From 3ee51d6fc2047d898b91f1a31481e35494ce2ac7 Mon Sep 17 00:00:00 2001
From: DrEntropy <DrEntropy@users.noreply.github.com>
Date: Sun, 8 Sep 2024 01:38:49 -0700
Subject: [PATCH] Add an example for using `offset`, addressing issue #418
 (#842)

---
 docs/_quarto.yml                      |    1 +
 docs/notebooks/count_roaches.ipynb    | 1036 +++++++++++++++++++++++++
 docs/notebooks/data/roaches.csv       |  263 +++++++
 docs/notebooks/gallery.yml            |    4 +
 docs/notebooks/thumbnails/roaches.png |  Bin 0 -> 25778 bytes
 5 files changed, 1304 insertions(+)
 create mode 100644 docs/notebooks/count_roaches.ipynb
 create mode 100644 docs/notebooks/data/roaches.csv
 create mode 100644 docs/notebooks/thumbnails/roaches.png

diff --git a/docs/_quarto.yml b/docs/_quarto.yml
index e76d98b87..aba08c6fa 100644
--- a/docs/_quarto.yml
+++ b/docs/_quarto.yml
@@ -73,6 +73,7 @@ website:
               - notebooks/alternative_links_binary.ipynb
               - notebooks/wald_gamma_glm.ipynb
               - notebooks/negative_binomial.ipynb
+              - notebooks/count_roaches.ipynb
               - notebooks/beta_regression.ipynb
               - notebooks/categorical_regression.ipynb
               - notebooks/circular_regression.ipynb
diff --git a/docs/notebooks/count_roaches.ipynb b/docs/notebooks/count_roaches.ipynb
new file mode 100644
index 000000000..82a038e89
--- /dev/null
+++ b/docs/notebooks/count_roaches.ipynb
@@ -0,0 +1,1036 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import arviz as az\n",
+    "import bambi as bmb\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "from scipy.stats import nbinom"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "az.style.use(\"arviz-darkgrid\")\n",
+    "SEED = 7355608"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Count Regression with Variable Exposure\n",
+    "\n",
+    "This example is based on the [\"Roaches\"](https://github.com/avehtari/ROS-Examples/tree/master/Roaches/) example from [Regression and Other Stories](https://avehtari.github.io/ROS-Examples/index.html) by Gelman, Hill, and Vehtari.   The example is a count regression model with an offset term. \n",
+    "\n",
+    "The data is the number of roaches caught in 262 apartments.  Some pest control treatment was applied to 158 (treatment=1) of the apartments, and 104 apartments received no treatment (treatment=0).   The other columns in the data are:\n",
+    "\n",
+    "- `y`: the number of roaches caught\n",
+    "- `roach1` : the pre-treatment roach level\n",
+    "- `senior` : indicator for whether the appartment is for seniors\n",
+    "- `exposure2` : the number of trap-days (number of traps x number of days).   \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>y</th>\n",
+       "      <th>roach1</th>\n",
+       "      <th>treatment</th>\n",
+       "      <th>senior</th>\n",
+       "      <th>exposure2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>153</td>\n",
+       "      <td>3.0800</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>127</td>\n",
+       "      <td>3.3125</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.600000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.0167</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.0300</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0200</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.142857</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     y  roach1  treatment  senior  exposure2\n",
+       "1  153  3.0800          1       0   0.800000\n",
+       "2  127  3.3125          1       0   0.600000\n",
+       "3    7  0.0167          1       0   1.000000\n",
+       "4    7  0.0300          1       0   1.000000\n",
+       "5    0  0.0200          1       0   1.142857"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roaches = pd.read_csv(\"data/roaches.csv\", index_col=0)\n",
+    "# rescale \n",
+    "roaches[\"roach1\"] = roaches[\"roach1\"] / 100\n",
+    "roaches.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Poisson regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "One way to model this is to say that there is some rate of roaches per trap-day , and that the number of roaches caught is a Poisson random variable with a rate that is proportional to the number of trap-days (the *exposure*).    That is:\n",
+    "\n",
+    "$$\n",
+    "\\begin{align*}\n",
+    "y_i &\\sim \\text{Poisson}(\\text{exposure2}_i \\times \\rho_i) \\\\\n",
+    "\\log(\\rho_i) &= \\beta_0 + \\beta_1 \\text{treatment}_i + \\beta_2 \\text{roach1}_i + \\beta_3 \\text{senior}_i\n",
+    "\\end{align*}\n",
+    "$$\n",
+    "\n",
+    "With a little algebra, we can rewrite this as a generalized linear model:\n",
+    "\n",
+    "$$\n",
+    "\\begin{align*}\n",
+    "y_i &\\sim \\text{Poisson}(\\lambda_i) \\\\\n",
+    "\\log(\\lambda_i) &= \\beta_0 + \\beta_1 \\text{treatment}_i + \\beta_2 \\text{roach1}_i + \\beta_3 \\text{senior}_i + \\log(\\text{exposure2}_i)\n",
+    "\\end{align*}\n",
+    "$$\n",
+    "\n",
+    "However, we don't want to estimate a coefficient for $\\log(\\text{exposure2})$, we want to simply add it as an *offset*.   In `bambi` we do this by using the `offset` function in the formula to specify that a term should not be multiplied by a coefficient to estimate and simply added.  The formula for the model is then:\n",
+    "\n",
+    "```python\n",
+    "\"y ~ roach1 + treatment + senior + offset(log(exposure2))\"\n",
+    "```\n",
+    "\n",
+    "If you are familiar with R this offset term is the same as the offset term in the `glm` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [Intercept, roach1, treatment, senior]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e96231dfcd6940598a91607e07325d09",
+       "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"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 18 seconds.\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# bambi poisson model\n",
+    "model_1 = bmb.Model(\"y ~ roach1 + treatment  + senior + offset(log(exposure2))\", family = \"poisson\", data = roaches)\n",
+    "idata_1 = model_1.fit()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sd</th>\n",
+       "      <th>hdi_3%</th>\n",
+       "      <th>hdi_97%</th>\n",
+       "      <th>mcse_mean</th>\n",
+       "      <th>mcse_sd</th>\n",
+       "      <th>ess_bulk</th>\n",
+       "      <th>ess_tail</th>\n",
+       "      <th>r_hat</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Intercept</th>\n",
+       "      <td>3.089</td>\n",
+       "      <td>0.020</td>\n",
+       "      <td>3.052</td>\n",
+       "      <td>3.128</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4436.0</td>\n",
+       "      <td>3676.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>roach1</th>\n",
+       "      <td>0.698</td>\n",
+       "      <td>0.009</td>\n",
+       "      <td>0.681</td>\n",
+       "      <td>0.714</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3891.0</td>\n",
+       "      <td>3221.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>senior</th>\n",
+       "      <td>-0.381</td>\n",
+       "      <td>0.032</td>\n",
+       "      <td>-0.442</td>\n",
+       "      <td>-0.321</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3889.0</td>\n",
+       "      <td>3001.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>treatment</th>\n",
+       "      <td>-0.517</td>\n",
+       "      <td>0.024</td>\n",
+       "      <td>-0.566</td>\n",
+       "      <td>-0.474</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4742.0</td>\n",
+       "      <td>3310.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
+       "Intercept  3.089  0.020   3.052    3.128      0.000      0.0    4436.0   \n",
+       "roach1     0.698  0.009   0.681    0.714      0.000      0.0    3891.0   \n",
+       "senior    -0.381  0.032  -0.442   -0.321      0.001      0.0    3889.0   \n",
+       "treatment -0.517  0.024  -0.566   -0.474      0.000      0.0    4742.0   \n",
+       "\n",
+       "           ess_tail  r_hat  \n",
+       "Intercept    3676.0    1.0  \n",
+       "roach1       3221.0    1.0  \n",
+       "senior       3001.0    1.0  \n",
+       "treatment    3310.0    1.0  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "az.summary(idata_1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The sampling seems to have gone well based on `ess` and `r_hat`.  If this were a real analysis one would also need to check priors, trace plots and other diagnostics.  However, lets see if the model predicts the distribution of roaches (`y`) observed. We can do this by looking at  the posterior predictive distribution for the model.  We plot the log of `y` to make the results easier to see."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_log_posterior_ppc(model, idata):\n",
+    "    # plot posterior predictive check\n",
+    "    model.predict(idata, kind='response', inplace=True)\n",
+    "    var_name = 'log(y+1)'\n",
+    "    # there is probably a better way\n",
+    "    idata.posterior_predictive[var_name] = np.log(idata.posterior_predictive['y'] + 1)\n",
+    "    idata.observed_data[var_name] = np.log(idata.observed_data['y'] + 1)\n",
+    "    \n",
+    "    return az.plot_ppc(idata, var_names=[var_name])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='log(y+1)'>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 720x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_log_posterior_ppc(model_1, idata_1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It appears that we are drastically under predicting the number of apartments with a small number of roaches.  This suggests creating a test statistic measuring the fraction of zeros, both in the observed data and in the simulated replications (posterior predictions).  We can then use this to check the model fit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fraction of zeros in the observed data: 0.35877862595419846\n",
+      "Fraction of zeros in the posterior predictive check: 0.0007022900763358779\n",
+      " 80% CI: [0.         0.00381679]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# check number of zeros in y\n",
+    "\n",
+    "def check_zeros(idata):\n",
+    "    # flatten over chains:\n",
+    "    sampled_zeros = (idata.posterior_predictive[\"y\"]==0).mean((\"__obs__\")).values.flatten()\n",
+    "    print(f\"Fraction of zeros in the observed data: {np.mean(roaches['y']==0)}\")\n",
+    "    print(f\"Fraction of zeros in the posterior predictive check: {np.mean(sampled_zeros)}\")\n",
+    "    print(f\" 80% CI: {np.percentile(sampled_zeros, [10, 90])}\")\n",
+    "\n",
+    "check_zeros(idata_1)\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Poisson model here does not succeed in reproducing the observed fraction of zeros. In the data we have about 36% zeros, while in the replications we almost always have no zeros or very few.  Gelman, Hill, and Vehtari suggest we try an overdispersed and more flexible model like the negative binomial.   \n",
+    "\n",
+    "## Negative Binomial Fit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [alpha, Intercept, roach1, treatment, senior]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9e50679163ce40f2a54806febb7cfa11",
+       "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"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 18 seconds.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# bambi poisson model\n",
+    "model_2 = bmb.Model(\"y ~ roach1 + treatment  + senior + offset(log(exposure2))\", family = \"negativebinomial\", data = roaches)\n",
+    "idata_2 = model_2.fit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sd</th>\n",
+       "      <th>hdi_3%</th>\n",
+       "      <th>hdi_97%</th>\n",
+       "      <th>mcse_mean</th>\n",
+       "      <th>mcse_sd</th>\n",
+       "      <th>ess_bulk</th>\n",
+       "      <th>ess_tail</th>\n",
+       "      <th>r_hat</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Intercept</th>\n",
+       "      <td>2.855</td>\n",
+       "      <td>0.234</td>\n",
+       "      <td>2.433</td>\n",
+       "      <td>3.306</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>5333.0</td>\n",
+       "      <td>3562.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>alpha</th>\n",
+       "      <td>0.272</td>\n",
+       "      <td>0.026</td>\n",
+       "      <td>0.224</td>\n",
+       "      <td>0.322</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>5228.0</td>\n",
+       "      <td>3369.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>roach1</th>\n",
+       "      <td>1.326</td>\n",
+       "      <td>0.258</td>\n",
+       "      <td>0.853</td>\n",
+       "      <td>1.806</td>\n",
+       "      <td>0.004</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>4783.0</td>\n",
+       "      <td>3311.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>senior</th>\n",
+       "      <td>-0.333</td>\n",
+       "      <td>0.267</td>\n",
+       "      <td>-0.824</td>\n",
+       "      <td>0.177</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>6074.0</td>\n",
+       "      <td>3265.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>treatment</th>\n",
+       "      <td>-0.787</td>\n",
+       "      <td>0.250</td>\n",
+       "      <td>-1.257</td>\n",
+       "      <td>-0.327</td>\n",
+       "      <td>0.003</td>\n",
+       "      <td>0.002</td>\n",
+       "      <td>5756.0</td>\n",
+       "      <td>3379.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
+       "Intercept  2.855  0.234   2.433    3.306      0.003    0.002    5333.0   \n",
+       "alpha      0.272  0.026   0.224    0.322      0.000    0.000    5228.0   \n",
+       "roach1     1.326  0.258   0.853    1.806      0.004    0.003    4783.0   \n",
+       "senior    -0.333  0.267  -0.824    0.177      0.003    0.003    6074.0   \n",
+       "treatment -0.787  0.250  -1.257   -0.327      0.003    0.002    5756.0   \n",
+       "\n",
+       "           ess_tail  r_hat  \n",
+       "Intercept    3562.0    1.0  \n",
+       "alpha        3369.0    1.0  \n",
+       "roach1       3311.0    1.0  \n",
+       "senior       3265.0    1.0  \n",
+       "treatment    3379.0    1.0  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "az.summary(idata_2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='log(y+1)'>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 720x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_log_posterior_ppc(model_2, idata_2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fraction of zeros in the observed data: 0.35877862595419846\n",
+      "Fraction of zeros in the posterior predictive check: 0.338175572519084\n",
+      " 80% CI: [0.28625954 0.39312977]\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "check_zeros(idata_2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1c437c642f0>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAHrCAYAAAAe4lGYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABkH0lEQVR4nO3dd3QUZdvH8d+mkhB6B+mQ0DsBkS5VOsKD9KagVFFEsD9gxYYUpUgR8QEEQXov0hMQpBcRQpXQIQVS9/0j7y4s2UACGQY23885e85kZnbmmtnMzLX3XnOPxWq1WgUAAAAg1bmZHQAAAADgqki2AQAAAIOQbAMAAAAGIdkGAAAADEKyDQAAABiEZBsAAAAwCMk2AAAAYBCSbQAAAMAgJNsAAACAQUi2obNnzyogIED169dPleUtWLBAAQEBGj58eKos73F6mmMHYAzOkU+X+vXrKyAgQGfPnjU7lFQ3btw4BQQEaNy4cWaHYoiAgAAFBASkyrJS+7h9FCTbj4HtwA8ICNDnn39+33l/+ukn+7yp9Q/nCmwnmLtfJUuWVPXq1dWzZ08tWbLE7BABPCTOkY/u7nNkv379kpxv0aJFCggIUNeuXR9jdKln7dq1GjdunA4fPmx2KA+ta9euia5nAQEBKl++vJo0aaJRo0bp/PnzZoeJVESy/ZgtXbpUcXFxSU5fvHjxY4zm6ePn56dKlSqpUqVKKlOmjOLj47Vt2zYNHTpUQ4cOldVqfaTlZ8iQQYULF1aOHDlSKWIAKcE58tGtW7dOBw8eNDsMQ6xdu1bjx4+/b7KdP39+FS5cWJ6eno8xspTLkyeP/XpWsWJF5cyZU6dPn9asWbPUokUL7d+/P9F7smTJosKFCytLliwmRGy8woULq3DhwmaHkeo8zA4gLSlcuLBOnjypbdu2qVatWommnzhxQgcOHLDPh8RKlSqln3/+2f53fHy8Zs6cqc8++0xLlixRvXr11KxZs4defsOGDdWwYcPUCBVACnGOfHTu7u6Ki4vT2LFjNWnSJLPDMcVPP/1kdgjJ8uKLL2rgwIEO406dOqUBAwbo2LFj+u9//6v58+c7TO/SpYu6dOnyOMN8rFauXGl2CIagZfsxatmypaSkW2YWLVokSWrVqtVji+lp5+bmph49ethrspYuXWpyRAAeFufIR1evXj35+vpq48aN2rdvn9nhIIUKFiyooUOHSpL279+v8PBwkyNCaiDZfowCAwOVJ08erV27VpGRkQ7TrFarlixZonTp0qlRo0b3XU5kZKS+//57tWjRQhUqVFClSpXUvn17/fLLL4qNjU3yfcHBwerRo4cqVaqkypUrq2vXrtq6desD475165YmT56stm3bqlKlSipfvrxatWqlH3/8UdHR0cnbeINVrVpVUkKrwN3Onz+vDz/8UPXr11eZMmVUrVo1vfzyy/rjjz+cLud+Ny7t2rVL/fv313PPPafSpUsrMDBQTZs21bvvvqu//vrLYd7Y2Fj99NNPateunSpWrKgyZcqoZs2aeumllzR27FjdvHkz0fKvXbum0aNHq3HjxipXrpyqVq2qrl27avHixU7LY+6ONTo6WuPGjVPDhg1VtmxZ1alTR5999lmi/zPgScY58tFlzpxZ3bp1kySNHTs2xe9/mG2xWq2aPXu2WrZsqXLlyqlGjRp68803debMmSTPqXFxcVq7dq1GjBihZs2aqXLlyipfvryaNm2q0aNH6+rVqw7z2252W7hwoSRpxIgRDvXOd98weO8NknFxcXruuecUEBDgtDTD5vPPP1dAQIA+++yzRNP27dunIUOGqFatWipTpoxq1KihQYMG6dChQ8nbsSmQN29e+/C9+zypGyQf9Xqwe/duDRgwQDVq1FCZMmVUu3ZtDRs2TP/884/T+W1150FBQTpy5Ihee+01VatWTZUqVVKPHj0c9vOuXbvUu3dvBQYGqmLFiurTp0+Sy03qXowzZ85o8uTJ6tq1q+rUqaMyZcqoevXq6t27tzZu3Jjkdj0pSLYfsxYtWigyMlJr1qxxGP/nn3/q3LlzatCggdKnT5/k+69evaoOHTrou+++0/Hjx1WgQAHlzp1b+/bt08iRI9WnTx9FRUUlet+yZcvUvXt3bd++XZ6enipUqJCOHTuml19+WcuXL09yfaGhoWrXrp2+/vprHT16VNmzZ1e+fPl0/Phxffnll+rRo4du376d7O0fPny4IXfhO0tG9+7dq1atWmnOnDm6du2a/P39lS5dOm3evFl9+vTRd999l+zlr127Vl27dtXatWsVGxurgIAAZcuWTRcuXND8+fO1bNkyh/nfeOMNffrpp9q/f7+yZ8+uEiVKyMPDQ/v27dOECRN07tw5h/lPnTqlNm3aaOrUqTp37pyKFSumTJkyKTg4WG+99ZaGDx+eZD16TEyMevXqpQkTJsjb21v58uXTxYsXNWPGDA0YMCDZ2wg8CThHPvo5smfPnvLz89PmzZu1Z8+eZL/vYbfl3Xff1UcffaSjR48qR44cyp07t9asWaMXX3xRp0+fdrquS5cuqX///vr9999148YNFSxYUHnz5tW5c+c0depUtWvXTpcvX7bP7+3trUqVKilbtmySpEKFCtnrnStVqqQ8efIkuV3u7u5q2rSpJCU6V9tYrVb753xvKeKMGTP0n//8R8uXL1d0dLSKFy+uuLg4rVq1Sv/5z3+0evXq++zVlDtw4ICkhPrsrFmzpui9D3M9+N///qdOnTrZj7mAgADdunVLixYtUps2be6bzO7du1cdOnTQrl27lC9fPrm5uWn79u3q3r27/v77b61YsULdu3fX4cOH9cwzz8hqteqPP/5Qly5dHD7fB5k4caK+/vprHThwQOnSpVNAQIA8PDy0ZcsW9e3bV5MnT07RfnrcSLYfM9vPn/f+TGr7edT2M2pSPvroIx07dkzFixfXqlWrtHjxYi1fvlzz589X9uzZtXXr1kStGaGhoXrvvfcUHx+vPn36aOvWrfrtt9+0ZcsWdevWLcnWj/j4eA0ePFjHjx9Xs2bN9Mcff2j16tVavny51q1bpypVqujPP/9MUdJqlF27dkmSChQoICmhdeb111/XzZs31bRpU23evFkLFizQH3/8oc8//1zu7u76/vvvk2zhvtd3332n+Ph4ffjhh9q2bZsWLFigFStWaPfu3Zo5c6aeffZZ+7wHDhzQqlWrlCdPHi1fvlxr1qzR/PnztXHjRgUHB+vjjz9W5syZ7fNbrVa98cYb+vfffxUYGKiNGzdqwYIFWrt2raZMmSJfX1/9/vvvmj17ttPYVq1apWvXrmnFihVaunSpVq5cqdmzZ8vPz09bt27Vpk2bHnKvAo8f58hHd3frdnK7iHvYbVm5cqV+++03eXl5acKECVq3bp0WLFigTZs2qWzZsvrxxx+dri99+vT6/PPPtX37dm3ZssV+Tt2yZYu6dOmic+fO6euvv7bPnyNHDs2ePVu1a9eWJPXt21ezZ8+2v9q1a3ff7WvevLkkafny5YqPj080fefOnQoNDVXBggVVrlw5+/hNmzbp888/V+bMmTVu3DgFBQVp4cKFCgoK0scffyyr1arhw4fr4sWLydrPSbFarbpy5YqWLFmiL774wr6NKZXS68Hhw4f1ySefyGq16q233tKWLVv022+/aevWrerUqZOioqI0dOjQJLdv7Nix6tixo7Zu3aoFCxZo27Ztev755xUREaGRI0fqgw8+0NChQ+2f8aZNm1SuXDldvXpV06dPT/Z2NWrUSL/++qt2796tVatW2Y/PX375RTly5NCYMWOS/GL3JCDZfsyKFSumUqVKafv27fZ/3ujoaK1cuVLZsmXTc889l+R7Q0JC7N+gR48ebU8sJals2bJ67733JCV8S727zmv27NmKjIxU2bJl9eabb8rDI+G+WE9PT40YMUJFihRxur6NGzdqz549Klu2rEaPHq3s2bPbp+XOnVvffvutfH19NWfOnGS33GTKlEm5cuVSpkyZkjX/g1itVs2YMUPr16+XJHvrxdKlS3X+/Hllz55dX3zxhfz8/OzvadOmjTp06CBJyf42HBISokyZMqlTp05yd3e3j7dYLKpWrZpDP562UpbGjRuraNGiDsvx8/NT+/btHVphtm/frgMHDsjLy0vffPONw36uXbu2+vfvL0n68ccfnbZux8bG6vPPP3e4g7tChQpq3769JJFs46nCOTJ1zpE9e/ZUhgwZtHXrVntjxP087LbYEqZ+/fqpQYMG9vGZM2fWN998Ix8fH6fry5Ahg9q0aePQ8CBJGTNm1Pvvv688efJoxYoV9y37SYkKFSoof/78Cg0Ndbo/bPf73NuqPWbMGFmtVn3yySeJypfat2+vbt26KSIiQvPmzUtxTOPHj7eXTZQoUUI1atTQ0KFD7fuuZ8+eKV5mSq8HU6dOVWxsrJ5//nm9/PLLcnNLSAu9vLz0wQcfqHjx4goLC0uysad48eJ6++235eXlZX/fW2+9JSmhLKtq1arq2bOnfbkZM2bUoEGDJEmbN29O9nbVqVNH5cuXl8VicRhfpUoVDR48WHFxcff9BcpsJNsmaNWqleLi4uw/Z23YsEE3b95Us2bN7Cd5Z7Zu3Sqr1arKlSurVKlSiaY3btxYuXPnVmRkpHbv3m0fv2XLFklSx44dnS63U6dOTsfbflJq06aN07hy5sypsmXLKjIy0v6z14OMGDFCmzZt0ogRI5I1/70OHTqkjh07qmPHjmrfvr2qV69ur69r3LixWrRoIenONrdv317e3t6JlmNr9dmzZ0+y6prz5MmjmzdvJqt+M3fu3JISkujr168/cH5brE2aNHHa5eBLL70kLy8vnTt3TidOnEg0vWTJkipbtmyi8bZxrvhgB7g2zpEPf460yZgxo3r06CEpebXbD7Mt4eHh2rt3rySpbdu2id6TKVMmhwTcme3bt+vTTz9Vnz591LlzZ/v5PSwsTLdu3Up0H86jsCXS995IHxsbq1WrVkm60wIuSefOndPBgweVLVs2Pf/8806XaWto2blzZ4rjubvrv0qVKsnf31++vr46efKkZs+e/VB9baf0emC7pjnrd91isdjHJ3Xta9u2baIEuHDhwvYvWc5+cbAdm2fOnLnvttzr6tWr+umnn/Tmm2+qR48e9v+VmTNnSpKOHDmSouU9TnT9Z4JmzZpp9OjRWrRokXr27Jnsn0dDQkIkJbT8OOPm5qYiRYrowoULCgkJsf/cZnvfva2sNkmNP3r0qCRpzpw5SfbyYVv2o/6Ellzh4eH2i6Sbm5syZsyowMBAtW7d2uGgf9C+KlSokDw9PRUTE6PTp0+rRIkS911v9+7dNXLkSPXq1UulS5dWjRo1VLlyZVWtWtWh1VySKlasqPLly2vv3r2qW7euatSooapVq6pq1aoqXbp0ohPTg2L18/NTnjx5dOrUKYWEhCT6vPLnz+/0fbZav4iIiPtuG/Ck4RyZOnr06KGZM2cqKChIwcHBCgwMTHLeh9mW06dPy2q1KnPmzMqVK5fT9yT14KHo6GgNGTJEa9euve82JKfBIrlatGihiRMnatWqVXr//fft/XBv3bpV169fV8mSJR0+62PHjkmSoqKikvwiZqv/Dw0NTXE8zrr+u337tsaPH68pU6aoc+fOWrZsmXx9fZO9zJRcD27evGm/ETWp/3HbsWT77O91969Hd8uSJYtu3brldLotlpTcwL9lyxa9/vrrCgsLS3KeGzduJHt5jxvJtgly5MihZ599Vlu2bNHOnTu1adMmFSlSxOm30bvZ/jHvd8OE7eaRuw+oB73P9p572X5mtZ1w7iclNwA9isDAQId+tpNi2+akts1isShr1qwKDQ1NVjLauXNnpU+fXtOnT9fBgwd18OBBTZkyRd7e3mrVqpWGDRumDBkySEq4oE+ZMkXjx4/X4sWLtW7dOq1bt06SlC9fPg0YMMChFSi5n+upU6ecxprUidj2sx3wtOEcmTr8/PzUs2dPjRkzRmPHjtWsWbOSnPdhtsW23+53w2pS0yZPnqy1a9cqR44cGjp0qKpWraocOXLYyxE6duyo3bt3p1oZiZSQOJYoUUJHjhzRtm3bVKdOHUl3WrrvbtWWZE/s7m7kSYqzm24fRrp06TR06FDt2LFD+/fv16+//mr/hSI5UnI9uDvZTep/3FZOlNR1Ml26dE7H2xqVnE2/t8HpQW7evKk33nhDYWFhat26tTp16qTChQvLz89Pbm5u2rZtm3r27Jmq/yupjWTbJK1atdKWLVs0bNgwxcTEJKvfWNtBdG+XSHe7cuWKJMcTnK+vr8LCwnT16lWn3zKTWp5tfdOnT1eNGjUeGN+TxBa7bX/cy2q12rf7fheKu7Vu3VqtW7fWpUuXtHPnTm3dulXLly/Xr7/+qkuXLmnixIn2eTNlyqR3331X77zzjo4cOaKdO3dq7dq1CgoK0ogRI+Tr66smTZo4xJrSzxVwZZwjU0fXrl01Y8YM7dy5U9u3b09yvofZFtt77tdCmVSStmTJEknSZ5995vQBRhcuXEhWDCnVrFkzHTlyREuXLlWdOnV0+/ZtrVu3ThaLJVG9tm37KlWqlGTNslEqVKig/fv337erwkd1d2J+5coV5cyZM9E8th5DzLz2bNq0STdu3FDFihX1+eefJ0rW//33X5MiSz6avkzSsGFD+fr66vz587JYLPZa4/spVKiQJOn48eNOp8fHx9trem3z3j3srN5XUpL9Xdp+VkpOS8eT5kH7KiQkRDExMXJ3d0/yZ7Ck5MiRQy+88II++eQTzZs3T25ubtqwYYPTn4ktFotKliypbt26aebMmerTp48kOdxM86BYw8PD7SeTuz9XwJVxjkwdfn5+6t27t6T7124/zLYUKFBAbm5uunbtWpJlMrbylHvZuj+tWLFiomnXrl1Lsiwjpa2i92revLksFovWrl2r27dva/369YqIiFDlypUTdR9oK6E4ceKE0x5MjGRbX2qW0dwrY8aM9l9zkvoftx1LZl57bP8rFSpUcPr5P8m12jYk2ybx8fFRr1699Oyzz6pDhw7Kly/fA99Ts2ZNWSwW/fnnn0470l+9erUuXLggX19fVapUyT7edvf+nDlznC43qW/stjuv586dm2o/kT0uNWvWlJSQ1DqL3VaKUqlSpRTVw92rWLFi9vKR5NRkli9fPtG8tladlStX6tKlS4neM3fuXEVHRytfvnxJ9ooAuBrOkamnS5cuypo1q3bv3p3kjW4Psy1+fn72c5rtYTN3CwsLs5fQ3ctWXuDs18fp06crLi7O6ftsN7w/bFlO3rx5ValSJUVGRmr9+vX2m3DvbdWWEhJMf39/Xb9+Xb///vtDre9hWK1W+4PSkqrBTi22a6Wz8kyr1WovPbLNZwbbZ+6sX+5r164leqT9k4hk20QDBw7UjBkz9N///jdZ8xcsWNB+Qnz77bcd7uQ9ePCgPv74Y0kJ9cV337TXsWNH+fr6au/evRozZoy9rikmJkZffPFFkq1ADRs2VIUKFXTixAm9+uqrie4Kj46O1saNG1N01/wXX3yh+vXr2/sRNUrz5s2VN29eXb58WcOHD3f4KXPRokWaO3euJOmVV1554LLCw8M1ZMgQBQUFObRuxMXFaebMmbpx44Z8fX3tXS0tXrxYEyZMSHTX97Vr1+wntLt7SqhevbrKli2r6OhovfHGGw4Xny1btmj8+PH2WB+1VQd4mnCOTB2+vr721u2kbn582G2x1RN///339i5YpYSb1YYMGZJkiUnlypUlJTy10XZ+tlqt+v333zVt2jSnvUhJd5LPXbt2Jfmgrwex1WbPmTNHmzZtkoeHh72s715Dhw6VxWLRyJEjNW/evER1wWfOnNEPP/yQag+2uX37tkaPHq2DBw9KevBNwY+qV69e8vDw0Lp16zRt2jT7NS46OlqffPKJjh07pgwZMiR5g+jjUKVKFUkJDVLbtm2zj7948aIGDRqU5BezJwk120+Zjz76SCdPntSxY8fUuHFjFS9eXLGxsfaLQY0aNRLd3Zw7d27997//1bBhw/TDDz9ozpw5euaZZ3TmzBndvHlTQ4YMcXh4gI2bm5vGjRunvn37atu2bWrUqJEKFiyozJkzKyIiQqdOnVJMTIxDf6wPcu3aNZ07d07Xrl17tB3xAD4+PhozZox69+6t5cuXa+PGjSpSpIiuXLliL8l47bXX7DfI3E98fLyWL1+u5cuXy9fXVwUKFJCHh4d9OywWi9555x17TdvVq1c1duxYjR07Vrly5VLOnDkVFRWlkydPKiYmRrly5dLgwYPty7dYLPr666/VrVs3BQcHq27duipevLjCw8PtF7xWrVrppZdeMmBPAa6Fc6RznTt31vTp05N8at/DbkuTJk304osv6rffftNrr72m/PnzK1OmTDp+/Li8vb3Vu3dvTZw4MdENegMHDtS2bdu0fv161a5dWwULFtSlS5d08eJFtWrVSv/++6+Cg4MTxdmwYUN9++23WrZsmf766y/lyZNHbm5uatOmjdPuB51p0qSJPvnkEwUFBUlKeJ5BUjfH1qlTR++//74++eQTvffee/rss89UqFAhWSwWXbhwwb4/P/roo2St+26//fabQ/IYHh6uM2fO6NatW5KkwYMHO/wCY4SSJUvq3Xff1ciRI/XFF19o6tSp9t6vbt68KS8vL3311VdOu6V9XMqUKaPGjRtr1apV6tmzpwoWLChfX1/9/fff8vb21ptvvqlPP/3UtPiSg2T7KZM1a1bNnTtX06dP18qVKxUSEiI3NzeVLVtWrVu3VocOHezdGd2tZcuWypUrlyZMmKD9+/frxIkTKl26tPr27atChQo5vZBICX2rzp07V/Pnz9fy5ct17Ngx+8NiypUrpxo1atgfJPOkKV++vBYtWqRJkyZpy5YtOnr0qHx9fVWzZk1169YtWYm2lHBjyOjRo7V161bt379f586dU0xMjHLnzq1atWqpd+/eDl0HNm7cWDExMdq+fbv9ou/j4yN/f381bNhQnTt3VsaMGR3WUbBgQS1cuFBTpkzR+vXr9ffff8vLy0tVq1ZV+/bt1bJlS1q1gWTgHOmcj4+PXnnlFftzCZx52G35+OOPVaZMGc2ePVshISGKiIhQvXr1NGTIEHsf5vfeYFemTBn98ssvGjNmjPbs2aOTJ0+qYMGC6tOnj7p06WJ/FsK9ChQooIkTJ2rSpEk6dOiQzp8/L6vVet9uDe+VNWtWPffcc/YnCN/bC8m9OnfurKpVq2rmzJnasWOHjh8/Li8vL+XOnVvVq1dXw4YNk309udu///7rcHOfp6ensmfPrnr16qlz5872Fl2jderUSQEBAZo2bZp2796tI0eOKEuWLKpbt6769u2bZFeaj9NXX32lokWLatGiRTp//rwyZ86sxo0ba8CAAU7LL580FuvD/g4DAABwH6NGjdKsWbM0YsSIFHVhB7gSarYBAECqi4iIsD+Z0ehyCOBJRrINAAAe2owZM3T48GGHcaGhoRo0aJAuXbqk0qVLq1y5ciZFB5iPMhIAAPDQunbtquDgYGXIkEH58+dXdHS0vW/qLFmyaObMmfL39zc7TMA03CAJAAAeWrdu3ZQxY0YdOnRIJ06ckNVqVYECBVSrVi298sorypUrl9khAqaiZRsAAAAwCDXbAAAAgEFItgEAAACDkGwDAAAABuEGSQCuKz5esnVJVrKk5Eb7ApBmcPzjCcENkgBcV0SE5OeXMBweLt3zyGgALozjH08IvuYBAAAABiHZBgAAAAxCsg0AAAAYhGQbAAAAMAjJNgAAAGAQkm0AAADAIPSzDcB1eXpKQ4feGQaQdnD84wlBP9sAAACAQSgjAQAAAAxCGQkA1xUfL50+nTBcoACPawbSEo5/PCEoIwHgunhcM5B2cfzjCcHXPAAAAMAgJNsAAACAQUi2AQAAAIOQbAMAAAAGIdkGAAAADPLYuv6b8EOkfbj/a76Pa7UAAMBAd1/fHwdyCDxt6GcbgOvy8JD69bszDCDt4PjHE4L/PgCuy9tbmjDB7CgAmIHjH08IarYBAAAAg9CyDcB1Wa3S5csJw9mzSxaLufEAeHw4/vGEINkG4LoiI6WcOROGeVwzkLZw/OMJQRkJAAAAYBCSbQAAAMAgJNsAAACAQUi2AQAAAIOQbAMAAAAGIdkGAAAADELXfwBcl4eH1L37nWEAaQfHP54Q/PcBcF3e3tKMGWZHAcAMHP94QlBGAgAAABiElm0ArstqTXiKnCT5+vK4ZiAt4fjHE4KWbQCuKzJS8vNLeNkuugDSBo5/PCFItgEAAACDkGwDAAAABiHZBgAAAAxCsg0AAAAYhGQbAAAAMAjJNgAAAGAQkm3Y1a9fX/Xr1zc7jCfa8OHDFRAQoLNnzz7Scs6ePauAgAANHz48lSKDU+7uUrt2CS93d7OjAfA4cfzjCcFDbVzUjh07NGfOHO3Zs0dXrlyRr6+vihYtqsaNG6tjx47y9vY2O0SkgoCAAAUGBurnn382O5QnU7p00rx5ZkcBwAwc/3hCkGy7mNjYWI0cOVJz586Vr6+vatWqpYIFCyosLExbt27VZ599ptmzZ2vy5MkqWLCg2eECAJAiE354/A+o6f+a72NfJ1wHybaL+frrrzV37lyVLVtWEyZMUK5cuezT4uLiNGHCBE2YMEGvvPKKFixYID8/PxOjBQAAcG3UbLuQkJAQzZgxQ5kzZ9bEiRMdEm1Jcnd316BBg9S8eXOdOnVKU6dOdbqcGzdu6L333lONGjVUrlw5tWvXTuvWrUs0X1RUlKZNm6aWLVuqcuXKqlixoho0aKA33nhDR48eTTT/2rVr1b17d1WtWlVly5ZV8+bNNXXqVMXFxTnMt2DBAgUEBGjBggXauHGjOnXqpIoVK6p+/frauXOnAgIC9M477ziN/cKFCypZsqS6d+/uMD48PFxjx45Vs2bNVK5cOVWpUkW9e/fWrl27nC7n77//Vt++fVWxYkVVrlxZr7zyio4dO+Z03vuJi4vT5MmT1bBhQ5UtW1YNGzbUpEmTZLVanc6/Y8cOjRgxQo0bN1bFihVVsWJFtW3bVnPnznWYLygoSAEBAZKk4OBgBQQE2F8LFiyQJIWFhWny5Mnq0qWLatasqTJlyqhmzZoaNmyYTp8+neJteSpFREgWS8IrIsLsaAA8Rh5REerfL73690svjyiOf5iHlm0XsnDhQsXHx+s///mPsmfPnuR8/fr109KlS/Xbb79p8ODBDtOio6PVs2dP3b59W61bt1ZYWJiWLVum/v37a/To0WrZsqV93rffflsrVqxQQECA2rZtKy8vL/37778KCgpSzZo17cmgJH3zzTeaNGmScufOrUaNGsnPz087d+7U6NGjtXfvXo0dOzZRnCtXrtTWrVtVt25dderUSREREapSpYry5cun1atX68MPP0xUe7548WLFx8erVatW9nHXr19Xly5d9Pfff6tKlSqqWbOmwsLCtG7dOnXv3l3fffedGjRoYJ//2LFj6tixoyIjI9WwYUMVKlRI+/btU8eOHVWiRInkfyCS3n//ff3222965pln1LlzZ0VFRWn69Onas2eP0/mnTJmi06dPq3z58sqdO7du3rypLVu26IMPPtDJkyftN1Tmy5dPAwYM0Pjx45UvXz61adPGvoySJUtKkv755x+NHTtW1apVU8OGDeXj46MTJ05o6dKl+uOPP7RgwQLly5cvRdsDAABSJm0l2/dr2XJ3T7iZIjnzurlJPj4PN29kpOSsVTN9+qSXkUy7d++WJD377LP3na9o0aLKmTOnQkND9e+//ypPnjz2aZcuXVLRokU1d+5ceXp6SpJ69Oihtm3batSoUapfv778/PwUFhamlStXqkyZMvr111/lfted3nFxcYq4a59s3bpVkyZNUu3atTV27Fj5/P/+sFqt+uijjzRnzhytWrVKjRs3dohz06ZNmjZtmmrUqOEwvkWLFpo4caLWr1+vpk2bOkxbsmSJ0qVLp0aNGtnHjRo1Sn///bc+/fRTvfjii/bxly9fVrt27fT++++rVq1a9sR91KhRCg8P15dffunw5cL2hSG5goKC9Ntvv6lEiRKaPXu2fH0Tav5effVVhy8Dd/voo4+UP39+h3GxsbHq06ePZs6cqW7duilv3rx65plnNHDgQHuyPXDgwETLKlq0qLZs2aLMmTM7jN+xY4d69uypH374QR9//HGytwcAAKRc2ioj8fNL+nVXEiZJypkz6XnvSfBUqFDS89au7ThvqVLO50sFly9fliTlzp37gfPaEuxLly4lmjZ48GB7oi0lJG0vvviibt68aS8nsVgsslqt8vLycki0pYRylYwZM9r/njVrliRp5MiR9kTbtoyhQ4fKYrFo2bJlieJo0KBBokRbkj1RXbx4scP4I0eO6NixY3r++efttehXr17VihUr9Oyzzzok2pKUPXt29e7dW1evXtW2bdskSefPn7eXZdydaEtS3759HbbrQX7//XdJUv/+/e2JtiTlypVL3bp1c/qeexNtSfLw8NBLL72kuLg4BQUFJXv9GTJkSJRoS1L16tVVrFgx+zYDAADjpK2WbdglVTPs6empChUqJBpfpUoV/fLLLzpy5IhatWolPz8/1apVS5s3b1abNm3UuHFjValSReXKlZOXl5fDe/fu3StfX1/Nnz/f6TrTpUunEydOJBpfrlw5p/MXKVJEZcqU0ebNm3X9+nV7Qrlo0SJJcmg13r9/v+Li4hQVFaVx48YlWlZISIgk6cSJE6pXr56OHDkiSapcuXKiedOnT68SJUooODjYaVz3stWtV6lSJdE0Z+OkhNryadOmae3atTpz5owiIx3vur948WKy1m0TFBSkn376Sfv27dO1a9cUGxtrn3b3FyoAAGCMtJVsh4cnPe3eDu/vl9S43fODwP8nbMma99Ah52UkqSB79uw6ceKELly4oCJFitx33gsXLkiScuTI4TA+c+bMcrs3ZknZsmWTlHDTnc3YsWM1adIkLV26VN9++62khIT0xRdf1BtvvGFvxb5x44ZiY2M1fvz4JOO5N6m8e53OtGrVSp988olWrFihjh07Kj4+XkuXLlW2bNn03HPP2ee7ceOGpIQSG1uZjTO3bt1y2L6k1n2/Wvh7hYWFyc3NTVmyZEk0zdnyo6Oj1a1bNx08eFClSpVSy5YtlTlzZnl4eOjcuXNauHChoqOjk73+FStWaMiQIfL19VXNmjWVL18++fj4yGKxaOHChTp37lyylwUAAB5O2kq2U1IXbdS8vsb11VmpUiUFBwdr+/btTssvbP755x9dvHhRuXLlcqjXlhJuJoyPj0+UcF+5ckVSQmmCja+vr4YMGaIhQ4bozJkzCgoK0pw5czRz5kxFRUVp5MiRkmQv6UhJCYSUUGaSlGbNmumLL77Q4sWL1bFjR+3YsUMXL15Ut27d5OFx59/atu5evXrp7bfffuA6bdtn29572Up1kiNDhgyKj4/XtWvXlDVrVodpzpa/bt06HTx4UO3bt09US71s2TItXLgw2euWpPHjx8vb21sLFixQoUKFEi0PAAAYL23VbLu41q1by83NTb/++quuXr2a5HwTJ06UpEQ1zJIUExOjv/76K9F4Wxd5SfXGkT9/frVr106zZs2Sr6+v1q9fb59Wrlw5Xb9+3V6ykRpsLdh79uzRmTNn7PXb99ZZly1bVhaLJcneP+5l274///wz0bSIiAh7mUly2Hpjcda9oLNxZ86ckSTVr18/WfNLkpubW6KuE21Onz6tokWLJkq0Q0ND7etyee7u0gsvJLx4XDOQpljd3BVSurFCSjeW1Y3jH+Yh2XYhhQsXVrdu3XT9+nW9+uqriep74+PjNWHCBC1evFgFChRQ7969nS7nu+++U0xMjP3vf/75R7/99psyZMig559/XlLCjYf79u1L9N4bN24oJibGoUu+rl27SpLeeecdXbt2LdF7Ll26pH/++SfF29uqVStZrVbNnz9fq1evVpEiRVS2bFmHeXLkyKGmTZtqz549+vHHH53Wqu/du9deRpI3b15VrVpVR48eTXQD5qRJk3Tz5s1kx9e6dWtJ0oQJExzKZEJDQzVz5sxE8+fNm1dS4kQ/ODhY85J45HCmTJnsJUHOlnfq1CmH1vioqCh99NFHDrXbLi1dOmnZsoTX3b0NAXB5cZ7ptKz/Ai3rv0Bxnhz/ME/aKiNJA9566y2FhYXpt99+U+PGjVWnTh0VKFBA4eHh2rp1q0JCQlSoUCFNnjzZ6dMjc+TIobCwMLVu3Vp16tRReHi4li5dqqioKI0aNcr+ntDQULVv317FixdXqVKllCtXLl2/fl3r1q1TTEyMXn75Zfsya9eurX79+un7779Xo0aNVKtWLeXNm1fXr1/XqVOn9Oeff+r1119X0aJFU7Sttl5Hpk6dqpiYmCS70/vwww918uRJffnll1q0aJEqVqwoPz8/XbhwQQcPHlRISIi2bNlirzH/4IMP1LFjR7399ttau3atChUqpP3792vfvn2qUqVKkq3M96pWrZratm2rBQsWqEWLFmrYsKGio6O1fPlyVahQQRs2bHCYv169esqXL59+/PFH/f333ypevLhOnjypjRs3qkGDBlq1alWidVSvXl0rVqzQoEGDVLJkSbm7u6tOnToKCAhQ165dNWrUKLVu3VpNmjRRbGystm3bJqvVqhIlSqSolR4AADwckm0X4+HhoU8//VTNmzfX3Llz9eeff2rt2rXy8fFR0aJF9dJLL6ljx45Kl0Qrn5eXl6ZNm6avvvpKv//+u8LCwuTv769+/frZW7Ul2ft23rFjh7Zt26br168rS5YsKlWqlHr06KGaNWs6LHfw4MGqWrWqZs6cqe3btyssLEyZM2fWM888owEDBqhFixYp3lZbf9oLFiyQxWJJchmZM2fWnDlzNGvWLC1fvlxLlixRfHy8smfPrhIlSui1115zuInR399fs2fP1ldffaXNmzdry5Ytqly5smbPnq1p06YlO9mWpI8//liFCxfWr7/+qlmzZil37tzq2bOnmjZtmijZTp8+vX766Sd9+eWX2rlzp4KDg1WsWDF99dVXypYtm9Nk+91335WU0Hf2mjVr7NsVEBCgzp07y8PDQ7NmzdKvv/6qjBkzqk6dOnrjjTf0+uuvJ3sbAADAw7NYk+oDLpVN+OHOz+j9XzPuJkEAsIuISOgzX0roYSgVHh4FwNHd1/cniUdUhHq9XUiSNO2LEMV6P/zxT96CR0HLNgDX5qRbSQBpg2c0xz/Mxw2SAAAAgEFItgEAAACDkGwDAAAABiHZBgAAAAxCsg0AAAAYhN5IALguNzepTp07wwDSDKvFTeeK17IPA2Yh2Qbgunx8pI0bzY4CgAnivHz0+5CVZocBUEYCAAAAGIVkGwAAADAIyTYA1xURIeXIkfCKiDA7GgCPkUdUhHoNK6hewwrKI4rjH+ahZhuAa7t82ewIAJjEJ5zjH+ajZRsAAAAwSJpq2Z7wQ6TZISSp/2u+ZofwVFqwYIFGjBihzz77TG3btjU7nCfKuHHjNH78eM2cOVPVqlWTJJ09e1bPP/+82rRpo88//zzV1xkUFKRu3bppwIABGjhwYKovHwCApw0t2y7k7NmzCggIcHiVKVNGderU0ZtvvqkjR44YHkNQUJACAgI0btw4w9cFcwQEBKhr165mhwEAwFMhTbVspxUFChRQy5YtJUmRkZH666+/tHTpUq1evVo//fSTKlWqZHKEqadhw4YqX768cubMaXYoT4VcuXJp+fLlypAhgyHLL1eunJYvX64sWbIYsnwAAJ42JNsuqECBAol+wv/22281ceJEffvtt/r5559Niiz1ZciQwbDE0RV5enqqaNGihi3fx8fH0OUDAPC0oYwkjbD97H/gwAH7uNjYWM2YMUMtW7ZUuXLlVLlyZXXt2lUbnTxxLz4+XvPmzVO7du0UGBio8uXLq379+urXr5927twpKaFGuFu3bpKk8ePHO5SznD171r6s6OhoTZ8+XW3atFGFChVUsWJFderUSevWrUu03uHDhysgIEBnzpzRjBkz1KxZM5UpU0bDhw+XlFCzHRAQoAULFiR67+7du9WnTx8FBgaqbNmyatKkicaNG6dbt24lmtdWGhEaGqrhw4frueeeU4kSJRQUFPTA/RoQEKCoqCiNHj1aderUUdmyZdWiRQvNnz8/0fx3x7tx40Z16tRJFStWVP369R9q/0jSv//+qzfeeEOBgYGqWLGiunTpYv9M7mUrNbLtv7uFh4dr/PjxatGihSpUqKDKlSurdevWGjNmjGJiYuwlQpIUHBzs8Pna9r+zMqIGDRqoYsWKTve7JPXs2VMlSpTQ+fPnHcavXbtW3bt3V9WqVVW2bFk1b95cU6dOVVxcnNPlOOXmJlWpkvDice1AmmK1uCm0QCWFFqjE49phKlq20wiLxeLwt9Vq1ZAhQ7R69WoVKlRInTt3VmRkpFauXKm+ffvq3XfftSfOkvT111/rxx9/VIECBdS8eXOlT59eoaGh2rVrl3bs2KGqVasqMDBQbdq00cKFCxUYGKjAwED7+zNmzCgpIZHs3bu3goODVapUKbVr104xMTH6448/1K9fP73//vvq0qVLovhHjRqlvXv3qk6dOqpbt66yZ89+3+1dtWqV3njjDXl6eqpp06bKli2btm3bpvHjx2vr1q2aOXOmvLy8HN5z/fp1dejQQZkyZVLTpk0VExMjPz+/ZO3fwYMH6+jRo2rSpIliY2O1YsUKvfvuu7py5Yr69u2baP6VK1dq69atqlu3rjp16qSI/+8DOqX75+LFi+rQoYNCQ0NVs2ZNlS5dWv/884969uxpvykyOa5evaquXbvq+PHjKlmypF566SXFx8frxIkT+vHHH9WrVy/ly5dPAwYM0Pjx45UvXz61adPG/v6SJUsmueyWLVtqwoQJWrdunZo3b+4w7eLFi/b/n7x589rHf/PNN5o0aZJy586tRo0ayc/PTzt37tTo0aO1d+9ejR07Nnkb5uMjJfHFA4Bri/Py0fzhm80OAyDZTitmzpwpSSpTpowkadGiRVq9erUCAwM1depUe+L52muvqW3btho9erTq1aun/PnzS5Lmz5+vXLlyafHixfLx8bEv12q16saNG5JkT+5sybaz3igmTJig4OBgDRw4UP3797d/CQgPD1f37t31+eefq2HDhsqVK5fD+44ePaqFCxc6JGRJCQ8P13vvvSc3NzfNmTNHJUqUsMc6dOhQLV26VD/++KP69evn8L5jx46pbdu2+vjjj+Xu7v7A9dztzJkzWrJkiT05f/XVV9WmTRuNHTtWL7zwgn0/2mzatEnTpk1TjRo1Hmn/fPPNNwoNDdXrr7+u1157zb6cuXPn6oMPPkh2/P/97391/PhxvfrqqxoyZIjDtMuXL8vX11cZM2bUwIED7cl2cnsbadWqlSZMmKDFixcnSraXLl2q+Ph4+z0GkrR161ZNmjRJtWvX1tixY+3/b1arVR999JHmzJmjVatWqXHjxsnePgAAzMLvKi7o9OnTGjdunMaNG6cvvvhCHTt21MSJE+Xt7a033nhDUkJCLElvvfWWQwtv7ty51aNHD8XExGjJkiUOy/X09EyUhFosFmXOnDlZccXHx2v27NkqWLCgQyIpSX5+furfv79iYmK0Zs2aRO/t3bt3shJtKaH84ObNm3rxxRftibYt1qFDh8rDw8O+/fdu31tvvZXiRFtKSK7vbgXPkSOHevbsqdjY2ET7UUoorbg30U7p/omOjtby5cuVLVs29erVy2FZ7du3V6FChZIV++XLl7Vq1SoVKFBAAwYMSDQ9e/bs8vB4+O/lBQsWVPny5bV161ZdvXrVYdrixYvl7e2tJk2a2MfNmjVLkjRy5EiHL3a2z89isWjZsmUPHQ8AAI8TLdsu6PTp0xo/frykhAQyW7Zsat68ufr06WOvuT18+LDSpUuncuXKJXq/rfzj7q4CmzRpojlz5qhFixZq2rSpAgMDVaFCBfn6Jr9/8JMnT+rGjRvKmTOnPb672RKxEydOJJrmLM6kHD582GE77pYnTx7lz59fJ0+eVHh4uEOC/Mwzzyhr1qzJXs/dqlSpkuQ4Z10uOtuelO6fkydPKioqStWrV5e3t7fDvG5ubqpUqZJCQkIeGPuBAwdktVpVrVo1eXp6PnD+h9GqVSvt3btXy5Yts98/8Pfff+vw4cNq0qSJw02ue/fula+vr9Oad0lKly6d0/8RpyIjpVKlEoYPHZJS8P8K4OnmER2pjiMrS5Jmf/CnYr04/mEOkm0XVLNmTU2dOvW+84SHhyt37txOp9nqocPCwuzj3nvvPeXPn18LFy7UDz/8oB9++EHe3t5q2rSp3n777WQlqdevX5eUkGT9/fffSc7n7Ea6bNmyPXD5NuHh4Q7bca8cOXLo5MmTioiIcEi2H1QHfj/O4rONu3s/3m/+lO4f23KT2jfJ3Wc3b96UpESlO6nphRde0GeffaYlS5bYk+1FixZJSkjE73bjxg3FxsY6/cJhExmZzAdUWa3SqVN3hgGkHVarMl49bR8GzEKynUb5+fnpypUrTqfZxt+diHp6eurll1/Wyy+/rNDQUO3cuVMLFizQ77//rsuXLz8wub97eY0bN07+DW7/794bPJOznsuXLzudbhufPn36h17Hva5cuaI8efIkGifJadeEztaV0v1jW+6DPscHsd28Ghoamqz5H0aWLFlUq1YtrV+/XqdOnVKBAgW0dOlS+/i72fbDg3qCAQDgaUCynUaVLFlSO3bs0L59+xKVNAQHB0uSQ73z3XLlyqXmzZvrhRdeUNOmTbVt2zbdvn1b6dKls9c7O+uerWjRovLz89OBAwcUExNjWMmCrWeM4OBgvfDCCw7TQkNDdebMGeXPnz/ZPY0kx65du9SiRYtE46Sk9+O9Urp/ChcuLG9vbx04cEBRUVEOpSTx8fHavXt3stZbpkwZubm5KSgoKFnrdXNzS1n3e/+vZcuWWr9+vRYvXqzAwED9+++/6ty5c6L1lStXTps2bVJISEiy684BJJjwQzJ/9QHw2HCDZBpl67bt66+/VkxMjH18aGioZsyYIQ8PD3sPEdHR0dq+fbus9/wMFxkZqYiICHl4eMjt//swzpQpk3059/Lw8FDHjh117tw5ffHFFw7rtTl27FiyW2ST0qBBA2XIkEELFixwKMewWq327b2727rUMHHiRHv5ipTQej59+nR5eHgkSsKTktL94+XlpaZNm+rKlSuaNm2aw3zz5s1LVr22lFA+06hRI4da/7tduXJFsbGx9r8zZcqkCxcuJGvZd6tfv74yZMigJUuWJFlCIt3pE/6dd97RtWvXEk2/dOmS/vnnnxSvHwAAM9CynUa1atVKq1ev1rp169SyZUvVrVtXt27d0ooVK3T9+nUNHz7c3l3d7du31aNHD+XPn1/ly5dXnjx5FBkZqY0bN+rSpUt65ZVX7D2aFClSRDlz5tSyZcuULl065c6dWxaLRR07dlSGDBk0aNAgHTp0SD///LP++OMPVa1aVVmzZlVoaKiOHTumI0eOaO7cuSmq0b6Xn5+fRo0apTfffFP/+c9/1LRpU2XNmlXbt2/XgQMHVK5cOb388supsh9t8ufPrxYtWqhRo0b2fravXLmiIUOGJOr2735Sun/efPNNbd++XWPGjNGff/6pUqVK6Z9//tEff/yhmjVrasuWLcla74cffqi///5bEydO1KZNm1S9enVZrVaFhIRoy5Yt2rZtm73cpHr16lqxYoUGDRqkkiVLyt3dXXXq1LHffJsUW68j8+bN0/nz51WoUCGVL18+0Xy1a9dWv3799P3336tRo0aqVauW8ubNq+vXr+vUqVP6888/9frrr/OkSgDAUyFNJdv9X+NOZBuLxaKxY8dq5syZWrhwoWbNmiVPT0+VLl1aPXr00PPPP2+f18fHR0OHDtWOHTu0a9cuXblyRZkyZVKRIkU0dOhQh1INd3d3jR8/Xl999ZUWLVpkf1jLCy+8oAwZMsjLy0tTpkzR/Pnz9fvvv2vVqlWKjo5W9uzZVbRoUb300kvy9/d/5O1r2rSpcuTIoUmTJmnNmjW6deuW8uXLp379+umVV15J1HvHoxozZoy+++47LVu2TFevXlWhQoU0ZMgQtW/fPkXLSen+yZkzp+bMmaMvv/xSW7Zs0a5du1S6dGlNnz5dO3bsSHaynTVrVv3666+aOnWqVq5cqVmzZsnb21vPPPOM+vTp49AF37vvvitJ2rFjh9asWaP4+Hhlz579gcm2lPAlb968eYqJiblvi//gwYNVtWpVzZw5U9u3b1dYWJgyZ86sZ555RgMGDEj2rwUAAJjNYr23NsAgd9eRkfTCVXTt2lXBwcE6evSo2aHAmchIqWrVhOGdO+n6Dy6Pmu07PKIj1f6L2pKkeW9veqSu/8hb8CjSVMs2gDTG11c6eNDsKACYINbLV7Pf32V2GAA3SAIAAABGIdkGAAAADEIZCfAIfv75Z7NDwP1Qsw2kWalZsw08CpJtAK7LapUOHbozDCDtsFqV9d/D9mHALJSRAAAAAAYh2QYAAAAMQrINAAAAGIRkGwAAADAIyTYAAABgEHojAeC6LBapYME7wwDSDotFN7MWsA8DZiHZBuC6fH2lkBCzowBgglgvX/388WGzwwAoIwEAAACMQrINAAAAGIRkG4DrunUr4XHtVasmDANIM9yjb6nd57XU7vNaco/m+Id5qNkG4Lri46Vdu+4MA0gzLNZ45Tq92z4MmIWWbQAAAMAgJNsAAACAQUi2AQAAAIOQbAMAAAAGIdkGAAAADEJvJABcW/bsZkcAwCS3/Dj+YT6SbQCuK3166dIls6MAYIJY7/SaNvqU2WEAlJEAAAAARiHZBgAAAAxCsg3Add26JdWtm/Dice1AmuIefUutv22i1t824XHtMBU12wBcV3y89Mcfd4YBpBkWa7zy/b3ZPgyYhZZtAAAAwCAk2wAAAIBBSLYBAAAAg5BsAwAAAAYh2QYAAAAMQm8kAFybr6/ZEQAwSYwXxz/MR7INwHWlTy9FRJgdBQATxHqn1+Qxl8wOA6CMBAAAADAKyTYAAABgEJJtAK7r9m2pWbOE1+3bZkcD4DFyj7mtZhPaqtmEtnKP4fiHeajZBuC64uKk5cvvDANIMyzxcSp0cJV9GDALLdsAAACAQUi2AQAAAIOQbAMAAAAGIdkGAAAADEKyDQAAABiEZBsAAAAwCF3/AXBd6dNLVqvZUQAwQax3ek34PsLsMABatgEAAACjkGwDAAAABiHZBuC6bt+W2rdPePG4diBNcY+5rcZTuqjxlC48rh2mItkG4Lri4qT58xNePK4dSFMs8XEqtmehiu1ZyOPaYSqSbQAAAMAgJNsAAACAQUi2AQAAAIOQbAMAAAAGIdkGAAAADEKyDQAAABiEx7UDcF2+vlJ4+J1hAGlGrJevJn170T4MmIVkG4Drslik9OnNjgKAGSwWxXpz/MN8lJEAAAAABiHZBuC6oqKkHj0SXlFRZkcD4DFyi4lS/Zl9VH9mH7nFcPzDPCTbAFxXbKz0008Jr9hYs6MB8Bi5xceq5I5fVHLHL3KL5/iHeUi2AQAAAIOQbAMAAAAGIdkGAAAADEKyDQAAABiEZBsAAAAwCMk2AAAAYBCeIAnAdfn6Shcv3hkGkGbEevlq6hch9mHALCTbAFyXxSLlyGF2FADMYLHodgaOf5iPMhIAAADAICTbAFxXVJTUv3/Ci8e1A2mKW0yUas8ZotpzhvC4dpiKZBuA64qNlb7/PuHF49qBNMUtPlZlN01W2U2TeVw7TEWyDQAAABiEZBsAAAAwCMk2AAAAYBCSbQAAAMAgJNsAAACAQUi2AQAAAIPwBEkArsvHRzp58s4wgDQj1tNHM0cdsg8DZiHZBuC63NykQoXMjgKAGdzcFJatoNlRAJSRAAAAAEYh2QbguqKjpbfeSnhFR5sdDYDHyC02WjUWvKMaC96RWyzHP8xDsg3AdcXESF99lfCKiTE7GgCPkVtcjCqu/U4V134ntziOf5iHZBsAAAAwCMk2AAAAYBCSbQAAAMAgJNsAAACAQUi2AQAAAIOQbAMAAAAG4QmSAFyXj4904MCdYQBpRqynj2a/t9M+DJiFZBuA63Jzk0qXNjsKAGZwc9PVvKXMjgKgjAQAAAAwCi3bAFxXdLT06acJw++8I3l5mRsPgMfGLTZalVd+KUn6s8lbivfg+Ic5SLYBuK6YGOm//00Yfustkm0gDXGLi1Hg8oQv23savk6yDdNQRgIAAAAYhGQbAAAAMAjJNgAAAGAQkm0AAADAICTbAAAAgEFItgEAAACD0PUfANeVLp0UHHxnGECaEeeZTvOGbbIPA2Yh2QbgutzdpapVzY4CgAmsbu66WKiy2WEAlJEAAAAARqFlG4Drio6WvvsuYXjwYJ4gCaQhbrHRKr9hgiRpb73+PEESpiHZBuC6YmKkYcMShvv1I9kG0hC3uBjVWPieJGl/7T4k2zANZSQAAACAQUi2AQAAAIOQbAMAAAAGIdkGAAAADEKyDQAAABiEZBsAAAAwCF3/AXBd6dJJGzbcGQaQZsR5ptPC11fYhwGzkGwDcF3u7lLdumZHAcAEVjd3nfevbXYYAGUkAAAAgFFo2QbgumJipMmTE4b79JE8Pc2NB8Bj4xYXo1JbpkmSDtXspXh3jn+Yg2QbgOuKjpYGDEgY7tGDZBtIQ9xio1Vn7huSpCPVu5BswzSUkQAAAAAGIdkGAAAADEKyDQAAABiEZBsAAAAwCMk2AAAAYBCSbQAAAMAgdP0HwHV5e0tLl94ZBpBmxHl4a+lrv9mHAbOQbANwXR4eUrNmZkcBwARWdw+dKtvE7DAAykgAAAAAo9CyDcB1xcRIv/ySMNy5M0+QBNIQt7gY+QfPkSQdC3yJJ0jCNCTbAFxXdLTUs2fCcPv2JNtAGuIWG63nf35VknS8UttHSrYn/BCZWmElW//XfB/7OmEMykgAAAAAg5BsAwAAAAYh2QYAAAAMQrINAAAAGIRkGwAAADAIyTYAAABgELr+A+C6vL2lX3+9MwwgzYjz8NbKl3+2DwNmIdkG4Lo8PBL61waQ5ljdPfRPpbZmhwFQRgIAAAAYhZZtAK4rNlZauDBhuE2bhJZuAGmCJS5WRfYuliSdKN9SVneOf5iD/zwArisqSvrPfxKGw8NJtoE0xD02Sk1+7CpJmvTtRcWSbMMklJEAAAAABiHZBgAAAAxCsg0AAAAYhGQbAAAAMAjJNgAAAGAQkm0AAADAIPSDA8B1eXlJ06ffGQaQZsR7eGld14n2YcAsJNsAXJenp9Sjh9lRADBBvLunjjzb1ewwAMpIAAAAAKPQsg3AdcXGSqtWJQw3bswTJIE0xBIXqwKH1kqSTpdqwOPaYRr+8wC4rqgoqXnzhGEe1w6kKe6xUWr+w4uSeFw7zEUZCQAAAGAQkm0AAADAICTbAAAAgEFItgEAAACDkGwDAAAABiHZBgAAAAxCPzgAXJeXlzR+/J1hAGlGvIeX/ujwjX0YMAvJNgDX5ekp9e9vdhQATBDv7qkDdfqaHQZAGQkAAABgFFq2AbiuuDhp8+aE4Vq1JHd3c+MB8NhY4uOU5/hWSdK/xZ6T1Y3jH+Yg2Qbgum7flurVSxgOD5fSpzc3HgCPjXvMbbUZ01TS/z+u3ZvjH+agjAQAAAAwCMk2AAAAYBCSbQAAAMAgJNsAAACAQUi2AQAAAIOQbAMAAAAGoes/AK7L01MaPfrOMIA0I97dU9vafGwfBsxCsg3AdXl5SW+9ZXYUAEwQ7+GlPQ2HmB0GQBkJAAAAYBRatgG4rrg4affuhOFKlXhcO5CGWOLjlOP0X5KkSwUq8Lh2mIZkG4Drun1bCgxMGOZx7UCa4h5zW+1H15bE49phLspIAAAAAIOQbAMAAAAGIdkGAAAADEKyDQAAABiEZBsAAAAwCMk2AAAAYBC6/gPgujw9pQ8/vDMMIM2Id/dU8Avv2IcBs5BsA3BdXl7SRx+ZHQUAE8R7eGln83fNDgOgjAQAAAAwCi3bAFxXfLx0+HDCcMmSkhvtC0CaER+vrBeOSJKu5i7B8Q/TkGwDcF23bkllyiQM87h2IE3xiLmljh9XlcTj2mEuvuYBAAAABiHZBgAAAAxCsg0AAAAYhGQbAAAAMAjJNgAAAGAQkm0AAADAIHT9B8B1eXpKQ4feGQaQZsS7e2pPg8H2YcAsJNsAXJeXl/Tll2ZHgTRqwg+RZoeQpsV7eGlb20/NDgOgjAQAAAAwCi3bAFxXfLx0+nTCcIECPK4ZSEvi45Xh2hlJUliW/Bz/MA3JNgDXdeuWVLhwwjCPawfSFI+YW+r2filJPK4d5uJrHgAAAGAQkm0AAADAICTbAAAAgEFItgEAAACDkGwDAAAABiHZBgAAAAxC138AXJeHh9Sv351hAGlGvJuH9tfuYx8GzMJ/HwDX5e0tTZhgdhQATBDv6a1NL31rdhgAZSQAAACAUWjZBuC6rFbp8uWE4ezZJYvF3HgAPD5Wq9KFJxz/t/04/mEekm0ArisyUsqZM2GYx7UDaYpHdKR6v11IEo9rh7koIwEAAAAMQrINAAAAGIRkGwAAADAIyTYAAABgEJJtAAAAwCAk2wAAAIBB6PoPgOvy8JC6d78zDCDNiHfz0OHqne3DgFn47wPgury9pRkzzI4CgAniPb21vttks8MAKCMBAAAAjELLNgDXZbUmPEVSknx9eVwzkJZYrfKITjj+Y704/mEeWrYBuK7ISMnPL+FlS7oBpAke0ZHqOySn+g7JaU+6ATOQbAMAAAAGIdkGAAAADEKyDQAAABiEZBsAAAAwCMk2AAAAYBCSbQAAAMAg9LMNwHW5u0vt2t0ZBpBmWN3cdbxiG/swYBaSbQCuK106ad48s6MAYII4z3Ra9coss8MAKCMBAAAAjEKyDQAAABiEZBuA64qIkCyWhFdEhNnRAHiMPKIi1L9fevXvl14eURz/MI8pNdsTfoi0D/d/zdeMEAAAAADD0bINAAAAGIRkGwAAADAIyTYAAABgEJJtAAAAwCAk2wAAAIBBeIIkANfl7i698MKdYQBphtXNXSGlG9uHAbOQbANwXenSScuWmR0FABPEeabTsv4LzA4DoIwEAAAAMArJNgAAAGAQkm0ArisiQkqfPuHF49qBNMUjKkJ9Xs+hPq/n4HHtMBU12wBcW2Sk2REAMIlnNMc/zEfLNgAAAGAQkm0AAADAICTbAAAAgEFItgEAAACDkGwDAAAABqE3EgCuy81NqlPnzjCANMNqcdO54rXsw4BZSLYBuC4fH2njRrOjAGCCOC8f/T5kpdlhAJSRAAAAAEahZRsAAOAJM+GHx/9Anv6v+T72daYFtGwDcF0REVKOHAkvHtcOpCkeURHqNaygeg0ryOPaYSpatgG4tsuXzY4AgEl8wjn+YT5atgEAAACDkGwDAAAABiHZBgAAAAxCsg0AAAAYhGQbAAAAMAi9kQBwXW5uUpUqd4YBpBlWi5tCC1SyDwNmIdkG4Lp8fKSdO82OAoAJ4rx8NH/4ZrPDACgjAQAAAIxCsg0AAAAYhGQbgOuKjJQKFUp4RUaaHQ2Ax8gjOlJd3yupru+VlEc0xz/MQ802ANdltUqnTt0ZBpB2WK3KePW0fRgwCy3bAAAAgEFItgEAAACDkGwDAAAABiHZBgAAAAxCsg0AAAAYhN5IALgui0UqVerOMIC0w2LR1Twl7cOAWUi2AbguX1/p4EGzowBgglgvX81+f5fZYQCUkQAAAABGIdkGAAAADEKyDcB1RUZKpUsnvHhcO5CmeERHquOoKuo4qgqPa4epqNkG4LqsVunQoTvDANIOq1VZ/z1sHwbMQss2AAAAYBCSbQAAAMAgJNsAAACAQUi2AQAAAIOQbAMAAAAGoTcSAK7LYpEKFrwzDCDtsFh0M2sB+zBgFpJtAK7L11cKCTE7CgAmiPXy1c8fHzY7DIAyEgAAAMAoJNsAAACAQSgjAeC6bt2SatdOGN60SfLxMTcemGbCDzyuO61xj76lNt80kiQtfGO14rw4/mEOkm0Aris+Xtq1684wgDTDYo1XrtO77cOAWSgjAQAAAAxCsg0AAAAYhGQbAAAAMAjJNgAAAGAQkm0AAADAIPRGAsC1Zc9udgQATHLLj+Mf5iPZBuC60qeXLl0yOwoAJoj1Tq9po0+ZHQZAGQkAAABgFJJtAAAAwCAk2wBc161bUt26Ca9bt8yOBsBj5B59S62/baLW3zaRezTHP8xDzTYA1xUfL/3xx51hAGmGxRqvfH9vtg8DZqFlGwAAADAIyTYAAABgEJJtAAAAwCAk2wAAAIBBSLYBAAAAg9AbCQDX5utrdgQATBLjxfGfEhN+iHzs6+z/mut/RiTbAFxX+vRSRITZUQAwQax3ek0ec8nsMADKSAAAAACjkGwDAAAABiHZBuC6bt+WmjVLeN2+bXY0AB4j95jbajahrZpNaCv3GI5/mIeabQCuKy5OWr78zjCANMMSH6dCB1fZhwGz0LINAAAAGIRkGwAAADAIyTYAAABgEGq2AQCPlRkPzgAAs9CyDQAAABiElm0AAACY4nH/0mXG4+EtVqvV+tjXCgAAAKQBlJEAAAAABiHZBgAAAAxCsg0AAAAYhGQbAAAAMAjJNgAAAGCQh+r6b9++fRo3bpz++usvxcTEqFixYurevbtatGiR7GXEx8frf//7n+bOnatTp07J19dX1apV05AhQ1SoUKGHCeup8qj78MqVK5o/f74OHjyoAwcO6Ny5c5Kko0ePGhn2E+NR99+uXbu0du1aBQcH69y5c4qMjFS+fPn0/PPPq2/fvsqYMaPBW2CuR91/QUFB+vXXX3Xo0CFdunRJMTExyp07typVqqRXXnlFRYoUMXgL8CTiuDQXx7X5UiM/ultMTIzatWunI0eOqHDhwlq5cmUqR+x6UuM46NatW5LT586dqwoVKqQophR3/RcUFKTevXvL09NTzZo1U4YMGbR69WqdPXtWQ4YM0auvvpqs5bz//vv69ddfVaxYMdWpU0dXrlzR8uXL5e3trTlz5qhYsWIp2pCnSWrsQ9s/g8ViUcGCBRUaGqpbt26liWQ7Nfbfc889p2vXrqly5coqWbKkLBaLgoODdejQIRUoUEBz5sxRtmzZHsPWPH6psf++/fZbLVq0SOXKlVOuXLnk6empEydOaNOmTXJ3d9eUKVNUvXr1x7A1eFJwXJqL49p8qZUf3e27777TjBkzFBkZSbKdDKmZXwUGBiowMDDR9Pbt2yt37twpC8yaAjExMdYGDRpYy5QpYz148KB9fFhYmLVZs2bWUqVKWU+ePPnA5Wzfvt3q7+9v7dSpkzUqKso+ftu2bdaAgABr586dUxLWUyW19uGlS5eswcHB1rCwMKvVarU2btzY6u/vb1TYT4zU2n+TJk2yhoaGOoyLj4+3fvjhh1Z/f3/rRx99lNqhPxFSa//dvn3b6fht27ZZ/f39rW3btk2tkPEU4Lg0F8e1+VLrM7jbgQMHrKVKlbLOnDnT6u/vb23cuHEqR+1aUusz2LFjh9Xf3986duzYVIstRTXbO3bs0OnTp9W8eXOVKlXKPt7Pz0/9+vVTbGysFixY8MDlzJs3T5L0+uuvy8vLyz7+2WefVc2aNbVz506dPHkyJaE9NVJrH2bPnl1Vq1aVn5+fkeE+cVJr//Xp00c5c+Z0GGexWNSvXz9J0s6dO1M38CdEau0/b29vp+OfffZZZcqUSadPn061mPHk47g0F8e1+VLrM7CJjo7W8OHDVb58eXXp0sWIkF1Oan8GqSlFNdvBwcGSpJo1ayaa9txzzznMcz9BQUHy9fVVpUqVEk2rWbOmNm/erJ07d6pw4cIpCe+pkFr7MK0yev95eCQcEu7u7g+9jCeZ0ftvz549unHjhipXrvzQy8DTh+PSXBzX5kvtz2D8+PE6deqUFi1aJIvFkjpBurjU/gxCQkI0c+ZM3b59W3nz5lWNGjWUNWvWh4otRcl2SEiIJKlgwYKJpmXKlElZsmTRqVOn7ruMyMhIXbp0Sf7+/k5PnLabI23rcjWpsQ/TMqP332+//SbpzoHpalJ7/wUFBSk4OFjR0dE6deqUNmzYoCxZsmjEiBGpFTKeAhyX5uK4Nl9qfgb79u3Tjz/+qCFDhrhko6NRUvs4WLp0qZYuXWr/O126dBo4cKBefvnlFMeWomQ7PDxckpQhQwan0/38/HThwoX7LiMsLMw+b1LLuHtdriY19mFaZuT+O3z4sCZMmKBs2bI91MH0NEjt/RccHKzx48fb/y5YsKC++eYblSlT5tECxVOF49JcHNfmS63PIDo6WiNGjFDJkiXVq1evVI3R1aXWZ5A1a1YNGzZMdevWVd68eXXz5k0FBQXpq6++0pdffik/Pz+99NJLKYqNfrYBSWfOnFHfvn0VFxenb7755qF/KkprBg4cqKNHj2rPnj2aN2+eihQpoo4dO2rJkiVmhwYXwHFpDo5r84wZM0anTp3Sp59+StmUSYoXL67evXuraNGi8vHxUa5cudSyZUv9+OOP8vT01Lhx4xQfH5+iZaYo2ba1Ottap+8VHh6e5DcKG9v0pFqubeNd9ca/1NiHaZkR++/cuXPq3r27rl69qrFjx7p011ZG/f/5+vqqXLlyGj9+vIoUKaIPPvhAV69efaRY8fTguDQXx7X5UuMzOHjwoGbMmKFXX31VAQEBqR6jqzM6v/L391f58uV1+fLlFJfFpSjZttVTO1vJjRs3dO3aNae1Mnfz9fVVjhw5dPbsWcXFxSWabqu5cdUH26TGPkzLUnv/nT17Vl27dtXFixc1ZswY1atXL7VCfSIZ/f/n4eGhatWqKTIyUvv373/o5eDpwnFpLo5r86XGZ3D06FHFxcVp3LhxCggIcHhJ0smTJxUQEKAqVaqkevyu4HHkV1myZJEk3b59O0XvS1GyXbVqVUnSli1bEk3bunWrJDntAPxegYGBioyM1O7duxNNsy3bti5Xk1r7MK1Kzf139uxZdevWTRcvXtS3336rBg0apF6gT6jH8f938eJFSXd6kIDr47g0F8e1+VLjMyhUqJDatWvn9CUlVAa0a9dOrVu3Tt3gXYTRx0FsbKwOHToki8WiPHnypOzNKemUOyYmxvr8889by5QpYz106JB9/N0dhp84ccI+/sqVK9bjx49br1y54rCctP5Qm9TYh/dKSw+1SY39d+bMGWu9evWspUqVsq5ateqxxW+21Np/wcHB1vj4+ETL37x5s7V06dLWypUrWyMiIozbEDxROC7NxXFtPqOu7TY81ObBUusz2L17d6LjICYmxvrJJ59Y/f39rb17905xbCl+XPuOHTv08ssvy9PTU82bN5efn5/9UZivv/66XnvtNfu848aN0/jx4zVgwAANHDjQYTnvvfee5s2blyYf155a+3D48OH24TVr1ig8PFxt2rSxjxs2bJhL3lCUGvuvfv36OnfunCpUqOC0T05Jifa3q0iN/VelShVlyZJFZcuWVe7cuRUVFaWjR49q586d8vT01FdffaUmTZqYsXkwCceluTiuzZda13ZnAgICeFx7MqTWeUiSKlasqFy5ciksLMz+sMW8efNq1qxZypcvX4riSvHvQdWrV9f//vc/jR07VitWrFBMTIyKFSumwYMHq2XLlslezsiRIxUQEKC5c+fq559/lq+vr+rVq5cm+pVMrX24cOHC+44bMGCASybbqbH/zp07J0n666+/9Ndffzmdx1Uv6qmx/wYOHKjNmzfrzz//1NWrV+0/q7Vv317du3dX8eLFDd4KPGk4Ls3FcW2+1Lq24+Glxmfw0ksvafPmzQoODta1a9fk4eGhAgUK6NVXX1WvXr2UKVOmFMeV4pZtAAAAAMlDP9sAAACAQUi2AQAAAIOQbAMAAAAGIdkGAAAADEKyDQAAABiEZBsAAAAwCMk2AAAAYBCSbQAAAMAgJNsAAACAQVL8uHYkT0BAwH2nBwYG6ueff35M0Tya4cOHa+HChZo5c6aqVatmdjiP7PTp0/riiy+0a9cu3bhxQ1ar1WW2DcCTh+vBk4vrAR4Hkm2DtWnTxun4IkWKPOZIkla/fn2dO3dOR48eNTsUw8XHx2vQoEE6fPiwKlSooIIFC8rNzU3Zs2c3OzQALo7rwZOF6wEeF4vVarWaHYQrsrVkPA0nrAedXC9evKiwsDDlzZtXPj4+jzm61HXmzBk1aNBAVapU0S+//GJ2OADSAK4HTyauB3hcaNnGA+XMmVM5c+Y0O4xUceHCBUlS/vz5TY4EAJ4+XA+AlKNl2yDJbcm4u/4tOjpakydP1uHDhxUWFqadO3cqY8aM2rVrl5YvX66dO3fqwoULioqKUt68edWgQQP16dNHGTNmdLrs48ePa+rUqdqxY4cuXbqkjBkzqnDhwmrUqJG6d++uoKAgdevWzel78+XLp/Xr1yeK8d46tn///Vc//PCDNm/erEuXLilDhgyqXLmy+vTpo3LlyjnMe/bsWT3//PMKDAzUlClTNH78eC1btkyXLl1Snjx51L59e73yyiuyWCzJ2sc2v//+u+bOnaujR48qLi5OBQoUUPPmzdWjRw95e3vb50uqbvJB9ZILFizQiBEj7hvDgAEDNHDgQPvf0dHRmj17thYvXqwTJ07IarWqWLFi6tChg9q1a5doGwMCApQvXz6tXLlSkydP1tKlS3X27FnVrl1b33//vaSU7WtJ+ueffzRx4kTt2bNHoaGhSp8+vXLlyqXAwEC98sorLnPBBJ50XA+4HnA9SNto2X5CLF26VPPmzVOZMmVUu3ZtnT592n4Ajh49WocPH1bx4sVVvXp1RUdH6+DBg5oyZYo2btyouXPnKn369A7LW7FihYYNG6bo6GgVL15cFStW1I0bN3Ts2DF9+umn6t69u7Jnz642bdpo1apVioyMdKgnzJIlywNjPnr0qLp3765r166pSJEiatSokc6fP681a9Zow4YN+uqrr9S0adNE74uJiVGvXr10/PhxlS1bVkWKFNHOnTv19ddfKyIiQkOGDEn2fvvggw80d+5ceXt7q3r16kqXLp2Cg4P1zTffaMOGDZoxY4bSpUsnKaFe8tKlS9qyZYsKFCigypUrS3pwvWSBAgWSrLXcvHmzLl++LDe3Ox37REZG6pVXXtGuXbuUJUsWVa5cWW5ubvrrr7/03nvvaf/+/Ro5cmSiZcXHx6t///7atWuXqlatqoCAAGXOnFlSyvf1wYMH1alTJ0VFRalcuXIqV66cIiIidObMGc2cOVMNGjTg5Ao8obgecD3geuBirDCEv7+/1d/f/4Hzvf322/Z5ly1b5nSejRs3Wq9fv+4wLioqyvr+++9b/f39rePGjXOYdvLkSWvZsmWtpUuXTrTMuLg46/r16x3G1atX776x2mLcsWOHfVx8fLy1efPmVn9/f+vXX39tjY+Pt09bsWKFtUSJEtaKFStaL126ZB9/5swZ+7Z26tTJeuXKFfu0ffv2WUuVKmUtX768NTw8PMlY7rZy5Uqrv7+/tVatWtaQkBD7+LCwMGvHjh2t/v7+1i+++MLhPTt27LD6+/tb33777WSt435WrVplDQgIsNarV89hWz788EOrv7+/9a233nLYlitXrljbt29v9ff3t27YsMFhWbb90rBhQ+uFCxccpj3MvrZ9ZqtWrUoU9/Hjx62hoaGPuvkAkonrAdcDrgdpG/1sGywgIMDp6+bNmw7z1a1bVy+88ILTZdSpU0eZMmVyGOfl5aV33nlHHh4e9p/3bGbMmKGoqCh16NAh0TLd3NxUr169R96uoKAgHTt2TM8884wGDx7s8DNYkyZN1KBBA0VERGjBggWJ3uvm5qaPP/5YWbNmtY8rW7asatWqpVu3bunAgQPJisH2U9+gQYNUsGBB+3g/Pz99+OGHslgsmjNnjqKjox92M5N05MgRvf322/Lx8dH3339v35YrV65o/vz5euaZZ/Txxx87tDBlzZrV3oIxZ84cp8t94403lCtXLodxD7Ovr1y5IkmqXr16onUULVqUVgzABFwPuB7YcD1IWygjMVhSPzd5eno6/F2/fv37Lic0NFTr16/XiRMnFB4eLuv/l9p7enoqJCTEYd7t27dLkjp06PCQUT/Yrl27JEkvvPCC3N3dE01v1aqVVq9erV27dqlPnz4O0/Lly6fChQsnek/hwoW1YcMGXbp06YHrj4mJ0V9//SWLxaIWLVokmm67iB05ckRHjhxxWsP2sK5evap+/frp1q1bGjdunEqUKGGfFhwcrJiYGNWqVUteXl6J3luiRAmlT5/e6QXEYrE4/T94mH1dunRpbdq0ScOGDVO/fv1UpkwZh582ATx+XA+4HtyN60HaQbJtsM8//zxZ8+XJkyfJadOnT9fXX3+tmJiYZC3r33//lWTsHdYXL16UlHCidMY23jbf3XLnzu30Pb6+vpKUrJaH69evKyYmRjly5HC46eXeGI4cOeI0hocVHR2tAQMG6Ny5cxo8eLAaNmzoMP3cuXOSpNmzZ2v27NlJLicqKirRuGzZsjk9IT/Mvn755Zf1559/asOGDdqwYYMyZMig8uXLq27dumrTpo38/PwesKUAUhvXA64HznA9cH0k20+IpE4Qf/31lz7//HNlyJBBo0aNUmBgoHLkyGE/CGvWrOn0m7/FYknxXdwP40HrcDb9ccRl1Pr++9//6s8//1TTpk3Vr1+/RNPj4+MlSaVKlXrgU+PuldT/gE1K9rWfn59mzpxpP8EGBwdr+/bt2rJliyZNmqT//e9/KlCgQIriA/B4cD0wDtcDrgdmINl+wq1Zs0aS9Prrryf6CfL27du6fPlyovfkyZNHISEhOn36tPz9/Q2Jy1bjdfbsWafTbd/oc+TIYcj6M2fOLE9PT12+fFm3b9+232F+t/Pnz6dqDDNmzND8+fNVunTpJFuobPV1gYGBD+weKrkedl9bLBZVqVJFVapUkZTwc+cnn3yipUuX6ptvvtGYMWNSJT4AjwfXA+e4HtzB9eDJRNHOE85244yzn9pWrlxpr9W727PPPitJ+vXXX5O1Dlu9YGxsbLLjsh2wy5cvV1xcXKLpixcvdpgvtXl6eqpChQqyWq1aunRpounHjh3TkSNHlD59eocauoe1ZcsWjR49Wjly5ND333/v9GQuJdyA4u7uro0bNzrdLw8jtfZ11qxZNWDAAEkJ+wfA04XrgXNcD+7gevBkItl+whUqVEiSNH/+fIcavePHj+urr75y+p7u3bvL29tbc+bM0apVqxymxcfH648//nAYZ/umfPLkyWTHVa1aNfn7++vs2bMaO3asw0l+7dq1WrNmjXx9fZO8ISg1dOnSRZI0btw4nTlzxj4+PDxco0aNktVqVYcOHZzWvaXEyZMnNWTIELm7u2v8+PFJ1hhKCS0Zbdq0UUhIiIYNG6arV68mmmf37t2JPoP7eZh9PXv2bId9YrNp0yZJ968JBfBk4nqQNK4HXA+eZJSRPOHatm2r6dOna8OGDWrSpInKli2rGzduaOfOnXr++ee1f/9++89GNoULF9ann36q4cOHa9CgQfL391fx4sXtDzG4ePGiw5PM6tevr+DgYPXo0UPVqlWTj4+PsmTJoqFDhyYZl8Vi0VdffaVu3bpp4sSJWrNmjUqWLKnz589r9+7d8vDw0KeffmrYz4ZSQjdHHTp00Ny5c9W8eXOHhxhcvXpVFSpU0KBBgx55PVOmTNHNmzdVsGBBzZkzx2k3TQ0aNFCDBg0kSe+9957OnDmjpUuXasOGDSpZsqRy5sypy5cv69SpUwoNDVW3bt1Up06dZK3/Yfb1nDlz9NFHH6lYsWIqWrSo3N3ddfLkSR0+fFjp0qWzt2gAeHpwPUga1wOuB08yku0nXJYsWTR//nx9+eWX2rlzp9avX69nnnlGgwYNUu/evRPd/WzTvHlzFStWTFOmTFFQUJBWr16tTJkyqUiRIom6Xuratatu3LihZcuWafXq1YqJiVG+fPnue3KVErpTWrhwof2RsatWrZKfn58aNGigvn37pmr3SkkZOXKkKlWqpDlz5ig4ONj+eN7u3burR48eSf68lxK2m1xOnTqlU6dOOZ0nX7589pOrj4+Ppk2bpoULF2rx4sU6evSo9u7dq2zZsqlAgQLq1q2bmjdvnqIYUrqvBw8erLVr12rfvn3avn27YmJilCtXLnXo0EG9e/d26IcWwNOB68H9cT3gevCkslidFXkBAAAAeGTUbAMAAAAGIdkGAAAADEKyDQAAABiEZBsAAAAwCMk2AAAAYBCSbQAAAMAgJNsAAACAQUi2AQAAAIOQbAMAAAAGIdkGAAAADEKyDQAAABiEZBsAAAAwyP8Bf8bOnHBAISAAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 720x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_zeros(ax, idata, model_label, **kwargs):\n",
+    "    data_zeros = np.mean(roaches['y']==0)\n",
+    "    # flatten over chains:\n",
+    "    sampled_zeros = (idata.posterior_predictive[\"y\"]==0).mean((\"__obs__\")).values.flatten()\n",
+    "    ax.hist(sampled_zeros, alpha=0.5, **kwargs)\n",
+    "    ax.axvline(data_zeros, color='red', linestyle='--')\n",
+    "    ax.set_xlabel(\"Fraction of zeros\")\n",
+    "    ax.set_title(f\"Model: {model_label}\")\n",
+    "    ax.yaxis.set_visible(False)\n",
+    "    ax.set_facecolor('white')\n",
+    "    return ax\n",
+    "\n",
+    "fig, ax = plt.subplots(1,2, gridspec_kw={'wspace': 0.2})\n",
+    "plot_zeros(ax[0],idata_1, \"Poisson\", bins= 2) # use 2 bins to make it more clear. Almost no zeros.\n",
+    "plot_zeros(ax[1],idata_2, \"Negative Binomial\")\n",
+    "\n",
+    "fig.legend([\"Observed data\", \"Posterior predictive\"], loc='center left', bbox_to_anchor=(0.05, 0.8)) \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The negative binomial distribution fit works much better, predicting the number of zeros consistent with the observed data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    " *Regression and Other Stories* introduces a further improvement by introducing a zero-inflated regression later in the chapter, but I will not persue that here, after all the point of this example is to illustrate the use of offsets. \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## PYMC equivalent model\n",
+    "\n",
+    "The model behind the scenes looks like this for the Poission model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$$\n",
+       "            \\begin{array}{rcl}\n",
+       "            \\text{Intercept} &\\sim & \\operatorname{Normal}(0,~4.52)\\\\\\text{roach1} &\\sim & \\operatorname{Normal}(0,~3.33)\\\\\\text{treatment} &\\sim & \\operatorname{Normal}(0,~5.11)\\\\\\text{senior} &\\sim & \\operatorname{Normal}(0,~5.43)\\\\\\text{mu} &\\sim & \\operatorname{Deterministic}(f(\\text{senior},~\\text{treatment},~\\text{roach1},~\\text{Intercept}))\\\\\\text{y} &\\sim & \\operatorname{Poisson}(\\text{mu})\n",
+       "            \\end{array}\n",
+       "            $$"
+      ],
+      "text/plain": [
+       "Intercept ~ Normal(0, 4.52)\n",
+       "   roach1 ~ Normal(0, 3.33)\n",
+       "treatment ~ Normal(0, 5.11)\n",
+       "   senior ~ Normal(0, 5.43)\n",
+       "       mu ~ Deterministic(f(senior, treatment, roach1, Intercept))\n",
+       "        y ~ Poisson(mu)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pymc_model = model_1.backend\n",
+    "pymc_model.model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "Let's look at the equivalent (Poisson) model in PYMC:   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using jitter+adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [Intercept, beta_roach1, beta_treatment, beta_senior]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "63ff23bd3eac429d87274d6d036dbef9",
+       "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"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 18 seconds.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sd</th>\n",
+       "      <th>hdi_3%</th>\n",
+       "      <th>hdi_97%</th>\n",
+       "      <th>mcse_mean</th>\n",
+       "      <th>mcse_sd</th>\n",
+       "      <th>ess_bulk</th>\n",
+       "      <th>ess_tail</th>\n",
+       "      <th>r_hat</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Intercept</th>\n",
+       "      <td>3.089</td>\n",
+       "      <td>0.022</td>\n",
+       "      <td>3.049</td>\n",
+       "      <td>3.131</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2079.0</td>\n",
+       "      <td>2250.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>beta_roach1</th>\n",
+       "      <td>0.698</td>\n",
+       "      <td>0.009</td>\n",
+       "      <td>0.681</td>\n",
+       "      <td>0.715</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2756.0</td>\n",
+       "      <td>2731.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>beta_senior</th>\n",
+       "      <td>-0.380</td>\n",
+       "      <td>0.033</td>\n",
+       "      <td>-0.444</td>\n",
+       "      <td>-0.318</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3018.0</td>\n",
+       "      <td>2363.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>beta_treatment</th>\n",
+       "      <td>-0.517</td>\n",
+       "      <td>0.025</td>\n",
+       "      <td>-0.565</td>\n",
+       "      <td>-0.469</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2520.0</td>\n",
+       "      <td>2340.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
+       "Intercept       3.089  0.022   3.049    3.131      0.000      0.0    2079.0   \n",
+       "beta_roach1     0.698  0.009   0.681    0.715      0.000      0.0    2756.0   \n",
+       "beta_senior    -0.380  0.033  -0.444   -0.318      0.001      0.0    3018.0   \n",
+       "beta_treatment -0.517  0.025  -0.565   -0.469      0.001      0.0    2520.0   \n",
+       "\n",
+       "                ess_tail  r_hat  \n",
+       "Intercept         2250.0    1.0  \n",
+       "beta_roach1       2731.0    1.0  \n",
+       "beta_senior       2363.0    1.0  \n",
+       "beta_treatment    2340.0    1.0  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# recreate the model using pymc\n",
+    "import pymc as pm\n",
+    "with pm.Model() as model_pymc:\n",
+    "    # priors\n",
+    "    alpha = pm.Normal(\"Intercept\", mu=0, sigma=4.5)\n",
+    "    beta_roach1 = pm.Normal(\"beta_roach1\", mu=0, sigma=3.3)\n",
+    "    beta_treatment = pm.Normal(\"beta_treatment\", mu=0, sigma=5.11)\n",
+    "    beta_senior = pm.Normal(\"beta_senior\", mu=0, sigma=5.43)\n",
+    "    \n",
+    "    # likelihood\n",
+    "    mu = pm.math.exp(alpha + beta_roach1 * roaches[\"roach1\"] +\n",
+    "                             beta_treatment * roaches[\"treatment\"] +\n",
+    "                             beta_senior * roaches[\"senior\"] +\n",
+    "                             pm.math.log(roaches[\"exposure2\"])) # no beta for exposure2 \n",
+    "    y = pm.Poisson(\"y\", mu=mu, observed=roaches[\"y\"])\n",
+    "\n",
+    "    idata_pymc = pm.sample(1000)  \n",
+    "\n",
+    "az.summary(idata_pymc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this model (`model_pymc`) we have the equivalent Poisson regression with everything explicit to illustrate what the 'offset' function is doing. It simply makes it possible to express a term like this in the `formulae` string in a `bambi` model.\n",
+    "\n",
+    " "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pymc_env",
+   "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": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/notebooks/data/roaches.csv b/docs/notebooks/data/roaches.csv
new file mode 100644
index 000000000..0ee0aa235
--- /dev/null
+++ b/docs/notebooks/data/roaches.csv
@@ -0,0 +1,263 @@
+"","y","roach1","treatment","senior","exposure2"
+"1",153,308,1,0,0.8
+"2",127,331.25,1,0,0.6
+"3",7,1.67,1,0,1
+"4",7,3,1,0,1
+"5",0,2,1,0,1.14285714285714
+"6",0,0,1,0,1
+"7",73,70,1,0,0.8
+"8",24,64.56,1,0,1.14285714285714
+"9",2,1,0,0,1
+"10",2,14,0,0,1.14285714285714
+"11",0,138.25,0,0,1
+"12",21,16,0,0,1
+"13",0,97,0,0,1
+"14",179,98,0,0,0.8
+"15",136,44,0,0,1
+"16",104,450,0,0,0.8
+"17",2,36.67,0,0,0.8
+"18",5,75,0,0,1
+"19",1,2,0,0,1
+"20",203,342.5,0,0,1
+"21",32,22.5,0,0,1
+"22",1,4,0,0,1
+"23",135,94,0,0,1
+"24",59,132.13,0,0,0.857142857142857
+"25",29,80,0,0,1
+"26",120,95,0,0,1
+"27",44,34.13,0,0,0.8
+"28",1,19,0,0,1
+"29",2,25,0,0,1
+"30",193,57.5,0,0,1
+"31",13,3.11,0,0,1
+"32",37,10.5,0,0,1
+"33",2,47,0,0,1
+"34",0,2.63,0,0,1
+"35",3,204,0,0,1
+"36",0,0,0,0,1
+"37",0,0,0,0,1
+"38",15,246.25,0,0,0.8
+"39",11,76.22,0,0,1.57142857142857
+"40",19,15,0,0,1.42857142857143
+"41",0,14.88,0,0,1
+"42",19,51,0,0,1
+"43",4,15,0,0,1
+"44",122,52.5,0,0,0.8
+"45",48,13.75,0,0,1
+"46",0,1.17,0,0,1
+"47",0,1,0,0,1
+"48",3,2,0,0,1
+"49",0,0,0,0,1
+"50",9,2,0,0,1
+"51",0,2,0,0,1.14285714285714
+"52",0,30,0,0,1.14285714285714
+"53",0,7,0,0,1
+"54",12,18,0,0,1
+"55",0,2,0,0,0.8
+"56",357,266,0,0,1
+"57",11,174,1,0,0.914285714285714
+"58",60,252,1,0,1
+"59",0,0.88,1,0,1.28571428571429
+"60",159,372.5,1,0,1
+"61",50,42,1,0,1
+"62",48,19,1,0,0.771428571428571
+"63",178,263,1,0,1
+"64",4,34,1,0,1.42857142857143
+"65",6,1.75,1,0,1
+"66",0,213.5,1,0,0.771428571428571
+"67",33,13.13,1,0,1
+"68",127,154,1,0,1
+"69",4,21,1,0,1
+"70",63,220,1,0,1
+"71",88,38.32,1,0,1
+"72",5,352.5,1,0,0.6
+"73",0,7,1,0,1
+"74",0,4,1,0,1
+"75",62,59.5,1,0,1
+"76",4,7.5,1,0,1
+"77",150,112.88,1,0,1.28571428571429
+"78",38,172,1,0,1
+"79",0,13,1,0,1
+"80",3,18,1,0,1
+"81",1,0,1,0,1
+"82",14,27,1,0,1
+"83",77,148,1,0,1
+"84",42,32,1,0,1.14285714285714
+"85",21,28,1,0,1
+"86",1,0,1,0,1
+"87",45,28,1,0,1
+"88",0,0,1,1,0.8
+"89",0,14,1,1,0.8
+"90",0,5,1,1,1
+"91",0,0,1,1,1
+"92",0,104,1,1,0.685714285714286
+"93",183,27,1,1,1
+"94",28,132,1,1,1
+"95",49,258,1,1,1
+"96",1,1,1,1,1
+"97",0,2,1,1,1
+"98",0,6,1,1,1
+"99",3,3,1,1,0.8
+"100",0,3,1,1,0.857142857142857
+"101",0,0,1,1,1
+"102",0,0,1,1,1
+"103",0,0,1,1,1
+"104",18,1.25,1,1,1
+"105",0,0,1,1,1
+"106",0,16,1,1,1
+"107",5,68,1,1,0.4
+"108",0,1,1,1,0.8
+"109",19,18,1,1,1.14285714285714
+"110",5,123.67,1,1,1
+"111",0,2,1,1,0.8
+"112",27,82.5,1,1,1
+"113",0,0,1,1,0.2
+"114",0,1.25,1,1,1
+"115",77,171,1,1,1
+"116",1,91.88,1,1,1
+"117",3,5,1,1,1
+"118",2,7,1,1,1
+"119",0,0,1,1,1
+"120",0,4,1,1,1
+"121",22,53.75,1,1,1
+"122",102,138.06,1,1,1.14285714285714
+"123",0,1,1,1,1.14285714285714
+"124",0,1.25,1,1,1
+"125",0,28,1,1,0.8
+"126",0,15,1,1,1
+"127",0,0.88,1,1,0.8
+"128",0,0,1,1,1
+"129",0,33,1,1,1
+"130",4,136.25,1,1,4.28571428571429
+"131",12,127.5,1,1,0.8
+"132",2,2,1,1,0.8
+"133",0,2,1,1,0.8
+"134",0,0,1,1,1
+"135",1,3,1,1,1
+"136",0,46,1,1,1.28571428571429
+"137",40,68,1,1,1
+"138",0,0,1,1,1
+"139",1,49,1,1,1
+"140",2,27.13,1,1,1
+"141",27,45.5,1,0,1
+"142",0,0,1,0,0.457142857142857
+"143",2,4,1,0,1
+"144",0,0,1,0,2.42857142857143
+"145",0,1,1,0,1.42857142857143
+"146",0,0,1,0,1
+"147",0,0,1,0,1
+"148",3,10.5,1,0,1.14285714285714
+"149",1,0,1,0,1
+"150",20,3,1,0,1.14285714285714
+"151",0,0,1,0,1
+"152",0,0,1,0,1
+"153",0,0,1,0,1
+"154",0,3.75,1,0,0.8
+"155",0,0,1,0,1.14285714285714
+"156",0,0,1,0,1
+"157",0,0,0,0,0.8
+"158",0,0.88,0,0,1.57142857142857
+"159",0,2,1,0,1
+"160",53,81,1,0,1.14285714285714
+"161",69,31,1,0,2.28571428571429
+"162",15,10,1,0,1
+"163",0,10.23,1,0,0.571428571428571
+"164",2,0,0,0,0.8
+"165",4,13,1,0,0.857142857142857
+"166",6,1,1,0,0.8
+"167",8,33.25,1,0,0.857142857142857
+"168",0,0,0,0,2.28571428571429
+"169",0,53,0,0,0.8
+"170",0,5,1,0,1
+"171",18,157,1,0,0.857142857142857
+"172",38,23.33,1,0,1
+"173",0,6,1,0,1.02857142857143
+"174",2,10,1,0,0.6
+"175",18,100,1,0,0.857142857142857
+"176",34,55,1,0,1
+"177",1,0,1,0,0.8
+"178",109,16.25,1,0,1
+"179",5,7.78,1,0,1.48571428571429
+"180",15,53,1,0,1
+"181",0,2,1,0,1
+"182",64,73,1,0,1
+"183",0,0,1,0,0.8
+"184",1,0,1,0,0.8
+"185",0,0,1,0,1
+"186",1,3.18,1,0,1
+"187",3,5,0,0,0.857142857142857
+"188",5,3,0,0,0.8
+"189",7,0,0,0,1
+"190",18,10,0,0,0.8
+"191",1,1,0,0,1
+"192",0,0,1,0,1
+"193",0,1,1,0,0.8
+"194",3,12,1,0,1
+"195",3,17,1,0,1
+"196",0,16,1,0,1
+"197",19,2.5,1,0,1.42857142857143
+"198",0,0,1,0,1.71428571428571
+"199",8,21.88,1,0,0.771428571428571
+"200",26,173,1,0,0.8
+"201",50,111,1,0,1
+"202",15,35,1,0,1.85714285714286
+"203",0,0,1,0,1
+"204",19,3,1,0,1
+"205",5,2.1,1,0,1
+"206",17,0,1,0,1
+"207",121,49,1,0,1
+"208",1,1.25,1,0,1
+"209",0,1,1,0,0.8
+"210",0,1,1,0,1
+"211",0,0,1,0,1
+"212",0,0,1,0,2.28571428571429
+"213",4,54,1,0,0.8
+"214",1,4.2,1,0,1.42857142857143
+"215",14,51.25,1,0,1.02857142857143
+"216",1,30,1,0,1.71428571428571
+"217",25,196,0,0,1.14285714285714
+"218",0,0,0,0,1.14285714285714
+"219",14,2,0,0,1
+"220",0,1.5,0,0,1
+"221",59,96.25,0,0,1.14285714285714
+"222",243,241.5,0,0,0.8
+"223",80,140,0,0,1.42857142857143
+"224",69,18,0,0,0.914285714285714
+"225",14,3,0,0,1.14285714285714
+"226",9,0.54,0,0,1.28571428571429
+"227",38,82,0,0,0.6
+"228",37,19,0,0,1
+"229",48,18.75,0,0,0.8
+"230",293,51,0,0,1.14285714285714
+"231",7,0,0,0,1
+"232",10,1,0,0,1.42857142857143
+"233",19,0,0,0,1
+"234",24,5.44,0,0,1
+"235",91,0,0,0,1
+"236",1,3,0,1,1
+"237",0,0,0,1,1
+"238",0,0,0,1,1
+"239",0,0,0,1,1
+"240",0,0,0,1,1
+"241",148,28.75,0,1,1.14285714285714
+"242",3,0,0,1,0.857142857142857
+"243",26,3,0,1,1
+"244",12,2,0,1,1
+"245",77,135,0,1,1
+"246",0,0,0,1,1
+"247",7,68.25,0,1,1
+"248",0,1,0,1,1
+"249",1,0,0,1,1
+"250",0,0,0,1,0.6
+"251",17,0,0,1,1.02857142857143
+"252",0,1,0,1,1
+"253",7,1,0,1,1
+"254",11,2.5,0,1,0.685714285714286
+"255",6,51.25,0,1,0.8
+"256",50,13.13,0,1,1
+"257",1,0,0,1,0.8
+"258",0,0,0,1,1.48571428571429
+"259",0,0,0,1,1
+"260",0,0,0,1,1
+"261",171,0,0,1,1
+"262",8,0,0,1,1
diff --git a/docs/notebooks/gallery.yml b/docs/notebooks/gallery.yml
index fbc30268f..05c316791 100644
--- a/docs/notebooks/gallery.yml
+++ b/docs/notebooks/gallery.yml
@@ -72,6 +72,10 @@
       subtitle: Students absence
       href: negative_binomial.ipynb
       thumbnail: thumbnails/negative_binomial.png
+    - title: Count Regression with Variable Exposure
+      subtitle: Accounting for varying exposure using offsets
+      href: count_roaches.ipynb
+      thumbnail: thumbnails/roaches.png
     - title: Beta regression
       subtitle: With many datasets
       href: beta_regression.ipynb
diff --git a/docs/notebooks/thumbnails/roaches.png b/docs/notebooks/thumbnails/roaches.png
new file mode 100644
index 0000000000000000000000000000000000000000..169d5ba951eb87c22a1a58983ca63afd28486f62
GIT binary patch
literal 25778
zcmb4rbzGI()-~!;kIAtUFg7A6pi)ZOut7j+Nl`%Q?$To*prTtsQ9w#cK<P$BL_|Tl
zO9Z7mH}Q>yC+>aU`+oP{KkkovRQ7(JwdR_0%rVAX?q_AhS1sSVoPmL16;<M-90S9`
zdIp9C-M=oupD=$t_Xt0ZSe;U_k~h_}vemZGWsugkGBYx@GSa`e!$#M_Qs2~shwBK}
zq5V6qSXr4_3UYHB|Md^JOf4>R@03|oiw{|5CUM@9fq_w*{BJ>|ScE<UgANPz<S_-i
zr@f7~R_k@^3kI8?(|&VU+Ijzr(1HhCih^&ik34^-aPH(wu?zXP)`v^18C>woF$eCl
zb-#seT65gz%g)7e<1@Sax%Sr{UAScle{;zZ$6_5ln-2flPoI*<H7&jJ4U$zC<I8tc
zraxxFPllFgiIfHSdHuf8BK%mgvzEN)%Hs*E@MH50oo)DW;MYQa{J8KN%@{vk@1S5o
z7_J_ZNC{57T*9$q$I;R5YFVFlPL-EO7c5w?Rn*BYdVjyUga4(a7giQOkoB7y?cRrf
zb-5&vPsm$Az@*{u;$MEb(vW7Z=XUY-)vFBnudt5^a`ZIk^^|(AYrU@-fBp5#_aIl7
zW_#DEPBwa0)F|Wk<wk6eGPPKxd=*B!Dk~E&%W|Gic-QmW0%1z=1Fyb5OPySo8QN3R
z(vVZjfBp4rb({vvC99~*y>-c31x%i<W)U^A*~=JfSRLmccNq`Kp_P;6&#NDDF7$Ng
z^yILfo4dlvgqcU28W{@a=H^rxtt`92OLsat+rEE~ko4h*k_(sQkbi#XYg=2gaXtSk
zCP9T0ZmWGN4V=T;FMqgy?B<G1O82%Okz>Ee!2agQP-(1Ms?1VGW?rF{Cthe|Dzz1O
z<(ei0h`G9K;8Ir!3=H%-nBZOXkj-nxLFv`SQ|+G~%SXrtRlPW$SP^n+MQ?MiSgB)z
zPGQK~n=AH7`R?Cy(kKi6H^!{>t)AP1F5yqBcW|<+r@fR36pk{kPl-~BRds(JblCj(
z($(8vmbksc1BVB>&eqP_Iyq(O6+ewC5v8BO=bX!S7_GpUxM{lN-C9-oQai8W*9}LP
zo8NP?>g((C@boONd3A|Tzl@TYcQ9X8Y(~fC*TSK%p4<j4udl6clL-(EU$AJ&5B!^{
ze%HB}$>HxpXZp*+Ju|F2hYsgCy-<o%*SGog)-zrDL4(?a@USp7y^Wlz6skdGbmb+_
zPW|s49nYCX9BK>`e7Q6XEKV{oZ~Xp2TsdA-WO8cCpzZzLx>VDk+SVO6mv7u38GOP`
zk~Y@g96vB%_Hg%cgB`UlcNQ*M<ijS*9E%lw@ZiDf@)t@mzWdLISap^~VIRjeWOQ`G
za^_|oldqH>-@u`8HeAxDBFEWrW~hkU<L+JQA?G)5-gK3Qx^XB*hkf+f7lUZ1%dqZV
z)Rtr2?Yl=d@I{JoeKgr=1cz#}K{$)+^tsZ~(!zSX>x!|eigL$eUN;s8iN!ODO`k=`
zD?dJ@s}QTompwgHG}Y!M_LS?pivSlQPHKieUTs+-5>XZ|weP@zv(iuaD>M6ZVsdAv
zinN<*YE))M%4H3z;{rI8UPK<!{TQkD+25fv+>h6$N2M{#o~LvuUL*5)X{dONdE0vq
z<+!KOO0m&#8kq)zU-M;(F7@5x6BJa%ddXuK6;q7PS4PQ;2RV<O51Hd>p}t4>=@vgd
z-<V<jkaCwvh(EvI^^x%_)mVQK#}w5RqbQ5^k5Q-ZZgm*`EMVR7=`p1w3K6fGX%l<;
z&gNh|3Hb-}$AjNE8qd((RT;B-xZcD@EzK;LqW|vpn$@Glw#^6Z`d88Liw=G1-`D^4
zTT2peUdM2WnDWu1S4HVPi6_g)dg~*0p12i>;3Ox<pdvE(*!3lyUxH5**1uxEFJP)k
zAIPQ)M{T39j5@q@95-3D`M^;=gNn0cRWogjS8d}zGdDf#RX%BK{r%XnV}cgnZfiRB
z@+rnBN&9dp%6T!1Y#$rOs#Ij!nl4_lB>G~$M^|}-EUieoOY;uT<>D(t-wM0xl4H6m
zqC&|cJB)U9HD;#rn>0N7W+TEbk;0OQ@Z5Xokep%F3-ir$NH|>$>6Qjxo?R=$Y5Ycy
z*UXMy%_R5(TUS2VR-pLo=z{j*05$qpZ~2D@yDKAPSx%ig#p=+Upj%Xt?PzP<oTGuG
z&7lx^{~nXjb3ZMY^F@B#vKG$4!NFv+b7rRunM52EY7;Lj=QvGTO;4CRPqzD!fHY~&
z3Fzr%5p_!2Y1=9!b$Yz7F$U|VU;Ok)hE1>k^vsM8mxiEmQ`ULssgY9KW@o>ic7%wM
zMy5?=U9#bZZQEjyMs<sPr8jY@e?dM=HmW`3HrZ^~w#s>`GominH8<gMN!(gi$q#vX
zC&&xjShmipui=Q>q%2M~zj56atdgK@-&2nb`<|b9e2D$<VHMNtQQupPEP*;A4rk8X
z!Rm!CTes&!eZAV`P<wg4=O(^O?^j~acGTAYD!lBlxaiaod`IBW@Njv@=O@YdzdE`7
z+Ntf0w#}8|EY4w};$9r$_qKlw5K2X&3*DsUbPlU*lMyH7#}(x|H#;>m-fZ3We$|Hk
zDrfD7J1WyHzw=smDI^<K%lL6?rIhsd_QoQyv8$yR(ZY`3TwdE9un*^0*nWtTu7UJr
zP?r=%VHH%aSBX`ke|Iuv|9WQ&&q&bRv^aJ_*~%)(V-t5=TH4`Q)nv)J$r9Hjyb-;p
z(p$)^<!acCsUGCT=RdL;Ek!1p=Vl|2>}tKzv8XskQl1+*<(}{x_dWU4*O;lyBxv;o
zVdu@QC7Pa|u8-C1YRb->ou!+`s=PYhm}y(xn&&q9iKpa)L2{Ru*z8$9E{*M?%}XC2
zHV>JPfx~7;JIli5aZ-eZg_CrPHjehzD?enDl__;`Xh2x<)RzQ}&z>=$+gFV77|4lE
z{n*VUWOD{fULCC%lVmq|ky9&2efWEc@>ox8vUPXW$=EH3F^B%_@%1b$@uJRCSGucW
zr+QPH$%o_bISZ@2CMN7wF$=2{c(Eua>U|z5U~#>II;xuElwK997J-cTqeN`(%(*fH
zZv+*UwtW5G9qhr@9i!D+)t{dTR3_=mlki)yasL-&#bEqfb*%Z+EWQ7sQEN^$LNWs1
z;5^alR+nP@WaEC7XB+k@T|pfCAF+HMh}=ZaLUH<n=l4biC(n;VS4tw)yzTVjrAxb!
zKb4S;dWY3wm9hulZ<`u#cHOXfbL8D^0tRn>Urh270&|y^mevful;pDV8XS>u<Z%6b
zj}59BR#B)wQ8-*hB_*<IsV0@rrJpcc#4q$>L6Nbw?Y6PVyzN+Hd_LVG+=bpNU~g})
z|Mtc*)l`!J^0Y|NQAV{1Q-cL80dd?oWmD6`CCSco8{^u9d>5I<1YJ_*LM*=*cXr3;
zrj!Kj7GM22P(%&k=}RH|2PBk|i?e9k+-umBl}^#eDe>^~lI3;&UL3G|)hb22gkqff
z5&8tas*_|&ye%JrLW>vcx4yl(eEIS+Y|8$a%4md7_t$*S!nzVF(sqVLy9cRq+&L3(
zbMCVHly$!$@m0tcMB*zpwW-XPQ*ET)G*=&+j#K<FBqW5_>c<yKs%cNntFD??p(!Sf
z(Ti7Xyi%WH>@Yj|eZ!hH!4GzecU@-`{eo=V8#IWOQoy5WWIHG#fy$2AAPq~PSjE`%
zH^m~~)_Rp_xlEoqV)5<Eql1@d(-+4nD94{ju0XXUCt%FA>Bo;NvFhn^wF$cAD1Cn4
z98VlS-qZX{+>6;*c0VgwToRxZRwt?K%wj`CuE(7_3i#Kf9ZyU@=Iw6HQIVCEy}S9~
z@lzh_%`<YyzLx}waHyrcIPJw8+f$oZA3?HjvkN`e$=R9ja`980)CQf8-qdu9b~%Iw
zjc@Kcj{fII2TycY$NNznyYo?gt!Jn0bxQ&@w44TSPESww^y`;Dzbk0<V+XZxmVf)&
zwIN7$QSuQo7PhvjUa2bkQEB~8hy|v|`M$rqE#^vT$m*iO;bD2aB9p3iifMD)qNS@p
z6ctG|I}P8>b3qzNnHulsrq0Jrib=uy_g&ap+qCPM$WR8&IoXs4Mr~1Vj~uctejwsF
zrf_TJmL%(LCC5weW`uiEYSun?nVE`0SsWXD%yYR)pxJ3R3kyp`chUWw*Xxq>+rA9!
zpl;#OtDyJi#v{>PL7<Ow(y8vtH*oBAot;KlS!akDjnf<Iqeb*SZ9X5X<UCZ!=Kj$S
zNGjf>F@t^AuH(ZUpQX&t0D-3ZNqg@-cN}{=zwPumq-b_cJ<0f`cz;DBqZfg$v-Skb
zSXfF)tjNyF8hNvnCDLy2>+%&Vq!M05;Qu+$dsExn+jU)=)l9Ql*@O4T0NZudCPp}?
zlgE5^>^g<|M8H&L%!WXc_98#O{EOzBwZbJgklX{bFg~;RqY<Tk&y~t(pRZrP_K00J
zu6cP(z_dvLNvVg;{xV{f14n?MGleKQ_vdI9R8R?&fb0;A#w@wWXjHj^drSdbsd#Le
zf_fJ@18iw|nnlN_C{$vOLx;*{6RWS^K_nq(m)%~o8<CNXIv$3q9DS+a9@&;?<#?0$
zc*BOYSE@-@c6Ju?r<yivHZ?W5`(82}MGlco=<*-_Rv3zx*%RZ!W82q2TTA<axDqpS
zQF|fn&ud=S-Xg`FP2uL|4iK`HVUu|(!JDm9NJG3Dg$$;c$4y*Vn#N!JfYJ3{QsJY6
zp$;QIcCy<i*E1$^wzjs`W!lD52y}OEddM!n$L*Jf$G!Eb-Q}`kib0~z73r3j>60S+
z_n%2MX_T~LDGq(fS~lJ!!Gbc6(@4!IU&uv8Rw;jTeW?$p8ovl&l8m3e;gwqaqDNbL
zW_K<MWnpu(R!&Zip#4yoUP<80S!d&j5r+$z&yRH5HeP&HQ^023Ylw^x@=F@piZSl<
z#V+PAnV#TSrT3>a5YPwU5Hy0r8I2=6Hs^x?m87A^p@p=I$XLx>{vA+Jf8<9g7Vv??
zSkL30AaR^{3Tx8z;WhyMU8g*Lpl4aWY}u>120h2-?ARCl)VpF1BasvWYp7#G+G=B=
zvp0Ozu^wZdVNaesIi&X~^7ZSZ$tI0;t<PM)<Pan!EF!XD-MUburI?fMYsWQAQBwFU
zzDb~VNrg-KDc#-5r;u);3wT}`T&YeYfO@FC$gsaDJ6bI@j(|>-oM=QThen1~$hz|D
z7e{fN1BLCax+>Uj-nc=T${PA!(m&#yqMCdA_HDY!wt=wL?T-bqU%_}|j=g&&&@?Px
zx$=xflUuDXo}<VhnZvT<6C%{Fe4H}c5{$&RYRjRMC`8%){FI0{yV9{F!S3tW@U4mt
zb2H<;GiIiZ8H&~MTFQkqRwmJI1R<4%G)<e{r57{DxN4QOxo~mDY^F0k<-He6JiGk!
zNE8}i^~cC`53@gct23H3W$EXx;ftCxuYaXO@9DQ5j~BNsqOp47l($=DaQb>Tye4NO
zdv>auoL7s$Z2~3~M|-brg(=G{zI_P39OSHo*eop$5K7iB-?M>T?p<eUX=d&0!G7Dk
z8@(M5G8rS6W|xcwI@Y@AB?p-Gv9Ss;h<IQ>++0!~Uaap3OZj~9)m~I>WF2XxLv@I?
zaM>VDtVlKD(0ZWdnDpaAr*MYw+CH~dZF|0Q%OMHLv}dyTQ)HiNR5a|YAnA;01Umsb
zz7F27H$A8u`S<C2{|fh-u|%eUB(nO*)D<{{cen7I##&VZn$ez)Yp3L=H22x&8OW8B
zUJaF(;K{9mR*|GV+J3~Mrf7|zO;02cScK{pckla$eUa|)PfF3&DMZLHQEgQ-`b{c*
zb!2<`OukV)5}%l`oJLhgwLIHf^XdeByuUg=ch+F;=B^`h79wdvwbEU#4N5ZJ71oCk
zJ;*g%eV$6^roQPshV6(FXtFz@t*vd)n2{`K-4%vlC&<IZ#DpLqaw4MV_C?**siy#O
zk{ZoxRC6Hz-8)K5vr7FjD^ouS+f0;pO2N9P@$vC*-@dgP?NW?rKJ-vE(AXKPs*KQP
z-rW4~-NsB~*9JsgT`JvWlSd{NQ5B0=h3HSR{4QgR$O6D64;~F9t^f6%`<OkC?#D1d
zGUfiP;d?l;AxKdM=t~L4KtBA|-5s+>+W+u7|FV(|X1Y&z%Zr09A#lF8Azi*c)l}9Z
z$E|i_y<=bX@y80Z6rIAg)H1|R$H;=y=r1IcR<dJDc$kh@oWwUO1_)YP(Z`I*#%}av
zJ(rzgOb49w*7=-p4k!2-dg12On788AwijL6KW+fm!BZ-n!hGUW)|gRU5)}#lEXvyG
z<o6&p$ts){t%tj$ME!Hll)cnG0bZaB7%@R*|CnkUogMjr(m65MCKFLmP*88^U_9I;
z((|H8S&lZNQRkv0DI-<zu;Un=gF-oZ@Zdp|blIWHFT=ilcyNL{Yv6a$$#3fbW#@P3
zrS|pmAa~A<o|3-%qi}@u0mjfgdQpxvoEd(ml9!j)%l0r+O!>8s`MontE-o&la}1+b
zn}~f%S-<z}QS34rGgX@Ip_CzS$Jz`rK>v#&`dCJ(g#Ek2V?)Lb00Q@K^sI{0h{1{)
z&P<JZ+`fIb{iDx1YnChff)AU2y;075=8le<mVs|+!@f)J?~G|#(xzBanWXeI9gXJp
zfxs}EniMNcuH#3+92^`xhkkUVC$S_N%+p6d?&d|!@`-xV=l5@%!P?0Pm1$)=By*{!
z?Epkpj8-TVZL%4keR{<5DVIQ$X-|q!JSxT$1F;7pTzrOA-rn~s1U(y-JmkP^t*^{$
zjKTAd4L$f)q2r6QS(=ri6yUQ@xeEx&y$R({{gk-)KCK)l>X-*Z+ScL+Q4!erkN$iF
zAqITAYl5<dMH;j8@73n9U9n<CqTiRsA|=h1p6Y_m@`!z0T*aMqBY`Y2qw_LNS@w!i
za;&FLpEkgvj6E^yH?H(uyY`H$tE)z?i$?C;Ed4}mzc%UOu9wlG-^g&L7=|Yfor$S5
z;1)^ve0fRz<-QkMeMq;g4q-D7Z|l^b1SYE>&0h1|tibu<P5V{8xEyx^RAK7-CSo`6
zOmueQ5_Q>VzHM_-^{Y$bgls~6*vg}KCp(UP&z{r3hNoz;M?p9!++4oF>g&5@*;8FH
z?tRO>?%k^b43xL3o<_SG3>veZzJ^Z{h{^}_g+`9k`8+p9+X;3{kdFRBwsDkBA7xGZ
zmV~!?d3-t_egm>Ul4SBOKc7TbhI!j9Wb<APPEO4%m34d0NLW>mBZu{{C1pBFMrvkf
zeDvjvK`kN8&+1J<oN6gorY8o+R9KP~m)iN8Aq-K?$Ete6OkA95j9*o*bH!m9<5CH~
z@uU*4lBVNMMRc*U3MZG^xTL7Yl75=cxQ;g>TY_p!D?wMsI{7Sq>8nb;lE*yafJQVo
zH@EB;{hEZx%)eL)N`ud|DeI*58(@G^P+a#77*o<wk_i01zLZhYVL<wg7l4!I(7?bK
z&}O{&a5^6ssHh;PA$LM~AmdZ)um5NR@wS1wY?U0~MWR=(-C)R)Ra?EkZAxTY##b-<
z*h_Ruf=xEC;?<>siZt^}8@M&qU%h%Y=6MVWZ*61aQz2WEnep5?LQjt5F^XLQT5#xi
zeA%kw(-x3p@+WSsG`F+6dKZ8JdlQl4OgF&ypwd4ev>~#~K7d~I?{Xk5PPo2cwCJtl
zBM=Q|(=mx@kU>;blx)4jcwa04sv&4J4!N+~aq8(8xeM)jldF3KJ~YUJH^n)K!#^mh
zu2x>WeEoeama`BJbd&veCP(S|${1xnuyx?qlt}-9XWX!1LpYjv4g?hbq^#x5_1+>h
z6jM{(@s+V^0_bsLkZ3qGv(gYAy_>Soyn^!6Jaj>JLEKHtFiSjR)ktUgLnnjgJQJyh
zd!$FeymqBFyObNoXFo?4+$aD1jG)VOT2^*;B`7cHr-u)gu5uu;kNTG6GUHGgr=f;E
zuL709YVhl+WP?gc9H#i+uU}u6UyE$&j3Yrn1t0=<d*MTIB(Ztu+_yA9zbwq1<VS=o
zRnji-n4CaC6%c%X?pzM}<LvP!$0?*9g~+ozk#IE9%--aPiB0_o)rqB><j#yeIWe@E
znK=%m+Yhh_v@ieyq3MN}ZqQhzx(NS+*c#2>q(w-kv8H$wPj=vD=g~@Knapr!^^e}{
zq1Z}8z$Ma4U>o<6HXrdjG&WZC#55-gTy#a!1ns&Go&6!7q0;97A!UL)fWbDbUAs#_
zKmagU2?a>WZ8cL*<Cex)P+<h1?K$&^)SvEm?(2a6lBULbW6+l!$-XbK%sB=~NEswb
zHSnT!Pt8Gk-bMUM#8y6o8WzfM7&|y%(1T&rhkNvkd^rIKBXN8ck#Py3a^5|~+w9VZ
zAElw-Uz9-DYazSJBjPN+7o*%8MA`N?t=3oyu2BV*s4mAjb0(y}t!+1w9G_9mS?sma
z`#W2hs?NQ<aEwzu?JTyo6zOFYq(>3DOQ3v3w49X*x>Bmi*K}I8cRuy<^3t?#-$1uq
z_SC+{cygl6OY!-cErf#DxpOBeE#UTfk#rncI>wtWDC6%4?M)&bt#&8^Gy1}t-${uB
z;JN6!@ohF3o@WH4mW5H#58nr`%^g$e&}_1*-v>)9GTvZ0#`bG-{ffeD@PaRZq79J6
z>Rw%PyT>BNh3zBFI8K2wIOvDYBI)%=+}&oDK@kxVglYu276lADwoxP0x~qZ;#l@fB
z*q`tTyT$MAg%&~JIrig;j<Ua3xw4jXc*rSF1LRRb*V(K?my2JJfOvg%flLv15bwtB
zyxG-Ir_5MFPv0#A8sxqB?)G|X?WJjJ4Io|}jrP2K`=!|9(jOMTwop$K<%+ou^}uRk
zK-<k6%-B}mEBCoHGQv>JV${<u_MBS!$m`Hr1_q=to8IV_mJ7O&a3E(8mN472YybI#
zD|pwT@o@p6Gp~6Yb}YjOf+%YAK6vRg*gpw$d-p316t68@tPrEbnLYGz9|^YFoeQ6b
z=>F8p`2JML{!v<MT|#Au+b61-U`<s@2>W)LzVN=D*>u~oH>+q2>dGf?eEaTqU^_Ec
z<M4+o3|x2GSY&_A=^(%VA8B$UiK|{)+dD{}aBJGK<h~90++$)tpJMNB@+m8iZ&}7r
zy@7>0Ks4Zh`ZESE!zE>F@3{)^#9y~$*s1f}w=pLVQ!V_@8s9&@!5`w_P))|{lYtXq
z&S`UoIbMdz&;*a#YBjSgwfI%o_{ZgKSG$J>?oW+o@~qs^+cHtG?)N!ShPa}7HU9%|
zdiF9rx<+1&Y#da_F9~?;^RZ>^Ma%MP6QwqKrVldMIUSxIys@KpNlV7A-QiD<th;58
zr%RcCCA`DG5+eV_LmsboWl;BXbF0Xv2WT_sFD(!Y{B3%Cftf~AQ`wy@dg2RSuiQRo
zcl_+>h4{qHH++rphz8Pj9#1KgQ$uNbJ2+d(laoK*cSvq%Sjw)JD}$kI30blKN-?de
zjMvhFS|lKJhpld)HE+XjzdZw4C|YJw94JC)`&BHW!alpM%lGxkW!fy`wzW+RT`+KX
zelJcKt5=|~@?I|9-dmd}t{kWS0KJ08M$Tv)j~KK@NRa$OC&PXxBI=SQU63&Ng}mA1
zGpx$8?EiYj`$n0MAUfTJK8YjggHJ$<7XJI<6&#=#2vZ7d{~V-;Ap{c+m6y+ITNg6i
z-?h%gZkjQ&bI!m2k`|l#(!vzaNt7}^U_p{>0btIeX7-eW#o^U{`-}9W1IOaChCINf
z?hq3bTNetE+03}YDD(dP`@a!Rj__MZ!%S<9sQ~8Hpo%K8>@CqdNP@<(&UlJ;^#{s~
zJZb2`MFJVgfX|fT)lyLK20!^64OBFQsc4l~Avn&@Nb?0=LYj9DoNFc27b4Ih>nj4B
z@j*7o&(A;dDfPmeg+yrv^;(x^9!k12aF)^F+|a#7EL^y7q*$o`@ZA}-ZXD+m!ZK|8
z`woZ-3#(&ck?22@=KxeamF+kV7@rAw5A{%~ImcNMnk~58^W=rGJsnm}ba#lS-wGcB
zw45Nz11wpA(xH>A8c>{jsbDqXHNj{qq0=G13=LTcTVef(8~g%tBP|R`2v1t01%mxP
z<>t+s`b4~fwrWuIBI*x?a4WyjBbgx4fUm~(9H&l7Y?OMtZ`a$ND-6P?na{21YUa+K
z8kges+I!FSk(;marUbLr<M{;z-C}c-UPPDq<^;4|jtn?TsN#W^H>=A-LPLp;-k9U8
zMrx;u$|EfD^Vu1?A!r?<;5s(}eyHtJdT|z71-jaONc%J3BGE_6efH<;Eqq3ChKdLs
zokr>&M8!gMz(Tft6{IlQ_G=RP@!ZpW%5fEd1&XOA8l)DiS+fQrs|U(%1nNgOAl?}T
z#~i2{cwEA7jhPds1ft0Zs$mS6tY|1R=#P`Y9$5D`X*4^J2OtD41kFK-Cj9oU-Mh)U
zqhXRj(Ub={s2Fn6{hR9!T9fOX^UPSiDZ!6~RfArqKQ+=>mv|YnV#JxpAwUr^PLo3)
zAxe>!9-#XNp~VXyvh@lmbQJl?E?Tnk3p!38G)16I-sju_q>-e7MQ0(<4~T_mu<dPa
zq3hUWu0YixI&c!g4#<)FR6S~z+#X}r`f<Zr3tQQ=$GeUUXQWd_MHvN5b;7>AP~>r*
z3iEq#Ceyaxmm&hybUYv)l=H{OJQ2rFoESy4amWS=kLhDA<WL&P*|T?WkfafE3YZU=
z_gu7AZVuRz^&2-nN52*gHhT(^O}k5VKojUzk2J#|BM<~YlXB?)31Xd`o|}D%?%+qL
z7fCBb4MSks+_o%#E=@`_KD6~f&MD80{*bGx;<e&&_yo_WCFr;#b|SFFwFNk$l4w3s
zMQ29jvnRfKjtxSfQ_8gI9f2}Kbjz`K&Um!tjEvO~K|2T9-;Ukfng09lzY~=r3N#*3
zZ=9Uh8O&-rk1D!xFS4cMX}sZ%a5G0IKtKvHEdVXEqah)C$7|Md)G<NCtmXB|A`aE0
z$P}a65FmY_JI%l&pjx6>Tqce|moiUCUCO`^re4RfdvI84%368t9x7I|)4rcN0PI>e
zTou=Q*fdJ6_`B3%JhLwaAclTzxNdC^+F4eAe!GDSjv<_^6k~`jAqWV4Znjq{`x8`S
zhlYkMB$C2!Soi=})XfF*8TwN6K`e}&tWUWEtHQ^NudYDLaQ}Mc%d=x4C)H>o=QOhH
zUgb>y?GVER(Or6zf1r7)L<S;Vydb5H<}KoAGuqdvM*bN4Nuho(2}<?TZ3ws`C5JS_
zl+u07Wp2inDmOPf)9ao>L4PL&J<@V2&~cqHm9$}?pu?bP0=Ba{&TKiPBjJr-1vjer
zLggTxAK}*)7VB$V!>Y*B)7cpX*?_W@u<-;M5<MMAl@-|<VJrovTM11e>BvaV!&g1@
z^YhDd1mC(3X#8`KSaYXfIYIz|jt@a<j2Pa;E_YJ*qjxFvUhA1DYscg1S4wwLEw1o2
zL2yQ4SdW$n4hB|LB?*b!luo37w2y%xq;^rGfx|-Vas1*TO7A^<xJF-nJfSjH8O%T_
z@8!0z8+AiaYV;Abf|eb3cI{^B&p;!2F`>$M-TL)mK`v8N9QATkiS^sI83x&mZ{h$f
zxRc~#<mSdE(%ZDUzu&JSt#SDJ>p3=~LjUnEJHP8vqINir^$3D^0ZZ}>{B(nD(~3=8
z5;V}Gtp`NG=~`q8=uxqa553LG13v%Zg&~$8B07cLxG{nrred3EmbPhr4%+#PmoD+S
zOixhS(gmWj90Q7z&~TAfSm}jIFv<dlsHi64#}KR|4VERf-!t($VK>%pQ+s@u2WZlw
zCMT@QIahVU5&6FG^JnSv2|85X&$UCL$jj&$nS||#gGCPAhtCm9J$pO5AFwB=!i9mv
z{V;o$2&^CO?;IK(RYWo-8ESH3;sXK^Y9b)JdV(KnFtbdZWQx4pCgX~%ocE)Z=dr%%
zL=CZcK-NYik0DOap<%U%toB`NvgZyYOK&r+bc<7R$Mt`|3#<TcAp|~-A2@G>KLn%1
zYuRyzWKzUGO^3Tvg1YR6_gOufHw1xBPwt#AV0tUTvk*#k6St-UUiDk6`S<3!ls?UD
zhfc`SE-iw_^_T0L9BU=5X}i(TLny7rUV5YPaMLgL%|)gsZx2mGRzWHoUIz3?2Wq>0
z!(hWGf&&3n@@(+&n`=240o}NbD~&SKP+*F%hD5cD)6Dk&=7c!)4m-*BiMAg40seSj
zsh!#;d+O->2S_voUZBF$@P5c~ra_R7kAQt@)J%A=)rhkOaZ(Dv(lc5=UUl9N^&G@z
zVUt*&Zhel}+%!*V((T*7IzFaa)Hqx+ugZRwXb#Hc0R=!$?EJ74?a33hkhpcmsMWD(
zYW7Gboj7r#x7)(Ludi$cHzkWe?dK|u4IAV#ULBVd@U6Vt#?rB~)@CO$kzui{aFpf=
zs4mC0wl+S1E5aCogE$M;DuC-d>?owuoauL+ofId{_aU7R&w%_i<=)<P!^TRDGyzQQ
z-LvO?uv89kizJP37T|w4OC3oUgkNLJudk0*+y}i7=UOX9IbP|(u2Twd61mwEtpF*X
zXs`34W>s{5V`jDHX&M+Wg0TqbvRc#uaAO6irBhIyVQok)gPmmiM;#O`J{+Ty!{0wY
zQI`99QVcGVVVd1HGsv?&tjCIdZ6Uh2RZT=j?sXn16<?iP&&8=Bs9&3Kqq;GB)dDkn
z^T5ue7jp|ayK|442X3D`Ad5s!UDV{z&50w@iNKKuU84vpe*-vJqN8P4#~eR?oX`@|
zfx@+&<-0BM_%B}`AS5}k#yLB?G<eOT@r5W_A7=x<d^wv8r4LpLWSVgAeac*fa|F;N
zE2`--sY^cuVlfs7u@g8)8SN(N@Y?Y@z$DcWDn^J)2o+Wt^;kYBHbqnXxlBOxqeqV@
zndS6>JVxuGZ&c#yA*vX}sJ+OpU`vj{3#fn`h*WSf{@`58bw;pX;@N;E#Mgkd90r!c
z5GkLy&uFoq#)%9Kz!d{JCla;Tpynkx^vO<>7K)K)k8R^OGJBMZ!vq>Q$Ypv^oNy(C
zSt5o&RP$&k%1G^sA=V1<ni{}wC`#8AkX_O<Gh^Uv`NJRyC3r7cD9D-TApfEQ?Sb@)
zd>j_(!_2@Syq#vesjam&80?Dzi1TV>pHaL%Q4)qgM`Hy>%cQuYfR-iwPVNgrxgbD2
zK4(f2TtFre+qeGycr0bDS~6=b-KGh(8sG68Dk?EG5{-ZW8Gk-Q!Tq)Xq|vI$(WLI<
z)bB%MLK+6165&u-4#^U*Hz}#P))9D|f01gjh5Ri^MTS-TiJ63$8^PKAVRHJBT&-!D
z=3WW~6rENTVHfcHhDf9+I|)!jV^LdJg=&4nV9!y(v8y8L$@xS*b_l(Mf+9d@Z!HdR
zwhR2HWIsv603walRl-|=t9??gLkG9z<Nd{U4DZlWPZ~RLN=0y3A+?l|j|5nV#(F!n
z-4z%?^Tg%|cHnhhqRPXrMO;486RpK1KW4Oe`t}@Sc<Fgt;s=kG<PYt=8<?3Fzb<X8
z9%pkGSGeJjtsz0<?e+XE-$878P`2rJ@nc2&4AmWBYE%aO>z8>8#6A^(Kltg#{a0=9
zA4c9L35GN-0f8u)0HHjFvXwFU(%(I*6Brlip{H-jaB>Q}yY2J6g;$q-&2k!{-}ljT
zTkuC`eDiM$aP@{YJKH}z81T4#Kxiw2*8}o+tM)}|<}LrY+XG1~SZ~`^!%U~zo?jUr
zy<>ZdKMDQGhe-Gv)CPhusNG440%f4*HlU#T^1?M@I3(6o2;qD}PuV`EnYZz}Oxt~a
zdPE+O<RK+M#8C-Oqbyh<Lh)0$vU&@OKap#|3~fDZb|IRZh2d(%D$aVYZ!m};tj+9v
z*yT^-<d+iK5a4Sw^NF)(@1xb@h1Djnda>m%zx)!3BaZiDcrCwEI@u^<14*rj4OS{l
zrP!t1B&$xjSj}uzv6+#Fh*BMk8&MJ3dAEos5+(zEo4v9(fnJEg=OcU?JBzsC&?Iaz
zc%#h;P7Dlz5Nx<2k6v5o?(%jH-3Oof*OP>Moq|py?XiCpY}1>U?@BiS%SQw5-{NeB
zob{!B5yL=I@EywCmoq)CEw2~Q)6lb%&KXS!B<^-1b6fY;DQ*i8Hs=PkK=zdf=3Wnv
z6k4emoU^X#_#m6U26pz~(`jhyX{1^Tc_%&<ajeRD0cAltNL0hPF6kUZ4h1x!q!%MC
zc2X1kMDzY^@cxKcID~n~Dni@v^FEu%gs?^$qB!)!{6<WV#A^vR4~Ro|LcrsjSpBP&
zwyxQ_b>6v@{yk9MeJ$(y_3KAo&uqmnljO@RGM|otttw$~M$r)~owh9iAR>E31(H!f
zcgkWgCsBsf5ks2$YuB!kUZ1d)b7%T=_wgn4=4Re>b2Ee}0-o?Dkru(a#ej_^B<ID8
z7kgNb)7Ed_-ZL;+H<p`th=>l6@Px{YXHrIcYUNoay}y9tV|q`FlhBBj5n{wV3(-TJ
z7+}=ZpO&{DZtvKm7)2ZSYTkR)|6+0)d(u(Xn;RqFwbf~JsHT+v>Y4v_Ahpt<^8U^f
z@DCc0mXmO~aHkOS8MFvOcPFzI(?(Duy5Z+i07*!Q4kDF<i3C~UXVX(dObHw?24go$
zK|T<l8-#W`ok#D}UiO_2dLTBEWD~8xPJjv+WYShv0A6H+&?ll?$b|Uat>GXhNO~bu
zDl(r-bFW6Sflm`mpCq!7;z*Vv85eP2h@B=@ysWIO?M;?I7}h_&9D~zV4s9O{7Q4m7
za&>x}!P|GEbthX3aGQKFe|a{&FOxXX2vvJ!7A>X$K8SoFAyaP8^)C_(r+)|F>-rLW
z9p#_!<FKl#65}rVGW}fkLq`C|h<UjSZ8Tw7G#xrw2{D0gfB+0o0NiMn|7W8;JBvnp
zPkIj+34=dudj0~YeFu8+U1M`WS@y$q1+M>=d$8Zu(YBj)Fn`fiL(k!%(&^SGpI0y1
zQ7f|#b%vcvY{?XTlrdE(r|^~&-pKLmWSwC=vF2<QaY%v-CIZTf6s@BqD4v{o7_d?L
zUU65$dWYbr>B#mO6{aLM?IlVBIGk1651&OTPjy}PD<h*adMBc5#~^tSf;f+Uk69!d
z<wlUALtTdM*s(vG4dTUT=y8b7mj&SA&ysdSY<40PYQXn_9Mo3wEK{KS+xz-vUynBQ
z0*9-j8y7j^iaG?7<2FJ>5PjQOtd0ok*ib^EG3(DcVSQlMs6<MOtJZY<obwb;Dq{PS
zu$i6%e6)rGnE=hLhjeb|C4sOYS{Lzgy}q`nx6sDIahpD@9OTV_m)ImzKtn{s<VhD<
zLL(+Nzy>P9vkHAu@0_Vpj3SHk$OG`^q2O?$(DoHJi83&JdfU&z^#i&VnZh9H0-g17
zxLGT({q1n{lA|tn{P=z<aT}jlVcOS_UKuLR2%p3icx6@>jgE|*@xGzDe<LjIN6wog
zaENt~u#_iuu~UgK-<+F^!=Hra<79s?&UM0CWA@<b7WAYJgZZ1#Hgi^y1|5=q&dj(8
zF|PT1J*g%s5!Y|Bxa7g^dHSHHrsgF5oaElvG=Wl?&RKWpzR0P>TZLY_Nc%h>i8A^P
z+6Fp86}lP+!JnXpBzR%!>lqrbo9J&xj#NNNAbJptl$F|bh*a{1a9Y$9ph1(O@xr&{
z#%+^QQu326U}Qr3;<XOdYkq;mT(ZJ${Bj5;9pW7TO?4?gUBqePCE;Dbe6l*^#!k2O
z#Vve-v6Um19rL&lo@WHEV({_BlT&+gT?e4^khmlal1NTw?b=tQTleqSUmUhw)Tu^W
zsLvPf+xC(mEeNm*0LLeOpN3JHxOxgTOB$mUqG;a^U)JCftPk|=3nFSJ2Ztn1s9$Po
zYF-xfLh>&FxS2Q8uPs^%b`u=|ACirz*{oYB8VanxGAe0ev)BN*yKb=EB$ebz$ALy5
zymPs~EmhKwE1-OCO$4ZYx$w2O7@25t19Qt;Iy=@dMcv!IPl~4JIuLO&`|zyxYT!@!
z0;Qz`1V?j*Z@9Vb13Z(7sO<{Qd;7Mht;w-pguDuR_zK9?&j<(91p5)zqzMjR9o8sd
zc0cd?Is_e4i@*1r&Oq4`F`MK^=diloJRX*zAK+{uo|(aQt+3QZtoUo<-AqTZCevB(
zK9+imhjjvljhEq0L?an46xDfiljaw~m1790=3y^DDOFP9UB3-X>-q2B3(maNUbp4(
zr=<#zGos{eZ!HL8Qi?gGDPqjB<kRtlfYXzclbuKdc!BrL0k<6-9J<>5w5p1@b6=n<
zA<i3s+b`h!;&2kh4y5Ng+5^hV%&sz->u=g7{4g;Pe=ZbYvJDQwDL2i6Y|!r^#QnaN
ztx&)EMsbx-*~}%izU!g|OJ6l+ZQZ)n|HUNBX*h>T2NSE6t4TC*R3n0J=o6W>V|mvd
zeqX@QqJI?6=Cl92oRCe=7gVsdKD@syT`EO8kv-gqunWgQ>c)$oMP4DJPlPQ7&zV>)
zB-9-k3;03|{|E+j*_Tube~-O<2Wc>S;O&al<){Z3Fxi4V5~~%tODRARz<87A$+obt
z@wRNa4A<qGxb~Zxnlix<f9~A5)!`JlT<XB(Cse6Hzl6D-eed4A=ew;fEv3$nJI=*}
z-&f6b$==AV`2yvVFwtaSBV-h1Ne0}NoP>l#LU-{3%A<i3hg?S&4Ahui*V(Guozja8
zsDuX3+3CuaNWD*wb}}+DIx?=oXb(|lUyM($+_Ys&6nc7aEFXI3=m(0u*&jL|83)65
z7HqOL1|^)4OICL$9|41!pr-S%?AftJL^Q;fne8nApE}l?omrnzaR5-yani&4ivu>v
z3Li(2eEX1AHq+ML9**Xy3;R!ci##WwHz}p;6z{X5<&o!hyQ$7)YJhVgK8Rdm-a<@M
z3~Oy;!0wXn9sx%=e|%SC=epSd+on=v-L=xWR-J^04q6}2!oa}u1)GS(B#B<W3f_zg
zD97NCltCwyfp8?00%WF!CXGg<GLm4)!9gxlh7)o<-W`!fV1+}wk8+QQ|9bFx$T0Yj
zB5%x{5T$pc=P6`u;$P;y@?{4RK|zEh&Q7#35>f!IlOmQDq}Ov`?kh;*?9rt;enCM&
zAdJIdYnCSy7Zn&H(EaSM2tQOcB8JE-#BLmwz+wy!W<1pPNQ`lfxul@f63U6vHP-Kf
zFlllcmL|hB@JN+ntc}d#b>e3!8AL$mWq^!RsM_zw;t~n)sD_MJfu&Z+c7U#^1dHbt
z{5`ScWjBODl~#Rq@g|UA7~sXn#zrDYylQrOvR*BIH@2i401*xLPC}-YI$@cWFjP2#
z^bvyx17`Cb>i8Uk{<XUzN`4AP?@_c1!(o#1odj$Bj8>bB&Nk2d5I9?iSWc)qlqFX9
z+6g8SaTxi6-!ToUE+`O(>829Pm1J1WMK+!wF;FnXiRdP`%GTEQE{m9^mh-3&7DdNZ
zrDz3r)-$+ki1VCS1%OFL5&gt5;c@St5-Or%(v@@BBV|$$tzEzM8X^Z0B8Qk4P<jv$
zzC>;zv<4hX)C$LfPy(8B=zT)yF3x~|MhUq|>;qs&L{Ak_J7)_~LP#;^&YhMczEK=r
zg$yfw(nBCoNZ2F<fUKLr`}+%8M-%Xi7$^iaS0X%o2eqs1%i?AbRr{#KF+k>h036`M
zpSf0;;yu6$THoHo-96pZa}GWkMNk!_=R;g5Lxdvg|K&jY)1<zR#e>6q^!Z|TD*x*p
zw$%?FjJ#E}LK6f}E^%bk2ETs%K_bea@<wZbQ0EjQmzRI9kZ{QE%304kTA015sY6)Y
z+y}LRy=4kT{8xG3S1SwJeIXL`j;bQ%#O7vQo6QUY5<~z#{pZ~4mSqFKy39^qPCW5?
z=4|4su=9AaRl>_L`3ia1XEjp>5w%~c`iwT`jcLE$@BIM3{BIO`2O_ZV-xzh_4Qahs
zd!7<V-1zdZ&f7Ws{%?t)3>SV^;ntd&9@I|EWB4i9-}!q6_iA^B^}bf~Ud5O_D;G)d
zG=!Q)HGWU7=o?nG@amXwvyUV_{MWW{jm$ps_xr2t+6A-gMOQw$3H|z?CU*Z7Ltsi_
zP*)r4(GzEQO-Y?b;T0usyU*F7vyAoUll71I^G|u#YBT(N-M<!#i1&>dtd+4(es)Y>
znVqF#bB#l)n)&gx2R6V4p|0$dg=r&F4!3MH%o>`>uKjPG>q&fk?HYyy)H|C%bcmDh
z3zy|Sb@CC@Vup3^93rZ}9rXLOdeO5xzu+|wEdP5<Ij(!m7&~{Py0>0Wd{N6m3$OH8
zy>)fbwKq~MyjuTSvaez7Mg8+jHfYXaW#;3&`M?U!f92iyGd%uzELvj@euB<&DW#a&
za|{QLy|8tVo)CT8bAUm0-rW%T_e^z5hDoz;U8=0zV4BSiX>&Zyd-;sWL2oYMIHu>j
z=C|)_*yQj!P5ifn{C+%5%H?2>-t!+w=ZWq3x10FKgB2t%GSkS(k+Yj1riarn{NndG
zJC>3a*MTh~K_zUYlh8a{_HWF0u$<0w`a6DGGD1Sam_^oVo;&w*#XQ~RW*$2$xazT_
z=bKIE31jM8UD@abMpu``*QgMa!shh+g6MDev=l4~xGg-tV^#kPXGGwRWv{I#rzAIV
z6T^z$JBLYwu1!11nId20m%pq1gSO3jhEEq>{jh=S;-_Qh=kU3@vVwO-uIpP*ysf^6
z+8J+yk}V;1mbV7dY#w=#_w=~ylQSpUn%`D;jW?fDR7qDrTB%n0p}o4$nN$C~gMUZN
z|6bvNU*Oj%DiFsAMe%{Fyt}K)#d*Y;{^HCU8Zd_Vi7aMA!+PJ?X9$76j|WyWG(5FQ
z((aV_6Q)&H3qEl2tMYBk3f;P*>4DmQ#PSIe%lYplw48kgJlqe?^J!}8cR1o^WHwa0
zu%#X*I{x3(+rKltv@U~($j{5ms^#_1jfaMsn_B+o##>lEl=R~lTF#(nW?5dD6^_UL
z^UNUJ)s?N1Mmi&w<@IOnwzq%0jqz&1dpxg?vI{$PDzQ+_W4vX;?FjZZE$-3g&fGca
z%`V-XG~*qs%soBNwG7?hu6Nv;Ba^VI{7*B@fAHL2Mp+oDeYgVFZ+E)9`KbSz?~i4M
zZyEo+xQ*e_it~iz3zNPKmB@s@lTWZN+2MP>Idm><2)2l$u$?BaN;z`1f2Zi1X3q*A
zTl;*T)Vz2xOgnGwE&Ay0+9hSbrKWAJRGA2*=)3lcQtA(^(xu=~kxLBzRZ9HlAo+RB
z%c$G<8|FjR{(1S^YULWY1&KJ|W*9z|Ys?^Sn_rX!Yy-yQjZsy8e)_p9<YePH3Lmb!
zMIU(}Eb{s8($|;THf{=)Q+rOOt*-0|x3k4gU!RZKd%h@noBK-fUlJ!8E`};z?%PHS
z+g+J?-zbmw?~{t>{{P?4U^TN8UNA=~DZigb%ihkg(=fO8&*x=kmP^$8`MkD_0UY)3
zkgz^V;8Phw=j~XC%G_z%frfM*+55lDFWltPk_Qx~6;AGK5^s6u^MF+n1~lH9(Z?wD
zN4I|6xGna(tBWGdDod_jTr!1i`nSib+N^4G)lMvQLG6`4Km0$sVIpI8QA<WvcDR;n
zHVW<M+rtA@AN^cY{;Zb5!fU6hm)!4AX%rce4Nv(8mU`Z(#jm8Ac0fB#`pFGn69)a?
zS-F%>jdA%>0`@k`Da|kQoG=O-hvZa=>)Km+dDN6^yzfN#C8b?@IH>+3y{GbNMtPL~
zVxdQ_FTz6GQ_CZo=L5shPG6(i{jVGr5m6E9HeVUCG9&H@*H`|}g?=v?qr|#Wz~s|9
z3Z7)`rkI6pRgyFrKZDmu`&(y+8Peh+_eR+d-_T~RULD5X_v)O%Y@R30PF?8JssF+W
zCw3UQ!Osue`h0H4a`T)0F>sgEI;6kq_D=TEep95{Gr#rsMJ=+P@@HCd^yjCqM;%Lx
zOFOJT+;O8-HJM^-YuSdjO66;t_xA}UYFTc6;KO<Oa^9?m3Wuqz&8r#x$|3{=m>In8
zI({-v-cqhmHYXO>_ylsmA35j$x=!RDPhxPl7?N+f{8o{v`Q+QwC|_UaWHjGPBXeTW
zO|x}>K6Goi$SSu-|NEiG`);&K`e3o{-y%=kHljrflUmF{s>CgF-ZGrkmajasxr1=M
zUzsh{dc~`K3De8<-^Q!3NI0Wd)_lC$jw=kT=>ID<Xlj;;vpk*$D~OHD!?xfb|0(f0
zWm!#_y)`SO(YE!jKpJF;f3fMF1TC(E>&+lz`FwgwHKFQ$q^uk!z+v!qdiXsSWuN)S
z{4d<0KQqFg?ohpVnT`gs*WdgVsD6GU?S6XV*LV%yGCt6AyZ<~NPpeih`!Ak$%IdAz
zwS~fQTO_2uw)wQyZK2Ak_)bHsT}5IEjfO&++1mqNhD*!Vdp%O?8bb*Ir0Mu-2u1N<
z+#vIhTmC1;`qQ`>-~2ONpSX=x_g%uyzjZ~;Xz%5eestmI&UeO2b2}UUk97M&sdK3P
zMr%(kt7m?L9fPo_QcT%Rw(WDNGk~_t^U(H?92hd~NAvJO+$zi=S3_d0m!VtwpHA6-
zPXGTBBm9_RoVU4V*yjG+_~9?DT>-s$SI<xM|4d<9cnCOH_EjW1Ij3IdA7DMv?{t&*
z2%nOpIXM$+tFKDz&~2H+vzG|##XNuJK1dXnaJTA8Wh@9=#$5WKONbINwYpGWHe&(L
z-=Ol}7J_+GaC)XxZ(SqFPcR)=XI<PzKX75={4zj$ZxWtI_=iiZR7XGV|A&YCYY9+m
zZ{OeVdjkgL+ENC)JHjEmDsr_PM*ICVna^S0z1aEc*SXwIVjOq<Tz%&^QbzJh|N3qG
z*Z*;QklpVTY-S%9xFWL6w<<qt><r&l|7VfvETb0r{yaG-w+XW={x!_sx9hlM(4R<e
zH^*^j&5~fz`0p)$b{}dwVWfX>pBGBSvGV^e2!jRIBJX^bj+XyU@=l(Cd3pNR<R0uR
z!9QFqEc$Jye=Zp}%Z#W__&?ziT&J{*r`4@s?r7j_{G{lZ_4CC^1R<<hBX@86GVb{#
zPRyEoFP=d-oyYcMw^NGzKHp=wN_BdB1kKma-r@gWEwdjmm}!`p$oX*0H&#lXPmZrp
z_zOHi%?%LxQ_badozVL~_VteU@(n<!r~T&<>TW?fjplt(as58t;zU+H@p(Q@B_m`=
z@YtxtezY4+`cE6AdAZs16o+CLRxv5>Vriu?D>)qVi>Rg5*>dd9p7*nhtKZ*?_-76v
z==juW$#5w)_+^CdKesz69^Dqn(RDa_ISy*ne8Y=_{v~+6rH)ZNKPNI@8~)=RzKC%l
zlCpbWs#x(DF8K3I6jduPUv>~cdC%?9p~?~#7Ji|XSIMjZ!|T0&TzfU|mp5so8C1(`
zZc3sK4IUI=ex&!7Q8-R0*4!&6oWGfuM&We2l>9a8Cy708`mamwo{y)Cca`JHGs#l?
zC&^~y^upK4x+Nwyzc8G!DDpxH1(t2eD2sp=bLi(&%Ear3%0B+P`u*K3s#nLV0hum~
zS4;hLZ^`RR-~R5-A2|NC!<Yz|87!DTg85tHom@=`*aF7>zfbNTkon6t%oB8U8<J;R
z+m=5Vv=(W=si(VV&cECB^gK$O8j(ZR__H-;wk-S`0R5fC=38UvBX*5B#x}eR%)7@Q
z)f?~!$NEcO**CtKv_bxaj^L6}&6%EG!nG_B$CoM-I19Ln$NZnSl#$T_FP|-p#S9F8
z{fl`X<k462xia1NZLOS2F_lv_)#Q%1um%4C-n;MCHDwBW4iT})^c3W&NDNGN;ocWw
z^y$%k2SL+yjvmxe91sIBxe!7cxVdu3#PB8syDjUE8j7S35cXHhHD@)ma3sdIh?|c_
zuEp89Wo%vBx$$ODY)Kd@CAXpAuRL%)2CfMj!964~`0kImMBt6ug*fp>S6qffOukTX
zcVaA=3VE3Lm*G?h!}Q@PT%U2!Zp8rQYZ~vSg5*iL-C`MP;J%?eM}V7+#IV0AoCF-=
zJq~hhhv&z&$}zhXLrh3}pRc!_*#wJ4Q?_GKdAU5OLwPX1MCm403>fuFW8!oCU$kdI
zh9-|d>@eh}2Mi{Zf?y<a7g46LH0*WvBd7*FC+fwn=U+Sr1O5is17H;hch?c(8*3()
zW5I<nf7KK;Va(M*?<Oxqgf&>m&cP|goL43667^~>XJ$-|Q#DBfmQ;CY9^YVO1;={@
zdIT9H@Mf0>)jL}7lFv@gOx@qJt<EXRho!NXT-pRP1ITS^Lcq$X$6By0Kyp7&I)aO-
zh>*m-;*<YEtX&n@V3i_o_Df_`)Y%CHWHPACDv-?QVZNSx-EP#J)7aJDZ`zByRrokK
zK8m_v-mnT27lvf!Yi*T0Yw}ntX6{~ql&pgH41=o482qXszoSqid#9q#75?jlTeofr
z-tqMEN_D{{IZ7A=>&C4}-gUuZYvc^)psLh&PC)5BKQl8Ei=Wc5_XpRv{IP}IJZbeH
z%wNC_ngAKJoP4q|uFvBT?^TuS=79j~R@vO#jN$hh1y9aXeDy@i!Q5>nj0d<X>!WBB
zZ22%)$iwA@0gZvz?Vm6QO(T{hOq&&n&emQIA|BFY%##NQ+aF>L7U7dKWPwpi16r;R
zX#5AQqULza(av&lY_Qel%db?39f4W~!;`$g0NY7}UNI;+5pCoeGFXDCxY|i3`O)CF
zw!dECB~ewO2rA|{*;{v(<=?)qA!FdWB#Ir+i<P5=fy&?GKL)4lkx!Olh;d?U#T&1c
zj)DK5-?ZrkoEFgtU-o7FQTJ8p2VxI7%-P#fwy(uSM2y*Y*dCYCP2pN3gSNOcZTd^m
z>vFfYGWG4%9a6m|7hoHKWx*SKI>j@eCU`6(tzvd1q)cLFC7KqloKl2r4hw3Pn+NBq
zLK|XP24(xZ`*ZR>@aC4n{@0gz=fejHa_VMhXJurcraakg+-<=Z^@_3lQ0REGY3|IP
z&f;sG26B#b@MM!)v>-B(@8vhDc}C{SaD7_ThC_i1xBGQ`P2<-xa9Kip%&?(FLhwk_
z2@O3bM?b8B%Z$hsd2>SeCitA5!I{{;zQ8v`M+nm?H1C}!mPC}#Oix?E=}YI@y?_5F
zQB%31G)IlNJ$z%@s(RmMg`gV~;hqSd6>mm(C3<HzZQuR^Mv(eBE{NwjlO5aO@#!dC
zm@j|xk41_t*@0Q!-KX!Ih4K#3H(udxX=QlT<@+G|$z^PC+9-mzw;t}0#=CVQ6O_T;
z&klzp5gyn*?z*Hbpuz}y4vBKZrcDv1_>y2(4oSl$tG7oY%@Io}JRE@@>)AsfeHh?d
z^xT$gdTp-1`kvu1&gy9>lw}b?E|Rt0RB~AtvJkm}hj<Nf?un!|10TfD$Oug8ZMf+w
zC`9-9-rAI8!Ftt*ez@iWzX>egRzDJ^8fV<5G5S8YqQ*`C@fvzRk}1j5#D@Z9Cc~;)
z>GaGTrof2@eJfOV@<5ED6GtI~IESeWj{JG-+IS8lB|t9>hJ#QMV>(gHp)ovyxzJdY
zq4Znhf9w77!|@1}@<M8b(9j@F@I@pavK>a~!sc!@)^i?%NUZ)QG3n-&OY6hli=HV$
zXpm$|u5KfjsNvcktTlJ?apzrIX6C-bZi(S6c3et_+bCRrw6}D=HP`3D`h>r~Y&P=a
z96nATCx71RMlHjf3UM8h)bB7I*JA!n8FBi6f_N4>)h~4}0e&DwD!F@Nt#o~cbqwW5
zFT=DZoK>hT6>xK{lTPHUPvKI2NKTXF=NVf(FLqfPuI&n#ZDWW|ruj@ZvS4t|76Zjp
zTZj8syZ=1VZ9IE%9|OwD_YqcBg(Xn3WXKf|jV&5CN(+u5)8NV-95o_QhxL4B@AMXe
zBY6tdOl>~dV&v3$x{>J?uC074I*ZC!g-b%zp!|J(`7~+v>=SI>xTF+gGWG6^EktBP
z<oNmg7pI!s^(x;q1zDBk+lh&ZlSR#0_KC^31+oesqqck3t~>R=37<R6^*P_%STr=Q
zmtVM|;|<A^kd{N*c96%w?O7@$)51X|V{XLmqE1Idj$kTczU(~_AkyW`xkvDDW{^7v
z=~?g{m&3rsci}YyJU(BvkKit=KuF_$dswOiRq19E1Fd7N$Q^reK;i0o<pq!KBdMmd
z-dpL!0!yyegJZ2FNmhpHK9(9Iad$=;a~QgXv{Phg4T32dA18+dHHAbt@vOo*^5~WQ
zfn^2Z>4)P!`>9sjT!xX+PTlcPSe3oR{9QMQV0Ik;*_ZinL}6C!JRHGV9$zH~`;Q^2
zs^@0M#fUwKjBo)1KnZ>hi&qT3vQV=aL)j#Fh$jfHUSHTVV0ZG{Gv#w8)|uQ1M8CJ~
zz=f+)d+Jexk>=T`#2YzMt>t<opg@tk3x7%uHjK61@Mc!v>w3jvh`E}0>)?}G)wEn#
z|K}#;RAi*IwIYHC;1>p$^m+N})m2bezaiQeE?)klQdvt8ms_BeKVM(q^K~ApF%+Ko
zV|?{LVQBwli!L{vW<!xZdi1q(#L}O`kuGOSh5g}nG(_SgZa7G~dNzl>df-o*!n8e6
zV;j@VPXHb2rmTAX(5?H;L(lF7%u8v;*U9u(=LDukggYc?kcFL5jbvbk=M!3=M_fs;
zSPLTQ5?dlBD?efs0TY!1Lc9!I_wXq<@hO`;0Sx?^ZtP1)))8LC_F`#(I=5z4BoG7f
zm*ZLjuOk^1#4y2k<;zic|4JdfUbH#PP{t6qRCZ6?Qakl4weJ^^yJAs-d4=vMn76)N
zOqH9Ro|L}#3qzo&I^$LEP~G|+g~o+xnX{b8<uLG6;cYiyI1EqA0sWl#4~bP3|JeoO
zp@)Zu98!8E+zh0&!~12G(Svhnh(UcK+>&tt;$cQKkP8+tAQ|`U*$$+L&z+9L5R@_2
z%H}cHB$$G6FlDjnf$OA9W9Ee&P!ZWR0YY#nJS0ax!6kK`TvLUMlq#^z#N|tbXLK=g
za3GLNTFDeO?s+M!E8r*YaRQ`q95}eRWQaW%F+*Gn_KuGA-=(5WhK`NWh+77i+4OD8
zySiWz84#m4qt=qqGU5*bLd5lWaFAXk&P4LsSGFx$xR6{tL~f}gD+9lSH6DxHn$YYz
zmmTKaYe=2KN#KQtqZiY*uxo-^$R-{bxJ}6Oaaq3kCD2NpF^$<wM56vmt;_NKaO>gO
z<e}e_J4*vZo$Fi};PO*-V`NdnAy9-10avGVL*@2Ar0W4#7>V=7f%^ql7pcLCK<-I`
zGnd?f0TXNmDIvIufZW+hDi*P`le;;I%zx*ORV3Rd8f(#_OQKM|$yN{mE%9I%*%@*x
zEwaxQ3?stO7Z0aJ`N@7yclUERR^*~lGLwqNfZYBx3fxYv$iNm+$6V-`Mc^fKg^rFf
za7Pex7LfSpXMtw&S^ftv-NG`lu?K4o;b$FgK_u4(Ak9iSm}Tq0O+<T<+<@%MrIoW^
zAXSKoe)9C`E?5kRUBH`FY9pDFg>7s;bHJ~qk3qJrM=W$gbjhrz>)b5a7;<|P&@{QT
zR_^2YC=wJ}3N{QFMIpP9+i!R6+C?t^AQpjDTX|c)Ef)?ZJJ90La`DB18@ai;0KaqB
zhRHa^m_4R-I;;Y*kgD-oxqSz?0NAH6mZ(OuJ(9TPBO6R;7{Z7g3&<{$^a;~*-SAp6
zy+;D;&v{jMu>o=Ktd3!VWI)1soQ{-dd>6o7mh4zE<`=7wNL9HtGIUs6@m%y2o34sN
z2i$E$PjaS@otN_A(8Wwb79Vc+EL7=Bv1pf!!9ylvFtQ56(DVtMcthBVVgVtm$gPg8
zBEah}r|5KYxnZwPY?}-IsqU&@e|=dMqg<k`COSQEf_%a#E_<}7@x?f;3ak>QqvPYL
zCN(eLXrE7+A(tg}!&$ohxyYxS6lXn5VM?wa2i%KA6nkY%GSJ++7gR5bTdKcp#%SCA
z9XnokcB6W&9wsIsVjeBmg{4ItVQjJaJG@G^P4?o<%*<m!<3<Gkz`{+Mv(iJ24GkD6
z$rf)Sc>so>GsNz(gps*tfJX&Q4sHNkbn4_uGH~tQ)Qwx<$b1F5sbsHxxW|!9oudWp
zguS%T<IR|SGnq=k1sHTYU`TRxD9*n>&*giVbYZdKd1&?R!y2-uz<;IV&90`+<jt=j
z-I0mV5m<+Xk`BO5RN3_VK@74+6ggwcf=u+dnKLHF14v-V5F;`n*6TS{y_HtJ{)L>|
z8XR1;y}J*p$cYKSw<tJ`@t+ww7aK3H0&G3ixDR7JGc#BBoL`-zg9Gl7dZ*n4!;};a
z&fJ0f=fr9d))XpPuZG6Pb<(D=$125Z;y!AdDA^#<)#b!wgS5)#Ad3Q%tev+6_`UO5
zx0kznsg~;u_~;PQfB2hZ-X@o4;c7B-)x5En1eg+Q4o27r!650qS377U(E6iCn{o1`
zG&MEdo76&1E`x`>sdMqWi?0sPh}k`I#%JpTYEW@b<?$8@u*|sy@h=c|Aurl+xBFP#
zu>|9nWNza8Fs^%nOOV`|f?$rtxRMTMH<F%@P7hNR(4M!qS%}qp{wDtBMhtTIj)>r@
zA)5CKUpF$Vg9~kN^QT!_XM59TfZDITx7;=`F~wpohcFmhh3#HqGX2ManRz;cTq6KG
z5|xV$*3r^5^dCn=c8Yz<K?CDAKRkq|t;6kG#MOq?B3I&&J7_S%%=|K(L2es~(PA?Z
zGiOS@?Cmg_-&|)#{Y2-cEs5C<(^zsBE<A&=Nn7k!SXMhnp5$T}Xi%epvF%v58UKv=
zJKYK#_U5@RytM)d0!-xPo-D@{9V-=`q(h5*Tc1zG+*N7G|JT{MzBCzzVcd`sAsZx_
zj<S<xm}Nx=?69Of@Ja_=Se7r9F{-T<rZjXp?LZAXP);MM)LI@A)0Jy#D<LbiC=^*)
zbWT5HnrlRxw4rugMIVAb^$)mr@B2LWd*9El-%pnX4IV<^%9Sl+e&dj=ZD_d48H^53
zyc~U^+uM<qzGB`uUeQkIPVd)lHpl&%nZw+^-*AXv1&w7KM1V9N29~8d_bq6e{qmn|
z?BQFb%C`4?FJQE^M{ZbgA`k(iE((^(hWBZ?jnK|ism3DhPgip?)<{U|HMR2qI&;aF
z;PBhQnr*2O0mN|81{4%IGCzft<rq<~`ardGLcuBzm3f`<dExyD54y$q;c-l7yMMc;
z=ajkZ6{1*5)AKv8%^kAY0?%j)E#p#~x2x6a`kwm9H;^ISB#NT(^T~;kn&L<cNdYKv
zfH=dTt2BN_U9v$ATO`wI^-`<e0;M3~4XR(U2fuz-%nf~WsM~I@Lxo-XHc~Rf;yQq~
zlYOz^>+!i~JJx?VQ<T_0XdQ~_xM?IL3=8vybM(??G#34s8Z+Fbm_3Mb;-Ckq;hIU6
z<N;7B(gSySDsx~yP_!{v{KMnv2i7B9rn&w=$OUz_#k})0jfi)r&02(ZKh{8~rz9$~
zRYq$AmWScFDj5o(OI^(s53%AY;R5nTWJKr+Oz@o(MEnJVJzu*RaA#iFg@}H>?5HQZ
zScfFHTCdw-h8$5CH2IEpD7v+07uNZRl2S!c!Sav9@o=WH2Md^?X4<-dS?)%^PPp5W
zH<Ih=h+9mU3?nQ_7`B8Q710I^Nai;|gY}^BDHt6S0<zBD&eho4g|Pb2^N)b~%pkZ@
zJK6S=Z-T{-UzT<{td%{uO48o=!>X4Z$K|V*#8X0z4aXB6y*^Ncxfgeum|t-A3MK^6
zmM5dM0xf7$E?JA$X5!H?ejx&<7Mvhm#adF~i;%~lHx;Xbkw$8Ykr;(5u|!cjlIC)P
zumh(#r5bG%v3nFf@{N;aIVp?)H}3Yz(xRYBxkc75Fbsr=h#T`^{~_kU)|wdQ!)MC@
zulm0oQrmFFXm!@$B9lO7v3yd&i#{gbx|O>2eQ%nsU_enKd5?%-5{^La2Q$u%w{BpZ
za;V@YUTp?!UP?faR<2YHCI~@M8ReXT(H9dXAI^G*1y+ORTo}H4etg445s*v;j%A}t
z!r-uT71rRk6IH~+qEeqpLKlqdC|1>=w)(niFY=ti7$~=z0B+JW31_U1Sz(X{e8mu+
z!RV;z)d$?O9qw-#+K+C;QC^=?G9jD4Xl5y%{l|aP_}6cte;sf?8X1+>+I}fLdfHTc
Ph?fw%E~akP_9H(5)BgVP

literal 0
HcmV?d00001