From 10b0ee25762f9270449f1a8900f44de078ecf889 Mon Sep 17 00:00:00 2001
From: Tim Swena <swast@google.com>
Date: Mon, 28 Apr 2025 14:59:59 -0500
Subject: [PATCH 1/2] docs: fix `bq_dataframes_template` notebook to work if
 partial ordering mode is enabled

---
 .../bq_dataframes_template.ipynb              | 578 +++++++-----------
 1 file changed, 211 insertions(+), 367 deletions(-)

diff --git a/notebooks/getting_started/bq_dataframes_template.ipynb b/notebooks/getting_started/bq_dataframes_template.ipynb
index c79efb63e5..7900d48829 100644
--- a/notebooks/getting_started/bq_dataframes_template.ipynb
+++ b/notebooks/getting_started/bq_dataframes_template.ipynb
@@ -114,7 +114,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 2,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -137,7 +137,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 1,
+      "execution_count": 3,
       "metadata": {
         "id": "oM1iC_MfAts1"
       },
@@ -158,7 +158,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": 4,
       "metadata": {
         "id": "PyQmSRbKA8r-"
       },
@@ -179,7 +179,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": 5,
       "metadata": {
         "id": "NPPMuw2PXGeo"
       },
@@ -249,7 +249,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 4,
+      "execution_count": 6,
       "metadata": {
         "id": "Vyex9BQI-BNa"
       },
@@ -271,7 +271,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": 7,
       "metadata": {},
       "outputs": [
         {
@@ -306,76 +306,76 @@
               "  </thead>\n",
               "  <tbody>\n",
               "    <tr>\n",
-              "      <th>198</th>\n",
+              "      <th>77</th>\n",
+              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
+              "      <td>Biscoe</td>\n",
+              "      <td>37.8</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>190.0</td>\n",
+              "      <td>4250.0</td>\n",
+              "      <td>MALE</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>97</th>\n",
               "      <td>Gentoo penguin (Pygoscelis papua)</td>\n",
               "      <td>Biscoe</td>\n",
-              "      <td>43.3</td>\n",
-              "      <td>13.4</td>\n",
-              "      <td>209.0</td>\n",
-              "      <td>4400.0</td>\n",
+              "      <td>44.9</td>\n",
+              "      <td>13.8</td>\n",
+              "      <td>212.0</td>\n",
+              "      <td>4750.0</td>\n",
               "      <td>FEMALE</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>235</th>\n",
+              "      <th>273</th>\n",
               "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
-              "      <td>Torgersen</td>\n",
-              "      <td>35.1</td>\n",
-              "      <td>19.4</td>\n",
-              "      <td>193.0</td>\n",
-              "      <td>4200.0</td>\n",
-              "      <td>MALE</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>317</th>\n",
-              "      <td>Chinstrap penguin (Pygoscelis antarctica)</td>\n",
               "      <td>Dream</td>\n",
-              "      <td>45.4</td>\n",
-              "      <td>18.7</td>\n",
-              "      <td>188.0</td>\n",
-              "      <td>3525.0</td>\n",
+              "      <td>36.9</td>\n",
+              "      <td>18.6</td>\n",
+              "      <td>189.0</td>\n",
+              "      <td>3500.0</td>\n",
               "      <td>FEMALE</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>117</th>\n",
-              "      <td>Chinstrap penguin (Pygoscelis antarctica)</td>\n",
-              "      <td>Dream</td>\n",
-              "      <td>48.5</td>\n",
-              "      <td>17.5</td>\n",
-              "      <td>191.0</td>\n",
-              "      <td>3400.0</td>\n",
-              "      <td>MALE</td>\n",
+              "      <th>286</th>\n",
+              "      <td>Gentoo penguin (Pygoscelis papua)</td>\n",
+              "      <td>Biscoe</td>\n",
+              "      <td>45.1</td>\n",
+              "      <td>14.4</td>\n",
+              "      <td>210.0</td>\n",
+              "      <td>4400.0</td>\n",
+              "      <td>FEMALE</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>159</th>\n",
-              "      <td>Chinstrap penguin (Pygoscelis antarctica)</td>\n",
-              "      <td>Dream</td>\n",
-              "      <td>45.6</td>\n",
-              "      <td>19.4</td>\n",
+              "      <th>65</th>\n",
+              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
+              "      <td>Torgersen</td>\n",
+              "      <td>46.0</td>\n",
+              "      <td>21.5</td>\n",
               "      <td>194.0</td>\n",
-              "      <td>3525.0</td>\n",
-              "      <td>FEMALE</td>\n",
+              "      <td>4200.0</td>\n",
+              "      <td>MALE</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
               "</div>"
             ],
             "text/plain": [
-              "                                       species     island  culmen_length_mm  \\\n",
-              "198          Gentoo penguin (Pygoscelis papua)     Biscoe              43.3   \n",
-              "235        Adelie Penguin (Pygoscelis adeliae)  Torgersen              35.1   \n",
-              "317  Chinstrap penguin (Pygoscelis antarctica)      Dream              45.4   \n",
-              "117  Chinstrap penguin (Pygoscelis antarctica)      Dream              48.5   \n",
-              "159  Chinstrap penguin (Pygoscelis antarctica)      Dream              45.6   \n",
+              "                                 species     island  culmen_length_mm  \\\n",
+              "77   Adelie Penguin (Pygoscelis adeliae)     Biscoe              37.8   \n",
+              "97     Gentoo penguin (Pygoscelis papua)     Biscoe              44.9   \n",
+              "273  Adelie Penguin (Pygoscelis adeliae)      Dream              36.9   \n",
+              "286    Gentoo penguin (Pygoscelis papua)     Biscoe              45.1   \n",
+              "65   Adelie Penguin (Pygoscelis adeliae)  Torgersen              46.0   \n",
               "\n",
               "     culmen_depth_mm  flipper_length_mm  body_mass_g     sex  \n",
-              "198             13.4              209.0       4400.0  FEMALE  \n",
-              "235             19.4              193.0       4200.0    MALE  \n",
-              "317             18.7              188.0       3525.0  FEMALE  \n",
-              "117             17.5              191.0       3400.0    MALE  \n",
-              "159             19.4              194.0       3525.0  FEMALE  "
+              "77              20.0              190.0       4250.0    MALE  \n",
+              "97              13.8              212.0       4750.0  FEMALE  \n",
+              "273             18.6              189.0       3500.0  FEMALE  \n",
+              "286             14.4              210.0       4400.0  FEMALE  \n",
+              "65              21.5              194.0       4200.0    MALE  "
             ]
           },
-          "execution_count": 5,
+          "execution_count": 7,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -424,7 +424,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": 8,
       "metadata": {
         "id": "YKwCW7Nsavap"
       },
@@ -433,7 +433,7 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "average_body_mass: 4201.754385964906\n"
+            "average_body_mass: 4201.754385964911\n"
           ]
         }
       ],
@@ -453,7 +453,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
+      "execution_count": 9,
       "metadata": {
         "id": "4PyKMR61-Mjy"
       },
@@ -514,7 +514,7 @@
               "[3 rows x 1 columns]"
             ]
           },
-          "execution_count": 7,
+          "execution_count": 9,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -543,7 +543,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 8,
+      "execution_count": 10,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -559,7 +559,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 9,
+      "execution_count": 11,
       "metadata": {},
       "outputs": [
         {
@@ -573,7 +573,7 @@
               "dtype: struct<culmen_length_mm: double, culmen_depth_mm: double, flipper_length_mm: double>[pyarrow]"
             ]
           },
-          "execution_count": 9,
+          "execution_count": 11,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -594,21 +594,21 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 10,
+      "execution_count": 12,
       "metadata": {},
       "outputs": [
         {
           "data": {
             "text/plain": [
-              "133    <NA>\n",
-              "279    18.6\n",
-              "34     18.3\n",
-              "96     18.7\n",
-              "208    18.9\n",
+              "116    <NA>\n",
+              "308    15.5\n",
+              "285    15.9\n",
+              "2      16.0\n",
+              "295    16.1\n",
               "dtype: Float64"
             ]
           },
-          "execution_count": 10,
+          "execution_count": 12,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -639,7 +639,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 11,
+      "execution_count": 13,
       "metadata": {},
       "outputs": [
         {
@@ -648,13 +648,13 @@
               "<Axes: title={'center': 'Numeric features'}>"
             ]
           },
-          "execution_count": 11,
+          "execution_count": 13,
           "metadata": {},
           "output_type": "execute_result"
         },
         {
           "data": {
-            "image/png": 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ne6rC9lmzUH7/A6iYO7dB2w0RIkSIhsb/ZB4YETUpAwNumd8s5/7uttEoiAf/GaZOnYqZM2fioYcewhFHHIHNmzfjhx9+yPn8q1atwptvvol58+Zh1apVOOOMM7B69Wr06dMHH3zwARYtWoRLLrkEo0aNwqGHHgoAOPPMM5Gfn48333wTpaWleOKJJ3DsscdixYoVaN26NW137ty5+M9//oOdO3dizJgxuOeee1wTdRBMnDgR3333HV566SV07twZr776Ko4//ngsW7YMvXv3BgBUV1fj/vvvx5w5cxCJRHD++efj+uuvx/PPPw8AuPfee/H8889j1qxZ6N+/P2bMmIG5c+fimGOOAQDMmDEDK1aswP7774/bbrsNANCuXTtqxPzxj3/EAw88gHbt2uGKK67AJZdcgoULFzbaPQaA22+/HQ8++CAefPBBTJkyBeeeey569OiBqVOnomvXrrjkkkswceJEvPnmm1nfUxXMyioAgFFZ2WBthggRIkRjIDRgWhD27NmDGTNm4JFHHsGFF14IAOjZsyeOOOIIjpHIBqZp4plnnkFxcTEGDBiAY445BsuXL8cbb7yBSCSCvn374t5778X777+PQw89FB9//DEWL16M8vJyJBIJAMD999+PuXPn4pVXXsFll11G2509ezaKi4sBABdccAHefffdrA2Y9evXY9asWVi/fj06d+4MALj++usxb948zJo1C3fddRcAIJVK4fHHH0fPnj0BZIweYogAwMMPP4ypU6fi1FNPBQA88sgjeOONN+j20tJSxONxFBQUoGPHjq5+3HnnnTj66KMBAH/4wx9wwgknoLa2NlCCuGzvMcHFF1+MMWPGAACmTJmC4cOH4+abb8bo0aMBAL///e9x8cUXB7+ZQUDqgpnZMYMhQoQI0dQIDRgA+TEd3902utnOHRTff/896urqcOyxxzbY+bt160aNDADo0KEDdF3nkqB16NAB5eXlAICvvvoKlZWVaNOmDddOTU0N584Q2+3UqRNtIxssW7YMhmGgT58+3Pd1dXVcHwoKCqjxIp6voqICW7duxSGHHEK367qOoUOHwjTNQP0YNGgQ1zYAlJeXo2vXrr7HZnuPZefs0KEDAOCAAw7gvqutrcXu3btRUlIS6Dr8YFmZ+2GZYYHTECFC7N0IDRhk9AbZuHGaC/n5+cptZDJkNTWpVMq3zVgsxn3WNE36HZnoKysr0alTJynjw4Zoe7WRDSorK6HrOpYuXQpd5429oqIiz/Nlqy/yAts+qZsV9Hqyvcde56xPPwLBsNsKGZgQIULs5dj7Z+0QFL1790Z+fj7effddjB8/ntvWrl07AMDmzZvRqlUrABkRb0NjyJAh2LJlC6LRKLp169bg7YsYPHgwDMNAeXk5jjzyyJzaKC0tRYcOHbBkyRIcddRRAADDMPDf//4XBx10EN0vHo/DMP63mQfKvIQMTIgQIfZyhAZMC0JeXh6mTJmCyZMnIx6P4/DDD8fPP/+Mb7/9FmPHjkWXLl0wbdo03HnnnVixYgUeeOCBBu/DqFGjMHz4cJxyyimYPn06+vTpg02bNuH111/HqaeeimHDhjXo+fr06YPzzjsPY8eOxQMPPIDBgwfj559/xrvvvotBgwbhhBNOCNTOVVddhbvvvhu9evVCv3798PDDD2Pnzp1cFfJu3brhs88+w9q1a1FUVEQFyf9TsJkXy2hAVidEiBAhGgFhGHULw80334zrrrsOt9xyC/r374+zzjoL5eXliMViePHFF/HDDz9g0KBBuPfee3HHHXc0+Pk1TcMbb7yBo446ChdffDH69OmDs88+G+vWraM6jYbGrFmzMHbsWFx33XXo27cvTjnlFCxZsiSQ/oRgypQpOOecczB27FgMHz4cRUVFGD16NCfCvf7666HrOgYMGIB27dph/fr1jXE5ezcoAxMaMCFChNi7oVkNKRTYi7B7926UlpaioqLCJXCsra3FmjVr0L1790BRJCF+eTBNE/3798eYMWNw++23N3d3GgW5POcbJkxE5bvvotXYC9DxxhsbuYchQoQI4YbX/M0idCGF+J/AunXr8NZbb+Hoo49GXV0dHnnkEaxZswbnnntuc3dt70IYRh0iRIgWgtCFFKJJsX79ehQVFSn/NZbbJhKJYPbs2Tj44INx+OGHY9myZXjnnXfQv3//erU7cOBA5bWQJHotCSSMOhTxhggRYm9HyMCEaFJ07tzZMzqKJKtraHTp0iVw5txs8MYbbyjD1RtLE9SosMW7oYg3RIgQeztCAyZEkyIajaJXr17N3Y0Gw3777dfcXWhYEPFuKOIN0QxIbS1HrEP75u5GiBaCrF1IGzduxPnnn482bdogPz8fBxxwAD7//HO63bIs3HLLLejUqRPy8/MxatQo/Pjjj1wbO3bswHnnnYeSkhKUlZVh3LhxqBRqr3z99dc48sgjkZeXhy5dumD69Ok5XmKIECGCwjLDTLwhmge7XnkFK48+Gjtffrm5uxKihSArA2bnzp04/PDDEYvF8Oabb+K7777DAw88QBOnAcD06dPxl7/8BY8//jg+++wzFBYWYvTo0aitraX7nHfeefj222/x9ttv4z//+Q8+/PBDWkMHyCiQjzvuOOy3335YunQp7rvvPkybNg1PPvlkA1xyiBAhlCDMS+hCCtHEqFuZKUWSbOAK6yF+ucjKhXTvvfeiS5cumDVrFv2ue/fu9G/LsvDnP/8ZN910E04++WQAwHPPPYcOHTpg7ty5OPvss/H9999j3rx5WLJkCU169vDDD+P//u//cP/996Nz5854/vnnkUwm8cwzzyAej2PgwIH48ssv8eCDD3KGTogQIRoWlHmx5AZMct06VH78McrOPBOReLzR+7N73nzoZWUoPOxQ/51DtGiQZ88KUAIlRAggSwbmtddew7Bhw3DmmWeiffv2GDx4MGbOnEm3r1mzBlu2bMGoUaPod6WlpTj00EPxySefAAA++eQTlJWVcRlbR40ahUgkgs8++4zuc9RRRyHODJCjR4/G8uXLsXPnTmnf6urqsHv3bu5fiBAhsoRPJt5Vo4/H1tvvwI6nn270rqR37sTGa67BxkmTGv1cIfYCEAF5Kt3MHQnRUpCVAbN69Wo89thj6N27N+bPn4/f/e53uPrqq/Hss88CALZs2QLAHX3RoUMHum3Lli1o354XaUWjUbRu3ZrbR9YGew4Rd999N0pLS+m/Ll26ZHNpIUKEAJg8MN4upOql/230rphV1YBlwRD0cSF+maAMTDo0YEIEQ1YGjGmaGDJkCO666y4MHjwYl112GS699FI8/vjjjdW/wJg6dSoqKirovw0bNjTKeVKbN6N2xQqkFUzQ3ozZs2dzFaNbEkaMGIFJTbQS1zQNc+fObZJz7W1wRLw+Ghi9CVJIGfZE1gQRUTtfegnrLxkHs6qq0c8VQgHCwIQGTIiAyGoU6tSpEwYMGMB9179/f5p8rGPHjgCArVu3cvts3bqVbuvYsSPKy8u57el0Gjt27OD2kbXBnkNEIpFASUkJ968xYKXTsJJJZ6UaosVi2rRpXDXqEGBEvN7PtxbRG70r1I1lmmjsiic7X3wJVYsWoearrxr1PCHUCDUwIbJFVgbM4YcfjuXLl3PfrVixgubC6N69Ozp27Ih3332Xbt+9ezc+++wzDB8+HAAwfPhw7Nq1C0uXLqX7vPfeezBNE4ceeijd58MPP+QShL399tvo27cvF/HULCDVi8NM6yF+gaAMjELES6BFG9+AoQwMADSyAUNW/WECv2YE0V+lQwMmRDBkZcBcc801+PTTT3HXXXdh5cqVeOGFF/Dkk09iwoQJADLU+6RJk3DHHXfgtddew7JlyzB27Fh07twZp5xyCoAMY3P88cfj0ksvxeLFi7Fw4UJMnDgRZ599Ns3Ceu655yIej2PcuHH49ttv8fe//x0zZszAtdde27BXT2BZQLIq2L90DZCugZUKuL/fvywHZtM0MX36dPTq1QuJRAJdu3bFnXfeiQULFkDTNOzatYvu++WXX0LTNKxdu1baFmEgnnnmGXTt2hVFRUW48sorYRgGpk+fjo4dO6J9+/a48847ueN27dqF8ePHo127digpKcHIkSPxFbNyJe3OmTMH3bp1Q2lpKc4++2zs2bMn0DVWVVVh7NixKCoqQqdOnfDAAw+49qmrq8P111+PffbZB4WFhTj00EOxYMECup24y+bOnYvevXsjLy8Po0ePpq7F2bNn409/+hO++uoraJoGTdMwe/Zsevy2bdtw6qmnoqCgAL1798Zrr70WqO/kd5g/fz4GDx6M/Px8jBw5EuXl5XjzzTfRv39/lJSU4Nxzz0V1dTU9bsSIEbjqqqswadIktGrVCh06dMDMmTNRVVWFiy++GMXFxejVqxfefPPNQP3IGUHDqJuCgWFdR43tRiJuizD/TfOBsH6hiDdEQGQVRn3wwQfj1VdfxdSpU3Hbbbehe/fu+POf/4zzzjuP7jN58mRUVVXhsssuw65du3DEEUdg3rx5XDXc559/HhMnTsSxxx6LSCSC008/HX/5y1/o9tLSUrz11luYMGEChg4dirZt2+KWW25pvBDqVDVwV7AU9g0eOHrjJiBeGHj3qVOnYubMmXjooYdwxBFHYPPmzfjhhx9yPv2qVavw5ptvYt68eVi1ahXOOOMMrF69Gn369MEHH3yARYsW4ZJLLsGoUaMoQ3bmmWciPz8fb775JkpLS/HEE0/g2GOPxYoVK9C6dWva7ty5c/Gf//wHO3fuxJgxY3DPPfe4jCEZbrjhBnzwwQf417/+hfbt2+PGG2/Ef//7X87dM3HiRHz33Xd46aWX0LlzZ7z66qs4/vjjsWzZMvTu3RsAUF1djTvvvBPPPfcc4vE4rrzySpx99tlYuHAhzjrrLHzzzTeYN28e3nnnHQCZ547gT3/6E6ZPn4777rsPDz/8MM477zysW7eOXp8fpk2bhkceeQQFBQUYM2YMxowZg0QigRdeeAGVlZU49dRT8fDDD2PKlCn0mGeffRaTJ0/G4sWL8fe//x2/+93v8Oqrr+LUU0/FjTfeiIceeggXXHAB1q9fj4KCgkD9yBqExveZyDW9CQwYRgthmSa0xjyXGZZQaG7Q3yDUwIQIiKxLCZx44ok48cQTlds1TcNtt92G2267TblP69at8cILL3ieZ9CgQfjoo4+y7d4vGnv27MGMGTPwyCOP4MILLwQA9OzZE0cccQTHPmQD0zTxzDPPoLi4GAMGDMAxxxyD5cuX44033kAkEkHfvn1x77334v3338ehhx6Kjz/+GIsXL0Z5eTkSiQQA4P7778fcuXPxyiuvUCPTNE3Mnj0bxcXFAIALLrgA7777rq8BU1lZiaeffhp/+9vfcOyxxwLITOz77rsv3Wf9+vWYNWsW1q9fT1m766+/HvPmzcOsWbNw1113AQBSqRQeeeQRang9++yz6N+/PxYvXoxDDjkERUVFiEajUl3V2LPOwtlnnQUtEsFdd92Fv/zlL1i8eDGOP/74QPf1jjvuwOGHHw4AGDduHKZOnYpVq1ahR48eAIAzzjgD77//PmfAHHjggbjpppsAZAzVe+65B23btsWll14KALjlllvw2GOP4euvv8Zhhx0WqB/ZwiJVqP2qUTeBAYOmZGBo9FXIwPihZtk3SK5di9KT1POAF/a88w4iJSUoPOQQfoMRamBCZIewFhIAxAoyTEgApLZsQXrHDkTbtkWsfQPU7IgFX0l///33qKuroxN7Q6Bbt27UyAAy4eq6riMSiXDfEeH1V199hcrKSrRp04Zrp6amBquYDJpiu506dXKJt2VYtWoVkskkNTqAjMHbt29f+nnZsmUwDAN9+vThjq2rq+P6FY1GcfDBB9PP/fr1Q1lZGb7//nscIg6eAgZ06gRzzx7opaUoLCxESUlJoP4TDBo0iP7doUMHFBQUUOOFfLd48WLlMbquo02bNjjggAO4YwBk1Y+sQSZyXxFv40chcSvxRhbNW2TyDGtA+WLT1D8guXIV8g/YH/Fu3bI6NvnTRvw08SoAQP8fvue2hQxMiGwRGjBARpgb1I0TKwCiNUA0PyvXT0MgPz9fuY0YHGy0hqpKMotYLMZ91jRN+p1pDy6VlZXo1KmTlPFhQ7S92qgvKisroes6li5dCl1gAoqKihrkHNFolE5qQPb9Z6/f757KjpEdp9kC8oa6jzIQ8a6fiLepGZjGjkKiLrPQgPGFWZFJEmoE1LSxSG92FoqWZdFnOtNgmAcmRHZogmQOvzDQKKSmD0Pq3bs38vPzuSgvgnbt2gEANm/eTL/78ssvG7wPQ4YMwZYtW2hVafZf27Zt691+z549EYvFaFZmIFODa8WKFfTz4MGDYRgGysvLXX1g3UHpdJorNLp8+XLs2rUL/fv3BwDE43EYYTg8DyOYiLdpNDDMb9PoLiSigWn658Gsrsb6yy/Hzn/8o8nPnQsoS5XDb+LFqjkMzN7vQrIMAxuvvRbb9oIcaP/LCA2YbKE1ppTQG3l5eZgyZQomT56M5557DqtWrcKnn36Kp59+Gr169UKXLl0wbdo0/Pjjj3j99del0Tv1xahRozB8+HCccsopeOutt7B27VosWrQIf/zjHzljIVcUFRVh3LhxuOGGG/Dee+/hm2++wUUXXcS5tPr06YPzzjsPY8eOxT//+U+sWbMGixcvxt13343XX3+d7heLxXDVVVfhs88+w9KlS3HRRRfhsMMOo+6jbt26Yc2aNfjyyy+xbds21NXV1bv/LR5Bq1E3RSI7pg+NblhQDUzTL0xqvvwSVR98iJ3Pe+sC9xoETXYoASfMFhliGoW09xswlR9+iN1vvImf/zyjubvyP43QgMkVzcDAAMDNN9+M6667Drfccgv69++Ps846C+Xl5YjFYnjxxRfxww8/YNCgQbj33ntxxx13NPj5NU3DG2+8gaOOOgoXX3wx+vTpg7PPPhvr1q1zlX/IFffddx+OPPJInHTSSRg1ahSOOOIIDB06lNtn1qxZGDt2LK677jr07dsXp5xyCpYsWYKuXbvSfQoKCjBlyhSce+65OPzww1FUVIS///3vdPvpp5+O448/HscccwzatWuHF198ke9IA/zGVjrd6O6PhoSzuvbus6Y3vveZY2Aa24XUjCJemoOmBTAPAPOM5PCbsHWORAPGSWS397uQ0j//3NxdCAFAs1rS6JoFdu/ejdLSUlRUVLiy8tbW1mLNmjXo3r07F94dBKnycqTLy6G3aoX4Pvs0ZJdDNCBmz56NSZMmcXlxgqJu9WqY1dWIdeqEqCBWzgZmXR3qVq6EXlbWLM9KLs/5j0cdjXR5OfIPPBDd/v4St82yLPzQP5OJu9XYC9DxxhsbvM8sKj/4ABsuvwIA0PvjjxBtABelCj8MGQqruhqd7roLZaed2mjnkWHP++/jp99diXj37uj55htNeu5csPzgQ2Du2YP9/jYHBUxR3iDY/dZb2Hj17wEAvRctRJRJS7B+3HhULVyI2L77otc7bzdonxsa259+BuX33QfALUYOUX94zd8sQgYmWzSjCylEE6Oetr1VVwdYFqza2gbqUOPDycQruXaG/m8SBsZgXUhNFEbtJ15uDAR12+0tqI8LiXkXlAxMCxDxGnt2N3cXQiA0YLKGhuYT8TYmzLo6pHfubHR3x/r161FUVKT8R+pqBYFRWdk4lYo97sEVV1yh7PsVV1whb6clPSsetZAszoBpgjBqtg+NbFjQMGqF1qbmq6+w++3GYQWoqyygkWbW1GDnP/6B1FZ3OH3tDz+ggtGBNQaCuhllMGvUBgwVUrcADYy5J6yQvjcgDKPOFoSAaUmTUgCkt2yBsWcPtGgMenHDhCLL0LlzZ8/oKJKYzg+WaSK5bh2gacjr18+Vl+Siiy7CRRddVI+eQvob33bbbbj++uulu4tUZ4v0znrkQ+EmliYoJcAZUU2WyE5+no3XXIvUpk3I/+ADxDo0QP4nFqa38SSi/L77sfOFFxDr3Bm93uMjEjdNvRF133+PvL59kejVq2H7SUANmOwZI7PGKZ/RkhkYszL7EPIQDY/QgMkWv1AXkrMCbdzBg4Rf1xummTEwGsNIsNuUtdy+fXu0D5rAsB5ix+aCV4gsN7E0MQPTmAnmLPIsQW1EGLaWytxdATSwAUPdYwENmD12GoXUJnfyTdLP9I4dSDRI79ygbsYcGBgvFxJldFoAA2MwDIwrn02IJkPoQsoWWeaBSe/cieS6dU2S4dMyTSTXr0d6+/YcDm45k6wLjdX3Bmq2Rd1aj9U1N+E0xUU1FQPDnUd+XVZjJlkz1ayXfH/1fiSSyUrW3wiwDAMbr78BPz/6qPz8Obj1zOoap31FGHWLYGDYJH4t6gX/ZSE0YHJFwIfW2L4dxp49MJnKw40Fq7YWxu7dSG/LwYChjbSQl7Ex+2m5/sgNdDJsIfcU8CxqyIW3NkG+FK4Pjc3A0PPIWRAn1LnhhbbZMjCW1/Nk989KJuvbLdR+9x12/+c/2PboY7yx4eNu84JZ6xgwItNCn70WwcAwBkyYDLPZEBow2cJmYIIO31ZAIWdDJOpyNBc5TC4txXCxwfW2gfvuOUFk1RD57RumOf/TWfV/jryyrLJ5SpoiWsfgq1E33nm8o50sy2LYgUaYXLNlYDzEvtTQagADpm6lXdssnUbSFtezuq6copBqGAZGZFpoJFgDPMeNDJaBCetnNR9CAyZbZFtKIMB+6W3bUPv99znVFuHPFfyc6jZaiCHTFAxMfcOo0bQMTLq8HLXf/wCjHmyfo2/wFvE2iUu0qRgYv2gndnsjTKz0/EHb9nIh0YrO9TdgkqtW0r+pMcPdq4aNQmKfqb3djcRFP+7lxtYvGaEBkyuyNWA89jdtYZvJrE5y7FQ9Dm0hhkuToKEZmKa5t5nnqJ55Z4KKeJvEhcQwMI2ZB8aPgWHT3zfGxJplJWzP6LYU0cA0IAMDoI4YM2b9jEp2jFOWEgD2+my8IQOzdyA0YLKEljUDk8U+kn2NyspM9t8dOzIuAsvCZZddhtatW0PTNJSVlWHSpEm0T/1Gj8bDzz4brG+ybjahIaNpGubOnZvbwSyV3QB9njZtGg466CDhHPVstKmNwoYwmOhk6iPibYqka6wxUQ+XVfWSJaj65BPldj8NDKt7aVQNTFDjyGPFTxmYhjBgVjkGTNI2ZjiWRJi4LdNExb/+Rd1N0v55hlEzbTcAg9RYMJNJpSFX+/33jZYvqClgWRYqXn8ddatXN3dXAiEMo84WWYfLBZlU5BZMJtfJejp4a9Eo3lq4ELNnz8aCBQvQo0cPnHHGGcwBFj568UUUFeWQx6WJ9RrNCU3T8Oqrr+KUU06R71AfLZGsnaYyZOrr8mKP92MimpiBydWFZCWTWHfBWABAnyWLoRcXu/dhr0tmHLBM0N6ggVHsZ5mmkyW3ngaMWVOD1E8/0c/UmPFgYHa98gq23HIrAHV6fd6FpNDAAMGNuWaAWVHBfWafmY3XXY/k6tXIe+dtxPfdt6m7Vm/UfvsdNl13PfKHDEG3F55v7u74ImRgckUDamCUE51hcCtPyzCwatUqdOrUCb/61a/QsWNHRKO8DdqudWsU5OUH65u8M/U41kGyAVaAnmDv1d7q/mqufuV6Xp+wZS40tyloc4N99nM7n1FVRf9Wumi5Sdl97zi3UaNoYLKMQlL9vkw/zXq+f8k1azLPkT2+JNesyRQm9TBgqj76yLddLxdSS9HAGIIBw94HkofH2Lmr6TrUgKD9z6GGXHMgNGCQGRCqU9WB/tUYtZl/6Zpg+6cz+6u2Wx4TsThQXXLllbjqqquwfv16aJqGbt26iReScSE9l3EhWaYJTdPwyAMP4De/+Q3y8/PRo0cPvPLKK/SQtWvXQtM0vPyf/+CY889H0b77Yv/998cHH3zANf3NN9/gN7/5DYqKitChQwdccMEF2LZtG90+YsQITJw4EZMmTULbtm0xevTorH+HDRs2YMyYMSgrK0PrsjL89v/+D2vXrqXbL7roIpx84om4549/xD7dumHfI47ApDvuQIoZCDdv3owTTjgB+fn56N69O1544QV069YNf/7znwGA3rNTTz1Veg/nzJmDviNHouPw4Tjv8suxJ6CwesSIEbjqqqswadIktGrVCh06dMBTf/sbqqqrcdmNN6K4uBi9evXCm2++SY9ZsGABNE3D/PnzMXjwYOTn52PkyJEoLy/Hm2++if79+6OkpATnnnsuqoMIc+trMHm4BwBhUmmCKKSGKCXAMhGarsgezCXM83EhNYY2g5zTdhH7QrEPxyTV04AhjEv+oEHQ8vJgJZMZRoZ7Rvh+BCnrYQXVwLQgA8aSibwbOSFoo4H0u4UIk0MXEoCadA0OfeHQ7A/8NIt9v5R//dm5nyGqYmCESeShO+9C7wED8OSTT2LJkiXQdR1nnnkm3S4OfiT3zLQ778Q906djxowZmDNnDs4++2wsW7YM/fv3p/veeN99uG/yZAw85BA8PHs2TjrpJKxZswZt2rTBrl27MHLkSIwfPx4PPfQQampqMGXKFIwZMwbvvfcebePZZ5/F7373OyxcuDCLG5NBKpXC6NGjMXz4cCyYPx/W1q2496mncPzxx+Prr79GPB4HACz48EO0LyzEWy+/jBXLlmHsDTdgyLPP4vIrrwQAjB07Ftu2bcOCBQsQi8Vw7bXXorzcqRmzZMkStG/fHrNmzcLxxx8PnZnUVq1ahblz5+Kfjz2GnTt34oLJk3HPPffgzjvvDHQNzz77LCZPnozFixfj73//O66aOhVzhw/Hb0eNws3Tp+Ohhx7CBRdcgPXr16OgoIAeN23aNDzyyCMoKCjAmDFjMGbMGCQSCbzwwguorKzEqaeeiocffhhTpkzx7kA9o6c4o0VWC4nRJTSJcNH0ZoSCgJswFYMyPwHJopBYF1PDT0wcu2QYlPVQIsB11DeRXXrrVgBAvMu+MPfsRt2PK5HcuBF5paXMCfl7ZVb5G9mBGZi9OBeMi4GRhJbv7WHgKlhZCsqbGyEDsxeAGB6iASI+RKXFRSguLoau6+jYsSPatWsnNiT9fNrxx2P8+PHo06cPbr/9dgwbNgwPP/wwt+sV556LU379a/Tv3RuPPfYYSktL8fTTTwMAHnnkEQwePBh33XUX+vXrh8GDB+OZZ57B+++/jxUrVtA2evfujenTp6Nv377o27dvVvfg73//O0zTxFNPPYUDBgxAvx498ORdd2P9+vVYsGAB3a9VSQkeuvFG9O3RE/939NE4/sgj8Z69/YcffsA777yDmTNn4tBDD8WQIUPw1FNPoYYZNMk9Kysrc91D0zQxe/ZsDOzdB4cPHYpzTz8d777L15rxwoEHHoibbroJvXv3xtSpU5GXSKBtq1a45Iwz0Lt3b9xyyy3Yvn07vv76a+64O+64A4cffjgGDx6McePG4YMPPsBjjz2GwYMH48gjj8QZZ5yB999/P0APyHMUuMs82AlE0kiTRyGxzEeOAyrnNlJpR3yYHu66GyUKKbt8NyqWpiEZGHrvYzFECjOaOqumhn+4hL6a1VXwg8mWEhD1RC0kCsnYJRgwEgamMcTeTQH6DLUQAyxkYADkR/Px2bmfBdrXrK5Gcu1aaPF4oGJptd99BwCIde4MvaxMem461PgwMFlH29j7HypE1wwfPtxVUPEwZp9oNIphw4bh++8zQryvvvoK77//vlQcvGrVKvTp0wcAMHTo0Oz6x+Crr77CypUrUUxElqYJaBpqa2uxiomG6N+nT4Y1sa+tY7t2+N4WGy5fvhzRaBRDhgyh+/fq1QutWrUK1Idu3bqhuLgYtbYh0Kl9e4698cOgQYPo37quo02rVhjYuzfta4cOHQDA1SZ7XIcOHVBQUIAePXpw3y1evNi/A/UUH/sxMFzm1KZIZNcADAwnGlVNKqzhJhUvN1EUEhBs4lDdi4Y0YOx7r0V0RGy20KyuFtxtAuPbkAxMY4ilGwguF5LM9dpSXUgeeaD2RoQGDDJRKQWxAv8dAZgxQNfzoEXiyPM5xrIsaHoeACCm5yGq2l8VASQ+RH4GTENEvUiOraysxEknnYR7773Xta1Tp07078LCwpxPW1lZiaFDh+L555+HsWcPUps3Q4vFkOjenWNJYoReJ5FZmgazgV62WCzGf5Fl2+LxmqbR/rIF38Q22eM0TZO2k9U15vrz+4l409kxBfVFwzAwzKSqLBPgk6iOi0JqRA0MAroeAjBJ9Q6jJkaVHkGk0DFgOKNFZGB8NDBWKsUZwe5ijkx7e7EGxqoVxOBSBmbv7b8X6LvQQhiY0IWUNUgYde5iO/d+rj8yn3I1YGCzNfbnxQLb8umnn3L6FwD47Kuv6N/pdBpLly6l+wwZMgTffvstunXrhl69enH/ghotRmWlZz2oIUOG4Mcff0T79u3Rq0cP9OzaFT27dkWvXr1QyvrdJddK0LdvX6TTaXzxxRf0u5UrV2Lnzp3cfrFYDMndu9WTRUNFDzVXHpgGYGD8MvF6pbP3Qs2336Lyww+571Ll5aj4178kE1oDaGBYl4Xq92YNCB8XUpNoYHJtp7EZmKpqjnkTBc9+9d5MMcGiqpQA9m4NjMjCsb8f1cB4MHWpreWoeO21vfMa7ee7pTAwoQGTLaj9kps7R7FRvk/WBoy4e+aLf86bh2eeeQYrVqzArbfeisWLF2PixIncvk+88AL+9e67+GHFCkyYMAE7d+7EJZdcAgCYMGECduzYgXPOOQdLlizBqlWrMH/+fFx88cUwAgy4VjqN5Nq1qFu9WukGO++889C2bVucfPLJ+GjhQqz96Sd8+NlnuPrqq/ETk4+C3ANZO/369cOoUaNw2WWXYfHixfjiiy9w2WWXIT8/nyt3v1+XLnjnjTewcflyl3EjuY05g2unCYwZx37J8Vy+It76RyGtPf0MbLjsciQ3bKDfrb/kEmya8gdX1WNuEsiVgakOooFhr9vbhdQozIBZf6YJAO9Cqm8iOIaB0RQuJJcOymcsEMPYW2oYtatvlvu98TJ0155xBjZNnoLttsZwb0LIwPzSQTPxBti3vuUGstbAyEOyb5p4FV566SUMGjQIzz33HF588UUMGDCAO/L2a67BA08/jWGjRuHjjz/Ga6+9hrZt2wIAOnfujIULF8IwDBx33HE44IADMGnSJJSVlSES8X+E+PBb+TUUFBTgww8/RNeuXXHm2LEYfPLJuOKmm1BbW4uSkhJJo/J2nnvuOXTo0AFHHXUUTj31VFx66aUoLi5GXl4e3efem2/Ge598gu4HHojBgwer225Io6NJ2JgGDKOWiXi5WkjZn4tlQEiUC+Bked3FhPdnzqHWWwQFW/1YycBwIlo/F9JeoIFRtdOAeWCg0MDwaR+yM7YsHwOmpYh4RX2LNHLH43dM//wzAGDP2+80fN/qCWJ4NUR286ZAqIHJFlqOLqQAD4RfFBIsC5MmTXJKBwBchI5lWfhh/nzXOTu1b4e33nrL89x9e/TAhy+8gGibNogxuhaC3r1745///KfyeLYfLrDZi00TsI0e8Xo7duyIZ599FumdO5HauBFaNIq8fv3o9tmzZ6Nu1arMSs4+9r4pU5Do0ZPu06lTJ7zxxhv0808//YTy8nL0YgTXJ44ahd8MG4Zou3aI2cLaadOmYdq0aVx/rh4/HtcL36kgu/4VH37ootXZax4xYoTrHlx00UW46KKLuO9kfZOinoaXbxg1F4WUQx0cRiMRKXDrwYyft/FfcAxMbhO7JYlC2nr3PYgUFaHdVRkW0p+BaZpaSK6+ZImG1MBYrAZGKeLN7LP96WeQZPI1qSC6kMRQ75Yi4nUZV1S4m53YuyHKPTQ4AhYWLb//fiAaRXtmLmoOhAZMrmiM1Xm9XUiCwbQXlgewLAu+xRiCTMTcitz5+7333kNlZSUOOOAAbN68GZMnT0a3bt1w1FFHBW+/vq4Y8TwN0VZTgH3e7KRqrOvNqmcUEldtXZVUjoHVAK4VLgrJMFC3Zg122LXC2v7uCmjRKG8cSTUwrEizMTQw9TfUAH5irW8eGJ6ByejcMiJed9bi8vvu449V/LYuY95LA7M3u5CEyV3GwAQxwPZGA4YYrl7vW6q8HNufyri/2l5+OSL59cn8Xj+ELqRsYQ/oyrnPshwhV7YTmMKAoRlEs6XRsxF1NrDb5Pnnn0dRURGKiopQ0qYN2h1yCNodcghKWrXCwIEDg/XFs5/MYMFsTqVSuPHGGzFw4ECceuqpaNeuHU1q525LeRLu0/r16+m1yP6tVxWvay4Rbw7nNWtr3S4W0YWZVruQglRSZyv4spNVRCUE94mKCgIuCskwYDCaJ2KQWT4lCzgGpjEz8SrOHxhGA4p4VQxMgGrUqt9TrJLupYHBXiBwtVIpqdDWZZxIGJggrsC9WcTrWTCUfdebeWEWMjDZwseFlN6yBekdOzI5YtjVq1ebiomHvtC67qqL5NkOg+ply6BF1Kvdbt26wTRN1H77raqJnPDb3/4Whx6ayW5s1tbSCrXxrl2RkBTUk0LWGXqr5Mbh6NGj/csYeBh2fLuZ/zp37uzKm8Oic+fOitM0DwOT7alSW7di5TEjkSdEpsEwuNU0X43aeRYrFy7EhsuvQIc//AGtzz9PeR5jt2PAsBN1tGNHJO1cP1YyCc3OusxN5jln4uUZGNaNZSWTQH4+z6r4hVE3Zi0k4VxZt9OQUUh2P3gNTJXA0sl/E022WIC/iHdvYmAs08Rqu9xI93/9Cxqr9VMY+jwD4/+cmHthxW3Sby8GpqmLunohNGCyBDVJFLOEWZ3RZ1i1tdAY4ajXrKKMHmEYGAvChOjZEHgXUlY+pIZ5IIuLi2lSOrO6GnX294kePaT6B74LlrInVqDq3j4IzFRktkejUU5Dk/V57JayrWOe+/myuze7Xv4HwBixTnOCuy8tF7vWfvMtkE6j5uuvAKgNGHPPbucDM1HrTJh8autWxLt0ce3TUJl4jQqnD2SS5xgYmQuJm1gbrxq12JdsweXNabQ8MGzIsOI5U/xWXgaM+Ps2t4jXrK6m4nKrpgYawyqJfZMV4wwSbl9vN18jwAqggeG1cM0brRS6kLIFy6pIJ8BcJla5VoVjYDInDNSM89li//M4zs06NCTY+xRoIvIyMGTGXtaeNY/7InxpVFX55rfwOJGy3UZBbvaL2v0j+vq5atTMb0omIp9Vs7GHYT8UrojUxo3OPg3AwIhRSMbOHc5n0m92EJa5kNgJq7GjkOqjgWFdfA2VB0aP0gWH5XIhKRIDqsLVRQPGo8p3czMwMrEy/exiYCSsRTOIeC3DwO5585BiIvyyBjG8TFO5YOa0Vs0cbh0aMNmCjaiRTrBkcrSCT2Cq3CaiBiaLMGrLsthPPsfJ22gwsP0OUuslyFbuXuTaZ58zGQaSa9ciuW5dbmGFTWzAWDlaMKoaNq6BWxGFRCZOP58+y8CosvryBkz9op4AuKKQ0jsYA4YyMOxk1QyZeBsoColrpyEZGCaRnZi0TfpeKCY1VlAN+DEwzctOSCtMk20CCydnYJpeA1O1aBE2TroGW++6O+c2giwauBxDzayBCQ2Y+iAbl06wBvmP2Row4vagUUiNPdHmKmb2MBAbzYUkGluWlRmM6mvANAVyvDfiypjCY1JhXS1UDOtD+7MaGFXSvNTGTcz3AdwVPhCjkIwdMhGvR3I2YXujZOINwGoEaofNA1NffYWqFhLrYjMtebi9cuITJn62jz5GQpPDg4FxsSuEgTHcRr0nGtgYTm/fDgAwGCM9W1h+ejAITGzIwLQwaD5KBnYSsSTf+x3Dfi26kHwGcUs8X46aiAZHri4kSFgp2ffZGgpNZdi1kDBqlYvMFS6qqkZtf88ZOIaBTX/8I3b+4x/OIXv2cNvp30oGpv7hxS4NDONCosnefFw4HGXe2NWos2Rgdr/xBjZef0MmiowT8dbPAAiSBwaWKX+fVQaM3T8q0vZgYBpicq/4z+vYeMNkmHV1/jsL4HVPguZF/Ez6bqpZmyYBeQ/rc+4A9cesZJ3vPk2FUMSbLXxcSBa3LceJlUBgYLLKxOtqms/poTxvYzMwgQyYAG3Vi4HxOJHKeDLNQLlL+KZaBgPDpduXtUc+KqKQyATPDuy1P/yAiv/3T1R/8ilanXkmACEPjIKiJ6tI8ftcB0qWXbLSaaRZBoa6kLwNCG5V2uiZeLObuDdeex0AINGrF+JduzhtNkImXiuZ5DL8WqapiNpSrNxtVkLLz8/0L9W4GphN118PACgYMhitzjknu4M9QvhdLByJQuIYmKY3YGgEUX0i2QKEglusQRgyMC0YPi6IwCyBauIhq1y7ovFx55/HZeFlz2PZ7g6uzQDnnz17NlrZJQMaC9mzJTm6nIL3KNhxAZigoMc3NgNTH2NJKVL2ovWlLiS3iJRza7AaGIXuRGRxnINzdSEJDAyrgaEiXp8U8I0d3tsAUUipTZsaPRMvAJiVjF7KtKQLEj8XEkl85qmBacAIHVF7EwSeE7nIwJDt3O/Y9CJk+mzWx3gKEPlncUZs8zLLIQOTJTRNy7AwtkjWxWlQr43gQvICewz900mIp/ms/FMbN8LcvQdafh6/YW/RbdTDhZTVthz6E3i/bHUlLj1SVodnj3r0VRWF5CWsZAcuMnDKokrYNpQamLTcgEFDuJBqRQ0MK+J1a2CkYdRc1EUjV6PO+TpreA2SYcAyDN+xQ90giULSMy6fWAxIpWBWVTL7KFxIqlW5/XxQRkfB6AH1NxRZpkiXVbP3gcrFCUjYFcrAMN83CwNjP8/1cOsEqctl1jHGcRhG3YLhG0YddFKx3LuwLiifQSiTn8GAVctQeypBrw8aZZ7lXDFZMipBDIGcNTASF6Dq3NkOCq62m26lku2ZlAyMeM0Kyp8yMGwYL5mAmP04DYzC187pOBrChcRcm5VKwaioYD4TDYxPGDXnQmreKCR1iHKty7iqDwtD+2EncKM6GDYRoGnw945JYCcvBmobMISBUfzW4rZcYDCuyEhBDqnuPaOQVAyM/DluMpB+1OPcVhAGhnEhhWHULRBUS+IXJcMyDx7tWbIJlXl42FVUOp3GxIkTUVpairZt2+Lmm2+mD9GOXTsx/sYb0flXv0JR69Y46bzzsHLdOu5cs2fPRteuXVFQUIBTTz0V25kXfd3Gjcjv3h2ff/45d8yf//xn7LfffjB9JpEFCxZA0zTMnz8fgwcPRn5+PkaOHIny8nLM/+gjDP7tb9Gmdy+ce+65qGYmlnnz5uGII45AWVkZ2rRpg5PHjsXqDRvo9mQyiYkTJ6JTp04oG3wQ+h53HO576il67/50993o2rUrEokEOnfujKuvvtqzn+Qub9q6FSeccALy8/PRvXt3vPDCC+jRuzcemTPH3rEBhML0YyMbMPViYFQi3oBRSGTAZLdLMnpyGhiF4FHNwOToQmIYGGM7H50hZWCkYdSsC6kRNDCKKKSab75F9X+/4PdVTE5mba3btVEfN5LhMDCAY8CwuXxgWlzfe3/4get4rj9ExGszxd4MTP1cSOltzrhmpQ1ULlyIutVrAh/Pl5cQDBiXBsZy7ReUqQtimCfXrUPlRx/5t0W0aA0k4lVHITEL5VDE2/ywLEsdSiqBWVeXSUteXeP6kUk0gFlTA80w6ACqVVdLV7oaVwjLKaBHH2zissp0FM8++yzGjRuHxYsX4/PPP8dll12GTrEYLj7tNFx2441YtX49/vHww2jTvz+mTJ6CU6+8Ev+dOxd5loXPPvsM48aNw913341TTjkF8+bNw6233krPvt8++2Dk4Ydj1qxZGDZsGP1+1qxZuOiiixCJBLN3p02bhkceeQQFBQUYM2YMzhk3DnEAs+69FzW6jjGXXYaHH34YU6ZMAQBUVVXh2muvxaBBg1BZWYmbJk/G2b//PT595RXAsvCXv/wFr732Gl5++WW0r63DT5s34actWwAAc99+GzMeewwvvfQSBg4ciC1btuCrr77y7qA9wY+79lrsqKqitZKuvfZalJeXu/bL/FlPQXZTIlt3l0rEa3qsPCWJ7HgXhpMQix6ym9XA8PlEnLYY/7qfYeEDyzC41WL655/57WSClxQo5PZrhigkyzSx/pJLYNXVoc8nixwdiiJ3iFVb6zKu6sXAkHsS4Q0YloGBxYh4NS1TGJM5XnSvE6Mkki9xIYlGQj1zpKS3O5XNa5Z9jZ3PZRYl/X/4PlgDXuUjXNWo3QyMlwtJi8Wcd6a2FppPZvKN112P2m++QY8330Cie3flftSV20AiXpVxxUV1hQZM88OqqcHyIUOb5dx9/7vUvXrWNCcCKRLh6nB06dIFDz30EDRNQ9++fbFs2TI8/OyzOHLoULy+YAHemzMHhx10EBI9e+LZRx5Gz4MPxr/few/nHnAAZsyYgeOPPx6TJ08GAPTp0weLFi3CvHnzaPsXn3UWrrrlFjz44INIJBL473//i2XLluFf//pX4Gu64447cPjhhwMAxo0bh6lTp+LbN95A9y5doJeV4YwzzsD7779PDZjTTz+dO/7J++/HPgceiO9XrcLQAQOwfv169O7dG0cccQRqv/sOXTt1pPtu2LwZHdu3x6hRoxCLxdC1a1cccsgh3h20LCxfvRrvLVyIJUuWUGPtqaeeQu/evZndsnR9cafIzYWXMxpDxFuPRHZiSnLLsmCwk58q3wRrLJj1Y2DE4oHpbdv47dT15b3q5HNjNI0GxqqrowafsafS0Y0ok8TVuF0bDcLAiC4kxg3IamB0HdA86gUBWYl46+uqY11INX4LGgm8Erq5GRm3BsaLBWENGLO21re0SnqHnd9l507Ay4Ahz2YDiXjVUUhCJFozIisX0rRp06BpGvevX79+dHttbS0mTJiANm3aoKioCKeffjq2CmmN169fjxNOOAEFBQVo3749brjhBqSFh3XBggUYMmQIEokEevXqhdmzZ+d+hXs5lBMdXQFFuNDtQwYNgsHQo4cddhhWrl+P71etQjQaxcEHHGC3A7QpK0Pvbt3ww+rVgGXh+++/pwUWCYYPH859/u2oUdB1Ha+++iqAjMvpmGOOQbdu3QJf06BBg+jfHTp0QEFBAbqT+jamiQ4dOnBMx48//ohzzjkHPXr0QElJCfrYfdqweTMA4KKLLsKXX36Jvn374rq77sI7ixbRY08bPRo1NTXo0aMHLr30Urz66quu58kFC1ixdi2i0SiGDBlCv+7VqxdatWrF7McalvXVwDQycnQhWem0cn9PES97P6gh4K7aTCuzCzlE1AwMs/JmB+IcGBhRnOwyYCgD47Pq5FxcjayBSTsGDP2ujhEiK11INS7jyqyXBsbuk8DAcEYok8hOi0SosZPZJrmPRMQr0cC4GZjMNrO2Fpum/AG733orq/5zLqS6HO6DBwND+00YJwkD48WCsGO+MoUB1xeTP68Kaf6dywWq95LbZy8Ko86agRk4cCDeeecdpwGGNrzmmmvw+uuv4x//+AdKS0sxceJEnHbaaVi4cCEAwDAMnHDCCejYsSMWLVqEzZs3Y+zYsYjFYrjrrrsAAGvWrMEJJ5yAK664As8//zzeffddjB8/Hp06dfKvMpwjtPz8DBMSELU//ggrlcoUJszjI39qf/gBlmki2q4dtHicJubSS0sR32cfd2PC8QQ0ykMwYMy6OqS2boHepnWGmVFOVnwUVNApLR6PY+zYsZg1axZOO+00vPDCC5gxY0bAozOIMWI+TdMQE6llTeP0NCeddBL2228/zJw5E507d0bdxo0Y/OtfI5lKwbIsDBkyBGvWrMEbb7yB+a+8gguuvx7HHHYYXnjwQezbsSO+XbwYC5Yuxdtvv40rr7wS9913Hz744AOuHzyaIDqriRkYS/nBG5xLwLXRi4FhXUiyKCTehcTpXyCyGoowatN/MPWCKTAwxq5dfB8kxRzlzEHj1n6RaWDYSA/OmFEYUFZtnYSBqYcbRsXA7FFEIUUifOVyWXi13XetIEAYtX0t1UuXouJf/0LdqlUoOe64wN1nXUi5MFGqWl2Zxm1DLB6HmU5LSwm43EyS4wHAqvU3YIIUWASYZ6OBRLxBNDAtLow6Go2iY8eOru8rKirw9NNP44UXXsDIkSMBZLQT/fv3x6efforDDjsMb731Fr777ju888476NChAw466CDcfvvtmDJlCqZNm4Z4PI7HH38c3bt3xwMPPAAA6N+/Pz7++GM89NBDjWfAaJqvH5JFJC8flq4jkpeHSEFBZlK23TxaIgHNshDJy4cWj1EDJ5KXL6UKXS86ZWCclQ1rwHy+bFlmt2QSWl4ePv30U/Tq2hX9e/ZEOp3GkmXLcNhBB8ECsH3nDvy4di369+wJWBb69++Pzz77jDvdp5984jr/+PHjsf/+++PRRx9FOp3GaaedFvje+EJ44Ldv347ly5dj5syZOPLIIwEA73/3neuwkpISnHXWWTh5//1x6q9/jZOvuAI7KirQurQU+fn5OOmkk3DSSSdhwoQJ6NevH5YtW8axK+I19unWDel0Gl988QWGDs24D1euXImdO3cqDtm7DRghhC3wYaJhwTXpEvGy4ZM+eWCEMGpTPI9CJMkzMIrMvz6wTBMwTZdrzBIyslqpVOZ87GpbFj3T2HlgJMwUe69NNrpQIW41G0sDo6s1MJZlKl3dXiLebDQwhD3J9loMjoHJIRNvgEKTWjwOMAUuOYPbw9hgtykLqbIg75KPa4i+cw2ViVcREccxey0tjPrHH39E586d0aNHD5x33nlYv349AGDp0qVIpVIYNWoU3bdfv37o2rUrPrEnyU8++QQHHHAAOnToQPcZPXo0du/ejW+//Zbuw7ZB9vlEnGgF1NXVYffu3dy/RgPV1FpIlZej9vvvJQ9iwDBaVbQK+V+LOLlnkHGrTJk+HT98+y1efPFFPPLXv+LK885Dr/32w4nHHIMJ06Zh0X//i6++/hoXXX01OrdvjxOPOQYAcPXVV2PevHm4//778eOPP+LhBx/EvDffdPWhf//+OOywwzBlyhScc845yM/PIQxRBcEV06pVK7Rp0wZPPvkkVq5ciffeew+Tb7+duz8PPvggXnzxRfzw/ff4ce1a/POtt9ChbVuUFRdjzty5eGbOHHzzzTdYvXo1/va3vyE/Px/77befRx+Avj16YOSvfoXLLrsMixcvxhdffIHLLrsM+fn58ozF9Q6jbkJkcWrD6z0RBydOn+KOQuJcSGl+1ehiYFRRSIoij9kMlBvGj8eq3/wfJxoG3JNg1cKFWD7sYOx4/gVpX5zvGjeMWnadHOvCrngV57dqatxFButTD0kVhVQlRCGR+6XrNOQaUDEwWSSyEwSp2U7KbEZnM5m9AQMPY4R8piURTP5Zz+yj+J0sPvlfEANGlihPuh/RotXDgAlSvoPTwLSkMOpDDz0Us2fPxrx58/DYY49hzZo1OPLII7Fnzx5s2bIF8XgcZWVl3DEdOnTAFjtiZMuWLZzxQraTbV777N69GzUeP/bdd9+N0tJS+q8L0Vw0BpgJLl1eDlgWUpsyeg0njJr5m3wOAvsYh5rVuHOee9JJqKmrw/BjjsGECRNw9cSJGGenan/i9tsxeMAAnD5xIo4YORKWBbz66KMZV4pl4bDDDsPMmTMxY8YMHHjggZj/+uuYctllQj/tCJ1x45BMJnHJJZcEvi2BIAxUkUgEL730EpYuXYr9998f11xzDe6eOpXbp7i4GNOnT8fBhxyCI885B+s3bsSrjz6KSCSC0uJiPP3cczj88MMxaNAgvPPOO/j3v/+NNm3aKLtAqjY/PX06OnTogKOOOgqnnnoqLr30UhQXFyNhD0z8Qb9MBsbFjLCteFUI9mNgyGRqZSLrXAO1Kg+MgoHJhqquWvI5Uhs2ICmkEHAZMEuWwKqrQ9rWWol9cfrU2FFIEgaG08D4GzBmba2bKWiQKCRRxMtm4jXUDIzsPtJEdvaCKJ12FmyqrM855jbhXEg5aGC4PEVeDAzgaFRYxkLFlohtZcPA+BkL6YZ1IakYGKulRiH95je/oX8PGjQIhx56KPbbbz+8/PLLDbtKzwFTp07FtddeSz/v3r270YwYDVpmihBEnpY4iQSZVFQTnf0/dU1pGubPmkV3++tDDyHeuTPMujrU/fgjAKBVaSmesrVE8W7dYGzf7lr5XnLJJdQoqf1hOax0Cr+/8EJXtzZu3IgDDjgABx98sLzfEowYMcJFwV900UU477jjkLYzoFqWhWnTpmHatGl0n1GjRuE7xm1Ut3Ytqm1XGSwLl156KS699FJYhoHa7/kwyN8eeyxOv+ACRFu3DtxPcm87tm+PN954g379008/oby8HD27dlUeE/wULSOM2suFxGYYrfzgQ6SYiV6atMvWLGma5qol46pEHCAPjKcOQQHLsqiomMv8C4moVRaqK5t4A0aX5ArZqpcNVWVdSEo3gmG49DENmQeG5G4xqxwDxjJNnoEh/xuGvKYUUwuJIpUC4nH3/lRXlZtbpL4uJI59UGhgtEQi0z4V8fo/J+L3QcocUNeQj2Eiy72UNQII5/emPDD1SmRXVlaGPn36YOXKlejYsSOSySR2CUK5rVu3Us1Mx44dXVFJ5LPfPiUlJZ5GUiKRQElJCfev0UAIGDFFvshk5LIophoY4kLiGRi6myyHhasp78gUGc25p7IS33zzDR555BFcddVVATvtDVdRRP8DGuZ7n/YXLFqE1157DWvWrMGiRYtw9tlno9t+++GIoe6Q+qwHhSwS2Vk2S1EvBEyaKMLc7WHA2APnnnffxU9XXsmfQ1ILCYATCSGGJouDryragZkU+aygAScwLvOv4EIKMpHJqlGnJcxSQ0JSC4mj6TkXkvr8LqagAfPAaHpmrcuzcIw7xBb7UhZG9nvRTLyOFtCpR6XQwOTgFrFSKU6wnZOIt54MjPJ3chkwwRkYXxFvQzAwAepymVwm3hZswFRWVmLVqlXo1KkThg4dilgshnfffZduX758OdavX09DdYcPH45ly5ZxIbRvv/02SkpKMGDAALoP2wbZRwz3bVbIMvFmU1aA/VbJwDCJ7Nj/yW5E3OY1qftNipLt19x2G4YOHYoRI0a43EdXXHEFioqKpP+uuOKKYOepbymBgPjoo4+UfW1n54lJpVK48cYbMXDgQJx66qlo164d3ps3Tx691EiJ7CzTRN2PPyK5dm127XudLysGRq2BIW6b2mXfuDdytZDcJQS472QMTJBU7VwujoD3kzVgGLYACDaRSQdk1hhromrUnNHCGl4ek5OY96Yh88BoUd3dpuUYm5pt6BCXk5SBETQwAPO8qCLecnAhsRXHM40JC80g8GBgyDVHiAFDxmrT/zlxGUNZRCH5ingbQAPD9VuROsLai2ohZeVCuv7662nI66ZNm3DrrbdC13Wcc845KC0txbhx43DttdeidevWKCkpwVVXXYXhw4fjsMMOAwAcd9xxGDBgAC644AJMnz4dW7ZswU033YQJEyYgYdNxV1xxBR555BFMnjwZl1xyCd577z28/PLLeP311xv+6nOGQuQpTCIWt2INOKlQD5IPA5NK0mgLv7ZIf4Jg5j33YM4//ynddtttt+F6u0S9CE/GS7FyzxpZMDDDhg3Dl19+6d7VNFG3ciUA4NdHHomTxo/PfG8YSG3eDC0ez+iagp47aF8Vx5s1tbCSycw/4n5pQngyMPbgFJclz1LoVmQ1kGAYbvqbiFXF94a0l0gIrpWALiTGjWLkYMD4upCaSAOjdiGpzy8KsuuVB0ZgYMj/LhefoJWhriRZUUwaheSkjpBWBGf2zcWFJGZc5mAYnNhYBY6BEcPTBQbGkjEwKheS0JZfHhhO9OtnLJA+2+HtWoDrdJ3PY2FB99mLGJisDJiffvoJ55xzDrZv34527drhiCOOwKeffop27doBAB566CFEIhGcfvrpqKurw+jRo/Hoo4/S43Vdx3/+8x/87ne/w/Dhw1FYWIgLL7wQt912G92ne/fueP3113HNNddgxowZ2HffffHUU081Wgh1TpC5kMRBGIAWZNJzHSdoYOwJTdM0d1xTKuUzqasmzRqP45g/TRNmZSUihYXQdB3t27dH+/btva7Goy/O374TtYJNyMaEyM/PR69evdxNmyZqycDOtJ0uL3flCeGQbb6DoCLe+hh0qvazMLbSO3eomyQDp2wy4hLZsQns3NltLcty5STxym1BJ7VcSgmwzI+YYTjAfZGKeD2KDjYE5FFI2eWBAQBjdwX3uTEYGFd9KkbES/63xP1If5gEcCQbLX1eFCxcLlFI6a1blNtkJQ6kCKKBIQyMVAOj+J1cpWd8GBhJkkMVXKHfORkwiihAdh+BhWtOZGXAvPTSS57b8/Ly8Ne//hV//etflfvst99+nHBShhEjRuCLL77Ipms5IWfdgT35urQdXpOITIMic/NIwqjZc3K7JpM+DIy7P5ZloW7VKvUxjJlg7NyJ1ObNiLZrh5gQGZYVxEs3Te8K21lrYLLpi/w3Mn30EdkyR4GfLfb3syzp7xzsfOyH4H1Ibdrk2zfp5CGphZT5W1KPxTDctW2EPDEspJNaTi4keYkELZFQ62H8wqjrWaPH75yyKCQzQCZeAFylbQD1SmTnYmBk76tpOitwnd/PyxDUorFM5WrGgHExMCQEnEzaWTBfqS1qAyZo5liVcNsyDGdx6cHAqF1IwnX6aGD43DJ+Il7e0NaUiTw9EKCYIxuW3qLCqH8pIBqHalUNGD8EnWQ8JhAzlULdDz8gtVGYQEQDJiJ3IQGZVZq3Bia7PonbpRlWc4AldsTPHZCl68vVvufOin39qNDGCqNuAL2PfbDrb/J8qzMSA2kPA8bJPyG5N6rcLWRCEvK5uGh4WfZSAgkDE3Slx55HVeMp4hEMIJ94mz4KiStqyWoOPCYxc1cjMjC6e62bSWRHNDBOtCR7PLe//btqsSidXFcdNxoV//63smCiw8RkwcBs2arcFlQDo3Jfst+TKCQpA6MaM8VyD35RSB4J9TzbzvE55Q2mIBqYFsTA/FKg6zrKysqomLigoCAr7UHSMGCaJoy6OqTZH7ymBkn7cySVgmaaMMjndJoT2SW3boWZSrlWdGZdHfRoFMlkCqZpIp1Ow6itRdK0aPp9LRqFlU4jXV2V0WxIHiIzmYRhpOkxZm0t9EgkowHxeOg00wTsfqaSdTBME5FUypWWPRsk004/gMx9ini8YHVG2sngWlcH3R4czWSS3l8W6VQKRsD+Wek0vX4NoNeaTKW4PorI9h6khWfDSCYRlRyfrq119qup4Sr6ZgOjthYp0o5homr7dpSXl6OsrAy6gu2yLAtJ24BmC8xR2KwHO6gR9oLTd3FRSPICie623QyMl1shqK+ddbGIIl56nvx8QOUu9HMhNUoiO28GJkgeGEDGwDSgBkaXrHUNLwZGwjiTMOpYjGMHNt0wGV1mPinsm7sLKWW7kKKdOnE5fgAEZ3JUofPMc6zFM9dA630F0cBkGYXEJ4z0cSE1RMkLLvt1EA1MCxLx/pJAwrbLZYJNH6S3b4dVVwc9meQGjWgkQgVkWmUltEiEDqJaPI4o83Ckf/5ZWjJeNwxE8vJg7NwJs6YGkdpa6Lt303MCziSi7d6d2VcYuABAT6dh7tlDBwLdNBFJJGCZprfILRJBzDbmjF27YFZXQ6usRDRIuJ8C6W3buME0CnjSm6mtW+kAolsWVftb6bS075HaWuiSeyCDZRhOG5qGmD3gsueUQduzR2qAqGBWVXG/S6SuDjob2mmamXuSTlPxZTQSoXk3soVZV+dU4NU0xCIaysrKpGU/CIxdu6hOJNalC5KrVwuNEp1KZhAtPv54tBk/HmvPOIN3L8my8hoiAyNoYIRMvQCg5eVlDBiZEDjoSs8IwMB4lA2Ram08Cvs1CCTVqNlaSJwLyUMDQw024p6pDwND3D0eDAxkDEyAMGotGnUZ6k7xyEjGNVWfKCSbgYnvs4/LgMmJgVEYM2IYNR+FFEwD4xuFxLmFsnMh5YIgiwY+kV090z/UE/+zBoymaejUqRPat2+PVJZ+7S3PP4+qhYvQduIEbHvE0fvsO+sZbLjqagBA0YijESkrw+65/wIA5PXrh30eepDuu/ra61xhjwDQ4Y83ouiII7D15ZdRueADtLnsUpSdeio2z34W1YsXAwDKzj4Lu176O2L7dEbx8b/BjqefdrXTfvIN2PniS0ht2AAA6HT33Sjo1w9GZSXWTrxKeW2R4mJ0f/nvAIDyhx7CnrfeRuGRR6LjjVOVx/hh48OPcAnoOs/4M/I8ysKvu+12pG0/dufp9yK/b18AQHLtWmy4407X/q3OPw+tzzsvUF9S5eVYb/9GiEXR/bXXAABrbvwjTA8Rb/7Qoeh8x+3K7SJ2vfoqtj85k35uPW4cWp1xOgAguWULNlx6GZBOI2/gQNTaZTS6zHkOsbZtA5+DRdXnn2OLfW8i+fno8+/XlMwLAXFf6u3aetfpYiYpWnGYGDCia4i6kHjjIxADk5cA9uyRMzABRbz1dSHJXIncypdN1tdQkAg1eQaGcScFmMgjBQUwKyrqVUpArIVERbzcPpaagZHeR8eASTM6lWiHDo4YOJHIlEUQxeCWFTiyhjAwsX33BT7/nN8YdGJXTOTs/Y8IpQRyYWCMXd4LL87g8mEhubZzZAo5I0nxzu1NtZD+Zw0YAl3XfQd6EdHKKkQ2b0asphYRxsLP0zT6Wd+1C1EtQj9H2rZFnl3Y0TIMaGvWSNXwsbo65OXlQd+2HZHNm5FARhwdq6mhbRV3747dmzfD+PlnRA89jOsDQTyVQmTrFrotYRrIy8tDmmlHhsiePbSfpA/6ju30u1wQKS/nzhm3r1G5/+bNiNgDXNwwuX1lfY9V1wTuX4RtQ9Pocdq6dYh4aKL0rVuzugexqiqur7HqauTl5cEyTWycMBER27BM7diBiD1ZJaAhnuN9TqXT9HxaQUGgZzq1KVMpPda5MzRVagCAn6RIng8iChezv0rqIslcSC4GRtMQicVhIGMEiTVjAot4GWNDacB43WMfFxLdJ0emTAZpFBIrlKxlRbz+i61Ifn7GgGmQatTeIl46gRGtHvlflhCQPAMC+2Ls2ePkVkkkYNTUSJm8IJE1lmVRBia2777u7YEZGPdvAjDPQiQCEFZKwsAoayEJbqCaZcsy84HKzZsFq8LlXsrZhZQdA9PcYdT/kyLe+oI+bEYaGjMY8oO2qaQhxRotHMiLa1u5hKbUmPo8iZ49M5/TaWUCNMsw5Q+j30vAugPIIOpVGj4AxMmLq64r21+xClCuarJYBXADiL2qA+AOuRWPs/O0lD/wIHb+/eUA55HnPTG2b0eSiQLjk5TVf8Jx/e0BwsDE99nHNakAzP22mFBZwjyQ+yas8h32RAjHFJkaumq1z6Hr1K2YqRAtXEPQiYe5hyoDRivIUsRryI20BoNfHhg2dXsQBoYslOoRMeXOxCuZYCWJ7GhCO68opBhfa8yqrqb5iGh6fklW2SCTsrFrF32nYvvs494hsAZGcV6GRaJskCWJ1lONmfZzr7dpg0hhIcw9e1C3fLm6HxwD49P3FG/spXfswKapN6L6v8Ejev0KqFrptLCwCKOQWhxoVsq0wa3m+DohhisbKQFxGchAVx4pwYBhJphIcTFiXbsAAOpWrJA3ZBry6AY/i5ml4G1Do95RSGLyphqf6C/VKkA1iWXjhxUHgYDXZiaTSG/ahO0zZ6L8/vvd22tqUPnxQidrppj3xL4mV5ZQdp966CtyqddDQqhjnTvLxcNExMswMHQiI7+Fy4Uk16+42ADBoNYiEUcUmUq7DIngeWBY49c2vAS9VSTPy4UkOY8onmxgA8Y/D0x2LiRioDVMNWqigZG7kGjkHHEt2v97hlHHosgbOJDbRtw+NLJHlk8oQBbktF2GRm/Txtst6gPVuEPfrWjU7S5TRCtx7drXoMXjyB86BABQvWSJuh9ZXL84Bux55x1UvPoqdjz7rOdxHDiDUfIbCukHsimy2hgIDZhcQOqCCLH2XPE4gQFhB0Yvi5tMAMTPSFYr7ACuFxUh3nW/TFt2IUdXO4YBaWVRpp1Od97hPo59CesayIAhKcTtAYXNfSBf8boNLwBqZiEbBkYU0QXNC1FXR6OQZJED22fNwobx47HpDxmtkErzYXgljqvPfc6JgbFdSPvsI19hi+GhEY3mJXJcSIKhRitT888em8Qs851tqLNaiyjDwLgYrOzzwBBogsvIW8Tr70LyLaqXZWipTDjJ0fScC8n/tyW1hhokDwx1IckMXA8GRnzPLItjL7rMfBL7/W0O4t26AXCEt5GEPd7ZbkTeheT/fpAcMLEOHaDFJH0OnAeGjTxjfh9WiEyMNrJoCbKIMIg4WkeBXSi3ysOACZQcj/aT7zNhILMpZqkce8lXojA8ZGBaHlgXEjtYscXjLCMtiBCZkNrKSmXblIGxBx+aLIlZhWn5+YjLKiazMC1pZVE64OTno/Dww93HsX1uKAPGZqIipaWZrtgGwJY77sSPRx6FNImeIVCsAtQupOAThjgBBDZgkkkux4l4zp3PzQEA7P73vzMDr0LzYezwMGDq4arjVkuMa8wLLAMDmUiTJuhyJiktwuf5cE/usiR0jgYmQlbYMgaGcSG5+h80D4zkHmp5Ce5zxMOFJC8lIOawUT8zNcu+wYpDD8OOvz3v01MGMqY0KXchBdXAABIjOhswvwugEvEazu9IJnNVLSTmOdGiUURbt0bBsGGI2lncHQaGMTYNQ2BjAzAwtiEU7dhRyioGHisUGhjWAKHGPJO+wOmIQgPDaIsKbQOmZsnnynxefDoCHxEv+2wYaadeXhbjt18UkouBCRPZtUAQF1IqzQ0+IgPjEqDZ4BIBCaArWEEDw4ZEapqGWCd1eCyQGVykDyM7YUj92k4NJ2Jt17cCL7km3a6XRDKk7vzb32Bs346dQoZn7oULoIHxTULHQryWdNo3Cy+QeXEtScp8gsJfOcVG65YvV2pgiAtJt405z75lA3F1FkQvYId5661bZ7KjirCEgVmPOCJKqoFx30+xP5bhRCERNsQVucFqYNJuBiZwHhjJPRRdRpqHC4md4OpWrcKmG/+I5Jq1/D4ehubmW2+BuWcPtt7hZjel5xPEylQDk5S7kAJpYPKJBqbhopDkIl6LiVAjDAyvC6HtsflTGNY6apcmIYYHdSEhMw5kW4fKrMyMwXpJiTRVQ9DJXMpeM8dr0ahjtBFjPohej3FBxXv0yBxeUaE2NoOMfwSCsZfTAtQnD4yLzWnmRHahAZMDyGBvGQbPrDC1SCxTWD1IXDMy0BUsNWBi3GcCvXVr704apvRhpC9jNCoVbmaOJX74hhHxkuP14uJMVwQXjJhjQsVcKV+WLNL8y1xI5h6PgoZkP5aBgfv3YIWJu+fPl1RfzvSRMDBxWZ2mHFYzlmWh6pNPkN62jf8+wMBCjYp4XK5xIAwMFfFKopAUYdS8WNpxIVEGhhaeYwxq4l5Kpdz3wud6zOpqVH70kTQ1gRh15B1G7Zx318svo+Kf/3Qbgx5UvjRfihfE6yKuNVb3wrmQAmhgCAPTEFFIEbUGhi8lwBdzdKXMFxgYAsLAEO0KcSGRY3hWwf/9cHQ2MYWuq34MDH2uo1FqtFmioQ+vxZb7eWe/F8EHgmSXB4ayeFRXmULlRx/DqJQneASEeUqyaBAXe9m6Sxsa//Nh1LmAdSFxoleRgZEICgEhqkCEEIVEcw0ID47eyseAsUwFA2O/4OILxB5qGJlsvw2tgSEuJCE6hPVVW5alTGCm8v8HXp2bptuFlDaUGVtZmMkkL8pWaD8AoOrDj5Cwc9dA0zKsln3fSfHERM+eqFm6VGgj+/tc8/nnWH/xJe4NQQZ7VmclcRFApMb1iJP/hDIw/mHUFhNGTaNMxBDtaJR3IYn99zFStz/1FLY9+hgKjz7Ktc2tgQnmQjIqdkt38Xof2GjBQHAZ1G4NDOdCCvCMUA1Mji4krkYbyQOjiEKiBqjmnciOu2csA2MbMKSQqhZnGJhUKmsXEtWoxOQLtMAuY2UeGPs55hgY+zdjr1lx71kDiGW1/Awe7lhVn4XUBabgQtr2+BPY9te/ovBXw9H1mWec49j8Or4MjMDqtaRq1CEyYKOQOAaG1cCYgoiWteI9XUjyMGpToIOjrVt59tESw7jFxGG6rk5bbx9HBs6GikKiLiQhCslrJeInKst87/8SGRUVWP3bk90TjJGmtLMXMi4kNQPD0vXG7t2cy8SqqaEvumG7kOI93In8crnPKUVOn0CDPcPySWvdCCJeTZO5kIT7kHSHUYPJxKvZ7g36O7MGtUcYtZ+RmrJX8ESYzEJkYDQvBgbOgK4ybL0mEpZBCALXCpZGIbEi3mClBGgf6quBYe49ndikz4ckkR3VwAi/HzG8dJ1LAhht347bT4tFnWy8oiEb4NrZXDNSt2hQxkCie8n87WhYKAMjZKwGPN4/kzmeNQp9NDOZv32uXxDxWknegNn1j38AAKoWfeJcTmUlVp94EgoPPRSd773HV4jsckuGIt4WCCYKiX3ZRQYGCgGWtwtJbsCIdLCvC8k05GJYQ/ECsX0gk1MOIjBpe4IGxqoWongYA8bLdVCfPDC1PyxHeutWmpmYbZPTLqlgmvykIhowSZ6doQwaTV6YuYfEhRRt286lg8lFa6Ssz5QlAyM1ZsUqu7pO84JQ0aEqQkdRCymScJI5im1TzUI67TZYfCYe6roSny1IGJh8dRQS6S8ADwNG/TuxDEIQuEXlMg0MM14EiMQhRmKQUgLbZ8/GdiGTN2dUeWTiZRPZOYZO5v+aL77EhokTsWHCRFQvXcoUcuSNCsLAUESYhVU6LWhRsnAhRWPU/c4hKAOjSOXAhoJTY17GwJimd4SlHsmagfHN4cUxMGmHQSdjfsL9bFZ99BHSW7ag4l//glgWRCpod4l4QwamxUEVhWQwWgqXiJZjYLI3YEp/+1uUf/898gYNAuBvwChTt7MvUEANTH1EvOxLESm1GZjqasEn7gw0LpdEkBeYCbFNb9+OulWrUHDwwdxKz6xWTUZpLnrMC1yeHw8XEquX0fLzgJ2ggxxxIUVbt4LeujVfxyoXDYwiKaCfb5pNSKXFY3IXEnHb0FVjxIlCUoh4qYaLNZ5Ni1LqdBAl16rQwIgTteWnc7LPJwtxj4hRSPne2Y4ty4IGtQHjWTNLMkl4QjS+JQwMZ8xIXEiRwkKur0FdSGZVFcrvnQ5YFsrGjKEaNSkDI8uAy07SxNCxDdztzzzDCLoNdPjDlMx2YcyJtmnDfdb0zHNgJZOZZyjLPDBcwch6uJC430WmgdGZMGoJA5P5bLjuGzWA9GhmfNL1jIGvdI/LDSnpvkoNDHn33Owgu4hiyzuI5yZwBTxkoT9sDIQMTC5QRCGZu5mJMM27kNiH2yvqhbyAJiOwBIDWYy9Al6efQtennwKQ0cZECgvV7YiJ1ChlTyYMPTM4SQYmyzQzocANIOJlXyq9xAmjZgdo3oUkRtN4i8oy/XV+g0033oj1Yy9E7bffcfuosrLCNIMxMBAMGJcLScXA5HN9NHbuApAxQEUjNBcNDFvoj4Pfao3pbyQel7uQ6MrSNhAZES/IM6IS8QrZlMm1ERGvFwOTcR2Ik0EwBkZmwHChufAR8QIOA6N4Zjw1MNm6kAJoYKzaWqVoGnAWBvRzQBdSescOEK0Lq/fh+kQZGMnzwURQaUIYNZ8Qs5aP3mHgcudpjCsxnXaxCr5gM+XWRwOjZGAYDQx1IZGq7Wr3N2UsGQac/V9Z+TmgiNcyTRdbTTUw9N1zG+5sIrq61Wv4jdIwamHcC0sJtDyQwV4cIHgGxnSldqbbvAwYkgNDMGA0XUfR4Yc7qyR4szBuXyVxBwgvkIyFSae5Cbo+LiR2UtYZBoZdVbL0tGtAV6yEOLAMzObMKiItVBlXT0ZGIA0MAJhM/h5fBkZwIcHWSxGxot6qtUvHlMt9VjIwfgYMe/8VUUjOytJhYDiDV5LvBhIXkiyMWkySJ2bidQ3WPis9qh2TRSEJjIsWj3vWMqJuHJGBIQyRx+8UYcOAg0yWrigkXn9G95HVmLKhFxZx10NEyn4uJDYnEcdCShgY6fNhMCyzGEbNwEozFcaF5HIRkbHSI1TkmzFks3QhUVeV3IAJXJJCNe5wLng+jFqc8Kn25P/9P6w4bDiqv/jCrRmKMnIEGYKKeMXngg2jJn2W1ABjn5HkGt6AkbnmLTEAJdTAtDxQEa8wQBjCIKAKB/bMjJhO8zkTPKIadA8hr4shoCteQoHyLxC3r6j3qEeyItaQckS8NXxoKJPEyZ0/hd0W4CVX5K5R1joy0oEZGCMoA8MYMDSk1TQz7iK7r9FWZa5IslxcdVauDAzb/2hUmrWUaqHIIKVF+CrMpul2+UkqSXNh1FQTJNHXsGHUYimBgAyMDK68L6zeRgZTroFxQsA9GBgmnN6sUfw2DFSZocWVrunlzo1GESkqcvqZDQNjw7X4YtoGIBfxWiadtDUhjJpDKg2VBkacVDVGA2OlRBdSEBEvw/TI8sAEHctUDEyKMcQoA8O7Wp02MvtWLVwEs6IiE3XIJsID5NE/XH8DCIPhvjdW2p0HhgtRJ4weY5DUrVnNNypjYFTMfjMhNGByAcl1ILAcrIhX1MBwriaPlZGVSrtWxypEPUKpXYOXUK+D1jiRijcN3oCpj4iXHKtpdJA1q6sEYSLzorhEvP4MDKuPoCtXoR0lA2MYuWlgPAwYWBYV11IGxkjD2JmJQIoUF0OLx90GaA6Dgaowpq8GhtFYZXzxahGvNJEdQCNFuHZTbqaAC6MWRM1cVAa78nYZssH9/yJEDYwWjXqHO9vnMsRw/zxegCwDlxLAr+YXcy7ns23AiM8XeV8k16npOnTGgNECGjAGU5eLy4UkZWBkGhiLM27Z/bm+p9OMYSHUpZIwMI6IN6UcQ1Xg8sDIjNSgLiRFFJJFDZCoU3lbdIfSfe3viXszmWTCqHkXkl/pAdffYn9dhnCaiSIlGhim8LDtauUYmNUBGJgcS3w0FkIDJgcQF5IrqY84KXNCRjcDIxtELcF947VS9HYhCRMLGRgZEVrmWmQpwk3qPwWgzGkQBOzKi9ZCqq7h2ufrjogiXn8NDMfSkDBesYCkh57B2KMu7cC14SXiFSYcsi+l8w2TUvbEcIm25SMwctHAqBgYP6PTFETiyjwfcJ4dNpEdAE8NjFhU0ckDYz/zhIGhz6NQjVpkYHxdSF4MjLDKZ3LOSNsyzcz9EdrUaJ0er4mEMaZ9KpwDkIaLc/oz8j1dTUsmOj2CCONapgyMnwuJqcvFspAuN4f4N4Fp8sYtINfUMQnpXAumWIw7RovovAZGkRFXBbbmVr1EvKqCsqzGxoeBIb8VZYWTSeddIgsG6pZUaWACjH+QvO+G4bAlNITduc+EceMNmAAMjMhshy6klgfHhaQeNDPVqL01MCztS7cxBowWi/GUvQC9rEx9flfRLXuSF8IelQxMsmEYGFa8RwZWs7qan3i5ZHWihR9EAyNzIQkMTJXKhWTAqnULP6WnYdoQWTRxAiUGDHVfmAal7AlzVvKb41F62mmIdupk9zkHEa+CgQkadkwNGJkLibJ2TsVhzcXACPeBaDWEfBIOjc2XEuA0MCrxJuAv4vVkYATfv657MzCGPLmhEwLu8Tsxz6JMUCzCnTbAyBhOxK1qv/9eleG1iM4J+oO7kFgGhmEhxdBoqES8Jm/cAtwkSfdjEn66RLyaxhuYuhCNlgrGQNBzkec6Wr9MvLIK4QAzrkR151rFpIxCf53yME5+I1eVb8W1WRL2R7qfpPaaqIFh5wQScMKOY+mffxYaldyrLN/LxkZowOQCXa6B4ZAWNDDE52hZjsBTasCkOHrfC5FCJp+FsEISJ9ja777D5mnTHHGrlwZGcCFlMsnmZmmzDIxmMzBmTQ2Xv8QScobwfTHl+7H7cAyMXAPjKeINWK01sAuJ2ZdqPtIGTDuFN1ktR9u0Qee77kTBkCHSPgeBTLQK+K80xWKhshX2rpdfxrbHn2AmNN3tQlLkgeGjkCzGhcSXEuCikGg16qTEtVIPBiYhYWBkOUJIW6YlNWDoROthLLHPqCnJSePaX/K8s+8ueVYsj6SSmq5zkVXBXUgKDYyEgZGLvC3OuAUYQ4aFhwYG4N1IWkSnQl9XLaRALiSH6ZHWQgo6jjHPb3r7Dmz+059Qs+wbPs+MmMhOkX2YrW8nMuA0olVlCHDsj0ffJYawqIFhF9wyBkaEtJij+Pw1cxh1mAcmB9AoJK8f3zShsRVGJVZwpEgSBs1oYPwNmELubzaMWxy8dv/nPwCA5GHr7GvwiEISDRhkHlxV4jsvONEHjgsJpsn73L0KlrHbVAwMubeMcRhcA5N2CSZVCJoHBnBYNppUzHQyY4qhtvQ3yEUDU08RLxnkZWHUqY0b8fOf/4y8Aw7IfCGE3VuW5XKzUEaGXTmbBp1YqEEn0O6uatRigjffPDDqydqlgQkg4pUzMHYIuMdEwhpugVxIkigk9t3Ti4th7t7tnZNJ17lIK+qqTaUyOW0ULG6acSFxSTglDIzShRSEgWEYNdl4wzMwrCGbfTFH8txp8Zi6zwHATt575s/PHFqxG/nDhmba1xkGRhSk0/7yGhgrmZSIeLNhYLIT8YqZ1DkGhhgwXmOfTAPjytMVMjAtDtSF5LVyF6KQSJVndsWsFzAGDBOiKeoTVKAGAdw0ucq4ooOqR40TlwYG7gc3KOhKPxbjVolE0AqILIvHC6LKA0MmNyY5m8uFpIxCchtrKmTDwBDQSshpxy0XEbO1xpioiyyhDqMOLuIF+FB2EaT0g6YLUUiG4cHACBoC8hwQNsQnE69r8PS9niyjkDzeLcsw5QwMNWCEa+YWKowGJoCI1502wOQ0cmRyp65CyTOi6Tp3jdxY4MHCsCJeuiJn2VY/BsZyF3MkNZG43RgRrxhGDYgMDOtKTPFsV1aZeO1EcYKhGigZHlO7jIVZXS1oYIREdq5nljccrGTSfb/8RLysweXF/LkMC2ZhJjFgiOYpKANjkflLorVpToQGTC4IyMC4HkqDcVdEIlwSJ1q0MZWNC8kxgMR9VQM6HRyJASMZUDKTurCyzzEbL0fp6jqdBEg+lMwH5qXwiD5R+oDJCojLXZOFC8mruCbbBpsHRgwnVBkwDANDfnsxW6vGGK/ZQsnA+IjrCFNCnxtVVmYw7Iwo4pVEIZEJU6xhRUsJ5PGJ7IIyMPWKQhLzwMRiiJBwZxkTYykMGMqUOeeqW7MGPx55FLY/MytzKGu45cTApLnnhAqHveqSCS4k9m8vN5KxfbvTjT17YBkG1o45CxsuvyJzfh8GxjJkDIxkP46Bcd9vNwPDhNML9X38wNZCypxPeK593oudL/0dPx5+hCsRJiC4gKJMWQ0VA2O4GRguionpp9I9HrSYpSsBKLMws41SdqFGUn54yiCYe7Xhiiuw9qyz3Qu3Zq5GHRowOYCsVr3CoWEYbnrdNBk3QoIzOmiIZjrt1icoUHzMMYjtsw+KRh3rosRVAxd9iGneBrl40xVhlaOQV/R9k8E1rWJgvBLZKaOQ7MRj7O/RhC4kznUlDOB0ZWyYSsOUuiRz0sAoGBi/yrVUxKt2IVEwpQTciezEKCT7M3t/GK2MkoGJ6tSYzkQhiS4k73DNrDQwuo6C4YchUlKCgsGD3QcoRLxUr8C8CzVffQVj2zZUfvCBfSzjQspBxGsZ/BhBhMNEMyYzWF0aGMYg8Lov7Dto7NmD9LZtqF22DKn16zNfsgyMQhBLfz8SFKAMo1ZEIUFgYLgwalEDE0DEK7iqXOOiD5NX+fFHMHbsQFpSJJWWN4CtgRFEvC79nuhCSiUdEa8QRq0UKAdxoUPCCqbSrlQYvIg3OANjGQaqPvgQtV9/jbRdNNVpKDRgWhzoCsEro66CgaERSPE493JReppNghbAhdTzrfnY9+GH3S+qyoVkr+TIikmugXFP6jlHIgkDCnF7kZT6AD8weUWfqF5gmnCNK6gYLIy6QVxIhgESNcK69QCGgTEMek8bUgOjEvH6MjBESB7zdyHR+xrRJYns7G1MBBEgMjBsJl6BgaHVqJnw2WRK+u54Xo/q+WSYHQpdR/tJk9Dnk0VI9Orpbss0pVFrVJvBropZfQP4CVIZ+cbCY4zQ4jFnXLCfHSoEZ8uI6LpT5Rv2pGgbEsSotywL1V98QY0Ws6aG5gIBMlEprjGD0bNIXc2WyRu3dl9cSKXA1igSwTEwbCK7dDp3FxJ9rrNjYLzKppgpQcMihFG72Ig0/2yYyaTzXBPGykfEyy1EvLRXogHjYtDTmf6Ta7Gzj7sytrMQGCQAruKxYRh1S0QAA8algYG9umLpYeZl1m1Br1lbw9D7HkJDG5pdnt7tQpI/mHQyElcA7D5CJl6g4RgYzc6LwmpgvBLZ8QyMt5+YfRnF1Zp3FJJ/xtRM+ynfv8X6VHRwFoxXbp96aGBUEVTBNTD2M8YO9GJEG7mvJHGXPTmyzIoYusutnJNOWHAkT6WBEcKoxefALw+M4vnMlEnghzm6Otd1LnMuhYKBoXqFtPu3dwyYejIwDEsbiTMuJPsZJW7MiJ3VGsiwHmKFbTq22P2rWrgI6845F6t/83+ZS2QikADAqKx0jRlcRJEiCslhYIgo1S0YZg0RuYhXYGCYkhK8iDfAZCnkmxHP58tMegllkyknIV+MKeZIjXFxvJeJeInBR8ZfUkogAAOThYhXjIBjmX3A0cB41uWjekLmmRZTToQi3pYHuhLzpN8MiZ6D10GwBkykyA6XrK5xrY4D9SlLFxIdnFQaGEEX4heSqYJI6UZs4TIv4vXwcwdIpU2T9HGJBIOKeBm2SRWZIoni4PQ2HgYMLeZoGIxhKoh466GBqS8DQ1eqjAvJNegTI1TUOTC1kKgBQ2shMYMeYyBSRsEVhcRrH9xhobm5kLRYzFntku9Y14hkkWCZlrR6uZQpEwWSXBh1blFI7BjhciHZBozOGDCI6lKdD+Dcl8oFCzLN29ozNgcMAC7SicKHgckkshMYGEkYNZeJVyri5RkYrqREti4kGjSQmwbG6x20WAZFj3KGPPu/05boQmK0XWQBGRGMIFd/2AWcx/X7JO600mmFBsZjXJcwMJZdHoM+XyED0/JAo5C8JnUpA+O4ESJ5ogGTyQlj1tQEdiFxfQroQhI1MKoqxC7LvIE1MCoGxqUDCaSBIXU9WKOCMYpMUymoZBM+ie4fAln1YqUBI+xLV5eGh4g3Cw2MlUph673TUfnRxwA8GBiflaYrEy/jQnIZMGSQo5EmGYNu+1NPY9dLLwGQMDCs+JJxR0aEUgJczhGuiF/DuJC0WEzJwGS2S94xRRg1V4WenJeZnADeyM49ConRwBAXZG2d7drK9EvnGBjdnW2YCQoA3AYIycJLsnkbe/a4niU/BsayTIcZ8wij5hIeBmFgoow7kh1js4xCAuBaoPkykx7vYEYDwzA8ovHhembFKKQUXKJniYi3cuFCbL13uksLxu5Tt3Ilttx2G1Jby+1tfgaMAU8NjJSJt8+XdrOKdG4KSwm0PEh1IwIsJqSXwjQps6HFE1w7enE9DZjALiS7bV2xQgHsJEjZaWBSGzeibuVK9/mE8EmqgWGikLzcRIGKmUly7HArNw8qn12ZqAwYTWbAyFxIsRhvnDDJtDgNjMKFFGSA3vniS9gxaxY2XHqpKyyfg6+vX53ITsnAkH3sgXvnnDnONlIyIW0XYmQLdDIToyPi9cgDk067nwMPEa8sHw09n4SBYSdRKQOjCqMWWA32b1n+IStAIjspA2MbPpG8PHq/zLpablLiXEhRHfkHHiTtKxW2Cxqn6sWLAQCJPn3oeY2KCr4N9pnQNPckZ1oQSwlIE9kBtLClPJGdSgOTytqFxNZCkp6vHhoYK5mkDBuJqgRAjTh3AVJJFBI9Xi3i/fnPM7Bj1ixUL12qjEJaP248dr7wIjZec01mm1/aCCZBKgAYlbwBo5eWui+YiHjZ5912IdFxLgyjbnlwTfoS5b1UnW2alAp2uZAKMwaMVVtLcz40BgND9ycrJYl405WJF/4GzMpjR2H1iSdxkQ2ABwPDDpZpj0FKkdKbOwfJUaAIo/ai8lmxG5fZmIHMsJExMJFYjJsQM/oLnfbdL5FdEA1M7XdOeKfFppwX++ez0nQzMMzzo3JHkudc8rwTDYaVSrnpbHKPdd1lrMnywGQYGBKdFMC483g2tXjcJVDmJmaWgaGuMbkBQxPZsWU27N+M6IQ4BiaAC0nG0pL8LHqrVo7ouabWCeMXciohoiOvbx90fe5Z9Jw/z74uXgPDMq3pnTux44UXAQCtx46l95gNq850QLhv4u9uGO5SAhINDMBMfH5h1BGNywfEF3MMHoXkhFFnF4XkrYFJMu0zKQVo0VPht0ylM+MSF0bNuKDAsPmSGlpmdY1ycUeigWr++9/M8WLaCGHRZtbVcWOFyMDIDBinfh7LwNjzF2H4wiikFgixoqpYa0UBnh7mo5DYYmykLkm9DBg/zQqNQpKn23ZrYLySKDnnSm3axG+jlKttwEiMBK7QpTBI8dvkL0vtN99g5VFHY+dLf3e+DKhF4Fa1bGJBBr4uJLZ2FRtZxhSqYwXcrgq8WWhgWMPPMwrOZ7B3ZeJlXUjiqpVqYHgXEgt6j1ISAW4dc3/o/bD3YatRRxmGwxBcAR4iXs8QahkDw2lg4u6/DQMGcdUw9cYoG1LLGjBkciIupOxEvLJq1NS906qMaqjMOseA0QsLebeY/XfhIYcgvt9+3LU44cvONe98/gVY1dVIDOiPomNGQLfHnvTP27iuuCpQi8ycxTAwtuHiy8DIwqhZF5KohcoxDwx5llzn83svfDUwTCI7ZnECwCU0twzeBSbLxAuJiJdzSxpyBsav36KGS4yIM+ys7WQMkdbVowVXJS6khPOuNCdCAyYHuFZ0kglOCoPPxsoZMMwq39iVmaQal4FRRyFlmCKRgVFPEuxqVYywEUOHpfeKKxnvoX1QCd3q6pD++Wfsfv1157uAK2H6Ymua1FDJ9NltoEpdSPGYMCE6BeUsI+1OHkf207MwYFjXm8p9BATOXBvEhUQR4V1I3CZGxOsO6axz2iW0u5gxWY9IGRhHLOjhQvJiYEQNjM6HgrOiUvpbMVqTNpdfjkhpKcrOPNNhQ7g6XoKIN8tq1LLM07ToZ+vWzDnrnAikoiKAyXgr07G5XEjM71v7zTcAgLJTT4OmaYiU2AaMyMB4iJ8zjTPFHOmErDJgCAMj6SvrQtIjXFRe9rWQeLGwyx1abw0Mk4hOcxYnANzvnGHw44QsE69ExEv64HKhMeMku+AVjwfcz55LE2P/HoQ5lBkwlIFhNV/2cSSbuG+Jj0ZGaMDkAPGlcK2oxf3pIGzyUUisuyEapZM7WWUHCaOmx4uGgx8D41ELSeZC8qLpjUomP4qwoiQvDjHQpO4Yw0TtDz8gvW2bOzFawDBCV5sBXUi0anQioZy4pX1OuV1IWizGGXCRWJwZoJzyDGIUkuNWyc6A8SxCGTQKibiQPKKQ6Pe6hwuJaGCE7KmZc9nPfCzG+PwFBobNA5NOOStdYph7rT69GJh4nJuIXdemS5gnw6CGbXy/rujz8UfodPttTkQQ43Z0h1GzIt7cNDCOC6k1E4VUQ9+zSFGRECHk/j1EvQ7LtJIaSHpZxm2gF2f0NMZ2noERBbkyA8ZhYISoGgHUhSQZ00QGhjKSyWQgFzIHMW1DthoYr1wrTHVsaRi1q5ijzIDxz8SrYmDYvkXbtGHOk3aPm4L+ysXICMyhJwMjyQPjaGBCA6bFwVUS3seFxA6MbIQB526I6i59SH0YGN/9PTQwmayxwTUwfII30RdrGzD5xIBxu2lSGzdizamnYcOVE9yuDyPLAYwgSxeSlkjI7wXgyrEB8NfJVdwWXUiUImYSlNVDA8O5kDwYGN9q1GImXgkT4YLHJKWxYdTiarDWMWAoAyPNA8OGUZtcX7xWetkwMOIkzLo8HL++U406UlDgTIYMG0LPTQxZEnXI0u2SUGxX36kmwmGmSI4WvXUrpxgox8AUChFCagbG0S85rBMxkMh4E7EDCNLbeAbG5Q4S7p1lWe5EduKzQZgg24UkjUJyMTD2MUJ+Jt8cLkytHicTb7YMjPc7SBO5sYnsbG2Jq20j7TJgoBDxcuOePQ5wWjDw73Sk1BFxp8vLfaOQXGOgXegziAaGKyNjG5TOuxK6kFoeBN2IFo2qa8mwtDmXyI7XwCDiNmBEd4wXlCsOFSgDI9nfNNzFHD0GDz7FPn8cZWDsa9NblbmOT23eDFhWRj/jkcgum8qnnN/WIyMq7V88rkynL9M4cQwMU7BS1FSwqzQnQZnwu2ajgWEZGEUZASCAAeNiYDw0MOR7DwaGusFkDEyto39wUeYSBgZJph4YufdeLiQ/DQw78YqLD1n4OBNGzVV8z5MwMKxGI5nkNVtZRCGxixwihI+2bs2xPmYV0cAUcb+B1KAkzxi5N8zzQAwkyoraBroYhRSEgaGuPWLcCsfQGm91ag0MH0bNaKFqBAPGj6FkMmLT87hcSLlrYABmwcOWElAlshNDl1MpZzyjGkTCSDLPDfnN0mmle92lOxQTdwrsH43EZH5DK5XKWgND4OjFQgamxcGVdt2juq1YtVSlgdGiOqXhc2JgyEQU8BhaSkBapE0WheSlgal09hOLHBKfqS3ejbZr5z6ffS6rrs4nkV3wXDS+GhhbB8EyMErXSYFbG2NKopC0WJyLatHicW5lbfnmgfEZXC2Lo9QtMSsmi6CZeMkzyBpvsuSGABOF5BbxpsszUREZDYzAwDAuJDqhWJZdbkORiTdF8iXxmXul1+JjwLATvOt5j7gNN8swHcOWLZiacNgQCoVIE8guCol1MzsMTGtpFFKkqMil6xEhamDY95K6TQkDY99jIuykbXiEn2ca8klkF3M0YCQzrGzBxC0QIk4tJHcuqixyAVERr3A+5r3YPW8etj3xJN+GzxhDK7OzxRyViex4BgaplFtU7SPiVTEwbJqL1MaN/gwMMcjtfGMAuLI1xJ3I9V8ShURAWeQwCqnlweVCktVbIdt0nWNguGRmbDsRHZqwEsrGgIm2bZv5X2IgSOFRjTpbDYwnA1PFMzCx9u3d5+MMGOE8rOsgKwbGWwNDVva8AZOFC0ki4pW5kOiEmU4zocsKDYyPAcNV8AZgMNWxXf0LGIVEVsi8C0nB4tEoJPewkX/ggZk/mAgiei6a6TjKswWGIdfApNwMjGceGD8NDGucuUKqI/y+gAcDIxHxpgQGRnBd+uq2BAbGSqXo76y3akXfG7Oulv7ekaIi3vDyMGBkiQXp9djuXOKmMrNkYCzbCM005k5kp0WjjguJsHCyWkgJgYEhrkSx7k4WyQypiNdDA7Pl9jvw80MPIbl2rbPdx43rMDBRx5AXGRhOuC8u6BgXFKAQ8RIXUpo3bDgDxhmfU5s2uTUwwr1zDHJnLGPfs6AaGAIq4g0NmJYH10o9qqsNmHjcealNg67etLwAGpgsSgm0Ouds7DNjBlpfdFGwaxDC+DgYJl01EwTWwAgPO40+KFAzMM4qMelieqwG1MBwk5E9WTkGTFx+L6AKoxZW3sjoSVxhucwqTVmNOqALKV3+M/fZ3LPHvROJsPEZWMSIKM6FpBTxuqOQ9NJS7PvooygeNcpu151FlzJPMf4ecwxMlA+fJYY+XZ17XU82GhjROJOIl82aWjqhyBgYlhlwizSZa7csd2SPAJrYzP4djB07qBsk2qqVU3qhto4v5MiyYDIRrxBGLZ2ECggDYxtJgqHv1sC488DQiZeEUWs820V/U48opIhYzFEwepzz+RjlzDUqayFJosTY38iXBSVuQZ1JZCcwMPT9ThscUwswhhx5l2iFc6IJM+DUdkvxizYuu7XzDCY3bvRnjux+RxJ5nEjay4CRRSERsCkHmhOhAZMLRA1MxMOASSToQGAx4tiIIOJFJMLk0hBCXAMgUlCAktHHUUGeL0gYnywKSZaJ16tKq6pKM1gNTMaA0Vu3dmkouLLvgt9blUrbD7wLqco5tw0yMdD+JfI8opCClRKQMjBRh4Fx8sDkpoFJ/8wbMIbEgKETl68GRgyjDiLidbuQYvvui+KRxzjnlUREEM2IK6SZ8fFz75BlOTVXCOvhYbxmo4FxiXjZ/ti/FcnDBPARaHIGhmH6mGJ9BOJv5oLJGzC06GVJSSaqjTCFtTVKEa9XGDWNjpIyMJlrYzUoHFwMjNuFRCdtBQNDjULKwHiLeDU94rwPoo7Dz4XEJFykaSI8aiGRe8KlJvB5b3gGhixM+UR2EfYddC3obCNRUcyR01SleQYGDOPFGkapjRt92VuOaWaeDRKN5JmJVyIfcNU0ayaEBkwOcGtgnAqqrn3jcedBtxgXkqiB0XXXRKlKbe/ZN0UeBvd+fCZIDoZJB2nSB6/JlXVlqBgYcm2arnMhgABvwLjyFbA+4CwYGPalM7bbeTU6OO6r7FxIwUoJyES8HANTTw2MOBmSbJpcWwHFdS4GxqMWktNPe9DVIq59OZeFwoXE5oEB4NbAMPeO6KoIO+Ap4vXLxBtUA2NfF6nUqxUU8MfmSRgYTsTrZp/8DBhRA0MQbdWKO6dVW+eIeIUwaikDI7qQBNcuwEQh5bmfb8DNwMhcSNRgo7XVWA0MY8AQY8QnkR1bSkDUwPhq4GQVr8UoJMJ0MOUnSN6tzHY/DQxhknRXSgAXAyNzIREtkDD+Gtu2Y+u9092ZtsX3mLiXmHuTLv85uPg4HncWG3V19B5IXUiSWkgEe4sGRrHUCuEFtwbGy4UU43ykqky80HVXkje9dascOhfQJqVh1My16LodDpp2KPzi4owvP2AiO5GBsUgYNWOMRdu35wd2NsU1MWCi0cyLE6SYowzMai21cSMAING9B2o+X5rpj/0COnlg4lAl4ZLWQlKJeMUoJJoq3FAya0HzwKTLy7nPBsMUOG0RIarPStKViTeAiJc8W8IkBYAX54rPAHUhxfgJTtTAMPfF2GMbMPn+LiQvdjCbKCSyn2nXiRGzRjvuHDkDw+b5iHbqhPTmzQEYGCEKiXTFZgtp8ctaRgNTWAQt4rQry34bRANDjCOxkjWFaNBLRLzuUgI8M0T1LGTil2lg8ngGhkYhuVxI7mfasizUfvMtEr16ugs5Ss5HF0FsVJbNwHjV1CLgxiehGjUVNDNspNKFJIh4K15/HWZFBZJr1jh9lblj7TpjXGXpHTv8i7dSA8Ypd8IlIBUT48FZPMo1ML+AMOp77rkHmqZh0qRJ9Lva2lpMmDABbdq0QVFREU4//XRstes2EKxfvx4nnHACCgoK0L59e9xwww1ICy/YggULMGTIECQSCfTq1QuzZ8+uT1cbFiINHdWVepVIPCGNRMm4kJjJTtddYtEo4/IICldSK0nadwBMFJLkZWcZGKJaz1UDI4h4AW+hMTUoKA0boJijBOyATcobxHv2oN8RIS2bWVIlXq2PiJeyb6x/XmRgAuaBSW/jE42Ze9wiXlaI6gVPF5JPGDWXyZZEezDPsqhdMNkQWobRsAyDZ2CYdASmHRFDI388DRjJpMOkFfBiYNiBm+xHGBhdyFkU8WNgUklqZMc6dADgNjpdfVcwMMSAccoX1DoaGFHEK2EORQ2MODGz7BKXh4XdJ0AmXncxR/bZiFJ3uyyhHgGXCJTVwLgiId3PdMU/X8XaM8/E+ksvdRVylJ5PEhpMXUhB2AQadeVOCUAZGGLoptwMDGWihChQIqA2mFpyVjrlMtpocjxm0Wfs2uVbAJhLF2HfH5Y5l6bsINf1SwyjXrJkCZ544gkMGjSI+/6aa67Bv//9b/zjH//ABx98gE2bNuG0006j2w3DwAknnIBkMolFixbh2WefxezZs3HLLbfQfdasWYMTTjgBxxxzDL788ktMmjQJ48ePx/z583PtboNC0zS+4F1E93QhsQ86zUoqJrLTdZerQs/BgHGl/1aGd7t9xGxyIsrAFGUGcU8RL5uJ15XIjriQGAamXVt1W+RFk2VgzcaAYQYVYsAkejAGjGhEJBJK91tWGhhXHhh3m+JgEVTEK4ZNUwZG9hsGDqOWuZBUUUhuEa8sYZgrBwVbC0nTnJWrwMAAzr0hgyvVZ3gZMBJ2kBgbLg2MwCLkH3QQWp1/PjrcONVhYOz7ygp4AcaY8mRgMtcT7dQRQAANjCFnYKI2+0rYEauuzulXkVALyYuBIRoYwThmxxolAyMuhiSuKlcpASHrsYutFvVfkDAwAmtDzyVhKHe9/DIAoObzpc41cgaM4EKShAaToIkgeZgIIvl5zsKUGBP2b6/blcLNykqJC4kJwwZc95R1ocsYGBhpd4SoZSFtZ1EWxzWxXS0WR8R+59lxWyzgCjgGmVzE669NawrkZMBUVlbivPPOw8yZM9GqlePmqKiowNNPP40HH3wQI0eOxNChQzFr1iwsWrQIn376KQDgrbfewnfffYe//e1vOOigg/Cb3/wGt99+O/76178iab9sjz/+OLp3744HHngA/fv3x8SJE3HGGWfgoYceUvaprq4Ou3fv5v41JriJSfcIo04wDIyQTp4TtEV0V80dvVUuBgzPuCiFwGIiJcgZGJ1UyQ4q4lUkstMYFkMqGBP3J6sYS6KBUaQr50AEetu3ZwbxSATxrl3pZlc23ERcKjAEVFFIimKOYi0kmVEkPitB88CIdDRhCkqcrJzUkA4YsSGLQoKctJMmspNFe7hyCDEuJPZcPz/wIKptlx5dwRNxtS1QpvqMLPPAkN/MZUSKE6qmoeNNf0TrsWMpe0AYGNGAoSLeujon+6or02rmvsc6dgLgLpDo6rupYGBa8QwMAKRtLZcehIFxaWD4e8QuKLTAGhjJ+0HGBcLmCKLoIGVXVIykKwqJYWB2/fPVTPFW9rmjLI+HIJ1oYJj7QV1IATJhE0QKC5UMDFl4GhUVbhEvm8kX7nvKVUEXXeiAK8UFYRBJhKJKkE3z1yQS9J0nonAtHpcWaKXRUFINDFlYqLVpTYGcDJgJEybghBNOwCg7dJJg6dKlSKVS3Pf9+vVD165d8cknnwAAPvnkExxwwAHoYFOsADB69Gjs3r0b3377Ld1HbHv06NG0DRnuvvtulJaW0n9dunTJ5dICg3tJPDUwDANj8unk3WHUBdxxssrNvv0S3VsqBkaigWH1E9TVZbuQsg2jTm3ahOSGDU47zLVEvAwY5qUCwFOUZLUaIDqL9JfoX6IdOnBJnMQ2IomEMoxaqoGRuZDiogvJravREgnXYEFXnH6hkIIBY9haDZ11g8SyZGCIwSOZCFyguSvcxRA1XaeTmClmUKUGTJRrp+Jf/0LdihX28XY7JKkaMWAIA+GRB0YqMsxXMDBeIvcIYWDkBgzLFNBJJC0wMPagH+tou5B8GRghCslGtA3RwDgTEs2gW1TEL1RkDIwYRp32YGACRyFJkl4Stw0Z4ziBd0zCwLjZHjaM2kqnnSgkRR4Ys64Om2+8EVumTeNTCaTdBky0bSZggJa6kAhTqQspi0SZkcJCV2FS8lsS9syoqHC9s1TzpLsXkIDAwCRTLreZxUQzaokElRmQ5yyicAdaVYyIl7joqoSxVjzGVGtgnIVS8zIwWYt4X3rpJfz3v//FkiVLXNu2bNmCeDyOMkHR3KFDB2zZsoXuwxovZDvZ5rXP7t27UVNTg3zJhDJ16lRce+219PPu3bsb1YjhrXyPTLxsNlbDpCm13WHUvAtJb91abhX7QWAnlJM9mTAk2gca6gfWgAku4rVSKawceSzfLfbaStQGDMkGSiYPWTFHLRbzrsTM7EvcR7HOnXmqGiJT5eVCkhiSpgkrnYYWjQoiXsaAkTAwst+DPks+K0CXP50wBTIGJmgxR+pCCmDAkOeRFYoLq10rmVRmCKYMTCQClzliT5YRcXVIBuQsNTBkMUDdVpoGWJa3AWP3gRpPwu/OsgdWbS2Ql+fOB5R2RLxAkCgkuVEe7dw58z1JBsdcY6SoiA+j9mJgFGHUuTAwslphTuiyIpGdcIzMWGIZGCuZlApKAcfA5yIVGcNWpoEpO/NMRNu1Q93qNdj2yCOeGpisXEhshJrIwLRyGBjVu6Sq3i0uBl0uGpNh8ROJDNuzbh3VWvm6kJgoJFFv6ALVwEjer70kjDorA2bDhg34/e9/j7fffht5PgUMmxqJRAIJn6rQDQrR/aNgYCIJnoFxHr48ZSkBIMcIJMgYGB9BZkzCwDADBF2Fempg+DBqV4ZYTeNeLL1EPkABTB0ocl6OgZGvVmUgLx01YPbpzE9AwhSaSSwY3IUEZIyt9ePGo+777zNtyMKoJQyMiMAamAAMDPFv++bMEBgY9rnxHXQj/Cqb/h2LZXJLiLl8CBT1aQBnsnSo6czvThkYTw2MJMcJ40ICkPkdmNW9DDSMWqWBIWyOkdGI6eDvlcloYGKMAWPW1GDd2AuR178/Ot32J5hVVVh7zrlIrl3rLulgI2YbMEBmsjBZA6ZQCKOW1ULydSFlr4GRRTtRo0ESRs3WNaLfyZ5/VtidSqk1WMT4YA0YZpEni0KKFBSg5P/+Dzvm/C2zT0NpYAoLnXtqWVxIuc4wMGo2072ABARmN512lU/I1FdyWHzqriLsnK8B44xRVGemOIa+c1IRL6lG3YI0MEuXLkV5eTmGDBmCaDSKaDSKDz74AH/5y18QjUbRoUMHJJNJ7BJSnm/duhUdO2ZEbR07dnRFJZHPfvuUlJRI2ZfmADdwe2lgYiwDY9AVmV5W5mJgWFdFNBf9CwAxjFpZEDLiXgFQy5wJFaR+fw92wKjmGRjOjwt7tcIMNIVHHqUUKJPoEyoeZhkYMc+CF+wXP2m7kGKdO/OTl+CS8HQhKWjZ6qVLqfECQJ7ILhLg9whqwIgMTIV9r0pZBiZoFJJQSoBlYETam4DoHCQaGPZvq05uwLAMjAuCBoYekyMDUzBsKBCLIW/gAO6c3gwMcSGRcOVC1y505WkzgFwUUl2S9jNKSmak09j18suoXbaMCk5rf/gBdStWcPdZHD/i++zjbGMNjGgUkYJ879w2THuOC8kdhUT/DhiFJGVgyPVLBN5sHhjnXN6LTCuVUi4kfBkY8hzIkuUJRRPZ8Yy6kLJkYLhrZZL6RRkNjOpdoi58iTCaQMrAMCLeSDxB3VW0XfH9IaUoWAaGMO2VhIFRGYz+mXi9qsQ3BbIyYI499lgsW7YMX375Jf03bNgwnHfeefTvWCyGd999lx6zfPlyrF+/HsOHDwcADB8+HMuWLUM5E1749ttvo6SkBAMGDKD7sG2QfUgbewN4EW9UaShkMvFmbnNqw4aMsj4WQ7xrF08NTE4RSADEMGpVeLcThSSE/YIJfWbqNXlrYHjlvGjAiMUQ9aJC9FrwPro88biyTSkDQ1ZYqheOgdSF5OGS83IhaVGdHxjsdrgaKsjcP/Y5iMgYGA8XUtDsuc5nOw04oymShp+n06j4z+uo+myxE50l5qRhJxqFsSpnYJjjSH4JRZVs+ix6MjCCNilIFJKkv23GjUPfJYtReMghmS9UmVm5PtgaHoUGBoCTzM6+Rm7FzLjOInl50O0Ah+ql/3X2MU0pQ8UZMJrG6cRYXUO0detMP1njQmbA+JQSYMeawAyMxMAnhhFlYAR2ziXi9WHurWRK/RvZixI+2SVrwBAGRjI+RJxFJNtvIKNnMmtqAjMwpPCrK6cRYWBYF1JSxcDIRbwspAyM4USIanl5rkAP0YChSUNJhmeJiFc1dzmlBGQamL0jjDorF1JxcTH2339/7rvCwkK0adOGfj9u3Dhce+21aN26NUpKSnDVVVdh+PDhOOywwwAAxx13HAYMGIALLrgA06dPx5YtW3DTTTdhwoQJ1AV0xRVX4JFHHsHkyZNxySWX4L333sPLL7+M119/vSGuuUHAi3gjyuRfbCbe2h9/BAAkunXLvABiKYEC50ESLevAECMHFMwQXU1LopCobzQvz9e9YRkGR+maCgbGdfp4XN032JEWUDAw5Dhb1yDtFxHx/kQYmH247XoR78bSEgk1axGJQEskMisfu76LVVcnNWCkDEwk4oj3PFxISKVgWZbS0FKt5vTSMqctasA4v1d6+3Zsuv56QNfRb9nXmSJ8Qt4ILRKh91Nv5bTH3weJASPJuaHUwJCIJQ8GJiIWuiT6DJuml90bKU0fjXGTJdXdeBgwoiEgN2BIJJKbgeHCx/Uoou3bw9i5EzVffOH0taaG05jRdpn7GO3Ykc+1w+hGdCJK1QMyMOSZEcOouRIJOeaBAZwJVprITnexKX4MTKSoSDkuUBEvK3Q1WQ2Mk85ABL1fZJEgjGdGRUVgA4beO+ZaLYaBIYYrUimYuyvEwzP9odF7akZQ9lzzIt64S2ogupD0Nq2R+ukn57wxloGppN9lDo7wCwWJXoi2k/gFJLKT4aGHHsKJJ56I008/HUcddRQ6duyIf/7zn3S7ruv4z3/+A13XMXz4cJx//vkYO3YsbrvtNrpP9+7d8frrr+Ptt9/GgQceiAceeABPPfUURo8e3dDdzRnci6kHK+ZYtyJjwMR79bTb4PMVcELXHF1IIgMjG4Qz+0nywJAHu9phYJw09/KVhKvScyrFaWIASBPBZTaoX15SKZcL0xM0MF6raaTTsNJpJNevBwAkuncDAHS45WaUnn4aikYcze3uysQr5Oih52SMlOTadXwbMg0MBIbEy4BhrlEGNt8MCxkDw66MiMAv2rZtxnhgk+qxqy+7H60vvBAl//cbRAUhvUYL9rmjkNjrUGlgaL9lEy55HoX7w7EDChZGOsAqssh6uZA0IQWB3IXkJJYDBA0Mc92aHkFev74AeCGvUVUlFaCzv0NMuO9sqv9om7aks8y5cgijZsYaTcWKuPLABNHAiCJe/zBqAOh0550oOekklJ50ovK99nUheRSMdEp6yJOzGbt2Ba61Rp4LbqxlGZiiQnr/VSJulYiXhZVKud1abIRoPOFKdpro04f7TJ8Xcl5GxGsIUUjd/vEyikYdi0533J45v+JeAczv2JIYGBkWLFjAfc7Ly8Nf//pX/PWvf1Ues99+++GNN97wbHfEiBH4glm57HUIqoFJxOlKhoSMJnr2ymwTWBw+CilXBkYwYJjQYQ6SF0g0YLS8PGeCUqxORLZF5kJS1XRyZQ1mj6EuJANWOo3tz8yidUKIUFWLxZQiuYzxsgFIpaAVFNCokNbnngsA2P3mm/z5EgmYzMvIRRfpOiLxOAz7e/LCs2m/SX9kBky0XTuaYVPq/mL1J3Zkk/SaiG6lqIjL2Cl1ITErIzKIkgzIJkNri0a0lUohUliIfR58EFun34cdzzzjdEDiQpKF4Zt+DIxs0FYYMJw+wzTlk6ikzo+73IebcXRBMKi9XEhkEuENGGdi1XQdBQcfjIp/vcb3tbqaVgaOdu6E9KbNmf0FBoY/p3NPqEvAL5GdXxg1K+KtBwPjaGBIjiA+545LxKs4V9npp6Hs9NPocVLIXEhsRlpbuyR7f9waGH7cMHbtUkY/iaDPBSs+ZhgY6DoiZaUwft6mzgMkyYQuwkqnXLq8TC4xJ4yaW+hGoygYNhTbZ850vhLqzvFRSHzOrfyBA9HlkUdQ8+WXmZ0DFHNs7lpIDc7A/K+A18DoSj9ihGFgyMor0cs2YLhaSFEu2VsuZQQyJwxmwDgMDBv2y4t4tbyEb5p70ViRupBUwmuPl9cJozbx84y/4OcHH3T6Se6bhwvKMgzUrVoJIJOB1+W2cGlTElI2gexLXljWSCHKf6cNIbeP/TdbOkF0kYjn8loFUgNGmFj1MsaAYZIREpAkV6Qf7ITPGVxCNlVXRBudpBRRSLSKsIKBoRFBkqgZkonXg4FRRSJJqX9xsqUGu4cGxsVeSlyfJNFebW2mPwxjxmWOjUZRcPDBruMzDExmvzxb8wfwKeRjggHDaWCIC4nTwHhEISWDJLJTMTDC7+8p4iXPBl9KgK+xpUjsqOi761zEhSRo7ghIDStPBkYhTDV2VQQW8dIq3oKIlzLEkQhdVIjlPwgcDaI3A+PKDWWkuXp67EI3f//9+fFe16EzAn+AH6NcObeY4wBIc+bQdki0Y2jAtEyIiexUk6kWT7hWMgnbhcS7KSL8qkjFnPj1S/TjF8ldSJ4aGOpCynNEvArXhugukjMwijwTXgwMuX7DwM6XXuKPY9w5KljpNJKrVgEAEj17us8trtDzEjwbJbBjrNtKHXGmZmDod34uJI98O2SwdhkwksSAnAZGYGCosFDXuefFua8k3FluBPC1kGQMjLcBIwvHVUYh5fm7kFwF+KJRd7JA+rx7kM5iBJ8XA1Nb5zKcOBdSJIJY165ONBLZp6qKamXYCsAsmxfrJDAwjBGnE5cAayhIjDK/Yo5sxKOmcIG7jA2ZiJdhKcVjRANGGRGp6LvrXBIXEqs7Ivl7ZCynqIGRupAChgTT50KsrM4wMESXpswDJCm94EIq7XLRWEw5mkiCdyEVHDyMu/96aalrXtISDAOjMmBofhubgRGFyLru1hQ1E0IDJke4VugBNDAAgGiUprTnairpUSE7Zo55doRBWFcyMEREJpmAqIjXKXCoEri5XEjJpCsPjMqFFISBSe/cyWfbRDADBuk06n7MMDBEc8S1IRp6jN7H1baCgXG1GcSAkR3LDoQeq0DWhcT1nUlkRyl1loERDZiUvPBb2yuuQOnppyHerVtmu+hCkDEwEtZKlWTQqXydjQaGMX5VDIywmpYaKYrMp/w+/gYMDaOuq3WniSeTKXGVaZqLhTGrq6mhw2rDSLQcAEQ7eDAwdoZePt+KVx4YUgtJZGCEHDcyltQVhSSZLmgiOxk7x4t4lUyP2Hfx9yOfJS4k1oChxU09NTByt4ixa1fgUgJSBobRwIBhYFhmjYUqEy8LaTVqVsQbT3DRqol+/bgxVS8tdUeBxZ1km44BIxg5tMq23NjTolGHpfHKkN0ECA2YXMG+mKILiV2hxuPcRBDr2JGf5Gh9mEw13sSA/tDbtEGif/+cuuWmwVUaGJKHwEvEy0YhyZkBQ6g5lWFgeGGvdHCU9JXrt80cGRIKNpABY1moI1FftuaIhbgyzhTXVLiQIhHnJY9FEVdkeJZWo4bIwEjCqDUtULi6w8DwBqFeWoZo507Qy8oQ67JvZl+ZBsa+ZlXl2tZjL0DnO+9U60XIgC0KNQns+ydWEab7kvPJVp122xFXjSom+aCfC4kaSLIwbdJ3LxEvv033CaN2Feoj9WaYc5SecjK3j8m4kCJ5eZkikgA63HwT8ocNRaSoCIVHHC6ck4lCIpoGzoXkvl6av4mEe3uUEiB9EeFiyjxFvBK3oyDiVQl4XeeVTLqAIgqJNWaoCynXKCQ1+8n1h30umCzrbJkAFysqMr5BRLzptJvhMAwuE28kkUDe/vsj2r49ikeM4N5ZvbTUxc6xLiQ6nrgYGEEvJBowuu7KQtxcqLeI938V3IMhiHhJRlIArugWvRUvzo22bo1UTQ1dRXf/+99hWVZgutWFAEJEtv+yTLz0RWRFvMzqpG71GkTbtYVeXIw00YHYGUqtpCQPjCIXjRd9Kps8xH56ugMAx4CRMDCJfv0Q22cfWitJi/MuJDB1ezRNo9oVLRpDm3GXoFIQr2faEBgY+7qj7RkNjGIQ16LRzOpKooGpW7MG0bZtlRqYSEE+es2fD8uysIu425h2aBQSYWDoCs4nn444MRJtjKqUABkYaxQiXso2ShgDMgEKGqFIABcSHYgLCmBWVHiLhD1Yv0AMDBNG7UosaItz2eeo6Mgj0e/rr/DT7yeh8v33MwxMNanQno/WY8ei7PTTESksRPExx8AyDLcOiI1CaptxIXH1qGT302Z3CEPhYmAEI1jKjgTIA0MZP/Iuu2ohMeNijgaMlpcHVFdTo8OsrpId5iniFesWyVxIQSdjjlGORDLHscYPw8DQrwoLYVYwIdVU0O4h4pUxMEyhXbIY6vbC87AAV94pvbRUGsYuLlzEd87P3YZYjC44Qg1MC4WXBoZ9QCJsMUfwfm8A2Pfhv6DL448hZq+OxWRoWfdLHISVLiR3ZlJZgUMxD0xy7VqsPvFE/DRhIgDA2JGhSKMd7NW9JIxanRdEYcB4uGnYfvoZMLAsaPE4YkxWU9qGpqGYCcuPJOK8CynKZ41lXUisW4CLABIZGOJCYtgedWJBQpPzE03dypVY/Zv/w5qTT1EbMHl5znMjUOWAJArJ/n10FTtH+qRyIShFvLwGRmTIPDUwwn3OfNC458CPgaETi4eew4uyd2VNlrg+NSaM2qWBIdctSV5IfjOzqsrZzzZMaFhuNCqv1sxGKMkYGMn9JLozylZ4hFEDwRgYz3snC6PW+WrUgV1ILt2GbTRKGBgWTl4TDz2P/Zu5Rby7gueBYd4/6m5hUxPoOiesB9wLsiBRcVYqJWFg0owGxi5YGo9LM2rrZW4Xkl5c7Jpf3BoYwdgTovy0aJTRyYQamBYJPg9MhHsouFV4IiEwMGVcO3n9+6PoaD4nSb3gikJSiXjdURmugSMvz+XaqPnmW8A0UfvNN7Asi0bixGy/vayUgOhSUvWVfp2f78nOBHIh2dBbt1ZGPpSMPs5pMxaTu5DISkkYIHr8+zUUHXMMOj/4gNMgKRZIJkuZC0mln1G4kHa/OQ8Ar5HgBkPRaKLhovbqyTCQ3r490w+bCSIrVd+wUVUkT8RPxGvn4xAHSjG5GruN3OeE8O6IkR4SUAaG1D+SrsDtdryMXvaZ0zQu3T79mnHNuBgYiQuJNm23lRHxVnP99QOr8yAMLp9vRX0+q6YmIzD1iEICwJcroCcTI/e87p07jFqLRbl3KqgLSTRAvVxILAJFIakYmIqK4BoYmQuJbS/idiG52DxyLz3GOSudljAwvAtJBGuER0pLXb9ZpKTUNW66ngUfdxuXhbglFXMMwYAduPWodOVN/mbpXpGBaWiIq0iliJeE8bEDjFh9VyLiTa7PJG8zq6th7NqF9M6MAUOSnsmikEyVS0ExmUTy8rhJEsjcN1KzJDADQ9pSIG/QIOQNGoT0tp8R7dSJMxLErLFkYiW/c6J3b3R57FFOxGbYtYm0eBxWTY1UxKusM6QwYNI7truviWFOXNdHVoT24GPs3JkZiDSNrt5NWgTSh4FRiHhZF5JUxGu7SLREAmCeBc8oJaKBYbPnxuNcJWlxsLQsCzuefhpVH3+cOZaIK2WJ3QLk3WCNArF+F/2eMDB1bgbGkriQ6HGEgamupmHmyhT+AlhGU1bOQbYQYI0jkREF4NKlRST1kFxMkodmzak1xbsXOQM3qAtJ06hLmjuOsCcKA8awq7PLaimRfqU2bUL5jBn0WYwUFsKsqrJdSFlm4gVohmeegZG7kLj+BAyjpvfAznklZuJ1gWVgJCJevbTEzbSLc4TobpOJ5CVMb3MgNGByhJ8Gxvk7zlnZjW3AuNKhq8KxJbk+9DZ87plMxWxexJtat55uT23aRF1IMQ8DpnD4YdIuSFPKIzP4ioNnvFs3mmCJVlAWGZhIJGPo7NxJffNiHSbu/JqGbn+b44QTy9goux9k9ejyzzOTXLxrRtwbbd0aqU2b6G/NTsqi6Jm2Qw0YfsVF7i93mcxg6HIF2NvqVq3ki4e2aUPPQcJNI0U+DIxKxKsq5iiKwPPzwV4NTa4mMWhlGhhH9JvRGYiDZdXHH6P8focBowZMrlFIjGGmek89GRiFC4ntGxtGrRK3ixDfp0yDCiE17WceNfzYZ06Lx2EZBqKCFk/KwAQQ8bq2cUUmo/w7lReQgUHmmizBgJHlgWFBDTWP39/cvRvbH3sceruMlkhv24YaMLm4kOhkzz4Luu7SOnLHaJozJviIeOk9iMepQUMz8coYGE4DU+Yy5vSSEjcDUyR3b6k0MFo06izKQxFvy0RQDYyWiHOrEnHgaPiO+fvxAWdFwl5HtHUbfh8mkR0R8ZLU/AAxYAQGJpmkKao733cfLCON0hNOkPdV8fJq+fmuVWW8e3dqwLC+XxZdnngCekkx1p1/geNWyPOeJHjRrZtNoBNrTO226v7av5Bcuxb5Bx4IANjnLzOQLi9HrEN7174GK+Rj+6HQwBjbBQYmEuEmG3HiKTp6BPTSUqTWrcfuefOg224ilgWi1Zb9GBgxgkFiwHBMJPmbFI4TqekADAznQiKGaiSSGcgFA0YMUXU0MLlFIbEuk8LDD5fuEqGZeGtd+TGcMGoPBqaqmrrYgrqQxMkQEIwkyUJAi0Sg5efDqq7mnrmuTz8Fs7bOZaDR9yQWo3oZtwbKg71SGLdcHhhF1Wtpe7GYm2nwcyGR9A8BctpQ7V7bdkitW2+7kAJGIQkMDCAwMJEI4t2788cwC8ni445jyhF4TMGMiJewmVbagJl0wqhdEES8XCK8WAxafr5r3HSx9C4Gxp1nSdynuRAaMDnCUwMjGjNNyMBwvnEJk+F0xP6eNWDa8gYMH0YtMWA2bkTankRiHVkGJjPAJHr3Ql6/fh59lfdNysAwA0LR0Ueh9rvvUDbmTFQtWuR8f+QRzjUJuogg4M7JRCEBDI0tmRzz+vRBHlODJH/gQGDgQOk5DFVxN5kvHXCivMh+YsVroc6UXlSI1hddiJ9n/AXbH38CrS8cCwCI2itOgHEh+SRL5CYwVuytEPGK90Zkv2j+CVktIIHpAkBLRtBJURgsRQbJcSF5MDBeEwaDYkYfxfWTinjr3GG3NKRYEtbMupCqszNg2l0zCcaOHSg752ymI7xYVoZIQQGM6mouj1L+0KFS5pNEV+lsmQohxwfHXkWjvDZClshO0MBky8DQvsWDiXidYyXaOPGa7baoKNowMtl4A4BjUyQGDHQd0Q4dqHsqs4NzL9v+7gqmrx4GNdiIQftdMA0uE68ITsRbWkLdakBGwKsJwnjAzdIHY2Dk72RTIxTx5oqAGphIIsGt7JrShRTJy1OzHJRSZx54CQPDajOMykqOEUj9tJEOdiwDQ15aVQi3rK8sIvl5bgam237072j79ujy+GMoHjlSfm3sbxNQZ5Dpj9sdotLAZIvW4y4BALS/7jr5DjHeUCQQU5GztUwAucan7OzMRFf3449Ibd4CAIgyNVNIokFfFxJ7PxSaC2kYPtktX2Rg7H7LVrqSKCT6tyJkU1wZJvr3gxaPI09iPNLfUVE1HgBq/uvUXis85BDpPk5+FXcYtXgu7jjWhSREIfkh1r49ujzxOIpHjHDO4VONGnAMJKLNApu/Q+yzEBEF2LlNuAYZ40RRa4p7b3WRgQluwOhMDZ+gUUiqvgEe96iwkEZHpUW2UwE2BN1hYOz3VtMyRoKmcQk0Cw8/HIjFUHryyfyizqe0ghgybaWDu5AiRcX8+G6n6nC9p2I0ophhWDDU2UR2pEp8cyFkYHIEF3LrpYEREtnJ6OAG7Rc7uYhRHCwkGpiooIGJ5OVxIt4Uw74AQN0PPzgrGWLAMKtrPwNGNahoeXINjNj3IO0qK2HLjuNcSPZvSMR2VDicmwHT/vrr0faKK6hLx3Vuep8dn7JRWQlT0MyI9ZZkZRr0sjIQ/UNqa8aAYSOOgruQ5AyMMgopKhowcgZGfi61BoYIJV0i3iSfMC/erRt6L1okrWFE3ToeE0bBIYeg5osvkOjd26M4KxHxujUwzrm8GRjialKV2AgELpGdmoEBHNbPy/gmhjC7Ghfr8HBlR3Qd3LRFfj9Rm8OJeIMvJgqGDKGlQChzQ/PA+BgwMiNVMWZo0Sj0sjKkt2xBepsi7b/YlJcGhvktEj17ofarrzN/9+6Nvos/c5fK8DNghGRzlpGGWUeMGm8XUqSoENoO5rNdFykwAwNkxneZiJcVuBuGd3RfIyJkYHIEn6lVF4wW0YXUdFFI3Oo4EVcbCcLEDNiRF2yNlEQel3a6btVqe0Pmemq++SZzXGGhPOlXzgyMoIGJRGj5hUxnfIRj7Kovi5IMYh0XAE6RQXuF6pv8TdW2pimNF/Z8bJRSauMm935iuQLJKl7TNDoopbdsBcAL9QwahZRFGDX7XGnCJMX0jYXLgPGYQB0XkiQdgYqBETL+atEo9KJCafRQkCikNpeOR8fb/oT95jyn7idhYGpqHBeKWHfJR8RLRMw5lwtBdgwMMYK9ovYIC8HplkQGRvJ+OF9IwqijfDXqSBYupIJDnFxLrAuJjcJRQaqBUbhqtFiUjsnGtqAMjDoPDDv5szXYtHg84xr3KSqrBC3SyriQZIVh2ZxjpWV8VFJJJjJKzEWli6k2RAZGNNRjUe6Za04dTMjA5Ah+ta4LLzeffZKtztuUYdSRhNsV42zMfK+XlqLV+ecjkp9Jhqa3KkOaDrAJxLt1g962LYxt27DticcBZKro1n77LWVb9Nat3QnafJLRiX3lvs/P46+jsBCRvDy0ueJyGLt2Idapk/sgmZgU2a1yNVkb9kRRPOpYVH3yCcpOPyNwe9kg1qkjar/5BqmffqLfpTZtdPdRZGAUOopIURHMPXuQthkYVu9CGRgfF5IWwIWkuu8AXEyIZ4JGSS0k0YARGRixZIEy4zNAf0cvzYFeVIRWY8ao2wCgF2dWsUZVJZ8BmI0U8gqj3r3bydKdhT7L3aCCHWN3IQwMCe/3YmBsVyurq3AxMKzx50qNLwujFhLZyUSnCrDJImkiNdOUR2SJkOaBUYyD0ahTOTqoC4k18rwYGMaFpGT0AjIX1IhjXUgSgzBSUIDWl1wCGGnEOrSn2cgBxoUkLML8GBi3BibGM1qhAdMCITAwqhdEi8W5MMb6rLoCgTWkEgk1A8N83/GmPzqHl5UhvWmzfXweIokE2lxyCcqnT0dyZYbSLT35t6j99lvnmNatEBFeCl2RR4ODKg9MfoFAhWZesPaTJimb4qhr9h4E1BmI/SEGKhm04127ouvMJ4O3lSXiPXsCb7+DOps2BwIyMAqNj15UiDRANTARzoAhYdR+DFkQFxKbRE+Y1MQJ2ks/RDUwbB4YXofkciHVSTKEKkDduEFXvAqQDKuZ2jl2BmBWrAkFA2Mbc6woOxuBubtB5nlXjT228U5dSAEYGG7R4WJgPEK3JbV9MpXbWRFv8LEv1tEpaFn77XdOl0jBRg9ko4HRojG6qBT1ZirwIt7MuyBjYOJMDTYtIh8L/VxIdD/GheREZ8kNwg6Tb3COYwz2SElmwSIa+qo8MABgmZZcxKvzRk5zIXQh5QiXBoZ9EBlRUyQRV+b+aJR+CS4kpe9X8eJEGYaIWPitzj6LVj0tO+dstLrgAuhtnaiWaKvW7uyZfu4jqAfeSJ7IwATQsSjo7ZyjkGgYddO8IoRurlu1kn6X2rDBtZ9LxKvQ+BBhHsmNwQr1iIjXz4WkYmCUiezElZ1LxJudCyniEvHyYkFRA+Pl3iOTVH0ZULJaN3dVOKUdXJlM1QwMHew1LXBiNxm4cyjcYtRttdufgYm2ybzPrNjbEly1fKkNhYhX441b7l3MwoUEOFXkCw//Ff3O3OM/lgaKQqL7OgyMK2UBA1a7yC0giHbN1qWw41Css8MUK/NxBTVgyLNimDCTaheS6ziZC4k1UqNRty6Hy34tYWB0fsEeupBaIEQNjBdFKQoxGxWCC0k5ASteHL2MeVEZv3jXp2aibuVKlJx4IjRNQ9lpp2H7kxlGQm/dOsO2MDkkghgwauo7n2dgCrI0hjgDJgsNjEyQ6iMYbigQAyb540pYlgVN01C3erW7j/FYYBcS/9m5h2bAKCSliFdhLLpcSGKSPS8XkiwKiYZR25OiMKG6XEiqdxBA++uuReGvfsVF8uQCMtlZqRQNUXXlWvIyYMjn/Hx/htILXBi1YiGQz7uQZCkACIqP+zU6Je9A4eGHo2Lu3MyXQlJFPoxauEZZGHVU51nNLFxIALDfc89hz9vvoHjUsdj5wouZa2HCglWQZuJVsE9aLBbIqI127IjO99yNiB2KTEDcMjRLuMCMdXvpRaR37JC7vZELA2PQTMTSTLzicT5RSHqhRDPGMjCGIdfAiELfZkLIwOQIUQPDPYiMRappWpMyMNzkIlQnZaEa7NmXmZ1M8gYMQOlvf0uPa33xRXQbSZQVYSdWnxwjQObeSJNw5fFit0AsimpSzYam99DANDbi3bsDmgajooImByRsDD+pxwQGRm6gifefaGAs03SKOfpEIXFh1Owg51MLie4m5oGxt3d99lkU/upX6HTH7c42Dw2MpkhbLrqQ0jvdWYsJYp07o+z003x1WX7Q8vPpdaS3Z1wOgRgYUdBcH/cRPIxLyTmNAAxMJJFA2emnc64bsQ4PPY+mKctMuBPZMS7GLBmYaOvWaHXWGDrxAgEZGJmIV+nijwYyYDRNQ9HRR6NgyBDue6qfIa5B4bfIP+ggZboHILgBQ5jJdHl5ZrzVNMQ6u4vUusC8w7okCkk6Tgv1x6QaGNbIacYw6tCAyREiAxPr0gWIRDIFtAQYe/xXDQ3WL5bCTSTUq1LFikTnXEhq9iLaqhXa2ZqUstNPz5yPtewl90F+QsVgz4VC+w/2Kg1Mri4kmgcmYOKz+iKSn4/YvvsCAOpWroJRWUW1SGxek4gg4lVNhKK+hQxUZnW1kynXz4UUVbAunAuJmSwCamAKDz0EXZ95Gom+TD4MGQPjI+Jlo1G0WMw1uTQGNE1DhOhgbM1EhJlgAUgz8Wq6zt2PeulfAD7aJ2gYdbYpAFwuJEbnooimEd2wXK21XPV/TJtBGBiWRZa1wYJ1IRHkDx0KACg99VTf46n7yc7s68UCShFQxEvYyJplmbDseLdu7ugh6XHM/ZeIeGUGDLuwtAzDlbeJS2QHhAxMi4SggYnE4+i79HP0/vAD18qleNSxAIC8Aw5o0i5GEvVlYLwHnLZXXI4+SxajeOQxmf0Zyz62T4DVAVSr1TyOIg8UScQOBLlqYGQi3mwHpHqA1cEk12TcR3rbtjTLcaZfYiI7+fXphaILyTZgiDEdi/lrMFRCUc5dJ89/BPAaGC0Wc1HVLHtEw9YlCSHJuetWruLeLaKBaXvVRPT+ZFFwo7meoKtuO+w2UlDgSmwpA8vUZOPalDfGLFS8XLEAzABRSDKIdbkoS6ZpLlEq/W09Sglk60Kix0UitF1VKQ6KWAz5Bw5yf+8VhdSqjPuq8NBD0OezT9HprjuZ4+XuPirqVjAwfsjWhVT79TIAGUY80HE+LiTl+Eiiq4RyGVyb1MgJNTAtDi4NDJyHQQw/7HjzzSgYOgzFx/26yfoHZAwQpZ9dFQHErMqDiO5YISj7YsQ6dw7WR5KkjP0uP5/PcxEgksiVQCuLY6XHCcUcmwKJXj1RuWABkitX0ck/0bMndw0ZDQybuyeoBibzmbCBUt+3AO4ZV0UhyZL/0b4xBozEdcMOnk6l64y41aqrc3z89rbNU6citXkT2l15JYBMOv/MeQp9yyI0JPTSMgBO1IoWi2UikcjkqtKkFBZSoaiWRYJFGXgRr2KRQhgY+zcPGrJLmy0VmCWdmbg0BQPjVQspSxcSC03XYZkm0lu3eu6Xv//+0knZKwpJFoUjGsOaeL02IoILKesFT7YiXps9DWrAcLWRbAOGK3ujcKnSKtuMUF6Lx2Elk847r+sZVtQvL1cjImRgcoQrDwwLYeWiFxej1VljGr+QowCvFba6EjSzKs6S8s2FgZFmLRVdSEH6wbo4OAFwFgZMPI7WF1+MVueeQwewoCukhkCid2/EunZFpKQYSVv/kujZkxv4MwwMqysIpoGheUiIgNcviR3gEiTSvzWVBkYQ8TL3XqpLYCcatk0xpJcxmGqXfYNtjz2GteecS8tYBBEzNiQcBoYxYFhjTcHAJHr0oH/XO51CgDBqyoCRgoABGZjO901H4VFHos2ll3Lfa2w2Y/GckmKZmZTzuYVRiyCGAkkzoMpozuaPYaE2YKIu4zebKCbyLJAacTIJgReCji9sYj8A0nIZ0vZZtoUmsuPzlElBGBjWTUve16jATodRSC0PPAPDP9xBy7I3NiLCwK7FYtKES9wx7Ko4S8Ejx8DsE5yBkfWBmzAD0O1iETnaVpaDZocpkwEAO56bk/miCQ2Y0pNPRunJJwMANlzxOwCZUNLUT05COy0mMDCqMGrGP67F43TV5RgwARgLXc7AsKI9TxFvHpvTxZuBYTMQR+JxmHBWiqxgNLVxIyrff59vpx7hyLmA6h6IARON8vVxFM9MwcEHo3LBAgD118Coou5YuMTFARmY0pNOQulJJ7k3MC4kzqUSiThGLcvq6UIemHr8TtH27WBs24baH77PfO7Y0VWNHFAbMEqWKhZ1GfPSPDJKA6YMAGhkULR9O+l+KgQyYDQNxaNGIdq+PdLl5QCAvAH9g52AcblSRk1Rt487ZSwGC3zZhkh+PsyKCsdAV2TIbkqEDEyu4Hze/EPoUu83E0QNS2w/Jx2/6sVhaw7VJ8wzMAPDsiX2QKK3biOIeIPkgWGjMnKMQpK015QaGBZkpZno2ctlCLBJA1VaCnZVybIx1IXkV8gRArPITliMz9srjJorSyFjYNjK06wr0v6eDK5s9erk2rWSdho5OaQANpQacFxIzg4KA4ZZRddnMgfAi3hVDIwiCixXsCJeTVGLyVWNOsdijiKi7TKGgfFzxmhko6VY5A8eLP1eGYUUjboz0cqyNfswMGI/A0NlVP7/9u47vqr6fvz469y9cm/2IgHCBlkCAnErlCFqrbZVa5U6vyrqz1FXW0cnVh9tta2tnWpbR6t1VMWBKKiAgCiywyZhJCHzZt39+f1xuYfcDMgiycX38/HII8k9557zOZ97xvt+Zos2fZrBQMZttwLR6nljy4bj7Wh+bLFzNK4EtZ2xk1r2YMNgODKhbcsSmD583kkJTBdp7Xw7BSDUuuFTX4jdJAc9/xyhykq8b7ypj6bb3k3WMmgQeb//XZcmnWw+FHdH39/8hjfg178iXOvFOqQgbp2ONHhsb5Ctrn7T1S/OY0x3f7ykXXcd/u3bsY4YTtOXXx5Jl8US/w2qA+PAGNqaRqADVUjxD8lmJTDN67zbm8S0xSjQ7XVtHfjss0QaGzC1MftwrKQpdPDgkX0H4rtOR9fv5Sqk5BbtI8ymFlVIbZ8zttFHvjUH9pV0Kw16G7GjlKq0Gp/mKOPAdGyfzUtgjt3Au1Uj3m5UIbUMDMw5RwIYo8dDzsKFGBz29nvmtFfS0UYVUpt52k67ppbthDobwBytB5k+AOHhPPVccgkGhyNuhN9jMaWlkfeHP0Rn3W6rtLudEhh9Hq1m7adiVWtxbWCQgewSUlzRaMsSmFD/KIGJVSHFupfWvf2OvuxoJQtJM2d2aX/hZsOkd7j0plne2cePb7MnSUdKUeLGxWg+EnJXS2Bik//10kB2LaVcemROnrgSmMO9eWLVge0OZOdsHsA0H8QuVgLTgS6Y7XSjbq8EJn7mYWt8ANTOjdI5bWrr/ca+6XXwG3tfVSHpWpTAtDtxYLP8CexsPUhhpxiOXULYmck0O8R45MEVd323UwKDMX4gu54ogdH/z2pWAmM26z0h23O0Rrya2Yxmt+uTbLbVhqm9RrzdLoE5SlW+HsA0Cxzd553Xue3DUfOmvevySAlM8wDmcL70ozYwUoXURUdtA9OHrbKba/UAaO+B1Ic6NCR6R6qQmpcQNBu3oKtVSNZhQ0HTsA4f3qX396TmbYBiNxzrqFEYPZ52i9KbBy3Nu1SHOziRY3Sldr5lNzu/43udNKvaslrjutp25uEZm7iuo22wuto9t6ta9VBp0Yj3aKM3Z/0wOu9Y1g9+0K00mNLTMaakHPX8bN0GpptVSLH7R7NuzdDiy0rL86FZF8PuBFBHK4Hp0HaP0gYG4qtc2xrJt6NVSObMzGOnpfn+Na39sbD0nRy/e3V7E6DG5tGKDRwYbXt3uARGulGfAI7SBoZg/2jEax0xMu7/5o2vujWM+bF05kbVvLt0O0XcHRozo9l24gKYLt40HZMnM3zF8uM+e3hHGGytG1YPeu5fqECgdTXBYe21gUn93nzc583tULfj9rpRt3fDatW7oeWo0B0Um3dLH5/DZIKjNIzv7Sqklj1NNFPHSmAAUr57Be65czA1m0usS2mw2xm6+L2j5mvLrtrdLoFpXurTzmzYcQGt2RTfE60bDZdbBgbNS2A60jj5aG1g4PA1cuhQu9tztFFSCIerYjVNL/XtdAnM4bS1bDepNc+34xnAtFMqpk9DERs40HxkXis9EI7dc/vwC7sEMF0UP5lj/AlmTE8neKD1TMK9ZfB//k1gbzGOSfEN2o4Mj398S1+a1/UfS/MqmvZuRB1psKa1VwLTjUCtt7u9t0eL60YdvXkYLBY4ysOrvTYwppSUDh9XXBuW5vnYzg2rZY+T+IdZxx+eWffcjeuss3CddhoAQ159hcYvviCwew9VTz/dav3er0JKjvtfM5sxdqARL0TzsbvBi76bYwShrRrxdnIcmJaOPLjiq5C09oIZoxFjUhL5f3oKjKYeq0IyOJ3xvey6EZjpAUzzNmHN7u1D332HhpWfknzJxW2/32jE4HbrYwB1JYDBZIJgMK6XaFyp83G8X5vSUtt8/UgV0pGZzFuWwOhTfEgJTOKJKy1ocYLl/vIRSh96mLT/u6GXUxVlHz8e+/g2RqM0tTHcdw/K/9NTVP7lr+Qs/EWH3xPf0yX+W1L6ggUEdu/CMX36sTfUTgnMiaCtEphjvqfZA7XltAId1s5gaR0pgTF0owTGMnhwXG846/DhWIcPp+pfz7W9394OYFo14jXrg8ZB220o+kJPt4GxjRqJ69xzcUyZoncHB9ovgTn8oHOddVa39gvxgYExPS2+tK87gZmpjSqkZtuzDBqEZdCgo27C6PEcCWC6EJxqRiOKwwMdHp4UsnnJansTQXZH9kMPUvfBh6RccUWby2Ol3pHmbWBio5Pr4wEd/qyVBDAJ52htYKwFBQz6x7O9nKJji91Yj1fXYNdZZ3X+ZhUrOWljqPmMW2/p8GaaPzROuADGHt+ItyM0oxHN4UA1NnZ5lFqtnQdTe432tJaNeNubM6mL2hvdudcDmBZVSNYhBfi3b2+2Qv9oWqiZTProqbH/u7U9s5n8PzwJQP3HHx15vd1pJnru8dI8MDClpfdYABMrVYrrxt/J3lpGj4cg0WlYujJZaOw6iwtgmgWfHR20rjNSLr+clMsvbz9NsRKYWC8ks/lI04BYCUzs+pa5kBLQUca/6K96qwqpM/QW9t29uRpO3BKY5uOctDduQ1ti1RoGZ9cCmHZLYNoLYJo/VGzW+OkgeiCAsRQMbnu/3ZxhurMMLpdewpV2/XU4Tz21X5bAQHyw1Zlz51jieuUY40tRDU4nGI3xY+N0d38Wiz40gymtRQlMd6qQzLEqpGbXSCfvj7E8NnWyAa8uVo3VvNQ0LoDp4LQBPShWhaX3hGo26aVehSrdqBNX3LfNPhrsrLO041yF1CVtzWLbje1A22OFJDJDG72QOvS+ww0T25pxtiPiSmCalyq0840rbvwdS4sqpB4I8q0FBeQ+9ihN676k+rkj1UndHpa/kzRNI+/J3xOuqSFp9myAuDYwR2vE29vcF15A1d/+Hv2nJ79oHWWaibzf/45IN0r+2mPKyCBcXd2qCqk749toehVSsxKYTvbW0gOYrrR/4Uj+xTUEb3bN28b0fAnMsbQayM5sIuO2/4d94kR9cuL+MJBdYjx5+6F2J7rrz4z9sAQm1rOhuyUwJ3AbmLjJHDvTHfnwA6SrbWDigspm37jbHSag+czUVmt8lWAPPdQ9F1yA8/TT4tPZy1VIAM7p03HPmaMfY1xpQx+NHdSWtKuv1v8OluzruQ0b2u46DeAsLCRpxoye29dhsQChVRVSt0pgDlchtdMGpiO6G8C0VQITrq7R/7aO6P2hHGINwI9MBGrGkjeA1O9858gXBr0EpuV0vL1HApguSswSmOPbBqZLYvnYzVFCOZHbwDTvhdSJEhjbqFGgadhGjOjajuOqkJqXwLRThWSJr0KKb9zZcw/1llUTvV2F1JaOjMTbF0zp6aReew0A7rlzemy78VVIvXM/sU+YEP09bmyLNjDd74VkTGoewHTu84u1UbGNH9e1NDRrAwOAyYR/xw59eXuj5R5PsTYwsSqktnqQ6eM8STfqxNPeCKT9mX5h9qP06iUw3Ww30PyhcaIFMM1LYDpzM8t++CEy/t9tXe62q7UTgLRXAtNy3pv4CQd7MIBxxE9UeVzHNOqguKCqH1UhAWR+//ukXXNN3HQN3RZXhdQ7x5t+6y0kX3op5qxMIodHzYVutq+KNeKNq0Lq3L0o+ZKLcZ5+eqcnctT3d/g6M6Wng6ZhdLtxTD2FwM6dWAoKjvHu40Pvxn14fJs2x8OSbtQJLAFLYDjOvZC6JNYup5tBVfO5l0ypqQQOF32eCAxtjAPTEZrR2L0xR9ppxNtuCUzcQHa2FmOC9NytJm7m5z6oPmpLXAlMP6pCgmi7lB4NXqDFQHa9cz/RNA1zVrShbI/1QmqrEW8XSnRi6eqSw+k3ZWcx4Ne/wpiahm30KKyDB3dp6oCe0HIMoZZDB0Rf7Ptu1P3oSZZYErENzJG5LPpPemM3+64Wu+c+9iiOKVPIvOtO/bUBv/0t9pNPZuAzz/REEvtc3CBSvVicrBkMcLh0I66XVwdKYDSrJf5beg9eI3ElMP0lgIkbyO7Ev602nybC6G7j4Xa8GY1Hzs0OBvWZd38f5xlnkDTnSFXakSqk5iUwvXt/1Htims24587FOW0qRreb1Pnzu96upptajiHU5hx1WmwqAWnEm3ASsw1M305Q2KbYzb6LbWA8F1zAoH/9M66kwTZyBINfeB7n9Gk9kcJ+ITaTb090R+6UFvOeAO2WwDQP5FtVIfVkANO8u2k/aP8C8Wk60aow29TsHtIXD1lN05oNbd+xe0fatdcy8C9/jh/FV59KoOtVSN3WLIDpL1pO+9By+gzgyDUtkzkmnkRsA6OfcP3oG6I+uF43J5o70RlTo1VkbX0TOp6OtFHqQAnM4Vmyoa0qpJ4sgWnWK6uflMA0v+Ern78PU9JLmpXA9FUpwZFSyc7dOwzNJv88EsC0Dmp6i9YPA5iWE+i2WQJj6PsSmAR58vZDzetgE6UKKRYs9KOBtmLBVKLkYV/JXfgIgT174obY7w2xYc7jugYfrdGe2Ryd18Ua37i2J4vlNYNBH2W43wQwzY5V+X19mJLe0bwXUl8HMJ39Ahl3zhzeRvMqpPZmnj5ejgQw/aM0EdpoA9Ni/q/oi7ESmATpRv3HP/6R8ePH43a7cbvdFBYW8vbbb+vLfT4fCxYsIC0tDZfLxSWXXEJZWVncNoqLi5k3bx4Oh4PMzEzuvvtuQi1mml26dCmTJk3CarUybNgwnumHbRkMFgvJl12K56KLOjTZYH+gD5HdL0tg+lFQ1Q85Jp1M8sXf6P0d6wMNdmAcGJqNq9FycLkeDppjDXl7eyLHjoh8JUpg+j6A4XDJS2dLLprPXt7WZI69PhBmrCqsX5XAdKQNTN93o+7UkywvL49HHnmEtWvX8tlnn3Huuefy9a9/nU2bNgFwxx138MYbb/DSSy+xbNkyDhw4wMUXH5nFMxwOM2/ePAKBACtWrODZZ5/lmWee4cEHH9TX2b17N/PmzeOcc85h3bp13H777Vx33XW8++67PXTIPSfn4YfJfWRhXyej44z9rw2M1s02MOL40kvGmp0z6TfeCID7/PNbrx+7GVviAwuth4PmWK+f/lIC05zynfglMHFVSF0dQr+bYtXOna1+1oNeg+HIKLhWqx6UtTfn1vFiHRLtKt3eVBl9oWUbmLZ7ISVYN+oLLrgg7v+f//zn/PGPf+TTTz8lLy+Pv/3tbzz//POce+65ADz99NOMHj2aTz/9lOnTp/Pee++xefNm3n//fbKyspg4cSI//elPuffee3n44YexWCw89dRTFBQU8Ktf/QqA0aNH88knn/Cb3/yG2YeH7RZdc6QKqf8EMOi9kCSA6Zf0RrxHHljuWbOwL/2wzQfXkTYwLYrDezhojjWa7Y8BTMR/4pfA9KcqpE6XwBwOrluW+o5csxoVDMZ1ie8N2Q8/TPott3avK3YPa5kHbZXAZD/wIyKNjZjz83srWa10+WtROBzmxRdfpKGhgcLCQtauXUswGGTmzJn6OqNGjWLgwIGsXLkSgJUrVzJu3DiysrL0dWbPno3X69VLcVauXBm3jdg6sW20x+/34/V6435EvK7WGR9PPTWZozg+9Ea8LQIQc3Z2m73v9CL5FlVIPd011eiIBTD9p91ATG/PzdQXVLNq/64O4NZdegDTxTYwLd9ncDrbHrDtONOMxn4VvMDh4RqaXd9tBTDWYcOwjx+PqdkYXL2t0wHMhg0bcLlcWK1WbrzxRl599VXGjBlDaWkpFouF5BYnQFZWFqWlpQCUlpbGBS+x5bFlR1vH6/XS1Gz0xZYWLlyIx+PRf/L7MCrsrxxTT8E+aRLJzar1+pwEMP2bqXM919qrQurpsZK0WBuYlvvpQ7mP/hL7ySeTcccdfZ2U4y5cXaX/3RcPfWh2rnWy+lnvtdSP2pz0N5qm6e1gNLO5VZVSf9Hpp8bIkSNZt24dtbW1vPzyy8yfP59ly5Ydj7R1yv3338+ddx4ZzMzr9UoQ04IpNZXBzz937BV7kV6dJQFMv6QPNNjBKiDL0KEESkpa1ef3dBWhsR9WIXkuvBDPhRf2dTJ6RaiiUv+7r6Zy6GoVkqGdEhgRT3PYoaEBQ7KnX0zX0ZZOf4IWi4Vhw4YBMHnyZNasWcMTTzzBpZdeSiAQoKamJq4UpqysjOzsbACys7NZvXp13PZivZSar9Oy51JZWRlutxv7UaJAq9WKtR/dzEQHxbpRy82kX9IDzA6WoOT95teEqmtaFYn3eCNeZ/+tQvoqCFVWHnul46ynq5BEPIPdQZjeH3uqM7p9V4lEIvj9fiZPnozZbGbJkiX6sqKiIoqLiyksLASgsLCQDRs2UF5erq+zePFi3G43Y8aM0ddpvo3YOrFtiBOL/g1fbib9kz6Dece+gWkWS9v1+T3djfpwG5j+2I36qyDcjwKYzlYFtdeIV8SLVSG1OQZMP9GpT/D+++9n7ty5DBw4kLq6Op5//nmWLl3Ku+++i8fj4dprr+XOO+8kNTUVt9vNrbfeSmFhIdOnTwdg1qxZjBkzhiuvvJJHH32U0tJSfvSjH7FgwQK99OTGG2/k97//Pffccw/XXHMNH3zwAf/5z3946623ev7oRd/TJ3PsRz2jhE5vqNvNXkQ9/fkmzZ5N49q1JEnPxK+szk4loL/vcKmdBDBHdySAOUFKYMrLy7nqqqsYOXIkM2bMYM2aNbz77rt87WtfA+A3v/kN559/Ppdccglnnnkm2dnZvPLKK/r7jUYjb775JkajkcLCQr773e9y1VVX8ZOf/ERfp6CggLfeeovFixczYcIEfvWrX/HXv/5VulCfoPS2FXIz6Z9iD4kuVgHFulo7Tz+jx5IE0YH9Cl5+CcekST26XdEx6bfdCkBGs3aHvc0yZAgA1oKCzr1v0GAwGrF08n1fNbHRePtzANOpp8bf/va3oy632Ww8+eSTPPnkk+2uM2jQIBYtWnTU7Zx99tl88cUXnUmaSFD6BJMyF1K/1N0SmKHvvUvY68XcR4OdieMj/aab8FxwAea8vD5LQ9b995F29fcwDxjQqfdZ8gYw7IMlGPuw+28i0A7Ph3TCBDBC9DiZSqB/62Q36pYMNttXYlyUrxpN07D0cS9PzWjsdPASY24xVIdo7UgVUv+dKqf/TIojvpKSZpyLbcwYkmbN6uukiDboozdrcqsQ4qvEPe88bGPG4Dp3Rl8npV3ytVf0Kfv48RS88t++ToZoh16F1I8mABVCHH9J55xD0jnn9HUyjkruSkKI9umNeKWXmBCif5EARgjRrp7qRi2EED1NAhghRPtivcSkCkkI0c/IXUkI0S59DiNpxCuE6GfkriSEaF9sriopgRFC9DNyVxJCtEsvgZE2MEKIfka6UQsh2uWeO5dAcTHO007t66QIIUQcCWCEEO1yz5mNe47MQyaE6H+kCkkIIYQQCUcCGCGEEEIkHAlghBBCCJFwJIARQgghRMKRAEYIIYQQCUcCGCGEEEIkHAlghBBCCJFwJIARQgghRMKRAEYIIYQQCUcCGCGEEEIkHAlghBBCCJFwJIARQgghRMKRAEYIIYQQCUcCGCGEEEIkHAlghBBCCJFwJIARQgghRMKRAEYIIYQQCUcCGCGEEEIkHAlghBBCCJFwJIARQgghRMKRAEYIIYQQCUcCGCGEEEIkHAlghBBCCJFwJIARQgghRMKRAEYIIYQQCUcCGCGEEEIkHAlghBBCCJFwJIARQgghRMKRAEYIIYQQCUcCGCGEEEIkHAlghBBCCJFwOhXALFy4kFNOOYWkpCQyMzO56KKLKCoqilvH5/OxYMEC0tLScLlcXHLJJZSVlcWtU1xczLx583A4HGRmZnL33XcTCoXi1lm6dCmTJk3CarUybNgwnnnmma4doRBCCCFOOJ0KYJYtW8aCBQv49NNPWbx4McFgkFmzZtHQ0KCvc8cdd/DGG2/w0ksvsWzZMg4cOMDFF1+sLw+Hw8ybN49AIMCKFSt49tlneeaZZ3jwwQf1dXbv3s28efM455xzWLduHbfffjvXXXcd7777bg8cshBCCCESnaaUUl1986FDh8jMzGTZsmWceeaZ1NbWkpGRwfPPP883v/lNALZu3cro0aNZuXIl06dP5+233+b888/nwIEDZGVlAfDUU09x7733cujQISwWC/feey9vvfUWGzdu1Pd12WWXUVNTwzvvvNOhtHm9XjweD7W1tbjd7q4eohBCCCF6UUef391qA1NbWwtAamoqAGvXriUYDDJz5kx9nVGjRjFw4EBWrlwJwMqVKxk3bpwevADMnj0br9fLpk2b9HWabyO2TmwbbfH7/Xi93rgfIYQQQpyYuhzARCIRbr/9dk477TTGjh0LQGlpKRaLheTk5Lh1s7KyKC0t1ddpHrzElseWHW0dr9dLU1NTm+lZuHAhHo9H/8nPz+/qoQkhhBCin+tyALNgwQI2btzIiy++2JPp6bL777+f2tpa/aekpKSvkySEEEKI48TUlTfdcsstvPnmm3z00Ufk5eXpr2dnZxMIBKipqYkrhSkrKyM7O1tfZ/Xq1XHbi/VSar5Oy55LZWVluN1u7HZ7m2myWq1YrdauHI4QQgghEkynSmCUUtxyyy28+uqrfPDBBxQUFMQtnzx5MmazmSVLluivFRUVUVxcTGFhIQCFhYVs2LCB8vJyfZ3FixfjdrsZM2aMvk7zbcTWiW1DCCGEEF9tneqFdPPNN/P888/z+uuvM3LkSP11j8ejl4zcdNNNLFq0iGeeeQa3282tt94KwIoVK4BoN+qJEyeSm5vLo48+SmlpKVdeeSXXXXcdv/jFL4BoN+qxY8eyYMECrrnmGj744ANuu+023nrrLWbPnt2htEovJCGEECLxdPj5rToBaPPn6aef1tdpampSN998s0pJSVEOh0N94xvfUAcPHozbzp49e9TcuXOV3W5X6enp6q677lLBYDBunQ8//FBNnDhRWSwWNWTIkLh9dERtba0CVG1tbafeJ4QQQoi+09Hnd7fGgenPpARGCCGESDy9Mg6MEEIIIURfkABGCCGEEAlHAhghhBBCJBwJYIQQQgiRcCSAEUIIIUTCkQBGCCGEEAlHAhghhBBCJBwJYIQQQgiRcCSAEUIIIUTCkQBGCCGEEAlHAhghhBBCJBwJYIQQQgiRcCSAEUIIIUTCkQBGCCGEEAlHAhghhBBCJBwJYIQQQgiRcCSAEUIIIUTCkQBGCCGEEAlHAhghhBBCJBwJYIQQQgiRcCSAEUIIIUTCkQBGCCGEEAlHAhghhBBCJBwJYIQQQgiRcCSAEUIIIUTCkQBGCCGEEAlHAhghhBBCJBwJYIQQQgiRcCSAEUIIIUTCkQBGCCGEEAlHAhghhBBCJBwJYIQQQgiRcCSAEUIIIUTCkQBGCCGEEAlHAhghhBBCJBwJYIQQQgiRcCSAEUIIIUTCkQBGCCGEEAlHAhghhBBCJBwJYIQQQgiRcCSAEUIIIUTCkQBGCCGEEAlHAhghhBBCJJxOBzAfffQRF1xwAbm5uWiaxmuvvRa3XCnFgw8+SE5ODna7nZkzZ7J9+/a4daqqqrjiiitwu90kJydz7bXXUl9fH7fO+vXrOeOMM7DZbOTn5/Poo492/uiEEEIIcULqdADT0NDAhAkTePLJJ9tc/uijj/Lb3/6Wp556ilWrVuF0Opk9ezY+n09f54orrmDTpk0sXryYN998k48++ogbbrhBX+71epk1axaDBg1i7dq1PPbYYzz88MP8+c9/7sIhCiGEEOKEo7oBUK+++qr+fyQSUdnZ2eqxxx7TX6upqVFWq1W98MILSimlNm/erAC1Zs0afZ23335baZqm9u/fr5RS6g9/+INKSUlRfr9fX+fee+9VI0eO7HDaamtrFaBqa2u7enhCCCGE6GUdfX73aBuY3bt3U1paysyZM/XXPB4P06ZNY+XKlQCsXLmS5ORkpkyZoq8zc+ZMDAYDq1at0tc588wzsVgs+jqzZ8+mqKiI6urqNvft9/vxer1xP0IIIYQ4MfVoAFNaWgpAVlZW3OtZWVn6stLSUjIzM+OWm0wmUlNT49ZpaxvN99HSwoUL8Xg8+k9+fn73D0gIIYQQ/dIJ0wvp/vvvp7a2Vv8pKSnp6yQJIYQQ4jjp0QAmOzsbgLKysrjXy8rK9GXZ2dmUl5fHLQ+FQlRVVcWt09Y2mu+jJavVitvtjvsRQgghxImpRwOYgoICsrOzWbJkif6a1+tl1apVFBYWAlBYWEhNTQ1r167V1/nggw+IRCJMmzZNX+ejjz4iGAzq6yxevJiRI0eSkpLSk0kWQgghRALqdABTX1/PunXrWLduHRBtuLtu3TqKi4vRNI3bb7+dn/3sZ/zvf/9jw4YNXHXVVeTm5nLRRRcBMHr0aObMmcP111/P6tWrWb58ObfccguXXXYZubm5AHznO9/BYrFw7bXXsmnTJv7973/zxBNPcOedd/bYgQshhBAigXW2e9OHH36ogFY/8+fPV0pFu1I/8MADKisrS1mtVjVjxgxVVFQUt43Kykp1+eWXK5fLpdxut7r66qtVXV1d3DpffvmlOv3005XValUDBgxQjzzySKfSKd2ohRBCiMTT0ee3ppRSfRg/HTderxePx0Ntba20hxFCCCESREef3ydMLyQhhBBCfHVIACOEEEKIhCMBjBBCCCESjgQwQgghhEg4EsAIIYQQIuFIACOEEEKIhCMBjBBCCCESjgQwQgghhEg4EsAIIYQQIuFIACOEEEKIhCMBjBBCCCESjgQwQgghhEg4EsAIIYQQIuFIACOEEEKIhCMBjBBCCCESjgQwQgghhEg4EsAIIYQQIuFIACOEEEKIhCMBjBBCCCESjgQwQgghhEg4EsAIIYQQIuFIACOEEEKIhCMBjBBCCCESjgQwQgghhEg4EsAIIYQQIuFIACOEEEKIhCMBjBBCCCESjgQwQgghhEg4EsAIIYQQIuFIACOEEEKIhCMBjBBCCCESjgQwQgghhEg4EsB0UigSQinV4fV9IR/ljeXHXK8z2+yM+kA9wUjwmOuFIiFq/bXHJQ1CHMvxOv87KqIi+MP+Ptt/IBwgoiLd3k44EqYh2NADKTpCKdXhtHkDXkKR0FHXaQg2tHtPqvXX9vm50FF1gbo+PWcEmPo6AYnm30X/5uVtL/Ptkd/momEXsaVyC2/uepNz8s/htAGnUe2rZq93LydnnkxIhbj2vWvZXLGZP8/6M6dkn6JvpyHYwEtFLzE2fSwbKzby2y9+y9n5Z3PrybdS4CkgGA7y3t73WLR7EREVYXLWZJpCTdT4akh3pDNr0CyGJg8FoKyhjP31+zk582TWV6wnx5lDpiOToqoirnz7Sk7NPZXHz3lc3/dnpZ9RUldCjiuHQ42HKMwt5LE1j/Henvf4zTm/4ez8swHYVbuLZzc9y7dHfpuT0k7qVr6VN5azpXILIRVidOpocl25fLL/EzYc2sB1467DbDTHrR9REQxa6/i6LlDHxoqNjE0fy88+/Rlmg5mHT30Yk+HYp3JlUyVJliRCkRAbKzYyKWuS/r66QB1LipcwJWsKZoOZz8o+4/QBp/PjlT/GYrTw4PQHcZgdeANe7EZ7q/QqpdhVu4u6QB2jUkehaRqLdi1i5cGVfG3Q1/jaoK+1Wj8QCWA1Wtnr3YvVaCXbmd3ZbCUYDrK+Yj3DU4bztw1/44WtL/C7c3/HtJxpx3yvUootVVtYU7qGxmAjpw44FaNm5M1db3LVmKvIdeXq65Z4SwhEAmys2Mge7x6uHns1SimMmpH6YD3/2vwvxmWM49z8czEbzSilWLZvGflJ+QxNHkpERfiw+ENe2v4SKHj0rEdxW9wA/Hn9n/n7xr9zxegrGJY8jPykfMamj6W0oZS/bvgr6fZ0bhh/Q5vnQ1ve3/s+FU0VXDTsIl7c+iJry9aS6cjknqn38GHxh3y8/2NynDlMzZ7KE58/wZyCOSwrWcb6ivU8f97zDEsZRkRFiKiIfn58evBTdtfu5qy8s+LyJZaPmqbp/wfCASxGS4fSCrClcgvfe+d7TM2ZyhPnPKEfZ62/loqmCga5B2EymAiEA+yq3cXOmp0cbDjI3IK5vFT0Eu8Xv8+sQbO4ePjFfH/Z99lRs4OHCh/igqEXdDgNLVX7qvn7xr+z/tB6tldvR6G4YvQVXDvuWgyagVp/LZmOTP34V5Wu4tlNz/LJ/k+YN2Qej5zxSKt82VWzi1+s+gVrytaQZEniqjFXMcA1gKHJQxmWPIynNz7Nb7/4LacPOJ2HCh866vUQjoQBMBqMca/X+Gr455Z/YjfZCUVCOEwOLhh6AS9sfYGRKSOZMWjGMY89GA5iNBj55+Z/oqFx5Zgr445jX90+fr7q56w4sIKRKSN5bt5zmA3R+8F/t/2XbdXbWHDyAv38bmlT5SYW7VrEtJxp+EI+cl25jE0f2256ShtKKW0oZWLmxLjXIypCQ7ABl9mFpmnUBer4T9F/KMwtZEzaGALhAEtLljI+YzzZzmz9vjc6bTRWo7VVfoZVGIXiX5v/xYSMCUzJntJmenZU7+Cp9U+RZEnih9N+2KF77/GiqUQJdzvJ6/Xi8Xiora3F7W77ROqKy968jE2VmwAYmzaW/fX7qfZXAzAiZQRljWXU+mv5v/H/h0Lx5/V/BiA/KZ9/zP0Hv1z9S9aUrkGhqPJVYTKYUEoRVtELMsuRxV1T7uI3a3/DwYaDR03Lt0Z8i6HJQ/ndF7+jIdjAqNRRbK3aSrI1mWfmPMOT655k8d7FAPzpa3/iUOMh3BY3dyy9Q99fbJ9ljWUAuC1uLht1GWm2NJ7d9CwHGg6QakvlpQte0m9YMRVNFXy07yMmZ02mvLGcYCSoB2nv7H6HhmADZ+efzZbKLdz90d1x31bOyjuL5QeWE4qEuHnCzXxzxDcxG8y8t/c9nlz3JDX+GuYWzOWhwocoqirSv1X+fNXPKakrwWV2UR+sB+CuyXcx/6T5hCIhfrbqZ6wtW8sjZzxCliMLs8FMsi2Zd/a8w/0f30+aLQ2H2cHu2t3MGjSLhWcs5Lktz/GX9X+hLlhHqi0Vk8FEeWN53D5OzjyZ206+jds+uA2nxcnPTvsZRVVFnJN/Dmn2NH7wyQ9YUrwEgBxnDun2dDZUbADAarTynwv+Q1VTFRsqNqCh8e6ed9latZVpOdNYcWAFdpOdJ2c8qd809tTuIc2ehtPsZPn+5WQ4MgiGg/y76N+U1JUwNWcqLrOLP335J+qCdaTZ0qj0VQKQ68zll2f+ErfFTYGnAE3TaAw2UheoI8uZRVlDGcv2LeOFrS+wo2aH/pkYNANmgxl/2M9Qz1Cen/c8VqOVX639Ff/c/M+4z35s2lh21e5C0zScJiflTdFSxjFpY3h69tP8Yd0feHbzs9hNdu6beh//KfqPft0ATM+Zzrj0cWQ7s1m4aiEhdeRbu0kzcf3463l649P4wj4Avjboa5w/5HymZE/BbXGjlKLaX02KNQV/2I8/7Mdj9fDpwU+5/r3r9Xw40HBA327zPALQ0FDE3/7OzT+XR896lJvfv5lNlZu4btx1aGg88fkTKBQGzcAD0x/gmyO+iVKKF4te5Lef/5ap2VO5bdJtvLXrLf6+8e9MyJjAteOuZVLmJCJEcJgcbK7cTI2/hmk507Aarby35z121OxgaclStlRtAeCWibcwOWsyRdVFPPH5EzSFmnCancwePJulJUup8lXpaU21pcb93/J4Tko7idMHnM6krEm4zC4qmyrJceUwxDOEd/e8yxOfP8Ep2adww/gbMBlM5Lny2FGzgypfFY+teYyi6iJampo9lX11+6hoquAvs/7CsJRh3PHhHawuXR233rwh8/h438fcPDF6bS8rWcaPV/4Yb8Dbapuxz6q0sVQv6cm0Z/KXWX9hbfnaaHCcPo67T7kbj9VDaUMpNyy+gUA4wJ2T7+SDkg8YlTKKmYNm8sNPfsjn5Z/HbdtisBCIBDAZTLx98dt6YBRREap91dQH68lz5aFQPLziYd7c9SajUkfp5+u07Glsr9nOpMxJfGP4N3hszWPs8e7Rt3/PKfdw5ZgreX/v+9yx9A4ABrsHc17BeXgDXorrimkKNXHF6CsYlz6Ob73xrbjPzaSZeOysx/BYPYxKHUWSJYmIinCo8RBrytbw05U/pTHUyKUjL+X6cdeTbk/nD1/+gWc3PYs/7CfPlcc5A8/h430fs8e7B7vJzq0n38obO99gS9UWbEYbswfPpqi6iK1VWxmdOpr/G/9/+MI+nGYnb+9+m4/3fwwKxqSPYdXBVViNVp477zlGpo5EKcVu7268fi+fl3/Obz//rf78uHHCjSyYuKDNz7Q7Ovr8lgCms9sNeHlj5xv8Yd0f9Isxy5FFQ7BBf9i15DA5aAw1YtAMcUWxNqNNv0Gfm38u26q3sa9+n748zZbGt0d+G4vRwrbqbbgtbpKtyWyp2sJH+z7q1nEMTBqIpmlU+6r14zAZTO0W/6baUkm3p7PXu5fJWZMZnTqaV3e8GnchArjMLqxGa9yDImawezB2k52i6qK4fDBqRsIqrP9uzml2tlskHls/9o01yZKkV4PF8tZkMDEyZSRbqra0WQxuNpj14uzmf8eYDCZsRlu7n63JYMJuslMXqMNkMOEwOfT8dFvcZDuz2Va9rc2HZUs2o43/m/B/XDnmSm56/ybWla8j25lNSV3JUd/XPN9anmOptlQag434wj48Vg8XDr0wLhixGW1Mz51OOBKO3sQ48iBMtiYTVmHqAnVA9LNNsaVQ0VRBU6gpLg0DXAOoC9ThDXjJtGfqAU1zTrOTrw/9Ov/d/t9WRe9j0saglKLWXxsXdIxJG8O2qm16gGMymJiQMYFafy07anYwLHkYZY1l1AXqGJ8xnmJvMTX+Gv39FoOFK8ZcwT83/ZOQCmE32Tmv4Dxe2/EaYRUmPymfkroS7CY7vpAPhWJK1hQ+K/usVfoHuwezx7sHo2bkhvE3sLp0NWvL1h71s2lLsjWZMWljWHFghf5aW+c+tD4n3RY3w5KHsb9+v/6lY+bAmZTUlVBUXYTVaGVuwVxe3/H6Mc+3lpLMSdQF6/T/02xp3DH5DkaljmKPdw8PLH8g7nPPdmbjNDnZWbsTu8nORcMuoriumOX7l7e7j/EZ4/nF6b9gdelqPtn3CTX+Goqqj3xBmTFwBrtrd7Ordler97otbgpzC9lYsZH99fvb3YfL7OKc/HMwGowsLVkadz7YTXZOSjuJgw0HKWss0+93LrOLJEtS3JfGo12z2c5svjHsG/zxyz+2ukfEAqa22E12mkJN5DhzAFAoShtK9eWptlSGJg9lw6EN+rOhpaPdp02aKf7LwFHWPZZUW6p+rrXM7wkZE/jy0JdoaPx51p+ZnjO9S/tojwQwxymAifn04KfcuPhGFIrnznuOXFcuz256FofJgS/s468b/ordZOe7o79LYW4h31/2fap8VaTaUnlw+oOYjWYmZk7k8bWPc7DhII+e+Shbq7ZyzbvXAHDh0At5YPoD2Ey2Nve/+uBq/rH5HzSGGpmaPZUJGRP47/b/MnvwbP68/s9srdoKwBDPEP1mEDuZhyUP47nznsNhdvDloS+5+p2rsRgt/HXWX3lh6wtYjBa2V2+n1l/LnZPv5Kef/pRDTYfaTEeGPYOKpgpSbCloaHrgkmJNIS8pTy9xuGjYRTxQ+ABmg5k1pWu47+P7yHPlYTVaWXlwZdw2b554MwXuAu79+F4iKoLH6iHbkU19sJ6hyUO5acJNvLfnPc4deC5PffkUyw8cuWHajDbykvLiShZiLhl+CUmWJL049perf4lCkWpL5fZJt3PagNO4+f2bCUaC3DXlLv677b9cOPRCBiQNYMH7CyhvKifHmYPdZGdX7S4GuQex17tXz4dfnf0rBrsHc/dHd1PWUMavz/41NpONS/53CU2hJlKsKUzJnoJJM5HrymVy1mTe2/sepw84ndd3vK4HEOMzxtMYbNSPwW6yEwwHCakQ5w85n7HpY/nDuj/QFGrinlPuYdbgWfz8058TVmEuHn4xd3x4B06zk/pgfPsnm9HGPVPv4acrf8q49HHMGhytdkiyJKGU4s1db3Kw4SAnZ57M7R/eHheIPVD4AHMGzwFgyd4l/Gj5j5gxcAYTMyeytWorN0+8mb3evVzzzjWEVAiTZuK2Sbfxxq432F2zm2+O+CY3TriRNHsai/cu5m8b/kaOM4cPSz7EZDDx8gUvM9gzmPpAPZe/dTl7vHu4cOiF/OTUn/B5+ef8d/t/2VSxKe6bb3sGuwdz0bCLeG3Ha9x9yt2cmXcmS0uW8vbut7l+3PUMSxnG6oOrWV26mvknzae4rhi3xc2T657krV1vAdGA4uqxV7O2bC3BcJDZg2cz/6T5/Gj5j/jfzv/p+7IYLFw77lpWHFjBtupteKwebhx/I9uqt/F+8ftx7d+cZmdcaRXA6NTRbK/Zzs9O+xkrDqxg1cFV2E12PdD67pjvsnz/cl7b8RqnZJ/Ct0Z+C7PBzK7aXdzw3g1k2DP4+5y/YzaYeX/v++Qn5XNS+kmUN5azfP9yPtn/CTtrdtIQaiDFmkJxXTENwQbMBjOXjbqM1QdX6/eHYCSIxWAhx5VDsjWZhwofYnjKcD2tS4qXcOfSO0m3p2M2mPWHWpotjT997U+MTB3JlsotfPvNbwNwWu5prC1biy/sI9WWysXDL+aG8TdgN9njPq/GYCP/2PwPyhrLuHvK3XgDXq56+yoONhwk15nLRcMvYtGuRXGfffNSzrPyzqLKV8Xmys0YNAOPn/M4Z+adCcD++v38b+f/yHJk8dCKh1qdKxoaFqNFD6hNBhO3T7qdjRUbmTV4Fo3BRt7e8zZzB89lTekavjz0JaFIiMfPeZxhycP47qLvsrFyo769s/LO4kfTf8Sbu95kX90+XGYXA90D2Ve3j39s/gdhFcZldvHCvBcY7BlMIBzg5iU3s+rgqrgS39g5mOvK5WuDvsb4jPE89eVTbK/eTliFsRltPFj4IGfln8UHxR+wvXo7dpOdS4Zfwr+2/IutVVsp8BRw/bjr2Va9LXof1jROzz2dJ9c9SXlTOUnmJCqaKpieM53zh57PO7vf4dUdr3LD+Bt4edvLcUGL2WAmxZZCKBJiwcQFfGvEt/jxyh+zaPcifnLqT5hTMOdol2SnSQBznAMYgHXl64ioCJOyJrVatrt2NznOHD0ACUfCFFUXkeXIIs2e1u42l+xdQo2/hm8M/0aH6/xbCoQDLNq9iLVla7lpwk38af2f2OvdyyNnPML++v2MSBlBkiUpLq0mzUS+O7/N7flCPjZUbMDr95LrymX5geWUN0Yf5t8Z/R3CkTBWoxWFYmfNTrwBL6NTR+OyuAhGgoQioVY3rYiKoKHhDXh5ZfsrnJp7KmajmaZgEyelR9vbLN+/nP31+zl/yPk4zI420+YP+9lTuwe3xU1xXTE5zhxSbCm8testpmZPJRgJsse7hyGeIXE3Y4i264gQIdeVq9dhK6X0qoLmyhrKeGXHK5xXcB5ZjiwONR0iz5XHlqotKKUYmToyri64ef3/zpqdHGo6xOSsyfp+WoqoCG/teovHP3+cOyffybwh89hVs4utVVuZmjOViIpQF6jT2z3VBerwhXxkODJabSsUCWHUjDQEG9hbtxe3xU2qLVUvCQyEA6TYUtpMR0xTqClaRYTGiJQRreq5w5Fwq/YHAMtKlvFZ2Wd8e+S3yU/KJxAO0BRqwmP1tLmfkroSIirCIPcg/bVafy1bqrYwNXtqq89hT+0e1h1aRzgSZlrONJbvX066I53RqaNZW7YWs9HMmQPObPd8OZpqXzX/2vIvvH4vMwbNaPNbZSAc4IWtL7CpchNptjTmnzS/3bYaSimaQk2YDCbqAnW4LW40TeOzss9YfXA1J6WdxIxBM1q1FemoYCSIAUObn0N7wpEw9cF6rEarfm9SSuEP+9lWvY3BnsHttt2AaECQYk2hpK6Ev238G6NTR3P+kPPjzsN/bf4XtYFabppwE8FIEF/Ih8vs6lQ66wJ1VPuqyU/KR9M0QpEQa8vWsrFiI26rmxkDZ+C2uDlYf1C/bwXDQfxhPy6Lq81t3vfxfaw5uIbLR1/OlKwpZDmySHekY8DAjpod1PprGeQeRJYzq8PpbAw2stu7G5fZhcvsItWW2u5nWeuvpbShlDR7Gun2dP31UCREla+KFGsKb+56E3/Yz9TsqeS781vdL/xhPxVNFXgsnnaPszti13VDsIEvyr+g2ldNhiODk9JOintmQPS5cKDhAEM8Q3o8HRLA9EIAI0RPawo1YTPauvRAE0KIE0FHn9/SC0mIfqRlSZUQQoi2yTgwQgghhEg4EsAIIYQQIuH06yqkJ598kscee4zS0lImTJjA7373O6ZOndqnaSoqreNATRONgTBjB7gxGjTWldRg0DQykqwYDdG2CxPyktl0oJZXPt/PoXo/PzhvNLkeG7VNQep8ISJKsWJnJeVeP42BEKGIYmiGi2lDUtlX3cTaPVWMy0tmb2UDFfUB0pwWThrgRimobAgQjkQYku7CH4pQ1RDAbNQYN8DD2r3VOKwmpg5Opc4X5JkVexiRlcT543NoCITx2M2UVDWyt7KRino/uyoamF6QChq8v7mcsjofLouJEdlJjMlxU9UQYEK+h7wUB2VeH9vK6hid48ZmNhKOKDz2aCMzXzBMYyBMisPcqv1GnS/IG18epDEQYnCaE02DsQM8mI0GGgMhcj12vtxXQ7LDwtq91fxj5R5Ozk/mwokD8AXDLN5cRpbbRo7Hhsmo0eAPUecL0eAPU5DhZN64nOhgWruraAyEGZHlYmCqQ09HOKI4UNPEx9srSHVayEuxs7uigVOHpuG2m3l57T5W7qxk7AA3WW4bXl+IcDjCeeNyeHP9QXYeqifVaeE70wZS5wvhtpnJ9hzpHRaJKLaV17HloJesJBuBcARN00hzWvh4ewV5KXaMBo3tZfVcMCEHl83EOxtL2VPRyBkj0gmEIgxItjN2gIc6X5BQWPHOplJC4QgT8pNZvLmMoRkuRmQl8dH2Q5ycn8zANAfLd1TyZUkNY3LduKwmlu+ooKisjttnjiDNaSEQjjAyKwmb2aiflwD1/hCvfrEfo6YxMT+Z6sYAn+6qZGiGi+FZLtburebk/BRG5SSxt7KRotI6BqTYqaz3U1RWhy8Y4dJT8llXXENFvR+P3Ux1Y4AhGS4m5ifjsZuJRBRflFRjMxsZle2m1Otjb2UDG/bV4gtGmDgwWV93+Y4K3t9SxpgcNwOS7QxOd5KZZGXV7ip2lNfTFAyTmWTljOEZPL+qGJfNxCWTBrDzUAP3/Xc9o3LcPHLxOJxWE5GIoqisjjpfiGSHmS9LathT2YC3KYTFZGBIhpNgKMLgdCejst38e00JjYGQ/nlddPIAMpKs7K1soLIhwJgcN2VeHx9tr6CqPkBBhpNhGS4GpjlwWU36ubXzUD0eu5mJ+cnsrWzk1S/247KauHRqPm6bmVA4wstr97G7ooGZY7IYlObgnY2lVDUE+PaUfD7efohkh4Wpg1PxhyIYNCj1+mjwh6nzBVm7txqr2Ui224bFZKDM6+PskRmUeX2s2VONy2riy5IajAaNYZkuBiTbKfX6KEh38rUxWZiN0e+qZqOBSERx0OujuiFATWOQLLeV4VlJNAZCLN5chtNiIsVp5p8r9+KymchKsuGwmnDbTJwxPINdFfVUNQSYOzYHgwaf7Khg/b5amgJhyrw+8lIc5Kfa0TQ4bWg6mW4bjYEQ28rqWb+vhg+3RnthjctLJsdjwxcMU9sUZM2eKtaX1DJ7bDZzx2ZjMhooSHNyqN7H4DQnaS4rgVBEP35NAxQ8t6qYsQM8nDsqk0AowoqdFew8VI9B08hPdTAiK4n1+2rIctuYVhBtZHugpglfMMz28nq2HPRy5ogMTs5Ppt4fYl1JDf5ghGA4Ql6Kg7ED3OyrbsLjMGM3G/lkRwVvbzhIisPC6cPTOX1YOpqmoZRi0YZSVuysoDEQxmI0cPbIDOwWI+9sLGVIhpO5Y3Pwh8Ks3FVFmjM62OHQDBcjs5OoqPfjtpmxmAzRzgQKPt1VyaF6P+kuK0MynLy1/iBGg8aYHDcum4l3N5biD0fw2M2kOCxkua3sqWgkHFHkpdjx+oLkpzgYl+dB0zQ+31vNwFQHmw54qaj3M3NMFqt3V1Lm9ZPmtDB9SBpLtx0i12PjnJGZGAwa/lAYXyDCoXofJVVNeH1B9lU3keGycsGEXOyWjjfO7mn9thHvv//9b6666iqeeuoppk2bxuOPP85LL71EUVERmZmZx3z/8WrEe/Nza1m0ofSY6zksRhoDR8Z18NjNGA0aVQ1tjw/Q0zQNzAYDgXB0TBCTQSMUUbhtJry+zo8LYDUZ8Idaj6OSmWQl2WFmT0UjgXAEo0HDoEW7J3L4mRmOKMKR9k8zp8VIQ6D1GBgdleW24g9FqGk80mU4223DaNDwNgWp87d9vBaTAaOm0RTs3L41DYZluKhqCKAAb1OQ0FGOr+V727viRmS52HmogQVnD+XVdfspqWpqe8Uu0LRoPv9w3hgee7fouJ2HmgaDUh1EFBRXNQJg0KCt7NE0KEhzsquiodXraU4LFfVHT2PzvHRZTfiCYTQNguGOfRaxa6Lla267uUP5E3vQNN9fy+veoIHNbESDbp3jPSXLbSUcUa3ydkiGk9JaX1zaj2VUdhIGTWPzwbYHp4txWow0BsPtnvcdYTZqZLisHKj1ke6yUlHvR9PAbj6S347DD9KjHUNGkhWTQeNgbesxVrLcVup8oVbvd1lN1LdzDwEYnuki022l3Otne3nb40XFGDRQtL4HDM90sb28HrNRw2w06F8Oy+t6ZqoCTYsGr4E27uHtyUiy4rAYKalqbPP6BUh2mHnogjF84+S8HklnTML3Qpo2bRqnnHIKv//97wGIRCLk5+dz6623ct999x3z/ccrgPn4b/fiPriSOkMSNU0hTITx2E34DE5y/bswE6RcJWOLNODRGjHZXFSF7VgCNYQxckh5MGowXCuhypKLy6xhVw0EDHb2h1NpaGwgQ6shzRJmU2QQFpudTLOPcKCJMr8FixbBZDKh0HD4y8imkoDBQYMykx/eR4kxn5DSSApHB3QzmwwEw2BQIUxaGK9yUoMLLE6MRhNmk4mGuhrcNJJj8+NUTfiNdirDTirDDsJGGyn+/TjwgQYmg4FQuPVFoGi/10wEjSaTB789g4qQA1ukgYGBHTQqK3vJIZMqRmkl1GPnkPIw0NpIuTkXf0Mt2VRiN0bwG+zU46BRi6bJjp+wwYq3sQl3xItHa8CruakyZ1HpN5JPGXZ8WLUgRiIEMFNpzUcLNmGK+PCa0kkNHiSChsWgyDT7KTYNwhqqw6KFKY+48fv9uIwhspwaQX8jwUCAYnKw4SOLKuqxYyJMBAN+zYrR6qQ+bMJNI0opdobSyHeGMflqcOPFo/kIhCNE0LAZFXYCEAmxy1iAN6iRQyW1uNiddhYTTXtQ1XsIBIK4LBq+ww8AuzkaSPqVCYcpgscUZKdhMLZIEymGRiKakUBDLQHNCpoBa6QRl+bDSRMhTHw54jYyip7DZQxRb0pmRygDg8FImsOMr64CU8SPze4k7KvDoRoxaYqI2UV12ErI5MJjM2D0VWPy15BiqMduhAbNwUHrEEr9FtL8+8nRKmnCyiEtFQV4VB0OzU+lKRu71UpKpAq7v5zGsIkmrDRhwe50Ux82UxexYPZXk6LVc8CYS5Zd4cBHfZOfYDiMwwQmTREIRj9Xt9VAIBhERSIYiGBAUaklEzbacIa9+GwZWG0OkiPV2AOVlJOGz+gkqWEvmVRTYikgx1CDwsjBiAf8dUTQqMKDxQAFqpgQJmotWTQ5B9DgC5LkO4A54kNDEcLEPjLJN9dhCDVSE3Hgw8IoWzXmUAMqEiSDWnxYqNccRCweKgJGkiJ1uIxBDEQIRaDKmI5ZBbCGo59hSBkoNWZTacrCRSMjTaU4QzXU4aLENBAbfoIN1UQw4PKkEVIaIwKbUQYjB0351IYt5EUOEAr4aAobCGPAQgibFsBGAIXGdsMQ8rRDVT5l2QAAEThJREFUeMLV+DERwEzEYAUVRkUi+JwDMFsshMMhVDhMMBTC5/fTqDlpxI450oRD86Mw4kpyo5ksJGlN0FSDXxmpwk2qbx8aChNhzAZFlTUPZ1IyBoMBrz9MYwgMBiM54f0kh6tRzgx2NDoxhppIDR/CGaljp2EwkXAQDw1ohweWi/02EcZj9FOjHAQiRkBRasoj1WHEqMIEfQ0kB8toMKehQn4cqpEmrAQwk284RDL1NBqTOBRyUKOc1CgnRrONDGM9AS0a0FjxU0MSVuUnRavHpoUwubMIK6j2NmAghJkwJkJYtAipdg2LFkaFQ/j9/ujrJiPlhkzKfSYU4LTbaNCcRFDUN/px4CNTqyGACTMh7ARoxEaTZsdgSyIU8JEVLsVq0jAYDATCikgkgt1swGTQCGDGqyURDIWxGRUmLYI/DPWmVAx+L2mRQ1gIst0wDGO4iQGGKpyan4qwk0aTB82ZTrCxhgGhEipMOTSFFDYVvWdE0BiolWEhzEFTHocseTjNilB9JbaQF23Wzxh3+vk99oyFBA9gAoEADoeDl19+mYsuukh/ff78+dTU1PD666+3eo/f78fvPxKter1e8vPze74b9b+vhC3/O/Z6QnTV5O/B3hVQsa1HN6s0I6Ez7sH80cIe3a4Q4qtLXfQU2sTLe3SbCd2NuqKignA4TFZW/IBCWVlZbN26tc33LFy4kB//+MfHP3Gn3w6jL4Smw0PoG80QCUNTNaQNA6sbGivB5o7+7a+L/jhSQUWgdh9EQpA1Fqr3gMkK9pToOt79YLJBUjZoRihdD5oBbJ7oej7vkf1FQuAZAO4B0FQDgTpIHwHlW8FogqToUNXRskoV3Z7RDL7aaPqCTaDC0TRZXNF92DxgcUKgMXo8TdUQqIfkgeBoY/C9NmPfNl6LhKGxAurLovs3OyBj1OFjPgCuTMgcE13WVB3dV9XOaP6lDAKjJZoOf100D0K+aD6F/WAwgSM9mvaGQ1BbEk1/6pDoZ2C0RNcJNsKhoui+zXaoOwgpBdGyVU0DsxPKN4E9NbpOY0X0vSbb4Z/Dk58d2hr9P7Uguh+jKZqHwabDP41gTYJIBLz7on/bU6Ofv9Ud3RdEPw+zPZqHxYdHIk4bGs2DpBwY9y0I+sBgjP5ohmb5rSAcPLzMBAfXgz0ZnBnRvLa6ou9FRT9bqwssLjRrEma/FwZNjR5D7b5o/sfYPNFjDzUdfp8bDAbw1x85jw3G6PHYU8CRAgYzNJRHz7tgI7iyoudhsAFq90fT7UiN7q8mOmoxjjRw50EkGH1PoPFw3jVE/7a6Dp8Duw6nIym6HYMxmm+x35ohmr7mr0H0OgoHoumsL43mlS05el3VHYyeS66s6LVTtgk8edHPsLEymgcqEl0vHIScidF8rNwZ3ZZS0esh9lkGG6PpdGVF88RXG80nT370GDRD9PwOB6LLfLUQaIiua3FE0xwJRvPK7IiesyoCIX/0fG2siJ4n6SOi+/AegOrd0TyxeaLnma8mmo7ck6Pbq94T3UfK4Oj2woHoeWGyRj8Hsz26fP/nkJwfvW+FA9F9hvzRvIyEoueHikTPMc1w5HfsGCzO6DFEwtH9h/yH7yPJ0TxuqIie0ybrkc+mamd0PRWJ5qU6fC9Lyonma8Oh6H3C7Ih+PhYnlG6I/u/KQK+Xbn4dWV3R+0YkFM2Pqp3Ra9dojv5250J9efTY7SnR8zvQGN1nUtaR+05TdfReGmwCZ3r0N0Tzq7Eq+tuRFt1uw6FoWozmaL4YzdFrwWg6/Nvc7DVz9FyqLYneu5SKptUXLSXHYDp8fJnRvDSaovej2D0vUB89ztSC6LaUiv5oWjQNGtG0NtUcuU4Mxug+68uj9wb3gOh7Sr8Eqyf67LAmRY+5oSJ67huM0ftwTXH0b0tS9NyMhKLXiMkGFduj57vJqt/XtOzxbTwHeke/LIE5cOAAAwYMYMWKFRQWFuqv33PPPSxbtoxVq1a1ek+vlcAIIYQQ4rhJ6BKY9PR0jEYjZWVlca+XlZWRnd32sN1WqxWr1drmMiGEEEKcWPrlODAWi4XJkyezZMkS/bVIJMKSJUviSmSEEEII8dXUL0tgAO68807mz5/PlClTmDp1Ko8//jgNDQ1cffXVfZ00IYQQQvSxfhvAXHrppRw6dIgHH3yQ0tJSJk6cyDvvvNOqYa8QQgghvnr6ZSPeniCzUQshhBCJp6PP737ZBkYIIYQQ4mgkgBFCCCFEwpEARgghhBAJRwIYIYQQQiQcCWCEEEIIkXAkgBFCCCFEwpEARgghhBAJRwIYIYQQQiScfjsSb3fFxufzer19nBIhhBBCdFTsuX2scXZP2ACmrq4OgPz8/D5OiRBCCCE6q66uDo/H0+7yE3YqgUgkwoEDB0hKSkLTtB7brtfrJT8/n5KSkq/0FAWSD1GSD1GSD5IHMZIPUZIPUV3JB6UUdXV15ObmYjC039LlhC2BMRgM5OXlHbftu93ur/RJGSP5ECX5ECX5IHkQI/kQJfkQ1dl8OFrJS4w04hVCCCFEwpEARgghhBAJRwKYTrJarTz00ENYrda+TkqfknyIknyIknyQPIiRfIiSfIg6nvlwwjbiFUIIIcSJS0pghBBCCJFwJIARQgghRMKRAEYIIYQQCUcCGCGEEEIkHAlghBBCCJFwJIDppCeffJLBgwdjs9mYNm0aq1ev7uskHTcPP/wwmqbF/YwaNUpf7vP5WLBgAWlpabhcLi655BLKysr6MMU946OPPuKCCy4gNzcXTdN47bXX4pYrpXjwwQfJycnBbrczc+ZMtm/fHrdOVVUVV1xxBW63m+TkZK699lrq6+t78Si671j58L3vfa/V+TFnzpy4dRI9HxYuXMgpp5xCUlISmZmZXHTRRRQVFcWt05HroLi4mHnz5uFwOMjMzOTuu+8mFAr15qF0S0fy4eyzz251Ptx4441x6yR6Pvzxj39k/Pjx+qiyhYWFvP322/ryr8K5cKw86NXzQIkOe/HFF5XFYlF///vf1aZNm9T111+vkpOTVVlZWV8n7bh46KGH1EknnaQOHjyo/xw6dEhffuONN6r8/Hy1ZMkS9dlnn6np06erU089tQ9T3DMWLVqkfvjDH6pXXnlFAerVV1+NW/7II48oj8ejXnvtNfXll1+qCy+8UBUUFKimpiZ9nTlz5qgJEyaoTz/9VH388cdq2LBh6vLLL+/lI+meY+XD/Pnz1Zw5c+LOj6qqqrh1Ej0fZs+erZ5++mm1ceNGtW7dOnXeeeepgQMHqvr6en2dY10HoVBIjR07Vs2cOVN98cUXatGiRSo9PV3df//9fXFIXdKRfDjrrLPU9ddfH3c+1NbW6stPhHz43//+p9566y21bds2VVRUpH7wgx8os9msNm7cqJT6apwLx8qD3jwPJIDphKlTp6oFCxbo/4fDYZWbm6sWLlzYh6k6fh566CE1YcKENpfV1NQos9msXnrpJf21LVu2KECtXLmyl1J4/LV8cEciEZWdna0ee+wx/bWamhpltVrVCy+8oJRSavPmzQpQa9as0dd5++23laZpav/+/b2W9p7UXgDz9a9/vd33nIj5UF5ergC1bNkypVTHroNFixYpg8GgSktL9XX++Mc/Krfbrfx+f+8eQA9pmQ9KRR9c/+///b9233Mi5oNSSqWkpKi//vWvX9lzQakjeaBU754HUoXUQYFAgLVr1zJz5kz9NYPBwMyZM1m5cmUfpuz42r59O7m5uQwZMoQrrriC4uJiANauXUswGIzLj1GjRjFw4MATOj92795NaWlp3HF7PB6mTZumH/fKlStJTk5mypQp+jozZ87EYDCwatWqXk/z8bR06VIyMzMZOXIkN910E5WVlfqyEzEfamtrAUhNTQU6dh2sXLmScePGkZWVpa8ze/ZsvF4vmzZt6sXU95yW+RDz3HPPkZ6eztixY7n//vtpbGzUl51o+RAOh3nxxRdpaGigsLDwK3kutMyDmN46D07Y2ah7WkVFBeFwOC7TAbKysti6dWsfper4mjZtGs888wwjR47k4MGD/PjHP+aMM85g48aNlJaWYrFYSE5OjntPVlYWpaWlfZPgXhA7trbOg9iy0tJSMjMz45abTCZSU1NPqLyZM2cOF198MQUFBezcuZMf/OAHzJ07l5UrV2I0Gk+4fIhEItx+++2cdtppjB07FqBD10FpaWmb50tsWaJpKx8AvvOd7zBo0CByc3NZv3499957L0VFRbzyyivAiZMPGzZsoLCwEJ/Ph8vl4tVXX2XMmDGsW7fuK3MutJcH0LvngQQwol1z587V/x4/fjzTpk1j0KBB/Oc//8Fut/dhykR/cNlll+l/jxs3jvHjxzN06FCWLl3KjBkz+jBlx8eCBQvYuHEjn3zySV8npU+1lw833HCD/ve4cePIyclhxowZ7Ny5k6FDh/Z2Mo+bkSNHsm7dOmpra3n55ZeZP38+y5Yt6+tk9ar28mDMmDG9eh5IFVIHpaenYzQaW7UoLysrIzs7u49S1buSk5MZMWIEO3bsIDs7m0AgQE1NTdw6J3p+xI7taOdBdnY25eXlcctDoRBVVVUndN4MGTKE9PR0duzYAZxY+XDLLbfw5ptv8uGHH5KXl6e/3pHrIDs7u83zJbYskbSXD22ZNm0aQNz5cCLkg8ViYdiwYUyePJmFCxcyYcIEnnjiia/UudBeHrTleJ4HEsB0kMViYfLkySxZskR/LRKJsGTJkri6vxNZfX09O3fuJCcnh8mTJ2M2m+Pyo6ioiOLi4hM6PwoKCsjOzo47bq/Xy6pVq/TjLiwspKamhrVr1+rrfPDBB0QiEf1iPhHt27ePyspKcnJygBMjH5RS3HLLLbz66qt88MEHFBQUxC3vyHVQWFjIhg0b4oK5xYsX43a79WL3/u5Y+dCWdevWAcSdD4meD22JRCL4/f6vzLnQllgetOW4ngddaHD8lfXiiy8qq9WqnnnmGbV582Z1ww03qOTk5LjW1CeSu+66Sy1dulTt3r1bLV++XM2cOVOlp6er8vJypVS0y+DAgQPVBx98oD777DNVWFioCgsL+zjV3VdXV6e++OIL9cUXXyhA/frXv1ZffPGF2rt3r1Iq2o06OTlZvf7662r9+vXq61//epvdqE8++WS1atUq9cknn6jhw4cnVPdhpY6eD3V1der73/++Wrlypdq9e7d6//331aRJk9Tw4cOVz+fTt5Ho+XDTTTcpj8ejli5dGtcttLGxUV/nWNdBrNvorFmz1Lp169Q777yjMjIyEqrr7LHyYceOHeonP/mJ+uyzz9Tu3bvV66+/roYMGaLOPPNMfRsnQj7cd999atmyZWr37t1q/fr16r777lOapqn33ntPKfXVOBeOlge9fR5IANNJv/vd79TAgQOVxWJRU6dOVZ9++mlfJ+m4ufTSS1VOTo6yWCxqwIAB6tJLL1U7duzQlzc1Nambb75ZpaSkKIfDob7xjW+ogwcP9mGKe8aHH36ogFY/8+fPV0pFu1I/8MADKisrS1mtVjVjxgxVVFQUt43Kykp1+eWXK5fLpdxut7r66qtVXV1dHxxN1x0tHxobG9WsWbNURkaGMpvNatCgQer6669vFcwnej60dfyAevrpp/V1OnId7NmzR82dO1fZ7XaVnp6u7rrrLhUMBnv5aLruWPlQXFyszjzzTJWamqqsVqsaNmyYuvvuu+PG/1Aq8fPhmmuuUYMGDVIWi0VlZGSoGTNm6MGLUl+Nc+FoedDb54GmlFKdK7MRQgghhOhb0gZGCCGEEAlHAhghhBBCJBwJYIQQQgiRcCSAEUIIIUTCkQBGCCGEEAlHAhghhBBCJBwJYIQQQgiRcCSAEUIIIUTCkQBGCCGEEAlHAhghhBBCJBwJYIQQQgiRcP4/QHHz2b93EKcAAAAASUVORK5CYII=",
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",
             "text/plain": [
               "<Figure size 640x480 with 1 Axes>"
             ]
@@ -664,12 +664,12 @@
         }
       ],
       "source": [
-        "df.plot(title=\"Numeric features\")"
+        "df.sort_values(\"body_mass_g\").plot(title=\"Numeric features\", logy=True)"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 12,
+      "execution_count": 14,
       "metadata": {},
       "outputs": [
         {
@@ -678,13 +678,13 @@
               "<Axes: xlabel='species'>"
             ]
           },
-          "execution_count": 12,
+          "execution_count": 14,
           "metadata": {},
           "output_type": "execute_result"
         },
         {
           "data": {
-            "image/png": 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",
+            "image/png": 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",
             "text/plain": [
               "<Figure size 640x480 with 1 Axes>"
             ]
@@ -709,12 +709,12 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 13,
+      "execution_count": 15,
       "metadata": {},
       "outputs": [
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
               "<Figure size 640x480 with 1 Axes>"
             ]
@@ -747,7 +747,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 14,
+      "execution_count": 16,
       "metadata": {},
       "outputs": [
         {
@@ -801,7 +801,26 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 15,
+      "execution_count": 17,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Drop any rows that has missing (NA) values\n",
+        "df = df.dropna()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Part of preparing data for a machine learning task is splitting it into subsets for training and testing to ensure that the solution is not overfitting. By default, BQML will automatically manage splitting the data for you. However, BQML also supports manually splitting out your training data.\n",
+        "\n",
+        "Performing a manual data split can be done with `bigframes.ml.model_selection.train_test_split` like so:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
       "metadata": {},
       "outputs": [
         {
@@ -809,39 +828,30 @@
           "output_type": "stream",
           "text": [
             "\n",
-            "    X shape: (334, 6)\n",
-            "    y shape: (334, 1)\n",
+            "    df_train shape: (267, 7)\n",
+            "    df_test shape: (67, 7)\n",
             "\n"
           ]
         }
       ],
       "source": [
-        "# Drop any rows that has missing (NA) values\n",
-        "df = df.dropna()\n",
+        "from bigframes.ml.model_selection import train_test_split\n",
         "\n",
-        "# Isolate input features and output variable into DataFrames\n",
-        "X = df[['island', 'culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm', 'sex', 'species']]\n",
-        "y = df[['body_mass_g']]\n",
         "\n",
-        "# Print the shapes of features and label\n",
+        "# This will split df into test and training sets, with 20% of the rows in the test set,\n",
+        "# and the rest in the training set\n",
+        "df_train, df_test = train_test_split(df, test_size=0.2)\n",
+        "\n",
+        "# Show the shape of the data after the split\n",
         "print(f\"\"\"\n",
-        "    X shape: {X.shape}\n",
-        "    y shape: {y.shape}\n",
+        "    df_train shape: {df_train.shape}\n",
+        "    df_test shape: {df_test.shape}\n",
         "\"\"\")"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Part of preparing data for a machine learning task is splitting it into subsets for training and testing to ensure that the solution is not overfitting. By default, BQML will automatically manage splitting the data for you. However, BQML also supports manually splitting out your training data.\n",
-        "\n",
-        "Performing a manual data split can be done with `bigframes.ml.model_selection.train_test_split` like so:"
-      ]
-    },
     {
       "cell_type": "code",
-      "execution_count": 16,
+      "execution_count": 19,
       "metadata": {},
       "outputs": [
         {
@@ -850,7 +860,7 @@
           "text": [
             "\n",
             "    X_train shape: (267, 6)\n",
-            "    X_test shape: (67, 6)\n",
+            "    X_test shape: (67, 7)\n",
             "    y_train shape: (267, 1)\n",
             "    y_test shape: (67, 1)\n",
             "\n"
@@ -858,13 +868,31 @@
         }
       ],
       "source": [
-        "from bigframes.ml.model_selection import train_test_split\n",
+        "# Isolate input features and output variable into DataFrames\n",
+        "X_train = df_train[[\n",
+        "    'island',\n",
+        "    'culmen_length_mm',\n",
+        "    'culmen_depth_mm',\n",
+        "    'flipper_length_mm',\n",
+        "    'sex',\n",
+        "    'species',\n",
+        "]]\n",
+        "y_train = df_train[['body_mass_g']]\n",
         "\n",
-        "# This will split X and y into test and training sets, with 20% of the rows in the test set,\n",
-        "# and the rest in the training set\n",
-        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
+        "X_test = df_test[[\n",
+        "    'island',\n",
+        "    'culmen_length_mm',\n",
+        "    'culmen_depth_mm',\n",
+        "    'flipper_length_mm',\n",
+        "    'sex',\n",
+        "    'species',\n",
+        "    # Include the actual body_mass_g so that we can compare with the predicted\n",
+        "    # without a join.\n",
+        "    'body_mass_g'\n",
+        "]]\n",
+        "y_test = df_test[['body_mass_g']]\n",
         "\n",
-        "# Show the shape of the data after the split\n",
+        "# Print the shapes of features and label\n",
         "print(f\"\"\"\n",
         "    X_train shape: {X_train.shape}\n",
         "    X_test shape: {X_test.shape}\n",
@@ -885,7 +913,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 18,
+      "execution_count": 20,
       "metadata": {},
       "outputs": [
         {
@@ -901,7 +929,7 @@
               "                ('linreg', LinearRegression(fit_intercept=False))])"
             ]
           },
-          "execution_count": 18,
+          "execution_count": 20,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -939,7 +967,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 19,
+      "execution_count": 21,
       "metadata": {},
       "outputs": [
         {
@@ -970,80 +998,86 @@
               "      <th>flipper_length_mm</th>\n",
               "      <th>sex</th>\n",
               "      <th>species</th>\n",
+              "      <th>body_mass_g</th>\n",
               "    </tr>\n",
               "  </thead>\n",
               "  <tbody>\n",
               "    <tr>\n",
-              "      <th>37</th>\n",
-              "      <td>-18640.718256</td>\n",
+              "      <th>20</th>\n",
+              "      <td>4636.821089</td>\n",
               "      <td>Biscoe</td>\n",
-              "      <td>44.5</td>\n",
-              "      <td>15.7</td>\n",
-              "      <td>217.0</td>\n",
-              "      <td>.</td>\n",
+              "      <td>42.9</td>\n",
+              "      <td>13.1</td>\n",
+              "      <td>215.0</td>\n",
+              "      <td>FEMALE</td>\n",
               "      <td>Gentoo penguin (Pygoscelis papua)</td>\n",
+              "      <td>5000.0</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>245</th>\n",
-              "      <td>3109.962252</td>\n",
-              "      <td>Dream</td>\n",
-              "      <td>33.1</td>\n",
-              "      <td>16.1</td>\n",
-              "      <td>178.0</td>\n",
+              "      <th>151</th>\n",
+              "      <td>4564.698311</td>\n",
+              "      <td>Biscoe</td>\n",
+              "      <td>42.0</td>\n",
+              "      <td>13.5</td>\n",
+              "      <td>210.0</td>\n",
               "      <td>FEMALE</td>\n",
-              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
+              "      <td>Gentoo penguin (Pygoscelis papua)</td>\n",
+              "      <td>4150.0</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>267</th>\n",
-              "      <td>3372.443434</td>\n",
-              "      <td>Torgersen</td>\n",
-              "      <td>41.1</td>\n",
-              "      <td>17.6</td>\n",
-              "      <td>182.0</td>\n",
+              "      <th>194</th>\n",
+              "      <td>4650.79465</td>\n",
+              "      <td>Biscoe</td>\n",
+              "      <td>45.3</td>\n",
+              "      <td>13.7</td>\n",
+              "      <td>210.0</td>\n",
               "      <td>FEMALE</td>\n",
-              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
+              "      <td>Gentoo penguin (Pygoscelis papua)</td>\n",
+              "      <td>4300.0</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>280</th>\n",
-              "      <td>3341.376012</td>\n",
-              "      <td>Torgersen</td>\n",
-              "      <td>36.6</td>\n",
-              "      <td>17.8</td>\n",
-              "      <td>185.0</td>\n",
+              "      <th>105</th>\n",
+              "      <td>4613.791453</td>\n",
+              "      <td>Biscoe</td>\n",
+              "      <td>40.9</td>\n",
+              "      <td>13.7</td>\n",
+              "      <td>214.0</td>\n",
               "      <td>FEMALE</td>\n",
-              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
+              "      <td>Gentoo penguin (Pygoscelis papua)</td>\n",
+              "      <td>4650.0</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>40</th>\n",
-              "      <td>3310.178937</td>\n",
+              "      <th>60</th>\n",
+              "      <td>4731.406935</td>\n",
               "      <td>Biscoe</td>\n",
-              "      <td>37.6</td>\n",
-              "      <td>17.0</td>\n",
-              "      <td>185.0</td>\n",
+              "      <td>45.2</td>\n",
+              "      <td>13.8</td>\n",
+              "      <td>215.0</td>\n",
               "      <td>FEMALE</td>\n",
-              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
+              "      <td>Gentoo penguin (Pygoscelis papua)</td>\n",
+              "      <td>4750.0</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
               "</div>"
             ],
             "text/plain": [
-              "     predicted_body_mass_g     island  culmen_length_mm  culmen_depth_mm  \\\n",
-              "37           -18640.718256     Biscoe              44.5             15.7   \n",
-              "245            3109.962252      Dream              33.1             16.1   \n",
-              "267            3372.443434  Torgersen              41.1             17.6   \n",
-              "280            3341.376012  Torgersen              36.6             17.8   \n",
-              "40             3310.178937     Biscoe              37.6             17.0   \n",
+              "     predicted_body_mass_g  island  culmen_length_mm  culmen_depth_mm  \\\n",
+              "20             4636.821089  Biscoe              42.9             13.1   \n",
+              "151            4564.698311  Biscoe              42.0             13.5   \n",
+              "194             4650.79465  Biscoe              45.3             13.7   \n",
+              "105            4613.791453  Biscoe              40.9             13.7   \n",
+              "60             4731.406935  Biscoe              45.2             13.8   \n",
               "\n",
-              "     flipper_length_mm     sex                              species  \n",
-              "37               217.0       .    Gentoo penguin (Pygoscelis papua)  \n",
-              "245              178.0  FEMALE  Adelie Penguin (Pygoscelis adeliae)  \n",
-              "267              182.0  FEMALE  Adelie Penguin (Pygoscelis adeliae)  \n",
-              "280              185.0  FEMALE  Adelie Penguin (Pygoscelis adeliae)  \n",
-              "40               185.0  FEMALE  Adelie Penguin (Pygoscelis adeliae)  "
+              "     flipper_length_mm     sex                            species  body_mass_g  \n",
+              "20               215.0  FEMALE  Gentoo penguin (Pygoscelis papua)       5000.0  \n",
+              "151              210.0  FEMALE  Gentoo penguin (Pygoscelis papua)       4150.0  \n",
+              "194              210.0  FEMALE  Gentoo penguin (Pygoscelis papua)       4300.0  \n",
+              "105              214.0  FEMALE  Gentoo penguin (Pygoscelis papua)       4650.0  \n",
+              "60               215.0  FEMALE  Gentoo penguin (Pygoscelis papua)       4750.0  "
             ]
           },
-          "execution_count": 19,
+          "execution_count": 21,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -1070,7 +1104,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 20,
+      "execution_count": 22,
       "metadata": {},
       "outputs": [
         {
@@ -1105,12 +1139,12 @@
               "  <tbody>\n",
               "    <tr>\n",
               "      <th>0</th>\n",
-              "      <td>582.272638</td>\n",
-              "      <td>8337651.200465</td>\n",
-              "      <td>0.004989</td>\n",
-              "      <td>193.446297</td>\n",
-              "      <td>-11.273389</td>\n",
-              "      <td>-11.091156</td>\n",
+              "      <td>236.397487</td>\n",
+              "      <td>87218.80835</td>\n",
+              "      <td>0.005214</td>\n",
+              "      <td>194.28155</td>\n",
+              "      <td>0.839539</td>\n",
+              "      <td>0.840426</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
@@ -1119,21 +1153,21 @@
             ],
             "text/plain": [
               "   mean_absolute_error  mean_squared_error  mean_squared_log_error  \\\n",
-              "0           582.272638      8337651.200465                0.004989   \n",
+              "0           236.397487         87218.80835                0.005214   \n",
               "\n",
-              "   median_absolute_error   r2_score  explained_variance  \n",
-              "0             193.446297 -11.273389          -11.091156  \n",
+              "   median_absolute_error  r2_score  explained_variance  \n",
+              "0              194.28155  0.839539            0.840426  \n",
               "\n",
               "[1 rows x 6 columns]"
             ]
           },
-          "execution_count": 20,
+          "execution_count": 22,
           "metadata": {},
           "output_type": "execute_result"
         }
       ],
       "source": [
-        "pipeline.score(X_test, y_test)"
+        "pipeline.score(X_test.drop(columns=[\"body_mass_g\"]), y_test)"
       ]
     },
     {
@@ -1145,16 +1179,16 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 21,
+      "execution_count": 23,
       "metadata": {},
       "outputs": [
         {
           "data": {
             "text/plain": [
-              "-11.273389374372979"
+              "np.float64(0.8395390042903277)"
             ]
           },
-          "execution_count": 21,
+          "execution_count": 23,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -1162,7 +1196,7 @@
       "source": [
         "from bigframes.ml.metrics import r2_score\n",
         "\n",
-        "r2_score(y_test, y_pred[\"predicted_body_mass_g\"])"
+        "r2_score(y_pred['body_mass_g'], y_pred[\"predicted_body_mass_g\"])"
       ]
     },
     {
@@ -1187,65 +1221,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 22,
+      "execution_count": 24,
       "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>prompt</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>What is BigQuery?</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>What is BQML?</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>What is BigQuery DataFrames?</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>3 rows × 1 columns</p>\n",
-              "</div>[3 rows x 1 columns in total]"
-            ],
-            "text/plain": [
-              "                         prompt\n",
-              "0             What is BigQuery?\n",
-              "1                 What is BQML?\n",
-              "2  What is BigQuery DataFrames?\n",
-              "\n",
-              "[3 rows x 1 columns]"
-            ]
-          },
-          "execution_count": 22,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
+      "outputs": [],
       "source": [
         "# df = bpd.DataFrame(\n",
         "#         {\n",
@@ -1265,105 +1243,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 23,
+      "execution_count": 25,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "/usr/local/google/home/shobs/code/bigframes/bigframes/core/__init__.py:114: PreviewWarning: Interpreting JSON column(s) as pyarrow.large_string. This behavior may change in future versions.\n",
-            "  warnings.warn(msg, bfe.PreviewWarning)\n"
-          ]
-        },
-        {
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>ml_generate_text_llm_result</th>\n",
-              "      <th>ml_generate_text_rai_result</th>\n",
-              "      <th>ml_generate_text_status</th>\n",
-              "      <th>prompt</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>## BigQuery: Your Data Warehouse in the Cloud\n",
-              "...</td>\n",
-              "      <td>[{\"category\":\"HARM_CATEGORY_HATE_SPEECH\",\"prob...</td>\n",
-              "      <td></td>\n",
-              "      <td>What is BigQuery?</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>## BQML - BigQuery Machine Learning\n",
-              "\n",
-              "BQML stan...</td>\n",
-              "      <td>[{\"category\":\"HARM_CATEGORY_HATE_SPEECH\",\"prob...</td>\n",
-              "      <td></td>\n",
-              "      <td>What is BQML?</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>## BigQuery DataFrames\n",
-              "\n",
-              "BigQuery DataFrames is...</td>\n",
-              "      <td>[{\"category\":\"HARM_CATEGORY_HATE_SPEECH\",\"prob...</td>\n",
-              "      <td></td>\n",
-              "      <td>What is BigQuery DataFrames?</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>3 rows × 4 columns</p>\n",
-              "</div>[3 rows x 4 columns in total]"
-            ],
-            "text/plain": [
-              "                         ml_generate_text_llm_result  \\\n",
-              "0  ## BigQuery: Your Data Warehouse in the Cloud\n",
-              "...   \n",
-              "1  ## BQML - BigQuery Machine Learning\n",
-              "\n",
-              "BQML stan...   \n",
-              "2  ## BigQuery DataFrames\n",
-              "\n",
-              "BigQuery DataFrames is...   \n",
-              "\n",
-              "                         ml_generate_text_rai_result ml_generate_text_status  \\\n",
-              "0  [{\"category\":\"HARM_CATEGORY_HATE_SPEECH\",\"prob...                           \n",
-              "1  [{\"category\":\"HARM_CATEGORY_HATE_SPEECH\",\"prob...                           \n",
-              "2  [{\"category\":\"HARM_CATEGORY_HATE_SPEECH\",\"prob...                           \n",
-              "\n",
-              "                         prompt  \n",
-              "0             What is BigQuery?  \n",
-              "1                 What is BQML?  \n",
-              "2  What is BigQuery DataFrames?  \n",
-              "\n",
-              "[3 rows x 4 columns]"
-            ]
-          },
-          "execution_count": 23,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
+      "outputs": [],
       "source": [
         "# from bigframes.ml.llm import GeminiTextGenerator\n",
         "\n",
@@ -1382,47 +1264,9 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 24,
+      "execution_count": 26,
       "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "## BigQuery DataFrames\n",
-            "\n",
-            "BigQuery DataFrames is a Python library that allows you to interact with BigQuery data using the familiar Pandas API. This means you can use all the powerful tools and methods from the Pandas library to explore, analyze, and transform your BigQuery data, without needing to learn a new language or API.\n",
-            "\n",
-            "Here are some of the key benefits of using BigQuery DataFrames:\n",
-            "\n",
-            "* **Ease of use:** If you're already familiar with Pandas, you can start using BigQuery DataFrames with minimal learning curve.\n",
-            "* **Speed and efficiency:** BigQuery DataFrames leverages the power of BigQuery to perform complex operations on large datasets efficiently.\n",
-            "* **Flexibility:** You can use BigQuery DataFrames for a wide range of tasks, including data exploration, analysis, cleaning, and transformation.\n",
-            "* **Integration with other tools:** BigQuery DataFrames integrates seamlessly with other Google Cloud tools like Colab and Vertex AI, allowing you to build end-to-end data analysis pipelines.\n",
-            "\n",
-            "Here are some of the key features of BigQuery DataFrames:\n",
-            "\n",
-            "* **Support for most Pandas operations:** You can use most of the DataFrame methods you're familiar with, such as `groupby`, `filter`, `sort_values`, and `apply`.\n",
-            "* **Automatic schema inference:** BigQuery DataFrames automatically infers the schema of your data, so you don't need to manually specify it.\n",
-            "* **Efficient handling of large datasets:** BigQuery DataFrames pushes computations to BigQuery, which allows you to work with large datasets without running out of memory.\n",
-            "* **Support for both public and private datasets:** You can use BigQuery DataFrames to access both public and private datasets stored in BigQuery.\n",
-            "\n",
-            "## Getting Started with BigQuery DataFrames\n",
-            "\n",
-            "Getting started with BigQuery DataFrames is easy. You just need to install the library and configure your authentication. Once you're set up, you can start using it to interact with your BigQuery data.\n",
-            "\n",
-            "Here are some resources to help you get started:\n",
-            "\n",
-            "* **Documentation:** https://cloud.google.com/bigquery/docs/reference/libraries/bigquery-dataframe\n",
-            "* **Quickstart:** https://cloud.google.com/bigquery/docs/reference/libraries/bigquery-dataframe-python-quickstart\n",
-            "* **Tutorials:** https://cloud.google.com/bigquery/docs/tutorials/bq-dataframe-pandas-tutorial\n",
-            "\n",
-            "## Conclusion\n",
-            "\n",
-            "BigQuery DataFrames is a powerful tool that can help you get the most out of your BigQuery data. If you're looking for a way to easily analyze and transform your BigQuery data using the familiar Pandas API, then BigQuery DataFrames is a great option.\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "# print(pred.loc[2][\"ml_generate_text_llm_result\"])"
       ]
@@ -1443,7 +1287,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 15,
+      "execution_count": 27,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -1491,7 +1335,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.10.12"
+      "version": "3.12.6"
     }
   },
   "nbformat": 4,

From 49672011a8b7c11d05a55220c6288ab06670dfab Mon Sep 17 00:00:00 2001
From: Tim Swena <swast@google.com>
Date: Mon, 28 Apr 2025 15:43:58 -0500
Subject: [PATCH 2/2] use reset_index in plot, show output for generative ai

---
 .../bq_dataframes_template.ipynb              | 479 +++++++++++++-----
 1 file changed, 341 insertions(+), 138 deletions(-)

diff --git a/notebooks/getting_started/bq_dataframes_template.ipynb b/notebooks/getting_started/bq_dataframes_template.ipynb
index 7900d48829..12847483ac 100644
--- a/notebooks/getting_started/bq_dataframes_template.ipynb
+++ b/notebooks/getting_started/bq_dataframes_template.ipynb
@@ -179,7 +179,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": null,
       "metadata": {
         "id": "NPPMuw2PXGeo"
       },
@@ -199,7 +199,7 @@
         "# Note: BigQuery DataFrames objects are by default fully ordered like Pandas.\n",
         "# If ordering is not important for you, you can uncomment the following\n",
         "# expression to run BigQuery DataFrames in partial ordering mode.\n",
-        "#bpd.options.bigquery.ordering_mode = \"partial\"\n",
+        "# bpd.options.bigquery.ordering_mode = \"partial\"\n",
         "\n",
         "# Note: By default BigQuery DataFrames emits out BigQuery job metadata via a\n",
         "# progress bar. But in this notebook let's disable the progress bar to keep the\n",
@@ -306,73 +306,73 @@
               "  </thead>\n",
               "  <tbody>\n",
               "    <tr>\n",
-              "      <th>77</th>\n",
+              "      <th>0</th>\n",
               "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
-              "      <td>Biscoe</td>\n",
-              "      <td>37.8</td>\n",
-              "      <td>20.0</td>\n",
-              "      <td>190.0</td>\n",
-              "      <td>4250.0</td>\n",
-              "      <td>MALE</td>\n",
+              "      <td>Dream</td>\n",
+              "      <td>36.6</td>\n",
+              "      <td>18.4</td>\n",
+              "      <td>184.0</td>\n",
+              "      <td>3475.0</td>\n",
+              "      <td>FEMALE</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>97</th>\n",
-              "      <td>Gentoo penguin (Pygoscelis papua)</td>\n",
-              "      <td>Biscoe</td>\n",
-              "      <td>44.9</td>\n",
-              "      <td>13.8</td>\n",
-              "      <td>212.0</td>\n",
-              "      <td>4750.0</td>\n",
-              "      <td>FEMALE</td>\n",
+              "      <th>1</th>\n",
+              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
+              "      <td>Dream</td>\n",
+              "      <td>39.8</td>\n",
+              "      <td>19.1</td>\n",
+              "      <td>184.0</td>\n",
+              "      <td>4650.0</td>\n",
+              "      <td>MALE</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>273</th>\n",
+              "      <th>2</th>\n",
               "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
               "      <td>Dream</td>\n",
-              "      <td>36.9</td>\n",
-              "      <td>18.6</td>\n",
-              "      <td>189.0</td>\n",
-              "      <td>3500.0</td>\n",
-              "      <td>FEMALE</td>\n",
+              "      <td>40.9</td>\n",
+              "      <td>18.9</td>\n",
+              "      <td>184.0</td>\n",
+              "      <td>3900.0</td>\n",
+              "      <td>MALE</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>286</th>\n",
-              "      <td>Gentoo penguin (Pygoscelis papua)</td>\n",
-              "      <td>Biscoe</td>\n",
-              "      <td>45.1</td>\n",
-              "      <td>14.4</td>\n",
-              "      <td>210.0</td>\n",
-              "      <td>4400.0</td>\n",
+              "      <th>3</th>\n",
+              "      <td>Chinstrap penguin (Pygoscelis antarctica)</td>\n",
+              "      <td>Dream</td>\n",
+              "      <td>46.5</td>\n",
+              "      <td>17.9</td>\n",
+              "      <td>192.0</td>\n",
+              "      <td>3500.0</td>\n",
               "      <td>FEMALE</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>65</th>\n",
+              "      <th>4</th>\n",
               "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
-              "      <td>Torgersen</td>\n",
-              "      <td>46.0</td>\n",
-              "      <td>21.5</td>\n",
-              "      <td>194.0</td>\n",
-              "      <td>4200.0</td>\n",
-              "      <td>MALE</td>\n",
+              "      <td>Dream</td>\n",
+              "      <td>37.3</td>\n",
+              "      <td>16.8</td>\n",
+              "      <td>192.0</td>\n",
+              "      <td>3000.0</td>\n",
+              "      <td>FEMALE</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
               "</div>"
             ],
             "text/plain": [
-              "                                 species     island  culmen_length_mm  \\\n",
-              "77   Adelie Penguin (Pygoscelis adeliae)     Biscoe              37.8   \n",
-              "97     Gentoo penguin (Pygoscelis papua)     Biscoe              44.9   \n",
-              "273  Adelie Penguin (Pygoscelis adeliae)      Dream              36.9   \n",
-              "286    Gentoo penguin (Pygoscelis papua)     Biscoe              45.1   \n",
-              "65   Adelie Penguin (Pygoscelis adeliae)  Torgersen              46.0   \n",
+              "                                     species island  culmen_length_mm  \\\n",
+              "0        Adelie Penguin (Pygoscelis adeliae)  Dream              36.6   \n",
+              "1        Adelie Penguin (Pygoscelis adeliae)  Dream              39.8   \n",
+              "2        Adelie Penguin (Pygoscelis adeliae)  Dream              40.9   \n",
+              "3  Chinstrap penguin (Pygoscelis antarctica)  Dream              46.5   \n",
+              "4        Adelie Penguin (Pygoscelis adeliae)  Dream              37.3   \n",
               "\n",
-              "     culmen_depth_mm  flipper_length_mm  body_mass_g     sex  \n",
-              "77              20.0              190.0       4250.0    MALE  \n",
-              "97              13.8              212.0       4750.0  FEMALE  \n",
-              "273             18.6              189.0       3500.0  FEMALE  \n",
-              "286             14.4              210.0       4400.0  FEMALE  \n",
-              "65              21.5              194.0       4200.0    MALE  "
+              "   culmen_depth_mm  flipper_length_mm  body_mass_g     sex  \n",
+              "0             18.4              184.0       3475.0  FEMALE  \n",
+              "1             19.1              184.0       4650.0    MALE  \n",
+              "2             18.9              184.0       3900.0    MALE  \n",
+              "3             17.9              192.0       3500.0  FEMALE  \n",
+              "4             16.8              192.0       3000.0  FEMALE  "
             ]
           },
           "execution_count": 7,
@@ -433,7 +433,7 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "average_body_mass: 4201.754385964911\n"
+            "average_body_mass: 4201.754385964914\n"
           ]
         }
       ],
@@ -565,11 +565,11 @@
         {
           "data": {
             "text/plain": [
-              "133    {'culmen_length_mm': None, 'culmen_depth_mm': ...\n",
-              "279    {'culmen_length_mm': 37.9, 'culmen_depth_mm': ...\n",
-              "34     {'culmen_length_mm': 37.8, 'culmen_depth_mm': ...\n",
-              "208    {'culmen_length_mm': 40.5, 'culmen_depth_mm': ...\n",
-              "96     {'culmen_length_mm': 37.7, 'culmen_depth_mm': ...\n",
+              "0    {'culmen_length_mm': 36.6, 'culmen_depth_mm': ...\n",
+              "1    {'culmen_length_mm': 39.8, 'culmen_depth_mm': ...\n",
+              "2    {'culmen_length_mm': 40.9, 'culmen_depth_mm': ...\n",
+              "3    {'culmen_length_mm': 46.5, 'culmen_depth_mm': ...\n",
+              "4    {'culmen_length_mm': 37.3, 'culmen_depth_mm': ...\n",
               "dtype: struct<culmen_length_mm: double, culmen_depth_mm: double, flipper_length_mm: double>[pyarrow]"
             ]
           },
@@ -600,11 +600,11 @@
         {
           "data": {
             "text/plain": [
-              "116    <NA>\n",
-              "308    15.5\n",
-              "285    15.9\n",
-              "2      16.0\n",
-              "295    16.1\n",
+              "0    18.4\n",
+              "1    19.1\n",
+              "2    18.9\n",
+              "3    17.9\n",
+              "4    16.8\n",
               "dtype: Float64"
             ]
           },
@@ -642,6 +642,16 @@
       "execution_count": 13,
       "metadata": {},
       "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/home/swast/src/github.com/googleapis/python-bigquery-dataframes/bigframes/core/array_value.py:263: AmbiguousWindowWarning: Window ordering may be ambiguous, this can cause unstable results.\n",
+            "  warnings.warn(msg, category=bfe.AmbiguousWindowWarning)\n",
+            "/home/swast/src/github.com/googleapis/python-bigquery-dataframes/bigframes/core/array_value.py:239: AmbiguousWindowWarning: Window ordering may be ambiguous, this can cause unstable results.\n",
+            "  warnings.warn(msg, bfe.AmbiguousWindowWarning)\n"
+          ]
+        },
         {
           "data": {
             "text/plain": [
@@ -654,7 +664,7 @@
         },
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
               "<Figure size 640x480 with 1 Axes>"
             ]
@@ -664,7 +674,12 @@
         }
       ],
       "source": [
-        "df.sort_values(\"body_mass_g\").plot(title=\"Numeric features\", logy=True)"
+        "(\n",
+        "    df\n",
+        "    .sort_values(\"body_mass_g\")\n",
+        "    .reset_index(drop=True)\n",
+        "    .plot(title=\"Numeric features\", logy=True)\n",
+        ")"
       ]
     },
     {
@@ -823,6 +838,16 @@
       "execution_count": 18,
       "metadata": {},
       "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/home/swast/src/github.com/googleapis/python-bigquery-dataframes/bigframes/core/array_value.py:263: AmbiguousWindowWarning: Window ordering may be ambiguous, this can cause unstable results.\n",
+            "  warnings.warn(msg, category=bfe.AmbiguousWindowWarning)\n",
+            "/home/swast/src/github.com/googleapis/python-bigquery-dataframes/bigframes/core/array_value.py:239: AmbiguousWindowWarning: Window ordering may be ambiguous, this can cause unstable results.\n",
+            "  warnings.warn(msg, bfe.AmbiguousWindowWarning)\n"
+          ]
+        },
         {
           "name": "stdout",
           "output_type": "stream",
@@ -1003,78 +1028,78 @@
               "  </thead>\n",
               "  <tbody>\n",
               "    <tr>\n",
-              "      <th>20</th>\n",
-              "      <td>4636.821089</td>\n",
-              "      <td>Biscoe</td>\n",
-              "      <td>42.9</td>\n",
-              "      <td>13.1</td>\n",
-              "      <td>215.0</td>\n",
+              "      <th>0</th>\n",
+              "      <td>3444.135246</td>\n",
+              "      <td>Dream</td>\n",
+              "      <td>42.2</td>\n",
+              "      <td>18.5</td>\n",
+              "      <td>180.0</td>\n",
               "      <td>FEMALE</td>\n",
-              "      <td>Gentoo penguin (Pygoscelis papua)</td>\n",
-              "      <td>5000.0</td>\n",
+              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
+              "      <td>3550.0</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>151</th>\n",
-              "      <td>4564.698311</td>\n",
-              "      <td>Biscoe</td>\n",
-              "      <td>42.0</td>\n",
-              "      <td>13.5</td>\n",
-              "      <td>210.0</td>\n",
-              "      <td>FEMALE</td>\n",
-              "      <td>Gentoo penguin (Pygoscelis papua)</td>\n",
-              "      <td>4150.0</td>\n",
+              "      <th>1</th>\n",
+              "      <td>3735.564386</td>\n",
+              "      <td>Torgersen</td>\n",
+              "      <td>39.1</td>\n",
+              "      <td>18.7</td>\n",
+              "      <td>181.0</td>\n",
+              "      <td>MALE</td>\n",
+              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
+              "      <td>3750.0</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>194</th>\n",
-              "      <td>4650.79465</td>\n",
-              "      <td>Biscoe</td>\n",
-              "      <td>45.3</td>\n",
-              "      <td>13.7</td>\n",
-              "      <td>210.0</td>\n",
-              "      <td>FEMALE</td>\n",
-              "      <td>Gentoo penguin (Pygoscelis papua)</td>\n",
-              "      <td>4300.0</td>\n",
+              "      <th>2</th>\n",
+              "      <td>3879.370094</td>\n",
+              "      <td>Dream</td>\n",
+              "      <td>40.9</td>\n",
+              "      <td>18.9</td>\n",
+              "      <td>184.0</td>\n",
+              "      <td>MALE</td>\n",
+              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
+              "      <td>3900.0</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>105</th>\n",
-              "      <td>4613.791453</td>\n",
+              "      <th>3</th>\n",
+              "      <td>3787.401253</td>\n",
               "      <td>Biscoe</td>\n",
-              "      <td>40.9</td>\n",
-              "      <td>13.7</td>\n",
-              "      <td>214.0</td>\n",
-              "      <td>FEMALE</td>\n",
-              "      <td>Gentoo penguin (Pygoscelis papua)</td>\n",
-              "      <td>4650.0</td>\n",
+              "      <td>38.2</td>\n",
+              "      <td>18.1</td>\n",
+              "      <td>185.0</td>\n",
+              "      <td>MALE</td>\n",
+              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
+              "      <td>3950.0</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>60</th>\n",
-              "      <td>4731.406935</td>\n",
-              "      <td>Biscoe</td>\n",
-              "      <td>45.2</td>\n",
-              "      <td>13.8</td>\n",
-              "      <td>215.0</td>\n",
+              "      <th>4</th>\n",
+              "      <td>3435.804331</td>\n",
+              "      <td>Dream</td>\n",
+              "      <td>36.0</td>\n",
+              "      <td>18.5</td>\n",
+              "      <td>186.0</td>\n",
               "      <td>FEMALE</td>\n",
-              "      <td>Gentoo penguin (Pygoscelis papua)</td>\n",
-              "      <td>4750.0</td>\n",
+              "      <td>Adelie Penguin (Pygoscelis adeliae)</td>\n",
+              "      <td>3100.0</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
               "</div>"
             ],
             "text/plain": [
-              "     predicted_body_mass_g  island  culmen_length_mm  culmen_depth_mm  \\\n",
-              "20             4636.821089  Biscoe              42.9             13.1   \n",
-              "151            4564.698311  Biscoe              42.0             13.5   \n",
-              "194             4650.79465  Biscoe              45.3             13.7   \n",
-              "105            4613.791453  Biscoe              40.9             13.7   \n",
-              "60             4731.406935  Biscoe              45.2             13.8   \n",
+              "   predicted_body_mass_g     island  culmen_length_mm  culmen_depth_mm  \\\n",
+              "0            3444.135246      Dream              42.2             18.5   \n",
+              "1            3735.564386  Torgersen              39.1             18.7   \n",
+              "2            3879.370094      Dream              40.9             18.9   \n",
+              "3            3787.401253     Biscoe              38.2             18.1   \n",
+              "4            3435.804331      Dream              36.0             18.5   \n",
               "\n",
-              "     flipper_length_mm     sex                            species  body_mass_g  \n",
-              "20               215.0  FEMALE  Gentoo penguin (Pygoscelis papua)       5000.0  \n",
-              "151              210.0  FEMALE  Gentoo penguin (Pygoscelis papua)       4150.0  \n",
-              "194              210.0  FEMALE  Gentoo penguin (Pygoscelis papua)       4300.0  \n",
-              "105              214.0  FEMALE  Gentoo penguin (Pygoscelis papua)       4650.0  \n",
-              "60               215.0  FEMALE  Gentoo penguin (Pygoscelis papua)       4750.0  "
+              "   flipper_length_mm     sex                              species  body_mass_g  \n",
+              "0              180.0  FEMALE  Adelie Penguin (Pygoscelis adeliae)       3550.0  \n",
+              "1              181.0    MALE  Adelie Penguin (Pygoscelis adeliae)       3750.0  \n",
+              "2              184.0    MALE  Adelie Penguin (Pygoscelis adeliae)       3900.0  \n",
+              "3              185.0    MALE  Adelie Penguin (Pygoscelis adeliae)       3950.0  \n",
+              "4              186.0  FEMALE  Adelie Penguin (Pygoscelis adeliae)       3100.0  "
             ]
           },
           "execution_count": 21,
@@ -1139,12 +1164,12 @@
               "  <tbody>\n",
               "    <tr>\n",
               "      <th>0</th>\n",
-              "      <td>236.397487</td>\n",
-              "      <td>87218.80835</td>\n",
-              "      <td>0.005214</td>\n",
-              "      <td>194.28155</td>\n",
-              "      <td>0.839539</td>\n",
-              "      <td>0.840426</td>\n",
+              "      <td>212.800303</td>\n",
+              "      <td>72655.272611</td>\n",
+              "      <td>0.004369</td>\n",
+              "      <td>144.426983</td>\n",
+              "      <td>0.877546</td>\n",
+              "      <td>0.877991</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
@@ -1152,11 +1177,11 @@
               "</div>[1 rows x 6 columns in total]"
             ],
             "text/plain": [
-              "   mean_absolute_error  mean_squared_error  mean_squared_log_error  \\\n",
-              "0           236.397487         87218.80835                0.005214   \n",
+              " mean_absolute_error  mean_squared_error  mean_squared_log_error  \\\n",
+              "          212.800303        72655.272611                0.004369   \n",
               "\n",
-              "   median_absolute_error  r2_score  explained_variance  \n",
-              "0              194.28155  0.839539            0.840426  \n",
+              " median_absolute_error  r2_score  explained_variance  \n",
+              "            144.426983  0.877546            0.877991  \n",
               "\n",
               "[1 rows x 6 columns]"
             ]
@@ -1185,7 +1210,7 @@
         {
           "data": {
             "text/plain": [
-              "np.float64(0.8395390042903277)"
+              "np.float64(0.8775458183087934)"
             ]
           },
           "execution_count": 23,
@@ -1223,13 +1248,69 @@
       "cell_type": "code",
       "execution_count": 24,
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>prompt</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>What is BigQuery?</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>What is BQML?</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>What is BigQuery DataFrames?</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>3 rows × 1 columns</p>\n",
+              "</div>[3 rows x 1 columns in total]"
+            ],
+            "text/plain": [
+              "                         prompt\n",
+              "0             What is BigQuery?\n",
+              "1                 What is BQML?\n",
+              "2  What is BigQuery DataFrames?\n",
+              "\n",
+              "[3 rows x 1 columns]"
+            ]
+          },
+          "execution_count": 24,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
       "source": [
-        "# df = bpd.DataFrame(\n",
-        "#         {\n",
-        "#             \"prompt\": [\"What is BigQuery?\", \"What is BQML?\", \"What is BigQuery DataFrames?\"],\n",
-        "#         })\n",
-        "# df"
+        "df = bpd.DataFrame(\n",
+        "        {\n",
+        "            \"prompt\": [\"What is BigQuery?\", \"What is BQML?\", \"What is BigQuery DataFrames?\"],\n",
+        "        })\n",
+        "df"
       ]
     },
     {
@@ -1243,9 +1324,98 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 25,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/home/swast/src/github.com/googleapis/python-bigquery-dataframes/bigframes/core/array_value.py:109: PreviewWarning: JSON column interpretation as a custom PyArrow extention in\n",
+            "`db_dtypes` is a preview feature and subject to change.\n",
+            "  warnings.warn(msg, bfe.PreviewWarning)\n",
+            "/home/swast/src/github.com/googleapis/python-bigquery-dataframes/bigframes/core/array_value.py:263: AmbiguousWindowWarning: Window ordering may be ambiguous, this can cause unstable results.\n",
+            "  warnings.warn(msg, category=bfe.AmbiguousWindowWarning)\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>ml_generate_text_llm_result</th>\n",
+              "      <th>ml_generate_text_rai_result</th>\n",
+              "      <th>ml_generate_text_status</th>\n",
+              "      <th>prompt</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>BQML stands for **BigQuery Machine Learning**....</td>\n",
+              "      <td>&lt;NA&gt;</td>\n",
+              "      <td></td>\n",
+              "      <td>What is BQML?</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>BigQuery is a fully managed, serverless data w...</td>\n",
+              "      <td>&lt;NA&gt;</td>\n",
+              "      <td></td>\n",
+              "      <td>What is BigQuery?</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>BigQuery DataFrames are a Python library that ...</td>\n",
+              "      <td>&lt;NA&gt;</td>\n",
+              "      <td></td>\n",
+              "      <td>What is BigQuery DataFrames?</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>3 rows × 4 columns</p>\n",
+              "</div>[3 rows x 4 columns in total]"
+            ],
+            "text/plain": [
+              "                         ml_generate_text_llm_result  \\\n",
+              "1  BQML stands for **BigQuery Machine Learning**....   \n",
+              "0  BigQuery is a fully managed, serverless data w...   \n",
+              "2  BigQuery DataFrames are a Python library that ...   \n",
+              "\n",
+              "  ml_generate_text_rai_result ml_generate_text_status  \\\n",
+              "1                        <NA>                           \n",
+              "0                        <NA>                           \n",
+              "2                        <NA>                           \n",
+              "\n",
+              "                         prompt  \n",
+              "1                 What is BQML?  \n",
+              "0             What is BigQuery?  \n",
+              "2  What is BigQuery DataFrames?  \n",
+              "\n",
+              "[3 rows x 4 columns]"
+            ]
+          },
+          "execution_count": 25,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
       "source": [
         "# from bigframes.ml.llm import GeminiTextGenerator\n",
         "\n",
@@ -1264,11 +1434,44 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 26,
+      "execution_count": null,
       "metadata": {},
-      "outputs": [],
+      "outputs": [
+        {
+          "data": {
+            "text/markdown": [
+              "BigQuery DataFrames are a Python library that provides a Pandas-like interface for interacting with BigQuery data.  Instead of loading entire datasets into memory (which is impossible for very large BigQuery tables), BigQuery DataFrames allow you to work with BigQuery data in a way that feels familiar if you've used Pandas, but leverages BigQuery's processing power for efficiency.  This means you can perform data analysis and manipulation on datasets that are too large for Pandas to handle directly.\n",
+              "\n",
+              "Key features and characteristics include:\n",
+              "\n",
+              "* **Lazy Evaluation:**  BigQuery DataFrames don't load the entire dataset into memory.  Operations are expressed as queries that are executed in BigQuery only when necessary (e.g., when you call `.to_dataframe()` to materialize a result, or when you explicitly trigger execution). This significantly reduces memory consumption.\n",
+              "\n",
+              "* **Pandas-like API:**  The library aims for a familiar API similar to Pandas.  You can use many of the same functions and methods you would use with Pandas DataFrames, such as filtering, selecting columns, aggregations, and joining.\n",
+              "\n",
+              "* **Integration with BigQuery:**  The library seamlessly integrates with BigQuery.  It allows you to read data from BigQuery tables and write data back to BigQuery.\n",
+              "\n",
+              "* **Scalability:**  Because the processing happens in BigQuery, BigQuery DataFrames can scale to handle datasets of virtually any size.  It's designed to efficiently process terabytes or even petabytes of data.\n",
+              "\n",
+              "* **Performance:**  While providing a user-friendly interface, BigQuery DataFrames leverages BigQuery's optimized query engine for fast execution of operations.\n",
+              "\n",
+              "* **SQL integration:** While providing a Pythonic interface, you can easily incorporate SQL queries directly within the DataFrame operations providing flexibility and control over the data manipulation.\n",
+              "\n",
+              "\n",
+              "**In short:** BigQuery DataFrames provide a powerful and efficient way to work with large BigQuery datasets using a familiar Pandas-like syntax without the memory limitations of loading the entire dataset into local memory.  They bridge the gap between the ease of use of Pandas and the scalability of BigQuery.\n"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ]
+          },
+          "execution_count": 26,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
       "source": [
-        "# print(pred.loc[2][\"ml_generate_text_llm_result\"])"
+        "# import IPython.display\n",
+        "\n",
+        "# IPython.display.Markdown(pred.loc[2][\"ml_generate_text_llm_result\"])"
       ]
     },
     {