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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "cf1403ce",
+   "metadata": {},
+   "source": [
+    "# Time Series Forecasting with BigFrames\n",
+    "\n",
+    "This notebook provides a comprehensive walkthrough of time series forecasting using the BigFrames library. We will explore two powerful models, TimesFM and ARIMAPlus, to predict bikeshare trip demand based on historical data from San Francisco. The process covers data loading, preprocessing, model training, and visualization of the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c0b2db75",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import bigframes.pandas as bpd\n",
+    "from bigframes.ml import forecasting\n",
+    "bpd.options.display.repr_mode = \"anywidget\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0eba46b9",
+   "metadata": {},
+   "source": [
+    "### 1. Data Loading and Preprocessing\n",
+    "\n",
+    "The first step is to load the San Francisco bikeshare dataset from BigQuery. We then preprocess the data by filtering for trips made by 'Subscriber' type users from 2018 onwards. This ensures we are working with a relevant and consistent subset of the data. Finally, we aggregate the trip data by the hour to create a time series of trip counts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "83928f4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = bpd.read_gbq(\"bigquery-public-data.san_francisco_bikeshare.bikeshare_trips\")\n",
+    "df = df[df[\"start_date\"] >= \"2018-01-01\"]\n",
+    "df = df[df[\"subscriber_type\"] == \"Subscriber\"]\n",
+    "df[\"trip_hour\"] = df[\"start_date\"].dt.floor(\"h\")\n",
+    "df_grouped = df[[\"trip_hour\", \"trip_id\"]].groupby(\"trip_hour\").count().reset_index()\n",
+    "df_grouped = df_grouped.rename(columns={\"trip_id\": \"num_trips\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c43b7e65",
+   "metadata": {},
+   "source": [
+    "### 2. Forecasting with TimesFM\n",
+    "\n",
+    "In this section, we use the TimesFM (Time Series Foundation Model) to forecast future bikeshare demand. TimesFM is a powerful model designed for a wide range of time series forecasting tasks. We will use it to predict the number of trips for the last week of our dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1096e154",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/google/home/shuowei/src/python-bigquery-dataframes/bigframes/dataframe.py:5340: FutureWarning: The 'ai' property will be removed. Please use 'bigframes.bigquery.ai'\n",
+      "instead.\n",
+      "  warnings.warn(msg, category=FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "✅ Completed. \n",
+       "    Query processed 58.7 MB in 19 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:eb026c28-038a-4ca7-acfa-474ed0be4119&page=queryresults\">Job bigframes-dev:US.eb026c28-038a-4ca7-acfa-474ed0be4119 details</a>]\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "✅ Completed. \n",
+       "    Query processed 7.1 kB in a moment of slot time.\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "✅ Completed. \n",
+       "    Query processed 7.1 kB in a moment of slot time.\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "929eda852e564b799cf76e62d9f7b46a",
+       "version_major": 2,
+       "version_minor": 1
+      },
+      "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>forecast_timestamp</th>\n",
+       "      <th>forecast_value</th>\n",
+       "      <th>confidence_level</th>\n",
+       "      <th>prediction_interval_lower_bound</th>\n",
+       "      <th>prediction_interval_upper_bound</th>\n",
+       "      <th>ai_forecast_status</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2018-04-24 14:00:00+00:00</td>\n",
+       "      <td>126.519211</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>96.837778</td>\n",
+       "      <td>156.200644</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2018-04-30 21:00:00+00:00</td>\n",
+       "      <td>82.266197</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>-7.690994</td>\n",
+       "      <td>172.223388</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2018-04-25 14:00:00+00:00</td>\n",
+       "      <td>130.057266</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>78.019585</td>\n",
+       "      <td>182.094948</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2018-04-26 06:00:00+00:00</td>\n",
+       "      <td>47.235214</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>-16.565634</td>\n",
+       "      <td>111.036063</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2018-04-28 01:00:00+00:00</td>\n",
+       "      <td>0.761139</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>-61.080531</td>\n",
+       "      <td>62.602809</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2018-04-27 11:00:00+00:00</td>\n",
+       "      <td>160.437042</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>80.767928</td>\n",
+       "      <td>240.106157</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2018-04-25 07:00:00+00:00</td>\n",
+       "      <td>321.418488</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>207.344246</td>\n",
+       "      <td>435.492729</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2018-04-24 16:00:00+00:00</td>\n",
+       "      <td>284.640564</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>198.550187</td>\n",
+       "      <td>370.730941</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2018-04-25 16:00:00+00:00</td>\n",
+       "      <td>329.653748</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>201.918472</td>\n",
+       "      <td>457.389023</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2018-04-26 10:00:00+00:00</td>\n",
+       "      <td>160.995972</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>67.706721</td>\n",
+       "      <td>254.285223</td>\n",
+       "      <td></td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10 rows × 6 columns</p>\n",
+       "</div>[168 rows x 6 columns in total]"
+      ],
+      "text/plain": [
+       "          forecast_timestamp  forecast_value  confidence_level  \\\n",
+       "0  2018-04-24 14:00:00+00:00      126.519211              0.95   \n",
+       "1  2018-04-30 21:00:00+00:00       82.266197              0.95   \n",
+       "2  2018-04-25 14:00:00+00:00      130.057266              0.95   \n",
+       "3  2018-04-26 06:00:00+00:00       47.235214              0.95   \n",
+       "4  2018-04-28 01:00:00+00:00        0.761139              0.95   \n",
+       "5  2018-04-27 11:00:00+00:00      160.437042              0.95   \n",
+       "6  2018-04-25 07:00:00+00:00      321.418488              0.95   \n",
+       "7  2018-04-24 16:00:00+00:00      284.640564              0.95   \n",
+       "8  2018-04-25 16:00:00+00:00      329.653748              0.95   \n",
+       "9  2018-04-26 10:00:00+00:00      160.995972              0.95   \n",
+       "\n",
+       "   prediction_interval_lower_bound  prediction_interval_upper_bound  \\\n",
+       "0                        96.837778                       156.200644   \n",
+       "1                        -7.690994                       172.223388   \n",
+       "2                        78.019585                       182.094948   \n",
+       "3                       -16.565634                       111.036063   \n",
+       "4                       -61.080531                        62.602809   \n",
+       "5                        80.767928                       240.106157   \n",
+       "6                       207.344246                       435.492729   \n",
+       "7                       198.550187                       370.730941   \n",
+       "8                       201.918472                       457.389023   \n",
+       "9                        67.706721                       254.285223   \n",
+       "\n",
+       "  ai_forecast_status  \n",
+       "0                     \n",
+       "1                     \n",
+       "2                     \n",
+       "3                     \n",
+       "4                     \n",
+       "5                     \n",
+       "6                     \n",
+       "7                     \n",
+       "8                     \n",
+       "9                     \n",
+       "...\n",
+       "\n",
+       "[168 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result = df_grouped.head(2842-168).ai.forecast(\n",
+    "    timestamp_column=\"trip_hour\",\n",
+    "    data_column=\"num_trips\",\n",
+    "    horizon=168\n",
+    ")\n",
+    "result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90e80a82",
+   "metadata": {},
+   "source": [
+    "### 3. Forecasting with ARIMAPlus\n",
+    "\n",
+    "Next, we will use the ARIMAPlus model, which is a BigQuery ML model available through BigFrames. ARIMAPlus is an advanced forecasting model that can capture complex time series patterns. We will train it on the same historical data and use it to forecast the same period as the TimesFM model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f41e1cf0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    Query processed 1.8 MB in 46 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:ac354d97-dc91-4d01-9dca-7069db6a26a7&page=queryresults\">Job bigframes-dev:US.ac354d97-dc91-4d01-9dca-7069db6a26a7 details</a>]\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "✅ Completed. \n",
+       "    Query processed 92.2 kB in a moment of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:e61f41af-8761-4853-ae41-d38760c966ed&page=queryresults\">Job bigframes-dev:US.e61f41af-8761-4853-ae41-d38760c966ed details</a>]\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "✅ Completed. \n",
+       "    Query processed 1.3 kB in a moment of slot time.\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "✅ Completed. \n",
+       "    Query processed 10.8 kB in a moment of slot time.\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "✅ Completed. \n",
+       "    Query processed 0 Bytes in a moment of slot time.\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0624fdda2be74b13bc6e6c30e38842b6",
+       "version_major": 2,
+       "version_minor": 1
+      },
+      "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>forecast_timestamp</th>\n",
+       "      <th>forecast_value</th>\n",
+       "      <th>standard_error</th>\n",
+       "      <th>confidence_level</th>\n",
+       "      <th>prediction_interval_lower_bound</th>\n",
+       "      <th>prediction_interval_upper_bound</th>\n",
+       "      <th>confidence_interval_lower_bound</th>\n",
+       "      <th>confidence_interval_upper_bound</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2018-04-24 00:00:00+00:00</td>\n",
+       "      <td>52.768335</td>\n",
+       "      <td>34.87452</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>-15.462203</td>\n",
+       "      <td>120.998872</td>\n",
+       "      <td>-15.462203</td>\n",
+       "      <td>120.998872</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2018-04-24 01:00:00+00:00</td>\n",
+       "      <td>67.3281</td>\n",
+       "      <td>48.075255</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>-26.729122</td>\n",
+       "      <td>161.385322</td>\n",
+       "      <td>-26.729122</td>\n",
+       "      <td>161.385322</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2018-04-24 02:00:00+00:00</td>\n",
+       "      <td>75.205573</td>\n",
+       "      <td>53.910921</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>-30.268884</td>\n",
+       "      <td>180.68003</td>\n",
+       "      <td>-30.268884</td>\n",
+       "      <td>180.68003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2018-04-24 03:00:00+00:00</td>\n",
+       "      <td>80.070922</td>\n",
+       "      <td>55.994076</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>-29.479141</td>\n",
+       "      <td>189.620985</td>\n",
+       "      <td>-29.479141</td>\n",
+       "      <td>189.620985</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2018-04-24 04:00:00+00:00</td>\n",
+       "      <td>75.161779</td>\n",
+       "      <td>56.583974</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>-35.542394</td>\n",
+       "      <td>185.865952</td>\n",
+       "      <td>-35.542394</td>\n",
+       "      <td>185.865952</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2018-04-24 05:00:00+00:00</td>\n",
+       "      <td>81.428432</td>\n",
+       "      <td>56.85087</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>-29.797913</td>\n",
+       "      <td>192.654778</td>\n",
+       "      <td>-29.797913</td>\n",
+       "      <td>192.654778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2018-04-24 06:00:00+00:00</td>\n",
+       "      <td>116.981445</td>\n",
+       "      <td>57.180767</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>5.109671</td>\n",
+       "      <td>228.853218</td>\n",
+       "      <td>5.109671</td>\n",
+       "      <td>228.853218</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2018-04-24 07:00:00+00:00</td>\n",
+       "      <td>237.222361</td>\n",
+       "      <td>57.770307</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>124.197176</td>\n",
+       "      <td>350.247546</td>\n",
+       "      <td>124.197176</td>\n",
+       "      <td>350.247546</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2018-04-24 08:00:00+00:00</td>\n",
+       "      <td>323.722572</td>\n",
+       "      <td>58.681662</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>208.91436</td>\n",
+       "      <td>438.530784</td>\n",
+       "      <td>208.91436</td>\n",
+       "      <td>438.530784</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2018-04-24 09:00:00+00:00</td>\n",
+       "      <td>357.288952</td>\n",
+       "      <td>59.806906</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>240.279247</td>\n",
+       "      <td>474.298656</td>\n",
+       "      <td>240.279247</td>\n",
+       "      <td>474.298656</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10 rows × 8 columns</p>\n",
+       "</div>[168 rows x 8 columns in total]"
+      ],
+      "text/plain": [
+       "          forecast_timestamp  forecast_value  standard_error  \\\n",
+       "0  2018-04-24 00:00:00+00:00       52.768335        34.87452   \n",
+       "1  2018-04-24 01:00:00+00:00         67.3281       48.075255   \n",
+       "2  2018-04-24 02:00:00+00:00       75.205573       53.910921   \n",
+       "3  2018-04-24 03:00:00+00:00       80.070922       55.994076   \n",
+       "4  2018-04-24 04:00:00+00:00       75.161779       56.583974   \n",
+       "5  2018-04-24 05:00:00+00:00       81.428432        56.85087   \n",
+       "6  2018-04-24 06:00:00+00:00      116.981445       57.180767   \n",
+       "7  2018-04-24 07:00:00+00:00      237.222361       57.770307   \n",
+       "8  2018-04-24 08:00:00+00:00      323.722572       58.681662   \n",
+       "9  2018-04-24 09:00:00+00:00      357.288952       59.806906   \n",
+       "\n",
+       "   confidence_level  prediction_interval_lower_bound  \\\n",
+       "0              0.95                       -15.462203   \n",
+       "1              0.95                       -26.729122   \n",
+       "2              0.95                       -30.268884   \n",
+       "3              0.95                       -29.479141   \n",
+       "4              0.95                       -35.542394   \n",
+       "5              0.95                       -29.797913   \n",
+       "6              0.95                         5.109671   \n",
+       "7              0.95                       124.197176   \n",
+       "8              0.95                        208.91436   \n",
+       "9              0.95                       240.279247   \n",
+       "\n",
+       "   prediction_interval_upper_bound  confidence_interval_lower_bound  \\\n",
+       "0                       120.998872                       -15.462203   \n",
+       "1                       161.385322                       -26.729122   \n",
+       "2                        180.68003                       -30.268884   \n",
+       "3                       189.620985                       -29.479141   \n",
+       "4                       185.865952                       -35.542394   \n",
+       "5                       192.654778                       -29.797913   \n",
+       "6                       228.853218                         5.109671   \n",
+       "7                       350.247546                       124.197176   \n",
+       "8                       438.530784                        208.91436   \n",
+       "9                       474.298656                       240.279247   \n",
+       "\n",
+       "   confidence_interval_upper_bound  \n",
+       "0                       120.998872  \n",
+       "1                       161.385322  \n",
+       "2                        180.68003  \n",
+       "3                       189.620985  \n",
+       "4                       185.865952  \n",
+       "5                       192.654778  \n",
+       "6                       228.853218  \n",
+       "7                       350.247546  \n",
+       "8                       438.530784  \n",
+       "9                       474.298656  \n",
+       "...\n",
+       "\n",
+       "[168 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = forecasting.ARIMAPlus(\n",
+    "    auto_arima_max_order=5,  # Reduce runtime for large datasets\n",
+    "    data_frequency=\"hourly\",\n",
+    "    horizon=168\n",
+    ")\n",
+    "X = df_grouped.head(2842-168)[[\"trip_hour\"]]\n",
+    "y = df_grouped.head(2842-168)[[\"num_trips\"]]\n",
+    "model.fit(\n",
+    "    X, y\n",
+    ")\n",
+    "predictions = model.predict(horizon=168, confidence_level=0.95)\n",
+    "predictions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec5a4513",
+   "metadata": {},
+   "source": [
+    "### 4. Compare and Visualize Forecasts\n",
+    "\n",
+    "Now we will visualize the forecasts from both TimesFM and ARIMAPlus against the actual historical data. This allows for a direct comparison of the two models' performance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7f5b5b1e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "✅ Completed. \n",
+       "    Query processed 31.7 MB in 11 seconds of slot time.\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "✅ Completed. \n",
+       "    Query processed 58.8 MB in 12 seconds of slot time.\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'Trip Forecasts Comparison'}, xlabel='trip_hour'>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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iv/7rvwAA3/ve99ClSxdMnDgRV1xxBQzDwKOPPpq36Ubnft58882YO3cuRo4ciUsvvRSxWAx//OMf0draipkzZ+Z97SFC7DG0SwxViBDtCCo0mwpjpUKz//rXv7LrrruO9ejRg1VWVrJx48axNWvWBB7fDnNdunSpsty8efPYiBEjWGVlJautrWUnn3wyW7FihVDGDs3++uuvpft45pln2MiRI1l1dTWrrq5mBx10EJs0aRJbuXKlUO6tt95ixx9/POvUqROrrq5mw4YNY/fcc4/z+5dffslOPfVU1rlzZ1ZXV8d+9KMfsXXr1gnhyK2trWzq1KnskEMOcfZzyCGHsHvvvVc41o4dO9jZZ5/NOnfuzAA4Ydp//OMf2THHHMO6devGysvL2YABA9jUqVPZtm3bAu8pY+mw6RtuuIENHTqUVVVVsYqKCjZkyBB23XXXsfXr1wtln3zySXbYYYex8vJy1rVrV3bOOeewL7/8UigzceJEVl1d7TsO1Vb69u3Lxo0b53y3n/OCBQvYxRdfzLp06cJqamrYOeecI4TcM8bYwoUL2VFHHcUqKyvZ3nvvza655hr2yiuvMADsjTfeCDy2/RvfTnXv57vvvsvGjBnDampqWFVVFfv+97/P3n77baEM1Wbtd4E/xxAh2gPh2kwhQmSJ+fPn4/vf/z6eeuopnH766e19OiEKFHZyvqVLl+6WZStChAjhIvSZCREiRIgQIUIUNUIyEyJEiBAhQoQoaoRkJkSIECFChAhR1Ah9ZkKECBEiRIgQRY1QmQkRIkSIECFCFDWKMs+MZVlYt24dOnXqpMwmGiJEiBAhQoQoHDDGsH37duy9996IRNpOTylKMrNu3bowzXaIECFChAhRpFi7di323XffNttfUZIZO3vl2rVrw3TbIUKECBEiRJGgqakJvXv3Vmb1zgVFSWZs01JtbW1IZkKECBEiRIgiQ1u7iIQOwCFChAgRIkSIokZIZkKECBEiRIgQRY2QzIQIESJEiBAhihpF6TMTIkR7wjRNJJPJ9j6NECHyRllZWZuGx4YI0V4IyUyIEJpgjGHDhg3YunVre59KiBBtgkgkgv79+6OsrKy9TyVEiLwQkpkQITRhE5kePXqgqqoqTNgYoqhhJx9dv349+vTpE7bnEEWNkMyECKEB0zQdItOtW7f2Pp0QIdoE3bt3x7p165BKpRCPx9v7dEKEyBmhsTRECA3YPjJVVVXtfCYhQrQdbPOSaZrtfCYhQuSHkMyECJEFQik+RCkhbM8hSgUhmQkRIkSIECFCFDVCMhMiRIgQIUKEKGqEZCZEiBAFjWnTpuHQQw9t79MIESJEASMkMyFChNjjOPbYY3HVVVdplb366qvx2muv7d4TChEiRFEjJDMhQoQoSDDGkEqlUFNTE4bDhwixG/HxhiZM/NO/8MGX29r7VHJGSGZChMgBjDE0J1Lt8scYy+pcjz32WFxxxRW45ppr0LVrV9TX12PatGkAgNWrV8MwDLz33ntO+a1bt8IwDMyfPx8AMH/+fBiGgVdeeQWHHXYYKisrcdxxx2HTpk146aWXMGjQINTW1uLss89Gc3Nz4PlccMEFWLBgAe666y4YhgHDMLB69WrnOC+99BKGDx+O8vJyvPXWWz4z0wUXXIBTTjkFN954I7p3747a2lr8/Oc/RyKRcMo8/fTTGDp0KCorK9GtWzeMHj0aO3fuzOq+hQjRUXDOg0uw4JOv8cNZb7X3qeSMMGleiBA5YFfSxOAbXmmXY6+YPgZVZdm9uo888gimTJmCJUuWYNGiRbjgggswYsQIDBw4UHsf06ZNwx/+8AdUVVXhjDPOwBlnnIHy8nI8/vjj2LFjB0499VTcc889uPbaa5X7ueuuu/DJJ59gyJAhmD59OoB08rbVq1cDAH7xi1/gd7/7Hfbbbz906dLFIVU8XnvtNVRUVGD+/PlYvXo1LrzwQnTr1g233HIL1q9fjx//+MeYOXMmTj31VGzfvh3//Oc/syaBIUJ0FGzemZ4IWEX8ioRkJkSIDoBhw4bh17/+NQBg4MCB+MMf/oDXXnstKzJz8803Y8SIEQCAiy66CNdddx1WrVqF/fbbDwBw+umn44033ggkM3V1dSgrK0NVVRXq6+t9v0+fPh3HH3+8ch9lZWX405/+hKqqKhx88MGYPn06pk6diptuugnr169HKpXCaaedhr59+wIAhg4dqn2dIQoTiZSFix99Bw37dcN/jxrQ3qcTosAQkpkQIXJAZTyKFdPHtNuxs8WwYcOE77169cKmTZty3kfPnj1RVVXlEBl727/+9a+sz82Lww8/PLDMIYccImRjbmhowI4dO7B27Voccsgh+MEPfoChQ4dizJgxOOGEE3D66aejS5cueZ9biPbD8+99hfkrv8b8lV+HZCaEDyGZCREiBxiGkbWppz3hXXfHMAxYloVIJO02x5tg7KUbVPswDIPcZ76orq7Oq340GsXcuXPx9ttv49VXX8U999yDX/7yl1iyZAn69++f9/mFaB/sSoRLLoSgEToAhwjRgdG9e3cAwPr1651tvDPw7kJZWVle6wG9//772LVrl/N98eLFqKmpQe/evQGkidWIESNw44034t///jfKysrw3HPP5X3eIdoP4coLIVQonqlliBAh2hyVlZU46qij8Jvf/Ab9+/fHpk2bcP311+/24/br1w9LlizB6tWrUVNTg65du2ZVP5FI4KKLLsL111+P1atX49e//jUuu+wyRCIRLFmyBK+99hpOOOEE9OjRA0uWLMHXX3+NQYMG7aarCbEnEHKZECqEykyIEB0cf/rTn5BKpTB8+HBcddVVuPnmm3f7Ma+++mpEo1EMHjwY3bt3R2NjY1b1f/CDH2DgwIE45phjcOaZZ+K//uu/nHDz2tpavPnmmzjppJNwwAEH4Prrr8ftt9+OsWPH7oYrCREiRCHAYEUYr9jU1IS6ujps27YNtbW17X06IToAWlpa8MUXX6B///6oqKho79Pp0LjggguwdetWPP/88+19KkWPYmrXjy5eg189/yEAYPVvxrXz2ZQW+v3iBefz7r63u2v8DpWZECFChAhR8AjNTLsPpeCPFJKZECFCtCkaGxtRU1ND/mVrUgoRAiiNAbdQESmBmxs6AIcIEaJNsffeeysjovbee++89j979uy86ofIHVt2JtCluqxdjm20gTbzzLIvUV0exYlDerXBGZUOIgZQ7IHvIZkJESJEmyIWi2H//fdv79MI0cZ47t9fYvKT7+OSYwfg2hMP2uPHz1c82NjUgv956n0AwBczToJRAmpEWyF9L4rOfVZAaGYKESJEiBCB+PXflgMA7pu/ql2Ony/1aNrlJoMsvrCX3YtSoHUhmQkRIkSIEAWPfIUUvr4VshkBpeAzE5KZECFChAgRiOI3y7jnX8yrQ+8OFP2jRUhmQoQIESJEESBfB+BQmaERKjMhQoQIEaJDoN3Hu3zNTNznkMuIaO9H2xYIyUyIEB0M8+fPh2EY2Lp1a7uex8KFCzF06FDE43Gccsopu+UYjDFcfPHF6Nq1KwzD2COLaIbYPch3wOXNZKEyI6LdiWobICsy069fPxiG4fubNGkSgHRq7EmTJqFbt26oqanBhAkTsHHjRmEfjY2NGDduHKqqqtCjRw9MnToVqVSq7a4oRIgQAo499lhcddVVzvfvfe97WL9+Perq6trvpABMmTIFhx56KL744ovdljvm5ZdfxuzZszFnzhysX78eQ4YM2S3H2RPo168f7rzzznY7fluMd9uak8h1BR2Vz86O1hSSpqWuz30OyYyISKT42UxWZGbp0qVYv3698zd37lwAwI9+9CMAwOTJk/GPf/wDTz31FBYsWIB169bhtNNOc+qbpolx48YhkUjg7bffxiOPPILZs2fjhhtuaMNLChEihAplZWWor69vd4fOVatW4bjjjsO+++6Lzp0777Zj9OrVC9/73vdQX1+PWCz71FqMsXDC1QZ4t3ELDpn+Ki77679zqi+aiVwysmVnAkN+/QqO//0CdX3BZyanUyhZdDifme7du6O+vt75mzNnDgYMGIBRo0Zh27ZteOihh/D73/8exx13HIYPH46HH34Yb7/9NhYvXgwAePXVV7FixQo89thjOPTQQzF27FjcdNNNmDVrFhKJxG65wBAhdgsYAxI72+cvi1nlBRdcgAULFuCuu+5ylNTZs2cLZqbZs2ejc+fOmDNnDg488EBUVVXh9NNPR3NzMx555BH069cPXbp0wRVXXAHTdPOEtra24uqrr8Y+++yD6upqHHnkkZg/f77z+5o1a3DyySejS5cuqK6uxsEHH4wXX3wRq1evhmEY2Lx5M37yk58452Sbv1555RUcdthhqKysxHHHHYdNmzbhpZdewqBBg1BbW4uzzz4bzc3NWtd++eWXo7GxEYZhoF+/fs55X3HFFejRowcqKiowcuRILF261Klnn8dLL72E4cOHo7y8HG+99RYsy8KMGTPQv39/VFZW4pBDDsHTTz8tHHP58uUYP348amtr0alTJxx99NFYtSqdl2Xp0qU4/vjjsddee6Gurg6jRo3Cu+++yzUphmnTpqFPnz4oLy/H3nvvjSuuuAJAWl1bs2YNJk+e7DzHPY18j/nAgs8BAC/8Z32Ox3c/82RkyRebAQCrN6vbBO9AXITrK+9WlIAwk3sG4EQigcceewxTpkyBYRhYtmwZkskkRo8e7ZQ56KCD0KdPHyxatAhHHXUUFi1ahKFDh6Jnz55OmTFjxuCSSy7B8uXLcdhhh0mP1draitbWVud7U1NTrqcdIkTbINkM3JpfWv6c8b/rgLJqraJ33XUXPvnkEwwZMgTTp08HkB5wvWhubsbdd9+NJ554Atu3b8dpp52GU089FZ07d8aLL76Izz//HBMmTMCIESNw5plnAgAuu+wyrFixAk888QT23ntvPPfcczjxxBPxwQcfYODAgZg0aRISiQTefPNNVFdXY8WKFaipqUHv3r2xfv16HHjggZg+fTrOPPNM1NXVYcmSJQCAadOm4Q9/+AOqqqpwxhln4IwzzkB5eTkef/xx7NixA6eeeiruueceXHvttYHXPmDAADzwwANYunQpotEoAOCaa67BM888g0ceeQR9+/bFzJkzMWbMGHz22Wfo2rWrU/8Xv/gFfve732G//fZDly5dMGPGDDz22GO4//77MXDgQLz55ps499xz0b17d4waNQpfffUVjjnmGBx77LF4/fXXUVtbi4ULFzqqzvbt2zFx4kTcc889YIzh9ttvx0knnYRPP/0UnTp1wjPPPIM77rgDTzzxBA4++GBs2LAB77+fzlj77LPP4pBDDsHFF1+Mn/3sZ1rPvtDQ1nliog450dtxqMyoUPxsJmcy8/zzz2Pr1q244IILAAAbNmxAWVmZTy7u2bMnNmzY4JThiYz9u/0bhRkzZuDGG2/M9VRDhOiwqKurQ1lZGaqqqlBfXw8A+Pjjj33lkskk7rvvPgwYMAAAcPrpp+PRRx/Fxo0bUVNTg8GDB+P73/8+3njjDZx55plobGzEww8/jMbGRmetpauvvhovv/wyHn74Ydx6661obGzEhAkTMHToUADAfvvt5xzPNnPV1dU552Xj5ptvxogRIwAAF110Ea677jqsWrXKqX/66afjjTfeCCQzdXV16NSpE6LRqHOMnTt34r777sPs2bMxduxYAMCDDz6IuXPn4qGHHsLUqVOd+tOnT8fxxx8PID2huvXWWzFv3jw0NDQ41/PWW2/hj3/8I0aNGoVZs2ahrq4OTzzxBOLxOADggAMOcPZ33HHHCef3wAMPoHPnzliwYAHGjx+PxsZG1NfXY/To0YjH4+jTpw+OOOIIAEDXrl0RjUbRqVMn3/3aU8jfATff48sdeHPZb+gzI6JDKzMPPfQQxo4dm/eicTq47rrrMGXKFOd7U1MTevfuvduPGyIEiXhVWiFpr2O3MaqqqhwiA6QnGf369UNNTY2wbdOmTQCADz74AKZpCoM1kB70u3XrBgC44oorcMkll+DVV1/F6NGjMWHCBAwbNizwXPgyPXv2RFVVlUCEevbsiX/96185XeeqVauQTCYdsgQA8XgcRxxxBD766COh7OGHH+58/uyzz9Dc3OyQGxuJRMJRlN977z0cffTRDpHxYuPGjbj++usxf/58bNq0CaZporm52VlF/Ec/+hHuvPNO7LfffjjxxBNx0kkn4eSTT87Jz2d3oC3JSL7H57mIrr8HXyckMyJKwWcmp7dkzZo1mDdvHp599llnW319PRKJBLZu3SqoMxs3bnRmEvX19b5OyI52Us02ysvLUV5ensuphgixe2AY2qaeYoB3ADYMQ7rNstIRIzt27EA0GsWyZcsc840NmwD99Kc/xZgxY/DCCy/g1VdfxYwZM3D77bfj8ssv1z6XoPPYnaiudp/vjh07AAAvvPAC9tlnH6Gc3TdVVlYq9zdx4kRs3rwZd911F/r27Yvy8nI0NDQ4/oK9e/fGypUrMW/ePMydOxeXXnopbrvtNixYsIAkSEUFxXi5+pudqK+rQEU8ShfiICgzmodn3EKKIZcRUQrKTE55Zh5++GH06NED48aNc7YNHz4c8Xgcr732mrNt5cqVaGxsdGTZhoYGfPDBB87sDgDmzp2L2tpaDB48ONdrCBEihAJlZWWC425b4LDDDoNpmti0aRP2339/4Y+fmPTu3Rs///nP8eyzz+J//ud/8OCDD7bpeWSLAQMGoKysDAsXLnS2JZNJLF26VNkHDR48GOXl5WhsbPRdr60SDxs2DP/85z+RTCal+1i4cCGuuOIKnHTSSTj44INRXl6Ob775RihTWVmJk08+GXfffTfmz5+PRYsW4YMPPgCwe55jdshTWSG2v7P6Wxz7u/k46e5/qusLeWLc7RHNUSxUZmi0d2RjWyBrZcayLDz88MOYOHGiIH/W1dXhoosuwpQpU9C1a1fU1tbi8ssvR0NDA4466igAwAknnIDBgwfjvPPOw8yZM7FhwwZcf/31mDRpUqi8hAixm9CvXz8sWbIEq1evRk1NTZuoGgcccADOOeccnH/++bj99ttx2GGH4euvv8Zrr72GYcOGYdy4cbjqqqswduxYHHDAAdiyZQveeOMNDBo0qA2uKHdUV1fjkksuwdSpU9G1a1f06dMHM2fORHNzMy666CKyXqdOnXD11Vdj8uTJsCwLI0eOxLZt27Bw4ULU1tZi4sSJuOyyy3DPPffgrLPOwnXXXYe6ujosXrwYRxxxBA488EAMHDgQjz76KA4//HA0NTVh6tSpgpoze/ZsmKaJI488ElVVVXjsscdQWVmJvn37Akg/xzfffBNnnXUWysvLsddee+32+9WWoEwZ/3g/ba79/OudyvpUnhhd8xVPXzqqA/DcFRvxy+c+wJ1nHYrvDXDbTwlwmeyVmXnz5qGxsRE/+clPfL/dcccdGD9+PCZMmIBjjjkG9fX1gikqGo1izpw5iEajaGhowLnnnovzzz/fibIIEWJ3gDGGGS99hEcXr2nvU2kXXH311YhGoxg8eDC6d+/u+Gjki4cffhjnn38+/ud//gcHHnggTjnlFCxduhR9+vQBkM4rNWnSJAwaNAgnnngiDjjgANx7771tcux88Jvf/AYTJkzAeeedh+985zv47LPP8Morr6BLly7KejfddBN+9atfYcaMGc41vfDCC+jfvz8AoFu3bnj99dexY8cOjBo1CsOHD8eDDz7omIgeeughbNmyBd/5zndw3nnnOeHhNjp37owHH3wQI0aMwLBhwzBv3jz84x//cHyQpk+fjtWrV2PAgAHo3r37bro7NNoyGomHbsI2wWeG5+N8lJKCpfDh2KpypYyf/fkdbNreirMfXCJsLwUyY7AiDLhvampCXV0dtm3bhtra2t1+PPsWlYIU1xHxwZfbcPIf3gIArP7NuIDScvxmzn9wdD3Ddw4+INA3IkSIYkFLSwu++OIL9O/fHxUVFcqy371lHr7enk6Rkct7dNUT/8bz763z1b95zgr8v7e+CNzvnP+sw2WPpxPu/ftXx6NLdRkAYP7KTbjg4XSeoM9uGYtYVD5HX/X1Dvzg9nRivTenfh99urW9I32ho98vXnA+8/d61G1vYE0mT0+ufaQudtf4Ha7NFADGGM744yL8cNbCDsvmix3bW+Q+DLpgjOEf769Dc8JEMrX7HU9DhChEtOXaSDyiusoMEZrNm69Mxdw89JmhUQrT9MKI+Stg7EyYWLp6CwBgQ1ML9u4czsqLDnm+qTyHDbvAwkFjY6PSaXfFihWOyStE+4N6DXNZF8giQrPV7mCcmSkkMwI6bGh2R0XY/NsHTS1JnPXHxThpaD0uO27gHj9+EVpiOwT23ntv5SrYeyIHVkdC3uMdUT+qmydGCK2WJ83TV2a0DtlhUAJcJiQzQaAWNwux5/Dnt1djxfomrFjflBOZyTdZV/jUCxOxWAz7779/e59GCE1Q76GuMsMIhVQgMyoHYO5zqMyIKAVlJvSZyQJh+28fJMz8bny+7ynViYYI0ZHQlhl8eegrMy6o0Gx1NJO8flvgi292YtGqzW26zz2JEuAyIZkJQjh4FT/yVddY2ApChMgb1HhJBB/5IIRWE6+kiqTw73FbJ5D+/u/m48cPLsbHG4pzEeRQmekACE1L7Y+2TLWdy+MU6oTNIUQHRb7jHTVg5uQATLCZ9o5m+nj99t2y392NUkg7EpKZAIRjV/sjf3lbHtKpi5DPFj8YY1izeSfWbd3V3qfSYZG3mYlRn/UUF6p+W6JYOUGHXZupI2FPvAAh1GjLDiKXKAbRzBQ2gmJES9LEtl1JfLOjtb1PpWiRf54Z+XbdPDOMCK3m30ilMqMIzX5m2Zd46p21WudRiihWEsYjJDMBCM1M7Y98Zw38ixoqM3qYPXs2Onfu3N6nAQCYNm0aDj300Lz2ofsIGWO4+OKL0bVrVxiGoQz9DpGGaTG8veob7GhNBZQkzEx80rscHHiFzznUb06k8D9PvY+pT/8H25rzS7BZrAh9ZjoAOuJAVmjI154rOgBnX78jhnGeeeaZ+OSTT9r7NPY4Xn75ZcyePRtz5szB+vXrMWTIkPY+pZzRr18/3HnnnW22P+o9fOitz3H2g0tw7v9bIv3drS/fziszKYWdiMoTw29XkSEefLEU92VHIoiQlSaKn8qEeWYCIRoYOt6gVgrIW5khPpcqkskkKisrS3YNKsYYOTCvWrUKvXr1wve+97289m+aJmKxjtG9/t87XwIA3lu7VVlOJwOwbp4YRpiZlNFMhJ+NmEG4I7zhfhiee5CLU3Z7I1RmAiC8NB2znbc78pdA294BmDGG5mRzu/xla/p8+eWXMXLkSHTu3BndunXD+PHjsWrVKgDA6tWrYRgGnnzySYwaNQoVFRX4y1/+4jMz2aaeP/3pT+jTpw9qampw6aWXwjRNzJw5E/X19ejRowduueUW4di///3vMXToUFRXV6N379649NJLsWPHDud3+zjPP/88Bg4ciIqKCowZMwZr19L+C8ceeyyuuuoqYdspp5yCCy64wPl+7733Ovvr2bMnzjnrzMD7dMEFF+Dyyy9HY2MjDMNAv379AACtra3OCtcVFRUYOXIkli5d6tSbP38+DMPASy+9hOHDh6O8vBxvvfUWLMvCjBkz0L9/f1RWVuKQQw7B008/LRxz+fLlGD9+PGpra9GpUyccffTRzrNZunQpjj/+eOy1116oq6vDqFGj8O677zp1GWOYNm0a+vTpg/Lycuy999644oornHu0Zs0aTJ48GYZh7NZoFd1xT8cBOKnIKUWFZlMmJ199yOvz+011WDLjfi5WJbpjTB3ygGpWfsVf/42vtu7C//13g7YTW4jskW8/LL6oOexAUmdXaheOfPzInM8pHyw5ewmq4vor/u7cuRNTpkzBsGHDsGPHDtxwww049dRTBX+QX/ziF7j99ttx2GGHoaKiAq+88opvP6tWrcJLL72El19+GatWrcLpp5+Ozz//HAcccAAWLFiAt99+Gz/5yU8wevRoHHlk+t5EIhHcfffd6N+/Pz7//HNceumluOaaa3Dvvfc6+21ubsYtt9yCP//5zygrK8Oll16Ks846CwsXLszp/rzzzju44oor8Oijj+J73/sevv32W7z2xvzAenfddRcGDBiABx54AEuXLkU0GgUAXHPNNXjmmWfwyCOPoG/fvpg5cybGjBmDzz77DF27dhXu4e9+9zvst99+6NKlC2bMmIHHHnsM999/PwYOHIg333wT5557Lrp3745Ro0bhq6++wjHHHINjjz0Wr7/+Ompra7Fw4UKkUmlTx/bt2zFx4kTcc889YIzh9ttvx0knnYRPP/0UnTp1wjPPPIM77rgDTzzxBA4++GBs2LAB77//PgDg2WefxSGHHIKLL74YP/vZz3K6j15Q76HuZIOKSuTzzOSUwVcwM9HHp31u9I5fyvAu1lmMxKAYz3mPQsX6//5+ejn7/3y5FYf16bJHz6sjoS1pYi4O3cU6U7ExYcIE4fuf/vQndO/eHStWrEBNTQ0A4KqrrsJpp52m3I9lWfjTn/6ETp06YfDgwfj+97+PlStX4sUXX0QkEsGBBx6I3/72t3jjjTccMsMrKP369cPNN9+Mn//85wKZSSaT+MMf/uDUeeSRRzBo0CD861//whFHHOGUa0maiEWDW0NjYyOqq6sxfvx4dOrUCX379sWBBw/FZ5vSihCDvE3V1dWhU6dOiEajqK+vB5Amgvfddx9mz56NsWPHAgAefPBBzJ07Fw899BCmTp3q1J8+fTqOP/54AGk159Zbb8W8efPQ0NAAANhvv/3w1ltv4Y9//CNGjRqFWbNmoa6uDk888QTi8TgA4IADDnD2d9xxxwnn98ADD6Bz585YsGABxo8fj8bGRtTX12P06NGIx+Po06ePc7+6du2KaDSKTp06Odeyu6Cr+ugUSynZiPuR6pfzJ0P5ZhsvzkktPxdv64SCewohmQkCIUfyCBrs7nntUyxr3IIHzz8ccd10lyEc5Gtm4mvnFprtR2WsEkvOVjs87i5UxrLzZfn0009xww03YMmSJfjmm29gZXorftXpww8/PHA//fr1Q6dOnZzvPXv2RDQaRSQSEbZt2rTJ+T5v3jzMmDEDH3/8MZqampBKpdDS0oLm5mZUVaXVpVgshu9+97tOnYMOOgidO3fGRx995AzOFmP4ZON2rbZw/PHHo2/fvthvv/1w4okn4sQTT8SYcScH1pNh1apVSCaTGDFihPP+x+NxHHHEEfjoo4+Esvw9/Oyzz9Dc3OyQGxuJRAKHHXYYAOC9997D0Ucf7RAZLzZu3Ijrr78e8+fPx6ZNm2CaJpqbm9HY2AgA+NGPfoQ777zTuc6TTjoJJ5988m7z1aFuve7bST07yhnXC3GhSUg/q31m5PUtwcyU30henFRGJGGq8PZCRkhmAiA6ncnLBA2Qt89NR4W8/OEGnHxIuJJvtlCNX1t2JlBdHkNZjCaJ+SfN89cxDCMrU0974uSTT0bfvn3x4IMPYu+994ZlWRgyZAgSiYRTprq6OnA/3kHXMAzpNpssrV69GuPHj8cll1yCW265BV27dsVbb72Fiy66CIlEwiEzOrDfMYsxRCIR3zNJJt2Q2k6dOuHdd9/F/Pnz8eqrr+KGG27Ar389DbP/Ng+1dXW0NBOANZubkTAtDOxRI/2dv4e2X9ALL7yAffbZRyhXXl4OAIEO1hMnTsTmzZtx1113oW/fvigvL0dDQ4Pz3Hr37o2VK1di3rx5mDt3Li699FLcdtttWLBgAUmQdgciec7PdKOR0uXSD68tlRkhT00HNTPxr0Ox3oNQJgiAzrLxug+/JWm2wRmFsPHV1l047Ka5OPGuN5XlmMJUqINijmbavHkzVq5cieuvvx4/+MEPMGjQIGzZsmWPHHvZsmWwLAu33347jjrqKBxwwAFYt26dr1wqlcI777zjfF+5ciW2bt2KQYMGSffbvXt3rF+/3vlumiY+/PBDoUwsFsPo0aMxc+ZM/Oc//8GaNavxr7fT7SSbZzhgwACUlZVh4cKFaGpJoiVpoqm5BUuXLnVULRkGDx6M8vJyNDY2Yv/99xf+evfuDQAYNmwY/vnPfwpEjMfChQtxxRVX4KSTTsLBBx+M8vJyfPPNN0KZyspKnHzyybj77rsxf/58LFq0CB988AEAoKysDKbZdn0Ouep1nmYmXnFJKsxM0eQOvFV+JW6N/T/RgZcroxvNRDkQF+tAni/4Z1isudVCZSYA1AwglwGyOJtI+4OyQ7/+0UYAwOdf71TW11HXlPWL+MF16dIF3bp1wwMPPIBevXqhsbERv/jFL/bIsffff38kk0ncc889OPnkk7Fw4ULcf//9vnLxeByXX3457r77bsRiMVx22WU46qijBH8ZHscddxymTJmCF154AQMGDMBvZt6GrVu3Or/PmTMHn3/+OY455hh06dIFL774IizLQr/99s/6Gqqrq3HJJZdg6tSpuP43ZajfZ1/MfPheNDc346KLLiLrderUCVdffTUmT54My7IwcuRIbNu2DQsXLkRtbS0mTpyIyy67DPfccw/OOussXHfddairq8PixYtxxBFH4MADD8TAgQPx6KOP4vDDD0dTUxOmTp0qqDmzZ8+GaZo48sgjUVVVhcceewyVlZXo27cvgLRZ8M0338RZZ52F8vJy7LXXXllfvw7y9RPRdcDt/9Xfsa/xDc6OvY53NUxGXlB9Nl+lo0YzRTSdsAsZoTITAFKaJGy22jsLoQ2yq8xhTZe2MjMVCyKRCJ544gksW7YMQ4YMweTJk3HbbbftkWMfcsgh+P3vf4/f/va3GDJkCP7yl79gxowZvnJVVVW49tprcfbZZ2PEiBGoqanBk08+Se73Jz/5CSZOnIjzzz8fx4wahbqe+2L4USOd3zt37oxnn30Wxx13HAYNGoT7778fjzz6GPY/0FZ6snuev/nNbzBhwgT88qqf46yTjsXnq1bhlVdeQZcuaqf/m266Cb/61a8wY8YMDBo0CCeeeCJeeOEF9O/fHwDQrVs3vP7669ixYwdGjRqF4cOH48EHH3RMRA899BC2bNmC73znOzjvvPOc8HD+Oh988EGMGDECw4YNw7x58/CPf/wD3bp1A5B2SF69ejUGDBiA7t27Z3XNMtDRTJr1qTdZMzTaglw9YIKyQh9fnNTI66uUIXK/XP0i9f8VEPrMlCgopzFdaZNHsUfFtBfy7URB5JfIvnZx8tHRo0djxYoVwjaqM7dxwQUXCHlbpk2bhmnTpgllZs+e7as3f/584fvkyZMxefJkYdt5553nq3faaaeR0VTTpk3DpP+5Duu3pReJjMfjuPfee3Hvvfdiy84E1m5pBgAM27czAGDkyJG+89iVSOFTLpqJwlVXXeXLYVNRUYG7774bP71mOgBgQPcaVJe7Xeexxx5L+lVdeeWVuPLKK8njDRs2TBoGDwCHHXaYkM8GAE4//XTn8ymnnIJTTjmF3PdRRx3lhGrvTuRrZhIcgBV5Zixu7k2ZmbSXQ+A4i+7xdfZbrKDuTTEhVGYCoLVSawk05kIG1VnqdqJUgixdsGJnM6UMbULLFczJ1Bg+eDKDr7YyI4eYtE5vJNVZp0l1HNEBOL9oplKYpOarXhcCQmUmANRDzkWZKc4m0v7gOQufil63E83JJMjXL3ptpvjRFvL9+q/W4tTjGsh2s2LFCvTp0yf/A3UwkOYjbzniPebfKHVoNq/MyBVz/Wgm+edEKvv3m6+vey8KDXwfV6w+MyGZCQC1bDzlDKzcV45t5Kutu/D+2q048eD6olwzI18IORAs5iRO03U8zDeaqS3e7ZakiUTKQm3lnguZLQZ4zVnZIpu3oXvPXvi/l9/E/j1qEJPke9p77zBtggrU+6ZLNL2rY9vvsb6ZSZ5iQV+Z4T/L++9QmSne6wnJTACocL5cbIy5LlQ54jevAwBuO30YfnR475z2Ucygkt7pDmTUjEwXbWFi+GTjdgB+f4sQew6xWAx9+u+HAb1qw+SVbQjtpJbEsiKiA69KWeEdgOVlclubyf2ci89MKUBX3SpkhG90AJjim4095f29aNXmPXKcQgO1CJpuJ5p/NJPd+bG8ic2uMNdQTmhvPbJUDY3ZtGd61Wvd+sHJK5OKmSEz5GYmMU+M4gQolwFuey7RTPy+ijWaiSJ6xYSQzASAWpCMkixVyJfzFGkbyxs8aRHIjGbrFdOgi3fRtBgsjbd3a4uFpMnQsqtF76AhCgy77+1pbk1h265EcMEChJ1N2F5UMxfkEs1ETTDMXKKZcvKZkZOhXPLMFOvgzyM0M3UAiOYkyk6rua+8z6U4G1m+oM1MuSgz7ueUaWH07xegU0Ucf79sBOkTYDGGXSmG1z7fgb07f43KsiiqqqqyShbGUulBI9kaQUusYz7HfJBoTTj3sKXFJZRJYrsXrYmUU27XrhaYiuUvZLAs5tRvbWlBjLld56cbmgAAfbtVoyKeOynY07AsC19//TWqqqr01nOi1mbSXjWbOzaZtE6hzBDKjn7SPOL4XBnlQpfkfjllJuvahQH+HhSrmSkkM4EglBmuhDaTzZOMFGcTyx+UmYmKjvCC6sRWb27G6s3Nme0AtSCzXeXZj3bilEP3ERZS1MWmLekcKYmqOLaGPjNZY2drClua02n/y3a5WXB3JUxs3pnwbfcikbKwaXsrACCyswKxLB3pGWPYtDVNllhTGco50mI/W3NbGSrLiofMAOmkin369Mkri69uTeo91l9okvOZ4UiP/tpM3DGFPDPu9kSAz8ysNz7Dms078dsJw5x7VqRjvwBdv6VCRtirBkDHA35PPfsOKsyIC0VyN5uKjvCC6sT48cy0GKLEAMe4/82xTjjggO7kejoUfvrsfADApcfujwkH7ZtV3RDAC/9Zh9+/kV6wdd6UUU6b+OcnX2PaG8t9271YuaEJ0/7+LgDgsYuORK/O2a083pIw8bPn/gkA+N3ph+Cgvm72X/vZTjv5YBzdP/9Mu3sSZWVlwqrnKuSfZ4ZXVtztumHBApnh1pzSnViSyoxHrVXhtldWAgDO/G4fDM+0gVJQzAWfsCK9nJDMBIBSY8SHr+kzk+e5FGkbyxukmYknI4yRjZnqxChfHH99kcRGo9GsfQy+2p7ufFtYFBUVFVnVDQGkjJhzD8vKKxziyaJxZ3u8rFwacg0AiLY45WJl5Vk/AyuScupb0bhQ395uRmIl/WwpopibzwzlgKsiM5zPjMWRGcYAyWra/vr88eXnouszs7M15Z4L0ScVEwS/oyJlM1k7AH/11Vc499xz0a1bN1RWVmLo0KHCireMMdxwww3o1asXKisrMXr0aHz66afCPr799lucc845qK2tRefOnXHRRRdhx44d+V/NbgCVZ4YRMqUKOo6mynMp0kaWL6iZl1eZ0anP30K+vlredpGDSV3cVwd9hvnCm2tIul1zIMs1ok32mSrTkZCbz4z7WTRx0C+Yxa/szJEZI9WK18quxu/j96rXZiIzALvQjWbi22ApvNOl4DOTFZnZsmULRowYgXg8jpdeegkrVqzA7bffLiy4NnPmTNx99924//77sWTJElRXV2PMmDGCc94555yD5cuXY+7cuZgzZw7efPNNXHzxxW13VW0IMs8MYbpQ7ivfc8mzftGCcML2monI6kQnZuRZP8SeA+U8yrcB1XuYr1mYSn8vHCP73RYV8jUz8RDJgLtdrczwD9tVRuo3vYUBkfU4LfqWcsIoElr+M6fMaOaZ4Sc/VGRVUaEE+riszEy//e1v0bt3bzz88MPONnsFWCDdYdx55524/vrr8cMf/hAA8Oc//xk9e/bE888/j7POOgsfffQRXn75ZSxduhSHH344AOCee+7BSSedhN/97ncFl4VTx866x2S54mxjeYPKgSD60ijqE89QcEjUXaAuXyfuDvoM8wWlwlFOpV7kYham6xNlOuiz1c73xH8mlBHVpIIPzeaVGaapzmnlmdGcmfIKEnUtxYRS8JnJSpn5+9//jsMPPxw/+tGP0KNHDxx22GF48MEHnd+/+OILbNiwAaNHj3a21dXV4cgjj8SiRYsAAIsWLULnzp0dIgOkV/WNRCJYsmSJ9Litra1oamoS/vYUhA6S8p/ZY3lmirSV5Qkdnxe1iSGYDOmamYr1RS928D6q/LPmnUqVbYBQWHVBdANimRJ/P8nV63XzPRHPQDfPC+8zA47M8CRHN5qJUluTmmszmcSq24WualAqWinkmcmKzHz++ee47777MHDgQLzyyiu45JJLcMUVV+CRRx4BAGzYsAEA0LNnT6Fez549nd82bNiAHj16CL/HYjF07drVKePFjBkzUFdX5/z17t0+Kf3p9UD06udtZirONpY3dGZuajMR/zm4Q1PVz1uZKfEBb3dBiIShlBlNU2FOPg5EGxKPkf1uSwHa+Z4o/0PuvqmiiQQywpmZLGGZg+wJrW6eGx58OZEkaVVvN5DpK4roGihkRWYsy8J3vvMd3HrrrTjssMNw8cUX42c/+xnuv//+3XV+AIDrrrsO27Ztc/7Wrl27W4/HQ+cF0HWYytdRrFgbWb6gnC9zWmCO2K6Ut9swDL9IfevaHaI5id+evRN4TsoM39lrHKMUQZEW7aWZzBR+HH0N/Yz15EK9ajMR//JzZMLQVGZ0zEyaPjMm4TNT6KoG9ah4DlesDs1ZkZlevXph8ODBwrZBgwahsbERAFBfXw8A2Lhxo1Bm48aNzm/19fW+pGOpVArffvutU8aL8vJy1NbWCn97CuRsgtjuq9+GDaOjzup1ZnTaA1meyk6hd1YdAdSzUpEUkdCKBRMpKzCKRdg3cZxiHQTyha7PzGEbn8KM+EOYX/4/WhMMP/h331VmTMYTWpWy44IK5tDNACyQmSKaoVCPSqN5FzyyIjMjRozAypUrhW2ffPIJ+vbtCyDtDFxfX4/XXnvN+b2pqQlLlixBQ0MDAKChoQFbt27FsmXLnDKvv/46LMvCkUcemfOF7C5QIYTQHUiLtWUUEChna+3VdgllJZfMoaG61j7QUeR0JxX8eJcyLXzvN6/h6N++oW+mKtruPj+QPjOaysw+Te87n6lJiRKkMsPlfDJToKCT9FTps6PRXxT6+02pa6UQsZlVNNPkyZPxve99D7feeivOOOMM/Otf/8IDDzyABx54AEBa8r3qqqtw8803Y+DAgejfvz9+9atfYe+998Ypp5wCIK3knHjiiY55KplM4rLLLsNZZ51VcJFMgPcFcLfzbTmXHCf8/nXzNBRpG8sb4j2Uy/2qF5AipLoRafk6jwr76qADYb4giWcu6hy3r693tOKbHenlEHYkUqitiAfWD6OZRAjJJy2GCJVJW0hSCe6zpsrBv7uWXJlhFp2Zm2oDfJ+gUuhE35rsCXVBQGOoKfRLoJCVMvPd734Xzz33HP76179iyJAhuOmmm3DnnXfinHPOccpcc801uPzyy3HxxRfju9/9Lnbs2IGXX35ZyIz5l7/8BQcddBB+8IMf4KSTTsLIkSMdQlRo0JEmdVUB70B205wVOHrmG9i2Sy81fhGpmW0LYhZFdS6SHcjrcyV0peIwNLt9QJkU9d9D+eeokIhN7/gkOe6gRFU3cSE/korPEOiGbYjAUt9B7gEZRDSTpVRm3M8W8VlFZnQCQAr9/aa4TCmY0rNezmD8+PEYP348+bthGJg+fTqmT59OlunatSsef/zxbA/dLtBxGtPPPCr+9tBbXwAAHl/SiEuOHaBzNhplSg/8VZvUjCgHnxfdkNC2dAAOkRsoZYT67K8vJx36GYTl7Ub3+KUMb/JKauFwRkQddd/xCZZVXIIl1kFYxB4ljyO0AT7PDEdmDIsmMyCeIb9dlTSP/4UvR2UTLkTQPjPFcw0Usl7OoKNBJwW2qXoBNFpGsTLhPQWSUHJlckllr+1ATMzEQ+SPzTta8cNZC/GXJWuU5QQzE0Eu9ROmuZ/1s0hz9S15Gyz1pkGZw/UTF8rNTMM2/Q0AcGTkY/U9ZHyiOj5pHleEW4DSV50gnoIyo9kGqAzGhd6X0z4z/OfCvgYKIZkJANVZ8R2aqhPVcXTTNXEUaRvLG5Sfi74DsPwzPVPz1Oc+e5/VC/9Zj//3z8/Juv5z6aAPkcBdr32K99duxS+f+1BdkBxI9NoA5aNBEV3f4UkTRfHPaPNFLmukCeTUcA0EyvdDeAgcmeHbg0KZod5j3YGcItRUYEghQiuaqcCvgUK4anYAxHYqf8raeWaI+rqmi0Jn/bsLlLM1EdzgA2UiyCWKwVts0uPvAgBG7L8XBvUKThnQQR8hiZ2t9EyaB50iAdLtqvoUOVZnn9UxM5X2w6X8LbSXFRGUGYLMqE5AWN3Xu2q2vT0Xn5ngCacXxZpnhgqjz3ftskJAqMwEgArpzM1EIS9T6C9Ae4M0E/HOnznMqimlzQudzurbnQmyvnAuWqU6DnQTrtGENj91TqivaS6m2mBHfbaCqU7TzCTcT0Sl22V7cD/yJJgnOapopmBCqqvOFa3PDLFdVGYK/SrkCMlMAGhJWXMg1WjeumSmOJtY/iDzxHB9mH4GX+IZakZD5fuee+u//vFG3DRnhXayrlKD7oLL+foriIRYvl21yCAlwxN+pCUJag0m7dXnuc+UMqPsC/n7zikzYuPQU2ZyWdaENjPJj1FUICZ8xYTQzBQAas0KYaaomNHpNAxdMlOsjawtkYuJgQeltO2pDMBecvuT2e8AAPbrXo1zjuyb176LEforLhODB1dGvXK6fMDSTZwo+MhR/hYlzmYMTzSSzCFYTSi5EGq+/+RXw1aeAUdgBDLDS+aaPjNEG9LkUkKm4WLKM6PjM1OsrDxUZoJAdFa664noLHBX4O2/3UGZg7Qz+FJmJm11TV6fh2pI1pFt123dFVimFJGTmYkiI5rPkJpJq8JyxXMhzEwd6D2m7mEuyozFZfDVVWYMRvjMKKOZggmt2v/Y/TFFEtrCBrnQZOgzU/rQYfPKHCf8Z6KYOtEUX79IW1meIEmHdica3HHpzspzmXnpyNAd9NFqkxn+BlHKiNJUSJgYciHENDkubVAh2KJioXoP5Sufp3gDgfImcsfkHX01lRkelO+cSl0TTdxUn1LYrYB634op8R+FkMwEQIfBqyNhgo9RrI1nT4F03tT0edFxANZfkiJ7aYYRn6kyHQt6bEYnHFo1kIiElq/jfk4pfWaCB6+O9B5T9z2XPDOmtjJjEZ/5nelGM1EqDX14CG2FaoOK+gUA2gE4vwlbISAkMwGgB0LNF1jD012XzRdpG8sbpDzN92GaUQhU+vlcyJAuLIqNEcfoSNBVZqioJe08M3zAS56TEioVfun7zLjIbfV6KjSbSxlsKUL1hYfAm5n4ZHoqnxmu7yDJiB4hpjKRF74yo5E0bw+dS1sjJDMBoBo6v12ZApuYDfDQDWQp9c6SAtVZUr40Xuikn9fNUUJ1XFRmTd8xyTLqZ7u1OYG3V31T8J1lttBdcZkitELb0PWZET6731TvMenkWQLyfC7I7T2SkxnezGQoQqvFm+2SGYPbV0RBhpjFcHv8Xvw69ohvYlqBVlSgVTlZoYhvMfmb8K+bTjLSYkJIZgJAP3D3cy6Ohzz0I3G0ipUc6IgyTWWF+5zL2kxU2KLu89AhoUElTrzznzj7wSX423vr9A5aJFCRQB5USL4426br086fbplc2lCHyjNjyPPEiIoFXZ3qC/k8MxGmIDOkMsNNKhTKTPWORkyIvoULY6/A4hoLM018WH4RPq64EBFFfWotuGJSNQxi5XIexTrOhGQmANTgSUmW/vpt19kVayPLF/QzkG/374AvJ92snQpf17woHF6DAAXNhjY0tQAAXvpwvdYxiwU5mZmICYZunhkqwlDpM0OogBYjCpU4qAlGLmYmy3CHoYhJkxlDyADMPytOpVGaqVyiwpOeSGI7YkZ6f7XmFro69zlpyttAoasaNWjGuMhiVKKFVJmL1WcmzDMTAMpxkH+X1Csu8zuTl8t3OYSOBJOQRtQzQvmLmreZKehksziObv9R6DJ2ttANZuJBkQntLNCC/wxXX9P7kxq8SuzR+CD6zLiftScVhAMw/zwimmYmnozwhzSYwszEf+aVGe68DM02lCLyzBQ6D5hh3o6GsvfwrDkSFjvF2S4+wz1+Wm2CUJkJgI7zqP5AKC8TmpnUIDMA5zCQkbNyTb8nk3AkVSkMbWFmkp1LKYBySPSCMimKSRD1yAgjtidzWs5AXqbUQZnfczHVGdxyBCozk7igIxXZpDAT8cMdR3osIRmgSp2T9xeUYl+IaGDvAQBOi75FR4kWaUMOyUwAyE5Md0anMyvXPRfNcqUGciYsDHB6qehzmVHSJgqyigCdXCTa/jdF2tFQ0DczuZ9zynGS53tMrulVAvK8LsQ8M+5n3QSiPJmg3iOVMsOrJoICo+kAzCtDzNII8/aA/yVhWtIfiknVECYFhNmwmBCSmQDoSIj6sxHiGLpvQJE2snxBOVuLJgK6vk6uoFzq60KHAOkvaVFajUDXAZginrqRJBQh5evo5pmhEjcW00CWN6hJgWZfyL9vvC+MoVj1WrzZ8uUMVA7AlmBOcsuJ0Wl6flP8WmrFmnCOjPIs0oEmJDMBoMhIbplD5eX0F5oszkaWL8iEZ1wZtRM2V458hnphGFR9FYSBNM9nWGoDpv5yBvyzcrfr+j3pdNw5KTv8MUrt4XhA55nRvIeEAzB/F5U+M8KDkyszKjIknBlPhrh3nzd5eUFFPxaTmYkHrVLv8VNpE4RkJgA68rTaATh4Vq9vYtArV2rQ8XnRD4/P3sSg40CsgjDZI+poJ07UO2TRIKdVs/M2M3GfNR35Kf+CjmRm4iGnIkHvobhQpexzVBWaTeSZ4V+wiNIBmGttQqI9wuREHx1JszgdgHnw5Lu/1YgH47/DwcbqoroGHmE0UyDkLx0le9O1acarvTaTVqnSAzVg6fstcQMOYSdWrTGoM5ApF5psUwfg0moF2mszcaDWxdF/D+VtQOkETqRioEhSqYOMCtTMM8PfK14NUZEZ0RzF5Zkhopx8x+fPmayvIrTytkJNlgod/DOcZU5H9+gWjIq8j/9j49vxrHJHqMwEgFyQTNvMFFyuozp/aoPwcdCPKANXTrrbgAzC/Gd5J6ZC2zoAax60SKAdzWTJ3yNdMxOVR0MkxJp5ZjqoMiMO2vLt6okZYWYSzES6odlyM5FKmRGOI9TnVR7Vqtvu52SRZgDmwZ9rd2wBAJQZZtFOmkMyEwC6E3M/5+sz02FJiiaoWXUuzyC3dXmIgZB3YlQMypSkzqOj+k1pRzNxn+lopuyPr98GuOOQqoT62Ty6eA1++sg7aEmqIm4KFzrmXuWkgFg1m2/TUQWZETP98n4uBEnx7YA3J3HKDE+GoEdmeOJr6QVDFRxot4ciuggOIZkJAD3711Vm3M90OKPuuXRMUAOGrplJx/kzJ2VH84lQ9akyKqhk/GKEQfhReEEqMJoDqY6fi67fFE1IyeoAgF89/yHmfbQRT72zVl2wQKGjcOo6ADMhAokjE6poJoqMCPtS+cwIHYl0vxGlzwzVjxDHKHDk68NZaAjJTADo2Yi7XelvoeE8qh+Wq1Ws5EBHM2mSEXIgc7fnoq4Jdn+FwtCWTuClZsqgcpd4QUUzaSdOFHxe+O0u9FfNJsiQ5rPZtktlSilgEL5GZNi6tzr/mSctXJ0IS6hOwPlkELlhVGsrGZacDFma9SllnupTCh35JnEtNIRkJgBUQxUet/ZAKt9vEZH5dkHeZiLuM6WOKReapAZCbUcXvo5GIb1dlQR4Dqibr0k08+RLaLk2oLBT6agSuu2hmAY8Hjr3QJ1nhlNmuBmgmAFYZSZyP/IOwEJ9ZdI8ngwRodmaGYBJ03EREQHBkV5QzdrjbPJHSGayABXRkG+yLt21mYq2leUJsRMlBjJNnxdqleNcHIB1k2VR5y+eI11fLFdabUBUZlQ3Uf6sdZ8haRbQJMRk4kWijAra73uBQU+lVpEZd7ixeAWEq6LymeFJC/9Z9KXR85lhZNI9zbWdiElqMT1ZQVUUnk0xXYWLkMwEQKfR6ibrIlfd1nb+7JigZn66GYCpfWnnqSHbADE786BtzUx65YoFEY7N6C4nkIupkCLB+lGJ3L6I0HDdNlisgwXtM5L9xI5XQ5jgs6KXNI9czkAZjcTVF0KzNX1mqEkR0TYLHeI7VfxUoPivYDdDz+dFUZ8aCHNg88X0orQlSJ8Z3RkhMRDpOy5SAyFfhoZ4HHkZ/cVGS6sNCGYmTZ8XaqFJte+aC2pSom1qpN7jDqXMUJ9V7yG/NhJnJuLqRBU+K6SZiCdD0HMgFpQd7nmo6uu4DBTT6xkqMx0MVNid7qwcBBkSPmuaSDoqSJ8Zwuznq69DhvIM7dY1M5Hh+XT1nMoVDQx+gKOL6cx+le8K8az4dqP0mSGPn73CWqRcRsuRXvkI+AS8RFpslZmJUmbIBSg164tkSM9vivLVKqZnK/rM8IuAtsfZ5I+QzARAmNERs3pNUz/5WW2m0jtOKUOn41Cu1kt2wvLZvr++/Ji6a7JQZgkeHdXMpKvMUCbFXEKrKZ8XXSdyaoZe+mYm+WddQkcpM3x9VTSRqMxwygpfX+UAzIj6mkn3dCYyxRWa7X4WlZl2OJk2QEhmAkAtYZALGdFxIlQd39vIPtu0HQ+++XnRJuHSBWnS01a3+M/y+64r/ZPPLU8yor02U5EOhDrId30sXVMjRY6VhJYgpLn4SxSvmSn4Hug2T2YSDriKF4lUYHifGWXSO3nUEiNIkr8+95nfDuKHAodJKTPFdBEcsiIz06ZNg2EYwt9BBx3k/N7S0oJJkyahW7duqKmpwYQJE7Bx40ZhH42NjRg3bhyqqqrQo0cPTJ06FamUio23LwRlhjJx5GDr185xoji30b9/E7e8+BHunb9KUar4QZEO/Xsor5PbQMh/1pOXdUyKut1HqXEZ3dBqEM9aeD9zCM/nf9BVZkyS2JQ2meFBDexqQsdXkpMRpa2RJB382k6KPDMgopa4z2oHYv4z/05zZ1JEL6gwNpWAmSnrhSYPPvhgzJs3z91BzN3F5MmT8cILL+Cpp55CXV0dLrvsMpx22mlYuHAhAMA0TYwbNw719fV4++23sX79epx//vmIx+O49dZb2+By2h6kw6fmbIQsp1lfp7P8d+MWegclAFpZ0VW33M85hXZrqAKq2Uze/h4ciqmz1IGuOkb6m2kSSh0SaqoWmhSeoXy79oKxknKMMe11qtoL5HtIvBPqHRBLyWuSGeEwvDKjSUYEMiQ4AOtlEC4FB2Ch7QpkpogugkPWZqZYLIb6+nrnb6+99gIAbNu2DQ899BB+//vf47jjjsPw4cPx8MMP4+2338bixYsBAK+++ipWrFiBxx57DIceeijGjh2Lm266CbNmzUIiocr82H6gnd7yG0h1/S2KtF21KUR1i9uuQRK8yCWShZp5aRNa4vhUGRVKYFIvQHvVa/5Z5xmRRoXXJ1ULTVIRbZphyTy8pGfmyx/jiFtfw6amFr0dtBOoyQMjynjBKyNibhdOcVGameRmIoMiRl4QagzTXc6AaDfUvSh0CO+UsAhoO5xMGyBrMvPpp59i7733xn777YdzzjkHjY2NAIBly5YhmUxi9OjRTtmDDjoIffr0waJFiwAAixYtwtChQ9GzZ0+nzJgxY9DU1ITly5eTx2xtbUVTU5Pwt6cgNNQcpFWdGaWu7NxRiQ1NCOVl/PWDZ5G5mCh0CW1b5pkp1lkTBeF+qqKZuM+UApKb7xq3X80kKaTPjOZ77HUUvnf+Kny9vRX3L/hcq357gVY4+e16O+CdbkWfE+UO5PvSrU/51gjRTHrKDpUJvJiU01IzM2VFZo488kjMnj0bL7/8Mu677z588cUXOProo7F9+3Zs2LABZWVl6Ny5s1CnZ8+e2LBhAwBgw4YNApGxf7d/ozBjxgzU1dU5f717987mtPMCOftvQ1VA1YnrKjgdBdQsMJeBTHtNGYoMEWX89eWfhTJkbXpfpQBdMw1NYjUnBaSi5n5RZwCWf85JHST9pgr74VITO111jYch+KnI87/4T0CugRjEdskO3DpC0j6O5OguVMmhWCNORWWm+PPMZOUzM3bsWOfzsGHDcOSRR6Jv3774v//7P1RWVrb5ydm47rrrMGXKFOd7U1PTHiM09NpM3HbN94+MoshzICx1aCkruiYK4nMuazvpKjP8r1TCN+1opgIf8LJFLhl4qeepvcgh5G1I6TMjvO9yQqwrzxfrasU6CkQuPjOisqLnMyPUJ01O9PFzyTOjFZla6A+RA5U0r3iuQEReodmdO3fGAQccgM8++wz19fVIJBLYunWrUGbjxo2or68HANTX1/uim+zvdhkZysvLUVtbK/ztKVCzb90FB3UiabTzW6hOtIRBzQh1o4loB175AKU6AdpnJntlKJc1XYrVnk1BJIqahC4XvyVKWeHOIKXymdF4htpmpiIa8HjQ7yH/mb42QXWx5NFMKjIi+tbw+9XrJUnSw32Oai6HQF4/WbvwwJ+ryTqgAzCPHTt2YNWqVejVqxeGDx+OeDyO1157zfl95cqVaGxsRENDAwCgoaEBH3zwATZt2uSUmTt3LmprazF48OB8TmX3gWLgXBH95QyIGaGiEy1W1t+WYMTgo9uJ6gx4un5PtLJCVldEwuSgzJRYG9BJKOj9jVJZ1MqO/Diiczd9ntT7nou/hC7pKTiQA7iuOiUnHbmYiUCQkVyUGWHNJs0MwPRaX/ThCw2CKtrRfGauvvpqLFiwAKtXr8bbb7+NU089FdFoFD/+8Y9RV1eHiy66CFOmTMEbb7yBZcuW4cILL0RDQwOOOuooAMAJJ5yAwYMH47zzzsP777+PV155Bddffz0mTZqE8vLy3XKB+UJHFVBmf6VmcbytXjMktEjbWN6gQyK5z5rOozmF9fKfieeh6gBINUhTVRDOpcQagXauIA1FTjdfE/UM1abK/CY1PKjXvdCJKj2Yc2W0Wb08A7D2rICoYyjq8wqOqBLpKzNTYv+Hu+P3kG2wmPxNOrTPzJdffokf//jH2Lx5M7p3746RI0di8eLF6N69OwDgjjvuQCQSwYQJE9Da2ooxY8bg3nvvdepHo1HMmTMHl1xyCRoaGlBdXY2JEydi+vTpbXtVbQhqlWZtZYb/THaCujugi5UyaElXc1asIw9r+9wED2q69SmVSYVi7WgoaPueUbN/TXWLJkN6z0Cn3eiaj4pVmdHLhk7XN4T2Lg+nVikjYtI7Kmuw6iFSIdj60UxXxJ4HADzPVgI4Pl1bsx8oNPDnWgo+M1mRmSeeeEL5e0VFBWbNmoVZs2aRZfr27YsXX3wxm8O2K0gykkMnSmWCVUdRFOeL0pagfBx0O1GdBFe5+dxwZVRtQIia4uvIz0uFUmsDgrlVaWaS19Hl+tTkQ/cZ0Kt28/vSezrFmgGYVGZymFSIC0XKc854YQg/8Tc++2goysykzDPDfS5jrbLdFry6xsMegxhj4arZHQE6Pi+6dmKqE9SXx+XlirTtZYHgwUOZeJA0U+l1wqRpKwdlhnQiVNQXz6W0HnYu0Uw6uZtUx8nF34E+Prddc6FJirQV+pOl1Cl9bsaTiVwyABMZM0n/G+/hCdLDOwArlBl+AmpoTHAKHfapMiaamQq+IRIIyUwAyBmdsD37gTAXx8UibWN5gyYw8jK51NdN9kU6QeYwEOZiZvKWe/7fX+Gku/6Jxs3NejsoMFDOtH4EE3n95Qzke9VfkoLfzr3HuupakU5KqCzIVLSfF9RK1boZgPldC6RFOwOwXJnhWah6OQP5vnT7gUKDfa4MnlWzdVl5gSEkMwHQGTxzGUh1HReL1bmsLUHPCDVn5dxnKvGh2kwkr5+LmYmOntEdCMXvVz35Hlasb8Ivn/9Aq36hQX/Vankd8Z3UewY6kU3++vLj6B6fB3WdhZ5DSIcEqn1m+EoUAdHzmRFfXq6MprIjECvuwqKqaCbhot1yuSQNLATY1215zEyic3bxICQzAWhbeVu+L3WeGb3OtpShk6tHPzye/6z7DPlzkZ+XqgujfXZyUWbkBZtaCnfleRX4q1EnvQueCesmr8zFZybfhS55KIIXCxrivc5FmSCUEUEx0VRWtJyBvdXd3yLkOlF6eWb444t9QmHDZC6ltM+VMcDi8swYipXHCxkhmQkAvcAcV0b1/nGfcwtn1CxXwhDJRDA59NUXCKH8GaoHwuDBS90Ha9SnqwsgF6os0rZBJaL0gnYCz0+dyy0iTb5jXcfeYo1m4kEN4EozE08ACAKiJDOUjVeTTgjmLIG08D4zKmVG/sIWi88MYwxMWFAyfbIMTFho0rBCMlOSoGZ+up2ozpoyupE4tONgAb9BbQCaOMoHNV99SlnRnpUT+yL8X3zHJ/al62sgnou8XNFGyGi0b285igTqkhGqnK7PDp1nJr9nWMgDIUD3edrJH3llhCAT6tBsJv0sKj6qWQkRmu1RbGifJkKZKZIJJ2MQSIvjM8NEn5lIqMyUJqgZoVgme2UlJ1WhcN+T3QqSQBBE018/eCavq67l5oBMKTPZz+ioYsVKZvT9nuT3Sl+ZCX4GuWTy1s9T4/5I+8wUNoh5hPZ7IO6MJxPuRyWZoUiTpplJID2EmSkGk7wGy5KHkxdLkAYDBGVGpIOcR1OozJQmdMwaOZmZCNOHr36RsP7dCco3JRefl1wi0nQcPnVVgVxUBepceBST4yEP3bWNaAIjL6OuH7xfX33usyWf1OcUWl5MoNox9dkLkUCYxHZdMkKoNJqNQMgzY4lmJq1WQF5/4T7bdPuWmJmYeA8jIZkpTeioArqdqBCRofn+5uJgWHLQGIi0szALP+iRIaKK9kCos5aPLlGlrrNYlRl9MhD87mk74hNZvdXmXuK5EWVU9UllpsAfoTipkD8EdWh28IusrawQZiJl0jwh0688MioKizYDWnLTWNH4zECurqU9abh7aCX35Gm1GUIykwV2XxSE3oyuWAesfEGrMVwZbWWm7QayXByAqcSJ+mSqOAdCCvk68OqaYYWxkzh+LouV5qbQ0uUKGfkrjHwl+dpM6lWzqd80yRB3AaLPjEeZoXbB5FeaiyN/e8BiogOw/e54lRlDkWunkBGSmQBQq/rqzsh0yul2boUsYe5OkGYewg/CV5+Qh/MdyPJ1ANbNfkvti4duwrZCg6hYqMq5n0Uywn/OgQxpDkT8byZRSb0cg05bLexnSJNA+XYvhOUIiCUM1MoMv6/sQ7NpZUfMAEw7aMt9ZnJReNsDjIkOwLZ1gAGICGamUJkpSVAdp77jIKUE6A2EOjO/An5/2gTUM6Ds1r765H3XrC+Uy16ZISM/NI8v7IsoWMidqArakwIN85yuuVfkIpqEmCCeHUlhzfce6uSGUSszxMvPkyHli8gfk89zoxeazb+k4nIG8tMqRMgcgBljiHBMMxImzStNkKYA5i1HsHnucy4mjlLoBPOFls+Joj4ZAUU9W199YjsRYSMpKD2OcC55KjPFmlBRP6pP/kWbDAnPingPNe8hPcFR1NF4jwufj8onYBTJUdU3qHWSNH1eSDKkrM8N2ISyEjUUPjPChYo+M4ON1TghsrSgJxWMechMpsEzeJ2oi9MBOKtVszsiyJfWM6wwBhhCvm6noLy+romD+1zIL8ruhM7ijLr3UGdQ80InFFeTy5CDt/66PvLtxUp09ReapBSt7JURnRW4vbCIEVs4F81M3sWqsIrkm9iuSWaElPmCskLXpk1IcsXEX0yuzHiXMyCfDx+a7VFmXiz/XwDALS0HARhOn0M7gnlGLStzP7w+M2E0U4lCNxyb9oeRd9b6yozm7L+EQZGRXJSVfDPo6vjPeCGu/0MNylqHJ6+zWH1mdJVH0lSo+x5xn2lymn0b0iVTpZD8Mt97SJqJxD1nfTYG4X/jr8KRFo8ylASQhNpnBkQIOQPDlkgEn8dj6Jlo1Dx/EY1NjfjHqn84BCNbfLLlE/z2X7/FlpYtZJl0G+Qy/doKDAPWlpm4qL4H/lNeplzSoZARKjNBIF5ab8dJD5LcZ1IV0OsEO6oyQ/nG6KbCp2bF+rN6qg3oEU3ymJb8swpaEngRIZd7SKtzigMR7y41QKuOnxOZoghtET03nbxK6qvhCYg7YJowcWvXLhi1axciKT2fGZ7ApJiJc3v1xMGtCRy/WS8DsHdphRN67wPTAK75wqSvTVhdm4ExBsMwwBhwTN99AQATm7fTx1dg3HPj0tdipXDqwFOzrj/h7xMAABubN+L3x/5eWoYxcdkC+3osxnDb3gnsjFbgnMp6/HeR2qxDZSYAVGfpnUWRgwxZX9xOd2qcskMOZETVEgFt6nOhOxDp+M8o64P6rDi+cJ7yMtrRTHnWLzQIyoyuukWZ+jRNjSAmFUoyJZyL/BcVIaWUmWJ6bGKfRbxHigsSTEDcgPl2zRb8ta4Tfl7fI2BtJnlumE/Lt+H9inI8XtdJUVcE7zPTwlrwTSyKLdEodkVToCPA3WOmyYx9Ke729ZGvtc9Bhnc3vZtX/eXfLCd/YwDETL+ms31nlC8YkpmSBG1W0K2v99Lr2NFLnbRQ0FG3dAci6nMuzqe6qkK+uYaEwxPFimlQ5KGTTA5QPENthZMnHXx9eRnf8QlTirZySqlzRWRGptUxPXImhmC7D2FbNCndrq7Pq0Q8ydBcm4lyJjYscmLCL44Z4RyFxbaZHxHI1czk1FculOntyzJkxtPwlOtbFTBCMhMAncyf3t/E7dS+sq8PFJcs3VbQIZS700xEkg5CsfGfALevfJPmEUcq1pWYtQklocDkoq7layqkPmsrS5Z8e6FDZxkJlUIpRszwZCKns3E+RQTzk2pnfDm5A3AEJt2OiGUPcpmUkGeYZ31LbW/3RDO5yox4DiGZKUlQna230dEdqbyz9JupqNp6Ck4pQ8fXSH8gpD5r1ue250KGqPp5m5mKtGHkFM1EqgJ6yk4u0Uw6C1XqqoNUFuhCf4KiAiFv0+pmKCcTuqHVYp4avg5fRkGmCGXG8ji80lm2xQUpHWWGf565MTMHZp7Ot6r63gzAjpnJc8q6iQsLDSGZCQA18/M2ANoxU14ml/qqcqUMqsPPd1bOI5dIGN0ZKdkGiDL++sEDdrH6zAi+JLpmJs3nLtTXIEA5OQBDvt0L/rcUlVW80B8h2RfqKhNyZYYfhCLqF8H5KIQSC89WRQZ4BUaeARiGpQjmEMmQczpcXpZ8X8N8zUxBvnuGhFD66oTKTGmCnoF4FBPi+VMzQu9LT73D3nKyl6XQQzrzBTmr5ssoZ+UEgSEGFf8OgmfS+mSI3y2nSigHwuDjFPxASEA/JD6YAOgnzZOX0VV2KEVPN7Sc31sxkVDqfaPUTi+oaKSIUEelzIjakGy7qVBGxGNyygyv0sCbjYU/JK/MuGTG4PLP5NsX50tmVMoMY+KCksxyQ7P52xb6zJQoSFu9txw1YBKmqVyVmVInLjLQ/hL8AKdXPxdlRGuBUc1ZOelvoWlioVCsZiZdB1p6YUOuDeglf83fZ4barqwvb6tFxGUUpj4X6uUM5GSGNxNFlP0bRYa4ewumMBPJlRnezMQiegtNRuA6CjMuyZzKAVcHu5MMMXjuL5cBmN8e+syUKGgfDbGcXp4ZvrxXcdGdoWoVKzEEE5DcZtW69YMJqbIL1iDEuv4WFIrV/Kh6p3joqAL5tgE1oSUIUA4+OxQhL/SJCkUidScVnh7Q+SQMsCoyRJiZonx1g5G7EJUh3gGYIzaK+vxCk3ymYIM3M+X5DM0810VSkhnmJTOuz4zgdxSSmdKE0FkKIZ16ZIQiMH5lRn78XElPKYEiA3sqNFocMOXnon4swcdUKTM6z7wUmoV+aHTwdlV9ikDpOxDLj6n7DEllpsCfIdkXiqXI+uJK0/x27rPm2kqUyckyVM+RcADmPptgdH0hmolzAE65ZKa9zUxqMsM8uX5cnxnB1BeSmdKEbl+jk5lVTGXPyHLidvn3jhSiTTvdysv4QM3Ehfpa1bV9qHjQfj7uZ5WZqJQfdb5ZlKln4z+OfF/6i4Xy77F8v/mGhhf6REUngkll6hOWruNUDn4gVUYjES8yf15pMkLsgNsu5qPhlRm6n/et4eRUSnBldl+eGK36gWYm/ndXmYn4ShYfQjITAF2fF1qa5D7z+7XocsJ24nyKydaeL6iOU9tfgagP4tl6oTOT1xwHSafjjkROeVD3xldOy9SoeZy8zVRUn6DXhmj/HbJ6QYAmlLqEjFNWuHL8IKReF0iuzDCIZiItZYYRPjOGRbZDnqhEDcsVMIRVpnefmSjf+l5zEp8BWCCUoTJTmtCZUXnLCduJmZ+u+YiKZiqqkM48ke+slvJL0HbE1CBQamVH/qx0TRSl/Hy1fWbI+67ZBshnkMPxIf+sq65RCfQK3YmbEV9ok5MI0YTEExN+q0KZYXwd4p1W+Lx4HXjdzXo+M7z5JSo4AOtlMNZB3qHZAe+AoMxYbgZgUTULyUzJgzIXpH+j2Ly8jL++/Ji+3UrITKmDGjx45JQwLQfnTRB18jYzZeEzU0oqjn40E3HfiX15QRMgoVT2xyfMyOrjE/steGnG/ag7+eIhRE0T0Uiq0GqqJ+Cdbk2FMkORKZ7MmKBDs3kyFQWXj4a1oZlpN4ZmW0zua+QzM4VkpjSh63CqQ2aoGaHsO/eL9DglNJ4FQid6RdffIpcF8vSjNXTqy2eUul04fw4lQWoU74RQTOP10L0fdKg9XYcqp2/qJJQEfiAvcDKj4y+mfgScskKYmVTRQGKiPDE3DP+ZJFTcZkHZMTiVxqB9bpjFRzOZ7nUXkJlJnbyTiSHYltxnJswAXKKgog10lxmgzSKK4yi2uz4zxdng8kW+YbmUaSkXMpSv86pwLlnkmbG/ewe/YiQ3uYRm55YnhlBTtM1U8n1Rq2F7odPuCpzL6Dlhq5QZyszE71dzoUnxvLjnoSAjOssZpM1MwcxZVGZcMpOvmSnf5QxUSJMWv00wjGYC8Jvf/AaGYeCqq65ytrW0tGDSpEno1q0bampqMGHCBGzcuFGo19jYiHHjxqGqqgo9evTA1KlTkUqlUIigZlHaygz/WTF46ifNs8tLi5ckSH8FzYFAJ/pFN2FbLgvs5euvQRFf7+CZKsJGQZEUXzni3cuF0JKftckQX1+TDOVJpgoBOn2ZctVsYWd80jy3kjKDr+Bnw6spnJnI8JyogOCOxILK5C+SGefZMz40Oz8ysrsnJMIzyNzDNMnh700HIzNLly7FH//4RwwbNkzYPnnyZPzjH//AU089hQULFmDdunU47bTTnN9N08S4ceOQSCTw9ttv45FHHsHs2bNxww035H4VuxG0vCw2Op1wQFXHpRPazZfTjaQpBeS70CRpitDshHUShOkrO/Jj6p4/X9YbEZcyi68l5OIATEfS5FA/BzJBR0Op6sjPhVfXCt7MRNw3XUInmi9Ep1t3q2YaZ+qdVJqZ5ASKP2bKYCB7VEtMmudwGc4BGO3sAKwCsxgiggzGRTNx5dQrjxcuciIzO3bswDnnnIMHH3wQXbp0cbZv27YNDz30EH7/+9/juOOOw/Dhw/Hwww/j7bffxuLFiwEAr776KlasWIHHHnsMhx56KMaOHYubbroJs2bNQiKRoA7ZbqBmjj7FhFRm5KRDNxrK2785ZtriJM85gSSURBlffUoZ4croJs2jlBXV60+TFro9CccnTJpeZSZhFl+joByyvaCetWqCIR4nmISqngFdn9uuGRKXS9K+QoD4vnDbiXdKuQfiuVuaDsB8MX6lajOiCs3m6gv9Mq/sKHxmKDMTR2byVWbyzTOj3Ldn0HDGEtaBzUyTJk3CuHHjMHr0aGH7smXLkEwmhe0HHXQQ+vTpg0WLFgEAFi1ahKFDh6Jnz55OmTFjxqCpqQnLly+XHq+1tRVNTU3C356Cbvp6nWgk3WgNoT4xkBV6x9eWIO+75r2lnoGQxFDzdtKDqur4ctKj3za858DkdYqxSSgmCDxoHw15Gd9hhDpyYqHrgExHU+k9w9xWnG5/6JhIlT4z3G+CmYgro14okqrPkRnQfTFZX3Amptuh4XUAlpiZrHzJjHKBsfzgi7RilDJTnGQmlm2FJ554Au+++y6WLl3q+23Dhg0oKytD586dhe09e/bEhg0bnDI8kbF/t3+TYcaMGbjxxhuzPdU2ATkT11RmtBYpBP0CUiYGYVZe2H1g3sjXzEQqK1yZ3MKCdZUVal/udnWOEnlb8SoBhT4YyqDvBM1/FqiJdF/q+rLaQabG4H3pHj/9ncEwDLENFLiZiQcV0q7OAMwrK3IyoswzQygrQjSTpgNvLkn3vD4zrgMwr8wUsAOwd92nzPUwBkSFay5OMpOVMrN27VpceeWV+Mtf/oKKiorddU4+XHfdddi2bZvzt3bt2j12bF0fBx1pMr2/zKza017ovA3yctSgWorQGYi0zUxEndzMTPQxxfpy0uPdTpsqRTiEtiTIjCYZ4T9rqByqPeSWeFF+HF8/QFwEZVamEugVGnwRdcJv/Ha9Z8B/FsxEhp7PDEVmTEOlzHA+M/x995iZaC7ERUMZlntP+GioHJSZPbXYqG/fbgrjjpc0b9myZdi0aRO+853vIBaLIRaLYcGCBbj77rsRi8XQs2dPJBIJbN26Vai3ceNG1NfXAwDq6+t90U32d7uMF+Xl5aitrRX+9hTIaCZPOd1oJCdHiK8cNZDJO0HdGWEpQOwCqYGIrq8j62s7ABNKQDY5UhxC69sur+8bCDN9jVfNKcZ2kIupjjINKQktdRyCHPvqk89dLKffD/gnJYWcAVilRAtzesUliCs2i6Yd2WdVfcqZWLlQJFXfY2ai67vlYtyq2YKZSUnG5ODVmN3pAOxly7bZiTEgyhfL01TWXsiKzPzgBz/ABx98gPfee8/5O/zww3HOOec4n+PxOF577TWnzsqVK9HY2IiGhgYAQENDAz744ANs2rTJKTN37lzU1tZi8ODBbXRZbQcy5T0x0/KCIj3ayg4x4BVTsq18oROWq0sm1ANe8EBEOazqmpn4fXiJqk6yL76errpXyMjFZ0U+v89NXWtLn5n0b/L6PkJqb7foMoUE/+SL/6x3D0UyQfi8qMgAt29RWXGhXDVbO5qJqu7+FhXIjGtmysWBVyTquzOaSb4gIIOYhZntRlPX7kRWPjOdOnXCkCFDhG3V1dXo1q2bs/2iiy7ClClT0LVrV9TW1uLyyy9HQ0MDjjrqKADACSecgMGDB+O8887DzJkzsWHDBlx//fWYNGkSysvL2+iy2g7ie5GLMkMpK3oDGU2G6GOUGnQGEvVAEDyrT/8GRMVEDM522UFVy1uI9f3POgqDVO28oAZMvzJT3O1AN1dQThl8CeKpXV/DVOg9TxVkk5pCXs5AdZ26ygyZ9M7jwEvWJ31eeDKknli49eVkSk2GuJW+YXHleDNTfsrM7k2aRzgAM28W5sLM+RaErB2Ag3DHHXcgEolgwoQJaG1txZgxY3Dvvfc6v0ejUcyZMweXXHIJGhoaUF1djYkTJ2L69OltfSptAnphQk1lxjdgMfl24h2gZ3Ty8ypFCFyCGEi0nT+57TJ/pCgkbIYgQ2DEdsXx+bJ5myh8PjPkKRQscvFZoe57ThFtmpMCsQ3JiY3qHHR8ZgrazKT4rquQUqYd3mdGHZotPwP+eaQUZESMZuL2xK/NpCBDfJ0YTPeogpkp+2fIqzG7dWJKKDMWYwKZydeJub2QN5mZP3++8L2iogKzZs3CrFmzyDp9+/bFiy++mO+h9wwoVUB7IKI6Mb36VDQT1TmXIvINbaYUHF0zjw4ZysZ8JRvIVMenyuk6nxYySDOuB4z4LJRR1KcmItpJ9zT2pdqH99xk/UBhOwCL32knanofsgy+jDGPmUhFhvjlCAhlBnSeGN4gJSoz3jwzRD9gicqMbG2mXJQZnszsVmUGXp8ZfizhnmFH8JnpiCDzkijK8aDK6Xqtk9FQmh1IKUAkMPJOVJtMKAiIjrpGOp/Shyf9DfxtQ6++vcG/NpPiJAoUuv4WOs9dWZ8wEfvbQDChVfnMUP5rFCEtljwzyrXotCdWHJmx3wEm3kNT8SbJV3xmnmgoRTvgtkfAuMGcJ0OKd5krF+NDs3kzUwE7APt9ZjKE0kNzGCtOM1NIZgJAdXy+TpCo7094Jt9OzsrlymDR2NrbAtR911VmqJm07jOgzAoqh3AofqKdwKmBVD77161fyNBW1zSeu2YCXqVpiToFL4ESZ7X0/iSHF/ZXLHlmVNepe9ay9X8sDxnRNdPY+7K8ZEgVmu3xuXH6UnjNTGRvzh3fgnNkQZnJ/hm2tZmJIkRenxmDi2ayDNeIZhmhMlOSoEwM1ErG/vrBM7L0duL4nu/uQObfZ6mCmATqR7IQn73f6EgUz/4cdY3ak7c+cVTvfolJGUWGvKsXFGM7EJ+hqpyctORialQNxLoKK2UqpAgJladFWJupiJ4fpYhpKzMZYmAyUfNJylzWMogIL4j9DjCfA7G2MuPsiY9mUii03MxSTJrHOfDmqcyk2kAVSVnyfVDKDOBVLkMyU5KgBkyKZATuL9N+VDZooXyRy9NtAtLEoDcr1zVN6TrgOmYibTOX/Di+gVDz+C6Z0SNjhQxdB15KTdFfaFJ+HF2fF+9DIOfuweNo+rvTD+i1ofaGvw1yv/HbVRmARUbp/BMXmtQkQ9w7JJqZaJ8ZQzSmuO+hR5nRaQMxw3S/Cknzsn+IfBugiEg2oPbhXaaAVxdNzr15t+a62Y0IyUwAqI7PRzLIGZnnO9yXUCwnr08lVtN1Pi0FSPpAyWe9m6CayZPKCKHniKqdnirAH0ebTBH+CrptqJCRi98TaerTfAZUG0qX01NmKIVV9z0uOjOT7x2QPw/d5QjA9YP8VlOhzBjCZ6q+QZNirzJjT0o0lR3DY2ayj2MIPjPZP0NemUkKK3DnBmofvuviTH0Wd3NDM1Opgug4VTMVcbt85ufv3DROAHwnqDejLQVQpj5dfwmaAInlso0o0z++fCCgBkgvKELrHfyKyUxhIyczEeQNQrcNqCKoNMbBTD0/GQFU6pqcEAuh2YVMZhT9la465iUDgN9MZCl9VtztNpkxLVGZSYGORvL5zDjvod6q2fxsR0yax5mZ8vSZyVWZMTiqR+3Dt4gl77fElyvS0OyQzASAXuSQLiduF0H6zOgqO5JZeQH3gW0C6hmIs23VQBhcH1CRCbkyQs32fccnBgL9XEXBhBZQS/yFClploctRSkAuPjPUZMELsg1oTkqotlYskxLfmRH9jzrPjL++xQDG/WByiom/vj+Dr2WJbUDl8yI68HLKDPPUpwgJ8/rM2OVc8qBa9ZuC4DOTA5lhTCSE5D58SfPcZ+AxwGV9DoWAkMwEQKcT9f6m2p5tJ0iRJr5+Ic/o2gI6piVl9lghZTz/2TuQEcf37k8yEKnNTPIB09sGSOdRjePLvhcDdH2/dPye1KtuU3XEctm2Ad+zzTo0my5TSFAGLBDvp2Qv3Ed5nhnToI2FPE/IxczEJ83jyYywUKUkM7cDz3IG3A/cp+yfoRAankOeGW8dOprJu2q2+wx4816xLmcQkpkA0D4zdDkeOnli+O1B9WWz+mL0lcgGFIn0E8Xge0iZKJT1dYiqikyRhFaPjNCRMHrnWcjQzsBLfNb3uSHqEO+nqj7/Pdfkl/Z3IZqpgCfEFJlL/6ZHSGULRaaVGa6+wchnYNdh3L5M5gnthippXvqHHYYBcOYoPmTZUiXNy5CWtbEoUgZnZgIfmh2M5d8sx1c7vnK+e/PMBPXn72x4B0vWL3HPy1OejIjKlFtcUY7HO9U4Z2tZDBanm7Ec1KVCQEhmAqArg+tGMdjl/J2Dbn23E9A5r1KAmFPH3Z7LPaSiMJT1ifPRTppHkCbf+KbZBuz6/mim4msIumSETJQH+WcvdFdbp9sAca811T3qOEVjZvK2QeI3dV+U6bu4z2mfGa6+Yh8GGBZUVmBEn32xqCo9dFkWE8xUqrWVDMawsLICDf164/Gu7jWIZEihzDCG98vLcFLvfXD9Pi7pMTgyEkQE1jatxVkvnIUTnznRPaZHSVFFE7WarbjwlQvx01d/ip3JnQCyUWbS5/azXj0xY6+uWGOtc8rzDsChmalE4R9w5MpKtqHVuvUpW32xdIJtDWpWLvsuq6OS9fPxl1DNpmgn8OzPP5f6hQzV8xTKEfda10wj1pd/9u6bPFHwz8C7nXqGVD/gbitoc7HiOnUJpQGGv3aqwcg++2JtND0Qp81M/L6Y717x9S+r74Ht0QhuqK8CYOepEc9F5UD8m65dAADPdzHcvpwnuqCfocFM/K2mGgCwptzw1PJ+kmP55uW+bdmQme2J7c7nllSLtDxpqrLE7U0s/Qwsywx9ZjoCKHnZ1951Z9XEdt2BSJaCu5D7wLaAzkAG6En8+dbny4kDsbyqfHf2M/TulyhPHL8U8szkslgoZZrSJjPE8b3leFBmYX2fGc/5SI5fyGTURzDId4q+hggYbt2rK7ZHI3iybgMA28zE7UvpwOv/wZ8B2AAjnoHBLLRE+Hwq9l5FM5OiFaGVz5TrnCifZ0aNVrPVt02bjABImAnns5E5Fx8ZIiIBGBM9eqIsalcQ8syEZKZEQSato2Rnb33Pd7uz0+1EqQGvY2UAlhMQbSdsz8xN9lm3Pl8vlxwp6Xr++oD+uj72V28YcDH6Tmn7zAhKgPx56mcQltf3/iYeX/5dn5DKSY/wHhcwG1URf0Zsl+zF+RTNMBhvBmBTciwbhozMeEKzTdCDOcDQYgj2FN95pc1MFKO1hPpuMX0zk4zM6JqJAFeN4ct565NkiInXX8Yizn6YEGoWkpmShL+zy2zPcVbtmom89XUHskwnyO2g5MkMQWD8kSjBA5HKPJfPQKaaz/metWbKe+r4VHh/QZspCGgrM9xnwW9Kmwxx9ZVtIPj4fD1tMxPZD+hdf3tDRfxVZjsefDRRPDN6pp1PXQT5vNgos9KOwqbljYYyQEXjGMySKitCNFOAMtQacYdMd9VsfTNTvsrMrtQut1zGbKRtprJM7OKuP+ZsFtfJ1l0EudAQkpkA0D4vkG73wkdGHC9+zU5UR5kp5F6wDSCoKYpL1UlcKNTPmZD6yYRamQk+L+XxCQXKvzYTfQ6FCl2FUeceKp8BX4cgQ+lyuoRSXp4Orw/uBwp5UkKpi7przAGislKWITOMiWoG7TEj1i9n6dDq9D3jlRWVMgNRWeGOKdanXkQLrTJhR8gATB4agB6ZUSkzzalmXzltB2Aw7OLNbI652wp9ZjoC6AyhcsXEC31lR69+tgNpKUB/XZ3ge6gKy80naZ4KunlmdGf1lM9MMZqZtHMFCe1d3vbzJUOqcmQIN9k/eI5DEE9dMtbe8PVDhN+XOmker8yk/1vMr8zoZAAuZ+lvFhNT8afNTISyAwYmmIn8fWlamaHWNRGVHbcal0GYOHMbvJnIDSbJUZnJlPMptER9ZlnYZfDKkuVsNwWSV8ANUYGQzASAUlYo2dgHYsDN2fHQ2U6XKTVQYorv3uisOs3fN1+eFl1C6p9Vq6OZ5N9zJTMyE4XsOMUAbTMR95kKr1c7EMtJMPVsVefJHyv3iLRMG7L82woRVBv0T7boffCiRRnvM+NRO6jnyOepqWDp0GjTkwHYMgwwiyID8vfF8oZmU8oOY2jhyID7vEQzk0op5x14SZ8X8vyB5mQeygyzBDOTXc5izNPHhspMh0DWPjPe704n6N2utwPZQFqMg1hWoMiILhkgylAJCX31CULJFOfi2YF0f76BUHOhSzqaqfgagq6ZRSA9gkM4oZh46xPH1HfE15vUUGsz6aRYKOS1tShlSjfxI0CZmTzKDJhC8uSUGcs1M3mVGZN4kXyrRtvP0BdaTTsQJ4RMuWItIK0sqZ5ji+l34M1VmbGT4+mHZltojvDKUkbZsUxBmdHXnAsLIZkJADXzyj1Hif0CaQ6k1GyiSDrBtoD2QETuQD4T11VGKNIkKgR6qgJfT1cV0Em4pqpfyBCerWJCSJmjtCcVhDlHWx2Dt5x8u34GYT8ZYkytTrUriPus24YBIMUN+q6ZCR4zk2I5Ao/PTFqZ8bwDBmCZhAMw4XsmrJptgAztTkcz+R2AvWRI5YjPKzM26cjZZ8YiyBCh7KSVGU5Zgj0pEjMG57IkQyEgJDMB8HVCmXaj+xJTna3feVNvIJN3gsXZ+HRBZdrNRZlRqSm0uux9CPY/+QDpBUlodU0URH2/zwx9DoUKXWWG8i3RXo5A2Jd8v959q7a7odWakxKinO7x2xs+0kaZ2xX7aOFGG5vMeFe9thTTAv71rOAmlaKyY9BkgLjXogOwQfvMwOMzIzkzZhhIKdalkCkz2qHVkPvM6CozzLIEB2BbmbE85Cc0M5UoqNkvZUP2giI9+YYFdyQzE0VGdM0s9ICpW1/+XRxUaVAdfs6mSvK8iq8hqFQSoZxQhyaROhFpqvr0e0wMhJ7yuouF2vvzqqqF+gyp+5wNGePJjD2kyhyAqXuQ5ErayozFIPi8pABYBJngV92OMTduyq/MZBfaDU9umYRi5evWlBvNlJMy04Y+M/ad9yk54dpMpQkyc6fmQEh1lilTc0ZIHKdDOQAT15rLrJoiRsr6vu9+ZUWtzHi/+5+hd3+q7Y4qUBIZgN3PSmWGuzhRZdF8D4ky+ikWvPvztwF1fe8zpI4vrd7u0E1QqXqGLYKjrzspFJ4N6Ay+uwRlJ+0z41vbyQAsjVWfyxlHyHgyA4XS7ckg7BJakTykCDMXIIZmU3lidJWZrH1umKjMuGTIY2ZSkKlCRkhmgkB0dtoDIdHZ6fvcyI/jld1L2dREmhg0ByJqVq6vjslJg/hs6PvvT3kv307O6okBzzurL8akeTk5ABNmR285cTtVJ7dJCe0zQx3fWz87MtTe8Pd32fWDgKjM2MOlaXkceBXKiDfHi5VRZ/hDmjBIZYZPplduuatzCxmEDQNQkImkVJkRj5fUJTOUMqNwHuN9ZrJVdphloZn3+XFWzU6K5UJlpjRBm5mynxGm66X/a6+ro318on4JQHcWmG3SPG9xfXXN35ErxyBNZUY/Ii69pSTyzHCfdfPMCOU0Ca1QxiL25d8d+Qs1qdFdkkJ2LrL9FQqofkxXoQaAXZKVmf0+L/592miNcPlcjAyh8frcGHQ0U8IQzVQyU6EFgHKeM5jXt8T+L55vwkMOeMiUlax8ZpK5KzPMq8zYZiYP+QvzzJQo6JdYvt1X32sKydJW7h9wxf9B9UsNqpk8vUyAvD4121Ydk6+Xi/MqeWJQmZk834n9FiOhzSVXj0pdo/ZAq3s5PoMsFVbqfLzPrFAjE8k16nJUZtw2DCHPTHqhSDmZEPxVADArfb9EM5Hhc2i1keRS2kUZ15cbYn3KAZgKBAC8ZiZaWdFJeqcy88jq6/vMMNFnxiZTHh+f0AG4REENZLmYONLlMv81Z9X+zfl1osUIOuOrt5y8vphbRr7dexzVfmXFVLefaiu6ygz1rIstz0xzIoVla7YIi62qnicPerFRETrvodpnhjg+UU53UkMRz2J5j/Wvnz5/3kxkO+3688yAJCNicrt0m/BlVgZIMmTy8UecozHzbKfqe/PUuM/Ko8yYtANwiiMO2TrwAkCSU33scroZgMFM0UxmKzNen5lQmSlNUAqIX1mR16c6W10zE90Jeo5TnO1PC0z4TM/kdZwvVfdJW5mROQDTuyUHTP3QbHn9YiMzZz+4BBPuextPLF0LIDt1kcr66zO1aUTliu2JLqc6N2pSo+v3RCk7xeJ7me0acwAE3xhHobY8C0UC5E3gh+g0GcmYqTw+N6RCy5mZ0mRIPBdnO6lseEiHTcq9yowigy9PRiiflxTTI0OkMkOQMWZZSEkyAHuvKzQzlSqIAcenrBANgOoEtc1MRIdfLI6DbQFq9q47mFMDSd7qGqPLqPbrDmRiOcrEoLu2U6E3gffWbgUAPLXMJjPyd0MGcn0u3TYAef1cfd+yfQ/pxIfy/RYaqLam+w4BgCmYc9x3wPKYmSgHXtPjG8MY85mZ0nlm5GSCf7/4NaAEcgx62WzLS1qc44jlkwoywxOPvJUZKmmewmeG9+axyZ03yWDoAFyioBQQXWWFnJHp1if2Vwr+ErqgTAy5m2mo7XpkQpbFWZkBmDi+/9nqkSm7YrHkKPEimpkdZmNioYijduJEok7uhNZVFqjzFOtThLY4nqH2QpuKfZjc6ky2nwxjntBqxV540pMmLcg4AHPHMAArRZmpOCIBOPUtH0miyICXdGS+ewb/VCo7ZcVnJtIkQ5QyQ5uZmKDMOIEEzOszU5htMAghmQkAneeFeLkD4IbVeo5DdqLE8T3kvRjDcnUh3gL5jJjaBsgIaXbKhtasWqUqEOfpzx9D1M9zIC00RGwy41vok64j+j1xM/xczL3cF90sylSelZzVQbt8kTxDSp3UzsDMGFKG/xmajMH0mYn8gzHzKTBppcGbZ8YE/Qz4tpImVnZoN78dtM8NQRr8ZqYEKAg+M1b2yoyMDGmHZjMLPG1xfWb80UyqxTILFSGZCQDV4fo6Mc2QTMpMpVufUmaKMSxXF8LMTUIkoplwQ+oOUMqMbiIw/731k6Fsopnsb9qqgI9QZ/4Xmc+MDft5ZaNKUMqKrrkXxLPykiHyFChCmaO6Z593qkjC6/XbsLw+Y/A5+trlfcsRSPK0MAZBQTEzjrpeM5Vq1WuvmcpimefH+/IYBlKEA69XmTEtK60seTMAJ+nQbB0yogrNzqu+ZQkLStpkhnmjmQxWNH0Jj5DMBICMJNF8iUmfmVzNVJKBVFW/FMBfq0yepwZHWX1AdQ/1lB2ZA6/q9vvqO46Dem1A2wm9SBpBtmSGDonVJyP5+sxQ77GXjNB5ZuTfvc+sUEOzqTboo/kkmYOgwNgqizfPTFpZ8ZMRizHRARhGWq3xKiuKDMC8z0ta2cn8wfsM5WTGhIfMmGamHejVB/KPZtKqTybdszxmJjtpnt/MVKjtUIWsyMx9992HYcOGoba2FrW1tWhoaMBLL73k/N7S0oJJkyahW7duqKmpwYQJE7Bx40ZhH42NjRg3bhyqqqrQo0cPTJ06VWljLDSQodVEeb8pQz4Q0aqC53umnRarv0Ru8JMG/npjEbnZwqmjqW7pOn867gLMv03n+Nk6f5LnXyQmCi/s/lTXzKS6T7rqlKDm8GRI9z327Y9Jj0+re979yZWZQn2G5GKrmmY+y2NOYlxoNu/zYhlpkuA7PkRlJpUxR3mjodKh3fKT8IZmM4mZCQBSplxZMb2kBclMO/DU59Zf8oKPVCJ9ZlTKTB71mSWamRhlZjL8Ie/FgKzIzL777ovf/OY3WLZsGd555x0cd9xx+OEPf4jly5cDACZPnox//OMfeOqpp7BgwQKsW7cOp512mlPfNE2MGzcOiUQCb7/9Nh555BHMnj0bN9xwQ9teVRuCMufYnWDEELf768u/6+aZIc1UHYjMCCYGyx5E3G1BygzlxK0b3k4lPqTy1/iO7x20s1SGqNlvsRLabJUZVWI8XSdwcuVxbUIpP46fjGSn7Oiaq9sbFKHUJeR+ZSUN04JHmTGkZMZLhtLKSoaM+KKhKAdg90gppN9L76rdAE1mLI8yw6wULAbh+ICongjlGdMLrdZVZrL0uUn7LYmJB+3rEMqhePoSHrFsCp988snC91tuuQX33XcfFi9ejH333RcPPfQQHn/8cRx33HEAgIcffhiDBg3C4sWLcdRRR+HVV1/FihUrMG/ePPTs2ROHHnoobrrpJlx77bWYNm0aysrKpMdtbW1Fa6vLdpuamrK9zpxBdWL2QBKLRpBIWYr8EvKBMNeByFEmfJ2gVvWiBJN8likz9Mxefq9zdd5011biy9DPj/LN0Y1I01V2CtXfwgs6mkleXkV6dMmAnxCmFSLt5JW++nJlRnfVbPsa/D4z0urtDjLPjmY4E2OaZiZDbiZhTMwzY8IAs0xf/bQyo5M0z8goM/5TphaK9JqvTDPJKTuGsF16fIJ07DGfGUYpM/48MyVvZuJhmiaeeOIJ7Ny5Ew0NDVi2bBmSySRGjx7tlDnooIPQp08fLFq0CACwaNEiDB06FD179nTKjBkzBk1NTY66I8OMGTNQV1fn/PXu3TvX084aVMI0uxMri0Yy5fTqO2TIo+yQ9YnOtljyU7QFhGuTEIGY8ww0lRnZfiXlpMdHDsqM9uxfVxWQD6QFOqn3IeIoM+J2/TxB7mfdTpe6h7rRTJQ6pEtGKFNhsSQ+pNugfLsXFmNIecxJ6f0yj1OqIV1bKU2GeDKS3ihTdqikd/5oKPjWdgL0fWbAUrAYg+FbzkBOZryKDblqtiI0Oy+fG+bxmXHWx/LnmSlUhVCFrMnMBx98gJqaGpSXl+PnP/85nnvuOQwePBgbNmxAWVkZOnfuLJTv2bMnNmzYAADYsGGDQGTs3+3fKFx33XXYtm2b87d27dpsTztn+GZUHp+VeDQ7E4fXTJTtQOwlQ979liIEM5OEzNnKDJl0Lk9THTWYCi+84vaTEWl5hvUWaxuglBmaCMi2MeG/DV1Tnatu6RFCfxi9nAxlm6cm5Rm4C/UZUmRONzTeYvCEBdv3T3w26dDoYDNTmrRklB1faHdwNJNpuPV9ygy1nII3U6+VhDeaCshfmVGamZifzGTlMyNVx/wLaBZjqo+szEwAcOCBB+K9997Dtm3b8PTTT2PixIlYsGDB7jg3B+Xl5SgvL9+tx6AQNCuOZ8iIbhpz+82xy8cjBhKycp7jefenOxCUAmRRQ/y2mE0oswxv9yYa1fVbcupLSJYMVBvy1qDWp/M1IVtV0BxICg35+swArpko56R1EnVNeQ4+BSL933t8ilDnG9XY3vATerEfdLYTrN5LOhi33fQoNqYk6Z3PnJRxALYkDsBU0ju/ApJKZxDW9JkxDQ8ZccxcnvrEcgQ+ZSZznknPKtsqMxOv2lD1lcoMeBWMWGjSYAXbDlXImsyUlZVh//33BwAMHz4cS5cuxV133YUzzzwTiUQCW7duFdSZjRs3or6+HgBQX1+Pf/3rX8L+7Ggnu0yhgR7IdMkMoQpk2ltamTG1bf0d3QHYJXPuNtvU55X8bVC+JdpZmAMIrayMrLy3bK6DOR1JUxxtIFszk5TMwHYAzW0fFJnQn1RQyoy8vl9dyygzpl4baG/ohqaT129BHEgzH03LEhY/NGFIB2MGj7KCjG+Ht75hgFH9gJe0WKbPlwcALEtOZvx9eSq9crfHvkEpMxTp0CYjkJuZfGSIUJaYx9Tn+m/6Q7MLtR2qkHeeGcuy0NraiuHDhyMej+O1115zflu5ciUaGxvR0NAAAGhoaMAHH3yATZs2OWXmzp2L2tpaDB48ON9T2S0I6oTKYkFmIvnL7jdTUcenOlHvcYgLKAEIobgSMmcTSu/AIKsPZE8mfA68mf/8QKZKAe732WHCebjnRRFiz/6KPDzf8RPTHAhl2y0JkQEU6hrh26HrRE2aezPlncSNuv1AkT1DXSd61fXLHIC9KoZlQO4zY8HnW8MsE8wykfREOVGDuS+02kr7vPi3U/W9SfPSykzKlxAxS2XG1FdmZA7AuvUNZgpLSjg+N14yJVE8iwFZKTPXXXcdxo4diz59+mD79u14/PHHMX/+fLzyyiuoq6vDRRddhClTpqBr166ora3F5ZdfjoaGBhx11FEAgBNOOAGDBw/Geeedh5kzZ2LDhg24/vrrMWnSpHYzIwWBIiP29rIAZYaaOdrlY5HsyBAjthdj49OFzDXFHggjhmtm8vofOHWoZ8ARyqTJsn6G/KxUNQZRyxbomxgoMiWWosxUhQbaZ4Zi9JJNDIK8H4sYSFksa3NtvmYquw3EowZMi25D1Pn4o7G0qu9xUE702ShbpsTMlPRk2zUBMEkGXl9otwEwi2XIjKjMUDfRq8yYVhKmJaoVgF+pcLfLQrOZT9mhMghTPjNekqMMrebzzFjZmZkYY8K9sh2fU0xcfqFYlZmsyMymTZtw/vnnY/369airq8OwYcPwyiuv4PjjjwcA3HHHHYhEIpgwYQJaW1sxZswY3HvvvU79aDSKOXPm4JJLLkFDQwOqq6sxceJETJ8+vW2vqg1BDoR2JxZTO58GqQL2QKw9I8xsKFYTQy6QRzOl/0cjBqKRLJUZezvn95Q0TVqZ0VB2VHefakO5Z4+1lZ3imNV7QZuZ5OVl12UxJtz0aIbMZBveTUWqBdfP/OfaUEvS0j6+/U03T017g0oPoOsAbVmeHCeZj0nPOkbpPDF+MsCY1wEYYMyCZaaEATp9TnrKDLNSXJQUR4gIZYXPUwMAFks7AJsSkiQDpcwkvPeAXPVbvjaUts8N8zgAO+ZqsT6j+WBBIysy89BDDyl/r6iowKxZszBr1iyyTN++ffHiiy9mc9h2Bb3abfq7razoTirt+vbAGw8I7fYPhOn/xer8mQv4S/UOQoZhIB6xlRmKjMi/i35PJkmG/M/WP6tWjUFkJIinx9A1M1H+GoVMaPlzzXbVbJ3t8WgErSlLUVb+PVdTl5eQ2gpt1s/Q9D5D+fHbG5Q5zlWYg8ikd5HDNHwDPCBNemd5B+LMcgbMsuClDjSZ8ZeTKjNknhnP+2qZPj8UgFZmKAXGayaiorHI+ro+N5boAGwrM0nmqd/R8sx0FASFRgeZmeiEZ/ZAGrSuEKEqEJ1rKUKmgNjbIobrr0CZmShzhl080Inbtz/JeSnuP3V8bedJr02+CAltkrOBOcqMz8SiR0SA9KDPP69YYIoE+TOw76GzxEKW9XWjGql+pFiUmaBACPv+p3/zX4NlmZ7ss+kyXlXCgiE1MzHPQJwy0soMY35lhnLg9SorKdvnRdfM5HsPTVgM/vq6PjOEmYhSVvL1uWGwRFOfk6cmXGiyQ4DK3uozM1GdGJHK3u8zIz9+UCdKlSslyEiDvSliGE5Hqhse7yVE5Rkn7nzCalV3P4gQU8eh6jtmriJqA/ygnRn3yevyQjo4MlEFCMoCTapzdlShsy6JvD4dzZPegd0P6BJS9/jFmWfGNbdnrj8a4X6T1Lf8Pi8AkDI9ZMaAL70+AEmOlPQ2M5WC5SEzKY2keQBgZTL4+skMtVCl1+clHdrtPVtqoUuvs3O2ZqK8lRnmXc4gM5Z4lBkGemJRyAjJTAAoBcRVVtomminr7K/e/RahjVMX/DPwqiIRw3AIoa7PjNfvyY5I0yVDMudN5XIG5ECqqcxQ50+QpEJEilNmSAdgoq5UmYHY4UZzXNLC8V0LcsQnogft//GA+lQbKpaFJn2rFnjIYJlAZiTk0xQddV0zk8dfBACThDZbliVEM6WT3lkwLf+ijlSeGZ+ZiaUIZYWq7yWeZsYXyHOu2ZqZNMkIRYZ8PjdUBmGfqc/uRzznBXpiV8gIyUwAggaybOVlr7KSdSp+DxnynlepIcgx2jDcWbW+mUnch00osw3t1lVmSDKiaWah9meXt/v4QvaZSUrurXb2XMndtZho1882KtBLimOEUzJd3yYjojJB5ovSbQMF+gxpn6H09dvqJkD0hZ5U+g6Z8SozMGBJBmMG0+dzwywLKdNPZigzk9dR18pEI3mpB+kATDgQJzXNVPmaibR9bnSXMzDkZIYZ+lF5hYSQzATA7oTcASP93xuaTXdCxECY+ZD9jDLzv0g6wXwh95dwXzbezKSbNM87K89WmZFl4FXdfnog1HuG+gNx4bYBnmjaZ0lNFLywrzci2PvddyBiABHHdKWnrHgdWKNZKqReB94gMxO13dcGCnQQoSMC0//LODIjew8ty5KamUyrRShnGnJlg3mS41mGkVZFJMqMSZmZvFFHjHAAppYz8OaZyazNlPJFU+WmzESMiLA9qL7X58auT5rJCL8lK0Nmona/iMJ1RFchJDMBsF9irzTuKjMBPjPUQOo4n2aXNM8diNXHKRXIw3Ld+xKNqM1MssHJq6w46ho5kFEduZ4yQ6pr2mYm+fl4/a4KOc9MMuVehJdM6i62GuXYDJ/9NxoxEDGye4+8zzBb3zWvudgNBMgu11GxLBZKO7HbykzU+c0boQXIBlK7bFqZKctcuAkAsjwzppdKAIylYGZMLDHGYHMVymfFq6yYpikoKxWWvdaRbmi3CYtL2lduXwNlprK8ZEgkI+XRcmV9igzZ2yuiFcJ+vfBFY3nMTOWZZ8qKNGleSGYCYD9Sr4LiHQiz7gR9nSgxkBLnVUxhuflAdl/4gUxMmicnPv764r6zj0SRKTP0/adymegqM/6BUCwf5HdVCEhyg7xNurz+KkGqSISXyBkfiWQ4cS76GXzFZxh0D4NMjZVl6cE8oeu3lbkH/lW3C/MZ+gl1+r9X3QTk5l6fA6+jzKTJiD2Qpn1hJIOxx0SSPo6JRIYMxRlzBjNy1WuvbwuSSJquA7FzDiQZ8qztxEzBF6jCJgOUmcnrm2KJyoxNRnSVGa+yUxFT1/f7xmT6oYyhzT7/MDS7ROGdFXqVkViArbytO1H7OFTId6lBdlsY3PtiGIYbmi2RJqRkyJZXMzfNtveTeWbISBT1eVJwMwBr5pnx1vepg0GmzvYHf2/dXE3ed0te11vO3ua8m4arzFB3gDbXQtg3XV++P4fMxNNkJknIY9792t+LZaFJ0lSaea6xqOGY4mWTAsZMzyKHNqFPm4kq7BWkASI02/Sbc1gKyYzPTJwBtjZEDubeBSFNEwnO36TcaZeaPjMsBVgpJGGTocxxCDJFRS3ZPi/lsXJhu+/8qaR5pkiGdOszj5nJuX4ULqlWISQzAQhSZspsM5OmicLJYOsZiPT9PSA9XjHKgjqQm5ncPAgRw40kkSsztFrjXyyUciCGcyyAMjPpKzPegcAdBPTqO23QIdRqMlAI4Ad5/4QgINdS5n+UV2bA+btEDCd3De3E7fluiYQqyO+IMvfaba4iiMzk6TfV3vCZSp3tLqGMKZJXeqN+7MgkO1uuM5AaBhnN5KUIqVQKyVRGmYFLZhjTcwBmzBQIRoXTNxNkwJcB2IRlWa6ZyXkvcwutzlaZ8ZmpMmSISrrnNb850UyQmZmkuyhohGQmAHbf4lVm9FfNFr9765cHOp96XkAnT426XKmAMhPxpoeoIhqJvy0OGfEMpvEAB2KqDQhOrYrb7881lIa2suIbSERlKchUWQjg763XiZ3PEUPllAHSpI93xLffoQgX0ab7Htlwk74FmYu9+xOvIZjMyM+nePPMpP+nOEIZVTwDb9I7x2fGMTNx+5aQGcbE0GwASFopJ5opzoBIABnxPhnLSiFh8cqMfW16yg5jJpjlJu0rY3YyyDx9ZggyRK2O7SVDpDLjoYOuz0y6fIVt/kVxTo5DMhMArwOwtxOL57pqtq3sxNTKDOUv0VHMTNR95aOZ4k4nqjYzec0ZlvMM0gNRkLrm+Gw4nR5fRv8avBmAA1PhUxmAPU7oBToOAhBNgK4qkv5ur60FEGZF+/5HRN8Yfrut7iQ1w/OtTH17s0uodN9j8RnaZqZESu8Z2rsrFmXGrzDbZMwlM64juoyQmr6kd4CbZ6ac27/MTMMs5guBTqaSSGTK8j4zzOcqnNmvJIQ6mUoTgShjzto+uj4zFjPBuLWhymA422Xw+cwQPi/ZOgB7yRDtM0MpM+ntjrJkFG47VCEkMwHIV5mhnD8tD5nRru/pRKhypQLZe8mbmQzDHQyTUlu9+9l1IBWfoROJQvrMpOFrA5pmJoqQeh14dVUBbzSQ97wKEQmpmUlUZvhtPFwzn+gbY+8yqpU40f9dthwCdQv5yCm+nH2+FfGI7zqDjg9I3uMClfeDzp9XZqQTM0/SO9sB2JIpM5I8MV6fGyCjzGR8bmIAIrCfDeUA7OlLLROtmWOlyVDmPSLIkIzMWGbK8eVxlJkclyNwyEyWPjO2E3Swz43HR8+JIsyQGfu6UNh9CYWQzARA5qTIz+jKsh6I0v9dM5NaniblbWKAKzXIo5nEwUW1nAGlzAjPMBaUp0auzukuNEnOyn2Ljeqpc96BtNgcgL1kjl/XRzoO2sQV4hpKjjoXMbjEh/rrc5lC2wh6BnLi6FVmkini+MTkw74vhU5IfW3QsyxLWplRvIeWfKFJM+PfUsFVYVIyY0kXdLSVnTgMzmdGb22lFLOQcsgM53NDqXu+99hCIuXmycmazBBmopyjmQLqex2b/cpMpn8Lk+aVJryzcj5hG6Cf+dPnr+FJNqWrzHhntVS5UoE8Gkk0/didqIwQ8reVn1Xz24MXC03/j3gGHP7c1GamTH1uIOaPF5jnxteJivWLwQGY9y8ynfuX/h6kzNib0iHY/DN0SWZMoc7x+3CPI26LR4ImJaKKxFj6nbf3UVmWNlLQkxLPM8z895oKC/UZUqHl/KrZqgVfmTcDsO30zjJ5Zrjdm56swECaYJheZSaVchyI44xTZnR9ZljKiWaKg1NmKDOTzwHYjaYCNMxMmmairBea1KzvVWa8ZMY5fxTneBKSmQD4Z2TioBOccA1CfWdW7zFxUKqA13pRbPJ0vqBm6vZ2fjkDeUioTJmRE9KghSa94bu6ZiZffWdWL6aCDzJxeL870UwRNaEuBCRlodmWeF/Sv/nr8pFrvDLDb3fVuSyUmSwmJY4SyCXP5NtLtmYmbxbpeIDfVHuDJGOSSYVcmRHNRPZwa5MRXpmRkQHL8iszJpdnJgZD6TPDGPPnmbFSSFp8nhr7/dZLmpcyTSRNTpnJksx4yUhQnpj888x4Q7Mz220yYytLRnGOJyGZCYC3E2NgwoMOzgAsl5Ht8uVx29av7oTdsOCOpcxQGXwtvhNVEEJ+kzCr5glpTO0zY7/1jr+GzGdGQ5mhyFDQQOrduTd7bPCSGu0PwczkSZoXD1ikkHH3X4hm4sxMrjqnfg/d7yJ5DXLEd97jqPsM+ecflGeGyhdl+p6htHq7w1XH7O9+dTCqiAr0J80z0kscZEwfvDLjXcU5vQN/npmklXTMVGkzEe0zw5jfAdhiluOvEmeMyyKtZ2ZKmkkkM2QqwhjiTL2cAOkAbOo58CY998Xrc1MZq0xvp5Qpw+MA7KzNZCszmTYIemJXyAjJTAB0lRk6min9n0+Xzg9aQcpMkOOht1ypwfJ0ogAAgcy49yYoaV6Em1Xz24Ofgd0GMof3qGuZUyLhtCH7+JatzGQG84B1fbybvepcMZiZ1m/b5Xz25pnxJsPzgn/WfBZgfqLgEFpNB+D0pMT/HpPKDmdOsY/Nk5kKJ5pJMzQ7899us4Xu9yQzswFiVKHdvuXKjGwNI8shLnEYznIETBrNZPkcgFOm6UQ+xQyDcwCW9wPeId6yUg4Z4cmQNpmxTCc0PMYQaKaSKTOMMWfV60AzkYekZGumoqOZ0vspy3gNWShslZdCSGYC4CcTTJjBB+eZ8SgrHnm6PNMJ0tlnkakvDoTUwnmlBq/zrb3Nvv6IYSjzxPBRT/xAyD8v129JbSJwTQz+fajuv9/nRqwfSIg91+XNoBu08np7w7IYHvzn5+53z/mLZEZS3yG0bmh2mkykP0eFNqCZtM4Sn19QvidvP2Axsb25eWbk9alMv04biLkkqRDh+n3JFWbBZ0aa78mb5STtw2E6ixy6ZiJpNJPUzJSCCVtZ4R2AJWYq5o9mMpmJhJOnhiGSOQM6tFusnzJdMlSmoezIyAiv1uRrZso+NDuz3bCjmTJkxijsiRGFkMwowHeAEcJW7uSZIbO3pv/zyorQiWqGdse8JooOo8xkyCDvV8FtTy9noFpoMv2fN1HwPjeARtI8iOfgDa+3zynoGrzPMKVJZryX5VXnvLPlQkNLysTGJtdR0olmkvrMyJ6hn5DypsK031TGAVg7NFtsA8Err9v32vVt4Z+/vTaTtgOw4zel1wbaG9625iUzES7PjOwaTDMJJkl6xyszjrIhMZMw5ncAbk2lHGUmDs4B2Eeb0ufEDO+2FFK2MgPGTVb0lJmUZaI1mfaZEUPD9cxMJjMdExGQvbJikyNnocmA0G5fIIHdj9nKjJHJt4XQzFRyYJLOzjujKwtMxW532O6LLpg4HFu9ekbpWxvK0+mqGt/HG5pw4z+WY/OOVrJMocK+LCGVPXcPoxG135LgPMrtU2bqCzIVUk7cfBl1fdHJ0+8voVb3vNfEz4pV9dsbMiLBb48HJM0TVAGekHJkyHU+VZMJMaLNbRuqhG/8eTnvocUE4mMrO/oOwBCO57SBAnW89PZDXkIuRjNJ3kPTP8CanM9MDIba58UynQgoG0+8swZbdqXNl3EYnPIqi6aCT28xmYUU85uZmKR+ert4XUkzhR0tu7j6ap8ZL0mymCVk9bV9XrwKjHu+6jwzQT4zPmXGjijLXG95Jm1g2gG4MPsSFWLBRTouZP4WvCrA2/DJSBhnITt7n3J5m5wRehfC88zKbajMHCff8xaSJsOazc340wXfJcsVIihTBK+42L+pQrMNw4ARoK5Rpj67D4saXkLJl6HvvzsQiNekG5ZLmSgcMlDgzqPe87e/u6qb+5vcAdj/vjF4ImnsDMBSdY4JZMS0GBhcnxeeDAUrM/5+IBYxnElJNmszMeYSorKATOLtDdJ3j7+HioiylMSp12Qp18zEkRlTUlZGRgxYQMZEEjMiiNsOrBIyZDHmI0MW5/MSZwxxI4CMeMiMyUzsTLjKTCzAZ0amrNhkxoARSGa8ay55o6Hs+t5lD2x4fYm8PjPlRia9gGHALMLFmUJlRgG+6coc//isl3RIp0eehug4WKa5YrM3rNdbXNUJ2h380tXfkmUKFbwDsLuaAJ8B2HBm9pTjISCG9fIDkWFAWR/gB12PAyK/NpPyGjLPkEi6VxYQTeWf1XuUmQB1sL3h9/nJbOeIqmsClNTniCsfTSOQEYUDsKCw8o78nPNqxFF21G1A5jMTiRiOspIkljOQOexbkvMq0EcoSTGR3iBbm0n2DFKSlbBTluWsFxRDxCUDssGcmbA8ZiYYFgwjEw0Fw60v8XmxmF9vSVmcmYkxxA3bAZYw0/h8ZlwyE2dpQgUAqSwceG3iEovEEI/EAdBkxKesMAuMMad8VaxKWd9LxpwlJRwyE898pwlZISMkMwoIyowkaZ7QCZImgvR/0mfGXhcoC8dD+zyEchpEujlRfA2UD8Hm10ZyBzj33qiWMzA4GTrt75DeHuWeIe0z45a1z4l5fC5Ug5DTBjwKjE2GHOdR3Rwpmf+umam4cpR4HYCFZQok12C5D1EwFfLvVtx5hgHrcwnP0K0ftFCl7z20XDNXLGI46hhlZvItDAsmnGux+My4voPidn7VbGptJi9My3RDq2E4ZpqUxOdFlgHYgOmEG8cRcciENE8Ng0+ZSZgpJDPLIZQx5tY3KGXGc/4shR2tzZn6BmJOaLOemSlpJbErlTZTVcQqEIuklZEgM5PtBp+0kkhYCWe/nco6Kevb52XYRDRDzpIZQlhjlGe+GwBhqipkhGRGAb5f4SNZ+FmaY2YiHYA9MzrLNXEYhmtioAYyX1ivZ1buPY4KxZiimjcxuJEsYueqkrcd511emWGciYIbyGifGVGZYZ5ZNX8cVX3qGVYGRML4UuF7zDRBuY7aG35n9cz5cxFpbnZkf33epCiuzeSqc7YyI7uH/D6j3BpMvJlKteJzurx9r13S4agSXDQVtZyBl6Sl/bbc766ZSVq93WGfv2tm8yozEaXPTIpTC2KZfbWYLUgi4+/BIi4ZkPnMmO7KSFXOTCYJFknvt5ozMzFDRoZcZaYmc+NbUrvQmkqTkWrOzMRgyUl15prt+q0sgZ3JnenzB5zzJ5WZzHabdDSnmtGcOX5VrCpQmfGSlp3JnWhONju/15bX6tXP3IiWDJlJZMhbVYbMWIaBVKr4/CtDMqOAIE9zUr7JdWLe3CG+fWT+u0nvIKgCqnWF0sfL1Fdkn7XPqxQhNTFwOUJ4503VQOYdCPn6Knkc4BUAd5/ZZGD2qnM27E6/wgnPp0Iqvc86s91nZqLPoT3hbZrepHm8L4w8Fb6fkPJ+T1EDSgdg/t3gV8c2hTYU5DOT/s9HvtnHikZdnxnaAdhPSItLmUn/d9IL2M/QITNQEkKLMzN1ylTekWxGIuOAWwlDTWaYu1BljX3bIq0OmalC1CEjcjOTq6zYx99ltaDFSisjVZaFeCY0mRmm/Boyg39t5hkn0YqdmbWZqpiBqOL4gGtmqi1Lkw6ejFTHq10yY6rNTA4ZSrpkqCJa4azNRPrMZO5ATebaWiIs7beTWaahJlLhXqsZkpmSgiyxWjJlCTN123kxKBU+H7bIqwKqsGJAMiPKfPcNEIXZB+YN3jfGMNxZNe/Yq+xEORXMUXYsLhoqQB4XlkPgzVSE2qC6Bu+yA7aPTEVg9lj5/uzthW5moog3r67FFT4vIqHl2oDE+VTuAOx+5s21vGoa1VwOgfdx4/PcOP2DbjQTxPsSL3S/J08/ZENUZuhgBnsgjjGGapvMJLYjifSgWcFirrIhXY7AdLbW2DHWkQSsjApTbUSd+rI8MbwDsE1GWsxdaDXTZKaSMec9gmHJsxhn/tdaNplJOGSiEoZDhkwiGsq+BzIyUxWrQjya8VmhzEwKMlQVd5Ud2syUhk0GTQNoSjQ5v9dEqpzPKW6ZhmJBGM2kAN+cnQyfpuV2YhHDZzrw7cMzK7f4TtjgoyjUnaB3RkRJ96UG1xTBm5lEMqJaMZlxxJF3AObzY0QUz4C/rbyZiYrQkV6D9xlmvvtWXNY0M7n7tU0fha3M0GQm/T3CkRGVz4s3aR5PcmLOQKrvMyM48jvKkPoZOKTLspxjRTmfGVu186pwsveVP5aT+LBAH6K/DWcIOafMOOZaGZnJDLARBlRnft+Z3IlkJuldJSKusiFbzsC0nDw1aTLDEIm2wLSVGSOSVmYYYBJkxt5ayykzLKMYVVlAa8YBmIFQZjIjQieHzCTRkhn0qxBFzM7TEhCaLZiJUi4ZidnRRAEOwDyZccxcsUrH54Z2AE7Xr+YubVPzJgDp5RgqDFeZSYbKTGmB7wTtNZQSKUtM4a0ZzSRbdVsMZwyoz60NxW/XGciqyqL0jwUOfvbO+7zwYb3K/BaSWb1/IAxWdgDxGXiPpcrz40akid/t49mLFGqv6+MZSAo9A7D3tGzOxpuZHAdaSTSQQ2YgJs3jfV5cQhvwDDmfGcHvKtBnJv2fX/rC5k2xiOGE96evgSbV7jmJeYK82aULDX6FOL3d7cvUPjN2hFIEQHVmUN+Z2IlEhsxUwFVWZGSATzhXnVkDCZEErEiGjBhRJxpJlieGMTjKjE1GWswWh4xUMjj1YVjydmQrOzaZMZL4pnl7+viIOg7EQcoMT2ZsMsIrM7o+M82pZqc+b6YilZlM/SgzUJm5hq93fZ0+PmOIRmOIOP5s/pXLCx0hmVGA73/sqKMEZ2aKRhAYzSRzHjW5Tjwoc6kNbzSTN3uqysRQzGTGdf4UlyPgnUJVS0qIfhl2ZY+JQfEM+V0KDsCEU64Mbq4g0cnTF82k7TNjdzgZQhsp7IHQe/7eVbMjhjoaya7NE1fT8iTN03QAFjP4wjl+VDM8v4xb9sA+10jEJVOA3G+GJz6ZExDCml0H6MJ8iP5opgyhlppr/ddvD+RRMMdnY2dyu0NmKg1X2ZBFI6U4P5IalunPIq0wM2SmJhJ388QEhGbbZKaVtaDVypAZC4hHXJ8ZeTsUlRkzkgSLpAf96ginzFA+Mx5lpTnZnJWZyN5ukxkA2NyyOV0/VhWozDjBEDAcJ+qvmzNkxrIAI+qYaszQAbi0wBMEe/bcarrKjOgATO0j/Z+PmJFnLtVTdrydSDwSLE9XlRWvNZE2MbjblUnzMpvS9d1Bn1fXYqrlEDhjIz979g7QlHkC4CPS3O98aLerzKhVAeea7Fmxo84Vtoki0MwUEI3EuIGUN0fJVs2WRrRJHID5Z6inkKb/xznfGCFpHrfytzx5o/c9dn2m+KjIAuUyrn8Wt2o44F5DLKrOACyamWyfmR2OmanKiLkZdGU+M5zaUWWvwhRJOGSmOhJzyIhlmHIlLHOPax0y04pW2wGYAfEMGYBhEWamNFwykwIz7GiqmJunxiAUVivAzBRkJsooK9XxakQzx3LICEeGLGZJswDb8WBRuOrYN7u+Sde3GBCJIO6oviGZKSlQyozowKsnT/Ozf74TjSpmpIBsRufZbnfCik6QV2YKdcCjwJuTZA7AaRNFsJlITJrHhO2qZyhzHvWuuAwEKDMOoeWcwLnygcqMZ99eiT9oSYz2BpUTiVctndDmAELKm6MsjiTa74cs15AleYb8MwgyVfLnKigzpnv+hhFwDZ5+wGLMSccQjYgm0EKEP/GjOKkKMtW5yoxLZtbuWOMkoqtEzFVmJGTGFMxM6UGbRRJIOaHZUcQzPicMfgdey+J9ZtK/tbIWbDPTykYFAxfNJHcAds1M6d9S0SSaqtL1a6Jl2mYmW5lJWAnM+XwOgOxCsyNGBFXxtLPuH//zR7d+xkwF+NeBStfPTAARcfyW7v733en6zAIiUcQc1bjEzUwzZszAd7/7XXTq1Ak9evTAKaecgpUrVwplWlpaMGnSJHTr1g01NTWYMGECNm7cKJRpbGzEuHHjUFVVhR49emDq1KlIpeTSWnuC71gqOJ8ZXlmxB8ggM5Pt5On1uVFFcfD1vY53zqwwGhzJUl3uKjM7EoV3n1UQfV7SnxlEE0VUYapjQn3eCTu9PRpASGUOwDJlRuUzwxMye598Z1kZsHK6L2mepw2Ux9TKTnuDNJMJhFQVzeQST95hXsw1ZNdXOwDLliXhFdagSUmc85mxr8s+p7giC7DPdw6ub015LKrMs1MIcN4jwmcmxkdmSsmMq8zYZqa/ffGc83tlNM5l4JWYqbjQ7upMptp1dV8hGbOVnTjKIq7Pi5dQMgZfNM8GfIltVsZMwwzEIi6ZUToAZ6KpErFWNJelfVZqonGHTOn6zADAmqY16WvS8HlxCGEkiup4NQA4Sfd4ZQeQh3ezjGIUgeEQShvVFoPBm5lKncwsWLAAkyZNwuLFizF37lwkk0mccMIJ2Llzp1Nm8uTJ+Mc//oGnnnoKCxYswLp163Daaac5v5umiXHjxiGRSODtt9/GI488gtmzZ+OGG25ou6tqI/Dt2ckjkfKYmTQdgO0BqyVpeToAd0YoXTE489/rIOg4AAckfLOPY2Nna3GRGd7EIDMz6Trw8onZGKeO8U7ccgdid5vjFuFRVtLHpq/BPytHVsqM1z/KSwbKA/LUtDdIMxNv6lMkj7SrGxBJj325vLITRIZsQsmbeyMRdRvg91HGRc7xkxJw55aQLKroOuy76pxLZiI+X5RCQ1DyTtHUJ3kGmXsSgZFWATyIRmJONI/MzMQ7BVcZZb7fqyJx1+RuWD5CaTEG03YAltziShiOmYlJyBDAmZkk9asjcdfMFUBmyqJlzgrZzvlr+Lw4ZMaIojpW7a9vcGRGsg+HUMNAtXeCAQBGBHF7fDGLj8xk5Uzx8ssvC99nz56NHj16YNmyZTjmmGOwbds2PPTQQ3j88cdx3HHHAQAefvhhDBo0CIsXL8ZRRx2FV199FStWrMC8efPQs2dPHHroobjppptw7bXXYtq0aSgr8zfU1tZWtLa6NrympiZfmd0BPntsmdNRecxMAZ2Q/V5XZkw9rSnTeVHKYhGBaFiMGzDtc5DM6AAuikAjmok/NXIxxQKFM5AZtJlJL6wXZDSUMs8M95kiI/xx5LCfoauiCcpMWdByBu7xTcv1tbHPwV2xuTCfLeXzo5tnhgnl0tv49zDK+T2plrSIcH5TXkLrJB4kXiS7bBm3MKyzYnTUJjO2mUlGqNxzsM+plSczAZOi9oaXkHv7oRif4kBy/byZqUZyjZFI1On7ZMoGv15TpYzMGDGUZZQHZjDfu5R2AE4foJPkNatghmumopQZ28zE/BpAVSQOk9lkjBoLMmZFI62stHLhz7zPC5k0L+MHEzEijjJjI8VSiEaiiBpRmMyUqjs2yYoYBio8ZG1TLNqxzExebNu2DQDQtWtXAMCyZcuQTCYxevRop8xBBx2EPn36YNGiRQCARYsWYejQoejZs6dTZsyYMWhqasLy5culx5kxYwbq6uqcv969e+dz2tqwO0HDMARlRrauT5A8bc++W1OW04nFoxGnIwTUtnY3WZclbI9zfhgU+N8KdeZHQaasAF5lhpa3eTLkmhg4J2zNpHuA6FuTS54ZfsVlQZnJ+GNRixR6zRmMiYNeUAbh9kawA7DbjmXvgF1bNCcxgejHFLmG5OochPdY5bvGr7rNJ81Lcu8xALUjue8ZMrSmTGefhW5m8jkwWyplRvYM3NDsGsnvRiSqVGbswTnKgEqj3Pd7hRF1lBVLoqykHYDTn52kexyqWMRRRizKZ8Yua0Sc9Y2c+tFyob4MNhmJRqLOopA2Ws1WN2mexN8lfQ3p/caMmOMzY8POF6NSd9xopojPzLQpGgWMqO1aDZN1IDJjWRauuuoqjBgxAkOGDAEAbNiwAWVlZejcubNQtmfPntiwYYNThicy9u/2bzJcd9112LZtm/O3du3aXE87KzCus3WUGa8DcEB+CLsTs81MrUnTmbnFoxE36yTUg6ntxLsraQrbg2aUfFnqGIUM0YHXvdduMjzOj0KqTLgDmV3ftMROWGVikDmB85Esznlq3H+eDDlhvYY7QFJmJtlAyPuhlMdoMlcI8PnMWDaZ8Ssjqkggw+DzvLizZ8OANqHl25Bsfa4gv6myqLswrH0sm4ipTWUeMsA4n5l4tODNTD5lxpnBu21TleLAURWY4UQD8YhEo0ozjWXXB1AZrfD9bkRjgs+Ld2LAmOsAXCchM5XMNTNZ8E9WADjLGUSNqE9dShkRlEXSilGQz0zEiKCuvE74rSXV4pA5KhqJr9+5vLPw27otaQKkciK2uNDsLp5nsG8qlYlmsscTuTpUyMiZzEyaNAkffvghnnjiibY8HynKy8tRW1sr/O0J8OG//NoruuuR8PuoLMuEdqcsZ0ZXFhOVGdVgWp0Jr7ZXvvaumKwax/jfCnS8I8GbCOzZK29qMYyg7LFu/cqME/eupCls1w3rrRASJ4rllA7AlsdEwYf3B0Ty2NcLiMnxZD43smRthQBZmCzA+cxEICguXgjPKnOvBN+1IGWG823h87mI77GKDLnb4jG3rdnPyzUz0dfgX0eLuWamaIRTjArzBfUvNJkG70StWt/K4sxMQ1rFWf+p23fAEHxmZO+xTYaAfSKdhd8GJhIAF81kwW9mMrkMwnWeYa9fIpnOIMz5zEidmDP/o0YUPTmH5PpUCkPLuiAasxdqpMYC18w0uNtgZ3t5tBynDTxNiEaSkRHeAfjgbgcLv733/gis27pLufK2E5ptRHCw5xnM3LRZdADuKMrMZZddhjlz5uCNN97Avvvu62yvr69HIpHA1q1bhfIbN25EfX29U8Yb3WR/t8sUCvjMo5QDcHDSvPR/O9dLa9JykmqVRSOOsgOo0/FXlWeUmQyZcaMrgmd0fAdZKDM/xhgu/csynPfQEi1VwzAMQRnRzRNj79swOELYmhIjWTRn5U54vum3qavzzKT/V3FO4Ckux0hckWMFkDsQ88SnvMBDs73NWpZnpkxB6EQVjsvAyz1DfgFIf337OLzfFfO0AftcaTIFcMqQ6YZmxxwzk8rUlf7PKxu2mak8HhEUo0IEle+KT/znrG+l8JkxANRzDtJdd3XCtG++RSQSRXksY2aRRTNlTC9RAOUeZeaxdRuBCKfsSMxM/KrdEUQFU9fT69YjYrhmLsuw5M8w01VHIzH0Trpk4ZW161AZrUA849RLKTOOqcyIYv/O+zvb3zrrLXSr7OaoKnxZ4fhcaPahPQ51tu9cNRks2Q1bmhOaykwEh3A+qBd80x0HJJNAJIqonYur1JUZxhguu+wyPPfcc3j99dfRv39/4ffhw4cjHo/jtddec7atXLkSjY2NaGhoAAA0NDTggw8+wKZNm5wyc+fORW1tLQYPHoxCAhM62/StWrGuCTsz4c2CmSlAmbEHnBaPA3AkIioO/vrp/3Z4tW1m4melfDkZeKJVKGamXUkTL36wAf/89Bus3dJMluP9KmxC2cov9mmIOUIoFSBiGKgqd9Ut0dYfnEEY4BxtU9mZmexfbEfflpTpmgkjEWG9HxnsffOz4jWb0/esS1UctZWZQaBAHYD9K4zbZMZPSOV5YuxJhYGymEsY+Ggm9crp7nH4tuKaqdQLvvLPWnQAzvjMZPbpJP6TEirxGVqMoTVZPNFMfkKdeYa8OqalzKTL3Pj1ZiBVie9u2g8RAEY0jopY2kwjIzO8zw1i5bhgazoIZOjmfVHFGGBEODLCfGYm3qk2Eongf77dAgDotG0/lDOkI3nspHOGf7kSwPWZiUSi+HFTehmDvc3q9DlFyxDPKDOmhjJz9L5HAwDKImWoiKXJmRBaLSETPBnilR0r2RkA8O3OhDK82w1oiWAv03L8fvZN2skCI4gxe3JefGQmq2imSZMm4fHHH8ff/vY3dOrUyfFxqaurQ2VlJerq6nDRRRdhypQp6Nq1K2pra3H55ZejoaEBRx11FADghBNOwODBg3Heeedh5syZ2LBhA66//npMmjQJ5eV+x672hOsA7HZi21tTuPKJ9wBkPPhtIkJ1QpnNTjRTkncAdjvBRMqSd4KZHdiqgq3MOE6hGnlm+DGyUMjM1mb3ZVGpGvxAZJOJ1qTlJiM0xFTypsUE0x3jSI+tjDQnUiIZUszqhVk5R6Ycf4mogaTJ1GYmx9RoKzOmMKPlV3xmjDmzdG993kSx6usdAID9e9Q4g4gsjX4hgDQzcc9AZSbi30ObtCRMMfGhWhVx1Tl+dWu7jpCJW/Ic+U088Uya4nNRLXhqXzNvErSfF59nRtWO2hNu0kAxJ5KwWKdyUuD6zADAaTt24uZvJqN7dD4QB6LRKCpjrpnJspijevPHjzDAipZh8patYNsPRLJl7/QoZkTcNBWG5TczcU61UcRw+vad+OvOs4DWvYD4/LSZKuKSIX+iSnfV7ZgRRUNLKx7pfSr6tDYDjR8B0Rhi0DMzRYwIenfqjSfHP+kk0LO329FI8tBqlwyVRcvw91P+jmf/vQb3fJROjfLtzoTSAdg2M9nJ/R5YY8L8yWNIzb4eQNoJ29aGSl6Zue+++7Bt2zYce+yx6NWrl/P35JNPOmXuuOMOjB8/HhMmTMAxxxyD+vp6PPvss87v0WgUc+bMQTQaRUNDA84991ycf/75mD59ettdVRuBcc6jZTH/rSqPRYUkUjJC4c0zw4dmxz3ytEyetd/JakdVSGWO55/pUeB/K5S+kiczqtw3PBlxQ5BN8OYnfoViX+ZPQZlJP4OdCbc+v2KyPDTbPT7vqGs6ZMYmkzShdJy44+lnSJmZ0vuWSfTp//y6Qp9tcslMUOLF9gadNM99hjzJ8ELmM5MyxeSVNqlXqZtCpuGUm6cmm4i2uGBmsn1mxPdYpQ7VZN7jnQnTUWbKuNDsQnk/vfCaSlMWE96DIGXG5Bx4rcywE4WJiB0uHI2hMu4qI9s9fYKZGVwjMGAZZYgA2L81ijL7UBHOZ8ZwI82c+h5lBgA6tXRDPJNNGEYEsYzPiiVZSNZinDKTOc53KntiL1sPiMRRFqtMHwvyvoD3eQGAwd0GY99O+wplVD4vvAMwAPSv649dO3o4v3+zI6EM77Y4ZQYAupsMAzsfDCNzZQZnZjKJiKpCRlbKjI5zWkVFBWbNmoVZs2aRZfr27YsXX3wxm0O3C/goiHIJmSmLiT4vFvPniXF9Zlx/Cd4BGBCzmvrPwVZmbFVBNDO5iyzS18E/tkKZ+W3d5TqY7WihXxw+lb094LUmLTGslyMDVBiwYRiCuuWYKAKS5rmqgCc835mpRoRnEvM2APjbwIJPvkafrunQyrJoRFjXJ2VZKPPMMfxhsXCUmQHdaxBXEIFCAJ+LJMU5b/Omi5gyRwtHKLlrleWJkUUS8XlqeEf+CllEXACZEReazLyDHjOT9D22RDLT3JpyfWZ4M1OBKKdeOFGZ3NIoaYUxoxbw0UzSfsxNmpcy4ihjrSg3Uog6ZCaKcttMA4btLUnUVca5+nb2WsDMkI4ypHDRiL7Av5BWZqK2zwzztSOT89Oxo54A5pCptM9Ner+mwXzXYHHRUJFoZthkFmArGNE44kibi5iRJh58EjtATHoHANiyBpg3DWi4DNh3OIB0NFKr2apUZmzCc+/8z/D/3vrC+f3bnVx4t9QBONMXZghLBBauffo/+ImdkDBir1xuCT5GxYK88syUOgQH4Kh/5Wk+2RWgntVVcMoM7wAM8J0gPZjaykxrxgHZLmrPNJVmpgLzmbn6qfdx/kP/cr57Z2E8hIFM6jPjUWY8nRhf3+6Id7aKZiYhcaFPXoZ7fD5xoodMAjRRlA0Ejy5eA8Af0SZNhe/xmbEYw4Zt6dV+9+1SyUVzybNItzfsU4p5nNVFM5Ne0jye9PDKTFzp8wLnOO7aTpaw34hyIHY/O47KFuPMTOl9qrMQp/93qkgPNjsTKU8GYP+xCgleMxmQmZhlrrUsGlFn0oarzCQzSe/KkETUcJUZ3melaZfYJzih2QwwM/XjSKG2PHPj+GgoSdI8nhzYykgEDBHbD8CIIB4tc47vJUMWY7AyhNMhKcwCbAUkEkdZ3HVM1lFW8Or1wPJngf93HJ5e9iW2NSeVPi980rykaWHmy+JSQpt3JJxzU+aZyRyjzEjhjZVfozXjzGxEIojDHouKj8wU73LKewDOQBahzEwRYSCVmXrsTZVc0jw+zwzAObAqVm2uLhdnRFSKexn4Qba9B7vmRApPL/tS2KYyM/EmBsdnJuWuisvPqgH/rJh34nbULc/948lEwrRQEXHvNe98KjgAW24n7pQlhBFvG+BR7skCrcpR4qh7KQvf7kwrW91qyp2BPH39TPAhKgQ4xC8SQQtcIsqHTJepVA1OIeV9VpwZpqGn7HjNWVITiYJMAbwyY3HmYtv3RmUqS++jU4XrhN7KKbSF7wBs36t0m21NWWhJiiZz9bIiGQUHBsxIGWAB5Ug6ykg0GkWMJzMt4mBqcg7EZqbc8dF3gV2HpgvEypHJnJAOzfaYmWwfEIMxGJn6BpijDCEScY5vSn1m3NDsiN0/MAuwSUc0hjiXyC5pJVEBMerKXjXbcfTd+bXz29VPvYd9OlehfD+ajPDKTnOrPw/N5p0JxKuCo5ns669EOqLJvgfxWAwxwyDrFzpCZUYB5gxkkJIZr5lJ5XPBO3+2esxMcWUnkP7Pz4iaE6bPAVg1oxPMTO089bP9BHjs0PKZMVAuyfNiZJLhUbNCfvZfxUn8vInCDrkG3BTzzvFhH0ec1XvT2wOq8HyRjPAoi0UCV1y2N9VkZvXNrSls3pEmM12ry5zcJ1T99gaV4JH3ZVE5MfOmPj6MnX+GqogwmbrnDe9XTQr4JiXzmbG3xVXKRGaT4zPTmuKWM3AdgNt7skGBnxRUSP3/1H5HJpcnxoykzUnlSDoDqRGJcdFAFpp2iYMpH82U4pczeOdP6f+xCkHZ8ZuZ3NBuQ1BmMsc3oo6JxjTk/YgTmm0vJdC6PT9lptsA57d6fIuvuDwxUp8XzoF4p2TB4H83boEB26dJYWbKJPerRLoPsZ9BLBbPmJmKU5kJyYwCdnOOeCJmbKQdgN3vssHMfifsWbnFgF2ZhugoMxqZQ2ORiLOPXQk+tDc7B+C28Jl5t3ELRt32Bl7+UJ6xWYWWlH9GoSIz/Kzc8ZnxhGYDtM+DxQ2Ets9M2gEYmfrpZ2tz0lbP+fF5anh/C9fMpCaz/DlUEmQmvZ/MICsxM9kDXKfMQLilOeGY5rpVlwlZpAtx5Wx3Vi8Sb10zk8wkmDQtN7Q57vodeWfkgDgQu4tBcnlmIkFLYrjb+HJ29GHMkwFYbi5Ob6uRKDPlseLJM2MYhpM8kjczxQKUGeb4vLjKSprMuP4adp4Wy2DY7vGjswdyA4BpxOFDrByxqGtm8pJadzkEBmTIhEBmIhHE7Qy+Up8ZzgG4JpPBfscmwF6QMVqGWLwyvX8ArSl/0jmfzwx3jCGR1ekyZoaMSBxw7W3RSNQJBLHRo1M5vtmRwNad6X1KzUx2lJVNZowEeL+hWCzmRDpRK3cXMkIyo4AsAzAPXh4G5M57Xp8ZwB28XWWGjsTgw1KrHDNJynkPnFV4NcKb0+XIYtq49LF3sWZzM37+2LKs60qVGZUDsERBaeX8HWxljArN5cNynSUhEqKZyfCEfcsgOI+mCJ8ZxSAGuIkTeTh+UxGa0NoE1J7Vf7lll1OntiIuEKpCXJ/JPqUyr88Mp4yowprFhSZd0rMrY+uvikcddUoaDWb56/PqHq/MqFded8mrafHKjMcBWKquic9wJ+cAXExmpojh9mUtSdO51rTPDG1mMzkHYCtDWsqNhGvmMaKIZ5LhmfCbmZw8NSxjpvIiVoG44Sor3mzYpun67EScTLtMOH7MITOS+pbl+MxEbTKz82vBzBSJVyBukxnJqtO8sgIALLHD+a2vsTFzHLsdq0Oz7aADGz8YlI5qSpl2riOJmcrxmXFToKRNfZm+LBZDPEO0ijGaKSQzCriRNEA54QDsjWbiwS9Qx8/KmzKDd5lja6dNDHxocmWZX5lxMwAHX0e6XP6d5Tc7WoMLEchembFfQDhmprQyk/7dntFSTtT8rNwmMzsTKSFpHiAuBCo7ftoJ3FVm7HK8H0xQ9ljKzMT/V4Um2/4W9rG7VJchkiFjqrDg9oZrZooI3/lnyCsm/vrp/zzpeXTxGiz5/FsA6fcizj0bf14bl9C6pMdyZt/8siRynxn3+Py76sszo9iH1GdGSJonlis08M/AXhi1JWk5zysW5U2lsut3lZmUxMyESBQxZzkA/wTHWfEZQL+enf0nGKtwzUSQOfCmB/cYSysQ6X0xGLb+Hok6DsAAkPQoEykuGipa2yv9YcdGwcyUJjPpr60pRdK7jJnrm2+/dX6zyYxlZbKcy8gI5wC80+MzYyfONE1bHaST5oEjg5VodZywy0IyU7qQ5RjhURYQzcT3S/zM3pZQHVu7xro0hvH/2/vuMDmKM/23e/Lu7GzSaldZKEsoIQFCIKKECYeBwzbBWBwcR/IJDrCJ5sBgzvjAh43B2MY+DJwB8+MczglsLMAEk0FCSCIISSivwuYwsev3R3dVV/d0qF7tamd36n2efXZ3Znq6qrq76q3ve7/vUyxuJrpbFxEA97ebaX++IR1YM6P/5kWiGS6DrlpkmbFfA/MasizK2QLsbipeXOx6fuMznzR34T+f/RCArsPxWoistZ2cCTHgcw/QXX3catmprzQnJa9CjYONYpeo9fWQIlb1Gpw7CgA27tWThcUjIYuFzCvXUIwTANN7MREJ+YhXiy07fLFQ081Ed8Xuri5KZvIawR/X7ARgFJrkwu5LEfxzFOf0f7wA2Fu3xEUzwQytVnnLjJEJN6+QogSiBY1qZhSMr3ayzMQ4AW9xG/IFejwBjAW7riJihobbyEzGZhnJc4UfQ0mj7E7XbvCh2aFIFGFjdkz7WFa2t/Vi804zCz4lM9Sy4lXOIKyEi9xMNIy94EGGmJtMDSMDKgLOmm6mSNj0EjhULi91SDLjAXMOVRzzzFC3hxuh4P/jk751GSZU5mby3JWblgG6s//+yk/McFehQpO8m6kfyMx+EKJMzsEyI+BmUjgBsD3PDMCXNCj2ddPjnUKzqWWNd2E5n9+0zHRl8qycQEUk5OOiMP92sszQ87ICih73AHVRUNQnzck3SK6Z9t4cbvr1GvzJWEwHGnaXXJEAWDFdrd61lawJBikqoiFLVJl9DPiFOGIhM/q9GOevocO9zVfn5oXmeRaVqFh+e1lmKl1cjUHdTK3dWVzz1Cq89PEe/w/3A/h8T3GuNAvvZvLSfZmh2Qqy0O/bGLIWywwlMwXF3V2sQoHjdoqzzOQV4uAmMgXAVDMTC8F6ft4yY9O88JaacGq0/kfHduDT543jw1AjMeZmSueKrde8AHh7ay+SSLP3xhtkJpc35riC9/HdnJvpV1ccycgMJUNZBzcXX84gTahuJsPcTKoaRlRx1+yUOiSZ8QDhJltVVfDEJYss71My4pZBlp+YFC4KoMNumfF0M9E2KGhM6Q/7m5tM8ySr1+NpmXH+u6/gvyIosUk7CDRFBMA6GSzWzNAdrZtlg7fA8NlX6aJJj3fTzPBkyMk6VxELsYXIL3usl5vJXAjc9RZVNsvM4RPr2d8RFzebE5b/9xt48s0t+MZv1vh+tj9Am1SUZ4a7BhEBMsdrVngkIiHL6/bF1OKm4nIV9XJkxlu8ah7Pa7PseWbMgqfursJIWC3aGMUipptJ1HL6P69/ht+8tx0XPPym/4f7ATypj3MFU3NObiZHy4zpZsoYGUFiSs5imUlEqwAAvUrxXMiimQiASccD88+3niAcQ0VIz8CbUR00L/zxxvMaDSvMzaSoKiJhk4z0Fqz14vJclexw1ajiAQpFEArHUWFc6K5st+sYhJUwurN5VHBkZoyyFwBBNmtELOaL69XxGYRpEMkJM0Zi4YRa0zKT10lKd674/LRmlKqoSMOMaDJ1QyqSNIOwIqOZhhV4qwAAHDl5hGUHSCclGkziRWZUBWhM6T5hmoo+YlvInPQCvPjxni/NK3rfrkNwAk849jc0m9cBASYxE0XawTJjF7Px4E38TgJcKlnyD83WxbKAPgY0tw1zMzE9jr0t3q7GimjIM6KMf8nRVckIrb9lotJmmfnnJRPZ32EPQmz9LoL3t7UDAFp7cvulfxJFcdI/43Xu2niJZ61kongME9GQIeTW/7cTIufaTIS5mXjLjJebiy+bwBeapP0ysxC7W3dURSm6jhrxjuZyAv8cHYhrSFulctFMvbkCIy68m8lRAEzzyUBBIqHnY4khhzCX5yUZqwEAdKsq8nm7ZoazzKgh4MwHgaknmR8Ix5E08rxoCpDJpy3Hm5YZAhgWoEo1Z3EzQQ2zatp2MlHgLDNqtBJI1Fo7qEagRqJIGoSlkxP3mt9hWlbaerKoUMw2RpUCqtCLTM5IquhARng3FdXM0A0SJTO5nDuZoZaZkBJCr2GZiSNjyYJcaZCZrCQzwwv8JEjB76rsegf7JGrXzCyd0Wh5n/rvKanx2tEpin7DNqWsiZjM6Ar3fvAEZn8FhnYryruftQY63u7GAVDk/+XhVJspky8wIbHdTWMfQ2t+DJWNV6tRGyrELDNubib9t6tlJhrmssd6W2ZokjcepqvR3TLBcgqp1uNpNlmAd594X99uG3E89M6/Yndn2uXT/QN7TiRAJzjWzLz+LhrFwzJjzUHj4qLgLDM5zjKja2bcIwr5Z5DXZpluJrvuyV3IH+KE6BQ9mbzZf0HRDO9ue+3TfULH7A+copm6M3n2fEVCiqt1FLCGZk9s0i2KlywejTmjk/oH1DCSsWr9e1UFxOYmoZYZyzMUNZPUIRxDIhRnlaC781YywWcQRkS34FQoWeZiUdQQoIRQaYx/umAlA7xlJqSGgKR1LkcoCoRiqDRuFkcyw4Vmt3TnUAkrCa1TOkAK+vze5XA81dGoisrmTOq2pGQmY1h2unLFxzPNjKKi1yiKmbBFlFUZrracki/ZnEdukGTGA/xuhILu4AFzIeIzw/KwupmAZTOtDwBdwKIe4k07oRpRZRW/8cUH3WB1M+3fDUq1IhQXPfJWoJs+uGVG/61wodmvb9yHX7y+BYD5ELu5CfjxUxSFfX5Ppz6R0AmYXkN7+3hXY9RFr+ElwuZfUYrX4SI3k3eeFfML7NmEvUKbebR0FfvSX/54r+cx+wu7AJi+ZrHM0LBeLzIBN82MPqG7Fau0ishN0phhbiaVWVe98szYQ7gp8aRE2rvQpNEHBZaaQwBw2rzRZv8FLTN86PLaHR1CxzihO5PHafe/jLsNQbsbLJsC4znktW6RkOqt++IsMyEjudyYqhBmjjQ2Z2oEybhu7UirKgrZXsvxBS6DsHnSSvPvcBxqKIJKo6FulhUVAAwLzqQaFSFFvwcUNQyoISSNC5W2HZ/L6/OFQghUNQxUNlg7GAoD4Siz7HRliq8Js6yoIbR396JC0b+zh+jEog6dgKb/7WeZoXNmwmaZSWc9LDvMMhNGL3MzmZoZqCpSRqRTXi04bjxLGZLMeIA4WmZCRX/7TaKAPglMa0paJnRa74lOZI5uJu54AKivjFnet+sQnNCftZnspQgA4JqnVjmSFCc4CYDF3Ewm4djLLchUR+KW9IzXSwBgrqYtLfpkVWNMAjFOS+F0frgupCGmmfJLuKY6sBl7riFHQqsVk5lUwuqqELXM7O0udkkM9KRF28+Pn0bsIdPumiE+qtDpGiSilBA6bwqslh1znJllJuptmeGtg2HOgmLPAOxZaJIjRBPrzUX4D1cuQTIW9tTsOIFPKvfRrr6Tmf99Zxs+2N6BB1/81PNzTknzOjlCZYlmciRzBmmAwtw8yKf1LLoAEE+hIl7NPp8ttFuOJ1yhSQabZQZKiJGJIssKTThHzOOOHF+BscalCMUqADXsbpkxQrt1AbGDZUaNAKEoO74ra20/3wZVUdHVaV6zrUQnRnVKB4hBZhwtK1yeGjpn0jI3lMxks1Hj/E6WGVMz02sQKD6aCYqKKiM8Pq/mhef0UoEkMx4gtoUQcHYzRQUtM7FwCJMazImMRUF4TeS2XfmIpI3M2HQIXv2wt6kvePkTPXrioeULkTKIxG9X7cDvVu8QOt4pNNvLzcSTESc3DyUnYRczvT0Eu8p46KmIurrCiKxwcTPxlhmniDY/NxPhvk5VgMuOnWR5P2ZzM3nlmeHrgKXi1t192EOvwMPJMrOrvdfhk/0HVjWbcxHplhn9b71QpLubxaKZcXIzGZYZMyGeO6Gl99DLn+xlWpN42DuaiT+ejx6kyQsjNsuMp3VNBSaOMBfhOiO8PmhoPZ/u/6NdnULHOMFLfM+DdwlSNxNfIDYS8i7JwQuAETasy6ueBHoMF1msCpFQFHHj+ufzVoJGLTsWN1OEJzMJXXdjHN+bt5IR6mYKccfFXrkb/zpVd5Mr0Urd1UWjkTSrZUYr0KR9RI+GStRYOxiKAKEYs+z0eFlmlBDS3R1Gv1TsILrbrSncbbqZHMgMdTOF1TDT/M1tWwn8cBGquj/TN93UspN3sMxQfaGiMstMXMlY3ExJg2hm1YLjXF3KkGTGA6Z520Q0EJkx/6ZkZHpTir1mCoDdJ3J7CDLvZqqKhVkbxC0zrh8TQnOHvgBMGZnEqOoEe71HcFIsFtjqBMdtR2qpzeRIZqhlxnkhsZNBu4mfWWZY2LetnAEvHnURAFPLjJPcwW6ZuemUmXj0nw9nr9Hv9HIz2MXOQHFkk6jmghao5LGtbWDJDItm4sS7GiHIGvdCmNvVOybN00wi4HSbU5ebr2ZGtVp23tqsL2RxTsRNSHH6At4qwZOpTwwhv72cgZebSVUUy4bEJDPBBMC8m2lHe9piJQkC+5zlBksmbkpm0nRxVYrqZhUdz9xMqmmZ6dwBbDeyiMf0SKakcWhOsy7mfKFJhijvZooZmheDjNiikfg8N1QzAwChtf+r/xFJ6AJY4/nJ2I7PGRoeFdDJjGoLsVdCQCiMCqrZyVkJJiGEkZlnPmhG88dv6J8L16AF+powPtbDLDNebiZVUdFjzFMnrb8Z2PMhQs/dgng4BKLpY9vtEE1Fr0pIDSMNB8uMGkIyRC0zBWmZGU5wFABzWgV79taMS34LwCRE88fVmN9lPPxRr0nArpnh3EyjauJmsi0PMtNfAuDuTJ7t5Eam4mjpMRdG0W/l2f6lx5hWil6XB4evjRRzSDqXYpoZbzcTjUirskWSVPu4mSjcLEO8ZsZ5V2+1zgHWfDFRm3jUSzcVUhVGYj53cJPlM25WCTuc3EzUwjBQsGer1l8zrQKpeNh1Q0A/C+jX0IkMU0GtWxZlntA66Z7iYZXVRwMc7iE61ysoEmHz/TLLkni7mRo5ET+1cnhZppxgr120o61vIm5+rLxyUFmS5tncTCwDsodlhlgsM7Gi92GIf2loc87m5iHEzzITNzQv1E3krJkJEVjIjOW7uGimjOZyPKBHUym2uchw6yQM80d3zpmMAcCtv12PL6srAQAfjTwF+4hOZi7LPYa5ZDsAZzcRLyBubk+jEtxzm+tBPKJ6uqlYOQOoLJrp9sijiCvGPK6oHJkh6MoOfJRcf0KSGQ/4u5msmhkRy8znZpm+1oxNQOg0kRPbYjyuznyAR1UnzGRbHnOgxc20H5qZ3YZotjIaQjIWxtXLprL3OnrFLDOU7V+85CDcdMoMtsC7uZosLgKHhajYzeS2q9b/t1ebramgZEa8nAGPimjYM2ke/wq9hjyZMa177gJefiH845VH466z5uBflhxk+UxYcDF0cjNt3FO8i+tPmG4mq2WGLshJzsKYdSAr/DVwqp0Vs1k4i0KzqRXdJelegrPMAF6uSsXi6qMossw4aac0kwwsnTkSiw6qw8XcNQwLap4o7LWLdvTRVcjPOV7aKX4M6TNHK7eLRHOxcgQKp5nhYVhmKo3u54ktGgkcGaLgSRGzzBhkxoWM8AJgCyIVhmVGb0DWfryR0VelbqbR863HR/WorLgLmdF4fzMULFA/0fu78Fw0kxr2zgr1GeN4DwGwGsLmfd04RN1gvrnpJTxMbsNordP1eELdTGoI9YrpBhut6C53qGFUcHWbWnv77r4cDBSno5RgcBYAF0czue0qicOufFxdBeaNrcb6XZ2YNUpn5CK7ckpaZjRVsfcaUzGhzKH9Vc5gd4e++xtp7CzPO2w8fvvedry1ubVocnUDnTArY2EoioKKSAjd2QJ6MgWgqvjzvOblsIm1OPewcfjbx3uws11vC7VUuOUJ4TUvgJ45lQclM3GXPDM8mXRayPyimeyaHcBalsDuZvKrTTS+vgLj68cXfcZLfMmDElIee7syaO5IWywG/QnafkuW3rzGomGq4hHTuukoANahKopjpAwliW5uDv4aOFnX4hFrBuFsXkMFFzTI30MRhzw3LJpJMFdOLBzCU5ctdvwOUcsM3TxMrK/A5n092NlHyww/nr25gmNld8C6Kag1BqfZmA/sAmhHVym9BlB0sawdjMzQcbCRCWrZIdyDpHDXglpmjPNkbJYdNzcTQ7TCIiDOalZymDfITJied/YX9XIG3bv1to9ZAACIE71NdjLBW2ZGkA7UKl3QoGLGnEMR2xLGh3t6MGPHb1BH9OfT0bJifEc6q2FvVxazQpst7x9C1uEGVcHNxvk1orGiloBVAPx+cglO6F1lPUEkgUgoipimIaOqaE8Xt6GUIS0zHuAnUQprNBMlM/prbpYZGhZM8f8uX4zXb1rKFg86kXolTKNHj+csM9m8xtUFcu+HpZxB37kMmo2FcGSVzt5VVcHxM/Rqre29/mSGEIKXDAExJQ8VMbPwnvMx+m+aWO07X5iLsw8dx96nZMYtA65dM5OyaWaqEzYBcFEGYFNvoSgKHjx/Ab5x6kz2fkhVzOytPtljKZJcSntqNvcktFoxIbLDK4Muj53GDv7GU2ZgxfFTMMIoibBmW3H0hRv2dmWwemub8Of50Gx6r6fzGku4mIyFubpbXiJ44PT5ozGmxmExgnsWZSY0VhS45akJh1RmnbELH3nNjOroZqIRae4C4ILtPiz6DlWMjAL6eFAXz3Rjc7Ozj5YZXgDs5uoFrISa6nzMTOY2N5NWXOyTMMuKCvS2Fp8grm/sKowlKUdsbiYny4zKES+bZSZLbJoZi5upEkUw3EyVBmnKuRzPMgirKnDkCuDEO4BjrmO71YRBZuyh4bxlZqqqlxFR6g6CEq3A9f94JGZc8nPklYgpIM65ZwDe2aZvyOZFtxd9ZqTRbgKC3rwtvN34HVLCOOfyW7B+xOeKx4ALb+9wcHWVMiSZ8YDTTntMjbl7paJRvpoyD/tCyo4Lh9iEAPB6B3/LDD+ZjqurYK8L55nZHzeTzTIDmJqTDgEy88qGvSxPDSUPVO/g7mYyFxIK3iVAd4NuVY/todm3ff5gy/t+mhk+xwkAnDpnlMU9oBGuNpdPjhIKGk4JmK5GrzwxvGbGDaLRTFRbcdjEOnz9pOk4brpORv/3nW3C+YKufOI9nPHDV/HXdc1Cn+crlFMSm84V0JXR75mqeNhVgA1Y74FUPIJXbjgez/zb0QB0ITqFe4oE8xo4uZlom8w0/c4icAejDAA+A7Bxfo/7wIXLuGawdsLK9buhEZ2EzTSsu9v7IOJ+/sNm/Ppdc0Hs9UiRwGfC5ucugCdz+m9CHPI9cXlm0O1QT8pw/SQNMpC3kwkmfuUtMxyZUUNGNJOLm8jPMsM0M87Hs4R1PurAuOHs6LVbdrgMwlOhR34qI81NERQFmXAVswx15jqLq78b7zV36GRmZqiYzKS1Sp0wAujM2kTICp1HQhhVncDMw5ZZD45WAuGY2Ybs0HIzSTLjAftCCABXLp2KifUVGFOTYDlf3JPm6b89NtQAvMMyTTeH+drj/7IIZx86FpccM4lNgk4TKFC8wO5PnplWQ/DLV2um/nMRN9Oa7ebuP81lXwXcLTP2aC7AtObwMPN0uOcYAfTF73crjmLvV9ujmTzKGVCoqoJpjUlUxcKYNSpl1mbyCOu1uPpt7hbAu6RFwYHQ2cHErx66h4JGsMsgpKMNUv6VIyYAAJ5du4uV2fDDaxv1cNrv/fVjoc87ZY/tSueZBaQqHvYUYNufQ0VRMHNUCn+99hg8zblr3LIo8xZSp4g42iZGtPJ2MmM9/yHjayzvh5lmxIuQ6r/dCKlo0kMA+PnfNwEA/unIiThohG5lWLejI3Dyyn9+5O2i19zAi7BdyQw3tkXaNaaZUYGJR6EIVKAPo+qzYiUDZmg3d/0U27VUQkhp1LLi7OZxJzN6NJPr8UYV6pDPEFdoRv23omgq87rWKcZ3V1lF/LlIip0/r+WLLCs0T006B6TQhXH5LfobiTr2mS5UIB7SrXUd2Q7L8bQFKiWBdZMs7+uWmRhrg/34UofUzHiAOOzIRiRj+Ms1xwLgs7c6C3idduVOENLMcJPgUVNG4KgpIwC4p/G3H+/2fxDQeiC8ZYG6bdoFBMC8LuEf5owyvou6mbwtM/wYnn3oWDzxxmc4wXBxAe7+eidCOmtUCodNrEVDVYzL4uxfzoDHn646GnmNWOv6+Ag/nZCzicAdF0LjJS/LjFdEHMWezgwKGkFIVTCySicz88fVYEZTFT7c1Yntbb2Y2uggXOLAL5iimWfpGIRUs9gqX08oGQuzdmcLuouCH283q8aUkda2+mtm3CwzepvoPWC3UNgtrP/vssVYvbUNX/zxa8Z5DcuMh6vIzUpLISoA7s0W8NYm3U3zpUPHor4yimhYxYe7OvHOZ604dGKd5/EU7zu4Fb3dTGb7a2yuWnueHUC/jnEu+pDwtZVmngGc+yTw7A1A2xbLd1UrYQA5ZGEjA05upqL6SCHUGPlgcrBaFUzNjOIsAI5WAmoItcbxWWI93iIg9kAVIgBy6CG9KGgFvfQBTDKlQEEMxlwXskZ15aMpVPcQhEgIBaWA1kwrKri2UkKULxD8c/hZRJADRs4CKuqBzS8DADKIIhGKoKfQjta01Z1HZ5YQDSu3kSlEKoBwDLXGHNSecXAHljCkZcYDpl7FOgFFw6pFSMgEwHbztvHbh8uYmhnHSZC2wftYN/eCfX3dHzJD/et8obwgbiZqfTnv8HGYaOwoTTeTm2ammFBWxSNY+bXj8I1/mMVec8ugyust2GdDKp6+/Eg8eP5C9ppb1Wwnyxj9DjpZe7mZKNwWsaZqq27KWwTu+vWurk4eNOKlKRW3ECOq3drjIA62w66NcgqVtoPXi1BLHD0X1avQZ4gQhwXdwTrnBJE8M05I2C0zrpXTzfPQ6wY4RTP554uyQzQa7e3PWpAtaBhVHcekEZWoqYji9HmjAQBff3q1kGWnI51Di0OIvpebiW9/2EYI7TWq+NcoCrxlRVWBGacCjXOKzlMDo8aQ3TLjRGamnQTMPRc4+T/1/5UQ6ozxK8AWGk2T5hF4WGbCbCG3kyFRMpNUdKsVAbFYNuj5FaiIUjITtpOZaigAEkbYtJ2MUEKUKwDzFCNj82H/YiFnFcggEdJdj602MlKg0UzUohVLmW+G4/p1CccYoevMievoSgGSzHiAOLgYnOAWieGUht4JXjVNzDa4mafd3RNAMXnZn6R5NOskH1pMk9aJkBmzsJ95vB+ZcbOM2MHq4niE1XrBzc3EhwW7gbmZBDUzAPDziw7DFcdNxmlz9YXIM+GYQB+8ooEoaFh2fdLqJmgwBN17BKov2wnPXodQbzt46xglDPR7qICbd/+4XQO/ayhSm8lesRrg3UyGZsZ2/oLDc8y7WnJFuicHVyHLl+T9HPsJgNcZ1rCFE2rZd93yDzOhKMDmfT2+2pntbb047M6/4vJfvFv0Hu8GtsPLskRdzLwYvuga8KHZFIdfov8edwR7qUYx0vKr1n5YBMQUagg46yfAEZez/6llpqDYQ6N5N5NTaLaeAbjWIB0FpJEpmPd6ocCRIQ+E1BiqjL7zZIJaVRRFRQzGM2MjM5pRzqGioN+jRZYV4zuyeSClGJar5Ehg+snsMxVII6ZUOR/PVc0GwETXAMCSAIbjqGW6nTbvzpYYpJvJA2xd9J1EDT+vT1iwG7zdTN7f4VVxm2+D+X39YJnhonFomHFXNl/kHrCjlxVHMyckWiTQ383k3TZKCAsubiY/MuQWzeQUjVR0bs/QbHp+6+vHTx+J46fzbjJ33RR9SYTMeGV0pdfPnj2YkpndHcHJzJ7OjGt0EYXpZjITH/7Xc7reJulIZjRLlL7oNfRz9yqKgoaqGH56waFIxsLozeURDZluQkpm7CJkJ+seX+iTNivsUl+Ld825WmYEyxls3qfrLSaNMCNyaiqimFCnh2jvak9jQr1DtI6B1z/dZyG8p88bjX3dGby6YR/u+fNHOHXOKKbD4WF39S2ZMgKvbNALlHbYCk5m8lqxdYwupDwZmXw8cPmrQO1E9lK1kecko1jvswKfdM8Nahh1xnmJ2mWZjyxkxilpX1QXAFdpBGFCkFcUtKZb0VSpu2LyhmbGb/dPQlHUagV0hlSdTFTT9tPzc5aZkHVTQRMHVhZUIGIlQ3wG4VxeQRV1w8VSwPRTgY+eAT75CyqUDCIGmWlLt1m+vsjNxFtmDBKHUBQ1xhh2ScvM8AGdgkQtM16TqOfxQhmAvYWDbpOgXZS6P9FM3Q5uJmqlIcTb5w6YZKYiGtwy42vdcs0ALEaGTAGq80LmdXqzNlPxeyKWHcCb0NI2eGpmApCZpM060ZAMYJnpKiYzfuB1X3FbFucqQ0CucBmW3RIXim8K3HRT+u8TZzVi8eR6nDCjEUumjmCf83Mz8fegoij499NmYfkREzBnjL4Ima4i5/MDHgJgl/vXjk17dTIz0UY4qKuQCrzdkLQR2VQibCkz8dbmFsfjNBuhvv+8Q9h7/D3neg24HCcWNM0GYmZEWm1IJ8YZ1Xpf0ePtLn8LwjHUsOx+Gjq5kgIaKzTpopmJJABFp0p0MW/LtLG3C4QLzfZCKMpcVTyZYJoZJYSYYlixbckDFaPeU9KYgnjLCp+nJlfgLDPxat1CdcQVAIBKpBGBPp4taeu1pLWZVBrSzoe202ircJy5mbrzQ4vMSMuMB4TN275kxvs8XkUG3TQb7Nw+wsEiN1M/CID5xTARCUFR9HZ2ZfIWomIHrSfCL2jCmhmfQQy5aA78xo/CtdCk8dvr/LQ2k9PYEm4h94Kni0JgMfciQxROmieAczMJEBMny4wfeMtSImJdzPhaWbGwimxec0g+qf/2f458NDM+XxAP+4Rm246/2JaF2c9VCbhvSkIuGaztoGTGbj2hGp5d7d5kxt63VDyCfd1t7H+naC+geAxqbRFNFG6bK8c8MQ6oDiUADciF8shpOUSMBHvs/F7771AMcUKQ0DT0qira0m1IRVOW4xUASDYAp9wD7FwFrHrcaHglc7XUaAXsRchCBiyFKr3AaU5aMubxpgBYRRSUzFjHUKnQBc0p6qbiyAwfDZXNw7TMUFeRkYG4AmmEiGGZ4cgYwFlmFIc5mpGZKHMz9RaGVjSTtMx4gAjuqt1Cs0UWQkDUPO0dBeG2Iye2l/fHMmMuhryJXWFuJ0p23GBaZngyI+Zm8l3IXBYDM1rI+1Y3FyLnhdALXgJgETcVwGufnKKZ/AmR2z3Ig1rW7PWpaOHDvQKWGbsA+ObfrCnKqmwHy5OjFFtmRnCLopt1bP81M2KWnb7mmaGg97JdSGstNup8rJlwz/36pXMFVuh1os2V1CRombH3rToRwa5287q39Thr30QJpVu+ozzNM2OvaWRDKhTXSwYAaM+YloECHApN2hHSiQ91NfFkxFJbCQAWXQoce715bCTOyEydk2VF0M2kcGSAP57miLEKgK2WmUhMv6apfLFliM9Tk8/mUUndcPEa/bdRdLNCSUPRdGJjFwCzqtmq0zUgrE2UjEkyM4wguhD5J83zPo+fedzrO/zcTMWh2dzfGsGne7qE81PQukZ2NwUlN90+lbN7c/r7CSfLjAsREnUzhVzCYlldIJ+LYEYzOZcz8Dq/6WZy18yI3gNOlhmRMRCJZqK1kOyWGXr90p5J06zfwcPNNUFhcTOFrRMpL0b2y8IsrJlxzTMjJgJPu1iG/O5BNysjbyz025RoxH3DwZMkeyZr6mZq9iEzdqKVSkQsJNapqrreLm/dEIWbpThnkJG4jzMgHImzPCc8GaDHR72ON7Qw1YYVpZU7Pqvp/Yrx5RBqJgDzzwcOWa6XJDDITLVWTCaoGDjmM1Uq4Zijm4oeryJiCoBtodmRuO7+qjLGjj8+p5kkM5TlxNFGGQjqOqtEBtB0YsOTQQCUQiHsVE6CfXmU9T9jq49V6pBkxgNOCc+c4O5m0n/7u6mcCYmIedo/NNvdzXTnH9dj6X/9Df/9yibP9lE4aWb4/33JDBMAi7uZ9lfzkmdkxscy41No0utwmiHfK5pJdCH2rJrdTwJgu27Cre9e33EyV7XbzzViRgOZuhSKOq4KvFviPBb66+uqM54FW+Vw0Xso4WOZ8buGCe5e5gkJ/wy6aWbCXJkFp9BuwByXSKi4TtjIlGFd6/S2ktmJWioewXmHj2f/t/U4H8+mQm4MpnF14ijc8iVlFH1MY04uDg5KOM4y0PL1ibKMzHhYdgxBbZUx9nxtoYxR8yhuqe2kAGc+CJzxgHG8vsjT8/MlCRiZ8XlE1HCMlUToyJjnpwnwQoghqpguHR7ReKXRfkOzwtV3oseHlTDCWb1duVCCtZm5mZQMSCFSdDwAZIxHryLkUYMtHGdZkPOkN1AixsGGJDMe2O/QbGEXiXeyL6/v8Avp9BIAP/yqTmL+40/rvRsInSTQ9tnJDLXU2CtS29Fr7LitlhnvY0V3xdS6YLfw5JmbyUcv4eJmcss1xINVzXaMZgpmnXOyrJhhve7Hi4Rmu7mZvLLv2kGLQx41dQSOmlIPwD8ChydjIRsr5C0zboSMEVIfV6E5ht4ZfN1gupncNiWeh1v0Ynx4t9BzzI2L27NMiXosXLyg02dKVITPjouq+PfTZuJwI9lei4ubycnVd/+5h2DJlBH4n4sPN/vhch9TMpLwIzORGFtMu7IOZMbLTaUoIGqE1Wdqz5gC4Kxh2fC0rCgKtHCcHc+fP1NI+x8PQI1EGRlau3M3ez1tHK8qUcTgLACOGGQmZbiUeDKSzuvHx8NxhAxhcy7Mkcmo6XYMGV/PH5/X8sgZ1y7uVLWcgitnQKCxdg8FBCYzL730Ej7/+c9j9OjRUBQFv/3tby3vE0Jw6623YtSoUUgkEli2bBk++eQTy2daWlpw/vnnI5VKoaamBhdffDG6ukrPpCVsWXF5gJmwvs+aGfNv1zwzLgn7nL4D8C6G6IUuzr1gdzNR60qXr2Ymb/k8/7dbwi6NLeR+Jn5nUpQXdjNR64TNzSRAaL3yzIiQIYCvuOz+HV7RTPsjABax6ti/oyoW9qwpxoNFwqhKUfvqBTQzoq5Cv+r1/mSGRjP1TbPDk3Te0qgJPMc82XYrGknJpldJBr8khkWlGjT92Tn3cL14q5tlxonQja+vwC/+ZRGOntpg9sOlRlrWsMzEfciMGklwZKSYzMR83FQKtxjzlpEcMdxMfmb2SAUjU508mWFuKu/D4/EKVBrH7+s1NSeUjKgkagqAbaHZoageyVVtkBneMkVJRTwcRySnf28hypGZSALE6JuadSJjpiuxwilpoNkIJAiBYtzzdutOKSMwmenu7sa8efPwwx/+0PH9u+++Gz/4wQ/w4x//GG+88QYqKytx0kknIZ02Gd7555+PtWvX4rnnnsMf/vAHvPTSS7j00kv73osBgkhYLsARij4shIC7m8lKZlzOvR+amSCg4t4El76fgpKbHl/NjEM0k0/VbPFdsbO7KqhmJlcgFlIikmuILkROYmHRXENRrzwzAZLm9SU0O8YRYj+BOK3WnOTIjK+biWu/vX11lQ6aGdtn6Jh4kTm+H27JK/2e47gboRUMBAhxhTR5ci4k5Of6tuzelxw/Q7VEzmTGOazcDl4XtWB8DY6epoem11bo18FNMyNKCKMuEWVZRf+/QnWOgqJQIzEkCSUTnGWFWmZ8jkcoyshEB388oZYZ7/YrkQQjQxbLDrPMeB9fl0qy4zXFXPOom0hB1DUDMM1MXG1YkbqzDpaZUBxhwzKTj3J5YhQFhbB+fMi4xlkti1whZz0/IYjzJOrgfzR+n2W0KQ4FYJWzhxKZCRyafcopp+CUU05xfI8Qgu9///u45ZZbcMYZZwAAHnvsMTQ2NuK3v/0tzj33XKxfvx7PPvss3nrrLRx66KEAgPvvvx+nnnoqvvvd72L06NFF35vJZJDJmMyyo+PAqKwDC4D7KBw0ff0emhmXqZSapzUCVnfH+h2w/d83NuO2q+df6/IhMz2emhnx2kxOMKOirAsRdY3Z3Rt2xDgtRzavsTbS0eqrZUZcM2PcA15ZhD26ICIApta1IjLDkctsQUPcMdpBRyenu/HTa1HwSfPsRIG/n9xCm+m4RnxchTGW9K5v2jU3NxO9h/xclYB+H6ZzWYuFkL8v3O4j+/2haaQoeo25mRyEtzGXsHI7aN+uO2k6/vX4Kez1mgpdZ+EWzSTqMmeZuG2WGaqZSah+lpm4i2VGH8+ol94DAEJRJIm+cPOWlaxhmYn7UFIlUoHKbFvR+allJuo3fYairP185W1qWVFIlMszYyMzhvunhuQARC2WGUpG4uE4woaWh0SsEW0kUgnke5DtTAMGz+nOdaMmVGOSIUIQ5jKw4/QHgJmnA1NPNNqgE51KTUOXqlraUOroV83Mpk2bsGvXLixbZpYWr66uxqJFi/Daa3pRttdeew01NTWMyADAsmXLoKoq3njjDcfvveuuu1BdXc1+xo0b15/NdoXowu8X1iselms9H/+fq6+d26U5ikf7qWq2GclUPJGaAmC/idQpNFs0z4x3+9wiqmgVbb+FiC+CmXHQO3gdzTQznmTG8/RmSKuDi0SEFAcJzbYLgC1999nZ84TILeOuHfxzwC9yM0elLJls3epj5TVBQuob2u15uKNVBeDLFfhPl05V4Pks0H6klsJJ+yLiZvIjM/R77d/Bwspdjhd1mTu5OwtaAXlFHwQ/y0woEkcVTafPWVZyRmh3RPUhM2GTTHRxVoWcoGUGkYSjmymrUQGwz/GhKDs+R0x3ISUTIBFTMxNytswcRFoB6AJkml+GHp8IJxCiOhZb8j/FSD7Y292NuEH6KBlhZIgQa2h2LAnMPsuMijIIFbVu8dahUke/kpldu3YBABobGy2vNzY2svd27dqFkSNHWt4Ph8Ooq6tjn7HjpptuQnt7O/vZunVrfzbbFWZuBTFfebGvXew8dCLqtVknNAHzNL9bdcxRUuRmGgDLjEFIvATAnekcW8icBMD7W5uJaW9s14D67v3cTOGQyj7D78xFiASNMnIaW1E3l5u70JI9dj/dTG6h2ZGQwshWpuC+GH64qwO7uZpKopWeC9wYjqk1/fV/vHKJRdTrVrCVibj76GYSfY5jLrWZnAopuoGSaic3k9+mhofT82AKgD3cTD7Eks5RvHWUP96emoCCWcf6oFuiCykAJHzIjBLhBbjmQko1N9GQd+kMhEwBcTdnVWBkxm/J4zQ7XQ6aGR8nF8BFMxXgQGY0dwEw/T/JJQfryelWGF4zozIyYx2LUFwnM3HSi0RYJzrUTdSTNcmMp5DecEFRV9mwdjMNBmKxGGIxh3oaAwyaTTbqs6tPuEZB+LsHAFM30J0tIJ0rMHLDJ7xzT7blHQXRX24mt7Bs/jWv0OwHXtgAQE/uRVPYAyYR6nGp7RTUzWS3DolGwgD6IpHPFiw7+/0tZ2C6KMQicez6E6uLou8C4FxBYy4iqo+gUBQFsbCKdE7ztMyc/sCr7O+qeERIdAzwbiYFK06YgtbuLE6fP7rIjeKWZ8a8hqIibpubSDNDmr3gZt3IFsSOB4CEAzkX1X3xcBLEm5qZYusobXs2rzm6qNj3Ut2a7TuYm8qFDAknn3S4BpTMqIQg7pXjBLCEBnfyodmGZcefzJiWGX4hzoGSGRHLDA3NNo83BcA+8whnmdGQZnMaHQOiRVwzAFNyEiVg9aG6cl1IRpNWzQx1WdnIjMKyAGcQURKWMejKGWRGIwhFPK6BoqCgRhkhK1s3U1OTnnuiubnZ8npzczN7r6mpCbt377a8n8/n0dLSwj5TKsgK7sgSLlYBUc1MKm6a7HkBnkieGVVVOD+1v2Wmz24mF/Eo/5oXmVm7Xdc5XXrMJIuuh46dRpxDg90KNdpR6ZJJWNQyAnCaC64ddLS8dvX0/nCKJBG3zLjlKuLIjJdmxie8utWIUlEVawkBCpFcM3zbKmMhT9EyDz5pXioewT1fmmeJgDHb4K2Z8RUAu2huaN4Zv4U4HnYW0eYFF3IAqIiY5JxCVDfFoydX/CwxN1PEyTITKvqcEyghcrPMFDTieD1zgu5ap7mQ5mtJEALVbwzDMZOMWEKzjY1l2KGukuV4k0zwZIRaZuK+lhkzGqmXOz5N3Ux+bqqQGZoNRWNRRNSyQrQwV2jSWTOjAEWEzKKZ0YzIqKiN2LHEeWmEoL9HyUhPjrPM2EmUDYQTUQ8ly0y/kpmDDjoITU1NWLlyJXuto6MDb7zxBhYvXgwAWLx4Mdra2vDOO++wzzz//PPQNA2LFi3qz+bsN6h+wZfMOExggLh5WVEUZp3hyQxPO7zm8bCHdsGe9IiSg6CkhoZdewuA3V0UdIEZVW01rfK5OZxM63lBvQI/ifI6IdFIGMBZsyGimal3uHbs/IKLgFmk0IPM7EcG4NZufTKvqYg6jkWQ8GxAJz9u+ZHs4JPmeX+nd9I8/8SHzpobeg9E/ciMi2WG9s/veMBZAybafx5B3Uz8a166GTOi0PodPBlyOt5MXChKCM3voAtxhaZB9TNTh8xoJqp5IYSwhG8xm+jV6XhqVeDJSM4gEH4CYEQSzM2TLpgCXjNPjZ/4LWaENsPog04mTDdTCCp90yWaCQDnKtP7QElRIpxA2CBWRWSGL2lA9HmWuqm6DTKTIBpCYW/rGAnFHK1bpY7AZKarqwurVq3CqlWrAOii31WrVmHLli1QFAVXX3017rzzTvzud7/DmjVrcMEFF2D06NE488wzAQAzZ87EySefjEsuuQRvvvkmXn31VaxYsQLnnnuuYyTTYEJU+EcX0nROs2X+1H+LzGE0EyqfWlzEMsO3zzGs1/YSbZ/diuRHbkzLTLGJm07gXpYZukhGbRNxSFXYROx0vKiJn2oVCLFqHkQjYQBny4CIdW2EkfjNqbZRQXAhjoadiYFIxWX+eDcysq9bb1udS4FAN6uI02coeXMr7GiHSAZjwNkyBpgE70C5mdyOF7HuOaUaYHmCAlhmHN1MTABc/AxGQiq7P7wsM2mH9Aj6d/JkyMEyQ5/DsOAY8paZHGeZ8YiUA2C1zBhEgM+REgsnHQ9jCEWYZYQnI3mDzMT8SkVympk8MUObTc2MHxmLQIGZKZiSAUpmlAJ3vJ3MhDj3uy0LMj0+FoohYrQlVERmTDeTSmKW47t5N5OvZSZWHpaZt99+G4cccggOOeQQAMC1116LQw45BLfeeisA4Prrr8eVV16JSy+9FIcddhi6urrw7LPPIh43d+SPP/44ZsyYgaVLl+LUU0/FkiVL8NBDD/VTl/oPbEfn8wAnXEy8otlnAXNBdHIz+ecocd8hu5UzsOeE8QurZpoZh6rYLM+MhwDYayJ2E+8C/DXw2xGGmCuK183kmItCRDNTvJiJ6J7qaaFGh1TyecGF0M3NxJNMr7Uw5kMsqGWmrsKPzLgvhPQzv7z0CEub/cmM/ruvKQ4C19fqq5vJJWkePT7icw8Cppup18HNtN8CYI88M4CzVcQOSvTtZIbqptyOzwmScraxc9DMVGjE3zITjqPeEKFv696ElnSLadUAEI34kBmuNlJXrg07u3bq7Wdkxu/8CVRqpmXl0/ZPAYjnqaGuo0qj+5vatgAw3UzIK0WfdQK1DjV365KN3oI+hhEljgQyxuE2K5Vhmbkp8iRixhjs7d2rH2+UQIgTAiXkQ2Y4EfOw1swcd9xxRrio9eeRRx4BoD8Ud9xxB3bt2oV0Oo2//vWvmDZtmuU76urq8MQTT6CzsxPt7e14+OGHkUz63KSDgKygm4mfGPgFOSO4EAPmbndfF+dmEozC8FpU3DQz3bbJ0o/M7G+eGTfLDOAd0ZTNi5n4VVUxU7pbTPzi4k0nzYXIPcCqTncXW2ZEc5TwJSF4AsO7Cb129n7ZeFuMttVWOpuYo2FTQOoGaiWj93vEQ6vFQ3RX70pGWK4gP6uAMyETtbC6uZkoIfWL5AGs9ZkoRMPzeThtDMw8My5kxiUai0evkR3WqUikVxbhvKCFlBIq+gzmtBw2d2zWz0k0qCE/y0wcczJZTM5oSBd68OtPfs2IQIQQRKI+mplQDE2FAup7aqGhgMfWPaa3X5TMRBIIA5jclQIA/PyDnwPgyIwvGdLngkN69HF6dO2jAExCtzT3mvlZD2J3RK/e5yfXPw6AyyCMCOKKvkZEYrax4EoaHN+qk7Dfbvgt8loePYb+KE6IxQLkiFAMqYKGUCHiW+W8lNCvmpnhBlEBcEhV2CJtITM5MV89YLqZ9nUXkxnfuj4uGYT577D/b3fp+BWJFBMAe2lm3HeVZuVsLzeTiF6huKRBXnAh5NvGay5E9BINVYabqdPBzaSJ3UNUlEuImWWXPx4QywCc14hjFt8WapmpdN4N+llmCCGM6NDPmpmvvV2UopsCRiZtbg7RMXSPhhKLSoyHTXcxj2D3oHueGbcIIycEdTMB7kn/LN/h4mbSX3MWQAPmc+B/Da2E8J637sF33vwOAKC2oFlznDghrDuCjjXmwU3tm0zxq0agRPyS5ulunontejDJJ62fIKfloBkZiOOKvwAYABZ06HlX1u1bB8AkMxG/AGCDKPxDh36+D/a9D0IIIyOHax95H2/g3A6dfKxv/QjtmXbTTUWiiBvRUG6aGQA4raMLlZFKbO/ajo3tG9HLufr8yIwSieHCjk4ctvEs3HD4DULtLQVIMuOBviTL4s3LdBJ0m3x41BoZONt7TTJDJ2Fxy4yAm4kq/W2TpVueFwovAbCIZibjZZnxKGmQ8zjOrR38rla0ajbgvLM1rQrux9cbBKEjnXewKogJkKNhlYWpt/dyZMYSzeRPZgBnETCNZqpzscz4aWbyGmGLMr2fRd1MogJcN80LfQ7EyxkULBatbFA3k+34vGB4PWA+H07RTPufZ8Y9mol/XUQAHNQyIxzNFDHdTJlCBk9++CR7b0E6I6SZAYAJOf1+3dKxxUwYRzSEvOoKccdX5/T7fGvnVmTy5iYjLqCZAYBRxiO4rWsbClohAJkx2p8vgBAF6UIv9qX3mX2gRRw9XEwAUKtpqDeuw7aubVzSvSjihpsJYedoJgBIK1WYmJqoH9+5DWlDDB3XCOATHq8aY6hqOd9nu5QgyYwHzElYQPjHCiaaF9/LtWKH066U7YT9cpS4FKoEioW9dHHc3WmthupXV8nMM1M8GfAuEreS8V6RGFRn4JR0L8iuOBEp3pmLFprU21a8sxVZiKsTEfb91zy1yvJeEDJFrTN8SnnRSBq/LL4dBkFyCssGzB21m5uJf53ez6Kh2WxX7/McuIWXBy0WqhFrvh7RTQkdA0KshDDIPOCcAViMzPzsAjMrumMGYJa918Uy41PSIFfQ2LjYo5msxxdbxuhj7RvNxFlm3thpzeh+WDotoJnRF9KJBpnZ2rmV6TYShECN+OQbM/QgdTl9TtrVswvt2XYAep6bqKBlZmReg4IQ8loezT3NyBrWkIji46Ix3GA1ShokXw3AIBOGq6xX060nbef9wft7AIzL6/Ph1s6tTDMDLcrcTPakeSiYG+E2ksTYqrHm+Vlotgb4EErV+N4Ycr5V2EsJksx4oE+WGe7iByIzDrtStpD5HE/dTM4ZgK3/U7Kxea9Vpe5nmTHLGbhrZjTibuI2d5VO5RCKtS4UoiJs/bvNnTUF9fWHBBYiJwGoSDSVqioYX69PYh/u7LS8l2cuEv/zVxvi3DbOMnfgILwAAFF5SURBVCN6D0VCCtNdbdhTLNpjO3IHATdgkiE3N5MTmRENzWZ98LXMOFuHRC0jvMXCIsQPmPwSsBNaccuMUxV4VjXc5xZYNqsRFx45EYCbZsZHAOxTbJK/r53cTG6WHZ6s+llm+Gdo1e5V7PXZnQnMyOYE8szobqRJhiViX3ofNrQZCTfzBShR/9pMAFCtKQgrcWhEw7vN7wIARhYKUPw0IMZCXoEs4tCLcH6w9wPkjUKXVfAhU3GdwFSRLmjZOgA6GaFC3BFGiHe0wl8jOtYgM9s6t6E13aq/SBKIw4XMdO9lf6qFLMYmx7Lzd2Y7AAAJTfEVbykGYYwpOUth0lKHJDMeyAaIYog7khkjBbcQmSleTMQXMmNREcgzQy01m/b2WF7v8WHgXgLgRMSMJHISAfN6C6cFLcG0Lk4CYHqcv6vOSfNipmHv2zVkkSw+k/D95+nRffb+5wXdTABQwywz5g7Ly6LFQ1EULJhQCwB497PWovdZkU+HRQzgLYPO9wG9L8OqwvoS1M0krHnpYzST1TrFP4diZCQSUhjh4I8PtKlxKO0RxM3kVassnfO+F+j4Xf6Ld/DQS586HE/d1i4lEVwsM/z1Fc25lc4VmN7k5sO/gfOa66ACQpoZAKghGhKqrlv5+3Y98/SYfB5hu07E5fgoCkiqetmcV3fox4/O5/0TFxoEIYEsotCPf33n6wCAEfmCf22oeI3+C1koWf15/LDlQ7Rl2gAA4w2LU8ytH+f/Ci9UnIRrsldgXM4kMzu6dujfq4xgmpmicgiTjmV/JrQujKsapx/ftQ17Mnqi2pE++jYAUIwxjCHnu8ktJUgy44G+TGL8joxpZkRS6TtF0hTEyJC3Zsb6P/3I5n02y8x+CIBVVTFdRR4iXsDZ3+8Uzmo/VixPTPFiaBYpFHcROLmZ/O4BtyzIQer60MrFHX2wzADAQoPMvONAZry0EoC5uH2y2zkU06kdYUE3U7Yg1gfXpHmCmhlVVRwtTKLXUFEUR0IrGskDmKkLnOaBQLovBzG9l3UTsLqOvv2nD4veZzlmwiHHRd3NMsOXSRGNCOvNFbB231oAwLSamVBBUxz4jAGnJWkITwQAvLjtbwB0MhIStMxEkUOVOgEA8Nzm5wAAY3IFXxcLIzNKBjFNz3u2cstKdn7iV44hlgLNLBbL6laaF7e+CACoiqRQT3S9SzjmQmamLsOTTddjM2nCxJw+D6zdtxZ7evfozSP1SCgZS1vNYz+HrkNXAAAqSDcjMxvbNmJPRj++0Xua1xGihDAv3UzDBX3xlfdyachZNFMf3UwZQfN81GOHbBcAU0vNZ/t0y8xBRtViXzeThwCYf91R98K7KBz6UkErXnu4mUSsY05uiiALkZebye8amv23ZiAOQqYomeE1M/YIIi/MaNJ3snaiCvAFBp2/h1oNHn9jC7a29BS970Ssve47y7F5sWvgZh0K4qpzIkT5AOH5ThFBopE8gLNlJcg84JXmwM/NZC9RYIeZMM/NTeUc2p1jgQjiJSV68+1oy7RBgYJxVZOhgLp7xSwzADBCHW95a0xOgMyEzYU4pUwCAGSNJHNj8nkovpoZ0zITzulkqCXdwo4nPjlaoKrM1ZRM65uLLZ1bAAANiSaEaAIbj+9JxsPYRepwcEZv94ctOjFNhBMo5CoQc3MzKQrU2Wfq34EeTErpKVF2dO/Anqzughopwk0Mi4+0zAwj9M0yw7mJCuIL0f64maIuWgMARWG6BY2goBGWd4SSGS8GTghhJMVJAAx4h2fzffIKzXZcRAUJHcBbt/bXMuOUZ0YsTwxgddkFIVPViWLNjFcUmB2ja/TJbXtbb9F7vczN5ExGt7WaY//axn1F7zsRa5bbxkczIxrN5FaSocDC6/tm4RR1FQLOief6Mg9YyIygqxAwcxbtccgmTa1+bta1VNzbauBnnYs7COiBYGSOfkePphOA+kQ9Qogwy4wvmVEU5BX9OajHWMtbY/J5ROyuFTsMkhBDDpWYZHlrdD4PCAqA48hAzU6wvDUqnwfxy9ECMDJTn7bmgamL1pn/2LP/ckjFI2hBFcbn8yz6CQBqYjXoyhTc3UwA4pU6gUqhB5oWZxFNFNUFgbwxYdO65RUZV2qQZMYDonlmgP0XADNC0ofjaRXkfQ61gYrcTBpBR2+OvT66Rn8gvMKqe7IFFs3g5GYCvCtn84TEybxN88P84f2deOEjaxFSURE04BzNIVrXBzDN97yLQFzvYaaT70qbY5Dbz2gm0zLjPwnRuled6XzRzt4UADt/z0VHHcT+Xrejo+h9p7Bgai1bvbUNV//yPdekiUFzlBQtpgHKCTjlmgkSEeeUOM+sGC2ieSkOzfazqPBgCRgdyIwZXu+8q3d7nYJam5zEvwBH5IoE2Mb4BdkQEJ3MNFY0Iq8RhECtO/5jUFD1fowpTMBlcy/DtNREnNjdg6lpgrDfGMZSAIAqpQexwnicNfUshJUwKgpxLEqnhTUzcSWHbDaFQxvNCLMF6QyI6mOZAYBEDQCgBlk0RmaxlyckzGfMM/tvLIwMosiSCBb1mlGns+pnoSuTYxmAiywzANQK/dxJJY3dbT2Y2zCXvTcxmwMRSYJHLTOKtMwMG7BCkwF83fxiHmRXHXPIwJoV3NE2VBm7OYekbXY3k0YImxSrYmG2m/O6aWmfVMV9V0dJTgeX8I3CbzKv4BbYB1/YYHlPtNgn4BzezsJ6A7gK033UW9A8MfyiXhDUewBm3SvrPSQuIq+KR1AV16/DTpt1ptdHAHzSwU343jnzAABrtrcXve9kIeOtTb9dtQMP/a1YdArsv2amEOAa7q+bKeZwD9B7qK+FJoMQUq8EjK09tFios3XAj8yYRSaDJd0LEs1FvyMPXbfVWNGIXEEzK0V7WCQo0tEaAEA004IVh6zA/y75L9y7ey8KiPjPAwaRqEY3sgWC24+8He9d8B7Obj4Ro/MFKH6bCsMyk0AGmZyGh096GO9f8D7u7DwMx/am/d1MABMBV6MbR1TegN+f+Xv89HM/xUkjTgUAFKACIfd8NZRIt6MSt+1rwR0HX4JrF16Lry38Gnp60wgbCQCdLDOUzAHAzt27ccW8K/CFqV/A7Pg0XNPahoLikycHsOiOpGZmmCCIZqaaJb3jdtXs+ACROH1wMwUjM+YOr6YywiwqTmHRFF1cXSa3nU0qoX9PZ9rJ1++9IPO5cMbXWeuNBCkJ4aQ7KgTY1ccdLTPiC5mTCNgUAAuIRx10R0F29QAwulrfre1ot+YR8nMxAMDUkbrmZouXu49bkO1jsrW12L3FR7KJWLcAh9DsAK5COoa8iDqQm8lJN0UjuQLkm8rkNXZfB9nUNCT1Baojnbe0gRCC1u5glhl7ziB/zYz++hab5iqIm41+R141yExlI/IFgih1jQiQgd5YIwCg0hCtFrL6fZVFxP85okRC6bbOA0TQMmQQhASySOcKUBQFiqIgRS+FiJuJEiqlG9lcCBOrJ+KIUUdAMfSUWXiPwYmz9P63k0qMKGj4x/r5uGj2RRiXGodcLyfQj1YWHxyOImu46fbs2Y2xVWPxzSO/iX9rWI4TenpR8Ev6B1g0M05BGaUKSWY8EOQhrqF6B85FEEgA7FEXyG8h8yIz9hx2BY1Yig7Sxc1JuEvhJ/4FTH+9k2XGrx/8pMOPFSGEuwZ9q3odJHsrFcdaI8rEr2Ey7kBmNPHzO4k/g7gqAWCU4TbkLTOEEHNX7iIABszFsK0nWxTS73QNRfrEJ6/z1cw4VP4OkrANMJ8F3k0TaDF2clUKllMATDcTYBLIIJqZVCLMxol3G3dl8qwdtS7FQmttZMaeqybt42pcOEHXdPzf6h34aJeZLykIIafzCQnp1j1qmYkpVOfhb5nJJPSQ6EojnDif1Yl5JoBlJgUrmaHJfhTfaCbdMhNTcshywRxqQZyMUc1MNbqtRW8z+j2Z90m8t2hSPU6Z3YR2GGSlt429V8joRFNTQq5tyYb1TUlLi5l3hhjtF7LMMM1M3nOTW2qQZMYDQTQzVO/AlyMIshB6+fp9LTMeokF7BuB0rmBaZiqijkm+7OjyyP5LUUXJTK97FIZbP849bBz7u8NWl4guZLE+WreCZACmC5nFMhPAzeVERljldIHzVzlYdoJaZmhV7HabiJiOY4VL0jzAXCRzBVLkdnS6hvbFzSn7syVHiW+hSdOqQb8rzwkgRRIfsmeBI/ZBwvtZ1WdHAbBYRBw1XlIywYigh1WMQlEU1CeLXU10kxSPqK5uonobmbFHB/Kh2U44eXYTls1sBCHA4298xl4XLWUAmPeXEjHITGUjcrxlxk/ACyBbqddVqspRy4xuKcyQiH8bXCwzRBO0zHA6FJIzNwSqkezOr+I0ACChi3BrlG6ry9nohx+ZAYBR1Ql0EENAnG4z25TVyUwhXOGa/E6L6q6m9lZTyK/lg5AZqpnJ+uYfKyVIMuOBQJYZJzdTAL0DEwBzk79oJI+Im4l+R0t31iIk9KqLROGVY4bCdDN5WWZcJuFkDP/1JV2v0eHgpgP8F0KguMgdIJ6jBADidCFzqs0kMJEnHchMEAFwpUNEmJN7R+g7uOtpyfzqcS/GIyq7D1t7rGJyGprNkyr7fdnlEMnmF5bPgxcX02vPk3ERQsieBY7YB8n1QzVHPCkPEs2jKGbOJZorRjTFAoXT89xCXUwuVhmg2GJjzx1FSXrcI4R7+WIjN8u6ZvYaI/QC93A0rCIWVpHdsxRXzbsJ8xvmI69xmhkBMpCv0N0s1TndslDI6CSgBzH/NhiWmSr0Ipvl5iLqZvK7hziyFdEy7PlXjfBuImBZQoWeObhO6bDWicsalhkBEXEyHna0zCg5wzITdq8ersR1MpPubGGvEYPMaGoQzUxeZgAeLgiSSr/GMxJFxDJjmtjprlTU104nv06bn53/jiYj0qWlO8sqKNdURMyJ14OBm2HZIm4md82MW4E8AEgZ48drbnJctkohAbCXZUZkV+1kmQkQTUUTpvGWlUJB/Px0fHlCGHQhrHAQEVOiGg2pnq4hRVFYwVP+PgbcBMDW7/rr+mac99DrTNsBmKREKEcJN8a033kLmRF3M/FEIJi7uHhTwqKZBM4PmBmt6bhTa6vX/c/DKaKJt6a6gVp0KIosM8aYullmAOCgeqN2EHf9g7jZAJ0QFnonYUnjaRhbNdZmmfEnA4WqUQCAmoJOZjRDM5NBzL/yuOHiURWCSMHUl2hEHwvfaCZVBQnTXDMZ5ipUCbXMCGhmKnUyU48OC8Ev5HR3WUHxJzNVsTDaiUFmOMsMDOsOibiTmZAR0VToNYX81M2kCVlmZAbgYYcgLgYqAG5zsCwEITOAOZGL6iVS8TBLw85bNgBgtzGpTzcSquU1gs8MgV9tRZQtfk4WFQqvUgasDYni7LUUIhYmtiPmF/ICnYAEw3Jtob0a56YSWYhMF0PfwnpNNxNHhgIIkJOcVYUSWtNFIfaoJh0IVa+P8JOHk/YLcA7NdtJevLZxH57/0Ayv560afguJU7FMPvtsIMtMH91MZn0srnp9gE0NwNVnMjQXQTQzADCCupkcyIxXxFIiErKcw26Z8UucCJjPYW+uwEhckNB0/TusG5N8QUNUEbfMqJUNAIBkoQMAoBmulbSPcFZvZAxayHCTGPWIAEDVjLlEhIyw8OwsS7MQ0vTfSligDcwy02nd2BikTMQyUxnjLDNpk5SoeUOc7yT+BX2rRu9GvovN3UQLQmb08ZPRTMMIfdPM5IoWIqGEb9xuiZEZwWgoRVFMzUraTmb03cDY2gRbLNft1B/yxlQMY2t0hr+ttbcowR6FiJuJToJOpMhpIbQj5aC5CbIQAsUC4ByntxCyzHhEsgRxNfK1lYIIkKkmqaAR7h4wrSoicHIzsbBsnwyxgNmHIjeTw708ssp5l91miSQyrr2gi4YSd3oN8lz2Wd9dOfbfzeRVuVzUMmMPzw4q4h7hoPtp7/Gueg7o4/f2LcswwSh6arfM9PpoZgBTxA6Yz32Qexgw54KujN7mvEYQC2CZiVXoG68o0ecuZplRBFw8AAox3ToTK5giZpUYZESAzCgR0zJD3XvMMiPiZqKWGZubqZDTr6cmQOiS8TA6SLGbKZTTyYwSdbfMhCv0/qfQjeYOfQypm4kECM0enVQwd2y1/+dLBJLMeCCYZka/AbJ5je3sg4RkRkIK03PRxTjIJEg1K+02Ae7uDv0BakzFmRmaljJoTMUxuiaOSEhBNq9hR3txaC1gJoHzEgBTMvLuljZGoCjowuS1IDtpbnIBXSx2N1NQvYVTBuAgmhm6uO/mFiHRIomA6aYCTGtYUBdFpYObifbHS/xLUeNgYQSc72VFUfCHK5dg9piUpX80uzTAWUUEF3Iq4N1lTMJBCoUCnIumH91MQaxzAJ9zyqqZEckzA/ARWSahpO5b+py4oSoewdhafTG2RzPRe8mL1EZCKiP11LISJEUFYG566PHZghZIAFxRqZOZuFHHiNDQbEEyQwwRcEWBs8wEIDN8SQNK6sNE/y1kmTHITB06GKEDgHxG74dIrppkLIQOWAXAmkYQLujfoXhYZljlbqUXzTRFA7XMiGhmjGs0uTaCcw4b7//5EoEkMx4QTcMOAJXREJvQqYk6CBlRFK5IXs5q3hUiM8y062yZGVkVKzJRj6pOIBxSMa5Of2g22yppU9AQ0fpKjxTc3CT77T+ut7xHFwavXSW1LGXyGlt8g0SDAeZiYe7qTTIjJADmsjhT61qQe6CBkRmTzAUx0auqmXiPkpEgxUoB54gqv2RpPKiItM2WTXqnQXTtItPZY6rxhyuPxoZvn4qrl00FAKbJAsTLQVDQ8hqb9uquhTwrZSB2PCUj3VndTUIICVTbyUnIH6QkBWCSxr67mYqtS9R961eygD+/vbQI/Q4vdzFQ7CYKImLXj7eSmXyBIBZAAFyZ1AWscWR0DaFhjRAlM1qVXiCyPr8bhBBoGkEIwd1MCcW0zFA3kxpAABxT8gjnuhkhzxpkBiF/QpeMRUzNjGGZSecLrMikGku6HxwzLTN0U0AKej/EyIxxjfLFASWlDElmPGAWOfSfxBRFKTJRB3EzAcWWBfN4//O7CXAtlhkbmWlK6Q8VFf1tcihQCJjm7gYXtwJ/fkDPBsuH6dLFrc6DDFXFwswyRV1lQRdCe20mS7VfgYmYLvYaMUkEc3UJLEQjq/TxpGMOcOJJwYXATkZMy0zAaCaOzNCFucpnEQPMXCX2dPprtuu73IPHuJud6f3FC4CDWEUAYOIISqwNMhPAsgWY2i1AX7z5SvIibhInMhMkmgkors8UJBAAcLYudTLLjP9iTJ9F+zWk5MjNPUhRZROi5wNqZpIxm2YmXwiUZ4aSmYSSRWdvBsQQveZUfxIAAGr9JADAWOxCJq8hrxGEA5EZWp8pa5IZw82kilhmohVMoFundLIAipxBZtSI/xgkY2G0E4OwGJqZnmwBFdDJSSjmZZnRx69K6UEznYsKhnZGICycWc8KkswMC+gJ24JNYvYswEHcTAAnYO2Dm8kMKbVaZqjPdGRVDJNHWtk8taaMr6e6GWfLDJ0EvcjM2NoETps7iv3PZ6Clboe6SvcHSVUVtuunE0hQ837clgE4qN6C1wRR11oQzczIVLGbKUj2Wr4NXWmrZUZYM2OLpAHgmzmWB3VR8Nl8cwUN6w2d1RwPMkOJUAsfzRTwGZhoEGta+bsQIMcJoI9zyngW2npzljw3ImPorJkJ1gZ77qaguYKcdD+U4FfF/QnpzFG6m+YDW1kKkU0Jfw67mylINJN+vJHbJM9Z+QQsM6GYqQfp6uoCyRtuJlXMMhMeoZOZCUozOtI5FDgyowYRACPLnp0wJTMCRAQAFCoChikCzhvRTGpExDLDC4DbAOj3U6VhmRFyM6GXzf8wopmIUGi20UdpmRke4Hd0og+xPTw7uJvEDM8GuFT+Audn0URpq3mcWmrqkzF89bgplmOoqJZaFJzy1PCve02CiqLggS8vYIvd+1vb2HvUMmPPUGqHPeFZLjAZtAqAmfBT0CoSUhW2K6WEtC+amfbeHNeGgLta20ISNJqJamZ4NxN1E9Yl/ReS8YbLkS9p8Nm+HmTzGiqjIUyocxceUrLU0sNbZsTLQQAmmaG6rhxzM4lPVVS/1tZjJTNBKpd3pHNMEB90Mbe7eYJqZpq4gqH0eaa/RdxMs41nsK9kJhm3WgdzATIAA7wA2LiH05wWT8RNEzYT13V3dbA8M1qouLCiE9T6yQCAiUozOtN55DQNEWqZCYtbZhJKlj07YVA3k4BlBmDWkaTSy+6DgpFnJiRAiKriZmg26W0DiJ7IkhWZ9BAA0/pMKaUbu2yaGaKKWGYomUl7f67EIMmMC/jEaaI7qhqWfVV/AOjuuiomcANx56GisyAJ01I2PzdgXdCSsTCqExE8d80xmDoyia+dOI29510OgZiTYNL/IaSah62clYf2x+7mssPejqBWCTp+dEdcCGgVAUxSaCczIvdAdSLC2kr7ECSSBjDHaJ9hzWJ1rQSPd6oP1SqQcI2CkpmtLT1sMadjUZeMelq46vrBzdRouD6piySIgJqCz8bNb0pE7oOaighiYRWEAC9v2It0rsAqzFcIRIPxn+uxa2ZEw+tjYeZqou62TkEBMAAcPFpfzHa0p5mVLFfQGMn0e46rmJvI7mbqm2amp5dzX3tUi2ZQVRaG3dPdCc0gM05Voh1Rq1enHqfsRmc6j0KBmGRExDLD6jNlOAGwPhYiRAQAENWt4JXoZc+iZlhmwlF/y0xNRQS9Id3Cpmg5INeDnmyeuZno9zuCt8wY+j0lCJmh45x1ttSXKiSZcQENhYyF3dOH28GHZ2fyBSa89BK+8phg7Ep/9vImAH1zM/3oxU+ZAJZOJrGwmdl1amMVnrv2WFy5dCo71ovMdGbybGfpt6MDgDGGm2I756agE6pbTRl7O+hCRqsEi+gEADAh894u3TxM2y1qFQE4NwMjM8GyvzbYIpqCZCAGiq9FUFdlJZfRmZIRurv0s4wBwOiaBFRFP+9GtpDqY5H0IeWMzPRkGYkJkuMFAEYYVaP3dWWhaSRQ0kMKXvfCC7hFwvsjIRXnL9Kz4P7Pa5uZRURRrNFmXrC7mYJq5wDgIEM7RIXQQQTAVfEIsxLS57ClOwtC9PvQ7zlk1kG7ZUbwHqYCYpqioLuHpvEPA4IWtoyiL/jp7g5oORrB42GN4GFkAa5EGh09WYtlRsiywipnZ1lEWQQByUxMJyJViklmFEPIHI7590NRFCSrUsgTY7x623TLjOFmgkfSPGoVSik9ZjQT1cyIuJkMMoRcN6DJPDNDHnQ3SidGEfD+dnq8ooj5uQHga5/TrSWvb9yHXEELRGb4sOnfvrcdgElmqnwmwAaHjKMUdFGtioeFSN2YGp3MbHMgM/YMpUXtsC3k+7rELUKAPtFPNPQ/a7a3s+P9LEI8qm3J/4LqdhiZ6aBkJpiJfr/JDLfg0qzOopYxvZ0qphjaqtt/vxaAaeHzu49HVMYQj6jQiLmIBtXMUEKU1wg60rlAta0o+OeQPgMVHmkF7Dh6mq532NGWZnmPqmJhId0VUCwAFsmzZAfTDhkRhh2CzzIFdVXRaBZ6P43wsa7p57BpZrRgzwCdA6irMt1ruIkEMt9SULFvb28XYJCAUFTQMmNYVsKKhq7eXvRkCgjBcDeKLOYsaV6GuWkiJEA0E8DITBK9yHTug/b3H2JUYYf+XZW1Ql/RVJ1AF4w+P3kOejJ5VAZxM6EHuzsz+qYmiGXGOB4AkOlw/1yJQZIZF4iEE9vB5+jgk1yJToIzm1KIhvTFYFd7OpCbJcERDbqjFl2E+LwWT721xZKfgi7KIlYZwBSQbjeqNucKGhtLX8uMTTNDydUIAa0HBdULrNnezqwjI1NiURBAcfLDIAJgwNTN7DHMu/mACdfsRUPpfUQz8/ohHlGZS8wkheKWGQC4eplOqt/5rBUAR4p9oqFUVWGLMI2MC+pmioVDTMC7tyvDnoEgrsIariQDHQO/CB4e9ZyQmVqlRK2DAC/CtkWkCWpmAGDiCKod6gYhhGuH2MaIuuvsZEbkOaYurg279XIAtD6PKBljruaWXhQ0grShmRFJFkeRN8KXs73dUAzLjGcEDw/OatHb3Yl93VmEaQbiIGQGWexqT+v5XQw3VTganMxMf+MmqH+5Gf8QegMAEE3WCH1FU3UcNYrhotu1BtnuVtQqRiLAhAchMiwrMSUHVdN1P7msYWGKimRRjpoRTWlJZoY82gIuIvpnjYWwJ8fcFDUBJkFVVZibZltrL2fe938AzzxkDPubLsCdghEQdZVRVg7hhl+twb1/+Zi9tyegdWQs135CCFtIQ6riWVcGMCdaSoT2dlKLjvhCNHOUvqvYuKfbEsklimruGvI1nkQtCzSiaY/NzSRqWWiwibFFIsl4KIqCqY26ZeWjXfpEFMQyAwDHTtPTyfdkC+jO5E3tl4CFkZGZPVath6iLBuAz4GbZzrixD4S0rSeLPV368aLjB5j3277ujBnWLmgRAZwsM+IFZylo2oQ9XRmkcxpz9Yi4mfjjqZshiO7t+OkjAQAvfbwHPdk89grkmeIxuiaBaEhFtqBhR1svenuDk5mCIfbN9nRCMYSoYVEyE4qgYCxt6Z4utHZnmZsJAUKzK5QssgUNm/d1s0KZ1ZWCbaBkRunF6F0rLW+FDTeYH5ps93zzru1oQov+j5FLx+vcAFCFHjR3pJHJ6Ne/Ii74HFHrjLTMDE1sb+vFr9/dhlc+2csS31UHcTNxvnpKhqoFRJc8TDLQw1kWRNTvEfz7abMAmBoJUctMSFUsD84jf9/M/g6yo9PbX4GQqqArk8fuzgzLKtyUivvurueMrYaiAG9tbsUH29uZCHZEADLDJvGONGt7kIXQzKScw05jIUhETGuBHxqSRq4ZmwBYVDzJu5lyBY256IIsxjOa9Inow12d0DRiVlwWJDOVsTDTfezpzHCk2P9ZoBYFGlrNkjYK3MMUIzgyQV0V4z2iqOwYV6t/9qPmzkCLOAUlfbkCwY42vf2i1x+wljMghDDdRZCNTT2rz5RlKROS3HXxA3Uz0Xs4CCmeOaoKY2oSyOQ1rNraxvLdjBC8B0OqwtI9bNrbbUYziYh/KQxC0dXVCbWg9yGaELwHFIW5qTI9XWjpzrLQbDHLjH5sbUSfP1dt3oM49GsYr6xyPcwCgwwkUZxVnVa19kNTdRz/L38s+3/Dpk1oUnRrKVIeZEYNAVG9nSlFJzM5I4tyRUJwLqRtlJaZoYk/rN6Ba//fajz+xmd9dDOZCcfa+2CZAUx/88a93YwQNVaJ3YAsEsaYuKifXcSy88WFY9nfvDsiKJmJR0JMt7J+Zwd2GFaWUdX+fZjckMQps5sAAM98sBN7jEUgiJuJ1wrsl2WmN8d0H2NqE0LiUaA410zQPDN0nHd1pNl3hFQl0H00wygq+uHOTuzuzCBXIAipSqBx4HOdsPtIYEGnehual6a5IzihpCLgPZ0mmRkXgMwsnKCb4FdvbWdkJAgZjEdCLBMzJWVB3EyUcOxqT6O1J8c2FUH6QK0gLd0Z5u6Z3FApfB82cqQeAHZ3iI+DoigseeHOtjRz9zYEeA75fEFZYyEVCss2oBpWmO6uDoQpmYl7RPDYQN1U6XQPWnr6ZpmpMcjMJxs3QlWIbu0x8sf4wsjQm1QcSsQIkpmJ9ZW4PX8B0tDHTdvzsZl8sGqUx5HgIpp6sLM9jXiuDQBQUdMgdG5pmRnioDlSPtjRzmkVxCexKQ36DbxxTzcjAUHIEGBOeG9v1s2J0bAq7Cenuzm6E+8KIBr86vFTcPahOqHZ02mSsaBkBgBmGK6etze3YsUT7wEARtWIifeOnKxPFmu2dwTeEQLcJN5ukoG+uCg+bu7E9jZ9IR0j2HbAJE7NHWkQIzcEIC4AHlebQGU0hHROw9837AUgJtrkMW9cDQDg9U372GLclIoLW4cAq35J1MIHAIeM18/9/rZ2ZPMauwZB7h/6HL340R5s7YNlZnJDEql4GL25Al76ZE/g8wOmq+n1jfsAiIv4ATPPzJaWHtz//CcA9KKuolGR+vnNZ5mRmZHiizkdL3psUHcxr7lh5UwCWLf4aCya+VaoSKOBSFwnM9t279OjagDEKgRdPACrnN3d1YHW7iwrZ4AAocnVYf2+37ZlIwCgLVQvHI1FXT3VcMiqHhMr3ji9qQrdSOA1Tbe4z4Qe5YrKBrPkgBu4LMDrd3agDnrOoWSdh0XH4XhpmRmioKnat7b0sqRdQaKZxtYmUJ2IIFvQ8OamfYGPB8xd9VubdXNiYyomvBurqzRN0wACaW7ikRDu/uI8Ngm+bCwCQSdBAJjRqPfhgRc2sNdGC1hmAJNQvvdZq6WulChYwrFMHu9tadPPHYCMLJpUj7CqYPW2dvz81c0ATNefCKhl4qNdnfj1u9uxtyujJ5urF5uIwyEVh4zXLQvX/e/7AIIvxAvG16K+Moq2nhx+9c42AMH6wJ/zj+/vNN1MAvfRpBGVqK2IIJPXsHZHO7MIBCGUXzCshC99soc9B0GsGqqqYI5R7Xejod0JOoa0z+9v0xcBUa0KYIrQAbB7KAgZA8xnOVcgeM9IQDklAJmZNToFRdHJyJ7ODLcpEbsOvLuWbSoCzAHM3bi3G/mMkfk2AJmJVeh9TaEHDcZCHK4WXIhh6mu27NqnC4D7ZJkxNE+tenRoV1TQqgEwMjNR2VX8nqBlZmxtAslYGPuI/l2z1M/0N/ysMoAlounxN7ZgBB3DVKPQuaVlJiB++MMfYuLEiYjH41i0aBHefPPNwWwOqhMRTDBcJM+u3cVeE4WiKGwxfuEjnQz4RfDYYU8XH4RE0MmmtSeLnmweD774KYBg/v4z5usTxuOvbwEA7DI0L0EWgxMPboSdf4m6WaY3VSGsKujM5JErEFQnIoEsI8lYmC26vbkCUvEw5huWChFMa6zCV47Q84x8uEuPHBgTgAiMr6vA2NoE8hrBrf/3AQBg+eKJge4j6iahCHIPAPpYn2y46542yEyQPgDmvfTHNTvx57XNAMQsfIqisPZf9MhbbAyDENIJ9ZWY0VQFWt4rpCqY1ii+kAOmdYdiWqOg1sHAgvHWaxDEzZSMhfGrKxZbXgtCxgB9c0E3Ic9/uBsAMG2keB+SsTCLKnrijS2MFIo+x9QtvGlvN7oN62IQdy+t9/bOZ62AUeQwJJAsjiJRqS+mU9VtUBWCXhJFvKZJ+PhYQj9/V3cn/r5hr+lmUgWsY4ZlJhXWCe1IpQ0AkE2MFD4/JTMHqc0O74mRGUVRML2pipGZGcpW/Y0qgXFglbP1TfkIxcgGXSnYB2aZaff+XAlh0MjMU089hWuvvRa33XYb3n33XcybNw8nnXQSdu/ePVhNAgAcNcX0iYZVBXPH1gQ6ftFBdexvRQFbVEQxMhVHLWfNGSm4kwJ04hRWFRQ0grMe/Dt7PUgkxjmHjUNYVfDaxn34xm/W4OPmLoRV/aESxYymFM473Fo6/qwFY10+bUU8EsKiSeYYzh6TErZMUUxqMK0gJ85qChRFAgDHTrfuwA6fWOfyyWIoioKjp+rH00WARgeJ4itHTLCQwYNHi5mleVx+7GSLa2tsAEIIAEdNqS96TdTVssAgM3x9oyBkBrCS+lmjUsx1IwreJXPe4eMDj+E1J06zWDSDbAiA4mt24kzBHTEHPoM3YI6rKBYahOx7fzWjE0UtdNSS9vInuqszGlaFLLwUE0dUIoYsMukeVBq6kXAAzUu4Sl90D1F06+5OZSSmBCCkNIw7gQx2tKe50GxxN1OlksXR6vu4MPRnAEBVwzjh83sSFkHLDACcOKsRLYS6jAz9TUXxs+l2jmp0I4YsUvTYpKhmxrh/pWXGH/feey8uueQSXHTRRZg1axZ+/OMfo6KiAg8//PBgNQkA8NXjJrNcHb+89AimPxDFFzgh7dkLx7FQ4SC45JhJqIiGUF8ZxZmHiJtWo2EVJ8zQJwG6Iw6rCo4JsJiOra3AlxfpROTxN3TrzLmHj8Oo6mCL4Z1nzMZPli/E05cvxsZvnxrIRH7ywSYBnO1R2NAN//GPczBnTDWWzRyJG0+ZEfj4RQfVMevO106chkMDkBkA+NfjJ1v+nzcuWB8aqmJ4/aaliEdUVCci+OclBwU6HtAtATSTLQCcEHAxPXn2KDx4/oKi7xTBQptV48RZjcKRVBTUTQQUW6pEQK0SAHDuYQEWIQOzx1Rj9W2fw8VLDsK0xmRgQhqPhHDpMZMwa1QKj/7z4ThljoBrwIZKW+RS0DG87qTpaOSiyG46ZYawy9X+vJ+/aHygTcWoZAivVV6HZ6M3YCTa9BcrA4yhYX2YaFg2Jk6ZFcjVR/OknHFwLa46YQoOqjXGTqicgd73cL4H/xP9DqaoerK7pjETvI6yYuRMd0Ljlb3Xhi8uHIsW2EhcQmA+qtbXoS9OyqMeOiHR1AgQrxE78RDUzATbbvQTstks3nnnHdx0003sNVVVsWzZMrz22mtFn89kMixOHgA6OgZugMfWVuAPVy6BoiiY3BDMtA3o+ox/P20WNuzuxG2fP7hPbfjqcVOKikKK4qKjDsJf1jVDVYDbz5iN5UcEeAANXH/yDLy5qQWf7O7CxUsOwrVcHSdRqKqCkw4OZpWi+McFY/Haxn3oyRbwZZuFRwSzx1Tj91cu6dO5AV3A+euvHom8RvpERsfWVuDxf1mEix55C4sn1Qe2KgD6zvj3K5YgHgkFXsQorl42FXu7MjhhxshArjaKE2c14rzDxyMeUfH5eaOFXTXzxtVgdHUcmbyG7549D8dMbQhsXTty8giEVAWNVTFcdNTEwG2fO7YG9ZVRjK5JYO7Y4IQY0N1bNN1BX3DzqTP7fCwAPPDlBbj2/61Ca08OXz1usv8BNoxMxfHg+Qtw3kNv4LjpDbjsWPHvOHh0CucdPg7bWntx9bKpWDghGKFX9n6MusIe1KnA8oMywGcISGasmzi1bmKg81PryknTqnHSYdOBjSrQjkCWGbRvtb7eOEf8/LEqYN65wJsPFb8X4FkYkYzhslMOB/7KfY9Xwjx2oD5nTw/txC3H1QOvA0pypPi5xx0OLLoCGH+EcFsHG4NCZvbu3YtCoYDGRutusbGxER9++GHR5++66y7cfvvtB6p5mBLAN+2Ei/uwk+4vLJ5cj1dvPAGqUry7EkUyFsb/rTgKPZmCcNbY/kQyFsaD5y884OflMTWgxsKOo6aMwOs3LQ1kmu/vNtRURPHAlxf4f9AFkZCKu84KMIEbiEdCeO5aPT9GZR/7P2VkEq/ftBSpRDhQ5lyK6kQEf7v+eIQUJTCRKhUcP2Mk3rv1c9jXlfFNOOmGhRPq8NpNJwiF1fNQVQV3nTW3T+cEAOz7hP3ZWDBEsMkAmhO7LmR0wPuYWj+M7MGsxlBIJM+McaxmdfMFXthruI3kjNOAEVOBhuCW4ikTbetJhQiZma7/3vsxTl1EdDIjorWhmHyC/jOEMChkJihuuukmXHvttez/jo4OjBsX3HRcLggimHVDLBzq0yIiYaKvFpXhgL6SGB5BI5Ds2B8iWUoIEhI9EMf3Cbu5TWmLHtosLD4FbBE7CjD9lGDnp9YVRmYM/VYQy4wdgpl7HT8frwaWfTPY8RR2jYyQZcYoJNzVDOzQ02OgdmLfzj9EMChP+4gRIxAKhdDcbFV6Nzc3o6mpmD3GYjHEYoPwQEpISEhIBMfudebfPbqIWFh8ClhdUuMOD04kmGVGj+ZBgZKZAJYZHlf8vfg1P/CkI7YfVtZKW6I+Ec1MPAXUTQZaPgXefUx/bZiTmUERAEejUSxcuBArV5o1KzRNw8qVK7F48WKPIyUkJCQkSh5O1o0glhlVBcYv1tPyn35/H85vRIEyy4zhMhJyM9naPv98oLEP+keezESD6y8tx/KlIEQsMwAw41T9d7eeJkSSmQHCtddei5/+9Kd49NFHsX79elxxxRXo7u7GRRddNFhNkpCQkJDoD5z1kE5GeATRzADA8t8A/7YaaJge/PyulhmRaKa4WTUaAJLBw+oB9J9lRlGs1pkKQTH2NJtrbpiTmUFzKp9zzjnYs2cPbr31VuzatQvz58/Hs88+WyQKlpCQkJAYguCtEaGYmHuERyThrl/xAyUPNE8K1cyIhGarKrDgn4A3f6L/Xxs8IhSAzTIjXorBER3bzb9F8swAwBhbEMWIPpDCIYRBVcitWLECK1asGMwmSEhISEgMBGIcmakeK17XqD9ANTddhotFC5A0DwBOuAUA0a0yc8/pWxv4nC77G1E39XPAJ38Bxh8pTowitoSrVcPbUDA85P4SEhISEqUFftGtOcDRp5TMdBsZ5QsBNDOALqA99Z79awNPJgp598+J4MRvAROXAIf9S8Dj7gCeuxU462f7d/4hAElmJCQkJCT6H1FOJ1J9gMkM1ed0GWRGCxDNNBAQJVFuGDlD/wmKxVcCc84GUsEzUA81yKrZEhISEhL9D97NVBM8k/d+gUZOpduAfDaYALg/cfTXgaa5wNxzD+x5KVS1LIgMIMmMhISEhMRAgBcA1/etPEufkagFFCPpZ1czAFqC/QCTmaX/Dlz+spXYSQwIJJmRkJCQkOh/dHFJUacsO7DnVlXT1dSxg3tdKiuGKySZkZCQkJDof8w6U/897RSzCvOBBCUzfMHIA22ZkThgkDRVQkJCQqL/MX4RcNV7QGrs4Jx/xHRg52qzNhFw4DUzEgcM0jIjISEhITEwqJsEhAep4Oqoefrv7e+Yr6myeO5whSQzEhISEhLDD5TMbHlN/x1L7X/yOomShSQzEhISEhLDD6PnW91K9vT+EsMKksxISEhISAw/xKqAScea/487fPDaIjHgkGRGQkJCQmJ4Yt555t9TThy8dkgMOGQ0k4SEhITE8MTsLwAjZ+lamZEzB7s1EgMISWYkJCQkJIYnFAVonDXYrZA4AJBuJgkJCQkJCYkhDUlmJCQkJCQkJIY0JJmRkJCQkJCQGNKQZEZCQkJCQkJiSEOSGQkJCQkJCYkhDUlmJCQkJCQkJIY0JJmRkJCQkJCQGNKQZEZCQkJCQkJiSEOSGQkJCQkJCYkhDUlmJCQkJCQkJIY0JJmRkJCQkJCQGNKQZEZCQkJCQkJiSEOSGQkJCQkJCYkhjSFZNZsQAgDo6OgY5JZISEhISEhIiIKu23Qd7y8MSTLT2dkJABg3btwgt0RCQkJCQkIiKDo7O1FdXd1v36eQ/qZHBwCapmHHjh2oqqqCoiiD3Zx+RUdHB8aNG4etW7cilUoNdnMOOMq9/4Acg3LvPyDHoNz7Dwz+GAzU+Qkh6OzsxOjRo6Gq/ad0GZKWGVVVMXbs2MFuxoAilUqV7UMMyP4DcgzKvf+AHINy7z8w+GMwEOfvT4sMhRQAS0hISEhISAxpSDIjISEhISEhMaQhyUyJIRaL4bbbbkMsFhvspgwKyr3/gByDcu8/IMeg3PsPDP4YDPb5g2JICoAlJCQkJCQkJCikZUZCQkJCQkJiSEOSGQkJCQkJCYkhDUlmJCQkJCQkJIY0JJmRkJCQkJCQGNKQZEZCQkJCQkJiSEOSGQkJibKEpmmD3QQJCYl+giQzZYLdu3cPdhNKDuW+mJVj/z/44AOcffbZANCvdWGGEso9G4ecC60YrHmgv+9DmWemDPDee+9h4cKFePHFF3HMMccMdnMGBZs2bcIrr7yClpYWzJo1CyeeeCIA/YEabsVKnfDpp5/i0UcfRVtbGyZMmICvfe1rg92kA47Vq1dj6dKlaGlpwe9+9zucdtppZXP9AaC1tRXxeByJRKKs+s2j3OfCUpgHB+o+LM+tSRlh9erVOPbYY3HNNdeU5cMLAGvWrMHhhx+OX//613jwwQdx44034vjjj0dHRwcURRn2O9U1a9Zg8eLFWL9+Pd5//3088cQTuPfeewe7WQcUq1evxhFHHIGvfOUrOOKII/D0008DQNks6OvXr8fnPvc53HPPPejp6SmL+96Ocp8LS2EeHND7kEgMW6xZs4ZUVFSQW265hRBCiKZp5OOPPyYvvvgi2bFjxyC37sBg3759ZP78+eSGG24ghBDS0dFBHn/8caIoCjnqqKPYOBQKhcFs5oDh448/JhMmTCA333wzIUTv/+mnn06+/e1vWz43XPtPCCHvvvsuSSQS5MYbbySEEPL000+TVCpFXnjhhcFt2AHCZ599RubNm0caGxvJkUceSe6++27S3d1NCNHnhHJAuc+FpTAPDvR9KC0zwxSZTAa33HILent78a1vfQsAcNppp+Gcc87B8ccfj89//vO4+uqrB7eRBwA7duxAPp/HxRdfDACoqqrCCSecgIMPPhgbN27EP/zDPwAYnvqJQqGAJ554AkuWLMEtt9wCQO9/Q0MDXnvtNSxfvhxf/epXkc/noarqsNTQ7NmzB1/5ylfwr//6r7jrrrsAAHPnzsWECRPwt7/9DcDw1g4RQvDMM8+gqakJf/zjHzF37lw8/fTT+OEPf8h2xsO5/4CcC4HBnwcPxH04/GZwCQBANBrFzTffjJkzZ2LRokU48cQTEQqFcM8992DNmjX4/Oc/jxdffBF33HHHYDd1wNHZ2Yk1a9aw/9vb26GqKr73ve+hra0N//mf/zmIrRs4hEIhLF++HF/72teQSCQAAN/5znfw85//HFOnTkVDQwNeeOEFLF68GISQYUnootEoHnroIdxzzz3stWnTpuHMM8/E97//fezatWtY9ptCURScfvrpuOyyy7Bw4UL86Ec/wsKFC9lC0t3dDVVVh7XLSc6FOgZzHjwg9+F+23YkShaFQoG8++67ZM6cOWTBggVk69at7L2enh6yfPlysnTpUpLJZAaxlQOL5uZmsnTpUnL66aeTu+66i/z+978nNTU15JprriGEEHLOOeeQCy+8cJBb2f+gZlvefLtlyxayePFi8swzz7DXVq5cSUaMGEFeeeWVA97GgYaTyZy+tmHDBjJ79mxy1113EU3ThrW7xd63XC5HLr/8cnLYYYdZTP0///nPB6F1BwZ0Lpw7d25ZzoW7d+8mS5cuJWecccagzYP257G/78Nw/3EvicHGzp078dFHHyEcDmPy5MkYNWoU5s+fj1/84hfYsWMHmpqaAOjuh0QigenTp2Pt2rXDyszMj8GkSZMwevRo3H///bj11lvx85//HIqiYMWKFczcPHLkSHz88ceD3Or+QyaTQSwWA1AcoTBu3Dg888wzqK6uZu8pioKGhgZ2bwwH0DFwEvdSK8ykSZMwa9Ys/OpXv8KNN94IYPhEtrW0tGD79u0AgLFjx6K2thaapkFVVRQKBYTDYfzgBz/AVVddhaeffhqapmHjxo347//+bxx//PGYMGHCIPdg/8GPwZgxY1BXV4c5c+bgf/7nf7Bz585hPxfy/R89ejQaGhpw33334bbbbsOjjz4KQsiAz4P8XDxlyhTLHJPP5/v/PtwfpiVROli9ejWZMGECmTJlChk9ejRpamoiTz/9NMnn84QQZ4HVRRddRC688EKSy+UOdHMHBE5j8NRTTxFC9N1XR0cH2bx5M/u8pmnkC1/4Avna1742WE3uV6xbt44sWbKECVudrrn9tRtuuIEcd9xxpKWl5UA0ccAhMgZ0h/jRRx+Ruro68qMf/ehANnFA8f7775MFCxaQ6dOnk3HjxpHTTz+dfPbZZ5bP0DmB7oxjsRhJpVLk3XffHYwm9zucxoA+9/l83tFiN5zmQnv/P//5z5NPP/2UEEJIe3s76ejosNwTAzEPOs3F//u//2uxfNGx7q/7UJKZYYDdu3eTadOmkRtuuIHs2LGDvP322+Saa64hoVCIfOc73yGdnZ2Wz+/bt4/cdNNNpKGhgaxdu3aQWt2/cBsDVVXJt7/9bdLe3m75/Mcff0xuuukmUltbS9avXz9Ire4/bNq0iUyZMoXU19eTBQsWkBdffJEQ4h4lsHXrVnLDDTeQ2tpasnr16gPZ1AFD0DHo7OwkRxxxBFm+fPmwcC989NFHpKGhgVx33XVkzZo15NFHHyUnnHAC+e53v0sIsY4DXdC/+tWvktraWvLBBx8MSpv7G0HGgJDhNxe69f+ee+4hhBS7egZiHvRbjzo6OthnKbHuj/tQkplhgI0bN5Lp06eTt99+2/L69773PaIoCrn//vsJIfqN/Mwzz5B/+qd/ImPHjh02OzFCgo1Bc3MzueOOO8j48ePJe++9Nwit7V+k02myYsUKctZZZ5Enn3ySnH322WTu3LmWxZyfxF999VWyYsUKMm3atGHRf0LExsAJzzzzzLAgs11dXeS8884jF198seX1Cy+8kCxZssTxmIcffpgoijJs5oGgY/Dss88Oq7kwaP937949IPNgkLmYkP67DyWZGQZYtWoViUaj5K233iKEEJLNZtl7d911FwmHw+zG2rVrF/nv//5vsnHjxkFp60AhyBjk83mydevWYZVf4k9/+hN56KGHCCGEvPbaa+RLX/qSZTHn0draSv785z+TLVu2HOhmDiiCjMFwE/zu3buXXHPNNeTxxx8nhJg73t/97ndk8eLFJJfLObpXNm3adCCbOaAIOgY7d+4cVnNh0P7ncjmyZcuWfp8Hg8zFFP1xH0oyM0xw+umnk0WLFpHm5mZCiH6j0h35aaedRpYvX07S6TQhZPhN5BR+Y3DBBReQbDY7bPvP45VXXimyTqTT6WHjThCB2xisW7dukFs2MKCLByHmM/6nP/2JzJs3j2QyGfbacNFHOUF0DPbu3UsIGX7JIkX7v2/fvgFth+hc3J/u3eGbYKHMcNlllyESieC6667D3r17EQ6HWXRGU1MT9u3bx6JchkPEhhP8xmDv3r2IRCLDtv+AmQDuqKOOwlVXXYUZM2bgqquuwsqVK3Hddddh6dKl6OzsHORWDiz8xuD4448flmNw6KGHArBGZXV3d6OrqwuhUAiKouCWW27BySefjGw2O5hNHTCIjsGpp56KbDY77OYC0f6fcsopyGazA5ZfSHQujkaj/XZOGZo9THDKKafg008/xWOPPYYrrrgCDzzwABobGwHo4ag1NTXIZrPDejEv5zGgE4WqqsjlcohEIjjqqKMAAPfffz9OOukkVFVV4c9//jOqqqoGubUDg3IfAxp+rSgKCoUCQqEQUqkUEokEQqEQbrnlFtx777146aWX+nURKSWU+xiUSv8HZS7uNxuPxKCA+kV7e3sJIYQ89thj5JhjjiH19fVk+fLl5PTTTyfJZJK8//77g9nMAUW5jwHtP2865l1pp512GqmpqRnWLqZyHwOn/hNCyIsvvkiOPvpocs0115BoNFqkVRhOKPcxKIX+D+ZcLMnMEMG+ffvInj17LK/RG2fz5s1k5MiR5Fe/+hUhhJBPP/2UfOtb3yLLly8nV1111bAIOSREjoFf/0eNGkV+8YtfWN779re/TSoqKoZN1FK5j0HQ/v/qV78iiqKQZDJJ3nnnnQPa1oFCuY9BKfR/w4YNRd812HOxJDNDAJ9++imZPHkyue2224qU51u2bCGjR48ml19++bBI+OSGch8D0f7bxc3PPPPMsBG8lvsY9KX/q1evJqeccsqwIPOEyDEohf6/9957JJVKkZ/+9KdF7w3mXCzJzBDAgw8+SBRFIQsWLCB33XUX2bVrFyFEN6PfeOON5KqrrrLcvMMxWqfcxyBo/4cjyn0M+tr/1tbWA9zSgUO5j8Fg93/VqlWkoqKCXHvttUXvaZpGvvGNb5B/+7d/G5S5WJKZIYD33nuP/NM//RO5/fbbyejRo8l//Md/DJuHUxTlPgbl3n9C5BgE7f9wJHblPgaD2f+PPvqIxGIxcssttxBC9Pwxv//978nPfvYz8vvf/77fzxcUMpppCIAQgtdffx2PPPIICoUCfvKTn6CqqgrPP/88Zs+ezYqFDWeU+xiUe/8BOQZB+z/cIvYAOQaD1f98Po8HHngAyWQSCxYsAACceeaZ2LZtG9rb27F161Z84QtfwDe+8Q3MmzevX84ZGINGoyQC4XOf+xwrDnbXXXeRZDJJqquryV/+8pdBbtmBQ7mPQbn3nxA5BuXef0LkGAxW/z/88ENyySWXkCOOOIKMGzeOnHrqqWTdunWkp6eHvPHGG2TUqFHkoosuGtA2eEEmzStx0ARg6XQaL7/8MgBgw4YNUBQFiUQCa9aswa5duwaziQOOch+Dcu8/IMeg3PsPyDEY7P5Pnz4d1157LSZPnoy5c+fi3nvvxcyZM5FIJHD44YfjwQcfxKOPPooNGzYMWBu8IN1MJYTNmzfjtddeQ3NzM44//nhMmTIFlZWVAIBFixZBVVVcddVVeOaZZ7Bq1So88cQTuPXWW6GqKq688kqEQqFB7sH+o9zHoNz7D8gxKPf+A3IMSqH/fBuOO+44TJ48GTNmzMA3v/lNbNiwAZMmTQJgJqvM5XKYPn06Ghoa9vvcfcKg2YQkLHj//ffJiBEjyNFHH01qamrI7NmzyRe+8AVW24Kq2EeNGmWpv/Htb3+bfPzxx4PV7H5FuY9BufefEDkG5d5/QuQYlEL/ndpw1llnsegpp5pKX//618nJJ59MOjo6+qUNQSHJTAmgq6uLLFmyhKxYsYL09vaSXC5HHnroIXL00UeTOXPmkObmZtLa2kquv/56lvhruBVIK/cxKPf+EyLHoNz7T4gcg1Lov1cb5s6dywgNxbp168g3vvENkkqlyJo1a/q1LUEgyUwJYM+ePWTGjBksYyIhepXR559/nhx11FFkyZIlg8Z2DxTKfQzKvf+EyDEo9/4TIsegFPrv14YjjzySVV7fsGEDOemkk8iUKVMGPcO2FACXAKqrq1FTU4O///3v7LVwOIzjjjsON998M9LpNL7//e8PWIXTUkC5j0G59x+QY1Du/QfkGJRC//3akM/ncf/994MQgsmTJ+M73/kOVq5cifnz5w9Ym0QgyUwJIBQKYcmSJXj55ZeZSh3QcwSceuqpWLBgAf785z8Pu5wJPMp9DMq9/4Acg3LvPyDHoBT679eG+fPn4y9/+Qt7ff78+Rg/fvyAtUcYg2cUkuDR2tpKZs+eTY444gjy9ttvs6JdhBDy1FNPkVmzZjHT3nBFuY9BufefEDkG5d5/QuQYlEL/S6ENQSEtMyWAbDaLmpoavPDCC9i7dy+uvPJK/PrXv0YulwMhBC+//DLq6+sRi8UGu6kDhnIfg3LvPyDHoNz7D8gxKIX+l0Ib+gKFkGHqfCxhECMuHwAKhQJCoRB27NiBdDqNuro6nH322dizZw+am5sxe/ZsvPXWW3jhhRcG3SfZnyj3MSj3/gNyDMq9/4Acg1Lofym0oT8gycwBQkdHBwqFAjKZDJqamqBpGjRNQzgcxmeffYYjjzwSN954I6688kp0d3fj3XffxSuvvIKRI0fi2GOPxZQpUwa7C/uNch+Dcu8/IMeg3PsPyDEohf6XQhv6HQfWq1We+OCDD8jRRx9NDjnkENLQ0ED+/Oc/s/e2bt1Kkskkueyyy4imacMqZwKPch+Dcu8/IXIMyr3/hMgxKIX+l0IbBgKSzAww1q9fT+rr68l1111HnnjiCXLppZeSqVOnslwBr7/+Orn++ustAqvhhnIfg3LvPyFyDMq9/4TIMSiF/pdCGwYKkswMIHK5HLngggvIBRdcwF577rnnyFlnnUVaWlrIli1bBrF1BwblPgbl3n9C5BiUe/8JkWNQCv0vhTYMJGQ00wAin89j06ZNrCAXALzyyit44YUXcPTRR2POnDm4/fbbkclkBrGVA4tyH4Ny7z8gx6Dc+w/IMSiF/pdCGwYSsmr2ACIej+OQQw7Bf/3Xf6GhoQHr1q3Dww8/jIcffhgzZszAunXr8JWvfAVz587FP/7jPw52cwcE5T4G5d5/QI5BufcfkGNQCv0vhTYMJGQ00wBA0zSoqm702rhxI+699160t7dj3bp1OO+88/D1r3+dfXbJkiWYM2cOfvSjHw1WcwcE5T4G5d5/QI5BufcfkGNQCv0vhTYcCEjLTD+ira0NNTU1UFWVxetPmjQJDzzwANLpNI499lg0NTUB0OP5CSGIxWI46KCDBrnl/YdyH4Ny7z8gx6Dc+w/IMSiF/pdCGw4kpGamn7B+/XosWLAAt956KwC9vkWhUGDvx+NxzJkzB7/85S+xefNmtLW14c4778RHH32Es846a7Ca3a8o9zEo9/4DcgzKvf+AHINS6H8ptOGAY7CUx8MJW7ZsIfPnzydTp04ls2fPJrfffjt7j4/T/8UvfkGOPfZYEo1GyRFHHEHGjx9P3n333cFocr+j3Meg3PtPiByDcu8/IXIMSqH/pdCGwYB0M+0nCCF48sknMXr0aFx99dV49dVX8eSTTwIAbr31Vqiqilwuh0gkgvPPPx/z5s3Dm2++iZqaGhx66KGlUW10P1HuY1Du/QfkGJR7/wE5BqXQ/1Jow6Bh8HjU8MHOnTvJI488QgghpLm5mdx2221kxowZ5Jvf/Cb7TDabHazmHRCU+xiUe/8JkWNQ7v0nRI5BKfS/FNowGJBkZgCwY8cOxxvoN7/5zZDMrNgXlPsYlHv/CZFjUO79J0SOQSn0vxTacCAg3Ux9wM6dO7F161a0trZi2bJlCIVCAPQQOEVRMGrUKFx66aUAgF/+8pcghKC9vR333Xcftm3bhtGjRw9m8/sF5T4G5d5/QI5BufcfkGNQCv0vhTaUBAaPRw1NrF69mkyYMIFMmzaNVFdXkxkzZpAnnniC7Nu3jxCiC6w0TSOE6Iz41ltvJYqikNraWvL2228PZtP7DeU+BuXef0LkGJR7/wmRY1AK/S+FNpQKJJkJgN27d5MZM2aQm2++mXz66adk+/bt5JxzziEzZ84kt912G9m9ezchhLCbhxBCli9fTlKpFFm7du1gNbtfUe5jUO79J0SOQbn3nxA5BqXQ/1JoQylBkpkAWLt2LZk4cWIRo73hhhvInDlzyN133026u7vZ6z/72c9ITU3NkA53s6Pcx6Dc+0+IHINy7z8hcgxKof+l0IZSgiQzAbBq1SoyduxY8tJLLxFCCOnp6WHvXXXVVeSggw4iq1evZq/t2rWLbNy48YC3cyBR7mNQ7v0nRI5BufefEDkGpdD/UmhDKUHWZgqIww8/HMlkEs8//zwAIJPJIBaLAQAOO+wwTJkyBU8++SRLHz0cUe5jUO79B+QYlHv/ATkGpdD/UmhDqUCWM/BAd3c3Ojs70dHRwV77yU9+grVr1+LLX/4yACAWiyGfzwMAjjnmGHR3dwPAsLlxyn0Myr3/gByDcu8/IMegFPpfCm0oZUgy44J169bhrLPOwrHHHouZM2fi8ccfBwDMnDkT9913H5577jl86UtfQi6XYxVJd+/ejcrKSuTzeQwHg1e5j0G59x+QY1Du/QfkGJRC/0uhDSWPwfJvlTLWrl1L6uvryTXXXEMef/xxcu2115JIJMKEU93d3eR3v/sdGTt2LJkxYwY588wzydlnn00qKyvJmjVrBrn1/YNyH4Ny7z8hcgzKvf+EyDEohf6XQhuGAqRmxoaWlhacd955mDFjBu677z72+vHHH485c+bgBz/4AXuts7MTd955J1paWhCPx3HFFVdg1qxZg9HsfkW5j0G59x+QY1Du/QfkGJRC/0uhDUMFMgOwDblcDm1tbfjiF78IQM+iqKoqDjroILS0tADQi3kRQlBVVYX//M//tHxuOKDcx6Dc+w/IMSj3/gNyDEqh/6XQhqGC8uqtABobG/GLX/wCRx99NACgUCgAAMaMGcNuDkVRoKqqRYilKMqBb+wAodzHoNz7D8gxKPf+A3IMSqH/pdCGoQJJZhwwdepUADq7jUQiAHT2u3v3bvaZu+66Cz/72c+Ycny43TzlPgbl3n9AjkG59x+QY1AK/S+FNgwFSDeTB1RVBSGE3RiUCd96662488478d577yEcHt5DWO5jUO79B+QYlHv/ATkGpdD/UmhDKUNaZnxA9dHhcBjjxo3Dd7/7Xdx99914++23MW/evEFu3YFBuY9BufcfkGNQ7v0H5BiUQv9LoQ2livKlcYKg7DcSieCnP/0pUqkUXnnlFSxYsGCQW3bgUO5jUO79B+QYlHv/ATkGpdD/UmhDqUJaZgRx0kknAQD+/ve/49BDDx3k1gwOyn0Myr3/gByDcu8/IMegFPpfCm0oNcg8MwHQ3d2NysrKwW7GoKLcx6Dc+w/IMSj3/gNyDEqh/6XQhlKCJDMSEhISEhISQxrSzSQhISEhISExpCHJjISEhISEhMSQhiQzEhISEhISEkMaksxISEhISEhIDGlIMiMhISEhISExpCHJjISEhISEhMSQhiQzEhIS+41vfvObmD9/fr9933HHHYerr766375PQkJieEOSGQkJCVeIkoqvf/3rWLly5cA3SEJCQsIBsjaThIREn0EIQaFQQDKZRDKZHOzm7Dey2Syi0ehgN0NCQiIgpGVGQkLCERdeeCH+9re/4b777oOiKFAUBY888ggURcEzzzyDhQsXIhaL4ZVXXilyM1144YU488wzcfvtt6OhoQGpVAqXX345stms8Pk1TcP111+Puro6NDU14Zvf/Kbl/S1btuCMM85AMplEKpXC2Wefjebm5qI28Lj66qtx3HHHsf+PO+44rFixAldffTVGjBjBat5ISEgMLUgyIyEh4Yj77rsPixcvxiWXXIKdO3di586dGDduHADgxhtvxHe+8x2sX78ec+fOdTx+5cqVWL9+PV588UU8+eST+PWvf43bb79d+PyPPvooKisr8cYbb+Duu+/GHXfcgeeeew6ATnTOOOMMtLS04G9/+xuee+45bNy4Eeecc07gfj766KOIRqN49dVX8eMf/zjw8RISEoMP6WaSkJBwRHV1NaLRKCoqKtDU1AQA+PDDDwEAd9xxB0488UTP46PRKB5++GFUVFTg4IMPxh133IHrrrsO3/rWt6Cq/vuouXPn4rbbbgMATJ06FQ888ABWrlyJE088EStXrsSaNWuwadMmRrAee+wxHHzwwXjrrbdw2GGHCfdz6tSpuPvuu4U/LyEhUXqQlhkJCYnAOPTQQ30/M2/ePFRUVLD/Fy9ejK6uLmzdulXoHHaLz6hRo7B7924AwPr16zFu3DhGZABg1qxZqKmpwfr164W+n2LhwoWBPi8hIVF6kGRGQkIiMCorKwf8HJFIxPK/oijQNE34eFVVQQixvJbL5Yo+dyD6IiEhMbCQZEZCQsIV0WgUhUKhT8euXr0avb297P/XX38dyWTSYk3pK2bOnImtW7darDzr1q1DW1sbZs2aBQBoaGjAzp07LcetWrVqv88tISFRepBkRkJCwhUTJ07EG2+8gc2bN2Pv3r2BLCPZbBYXX3wx1q1bhz/96U+47bbbsGLFCiG9jB+WLVuGOXPm4Pzzz8e7776LN998ExdccAGOPfZY5gI74YQT8Pbbb+Oxxx7DJ598gttuuw0ffPDBfp9bQkKi9CDJjISEhCu+/vWvIxQKYdasWWhoaMCWLVuEj126dCmmTp2KY445Bueccw5OP/30ovDqvkJRFPzf//0famtrccwxx2DZsmWYNGkSnnrqKfaZk046Cf/+7/+O66+/Hocddhg6OztxwQUX9Mv5JSQkSgsKsTuVJSQkJPYTF154Idra2vDb3/52sJsiISFRBpCWGQkJCQkJCYkhDUlmJCQkDii2bNnCyh84/QRxZUlISEgA0s0kISFxgJHP57F582bX9ydOnIhwWObzlJCQEIckMxISEhISEhJDGtLNJCEhISEhITGkIcmMhISEhISExJCGJDMSEhISEhISQxqSzEhISEhISEgMaUgyIyEhISEhITGkIcmMhISEhISExJCGJDMSEhISEhISQxr/H1aA8JlVgtCdAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "timesfm_result = result.sort_values(\"forecast_timestamp\")[[\"forecast_timestamp\", \"forecast_value\"]]\n",
+    "timesfm_result = timesfm_result.rename(columns={\n",
+    "    \"forecast_timestamp\": \"trip_hour\",\n",
+    "    \"forecast_value\": \"timesfm_forecast\"\n",
+    "})\n",
+    "arimaplus_result = predictions.sort_values(\"forecast_timestamp\")[[\"forecast_timestamp\", \"forecast_value\"]]\n",
+    "arimaplus_result = arimaplus_result.rename(columns={\n",
+    "    \"forecast_timestamp\": \"trip_hour\",\n",
+    "    \"forecast_value\": \"arimaplus_forecast\"\n",
+    "})\n",
+    "df_all = df_grouped.merge(timesfm_result, on=\"trip_hour\", how=\"left\")\n",
+    "df_all = df_all.merge(arimaplus_result, on=\"trip_hour\", how=\"left\")\n",
+    "df_all.tail(672).plot.line(\n",
+    "    x=\"trip_hour\",\n",
+    "    y=[\"num_trips\", \"timesfm_forecast\", \"arimaplus_forecast\"],\n",
+    "    rot=45,\n",
+    "    title=\"Trip Forecasts Comparison\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "015804c3",
+   "metadata": {},
+   "source": [
+    "### 5. Multiple Time Series Forecasting\n",
+    "\n",
+    "This section demonstrates a more advanced capability of ARIMAPlus: forecasting multiple time series simultaneously. This is useful when you have several independent series that you want to model together, such as trip counts from different bikeshare stations. The `id_col` parameter is key here, as it is used to differentiate between the individual time series."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6dbe6c48",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/google/home/shuowei/src/python-bigquery-dataframes/bigframes/core/log_adapter.py:182: TimeTravelCacheWarning: Reading cached table from 2025-12-12 23:04:48.874384+00:00 to avoid\n",
+      "incompatibilies with previous reads of this table. To read the latest\n",
+      "version, set `use_cache=False` or close the current session with\n",
+      "Session.close() or bigframes.pandas.close_session().\n",
+      "  return method(*args, **kwargs)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "✅ Completed. \n",
+       "    Query processed 69.8 MB in a moment of slot time.\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of stations: 41\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "✅ Completed. \n",
+       "    Query processed 69.8 MB in a moment of slot time.\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "✅ Completed. \n",
+       "    Query processed 69.8 MB in a moment of slot time.\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Date range: 2013-08-29 to 2018-04-30\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    Query processed 18.8 MB in 2 minutes of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:74ada07a-98ad-4d03-90bb-2b98f1d8b558&page=queryresults\">Job bigframes-dev:US.74ada07a-98ad-4d03-90bb-2b98f1d8b558 details</a>]\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "✅ Completed. \n",
+       "    Query processed 1.4 MB in 4 seconds of slot time. [<a target=\"_blank\" href=\"https://console.cloud.google.com/bigquery?project=bigframes-dev&j=bq:US:a292f715-1d9c-406d-a7d5-f99b2ba71660&page=queryresults\">Job bigframes-dev:US.a292f715-1d9c-406d-a7d5-f99b2ba71660 details</a>]\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "✅ Completed. \n",
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+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "✅ Completed. \n",
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+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "✅ Completed. \n",
+       "    Query processed 0 Bytes in a moment of slot time.\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "00fc1edbf6fd40dfb949a3e3a30b6c3e",
+       "version_major": 2,
+       "version_minor": 1
+      },
+      "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>forecast_timestamp</th>\n",
+       "      <th>start_station_name</th>\n",
+       "      <th>forecast_value</th>\n",
+       "      <th>standard_error</th>\n",
+       "      <th>confidence_level</th>\n",
+       "      <th>prediction_interval_lower_bound</th>\n",
+       "      <th>prediction_interval_upper_bound</th>\n",
+       "      <th>confidence_interval_lower_bound</th>\n",
+       "      <th>confidence_interval_upper_bound</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2016-09-01 00:00:00+00:00</td>\n",
+       "      <td>Beale at Market</td>\n",
+       "      <td>27.911417</td>\n",
+       "      <td>3.422434</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>21.215568</td>\n",
+       "      <td>34.607265</td>\n",
+       "      <td>21.215568</td>\n",
+       "      <td>34.607265</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2016-09-01 00:00:00+00:00</td>\n",
+       "      <td>Civic Center BART (7th at Market)</td>\n",
+       "      <td>17.09455</td>\n",
+       "      <td>4.266287</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>8.74774</td>\n",
+       "      <td>25.441361</td>\n",
+       "      <td>8.74774</td>\n",
+       "      <td>25.441361</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2016-09-01 00:00:00+00:00</td>\n",
+       "      <td>Embarcadero at Bryant</td>\n",
+       "      <td>22.343648</td>\n",
+       "      <td>3.393702</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>15.704012</td>\n",
+       "      <td>28.983284</td>\n",
+       "      <td>15.704012</td>\n",
+       "      <td>28.983284</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2016-09-01 00:00:00+00:00</td>\n",
+       "      <td>Embarcadero at Folsom</td>\n",
+       "      <td>28.25329</td>\n",
+       "      <td>3.382158</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>21.63624</td>\n",
+       "      <td>34.870339</td>\n",
+       "      <td>21.63624</td>\n",
+       "      <td>34.870339</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2016-09-01 00:00:00+00:00</td>\n",
+       "      <td>Embarcadero at Sansome</td>\n",
+       "      <td>52.538083</td>\n",
+       "      <td>6.269291</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>40.272477</td>\n",
+       "      <td>64.803689</td>\n",
+       "      <td>40.272477</td>\n",
+       "      <td>64.803689</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2016-09-01 00:00:00+00:00</td>\n",
+       "      <td>Embarcadero at Vallejo</td>\n",
+       "      <td>16.513233</td>\n",
+       "      <td>2.953683</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>10.734476</td>\n",
+       "      <td>22.29199</td>\n",
+       "      <td>10.734476</td>\n",
+       "      <td>22.29199</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2016-09-01 00:00:00+00:00</td>\n",
+       "      <td>Market at 10th</td>\n",
+       "      <td>34.051274</td>\n",
+       "      <td>6.205798</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>21.90989</td>\n",
+       "      <td>46.192658</td>\n",
+       "      <td>21.90989</td>\n",
+       "      <td>46.192658</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2016-09-01 00:00:00+00:00</td>\n",
+       "      <td>Market at 4th</td>\n",
+       "      <td>25.746029</td>\n",
+       "      <td>4.001592</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>17.917082</td>\n",
+       "      <td>33.574977</td>\n",
+       "      <td>17.917082</td>\n",
+       "      <td>33.574977</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2016-09-01 00:00:00+00:00</td>\n",
+       "      <td>Market at Sansome</td>\n",
+       "      <td>46.134368</td>\n",
+       "      <td>5.071852</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>36.211503</td>\n",
+       "      <td>56.057233</td>\n",
+       "      <td>36.211503</td>\n",
+       "      <td>56.057233</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2016-09-01 00:00:00+00:00</td>\n",
+       "      <td>Mechanics Plaza (Market at Battery)</td>\n",
+       "      <td>23.269941</td>\n",
+       "      <td>3.194675</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>17.019692</td>\n",
+       "      <td>29.520189</td>\n",
+       "      <td>17.019692</td>\n",
+       "      <td>29.520189</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10 rows × 9 columns</p>\n",
+       "</div>[123 rows x 9 columns in total]"
+      ],
+      "text/plain": [
+       "          forecast_timestamp                   start_station_name  \\\n",
+       "0  2016-09-01 00:00:00+00:00                      Beale at Market   \n",
+       "1  2016-09-01 00:00:00+00:00    Civic Center BART (7th at Market)   \n",
+       "2  2016-09-01 00:00:00+00:00                Embarcadero at Bryant   \n",
+       "3  2016-09-01 00:00:00+00:00                Embarcadero at Folsom   \n",
+       "4  2016-09-01 00:00:00+00:00               Embarcadero at Sansome   \n",
+       "5  2016-09-01 00:00:00+00:00               Embarcadero at Vallejo   \n",
+       "6  2016-09-01 00:00:00+00:00                       Market at 10th   \n",
+       "7  2016-09-01 00:00:00+00:00                        Market at 4th   \n",
+       "8  2016-09-01 00:00:00+00:00                    Market at Sansome   \n",
+       "9  2016-09-01 00:00:00+00:00  Mechanics Plaza (Market at Battery)   \n",
+       "\n",
+       "   forecast_value  standard_error  confidence_level  \\\n",
+       "0       27.911417        3.422434              0.95   \n",
+       "1        17.09455        4.266287              0.95   \n",
+       "2       22.343648        3.393702              0.95   \n",
+       "3        28.25329        3.382158              0.95   \n",
+       "4       52.538083        6.269291              0.95   \n",
+       "5       16.513233        2.953683              0.95   \n",
+       "6       34.051274        6.205798              0.95   \n",
+       "7       25.746029        4.001592              0.95   \n",
+       "8       46.134368        5.071852              0.95   \n",
+       "9       23.269941        3.194675              0.95   \n",
+       "\n",
+       "   prediction_interval_lower_bound  prediction_interval_upper_bound  \\\n",
+       "0                        21.215568                        34.607265   \n",
+       "1                          8.74774                        25.441361   \n",
+       "2                        15.704012                        28.983284   \n",
+       "3                         21.63624                        34.870339   \n",
+       "4                        40.272477                        64.803689   \n",
+       "5                        10.734476                         22.29199   \n",
+       "6                         21.90989                        46.192658   \n",
+       "7                        17.917082                        33.574977   \n",
+       "8                        36.211503                        56.057233   \n",
+       "9                        17.019692                        29.520189   \n",
+       "\n",
+       "   confidence_interval_lower_bound  confidence_interval_upper_bound  \n",
+       "0                        21.215568                        34.607265  \n",
+       "1                          8.74774                        25.441361  \n",
+       "2                        15.704012                        28.983284  \n",
+       "3                         21.63624                        34.870339  \n",
+       "4                        40.272477                        64.803689  \n",
+       "5                        10.734476                         22.29199  \n",
+       "6                         21.90989                        46.192658  \n",
+       "7                        17.917082                        33.574977  \n",
+       "8                        36.211503                        56.057233  \n",
+       "9                        17.019692                        29.520189  \n",
+       "...\n",
+       "\n",
+       "[123 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_multi = bpd.read_gbq(\"bigquery-public-data.san_francisco_bikeshare.bikeshare_trips\")\n",
+    "df_multi = df_multi[df_multi[\"start_station_name\"].str.contains(\"Market|Powell|Embarcadero\")]\n",
+    "  \n",
+    "# Create daily aggregation\n",
+    "features = bpd.DataFrame({\n",
+    "    \"start_station_name\": df_multi[\"start_station_name\"],\n",
+    "    \"date\": df_multi[\"start_date\"].dt.date,\n",
+    "})\n",
+    "\n",
+    "# Group by station and date\n",
+    "num_trips = features.groupby(\n",
+    "    [\"start_station_name\", \"date\"], as_index=False\n",
+    ").size()\n",
+    "# Rename the size column to \"num_trips\"\n",
+    "num_trips = num_trips.rename(columns={num_trips.columns[-1]: \"num_trips\"})\n",
+    "\n",
+    "# Check data quality\n",
+    "print(f\"Number of stations: {num_trips['start_station_name'].nunique()}\")\n",
+    "print(f\"Date range: {num_trips['date'].min()} to {num_trips['date'].max()}\")\n",
+    "\n",
+    "# Use daily frequency  \n",
+    "model = forecasting.ARIMAPlus(\n",
+    "    data_frequency=\"daily\",\n",
+    "    horizon=30,\n",
+    "    auto_arima_max_order=3,\n",
+    "    min_time_series_length=10,\n",
+    "    time_series_length_fraction=0.8\n",
+    ")\n",
+    "\n",
+    "model.fit(\n",
+    "    num_trips[[\"date\"]],\n",
+    "    num_trips[[\"num_trips\"]],\n",
+    "    id_col=num_trips[[\"start_station_name\"]]\n",
+    ")\n",
+    "\n",
+    "predictions_multi = model.predict()\n",
+    "predictions_multi"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}