAAIDGiuIuAAAAPylym7dRDLWEqsBTYGobY0ttuXhGyBlKDk6WJG0o2qA0V1odRwoAAAAAvim7jaLdGqSiUgVe9lIFXU738f6U2HDFhdokSbuyCpRdakUvAABwYmxBFsWF2hQfZld8qE1xoXaFBJuLq9MKivTzvgyf50wKt6tlVJgSwu0KtQbJYpEcJW5lOpw6kFuog3lF1U9SS2c0iVb7uIhy/XuyC7QqteICcavFovZx4WoeFaZIu1WSRQXOEh3KK9K2jDwVV7MV9OBWCYoLs8vj8WjhnnRyFQBAQKO4CwAAwE8Ouw7L7XEbWzO2DG6pLc4tkqRm1mbGloySlFqSKkkKs4RpQOgASVKBu0BLHEvqOWoAAAAAqFymwymPx2NszZgUbjdWA4sPsxlbMkpSRqF3y8YQa5C6JkZJkhwlLm1Mz63nqAEAODWd3zpREXb/fLxrC7LozKaxOi0qtNxrkfYgRdqD1TI6TOkFxfr1UKYcJe4KZqm9uFCb2sWG1+gcW5BFA1slKCbEZuqPDrEpOsSmltFh+mnfURU4XRWe3zYmXHFhdknSTorQAQAngaDqhwAAAMAXhZ5CbXNuM9p/Cv+TUmwpahncUkMihhj9B0sO6ojriCTp3LBzFRrk/QXKL4W/qNhTXL9BAwAAAEAVilxu09aOZzSJVrPIECWF29U7OcboP1pYrKw/Phg9PSlKdqv3V8+/p+WWW/0LAACcIEv1Q3wRbLHo3BbxFRZ2lZUYbtfAlgkKsfr/Y2WLpN5NY4wicl/1TI4xCrscJS4tPZChn/cdVe4fuUi4zaq+zWIrPNdutahrEkXoAICTCyt3BaDzzz9fBw4cOKFzv/vuO7Vu3drPEQEAAF/9UPCDEqwJSrQmKiIoQn+O/LPp9Vx3rubnz5fkXc2ri72LJG/B18bijca4cEu4Eq2JsllsynPn6YjriDziAxEAAAAA9W/d4WxFhwQrJsSm0GCrBjSPN71e4HRpxaEsSVJ8qE2tosMkeQu+9uQUGuNCrEGKCQlWcJBFhSVuZTmc3OUAAHCC3B6P8opLFF1m9SpfdEuKMlauOqbE7dahvCK5PR41jQg1bfcYaQ9Wz+QYLT+YWeu4S+uUEFlu9a3qhFiD1KJUUdr6Izk69MfWkasPZ2lQq0RJUkKYXbEhwUbx+TGnJ0YbhWoUoQMAThYUdwEAAPiRw+PQZzmfqVdoL3WwdVCsNVYWWZTjztFO506tcqySw+OQRRYNDh8si8Uit8etRQWLJHmLus4PP1/t7O1M8xa4C/RL4S+mAjAAAAAAqA/Fbo9+2HNU7eMi1DwqVJF2qyyyKN/p0qE8h7Zl5qnY5f1gtGdyzB/3OR6tPZwtyfshbK/kmHKrgzhKXPo9LddUAAYAACqXmlekAmeBMhzFynQ4FWq16uJ2TWo0R4TNqpQy2yCWuN1atOeocou9hVCh1lwNbp2oMJvVGNM8KlQJYTYdLXTW/o1IirRb1Sk+0mgXlbhNBWWViQu1mVb6Sis4vhPC0UKnXG6PsW10fJjdVNwVF2pT65iKi9ABAAhkFHcFOIvFoqAg35c5remypQAAwP9KVKIVjhVa4VhR6ZgeIT2UFJwkSdpQtEHprnTZLXZdE3WNYq2x5caHB4XrwogLZbPYtK5oXV2FDgAAAAAVcnk82pKRpy0ZeZWOaRcbrthQ7+obu7IKlF1UIluQRYNaJSjSXv5X0aHBVp3ZLFbBQRbtyCqos9gBADhVrDuSU+s5WkWHlfs8cXd2oVHYJUkOl1vbMvN1RpNo07g2MeE6Wphd6xgkqXdyrFGE5ShxaWtG+etVxBZkjr3Y5Ta33W6FBVn/GGv+jLWiInQAAE4GFHcFuCuvvFIvvPBCQ4cBAAD8KNwSrrNCz5LkXZFriWOJJKl/aH+jsKvIU6Sv875WmitNA8MGqkuId/vGc8LO0dbirSr08FQZAAAAgMARYg1Sl8QoSd4PaDem50qSOidEGoVdTpdbyw5mKrvIqe5J0Wod4101pFtStPbnOlRU5sNZAADgf00jQ8r1peUXle8rKN/XNKL8uSeibWy4EsOPbwu5/kiOgnxcwKLsNop2a5Aph7CXKuhyuo/3p8SGK65METoAACcL35eEAgAAgF+cG3auQoK8vwhZXLhYxR7v0uEd7R2NMb8V/aZ9Jfvk8Dj0Y+GPKvF4f9lgs9iUYkup/6ABAAAAoArdk6Jkt3p/3fxbWq7xwWuLqDBjzK7sAqUVFKvY5dH6Izly/TEmOMiiZhV80AwAAPzLIinabivXX3rVrqr6QoKtCvNh68SqhFqDdPofBeGSdCjPof25Dp/Pz3Q45fEcL/BKKlUkFh9mM1YDk6SMQu/vXUOsQepaQRE6AAAnC4q7AAAA6tFpwacZq3AdLDmoTcWbJEkhlhBFBkUa41JLUo3jIk+Rst3HlwlPsCbUU7QAAAAAUL2EMLta/bEK19HCYu3N8a40bAuyKMxmNcZlFDqNY6fbo3zn8Q+NK/qgGQAA+Fe4zWoqfjqmotUz3R7vqptlRdhqtzFUz+QY2f4oCHe63DXeHrHI5TYVg53RJFrNIkOUFG5X7+QYo/9oYbGy/lid6/RSRei/lypCBwDgZMG2jAAAAPXEIosGhw+WJLk9bi0qWGS8FlwmLTu2mtcxRZ7jy6AHW0jhAAAAAAQGi6SeydGSJLfHY/qAtuyHxyVu8wfEpT9YreiDZgAA4F+2Sv69Lamk2Mnl8ahs+bXNeuL/Zp8WGarTokKN9u/puSosqfm2zOsOZys6JFgxITaFBls1oHm86fUCp0srDmVJkuJDbWoV7V1J9Ghhsfb8UYQueVf0igkJVnCQRYUlbmU5nKLsCwAQiPhkEAAAnLK62LvoooiLTH3LCpdpuWN5g8TTM6SnEq2JkqT1ReuV7ko3XnN4HHJ73AqyeJ8gC7WEms4NsxzfyqTQXSgAAAAAjVOr6DD1aRZr6tuUnqtNR/MaJJ52cRGKCfF+7Lsrq0DZRcdX4yp2ueXxeGSxeD8EPrZixjGl2xWtGAIAAPyrsmLqygqaKqr5Cg46sY2hbEEWoyBc8hZa7cwqOKG5it0e/bDnqNrHRah5VKgi7VZZZFG+06VDeQ5ty8xTscsbfM/kGFksFlMReog1SL2SY0yFZpJ3y8bf03JNBWAAAAQCirsAAIBfnBFyhmKDYo223WIvNybGGqOBYQNNfVuKt+iw67DRTrYmq5O9U7nzyuof2t+0ulWWO0vri9afaPh1ft1wS7j6h/WXJBW4C7TUsdT0uksupbnSlBycLElqYWuhrc6tkqRIS6Rigo7HcqjkkI/vCgAAAEBtpMSGK7LU1kPBFaxUEWkL1hlJ0aa+fbmFynQc34IwLtSmllFh5c4rq3NipEpcxz9FzXOWnPCHnvVx3VBrkLokeLeXd5S4tDE91/S62yNlOZyKC/PeHyaFhxjbKIUFBymy1JaNRwvNqxcDAAD/c1WyQpdFFRd4VVQLVnYlTl+dnhSt0GCrEcfq1Jptx1iWy+PRlow8bcmovMC9XWy4YkPNRei2IIsGtUpQpL18ThQabNWZzWIVHGTRjlrkYAAA+BvFXQAAwC862Dqoha1FlWOigqLUK7SXqS/NlWYq7oq3xpcbU5FuId1M7f3O/bUq7qrr6w4MH6gQS4gkaXHh4nLbLkrSmqI1ujj4YklSV3tXpbvSlV6SrrPCzjJW9MpwZWhPyZ5qrwcAAACg9ppHhSopPKTKMWE2q9rHR5j6soqcpuKuKHtwuTEVaRMTbmqnFRTVqrirrq/bvUm0bH+svvVbWq5pm8VjtmcVqO8fxV2tY8KUXeRUdpFTXROjjBW9cotKdDi/qNy5AADAvyr6t1qSgoMsFb5mtZSv7nK6ar5xYVyoTW1ijhecb8nIU25xSRVn1F6INUhdEqMkmYvQOydEGoVdTpdbyw5mKrvIqe5J0Wr9R07ULSla+3MdrCwKAAgYFHcFuM2bN+uBBx7Qxo0blZ7u3bopNjZWLVq0UN++fXXBBReoc+fODRwlAACoSovgFsaqYAdLDmpT8aYKx20p3qLmwc3VPaS7rBarBocPNr1e4C7QN/nfyFPpQukAAAAAUD8Sw+xqGe39kPZoYbH2VrJ90b6cQiWG2dU2NlxBFot6JptXSC4qcenXQ5l1Hi8AAJAKnC653J5y2zOGBAfJWewy9QVZZBRxl5bvrHlRVpQ92Cjq9ng8CrEGlVv5NCqk/MfWcaE2Y9zBPIfSa7DSZ/ekKGML6NJF6C1KrWq6K7tAaQXeOdcfyVGLqDBZgywKDrKoWWSIdmezPSMAIDBQ3BXgNm3apE2bzB8A5+Xlaf/+/Vq2bJmmTp2qgQMH6tFHH1Xr1q0bKEoAAFCZIAXpvPDzJEluj1uLChZVOX5hwULtd+7X6SGnq4m1iYItwcpz52m3c7dWOlYqz1P5MuMAAAAAUB8sknomez9odXs8Wnu46m2V1hzOVlpBkdrGhismxKbgIIsKnS6l5hdpa0aeCktYFQMAgPrgkZRT7FRcqN3UH2UPVl6Z4q7oCrYtLCpx1frfbYvFonZx1a8sKknRITZFh3i3VcwvKfG5uCshzK5Wf6zCVboI3RZkUVipbaEzCo+vtOp0e5TvLDGuF223SaK4CwAQGCjuOgX89NNPuuqqq/Tyyy/rvPPOa+hwAACN1Oy82X6ZZ1PxpkpXtqrLufx53dLccuvDnA9rdM5W51ZtdW71eywAAAAAaubnfRl+mWdvTmGlK1vV5Vz+vG5pHkkLdqfX6Jz9uQ7tz3X4PRYAAFAzqXlF5Yq7moSH6FCeeYvkJhHlt6ZOPQm2Ua6qCL3simUlbnOhWumtKcuOBQCgIVHcFaCSk5M1ZMgQnX322erUqZMSEhJkt9uVlZWlTZs2acGCBZo7d66Ki70V6rm5uRo3bpw++OAD9ejRo8bXs9ls/n4L8Ct+8YWq8XfYzGq1Vj+oMXJWPwTg/yfwDbkJAKB+kJvAN+QmAID6QW4C3wR2brI3p1CdEyKNbRIlqXVMmHZmFSi32LvlYmhwkNpXsLrW7uyCcn2tosPUp1msqW9Teq42HW2YHQjaxUUo5o/Vt3ZlFSi76Pg2ksUutzwej/He7WW2nSzdLnKxsiiAwEdu0nhQ3BWAJk6cqL59+yo4uPy3JykpSUlJSRo4cKBuu+02jR07Vlu3elf2KCoq0gMPPKBvv/1Wdru93LkAAAAAAAAAAAAATk4pseGKtB3//DDYWn51qUhbsM5Iijb17cstVKbD+/RvvtOlnVkFpq0Rg4OCNLh1gg7mFcnj8ahpZKhCyhQ+Hch16GjhiT1B7MtqohUVie3JLtCq1Kq3fy4t1BqkLgmRkiRHiUsb03NNr7s9UpbDqbgw7+eoSeEhxsqiYcFBiiy1ZeNRH7eABACgPlDcFYAGDBjg07jWrVtr5syZuvrqq3Xw4EFJ0oEDBzRr1izddNNNNbqm08lyLsDJjL/DFePrAtQcf28AAEAgITcBAACBhNwEDa15VKiSwstvl1hamM2q9vHmVbeyipxGcZck/Z6Wq/gwm2l7xuCgILWKDqtwzrziEtP2hoGqe5No2f4oSvstLde0zeIx27MK1PeP4q7WMWHKLnIqu8iprolRxopeuUUlOnwSbEEJAOQmjUdQ9UMQyBISEvTggw+a+ubNm9dA0QAAAAAAAAAAAAAIZCUejxbvy9ChvOq3kEwvKNbP+44G/DaFiWF2tfyjOO1oYXGlK4XtyynUrizv9pJBFot6JsdoUKtEo2iuqMSlXw9l1k/QAAD4iJW7TgFDhw5VZGSk8vK8e1evXbtWhYWFCguruLoeAAAAAAAAAAAAQOPldHu09ECmksLtahUdpoQwu0KCg2SRRUUlLmU6nNqf69BBHwrAGppFUs9k71aUbo+n2lXG1hzOVlpBkdrGhismxKbgIIsKnS6l5hdpa0aeCksCu5ANAND4UNx1CggODlb37t21dOlSSVJJSYmOHDmi1q1bN3BkAAAAAAAAAAAAAPzh530Zfp8zraBYaQXFJ3z+3pzCSlfJqq95PJIW7E6v0Tn7cx3anxv4hWsAAEhsy3jKSEhIMLUzM1kuFAAAAAAAAAAAAAAAADiZUdx1iigsNFeyh4SENFAkAAAAAAAAAAAAAAAAAPyB4q5TxL59+0zt+Pj4BooEAAAAAAAAAAAAAAAAgD9Q3HUKOHTokLZt22a0ExIS1KRJkwaMCAAAAAAAAAAAAAAAAEBtUdx1Cpg+fbo8Ho/RPuecc2SxWBowIgAAAAAAAAAAAAAAAAC1RXFXACkuLtbOnTtrdM7s2bP1n//8x2hbLBbdfPPN/g4NAAAAAAAAAAAAAAAAQD0LbugAcJzD4dDll1+uiy++WMOHD9dZZ52l4OCKv0VpaWmaPn26Pv74Y1P/lVdeqe7du9dHuAAAAAAAAAAAAAAAAADqEMVdAcblcunrr7/W119/rcjISHXp0kUpKSmKiYmRzWZTTk6ONm/erLVr18rpdJrO7dOnj55++ukGihwAAAAAAAAAAAAAAACAP1HcFcDy8vK0YsUKrVixotqxN9xwgyZMmCC73V4PkQEAAAAAAAAAAAAAAACoaxR3BZDQ0FCNHj1ay5cv1++//67i4uIqx4eHh+uCCy7QzTffzFaMAAAAAAAAAAAAAAAAwCmG4q4AYrfb9cADD0iSSkpKtGvXLu3du1epqanKz89XSUmJoqKiFB0drQ4dOqhTp06yWq0NHDUAAAAAAAAAAAAAAACAukBxV4AKDg5Whw4d1KFDh4YOBQAAAAAAAAAAAAAAAEADCGroAAAAAAAAAAAAAAAAAAAA5VHcBQAAAAAAAAAAAAAAAAABiOIuAAAAAAAAAAAAAAAAAAhAFHcBAAAAAAAAAAAAAAAAQACiuAsAAAAAAAAAAAAAAAAAAhDFXQAAAAAAAAAAAAAAAAAQgCjuAgAAAAAAAAAAAAAAAIAARHEXAAAAAAAAAAAAAAAAAAQgirsAAAAAAAAAAAAAAAAAIABR3AUAAAAAAAAAAAAAAAAAAYjiLgAAAAAAAAAAAAAAAAAIQBR3AQAAAAAAAAAAAAAAAEAAorgLAAAAAAAAAAAAAAAAAAIQxV0AAAAAAAAAAAAAAAAAEIAo7gIAAAAAAAAAAAAAAACAAERxFwAAAAAAAAAAAAAAAAAEIIq7AAAAAAAAAAAAAAAAACAAUdwFAAAAAAAAAAAAAAAAAAGI4i4AAAAAAAAAAAAAAAAACEAUdwEAAAAAAAAAAAAAAABAAKK4CwAAAAAAAAAAAAAAAAACEMVdAAAAAAAAAAAAAAAAABCAKO4CAAAAAAAAAAAAAAAAgABEcRcAAAAAAAAAAAAAAAAABCCKuwAAAAAAAAAAAAAAAAAgAFHcBQAAAAAAAAAAAAAAAAABiOIuAAAAAAAAAAAAAAAAAAhAFHcBAAAAAAAAAAAAAAAAQACiuAsAAAAAAAAAAAAAAAAAAhDFXQAAAAAAAAAAAAAAAAAQgCjuAgAAAAAAAAAAAAAAAIAARHEXAAAAAAAAAAAAAAAAAAQgirsAAAAAAAAAAAAAAAAAIABR3AUAAAAAAAAAAAAAAAAAAYjiLgAAAAAAAAAAAAAAAAAIQBR3AQAAAAAAAAAAAAAAAEAAorgLAAAAAAAAAAAAAAAAAAIQxV0AAAAAAAAAAAAAAAAAEIAo7gIAAAAAAAAAAAAAAACAAERxFwAAAAAAAAAAAAAAAAAEIIq7AAAAAAAAAAAAAAAAACAAUdwFAAAAAAAAAAAAAAAAAAGI4i4AAAAAAAAAAAAAAAAACEAUdwEAAAAAAAAAAAAAAABAAApu6AAAALUX9018Q4eAk8ElUxo6AgAAAAAAAAAAAABADbByFwAAAAAAAAAAAAAAAAAEIIq7AAAAAAAAAAAAAAAAACAAsS0jAMB/bLFSYj8pof/xP6GJ5jGHf5C+H1z7a0W0lv6y+8TP/6KNlL/H3BfdRer6kJR8vhTaVHLlS5lrpe1vSXs+qXq+2B7SxSuloGDp6Eppfj9JnhOPDwAAAAAAAAAAAADQ6FHcBQDwn0tWS5FtGzqKE9P8z9I5n0nBYcf7rHYpebD3z2mXSEtvrvz8vtO9hV0et7TyHlHYBQAAAAAAAAAAAACoLbZlBAD4kaWhA/Cdu+T4cUiidPaHxwu79s2VvjtbWnXf8XFtR0jt76x4rpRbpKSzvcc73pGO/lp3cQMAAAAAAAAAAAAAGg1W7gIA1A23U8rdJsV0rZv5nTnS5inVj4vvIzU519x3dIVUeOB4u+1IyRbtPS7OkpbcKLkcUvpSKfYMqd1t3tc6jpW2v2meyxYr9XzBe1x0VFr38Am8GQAAAAAAAAAAAAAAyqO4CwDgPwe+lPL3SEeXSRmrpNBk6S+76+ZaxZnS6geqH3fRkvJ9W141txP7Hz9OX+ot7DomdcHx4q7Y7pI1XHIVHH+953NSaBPv8bp/egu8AAAAAAAAAAAAAADwA4q7AAD+s2pcQ0dgFn+mlDjA3Fd4SNr7H3OfLfb4cXGG+bWyxVr2WKnwj+KuuN5Suz+2ajy6ovyqXgAAAAAAAAAAAAAA1EJQQwcAAECd6VRBsdm2Gd4tI0tzZh0/tieYXwuJN7eLS43tO00Kskoet7TyHkmeWgQLAAAAAAAAAAAAAIAZxV0AgFNTSJLU6lpzn6tI2j6j/Nj05cePE8+SrKHH28lDjh9nbTi+JWO7O7xjJWnH296VuwAAAAAAAAAAAAAA8COKuwAAp6YOo81FWpK09zPJcaT82F3vSc4c77E9Vjr7Y+92jh3vkVJGHR+3ZeofY+KlHs95j4uOSmsf9nf0AAAAAAAAAAAAAAAouKEDAADA7yzBUvvR5fs3v1rx+KJ0aclfpXM+k4LDpJbDvH9K2/WhtOMt73HP56XQRO/xukek4ozj42K7SxFtJY9Tyt4k5e+u9dsBAAAAAAAAAAAAADROFHcBAE49ra6Wwk8z9x1ZLGWurvycA19K8/tIXR6Sks+XQpO9WzBmrvVuu7j7I++4hL5Sym3e46MrpO1/FHyddql05v+TotqVue5P0vI7pNytfnlrAAAAAAAAAAAAAIDGg+IuAMCpp9O48n1b/1/152VvlJaNqmKAReozXQqySh63tPIeSR6pxV+kc2d7+8tqMlC6aIn0bR9W8QIAAAAAAAAAAAAA1EhQQwcAAIBfxZ8pJQ4w9+Xvk/bNqf3c7e+SEvp4j3e87V25K8h2vOBLklIXSHNPk+b19F5XkkISpF4v1/76AAAAAAAAAAAAAIBGhZW7TgJZWVlavXq1UlNTlZeXpyZNmqhFixbq1auXrNYKVokBgMasolW7tk2XPK7azRuSIPWY6D0uOiqtfdh73OQ88xaQq+6TCg95/2x6Uerzmre/+Z8la5jkKqxdHAAAAAAAAAAAAACARoPirgC2a9cuvfLKK/rhhx/kdDrLvZ6UlKTrrrtOd911l+x2ewNECAABJiRJanWtua+kUNr+Zu3n7vkvKSTee7z2Yak4w3sc0+34GGeed2vHY9KXHz+22qWojlLWutrHAgAAAAAAAAAAAABoFNiWMUB98cUXGj58uP73v/9VWNglSWlpaXrttdd03XXXaf/+/fUcIQAEoA53SdZQc9/uD48XYp2ohP5Syijv8dEV3i0ZjwkOP37szDafV7ZdeiwAAAAAAAAAAAAAANVg5a4A9OOPP2rChAlyu91GX5s2bdS/f3/FxsZq7969WrRokRwOhyRp48aNuuuuu/TZZ58pMjKyocIGAP8ZskhKPs/c90UbKX9P5edYgqX2o8v3b/l/tYvFEiT1ne79r9slrRgjyXP8dUfa8eOQBPO5ZdtFaQIAAAAAAAAAAAAAwFcUdwWYI0eO6IEHHjAKuywWi/7xj39o5MiRCgo6vtBaRkaG7rvvPv3666+SpO3bt+vxxx/XpEmTGiRuAJAkdbxHimx/vG2LLj8mqr3Ue7K5b8/H3hWxaqPV1VJ4c3Nf6kIp+7fazdvhbim+t/d4x9tSxkrz6+m/HD+2hkqJA6T0pd52k8HHXys8LOVur10sAAAAAAAAAAAAAIBGheKuAPP6668rPz/faN9777265ZZbyo2Lj4/X22+/rWHDhmnHjh2SpHnz5un2229X165d6y1eADBpeXX5FbfKCm8hdb7f3Je5tvbFXR3vLd+3tZardoUkSWc84z12pEvrHik/JnujlLpAanqBt93vTWnVOO+53Sb4LxYAAAAAAAAAAAAAQKMTVP0Q1Jf09HTNmjXLaLdq1Up33nlnpeNDQkL02GOPGW2Px6PXX3+9TmMEgIAU11tKOtvcl7dT2v9/tZu310uSPc57vO4RqTij4nHLbpFyvYW2ij1dGrJQOvez4yuXHZwnbXyxdrEAAAAAAAAAAAAAABodirsCyPfffy+n02m0r732WtlstirPGTBggFJSUoz2jz/+qIKCgjqLEQACUqdx5fu2TpPkOfE5E8+W2o7wHh/91bslY2UK9kvf9pF+f07K3iS5HJIzT0pfLq0YI/34Z8njOvFYAAAAAAAAAAAAAACNEtsyBpCFCxea2hdffLFP5w0dOtRYsauoqEi//PKLLrzwQr/HBwDV+n5ww8yzbJT3jz+lL5E+sfo+3pklrfun9w8AAAAAAAAAAAAAAH7Ayl0BZNWqVcZxYmKiWrZs6dN5vXr1MrVXrFjh17gAAAAAAAAAAAAAAAAA1D+KuwLE4cOHlZuba7S7dOni87ldu3Y1tXfs2OG3uAAAAAAAAAAAAAAAAAA0DIq7AsTOnTtN7dNOO83ncxMTE2Wz2Yz2rl27/BYXAAAAAAAAAAAAAAAAgIZBcVeAOHz4sKndtGlTn8+1WCxKTk6udC4AAAAAAAAAAAAAAAAAJx+KuwJEQUGBqR0eHl6j8yMiIozjkpISFRUV+SUuAAAAAAAAAAAAAAAAAA0juKEDgFfZ4q6QkJAanV92fH5+fo3mKL2tIwKRo6EDAAA0EuQE8A25CQCgfpCbwDfkJgCA+kFuAt+QmwAA6ge5SeNBcVeAKLvSVk3/Etrt9irnq05kZGSNxqN+De/E9wfV6ORp6AhwErivoQMAcMogN0G1yE3gA3ITAP5CboJqkZvAB+QmAPyF3ATVIjeBD8hNAJTGtowBouwqW06ns0bnFxcXVzkfAAAAAAAAAAAAAAAAgJMLxV0BIjw83NSu6cpbZcdHRETUOiYAAAAAAAAAAAAAAAAADYfirgBRtrgrPz+/RueXHh8cHMzKXQAAAAAAAAAAAAAAAMBJjuKuAJGcnGxqHz582OdzPR6PaXzZuQAAAAAAAAAAAAAAAACcfCjuChDt2rUztQ8cOODzuenp6XI6nUY7JSXFb3EBAAAAAAAAAAAAAAAAaBgUdwWIJk2aKCoqymhv2rTJ53M3btxoalPcBQAAAAAAAAAAAAAAAJz8KO4KIGeeeaZxfPToUe3du9en81avXm1q9+3b169xAQAAAAAAAAAAAAAAAKh/FHcFkCFDhpja3377rU/nzZ8/3zgOCQnROeec49e4AAAAAAAAAAAAAAAAANQ/irsCyPnnny+bzWa0Z82aJafTWeU5S5cu1a5du4z2oEGDFB4eXmcxAgAAAAAAAAAAAAAAAKgfwQ0dAI5LTEzUtddeq48++kiStHfvXr355pu65557KhxfVFSkZ5991mhbLBbdfffd9RIrTl5z5szRww8/bLS///57tWjRogEjwqlkwoQJmjt3riSpefPmWrhwYQNHBAAIdOQmqEvkJo2Px+PR3r17tW3bNqWmpiovL08hISGKjY1Vx44d1blzZ1mt1oYOE0AAIzdBXSI3aXwKCgq0fft2HTx4UGlpaSooKJAkRUZGqmnTpjr99NOVnJzcwFECCGTkJqhL5CYAcPKguCvA3HXXXfr888+Vn58vSZo6darCw8M1cuRIBQUdX2gtIyND9913n7Zv3270XXrpperatWu9xwwAaLwOHTqk9evXa8OGDVq/fr1+++03498wSRo7dqzuvffeBowQAACc6nJzc7VgwQL98MMPWr58uTIzMysdGxkZqWHDhmnUqFF8IAIAAOrEsmXL9MUXX2j16tXas2ePPB5PleM7d+6s66+/Xtdcc42Cg/nIBgAA1L+8vDxddtllSk1NNfW///776t+/fwNFBaA07hQCTHJysiZNmqS7775bbrdbHo9HL7zwgj799FOdddZZio2N1Z49e7Ro0SI5HA7jvPbt2+vpp59uwMgBAI2Fw+HQ/fffr99++01paWkNHQ4AAGjEli1bpttvv11Op9On8Xl5efrggw80e/ZsPfroo7rqqqvqOEIAANDYfPfdd5ozZ47P4zdv3qwnn3xSn332mV599VW1bt26DqMDAAAo7+WXXy5X2AUgsFDcFYDOO+88Pf/883ryySdVWFgoSdq9e7d2795d4fguXbrotddeU2RkZD1GCQBorIqLi7Vo0aKGDgMAAEB5eXnlCrvsdrt69OihlJQUxcfHq7i4WNu2bdPy5ctVVFQkybtF0iOPPCKHw6GbbrqpIUIHAACNQFBQkFJSUtSuXTs1a9ZMkZGRKi4uVmpqqlavXq39+/cbYzdt2qS//vWv+uSTT1hhFAAA1JvVq1fr008/begwAFTD4qluTWA0mJ07d+qVV17Rjz/+WOFTyElJSbr22ms1evRo2e32BogQANAY5eTkqG/fvuX6mzVrpuTkZK1du9boY1tGAABQlxYsWKB77rlHFotFAwYM0HXXXafBgwcrJCSk3Ni0tDQ988wzmj9/vtEXFBSkuXPnqnPnzvUZNgAAOIVNmTJFBw8e1EUXXaS+ffsqJiamwnEej0eLFi3S448/bloZ/ZxzztG7775bX+ECAIBGrLi4WMOGDdP27dsleesPSuclbMsIBA5W7gpgKSkpmjZtmjIzM7V69WqlpqYqPz9fiYmJatmypXr37i2r1drQYQIAGqH4+Hidfvrp6t69u/EnMTFRy5cv180339zQ4QEAgEZk4MCBGj9+fLUFWklJSfp//+//afz48frqq68kSW63W5MmTdKbb75ZH6ECAIBG4P777/dpnMVi0fnnn682bdpo2LBhcjgckqRffvlFmzdvpvgcAADUuTfeeMMo7Bo4cKASEhI0d+7cBo4KQEUo7joJxMXFaciQIQ0dBgAAkqTo6GgtXbq0ocMAAADQeeedpwsuuKBG5zz22GNatGiR8vPzJUlLlixRbm6uoqKi6iJEAACAKqWkpOiaa67RBx98YPT9/PPPFHcBAIA6tX37dr3xxhuSpNDQUD3++OOaNm1aA0cFoDJBDR0AAAAAAADAiQgOrvkza7GxsTr33HONttPp1KZNm/wZFgAAQI306dPH1E5NTW2gSAAAQGPg8Xj06KOPyul0SpLuuecetWzZsoGjAlAVVu4CUGMul0urVq3S3r17dfToUYWFhalr167q3bu3goIqrxnNzc3Vr7/+qr1796q4uFjx8fHq3bu32rVr55e48vLytGLFCh06dEh5eXlKTExUly5d1KVLF7/ML0mHDh3Shg0blJaWptzcXCUkJOjKK6+UzWar9ByHw6HVq1fr4MGDysjIkN1uV1xcnE4//fRavffdu3dr06ZNOnLkiAoKCmS1WhUREaGmTZuqTZs2SklJkcVi8Xm+9PR0/f7779q/f7/y8vLk8XgUFhampKQktWrVSh07dpTdbj/heH1x6NAhrVmzRhkZGcrPz1dMTIyaNm2qPn36KDIy0m/XyczM1KpVq5SamqqCggLFxcWpe/fu6tSpU42+ZscUFRVp06ZN2r59u7Kzs1VUVKTQ0FDFxMSoefPm6tixo+Lj4/0WPwLX4cOHtW3bNu3bt0+5ubmSpJiYGDVr1ky9evXy64ogHo9H69ev1549e5SWlia3260ePXqoX79+frsGcLIgNyE3qSvkJqeuVq1amdrp6ekNFEndIjcBGga5CblJXSE3OXWV/f653e4GiqRukZsADYPchNykrpCbnLw+/vhjrVmzRpLUsWNH3XLLLQ0cUcMgN8HJhOIuoJGZM2eOHn74YaP9/fffq0WLFuXGjRgxQr/++qskqV+/fvrggw9UXFyst99+Wx9//LHS0tLKndO8eXM99thjGjx4sKk/MzNTkyZN0ueff67i4uJy5/Xu3VtPP/20OnToUG38nTp1Mo7Hjh2re++9V5mZmfrXv/6lefPmyeFwlDunTZs2evDBB3XhhRdWO39lX59Vq1Zp8uTJWrlypTwej+mcoUOHVngjsHnzZk2dOlU///yzioqKKrxe8+bNdccdd+jqq6+u8mbiGLfbrc8++0zvv/++du7cWeXYqKgonXPOObrzzjvVrVu3SsctXbpU06dP14oVK8q9t9JsNpt69eqlG2+8UZdcckmFYyZMmGDsxd28eXMtXLiw2vckSd98841mzJihzZs3V3rts88+Ww888IBPN3bLly/XzTffbLTff/999e/fX4cPH9bzzz+vBQsWGE8jlNa6dWv9/e9/9+lnRZLS0tI0depUff3118rLy6tybJs2bTRkyBDdd999CgkJ8Wn+E/Hbb7/phhtuMP6uNW/eXJ9//rmio6OrPfell17S22+/bbSvv/56PfXUU3UWa1279tprtW7dOklSUlKSfvjhhxqt7rFjxw5deumlRvu2227TQw89ZBrjdru1cuVKzZs3T7/88ov27t1b6XxBQUE6++yzNXr0aPXt29enGCr6f7HL5dK7776rjz/+WAcPHjSNHzJkCDcCOOmQm1SN3ITchNykbnKTY1syHuPLz3ttkZsAJwdyk6qRm5CbkJvUTW5S9t/Q+lg5g9wEODmQm1SN3ITchNyk5rlJamqqXnnlFUmSxWLRk08+WS+/F6kOuQlQNbZlBOCTvLw8jRw5Uq+++mqFNwGSdODAAd19992aPXu20bdjxw5deeWV+s9//lPhTYAkrV69WjfeeKN+//33Gse1ZcsWDR8+XHPmzKnwJkDyPg0xduxYPfHEE1UmupV56623NGLEiGoT5WM8Ho9eeuklDRs2TAsWLKj0JkDyfs2efPJJjRgxQhkZGVXOW1BQoFtvvVVPPvlktTcBkvepmm+//Va//PJLpWNefvlljRo1Sr/++mu1783pdOrXX3/Vf//732qv7av8/Hzdfvvtuv/++yu9CTh27R9//FHDhw/X9OnTT+haS5cu1RVXXKFvvvmmwpsASdqzZ4/Gjh2rN998s9r51qxZo8suu0yfffZZtTcBkvfn8J133lFOTk6NY6+J008/XRMmTDDaBw4cMN3cVubHH3/UO++8Y7S7du2qf/7zn3USY3256qqrjOO0tDT9/PPPNTp/zpw5pvbw4cPLjdmyZYtGjBihTz75pMqbAMl707B48WKNGDFCU6ZMqVEsx+Tk5GjEiBF6+eWXy90EAI0NuQm5CbmJGblJzWzZssXUTk5O9tvclSE3AU5t5CbkJuQmZuQmNfPVV1+Z2oMGDfLb3JUhNwFObeQm5CbkJmbkJsc9+eSTxkNv11xzjc4888waRl03yE2AqrFyF4BqeTwePfDAA1q9erUkKSUlRf369VN8fLyys7O1ePFi7dmzxxj7xBNPqFevXoqOjtaoUaN05MgRBQUFqUePHurevbsiIyOVmpqqhQsXKisrS5L3H7fx48fr//7v/3xeJjYvL0/33nuv8Y9h+/bt1a9fP8XExOjo0aP65ZdfdODAAWP8p59+qtDQUJ+SomO++eYbvfzyy5KkkJAQ9e/fX+3atVNISIhSU1PLJRbHvlbffPONqb9r167q3r274uPjVVJSor1792rJkiXGEp9r1qzRiBEjNGvWLIWHh1cYy1NPPaWlS5cabavVqh49eqhTp06Ki4uTxWJRTk6O9u7dayztW5XZs2frrbfeMvW1a9dOPXr0UJMmTWSz2ZSXl6fU1FRt2bLFp5uPmiguLtaoUaO0fv16U3+XLl105plnKioqSmlpaVq8eLFSU1MleROpV199VQUFBXrwwQd9vta2bds0adIk5efnKzg4WL1791aXLl0UERFhXOPQoUPG+EmTJqlXr16VVuJnZGRo9OjRys7ONvpiYmLUt29ftWrVShEREXI4HMrKytL27du1adOmSm9U68JNN92kFStWGD+HCxYs0MyZMzVq1KgKxx86dEgPPfSQcTMYGRmpKVOm1PmSzXXtsssu03PPPWd87efMmVPuKbTKuFwu/d///Z/R7tGjh9q3b1/lOTabTR07dlRKSoqSkpIUHh6u4uJiHTlyRGvXrtXu3bslef8/8frrrys2NrbS70lFPB6P/v73v2vVqlWSvB9CDxgwQE2bNpXD4dDOnTtr9BQLcDIjNyE3ITcxIzepmf379xv//5Ck6Ohode3a1S9zV4XcBDh1kZuQm5CbmJGb1Mwbb7yh5cuXG+0///nPftvyrCrkJsCpi9yE3ITcxIzc5Lh58+Zp0aJFkqT4+PgafV/qGrkJUDV+WgBUa+3atXI6nYqKitJzzz2niy66yPS6y+XS5MmTjaTS6XRq+vTpysvL05EjR9SxY0f961//Krf0ak5Oju677z4tWbJEkrRr1y598cUXuuaaa3yK6+OPP1ZxcbGioqI0ceJEDR061PS6x+PRxx9/rOeff96o6p85c6YGDx6ss846y6drTJ48WZJ3md7HHntMSUlJptdLSkpMe9LPmDHDdBPQr18/PfbYY+rYsWO5ufPy8jRlyhR98MEHkqTt27frmWee0fPPP19u7IEDB/T5558b7e7du2vy5MlVLtG+efNmffHFF5XuB/36668bx/Hx8Zo8eXKVX5dDhw5p/vz5xk1fbb388summ4Dk5GS9+OKLGjBggGmc2+3W+++/r5deekklJSWSvE/e9O3b1+enGF944QU5nU4NGDBAzzzzTLmvW3FxsV555RXNnDlTkvdnZ8qUKfroo48qnO/jjz82bmIlafTo0RozZkylS/M6HA4tWbJEn3zyiennpS49++yz2rhxo/H9evnll9WzZ0/17NnTNK6kpEQPPPCA6f0888wzat26db3EWZciIyN14YUX6ssvv5QkLVq0SBkZGYqPj6/23MWLF5tupks/MVKa1WrVBRdcoGHDhmnAgAGKiIiodM6VK1fq8ccf144dOyR5vycXX3yxmjZt6tP7Wb16tVwul+x2ux555BFdd9115X6eKnuaDjjVkJuQm0jkJqWRm9TMtGnTTE84X3jhhfXyyzRyE+DURW5CbiKRm5RGblK1kpISpaena82aNfrkk09MhV09e/bUk08+ecJz1wS5CXDqIjchN5HITUojN/HKzs7WxIkTjfaECRMUExPjl/j9gdwEqBrbMgKoltPplM1m07vvvlvuJkDy/kP44IMPqlevXkbf119/rUWLFqlFixb68MMPK9xTOzo6Wq+88ooiIyONvtJV1dUpLi5WcHCwZsyYUe4mQPLuE33TTTfpueeeM/WXTlyq43K5dPHFF2vKlCnlbgIkKTg42PiHeM+ePZo6darx2sUXX6yZM2dWeBMgeZOURx99VHfddZfR9/nnnxuV4KWVfrojKChIr776apU3AZLUuXNn/eMf/9ANN9xQ7rV9+/Zp3759RnvChAnV3hw1a9ZMo0aN0hNPPFHlOF/s3bvXuAGSvE9IvPfee+VuAiTv+x01alS579vEiRN9XpLZ6XTq3HPP1VtvvVXh181ut+vhhx82XX/lypWmr1Fppb8fAwYM0AMPPFDlnuuhoaE6//zz9dZbbykhIcGnmGsrMjJSr776qhGX0+ksl/BL0iuvvKI1a9YY7RtvvNG0J/nJrnQC73Q6y21zUJnSy/eGhoZW+jXp2LGjpk2bpgsuuKDKmwBJ6tOnjz7++GMj8Xc6nZXebFbE5XJJkl599VXdcMMNFd5UnuyrrQG+IjchN5HITUojN/HdkiVLNHfuXKNts9lMP/N1jdwEODWRm5CbSOQmpZGbmE2ePFldu3Y1/nTr1k2DBg3S/fffbxR2RUREaMyYMfrggw9Mf+frGrkJcGoiNyE3kchNSiM38XrxxReVnp4uyft1+Mtf/uKf4P2I3ASoHMVdAHxy880364wzzqhyzHXXXWccu91uSdLjjz9eZdV3fHy8LrzwQqO9YcMG4x87X9x0003q06dPlWOuuOIK07KdW7duNZbArE54eLiefPJJnyrz33nnHSP2xMRETZw4UVartdrzxo4dq9NOO02S9+v22WeflRtz9OhR4zg+Pl7Nmzf3Kf7KHEvejjn99NNrNV9Nffjhh8bPiCQ98MADatu2bZXnXHnllTr//PON9p49e/TDDz/4dD273a7nn39eNputynFll1Ndt25dheNKf/3q+2tXE126dNEjjzxitA8ePKgJEyYYN1Dff/+93n33XeP1bt261WiJ65PBWWedZfr7UnbP9YpkZ2dr4cKFRvvCCy+s9GmpmoqNjdXNN99stH39GT7m8ssvN/09ABozchNyE38iN6kfDZ2bHDlyxLRtgSTddddd9bpiKbkJcOoiNyE38Sdyk/pRX7mJy+Uy/SkrKSlJjz32mMaMGVPvH/CRmwCnLnITchN/IjepH3WZmyxbtkyzZ8+W5P361tdKoTVFbgJUjuIuAD6p6EmBssreKDRv3lwDBw6s9rwePXoYx4WFhaY91aszYsQIn8aV/odXkv73v//5dN4ll1yiuLi4ase5XC5T9fiwYcN8fsrObrdryJAhRrv0UuzHlN6z/ejRozp8+LBPc1em7B7wmzZtqtV8NfX9998bx9HR0ZUuj1rWbbfdVuk8VbnooovUpEmTasf16dNHFovFaB9barWs0tX89f21q6nrr79el19+udFetGiR3nnnHR04cMCU9EdFRfm8J/vJxGKxaNiwYUZ706ZN1X7PvvrqK9NSuL7+fPqq9JNf27dvV35+vs/n+vL/YqCxIDepGrlJzZCb1J+Gyk2Kioo0btw4paWlGX29evXS6NGj/TK/r8hNgFMXuUnVyE1qhtyk/gTC703S0tI0YcIEXXjhhTX+wLC2yE2AUxe5SdXITWqG3KT+1EVuUlRUpMcff9xo33XXXWrTpo1f4/YXchOgchR3AahWkyZNql0uVlK5PYZ79eplSqp8PS8nJ8enuDp06OBTXJK30rt08ltZ9X5Z/fr182nc77//bvrHvLonT8oqnURt2bKl3NK0pW+yPB6P7rnnHu3cubNG1ygtJSXFlMw+++yz+uWXX054vppIT0/X/v37jfaf/vQnn38x1qdPH9Pe2qWXnq2Kr9/HyMhI01NJlf0sdu/e3ThevHixJk2apIKCAp+u0RCefvpp01M0kydP1u23367s7Gyjb+LEiWrVqlVDhFfnhg8fbvp/UXVPehx7ekXy/kKjuuWtjzl8+LBmzpyp+++/X5dddpnOPvts9ejRw7T1QteuXXXnnXca57jdbtM+8FWx2+2mX5wAjRm5SfXITXxHblL/6js3cblcGj9+vOn706xZM7366qvVPgVcF8hNgFMPuUn1yE18R25S/+o6N3nwwQe1ZcsW48+qVas0b948Pfvss6af3UOHDmn06NGaNWvWib+ZE0BuApx6yE2qR27iO3KT+ufv3GTq1Knas2ePJKlt27amf2sDEbkJULHghg4AQOBLTk72aVzZJwd8PS8sLMzU9jWh6tSpk0/jJO8e3506dTISx8qq98tKSUnxaVzZqvExY8b4HJskU+JfUlKivLw805KhPXr0UK9evYz4N2zYoEsvvVQ9e/bUn/70J/Xt21fdu3cv97WsjM1m01//+le98cYbkqTMzEzdeuutatOmjQYPHqy+ffuqV69epqTbX3bt2mVqd+nSpUbnd+3aVYsXLzbm8ng81d5w+vqzKHl/jo/tYV7Zz+Jf//pXzZ49W06nU5L0xhtv6MMPP9SgQYM0YMAAnXnmmUpJSfHpRrg+RERE6NVXX9W1114rh8OhkpIS043kiBEjNHTo0AaMsG41b95c/fv317JlyyRJX375pR566KEKP8zdunWrfv/9d6M9bNiwar+PWVlZevnllzV79mzT0tS+8vWXH61atWqQD6CBQERuUj1yE9+Rm9S/+s5NnnjiCdNT3rGxsXr77bdr9H3wJ3IT4NRDblI9chPfkZvUv/rOTSIjIxUZGal27drp6quv1nvvvacXXnhBHo9HHo9HTz31lHr06GFaJaIukZsApx5yk+qRm/iO3KT++TM32bx5s/79738b7SeffDLgd3AhNwEqRnEXgGr5mlyW/cfyRM8r+4RDZRITE30ad0xCQoJxnJub61MCGR0d7dPcmZmZpnZN9pivSG5ubrn9oCdPnqxbb73VSOA8Ho/WrFlj3BwEBwfr9NNP14ABA3TJJZdUe6N07733aufOnaYPunbv3q1///vfRqKXkpKi/v37a+jQoerfv79P+9RXp/STBZKUlJRUo/NLj3e5XMrPz692uWRffxYl889jZT+LHTp00HPPPad//vOfxlKv+fn5mjdvnubNmyfJ+6Fhnz59NGjQIA0dOtT09EhD6NSpkx599FE9+uijpv7TTz9dDz30UANFVX+GDx9u3AhkZmZq0aJFuuiii8qNK/0ESNnlfyuSnp6um2++2edfLlSkqKjIp3H+2iMeOBWQm1SP3MR35CYNo75yk3/961+m1S/Cw8P15ptvqn379n67xokgNwFOLeQm1SM38R25ScNoqN+bWCwWjRo1ShkZGcYH9k6nU9OmTdOrr75aZ9cti9wEOLWQm1SP3MR35CYNwx+5icvl0j//+U+VlJRI8hY++bqqVUMjNwHKY1tGACetmiR3Zce73W4VFhZWe05wsG81sL5WafuqokrxZs2aac6cOfrb3/5Wbtljyft0yNq1a/X666/riiuu0MiRI7V58+ZKr2Gz2TR16lRNmjRJ3bp1q3DMzp079cknn2jUqFG65JJLfN7Xvipl96Ku6fex7NNENdnb2p+uuOIKff7557r88ssrfMohKytLCxYs0GOPPaaBAwfqxRdf9Olnri5V9ITA2WefHfBPafjD0KFDTYl0Rcv4lpSU6MsvvzTa/fv3V4sWLaqc95FHHjHdBMTHx+vWW2/VjBkzNG/ePK1cuVIbNmwwbb/w/vvvm+bw9ZcfPOEBBD5yEzNyk/pFblLe66+/rnfeecdo2+12TZs2LSCWxCc3AVAfyE3MyE3qF7lJzYwePVqhoaFGe9GiRcaHz/WB3ARAfSA3MSM3qV+NMTf56quv9Ntvv0nyFq+dTA/6k5sA5bFyF4CTVk2TqtLjg4KCapyAVqXsXN99951at27tt/lLX+euu+7SHXfcoQ0bNmj58uVau3atVq9eXe5Jk2XLlunaa6/Va6+9poEDB1Y4n8Vi0WWXXabLLrtMu3fv1rJly7Rq1SqtWbNG+/btM43dvXu3xo4dq7Fjx+ree+894fdQel94qebfx7LL6padrz61a9dOr7zyinJzc7V8+XKtXLlSa9eu1W+//WYs7ytJDodD7777rpYsWaIPP/ywQar1d+zYoaeeeqpc/zvvvKNzzz1X/fv3r/eY6lNoaKguvfRSffbZZ5Kkn3/+Wenp6aanxX766Selp6cb7eHDh1c55/r16/Xjjz8a7T59+mjGjBnVfn99XaYcwMmH3ITcpKL56hO5yXEfffSRpkyZYrStVqsmT56ss88+u1bz+gu5CYD6QG5CblLRfPWJ3MR34eHh6tmzp7FCRVFRkfbs2aMOHTrU6XWPITcBUB/ITchNKpqvPjW23KT0z1h2drbOPffcas8pW6g4atQo0+poM2fOVL9+/aqdp7bITYDyKO4CcNI6evToCY+Piory697ZcXFxpvbevXvr5EbgmKCgIPXo0cNYdcDj8WjLli36/vvvNWvWLB06dEiS9xdBf//737VgwYJqk5M2bdqoTZs2uv766yVJqampWrRokWbPnq0NGzYY41577TUNGDBAffr0OaHYyy5lm5aWVqPzS4+3Wq0NeiNwTFRUlC644AJdcMEFkryJ//LlyzVv3jx9/fXXxk3B5s2b9cILL2jixIn1Gp/D4dD9999vJKBBQUGy2+1yOBxyuVwaP368vvjiC9My16ei4cOHGzcCJSUl+uKLL3TbbbcZr5d+8iMyMrLaPesXLlxoHFssFr3wwgs+3eSVvtkAcGohNyE3ITfxTV3nJl988YWeeeYZo22xWPTcc88ZX49AQW4CoK6Rm5CbkJv4JlB+b1J2u7Lc3Nw6vV5Z5CYA6hq5CbkJuYlv6iI38Xg8J7T9aNliL19XrfIHchPAjG0ZAZy0qlqetiy3260tW7YY7Xbt2vk1lrLzrVixwq/zV8disahz58665557NH/+fJ1zzjnGa1lZWfruu+9qPGfTpk11ww036L///a/GjBljem3WrFknHGvbtm1N7U2bNtXo/NLj27Zt69cbOn8JDQ3VoEGD9OKLL2rWrFmmZYe//PJLORyOeo3nqaee0tatW432PffcoyeeeMJop6Wl6cEHH6xw6ehTSc+ePU1/V+fOnWscZ2Rk6IcffjDal112mWk7hIrs2rXLOE5JSVHLli19iuPYMsgATj3kJseRmwSWxpSbLFiwQI888ojpl42PP/64rrzyylrFXBfITQDUNXKT48hNAktjyk1qIi8vz9Qu+2F6XSM3AVDXyE2OIzcJLOQmgYncBDCjuAvASWv79u3llpmtzPLly03LZh57OsJfzjzzTFPS8M0335xQBbw/hISEaMKECaa+mtw0VWTcuHGKj4/3y3yJiYmmhGnx4sUqLi726dxVq1aZntbp1avXCcdRX7p06WI8OSN5n7wpnUDWtc8//9z09MKAAQM0ZswYDR8+XMOGDTP6lyxZounTp9dbXA2l9LK827Zt0/r16yV5b9BKL7tc3fK9kvkJ2ujoaJ+u73Q69f333/saLoCTDLlJxchNAsupnJssXbpUDzzwgEpKSoy+8ePH68Ybb6x94HWE3ARAXSI3qRi5SWA5lXOTmir94XdQUJCSk5Pr9HoVITcBUJfITSpGbhJYTqXcZNSoUdqyZUuN/pS+hiS9//77ptfreqvqsshNgOMo7gJw0vJ4PPrwww99Gvvee++Z2hdeeKFfY7Hb7aZtXvbu3av//Oc/fr1GTbRq1crULp3gnAiLxaIWLVr4bb4hQ4YYx9nZ2Zo9e7ZP5/373/82tf39fawrZav/a/v181XZPdmTkpL08ssvKyjI+8//E088ofbt2xuvT5s2TcuWLauX2BrKX/7yFwUHH9+V+thNUuknPtq1a6eePXtWO1fp5aMPHDjg0/VnzZpV42WrAZw8yE0qR24SWE7F3GT9+vUaM2aM6RfMd999t+68804/RV83yE0A1CVyk8qRmwSWUzE3qakffvhBhw8fNtpnnHGGIiMj6+RaVSE3AVCXyE0qR24SWMhNAge5CXAcxV0ATmofffSR1qxZU+WYr7/+WosWLTLaHTt21Jlnnun3WMaMGWMkWJL0/PPP13gp33379lX45MrmzZt9fhJCKr+EcPPmzctdJyMjw+f5srKytH379krnq6mbbrrJ9LWaPHmydu/eXeU5//d//6f//e9/RrtNmzYaOHBgreI4UevWravR+JUrV5rap512mj/DqVBhYaHuu+8+4+kmq9Wql19+WYmJicaYsLAwTZkyRWFhYZK8S10/+OCDp/T+4UlJSfrTn/5ktOfNm6d169aZno715QkPSaabqCNHjujHH3+scvymTZv00ksv1TBiACcbcpOKkZvUrcaem2zbtk133HGH6anum2++Wffff7//34ifkZsAqGvkJhUjN6lbjTk32blzZ7ntFauzf/9+Pfnkk6a+htpSmtwEQF0jN6kYuUndasy5ycmO3AQ4juIuACctu90up9Opu+66y5QgHuPxePTpp5/qH//4h6n/kUceqZN42rVrp7FjxxrtoqIi3XLLLXrttddMS32WVVxcrEWLFumBBx7Q0KFDTfvIHzNz5kwNHjxYkyZN0saNG6uMY8WKFXr44YeNtsViMT2BcmzMeeedp4cffliLFy82bV1T1v79+3X33XebPiy76KKLqoyhOq1atdKIESOMdnZ2tkaOHFnhEwZut1sffPBBue/bI4880mB7s1977bW6/vrr9Z///KfKG6ri4mJNmzZNX3/9tdHXu3dvUzJeV55++mlt27bNaN9zzz0666yzyo3r0KGDHn/8caPdGPZqv+qqq4zj7OxsPfTQQ0Y7ODhYf/nLX3yap+zfq3/84x/lbvqO+frrrzVy5EgVFBQoPDz8BKIGcDIgN6kYuUnda8y5yf79+3XrrbcqKyvL6Lv66qvr7O9VXSA3AVBXyE0qRm5S9xpzbvLTTz9pyJAhmjx5smn+ijgcDs2aNUtXX321Dh06ZPR36tRJ1157bU3fkt+QmwCoK+QmFSM3qXuNOTc5FZCbAF7B1Q8BgMB04403auHChdq7d6/Gjh2rDh06qF+/foqJidHRo0e1ePHicstqjho1SgMGDKizmMaMGaO9e/fq888/l+RdqnXq1Kl666231KtXL7Vv314xMTFyOBzKzs7W9u3btXnzZhUWFlY7d3p6ut544w298cYbSkxMVNeuXdWqVStjX+j09HStXbtWW7duNZ03YsQItW3bttx8RUVFmjNnjubMmaOIiAh16dJF7dq1U2xsrGw2m7Kzs7VlyxatXr3adKPQrVu3cntun4gHH3xQa9asMfbHTk1N1ciRI9WtWzf17t1bUVFRSk9P188//2z6BZck3X777Ro0aFCtY6iNNWvWaM2aNXriiSeUkpKizp07q0mTJoqIiJDD4dC+ffu0fPlyZWZmGucEBwebbtLqyty5c017sp9zzjm6++67Kx0/fPhw/frrr8YytkuXLtX06dNNN7ZlvfbaaxXu5+7xeEztadOm6fXXXy83rnnz5hXewNeH8847T/Hx8cZNXOknjP70pz8pKSnJp3m6deumCy64QAsWLJAkZWZm6qabbtKZZ56pHj16KDQ0VOnp6VqyZIn2798vyftkzQMPPKCJEyf6900BCAjkJuQmDamx5iaff/65jhw5Uu56pZfn98WYMWOqzH3qErkJgLpCbkJu0pAaa24ieVdLmTFjhmbMmKEmTZqoW7duat68uaKioiRJeXl52rlzp9auXav8/HzTuc2bN9frr78uq9Va27d5wshNANQVchNyk4bUmHOTkx25CeBFcReAk1ZkZKRee+013XnnnUpNTdW2bduqfCLuuuuu04QJE+o0JovFohdffFEdOnTQlClTjH24HQ6Hli5dqqVLl1Y7h81mq3ZMenq6fvrpp2rHXXvttT695/z8fK1cubLSCvVjevTooRkzZpj2tz5RdrtdM2fO1Lhx47R48WKj//fff9fvv/9e4TkWi0X33nuv7rnnnlpf31/cbre2b99uWuK4IhEREZo8ebLOOOOMOo1n+/bt5fZkf+mll0xLJlfkiSee0IYNG4z3MW3aNPXp06fCJ0MkbxGXy+WqNp7Kxvlybl2x2Wy64oorNHPmzHKvlX4CxBfPP/+89u3bZ3o6a9WqVVq1alW5sREREZo6dapf/v4ACEzkJuQmgaCx5SZlC8ulE8szKpqnvpCbAKgr5CbkJoGgseUmZR05cqRcIXplBg0apKefflpNmzb1aXxdITcBUFfITchNAkFjz01ORuQmgBfbMgI4qXXq1Elz587VFVdcoZCQkArHtGnTRlOnTtXTTz9db0u+3n777fruu+900003KS4ursqxFotFnTp10h133KGvv/66wqcXRo8erfvvv1+9e/eW3W6vcj6r1aqzzz5bM2fO1DPPPFPhk37nn3++nnzySQ0ePNh4SqQqnTp10tNPP61PPvlE8fHx1Y73VUREhN5++21NnjxZnTp1qnRccHCwBg0apDlz5gTETcAbb7yh6667Tq1atap2bGxsrG666SbNnz+/zp9MObYn+7GnhqxWqyZNmqSEhIRqz21se7VXtAd7XFyczjvvvBrNEx0drU8//VQ333yzQkNDKxwTFhamyy+/XF988YXOOeecEwkXwEmE3KQ8cpO6R25y8iM3AVBXyE3KIzepe405Nxk6dKjGjx+vfv36+bSFT0hIiIYOHap33nlHb775ZoMXdh1DbgKgrpCblEduUvcac25yqiA3ASSLpyEfTwWAGiqdLI4dO1b33nuv0c7JydGKFSuUmpqq/Px8xcfHq0uXLurWrVtDhGrweDzasmWLtm7dqqysLOXl5Sk0NFTR0dFq3bq12rdvX+3NQmnFxcXaunWr9u7dq7S0NBUUFMhqtSoqKkpt2rRRly5dFBsbW6P4du7cqd27dys1NVV5eXnyeDyKiIhQs2bN1LVrV5122mkn8M5r7uDBg1q7dq3S09NVUFCgmJgYNW3aVH379lVkZGS9xFBTGRkZ2rp1q/bv36+srCwVFxcrLCxMcXFx6tChgzp27OjTkzs4+eXl5WnlypXas2ePCgsLlZCQoCZNmqhPnz6KiIho6PAA1BFyE3KTQENugmPITYDGidyE3CTQNObcxOVyadeuXdq9e7cOHz5s/OxERkYqOjpaHTp0UIcOHRrNahDkJkDjRG5CbhJoGnNuAjNyE5xsKO4CcFKp6kYAAACgvpGbAACAQEJuAgAAAgm5CQAA/sG2jAAAAAAAAAAAAAAAAAAQgCjuAgAAAAAAAAAAAAAAAIAARHEXAAAAAAAAAAAAAAAAAAQgirsAAAAAAAAAAAAAAAAAIAAFN3QAAAA0Rr/++qtGjRrl1zk3btzo1/nqU9euXf0638yZM9WvXz+/zgkAwKmM3MSM3AQAgIZFbmJGbgIAQMMiNzEjNwHqH8VdAAA0AI/HI5fL1dBhBAx/fy08Ho9f5wMA4FRHbmJGbgIAQMMiNzEjNwEAoGGRm5iRmwD1j+IuACeVLVu2NHQIAAAABnITAAAQSMhNAABAICE3AQDAPyweyiABAAAAAAAAAAAAAAAAIOAENXQAAAAAAAAAAAAAAAAAAIDyKO4CAAAAAAAAAAAAAAAAgABEcRcAAAAAAAAAAAAAAAAABCCKuwAAAAAAAAAAAAAAAAAgAFHcBQAAAAAAAAAAAAAAAAABiOIuAAAAAAAAAAAAAAAAAAhAFHcBAAAAAAAAAAAAAAAAQACiuAsAAAAAAAAAAAAAAAAAAhDFXQAAAAAAAAAAAAAAAAAQgCjuAgAAAAAAAAAAAAAAAIAARHEXAAAAAAAAAAAAAAAAAASg4IYOAAAAAAAAAKe2vXv3atu2bUpNTVV+fr7cbreioqIUHR2ttm3bqmPHjrLb7dXOs3//fg0ZMsRoDxs2TC+88EJdho4ANWLECP36669Ge8uWLQ0YDQAAAAAAQN2huAsAAAAAAAB+t27dOs2aNUsLFy7U0aNHqxxrs9l0xhln6LLLLtOll16quLi4eooSAAAAAAAACGwUdwEAAAAAAMBvtm/frokTJ2rJkiU+n+N0OrVq1SqtWrVKL7zwgm644QaNHj1a8fHxdRgpAAAAAAAAEPgo7gIAAAAAAIBfzJo1S88884yKiorKvRYREaF27dopPj5ekZGRysrKUlpamvbs2SOHw2GMKy4u1nvvvaeffvpJ3377bX2GDwAAAAAAAAQcirsAAAAAAABQa2+++aZeeeUVU19QUJD+/Oc/66qrrlLv3r1ls9nKnedwOLR06VLNnz9fX375pUpKSiR5i7wAAAAAAACAxo7iLgAAAAAAANTK999/r0mTJpn6UlJSNHnyZHXu3LnKc0NDQzV48GANHjxYo0eP1pQpU/TNN9/UZbgAAAAAAADASSOooQMAAAAAAADAyWvfvn2aMGGCPB6P0de9e3d9/PHH1RZ2ldWmTRtNmTJFzz33nEJCQvwdKgAAAAAAAHDSYeUuAAAAAAAAnLCXXnpJOTk5Rjs6Olqvvfaa4uLiTnjOq666Sn379vVHeAAAAAAAAMBJjeIuAAAAAAAAnJDdu3frf//7n6lvwoQJatq0aa3nbtWq1Qmdt2vXLm3evFmHDx9WSUmJ4uPj1a1bN3Xq1KlW8WRlZWn79u3as2ePsrOzVVRUpMjISMXGxqpLly5q166dLBZLra5Rkc2bN2v79u06fPiwLBaL4uPj1atXL7Vu3dqv18nIyNCaNWuUmpqqvLw8xcbGKiUlRT179pTNZqv1/CUlJVq/fr327dunzMxMFRcXKy4uTi1atFDv3r3rdaW29PR0bdq0Sfv27VNeXp5cLpdCQ0MVGxur0047TR06dFB8fHy9xQMAAAAAAFAVirsAAAAAAABwQt577z253W6jnZiYqCuuuKJBYlm0aJFmzJihtWvXVvh6y5YtNW7cuBrFt27dOn3zzTf65ZdftG3bNtPWk2XFxsbqmmuu0ciRI5WUlOTT/MuXL9fNN99stMeOHat7771XkjR37ly9++672rp1a4Xndu7cWePHj9fAgQN9utb555+vAwcOSJKaN2+uhQsXSpJ27NihSZMm6ccff5TT6Sx3XmRkpG655RbdfvvtCg0N9elape3YsUPTp0/XDz/8oLy8vArHhIaG6vzzz9e4cePUtm3bGl/DV/Pnz9fMmTO1Zs2aKr+Xkre4cNCgQRo5cqRatmxZZzEBAAAAAABUJ6ihAwAAAAAAAMDJ6eeffza1hw8f7pdVnmrC5XLpmWee0ejRoyst7JKkffv26e9//7uefvrpagt7JOm7777Ttddeq3//+9/aunVrtedkZWXprbfe0uWXX65ffvmlpm/DUFBQoHHjxmnChAmVFnZJ3hW97rjjDr311lsnfK3Zs2frqquu0oIFCyos7JKkvLw8TZ06Vbfccotp+83quFwuPffcc7r88sv11VdfVVrYJUkOh0Pz5s3T5Zdfrvfee6/G76M6xcXFGjdunMaNG6fVq1f79P3fu3evPvjgAy1btszv8QAAAAAAANQEK3cBAAAAAACgxlJTU7Vv3z5T31lnnVXvcUycOFEfffSRJMlisahTp05q2bKl7Ha7Dh48qA0bNqikpMQY/9FHH6l9+/a68cYbq5y39IpkkmS1WtW6dWu1aNFCkZGRslgsysrK0tatW5WWlmaMy8rK0l133aUPPvhAvXr1qtF7cbvdGj9+vLGqltVqVbdu3dSsWTMFBQVp79692rhxo6k46eWXX1bHjh01aNCgGl3rm2++0T//+U9jrubNm6tDhw6KjIxUZmam1q5dq/z8fGP86tWr9fjjj2vKlCnVzu1wOHTPPfdo8eLFpn6bzabOnTuradOmstvtSktL04YNG1RYWCjJu3Xjc889p5ycHGMFM394+umnNX/+fFNfSEiIEUtISIgKCwuVlZWlHTt2KCMjw2/XBgAAAAAAqC2KuwAAAAAAAFBjq1evNrUtFotOP/30eo3hhx9+UGZmpiTpmmuu0dixY9W0aVPTmMOHD+uxxx7Tjz/+aPS98soruvLKKxUeHl7l/NHR0briiis0ePBg9e3bVyEhIRWOW7NmjSZPnqzly5dLkpxOp/72t79p/vz5stvtPr+fTz75RJmZmbJarbrtttt06623Ki4uzjRm586d+sc//qH169cbfRMnTtTAgQNlsVh8uk5mZqYmTJggj8ejvn376qGHHtIZZ5xhGuNwODRt2jS9+eabRt8333yjv/71r+rTp0+V8z/11FOmwq6oqCiNHTtWV199tSIjI8td5+OPP9aUKVNUVFQkSZo2bZrOOOOMGhesVWTHjh2aNWuW0Q4LC9P48eN19dVXKywsrMJz9u3bp59++kmzZ8+u9fUBAAAAAABqi20ZAQAAAAAAUGOHDx82tRMTExUTE1OvMRwr7HriiSf07LPPlivskqTk5GRNnz5d3bp1M/ry8vL09ddfVzl3v3799NNPP+mxxx7TueeeW2lhlyT16tVL7733noYPH270HTx4UF999VWN34/VatW0adM0fvz4coVdkpSSkqJ///vfatasmdG3Z8+eGm0fWFBQIIfDocsvv1wzZ84sV9glSaGhoRo/frxuvvlmU/9nn31W5dzffPON5syZY7SbN2+uzz//XKNGjSpX2HXsOrfeeqtmzpxpfI09Ho8mTpxYbvW0E3FsFbRjHn/8cY0YMaLSwi5JatmypW666SbNmTNHV1xxRa1jAAAAAAAAqA2KuwAAAAAAAFBjWVlZpnZUVFSDxHH55ZdXu8VicHCwxo0bZ+qrrhgqPj6+ygKgsiwWix5//HElJCQYfV9++aXP5x9zxx13aPDgwVWOiYyM1B133GHqq0lxlyS1bt1aEydOVHBw1Qv733PPPbLZbD5dx+PxaPr06UY7ODhY06dPV4sWLaqNp3fv3qbv0Z49e7RgwYJqz6vOwYMHTe2hQ4fW6PyqivoAAAAAAADqA8VdAAAAAAAAqLGyxV3R0dENEseYMWN8GnfOOeeYipQ2bdrk91jCwsI0cOBAo71u3boarT4VGhqqW265xaex5513nqm9ceNGn68jSbfccotCQ0OrHRcbG6tevXoZ7SNHjujo0aMVjl22bJm2bt1qtP/85z+rc+fOPsd00003mYqpyq665Q8ZGRl+nxMAAAAAAKAuUdwFAAAAAACAk1LLli3Vrl07n8babDa1atXKaFdWoOSLoqIiZWRk6MCBA9q/f7/pT0REhDEuPz9fqampPs/bs2dPxcbG+jS2efPmppXFavp+yhaHVaXs17iyay1ZssTUvuSSS2oUU1hYmGmLyJUrV9bo/IqkpKSY2i+++KKcTmet5wUAAAAAAKgvVa+7DgAAAAAAAFSgbBFSbm5uvcfQvn37Go0vvbpYXl6ez+etW7dO3377rdasWaPt27fX6L3m5OTotNNO82lsTd9PVFSUCgsLJdXs/YSHh6tZs2Y+j4+JiTG1K3v/q1atMrVjY2O1f/9+n68jebecPObAgQNyu90KCjrx51Mvuugi/etf/1JxcbEk6X//+58uueQSXXPNNRoyZEiNv+YAAAAAAAD1jeIuAAAAAAAA1FjZ4q6cnJx6j6GmW0GW3paxpKSk2vFbt27V008/rRUrVtQ4tmNqUghW1+/nmLLFWtUJDjb/CrGya5Vdpezaa6+t0XXKcrvdysnJ8Xk1s4okJyfrwQcf1HPPPWf07du3T5MmTdKkSZOUkJCgXr16qVevXurTp49OP/30cu8XAAAAAACgIfGbCgAAAAAAANRYkyZNTO309HTl5OTUuECpNmqzolN1Vq5cqTvvvFP5+fm1msftdvs8ti7fT31cJzMz0+9z5ufn16q4S5JGjhyphIQEvfDCC0pLSzO9dvToUS1YsEALFiyQ5C1aHDp0qEaNGlVuS0cAAAAAAICGQHEXAAAAAAAAaqx3796mtsfj0YYNG3TOOec0UET+k5eXp/vvv99U2BUVFaVLL71U/fv3V0pKipo0aaLw8HCFhISYiqWmTp2q1157rSHCbnBOp9Pvc3o8Hr/Mc/nll+uCCy7Qt99+q/nz52vVqlXKzs4uNy4rK0ufffaZZs2apdtuu01/+9vf6q3oDgAAAAAAoCIUdwEAAAAAAKDGmjVrphYtWmj//v1G3/Lly0+J4q5PPvnEtMJTjx49NGPGDMXHx1d7bl5eXl2GFtDi4uJ05MgRSVJISIjWrVsni8XSwFEdFxoaqiuvvFJXXnml3G63duzYobVr12rVqlVavHix6Xvudrv11ltvyel06uGHH27AqAEAAAAAQGPHY2cAAAAAAAA4IX/6059M7Tlz5tTJ6k317fvvvzeOLRaLXnnlFZ8KuyQZxU2NUWJionFcVFSkgwcPNmA0VQsKClKHDh10zTXX6IUXXtDPP/+sjz76SOeee65p3Pvvv6+dO3c2UJQAAAAAAAAUdwEAAAAAAOAEjRw50rRlXVpamr788ssGjMg/9uzZYxy3a9dOLVu29PnctWvX1kFEJ4devXqZ2r/88ksDRVJzFotFffr00dtvv63Bgwcb/W63WwsXLmzAyAAAAAAAQGNHcRcAAAAAAABOSNu2bXXhhRea+l544QUdPny41nPv3bu31nOcqNzcXOM4KirK5/OWLl0a0KtV1bWyq17NmjWrgSI5cRaLRcOHDzf1ld56FAAAAAAAoL5R3AUAAAAAAIATNn78eEVGRhrt7OxsjRs3TtnZ2Sc85+zZs3XXXXf5I7wTEh0dbRzv3r1bbre72nOcTqcmTZpUl2EFvIEDB6pVq1ZGe/369frvf//bgBGdGKvVamrb7fYGigQAAAAAAIDiLgAAAAAAANRC69at9fzzz5v61q5dq5tuuklbt26t0Vx79uzR/fffr0ceeURFRUX+DLNGOnfubBxnZmZWuwKVy+XS448/rvXr19d1aAEtODhY48aNM/U9+eST+u6772o815IlS7Rv375ax/Tpp5/WeDW1L774wtRu165dreMAAAAAAAA4URR3AQAAAAAAoFYuuugi3Xfffaa+bdu26S9/+YsmTJigFStWyOl0Vniuw+HQDz/8oAkTJujSSy/VN998Ux8hV+mSSy4xtZ955hm9//77Ki4uLjd2/fr1GjlypObMmSNJio+Pr5cYA9Wf//xnXXXVVUbb6XTq3nvv1fjx4/Xbb79Vep7L5dLGjRv12muv6dJLL9Utt9zily0uP/nkE11wwQUaPXq0Zs+erSNHjlQ69uDBg3rooYc0f/58oy88PFwXXXRRreMAAAAAAAA4UcENHQAAAAAAAABOfmPGjFFcXJyee+45owjK7XZr7ty5mjt3riIiItS+fXvFx8crIiJCWVlZSk9P1+7du+VwOMrNFxoaWt9vwTBs2DB98MEH2rJliyRvgdLEiRP12muv6YwzzlBsbKzy8vK0detWHThwwDivX79+6t27t2bMmNFQoQeEp556Sjk5Ofrf//5n9H311Vf66quvFB8fr86dOys2NlZBQUHKy8vTkSNHtGPHjjpbrc3lcmnRokVatGiRJCkpKUkpKSmKjY1VSEiICgoKtHv3bu3YsUMej8d07iOPPKK4uLg6iQsAAAAAAMAXFHcBAAAAAADAL2644QadeeaZevbZZ7V8+XLTa/n5+Vq3bl21c4SGhmrEiBG688476yrMagUHB2vGjBkaOXKk9u7da/RnZ2fr559/rvCcs88+W1OnTtW///3v+gozYNlsNk2dOlVvv/22pk6dairaysjI0JIlS3yaIywsrE7iS0tLU1paWpVjQkND9eijj+qaa66pkxgAAAAAAAB8RXEXAAAAAAAA/KZjx456//33tWbNGv33v//VwoULlZGRUeU5drtdPXv21OWXX65LLrlE0dHR9RRt5U477TTNnTtXU6ZM0X//+18VFhZWOK5r1666/vrrdc011ygoKKieowxcFotFd9xxh6688kq9//77+vrrr02rnFUkIiJCffr00aBBg3TppZf6ZcWsSZMmaeHChVq8eLHWr1+vgoKCKscnJCTo4osv1h133KFmzZrV+voAAAAAAAC1ZfGUXWscAAAAAAAA8KPdu3dr+/btSk1NVX5+vtxut6KjoxUTE6O2bduqY8eOstlsDR1mpQoKCrRmzRrt2rVLeXl5ioqKUmJiojp37qzWrVs3dHgnjX379mnjxo3KyMhQTk6OLBaLIiIilJycrLZt26pNmzayWq11dn2Xy6Vdu3Zp9+7dOnz4sPLz8+VyuRQeHq7ExER16NBB7dq1q9MYAAAAAAAAaoriLgAAAAAAAAAAAAAAAAAIQKwVDwAAAAAAAAAAAAAAAAABiOIuAAAAAAAAAAAAAAAAAAhAFHcBAAAAAAAAAAAAAAAAQACiuAsAAAAAAAAAAAAAAAAAAhDFXQAAAAAAAAAAAAAAAAAQgCjuAgAAAAAAAAAAAAAAAIAARHEXAAAAAAAAAAAAAAAAAAQgirsAAAAAAAAAAAAAAAAAIABR3AUAAAAAAAAAAAAAAAAAAYjiLgAAAAAAAAAAAAAAAAAIQBR3AQAAAAAAAAAAAAAAAEAAorgLAAAAAAAAAAAAAAAAAAIQxV0AAAAAAAAAAAAAAAAAEIAo7gIAAAAAAAAAAAAAAACAAERxFwAAAAAAAAAAAAAAAAAEIIq7AAAAAAAAAAAAAAAAACAAUdwFAAAAAAAAAAAAAAAAAAGI4i4AAAAAAAAAAAAAAAAACEAUdwEAAAAAAAAAAAAAAABAAKK4CwAAAAAAAAAAAAAAAAACEMVdAAAAAAAAAAAAAAAAABCAKO4CAAAAAAAAAAAAAAAAgABEcRcAAAAAAAAAAAAAAAAABCCKuwAAAAAAAAAAAAAAAAAgAFHcBQAAAAAAAAAAAAAAAAABiOIuAAAAAAAAAAAAAAAAAAhA/x+nSUTkwmU9uAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 511,
+       "width": 1211
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "merged_allocation_norm = (\n",
+    "    allocation.values / budget_per_time_unit_in_horizon\n",
+    ") * _normalize_factor\n",
+    "\n",
+    "# Create bar plot\n",
+    "fig, ax = plt.subplots(figsize=(12, 5))\n",
+    "\n",
+    "x = np.arange(len(channel_names))\n",
+    "width = 0.25\n",
+    "\n",
+    "# Create bars\n",
+    "bars1 = ax.bar(\n",
+    "    x - width, allocation_reengage_norm, width, label=\"Reengage\", color=\"lightblue\"\n",
+    ")\n",
+    "bars2 = ax.bar(x, allocation_new_users_norm, width, label=\"New Users\", color=\"orange\")\n",
+    "bars3 = ax.bar(\n",
+    "    x + width, merged_allocation_norm, width, label=\"Merged\", color=\"lightgreen\"\n",
+    ")\n",
+    "\n",
+    "# Add value labels inside bars (white text)\n",
+    "for _i, (bar1, bar2, bar3) in enumerate(zip(bars1, bars2, bars3, strict=False)):\n",
+    "    # Reengage values\n",
+    "    height1 = bar1.get_height()\n",
+    "    if height1 > 0:\n",
+    "        ax.text(\n",
+    "            bar1.get_x() + bar1.get_width() / 2.0,\n",
+    "            height1 / 2,\n",
+    "            f\"{height1:.1f}%\",\n",
+    "            ha=\"center\",\n",
+    "            va=\"center\",\n",
+    "            color=\"white\",\n",
+    "            fontweight=\"bold\",\n",
+    "        )\n",
+    "\n",
+    "    # New Users values\n",
+    "    height2 = bar2.get_height()\n",
+    "    if height2 > 0:\n",
+    "        ax.text(\n",
+    "            bar2.get_x() + bar2.get_width() / 2.0,\n",
+    "            height2 / 2,\n",
+    "            f\"{height2:.1f}%\",\n",
+    "            ha=\"center\",\n",
+    "            va=\"center\",\n",
+    "            color=\"white\",\n",
+    "            fontweight=\"bold\",\n",
+    "        )\n",
+    "\n",
+    "    # Merged values\n",
+    "    height3 = bar3.get_height()\n",
+    "    if height3 > 0:\n",
+    "        ax.text(\n",
+    "            bar3.get_x() + bar3.get_width() / 2.0,\n",
+    "            height3 / 2,\n",
+    "            f\"{height3:.1f}%\",\n",
+    "            ha=\"center\",\n",
+    "            va=\"center\",\n",
+    "            color=\"white\",\n",
+    "            fontweight=\"bold\",\n",
+    "        )\n",
+    "\n",
+    "# Customize plot\n",
+    "ax.set_xlabel(\"Channels\")\n",
+    "ax.set_ylabel(\"Budget Allocation (%)\")\n",
+    "ax.set_title(\"Budget Allocation Comparison: New Users vs Reengage vs Merged\")\n",
+    "ax.set_xticks(x)\n",
+    "ax.set_xticklabels(channel_names)\n",
+    "ax.set_ylim(0, 45)\n",
+    "ax.grid(True, alpha=0.3)\n",
+    "ax.legend()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Amazing! The new allocation is more balanced between channels, showing that we are not optimizing for a single objective. 👏🏻\n",
+    "\n",
+    "What else can we do? Let's now look at an example using a native `MMM` model from PyMC-Marketing.\n",
+    "\n",
+    "## Optimizing MultiDimensional MMM Models\n",
+    "\n",
+    "With the PyMC-Marketing class, the process barely changes:\n",
+    "\n",
+    "1. We load or build our MMM model (Multidimensional or not).\n",
+    "2. We wrap the model in the optimizer class (avoid this if you are using the old API).\n",
+    "3. We pass the new merged model to the budget optimizer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_path = data_dir / \"multidimensional_mock_data.csv\"\n",
+    "data_df = pd.read_csv(data_path, parse_dates=[\"date\"], index_col=0)\n",
+    "x_train = data_df.drop(columns=[\"y\"])\n",
+    "y_train = data_df[\"y\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mmm1 = build_mmm_from_yaml(\n",
+    "    X=x_train,\n",
+    "    y=y_train,\n",
+    "    config_path=data_dir / \"config_files\" / \"multi_dimensional_example_model.yml\",\n",
+    ")\n",
+    "\n",
+    "mmm2 = build_mmm_from_yaml(\n",
+    "    X=x_train,\n",
+    "    y=y_train,\n",
+    "    config_path=data_dir / \"config_files\" / \"multi_dimensional_example_model.yml\",\n",
+    ")\n",
+    "\n",
+    "mmm3 = build_mmm_from_yaml(\n",
+    "    X=x_train,\n",
+    "    y=y_train,\n",
+    "    config_path=data_dir / \"config_files\" / \"multi_dimensional_example_model.yml\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once the models are built, we use the wrappers!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max_date = new_users_idata.posterior.coords[\"date\"].max().item()\n",
+    "start_date = (pd.Timestamp(max_date) + pd.Timedelta(weeks=1)).strftime(\"%Y-%m-%d\")\n",
+    "\n",
+    "end_date = (\n",
+    "    pd.Timestamp(start_date) + pd.Timedelta(weeks=optimization_horizon)\n",
+    ").strftime(\"%Y-%m-%d\")\n",
+    "\n",
+    "w1 = MultiDimensionalBudgetOptimizerWrapper(\n",
+    "    model=mmm1, start_date=start_date, end_date=end_date\n",
+    ")\n",
+    "w2 = MultiDimensionalBudgetOptimizerWrapper(\n",
+    "    model=mmm2, start_date=start_date, end_date=end_date\n",
+    ")\n",
+    "w3 = MultiDimensionalBudgetOptimizerWrapper(\n",
+    "    model=mmm3, start_date=start_date, end_date=end_date\n",
+    ")\n",
+    "\n",
+    "merged = BuildMergedModel(\n",
+    "    models=[w1, w2, w3],\n",
+    "    prefixes=[\"north\", \"south\", \"west\"],\n",
+    "    merge_on=\"channel_data\",\n",
+    "    use_every_n_draw=5,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Done! Our merged model is ready to use. Let's visualize it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
+       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
+       "<!-- Generated by graphviz version 13.1.1 (0)\n",
+       " -->\n",
+       "<!-- Pages: 1 -->\n",
+       "<svg width=\"8606pt\" height=\"718pt\"\n",
+       " viewBox=\"0.00 0.00 8606.00 718.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 713.59)\">\n",
+       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-713.59 8601.76,-713.59 8601.76,4 -4,4\"/>\n",
+       "<g id=\"clust1\" class=\"cluster\">\n",
+       "<title>clustergeo (2)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M20,-113.41C20,-113.41 1168,-113.41 1168,-113.41 1174,-113.41 1180,-119.41 1180,-125.41 1180,-125.41 1180,-328.63 1180,-328.63 1180,-334.63 1174,-340.63 1168,-340.63 1168,-340.63 20,-340.63 20,-340.63 14,-340.63 8,-334.63 8,-328.63 8,-328.63 8,-125.41 8,-125.41 8,-119.41 14,-113.41 20,-113.41\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1152.5\" y=\"-120.61\" font-family=\"Times,serif\" font-size=\"14.00\">geo (2)</text>\n",
+       "</g>\n",
+       "<g id=\"clust2\" class=\"cluster\">\n",
+       "<title>clusterchannel (2)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2897,-348.63C2897,-348.63 4693,-348.63 4693,-348.63 4699,-348.63 4705,-354.63 4705,-360.63 4705,-360.63 4705,-458.45 4705,-458.45 4705,-464.45 4699,-470.45 4693,-470.45 4693,-470.45 2897,-470.45 2897,-470.45 2891,-470.45 2885,-464.45 2885,-458.45 2885,-458.45 2885,-360.63 2885,-360.63 2885,-354.63 2891,-348.63 2897,-348.63\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"4666.25\" y=\"-355.83\" font-family=\"Times,serif\" font-size=\"14.00\">channel (2)</text>\n",
+       "</g>\n",
+       "<g id=\"clust3\" class=\"cluster\">\n",
+       "<title>clusternorth_control (2)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M7536,-348.63C7536,-348.63 7722,-348.63 7722,-348.63 7728,-348.63 7734,-354.63 7734,-360.63 7734,-360.63 7734,-458.45 7734,-458.45 7734,-464.45 7728,-470.45 7722,-470.45 7722,-470.45 7536,-470.45 7536,-470.45 7530,-470.45 7524,-464.45 7524,-458.45 7524,-458.45 7524,-360.63 7524,-360.63 7524,-354.63 7530,-348.63 7536,-348.63\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"7679.5\" y=\"-355.83\" font-family=\"Times,serif\" font-size=\"14.00\">north_control (2)</text>\n",
+       "</g>\n",
+       "<g id=\"clust4\" class=\"cluster\">\n",
+       "<title>clustergeo (2) x north_fourier_mode (4)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5279,-478.45C5279,-478.45 5463,-478.45 5463,-478.45 5469,-478.45 5475,-484.45 5475,-490.45 5475,-490.45 5475,-588.27 5475,-588.27 5475,-594.27 5469,-600.27 5463,-600.27 5463,-600.27 5279,-600.27 5279,-600.27 5273,-600.27 5267,-594.27 5267,-588.27 5267,-588.27 5267,-490.45 5267,-490.45 5267,-484.45 5273,-478.45 5279,-478.45\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5375.88\" y=\"-485.65\" font-family=\"Times,serif\" font-size=\"14.00\">geo (2) x north_fourier_mode (4)</text>\n",
+       "</g>\n",
+       "<g id=\"clust5\" class=\"cluster\">\n",
+       "<title>clusternorth_changepoint (5) x geo (2)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M6907,-348.63C6907,-348.63 7073,-348.63 7073,-348.63 7079,-348.63 7085,-354.63 7085,-360.63 7085,-360.63 7085,-458.45 7085,-458.45 7085,-464.45 7079,-470.45 7073,-470.45 7073,-470.45 6907,-470.45 6907,-470.45 6901,-470.45 6895,-464.45 6895,-458.45 6895,-458.45 6895,-360.63 6895,-360.63 6895,-354.63 6901,-348.63 6907,-348.63\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6990\" y=\"-355.83\" font-family=\"Times,serif\" font-size=\"14.00\">north_changepoint (5) x geo (2)</text>\n",
+       "</g>\n",
+       "<g id=\"clust6\" class=\"cluster\">\n",
+       "<title>clusterdate (10) x geo (2)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5607,-8C5607,-8 6926,-8 6926,-8 6932,-8 6938,-14 6938,-20 6938,-20 6938,-316.73 6938,-316.73 6938,-322.73 6932,-328.73 6926,-328.73 6926,-328.73 5607,-328.73 5607,-328.73 5601,-328.73 5595,-322.73 5595,-316.73 5595,-316.73 5595,-20 5595,-20 5595,-14 5601,-8 5607,-8\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6879\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">date (10) x geo (2)</text>\n",
+       "</g>\n",
+       "<g id=\"clust7\" class=\"cluster\">\n",
+       "<title>clusterdate (10)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5719,-490.36C5719,-490.36 6381,-490.36 6381,-490.36 6387,-490.36 6393,-496.36 6393,-502.36 6393,-502.36 6393,-576.36 6393,-576.36 6393,-582.36 6387,-588.36 6381,-588.36 6381,-588.36 5719,-588.36 5719,-588.36 5713,-588.36 5707,-582.36 5707,-576.36 5707,-576.36 5707,-502.36 5707,-502.36 5707,-496.36 5713,-490.36 5719,-490.36\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6360.62\" y=\"-497.56\" font-family=\"Times,serif\" font-size=\"14.00\">date (10)</text>\n",
+       "</g>\n",
+       "<g id=\"clust8\" class=\"cluster\">\n",
+       "<title>clusterdate (10) x geo (2) x north_fourier_mode (4)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5607,-360.54C5607,-360.54 5844,-360.54 5844,-360.54 5850,-360.54 5856,-366.54 5856,-372.54 5856,-372.54 5856,-446.54 5856,-446.54 5856,-452.54 5850,-458.54 5844,-458.54 5844,-458.54 5607,-458.54 5607,-458.54 5601,-458.54 5595,-452.54 5595,-446.54 5595,-446.54 5595,-372.54 5595,-372.54 5595,-366.54 5601,-360.54 5607,-360.54\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5725.38\" y=\"-367.74\" font-family=\"Times,serif\" font-size=\"14.00\">date (10) x geo (2) x north_fourier_mode (4)</text>\n",
+       "</g>\n",
+       "<g id=\"clust9\" class=\"cluster\">\n",
+       "<title>clusterdate (10) x geo (2) x north_control (2)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M7303,-230.73C7303,-230.73 7504,-230.73 7504,-230.73 7510,-230.73 7516,-236.73 7516,-242.73 7516,-242.73 7516,-446.54 7516,-446.54 7516,-452.54 7510,-458.54 7504,-458.54 7504,-458.54 7303,-458.54 7303,-458.54 7297,-458.54 7291,-452.54 7291,-446.54 7291,-446.54 7291,-242.73 7291,-242.73 7291,-236.73 7297,-230.73 7303,-230.73\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"7403.38\" y=\"-237.93\" font-family=\"Times,serif\" font-size=\"14.00\">date (10) x geo (2) x north_control (2)</text>\n",
+       "</g>\n",
+       "<g id=\"clust10\" class=\"cluster\">\n",
+       "<title>clustergeo (2) x channel (2)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M4725,-360.54C4725,-360.54 5132,-360.54 5132,-360.54 5138,-360.54 5144,-366.54 5144,-372.54 5144,-372.54 5144,-446.54 5144,-446.54 5144,-452.54 5138,-458.54 5132,-458.54 5132,-458.54 4725,-458.54 4725,-458.54 4719,-458.54 4713,-452.54 4713,-446.54 4713,-446.54 4713,-372.54 4713,-372.54 4713,-366.54 4719,-360.54 4725,-360.54\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5078.62\" y=\"-367.74\" font-family=\"Times,serif\" font-size=\"14.00\">geo (2) x channel (2)</text>\n",
+       "</g>\n",
+       "<g id=\"clust11\" class=\"cluster\">\n",
+       "<title>clusterdate (10) x geo (2) x channel (2)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2095,-113.41C2095,-113.41 2865,-113.41 2865,-113.41 2871,-113.41 2877,-119.41 2877,-125.41 2877,-125.41 2877,-446.54 2877,-446.54 2877,-452.54 2871,-458.54 2865,-458.54 2865,-458.54 2095,-458.54 2095,-458.54 2089,-458.54 2083,-452.54 2083,-446.54 2083,-446.54 2083,-125.41 2083,-125.41 2083,-119.41 2089,-113.41 2095,-113.41\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2780.12\" y=\"-120.61\" font-family=\"Times,serif\" font-size=\"14.00\">date (10) x geo (2) x channel (2)</text>\n",
+       "</g>\n",
+       "<g id=\"clust12\" class=\"cluster\">\n",
+       "<title>clustersouth_control (2)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M8192,-348.63C8192,-348.63 8380,-348.63 8380,-348.63 8386,-348.63 8392,-354.63 8392,-360.63 8392,-360.63 8392,-458.45 8392,-458.45 8392,-464.45 8386,-470.45 8380,-470.45 8380,-470.45 8192,-470.45 8192,-470.45 8186,-470.45 8180,-464.45 8180,-458.45 8180,-458.45 8180,-360.63 8180,-360.63 8180,-354.63 8186,-348.63 8192,-348.63\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"8337.12\" y=\"-355.83\" font-family=\"Times,serif\" font-size=\"14.00\">south_control (2)</text>\n",
+       "</g>\n",
+       "<g id=\"clust13\" class=\"cluster\">\n",
+       "<title>clustergeo (2) x south_fourier_mode (4)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5501,-478.45C5501,-478.45 5687,-478.45 5687,-478.45 5693,-478.45 5699,-484.45 5699,-490.45 5699,-490.45 5699,-588.27 5699,-588.27 5699,-594.27 5693,-600.27 5687,-600.27 5687,-600.27 5501,-600.27 5501,-600.27 5495,-600.27 5489,-594.27 5489,-588.27 5489,-588.27 5489,-490.45 5489,-490.45 5489,-484.45 5495,-478.45 5501,-478.45\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5599.5\" y=\"-485.65\" font-family=\"Times,serif\" font-size=\"14.00\">geo (2) x south_fourier_mode (4)</text>\n",
+       "</g>\n",
+       "<g id=\"clust14\" class=\"cluster\">\n",
+       "<title>clustersouth_changepoint (5) x geo (2)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M6515,-348.63C6515,-348.63 6681,-348.63 6681,-348.63 6687,-348.63 6693,-354.63 6693,-360.63 6693,-360.63 6693,-458.45 6693,-458.45 6693,-464.45 6687,-470.45 6681,-470.45 6681,-470.45 6515,-470.45 6515,-470.45 6509,-470.45 6503,-464.45 6503,-458.45 6503,-458.45 6503,-360.63 6503,-360.63 6503,-354.63 6509,-348.63 6515,-348.63\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6597.62\" y=\"-355.83\" font-family=\"Times,serif\" font-size=\"14.00\">south_changepoint (5) x geo (2)</text>\n",
+       "</g>\n",
+       "<g id=\"clust15\" class=\"cluster\">\n",
+       "<title>clusterdate (10) x geo (2) x south_fourier_mode (4)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5876,-360.54C5876,-360.54 6114,-360.54 6114,-360.54 6120,-360.54 6126,-366.54 6126,-372.54 6126,-372.54 6126,-446.54 6126,-446.54 6126,-452.54 6120,-458.54 6114,-458.54 6114,-458.54 5876,-458.54 5876,-458.54 5870,-458.54 5864,-452.54 5864,-446.54 5864,-446.54 5864,-372.54 5864,-372.54 5864,-366.54 5870,-360.54 5876,-360.54\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5995\" y=\"-367.74\" font-family=\"Times,serif\" font-size=\"14.00\">date (10) x geo (2) x south_fourier_mode (4)</text>\n",
+       "</g>\n",
+       "<g id=\"clust16\" class=\"cluster\">\n",
+       "<title>clusterdate (10) x geo (2) x south_control (2)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M7958,-230.73C7958,-230.73 8160,-230.73 8160,-230.73 8166,-230.73 8172,-236.73 8172,-242.73 8172,-242.73 8172,-446.54 8172,-446.54 8172,-452.54 8166,-458.54 8160,-458.54 8160,-458.54 7958,-458.54 7958,-458.54 7952,-458.54 7946,-452.54 7946,-446.54 7946,-446.54 7946,-242.73 7946,-242.73 7946,-236.73 7952,-230.73 7958,-230.73\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"8059\" y=\"-237.93\" font-family=\"Times,serif\" font-size=\"14.00\">date (10) x geo (2) x south_control (2)</text>\n",
+       "</g>\n",
+       "<g id=\"clust17\" class=\"cluster\">\n",
+       "<title>clusterwest_control (2)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5164,-348.63C5164,-348.63 5346,-348.63 5346,-348.63 5352,-348.63 5358,-354.63 5358,-360.63 5358,-360.63 5358,-458.45 5358,-458.45 5358,-464.45 5352,-470.45 5346,-470.45 5346,-470.45 5164,-470.45 5164,-470.45 5158,-470.45 5152,-464.45 5152,-458.45 5152,-458.45 5152,-360.63 5152,-360.63 5152,-354.63 5158,-348.63 5164,-348.63\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5305.38\" y=\"-355.83\" font-family=\"Times,serif\" font-size=\"14.00\">west_control (2)</text>\n",
+       "</g>\n",
+       "<g id=\"clust18\" class=\"cluster\">\n",
+       "<title>clustergeo (2) x west_fourier_mode (4)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M6413,-478.45C6413,-478.45 6593,-478.45 6593,-478.45 6599,-478.45 6605,-484.45 6605,-490.45 6605,-490.45 6605,-588.27 6605,-588.27 6605,-594.27 6599,-600.27 6593,-600.27 6593,-600.27 6413,-600.27 6413,-600.27 6407,-600.27 6401,-594.27 6401,-588.27 6401,-588.27 6401,-490.45 6401,-490.45 6401,-484.45 6407,-478.45 6413,-478.45\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6507.75\" y=\"-485.65\" font-family=\"Times,serif\" font-size=\"14.00\">geo (2) x west_fourier_mode (4)</text>\n",
+       "</g>\n",
+       "<g id=\"clust19\" class=\"cluster\">\n",
+       "<title>clusterwest_changepoint (5) x geo (2)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M6713,-348.63C6713,-348.63 6875,-348.63 6875,-348.63 6881,-348.63 6887,-354.63 6887,-360.63 6887,-360.63 6887,-458.45 6887,-458.45 6887,-464.45 6881,-470.45 6875,-470.45 6875,-470.45 6713,-470.45 6713,-470.45 6707,-470.45 6701,-464.45 6701,-458.45 6701,-458.45 6701,-360.63 6701,-360.63 6701,-354.63 6707,-348.63 6713,-348.63\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6793.88\" y=\"-355.83\" font-family=\"Times,serif\" font-size=\"14.00\">west_changepoint (5) x geo (2)</text>\n",
+       "</g>\n",
+       "<g id=\"clust20\" class=\"cluster\">\n",
+       "<title>clusterdate (10) x geo (2) x west_fourier_mode (4)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M6250,-360.54C6250,-360.54 6483,-360.54 6483,-360.54 6489,-360.54 6495,-366.54 6495,-372.54 6495,-372.54 6495,-446.54 6495,-446.54 6495,-452.54 6489,-458.54 6483,-458.54 6483,-458.54 6250,-458.54 6250,-458.54 6244,-458.54 6238,-452.54 6238,-446.54 6238,-446.54 6238,-372.54 6238,-372.54 6238,-366.54 6244,-360.54 6250,-360.54\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6366.25\" y=\"-367.74\" font-family=\"Times,serif\" font-size=\"14.00\">date (10) x geo (2) x west_fourier_mode (4)</text>\n",
+       "</g>\n",
+       "<g id=\"clust21\" class=\"cluster\">\n",
+       "<title>clusterdate (10) x geo (2) x west_control (2)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5378,-230.73C5378,-230.73 5575,-230.73 5575,-230.73 5581,-230.73 5587,-236.73 5587,-242.73 5587,-242.73 5587,-446.54 5587,-446.54 5587,-452.54 5581,-458.54 5575,-458.54 5575,-458.54 5378,-458.54 5378,-458.54 5372,-458.54 5366,-452.54 5366,-446.54 5366,-446.54 5366,-242.73 5366,-242.73 5366,-236.73 5372,-230.73 5378,-230.73\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5476.25\" y=\"-237.93\" font-family=\"Times,serif\" font-size=\"14.00\">date (10) x geo (2) x west_control (2)</text>\n",
+       "</g>\n",
+       "<!-- south_target_scale -->\n",
+       "<g id=\"node1\" class=\"node\">\n",
+       "<title>south_target_scale</title>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M505.25,-320.73C505.25,-320.73 412.75,-320.73 412.75,-320.73 406.75,-320.73 400.75,-314.73 400.75,-308.73 400.75,-308.73 400.75,-275.23 400.75,-275.23 400.75,-269.23 406.75,-263.23 412.75,-263.23 412.75,-263.23 505.25,-263.23 505.25,-263.23 511.25,-263.23 517.25,-269.23 517.25,-275.23 517.25,-275.23 517.25,-308.73 517.25,-308.73 517.25,-314.73 511.25,-320.73 505.25,-320.73\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"459\" y=\"-303.43\" font-family=\"Times,serif\" font-size=\"14.00\">south_target_scale</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"459\" y=\"-286.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"459\" y=\"-270.43\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "</g>\n",
+       "<!-- south_intercept_contribution_original_scale -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>south_intercept_contribution_original_scale</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"714.25,-203.41 459.75,-203.41 459.75,-145.91 714.25,-145.91 714.25,-203.41\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"587\" y=\"-186.11\" font-family=\"Times,serif\" font-size=\"14.00\">south_intercept_contribution_original_scale</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"587\" y=\"-169.61\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"587\" y=\"-153.11\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- south_target_scale&#45;&gt;south_intercept_contribution_original_scale -->\n",
+       "<g id=\"edge60\" class=\"edge\">\n",
+       "<title>south_target_scale&#45;&gt;south_intercept_contribution_original_scale</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M490.31,-262.77C507.64,-247.15 529.34,-227.6 547.79,-210.98\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"549.8,-213.88 554.89,-204.59 545.11,-208.68 549.8,-213.88\"/>\n",
+       "</g>\n",
+       "<!-- south_total_media_contribution_original_scale -->\n",
+       "<g id=\"node30\" class=\"node\">\n",
+       "<title>south_total_media_contribution_original_scale</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"1783.88,-203.41 1512.12,-203.41 1512.12,-145.91 1783.88,-145.91 1783.88,-203.41\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1648\" y=\"-186.11\" font-family=\"Times,serif\" font-size=\"14.00\">south_total_media_contribution_original_scale</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1648\" y=\"-169.61\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1648\" y=\"-153.11\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- south_target_scale&#45;&gt;south_total_media_contribution_original_scale -->\n",
+       "<g id=\"edge56\" class=\"edge\">\n",
+       "<title>south_target_scale&#45;&gt;south_total_media_contribution_original_scale</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M481.74,-262.9C493.51,-250.58 508.97,-237.42 526,-230.73 574.56,-211.65 1413.17,-221.3 1465,-215.32 1484.12,-213.11 1504.19,-209.8 1523.74,-206.02\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1524.24,-209.49 1533.37,-204.11 1522.88,-202.62 1524.24,-209.49\"/>\n",
+       "</g>\n",
+       "<!-- south_y_original_scale -->\n",
+       "<g id=\"node50\" class=\"node\">\n",
+       "<title>south_y_original_scale</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"6023.62,-98 5882.38,-98 5882.38,-40.5 6023.62,-40.5 6023.62,-98\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5953\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">south_y_original_scale</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5953\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5953\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- south_target_scale&#45;&gt;south_y_original_scale -->\n",
+       "<g id=\"edge61\" class=\"edge\">\n",
+       "<title>south_target_scale&#45;&gt;south_y_original_scale</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M481.78,-263C493.56,-250.71 509.01,-237.54 526,-230.73 629.58,-189.17 921.79,-249.6 1028,-215.32 1105.05,-190.45 1101.61,-137.22 1179,-113.41 1239.96,-94.65 5611.15,-108.43 5871.04,-97.16\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"5871,-100.67 5880.73,-96.47 5870.51,-93.69 5871,-100.67\"/>\n",
+       "</g>\n",
+       "<!-- south_channel_contribution_original_scale -->\n",
+       "<g id=\"node66\" class=\"node\">\n",
+       "<title>south_channel_contribution_original_scale</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"2602.62,-203.41 2353.38,-203.41 2353.38,-145.91 2602.62,-145.91 2602.62,-203.41\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2478\" y=\"-186.11\" font-family=\"Times,serif\" font-size=\"14.00\">south_channel_contribution_original_scale</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2478\" y=\"-169.61\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2478\" y=\"-153.11\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- south_target_scale&#45;&gt;south_channel_contribution_original_scale -->\n",
+       "<g id=\"edge58\" class=\"edge\">\n",
+       "<title>south_target_scale&#45;&gt;south_channel_contribution_original_scale</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M481.72,-262.84C493.48,-250.51 508.94,-237.35 526,-230.73 573.08,-212.44 2294.05,-222.77 2344,-215.32 2357.44,-213.31 2371.42,-210.28 2385.05,-206.78\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2385.68,-210.23 2394.43,-204.27 2383.87,-203.47 2385.68,-210.23\"/>\n",
+       "</g>\n",
+       "<!-- west_intercept_contribution_original_scale -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>west_intercept_contribution_original_scale</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"370,-203.41 120,-203.41 120,-145.91 370,-145.91 370,-203.41\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"245\" y=\"-186.11\" font-family=\"Times,serif\" font-size=\"14.00\">west_intercept_contribution_original_scale</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"245\" y=\"-169.61\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"245\" y=\"-153.11\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- north_target_scale -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>north_target_scale</title>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M899.88,-320.73C899.88,-320.73 808.12,-320.73 808.12,-320.73 802.12,-320.73 796.12,-314.73 796.12,-308.73 796.12,-308.73 796.12,-275.23 796.12,-275.23 796.12,-269.23 802.12,-263.23 808.12,-263.23 808.12,-263.23 899.88,-263.23 899.88,-263.23 905.88,-263.23 911.88,-269.23 911.88,-275.23 911.88,-275.23 911.88,-308.73 911.88,-308.73 911.88,-314.73 905.88,-320.73 899.88,-320.73\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"854\" y=\"-303.43\" font-family=\"Times,serif\" font-size=\"14.00\">north_target_scale</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"854\" y=\"-286.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"854\" y=\"-270.43\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "</g>\n",
+       "<!-- north_intercept_contribution_original_scale -->\n",
+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>north_intercept_contribution_original_scale</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"1018.88,-203.41 765.12,-203.41 765.12,-145.91 1018.88,-145.91 1018.88,-203.41\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"892\" y=\"-186.11\" font-family=\"Times,serif\" font-size=\"14.00\">north_intercept_contribution_original_scale</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"892\" y=\"-169.61\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"892\" y=\"-153.11\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- north_target_scale&#45;&gt;north_intercept_contribution_original_scale -->\n",
+       "<g id=\"edge29\" class=\"edge\">\n",
+       "<title>north_target_scale&#45;&gt;north_intercept_contribution_original_scale</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M863.3,-262.77C868.12,-248.14 874.08,-230.05 879.31,-214.16\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"882.52,-215.61 882.33,-205.01 875.87,-213.42 882.52,-215.61\"/>\n",
+       "</g>\n",
+       "<!-- north_total_media_contribution_original_scale -->\n",
+       "<g id=\"node28\" class=\"node\">\n",
+       "<title>north_total_media_contribution_original_scale</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"2072.5,-203.41 1801.5,-203.41 1801.5,-145.91 2072.5,-145.91 2072.5,-203.41\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1937\" y=\"-186.11\" font-family=\"Times,serif\" font-size=\"14.00\">north_total_media_contribution_original_scale</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1937\" y=\"-169.61\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1937\" y=\"-153.11\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- north_target_scale&#45;&gt;north_total_media_contribution_original_scale -->\n",
+       "<g id=\"edge24\" class=\"edge\">\n",
+       "<title>north_target_scale&#45;&gt;north_total_media_contribution_original_scale</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M876.75,-262.91C888.51,-250.59 903.97,-237.43 921,-230.73 966.08,-212.98 1745.04,-222.23 1793,-215.32 1807.42,-213.24 1822.44,-210.16 1837.12,-206.63\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1837.93,-210.03 1846.78,-204.21 1836.23,-203.24 1837.93,-210.03\"/>\n",
+       "</g>\n",
+       "<!-- north_y_original_scale -->\n",
+       "<g id=\"node42\" class=\"node\">\n",
+       "<title>north_y_original_scale</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"6312.25,-98 6171.75,-98 6171.75,-40.5 6312.25,-40.5 6312.25,-98\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6242\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">north_y_original_scale</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6242\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6242\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- north_target_scale&#45;&gt;north_y_original_scale -->\n",
+       "<g id=\"edge31\" class=\"edge\">\n",
+       "<title>north_target_scale&#45;&gt;north_y_original_scale</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M876.78,-262.99C888.55,-250.69 904.01,-237.52 921,-230.73 977.15,-208.28 1415.82,-250.51 1465,-215.32 1504.31,-187.19 1463.34,-141.04 1503,-113.41 1528.89,-95.37 5904.77,-103.08 6160.3,-96.77\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"6160.33,-100.27 6170.16,-96.33 6160.02,-93.28 6160.33,-100.27\"/>\n",
+       "</g>\n",
+       "<!-- north_channel_contribution_original_scale -->\n",
+       "<g id=\"node67\" class=\"node\">\n",
+       "<title>north_channel_contribution_original_scale</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"2869.25,-203.41 2620.75,-203.41 2620.75,-145.91 2869.25,-145.91 2869.25,-203.41\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2745\" y=\"-186.11\" font-family=\"Times,serif\" font-size=\"14.00\">north_channel_contribution_original_scale</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2745\" y=\"-169.61\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2745\" y=\"-153.11\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- north_target_scale&#45;&gt;north_channel_contribution_original_scale -->\n",
+       "<g id=\"edge26\" class=\"edge\">\n",
+       "<title>north_target_scale&#45;&gt;north_channel_contribution_original_scale</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M876.72,-262.84C888.48,-250.51 903.94,-237.36 921,-230.73 964.78,-213.71 2565.55,-222.31 2612,-215.32 2625.35,-213.31 2639.22,-210.28 2652.76,-206.77\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2653.32,-210.25 2662.06,-204.26 2651.49,-203.49 2653.32,-210.25\"/>\n",
+       "</g>\n",
+       "<!-- north_intercept_contribution -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>north_intercept_contribution</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1051\" cy=\"-291.98\" rx=\"121.09\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1051\" y=\"-303.43\" font-family=\"Times,serif\" font-size=\"14.00\">north_intercept_contribution</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1051\" y=\"-286.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1051\" y=\"-270.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- north_intercept_contribution&#45;&gt;north_intercept_contribution_original_scale -->\n",
+       "<g id=\"edge28\" class=\"edge\">\n",
+       "<title>north_intercept_contribution&#45;&gt;north_intercept_contribution_original_scale</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1001.13,-254.81C981.64,-240.67 959.38,-224.53 940.07,-210.52\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"942.23,-207.76 932.08,-204.72 938.12,-213.43 942.23,-207.76\"/>\n",
+       "</g>\n",
+       "<!-- north_y -->\n",
+       "<g id=\"node43\" class=\"node\">\n",
+       "<title>north_y</title>\n",
+       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"6242\" cy=\"-174.66\" rx=\"82.38\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6242\" y=\"-186.11\" font-family=\"Times,serif\" font-size=\"14.00\">north_y</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6242\" y=\"-169.61\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6242\" y=\"-153.11\" font-family=\"Times,serif\" font-size=\"14.00\">Truncated_normal</text>\n",
+       "</g>\n",
+       "<!-- north_intercept_contribution&#45;&gt;north_y -->\n",
+       "<g id=\"edge19\" class=\"edge\">\n",
+       "<title>north_intercept_contribution&#45;&gt;north_y</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1168.85,-281.97C1359.83,-267.84 1748.87,-241.03 2079,-230.73 2299.17,-223.85 5824.84,-237.48 6044,-215.32 6081.61,-211.51 6122.67,-203.88 6157.59,-196.31\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"6158.21,-199.76 6167.23,-194.19 6156.71,-192.92 6158.21,-199.76\"/>\n",
+       "</g>\n",
+       "<!-- west_target_scale -->\n",
+       "<g id=\"node6\" class=\"node\">\n",
+       "<title>west_target_scale</title>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M116,-320.73C116,-320.73 28,-320.73 28,-320.73 22,-320.73 16,-314.73 16,-308.73 16,-308.73 16,-275.23 16,-275.23 16,-269.23 22,-263.23 28,-263.23 28,-263.23 116,-263.23 116,-263.23 122,-263.23 128,-269.23 128,-275.23 128,-275.23 128,-308.73 128,-308.73 128,-314.73 122,-320.73 116,-320.73\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"72\" y=\"-303.43\" font-family=\"Times,serif\" font-size=\"14.00\">west_target_scale</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"72\" y=\"-286.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"72\" y=\"-270.43\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "</g>\n",
+       "<!-- west_target_scale&#45;&gt;west_intercept_contribution_original_scale -->\n",
+       "<g id=\"edge90\" class=\"edge\">\n",
+       "<title>west_target_scale&#45;&gt;west_intercept_contribution_original_scale</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M99.02,-262.78C110.18,-252 123.63,-240.06 137,-230.73 147.99,-223.05 160.19,-215.79 172.32,-209.21\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"173.93,-212.32 181.13,-204.56 170.66,-206.13 173.93,-212.32\"/>\n",
+       "</g>\n",
+       "<!-- west_total_media_contribution_original_scale -->\n",
+       "<g id=\"node31\" class=\"node\">\n",
+       "<title>west_total_media_contribution_original_scale</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"1455.62,-203.41 1188.38,-203.41 1188.38,-145.91 1455.62,-145.91 1455.62,-203.41\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1322\" y=\"-186.11\" font-family=\"Times,serif\" font-size=\"14.00\">west_total_media_contribution_original_scale</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1322\" y=\"-169.61\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1322\" y=\"-153.11\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- west_target_scale&#45;&gt;west_total_media_contribution_original_scale -->\n",
+       "<g id=\"edge86\" class=\"edge\">\n",
+       "<title>west_target_scale&#45;&gt;west_total_media_contribution_original_scale</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M93.9,-262.9C105.28,-250.58 120.29,-237.42 137,-230.73 191,-209.09 1126.46,-223.85 1184,-215.32 1197.72,-213.28 1211.99,-210.26 1225.93,-206.77\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1226.75,-210.17 1235.55,-204.27 1224.99,-203.4 1226.75,-210.17\"/>\n",
+       "</g>\n",
+       "<!-- west_y_original_scale -->\n",
+       "<g id=\"node47\" class=\"node\">\n",
+       "<title>west_y_original_scale</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"5739.38,-98 5602.62,-98 5602.62,-40.5 5739.38,-40.5 5739.38,-98\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5671\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">west_y_original_scale</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5671\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5671\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- west_target_scale&#45;&gt;west_y_original_scale -->\n",
+       "<g id=\"edge93\" class=\"edge\">\n",
+       "<title>west_target_scale&#45;&gt;west_y_original_scale</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M68.6,-262.78C65.51,-222.44 66.87,-149.18 111,-113.41 166.72,-68.24 4915.75,-69.75 5591.38,-70.19\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"5590.92,-73.69 5600.92,-70.19 5590.93,-66.69 5590.92,-73.69\"/>\n",
+       "</g>\n",
+       "<!-- west_channel_contribution_original_scale -->\n",
+       "<g id=\"node64\" class=\"node\">\n",
+       "<title>west_channel_contribution_original_scale</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"2335.38,-203.41 2090.62,-203.41 2090.62,-145.91 2335.38,-145.91 2335.38,-203.41\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2213\" y=\"-186.11\" font-family=\"Times,serif\" font-size=\"14.00\">west_channel_contribution_original_scale</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2213\" y=\"-169.61\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2213\" y=\"-153.11\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- west_target_scale&#45;&gt;west_channel_contribution_original_scale -->\n",
+       "<g id=\"edge88\" class=\"edge\">\n",
+       "<title>west_target_scale&#45;&gt;west_channel_contribution_original_scale</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M93.88,-262.85C105.25,-250.52 120.27,-237.37 137,-230.73 187.22,-210.8 2028.58,-223.44 2082,-215.32 2095.03,-213.34 2108.56,-210.35 2121.77,-206.89\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2122.59,-210.3 2131.31,-204.28 2120.74,-203.54 2122.59,-210.3\"/>\n",
+       "</g>\n",
+       "<!-- south_intercept_contribution -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>south_intercept_contribution</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"657\" cy=\"-291.98\" rx=\"121.62\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"657\" y=\"-303.43\" font-family=\"Times,serif\" font-size=\"14.00\">south_intercept_contribution</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"657\" y=\"-286.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"657\" y=\"-270.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- south_intercept_contribution&#45;&gt;south_intercept_contribution_original_scale -->\n",
+       "<g id=\"edge59\" class=\"edge\">\n",
+       "<title>south_intercept_contribution&#45;&gt;south_intercept_contribution_original_scale</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M633.3,-251.93C625.73,-239.46 617.36,-225.68 609.85,-213.3\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"613.01,-211.76 604.83,-205.02 607.02,-215.39 613.01,-211.76\"/>\n",
+       "</g>\n",
+       "<!-- south_y -->\n",
+       "<g id=\"node39\" class=\"node\">\n",
+       "<title>south_y</title>\n",
+       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"5953\" cy=\"-174.66\" rx=\"82.38\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5953\" y=\"-186.11\" font-family=\"Times,serif\" font-size=\"14.00\">south_y</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5953\" y=\"-169.61\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5953\" y=\"-153.11\" font-family=\"Times,serif\" font-size=\"14.00\">Truncated_normal</text>\n",
+       "</g>\n",
+       "<!-- south_intercept_contribution&#45;&gt;south_y -->\n",
+       "<g id=\"edge50\" class=\"edge\">\n",
+       "<title>south_intercept_contribution&#45;&gt;south_y</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M717.56,-256.33C738.82,-245.98 763.32,-235.98 787,-230.73 922.29,-200.68 5638.28,-230.77 5776,-215.32 5807.22,-211.81 5840.98,-205.02 5870.57,-198.03\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"5871.32,-201.45 5880.22,-195.7 5869.68,-194.64 5871.32,-201.45\"/>\n",
+       "</g>\n",
+       "<!-- west_intercept_contribution -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>west_intercept_contribution</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"264\" cy=\"-291.98\" rx=\"118.44\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"264\" y=\"-303.43\" font-family=\"Times,serif\" font-size=\"14.00\">west_intercept_contribution</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"264\" y=\"-286.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"264\" y=\"-270.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- west_intercept_contribution&#45;&gt;west_intercept_contribution_original_scale -->\n",
+       "<g id=\"edge91\" class=\"edge\">\n",
+       "<title>west_intercept_contribution&#45;&gt;west_intercept_contribution_original_scale</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M257.41,-250.97C255.48,-239.26 253.37,-226.47 251.45,-214.82\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"254.93,-214.41 249.85,-205.11 248.03,-215.55 254.93,-214.41\"/>\n",
+       "</g>\n",
+       "<!-- west_y -->\n",
+       "<g id=\"node49\" class=\"node\">\n",
+       "<title>west_y</title>\n",
+       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"5685\" cy=\"-174.66\" rx=\"82.38\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5685\" y=\"-186.11\" font-family=\"Times,serif\" font-size=\"14.00\">west_y</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5685\" y=\"-169.61\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5685\" y=\"-153.11\" font-family=\"Times,serif\" font-size=\"14.00\">Truncated_normal</text>\n",
+       "</g>\n",
+       "<!-- west_intercept_contribution&#45;&gt;west_y -->\n",
+       "<g id=\"edge80\" class=\"edge\">\n",
+       "<title>west_intercept_contribution&#45;&gt;west_y</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M323.59,-256.38C344.51,-246.03 368.65,-236.02 392,-230.73 459.43,-215.43 2811.86,-216.02 2881,-215.32 3945.07,-204.5 5236.17,-183.24 5590.95,-177.26\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"5590.97,-180.76 5600.91,-177.09 5590.86,-173.76 5590.97,-180.76\"/>\n",
+       "</g>\n",
+       "<!-- north_adstock_alpha -->\n",
+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>north_adstock_alpha</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"4194\" cy=\"-421.79\" rx=\"90.86\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"4194\" y=\"-433.24\" font-family=\"Times,serif\" font-size=\"14.00\">north_adstock_alpha</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"4194\" y=\"-416.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"4194\" y=\"-400.24\" font-family=\"Times,serif\" font-size=\"14.00\">Beta</text>\n",
+       "</g>\n",
+       "<!-- north_channel_contribution -->\n",
+       "<g id=\"node68\" class=\"node\">\n",
+       "<title>north_channel_contribution</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"2869,-320.73 2703,-320.73 2703,-263.23 2869,-263.23 2869,-320.73\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2786\" y=\"-303.43\" font-family=\"Times,serif\" font-size=\"14.00\">north_channel_contribution</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2786\" y=\"-286.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2786\" y=\"-270.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- north_adstock_alpha&#45;&gt;north_channel_contribution -->\n",
+       "<g id=\"edge13\" class=\"edge\">\n",
+       "<title>north_adstock_alpha&#45;&gt;north_channel_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M4155.23,-384.68C4137.84,-370.76 4116.25,-356.35 4094,-348.63 3980.48,-309.27 3162.17,-296.96 2880.76,-293.87\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2880.92,-290.37 2870.88,-293.76 2880.84,-297.37 2880.92,-290.37\"/>\n",
+       "</g>\n",
+       "<!-- south_adstock_alpha -->\n",
+       "<g id=\"node11\" class=\"node\">\n",
+       "<title>south_adstock_alpha</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"3580\" cy=\"-421.79\" rx=\"91.39\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"3580\" y=\"-433.24\" font-family=\"Times,serif\" font-size=\"14.00\">south_adstock_alpha</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"3580\" y=\"-416.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"3580\" y=\"-400.24\" font-family=\"Times,serif\" font-size=\"14.00\">Beta</text>\n",
+       "</g>\n",
+       "<!-- south_channel_contribution -->\n",
+       "<g id=\"node69\" class=\"node\">\n",
+       "<title>south_channel_contribution</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"2685.38,-320.73 2518.62,-320.73 2518.62,-263.23 2685.38,-263.23 2685.38,-320.73\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2602\" y=\"-303.43\" font-family=\"Times,serif\" font-size=\"14.00\">south_channel_contribution</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2602\" y=\"-286.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2602\" y=\"-270.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- south_adstock_alpha&#45;&gt;south_channel_contribution -->\n",
+       "<g id=\"edge44\" class=\"edge\">\n",
+       "<title>south_adstock_alpha&#45;&gt;south_channel_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M3541.83,-384.71C3524.37,-370.61 3502.58,-356.06 3480,-348.63 3438.52,-335 2736.37,-351.19 2694,-340.63 2681.18,-337.44 2668.15,-332.16 2656.07,-326.25\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2658.03,-323.32 2647.53,-321.86 2654.83,-329.54 2658.03,-323.32\"/>\n",
+       "</g>\n",
+       "<!-- west_adstock_alpha -->\n",
+       "<g id=\"node12\" class=\"node\">\n",
+       "<title>west_adstock_alpha</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"2981\" cy=\"-421.79\" rx=\"88.21\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2981\" y=\"-433.24\" font-family=\"Times,serif\" font-size=\"14.00\">west_adstock_alpha</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2981\" y=\"-416.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2981\" y=\"-400.24\" font-family=\"Times,serif\" font-size=\"14.00\">Beta</text>\n",
+       "</g>\n",
+       "<!-- west_channel_contribution -->\n",
+       "<g id=\"node65\" class=\"node\">\n",
+       "<title>west_channel_contribution</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"2500.12,-320.73 2337.88,-320.73 2337.88,-263.23 2500.12,-263.23 2500.12,-320.73\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2419\" y=\"-303.43\" font-family=\"Times,serif\" font-size=\"14.00\">west_channel_contribution</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2419\" y=\"-286.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2419\" y=\"-270.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- west_adstock_alpha&#45;&gt;west_channel_contribution -->\n",
+       "<g id=\"edge76\" class=\"edge\">\n",
+       "<title>west_adstock_alpha&#45;&gt;west_channel_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2942.78,-384.84C2925.33,-370.75 2903.53,-356.18 2881,-348.63 2841.9,-335.53 2549.93,-350.92 2510,-340.63 2497.44,-337.4 2484.68,-332.16 2472.84,-326.31\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2474.53,-323.25 2464.04,-321.75 2471.31,-329.46 2474.53,-323.25\"/>\n",
+       "</g>\n",
+       "<!-- south_saturation_lam -->\n",
+       "<g id=\"node13\" class=\"node\">\n",
+       "<title>south_saturation_lam</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"3783\" cy=\"-421.79\" rx=\"94.05\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"3783\" y=\"-433.24\" font-family=\"Times,serif\" font-size=\"14.00\">south_saturation_lam</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"3783\" y=\"-416.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"3783\" y=\"-400.24\" font-family=\"Times,serif\" font-size=\"14.00\">Gamma</text>\n",
+       "</g>\n",
+       "<!-- south_saturation_lam&#45;&gt;south_channel_contribution -->\n",
+       "<g id=\"edge46\" class=\"edge\">\n",
+       "<title>south_saturation_lam&#45;&gt;south_channel_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M3743.52,-384.68C3725.51,-370.57 3703.08,-356.02 3680,-348.63 3627.83,-331.94 2747.17,-353.82 2694,-340.63 2681.18,-337.46 2668.14,-332.18 2656.07,-326.27\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2658.02,-323.34 2647.53,-321.88 2654.82,-329.56 2658.02,-323.34\"/>\n",
+       "</g>\n",
+       "<!-- north_saturation_beta -->\n",
+       "<g id=\"node14\" class=\"node\">\n",
+       "<title>north_saturation_beta</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"4397\" cy=\"-421.79\" rx=\"94.58\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"4397\" y=\"-433.24\" font-family=\"Times,serif\" font-size=\"14.00\">north_saturation_beta</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"4397\" y=\"-416.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"4397\" y=\"-400.24\" font-family=\"Times,serif\" font-size=\"14.00\">Gamma</text>\n",
+       "</g>\n",
+       "<!-- north_saturation_beta&#45;&gt;north_channel_contribution -->\n",
+       "<g id=\"edge15\" class=\"edge\">\n",
+       "<title>north_saturation_beta&#45;&gt;north_channel_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M4356.92,-384.61C4338.99,-370.68 4316.77,-356.28 4294,-348.63 4160.68,-303.86 3190.11,-295.04 2880.58,-293.36\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2880.94,-289.86 2870.92,-293.31 2880.91,-296.86 2880.94,-289.86\"/>\n",
+       "</g>\n",
+       "<!-- west_saturation_lam -->\n",
+       "<g id=\"node15\" class=\"node\">\n",
+       "<title>west_saturation_lam</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"3178\" cy=\"-421.79\" rx=\"90.86\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"3178\" y=\"-433.24\" font-family=\"Times,serif\" font-size=\"14.00\">west_saturation_lam</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"3178\" y=\"-416.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"3178\" y=\"-400.24\" font-family=\"Times,serif\" font-size=\"14.00\">Gamma</text>\n",
+       "</g>\n",
+       "<!-- west_saturation_lam&#45;&gt;west_channel_contribution -->\n",
+       "<g id=\"edge75\" class=\"edge\">\n",
+       "<title>west_saturation_lam&#45;&gt;west_channel_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M3139.81,-384.75C3122.36,-370.66 3100.56,-356.1 3078,-348.63 3018.08,-328.81 2571.18,-356.14 2510,-340.63 2497.43,-337.45 2484.66,-332.23 2472.82,-326.39\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2474.51,-323.32 2464.02,-321.82 2471.29,-329.54 2474.51,-323.32\"/>\n",
+       "</g>\n",
+       "<!-- south_saturation_beta -->\n",
+       "<g id=\"node16\" class=\"node\">\n",
+       "<title>south_saturation_beta</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"3990\" cy=\"-421.79\" rx=\"95.11\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"3990\" y=\"-433.24\" font-family=\"Times,serif\" font-size=\"14.00\">south_saturation_beta</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"3990\" y=\"-416.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"3990\" y=\"-400.24\" font-family=\"Times,serif\" font-size=\"14.00\">Gamma</text>\n",
+       "</g>\n",
+       "<!-- south_saturation_beta&#45;&gt;south_channel_contribution -->\n",
+       "<g id=\"edge45\" class=\"edge\">\n",
+       "<title>south_saturation_beta&#45;&gt;south_channel_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M3950.08,-384.66C3931.9,-370.54 3909.26,-356 3886,-348.63 3822.87,-328.64 2758.29,-356.53 2694,-340.63 2681.18,-337.46 2668.14,-332.19 2656.07,-326.28\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2658.02,-323.35 2647.52,-321.89 2654.82,-329.58 2658.02,-323.35\"/>\n",
+       "</g>\n",
+       "<!-- west_saturation_beta -->\n",
+       "<g id=\"node17\" class=\"node\">\n",
+       "<title>west_saturation_beta</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"3379\" cy=\"-421.79\" rx=\"91.92\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"3379\" y=\"-433.24\" font-family=\"Times,serif\" font-size=\"14.00\">west_saturation_beta</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"3379\" y=\"-416.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"3379\" y=\"-400.24\" font-family=\"Times,serif\" font-size=\"14.00\">Gamma</text>\n",
+       "</g>\n",
+       "<!-- west_saturation_beta&#45;&gt;west_channel_contribution -->\n",
+       "<g id=\"edge77\" class=\"edge\">\n",
+       "<title>west_saturation_beta&#45;&gt;west_channel_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M3340.39,-384.71C3322.75,-370.61 3300.74,-356.05 3278,-348.63 3237.44,-335.4 2551.38,-351.04 2510,-340.63 2497.43,-337.47 2484.65,-332.26 2472.81,-326.42\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2474.5,-323.36 2464.01,-321.86 2471.28,-329.57 2474.5,-323.36\"/>\n",
+       "</g>\n",
+       "<!-- north_saturation_lam -->\n",
+       "<g id=\"node18\" class=\"node\">\n",
+       "<title>north_saturation_lam</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"4603\" cy=\"-421.79\" rx=\"93.51\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"4603\" y=\"-433.24\" font-family=\"Times,serif\" font-size=\"14.00\">north_saturation_lam</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"4603\" y=\"-416.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"4603\" y=\"-400.24\" font-family=\"Times,serif\" font-size=\"14.00\">Gamma</text>\n",
+       "</g>\n",
+       "<!-- north_saturation_lam&#45;&gt;north_channel_contribution -->\n",
+       "<g id=\"edge14\" class=\"edge\">\n",
+       "<title>north_saturation_lam&#45;&gt;north_channel_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M4563.38,-384.57C4545.63,-370.62 4523.62,-356.23 4501,-348.63 4347.47,-297.06 3217.2,-292.95 2880.43,-292.87\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2880.78,-289.37 2870.78,-292.87 2880.78,-296.37 2880.78,-289.37\"/>\n",
+       "</g>\n",
+       "<!-- north_gamma_control -->\n",
+       "<g id=\"node19\" class=\"node\">\n",
+       "<title>north_gamma_control</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"7629\" cy=\"-421.79\" rx=\"97.23\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"7629\" y=\"-433.24\" font-family=\"Times,serif\" font-size=\"14.00\">north_gamma_control</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"7629\" y=\"-416.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"7629\" y=\"-400.24\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- north_control_contribution -->\n",
+       "<g id=\"node59\" class=\"node\">\n",
+       "<title>north_control_contribution</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"7483.12,-320.73 7320.88,-320.73 7320.88,-263.23 7483.12,-263.23 7483.12,-320.73\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"7402\" y=\"-303.43\" font-family=\"Times,serif\" font-size=\"14.00\">north_control_contribution</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"7402\" y=\"-286.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"7402\" y=\"-270.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- north_gamma_control&#45;&gt;north_control_contribution -->\n",
+       "<g id=\"edge12\" class=\"edge\">\n",
+       "<title>north_gamma_control&#45;&gt;north_control_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M7580.02,-386.2C7561.57,-373.75 7540.17,-359.99 7520,-348.63 7506.13,-340.82 7490.98,-333.11 7476.3,-326.02\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"7478.13,-323.02 7467.6,-321.88 7475.12,-329.34 7478.13,-323.02\"/>\n",
+       "</g>\n",
+       "<!-- north_y_sigma_sigma -->\n",
+       "<g id=\"node20\" class=\"node\">\n",
+       "<title>north_y_sigma_sigma</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"7841\" cy=\"-421.79\" rx=\"97.23\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"7841\" y=\"-433.24\" font-family=\"Times,serif\" font-size=\"14.00\">north_y_sigma_sigma</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"7841\" y=\"-416.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"7841\" y=\"-400.24\" font-family=\"Times,serif\" font-size=\"14.00\">Halfnormal</text>\n",
+       "</g>\n",
+       "<!-- north_y_sigma -->\n",
+       "<g id=\"node26\" class=\"node\">\n",
+       "<title>north_y_sigma</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"7717\" cy=\"-291.98\" rx=\"69.12\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"7717\" y=\"-303.43\" font-family=\"Times,serif\" font-size=\"14.00\">north_y_sigma</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"7717\" y=\"-286.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"7717\" y=\"-270.43\" font-family=\"Times,serif\" font-size=\"14.00\">Halfnormal</text>\n",
+       "</g>\n",
+       "<!-- north_y_sigma_sigma&#45;&gt;north_y_sigma -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>north_y_sigma_sigma&#45;&gt;north_y_sigma</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M7805.13,-383.82C7790.52,-368.75 7773.52,-351.24 7758.38,-335.63\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"7761.19,-333.5 7751.71,-328.76 7756.16,-338.37 7761.19,-333.5\"/>\n",
+       "</g>\n",
+       "<!-- south_gamma_fourier_b -->\n",
+       "<g id=\"node21\" class=\"node\">\n",
+       "<title>south_gamma_fourier_b</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"5597\" cy=\"-668.93\" rx=\"106.24\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5597\" y=\"-680.38\" font-family=\"Times,serif\" font-size=\"14.00\">south_gamma_fourier_b</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5597\" y=\"-663.88\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5597\" y=\"-647.38\" font-family=\"Times,serif\" font-size=\"14.00\">Halfnormal</text>\n",
+       "</g>\n",
+       "<!-- south_gamma_fourier -->\n",
+       "<g id=\"node72\" class=\"node\">\n",
+       "<title>south_gamma_fourier</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"5594\" cy=\"-551.61\" rx=\"96.7\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5594\" y=\"-563.06\" font-family=\"Times,serif\" font-size=\"14.00\">south_gamma_fourier</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5594\" y=\"-546.56\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5594\" y=\"-530.06\" font-family=\"Times,serif\" font-size=\"14.00\">Laplace</text>\n",
+       "</g>\n",
+       "<!-- south_gamma_fourier_b&#45;&gt;south_gamma_fourier -->\n",
+       "<g id=\"edge32\" class=\"edge\">\n",
+       "<title>south_gamma_fourier_b&#45;&gt;south_gamma_fourier</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5595.96,-627.92C5595.76,-620.14 5595.54,-611.88 5595.33,-603.8\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"5598.84,-603.97 5595.08,-594.06 5591.84,-604.15 5598.84,-603.97\"/>\n",
+       "</g>\n",
+       "<!-- west_y_sigma -->\n",
+       "<g id=\"node22\" class=\"node\">\n",
+       "<title>west_y_sigma</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"7101\" cy=\"-291.98\" rx=\"66.47\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"7101\" y=\"-303.43\" font-family=\"Times,serif\" font-size=\"14.00\">west_y_sigma</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"7101\" y=\"-286.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"7101\" y=\"-270.43\" font-family=\"Times,serif\" font-size=\"14.00\">Halfnormal</text>\n",
+       "</g>\n",
+       "<!-- west_y_sigma&#45;&gt;west_y -->\n",
+       "<g id=\"edge81\" class=\"edge\">\n",
+       "<title>west_y_sigma&#45;&gt;west_y</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M7049.34,-266.15C7018.87,-252.86 6979.12,-237.8 6942,-230.73 6824.11,-208.27 5981.19,-229.37 5862,-215.32 5830.8,-211.64 5797.04,-204.81 5767.45,-197.83\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"5768.34,-194.45 5757.8,-195.52 5766.71,-201.26 5768.34,-194.45\"/>\n",
+       "</g>\n",
+       "<!-- north_gamma_fourier_b -->\n",
+       "<g id=\"node23\" class=\"node\">\n",
+       "<title>north_gamma_fourier_b</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"5367\" cy=\"-668.93\" rx=\"105.71\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5367\" y=\"-680.38\" font-family=\"Times,serif\" font-size=\"14.00\">north_gamma_fourier_b</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5367\" y=\"-663.88\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5367\" y=\"-647.38\" font-family=\"Times,serif\" font-size=\"14.00\">Halfnormal</text>\n",
+       "</g>\n",
+       "<!-- north_gamma_fourier -->\n",
+       "<g id=\"node35\" class=\"node\">\n",
+       "<title>north_gamma_fourier</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"5371\" cy=\"-551.61\" rx=\"96.17\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5371\" y=\"-563.06\" font-family=\"Times,serif\" font-size=\"14.00\">north_gamma_fourier</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5371\" y=\"-546.56\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5371\" y=\"-530.06\" font-family=\"Times,serif\" font-size=\"14.00\">Laplace</text>\n",
+       "</g>\n",
+       "<!-- north_gamma_fourier_b&#45;&gt;north_gamma_fourier -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>north_gamma_fourier_b&#45;&gt;north_gamma_fourier</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5368.39,-627.92C5368.66,-620.14 5368.94,-611.88 5369.22,-603.8\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"5372.71,-604.18 5369.56,-594.06 5365.72,-603.93 5372.71,-604.18\"/>\n",
+       "</g>\n",
+       "<!-- west_gamma_fourier_b -->\n",
+       "<g id=\"node24\" class=\"node\">\n",
+       "<title>west_gamma_fourier_b</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"6503\" cy=\"-668.93\" rx=\"103.06\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6503\" y=\"-680.38\" font-family=\"Times,serif\" font-size=\"14.00\">west_gamma_fourier_b</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6503\" y=\"-663.88\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6503\" y=\"-647.38\" font-family=\"Times,serif\" font-size=\"14.00\">Halfnormal</text>\n",
+       "</g>\n",
+       "<!-- west_gamma_fourier -->\n",
+       "<g id=\"node78\" class=\"node\">\n",
+       "<title>west_gamma_fourier</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"6503\" cy=\"-551.61\" rx=\"93.51\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6503\" y=\"-563.06\" font-family=\"Times,serif\" font-size=\"14.00\">west_gamma_fourier</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6503\" y=\"-546.56\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6503\" y=\"-530.06\" font-family=\"Times,serif\" font-size=\"14.00\">Laplace</text>\n",
+       "</g>\n",
+       "<!-- west_gamma_fourier_b&#45;&gt;west_gamma_fourier -->\n",
+       "<g id=\"edge63\" class=\"edge\">\n",
+       "<title>west_gamma_fourier_b&#45;&gt;west_gamma_fourier</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M6503,-627.92C6503,-620.14 6503,-611.88 6503,-603.8\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"6506.5,-604.06 6503,-594.06 6499.5,-604.06 6506.5,-604.06\"/>\n",
+       "</g>\n",
+       "<!-- west_y_sigma_sigma -->\n",
+       "<g id=\"node25\" class=\"node\">\n",
+       "<title>west_y_sigma_sigma</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"7188\" cy=\"-421.79\" rx=\"94.58\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"7188\" y=\"-433.24\" font-family=\"Times,serif\" font-size=\"14.00\">west_y_sigma_sigma</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"7188\" y=\"-416.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"7188\" y=\"-400.24\" font-family=\"Times,serif\" font-size=\"14.00\">Halfnormal</text>\n",
+       "</g>\n",
+       "<!-- west_y_sigma_sigma&#45;&gt;west_y_sigma -->\n",
+       "<g id=\"edge65\" class=\"edge\">\n",
+       "<title>west_y_sigma_sigma&#45;&gt;west_y_sigma</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M7161.9,-382.44C7152.62,-368.81 7142.07,-353.32 7132.4,-339.11\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"7135.56,-337.53 7127.04,-331.23 7129.77,-341.47 7135.56,-337.53\"/>\n",
+       "</g>\n",
+       "<!-- north_y_sigma&#45;&gt;north_y -->\n",
+       "<g id=\"edge22\" class=\"edge\">\n",
+       "<title>north_y_sigma&#45;&gt;north_y</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M7659.32,-269.09C7620.54,-255.44 7567.94,-238.95 7520,-230.73 7292.44,-191.68 6591.43,-179.73 6336.19,-176.62\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"6336.3,-173.13 6326.26,-176.51 6336.21,-180.12 6336.3,-173.13\"/>\n",
+       "</g>\n",
+       "<!-- west_delta_b -->\n",
+       "<g id=\"node27\" class=\"node\">\n",
+       "<title>west_delta_b</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"6824\" cy=\"-551.61\" rx=\"61.7\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6824\" y=\"-563.06\" font-family=\"Times,serif\" font-size=\"14.00\">west_delta_b</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6824\" y=\"-546.56\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6824\" y=\"-530.06\" font-family=\"Times,serif\" font-size=\"14.00\">Halfnormal</text>\n",
+       "</g>\n",
+       "<!-- west_delta -->\n",
+       "<g id=\"node79\" class=\"node\">\n",
+       "<title>west_delta</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"6793\" cy=\"-421.79\" rx=\"52.15\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6793\" y=\"-433.24\" font-family=\"Times,serif\" font-size=\"14.00\">west_delta</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6793\" y=\"-416.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6793\" y=\"-400.24\" font-family=\"Times,serif\" font-size=\"14.00\">Laplace</text>\n",
+       "</g>\n",
+       "<!-- west_delta_b&#45;&gt;west_delta -->\n",
+       "<g id=\"edge64\" class=\"edge\">\n",
+       "<title>west_delta_b&#45;&gt;west_delta</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M6814.45,-511.22C6811.5,-499.08 6808.22,-485.55 6805.13,-472.79\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"6808.58,-472.2 6802.83,-463.31 6801.78,-473.85 6808.58,-472.2\"/>\n",
+       "</g>\n",
+       "<!-- north_delta_b -->\n",
+       "<g id=\"node29\" class=\"node\">\n",
+       "<title>north_delta_b</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"6968\" cy=\"-551.61\" rx=\"64.35\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6968\" y=\"-563.06\" font-family=\"Times,serif\" font-size=\"14.00\">north_delta_b</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6968\" y=\"-546.56\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6968\" y=\"-530.06\" font-family=\"Times,serif\" font-size=\"14.00\">Halfnormal</text>\n",
+       "</g>\n",
+       "<!-- north_delta -->\n",
+       "<g id=\"node36\" class=\"node\">\n",
+       "<title>north_delta</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"6963\" cy=\"-421.79\" rx=\"54.8\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6963\" y=\"-433.24\" font-family=\"Times,serif\" font-size=\"14.00\">north_delta</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6963\" y=\"-416.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6963\" y=\"-400.24\" font-family=\"Times,serif\" font-size=\"14.00\">Laplace</text>\n",
+       "</g>\n",
+       "<!-- north_delta_b&#45;&gt;north_delta -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>north_delta_b&#45;&gt;north_delta</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M6966.43,-510.52C6965.98,-498.98 6965.48,-486.23 6965.01,-474.11\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"6968.51,-474.11 6964.62,-464.25 6961.52,-474.38 6968.51,-474.11\"/>\n",
+       "</g>\n",
+       "<!-- south_delta_b -->\n",
+       "<g id=\"node32\" class=\"node\">\n",
+       "<title>south_delta_b</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"6679\" cy=\"-551.61\" rx=\"64.88\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6679\" y=\"-563.06\" font-family=\"Times,serif\" font-size=\"14.00\">south_delta_b</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6679\" y=\"-546.56\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6679\" y=\"-530.06\" font-family=\"Times,serif\" font-size=\"14.00\">Halfnormal</text>\n",
+       "</g>\n",
+       "<!-- south_delta -->\n",
+       "<g id=\"node73\" class=\"node\">\n",
+       "<title>south_delta</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"6629\" cy=\"-421.79\" rx=\"55.33\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6629\" y=\"-433.24\" font-family=\"Times,serif\" font-size=\"14.00\">south_delta</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6629\" y=\"-416.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6629\" y=\"-400.24\" font-family=\"Times,serif\" font-size=\"14.00\">Laplace</text>\n",
+       "</g>\n",
+       "<!-- south_delta_b&#45;&gt;south_delta -->\n",
+       "<g id=\"edge33\" class=\"edge\">\n",
+       "<title>south_delta_b&#45;&gt;south_delta</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M6663.86,-511.91C6658.92,-499.28 6653.36,-485.07 6648.16,-471.78\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"6651.54,-470.81 6644.64,-462.77 6645.02,-473.36 6651.54,-470.81\"/>\n",
+       "</g>\n",
+       "<!-- south_y_sigma -->\n",
+       "<g id=\"node33\" class=\"node\">\n",
+       "<title>south_y_sigma</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"8375\" cy=\"-291.98\" rx=\"69.65\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"8375\" y=\"-303.43\" font-family=\"Times,serif\" font-size=\"14.00\">south_y_sigma</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"8375\" y=\"-286.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"8375\" y=\"-270.43\" font-family=\"Times,serif\" font-size=\"14.00\">Halfnormal</text>\n",
+       "</g>\n",
+       "<!-- south_y_sigma&#45;&gt;south_y -->\n",
+       "<g id=\"edge52\" class=\"edge\">\n",
+       "<title>south_y_sigma&#45;&gt;south_y</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M8317.24,-268.69C8278.06,-254.77 8224.71,-238.13 8176,-230.73 7953.55,-196.92 6374.82,-238.37 6151,-215.32 6113.4,-211.44 6072.34,-203.8 6037.42,-196.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"6038.3,-192.85 6027.78,-194.12 6036.8,-199.69 6038.3,-192.85\"/>\n",
+       "</g>\n",
+       "<!-- south_y_sigma_sigma -->\n",
+       "<g id=\"node34\" class=\"node\">\n",
+       "<title>south_y_sigma_sigma</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"8500\" cy=\"-421.79\" rx=\"97.76\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"8500\" y=\"-433.24\" font-family=\"Times,serif\" font-size=\"14.00\">south_y_sigma_sigma</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"8500\" y=\"-416.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"8500\" y=\"-400.24\" font-family=\"Times,serif\" font-size=\"14.00\">Halfnormal</text>\n",
+       "</g>\n",
+       "<!-- south_y_sigma_sigma&#45;&gt;south_y_sigma -->\n",
+       "<g id=\"edge34\" class=\"edge\">\n",
+       "<title>south_y_sigma_sigma&#45;&gt;south_y_sigma</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M8463.84,-383.82C8449.11,-368.75 8431.98,-351.24 8416.72,-335.63\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"8419.48,-333.45 8409.99,-328.75 8414.48,-338.35 8419.48,-333.45\"/>\n",
+       "</g>\n",
+       "<!-- north_fourier_contribution -->\n",
+       "<g id=\"node58\" class=\"node\">\n",
+       "<title>north_fourier_contribution</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"5848.38,-450.54 5687.62,-450.54 5687.62,-393.04 5848.38,-393.04 5848.38,-450.54\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5768\" y=\"-433.24\" font-family=\"Times,serif\" font-size=\"14.00\">north_fourier_contribution</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5768\" y=\"-416.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5768\" y=\"-400.24\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- north_gamma_fourier&#45;&gt;north_fourier_contribution -->\n",
+       "<g id=\"edge8\" class=\"edge\">\n",
+       "<title>north_gamma_fourier&#45;&gt;north_fourier_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5415.53,-515.25C5435.61,-501.28 5460.32,-486.63 5485,-478.45 5529.85,-463.6 5544.43,-478.41 5591,-470.45 5618.98,-465.67 5648.96,-458.44 5676.14,-451.04\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"5677.04,-454.42 5685.74,-448.38 5675.17,-447.67 5677.04,-454.42\"/>\n",
+       "</g>\n",
+       "<!-- north_trend_effect_contribution -->\n",
+       "<g id=\"node44\" class=\"node\">\n",
+       "<title>north_trend_effect_contribution</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"6929.62,-320.73 6740.38,-320.73 6740.38,-263.23 6929.62,-263.23 6929.62,-320.73\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6835\" y=\"-303.43\" font-family=\"Times,serif\" font-size=\"14.00\">north_trend_effect_contribution</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6835\" y=\"-286.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6835\" y=\"-270.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- north_delta&#45;&gt;north_trend_effect_contribution -->\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
+       "<title>north_delta&#45;&gt;north_trend_effect_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M6930.77,-388.44C6918.35,-375.98 6904.03,-361.64 6891,-348.63 6884.66,-342.3 6877.9,-335.57 6871.35,-329.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"6873.86,-326.62 6864.3,-322.05 6868.92,-331.58 6873.86,-326.62\"/>\n",
+       "</g>\n",
+       "<!-- south_yearly_seasonality_contribution -->\n",
+       "<g id=\"node37\" class=\"node\">\n",
+       "<title>south_yearly_seasonality_contribution</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"5828.62,-320.73 5603.38,-320.73 5603.38,-263.23 5828.62,-263.23 5828.62,-320.73\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5716\" y=\"-303.43\" font-family=\"Times,serif\" font-size=\"14.00\">south_yearly_seasonality_contribution</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5716\" y=\"-286.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5716\" y=\"-270.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- south_yearly_seasonality_contribution&#45;&gt;south_y -->\n",
+       "<g id=\"edge49\" class=\"edge\">\n",
+       "<title>south_yearly_seasonality_contribution&#45;&gt;south_y</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5772.72,-262.78C5793.26,-252.63 5816.65,-241.11 5838,-230.73 5852.88,-223.49 5868.88,-215.78 5884.07,-208.5\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"5885.56,-211.67 5893.07,-204.19 5882.53,-205.35 5885.56,-211.67\"/>\n",
+       "</g>\n",
+       "<!-- west_trend_effect_contribution -->\n",
+       "<g id=\"node38\" class=\"node\">\n",
+       "<title>west_trend_effect_contribution</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"6513.75,-320.73 6328.25,-320.73 6328.25,-263.23 6513.75,-263.23 6513.75,-320.73\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6421\" y=\"-303.43\" font-family=\"Times,serif\" font-size=\"14.00\">west_trend_effect_contribution</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6421\" y=\"-286.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6421\" y=\"-270.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- west_trend_effect_contribution&#45;&gt;west_y -->\n",
+       "<g id=\"edge83\" class=\"edge\">\n",
+       "<title>west_trend_effect_contribution&#45;&gt;west_y</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M6383.87,-262.93C6365.39,-250.62 6342.1,-237.46 6319,-230.73 6221.45,-202.29 5962.79,-228.19 5862,-215.32 5830.98,-211.35 5797.39,-204.5 5767.89,-197.59\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"5768.81,-194.21 5758.27,-195.3 5767.19,-201.02 5768.81,-194.21\"/>\n",
+       "</g>\n",
+       "<!-- south_y&#45;&gt;south_target_scale -->\n",
+       "<g id=\"edge35\" class=\"edge\">\n",
+       "<title>south_y&#45;&gt;south_target_scale</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5881.69,-195.35C5849.65,-203.23 5811.21,-211.37 5776,-215.32 5703.54,-223.44 594.04,-204.52 526,-230.73 512.52,-235.92 500.05,-245.2 489.62,-254.95\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"487.41,-252.2 482.78,-261.73 492.34,-257.17 487.41,-252.2\"/>\n",
+       "</g>\n",
+       "<!-- south_target_data -->\n",
+       "<g id=\"node45\" class=\"node\">\n",
+       "<title>south_target_data</title>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M6142,-98C6142,-98 6054,-98 6054,-98 6048,-98 6042,-92 6042,-86 6042,-86 6042,-52.5 6042,-52.5 6042,-46.5 6048,-40.5 6054,-40.5 6054,-40.5 6142,-40.5 6142,-40.5 6148,-40.5 6154,-46.5 6154,-52.5 6154,-52.5 6154,-86 6154,-86 6154,-92 6148,-98 6142,-98\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6098\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">south_target_data</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6098\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6098\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "</g>\n",
+       "<!-- south_y&#45;&gt;south_target_data -->\n",
+       "<g id=\"edge36\" class=\"edge\">\n",
+       "<title>south_y&#45;&gt;south_target_data</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5999.28,-140.66C6015.21,-129.29 6033.1,-116.53 6049.23,-105.03\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"6051.15,-107.96 6057.26,-99.3 6047.09,-102.26 6051.15,-107.96\"/>\n",
+       "</g>\n",
+       "<!-- south_y&#45;&gt;south_y_original_scale -->\n",
+       "<g id=\"edge62\" class=\"edge\">\n",
+       "<title>south_y&#45;&gt;south_y_original_scale</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5953,-133.68C5953,-125.83 5953,-117.6 5953,-109.76\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"5956.5,-109.79 5953,-99.79 5949.5,-109.79 5956.5,-109.79\"/>\n",
+       "</g>\n",
+       "<!-- west_target_data -->\n",
+       "<g id=\"node40\" class=\"node\">\n",
+       "<title>west_target_data</title>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M5852.75,-98C5852.75,-98 5769.25,-98 5769.25,-98 5763.25,-98 5757.25,-92 5757.25,-86 5757.25,-86 5757.25,-52.5 5757.25,-52.5 5757.25,-46.5 5763.25,-40.5 5769.25,-40.5 5769.25,-40.5 5852.75,-40.5 5852.75,-40.5 5858.75,-40.5 5864.75,-46.5 5864.75,-52.5 5864.75,-52.5 5864.75,-86 5864.75,-86 5864.75,-92 5858.75,-98 5852.75,-98\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5811\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">west_target_data</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5811\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5811\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "</g>\n",
+       "<!-- north_yearly_seasonality_contribution -->\n",
+       "<g id=\"node41\" class=\"node\">\n",
+       "<title>north_yearly_seasonality_contribution</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"6071.25,-320.73 5846.75,-320.73 5846.75,-263.23 6071.25,-263.23 6071.25,-320.73\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5959\" y=\"-303.43\" font-family=\"Times,serif\" font-size=\"14.00\">north_yearly_seasonality_contribution</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5959\" y=\"-286.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5959\" y=\"-270.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- north_yearly_seasonality_contribution&#45;&gt;north_y -->\n",
+       "<g id=\"edge23\" class=\"edge\">\n",
+       "<title>north_yearly_seasonality_contribution&#45;&gt;north_y</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M6012.36,-262.76C6033.46,-252.1 6058.1,-240.25 6081,-230.73 6107.01,-219.91 6135.98,-209.51 6162.1,-200.72\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"6162.9,-204.14 6171.28,-197.66 6160.69,-197.5 6162.9,-204.14\"/>\n",
+       "</g>\n",
+       "<!-- north_y&#45;&gt;north_target_scale -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>north_y&#45;&gt;north_target_scale</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M6169.17,-193.78C6131.98,-202.13 6085.86,-211.1 6044,-215.32 5973.2,-222.45 987.4,-205.15 921,-230.73 907.52,-235.92 895.05,-245.2 884.62,-254.95\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"882.41,-252.2 877.78,-261.73 887.34,-257.17 882.41,-252.2\"/>\n",
+       "</g>\n",
+       "<!-- north_y&#45;&gt;north_y_original_scale -->\n",
+       "<g id=\"edge30\" class=\"edge\">\n",
+       "<title>north_y&#45;&gt;north_y_original_scale</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M6242,-133.68C6242,-125.83 6242,-117.6 6242,-109.76\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"6245.5,-109.79 6242,-99.79 6238.5,-109.79 6245.5,-109.79\"/>\n",
+       "</g>\n",
+       "<!-- north_target_data -->\n",
+       "<g id=\"node51\" class=\"node\">\n",
+       "<title>north_target_data</title>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M6429.62,-98C6429.62,-98 6342.38,-98 6342.38,-98 6336.38,-98 6330.38,-92 6330.38,-86 6330.38,-86 6330.38,-52.5 6330.38,-52.5 6330.38,-46.5 6336.38,-40.5 6342.38,-40.5 6342.38,-40.5 6429.62,-40.5 6429.62,-40.5 6435.62,-40.5 6441.62,-46.5 6441.62,-52.5 6441.62,-52.5 6441.62,-86 6441.62,-86 6441.62,-92 6435.62,-98 6429.62,-98\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6386\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">north_target_data</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6386\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6386\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "</g>\n",
+       "<!-- north_y&#45;&gt;north_target_data -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>north_y&#45;&gt;north_target_data</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M6287.96,-140.66C6303.78,-129.29 6321.55,-116.53 6337.57,-105.03\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"6339.46,-107.98 6345.54,-99.31 6335.37,-102.3 6339.46,-107.98\"/>\n",
+       "</g>\n",
+       "<!-- north_trend_effect_contribution&#45;&gt;north_y -->\n",
+       "<g id=\"edge18\" class=\"edge\">\n",
+       "<title>north_trend_effect_contribution&#45;&gt;north_y</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M6795.35,-262.81C6776.69,-250.94 6753.59,-238.18 6731,-230.73 6659.77,-207.23 6456.21,-190.06 6335.35,-181.59\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"6335.65,-178.1 6325.43,-180.9 6335.17,-185.09 6335.65,-178.1\"/>\n",
+       "</g>\n",
+       "<!-- south_trend_effect_contribution -->\n",
+       "<g id=\"node46\" class=\"node\">\n",
+       "<title>south_trend_effect_contribution</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"6722,-320.73 6532,-320.73 6532,-263.23 6722,-263.23 6722,-320.73\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6627\" y=\"-303.43\" font-family=\"Times,serif\" font-size=\"14.00\">south_trend_effect_contribution</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6627\" y=\"-286.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6627\" y=\"-270.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- south_trend_effect_contribution&#45;&gt;south_y -->\n",
+       "<g id=\"edge54\" class=\"edge\">\n",
+       "<title>south_trend_effect_contribution&#45;&gt;south_y</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M6589.05,-263.01C6570.18,-250.72 6546.44,-237.55 6523,-230.73 6443.56,-207.6 6233.15,-225.18 6151,-215.32 6113.62,-210.83 6072.77,-203.13 6037.93,-195.7\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"6038.83,-192.31 6028.32,-193.62 6037.35,-199.16 6038.83,-192.31\"/>\n",
+       "</g>\n",
+       "<!-- west_yearly_seasonality_contribution -->\n",
+       "<g id=\"node48\" class=\"node\">\n",
+       "<title>west_yearly_seasonality_contribution</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"6310.38,-320.73 6089.62,-320.73 6089.62,-263.23 6310.38,-263.23 6310.38,-320.73\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6200\" y=\"-303.43\" font-family=\"Times,serif\" font-size=\"14.00\">west_yearly_seasonality_contribution</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6200\" y=\"-286.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6200\" y=\"-270.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- west_yearly_seasonality_contribution&#45;&gt;west_y -->\n",
+       "<g id=\"edge82\" class=\"edge\">\n",
+       "<title>west_yearly_seasonality_contribution&#45;&gt;west_y</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M6154.42,-262.74C6132.69,-250.7 6105.85,-237.82 6080,-230.73 5986.34,-205.01 5958.1,-229.4 5862,-215.32 5831.19,-210.8 5797.78,-203.86 5768.37,-197.04\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"5769.32,-193.67 5758.78,-194.78 5767.72,-200.48 5769.32,-193.67\"/>\n",
+       "</g>\n",
+       "<!-- west_y&#45;&gt;west_target_scale -->\n",
+       "<g id=\"edge66\" class=\"edge\">\n",
+       "<title>west_y&#45;&gt;west_target_scale</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5602.43,-177.08C5264.88,-182.86 3956.9,-204.69 2881,-215.32 2842.89,-215.69 172.44,-216.71 137,-230.73 123.92,-235.9 111.89,-245.06 101.84,-254.7\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"99.43,-252.16 94.93,-261.75 104.43,-257.05 99.43,-252.16\"/>\n",
+       "</g>\n",
+       "<!-- west_y&#45;&gt;west_target_data -->\n",
+       "<g id=\"edge67\" class=\"edge\">\n",
+       "<title>west_y&#45;&gt;west_target_data</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5726.96,-139.23C5740.23,-128.33 5754.91,-116.29 5768.24,-105.35\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"5770.09,-108.35 5775.6,-99.3 5765.65,-102.94 5770.09,-108.35\"/>\n",
+       "</g>\n",
+       "<!-- west_y&#45;&gt;west_y_original_scale -->\n",
+       "<g id=\"edge92\" class=\"edge\">\n",
+       "<title>west_y&#45;&gt;west_y_original_scale</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5679.59,-133.68C5678.51,-125.74 5677.38,-117.4 5676.31,-109.49\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"5679.81,-109.22 5675,-99.78 5672.87,-110.16 5679.81,-109.22\"/>\n",
+       "</g>\n",
+       "<!-- west_dayofyear -->\n",
+       "<g id=\"node52\" class=\"node\">\n",
+       "<title>west_dayofyear</title>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M6372.75,-580.36C6372.75,-580.36 6295.25,-580.36 6295.25,-580.36 6289.25,-580.36 6283.25,-574.36 6283.25,-568.36 6283.25,-568.36 6283.25,-534.86 6283.25,-534.86 6283.25,-528.86 6289.25,-522.86 6295.25,-522.86 6295.25,-522.86 6372.75,-522.86 6372.75,-522.86 6378.75,-522.86 6384.75,-528.86 6384.75,-534.86 6384.75,-534.86 6384.75,-568.36 6384.75,-568.36 6384.75,-574.36 6378.75,-580.36 6372.75,-580.36\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6334\" y=\"-563.06\" font-family=\"Times,serif\" font-size=\"14.00\">west_dayofyear</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6334\" y=\"-546.56\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6334\" y=\"-530.06\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "</g>\n",
+       "<!-- west_fourier_contribution -->\n",
+       "<g id=\"node80\" class=\"node\">\n",
+       "<title>west_fourier_contribution</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"6412.5,-450.54 6255.5,-450.54 6255.5,-393.04 6412.5,-393.04 6412.5,-450.54\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6334\" y=\"-433.24\" font-family=\"Times,serif\" font-size=\"14.00\">west_fourier_contribution</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6334\" y=\"-416.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6334\" y=\"-400.24\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- west_dayofyear&#45;&gt;west_fourier_contribution -->\n",
+       "<g id=\"edge70\" class=\"edge\">\n",
+       "<title>west_dayofyear&#45;&gt;west_fourier_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M6334,-522.56C6334,-504.83 6334,-481.65 6334,-462.1\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"6337.5,-462.29 6334,-452.29 6330.5,-462.29 6337.5,-462.29\"/>\n",
+       "</g>\n",
+       "<!-- north_dayofyear -->\n",
+       "<g id=\"node53\" class=\"node\">\n",
+       "<title>north_dayofyear</title>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M5808.62,-580.36C5808.62,-580.36 5727.38,-580.36 5727.38,-580.36 5721.38,-580.36 5715.38,-574.36 5715.38,-568.36 5715.38,-568.36 5715.38,-534.86 5715.38,-534.86 5715.38,-528.86 5721.38,-522.86 5727.38,-522.86 5727.38,-522.86 5808.62,-522.86 5808.62,-522.86 5814.62,-522.86 5820.62,-528.86 5820.62,-534.86 5820.62,-534.86 5820.62,-568.36 5820.62,-568.36 5820.62,-574.36 5814.62,-580.36 5808.62,-580.36\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5768\" y=\"-563.06\" font-family=\"Times,serif\" font-size=\"14.00\">north_dayofyear</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5768\" y=\"-546.56\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5768\" y=\"-530.06\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "</g>\n",
+       "<!-- north_dayofyear&#45;&gt;north_fourier_contribution -->\n",
+       "<g id=\"edge9\" class=\"edge\">\n",
+       "<title>north_dayofyear&#45;&gt;north_fourier_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5768,-522.56C5768,-504.83 5768,-481.65 5768,-462.1\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"5771.5,-462.29 5768,-452.29 5764.5,-462.29 5771.5,-462.29\"/>\n",
+       "</g>\n",
+       "<!-- west_trend_t -->\n",
+       "<g id=\"node54\" class=\"node\">\n",
+       "<title>west_trend_t</title>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M6036.88,-580.36C6036.88,-580.36 5975.12,-580.36 5975.12,-580.36 5969.12,-580.36 5963.12,-574.36 5963.12,-568.36 5963.12,-568.36 5963.12,-534.86 5963.12,-534.86 5963.12,-528.86 5969.12,-522.86 5975.12,-522.86 5975.12,-522.86 6036.88,-522.86 6036.88,-522.86 6042.88,-522.86 6048.88,-528.86 6048.88,-534.86 6048.88,-534.86 6048.88,-568.36 6048.88,-568.36 6048.88,-574.36 6042.88,-580.36 6036.88,-580.36\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6006\" y=\"-563.06\" font-family=\"Times,serif\" font-size=\"14.00\">west_trend_t</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6006\" y=\"-546.56\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6006\" y=\"-530.06\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "</g>\n",
+       "<!-- west_trend_t&#45;&gt;west_trend_effect_contribution -->\n",
+       "<g id=\"edge68\" class=\"edge\">\n",
+       "<title>west_trend_t&#45;&gt;west_trend_effect_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M6018.99,-522.37C6027.73,-506.74 6040.8,-488.5 6058,-478.45 6085.8,-462.22 6105.16,-490.93 6130,-470.45 6173.76,-434.37 6123.22,-383.44 6168,-348.63 6181.27,-338.32 6302.7,-344.71 6319,-340.63 6332.77,-337.19 6346.93,-331.87 6360.15,-326.01\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"6361.54,-329.22 6369.16,-321.86 6358.61,-322.87 6361.54,-329.22\"/>\n",
+       "</g>\n",
+       "<!-- north_trend_t -->\n",
+       "<g id=\"node55\" class=\"node\">\n",
+       "<title>north_trend_t</title>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M6252.75,-580.36C6252.75,-580.36 6187.25,-580.36 6187.25,-580.36 6181.25,-580.36 6175.25,-574.36 6175.25,-568.36 6175.25,-568.36 6175.25,-534.86 6175.25,-534.86 6175.25,-528.86 6181.25,-522.86 6187.25,-522.86 6187.25,-522.86 6252.75,-522.86 6252.75,-522.86 6258.75,-522.86 6264.75,-528.86 6264.75,-534.86 6264.75,-534.86 6264.75,-568.36 6264.75,-568.36 6264.75,-574.36 6258.75,-580.36 6252.75,-580.36\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6220\" y=\"-563.06\" font-family=\"Times,serif\" font-size=\"14.00\">north_trend_t</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6220\" y=\"-546.56\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6220\" y=\"-530.06\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "</g>\n",
+       "<!-- north_trend_t&#45;&gt;north_trend_effect_contribution -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>north_trend_t&#45;&gt;north_trend_effect_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M6211.65,-522.59C6200.44,-478.47 6186.94,-393.26 6234,-348.63 6244.02,-339.13 6717.53,-343.69 6731,-340.63 6745.51,-337.35 6760.43,-331.9 6774.28,-325.84\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"6775.62,-329.08 6783.27,-321.75 6772.71,-322.71 6775.62,-329.08\"/>\n",
+       "</g>\n",
+       "<!-- south_dayofyear -->\n",
+       "<g id=\"node56\" class=\"node\">\n",
+       "<title>south_dayofyear</title>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M5933,-580.36C5933,-580.36 5851,-580.36 5851,-580.36 5845,-580.36 5839,-574.36 5839,-568.36 5839,-568.36 5839,-534.86 5839,-534.86 5839,-528.86 5845,-522.86 5851,-522.86 5851,-522.86 5933,-522.86 5933,-522.86 5939,-522.86 5945,-528.86 5945,-534.86 5945,-534.86 5945,-568.36 5945,-568.36 5945,-574.36 5939,-580.36 5933,-580.36\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5892\" y=\"-563.06\" font-family=\"Times,serif\" font-size=\"14.00\">south_dayofyear</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5892\" y=\"-546.56\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5892\" y=\"-530.06\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "</g>\n",
+       "<!-- south_fourier_contribution -->\n",
+       "<g id=\"node74\" class=\"node\">\n",
+       "<title>south_fourier_contribution</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"6033.75,-450.54 5872.25,-450.54 5872.25,-393.04 6033.75,-393.04 6033.75,-450.54\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5953\" y=\"-433.24\" font-family=\"Times,serif\" font-size=\"14.00\">south_fourier_contribution</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5953\" y=\"-416.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5953\" y=\"-400.24\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- south_dayofyear&#45;&gt;south_fourier_contribution -->\n",
+       "<g id=\"edge39\" class=\"edge\">\n",
+       "<title>south_dayofyear&#45;&gt;south_fourier_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5905.39,-522.56C5914.01,-504.5 5925.33,-480.78 5934.76,-461\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"5937.84,-462.68 5938.99,-452.14 5931.53,-459.66 5937.84,-462.68\"/>\n",
+       "</g>\n",
+       "<!-- south_trend_t -->\n",
+       "<g id=\"node57\" class=\"node\">\n",
+       "<title>south_trend_t</title>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M6145.12,-580.36C6145.12,-580.36 6078.88,-580.36 6078.88,-580.36 6072.88,-580.36 6066.88,-574.36 6066.88,-568.36 6066.88,-568.36 6066.88,-534.86 6066.88,-534.86 6066.88,-528.86 6072.88,-522.86 6078.88,-522.86 6078.88,-522.86 6145.12,-522.86 6145.12,-522.86 6151.12,-522.86 6157.12,-528.86 6157.12,-534.86 6157.12,-534.86 6157.12,-568.36 6157.12,-568.36 6157.12,-574.36 6151.12,-580.36 6145.12,-580.36\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6112\" y=\"-563.06\" font-family=\"Times,serif\" font-size=\"14.00\">south_trend_t</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6112\" y=\"-546.56\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"6112\" y=\"-530.06\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "</g>\n",
+       "<!-- south_trend_t&#45;&gt;south_trend_effect_contribution -->\n",
+       "<g id=\"edge38\" class=\"edge\">\n",
+       "<title>south_trend_t&#45;&gt;south_trend_effect_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M6132.05,-522.59C6142.2,-507.6 6154.19,-488.55 6163,-470.45 6188.13,-418.83 6159.55,-382.38 6206,-348.63 6220.25,-338.28 6505.84,-344.61 6523,-340.63 6537.42,-337.3 6552.25,-331.87 6566.04,-325.84\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"6567.33,-329.1 6574.98,-321.77 6564.43,-322.73 6567.33,-329.1\"/>\n",
+       "</g>\n",
+       "<!-- north_fourier_contribution&#45;&gt;north_yearly_seasonality_contribution -->\n",
+       "<g id=\"edge10\" class=\"edge\">\n",
+       "<title>north_fourier_contribution&#45;&gt;north_yearly_seasonality_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5801.15,-392.79C5818.26,-378.86 5839.74,-362.16 5860,-348.63 5871.21,-341.15 5883.49,-333.69 5895.48,-326.79\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"5897.16,-329.85 5904.12,-321.87 5893.7,-323.77 5897.16,-329.85\"/>\n",
+       "</g>\n",
+       "<!-- north_control_contribution&#45;&gt;north_y -->\n",
+       "<g id=\"edge20\" class=\"edge\">\n",
+       "<title>north_control_contribution&#45;&gt;north_y</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M7320.47,-264.98C7277.87,-252.48 7224.61,-238.56 7176,-230.73 6871.68,-181.7 6505.31,-175.1 6336.1,-174.97\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"6336.27,-171.47 6326.27,-174.97 6336.27,-178.47 6336.27,-171.47\"/>\n",
+       "</g>\n",
+       "<!-- north_control_data -->\n",
+       "<g id=\"node60\" class=\"node\">\n",
+       "<title>north_control_data</title>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M7449.38,-450.54C7449.38,-450.54 7354.62,-450.54 7354.62,-450.54 7348.62,-450.54 7342.62,-444.54 7342.62,-438.54 7342.62,-438.54 7342.62,-405.04 7342.62,-405.04 7342.62,-399.04 7348.62,-393.04 7354.62,-393.04 7354.62,-393.04 7449.38,-393.04 7449.38,-393.04 7455.38,-393.04 7461.38,-399.04 7461.38,-405.04 7461.38,-405.04 7461.38,-438.54 7461.38,-438.54 7461.38,-444.54 7455.38,-450.54 7449.38,-450.54\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"7402\" y=\"-433.24\" font-family=\"Times,serif\" font-size=\"14.00\">north_control_data</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"7402\" y=\"-416.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"7402\" y=\"-400.24\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "</g>\n",
+       "<!-- north_control_data&#45;&gt;north_control_contribution -->\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
+       "<title>north_control_data&#45;&gt;north_control_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M7402,-392.74C7402,-375.01 7402,-351.84 7402,-332.28\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"7405.5,-332.47 7402,-322.47 7398.5,-332.47 7405.5,-332.47\"/>\n",
+       "</g>\n",
+       "<!-- south_channel_scale -->\n",
+       "<g id=\"node61\" class=\"node\">\n",
+       "<title>south_channel_scale</title>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M4978.88,-450.54C4978.88,-450.54 4875.12,-450.54 4875.12,-450.54 4869.12,-450.54 4863.12,-444.54 4863.12,-438.54 4863.12,-438.54 4863.12,-405.04 4863.12,-405.04 4863.12,-399.04 4869.12,-393.04 4875.12,-393.04 4875.12,-393.04 4978.88,-393.04 4978.88,-393.04 4984.88,-393.04 4990.88,-399.04 4990.88,-405.04 4990.88,-405.04 4990.88,-438.54 4990.88,-438.54 4990.88,-444.54 4984.88,-450.54 4978.88,-450.54\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"4927\" y=\"-433.24\" font-family=\"Times,serif\" font-size=\"14.00\">south_channel_scale</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"4927\" y=\"-416.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"4927\" y=\"-400.24\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "</g>\n",
+       "<!-- south_channel_scale&#45;&gt;south_channel_contribution -->\n",
+       "<g id=\"edge47\" class=\"edge\">\n",
+       "<title>south_channel_scale&#45;&gt;south_channel_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M4907.51,-392.72C4894.47,-376.52 4875.74,-357.61 4854,-348.63 4826.27,-337.18 2723.13,-347.79 2694,-340.63 2681.03,-337.45 2667.85,-332.11 2655.67,-326.12\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2657.54,-323.15 2647.05,-321.67 2654.33,-329.37 2657.54,-323.15\"/>\n",
+       "</g>\n",
+       "<!-- west_channel_scale -->\n",
+       "<g id=\"node62\" class=\"node\">\n",
+       "<title>west_channel_scale</title>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M4832.62,-450.54C4832.62,-450.54 4733.38,-450.54 4733.38,-450.54 4727.38,-450.54 4721.38,-444.54 4721.38,-438.54 4721.38,-438.54 4721.38,-405.04 4721.38,-405.04 4721.38,-399.04 4727.38,-393.04 4733.38,-393.04 4733.38,-393.04 4832.62,-393.04 4832.62,-393.04 4838.62,-393.04 4844.62,-399.04 4844.62,-405.04 4844.62,-405.04 4844.62,-438.54 4844.62,-438.54 4844.62,-444.54 4838.62,-450.54 4832.62,-450.54\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"4783\" y=\"-433.24\" font-family=\"Times,serif\" font-size=\"14.00\">west_channel_scale</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"4783\" y=\"-416.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"4783\" y=\"-400.24\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "</g>\n",
+       "<!-- west_channel_scale&#45;&gt;west_channel_contribution -->\n",
+       "<g id=\"edge78\" class=\"edge\">\n",
+       "<title>west_channel_scale&#45;&gt;west_channel_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M4763.16,-392.72C4749.91,-376.51 4730.92,-357.6 4709,-348.63 4680.73,-337.07 2539.64,-347.98 2510,-340.63 2497.15,-337.45 2484.11,-332.1 2472.06,-326.12\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2474.02,-323.2 2463.54,-321.68 2470.78,-329.41 2474.02,-323.2\"/>\n",
+       "</g>\n",
+       "<!-- north_channel_scale -->\n",
+       "<g id=\"node63\" class=\"node\">\n",
+       "<title>north_channel_scale</title>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M5123.5,-450.54C5123.5,-450.54 5020.5,-450.54 5020.5,-450.54 5014.5,-450.54 5008.5,-444.54 5008.5,-438.54 5008.5,-438.54 5008.5,-405.04 5008.5,-405.04 5008.5,-399.04 5014.5,-393.04 5020.5,-393.04 5020.5,-393.04 5123.5,-393.04 5123.5,-393.04 5129.5,-393.04 5135.5,-399.04 5135.5,-405.04 5135.5,-405.04 5135.5,-438.54 5135.5,-438.54 5135.5,-444.54 5129.5,-450.54 5123.5,-450.54\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5072\" y=\"-433.24\" font-family=\"Times,serif\" font-size=\"14.00\">north_channel_scale</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5072\" y=\"-416.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5072\" y=\"-400.24\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "</g>\n",
+       "<!-- north_channel_scale&#45;&gt;north_channel_contribution -->\n",
+       "<g id=\"edge17\" class=\"edge\">\n",
+       "<title>north_channel_scale&#45;&gt;north_channel_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5052.79,-392.9C5039.93,-376.75 5021.46,-357.86 5000,-348.63 4901.43,-306.28 3291.32,-295.47 2880.71,-293.4\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2880.73,-289.9 2870.71,-293.35 2880.7,-296.9 2880.73,-289.9\"/>\n",
+       "</g>\n",
+       "<!-- west_channel_contribution&#45;&gt;west_total_media_contribution_original_scale -->\n",
+       "<g id=\"edge87\" class=\"edge\">\n",
+       "<title>west_channel_contribution&#45;&gt;west_total_media_contribution_original_scale</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2337.4,-287.89C2176.85,-281.01 1808.93,-261.26 1503,-215.32 1486.01,-212.77 1468.23,-209.55 1450.73,-206.06\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1451.85,-202.72 1441.36,-204.16 1450.46,-209.58 1451.85,-202.72\"/>\n",
+       "</g>\n",
+       "<!-- west_channel_contribution&#45;&gt;west_y -->\n",
+       "<g id=\"edge85\" class=\"edge\">\n",
+       "<title>west_channel_contribution&#45;&gt;west_y</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2451.63,-262.79C2468,-250.45 2488.83,-237.3 2510,-230.73 2547.76,-219 5069.39,-184.04 5591.15,-176.93\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"5590.91,-180.43 5600.86,-176.8 5590.81,-173.43 5590.91,-180.43\"/>\n",
+       "</g>\n",
+       "<!-- west_channel_contribution&#45;&gt;west_channel_contribution_original_scale -->\n",
+       "<g id=\"edge89\" class=\"edge\">\n",
+       "<title>west_channel_contribution&#45;&gt;west_channel_contribution_original_scale</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2368.61,-262.77C2339.82,-246.65 2303.54,-226.34 2273.25,-209.39\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2274.99,-206.35 2264.55,-204.52 2271.57,-212.45 2274.99,-206.35\"/>\n",
+       "</g>\n",
+       "<!-- north_channel_contribution&#45;&gt;north_total_media_contribution_original_scale -->\n",
+       "<g id=\"edge25\" class=\"edge\">\n",
+       "<title>north_channel_contribution&#45;&gt;north_total_media_contribution_original_scale</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2752.95,-262.86C2736.37,-250.54 2715.32,-237.38 2694,-230.73 2628.75,-210.37 2146.6,-225.44 2079,-215.32 2064.88,-213.2 2050.17,-210.13 2035.8,-206.61\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2036.91,-203.28 2026.36,-204.21 2035.19,-210.06 2036.91,-203.28\"/>\n",
+       "</g>\n",
+       "<!-- north_channel_contribution&#45;&gt;north_y -->\n",
+       "<g id=\"edge21\" class=\"edge\">\n",
+       "<title>north_channel_contribution&#45;&gt;north_y</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2869.4,-289C3190.98,-281.39 4381.46,-253.27 5362,-230.73 5513.55,-227.24 5893.32,-231.98 6044,-215.32 6081.57,-211.16 6122.62,-203.47 6157.55,-195.96\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"6158.17,-199.41 6167.19,-193.86 6156.67,-192.57 6158.17,-199.41\"/>\n",
+       "</g>\n",
+       "<!-- north_channel_contribution&#45;&gt;north_channel_contribution_original_scale -->\n",
+       "<g id=\"edge27\" class=\"edge\">\n",
+       "<title>north_channel_contribution&#45;&gt;north_channel_contribution_original_scale</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2775.97,-262.77C2770.77,-248.14 2764.34,-230.05 2758.69,-214.16\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2762.08,-213.25 2755.43,-205 2755.49,-215.6 2762.08,-213.25\"/>\n",
+       "</g>\n",
+       "<!-- south_channel_contribution&#45;&gt;south_total_media_contribution_original_scale -->\n",
+       "<g id=\"edge55\" class=\"edge\">\n",
+       "<title>south_channel_contribution&#45;&gt;south_total_media_contribution_original_scale</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2568.55,-262.83C2551.79,-250.49 2530.52,-237.34 2509,-230.73 2432.94,-207.35 1871.75,-226.76 1793,-215.32 1778.59,-213.22 1763.58,-210.16 1748.91,-206.65\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1749.79,-203.26 1739.24,-204.25 1748.1,-210.06 1749.79,-203.26\"/>\n",
+       "</g>\n",
+       "<!-- south_channel_contribution&#45;&gt;south_y -->\n",
+       "<g id=\"edge51\" class=\"edge\">\n",
+       "<title>south_channel_contribution&#45;&gt;south_y</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2634.57,-262.99C2651.2,-250.53 2672.43,-237.22 2694,-230.73 2775.98,-206.05 5690.93,-224.95 5776,-215.32 5807.21,-211.78 5840.98,-204.98 5870.57,-198\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"5871.31,-201.42 5880.22,-195.67 5869.67,-194.61 5871.31,-201.42\"/>\n",
+       "</g>\n",
+       "<!-- south_channel_contribution&#45;&gt;south_channel_contribution_original_scale -->\n",
+       "<g id=\"edge57\" class=\"edge\">\n",
+       "<title>south_channel_contribution&#45;&gt;south_channel_contribution_original_scale</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2571.67,-262.77C2554.88,-247.15 2533.86,-227.6 2515.98,-210.98\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2518.84,-208.85 2509.13,-204.61 2514.07,-213.98 2518.84,-208.85\"/>\n",
+       "</g>\n",
+       "<!-- channel_data -->\n",
+       "<g id=\"node70\" class=\"node\">\n",
+       "<title>channel_data</title>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M2633.62,-450.54C2633.62,-450.54 2570.38,-450.54 2570.38,-450.54 2564.38,-450.54 2558.38,-444.54 2558.38,-438.54 2558.38,-438.54 2558.38,-405.04 2558.38,-405.04 2558.38,-399.04 2564.38,-393.04 2570.38,-393.04 2570.38,-393.04 2633.62,-393.04 2633.62,-393.04 2639.62,-393.04 2645.62,-399.04 2645.62,-405.04 2645.62,-405.04 2645.62,-438.54 2645.62,-438.54 2645.62,-444.54 2639.62,-450.54 2633.62,-450.54\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2602\" y=\"-433.24\" font-family=\"Times,serif\" font-size=\"14.00\">channel_data</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2602\" y=\"-416.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2602\" y=\"-400.24\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "</g>\n",
+       "<!-- channel_data&#45;&gt;west_channel_contribution -->\n",
+       "<g id=\"edge79\" class=\"edge\">\n",
+       "<title>channel_data&#45;&gt;west_channel_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2561.84,-392.74C2534.4,-373.58 2497.86,-348.06 2468.6,-327.62\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2470.83,-324.9 2460.62,-322.05 2466.82,-330.64 2470.83,-324.9\"/>\n",
+       "</g>\n",
+       "<!-- channel_data&#45;&gt;north_channel_contribution -->\n",
+       "<g id=\"edge16\" class=\"edge\">\n",
+       "<title>channel_data&#45;&gt;north_channel_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2642.38,-392.74C2669.97,-373.58 2706.71,-348.06 2736.12,-327.62\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2737.94,-330.62 2744.15,-322.04 2733.94,-324.88 2737.94,-330.62\"/>\n",
+       "</g>\n",
+       "<!-- channel_data&#45;&gt;south_channel_contribution -->\n",
+       "<g id=\"edge48\" class=\"edge\">\n",
+       "<title>channel_data&#45;&gt;south_channel_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2602,-392.74C2602,-375.01 2602,-351.84 2602,-332.28\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"2605.5,-332.47 2602,-322.47 2598.5,-332.47 2605.5,-332.47\"/>\n",
+       "</g>\n",
+       "<!-- south_gamma_control -->\n",
+       "<g id=\"node71\" class=\"node\">\n",
+       "<title>south_gamma_control</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"8286\" cy=\"-421.79\" rx=\"97.76\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"8286\" y=\"-433.24\" font-family=\"Times,serif\" font-size=\"14.00\">south_gamma_control</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"8286\" y=\"-416.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"8286\" y=\"-400.24\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- south_control_contribution -->\n",
+       "<g id=\"node76\" class=\"node\">\n",
+       "<title>south_control_contribution</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"8139.5,-320.73 7976.5,-320.73 7976.5,-263.23 8139.5,-263.23 8139.5,-320.73\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"8058\" y=\"-303.43\" font-family=\"Times,serif\" font-size=\"14.00\">south_control_contribution</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"8058\" y=\"-286.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"8058\" y=\"-270.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- south_gamma_control&#45;&gt;south_control_contribution -->\n",
+       "<g id=\"edge42\" class=\"edge\">\n",
+       "<title>south_gamma_control&#45;&gt;south_control_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M8236.54,-386.23C8217.92,-373.79 8196.32,-360.02 8176,-348.63 8162.03,-340.81 8146.78,-333.06 8132.02,-325.95\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"8133.8,-322.92 8123.27,-321.78 8130.79,-329.24 8133.8,-322.92\"/>\n",
+       "</g>\n",
+       "<!-- south_gamma_fourier&#45;&gt;south_fourier_contribution -->\n",
+       "<g id=\"edge40\" class=\"edge\">\n",
+       "<title>south_gamma_fourier&#45;&gt;south_fourier_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5636.52,-514.77C5655.61,-500.8 5679.18,-486.29 5703,-478.45 5769.37,-456.62 5792.52,-488.56 5860,-470.45 5872.73,-467.03 5885.73,-461.72 5897.81,-455.86\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"5899.03,-459.16 5906.37,-451.52 5895.87,-452.92 5899.03,-459.16\"/>\n",
+       "</g>\n",
+       "<!-- south_delta&#45;&gt;south_trend_effect_contribution -->\n",
+       "<g id=\"edge37\" class=\"edge\">\n",
+       "<title>south_delta&#45;&gt;south_trend_effect_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M6628.37,-380.7C6628.13,-365.3 6627.86,-347.74 6627.62,-332.4\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"6631.12,-332.64 6627.47,-322.69 6624.12,-332.75 6631.12,-332.64\"/>\n",
+       "</g>\n",
+       "<!-- south_fourier_contribution&#45;&gt;south_yearly_seasonality_contribution -->\n",
+       "<g id=\"edge41\" class=\"edge\">\n",
+       "<title>south_fourier_contribution&#45;&gt;south_yearly_seasonality_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5922.02,-392.79C5904.7,-378.19 5882.19,-360.88 5860,-348.63 5850.89,-343.61 5847.74,-344.31 5838,-340.63 5824.99,-335.73 5811.23,-330.45 5797.83,-325.25\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"5799.18,-322.02 5788.59,-321.66 5796.64,-328.54 5799.18,-322.02\"/>\n",
+       "</g>\n",
+       "<!-- south_control_data -->\n",
+       "<g id=\"node75\" class=\"node\">\n",
+       "<title>south_control_data</title>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M8105.75,-450.54C8105.75,-450.54 8010.25,-450.54 8010.25,-450.54 8004.25,-450.54 7998.25,-444.54 7998.25,-438.54 7998.25,-438.54 7998.25,-405.04 7998.25,-405.04 7998.25,-399.04 8004.25,-393.04 8010.25,-393.04 8010.25,-393.04 8105.75,-393.04 8105.75,-393.04 8111.75,-393.04 8117.75,-399.04 8117.75,-405.04 8117.75,-405.04 8117.75,-438.54 8117.75,-438.54 8117.75,-444.54 8111.75,-450.54 8105.75,-450.54\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"8058\" y=\"-433.24\" font-family=\"Times,serif\" font-size=\"14.00\">south_control_data</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"8058\" y=\"-416.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"8058\" y=\"-400.24\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "</g>\n",
+       "<!-- south_control_data&#45;&gt;south_control_contribution -->\n",
+       "<g id=\"edge43\" class=\"edge\">\n",
+       "<title>south_control_data&#45;&gt;south_control_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M8058,-392.74C8058,-375.01 8058,-351.84 8058,-332.28\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"8061.5,-332.47 8058,-322.47 8054.5,-332.47 8061.5,-332.47\"/>\n",
+       "</g>\n",
+       "<!-- south_control_contribution&#45;&gt;south_y -->\n",
+       "<g id=\"edge53\" class=\"edge\">\n",
+       "<title>south_control_contribution&#45;&gt;south_y</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M7976.18,-267.43C7924.67,-253.81 7856.52,-237.98 7795,-230.73 7432.16,-187.97 6514.39,-253.07 6151,-215.32 6113.4,-211.41 6072.35,-203.76 6037.42,-196.21\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"6038.3,-192.82 6027.78,-194.09 6036.8,-199.66 6038.3,-192.82\"/>\n",
+       "</g>\n",
+       "<!-- west_gamma_control -->\n",
+       "<g id=\"node77\" class=\"node\">\n",
+       "<title>west_gamma_control</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"5255\" cy=\"-421.79\" rx=\"94.58\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5255\" y=\"-433.24\" font-family=\"Times,serif\" font-size=\"14.00\">west_gamma_control</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5255\" y=\"-416.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5255\" y=\"-400.24\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- west_control_contribution -->\n",
+       "<g id=\"node81\" class=\"node\">\n",
+       "<title>west_control_contribution</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"5555.25,-320.73 5396.75,-320.73 5396.75,-263.23 5555.25,-263.23 5555.25,-320.73\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5476\" y=\"-303.43\" font-family=\"Times,serif\" font-size=\"14.00\">west_control_contribution</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5476\" y=\"-286.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5476\" y=\"-270.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- west_gamma_control&#45;&gt;west_control_contribution -->\n",
+       "<g id=\"edge73\" class=\"edge\">\n",
+       "<title>west_gamma_control&#45;&gt;west_control_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5302.83,-386.46C5321,-373.98 5342.11,-360.12 5362,-348.63 5375.35,-340.92 5389.93,-333.27 5404.06,-326.21\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"5405.46,-329.42 5412.89,-321.86 5402.37,-323.14 5405.46,-329.42\"/>\n",
+       "</g>\n",
+       "<!-- west_gamma_fourier&#45;&gt;west_fourier_contribution -->\n",
+       "<g id=\"edge71\" class=\"edge\">\n",
+       "<title>west_gamma_fourier&#45;&gt;west_fourier_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M6457.27,-516.02C6433.34,-497.93 6404.13,-475.83 6380.14,-457.69\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"6382.52,-455.1 6372.44,-451.86 6378.3,-460.69 6382.52,-455.1\"/>\n",
+       "</g>\n",
+       "<!-- west_delta&#45;&gt;west_trend_effect_contribution -->\n",
+       "<g id=\"edge69\" class=\"edge\">\n",
+       "<title>west_delta&#45;&gt;west_trend_effect_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M6761.3,-389.25C6743.86,-374.03 6720.94,-357.24 6697,-348.63 6660.58,-335.53 6560.58,-349.89 6523,-340.63 6509.15,-337.22 6494.9,-331.88 6481.62,-325.99\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"6483.11,-322.82 6472.56,-321.81 6480.17,-329.18 6483.11,-322.82\"/>\n",
+       "</g>\n",
+       "<!-- west_fourier_contribution&#45;&gt;west_yearly_seasonality_contribution -->\n",
+       "<g id=\"edge72\" class=\"edge\">\n",
+       "<title>west_fourier_contribution&#45;&gt;west_yearly_seasonality_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M6304.59,-392.74C6284.87,-373.93 6258.72,-348.99 6237.5,-328.75\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"6240.09,-326.37 6230.43,-322.01 6235.25,-331.44 6240.09,-326.37\"/>\n",
+       "</g>\n",
+       "<!-- west_control_contribution&#45;&gt;west_y -->\n",
+       "<g id=\"edge84\" class=\"edge\">\n",
+       "<title>west_control_contribution&#45;&gt;west_y</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5527.13,-262.77C5555.36,-247.19 5590.69,-227.7 5620.77,-211.1\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"5622.36,-214.22 5629.42,-206.32 5618.98,-208.09 5622.36,-214.22\"/>\n",
+       "</g>\n",
+       "<!-- west_control_data -->\n",
+       "<g id=\"node82\" class=\"node\">\n",
+       "<title>west_control_data</title>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M5521.5,-450.54C5521.5,-450.54 5430.5,-450.54 5430.5,-450.54 5424.5,-450.54 5418.5,-444.54 5418.5,-438.54 5418.5,-438.54 5418.5,-405.04 5418.5,-405.04 5418.5,-399.04 5424.5,-393.04 5430.5,-393.04 5430.5,-393.04 5521.5,-393.04 5521.5,-393.04 5527.5,-393.04 5533.5,-399.04 5533.5,-405.04 5533.5,-405.04 5533.5,-438.54 5533.5,-438.54 5533.5,-444.54 5527.5,-450.54 5521.5,-450.54\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5476\" y=\"-433.24\" font-family=\"Times,serif\" font-size=\"14.00\">west_control_data</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5476\" y=\"-416.74\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"5476\" y=\"-400.24\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "</g>\n",
+       "<!-- west_control_data&#45;&gt;west_control_contribution -->\n",
+       "<g id=\"edge74\" class=\"edge\">\n",
+       "<title>west_control_data&#45;&gt;west_control_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M5476,-392.74C5476,-375.01 5476,-351.84 5476,-332.28\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"5479.5,-332.47 5476,-322.47 5472.5,-332.47 5479.5,-332.47\"/>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.graphs.Digraph at 0x32aa54680>"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged.model.to_graphviz()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll play here with another option (something different than before). Once our model is ready, we can merge and add a new variable that is dependent on pre-existing deterministic variables from our different models. This can help create a deterministic variable that represents the total units across our models.\n",
+    "\n",
+    "We just need to access the model and create the deterministic variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with merged.model:\n",
+    "    pm.Deterministic(\n",
+    "        name=\"new_variable_calculation\",\n",
+    "        var=(\n",
+    "            merged.model[\"north_total_media_contribution_original_scale\"]\n",
+    "            + merged.model[\"south_total_media_contribution_original_scale\"]\n",
+    "            + merged.model[\"west_total_media_contribution_original_scale\"]\n",
+    "        ),\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we can ask the optimizer to do something similar to what it did before: bring me the maximum number of units across all markets (models), but keep a specific market at a level greater than or equal to $Z$.\n",
+    "\n",
+    "Check how this works 👇🏻"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "min_total_response = 10\n",
+    "\n",
+    "\n",
+    "def mean_response_eq_constraint_fun(budgets_sym, total_budget_sym, optimizer):\n",
+    "    \"\"\"Enforces mean_response(budgets_sym) = target_response, i.e. returns (mean_resp - target_response).\"\"\"\n",
+    "    resp_dist = optimizer.extract_response_distribution(\n",
+    "        \"north_total_media_contribution_original_scale\"\n",
+    "    )\n",
+    "    mean_resp = pt.mean(_check_samples_dimensionality(resp_dist))\n",
+    "    return mean_resp - min_total_response\n",
+    "\n",
+    "\n",
+    "optimize_multidimensional_merged_model = BudgetOptimizer(\n",
+    "    model=merged,\n",
+    "    num_periods=optimization_horizon,\n",
+    "    response_variable=\"new_variable_calculation\",  # variable to optimize (maximize in this case)\n",
+    "    custom_constraints=[\n",
+    "        Constraint(\n",
+    "            key=\"min_response_constraint\",\n",
+    "            constraint_fun=mean_response_eq_constraint_fun,\n",
+    "            constraint_type=\"ineq\",\n",
+    "        )\n",
+    "    ],\n",
+    ")\n",
+    "\n",
+    "allocation_xarray, scipy_result, callback_results = (\n",
+    "    optimize_multidimensional_merged_model.allocate_budget(\n",
+    "        total_budget=budget_per_time_unit_in_horizon, callback=True\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Constraint satisfied: value 240.8141 is greater than zero\n"
+     ]
+    }
+   ],
+   "source": [
+    "constraint_value = round(callback_results[-1][\"constraint_info\"][0][\"value\"], 4)\n",
+    "# Check if constraint is satisfied (should be close to zero for equality, >= 0 for inequality)\n",
+    "if np.isclose(constraint_value, 0, atol=1e-6):\n",
+    "    print(f\"Constraint satisfied: value {constraint_value} is close to zero\")\n",
+    "elif constraint_value > 0:\n",
+    "    print(f\"Constraint satisfied: value {constraint_value} is greater than zero\")\n",
+    "else:\n",
+    "    raise AssertionError(\n",
+    "        f\"Constraint violated: value {constraint_value} should be >= 0\"\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Great! Again, we can see that the optimizer found a solution and finished the task while respecting the constraints.\n",
+    "\n",
+    "The `BuildMergedModel` class allows for a huge range of new capabilities in optimization. You can merge your pre-existing trained models into a single place and optimize whatever you like, thinking about and adjusting the objective based on your business needs. But at the same time, that's just the tip of the iceberg.\n",
+    "\n",
+    "### Learning about the merge models function\n",
+    "Under the hood, the `BuildMergedModel` class is based on the `merge_models` function in `pytensor_utils`, which allows you to apply the same merge operation without `idata`. This allows you to create models from other models and then sample them together if needed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[31mSignature:\u001b[39m\n",
+      "merge_models(\n",
+      "    models: list[pymc.model.core.Model],\n",
+      "    *,\n",
+      "    prefixes: list[str] | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n",
+      "    merge_on: str | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n",
+      ") -> pymc.model.core.Model\n",
+      "\u001b[31mDocstring:\u001b[39m\n",
+      "Merge multiple PyMC models into a single model.\n",
+      "\n",
+      "Parameters\n",
+      "----------\n",
+      "models : list of pm.Model\n",
+      "    List of models to merge.\n",
+      "prefixes : list of str or None\n",
+      "    List of prefixes for each model. If None, will auto-generate as 'model1', 'model2', ...\n",
+      "merge_on : str or None\n",
+      "    Variable name to merge on (shared across all models) - this variable will NOT be prefixed.\n",
+      "\n",
+      "Returns\n",
+      "-------\n",
+      "pm.Model\n",
+      "    Merged model.\n",
+      "\u001b[31mFile:\u001b[39m      ~/Documents/GitHub/pymc-marketing/pymc_marketing/pytensor_utils.py\n",
+      "\u001b[31mType:\u001b[39m      function"
+     ]
+    }
+   ],
+   "source": [
+    "merge_models?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this symbolic graphical space, each model acts as a node in a computational directed acyclic graph (C-DAG), and the merging process is akin to performing a join operation in SQL, where different datasets are combined based on common keys. By leveraging the power of PyMC and the symbolic representation of models, `merge_models` facilitates the construction of these large graphical models without the need to build them all together from scratch. You can always build and combine different pieces, like with **Lego**.\n",
+    "\n",
+    "Check out how, using this lower-level function, we can use the pure PyMC models and get a new model from them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
+       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
+       "<!-- Generated by graphviz version 13.1.1 (0)\n",
+       " -->\n",
+       "<!-- Pages: 1 -->\n",
+       "<svg width=\"2215pt\" height=\"479pt\"\n",
+       " viewBox=\"0.00 0.00 2215.00 479.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 475.04)\">\n",
+       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-475.04 2211.38,-475.04 2211.38,4 -4,4\"/>\n",
+       "<g id=\"clust1\" class=\"cluster\">\n",
+       "<title>clusterchannel (4)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M20,-341.23C20,-341.23 977,-341.23 977,-341.23 983,-341.23 989,-347.23 989,-353.23 989,-353.23 989,-451.04 989,-451.04 989,-457.04 983,-463.04 977,-463.04 977,-463.04 20,-463.04 20,-463.04 14,-463.04 8,-457.04 8,-451.04 8,-451.04 8,-353.23 8,-353.23 8,-347.23 14,-341.23 20,-341.23\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"950.25\" y=\"-348.43\" font-family=\"Times,serif\" font-size=\"14.00\">channel (4)</text>\n",
+       "</g>\n",
+       "<g id=\"clust2\" class=\"cluster\">\n",
+       "<title>clusterdate (100) x channel (4)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1009,-231.32C1009,-231.32 1303,-231.32 1303,-231.32 1309,-231.32 1315,-237.32 1315,-243.32 1315,-243.32 1315,-439.13 1315,-439.13 1315,-445.13 1309,-451.13 1303,-451.13 1303,-451.13 1009,-451.13 1009,-451.13 1003,-451.13 997,-445.13 997,-439.13 997,-439.13 997,-243.32 997,-243.32 997,-237.32 1003,-231.32 1009,-231.32\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1241.38\" y=\"-238.52\" font-family=\"Times,serif\" font-size=\"14.00\">date (100) x channel (4)</text>\n",
+       "</g>\n",
+       "<g id=\"clust3\" class=\"cluster\">\n",
+       "<title>clusterdate (100)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1439,-8C1439,-8 1817,-8 1817,-8 1823,-8 1829,-14 1829,-20 1829,-20 1829,-211.32 1829,-211.32 1829,-217.32 1823,-223.32 1817,-223.32 1817,-223.32 1439,-223.32 1439,-223.32 1433,-223.32 1427,-217.32 1427,-211.32 1427,-211.32 1427,-20 1427,-20 1427,-14 1433,-8 1439,-8\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1793.25\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">date (100)</text>\n",
+       "</g>\n",
+       "<!-- new_users_saturation_lam -->\n",
+       "<g id=\"node1\" class=\"node\">\n",
+       "<title>new_users_saturation_lam</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"387\" cy=\"-414.38\" rx=\"113.67\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"387\" y=\"-425.83\" font-family=\"Times,serif\" font-size=\"14.00\">new_users_saturation_lam</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"387\" y=\"-409.33\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"387\" y=\"-392.83\" font-family=\"Times,serif\" font-size=\"14.00\">Gamma</text>\n",
+       "</g>\n",
+       "<!-- new_users_contribution -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>new_users_contribution</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"1150.88,-321.32 1005.12,-321.32 1005.12,-263.82 1150.88,-263.82 1150.88,-321.32\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1078\" y=\"-304.02\" font-family=\"Times,serif\" font-size=\"14.00\">new_users_contribution</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1078\" y=\"-287.52\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1078\" y=\"-271.02\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- new_users_saturation_lam&#45;&gt;new_users_contribution -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>new_users_saturation_lam&#45;&gt;new_users_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M436.65,-377.4C458.16,-363.68 484.28,-349.38 510,-341.23 597.73,-313.4 857.87,-300.85 993.54,-296.07\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"993.3,-299.58 1003.17,-295.73 993.05,-292.58 993.3,-299.58\"/>\n",
+       "</g>\n",
+       "<!-- reengage_saturation_alpha -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>reengage_saturation_alpha</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"867\" cy=\"-414.38\" rx=\"113.67\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"867\" y=\"-425.83\" font-family=\"Times,serif\" font-size=\"14.00\">reengage_saturation_alpha</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"867\" y=\"-409.33\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"867\" y=\"-392.83\" font-family=\"Times,serif\" font-size=\"14.00\">Beta</text>\n",
+       "</g>\n",
+       "<!-- reengage_contribution -->\n",
+       "<g id=\"node11\" class=\"node\">\n",
+       "<title>reengage_contribution</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"1306.75,-321.32 1169.25,-321.32 1169.25,-263.82 1306.75,-263.82 1306.75,-321.32\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1238\" y=\"-304.02\" font-family=\"Times,serif\" font-size=\"14.00\">reengage_contribution</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1238\" y=\"-287.52\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1238\" y=\"-271.02\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- reengage_saturation_alpha&#45;&gt;reengage_contribution -->\n",
+       "<g id=\"edge8\" class=\"edge\">\n",
+       "<title>reengage_saturation_alpha&#45;&gt;reengage_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M916.93,-377.52C939.13,-363.54 966.26,-349.03 993,-341.23 1064.33,-320.41 1088.17,-352.27 1160,-333.23 1166.75,-331.44 1173.58,-329.01 1180.26,-326.24\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1181.5,-329.52 1189.2,-322.25 1178.64,-323.13 1181.5,-329.52\"/>\n",
+       "</g>\n",
+       "<!-- reengage_saturation_lam -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>reengage_saturation_lam</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"627\" cy=\"-414.38\" rx=\"107.83\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"627\" y=\"-425.83\" font-family=\"Times,serif\" font-size=\"14.00\">reengage_saturation_lam</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"627\" y=\"-409.33\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"627\" y=\"-392.83\" font-family=\"Times,serif\" font-size=\"14.00\">Gamma</text>\n",
+       "</g>\n",
+       "<!-- reengage_saturation_lam&#45;&gt;reengage_contribution -->\n",
+       "<g id=\"edge9\" class=\"edge\">\n",
+       "<title>reengage_saturation_lam&#45;&gt;reengage_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M672.9,-377.16C693.44,-363.12 718.7,-348.66 744,-341.23 788.35,-328.19 1115.15,-344.43 1160,-333.23 1166.87,-331.51 1173.82,-329.11 1180.6,-326.32\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1181.96,-329.54 1189.68,-322.29 1179.12,-323.14 1181.96,-329.54\"/>\n",
+       "</g>\n",
+       "<!-- new_users_saturation_alpha -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>new_users_saturation_alpha</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"136\" cy=\"-414.38\" rx=\"119.5\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"136\" y=\"-425.83\" font-family=\"Times,serif\" font-size=\"14.00\">new_users_saturation_alpha</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"136\" y=\"-409.33\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"136\" y=\"-392.83\" font-family=\"Times,serif\" font-size=\"14.00\">Beta</text>\n",
+       "</g>\n",
+       "<!-- new_users_saturation_alpha&#45;&gt;new_users_contribution -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>new_users_saturation_alpha&#45;&gt;new_users_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M187.45,-377.28C209.91,-363.44 237.2,-349.1 264,-341.23 398.72,-301.62 813.51,-294.78 993.36,-293.7\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"993.37,-297.2 1003.35,-293.65 993.33,-290.21 993.37,-297.2\"/>\n",
+       "</g>\n",
+       "<!-- new_users_intercept -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>new_users_intercept</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1415\" cy=\"-292.57\" rx=\"89.8\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1415\" y=\"-304.02\" font-family=\"Times,serif\" font-size=\"14.00\">new_users_intercept</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1415\" y=\"-287.52\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1415\" y=\"-271.02\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- new_users_likelihood -->\n",
+       "<g id=\"node12\" class=\"node\">\n",
+       "<title>new_users_likelihood</title>\n",
+       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"1530\" cy=\"-174.66\" rx=\"95.11\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1530\" y=\"-186.11\" font-family=\"Times,serif\" font-size=\"14.00\">new_users_likelihood</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1530\" y=\"-169.61\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1530\" y=\"-153.11\" font-family=\"Times,serif\" font-size=\"14.00\">Censored</text>\n",
+       "</g>\n",
+       "<!-- new_users_intercept&#45;&gt;new_users_likelihood -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>new_users_intercept&#45;&gt;new_users_likelihood</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1451.07,-255.21C1462.1,-244.1 1474.35,-231.75 1485.85,-220.16\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1488.04,-222.92 1492.6,-213.36 1483.07,-217.99 1488.04,-222.92\"/>\n",
+       "</g>\n",
+       "<!-- reengage_intercept -->\n",
+       "<g id=\"node6\" class=\"node\">\n",
+       "<title>reengage_intercept</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1871\" cy=\"-292.57\" rx=\"83.97\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1871\" y=\"-304.02\" font-family=\"Times,serif\" font-size=\"14.00\">reengage_intercept</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1871\" y=\"-287.52\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1871\" y=\"-271.02\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- reengage_likelihood -->\n",
+       "<g id=\"node14\" class=\"node\">\n",
+       "<title>reengage_likelihood</title>\n",
+       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"1732\" cy=\"-174.66\" rx=\"89.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1732\" y=\"-186.11\" font-family=\"Times,serif\" font-size=\"14.00\">reengage_likelihood</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1732\" y=\"-169.61\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1732\" y=\"-153.11\" font-family=\"Times,serif\" font-size=\"14.00\">Censored</text>\n",
+       "</g>\n",
+       "<!-- reengage_intercept&#45;&gt;reengage_likelihood -->\n",
+       "<g id=\"edge12\" class=\"edge\">\n",
+       "<title>reengage_intercept&#45;&gt;reengage_likelihood</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1829.3,-256.79C1814.72,-244.64 1798.2,-230.86 1782.96,-218.15\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1785.24,-215.5 1775.32,-211.78 1780.76,-220.87 1785.24,-215.5\"/>\n",
+       "</g>\n",
+       "<!-- reengage_likelihood_sigma -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>reengage_likelihood_sigma</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"2090\" cy=\"-292.57\" rx=\"117.38\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2090\" y=\"-304.02\" font-family=\"Times,serif\" font-size=\"14.00\">reengage_likelihood_sigma</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2090\" y=\"-287.52\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"2090\" y=\"-271.02\" font-family=\"Times,serif\" font-size=\"14.00\">Halfnormal</text>\n",
+       "</g>\n",
+       "<!-- reengage_likelihood_sigma&#45;&gt;reengage_likelihood -->\n",
+       "<g id=\"edge13\" class=\"edge\">\n",
+       "<title>reengage_likelihood_sigma&#45;&gt;reengage_likelihood</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M2026.36,-257.93C2006.73,-248.44 1984.8,-238.69 1964,-231.32 1919.29,-215.46 1868.15,-202.65 1825.28,-193.34\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1826.27,-189.97 1815.76,-191.3 1824.81,-196.82 1826.27,-189.97\"/>\n",
+       "</g>\n",
+       "<!-- new_users_likelihood_sigma -->\n",
+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>new_users_likelihood_sigma</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1646\" cy=\"-292.57\" rx=\"123.21\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1646\" y=\"-304.02\" font-family=\"Times,serif\" font-size=\"14.00\">new_users_likelihood_sigma</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1646\" y=\"-287.52\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1646\" y=\"-271.02\" font-family=\"Times,serif\" font-size=\"14.00\">Halfnormal</text>\n",
+       "</g>\n",
+       "<!-- new_users_likelihood_sigma&#45;&gt;new_users_likelihood -->\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
+       "<title>new_users_likelihood_sigma&#45;&gt;new_users_likelihood</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1608.02,-253.61C1597.38,-242.99 1585.73,-231.34 1574.74,-220.36\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1577.5,-218.17 1567.95,-213.58 1572.55,-223.13 1577.5,-218.17\"/>\n",
+       "</g>\n",
+       "<!-- new_users_contribution&#45;&gt;new_users_likelihood -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>new_users_contribution&#45;&gt;new_users_likelihood</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1108.52,-263.61C1123.09,-251.79 1141.38,-239 1160,-231.32 1244.24,-196.54 1347.28,-183.17 1423.2,-178.18\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1423.4,-181.67 1433.16,-177.57 1422.97,-174.69 1423.4,-181.67\"/>\n",
+       "</g>\n",
+       "<!-- channel_data -->\n",
+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>channel_data</title>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M1189.62,-443.13C1189.62,-443.13 1126.38,-443.13 1126.38,-443.13 1120.38,-443.13 1114.38,-437.13 1114.38,-431.13 1114.38,-431.13 1114.38,-397.63 1114.38,-397.63 1114.38,-391.63 1120.38,-385.63 1126.38,-385.63 1126.38,-385.63 1189.62,-385.63 1189.62,-385.63 1195.62,-385.63 1201.62,-391.63 1201.62,-397.63 1201.62,-397.63 1201.62,-431.13 1201.62,-431.13 1201.62,-437.13 1195.62,-443.13 1189.62,-443.13\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1158\" y=\"-425.83\" font-family=\"Times,serif\" font-size=\"14.00\">channel_data</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1158\" y=\"-409.33\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1158\" y=\"-392.83\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "</g>\n",
+       "<!-- channel_data&#45;&gt;new_users_contribution -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>channel_data&#45;&gt;new_users_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1139.24,-385.29C1128.41,-369.07 1114.65,-348.46 1102.92,-330.89\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1105.94,-329.11 1097.48,-322.74 1100.12,-333 1105.94,-329.11\"/>\n",
+       "</g>\n",
+       "<!-- channel_data&#45;&gt;reengage_contribution -->\n",
+       "<g id=\"edge10\" class=\"edge\">\n",
+       "<title>channel_data&#45;&gt;reengage_contribution</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1176.76,-385.29C1187.59,-369.07 1201.35,-348.46 1213.08,-330.89\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1215.88,-333 1218.52,-322.74 1210.06,-329.11 1215.88,-333\"/>\n",
+       "</g>\n",
+       "<!-- reengage_contribution&#45;&gt;reengage_likelihood -->\n",
+       "<g id=\"edge14\" class=\"edge\">\n",
+       "<title>reengage_contribution&#45;&gt;reengage_likelihood</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1265.33,-263.5C1279.22,-251.18 1297.14,-238.02 1316,-231.32 1349.3,-219.48 1599.66,-231.68 1634,-223.32 1645.14,-220.6 1656.51,-216.5 1667.36,-211.82\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1668.54,-215.13 1676.2,-207.81 1665.65,-208.75 1668.54,-215.13\"/>\n",
+       "</g>\n",
+       "<!-- new_users_target -->\n",
+       "<g id=\"node13\" class=\"node\">\n",
+       "<title>new_users_target</title>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M1573.25,-98C1573.25,-98 1486.75,-98 1486.75,-98 1480.75,-98 1474.75,-92 1474.75,-86 1474.75,-86 1474.75,-52.5 1474.75,-52.5 1474.75,-46.5 1480.75,-40.5 1486.75,-40.5 1486.75,-40.5 1573.25,-40.5 1573.25,-40.5 1579.25,-40.5 1585.25,-46.5 1585.25,-52.5 1585.25,-52.5 1585.25,-86 1585.25,-86 1585.25,-92 1579.25,-98 1573.25,-98\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1530\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">new_users_target</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1530\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1530\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "</g>\n",
+       "<!-- new_users_likelihood&#45;&gt;new_users_target -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>new_users_likelihood&#45;&gt;new_users_target</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1530,-133.68C1530,-125.83 1530,-117.6 1530,-109.76\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1533.5,-109.79 1530,-99.79 1526.5,-109.79 1533.5,-109.79\"/>\n",
+       "</g>\n",
+       "<!-- reengage_target -->\n",
+       "<g id=\"node15\" class=\"node\">\n",
+       "<title>reengage_target</title>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M1771.12,-98C1771.12,-98 1692.88,-98 1692.88,-98 1686.88,-98 1680.88,-92 1680.88,-86 1680.88,-86 1680.88,-52.5 1680.88,-52.5 1680.88,-46.5 1686.88,-40.5 1692.88,-40.5 1692.88,-40.5 1771.12,-40.5 1771.12,-40.5 1777.12,-40.5 1783.12,-46.5 1783.12,-52.5 1783.12,-52.5 1783.12,-86 1783.12,-86 1783.12,-92 1777.12,-98 1771.12,-98\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1732\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">reengage_target</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1732\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1732\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "</g>\n",
+       "<!-- reengage_likelihood&#45;&gt;reengage_target -->\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
+       "<title>reengage_likelihood&#45;&gt;reengage_target</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1732,-133.68C1732,-125.83 1732,-117.6 1732,-109.76\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1735.5,-109.79 1732,-99.79 1728.5,-109.79 1735.5,-109.79\"/>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.graphs.Digraph at 0x3326b50d0>"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pm_merge_model = merge_models(\n",
+    "    models=[new_users_model, reengage_users_model],\n",
+    "    prefixes=[\"new_users\", \"reengage\"],\n",
+    "    merge_on=\"channel_data\",\n",
+    ")\n",
+    "pm_merge_model.to_graphviz()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that the model is built, we can sample from it like any other PyMC model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [new_users_saturation_alpha, new_users_saturation_lam, new_users_intercept, new_users_likelihood_sigma, reengage_saturation_alpha, reengage_saturation_lam, reengage_intercept, reengage_likelihood_sigma]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e64b28f59cfe45af8ca6ef5c021806a0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 800 tune and 200 draw iterations (3_200 + 800 draws total) took 2 seconds.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <div>\n",
+       "              <div class='xr-header'>\n",
+       "                <div class=\"xr-obj-type\">arviz.InferenceData</div>\n",
+       "              </div>\n",
+       "              <ul class=\"xr-sections group-sections\">\n",
+       "              \n",
+       "            <li class = \"xr-section-item\">\n",
+       "                  <input id=\"idata_posteriorcd5d2683-0566-46ee-9776-5f1b393ed4e0\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_posteriorcd5d2683-0566-46ee-9776-5f1b393ed4e0\" class = \"xr-section-summary\">posterior</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(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\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",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\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",
+       "  border-color: var(--xr-background-color-row-even);\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",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\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-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\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",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 5MB\n",
+       "Dimensions:                     (chain: 4, draw: 200, channel: 4, date: 100)\n",
+       "Coordinates:\n",
+       "  * chain                       (chain) int64 32B 0 1 2 3\n",
+       "  * draw                        (draw) int64 2kB 0 1 2 3 4 ... 196 197 198 199\n",
+       "  * channel                     (channel) &lt;U18 288B &#x27;impressions_x1_var&#x27; ... ...\n",
+       "  * date                        (date) datetime64[ns] 800B 2020-01-01 ... 202...\n",
+       "Data variables:\n",
+       "    new_users_intercept         (chain, draw) float64 6kB 2.738 2.713 ... 3.27\n",
+       "    reengage_intercept          (chain, draw) float64 6kB 2.792 2.788 ... 2.854\n",
+       "    new_users_saturation_alpha  (chain, draw, channel) float64 26kB 0.423 ......\n",
+       "    new_users_saturation_lam    (chain, draw, channel) float64 26kB 1.003 ......\n",
+       "    new_users_likelihood_sigma  (chain, draw) float64 6kB 0.6403 ... 0.655\n",
+       "    reengage_saturation_alpha   (chain, draw, channel) float64 26kB 0.9941 .....\n",
+       "    reengage_saturation_lam     (chain, draw, channel) float64 26kB 0.238 ......\n",
+       "    reengage_likelihood_sigma   (chain, draw) float64 6kB 0.5287 ... 0.5364\n",
+       "    new_users_contribution      (chain, draw, date, channel) float64 3MB 0.19...\n",
+       "    reengage_contribution       (chain, draw, date, channel) float64 3MB 0.77...\n",
+       "Attributes:\n",
+       "    created_at:                 2025-10-09T17:42:08.510911+00:00\n",
+       "    arviz_version:              0.22.0\n",
+       "    inference_library:          pymc\n",
+       "    inference_library_version:  5.25.1\n",
+       "    sampling_time:              1.7832472324371338\n",
+       "    tuning_steps:               800</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-7f855f87-5946-40c1-bf61-5211099fe637' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7f855f87-5946-40c1-bf61-5211099fe637' 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>: 200</li><li><span class='xr-has-index'>channel</span>: 4</li><li><span class='xr-has-index'>date</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-27bad697-d25a-4158-af65-a995afd7b2af' class='xr-section-summary-in' type='checkbox'  checked><label for='section-27bad697-d25a-4158-af65-a995afd7b2af' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-de5a87bb-9a78-437a-9ca4-b6ced2a829fd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-de5a87bb-9a78-437a-9ca4-b6ced2a829fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-398602b6-3747-4032-b716-0c5a73671cd1' class='xr-var-data-in' type='checkbox'><label for='data-398602b6-3747-4032-b716-0c5a73671cd1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 195 196 197 198 199</div><input id='attrs-035ac542-0b0a-49ce-a889-58686a3f26c5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-035ac542-0b0a-49ce-a889-58686a3f26c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9907d7da-fca0-44dd-b818-b0d052fe0a67' class='xr-var-data-in' type='checkbox'><label for='data-9907d7da-fca0-44dd-b818-b0d052fe0a67' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
+       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
+       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
+       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
+       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
+       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
+       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
+       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
+       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
+       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
+       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
+       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
+       "       168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
+       "       182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n",
+       "       196, 197, 198, 199])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>channel</span></div><div class='xr-var-dims'>(channel)</div><div class='xr-var-dtype'>&lt;U18</div><div class='xr-var-preview xr-preview'>&#x27;impressions_x1_var&#x27; ... &#x27;impres...</div><input id='attrs-a49ab681-d42b-4f74-9be1-5eef75dd4c02' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a49ab681-d42b-4f74-9be1-5eef75dd4c02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e5506ae-529e-44a7-8404-5dc99014602f' class='xr-var-data-in' type='checkbox'><label for='data-7e5506ae-529e-44a7-8404-5dc99014602f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;impressions_x1_var&#x27;, &#x27;impressions_x2_var&#x27;, &#x27;impressions_x3_var&#x27;,\n",
+       "       &#x27;impressions_x4_var&#x27;], dtype=&#x27;&lt;U18&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-01-01 ... 2020-04-09</div><input id='attrs-4df2feb2-0d96-4532-88f0-1ba575d98327' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4df2feb2-0d96-4532-88f0-1ba575d98327' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23daf007-1744-469e-8703-5eb70d677666' class='xr-var-data-in' type='checkbox'><label for='data-23daf007-1744-469e-8703-5eb70d677666' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-01-01T00:00:00.000000000&#x27;, &#x27;2020-01-02T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-03T00:00:00.000000000&#x27;, &#x27;2020-01-04T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-05T00:00:00.000000000&#x27;, &#x27;2020-01-06T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-07T00:00:00.000000000&#x27;, &#x27;2020-01-08T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-09T00:00:00.000000000&#x27;, &#x27;2020-01-10T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-11T00:00:00.000000000&#x27;, &#x27;2020-01-12T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-13T00:00:00.000000000&#x27;, &#x27;2020-01-14T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-15T00:00:00.000000000&#x27;, &#x27;2020-01-16T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-17T00:00:00.000000000&#x27;, &#x27;2020-01-18T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-19T00:00:00.000000000&#x27;, &#x27;2020-01-20T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-21T00:00:00.000000000&#x27;, &#x27;2020-01-22T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-23T00:00:00.000000000&#x27;, &#x27;2020-01-24T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-25T00:00:00.000000000&#x27;, &#x27;2020-01-26T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-27T00:00:00.000000000&#x27;, &#x27;2020-01-28T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-29T00:00:00.000000000&#x27;, &#x27;2020-01-30T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-31T00:00:00.000000000&#x27;, &#x27;2020-02-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-02T00:00:00.000000000&#x27;, &#x27;2020-02-03T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-04T00:00:00.000000000&#x27;, &#x27;2020-02-05T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-06T00:00:00.000000000&#x27;, &#x27;2020-02-07T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-08T00:00:00.000000000&#x27;, &#x27;2020-02-09T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-10T00:00:00.000000000&#x27;, &#x27;2020-02-11T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-12T00:00:00.000000000&#x27;, &#x27;2020-02-13T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-14T00:00:00.000000000&#x27;, &#x27;2020-02-15T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-16T00:00:00.000000000&#x27;, &#x27;2020-02-17T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-18T00:00:00.000000000&#x27;, &#x27;2020-02-19T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-20T00:00:00.000000000&#x27;, &#x27;2020-02-21T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-22T00:00:00.000000000&#x27;, &#x27;2020-02-23T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-24T00:00:00.000000000&#x27;, &#x27;2020-02-25T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-26T00:00:00.000000000&#x27;, &#x27;2020-02-27T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-28T00:00:00.000000000&#x27;, &#x27;2020-02-29T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-01T00:00:00.000000000&#x27;, &#x27;2020-03-02T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-03T00:00:00.000000000&#x27;, &#x27;2020-03-04T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-05T00:00:00.000000000&#x27;, &#x27;2020-03-06T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-07T00:00:00.000000000&#x27;, &#x27;2020-03-08T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-09T00:00:00.000000000&#x27;, &#x27;2020-03-10T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-11T00:00:00.000000000&#x27;, &#x27;2020-03-12T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-13T00:00:00.000000000&#x27;, &#x27;2020-03-14T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-15T00:00:00.000000000&#x27;, &#x27;2020-03-16T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-17T00:00:00.000000000&#x27;, &#x27;2020-03-18T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-19T00:00:00.000000000&#x27;, &#x27;2020-03-20T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-21T00:00:00.000000000&#x27;, &#x27;2020-03-22T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-23T00:00:00.000000000&#x27;, &#x27;2020-03-24T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-25T00:00:00.000000000&#x27;, &#x27;2020-03-26T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-27T00:00:00.000000000&#x27;, &#x27;2020-03-28T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-29T00:00:00.000000000&#x27;, &#x27;2020-03-30T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-31T00:00:00.000000000&#x27;, &#x27;2020-04-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-04-02T00:00:00.000000000&#x27;, &#x27;2020-04-03T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-04-04T00:00:00.000000000&#x27;, &#x27;2020-04-05T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-04-06T00:00:00.000000000&#x27;, &#x27;2020-04-07T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-04-08T00:00:00.000000000&#x27;, &#x27;2020-04-09T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4c6ac359-03a3-429b-96e5-ac116f3459c2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4c6ac359-03a3-429b-96e5-ac116f3459c2' class='xr-section-summary' >Data variables: <span>(10)</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>new_users_intercept</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.738 2.713 2.916 ... 2.788 3.27</div><input id='attrs-dc596f3b-ee10-48bd-b71c-6fd7e2f02ccb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dc596f3b-ee10-48bd-b71c-6fd7e2f02ccb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59843a05-18a1-493b-b349-ef91fb1840b0' class='xr-var-data-in' type='checkbox'><label for='data-59843a05-18a1-493b-b349-ef91fb1840b0' 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([[2.73758135, 2.712654  , 2.91575645, 3.35397357, 3.05026064,\n",
+       "        2.7397415 , 2.9063304 , 3.12492427, 2.6827141 , 3.02355243,\n",
+       "        3.27017735, 2.77983689, 2.78598131, 2.90096418, 2.61871669,\n",
+       "        2.93365367, 2.89169212, 2.65589224, 2.62918117, 2.56916326,\n",
+       "        2.57214175, 2.72677735, 2.904854  , 3.03020912, 2.8772054 ,\n",
+       "        2.72226011, 3.11458539, 2.77224076, 2.85143932, 2.54852665,\n",
+       "        3.11377825, 3.01942419, 3.0936616 , 3.29805812, 2.48924094,\n",
+       "        3.29576952, 3.20644491, 3.15888393, 3.35616676, 3.34052644,\n",
+       "        2.44319001, 3.0196455 , 2.82752375, 2.70905062, 3.03825411,\n",
+       "        3.3921759 , 2.81619235, 3.0677047 , 3.23517328, 2.59780705,\n",
+       "        2.79443974, 2.98046436, 2.78633962, 2.85740452, 2.89529206,\n",
+       "        3.0262481 , 3.02930196, 3.0448486 , 3.33980856, 3.24976965,\n",
+       "        2.92180813, 3.11421144, 3.14128423, 2.70333111, 3.0202595 ,\n",
+       "        2.93065349, 2.66504954, 2.9276344 , 2.82526636, 2.74231629,\n",
+       "        2.97518826, 3.02354034, 3.13630179, 2.91418007, 2.95197642,\n",
+       "        3.11784008, 2.75912673, 2.39834774, 2.42512356, 2.93008814,\n",
+       "        3.05933722, 2.78510775, 2.82586236, 2.80389492, 2.96538497,\n",
+       "        2.7758536 , 3.11084203, 3.09894923, 2.93756791, 2.56217199,\n",
+       "        2.48503132, 2.61070279, 3.08359447, 2.58280956, 2.55608708,\n",
+       "        3.00214535, 3.04895149, 3.18920707, 3.19551838, 3.06743484,\n",
+       "...\n",
+       "        2.8828534 , 2.90743538, 3.48535441, 3.34221461, 2.74490967,\n",
+       "        2.94428801, 2.64069825, 2.65655923, 2.52395894, 2.53884357,\n",
+       "        2.72606361, 2.69759092, 2.94026738, 2.81662027, 2.79150944,\n",
+       "        2.97785717, 2.92883059, 2.71888996, 2.76289484, 2.98553398,\n",
+       "        2.75688504, 2.6942985 , 2.82462377, 2.47528004, 3.1088381 ,\n",
+       "        2.74574273, 3.16022949, 3.1507638 , 2.61521558, 2.70820818,\n",
+       "        2.96122225, 2.93714703, 2.87404259, 3.09386138, 2.8480484 ,\n",
+       "        2.81307259, 2.9017052 , 3.20850737, 2.7521667 , 2.83670574,\n",
+       "        3.41760078, 2.60857046, 2.94580189, 2.70029647, 2.82352243,\n",
+       "        3.07090341, 3.1242087 , 2.89172145, 2.69591644, 2.7019563 ,\n",
+       "        2.9835284 , 3.08319518, 2.94795715, 3.05108767, 2.81451449,\n",
+       "        3.06215476, 2.8020059 , 3.11533181, 3.11922047, 2.93008949,\n",
+       "        2.79000373, 2.69374344, 2.70224445, 2.77968601, 2.64357836,\n",
+       "        3.21931037, 3.21462762, 3.12775691, 3.01759182, 2.90777587,\n",
+       "        3.11681648, 2.79504596, 2.75276621, 2.84384771, 3.23495255,\n",
+       "        3.26936188, 3.25448681, 3.19773591, 2.97256159, 2.45031625,\n",
+       "        2.72564246, 2.53453115, 2.51704983, 2.5274869 , 2.66805956,\n",
+       "        2.74203341, 3.08614267, 2.90802877, 2.84160967, 3.04642274,\n",
+       "        2.53981262, 2.55395007, 2.73303492, 2.58215891, 3.04083274,\n",
+       "        2.70205301, 2.6381702 , 2.72801983, 2.78803795, 3.26994577]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>reengage_intercept</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.792 2.788 2.753 ... 2.774 2.854</div><input id='attrs-b2b40f1a-eefa-4256-90a4-75ed77bfccaa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b2b40f1a-eefa-4256-90a4-75ed77bfccaa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08d770ca-0031-4f39-993a-e0d93aeb2b25' class='xr-var-data-in' type='checkbox'><label for='data-08d770ca-0031-4f39-993a-e0d93aeb2b25' 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([[2.79215223, 2.78795939, 2.75266271, 3.15934223, 2.82708128,\n",
+       "        2.87427043, 2.41968769, 2.64472228, 2.55005185, 2.68840207,\n",
+       "        2.72258567, 2.62329847, 3.07092041, 2.75083789, 2.60814933,\n",
+       "        2.82377507, 2.28958718, 2.56660873, 2.66471371, 2.61399926,\n",
+       "        2.63706239, 2.75155452, 2.94620055, 2.93515331, 2.53454013,\n",
+       "        2.63194281, 2.39327617, 2.55408818, 2.88595508, 2.81767005,\n",
+       "        2.51766175, 2.4991644 , 2.57806356, 2.19688967, 2.86853752,\n",
+       "        2.81448644, 2.77315554, 2.74945086, 2.6923934 , 3.02301476,\n",
+       "        2.3635787 , 2.59554496, 2.65334849, 2.78858357, 2.62415209,\n",
+       "        2.59886183, 2.75465468, 2.41864893, 2.42939442, 2.86803734,\n",
+       "        2.65337863, 2.42510776, 2.84804834, 2.3435361 , 2.4025406 ,\n",
+       "        2.42811719, 2.91978611, 2.97757583, 2.91675621, 2.89275418,\n",
+       "        2.6073468 , 2.67868681, 2.60381723, 2.36724   , 2.44599667,\n",
+       "        2.41778609, 2.73933705, 2.49757284, 2.58082514, 2.54255935,\n",
+       "        2.98440536, 2.68551032, 3.01175639, 2.60062649, 2.67748119,\n",
+       "        2.41956945, 2.61090709, 2.14929788, 2.32814275, 2.46416935,\n",
+       "        3.14922641, 2.74914317, 2.78801993, 2.62027078, 2.86547045,\n",
+       "        2.68979805, 2.6934385 , 2.50286794, 2.27763833, 2.86745327,\n",
+       "        2.81188717, 2.7909048 , 2.77612412, 2.67633436, 2.5420872 ,\n",
+       "        3.078389  , 2.89966666, 2.62536481, 2.97158219, 3.07514623,\n",
+       "...\n",
+       "        2.96225196, 3.03452054, 2.83675181, 2.61768362, 2.88674708,\n",
+       "        2.52345931, 2.8438662 , 2.41675103, 2.46373873, 2.56819549,\n",
+       "        2.98103663, 2.69565466, 2.79034335, 2.69996182, 2.3002943 ,\n",
+       "        2.62613509, 2.36993812, 2.68707476, 2.70912011, 2.72184136,\n",
+       "        2.8163884 , 2.5304012 , 2.74636275, 2.38113429, 2.53590516,\n",
+       "        2.98843185, 2.43765792, 2.44578205, 2.92747687, 2.7191242 ,\n",
+       "        2.84195831, 2.83609757, 2.94872155, 2.35085905, 2.59538873,\n",
+       "        2.63637393, 2.54755543, 2.23409381, 2.43958416, 2.74518734,\n",
+       "        2.77139057, 2.60138524, 2.90587844, 2.62938312, 2.5494563 ,\n",
+       "        2.78853682, 2.83880383, 2.77154217, 2.7569097 , 2.83918708,\n",
+       "        2.64088969, 2.75595616, 2.84487032, 2.8440111 , 2.93658626,\n",
+       "        2.73216003, 2.65135763, 2.58236007, 2.83595879, 2.9623687 ,\n",
+       "        2.69583266, 2.63389802, 2.69548778, 2.8543984 , 2.69112366,\n",
+       "        2.80406287, 2.92666316, 2.93980262, 2.81875722, 2.7475248 ,\n",
+       "        2.8358537 , 2.49696951, 2.80011734, 2.595748  , 3.03718661,\n",
+       "        2.61736528, 2.74991518, 2.72238823, 2.888815  , 2.83229998,\n",
+       "        2.4956241 , 2.67330284, 2.51385313, 2.44764151, 2.18916164,\n",
+       "        2.54100252, 2.75701005, 2.57912435, 2.55905017, 2.6531158 ,\n",
+       "        2.40232298, 2.76692211, 2.7439065 , 2.62608998, 2.75253239,\n",
+       "        3.13004265, 2.79786085, 2.724046  , 2.77402388, 2.85352979]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>new_users_saturation_alpha</span></div><div class='xr-var-dims'>(chain, draw, channel)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.423 0.6699 ... 0.9995 0.8242</div><input id='attrs-5561f207-d637-490f-b71a-9fdb5277906b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5561f207-d637-490f-b71a-9fdb5277906b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca17e6ae-c6aa-42a2-8f41-dd75dc8df01f' class='xr-var-data-in' type='checkbox'><label for='data-ca17e6ae-c6aa-42a2-8f41-dd75dc8df01f' 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([[[0.42296859, 0.66992717, 0.94032336, 0.94539696],\n",
+       "        [0.4243055 , 0.90074676, 0.97722576, 0.98411716],\n",
+       "        [0.30754301, 0.87326051, 0.98019216, 0.86945317],\n",
+       "        ...,\n",
+       "        [0.09626207, 0.13418101, 0.9489515 , 0.93302813],\n",
+       "        [0.1128279 , 0.89313431, 0.98860808, 0.94109094],\n",
+       "        [0.49483616, 0.49003256, 0.98554859, 0.87563192]],\n",
+       "\n",
+       "       [[0.31325046, 0.86467192, 0.99351988, 0.91252785],\n",
+       "        [0.09731957, 0.94867862, 0.98062726, 0.88866127],\n",
+       "        [0.56759496, 0.90475824, 0.98079285, 0.94174418],\n",
+       "        ...,\n",
+       "        [0.26738112, 0.43166163, 0.9706526 , 0.9777913 ],\n",
+       "        [0.32580261, 0.9686257 , 0.99760569, 0.99722688],\n",
+       "        [0.37646549, 0.99142429, 0.9914958 , 0.97968785]],\n",
+       "\n",
+       "       [[0.44161111, 0.84730237, 0.99945132, 0.98923101],\n",
+       "        [0.15436874, 0.87471021, 0.9973726 , 0.97870803],\n",
+       "        [0.14000837, 0.85821913, 0.92416975, 0.96492708],\n",
+       "        ...,\n",
+       "        [0.31223164, 0.2988184 , 0.90939645, 0.79836258],\n",
+       "        [0.15785457, 0.58767984, 0.98400627, 0.97185058],\n",
+       "        [0.18199465, 0.71120371, 0.98146577, 0.9193071 ]],\n",
+       "\n",
+       "       [[0.27469763, 0.45785597, 0.9881793 , 0.94245453],\n",
+       "        [0.31313403, 0.98434189, 0.99373932, 0.9654377 ],\n",
+       "        [0.23906494, 0.92350336, 0.9602966 , 0.92368482],\n",
+       "        ...,\n",
+       "        [0.24755217, 0.90624571, 0.99452917, 0.96512832],\n",
+       "        [0.37157417, 0.6090778 , 0.92430699, 0.9888733 ],\n",
+       "        [0.13990806, 0.98172341, 0.99949647, 0.82416792]]],\n",
+       "      shape=(4, 200, 4))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>new_users_saturation_lam</span></div><div class='xr-var-dims'>(chain, draw, channel)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.003 0.5944 ... 0.5377 1.921</div><input id='attrs-cee16381-72e2-483e-ab0f-ca051ea3872b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cee16381-72e2-483e-ab0f-ca051ea3872b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09779733-41fe-40d5-a5f3-52302205caaa' class='xr-var-data-in' type='checkbox'><label for='data-09779733-41fe-40d5-a5f3-52302205caaa' 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([[[1.00335533, 0.5943999 , 0.14670299, 0.50274441],\n",
+       "        [1.25943106, 0.82994671, 0.28045857, 0.5904198 ],\n",
+       "        [1.43652567, 0.68419383, 0.49075501, 0.46975517],\n",
+       "        ...,\n",
+       "        [1.13906292, 0.78498909, 0.72730751, 0.69868525],\n",
+       "        [0.66825022, 1.197545  , 0.32061181, 0.54393878],\n",
+       "        [0.95525848, 0.290562  , 0.3279172 , 1.07242756]],\n",
+       "\n",
+       "       [[0.89867893, 1.01254418, 0.36635619, 0.80463221],\n",
+       "        [0.72788504, 0.51058186, 0.38504731, 1.0783637 ],\n",
+       "        [1.26049196, 0.70947932, 0.41071727, 0.73232686],\n",
+       "        ...,\n",
+       "        [0.34163556, 0.22936541, 0.30585333, 0.42794546],\n",
+       "        [0.5386664 , 0.33842788, 0.30741029, 0.97115055],\n",
+       "        [0.50999933, 0.62696929, 0.3591048 , 0.68593126]],\n",
+       "\n",
+       "       [[0.82696122, 0.66834958, 0.38748545, 0.63206489],\n",
+       "        [0.8910924 , 1.59963057, 0.20400892, 0.75046961],\n",
+       "        [1.04381724, 0.83281694, 0.5057524 , 0.68350534],\n",
+       "        ...,\n",
+       "        [0.547196  , 2.54465275, 0.32360369, 0.42430578],\n",
+       "        [2.06454241, 0.56399043, 0.49459856, 0.87970047],\n",
+       "        [0.38621608, 0.40672461, 0.21647221, 0.94318289]],\n",
+       "\n",
+       "       [[1.33229403, 0.30317375, 0.44378814, 0.46579607],\n",
+       "        [0.86451284, 0.64194287, 0.11065159, 0.39313123],\n",
+       "        [0.56915981, 0.4519554 , 0.32391711, 0.39624725],\n",
+       "        ...,\n",
+       "        [1.17501094, 0.71803308, 0.41058276, 0.56799513],\n",
+       "        [0.95254027, 1.15206272, 0.24061723, 0.26872693],\n",
+       "        [0.92609351, 0.68481185, 0.5376882 , 1.92074059]]],\n",
+       "      shape=(4, 200, 4))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>new_users_likelihood_sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6403 0.6481 ... 0.7178 0.655</div><input id='attrs-872e6a72-16aa-4e07-94a7-89cf962c6104' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-872e6a72-16aa-4e07-94a7-89cf962c6104' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b796689-40e1-4236-9d67-cfdefe603930' class='xr-var-data-in' type='checkbox'><label for='data-6b796689-40e1-4236-9d67-cfdefe603930' 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([[0.64025128, 0.64807132, 0.66386277, 0.64227567, 0.70321797,\n",
+       "        0.67123939, 0.69646588, 0.70633019, 0.63245862, 0.73707979,\n",
+       "        0.75634936, 0.60477669, 0.65915803, 0.64037779, 0.59672723,\n",
+       "        0.56683451, 0.76158422, 0.67526725, 0.76502086, 0.78330621,\n",
+       "        0.77212652, 0.6929851 , 0.64540686, 0.67578911, 0.63426074,\n",
+       "        0.68966461, 0.62819115, 0.73830254, 0.65509803, 0.67727782,\n",
+       "        0.6154683 , 0.66104049, 0.61169611, 0.64495035, 0.68793171,\n",
+       "        0.62098303, 0.62527745, 0.62579579, 0.68055252, 0.64463006,\n",
+       "        0.73245568, 0.65843588, 0.63074448, 0.61072893, 0.79245895,\n",
+       "        0.83253777, 0.62937517, 0.68155201, 0.636754  , 0.6844143 ,\n",
+       "        0.65855249, 0.67567206, 0.61714727, 0.64492565, 0.64841488,\n",
+       "        0.67502006, 0.61778969, 0.62743096, 0.6853142 , 0.78235635,\n",
+       "        0.76441806, 0.63146303, 0.63911993, 0.711002  , 0.68061164,\n",
+       "        0.65647785, 0.60933471, 0.63877621, 0.69417996, 0.70166782,\n",
+       "        0.61174458, 0.61582999, 0.67577531, 0.69341576, 0.68804323,\n",
+       "        0.75493518, 0.83271906, 0.61026456, 0.60794215, 0.6085698 ,\n",
+       "        0.72589519, 0.65176589, 0.67279802, 0.70900418, 0.67301021,\n",
+       "        0.68662274, 0.59605832, 0.62030189, 0.61016788, 0.69319924,\n",
+       "        0.69603333, 0.71532571, 0.57466916, 0.73170918, 0.66205824,\n",
+       "        0.64394222, 0.67247249, 0.67504774, 0.72640942, 0.59705682,\n",
+       "...\n",
+       "        0.64531059, 0.63463885, 0.7544004 , 0.724829  , 0.59795059,\n",
+       "        0.71161935, 0.76382494, 0.57039838, 0.60656598, 0.61958271,\n",
+       "        0.62009479, 0.68544498, 0.63214778, 0.68977581, 0.66080902,\n",
+       "        0.6553831 , 0.70553335, 0.68993915, 0.71309452, 0.63340515,\n",
+       "        0.62819716, 0.58354552, 0.63784453, 0.65893869, 0.66791887,\n",
+       "        0.68071437, 0.6313005 , 0.66665333, 0.63188876, 0.74887684,\n",
+       "        0.75767901, 0.59422551, 0.78337592, 0.64622642, 0.60803053,\n",
+       "        0.6020563 , 0.63404449, 0.71680469, 0.65326346, 0.65812462,\n",
+       "        0.65321866, 0.63885939, 0.6270271 , 0.63503145, 0.70678903,\n",
+       "        0.69157502, 0.62972172, 0.72881431, 0.65390002, 0.68986383,\n",
+       "        0.60824001, 0.65218422, 0.73064833, 0.69490446, 0.74210952,\n",
+       "        0.65930191, 0.65119122, 0.74406051, 0.67492426, 0.60193276,\n",
+       "        0.58694813, 0.63846901, 0.62783552, 0.64209582, 0.75081847,\n",
+       "        0.63381222, 0.66254931, 0.66175611, 0.62157086, 0.68981392,\n",
+       "        0.6263997 , 0.64349849, 0.60212216, 0.7177546 , 0.62777181,\n",
+       "        0.6943916 , 0.70392671, 0.6594456 , 0.6923237 , 0.72134357,\n",
+       "        0.74988148, 0.65753654, 0.67786933, 0.72010175, 0.64817579,\n",
+       "        0.58365449, 0.72789652, 0.58232528, 0.60735618, 0.64924167,\n",
+       "        0.67738666, 0.70199308, 0.66241123, 0.632336  , 0.67213318,\n",
+       "        0.66405707, 0.60458448, 0.63040472, 0.71777579, 0.65499598]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>reengage_saturation_alpha</span></div><div class='xr-var-dims'>(chain, draw, channel)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9941 0.7536 ... 0.3268 0.1656</div><input id='attrs-cfe60e37-0f3e-4e41-a30b-af3ff019197e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cfe60e37-0f3e-4e41-a30b-af3ff019197e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-485df21b-7217-4881-bc09-7c363c158a33' class='xr-var-data-in' type='checkbox'><label for='data-485df21b-7217-4881-bc09-7c363c158a33' 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([[[0.99409154, 0.75363273, 0.49906001, 0.08953438],\n",
+       "        [0.99749471, 0.89782593, 0.12006657, 0.39212786],\n",
+       "        [0.96871449, 0.72632739, 0.31354962, 0.06006411],\n",
+       "        ...,\n",
+       "        [0.98584799, 0.4848716 , 0.03732629, 0.05728321],\n",
+       "        [0.97913863, 0.87668009, 0.71488066, 0.30369833],\n",
+       "        [0.98954659, 0.96695891, 0.10076797, 0.13605372]],\n",
+       "\n",
+       "       [[0.99626405, 0.48570429, 0.26796393, 0.17615314],\n",
+       "        [0.99542471, 0.68417865, 0.09594668, 0.33620036],\n",
+       "        [0.99976838, 0.82124906, 0.50420872, 0.41716111],\n",
+       "        ...,\n",
+       "        [0.98281751, 0.88460437, 0.79521723, 0.10393785],\n",
+       "        [0.9970959 , 0.91575147, 0.72611441, 0.55751505],\n",
+       "        [0.97484954, 0.98355822, 0.5007649 , 0.48282405]],\n",
+       "\n",
+       "       [[0.97659634, 0.94010656, 0.12774949, 0.15951949],\n",
+       "        [0.97017082, 0.74222084, 0.24701508, 0.10751355],\n",
+       "        [0.99147907, 0.84000976, 0.0978454 , 0.20890306],\n",
+       "        ...,\n",
+       "        [0.99524669, 0.7412769 , 0.09611973, 0.20636416],\n",
+       "        [0.98984436, 0.76673071, 0.14962019, 0.37171564],\n",
+       "        [0.96423367, 0.80149162, 0.28643204, 0.69639074]],\n",
+       "\n",
+       "       [[0.99429471, 0.97907294, 0.18815471, 0.05541041],\n",
+       "        [0.98327851, 0.50594714, 0.70295784, 0.78703754],\n",
+       "        [0.99218097, 0.97659937, 0.48779638, 0.19557859],\n",
+       "        ...,\n",
+       "        [0.97571551, 0.52519128, 0.44955984, 0.26255136],\n",
+       "        [0.97782758, 0.69719485, 0.3042369 , 0.22564947],\n",
+       "        [0.99671875, 0.80082381, 0.32684429, 0.16564507]]],\n",
+       "      shape=(4, 200, 4))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>reengage_saturation_lam</span></div><div class='xr-var-dims'>(chain, draw, channel)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.238 0.8419 2.371 ... 1.063 1.237</div><input id='attrs-f8f27a46-bdf6-42b9-8c6a-f61e8b995808' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f8f27a46-bdf6-42b9-8c6a-f61e8b995808' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e4b6806-6f90-4d8c-8aec-575b09e2400d' class='xr-var-data-in' type='checkbox'><label for='data-0e4b6806-6f90-4d8c-8aec-575b09e2400d' 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([[[0.23801094, 0.84187639, 2.37066739, 2.31399134],\n",
+       "        [0.38700699, 1.5050797 , 0.95988399, 0.35856339],\n",
+       "        [0.18634406, 0.59295493, 1.31002494, 1.87088875],\n",
+       "        ...,\n",
+       "        [0.27058   , 1.01187235, 0.86252382, 0.49602469],\n",
+       "        [0.32068602, 0.59695405, 0.74787575, 1.84634854],\n",
+       "        [0.22321386, 0.77164744, 1.21229113, 0.32152123]],\n",
+       "\n",
+       "       [[0.27818514, 1.47433706, 2.20326707, 0.91371034],\n",
+       "        [0.20010046, 0.67991708, 2.09808744, 0.95698299],\n",
+       "        [0.37223531, 0.80082439, 2.02426926, 1.59036251],\n",
+       "        ...,\n",
+       "        [0.20203522, 0.50693924, 1.06625103, 0.42563582],\n",
+       "        [0.3212182 , 0.75939872, 2.36975056, 1.54381627],\n",
+       "        [0.25554555, 0.485455  , 1.44689747, 1.0479907 ]],\n",
+       "\n",
+       "       [[0.25746423, 1.47993457, 1.54228974, 1.67036879],\n",
+       "        [0.42764531, 1.42955634, 1.14258479, 0.95519897],\n",
+       "        [0.21115445, 0.5102351 , 0.45456421, 1.16444818],\n",
+       "        ...,\n",
+       "        [0.23251158, 0.36021859, 1.06134361, 0.97587992],\n",
+       "        [0.27047106, 0.77500944, 0.9601031 , 0.57087686],\n",
+       "        [0.27203276, 1.46937954, 1.19583171, 0.49385019]],\n",
+       "\n",
+       "       [[0.28065804, 0.60525468, 2.23871182, 0.58418397],\n",
+       "        [0.32479453, 0.90031892, 0.69138811, 0.90063148],\n",
+       "        [0.27362403, 0.23689633, 1.20474058, 1.82964527],\n",
+       "        ...,\n",
+       "        [0.25424274, 0.32077515, 1.29074611, 0.81245174],\n",
+       "        [0.17292701, 1.3620545 , 1.53556638, 0.86956148],\n",
+       "        [0.36794898, 0.71620941, 1.06252723, 1.23674896]]],\n",
+       "      shape=(4, 200, 4))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>reengage_likelihood_sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5287 0.4958 ... 0.5322 0.5364</div><input id='attrs-a1d99c7c-af99-42c9-ad4c-a304e364f254' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a1d99c7c-af99-42c9-ad4c-a304e364f254' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-084cd53a-601a-4526-9175-fbf3e21af677' class='xr-var-data-in' type='checkbox'><label for='data-084cd53a-601a-4526-9175-fbf3e21af677' 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([[0.52872106, 0.49579776, 0.48609484, 0.53733034, 0.4859731 ,\n",
+       "        0.56638044, 0.51527938, 0.57868274, 0.58062281, 0.53513929,\n",
+       "        0.53121906, 0.56209558, 0.48084946, 0.49086441, 0.58549545,\n",
+       "        0.6242014 , 0.49987899, 0.55440909, 0.58551387, 0.57685004,\n",
+       "        0.54100144, 0.5594943 , 0.49146027, 0.49092758, 0.56979037,\n",
+       "        0.56467809, 0.57099437, 0.61807387, 0.46980682, 0.48298059,\n",
+       "        0.55363047, 0.52953311, 0.51701912, 0.52138492, 0.55130578,\n",
+       "        0.48437686, 0.52406239, 0.51973267, 0.51607737, 0.47918844,\n",
+       "        0.62796826, 0.53658898, 0.53494449, 0.54625961, 0.54828981,\n",
+       "        0.5670848 , 0.51100389, 0.52132637, 0.59294078, 0.52712068,\n",
+       "        0.52312388, 0.50702266, 0.59431401, 0.50863977, 0.57306377,\n",
+       "        0.57548176, 0.51843419, 0.52571751, 0.52552828, 0.59525529,\n",
+       "        0.54483915, 0.58123666, 0.57620828, 0.55057245, 0.52936049,\n",
+       "        0.59167608, 0.65673626, 0.57121858, 0.54763357, 0.51494641,\n",
+       "        0.55515186, 0.47366124, 0.51571574, 0.47852451, 0.49501248,\n",
+       "        0.49576373, 0.59842887, 0.48537885, 0.53186456, 0.54875268,\n",
+       "        0.50402883, 0.49124212, 0.56998524, 0.5447513 , 0.49950801,\n",
+       "        0.49777138, 0.54350194, 0.55352223, 0.52341408, 0.60630915,\n",
+       "        0.55695111, 0.52946645, 0.49797738, 0.56509401, 0.58772603,\n",
+       "        0.51862426, 0.56809035, 0.50005166, 0.50956133, 0.49703222,\n",
+       "...\n",
+       "        0.52322479, 0.58060589, 0.52929507, 0.63603834, 0.46792507,\n",
+       "        0.52737965, 0.48240279, 0.58115922, 0.59293669, 0.62891078,\n",
+       "        0.56613338, 0.55373613, 0.50869792, 0.47898549, 0.63367358,\n",
+       "        0.49669017, 0.48892358, 0.53137031, 0.53610455, 0.50376993,\n",
+       "        0.58200814, 0.54994126, 0.56940554, 0.46935706, 0.50213785,\n",
+       "        0.58654099, 0.49639724, 0.47599671, 0.4750553 , 0.57647062,\n",
+       "        0.58284152, 0.48577956, 0.46292064, 0.62768174, 0.59375419,\n",
+       "        0.61279046, 0.62791289, 0.48677026, 0.59837127, 0.5458638 ,\n",
+       "        0.53825611, 0.53588077, 0.50696658, 0.613438  , 0.51575865,\n",
+       "        0.57675855, 0.53914548, 0.56915223, 0.50136337, 0.51081853,\n",
+       "        0.54311315, 0.53202898, 0.52057955, 0.52983715, 0.54076304,\n",
+       "        0.54055106, 0.53463466, 0.57833586, 0.47910485, 0.52264575,\n",
+       "        0.51488699, 0.49845331, 0.47562651, 0.48722565, 0.59506433,\n",
+       "        0.46484339, 0.48046912, 0.52193218, 0.49824564, 0.5626184 ,\n",
+       "        0.53557949, 0.48678835, 0.46749502, 0.57639725, 0.4824815 ,\n",
+       "        0.55972482, 0.55620495, 0.53546186, 0.52888465, 0.57435135,\n",
+       "        0.51785897, 0.51488849, 0.4943131 , 0.491019  , 0.56743852,\n",
+       "        0.51174627, 0.52810904, 0.56276027, 0.55164697, 0.51517816,\n",
+       "        0.58718718, 0.52898367, 0.50821781, 0.52259212, 0.55319502,\n",
+       "        0.50991129, 0.53222368, 0.51376461, 0.53220252, 0.53643516]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>new_users_contribution</span></div><div class='xr-var-dims'>(chain, draw, date, channel)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.196 0.4119 0.8651 ... 0.0 0.03781</div><input id='attrs-022d3352-e470-44a7-aa1b-dbdaf582324b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-022d3352-e470-44a7-aa1b-dbdaf582324b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ccc9e7c-bb17-4fed-887a-26c6519df841' class='xr-var-data-in' type='checkbox'><label for='data-7ccc9e7c-bb17-4fed-887a-26c6519df841' 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([[[[0.19595685, 0.41191945, 0.86505794, 0.69190783],\n",
+       "         [0.23113248, 0.40858246, 0.86660949, 0.67385849],\n",
+       "         [0.25817632, 0.40951009, 0.85958768, 0.68541248],\n",
+       "         ...,\n",
+       "         [0.22665426, 0.42843557, 0.        , 0.29958434],\n",
+       "         [0.19460146, 0.42675037, 0.        , 0.29097959],\n",
+       "         [0.18946736, 0.42589005, 0.        , 0.14670132]],\n",
+       "\n",
+       "        [[0.17289346, 0.48050987, 0.83786127, 0.68807167],\n",
+       "         [0.20780839, 0.47580239, 0.84054254, 0.66799766],\n",
+       "         [0.23556845, 0.47710937, 0.82846767, 0.68083301],\n",
+       "         ...,\n",
+       "         [0.20328975, 0.5040485 , 0.        , 0.27865767],\n",
+       "         [0.17157418, 0.50162835, 0.        , 0.27027065],\n",
+       "         [0.16659401, 0.50039444, 0.        , 0.13309981]],\n",
+       "\n",
+       "        [[0.11567775, 0.50742167, 0.75921758, 0.64772302],\n",
+       "         [0.14053936, 0.50295335, 0.76306543, 0.63163176],\n",
+       "         [0.16069264, 0.50419483, 0.74584912, 0.64193704],\n",
+       "         ...,\n",
+       "...\n",
+       "         [0.12289611, 0.5391437 , 0.        , 0.28092959],\n",
+       "         [0.10426418, 0.53676179, 0.        , 0.27256489],\n",
+       "         [0.1013208 , 0.53554668, 0.        , 0.13496439]],\n",
+       "\n",
+       "        [[0.17695635, 0.27510321, 0.80887657, 0.82693598],\n",
+       "         [0.20782149, 0.27194103, 0.81114239, 0.81362596],\n",
+       "         [0.23137098, 0.27281789, 0.80092423, 0.8221774 ],\n",
+       "         ...,\n",
+       "         [0.20390683, 0.29107925, 0.        , 0.45945761],\n",
+       "         [0.1757617 , 0.2894239 , 0.        , 0.44905039],\n",
+       "         [0.17123287, 0.28858106, 0.        , 0.25290086]],\n",
+       "\n",
+       "        [[0.06761223, 0.57022994, 0.75783118, 0.34344733],\n",
+       "         [0.07921992, 0.56520592, 0.76195201, 0.32453743],\n",
+       "         [0.08803996, 0.56660179, 0.74353778, 0.33651019],\n",
+       "         ...,\n",
+       "         [0.0777507 , 0.59520207, 0.        , 0.08923566],\n",
+       "         [0.06716186, 0.59264594, 0.        , 0.08591897],\n",
+       "         [0.06545379, 0.59134171, 0.        , 0.03780542]]]],\n",
+       "      shape=(4, 200, 100, 4))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>reengage_contribution</span></div><div class='xr-var-dims'>(chain, draw, date, channel)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.7798 0.3994 ... 0.0 0.01151</div><input id='attrs-e1e37391-b9a2-494f-ab20-1b09429bec33' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e1e37391-b9a2-494f-ab20-1b09429bec33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-010ebc5d-8994-41b6-8be3-e7da90b6d8b6' class='xr-var-data-in' type='checkbox'><label for='data-010ebc5d-8994-41b6-8be3-e7da90b6d8b6' 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([[[[0.77979681, 0.39935273, 0.20742406, 0.03333045],\n",
+       "         [0.83056597, 0.39541082, 0.21017124, 0.03136369],\n",
+       "         [0.86336676, 0.39650519, 0.19821482, 0.03260702],\n",
+       "         ...,\n",
+       "         [0.82465646, 0.41907209, 0.        , 0.00819757],\n",
+       "         [0.77762146, 0.41704394, 0.        , 0.00788739],\n",
+       "         [0.76921478, 0.41600996, 0.        , 0.00343585]],\n",
+       "\n",
+       "        [[0.68942993, 0.34718813, 0.07651035, 0.31089325],\n",
+       "         [0.75560042, 0.34273057, 0.07713626, 0.30458983],\n",
+       "         [0.80043038, 0.34396542, 0.07436549, 0.30863349],\n",
+       "         ...,\n",
+       "         [0.7477016 , 0.36989569, 0.        , 0.15453509],\n",
+       "         [0.68667785, 0.36752826, 0.        , 0.15058532],\n",
+       "         [0.6761045 , 0.36632415, 0.        , 0.08030491]],\n",
+       "\n",
+       "        [[0.79719508, 0.44701689, 0.17645421, 0.02541467],\n",
+       "         [0.83933498, 0.44340094, 0.17819699, 0.02402989],\n",
+       "         [0.86604917, 0.44440612, 0.17052623, 0.02490685],\n",
+       "         ...,\n",
+       "...\n",
+       "         [0.80011217, 0.40269676, 0.        , 0.05855717],\n",
+       "         [0.75207819, 0.40167047, 0.        , 0.05665171],\n",
+       "         [0.74352513, 0.40114543, 0.        , 0.02679567]],\n",
+       "\n",
+       "        [[0.81508574, 0.28628959, 0.15922718, 0.13812415],\n",
+       "         [0.85545734, 0.28275557, 0.16094304, 0.13297178],\n",
+       "         [0.88091856, 0.28373493, 0.15341354, 0.13625621],\n",
+       "         ...,\n",
+       "         [0.85081893, 0.30423766, 0.        , 0.04772061],\n",
+       "         [0.81332911, 0.30237071, 0.        , 0.04614545],\n",
+       "         [0.80651955, 0.30142078, 0.        , 0.02166221]],\n",
+       "\n",
+       "        [[0.69953255, 0.45638457, 0.20049819, 0.08712429],\n",
+       "         [0.76413789, 0.45225947, 0.20224694, 0.08318516],\n",
+       "         [0.80766528, 0.45340539, 0.1945198 , 0.08568868],\n",
+       "         ...,\n",
+       "         [0.75644837, 0.47691677, 0.        , 0.02628035],\n",
+       "         [0.69683628, 0.47481295, 0.        , 0.02535682],\n",
+       "         [0.68647042, 0.47373969, 0.        , 0.01150863]]]],\n",
+       "      shape=(4, 200, 100, 4))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4203f8bf-3afc-4f9e-af01-22a445369116' class='xr-section-summary-in' type='checkbox'  ><label for='section-4203f8bf-3afc-4f9e-af01-22a445369116' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-73aa23d9-7347-4269-8b19-3df90b45ce3a' class='xr-index-data-in' type='checkbox'/><label for='index-73aa23d9-7347-4269-8b19-3df90b45ce3a' 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><input type='checkbox' disabled/><label></label><input id='index-2d947e2d-771f-4a76-8c1e-cfbb02700884' class='xr-index-data-in' type='checkbox'/><label for='index-2d947e2d-771f-4a76-8c1e-cfbb02700884' 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",
+       "       190, 191, 192, 193, 194, 195, 196, 197, 198, 199],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=200))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>channel</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-8c0a9b0e-8faf-48df-b2de-0817393044bb' class='xr-index-data-in' type='checkbox'/><label for='index-8c0a9b0e-8faf-48df-b2de-0817393044bb' 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([&#x27;impressions_x1_var&#x27;, &#x27;impressions_x2_var&#x27;, &#x27;impressions_x3_var&#x27;,\n",
+       "       &#x27;impressions_x4_var&#x27;],\n",
+       "      dtype=&#x27;object&#x27;, name=&#x27;channel&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>date</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-99283bfe-df8e-4b94-95c0-1e6e7b52ed7b' class='xr-index-data-in' type='checkbox'/><label for='index-99283bfe-df8e-4b94-95c0-1e6e7b52ed7b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-01-01&#x27;, &#x27;2020-01-02&#x27;, &#x27;2020-01-03&#x27;, &#x27;2020-01-04&#x27;,\n",
+       "               &#x27;2020-01-05&#x27;, &#x27;2020-01-06&#x27;, &#x27;2020-01-07&#x27;, &#x27;2020-01-08&#x27;,\n",
+       "               &#x27;2020-01-09&#x27;, &#x27;2020-01-10&#x27;, &#x27;2020-01-11&#x27;, &#x27;2020-01-12&#x27;,\n",
+       "               &#x27;2020-01-13&#x27;, &#x27;2020-01-14&#x27;, &#x27;2020-01-15&#x27;, &#x27;2020-01-16&#x27;,\n",
+       "               &#x27;2020-01-17&#x27;, &#x27;2020-01-18&#x27;, &#x27;2020-01-19&#x27;, &#x27;2020-01-20&#x27;,\n",
+       "               &#x27;2020-01-21&#x27;, &#x27;2020-01-22&#x27;, &#x27;2020-01-23&#x27;, &#x27;2020-01-24&#x27;,\n",
+       "               &#x27;2020-01-25&#x27;, &#x27;2020-01-26&#x27;, &#x27;2020-01-27&#x27;, &#x27;2020-01-28&#x27;,\n",
+       "               &#x27;2020-01-29&#x27;, &#x27;2020-01-30&#x27;, &#x27;2020-01-31&#x27;, &#x27;2020-02-01&#x27;,\n",
+       "               &#x27;2020-02-02&#x27;, &#x27;2020-02-03&#x27;, &#x27;2020-02-04&#x27;, &#x27;2020-02-05&#x27;,\n",
+       "               &#x27;2020-02-06&#x27;, &#x27;2020-02-07&#x27;, &#x27;2020-02-08&#x27;, &#x27;2020-02-09&#x27;,\n",
+       "               &#x27;2020-02-10&#x27;, &#x27;2020-02-11&#x27;, &#x27;2020-02-12&#x27;, &#x27;2020-02-13&#x27;,\n",
+       "               &#x27;2020-02-14&#x27;, &#x27;2020-02-15&#x27;, &#x27;2020-02-16&#x27;, &#x27;2020-02-17&#x27;,\n",
+       "               &#x27;2020-02-18&#x27;, &#x27;2020-02-19&#x27;, &#x27;2020-02-20&#x27;, &#x27;2020-02-21&#x27;,\n",
+       "               &#x27;2020-02-22&#x27;, &#x27;2020-02-23&#x27;, &#x27;2020-02-24&#x27;, &#x27;2020-02-25&#x27;,\n",
+       "               &#x27;2020-02-26&#x27;, &#x27;2020-02-27&#x27;, &#x27;2020-02-28&#x27;, &#x27;2020-02-29&#x27;,\n",
+       "               &#x27;2020-03-01&#x27;, &#x27;2020-03-02&#x27;, &#x27;2020-03-03&#x27;, &#x27;2020-03-04&#x27;,\n",
+       "               &#x27;2020-03-05&#x27;, &#x27;2020-03-06&#x27;, &#x27;2020-03-07&#x27;, &#x27;2020-03-08&#x27;,\n",
+       "               &#x27;2020-03-09&#x27;, &#x27;2020-03-10&#x27;, &#x27;2020-03-11&#x27;, &#x27;2020-03-12&#x27;,\n",
+       "               &#x27;2020-03-13&#x27;, &#x27;2020-03-14&#x27;, &#x27;2020-03-15&#x27;, &#x27;2020-03-16&#x27;,\n",
+       "               &#x27;2020-03-17&#x27;, &#x27;2020-03-18&#x27;, &#x27;2020-03-19&#x27;, &#x27;2020-03-20&#x27;,\n",
+       "               &#x27;2020-03-21&#x27;, &#x27;2020-03-22&#x27;, &#x27;2020-03-23&#x27;, &#x27;2020-03-24&#x27;,\n",
+       "               &#x27;2020-03-25&#x27;, &#x27;2020-03-26&#x27;, &#x27;2020-03-27&#x27;, &#x27;2020-03-28&#x27;,\n",
+       "               &#x27;2020-03-29&#x27;, &#x27;2020-03-30&#x27;, &#x27;2020-03-31&#x27;, &#x27;2020-04-01&#x27;,\n",
+       "               &#x27;2020-04-02&#x27;, &#x27;2020-04-03&#x27;, &#x27;2020-04-04&#x27;, &#x27;2020-04-05&#x27;,\n",
+       "               &#x27;2020-04-06&#x27;, &#x27;2020-04-07&#x27;, &#x27;2020-04-08&#x27;, &#x27;2020-04-09&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;date&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-946396ba-1dd9-425b-be1b-bcc6fa415d26' class='xr-section-summary-in' type='checkbox'  checked><label for='section-946396ba-1dd9-425b-be1b-bcc6fa415d26' 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>2025-10-09T17:42:08.510911+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.22.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.25.1</dd><dt><span>sampling_time :</span></dt><dd>1.7832472324371338</dd><dt><span>tuning_steps :</span></dt><dd>800</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_stats467cbba2-7941-45c1-a617-5d8c99d9183b\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_sample_stats467cbba2-7941-45c1-a617-5d8c99d9183b\" class = \"xr-section-summary\">sample_stats</label>\n",
+       "                  <div class=\"xr-section-inline-details\"></div>\n",
+       "                  <div class=\"xr-section-details\">\n",
+       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
+       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\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",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\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",
+       "  border-color: var(--xr-background-color-row-even);\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",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\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-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\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",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 106kB\n",
+       "Dimensions:                (chain: 4, draw: 200)\n",
+       "Coordinates:\n",
+       "  * chain                  (chain) int64 32B 0 1 2 3\n",
+       "  * draw                   (draw) int64 2kB 0 1 2 3 4 5 ... 195 196 197 198 199\n",
+       "Data variables: (12/18)\n",
+       "    tree_depth             (chain, draw) int64 6kB 5 4 4 5 5 4 4 ... 5 4 4 4 4 5\n",
+       "    perf_counter_start     (chain, draw) float64 6kB 1.071e+06 ... 1.071e+06\n",
+       "    acceptance_rate        (chain, draw) float64 6kB 0.9271 0.583 ... 0.5379\n",
+       "    step_size              (chain, draw) float64 6kB 0.1555 0.1555 ... 0.1949\n",
+       "    energy_error           (chain, draw) float64 6kB -0.8538 -0.305 ... 0.865\n",
+       "    smallest_eigval        (chain, draw) float64 6kB nan nan nan ... nan nan nan\n",
+       "    ...                     ...\n",
+       "    index_in_trajectory    (chain, draw) int64 6kB 9 -7 -11 -11 7 ... 8 4 -10 13\n",
+       "    step_size_bar          (chain, draw) float64 6kB 0.1973 0.1973 ... 0.1935\n",
+       "    divergences            (chain, draw) int64 6kB 0 0 0 0 0 0 0 ... 0 0 0 0 0 0\n",
+       "    energy                 (chain, draw) float64 6kB 231.9 228.3 ... 227.9 236.5\n",
+       "    lp                     (chain, draw) float64 6kB -220.2 -219.4 ... -230.2\n",
+       "    process_time_diff      (chain, draw) float64 6kB 0.001645 ... 0.001607\n",
+       "Attributes:\n",
+       "    created_at:                 2025-10-09T17:42:08.522281+00:00\n",
+       "    arviz_version:              0.22.0\n",
+       "    inference_library:          pymc\n",
+       "    inference_library_version:  5.25.1\n",
+       "    sampling_time:              1.7832472324371338\n",
+       "    tuning_steps:               800</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-b70c792b-6f6e-41f9-a2a8-f36d3c4ec3ad' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b70c792b-6f6e-41f9-a2a8-f36d3c4ec3ad' 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>: 200</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-df2188ed-3c62-46ec-bbea-d7dd15e8793b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-df2188ed-3c62-46ec-bbea-d7dd15e8793b' 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-78c30f3e-5c13-43b1-9dbd-da5ae6825376' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-78c30f3e-5c13-43b1-9dbd-da5ae6825376' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f710198-2d66-412b-9ba5-c52196bb6a39' class='xr-var-data-in' type='checkbox'><label for='data-3f710198-2d66-412b-9ba5-c52196bb6a39' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 195 196 197 198 199</div><input id='attrs-0f7932ad-cec4-4357-93ea-289a3092e22f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0f7932ad-cec4-4357-93ea-289a3092e22f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d943cfd-dd2c-442f-8e44-79417a8cd772' class='xr-var-data-in' type='checkbox'><label for='data-5d943cfd-dd2c-442f-8e44-79417a8cd772' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,\n",
+       "        14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,\n",
+       "        28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,\n",
+       "        42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,\n",
+       "        56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,\n",
+       "        70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,\n",
+       "        84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,\n",
+       "        98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,\n",
+       "       112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
+       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,\n",
+       "       140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,\n",
+       "       154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,\n",
+       "       168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,\n",
+       "       182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195,\n",
+       "       196, 197, 198, 199])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-590bb833-39a3-4d86-b5dc-03fcb927ed33' class='xr-section-summary-in' type='checkbox'  ><label for='section-590bb833-39a3-4d86-b5dc-03fcb927ed33' class='xr-section-summary' >Data variables: <span>(18)</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>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'>5 4 4 5 5 4 4 5 ... 4 4 5 4 4 4 4 5</div><input id='attrs-a08587d9-b13e-418b-aad7-17b50616ed66' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a08587d9-b13e-418b-aad7-17b50616ed66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f38fd880-6802-4ff5-bac1-f2e2c3d9ed4c' class='xr-var-data-in' type='checkbox'><label for='data-f38fd880-6802-4ff5-bac1-f2e2c3d9ed4c' 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, 4, 4, 5, 5, 4, 4, 5, 4, 5, 5, 5, 4, 5, 4, 4, 5, 4, 5, 5, 4, 4,\n",
+       "        5, 4, 4, 5, 5, 4, 4, 5, 5, 5, 4, 5, 5, 5, 4, 5, 4, 4, 5, 4, 4, 5,\n",
+       "        4, 4, 4, 4, 4, 5, 5, 5, 5, 4, 4, 4, 5, 5, 4, 4, 4, 5, 4, 5, 4, 4,\n",
+       "        4, 4, 4, 4, 4, 5, 4, 5, 4, 4, 4, 5, 4, 5, 4, 4, 5, 5, 5, 4, 5, 5,\n",
+       "        4, 5, 4, 4, 4, 5, 5, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 4, 5, 4, 5, 4,\n",
+       "        4, 5, 5, 4, 4, 5, 5, 4, 4, 4, 4, 5, 4, 5, 4, 4, 5, 5, 4, 5, 5, 5,\n",
+       "        5, 4, 4, 4, 4, 4, 5, 4, 5, 5, 4, 5, 5, 4, 4, 5, 5, 5, 4, 5, 4, 4,\n",
+       "        4, 4, 4, 4, 5, 4, 5, 4, 4, 4, 4, 5, 5, 5, 4, 5, 5, 5, 4, 5, 5, 4,\n",
+       "        5, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 5, 4, 5, 5, 4, 4, 4, 4, 4, 4, 4,\n",
+       "        4, 4],\n",
+       "       [4, 5, 5, 5, 5, 5, 4, 4, 4, 5, 5, 4, 4, 5, 5, 4, 4, 4, 5, 4, 4, 4,\n",
+       "        4, 5, 4, 4, 4, 4, 5, 4, 5, 5, 4, 5, 5, 4, 5, 5, 4, 4, 5, 4, 5, 5,\n",
+       "        4, 5, 5, 4, 4, 4, 4, 5, 4, 4, 4, 5, 5, 5, 4, 4, 4, 4, 4, 4, 5, 4,\n",
+       "        4, 4, 4, 5, 5, 4, 4, 4, 5, 4, 4, 5, 4, 5, 5, 4, 5, 4, 4, 5, 5, 5,\n",
+       "        5, 4, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 4, 5,\n",
+       "        4, 4, 5, 5, 5, 4, 5, 5, 4, 5, 4, 4, 4, 4, 5, 4, 5, 4, 4, 5, 5, 5,\n",
+       "        5, 4, 4, 5, 4, 5, 5, 5, 4, 4, 4, 4, 5, 5, 4, 4, 4, 4, 5, 5, 4, 4,\n",
+       "        4, 4, 4, 4, 4, 4, 5, 4, 5, 5, 5, 4, 5, 4, 5, 5, 4, 5, 5, 4, 4, 5,\n",
+       "        4, 5, 4, 4, 4, 4, 5, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 5, 4,\n",
+       "        5, 5],\n",
+       "       [4, 4, 4, 4, 4, 5, 4, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 4,\n",
+       "        5, 5, 5, 4, 4, 5, 5, 5, 4, 5, 5, 4, 5, 4, 5, 4, 4, 4, 5, 4, 4, 4,\n",
+       "        4, 5, 4, 5, 5, 5, 4, 5, 4, 4, 5, 4, 4, 5, 4, 5, 5, 4, 5, 4, 4, 4,\n",
+       "        5, 4, 5, 5, 5, 4, 4, 4, 6, 4, 4, 4, 5, 4, 4, 5, 4, 4, 5, 4, 4, 5,\n",
+       "        4, 5, 4, 4, 5, 5, 4, 5, 4, 5, 4, 5, 4, 4, 5, 4, 4, 4, 4, 5, 5, 4,\n",
+       "        5, 4, 5, 4, 4, 5, 5, 5, 4, 4, 4, 4, 4, 4, 5, 4, 5, 5, 4, 5, 4, 4,\n",
+       "        4, 4, 4, 4, 5, 5, 5, 4, 5, 5, 4, 4, 5, 4, 4, 4, 5, 4, 5, 4, 4, 4,\n",
+       "        4, 5, 4, 5, 4, 5, 4, 4, 5, 5, 4, 4, 4, 5, 5, 5, 5, 4, 4, 6, 5, 4,\n",
+       "        4, 4, 5, 5, 4, 5, 4, 5, 4, 4, 5, 5, 5, 5, 4, 5, 5, 6, 5, 5, 4, 4,\n",
+       "        4, 5],\n",
+       "       [4, 4, 4, 4, 5, 5, 4, 4, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 5, 4, 4, 4,\n",
+       "        4, 4, 4, 4, 4, 4, 5, 4, 4, 5, 5, 4, 4, 4, 5, 4, 4, 5, 5, 5, 5, 4,\n",
+       "        4, 5, 5, 5, 5, 5, 4, 5, 4, 4, 5, 4, 4, 4, 4, 5, 4, 4, 5, 4, 4, 4,\n",
+       "        4, 5, 5, 5, 4, 5, 4, 4, 4, 4, 5, 4, 5, 4, 5, 4, 4, 4, 4, 4, 4, 5,\n",
+       "        4, 4, 4, 4, 5, 4, 4, 4, 4, 5, 5, 4, 5, 4, 5, 4, 5, 5, 4, 5, 5, 5,\n",
+       "        4, 4, 4, 4, 5, 5, 4, 5, 4, 5, 4, 4, 5, 5, 5, 5, 4, 4, 5, 5, 4, 4,\n",
+       "        4, 5, 4, 4, 4, 5, 4, 4, 4, 5, 4, 5, 4, 4, 4, 4, 4, 4, 5, 4, 5, 4,\n",
+       "        5, 5, 4, 4, 5, 4, 4, 5, 4, 5, 5, 5, 5, 5, 4, 5, 4, 5, 4, 4, 4, 5,\n",
+       "        4, 4, 4, 4, 5, 5, 4, 4, 4, 4, 5, 4, 4, 4, 4, 5, 4, 4, 5, 4, 4, 4,\n",
+       "        4, 5]])</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'>1.071e+06 1.071e+06 ... 1.071e+06</div><input id='attrs-26bfee1a-2e35-490f-b528-9bfcfb3121f0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-26bfee1a-2e35-490f-b528-9bfcfb3121f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd4e829e-d668-403a-bee8-c3be81889c09' class='xr-var-data-in' type='checkbox'><label for='data-dd4e829e-d668-403a-bee8-c3be81889c09' 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([[1071333.32294992, 1071333.32463463, 1071333.32718646,\n",
+       "        1071333.32809475, 1071333.32994446, 1071333.33163521,\n",
+       "        1071333.33249996, 1071333.33335333, 1071333.33502696,\n",
+       "        1071333.33590204, 1071333.33770496, 1071333.33941842,\n",
+       "        1071333.341109  , 1071333.34197292, 1071333.34372867,\n",
+       "        1071333.34452946, 1071333.34538   , 1071333.34701325,\n",
+       "        1071333.34798142, 1071333.34974304, 1071333.35152033,\n",
+       "        1071333.35240729, 1071333.35328258, 1071333.35493729,\n",
+       "        1071333.35577988, 1071333.35662071, 1071333.35826671,\n",
+       "        1071333.35992921, 1071333.36075187, 1071333.36159646,\n",
+       "        1071333.36322996, 1071333.36486421, 1071333.36649979,\n",
+       "        1071333.36733733, 1071333.36901175, 1071333.37057475,\n",
+       "        1071333.37215454, 1071333.37295642, 1071333.37451333,\n",
+       "        1071333.37531696, 1071333.37611583, 1071333.37770463,\n",
+       "        1071333.37868996, 1071333.37957129, 1071333.38125354,\n",
+       "        1071333.38210288, 1071333.38296062, 1071333.38383183,\n",
+       "        1071333.38469142, 1071333.3856215 , 1071333.38732533,\n",
+       "        1071333.38900442, 1071333.39072908, 1071333.39240154,\n",
+       "        1071333.39332067, 1071333.39420433, 1071333.39508012,\n",
+       "        1071333.396796  , 1071333.398501  , 1071333.39937054,\n",
+       "...\n",
+       "        1071333.36380471, 1071333.36539533, 1071333.36621883,\n",
+       "        1071333.3679665 , 1071333.36892275, 1071333.3697835 ,\n",
+       "        1071333.37063792, 1071333.37150108, 1071333.372368  ,\n",
+       "        1071333.37321512, 1071333.37486217, 1071333.375722  ,\n",
+       "        1071333.37746942, 1071333.37835004, 1071333.38004158,\n",
+       "        1071333.38172971, 1071333.38261129, 1071333.38348108,\n",
+       "        1071333.38525392, 1071333.38612967, 1071333.38698071,\n",
+       "        1071333.38864204, 1071333.38949433, 1071333.39115479,\n",
+       "        1071333.39280654, 1071333.39448683, 1071333.39613917,\n",
+       "        1071333.39779308, 1071333.39863942, 1071333.40030104,\n",
+       "        1071333.40115067, 1071333.40284237, 1071333.40369408,\n",
+       "        1071333.40456004, 1071333.40540342, 1071333.40705742,\n",
+       "        1071333.40790467, 1071333.40875021, 1071333.40959213,\n",
+       "        1071333.41044779, 1071333.41210475, 1071333.41374504,\n",
+       "        1071333.41458496, 1071333.41542725, 1071333.41925971,\n",
+       "        1071333.42039246, 1071333.42204488, 1071333.42286588,\n",
+       "        1071333.42372054, 1071333.42454392, 1071333.42536488,\n",
+       "        1071333.42700296, 1071333.42783158, 1071333.42869496,\n",
+       "        1071333.43057975, 1071333.43157779, 1071333.43250792,\n",
+       "        1071333.43336704, 1071333.43422112]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>acceptance_rate</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9271 0.583 ... 0.7786 0.5379</div><input id='attrs-5e57ce53-6fed-4236-a171-69eccddfef0c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5e57ce53-6fed-4236-a171-69eccddfef0c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0abd2be-9b22-45f1-8926-26cc7b4353fc' class='xr-var-data-in' type='checkbox'><label for='data-e0abd2be-9b22-45f1-8926-26cc7b4353fc' 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([[0.92711104, 0.58298019, 0.9540226 , 0.49431187, 0.62193668,\n",
+       "        0.77821146, 0.97087443, 0.80277961, 0.99056511, 0.90649808,\n",
+       "        0.75574674, 0.98570213, 0.9905559 , 0.69918665, 0.95281404,\n",
+       "        0.83870589, 0.58103188, 0.97629318, 0.88680441, 0.97410989,\n",
+       "        0.93915511, 0.96152594, 0.85461204, 0.55490258, 0.92808895,\n",
+       "        0.99520566, 0.86585471, 0.7377144 , 0.99006481, 0.8212913 ,\n",
+       "        0.9485516 , 0.99791252, 0.96329112, 0.73466757, 0.63041619,\n",
+       "        0.96641757, 0.88279785, 0.55462421, 0.98432748, 0.91016567,\n",
+       "        0.99289881, 0.99907718, 0.89896303, 0.93670723, 0.99399897,\n",
+       "        0.91314612, 0.79297846, 0.98656645, 0.78899328, 0.96763122,\n",
+       "        0.91363684, 0.87918884, 0.69045135, 0.70836722, 0.94526283,\n",
+       "        0.09186146, 0.99449252, 0.9888414 , 0.87196658, 0.97264102,\n",
+       "        0.81889458, 0.97710961, 0.94189635, 0.99567135, 0.8862757 ,\n",
+       "        0.98468768, 0.89494076, 0.96742157, 0.78478421, 0.77891693,\n",
+       "        1.        , 0.91961087, 0.89231253, 0.81701319, 0.98275097,\n",
+       "        0.70559713, 0.99158665, 0.96018338, 0.99099187, 0.9782792 ,\n",
+       "        0.9986027 , 0.86372903, 1.        , 0.95360423, 0.58138936,\n",
+       "        0.8402626 , 1.        , 0.81003759, 0.96745942, 0.97257913,\n",
+       "        0.88296288, 0.70262271, 0.99801443, 0.67668017, 0.74326728,\n",
+       "        0.77789657, 0.97092213, 0.67844371, 0.83112493, 0.51022191,\n",
+       "...\n",
+       "        0.89118414, 0.95451915, 0.88695576, 0.53903297, 0.83865986,\n",
+       "        0.83350067, 0.96647952, 0.80807397, 0.64580441, 0.85777648,\n",
+       "        0.83605073, 0.93435739, 0.96139627, 0.7689633 , 0.98542085,\n",
+       "        0.91523163, 0.99918768, 1.        , 0.86343828, 0.91057293,\n",
+       "        0.97116521, 0.98675094, 0.97365986, 0.61712162, 0.93287573,\n",
+       "        0.61448102, 0.99778037, 0.77301647, 0.87469642, 0.97188656,\n",
+       "        0.92858207, 0.97594458, 0.96067613, 0.88352115, 0.98940871,\n",
+       "        0.70382195, 0.36129321, 0.62782447, 0.2739989 , 0.96271699,\n",
+       "        0.96140485, 0.93853016, 0.4814028 , 0.76892533, 0.98822821,\n",
+       "        1.        , 0.96817459, 0.87432003, 0.798095  , 0.81536989,\n",
+       "        0.99175912, 0.92880048, 0.80056288, 0.9638234 , 0.97176439,\n",
+       "        0.72150542, 0.79346004, 0.7102407 , 0.91417547, 0.95238047,\n",
+       "        0.7979254 , 0.91899261, 0.91484605, 0.99883125, 0.66237511,\n",
+       "        0.98668638, 0.79477707, 0.95892512, 0.99875852, 0.95211029,\n",
+       "        0.96272933, 0.92161876, 0.96830966, 0.89015367, 0.86299944,\n",
+       "        0.54705854, 0.96466891, 0.96703337, 0.84087745, 0.57232227,\n",
+       "        0.75900999, 0.97093432, 0.94329054, 0.7995317 , 0.99705449,\n",
+       "        0.90466003, 0.84600253, 0.99649769, 0.69826807, 0.89901921,\n",
+       "        0.85005348, 0.99654895, 0.7611201 , 0.99324212, 0.7935015 ,\n",
+       "        0.99092692, 0.95498915, 0.71333631, 0.77864017, 0.5379404 ]])</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'>0.1555 0.1555 ... 0.1949 0.1949</div><input id='attrs-262dbdb7-0b71-4662-9feb-b721473664d8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-262dbdb7-0b71-4662-9feb-b721473664d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-477a1ac3-0c57-4ba6-ab77-937a10509f7d' class='xr-var-data-in' type='checkbox'><label for='data-477a1ac3-0c57-4ba6-ab77-937a10509f7d' 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([[0.15545534, 0.15545534, 0.15545534, 0.15545534, 0.15545534,\n",
+       "        0.15545534, 0.15545534, 0.15545534, 0.15545534, 0.15545534,\n",
+       "        0.15545534, 0.15545534, 0.15545534, 0.15545534, 0.15545534,\n",
+       "        0.15545534, 0.15545534, 0.15545534, 0.15545534, 0.15545534,\n",
+       "        0.15545534, 0.15545534, 0.15545534, 0.15545534, 0.15545534,\n",
+       "        0.15545534, 0.15545534, 0.15545534, 0.15545534, 0.15545534,\n",
+       "        0.15545534, 0.15545534, 0.15545534, 0.15545534, 0.15545534,\n",
+       "        0.15545534, 0.15545534, 0.15545534, 0.15545534, 0.15545534,\n",
+       "        0.15545534, 0.15545534, 0.15545534, 0.15545534, 0.15545534,\n",
+       "        0.15545534, 0.15545534, 0.15545534, 0.15545534, 0.15545534,\n",
+       "        0.15545534, 0.15545534, 0.15545534, 0.15545534, 0.15545534,\n",
+       "        0.15545534, 0.15545534, 0.15545534, 0.15545534, 0.15545534,\n",
+       "        0.15545534, 0.15545534, 0.15545534, 0.15545534, 0.15545534,\n",
+       "        0.15545534, 0.15545534, 0.15545534, 0.15545534, 0.15545534,\n",
+       "        0.15545534, 0.15545534, 0.15545534, 0.15545534, 0.15545534,\n",
+       "        0.15545534, 0.15545534, 0.15545534, 0.15545534, 0.15545534,\n",
+       "        0.15545534, 0.15545534, 0.15545534, 0.15545534, 0.15545534,\n",
+       "        0.15545534, 0.15545534, 0.15545534, 0.15545534, 0.15545534,\n",
+       "        0.15545534, 0.15545534, 0.15545534, 0.15545534, 0.15545534,\n",
+       "        0.15545534, 0.15545534, 0.15545534, 0.15545534, 0.15545534,\n",
+       "...\n",
+       "        0.19491675, 0.19491675, 0.19491675, 0.19491675, 0.19491675,\n",
+       "        0.19491675, 0.19491675, 0.19491675, 0.19491675, 0.19491675,\n",
+       "        0.19491675, 0.19491675, 0.19491675, 0.19491675, 0.19491675,\n",
+       "        0.19491675, 0.19491675, 0.19491675, 0.19491675, 0.19491675,\n",
+       "        0.19491675, 0.19491675, 0.19491675, 0.19491675, 0.19491675,\n",
+       "        0.19491675, 0.19491675, 0.19491675, 0.19491675, 0.19491675,\n",
+       "        0.19491675, 0.19491675, 0.19491675, 0.19491675, 0.19491675,\n",
+       "        0.19491675, 0.19491675, 0.19491675, 0.19491675, 0.19491675,\n",
+       "        0.19491675, 0.19491675, 0.19491675, 0.19491675, 0.19491675,\n",
+       "        0.19491675, 0.19491675, 0.19491675, 0.19491675, 0.19491675,\n",
+       "        0.19491675, 0.19491675, 0.19491675, 0.19491675, 0.19491675,\n",
+       "        0.19491675, 0.19491675, 0.19491675, 0.19491675, 0.19491675,\n",
+       "        0.19491675, 0.19491675, 0.19491675, 0.19491675, 0.19491675,\n",
+       "        0.19491675, 0.19491675, 0.19491675, 0.19491675, 0.19491675,\n",
+       "        0.19491675, 0.19491675, 0.19491675, 0.19491675, 0.19491675,\n",
+       "        0.19491675, 0.19491675, 0.19491675, 0.19491675, 0.19491675,\n",
+       "        0.19491675, 0.19491675, 0.19491675, 0.19491675, 0.19491675,\n",
+       "        0.19491675, 0.19491675, 0.19491675, 0.19491675, 0.19491675,\n",
+       "        0.19491675, 0.19491675, 0.19491675, 0.19491675, 0.19491675,\n",
+       "        0.19491675, 0.19491675, 0.19491675, 0.19491675, 0.19491675]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy_error</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.8538 -0.305 ... -0.1033 0.865</div><input id='attrs-05e06f5b-6e5b-45f7-b55b-bf776920df0b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-05e06f5b-6e5b-45f7-b55b-bf776920df0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf2ca0d4-9ab3-4c94-8784-f236f4313012' class='xr-var-data-in' type='checkbox'><label for='data-cf2ca0d4-9ab3-4c94-8784-f236f4313012' 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([[-8.53803138e-01, -3.05024504e-01,  2.83538924e-02,\n",
+       "         4.61393244e-01, -5.23144053e-01,  1.79193898e-01,\n",
+       "         1.57602762e-01,  2.52372413e-01, -3.65316118e-01,\n",
+       "        -8.28791930e-02,  9.21862680e-02, -6.51469646e-02,\n",
+       "        -9.50982515e-02,  9.44600828e-02, -1.62798211e-02,\n",
+       "         3.48394609e-03, -1.53919518e-01, -5.44051822e-02,\n",
+       "         1.37092879e-01, -6.31224302e-02,  1.17102045e-01,\n",
+       "        -3.33261117e-02, -1.31770839e-02, -1.67342987e-01,\n",
+       "         4.85960711e-02,  4.80959930e-03, -1.35351758e-01,\n",
+       "         4.86607308e-01, -4.42930615e-01, -1.95197939e-01,\n",
+       "         8.28908176e-03, -1.54755134e-01, -1.04637284e-01,\n",
+       "        -2.24254448e-01,  4.09789543e-01,  1.17279305e-01,\n",
+       "         2.65816967e-02, -1.24053305e-01, -8.22510094e-02,\n",
+       "         2.30488683e-01, -1.64870387e-01, -1.67574502e-01,\n",
+       "         4.23510375e-02,  1.36548919e-01,  5.67693121e-02,\n",
+       "         9.75864724e-02,  6.44289025e-01, -5.31527231e-01,\n",
+       "         4.24358424e-01, -4.12616940e-01,  8.60562898e-02,\n",
+       "         1.05726164e-01,  3.74529037e-01,  5.82155928e-03,\n",
+       "         5.27961850e-02,  1.81290658e+00, -1.10255891e+00,\n",
+       "        -8.26032936e-01,  3.98626004e-02,  3.27212424e-02,\n",
+       "...\n",
+       "        -1.59828534e-01,  2.02781285e-01,  2.67855615e-01,\n",
+       "        -2.14434139e-01, -5.72318061e-02,  1.28128011e-01,\n",
+       "        -9.37933855e-02, -2.17771305e-01, -1.11500052e-02,\n",
+       "        -1.05208141e-01,  6.11792488e-02,  1.69266627e-01,\n",
+       "         7.29352690e-02, -5.25776076e-02,  3.86966154e-01,\n",
+       "         1.55194953e-01, -1.98962956e-01,  1.79722129e-01,\n",
+       "        -2.57108605e-01,  3.31870585e-01,  1.75023997e-01,\n",
+       "         2.15632768e-01, -4.91786399e-01,  2.87123477e-01,\n",
+       "        -4.73114982e-02,  2.50321647e-01, -3.09312511e-01,\n",
+       "        -1.64465230e-02,  9.27769134e-03, -1.50864440e-02,\n",
+       "         2.07753348e-01, -8.71199514e-02,  6.01227115e-02,\n",
+       "         2.27616814e-01,  4.76110408e-01, -2.49423989e-01,\n",
+       "        -6.93512569e-02, -5.85640717e-02,  4.54638823e-01,\n",
+       "         1.12205259e-02, -2.13208323e-01,  1.35682204e-03,\n",
+       "         1.07842164e-01, -6.43625209e-02, -4.74664905e-02,\n",
+       "         3.38876879e-01, -8.67781626e-02,  7.16658491e-01,\n",
+       "        -5.37545165e-01,  1.45344235e-01, -2.54118703e-01,\n",
+       "         4.01340453e-01,  1.33497142e-02,  5.73962930e-01,\n",
+       "        -8.78159883e-01,  8.86084490e-03,  6.53268060e-02,\n",
+       "        -1.03273649e-01,  8.65018772e-01]])</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-e52c202f-22b3-4b6c-836b-35df94c8c3af' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e52c202f-22b3-4b6c-836b-35df94c8c3af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f11fd498-513b-4845-966c-332538beebe2' class='xr-var-data-in' type='checkbox'><label for='data-f11fd498-513b-4845-966c-332538beebe2' 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, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan],\n",
+       "       [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "...\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan],\n",
+       "       [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan]])</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-f9ff4a07-fba8-4904-ab92-0fe91c19f107' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f9ff4a07-fba8-4904-ab92-0fe91c19f107' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f27fe663-5998-4de9-b44f-5b4f126cbd9f' class='xr-var-data-in' type='checkbox'><label for='data-f27fe663-5998-4de9-b44f-5b4f126cbd9f' 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, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan],\n",
+       "       [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "...\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan],\n",
+       "       [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
+       "        nan, nan, nan, nan, nan]])</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'>31.0 15.0 15.0 ... 15.0 15.0 31.0</div><input id='attrs-3a5767ba-685a-415b-a296-a7a5ce824ef0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3a5767ba-685a-415b-a296-a7a5ce824ef0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34c49734-9f29-42ba-a321-f6ac196a9435' class='xr-var-data-in' type='checkbox'><label for='data-34c49734-9f29-42ba-a321-f6ac196a9435' 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([[31., 15., 15., 31., 31., 15., 15., 31., 15., 31., 31., 31., 15.,\n",
+       "        31., 15., 15., 31., 15., 31., 31., 15., 15., 31., 15., 15., 31.,\n",
+       "        31., 15., 15., 31., 31., 31., 15., 31., 31., 31., 15., 31., 15.,\n",
+       "        15., 31., 15., 15., 31., 15., 15., 15., 15., 15., 31., 31., 31.,\n",
+       "        31., 15., 15., 15., 31., 31., 15., 15., 15., 31., 15., 31., 15.,\n",
+       "        15., 15., 15., 15., 15., 15., 31., 15., 31., 15., 15., 15., 31.,\n",
+       "        15., 31., 15., 15., 31., 31., 31., 15., 31., 31., 15., 31., 15.,\n",
+       "        15., 15., 31., 31., 15., 15., 15., 15., 15., 15., 15., 31., 31.,\n",
+       "        31., 15., 31., 15., 31., 15., 15., 31., 31., 15., 15., 31., 31.,\n",
+       "        15., 15., 15., 15., 31., 15., 31., 15., 15., 31., 31., 15., 31.,\n",
+       "        31., 31., 31., 15., 15., 15., 15., 15., 31., 15., 31., 31., 15.,\n",
+       "        31., 31., 15., 15., 31., 31., 31., 15., 31., 15., 15., 15., 15.,\n",
+       "        15., 15., 31., 15., 31., 15., 15., 15., 15., 31., 31., 31., 15.,\n",
+       "        31., 31., 31., 15., 31., 31., 15., 31., 15., 15., 15., 31., 15.,\n",
+       "        15., 15., 15., 15., 15., 31., 15., 31., 31., 15., 15., 15., 15.,\n",
+       "        15., 15., 15., 15., 15.],\n",
+       "       [15., 31., 31., 31., 31., 31., 15., 15., 15., 31., 31., 15., 15.,\n",
+       "        31., 31., 15., 15., 15., 31., 15., 15., 15., 15., 31., 15., 15.,\n",
+       "        15., 15., 31., 15., 31., 31., 15., 31., 31., 15., 31., 31., 15.,\n",
+       "        15., 31., 15., 31., 31., 15., 31., 31., 15., 15., 15., 15., 31.,\n",
+       "...\n",
+       "        15., 31., 15., 31., 15., 15., 31., 31., 15., 15., 15., 31., 31.,\n",
+       "        31., 31., 15., 15., 47., 31., 15., 15., 15., 31., 31., 15., 31.,\n",
+       "        15., 31., 15., 15., 31., 31., 31., 31., 15., 31., 31., 47., 31.,\n",
+       "        31., 15., 15., 15., 31.],\n",
+       "       [15., 15., 15., 15., 31., 31., 15., 15., 15., 31., 15., 31., 15.,\n",
+       "        31., 15., 31., 15., 31., 31., 15., 15., 15., 15., 15., 15., 15.,\n",
+       "        15., 15., 31., 15., 15., 31., 31., 15., 15., 15., 31., 15., 15.,\n",
+       "        31., 31., 31., 31., 15., 15., 31., 31., 31., 31., 31., 15., 31.,\n",
+       "        15., 15., 31., 15., 15., 15., 15., 31., 15., 15., 31., 15., 15.,\n",
+       "        15., 15., 31., 31., 31., 15., 31., 15., 15., 15., 15., 31., 15.,\n",
+       "        31., 15., 31., 15., 15., 15., 15., 15., 15., 31., 15., 15., 15.,\n",
+       "        15., 31., 15., 15., 15., 15., 31., 31., 15., 31., 15., 31., 15.,\n",
+       "        31., 31., 15., 31., 31., 31., 15., 15., 15., 15., 31., 31., 15.,\n",
+       "        31., 15., 31., 15., 15., 31., 31., 31., 31., 15., 15., 31., 31.,\n",
+       "        15., 15., 15., 31., 15., 15., 15., 31., 15., 15., 15., 31., 15.,\n",
+       "        31., 15., 15., 15., 15., 15., 15., 31., 15., 31., 15., 31., 31.,\n",
+       "        15., 15., 31., 15., 15., 31., 15., 31., 31., 31., 31., 31., 15.,\n",
+       "        31., 15., 31., 15., 15., 15., 31., 15., 15., 15., 15., 31., 31.,\n",
+       "        15., 15., 15., 15., 31., 15., 15., 15., 15., 31., 15., 15., 31.,\n",
+       "        15., 15., 15., 15., 31.]])</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'>0.001645 0.0008217 ... 0.001606</div><input id='attrs-34b78f24-adfa-4d52-94c6-52555b210f54' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-34b78f24-adfa-4d52-94c6-52555b210f54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8520baf5-523f-4be7-9660-b7bc3ece0c3c' class='xr-var-data-in' type='checkbox'><label for='data-8520baf5-523f-4be7-9660-b7bc3ece0c3c' 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([[0.00164488, 0.00082175, 0.0008635 , 0.00180133, 0.00164962,\n",
+       "        0.00082217, 0.00081562, 0.00163558, 0.00083833, 0.00171858,\n",
+       "        0.00166788, 0.00164771, 0.00082225, 0.00170021, 0.00076183,\n",
+       "        0.0008105 , 0.00159688, 0.00088683, 0.00171146, 0.0017235 ,\n",
+       "        0.00084196, 0.0008325 , 0.00161329, 0.00080579, 0.00080321,\n",
+       "        0.00160625, 0.00162438, 0.00078675, 0.00080929, 0.00159413,\n",
+       "        0.00159858, 0.00159975, 0.00080217, 0.00162908, 0.00152563,\n",
+       "        0.00154412, 0.00076662, 0.00152167, 0.00076471, 0.00076429,\n",
+       "        0.00154337, 0.00089846, 0.00083621, 0.00164075, 0.00081088,\n",
+       "        0.00082167, 0.00083292, 0.00081992, 0.00088233, 0.00166579,\n",
+       "        0.00164146, 0.00167942, 0.00163221, 0.0008735 , 0.00084625,\n",
+       "        0.00083879, 0.00167858, 0.00166737, 0.00082983, 0.00081846,\n",
+       "        0.00082838, 0.00165288, 0.00081558, 0.00162729, 0.00081063,\n",
+       "        0.00082992, 0.00081558, 0.00081783, 0.00081142, 0.00084608,\n",
+       "        0.00078337, 0.00153667, 0.00077054, 0.00153033, 0.00076579,\n",
+       "        0.00095388, 0.00075638, 0.00153471, 0.00080004, 0.00158279,\n",
+       "        0.00080242, 0.00080133, 0.00159717, 0.00173187, 0.00175779,\n",
+       "        0.00082396, 0.00162112, 0.00163096, 0.00083392, 0.00196604,\n",
+       "        0.00082279, 0.00079696, 0.00081925, 0.00157108, 0.001554  ,\n",
+       "        0.00078271, 0.00077533, 0.00080058, 0.00079733, 0.00079467,\n",
+       "...\n",
+       "        0.00165192, 0.00082862, 0.00167458, 0.00083838, 0.00166608,\n",
+       "        0.00168408, 0.00082129, 0.00162025, 0.00162071, 0.00163612,\n",
+       "        0.00081787, 0.00082487, 0.00082713, 0.00086083, 0.00164083,\n",
+       "        0.00164312, 0.00082796, 0.00165804, 0.00081733, 0.00179538,\n",
+       "        0.00087704, 0.00084629, 0.00166317, 0.00165354, 0.00157021,\n",
+       "        0.00157633, 0.00094367, 0.00086154, 0.00166342, 0.00165717,\n",
+       "        0.00079054, 0.00079637, 0.00079929, 0.00159954, 0.00085425,\n",
+       "        0.00082687, 0.00082221, 0.00170608, 0.00079917, 0.000783  ,\n",
+       "        0.0007895 , 0.00155483, 0.00078963, 0.00166983, 0.00090304,\n",
+       "        0.00082017, 0.00081592, 0.00082075, 0.00081946, 0.00081012,\n",
+       "        0.00160658, 0.00081588, 0.00170054, 0.00083983, 0.00165117,\n",
+       "        0.00164771, 0.00084317, 0.00083062, 0.00172446, 0.00083479,\n",
+       "        0.00081158, 0.001623  , 0.00081475, 0.00162125, 0.0016155 ,\n",
+       "        0.00164229, 0.00161383, 0.00161679, 0.00080904, 0.00162375,\n",
+       "        0.00081433, 0.00165083, 0.00081433, 0.00082646, 0.00080825,\n",
+       "        0.00161929, 0.00081183, 0.00081058, 0.0008075 , 0.00081567,\n",
+       "        0.00162204, 0.00160563, 0.00080483, 0.00080737, 0.00082812,\n",
+       "        0.00107962, 0.00161362, 0.00078408, 0.00081479, 0.00078696,\n",
+       "        0.00078246, 0.00159775, 0.00079246, 0.00082879, 0.00161667,\n",
+       "        0.00094513, 0.00087917, 0.00081217, 0.00080908, 0.00160625]])</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-dfcc0467-0c2b-4653-8015-24c642efb7a8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dfcc0467-0c2b-4653-8015-24c642efb7a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bff1344c-302b-46c3-973a-d17b881ed6ba' class='xr-var-data-in' type='checkbox'><label for='data-bff1344c-302b-46c3-973a-d17b881ed6ba' 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, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "...\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False]])</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-e5ce0260-aceb-4eb3-b815-6ecf04147ed9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e5ce0260-aceb-4eb3-b815-6ecf04147ed9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48570c9b-7182-4d17-8ec4-e1ab019d787f' class='xr-var-data-in' type='checkbox'><label for='data-48570c9b-7182-4d17-8ec4-e1ab019d787f' 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, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "...\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        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'>-1.126 1.554 0.129 ... 0.5848 1.307</div><input id='attrs-a8d39456-4f18-414c-801b-40dc44bf36e2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a8d39456-4f18-414c-801b-40dc44bf36e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-076a4d12-4e47-47a9-9cb4-7d1a1ac3e394' class='xr-var-data-in' type='checkbox'><label for='data-076a4d12-4e47-47a9-9cb4-7d1a1ac3e394' 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([[-1.125625  ,  1.55368648,  0.12895118,  1.82612819,  1.29422653,\n",
+       "         0.51193528,  0.15760276,  0.57250462, -0.36531612,  0.348373  ,\n",
+       "         0.5230448 , -0.18986717, -0.18365546,  0.88646636, -0.24134388,\n",
+       "         0.5364363 ,  1.30924288,  0.08682219,  0.44098282, -0.55700175,\n",
+       "        -0.18295374, -0.47238409,  0.50967482,  1.48402668,  0.23227325,\n",
+       "        -0.16782572,  0.39606183,  0.79451361, -0.53380503,  0.60567249,\n",
+       "        -0.23775485, -0.2531581 ,  0.14197719,  0.78457932,  0.90338719,\n",
+       "        -0.24007048,  0.53260382,  1.54220044, -0.41091974, -0.28057765,\n",
+       "        -0.60727709, -0.17388969,  0.23579441,  0.21288489, -0.1360399 ,\n",
+       "         0.25229528,  0.64428902, -0.791185  ,  0.63578713, -0.84416311,\n",
+       "         0.18159508,  0.37397403,  1.04913236,  0.69829412, -0.29406663,\n",
+       "         6.32419271, -1.36111129, -0.82603294,  0.28520376,  0.08600681,\n",
+       "         0.45503664,  0.10321723,  0.10123666, -0.07117357,  0.29462363,\n",
+       "        -0.11666267,  0.22921612, -0.40456948,  0.42566798,  0.54800675,\n",
+       "        -0.59551871,  0.35094019, -0.85310229,  0.76008633,  0.06377656,\n",
+       "         1.19261102, -0.21823832, -0.33991369, -0.53156934, -0.42640762,\n",
+       "        -0.35604543,  0.37561785, -0.52746231,  0.22092813,  1.18333873,\n",
+       "         0.60469671, -0.41907402,  0.64133659, -0.41665751, -0.25049794,\n",
+       "         0.37211227,  0.74975916, -0.42087714,  0.72755303,  0.96870328,\n",
+       "         0.9549737 , -0.30924257,  0.76578054, -1.08525641,  2.13533386,\n",
+       "...\n",
+       "        -0.67275077, -0.20923751,  0.36571012,  1.30759265,  0.87107842,\n",
+       "         0.40705168, -0.35694111,  0.61465088,  1.22860243,  0.92774632,\n",
+       "         0.45168176,  0.2423953 ,  0.13018888,  0.66409488,  0.10565747,\n",
+       "         0.26861848, -0.249984  , -0.25659043,  0.32926018, -0.52732929,\n",
+       "        -1.28845708, -0.68208935, -0.1368675 ,  0.94193292, -0.69759718,\n",
+       "         1.64322969, -0.35529324,  0.52894226,  0.39305959, -0.60661923,\n",
+       "         0.15647035,  0.05257335,  0.09503423,  0.36594969, -0.14279257,\n",
+       "         0.70809719,  2.20854489,  1.84263165,  3.39661424,  0.12697753,\n",
+       "        -0.40069353,  0.20511539,  1.38594667,  0.67728747, -0.35685536,\n",
+       "        -0.15901971,  0.12812801,  0.37221572,  0.56268019,  0.48637709,\n",
+       "        -0.14081213,  0.1849613 ,  0.53035786, -0.22251247, -0.2401685 ,\n",
+       "         1.00570341,  0.84030145,  0.89881965,  0.28147031, -0.31594156,\n",
+       "         0.53168274,  0.38185996, -0.4454115 , -0.59942291,  1.13555204,\n",
+       "        -0.22645824,  0.59759504, -0.5385982 , -0.08998669,  0.14193258,\n",
+       "         0.1078299 ,  0.22537718, -0.25243555,  0.3650243 ,  0.38506837,\n",
+       "         1.64843196, -0.45365853, -0.17264215,  0.46271338,  1.16779244,\n",
+       "         0.80607217,  0.28521269,  0.14582728,  0.48119243, -0.10251196,\n",
+       "         0.22262029,  0.49012174, -0.24838666,  1.18483291, -0.53754516,\n",
+       "         0.33375727, -0.31289031,  0.48425564, -0.43711586,  0.94494891,\n",
+       "        -1.26835279,  0.10724111,  0.61417514,  0.58480538,  1.3069295 ]])</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'>9 -7 -11 -11 7 9 ... 8 8 4 -10 13</div><input id='attrs-44d9e7fb-5eef-474d-bb88-241a47addfc7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-44d9e7fb-5eef-474d-bb88-241a47addfc7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40279f0c-2f5b-4713-b155-ce54a30f6727' class='xr-var-data-in' type='checkbox'><label for='data-40279f0c-2f5b-4713-b155-ce54a30f6727' 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([[  9,  -7, -11, -11,   7,   9, -14,  -5,  12,  15,   3,  18,   9,\n",
+       "          6, -12,  -8,  15,  -9,  -6,  -2,  -4,  -6, -14,   4, -10,  14,\n",
+       "         23,  -4,  10,   6, -20,  -3,  -3,  -9, -21,  13,  -6,   4, -11,\n",
+       "         -5,  17,  10,  -5,   6, -11,   5, -11,  10,   9, -18,  -8,  -5,\n",
+       "        -10, -13,   8,  -5,  19,  -6,  -6,  -7,  -8, -28,   1, -10,  10,\n",
+       "          8,   6, -11,  -6, -11,  12, -29, -10, -10,  -3,   9,   7, -10,\n",
+       "          8,  -8, -13,  10,  16, -26,   7,   9, -11,   8,   9,  12,  -8,\n",
+       "          5,  13,  10,   6, -11,   3, -13,   8,  12,  -7,  -9,  -7,  13,\n",
+       "         -6,  -8,  25,  12, -11,  -9,  -6,  15,  -8,   8,   7,  -9,  16,\n",
+       "          7,   4,   9,  -3,  -9,   4,  25,  10,  10,  23,  13,   7,  -2,\n",
+       "         13,  25, -19,  -6,  -3, -10,  12,  -5,   8,  -6,   6, -13,   8,\n",
+       "         -9,   2, -13, -13,   9,  -2,  16,   7,  11,   5, -14,   6,   6,\n",
+       "          8,   5,  20,   2,   8, -11,  -8,  12,   8, -10,   4,  -7,  10,\n",
+       "          7, -28, -22,  10, -11,  -5,  -6,  23, -13,  -5,  -5,   8,  -3,\n",
+       "         -5,  -2, -12,   2,  -9,  -9,  -5,   3,  -2,   9,   9,  -7,   8,\n",
+       "         -6,   8,   7, -11,  -9],\n",
+       "       [ 13,   4, -25, -16,   3,  10,   8,  12,  15,  17, -19,   3,  -4,\n",
+       "          4,   6,  -5, -10,   8,  -4,   6,   8,  -8, -10,   8,   8,   6,\n",
+       "         -5,   6, -12,  -8,  14,  -9, -14,  -6,  16,  -5, -16, -15,  -2,\n",
+       "        -11, -14,  11,  -9,  26, -11, -10,   8,  -7,  -9,   4,  -9,  -7,\n",
+       "...\n",
+       "          2,  15,   7,   9,  10,  -9,  22,  -9,   7,   4,   8,  -6, -20,\n",
+       "        -25, -10, -11,   4, -15,   3, -10,   5,   5,  -7,   5,  10,  -3,\n",
+       "         -4,  11,  12,  15,   5,   8,  -5,  12,  -6,  17,  24, -31,  14,\n",
+       "          7,   5,   4,  -9,   8],\n",
+       "       [ -9, -11,  -8,   8, -15, -14,   6,  11,   7,  -1,   4,  15,  -2,\n",
+       "        -25,  12, -14,   6,  11,  -6,  -6, -10,  -5,  -8,   7,   7,  -7,\n",
+       "         -8,  -3, -19,   5,  13, -21,  26,   4,  -9,   3, -18,  -5,  -1,\n",
+       "          8, -10, -23,   8,   9,  -3,  -7, -11, -21, -14, -12,   9,  -3,\n",
+       "        -10,  -8,  20, -11, -11,  12,   6,  14,  -8, -15, -12,  12, -12,\n",
+       "         -9,   8, -15,  -8,  17,   8,  16,   5,   4,   6,  12,   8, -15,\n",
+       "        -11,  11,  16, -15,   5,   6,  -9,  -9,   9,  -2, -12,  14,  -7,\n",
+       "         -6, -16,  -7,  -8, -11,  -2,   8,   8, -12,  -6,  -5, -13,   7,\n",
+       "         16, -25,  -6, -14,  -6,  -4,   6,   4,  -8,   6, -12,  12,   7,\n",
+       "         -9,  -2,   6,  10,  -4,   7,   8,   9,  16,  13,  -5, -27,  10,\n",
+       "          8,  15,  -8, -19,   9,  -3,  -5,  16,   8,  -9,   8, -12, -12,\n",
+       "        -20, -12,  -8,  -3,  -9,  -7,   6, -14,  -6,   9,   2,   5,   6,\n",
+       "         15, -12,   9,   8,  -5, -27,   3,  -4,  11, -21,  -3,  -4,  -8,\n",
+       "         15,  10, -25,   8,  13, -14,  11,  -6,   6,  -4,   6, -26,   5,\n",
+       "         -7,   3,  -6,  -7,  14,  15,   2,  -6, -11, -10,   9,  -2,  19,\n",
+       "          8,   8,   4, -10,  13]])</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'>0.1973 0.1973 ... 0.1935 0.1935</div><input id='attrs-33ae95dd-8dce-452b-ac35-bfc3e433cbcb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-33ae95dd-8dce-452b-ac35-bfc3e433cbcb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3cdd3e64-4a76-41b0-866d-e90ae82fafc3' class='xr-var-data-in' type='checkbox'><label for='data-3cdd3e64-4a76-41b0-866d-e90ae82fafc3' 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([[0.19734035, 0.19734035, 0.19734035, 0.19734035, 0.19734035,\n",
+       "        0.19734035, 0.19734035, 0.19734035, 0.19734035, 0.19734035,\n",
+       "        0.19734035, 0.19734035, 0.19734035, 0.19734035, 0.19734035,\n",
+       "        0.19734035, 0.19734035, 0.19734035, 0.19734035, 0.19734035,\n",
+       "        0.19734035, 0.19734035, 0.19734035, 0.19734035, 0.19734035,\n",
+       "        0.19734035, 0.19734035, 0.19734035, 0.19734035, 0.19734035,\n",
+       "        0.19734035, 0.19734035, 0.19734035, 0.19734035, 0.19734035,\n",
+       "        0.19734035, 0.19734035, 0.19734035, 0.19734035, 0.19734035,\n",
+       "        0.19734035, 0.19734035, 0.19734035, 0.19734035, 0.19734035,\n",
+       "        0.19734035, 0.19734035, 0.19734035, 0.19734035, 0.19734035,\n",
+       "        0.19734035, 0.19734035, 0.19734035, 0.19734035, 0.19734035,\n",
+       "        0.19734035, 0.19734035, 0.19734035, 0.19734035, 0.19734035,\n",
+       "        0.19734035, 0.19734035, 0.19734035, 0.19734035, 0.19734035,\n",
+       "        0.19734035, 0.19734035, 0.19734035, 0.19734035, 0.19734035,\n",
+       "        0.19734035, 0.19734035, 0.19734035, 0.19734035, 0.19734035,\n",
+       "        0.19734035, 0.19734035, 0.19734035, 0.19734035, 0.19734035,\n",
+       "        0.19734035, 0.19734035, 0.19734035, 0.19734035, 0.19734035,\n",
+       "        0.19734035, 0.19734035, 0.19734035, 0.19734035, 0.19734035,\n",
+       "        0.19734035, 0.19734035, 0.19734035, 0.19734035, 0.19734035,\n",
+       "        0.19734035, 0.19734035, 0.19734035, 0.19734035, 0.19734035,\n",
+       "...\n",
+       "        0.19347807, 0.19347807, 0.19347807, 0.19347807, 0.19347807,\n",
+       "        0.19347807, 0.19347807, 0.19347807, 0.19347807, 0.19347807,\n",
+       "        0.19347807, 0.19347807, 0.19347807, 0.19347807, 0.19347807,\n",
+       "        0.19347807, 0.19347807, 0.19347807, 0.19347807, 0.19347807,\n",
+       "        0.19347807, 0.19347807, 0.19347807, 0.19347807, 0.19347807,\n",
+       "        0.19347807, 0.19347807, 0.19347807, 0.19347807, 0.19347807,\n",
+       "        0.19347807, 0.19347807, 0.19347807, 0.19347807, 0.19347807,\n",
+       "        0.19347807, 0.19347807, 0.19347807, 0.19347807, 0.19347807,\n",
+       "        0.19347807, 0.19347807, 0.19347807, 0.19347807, 0.19347807,\n",
+       "        0.19347807, 0.19347807, 0.19347807, 0.19347807, 0.19347807,\n",
+       "        0.19347807, 0.19347807, 0.19347807, 0.19347807, 0.19347807,\n",
+       "        0.19347807, 0.19347807, 0.19347807, 0.19347807, 0.19347807,\n",
+       "        0.19347807, 0.19347807, 0.19347807, 0.19347807, 0.19347807,\n",
+       "        0.19347807, 0.19347807, 0.19347807, 0.19347807, 0.19347807,\n",
+       "        0.19347807, 0.19347807, 0.19347807, 0.19347807, 0.19347807,\n",
+       "        0.19347807, 0.19347807, 0.19347807, 0.19347807, 0.19347807,\n",
+       "        0.19347807, 0.19347807, 0.19347807, 0.19347807, 0.19347807,\n",
+       "        0.19347807, 0.19347807, 0.19347807, 0.19347807, 0.19347807,\n",
+       "        0.19347807, 0.19347807, 0.19347807, 0.19347807, 0.19347807,\n",
+       "        0.19347807, 0.19347807, 0.19347807, 0.19347807, 0.19347807]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>divergences</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-fdd9f2d6-60fe-46e1-a438-ede0334cbabb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fdd9f2d6-60fe-46e1-a438-ede0334cbabb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce993fd2-e04f-45db-a200-e256b02c8cc9' class='xr-var-data-in' type='checkbox'><label for='data-ce993fd2-e04f-45db-a200-e256b02c8cc9' 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0],\n",
+       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "        0, 0]])</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'>231.9 228.3 228.7 ... 227.9 236.5</div><input id='attrs-f8e1bc5f-5dcd-4889-93b8-95288475d824' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f8e1bc5f-5dcd-4889-93b8-95288475d824' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-726c23fd-8ee7-4f57-9985-ee68bd76e7ee' class='xr-var-data-in' type='checkbox'><label for='data-726c23fd-8ee7-4f57-9985-ee68bd76e7ee' 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([[231.94463701, 228.26743117, 228.68916696, 229.20696899,\n",
+       "        233.65387306, 233.90036611, 237.7752686 , 243.17795326,\n",
+       "        241.30169911, 234.75238961, 236.38961654, 236.68017555,\n",
+       "        234.27720876, 229.10673424, 234.646742  , 240.34485897,\n",
+       "        246.25748063, 236.40590264, 233.5510564 , 235.73446073,\n",
+       "        229.55303209, 225.63376317, 228.51006706, 228.57976591,\n",
+       "        229.69860146, 231.22463774, 241.35179378, 238.38064627,\n",
+       "        238.29332398, 234.87984596, 228.49399267, 225.90421126,\n",
+       "        231.79494001, 232.71413682, 235.22020109, 235.88617529,\n",
+       "        239.4392827 , 238.77461096, 226.32160375, 229.04233909,\n",
+       "        233.01236944, 233.18193326, 226.88248673, 232.73463689,\n",
+       "        229.77414003, 235.86731303, 237.1248806 , 229.91594287,\n",
+       "        230.39594208, 233.81102984, 230.99495913, 232.69832298,\n",
+       "        233.28013734, 239.07433906, 226.45928289, 231.68105218,\n",
+       "        234.86343863, 233.37955447, 225.71972796, 235.02359215,\n",
+       "        235.46503882, 228.4294958 , 230.1125694 , 228.25019241,\n",
+       "        228.44629253, 225.63408864, 238.05189184, 238.75408331,\n",
+       "        232.67242772, 229.56193176, 228.13556539, 235.23358221,\n",
+       "        232.00970211, 232.2726912 , 228.09853349, 237.55411554,\n",
+       "        247.52848163, 244.32066447, 233.68856793, 234.26905252,\n",
+       "...\n",
+       "        233.0714502 , 237.51096933, 233.44323816, 229.94989044,\n",
+       "        233.18440263, 231.49637199, 228.99316419, 229.22207134,\n",
+       "        230.02610575, 234.55166074, 226.92388875, 226.03349943,\n",
+       "        226.03251094, 229.02997184, 227.05693198, 224.9550415 ,\n",
+       "        230.5976649 , 230.60213078, 229.95556446, 225.35557309,\n",
+       "        230.96699212, 230.28247185, 240.30582332, 243.20456246,\n",
+       "        231.04363252, 227.03800687, 232.09179179, 231.87593766,\n",
+       "        233.33402554, 235.77626699, 235.8954481 , 228.97483282,\n",
+       "        233.36691554, 232.33354867, 232.258353  , 232.36540103,\n",
+       "        232.42546491, 227.35915562, 235.7988404 , 233.61740235,\n",
+       "        232.69902338, 230.02440952, 236.20624922, 231.42890495,\n",
+       "        233.87633424, 236.54142626, 240.25688665, 239.26162913,\n",
+       "        232.94710812, 229.79446387, 230.58832556, 242.39861253,\n",
+       "        235.85929265, 232.84964574, 227.32309788, 234.77748736,\n",
+       "        229.93645702, 226.3195362 , 230.84710244, 241.32965828,\n",
+       "        238.29544446, 231.08850949, 227.66127143, 234.25818048,\n",
+       "        235.53843613, 227.3790214 , 225.46717811, 229.70967972,\n",
+       "        227.50154818, 231.14520752, 230.05029055, 229.54288778,\n",
+       "        227.64436036, 227.90215971, 231.19041386, 231.92262545,\n",
+       "        226.76293453, 226.45864545, 227.91534403, 236.5483602 ]])</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'>-220.2 -219.4 ... -219.6 -230.2</div><input id='attrs-851acb79-e461-4503-8f63-48aa53d59243' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-851acb79-e461-4503-8f63-48aa53d59243' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9af3cb21-2d67-4e0e-b2d4-45ea5912e609' class='xr-var-data-in' type='checkbox'><label for='data-9af3cb21-2d67-4e0e-b2d4-45ea5912e609' 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([[-220.18662723, -219.41753556, -218.69585661, -222.58095396,\n",
+       "        -222.38416762, -223.28281279, -230.5932141 , -228.48087106,\n",
+       "        -225.99453693, -225.64206009, -225.96565464, -226.70958779,\n",
+       "        -218.97565246, -220.59797281, -229.79444966, -229.96343272,\n",
+       "        -229.87416429, -224.14080001, -226.09587336, -224.95501451,\n",
+       "        -219.99088727, -220.77972188, -218.40046719, -219.03575412,\n",
+       "        -220.84805558, -223.7431758 , -219.9548437 , -224.82564806,\n",
+       "        -227.94177167, -221.27272146, -218.80914908, -219.53720168,\n",
+       "        -220.60778171, -223.70597943, -221.2395721 , -226.54822201,\n",
+       "        -228.09144441, -219.15787812, -218.97265155, -223.09801939,\n",
+       "        -228.11249485, -218.14229981, -222.27705719, -222.51359328,\n",
+       "        -223.56884104, -226.4883061 , -222.54993244, -222.35873118,\n",
+       "        -225.01853879, -222.25059698, -222.39504069, -221.50426443,\n",
+       "        -228.04466887, -221.65264542, -218.0957078 , -221.73920531,\n",
+       "        -225.06477598, -217.49636461, -220.52088935, -227.00952452,\n",
+       "        -220.79050407, -220.65393435, -220.60944275, -220.78601532,\n",
+       "        -218.03819797, -220.66632517, -229.79951473, -219.42875913,\n",
+       "        -223.30458991, -222.63895249, -219.81130021, -225.4349439 ,\n",
+       "        -219.59073823, -220.17055172, -221.8522251 , -228.04761956,\n",
+       "        -230.90732557, -225.91914751, -225.94486614, -222.66506991,\n",
+       "...\n",
+       "        -227.10597706, -226.58119277, -218.40626314, -224.8244412 ,\n",
+       "        -222.28620737, -223.26857184, -221.45087184, -221.41593387,\n",
+       "        -223.44953344, -221.39192128, -217.82511434, -217.17186874,\n",
+       "        -219.78071644, -221.08134321, -218.60072711, -219.77396182,\n",
+       "        -221.20672188, -220.89299041, -217.63533524, -220.65249572,\n",
+       "        -222.01528822, -220.67582672, -228.42874183, -226.70021152,\n",
+       "        -220.23873694, -219.69429745, -222.72817576, -220.71663386,\n",
+       "        -224.61852312, -225.66178337, -219.27322846, -221.85181966,\n",
+       "        -224.99890428, -226.46820474, -224.57656538, -220.13057198,\n",
+       "        -217.11235121, -220.34491276, -223.9420048 , -224.69982075,\n",
+       "        -222.63967438, -222.09085067, -223.81695866, -217.87565666,\n",
+       "        -224.95178461, -224.73786972, -230.36797401, -223.80417455,\n",
+       "        -220.33119926, -221.34530628, -222.6448904 , -228.03275324,\n",
+       "        -221.33604685, -219.44189706, -221.13826307, -223.06139011,\n",
+       "        -223.46061632, -218.24933835, -221.88115335, -227.73165469,\n",
+       "        -223.53763277, -218.65818865, -222.986932  , -227.04036945,\n",
+       "        -222.08903776, -217.8758162 , -220.33093897, -218.07211514,\n",
+       "        -220.84552929, -222.38883484, -220.13235679, -219.16318515,\n",
+       "        -221.33168581, -220.53285009, -222.3652496 , -220.87337072,\n",
+       "        -219.20325279, -216.84256872, -219.58323401, -230.22965435]])</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'>0.001645 0.000821 ... 0.001607</div><input id='attrs-fdb12cc4-1751-4d89-b1bd-657c762c1373' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fdb12cc4-1751-4d89-b1bd-657c762c1373' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62e652ef-e2e4-4dfa-a312-688f184526e5' class='xr-var-data-in' type='checkbox'><label for='data-62e652ef-e2e4-4dfa-a312-688f184526e5' 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([[0.001645, 0.000821, 0.000864, 0.001784, 0.001649, 0.000822,\n",
+       "        0.000816, 0.001635, 0.000838, 0.001711, 0.001667, 0.001647,\n",
+       "        0.000823, 0.001696, 0.000761, 0.000811, 0.001597, 0.000881,\n",
+       "        0.001712, 0.001706, 0.000842, 0.000832, 0.001613, 0.000807,\n",
+       "        0.000804, 0.001607, 0.001624, 0.000786, 0.000809, 0.001594,\n",
+       "        0.001599, 0.001599, 0.000802, 0.001623, 0.001527, 0.001544,\n",
+       "        0.000766, 0.001521, 0.000765, 0.000764, 0.001544, 0.000891,\n",
+       "        0.000835, 0.00164 , 0.000812, 0.000821, 0.000832, 0.00082 ,\n",
+       "        0.000859, 0.001667, 0.001642, 0.001679, 0.001632, 0.000868,\n",
+       "        0.000846, 0.000838, 0.001679, 0.001667, 0.00083 , 0.000818,\n",
+       "        0.000828, 0.001645, 0.000815, 0.001627, 0.00081 , 0.00083 ,\n",
+       "        0.000817, 0.000818, 0.000811, 0.000834, 0.000783, 0.001536,\n",
+       "        0.000771, 0.00153 , 0.000766, 0.000942, 0.000756, 0.001534,\n",
+       "        0.0008  , 0.001582, 0.000803, 0.000802, 0.001596, 0.001725,\n",
+       "        0.001748, 0.000825, 0.001621, 0.001632, 0.000835, 0.00195 ,\n",
+       "        0.000823, 0.000798, 0.00082 , 0.001571, 0.001555, 0.000783,\n",
+       "        0.000776, 0.000801, 0.000797, 0.000796, 0.00079 , 0.000819,\n",
+       "        0.001589, 0.001608, 0.001572, 0.000836, 0.00155 , 0.000777,\n",
+       "        0.001535, 0.000791, 0.000777, 0.001529, 0.001613, 0.00078 ,\n",
+       "        0.000771, 0.001526, 0.001538, 0.000774, 0.000772, 0.000771,\n",
+       "...\n",
+       "        0.000732, 0.000768, 0.000767, 0.001759, 0.000833, 0.000825,\n",
+       "        0.000807, 0.000894, 0.001468, 0.000737, 0.000786, 0.000789,\n",
+       "        0.00079 , 0.001726, 0.001686, 0.000843, 0.001652, 0.000828,\n",
+       "        0.001675, 0.000838, 0.001666, 0.001669, 0.000822, 0.00162 ,\n",
+       "        0.00162 , 0.001637, 0.000818, 0.000825, 0.000828, 0.00086 ,\n",
+       "        0.001641, 0.001642, 0.000828, 0.001659, 0.000817, 0.001789,\n",
+       "        0.000878, 0.000846, 0.001662, 0.001646, 0.00157 , 0.001576,\n",
+       "        0.000938, 0.000861, 0.001664, 0.001643, 0.00079 , 0.000796,\n",
+       "        0.000799, 0.001599, 0.000848, 0.000826, 0.000823, 0.001694,\n",
+       "        0.000799, 0.000783, 0.000789, 0.001555, 0.000791, 0.001664,\n",
+       "        0.000902, 0.00082 , 0.000817, 0.000821, 0.00082 , 0.000811,\n",
+       "        0.001607, 0.000816, 0.001696, 0.000839, 0.001651, 0.001648,\n",
+       "        0.000844, 0.00083 , 0.001719, 0.000835, 0.000812, 0.001623,\n",
+       "        0.000815, 0.001622, 0.001617, 0.001642, 0.001614, 0.001617,\n",
+       "        0.000809, 0.001624, 0.000815, 0.001646, 0.000815, 0.000827,\n",
+       "        0.000809, 0.00162 , 0.000811, 0.000811, 0.000808, 0.000816,\n",
+       "        0.001622, 0.001605, 0.000805, 0.000808, 0.000827, 0.001049,\n",
+       "        0.001614, 0.000785, 0.000815, 0.000787, 0.000782, 0.001598,\n",
+       "        0.000793, 0.000829, 0.001616, 0.000942, 0.000875, 0.000813,\n",
+       "        0.000809, 0.001607]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a89d148d-df29-475d-8025-57a092615516' class='xr-section-summary-in' type='checkbox'  ><label for='section-a89d148d-df29-475d-8025-57a092615516' 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><input type='checkbox' disabled/><label></label><input id='index-f68918fe-57d6-413a-94e1-20e4a8dd5131' class='xr-index-data-in' type='checkbox'/><label for='index-f68918fe-57d6-413a-94e1-20e4a8dd5131' 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><input type='checkbox' disabled/><label></label><input id='index-b021c117-51db-425b-8566-06d2c3383a81' class='xr-index-data-in' type='checkbox'/><label for='index-b021c117-51db-425b-8566-06d2c3383a81' 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",
+       "       190, 191, 192, 193, 194, 195, 196, 197, 198, 199],\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=200))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a7a22829-0b56-40c3-ae73-481b45a49502' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a7a22829-0b56-40c3-ae73-481b45a49502' 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>2025-10-09T17:42:08.522281+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.22.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.25.1</dd><dt><span>sampling_time :</span></dt><dd>1.7832472324371338</dd><dt><span>tuning_steps :</span></dt><dd>800</dd></dl></div></li></ul></div></div><br></div>\n",
+       "                      </ul>\n",
+       "                  </div>\n",
+       "            </li>\n",
+       "            \n",
+       "            <li class = \"xr-section-item\">\n",
+       "                  <input id=\"idata_observed_data6b115f82-eb7d-49c7-b721-df47e93abc07\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_observed_data6b115f82-eb7d-49c7-b721-df47e93abc07\" class = \"xr-section-summary\">observed_data</label>\n",
+       "                  <div class=\"xr-section-inline-details\"></div>\n",
+       "                  <div class=\"xr-section-details\">\n",
+       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
+       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\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",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\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",
+       "  border-color: var(--xr-background-color-row-even);\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",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\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-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\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",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2kB\n",
+       "Dimensions:               (date: 100)\n",
+       "Coordinates:\n",
+       "  * date                  (date) datetime64[ns] 800B 2020-01-01 ... 2020-04-09\n",
+       "Data variables:\n",
+       "    new_users_likelihood  (date) float64 800B 5.507 5.487 5.581 ... 1.819 1.432\n",
+       "    reengage_likelihood   (date) float64 800B 4.623 4.903 5.35 ... 4.338 3.943\n",
+       "Attributes:\n",
+       "    created_at:                 2025-10-09T17:42:08.525115+00:00\n",
+       "    arviz_version:              0.22.0\n",
+       "    inference_library:          pymc\n",
+       "    inference_library_version:  5.25.1</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-f0291a4d-86d9-4bbb-af7c-681a248efdf1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f0291a4d-86d9-4bbb-af7c-681a248efdf1' 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'>date</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a8de4e7c-8859-4b1d-9c50-816d538b32d6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a8de4e7c-8859-4b1d-9c50-816d538b32d6' 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'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-01-01 ... 2020-04-09</div><input id='attrs-57567e03-d893-4b60-ba51-9361b720191d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-57567e03-d893-4b60-ba51-9361b720191d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b61d1d65-9c95-46f0-a792-3b041fcd4be0' class='xr-var-data-in' type='checkbox'><label for='data-b61d1d65-9c95-46f0-a792-3b041fcd4be0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-01-01T00:00:00.000000000&#x27;, &#x27;2020-01-02T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-03T00:00:00.000000000&#x27;, &#x27;2020-01-04T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-05T00:00:00.000000000&#x27;, &#x27;2020-01-06T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-07T00:00:00.000000000&#x27;, &#x27;2020-01-08T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-09T00:00:00.000000000&#x27;, &#x27;2020-01-10T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-11T00:00:00.000000000&#x27;, &#x27;2020-01-12T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-13T00:00:00.000000000&#x27;, &#x27;2020-01-14T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-15T00:00:00.000000000&#x27;, &#x27;2020-01-16T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-17T00:00:00.000000000&#x27;, &#x27;2020-01-18T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-19T00:00:00.000000000&#x27;, &#x27;2020-01-20T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-21T00:00:00.000000000&#x27;, &#x27;2020-01-22T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-23T00:00:00.000000000&#x27;, &#x27;2020-01-24T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-25T00:00:00.000000000&#x27;, &#x27;2020-01-26T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-27T00:00:00.000000000&#x27;, &#x27;2020-01-28T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-29T00:00:00.000000000&#x27;, &#x27;2020-01-30T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-31T00:00:00.000000000&#x27;, &#x27;2020-02-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-02T00:00:00.000000000&#x27;, &#x27;2020-02-03T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-04T00:00:00.000000000&#x27;, &#x27;2020-02-05T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-06T00:00:00.000000000&#x27;, &#x27;2020-02-07T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-08T00:00:00.000000000&#x27;, &#x27;2020-02-09T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-10T00:00:00.000000000&#x27;, &#x27;2020-02-11T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-12T00:00:00.000000000&#x27;, &#x27;2020-02-13T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-14T00:00:00.000000000&#x27;, &#x27;2020-02-15T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-16T00:00:00.000000000&#x27;, &#x27;2020-02-17T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-18T00:00:00.000000000&#x27;, &#x27;2020-02-19T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-20T00:00:00.000000000&#x27;, &#x27;2020-02-21T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-22T00:00:00.000000000&#x27;, &#x27;2020-02-23T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-24T00:00:00.000000000&#x27;, &#x27;2020-02-25T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-26T00:00:00.000000000&#x27;, &#x27;2020-02-27T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-28T00:00:00.000000000&#x27;, &#x27;2020-02-29T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-01T00:00:00.000000000&#x27;, &#x27;2020-03-02T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-03T00:00:00.000000000&#x27;, &#x27;2020-03-04T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-05T00:00:00.000000000&#x27;, &#x27;2020-03-06T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-07T00:00:00.000000000&#x27;, &#x27;2020-03-08T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-09T00:00:00.000000000&#x27;, &#x27;2020-03-10T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-11T00:00:00.000000000&#x27;, &#x27;2020-03-12T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-13T00:00:00.000000000&#x27;, &#x27;2020-03-14T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-15T00:00:00.000000000&#x27;, &#x27;2020-03-16T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-17T00:00:00.000000000&#x27;, &#x27;2020-03-18T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-19T00:00:00.000000000&#x27;, &#x27;2020-03-20T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-21T00:00:00.000000000&#x27;, &#x27;2020-03-22T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-23T00:00:00.000000000&#x27;, &#x27;2020-03-24T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-25T00:00:00.000000000&#x27;, &#x27;2020-03-26T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-27T00:00:00.000000000&#x27;, &#x27;2020-03-28T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-29T00:00:00.000000000&#x27;, &#x27;2020-03-30T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-31T00:00:00.000000000&#x27;, &#x27;2020-04-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-04-02T00:00:00.000000000&#x27;, &#x27;2020-04-03T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-04-04T00:00:00.000000000&#x27;, &#x27;2020-04-05T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-04-06T00:00:00.000000000&#x27;, &#x27;2020-04-07T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-04-08T00:00:00.000000000&#x27;, &#x27;2020-04-09T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-914ebfac-e6aa-4392-b068-c35544f382a6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-914ebfac-e6aa-4392-b068-c35544f382a6' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>new_users_likelihood</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.507 5.487 5.581 ... 1.819 1.432</div><input id='attrs-89d70916-ac6a-471a-9512-e4071362005d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-89d70916-ac6a-471a-9512-e4071362005d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b565dba8-0ba9-4915-a326-076c533a9847' class='xr-var-data-in' type='checkbox'><label for='data-b565dba8-0ba9-4915-a326-076c533a9847' 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([5.50695735, 5.48653634, 5.58058615, 5.58545299, 5.50495344,\n",
+       "       5.52857717, 5.66096203, 5.42295214, 5.46207481, 5.50089565,\n",
+       "       5.33828226, 5.05234246, 4.70516842, 4.74783578, 4.89714433,\n",
+       "       5.1350657 , 5.30958605, 5.08685801, 5.01798693, 5.05407644,\n",
+       "       4.89685798, 4.71401087, 4.79703673, 4.81245754, 4.90115243,\n",
+       "       4.93804937, 4.93825049, 5.05043055, 5.28505962, 5.20965063,\n",
+       "       5.47978184, 5.4228504 , 5.27304099, 4.94440847, 4.74325331,\n",
+       "       4.96040917, 5.11534614, 5.06264276, 5.11865856, 5.43500978,\n",
+       "       5.37237326, 5.26435959, 5.23238283, 5.24610824, 5.29818757,\n",
+       "       4.8845374 , 4.33249875, 3.89837781, 3.19223993, 3.60668006,\n",
+       "       3.40710784, 4.17167881, 4.6915189 , 4.99666025, 5.14056989,\n",
+       "       5.11313651, 5.21641036, 5.17817333, 5.23402521, 4.8020877 ,\n",
+       "       4.83601764, 4.87607757, 4.93569074, 4.88644524, 5.07662041,\n",
+       "       4.98740468, 4.71127838, 4.67580082, 4.59638821, 4.66732195,\n",
+       "       4.61786276, 4.65294467, 4.93925773, 4.87719253, 4.80303491,\n",
+       "       5.15174274, 5.16839057, 4.93930871, 5.06488478, 5.08253027,\n",
+       "       4.63761199, 4.40169568, 4.3706577 , 4.38587306, 4.4123177 ,\n",
+       "       4.35266444, 4.5046729 , 4.42703707, 4.21341107, 3.73537545,\n",
+       "       3.60737728, 3.32483493, 3.12590833, 2.55991822, 2.34595443,\n",
+       "       1.63205973, 1.59403515, 1.86849091, 1.81939968, 1.43174295])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>reengage_likelihood</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.623 4.903 5.35 ... 4.338 3.943</div><input id='attrs-8c77fd21-626d-4dc9-bef5-4d86807ea0c1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8c77fd21-626d-4dc9-bef5-4d86807ea0c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4bdc6f8-0fde-45cf-bc57-81e7ff0ed106' class='xr-var-data-in' type='checkbox'><label for='data-f4bdc6f8-0fde-45cf-bc57-81e7ff0ed106' 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([4.62322943, 4.90334965, 5.34982733, 5.06180134, 5.1573303 ,\n",
+       "       4.887907  , 4.56360537, 4.70913773, 4.4507765 , 4.66490142,\n",
+       "       4.49490369, 3.83045458, 4.20770774, 3.89200151, 3.70474548,\n",
+       "       4.20052689, 3.98573834, 3.4815218 , 3.41343044, 3.53387376,\n",
+       "       2.97318722, 3.73603046, 3.8217979 , 3.63546621, 3.70099037,\n",
+       "       4.22652458, 4.3796119 , 3.98609567, 4.32407451, 4.06171702,\n",
+       "       3.82584636, 3.42169886, 2.88614579, 2.72891128, 2.91567303,\n",
+       "       2.72667331, 2.83297271, 2.84777618, 2.81129293, 3.46030856,\n",
+       "       3.62916448, 3.39594452, 4.15372689, 4.1053394 , 3.42681489,\n",
+       "       3.73701159, 2.92196691, 2.90794738, 2.85548545, 2.56368267,\n",
+       "       2.68395366, 2.73047218, 3.14555156, 3.18864933, 3.63063329,\n",
+       "       3.61976479, 3.90189359, 2.88253987, 3.78100527, 3.17183785,\n",
+       "       3.5347323 , 3.22419961, 3.4638064 , 2.86412355, 3.20240322,\n",
+       "       3.23264204, 2.89224803, 3.35621407, 2.91147925, 3.6234293 ,\n",
+       "       3.34125041, 3.64628538, 3.71725908, 4.25434512, 4.30573063,\n",
+       "       4.04034794, 3.88466986, 4.32572007, 4.60616849, 4.6746703 ,\n",
+       "       5.00824199, 5.01185113, 5.12999187, 5.14451469, 5.25050436,\n",
+       "       5.28072318, 5.08679522, 5.12657331, 5.03140319, 4.93688946,\n",
+       "       4.86153547, 4.85448487, 4.85433419, 4.78807128, 4.73518434,\n",
+       "       4.13793065, 4.17770449, 4.63216849, 4.33750418, 3.94251142])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4aa89ed3-8349-4577-a874-9229e53e5676' class='xr-section-summary-in' type='checkbox'  ><label for='section-4aa89ed3-8349-4577-a874-9229e53e5676' 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>date</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-a6e6f46c-a61a-4928-af28-dee29cc4b7af' class='xr-index-data-in' type='checkbox'/><label for='index-a6e6f46c-a61a-4928-af28-dee29cc4b7af' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-01-01&#x27;, &#x27;2020-01-02&#x27;, &#x27;2020-01-03&#x27;, &#x27;2020-01-04&#x27;,\n",
+       "               &#x27;2020-01-05&#x27;, &#x27;2020-01-06&#x27;, &#x27;2020-01-07&#x27;, &#x27;2020-01-08&#x27;,\n",
+       "               &#x27;2020-01-09&#x27;, &#x27;2020-01-10&#x27;, &#x27;2020-01-11&#x27;, &#x27;2020-01-12&#x27;,\n",
+       "               &#x27;2020-01-13&#x27;, &#x27;2020-01-14&#x27;, &#x27;2020-01-15&#x27;, &#x27;2020-01-16&#x27;,\n",
+       "               &#x27;2020-01-17&#x27;, &#x27;2020-01-18&#x27;, &#x27;2020-01-19&#x27;, &#x27;2020-01-20&#x27;,\n",
+       "               &#x27;2020-01-21&#x27;, &#x27;2020-01-22&#x27;, &#x27;2020-01-23&#x27;, &#x27;2020-01-24&#x27;,\n",
+       "               &#x27;2020-01-25&#x27;, &#x27;2020-01-26&#x27;, &#x27;2020-01-27&#x27;, &#x27;2020-01-28&#x27;,\n",
+       "               &#x27;2020-01-29&#x27;, &#x27;2020-01-30&#x27;, &#x27;2020-01-31&#x27;, &#x27;2020-02-01&#x27;,\n",
+       "               &#x27;2020-02-02&#x27;, &#x27;2020-02-03&#x27;, &#x27;2020-02-04&#x27;, &#x27;2020-02-05&#x27;,\n",
+       "               &#x27;2020-02-06&#x27;, &#x27;2020-02-07&#x27;, &#x27;2020-02-08&#x27;, &#x27;2020-02-09&#x27;,\n",
+       "               &#x27;2020-02-10&#x27;, &#x27;2020-02-11&#x27;, &#x27;2020-02-12&#x27;, &#x27;2020-02-13&#x27;,\n",
+       "               &#x27;2020-02-14&#x27;, &#x27;2020-02-15&#x27;, &#x27;2020-02-16&#x27;, &#x27;2020-02-17&#x27;,\n",
+       "               &#x27;2020-02-18&#x27;, &#x27;2020-02-19&#x27;, &#x27;2020-02-20&#x27;, &#x27;2020-02-21&#x27;,\n",
+       "               &#x27;2020-02-22&#x27;, &#x27;2020-02-23&#x27;, &#x27;2020-02-24&#x27;, &#x27;2020-02-25&#x27;,\n",
+       "               &#x27;2020-02-26&#x27;, &#x27;2020-02-27&#x27;, &#x27;2020-02-28&#x27;, &#x27;2020-02-29&#x27;,\n",
+       "               &#x27;2020-03-01&#x27;, &#x27;2020-03-02&#x27;, &#x27;2020-03-03&#x27;, &#x27;2020-03-04&#x27;,\n",
+       "               &#x27;2020-03-05&#x27;, &#x27;2020-03-06&#x27;, &#x27;2020-03-07&#x27;, &#x27;2020-03-08&#x27;,\n",
+       "               &#x27;2020-03-09&#x27;, &#x27;2020-03-10&#x27;, &#x27;2020-03-11&#x27;, &#x27;2020-03-12&#x27;,\n",
+       "               &#x27;2020-03-13&#x27;, &#x27;2020-03-14&#x27;, &#x27;2020-03-15&#x27;, &#x27;2020-03-16&#x27;,\n",
+       "               &#x27;2020-03-17&#x27;, &#x27;2020-03-18&#x27;, &#x27;2020-03-19&#x27;, &#x27;2020-03-20&#x27;,\n",
+       "               &#x27;2020-03-21&#x27;, &#x27;2020-03-22&#x27;, &#x27;2020-03-23&#x27;, &#x27;2020-03-24&#x27;,\n",
+       "               &#x27;2020-03-25&#x27;, &#x27;2020-03-26&#x27;, &#x27;2020-03-27&#x27;, &#x27;2020-03-28&#x27;,\n",
+       "               &#x27;2020-03-29&#x27;, &#x27;2020-03-30&#x27;, &#x27;2020-03-31&#x27;, &#x27;2020-04-01&#x27;,\n",
+       "               &#x27;2020-04-02&#x27;, &#x27;2020-04-03&#x27;, &#x27;2020-04-04&#x27;, &#x27;2020-04-05&#x27;,\n",
+       "               &#x27;2020-04-06&#x27;, &#x27;2020-04-07&#x27;, &#x27;2020-04-08&#x27;, &#x27;2020-04-09&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;date&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bdd200a1-336a-4ff5-beaa-0c68508397d1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bdd200a1-336a-4ff5-beaa-0c68508397d1' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2025-10-09T17:42:08.525115+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.22.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.25.1</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_constant_dataa674ad8c-4ec2-4289-99b1-4b7eb2775668\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_constant_dataa674ad8c-4ec2-4289-99b1-4b7eb2775668\" class = \"xr-section-summary\">constant_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(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\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",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\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",
+       "  border-color: var(--xr-background-color-row-even);\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",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\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-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\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",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 4kB\n",
+       "Dimensions:       (date: 100, channel: 4)\n",
+       "Coordinates:\n",
+       "  * date          (date) datetime64[ns] 800B 2020-01-01 ... 2020-04-09\n",
+       "  * channel       (channel) &lt;U18 288B &#x27;impressions_x1_var&#x27; ... &#x27;impressions_x...\n",
+       "Data variables:\n",
+       "    channel_data  (date, channel) float64 3kB 0.8661 0.949 1.686 ... 0.0 0.09234\n",
+       "Attributes:\n",
+       "    created_at:                 2025-10-09T17:42:08.526460+00:00\n",
+       "    arviz_version:              0.22.0\n",
+       "    inference_library:          pymc\n",
+       "    inference_library_version:  5.25.1</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-4062fa77-4f74-4746-ae81-ad977779e71a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4062fa77-4f74-4746-ae81-ad977779e71a' 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'>date</span>: 100</li><li><span class='xr-has-index'>channel</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6220095d-aeea-40cb-b45c-1d6e9ed10d81' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6220095d-aeea-40cb-b45c-1d6e9ed10d81' 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'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-01-01 ... 2020-04-09</div><input id='attrs-e0898ef9-adf6-459f-9575-85a0be157f46' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e0898ef9-adf6-459f-9575-85a0be157f46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a6b7a6d-348a-4e06-834c-10180811378d' class='xr-var-data-in' type='checkbox'><label for='data-4a6b7a6d-348a-4e06-834c-10180811378d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-01-01T00:00:00.000000000&#x27;, &#x27;2020-01-02T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-03T00:00:00.000000000&#x27;, &#x27;2020-01-04T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-05T00:00:00.000000000&#x27;, &#x27;2020-01-06T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-07T00:00:00.000000000&#x27;, &#x27;2020-01-08T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-09T00:00:00.000000000&#x27;, &#x27;2020-01-10T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-11T00:00:00.000000000&#x27;, &#x27;2020-01-12T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-13T00:00:00.000000000&#x27;, &#x27;2020-01-14T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-15T00:00:00.000000000&#x27;, &#x27;2020-01-16T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-17T00:00:00.000000000&#x27;, &#x27;2020-01-18T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-19T00:00:00.000000000&#x27;, &#x27;2020-01-20T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-21T00:00:00.000000000&#x27;, &#x27;2020-01-22T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-23T00:00:00.000000000&#x27;, &#x27;2020-01-24T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-25T00:00:00.000000000&#x27;, &#x27;2020-01-26T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-27T00:00:00.000000000&#x27;, &#x27;2020-01-28T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-29T00:00:00.000000000&#x27;, &#x27;2020-01-30T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-01-31T00:00:00.000000000&#x27;, &#x27;2020-02-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-02T00:00:00.000000000&#x27;, &#x27;2020-02-03T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-04T00:00:00.000000000&#x27;, &#x27;2020-02-05T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-06T00:00:00.000000000&#x27;, &#x27;2020-02-07T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-08T00:00:00.000000000&#x27;, &#x27;2020-02-09T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-10T00:00:00.000000000&#x27;, &#x27;2020-02-11T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-12T00:00:00.000000000&#x27;, &#x27;2020-02-13T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-14T00:00:00.000000000&#x27;, &#x27;2020-02-15T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-16T00:00:00.000000000&#x27;, &#x27;2020-02-17T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-18T00:00:00.000000000&#x27;, &#x27;2020-02-19T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-20T00:00:00.000000000&#x27;, &#x27;2020-02-21T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-22T00:00:00.000000000&#x27;, &#x27;2020-02-23T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-24T00:00:00.000000000&#x27;, &#x27;2020-02-25T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-26T00:00:00.000000000&#x27;, &#x27;2020-02-27T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-02-28T00:00:00.000000000&#x27;, &#x27;2020-02-29T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-01T00:00:00.000000000&#x27;, &#x27;2020-03-02T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-03T00:00:00.000000000&#x27;, &#x27;2020-03-04T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-05T00:00:00.000000000&#x27;, &#x27;2020-03-06T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-07T00:00:00.000000000&#x27;, &#x27;2020-03-08T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-09T00:00:00.000000000&#x27;, &#x27;2020-03-10T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-11T00:00:00.000000000&#x27;, &#x27;2020-03-12T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-13T00:00:00.000000000&#x27;, &#x27;2020-03-14T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-15T00:00:00.000000000&#x27;, &#x27;2020-03-16T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-17T00:00:00.000000000&#x27;, &#x27;2020-03-18T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-19T00:00:00.000000000&#x27;, &#x27;2020-03-20T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-21T00:00:00.000000000&#x27;, &#x27;2020-03-22T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-23T00:00:00.000000000&#x27;, &#x27;2020-03-24T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-25T00:00:00.000000000&#x27;, &#x27;2020-03-26T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-27T00:00:00.000000000&#x27;, &#x27;2020-03-28T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-29T00:00:00.000000000&#x27;, &#x27;2020-03-30T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-03-31T00:00:00.000000000&#x27;, &#x27;2020-04-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-04-02T00:00:00.000000000&#x27;, &#x27;2020-04-03T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-04-04T00:00:00.000000000&#x27;, &#x27;2020-04-05T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-04-06T00:00:00.000000000&#x27;, &#x27;2020-04-07T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-04-08T00:00:00.000000000&#x27;, &#x27;2020-04-09T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>channel</span></div><div class='xr-var-dims'>(channel)</div><div class='xr-var-dtype'>&lt;U18</div><div class='xr-var-preview xr-preview'>&#x27;impressions_x1_var&#x27; ... &#x27;impres...</div><input id='attrs-6cab45ad-2bb0-4320-94c4-60053386a11c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6cab45ad-2bb0-4320-94c4-60053386a11c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8bdfed3-01f6-455c-b080-e6bbdf6faa73' class='xr-var-data-in' type='checkbox'><label for='data-f8bdfed3-01f6-455c-b080-e6bbdf6faa73' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;impressions_x1_var&#x27;, &#x27;impressions_x2_var&#x27;, &#x27;impressions_x3_var&#x27;,\n",
+       "       &#x27;impressions_x4_var&#x27;], dtype=&#x27;&lt;U18&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0c308f39-cceb-4340-ae20-043aa79a7df3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0c308f39-cceb-4340-ae20-043aa79a7df3' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>channel_data</span></div><div class='xr-var-dims'>(date, channel)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.8661 0.949 1.686 ... 0.0 0.09234</div><input id='attrs-aae6c944-15a2-4273-9f11-49423f2918bc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aae6c944-15a2-4273-9f11-49423f2918bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b765dc9-b790-46ae-82fd-5aac04dae4e6' class='xr-var-data-in' type='checkbox'><label for='data-8b765dc9-b790-46ae-82fd-5aac04dae4e6' 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([[8.66097713e-01, 9.48982809e-01, 1.68612082e+00, 1.37225917e+00],\n",
+       "       [1.20888607e+00, 9.29276047e-01, 1.72469868e+00, 1.24762646e+00],\n",
+       "       [1.57193416e+00, 9.34703500e-01, 1.56193760e+00, 1.32541480e+00],\n",
+       "       [1.31640785e+00, 9.42520855e-01, 1.69781213e+00, 1.47312744e+00],\n",
+       "       [1.46891019e+00, 8.99848967e-01, 1.71296807e+00, 1.36572415e+00],\n",
+       "       [1.09154067e+00, 9.04968633e-01, 1.60462328e+00, 1.46779009e+00],\n",
+       "       [7.33968636e-01, 9.35314266e-01, 1.69095168e+00, 1.42985231e+00],\n",
+       "       [9.13045068e-01, 9.09220686e-01, 1.67805721e+00, 1.16194654e+00],\n",
+       "       [7.57163218e-01, 9.40267279e-01, 1.60065243e+00, 1.20463064e+00],\n",
+       "       [8.10737403e-01, 9.69077536e-01, 1.88052919e+00, 1.05269134e+00],\n",
+       "       [6.85751132e-01, 9.44335494e-01, 1.80589837e+00, 8.61428742e-01],\n",
+       "       [3.88951389e-01, 9.52580464e-01, 1.84384186e+00, 5.85919087e-01],\n",
+       "       [5.31234337e-01, 9.39189439e-01, 1.67570962e+00, 4.38622847e-01],\n",
+       "       [3.69568278e-01, 9.51486028e-01, 1.42544760e+00, 5.60742520e-01],\n",
+       "       [3.31033682e-01, 9.17916024e-01, 1.63491405e+00, 5.80888133e-01],\n",
+       "       [5.86150549e-01, 8.59843906e-01, 1.60114525e+00, 7.80928938e-01],\n",
+       "       [4.56865491e-01, 8.65714850e-01, 1.61622352e+00, 1.01400714e+00],\n",
+       "       [2.14526688e-01, 9.13323192e-01, 1.51650008e+00, 8.62123694e-01],\n",
+       "       [1.81682786e-01, 9.14948627e-01, 1.55104226e+00, 7.40673433e-01],\n",
+       "       [2.03158068e-01, 9.22808933e-01, 1.43579914e+00, 8.61391680e-01],\n",
+       "...\n",
+       "       [1.29175692e+00, 9.90667738e-01, 7.24218622e-01, 1.28299367e+00],\n",
+       "       [1.28902203e+00, 9.99928086e-01, 6.33073650e-01, 1.09562779e+00],\n",
+       "       [1.34609464e+00, 1.02887909e+00, 7.03447336e-01, 1.00303958e+00],\n",
+       "       [1.41337664e+00, 1.02970439e+00, 7.44942022e-01, 8.83866695e-01],\n",
+       "       [1.63001251e+00, 1.04266814e+00, 8.21608327e-01, 6.90547890e-01],\n",
+       "       [1.54862669e+00, 1.03125387e+00, 8.02402918e-01, 6.92223946e-01],\n",
+       "       [1.31854501e+00, 1.05363392e+00, 7.49661552e-01, 8.18574535e-01],\n",
+       "       [1.26237014e+00, 1.06697753e+00, 6.93231346e-01, 8.87287196e-01],\n",
+       "       [1.29684672e+00, 1.05480038e+00, 6.77698486e-01, 7.72896091e-01],\n",
+       "       [1.03626110e+00, 1.05776865e+00, 3.87999829e-01, 6.61586443e-01],\n",
+       "       [1.16664991e+00, 1.05787485e+00, 4.82686500e-01, 5.19942920e-01],\n",
+       "       [1.18175268e+00, 1.05773088e+00, 3.79017310e-01, 4.70070090e-01],\n",
+       "       [1.12472806e+00, 1.07903479e+00, 1.73569074e-01, 6.36298175e-01],\n",
+       "       [1.20882001e+00, 1.07959460e+00, 1.16368822e-02, 6.30386768e-01],\n",
+       "       [1.13746895e+00, 1.07340207e+00, 1.25575134e-02, 4.59587986e-01],\n",
+       "       [8.25244474e-01, 1.05048582e+00, 0.00000000e+00, 1.55410928e-01],\n",
+       "       [9.38242099e-01, 1.05103181e+00, 5.84336557e-02, 7.75183010e-02],\n",
+       "       [1.15842162e+00, 1.05453798e+00, 0.00000000e+00, 2.33216802e-01],\n",
+       "       [8.55002282e-01, 1.04311096e+00, 0.00000000e+00, 2.23539851e-01],\n",
+       "       [8.14141663e-01, 1.03733810e+00, 0.00000000e+00, 9.23421438e-02]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9d1b4a3a-ffbc-4347-b924-a3a4615508aa' class='xr-section-summary-in' type='checkbox'  ><label for='section-9d1b4a3a-ffbc-4347-b924-a3a4615508aa' 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>date</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f40aa38a-ea65-4b8c-b138-cf1937b55d14' class='xr-index-data-in' type='checkbox'/><label for='index-f40aa38a-ea65-4b8c-b138-cf1937b55d14' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-01-01&#x27;, &#x27;2020-01-02&#x27;, &#x27;2020-01-03&#x27;, &#x27;2020-01-04&#x27;,\n",
+       "               &#x27;2020-01-05&#x27;, &#x27;2020-01-06&#x27;, &#x27;2020-01-07&#x27;, &#x27;2020-01-08&#x27;,\n",
+       "               &#x27;2020-01-09&#x27;, &#x27;2020-01-10&#x27;, &#x27;2020-01-11&#x27;, &#x27;2020-01-12&#x27;,\n",
+       "               &#x27;2020-01-13&#x27;, &#x27;2020-01-14&#x27;, &#x27;2020-01-15&#x27;, &#x27;2020-01-16&#x27;,\n",
+       "               &#x27;2020-01-17&#x27;, &#x27;2020-01-18&#x27;, &#x27;2020-01-19&#x27;, &#x27;2020-01-20&#x27;,\n",
+       "               &#x27;2020-01-21&#x27;, &#x27;2020-01-22&#x27;, &#x27;2020-01-23&#x27;, &#x27;2020-01-24&#x27;,\n",
+       "               &#x27;2020-01-25&#x27;, &#x27;2020-01-26&#x27;, &#x27;2020-01-27&#x27;, &#x27;2020-01-28&#x27;,\n",
+       "               &#x27;2020-01-29&#x27;, &#x27;2020-01-30&#x27;, &#x27;2020-01-31&#x27;, &#x27;2020-02-01&#x27;,\n",
+       "               &#x27;2020-02-02&#x27;, &#x27;2020-02-03&#x27;, &#x27;2020-02-04&#x27;, &#x27;2020-02-05&#x27;,\n",
+       "               &#x27;2020-02-06&#x27;, &#x27;2020-02-07&#x27;, &#x27;2020-02-08&#x27;, &#x27;2020-02-09&#x27;,\n",
+       "               &#x27;2020-02-10&#x27;, &#x27;2020-02-11&#x27;, &#x27;2020-02-12&#x27;, &#x27;2020-02-13&#x27;,\n",
+       "               &#x27;2020-02-14&#x27;, &#x27;2020-02-15&#x27;, &#x27;2020-02-16&#x27;, &#x27;2020-02-17&#x27;,\n",
+       "               &#x27;2020-02-18&#x27;, &#x27;2020-02-19&#x27;, &#x27;2020-02-20&#x27;, &#x27;2020-02-21&#x27;,\n",
+       "               &#x27;2020-02-22&#x27;, &#x27;2020-02-23&#x27;, &#x27;2020-02-24&#x27;, &#x27;2020-02-25&#x27;,\n",
+       "               &#x27;2020-02-26&#x27;, &#x27;2020-02-27&#x27;, &#x27;2020-02-28&#x27;, &#x27;2020-02-29&#x27;,\n",
+       "               &#x27;2020-03-01&#x27;, &#x27;2020-03-02&#x27;, &#x27;2020-03-03&#x27;, &#x27;2020-03-04&#x27;,\n",
+       "               &#x27;2020-03-05&#x27;, &#x27;2020-03-06&#x27;, &#x27;2020-03-07&#x27;, &#x27;2020-03-08&#x27;,\n",
+       "               &#x27;2020-03-09&#x27;, &#x27;2020-03-10&#x27;, &#x27;2020-03-11&#x27;, &#x27;2020-03-12&#x27;,\n",
+       "               &#x27;2020-03-13&#x27;, &#x27;2020-03-14&#x27;, &#x27;2020-03-15&#x27;, &#x27;2020-03-16&#x27;,\n",
+       "               &#x27;2020-03-17&#x27;, &#x27;2020-03-18&#x27;, &#x27;2020-03-19&#x27;, &#x27;2020-03-20&#x27;,\n",
+       "               &#x27;2020-03-21&#x27;, &#x27;2020-03-22&#x27;, &#x27;2020-03-23&#x27;, &#x27;2020-03-24&#x27;,\n",
+       "               &#x27;2020-03-25&#x27;, &#x27;2020-03-26&#x27;, &#x27;2020-03-27&#x27;, &#x27;2020-03-28&#x27;,\n",
+       "               &#x27;2020-03-29&#x27;, &#x27;2020-03-30&#x27;, &#x27;2020-03-31&#x27;, &#x27;2020-04-01&#x27;,\n",
+       "               &#x27;2020-04-02&#x27;, &#x27;2020-04-03&#x27;, &#x27;2020-04-04&#x27;, &#x27;2020-04-05&#x27;,\n",
+       "               &#x27;2020-04-06&#x27;, &#x27;2020-04-07&#x27;, &#x27;2020-04-08&#x27;, &#x27;2020-04-09&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;date&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>channel</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-103ab9e0-cd4d-4cff-8c53-f8d44c670379' class='xr-index-data-in' type='checkbox'/><label for='index-103ab9e0-cd4d-4cff-8c53-f8d44c670379' 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([&#x27;impressions_x1_var&#x27;, &#x27;impressions_x2_var&#x27;, &#x27;impressions_x3_var&#x27;,\n",
+       "       &#x27;impressions_x4_var&#x27;],\n",
+       "      dtype=&#x27;object&#x27;, name=&#x27;channel&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c9376843-84c6-4da0-8fb2-daabfaa42c02' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c9376843-84c6-4da0-8fb2-daabfaa42c02' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2025-10-09T17:42:08.526460+00:00</dd><dt><span>arviz_version :</span></dt><dd>0.22.0</dd><dt><span>inference_library :</span></dt><dd>pymc</dd><dt><span>inference_library_version :</span></dt><dd>5.25.1</dd></dl></div></li></ul></div></div><br></div>\n",
+       "                      </ul>\n",
+       "                  </div>\n",
+       "            </li>\n",
+       "            \n",
+       "              </ul>\n",
+       "            </div>\n",
+       "            <style> /* CSS stylesheet for displaying InferenceData objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-sections.group-sections {\n",
+       "  grid-template-columns: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\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-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       ".xr-wrap{width:700px!important;} </style>"
+      ],
+      "text/plain": [
+       "Inference data with groups:\n",
+       "\t> posterior\n",
+       "\t> sample_stats\n",
+       "\t> observed_data\n",
+       "\t> constant_data"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "with pm_merge_model:\n",
+    "    merged_model_idata = pm.sample(**sample_grid)\n",
+    "merged_model_idata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Is this model different from sampling two models separately? It shouldn't be. This is merging all your variables and keeping every deterministic or random variable with a prefix. This means the nodes don't interact or pull information from each other; the models are fully independent even when they share the same variable. This is equivalent to running separate models in parallel.\n",
+    "\n",
+    "Let's see how the posterior for a given parameter looks against the individual model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([<Axes: title={'center': '94.0% HDI'}>], dtype=object)"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x580 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 591,
+       "width": 611
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "temp = merged_model_idata.copy()\n",
+    "# rename new_users_saturation_lam to saturation_lam\n",
+    "temp.rename({\"new_users_saturation_lam\": \"saturation_lam\"}, inplace=True)\n",
+    "\n",
+    "az.plot_forest(\n",
+    "    data=[temp.posterior.saturation_lam, new_users_idata.posterior.saturation_lam],\n",
+    "    model_names=[\"Merged model\", \"New users model\"],\n",
+    "    var_names=[\"saturation_lam\"],\n",
+    "    combined=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The recovered parameter is the same!\n",
+    "\n",
+    "Nevertheless, the biggest benefit of combining the models is being able to connect or relate the variables. Not only for optimization, but during sampling, you could add penalty terms on a variable of a model A, which, if related to model B, could condition the values for the parameters of model B.\n",
+    "\n",
+    "Depending on the model, a possible benefit or problem could be the sampling time. This can vary quite a bit once you start making these large graphical models.\n",
+    "\n",
+    "To estimate sampling times for any PyMC model, you can use the `ModelSamplerEstimator` utility class from `pytensor_utils`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[31mInit signature:\u001b[39m\n",
+      "ModelSamplerEstimator(\n",
+      "    *,\n",
+      "    tune: int = \u001b[32m1000\u001b[39m,\n",
+      "    draws: int = \u001b[32m1000\u001b[39m,\n",
+      "    chains: int = \u001b[32m1\u001b[39m,\n",
+      "    sequential_chains: int = \u001b[32m1\u001b[39m,\n",
+      "    seed: int | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n",
+      ") -> \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "\u001b[31mDocstring:\u001b[39m     \n",
+      "Estimate computational characteristics of a PyMC model using JAX/NumPyro.\n",
+      "\n",
+      "This utility measures the average evaluation time of the model's logp and gradients\n",
+      "and estimates the number of integrator steps taken by NUTS during warmup + sampling.\n",
+      "It then compiles the information into a single-row pandas DataFrame with helpful\n",
+      "metadata to guide planning and benchmarking.\n",
+      "\n",
+      "Parameters\n",
+      "----------\n",
+      "tune : int, default 1000\n",
+      "    Number of warmup iterations to use when estimating NUTS steps.\n",
+      "draws : int, default 1000\n",
+      "    Number of sampling iterations to use when estimating NUTS steps.\n",
+      "chains : int, default 1\n",
+      "    Intended number of chains (metadata only; not used in JAX runs here).\n",
+      "sequential_chains : int, default 1\n",
+      "    Number of chains expected to run sequentially on the target environment.\n",
+      "    Used to scale the wall-clock time estimate.\n",
+      "seed : int | None, default None\n",
+      "    Random seed used for the step estimation runs.\n",
+      "\n",
+      "Examples\n",
+      "--------\n",
+      ".. code-block:: python\n",
+      "\n",
+      "    est = ModelSamplerEstimator(\n",
+      "        tune=1000, draws=1000, chains=4, sequential_chains=1, seed=1\n",
+      "    )\n",
+      "    df = est.run(model)\n",
+      "    print(df)\n",
+      "\u001b[31mFile:\u001b[39m           ~/Documents/GitHub/pymc-marketing/pymc_marketing/pytensor_utils.py\n",
+      "\u001b[31mType:\u001b[39m           type\n",
+      "\u001b[31mSubclasses:\u001b[39m     "
+     ]
+    }
+   ],
+   "source": [
+    "from pymc_marketing.pytensor_utils import ModelSamplerEstimator\n",
+    "\n",
+    "ModelSamplerEstimator?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once we initialize the class, we can pass a model to it and get back the sampling estimation in different time units. See the example below!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "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>model_name</th>\n",
+       "      <th>num_steps</th>\n",
+       "      <th>eval_time_seconds</th>\n",
+       "      <th>sequential_chains</th>\n",
+       "      <th>estimated_sampling_time_seconds</th>\n",
+       "      <th>estimated_sampling_time_minutes</th>\n",
+       "      <th>estimated_sampling_time_hours</th>\n",
+       "      <th>tune</th>\n",
+       "      <th>draws</th>\n",
+       "      <th>chains</th>\n",
+       "      <th>seed</th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td></td>\n",
+       "      <td>62551</td>\n",
+       "      <td>0.000057</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.557603</td>\n",
+       "      <td>0.059293</td>\n",
+       "      <td>0.000988</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2025-10-09 17:42:17+00:00</td>\n",
+       "      <td>pm_merge_model</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  model_name  num_steps  eval_time_seconds  sequential_chains  \\\n",
+       "0                 62551           0.000057                  1   \n",
+       "\n",
+       "   estimated_sampling_time_seconds  estimated_sampling_time_minutes  \\\n",
+       "0                         3.557603                         0.059293   \n",
+       "\n",
+       "   estimated_sampling_time_hours  tune  draws  chains  seed  \\\n",
+       "0                       0.000988  1000   1000       1  None   \n",
+       "\n",
+       "                  timestamp           model  \n",
+       "0 2025-10-09 17:42:17+00:00  pm_merge_model  "
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "estimator = ModelSamplerEstimator()\n",
+    "estimated_model_time = estimator.run(pm_merge_model)\n",
+    "# change model name of index zero to pm_merge_model\n",
+    "estimated_model_time.loc[0, \"model\"] = \"pm_merge_model\"\n",
+    "estimated_model_time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Doing the same, we can now estimate the time for our individual models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_users_time = estimator.run(new_users_model)\n",
+    "new_users_time.loc[0, \"model\"] = \"new_users_model\"\n",
+    "estimated_model_time = pd.concat(\n",
+    "    [estimated_model_time, new_users_time], ignore_index=True\n",
+    ")\n",
+    "\n",
+    "reengage_users_time = estimator.run(reengage_users_model)\n",
+    "reengage_users_time.loc[0, \"model\"] = \"reengage_users_model\"\n",
+    "estimated_model_time = pd.concat(\n",
+    "    [estimated_model_time, reengage_users_time], ignore_index=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Merged model: Assuming 1000 draws and tunes over 4 chains, the minimum time to sample the merged model is:  [3.55760328] seconds\n",
+      "New users model: Assuming 1000 draws and tunes over 4 chains, the minimum time to sample the new users model is:  [1.49876236] seconds\n",
+      "Re-engage users model: Assuming 1000 draws and tunes over 4 chains, the minimum time to sample the re-engage users model is:  [2.036205] seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\n",
+    "    \"Merged model: Assuming 1000 draws and tunes over 4 chains, the minimum time to sample the merged model is: \",\n",
+    "    estimated_model_time.query(\"model == 'pm_merge_model'\")[\n",
+    "        \"estimated_sampling_time_seconds\"\n",
+    "    ].values,\n",
+    "    \"seconds\",\n",
+    ")\n",
+    "\n",
+    "print(\n",
+    "    \"New users model: Assuming 1000 draws and tunes over 4 chains, the minimum time to sample the new users model is: \",\n",
+    "    estimated_model_time.query(\"model == 'new_users_model'\")[\n",
+    "        \"estimated_sampling_time_seconds\"\n",
+    "    ].values,\n",
+    "    \"seconds\",\n",
+    ")\n",
+    "\n",
+    "print(\n",
+    "    \"Re-engage users model: Assuming 1000 draws and tunes over 4 chains, \"\n",
+    "    \"the minimum time to sample the re-engage users model is: \",\n",
+    "    estimated_model_time.query(\"model == 'reengage_users_model'\")[\n",
+    "        \"estimated_sampling_time_seconds\"\n",
+    "    ].values,\n",
+    "    \"seconds\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "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>model_name</th>\n",
+       "      <th>num_steps</th>\n",
+       "      <th>eval_time_seconds</th>\n",
+       "      <th>sequential_chains</th>\n",
+       "      <th>estimated_sampling_time_seconds</th>\n",
+       "      <th>estimated_sampling_time_minutes</th>\n",
+       "      <th>estimated_sampling_time_hours</th>\n",
+       "      <th>tune</th>\n",
+       "      <th>draws</th>\n",
+       "      <th>chains</th>\n",
+       "      <th>seed</th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td></td>\n",
+       "      <td>62551</td>\n",
+       "      <td>0.000057</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.557603</td>\n",
+       "      <td>0.059293</td>\n",
+       "      <td>0.000988</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2025-10-09 17:42:17+00:00</td>\n",
+       "      <td>pm_merge_model</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td></td>\n",
+       "      <td>42407</td>\n",
+       "      <td>0.000035</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.498762</td>\n",
+       "      <td>0.024979</td>\n",
+       "      <td>0.000416</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2025-10-09 17:42:23+00:00</td>\n",
+       "      <td>new_users_model</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td></td>\n",
+       "      <td>60072</td>\n",
+       "      <td>0.000034</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.036205</td>\n",
+       "      <td>0.033937</td>\n",
+       "      <td>0.000566</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>None</td>\n",
+       "      <td>2025-10-09 17:42:28+00:00</td>\n",
+       "      <td>reengage_users_model</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  model_name  num_steps  eval_time_seconds  sequential_chains  \\\n",
+       "0                 62551           0.000057                  1   \n",
+       "1                 42407           0.000035                  1   \n",
+       "2                 60072           0.000034                  1   \n",
+       "\n",
+       "   estimated_sampling_time_seconds  estimated_sampling_time_minutes  \\\n",
+       "0                         3.557603                         0.059293   \n",
+       "1                         1.498762                         0.024979   \n",
+       "2                         2.036205                         0.033937   \n",
+       "\n",
+       "   estimated_sampling_time_hours  tune  draws  chains  seed  \\\n",
+       "0                       0.000988  1000   1000       1  None   \n",
+       "1                       0.000416  1000   1000       1  None   \n",
+       "2                       0.000566  1000   1000       1  None   \n",
+       "\n",
+       "                  timestamp                 model  \n",
+       "0 2025-10-09 17:42:17+00:00        pm_merge_model  \n",
+       "1 2025-10-09 17:42:23+00:00       new_users_model  \n",
+       "2 2025-10-09 17:42:28+00:00  reengage_users_model  "
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "estimated_model_time.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "The main focus of this notebook is a powerful technique: combining different probabilistic models to create a unified framework for decision-making. We showed how the `BuildMergedModel` class can smoothly integrate various models, whether they are custom PyMC models or complex MultiDimensional MMMs from the `pymc-marketing` library. This method allows us to tackle tricky trade-offs—like maximizing new users while also re-engaging existing ones—by reformulating the problem as a constrained optimization. We can maximize one objective while ensuring another meets a minimum performance level, which helps us find a balanced budget allocation without compromising important business objectives.\n",
+    "\n",
+    "However, this is not just about marketing. The techniques presented here demonstrate a flexible and scalable approach to computational modeling. By building models in a modular, 'Lego-like' fashion using the `merge_models` function, we can create complex graphical models that truly capture the connections within business systems. This opens up possibilities for detailed scenario planning and optimization in various areas, such as finance, operations, or product development. Plus, with tools like `ModelSamplerEstimator` to estimate computational costs, practitioners can handle the complexities of larger models. This provides a practical and powerful framework for data-driven strategic planning in any organization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Last updated: Thu Oct 09 2025\n",
+      "\n",
+      "Python implementation: CPython\n",
+      "Python version       : 3.12.11\n",
+      "IPython version      : 9.4.0\n",
+      "\n",
+      "pytensor: 2.31.7\n",
+      "\n",
+      "arviz         : 0.22.0\n",
+      "numpy         : 2.2.6\n",
+      "pymc_marketing: 0.16.0\n",
+      "pandas        : 2.3.1\n",
+      "pymc          : 5.25.1\n",
+      "pymc_extras   : 0.4.0\n",
+      "pytensor      : 2.31.7\n",
+      "matplotlib    : 3.10.3\n",
+      "\n",
+      "Watermark: 2.5.0\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext watermark\n",
+    "%watermark -n -u -v -iv -w -p pytensor"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pymc-marketing-dev",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/pymc_marketing/mmm/budget_optimizer.py b/pymc_marketing/mmm/budget_optimizer.py
index 287232e2..13893ec8 100644
--- a/pymc_marketing/mmm/budget_optimizer.py
+++ b/pymc_marketing/mmm/budget_optimizer.py
@@ -141,30 +141,7 @@
 
 
     # 2) Create a minimal wrapper satisfying OptimizerCompatibleModelWrapper
-    class SimpleWrapper:
-        def __init__(self, base_model, idata, channels):
-            # required attributes
-            self._base_model = base_model
-            self.idata = idata
-            self.channel_columns = list(channels)  # used if bounds is a dict
-            self._channel_scales = 1.0  # scalar or array broadcastable to channel dims
-            self.adstock = type("Adstock", (), {"l_max": 0})()  # no carryover
-
-        def _set_predictors_for_optimization(self, num_periods: int) -> pm.Model:
-            coords = {"date": np.arange(num_periods), "channel": self.channel_columns}
-            # clone model
-            m = clone_model(self._base_model)
-
-            # Set the channel_data for optimization
-            pm.set_data(
-                {"channel_data": np.zeros((num_periods, len(self.channel_columns)))},
-                model=m,
-                coords=coords,
-            )
-            return m
-
-
-    wrapper = SimpleWrapper(base_model=train_model, idata=idata, channels=channels)
+    wrapper = CustomModelWrapper(base_model=train_model, idata=idata, channels=channels)
 
     # 3) Optimize N future periods with optional bounds and/or masks
     optimizer = BudgetOptimizer(model=wrapper, num_periods=8)
@@ -214,11 +191,13 @@ def _set_predictors_for_optimization(self, num_periods: int) -> pm.Model:
 from typing import Any, ClassVar, Protocol, cast, runtime_checkable
 
 import numpy as np
+import pymc as pm
 import pytensor.tensor as pt
 import xarray as xr
 from arviz import InferenceData
-from pydantic import BaseModel, ConfigDict, Field, InstanceOf
+from pydantic import BaseModel, ConfigDict, Field, InstanceOf, PrivateAttr
 from pymc import Model, do
+from pymc.model.fgraph import clone_model
 from pymc.model.transform.optimization import freeze_dims_and_data
 from pymc.pytensorf import rewrite_pregrad
 from pytensor import function
@@ -699,15 +678,28 @@ def __init__(self, **data):
         self._budget_shape = tuple(len(coord) for coord in self._budget_coords.values())
 
         # 4. Ensure that we only optmize over non-zero channels
+        # Only perform non-zero channel detection for MMM instances.
+        # For OptimizerCompatibleModelWrapper, default to optimizing all channels unless a mask is provided.
+        is_wrapper = (
+            False if "channel_contribution" in self.mmm_model.idata.posterior else True
+        )
+
         if self.budgets_to_optimize is None:
-            # If no mask is provided, we optimize all channels
-            self.budgets_to_optimize = (
-                self.mmm_model.idata.posterior.channel_contribution.mean(
-                    ("chain", "draw", "date")
-                ).astype(bool)
-            )
-        else:
-            # If a mask is provided, ensure it has the correct shape
+            if is_wrapper:
+                # Wrapper path: default to all True over budget dims
+                ones = np.ones(self._budget_shape, dtype=bool)
+                self.budgets_to_optimize = xr.DataArray(
+                    ones, coords=self._budget_coords, dims=self._budget_dims
+                )
+            else:
+                # If no mask is provided, optimize all non-zero channels in the model
+                self.budgets_to_optimize = (
+                    self.mmm_model.idata.posterior.channel_contribution.mean(
+                        ("chain", "draw", "date")
+                    ).astype(bool)
+                )
+        elif not is_wrapper:
+            # If a mask is provided for MMM instances, ensure it has the correct shape
             expected_mask = self.mmm_model.idata.posterior.channel_contribution.mean(
                 ("chain", "draw", "date")
             ).astype(bool)
@@ -1215,3 +1207,50 @@ def track_progress(xk):
 
         else:
             raise MinimizeException(f"Optimization failed: {result.message}")
+
+
+class CustomModelWrapper(BaseModel):
+    """Wrapper for the BudgetOptimizer to handle custom PyMC models."""
+
+    model_config = ConfigDict(arbitrary_types_allowed=True, extra="forbid")
+
+    base_model: Model = Field(
+        ...,
+        description="Underlying PyMC model to be cloned for optimization.",
+    )
+    idata: InferenceData
+    channel_columns: list[str] = Field(
+        ...,
+        description="Channel labels used for budget optimization.",
+    )
+    adstock: Any = Field(
+        default_factory=lambda: type("Adstock", (), {"l_max": 0})(),
+        description="Default adstock placeholder with zero carryover.",
+    )
+
+    _channel_scales: int = PrivateAttr(default=1.0)
+
+    def __init__(
+        self,
+        base_model: Model,
+        idata: InferenceData,
+        channels: Sequence[str],
+    ) -> None:
+        super().__init__(
+            base_model=base_model,
+            idata=idata,
+            channel_columns=list(channels),
+        )
+
+    def _set_predictors_for_optimization(self, num_periods: int) -> pm.Model:
+        coords = {"date": np.arange(num_periods), "channel": self.channel_columns}
+        model_clone = clone_model(self.base_model)
+        pm.set_data(
+            {"channel_data": np.zeros((num_periods, len(self.channel_columns)))},
+            model=model_clone,
+            coords=coords,
+        )
+        return model_clone
+
+
+OptimizerCompatibleModelWrapper.register(CustomModelWrapper)
diff --git a/pymc_marketing/pytensor_utils.py b/pymc_marketing/pytensor_utils.py
index dad40ece..aa53784d 100644
--- a/pymc_marketing/pytensor_utils.py
+++ b/pymc_marketing/pytensor_utils.py
@@ -14,6 +14,8 @@
 
 """PyTensor utility functions."""
 
+from collections import Counter
+
 import arviz as az
 import pandas as pd
 import pytensor
@@ -54,17 +56,39 @@ def _prefix_model(f2, prefix: str, exclude_vars: set | None = None):
             for dim in v_dims:
                 exclude_dims.add(dim.data)
 
+    # Track dims and build a mapping from base variable names to prefixed names
     dims = set()
+    base_to_prefixed: dict[str, str] = {}
     for v in f2.outputs:
-        # Only prefix if not in exclude_vars
-        if v.name not in exclude_vars:
-            new_name = f"{prefix}_{v.name}"
+        # Only prefix if not in exclude_vars and has a valid name
+        old_name = getattr(v, "name", None)
+        if old_name and (old_name not in exclude_vars):
+            new_name = f"{prefix}_{old_name}"
             v.name = new_name
             if isinstance(v.owner.op, ModelVar):
                 rv = v.owner.inputs[0]
                 rv.name = new_name
+            # Record base to prefixed mapping for subsequent value-var renaming
+            base_to_prefixed[old_name] = new_name
         dims.update(extract_dims(v))
 
+    # Also collect ModelVar outputs that may not be listed among f2.outputs
+    # (e.g., observed RVs or deterministics created internally)
+    for var in list(f2.variables):
+        if (
+            (owner := getattr(var, "owner", None)) is not None
+            and isinstance(owner.op, ModelVar)
+            and isinstance(name := getattr(var, "name", None), str)
+            and name
+            and name not in exclude_vars
+            and name not in base_to_prefixed
+            and not name.startswith(prefix + "_")
+        ):
+            new_name = f"{prefix}_{name}"
+            var.name = new_name
+            owner.inputs[0].name = new_name
+            base_to_prefixed[name] = new_name
+
     # Don't rename dimensions that belong to excluded variables
     dims_rename = {
         dim: pytensor.as_symbolic(f"{prefix}_{dim.data}")
@@ -83,6 +107,35 @@ def _prefix_model(f2, prefix: str, exclude_vars: set | None = None):
             new_coords[k] = v
     f2._coords = new_coords  # type: ignore[attr-defined]
 
+    # Also rename associated transformed/value variables to keep names unique across merged graphs.
+    # Example patterns include: "<base>", "<base>_log__", "<base>_logodds__", etc.
+    # We only attempt renames for bases we actually prefixed above.
+    if base_to_prefixed:
+        for var in list(f2.variables):
+            if (
+                isinstance(name := getattr(var, "name", None), str)
+                and name
+                and name not in exclude_vars
+                and (
+                    match := next(
+                        (
+                            (base, prefixed)
+                            for base, prefixed in base_to_prefixed.items()
+                            if isinstance(base, str)
+                            and base
+                            and (
+                                name == base
+                                or name.startswith(base + "_")
+                                or name.startswith(base + "__")
+                            )
+                        ),
+                        None,
+                    )
+                )
+            ):
+                base, prefixed = match
+                var.name = name.replace(base, prefixed, 1)
+
     return f2
 
 
@@ -162,6 +215,56 @@ def merge_models(
     return model_from_fgraph(f, mutate_fgraph=True)
 
 
+def validate_unique_value_vars(model: Model) -> None:
+    """Validate that a model has unique, non-null value var names and 1:1 mappings.
+
+    This checks that:
+    - All entries in ``model.value_vars`` have unique, non-empty names
+    - Keys of ``model.values_to_rvs`` (value vars) also have unique names
+    - ``model.rvs_to_values`` mapping is consistent (bijection by names)
+    """
+    # Check value_vars names are unique and non-empty
+    value_vars = list(getattr(model, "value_vars", []))
+    value_var_names = [getattr(v, "name", None) for v in value_vars]
+    if any(n is None or n == "" for n in value_var_names):
+        raise ValueError("Found unnamed value variables in model.value_vars")
+    dup_vnames = [n for n, c in Counter(value_var_names).items() if c > 1]
+    if dup_vnames:
+        raise ValueError(f"Duplicate value variable names: {dup_vnames}")
+
+    # Check values_to_rvs keys are unique by name
+    v2r = getattr(model, "values_to_rvs", {})
+    v2r_value_vars = list(v2r.keys())
+    v2r_value_names = [getattr(v, "name", None) for v in v2r_value_vars]
+    if any(n is None or n == "" for n in v2r_value_names):
+        raise ValueError("Found unnamed value variables in values_to_rvs")
+    # Some observed/deterministic value-vars may legitimately share names across merged models
+    # if they were intentionally merged on (e.g., merge_on) or are non-free and identical.
+    # Only enforce uniqueness among value vars that correspond to free RVs.
+    _ = {
+        getattr(v2r[v], "name", None)
+        for v in v2r_value_vars
+        if v in getattr(model, "value_vars", [])
+        and v2r.get(v) in getattr(model, "free_RVs", [])
+    }
+    # Map back to the value-var names for those free RVs
+    free_value_var_names = [
+        getattr(model.rvs_to_values[rv], "name", None) for rv in model.free_RVs
+    ]
+    dup_map_names = [n for n, c in Counter(free_value_var_names).items() if n and c > 1]
+    if dup_map_names:
+        raise ValueError("Duplicate value variable names for free RVs: {dup_map_names}")
+
+    # Check consistency of reverse mapping by names
+    r2v = getattr(model, "rvs_to_values", {})
+    # Names on the value side of both dicts should align set-wise
+    r2v_value_names = [getattr(v, "name", None) for v in r2v.values()]
+    if set(r2v_value_names) != set(v2r_value_names):
+        raise ValueError(
+            "Mismatch between values_to_rvs and rvs_to_values by value var names"
+        )
+
+
 def extract_response_distribution(
     pymc_model: Model,
     idata: InferenceData,
diff --git a/tests/mmm/test_budget_optimizer.py b/tests/mmm/test_budget_optimizer.py
index f69f3e6c..c85a5614 100644
--- a/tests/mmm/test_budget_optimizer.py
+++ b/tests/mmm/test_budget_optimizer.py
@@ -21,11 +21,11 @@
 import pytensor.tensor as pt
 import pytest
 import xarray as xr
-from pymc.model.fgraph import clone_model as cm
 
 from pymc_marketing.mmm import MMM
 from pymc_marketing.mmm.budget_optimizer import (
     BudgetOptimizer,
+    CustomModelWrapper,
     MinimizeException,
     optimizer_xarray_builder,
 )
@@ -910,15 +910,10 @@ def test_budget_distribution_over_period_integration(dummy_df, dummy_idata):
 
 
 def test_custom_protocol_model_budget_optimizer_works():
-    """Validate the optimizer works with a custom model that follows the protocol.
-
-    This serves as an example for users wanting to plug in their own PyMC models.
-    Requirements implemented here:
-    - The model has a variable named 'channel_data' with dims ("date", "channel").
-    - Deterministics 'channel_contribution' ("date", "channel") and 'total_contribution' ("date").
-    - A wrapper object exposes: idata, channel_columns, _channel_scales, adstock.l_max, and
-      a method `_set_predictors_for_optimization(num_periods) -> pm.Model` that returns a PyMC model
-      where 'channel_data' is set for the optimization horizon.
+    """Validate the optimizer works with the built-in CustomModelWrapper.
+
+    This serves as an example for users wanting to plug in their own PyMC models via
+    ``CustomModelWrapper``, which satisfies the OptimizerCompatibleModelWrapper protocol.
     """
     # 1) Build and fit a tiny custom PyMC model
     rng = np.random.default_rng(0)
@@ -944,33 +939,18 @@ def test_custom_protocol_model_budget_optimizer_works():
 
         idata = pm.sample(50, tune=50, chains=1, progressbar=False, random_seed=1)
 
-    # 2) Minimal wrapper satisfying the optimizer protocol
-    class SimpleWrapper:
-        def __init__(self, base_model: pm.Model, idata, channels):
-            self._base_model = base_model
-            self.idata = idata
-            self.channel_columns = list(channels)
-            self._channel_scales = 1.0
-            self.adstock = type("Adstock", (), {"l_max": 0})()  # no carryover
-
-        def _set_predictors_for_optimization(self, num_periods: int) -> pm.Model:
-            m = cm(self._base_model)
-            pm.set_data(
-                {
-                    "channel_data": np.zeros(
-                        (num_periods, len(self.channel_columns)),
-                        dtype=m["channel_data"].dtype,
-                    )
-                },
-                coords={
-                    "date": np.arange(num_periods),
-                    "channel": self.channel_columns,
-                },
-                model=m,
-            )
-            return m
+    # 2) Wrap the model with CustomModelWrapper
+    wrapper = CustomModelWrapper(
+        base_model=train_model,
+        idata=idata,
+        channels=channels,
+    )
 
-    wrapper = SimpleWrapper(base_model=train_model, idata=idata, channels=channels)
+    # Ensure the wrapper produces correctly shaped optimization models
+    opt_model = wrapper._set_predictors_for_optimization(num_periods=6)
+    assert tuple(opt_model.named_vars_to_dims["channel_data"]) == ("date", "channel")
+    assert list(opt_model.coords["channel"]) == channels
+    assert len(opt_model.coords["date"]) == 6
 
     # 3) Optimize budgets over a small future horizon
     optimizer = BudgetOptimizer(model=wrapper, num_periods=6)
diff --git a/tests/test_pytensor_utils.py b/tests/test_pytensor_utils.py
index 71134f44..7b55a684 100644
--- a/tests/test_pytensor_utils.py
+++ b/tests/test_pytensor_utils.py
@@ -35,6 +35,7 @@
     ModelSamplerEstimator,
     _prefix_model,
     merge_models,
+    validate_unique_value_vars,
 )
 
 
@@ -470,6 +471,35 @@ def test_merge_models_prefix_and_merge_on_channel_data(
         assert d in channel_data_dims
 
 
+def test_merge_models_value_vars_unique_and_logp_compiles(
+    fitted_multidim_mmm, sample_multidim_data
+):
+    # Build two wrapped models with identical structure to force potential name collisions
+    dates = sample_multidim_data["date"].unique()
+    start_date = dates[-1] + pd.Timedelta(days=7)
+    end_date = start_date + pd.Timedelta(weeks=4)
+
+    wrapper1 = MultiDimensionalBudgetOptimizerWrapper(
+        model=fitted_multidim_mmm, start_date=start_date, end_date=end_date
+    )
+    wrapper2 = MultiDimensionalBudgetOptimizerWrapper(
+        model=fitted_multidim_mmm, start_date=start_date, end_date=end_date
+    )
+
+    m1 = wrapper1._set_predictors_for_optimization(num_periods=wrapper1.num_periods)
+    m2 = wrapper2._set_predictors_for_optimization(num_periods=wrapper2.num_periods)
+
+    merged = merge_models(
+        models=[m1, m2], prefixes=["model1", "model2"], merge_on="channel_data"
+    )
+
+    # Validate uniqueness of value var names and mapping consistency
+    validate_unique_value_vars(merged)
+
+    # Additionally ensure PyMC can create the logp+dlogp function without raising
+    _ = merged.logp_dlogp_function(ravel_inputs=True)
+
+
 def test_merge_models_raises_with_too_few_models(fitted_multidim_mmm):
     m1 = fitted_multidim_mmm.model
     with pytest.raises(ValueError, match="Need at least 2 models to merge"):