diff --git a/docs/notebooks/mister_p.ipynb b/docs/notebooks/mister_p.ipynb
index 69259bac7..d3ac09be1 100644
--- a/docs/notebooks/mister_p.ipynb
+++ b/docs/notebooks/mister_p.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "## Regression and Effect Modification\n",
     "\n",
-    "What are we even doing when we fit a regression model? \n",
+    "What are we even doing when we fit a regression model? Is a question that arises when first learning the tools of the trade and again when debugging strange results of your thousandth logistic regression model.\n",
     "\n",
     "This notebook is intended to showcase how regression can be seen as a method for automating the calculation of stratum specific conditional effects. Additionally, we'll see how we can enrich regression models by a post-stratification adjustment with knowledge of the appropriate stratum specific weights. This technique of multilevel regression and post stratification (MrP) is often used in the context of national surveys where we have knowledge of the population weights appropriate to different demographic groups. It can be used in wide variety of areas ranging from political polling to online market research. We will demonstrate how to fit and and assess these models using Bambi. "
    ]
@@ -38,7 +38,7 @@
    "source": [
     "## Risk Stratification\n",
     "\n",
-    "First consider this example of heart transplant patients adapted from Hernan and Robins' excellent book *Causal Inference: What if*. Here we have a number of patients (anaoymised with named for the Greek Gods). The data records the outcomes of a heart transplant program for those who were part of the program and those who were not. We also see the different risk levels of each patient assigned the treatment. \n",
+    "First consider this example of heart transplant patients adapted from Hernan and Robins' excellent book *Causal Inference: What if*. Here we have a number of patients (anonymised with named for the Greek Gods). The data records the outcomes of a heart transplant program for those who were part of the program and those who were not. We also see the different risk levels of each patient assigned the treatment. \n",
     "\n",
     "What we want to show here is that a regression model fit to this data automatically accounts for the weighting appropriate to the different risk strata."
    ]
@@ -710,38 +710,38 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Intercept</th>\n",
-       "      <td>0.424</td>\n",
-       "      <td>0.203</td>\n",
-       "      <td>0.001</td>\n",
-       "      <td>0.787</td>\n",
+       "      <td>0.432</td>\n",
+       "      <td>0.211</td>\n",
+       "      <td>0.065</td>\n",
+       "      <td>0.875</td>\n",
        "      <td>0.003</td>\n",
        "      <td>0.002</td>\n",
-       "      <td>4666.0</td>\n",
-       "      <td>3125.0</td>\n",
+       "      <td>4393.0</td>\n",
+       "      <td>2686.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Treatment</th>\n",
-       "      <td>0.113</td>\n",
-       "      <td>0.252</td>\n",
-       "      <td>-0.358</td>\n",
-       "      <td>0.593</td>\n",
+       "      <td>0.103</td>\n",
+       "      <td>0.266</td>\n",
+       "      <td>-0.416</td>\n",
+       "      <td>0.592</td>\n",
        "      <td>0.004</td>\n",
        "      <td>0.004</td>\n",
-       "      <td>4542.0</td>\n",
-       "      <td>3240.0</td>\n",
+       "      <td>4516.0</td>\n",
+       "      <td>2237.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Outcome_sigma</th>\n",
-       "      <td>0.542</td>\n",
-       "      <td>0.090</td>\n",
-       "      <td>0.391</td>\n",
-       "      <td>0.716</td>\n",
-       "      <td>0.001</td>\n",
+       "      <td>0.544</td>\n",
+       "      <td>0.093</td>\n",
+       "      <td>0.390</td>\n",
+       "      <td>0.723</td>\n",
+       "      <td>0.002</td>\n",
        "      <td>0.001</td>\n",
-       "      <td>3732.0</td>\n",
-       "      <td>2344.0</td>\n",
+       "      <td>3636.0</td>\n",
+       "      <td>2846.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -750,14 +750,14 @@
       ],
       "text/plain": [
        "                mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
-       "Intercept      0.424  0.203   0.001    0.787      0.003    0.002    4666.0   \n",
-       "Treatment      0.113  0.252  -0.358    0.593      0.004    0.004    4542.0   \n",
-       "Outcome_sigma  0.542  0.090   0.391    0.716      0.001    0.001    3732.0   \n",
+       "Intercept      0.432  0.211   0.065    0.875      0.003    0.002    4393.0   \n",
+       "Treatment      0.103  0.266  -0.416    0.592      0.004    0.004    4516.0   \n",
+       "Outcome_sigma  0.544  0.093   0.390    0.723      0.002    0.001    3636.0   \n",
        "\n",
        "               ess_tail  r_hat  \n",
-       "Intercept        3125.0    1.0  \n",
-       "Treatment        3240.0    1.0  \n",
-       "Outcome_sigma    2344.0    1.0  "
+       "Intercept        2686.0    1.0  \n",
+       "Treatment        2237.0    1.0  \n",
+       "Outcome_sigma    2846.0    1.0  "
       ]
      },
      "execution_count": 9,
@@ -809,62 +809,62 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Intercept</th>\n",
-       "      <td>0.239</td>\n",
-       "      <td>0.266</td>\n",
-       "      <td>-0.264</td>\n",
-       "      <td>0.739</td>\n",
-       "      <td>0.005</td>\n",
+       "      <td>0.251</td>\n",
+       "      <td>0.268</td>\n",
+       "      <td>-0.281</td>\n",
+       "      <td>0.742</td>\n",
+       "      <td>0.006</td>\n",
        "      <td>0.004</td>\n",
-       "      <td>2447.0</td>\n",
-       "      <td>2806.0</td>\n",
+       "      <td>2220.0</td>\n",
+       "      <td>2469.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Treatment</th>\n",
-       "      <td>0.012</td>\n",
-       "      <td>0.370</td>\n",
-       "      <td>-0.692</td>\n",
-       "      <td>0.696</td>\n",
+       "      <td>-0.002</td>\n",
+       "      <td>0.381</td>\n",
+       "      <td>-0.708</td>\n",
+       "      <td>0.761</td>\n",
        "      <td>0.008</td>\n",
-       "      <td>0.005</td>\n",
-       "      <td>2299.0</td>\n",
-       "      <td>2814.0</td>\n",
+       "      <td>0.006</td>\n",
+       "      <td>2057.0</td>\n",
+       "      <td>2134.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Risk_Strata</th>\n",
-       "      <td>0.423</td>\n",
-       "      <td>0.390</td>\n",
-       "      <td>-0.286</td>\n",
-       "      <td>1.151</td>\n",
-       "      <td>0.009</td>\n",
-       "      <td>0.006</td>\n",
-       "      <td>1921.0</td>\n",
-       "      <td>2056.0</td>\n",
+       "      <td>0.406</td>\n",
+       "      <td>0.422</td>\n",
+       "      <td>-0.374</td>\n",
+       "      <td>1.180</td>\n",
+       "      <td>0.010</td>\n",
+       "      <td>0.007</td>\n",
+       "      <td>1973.0</td>\n",
+       "      <td>2290.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Treatment_x_Risk_Strata</th>\n",
-       "      <td>-0.006</td>\n",
-       "      <td>0.491</td>\n",
-       "      <td>-0.928</td>\n",
-       "      <td>0.929</td>\n",
-       "      <td>0.012</td>\n",
-       "      <td>0.008</td>\n",
-       "      <td>1781.0</td>\n",
-       "      <td>2068.0</td>\n",
+       "      <td>0.011</td>\n",
+       "      <td>0.527</td>\n",
+       "      <td>-1.047</td>\n",
+       "      <td>0.948</td>\n",
+       "      <td>0.013</td>\n",
+       "      <td>0.010</td>\n",
+       "      <td>1690.0</td>\n",
+       "      <td>1718.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Outcome_sigma</th>\n",
-       "      <td>0.529</td>\n",
-       "      <td>0.097</td>\n",
-       "      <td>0.374</td>\n",
-       "      <td>0.719</td>\n",
+       "      <td>0.535</td>\n",
+       "      <td>0.098</td>\n",
+       "      <td>0.364</td>\n",
+       "      <td>0.708</td>\n",
        "      <td>0.002</td>\n",
        "      <td>0.001</td>\n",
-       "      <td>2747.0</td>\n",
-       "      <td>2252.0</td>\n",
+       "      <td>2601.0</td>\n",
+       "      <td>2314.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -873,18 +873,18 @@
       ],
       "text/plain": [
        "                          mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  \\\n",
-       "Intercept                0.239  0.266  -0.264    0.739      0.005    0.004   \n",
-       "Treatment                0.012  0.370  -0.692    0.696      0.008    0.005   \n",
-       "Risk_Strata              0.423  0.390  -0.286    1.151      0.009    0.006   \n",
-       "Treatment_x_Risk_Strata -0.006  0.491  -0.928    0.929      0.012    0.008   \n",
-       "Outcome_sigma            0.529  0.097   0.374    0.719      0.002    0.001   \n",
+       "Intercept                0.251  0.268  -0.281    0.742      0.006    0.004   \n",
+       "Treatment               -0.002  0.381  -0.708    0.761      0.008    0.006   \n",
+       "Risk_Strata              0.406  0.422  -0.374    1.180      0.010    0.007   \n",
+       "Treatment_x_Risk_Strata  0.011  0.527  -1.047    0.948      0.013    0.010   \n",
+       "Outcome_sigma            0.535  0.098   0.364    0.708      0.002    0.001   \n",
        "\n",
        "                         ess_bulk  ess_tail  r_hat  \n",
-       "Intercept                  2447.0    2806.0    1.0  \n",
-       "Treatment                  2299.0    2814.0    1.0  \n",
-       "Risk_Strata                1921.0    2056.0    1.0  \n",
-       "Treatment_x_Risk_Strata    1781.0    2068.0    1.0  \n",
-       "Outcome_sigma              2747.0    2252.0    1.0  "
+       "Intercept                  2220.0    2469.0    1.0  \n",
+       "Treatment                  2057.0    2134.0    1.0  \n",
+       "Risk_Strata                1973.0    2290.0    1.0  \n",
+       "Treatment_x_Risk_Strata    1690.0    1718.0    1.0  \n",
+       "Outcome_sigma              2601.0    2314.0    1.0  "
       ]
      },
      "execution_count": 10,
@@ -903,7 +903,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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jl19+iclk4qqrrqp138LDw2nXrl3NfVu2bBmFhYXccMMNNS2eZ6Nz584AjB49mg8//JAjR46cU71nYvjw4bV+b9u2LeXl5WRmZgKwePFiACZMmFBrvfHjx9f6vby8nEWLFjFy5Ei8vLxqPT5Dhw6lvLycFStW1Pqbyy+//Izr/Otf/8rBgwd5/fXXue666/D29ubll1+mY8eOvPfee2e8nbZt25KQkHDC8vP1ujkXZ3q8FBcXc/fdd9O8eXNcXFxwcXHB29ubkpIStm3bdk77Pt5q8vtR/K688kqsViuLFi2qtTwlJYUmTZrU/O7h4UFCQsIJr9ePP/6Yyy677KTnIAIkJiYydepUnn/++Zr3jpPV1rp161rnZB2v1TCME1qaL7nkEiwWS83vbdu2BaipzTCMWsflb1vdu3Tpwptvvsmjjz7KihUrqKqqOmlNpzN8+HBcXV1PWD537lx69uyJt7c3Li4uuLq68tprr53zc3Y2vv76awBuvPHG0663d+9exo8fT3h4eM3x37dvX4A/dWydz+dPxJkorIk0YifrUnL06FE2btyIq6trrYuPjw+GYZCdnQ0cC0y9e/fmyJEjPPvssyxdupTVq1fzwgsvAMcGUzhTv++a5Obmdtrl5eXlNbXCscDx+3o/+OCDmlqP8/LywsPDo9Yyd3f3mu39GdHR0XTq1OmEi7e39zlt7+jRoxiGQVhY2An3bcWKFTX3LSsrq2b/56JPnz7MmzevJvRGR0eTlJR0VqHkTP2+O+HxLm/Hj5WcnBxcXFxOWC88PLzW7zk5OVRXV/Pcc8+d8NgMHToU4ITn/lTdp04lLCyMyZMn8/LLL7Nx40Z++OEH3NzcaoaGPxMn2+f5fN2cizM9XsaPH8/zzz/PtGnT+Oabb1i1ahWrV68mJCTknGs8/vz+vhudyWQiPDy8phvgcSfrfuru7l5r/xkZGfz8889/GMYfeughLBZLTbe/k9V2sucrMjKy5vbT1fb7Y/mHH3444dg83hXwgw8+YNKkSbz66qt0796dwMBAJk6cSEZGxmnvw2+drNZPPvmE0aNHExUVxTvvvMPy5ctZvXo1U6ZMOS/vcX8kKysLi8Vywuv1t4qLi+nduzcrV67k0UcfZcmSJaxevZpPPvkEOPfj/3w/fyLOROesiTRiJ/tmPTg4GE9Pz5pzZ052O8C8efMoKSnhk08+ITY2tub29evXX5BaT1fLRx99VKuGhiA4OBiTycTSpUtPev7R8WXH//E9fPjwOe/rsssu47LLLqOiooIVK1bwt7/9jfHjxxMXF1dzDtfvHQ+9FRUVtZb//p+isxEUFER1dTU5OTm1/pn6/T+xAQEBWCwWrr766lN+ix8fH1/r93NpdfytPn36MGjQIObNm0dmZuYpz3n7o306+nVzJsdLQUEBX375JQ8++CD33HNPzfLj50Sdq+PPb1ZWVq3AZhgGGRkZNa28Z+PTTz/FarUycODA064XERHBzJkz+fvf/86sWbNOWlt6evoJy9PS0oD/vdecqY4dO7J69epay44Hh+DgYJ555hmeeeYZDh48yOeff84999xDZmYmCxYsOKPtn+zYeuedd4iPj+eDDz6odfvvX6MXSkhICDabjYyMjFN+OfL999+TlpbGkiVLalrTgD89V+H5fv5EnIla1kSklmHDhrFnzx6CgoJO2lJ0fBLU4/8M/DZIGIbBf//73xO2+ftvw8+X1NRUXFxc2LNnz0lr7dSp01lv01m+YR02bBiGYXDkyJGT3q/k5GQAevTogZ+fHy+//DKGYZxye2dyv9zd3enbty9PPPEEcKy73qmEhYXh4eFRa5JogM8+++yM7+PvHR8qf86cObWWv/vuu7V+9/Lyol+/fqxbt462bdue9PE5WavMmTh69OhJh+e32Wzs2rULLy+vmsnMz+VYcfTr5kyOF5PJhGEYJ3xJ8Oqrr54wwfTZPAbHpxR45513ai3/+OOPKSkpOWHKgTPx8ccfM2zYsDMatv7uu+8mMDCwVgD9bW1bt25l7dq1tZbPnj0bk8l0wjQOf8THx+eEY/J4z4DfatKkCTfddBMDBw6ste9zee5NJlPNpNXHZWRknPQ1eSGOreMDAb300kunrfH4/n/rP//5zwnrnu2xdT6fPxFnopY1Eall5syZfPzxx/Tp04fbbruNtm3bYrfbOXjwIAsXLmTWrFl07dqVgQMH4ubmxrhx47jrrrsoLy/npZdeIi8v74RtJicn88knn/DSSy/RsWNHzGbzOQWp34uLi+Phhx/mvvvuY+/evQwePJiAgACOHj3KqlWrsFqt/N///d9ZbdPHx4fY2Fg+++wzBgwYQGBgIMHBwTUh9VQOHjx4wnlScOzb5nOZj6hnz55ce+21TJ48mV9++YU+ffpgtVpJT0/np59+Ijk5meuvvx5vb2+efvpppk2bxsUXX8z06dMJCwtj9+7dbNiwgeeffx6gJtw98cQTDBkyBIvFQtu2bXn00Uc5fPgwAwYMIDo6mvz8fJ599tla55GczPHz6V5//XWaNWtGu3btWLVq1QnB6mwMGjSIPn36cNddd1FSUkKnTp34+eefefvtt09Y99lnn6VXr1707t2b66+/nri4OIqKiti9ezdffPHFOY9m+fbbb/Of//yH8ePH07lzZ/z8/Dh8+DCvvvpqzaiox//pPv6YPvvss0yaNAlXV1cSExPx8fE55fYd/bo5k+PF19eXPn368OSTT9Yc+z/88AOvvfZaTVA9LikpCYBXXnkFHx8fPDw8iI+PP2lYHjhwIKmpqdx9990UFhbSs2fPmtEg27dvz9VXX31W9yUnJ4cffviB999//4zW9/X15b777uO222474bbbbruN2bNnc8kll/Dwww8TGxvLV199xYsvvsj1119/0nMPz0VBQQH9+vVj/PjxtGzZEh8fH1avXs2CBQsYNWpUzXrn8twPGzaMTz75hBtuuIErrriCQ4cO8cgjjxAREcGuXbtqrZucnMySJUv44osviIiIwMfHh8TExD9133r37s3VV1/No48+ytGjR2tC9Lp16/Dy8uLmm2+mR48eBAQEMGPGDB588EFcXV2ZM2cOGzZsOGF7p3rPOlnoravnT8QhHDOuiYjUpdPNs3YyxcXFxv33328kJiYabm5uhp+fn5GcnGzcdtttRkZGRs16X3zxRc18OlFRUcadd95ZM6Lfb0cTy83NNa644grD39/fMJlMJ8wZ9PsR7I6PSDZ37txay081Ktm8efOMfv36Gb6+voa7u7sRGxtrXHHFFcZ333132sfAMP43UuFvfffdd0b79u0Nd3f3WvMKncwfjQY5YcKEE/Z1JqNBHvf6668bXbt2NaxWq+Hp6Wk0a9bMmDhxovHLL7/UWm/+/PlG3759DavVanh5eRmtW7c2nnjiiZrbKyoqjGnTphkhISE1z8G+ffuML7/80hgyZIgRFRVluLm5GaGhocbQoUNrjWJ5KsfnRAsLCzOsVqtx6aWXGvv37z/laJC/v98nmwMsPz/fmDJliuHv7294eXkZAwcONLZv337SETr37dtnTJkyxYiKijJcXV2NkJAQo0ePHsajjz5as86pjqVT2bp1qzFr1iyjU6dORkhIiOHi4mIEBAQYffv2Nd5+++0T1r/33nuNyMhIw2w2n3SetZP5s68bwzj30SCP+6Pj5fDhw8bll19uBAQEGD4+PsbgwYONzZs3n3R+q2eeecaIj483LBbLGc2zdvfddxuxsbGGq6urERERYVx//fWnnGft9/r27VszJ9irr75qeHl5GSUlJSesd6rXVEVFhREfH3/KedbGjx9vBAUFGa6urkZiYqLx5JNPnnKert872TH6e+Xl5caMGTOMtm3b1sxxl5iYaDz44IO17sfZvmce9/e//92Ii4sz3N3djVatWhn//e9/T/oet379eqNnz56Gl5fXaedZM4wzHw3SMI6Nbvqvf/3LSEpKqvns6N69u/HFF1/UrLNs2TKje/fuhpeXlxESEmJMmzbNWLt27Qmjy57qPcswTj3P2oV+/kQcwWQYp+k3IyIiIuKEhg4diqenJx9//LGjSxERuWAU1kRERERERJyQBhgRERERERFxQgprIiIiIiIiTkhhTURERERExAkprImIiIiIiDghhTUREREREREnpEmx64hhGBQVFTm6DBERERERcRI+Pj6YTKZT3q6wVkeKiorw8/NzdBkiIiIiIuIkCgoK8PX1PeXtmmetjqhlTUSkcauqquKNN94AYPLkybi6ujq4IhERcbQ/allTWBMREakDJSUleHt7A1BcXIzVanVwRSIi4uw0wIiIiIiIiIgTUlgTERERERFxQgprIiIiIiIiTkijQYqIiIhIg2Sz2aiqqnJ0GdIIubq6YrFY/vR2FNZEREREpEExDIOMjAzy8/MdXYo0Yv7+/oSHh592tMc/orAmIiIiIg3K8aAWGhqKl5fXn/pnWeRsGYZBaWkpmZmZAERERJzzthTWRERE6oC7uztffvllzXURuTBsNltNUAsKCnJ0OdJIeXp6ApCZmUloaOg5d4lUWBMREakDLi4uXHLJJY4uQ6TBO36OmpeXl4Mrkcbu+DFYVVV1zmFNo0GKiIiISIOjro/iaOfjGFTLmoiISB2oqqpizpw5AEyYMAFXV1cHVyQiIs5OLWsiIiJ1oLKyksmTJzN58mQqKysdXY6ISC1LlizBZDLVGkFz3rx5NG/eHIvFwsyZM3nzzTfx9/f/0/symUzMmzfvT2/nfNq/fz8mk4n169ef8d9cdNFFzJw584LVBGpZExERERFxatdccw35+fnnLeBcdNFFpKSk8Mwzz9Qs69GjB+np6fj5+dUsu+6665g8eTK33HILPj4+uLi4MHTo0PNSg5wZhTURERERkQagqqrqnLtYu7m5ER4eXvN7cXExmZmZpKamEhkZWbP8+CiHUjfUDVJERERExAl89NFHJCcn4+npSVBQEBdffDF33nknb731Fp999hkmkwmTycSSJUtquu19+OGHXHTRRXh4ePDOO++Qk5PDuHHjiI6OxsvLi+TkZN57772afVxzzTX88MMPPPvsszXb279/f61ukEuWLMHHxweA/v371+zzZN0gv/jiCzp27IiHhwdNmzbl//7v/6iurq65fdeuXfTp0wcPDw9at27Nt99+e8aPx2/vY+/evfH09KRz587s3LmT1atX06lTJ7y9vRk8eDBZWVk1f2e323n44YeJjo7G3d2dlJQUFixYUGvbq1aton379nh4eNCpUyfWrVt3wv63bt3K0KFD8fb2JiwsjKuvvprs7Owzrv98UFgTERERkYbLMKCyxDEXwzjjMtPT0xk3bhxTpkxh27ZtLFmyhFGjRvHggw8yevRoBg8eTHp6Ounp6fTo0aPm7+6++25uueUWtm3bRmpqKuXl5XTs2JEvv/ySzZs3c+2113L11VezcuVKAJ599lm6d+/O9OnTa7YXExNTq5YePXqwY8cOAD7++OMT9nncN998w1VXXcUtt9zC1q1b+c9//sObb77JY489BhwLTaNGjcJisbBixQpefvll7r777rN+Ch988EHuv/9+1q5di4uLC+PGjeOuu+7i2WefZenSpezZs4cHHnigZv1nn32Wp59+mqeeeoqNGzeSmprK8OHD2bVrFwAlJSUMGzaMxMRE1qxZw0MPPcQdd9xxwvPRt29fUlJS+OWXX1iwYAFHjx5l9OjRZ13/n6FukCIiIiLScFWVwuORf7zehfCXNHCzntGq6enpVFdXM2rUKGJjYwFITk4GjnU9rKioqNVN8biZM2cyatSoWst+GzxuvvlmFixYwNy5c+natSt+fn64ubnh5eV10u3BsS6RoaGhAAQGBp5yvccee4x77rmHSZMmAdC0aVMeeeQR7rrrLh588EG+++47tm3bxv79+4mOjgbg8ccfZ8iQIWf0mPz2/qSmpgJw6623Mm7cOBYtWkTPnj0BmDp1Km+++WbN+k899RR33303Y8eOBeCJJ55g8eLFPPPMM7zwwgvMmTMHm83G66+/jpeXF23atOHw4cNcf/31Ndt46aWX6NChA48//njNstdff52YmBh27txJQkLCWd2Hc6WwJiIiIiLiYO3atWPAgAEkJyeTmprKoEGDuOKKKwgICDjt33Xq1KnW7zabjb///e988MEHHDlyhIqKCioqKrBazyw0no01a9awevXqmpa04/svLy+ntLSUbdu20aRJk5qgBtC9e/ez3k/btm1rroeFhQH/C7LHl2VmZgJQWFhIWlpaTZA7rmfPnmzYsAGAbdu20a5du1oTp/++rjVr1rB48WK8vb1PqGfPnj0KayIiIg2Ju7s7H374Yc11Eakjrl7HWrgcte8zZLFY+Pbbb1m2bBkLFy7kueee47777qvpvngqvw9hTz/9NP/617945plnSE5Oxmq1MnPmzAsyZYjdbuf//u//TmjZA/Dw8MA4STfQc5ko+reDphz/+98vs9vtp92PYRg1y05W1+/Z7XYuvfRSnnjiiRNui4iIOPPi/ySFNRERkTrg4uLClVde6egyRBofk+mMuyI6mslkomfPnvTs2ZMHHniA2NhYPv30U9zc3LDZbGe0jaVLl3LZZZdx1VVXAcdCx65du2jVqlXNOmezvdPp0KEDO3bsoHnz5ie9vXXr1hw8eJC0tLSaESWXL1/+p/d7Or6+vkRGRvLTTz/Rp0+fmuXLli2jS5cuNXW9/fbblJWV1YxuuWLFilrb6dChAx9//DFxcXG4uDguMmmAERERERERB1u5ciWPP/44v/zyCwcPHuSTTz4hKyuLVq1aERcXx8aNG9mxYwfZ2dlUVVWdcjvNmzevaaHbtm0b1113HRkZGbXWiYuLY+XKlezfv5/s7OwTWqXO1AMPPMDs2bN56KGH2LJlC9u2beODDz7g/vvvB+Diiy8mMTGRiRMnsmHDBpYuXcp99913Tvs6G3feeSdPPPEEH3zwATt27OCee+5h/fr13HrrrQCMHz8es9nM1KlT2bp1K/Pnz+epp56qtY0bb7yR3Nxcxo0bx6pVq9i7dy8LFy5kypQp5yXonimFNRERkTpQXV3N3LlzmTt3bq1hrUVE4FiL0I8//sjQoUNJSEjg/vvv5+mnn2bIkCFMnz6dxMREOnXqREhICD///PMpt/PXv/6VDh06kJqaykUXXUR4eDgjRoyotc4dd9yBxWKhdevWhISEcPDgwXOqOTU1lS+//JJvv/2Wzp07061bN/75z3/WDJBiNpv59NNPqaiooEuXLkybNq3W+W0Xyi233MKsWbOYNWsWycnJLFiwgM8//5wWLVoA4O3tzRdffMHWrVtp374999133wndHSMjI/n555+x2WykpqaSlJTErbfeip+fH2Zz3UUok3EmnTZFRETkTykpKak5Ub24uPiCnOwvIlBeXs6+ffuIj4/Hw8PD0eVII3Y+jkW1rImIiIiIiDghhTUREREREalzjz/+ON7e3ie9nO1cbA2VRoMUEREREZE6N2PGDEaPHn3S246P0tjYKayJiIiIiEidCwwMJDAw0NFlODV1gxQREREREXFCCmsiIiIiIiJOSN0gRURE6oCbmxtvvPFGzXUREZE/orAmIiJSB1xdXbnmmmscXYaIiNQj6gYpIiIiIiLihBTWRERE6kB1dTVfffUVX331FdXV1Y4uR0QaoDfffBN/f39Hl3FBXHTRRcycOfOM128oj4W6QYqIiNSBiooKhg0bBkBxcTEuLvoIFpHza8yYMQwdOtTRZch5pE8KEREREZEGwNPTU5NJNzDqBikiIiIi4gQuuugibrnlFu666y4CAwMJDw/noYceqrn9n//8J8nJyVitVmJiYrjhhhsoLi6uuf23Xf927NiByWRi+/bttfbxz3/+k7i4OAzDAGDr1q0MHToUb29vwsLCuPrqq8nOzj7jem+++WZmzpxJQEAAYWFhvPLKK5SUlDB58mR8fHxo1qwZX3/9da2/++GHH+jSpQvu7u5ERERwzz331OoeXlJSwsSJE/H29iYiIoKnn376hH1XVlZy1113ERUVhdVqpWvXrixZsuSM6q5PFNZEREREpMEyDIPSymqHXI4HorPx1ltvYbVaWblyJf/4xz94+OGH+fbbbwEwm838+9//ZvPmzbz11lt8//333HXXXSfdTmJiIh07dmTOnDm1lr/77ruMHz8ek8lEeno6ffv2JSUlhV9++YUFCxZw9OhRRo8efVb1BgcHs2rVKm6++Wauv/56rrzySnr06MHatWtJTU3l6quvprS0FIAjR44wdOhQOnfuzIYNG3jppZd47bXXePTRR2u2eeedd7J48WI+/fRTFi5cyJIlS1izZk2t/U6ePJmff/6Z999/n40bN3LllVcyePBgdu3adca11wcm41yOIhERETkrJSUleHt7A8fOWbNarQ6uSKRhKi8vZ9++fcTHx+Ph4UFpZTWtH/jGIbVsfTgVL7czP+vooosuwmazsXTp0pplXbp0oX///vz9738/Yf25c+dy/fXX17SEvfnmm8ycOZP8/HwA/vWvf/H888+zZ88eAHbu3EliYiJbtmyhdevWPPDAA6xcuZJvvvnf43P48GFiYmLYsWMHCQkJZ1WvzWbDz8+PUaNGMXv2bAAyMjKIiIhg+fLldOvWjfvuu4+PP/6Ybdu2YTKZAHjxxRe5++67KSgooLS0lKCgIGbPns2YMWMAyM3NJTo6mmuvvZZnnnmGPXv20KJFCw4fPkxkZGRNPRdffDFdunTh8ccfP+GxcITfH4vnQuesiYiIiIg4ibZt29b6PSIigszMTAAWL17M448/ztatWyksLKS6upry8nJKSkpO+gXQ2LFjufPOO1mxYgXdunVjzpw5pKSk0Lp1awDWrFnD4sWLa75I+q09e/b8YVj7fb0Wi4WgoCCSk5NrloWFhQHU3Idt27bRvXv3mqAG0LNnT4qLizl8+DB5eXlUVlbSvXv3mtsDAwNJTEys+X3t2rUYhnFCfRUVFQQFBf1hzfWJwpqIiIiINFierha2PpzqsH2fLVdX11q/m0wm7HY7Bw4cYOjQocyYMYNHHnmEwMBAfvrpJ6ZOnUpVVdVJtxUREUG/fv1499136datG++99x7XXXddze12u51LL72UJ5544qR/e671/nbZ8VBmt9uBY91SfxvUji87vu6ZdPqz2+1YLBbWrFmDxVL7MT5Z8KzPFNZERETqgJubG88//3zNdRGpGyaT6ay6IjqrX375herqap5++mnM5mPDTnz44Yd/+HcTJkzg7rvvZty4cezZs4exY8fW3NahQwc+/vhj4uLi6mw6kdatW/Pxxx/XCm3Lli3Dx8eHqKgoAgICcHV1ZcWKFTRp0gSAvLw8du7cSd++fQFo3749NpuNzMxMevfuXSd1O4oGGBEREakDrq6u3Hjjjdx4440nfBMtIvJHmjVrRnV1Nc899xx79+7l7bff5uWXX/7Dvxs1ahSFhYVcf/319OvXj6ioqJrbbrzxRnJzcxk3bhyrVq1i7969LFy4kClTpmCz2S7I/bjhhhs4dOgQN998M9u3b+ezzz7jwQcf5Pbbb8dsNuPt7c3UqVO58847WbRoEZs3b+aaa66pCagACQkJTJgwgYkTJ/LJJ5+wb98+Vq9ezRNPPMH8+fMvSN2OorAmIiIiIuLkUlJS+Oc//8kTTzxBUlISc+bM4W9/+9sf/p2vry+XXnopGzZsYMKECbVui4yM5Oeff8Zms5GamkpSUhK33norfn5+tcLR+RQVFcX8+fNZtWoV7dq1Y8aMGUydOpX777+/Zp0nn3ySPn36MHz4cC6++GJ69epFx44da23njTfeYOLEicyaNYvExESGDx/OypUriYmJuSB1O4pGgxQREakDvx0xrXfv3iecZyEi58f5GIFP5HzQaJAiIiL1RHl5Of369QM0dL+IiJwZdYMUEREREZFaDh48iLe39ykvBw8edHSJjYJa1kREREREpJbIyEjWr19/2tvlwlNYExERERGRWlxcXGjevLmjy2j01A1SRERERETECSmsiYiIiEiDowHPxdHOxzGosCYiIiIiDcbxSedLS0sdXIk0dsePwePH5LnQOWsiIiJ1wNXVlX/84x8110XkwrBYLPj7+5OZmQmAl5cXJpPJwVVJY2IYBqWlpWRmZuLv7/+n5tXUpNgiIiIi0qAYhkFGRgb5+fmOLkUaMX9/f8LDw//UlwUKayIiIiLSINlsNqqqqhxdhjRCrq6uf6pF7TiFNRERkTpgs9lYu3YtAB06dDgvH+IiItKwKayJiIjUgZKSEry9vQEoLi7GarU6uCIREXF2Gg1SRERERETECSmsiYiIiIiIOCGFNRERERERESeksCYiIiIiIuKEFNZERERERESckMKaiIiIiIiIE3JxdAEiIiKNgaurKw8++GDNdRERkT+iedZERERERESckLpBioiIiIiIOCF1gxQREakDdrudbdu2AdCqVSvMZn1fKiIip6dukCIiInWgpKQEb29vAIqLi7FarQ6uSEREnJ2+1hMREREREXFCCmsiIiIiIiJOSGFNRERERETECSmsiYiIiIiIOCGFNRERERERESeksCYiIiIiIuKENM+aiIhIHXB1deWOO+6ouS4iIvJHNM+aiIiIiIiIE1I3SBERERERESekbpAiIiJ1wG63c/DgQQCaNGmC2azvS0VE5PTUDVJERKQOlJSU4O3tDUBxcTFWq9XBFYmIiLPT13oiIiIiIiJOSGFNRERERETECSmsiYiIiIiIOCGFNRERERERESeksCYiIiIiIuKEFNZERERERESckOZZExERqQMuLi7ccMMNNddFRET+iOZZExERERERcULqBikiIiIiIuKE1A9DRESkDhiGQXZ2NgDBwcGYTCYHVyQiIs5O3SBFRETqQElJCd7e3gAUFxdjtVodXJGIiDg7dYMUERERERFxQgprIiIiIiIiTkhhTURERERExAkprImIiIiIiDghhTUREREREREnpLAmIiIiIiLihDTPmoiISB1wcXFh0qRJNddFRET+iOZZExERERERcULqBikiIiIiIuKE1A9DRESkDhiGQWlpKQBeXl6YTCYHVyQiIs5OLWsiIiJ1oLS0FG9vb7y9vWtCm4iIyOkorImIiIiIiDghhTUREREREREnpLAmIiIiIiLihBTWREREREREnJDCmoiIiIiIiBNSWBMREREREXFCmmdNRESkDlgsFq644oqa6yIiIn/EZBiG4egiREREREREpDZ1gxQREREREXFCCmsiIiIiIiJOSGFNRESkDpSUlGAymTCZTJSUlDi6HBERqQcU1kRERERERJyQwpqIiIiIiIgTUlgTERERERFxQgprIiIiIiIiTkhhTURERERExAkprImIiIiIiDghF0cXICIi0hhYLBaGDh1ac11EROSPmAzDMBxdhIiIiIiIiNSmbpAiIiIiIiJOSGFNRERERETECSmsiYiI1IGSkhKsVitWq5WSkhJHlyMiIvWABhgRERGpI6WlpY4uQURE6hG1rImIiIiIiDghhTUREREREREnpLAmIiIiIiLihBTWREREREREnJDCmoiIiIiIiBPSaJAiIiJ1wGw207dv35rrIiIif8RkGIbh6CJERERERESkNn21JyIiIiIi4oQU1kRERERERJyQwpqIiEgdKCkpISQkhJCQEEpKShxdjoiI1AMaYERERKSOZGdnO7oEERGpR9SyJiIiIiIi4oQU1kRERERERJyQwpqIiIiIiIgTUlgTERERERFxQgprIiIiIiIiTkijQYqIiNQBs9lMp06daq6LiIj8EZNhGIajixAREREREZHa9NWeiIiIiIiIE1JYExERERERcUIKayIiInWgtLSUuLg44uLiKC0tdXQ5IiJSD2iAERERkTpgGAYHDhyouS4iIvJH1LImIiIiIiLihBTWREREREREnJDCmoiIiIiIiBNSWBMREREREXFCGmBEREREarHb7ZSWllJWVkZ5eTllZWVUVVVhsVhwcXHBYrHUXFxcXHBzc8PDwwN3d3fMZn0PLCJyviisiYiI1AGTyUTr1q1rrjuK3W5n//797N69mz179rB371727t1LZmYm2dnZZOfmkVdQhGGygMWCyWwBkxmTqXYIMwwDDDsYdgy7Hew2DMOOi8WMm5sr7q6uuLu54ubmiquLCy6/hjuz2Vwr7P32YjabMZlMNReg1u8nW+bu7o6fn1/NJSgoiJiYmJpLSEiIQx9vEZE/w2Ro/GAREZEGyTAM9u7dy7Jly1i7di1r165l3foNlFQZmD28Mbt7YXa3YnL3xuLpjcndisnieiygmc1gMmNxccXV1RWz2YJhGBiGgd1ux263Yxg27Dbbb4KbceyC8euy48s5Fuqwg92OYRz7id0GGGA6TWucyQwm07HL/+7Ysb+z2TDsNgxbJUZ1FUZV+bGf1RUYVRUYVeW4mmwkNo0lqXUr2rRpQ1JSEl27diUkJOTCPvgiIueBwpqIiEgDcvjwYb799lsWL17MkiVLOJKVh8UagNnLH4tPMC4+Qbh4ehMQHIqffwB+fr4E+Pnh5+ONj9UDX28r3lYrVi9PPNxcsVjMmE0mTADH85Lxa/4ywGbYqa62UVlZRVV19bFLVTVVVVVU22xUVlVRbbNj2A1shoFht2Oz2zGMY618dsOO3Q6GYf91/rljIc/4dT//cywAHltuHMuBQFV1NeUVFZRXVFJRUUVZWRlFxUUUFRVTWlyIvboSo7oSe1kRtqIcbGUF2EsLaBYdRp/uXejduzcDBw4kPDy87p4kEZEzpLAmIiJSjxmGwdatW5k3bx7z5s1jzfpNWHxDcPEJxiUoGlfvQCJi4ggNCycyPJi4qAhiIsOwurthMTfs7oFllZUczczhcMZRjmRkkZmTR1Z2DjnZmRiVZdiK86nOO4KtJJcuSS0YPWoEo0aNIjo62tGli4gACmsiIiJ1orS0lM6dOwOwevVqvLy8/tT29u7dy5w5c5gzZw479x3CxT8Ml4BI3IKiiYxrQXRME5rHRtOyeRwB3l4NPpidjYLCQrbs2sfu/YfYfzidzKMZ2MoKqco+RHXeEbq0acYN107j8ssvx93d3dHlikgjprAmIiJSB0pKSvD29gaguLgYq9V61tsoKCjg/fff5+2332bZyl9wCYjANbgJHiFNiG3RmmbNmtK+dQJRIQG4WjQq45nKys5h+dqNbN65l7S0NGylBVQe3YuvUcLkMSOYMeM64uLiHF2miDRCCmsiIiJ14FzDmmEYLFu2jP/+9798OPcjqtx8cQmKxj20KXGJSbRq1ZJOya0I9fdW69l5kJWdy+Llq1izaTtFBblU56Zhy9rL2KEX8eBf71doE5E6pbAmIiJSB842rGVnZzN79mxeffVVduw/gmtgNK5hzQiLT6R1UjKd27UhNjwIF81rdkHY7TZWrdvID6s2cOjQIaryj2LP3MXVwy/mr3+5h6ioKEeXKCKNgMKaiIhIHTjTsLZmzRqeeeYZPvhwLoZ3MK4h8VgjmpGQ3J5O7drQNrEpnq6aJrUu7dyzh8+++YH9h9OozkuDrN3cMulK7vvLvX/63EMRkdNRWBMREakDpwtr1dXVzJs3j2effZafV63BNTgWt/AWRCUkkZzclu7tkwgN8MGsyZ0dauv27Xz27VKOpKVTmX2QwOocnv6/e7jiiis08baIXBAKayIiInXgZGEtPz+fV199leeee44juSW4hsbjGZlAYttOdOuUQtsWcbi7WhxcufyWgcHqNeuYt/BH8vNzqUzbSZe4AF7+99O0bNnS0eWJSAOjsCYiIlIHSktLad26NQCff/45r7zyCm++9RYVbn64hTXHLyaR5I6d6dW5PXHhwRosxMlVVlbyxYJv+XHNZioLsrCnb+PhmdO49ZabMes8QhE5TxTWRERE6siGDRt4/PHHmfvJPFyDYnCLSCCiRTIpHTrSu2MyQb5nP5y/OFZWVhZvzf2M/YfTqTy6hw6RXrz93xeIjY11dGki0gAorImIiFxgK1as4LHHHuOrBd/iGhqPe1RLmrfrQueO7emSlKABQ+o5A4NFi3/kyyUrqCjMxnx0B888OItJE692dGkiUs8prImIiFwga9eu5a9//Stff7sIt9CmeES3pmWH7vTq1oXk5jGauLqBOZp5lNfe+4S0jCzK07YzuncSL/37X3h6ejq6NBGppxTWREREzrOtW7fywAMP8MlnX+IWFo9HTDIt23fmwDev4+nmxgMvz8XNw8PRZcoFYLfb+Wz+Ar5fuZ7KnDTiXAv5ePYrJCQkOLo0EamHFNZERETOk8zMTO6//35ee/MtXILj8IhuQ+vOvejXuztNQ/2ZPuDYACOvLd6Gh6fm52rItmzfxltz51NckIMpYxsvPXo3Y8eMdnRZIlLPKKyJiIj8SRUVFTz33HM88sijlLn54x6TRMvOvbmoVw+Sm8fgYjZTXlbK1H6tAIW1xqKgoICX33qXQxnZlB/eyi1XDODxRx7SaJEicsYU1kRERP6E+fPnc8stt7A/swCPJslEJ3ejf79+dGnTAjeX//1TrrDWONntNubO+4Kla7dSmbWffi0Cee+NV2rm3BMROR19tSMiInIOMjIyGDt2LMNGXM4Ruz/hPS9n6FXXM+v6KfRql1grqEnjZTZbGDNqBGMv6YdHaFN+OFRJj9QRHDp0yNGliUg9oLGCRUREzoJhGLz++uvcccedlLj64dN+CB37DOKS/r2JCPRxdHnipHp1705YSAivvPspBws96DL4Cr6c/SIdO3Z0dGki4sT0tZ+IiMgZOnLkCBdffDHX3nQbFeFJNO0/jskzbmHyqCEKavKHWjRvzj03TSEsLIzykDZcPOFGvlm40NFliYgTU1gTERE5A/PmzaNt23b8tOUAAZ2HcfGYa7ltxmS6tG6KxWw6o234+Afi4x94gSsVZxYUGMTdN19LfHQYpqhkrrjlId6e856jyxIRJ6UBRkRERE6jtLSUWbNm8Z/X38Ijrj1NOvTlsmHDSG4Whdl0ZiFN5Peqq6v471tz2LL3CBVHtvHQ9FHceftMR5clIk5GYU1EROQUdu/ezYgRI9h+KAvP5l3pOuASRg3uh79VE1rLn2cYdt6b+zHLNu6iMmM3N13Wk7898hAmfQkgIr9SWBMRETmJBQsWMG7ceEq9QglJ7svgS0fQp0MrXDRHlpxHBgZfff0NC35aS2XWfqaltudfTzyuwCYigMKaiIhILYZh8OSTT3Lv/Q/gHptCfNeBjBk1guZRIX9qu5Xl5fzjtkkA3PWvt3DzUOuc/M9333/PvO9XUJV9kKv7tuGFf/1DgU1ENHS/iIjIcWVlZUyZMoUP532JZ8s+tO83jLHDUwnw9vzT27YbdratW1FzXeS3Lu7fH4vFhY+/XcrbP26j4qbb+O9z/8SsllyRRk3vACIiIkBubi4DBw5k7leL8Gk7kIGjpzB59LDzEtREzkS/vn0YM/giXIOi+XDFXq657iZsNpujyxIRB1JYExGRRu/gwYP06tWLVdsPEtRpCKOvuY5RA7rj4aIOKFK3evfqyYRh/XELjGDe+iNMmDpDgU2kEVNYExGRRm3Tpk10796dPXnVhHUZxlXXTKVv+5ZnPHeayPnWvVs3rr5sEK7+EczfksmYSdMV2EQaKYU1ERFptH766Sd69+5DjjmAmJ6XMWXKZDomxmlgB3G4Lp07c82owbgFRLBwRw4Tr70Ru13nOoo0NgprIiLSKC1ZsoTU1MFUBMTTot/lTL/mKhJjwhxdlkiNjh06MHHEYFz9I/h8fRrTbroNDeIt0rgorImISKOzaNEihl4yDHt4K1r2G8W0q8YSExJwwffr7uGJu4cGLJEz16ljByZcOgDXgAjmrtzHDbfdpcAm0ohonjUREWlUFi5cyGUjRkJkG1r2HcHksaMI9vVydFkip/XTzz/z/vwlVOUc5trU9jz990cdXZKI1AG1rImISKOxYMEChl82AiLb0PqikUwdr6Am9UOvnj25fFAvXINj+M+CX3jk7086uiQRqQMak1hERBqF77//npGjLscUlUSb/pdzzdgRBFrVJVHqj359+1JRUcGXP8IT7y4kwN+fm2ZMd3RZInIBKayJiEiDt2zZMoZfdhlEtKZ1v1EOCWqVFeU8e+8MAG7928u4uXvU6f6lYRg8aBAlJaUsXgP3PP8eAf5+TBg72tFlicgForAmIiIN2po1axgyZCi2kEQS+lzGNaMd06Jmt9tZv2xxzXWRczVq5GWUlJayCrj+0RcJ8PNl6JDBji5LRC4AnbMmIiIN1qZNmxg0aBCVAXE07zWMKeMuJ0jnqEk9Z8LEVePGkhQXgUt4IhPu/Bs//fyzo8sSkQtAYU1ERBqkffv2MWjQIEq8IojvMYxrxl1JiJ/V0WWJnBdms5lpk6+mWUQQhCUycsa9rN+wwdFlich5prAmIiINTlZWFqmpqeTiQ5NuQ5k0/koiAn0cXZbIeeViceGGaycTE+yDLSSBoZNuZffu3Y4uS0TOI4U1ERFpUIqLi7nkkkvYn1dJRKdBjB83uk4mvBZxBHc3d26ZMY1gPy/KApoycOy1pKWlObosETlPFNZERKTBqKqq4sorr2TdrsMEpQxgzNhxJESFOroskQvKy8vKbddPx9/bkzyvGC6+4hpyc3MdXZaInAcKayIi0iAYhsG0adP49ufV+CZdxKgx42nXPMbRZYnUCT8/P26bMRVvbytp5hBSr5hIcXGxo8sSkT/JZBiG4egiRERE/qx77rmHJ//9Et7JFzNywhQu7pKE2WRydFkidSotLY2nX5lNaUEOHQKrWfDxu7i7uzu6LBE5R2pZExGReu/ZZ5/lH//6N16JvUgdOZYBnRXUpHGKjIzkxomjcfcJZE22iSsnTsNmszm6LBE5RwprIiJSr33wwQfcfufdeCX0oPew0VzSpzMWs4KaNF5Nmzbl2rGX4eYbwpK9hUyecTPqSCVSPymsiYhIvfX9999z9cRJuDftRKeBo7gitTeuFuf8aKusKOfZv1zPs3+5nsqKckeXIw1c69atmTQyFRf/CD79ZT+33XWvAptIPeScn2giIiJ/YP369YwYMRJLdDJJ/UYwbvggPFxdHF3WKdntdlZ9P59V38/Hbrc7uhxpBDp26MDowX1xDWrCq9+s4dG//8PRJYnIWXLeTzUREZFTOHDgAEOGDKHSL4aEXsO4atQwfDzdHF2WiNPp06snJaWlfPWjib+//TWBAf7cOOM6R5clImdIYU1EROqV3NxcBg8eTK5hJbbbECaMHkmwr5ejyxJxWkMGDaSkpIwlhp27/vU2Af7+jB87xtFlicgZUFgTEZF6o6ysjOHDh7P7aCER3YYzdvRoYoL9HV2WiNO7fOSllJSWshq49sFn8PfzZeiQIY4uS0T+gM5ZExGResFms3HVVVexYuN2Atv25/Ix42nZJMzRZYnUCyZMXD1uNEnNonCLTmLczP/jp59+cnRZIvIHFNZERMTpGYbBzJkzmff1d/i07svwMVfTMbGJo8sSqVfMZjPTJl1Fs+gwLFFtuGz6HWzYsMHRZYnIaSisiYiI03vyySd54ZXX8ErozpArr6ZPSqImvRY5By4WF26cdg1RoUHYw1oz+Krr2b17t6PLEpFTMBmadENERJzYnDlzuGrSNVhb9qb/lVO4fGAvp51L7XQMw6CivAwAdw9PTAqb4kClZaU8+dwrHM3Owb9oLz9/8T5RUVGOLktEfkdhTUREnNaiRYsYMvQSXGI70G34RK4akYqHq8XRZYk0CIWFhfzj+VfIzcsjvDKNHz9/j9DQUEeXJSK/Uf++mhQRkUZhw4YNjBw5CktUEskDRjLm0osV1ETOI19fX2677hp8fPzIcI3gouFjyczMdHRZIvIbCmsiIuJ0du/eTWpqKpV+MTTvdSlXXX4pPh71e9LrqsoKXn54Fi8/PIuqygpHlyMCQFBQMLNmTMLbz580l3AuGj6WrKwsR5clIr9SWBMREady6NAhLr74YvJMvjTpNpSrxowkyKf+T3pts9lYOv8jls7/CJvN5uhyRGqEhoYx69pJePsHccQSxkXDxyiwiTgJhTUREXEamZmZDBw4kLQSiOo6hKvGjdWk1yJ1ICwsjNunX4VPQDCHzWH0Gz6G7OxsR5cl0ugprImIiFPIz88nNTWVPZlFhHcezLhxE2geFezoskQajfDwiJrAdujXwJaTk+PoskQaNYU1ERFxuJKSEoYNG8bmfemEdExl9PiraRMX7uiyRBqd8PAIbpt2FT7+QRw0hXKRWthEHEphTUREHKqiooJRo0axYuNOAlMGcsX4ibRvHu3oskQarYiICGZOPxbYDhFKn2GjSU9Pd3RZIo2SwpqIiDhMVVUVEyZMYNGyX/Bv259R4yfRpVW8JowWcbDIiEhuv3Yi3gFBpLlE0HvYaA4cOODoskQaHYU1ERFxiKqqKsaNG8enCxbhk9SP4eMm0bNtC8wKaiJOITw8nLuuvwb/oGCyvWLpfek4du3a5eiyRBoVk2EYhqOLEBGRxqWyspIxY8bw+cIl+Cb3Z/i4yVzcJRmLueEGNcMwKMrPBcDHP1Cth1Jv5OXl8c+X3yQnNxevvF188+5/SE5OdnRZIo2CwpqIiNSpiooKrrzySr5atBS/5P4MHz+FAZ2TGnRQE6nviooK+edLr5OZk4c5YytzX/wb/fv3d3RZIg2eukGKiEidKS8v5/LLL+er75fi13YAl02YqqAmUg/4+Phy503TaRIZhhGZxGXX3cO7773v6LJEGjy1rImISJ0oLy9n5MiRfLt0JX5tL2bE+Mlc1LF1owlqVZUVvPPsIwBcdetfcXVzd3BFImevqqqKV96cw9Z9h6k4uJH/mzGaO2fd7uiyRBoshTUREbngSktLGTlyJIuWr8E/eQAjJkymb/tWjSaoAZSXlTK1XysAXlu8DQ9PLwdXJHJuDMPOu3M/YfmGnVSkbWdKakeeffpJzGZ12BI53/SqEhGRCyo/P5/U1FQWrVhHQMogRl41lYs6NK6gJtKQmExmxo++nKF9O+Me1ZI3Fm3iivETKS8vd3RpIg2OwpqIiFwwGRkZ9O3blxVb9hLUYTCXT5hM3/aJGp5fpJ4zYWLooIFMuPRiPMKb892eIvpdMoqsrCxHlybSoCisiYjIBbF371569erFtrR8QjsPZeykqfRql6CgJtKAdO/ahesnjMArJJqtpT50uXg4W7ZscXRZIg2GwpqIiJx3GzdupFevXhwqMRHZbThXT5pC11bxCmoiDVCrli2549qrCQiLJM+vOb1GXM38+fMdXZZIg6CwJiIi59U333xDr169ybUEEttrBJOvuYa2zaIcXZaIXECREZHcd8t0YmNiIKY9V97yEE/9819oHDuRP0dhTUREzptXXnmFSy4dTlVIAgn9r2DqpKtoER3i6LJEpA5Yrd7MunEaHVon4B7Xnode+4JxE6dQWlrq6NJE6i0N3S8iIn+a3W7n3nvv5cl/PotH8y6kDBjJuJFDCfL2dHRpTsNut5OTcQSAoPAoDXMuDZaBwcJFi/lqyUoqco4Q71rAvHffoGnTpo4uTaTeUVgTEZE/paSkhEmTJvHpVwvxTOhB72FjGJXaB6u7q6NLExEH2rZ9B6/P/YLivCxcMrfz9rOPMXToUEeXJVKvKKyJiMg5O3z4MMOHD2fjnsP4tOrNoFHjGNKzE24uajUSEcjLy+Wlt97nSEYmFQc3cdeky3jg/r+oZVnkDCmsiYjIOVm5ciUjRowgp9qd4JQBXHrFWHq1TdBk16dQXVXJhy8/CcDoGXfi4urm4IpE6kZ1dRVvf/gpa7bsovLoHvo0C2DOG68QEBDg6NJEnJ7CmoiInLV33nmHadOvhZBmNOkymCuuuJw2cRGYNDT/KZWXlTK1XysAXlu8DQ9PLwdXJFK3fvjpZz75dikVuekElqXx4WvP0aVLF0eXJeLU1AYtIiJnrLKykptvvpmJU6Zhju1AUuoEpk+ZRFJ8pIKaiJxW3149mTllLIGRceQHJNB/7HX881/PaHh/kdNQy5qIiJyRw4cPc+WVV7Jq03a8WnSn++BRXDH4Irw91J3vTKhlTeSY0rJSXp/zIdv3HqYifSf9W4Yw+zV1ixQ5GbWsiYjIH1q0aBHt27dnze50gjsPY9Q1NzDh0osV1ETkrHl5enHjtElcdnEvvGJa88OhStr3upiVK1c6ujQRp6OwJiIip2S32/nb3/7GoMFDKLZGEd9vDJOvvYGBXZNxtegjRETOjQkTA/tfxMyp4wiKaUF+YCv6j5nOU0//U90iRX5D3SBFROSk8vPzmTRpEl98swivZl1oN+AyrrhkIGH+3o4urV5SN0iRkysrK+O1d+eybc9BKtO2069lKLNfe4XAwEBHlybicPpaVERETrBhwwY6derE/B9W4p+SyuAJM5hy5XAFNRE57zw9Pblx6tWMGNgbrybJ/Hi4iva9B7F8+XJHlybicGpZExGRWt566y1mzLgeIyiW8PYDGDbicrq2bqr50/4ku91O2v7dAETGNdekwCInsf/AAV59bx65OZlUHdrEgzdcxR2zbtfrRRothTUREQGgoqKCW2+9lVdefwuP+I607DWEKy4bRlyYRmgTkbpTXl7G63M+YuveA1Sm76Z3i0Deee0/BAUFObo0kTqnsCYiIuzevZvRo0ezcfdBvFp0o8fgUYwc1AcfjfYoIg5gYPD9kqV8vng5Fblp+Jce5v1XnqVHjx6OLk2kTimsiYg0ch9++CHTpk2nwhpGYFJvhgy/nL4dW2u0x/OsuqqSz958AYDLrrkRF1cFYZE/cuDgQf777qfk5mZTdXAT988Yy9133qFukdJoKKyJiDRS5eXl3H777bz839fxiEuhWbdURl02jIToUEwmnZ92vmk0SJFzU15exhvvzmXLnoNUZuymR7wfc15/hZCQEEeXJnLB6WsJEZFGaNeuXXTv3p1X3v4Qa1J/el0+jRumTCAxJkxBTUScioeHJzOmXM2oQX3wjG7FikwzKX2HsGTJEkeXJnLBKayJiDQy77//Ph06dGBrRjEh3S7jyqk3MWH4xfh5eTi6NBGRkzJhon/fPtwxfQIh0XGUhCYxdMosHnjoYWw2m6PLE7lgFNZERBqJsrIyZsyYwfirr8EW3oaEgeOZft0MBnRqo/PTRKReiImO5v6ZM2jfphUeTTvxz49/pN/gS0lLS3N0aSIXhD6dRUQagZ07d9K9e3deffcjvJP70/uKadwweQIt1e1RROoZdzd3pk8cx9hhA7BGt2R9kRcpFw1lwYIFji5N5LxTWBMRaeDeffddOnbsyLajJYR2u4wrptzM+EvV7VFE6rde3btx14yJRDRpRmVkCiOvv49Zd91NVVWVo0sTOW8U1kREGqiysjKuvfZarpo05ddujxOYft31DOikYflFpGGICA/nL7dcS9f2bfFs1pn/LFhLzwGD2b9/v6NLEzkvNHS/iEgDtH37dkaPHs3W/Wl4Ne9K99RRjBjUW61pDmS32di3YzMA8YlJmC0WB1ck0rCsWrOWD776npKcNFwzd/Dfp/6PUaNGOboskT9FYU1EpIGZM2cO1103gyqfCIKT+zL4slH0ad9KrWki0uBlZ2fx8jsfkZaeTsWBjUwZ1ounn/wHHh76okrqJ4U1EZEGorS0lFtuuYXXZ8/BI649LbqnMnL4MFpEBWsQERFpNGw2Gx9+9iU/r9lMZeZemnmWMfft10lISHB0aSJnTWFNRKQB2LJlC2PHjmXbwaPHuj0OHsmIi3vj5+Xu6NLkV9VVlSz44HUABo+Zgourm4MrEmnY1m3cxLuff0tx1hFMGVt54bG/MGHCBEeXJXJWFNZEROoxwzD4z3/+w223z8Ie0ITgpN4MGT6S3ur26HTKy0qZ2q8VAK8t3oaHp5eDKxJp+PLy8/jPOx9z6NBhyg9tYnTfdrz43LNYrVZHlyZyRvRJLiJST+Xk5DBq1ChuuPV2TLEdaZ06getmXEd/TXItIgJAgH8Ad90whf69uuDVrCMfrzlMp1792bx5s6NLEzkj+jQXEamHFi9eTLt27fhyyUr8UgYzYOx0rp80luaRIY4uTUTEqZjNZi6/dCgzJowiIK4VaZ5xdL9kDK+88l/UwUycncKaiEg9UlVVxX333ceAgYPINgfSpO8Yrr7uFkYP6oO3h86BEhE5lTYtW3LfzVNpkdgKl2bdmPnUG4wefzWFhYWOLk3klHTOmohIPbF3717Gjx/P6k3b8Wzaifb9L+WywRcTGejj6NLkDOicNRHnYBh2vvxmEd/+/AsVOUcIqTrKnP88Q/fu3R1dmsgJ1LImIuLkDMNg9uzZpKS0Z93+bIK7XMZl19zE5CuHK6iJiJwlk8nMpYMHcvM1owmKaUaufwIDxs3gvvsfoKqqytHlidSiljURESeWnp7Oddddx5fffIdHXHuadh7AZcMvoVWTcMyaO61eUcuaiPMpKytj9oefsGnnPiqP7iXBp4p3X/8PLVu2dHRpIoDCmoiIUzIMg3fffZebb76ZYrM31hZd6HHxMIb176m50+opu83G9vWrAGiZ0gWzxeLgikTkuBWrf2HugiWU5h7FnraFx2fN4KabbsSkL8XEwRTWREScTEZGBjNmzODzrxfi0SSZ2I79GDJkKO0TYrGY9Y+DiMiFkJeXy6vvfsKBI2mUp+2kZ9MA3vjPC0RHRzu6NGnEFNZERJyEYRi899573HzzzRSZrFibd6b7xcMY2q8ngd4eji5PRKTBMww7C7//gfk/rqIi/ygumTv42903M336NMxmDfUgdU9hTUTECezevZsbbriB735chkeTZGJS+jLkkiF0SIjFRf8gNAjV1VV8P+9dAPqPGI+Li6uDKxKRUzmSnsYbH3xGekYmFWk76BTjzRv/eYFmzZo5ujRpZBTWREQcqKKign/84x889vjfMPwisTbrRNd+qQzr35MAq1rTGhINMCJSv9jtdhZ8t4SFy1ZTnpsB6dt48NZpzJx5Kxadcyp1RGFNRMRBFixYwMyZM9l9JBuP2LY069SPIYMG0CY+SuemNUAKayL1U2ZWJm9++DkHDx+hPH0XbYLMvPbiv2nbtq2jS5NGQH1rRETq2M6dOxk2bBhDh4/kQLknYT0v59JJN3Hz1Am0bRatoCYi4kRCQ0K588apjBgyAN/4FHbaw+h6yVhuvPkW8vLyHF2eNHBqWRMRqSN5eXk8/vjjPPvv5zAFROMZ146OvQeSelEPooP9HV2eXGBqWROp//Lycnnnky/ZsfcgldkH8So8yGP33saUKVPUNVIuCIU1EZELrKioiGeffZannnqKErMV95gkEjr15eL+fUlqGqUBRBoJhTWRhmPjlq189PUSsjPTqUzbQcsgV57/5xP06NHD0aVJA+Pi6AJERBqqsrIyXnrpJf72t7+RV2HCPTqFuDad6d37Inp1aI2Hq76FFRGpj9q2aU2bloksWPwji5b5sSf3KP1HT2fURR157JH/Iz4+3tElSgOhljURkfOssrKS119/nUceeYSjhRW4RSYS3qozXXv14aLObfHxcHN0ifIn2e12KivKqSgrpaKslPKyUirKS6koLaW8/Niy6qpKqquqsFVXUV1VRUVZKR/9958ADBk7DTCwVVdTXf2/daqrq49dr67CbrMBx+bfq/n52+sYtW8DzGYLLq6uuLi6Hfvp4oqLqysWV7ea6y61rrvi7uGFp7cPnlYfvLx98LR642U99run1RuLi77XFTmdwsIC3v/8GzZu201VXhq2jO1cMzKVv973FyIiIhxdntRzCmsiIudJcXExr776Kk8//TTp+aW4RSQSnNCerj37clHXFA3F76SqKisoyM2mICeLgtxs8nOyKMjNoqQwn+LCAkqKCigpzP/1Zx628hKMylJcLSZczeBiBlcLuJpNuPz6+/GL2WTCYgaLCUwmg4JyMJkgzGrCzXJsfYvp2LoWM5hNtS8mwGI2YQIw1R4VzPSbcWiOf5LbAZsd7IaB3Th23fbrzyr7/35W2Q1sdqj+zaXKblBlO3Z7pe3Y9Qob4OKBxdMHN58gfAND8AsMxq/mZ+3rPv6BmHXejjRSe/bv5+OvF3Pw0CEqsw9hyt7DTRNHc9dddxIQEODo8qSeUlgTEfmTcnJyeO6553juuecoqLbgFp5AYNNkOnXvTf/uHQj21blJjmIYBnlZR8nOOEx2xhGyM46Q8+vP7IwjFGRnQHkhXq4mvFzBy9WEhwt4uprwdAGrqwkfd7C6Hfvdw+V/AetYCDPh5uqCm5sLbq6uuLi4YHFxwWJxxWyxYLaYMZnMmM1mTGYzll9/mkwWMJnBbMFkPrYeJjNmswXMFswmM5jNwK+JzWTi+If1sehm+vV6zVXAhGHYMew2DLsNu80OdhuGYTvWSvfr8uPLDLsdu91OdXUV1ZVVVFVXYquqwlZVRXllBZVVNqrtxv/CnA1KqgyKKgwKK6Co0qC0Csqqj/0sqTSosFvwCIzAJ7QJIRHRhETGHLtERBMR2wwfP/3DKg3fpq3b+ey7H0lPS6Mqaz/uRUeYdd013HzzTfj4+Di6PKlnFNZERM7RunXreP7553n3vfep9gzAPTyBkObJdOzagz6d2ymk1aHSkiLS9u0m/dA+Mg7uJf3gPjIO7SX78D7cjXJ83cHHzYSPuwlfNwjyMhHgacLXzYSbBTzcLPhYPfHy9MTVwxN3D0/c3NyxuHlgdvPE7O6Jxc0LV3cvLO5emF09cHF1x+TmASYLhsmMYbLUbu6qrwwDE3aM6kpsFaVUV5RiKy+hsqyI6rIiqstKqK4owVZRQkVZGRUV5VRWlFFaWk55tUGFDUqrILfUTlYp5JcbFFYYFFQY2N398Y5oRkRcCyJjmxEZ14yo+ASCw6MwNYTHTuRXBgar127gy8XLyclMpzJzPx4l6dw4aSy33noLQUFBji5R6gmFNRGRs1BZWcnHH3/M888/z/LVa3ANisEtPIGoxHZ07NyZXh2S8PNyd3SZDZbdZiPj8H4O7d7Owd3bOLh7O4d2b6M0+zCBnib8PY5dgr1MhHmbCPQw4eVmxt/XGx8fKx5e3nh6WXH18sPVOwA3b3/crP5YPH2xm1wxzBeuC191tY0FP60FYHCvDri4NKDugoaByVZJRXEuZYU5VBZmU1WcT3VJPmUlxZSVFFJYVEJplUFpNWSV2EkrMsgtM8grh3KLN77RLYlp0YYmLVoR26I1UfEtcHNX12Gp3+x2Oz8uX8V3y34hNzuTqqwDWPIOMm3cCGbdfjtRUVGOLlGcnMKaiMgZ2Lt3L2+99RavvPIKWUUVuAbH4hGVSGK7znRq3472rZri6aqBGM4nwzDIzjjC3q0b2LN1Pbu3rOfQjk1YTeUEeZkI8jQR4W0iyteEn7uJID8rAf5+ePn44enth5tvMB7+obj7h2K4eGGYHfv8lJVX0G/ifQAsnv0Ynh6NK9TbK0oozUmjLC+DysIsKotyKCksoKiwgKIKG0UVkFZ0LMRllxrkVVjwjGhBk1YdaNYmhWZt2hEZ2xyzprqQeshut7NizTq+/fkXMtPTqMo5hJG9l/GXXswds2aRmJjo6BLFSSmsiYicQn5+PnPnzmX27Nn8tGwFroGRuIY2JaBJS5I6dKZHx3bERQRpnrTzpKSogH3bNrF7yzr2bFnP3q3rsZTlEGI1EeJlItrXRIyfmQAvF8KCA/DxD8TqH4SHfwRewdGYvQKwm12dtitiYw9rp2JUV1KSfYjS7MNU5KZTVphNUV4uBSUVFFcapBXZOVBgkFliUIQV39hkmid3JDGlCy2SOuBp9Xb0XRA5YwYGazduZuGPqzh85DDVeelUpO2gX8dW3HbLTQwaNEhfSEgtCmsiIr9RVVXFwoULmT17Np999hnVLl64BEbjHt6c+DbtaZOURPeU1gR6ezq61HovK/0wO9avYseG1ezYsJrcQ7sItZoI8zYT5WMizt9EoJeFyJBA/INC8A4KwyukCR5BUdhcrMcG6KhHFNbOgmFQXnCUoox9lGUdojwvg/zcbArLqskrh315dg4XGWSWmfCIaEl82260TOlCYrvO+PgHOrp6kTOydccuFvy4gr37D1BdlE1l2k5i/V24ZcZ0Jk2ahLe3vogQhTUREQzDYN26dcyePZv33nuPrLwCXAKicAuNJ7xFW1omp9AhqRVNI0Nwc6lfAcFZ2O12juzbxY71q9i+YRU7N6zGKMwg3NtEmNVEvL+JSF8z4YG+hISE4BsYgkdIDNbQOOzufvUumJ2MwtqfY9hsFGftpyRjP+XZBynIziC/sJiCcoODBXb25RukF9uxhLWkRYdeJHXuRWJKFzw8NdCPOLe0o0dZ+MNKNm7bQVlBNpUZu3Evy2by2JHcfNNNNG3a1NEligMprIlIo3XkyBHmzJnD7Nmz2bJ1Gy5+YbgERRMQn0zLdh1p07o1rZtF46dJrM9adVUl+7ZvYvv61exYv4rdm1fjVV1EuLeJSB8TLQLNhHhbiAoLIjAkHO+QaKzh8Vh8QrGbG+bjrbB2/pXlZ1J0ZCelmfspzkknLy+fgnKD/fl29uQZHC2z4B3bnoROPUnu0oemrdpqHjhxWqVlZXz/8ypWrNtMblYG1TmHqcrcw6Du7bnt1pvp16+fRk1thBTWRKRRKSkp4dNPP2X27Nl89913mL38cQmIxCsqkYSULiS2SqJdq2YE+3joXLSzUFZSzK7Na2u6NR7Ytp4AlwoifEzE+JppGmAmxNuVyIhQAoLD8AptgndEcwwP/wbRanYmFNYuvIriPAoObqUkfQ+FWUfILywmr8xgZ46d/QV28vAjsm0f2vXoT9tufTXvmzglw7Dzy4YtLFmxjgMHD1BdmEll+i6ah3hzy/XTuOqqq/DyUotxY6GwJiINnt1uZ8mSJcyePZuPPvqI0mpwDYjENSSW+OQuJLZpS9s2iUQH+eHhqm/dz0Re9lF2b1rL9g3HWs4y9m4l1NNOuLeZJn4mmgWYCfXzJCI8DL/gMLzC4vAKa4rd1dtpBwC50BTW6l5JTjqFh7ZRenQPeZnp5BWXk1FssDXLzpEiA9eoZFp1G0BKj/7EJrTRwA7idA4dSWfh0hVs3r6LisJcKjP34FWZz9RxI7npxhuJiYlxdIlygSmsiUiDtW3bNt5++23eeecdDqVl4BoQgUtQE8JatKVl244ktW5F8+hQvN1d1LXkNKqrKjmwcyu7Nq9l16a17Nq8lrKcI0R4mwj3NhPnbyLWz0xEkC9h4aH4BkdgDW+KR3AsNovmyTqu2mbjh1WbAejbJQkXdcerU3ZbNQWHd1J4aCvFRw+Ql5tLXrnBrhw7e/Ls5JkCiEq5iA69LqZtt754eFkdXbJIjZLSEhb9tJIV67aQn5tFdW4atqx9DO3dkdtuuYmePXvqc6yBUlgTkQYlKyuL999/n9mzZ/PLmrXHzkMLjMK3SStatutEq1ataNMijgAvN8z6YDupvKyj7Nq0piac7d+xGau5gnBvM+HeJpr6m4jysxATFkRwSDA+wVFYI5rh4heB3aLWIqkfygpyyNu3ibKMXeRlHiGvpJIjhQZbs+2klbrgl9iD9r1T6dDrYvyCQhxdrghwrKfI6vUbWbxiLYcOp2ErzqEyfSetIv2Yef10xo4di7u73ocbEoU1Ean3Kioq+PLLL5k9ezbz58/H8PTDJSASj/DmNE/uRGKr1qS0TiDUzwtXi7o5/VZ1VSX7d2ypCWa7N68lPzONEOuxURojfUw0CzQTFeBFdEQIgcEheAdH4RkWj+EZiGF2dfRdEPnT7NXV5B/aTuGhrRRl7CU3v4jMEoOtWTb2F4BLVFva9BxMp76DiGiikfnEORw4dJiFPy5n8869VBXnU5m5D19bIdPHj+KGG64nIiLC0SXKeaCwJiL1kmEYrFixgtmzZ/PBBx9QUG47Nml1SDzRCckktmlL+6SWxIQG6jy038jNzGDX5jU1wWz/ji24GRWEeZsIs5qJ9jURH2ihSXgwEWHB+AWF4hXSBPfAaKpdvDBMeizPlbpB1h9FmYfI27uB4vRd5OVkk1tmsCPbzq5cOxW+sbToPoSOfQbSrHWKznMThysqKuK7pctZuWEbRfk5VOWmYc/ez4j+3Zh5y0106dLF0SXKn6CwJiL1yr59+3jnnXeYPXs2ew4ewcU/AreQWAKaJNKybXvatm5FYlwU3u4uji7V4aoqK9i/cwu7fz3PbNemtRRkpRPsZfp18mkTTf3NNAn2IiYylODgYHyDw/EMiQPPAKotno12MJALQQOM1E/lhbnk7FlHyZGd5GWlkVtqY2+ene3ZdnJNgcR2HkTHPoNo06kHrm56TsVxbDYbK9esY/HK9aSnp9dMtN0uLoSZN17HFVdcgaurekPUNwprIuL0CgoKmDt3Lm+//TY//rwcV/9wXAKjsUY2p0VSCq1btaJ9q+b4NfLz0HIy048Fs1/PN9u/Ywte5kpCrWZCrSZifE3EBvyv1cw/OBRrcAxuAZFUuVjVpfECU1ir/yrLS8ndu4GSw9vIzzhETkkFhwrsbMkyyKjwICSpDx37pJLSoz9WXz9HlyuN2J79+1n44wq27d5PVXEelRm7CTSXcuPkCcyYMYPAwEBHlyhnSGFNRJxSdXU1CxcuZPbs2Xz2+RdUu/ngEhiFW2hT4lsl06pVazomtyTM3weLufEFtKrKCvbv2FwzOuOuTWspy8sg1Hqs1SzcaiY+wEREgBcxEaEEhoTgGxSOV0gchqe/Ws0cQGGtYbFXV5N3YAuFBzdTmLGfnIJS0ovtbMq0c6jIjLVpJ1J6p9KxzyCCw6McXa40UvkF+Xz743JWbdxOSX4OVdkHsRQcZuKoIdx+2200bapzMJ2dwpqIOKV9+/bRtGkz3KNa4RrejLDYRFq2SaJT2zbERwY3uoFCco6m1Qpmh3dtwd+1qiacxfubiPC1EBUaTGhoMH7BoXgFx+IeGEmVxQvDrG6hjqaw1nAZhkFB2h4K92+kKGMv2Tl5ZJYYbM60sS/fgJBEknoNpmOfgcS2aK0h1qXOVduq+WnFGpasWkfW0Qyq8tKoPrqbS3p15K47bqdr166OLlFOQWFNRJzWiNHjKY3pRss2ybRtEYuXW+MIHLbqag7u3saODavZsWE1uzatpboggzBvM2FWE9G+x+Y1C/X3JDI8FP+gUKzBUVhDY7F7+Gv4fCelsNZ4lOZmkLdnPcXpu8jJOkpWiZ1tWXZ259kpco+gZfdBdOwzkMSULri4qPux1B0Dg41btvHtT6vZf+Ag1YVZVKbvoGOzCO66/VYuvfRSDZrjZBTWRMRpFZRV8d+le/Fxd8HHo+H+Q1NeWsLuzetqwtm+revwM5cR5m0i3Hps6PwQbzNRocEEBYfgExyONTQOt4BIdWesRxTWGqfK4nzy9m2g+MgOcjIOk1Vcze5cOztz7GTZvInpMICOfQbStmtfPK3eji5XGpH9Bw+z4IdlbN21l6rCY4ORRPuYuOPmGUycOBFPT09HlygorImIE2uoYa2kqICta5azfd1KdmxYTcberYR62onwMRPja6JpgJlgHw8iI0LwDw7DGtwEr7A4DA9/dWesxxTWxFZVTv6+TRQd3kpu2kGyC8s4WGBnW7bB4RILgYnd6dB3MO17DSAgOMzR5UojkZuXx4IlP7Nm0w7KCrKozNiDd3U+N1wzjhtvvJGQEE0K70gKayLitBpKWKuurmLP5vVsWr2UTSt/5PCODURYDSJ9zMT7m4j1NxMe5ENYaOixQUDCm+IREotdrWYNSnW1jQU/rQVgcK8OuLhonrXGzLDbKDi8g8IDmylI30dWbiFHS+xsybSzv8DANSqZ5F6ptO/Rn5jmLXWem1xw5eVlfLd0JcvWbiI/J5Pq7EOQd4AZV13OU//4h6PLa7QU1kTEadXXsGYYBukH9rBp1VI2r/qJHetW4G8uIdr3WDhrGmAmKjSAiIgwfIKj8Qpviqt/JHaLm6NLFxEHKck6QN7ejRSn7yE7K4vsUoMdOXb25Nkpcg0lvlN/2vXoR1Knnnh4WR1drjRgdruNZb+s4/vlazmans6wzs15+x/3OrqsRkthTUScVn0Ka5UV5Wxds5y1S79j/bLvsRWkEeNrJtbPTGKwmcgAT6KjIvAPi8Y7MgHXwGjsZoUzETlReWEOuXvWU5q+i9zMNHJLqjlQYGdHjkFaiQXf5l1o13MAKd37Ed4k3tHlSgNlYLB03Q56t4piQt82ji6n0VJYExGn5exhrSg/l3U/f8/apd+yZdWPBLmU0cTPTMtgE3EBrsRGhREUHoVPRHM8wpthd/FydMniQNU2Gys37ACga7tEXCzqBil/zFZVQf6BLRQf2U5BxgFy8ovJLLGzNcvOgQKDCu8YErsOoF2PfrRq3xVXN50LKefP3qxiOsQGMDQ5wtGlNFoKayLitJwxrOXnZLJq8desXPQV+zavJsYHmvqbaBNqJjbEmyYxTfCPaop3dEvsHgFg0hDIcowGGJHzoSTrEAUHNlOcsYfczKPklNrYnWtnd66dzAp3glv3pF2P/qT06EdQWKSjy5V6TmHN8TSsmIjIHyjIyWL1kgWsWPQl+zauJNbPRItAMyO6uNA0KpjI6Bh8oxPxjEjAZvEAwO7gmkWkYbKGxGANiQGgqryYggNbaHN4B3lHD5FbWMLR4h/Y+uFifnrVwAhqTsuuA0jp2Z8WSR2wuOjfPpH6Rq9aEZGTKC0pYtX381n2zTx2b1hBnC8kBJm5rKsrLZqEEdkkDv/YJCyBTbCbj7X62Rxcs4g0Lq4e3gQndiU4sStxhkHR0b0U7N9Ch6P7yM3OIrd0P7vW/pevF/2Hd2zehCX1JqXnANp264tfYLCjyxeRM6CwJiLyK1t1NZtW/chPX3/KuqXfEOlZSUKQmUs6u5DQJORYQItriyUwtma+M7WgiYhTMJnwCW+GT3gzACpLCsnfv5E2aTvJO3qE3KIyMgoXsmX2Aha9YOASkUSbHheT0qM/cYlJmhpAxEkprIlIo7d/5xaWzv+Y5Qs/w6syh4QgM9e1M5MQHUpcfFMC4tviEhRX04KmE31FxNm5WX0JbdML2vQizm6j4MhuCg9upePRfeTk5JBbtp2dS7fyyRfPUOgSTEyH/qT0HEBS5154eGowJBFnobAmIo1SaXEhyxZ+zpLP3yd77yYSgsxcHmumVbgvLZrFExifhGdkS52DJiL1nslswT8mEf+YRODY1AB5+zbSOn0X+UfTyCvN58jhj1nzwly+LHXBp3lXknscO9ctNLKJg6sXadwU1kSk0TAMgx0bVrPk8/f55fuviPSsIDnUQvsenrRqHktoXCI+cW2xufkBOgdNRBomD98gItr1g3b9sFVVkn9oG80PbqPD0X3kFRSTXbqCbZ8v47+z/w9bYHNa9xxMxz6D1F1SxAEU1kSkwSspLODH+R+x6NM5lB/dQ8tgC1OTzbSJDSO+WQIBLTpg+ESCyayAJheMq4sLd0wZWXNdxBlYXN0IatqOoKbtACjKOkzB/o0kpe8hNzvz2CAlK1/k06+fp8gtjNjOA+nUJ5WWHbri4uIcU6qINGSaZ01EnNafnWdt/47NfPvx26xcOI8or0rahppJiXKndUI8oc2S8YxKwmbRXFciIidTWVpM7r71FB/aRt7RI+SVVnKowM7WLIOMaivhyRfRse9g2nbri6fV29HlygWgedYcT2FNRJzWuYS1qsoKVi76iu8+eZuMHWtpHWKhU6SZ5Ngg4hNaEdCiE3avUFBXHhGRM2arqiRn/xaKD26hMGMfuUXlpBfb2ZRp50iJC/6tetOx3yV06DUAq4+fo8uV80RhzfEU1kTEaZ1NWMtKP8z3895lyefv4WfLo22YhY6RLrRJiCe8RVu8YpJrBgsRcQSb3c76bXsBSGnVFIvZ7OCKRM6NYdgpOLyL/P2bKErfS25+ARnFdjZn2jlQZME3oTsd+l1Kx94D8fbzd3S58icorDmewpqIOK0/CmuGYbBt7QoWfPA6m5d/R0IAdIww066JLwmJiQQldAa/aDDpn2JxvLLyCvpNvA+AxbMfw9NDXXClYSg6epC8vesoOrKL3Lw8jhYbbDxqY3+hGd+WPek6aBQd+wzC3cPT0aXKWVJYczyd4Swi9U51VSXLv/2Cr99/lcKDW2kTauHGjhbatYgmpkVrfJp2wubm4+gyRUQaBZ+wJviEHRvivyjrCHm719I6bQc5uXlkFC9j45tL+e4lD8Lbp9ItdSRtOvbAokF2RM6IXikiUm8U5efy3Sfv8N3Hb+FbnUNKuIWOPbxo26o5YYmdsIS2wDC7akRHEREH8QmJwickCriU4uw0sneuIvHQdvIKijiS/QXL//kZnxkBxPcYTs/BozQdgMgfUFgTEaeXtn8XSz+dzfJvPqGpTxXDo8ykNAmgZauWBCV2w/CJAJMJ9ekWEXEe3sGReAePACD30C6id/9Cq7Rd5BYVsn/nbD5f+hZFXk1I6n85vYdeTlBYpGMLFnFCCmsi4pQMw2DRtwv578N/5/DGn0gKtTAt2UxKswjiEpPwa9EFm5ufApqISD0QGNOCwJgW2G02svZuImbvWtpm7Ce7+Ajblj7DK5/+C88WPelxyTg69L4YF1c3R5cs4hQU1kTEqZSVlfHOO+/wzDPPkLl/G21DzYzq6ka7hDiiWrbHI6Ytdou7ujqKiNRDZouFsBYphLVIobKijOyda4jbt55uWelkFK1g3as/8+1LfsT3HkXfS8cSFd/C0SWLOJTCmog4hYyMDF544QVefvklrFW5dIiwcFVPKy1aNCOkZWc8w1pgmCzYHV2oiIicF27unkQm9yIyuRfFuUcJ37qMZge3kFNYwt5tb/LB4texR6TQeeg4ug4Yhoenl6NLFqlzGrpfRBxq/fr1PPPMM3zw3rs09bPROdJMl6YBdO3YjqYpvVid7YaHmwV3F4ujSxX5U6qqq/lg/lIAxgztjatGwxM5gWG3c3T3RvJ3r6bg6EGyim2sz7Czq9iD6K6XMuDyicS2aO3oMhsNDd3veAprIlLnbDYbX375Jf/6179Y9fMPJIWa6RploUebaDp2aE+zlN5YPP0orbLx065sPFzNCmsiIo1MWVE+mVuXU3hgAzn5RezLs7Mm3YYtPIXul02iS78huLppvsILSWHN8RTWRKTOFBUV8frrr/Pvf/+bgvR9JIeZ6R7jQp+UBJI7diUqsQO4eABgAAUlZSzfm4u7iwV3V01sLSLSGBmGQe7B7eTvWEl+xgEyi22sy7Czv9yX+Iuu5KLh4zSS5AWyP6eUlGg/RnSM1RQLDqKwJiIX3L59+3juued47bXXCDAVkRJuoXucld6d2tCmcy/8IpqDuXaXsIrKSm657Q484lIwqiuhutJB1YucH3bDICu/DIAQf0/M+sdH5OzZqgiy5xBoKqS43MauXBtr0u3srgzCHtAEi3eQQsV55BIYRcWRbWSt/Rar1erocholdZgXkQvCMAyWLl3KM888w1dffEaLABgZb6ZnYgTdOrQloWNv3P3CwPRHLWYGFk8f0PdKUs/Zq218+MUGAG4c3ReLuvaKnJN8wsgzDDzdM0nyyqBtWDGHCwtYnbaebYetFAe1xj0yEZOLhv//s0wWNzRHjmOpZU1EzqvKyko++OADnnnmGXZtXkubUAtdIs30aRdPhw4diU3qitnD9w+3YwAVFRUcyS/HZtfblNR/peUVtB99LwDrPvwbXh4610bkfMjNSuPQ+h/J2b+No4UVbDxqZ0ehO636j2bE+ClEx8Y7usR6LdzXnYQotVg6isKaiJwXhw8f5pVXXuG///0v5pKjtA0z0y3Gnd4dW5HcsRshcUngon9OpfEqKavAu8/1ABT/+BJWT70eRM6n8vIyNqxcws41P5GelcueXDu/pNuJ6zSI62+5nQEDBihwSL2jsCYi58wwDBYtWsSLL77I/C8/p6mfQftwM12b+tGjYzKtOvbGGtIEzOruJaKwJlI3DGDH5nVsWf4dhw/sJa3IYOURG9WBLZg44zauuuoqvLw0Z5vUDwprInLW8vPzefPNN3nppZc4emAnrUMsdI400zOpCSnt2tG0XQ9crIGOLlPEqSisidS9oxlprP1xAQe2r+doYSUbjtrZX+5D6pjp3HDjTcTExDi6RJHTUlgTkTO2bt06XnzxRd6dM4cw93KSwyx0beJJj/YtadO+i7o6ipyGwpqI45SWlrL250XsWvcTmbmF7My1sS7DRMvew7nxltvo0aOHukiKU1JYE5HTKiws5P333+e1115jy7pVtAox0yHcQteEUDqlJJPYoQfu/pFnMKqjSOOmsCbieHbDzpZ1q9m6chHpRw5xsMDOqiN2XCKTmXrj7YwePRp3d702xXkorInICQzD4Oeff+a1115j7ocfEuxaRlKohY6RrnRv14I27ToS3bIDJjfNuSJypiqrqnn8jS8B+MvkYbi5avYcEUc6fGg/65cu4MDOzRwtqmZNmo00exAjJt7AddddR3h4uKNLFFFYE5H/OXr0KG+99Ravv/46aft2kBh8rBWtU/MQOia3IqF9N7xD4jRgiIiINBhFRYWsXbqQPRtXkJ5bzNYsO5uyzaQMuJwbb76Vrl27OrpEacQU1kQauerqahYsWMBrr73G1199SYyPndYhZjpFu9OtbQKt26YQmdAek5u3o0sVERG5YKptNjat/pkdvyzhSFo6+/LtrDpiwyu2A9NuuJUrr7xSXSSlzimsiTRChmHwyy+/8M477/D+++9jKskiIchMhwgznROiaJfcmhbtuuHuH6FWNJHzxG63s21fOgCt4iMwm3Wep4izOrB3J5uWfcv+XdtIK6xm9REbGUYQoybOYMaMGURERDi6RGkkFNZEGpE9e/YwZ84c3nnnHTIO7CIhyEzbMDNJkd50aptIq3adCYltDa4eji5VpMHRACMi9U9hQT7rflrInk2rSMstZkumnY1ZJtoPGMWNN99Kt27dNIqkXFAKayINXHZ2Nh9++CHvvPMOa1ctp1mgmdbBZtpGuNE5qTktWrYmtnVnXLz8QR84IheMwppI/VVdXc3mNT+z45cfOXQkjX35dlYfseEZk8LUG25lzJgx6iIpF4TCmkgDlJ+fz+eff86HH37Iom+/IdrbTkKgiXbhLnRs2YRWrVrSLLkL7n7h6uYoUkcU1kQahoP7dv3aRXIr6YXVrEm3caTKn9Qrr2H69GtJSEhwdInSgCisiTQQeXl5fPbZZ3z00Ucs+nYh4V7VNA841s0xpXk4bVol0jypM75hcWBxdXS5Io2OwppIw1JUWMDanxayd9Mq0nOL2JljZ226nYik3ky59npGjhyJm5ubo8uUek5hTaQey83N5bPPPmPu3LksXvQdYZ7VNAs00y7MTFJsCK0Tm9G0dXtCY1uBi/4xFHEkhTWRhslmt7N9w0p2rlnKgf37OFJo8Eu6jSx7AJeMncL06dNp3ry5o8uUekphTaSeOXr0KF988QUfffQRPyxeRISXjfgAM21DzSTHh9ImsRlNW7UjpElLcPV0dLki8iuFNZGGLyc7k43LvmXf1nWk5RazLcvOhqN2mqRcxOTpM7jsssvU2iZnRWFNpB7Yvn07n332GZ999hlrV6+gia+JeH8TyWEWkuJCaJ3QnPjW7QiJSVRAE3FSCmsijYfNZmPb+hXsXPsTBw7sJ63QYG26jaN2PwaMmMA110wmJSVFI0nKH1JYE3FCNpuNlStXMm/ePD777DOO7NtJnL+ZpgFm2oSYSWoaTovmTWnaqj3B0S001L5IPVBZVc19L34CwGM3jMLN1cXBFYlIXcjOzGDj8kXs27aOjNxi9uTZWZ9hxy2iNWOunsKECRMIDQ11dJnipBTWRJxEcXExixYt4osvvuCLL77AVpRFnL+JFoFmWoa60rZFDE2bNiWuVQp+YXFgUTcKERGR+sJmt7Nr0y/sWr+MA3t3c7Somo2ZdnblmUjsOojR4yYwfPhwrFaro0sVJ6KwJuJAu3bt4quvvmL+/Pn89OMPBLtXEetvplWwiRahXiQnxBHfrDmxLdvj4R8GZn0TLyIiUt+VFBeyefWP7NvyC0fSjnKk6Fhr2+Eydzr1u5Sx48aTmpqqudtEYU2kLlVUVPDDDz8wf/58vvrqK9IP7KaJn5k4fxOtgs0kRAXSsnkssc1aEp3Q9teJqs2OLltEzgO73c7BjFwAmoQHYjbrtS0ikH74ANvXLOXgrk2kZRVysMDOxkw72TZveg0exbhx4+nXrx8Wi+ZFbYwU1kQusMOHDzN//nzmz5/Pou++w0opTfxMNA80kxDsSnLzKOLj42nSIomg6GaYXL0cXbKIXAAaYERETscADu3Zxs51yzm4eytpucXsz7ezOdOgyCWIAcPHcOWVV9KjRw8Ft0ZEYU3kPKuoqODnn39m4cKFLFiwgJ1bNhDjZybG10TrEDPNw31o0zyOmPimNEloh4dfGFjUvVGkoVNYE5EzZbfb2Lt9E3s2LOfA3p0czS9jX76dzZl2il2C6JU6gpEjR9K/f391lWzgFNZE/iTDMNi5cyfffPMN33zzDT8sWYKPuYwYXxPNAsw0DzLTOj6SpnGxNGnemrC4RMzuPqDhekUaFYU1ETkX1dVV7Ny0hn1bVnNo3x6yiso5kG9nS5ZBVpUXnS4awsiRoxg6dCg+Pj6OLlfOM4U1kXOQl5fH999/XxPQcjMOEu1rpomviZbBZpqG+dCqaQzRsXE0SUzBOygSLK6OLltEHEhhTUT+rOrqKvZu28D+bWs5vHcX6blFHC402JJlJ63UQqsuA7hs5CguueQSIiIiHF2unAcKayJnoLq6mtWrV7Nw4UK++eYb1qxeSZiXQYyfmeaBJpoFutK6aRRxcU2IbtqS0CaJmN291XomIjUU1kTkfLLb7Rzcs419W37h8J7tpGXlc7TkWFfJ/fkGQfHJDBg8jKFDh9K1a1ed51ZPKayJnITdbmfz5s18//33LFq0iB9/+AEPezFRPibi/M20CDTTIjqIFvFNiI5rRnTzJNx9gzW0voicksKaiFwoBpBxaB+7N60kbe920jKOkl1isCvXzp48O0UmPzr1TWXo0EtITU0lJCTE0SXLGTrjsGb6gxaCSZMm8eabb56Pmmp56KGHmDdvHuvXrz/v2z4XS5YsoV+/fuTl5eHv7+/ocuQ8MQyDPXv21ISzxYsXU1WURZSPmehfuzbGBHrSslkMTZrEEtMiGf+wJuDq6ejSRaSeUFgTkbpSWJDL3s2/cGTPVg4fPEBmYRlphQbbsu0cLjIIT+jAxamXMHDgQLp06YKrq07VcFZn3AyQnp5ec/2DDz7ggQceYMeOHTXLPD1r/9NaVVWlJ16cWlpaWk04+/7778lJP0iUr5lIHxOjmpiJDbTSMj6KmJgYIuMTCY9t8evAIJobSUTOnovFzA1X9Ku5LiJyofj6BZLScxApPQdhs1VzcNdWDm5fz5EDu0k7mkVO6Ua2fLieL155hHy7laQufbl44CAGDhxI4v+3d+/RUdX3u8efPblnkgyS+z1cBRQEuSuIWhQQi0tt0Uojglh7WhdF2yK/IgKt/n4VF5bWip56ENZZWuFI0RbBa1VQCXILgiD3EBLIhUsSksk9+Z4/UqZEYzExmdnJvF9rZXW6s2d4Jh/i4pnvnu9cfvklF2ngPW26DHLVqlWaM2eOSktLJUnHjx9Xjx49tGbNGi1fvlxbt27V888/rxkzZmjlypVasmSJcnJylJGRodmzZ+tnP/uZ57EeffRRvf7668rPz1dCQoKmTZumxx9/XEFBQVq1apVmzJjR7M9euXKl7rvvPlmWpRdeeEHr16/XBx98oPT0dL300kuKjY3VrFmztH37dg0aNEgvv/yyevXq5bn/+vXrtWjRIu3bt09JSUmaPn265s+fr8DApt5qWZZefPFFbdiwQe+8846Sk5O1dOlSTZkyxfM8L9ZRK4pofydPntTmzZu1adMmbdq0SSeOHlBSpEOJEZZ6d7eUflmQ+vdIUnpqshLTeyupR/+mD6V2cI03AADoGkrOFuvYvp0qyDmgU/knVFxWpWK30YEzjco7b6TIRI0ad5PGjx+v8ePHKz4+3teR/Vq7lrWMjAwtXbpUQ4YMUUhIiDZs2KCFCxfqz3/+s4YMGaLs7Gw98MADeuaZZzR9+nRJ0hNPPKEbb7xRSUlJ2rt3rx544AE98sgjmjt3rqqqqrRgwQK9/fbbev/99yVJLpdLYWFhsixLycnJeuaZZzR48GA9+uij2r17t3r27Km5c+cqLS1NM2fOVLdu3fTWW29Jkt555x1NnTpVf/rTnzR27FgdPXpUP/nJT3Tfffdp4cKFTT8Qy1JKSoqWLFmi4cOH69lnn9VLL72k3NxcuVwu/f3vf9edd96pgwcPKioqSmFhYXK5XO0xC7QjY4yOHz/uKWebN29WwYmjSoywlBTpUK/LLKW6AnR5erzSU1OUlN5Tyb0GKCQihs88AwAAfqGxsUEnjx9W3qE9Ksw9opMnT+mMu1555xt1+JzRyfONis64Qtfd2LTqNnbsWDmdTl/H9ivtWtaWLVumX/ziF57z0tLS9NRTT+lHP/qR59gTTzyhjRs3asuWLS0+9tNPP601a9Zox44dkr75PWuWZemxxx7T7373O0nS1q1bNXr0aK1YsUIzZ86UJK1evVozZsxQVVWVJOm6667TpEmT9F//9V+ex3n55Zc1d+5cnTp1qsXHdbvdioyM1MaNGzVx4kTes2ZTjY2N2r9/v7Zs2aLNmzdr8+bNKi3KU0KEQ4mRlqec9UmNV3pKkhJSM5Tca4DCu8WzpT4ArzDG6ExpuSQpplsklxkBsJ3amirlHtyrk0e+UEH+cZ0sPK2SKqNjJY3KKTU6XR2glP7DNHbcDbr++ut1zTXXKDw83Nexu7R2XUIYNmyY5/bp06eVl5en+++/Xw888IDneH19fbOVqLVr12rZsmU6cuSIKioqVF9fr6ioqG/15w0aNMhz+8IS7cCBA5sdq66u1vnz5xUVFaWdO3dq+/btevLJJz3nNDQ0qLq6WpWVlZ6/bBc/rtPpVGRkpIqLi7/tjwFeUF5erm3btmnLli3asmWLsrK2KKC2XIkRlhIiLN0S51BS71D1SUtQWkqSElJ7KLlHP4W6YqWAYF/HB+CHKqtrFXfzHElsMALAnoJDwtRn0Aj1GTRCklRedk45X+5WQc4Bncw7rtPnylVSvUMH1m3TOyv/R6erApRx5QhPeRs9ejTlrZ21a1m7eFm0sbFRkvTiiy9q5MiRzc678DkPW7du1d13363FixdrwoQJcrlcWr16tZYuXfqt/ryLNzC58AplS8cuZGlsbNTixYt1xx13fO2xQkNDW3zcC49z4THgfcYY5ebmeorZp59+qv1f7FFMmFG801JypEPTL7eU1M2p3mkJSkpMVEJaTyX16KeQiGguawQAAGiDSFd3DRp1owaNulFG0tnCfJ04vFfFuYd16mSeis5VqKR6u/a+9pk2rvhvna4KUI8rR2jcDTd6yttXNyFE63TYv2Lj4+OVnJysY8eOadq0aS2e8+mnnyo9PV3z58/3HMvNzW12TnBwsBoaGtol09VXX62DBw+qd+/ebX6M4OCmVZn2yoSvc7vd2rVrl7Zt26asrCxt2bJF7nMFinc6FOe01C/K0sSRgUqNc6lnaqISEhMVn9pLcWm9FRASyYYgAAAA7cySFJOQopiEFGnsJBljdLYoX3mH9qroxBGdzM9TUUm5zlVt157/95k2/p8ndaYmUD2+svJGeWudDl1yWLRokWbPnq2oqChNmjRJNTU12rFjh0pKSvTII4+od+/eOnHihFavXq3hw4drw4YNev3115s9RkZGhnJycrR7926lpKQoMjJSISFtu3Tk8ccf16233qrU1FT98Ic/lMPh0J49e7R371498cQT3+ox0tPTZVmW3nzzTd1yyy0KCwtTREREm/Kg6SMe9u7dq23btmn79u3atm2bDh/Yr5gwozinpXinpdtTHEroF6peqfFKSUpQfFKqEtL7yBWT3PQ5Z7zvAwAAwKssy1JMQqpiElIl/et9uQUnlHdkn4r/Vd6KSypUUr1NX7y2VW+teFJnqgOU0m+orhlzncaMGaNrr71W3bt39/EzsbcOLWuzZs1SeHi4nn76ac2dO1dOp1MDBw7UnDlzJEm33XabHn74YT300EOqqanR5MmTtWDBAi1atMjzGHfeeafWrVunG264QaWlpZ6t+9tiwoQJevPNN/Xb3/5WS5YsUVBQkPr166dZs2Z968dITk7W4sWLNW/ePM2YMUP33nsvW/d/S42NjTp8+LCnlG3fvl2f785WhKNWsU5LseGWhrgsTRkVqKSYKKUnxSk+PkHxqT2UkN5Xwc7LuKQRAADAhizLUmxSumKT0iX9q7wV5unEob0qPnFEp07m63RphUqqdunY+h366JWlKqxoVPe0/hp5zViNGTNGY8aM8SyMoEmbdoMELqWhoUGHDh1Sdna2du3a1fS/O3fIUVuu2HBLMeGWUqIspbkcinOFqmdKvBLi4xWbmKL41F6KikmUAlk1A9B1uKtqFHHd/5LEBiMA/E/Te97ylH9kv4rzjqiw4KQKT5eorNoot6xRx0uNitxGAa5EDRn57/J25ZVXeva78EeUNXxntbW12rdv379L2a5d2vP55wpurFSs06GYcEtJEZbSuzkU7QxUz5Q4JSQkKDY+UXEpPRWdmCYr2ClZDl8/FQDoMJQ1AGiuvPSs8o/sU2HuYRUX5OtUYbFKqxpUWGF0tKRRRW6jCjnVe+BwjRg5SiNHjtTIkSP96oO6KWtoFbfbrc8//7zZitmX+75QZGC9ov+1YpYY0bRiFhMRpIykWMXHxSo2PkExSemKSe6hwNAoNgEB4Hdqauv04H//X0nS//7NvQoJ5jMeAeBiNVVu5R87oIKcgzp9KlcFBQU6565VabV0rKRRJ8uNiioaFRaTpquGjdKoUU0FbsiQIc12du9KKGtokTFGJ0+e1J49ezxf2dnZOn7koC4LlWLCLUWHW0qJtJQc5VBsVKgykuMUGxurmLgExSZnKDohVQ52ZwQAAEAbNDTUqyjvmE4dP6Rzp46rqKhARafP6XyNUUF50wd1F7uNSmoDlNL3Kg0b0VTeRowYod69e8vh6PxXbVHWILfbrX379jUrZnv2fK6GylJFh1nqHta0+Ueqy1Kc06H4yyKUlhiruLg4dY9LVFxyhrrFpcgKDudSRgAAAHSYyooynco5oOK8ozpTcFKFRQU6U1apsmrpRFnT6lux26gmIEIZ/QdryNVDNXRo01efPn063fvfKGt+pry8XO+//77nIwv27NmjEzlHdFmoPMUs3tlUzC4Lcyg1IVrxsdGKiY7VZfFJik/pqcjucWz+AQCtZIxRZXWtJCk8NJjdzgCgHRjTqHNF+Tp1/JBO5+fodFGBiouKVVpdr7JqKbe0UQUVRmcqjWoc4frxT2brd0886evY3xr7oPuZoqIi/fDOO3R1okNxToe+F2UpZnSQYrtFKDUhWjExMeoeE6foxFRFJ6YrKCxSCgj2dWwA6PQqq2vZYAQA2pllORSdkKbohDTPsfq6WhWfzFFx3jGdLTyhM8VFKj59WmWVtUoPPefDtK1HWfMzPXv21KTrhumBwZZSY6PULT5VMUlpiuwWJwWFchkjAAAAOrXAoGAlZVyupIzLPccaG+pUcihLpu8NPkzWepQ1P+NwOLR+/Xpp6/NSRLwUFObrSAAAAECHcgQEKTo6VoqO9nWUVmEZBQAAAABsiLIGAAAAADZEWQMAAAAAG6KsAQAAAIANscEIAABeEOBw6AffG+a5DQDApVDWAADwgtCQIL32+5/5OgYAoBPhpT0AAAAAsCHKGgAAAADYEGUNAAAvcFfVyBo+U9bwmXJX1fg6DgCgE6CsAQAAAIANUdYAAAAAwIYoawAAAABgQ5Q1AAAAALAhyhoAAAAA2BBlDQAAAABsKNDXAeBdxhhVVVYqsK5OprZWMgG+jgQAfqG+vl4TR1/puV1Ta/k4EQD4F6u+XkGSOtN/fS1jjPF1CHiP2+1W74RIzR4RrPJao5p6xg8AAICur+dlDt2/5G8KvXKyr6N8a6ys+aG6Bqm02igyRAoP6kyvLQAAAABtU1JtZAKCfB2jVVhZ8zPGGFVWVko15VJjva/jAAAAAF4Tflm8rE5U2ChrAAB4gdvtVlxcnCSpuLhYTqfTx4kAAHbHZZAAAHhJZWWlryMAADoRtu4HAAAAABuirAEAAACADVHWAAAAAMCGKGsAAAAAYEOUNQAAAACwIXaDBADACxwOh8aNG+e5DQDApfA5awAAAABgQ7y0BwAAAAA2RFkDAAAAABuirAEA4AVut1uxsbGKjY2V2+32dRwAQCfABiMAAHjJmTNnfB0BANCJsLIGAAAAADZEWQMAAAAAG6KsAQAAAIANUdYAAAAAwIYoawAAAABgQ+wGCQCAFzgcDg0bNsxzGwCAS7GMMcbXIQAAAAAAzfHSHgAAAADYEGUNAAAAAGyIsgYAgBdUVlYqIyNDGRkZqqys9HUcAEAnwAYjAAB4gTFGubm5ntsAAFwKK2sAAAAAYEOUNQAAAACwIcoaAAAAANgQZQ0AAAAAbIiyBgAAAAA2xG6QAAB4gWVZGjBggOc2AACXYhn2DwYAAAAA2+EySAAAAACwIcoaAAAAANgQZQ0AAC+orKzUFVdcoSuuuEKVlZW+jgMA6ATYYAQAAC8wxmj//v2e2wAAXAorawAAAABgQ5Q1AAAAALAhyhoAAAAA2BBlDQAAAABsiLIGAAAAADbEbpAAAHiBZVlKT0/33AYA4FIsw/7BAAAAAGA7XAYJAAAAADZEWQMAAAAAG6KsAQDgBVVVVRo+fLiGDx+uqqoqX8cBAHQCvGcNAAAvcLvdioiIkCRVVFTI6XT6OBEAwO5YWQMAAAAAG6KsAQAAAIANUdYAAAAAwIYoawAAAABgQ5Q1AAAAALChQF8HAADAX8TExPg6AgCgE2HrfgAAAACwIS6DBAAAAAAboqwBAAAAgA1R1gAA8IKqqipdf/31uv7661VVVeXrOACAToD3rAEA4AVut1sRERGSpIqKCjmdTh8nAgDYHStrAAAAAGBDlDUAAAAAsCHKGgAAAADYEGUNAAAAAGyIsgYAAAAANhTo6wAAAPiL8PBwX0cAAHQibN0PAAAAADbEZZAAAAAAYEOUNQAAAACwIcoaAABeUF1drcmTJ2vy5Mmqrq72dRwAQCfAe9YAAPACt9utiIgISVJFRYWcTqePEwEA7I6VNQAAAACwIcoaAAAAANgQZQ0AAAAAbIiyBgAAAAA2RFkDAAAAABsK9HUAf2GMUXl5ua9jAAB8xO12e26fP39eDQ0NPkwDALCDyMhIWZb1jd9n634vOX/+vFwul69jAAAAALCJsrIyRUVFfeP3KWtewsrad3f+/HmlpqYqLy/vP/6lhncxF3tiLvbEXOyJudgTc7En5tK+LrWyxmWQXmJZFn+h20lUVBQ/SxtiLvbEXOyJudgTc7En5mJPzMU72GAEAAAAAGyIsgYAAAAANkRZQ6cREhKihQsXKiQkxNdRcBHmYk/MxZ6Yiz0xF3tiLvbEXLyLDUYAAAAAwIZYWQMAAAAAG6KsAQAAAIANUdYAAAAAwIYoawAAAABgQ5Q12FZJSYkyMzPlcrnkcrmUmZmp0tLSS97vyy+/1JQpU+RyuRQZGalRo0bpxIkTHR/YT7R1Lhc8+OCDsixLy5Yt67CM/qi1c6mrq9Ojjz6qgQMHyul0KikpSffee69OnTrlvdBd0PLly9WjRw+FhoZq6NCh+vjjj//j+Zs2bdLQoUMVGhqqnj176oUXXvBSUv/SmrmsW7dON910k2JjYxUVFaXRo0frnXfe8WJa/9Ha35cLPv30UwUGBmrw4MEdG9BPtXYuNTU1mj9/vtLT0xUSEqJevXrppZde8lLaro+yBtu65557tHv3br399tt6++23tXv3bmVmZv7H+xw9elRjxoxRv3799NFHH+nzzz/XggULFBoa6qXUXV9b5nLBG2+8oc8++0xJSUkdnNL/tHYulZWV2rVrlxYsWKBdu3Zp3bp1OnTokKZMmeLF1F3LmjVrNGfOHM2fP1/Z2dkaO3asJk2a9I0vFuXk5OiWW27R2LFjlZ2drd/85jeaPXu2/va3v3k5edfW2rls3rxZN910kzZu3KidO3fqhhtu0Pe//31lZ2d7OXnX1tq5XFBWVqZ7771X3/ve97yU1L+0ZS5Tp07VP//5T61YsUIHDx7Uq6++qn79+nkxdRdnABvav3+/kWS2bt3qOZaVlWUkmQMHDnzj/e666y7z4x//2BsR/VJb52KMMfn5+SY5Odl88cUXJj093fzhD3/o4LT+47vM5WLbtm0zkkxubm5HxOzyRowYYX760582O9avXz8zb968Fs+fO3eu6devX7NjDz74oBk1alSHZfRHrZ1LSwYMGGAWL17c3tH8Wlvnctddd5nHHnvMLFy40Fx11VUdmNA/tXYub731lnG5XObs2bPeiOeXWFmDLWVlZcnlcmnkyJGeY6NGjZLL5dKWLVtavE9jY6M2bNigvn37asKECYqLi9PIkSP1xhtveCl119eWuUhNs8nMzNSvf/1rXXHFFd6I6lfaOpevKisrk2VZ6tatWwek7Npqa2u1c+dO3Xzzzc2O33zzzd84g6ysrK+dP2HCBO3YsUN1dXUdltWftGUuX9XY2Kjy8nJ17969IyL6pbbOZeXKlTp69KgWLlzY0RH9Ulvm8o9//EPDhg3TkiVLlJycrL59++pXv/qVqqqqvBHZL1DWYEuFhYWKi4v72vG4uDgVFha2eJ/i4mJVVFTo97//vSZOnKh3331Xt99+u+644w5t2rSpoyP7hbbMRZKeeuopBQYGavbs2R0Zz2+1dS4Xq66u1rx583TPPfcoKiqqvSN2eWfOnFFDQ4Pi4+ObHY+Pj//GGRQWFrZ4fn19vc6cOdNhWf1JW+byVUuXLpXb7dbUqVM7IqJfastcDh8+rHnz5umVV15RYGCgN2L6nbbM5dixY/rkk0/0xRdf6PXXX9eyZcu0du1a/fznP/dGZL9AWYNXLVq0SJZl/cevHTt2SJIsy/ra/Y0xLR6Xml79lKTbbrtNDz/8sAYPHqx58+bp1ltv5U37l9CRc9m5c6f++Mc/atWqVd94DlrWkXO5WF1dne6++241NjZq+fLl7f48/MlXf96XmkFL57d0HN9Na+dywauvvqpFixZpzZo1Lb4ggu/m286loaFB99xzjxYvXqy+fft6K57fas3vS2NjoyzL0iuvvKIRI0bolltu0TPPPKNVq1axutZOeGkCXvXQQw/p7rvv/o/nZGRkaM+ePSoqKvra906fPv21V3wuiImJUWBgoAYMGNDseP/+/fXJJ5+0PbQf6Mi5fPzxxyouLlZaWprnWENDg375y19q2bJlOn78+HfK3pV15FwuqKur09SpU5WTk6MPPviAVbU2iomJUUBAwNdefS4uLv7GGSQkJLR4fmBgoKKjozssqz9py1wuWLNmje6//3699tprGj9+fEfG9DutnUt5ebl27Nih7OxsPfTQQ5KaSoIxRoGBgXr33Xd14403eiV7V9aW35fExEQlJyfL5XJ5jvXv31/GGOXn56tPnz4dmtkfUNbgVTExMYqJibnkeaNHj1ZZWZm2bdumESNGSJI+++wzlZWV6ZprrmnxPsHBwRo+fLgOHjzY7PihQ4eUnp7+3cN3YR05l8zMzK/9Q2fChAnKzMzUjBkzvnv4Lqwj5yL9u6gdPnxYH374IQXhOwgODtbQoUP13nvv6fbbb/ccf++993Tbbbe1eJ/Ro0dr/fr1zY69++67GjZsmIKCgjo0r79oy1ykphW1mTNn6tVXX9XkyZO9EdWvtHYuUVFR2rt3b7Njy5cv1wcffKC1a9eqR48eHZ7ZH7Tl9+Xaa6/Va6+9poqKCkVEREhq+neXw+FQSkqKV3J3eb7a2QS4lIkTJ5pBgwaZrKwsk5WVZQYOHGhuvfXWZudcfvnlZt26dZ7/v27dOhMUFGT+8pe/mMOHD5tnn33WBAQEmI8//tjb8bustszlq9gNsv21di51dXVmypQpJiUlxezevdsUFBR4vmpqanzxFDq91atXm6CgILNixQqzf/9+M2fOHON0Os3x48eNMcbMmzfPZGZmes4/duyYCQ8PNw8//LDZv3+/WbFihQkKCjJr16711VPoklo7l7/+9a8mMDDQPPfcc81+L0pLS331FLqk1s7lq9gNsmO0di7l5eUmJSXF/OAHPzD79u0zmzZtMn369DGzZs3y1VPocihrsK2zZ8+aadOmmcjISBMZGWmmTZtmSkpKmp0jyaxcubLZsRUrVpjevXub0NBQc9VVV5k33njDe6H9QFvncjHKWvtr7VxycnKMpBa/PvzwQ6/n7yqee+45k56eboKDg83VV19tNm3a5Pne9OnTzbhx45qd/9FHH5khQ4aY4OBgk5GRYZ5//nkvJ/YPrZnLuHHjWvy9mD59uveDd3Gt/X25GGWt47R2Ll9++aUZP368CQsLMykpKeaRRx4xlZWVXk7ddVnG/OvdzAAAAAAA22A3SAAAAACwIcoaAAAAANgQZQ0AAAAAbIiyBgAAAAA2RFkDAAAAABuirAEAAACADVHWAAAAAMCGKGsAAAAAYEOUNQAAAACwIcoaAAAAANgQZQ0AAAAAbIiyBgAAAAA29P8BVLLZQtQjWogAAAAASUVORK5CYII=",
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Kjt9//z0mk4kbb7yxxm0LCQmhVatW1bdt2bJlFBYWcuedd1aPeJ6NDh06AHDttdfy5ZdfkpqaWqu8Z2Lo0KE1fk5JSaGsrIzMzEwAFi1aBMANN9xQY7uRI0fW+LmsrIyFCxdy5ZVX4uHhUeP+GTx4MGVlZaxYsaLG71x11VVnnPPJJ5/k4MGDfPjhh9x22214eXnx7rvv0q5dO6ZPn37Gl5OSkkJcXNwJp5+v101tnOnzpbi4mAkTJhAbG4uTkxNOTk54eXlRUlLCtm3banXdx0dN/rqK3zXXXIOnpycLFy6scXrr1q2JjIys/tnNzY24uLgTXq8zZ87kiiuuOOk+iADx8fGMGzeON998s/q942TZEhMTa+yTdTyrYRgnjDQPGTIEi8VS/XNKSgpAdTbDMGo8L/886t6xY0c+/vhjnnvuOVasWEFlZeVJM53O0KFDcXZ2PuH0r776im7duuHl5YWTkxPOzs588MEHtX7Mzsa8efMAuOuuu0673d69exk5ciQhISHVz/9evXoBnNNz63w+fiKORGVNpAE72ZSSjIwMNm3ahLOzc40vb29vDMMgOzsbOFaYevToQWpqKlOnTuW3335j9erVvPXWW8CxxRTO1F+nJrm4uJz29LKysuqscKxw/DXvjBkzqrMe5+HhgZubW43TXF1dqy/vXERERNC+ffsTvry8vGp1eRkZGRiGQXBw8Am3bcWKFdW3LSsrq/r6a6Nnz57Mnj27uvRGRESQlJR0VqXkTP11OuHxKW/Hnys5OTk4OTmdsF1ISEiNn3NycqiqquKNN9444b4ZPHgwwAmP/ammT51KcHAwY8eO5d1332XTpk0sWbIEFxeX6qXhz8TJrvN8vm5q40yfLyNHjuTNN99k/Pjx/Pjjj6xatYrVq1cTFBRU64zHH9+/TqMzmUyEhIRUTwM87mTTT11dXWtcf3p6OkuXLv3bMv70009jsViqp/2dLNvJHq+wsLDq80+X7a/P5SVLlpzw3Dw+FXDGjBncdNNNvP/++3Tp0oWAgABGjx5Nenr6aW/Dn50s6zfffMO1115LeHg4n332GcuXL2f16tXcfPPN5+U97u9kZWVhsVhOeL3+WXFxMT169GDlypU899xzLF68mNWrV/PNN98AtX/+n+/HT8SRaJ81kQbsZJ+sN2rUCHd39+p9Z052PsDs2bMpKSnhm2++ISoqqvr8DRs2XJCsp8vy9ddf18hQHzRq1AiTycRvv/120v2Pjp92/B/fw4cP1/q6rrjiCq644grKy8tZsWIFL7zwAiNHjiQ6Orp6H66/Ol56y8vLa5z+13+KzkZgYCBVVVXk5OTU+Gfqr//E+vv7Y7FYGDVq1Ck/xY+Jianxc21GHf+sZ8+e9O/fn9mzZ5OZmXnKfd7+7jrt/bo5k+dLQUEB33//PRMnTuSRRx6pPv34PlG1dfzxzcrKqlHYDMMgPT29epT3bMyaNQtPT08uvfTS024XGhrK/fffz4svvsgDDzxw0mxpaWknnH7kyBHg/7/XnKl27dqxevXqGqcdLw6NGjXitdde47XXXuPgwYN89913PPLII2RmZjJ//vwzuvyTPbc+++wzYmJimDFjRo3z//oavVCCgoKwWq2kp6ef8sORX375hSNHjrB48eLq0TTgnI9VeL4fPxFHopE1EanhsssuY8+ePQQGBp50pOj4QVCP/zPw5yJhGAb//e9/T7jMv34afr4MGDAAJycn9uzZc9Ks7du3P+vLdJRPWC+77DIMwyA1NfWktys5ORmArl274uvry7vvvothGKe8vDO5Xa6urvTq1YvJkycDx6brnUpwcDBubm41DhIN8O23357xbfyr40vlf/755zVOnzZtWo2fPTw86NOnD+vXryclJeWk98/JRmXOREZGxkmX57darezatQsPD4/qg5nX5rli79fNmTxfTCYThmGc8CHB+++/f8IBps/mPjh+SIHPPvusxukzZ86kpKTkhEMOnImZM2dy2WWXndGy9RMmTCAgIKBGAf1ztq1bt7Ju3boap3/yySeYTKYTDuPwd7y9vU94Th6fGfBnkZGR3H333Vx66aU1rrs2j73JZKo+aPVx6enpJ31NXojn1vGFgN55553TZjx+/X/2n//854Rtz/a5dT4fPxFHopE1Eanh/vvvZ+bMmfTs2ZN//OMfpKSkYLPZOHjwID/99BMPPPAAnTp14tJLL8XFxYURI0bw8MMPU1ZWxjvvvENeXt4Jl5mcnMw333zDO++8Q7t27TCbzbUqUn8VHR3NM888w+OPP87evXsZOHAg/v7+ZGRksGrVKjw9PfnXv/51Vpfp7e1NVFQU3377LZdccgkBAQE0atSouqSeysGDB0/YTwqOfdpcm+MRdevWjVtvvZWxY8eyZs0aevbsiaenJ2lpafz+++8kJydzxx134OXlxZQpUxg/fjz9+vXjlltuITg4mN27d7Nx40befPNNgOpyN3nyZAYNGoTFYiElJYXnnnuOw4cPc8kllxAREUF+fj5Tp06tsR/JyRzfn+7DDz+kWbNmtGrVilWrVp1QrM5G//796dmzJw8//DAlJSW0b9+epUuX8umnn56w7dSpU+nevTs9evTgjjvuIDo6mqKiInbv3s2cOXNqvZrlp59+yn/+8x9GjhxJhw4d8PX15fDhw7z//vvVq6Ie/6f7+H06depUbrrpJpydnYmPj8fb2/uUl2/v182ZPF98fHzo2bMnL730UvVzf8mSJXzwwQfVRfW4pKQkAN577z28vb1xc3MjJibmpGX50ksvZcCAAUyYMIHCwkK6detWvRpkmzZtGDVq1FndlpycHJYsWcIXX3xxRtv7+Pjw+OOP849//OOE8/7xj3/wySefMGTIEJ555hmioqL44YcfePvtt7njjjtOuu9hbRQUFNCnTx9GjhxJQkIC3t7erF69mvnz5zN8+PDq7Wrz2F922WV888033HnnnVx99dUcOnSIZ599ltDQUHbt2lVj2+TkZBYvXsycOXMIDQ3F29ub+Pj4c7ptPXr0YNSoUTz33HNkZGRUl+j169fj4eHBPffcQ9euXfH39+f2229n4sSJODs78/nnn7Nx48YTLu9U71knK70X6/ETsQv7rGsiIhfT6Y6zdjLFxcXGE088YcTHxxsuLi6Gr6+vkZycbPzjH/8w0tPTq7ebM2dO9fF0wsPDjYceeqh6Rb8/ryaWm5trXH311Yafn59hMplOOGbQX1ewO74i2VdffVXj9FOtSjZ79myjT58+ho+Pj+Hq6mpERUUZV199tbFgwYLT3geG8f9XKvyzBQsWGG3atDFcXV1rHFfoZP5uNcgbbrjhhOs6k9Ugj/vwww+NTp06GZ6enoa7u7vRrFkzY/To0caaNWtqbDd37lyjV69ehqenp+Hh4WEkJiYakydPrj6/vLzcGD9+vBEUFFT9GOzbt8/4/vvvjUGDBhnh4eGGi4uL0bhxY2Pw4ME1VrE8lePHRAsODjY8PT2Nyy+/3Ni/f/8pV4P86+0+2THA8vPzjZtvvtnw8/MzPDw8jEsvvdTYvn37SVfo3Ldvn3HzzTcb4eHhhrOzsxEUFGR07drVeO6556q3OdVz6VS2bt1qPPDAA0b79u2NoKAgw8nJyfD39zd69eplfPrppyds/+ijjxphYWGG2Ww+6XHWTuZcXzeGUfvVII/7u+fL4cOHjauuusrw9/c3vL29jYEDBxqbN28+6fGtXnvtNSMmJsawWCxndJy1CRMmGFFRUYazs7MRGhpq3HHHHac8ztpf9erVq/qYYO+//77h4eFhlJSUnLDdqV5T5eXlRkxMzCmPszZy5EgjMDDQcHZ2NuLj442XXnrplMfp+quTPUf/qqyszLj99tuNlJSU6mPcxcfHGxMnTqxxO872PfO4F1980YiOjjZcXV2NFi1aGP/9739P+h63YcMGo1u3boaHh8dpj7NmGGe+GqRhHFvd9NVXXzWSkpKq/3Z06dLFmDNnTvU2y5YtM7p06WJ4eHgYQUFBxvjx441169adsLrsqd6zDOPUx1m70I+fiD2YDOM082ZEREREHNDgwYNxd3dn5syZ9o4iInLBqKyJiIiIiIg4IC0wIiIiIiIi4oBU1kRERERERByQypqIiIiIiIgDUlkTERERERFxQCprIiIiIiIiDkgHxb5IDMOgqKjI3jFERERERMRBeHt7YzKZTnm+ytpFUlRUhK+vr71jiIiIiIiIgygoKMDHx+eU5+s4axeJRtZEROqXyspKPvroIwDGjh2Ls7OznROJiEhd83cjayprIiIitVBSUoKXlxcAxcXFeHp62jmRiIjUN1pgRERERERExAGprImIiIiIiDgglTUREREREREHpNUgRURERKReslqtVFZW2juGNEDOzs5YLJZzvhyVNRERERGpVwzDID09nfz8fHtHkQbMz8+PkJCQ0672+HdU1kRERESkXjle1Bo3boyHh8c5/bMscrYMw6C0tJTMzEwAQkNDa31ZKmsiIiK14Orqyvfff1/9vYg4BqvVWl3UAgMD7R1HGih3d3cAMjMzady4ca2nRKqsiYiI1IKTkxNDhgyxdwwR+Yvj+6h5eHjYOYk0dMefg5WVlbUua1oNUkRERETqHU19FHs7H89BjayJiIjUQmVlJZ9//jkAN9xwA87OznZOJCIi9Y1G1kRERGqhoqKCsWPHMnbsWCoqKuwdR0TknCxevBiTyVRjBc3Zs2cTGxuLxWLh/vvv5+OPP8bPz++cr8tkMjF79uxzvpzzaf/+/ZhMJjZs2HDGv9O7d2/uv//+C5YJNLImIiIiIuLQxowZQ35+/nkrOL1796Z169a89tpr1ad17dqVtLQ0fH19q0+77bbbGDt2LPfeey/e3t44OTkxePDg85JBzozKmoiIiIhIPVBZWVnrKdkuLi6EhIRU/1xcXExmZiYDBgwgLCys+vTjqxzKxaFpkCIiIiIiDuDrr78mOTkZd3d3AgMD6devHw899BD/+9//+PbbbzGZTJhMJhYvXlw9be/LL7+kd+/euLm58dlnn5GTk8OIESOIiIjAw8OD5ORkpk+fXn0dY8aMYcmSJUydOrX68vbv319jGuTixYvx9vYGoG/fvtXXebJpkHPmzKFdu3a4ubnRtGlT/vWvf1FVVVV9/q5du+jZsydubm4kJiby888/n/H98efb2KNHD9zd3enQoQM7d+5k9erVtG/fHi8vLwYOHEhWVlb179lsNp555hkiIiJwdXWldevWzJ8/v8Zlr1q1ijZt2uDm5kb79u1Zv379Cde/detWBg8ejJeXF8HBwYwaNYrs7Owzzn8+qKyJiIiISP1lGFBRYp8vwzjjmGlpaYwYMYKbb76Zbdu2sXjxYoYPH87EiRO59tprGThwIGlpaaSlpdG1a9fq35swYQL33nsv27ZtY8CAAZSVldGuXTu+//57Nm/ezK233sqoUaNYuXIlAFOnTqVLly7ccsst1ZfXpEmTGlm6du3Kjh07AJg5c+YJ13ncjz/+yI033si9997L1q1b+c9//sPHH3/M888/DxwrTcOHD8disbBixQreffddJkyYcNYP4cSJE3niiSdYt24dTk5OjBgxgocffpipU6fy22+/sWfPHp566qnq7adOncqUKVN4+eWX2bRpEwMGDGDo0KHs2rULgJKSEi677DLi4+NZu3YtTz/9NA8++OAJj0evXr1o3bo1a9asYf78+WRkZHDttdeedf5zoWmQIiIiIlJ/VZbCpLC/3+5CeOwIuHie0aZpaWlUVVUxfPhwoqKiAEhOTgaOTT0sLy+vMU3xuPvvv5/hw4fXOO3PxeOee+5h/vz5fPXVV3Tq1AlfX19cXFzw8PA46eXBsSmRjRs3BiAgIOCU2z3//PM88sgj3HTTTQA0bdqUZ599locffpiJEyeyYMECtm3bxv79+4mIiABg0qRJDBo06Izukz/fngEDBgBw3333MWLECBYuXEi3bt0AGDduHB9//HH19i+//DITJkzg+uuvB2Dy5MksWrSI1157jbfeeovPP/8cq9XKhx9+iIeHBy1btuTw4cPccccd1Zfxzjvv0LZtWyZNmlR92ocffkiTJk3YuXMncXFxZ3UbaktlTURERETEzlq1asUll1xCcnIyAwYMoH///lx99dX4+/uf9vfat29f42er1cqLL77IjBkzSE1Npby8nPLycjw9z6w0no21a9eyevXq6pG049dfVlZGaWkp27ZtIzIysrqoAXTp0uWsryclJaX6++DgYOD/F9njp2VmZgJQWFjIkSNHqovccd26dWPjxo0AbNu2jVatWtU4cPpfc61du5ZFixbh5eV1Qp49e/aorImIiDgyV1dXvvzyy+rvRcRBOXscG+Gy13WfIYvFws8//8yyZcv46aefeOONN3j88cerpy+eyl9L2JQpU3j11Vd57bXXSE5OxtPTk/vvv/+CHGLEZrPxr3/964SRPQA3NzeMk0wDrc2Bov+8aMrx3//raTab7bTXYxhG9Wkny/VXNpuNyy+/nMmTJ59wXmho6JmHP0cqayIiIrXg5OTENddcY+8YIvJ3TKYznopobyaTiW7dutGtWzeeeuopoqKimDVrFi4uLlit1jO6jN9++40rrriCG2+8EThWOnbt2kWLFi2qtzmbyzudtm3bsmPHDmJjY096fmJiIgcPHuTIkSPVK0ouX778nK/3dHx8fAgLC+P333+nZ8+e1acvW7aMjh07Vuf69NNPOXr0aPXqlitWrKhxOW3btmXmzJlER0fj5GS/yqQFRkRERERE7GzlypVMmjSJNWvWcPDgQb755huysrJo0aIF0dHRbNq0iR07dpCdnU1lZeUpLyc2NrZ6hG7btm3cdtttpKen19gmOjqalStXsn//frKzs08YlTpTTz31FJ988glPP/00W7ZsYdu2bcyYMYMnnngCgH79+hEfH8/o0aPZuHEjv/32G48//nitrutsPPTQQ0yePJkZM2awY8cOHnnkETZs2MB9990HwMiRIzGbzYwbN46tW7cyd+5cXn755RqXcdddd5Gbm8uIESNYtWoVe/fu5aeffuLmm28+L0X3TKmsiYiI1EJVVRVfffUVX331VY1lqkVEasPHx4dff/2VwYMHExcXxxNPPMGUKVMYNGgQt9xyC/Hx8bRv356goCCWLl16yst58sknadu2LQMGDKB3796EhIQwbNiwGts8+OCDWCwWEhMTCQoK4uDBg7XKPGDAAL7//nt+/vlnOnToQOfOnXnllVeqF0gxm83MmjWL8vJyOnbsyPjx42vs33ah3HvvvTzwwAM88MADJCcnM3/+fL777juaN28OgJeXF3PmzGHr1q20adOGxx9//ITpjmFhYSxduhSr1cqAAQNISkrivvvuw9fXF7P54lUok3EmkzZFRESkhpKSkuodz4uLiy/IzvsicvbKysrYt28fMTExuLm52TuONGDn47mokTUREREREREHpLImIiIiIiIX3aRJk/Dy8jrp19kei62+0mqQIiIiIiJy0d1+++1ce+21Jz3v+CqNDZ3KmoiIiIiIXHQBAQEEBATYO4ZD0zRIERERERERB6SyJiIiIiIi4oA0DVJERKQWXFxc+Oijj6q/FxEROd9U1kRERGrB2dmZMWPG2DuGiIjUY5oGKSIiIiIi4oBU1kRERGqhqqqKH374gR9++IGqqip7xxER4eOPP8bPz8/eMS6I3r17c//995/x9vXlvtA0SBERkVooLy/nsssuA6C4uBgnJ/1JFRH7uu666xg8eLC9Y8h5pL8sIiIiIiL1gLu7uw4mXc9oGqSIiIiIiAPo3bs39957Lw8//DABAQGEhITw9NNPV5//yiuvkJycjKenJ02aNOHOO++kuLi4+vw/T/3bsWMHJpOJ7du317iOV155hejoaAzDAGDr1q0MHjwYLy8vgoODGTVqFNnZ2Wec95577uH+++/H39+f4OBg3nvvPUpKShg7dize3t40a9aMefPm1fi9JUuW0LFjR1xdXQkNDeWRRx6pMZ28pKSE0aNH4+XlRWhoKFOmTDnhuisqKnj44YcJDw/H09OTTp06sXjx4jPKXZeorImIiIhIvWUYBqUVVXb5Ol6Izsb//vc/PD09WblyJf/+97955pln+PnnnwEwm828/vrrbN68mf/973/88ssvPPzwwye9nPj4eNq1a8fnn39e4/Rp06YxcuRITCYTaWlp9OrVi9atW7NmzRrmz59PRkYG11577VnlbdSoEatWreKee+7hjjvu4JprrqFr166sW7eOAQMGMGrUKEpLSwFITU1l8ODBdOjQgY0bN/LOO+/wwQcf8Nxzz1Vf5kMPPcSiRYuYNWsWP/30E4sXL2bt2rU1rnfs2LEsXbqUL774gk2bNnHNNdcwcOBAdu3adcbZ6wKTUZtnkYiISANXUlKCl5cXcGyfNU9PTzsnEhGAsrIy9u3bR0xMDG5ubpRWVJH41I92ybL1mQF4uJz5Xke9e/fGarXy22+/VZ/WsWNH+vbty4svvnjC9l999RV33HFH9UjYxx9/zP33309+fj4Ar776Km+++SZ79uwBYOfOncTHx7NlyxYSExN56qmnWLlyJT/++P/vn8OHD9OkSRN27NhBXFzcWeW1Wq34+voyfPhwPvnkEwDS09MJDQ1l+fLldO7cmccff5yZM2eybds2TCYTAG+//TYTJkygoKCA0tJSAgMD+eSTT7juuusAyM3NJSIigltvvZXXXnuNPXv20Lx5cw4fPkxYWFh1nn79+tGxY0cmTZp0wn1hD399LtaG9lkTEREREXEQKSkpNX4ODQ0lMzMTgEWLFjFp0iS2bt1KYWEhVVVVlJWVUVJSctIPjK6//noeeughVqxYQefOnfn8889p3bo1iYmJAKxdu5ZFixZVf/D0Z3v27PnbsvbXvBaLhcDAQJKTk6tPCw4OBqi+Ddu2baNLly7VRQ2gW7duFBcXc/jwYfLy8qioqKBLly7V5wcEBBAfH1/987p16zAM44R85eXlBAYG/m3mukRlTURERETqLXdnC1ufGWC36z5bzs7ONX42mUzYbDYOHDjA4MGDuf3223n22WcJCAjg999/Z9y4cVRWVp70skJDQ+nTpw/Tpk2jc+fOTJ8+ndtuu636fJvNxuWXX87kyZNP+ru1zfvn046XMpvNBhyblvrnonb8tOPbnsmkP5vNhsViYe3atVgsNe/jkxXPukxlTUREpBZcXFx48803q78XEcdkMpnOaiqio1qzZg1VVVVMmTIFs/nYshNffvnl3/7eDTfcwIQJExgxYgR79uzh+uuvrz6vbdu2zJw5k+jo6It2+JHExERmzpxZo7QtW7YMb29vwsPD8ff3x9nZmRUrVhAZGQlAXl4eO3fupFevXgC0adMGq9VKZmYmPXr0uCi57UULjIiIiNSCs7Mzd911F3fdddcJnyyLiJxvzZo1o6qqijfeeIO9e/fy6aef8u677/7t7w0fPpzCwkLuuOMO+vTpQ3h4ePV5d911F7m5uYwYMYJVq1axd+9efvrpJ26++WasVusFuR133nknhw4d4p577mH79u18++23TJw4kX/+85+YzWa8vLwYN24cDz30EAsXLmTz5s2MGTOmuqACxMXFccMNNzB69Gi++eYb9u3bx+rVq5k8eTJz5869ILntRWVNRERERMTBtW7dmldeeYXJkyeTlJTE559/zgsvvPC3v+fj48Pll1/Oxo0bueGGG2qcFxYWxtKlS7FarQwYMICkpCTuu+8+fH19a5Sj8yk8PJy5c+eyatUqWrVqxe233864ceN44oknqrd56aWX6NmzJ0OHDqVfv350796ddu3a1bicjz76iNGjR/PAAw8QHx/P0KFDWblyJU2aNLkgue1Fq0GKiIjUwp9XQOvRo8cJ+02IiH2cjxX4RM4HrQYpIiJiJ2VlZfTp0wfQ0v0iInJhaBqkiIiIiIjUcPDgQby8vE75dfDgQXtHbBA0siYiIiIiIjWEhYWxYcOG054vF57KmoiIiIiI1ODk5ERsbKy9YzR4mgYpIiIiIiLigFTWRERERKTe0YLnYm/n4zmosiYiIiIi9cbxg9SXlpbaOYk0dMefg8efk7WhfdZERERqwdnZmX//+9/V34uIY7BYLPj5+ZGZmQmAh4cHJpPJzqmkITEMg9LSUjIzM/Hz8zun43DqoNgiIiIiUq8YhkF6ejr5+fn2jiINmJ+fHyEhIef0YYHKmoiIiIjUS1arlcrKSnvHkAbI2dn5nEbUjlNZExERqQWr1cq6desAaNu27Xn5oywiIvJnKmsiIiK1UFJSgpeXFwDFxcV4enraOZGIiNQ3Wg1SRERERETEAamsiYiIiIiIOCCVNREREREREQeksiYiIiIiIuKAVNZEREREREQckMqaiIiIiIiIA3KydwAREZG6yNnZmYkTJ1Z/LyIicr7pOGsiIiIiIiIOSNMgRUREREREHJCmQYqIiNSCzWZj27ZtALRo0QKzWZ9/iojI+aVpkCIiIrVQUlKCl5cXAMXFxXh6eto5kYiI1Df6GFBERERERMQBqayJiIiIiIg4IJU1ERERERERB6SyJiIiIiIi4oBU1kRERERERByQypqIiIiIiIgD0nHWREREasHZ2ZkHH3yw+nsREZHzTcdZExERERERcUCaBikiIiIiIuKANA1SRESkFmw2GwcPHgQgMjISs1mff4qIyPmlaZAiIiK1UFJSgpeXFwDFxcV4enraOZGIiNQ3+hhQRERERETEAamsiYiIiIiIOCCVNREREREREQeksiYiIiIiIuKAVNZEREREREQckMqaiIiIiIiIA9Jx1kRERGrBycmJO++8s/p7ERGR803HWRMREREREXFAmgYpIiIiIiLigDRvQ0REpBYMwyA7OxuARo0aYTKZ7JxIRETqG02DFBERqYWSkhK8vLwAKC4uxtPT086JRESkvtE0SBEREREREQeksiYiIiIiIuKAVNZEREREREQckMqaiIiIiIiIA1JZExERERERcUAqayIiIiIiIg5Ix1kTERGpBScnJ2666abq70VERM43HWdNRERERETEAWkapIiIiIiIiAPSvA0REZFaMAyD0tJSADw8PDCZTHZOJCIi9Y1G1kRERGqhtLQULy8vvLy8qkubiIjI+aSyJiIiIiIi4oBU1kRERERERByQypqIiIiIiIgDUlkTERERERFxQCprIiIiIiIiDkhlTURERERExAHpOGsiIiK1YLFYuPrqq6u/FxEROd9MhmEY9g4hIiIiIiIiNWkapIiIiIiIiANSWRMREREREXFAKmsiIiK1UFJSgslkwmQyUVJSYu84IiJSD6msiYiIiIiIOCCVNREREREREQeksiYiIiIiIuKAVNZEREREREQckMqaiIiIiIiIA1JZExERERERcUBO9g4gIiJSF1ksFgYPHlz9vYiIyPlmMgzDsHcIERERERERqUnTIEVERERERByQypqIiIiIiIgDUlkTERGphZKSEjw9PfH09KSkpMTecUREpB7SAiMiIiK1VFpaau8IIiJSj2lkTURERERExAGprImIiIiIiDgglTUREREREREHpLImIiIiIiLigFTWREREREREHJBWgxQREakFs9lMr169qr8XERE530yGYRj2DiEiIiIiIiI16aNAERERERERB6SyJiIiIiIi4oBU1kRERGqhpKSEoKAggoKCKCkpsXccERGph7TAiIiISC1lZ2fbO4KIiNRjGlkTERERERFxQCprIiIiIiIiDkhlTURERERExAGprImIiIiIiDgglTUREREREREHpNUgRUREasFsNtO+ffvq70VERM43k2EYhr1DiIiIiIiISE36KFBERERERMQBqayJiIiIiIg4IJU1ERGRWigtLSU6Opro6GhKS0vtHUdEROohLTAiIiJSC4ZhcODAgervRUREzjeNrImIiIiIiDgglTUREREREREHpLImIiIiIiLigFTWREREREREHJAWGBEREWngrFYrpaWlHD16tPqrsrISACcnJ/z8/PD398fNzc3OSUVEGhaVNRERkVowmUwkJiZWf++oSkpKOHDgQPXX/v37SU1NJT09/dhXRiY5+YWYnFwwmS1gdgKTqcZtMmw2DFslbs7O+Pt40rhRIE1jomnWrBkpKSm0bt2aFi1aYDZrwo6IyPlkMrTesIiISJ1XWVnJ5s2b2bRpE1u2bGHz5s1s2bKFQ2mZmF09MLl6YHbxwOTsitnFHbObN2Y3L0zOrpjMTmA2g9mCs5MLTs7OmC0WAGw2K+VHy7DZqsBmw7BVgdWKrawIa2kBtqOF2I4W4ufhTP9eXRk0cCD9+/cnKCjIzveIiEjdp7ImIiJSxxw/xtvKlSurv9Zt2ESl2eVYCXP3xuLpj8UrALOLB66e3vj6B+Lj44O3jzfeXl74eHni5+2Jn683vt5eeHq44ersjMVswWSC6oE1A6w2G6VHyygsLqGopJT8gkKycvPJzisgKzubzMxMKkqLsJYUUJWbirUoizZxkdx80yhGjBiBj4+PXe8vEZG6SmVNRETEwRmGwc6dO1mwYAELFixg2bJlZBeWYvb0w+Lhj5NvYyzegXh4+xMUGkZAYCCNAwMJa9yIiNBgAny9cHEyY75A0zUrq6rYunMPm3fsYfeBw2RlZmA9WkRl1n6cSrMZPqAXt90yns6dOzv0lFEREUejsiYiIlILpaWldOjQAYDVq1fj4eFxXi+/qKiIH3/8kblz57JgwQJSs/KweAVi8Q7EKSAcZ09/gsObEBwSSkRoME0jw4kMC8bN2XLBStmZysnN5/c161izaRu5OdlYi7KpSNtFUmQgzzzxKIMGDVJpExE5AyprIiIitVBSUoKXlxcAxcXFeHp6nvNlpqWl8d133/Htt9+ycNFirC7eOPk2xjkgAhe/xoRHxxIZ2YRmkU1oHh2Br6cbFrPjlh4Dgx07d7N45Tq27zlAeWEOFanbSQr35ZknJjBw4ECVNhGR01BZExERqYXzVdZKSkqYNWsWn3zyCQsWLcbiE4yTXwjOjSIJDIuiaWwczZtG0aJZNAHeHg5dzk6noLCA739ewupN26koyqX8yHZSInx55cXn6Natm73jiYg4JJU1ERGRWjiXsmYYBr///jsffvghX389kzKLB07+4TgHxxDeNIGmsbGkJMQSGxmGu3P9OspOQUEBc35exJrNOykvyKL88FaG9WzDyy9OIiIiwt7xREQcisqaiIhILdSmrBUUFPDJJ5/w7rvvsn3vQZwDI3Bu3IxGkc1JaJlM+5REmkWE4OJU/49Xll+Qz1ff/8TGbbupyk+DzF08OH4kDz34AO7u7vaOJyLiEFTWREREauFsytqWLVt47bXXmDZ9OhUuvjg3jsEzNJb4lLa0SU6kVXxTPFzq1wjamdq3/wDTv/uRI2npVGTtp5EtlzcnP8Nll11m72giInansiYiIlILf1fWDMNg0aJFvPzyy8z/+RecG0XiEhpHSLNEklu1plvbFIIDvO2+cqMjMDBYtnINcxYupTAvi/LD2+jXKpo3X5tCVFSUveOJiNiNypqIiEgtlJaWkpiYCMDWrVurl+6vrKzkq6++4uWXX2bj9j24BEXjEhZPfEp72rdrQ7vEZvVuP7Tzpby8jJnf/8SKjVuoyEuH9O08cvtoHnzwAVxcXOwdT0TkolNZExEROQ9KSkp47733ePXVVzmSX4pL42Z4RCTQsm1HenZqR/PIEJzM9X9ftPPh0OFUPp89l8OpRyhP30OEUzFvvzqZSy65xN7RREQuKpU1ERGRc1BUVMRbb73FlClTyK+04Bwah390S1q160jvzm0Ja+SnqY61YGDw27KVzFm0nOKcdMoPbOSKHm14dcpLhIWF2TueiMhFobImIiJSCwUFBbzxxhu8+uprFOKGa2g8QbEptO/UmZ7tWxHo42HviPVCSWkJX875kXV/bKM86wBO2bt5+sG7ufvuu3Fy0nRSEanfVNZERETOQl5eHq+99hqvvTaVwpJSzC7uRF46hs7detKrYwo+7q72jlgv7dl3gGlzfiLt0AHKU7fR3M/Eu2+8RteuXe0dTUTkglFZExEROQPZ2dm8+uqrvPHGmxx19sEltDmlWxYB8Pq8jQT6+9k3YANgGDZ++nU5P/26gpKsw5Qf3MTIwb3494svEBQUZO94IiLnncqaiIjIaeTn5zNlyhReffU1yl39cA1PICyhLe3atOGLx0YA8MGibbi5a9rjxVJYWMT0737kj63bqMg6gFvhIZ64/3Zuv/023Nzc7B1PROS8UVkTERE5idLSUt58801efHEyRSYPXMMTCE9sT+fOXejeNhGLrYpxfVoAKmv2sm3nbmZ8/zNZGWmUZ+wl0JrHUw/dy5gxY3B2drZ3PBGRc6ayJiIi8icVFRV88MEHPPvss2QdBdeIRELi29C1W3d6tE/C0+VYCSg7Wqqy5gBsNiu//LaMBcvWUZiTQXnaTsJcK3j6kQe4/vrrVdpEpE5TWRMREQGsVivTp09n4sSJHMguwjU8kaDYVnTu1p2+ndvg5VbzoMwqa46lsrKSH5f8zq8rN1Ccm0l5+i4amUq499Yx3HLLLfj5+dk7oojIWVNZExGRBs0wDL777jueeOIJtu0/gkt4C/ybptCxS3cu7dYOP0/3k/6eyppjKis7yrxffmPZ+q2U5GVSkbkPp8IjjLl2KPfcfTfNmze3d0QRkTOmsiYiIg3WokWLeOyxx1i1YTMuYQn4Nm1Fuy496N+jA0E+Xqf93bKjpdx/ZTcAXpu1VGXNwVRUVPDbyjX8unoj2RlpVOYeoSJjNx1bRHPL2Ju4+uqr8fT0tHdMEZHTUlkTEZEGZ+PGjTz88MP8vPg3XEKa4x2TQuvOvejfoyPhjfzsHU/OIwODDX9s5Zfl69h/4ACVxblUpu/BpTyX64YOZPy4cXTo0AGTyWTvqCIiJ1BZExGRBuPQoUM8+eSTfDrtC5wbx+ARlUJyp55c2qML0aGBmPUPe72WlZ3L4hVrWb91B/nZGVTlpVGZtY/YYB9uGTOKUaNGERgYaO+YIiLVVNZERKTeKygo4MUXX+S1qa9j8wnFtUlLEjv0pF+vbsRHhmIxq6Q1JIZhY+PWnSxdu4ldu/dSXpxHZdYBTEXpXN6nC7fdMp7evXtrtE1E7E5lTURE6q2Kigr+85//8Mwzz5Bvc8OtSUuiW3Whb68etE1oirPFXPvLLivj3/+4CYCHX/0fLjoYc51UXFLCryvXsnrjVjIzM7AW51KRtosIHyduGT2CMWPGEBwcbO+YItJAqayJiEi9tGDBAu655x52Hc7EtUkSoS060KNnL3q0a4mbs9M5X75Wg6xfDAz27N3P4hXr2LprL2VFeVRmH8SWe4grL+3OhIcepFWrVvaOKSINjMqaiIjUK4cOHeKf//wnM7/7AdewBALi2tOl1yX069oWH3fX83Y9Kmv1V1nZUX5ftZ4VG7aSnnaEysJMKtJ20jMllgkP3M8ll1yiKZIiclGorImISL1QWVnJK6+8wjPPPkuVVwjuUa1o0+MSBvXpQUQj3/N+fSprDcPuffuZt3g5O/fuo6ool/Ij20kM9eGFfz1J//79VdpE5IJSWRMRkTpv3bp13HzzzWzecwi3qFZEt+nOgH6X0Dou+oItHqKy1rBkZmUx95ff2bBtF+WFOVSkbqNTbAhTp0zW9EgRuWBU1kREpM46evQo//rXv3j51ak4Bcfin9CJnv0GcmmXtni4nPt+aaejstYwFRUVMXv+QtZs3kFFfiaVqVu55eoBPPuvp/Hx8bF3PBGpZ1TWRESkTlq2bBljx45lT3o+7k3bkdjlEq4Y0Jeo4ICLMjVNZa1hy8rJ5ovZ89ix9wCV2QcJrMrmk7dfoWfPnvaOJiL1SO3XLBYREbEDq9XKM888Q49efThY5k5It+FcedPt3HHDVUSHBF7UfYhc3dxxdXO/aNcnjiMosBH3jBvF+BHDCIxJJD+gJQPH/pMHJjxKeXm5veOJSD2hkTUREakzDh06xI033sjSdZtxi2lHcs+BXDmwHxFBfvaOJg1YWdlRPvtqNht27KMy5zDRLkVM+++bJCcn2zuaiNRxKmsiIlInfPPNN4wffwslroH4JHThkkFDGdCtLa5OFntHEwFgzbr1fPH9AkoL87CmbuaVx+7hlvHj7B1LROowlTUREXFoVquVRx99lJdffR23mDZEte/LlUMvo2V0mJZNF4dTWFjA+59/zZ5DaZQf2c4NvVN4a+oruLi42DuaiNRBKmsiIuKwCgsLueGGG5j7y+94NO9Ex/7DGT6wN/6ebvaORkV5GVMfvR2A+154FxdX+2cSx2AYNr6b+yM/L99AZc4h2ja2MOfLT7VapIicNZU1ERFxSHv37mXo0KHsSCvAp0V3Bgy7hgFd2+JscYy1sbQapPydjZv+4ONv5lKWl0mkOZefZn5GaGiovWOJSB3iGH/xRERE/uTXX3+lY8eO7MqrIrjTZdww9hYGd2/nMEVN5Ey0Sknm/rHX490ohEOWULoMuY4dO3bYO5aI1CH6qyciIg5l9uzZXNp/ACU+UcT0HM4t48bROSkWs/ZPkzooKiqKh+8YQ0BAAAU+TekxfAwrVqywdywRqSNU1kRExGF88sknXH3d9Vii2pJ06bXcPnYUzSMa2zuWyDlp1CiICffcSmjjRlQFJzJwzD/4ZdEie8cSkTpAZU1ERBzCG2+8wZjxt+Ia24U2/a9h3IjhNPb1tHcskfPC29uHh+6+labhwZjDkxh+5xMsWLjQ3rFExMGprImIiF0ZhsFzzz3HfQ9OwCOhB50GXcvYay7Hx93V3tFEzitXVzfuuW0csRGNMYW15Op7JvLTzwvsHUtEHJjKmoiI2NUTTzzBU8++gEd8d3pcPoIbrxyAh6uzvWOJXBDOzs7cfevNxEUEYQ5N5Jp7n+bHn362dywRcVBaul9EROxm0qRJPPGv5/Bo0ZNLrhzFlf264eKkzxGl/quqquTt/37EzkOZWI9s5avXJ9L/0kvtHUtEHIzKmoiI2MXrr7/O/Q89gkdCD/pceSNX9++hpfmlQTlW2D5m56EMjLRtfP/+ZLp17WrvWCLiQFTWRETkovvggw+45Y678UjoQffLr+f6wX01oiYNUpW1ijfefZ/dqdk4ZW7n58/eoHWrVvaOJSIOQn8ZRUTkopo+fTq33H4nHs270GngVVw3uE+dLGoV5WVMfewOpj52BxXlZfaOI3WUk8WJO28ZS5NGPliD4hh8033s2rXL3rFExEHUvb+OIiJSZ/3000+MHjMWt2YdaNt/OCOHXoqrk8XesWrFZrOx6pe5rPplLjabzd5xpA5zdXHl3jvGE+TnyVH/Zlw68nZSU1PtHUtEHIDKmoiIXBR//PEHV199DU5NUkjpeyU3DhuIu7OTvWOJOAQPdw/+ccct+Hu7ke/ZhEuuGUtOTo69Y4mInamsiYjIBXfkyBGGDBlChV8UsV0Hc+NVQ/Byc7F3LBGH4uPjwz9uH4e3hwfpTsH0v+YmioqK7B1LROxIZU1ERC6o4uJiLrvsMjIqXQlv358brx1OgJeHvWOJOKTAwEbcf+to3D082VPuw5DrxlBWpn0iRRoqlTUREblgqqqquP766/ljfwaBKX0Ycd3VNAnys3csEYcWGhrGPWNH4uLlw8ZcM1eNGk9VVZW9Y4mIHaisiYjIBfPAAw8wb/FSfBJ7MPya60mMDrN3JJE6ITo6mttHDsfFJ5DfDpRw0213ayEbkQZIZU1ERC6ITz/9lDfe/S8ezbsyaPgIOic1s3ckkTolISGBMVcNxtk3mO/WH+a+hx5Fh8cVaVh0UGwRETnv1q9fT9fuPTBHtaf7sNGMvKwvzpb69fmgYRiUlx0FwNXNHZPJZOdEUl8tXbaM6XMXU5G1nwev6c3Tjz9i70gicpFozWQRETmvcnJyuHL4cAhOIL5rf64Z2KveFTUAk8mEm7sWSpELr1vXrpSUlvLdInh5+k8E+Plx71232zuWiFwEKmsiInLeWK1WRo4cSVqZM2Gde3LD8MvxcHW2dyyROq9/v34UlZSyaDU8+uY0Avz9uHHk9faOJSIXmMqaiIicN0899RS/rNyIb+sBXHPVMEIDfe0d6YKprCjngxcfA2DcI5NwdnG1cyKp74ZfcTmlpUdZaRjc8eyb+Pn5cNngwfaOJSIXUP2blyIiInYxa9YsXnzlddzjujDgsito3Tza3pEuKKvVym9zv+a3uV9jtVrtHUcaABMmbrz+GpKaRuAclsiNDzzPb7//bu9YInIBqayJiMg52759OzeNHYd7bCfa9+rPwG7t7B1JpF4ymczcMuZGYiOCMIe14MpbH2bDhg32jiUiF4jKmoiInJOioiKGXXkllYHNiGnbkxGXXYrFrJURRS4Ui8XCnePHEh4UgDW4BYNG383u3bvtHUtELgCVNRERqTXDMBgzZgz78ioJatmVUVcPxdPNxd6xROo9FxcX7rv9Zhr7+1AW0JxLrrmZ/fv32zuWiJxnKmsiIlJr//73v/n251/xiuvMVcOuIKJxgL0jiTQYHu4e/OOOcQT4elPgE0Of4aM5dOiQvWOJyHmksiYiIrXy888/8/jEZ3CP7UTfgZfRPrGZvSOJNDg+Pj48cOc4/H19yPOKovewG0hNTbV3LBE5T1TWRETkrO3fv58RI0fiEt2GVj36c1nvTphM2k9NxB78/Px58M5x+Pn5k+MRSe9hN5CWlmbvWCJyHpgMwzDsHUJEROqOo0eP0r17d7ZklhPTfRj33DIKP093e8e66AzDoCg/FwBvvwCVVbG7nJxsXn7nIwry8wipSGXxt9MJCQmxdywROQcaWRMRkTNmGAZ33HEHm/alE5DYjeuvvqJBFjUAk8mEj38gPv6BKmriEAIDG/HA7WPw8fUj3SWcHpdfz+HDh+0dS0TOgcqaiIicsXfeeYdPZ3yDZ/POXDbsSppHBNs7koj8SaNGQTxw+1h8/fzJcougx+Uj2Ldvn71jiUgtaRqkiIickWXLltGrzyW4xnWjz1U3ce2Ang36eGqVFeV8NvVZAG6870mcXVztnEjk/8vNzeWV/3xEbm4uXvm7+eXr/xEXF2fvWCJyllTWRETkb6WlpdGuXTvy3MJIGXA9t94wHHdnJ3vHsquyo6WM69MCgA8WbcPN3cPOiURqKigoYMq7H5Kdk4t7zk5+mv4eycnJ9o4lImdB0yBFROS0KioquOaaa8i2uhHRtg8jhg1q8EVNpC7w9fXlobvGExIUSFmjFvS59hZ+++03e8cSkbOgsiYiIqf14IMPsmLjdvwSe3D18Ctp7Odt70gicoa8vbx58K5biAhphC20JUNufoCvZ35j71gicoZU1kRE5JQ+/fRT3nz3v7g378ygYVeT1DTc3pFE5Cx5eHjy4N23EhcdgSWyNaMf+TdT33jT3rFE5AyorImIyEmtX7+eW2+7Hbem7eg68Er6dkjWEvUidZSzswt33zKWDi2b4xbVisfe+ZqHHnkcLV0g4thU1kRE5AS5ubkMHz4cIzCGhO6DuWpAL5wt+pMhUpeZzWZuuuFa+ndti2t4Au/MW8u1o8ZSVlZm72gicgr6yysiIjVYrVZGjhzJ4cIqQtv25bphQ/Byc7F3LBE5D0yYGDpkENcN7o1bcAw/7sij9+ArycrKsnc0ETkJLd0vIiI1PPzww0x56z382gxi9C130CEh2t6RHJLNZiMnPRWAwJBwzGZ9/il1y9bt23h/xhyO5mYQePQw8778mPj4eHvHEpE/UVkTEZFqn3/+OaPGjMOjZW+uGHU7g7q1waz91ETqrbS0NN74+Avyc7JwytjGl+++RN++fe0dS0T+j8qaiIgAsGbNGnr06ImpSWu6D7+ZkZf3w8VJo0Ui9V1RcRFv/PdTUjOyqDz8B5Puu4l77rpTCwqJOACVNRERIT09nQ4dOpCFLy0HjOTWUdfirf3UTquqsoIv330JgGtvfwgnZ91fUndVVlbywWfT2bz7IOVHdnJVlzjee+t13Nzc7B1NpEFTWRMRaeDKy8vp27cvq3ceJrLHVdxy81iaBPnaO5bDKztayrg+LQD4YNE23Nw97JxI5NwYGMz7aQHzfltNRfZh4j2PMnv6/4iIiLB3NJEGS/NbREQaMMMwuOOOO1i5aQf+Sb0ZftXVKmoiDZQJE4P7X8pt11+BV2hTdlUF0qHfFfz+++/2jibSYKmsiYg0YC+++CIfT/sSz/iuDBl+LSnNwu0dSUTsLKllSx65YzTB4ZEcDUlmwOh7eevtd3QAbRE7UFkTEWmgZsyYwWNPTsSjeWf6DhtB7/YttfKjiAAQFNSYR++7jaS4WFxj2vPwG9MZc8vtHD161N7RRBoUlTURkQZo2bJl3DRmLO5N29FxwFVc3rszTjpOmIj8iauLK3eMG8XAnh1wjUjkm3WpdOw9gJ07d9o7mkiDob/MIiINzJ49e7jiiiugcXMSew3lmiH9cHO22DuWiDggEyYuGziA20cMwyesKYecIug06FqmfzHD3tFEGgSVNRGRBiQnJ4chQ4ZQ6ORHdJdBjLhqKL4ervaOJSIOLqllIo/dM47I6BhMUe0Z99Rr3HH3vZSXl9s7mki9pqX7RUQaiJKSEi655BLW7koltNNljB03jviIxvaOVWfZbDaO7N8NQFh0LGZNI5UGwGaz8vW3P/Dr2s1UZh8k1uMoMz/9gKZNm9o7mki9pLImItIAVFZWMnToUH5etpZG7QYzcsw42sdH2TuWiNRRGzb9wWez51OSn41T5nb+88ITXH31VfaOJVLvqKyJiNRzNpuN0aNHM33WD/i17s+1Y26he3JzTFr5UUTOQW5uDu9+8iWpGZmUH9rCVd1b8uZrr+Drq2M1ipwvKmsiIvWYYRj885//5PV338crqS9XjrqVfh2TtET/eVBVWcG3H78FwBVj7sLJ2cXOiUQuPqvVyszvfuC3tVuoyDlEYHka/3v7FXr16mXvaCL1gsqaiEg99vzzz/PkM8/j0aInQ0aOZ0iP9lqi/zwpO1rKuD4tAPhg0Tbc3D3snEjEfnbu2sXH38wjPyuD8oObuPPaAUx67llcXbWAkci50F9sEZF6avLkyTz5r2fxiOtKnytvYHB3FTURuTDimjfnqftupW3rFNybd+Y/P26gXfe+bNq0yd7RROo0/dUWEamHXnrpJR59YiIecV3pccUNDO/XHWeL3vJF5MJxc3Nj/I3XMebqy/CNSuSwSxRdh97Ic89PorKy0t7xROok/eUWEalnpkyZwoTHnsA9rgs9rhjFNQN74+qkg16LyMXRvk0rnrx3HPEJCTg368yLX/xCu+59Wbt2rb2jidQ5KmsiIvXIq6++ykOPPI57XFd6DLuRqwf2xs1ZRU1ELi4fH1/uvfUmRlzeD5+olhxwiqD7lTfx4MMTKC0ttXc8kTpDZU1EpB4wDINJkybxwMOP4hHfle5Db+TqgX1wd1FRExH7MGGie5fOPHXfeJKTU3CP68a7P24guXMv5s2bZ+94InWCypqISB1ns9n4xz/+wRP/eg6PhO50u/wGrh3SBw8XJ3tHExHBx8eXO8fewNhrL8c/OpEc3wSG3/kEl195Nfv377d3PBGHpqX7RUTqsMrKSm6++WamfTUL94TuXHLlKIZd0hVXTX284GxWK/t2bAYgJj4Js0X3ucjfKSsr45sffmTFhm1U5KdhpG3jwfEjmTDhYdzc3OwdT8ThqKyJiNRRJSUlXHPNNfy4ZDneiT0ZcM1oBnVrh4uTJk2IiGM7nJrK57PncejwESoy99LIlsukJx7m+uuvx6xDjIhUU1kTEamD0tLSuPLKK1mzbR++Sb25/LrR9O3QUsdRE5E6w8Bg2co1fLdwKUV5WZSnbiexsRuvvPgcvXr1snc8EYegsiYiUsesWbOGYcOGkVnuREBKb4ZfdyNdU5pjNpnsHa1BqaqsYP6MDwEYeN3NODm72DmRSN1UUVHB9z8v4rc1myjLy6T88Bb6tY3jpRefp0WLFvaOJ2JXKmsiInXIF198wdixN2MExhDRcQBXXX01KU3DMamonXdVVZVkph4k4/B+CvNyKS7IpSg/j6L8XIoK8ig/Wsrm1b8D0DypLQYGFidnPL198fT2wdPbFw8vH7z9/AkKa0JweBSNQiNwcdV+OSInU1hYwMy5C1i3ZSeV+elUpm5l1OV9eebpiYSEhNg7nohdqKyJiNQBNpuNJ598kkmTX8I9pi0JPS/nuiuH0iTI197R6jzDMMhOO8zuLevZt30zaQf2kHZwL8WZB/F0suLjasLLBTycTbg7gZeLCQ9nsJgMPtlkBeCu9k5YLCYwoNwKZVUGZVVQUGZwtApKKg2KK6C4wsDs1RiP4CgiYxOJSUimaYsUwqKaaYESkf9z+EgaX/2wgN379lOVm4opey93jbqKCQ8/hJ+fn73jiVxUKmsiIg4uLS2N0aNH88vSVbjHdqLzoKu5amAffD1c7R2tTrJWVbF783q2b1zF7s3r2bN5HU5luQR5mgjyMBHsaSLcx4SvqwlPNycC/Xxxc/fA2dUVF1c3LC7uWFzdqbCZuPLFnwGY9cTluLs6Y9hsVJUfxVpRhrWyDKOyjIqyoxwtLaK0qIjS8kpKKw1KKiGtyMaRIoOcowaFVlfcw1rQNLkDSR26EdeqA27uHna+p0Tsa+uOncz6cTFpaWlUZB/CregwD952E/feew8eHnp9SMOgsiYi4sDmzp3LTTfdRAEeeMd3pd/lVzGweztcnTQKczay0g7zx8pf2bRiCdvWLsPLKCLc20S4j5lm/iYaeVgICQ7EL6AR7j4BuPg0xi0gBBfvRtgsrmA6ceGWo2Xl9Bn9OACLPnked7e/Kc+GQVVpPkfzMyjLz+BoTjpH8zMozM+lqLSSwnKD/fk2DhQYZB614BGVQvM23Unp3JNmLdtohTxpkAwM1qzbyPe/LCU7K4vK7P34VGTz5D/uZPz4cTg7O9s7osgFpbImIuKAysvLmTBhAlPffAu3JslEdujH5UOH0jq2iRYSOQOGYXB4705W/vIDqxfNI//wTiJ9zUT5mkhoZCbMz50mEaH4BgbjHtQEj8bR2Fx9T1rKTuWsy9qpstqslOakUpKxn9KMfeRnHaGwsJj8MoNduTb25dvINwcQ2aE/HXoNJLFdFy1mIg2OzWbjt+Wr+PG3VeTnZVOZuZ8QpxKefeQfWu5f6jWVNRERB7N69WrGjx/Plr2puDVrT4dLhnLFgD409vW0dzSHd2jPDlYu/J6VC3+gNGMPMX5m4gPNxDWyEB3emMah4XiFNcczpBlWJ084h+J7vsrayZTmplNwcBtHM/eSl5FKbnEZhwoMtmbbSKvwJLR1X7oNHE5Sh+7a100alKqqShYsWcrC5esoKcyjImMPsb5mXnhqAoMHD9ZiS1LvqKyJiDiIoqIinnjiCd58+12cg2PxT+zCgCHD6N0+SQe6Po2i/FyW/vgtv/7wJbn7txIbYCahkZkWjZ2JjQonKLIZvpFJGJ6NMcznr9hcyLL2Z7aqKvIObqHgwGYK0/aRW3iUw0U2NmXYyCKA2B7D6DH4Gpo0i78g1y/iiMrLy5i7YDG/rfmDsuJ8KtJ30zrCh8n/eoIePXrYO57IeaOyJiLiAL777jvuuusu0grKcYtuTXKPgQy5tDcxIYH6pPgkrFVVbFy+mF9/+Io/li0kxsdKYpCZpBBn4mMiCGrSFJ+oFAzPoLOa2nhWGWw2NmzbC0DrFk2xXIRpWIZhI//QTvL2rqcgdRc5hWXszbexMd1GuX887QePpNugK/Hw9L7gWUQcQUlJMd/NX8iKTdupLM6n/Mh2erWM4sVnn6J169b2jidyzlTWRETsaOPGjTz66KPMX7AI14hEQpK60af/IHq0SdAiIieRn5PJom+ns3DWNFxKM2gRZKZdqIUWUUFExMTi36wdhnfIBStojsRWVUXW3k0U7F1LXtpBskusbMywsavQhbCOQ+lz5Y3EJCTbO6bIRZGXl8usuQtYv30vVcU5lB/eyhU9WvP8vyYSGxtr73gitaayJiJiB5s3b2bSpEl88dVMnBvH4BGZTPte/RnUpxshfl72judQDMNg58Y1/DzzE9YvmUdTXyutgs20jvAkoXkzApql4BoSj83ccFeFKy8tJnPbCvL3ricvL5+DBTZWH7FxNDCRrsPG0uXSy7UoiTQIGRnpzJy7gK17DlJVkEHVke3cPLw/z/7rafz9/e0dT+SsqayJiFxEq1atYtKkSXw75wdcGkfjEt6CxA496durG4lRYVjMmvJ4XFVVJSsXfM/caf8l78AWWja20CHMTKtmITRp3gK/Zh2wuvqd0yIh55bPyuwFKwAY1q8zTg4yEpp9cAc525ZRkLaXzGIr69Ns7Cv3o+WgMVwy/Ea8ffUPq9R/+w8cYOa8X9h7MJWq3MO4FR7i2Yfu5pZbxmPRojxSh6isiYhcYFVVVcybN4/XX3+dhYt/wzkoCtewBBLad6Nb5460jovG2VL/p+2dqdLiQhZ9O535Mz7C/Wg6rUMsdIhwITmhKcHNW+MSlojNbP9Roou1wEhtlZUUkr75dwr2rie7oIRt2TY2ZTsR3Gk4/a8fT1hUM3tHFLngNm3ZytfzfiEnO/vYypHeNt6a8gLdu3e3dzSRM6KyJiJygezfv58PPviADz/8kPT8EpwDI3ELT6Bl+y507dSRljHhWuXxT7LTU5k/40OWfPcFEW6ltA810zbSmxaJCQTFd8LmE+ZQ+6I5elk7zma1krZ9NXnbl5Gfm8OePBurj1hxataDvtfdRmK7rlrERuo1q9XKT4uWsGDZOo4WHNufbWi3ZKZMnkRERIS944mclsqaiMh5lJOTw+zZs/niiy9YuHgJTn6hODWKwi8ygRat2tG1fWuaNwnGSQdwrXZg11a+//Rd1i76noQAg07hFto0DSQ2IQnf+M7YXP3sHfGk6kpZ+7PsAzvI2vIrBen7OVJosPywlQLfBHqPuIsOvQbqmG1SrxUWFfLVd/PZsG03lQUZkLGDJ+4ayz//+Q+cnJzsHU/kpFTWRETOUVZWFnPmzOHLL79k4S+LwMMfJ/8wnBs3JSYxhaSWyXRq1YJAb3eNYPzJnq0bmf3h62xbsYDkYAtdIsy0jQ8nKr4VHjHtsTm52zviadXFsnZcUU4GGZsWkX9wGxlFlaw+YiXV3ITO19xJ94FXajESqdf27T/A9O/mcyQtg4qMvTTzquSDt16lffv29o4mcgKVNRGRs2Sz2Vi7di1z585l7ty5rF63HotXI5x8g3FuHE1odByx8S1ok5RA8yYh2h/tL3ZuWsOsD19nz9oltAqx0K2JhfYtmxKe0B6X8JZ1ZlXHulzWjisvLebw+l8o2LeB7MIy1qfb2FUeSOsrbqPPFSNwc/ewd0SRC8LAYMnvy5jzy3KOFmRTcWgz44dfyqRn/4WXl1bkFcehsiYicgZyc3P5+eefmTt3LvPmzSOn6CgW7yCc/EJwDowgJCqW2ObNaZUYT7OIUNxdNJ3szwzDYNu6Fcz+6HUOblpG6xAL3aKcaNcylvCWnbAExWOY69Z9Vh/K2nGV5WUc3vQrBbtWkVNYysZ0G1uLPIkbMJYB14zFy9fP3hFFLojCwgI+nzmHLbsPUJlziICKTN7+978YMmSIvaOJACprIiInZbVaWbVqFT/++CPz589n9boNmD39sXg3wjkgHPfAMKJjE4iOiSY5vhlNGgdqsZCTMAyDP1b9xuwPXyd9+2rahFroFulM26TmhLXsgrlRMwxT3Sppx9WnsnactaqS1M3Lyd+xnNz8QjZnWdmQ40JkzxEMGnErAY1D7B1R5ILY+McfzPhhIfm5xxYgGdIhjremvkJwcLC9o0kDp7ImIvJ/UlNTq8vZggULKawEi/ex6Y1O/qEEhUcTHdOUuGbRtIiNxt/TDbP2QTspwzBYv/QXZn/0Bjl71tMu1EK3KBfatGxOSMuumAKbOtTKjrVRZbWycuMOADq1isepHi3OYdhsHNm+htxtv5OXk8P2HCtrMywEthvKoBvv0LL/Ui+VV5Qzc848lm/YRmV+Oq65e3hl4sPceOMN2t9Y7EZlTUQaLMMwWLNmDbNmzWLOnDls2bEbJ58gLD5BOAc2wcO/MZHNmhMTFUlSXDOaBDfS6NnfsNlsrF3yI7M/eoPCQ1tpG2Kme7QrbZLiCW7ZFfyj6nxJa2gy9mwiZ/MS8rLS2Z1rY2WqDbeESxg46m6aJbaydzyR827f/gN88s33ZGZmUZ66nZ7xwfz37ddp0qSJvaNJA6SyJiINis1m49dff+Wbb75h1qxZHMkp/L+RszCc/cMIi25GZFQMLWKjSGgaiZebiz5RPQM2q5WVv8xl9sdvUJ62k3ZhZrpGudE6KYHgxG4Yfk1A92Odln1oF9mbFpGXfoD9+TaWH7ZhDe/ApTfcTVLH7nqdSL1itVr5bv7PLFq5gYq8NCxZu3jxkXu59dZb9FyXi0plTUQahM2bN/PZZ5/x+eefcyS3CCe/UJwbReLeKJymzVsQG9uUVgmxhAT66hhoZ8FaVcWyn7/ju4/fpCp7Lx3DLHSJdqdVUgJBLbtheIfX25JWVWVl/u/rABjYvS1OTvVnGuTpFGalkr5hIfmHd3Ew38qKVCv5XnH0GXk3HfsM0rHapF5JPZLKx199z5G0dMqP7KBDpDcfvvsmzZppKrBcHCprIlJv5eXl8emnn/LRRx+xcesOnAPCcW4UhVdIDLEtkohv3ozWLWLx93LXvmdnqaqygt/nfcN3n7wNeQfpGG6hS7QHrZJaENSyOzavkHpb0o6rjwuMnI2Sghwy1i8k7+AWUvMrWXHYSqo5jG7X3EGPwVfh4upm74gi54XNZmP+wsX8tHQNZblpGEe28Mw/b+fee+/Bog8n5AJTWROResUwDFavXs27777LFzO+pNLVF+dGUbgFN6VZi2QSWyTQPjmeAC8doLo2KsrKWDznC77/7D+4lKTRIdxC1xhPWiUnEpDYDcMjuN6XtOMaelk7rry0mLQNi8jbu560/DJWH7Gyu8yftkPH0W/4jXh4+dg7osh5kZGZycdffcfBQ6mUH9lBSmNnPnrvbVq0aGHvaFKPqayJSL1QUVHBjBkzePXVV9mwZQfOjSJxCWlOaLMWJCW3olObloQF+mExN4wicb4dLSlm4azPmDvtfbwqs4+NpDX1JjmpJY0Su2J1D2owJe04lbWaqirKSfvjV/J2rSY9t5j16TY257uTcOkNDLjuZvwbaQl0qfsMw8bCX5cyd8lKSrOPYE39g8fuuImHH34IZ2dne8eTekhlTUTqtIKCAt577z2mTp1KemEZzkExeIQnEJfSlvatk2md0BR3Zyd7x6yzSgoLmP/lR/z45YcEUEincAtdYv1ITk7CP74zVvdG9o5oNyprJ2ezVpGxbQU525eTkZ3Hlkwb67MshHUZzuAbbiekSbS9I4qcs5ycHD7+eg779h+gLG0XcT42Pv7Pm7Ru3dre0aSeUVkTkTopKyuLl19+mXfeeZejTl64hDTHPzqR1u070r1DGyKC/LQf2jkoyM1m3vT3+Xnmp4S4lNAxzELXuECSk5PxjetElau/vSPancra6RmGQebuDeRu/ZWsjEx25lhZlQbeSZfS//rbiE1qY++IIufEwODXpSv5btFSSrPTqTz8B/ePHs7Ep57E1VXvB3J+qKyJSJ2Sk5PDyy+/zBtvvkmFqz8uYQmExLWiXfsO9OiQQoCXh70j1mk5mWn88Nl/WPztdCI9K+gYbqZrXGMSk5Pxju1IlYuvvSM6DJW1M5d7aAeZfywmN+0gB/KPHavtaKNkel59Cx16D8TipNFvqbsKCgr45Os57Nizj4qMvTRxKeHjd6bSqVMne0eTekBlTUTqhLy8PF555RWmvv4GZS5+uIYnEN6iLV26dKVr6wQ8XV3sHbFOyzh8gDmfvsPSuV8T62elc7iZzvGhxCel4Nm0PVXO3vaO6HBU1s5efuZhsjYuIi91F2mFVaxKtXLYFkTby8fS54oReHrrwwCpu1asWcc3Py6hODeD8oN/cNPlvXnhuWfw99dMBKk9lTURcWgFBQVMnTqVV159jVInH1wjEgmNa0W3rl3p3q6l9kc7R3u2buSHz//D+iXzaBEIXZtY6BgfTrOkNrhHtcXq5G7viA6rymplyarNAPTqmISTlvA+Y2XF+aRv+pX8fRtJzz/KpgwbW/NdiOx+FQOuHUdIZIy9I4rUSnFxMZ/NnMPmnXuozDmMR+FBJj/5IKNHj8asY3hKLaisiYhDKioq4o033uDlKVMoNnvhGt6CkLjWdOvahR7tknB30apbtWWz2di4fBE/fPYe+/9YQVJjM10iLHRoEUl0y7a4NWmN1aJRIrnwrJUVZGxfSd7OVWTl5LI7x8qaNBtOTbvRc9hNtOnWVwfZljppy7btzPh+AdlZGVSkbqd1uBfvvvEqKSkp9o4mdYzKmog4lJKSEt566y3+/e+XKDDcqktal65d6N0hRSXtHFRWlLPsp2/54fP3KD6yi5RgC10jnWnXshnhCe1wDm2Bzaz7V+wje/9Wcrb8Rl76QVKLDFalWkkzNab1wBvpffl1+AYG2TuiyFmpslbxw0+LWLxqPUdz06k8vJlbrh7Ec8/8Cx8fHX9QzozKmog4hNLSUt59911efPFF8iqdcI1IJLh5azp36ULvTq3xdFWJqK2SogJ+mTWN+V9+iFtZFm1CzHSOcqd1UhwhCR3BPwbDrNGLs6VpkBdGSV4mGZt/p+DgZjILyticaeOPbDOBrQdwyfCbiGvVXge0lzolJyeHz2fPY8ee/VRmH8D7aBrPP3I/N910Exa9b8jfUFkTEbsqKyvjvffe44UXXiCr1IZreAsax7Whc9du9OnUCi8t2lBrWWmH+fHLj1j87ReEuJTQLtRCp6Y+JLVsQaP4jli9QsGkfShqSwuMXFjWygoyd64mf9dqsrOy2JtnY80RK0XesXS54ia6DRiGu6eXvWOKnLENf2zm63mLyM3OpCJtF7G+8Nrk5+jbt6+9o4kDU1kTEbsoLy/n/fffZ9KkSWQUleMSlkDjuLZ07NqdPh3b4Oupf3xrwzAMtq1bwY9ffsSmpT8TFwAdwsy0b9aI+MQk/Jp3pMotwN4x6wWVtYun4Mg+srctJe/wTtIKq1iXZmV3kTvRPa6k3/DRRDSNs3dEkTNSVVXJ/F9+5ZcV6zman0X5oS30axPLy5MnkZCQYO944oBU1kTkoqqoqODDDz/k+eefJy2/FJfQeBrFtaVDl+706dKGAE+tPlgb5WVHWTp/Nj999TH5h7aT1NhCx3AzbePCiU5IwTOmPVVOOgbd+aSydvFVlJWQuWUp+Xs3kJlTwO48K+vSbBDRjm5DR9Gh90CcXfQ4iOMrKipi5twFrN28ncr8dCpTt3L9wB48/dSTREVF2TueOBCVNRG5KCoqKvjf//7Hc889R2pOES6hcQQ2b0uHrj3p07k1gd4qErWRdeQQP8/8lMVzpuNnFJESbKZjE1datYglJL4NTsEJWjTkAlFZs6/sfZvJ27GcnCMHSSuysi7Nyv4yb2J7XUXfK0YSFh1r74gif+tIWhpffv8zu/YdpCrvCLb0HYy7ZjCPP/YYwcHB9o4nDkBlTUQuqIKCAt577z2mTp1KesFRXELjCGjWmg5de9C7U1uCfFXSzpbNamXD8sX8Mutztq5aRHN/aBdqpk20PwkJ8QQ274DVW/ujXWgqa46htDCXzC1LKTzwB1n5xezMsbE+/dhoW/fLb6BDn0G4uLrZO6bIae3cvZdZPy3m0KHDVOQcwpyzj/HXXc6DDzxAWFiYveOJHamsicgFkZqaytSpU3n33XcpNbnhEhJHQLNk2nXuTp/ObWns62nviHVObmY6i777gsXffQFF6bQMMtMhzEyruCZExiXhHdOaKmctB32xqKw5FsOwkbVnIwW7VpN95CBpRTbWplk5UOZFbK+r6XPF9UTEaN82cWybtmzl2wW/kZ6WTmXOIYysPYwa1p8JDz9MTIwOFt8QqayJyHm1ZcsWXn75ZT6fNh3DKwjX0OaExLehbYfO9GifrOmOZ8lmtbJp5RIWzprGpmULifGFVsFm2kZ60jK+GUHN2+AUFKupjnagsua4yv5vtC3/4GYyc4vYmWNjQ4YNwtrQdeiNdOozGBc3jbaJYzIwWL9xM/N+XcmRI6lU5qZiTd/FVf2789LkFwkNDbV3RLmIVNZE5JyVl5cza9Ys/vOf/7Dk9+U4N2qCS0gc0cntad++A51S4vFx1z+yZyMvO4PF381g8XczqMo/TItGFtqFHlswJCo2Dt+mbahyawQ63pTdVFVZmf/7OgAGdm+Lk5OOl+RoDMNGzt5Nx5b/P3KQI4XH923zpFmP4fS+/Hoim7ewd0yRkzIw2LxtJ/OXLOfAwUNYSrL5Y877hAQF2juaXEQqayJSa7t27eL999/no48+Ire0CudGkbiGxhGX0p5OHdrTJiEGN2f9A3umbDYbm1f9xsJZn7Nx6QKifQySGptpH+lBy/hYgmNTcGrcHKtFxVfkbB0tyiVzyzIK9v9BVn4Re/JsbEi3UR7Ygg4Dr6dr/yvw9PG1d0yRk9q8cy9HMnOYeudQfN01k6IhUVkTkbNy6NAhZsyYwRdffMG6jZtx8g/DpXEMfpHxJLVuR6c2KTQLD8LJosUtzlRW2mF+n/cNS77/ElveYRIamWkfZialWShRzePxi2lDlXuQRtFEzgPDsJGzbzO5O1eRn36AjCIrG9Nt7CywENSqPz0uu46WHbphNus9TBxHUVklReVV3NKjqcpaA6OyJiJ/KyMjg6+//povvviC35etwMm3MU4B4bgGNyMmIYmU5GQ6pSTg6+GKSYXijJQdLWX14vn89sNX7Fq/jNgAM0lBZlqHe5CY0JTGsa1xatwcm0bRHFaV1crKjTsA6NQqHieLRpHrmorSQjK2raBw/x9k5+RyqMDGunQbWZbGJF1yLT2HXE3jsEh7xxRRWWvAVNZE5JSKioq46qqrWLhoCWbvRjj5huASHENkXBLN4+Npn5xARJA/TvoE+owYhsGOjav59YevWLXwBwKdSkkINNM6xEJSbDgRMbH4NmuH1S1Qo2h1gBYYqV8Kjuwld+dK8g7vIrOgjO3ZNv7ItOEc3YEug6+nQ59BuLq52zumNFAqaw2Xk70DiIjj8vLy4nDqETySLiEioQ1xCS1o3TKemNAgXJxU0M5U6r5dLP/5O5b+OJvKnIPEBVoYlWCmZZNGxDZvRmBMK0yB0RhmZ6z2DivSQPmGNcU3rCmRlRVk7VpL030b6Jh+mPSi9az7ZA0//+cpIjtfRq/LrqNZy9aaRSAiF4XKmoickslk4p133mFxhgu+/v409tZS12cqOz2V5T/PYdlP35K1byuxAWb6BplJauFFQvNoGsck4hGRRJXTsUMZaIqDiGOwOLsQktiFkMQuRBRkk711Oc0PbiErr4j9B7/m+6dmUOwVQ5sB19F1wDD8GwXbO7KI1GOaBikip1VltfHO4j0ABHppmtfpFORms/KXH1j+03fs27yGGH8z8YFmUkKcaNE0gpCoZvjGtMKqxULqBU2DbDgMwyD3wFbydq0m/8heMouq2JxpY2uOgUdsVzoPvJr2vQZomqRcMJoG2XBpZE1E5ByUlhSxdslPLPvpW7at+Z0m3jZi/c0M6+xCi6ZhhEfF4BedguEdhmG2aJqjSB1kMpkIjG5JYHRLKspKydq+iqYHNtE1K4O0opWs+2gZP73tRnj7QXQbfBUt2nTWapIicl6orImInKWigjzW/voTqxfPZ9vq3wn1qCTW30y/9mYSokKJjI7BPzoZk38ENrMLNnsHFpHzxsXNg/DWvaF1b4pz0oncuZL4g9vIziviQPq3/PLCLL40Nyaxz3C6D7yS8Jjm9o4sInWYypqIyBnIy85gzeIfWb14Hrs2riTc00YzfzN3tjUTG9aY6JgY/KOTcG4Ug9XiioH2QxOp77wCQ/DqcgWRXa4g7+B2onevoVXqXjILc9ix9j98Ou8drMEtaX/pVXS5dCg+/oH2jiwidYz2WROR02rI+6xlHTnE6sXzWbVoHge3raOJj4mmfiaSgy00j2hEk6hI/CJb4BLUDKtFi680NFVVVmYvWAHAsH6dcXLScdYErJUVZO9ZT9HeDWSnHyaz2MqmDBs780z4Jvai68CraNP9EpxdGtb7qZwb7bPWcKmsichpNaSyZrNa2bNtIxuXLWL90l/I2LOZaD8zzfzNJAaZiYsKJrxJJH5RLbEEROmA1SJyWuUlhWTvWEnhgc1kZ2eTWmhjfbqN1ApPIjsNoWv/YcS37qj92+Rvqaw1XCprInJa9b2sFeXnsmnFEjYuX8zGFUtwLs8jys9MrL+ZhCALsZGhhERE4RfVEvP/7YMmInK2irIOkbNjNYWHt5OVX8yBfINNmTayTY2I6zGUrv2vIDo+Scdvk5NSWWu4VNZE5LTqW1mz2Wzs37GZDcsWsXH5IvZt3UCIJ0T5mYkPNNE8yIOmUaEEhETgE5WEyTsUw6zde+VEVpuNDdv2AtC6RVMsGh2RM3D8MAAFe9aRn7aP7MIydufa2Jxlo9gziqTew+jafyihkU3tHVUciMpaw6WyJiKnVR/KWklhAZtW/nps9Gz5YiqLson0NRPla6JlYzOx4UE0aRKGX2gMHmFxVLn4gkn/eMvp6Thrcq6sVZXk7N1I4b6N5KUdIKu4iu3ZNrZl27A1TqR132F07nc5AY1D7B1V7ExlreHSx8UiUu8YhsGBXVuPlbNli9j5x1qC3A2a+JgYFGamRbAXsdFhBIdG4BMRh9k3HKvTsYPZVtk5u4g0HBYnZxrHtadxXHsqy4+Ss3stzff/QdeMI2SX7GTzjy/y9vQXcI1uT/t+V9Kxz2A8fXztHVtELiKVNRGpF0pLitiy6vdj0xtXLKYsL4MIHzNNfE1c2s5C84hAopuEExAaiWdYHJWu/hhmJwzQgapFxO6cXd0JadmdkJbdiSwpJHvnamIPbiEnK4OM4vX88dUaXn7/KXxb9KTTpcNo26Mfrm7u9o4tIheYypqI1EmGYXB47042Ll/EhmWL2LVxDY3crTTxMTGgsZn4JA9ioyIIDgvDNzwWi38EVRZPMJmosHd4EZHTcPX0IbzNJdDmEkrzs8jZuZr4Q9vIzc3lSNESNnywiHlvuBLatj9d+l9BUsceODlpapxIfaSyJiJ1RllpCVvWLGPD8kVsXLaI8rwjNPEx08THRP/2ZpqGNiaqSTj+oZF4hTWnyi0Aw+yMgaY3ikjd5OEXhEfHwdBxMIUZh4jctZrE1B1k5RVzOOcHlr82h28qvYnpMoSu/a+keUo7HQpApB5RWRMRh2UYBmkH9rBx+eL/Gz1bRZBbJU18zAwJMdGspQdNI0NpHBqBb3hznAIiqbK4g8lEpb3Di4icZz7BTfAJbgJA3uGdRO9eR0rqLrILS9m3bwbzn/2CaU7BJPQYStcBVxAZ20KHAhCp41TWRMShVJSXsW3dCjYs/YX1y36hPOcQTXzMxPiZGdDOTFRwI5pEhOMfGoVneAI2twAMswXQ6JmINBz+EXH4R8RhWK3kHNhK073raZu2j6zCbHZvep+vFrxHqU8Myb2H0bX/FQRHRNk7sojUgpbuF5HTuhhL9+dmprNh2SLWL13ItjW/E+BURqTvseOeNQ10JiYilEb/t3KjS6MorBbtVC/2V1lVxYy5vwFw3eAeODvp80+xL2tlBbl7NlCwfyO56YfIKq5iS+axQwEQmkz7flfSud8Q/AIb2zuqnCUt3d9wqayJyGldiLJms9nYt20T65cuZP3vC8nat+XYcc/8TLRoZCYyyIuoJk3wDY3BJyIBq0egjnsmInIWKspKydm1huKDm8lKP0JGsY1NGVZ25YFns8506j+MDr0H4uHlY++ocgZU1houlTUROSXDMCgsLub9pQcBCPB0qfVllZcdZevqpaz7fQF/rFiMa3keUX4mYv3NNA+0EBMRRFBION7hcbg1jqHKyeN83QwRkQatvDif3F1rKD68nazMLI4U2diQYWNfkYWglr3o2O9yUrr0xsXVzd5R5RSKy6ooLq/izr7x+HrU/m+x1D0qayJySiUlJXh5++DT+WoAbKUFZ/X7RlUFVYVZVBVmQlE24d4GUb5mWgaZCfe14Ovljs3ZgzzDiwKrG1ZDO8JL3WEzDLLyjwIQ5OeOWQs5SB3gYisj0JSPl62Y4qOVHCw4Vtx25UGBSzBO/qFYvAIwaTaDQzG5eGB29WDb7DcJDfSzdxy5iDTBXkT+ns3A4hOIxcP37zctK6Yi6wCVWfuxFKUR6WMiJtBEUgsLwb6ueHr7UO7iR745gH02Zwz+/z+4lgt5G0TOM1uVlS/nbATgrmt7YXHSM1gcnxXIJIwMDDw8C2jml0liaD75xRXsys1kc2Y6ezJcKfePwSUkFotPY60o6QhMZmxHC+2dQuxAI2sickqGYVBaWsrWtCJKK6yn3O7Q/r0s+ekHflswn0M7NxLtZyY2wExiIzOxEYE0iYyiUXQLvENjMZy0OIjUD6Vl5bS59lEA1n/5Ah5uF2YBHpELzWazkXtgG5m715F+cC9H8o6yLcvG1mwbJe5hdB8wjEuGDKNp8wR7R23QnC0mOsWGYLFo1LMhUVkTkVpJTU1lxowZTJs2jR1/rKWpv5m4ADMJQWaSmoYT26wZ0YltCAxvBhbNr5f6p+RoOV497wCg+Nd38HRXWZO6r6qygr1bVrN381p2795Nan4FGzOs7Myx4d2kJddeP5IRI0YQHR1t76giDYLKmoicsby8PGbOnMm0adNYvXQxMX4m4gPNtGxsoVV8FLGxzYlJbItXUBMwa5a11G8qa1LflR8tYdfGFezftp5de/ZxqKCKDek2dufaiEzqzIiRN3DNNdfQuLEOBSByoaisichplZaWMmfOHKZPn87in+fSxMtKQuCxEbTkpmHEJSQQm9IJr8AIMGufHWk4VNakISkpzGXn+mXs376RnfsPc7DAxsZ0G/sKTCR36cvIkTcwbNgwfHx0KACR80llTUROUFlZyYIFC5g2bRrzv59NkFMpCY3MJAaZSYgMIj7+WEHzD43WCJo0WCpr0lAVZKexY/1S9u3YzK6D6RzIt/FHpkFqqTNd+l3OiBEjGDRoEG5uOhSAyLlSWRMR4NgO5suXL2fatGnM+noG3tY84gLMJDU2ExvmS0J8ArHJHQiOTlBBE0FlTQTDICt1Hzs3LGfvji3sz8hlX56NLVkGWVWe9BlyNSNHjqR3795YLJp5IVIbKmsiDdwff/zBtGnTmPHFNGx5h4gLNNM6xEJMkCct4pvTNKk9TeJaYbI42zuqiEOpqKxi0kffA/DY2MtwcdaHGNJwGYbBkX3b2bVhOXt3bSM1u4i9eTa2ZNkocQniksuvZfjw4XTv3h0nJ71WRM6UyppIA7Rv3z6++OILpk37nOz9W2keYKZ1iJmmjVxJjGtG08S2xCR1wOKsKSwiInJ2bDYr+7dvYvemlezfs4OM/KPszbOxNdugwORL10uv4Morr6Rfv364u+twLiKno7Im0kBkZmby5ZdfMm3aNLavX05sgJmUYDOxgc4kx8UQk5BMbKsuuHho53ARETk/qqoq2fXHWvZvXcuh/bvJKDjKwfxjUyUzK9xo02MAw64czpAhQ/Dz87N3XBGHo7ImUo8VFhYye/Zspk2bxtLFC4jxNWgZZCahkYXkuCbExrckNqUzHv7B9o4qUufYbDa27UsDoEVMKGazDlQrcjpWaxX7tm86Vtz27SQtp4jUIoMtmTZSSyzEtu3FFVcO57LLLiMiIsLecUUcgsqaSD1TXl7OvHnzmDZtGvN+mEOIWwUJgcdG0ZKahhxbybFVZ3waR4JJ/1yK1JYWGBGpPQM4vHc7e/5YzeG92zmSmUtG8bF93PbnG/hFteSSgZczePBgOnfurAVKpMFSWROpB6xWK0uWLGHatGnM/PorvIyi6v3QkiIDadkinmbJHWjUJE4rOYqcJyprIudP5pED7Ny0iiN7tnH4SBp5Rw125NjYk2ej0ORLx96DGDx4MAMHDiQwMNDecUUuGpU1kTrKMAzWrFnD9OnT+eKLL6gqSKd5oJlWwWYSw31ondicpi3bEtosCZOTFgoROd9U1kQujKKCPPZsXkvqns0cPniAzIKjHC402JZt43ARRCR2ZMCgIQwZMoRWrVphMpnsHVnkglFZE6ljtm7dWl3Qsg7tJjbATHJjMy3DPGmb2IxmLVJoktAas6sWChG5kFTWRC48q7WKAzs3c2DHRo7s301qRjY5pQbbc2zsy7Nh9QyhW78hDBw4kEsuuQRfX197RxY5r1TWROqAffv2MWPGDKZPn87ubZto5m+mZZCZliEutEtsRvOERKIS2+PsGQD6hFHkolBZE7n4crPS2btlDUf2buPQoUNkF1VwqNDGjhyDtBIT4QntuaT/IPr370+HDh20r5vUeSprIg4qIyOjeqn99atXEONvJj7QTOtQZ9okRBEXn0DTpA64+gZroRARO1BZE7Gvyooy9m7fxMEdm0g/tJf0rDxy/29ft/35NorNPrTqegkDBgxkwIABNGnSxN6RRc6aypqIA8nIyGDWrFl8/fXX/L5kEZE+EBtgIiXEQrv4SBLi42iW3AGPgHAtFCJiZyprIo4lJyuN/VvXk7Z/O4cPHiS7qIyMYoOt2TYOFRh4hcbSs9+xUbdevXrh6elp78gif0tlTcTODh06xDfffMPMmTNZuex3In1NNA8wkdzYQkpsGC3jm9M0qT0+wVFgcbF3XBH5PxWVVTz+9jcAPH/ncFyc9QGKiKOwWqs4tGcbB3f+QfqB3aSmpZN71Mb+PBu78wwyjlqIbd2dfv0HMGDAAFJSUrRQiTgklTURO9izZw8zZ85k5syZbFi7iihfM7EBJpIaW0hpFkpC82bEJLYhMDwWnPRpvYiIyLkoLS5k77b1pO7eypFD+8jILSK71GBnjo0DBTYqXBvRodcA+vfvz6WXXkpwcLC9I4sAKmsiF4VhGGzZsoVZs2Yxc+ZMdm7ZSLSfmdgAMy0bm2kVG0F882bEtGiNX2hTFTQREZELxAAyU/ezf/tG0vfv4PChw+SWVJBaZLA928bhQhuNYpLpfemxKZPdunXD1VV/l8U+VNZELpDKykp+/fVXvvvuO+bMmUPOkX1E+5lpHmAmMchCq7gmNI+NJbpFG01xFKmDbDYbB9NzAYgMCcBs1kI/InVRZWUFB3Zu5tCuP8g4uIfU9Czyygx259rYm2cjp9KNxI69ufT/pkzGxcVpyqRcNCprIudRXl4e8+bN47vvvmPevHm4WYuI8jWR0MhM80YupDRvQrPmcUS3aINXYARYtI+LSF2lBUZE6qfC/Gz2bd1A6t5tHDm4n6yCUrJKbWzPtnGgwMDsG0GXPseKW9++ffH397d3ZKnHVNZEzoHNZmPdunX8+OOPzJ8/nzUrlxPqaSPS10yLRmbiQrxIjo8hKqYZkS3a4OodBGYd80WkPlBZE6n/bDYbaQd3c2D7RtIP7iI19Qg5JVUcLrSxM8cgtRjC49vRu98ALr30Ujp37oyzs7O9Y0s9orImcpYyMjL46aefmD9/Pj//v/buPDrKKl/3+FNF5qkSyEBSmZmDTBKGoIAiCIqt7dBqSyOi2Hq6XTj0EfE6AN30acWFjdqip7kK9yxp5cpFuxUFaVEEAgrIpGEmZICMkITMU+37R6Q0ikoKkqoU389atdbrzlvF7/1RuPLU3u+udevUVFmiBJtVyeFW9e1mVd/ESPXpmaLEHv0U1yNN1gAbX1QNeCHCGnDxqa+r1bEDu5V36CsV5H773W6HTzmUXe5QWXOQ+g8fq/ETWjYq6du3L0smcV4Ia8DPaGho0JYtW7RmzRqtXbtWX+/ZKXuoRfFhVvWNtCilW4D690pQclKyEnpdoojYFMknwN1lA2hnhDUAJ0sKdWzfThVm71d+Xq5KT9equLplo5K800YmNFYjx07QhAkTNH78eEVHR7u7ZHQyhDXge4wxOnr0qNatW6c1a9bok/XrFeCoUnyYRanf7ODYLylGPVMTFZ/SS3Gp/eUTHCFZ2FwAuJgQ1gB8l8Ph0PFjB5V7cI+Kcg4r/8QJnaxqUm6FQ4dOGecuk1eOb5l1Gz16tAIDA91dNjwcYQ2QdPz4ca1fv975KC/KlT3UqoSwls1BkqNClNYzQQlJqUrs3b9lcxC21wcuaoQ1AD+lob5Wx/bv0fEjX6sgL1v5haUqq235brdjFQ6V1vuq95DLnEsmBw8ezK6y+IFzDms/t9522rRpWrZs2YWoqZW5c+fq3Xff1a5duy74a7vi008/1ZVXXqmysjKFh4e7uxy4qLS0VJ9++qk+/vhjrV+/XrlHD8oeapE9zKreXS1KjPBVn+RYJSclKSG1r2KS+8jqH8LsGQAnwhqAtjhdVqzsfbtVeGy/jufmqOBUlUq++WLu3AqjOr8IpV8+XhMmtCybTExMdHfJ8ADnvG94QUGB83jFihV6+umndeDAAefY96dxGxsb2Q0HHuP06dP67LPPnDNnWXt3Ky7UorhQq9IjLLptlL/6JHVXUoJd9uQeiktNk39oJDs3AvhRPl2s+t0tVzqPAeCnhEVEa9CoCRo0aoKMMSrOz9axA7tVlHNI+fn5Kqms1IlDq/TG5yv1zCyj4JhUXT5uoq666iqNHTtWXbt2dfclwA1cWga5bNkyPfTQQyovL5ckHTt2TCkpKVqxYoUWL16srVu36pVXXtH06dO1dOlSLViwQNnZ2UpOTtbMmTP1u9/9zvlajz32mN555x3l5+ere/fumjJlip5++mn5+vpq2bJlmj59eqs/e+nSpbrrrrtksVj06quv6r333tP69euVlJSk119/XVFRUZoxY4a2bdumgQMH6o033lCPHj2cz3/vvfc0d+5cff3114qLi9O0adP0xBNPyMenJbdaLBYtWbJEq1ev1tq1a2W327Vw4UJdf/31zuv8rvaaUcT5qa2tVWZmpjOc7dyxTVGBDsWFWpVks6hHhFW9E6LUIylecYnJsvdIU2B4DF9MDQAAOlxTY4PyDmcp79BeFeQe1YnCIpXVOnSs3KGjZUYnqowiUwbo8rHjNG7cOI0ePVo2m83dZaMDXNCwlpycrIULF2rIkCHy9/fX6tWrNWfOHP3tb3/TkCFDtHPnTt177716/vnnNW3aNEnS/PnzNW7cOMXFxWnv3r2699579cgjj2jWrFmqra3VU089pTVr1ujf//63JMlmsykwMFAWi0V2u13PP/+8Bg8erMcee0y7du1SamqqZs2apcTERN19990KDw/Xhx9+KElau3atbr31Vr344osaPXq0jhw5ot/+9re66667NGfOnJaGWCyKj4/XggULNGzYML300kt6/fXXlZOTI5vNpn/+85+6+eabdeDAAYWFhSkwMJB/LB6gsbFR27Zt0/r16/Xxxx9r65ZMRfg2Ki7UoiRby6YgqXFd1SvZrrj4JMX3TFNItzh2bQQAAB6npuq0svfv1omjWSrKz1VBySmV1xkdKXPoWLlRQbUU13uIxlwxTldeeaUuv/xyhYSEuLtstIMLGtYWLVqkBx980HleYmKinn32Wf361792js2fP18ffPCBMjMzz/razz33nFasWKHt27dL+vF71iwWi5588kn96U9/kiRt3bpVGRkZeu2113T33XdLkt566y1Nnz5dtbW1kqQxY8bommuu0eOPP+58nTfeeEOzZs3SiRMnzvq61dXVCg0N1QcffKBJkyZxz5qHaG5u1u7du50zZxs/+0zBqlFsqEUJYVb17mZVUlSoeqfEy56QqPgeaQqPSZB8g9xdOgAvYYxRaXmlJCkyPJTvUgLQbspPFSv3wF4V5hxQQX6uCksrdKq2JbzlVDhUXNtFif2GOsPbqFGjFBTE7zze4JzvWTsX6enpzuOSkhLl5eXpnnvu0b333uscb2pqajUTtXLlSi1atEiHDx9WVVWVmpqaFBYWdk5/3sCBA53HMTExkqQBAwa0Gqurq9Pp06cVFhamHTt2aNu2bfrzn//sPKe5uVl1dXWqqalxvqm/+7rBwcEKDQ1VcXHxubYB7aCpqUk7d+7Uhg0btGHDBm3a+Jl8GysV9833nd07wKKEbmHqkxKv+IRE2VP7qmtcqiy+QXwhNYB2UVPXoOirH5LEBiMA2ld412iFZ1ylgRlXSZLKSwuUc2CPinIO6nh+vgpPnlZZ3Zfat2q71i59RifrfZR8yXCNuWKcxo4dq5EjRxLeOqkLGtaCg4Odxw6HQ5K0ZMkSjRgxotV5Xbq0bNqwdetW3X777Zo3b54mTpwom82mt956SwsXLjynP++7G5ic+UTzbGNnanE4HJo3b55uuummH7xWQMC3y+G+vzGKxWJxvgY6RmNjo3bs2PFtONu0SX5NVd+EM4um9bUqLjxEfVPsik9IUFxyH0Un9mTHRgAA4PXCI2MVHhkrXTZRknSyKO+bmbfDOnE8T8VlVSqr+0J7/+9WffC/56uktovi+16qUZeP0ZgxY3TZZZcpIiLCzVeBc3FBw9p3xcTEyG636+jRo5oyZcpZz9m8ebOSkpL0xBNPOMdycnJanePn56fm5uYLUtOll16qAwcOqGfPni6/hp9fywYUF6omtGhoaNC2bduc4Sxz82YFmpZljfZQi6b3syqha6h6J9tlj7crNqmXYpJ6q0tAKOEMAABc1LrFJKhbTIKkliXaJQW5yj34lUryDqngxPGW8Fa7U0ff36FPly9UYbUUldxfGd+Et9GjRys2NtbNV4GzabewJrXcbzZz5kyFhYXpmmuuUX19vbZv366ysjI98sgj6tmzp3Jzc/XWW29p2LBhWr16td55551Wr5GcnKzs7Gzt2rVL8fHxCg0Nlb+/a0tNnn76aV133XVKSEjQr371K1mtVu3Zs0d79+7V/Pnzz+k1kpKSZLFY9P777+vaa69VYGAgN3S6oKamRtu2bdNnn32mDRs2aEtmpkKsdYoNafmus3su+WZZY7Jddrtd3ZN6KSaxp6wBYYQzAACAH2GxWBQdl6TouCRJkpF0qihfeYe+VnHeYRWeOK6C0jKV1x7QsU/3adE/X9VjVQ4FRadq5OVjNXr0aI0ZM0YpKSnci+sB2jWszZgxQ0FBQXruuec0a9YsBQcHa8CAAXrooYckSTfccIMefvhhPfDAA6qvr9fkyZP11FNPae7cuc7XuPnmm7Vq1SpdeeWVKi8vd27d74qJEyfq/fff1x//+EctWLBAvr6+6tu3r2bMmHHOr2G32zVv3jzNnj1b06dP15133snW/efg+PHjyszM1ObNm5WZmam9u3eqq3+zM5z9x2Cr7F1t6pVsV7zdrtjk3opKYFkjAADA+bBI6hYTr24x8ZJalk1WnCpW3uGvVZxzSIUn8nWiqETldXnK3f4/ev3f/0f/VWVkDY3R4JFjlJGRoVGjRmnw4MF8h7IbuLQbJPBTmpqatGfPHmVmZjoDWmlBrmKCLeoeYlWizaKUcKviI0OVmhirWHu84pJ7Kyq+pyx+QYQzAJ1CdW29Qsb8hyQ2GAHQudVUlivv8NcqzDmkouO5KigsUllts4qrjY6ccqigyqi8yU8paUM1ctRlysjIUEZGhnODP7QfwhrOizFGR44c0bZt27R9+3Zt27ZNu3Z+qQBHjWJCLIoJtig1wqLY0C7qER+tRHt3xcQlKDapl2wxCbL4BLJbI4BOibAGwFvV19Uo7/A+FeYc1MmCXBUUFOpkZa3K6qSjZQ4drzQqqnIoODpZQ4aPcs6+DRgwQD4+7bpw76JDWMM5czgcys7O1u7du53BbPv2bVJdhaKCrIoKbtkMJDncquiwAPVMjFVsbKxiEpIVm9RL/qHdpC5+7r4MALgg6hsadd9//Y8k6b//153y92N5EADv5HA0q+R4jk5kH1Dp8aMqKixQYclJVdQbnTjtUHa5UXG1UbUCldo/XUOHDVd6erqGDRum5ORk7n07D4Q1nFVFRYX27t2rPXv2aM+ePdq9e7e+2rtXfo4adQu0KDLIou4hFiXZLIoK9VWKPVqxMdGKjOmu6PhURcalyOofzJJGAAAAL1RTWa78o/tVnHdYpQV5Kigo0qmqOpXXS8e+mX0rrnaoOSBCfQYOc4a39PR02e12d5ffaRDWLmLGGBUVFWn//v2tHvv27VNpQa66Blqcj7hQi+yhVkUE+Sixezd1j4lWVEx3RdmTFWVPkU9gmGRl2hsAAOBi5HA0qSgvWwU5h3SqIFclxQUqKi5VeW2zTtYaHSszKqp2qLjayCcsRmmDvw1wQ4cOVXR0tLsvwSMR1i4y+fn5euqpp7R//34d2L9PzbUVsvlbFBFoUXiARdFBLcHMFmBRbLcw2WO6qVtklLpGdVdkbIK6dk9QF/9QydrF3ZcCAG5ljFFNXYMkKSjAj2U+APA9jQ11Ksw9rKLcIyorylNxUaEKik+qst6otNboWHnL7FtpTUuA65k2WAMHDtSgQYM0aNAg9enT56K/B46wdpEpLi5WSnyMbujjo/gwq0L8LYrtFq7oyAhFREQoPKKbbFGxiopNUJAtUurizwYgAHAWbDACAG1XX1OlgtxDKs7L1qmiPBUXFan4ZJmqGozKao1yKlrufztZa1TV7KvYlH4aMGiwM8ANHDhQ3bp1c/dldBjC2kXGGKO/PfOUxoccUtfIaHWNTZRvQKjkwy8ZANAWhDUAuDDqaipVmHdUpSdyVFF8XCdLS1RSWqqy6gZVNUjHKx06ftroVG3LIyAiVj3TBql///7q16+f8xEeHu7uS7ngCGsXo8pCaesrUkiM5Bvo7moAoFMirAFA+3E0N+lU8XEV52errChPZSVFKiktVcmpClU3GJXXmZavEKhumZErqzMKsMUopc+3AS4tLU39+vVTdHR0p12qfnEvAgUAAADgcaxdfBQZm6TI2KRW43XVFSrKz1bJiVydLi1URdlJnTp1SiXlVappPKWKys904uMN2vteS6ArrzMy/mGKS+mj3n366he/+IVuueUWN11V2xHWAAAAAHQKAcE2JfUZrKQ+g78dNA7VVFWo9ESuThUfV0VJQUuIKzul0lMVqmmsVXXjLpXs+VI5sf4SYQ0AAAAAOoDFqqDQCCX2iVBin0GtftRQV63SwnyVFR1XQ+kx+QxMc1ORriGsAQAAAPBKfgHBikvuo7jkPlLxPqn3AHeX1CaENQAAXNDFatUtV6U7jwEAuNAIawAAuCDA31dvP/M7d5cBAPBifBQIAAAAAB6IsAYAAAAAHoiwBgCAC6pr62UZdrcsw+5WdW29u8sBAHghwhoAAAAAeCDCGgAAAAB4IMIaAAAAAHggwhoAAAAAeCDCGgAAAAB4IMIaAAAAAHggH3cXgI5ljFFtTY18GhtlGhok08XdJQFAp9TU1KRJGZc4j+sbLG6uCADwUyxNTfKV1Jn+b20xxhh3F4GOU11drZ7dQzVzuJ8qG4zqm/jrBwAAgPdLjbDqngX/TwGXTHZ3KeeMmbWLUGOzVF5nFOovBfl2ps8WAAAAANeU1RmZLr7uLqNNmFm7yBhjVFNTI9VXSo4md5cDAAAAdJigiBhZOlFgI6wBAOCC6upqRUdHS5KKi4sVHBzs5ooAAN6GZZAAALiopqbG3SUAALwYW/cDAAAAgAcirAEAAACAByKsAQAAAIAHIqwBAAAAgAcirAEAAACAB2I3SAAAXGC1WjV27FjnMQAAFxrfswYAAAAAHoiPAgEAAADAAxHWAAAAAMADEdYAAHBBdXW1oqKiFBUVperqaneXAwDwQmwwAgCAi0pLS91dAgDAizGzBgAAAAAeiLAGAAAAAB6IsAYAAAAAHoiwBgAAAAAeiLAGAAAAAB6I3SABAHCB1WpVenq68xgAgAvNYowx7i4CAAAAANAaHwUCAAAAgAcirAEAAACAByKsAQDggpqaGiUnJys5OVk1NTXuLgcA4IXYYAQAABcYY5STk+M8BgDgQmNmDQAAAAA8EGENAAAAADwQYQ0AAAAAPBBhDQAAAAA8EGENAAAAADwQu0ECAOACi8WitLQ05zEAABeaxbDfMAAAAAB4HJZBAgAAAIAHIqwBAAAAgAcirAEA4IKamhr1799f/fv3V01NjbvLAQB4ITYYAQDABcYYZWVlOY8BALjQmFkDAAAAAA9EWAMAAAAAD0RYAwAAAAAPRFgDAAAAAA9EWAMAAAAAD8RukAAAuMBisSgpKcl5DADAhWYx7DcMAAAAAB6HZZAAAAAA4IEIawAAAADggQhrAAC4oLa2VsOGDdOwYcNUW1vr7nIAAF6Ie9YAAHBBdXW1QkJCJElVVVUKDg52c0UAAG/DzBoAAAAAeCDCGgAAAAB4IMIaAAAAAHggwhoAAAAAeCDCGgAAAAB4IB93FwAAQGcVGRnp7hIAAF6MrfsBAAAAwAOxDBIAAAAAPBBhDQAAAAA8EGENAAAX1NbW6oorrtAVV1yh2tpad5cDAPBC3LMGAIALqqurFRISIkmqqqpScHCwmysCAHgbZtYAAAAAwAMR1gAAAADAAxHWAAAAAMADEdYAAAAAwAMR1gAAAADAA/m4uwAAADqroKAgd5cAAPBibN0PAAAAAB6IZZAAAAAA4IEIawAAAADggQhrAAC4oK6uTpMnT9bkyZNVV1fn7nIAAF6Ie9YAAHBBdXW1QkJCJElVVVUKDg52c0UAAG/DzBoAAAAAeCDCGgAAAAB4IMIaAAAAAHggwhoAAAAAeCDCGgAAAAB4IB93F3CxMMaosrLS3WUAAC6Q6upq5/Hp06fV3NzsxmoAAJ1RaGioLBbLj/6crfs7yOnTp2Wz2dxdBgAAAAAPUVFRobCwsB/9OWGtgzCz1n5Onz6thIQE5eXl/eSbHe2D/rsX/Xcv+u9e9N+96L970X/3ulD9/7mZNZZBdhCLxcI/pHYWFhZGj92I/rsX/Xcv+u9e9N+96L970X/3au/+s8EIAAAAAHggwhoAAAAAeCDCGjo9f39/zZkzR/7+/u4u5aJE/92L/rsX/Xcv+u9e9N+96L97dVT/2WAEAAAAADwQM2sAAAAA4IEIawAAAADggQhrAAAAAOCBCGsAAAAA4IEIa+h0ysrKNHXqVNlsNtlsNk2dOlXl5eU/+7x9+/bp+uuvl81mU2hoqEaOHKnc3Nz2L9jLuNr/M+677z5ZLBYtWrSo3Wr0Zm3tf2Njox577DENGDBAwcHBiouL05133qkTJ050XNGd3OLFi5WSkqKAgAANHTpUGzdu/MnzN2zYoKFDhyogIECpqal69dVXO6hS79SW/q9atUoTJkxQVFSUwsLClJGRobVr13Zgtd6nre//MzZv3iwfHx8NHjy4fQv0cm3tf319vZ544gklJSXJ399fPXr00Ouvv95B1XqftvZ/+fLlGjRokIKCghQbG6vp06fr5MmT51eEATqZSZMmmUsuucRkZmaazMxMc8kll5jrrrvuJ59z+PBh07VrV/Poo4+aL7/80hw5csS8//77pqioqIOq9h6u9P+Md955xwwaNMjExcWZv/71r+1bqJdqa//Ly8vN+PHjzYoVK8z+/fvNli1bzIgRI8zQoUM7sOrO66233jK+vr5myZIlJisryzz44IMmODjY5OTknPX8o0ePmqCgIPPggw+arKwss2TJEuPr62tWrlzZwZV7h7b2/8EHHzTPPvus+eKLL8zBgwfN448/bnx9fc2XX37ZwZV7h7b2/4zy8nKTmppqrr76ajNo0KCOKdYLudL/66+/3owYMcKsW7fOZGdnm88//9xs3ry5A6v2Hm3t/8aNG43VajUvvPCCOXr0qNm4caPp37+/+eUvf3ledRDW0KlkZWUZSWbr1q3OsS1bthhJZv/+/T/6vNtuu8385je/6YgSvZqr/TfGmPz8fGO3281XX31lkpKSCGsuOJ/+f9cXX3xhJP3sL1wwZvjw4eb+++9vNda3b18ze/bss54/a9Ys07dv31Zj9913nxk5cmS71ejN2tr/s0lLSzPz5s270KVdFFzt/2233WaefPJJM2fOHMLaeWhr/z/88ENjs9nMyZMnO6I8r9fW/j/33HMmNTW11diLL75o4uPjz6sOlkGiU9myZYtsNptGjBjhHBs5cqRsNpsyMzPP+hyHw6HVq1erd+/emjhxoqKjozVixAi9++67HVS193Cl/1LL38HUqVP16KOPqn///h1Rqldytf/fV1FRIYvFovDw8Hao0ns0NDRox44duvrqq1uNX3311T/a7y1btvzg/IkTJ2r79u1qbGxst1q9kSv9/z6Hw6HKykp17dq1PUr0aq72f+nSpTpy5IjmzJnT3iV6NVf6/69//Uvp6elasGCB7Ha7evfurf/8z/9UbW1tR5TsVVzp/6hRo5Sfn68PPvhAxhgVFRVp5cqVmjx58nnVQlhDp1JYWKjo6OgfjEdHR6uwsPCszykuLlZVVZWeeeYZTZo0SR999JFuvPFG3XTTTdqwYUN7l+xVXOm/JD377LPy8fHRzJkz27M8r+dq/7+rrq5Os2fP1h133KGwsLALXaJXKS0tVXNzs2JiYlqNx8TE/Gi/CwsLz3p+U1OTSktL261Wb+RK/79v4cKFqq6u1q233toeJXo1V/p/6NAhzZ49W8uXL5ePj09HlOm1XOn/0aNHtWnTJn311Vd65513tGjRIq1cuVK///3vO6Jkr+JK/0eNGqXly5frtttuk5+fn7p3767w8HC99NJL51ULYQ0eYe7cubJYLD/52L59uyTJYrH84PnGmLOOSy2frErSDTfcoIcffliDBw/W7Nmzdd1113Hj/zfas/87duzQCy+8oGXLlv3oORe79uz/dzU2Nur222+Xw+HQ4sWLL/h1eKvv9/bn+n228882jnPT1v6f8eabb2ru3LlasWLFWT/kwLk51/43Nzfrjjvu0Lx589S7d++OKs/rteX973A4ZLFYtHz5cg0fPlzXXnutnn/+eS1btozZNRe1pf9ZWVmaOXOmnn76ae3YsUNr1qxRdna27r///vOqgY894BEeeOAB3X777T95TnJysvbs2aOioqIf/KykpOQHn36cERkZKR8fH6WlpbUa79evnzZt2uR60V6kPfu/ceNGFRcXKzEx0TnW3NysP/zhD1q0aJGOHTt2XrV7g/bs/xmNjY269dZblZ2drfXr1zOrdg4iIyPVpUuXH3yKWlxc/KP97t69+1nP9/HxUbdu3dqtVm/kSv/PWLFihe655x69/fbbGj9+fHuW6bXa2v/Kykpt375dO3fu1AMPPCCpJTwYY+Tj46OPPvpI48aN65DavYEr7//Y2FjZ7XbZbDbnWL9+/WSMUX5+vnr16tWuNXsTV/r/l7/8RZdddpkeffRRSdLAgQMVHBys0aNHa/78+YqNjXWpFsIaPEJkZKQiIyN/9ryMjAxVVFToiy++0PDhwyVJn3/+uSoqKjRq1KizPsfPz0/Dhg3TgQMHWo0fPHhQSUlJ51+8F2jP/k+dOvUHvyxNnDhRU6dO1fTp08+/eC/Qnv2Xvg1qhw4d0ieffEJoOEd+fn4aOnSo1q1bpxtvvNE5vm7dOt1www1nfU5GRobee++9VmMfffSR0tPT5evr2671ehtX+i+1zKjdfffdevPNN8/7XpGLWVv7HxYWpr1797YaW7x4sdavX6+VK1cqJSWl3Wv2Jq68/y+77DK9/fbbqqqqUkhIiKSW33WsVqvi4+M7pG5v4Ur/a2pqfrD8t0uXLpK+XWHhkvPangRwg0mTJpmBAweaLVu2mC1btpgBAwb8YOvyPn36mFWrVjn/e9WqVcbX19f8/e9/N4cOHTIvvfSS6dKli9m4cWNHl9/pudL/72M3SNe1tf+NjY3m+uuvN/Hx8WbXrl2moKDA+aivr3fHJXQqZ7Zufu2110xWVpZ56KGHTHBwsDl27JgxxpjZs2ebqVOnOs8/s3X/ww8/bLKyssxrr73G1v3noa39/8c//mF8fHzMyy+/3Oq9Xl5e7q5L6NTa2v/vYzfI89PW/ldWVpr4+Hhzyy23mK+//tps2LDB9OrVy8yYMcNdl9CptbX/S5cuNT4+Pmbx4sXmyJEjZtOmTSY9Pd0MHz78vOogrKHTOXnypJkyZYoJDQ01oaGhZsqUKaasrKzVOZLM0qVLW4299tprpmfPniYgIMAMGjTIvPvuux1XtBdxtf/fRVhzXVv7n52dbSSd9fHJJ590eP2d0csvv2ySkpKMn5+fufTSS82GDRucP5s2bZoZO3Zsq/M//fRTM2TIEOPn52eSk5PNK6+80sEVe5e29H/s2LFnfa9Pmzat4wv3Em19/38XYe38tbX/+/btM+PHjzeBgYEmPj7ePPLII6ampqaDq/Yebe3/iy++aNLS0kxgYKCJjY01U6ZMMfn5+edVg8WY85mXAwAAAAC0B3aDBAAAAAAPRFgDAAAAAA9EWAMAAAAAD0RYAwAAAAAPRFgDAAAAAA9EWAMAAAAAD0RYAwAAAAAPRFgDAAAAAA9EWAMAAAAAD0RYAwAAAAAPRFgDAAAAAA9EWAMAAAAAD/T/AW1ZqzezC/+dAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -949,7 +949,7 @@
     {
      "data": {
       "text/plain": [
-       "0.4976216275056784"
+       "0.500911899831879"
       ]
      },
      "execution_count": 12,
@@ -979,7 +979,7 @@
     {
      "data": {
       "text/plain": [
-       "0.4910643187175053"
+       "0.49462444720219717"
       ]
      },
      "execution_count": 13,
@@ -1000,7 +1000,40 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Hernan and Robins expand on these foundational observations and elaborate the implications for causal inference and the bias of confounding variables. We won't go into these details, as we instead we want to draw out the connection with controlling for the risk of non-representative sampling.\n",
+    "We can see these results more clearly using `bambi` model interpretation functions to see the predictions within a specific strata. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 2, figsize=(20, 6))\n",
+    "axs = axs.flatten()\n",
+    "bmb.interpret.plot_predictions(reg, results, covariates=['Treatment'], ax=axs[0]);\n",
+    "bmb.interpret.plot_predictions(reg_strata, results_strata, covariates=['Treatment'], ax=axs[1]);\n",
+    "axs[0].set_title(\"Non Stratified Regression \\n Model Predictions\")\n",
+    "axs[1].set_title(\"Stratified Regression \\n Model Predictions\");"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hernan and Robins expand on these foundational observations and elaborate the implications for causal inference and the bias of confounding variables. We won't go into these details, as we instead we want to draw out the connection with controlling for the risk of non-representative sampling. The usefulness of \"representative-ness\" as an idea is disputed in the statistical literature due to the vagueness of the term. To say a sample is representative is ussually akin to meaning that it was generated from a high-quality probability sampling design. This design is specified to avoid the creep of bias due to selection effects contaminating the results.\n",
     "\n",
     "We've seen how regression can automate stratification across the levels of covariates in the model conditional on the sample data. But what if the prevalence of the risk-profile in your data does not reflect the prevalance of risk in the wider population? Then the regression model will automatically adjust to the prevalence in the sample, but it is not adjusting to the correct weights. "
    ]
@@ -1012,17 +1045,16 @@
    "source": [
     "## The Need for Post-Stratification\n",
     "\n",
-    "In the context of national survey design there is always a concern that the sample respondents may be more or less representative of the population across different key demographics e.g. it's unlikely we would put much faith in the survey's representativeness if it had 90% male respondents. Given that we can know before hand that certain demographic splits are non-representative according to census data, we might hope to use this information to appropriately re-weight the regressions fit to non-representative surveys. \n",
+    "In the context of national survey design there is always a concern that the sample respondents may be more or less representative of the population across different key demographics e.g. it's unlikely we would put much faith in the survey's accuracy if it had 90% male respondents on a question about the lived experience of women. Given that we can know before hand that certain demographic splits are not relective of the census data, we can use this information to appropriately re-weight the regressions fit to non-representative survey data. \n",
     "\n",
     "We'll demonstrate the idea of multi-level regression and post-stratification adjustment by replicating some of the steps discussed in Martin, Philips and Gelmen's [\"Multilevel Regression and Poststratification Case Studies\"](https://bookdown.org/jl5522/MRP-case-studies/). \n",
     "\n",
-    "They cite data from the Cooperative Congressional Election Study (Schaffner, Ansolabehere, and Luks (2018)), a US nationwide survey designed by a consortium of 60 research teams and administered by YouGov. The outcome of interest is a binary question: should employers decline coverage of abortions in insurance plans?\n",
-    "\n"
+    "They cite data from the Cooperative Congressional Election Study (Schaffner, Ansolabehere, and Luks (2018)), a US nationwide survey designed by a consortium of 60 research teams and administered by YouGov. The outcome of interest is a binary question: Should employers decline coverage of abortions in insurance plans?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -1227,7 +1259,7 @@
        "[5 rows x 526 columns]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1238,17 +1270,18 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Cleaning Census Data\n",
     "\n",
-    "To prepare the census data for modelling we need to break the demographic data into appropriate stratum. We will break out these groupings as along broad categories. Even these steps amount to a significant choice where we use our knowledge of pertinent demographics to decide upon the key strata we wish to represent in our model, as we seek to better predict and understand the voting outcome. "
+    "To prepare the census data for modelling we need to break the demographic data into appropriate stratum. We will break out these groupings as along broad categories familiar to audiences of election coverage news. Even these steps amount to a significant choice where we use our knowledge of pertinent demographics to decide upon the key strata we wish to represent in our model, as we seek to better predict and understand the voting outcome. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -1345,7 +1378,7 @@
        "4       1.0    MA  White  -0.5  40-49            HS  202120533"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1483,7 +1516,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -1605,7 +1638,7 @@
        "48197  0.416893  Northeast   7.134465  "
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1640,7 +1673,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -1699,7 +1732,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1751,7 +1784,7 @@
        "1  0.465000  0.007054  Biased Data"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1793,7 +1826,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1902,7 +1935,7 @@
        "4    ID  White   0.5  70+       Post-grad  17        11  0.683102   West"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1933,7 +1966,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1960,7 +1993,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1994,17 +2027,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "More interesting we can use the comparison functionality to compare differences in `eth` conditional on `age` and `edu`. Where we can see that the variances between are pretty stable across all age groups, slightly shifted by within the `Post-grad` level of education. "
+    "More interesting we can use the comparison functionality to compare differences in `eth` conditional on `age` and `edu`. Where we can see that the differences between ethnicities are pretty stable across all age groups, slightly shifted by within the `Post-grad` level of education. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x500 with 1 Axes>"
       ]
@@ -2023,7 +2056,8 @@
     "    subplot_kwargs={\"main\": \"age\", \"group\": \"edu\"},\n",
     "    fig_kwargs={\"figsize\": (12, 5), \"sharey\": True},\n",
     "    legend=True,\n",
-    ")"
+    ")\n",
+    "ax[0].set_title(\"Comparison of Difference in Ethnicity \\n within Age and Educational Strata\");"
    ]
   },
   {
@@ -2036,7 +2070,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -2203,7 +2237,7 @@
        "7  0.099906    0.004498     0.194574  "
       ]
      },
-     "execution_count": 23,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2233,7 +2267,7 @@
    "source": [
     "### Fit Final Model to Biased Sample\n",
     "\n",
-    "We can specify these features of our model as follows: \n",
+    "We can specify these features of our model using a hierarchical structure as follows: \n",
     "\n",
     "$$ Pr(y_i = 1) = logit^{-1}(\n",
     "\\alpha_{\\rm s[i]}^{\\rm state}\n",
@@ -2247,12 +2281,12 @@
     ")\n",
     "$$\n",
     "\n",
-    "using the formula syntax. We have:\n"
+    "Here we have used the fact that we can add components to the $\\alpha$ intercept terms and interaction effects to express the stratum specific variation in the outcomes that we've seen in our exploratory work. Using the `bambi` formula syntax. We have:\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2271,7 +2305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -2285,8 +2319,8 @@
        "              <ul class=\"xr-sections group-sections\">\n",
        "              \n",
        "            <li class = \"xr-section-item\">\n",
-       "                  <input id=\"idata_posterior11cf74fb-14e4-4c84-91ff-e1b39e315a4d\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
-       "                  <label for=\"idata_posterior11cf74fb-14e4-4c84-91ff-e1b39e315a4d\" class = \"xr-section-summary\">posterior</label>\n",
+       "                  <input id=\"idata_posterior96eda290-e84d-4068-a156-c32877887247\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_posterior96eda290-e84d-4068-a156-c32877887247\" class = \"xr-section-summary\">posterior</label>\n",
        "                  <div class=\"xr-section-inline-details\"></div>\n",
        "                  <div class=\"xr-section-details\">\n",
        "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
@@ -2682,15 +2716,15 @@
        "    1|edu:age             (chain, draw, edu:age__factor_dim) float64 -0.1959 ...\n",
        "    1|edu:eth             (chain, draw, edu:eth__factor_dim) float64 -0.1216 ...\n",
        "Attributes:\n",
-       "    created_at:                  2023-09-09T21:52:09.389695\n",
+       "    created_at:                  2023-09-17T17:39:00.290773\n",
        "    arviz_version:               0.16.1\n",
        "    modeling_interface:          bambi\n",
-       "    modeling_interface_version:  0.13.0.dev</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a1e4b8fc-f54b-4c1f-b2bd-7c843ef5c710' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a1e4b8fc-f54b-4c1f-b2bd-7c843ef5c710' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>state__factor_dim</span>: 46</li><li><span class='xr-has-index'>eth__factor_dim</span>: 4</li><li><span class='xr-has-index'>edu__factor_dim</span>: 5</li><li><span class='xr-has-index'>male:eth__factor_dim</span>: 8</li><li><span class='xr-has-index'>edu:age__factor_dim</span>: 30</li><li><span class='xr-has-index'>edu:eth__factor_dim</span>: 20</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6c2bd036-fcd0-423b-b75f-8cb199cb5ffb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6c2bd036-fcd0-423b-b75f-8cb199cb5ffb' class='xr-section-summary' >Coordinates: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-0ad1e444-4973-4c72-8ee6-8b0aa167eb6f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0ad1e444-4973-4c72-8ee6-8b0aa167eb6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22ed7848-25eb-46ac-8ddd-6034dd3d2c3e' class='xr-var-data-in' type='checkbox'><label for='data-22ed7848-25eb-46ac-8ddd-6034dd3d2c3e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-4853bfe3-f43b-4841-9f4a-8d25103cf6ba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4853bfe3-f43b-4841-9f4a-8d25103cf6ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-451f8ce5-19ac-4cbc-9f99-7b21947cf6ad' class='xr-var-data-in' type='checkbox'><label for='data-451f8ce5-19ac-4cbc-9f99-7b21947cf6ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>state__factor_dim</span></div><div class='xr-var-dims'>(state__factor_dim)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;AK&#x27; &#x27;AL&#x27; &#x27;AR&#x27; ... &#x27;WI&#x27; &#x27;WV&#x27; &#x27;WY&#x27;</div><input id='attrs-79ebc4b1-5e8d-47d3-aaad-873fff2f771b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-79ebc4b1-5e8d-47d3-aaad-873fff2f771b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ce31542-bc08-4134-a956-a3ca846ab379' class='xr-var-data-in' type='checkbox'><label for='data-0ce31542-bc08-4134-a956-a3ca846ab379' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;AK&#x27;, &#x27;AL&#x27;, &#x27;AR&#x27;, &#x27;CA&#x27;, &#x27;CO&#x27;, &#x27;DE&#x27;, &#x27;FL&#x27;, &#x27;GA&#x27;, &#x27;HI&#x27;, &#x27;IA&#x27;, &#x27;ID&#x27;, &#x27;IL&#x27;,\n",
+       "    modeling_interface_version:  0.13.0.dev</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-155f0f0c-9c60-4a7d-a33c-0c2815dce7a2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-155f0f0c-9c60-4a7d-a33c-0c2815dce7a2' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>state__factor_dim</span>: 46</li><li><span class='xr-has-index'>eth__factor_dim</span>: 4</li><li><span class='xr-has-index'>edu__factor_dim</span>: 5</li><li><span class='xr-has-index'>male:eth__factor_dim</span>: 8</li><li><span class='xr-has-index'>edu:age__factor_dim</span>: 30</li><li><span class='xr-has-index'>edu:eth__factor_dim</span>: 20</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-756cb89e-3909-4bf1-bc42-46f9b7c1c5ad' class='xr-section-summary-in' type='checkbox'  checked><label for='section-756cb89e-3909-4bf1-bc42-46f9b7c1c5ad' class='xr-section-summary' >Coordinates: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-3270a688-01ed-4df9-adbd-ba38a4a483b4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3270a688-01ed-4df9-adbd-ba38a4a483b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e299c986-fbc2-46b6-aaff-846ee0bcec36' class='xr-var-data-in' type='checkbox'><label for='data-e299c986-fbc2-46b6-aaff-846ee0bcec36' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-80ab3895-5511-476e-a057-98a001a1a3cc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-80ab3895-5511-476e-a057-98a001a1a3cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cae8803a-746d-4780-a2b4-da25329af2c8' class='xr-var-data-in' type='checkbox'><label for='data-cae8803a-746d-4780-a2b4-da25329af2c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>state__factor_dim</span></div><div class='xr-var-dims'>(state__factor_dim)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;AK&#x27; &#x27;AL&#x27; &#x27;AR&#x27; ... &#x27;WI&#x27; &#x27;WV&#x27; &#x27;WY&#x27;</div><input id='attrs-2e725ec4-5346-4350-b8dc-d7923d07bd0e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2e725ec4-5346-4350-b8dc-d7923d07bd0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-edf6f2e2-3a84-4fdb-bbbf-775038bb2735' class='xr-var-data-in' type='checkbox'><label for='data-edf6f2e2-3a84-4fdb-bbbf-775038bb2735' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;AK&#x27;, &#x27;AL&#x27;, &#x27;AR&#x27;, &#x27;CA&#x27;, &#x27;CO&#x27;, &#x27;DE&#x27;, &#x27;FL&#x27;, &#x27;GA&#x27;, &#x27;HI&#x27;, &#x27;IA&#x27;, &#x27;ID&#x27;, &#x27;IL&#x27;,\n",
        "       &#x27;KS&#x27;, &#x27;KY&#x27;, &#x27;LA&#x27;, &#x27;MA&#x27;, &#x27;MD&#x27;, &#x27;ME&#x27;, &#x27;MI&#x27;, &#x27;MN&#x27;, &#x27;MO&#x27;, &#x27;MS&#x27;, &#x27;MT&#x27;, &#x27;NC&#x27;,\n",
        "       &#x27;ND&#x27;, &#x27;NE&#x27;, &#x27;NH&#x27;, &#x27;NJ&#x27;, &#x27;NM&#x27;, &#x27;NV&#x27;, &#x27;NY&#x27;, &#x27;OH&#x27;, &#x27;OK&#x27;, &#x27;OR&#x27;, &#x27;PA&#x27;, &#x27;RI&#x27;,\n",
-       "       &#x27;SC&#x27;, &#x27;SD&#x27;, &#x27;TN&#x27;, &#x27;UT&#x27;, &#x27;VA&#x27;, &#x27;VT&#x27;, &#x27;WA&#x27;, &#x27;WI&#x27;, &#x27;WV&#x27;, &#x27;WY&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>eth__factor_dim</span></div><div class='xr-var-dims'>(eth__factor_dim)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;Black&#x27; &#x27;Hispanic&#x27; &#x27;Other&#x27; &#x27;White&#x27;</div><input id='attrs-86d00dd6-09e6-424c-9380-933dca877a57' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-86d00dd6-09e6-424c-9380-933dca877a57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7371503-b916-41d6-9afb-474ebdf0d390' class='xr-var-data-in' type='checkbox'><label for='data-f7371503-b916-41d6-9afb-474ebdf0d390' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Black&#x27;, &#x27;Hispanic&#x27;, &#x27;Other&#x27;, &#x27;White&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>edu__factor_dim</span></div><div class='xr-var-dims'>(edu__factor_dim)</div><div class='xr-var-dtype'>&lt;U14</div><div class='xr-var-preview xr-preview'>&#x27;4-Year College&#x27; ... &#x27;Some college&#x27;</div><input id='attrs-e8500332-d32e-44c9-b406-e827a0f4f656' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e8500332-d32e-44c9-b406-e827a0f4f656' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c14fc9fd-6b0d-462d-92b3-9648fdb63a87' class='xr-var-data-in' type='checkbox'><label for='data-c14fc9fd-6b0d-462d-92b3-9648fdb63a87' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;4-Year College&#x27;, &#x27;HS&#x27;, &#x27;No HS&#x27;, &#x27;Post-grad&#x27;, &#x27;Some college&#x27;],\n",
-       "      dtype=&#x27;&lt;U14&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>male:eth__factor_dim</span></div><div class='xr-var-dims'>(male:eth__factor_dim)</div><div class='xr-var-dtype'>&lt;U13</div><div class='xr-var-preview xr-preview'>&#x27;-0.5:Black&#x27; ... &#x27;0.5:White&#x27;</div><input id='attrs-07894e32-cb5c-40c6-a6f8-6a929a37bd6d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-07894e32-cb5c-40c6-a6f8-6a929a37bd6d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a500053d-c3ce-4fe5-a7c2-f3aa2991622a' class='xr-var-data-in' type='checkbox'><label for='data-a500053d-c3ce-4fe5-a7c2-f3aa2991622a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;-0.5:Black&#x27;, &#x27;-0.5:Hispanic&#x27;, &#x27;-0.5:Other&#x27;, &#x27;-0.5:White&#x27;, &#x27;0.5:Black&#x27;,\n",
-       "       &#x27;0.5:Hispanic&#x27;, &#x27;0.5:Other&#x27;, &#x27;0.5:White&#x27;], dtype=&#x27;&lt;U13&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>edu:age__factor_dim</span></div><div class='xr-var-dims'>(edu:age__factor_dim)</div><div class='xr-var-dtype'>&lt;U20</div><div class='xr-var-preview xr-preview'>&#x27;4-Year College:18-29&#x27; ... &#x27;Some...</div><input id='attrs-523e2c9a-fb3d-4156-9e20-01b94d818dd0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-523e2c9a-fb3d-4156-9e20-01b94d818dd0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce27a6b6-c0b9-41cc-afe8-a53719a48d47' class='xr-var-data-in' type='checkbox'><label for='data-ce27a6b6-c0b9-41cc-afe8-a53719a48d47' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;4-Year College:18-29&#x27;, &#x27;4-Year College:30-39&#x27;, &#x27;4-Year College:40-49&#x27;,\n",
+       "       &#x27;SC&#x27;, &#x27;SD&#x27;, &#x27;TN&#x27;, &#x27;UT&#x27;, &#x27;VA&#x27;, &#x27;VT&#x27;, &#x27;WA&#x27;, &#x27;WI&#x27;, &#x27;WV&#x27;, &#x27;WY&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>eth__factor_dim</span></div><div class='xr-var-dims'>(eth__factor_dim)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;Black&#x27; &#x27;Hispanic&#x27; &#x27;Other&#x27; &#x27;White&#x27;</div><input id='attrs-27b0b65e-5776-4d4a-a5bd-76428c58e650' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-27b0b65e-5776-4d4a-a5bd-76428c58e650' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-792fc325-a5af-419a-87f6-f1c75182891a' class='xr-var-data-in' type='checkbox'><label for='data-792fc325-a5af-419a-87f6-f1c75182891a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Black&#x27;, &#x27;Hispanic&#x27;, &#x27;Other&#x27;, &#x27;White&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>edu__factor_dim</span></div><div class='xr-var-dims'>(edu__factor_dim)</div><div class='xr-var-dtype'>&lt;U14</div><div class='xr-var-preview xr-preview'>&#x27;4-Year College&#x27; ... &#x27;Some college&#x27;</div><input id='attrs-36d3abe9-74b4-4189-8719-dff236e9b984' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36d3abe9-74b4-4189-8719-dff236e9b984' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1256c933-3abb-4a2b-b6a0-c48cc508f6ff' class='xr-var-data-in' type='checkbox'><label for='data-1256c933-3abb-4a2b-b6a0-c48cc508f6ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;4-Year College&#x27;, &#x27;HS&#x27;, &#x27;No HS&#x27;, &#x27;Post-grad&#x27;, &#x27;Some college&#x27;],\n",
+       "      dtype=&#x27;&lt;U14&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>male:eth__factor_dim</span></div><div class='xr-var-dims'>(male:eth__factor_dim)</div><div class='xr-var-dtype'>&lt;U13</div><div class='xr-var-preview xr-preview'>&#x27;-0.5:Black&#x27; ... &#x27;0.5:White&#x27;</div><input id='attrs-71197d48-834f-4cb2-a8e9-42e4901e678e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-71197d48-834f-4cb2-a8e9-42e4901e678e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5acac7f-1408-4e9a-8959-4fac2b064901' class='xr-var-data-in' type='checkbox'><label for='data-d5acac7f-1408-4e9a-8959-4fac2b064901' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;-0.5:Black&#x27;, &#x27;-0.5:Hispanic&#x27;, &#x27;-0.5:Other&#x27;, &#x27;-0.5:White&#x27;, &#x27;0.5:Black&#x27;,\n",
+       "       &#x27;0.5:Hispanic&#x27;, &#x27;0.5:Other&#x27;, &#x27;0.5:White&#x27;], dtype=&#x27;&lt;U13&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>edu:age__factor_dim</span></div><div class='xr-var-dims'>(edu:age__factor_dim)</div><div class='xr-var-dtype'>&lt;U20</div><div class='xr-var-preview xr-preview'>&#x27;4-Year College:18-29&#x27; ... &#x27;Some...</div><input id='attrs-15ce3aa8-80f6-4e58-a604-0461b07a06f8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-15ce3aa8-80f6-4e58-a604-0461b07a06f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37d3e949-94b4-4442-bed2-0848ba2340e9' class='xr-var-data-in' type='checkbox'><label for='data-37d3e949-94b4-4442-bed2-0848ba2340e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;4-Year College:18-29&#x27;, &#x27;4-Year College:30-39&#x27;, &#x27;4-Year College:40-49&#x27;,\n",
        "       &#x27;4-Year College:50-59&#x27;, &#x27;4-Year College:60-69&#x27;, &#x27;4-Year College:70+&#x27;,\n",
        "       &#x27;HS:18-29&#x27;, &#x27;HS:30-39&#x27;, &#x27;HS:40-49&#x27;, &#x27;HS:50-59&#x27;, &#x27;HS:60-69&#x27;, &#x27;HS:70+&#x27;,\n",
        "       &#x27;No HS:18-29&#x27;, &#x27;No HS:30-39&#x27;, &#x27;No HS:40-49&#x27;, &#x27;No HS:50-59&#x27;,\n",
@@ -2698,76 +2732,76 @@
        "       &#x27;Post-grad:40-49&#x27;, &#x27;Post-grad:50-59&#x27;, &#x27;Post-grad:60-69&#x27;,\n",
        "       &#x27;Post-grad:70+&#x27;, &#x27;Some college:18-29&#x27;, &#x27;Some college:30-39&#x27;,\n",
        "       &#x27;Some college:40-49&#x27;, &#x27;Some college:50-59&#x27;, &#x27;Some college:60-69&#x27;,\n",
-       "       &#x27;Some college:70+&#x27;], dtype=&#x27;&lt;U20&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>edu:eth__factor_dim</span></div><div class='xr-var-dims'>(edu:eth__factor_dim)</div><div class='xr-var-dtype'>&lt;U23</div><div class='xr-var-preview xr-preview'>&#x27;4-Year College:Black&#x27; ... &#x27;Some...</div><input id='attrs-73eda926-325f-46c8-be6b-69ec18fcbef6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-73eda926-325f-46c8-be6b-69ec18fcbef6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb6b274e-3d6f-465a-8c86-76f3d951df63' class='xr-var-data-in' type='checkbox'><label for='data-cb6b274e-3d6f-465a-8c86-76f3d951df63' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;4-Year College:Black&#x27;, &#x27;4-Year College:Hispanic&#x27;,\n",
+       "       &#x27;Some college:70+&#x27;], dtype=&#x27;&lt;U20&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>edu:eth__factor_dim</span></div><div class='xr-var-dims'>(edu:eth__factor_dim)</div><div class='xr-var-dtype'>&lt;U23</div><div class='xr-var-preview xr-preview'>&#x27;4-Year College:Black&#x27; ... &#x27;Some...</div><input id='attrs-e79a5056-78dd-4ded-a46d-7d635c4ee46f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e79a5056-78dd-4ded-a46d-7d635c4ee46f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd242cfb-d867-4a2f-b3be-8562ff14fcc0' class='xr-var-data-in' type='checkbox'><label for='data-bd242cfb-d867-4a2f-b3be-8562ff14fcc0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;4-Year College:Black&#x27;, &#x27;4-Year College:Hispanic&#x27;,\n",
        "       &#x27;4-Year College:Other&#x27;, &#x27;4-Year College:White&#x27;, &#x27;HS:Black&#x27;,\n",
        "       &#x27;HS:Hispanic&#x27;, &#x27;HS:Other&#x27;, &#x27;HS:White&#x27;, &#x27;No HS:Black&#x27;, &#x27;No HS:Hispanic&#x27;,\n",
        "       &#x27;No HS:Other&#x27;, &#x27;No HS:White&#x27;, &#x27;Post-grad:Black&#x27;, &#x27;Post-grad:Hispanic&#x27;,\n",
        "       &#x27;Post-grad:Other&#x27;, &#x27;Post-grad:White&#x27;, &#x27;Some college:Black&#x27;,\n",
        "       &#x27;Some college:Hispanic&#x27;, &#x27;Some college:Other&#x27;, &#x27;Some college:White&#x27;],\n",
-       "      dtype=&#x27;&lt;U23&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b20921ad-485a-4f99-b415-606f7c563b32' class='xr-section-summary-in' type='checkbox'  ><label for='section-b20921ad-485a-4f99-b415-606f7c563b32' class='xr-section-summary' >Data variables: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Intercept</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2358 0.4365 ... -0.06399 0.2565</div><input id='attrs-716257ef-9779-4e4c-ac2a-4509c447fa7c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-716257ef-9779-4e4c-ac2a-4509c447fa7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4124620b-0f36-4ef0-8f1c-02be4470677a' class='xr-var-data-in' type='checkbox'><label for='data-4124620b-0f36-4ef0-8f1c-02be4470677a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.2357953 ,  0.43651919,  0.8163025 , ...,  0.3761122 ,\n",
+       "      dtype=&#x27;&lt;U23&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-55b9b773-b138-4ff8-a410-52e0cc472348' class='xr-section-summary-in' type='checkbox'  ><label for='section-55b9b773-b138-4ff8-a410-52e0cc472348' class='xr-section-summary' >Data variables: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Intercept</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2358 0.4365 ... -0.06399 0.2565</div><input id='attrs-a0925a56-173f-4972-b5e6-9a02a5d5db5d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a0925a56-173f-4972-b5e6-9a02a5d5db5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55503251-823f-4ee2-9ace-fb9cf606310c' class='xr-var-data-in' type='checkbox'><label for='data-55503251-823f-4ee2-9ace-fb9cf606310c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.2357953 ,  0.43651919,  0.8163025 , ...,  0.3761122 ,\n",
        "         0.82908072,  0.45231933],\n",
        "       [ 1.08283544,  0.04883994,  0.54556097, ..., -3.01791601,\n",
        "        -1.70137604,  0.16253107],\n",
        "       [ 0.33472456,  0.06603835,  0.10798744, ...,  0.57547102,\n",
        "        -1.00089016, -0.92153227],\n",
        "       [ 0.54653198, -0.09063433, -0.0962701 , ..., -0.07409252,\n",
-       "        -0.06398592,  0.25649592]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>male</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1855 0.0546 ... 0.1347 0.1717</div><input id='attrs-2be4c3f1-be62-4d51-8582-301591683f28' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2be4c3f1-be62-4d51-8582-301591683f28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df98fae9-d85b-461e-aaf5-da516e339314' class='xr-var-data-in' type='checkbox'><label for='data-df98fae9-d85b-461e-aaf5-da516e339314' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.18554999, 0.05460117, 0.15523781, ..., 0.26503596, 0.17864307,\n",
+       "        -0.06398592,  0.25649592]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>male</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1855 0.0546 ... 0.1347 0.1717</div><input id='attrs-6f490e5a-b527-4489-9260-264b044c6f28' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6f490e5a-b527-4489-9260-264b044c6f28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fea93690-990f-45ab-b5e4-9cd872aae42f' class='xr-var-data-in' type='checkbox'><label for='data-fea93690-990f-45ab-b5e4-9cd872aae42f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.18554999, 0.05460117, 0.15523781, ..., 0.26503596, 0.17864307,\n",
        "        0.13248461],\n",
        "       [0.17190392, 0.28717712, 0.28285475, ..., 0.23358331, 0.52666889,\n",
        "        0.06526399],\n",
        "       [0.25442286, 0.18649074, 0.14598336, ..., 0.17109997, 0.31019605,\n",
        "        0.3536368 ],\n",
        "       [0.22267076, 0.26419163, 0.15164258, ..., 0.18046546, 0.13468066,\n",
-       "        0.17170458]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>repvote</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.333 -1.437 ... -1.032 -1.216</div><input id='attrs-76ff0f8f-53ad-4114-a7d0-15a1829bb4fc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-76ff0f8f-53ad-4114-a7d0-15a1829bb4fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71b7ede8-d017-4db3-a251-63374169d3ba' class='xr-var-data-in' type='checkbox'><label for='data-71b7ede8-d017-4db3-a251-63374169d3ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-1.33328689, -1.43728603, -1.89844705, ..., -1.60984098,\n",
+       "        0.17170458]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>repvote</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.333 -1.437 ... -1.032 -1.216</div><input id='attrs-8b114dba-789e-404f-ba17-4516713235d2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8b114dba-789e-404f-ba17-4516713235d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15d8a6e7-7a19-4790-98d9-4d5a0a7b17f9' class='xr-var-data-in' type='checkbox'><label for='data-15d8a6e7-7a19-4790-98d9-4d5a0a7b17f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-1.33328689, -1.43728603, -1.89844705, ..., -1.60984098,\n",
        "        -0.72676365, -1.26571675],\n",
        "       [-1.28759373, -1.39636135, -1.13573472, ..., -0.61737146,\n",
        "        -1.57143157, -0.40090848],\n",
        "       [-0.92257964, -0.9340195 , -0.84808671, ..., -1.03842182,\n",
        "        -0.74135311, -0.93448461],\n",
        "       [-1.03925721, -1.03933099, -0.85007501, ..., -1.09283078,\n",
-       "        -1.03225784, -1.21606504]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|state_sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2279 0.2581 ... 0.357 0.3526</div><input id='attrs-b210aeb5-e9d8-4827-8b9f-6208bf6fb01e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b210aeb5-e9d8-4827-8b9f-6208bf6fb01e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5aa14fc-447e-4f74-9a22-e884b89d5823' class='xr-var-data-in' type='checkbox'><label for='data-e5aa14fc-447e-4f74-9a22-e884b89d5823' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.22788663, 0.25814198, 0.30612645, ..., 0.28422744, 0.30855403,\n",
+       "        -1.03225784, -1.21606504]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|state_sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2279 0.2581 ... 0.357 0.3526</div><input id='attrs-02be3225-a909-4576-88ab-f657c198ab93' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-02be3225-a909-4576-88ab-f657c198ab93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d076e1a-6473-45a9-9aea-4a6f02ba05f8' class='xr-var-data-in' type='checkbox'><label for='data-7d076e1a-6473-45a9-9aea-4a6f02ba05f8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.22788663, 0.25814198, 0.30612645, ..., 0.28422744, 0.30855403,\n",
        "        0.28674029],\n",
        "       [0.25427811, 0.2869506 , 0.29105246, ..., 0.32861627, 0.27543615,\n",
        "        0.26341471],\n",
        "       [0.35677381, 0.30736264, 0.36516731, ..., 0.27166973, 0.28866001,\n",
        "        0.31776415],\n",
        "       [0.20504879, 0.25572048, 0.26693605, ..., 0.35942905, 0.35696505,\n",
-       "        0.35264898]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|eth_sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2922 0.6481 ... 1.001 0.5405</div><input id='attrs-240c0fcf-3ac3-4ee4-8c3e-d0a8b2686880' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-240c0fcf-3ac3-4ee4-8c3e-d0a8b2686880' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f80f314a-de0e-4d54-a2e0-279edbd9b6d8' class='xr-var-data-in' type='checkbox'><label for='data-f80f314a-de0e-4d54-a2e0-279edbd9b6d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.2921631 , 0.64811353, 0.46894068, ..., 0.36037196, 1.1757669 ,\n",
+       "        0.35264898]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|eth_sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2922 0.6481 ... 1.001 0.5405</div><input id='attrs-7a6f6afa-64c9-46f8-a44a-13a80e166c44' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7a6f6afa-64c9-46f8-a44a-13a80e166c44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb0c0afd-56e0-4766-b9f4-7478ba5356c4' class='xr-var-data-in' type='checkbox'><label for='data-bb0c0afd-56e0-4766-b9f4-7478ba5356c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.2921631 , 0.64811353, 0.46894068, ..., 0.36037196, 1.1757669 ,\n",
        "        0.79513062],\n",
        "       [0.59163681, 0.71757778, 0.39237225, ..., 2.53012159, 2.58015772,\n",
        "        0.17337584],\n",
        "       [0.13887082, 0.35217904, 0.39475355, ..., 0.68786625, 2.04625487,\n",
        "        1.86577226],\n",
        "       [0.24614945, 0.54402931, 0.40870221, ..., 0.92293547, 1.00104249,\n",
-       "        0.54051062]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|edu_sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4018 0.3691 ... 0.1152 0.08889</div><input id='attrs-b8769592-b320-448d-b960-0110e5ac1a6a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b8769592-b320-448d-b960-0110e5ac1a6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2afbc88b-da88-42b7-a3a7-405ec2347dc4' class='xr-var-data-in' type='checkbox'><label for='data-2afbc88b-da88-42b7-a3a7-405ec2347dc4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.40177726, 0.36906181, 0.12511795, ..., 0.07212523, 0.44653234,\n",
+       "        0.54051062]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|edu_sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4018 0.3691 ... 0.1152 0.08889</div><input id='attrs-dbbae105-966e-4c2a-ae0d-69d5443ccc78' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dbbae105-966e-4c2a-ae0d-69d5443ccc78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fcb1390a-a771-41ce-b0bb-418b14e98494' class='xr-var-data-in' type='checkbox'><label for='data-fcb1390a-a771-41ce-b0bb-418b14e98494' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.40177726, 0.36906181, 0.12511795, ..., 0.07212523, 0.44653234,\n",
        "        0.66976489],\n",
        "       [0.00631078, 0.17373649, 0.57101734, ..., 0.31783469, 0.13025087,\n",
        "        0.62022555],\n",
        "       [0.08792224, 0.10185938, 0.26869272, ..., 0.09290535, 0.03162795,\n",
        "        0.20758365],\n",
        "       [0.73962758, 0.23081107, 0.28538795, ..., 0.1436684 , 0.11522592,\n",
-       "        0.08888835]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|male:eth_sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.09973 0.09009 ... 0.1604 0.2017</div><input id='attrs-b4f676d6-a05d-40b2-9f3c-8cb514f7f952' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b4f676d6-a05d-40b2-9f3c-8cb514f7f952' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f2c865d-8f4d-46cf-9352-f481d6f1bd43' class='xr-var-data-in' type='checkbox'><label for='data-6f2c865d-8f4d-46cf-9352-f481d6f1bd43' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.09972931, 0.09008741, 0.0104649 , ..., 0.24436277, 0.30843254,\n",
+       "        0.08888835]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|male:eth_sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.09973 0.09009 ... 0.1604 0.2017</div><input id='attrs-c7487f0a-bcac-4fcf-8075-d72bff0fe7d6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c7487f0a-bcac-4fcf-8075-d72bff0fe7d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d249257d-03f5-46c8-8a95-0387b28fca45' class='xr-var-data-in' type='checkbox'><label for='data-d249257d-03f5-46c8-8a95-0387b28fca45' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.09972931, 0.09008741, 0.0104649 , ..., 0.24436277, 0.30843254,\n",
        "        0.11575019],\n",
        "       [0.08582021, 0.05566894, 0.0044242 , ..., 0.10406267, 0.24083354,\n",
        "        0.13214042],\n",
        "       [0.01613223, 0.08062836, 0.20829874, ..., 0.02283508, 0.04888584,\n",
        "        0.11778812],\n",
        "       [0.23467763, 0.11682297, 0.00889169, ..., 0.14732406, 0.16038902,\n",
-       "        0.20172304]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|edu:age_sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.139 0.07881 ... 0.1752 0.1378</div><input id='attrs-f44d523c-c44b-4f8d-bc37-03dd75afa7d4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f44d523c-c44b-4f8d-bc37-03dd75afa7d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21ee4760-685a-4173-913e-abb8fbd08180' class='xr-var-data-in' type='checkbox'><label for='data-21ee4760-685a-4173-913e-abb8fbd08180' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.13896325, 0.07880674, 0.15077977, ..., 0.2274383 , 0.19976934,\n",
+       "        0.20172304]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|edu:age_sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.139 0.07881 ... 0.1752 0.1378</div><input id='attrs-3d2b9ee7-0a60-414f-9180-1470845dd02f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3d2b9ee7-0a60-414f-9180-1470845dd02f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acc2bff2-504a-4e4b-90e0-05be25642a58' class='xr-var-data-in' type='checkbox'><label for='data-acc2bff2-504a-4e4b-90e0-05be25642a58' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.13896325, 0.07880674, 0.15077977, ..., 0.2274383 , 0.19976934,\n",
        "        0.26169646],\n",
        "       [0.04594554, 0.25734548, 0.14074699, ..., 0.12098062, 0.20249964,\n",
        "        0.12780594],\n",
        "       [0.23094571, 0.21564635, 0.14661088, ..., 0.15523845, 0.24861837,\n",
        "        0.20299868],\n",
        "       [0.23613126, 0.21425098, 0.16440101, ..., 0.17224596, 0.17520894,\n",
-       "        0.13778684]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|edu:eth_sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1356 0.1101 ... 0.2902 0.2252</div><input id='attrs-e9d65921-34ba-4450-99ee-cf19c3f25208' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e9d65921-34ba-4450-99ee-cf19c3f25208' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-418dfc38-983e-46a7-bcfa-f367fc8dca62' class='xr-var-data-in' type='checkbox'><label for='data-418dfc38-983e-46a7-bcfa-f367fc8dca62' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.13564227, 0.11014919, 0.25155446, ..., 0.2837875 , 0.21216988,\n",
+       "        0.13778684]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|edu:eth_sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1356 0.1101 ... 0.2902 0.2252</div><input id='attrs-b1d04ead-1243-44bf-8f66-c97798817322' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b1d04ead-1243-44bf-8f66-c97798817322' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58bfd003-2392-4b58-974b-fc4f3c4b53d8' class='xr-var-data-in' type='checkbox'><label for='data-58bfd003-2392-4b58-974b-fc4f3c4b53d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.13564227, 0.11014919, 0.25155446, ..., 0.2837875 , 0.21216988,\n",
        "        0.23004101],\n",
        "       [0.11604053, 0.01230621, 0.24543241, ..., 0.10798228, 0.18381797,\n",
        "        0.25597436],\n",
        "       [0.25055064, 0.18110486, 0.15498204, ..., 0.10596968, 0.18290151,\n",
        "        0.22959195],\n",
        "       [0.21238655, 0.08477365, 0.14353349, ..., 0.36743476, 0.29022249,\n",
-       "        0.22517211]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|state</span></div><div class='xr-var-dims'>(chain, draw, state__factor_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3825 0.02488 ... 0.393 -0.4676</div><input id='attrs-026411a3-3cec-4331-9134-dd1f4936bacf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-026411a3-3cec-4331-9134-dd1f4936bacf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-425ca5bf-4032-4329-a65e-60f2c3ac0640' class='xr-var-data-in' type='checkbox'><label for='data-425ca5bf-4032-4329-a65e-60f2c3ac0640' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.38250375,  0.02488263,  0.21664533, ..., -0.22699524,\n",
+       "        0.22517211]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|state</span></div><div class='xr-var-dims'>(chain, draw, state__factor_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3825 0.02488 ... 0.393 -0.4676</div><input id='attrs-e4e06ca8-9050-4ef7-9c04-e13b9a3f58cd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e4e06ca8-9050-4ef7-9c04-e13b9a3f58cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ff1a863-4d0c-4cb9-810d-2d77ea22644a' class='xr-var-data-in' type='checkbox'><label for='data-5ff1a863-4d0c-4cb9-810d-2d77ea22644a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.38250375,  0.02488263,  0.21664533, ..., -0.22699524,\n",
        "          0.42690621,  0.08939394],\n",
        "        [ 0.09604889,  0.61553696, -0.03211364, ..., -0.0214277 ,\n",
        "          0.35967063,  0.08878077],\n",
@@ -2807,7 +2841,7 @@
        "        [ 0.65858299,  0.17171798,  0.21039919, ...,  0.15819054,\n",
        "          0.27328158, -0.3036795 ],\n",
        "        [ 0.46501501,  0.2624723 ,  0.15400031, ...,  0.32887312,\n",
-       "          0.39299399, -0.4676157 ]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|eth</span></div><div class='xr-var-dims'>(chain, draw, eth__factor_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.3689 -0.01268 ... 0.5365</div><input id='attrs-27382a74-8b4e-4a09-be07-49c47b013d8a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-27382a74-8b4e-4a09-be07-49c47b013d8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8205129a-b97e-4abc-863d-b8f6cde1b6c1' class='xr-var-data-in' type='checkbox'><label for='data-8205129a-b97e-4abc-863d-b8f6cde1b6c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-3.68853010e-01, -1.26764249e-02,  1.05940562e-01,\n",
+       "          0.39299399, -0.4676157 ]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|eth</span></div><div class='xr-var-dims'>(chain, draw, eth__factor_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.3689 -0.01268 ... 0.5365</div><input id='attrs-bc873a9a-fe5e-4e34-b3dc-8ebbd1ee47e4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bc873a9a-fe5e-4e34-b3dc-8ebbd1ee47e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-277b19cf-a825-4065-8b59-7b65f29dfa33' class='xr-var-data-in' type='checkbox'><label for='data-277b19cf-a825-4065-8b59-7b65f29dfa33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-3.68853010e-01, -1.26764249e-02,  1.05940562e-01,\n",
        "          1.95515035e-01],\n",
        "        [-4.07408561e-01, -7.32408287e-02,  2.57043589e-01,\n",
        "          2.78638817e-01],\n",
@@ -2847,7 +2881,7 @@
        "        [-4.39739910e-02,  4.91938969e-01,  3.05741057e-01,\n",
        "          7.21111591e-01],\n",
        "        [-4.96597357e-01,  3.24731626e-01, -2.82345008e-02,\n",
-       "          5.36478559e-01]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|edu</span></div><div class='xr-var-dims'>(chain, draw, edu__factor_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1912 0.4088 ... -0.02438 -0.07707</div><input id='attrs-e36fe1cc-e97e-4e84-be08-c768b0bad110' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e36fe1cc-e97e-4e84-be08-c768b0bad110' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b274a798-0037-406f-8b03-0acd04e6964a' class='xr-var-data-in' type='checkbox'><label for='data-b274a798-0037-406f-8b03-0acd04e6964a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.19120976,  0.40883343,  0.32287821, -0.2609866 ,\n",
+       "          5.36478559e-01]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|edu</span></div><div class='xr-var-dims'>(chain, draw, edu__factor_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1912 0.4088 ... -0.02438 -0.07707</div><input id='attrs-10d4c4cd-6947-4c2a-8dd4-97938b198f16' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-10d4c4cd-6947-4c2a-8dd4-97938b198f16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d37ae84-7190-447b-988b-34024b43f8d3' class='xr-var-data-in' type='checkbox'><label for='data-7d37ae84-7190-447b-988b-34024b43f8d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.19120976,  0.40883343,  0.32287821, -0.2609866 ,\n",
        "          0.34378828],\n",
        "        [-0.08894206,  0.22194809,  0.34861253, -0.34084547,\n",
        "          0.12977861],\n",
@@ -2887,7 +2921,7 @@
        "        [ 0.05984333,  0.10854492, -0.11492089, -0.02351373,\n",
        "         -0.07170203],\n",
        "        [ 0.06333353,  0.07045411, -0.0609233 , -0.02437717,\n",
-       "         -0.07707395]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|male:eth</span></div><div class='xr-var-dims'>(chain, draw, male:eth__factor_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.003232 -0.07587 ... -0.1035</div><input id='attrs-aef1cd50-0eb9-4b3a-bc35-e0aea816fa29' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aef1cd50-0eb9-4b3a-bc35-e0aea816fa29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a460f8e4-5520-4bb6-bcb9-e21af9e72fd9' class='xr-var-data-in' type='checkbox'><label for='data-a460f8e4-5520-4bb6-bcb9-e21af9e72fd9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 3.23210975e-03, -7.58727100e-02, -3.93659154e-02, ...,\n",
+       "         -0.07707395]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|male:eth</span></div><div class='xr-var-dims'>(chain, draw, male:eth__factor_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.003232 -0.07587 ... -0.1035</div><input id='attrs-ee868dc3-5826-4df7-83ce-fb7c52e79d5d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ee868dc3-5826-4df7-83ce-fb7c52e79d5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c40db0b9-9583-4af1-a134-724ca507da1f' class='xr-var-data-in' type='checkbox'><label for='data-c40db0b9-9583-4af1-a134-724ca507da1f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 3.23210975e-03, -7.58727100e-02, -3.93659154e-02, ...,\n",
        "          4.66359832e-02,  6.55923347e-02, -1.37860640e-02],\n",
        "        [-3.15997696e-02, -1.91314387e-02, -2.28263982e-05, ...,\n",
        "         -1.98865846e-02, -3.34916561e-02,  1.52312912e-01],\n",
@@ -2927,7 +2961,7 @@
        "        [ 3.65321648e-02, -5.14534881e-02,  2.85102853e-01, ...,\n",
        "          6.69374138e-02,  1.33906766e-03, -7.85379772e-02],\n",
        "        [ 2.26404415e-01, -7.23006452e-02,  1.42095007e-01, ...,\n",
-       "          7.10830962e-02,  1.33409009e-02, -1.03505741e-01]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|edu:age</span></div><div class='xr-var-dims'>(chain, draw, edu:age__factor_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.1959 0.01111 ... 0.07365 0.1426</div><input id='attrs-090e8769-4c9d-4f61-8a32-350a6f1f5b55' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-090e8769-4c9d-4f61-8a32-350a6f1f5b55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ad0292c-86cb-4231-a597-1cb4bcc21796' class='xr-var-data-in' type='checkbox'><label for='data-7ad0292c-86cb-4231-a597-1cb4bcc21796' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-0.19587975,  0.01110816,  0.0529932 , ...,  0.06955911,\n",
+       "          7.10830962e-02,  1.33409009e-02, -1.03505741e-01]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|edu:age</span></div><div class='xr-var-dims'>(chain, draw, edu:age__factor_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.1959 0.01111 ... 0.07365 0.1426</div><input id='attrs-2d1ab329-14ad-4a84-be03-a4e86428af6a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2d1ab329-14ad-4a84-be03-a4e86428af6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dcc2c7f0-9012-401c-b865-fa9786c94b39' class='xr-var-data-in' type='checkbox'><label for='data-dcc2c7f0-9012-401c-b865-fa9786c94b39' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-0.19587975,  0.01110816,  0.0529932 , ...,  0.06955911,\n",
        "          0.09093148,  0.04398449],\n",
        "        [ 0.00346945,  0.06466284,  0.11923803, ...,  0.00927192,\n",
        "          0.00745912,  0.09853649],\n",
@@ -2967,7 +3001,7 @@
        "        [ 0.21476261,  0.08806381,  0.2784128 , ...,  0.00388492,\n",
        "          0.10326975,  0.0531971 ],\n",
        "        [ 0.10948511,  0.01465483,  0.10216166, ..., -0.01694412,\n",
-       "          0.07364913,  0.14258407]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|edu:eth</span></div><div class='xr-var-dims'>(chain, draw, edu:eth__factor_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.1216 0.02276 ... 0.1539 0.2324</div><input id='attrs-3f703531-d822-4653-9ec6-41ceaaf39d87' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3f703531-d822-4653-9ec6-41ceaaf39d87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e9f2f1d-74ec-493d-bd94-dd0365d2559c' class='xr-var-data-in' type='checkbox'><label for='data-7e9f2f1d-74ec-493d-bd94-dd0365d2559c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-1.21630654e-01,  2.27589260e-02, -8.09307811e-02, ...,\n",
+       "          0.07364913,  0.14258407]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|edu:eth</span></div><div class='xr-var-dims'>(chain, draw, edu:eth__factor_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.1216 0.02276 ... 0.1539 0.2324</div><input id='attrs-8f50467a-df1d-42bd-9571-dc5e5d529ff1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8f50467a-df1d-42bd-9571-dc5e5d529ff1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f68205ee-87ff-4370-a456-84efb9fbd1e2' class='xr-var-data-in' type='checkbox'><label for='data-f68205ee-87ff-4370-a456-84efb9fbd1e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-1.21630654e-01,  2.27589260e-02, -8.09307811e-02, ...,\n",
        "          1.18414504e-01, -2.94369545e-01,  4.28628830e-02],\n",
        "        [ 8.79262093e-02, -2.40069971e-02,  1.75872143e-01, ...,\n",
        "         -1.22918338e-02,  1.79839841e-02,  1.11624648e-01],\n",
@@ -3007,16 +3041,16 @@
        "        [-2.96442670e-02, -5.23772125e-01,  3.35574294e-01, ...,\n",
        "          2.39128705e-01,  2.95420812e-01,  3.14745957e-01],\n",
        "        [ 6.41359939e-03, -4.59543427e-01,  2.75663034e-01, ...,\n",
-       "          1.64926517e-01,  1.53933961e-01,  2.32381755e-01]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8eec24f4-91f8-46a3-bf76-ddab167f7712' class='xr-section-summary-in' type='checkbox'  ><label for='section-8eec24f4-91f8-46a3-bf76-ddab167f7712' class='xr-section-summary' >Indexes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8fff3a6c-650f-4123-9ca0-7f3bef219002' class='xr-index-data-in' type='checkbox'/><label for='index-8fff3a6c-650f-4123-9ca0-7f3bef219002' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2180a352-1ef4-4b80-b607-769ddc385a54' class='xr-index-data-in' type='checkbox'/><label for='index-2180a352-1ef4-4b80-b607-769ddc385a54' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "          1.64926517e-01,  1.53933961e-01,  2.32381755e-01]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b468b8fa-243a-4d9e-ad28-04bbe67542e0' class='xr-section-summary-in' type='checkbox'  ><label for='section-b468b8fa-243a-4d9e-ad28-04bbe67542e0' class='xr-section-summary' >Indexes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d23418bf-be3e-4765-a97e-d30c96ca546d' class='xr-index-data-in' type='checkbox'/><label for='index-d23418bf-be3e-4765-a97e-d30c96ca546d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0b908011-6273-4862-a8fc-ed4ee22edfc0' class='xr-index-data-in' type='checkbox'/><label for='index-0b908011-6273-4862-a8fc-ed4ee22edfc0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
        "       ...\n",
        "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
-       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>state__factor_dim</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-299694d0-d9e6-4dec-a31d-3de3bcbc90bc' class='xr-index-data-in' type='checkbox'/><label for='index-299694d0-d9e6-4dec-a31d-3de3bcbc90bc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;AK&#x27;, &#x27;AL&#x27;, &#x27;AR&#x27;, &#x27;CA&#x27;, &#x27;CO&#x27;, &#x27;DE&#x27;, &#x27;FL&#x27;, &#x27;GA&#x27;, &#x27;HI&#x27;, &#x27;IA&#x27;, &#x27;ID&#x27;, &#x27;IL&#x27;,\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>state__factor_dim</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7d17c3f3-6676-4768-9e60-1161e35ae10d' class='xr-index-data-in' type='checkbox'/><label for='index-7d17c3f3-6676-4768-9e60-1161e35ae10d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;AK&#x27;, &#x27;AL&#x27;, &#x27;AR&#x27;, &#x27;CA&#x27;, &#x27;CO&#x27;, &#x27;DE&#x27;, &#x27;FL&#x27;, &#x27;GA&#x27;, &#x27;HI&#x27;, &#x27;IA&#x27;, &#x27;ID&#x27;, &#x27;IL&#x27;,\n",
        "       &#x27;KS&#x27;, &#x27;KY&#x27;, &#x27;LA&#x27;, &#x27;MA&#x27;, &#x27;MD&#x27;, &#x27;ME&#x27;, &#x27;MI&#x27;, &#x27;MN&#x27;, &#x27;MO&#x27;, &#x27;MS&#x27;, &#x27;MT&#x27;, &#x27;NC&#x27;,\n",
        "       &#x27;ND&#x27;, &#x27;NE&#x27;, &#x27;NH&#x27;, &#x27;NJ&#x27;, &#x27;NM&#x27;, &#x27;NV&#x27;, &#x27;NY&#x27;, &#x27;OH&#x27;, &#x27;OK&#x27;, &#x27;OR&#x27;, &#x27;PA&#x27;, &#x27;RI&#x27;,\n",
        "       &#x27;SC&#x27;, &#x27;SD&#x27;, &#x27;TN&#x27;, &#x27;UT&#x27;, &#x27;VA&#x27;, &#x27;VT&#x27;, &#x27;WA&#x27;, &#x27;WI&#x27;, &#x27;WV&#x27;, &#x27;WY&#x27;],\n",
-       "      dtype=&#x27;object&#x27;, name=&#x27;state__factor_dim&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>eth__factor_dim</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-eca39345-6f76-433d-a914-bc2976131369' class='xr-index-data-in' type='checkbox'/><label for='index-eca39345-6f76-433d-a914-bc2976131369' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Black&#x27;, &#x27;Hispanic&#x27;, &#x27;Other&#x27;, &#x27;White&#x27;], dtype=&#x27;object&#x27;, name=&#x27;eth__factor_dim&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>edu__factor_dim</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-88789886-e06b-4884-8d78-cc4ada630b3d' class='xr-index-data-in' type='checkbox'/><label for='index-88789886-e06b-4884-8d78-cc4ada630b3d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;4-Year College&#x27;, &#x27;HS&#x27;, &#x27;No HS&#x27;, &#x27;Post-grad&#x27;, &#x27;Some college&#x27;], dtype=&#x27;object&#x27;, name=&#x27;edu__factor_dim&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>male:eth__factor_dim</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e1395943-10c6-45db-8b68-3187265db530' class='xr-index-data-in' type='checkbox'/><label for='index-e1395943-10c6-45db-8b68-3187265db530' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;-0.5:Black&#x27;, &#x27;-0.5:Hispanic&#x27;, &#x27;-0.5:Other&#x27;, &#x27;-0.5:White&#x27;, &#x27;0.5:Black&#x27;,\n",
+       "      dtype=&#x27;object&#x27;, name=&#x27;state__factor_dim&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>eth__factor_dim</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9afc5ee3-2bc0-4e65-8fb5-b3653f819901' class='xr-index-data-in' type='checkbox'/><label for='index-9afc5ee3-2bc0-4e65-8fb5-b3653f819901' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Black&#x27;, &#x27;Hispanic&#x27;, &#x27;Other&#x27;, &#x27;White&#x27;], dtype=&#x27;object&#x27;, name=&#x27;eth__factor_dim&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>edu__factor_dim</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-334d8563-4fbb-4388-87a8-b6d16d96a7a0' class='xr-index-data-in' type='checkbox'/><label for='index-334d8563-4fbb-4388-87a8-b6d16d96a7a0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;4-Year College&#x27;, &#x27;HS&#x27;, &#x27;No HS&#x27;, &#x27;Post-grad&#x27;, &#x27;Some college&#x27;], dtype=&#x27;object&#x27;, name=&#x27;edu__factor_dim&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>male:eth__factor_dim</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bea619a7-6301-477a-ab14-67ac6d6bbdcf' class='xr-index-data-in' type='checkbox'/><label for='index-bea619a7-6301-477a-ab14-67ac6d6bbdcf' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;-0.5:Black&#x27;, &#x27;-0.5:Hispanic&#x27;, &#x27;-0.5:Other&#x27;, &#x27;-0.5:White&#x27;, &#x27;0.5:Black&#x27;,\n",
        "       &#x27;0.5:Hispanic&#x27;, &#x27;0.5:Other&#x27;, &#x27;0.5:White&#x27;],\n",
-       "      dtype=&#x27;object&#x27;, name=&#x27;male:eth__factor_dim&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>edu:age__factor_dim</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d92aec5c-e3ab-4d71-a024-b2c66238378d' class='xr-index-data-in' type='checkbox'/><label for='index-d92aec5c-e3ab-4d71-a024-b2c66238378d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;4-Year College:18-29&#x27;, &#x27;4-Year College:30-39&#x27;, &#x27;4-Year College:40-49&#x27;,\n",
+       "      dtype=&#x27;object&#x27;, name=&#x27;male:eth__factor_dim&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>edu:age__factor_dim</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e8e3642f-955d-483a-ba4b-8d13c692cc08' class='xr-index-data-in' type='checkbox'/><label for='index-e8e3642f-955d-483a-ba4b-8d13c692cc08' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;4-Year College:18-29&#x27;, &#x27;4-Year College:30-39&#x27;, &#x27;4-Year College:40-49&#x27;,\n",
        "       &#x27;4-Year College:50-59&#x27;, &#x27;4-Year College:60-69&#x27;, &#x27;4-Year College:70+&#x27;,\n",
        "       &#x27;HS:18-29&#x27;, &#x27;HS:30-39&#x27;, &#x27;HS:40-49&#x27;, &#x27;HS:50-59&#x27;, &#x27;HS:60-69&#x27;, &#x27;HS:70+&#x27;,\n",
        "       &#x27;No HS:18-29&#x27;, &#x27;No HS:30-39&#x27;, &#x27;No HS:40-49&#x27;, &#x27;No HS:50-59&#x27;,\n",
@@ -3025,20 +3059,20 @@
        "       &#x27;Post-grad:70+&#x27;, &#x27;Some college:18-29&#x27;, &#x27;Some college:30-39&#x27;,\n",
        "       &#x27;Some college:40-49&#x27;, &#x27;Some college:50-59&#x27;, &#x27;Some college:60-69&#x27;,\n",
        "       &#x27;Some college:70+&#x27;],\n",
-       "      dtype=&#x27;object&#x27;, name=&#x27;edu:age__factor_dim&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>edu:eth__factor_dim</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0740137b-4abd-47f1-9c8e-8c20120b2baa' class='xr-index-data-in' type='checkbox'/><label for='index-0740137b-4abd-47f1-9c8e-8c20120b2baa' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;4-Year College:Black&#x27;, &#x27;4-Year College:Hispanic&#x27;,\n",
+       "      dtype=&#x27;object&#x27;, name=&#x27;edu:age__factor_dim&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>edu:eth__factor_dim</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ca939d6c-8647-47fc-9cbf-ac0f6d5782c9' class='xr-index-data-in' type='checkbox'/><label for='index-ca939d6c-8647-47fc-9cbf-ac0f6d5782c9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;4-Year College:Black&#x27;, &#x27;4-Year College:Hispanic&#x27;,\n",
        "       &#x27;4-Year College:Other&#x27;, &#x27;4-Year College:White&#x27;, &#x27;HS:Black&#x27;,\n",
        "       &#x27;HS:Hispanic&#x27;, &#x27;HS:Other&#x27;, &#x27;HS:White&#x27;, &#x27;No HS:Black&#x27;, &#x27;No HS:Hispanic&#x27;,\n",
        "       &#x27;No HS:Other&#x27;, &#x27;No HS:White&#x27;, &#x27;Post-grad:Black&#x27;, &#x27;Post-grad:Hispanic&#x27;,\n",
        "       &#x27;Post-grad:Other&#x27;, &#x27;Post-grad:White&#x27;, &#x27;Some college:Black&#x27;,\n",
        "       &#x27;Some college:Hispanic&#x27;, &#x27;Some college:Other&#x27;, &#x27;Some college:White&#x27;],\n",
-       "      dtype=&#x27;object&#x27;, name=&#x27;edu:eth__factor_dim&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e2ef75d8-d32f-48c1-9217-38049deed1cc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e2ef75d8-d32f-48c1-9217-38049deed1cc' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-09-09T21:52:09.389695</dd><dt><span>arviz_version :</span></dt><dd>0.16.1</dd><dt><span>modeling_interface :</span></dt><dd>bambi</dd><dt><span>modeling_interface_version :</span></dt><dd>0.13.0.dev</dd></dl></div></li></ul></div></div><br></div>\n",
+       "      dtype=&#x27;object&#x27;, name=&#x27;edu:eth__factor_dim&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dcd44c28-1b0c-4d95-9112-727c0b0d183a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dcd44c28-1b0c-4d95-9112-727c0b0d183a' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-09-17T17:39:00.290773</dd><dt><span>arviz_version :</span></dt><dd>0.16.1</dd><dt><span>modeling_interface :</span></dt><dd>bambi</dd><dt><span>modeling_interface_version :</span></dt><dd>0.13.0.dev</dd></dl></div></li></ul></div></div><br></div>\n",
        "                      </ul>\n",
        "                  </div>\n",
        "            </li>\n",
        "            \n",
        "            <li class = \"xr-section-item\">\n",
-       "                  <input id=\"idata_log_likelihood2db84b08-ccbc-48bf-99bc-f5dfd9c1df3a\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
-       "                  <label for=\"idata_log_likelihood2db84b08-ccbc-48bf-99bc-f5dfd9c1df3a\" class = \"xr-section-summary\">log_likelihood</label>\n",
+       "                  <input id=\"idata_log_likelihood76ea0911-fd70-430d-a5f0-822acae96161\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_log_likelihood76ea0911-fd70-430d-a5f0-822acae96161\" class = \"xr-section-summary\">log_likelihood</label>\n",
        "                  <div class=\"xr-section-inline-details\"></div>\n",
        "                  <div class=\"xr-section-details\">\n",
        "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
@@ -3414,10 +3448,10 @@
        "Data variables:\n",
        "    p(abortion, n)      (chain, draw, p(abortion, n)_obs) float64 -2.099 ... 0.0\n",
        "Attributes:\n",
-       "    created_at:                  2023-09-09T21:52:09.396305\n",
+       "    created_at:                  2023-09-17T17:39:00.297597\n",
        "    arviz_version:               0.16.1\n",
        "    modeling_interface:          bambi\n",
-       "    modeling_interface_version:  0.13.0.dev</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4bf863b7-0c33-4cbc-94d3-26c42a25591a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4bf863b7-0c33-4cbc-94d3-26c42a25591a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>p(abortion, n)_obs</span>: 11040</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-4e9b941b-771b-4cfb-9040-1467ad7f8207' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4e9b941b-771b-4cfb-9040-1467ad7f8207' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-dd969f3b-4072-4e13-8978-470cd703ebbc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dd969f3b-4072-4e13-8978-470cd703ebbc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c187103c-b662-4a9c-b1d0-39680bdb0fee' class='xr-var-data-in' type='checkbox'><label for='data-c187103c-b662-4a9c-b1d0-39680bdb0fee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-0ae47fce-7bcd-4c24-8378-f094dff767d3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0ae47fce-7bcd-4c24-8378-f094dff767d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf2cbe93-a108-48d6-9c4b-e698a850de02' class='xr-var-data-in' type='checkbox'><label for='data-bf2cbe93-a108-48d6-9c4b-e698a850de02' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>p(abortion, n)_obs</span></div><div class='xr-var-dims'>(p(abortion, n)_obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 11036 11037 11038 11039</div><input id='attrs-2b8d4abc-fe00-4bf2-a0db-d9f6319312db' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2b8d4abc-fe00-4bf2-a0db-d9f6319312db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6da0c8fe-15d8-4dbe-ae89-d8135e2ddefa' class='xr-var-data-in' type='checkbox'><label for='data-6da0c8fe-15d8-4dbe-ae89-d8135e2ddefa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([    0,     1,     2, ..., 11037, 11038, 11039])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c933ea89-d0d8-4c8d-988d-5ed898331733' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c933ea89-d0d8-4c8d-988d-5ed898331733' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>p(abortion, n)</span></div><div class='xr-var-dims'>(chain, draw, p(abortion, n)_obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.099 -6.533 -2.53 ... 0.0 0.0 0.0</div><input id='attrs-6f6bcc77-2789-4d1e-9d88-15f7cac3f925' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6f6bcc77-2789-4d1e-9d88-15f7cac3f925' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9accbedb-d4c0-4c4e-9bf4-6b2de0dd6e1d' class='xr-var-data-in' type='checkbox'><label for='data-9accbedb-d4c0-4c4e-9bf4-6b2de0dd6e1d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-2.09936954, -6.53260554, -2.53030632, ...,  0.        ,\n",
+       "    modeling_interface_version:  0.13.0.dev</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-78626919-0065-4c17-befe-7131cef13a57' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-78626919-0065-4c17-befe-7131cef13a57' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>p(abortion, n)_obs</span>: 11040</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d9be4518-d894-45bd-8f3e-9e78a1116029' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d9be4518-d894-45bd-8f3e-9e78a1116029' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-9428740c-5496-4fdb-9f75-37e5444bd84b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9428740c-5496-4fdb-9f75-37e5444bd84b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28d61cc7-7b43-4890-a3eb-1fedea0fd6be' class='xr-var-data-in' type='checkbox'><label for='data-28d61cc7-7b43-4890-a3eb-1fedea0fd6be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-1e959579-e0ba-4327-9b4c-53a6a57334c4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e959579-e0ba-4327-9b4c-53a6a57334c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9bafef64-338c-4329-abb9-e446ca324b1d' class='xr-var-data-in' type='checkbox'><label for='data-9bafef64-338c-4329-abb9-e446ca324b1d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>p(abortion, n)_obs</span></div><div class='xr-var-dims'>(p(abortion, n)_obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 11036 11037 11038 11039</div><input id='attrs-4d26a9a6-07cf-4306-b21a-18b7eb11e86a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4d26a9a6-07cf-4306-b21a-18b7eb11e86a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5f554f1-a008-4aea-9984-56cadd116294' class='xr-var-data-in' type='checkbox'><label for='data-f5f554f1-a008-4aea-9984-56cadd116294' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([    0,     1,     2, ..., 11037, 11038, 11039])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-85433ea0-3e23-4d56-82c0-0b0a8b3c72fd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-85433ea0-3e23-4d56-82c0-0b0a8b3c72fd' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>p(abortion, n)</span></div><div class='xr-var-dims'>(chain, draw, p(abortion, n)_obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.099 -6.533 -2.53 ... 0.0 0.0 0.0</div><input id='attrs-35252390-a6d9-4e45-83c5-fa6f6cf418cf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-35252390-a6d9-4e45-83c5-fa6f6cf418cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-902ea28a-4ae2-4541-b183-e31804a3fa7c' class='xr-var-data-in' type='checkbox'><label for='data-902ea28a-4ae2-4541-b183-e31804a3fa7c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-2.09936954, -6.53260554, -2.53030632, ...,  0.        ,\n",
        "          0.        ,  0.        ],\n",
        "        [-2.82166815, -5.81761829, -2.15259044, ...,  0.        ,\n",
        "          0.        ,  0.        ],\n",
@@ -3457,20 +3491,20 @@
        "        [-2.36708357, -4.65351176, -2.35737355, ...,  0.        ,\n",
        "          0.        ,  0.        ],\n",
        "        [-2.52597843, -5.57143406, -2.08559554, ...,  0.        ,\n",
-       "          0.        ,  0.        ]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e85798fc-7dbf-4ee2-b8d2-f5074d767a94' class='xr-section-summary-in' type='checkbox'  ><label for='section-e85798fc-7dbf-4ee2-b8d2-f5074d767a94' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e18d530a-54d2-4dc4-9b55-cb740403cf05' class='xr-index-data-in' type='checkbox'/><label for='index-e18d530a-54d2-4dc4-9b55-cb740403cf05' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-93ee3e29-bc1c-41b4-9981-540e8980c7a8' class='xr-index-data-in' type='checkbox'/><label for='index-93ee3e29-bc1c-41b4-9981-540e8980c7a8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "          0.        ,  0.        ]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0daf72b8-f73a-4a7f-8c9a-d20670570836' class='xr-section-summary-in' type='checkbox'  ><label for='section-0daf72b8-f73a-4a7f-8c9a-d20670570836' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-299281f7-4784-4126-ba15-ed953ac8493e' class='xr-index-data-in' type='checkbox'/><label for='index-299281f7-4784-4126-ba15-ed953ac8493e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-af345c5c-972e-4a39-942d-3bbb52fcb6fb' class='xr-index-data-in' type='checkbox'/><label for='index-af345c5c-972e-4a39-942d-3bbb52fcb6fb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
        "       ...\n",
        "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
-       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>p(abortion, n)_obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4d0aadc8-ac96-4695-83cf-a87f842133a9' class='xr-index-data-in' type='checkbox'/><label for='index-4d0aadc8-ac96-4695-83cf-a87f842133a9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    0,     1,     2,     3,     4,     5,     6,     7,     8,     9,\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>p(abortion, n)_obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d5adf172-e877-42fa-be6c-6e831100580c' class='xr-index-data-in' type='checkbox'/><label for='index-d5adf172-e877-42fa-be6c-6e831100580c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    0,     1,     2,     3,     4,     5,     6,     7,     8,     9,\n",
        "       ...\n",
        "       11030, 11031, 11032, 11033, 11034, 11035, 11036, 11037, 11038, 11039],\n",
-       "      dtype=&#x27;int64&#x27;, name=&#x27;p(abortion, n)_obs&#x27;, length=11040))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c9851e38-a65d-46ac-9a06-0f42416915f0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c9851e38-a65d-46ac-9a06-0f42416915f0' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-09-09T21:52:09.396305</dd><dt><span>arviz_version :</span></dt><dd>0.16.1</dd><dt><span>modeling_interface :</span></dt><dd>bambi</dd><dt><span>modeling_interface_version :</span></dt><dd>0.13.0.dev</dd></dl></div></li></ul></div></div><br></div>\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;p(abortion, n)_obs&#x27;, length=11040))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fbcc7a41-eae8-46a6-9f53-dacc5773fe3d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fbcc7a41-eae8-46a6-9f53-dacc5773fe3d' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-09-17T17:39:00.297597</dd><dt><span>arviz_version :</span></dt><dd>0.16.1</dd><dt><span>modeling_interface :</span></dt><dd>bambi</dd><dt><span>modeling_interface_version :</span></dt><dd>0.13.0.dev</dd></dl></div></li></ul></div></div><br></div>\n",
        "                      </ul>\n",
        "                  </div>\n",
        "            </li>\n",
        "            \n",
        "            <li class = \"xr-section-item\">\n",
-       "                  <input id=\"idata_sample_stats804e9719-e78c-42f9-8b51-7d817d667c84\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
-       "                  <label for=\"idata_sample_stats804e9719-e78c-42f9-8b51-7d817d667c84\" class = \"xr-section-summary\">sample_stats</label>\n",
+       "                  <input id=\"idata_sample_statsd69ed40b-6012-44b9-84d9-ded6dfb20c34\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_sample_statsd69ed40b-6012-44b9-84d9-ded6dfb20c34\" class = \"xr-section-summary\">sample_stats</label>\n",
        "                  <div class=\"xr-section-inline-details\"></div>\n",
        "                  <div class=\"xr-section-details\">\n",
        "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
@@ -3851,57 +3885,57 @@
        "    tree_depth       (chain, draw) int64 9 9 9 9 9 9 9 9 9 ... 9 9 9 9 9 9 9 9 9\n",
        "    lp               (chain, draw) float64 2.205e+03 2.206e+03 ... 2.175e+03\n",
        "Attributes:\n",
-       "    created_at:                  2023-09-09T21:52:09.395016\n",
+       "    created_at:                  2023-09-17T17:39:00.296422\n",
        "    arviz_version:               0.16.1\n",
        "    modeling_interface:          bambi\n",
-       "    modeling_interface_version:  0.13.0.dev</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0d8b1012-7363-4c69-a792-2ffc97ae96a4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0d8b1012-7363-4c69-a792-2ffc97ae96a4' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8e7c0e08-64cb-4ef3-bbcd-bedfb173cd37' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8e7c0e08-64cb-4ef3-bbcd-bedfb173cd37' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-7a285963-28c5-4190-8edb-47d980d91b6a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7a285963-28c5-4190-8edb-47d980d91b6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-466f1676-b682-4f30-ba1c-bff3379f5e9e' class='xr-var-data-in' type='checkbox'><label for='data-466f1676-b682-4f30-ba1c-bff3379f5e9e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-9e23416a-f5ea-4149-b9b7-40ed76c68f58' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9e23416a-f5ea-4149-b9b7-40ed76c68f58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-279ab9b8-264a-48e8-a5b3-8c8a44497ce0' class='xr-var-data-in' type='checkbox'><label for='data-279ab9b8-264a-48e8-a5b3-8c8a44497ce0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4a25267c-7b75-4299-bb81-68b8aa01b30e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4a25267c-7b75-4299-bb81-68b8aa01b30e' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>acceptance_rate</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9995 0.9926 1.0 ... 0.9999 0.9988</div><input id='attrs-914b270d-06ba-499a-9ab1-57f77f191403' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-914b270d-06ba-499a-9ab1-57f77f191403' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89620bfe-f54a-4e22-94ca-9c474fe360c8' class='xr-var-data-in' type='checkbox'><label for='data-89620bfe-f54a-4e22-94ca-9c474fe360c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.99946526, 0.99259021, 0.99998277, ..., 0.99903086, 0.98809861,\n",
+       "    modeling_interface_version:  0.13.0.dev</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c8097364-3861-4225-9eb7-ce9275d66ce3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c8097364-3861-4225-9eb7-ce9275d66ce3' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7fb67d3b-96df-49cd-9f47-498e00747b54' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7fb67d3b-96df-49cd-9f47-498e00747b54' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-d7c7c08c-9a9d-43e5-8c08-2ed81c053130' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d7c7c08c-9a9d-43e5-8c08-2ed81c053130' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-636198c3-a476-4db3-a86c-0e1fa1992207' class='xr-var-data-in' type='checkbox'><label for='data-636198c3-a476-4db3-a86c-0e1fa1992207' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-47b08ca2-ab86-4690-a7f7-7e1359d3ab56' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-47b08ca2-ab86-4690-a7f7-7e1359d3ab56' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73cfae88-9e00-436e-ab2a-4bc2d9cfdb6a' class='xr-var-data-in' type='checkbox'><label for='data-73cfae88-9e00-436e-ab2a-4bc2d9cfdb6a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b158c70d-b0a3-439f-9cb8-f3614e096361' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b158c70d-b0a3-439f-9cb8-f3614e096361' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>acceptance_rate</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9995 0.9926 1.0 ... 0.9999 0.9988</div><input id='attrs-b47b586e-fcc5-4579-b186-55456bfd8e2b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b47b586e-fcc5-4579-b186-55456bfd8e2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa29a151-1572-4fb1-b55f-713ecdb73256' class='xr-var-data-in' type='checkbox'><label for='data-fa29a151-1572-4fb1-b55f-713ecdb73256' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.99946526, 0.99259021, 0.99998277, ..., 0.99903086, 0.98809861,\n",
        "        0.99689415],\n",
        "       [0.99913792, 0.9997045 , 0.99997501, ..., 0.98352235, 0.93096018,\n",
        "        0.85187495],\n",
        "       [0.98668511, 0.99626539, 0.99994987, ..., 0.99803789, 0.99652942,\n",
        "        0.99404411],\n",
        "       [0.98789206, 0.99794272, 0.99994925, ..., 0.99995445, 0.99990492,\n",
-       "        0.99879618]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.009849 0.009849 ... 0.007358</div><input id='attrs-813ea963-e581-4d23-82b3-b4c39976f06d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-813ea963-e581-4d23-82b3-b4c39976f06d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2328571-da14-45d6-be1a-f1f45e161d9a' class='xr-var-data-in' type='checkbox'><label for='data-f2328571-da14-45d6-be1a-f1f45e161d9a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.00984927, 0.00984927, 0.00984927, ..., 0.00984927, 0.00984927,\n",
+       "        0.99879618]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.009849 0.009849 ... 0.007358</div><input id='attrs-63e99788-b5f8-41d5-a4d0-b97d83cf8f8e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-63e99788-b5f8-41d5-a4d0-b97d83cf8f8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31165942-6da5-4523-a7f4-226606bfd06c' class='xr-var-data-in' type='checkbox'><label for='data-31165942-6da5-4523-a7f4-226606bfd06c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.00984927, 0.00984927, 0.00984927, ..., 0.00984927, 0.00984927,\n",
        "        0.00984927],\n",
        "       [0.0107935 , 0.0107935 , 0.0107935 , ..., 0.0107935 , 0.0107935 ,\n",
        "        0.0107935 ],\n",
        "       [0.01346049, 0.01346049, 0.01346049, ..., 0.01346049, 0.01346049,\n",
        "        0.01346049],\n",
        "       [0.0073581 , 0.0073581 , 0.0073581 , ..., 0.0073581 , 0.0073581 ,\n",
-       "        0.0073581 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverging</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-48707aa2-9d51-4a80-b6cb-73a440818458' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-48707aa2-9d51-4a80-b6cb-73a440818458' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70e35907-c78a-4bd8-8cba-a380ef41846b' class='xr-var-data-in' type='checkbox'><label for='data-70e35907-c78a-4bd8-8cba-a380ef41846b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
+       "        0.0073581 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverging</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-c7710153-3087-4670-a25e-c98e910bf05e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c7710153-3087-4670-a25e-c98e910bf05e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-506b165b-6406-4385-8f82-d1e256bc5a6c' class='xr-var-data-in' type='checkbox'><label for='data-506b165b-6406-4385-8f82-d1e256bc5a6c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
        "       [False, False, False, ..., False, False, False],\n",
        "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.255e+03 2.272e+03 ... 2.239e+03</div><input id='attrs-83564b62-f86a-40ce-9f3a-c2cab7417822' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-83564b62-f86a-40ce-9f3a-c2cab7417822' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bde9ee9d-00d4-49f8-9ca9-18bdecb38954' class='xr-var-data-in' type='checkbox'><label for='data-bde9ee9d-00d4-49f8-9ca9-18bdecb38954' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2254.80380148, 2271.7792398 , 2262.62856249, ..., 2257.72468109,\n",
+       "       [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.255e+03 2.272e+03 ... 2.239e+03</div><input id='attrs-3f002816-e2ba-4a16-bffd-53ace644d959' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3f002816-e2ba-4a16-bffd-53ace644d959' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d55522e3-1c88-4b1b-a200-62df2ab3ff29' class='xr-var-data-in' type='checkbox'><label for='data-d55522e3-1c88-4b1b-a200-62df2ab3ff29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2254.80380148, 2271.7792398 , 2262.62856249, ..., 2257.72468109,\n",
        "        2230.11267142, 2237.93805661],\n",
        "       [2269.06513073, 2263.36956843, 2245.93272568, ..., 2261.17563583,\n",
        "        2262.64441608, 2253.12366973],\n",
        "       [2264.71522209, 2252.51613169, 2243.13421993, ..., 2258.2260048 ,\n",
        "        2259.74040336, 2243.20097056],\n",
        "       [2248.65859906, 2246.09743317, 2267.83771194, ..., 2254.07769144,\n",
-       "        2245.89919424, 2239.36666182]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_steps</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>511 511 511 511 ... 511 511 511 511</div><input id='attrs-ff818bb1-7932-48b4-8825-0b9a43c8a27d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ff818bb1-7932-48b4-8825-0b9a43c8a27d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b8fbc43-45f4-4ee7-82b0-c03223dd4a73' class='xr-var-data-in' type='checkbox'><label for='data-8b8fbc43-45f4-4ee7-82b0-c03223dd4a73' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[511, 511, 511, ..., 511, 511, 511],\n",
+       "        2245.89919424, 2239.36666182]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_steps</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>511 511 511 511 ... 511 511 511 511</div><input id='attrs-65a77267-7d8d-45ad-8f78-3682916561bb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-65a77267-7d8d-45ad-8f78-3682916561bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d2774af-fdde-4363-bd8d-a5472fca526b' class='xr-var-data-in' type='checkbox'><label for='data-7d2774af-fdde-4363-bd8d-a5472fca526b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[511, 511, 511, ..., 511, 511, 511],\n",
        "       [511, 511, 511, ..., 511, 511, 511],\n",
        "       [255, 255, 255, ..., 255, 255, 255],\n",
-       "       [511, 511, 511, ..., 511, 511, 511]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tree_depth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>9 9 9 9 9 9 9 9 ... 9 9 9 9 9 9 9 9</div><input id='attrs-26955718-9764-4396-b854-cc5817396af9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-26955718-9764-4396-b854-cc5817396af9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0020683-009e-40f0-9755-c1f36026d8bf' class='xr-var-data-in' type='checkbox'><label for='data-a0020683-009e-40f0-9755-c1f36026d8bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[9, 9, 9, ..., 9, 9, 9],\n",
+       "       [511, 511, 511, ..., 511, 511, 511]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tree_depth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>9 9 9 9 9 9 9 9 ... 9 9 9 9 9 9 9 9</div><input id='attrs-e6fb9f93-2b82-4dfb-8dde-8fa939c85ae9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6fb9f93-2b82-4dfb-8dde-8fa939c85ae9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-044e3c8c-8de2-4ec0-9731-fe7d736d000c' class='xr-var-data-in' type='checkbox'><label for='data-044e3c8c-8de2-4ec0-9731-fe7d736d000c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[9, 9, 9, ..., 9, 9, 9],\n",
        "       [9, 9, 9, ..., 9, 9, 9],\n",
        "       [8, 8, 8, ..., 8, 8, 8],\n",
-       "       [9, 9, 9, ..., 9, 9, 9]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.205e+03 2.206e+03 ... 2.175e+03</div><input id='attrs-38039be2-d45e-468f-8bc5-5e74a66c6b2c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-38039be2-d45e-468f-8bc5-5e74a66c6b2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fb1d152-4452-4e54-ac4f-eef361ead39d' class='xr-var-data-in' type='checkbox'><label for='data-7fb1d152-4452-4e54-ac4f-eef361ead39d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2205.14758575, 2206.32053817, 2203.18323165, ..., 2185.99408105,\n",
+       "       [9, 9, 9, ..., 9, 9, 9]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.205e+03 2.206e+03 ... 2.175e+03</div><input id='attrs-bffb22a3-50d1-48d6-a76d-48b2cb735a90' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bffb22a3-50d1-48d6-a76d-48b2cb735a90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4548e0b-20aa-49e1-8565-7fc887496516' class='xr-var-data-in' type='checkbox'><label for='data-a4548e0b-20aa-49e1-8565-7fc887496516' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2205.14758575, 2206.32053817, 2203.18323165, ..., 2185.99408105,\n",
        "        2172.48562089, 2185.00510852],\n",
        "       [2208.18815797, 2193.96415489, 2188.18604319, ..., 2200.69704711,\n",
        "        2190.64594014, 2191.29041039],\n",
        "       [2200.43179845, 2194.72819558, 2178.81994092, ..., 2200.06509338,\n",
        "        2187.73927464, 2192.0455093 ],\n",
        "       [2190.22894365, 2189.6562415 , 2213.11595564, ..., 2188.1258582 ,\n",
-       "        2187.37140655, 2175.15102085]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-14d5aa77-6a21-4169-b579-eca7782f3a08' class='xr-section-summary-in' type='checkbox'  ><label for='section-14d5aa77-6a21-4169-b579-eca7782f3a08' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-61978e24-759c-437e-9927-de137a26e6ed' class='xr-index-data-in' type='checkbox'/><label for='index-61978e24-759c-437e-9927-de137a26e6ed' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5168f704-0f4a-45c4-b1f9-ce8d02f4f5fd' class='xr-index-data-in' type='checkbox'/><label for='index-5168f704-0f4a-45c4-b1f9-ce8d02f4f5fd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "        2187.37140655, 2175.15102085]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-864f0a1c-1f36-4b1e-9faf-037ea48e4b2f' class='xr-section-summary-in' type='checkbox'  ><label for='section-864f0a1c-1f36-4b1e-9faf-037ea48e4b2f' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-358462c8-3d8d-4d60-a0f7-9504faf51748' class='xr-index-data-in' type='checkbox'/><label for='index-358462c8-3d8d-4d60-a0f7-9504faf51748' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ea33cc96-dce7-4440-a53a-d86c9c60788a' class='xr-index-data-in' type='checkbox'/><label for='index-ea33cc96-dce7-4440-a53a-d86c9c60788a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
        "       ...\n",
        "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
-       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b694b773-66cb-4043-9553-df416cbc9dbd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b694b773-66cb-4043-9553-df416cbc9dbd' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-09-09T21:52:09.395016</dd><dt><span>arviz_version :</span></dt><dd>0.16.1</dd><dt><span>modeling_interface :</span></dt><dd>bambi</dd><dt><span>modeling_interface_version :</span></dt><dd>0.13.0.dev</dd></dl></div></li></ul></div></div><br></div>\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c9407a7b-f2d9-4b46-a56a-397d64313c43' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c9407a7b-f2d9-4b46-a56a-397d64313c43' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-09-17T17:39:00.296422</dd><dt><span>arviz_version :</span></dt><dd>0.16.1</dd><dt><span>modeling_interface :</span></dt><dd>bambi</dd><dt><span>modeling_interface_version :</span></dt><dd>0.13.0.dev</dd></dl></div></li></ul></div></div><br></div>\n",
        "                      </ul>\n",
        "                  </div>\n",
        "            </li>\n",
        "            \n",
        "            <li class = \"xr-section-item\">\n",
-       "                  <input id=\"idata_observed_data974abc87-b5f1-4ea0-8222-44d60934572f\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
-       "                  <label for=\"idata_observed_data974abc87-b5f1-4ea0-8222-44d60934572f\" class = \"xr-section-summary\">observed_data</label>\n",
+       "                  <input id=\"idata_observed_data42c5d0d2-63f0-4458-a20b-5f23774d696f\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_observed_data42c5d0d2-63f0-4458-a20b-5f23774d696f\" class = \"xr-section-summary\">observed_data</label>\n",
        "                  <div class=\"xr-section-inline-details\"></div>\n",
        "                  <div class=\"xr-section-details\">\n",
        "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
@@ -4275,16 +4309,16 @@
        "Data variables:\n",
        "    p(abortion, n)      (p(abortion, n)_obs) int64 18 16 13 12 11 ... 0 0 0 0 0\n",
        "Attributes:\n",
-       "    created_at:                  2023-09-09T21:52:09.396647\n",
+       "    created_at:                  2023-09-17T17:39:00.297909\n",
        "    arviz_version:               0.16.1\n",
        "    inference_library:           numpyro\n",
        "    inference_library_version:   0.13.0\n",
-       "    sampling_time:               440.235424\n",
+       "    sampling_time:               751.216233\n",
        "    modeling_interface:          bambi\n",
-       "    modeling_interface_version:  0.13.0.dev</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d919623d-b673-4df9-a7fe-897072c96d95' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d919623d-b673-4df9-a7fe-897072c96d95' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>p(abortion, n)_obs</span>: 11040</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b062d9d9-110e-41a1-9b07-c8a60eaabb6b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b062d9d9-110e-41a1-9b07-c8a60eaabb6b' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>p(abortion, n)_obs</span></div><div class='xr-var-dims'>(p(abortion, n)_obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 11036 11037 11038 11039</div><input id='attrs-9a43d875-b676-4a09-9407-47d3cb1cc4c5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9a43d875-b676-4a09-9407-47d3cb1cc4c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4be56d3-7c01-4cc6-a6a4-35ebd1ee6395' class='xr-var-data-in' type='checkbox'><label for='data-c4be56d3-7c01-4cc6-a6a4-35ebd1ee6395' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([    0,     1,     2, ..., 11037, 11038, 11039])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0bf4ef3d-505a-4a28-83b5-56023015b580' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0bf4ef3d-505a-4a28-83b5-56023015b580' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>p(abortion, n)</span></div><div class='xr-var-dims'>(p(abortion, n)_obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>18 16 13 12 11 11 ... 0 0 0 0 0 0</div><input id='attrs-a02ba8a5-20bd-4903-9b07-d32fc8a3e9c8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a02ba8a5-20bd-4903-9b07-d32fc8a3e9c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed295698-e084-4ecc-bbed-d68c9982beb9' class='xr-var-data-in' type='checkbox'><label for='data-ed295698-e084-4ecc-bbed-d68c9982beb9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([18, 16, 13, ...,  0,  0,  0])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ef00d2a4-b6bb-4077-aad7-50d82644ae53' class='xr-section-summary-in' type='checkbox'  ><label for='section-ef00d2a4-b6bb-4077-aad7-50d82644ae53' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>p(abortion, n)_obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5bc9cff2-120e-490a-a3a9-459741cc79c2' class='xr-index-data-in' type='checkbox'/><label for='index-5bc9cff2-120e-490a-a3a9-459741cc79c2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    0,     1,     2,     3,     4,     5,     6,     7,     8,     9,\n",
+       "    modeling_interface_version:  0.13.0.dev</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-26970fba-5af2-42b3-adb7-b4648ab9e55d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-26970fba-5af2-42b3-adb7-b4648ab9e55d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>p(abortion, n)_obs</span>: 11040</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6ed33182-a816-421f-a9bf-efed1daef46f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6ed33182-a816-421f-a9bf-efed1daef46f' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>p(abortion, n)_obs</span></div><div class='xr-var-dims'>(p(abortion, n)_obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 11036 11037 11038 11039</div><input id='attrs-643de0ab-fc3f-4c68-8532-ee558277837e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-643de0ab-fc3f-4c68-8532-ee558277837e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac883d73-7a7c-4555-bc9f-bcd29df0815f' class='xr-var-data-in' type='checkbox'><label for='data-ac883d73-7a7c-4555-bc9f-bcd29df0815f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([    0,     1,     2, ..., 11037, 11038, 11039])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5312bc76-2926-4951-97de-9b6ff1edf640' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5312bc76-2926-4951-97de-9b6ff1edf640' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>p(abortion, n)</span></div><div class='xr-var-dims'>(p(abortion, n)_obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>18 16 13 12 11 11 ... 0 0 0 0 0 0</div><input id='attrs-e3198508-ff26-42cd-a803-3e441d3a1e78' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e3198508-ff26-42cd-a803-3e441d3a1e78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c6041c7-3420-4fe5-b626-78c18b95d523' class='xr-var-data-in' type='checkbox'><label for='data-1c6041c7-3420-4fe5-b626-78c18b95d523' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([18, 16, 13, ...,  0,  0,  0])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4b351356-b5e1-403d-8f6f-b608cbf2c68f' class='xr-section-summary-in' type='checkbox'  ><label for='section-4b351356-b5e1-403d-8f6f-b608cbf2c68f' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>p(abortion, n)_obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0f3023da-5189-4c1f-bfaa-cdfc0023bd24' class='xr-index-data-in' type='checkbox'/><label for='index-0f3023da-5189-4c1f-bfaa-cdfc0023bd24' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    0,     1,     2,     3,     4,     5,     6,     7,     8,     9,\n",
        "       ...\n",
        "       11030, 11031, 11032, 11033, 11034, 11035, 11036, 11037, 11038, 11039],\n",
-       "      dtype=&#x27;int64&#x27;, name=&#x27;p(abortion, n)_obs&#x27;, length=11040))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b8a30f76-3073-4add-bfd5-4857ed194baf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b8a30f76-3073-4add-bfd5-4857ed194baf' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-09-09T21:52:09.396647</dd><dt><span>arviz_version :</span></dt><dd>0.16.1</dd><dt><span>inference_library :</span></dt><dd>numpyro</dd><dt><span>inference_library_version :</span></dt><dd>0.13.0</dd><dt><span>sampling_time :</span></dt><dd>440.235424</dd><dt><span>modeling_interface :</span></dt><dd>bambi</dd><dt><span>modeling_interface_version :</span></dt><dd>0.13.0.dev</dd></dl></div></li></ul></div></div><br></div>\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;p(abortion, n)_obs&#x27;, length=11040))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-034c723d-8bdb-45c4-b635-5621aea2ea60' class='xr-section-summary-in' type='checkbox'  checked><label for='section-034c723d-8bdb-45c4-b635-5621aea2ea60' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-09-17T17:39:00.297909</dd><dt><span>arviz_version :</span></dt><dd>0.16.1</dd><dt><span>inference_library :</span></dt><dd>numpyro</dd><dt><span>inference_library_version :</span></dt><dd>0.13.0</dd><dt><span>sampling_time :</span></dt><dd>751.216233</dd><dt><span>modeling_interface :</span></dt><dd>bambi</dd><dt><span>modeling_interface_version :</span></dt><dd>0.13.0.dev</dd></dl></div></li></ul></div></div><br></div>\n",
        "                      </ul>\n",
        "                  </div>\n",
        "            </li>\n",
@@ -4640,7 +4674,7 @@
        "\t> observed_data"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4651,7 +4685,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -4865,7 +4899,7 @@
        "repvote                  1749.0    2462.0    1.0  "
       ]
      },
-     "execution_count": 26,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4884,7 +4918,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -4896,355 +4930,355 @@
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        "<title>clusterstate__factor_dim (46)</title>\n",
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        "<title>clusteredu:eth__factor_dim (20)</title>\n",
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        "<title>clusterp(abortion, n)_obs (11040)</title>\n",
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-       "<title>p(abortion, n)</title>\n",
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-       "<title>Intercept&#45;&gt;p(abortion, n)</title>\n",
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+       "<g id=\"edge9\" class=\"edge\">\n",
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        "<title>1|edu&amp;eth_sigma&#45;&gt;1|edu&amp;eth</title>\n",
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-       "<!-- 1|edu&amp;age_sigma -->\n",
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-       "<text text-anchor=\"middle\" x=\"1725.25\" y=\"-311.33\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
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-       "<title>1|edu&amp;age</title>\n",
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-       "<text text-anchor=\"middle\" x=\"1557.25\" y=\"-177.52\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
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-       "<!-- 1|edu&amp;age_sigma&#45;&gt;1|edu&amp;age -->\n",
-       "<g id=\"edge10\" class=\"edge\">\n",
-       "<title>1|edu&amp;age_sigma&#45;&gt;1|edu&amp;age</title>\n",
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-       "<polygon fill=\"black\" stroke=\"black\" points=\"1609.46,-230.97 1599.23,-228.19 1605.5,-236.74 1609.46,-230.97\"/>\n",
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        "</g>\n",
        "<!-- repvote -->\n",
-       "<g id=\"node4\" class=\"node\">\n",
+       "<g id=\"node3\" class=\"node\">\n",
        "<title>repvote</title>\n",
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-       "<text text-anchor=\"middle\" x=\"149.25\" y=\"-177.52\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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+       "<text text-anchor=\"middle\" x=\"375.01\" y=\"-194.02\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"375.01\" y=\"-177.52\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- p(abortion, n) -->\n",
+       "<g id=\"node22\" class=\"node\">\n",
+       "<title>p(abortion, n)</title>\n",
+       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"710.01\" cy=\"-81.16\" rx=\"64.35\" ry=\"40.66\"/>\n",
+       "<text text-anchor=\"middle\" x=\"710.01\" y=\"-92.61\" font-family=\"Times,serif\" font-size=\"14.00\">p(abortion, n)</text>\n",
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+       "<text text-anchor=\"middle\" x=\"710.01\" y=\"-59.61\" font-family=\"Times,serif\" font-size=\"14.00\">Binomial</text>\n",
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        "<!-- repvote&#45;&gt;p(abortion, n) -->\n",
-       "<g id=\"edge16\" class=\"edge\">\n",
+       "<g id=\"edge18\" class=\"edge\">\n",
        "<title>repvote&#45;&gt;p(abortion, n)</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M170.26,-163.89C178.11,-153.9 187.92,-144.01 199.25,-137.82 263.1,-102.96 467.51,-89.52 579.73,-84.67\"/>\n",
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        "</g>\n",
-       "<!-- male -->\n",
-       "<g id=\"node5\" class=\"node\">\n",
-       "<title>male</title>\n",
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-       "<text text-anchor=\"middle\" x=\"249.25\" y=\"-210.52\" font-family=\"Times,serif\" font-size=\"14.00\">male</text>\n",
-       "<text text-anchor=\"middle\" x=\"249.25\" y=\"-194.02\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text text-anchor=\"middle\" x=\"249.25\" y=\"-177.52\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<!-- Intercept -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>Intercept</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"479.01\" cy=\"-199.07\" rx=\"45.25\" ry=\"40.66\"/>\n",
+       "<text text-anchor=\"middle\" x=\"479.01\" y=\"-210.52\" font-family=\"Times,serif\" font-size=\"14.00\">Intercept</text>\n",
+       "<text text-anchor=\"middle\" x=\"479.01\" y=\"-194.02\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"479.01\" y=\"-177.52\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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-       "<!-- male&#45;&gt;p(abortion, n) -->\n",
-       "<g id=\"edge18\" class=\"edge\">\n",
-       "<title>male&#45;&gt;p(abortion, n)</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M268.55,-162.87C275.44,-153.32 284.07,-143.94 294.25,-137.82 341.2,-109.61 489.14,-94.13 580.42,-87.04\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"580.55,-90.46 590.26,-86.22 580.02,-83.48 580.55,-90.46\"/>\n",
+       "<!-- Intercept&#45;&gt;p(abortion, n) -->\n",
+       "<g id=\"edge17\" class=\"edge\">\n",
+       "<title>Intercept&#45;&gt;p(abortion, n)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M503.46,-164.5C511.9,-154.78 522.03,-144.89 533.01,-137.82 564.67,-117.42 604.33,-104.05 638.07,-95.53\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"638.45,-98.8 647.34,-93.04 636.8,-92 638.45,-98.8\"/>\n",
        "</g>\n",
        "<!-- 1|male&amp;eth_sigma -->\n",
-       "<g id=\"node6\" class=\"node\">\n",
+       "<g id=\"node5\" class=\"node\">\n",
        "<title>1|male&amp;eth_sigma</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1385.25\" cy=\"-316.38\" rx=\"79.73\" ry=\"40.66\"/>\n",
-       "<text text-anchor=\"middle\" x=\"1385.25\" y=\"-327.83\" font-family=\"Times,serif\" font-size=\"14.00\">1|male:eth_sigma</text>\n",
-       "<text text-anchor=\"middle\" x=\"1385.25\" y=\"-311.33\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text text-anchor=\"middle\" x=\"1385.25\" y=\"-294.83\" font-family=\"Times,serif\" font-size=\"14.00\">HalfNormal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1719.01\" cy=\"-316.38\" rx=\"79.73\" ry=\"40.66\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1719.01\" y=\"-327.83\" font-family=\"Times,serif\" font-size=\"14.00\">1|male:eth_sigma</text>\n",
+       "<text text-anchor=\"middle\" x=\"1719.01\" y=\"-311.33\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"1719.01\" y=\"-294.83\" font-family=\"Times,serif\" font-size=\"14.00\">HalfNormal</text>\n",
        "</g>\n",
        "<!-- 1|male&amp;eth -->\n",
-       "<g id=\"node16\" class=\"node\">\n",
+       "<g id=\"node17\" class=\"node\">\n",
        "<title>1|male&amp;eth</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"1254.38,-227.82 1164.13,-227.82 1164.13,-170.32 1254.38,-170.32 1254.38,-227.82\"/>\n",
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-       "<text text-anchor=\"middle\" x=\"1209.25\" y=\"-194.02\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text text-anchor=\"middle\" x=\"1209.25\" y=\"-177.52\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
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+       "<text text-anchor=\"middle\" x=\"1543.01\" y=\"-210.52\" font-family=\"Times,serif\" font-size=\"14.00\">1|male:eth</text>\n",
+       "<text text-anchor=\"middle\" x=\"1543.01\" y=\"-194.02\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"1543.01\" y=\"-177.52\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
        "</g>\n",
        "<!-- 1|male&amp;eth_sigma&#45;&gt;1|male&amp;eth -->\n",
-       "<g id=\"edge7\" class=\"edge\">\n",
+       "<g id=\"edge8\" class=\"edge\">\n",
        "<title>1|male&amp;eth_sigma&#45;&gt;1|male&amp;eth</title>\n",
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-       "<polygon fill=\"black\" stroke=\"black\" points=\"1263.47,-230.75 1253.18,-228.19 1259.63,-236.6 1263.47,-230.75\"/>\n",
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        "</g>\n",
        "<!-- 1|eth_sigma -->\n",
-       "<g id=\"node7\" class=\"node\">\n",
+       "<g id=\"node6\" class=\"node\">\n",
        "<title>1|eth_sigma</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"782.25\" cy=\"-316.38\" rx=\"57.98\" ry=\"40.66\"/>\n",
-       "<text text-anchor=\"middle\" x=\"782.25\" y=\"-327.83\" font-family=\"Times,serif\" font-size=\"14.00\">1|eth_sigma</text>\n",
-       "<text text-anchor=\"middle\" x=\"782.25\" y=\"-311.33\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text text-anchor=\"middle\" x=\"782.25\" y=\"-294.83\" font-family=\"Times,serif\" font-size=\"14.00\">HalfNormal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1116.01\" cy=\"-316.38\" rx=\"57.98\" ry=\"40.66\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1116.01\" y=\"-327.83\" font-family=\"Times,serif\" font-size=\"14.00\">1|eth_sigma</text>\n",
+       "<text text-anchor=\"middle\" x=\"1116.01\" y=\"-311.33\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
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        "</g>\n",
        "<!-- 1|eth -->\n",
-       "<g id=\"node13\" class=\"node\">\n",
+       "<g id=\"node12\" class=\"node\">\n",
        "<title>1|eth</title>\n",
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-       "<text text-anchor=\"middle\" x=\"655.25\" y=\"-194.02\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text text-anchor=\"middle\" x=\"655.25\" y=\"-177.52\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
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+       "<text text-anchor=\"middle\" x=\"984.01\" y=\"-194.02\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"984.01\" y=\"-177.52\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
        "</g>\n",
        "<!-- 1|eth_sigma&#45;&gt;1|eth -->\n",
-       "<g id=\"edge4\" class=\"edge\">\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
        "<title>1|eth_sigma&#45;&gt;1|eth</title>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"1026.99,-232.09 1017.15,-228.14 1022.38,-237.36 1026.99,-232.09\"/>\n",
        "</g>\n",
        "<!-- 1|edu_sigma -->\n",
-       "<g id=\"node8\" class=\"node\">\n",
+       "<g id=\"node7\" class=\"node\">\n",
        "<title>1|edu_sigma</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1053.25\" cy=\"-316.38\" rx=\"60.1\" ry=\"40.66\"/>\n",
-       "<text text-anchor=\"middle\" x=\"1053.25\" y=\"-327.83\" font-family=\"Times,serif\" font-size=\"14.00\">1|edu_sigma</text>\n",
-       "<text text-anchor=\"middle\" x=\"1053.25\" y=\"-311.33\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text text-anchor=\"middle\" x=\"1053.25\" y=\"-294.83\" font-family=\"Times,serif\" font-size=\"14.00\">HalfNormal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1387.01\" cy=\"-316.38\" rx=\"60.1\" ry=\"40.66\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1387.01\" y=\"-327.83\" font-family=\"Times,serif\" font-size=\"14.00\">1|edu_sigma</text>\n",
+       "<text text-anchor=\"middle\" x=\"1387.01\" y=\"-311.33\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"1387.01\" y=\"-294.83\" font-family=\"Times,serif\" font-size=\"14.00\">HalfNormal</text>\n",
        "</g>\n",
        "<!-- 1|edu -->\n",
-       "<g id=\"node15\" class=\"node\">\n",
+       "<g id=\"node14\" class=\"node\">\n",
        "<title>1|edu</title>\n",
-       "<polygon fill=\"none\" stroke=\"black\" points=\"961.38,-227.82 871.13,-227.82 871.13,-170.32 961.38,-170.32 961.38,-227.82\"/>\n",
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-       "<text text-anchor=\"middle\" x=\"916.25\" y=\"-194.02\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text text-anchor=\"middle\" x=\"916.25\" y=\"-177.52\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
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+       "<text text-anchor=\"middle\" x=\"1250.01\" y=\"-194.02\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"1250.01\" y=\"-177.52\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
        "</g>\n",
        "<!-- 1|edu_sigma&#45;&gt;1|edu -->\n",
-       "<g id=\"edge6\" class=\"edge\">\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
        "<title>1|edu_sigma&#45;&gt;1|edu</title>\n",
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        "</g>\n",
        "<!-- 1|state_sigma -->\n",
-       "<g id=\"node9\" class=\"node\">\n",
+       "<g id=\"node8\" class=\"node\">\n",
        "<title>1|state_sigma</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"512.25\" cy=\"-316.38\" rx=\"63.82\" ry=\"40.66\"/>\n",
-       "<text text-anchor=\"middle\" x=\"512.25\" y=\"-327.83\" font-family=\"Times,serif\" font-size=\"14.00\">1|state_sigma</text>\n",
-       "<text text-anchor=\"middle\" x=\"512.25\" y=\"-311.33\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text text-anchor=\"middle\" x=\"512.25\" y=\"-294.83\" font-family=\"Times,serif\" font-size=\"14.00\">HalfNormal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"846.01\" cy=\"-316.38\" rx=\"63.82\" ry=\"40.66\"/>\n",
+       "<text text-anchor=\"middle\" x=\"846.01\" y=\"-327.83\" font-family=\"Times,serif\" font-size=\"14.00\">1|state_sigma</text>\n",
+       "<text text-anchor=\"middle\" x=\"846.01\" y=\"-311.33\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"846.01\" y=\"-294.83\" font-family=\"Times,serif\" font-size=\"14.00\">HalfNormal</text>\n",
        "</g>\n",
        "<!-- 1|state -->\n",
        "<g id=\"node11\" class=\"node\">\n",
        "<title>1|state</title>\n",
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-       "<text text-anchor=\"middle\" x=\"385.25\" y=\"-210.52\" font-family=\"Times,serif\" font-size=\"14.00\">1|state</text>\n",
-       "<text text-anchor=\"middle\" x=\"385.25\" y=\"-194.02\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text text-anchor=\"middle\" x=\"385.25\" y=\"-177.52\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
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+       "<text text-anchor=\"middle\" x=\"710.01\" y=\"-210.52\" font-family=\"Times,serif\" font-size=\"14.00\">1|state</text>\n",
+       "<text text-anchor=\"middle\" x=\"710.01\" y=\"-194.02\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"710.01\" y=\"-177.52\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
        "</g>\n",
        "<!-- 1|state_sigma&#45;&gt;1|state -->\n",
        "<g id=\"edge2\" class=\"edge\">\n",
        "<title>1|state_sigma&#45;&gt;1|state</title>\n",
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-       "<polygon fill=\"black\" stroke=\"black\" points=\"426.98,-233.32 417.23,-229.19 422.28,-238.5 426.98,-233.32\"/>\n",
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+       "</g>\n",
+       "<!-- male -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>male</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"583.01\" cy=\"-199.07\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text text-anchor=\"middle\" x=\"583.01\" y=\"-210.52\" font-family=\"Times,serif\" font-size=\"14.00\">male</text>\n",
+       "<text text-anchor=\"middle\" x=\"583.01\" y=\"-194.02\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"583.01\" y=\"-177.52\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- male&#45;&gt;p(abortion, n) -->\n",
+       "<g id=\"edge21\" class=\"edge\">\n",
+       "<title>male&#45;&gt;p(abortion, n)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M604.99,-164.54C611.8,-155.36 619.71,-145.76 628.01,-137.82 636.12,-130.07 645.41,-122.66 654.76,-115.92\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"656.52,-118.26 662.71,-109.67 652.51,-112.53 656.52,-118.26\"/>\n",
        "</g>\n",
        "<!-- 1|state_offset -->\n",
        "<g id=\"node10\" class=\"node\">\n",
        "<title>1|state_offset</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"368.25\" cy=\"-316.38\" rx=\"62.23\" ry=\"40.66\"/>\n",
-       "<text text-anchor=\"middle\" x=\"368.25\" y=\"-327.83\" font-family=\"Times,serif\" font-size=\"14.00\">1|state_offset</text>\n",
-       "<text text-anchor=\"middle\" x=\"368.25\" y=\"-311.33\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text text-anchor=\"middle\" x=\"368.25\" y=\"-294.83\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"702.01\" cy=\"-316.38\" rx=\"62.23\" ry=\"40.66\"/>\n",
+       "<text text-anchor=\"middle\" x=\"702.01\" y=\"-327.83\" font-family=\"Times,serif\" font-size=\"14.00\">1|state_offset</text>\n",
+       "<text text-anchor=\"middle\" x=\"702.01\" y=\"-311.33\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"702.01\" y=\"-294.83\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- 1|state_offset&#45;&gt;1|state -->\n",
        "<g id=\"edge1\" class=\"edge\">\n",
        "<title>1|state_offset&#45;&gt;1|state</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M374.11,-275.7C375.87,-263.73 377.8,-250.61 379.56,-238.71\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"383.11,-239.59 381.11,-229.18 376.19,-238.57 383.11,-239.59\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M704.79,-275.38C705.61,-263.54 706.51,-250.62 707.32,-238.86\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"710.87,-239.25 708.07,-229.03 703.89,-238.76 710.87,-239.25\"/>\n",
        "</g>\n",
        "<!-- 1|state&#45;&gt;p(abortion, n) -->\n",
        "<g id=\"edge20\" class=\"edge\">\n",
        "<title>1|state&#45;&gt;p(abortion, n)</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M430.75,-178.54C475.18,-159.46 543.15,-130.28 592.67,-109.03\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"593.81,-111.91 601.62,-104.75 591.05,-105.48 593.81,-111.91\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M710.01,-170.01C710.01,-158.92 710.01,-145.82 710.01,-133.17\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"713.51,-133.23 710.01,-123.23 706.51,-133.23 713.51,-133.23\"/>\n",
+       "</g>\n",
+       "<!-- 1|eth&#45;&gt;p(abortion, n) -->\n",
+       "<g id=\"edge14\" class=\"edge\">\n",
+       "<title>1|eth&#45;&gt;p(abortion, n)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M938.46,-178.8C893.24,-159.67 823.55,-130.19 773.03,-108.81\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"774.77,-105.33 764.19,-104.65 772.04,-111.77 774.77,-105.33\"/>\n",
        "</g>\n",
        "<!-- 1|eth_offset -->\n",
-       "<g id=\"node12\" class=\"node\">\n",
+       "<g id=\"node13\" class=\"node\">\n",
        "<title>1|eth_offset</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"650.25\" cy=\"-316.38\" rx=\"56.39\" ry=\"40.66\"/>\n",
-       "<text text-anchor=\"middle\" x=\"650.25\" y=\"-327.83\" font-family=\"Times,serif\" font-size=\"14.00\">1|eth_offset</text>\n",
-       "<text text-anchor=\"middle\" x=\"650.25\" y=\"-311.33\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text text-anchor=\"middle\" x=\"650.25\" y=\"-294.83\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"984.01\" cy=\"-316.38\" rx=\"56.39\" ry=\"40.66\"/>\n",
+       "<text text-anchor=\"middle\" x=\"984.01\" y=\"-327.83\" font-family=\"Times,serif\" font-size=\"14.00\">1|eth_offset</text>\n",
+       "<text text-anchor=\"middle\" x=\"984.01\" y=\"-311.33\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"984.01\" y=\"-294.83\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- 1|eth_offset&#45;&gt;1|eth -->\n",
-       "<g id=\"edge3\" class=\"edge\">\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
        "<title>1|eth_offset&#45;&gt;1|eth</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M651.99,-275.38C652.5,-263.54 653.06,-250.62 653.57,-238.86\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"657.11,-239.17 654.04,-229.03 650.11,-238.87 657.11,-239.17\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M984.01,-275.38C984.01,-263.54 984.01,-250.62 984.01,-238.86\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"987.51,-239.03 984.01,-229.03 980.51,-239.03 987.51,-239.03\"/>\n",
        "</g>\n",
-       "<!-- 1|eth&#45;&gt;p(abortion, n) -->\n",
-       "<g id=\"edge21\" class=\"edge\">\n",
-       "<title>1|eth&#45;&gt;p(abortion, n)</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M655.25,-170.01C655.25,-158.92 655.25,-145.82 655.25,-133.17\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"658.75,-133.23 655.25,-123.23 651.75,-133.23 658.75,-133.23\"/>\n",
+       "<!-- 1|edu&#45;&gt;p(abortion, n) -->\n",
+       "<g id=\"edge19\" class=\"edge\">\n",
+       "<title>1|edu&#45;&gt;p(abortion, n)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1204.48,-182.31C1164.65,-168.98 1105.04,-150.14 1052.01,-137.82 960.66,-116.59 853.73,-100.57 784.08,-91.29\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"784.6,-87.7 774.23,-89.86 783.68,-94.64 784.6,-87.7\"/>\n",
        "</g>\n",
        "<!-- 1|edu_offset -->\n",
-       "<g id=\"node14\" class=\"node\">\n",
+       "<g id=\"node15\" class=\"node\">\n",
        "<title>1|edu_offset</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"916.25\" cy=\"-316.38\" rx=\"58.51\" ry=\"40.66\"/>\n",
-       "<text text-anchor=\"middle\" x=\"916.25\" y=\"-327.83\" font-family=\"Times,serif\" font-size=\"14.00\">1|edu_offset</text>\n",
-       "<text text-anchor=\"middle\" x=\"916.25\" y=\"-311.33\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text text-anchor=\"middle\" x=\"916.25\" y=\"-294.83\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1250.01\" cy=\"-316.38\" rx=\"58.51\" ry=\"40.66\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1250.01\" y=\"-327.83\" font-family=\"Times,serif\" font-size=\"14.00\">1|edu_offset</text>\n",
+       "<text text-anchor=\"middle\" x=\"1250.01\" y=\"-311.33\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"1250.01\" y=\"-294.83\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- 1|edu_offset&#45;&gt;1|edu -->\n",
-       "<g id=\"edge5\" class=\"edge\">\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
        "<title>1|edu_offset&#45;&gt;1|edu</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M916.25,-275.38C916.25,-263.54 916.25,-250.62 916.25,-238.86\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"919.75,-239.03 916.25,-229.03 912.75,-239.03 919.75,-239.03\"/>\n",
-       "</g>\n",
-       "<!-- 1|edu&#45;&gt;p(abortion, n) -->\n",
-       "<g id=\"edge17\" class=\"edge\">\n",
-       "<title>1|edu&#45;&gt;p(abortion, n)</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M870.79,-177.88C828.25,-158.98 764.35,-130.61 717.17,-109.65\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"718.93,-106.16 708.37,-105.3 716.09,-112.56 718.93,-106.16\"/>\n",
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-       "<!-- 1|male&amp;eth&#45;&gt;p(abortion, n) -->\n",
-       "<g id=\"edge13\" class=\"edge\">\n",
-       "<title>1|male&amp;eth&#45;&gt;p(abortion, n)</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1163.66,-184.27C1119.01,-171.1 1048.89,-151.31 987.25,-137.82 898.92,-118.48 796.09,-102.18 728.55,-92.32\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"729.42,-88.76 719.02,-90.79 728.42,-95.69 729.42,-88.76\"/>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1250.01,-275.38C1250.01,-263.54 1250.01,-250.62 1250.01,-238.86\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1253.51,-239.03 1250.01,-229.03 1246.51,-239.03 1253.51,-239.03\"/>\n",
        "</g>\n",
        "<!-- 1|male&amp;eth_offset -->\n",
-       "<g id=\"node17\" class=\"node\">\n",
+       "<g id=\"node16\" class=\"node\">\n",
        "<title>1|male&amp;eth_offset</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1209.25\" cy=\"-316.38\" rx=\"78.14\" ry=\"40.66\"/>\n",
-       "<text text-anchor=\"middle\" x=\"1209.25\" y=\"-327.83\" font-family=\"Times,serif\" font-size=\"14.00\">1|male:eth_offset</text>\n",
-       "<text text-anchor=\"middle\" x=\"1209.25\" y=\"-311.33\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text text-anchor=\"middle\" x=\"1209.25\" y=\"-294.83\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1543.01\" cy=\"-316.38\" rx=\"78.14\" ry=\"40.66\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1543.01\" y=\"-327.83\" font-family=\"Times,serif\" font-size=\"14.00\">1|male:eth_offset</text>\n",
+       "<text text-anchor=\"middle\" x=\"1543.01\" y=\"-311.33\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"1543.01\" y=\"-294.83\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- 1|male&amp;eth_offset&#45;&gt;1|male&amp;eth -->\n",
-       "<g id=\"edge8\" class=\"edge\">\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
        "<title>1|male&amp;eth_offset&#45;&gt;1|male&amp;eth</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1209.25,-275.38C1209.25,-263.54 1209.25,-250.62 1209.25,-238.86\"/>\n",
-       "<polygon fill=\"black\" stroke=\"black\" points=\"1212.75,-239.03 1209.25,-229.03 1205.75,-239.03 1212.75,-239.03\"/>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"1546.51,-239.03 1543.01,-229.03 1539.51,-239.03 1546.51,-239.03\"/>\n",
+       "</g>\n",
+       "<!-- 1|male&amp;eth&#45;&gt;p(abortion, n) -->\n",
+       "<g id=\"edge15\" class=\"edge\">\n",
+       "<title>1|male&amp;eth&#45;&gt;p(abortion, n)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1497.63,-183.25C1453.13,-169.32 1383.12,-148.99 1321.01,-137.82 1129.83,-103.44 900.97,-89.79 785.13,-84.79\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"785.42,-81.26 775.28,-84.33 785.12,-88.25 785.42,-81.26\"/>\n",
+       "</g>\n",
+       "<!-- 1|edu&amp;age&#45;&gt;p(abortion, n) -->\n",
+       "<g id=\"edge13\" class=\"edge\">\n",
+       "<title>1|edu&amp;age&#45;&gt;p(abortion, n)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M274.09,-170.06C288.46,-158.22 306.55,-145.44 325.01,-137.82 426.67,-95.85 554.77,-84.75 634.55,-82.23\"/>\n",
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        "<!-- 1|edu&amp;age_offset -->\n",
-       "<g id=\"node18\" class=\"node\">\n",
+       "<g id=\"node19\" class=\"node\">\n",
        "<title>1|edu&amp;age_offset</title>\n",
-       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1557.25\" cy=\"-316.38\" rx=\"74.42\" ry=\"40.66\"/>\n",
-       "<text text-anchor=\"middle\" x=\"1557.25\" y=\"-327.83\" font-family=\"Times,serif\" font-size=\"14.00\">1|edu:age_offset</text>\n",
-       "<text text-anchor=\"middle\" x=\"1557.25\" y=\"-311.33\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text text-anchor=\"middle\" x=\"1557.25\" y=\"-294.83\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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+       "<text text-anchor=\"middle\" x=\"244.01\" y=\"-327.83\" font-family=\"Times,serif\" font-size=\"14.00\">1|edu:age_offset</text>\n",
+       "<text text-anchor=\"middle\" x=\"244.01\" y=\"-311.33\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"244.01\" y=\"-294.83\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
        "</g>\n",
        "<!-- 1|edu&amp;age_offset&#45;&gt;1|edu&amp;age -->\n",
-       "<g id=\"edge9\" class=\"edge\">\n",
+       "<g id=\"edge10\" class=\"edge\">\n",
        "<title>1|edu&amp;age_offset&#45;&gt;1|edu&amp;age</title>\n",
-       "<path fill=\"none\" stroke=\"black\" d=\"M1557.25,-275.38C1557.25,-263.54 1557.25,-250.62 1557.25,-238.86\"/>\n",
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-       "<!-- 1|edu&amp;age&#45;&gt;p(abortion, n) -->\n",
-       "<g id=\"edge15\" class=\"edge\">\n",
-       "<title>1|edu&amp;age&#45;&gt;p(abortion, n)</title>\n",
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+       "<polygon fill=\"black\" stroke=\"black\" points=\"247.51,-239.03 244.01,-229.03 240.51,-239.03 247.51,-239.03\"/>\n",
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        "<!-- 1|edu&amp;eth_offset -->\n",
        "<g id=\"node20\" class=\"node\">\n",
        "<title>1|edu&amp;eth_offset</title>\n",
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-       "<text text-anchor=\"middle\" x=\"1892.25\" y=\"-327.83\" font-family=\"Times,serif\" font-size=\"14.00\">1|edu:eth_offset</text>\n",
-       "<text text-anchor=\"middle\" x=\"1892.25\" y=\"-311.33\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
-       "<text text-anchor=\"middle\" x=\"1892.25\" y=\"-294.83\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1890.01\" cy=\"-316.38\" rx=\"72.83\" ry=\"40.66\"/>\n",
+       "<text text-anchor=\"middle\" x=\"1890.01\" y=\"-327.83\" font-family=\"Times,serif\" font-size=\"14.00\">1|edu:eth_offset</text>\n",
+       "<text text-anchor=\"middle\" x=\"1890.01\" y=\"-311.33\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text text-anchor=\"middle\" x=\"1890.01\" y=\"-294.83\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
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        "<!-- 1|edu&amp;eth_offset&#45;&gt;1|edu&amp;eth -->\n",
-       "<g id=\"edge12\" class=\"edge\">\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
        "<title>1|edu&amp;eth_offset&#45;&gt;1|edu&amp;eth</title>\n",
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        "<!-- 1|edu&amp;eth&#45;&gt;p(abortion, n) -->\n",
-       "<g id=\"edge19\" class=\"edge\">\n",
+       "<g id=\"edge16\" class=\"edge\">\n",
        "<title>1|edu&amp;eth&#45;&gt;p(abortion, n)</title>\n",
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        "</g>\n",
        "</svg>\n"
       ],
       "text/plain": [
-       "<graphviz.graphs.Digraph at 0x2966eebe0>"
+       "<graphviz.graphs.Digraph at 0x2a01ebc10>"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5263,12 +5297,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
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DjD5uKT+F3c5ef/11fPvtt+jRowemTZuGRo0aITMzEwcPHkTXrl3RqlUrg77fe+89ODg44JVXXsG9e/cwZcoUAEDjxo3RtWtXhISEoFKlSjh37hy++uqrUv3jh6jcKNXbnYjIbLq73Y8dO5ZvvczMTPHGG28If39/oVKphJeXl3j55ZdFSkqKXCcmJkb06tVL+Pv7C7VaLVxcXETLli3Fli1b9Pr66aefRN26dYVarRYAxPDhw+VxV69eFS+88IKoXLmyUKlUws3NTYSGhoq33npLrqO7y3z9+vUGcea9211n8+bNonHjxsLGxkbY29uLNm3aiF9++UWvju6u9Dt37hSw1AqOw5x+v/jiC9G4cWNhb28vbG1tRVBQkBg2bJg4fvy4XCcnJ0fMmzdP+Pr6CmtraxESEiK2bt0qWrZsWeDd7kIIcfbsWdGvXz/h4uIirK2thZ+fnxgxYoTIysqS6yxZskQEBgYKS0tLvT7y3u2eW2hoqAAghgwZYnR8dna2WLRokahdu7awsbERFSpUENWrVxejR48WFy9ezHe56Rw8eFD07dtXeHl5CZVKJRwdHUXTpk3Fu+++q3dnOwDx6quvGrT39/fX28aEKNx2JsSjJw2MHz9e+Pn5CZVKJdzd3UWXLl3E+fPn5X7w793uub377rt6TziYNm2aaNCggahUqZJQq9WiSpUqYsKECSIpKalQy4CITJOEMHJuhIiIiIioBPCaTyIiIiJSDJNPIiIiIlIMk08iIiIiUgyTTyIiIiJSDJNPIipRN2/exNixYxEUFAQbGxtUqlQJ4eHhWLNmjdFnQT6O6OhoSJKk93Fzc0N4eDi2bdtmUF+SJERFRRVrDMXl2rVrkCQJ0dHRBdY9d+4chg4diipVqsDGxgaurq6oV68exo4di7S0tJIP9jFERUWZ/ZIAIirb+JxPIioxv/zyC7p27YoKFSpgypQpCAkJQWpqKr777js8//zz2Lp1K9auXav3DvfisGrVKlSvXl1+o9NHH32Ebt26YcuWLejWrZtcLyYmpthfY6m0kydPolmzZggODsabb76JgIAAJCUl4Y8//sA333yDyZMnw9HRsbTDJCKSMfkkohJx9+5d9O7dG05OTjh69Cg8PDzkcT169EBISAimTZuGOnXqYNq0acU67Zo1a+q9PrNjx46oVKkS1q1bp5d8NmnSpFinWxqWLFkCCwsLHDhwQO/1l3379sXcuXOL/egyEdHj4ml3IioRn332GW7fvo358+frJZ46U6dORfXq1fHuu+8iOzsbAHDgwAFIkoR169Zh5syZ8Pb2hqOjI9q2bYsLFy4UORYbGxtYW1tDpVLplec97X7nzh288soreO6551ChQgW4u7ujdevWOHz4sEGfy5cvR+3atVGhQgU4ODigevXqmDFjhl6dhIQEjB49Gj4+PrC2tkZgYCBmz54NjUajVy8uLg79+/eHg4MDnJycMGDAAKPvhTcmOTkZjo6OqFChgtHxuU9p79mzBz169ICPjw9sbGzwzDPPYPTo0Qav8dSdCv/zzz/Rr18/ODk5wdnZGRMnToRGo8GFCxfQsWNHODg4ICAgAAsXLtRrr1uPX3/9NSZOnAhPT0/5taEnT54s1Hx9++23aNq0Kezt7VGhQgV06NCh0G2J6MnG5JOISsSePXtgaWmpd6QxN0mS0L17d/zzzz/4/fff9cbNmDED169fx2effYZPPvkEFy9eRLdu3aDVags1ba1WC41Gg+zsbNy6dQuvv/467t+/j8GDB+fb7p9//gEAREZGYvv27Vi1ahWqVKmC8PBwHDhwQK73zTff4JVXXkHLli2xadMmbN68GRMmTMD9+/flOgkJCWjUqBF+/PFHvPnmm9i5cydefPFFzJs3D6NGjZLrZWZmom3btti9ezfmzZuH9evXw9PT0+DVl6Y0bdoU8fHxGDJkCA4ePJjvu94vX76Mpk2bYvny5di9ezfefPNNHD16FM2bN5f/AMitf//+qF27NjZu3IhRo0bh/fffx4QJE9CzZ0906dIFmzZtQuvWrfHGG2/g+++/N2g/Y8YMXLlyBZ999hk+++wzxMXFITw8HFeuXMl3nt555x0MGjQIzz33HL777jt89dVXSE9PR4sWLXD27NlCLRcieoKV6vuViKjcql69uvD09My3zvLlywUA8e233woh/nv9ZefOnfXqfffddwKAiImJybc/3atH837UarX4+OOPDeoDEJGRkSb702g0Ijs7W7Rp00b06tVLLh87dqyoWLFivrGMHj1aVKhQQVy/fl2vfNGiRQKA+Ouvv/SWwQ8//KBXb9SoUUZfu5lXVlaW6NmzpzyvlpaWom7dumLmzJni9u3bJtvl5OSI7Oxscf36dYPp614v+t577+m1qVOnjgAgvv/+e7ksOztbuLm5id69e8tluvVYr149kZOTI5dfu3ZNqFQqMXLkSINp6dy4cUNYWVmJ1157TW/a6enpwtPTU/Tv3z/f5UFETz4e+SSiUiP+vR4x793O3bt31/seEhICALh+/Xqh+l29ejWOHTuGY8eOYefOnRg+fDheffVVfPTRRwW2XbFiBerVqwcbGxtYWVlBpVJh7969OHfunFynUaNGuHv3LgYNGoQffvjB4LQ1AGzbtg2tWrWCt7c3NBqN/OnUqRMA4ODBgwCA/fv3w8HBwWCeCzpKq6NWq7Fp0yacPXsW77//PgYOHIg7d+7g7bffRnBwsN7lCrdv38aYMWPg6+srz5u/vz8A6M2fTteuXfW+BwcHQ5IkeR4AwMrKCs8884zRdTN48GC9devv74/Q0FDs37/f5Pz8+OOP0Gg0GDZsmN5ys7GxQcuWLfWOQBNR2cQbjoioRPj5+eHixYu4f/8+7O3tjda5du0aAMDX11ev3MXFRe+7Wq0GgHxPKecWHBxscMPR9evXMXXqVDz//POoWLGi0XaLFy/GpEmTMGbMGMydOxeurq6wtLTErFmz9JKzoUOHQqPR4NNPP0WfPn2Qk5ODhg0b4q233kK7du0AAImJidi6davBdaY6uoQ1OTnZ6DWxnp6ehZrX3PMcHBwM4FFSv2TJEkycOBGzZs3Cd999h5ycHLRv3x5xcXGYNWsWatWqBXt7e+Tk5KBJkyZGl62zs7Ped2tra9jZ2cHGxsag3NgjnYzNg6enJ/744w+T85GYmAgAaNiwodHxxf1kBCJSHpNPIioR7dq1w+7du7F161YMHDjQYLwQAlu2bIGzszPq169f4vGEhITgxx9/xN9//41GjRoZrfP1118jPDwcy5cv1ytPT083qBsREYGIiAjcv38fhw4dQmRkJLp27Yq///4b/v7+cHV1RUhICN5++22j0/L29gbwKNH+7bffDMYX9oYjYyRJwoQJEzBnzhycOXMGAHDmzBn88ccfiI6OxvDhw+W6ly5dKvJ0CmJsHhISEgz+uMjN1dUVALBhwwb5qCwRlS/8E5KISsTIkSPh7u6O6dOn4/bt2wbjFy5ciPPnz2Pq1Kkmjw4Wp1OnTgEA3NzcTNaRJEk+yqrz559/IiYmxmQbe3t7dOrUCTNnzsTDhw/x119/AXh0yvrMmTMICgpCgwYNDD665LNVq1ZIT0/Hli1b9Ppdu3ZtoeYrPj7eaHlcXBzS0tLk6ehOf+edv5UrVxZqOkWxbt06vUc9Xb9+HUeOHEF4eLjJNh06dICVlRUuX75sdLnlPqJNRGUTj3wSUYmoWLEivv/+e3Tt2hX169fHlClTULt2baSlpeHbb7/FmjVrMGDAAEyZMqXYp33mzBn5cUbJycn4/vvvsWfPHvTq1QuBgYEm23Xt2hVz585FZGQkWrZsiQsXLmDOnDkIDAzUezzSqFGjYGtri2bNmsHLywsJCQmYN28enJyc5NPFc+bMwZ49exAaGopx48ahWrVqyMrKwrVr17Bjxw6sWLECPj4+GDZsGN5//30MGzYMb7/9NqpWrYodO3bgxx9/LNS8vvTSS7h79y769OmDmjVrwtLSEufPn8f7778PCwsLvPHGGwCA6tWrIygoCNOmTYMQAs7Ozti6dSv27NlT1MVcoNu3b6NXr14YNWoUUlNTERkZCRsbG0yfPt1km4CAAMyZMwczZ87ElStX5Ge0JiYm4rfffoO9vT1mz55dYjETUclj8klEJaZZs2b4888/sWDBAnzwwQe4desWbG1tUbt2bXz99dcGN6QUl4iICHnYyckJgYGBWLx4MV555ZV8282cORMZGRn4/PPPsXDhQjz33HNYsWIFNm3apHejS4sWLRAdHY3vvvsOKSkpcHV1RfPmzbF69Wr5yKqXlxeOHz+OuXPn4t1338WtW7fg4OCAwMBAOaECADs7O+zbtw/jx4/HtGnTIEkS2rdvj2+++QahoaEFzutrr72Gb7/9Fp9++iliY2Nx//59uLm5oWnTpli9erX8IH2VSoWtW7di/PjxGD16NKysrNC2bVv89NNP8PPzM3cRF8o777yDY8eOISIiAmlpaWjUqBG++eYbBAUF5dtu+vTpeO655/DBBx9g3bp1ePDgATw9PdGwYUOMGTOmRGIlIuVIQvD1F0REVHwOHDiAVq1aYf369ejbt29ph0NETxhe80lEREREimHySURERESK4Wl3IiIiIlIMj3wSERERkWKYfBIRERGRYph8EhEREZFimHwSERERkWLKxEPmc3JyEBcXBwcHhxJ5IDURERERPR4hBNLT0+Ht7Q0LC9PHN8tE8hkXFwdfX9/SDoOIiIiICnDz5k34+PiYHF8mkk8HBwcAj2bG0dGxlKMhIiIiorzS0tLg6+sr522mlInkU3eq3dHRkcknERER0ROsoEskecMRERERESmGyScRERERKYbJJxEREREphsknERERESmGyScRERERKYbJJxEREREphsknERERESmGyScRERERKYbJJxEREREphsknERERESnG7OTz0KFD6NatG7y9vSFJEjZv3lxgm4MHD6J+/fqwsbFBlSpVsGLFiqLESkRERERlnNnJ5/3791G7dm189NFHhap/9epVdO7cGS1atMDJkycxY8YMjBs3Dhs3bjQ7WCIiIiIq26zMbdCpUyd06tSp0PVXrFgBPz8/LFmyBAAQHByM48ePY9GiRejTp4+5kycqX3JygOwM89tZ2wOSpF927zaQetPMfioAbtUMy1NjgXsJwD9XAQtLQLIAhPh35L//5v3u8gzgVduwrysHH8Vmqp2xMq86gMdzhn2d/QHISjUeS8o1QOQA9q76bap3BVyCDPv65YNc/RRS7UGAg4d+2cP7wNGV5vUDAI1GAWoH/bL0RODU1+b31Xyi4faQfBn463vz+rGuADR52bA8/g/g790mGplYhhXcgfojDMuv/QJc+9m8uFyCgFp9Dcsv7ALiTprXl3cdoJqR37A/1wPJF83rK6A5EBhmWH7scyA93ry+qnUCKtc3LP/5feDBPSMN8tl2QwYCbs8alu+dY/4232gU4OitX5aVBhx+z7x+AKDFJMDGUb8sNRb4rQj7T7s5hmW3z5u//6gdgZZTDctvHgPObjavL8fKQNNXDMsv/QRc2mdeX+7VgXrDDMvPbHwUW2F51gLqDjFv2gowO/k0V0xMDNq3b69X1qFDB3z++efIzs6GSqUyaPPgwQM8ePBA/p6WllbSYVJZlhqLCQuWwkrSykXrtS0B6P8YeyEZzS1PF6pLR9zH8AffYML1MBzOCTEYP9JqOzzxj1lhbstpgpOaQGRdPQELtT0AoCLS0d/yIADDn5LcvxEiT/mX2vbQwFKvUnXpOppanDNS/79vefu5g4rYqW1kMPU60iXUlK7i55ta1HCzgEWevMaYy8IbZ3ICDcpDLc7ATUqVv6c9AHZf1iDEw0Keqi5E3feEnEpIhqPBD2Ug4mEtZRvUz+97opiL+0KtvwQE4C8lQvz7Pb/2N1IFXGwlWEhAKmY9Wu65WECgItJNLJX/5P3NT0UkcvKcfLIUGjhImQX0Y5g8pCLKYEJWkhZ2yCpULHI5JKRjikEllaSBDR4Wuh8AyIEFMmD4Q2yNbKigKVQfOlpY4AEMfyusoYEltIXqRzdaCwtojPz0qaCBJHIKjGVxBxu80tAaALBk70Us0Riu+y3WSxFicbXAvnKbvvcfrNMmGJQfVS+Bh3TXrL5e2CthX049g/JrNuYnjF32uuAvob9fuyEFx2yWmN1Xw71VcQeV9MpqSlewTf2B2X0F7G1sUNbG4nd8br3UrH5uoxLa7Tf8o7kPfsKb+MSsvhIdnsOGrHYG5Q2v7UKj65+a1Veafzvc8OhhUO59Zg+cz68rdD+pgV1w27s7qno4FFxZQSWefCYkJMDDQ/9IgYeHBzQaDZKSkuDl5WXQZt68eZg9e3ZJh0YKO7R9Hc4d2QIn8eiveJEr4RECSIEDFmoG6krkcU2lM+hmEZOn9NEXD+kfWMT9ib9/fYhs7X9jE3N+Qt7k8yHS4GBxrVCxPtQCb17XAvjh34++eYXqJS/DaacBWFSkvrYZlBz792OeOwC2G5T+/O8HAM7cLvgH+ZEb/370/WSi9u/x+fWb/O9H39+FjMSwL0OXzekhU7dtGR6BygGQZHZMpvsy708a0309/PdjHgHgfjH1lQPAMJF+8O/H/L4MW5nfj64vw7kp7PxpC7s7UJkgxKMtwqAcyPsTUojOAK3WSHFRthlh4g8qMw9cCwA5ZrZRQoknnwAg5TkdpPvLPW+5zvTp0zFx4kT5e1paGnx9fUsuQDJw+/ZtrD18FoDxHRMA7t69C82/O9pDSY2HFjbyuJwcAQEB25x7sBSPKvU6/TrmfHUHcen57QnfGJTcAPCt2XNwyqAk/t/Pk8LK2QfWkgb+0m25LPcukXvvyFt+QfhA5Dlq5izdg6f0T6H7AIAM2OCyqGwwwhP/wNMiBQDwW2wOWvhZwqaA/y1ihSuuCG+D8lrSFVSU9JMjrQCupuSgc1UrORbd/wcSgGM5z+KMqGIQeB/Lw3D+ty9d8X/tjX/fmdMI14Tnf2P+HfGq5Q+wkESB7SVIuJySg2BXC6zOaY8E4aI3L/bIwmtWmwzOehuTu8qHml64B1u98Z5SCl602mlWPwDwtibvaTUJz1jEYqDl/kL3AQDpsMUSTV+DWnUtLqKbZUyh+wGAOOGCT7VdDGq0tPgDrSxP5ekj/4V3QfhgrbaNQXk3ixg0tPzvT5KCVoEkAce0z2JLTjODcYMtf0Kwxc0C+2juZ1lADVKCu73hf0hOwtL0D5YJaisLtAx0Nljvz6baPfrb3AwV1JYIDXI2KPdOtwXMvCLKRmUJ74o2BuV2avPSNluVJTwc1QVXVJgkjJ3DKWxjScKmTZvQs2dPk3XCwsJQt25dfPDBf4fVN23ahP79+yMjI8Poafe80tLS4OTkhNTUVDg6OhZYnx7P3TN7kfjlUFyN/+9UaX6nbgHgB20oNmtz/4f+aMRMq68RKD06lfT+rw9x4JqRPwuL0aCaVnI8eX+KPKR/0OTf09KFVdnBAppGw7FBtNIrn20ZDT+L2yZaGbdK0wGHRG1AsoClTQUAgDPS8EIhko28PtD0QXaevx2rSHH4aUCFQrW3kP5NXO1cgKptDSvE/wnc/ndZZSQDnjUByTJXIqifyAHSo2shKwUY9nXnAvAgPU87E33ovjt4PbpmMK+ki4Dmgel2ORrAthL01r2dM6DST/IAPLrWrDAZY252roCVtX5Zjha4b+avFADYuz26njY3bTaQkc+xT1Px2rsZjtM8eHRtnjkkC8DexbA8OxN4aOa1yRYW/66LPB5mABrjlwOY7svK8FpB4NH1tlozj8laWj+6ZjqvB/cebT/msLIBVIYJAh7cM/9wl5WN4bal68tcVjaApZEk5cE987d5KxvD7VQI89ehrq+808/JMX8dAsaXe06O+esQML7cc7QFrEMTy9HYcs/JgdmHLAHD5Q6Yf80uYP46fwyFzddK/Mhn06ZNsXXrVr2y3bt3o0GDBoVKPEl5e9fMw+hlCblONRaq1b8ffa+bqP1lTxtkCZXePnFXOGCBdpBB3WYWf6GH5RH5u7EjeN52Gjzwa4JOryx+VOBR03CHe5AGpFwvcE5ys3TyhlTBDe8bjDGMsyBGUrwi9zXW7BZm8gp59CkOxm5oKirXqsXXl1PlgusUhoUl4OBZPH1ZqgxvaCoqKzVQwa14+lLZGk/gi8La7tGnWPqyB2AkkSwKdeH+cGNfePR/a3FtDxYWgIWRRLLIfRlJJIvUlyWAYjrKbVGMj1RXMJEsSWYnn/fu3cOlS5fk71evXsWpU6fg7OwMPz8/TJ8+HbGxsVi9ejUAYMyYMfjoo48wceJEjBo1CjExMfj888+xbl3hL5gl0+5nPkTY7D1I+fe73lHJIvY5/36qmYmneV6qp8Kw93YBVVoajJtSYlMFYFfx0YeIiIhKjdnJ5/Hjx9Gq1X+nIHXXZg4fPhzR0dGIj4/HjRv/3XQQGBiIHTt2YMKECVi2bBm8vb3x4Ycf8jFLxeS5WduRdeM0hEZ32kKgo8Vv6GYZk+/d0nnLz+b4Y4WmGwBgSbqTPH55Hw/EVW4tf899bVbuP8BOiqo4IfI82kOS0MPiF7jhrnw9n5As8Jd1LaOJJxEREZV/j3XNp1J4zadp9jXCkXH2YIn17zHoHSSsnV5i/RMREVH5UNh8je92L+M0ybdKrG8r58qw9izGa+yIiIjoqafIo5aohFw9jMq4g6sAWtfxxd2WM3AHldDc4jTCLf6Qq5m6Pjn3o64uicpYq/33lhjdzcNW1jgfZfo2GSIiIiJzMfksy5Ivyc9PfE6VgKUzWgLuwQAGFKm7+cUYGhEREZExPO1ORERERIrhkU+F/H3zDtov+61Y+xxUyPeUExERET0pmHwq5IOPlyAq9muk38v/LSGzs4cZlD0r3cQgK8PX5FXEPcxP4IuGiYiIqOxg8qkEbTYaXVyKERvTC66LxQYlMf9+8iOVk7ceEBERUfnGaz6V8CAdt/4pwrtrC6miDdCxmi1Q0b/EpkFERERUHHjkU2FeFSTsHV8Nl4WX0fGvZr9uUFZTuoKXrbaY7DNDssPn2tboXFzvSyYiIiIqIUw+FSZJwFWXcOx2GmR0fA+jpfWwG/VM9lnVww4bOtcvlviIiIiIShKTz1LQuW4AOoeHlXYYRERERIrjNZ9KkCwQLyoBALJhBTh4lnJARERERKWDyacSbCtijbYdAOAf4QDUH17KARERERGVDiafRERERKQYJp9EREREpBgmn0RERESkGCafRERERKQYJp9KeJiBUIszAIAKyAKu/VzKARERERGVDiafStBkIdTiLADAXsoErv1SygERERERlQ4mn0RERESkGCafRERERKQYJp9EREREpBgmn0RERESkGCafRERERKQYJp9EREREpBgmn0RERESkGCafRERERKQYq9IO4En0v40nsPdsovxd5FNXN04I4zWFEHAS6WhVnAESERERlVFMPvPYduw8vj4WX6x9Zhdrb0RERERlF5PPPF7dcAkpez9F1o0/5TJP6R+ooPn36KY+kasw9+gUUQEZQg0ASEAO/vfPg5IKmYiIiKjMYPKZhyY1Eem/b9Eru1mknjL/6zNXqa2dPeASVKQeiYiIiMo6Jp95af47SW5XvQUsKzijt+VhVMI9SNJ/1XSDxsoAYH9OHVwUPvqVLCyRERwG1OpbIqETERERPemYfObDsUEPqCtXx/OquwiQEgtukMtdTWck5TQoociIiIiIyiYmn3k0tDiLH/4dbmRxDn+gOkZmTynVmIiIiIjKCyafeQy0PCAnnwMtD6DycyON1tOdSZdynWzP/dBUCwsJeamsLPB8aJViiZOIiIioLGLyWYCPhzUp7RCIiIiIyg2+4YiIiIiIFMPkk4iIiIgUw+STiIiIiBTD5JOIiIiIFMPkk4iIiIgUw+STiIiIiBTD5JOIiIiIFMPkk4iIiIgUw+STiIiIiBTD5JOIiIiIFMPkM4844SoPxwqXUoyEiIiIqPxh8pnHe5r+RoeJiIiI6PEx+SQiIiIixTD5JCIiIiLFMPkkIiIiIsUw+SQiIiIixTD5zKOtxfH/hi1/L8VIiIiIiMofJp95dLL8TR7ubPFbPjWJiIiIyFxMPomIiIhIMUw+iYiIiEgxTD6JiIiISDFMPomIiIhIMUw+iYiIiEgxTD6JiIiISDFMPomIiIhIMUw+iYiIiEgxTD6JiIiISDFMPomIiIhIMUw+iYiIiEgxTD7zOCf8cg37lmIkREREROUPk888PtN0lYc/zTVMRERERI+PyScRERERKYbJJxEREREphsknERERESmGyScRERERKYbJZx59LA/Kw70tD5ViJERERETlD5PPPEIt/pKHm+caJiIiIqLHx+STiIiIiBTD5JOIiIiIFMPkk4iIiIgUw+STiIiIiBTD5JOIiIiIFMPkk4iIiIgUw+STiIiIiBTD5JOIiIiIFMPkk4iIiIgUw+STiIiIiBTD5JOIiIiIFMPkM4/fcqrLw0dFtVKMhIiIiKj8YfKZx7fa1v8Na1rnU5OIiIiIzMXkk4iIiIgUw+STiIiIiBTD5JOIiIiIFMPkk4iIiIgUw+QzjxFWO+XhiFzDRERERPT4mHzmUUu6mmv4WukFQkRERFQOMfkkIiIiIsUw+SQiIiIixTD5JCIiIiLFMPkkIiIiIsUw+SQiIiIixTD5JCIiIiLFMPkkIiIiIsUw+SQiIiIixTD5JCIiIiLFMPkkIiIiIsUw+SQiIiIixTD5zGNPTn15eHdOvVKMhIiIiKj8YfKZxy5tY6PDRERERPT4mHwSERERkWKYfBIRERGRYph8EhEREZFimHwSERERkWKYfOYxzmqDPDzeamMpRkJERERU/jD5zMNfum10mIiIiIgeH5NPIiIiIlIMk08iIiIiUgyTTyIiIiJSDJNPIiIiIlIMk08iIiIiUgyTTyIiIiJSDJNPIiIiIlIMk08iIiIiUgyTTyIiIiJSDJNPIiIiIlIMk08iIiIiUgyTzzw2alvIwxtyDRMRERHR42PymceRnFq5hmuWYiRERERE5Q+TTyIiIiJSDJNPIiIiIlIMk08iIiIiUgyTTyIiIiJSDJPPPGapVv83bLU6n5pEREREZC4mn3lUxD15uJJ0vxQjISIiIip/rEo7ACIiUpZWq0V2dnZph0FEZYSVlRUsLS0hSVLx9FcsvRAR0RNPCIGEhATcvXu3tEMhojLG0tIS7u7ucHJyeuwklMknEdFTQpd4uru7w87OrtiOYhBR+SWEgEajQVpaGuLj45GZmQkvL6/H6pPJJxHRU0Cr1cqJp4uLS2mHQ0RljIODA9RqNZKSkuDu7g5LS8si98UbjoiIngK6azzt7OxKORIiKqvs7e0hhHjsa8aZfBIRPUV4qp2Iiqq4/v9g8klEREREimHySURE5cavv/6Kfv36wcvLC9bW1vD09ETfvn0RExOjVy8qKgqSJCEpKamUIi0d165dgyRJiI6OLu1Q6CnG5JOIiMqFpUuXolmzZrhx4wYWLlyIn376CYsWLUJsbCyaN2+Ojz76qLRDJCLwbnciIioHfvnlF7z++uvo2LEjfvjhB1hZ/ffzNnDgQPTo0QPjx49H3bp10axZs1KMVJ9Go4EQAiqVqrRDIVIMj3wSEVGZN2/ePEiShBUrVuglnsCjt7MsX74ckiRh/vz5euNu3ryJHj16wMHBAZUqVcLIkSORlpamV2f9+vVo1KgRHB0dUaFCBVStWhUTJkzQqxMXF4dRo0bB29sb1tbWCAoKwrx585CTkyPX0Z3yXrBgAaKiouDv7w+1Wo1Tp07B2toas2bNMpivM2fOQJIkrFy50qxp6er1798fDg4OcHJywoABA5CQkGDegiUqATzymccXmo4ATj8a1nbAoNINh4iICqDVarF//37Ur18fvr6+Ruv4+fmhfv362LdvH7RarVzeu3dvDBw4EGPHjsXvv/+OqKgoXLlyBXv37oUkSThy5AgGDBiAAQMGYPbs2VCr1bh69SqOHz8u9xEfH49GjRpBpVJh9uzZCAwMxOHDhxEZGYm4uDgsXbpUL5YPP/wQzz33HJYsWQJra2v4+/ujS5cu+PLLLzF79mxYWPx3XGjVqlWwtbXFwIEDzZpWZmYm2rZti7i4OMybNw9Vq1bFtm3bMGDAgGJb7kRFxeQzj79EFXn4TE6VfGoSEZVtmZmZuHDhQmmHoadatWqwtbU1q01SUhIyMjJQpUr+/2cHBgbit99+Q3JyslzWt29fzJs3DwDQrl072NnZYfz48di9ezc6dOiAmJgYCCGwYsUKODk5ye1efPFFeTgqKgqpqan466+/4OfnBwBo27YtLCwsMGfOHEyePBn+/v5yfTs7O+zcuVPvCG1ERAQ2b96Mffv2oW3btgAenZJfs2YNevXqJU+7sNP68ssvce7cOfzwww/o3r07AKBDhw7IyMjAF198YdbyJSpuTD6JiJ5SFy5cQN26dUs7DD0nT55EnTp1SqRvIQQA/WcV6o4o6gwePBjjx4/H/v370aFDBzRs2BAAMGDAALz00ksIDQ2Fp6enXpvt27ejVatW8Pb2hkajkcs7deqEqKgoHDp0CEOHDpXLu3XrZnBpQOfOneHh4YFVq1bJyeeOHTuQmJiIiIgIs6e1f/9+ODg4yImnzvPPP8/kk0odr/kkIqIyzdXVFXZ2drh69Wq+9a5duwY7Ozs4OzvLZXkTSVdXV1hZWclHR8PCwrBp0yY8ePAAgwYNgpeXF+rVq4etW7fKbRITE7F161aoVCq9T+PGjQHA4HFOxt6LbWVlheeffx6bNm1CamoqACA6Ohp+fn5o3bq12dNKTk6Gh4eHwXQe953cRMWBRz6JiJ5S1apVw8mTJ0s7DD3VqlUzu42lpSVatWqFXbt24datW/Dx8TGoc+vWLfz+++/o1KmT3jupExISULlyZfl7UlISNBoNXFxc5LKePXuiZ8+eePDgAX755RdERUWhd+/eOH36NKpXrw5XV1eEhITg7bffNhpf3utQTb0lJiIiAu+99x6+/fZb9O7dG9u2bcO0adP0rgEt7LRcXFzw22+/GYyPj4832o5ISUw+iYieUra2tiV2iltp06dPx86dO/Hyyy9j8+bNegmmVqvFyy+/DCEEpk+frtfum2++Qf369eXva9euBQCEh4cbTEOtVqN169YQQqBt27Y4d+4cqlevjs6dO2PXrl0ICgpCpUqVijwPNWrUQMOGDREdHY3MzExoNBoMHz5cr05hp9WqVSt899132LJli96p96+//rrI8REVFyafebyrWoEh8vByAJNKMxwiIiqEZs2aYcmSJXj99dfRvHlzvPrqq/D398eNGzewbNkyHD16FEuWLEFoaKheu/Xr18PKygqtW7fGiRMnEBkZiZYtW6JDhw4AgDfffBNxcXFo3bo1KleujOTkZLz77ruoWLEimjZtCgCYO3cu9uzZg2bNmmHcuHGoVq0aMjIycOXKFWzfvh3r1q0rdFIaERGBV155BTdv3kRYWBiCgoL0xhd2WsOGDcP777+PYcOG4e2335bvdv/pp5+KYWkTPZ4iXfP58ccfIzAwEDY2Nqhfvz4OHz5ssu6BAwcgSZLB5/z580UOuiRZ4L/npFnmU4+IiJ4sr732Gn755Rf4+Phg8uTJaN26NSZOnAgvLy/8/PPPeO211wzafP/99zh9+jR69eqFefPmYdCgQdi8ebN8arxx48aIjY3FlClT0L59e7zyyivw8PDAvn375OtFvb29cfz4cbRp0wYLFixAhw4dMHz4cHz11Vdo1KgR7O3tCz0PgwYNgo2NDW7duoURI0YYjC/stOzs7OQ756dNm4a+ffsiLi4O33zzTRGWLFHxkoTu9r9C+vbbbzF06FB8/PHHaNasGVauXInPPvsMZ8+elR/7kNuBAwfQqlUrXLhwAY6OjnK5m5ub3mmR/KSlpcHJyQmpqal6fZSEb14OwaAVj57zuTYiCIO+uFSi0yMiUkJWVhauXr0qHzggIjJXQf+PFDZfM/vI5+LFi/Hiiy9i5MiRCA4OxpIlS+Dr64vly5fn287d3R2enp7yp7CJJxERERGVH2Ylnw8fPsTvv/+O9u3b65W3b98eR44cybdt3bp14eXlhTZt2mD//v351n3w4AHS0tL0PkRERERU9pmVfCYlJUGr1Ro8O8zDw8Pk+2K9vLzwySefYOPGjfj+++9RrVo1tGnTBocOHTI5nXnz5sHJyUn+mHpdGhERERGVLUW62z3vM8qEECafW1atWjW957Y1bdoUN2/exKJFixAWFma0zfTp0zFx4kT5e1paGhNQIiIionLArCOfrq6usLS0NDjKefv2baNvUjClSZMmuHjxosnxarUajo6Oeh8iIiIiKvvMSj6tra1Rv3597NmzR698z549Bs9Oy8/Jkyf5ii8iIiKip5DZp90nTpyIoUOHokGDBmjatCk++eQT3LhxA2PGjAHw6JR5bGwsVq9eDQBYsmQJAgICUKNGDTx8+BBff/01Nm7ciI0bNxbvnBARERHRE8/s5HPAgAFITk7GnDlzEB8fj5o1a2LHjh3w9/cH8Oi9sTdu3JDrP3z4EJMnT0ZsbCxsbW1Ro0YNbN++HZ07dy6+uSAiIiKiMqFINxy98soreOWVV4yOi46O1vs+depUTJ06tSiTISIiIqJypkiv1yQiIiIiKgomn3ks0fSRh9/X9C7FSIiIiIjKHyafedwUHkaHiYjoyRQdHQ1JkuSPlZUVfHx8EBERgdjY2GKfXlxcHKKionDq1Kli7xsAoqKiTD47uzwyNr8BAQEYMWKEWf3kt15GjBiBgICAogdJxapI13wSERE9aVatWoXq1asjMzMThw4dwrx583Dw4EGcPn0a9vb2xTaduLg4zJ49GwEBAahTp06x9aszcuRIdOzYsdj7LUs2bdpk9jO+81svs2bNwvjx44sxQnocTD6JiKhcqFmzJho0aAAAaNWqFbRaLebOnYvNmzdjyJAhpRxdwTIyMmBnZwcfHx/4+PgUS59CCGRlZcHW1rZY+sstKysL1tbWsLAo/pOodevWLdb+goKCirU/ejw87U5EROVSkyZNAADXr18HAGRmZuKNN95AQEAArK2t4evri/HjxyM9PV2v3b59+xAeHg5nZ2fY2toiICAAAwcOBAAcOHAADRs2BABERETIp/pzP+nlxx9/RKtWreDg4AA7OzuEhYXh8OHDetPQnWo+fvw4evbsCScnJzRr1kxvXG6FjT0gIABdu3bFd999h5CQENjY2GDlypUml1F4eDhq1qyJQ4cOoVGjRrCxsYGvry/mz58PIYRc78CBA5AkCatXr8b48ePh5eUFOzs7pKWlFXqeAWD79u2oU6cO1Go1AgMDsWjRIqNxGTvtHhsbi5deegm+vr6wtraGt7c3+vbti8TExALXS+7T7tnZ2XB3d8fQoUMNppucnAy1Wo3p06fLZampqZg4cSICAwPlZT9lyhRkZWWZXK6UPx75zMMeGbmGM0sxEiKikiWEQGa2trTD0GOrsiy26x0vX74MAHBzc4MQAt27d8fBgwcxc+ZMhIaG4sSJE4iKisLRo0dx+PBhqFQqXL16FV26dEGLFi0QHR0NJycn3Lx5E9u2bQMA1KtXD6tWrUJERAT+97//oUuXLgD+O7L25ZdfIiIiAr169cJXX30FS0tLfPTRR2jbti0OHDiApk2b6sXYt29fPP/88xg7diwePnxodD4KG7vO77//jgsXLmDWrFnw8fGBi4tLvsspISEBgwcPxowZMxAUFIQNGzZg+vTpyMrKQlRUlF7d6dOnIywsDJ999pl8RLWw87x371706NEDTZs2xTfffAONRoMFCxbgzp07Ba7L2NhYNGzYEBqNBjNmzECtWrWQnJyMXbt2ISUlpcD1kptKpcKQIUOwcuVKLFu2TO/0/tq1a/Hw4UM58b1//z5atGiB+Ph4zJw5E7Vq1cKpU6cQFRWF8+fPY+vWrQXGToaYfOYxRxWNQf8Oz1VFA/hfKUZDRFRyMrO1eO7NH0s7DD1n53SAnXXRfpq0Wi00Gg2ysrJw+PBhvPXWW3BwcED37t3x448/4qeffsL777+P119/HQDQrl07uLm54cUXX8T69esxePBgnDhxAllZWXj33XdRu3Ztue/nn38eAODo6IiaNWsCeJTY6I6uAo9Om0+YMAFdu3bVe4tfx44dUaNGDfzvf//D3r179WKOiIhAZGRkvvNV2Nh1kpKS8PPPPxf6VHNycjK2bdsmJ2wdOnRAcnIyFi5ciNdffx0VK1aU6z777LNYt25dkeZ55syZ8PDwwJ49e2BjYwMAaN++PQIDAwuM8c0330RSUhJOnz6NatWqyeX9+/eXh02tF2MiIiKwZMkSfPfddxg5cqRcHh0djdDQUHkaH374If766y8cPXpUvqSjTZs2cHV1xYgRI3D48GG0aNGiwPhJH0+7ExFRudCkSROoVCo4ODigc+fO8PDwwM6dO+Hh4YH9+/cD+C+J1Bk6dCgsLS2xb98+AECdOnVgbW2N0aNHY82aNXpv7CvIkSNHkJKSguHDh0Oj0cgfSZLQvn17/Pzzz9Bq9Y809+rVq8B+Cxu7Tq1atcy6xtHBwUFOPHUGDx6MzMxM/Prrr/nGW9h5vn//Po4dO4bevXvLiScAODk5oVu3bgXGuHPnTrRq1Uov8XwcISEh8tFSndOnT+PEiROIiIiQy7Zv346aNWuiTp06evPXqVMnAI8uRyDz8cgnEdFTylZlibNzOpR2GHpsVZZFbrt69WoEBwfDysoKHh4e8PLyksclJyfDysoKrq6uem1UKhVcXV2RnJwM4NFRsz179mDBggV46aWXkJGRgWrVquGNN97QS0qMSUxMBPDoVLopaWlpqFSpkvw9d4ymFDZ2c/rMzcPD8LGCnp6e8rTz67uw83z//n3k5OTI/Zob7507d4rtJiydiIgIvPbaa/j777/x7LPPYtWqVbCzs9M7mpqYmIhLly7pXdaQW1JSUrHG9LRg8klE9JSSJKnIp7ifRMHBwfKp0bxcXFyg0WiQlJSkl8RlZ2cjKSlJ77rIsLAwhIWFQavV4tixY1iwYAFeeOEF+Pr6om3btianr+t36dKlJk/7Ojg46H0vzPWt5sRe2D5z0yWQuSUkJMjTzq/vws6ztbU1JEmS+80tPj6+wBjd3Nxw69atAuuZY/DgwZg8eTKio6MxZ84crFmzBn369NG7BtTV1RW2trb44osvjPbh7u5erDE9LXjanYiIyr3WrVsDAL7++mu98jVr1kCr1crjc7O0tESTJk3kO7JPnz4NAFCr1QAe3YGeW7NmzeDk5ISzZ8+iQYMGRj9WVuYn+0WJ3Rzp6enYvn27XtnatWthY2NT4LWThZ1ne3t7NGrUCN9//73eXeJpaWmFummnU6dO2L9/Py5cuGCyjqn1YoqzszO6d++Or776Clu2bMHt27cNjm537twZly9fhouLi9F58/PzK9S0SF/5+ZOXiIjIhPbt26Nt27Z44403kJaWhtDQUJw8eRKRkZFo3Lgx+vXrBwBYsWIFDhw4gM6dO8PPzw/37t3D8uXLYW1tLSd5QUFBsLW1xbp161CjRg3Y2dkhMDAQLi4u+PDDDxEREYGUlBT07dsXbm5uuH37Nk6ePInMzEwsXry4xGIvKhcXF4wePRozZ85ElSpVsGHDBmzatAmzZs3Su9nImAoVKhR6nufOnYuOHTuiXbt2mDRpEjQaDebPn48KFSogJSUl3+nMmTMHO3fuRFhYmHy3e0pKCnbt2oVJkyahevXq+a4XUyIiIrB+/XqMHz8eAQEBCA8P1xs/YcIEbNy4EWFhYZg4cSJq1aoFrVaL69evY9euXZg9ezZq1apVqOVMuYgyIDU1VQAQqampJT6tdWNqCQACgFgbEVTi0yMiUkJmZqY4e/asyMzMLO1Qit2qVasEAHHs2LF8692/f19MmTJF+Pn5CSsrK1G5cmXx2muv6f22xMTEiJ49ewpfX1+hVquFq6uraNu2rdi3b59eX19//bWoWrWqsLKyEgDEqlWr5HH79+8XnTp1EpUqVRLW1tbCx8dHdO/eXfzwww9yncjISAFA3LlzxyBO3ThzYxdCCH9/f9GlS5cCl5lOy5YtRY0aNcS+fftE/fr1hVqtFpUrVxZvv/22yMnJ0ZsnAGL9+vVG+ynMPAshxJYtW0RISIiwtrYWfn5+Yv78+Ubn19/fXwwfPlyv7ObNm+KFF14Qnp6eQqVSCW9vb9G/f3+RmJgo1zG1XoYPHy78/f0N4tZoNMLb21sAEJGRkUbnLT09XcyYMUM8++yzwtraWjg5OYnatWuLyZMni+TkZBNLtnwq6P+RwuZrkhC5niL7hEpLS4OTkxNSU1PNft2Wub55OQSDVjw6tbI2IgiDvrhUotMjIlJCVlYWrl69isDAQL27jenpFh4ejqSkJJw5c6a0Q6EyoKD/Rwqbr/GaTyIiIiJSDJNPIiIiIlIMbzgiIiJ6SvEh6VQaeOSTiIiIiBTD5JOIiIiIFMPkM4852UPl4dm5homIiIjo8TH5zCMVDrmGK5RiJERERETlD5NPIiIiIlIMk08iIiIiUgyTTyIiIiJSDJPPPLylO7mGk0oxEiIiKozo6GhIkiR/rKys4OPjg4iICMTGxhb79OLi4hAVFYVTp04Ve98AEBUVBUmSSqTvJ5Gx+Q0ICMCIESPM6ie/9TJixAgEBAQUPUgqVkw+85hktV4enpxrmIiInmyrVq1CTEwM9uzZg1GjRmHdunVo0aIF7t+/X6zTiYuLw+zZs0ss+Rw5ciRiYmJKpO+yYtOmTZg1a5ZZbfJbL7NmzcKmTZuKKTp6XHzDERERlQs1a9ZEgwYNAACtWrWCVqvF3LlzsXnzZgwZMqSUoytYRkYG7Ozs4OPjAx8fn2LpUwiBrKws2NraFkt/uWVlZcHa2hoWFsV/HKtu3brF2l9QUFCx9kePh0c+iYioXGrSpAkA4Pr16wCAzMxMvPHGGwgICIC1tTV8fX0xfvx4pKen67Xbt28fwsPD4ezsDFtbWwQEBGDgwIEAHr2OsmHDhgCAiIgI+VR/dHS03P7HH39Eq1at4ODgADs7O4SFheHw4cN609Cdaj5+/Dh69uwJJycnNGvWTG9cboWNPSAgAF27dsV3332HkJAQ2NjYYOXKlSaXUXh4OGrWrIlDhw6hUaNGsLGxga+vL+bPnw8hhFzvwIEDkCQJq1evxvjx4+Hl5QU7OzukpaUVep4BYPv27ahTpw7UajUCAwOxaNEio3EZO+0eGxuLl156Cb6+vrC2toa3tzf69u2LxMTEAtdL7tPu2dnZcHd3x9Chhs/yTk5OhlqtxvTp0+Wy1NRUTJw4EYGBgfKynzJlCrKyskwu17zL95dffkGTJk1ga2sLf39/fPbZZwCAbdu2oV69erCzs0OtWrWwe/dugz4uXLiAgQMHwt3dHWq1GjVq1JDb62RlZWHixIkICQmBo6MjXF1d0bJlS4PXp167dg2SJGHRokV47733EBgYiAoVKqBp06b49ddfC5yf4sIjn0RET7tP25jfpudywO1Z/bL7ScDaAeb3NWQ9YOdsfrsCXL58GQDg5uYGIQS6d++OgwcPYubMmQgNDcWJEycQFRWFo0eP4vDhw1CpVLh69Sq6dOmCFi1aIDo6Gk5OTrh58ya2bdsGAKhXrx5WrVqFiIgI/O9//0OXLl0A/Hdk7csvv0RERAR69eqFr776CpaWlvjoo4/Qtm1bHDhwAE2bNtWLsW/fvnj++ecxduxYPHz40Oh8FDZ2nd9//x0XLlzArFmz4OPjAxcXl3yXU0JCAgYPHowZM2YgKCgIGzZswPTp05GVlYWoqCi9utOnT0dYWBg+++wz+YhqYed579696NGjB5o2bYpvvvkGGo0GCxYswJ07d4xEpS82NhYNGzaERqPBjBkzUKtWLSQnJ2PXrl1ISUkpcL3kplKpMGTIEKxcuRLLli2Do6OjPG7t2rV4+PChnPjev38fLVq0QHx8PGbOnIlatWrh1KlTiIqKwvnz57F169YCY09ISMDIkSMxdepUVK5cGUuXLsWoUaMQGxuLjRs3YsaMGXB0dMTs2bPRo0cPXLlyBV5eXgCAM2fOoFmzZggICMDixYvh4eGBrVu34qWXXkJqaiomTZoEAHjw4AFSUlIwZcoU+Pj4IDMzEz/88ANat26Nn376Ca1bt9aLadmyZahevTqWLFkC4NFlCZ07d8bVq1fh5ORU4Dw9NlEGpKamCgAiNTW1xKe1bkwtAUAAEGsjgkp8ekRESsjMzBRnz54VmZmZhiMjHc3/xJ407Cc1rmh9pSU81rytWrVKABC//vqryM7OFunp6WLHjh3C3d1dODg4iISEBLFz504BQLz//vt6bT///HMBQKxZs0YIIcSGDRsEAHHq1CmT0zt27JgAIFatWqVXfv/+fVGpUiXRrVs3vfKHDx+KqlWritatW8tlkZGRAoCIiooy6F83TqewsQshhL+/v7CyshKXLl0yGX9uLVu2FADEtm3b9Mp79eolbG1tRUpKihBCiP379wsAIjw8vMjz3LhxY+Ht7a23Dd69e1dUqlRJ5E1H/P39xfDhw+XvL7zwglCpVOL8+fMm58XUehFCiOHDhwt/f3/5+x9//CEAiE8//VSvXr169URoaKj8/Z133hEWFhbi2LFjevWio6MFAHHo0CGT8Qjx3/L9/fff5bKkpCRhaWkpbG1tRWxsrFx+4sQJAUAsXbpULmvfvr3w8fExyH9GjBghHBwcxL1794xOV6PRiOzsbNG2bVvRs2dPufzq1asCgKhVq5bQaDRy+dGjRwUAsW7dunznJ9//R0Th8zWedicionKhSZMmUKlUcHBwQOfOneHh4YGdO3fCw8MD+/fvBwA8//zzem2GDh0KS0tL7Nu3DwBQp04dWFtbY/To0VizZg1u3LhR6OkfOXIEKSkpGD58ODQajfyRJAnt27fHzz//DK1Wq9emV69eBfZb2Nh1atWqZdY1jg4ODvKRQp3BgwcjMzPT4FRs3ngLO8/379/HsWPH0Lt3b9jY2MjtnZyc0K1btwJj3LlzJ1q1aoVq1aoVer7yExISIh8t1Tl9+jROnDiBiIgIuWz79u2oWbMm6tSpozd/nTp1AgCD09rGeHl5oV69evJ3FxcXuLu7o06dOvD29pbLn3vuOQD/XSaSlZWFffv2oVevXrCzs9ObfufOnZGeno4TJ07I7Tdu3IjmzZvDxcUFVlZWUKlU+Omnn3D+/HmDmLp06QJLS0u95ZF72iWNp92JiJ52lRuY30ZlZ1hmqSpaX5aqgusUwurVqxEcHAwrKyt4eHjIpy6BR9fyWVlZwdXVVa+NSqWCq6srkpOTATw6Tbtnzx4sWLAAL730EjIyMlCtWjW88cYbekmJMYmJiQAenUo3JS0tDZUqVZK/547RlMLGbk6fuXl4eBiUeXp6ytPOr+/CzvP9+/eRk5Mj92tuvHfu3Cm2m7B0IiIi8Nprr+Hvv//Gs88+i1WrVsHOzg79+/eX6yQmJuLSpUt6lzXklpRU8CMZnZ0NLymxtrY2KFer1QAeXd8LPFr2Go0GS5cuxdKlS/Od/vr169G/f38MGDAAU6dOhYeHBywtLTFr1iycO3fOoF3eSzF0fxDopl3SmHwSET3tRu0tnn7sXYuvryIIDg6W73bPy8XFBRqNBklJSXpJXHZ2NpKSkvR+jMPCwhAWFgatVotjx45hwYIFeOGFF+Dr64u2bduanL6u36VLl8o3O+Xl4OCg970wz/M0J/bC9pmbLoHMLSEhQZ52fn0Xdp6tra0hSZLcb27x8fEFxujm5oZbt24VWM8cgwcPxuTJkxEdHY05c+ZgzZo16NOnj941oK6urrC1tcUXX3xhtA93d/dijSm3SpUqwcLCAsOGDcOrr75qtI7uCPfatWsRGBiIdevW6a2je/fulVh8j4On3YmIqNzT3XDx9ddf65WvWbMGWq3W4IYMALC0tESTJk3kO7JPnz4NwPAIlU6zZs3g5OSEs2fPokGDBkY/VlbmH/MpSuzmSE9Px/bt2/XK1q5dCxsbG5MJpU5h59ne3h6NGjXC999/r3eXeFpaWqFu2unUqRP279+PCxcumKxjar2Y4uzsjO7du+Orr77Cli1bcPv2bYOj2507d8bly5fh4uJidN78/PwKNa2isLOzQ3h4OE6ePImQkBCj09cdRZckSU7wdf76668n9nmxPPJJRETlXvv27dG2bVu88cYbSEtLQ2hoKE6ePInIyEg0btwY/fr1AwCsWLECBw4cQOfOneHn54d79+5h+fLlsLa2lpO8oKAg2NraYt26dahRowbs7OwQGBgIFxcXfPjhh4iIiEBKSgr69u0LNzc33L59GydPnkRmZiYWL15cYrEXlYuLC0aPHo2ZM2eiSpUq2LBhg/yQ94oVK+bbtkKFCoWe57lz56Jjx45o164dJk2aBI1Gg/nz56NChQpISUnJdzpz5szBzp07ERYWJt/tnpKSgl27dmHSpEmoXr16vuvFlIiICKxfvx7jx49HQEAAwsPD9cZPmDABGzduRFhYGCZOnIhatWpBq9Xi+vXr2LVrF2bPno1atWoVajkXxQcffIDmzZujRYsWePnllxEQEIC0tDT8/fff2Lt3L3bu3Ang0TWcmzZtwquvvorevXvjypUriIqKgr+/v8F1xk+EfG9HekLwbnciosdT0F2qZZnubve8dyTndf/+fTFlyhTh5+cnrKysROXKlcVrr72m99sSExMjevbsKXx9fYVarRaurq6ibdu2Yt++fXp9ff3116Jq1arCysrK4A7r/fv3i06dOolKlSoJa2tr4ePjI7p37y5++OEHuY7ujvY7d+4YxJn3bvfCxi7Eo7vEu3TpUuAy02nZsqWoUaOG2Ldvn6hfv75Qq9WicuXK4u233xY5OTl68wRArF+/3mg/hZlnIYTYsmWLCAkJEdbW1sLPz0/Mnz/f6PzmvdtdCCFu3rwpXnjhBeHp6SlUKpXw9vYW/fv3F4mJiXIdU+sl793uOhqNRnh7ewsAIjIy0ui8paenixkzZohnn31WWFtbCycnJ1G7dm0xefJkkZycbGLJPqJbvnmZWk8AxKuvvqpXdvnyZTFixAjh7e0trKyshLu7u2jevLlYtGiRXCcnJ0e89dZbws/PT6jValG7dm2xYcMGg/nW3e3+7rvvGp22qWWgU1x3u0v/TvCJlpaWBicnJ6Smpupdi1ESvnk5BINWPDq1sjYiCIO+uFSi0yMiUkJWVhauXr2KwMBAvbuN6ekWHh6OpKQknDlzprRDoTKgoP9HCpuv8ZpPIiIiIlIMk888JmWPkYcnZo8uxUiIiIiIyh/ecGTAwsQwERFR+VKYh6QTFTdmV0RERESkGCafRERERKQYJp9ERE+RMvCAEyJ6QhXX/x9MPvMIlq7Lw9VzDRMRlWW6N+toNJpSjoSIyqrs7GwAj97+9TiYfOYx0uq/V4y9ZLWjFCMhIio+lpaWsLS0RFpaWmmHQkRlkBACqampUKvVUKlUj9UX73YnInoKSJIEd3d3xMfHQ61Ww97eXu890ERExgghkJ2djdTUVNy7dw+VK1d+7D6ZfBIRPSWcnJyQmZmJpKQk3Llzp7TDIaIyRK1Wo3LlysXypkkmn0RETwlJkuDl5QV3d3f52i0iooJYWlo+9qn23Jh8EhE9ZXTXfxIRlQbecEREREREimHySURERESKYfJJRERERIph8klEREREimHySURERESKYfJJRERERIph8klEREREimHymUc67OThNGFbipEQERERlT9MPvOIyh7x37BmhMl6RERERGQ+Jp9EREREpBgmn0RERESkGCafRERERKQYJp9EREREpBgmn3k0sjgrDze2OFeKkRARERGVP0w+8xhgecDoMBERERE9PiafRERERKQYJp9EREREpBgmn0RERESkGCafRERERKQYJp9EREREpBgmn0RERESkGCafRERERKQYJp9EREREpBgmn0RERESkGCafRERERKQYJp9EREREpBgmn3nECVd5OFa4lGIkREREROUPk8883tP0NzpMRERERI+PyScRERERKYbJJxEREREphsknERERESmGyScRERERKYbJZx7tLI7/N2x5PJ+aRERERGQuJp95dLT8TR7uZHGsFCMhIiIiKn+YfBIRERGRYph8EhEREZFimHwSERERkWKYfBIRERGRYph8EhEREZFimHwSERERkWKYfBIRERGRYph8EhEREZFimHwSERERkWKYfBIRERGRYph8EhEREZFimHzmcU74y8NnhW8pRkJERERU/jD5zOMzTZdcw11LMRIiIiKi8ofJJxEREREphsknERERESmGyScRERERKYbJJxEREREphslnHn0sD+YaPlSKkRARERGVP0w+8wi1+EsebpZrmIiIiIgeH5NPIiIiIlIMk08iIiIiUgyTTyIiIiJSDJNPIiIiIlIMk08iIiIiUgyTTyIiIiJSDJNPIiIiIlIMk08iIiIiUgyTTyIiIiJSDJNPIiIiIlIMk08iIiIiUgyTzzx+zQk2OkxEREREj4/JZx7rta3k4e+04aUXCBEREVE5xOSTiIiIiBTD5JOIiIiIFMPkk4iIiIgUw+STiIiIiBTD5DOPCKudRoeJiIiI6PEx+cyjpnRVHq4lXSu9QIiIiIjKISafRERERKQYJp9EREREpBgmn0RERESkGCafRERERKQYJp9EREREpBgmn0RERESkGCafRERERKQYJp9EREREpBgmn0RERESkGCafRERERKQYJp9EREREpBgmn3nszmmQa7h+KUZCREREVP4w+czjR20jeXhXrmEiIiIienxMPomIiIhIMUw+iYiIiEgxTD6JiIiISDFMPomIiIhIMUw+83jdaoPRYSIiIiJ6fEw+8/CVbsvDftKdUoyEiIiIqPxh8klEREREimHySURERESKYfJJRERERIph8klEREREimHySURERESKYfJJRERERIph8klEREREiilS8vnxxx8jMDAQNjY2qF+/Pg4fPpxv/YMHD6J+/fqwsbFBlSpVsGLFiiIFS0RERERlm9nJ57fffovXX38dM2fOxMmTJ9GiRQt06tQJN27cMFr/6tWr6Ny5M1q0aIGTJ09ixowZGDduHDZu3PjYwRMRERFR2WJ28rl48WK8+OKLGDlyJIKDg7FkyRL4+vpi+fLlRuuvWLECfn5+WLJkCYKDgzFy5Ei88MILWLRo0WMHT0RERERli1nJ58OHD/H777+jffv2euXt27fHkSNHjLaJiYkxqN+hQwccP34c2dnZRts8ePAAaWlpeh8iIiIiKvvMSj6TkpKg1Wrh4eGhV+7h4YGEhASjbRISEozW12g0SEpKMtpm3rx5cHJykj++vr7mhPlYNmhbysPrtWGKTZeIiIjoaVCkG44kSdL7LoQwKCuovrFynenTpyM1NVX+3Lx5syhhFsmv1o1QoU5HVKjTEUdtmyk2XSIiIqKngZU5lV1dXWFpaWlwlPP27dsGRzd1PD09jda3srKCi4uL0TZqtRpqtdqc0IrNrY+GAhhaKtMmIiIiKu/MOvJpbW2N+vXrY8+ePXrle/bsQWhoqNE2TZs2Nai/e/duNGjQACqVysxwiYiIiKgsM/u0+8SJE/HZZ5/hiy++wLlz5zBhwgTcuHEDY8aMAfDolPmwYcPk+mPGjMH169cxceJEnDt3Dl988QU+//xzTJ48ufjmgoiIiIjKBLNOuwPAgAEDkJycjDlz5iA+Ph41a9bEjh074O/vDwCIj4/Xe+ZnYGAgduzYgQkTJmDZsmXw9vbGhx9+iD59+hTfXBARERFRmSAJ3d0/T7C0tDQ4OTkhNTUVjo6OpR0OEREREeVR2HyN73YnIiIiIsUw+SQiIiIixTD5JCIiIiLFMPkkIiIiIsUw+SQiIiIixTD5JCIiIiLFMPkkIiIiIsUw+SQiIiIixTD5JCIiIiLFMPkkIiIiIsUw+SQiIiIixTD5JCIiIiLFMPkkIiIiIsUw+SQiIiIixTD5JCIiIiLFMPkkIiIiIsUw+SQiIiIixTD5JCIiIiLFMPkkIiIiIsUw+SQiIiIixTD5JCIiIiLFMPk0QpIk+fOk91uWYi2pfitWrAhJklCxYsVi6xMAfH19IUkSfH19i7XfcePGQZIkjBs3rtj6PHXqFMLDw3Hq1Kli67Mk+12+fDlsbGywfPnyYutzz549CAgIwJ49e4qtTwCIj49HVFQU4uPjy0S/ZUlZWgZlKVaiJx2TTyrzUlNT9f4tLrGxsXr/Fpe9e/fq/Vsc/vrrLxw8eBB//fVXsfVZkv3u27cPDx48wL59+4qtz6NHj+L69es4evRosfUJPEo6Zs+eXSLJZ0n0W5aUpWVQlmIletIx+SQiIiIixTD5JCIiIiLFMPkkIiIiIsUw+SQiIiIixTD5JCIiIiLFMPkkIiIiIsUw+aQyz8nJSe/f4lK5cmW9f4tLmzZt9P4tDjVq1EDLli1Ro0aNYuuzJPtt3bo11Go1WrduXWx9Nm7cGP7+/mjcuHGx9QkAXl5eiIyMhJeXV5notywpS8ugLMVK9KSThBCitIMoSFpaGpycnJCamgpHR8fSDoeIiIiI8ihsvsYjn0RERESkGCafRERERKQYJp9EREREpBgmn0RERESkGCafRERERKQYJp9EREREpBgmn0RERESkGCafRERERKQYJp9EREREpBgmn0RERESkGCafRERERKQYq9IOoDB0r59PS0sr5UiIiIiIyBhdnqbL20wpE8lneno6AMDX17eUIyEiIiKi/KSnp8PJycnkeEkUlJ4+AXJychAXFwcHBwdIklTi00tLS4Ovry9u3rwJR0fHEp8ePR6ur7KH66xs4foqe7jOypbysr6EEEhPT4e3tzcsLExf2VkmjnxaWFjAx8dH8ek6OjqW6Y3gacP1VfZwnZUtXF9lD9dZ2VIe1ld+Rzx1eMMRERERESmGyScRERERKYbJpxFqtRqRkZFQq9WlHQoVAtdX2cN1VrZwfZU9XGdly9O2vsrEDUdEREREVD7wyCcRERERKYbJJxEREREphsknERERESmGyWceH3/8MQIDA2FjY4P69evj8OHDpR0SmRAVFQVJkvQ+np6epR0W/evQoUPo1q0bvL29IUkSNm/erDdeCIGoqCh4e3vD1tYW4eHh+Ouvv0onWAJQ8DobMWKEwT7XpEmT0gmWMG/ePDRs2BAODg5wd3dHz549ceHCBb063M+eHIVZX0/LPsbkM5dvv/0Wr7/+OmbOnImTJ0+iRYsW6NSpE27cuFHaoZEJNWrUQHx8vPw5ffp0aYdE/7p//z5q166Njz76yOj4hQsXYvHixfjoo49w7NgxeHp6ol27dvLrdEl5Ba0zAOjYsaPePrdjxw4FI6TcDh48iFdffRW//vor9uzZA41Gg/bt2+P+/ftyHe5nT47CrC/gKdnHBMkaNWokxowZo1dWvXp1MW3atFKKiPITGRkpateuXdphUCEAEJs2bZK/5+TkCE9PTzF//ny5LCsrSzg5OYkVK1aUQoSUV951JoQQw4cPFz169CiVeKhgt2/fFgDEwYMHhRDcz550edeXEE/PPsYjn/96+PAhfv/9d7Rv316vvH379jhy5EgpRUUFuXjxIry9vREYGIiBAwfiypUrpR0SFcLVq1eRkJCgt7+p1Wq0bNmS+9sT7sCBA3B3d8ezzz6LUaNG4fbt26UdEv0rNTUVAODs7AyA+9mTLu/60nka9jEmn/9KSkqCVquFh4eHXrmHhwcSEhJKKSrKT+PGjbF69Wr8+OOP+PTTT5GQkIDQ0FAkJyeXdmhUAN0+xf2tbOnUqRPWrFmDffv24b333sOxY8fQunVrPHjwoLRDe+oJITBx4kQ0b94cNWvWBMD97ElmbH0BT88+ZlXaATxpJEnS+y6EMCijJ0OnTp3k4Vq1aqFp06YICgrCl19+iYkTJ5ZiZFRY3N/KlgEDBsjDNWvWRIMGDeDv74/t27ejd+/epRgZjR07Fn/++Sd+/vlng3Hcz548ptbX07KP8cjnv1xdXWFpaWnw1+Dt27cN/mqkJ5O9vT1q1aqFixcvlnYoVADdUwm4v5VtXl5e8Pf35z5Xyl577TVs2bIF+/fvh4+Pj1zO/ezJZGp9GVNe9zEmn/+ytrZG/fr1sWfPHr3yPXv2IDQ0tJSiInM8ePAA586dg5eXV2mHQgUIDAyEp6en3v728OFDHDx4kPtbGZKcnIybN29ynyslQgiMHTsW33//Pfbt24fAwEC98dzPniwFrS9jyus+xtPuuUycOBFDhw5FgwYN0LRpU3zyySe4ceMGxowZU9qhkRGTJ09Gt27d4Ofnh9u3b+Ott95CWloahg8fXtqhEYB79+7h0qVL8verV6/i1KlTcHZ2hp+fH15//XW88847qFq1KqpWrYp33nkHdnZ2GDx4cClG/XTLb505OzsjKioKffr0gZeXF65du4YZM2bA1dUVvXr1KsWon16vvvoq1q5dix9++AEODg7yEU4nJyfY2tpCkiTuZ0+QgtbXvXv3np59rBTvtH8iLVu2TPj7+wtra2tRr149vUcg0JNlwIABwsvLS6hUKuHt7S169+4t/vrrr9IOi/61f/9+AcDgM3z4cCHEo8fAREZGCk9PT6FWq0VYWJg4ffp06Qb9lMtvnWVkZIj27dsLNzc3oVKphJ+fnxg+fLi4ceNGaYf91DK2rgCIVatWyXW4nz05ClpfT9M+JgkhhJLJLhERERE9vXjNJxEREREphsknERERESmGyScRERERKYbJJxEREREphsknERERESmGyScRERERKYbJJxEREREphsknERERESmGySdRHlqtFiEhIahQoQISExOLpc9r165BkiRIklQs/ZWG6OhoSJKE8PDw0g6lVG3ZsgWSJOHVV18t7VAey40bN2BnZ4eGDRsWW5/lYRsZMWIEJElCVFRUaYdSqiZOnAhJkrB+/frSDoXKISafRHmsXLkSp0+fxrhx4+Dh4VHa4SgiOjoaUVFROHXqVGmH8sTr3r07mjRpgpUrV+LMmTPF0mdAQAAsLCzkdz0rYfLkycjMzMTbb7+t2DRLW1RUFKKionD37t3SDuWJN2PGDDg4OGDKlCnIysoq7XConGHySZRLWloa3nzzTTg4OGDq1KmlHY5ioqOjMXv27HyTTycnJ1SrVg1+fn7KBfaEmjNnDrRaLSZNmvTYfZ04cQLXr19H06ZN4enpWQzRFezIkSNYv349mjdvjvbt2ysyzSfB7NmzMXv27HyTTy8vL1SrVg2urq7KBfYEcnV1xWuvvYbr16/j/fffL+1wqJxh8kmUS3R0NJKTk9G/f39UrFixtMN5ovTq1Qvnz5/H6tWrSzuUUte2bVsEBgZi9+7dOH369GP1tWnTJgCPlq9SFi1aBAAYPXq0YtMsK+bNm4fz589j7NixpR1KqRs1ahQkScKHH36I7Ozs0g6HyhEmn0S5LF++HMCj676ITJEkCcOGDQMALFu27LH6+v777wEol3zeunULW7ZsgYODA3r37q3INKlsCggIQFhYGBISErBx48bSDofKESafVK4EBARAkiQcOHAAf//9N4YOHQpvb2+o1WoEBQVh6tSpSE1NNdr2t99+w/nz5+Hm5obmzZsbrZOUlITPPvsMffr0QbVq1eDo6AhbW1tUrVoVo0ePxt9//12oOHfv3o327dvDxcUFdnZ2qFevHpYtWwatVmuyTXx8PCZNmoTg4GDY2dnBwcEBderUyfcatvDwcEiShOjoaCQmJmLcuHF45plnYGNjg4CAAPkGkYMHDwIAIiIi5Buj8t44UtDNJFlZWVi8eDEaN24MJycn2NjYICgoCKNHj8alS5eMtomKioIkSRgxYgS0Wi0++OAD1KlTB/b29nByckLbtm2xf//+Qi1Tc+Sdl6+++gpNmzaFg4MDKlSogNDQUGzYsCHfPnSJ29q1a6HRaIoUx8WLF3H27FmEhIQgKCioUG3y3rxm7ra0du1aaLVadOzYEXZ2dibjWrhwoXyE19bWFo6Ojqhbt26hr5kUQuDjjz9G/fr1UaFCBVSsWBFt27bFjz/+mG+7EydO4Pnnn4evry/UajWcnZ0RHh6Ozz//3OQ86ZbHtWvX8Pvvv2PgwIHw9vaGlZUVRowYId9EpBMYGKi3nee+uaigG44edz9MSUnBpEmTUKVKFajVanh4eGDIkCG4cuVKvsulKHLPS1ZWFmbPno3g4GDY2trCxcUFPXr0KPA6b912zjMeVKwEUTni7+8vAIiFCxcKe3t7YW1tLerWrSuqV68uAAgAomrVqiI2Ntag7TvvvCMAiO7du5vsf+nSpQKAUKlUwsfHR9SvX18EBwcLOzs7AUDY29uLvXv3GrS7evWqPP1ly5YJSZJExYoVRYMGDUTlypXlcT169BDZ2dkG7Y8cOSKcnZ0FAGFlZSXq1KkjatSoISRJEgCEv7+/uHDhgkG7li1bCgBi1qxZwt3dXVhYWIgaNWqIunXriuDgYLFjxw7RrFkz4ejoKC+bZs2ayZ+xY8fKfa1atUoAEC1btjSYTkJCgqhdu7beMq5Xr56wsbERAISNjY3YvHmzQbvIyEgBQAwZMkR06tRJABCBgYGiTp068jK1tLQUW7duNblOiiL3vIwaNUoAEJUrVxb169eXlwUA8fHHH5vsQ6vVCgcHBwFAxMTEFCmO+fPnCwAiMjKy0G0ed1tq3769ACAWL15schp9+vQRAISdnZ2oUqWKaNiwoQgKChJWVlYCgKhSpYrRfSj3ch0wYIC8XBs0aCAqVqwox7Zw4UKj0/3444+FhYWFACAcHR1FgwYN5H0agGjbtq24f/++QTvd+MWLFwsrKythZ2cn6tWrJ2rUqCEiIiLE22+/LZo1aybXa9Cggd52/vnnn8t9DR8+3OQ6edz98K233hIBAQHC0tJS1KxZUwQHBwtLS0sBQLi5uYkbN26YXCdFoZuXiRMninr16glJkkS1atVErVq1hLW1tbyOjx8/brKP3377TQAQFSpUMLo9ERUFk08qV3Q/VCqVSnTs2FHcvn1bHnfq1CkRGBgoAIh27doZtO3QoYP8A2HK0aNHxbZt20RGRoZeeUZGhnj//feFhYWF8PT0FJmZmXrjcycMKpVKTJs2TTx48EAe/91338mJ2ttvv63XNjk5WXh6egoAok2bNiI+Pl4ed+HCBVGzZk0BQNSoUUM8fPhQr63uR8/S0lKEh4fr/bjlngddvVWrVpmc9/ySz3bt2gkAws/PT++HLCUlRfTr10/+kbt48aJeO13yqVKphJ+fnzh69Kg8Lj09XXTv3l1OSHNyckzGZi7dvKhUKlGpUiWxY8cOedzDhw/F6NGjBQDh4OAg0tPTTfbTpk0bAUDMmzevSHE0btxYABCnTp0qdJvH2ZY0Go2wt7cXAMTPP/9schqbN28WMTExQqvV6pXHx8eLESNGCACiZ8+eBu1yL1cbGxvx3XffyeMePHggpk2bJgAISZLE4cOH9doePnxYTjwnTZqktw/t3LlT/qPgpZdeMpiubnlYWlqKcePGiXv37snjcm/nunpXr141Oe+mks/i2A9VKpVo06aNuHXrljzu4sWLomrVqgKAGDFihMm4ikI3LyqVStSuXVucP39eHpeYmCiaNGkiAIjw8HCTfWRnZwu1Wv1Yf2QR5cXkk8oVXfJZsWJFkZqaajD+0KFD8g9Q3v9IdT8A+SVgBRk0aJAAIDZs2KBXnjthaN26tdG2s2fPFgBEpUqV9I7uvP322wKAcHJyEikpKQbtzp07J/9or1mzRm+c7kfPxcXFaNu89YqSfP7yyy/yvO3fv9+g3cOHD+X1MmrUKL1xuuQTgDhw4IBB27i4OKFSqQQA8eeff5qMzVy6eQEgoqOjDcZnZGQINzc3AUBs2bLFZD/Dhg0zmRAV5NatW0KSJFGlShWz2j3OthQbG1uoBCw/OTk5wtvbW0iSpPfHnRD6y3XOnDlG27dq1UoAEB06dNAr1/0BY+yPGyGEWLlypZxg5j1CqJtmmzZt8o39cZLP4tgPnZ2dRXJyskHb9evXy+OLk25erK2txd9//20w/tixY/IfA3fv3jXZj5+fnwAg1q5dW6zx0dOL13xSuTRy5Eg4OjoalLdo0QINGjQAAGzbtk1v3O3btwEALi4u+fadnZ2NTZs24bXXXkOXLl0QFhaG5s2bo3nz5vj5558BAMePHzfZfuLEiUbLx40bBysrK6SkpODIkSNyuS7OkSNHGr0Dv3r16ujevTsAYPv27Ub77tevX4ndva+Lr169ekavB1WpVJgwYUK+8dWqVQstW7Y0KPfy8kJgYCCAR9chFjdHR0f5xqHcbG1tUbdu3QKnq9tWdNuOOTZv3gwhxGPdaGTutpQ7zoK287t37+KTTz5BREQEOnTogBYtWqB58+Zo0aIF7t27ByEETpw4YbStlZUVxo0bZ3Sc7vFUe/fuRWZmJgAgIyNDvrZ38uTJRttFRETAzc0NWq0Wu3btMlpn1KhR+c7T4yiO/XDIkCFwdnY2KG/WrBkA4J9//sE///xTTBH/p0OHDqhatapBef369aFWqyGEwOXLl022f5ztnMgYq9IOgKgk1KxZ0+S4GjVq4Pjx4zh79qxeue6H0MbGxmTbK1euoFu3bgZt80pKSjI7tooVK6Jy5cq4fv06zp49i7Zt2wIAzp8/D+BRgmZKSEgINm/eLNct7DSLg26aISEhJuvoxsXFxSE9PR0ODg5646tVq2ayrYeHB/7++2/cu3evGKLV9+yzz5p865TuBQP5TVe3rei2HXMUxyOWzN2WcseZ33a+Y8cODB06tMBEyNR27uPjAycnJ6PjatSoAQDQaDS4ePEiQkJCcOnSJfmmLVPbuUqlQnBwMO7cuVOq2/nj7IemtvPcL7O4d++e0QT1cZiariRJcHd3x82bN0tsOycyhkc+qVzK72HdunHp6el65bqHSpv6wRVCoHfv3vLdyRs3bsSNGzeQlZUF8egSFsyaNQsA8n0mnrmxpaWlFdjOy8tLr25e9vb2Jts+LnPiy10/t/zis7B49N9UTk5OUUM06XGnq9tWzH0geUpKCg4ePAgPDw80bdrUrLa5mbst5Y7T1HZ+69Yt9OvXD//88w969eqF/fv3IzExEQ8fPpS38xYtWgAwvZ0XJq7cseXeJsrDdm5ufLptDShf2zmRKTzySeVSfu9k143Le/TN09MTcXFxSE5ONtru+PHj+OOPP2BjY4OffvoJbm5uBnXyO+KZe/qm3hJkLDZHR0ekpKTk++rF+Ph4ua7SdNMsTHy565cHum3F3NewbtmyBRqNBj169NBLPMxl7raUO3FKTk42Gve6deuQkZGBhg0bYsOGDUbjK2g7L8z+lzu23NtEQkIC/P39jbYt7e38Sd4PS1JRt3MiU3jkk8ql/N65rRv33HPP6ZXrrgU11VZ3TVRwcLDRxBMAfvnllyLHlpqailu3bhnEVr16dQDI9006unHBwcEFTt8YU6eeC8Oc+CpXrmyQ9Jdluvlq2LChWe10p9wf9yHv5m5LDg4OePbZZ/Ntq9vOmzdvbjLxLOh5tjdv3jT5PF3ddK2srOTrEJ955hlYWT06FmJqO8rOzpZPZxd1O38cSuyHT6KEhAT5jw3d/5FEj4vJJ5VLn332mcFpdQD4+eef5ZuBunTpojeuVatWAIBff/3VaJ+2trYAHv1nbOwU1fbt2/Hnn38WGJup9yQvXboUGo0GFStWRGhoqFzetWtXAI/mydhDrP/++29s2bIFgOE8FZbuYeNFuaZLF9/vv/+OAwcOGIzPzs6W57mo8T2J/vnnHzkJM/XgfWMyMjKwe/duODk5ydtcUZm7LQGF385jY2ONjl+wYEG+L0MAHl3P+dFHH+Ubc+vWreVp2dnZyXHpXv2Z15dffonbt2/DysoKHTp0yHf6phTHdl6S++GTSHfDWvXq1fUunyF6HEw+qVy6d+8eBg4cqHd68PTp0/JrM9u0aWNwrV27du2gVqtx+vRpo9fDhYaGwtraGvHx8Zg6dare9W7btm3DkCFD5B/T/Bw8eBCzZs3Sa79x40a88847AB7dDZz7zTNjxoyBp6cnUlNT0b9/f73TlhcvXkS/fv2g1WpRs2ZN9OvXr8DpG/PMM88AAA4cOAAhhFltQ0ND0b59ewCP3qiS+w7o1NRUDBs2DNeuXYOdnR2mTp1apPhM0b3RqjReh6pbVvXr18/3OsC8du3ahczMTHTp0gXW1taPFYO52xLwXxJl7A8F4L9EeuPGjVi/fr1cnp2djfnz5+O9994rcDtXqVR4++235VeH6tr/73//w969eyFJEmbOnKnX5s0334SFhQUOHjyIKVOm4MGDB/K43bt3y3fBv/jii/D19c13+qbotvN9+/aZ3VaJ/dCY3G+0io6OLrZ+C0u3nZSnhJqeAKX1jCeikqB7nuSCBQuEnZ2dsLa2FvXq1RPBwcHyM/6CgoL0HvKc2/PPPy8AiKVLlxodP3fuXLkfZ2dnvbfK1K9fX0yePFkAEMOHD9drl/vZjLq3JFWsWFE0bNhQ+Pj4yOO6du1q8g1HlSpVkh8YXadOHVGzZk35uYIFvVmloGeXHj16VO7Lx8dHNG/eXLRs2VKMHz9ermPOG46qVasm6tevL2xtbQXw6A1HmzZtMmine85n3uVV2HnQre/82huT37zo5PemGyGE6NmzpwCg93acwhgyZIgAINavX29WO53H3Za0Wq283E6fPm10vO6Zm7rtoUGDBsLJyUkAEGPGjDG5TnIvV93LBXx8fETDhg313nBk6qH8y5Yt03vDUcOGDUVAQIDeczzze8NRQc8uXbRokVw3ODhYhIWFiZYtW+rNR0FvOCqp/dDUPORe3+Y+g7igbViI//YhY8/ozc7OFm5ubkKSJIMXRBA9Dh75pHKpUaNGOH78OHr37o24uDhcvnwZAQEBmDRpEo4dO4bKlSsbbTd27FgAMHmE4X//+x++/vprNGzYEJmZmTh37hwqVaqEOXPm4Oeffy7U3bZjx47F9u3bUa9ePVy8eBFJSUmoXbs2PvzwQ2zevFm+9i23pk2b4syZM5gwYQKqVKmC8+fP48qVK6hZsybefPNNnDp1Sr6WrygaNWqEzZs3Izw8HPfu3cORI0dw8ODBAt/7rOPh4YGYmBgsWrQIDRs2RFxcHE6fPg0PDw+MGjUKf/75J3r27Fnk+IzRaDTykW1zr7l8XElJSdi+fTucnZ0xaNCgQrfLzs7G9u3bYWNjg06dOj12HEXZliwsLPDyyy8DML6dW1hYYOvWrYiKikLVqlWRmJiIy5cvIyQkBKtXr8by5csLFds333yDDz74AK6urjh79iy0Wi1at26NHTt2YNq0aUbbvPLKK/jtt98wePBgODg44NSpU0hJSUFYWBg+/fRT7Nq1y+T76AtjwoQJWLRoEWrXro3r16/j0KFDOHjwIK5du1ao9iW9Hxqju4lJkiTUr1+/WPsuyI4dO3Dnzh106NBBPmpMVCxKO/slKk75/RVfWF27dhUAjL6jnZ4cMTExAoDw9vYWWVlZik575syZAoB47733zGr3448/CgCie/fuRZ527iNhRZWeni68vLyEk5OT0Tfu0JNj3rx5AoDo06eP4tNu3ry5kCRJHDt2TPFpU/nGI59EeSxevBgqlQozZswo7VAoHwcPHgQATJs2DWq1WrHp3rlzBx988AGeffZZvPbaa2a1LY4HyxeHChUq4J133kFqaioWLFhQqrFQ/g4ePAhJkuRnCCtlx44d+PnnnzF8+HDe5U7Fjs/5JMqjatWqWL16Nc6fP4/ExEQ+2+4JdejQIXh5eZXoKxWNuXLlCiZNmoSOHTtCpVKZ1Xb58uWFPm1d0oYPH87XJT7htFotjhw5gh49eqB27dqKTjs9PR2RkZEYM2aMotOlp4MkhJm3thI9wQICAnD9+nXs37/frMffEJUV165dk993z/++iags4ml3IiIiIlIMj3wSERERkWJ45JOIiIiIFMPkk4iIiIgUw+STiIiIiBTD5JOIiIiIFMPkk4iIiIgUw+STiIiIiBTD5JOIiIiIFMPkk4iIiIgUw+STiIiIiBTzfymgRBgNSjp/AAAAAElFTkSuQmCC",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 800x500 with 1 Axes>"
       ]
@@ -5295,7 +5329,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -5411,7 +5445,7 @@
        "4       0.016667  "
       ]
      },
-     "execution_count": 29,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5442,7 +5476,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -5456,8 +5490,8 @@
        "              <ul class=\"xr-sections group-sections\">\n",
        "              \n",
        "            <li class = \"xr-section-item\">\n",
-       "                  <input id=\"idata_posterior3f28aade-d843-4c8d-bb3f-e509f9bccc65\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
-       "                  <label for=\"idata_posterior3f28aade-d843-4c8d-bb3f-e509f9bccc65\" class = \"xr-section-summary\">posterior</label>\n",
+       "                  <input id=\"idata_posteriore9a2725a-93d8-49d9-a249-39cb59eb7843\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_posteriore9a2725a-93d8-49d9-a249-39cb59eb7843\" class = \"xr-section-summary\">posterior</label>\n",
        "                  <div class=\"xr-section-inline-details\"></div>\n",
        "                  <div class=\"xr-section-details\">\n",
        "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
@@ -5854,15 +5888,15 @@
        "    1|edu:eth             (chain, draw, edu:eth__factor_dim) float64 -0.1216 ...\n",
        "    p(abortion, n)_mean   (chain, draw, p(abortion, n)_obs) float64 0.5157 .....\n",
        "Attributes:\n",
-       "    created_at:                  2023-09-09T21:52:09.389695\n",
+       "    created_at:                  2023-09-17T17:39:00.290773\n",
        "    arviz_version:               0.16.1\n",
        "    modeling_interface:          bambi\n",
-       "    modeling_interface_version:  0.13.0.dev</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-26099e04-2187-40b5-83d5-73bec17019f5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-26099e04-2187-40b5-83d5-73bec17019f5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>state__factor_dim</span>: 46</li><li><span class='xr-has-index'>eth__factor_dim</span>: 4</li><li><span class='xr-has-index'>edu__factor_dim</span>: 5</li><li><span class='xr-has-index'>male:eth__factor_dim</span>: 8</li><li><span class='xr-has-index'>edu:age__factor_dim</span>: 30</li><li><span class='xr-has-index'>edu:eth__factor_dim</span>: 20</li><li><span class='xr-has-index'>p(abortion, n)_obs</span>: 11040</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2449f36d-90fd-4ab0-956e-cde364fc2c6c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2449f36d-90fd-4ab0-956e-cde364fc2c6c' class='xr-section-summary' >Coordinates: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-0de6939e-80fd-4f0b-a544-71d0fe87b5e4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0de6939e-80fd-4f0b-a544-71d0fe87b5e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06501a7c-9478-4487-82c7-c3bfacdad816' class='xr-var-data-in' type='checkbox'><label for='data-06501a7c-9478-4487-82c7-c3bfacdad816' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-801b2d10-c5ce-41e9-a224-ba263e431ee7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-801b2d10-c5ce-41e9-a224-ba263e431ee7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d218c30-9292-40d9-8633-4f258eeb6f85' class='xr-var-data-in' type='checkbox'><label for='data-0d218c30-9292-40d9-8633-4f258eeb6f85' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>state__factor_dim</span></div><div class='xr-var-dims'>(state__factor_dim)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;AK&#x27; &#x27;AL&#x27; &#x27;AR&#x27; ... &#x27;WI&#x27; &#x27;WV&#x27; &#x27;WY&#x27;</div><input id='attrs-7c497431-9954-4222-9e94-e8ef0c49ca82' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7c497431-9954-4222-9e94-e8ef0c49ca82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d185197-6c63-4d24-a71c-c00867b9bf20' class='xr-var-data-in' type='checkbox'><label for='data-1d185197-6c63-4d24-a71c-c00867b9bf20' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;AK&#x27;, &#x27;AL&#x27;, &#x27;AR&#x27;, &#x27;CA&#x27;, &#x27;CO&#x27;, &#x27;DE&#x27;, &#x27;FL&#x27;, &#x27;GA&#x27;, &#x27;HI&#x27;, &#x27;IA&#x27;, &#x27;ID&#x27;, &#x27;IL&#x27;,\n",
+       "    modeling_interface_version:  0.13.0.dev</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b7528326-e50c-4902-b25e-ddc0b436b375' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b7528326-e50c-4902-b25e-ddc0b436b375' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>state__factor_dim</span>: 46</li><li><span class='xr-has-index'>eth__factor_dim</span>: 4</li><li><span class='xr-has-index'>edu__factor_dim</span>: 5</li><li><span class='xr-has-index'>male:eth__factor_dim</span>: 8</li><li><span class='xr-has-index'>edu:age__factor_dim</span>: 30</li><li><span class='xr-has-index'>edu:eth__factor_dim</span>: 20</li><li><span class='xr-has-index'>p(abortion, n)_obs</span>: 11040</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e2af7651-1f73-41dd-93c1-820c66545655' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e2af7651-1f73-41dd-93c1-820c66545655' class='xr-section-summary' >Coordinates: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-e2db81de-e251-4df6-b235-4bfc94ea4845' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e2db81de-e251-4df6-b235-4bfc94ea4845' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d984d8e1-db2b-473d-99d7-7be3449945f0' class='xr-var-data-in' type='checkbox'><label for='data-d984d8e1-db2b-473d-99d7-7be3449945f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-baf526fa-9fc6-4511-b920-efb02ed13d63' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-baf526fa-9fc6-4511-b920-efb02ed13d63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-110e9bae-3eed-458c-8c28-4e99f170e6a1' class='xr-var-data-in' type='checkbox'><label for='data-110e9bae-3eed-458c-8c28-4e99f170e6a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>state__factor_dim</span></div><div class='xr-var-dims'>(state__factor_dim)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;AK&#x27; &#x27;AL&#x27; &#x27;AR&#x27; ... &#x27;WI&#x27; &#x27;WV&#x27; &#x27;WY&#x27;</div><input id='attrs-91f97281-fa4d-44b2-8925-3898bd0437d6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-91f97281-fa4d-44b2-8925-3898bd0437d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55a8ef30-c267-4d8b-90e4-6d1d99468f54' class='xr-var-data-in' type='checkbox'><label for='data-55a8ef30-c267-4d8b-90e4-6d1d99468f54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;AK&#x27;, &#x27;AL&#x27;, &#x27;AR&#x27;, &#x27;CA&#x27;, &#x27;CO&#x27;, &#x27;DE&#x27;, &#x27;FL&#x27;, &#x27;GA&#x27;, &#x27;HI&#x27;, &#x27;IA&#x27;, &#x27;ID&#x27;, &#x27;IL&#x27;,\n",
        "       &#x27;KS&#x27;, &#x27;KY&#x27;, &#x27;LA&#x27;, &#x27;MA&#x27;, &#x27;MD&#x27;, &#x27;ME&#x27;, &#x27;MI&#x27;, &#x27;MN&#x27;, &#x27;MO&#x27;, &#x27;MS&#x27;, &#x27;MT&#x27;, &#x27;NC&#x27;,\n",
        "       &#x27;ND&#x27;, &#x27;NE&#x27;, &#x27;NH&#x27;, &#x27;NJ&#x27;, &#x27;NM&#x27;, &#x27;NV&#x27;, &#x27;NY&#x27;, &#x27;OH&#x27;, &#x27;OK&#x27;, &#x27;OR&#x27;, &#x27;PA&#x27;, &#x27;RI&#x27;,\n",
-       "       &#x27;SC&#x27;, &#x27;SD&#x27;, &#x27;TN&#x27;, &#x27;UT&#x27;, &#x27;VA&#x27;, &#x27;VT&#x27;, &#x27;WA&#x27;, &#x27;WI&#x27;, &#x27;WV&#x27;, &#x27;WY&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>eth__factor_dim</span></div><div class='xr-var-dims'>(eth__factor_dim)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;Black&#x27; &#x27;Hispanic&#x27; &#x27;Other&#x27; &#x27;White&#x27;</div><input id='attrs-a8a70b74-5e2a-4485-8028-f94b266514a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a8a70b74-5e2a-4485-8028-f94b266514a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bdca3648-71e8-4b43-9aa3-e58857069a26' class='xr-var-data-in' type='checkbox'><label for='data-bdca3648-71e8-4b43-9aa3-e58857069a26' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Black&#x27;, &#x27;Hispanic&#x27;, &#x27;Other&#x27;, &#x27;White&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>edu__factor_dim</span></div><div class='xr-var-dims'>(edu__factor_dim)</div><div class='xr-var-dtype'>&lt;U14</div><div class='xr-var-preview xr-preview'>&#x27;4-Year College&#x27; ... &#x27;Some college&#x27;</div><input id='attrs-4442da6b-5104-448f-bec2-9753b67ded3a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4442da6b-5104-448f-bec2-9753b67ded3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a79746b0-24c9-4d08-9ccb-bcf46d055007' class='xr-var-data-in' type='checkbox'><label for='data-a79746b0-24c9-4d08-9ccb-bcf46d055007' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;4-Year College&#x27;, &#x27;HS&#x27;, &#x27;No HS&#x27;, &#x27;Post-grad&#x27;, &#x27;Some college&#x27;],\n",
-       "      dtype=&#x27;&lt;U14&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>male:eth__factor_dim</span></div><div class='xr-var-dims'>(male:eth__factor_dim)</div><div class='xr-var-dtype'>&lt;U13</div><div class='xr-var-preview xr-preview'>&#x27;-0.5:Black&#x27; ... &#x27;0.5:White&#x27;</div><input id='attrs-bdccdcc4-0955-4c73-a096-48d604337638' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bdccdcc4-0955-4c73-a096-48d604337638' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b962e3e-dc5d-4b91-af5e-c90b78f95df0' class='xr-var-data-in' type='checkbox'><label for='data-5b962e3e-dc5d-4b91-af5e-c90b78f95df0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;-0.5:Black&#x27;, &#x27;-0.5:Hispanic&#x27;, &#x27;-0.5:Other&#x27;, &#x27;-0.5:White&#x27;, &#x27;0.5:Black&#x27;,\n",
-       "       &#x27;0.5:Hispanic&#x27;, &#x27;0.5:Other&#x27;, &#x27;0.5:White&#x27;], dtype=&#x27;&lt;U13&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>edu:age__factor_dim</span></div><div class='xr-var-dims'>(edu:age__factor_dim)</div><div class='xr-var-dtype'>&lt;U20</div><div class='xr-var-preview xr-preview'>&#x27;4-Year College:18-29&#x27; ... &#x27;Some...</div><input id='attrs-3a4330ca-c364-4c76-90a1-7a12bf450037' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3a4330ca-c364-4c76-90a1-7a12bf450037' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9d77859-4b61-4c3d-a3cf-941b242e016a' class='xr-var-data-in' type='checkbox'><label for='data-b9d77859-4b61-4c3d-a3cf-941b242e016a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;4-Year College:18-29&#x27;, &#x27;4-Year College:30-39&#x27;, &#x27;4-Year College:40-49&#x27;,\n",
+       "       &#x27;SC&#x27;, &#x27;SD&#x27;, &#x27;TN&#x27;, &#x27;UT&#x27;, &#x27;VA&#x27;, &#x27;VT&#x27;, &#x27;WA&#x27;, &#x27;WI&#x27;, &#x27;WV&#x27;, &#x27;WY&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>eth__factor_dim</span></div><div class='xr-var-dims'>(eth__factor_dim)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;Black&#x27; &#x27;Hispanic&#x27; &#x27;Other&#x27; &#x27;White&#x27;</div><input id='attrs-80ed445a-2a94-4f24-b64a-d7ab6e0f8be1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-80ed445a-2a94-4f24-b64a-d7ab6e0f8be1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9eae95ee-67a0-41f9-ad15-2cb4987dbe10' class='xr-var-data-in' type='checkbox'><label for='data-9eae95ee-67a0-41f9-ad15-2cb4987dbe10' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Black&#x27;, &#x27;Hispanic&#x27;, &#x27;Other&#x27;, &#x27;White&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>edu__factor_dim</span></div><div class='xr-var-dims'>(edu__factor_dim)</div><div class='xr-var-dtype'>&lt;U14</div><div class='xr-var-preview xr-preview'>&#x27;4-Year College&#x27; ... &#x27;Some college&#x27;</div><input id='attrs-0ab76e36-7d89-418d-bfce-26067a5b76dc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0ab76e36-7d89-418d-bfce-26067a5b76dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0996d86d-9a7a-4ff1-acbb-3220b7d4b89e' class='xr-var-data-in' type='checkbox'><label for='data-0996d86d-9a7a-4ff1-acbb-3220b7d4b89e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;4-Year College&#x27;, &#x27;HS&#x27;, &#x27;No HS&#x27;, &#x27;Post-grad&#x27;, &#x27;Some college&#x27;],\n",
+       "      dtype=&#x27;&lt;U14&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>male:eth__factor_dim</span></div><div class='xr-var-dims'>(male:eth__factor_dim)</div><div class='xr-var-dtype'>&lt;U13</div><div class='xr-var-preview xr-preview'>&#x27;-0.5:Black&#x27; ... &#x27;0.5:White&#x27;</div><input id='attrs-57e69cd4-fb88-4932-92ee-024957c566cf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-57e69cd4-fb88-4932-92ee-024957c566cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43b8dbb9-237d-4dda-8e9f-68ef61e3d18e' class='xr-var-data-in' type='checkbox'><label for='data-43b8dbb9-237d-4dda-8e9f-68ef61e3d18e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;-0.5:Black&#x27;, &#x27;-0.5:Hispanic&#x27;, &#x27;-0.5:Other&#x27;, &#x27;-0.5:White&#x27;, &#x27;0.5:Black&#x27;,\n",
+       "       &#x27;0.5:Hispanic&#x27;, &#x27;0.5:Other&#x27;, &#x27;0.5:White&#x27;], dtype=&#x27;&lt;U13&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>edu:age__factor_dim</span></div><div class='xr-var-dims'>(edu:age__factor_dim)</div><div class='xr-var-dtype'>&lt;U20</div><div class='xr-var-preview xr-preview'>&#x27;4-Year College:18-29&#x27; ... &#x27;Some...</div><input id='attrs-9a670e2d-6422-4b2f-a663-f8ef83d585ed' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9a670e2d-6422-4b2f-a663-f8ef83d585ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e152ed4-3386-45b6-b2d1-d59a5c09d2c9' class='xr-var-data-in' type='checkbox'><label for='data-4e152ed4-3386-45b6-b2d1-d59a5c09d2c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;4-Year College:18-29&#x27;, &#x27;4-Year College:30-39&#x27;, &#x27;4-Year College:40-49&#x27;,\n",
        "       &#x27;4-Year College:50-59&#x27;, &#x27;4-Year College:60-69&#x27;, &#x27;4-Year College:70+&#x27;,\n",
        "       &#x27;HS:18-29&#x27;, &#x27;HS:30-39&#x27;, &#x27;HS:40-49&#x27;, &#x27;HS:50-59&#x27;, &#x27;HS:60-69&#x27;, &#x27;HS:70+&#x27;,\n",
        "       &#x27;No HS:18-29&#x27;, &#x27;No HS:30-39&#x27;, &#x27;No HS:40-49&#x27;, &#x27;No HS:50-59&#x27;,\n",
@@ -5870,76 +5904,76 @@
        "       &#x27;Post-grad:40-49&#x27;, &#x27;Post-grad:50-59&#x27;, &#x27;Post-grad:60-69&#x27;,\n",
        "       &#x27;Post-grad:70+&#x27;, &#x27;Some college:18-29&#x27;, &#x27;Some college:30-39&#x27;,\n",
        "       &#x27;Some college:40-49&#x27;, &#x27;Some college:50-59&#x27;, &#x27;Some college:60-69&#x27;,\n",
-       "       &#x27;Some college:70+&#x27;], dtype=&#x27;&lt;U20&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>edu:eth__factor_dim</span></div><div class='xr-var-dims'>(edu:eth__factor_dim)</div><div class='xr-var-dtype'>&lt;U23</div><div class='xr-var-preview xr-preview'>&#x27;4-Year College:Black&#x27; ... &#x27;Some...</div><input id='attrs-3d0c62c0-6cad-454d-ad8c-9cd632cf141d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3d0c62c0-6cad-454d-ad8c-9cd632cf141d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9019821-ba2d-4024-9da0-1cde2d4a2282' class='xr-var-data-in' type='checkbox'><label for='data-d9019821-ba2d-4024-9da0-1cde2d4a2282' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;4-Year College:Black&#x27;, &#x27;4-Year College:Hispanic&#x27;,\n",
+       "       &#x27;Some college:70+&#x27;], dtype=&#x27;&lt;U20&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>edu:eth__factor_dim</span></div><div class='xr-var-dims'>(edu:eth__factor_dim)</div><div class='xr-var-dtype'>&lt;U23</div><div class='xr-var-preview xr-preview'>&#x27;4-Year College:Black&#x27; ... &#x27;Some...</div><input id='attrs-cd87bd9e-aef6-4aa5-a7a6-93c97239970e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cd87bd9e-aef6-4aa5-a7a6-93c97239970e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9503aaa8-2286-4818-baa9-dd49b5e6e9b9' class='xr-var-data-in' type='checkbox'><label for='data-9503aaa8-2286-4818-baa9-dd49b5e6e9b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;4-Year College:Black&#x27;, &#x27;4-Year College:Hispanic&#x27;,\n",
        "       &#x27;4-Year College:Other&#x27;, &#x27;4-Year College:White&#x27;, &#x27;HS:Black&#x27;,\n",
        "       &#x27;HS:Hispanic&#x27;, &#x27;HS:Other&#x27;, &#x27;HS:White&#x27;, &#x27;No HS:Black&#x27;, &#x27;No HS:Hispanic&#x27;,\n",
        "       &#x27;No HS:Other&#x27;, &#x27;No HS:White&#x27;, &#x27;Post-grad:Black&#x27;, &#x27;Post-grad:Hispanic&#x27;,\n",
        "       &#x27;Post-grad:Other&#x27;, &#x27;Post-grad:White&#x27;, &#x27;Some college:Black&#x27;,\n",
        "       &#x27;Some college:Hispanic&#x27;, &#x27;Some college:Other&#x27;, &#x27;Some college:White&#x27;],\n",
-       "      dtype=&#x27;&lt;U23&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>p(abortion, n)_obs</span></div><div class='xr-var-dims'>(p(abortion, n)_obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 11036 11037 11038 11039</div><input id='attrs-29e4c65d-a7eb-4999-9492-be017e720b36' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-29e4c65d-a7eb-4999-9492-be017e720b36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68be4ab0-520d-4cd3-9906-48f041737c71' class='xr-var-data-in' type='checkbox'><label for='data-68be4ab0-520d-4cd3-9906-48f041737c71' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([    0,     1,     2, ..., 11037, 11038, 11039])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-84af6840-95a2-4420-8e62-fc214c7017a8' class='xr-section-summary-in' type='checkbox'  ><label for='section-84af6840-95a2-4420-8e62-fc214c7017a8' class='xr-section-summary' >Data variables: <span>(16)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Intercept</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2358 0.4365 ... -0.06399 0.2565</div><input id='attrs-ea1c8ee3-1fd4-4506-899b-883ccdc1c5cc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ea1c8ee3-1fd4-4506-899b-883ccdc1c5cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b69cd2a-bef8-4b05-9f00-e776ac3acc77' class='xr-var-data-in' type='checkbox'><label for='data-1b69cd2a-bef8-4b05-9f00-e776ac3acc77' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.2357953 ,  0.43651919,  0.8163025 , ...,  0.3761122 ,\n",
+       "      dtype=&#x27;&lt;U23&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>p(abortion, n)_obs</span></div><div class='xr-var-dims'>(p(abortion, n)_obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 11036 11037 11038 11039</div><input id='attrs-9e014ac3-a5ee-4310-95ff-7be2eb39e917' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9e014ac3-a5ee-4310-95ff-7be2eb39e917' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0978eae5-adbf-4a39-9b47-156716e3151e' class='xr-var-data-in' type='checkbox'><label for='data-0978eae5-adbf-4a39-9b47-156716e3151e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([    0,     1,     2, ..., 11037, 11038, 11039])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8e32d188-61c7-4033-b939-0b48485e2de3' class='xr-section-summary-in' type='checkbox'  ><label for='section-8e32d188-61c7-4033-b939-0b48485e2de3' class='xr-section-summary' >Data variables: <span>(16)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>Intercept</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2358 0.4365 ... -0.06399 0.2565</div><input id='attrs-7f8752b3-8701-41ef-9d42-19ca0dd9baae' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7f8752b3-8701-41ef-9d42-19ca0dd9baae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcbd61f9-3c43-41b3-bd2a-25328aa24c70' class='xr-var-data-in' type='checkbox'><label for='data-bcbd61f9-3c43-41b3-bd2a-25328aa24c70' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.2357953 ,  0.43651919,  0.8163025 , ...,  0.3761122 ,\n",
        "         0.82908072,  0.45231933],\n",
        "       [ 1.08283544,  0.04883994,  0.54556097, ..., -3.01791601,\n",
        "        -1.70137604,  0.16253107],\n",
        "       [ 0.33472456,  0.06603835,  0.10798744, ...,  0.57547102,\n",
        "        -1.00089016, -0.92153227],\n",
        "       [ 0.54653198, -0.09063433, -0.0962701 , ..., -0.07409252,\n",
-       "        -0.06398592,  0.25649592]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>male</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1855 0.0546 ... 0.1347 0.1717</div><input id='attrs-6efba9f8-10d1-4dcd-bd83-896b59f736db' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6efba9f8-10d1-4dcd-bd83-896b59f736db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4a4e4b8-fdf2-43fa-9b00-a1c2b6c390b4' class='xr-var-data-in' type='checkbox'><label for='data-a4a4e4b8-fdf2-43fa-9b00-a1c2b6c390b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.18554999, 0.05460117, 0.15523781, ..., 0.26503596, 0.17864307,\n",
+       "        -0.06398592,  0.25649592]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>male</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1855 0.0546 ... 0.1347 0.1717</div><input id='attrs-da8a525e-03e1-4f86-a6d6-4ec1064875aa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-da8a525e-03e1-4f86-a6d6-4ec1064875aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b0cd5f6-6fd9-4f57-937c-e208f8afce33' class='xr-var-data-in' type='checkbox'><label for='data-3b0cd5f6-6fd9-4f57-937c-e208f8afce33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.18554999, 0.05460117, 0.15523781, ..., 0.26503596, 0.17864307,\n",
        "        0.13248461],\n",
        "       [0.17190392, 0.28717712, 0.28285475, ..., 0.23358331, 0.52666889,\n",
        "        0.06526399],\n",
        "       [0.25442286, 0.18649074, 0.14598336, ..., 0.17109997, 0.31019605,\n",
        "        0.3536368 ],\n",
        "       [0.22267076, 0.26419163, 0.15164258, ..., 0.18046546, 0.13468066,\n",
-       "        0.17170458]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>repvote</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.333 -1.437 ... -1.032 -1.216</div><input id='attrs-0b9cc86c-66b4-4276-a9c3-dabcbb612575' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0b9cc86c-66b4-4276-a9c3-dabcbb612575' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acae049b-125d-4f45-baaa-c61a82562013' class='xr-var-data-in' type='checkbox'><label for='data-acae049b-125d-4f45-baaa-c61a82562013' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-1.33328689, -1.43728603, -1.89844705, ..., -1.60984098,\n",
+       "        0.17170458]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>repvote</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.333 -1.437 ... -1.032 -1.216</div><input id='attrs-117ffa88-f563-4cc1-8b54-db81453327cd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-117ffa88-f563-4cc1-8b54-db81453327cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ac16f7d-b063-4f9e-b1f5-7a10b30a364e' class='xr-var-data-in' type='checkbox'><label for='data-6ac16f7d-b063-4f9e-b1f5-7a10b30a364e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-1.33328689, -1.43728603, -1.89844705, ..., -1.60984098,\n",
        "        -0.72676365, -1.26571675],\n",
        "       [-1.28759373, -1.39636135, -1.13573472, ..., -0.61737146,\n",
        "        -1.57143157, -0.40090848],\n",
        "       [-0.92257964, -0.9340195 , -0.84808671, ..., -1.03842182,\n",
        "        -0.74135311, -0.93448461],\n",
        "       [-1.03925721, -1.03933099, -0.85007501, ..., -1.09283078,\n",
-       "        -1.03225784, -1.21606504]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|state_sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2279 0.2581 ... 0.357 0.3526</div><input id='attrs-f22ce281-8280-48c1-8ab8-4c839a54fc7d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f22ce281-8280-48c1-8ab8-4c839a54fc7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53b8cee2-af32-4665-b8c5-71b63a8371e5' class='xr-var-data-in' type='checkbox'><label for='data-53b8cee2-af32-4665-b8c5-71b63a8371e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.22788663, 0.25814198, 0.30612645, ..., 0.28422744, 0.30855403,\n",
+       "        -1.03225784, -1.21606504]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|state_sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2279 0.2581 ... 0.357 0.3526</div><input id='attrs-aaabf35b-7c99-4d26-9aa1-7e60d43f7550' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aaabf35b-7c99-4d26-9aa1-7e60d43f7550' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7f287d5-5f2d-4f83-a71f-1ebc237b0aa1' class='xr-var-data-in' type='checkbox'><label for='data-a7f287d5-5f2d-4f83-a71f-1ebc237b0aa1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.22788663, 0.25814198, 0.30612645, ..., 0.28422744, 0.30855403,\n",
        "        0.28674029],\n",
        "       [0.25427811, 0.2869506 , 0.29105246, ..., 0.32861627, 0.27543615,\n",
        "        0.26341471],\n",
        "       [0.35677381, 0.30736264, 0.36516731, ..., 0.27166973, 0.28866001,\n",
        "        0.31776415],\n",
        "       [0.20504879, 0.25572048, 0.26693605, ..., 0.35942905, 0.35696505,\n",
-       "        0.35264898]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|eth_sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2922 0.6481 ... 1.001 0.5405</div><input id='attrs-87960195-a7f4-4bc2-8a51-3fc6054bcb48' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-87960195-a7f4-4bc2-8a51-3fc6054bcb48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eeceaa74-081b-48d2-866e-10b5913e2070' class='xr-var-data-in' type='checkbox'><label for='data-eeceaa74-081b-48d2-866e-10b5913e2070' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.2921631 , 0.64811353, 0.46894068, ..., 0.36037196, 1.1757669 ,\n",
+       "        0.35264898]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|eth_sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2922 0.6481 ... 1.001 0.5405</div><input id='attrs-db0bab06-df6d-4b0b-b4ff-514829700ee7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-db0bab06-df6d-4b0b-b4ff-514829700ee7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbc374b5-e5b6-4168-b1dc-902a3298603d' class='xr-var-data-in' type='checkbox'><label for='data-dbc374b5-e5b6-4168-b1dc-902a3298603d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.2921631 , 0.64811353, 0.46894068, ..., 0.36037196, 1.1757669 ,\n",
        "        0.79513062],\n",
        "       [0.59163681, 0.71757778, 0.39237225, ..., 2.53012159, 2.58015772,\n",
        "        0.17337584],\n",
        "       [0.13887082, 0.35217904, 0.39475355, ..., 0.68786625, 2.04625487,\n",
        "        1.86577226],\n",
        "       [0.24614945, 0.54402931, 0.40870221, ..., 0.92293547, 1.00104249,\n",
-       "        0.54051062]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|edu_sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4018 0.3691 ... 0.1152 0.08889</div><input id='attrs-05b7b716-ecc0-4d13-9145-f3165f21b875' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-05b7b716-ecc0-4d13-9145-f3165f21b875' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cd443da-916d-4e34-8577-a2120df4acfb' class='xr-var-data-in' type='checkbox'><label for='data-6cd443da-916d-4e34-8577-a2120df4acfb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.40177726, 0.36906181, 0.12511795, ..., 0.07212523, 0.44653234,\n",
+       "        0.54051062]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|edu_sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4018 0.3691 ... 0.1152 0.08889</div><input id='attrs-9f76899a-ab44-4577-b446-8e8475b89ae8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9f76899a-ab44-4577-b446-8e8475b89ae8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f063d9e0-44d7-4dff-ae07-f2aa10f7ef14' class='xr-var-data-in' type='checkbox'><label for='data-f063d9e0-44d7-4dff-ae07-f2aa10f7ef14' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.40177726, 0.36906181, 0.12511795, ..., 0.07212523, 0.44653234,\n",
        "        0.66976489],\n",
        "       [0.00631078, 0.17373649, 0.57101734, ..., 0.31783469, 0.13025087,\n",
        "        0.62022555],\n",
        "       [0.08792224, 0.10185938, 0.26869272, ..., 0.09290535, 0.03162795,\n",
        "        0.20758365],\n",
        "       [0.73962758, 0.23081107, 0.28538795, ..., 0.1436684 , 0.11522592,\n",
-       "        0.08888835]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|male:eth_sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.09973 0.09009 ... 0.1604 0.2017</div><input id='attrs-67dfd0cd-2848-4c51-aa2f-17f3af3229d1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-67dfd0cd-2848-4c51-aa2f-17f3af3229d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5730f63-b28f-4e40-a653-8e4edc088d84' class='xr-var-data-in' type='checkbox'><label for='data-e5730f63-b28f-4e40-a653-8e4edc088d84' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.09972931, 0.09008741, 0.0104649 , ..., 0.24436277, 0.30843254,\n",
+       "        0.08888835]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|male:eth_sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.09973 0.09009 ... 0.1604 0.2017</div><input id='attrs-fe3ead35-9d4a-4a24-b91c-894f693fe628' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fe3ead35-9d4a-4a24-b91c-894f693fe628' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae1bece4-b01f-432f-981e-e88174e1c5b4' class='xr-var-data-in' type='checkbox'><label for='data-ae1bece4-b01f-432f-981e-e88174e1c5b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.09972931, 0.09008741, 0.0104649 , ..., 0.24436277, 0.30843254,\n",
        "        0.11575019],\n",
        "       [0.08582021, 0.05566894, 0.0044242 , ..., 0.10406267, 0.24083354,\n",
        "        0.13214042],\n",
        "       [0.01613223, 0.08062836, 0.20829874, ..., 0.02283508, 0.04888584,\n",
        "        0.11778812],\n",
        "       [0.23467763, 0.11682297, 0.00889169, ..., 0.14732406, 0.16038902,\n",
-       "        0.20172304]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|edu:age_sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.139 0.07881 ... 0.1752 0.1378</div><input id='attrs-ac50efcc-c820-459f-814b-df9ee005d1fb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ac50efcc-c820-459f-814b-df9ee005d1fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5fcdeedd-536a-4819-8176-5afcb24406c6' class='xr-var-data-in' type='checkbox'><label for='data-5fcdeedd-536a-4819-8176-5afcb24406c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.13896325, 0.07880674, 0.15077977, ..., 0.2274383 , 0.19976934,\n",
+       "        0.20172304]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|edu:age_sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.139 0.07881 ... 0.1752 0.1378</div><input id='attrs-277f85ee-437b-40b6-943c-2574af9ce814' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-277f85ee-437b-40b6-943c-2574af9ce814' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-edd239c8-a6a1-4e2c-9b88-d8081f07d092' class='xr-var-data-in' type='checkbox'><label for='data-edd239c8-a6a1-4e2c-9b88-d8081f07d092' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.13896325, 0.07880674, 0.15077977, ..., 0.2274383 , 0.19976934,\n",
        "        0.26169646],\n",
        "       [0.04594554, 0.25734548, 0.14074699, ..., 0.12098062, 0.20249964,\n",
        "        0.12780594],\n",
        "       [0.23094571, 0.21564635, 0.14661088, ..., 0.15523845, 0.24861837,\n",
        "        0.20299868],\n",
        "       [0.23613126, 0.21425098, 0.16440101, ..., 0.17224596, 0.17520894,\n",
-       "        0.13778684]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|edu:eth_sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1356 0.1101 ... 0.2902 0.2252</div><input id='attrs-c1c5332e-f628-42fb-83d8-e77a766c4db9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c1c5332e-f628-42fb-83d8-e77a766c4db9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2f51c53-f45e-4b57-8176-de682c94fe9b' class='xr-var-data-in' type='checkbox'><label for='data-d2f51c53-f45e-4b57-8176-de682c94fe9b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.13564227, 0.11014919, 0.25155446, ..., 0.2837875 , 0.21216988,\n",
+       "        0.13778684]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|edu:eth_sigma</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1356 0.1101 ... 0.2902 0.2252</div><input id='attrs-208eee7b-f4e2-48c4-b543-8961f6463df9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-208eee7b-f4e2-48c4-b543-8961f6463df9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-701db0f4-c6ac-49fa-9bb0-ae869f0e83e9' class='xr-var-data-in' type='checkbox'><label for='data-701db0f4-c6ac-49fa-9bb0-ae869f0e83e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.13564227, 0.11014919, 0.25155446, ..., 0.2837875 , 0.21216988,\n",
        "        0.23004101],\n",
        "       [0.11604053, 0.01230621, 0.24543241, ..., 0.10798228, 0.18381797,\n",
        "        0.25597436],\n",
        "       [0.25055064, 0.18110486, 0.15498204, ..., 0.10596968, 0.18290151,\n",
        "        0.22959195],\n",
        "       [0.21238655, 0.08477365, 0.14353349, ..., 0.36743476, 0.29022249,\n",
-       "        0.22517211]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|state</span></div><div class='xr-var-dims'>(chain, draw, state__factor_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3825 0.02488 ... 0.393 -0.4676</div><input id='attrs-7043cbaa-686c-4df7-8433-9ca2ddb6a0ad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7043cbaa-686c-4df7-8433-9ca2ddb6a0ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3d6b9a3-017c-4383-b571-55eaab7cd8ee' class='xr-var-data-in' type='checkbox'><label for='data-a3d6b9a3-017c-4383-b571-55eaab7cd8ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.38250375,  0.02488263,  0.21664533, ..., -0.22699524,\n",
+       "        0.22517211]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|state</span></div><div class='xr-var-dims'>(chain, draw, state__factor_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.3825 0.02488 ... 0.393 -0.4676</div><input id='attrs-c4ec443c-6df5-480e-83e8-3a9675bd3458' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c4ec443c-6df5-480e-83e8-3a9675bd3458' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73d8283d-3399-435c-9f24-ae34b8930722' class='xr-var-data-in' type='checkbox'><label for='data-73d8283d-3399-435c-9f24-ae34b8930722' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.38250375,  0.02488263,  0.21664533, ..., -0.22699524,\n",
        "          0.42690621,  0.08939394],\n",
        "        [ 0.09604889,  0.61553696, -0.03211364, ..., -0.0214277 ,\n",
        "          0.35967063,  0.08878077],\n",
@@ -5979,7 +6013,7 @@
        "        [ 0.65858299,  0.17171798,  0.21039919, ...,  0.15819054,\n",
        "          0.27328158, -0.3036795 ],\n",
        "        [ 0.46501501,  0.2624723 ,  0.15400031, ...,  0.32887312,\n",
-       "          0.39299399, -0.4676157 ]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|eth</span></div><div class='xr-var-dims'>(chain, draw, eth__factor_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.3689 -0.01268 ... 0.5365</div><input id='attrs-111852a2-09d1-487a-9892-413f75d6cd77' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-111852a2-09d1-487a-9892-413f75d6cd77' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65eaa578-0be8-4176-a6b7-8b995d5f86ab' class='xr-var-data-in' type='checkbox'><label for='data-65eaa578-0be8-4176-a6b7-8b995d5f86ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-3.68853010e-01, -1.26764249e-02,  1.05940562e-01,\n",
+       "          0.39299399, -0.4676157 ]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|eth</span></div><div class='xr-var-dims'>(chain, draw, eth__factor_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.3689 -0.01268 ... 0.5365</div><input id='attrs-93d5968a-e479-42cb-8a3a-75ddeb580c3d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-93d5968a-e479-42cb-8a3a-75ddeb580c3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6a09c7b-acb5-4290-a92a-36146e4d0b76' class='xr-var-data-in' type='checkbox'><label for='data-d6a09c7b-acb5-4290-a92a-36146e4d0b76' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-3.68853010e-01, -1.26764249e-02,  1.05940562e-01,\n",
        "          1.95515035e-01],\n",
        "        [-4.07408561e-01, -7.32408287e-02,  2.57043589e-01,\n",
        "          2.78638817e-01],\n",
@@ -6019,7 +6053,7 @@
        "        [-4.39739910e-02,  4.91938969e-01,  3.05741057e-01,\n",
        "          7.21111591e-01],\n",
        "        [-4.96597357e-01,  3.24731626e-01, -2.82345008e-02,\n",
-       "          5.36478559e-01]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|edu</span></div><div class='xr-var-dims'>(chain, draw, edu__factor_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1912 0.4088 ... -0.02438 -0.07707</div><input id='attrs-a612df3e-b20f-40b4-9c7d-d619caf8f4c1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a612df3e-b20f-40b4-9c7d-d619caf8f4c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c4f7b44-3ffc-42b1-80dd-4a10dc29f6f9' class='xr-var-data-in' type='checkbox'><label for='data-3c4f7b44-3ffc-42b1-80dd-4a10dc29f6f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.19120976,  0.40883343,  0.32287821, -0.2609866 ,\n",
+       "          5.36478559e-01]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|edu</span></div><div class='xr-var-dims'>(chain, draw, edu__factor_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1912 0.4088 ... -0.02438 -0.07707</div><input id='attrs-dc393d1b-f467-4963-b076-bf0b06570f70' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dc393d1b-f467-4963-b076-bf0b06570f70' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1548a8d7-a726-49ba-88a1-5b39917c17a0' class='xr-var-data-in' type='checkbox'><label for='data-1548a8d7-a726-49ba-88a1-5b39917c17a0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.19120976,  0.40883343,  0.32287821, -0.2609866 ,\n",
        "          0.34378828],\n",
        "        [-0.08894206,  0.22194809,  0.34861253, -0.34084547,\n",
        "          0.12977861],\n",
@@ -6059,7 +6093,7 @@
        "        [ 0.05984333,  0.10854492, -0.11492089, -0.02351373,\n",
        "         -0.07170203],\n",
        "        [ 0.06333353,  0.07045411, -0.0609233 , -0.02437717,\n",
-       "         -0.07707395]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|male:eth</span></div><div class='xr-var-dims'>(chain, draw, male:eth__factor_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.003232 -0.07587 ... -0.1035</div><input id='attrs-e595d545-9a01-4219-bc5e-f7aa7c0e7368' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e595d545-9a01-4219-bc5e-f7aa7c0e7368' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20e9d76c-8561-4397-b951-b1ee6271dd39' class='xr-var-data-in' type='checkbox'><label for='data-20e9d76c-8561-4397-b951-b1ee6271dd39' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 3.23210975e-03, -7.58727100e-02, -3.93659154e-02, ...,\n",
+       "         -0.07707395]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|male:eth</span></div><div class='xr-var-dims'>(chain, draw, male:eth__factor_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.003232 -0.07587 ... -0.1035</div><input id='attrs-7c3c4d88-ae7d-4bff-861d-43cba612c06f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7c3c4d88-ae7d-4bff-861d-43cba612c06f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5354e208-1151-45a3-808c-c2ce4fdbca11' class='xr-var-data-in' type='checkbox'><label for='data-5354e208-1151-45a3-808c-c2ce4fdbca11' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 3.23210975e-03, -7.58727100e-02, -3.93659154e-02, ...,\n",
        "          4.66359832e-02,  6.55923347e-02, -1.37860640e-02],\n",
        "        [-3.15997696e-02, -1.91314387e-02, -2.28263982e-05, ...,\n",
        "         -1.98865846e-02, -3.34916561e-02,  1.52312912e-01],\n",
@@ -6099,7 +6133,7 @@
        "        [ 3.65321648e-02, -5.14534881e-02,  2.85102853e-01, ...,\n",
        "          6.69374138e-02,  1.33906766e-03, -7.85379772e-02],\n",
        "        [ 2.26404415e-01, -7.23006452e-02,  1.42095007e-01, ...,\n",
-       "          7.10830962e-02,  1.33409009e-02, -1.03505741e-01]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|edu:age</span></div><div class='xr-var-dims'>(chain, draw, edu:age__factor_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.1959 0.01111 ... 0.07365 0.1426</div><input id='attrs-12f80b38-3d82-475a-abbe-fda3f34b2295' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-12f80b38-3d82-475a-abbe-fda3f34b2295' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7658758a-8887-4602-a328-6302b4923234' class='xr-var-data-in' type='checkbox'><label for='data-7658758a-8887-4602-a328-6302b4923234' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-0.19587975,  0.01110816,  0.0529932 , ...,  0.06955911,\n",
+       "          7.10830962e-02,  1.33409009e-02, -1.03505741e-01]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|edu:age</span></div><div class='xr-var-dims'>(chain, draw, edu:age__factor_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.1959 0.01111 ... 0.07365 0.1426</div><input id='attrs-0d51b080-5940-4e2a-836a-bad5d6f3bf46' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0d51b080-5940-4e2a-836a-bad5d6f3bf46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09cf447a-a3a2-4a9c-9333-378f68e380a9' class='xr-var-data-in' type='checkbox'><label for='data-09cf447a-a3a2-4a9c-9333-378f68e380a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-0.19587975,  0.01110816,  0.0529932 , ...,  0.06955911,\n",
        "          0.09093148,  0.04398449],\n",
        "        [ 0.00346945,  0.06466284,  0.11923803, ...,  0.00927192,\n",
        "          0.00745912,  0.09853649],\n",
@@ -6139,7 +6173,7 @@
        "        [ 0.21476261,  0.08806381,  0.2784128 , ...,  0.00388492,\n",
        "          0.10326975,  0.0531971 ],\n",
        "        [ 0.10948511,  0.01465483,  0.10216166, ..., -0.01694412,\n",
-       "          0.07364913,  0.14258407]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|edu:eth</span></div><div class='xr-var-dims'>(chain, draw, edu:eth__factor_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.1216 0.02276 ... 0.1539 0.2324</div><input id='attrs-e561d8f8-12fe-476f-a34d-a3c458d15ce1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e561d8f8-12fe-476f-a34d-a3c458d15ce1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1132753-073f-4c3a-a382-2f33740bb1cf' class='xr-var-data-in' type='checkbox'><label for='data-f1132753-073f-4c3a-a382-2f33740bb1cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-1.21630654e-01,  2.27589260e-02, -8.09307811e-02, ...,\n",
+       "          0.07364913,  0.14258407]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>1|edu:eth</span></div><div class='xr-var-dims'>(chain, draw, edu:eth__factor_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.1216 0.02276 ... 0.1539 0.2324</div><input id='attrs-70322526-ee0f-450a-b12f-cd297ea40d49' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-70322526-ee0f-450a-b12f-cd297ea40d49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89e96a00-f67a-47e9-a034-df6a1b4f03ef' class='xr-var-data-in' type='checkbox'><label for='data-89e96a00-f67a-47e9-a034-df6a1b4f03ef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-1.21630654e-01,  2.27589260e-02, -8.09307811e-02, ...,\n",
        "          1.18414504e-01, -2.94369545e-01,  4.28628830e-02],\n",
        "        [ 8.79262093e-02, -2.40069971e-02,  1.75872143e-01, ...,\n",
        "         -1.22918338e-02,  1.79839841e-02,  1.11624648e-01],\n",
@@ -6179,7 +6213,7 @@
        "        [-2.96442670e-02, -5.23772125e-01,  3.35574294e-01, ...,\n",
        "          2.39128705e-01,  2.95420812e-01,  3.14745957e-01],\n",
        "        [ 6.41359939e-03, -4.59543427e-01,  2.75663034e-01, ...,\n",
-       "          1.64926517e-01,  1.53933961e-01,  2.32381755e-01]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p(abortion, n)_mean</span></div><div class='xr-var-dims'>(chain, draw, p(abortion, n)_obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5157 0.4553 ... 0.6461 0.4508</div><input id='attrs-4d8cd796-2a2c-48e6-bf1d-20d0c5b9c268' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4d8cd796-2a2c-48e6-bf1d-20d0c5b9c268' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0b38afe-6c4e-4cdb-a43b-d297b11418f4' class='xr-var-data-in' type='checkbox'><label for='data-b0b38afe-6c4e-4cdb-a43b-d297b11418f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[0.51573153, 0.45531986, 0.43573786, ..., 0.61467966,\n",
+       "          1.64926517e-01,  1.53933961e-01,  2.32381755e-01]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p(abortion, n)_mean</span></div><div class='xr-var-dims'>(chain, draw, p(abortion, n)_obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5157 0.4553 ... 0.6461 0.4508</div><input id='attrs-61d04412-61c9-4f23-9a6d-4e9e1fa715d6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-61d04412-61c9-4f23-9a6d-4e9e1fa715d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cea9e7a7-f3b1-41d4-b6dc-943fb551cf0f' class='xr-var-data-in' type='checkbox'><label for='data-cea9e7a7-f3b1-41d4-b6dc-943fb551cf0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[0.51573153, 0.45531986, 0.43573786, ..., 0.61467966,\n",
        "         0.66570024, 0.43093261],\n",
        "        [0.44660884, 0.48218763, 0.39812479, ..., 0.58403712,\n",
        "         0.58299891, 0.3640184 ],\n",
@@ -6219,16 +6253,16 @@
        "        [0.48269555, 0.53180137, 0.44030393, ..., 0.67235906,\n",
        "         0.74140405, 0.55761303],\n",
        "        [0.46845058, 0.49201557, 0.45228162, ..., 0.60406424,\n",
-       "         0.64609974, 0.45081273]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-27c8aaa3-8987-4bc9-82f4-0e9adadc74ef' class='xr-section-summary-in' type='checkbox'  ><label for='section-27c8aaa3-8987-4bc9-82f4-0e9adadc74ef' class='xr-section-summary' >Indexes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ed02c144-10ca-4532-842d-af070ee5edc9' class='xr-index-data-in' type='checkbox'/><label for='index-ed02c144-10ca-4532-842d-af070ee5edc9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1b8d7e50-1f95-4145-b1c6-32b4917278a6' class='xr-index-data-in' type='checkbox'/><label for='index-1b8d7e50-1f95-4145-b1c6-32b4917278a6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "         0.64609974, 0.45081273]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8952a526-d82f-4df6-9191-e95338c92b3e' class='xr-section-summary-in' type='checkbox'  ><label for='section-8952a526-d82f-4df6-9191-e95338c92b3e' class='xr-section-summary' >Indexes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-12e3cea7-1248-49fe-9f0b-b6a41a0b2bff' class='xr-index-data-in' type='checkbox'/><label for='index-12e3cea7-1248-49fe-9f0b-b6a41a0b2bff' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c3294668-e65c-46ac-a1ac-2834c18d5781' class='xr-index-data-in' type='checkbox'/><label for='index-c3294668-e65c-46ac-a1ac-2834c18d5781' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
        "       ...\n",
        "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
-       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>state__factor_dim</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b7c531ef-7851-49d5-acd3-9878da51ceed' class='xr-index-data-in' type='checkbox'/><label for='index-b7c531ef-7851-49d5-acd3-9878da51ceed' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;AK&#x27;, &#x27;AL&#x27;, &#x27;AR&#x27;, &#x27;CA&#x27;, &#x27;CO&#x27;, &#x27;DE&#x27;, &#x27;FL&#x27;, &#x27;GA&#x27;, &#x27;HI&#x27;, &#x27;IA&#x27;, &#x27;ID&#x27;, &#x27;IL&#x27;,\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>state__factor_dim</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-257eca6b-52cf-4ed1-80f7-301deac81997' class='xr-index-data-in' type='checkbox'/><label for='index-257eca6b-52cf-4ed1-80f7-301deac81997' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;AK&#x27;, &#x27;AL&#x27;, &#x27;AR&#x27;, &#x27;CA&#x27;, &#x27;CO&#x27;, &#x27;DE&#x27;, &#x27;FL&#x27;, &#x27;GA&#x27;, &#x27;HI&#x27;, &#x27;IA&#x27;, &#x27;ID&#x27;, &#x27;IL&#x27;,\n",
        "       &#x27;KS&#x27;, &#x27;KY&#x27;, &#x27;LA&#x27;, &#x27;MA&#x27;, &#x27;MD&#x27;, &#x27;ME&#x27;, &#x27;MI&#x27;, &#x27;MN&#x27;, &#x27;MO&#x27;, &#x27;MS&#x27;, &#x27;MT&#x27;, &#x27;NC&#x27;,\n",
        "       &#x27;ND&#x27;, &#x27;NE&#x27;, &#x27;NH&#x27;, &#x27;NJ&#x27;, &#x27;NM&#x27;, &#x27;NV&#x27;, &#x27;NY&#x27;, &#x27;OH&#x27;, &#x27;OK&#x27;, &#x27;OR&#x27;, &#x27;PA&#x27;, &#x27;RI&#x27;,\n",
        "       &#x27;SC&#x27;, &#x27;SD&#x27;, &#x27;TN&#x27;, &#x27;UT&#x27;, &#x27;VA&#x27;, &#x27;VT&#x27;, &#x27;WA&#x27;, &#x27;WI&#x27;, &#x27;WV&#x27;, &#x27;WY&#x27;],\n",
-       "      dtype=&#x27;object&#x27;, name=&#x27;state__factor_dim&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>eth__factor_dim</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9a9720bf-233c-46e5-8c86-fa235f25f7bb' class='xr-index-data-in' type='checkbox'/><label for='index-9a9720bf-233c-46e5-8c86-fa235f25f7bb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Black&#x27;, &#x27;Hispanic&#x27;, &#x27;Other&#x27;, &#x27;White&#x27;], dtype=&#x27;object&#x27;, name=&#x27;eth__factor_dim&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>edu__factor_dim</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-241cf155-adc8-4e3b-94be-4e88907b093c' class='xr-index-data-in' type='checkbox'/><label for='index-241cf155-adc8-4e3b-94be-4e88907b093c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;4-Year College&#x27;, &#x27;HS&#x27;, &#x27;No HS&#x27;, &#x27;Post-grad&#x27;, &#x27;Some college&#x27;], dtype=&#x27;object&#x27;, name=&#x27;edu__factor_dim&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>male:eth__factor_dim</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cda4e659-c8d5-428c-bc02-48ac45e0857a' class='xr-index-data-in' type='checkbox'/><label for='index-cda4e659-c8d5-428c-bc02-48ac45e0857a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;-0.5:Black&#x27;, &#x27;-0.5:Hispanic&#x27;, &#x27;-0.5:Other&#x27;, &#x27;-0.5:White&#x27;, &#x27;0.5:Black&#x27;,\n",
+       "      dtype=&#x27;object&#x27;, name=&#x27;state__factor_dim&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>eth__factor_dim</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2c177aea-fe34-48aa-b8ff-0400b20f2f65' class='xr-index-data-in' type='checkbox'/><label for='index-2c177aea-fe34-48aa-b8ff-0400b20f2f65' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Black&#x27;, &#x27;Hispanic&#x27;, &#x27;Other&#x27;, &#x27;White&#x27;], dtype=&#x27;object&#x27;, name=&#x27;eth__factor_dim&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>edu__factor_dim</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-991a85e5-f760-4aaa-b8dd-47e8a18998a5' class='xr-index-data-in' type='checkbox'/><label for='index-991a85e5-f760-4aaa-b8dd-47e8a18998a5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;4-Year College&#x27;, &#x27;HS&#x27;, &#x27;No HS&#x27;, &#x27;Post-grad&#x27;, &#x27;Some college&#x27;], dtype=&#x27;object&#x27;, name=&#x27;edu__factor_dim&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>male:eth__factor_dim</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c3fabc5a-f4eb-43e6-a79b-557d68440a95' class='xr-index-data-in' type='checkbox'/><label for='index-c3fabc5a-f4eb-43e6-a79b-557d68440a95' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;-0.5:Black&#x27;, &#x27;-0.5:Hispanic&#x27;, &#x27;-0.5:Other&#x27;, &#x27;-0.5:White&#x27;, &#x27;0.5:Black&#x27;,\n",
        "       &#x27;0.5:Hispanic&#x27;, &#x27;0.5:Other&#x27;, &#x27;0.5:White&#x27;],\n",
-       "      dtype=&#x27;object&#x27;, name=&#x27;male:eth__factor_dim&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>edu:age__factor_dim</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2c0a8a4b-b5fe-4b37-a046-6c9305d690ca' class='xr-index-data-in' type='checkbox'/><label for='index-2c0a8a4b-b5fe-4b37-a046-6c9305d690ca' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;4-Year College:18-29&#x27;, &#x27;4-Year College:30-39&#x27;, &#x27;4-Year College:40-49&#x27;,\n",
+       "      dtype=&#x27;object&#x27;, name=&#x27;male:eth__factor_dim&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>edu:age__factor_dim</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-454dee77-20b3-4e70-bf12-c1d4df67a6cb' class='xr-index-data-in' type='checkbox'/><label for='index-454dee77-20b3-4e70-bf12-c1d4df67a6cb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;4-Year College:18-29&#x27;, &#x27;4-Year College:30-39&#x27;, &#x27;4-Year College:40-49&#x27;,\n",
        "       &#x27;4-Year College:50-59&#x27;, &#x27;4-Year College:60-69&#x27;, &#x27;4-Year College:70+&#x27;,\n",
        "       &#x27;HS:18-29&#x27;, &#x27;HS:30-39&#x27;, &#x27;HS:40-49&#x27;, &#x27;HS:50-59&#x27;, &#x27;HS:60-69&#x27;, &#x27;HS:70+&#x27;,\n",
        "       &#x27;No HS:18-29&#x27;, &#x27;No HS:30-39&#x27;, &#x27;No HS:40-49&#x27;, &#x27;No HS:50-59&#x27;,\n",
@@ -6237,23 +6271,23 @@
        "       &#x27;Post-grad:70+&#x27;, &#x27;Some college:18-29&#x27;, &#x27;Some college:30-39&#x27;,\n",
        "       &#x27;Some college:40-49&#x27;, &#x27;Some college:50-59&#x27;, &#x27;Some college:60-69&#x27;,\n",
        "       &#x27;Some college:70+&#x27;],\n",
-       "      dtype=&#x27;object&#x27;, name=&#x27;edu:age__factor_dim&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>edu:eth__factor_dim</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6281b393-5faf-4b44-a25b-cd0c119e5980' class='xr-index-data-in' type='checkbox'/><label for='index-6281b393-5faf-4b44-a25b-cd0c119e5980' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;4-Year College:Black&#x27;, &#x27;4-Year College:Hispanic&#x27;,\n",
+       "      dtype=&#x27;object&#x27;, name=&#x27;edu:age__factor_dim&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>edu:eth__factor_dim</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5d4da39c-be25-4ebd-9203-d4db83a80cc5' class='xr-index-data-in' type='checkbox'/><label for='index-5d4da39c-be25-4ebd-9203-d4db83a80cc5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;4-Year College:Black&#x27;, &#x27;4-Year College:Hispanic&#x27;,\n",
        "       &#x27;4-Year College:Other&#x27;, &#x27;4-Year College:White&#x27;, &#x27;HS:Black&#x27;,\n",
        "       &#x27;HS:Hispanic&#x27;, &#x27;HS:Other&#x27;, &#x27;HS:White&#x27;, &#x27;No HS:Black&#x27;, &#x27;No HS:Hispanic&#x27;,\n",
        "       &#x27;No HS:Other&#x27;, &#x27;No HS:White&#x27;, &#x27;Post-grad:Black&#x27;, &#x27;Post-grad:Hispanic&#x27;,\n",
        "       &#x27;Post-grad:Other&#x27;, &#x27;Post-grad:White&#x27;, &#x27;Some college:Black&#x27;,\n",
        "       &#x27;Some college:Hispanic&#x27;, &#x27;Some college:Other&#x27;, &#x27;Some college:White&#x27;],\n",
-       "      dtype=&#x27;object&#x27;, name=&#x27;edu:eth__factor_dim&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>p(abortion, n)_obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e16fe7b2-912e-404f-82d9-43732ec4f652' class='xr-index-data-in' type='checkbox'/><label for='index-e16fe7b2-912e-404f-82d9-43732ec4f652' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    0,     1,     2,     3,     4,     5,     6,     7,     8,     9,\n",
+       "      dtype=&#x27;object&#x27;, name=&#x27;edu:eth__factor_dim&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>p(abortion, n)_obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9c84f727-048a-49cb-aad7-a3f9a743aa75' class='xr-index-data-in' type='checkbox'/><label for='index-9c84f727-048a-49cb-aad7-a3f9a743aa75' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    0,     1,     2,     3,     4,     5,     6,     7,     8,     9,\n",
        "       ...\n",
        "       11030, 11031, 11032, 11033, 11034, 11035, 11036, 11037, 11038, 11039],\n",
-       "      dtype=&#x27;int64&#x27;, name=&#x27;p(abortion, n)_obs&#x27;, length=11040))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e89db651-93d5-4284-aedd-abc93193f7f9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e89db651-93d5-4284-aedd-abc93193f7f9' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-09-09T21:52:09.389695</dd><dt><span>arviz_version :</span></dt><dd>0.16.1</dd><dt><span>modeling_interface :</span></dt><dd>bambi</dd><dt><span>modeling_interface_version :</span></dt><dd>0.13.0.dev</dd></dl></div></li></ul></div></div><br></div>\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;p(abortion, n)_obs&#x27;, length=11040))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c003013c-2be0-47da-ae9c-6378bbf295d9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c003013c-2be0-47da-ae9c-6378bbf295d9' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-09-17T17:39:00.290773</dd><dt><span>arviz_version :</span></dt><dd>0.16.1</dd><dt><span>modeling_interface :</span></dt><dd>bambi</dd><dt><span>modeling_interface_version :</span></dt><dd>0.13.0.dev</dd></dl></div></li></ul></div></div><br></div>\n",
        "                      </ul>\n",
        "                  </div>\n",
        "            </li>\n",
        "            \n",
        "            <li class = \"xr-section-item\">\n",
-       "                  <input id=\"idata_posterior_predictive99ca945a-6c1c-49f7-ba96-10f156a817e2\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
-       "                  <label for=\"idata_posterior_predictive99ca945a-6c1c-49f7-ba96-10f156a817e2\" class = \"xr-section-summary\">posterior_predictive</label>\n",
+       "                  <input id=\"idata_posterior_predictive88c04eee-ba98-44c1-9db7-0320af6a3b20\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_posterior_predictive88c04eee-ba98-44c1-9db7-0320af6a3b20\" class = \"xr-section-summary\">posterior_predictive</label>\n",
        "                  <div class=\"xr-section-inline-details\"></div>\n",
        "                  <div class=\"xr-section-details\">\n",
        "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
@@ -6627,53 +6661,53 @@
        "  * draw                (draw) int64 0 1 2 3 4 5 6 ... 994 995 996 997 998 999\n",
        "  * p(abortion, n)_obs  (p(abortion, n)_obs) int64 0 1 2 3 ... 11037 11038 11039\n",
        "Data variables:\n",
-       "    p(abortion, n)      (chain, draw, p(abortion, n)_obs) int64 16201 ... 398\n",
+       "    p(abortion, n)      (chain, draw, p(abortion, n)_obs) int64 16147 ... 382\n",
        "Attributes:\n",
        "    modeling_interface:          bambi\n",
-       "    modeling_interface_version:  0.13.0.dev</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-59186762-de16-442c-a397-ce7ec4c637eb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-59186762-de16-442c-a397-ce7ec4c637eb' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>p(abortion, n)_obs</span>: 11040</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8de14c86-c63c-4406-bd52-005a3f9d7f4f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8de14c86-c63c-4406-bd52-005a3f9d7f4f' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-6619d713-2c02-4983-8107-df0571a02b87' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6619d713-2c02-4983-8107-df0571a02b87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b21c363-4284-43ff-8a54-41edc48e1cb0' class='xr-var-data-in' type='checkbox'><label for='data-1b21c363-4284-43ff-8a54-41edc48e1cb0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-9a61b8be-de73-451f-b361-1a1c3348a2e1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9a61b8be-de73-451f-b361-1a1c3348a2e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d158c63e-c1a2-449c-ac5c-b901ff5c5427' class='xr-var-data-in' type='checkbox'><label for='data-d158c63e-c1a2-449c-ac5c-b901ff5c5427' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>p(abortion, n)_obs</span></div><div class='xr-var-dims'>(p(abortion, n)_obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 11036 11037 11038 11039</div><input id='attrs-036c90a4-da44-4686-9dc6-1c43bb9b5a9e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-036c90a4-da44-4686-9dc6-1c43bb9b5a9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d89910c8-d3bc-401e-a622-610e0d2e1e1c' class='xr-var-data-in' type='checkbox'><label for='data-d89910c8-d3bc-401e-a622-610e0d2e1e1c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([    0,     1,     2, ..., 11037, 11038, 11039])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7f48c655-d29a-400b-8485-389537d28fb2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7f48c655-d29a-400b-8485-389537d28fb2' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>p(abortion, n)</span></div><div class='xr-var-dims'>(chain, draw, p(abortion, n)_obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>16201 5306 4293 ... 4222 639 398</div><input id='attrs-cfb4431a-4a1a-4c62-858f-e9d164033cf4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cfb4431a-4a1a-4c62-858f-e9d164033cf4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c44c7698-c593-40f6-b245-46724d901934' class='xr-var-data-in' type='checkbox'><label for='data-c44c7698-c593-40f6-b245-46724d901934' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[16201,  5306,  4293, ...,  4339,   638,   351],\n",
-       "        [14016,  5642,  3923, ...,  4099,   572,   295],\n",
-       "        [16543,  6014,  3995, ...,  3172,   556,   207],\n",
+       "    modeling_interface_version:  0.13.0.dev</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-246c7eaf-34a2-434f-953f-5b69daad4d11' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-246c7eaf-34a2-434f-953f-5b69daad4d11' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>p(abortion, n)_obs</span>: 11040</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-cfdca75b-73c9-4ebb-beca-5798fd21e94e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cfdca75b-73c9-4ebb-beca-5798fd21e94e' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-a0b560dd-1907-4b72-9eae-1b4ea33c5ec0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a0b560dd-1907-4b72-9eae-1b4ea33c5ec0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b48d80be-68f0-43c7-8151-f8f5034368df' class='xr-var-data-in' type='checkbox'><label for='data-b48d80be-68f0-43c7-8151-f8f5034368df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-5edfc52c-2c05-437a-ac35-543e7dee731e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5edfc52c-2c05-437a-ac35-543e7dee731e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-240e3f5f-9d3c-4df3-a734-68990c3f3808' class='xr-var-data-in' type='checkbox'><label for='data-240e3f5f-9d3c-4df3-a734-68990c3f3808' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>p(abortion, n)_obs</span></div><div class='xr-var-dims'>(p(abortion, n)_obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 11036 11037 11038 11039</div><input id='attrs-a45b97b1-e7ae-4a9f-94c2-b39ce33e611e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a45b97b1-e7ae-4a9f-94c2-b39ce33e611e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6116c8a-70a4-4690-a084-fecea7e4388c' class='xr-var-data-in' type='checkbox'><label for='data-a6116c8a-70a4-4690-a084-fecea7e4388c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([    0,     1,     2, ..., 11037, 11038, 11039])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-de0cb3b4-085b-4477-98e6-ebf5a3c5c73c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-de0cb3b4-085b-4477-98e6-ebf5a3c5c73c' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>p(abortion, n)</span></div><div class='xr-var-dims'>(chain, draw, p(abortion, n)_obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>16147 5420 4308 ... 4353 635 382</div><input id='attrs-26a5a164-ae47-4ffc-b89d-bfbfc0e0c058' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-26a5a164-ae47-4ffc-b89d-bfbfc0e0c058' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f80cb51c-d0ef-4a9c-a4da-dd1cda3202a6' class='xr-var-data-in' type='checkbox'><label for='data-f80cb51c-d0ef-4a9c-a4da-dd1cda3202a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[16147,  5420,  4308, ...,  4317,   657,   354],\n",
+       "        [14007,  5735,  3898, ...,  4118,   600,   297],\n",
+       "        [16428,  6058,  3948, ...,  3111,   560,   193],\n",
        "        ...,\n",
-       "        [15092,  6635,  3637, ...,  3187,   513,   220],\n",
-       "        [14080,  5240,  4521, ...,  4265,   725,   337],\n",
-       "        [13383,  6223,  3975, ...,  4332,   763,   234]],\n",
+       "        [15151,  6761,  3645, ...,  3173,   523,   190],\n",
+       "        [14109,  5255,  4410, ...,  4313,   689,   321],\n",
+       "        [13224,  6184,  3975, ...,  4417,   783,   247]],\n",
        "\n",
-       "       [[14805,  5305,  4203, ...,  4328,   669,   312],\n",
-       "        [14854,  6917,  4815, ...,  3399,   630,   242],\n",
-       "        [15282,  6194,  4626, ...,  3921,   632,   220],\n",
+       "       [[14681,  5225,  4102, ...,  4248,   672,   297],\n",
+       "        [14859,  6845,  4790, ...,  3386,   624,   236],\n",
+       "        [15174,  6144,  4762, ...,  3899,   605,   216],\n",
        "        ...,\n",
-       "        [13333,  6014,  3419, ...,  3138,   448,   147],\n",
-       "        [15618,  5646,  4194, ...,  4768,   769,   271],\n",
-       "        [15268,  5209,  4298, ...,  4521,   645,   308]],\n",
+       "        [13487,  5978,  3460, ...,  3071,   451,   144],\n",
+       "        [15743,  5680,  4126, ...,  4731,   789,   277],\n",
+       "        [15293,  5116,  4142, ...,  4520,   631,   321]],\n",
        "\n",
-       "       [[16591,  6305,  4384, ...,  3997,   695,   333],\n",
-       "        [13959,  5931,  4756, ...,  4323,   757,   278],\n",
-       "        [15338,  6113,  4094, ...,  3647,   525,   216],\n",
+       "       [[16424,  6229,  4444, ...,  4046,   685,   351],\n",
+       "        [14011,  5974,  4711, ...,  4328,   763,   259],\n",
+       "        [15307,  6193,  4060, ...,  3594,   542,   217],\n",
        "        ...,\n",
-       "        [14180,  5943,  4542, ...,  3927,   551,   275],\n",
-       "        [13126,  5090,  4374, ...,  3628,   661,   193],\n",
-       "        [16496,  6452,  4425, ...,  4255,   562,   343]],\n",
+       "        [13984,  5965,  4642, ...,  3940,   533,   288],\n",
+       "        [13166,  5079,  4300, ...,  3676,   661,   232],\n",
+       "        [16446,  6458,  4447, ...,  4345,   562,   334]],\n",
        "\n",
-       "       [[16554,  6148,  4266, ...,  3899,   578,   323],\n",
-       "        [15216,  6100,  4943, ...,  3696,   462,   226],\n",
-       "        [14832,  5924,  4132, ...,  4549,   747,   337],\n",
+       "       [[16557,  6249,  4359, ...,  3904,   589,   313],\n",
+       "        [15038,  6083,  4872, ...,  3754,   444,   210],\n",
+       "        [15089,  5910,  3954, ...,  4600,   738,   431],\n",
        "        ...,\n",
-       "        [14616,  6207,  4161, ...,  4574,   712,   369],\n",
-       "        [15259,  6350,  4353, ...,  4744,   741,   470],\n",
-       "        [14782,  5854,  4393, ...,  4222,   639,   398]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-702644eb-8c95-4194-87a1-a465ca16b8ba' class='xr-section-summary-in' type='checkbox'  ><label for='section-702644eb-8c95-4194-87a1-a465ca16b8ba' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ffc602f6-c59e-4f8e-8f58-8a7f86100197' class='xr-index-data-in' type='checkbox'/><label for='index-ffc602f6-c59e-4f8e-8f58-8a7f86100197' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f139e037-e28f-423b-adb4-46e12091a473' class='xr-index-data-in' type='checkbox'/><label for='index-f139e037-e28f-423b-adb4-46e12091a473' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "        [14591,  6141,  4224, ...,  4546,   738,   358],\n",
+       "        [15271,  6266,  4471, ...,  4773,   735,   468],\n",
+       "        [14713,  5961,  4504, ...,  4353,   635,   382]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-56d40af6-a75a-4b49-9f93-59ceb43fbdaf' class='xr-section-summary-in' type='checkbox'  ><label for='section-56d40af6-a75a-4b49-9f93-59ceb43fbdaf' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-88213738-e2a2-42b4-bd7c-b38575962eff' class='xr-index-data-in' type='checkbox'/><label for='index-88213738-e2a2-42b4-bd7c-b38575962eff' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c6453153-b8b9-4b14-b8a6-f1f022fd4b56' class='xr-index-data-in' type='checkbox'/><label for='index-c6453153-b8b9-4b14-b8a6-f1f022fd4b56' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
        "       ...\n",
        "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
-       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>p(abortion, n)_obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4bc735f1-b220-41e5-8998-ab95c7ab7da6' class='xr-index-data-in' type='checkbox'/><label for='index-4bc735f1-b220-41e5-8998-ab95c7ab7da6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    0,     1,     2,     3,     4,     5,     6,     7,     8,     9,\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>p(abortion, n)_obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bfd7376e-ba71-492b-b969-95f68f6b1f48' class='xr-index-data-in' type='checkbox'/><label for='index-bfd7376e-ba71-492b-b969-95f68f6b1f48' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    0,     1,     2,     3,     4,     5,     6,     7,     8,     9,\n",
        "       ...\n",
        "       11030, 11031, 11032, 11033, 11034, 11035, 11036, 11037, 11038, 11039],\n",
-       "      dtype=&#x27;int64&#x27;, name=&#x27;p(abortion, n)_obs&#x27;, length=11040))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e1f33e96-3e82-4b4b-9e00-c16a782f3fc3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e1f33e96-3e82-4b4b-9e00-c16a782f3fc3' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>modeling_interface :</span></dt><dd>bambi</dd><dt><span>modeling_interface_version :</span></dt><dd>0.13.0.dev</dd></dl></div></li></ul></div></div><br></div>\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;p(abortion, n)_obs&#x27;, length=11040))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c5508e05-28be-451c-a68d-37874db05260' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c5508e05-28be-451c-a68d-37874db05260' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>modeling_interface :</span></dt><dd>bambi</dd><dt><span>modeling_interface_version :</span></dt><dd>0.13.0.dev</dd></dl></div></li></ul></div></div><br></div>\n",
        "                      </ul>\n",
        "                  </div>\n",
        "            </li>\n",
        "            \n",
        "            <li class = \"xr-section-item\">\n",
-       "                  <input id=\"idata_log_likelihood26007cd7-d2ec-4043-a8f7-2797f4fee241\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
-       "                  <label for=\"idata_log_likelihood26007cd7-d2ec-4043-a8f7-2797f4fee241\" class = \"xr-section-summary\">log_likelihood</label>\n",
+       "                  <input id=\"idata_log_likelihoodefb2c919-6145-4f60-8f07-74c67bcc3a01\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_log_likelihoodefb2c919-6145-4f60-8f07-74c67bcc3a01\" class = \"xr-section-summary\">log_likelihood</label>\n",
        "                  <div class=\"xr-section-inline-details\"></div>\n",
        "                  <div class=\"xr-section-details\">\n",
        "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
@@ -7049,10 +7083,10 @@
        "Data variables:\n",
        "    p(abortion, n)      (chain, draw, p(abortion, n)_obs) float64 -2.099 ... 0.0\n",
        "Attributes:\n",
-       "    created_at:                  2023-09-09T21:52:09.396305\n",
+       "    created_at:                  2023-09-17T17:39:00.297597\n",
        "    arviz_version:               0.16.1\n",
        "    modeling_interface:          bambi\n",
-       "    modeling_interface_version:  0.13.0.dev</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-cd1ae16f-b4a4-4596-85d1-69530c874de3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cd1ae16f-b4a4-4596-85d1-69530c874de3' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>p(abortion, n)_obs</span>: 11040</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d094a952-2eaa-4359-a8f7-343377ca6842' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d094a952-2eaa-4359-a8f7-343377ca6842' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-c79b1ba7-092f-4787-9c3f-65e5ed612d01' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c79b1ba7-092f-4787-9c3f-65e5ed612d01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ee49e76-9825-446f-a74a-57b05c841b03' class='xr-var-data-in' type='checkbox'><label for='data-3ee49e76-9825-446f-a74a-57b05c841b03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-b03fd8c9-ded2-4472-873d-0725e24ea252' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b03fd8c9-ded2-4472-873d-0725e24ea252' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a49a4ea9-afe2-42d4-9cac-ec1d5e4c10bd' class='xr-var-data-in' type='checkbox'><label for='data-a49a4ea9-afe2-42d4-9cac-ec1d5e4c10bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>p(abortion, n)_obs</span></div><div class='xr-var-dims'>(p(abortion, n)_obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 11036 11037 11038 11039</div><input id='attrs-45cab74f-2ba2-4622-941d-b5e3e48cee87' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-45cab74f-2ba2-4622-941d-b5e3e48cee87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-064c2168-818c-4182-86cd-27ff83fd69f5' class='xr-var-data-in' type='checkbox'><label for='data-064c2168-818c-4182-86cd-27ff83fd69f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([    0,     1,     2, ..., 11037, 11038, 11039])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-60b29624-efbc-4a27-9846-bc7fc1eaa7e6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-60b29624-efbc-4a27-9846-bc7fc1eaa7e6' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>p(abortion, n)</span></div><div class='xr-var-dims'>(chain, draw, p(abortion, n)_obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.099 -6.533 -2.53 ... 0.0 0.0 0.0</div><input id='attrs-29193db7-60e4-41e5-b4a1-89d46a37143d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-29193db7-60e4-41e5-b4a1-89d46a37143d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e17c3486-d154-4e1c-9a0b-fb5aca68e2c6' class='xr-var-data-in' type='checkbox'><label for='data-e17c3486-d154-4e1c-9a0b-fb5aca68e2c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-2.09936954, -6.53260554, -2.53030632, ...,  0.        ,\n",
+       "    modeling_interface_version:  0.13.0.dev</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-7df85ddc-4515-4e6c-9b74-ca28b2c50e7a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7df85ddc-4515-4e6c-9b74-ca28b2c50e7a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li><li><span class='xr-has-index'>p(abortion, n)_obs</span>: 11040</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ff81f103-38ea-4218-8676-766948587b85' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ff81f103-38ea-4218-8676-766948587b85' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-aa2e36ca-a967-45ad-b3c1-bda721f09ab4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aa2e36ca-a967-45ad-b3c1-bda721f09ab4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7af93789-0ea5-49fe-ab07-b94e3ae5309e' class='xr-var-data-in' type='checkbox'><label for='data-7af93789-0ea5-49fe-ab07-b94e3ae5309e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-31f161c7-cf35-4591-afa1-8a0f78d9ea93' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-31f161c7-cf35-4591-afa1-8a0f78d9ea93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab4c61ee-dfc5-4b99-935e-2d29e61d8b84' class='xr-var-data-in' type='checkbox'><label for='data-ab4c61ee-dfc5-4b99-935e-2d29e61d8b84' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>p(abortion, n)_obs</span></div><div class='xr-var-dims'>(p(abortion, n)_obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 11036 11037 11038 11039</div><input id='attrs-f0c2020b-8752-4880-9485-0d9213418788' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f0c2020b-8752-4880-9485-0d9213418788' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90a19bad-8123-4cfa-949f-854acf443c85' class='xr-var-data-in' type='checkbox'><label for='data-90a19bad-8123-4cfa-949f-854acf443c85' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([    0,     1,     2, ..., 11037, 11038, 11039])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6e5a5b3d-855d-447f-acd6-3ec9230e8ddb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6e5a5b3d-855d-447f-acd6-3ec9230e8ddb' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>p(abortion, n)</span></div><div class='xr-var-dims'>(chain, draw, p(abortion, n)_obs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.099 -6.533 -2.53 ... 0.0 0.0 0.0</div><input id='attrs-9efeebb3-5bcf-4c56-9271-54d0bc0250b5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9efeebb3-5bcf-4c56-9271-54d0bc0250b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3487bab2-ec0a-4470-b970-f70127bb082d' class='xr-var-data-in' type='checkbox'><label for='data-3487bab2-ec0a-4470-b970-f70127bb082d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-2.09936954, -6.53260554, -2.53030632, ...,  0.        ,\n",
        "          0.        ,  0.        ],\n",
        "        [-2.82166815, -5.81761829, -2.15259044, ...,  0.        ,\n",
        "          0.        ,  0.        ],\n",
@@ -7092,20 +7126,20 @@
        "        [-2.36708357, -4.65351176, -2.35737355, ...,  0.        ,\n",
        "          0.        ,  0.        ],\n",
        "        [-2.52597843, -5.57143406, -2.08559554, ...,  0.        ,\n",
-       "          0.        ,  0.        ]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7aea7385-430a-4944-b0f8-a5f6af105b09' class='xr-section-summary-in' type='checkbox'  ><label for='section-7aea7385-430a-4944-b0f8-a5f6af105b09' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e27831ce-bdf1-4670-80c2-e764235bb3b8' class='xr-index-data-in' type='checkbox'/><label for='index-e27831ce-bdf1-4670-80c2-e764235bb3b8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e2531018-0e67-4538-a78c-e74e40d5952a' class='xr-index-data-in' type='checkbox'/><label for='index-e2531018-0e67-4538-a78c-e74e40d5952a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "          0.        ,  0.        ]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a2f8ca72-a6d8-4f36-a3d7-cec71972caed' class='xr-section-summary-in' type='checkbox'  ><label for='section-a2f8ca72-a6d8-4f36-a3d7-cec71972caed' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d77cb689-a64a-40b5-a6cb-7ecbd97a0670' class='xr-index-data-in' type='checkbox'/><label for='index-d77cb689-a64a-40b5-a6cb-7ecbd97a0670' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-01813c69-27ed-4c03-a3f4-90f45e1b00c9' class='xr-index-data-in' type='checkbox'/><label for='index-01813c69-27ed-4c03-a3f4-90f45e1b00c9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
        "       ...\n",
        "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
-       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>p(abortion, n)_obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c29e1e67-1920-4dc9-9075-e072a2a151d8' class='xr-index-data-in' type='checkbox'/><label for='index-c29e1e67-1920-4dc9-9075-e072a2a151d8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    0,     1,     2,     3,     4,     5,     6,     7,     8,     9,\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>p(abortion, n)_obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3c59e697-6e12-4507-ad9d-3d9952974066' class='xr-index-data-in' type='checkbox'/><label for='index-3c59e697-6e12-4507-ad9d-3d9952974066' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    0,     1,     2,     3,     4,     5,     6,     7,     8,     9,\n",
        "       ...\n",
        "       11030, 11031, 11032, 11033, 11034, 11035, 11036, 11037, 11038, 11039],\n",
-       "      dtype=&#x27;int64&#x27;, name=&#x27;p(abortion, n)_obs&#x27;, length=11040))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-74fe772b-98e0-4d32-ad27-1985527a5807' class='xr-section-summary-in' type='checkbox'  checked><label for='section-74fe772b-98e0-4d32-ad27-1985527a5807' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-09-09T21:52:09.396305</dd><dt><span>arviz_version :</span></dt><dd>0.16.1</dd><dt><span>modeling_interface :</span></dt><dd>bambi</dd><dt><span>modeling_interface_version :</span></dt><dd>0.13.0.dev</dd></dl></div></li></ul></div></div><br></div>\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;p(abortion, n)_obs&#x27;, length=11040))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fa6039ee-85ff-44fa-8004-a10e0fef258e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fa6039ee-85ff-44fa-8004-a10e0fef258e' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-09-17T17:39:00.297597</dd><dt><span>arviz_version :</span></dt><dd>0.16.1</dd><dt><span>modeling_interface :</span></dt><dd>bambi</dd><dt><span>modeling_interface_version :</span></dt><dd>0.13.0.dev</dd></dl></div></li></ul></div></div><br></div>\n",
        "                      </ul>\n",
        "                  </div>\n",
        "            </li>\n",
        "            \n",
        "            <li class = \"xr-section-item\">\n",
-       "                  <input id=\"idata_sample_statsbef55e74-9ce6-4528-ab34-40669faba21c\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
-       "                  <label for=\"idata_sample_statsbef55e74-9ce6-4528-ab34-40669faba21c\" class = \"xr-section-summary\">sample_stats</label>\n",
+       "                  <input id=\"idata_sample_stats6fb8978e-58af-4971-be69-d2b1f7c2fde1\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_sample_stats6fb8978e-58af-4971-be69-d2b1f7c2fde1\" class = \"xr-section-summary\">sample_stats</label>\n",
        "                  <div class=\"xr-section-inline-details\"></div>\n",
        "                  <div class=\"xr-section-details\">\n",
        "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
@@ -7486,57 +7520,57 @@
        "    tree_depth       (chain, draw) int64 9 9 9 9 9 9 9 9 9 ... 9 9 9 9 9 9 9 9 9\n",
        "    lp               (chain, draw) float64 2.205e+03 2.206e+03 ... 2.175e+03\n",
        "Attributes:\n",
-       "    created_at:                  2023-09-09T21:52:09.395016\n",
+       "    created_at:                  2023-09-17T17:39:00.296422\n",
        "    arviz_version:               0.16.1\n",
        "    modeling_interface:          bambi\n",
-       "    modeling_interface_version:  0.13.0.dev</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4495842f-8736-48fd-a128-9a70698cd846' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4495842f-8736-48fd-a128-9a70698cd846' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-798bbe0e-6252-494c-b7b2-c1e86548c603' class='xr-section-summary-in' type='checkbox'  checked><label for='section-798bbe0e-6252-494c-b7b2-c1e86548c603' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-f4c7fdcf-f794-4552-bfda-b899f84238fd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f4c7fdcf-f794-4552-bfda-b899f84238fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5779e76-c9a6-483d-b0aa-5b4374d277ab' class='xr-var-data-in' type='checkbox'><label for='data-d5779e76-c9a6-483d-b0aa-5b4374d277ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-13bf1310-01d2-4f48-b461-08692270ada1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-13bf1310-01d2-4f48-b461-08692270ada1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e237d637-655b-4196-92f0-61fe9fc7165e' class='xr-var-data-in' type='checkbox'><label for='data-e237d637-655b-4196-92f0-61fe9fc7165e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-687719cc-5676-4fbb-9152-e42e8488d07b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-687719cc-5676-4fbb-9152-e42e8488d07b' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>acceptance_rate</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9995 0.9926 1.0 ... 0.9999 0.9988</div><input id='attrs-0ba9ee07-8bb0-40ff-8d21-846e5db0d889' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0ba9ee07-8bb0-40ff-8d21-846e5db0d889' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd5b96a1-051b-4d38-b6de-0ee29ec259fe' class='xr-var-data-in' type='checkbox'><label for='data-bd5b96a1-051b-4d38-b6de-0ee29ec259fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.99946526, 0.99259021, 0.99998277, ..., 0.99903086, 0.98809861,\n",
+       "    modeling_interface_version:  0.13.0.dev</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1cd97867-9e37-425e-a73a-d244f850ee82' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1cd97867-9e37-425e-a73a-d244f850ee82' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 4</li><li><span class='xr-has-index'>draw</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-03cedb4f-e5c9-4b06-a3b6-60e97c4deefb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-03cedb4f-e5c9-4b06-a3b6-60e97c4deefb' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-edc257f5-ca69-4e49-856a-9bdbb39093d2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-edc257f5-ca69-4e49-856a-9bdbb39093d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c63efbde-c041-4b5e-aa01-6ebd6c1b05fc' class='xr-var-data-in' type='checkbox'><label for='data-c63efbde-c041-4b5e-aa01-6ebd6c1b05fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-32fdb79d-df9f-4c75-9e26-6e4646320c4d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-32fdb79d-df9f-4c75-9e26-6e4646320c4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c409e59-7fe7-4cb9-8db9-0d8028368e1f' class='xr-var-data-in' type='checkbox'><label for='data-5c409e59-7fe7-4cb9-8db9-0d8028368e1f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-94aa5683-d350-4200-bbd6-99ea3842a4b6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-94aa5683-d350-4200-bbd6-99ea3842a4b6' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>acceptance_rate</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9995 0.9926 1.0 ... 0.9999 0.9988</div><input id='attrs-ef7fa766-3e73-4103-8d9d-4f20618bd72d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ef7fa766-3e73-4103-8d9d-4f20618bd72d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-391fea33-37bf-489e-af98-2ed271cbaef5' class='xr-var-data-in' type='checkbox'><label for='data-391fea33-37bf-489e-af98-2ed271cbaef5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.99946526, 0.99259021, 0.99998277, ..., 0.99903086, 0.98809861,\n",
        "        0.99689415],\n",
        "       [0.99913792, 0.9997045 , 0.99997501, ..., 0.98352235, 0.93096018,\n",
        "        0.85187495],\n",
        "       [0.98668511, 0.99626539, 0.99994987, ..., 0.99803789, 0.99652942,\n",
        "        0.99404411],\n",
        "       [0.98789206, 0.99794272, 0.99994925, ..., 0.99995445, 0.99990492,\n",
-       "        0.99879618]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.009849 0.009849 ... 0.007358</div><input id='attrs-928825dc-7961-4731-ae99-d06dc24e6166' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-928825dc-7961-4731-ae99-d06dc24e6166' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ef79693-c43a-4291-b582-aafbcebf5f8f' class='xr-var-data-in' type='checkbox'><label for='data-9ef79693-c43a-4291-b582-aafbcebf5f8f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.00984927, 0.00984927, 0.00984927, ..., 0.00984927, 0.00984927,\n",
+       "        0.99879618]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>step_size</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.009849 0.009849 ... 0.007358</div><input id='attrs-cca9c110-33aa-4a1b-ade4-a603babc8dd6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cca9c110-33aa-4a1b-ade4-a603babc8dd6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5034d825-1297-4337-baef-c534d9a1808d' class='xr-var-data-in' type='checkbox'><label for='data-5034d825-1297-4337-baef-c534d9a1808d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.00984927, 0.00984927, 0.00984927, ..., 0.00984927, 0.00984927,\n",
        "        0.00984927],\n",
        "       [0.0107935 , 0.0107935 , 0.0107935 , ..., 0.0107935 , 0.0107935 ,\n",
        "        0.0107935 ],\n",
        "       [0.01346049, 0.01346049, 0.01346049, ..., 0.01346049, 0.01346049,\n",
        "        0.01346049],\n",
        "       [0.0073581 , 0.0073581 , 0.0073581 , ..., 0.0073581 , 0.0073581 ,\n",
-       "        0.0073581 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverging</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-e9bfbb2f-ecf7-4130-9524-f559f85fd065' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e9bfbb2f-ecf7-4130-9524-f559f85fd065' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2baec3a-73ea-4a3b-89d7-e2563ec108ed' class='xr-var-data-in' type='checkbox'><label for='data-b2baec3a-73ea-4a3b-89d7-e2563ec108ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
+       "        0.0073581 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diverging</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-86c6eb30-f36c-4c9a-be0b-e6cfa1c6e044' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-86c6eb30-f36c-4c9a-be0b-e6cfa1c6e044' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1992abd-2e8e-473a-ba44-b2dbbf81d661' class='xr-var-data-in' type='checkbox'><label for='data-c1992abd-2e8e-473a-ba44-b2dbbf81d661' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[False, False, False, ..., False, False, False],\n",
        "       [False, False, False, ..., False, False, False],\n",
        "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.255e+03 2.272e+03 ... 2.239e+03</div><input id='attrs-39cf37df-7271-46ab-ab0f-54ff8e0c2470' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-39cf37df-7271-46ab-ab0f-54ff8e0c2470' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f0e9dc1-3039-4583-9a53-8a7fa04481be' class='xr-var-data-in' type='checkbox'><label for='data-5f0e9dc1-3039-4583-9a53-8a7fa04481be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2254.80380148, 2271.7792398 , 2262.62856249, ..., 2257.72468109,\n",
+       "       [False, False, False, ..., False, False, False]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>energy</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.255e+03 2.272e+03 ... 2.239e+03</div><input id='attrs-79abb63b-5ceb-453f-9c64-7aac28b56d63' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-79abb63b-5ceb-453f-9c64-7aac28b56d63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bde073a3-15a6-461f-8bc4-5c4cf4c36147' class='xr-var-data-in' type='checkbox'><label for='data-bde073a3-15a6-461f-8bc4-5c4cf4c36147' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2254.80380148, 2271.7792398 , 2262.62856249, ..., 2257.72468109,\n",
        "        2230.11267142, 2237.93805661],\n",
        "       [2269.06513073, 2263.36956843, 2245.93272568, ..., 2261.17563583,\n",
        "        2262.64441608, 2253.12366973],\n",
        "       [2264.71522209, 2252.51613169, 2243.13421993, ..., 2258.2260048 ,\n",
        "        2259.74040336, 2243.20097056],\n",
        "       [2248.65859906, 2246.09743317, 2267.83771194, ..., 2254.07769144,\n",
-       "        2245.89919424, 2239.36666182]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_steps</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>511 511 511 511 ... 511 511 511 511</div><input id='attrs-e93118a1-5e56-420f-a4c5-bc3a01eb3249' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e93118a1-5e56-420f-a4c5-bc3a01eb3249' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74908456-e75a-41c2-8dde-2904bfdaeba4' class='xr-var-data-in' type='checkbox'><label for='data-74908456-e75a-41c2-8dde-2904bfdaeba4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[511, 511, 511, ..., 511, 511, 511],\n",
+       "        2245.89919424, 2239.36666182]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_steps</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>511 511 511 511 ... 511 511 511 511</div><input id='attrs-35226ec2-f95b-4bd2-91e5-1abbabcf3f5e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-35226ec2-f95b-4bd2-91e5-1abbabcf3f5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7dcd901-2941-4a23-900c-5f28aea58700' class='xr-var-data-in' type='checkbox'><label for='data-f7dcd901-2941-4a23-900c-5f28aea58700' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[511, 511, 511, ..., 511, 511, 511],\n",
        "       [511, 511, 511, ..., 511, 511, 511],\n",
        "       [255, 255, 255, ..., 255, 255, 255],\n",
-       "       [511, 511, 511, ..., 511, 511, 511]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tree_depth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>9 9 9 9 9 9 9 9 ... 9 9 9 9 9 9 9 9</div><input id='attrs-a689a831-fa2b-4388-a971-95ae9e7578b6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a689a831-fa2b-4388-a971-95ae9e7578b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f25462a7-8d84-48bb-9e9e-34c0ef8a9c31' class='xr-var-data-in' type='checkbox'><label for='data-f25462a7-8d84-48bb-9e9e-34c0ef8a9c31' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[9, 9, 9, ..., 9, 9, 9],\n",
+       "       [511, 511, 511, ..., 511, 511, 511]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tree_depth</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>9 9 9 9 9 9 9 9 ... 9 9 9 9 9 9 9 9</div><input id='attrs-ca4cc4d8-6b0e-49e1-850a-2ceced7148f0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ca4cc4d8-6b0e-49e1-850a-2ceced7148f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f643e223-3987-44f3-b0ba-efd09b2d49c0' class='xr-var-data-in' type='checkbox'><label for='data-f643e223-3987-44f3-b0ba-efd09b2d49c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[9, 9, 9, ..., 9, 9, 9],\n",
        "       [9, 9, 9, ..., 9, 9, 9],\n",
        "       [8, 8, 8, ..., 8, 8, 8],\n",
-       "       [9, 9, 9, ..., 9, 9, 9]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.205e+03 2.206e+03 ... 2.175e+03</div><input id='attrs-01fb523f-ea5d-48b7-b287-de6a7359d1a8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-01fb523f-ea5d-48b7-b287-de6a7359d1a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3930fbc-6af5-475a-8443-fea601cb5466' class='xr-var-data-in' type='checkbox'><label for='data-e3930fbc-6af5-475a-8443-fea601cb5466' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2205.14758575, 2206.32053817, 2203.18323165, ..., 2185.99408105,\n",
+       "       [9, 9, 9, ..., 9, 9, 9]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.205e+03 2.206e+03 ... 2.175e+03</div><input id='attrs-738a478f-37a9-468f-8bb8-b32dbeb14d07' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-738a478f-37a9-468f-8bb8-b32dbeb14d07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f34f4066-8d72-45d8-83cc-00625009fec3' class='xr-var-data-in' type='checkbox'><label for='data-f34f4066-8d72-45d8-83cc-00625009fec3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2205.14758575, 2206.32053817, 2203.18323165, ..., 2185.99408105,\n",
        "        2172.48562089, 2185.00510852],\n",
        "       [2208.18815797, 2193.96415489, 2188.18604319, ..., 2200.69704711,\n",
        "        2190.64594014, 2191.29041039],\n",
        "       [2200.43179845, 2194.72819558, 2178.81994092, ..., 2200.06509338,\n",
        "        2187.73927464, 2192.0455093 ],\n",
        "       [2190.22894365, 2189.6562415 , 2213.11595564, ..., 2188.1258582 ,\n",
-       "        2187.37140655, 2175.15102085]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-066cb8f6-ad2c-4637-bc6c-53692fc931f9' class='xr-section-summary-in' type='checkbox'  ><label for='section-066cb8f6-ad2c-4637-bc6c-53692fc931f9' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1bb1cae5-1e0d-4e15-8b66-ce8ccc0f94bf' class='xr-index-data-in' type='checkbox'/><label for='index-1bb1cae5-1e0d-4e15-8b66-ce8ccc0f94bf' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b1811b74-a59c-45bd-9892-3873e67ac100' class='xr-index-data-in' type='checkbox'/><label for='index-b1811b74-a59c-45bd-9892-3873e67ac100' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
+       "        2187.37140655, 2175.15102085]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b8c05e5d-8ed0-4071-b5bc-6493a2135892' class='xr-section-summary-in' type='checkbox'  ><label for='section-b8c05e5d-8ed0-4071-b5bc-6493a2135892' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>chain</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-28b08cea-ea9a-4497-9efc-58fb3bc4d11e' class='xr-index-data-in' type='checkbox'/><label for='index-28b08cea-ea9a-4497-9efc-58fb3bc4d11e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0, 1, 2, 3], dtype=&#x27;int64&#x27;, name=&#x27;chain&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>draw</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-376610de-313b-4854-aaf2-5fcc41029149' class='xr-index-data-in' type='checkbox'/><label for='index-376610de-313b-4854-aaf2-5fcc41029149' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,\n",
        "       ...\n",
        "       990, 991, 992, 993, 994, 995, 996, 997, 998, 999],\n",
-       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5fb5b294-25e8-4a7b-93aa-db6825431b8a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5fb5b294-25e8-4a7b-93aa-db6825431b8a' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-09-09T21:52:09.395016</dd><dt><span>arviz_version :</span></dt><dd>0.16.1</dd><dt><span>modeling_interface :</span></dt><dd>bambi</dd><dt><span>modeling_interface_version :</span></dt><dd>0.13.0.dev</dd></dl></div></li></ul></div></div><br></div>\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;draw&#x27;, length=1000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-21897919-fc23-41ca-a9f4-78de103585e5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-21897919-fc23-41ca-a9f4-78de103585e5' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-09-17T17:39:00.296422</dd><dt><span>arviz_version :</span></dt><dd>0.16.1</dd><dt><span>modeling_interface :</span></dt><dd>bambi</dd><dt><span>modeling_interface_version :</span></dt><dd>0.13.0.dev</dd></dl></div></li></ul></div></div><br></div>\n",
        "                      </ul>\n",
        "                  </div>\n",
        "            </li>\n",
        "            \n",
        "            <li class = \"xr-section-item\">\n",
-       "                  <input id=\"idata_observed_data2999d351-6791-440b-991e-99edc28cbe23\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
-       "                  <label for=\"idata_observed_data2999d351-6791-440b-991e-99edc28cbe23\" class = \"xr-section-summary\">observed_data</label>\n",
+       "                  <input id=\"idata_observed_data80b12b1d-b8ad-4c33-8be5-70db9b6c58a2\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
+       "                  <label for=\"idata_observed_data80b12b1d-b8ad-4c33-8be5-70db9b6c58a2\" class = \"xr-section-summary\">observed_data</label>\n",
        "                  <div class=\"xr-section-inline-details\"></div>\n",
        "                  <div class=\"xr-section-details\">\n",
        "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
@@ -7910,16 +7944,16 @@
        "Data variables:\n",
        "    p(abortion, n)      (p(abortion, n)_obs) int64 18 16 13 12 11 ... 0 0 0 0 0\n",
        "Attributes:\n",
-       "    created_at:                  2023-09-09T21:52:09.396647\n",
+       "    created_at:                  2023-09-17T17:39:00.297909\n",
        "    arviz_version:               0.16.1\n",
        "    inference_library:           numpyro\n",
        "    inference_library_version:   0.13.0\n",
-       "    sampling_time:               440.235424\n",
+       "    sampling_time:               751.216233\n",
        "    modeling_interface:          bambi\n",
-       "    modeling_interface_version:  0.13.0.dev</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-03d16921-3be2-4076-8615-fab2a08dd35b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-03d16921-3be2-4076-8615-fab2a08dd35b' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>p(abortion, n)_obs</span>: 11040</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-18130ffd-eaa1-47ec-b0d6-ae967d1c74bb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-18130ffd-eaa1-47ec-b0d6-ae967d1c74bb' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>p(abortion, n)_obs</span></div><div class='xr-var-dims'>(p(abortion, n)_obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 11036 11037 11038 11039</div><input id='attrs-25457756-02e7-4163-99b0-2bb4803771d6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-25457756-02e7-4163-99b0-2bb4803771d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6bda2da3-bf74-4835-aa15-a4f7bd261808' class='xr-var-data-in' type='checkbox'><label for='data-6bda2da3-bf74-4835-aa15-a4f7bd261808' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([    0,     1,     2, ..., 11037, 11038, 11039])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dc8485a6-6ffd-4965-8d9c-df03178d7a9d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dc8485a6-6ffd-4965-8d9c-df03178d7a9d' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>p(abortion, n)</span></div><div class='xr-var-dims'>(p(abortion, n)_obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>18 16 13 12 11 11 ... 0 0 0 0 0 0</div><input id='attrs-6d71734d-aa31-4beb-8e5b-18959e9cfc2c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6d71734d-aa31-4beb-8e5b-18959e9cfc2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18f77008-463b-483e-b7e5-acff9273d023' class='xr-var-data-in' type='checkbox'><label for='data-18f77008-463b-483e-b7e5-acff9273d023' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([18, 16, 13, ...,  0,  0,  0])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3281783e-c381-411e-b30e-c31ca5c5e412' class='xr-section-summary-in' type='checkbox'  ><label for='section-3281783e-c381-411e-b30e-c31ca5c5e412' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>p(abortion, n)_obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e943b4e7-7b5f-455e-93aa-de97b0c7bb51' class='xr-index-data-in' type='checkbox'/><label for='index-e943b4e7-7b5f-455e-93aa-de97b0c7bb51' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    0,     1,     2,     3,     4,     5,     6,     7,     8,     9,\n",
+       "    modeling_interface_version:  0.13.0.dev</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-bb33cf80-1097-4348-9f48-2019586ebe23' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bb33cf80-1097-4348-9f48-2019586ebe23' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>p(abortion, n)_obs</span>: 11040</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-38df5cfb-457b-40e7-ae93-70a499159d0a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-38df5cfb-457b-40e7-ae93-70a499159d0a' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>p(abortion, n)_obs</span></div><div class='xr-var-dims'>(p(abortion, n)_obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 11036 11037 11038 11039</div><input id='attrs-8f982d71-7a4a-4dfe-a6bd-71feac72e943' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8f982d71-7a4a-4dfe-a6bd-71feac72e943' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f688da24-4510-414b-ae79-7e5d16d0860e' class='xr-var-data-in' type='checkbox'><label for='data-f688da24-4510-414b-ae79-7e5d16d0860e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([    0,     1,     2, ..., 11037, 11038, 11039])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-81fef7b9-9453-446a-a282-32ff45196f67' class='xr-section-summary-in' type='checkbox'  checked><label for='section-81fef7b9-9453-446a-a282-32ff45196f67' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>p(abortion, n)</span></div><div class='xr-var-dims'>(p(abortion, n)_obs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>18 16 13 12 11 11 ... 0 0 0 0 0 0</div><input id='attrs-e464dcca-cf14-4049-9e01-34fb0c19feb4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e464dcca-cf14-4049-9e01-34fb0c19feb4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e427ae8-197f-4b64-a371-7512f0ba00ea' class='xr-var-data-in' type='checkbox'><label for='data-1e427ae8-197f-4b64-a371-7512f0ba00ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([18, 16, 13, ...,  0,  0,  0])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-230a06b8-2b3d-4fd9-a47e-518dee4f547e' class='xr-section-summary-in' type='checkbox'  ><label for='section-230a06b8-2b3d-4fd9-a47e-518dee4f547e' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>p(abortion, n)_obs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-369c7ab1-d7b7-444a-88e1-6523c3636b01' class='xr-index-data-in' type='checkbox'/><label for='index-369c7ab1-d7b7-444a-88e1-6523c3636b01' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    0,     1,     2,     3,     4,     5,     6,     7,     8,     9,\n",
        "       ...\n",
        "       11030, 11031, 11032, 11033, 11034, 11035, 11036, 11037, 11038, 11039],\n",
-       "      dtype=&#x27;int64&#x27;, name=&#x27;p(abortion, n)_obs&#x27;, length=11040))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-23cfeaed-6ce2-46f2-a7ec-9c6f09dc52b0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-23cfeaed-6ce2-46f2-a7ec-9c6f09dc52b0' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-09-09T21:52:09.396647</dd><dt><span>arviz_version :</span></dt><dd>0.16.1</dd><dt><span>inference_library :</span></dt><dd>numpyro</dd><dt><span>inference_library_version :</span></dt><dd>0.13.0</dd><dt><span>sampling_time :</span></dt><dd>440.235424</dd><dt><span>modeling_interface :</span></dt><dd>bambi</dd><dt><span>modeling_interface_version :</span></dt><dd>0.13.0.dev</dd></dl></div></li></ul></div></div><br></div>\n",
+       "      dtype=&#x27;int64&#x27;, name=&#x27;p(abortion, n)_obs&#x27;, length=11040))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3ea9221b-cb8f-48ea-a8fe-af1b65e9d60e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3ea9221b-cb8f-48ea-a8fe-af1b65e9d60e' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2023-09-17T17:39:00.297909</dd><dt><span>arviz_version :</span></dt><dd>0.16.1</dd><dt><span>inference_library :</span></dt><dd>numpyro</dd><dt><span>inference_library_version :</span></dt><dd>0.13.0</dd><dt><span>sampling_time :</span></dt><dd>751.216233</dd><dt><span>modeling_interface :</span></dt><dd>bambi</dd><dt><span>modeling_interface_version :</span></dt><dd>0.13.0.dev</dd></dl></div></li></ul></div></div><br></div>\n",
        "                      </ul>\n",
        "                  </div>\n",
        "            </li>\n",
@@ -8276,7 +8310,7 @@
        "\t> observed_data"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8304,7 +8338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -8330,7 +8364,11 @@
        "      <th></th>\n",
        "      <th>state</th>\n",
        "      <th>base_expected</th>\n",
+       "      <th>base_lb</th>\n",
+       "      <th>base_ub</th>\n",
        "      <th>adjusted</th>\n",
+       "      <th>ub</th>\n",
+       "      <th>lb</th>\n",
        "      <th>census_share</th>\n",
        "    </tr>\n",
        "  </thead>\n",
@@ -8338,52 +8376,79 @@
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>OK</td>\n",
-       "      <td>0.421770</td>\n",
-       "      <td>0.325762</td>\n",
+       "      <td>0.422344</td>\n",
+       "      <td>0.209548</td>\n",
+       "      <td>0.658459</td>\n",
+       "      <td>0.326496</td>\n",
+       "      <td>0.412243</td>\n",
+       "      <td>0.246757</td>\n",
        "      <td>0.321553</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>34</th>\n",
        "      <td>MS</td>\n",
-       "      <td>0.437283</td>\n",
-       "      <td>0.380593</td>\n",
+       "      <td>0.437840</td>\n",
+       "      <td>0.215256</td>\n",
+       "      <td>0.682492</td>\n",
+       "      <td>0.380658</td>\n",
+       "      <td>0.492824</td>\n",
+       "      <td>0.278824</td>\n",
        "      <td>0.374640</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>CO</td>\n",
-       "      <td>0.475645</td>\n",
-       "      <td>0.397006</td>\n",
+       "      <td>0.475468</td>\n",
+       "      <td>0.251428</td>\n",
+       "      <td>0.697159</td>\n",
+       "      <td>0.397698</td>\n",
+       "      <td>0.483291</td>\n",
+       "      <td>0.315567</td>\n",
        "      <td>0.354857</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>MO</td>\n",
-       "      <td>0.512517</td>\n",
-       "      <td>0.420937</td>\n",
-       "      <td>0.302954</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>24</th>\n",
        "      <td>ME</td>\n",
-       "      <td>0.438816</td>\n",
-       "      <td>0.421903</td>\n",
+       "      <td>0.437219</td>\n",
+       "      <td>0.235827</td>\n",
+       "      <td>0.665177</td>\n",
+       "      <td>0.421139</td>\n",
+       "      <td>0.539669</td>\n",
+       "      <td>0.298316</td>\n",
        "      <td>0.403636</td>\n",
        "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>MO</td>\n",
+       "      <td>0.510291</td>\n",
+       "      <td>0.225359</td>\n",
+       "      <td>0.744009</td>\n",
+       "      <td>0.421991</td>\n",
+       "      <td>0.527927</td>\n",
+       "      <td>0.320247</td>\n",
+       "      <td>0.302954</td>\n",
+       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   state  base_expected  adjusted  census_share\n",
-       "9     OK       0.421770  0.325762      0.321553\n",
-       "34    MS       0.437283  0.380593      0.374640\n",
-       "2     CO       0.475645  0.397006      0.354857\n",
-       "25    MO       0.512517  0.420937      0.302954\n",
-       "24    ME       0.438816  0.421903      0.403636"
+       "   state  base_expected   base_lb   base_ub  adjusted        ub        lb  \\\n",
+       "9     OK       0.422344  0.209548  0.658459  0.326496  0.412243  0.246757   \n",
+       "34    MS       0.437840  0.215256  0.682492  0.380658  0.492824  0.278824   \n",
+       "2     CO       0.475468  0.251428  0.697159  0.397698  0.483291  0.315567   \n",
+       "24    ME       0.437219  0.235827  0.665177  0.421139  0.539669  0.298316   \n",
+       "25    MO       0.510291  0.225359  0.744009  0.421991  0.527927  0.320247   \n",
+       "\n",
+       "    census_share  \n",
+       "9       0.321553  \n",
+       "34      0.374640  \n",
+       "2       0.354857  \n",
+       "24      0.403636  \n",
+       "25      0.302954  "
       ]
      },
-     "execution_count": 31,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8396,15 +8461,28 @@
     "for s in new_data[\"state\"].unique():\n",
     "    idx = new_data.index[new_data[\"state\"] == s].tolist()\n",
     "    predicted_mrp = (\n",
-    "        ((abortion_posterior_mrp[idx].mean(axis=1) * new_data.iloc[idx][\"state_percent\"]))\n",
+    "        ((abortion_posterior_mrp[idx].mean(dim='sample') * new_data.iloc[idx][\"state_percent\"]))\n",
+    "        .sum()\n",
+    "        .item()\n",
+    "    )\n",
+    "    predicted_mrp_lb = (\n",
+    "        ((abortion_posterior_mrp[idx].quantile(0.025, dim='sample') * new_data.iloc[idx][\"state_percent\"]))\n",
+    "        .sum()\n",
+    "        .item()\n",
+    "    )\n",
+    "    predicted_mrp_ub = (\n",
+    "        ((abortion_posterior_mrp[idx].quantile(0.975, dim='sample') * new_data.iloc[idx][\"state_percent\"]))\n",
     "        .sum()\n",
     "        .item()\n",
     "    )\n",
     "    predicted = abortion_posterior_base[idx].mean().item()\n",
-    "    estimates.append([s, predicted, predicted_mrp])\n",
+    "    base_lb = abortion_posterior_base[idx].quantile(0.025).item()\n",
+    "    base_ub = abortion_posterior_base[idx].quantile(0.975).item()\n",
+    "    \n",
+    "    estimates.append([s, predicted, base_lb, base_ub, predicted_mrp, predicted_mrp_ub, predicted_mrp_lb])\n",
     "\n",
     "\n",
-    "state_predicted = pd.DataFrame(estimates, columns=[\"state\", \"base_expected\", \"adjusted\"])\n",
+    "state_predicted = pd.DataFrame(estimates, columns=[\"state\", \"base_expected\", \"base_lb\", \"base_ub\", \"adjusted\", \"ub\", \"lb\"])\n",
     "\n",
     "state_predicted = (\n",
     "    state_predicted.merge(cces_all_df.groupby(\"state\")[[\"abortion\"]].mean().reset_index())\n",
@@ -8426,14 +8504,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 1700x700 with 1 Axes>"
+       "<Figure size 1700x1000 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -8441,17 +8519,20 @@
     }
    ],
    "source": [
-    "fig, ax = plt.subplots(figsize=(17, 7))\n",
+    "fig, axs = plt.subplots(2, 1, figsize=(17, 10))\n",
+    "axs = axs.flatten()\n",
+    "ax = axs[0]\n",
+    "ax1 = axs[1]\n",
     "ax.scatter(\n",
     "    state_predicted[\"state\"], state_predicted[\"base_expected\"], color=\"red\", label=\"Biased Sample\"\n",
     ")\n",
     "ax.scatter(\n",
-    "    state_predicted[\"state\"], state_predicted[\"adjusted\"], color=\"blue\", label=\"Mr P Adjusted\"\n",
+    "    state_predicted[\"state\"], state_predicted[\"adjusted\"], color=\"slateblue\", label=\"Mr P Adjusted\"\n",
     ")\n",
     "ax.scatter(\n",
     "    state_predicted[\"state\"],\n",
     "    state_predicted[\"census_share\"],\n",
-    "    color=\"green\",\n",
+    "    color=\"darkgreen\",\n",
     "    label=\"Census Aggregates\",\n",
     ")\n",
     "ax.legend()\n",
@@ -8462,9 +8543,34 @@
     "    color=\"black\",\n",
     "    linestyles=\"--\",\n",
     ")\n",
+    "\n",
+    "\n",
+    "\n",
+    "ax1.scatter(\n",
+    "    state_predicted[\"state\"], state_predicted[\"base_expected\"], color=\"red\", label=\"Biased Sample\"\n",
+    ")\n",
+    "ax1.scatter(\n",
+    "    state_predicted[\"state\"], state_predicted[\"adjusted\"], color=\"slateblue\", label=\"Mr P Adjusted\"\n",
+    ")\n",
+    "ax1.legend()\n",
+    "\n",
+    "ax1.vlines(\n",
+    "    state_predicted[\"state\"],\n",
+    "    state_predicted[\"base_ub\"],\n",
+    "    state_predicted[\"base_lb\"],\n",
+    "    color='red'\n",
+    ")\n",
+    "ax1.vlines(\n",
+    "    state_predicted[\"state\"],\n",
+    "    state_predicted[\"ub\"],\n",
+    "    state_predicted[\"lb\"],\n",
+    "    color=\"slateblue\",\n",
+    ")\n",
     "ax.set_xlabel(\"State\")\n",
     "ax.set_ylabel(\"Proportion\")\n",
-    "ax.set_title(\"Comparison of Base Predictions and Post-stratified Adjustment\", fontsize=20);"
+    "ax1.set_title(\"Comparison of Uncertainty in Biased Predictions and Post-stratified Adjustment\", fontsize=15);\n",
+    "ax.set_title(\"Comparison of Post-stratified Adjustment and Census Report\", fontsize=15);\n",
+    "ax1.set_ylabel(\"Proportion\");\n"
    ]
   },
   {
@@ -8476,7 +8582,11 @@
     "\n",
     "In this notebook we have seen how to use `bambi` to concisely and quickly apply the technique of multilevel regression and post-stratification. We've seen how this technique is a natural and compelling extension to regression modelling in general, that incorporates prior knowledge in an interesting and flexible manner. \n",
     "\n",
-    "The problems of representation in data are serious. Policy gets made and changed on the basis of anticipated policy effects. Without the ability to control and adjust for non-representative samples, politicians and policy makers risk prioritising initiatives for a vocal majority among the represented in the sample. Multilevel regression and post-stratification is an apt tool for making the adjustments required and guiding decisions makers in crucial policy choices. "
+    "The problems of representation in data are serious. Policy gets made and changed on the basis of anticipated policy effects. Without the ability to control and adjust for non-representative samples, politicians and policy makers risk prioritising initiatives for a vocal majority among the represented in the sample.\n",
+    "The question of whether a given sample is \"good\" or \"bad\" cannot (at the time) ever be known, so some care needs to be taken when choosing to adjust your model of the data. Predictions made from sample data are powerful. It's not even an exaggeration to say that the fates of entire nations can hang on decisions made from poorly understood sampling procedures. \n",
+    "\n",
+    "Multilevel regression and post-stratification is an apt tool for making the adjustments required and guiding decisions makers in crucial policy choices, but it should be used carefully. \n",
+    "\n"
    ]
   }
  ],