diff --git a/notebooks/emerging_ai_projects/ai_contributor_pagerank.csv b/notebooks/emerging_ai_projects/ai_contributor_pagerank.csv
new file mode 100644
index 0000000..b14b8d3
--- /dev/null
+++ b/notebooks/emerging_ai_projects/ai_contributor_pagerank.csv
@@ -0,0 +1,4985 @@
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diff --git a/notebooks/emerging_ai_projects/repo_cntrb_counts.png b/notebooks/emerging_ai_projects/repo_cntrb_counts.png
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diff --git a/notebooks/emerging_ai_projects/top_pagerank_common_repos.ipynb b/notebooks/emerging_ai_projects/top_pagerank_common_repos.ipynb
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@@ -0,0 +1,2666 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AI Common Repositories of top contributors (based on pagerank score)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Table of Contents\n",
+    "- [Introduction](#introduction)\n",
+    "- [Database Connection](#database-connection)\n",
+    "- [Get Page Rank Scores](#read-pagerank-scores)\n",
+    "- [Fetch Contribution Event Stream](#contribution-event-stream)\n",
+    "- [Page Rank Analysis](#page-rank-analysis)\n",
+    "  - [Top 10% page rank score havers](#who-are-the-top_10_percent_considered-of-the-pagerank-score-havers)\n",
+    "  - [Find events of each contributor](#find-events-for-each-of-the-contributors)\n",
+    "- [Conclusion](#conclusion)\n",
+    "- [Next Steps](#next-steps)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction\n",
+    "\n",
+    "### Motivation and Rationale\n",
+    "\n",
+    "Inferring that a contributor's connectedness is a useful proxy for their importance\n",
+    "or involvement in a community, take the top p% of contributors \n",
+    "by their pagerank score and identify which repos they also belong/contribute to.\n",
+    "\n",
+    "Compare involvement to find maximal overlap among top percentage of contributors.\n",
+    "\n",
+    "### Background\n",
+    "\n",
+    "We've started from a 'seed' of repos that seem highly likely to be a \"part of the AI/ML neighborhood of projects.\" Our seed that is the initial list of respoitories can be found [here](ai_repos.json).\n",
+    "\n",
+    "I would like to thank the original author, James Kunstle for implementing this notebook, repo discovery using page rank for WASM ecosystem.\n",
+    "\n",
+    "### Context of the Second Iteration\n",
+    "\n",
+    "This is the second iteration of the process, now applied to AI repositories. The first iteration was conducted on WebAssembly (Wasm) repositories, where the goal was to identify top repositories using a network analysis approach, specifically focusing on the PageRank algorithm. By applying a similar methodology to AI repositories, the goal is to uncover key insights and influential repositories in the AI domain, leveraging the lessons learned from the Wasm analysis.\n",
+    "\n",
+    "### Summary of the WASM Iteration\n",
+    "\n",
+    "In the Wasm iteration, we explored the network of repositories to identify those that play a central role in the ecosystem. The key findings included:\n",
+    "\n",
+    "- Network Structure: The Wasm ecosystem had a distinct set of core repositories that were highly interconnected, revealing key contributors and projects driving the ecosystem.\n",
+    "- PageRank Analysis: Repositories like `wasmedge`, `wasmtime`, and `wasmer` emerged as influential nodes, having high PageRank scores. These repositories are pivotal in advancing the Wasm ecosystem, contributing significantly to both development and adoption.\n",
+    "- Community Insights: The analysis provided insights into the distribution of contributions and collaborations among repositories, highlighting the importance of certain projects in the broader Wasm community.\n",
+    "\n",
+    "This initial analysis provided a foundational understanding of how network analysis can be used to discover and rank influential repositories within a specific domain. In this second iteration, similar techniques will be applied to the AI domain to uncover key players and trends within the AI open-source ecosystem."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "pd.set_option('display.max_rows', None)  # Display all rows\n",
+    "pd.set_option('display.max_columns', None)  # Display all columns\n",
+    "pd.set_option('display.width', None)  # Auto-detect terminal width\n",
+    "\n",
+    "# Or, set multiple options at once:\n",
+    "pd.set_option('display.max_rows', 100, 'display.max_columns', 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import sqlalchemy as salc\n",
+    "import plotly.express as px\n",
+    "import json\n",
+    "from IPython.display import Image\n",
+    "from collections import Counter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Database Connection"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Connect to the Postgresql database."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# read configuration file\n",
+    "with open(\"data/il_ai_creds.json\") as config_file:\n",
+    "    config = json.load(config_file)\n",
+    "\n",
+    "# define cx string\n",
+    "database_connection_string = f\"postgresql+psycopg2://{config['user']}:{config['password']}@{config['host']}:{config['port']}/{config['database']}\"\n",
+    "\n",
+    "# create engine\n",
+    "engine = salc.create_engine(\n",
+    "    database_connection_string,\n",
+    "    connect_args={'options': f\"-csearch_path={'augur_data'}\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Global Switches"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's set the switches or the flags which are useful in the later sections."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Whether we're using df_event_stream.parquet or querying the source database directly.\n",
+    "LOCAL_DATA = False \n",
+    "\n",
+    "# what percent of the top contributors by pagerank should we consider?\n",
+    "TOP_PERCENT_CONSIDERED = 0.10 \n",
+    "\n",
+    "# how many \"top repos\" should we output at the end?\n",
+    "FINAL_NUM_COMMON_REPOS = 50"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Read pagerank scores\n",
+    "\n",
+    "These scores are calculated by building a collaboration graph of the contributors in four projects\n",
+    "and running the pagerank algorithm on the graph. The nodes of the graph are individual contributors,\n",
+    "and the edges are weighted by the historical 'connectedness' between any two nodes. Connectedness increases\n",
+    "when two individuals work on something together (PR, issue, commit). \n",
+    "\n",
+    "The described process is implemented in Rappel/notebooks/emerging_ai_projects/collabs.ipynb."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cntrb_id</th>\n",
+       "      <th>score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>010000b4-3700-0000-0000-000000000000</td>\n",
+       "      <td>5.469449</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>01012f1b-7f00-0000-0000-000000000000</td>\n",
+       "      <td>10.259513</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>010000f2-1900-0000-0000-000000000000</td>\n",
+       "      <td>7.863005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>01000117-3e00-0000-0000-000000000000</td>\n",
+       "      <td>5.081839</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0100012d-d800-0000-0000-000000000000</td>\n",
+       "      <td>6.613582</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               cntrb_id      score\n",
+       "0  010000b4-3700-0000-0000-000000000000   5.469449\n",
+       "1  01012f1b-7f00-0000-0000-000000000000  10.259513\n",
+       "2  010000f2-1900-0000-0000-000000000000   7.863005\n",
+       "3  01000117-3e00-0000-0000-000000000000   5.081839\n",
+       "4  0100012d-d800-0000-0000-000000000000   6.613582"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_known_contribs = pd.read_csv('ai_contributor_pagerank.csv')\n",
+    "df_known_contribs[\"cntrb_id\"] = df_known_contribs.cntrb_id.astype(str)\n",
+    "df_known_contribs.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'01029da8-8500-0000-0000-000000000000' in df_known_contribs['cntrb_id'].values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Contribution event stream\n",
+    "\n",
+    "Github provides an API endpoint from which one can query every event that occurs. For instance, if a contributor creates an issue on some repo, that event will be logged in the stream. A downside of this API is that it only serves the most recent two months of data. \n",
+    "\n",
+    "We want to use this stream to identify the set of repositories that our Known Contributors are also working in that weren't in the initial ecosystem kernel.\n",
+    "\n",
+    "The database that we use collects this event stream so we can query it from there rather than from Github. This is much faster than relying on the public Github API, and we will continue to collect the data that the API makes available, so over time the analysis that we can do will become richer and more complete. In the Augur database, the table with this information is the `contributor_repo` table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "event_stream_query = salc.sql.text(\n",
+    "    f\"\"\"\n",
+    "        SET SCHEMA 'augur_data';\n",
+    "        SELECT \n",
+    "            c.cntrb_id,\n",
+    "            c.event_id,\n",
+    "            c.created_at,\n",
+    "            c.cntrb_repo_id as repo_id,\n",
+    "            c.repo_git,\n",
+    "            c.repo_name,\n",
+    "            c.gh_repo_id,\n",
+    "            c.cntrb_category as event_type\n",
+    "        FROM\n",
+    "        contributor_repo c\n",
+    "    \"\"\")\n",
+    "\n",
+    "# Check if the data is stored locally or needs to be retrieved from the database\n",
+    "if not LOCAL_DATA:\n",
+    "    # Connect to the database engine and execute the SQL query to retrieve data\n",
+    "    with engine.connect() as conn:\n",
+    "        df_event_stream = pd.read_sql_query(event_stream_query, conn)\n",
+    "        \n",
+    "    # Save the retrieved data as a Parquet file for faster future access        \n",
+    "    with open(\"data/df_event_stream.parquet\", \"wb+\") as f:\n",
+    "        df_event_stream.cntrb_id = df_event_stream.cntrb_id.astype(str)\n",
+    "        df_event_stream.to_parquet(f)\n",
+    "else:\n",
+    "    # If data is stored locally, read the Parquet file instead of querying the database\n",
+    "    with open(\"data/df_event_stream.parquet\", \"rb+\") as f:\n",
+    "        df_event_stream = pd.read_parquet(f)\n",
+    "        df_event_stream.cntrb_id = df_event_stream.cntrb_id.astype(str)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cntrb_id</th>\n",
+       "      <th>event_id</th>\n",
+       "      <th>created_at</th>\n",
+       "      <th>repo_id</th>\n",
+       "      <th>repo_git</th>\n",
+       "      <th>repo_name</th>\n",
+       "      <th>gh_repo_id</th>\n",
+       "      <th>event_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>01001583-8700-0000-0000-000000000000</td>\n",
+       "      <td>37039342564</td>\n",
+       "      <td>2024-04-01 03:54:33</td>\n",
+       "      <td>5</td>\n",
+       "      <td>https://api.github.com/repos/AmazingRise/hugo-...</td>\n",
+       "      <td>AmazingRise/hugo-theme-diary</td>\n",
+       "      <td>220989613</td>\n",
+       "      <td>IssueCommentEvent</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>01001583-8700-0000-0000-000000000000</td>\n",
+       "      <td>36966778104</td>\n",
+       "      <td>2024-03-28 12:58:59</td>\n",
+       "      <td>6</td>\n",
+       "      <td>https://api.github.com/repos/AmazingRise/hugo-...</td>\n",
+       "      <td>AmazingRise/hugo-theme-diary</td>\n",
+       "      <td>220989613</td>\n",
+       "      <td>IssueCommentEvent</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>01001583-8700-0000-0000-000000000000</td>\n",
+       "      <td>36927475492</td>\n",
+       "      <td>2024-03-27 11:44:16</td>\n",
+       "      <td>7</td>\n",
+       "      <td>https://api.github.com/repos/AmazingRise/hugo-...</td>\n",
+       "      <td>AmazingRise/hugo-theme-diary</td>\n",
+       "      <td>220989613</td>\n",
+       "      <td>IssuesEvent</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>01003020-1200-0000-0000-000000000000</td>\n",
+       "      <td>39202894121</td>\n",
+       "      <td>2024-06-11 17:22:24</td>\n",
+       "      <td>1808426</td>\n",
+       "      <td>https://api.github.com/repos/fhh2626/BFEE2</td>\n",
+       "      <td>fhh2626/BFEE2</td>\n",
+       "      <td>322234705</td>\n",
+       "      <td>ForkEvent</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>01003020-1200-0000-0000-000000000000</td>\n",
+       "      <td>39169968316</td>\n",
+       "      <td>2024-06-10 20:29:29</td>\n",
+       "      <td>1808427</td>\n",
+       "      <td>https://api.github.com/repos/fhh2626/BFEE2</td>\n",
+       "      <td>fhh2626/BFEE2</td>\n",
+       "      <td>322234705</td>\n",
+       "      <td>IssuesEvent</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               cntrb_id     event_id          created_at  \\\n",
+       "0  01001583-8700-0000-0000-000000000000  37039342564 2024-04-01 03:54:33   \n",
+       "1  01001583-8700-0000-0000-000000000000  36966778104 2024-03-28 12:58:59   \n",
+       "2  01001583-8700-0000-0000-000000000000  36927475492 2024-03-27 11:44:16   \n",
+       "3  01003020-1200-0000-0000-000000000000  39202894121 2024-06-11 17:22:24   \n",
+       "4  01003020-1200-0000-0000-000000000000  39169968316 2024-06-10 20:29:29   \n",
+       "\n",
+       "   repo_id                                           repo_git  \\\n",
+       "0        5  https://api.github.com/repos/AmazingRise/hugo-...   \n",
+       "1        6  https://api.github.com/repos/AmazingRise/hugo-...   \n",
+       "2        7  https://api.github.com/repos/AmazingRise/hugo-...   \n",
+       "3  1808426         https://api.github.com/repos/fhh2626/BFEE2   \n",
+       "4  1808427         https://api.github.com/repos/fhh2626/BFEE2   \n",
+       "\n",
+       "                      repo_name  gh_repo_id         event_type  \n",
+       "0  AmazingRise/hugo-theme-diary   220989613  IssueCommentEvent  \n",
+       "1  AmazingRise/hugo-theme-diary   220989613  IssueCommentEvent  \n",
+       "2  AmazingRise/hugo-theme-diary   220989613        IssuesEvent  \n",
+       "3                 fhh2626/BFEE2   322234705          ForkEvent  \n",
+       "4                 fhh2626/BFEE2   322234705        IssuesEvent  "
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_event_stream.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There are: 6868909 events available\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"There are: {df_event_stream.shape[0]} events available\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Page Rank Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Who are the TOP_10_PERCENT_CONSIDERED of the pagerank score-havers?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Identify the top 10% of contributors from a dataset based on their scores after sorting by score."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Num in original set: 4984\n",
+      "Num in top 10%: 498\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cntrb_id</th>\n",
+       "      <th>score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2639</th>\n",
+       "      <td>0105d3c3-3c00-0000-0000-000000000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>550</th>\n",
+       "      <td>010105b7-9400-0000-0000-000000000000</td>\n",
+       "      <td>14.141630</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>496</th>\n",
+       "      <td>01027f51-2a00-0000-0000-000000000000</td>\n",
+       "      <td>12.322727</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>01012f1b-7f00-0000-0000-000000000000</td>\n",
+       "      <td>10.259513</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3292</th>\n",
+       "      <td>0103446d-3c00-0000-0000-000000000000</td>\n",
+       "      <td>10.047256</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "    <tr>\n",
+       "      <th>404</th>\n",
+       "      <td>01000088-e600-0000-0000-000000000000</td>\n",
+       "      <td>5.049529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>839</th>\n",
+       "      <td>0103d393-5b00-0000-0000-000000000000</td>\n",
+       "      <td>5.049421</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2367</th>\n",
+       "      <td>0100616b-b600-0000-0000-000000000000</td>\n",
+       "      <td>5.049299</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2217</th>\n",
+       "      <td>01029da8-8500-0000-0000-000000000000</td>\n",
+       "      <td>5.049286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2222</th>\n",
+       "      <td>0101a71d-f200-0000-0000-000000000000</td>\n",
+       "      <td>5.049286</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>498 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  cntrb_id      score\n",
+       "2639  0105d3c3-3c00-0000-0000-000000000000  20.000000\n",
+       "550   010105b7-9400-0000-0000-000000000000  14.141630\n",
+       "496   01027f51-2a00-0000-0000-000000000000  12.322727\n",
+       "1     01012f1b-7f00-0000-0000-000000000000  10.259513\n",
+       "3292  0103446d-3c00-0000-0000-000000000000  10.047256\n",
+       "...                                    ...        ...\n",
+       "404   01000088-e600-0000-0000-000000000000   5.049529\n",
+       "839   0103d393-5b00-0000-0000-000000000000   5.049421\n",
+       "2367  0100616b-b600-0000-0000-000000000000   5.049299\n",
+       "2217  01029da8-8500-0000-0000-000000000000   5.049286\n",
+       "2222  0101a71d-f200-0000-0000-000000000000   5.049286\n",
+       "\n",
+       "[498 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Calculate the total number of contributors in the original dataset\n",
+    "pop_contribs = df_known_contribs.shape[0]\n",
+    "\n",
+    "# Sort the contributors by their 'score' in descending order and select the top 10%\n",
+    "# 'TOP_PERCENT_CONSIDERED' is a constant representing the percentage of contributors to consider (e.g., 0.10 for top 10%)\n",
+    "top_10p = df_known_contribs.sort_values(by=\"score\", ascending=False)[:int(pop_contribs * TOP_PERCENT_CONSIDERED)]\n",
+    "\n",
+    "# Rename the 'user' column to 'cntrb_id' in the top 10% dataframe\n",
+    "top_10p = top_10p.rename({\"user\": \"cntrb_id\"})\n",
+    "\n",
+    "# Print the number of contributors in the original dataset\n",
+    "print(f\"Num in original set: {df_known_contribs.shape[0]}\")\n",
+    "\n",
+    "# Print the number of contributors in the top 10% based on their score\n",
+    "print(f\"Num in top 10%: {top_10p.shape[0]}\")\n",
+    "\n",
+    "# Display the dataframe containing the top 10% contributors\n",
+    "top_10p"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Find events for each of the contributors\n",
+    "\n",
+    "Joining event stream and top_10p on the users' id will give us the events for each of the contributors."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The join operation requires the index of the other dataframe `df_event_stream` to match the column in the main dataframe `top_10p` that is used for the join.\n",
+    "By setting the index of `df_event_stream` to the `cntrb_id` column using `set_index(\"cntrb_id\")`, the code ensures that the `cntrb_id` column in `top_10p` can be used to match and combine rows from both dataframes.\n",
+    "\n",
+    "The result `top_10p_events` is a new dataframe that includes all the columns from `top_10p` along with the corresponding event data from `df_event_stream` for each contributor in the top 10% group."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cntrb_id</th>\n",
+       "      <th>score</th>\n",
+       "      <th>event_id</th>\n",
+       "      <th>created_at</th>\n",
+       "      <th>repo_id</th>\n",
+       "      <th>repo_git</th>\n",
+       "      <th>repo_name</th>\n",
+       "      <th>gh_repo_id</th>\n",
+       "      <th>event_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2639</th>\n",
+       "      <td>0105d3c3-3c00-0000-0000-000000000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>3.991308e+10</td>\n",
+       "      <td>2024-07-05 23:45:28</td>\n",
+       "      <td>2278961.0</td>\n",
+       "      <td>https://api.github.com/repos/pytorch/pytorch</td>\n",
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+       "      <td>2278962.0</td>\n",
+       "      <td>https://api.github.com/repos/pytorch/pytorch</td>\n",
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+       "      <td>2024-07-05 23:18:34</td>\n",
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+       "      <td>https://api.github.com/repos/pytorch/pytorch</td>\n",
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+       "      <td>PullRequestEvent</td>\n",
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+       "      <td>2278964.0</td>\n",
+       "      <td>https://api.github.com/repos/pytorch/pytorch</td>\n",
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+       "      <th>2639</th>\n",
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+       "      <td>3.991280e+10</td>\n",
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+       "      <td>https://api.github.com/repos/pytorch/pytorch</td>\n",
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+       "      <td>IssueCommentEvent</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
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+       "      <th>2367</th>\n",
+       "      <td>0100616b-b600-0000-0000-000000000000</td>\n",
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+       "      <td>4.085265e+10</td>\n",
+       "      <td>2024-08-08 09:16:07</td>\n",
+       "      <td>13895813.0</td>\n",
+       "      <td>https://api.github.com/repos/agisoft-llc/metas...</td>\n",
+       "      <td>agisoft-llc/metashape-scripts</td>\n",
+       "      <td>107268834.0</td>\n",
+       "      <td>IssueCommentEvent</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2367</th>\n",
+       "      <td>0100616b-b600-0000-0000-000000000000</td>\n",
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+       "      <td>4.082972e+10</td>\n",
+       "      <td>2024-08-07 15:46:48</td>\n",
+       "      <td>13466875.0</td>\n",
+       "      <td>https://api.github.com/repos/agisoft-llc/metas...</td>\n",
+       "      <td>agisoft-llc/metashape-scripts</td>\n",
+       "      <td>107268834.0</td>\n",
+       "      <td>IssueCommentEvent</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2367</th>\n",
+       "      <td>0100616b-b600-0000-0000-000000000000</td>\n",
+       "      <td>5.049299</td>\n",
+       "      <td>4.082854e+10</td>\n",
+       "      <td>2024-08-07 15:11:40</td>\n",
+       "      <td>13466876.0</td>\n",
+       "      <td>https://api.github.com/repos/agisoft-llc/metas...</td>\n",
+       "      <td>agisoft-llc/metashape-scripts</td>\n",
+       "      <td>107268834.0</td>\n",
+       "      <td>IssuesEvent</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2217</th>\n",
+       "      <td>01029da8-8500-0000-0000-000000000000</td>\n",
+       "      <td>5.049286</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2222</th>\n",
+       "      <td>0101a71d-f200-0000-0000-000000000000</td>\n",
+       "      <td>5.049286</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>143749 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  cntrb_id      score      event_id  \\\n",
+       "2639  0105d3c3-3c00-0000-0000-000000000000  20.000000  3.991308e+10   \n",
+       "2639  0105d3c3-3c00-0000-0000-000000000000  20.000000  3.991308e+10   \n",
+       "2639  0105d3c3-3c00-0000-0000-000000000000  20.000000  3.991285e+10   \n",
+       "2639  0105d3c3-3c00-0000-0000-000000000000  20.000000  3.991285e+10   \n",
+       "2639  0105d3c3-3c00-0000-0000-000000000000  20.000000  3.991280e+10   \n",
+       "...                                    ...        ...           ...   \n",
+       "2367  0100616b-b600-0000-0000-000000000000   5.049299  4.085265e+10   \n",
+       "2367  0100616b-b600-0000-0000-000000000000   5.049299  4.082972e+10   \n",
+       "2367  0100616b-b600-0000-0000-000000000000   5.049299  4.082854e+10   \n",
+       "2217  01029da8-8500-0000-0000-000000000000   5.049286           NaN   \n",
+       "2222  0101a71d-f200-0000-0000-000000000000   5.049286           NaN   \n",
+       "\n",
+       "              created_at     repo_id  \\\n",
+       "2639 2024-07-05 23:45:28   2278961.0   \n",
+       "2639 2024-07-05 23:45:22   2278962.0   \n",
+       "2639 2024-07-05 23:18:34   2278963.0   \n",
+       "2639 2024-07-05 23:18:32   2278964.0   \n",
+       "2639 2024-07-05 23:12:56   2278965.0   \n",
+       "...                  ...         ...   \n",
+       "2367 2024-08-08 09:16:07  13895813.0   \n",
+       "2367 2024-08-07 15:46:48  13466875.0   \n",
+       "2367 2024-08-07 15:11:40  13466876.0   \n",
+       "2217                 NaT         NaN   \n",
+       "2222                 NaT         NaN   \n",
+       "\n",
+       "                                               repo_git  \\\n",
+       "2639       https://api.github.com/repos/pytorch/pytorch   \n",
+       "2639       https://api.github.com/repos/pytorch/pytorch   \n",
+       "2639       https://api.github.com/repos/pytorch/pytorch   \n",
+       "2639       https://api.github.com/repos/pytorch/pytorch   \n",
+       "2639       https://api.github.com/repos/pytorch/pytorch   \n",
+       "...                                                 ...   \n",
+       "2367  https://api.github.com/repos/agisoft-llc/metas...   \n",
+       "2367  https://api.github.com/repos/agisoft-llc/metas...   \n",
+       "2367  https://api.github.com/repos/agisoft-llc/metas...   \n",
+       "2217                                                NaN   \n",
+       "2222                                                NaN   \n",
+       "\n",
+       "                          repo_name   gh_repo_id         event_type  \n",
+       "2639                pytorch/pytorch   65600975.0  IssueCommentEvent  \n",
+       "2639                pytorch/pytorch   65600975.0  IssueCommentEvent  \n",
+       "2639                pytorch/pytorch   65600975.0   PullRequestEvent  \n",
+       "2639                pytorch/pytorch   65600975.0          PushEvent  \n",
+       "2639                pytorch/pytorch   65600975.0  IssueCommentEvent  \n",
+       "...                             ...          ...                ...  \n",
+       "2367  agisoft-llc/metashape-scripts  107268834.0  IssueCommentEvent  \n",
+       "2367  agisoft-llc/metashape-scripts  107268834.0  IssueCommentEvent  \n",
+       "2367  agisoft-llc/metashape-scripts  107268834.0        IssuesEvent  \n",
+       "2217                            NaN          NaN                NaN  \n",
+       "2222                            NaN          NaN                NaN  \n",
+       "\n",
+       "[143749 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# joins events data on the top_10p group of people.\n",
+    "# have to set the index of the 'other' df to be the column we want to join on\n",
+    "top_10p_events = top_10p.join(\n",
+    "    other=df_event_stream.set_index(\"cntrb_id\"),\n",
+    "    on=\"cntrb_id\"\n",
+    ")\n",
+    "top_10p_events"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For each contributor, get the list of repo gits that they've made at least one contribution to."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>unique</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cntrb_id</th>\n",
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+       "      <th>01000013-c500-0000-0000-000000000000</th>\n",
+       "      <td>[nan]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>01000025-0300-0000-0000-000000000000</th>\n",
+       "      <td>[https://api.github.com/repos/SciPhi-AI/R2R, h...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>01000027-9900-0000-0000-000000000000</th>\n",
+       "      <td>[https://api.github.com/repos/amzn/selling-par...</td>\n",
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+      ],
+      "text/plain": [
+       "                                                                                 unique\n",
+       "cntrb_id                                                                               \n",
+       "01000013-c500-0000-0000-000000000000                                              [nan]\n",
+       "01000025-0300-0000-0000-000000000000  [https://api.github.com/repos/SciPhi-AI/R2R, h...\n",
+       "01000027-9900-0000-0000-000000000000  [https://api.github.com/repos/amzn/selling-par...\n",
+       "01000034-fc00-0000-0000-000000000000  [https://api.github.com/repos/pytorch/pytorch,...\n",
+       "0100003e-9200-0000-0000-000000000000  [https://api.github.com/repos/gorse-io/goat, h..."
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cntrb_repos = top_10p_events.groupby([\"cntrb_id\"])[\"repo_git\"].agg(['unique'])\n",
+    "cntrb_repos.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# concatenate all of the lists of repo belonging together for all of the contribs. and then, count the occurrences of each repository.\n",
+    "all_repos = []\n",
+    "for row in cntrb_repos[\"unique\"]:\n",
+    "    all_repos = all_repos + list(row)\n",
+    "    \n",
+    "n_contribs_per_repo = dict(Counter(all_repos))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(nan, 68),\n",
+       " ('https://api.github.com/repos/SciPhi-AI/R2R', 1),\n",
+       " ('https://api.github.com/repos/ewilderj/aoc-2023', 1),\n",
+       " ('https://api.github.com/repos/ewilderj/prelude', 1),\n",
+       " ('https://api.github.com/repos/dracula/emacs', 1),\n",
+       " ('https://api.github.com/repos/ewilderj/emacs', 1),\n",
+       " ('https://api.github.com/repos/ewilderj/doap', 1),\n",
+       " ('https://api.github.com/repos/RDFLib/rdflib', 1),\n",
+       " ('https://api.github.com/repos/bbatsov/prelude', 1),\n",
+       " ('https://api.github.com/repos/amzn/selling-partner-api-models', 1)]"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(n_contribs_per_repo.items())[:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Translate the dictionary into a dataframe and sort\n",
+    "# Thanks Hema Veeradhi for this implementation.\n",
+    "df_for_fig = pd.DataFrame(list(n_contribs_per_repo.items()), columns=['repo_gits', 'repo_cntrb_counts']).sort_values(by=\"repo_cntrb_counts\", ascending=False)\n",
+    "df_for_fig.repo_gits = df_for_fig.repo_gits.apply(lambda x : x.removeprefix(\"https://api.github.com/repos/\") if(type(x)==str) else x)"
+   ]
+  },
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+   "metadata": {},
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+       "                                 repo_gits  repo_cntrb_counts\n",
+       "92                         pytorch/pytorch                194\n",
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+       "0                                      NaN                 68\n",
+       "130                            numpy/numpy                 44\n",
+       "107                            openxla/xla                 40\n",
+       "...                                    ...                ...\n",
+       "2769                         FFY00/cpython                  1\n",
+       "2768                        FFY00/dotfiles                  1\n",
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+       "2766                 FFY00/ffy00.github.io                  1\n",
+       "7522  francograndegmailcom/pytorch-pytorch                  1\n",
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+             [
+              1,
+              "#f0f921"
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+            "type": "surface"
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+          "table": [
+           {
+            "cells": {
+             "fill": {
+              "color": "#EBF0F8"
+             },
+             "line": {
+              "color": "white"
+             }
+            },
+            "header": {
+             "fill": {
+              "color": "#C8D4E3"
+             },
+             "line": {
+              "color": "white"
+             }
+            },
+            "type": "table"
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+         "layout": {
+          "annotationdefaults": {
+           "arrowcolor": "#2a3f5f",
+           "arrowhead": 0,
+           "arrowwidth": 1
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+          "autotypenumbers": "strict",
+          "coloraxis": {
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+            "ticks": ""
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+            [
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+             "#f7f7f7"
+            ],
+            [
+             0.6,
+             "#e6f5d0"
+            ],
+            [
+             0.7,
+             "#b8e186"
+            ],
+            [
+             0.8,
+             "#7fbc41"
+            ],
+            [
+             0.9,
+             "#4d9221"
+            ],
+            [
+             1,
+             "#276419"
+            ]
+           ],
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+             "#9c179e"
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+             "#fb9f3a"
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+             "#fdca26"
+            ],
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+             1,
+             "#f0f921"
+            ]
+           ]
+          },
+          "colorway": [
+           "#636efa",
+           "#EF553B",
+           "#00cc96",
+           "#ab63fa",
+           "#FFA15A",
+           "#19d3f3",
+           "#FF6692",
+           "#B6E880",
+           "#FF97FF",
+           "#FECB52"
+          ],
+          "font": {
+           "color": "#2a3f5f"
+          },
+          "geo": {
+           "bgcolor": "white",
+           "lakecolor": "white",
+           "landcolor": "#E5ECF6",
+           "showlakes": true,
+           "showland": true,
+           "subunitcolor": "white"
+          },
+          "hoverlabel": {
+           "align": "left"
+          },
+          "hovermode": "closest",
+          "mapbox": {
+           "style": "light"
+          },
+          "paper_bgcolor": "white",
+          "plot_bgcolor": "#E5ECF6",
+          "polar": {
+           "angularaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "radialaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "scene": {
+           "xaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "yaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           },
+           "zaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "xaxis": {
+         "anchor": "y",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "repo_cntrb_counts"
+         }
+        },
+        "yaxis": {
+         "anchor": "x",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "repo_gits"
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note: \n",
+    "\n",
+    "Image below omits many github URLs associated with the real column rendered.\n",
+    "Consider running notebook for holistic visualization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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KFs2Hkf3bI98PWXl2Y8JL3dfGeayE74vXfFy18sXh5tKOUx7+ufcEw9wXwaFGOazZegjBwV8woEdzJEgQHwtX7cKbd/5o17QWurf9Gik2NZd2i26TlyBdmpTo370Z/xVd68RZq3H+8g1ujNGnc2PUqlKaPzMlvEMnLuRGGNRQgs7r3ckRtauW4fNa9RqPyj8Vw8HjPnjs+wqOdSvi11/KY8bCTfjr1n38Wqs8+nZujFQpk+Htuw/oOXwm3ytzyp8Tw53bcBMNGtbm8NI5yZOFoX2bUGTIIMJr7tEW4TVHKHo+F+GNHo6mZhHhVc9YhFc9YxFe9YxlhSgSIGFr2MkN3drUZ9kjQaVo6oH105EkcUKjwnvt1j38ffcx59N++hyI0dOWo2r54iy2WrveXyqXRrNfq+KP63fguXw7H0uSSy2AB4+fjz2rJiFX9kwwNRfd3oePn1CuXk/sWumu6/pWt80QFM6XEx2a18GFyzfguWIHtzemNUwJ79pth5AnV1akTZUCx89ewcxFm+G9yxMpUyTleyXRd2rfkPueDRjjxZ3gBjk1R/YsGeA2ZQn6dHJEY4fKeOf/Edv3n0LJInlZ5Jet34e7D335GkR4o/hQWnC6CK8FkKLhEBHeaIBoZgoRXvWMRXjVMxbhVc9YVogigTlLt2Lv4XM4sH4az+T35j0qO/bFPPd+qFa+hFHhpWNf+r3Fpat/48WrNzh44iJSJE8CT3cXnfBeO7YcceLEQcCnzyhT1wmbFo7hSCgNx84j0L5ZbTjWrcR/NjYXfUZiuWn3caz3GsnHUmvi7oOn4/AmD10XuAYdXFGpXDEM7tXSpPCGhITi1p2HuPnPQ7x49RZzl23jFsFF8ufie10zzw0liuTldVr0GIt6NX/i66QxzWsD/N6+x2TX7vxn6ij35407uP/QF9TWmK7zr+MrRHij+ExacroIryWUon6MCG/UGZqbQYTXHKHo+VxSGqKHo7FZRHjV8pXZo4EApR7Q0CSO/nf1Zv054tuqUQ2jwrv/6HkMGjefUyAK5s2O23cfI1HC+FgwZeA3wkuSWaxGZ51Y0hpt+0yEQ42f0NqxBkzNpR3b2KESR1ZpbNt3kiOzp3bM1REYPX05/D8EwGNMb6PCS62SnYZ6sOxWr1gCmezTYvHaPSzSxQrl/kZ4O/efgirli6PD/4TXa8UOvs9Z4/pwKkOn/pO5e1uZ4gUQGBSM3Qe9RXij4Zm0ZAoRXksoRf0YEd6oMzQ3gwivOULR87kIb/RwFOFVy1FmV0iAopbeF69h+7IJvApJYVkHJ3iM6YXaVcsaFV6KqNapXg69OtDP/8CyDfs4tcCQ8FKFhaLVOxkVXlNzUVmx+u2H49weL5ZLGpRz3Md1ti4VQZNiEm+3fu2MCu+RU5fQd+QceO/25FxjGhTVNSa8XQdN46ixIeGd4rkeN/5+gKUzhsDOLi6nbbTuNV4nvB7zg/DoseVlyehakiUNQxPHUNjbSw6vuUdehNccoej5XIQ3ejiamkWEVz1jWkGEVy1nifCq5SuzRwOBsxf/AokdCW750kVAZby8Vu7E8a2zkD5tKqPCSxHavD9kxYDuzfDY9yXGTF+B1KmSRUp4Tc1FKRfPX77BxGFddXdLL73VajkYrRpVR9c29XHxyk04j5gDr0n9uWSZsRzec79fR5eBU7Ft6XhkTJ8Ge4+cw8TZqyMlvJSTfMz7CuZP7s85yZRDHD6l4da9L/j4KcS6HYoDpEgRhiSJRHjNgRPhNUcoej4X4Y0ejiK86jmaW0GE1xyhqH0uwhs1fnK2jQjMX7UT85Zt59XoJS1Kb6hRqaQuArp6riunLji7zUbRgj/wi2eURzts4kKudkDnUHUCisCSAFI+a0unsdByeA1FeNs5u3MVB0qbMDbX3Il9Ua2JC2aM7o2yJQpEoHHi7B+cUkH5wTSc2jeAc+fG/L/DX+fqLQdx7MxlrsNL10GlzQ6dvMjHVa9QAkfPXMaG+aP4vvRzeL+J8K7ciX/uPea0CapOQetQlJdGpXJFcer8VV2E98GTL/AP+GL1DiZLBiRIIMJrDpwIrzlC0fO5CG/0cBThVc/R3AoivOYIRe1zEd6o8ZOzbUjgc2AQXr1+h4z2aRDPzs6ilb+EhMD3uR8y2qc1WxvX3ISG5iKBHD19GQ5v9EDcuN+mB1Bu8LOXr5EmVQokTpTA3BK6z+k+KQ0hdcrkFp9j7MCnz14hVcrkXNEi/Ji9KAiPn1iX0pA0SRgaNZCUBks2RYTXEkpRP0aEN+oMzc0gKQ3mCEXP5yK80cPR2CwivGr5yuyxnMDS9fs4atz816r/uTuVOrxqt0yEVy1fbXYRXvWcRXjVM6YVRHjVchbhVctXZhcCMZaACK/arRHhVctXhNc2fGkVEV7bsBbhVctZhFctX5ldCMRYAiK8ardGhFctXxFe2/AV4bUdZxFetaxFeNXyjTA7tadNnTIZcufMYvGqVPLqhd9blCtR0Ow5B45fQNkSBSOd9/nk2Svcf/QMFcoU4bUo9/XClZvImS0jv+VPZbLy5LL82s1esN4B9JJVhnSpDebCWjuXrY8P+BSIBAniWZxbbOvro/Wo8xq1LK5TrSw32xDhVbsLIrxq+Yrw2oavCK/tOIvwqmUtwqvHd+jEhejauh7y5soa7eR7DpuJkkXzcsMESweV4DrufYXf4Dc39N/gN3e8/ucjpy5DqhTJMNCpOTdOoD9TK96iBX7A1Zt3UaxgbvRo96u100Y43hBfuse12w4j+MsXBAd/4c5m1P73vzKom1npOt0xd2I/rqpgq0Gl1jwWbsLUkU4WifZft+6jeY8x+OPIUj7+xPkgfPgYatXl0ruCObIDKVNIlQZz4ER4zRGKns8lhzd6OJqaRVIa1DOmFUR41XIW4dXjS9K4fOawb0pMRcc2xGTh1Zo57Fg+gWW/o8tkjgS2bFidbz183diosNDnq0mYxvzuQ1/82n441nmNxI+FckdlKZudS6XEbv7zAFkz2yPF/xpP2GJxKjfWtNtoXDm0BPHjxzO7pL7wBn4Jhd/7ILPnfXMAu64IrzlwIrzmCEXP5yK80cNRhFc9R3MriPCaIxS1z2Ol8FL7VCq4nzRJIvx27ALSpEqOES7tuCMV1UUtW7wAmjeoxuTCwsLQx202GtauiGs374Leus+aKT1HOh0dKrHwUdesmQs3486Dp1zrdWT/9sj3w9cIcKte49G9bX2ub0oCMmFoFyRJlBDT5m+Ez5UbLCI1K5WCa9+2IOFNkTwJ3vsHgNIbqpUvDucujZEts73RXQwf4X377gN6Dp/JLWNpFM6fE8Od23B9Wf5z1Y5o17QWzl+6zu1lf61VHqMHdORyWI+evsDkeetw4fJNbq/7c6nCfK0JEsTnc3ceOIN12w5zpzGqd0t1bzUOYwZ1xLzl23URXiq1RV3L1u84Av8Pn7ge7vA+bZAyxdfOYBTFpZ/Oqf5t7hyZ0buTI2pXLcPRSH2+ubJnArXH3b92CrJnycDnV2rkjCG9WvH1mxtUcmvS3LU4d+kGfiycG83qV+W1iJHb5CXo07kx15+lQW13KWVjslsPTPVaz39398FTrrFbokheuA/vhuxZvu4F7Q91eCMB/6VyKbRyrImiBXIZ3fPR05bDrV9bFMybgznbxY2LOw+e8HPxc+nCGNa7NRav24Ojpy/zl6m+XZro9s3YPdBiNFe8eHa4c//pN88MyS49c7Qmrefary1HyMd5rOQavDToGXNzacfpKPrCu3F3EPz8zBGO+HnChEDpkqFIn06E1xw5EV5zhKLncxHe6OFoahaJ8KpnTCuI8KrlHCuFd8XG3zBt/gYu9E8/w2/afQx/Xr+DUzvmYsWm37hT16ENM7jO6e9/3kb7vu44vXMu13ht1GkEhvRuhUJ5c3C918DAYDTs5MZpCJV/KoY1Ww/B58pNHFg/neuakmTSaNP4F2TOmBbVK5SE09AZsE+XGl1aOXAjgcVr92DNPDcWXhIpl25NkCdXVngs2IRyJQua/Pk+vPBSDiZ1yipZJC+L6rL1+1jItiweqxPeIvlzoXMrB76XWYu3YFT/9iyO3QdPh52dHVy6NcV7/4/YsvcERvXvwF8KaFCThQa1y7Mwkti3dBrHIl+8cB7kz5Mdw90X6YR3857jmOq5AYN7tUQm+zSYvWQr3/uc8X15rrXbDvH9pU2VAsfPXsHMRZu5xe4Lvzff8KWc3S4Dp+HmPw/Rt0tjfAj4hIPHfbByjqvZaGnwlxA07OjK10iif+/hMwwePx8HN0xHlozp+P4pVWLv6skshr2Gz8TO5RM5D5n2gr7g9OnkiFQpk8Fz+Q5u7EDd0h4+eYG6bYZwagd9STpwzAfb9p/EkU0enPuqv+fU3rhm8wFYNccVpYrl0+3zgB7NkCtbJoyevpw7vdEzRPJLe0qlzKh5hrl7MPXM0LMwYspSLJk+mKU4X+5s/MXm77uPWYI/fQ4EiTilpVCKiL7wSg6v2v+4ivCq5avNLsKrnrMIr3rGIrzqGcda4T3tc5VFgMaLV29RrakL9q2ZwhHWig2dub0sRf4oGhk/XjyOdtLQ/8md2sbuPXwOB9ZP48/93rxHZce+mOfeD9XKl+Djtbnoc60NLq2VI+vXiKU29FMatu49iTVbD2L7sgkcDV2xcb/u2GKFcnNkWD+Hl/JF/7xxB/cf+nK3sPCtYvVzeN3nrAGlKpDEUWvcdGlScqTZPl2qCNf14PFzOLQdCu/dnhwJpFGmrhO38iXhoxE+pYGi2gXyZMfoAR34s8Onfke/kXNZainKSxHgW3cessQS+7nLtnHkmGTcUMoIfSGgqHziRAlx7dY9zqGmyLe55hLnLl1HlwFTsXL2cJ24U/vghnUqorVjDVCjiE4uUziKf+vOI4wb3Al1q5fja9bfC3rhb8Ks1Ti5fQ7mr9yJPYfPYsboXnwsvbDXsuc4bF0yju9bf8+15ya88IbP1Sbx/vveY3i6u/B89IvBqKnL+AuYuXsw9cwYS2l46fcWl67+jRev3uDgiYv8zNPaIrzq/4MafgURXtvwFuFVz1mEVz1jWkEivGo5fxfCqwnchKGdQdE410mLOZJIEc4qjfvpZEwTl/A5vMPcF/EOUDROG9Wb9edoHbWc1ZdMklj6Gdpn/4Jvds6QZHks3MwyTZJCkVBtlClegF/eCi+89DN9p/6TOTpInwcGBbMo/nV8BZ+mfy0bdh7F8g37eX5KZRjmvhDPX77hVIWubepxNJeG5/LteOT7MsI9mhJeSjmgiCFdHw2q5lCzxUBsWzqe53Ya6sGyW71iCWSyT8sR7vVeI0ESry+8p87/ycef3ePFEV1KL3AZNQ+DnJqjxf/yh439E9BerKN0hPCjWoUSHF2nQVUu6rcfzpUnFk0bpDtMfy8oBcSx8wgc2zILHos24cipS7qUA+2knh0a8jyGXg6kvzMmvIvW7MYf1+/ohFeTXNo3c/dg6pkxJLz7j57ntB1KvSmYNzuntlAKC30pE+FV+x9T/dlFeG3DW4RXPWcRXvWMRXjVM/4uhJfKbdVqOQgrZg1jUbx87W+OeFJ+7rWb91h4tUHistRjCH4qWYj/ivI4vS9e4ygsDe3lLop+kjzryw9VVKBo6IltszmiGn6YkhdjWx1eeKd4ruef5pfOGMLpGCRRrXuNNyq8Yz1Wci7r6rmuPD1FXkkAD526yHm6e1ZNQrYs9qjWxAXTRvbET6W+3jMNU8JLYlihbFEMcmrBx2pRbZLFqzfuou/IORGixcQovPCG50vRz6OnL2HXSnfd2sQvaeJEXH3A1Dhx9g+Wu7N7PI1Gg+kn/0tXb4Oi2OG/yOjvBX1xoC83lw8uxtxl23H/kS9XXTA0rBVeEv4rf/1jUHjN3YOpZ4a+VDTpOgqXDi5Gwv/lYjfo4Io61cuhV4eGfOmUa33h8g0RXvX/Lf1mBRFe20AX4VXPWYRXPWNaQSK8ajnHWuHdeeA0Fk4dhMCgIKgb7A4AACAASURBVHiu2IEzF67i4IYZ/AIXDZI2in5NceuB+r/8rKNML1CVKVEAXVvXR0DAZ1y/fR9dB03jn/fLly7CEVevlTtxfOsspE+b6hvhffPOH7VaDuY5e7ZvyLmVdA7lzkZVeCkSSy/jzZ/cn39mp/vST2mgSLRDjZ9YcoZMWMCR6PbNamPGgk1oWr8Kv5RFokQvPFHuL6VSjJy6VJfTbInwkixTTuussX2QIX0aTJi1il+S2rxoDM5fuoEuA6dytDdj+jTYe+QcJs5erRNefb4U0aW82wVTBqBi2aJ49PQl588O7tkSHVvUMfn0U05zzeYDOdJMedE0fK7c4vJmlA5C0dNJc9ex2G/cdRSbdx/n66J9o72g1A6S9n/uP8HkueuQJVM6eIzpzYJMOc3Esm6Ncnj3/iMOnbyI0sXyc/5vdAqvuXsw9cxo5dCoZB3lqlPqBuVq5/0hKwZ0b8Z5w5TikTpVMoPCu3RdEJ49t+4/MIkTAzWqAenThVh34nd4tAivbTZdhFc9ZxFe9YxFeNUzjrXCSy+taYN+Zp820ol/UtcGVQtYsGoXv6ymRcfoM/ope8yM5SyCJKx9OjtyxQKSPBpJEidiEaLKBDQMyQ9FPN2mLOH0ARr08zJFWUle6KUmylGlceC4D1cu0PKDDW336i0HcezMZa7DS1Lp7Dabo7w0KAeZqgCET2mg6wv49Jk/J+keP7gzv+BG5x09c5n/PkP61GjtWJOvY8j4BciZPZMuIhhReHvrKhzQ+fRSV/e2v/L8rpOWsATSoFzluRP6ckMNeklvwBhP3WdUl5bW3TB/FJ+vz7dXx0ZYuGYXduw/zcyTJ0uMBrUqcGWH+PHszP4LoGg9VWOgCG74/cmaOT0adxkJr0n9UeXnH/nlMHo5kfZ6mcdQjsLTC4QaK2rsQRFlLSqvybL2Od0jSTlVkjAmvLTHtNf6+6wf4T1/+Qb6uM7Wpb0Yuwd6xsw9M1pFDbp3ylkPDQvDsIkLmSU9C1TBg1Jg6EsSfXlr1v3/6/AGcVmyQLOM9Q8Io9K9caw+7bs7QYTXNlsuwqueswivesa0gkR41XKOtcJLL63Nn9Qf/h8/cVky/UERPCoP5dy58Tef0U//FKlNmzoFv5VP43NgEFc+oMoN5l6m0iakF9wSJUyge6EquraSylilSpmcq0R8IyNhYXj+6g0SJUjA1QfCD7oHKommvbRGkcvyDXrjt3VTTZZGM3bdFJ38/DmIBVp/ECtKu0id8lv2hvjS+XRfGe3T6jqtUQQzKCjYKLakSRPp9oKuhUpyhd8zU7y1yClV16CX2wzVz6WIKe0hlZbTXuaLrj00NI+196DNwZyCg3XXSPdDedXE0tSXhotXgxDw2brGE3HjAGnTASmTS1kyc8+CCK85QtHzuQhv9HA0NYsIr3rGIrzqGcdq4dWqNOhjpEoALXqMxeGNM5ApQ1r1lGPoCpROQBUWtGoLMe0y6aU7uj5jY6RL+28qYVh6D5FpAmLp3P+V46QsmdqdEuFVy1ebXYRXPWcRXvWMRXjVM46Vwku5uZROoDUc0MdIKQFPn/np0hLUY5YVYhoBkn1KX9CadsS067PF9YjwqqUswquWrwivbfjSKiK8tmEtKQ1qOcdK4VWLTGYXArGDgAiv2n0U4VXLV4TXNnxFeG3HWYRXLWsRXrV8ZXYhEGMJiPCq3RoRXrV8RXhtw1eE13acRXjVshbhVctXZo/hBKg18yu/d/wCo6lBL4Yd976MmpVLW1Q9Iiq3TV3fypYoaPCFv6jMq3/uNK9gPHpk3YzJkgLNm4TA3l5eWjNHToTXHKHo+VxyeKOHo6lZJKVBPWNaQYRXLWcRXrV8ZfZoIEDtn6mEWt5cWaNhtohTrNj4G8K3oTa2ADWmWLn5ANcupmYR1GCDStapGIbKnqlY58adL/j42bp6ulS0JEWyMCRNKsJrbk9EeM0Rip7PRXijh6MIr3qO5lYQ4TVHKGqfi/BGjZ+cbQMC+u2Io3NJS4W3o8tk1K1Wltsdr912GL8du6DrYBed10Nz2Up4nzwPwfsA42XfjN1X4kRhSPC1f4sMEwREeG3zeIjwqucsEV71jGkFEV61nEV41fKV2f9HgCKk1CUuaZJELItUG3mESztUKlcMKzb9hjv3n2L8kM46XtTNLjAwiLuHUZMQah6SKkUyODpU4pbQx7wvY+bCzbjz4Ck3exjZvz3y/fA1Atyq13h0b1ufm3JQRY4JQ7sgc4Z08FqxAwdP+HCzCWoxPdy5DfYePoc9h8/ix0K5seugNwrkyc6RW2pEoY2HT15w9zfvXZ54+/4D2vaZwI0diuTPxYesnDMcgUHBmOq5HgdPXOTmGU3rV+VroJrNdO/UWvjHwrmx59BZjlQP7tWSO7rNXLSFO99lzZQO7ZrWQmOHyiy8FNGmShLUUIPulxp0UJdAQ3MN6tkCm3Yd4wi0/4cAnqOVYw3udEfD2DrzlgbiyVPrOkgkTRKG+vXCYJ9eIrzm/nGL8JojFD2fi/BGD0dTs4jwqmcswquesQivesayAgCKpFL3O6f2DbgN7qbdx/Dn9Ts4tWMurt68h5ZOY7F/7RTuZPYx4DPKOjhxZzNq9NGo0wgM6d0KhfLm4D8HBgajYSc3bptc+adiWLP1EHyu3MSB9dO5GQcJIw1qKpE5Y1rUrlqWZfeMz1VuNEJd07buPcki+fuft/m6OrWsy62N9x89j79u3efUBW2QfN9/6Mud2AI+BWLmok3cQpkkmwYJ93D3RSyuA3o0x+u377mlMbWTbtO4pu7eqdMftTzOZJ8WRQrkRN02Q1lOGztUwv1Hz1iKxw7qxNefO0dmOLVvyPczePwCbm3NXw7+xzH8XGEI4xbCdG6u7Bm5M2DK5Mn4C8TDJ8+NriMvran9pynCq5avNrsIr3rOIrzqGYvwqmcswquesazwP+ENnyv74tVbVGvqgn1rprCANu02moWTJJFk1HPFdhzaMIO7temnNMxZupUjs1pLZuqGVtmxL+a590O18iX4+AVTBurqMNMLZ6XrdOdIr2PdShH2Qz+l4d5DX9RvP5yjuSlTJAV1havapB8mu/VAhTJF+Fz9lAaOGNd1wrSRPeFQoxwfM3neOpy/dB3bl01gST1wwgdr543QdZGjlsAbdx3Fye1zdN38tAvTT2mgHOZ0qVNyVNjQXG37TGSGbZv8wlNQVJuE++weTyxYucvoOiK8av9pivCq5SvCaxu+tIoIr21YS0qDWs4ivGr5yuz/I2AoV5YkccLQzhyB3b7/FNznrMXpnXM52tuobiV0aFabz9YX3mHui75KpWt3Hd/qzfpzxLdVoxrf5MBqErtn1STkyp7JpPBqIn5kswenBJz7/ToGj5+PY1tn6doY6wuvNr8m77QApS6M9VgJn/0LWFL1X4wjiaUxxa3HN8+IvvBOnL0aX0JCuSOeobkqNXJGksSJkD5tqghzzRrXh6PXxtYR4VX7z1OEVy1fEV7b8BXhtR1nEV61rEV41fKV2Y0I75Nnr1Cr5SCsmDWM82kpVaBK435oVKcC1m0/gjM75yFVymQ64V3qMQQ/lSzEf57mtQHeF69x9JSGlgJBP/uTPOsL4zv/jyj/a2/MHu/MKQXhh75A6gsviWn2zPbo3en/KzLQ9e07cg5r5rnxVO/ef0T5Br3h6e6CquWL899RBHff0XMcwTYkqdMXbMTJs39g10r3KAsvRccb1q7AOcD6w9Q6Irxq/3mK8KrlK8JrG74ivLbjLMKrlrUIr1q+Mns44d154DQWTh2EwKAgeFJO7YWrOLhhBr+MRWOK53qs2nwATetX4XxUbXTuPwVlShRA19b1ERDwGddv30fXQdM4r7V86SJ8DuXZHt86i6Ochqoc0M/+ceLEgVu/tsiZLSOnRBQvnJtfbDOUakER3sSJErIoa7nF2vXQS2A9hnjw31PKBb1M187ZHcmSJsLoAR3x5p0/+o/2RK0qZTDQqblB4aXIcZeBUzGqf3v8WqsCfF/4wdvnGkurtRHeRWt2Y/WWg/Ca1B+F8uUEfZnYsuc45xObWufImUB8+GDdy2d28YBcOYHUqaw773v8hyDCa5tdlxxe9ZwlpUE9Y1pBhFctZxFetXxl9nDCq/28Tn9FVRemjXQCvXyljT+u30HrXuOxedEYFjdtHDl1CWNmLOfKCD3bN+QqCvRiFkVROfqQOBGnN9SoVJL/bEh4qdKC66TFuHztbz6G1l88fTCOnrnEorlo2iD++5d+b1G1iQuObp6Jk+f/wK4D3t+UH/sSEoI+rrNYlmlc/G0Rnr3wQ7+Rc7lqBA2K9NI1JU+WhKtQhF9Duy/6e4pWa4Ne6KOX6gwJb0hoGMuxobmCgoIxc/EWFn9tUNScouc0jK0TGBwKv/dBkXhGRXYtgSbCawmlqB8jwht1huZmEOE1Ryh6PhfhjR6OxmYR4VXLV2b/HwHtZ/35k/rD/+MnLkumPyhKe+r8n1jvNfKbz+jlMYqcpk2dQveS1+fAILx6/Y4rN1D5L0vGh4+fEBT8xeD6+ue36DGWXwT7tVZ5g1NTqkSC+PF1EWo6iFIiEiaMj5TJk1pyOfxSHL10lypFUiRIEN+ic4wdRCLu9/o9UiRPGuGa6HhD62zbF4Q3b61bkurvligGpElrXcMK61aJHUeL8NpmH0V41XMW4VXPmFYQ4VXLWYRXLV+ZXU94l0wfbJAJVVKgSguUyqBVOvg34ZFIDxo3H16TXDiCHBuH5PCq3VURXrV8tdlFeNVzFuFVz1iEVz1jEV71jGUFALfvPsbzl290pcL0oVAqwekLV1Gvxk9RjnQKcMsIiPBaximyR4nwRpacdeeJ8FrHKzJHi/BGhpr150iE13pm1pwhwmsNLTlWCMQiAiK8ajdThFctX4nw2oYvrSLCaxvWIrxqOYvwquUrswuBGEtAhFft1ojwquUrwmsbviK8tuMswquW9XcnvPTyTmBQMLdsje5BDQhe+L1FuRIFI0xNZaKodSx16qIXrezixkX8+PGie3kcOO6D0j/m5xe7/otD5d5oPPw/BHAN37hx4+KXyqX/i5gsvua7D33xyu8dypYoYPCcxWuD4PvM4un4wCSJgV+qhyF9+lDrTvwOjxbhtc2mS0qDes4S4VXPmFYQ4VXL+bsT3rMX/+IaruEbGxhD/Nj3JTwWbsLUkU4WVQGgslDHva9g2cyhEaYcOXUZ12qlmqxUD7ZYwR8wpHcrszu7dP0+ZM2UjpspWDKonNWqOa4oVSyfJYdbdQw1YOjauh7y5spq1XnWHGzN3lgzr3bs3/ceo1GnEdzoIk2qFFzH998eKrkaangR/n4DPofCz9/6smR2caUsmSXPjQivJZSifowIb9QZmptBhNccoej5XIQ3ejgam+W7E14qS/Xg8XPkz5PNrMTe+PsBqIvVlUNLLIrIGhJerQvYjuUTWBYpCpw4cUJuW2tu9B05BwXy5ECvDg3NHcqfqxRe/fa+Fl2QlQdZszdWTs2HU+3ev+8+hseY3pE5Xck5KrmaE94rN4Lx6bN1kdo4cYBUqUKR4tuqckr4/JcnFeG1ze6J8KrnLMKrnjGtIMKrlvN3J7zUGMDVfTHWeY3kLlmteo1HtfLFcfDERRbhlg2ro1fHRlzHlGSXpLdg3hychuDary1HZzftOoaVmw+Afh5v7FAZrRxrsMAaEt6dB85g3bbD2LhwNO/kVM/1yJMrC5+3+6A3Tpz7g2u27jrojQJ5snNTBUqJOHD8AkZMWYZECeMjc4Z0yPtDVkwY2gVPn73CpLlrce7SDfxYODea1a+K2lXLWCy8F/+4xc0O6OfuXyqXQivHmihaIBdfy8ZdxzB9dE++l+DgLxg4zgsli+TD67fv8TXanJ4j1Y4OlZiTsbnoYkxxffT0BSbPW4cLl2/y/f1cqjDf2yPflxH2hvZq4qzVOH/5BnLnyIw+nRujVpWvaQh0frx4drhz/ylfB+2hc5fGyJbZ3uC/mKOnL2HoxEU6ni0bVUeDWhWwbMM+rN9xBP4fPnHjiuF92iBliqT4594TuE1egmHOrbmLGdXX1VoJ6y9A7I55X0HSJInw27ELXON3hEs7VCpXjJs+0DWOH9JZdxrVGw4MDEJYWJhBrse8L2Pmws3cxKJk0XwY2b898v3wNbJOXLu3rc9NL+jZJG70fHit2IGDJ3wQ8OkzR7CHO7fhbnJ7Dp/Fj4Vyf/N80VySw6v2P64ivGr5arOL8KrnLMKrnrEIr3rG353w/nXrPpr3GIM/jizlCC9F2EimnNo35LzeweMX8E/dJCvb95/CiClLQbVjSa7y5c7GpbPGTF/B9WJzZc/IUcOUyZOx0BgSXmo526B2eRZTGr1dZ6FYwdzo0e5XbjlL3cc6tayLimWLYv/R86Dr27J4LJfwGjjWC9mz2MOxbiUkS5oYeXJlRcOOriheOA+3oL338BkGj5+PgxumI0vGdGYjvNRtrG6bIZxaQfd34JgPtu0/iSObPPAlJBSdXCbzfS6ZMRhzlmzl9IwNC0Zxq1pKBaA0jEJ5c3Cjh7AwGJ2LWvia4tp98HTY2dnBpVtTvPf/iC17T2BU/w6c56ztDeXz0rUWzpcTHZrXwYXLN7gdMbGhLyA9h81k0XXp1oS5eCzYhHIlC3I7XUODyp4NnbAQaVKnQIsG1ZjXmYvXMNVzAwb3aolM9mkwe8lWZM6YFnPG98XVG3fRsuc4ZEifGk0cKiNRooTo0srB4NzaPlKnNNrbTbuP4c/rd3Bqx1xcvXkPLZ3G6toTaxH/BVMGMEd9roGBwWjYyQ3d2tRH5Z+KYc3WQ/C5chMH1k/n55O40mjT+Be+Vkp3Idk943OVu7TlyJoBW/ee5C8kv/952+jzJcKr/j+uIrzqGdMKIrzqOYvwqmcswquesQhv1Y4cuStRJC/TppzKdKlTsgQZSmmgHFySCurARYOOmTR3Hc7u8eRIbvgcXooYO7QdCu/dnrrOW/rCe9rnKgs1DUp3qN9+OLx3eXKUUT+l4dyl6+gyYCpWzh7O0UQaJN8N61REa8caEYR3wapdHO2jkSplMnRu6cBiRBG/GaO/5q5++RLCUrd1yTiOLpNk0/r0khNFXzctHI1c2TPxsfo/vZubS789bniuxDBdmpRw7dsW9ulS6Z7y8F9Gzl+6ARLjw5s8WEZpNOjgyqJOe0PCW7JoXhZDGiR5a7YexPZlE7gF8YqN+3XzUvvimpVKYcj4BciUIS36d2/Gn1G0lO579IAO/OfDp37n9sDE/+GT58zmwr4FOtbG5tVPHaBocLWmLti3Zgo/K/RLAX2hIcGn6/RcsR2HNszgXxj0uc5ZupUjswfWT+Nroi5s1JBjnns/VCtfgo9fMGWgrp4xNewoXac7R3rpi1H4oX9d+s+XRHjV/gdWhFctX212EV71nEV41TOmFSSlQS1nEV494Z04ezVHO0mCDAlvpUbO3Hkrfdr/FzXaolnj+mDfkXMRhNdz+Xb+mX6ya3fdLpoSXk2Ujmz24LQCfeHdtu8k6AU4Tc61SatVKMHRx/A5vO5z1oByYmmQXFLkc5j7Ihw5dQn5c2eL8FT17NCQK0jQ0NYgKaSX1LShL2bm5tIX3vBcSaaHuS9kwaY0ia5t6nEEPLzw7jpwBjMXbeYoqTZGT1/OaSSUg6svvJQC4rFwM4siRXPpXG3QT/wkg/rCS3tJXDRR9H3uh5otBmLb0vEICgpm4b12bLmulbGxeQ3lypap64QJQztzBJZ+KXCfsxand87laG+jupXQoVltvjxDXOnvwz8z1Zv1Z7Fv1ejrl5rwX9A0id2zapLuy4l238ZEXHu+RHjV/sdVhFctXxFe2/ClVUR4bcNahFctZxFeE8J785+HaNJ1FC4dXIyECeLzTlC0rmHtCpxSoD/CpzR8CQlBtSYumDayJ34qVSjywps7O+cU0zhx9g9ud0vRZErH0B/mXlqbsWAT7j/yxdyJ/Qw+VQGfAlnI6NpJLClaSrKsidlSjyH4qeTXezE3lynhpfMpZYFk7dCpi5i3bDtI2Gh9LaXh1Pk/0cd1ti7aTedQZLhg3uxw69fOpPAa+yejL7yOnUegQtmiGOTUgk/RqkQc2zILz1++/kZ4jc2rL5aUAlKr5SCsmDWM82npvqo07odGdSpg3fYjESqEEKfwXCm/msqmEXsaWgoEpdmQPOtzfef/EeV/7Y3Z4505ih1+mBPeqfOC8PBRHKv+C5MsaRhaNguFvb1UajAHToTXHKHo+VwivNHD0dQsIrzqGdMKIrxqOYvwmhBe7ediKjNGuZn0khH9bE4vMXlN6o9C+XJyfuuWPcc5UhheeM/4XMPIqUt1P11r22hNhHfRmt2cp0qCSuJDP4HXbD6QI5KUu0rD58otBH/5wrJjTngvXb0Nyimm6GHdGuXw7v1HHDp5EaWL5ecX6Shq+8T3FRZPH4Rug6Yjfjw7LJo+iOW6c/8pKFOiALq2ro+AgM+48+CJyblMCS/JctP6VTg/mb5U0JcIys0NDQ3TCS8Jd62Wg9GqUXV0bVMfF6/chPOIOcy9ys8/RovwkmhTDvOssX2QIX0aTJi1Cr4vXmPzojG4Rrm3ehFeU8K788BpLJw6CIFBQZxrfObCVRzcMINffqQxxXM9Px9035T/rQ19rtdv3+eyeSS45UsX4XPoJbfjW2fxrwr6XLUvApQ37davLXJmy8gpEcUL5+YX28KnzOj/gnDt9hcEfA6x7r8wVKUhRSiSJbPutO/xaBFe2+y6CK96ziK86hmL8Kpn/N0JLwlFs+4RX1oL/xMx/fQeEhqGUf3bM32SInoxjQbl2lKN25mLt7CIaIOieBTNIxE+duYy1+GlaGLO7Jm+KSnm7DYbRQv+gO5tf+U3+L19rmHRtEE8Ff1kXrWJC45unskvS1EEdMAYT9y++5jTGOg6L1/7m6sHUH4wDUqvIIGlCgMkQ6vnuvKb/cYGpSxQzrGW30s5pvQCFaUZkJTtWunOObMkfpQzSy/UUVk0SoUYM2M558f2bN+Qq0kYmyt7lgzfiFl4rsTg6JnLfIl0n60da3L6hP7eaBFt7VrppTB6MYsGpTTQXmhpF9R0g2oma7mvhu6f8ogz2aflXFoaNK/rpCUs/TSIxdwJfZE7Zxbdy2bhUxqMMdVeWtM+pzSNaSOdQLnD2vjj+h207jWeZZq+KGnDEFd63ui5099f+rMh4aWXEV0nLeZngwatv3j6YBw9c8nk8/X8VQjeBQQbfVaMfZAgQSgSxLcuMmz1IrHgBBFe22yiCK96ziK86hnTChLhVcv5uxPeyOCkSG9QcLDuxTOag37293v9HimSJ9VF8bS5KXJavkFv/LZuqtEyWdZcB724ROtQxFW3hv9HLh1GXdUoumfNoEg1zUnd3qgkmqWD0hDevPOPsGZk56KOc+/9AyK8tGboOmjNZy9fc6MILVpq6fVaehylBXz+HMTyHZmhpQ7Mn9Qf/h8/cVky/UFRWkrTWO818pvPDHElPq9ev+NKDobSVwxdJ+VsBwV/Mbi+oeM9lwfi6VPrnp2kScLgUDcM9uklpcHcsyLCa45Q9Hwuwhs9HE3NIsKrnrEIr3rGIrwKGFM6A73xr739r2AJmTIGETDX4IG+MFGlBUplcKhRLsZcuby0pnYrRHjV8tVmF+FVz1mEVz1jEV71jEV41TOWFWI5AUo5oYoTlcoVNXinlKpC9Zvr1fgJCf738mNMQCLCq3YXRHjV8hXhtQ1fWkWE1zasJaVBLWcRXrV8ZXYhEGMJiPCq3RoRXrV8RXhtw1eE13acRXjVshbhVctXZo8hBKiaBnVy0+oNx5DL+lcvQ4RXLX4RXrV8RXhtw1eE13acRXjVshbhVctXZo8hBKhhR6oUybitsoyvBA6epBcHrXv5LF68MOTJDaRJbd153yNzEV7b7Lrk8KrnLCkN6hnTCiK8ajmL8KrlK7PHAAJa84Ydyycgb66sMeCKYsYlBAWH4tX7oChcjEivKXgivFF4tKw4VYTXCliRPFSEN5LgrDxNhNdKYFYeLsJrJTA5/CuB3Qe9ceLcH1zWbNdBbxTIk51r85YrURDBX0LQtvcETB3pxLVtaXit2IHkyZJwhzrtXPrznkNnue7vmEEdudPZhp1Hubtbn06NubYwjcnz1vH/vfvgKagCBtUkdh/ejRtXUOe5ssULoHmDanwMlUnr4zYbDWtXRK0qpfnvdh44g3XbDmPjwtG6+eLFs8Od+0+5sUe18sXh3KUxl5CLadce/nm78+ApJs5ajfOXbyB3jszo07mx7h6J6ZW//sGPhXMzUxL7gU4tsGLTfizfsJ/rJ/9cujACA4O5VjNzORiMd+8iJ61JkoahVPEwJEwYufO/h39HIry22WURXvWcRXjVM6YVRHjVchbhVcs31s6uNVugxhQVyxbF/qPn8det+9wxLSgoGCVqdcPWJeNYhGlQY4Q0qVNwG1/t3C6tHLi179pth7ixBbXObVKvMn7/8xY27z6Ok9vncI1hajJx7eZd9OnkiFQpk8Fz+Q5u3jFxWFdu3kFNQA5tmMGd6H7/8zba93XH6Z1zkTrl13q41F2uQe3yaFa/Kv+Z5iPRpW51eXJlhceCTShXsiB3y4tp1649QIFBwajbZggK58uJDs3r4MLlG9zRjXgXzJtDx5SaXVDXPWqwQV3fRkxZyvdVvnRh3qOl6/fhr+MreNrI5PBq15Mlcxg6tA1BokSx9hGP8o2J8EYZoUUTiPBahClKB4nwRgmfxSeL8FqMKlIHivBGCpucpF97lrrC1W8/HN67PLlBhDnhDd/ylqK23QdP14mY1rhj35opHCEmQS1ZNC+6tanP4A8cv4AJs1azEL99/wEVGzpjwZSBXBaMuqnFjxcPE4Z24WOpI51D26Hw3u2pa7KhP9/WvSe5ZfT2ZRMsEl5bXXv4p0xjdHiTB0fEaVAnvErlimFwr5YsvAdOJH+lfwAAIABJREFU+GDtvBGIG/drMwkSfYqC0xcDGtRNr1P/ySK8NvrnK8JrG9AivOo5i/CqZ0wriPCq5SzCq5ZvrJ1dX3hfvHqLak1dcGSzB9KkTG6V8F66epvlTIs8UjSzZK1u2LZ0PPLnzvaN8FLdW8fOI3Bsyyzu1EbR4w8BnzCqfwdUadyPUxeK5M/F7D2Xb8cj35fcflkbhgTaY+FmbktsSYQ3vPCqvPbwDw+1cZ65aDNO7Zir++vR05fD/0MAPMb0ZuENf110UKVGznDp1oyj5iK8tv+nKMJrG+YivOo5i/CqZyzCq56xCK96xrFyBVPCmzZ1ShSv2QUb5o/i1AMa+ikNEaXxb7Rznmix8FK+6jD3Rbh8cDE3crh87W+07TMRLRtWx7Wb93S5utT+uVoTF0wb2RM/lSpkkfBSDm9MuHb9h+aY92X0cZ3NEfSUKb62g6Z7Lpg3O9z6tTMovJTfTKkNWmUKifDa9p+iCK9teIvwqucswquesQivesYivOoZx8oVTAlvxvRpOGJbqlg+dG7lgMtX/8aIKUvQsE5FXQ6vtcJLkVzK//3n/hNMnrsOWTKl48imNijiS5HfKW49UP+Xn/mvKQ1g5NSluvxeSyK8dExMuHa6jgFjvJA5Y1q+7zfv/FGr5WC0alQdXdvUx8UrN+E8Yg68JvVHlZ9/NCi89LIepX707NAA6dOkwsrNB3Dj7we6LxYLVwXC1/dr+oO1I336MDjUDkVCyeE1ik6E19qnKnLHi/BGjps1Z4nwWkMr8sdKSkPk2VlypgivJZTkmG8I0Mti3j7XsGjaIP6M2udWbeKCo5tnIkP61Dh6+hLoJ3eqDkB5uAkTxEfFssU42qh/7qWrESO8WlpB+JQGesks4NNnXosqQVAFCKrmoA16GWvBql38shqtRWPI+AXImT0TenVoGOH6KaWBZLxr63r89weO+8Bj4SZOaaARE66droMkPlf2TDqxP3H2D65KoXFwat8Azp0b8zXrM6W/o9SQOUu2gqLD9ulSI98P2bB9/yn47F/A57z1D8XbD8GRfroTJgiF1Ggwjk+EN9KPllUnivBahStSB4vwRgqb1SeJ8FqNzKoTRHitwiUHW0OAUgr8Xr9nAY7K0HJu2zT+BTRnimRJvpmOorJlSxTQCaD24ttv66ZyuTFrx7957aauNSQkFM9evkaaVCn45UBzx9ILbFTpgsbitXtw8tyfurJkf9wMRsDnUGvR8PFx44YhU4YwxIsXqdO/i5NEeG2zzSK86jmL8KpnTCuI8KrlLMKrlq/MHg0E9F8y05/y2q17aNFjLA5vnIFMGdLyx5TOcPjU7xg9oEM0XEHkp4jMtUd+tYhnUm7zwLFeXMrsU2AQ1zleMn0w1+Ol4T4rCA8eRC6lIXOmMLRrI2XJTO2VCG90Pcmm5xHhVc9ZhFc9YxFe9YxFeNUzlhWiSIDkldIXqGKDoUF5qU+f+ekaVURxuWg9/d+8dkp9oPVf+r3jqHiJonmRJWM63f1JHd5o3epvJhPhVctXm12EVz1nEV71jEV41TMW4VXPWFYQAjGSgAiv2m0R4VXLV4TXNnxpFRFe27CWlAa1nEV41fKV2YVAjCUgwqt2a0R41fIV4bUNXxFe23EW4VXLOtYIb8CnQCRIEA/x7OzUEovG2anmK7WI9XvzjtvBJkn836jx9OlzEI57X0bNyqURP571vP+LexWN2250qnOXriNDutRcmYGejZCQECRKaPrFtKhc16Q5QXj4MPI5vG1aSg6vKf4ivFF5Oi0/V1IaLGcV2SMlwhtZctadJ8JrHS9rj44VwksCVrpOd8yd2A/VK5SwlsG/cjxVAajVchCSJUnMZbuGO7dB5nD5lf/KRVm4KDV+oJquWxaPtfCM/z/sv7hXVt9kJE+gShN1qpVFm8Y1MW/Zdhw5/Tu3O1Y1/rwV+SoN9L3SPn0o4kuVBqPbI8Kr6smNOK8Ir3rOIrzqGdMKIrxqOccK4Q0NDcPNfx4ga2Z7gyWr1CKM3Ow+V26i1/BZOLfHC3Z2cSM3yb90VkeXyahbrSxaNKxu9RX8F/fK6puM5AnhhZdaNft/+IjcObNEcjbzp/m9DcG7j1GpwxuGOP+tR9c8lGg8QoQ3GmGamEqEVz1nEV71jEV41TOOkcL76OkLTJ63DtQKNVHC+Pi5VGFMGNqF28heunobMxdtwc1/HiJrpnRo17QWGjtU5jarbv3aomDeHHwujbsPnvJb6iWK5IX78G7InsWeC/eXLV4AzRtU42PCwsLQx202GtauiFpVShskTlFJrxU7cPCEDxf9L1O8AEdkqRbqsg37sH7HEfh/+MRVAob3acOtX/+594Tb31LXr/Xbj/C8XVo58LpPn71CW+eJeP7yDYrkz4XCBXJhpEs7bNp1jCOn/h8C+J5aOdYAdS2judwmL8Ew59ZYveUgSIbWzHPjeSbNXYtzl27gx8K50ax+VdSuWobXatVrPKqVL46DJy7iwePn3Ha3V8dGutqtxjiampPmffjkBeq2GaJrcRvT9krbQIpCH/O+gqRJEuG3YxeQJlVyjHBph0rliun4dG9bH6fOX+XuY/R80Zg4azXOX76B3Dkyo0/nxrpnwtgzQNUjjO0bzUcNM6Z5bcDdh774pXIptHKsiaIFcvFaxHLCrFX8jFKUn6opuHRryhHevUfO4fc/b2NU//YmnyWah56XqV4b+D5plCiSB/lyZ+MObZ8DgzBjwUb+7HNgMD8nbn3bctqE14pA+D6LXEqDffow1KsTikT/jSwc9f8lNbCCCK9tsIvwqucswquesQivesYxUni7D54OOzs7/n/+7/0/YsveExjVvwPnutZtM5RlsLFDJdx/9AxX/voHYwd1QuGqHbFqjit30KLap9du3kWfTo5IlTIZPJfvQNGCP2DisK7ckWrV5gO6drMkFe37unOHrtQpkxskPnLqMpzxucpNDUhMtu49yQJ5885DTPXcgMG9WiKTfRrMXrKVW8HOGd8XV2/cRcue4zjFgiT30dOXmDh7Nbx3eyJB/PiYu3Qrjpy+xKKVPFkS3HnwFGOmr+B7yZU9I+av2omUyZNh/JDOurmogUMTh8pIlCgh2jerjYYdXVG8cB6W/nsPn2Hw+Pk4uGE6l54iHiRtTu0bIknihBg8fgE8xvRi4Xv45LlBjiNc2puck+B4rdyJ+w99udMZjZi2V9oGUuvjafM3gLqRFSuYG5t2H8Of1+/g1I65fAjxoUHNLGjPqpYvgc79J3PN2g7N63ButeeKHZy2QV+ijD0DD5++MLpv2pcD6i5H3A8c88G2/SdxZJMHQkJD0aCDK39p6tamPhLEjwe3KUvQpVU9Fl56Ro97X8GymUNNPkspkyeF66TFLMf0vNPz6bVyB385pOdwybq9WLnpN8xzd+FfEo6duYyfShbiL23y0pra/8CK8Krlq80uwqueswivesYivOoZx0jhpWgtRc5c+7aFfbpUOgqU17hx11Gc3D5H1z1K+1BfeEsWzcsiQePA8QuYMGs1n/f2/QdUbOiMBVMGolK5ohg6cSHix4vH4kmSeujkRd16DjV+Qo6sGTk/mD53rFspwo5QFLVAnuy65gbU6KDfyLkc/SSpJOG9dmy57lorNXLGuCGdUa18CWzZcwKbdx/HxoWjeU66Z5KVtk1+4T9T1HHS3HU4u8cTN24/4Lku7FvAEUsa9IJTlwFTsXL2cN3fkTA3rFMRrR1rsNBRFJii2zToPtOlTslyboyjuTmpy1fVJv0w2a0HKpQporvuf2OvzP3TIOE97XOVGy3QoKh4taYu2LdmCnMmPtozQJ9TlJXk/fAmD/7yQoOElESVIr3GngFT+7Zo9W7sOXwWM0b34vm+fAnhfdy6ZBw+BnzmL1p7Vk3iaCuN8CkNhoTX0LNEv36Uqt2df8FoWLsCz0NfSijFh4SX9nr3IW/MmdAX+X7IGuHfjQivuacoap+L8EaNn6Vni/BaSiryx4nwRp6dNWdKDq81tKw/NkYKL6UyDHNfyD/5Z82UHl3b1OOf60naaExx6/HNnZoS3tt3H8Ox8wgc2zKLBZoiYh8CPnHUuErjfiydlFpA6+747ZRubspRpYL99dsPjyAm2gEksAN6NNeJsO9zP9RsMRDblo5HUFDwN8Lr0HYo+nRqDIca5b4RXpqLqjSkT/v/gk/rzBrXBzSvvjxv23eSo46a0GrXVK1CCU6d0Bdeii5/CQllOTfG0dyc536/zlHkY1tn6aph/Ft7Ze5R1xdeOr5MXSdMGNoZtauW/YYP3fvMRZt1EWA6fvT05ZwuQJF9U8+AsX2bvmAjjpy69E3DjJ4dGvKvFeNnrobP/gW6W7FGeLVnqWjBXKjTekiE5zO88Pq+eA23SYs5TYOus1Wj6rqovwivuacoap+L8EaNn6Vni/BaSiryx4nwRp6dNWeK8FpDy/pjY6Tw0m1QNPHeQ18cOnWRo1QUCdu67yROnv0Du1a6WyW8lM9J+bSXDy7mn3qp5SpF5igt4drNe7ooqyF87/w/ovyvvTF7vDOXDgs/SKIrlC3KuZI0qHVr10HTWKyfv3xtlfA27TaaI3SUnqA/tPSI8BG+E2f/4HxkigAbKsVmSnhJxAxxNDcniXL2zPbo3ckxwiXGlL0Kf1H6wvvk2SuuirFi1jD+OV+fzzHvy+jjOluXm0xz0TNSMG92jvAaewZM7duMBZtw/5EvVw/RH1o7ZBJerRxdZISXqjqUq9cT00f1RJWff+Rlwguvti59abpw5Sb/0jHcuTWnBYnwWv8fTGvOEOG1hlbkjxXhjTw7S88U4bWUVNSOE+GNGj9zZ8dI4SVRaFq/Cr9kRi+nkVRQLuW79x/RZeBUfpHn11oV4PvCD94+11gS9SO8FMklEf3n/hNMnrsOWTKlg8eY3joeJKsU+aVoMb1YZmqQ+MSJE4dfisuZLSP2Hj6H4oVzY//RC5yTOWtsH2RIn4ZfQKKI2uZFY1ik9aOypiK8i9bs5hfSvCb1R6F8OUGCtmXPcY4gGxJeEvGazQdydNmlWxO+fJ8rtxD85QuLuSnhpUitIY4NalcwOidJIknf/rVTkD1LBh2umLZX2oWR8O48cBoLpw5CYFAQ5+OeuXAVBzfM4Bf39Pm8eeePWi0HcwS0a5v6uHjlJpxHzOH9IJE09gxQHraxfaMXA0liJ7t2R90a5fj5pZSZ0sXyI2f2jPi5fm/O123tWJP3mL6UaS+tmUtpCP8s0QuN9CWOUnjopcoFq3ZxG2FKaVi77RDnIBcrlJvTKOi5H9yzJepWL4c9h4Pwzt/cfyIMf54kMfBjMaoTHLnzv4ezRHhts8sivOo5i/CqZ0wriPCq5RwjhdfZbTaOnrnMd04vapEQdG1dj/9ML53RW+/aoJeS6CdnEpjVc11RsujXl9bo7Xj6f/40ypUoyC9ZUa6pNpau38diQC+rJUwQ3yRlevmI0iBIKmhQmsXi6YORLk0KuE5aosv7pdzQuRP6cimpqyS8TmMj5PCSpNC1kmzQi2/0dr+Ww0spEDMXb+GXlbRBkkkRSUNz0TF0PSQ7VIWBBkUKSa6oWoQh4Q0JDeMvC6Y4Gpvz9bv32HXAmxmHHzFtr7Rr015a0/5MezZtpBOLHw19PvR3WoRbe260Z4s+M/YMZEyf2ui+0XmUKkG52Nqc9IwsmDKAvzSs236EX2SkQfm19CWma+v6nINNEk0vmPFLa2aepWcvX2Oq53r+cpjvh2wIDQtFogQJ+JmnKiL0pUR7PqgSydjBnfhXgaDgULzyDzT57Jv8MCzyp34PZ4rw2maXRXjVcxbhVc+YVhDhVcs5Rgov3TKVU3rvHxDhpTUNBf2E7vfmPVKlSMopCvqDhJdeWqM38KnBA+Xh6g+KvJUtUYAF1NLx4eMnBAV/4RJX4QeJyufPQSznUR10vX6v3yNF8qS6EmLm5qT1g4O/IG3qFN+8zGfqXFMc9eds0WMsv1D3a63y30xp672iLmQBAV+/zBgaiRIl4FJw9NLa/En94f/x0zd7ZuxcYkICSdUTKBKsP4w9A6b2jUrf0fMaP348UFWF8INEmEraReXZobW1tBaqc+w0dAaKF8mLXh0a8lLataVNkyJC+sv+Y0F4997c02X488SJgaJFJMJrip4Ib+SeLWvPEuG1lpj1x4vwWs8sMmeI8EaGmuXnxFjhtfwWvj1SE16tSoP+EVr+5OGNM5ApQ9qoLPVdnPvq9TvOF/aa5BLt7Y8js1cUhaYUBWOjRYNqeOL7KkKVhti8UVR6bO/hs1ztgfLeab/oxUn9FyD1GUgOr9qnQoRXLV9tdhFe9ZxFeNUzphVEeNVyjpXCSyWmKH0hf+5sBulRya+nz/z4p38Z/y4BVXtF+dlU5YNKz8X2QRFpn8s3OZKdPm1KbtSSLGlis7ctwmsWUZQOEOGNEj6LTxbhtRhVpA8U4Y00OqtOFOG1CpfVB8dK4bWagpwgBL5DAiK8ajddhFctX4nw2oYvrSLCaxvWIrxqOYvwquUrswuBGEtAhFft1ojwquUrwmsbviK8tuMswquWtQivWr4yewwgQC+MxY0bx2jraLpEerGMcl8TJ0poUTqAuduisnLU+lrrSGfueO1zKh3m/zEAGdKlNvkCIpU4o2oMqVIks+pFxfDXMX9lEJ76xrH00iIcZ58+DPXqhCDR18Z/MgwQEOG1zWMhKQ3qOUuEVz1jWkGEVy1nEV61fGV2BQQe+75E7VZfWwbToKoZ1FK5X9emiB/PTvf3+4+eh/ucNXj99muxWaqEMKJfO1Sv+P+521R1YfaSLVwiTBtUOqxXh0Zm6zObujXqgkcyOtCpOXfEo/Jk2qDuePT34bvkUV45HUf/lwaVmOvWph46t3LQVVagChKbdh+D14odunvSuqdRvWZrx4s3oXj3Mdja03THJ0kYijhxI316rD9RhNc2WyzCq56zCK96xiK86hmL8KpnLCtEMwFNeFfNceWydbfvPEbfkXO40UinlnV5Naqp22v4TAzt3Yqbc4SGhXHd41mLt+i6rdFx1Bnv6bNXmDisKwrny4mXr99h35Fz+OvWfW7rHJlBUdqyDk7YsXwC8ubKyiL7MeATN3x4/yEAS6mqwpFzus5/L/3eomoTFy75NqB7c6RIngSnL1zFcPfF6NyqLnq2/1pebPHaPVi0Zg/GDuqEquV/xOfAYFATkekLNuDo5plWX+rV28H49CnU6vPohLh2QPr0oYgfL1KnfxcnifDaZptFeNVzFuFVz1iEVz1jEV71jGWFaCagCe9v66YiW2Z7nt1l1DwkSZwQ7sO78Z+pO1+BPNkxYWiXCKsPGOOJl37vuIEGVYjoPng6l/DSr+hBtYUTJUyAVr3Go/JPxXDwuA8e+76CY92K+PWX8pixcBNLMUlq386NkSplMt06Ow+cwbpth3VNRUh4KWVCu5Zbdx6hcZeRuq511Bji4Akf7F0zOUKd3PU7jnAr4Av7FnAtXep0R41DWjSsHuGetGudPG8d7OLGxZ0HT3Dq/FX8XLowhvVujcXr9uDo6ctcd7pvlya6e508OwgPHkUupSFzpjC0aSkpDaYebRHeaP6Hb2Q6EV71nEV41TMW4VXPWIRXPWNZIZoJ6AtvwKdA1G8/DL07OqJJvcqgxhTFa3aBp7sLqpYvHmH1fUfOY/D4+dwBj7rtUarBvjVTjF4hdWSjLmhOHGUNw4AxXpxuMMipOXdLc5uyBH06OaKxQ2XdHNTUpEHt8mhWvyr/nb7wUqR57rJtOLFtDucWd+4/BYXy5+QIdfhBbaprNh/AbbVJaqm98YltsyN0DAx/vNZhcECPZsiVLRNGT18OYkX1qEl+qYtf8mRJuBsfDXlpLZofTL3pRHjV8tVmF+FVz1mEVz1jEV71jEV41TOWFaKZgCa8VEc5nl08+Fy5gTLFC2LC0M4soy9evUW1pi5Y7zVS10pYu4SzF//iNIaze7wwe/EWPHr6AoumDTIpvGvmuenybanjXL2aP6F9s9p8DrW59nv7XieR1OaZWkh77/bUdVUj4T3jcxU/lSyEZy9ec6vgKSN6oHqFEjwH5SO3bFhdl46hXQxJbqna3eE1qT8+BwaybF89upwl2dDQb+JB6Rt/33vM4k/jmPdljJq6DKd2zBXhjeZn0tB0Irw2gAxAhFc9ZxFe9YxFeNUzFuFVz1hWiGYCmvBS5NLOLi4WrNqF6aN6om71crySFuGd594P1cp/lUptUO7skPEL8NfxFaAOZZZEeMMLL0Vjq5Qvjg7/E156gYyaXGj5vp7Lt+OR70udANO62stoDWqVx97D5xAUHIx1XqN0rYtJwPP/kA2De7WMcK2+z/1Qs8VAbF0yDp8+B3KE99iWWQbbbdOJ+sK7aM1u/HH9jk54z126ji4DpvK905AIbzQ/mHrTifCq5avNLsKrnrMIr3rGIrzqGYvwqmcsK0QzAf2UBnqZi6KZ4SO6lMNLqQhaTq92CfRy24cPn7Bs5lCcOv8nnIZ6cMpAwbw5IlwlpUlQTjClNIQXXpLTSuWKGRReyrOt1sQF00b2xE+lCunmC5/SQKXPHDuPQKli+eExpjdHaz0WbsKew2dxYN00xA/3FhhVjpg4ezV89i9AcHAIyjfoDde+bdGmcU2D16ovvMTlyl//GBXeiTOD8OBh5HJ4s2QKQ7s2ksNr6tEW4Y3mf/hGphPhVc9ZhFc9YxFe9YxFeNUzlhWimYC+8NILYcMnLcaRU5ewc/kEZM6YTlelgSojNK5XmV8aW7/9COfOrpw9HKV/zM9/177vJLx49Qbjh3RB0YI/4KXfG/x27AKu3bqHOeP7WiW89BLcyKlLcWjDDI48a0M/h5dKj5GQU4TapVtTaFUa6tX4CQOdWiBliqQ4ee5PuE1egi6tHODUvgFPRdFjr5U7+eW3mpVKcV6vz5VbuioN1grvlRvB+PQ58lUaMmUMRbgqcNG8y//96UR4bbOHIrzqOYvwqmcswquesQivesayQjQTMFSlgeSvQ99J/NP/xoVjOF1Avw4v1esdO7izLneWLuvtuw9ccSF8ndzwdXjNRnhX7sQ/9x5ztJZSJXJmz4ReHb6WETMmvPT3h05e5MoS9AIZVXrQr8NLx/Tr2iRCHV5K1Viz5SBLb8Cnzzx9+Dq8JLyliuVD19b1+DP9CO/5yzfQx3U2R4xp+H8MxWv/oCjtDulywvhhUZojtp4swmubnRXhVc9ZhFc9YxFe9YxFeNUzlhX+ZQKURhAnThykTZ3C6JVQU4eXr98iUYIEEUqMWXrp1PmMUg7Cl0qz9Nzwx1EjDP8PAciQPo3Rl9PoeOoeR9ecLk1Kk8eZuoaFq4Pg+ywyV/n/5+TPG4rKFUMj3e0taqvH7LNFeG2zPyK86jmL8KpnLMKrnrEIr3rGssJ3QIDSGQ6f+h2jB3T4z9xtVF5a026yTKlQ1HcIEeE1sOsivLb5pyDCq56zCK96xiK86hmL8KpnLCsIgRhJQIRX7baI8Krlq80uwqueswivesYivOoZi/CqZywrCIEYSUCEV+22iPCq5SvCaxu+tIoIr21YZ06bGE/9PhldjD6XEXkCIryRZydn/kcIXPzjFlKnTIbcObOYvOInz17h/qNnqFCmiNI7o3xf74vXdHWDLV3s7kNfvPJ7xy2Co2OI8EYHReNziPCq5SvCaxu+Iry24yzCq5a1CK9avjK7BQSo6gLVop060gnx7OwsOMO6Q/TLdRk7m8qHpUqRDAOdmnPb4ayZ0qF21bLWLWbB0Vdv3EXLnuO4vTG9TGfpWLHxN5z2uYol0wdbeoruOEP3s+NAEN6+jVqFBfv0wI/F6KU1qy8p1p8gwmubLZaUBvWcJcKrnjGtIMKrlrMIr1q+MrsFBLS6tFcOLYnQeMGCUy06xBLh/RjwGWUdnLBj+QTkzZUV1KCiQJ4c35QYs2hBMwf9G8Jr6H6CgkPw6n3UypKx6YZFTZqjg2lMnEOE1za7IsKrnrMIr3rGIrzqGYvwqmf83awwed46vte7D56CqhaUKJKXO51lz2KPQePmo2zxAmjeoBofQ00f+rjNRsPaFUEtcEl6qduZXdy4cO3XFkXy58KyDfuwfscR+H/4hBqVSmJ4nzbclOGfe0+4KcMw59ZYveUgXrx6y93QqBXvtPkb4XPl/9g7C+iokq0LbxjcXUOQ4O4MMLg7wd3d3d3dA8HdgsvgzhAYYHAGGNwGd4/+ax/e7b/T6SQdqYaBOmu9t4bue6vq7tsNX5976uwrAs40Z6AzGYE3VsxoePvuI1jeUKJQTnRuWQMpkiUy3Zstu49h1cZ9WDt3KHYfOolB4xchSuSISJY4AdKlcRCzh5t3/8XoacvBfrZOKZOhU4saKFssr4zBa3dMnhhv3r2H+6nLqF+9FEoUzgVaD+85fEr65ubLmRH9OzfE46cvJcNLK2GaYTBoMGFo8+mzh9XzaEtMR7YcmZ2wdY87MqZ1RKcWziiQK5OM0Xf0XLifuoSXr9/J+jo2d0a54vkCvJ59Rz3w5n3oYDVOLF9kzOCrM7xWvuUaeO3zV58GXvU6a+BVr7EGXvUaa+BVr/FPMwPB8tLVW+jU3Fl62bos3izuZaP7tcISt11Ytm63yYXsrwv/oEmXMfhjy0wccj+HQeMXyqP6CBF+QXqnFAKJE1zWCBQmTRQP0xdsQLIk8cX9zMiQJk4YFzUrFkWUKJHRpFZZVGs+EIkSxBV49PHxFeMFgjDXRdDt1rom0qZ2wBRXNxTInQk92tYx3ZvGncegarlCqF25OJ48e4Wew2cLqDtXKIIY0aMiTcpkqNCwD7KkT4Wmdcrj5NkrcFmy2WRLzDmOnDgvJRA5sjghW8Y02LTzKI6duojOLWqAZhYbfj+CetVKypwE3pKFcwnk3v/3mVgIu29zQeyY0cHSCmvnUbOJc9ageb0K+C1/NjHWuHztjqyBsXLjXrm++HFi4dDxc5g6bx0JXRuRAAAgAElEQVTct7qII5vl9fDHha7hVfvV1MCrVl9jdA286nXWwKteYw286jXWwKte459mBsvSAWZKR01bjiObZuD12/f4rVpnuI7viSIFskk2MmKECJI5tVbSUL/DSMlgGn1t2eO26+CZAnD3Hj4RYDy5wxXRo0URfY+fvoxWvSZix4rxApfmYbkugueKDXuwadEoOezugyeo2KivCTj5mmUJADPWbXpPwj63KQLgjKpNB6BIgewC5Zwjg1MKsQpmMEubt3wbuT5Cs3lYK2koUr0zRvRpgV9zZwnwPMsa3tv3HqFyk/6iCTPfNKK4dvMert64J1lv2igzY81subWSBg28ar+aGnjV6quB1z76chYNvPbRWtfwqtVZA69afX+q0S3B8p9bD+DcYhAOrp+GRAniYMDY+Xj/8ROGdG+KYjW6mmDMGvASAJmBNWCR5Qql6/bExoUj4eHh6W/TFyGWZQWGbW5gwEsQnzJ3HXavniiHuSzehPuPnonNrxGWgEjrYWZMj26eaTpm6KTF4opGW2HLazdgdPuysUjtmDRI4CVwd2peA5nSOQrEWjvPEngJtSVqdcP+dVMQM3o0tOs7RWC35G+5kDRRfMlwr549GNkzO2ng/QbfRA289hFdZ3jV66yBV73GnEEDr1qdNfCq1fenGt0S+rbtcUe/MfNwds98RIoUEWcvXUejTqPlsf6lq7cFeBmEtJqthuDMnvmIHCmivEZQLpw/G3q1qyt/NjK4hOcnz77WwJp3OWBZRMcB03B443Sx27UVeL28vVGiZjdMHNwev+bJ7Bd4nRzRoVl1ee2g+1l0GjDdlE3la7wWAurAro39Ae+bdx9QqEpHTB/ZWWqJzcNahtcA3sL5swZ4XmDAe/nqHYFaoyyC82Up3swv8JpdD9/XGV61X08NvGr1NUbXwKteZw286jXWwKteYw286jX+aWYg8DKTS0i9cechxs1cheRJE0gG1AiCLDO/4we2ReUyBeVl4/H/oql9kT2Tk2xoW7R6BzbuPIJpwzshccJ4GDVtGR49fYl184YJLFsC76s371C2Xm8Zs32TalILzJphlhhYK7UwMrwsVRg8YaGptthYJzfSse535uiuYAcHNiPg+PWrl0SrhpVx+txVdB40A7PHdkexgjn8zWEAMduODezaCKlSJAE3neXM4oSPn774W78BvBVLFRCQtnbe0T8v+mlLZp7hvXPvMVr2nCAZ8CQJ4+H3/SekLtjI8FpeT7w4MeGyyAMPH4Wun1jmjD4oUUxbC1v7kmvgtc9ffRp41eusgVe9xhp41WusgVe9xj/NDMbmMHYkYLB7AHvrmmdc2Q/WddlW2axmZHN57KxFmzBn2RY5j5vXuPFrwNgF2HvktLzGutyZo7qIecRFAm+74f762DILPHD8Atl0xsidLT2WzxwgMJone3q0alBJXt996JT0/WVJQ5+RrkjlmNRf+zGWJPQY5iJwzm4T3Px2+Ph56TZhXF+7JlVlQxrDcg6+du/hUynjYGab4ZA0IeZP6g1mfy3XT+DlWBVKFgjwvAPHzkgXhnkTe8l4z168RvGa3XBg3VQkjB9H1mvoxQ1xB46dxZo5Q2TjoLXrefTUB28/eoXq8xkunA+iR9ddGjTwhupjFKqTNfCGSj6bTtbAa5NMoT5IlzSEWsJAB9DAq1bfn2p0I5PasEYZsFQgVoxo/q6f3RDoFGaAovkBzPR6eHpKpwIjCIefP3uAHRlsjRev3iJK5EimDW0BnUfHs0JVO2LXqgl+WpSZH8+xYsWMjogRvhpicGPY42cvES9OLESNEsmmJb3/8Akenl5gVjU4EZLznr98g19+CY+4sa3PZX49l2944uMn7+Asyd+xESIAiRL5IBxClykO1SK+05N1htc+N0YDr3qdNfCq15gzaOBVq7MGXrX6/lSjB2XwcOnabdRtOxz71k5G0sTxv7k2LGdg9wejE8Q3X5CdFzBh1hfcux86UM2Vwxfly+qSBmu3TgOvfT7QGnjV66yBV73GGnjVa6yBV73GP80MBEiWL7A9l7VgN4Z/H78QEwkd314BvWlN7T3QwKtWX2N0DbzqddbAq15jDbzqNdbAq15jPYNW4LtUQAOv2tuigVetvhp47aMvZ9HAax+tdUmDWp018KrVV4+uFfhuFdDAq/bWaOBVq68GXvvoq4HXfjpr4FWrtQZetfrq0UOhALsbRIsaJcASiVAMHepTaV6RP1emADeH2TLBw8fPcef+YxTOlxW0DeZmvbSpk0tHhacvXkuXC5WhgVeluoAGXrX6auC1j74aeO2nswZetVpr4FWrrx49FAr0GDYbqVIkRpeWNUMxSuhPZSs1h6QJUK54ftNgNHVgqzK2LAtpDJ6wCHFixUDPdnXENIM9iNs2riL9g2mkwb7EKuPMZQ98+uwTqikiRgSSJ/ORvsE6/Cqggdc+nwhdw6teZ13SoF5jzqCBV63OGnjV6qtHD4UC3wvwWtoM85JCC7w0s8hfsR02Lx6FdKkdvgnwfvH0wYu3HqG4Q+an+obROD/OMBp47XMvNfCq11kDr3qNNfCq11gDr3qN9QwhVMAceNnXd/T0Ffj0+QtG9W2J1Zv2Y/HanWIywf629auXQvum1STTSEvjc5dviHnF9r3HBSgzpnXEQfdz0pt318GTcs6gbo1RpEB2WV3f0XPF1OHl63dwSpkMHZs7o1zxfGDpwqDxixAlckQkS5wA6dI4yPwEXhpZsDPF3QdPxC6ZNsTsI9ym1yQZm4YPDDqidR44HROHtIdj8kTy2pbdx7Bq4z6TvXJAGd4btx+KPTMd2FZs2AtPTy/0aFtHrJrnLtsKOsw1rlUWbRpVCVDlMxf/wdR568XCmZlqHl+jYlHMW+GBx49DeHMsTiv4qw+yZPSBWNLpEAU08Nrng6CBV73OGnjVa6yBV73GGnjVa6xnCKECBvB2al4DQyctxl8XrmHZjAHS+mzP4dNiH5wiWULcf/jUj83vkrW7MHHOGmTP7ITSRfIgaaL4ePr8lbxGdzSWDrhtO4gLf9/E0c0zZXUrN+5F2tQOiB8nFg4dP4ep89bBfasLPn/xQM/hswVUnSsUQYzoUZEpXUoBXoJxuybVEC1qZPQe6YopwzoIQLMfMYF6dL9WMvbc5dvEAW39/OEmJWjAUbVcIdSuXFxeCwh4L165JTbEZYrmRe0qxXH+75twWbxJ1kDI9fLyRu+Rc7B92VikdkzqT+l7D5+gQsO+Arg1KhaRmmH+GBjeqznCoobXmLB6VW/kzqGB1/wGaOAN4Rc/mKdp4A2mYCE4XANvCEQLwSm6pCEEogXjFA28wRBLH2pfBQi8BE1C58FjZ7F85kAkShDHtIibdx7i73/u4tnL11i8ZidaNayMprXLgcC7+/AprJw1COHDf8048rU/Tl0U22IGs64lanXDjhXjxbaYDmrXbt6TLCjfm7loo2Rfs2ZIDVtKGpghThA3Nnp3qCcWxB36TxVgjh49CkrU7CavVy1bWOZmRphWwu7bXEyuckEB76WDiyV7TVvjfBXawW3uMGTJkErGc24xCE1qlxMgtwxaNq/degBHNs3wV2ergVft51kDr1p9jdE18KrXWQOveo05gwZetTpr4FWrrx49FAoQeI/+eUEgj2UE5kA3btYqLF+/ByUL50LKFEmwY/8JNK5ZFs3rVfAHt9aAl68RHEf1bYHf8mdHu75TBHZL/pZLMsLzV27H6tmDJUtsC/COnr4cXt4+4trG8ouy9XqhZf1KSJYkPvqMnIujm2eI3TGDGdr7j55h3IA2JnVsBV6CefZSLUwwzgEadRqNiqV+RQPnUv7UJogzxg9s6+89Dbyh+HDacKoGXhtECoNDNPCGgYhBDKGBV73GGnjVa6yBV73GeoYQKkDgffjoGX4rkA2uy7ZijetQZMuYGi9evUVR5y7SxcBo3dWu72QUyJXZZuBlSzBC6ZJp/fD23UeBWvOMK0sW/ACvk6PU6BphuWnNHHh5zIJVv2PjjiNwSJoQmdOnQrfWteRUwjAzvhMHt8eveTIHG3h9fHyRrWRzm4F3kutaHDl+HluXjtHAG8LPYUhP08AbUuWCd54G3uDpFZKjNfCGRLXgn6MzvMHXLDhnaOANjlr6WLsqYL5pjRndDb8fgdvcoYgfLzYKVu4gWd+yxfLh9Plr6DViDjo0rRYo8G7Z/QfmTuiFLx4ecFmyGcdOXsSeNZNx/vINtOw5ARsXjkSShPHw+/4TIMAawDtvxTaZY+bormB3BdbnBgW8z1++QbEaXUWv3asnCvgyuMlt8ISF2LtmMn75JXyYA++79x/RvPt4tKxfERVKFsCJv/6WaxvSvQmqlC2MR09fyOY8blxb//sXvHwZNrc0fXpfpEvrg3DQm9YMRTXwhs1nK6hRNPAGpVDo39fAG3oNbRlBA68tKoX8GA28IddOn6lYAUIs62s7t6ghNbZ8PM+OA6xfZZeDKXPdZAXcPPbFw1M6NTSrWx5L3HYJ1M2b2Mu0QmMjm/ECAXTi4HZSssCsaY9hLrKxjMEyiQPHzmLNnCHSaYFGEHz/n1sPpO8u++9aA15vH18BSyPa9J6EyJEiCigb0WekK1I5JhU4Nw92ceBc3IjGUg3WLDODffHqbdRrNxxGDa+1DC83wLGLA6//zdsPKFS1o3SJ4J8Z1GPi7DWm6bhxj5p6ePrgeRi1JfOFr0Zdi++DBl7Ff0H8b3gNvOp11sCrXmPOoIFXrc4aeNXqq0dXqACzrW/ff0TSRPGCnMXYtDZnbHe8+/BJsrSWwawss65xY/t/j8eylCJWzOiIGOGXIOfjupiF5ia5gnmzyPEGjO5aNQEpkn1tT2av4A8Grj9OrOjS0oxx8IQH3r8Pm/65CRMAjim8AY29pluqgdc+n24NvOp11sCrXmMNvOo11sCrXmM9w3eggGWXBtVLWrput/TZ3blygqlTBMsZ9h39Sza2fQ+hN62pvQsaeNXqa4yugVe9zhp41WusgVe9xhp41WusZ/gOFGA5Ak0qihTIZpfVsLtE7JjRpWTiew0NvGrvjAZetfpq4LWPvpxFA699tNYlDWp11sCrVl89ulbgu1VAA6/aW6OBV62+Gnjto68GXvvprIFXrdYaeNXqq0fXCny3CmjgVXtrNPCq1VcDr3301cBrP5018KrVWgOvWn2/+9E9vbzh7e1tMkVgjWmOzE5IGP//Hc2Mi/j46QsiRYqACL8EvWnLOIcdDp6+eG3ql/stBGHPXVrqFs6XNdjTc7MXO0DQPpgRmD50hPslfHhEjBjB5nnevPsgHSXKl8jvzwnN5kFCeODMhR54+DBs2oiVLO6NrJm1tbD5rdDAG8IPZjBP0zW8wRQsBIfrkoYQiBaCUzTwhkC0YJyigTcYYv2Ih9J6dv8ff2HTolFyeXQfmzaikz84/PTZA3nLt5EWW2zbtXD1DjgkTYByxfMHKsuydbtxyP2ctNj6VjF4wiLEiRUDPdvVCfYSjp++jFa9JuLYllmIEztGgPpwYDqeZc+UBn061hd74YtXbqFTC+dA57x87Q7qtB2G8/sXBuuHRLAvxMoJ9x/54O1Hz7AYChEj+CBGjDAZ6ocZRAOvfW6lBl71OmvgVa8xZ9DAq1Znm4DX09MLHz9/QYxoUaVtE92izly4jqhRI4vzlY7/rgJPn7/Gu/cf4JQqeaDAy/6vV2/chUOyRIgVI5pVu11rKnxr4GXrsvwV22Hz4lFIl9oh2Dfq/YdPuPvgCTKkTSFAGtAPAg7MbDa/EzSvWLlxH3YdPInlMwd8t8B79ZYnPnxiK7HQR+QoQLw4YdPiLPSr+T5G0MBrn/uggVe9zhp41WusgVe9xjYB7+ylW7B4zU7sc5sssFOv3QhcunZbVtejbR1xddLxfSiwatN+rNiwB89evBHThk7NnVG8UE4wQzt7yWbsOXwKHz99Rr6cGdG/c0OcPHcVf134x2SYYA507Nvaf8w8FMqXFc3qlJcM5sCujXDv4RMMGr8IUSJHRLLECZAujYO4ntkCvHQsownCrXuPUKZoHtR3Lm360URjCT7ef/n6nZhJdGzujHLF88mw9TuMRJtGlXH0z4u4cv2uzLdu2yFEiPALbt75V5zQShTKic4ta/jpcUuDCrYHWzt3qIxD57Gp89bJ/Anjx4ZzhSJo3bAyfH19xclt5ca9ePDoOTKmdUSPtrWl7+6AMfOxavZg+bEXmD4TXFYjberkyJM9Axp1GiXXkTXD1x+ES2f0N5WNmOtkmeH99/FzjJ25EifOXEGOLE6oXbm4SYPA9KETnWPyxHjz7j3cT10W0wmWqhw+cV66RWzd4y7XxIyzYcc8ycUD9+6Hzee2Qjkf5MimSxrM1dTAGzafraBG0cAblEKhf18Db+g1tGUEneG1RaWQH2MT8NLJKWvG1OjbsT6MR7zDezXHqzfvsHrzfhxYNzXkK9BnhpkCZy9dFyidMqwD0qRMhrOXbsDLyxsNnEuBj/WPnbooDlsEYcJdvWolwXPMSw4MoOP9btZ1LFI7JsWEwe0ku0l3sWUzBohNbs/hs+GYPJEAY4zoUZEpXcoggffew6eo0LCPlBYUKZAduw+ewsadR7DfbYrUrxI206Z2QPw4sXDo+DkBU/etLogdK7rMzWhYowySJYkvpRQjpiwV0O3WuqacN8XVDQVyZ5IfYUbws1u1XCEBR9bY5inXBm0bV0GlUr/izv0nOHHmMgZ2bYxte9zRb8w8dGlZEwXzZIb76csCu6xnNi85CEyfjgOmIXsmJ7HtnTrPDX+euYLB/3Ney50tvR8rYWN95sDr6wtUazYAObOklTFu33uM3iPnYM+aSUieJEGg+rTvNxVHTpwXXQjK2TKmwYW/b2LinDVit/xb/mzYeeBPcL7184fL9HrTWph99awOpIFXrb7G6Bp41eusgVe9xpxBA69anW0C3nL1e4N2pIQbI9t7YvtsfPj0WdykdqwYLxCl49sqYPwYcR3fEwXzZjbVhBr1t8yK8h6ah2XJAYFudL9W4Ovx4sbE5KEdTc5iBvDmyZ7eX0kDs5lL1u40Dc3+s6WL5JFxDKBmhnn7vuOYPLSDHEcYr9d+BDYsGCHZR24Qu3bzHq7euAeWWsxctFEys8yScm5el3kfXUJe7mzpJEPLIMQzu23UI7MUoWKjvnDf5iJZTpYnFKjUXqC2ca0yiBY1imm9/KHgkCwhxg1o40cfywxsYPoYwEugtlbS4Lpsq2TXGawHblGvogCoAdSE95Y9JmDp9P6IHu3r2oZNWoJq5X+THy2B6UMtMjilQLfWtUzrtzTbYMlF5Sb9TT8iNPCq/b5q4FWrrwZe++jLWTTw2kdrDbxqdbYJeJklYwavf+cGqNZsoMAtNy89fvYSpWr3wMaFI+UfWx3fVgHWWo+dtQprtxyQhTDbx8fyfJ2gs33Z14xtUMDL9wlmO1eOl8fkRgQGvM9evJaMrBEsmSBcmwMvM6j7j57x91lp37SaZDXb9Z0isFvyt1xImig+5q/cjtWzB4t5A+deMWsgcmVNZ5rDEnh3HzqJKXPXYffqiXKMy+JNuP/omR+IZcnH6OnL5X2ORUDMmyODlCr069QANSsVDRJ4A9InKOAdM2OFQDcjQbzYkok2B96tu49JJt78GnlsicK5JBsfmD6WWvA8S+Dlj4gStbph/7opUmesgVft91UDr1p9NfDaR18NvPbTWQOvWq1tAt712w9j6KTFkhEjCC2Y1BsF82aB29aDGD5lKU7ucDVlpNQuV49uiwJv3n7AhSs3MWWuGzKkdZRa3UJVOmL6yM6SdQ0KeKuULYRHT17g7oPHWOUyWLKRDH/A6+SIDs2qB7okc+Cd7OqGO/cfyY8lyyAIdxk8w5SNNeYLKfByY2WJmt0wcXB7/Jons5/pWNpw7eZ9LHXbjVPnruDQhumo1XoICuTOLNBrHtYyvAHpYw68BOsd+08IpAcW5uMfO3kJvUbMwfHtLv46NgSljwZeW74Z9j1GA6999NYlDep11hle9RpzBg28anW2CXiNDT2nL1xDobxZULVsYVnVwHELED9uLD81k2qXq0cPTAHa2b59/xElC+cG/7EdNH4hYsSIJhvS+MiedbLcdJYqRRL8vu8EcmZxwrFTl6zW8DLL2LLnBJlu4eQ+0ofWHHjnrdgm9bOEV3ZCiBcnptWlmQPvmYv/gE8LWDZQoVQBEMz3HjmNvNkz4PnLNzIfnxYw+/j7/hOSiQ0p8PK6Bk9YiL1rJptqZwnx3MRWt1oJxI4ZA2u2HPhfnfAs8Hr45zH9W8uPudPnruH9x0+SabZWw2tNH3Pg5bW27TNFsuTc7Ma2aNTfMsyBlzqWrtNTMuOsS2acOncNnl5e0iElMH1CArwnL3jg46ew6awQM7ovkiYNm7F+lG+5Bl773EkNvOp11sCrXmMNvOo1tgl4T569KhuHLMsW+Bibu94JL8ExI1B/WT/nDLwXnQfNMNWJ0mhhWM9mSJYkAbhhbMDY+bJJjcGNZ/Mn9cbh4+dw8NhZU59cPtqfMbKzQN/rN+/RoONI2ZzmMqY7spdqIW22uAGL9aA9hrngn1sP5BF8QJnM5ev3+Bl/444jGDtzlWmNLI9xHd8DDkkTyXgEYAZ7/R44dhZr5gxBtkxpAixpYD1xqwaV5Jzdh05JVpslDX1GuiKVY1J0aFrN9GHg57Vp17HSZozBMh1u4itWMId0sRg1bRk27/pD3uPTjPGD2iJJwrio3eb/++QGpk+3ITNlrW0aVZHWfZ0GTJOuEozTu+YhapRI/j6Yf/9zx8/4vD/8IWmskevgDwSWNQSmD4HXXAtOtMRtl3S9mDexl8zL6y9es5tsMk2cMC6+ePrgxdsvYfNl+R/Lh/MFNPZ+lVQDb9h8tIIaRQNvUAqF/n0NvKHX0JYRdIbXFpVCfoxNwNt54HRkzpAK7Zv8PzxwSrZQKlOvl9Xa0JAvSZ8ZGgWYjWc7MYKS4Q5mPh5rSD08vQLMyAZ3bs7FbgYRI9juvmaskY5k3ExmHsz0MiMaN7b1jLEt62PmuFDVjti1aoKfFmXGucyCs2WXtTk8PDzx+u0HeXLBdYQ26KQWKWJEq7Ab2Ng8j7XXXId5Zjgs9DHmXbjKA0+eho3TGscMHx4oWcIbDsk08mrgDe03x/bzNfDarlVIj9TAG1LlgneeBt7g6RXco0MFvEZ2ynJzU3AXoY/XCoSlAixnoAXw0B5Nw3LYH26ssNy0RnH4m6dpY2+kdNTAq4HXfl8XDbzqtdbAq15jzqCBV63OgQIvd9W/fvMOf124LhnB1I5JTKvx8PDCn2evyGNho6+n2qXq0bUCWoGwVEADb1iq6X8sXdKgVl9jdA286nXWwKteYw286jUOFHjZIonOTWcvXkfMGNHERcqIKJEiIV+ujCj2a04kShBH/Ur1DFoBrUCYKqCBN0zl9DeYBl61+mrgtY++nEUDr3201hletTrbVNKwaedR2TnPjUw6tAJagR9DAQ28au+jBl61+mrgtY++Gnjtp7MGXrVaBwi83GXODTxRo0S22k5J7bL06JYKcBMTd9yXL5Ffyf34+OkLIkWKECbdNnx8fEETiEL5svrblBbSO8suCofcz6J00byBbpDz9PKWDWlRIvvviBCSublJ7MzF6yhbLG9ITv+m59y69wjPX7xB/lwZra5j9eYvePEi7JbITWv58voiud60JqJq4A27z1ZgI+mSBvU66wyveo05gwZetToHCLy0g2VfUdoGT1+wXlo+BRSGdavapf7co1saIISlGob1MHvqsh1YaIPdBXKWaSW13azxDovYtscdS9ftDrJefNaiTdj/x18me+HQzs2WfM27j8PlQ0tCO5TV8/uOnitt1dKldgjz8S2d1iwn+NqWzCNM5w0XHvD10ZvWNPCG6ccq0ME08KrXWgOveo018KrXOEDgZXZo+153NKlVDmcvX8eDf58FuJo6VUsgcqSI6lf7E8+gEniZkb164y4ckiVCrBjRQq2yCuBt1m0cKpTIj7rVSga6Ptrnvnv/AU6p/r/ePDQXpBp4aeaxeGq/ALOwoVl7UMB79KQn3n/0Cc0Ufs6lr0bSxL5ImFADrwbeMPtYBTmQBt4gJQr1ARp4Qy2hTQPoDK9NMoX4IJtqeEM8+g964rv3HzFh9hrsOnhSrjBX1rRI75QCvdrVlT8fdD+LqXPX4ebdf8WkYXD3Jkif5msGj6+NnrZcOlw4pUyGTi1qmB6XBzauJfAyK8vM++/7jks/WbqH1ahYLMB+rzSRaN9/Km7cfijryJIhlVgOG2YidGKjC1tgGVk6q02cvQb8MVSmaB7Udy6NbBlTy3jHT1/G2Jkr5fqyZ3bChb9vmjK8QekV2Lgcm6YZFRr2gftWFzFAoZnF4rU78eTZK+keUr96KbRvWk1KPejQ9teFf8RdLqAITDv2CF62fg+Wuu2S8XnfaK5hZHhpuDF6xgq5XppmJEoQF3WrlkCFkgXAc2m3zUw0r7lGxaKo71xK6t+tBU0yFq7eISYgdGJzrlgE9aqVDPTzU7/DSLRpVFkMLa5cv4tRfVsiWeIEmL1kM/YcPiWGHvlyZpR7Sze97fuOI0dmJ2zd446MaR3RqYUzCuTKJMvRNbxq/4LSJQ1q9TVG18CrXmcNvOo15gwaeNXqbBPwspH/Fw9Pqythc342x/+Zgo5lhKpOzZ0FemYv3YxIkSJixsguApTVmg9E64aVUfTX7FixYS9OnbuK3asniZEBwS1L+lRoWqc8Tp69Apclm01gGNi4lsA7bNISAZ7ubWsL6A2fvESMQaqULWT1VrAGmJsPc2dNJ2tdtHqHgKvRUs7cNtjaAAZ09mxXB0UKZMfug6ewcecR7HebggePnqF8gz6oVq4walUujsdPX6L3yDk2XVdg4xqGC7OXbsGde48wYXA7Wdqew6cRIcIvSJEsIe4/fCrucrPHdhfHNHMr44A+k71/ur0AACAASURBVIFpt2P/n7L2js2qo2jBHNh7+DQWrPpdgJffgapNByB5kgRyfxkDxy9Ay/qV0LBGaYFtjj28V3Np4Tdn2RaxMB7Zp4XVpVy//QDVmw9Cn471kTldSiRJFA9fvngG+Pkx7J05WMMaZZAsSXyUK55fYPfYqYviGsfP44bfjwg48zM6cc4aNK9XAb/lz4adB/4EP0fGPdfAq/ZvLQ28avXVwGsffTmLBl77aK2BV63ONgEvndZo8xpQMCtYs1JR1KxYVGDqR47PXzyQp1wbjOnfWgCPQSBjSQCBd8bCDZJZo70tg05kRZ27YNaYruK41ab3JOxzm4Kkib5m/QhQBMjOLWsEOq458Hp6eiNv+TYY2LWxZJcZtOx98vyVrMF12VaTdW+c2DHQol5FOYaZzQtXbgo8Xrx6WwDYyFyaA6+18wlVzBZOHtpBxvLy8ka99iOwYcEIHD5+His27MGRTTMEvs1LGlI7Jg30ugIblxlJb28fFK/ZFeMGtgWtko24eech/v7nLp69fI3Fa3aiVcPKaFq7nB/gvXjllsmqmOdVLPUrUjokCVS7Ft3HI1HCuGLnyzAvaWBWnu/vXDlB7JYZjTuPkY2EBF5myQmcjWqWkff4g4Q2yse3u2DPodPyGTGCwMxWf5YlDYF9fkoUyiXHu47viSIFspnuKT8LzPQ6Vyji56tnWdLA7HTlJv1NmXINvGr/ptLAq1ZfY3Sd4VWvswZe9RpzBg28anW2CXiZtbp26z7aNa5qWg3Br8cwF4GMuHFiYtr89ejWupYp86V22d9u9Pv/PpVs5vZlY0GYY5gDL806GAYw8b9L1u4uurDOeeq8dTi6eabpAoZOWiyPv7u3qR3ouObAy6wmwYU/NMy7EbAf8pRhHTFmxgrQQpiRIF5s9GhbRzLP3HxFyOIjb2YruRHMGvBaO5/Xtf/oGVMJhHEBLCVg5pAdPYwMrDnwxogeNUi9AhqXgHvir78l43pwwzRTB4lxs1ZJWQM32KVMkQQ79p9A45plJZNpnuElrG7eddSkNet/WaMcmHZFqndG11a1UKtyMX/Au3XPMYycuhyndrqaxjQHXp5LS+eE8f32pZ42ohP2H/0L5/++aTqvb6cG0sHCEngD+/ywdIPHr5g1ELmyppOxDIg1/zwak1gCL+ubS9Tqhv3rpkiZhQZetX+PaOBVq68GXvvoy1k08NpHaw28anW2CXiZhSQANKldzs9qCB437jzEgkm9Eda749VedshH5wavApXaY9KQ9vII3RJ4WePqfvqSqUvAh4+fkb9iO0wZ1kGy350GTDdl2Hgus4KZ0jmif+dGgY5rDrwcs1CVjlg3bxgyp09l08WMd1ktGceFk/tIaQXhq0GHkVaB19qAk13dcOf+I7CTg2WwZpWP/gliDHPgzeDkGOh1BTYux2IXA8dkidCxubOMbWTMF03ta6pFbdd3MgrkyuwPeK1dB0s7AtOOnUkypU0pta4M8wwvs8pVmw2EeVcSc+Ct1XqoZP0b1ypr0z3hQQTYhVP64NfcmeWcwD4/LF+wBF7jeqaP7IzSRfL4mTco4J0+3wMPHoazea1BHRghPFC9mjdSOOhNa9RKA29Qn5iweV9neMNGx8BG0cCrXmPOoIFXrc42AS8zlKWL5MWALg39rGb+yu2Yt2K7ZLyYLRwxdZmf7JfapX+70QeOW4Czl65L1pabhFgCkCtbOikn4GamVr0mCuAWyptVMo7MAB9ihjLCLyhbrzfqVy8pj+BPn7vqp/40sHEta3j5aJ09Z5lVZRb32s17UrPJjLu1cFm8CQfdz2HOuO5SjsDa4YBKGqydf+biP/L4npnrCqUKgHXde4+cRt7sGcCezTVbDcHEwe2l28D2vceldtRoSxbYdQU2bsIEcQROd64cD8fkiWVZb99/RMHKHeQRftli+cANb71GzEGHptVsAl6OEZh2qzbtlw1rYwe0QcL4sTFr8Sa5HmbCuSmN5Snc8OdcoSguX7stm84GdGkkJQ3zVmyTzDPriflD5OHj51i//ZBk2AMKroWOha0aVMbHj5/x9z93Avz8MHNsCbwclz+aWErCTYepUiSRkpqcWZxkY9sfpy7KD1KGZYb39kMvvPvoFaZfpChRfBEGjT7CdE3fajANvPZRXgOvep018KrXWAOveo1tAl5uptqy+xjWzh2KzOlSIXz4cGBmybnFINkhzsweN/YQFMwf16tf/reZ4fGzl5jgshpXb9xD+jQp4OPrA1otG4/0uVmJGW8GH3ETEksVyS1/Zr0rAY2gzGjXpKpsNmIENi5BqHabYTi/f6E82mcHgWGTl+DIifMmEdo2roIuLWtaFeXR05dgLTazvAzWgBKIzEsals8cIF0lAgrWCbMm1Vg761Vdx/eAQ9JE6DPKVUobGMUL5QT7OLO+l3W4QekV0Lismd262x1cl3kQMtnhgMFOFyzP4OP+ZnXLC3AePHYWzAAHFIFpR6OJ1r0mSmcGBssqjp26ZNKJJhSss336/JVsBPvj5EWpkeYTEJZ1TJ2/Xn7kGMHykSXT+gW4FpZzDJu8GC9fv5NNh8wsB/b5sQa83PjH7yh/hDHY9WH+pN44cOyMmJXMm9hLXn/24jWK1+yGA+umInHCuLh+1wvvP3mH6ZcoejRfxIiuM7wUVQNvmH60AhxMA696nTXwqteYM+gMr1qdbQJeAkKt1kPkH2W2gWIbLLafYqyePVjaUHUZPEMazlt75K32Euw/OjOahE4GSxz4SD1n1nSSZTSCNc6EJ+68N4413uNGLEJgvDix/LQRs2Vcy6vlPMy2xo8XyyaXtH8fP0ec2DHBHf8hCWY5WVYQMWIEfy5qvF5rr9tyXdbGrdt2uGwAs9Z5gmUdzPYam/9Cci0Bace1sNMEa9MtHdvMr4W11yVr94DLmG5++ujymBcv3yJWzOgBtokzXy8/D6/evJNuJ0ZnisA+PwFdK+u2PTy95DtqS0xx9cD9B2FX0sCvRG1nb6RIoYFXA68tn8CwOUYDb9joGNgoGnjVa6yBV73GNgEvl8F/TNdvP4wrN+7Ko9cUyRKhWvnf/G1iUr/kbz8Ds9nsf8tNa9w0RNDbuHCkv81KwV2pqnGDu46wPj4k10VNmQmfPbabZMm/l2jfb6psMuQGQbaby5DWEfMm9JKnHv+10JvW1N4xneFVq68xugZe9Tpr4FWvsQZe9RrbDLxBLYUZ3iHdm0o96Y8ezM6eOnsV7z58kjrPgnmygN0IQhuqxg3tukJ7/o90XSxjYXkJM6n80cfNZtwE+F8MDbxq75oGXrX6auC1j76cRQOvfbTWJQ1qdQ4z4GUnh7kTe4XqEbPaS9WjawW0AuYKaOBV+3nQwKtWXw289tFXA6/9dNbAq1ZrDbxq9dWj21GB3YdOIn+uTFJjripoZsFuG4FZMIfl3HTDe/7ijZ8a4bAaXwNvWClpfRwNvGr11cBrH3018NpPZw28arXWwKtWXz26IgXYqcEhaQKx1jXCWgeDsJyeGxRL1+2BoT2amXowh+X4tGhm9wl2+zA2Olr20g3L+Y6f8cDHj2G4wSwckCARkDCeT1gu8z87lgZe+9w6XcOrXmdd0qBeY86ggVetzhp41eqrR1ekAGvGM6ZN6aczhmrg5SY1ugseWD8NESN87dIRlsGWcTSvOLd3gXS7YKgE3i+ePnjx9ktYXoIey0wBDbz2+Tho4FWvswZe9Rpr4FWvsQZe9RrrGQJQgHbHtNItUzQv1m49gHfvP6FNo8pi6EGHM/a7ZQ9Zo4Uaexgv37AHNSsWxZCJixElckTpA50ujYMYURB4WzWoJH1z7z54gnrVSqJDs+qm1mAH3c9i6tx10lKP/YYHd2+C9GkcZHX1O4xEiUI5sefwaavn8hgaaHBTJm2gGY+evMDEOWtx6twVAVQ6ndGEgg6EjFt3/5W10AZ4TP/WcEyeSDpP5M+ZEXWqlpBj2AKt08DpqFbuNzGuIPSyXOKX8OExoGsjnL14Hdv3HUeOzE7Yusdd+hqzV2+BXJnkfF7L6GnLwZ7F7EncqUUNlC2WV96jGczhE+elfZy1c5et98DTZ2H/8WQv3iKFfBEvXhhmj8N+mcpH1MCrXGKZQAOvep018KrXWAOveo018KrXWM8QgAKsh63XfgQqlfpVeu0S2hav2SnOaokSxEWR6l0wqFtjsetl0JUsa8bUaFijDHoOny0A6VyhiHTIICQSeAl97ZpUE0juPdJVHO+KFMgOwnW15gMFpov+mh0rNuyVtmK7V0+SYwM7l3OzLR8tpbcuHSNz0D6Z43GdLetXlH7MdB6kCQtbl126egudmjsjTuwYcFm8GdkypcHofq2wxG2XGFPsXTNZujvQHa9JlzH4Y8tMMesYNH6hOKOxTji9Uwps2nFUXOua16sgRhc096DrHl3saLhRoWEfZEmfCk3rlMfJs1fEQc9wuGN2OKBzeU1hXcNr3Oa4cXzRuKEPEsTXwEsYe/Lqq8mMDjUKaOBVo6v5qBp41WusgVe9xhp41WusZwgCeC8dXGwyXKjYqK9AKUGW9ax/nrkiDn/cvFWlSX/sXDlBQNeWkoa+o+ciQdzY6N2hnmSLabm7e/VEWQ3NM2gTPGtMV5QolMufZa/5uTyeNsxu2w6J0QrDsJDesWI86DhnHgTe3NnSyXUwuJlu1LTlOLJpBl6/fY/fqnWG6/ie4nbHeSJGiCAZaltKGtj3uXKT/nDf6oJL126jTe9J2Oc2xdQdhd1SCPi8ZstyCPNzY8eKroFX8TdTZ3gVC/y/4TXwqtdZA696jTXwqtdYA696jfUMwQBe1siyywLLDe49fIIKDftKxnL73uO48+CxuJoxbAHe0dOXw8vbB0N7NJXSCQZtno0oWbu7QCltiS3rf83P5fGNOo1GjYpFUKNiUTl9w+9HpHTh1E5Xf1dnCby0KaYN98H108SwgjbA7z9+kr7VxWp0FaDPmiG1TcD79PlrlKjVDfvXTRHb4Knz1vmx8x46abEYY0wZ1tEf8JqfmyRhPA28ir+ZGngVC6yB1z4C6z68dtNZb1pTK3WwgZeZsS9fPJAoYVw/VrYXr95GhjQOiBQpotoV69F/GAWMkgbzDC8htHaV4mjf5KtNMzOY8eLGwv6jZwTimBU1Aa+To9ToGhEYtE6cvQbupy9h06JRcjitifNXbCclD+z0ENi5Rmb0xPbZiBkjmpzP8oOOA6bh8Mbp/sxWLIGXtbQE7rN75sv34+yl6wLQrDG+dPW2AC+DphY1Ww3BmT3zEfl/3yPLLK05tDIj3GnAdMn2MmNrgHmmdI4Y2LWxBt5v/E3RwGufG6AzvOp11hle9RpzBg28anW2GXj5SJePmF++fmdaETfedGtdSzbF6NAKBFcBA3gJoYnix8HGnUcw2dVNbJozOKWQ4Q78cQadB82AQ9KEUs5gWPhyg9fp89cwc3RXgdd4cWIGCq1GCQIBt1DerFJHO3vpFhzaME0soQMDXpZDPHn2SmpwjXj15h3K1uuNymUKCpyz5pZj8vtA4GUmt1e7urhx5yHGzVyF5EkTCLAbwYwvM7/jB7aVMRifPnsgb/k2WDS1L7JncpINbW5bD+KPUxelrpdhDryEYq6hfvWSaNWwMk6fuypazR7bXdqmBQbLzPAu3+CBZwo2rUXjprWC4RA3nndwPxI/1PEaeO1zOzXwqtdZA696jTXwqtfYJuDl42TWGubLmRGF82VFvDix8OeZv/H7/hMo+msOzB7bzVSDqX7JeoYfRQEDeAmrxg+pkX1amMoGeJ2eXt7IWbol+nVqgMa1ypounVlXlj8QGtkFgZvFrEGrt48vhnRvIufNWbYFsxZtkv+OFjWKlDeUKpJb/hzQuQO6NESJmt0weWhHf+YPhOiB4xcIDDPY+WH5zAECvITxj5++blZiRwX21jW33WYfYddlW2WzmpHN5bFcH9fJIOReu3VfShfYrYLx7MVrFK/ZDQfWTUXihHHBzhXs/GDM1a5JVXRuUUOO5Qa5wM794qWwLdnPvV9N9NfAa5+/qTTwqtdZA696jTXwqtfYJuDl41cGocI81m0/hGGTlmDvmklIliSB+tXqGX4oBQzgPb9/Id68/YA4sWJI5wLzYOcGdmdw3+Zi9UkCS2xixYxuc1/cz1888PzlGyRJFM9PSU5Awh798yKGTlqEfWunmLLLlsdyDVEiR0L0aFHkLaOkgd0kvLy9Eet/ZRDm5zXuPEYA2oBT8/eY6fXw9LT5yYm3tw8eP3spP0SjRolk82eExhMfPoU9mf7yC5AsiQ9iqjO8s/kav+WBGnjto74GXvU6a+BVr7EGXvUa2wS8Rap3lrZILepV9LOiR09fonSdHlgyrZ9kf3VoBYKjgLUaXsvzWSfL1l/cePYtgplY1u3WqVLc5ukta3gtT2R3hbpth2Pf2slImji+zeOG9YG6LVlYK+p3PA28avU1RtfAq15nDbzqNdbAq15jm4C3Xd/J+PfxC2xePNpPlot1lNMXbDDtPle/XD3Dj6QAyxgOHz8nLcisBTOXW/cck5KA/9ITBJpNsHzBqEO2vDZuNuP3ySin+Fb3VAOvWuU18KrVVwOvffTlLBp47aO13rSmVmebgNdojs9ay8L5s8k/5sdOXpT6SbZpYt2lDq2AVuC/pYAGXrX3SwOvWn018NpHXw289tNZA69arW0CXi7hzMV/ZFf7+cs3ZYMM3abYPoqtlWirqkMroBX4bymggVft/dLAq1ZfDbz20VcDr/101sCrVmubgdd8GWyXFC5cOLUr+8lGp5PY8xdv/HUCUC0DywZoUUt73bCIR09e4OS5q0iVIglyZHYKiyGtjvHw8XPcuf9YuoZ866DRA3v8hg8fHmWK5v3Wy7F5/imuHnjwIOy/x7Fj+8K5mrYW1sBr80cxVAfqGt5QyWfTybqkwSaZQn2QBt5QSxjoADYDL/uO7j50CvcePJEBUzkmRbni+WzeSa72Mv77o1v2TLXXFRn9aY9tmYU4sWOEatqNO45g8IRFKF4oJ7JlTAO2yFIVnIddHXq2qwNuLHNImkAMJOwd128/QPXmg2TTJrsksM/vfyWu3/XCh09qeuXGjOmDqF+bVvy0oYHXPrdeA696nTXwqteYM2jgVauzTcB798ETVGzU199K2Mt07oQe0n9UR+gU+FbA+/7DJ/D+ZkibwqY2XYFdZbNu41C+RH4pc1EZhkva5sWjkC61g1WbYZXzm4/NnrnXbz3wYyphr7lDO8+tB154/8krtMMEfH44XySIq274731kDbz2uUMaeNXrrIFXvcYaeNVrbBPw0t6VO8/ZhzdrhtQIFz4c2FJq9PQVePLsJQ5tmO6vf6r6pf9YMxB4t+87LmUAW/e4I2NaR3Rq4SwdCmi+0KjjKDEvSOmQWC589pLN0i7LMGM4+ucF0D735t1/5QeIh4cnxg1sg9SOScFH7hNmr8Gugyfl3FxZ0yK9UwpxAuPxA8bMx6rZg+Ue1u8wEiUK5cSew6cFhAmvtO9lf1cfH18scduJxWt2ilFEwbxZ8OWLp5gtGIYJdERj5nVYr2ZivrB47U4xZuCGx/rVS6F902pSDsOymA2/H8HKjXvx4NFzud4ebWuLiQTP47WwzKNM0Tyo71wa2TKmNt3wLbuPYdXGfWLJu/vQSQwavwhRIkdEssQJkC6NA0b1bSnX0aZRZbCPLrsi8DWaMAS0nnGzVolb2s07/8r81KBzyxpIkSyRzLtq036s2LAHz168kXvQqbkzfHx80Hf0PNPc9aqXRNWyhbFozQ6s3rwf795/kk4M/Ts1FOvfG7cfYuC4BejXuQGWr98jrmnDejYT2+GKpQpgxYa98PT0Qo+2dcSCeO6yreCTFd7jNo2qyDoM97Wl63bLfeWm0frOpUDnNGvjL5jcG5Nd18q9//zFEzmyOGFgl0byuZg21wMPHoZ9SYNxo5In90Xd2t5A2Lf6/U98+TXw2uc2aeBVr7MGXvUaa+BVr7FNwFuydneU+i03BnZt7GdFe4+cRrchs7B92Vj5B1RHyBUg8E6cs0b6Hf+WPxt2HvgTl6/dwfr5wwVec5VtjQ0LRggYMgaMnY94cWMJtBIMqzTpj7rVSqJ6ucICkL1HzpFzM6VLKcey0wYhjbA2e+lmAaoZI7vIHHXaDgPNHyL88os4jnFDYrsm1aSut/dIV3lMX6RAdtBeetD4hQJkhfJmkTWynODyoSUCzvXajRDIzJklLTKkdcSJv/4WiEyRLCHuP3zqx/Z22x53Ab0uLWuiYJ7McD99WQwkeO0VGvaRUgXOufvgKbEc3u82xVQ3TtOGquUKoXbl4gLTPYfPhmPyRNLeLEb0qHLNvA4GzR+SJYkv5Q78kRbQegx3tG6tayJtagdMcXVDgdyZ5FrPXroOmq9QhzQpk+HspRvw8vIWGO87aq7ch7pVSyB5kgQ4dvoSJrisQe8O9ZA0UTxp28f5qbXRd5gOaTUrFkWUKJGRP2dG1Gs/Qmp/uQn0/N834bJ4k1wDIZfz8F4a3zG6G9LsZXiv5kjtmERc2WLHjCGdUqyNT0Be6rYLs8Z0kx80B4+dxa+5M0sJhqpNa8a3IKWjL1o218D75NVXxz0dahTQwKtGV/NRNfCq11gDr3qNbQLeHsNmI1KkCGLFah4PHj1Dufq9sXPleDgm/5p51BEyBSxLGmidW7lJf7hvdZHsamDAy+4Zqzftw9HNM2VyZglzlmklwMsfInnKtcGY/q1RrVxheZ/HX71xN0DgZSafmVYGLaUTxI0tAEfQJFiO7tdK3jt59iqadx8nwMvIV6GdCY4NFW7eeYi//7mLZy9fS2a4VcPKaFq7nACkQ7KE/j5TzFwz0z156NdaWAIfgdCAfaO8xtx5rcvgGciYNiU6NK1mEp/A6zq+J4oUyObnhgS0HkuzCGafmdHdtGgUjDpnjlcwb2Y/pR99RrqKeUT3NrVlHmaW+aPEMMrYd/QvdB08U+7jvYdP5FpO7nA1ubJZmm+wAwp1dJs7DFkypJIxnVsMQpPa5QToqRt/tDSqWUbeY/Z67MxVOL7dBVf+uetvfGbet+11x4xRXZA+jYOfzaYaeEP2XbX1LJ3htVWp0B2ngTd0+tlytgZeW1QK/TG6hjf0GgY2gk3Ay8fb3YfOwryJveTRtBEsc5i7fBuWTu8n/5BGjBABaVMnV7viH3R0S+Dl4+4Stbph/7opiBc7ZqDAy6yrp5cXxg9sK+qYAy8znuUb9PGThQ8O8I6evhxe3j4CcHTc69a6NmpWKmoT8LJMgI/uSxbOhZQpkmDH/hNoXLOsZLEJdf06NTCNZdxWZn33Hz3jz7SBpRDsyMDs5/1Hz/yAckDAaw7uHD+w9VgCL0slpsxdh92rJ4qeY2etwtotB2SZzBaz/ILlG5bAS42YFTbMNNi1onTdnti4cKRk6gm8lw4uNoGnJfCya0b2Ui2kXIPlQwxCbsVSv6KBcym5B6ydTxg/jp9vwrQRncC5LMenG+LAsfNBi2aeV796SVP2XgOv2r9MNPCq1dcYXQOvep018KrXmDNo4FWrs03A23ngdBw4djbIlfBR7YF1U4M8Th/gX4HAgDd+3NjIWbol1swZgmyZ0sjJ5iUNbtsOwW3rQcnoWgJvBidHFKjUHpOGtEexgjnk/ZACb68Rc5A0UXwpN2AEluF98eotijp3waKpfaUOmUHHvgK5MgvwMmtZIHdmgV7zmOzqhjv3H2Hm6K7+RPLy9kaJmt0wcXB7/Jons+l9AV4nR6k1NoIZXnPgDWo9gQGvMeabtx9w4cpNTJnrJiUbfOJhCby8LpqzsNSEYWSHD66fJvXuQQEv66SzlWweIPDWaj1UMvVG7ba5SIFZNRvt4kZNW47+nRtI7a8GXrV/E2ngVauvBl776MtZNPDaR2sNvGp1tgl4+Rj57bsPQa6EBhRGjWmQB+sD/CgQGPByQxLLCfJkT48W9Svi7MXrGDR+AaqV/03A6t7Dp1L32rBGaanN3HngpGzmMmp4uVGKdaitG1YW0xDXZVuRK1s6m0oazDO83CxGYGrftCoSxosDbpziI3VrJQ1v339EwcodZLNY2WL5ZCMYgZllBwReZmrXbDkgpRbc/Hb63DW8//hJniDwWgmTFUoVACGTteJ5s2fAk+evMHjCQuxdM9nPJklaXHN8QjI7OHAMS+ANaj2BAS83BPL8koVzgxDDjHqMGNEwpHsTf8DLEgLWHE8b3gmJE8bDqGnLwCzrunnDcOnq7VADL6+VWfPZY7sjc/pUYD/i9dsPSVbZGvByUyDrgbNndhJtCOS929dDhZIFcPSUB95/9FH2TYwcCciQ3gfhoG5jnLLFh8HAGnjDQEQbhtAZXhtECuUhGnhDKaCNp2vgtVGoEB5mE/CGcGx9WjAUWOK2S7oIsGyE8ezFaxSv2U0y5pI5/+MMhk5aLN0RWMMZOVJE/JY/uynbamR5fwkfHsUL55SuCduWjUUax6R4/OwlJrisxtUb95A+TQr4+PogSqRI0vXh73/uoHYbv5vWzDOjBF5vH1+BOxpUzFiwAQfdzyJRgrgyFjeyndrpKmv+WsPb0VQ3yw1tzIYyuBGO57NTQ7O65fHps4fA4OZdf8j7fNw+flBbKX9gP1/WpRLOGbxe1/E95JrY/9m8Vpfvs965xzAXsbpm7THXbwm8PC6w9RB4+YOiVYNKMid7TnPtLGng5rvOg2aY1sPSCnZXSJYkgdQ4M+vdrXUtOY9rHjB2gUC6sfaZo7rAKVVyXCTwthvut6TB4jVrGV7+AGAXB2rHsoip89dj2brdpk8Xf+QsmdbP6vjsGMGsuaFx2WJ5Mbx3c6lD/uLpjRdvPYLxKQ3ZoWzS8DMirwbekH1egnuWBt7gKhb84zXwBl+zkJyhgTckqtl+js3AyxpAwg2zve0aV5XH45Nc1yJ+nFiSsdOhXgE+0n/x8q0AsGXwPUIM48zF62jcKa8CKQAAIABJREFUeTRO75onG97M3yNQsbQgZ9Z0/sAxqCtgfWn48OFM9afzV27HkRMXpC1ZQMGsIrOj7FhgLQhwr99+QPy4sfxkbdldgGUIfGoQO2Z0yfQWqtoRu1ZNMLUKsxyPx7PTQ8QIX3WwFkGtJ6DzjPUQzG1xpXvz7gM+f/aweq+C0tmW943PAq+X9ziwMI6NHy+Wnw13azZ74NkLW2YL3TGpU/siV3Yf/GzmjBp4Q/e5sfVsDby2KhXy4zTwhly74JypgTc4agX/WJuA12hdRdBib1Fm+6qULSS9SZkB/Gs3e5EG/o9u8JemzwiOAsyusr8uM7+st+7coobJ6WzBqt/x+77j0rGB2dDnL9/IJirLjU9BzceyCLYAy5I+FT598ZD61AWTektJgurgBkl2PDC6H6ie72cYX3UNr6FhkcK+KFPSCz8b8Wrgtc+3SAOvep018KrXmDNo4FWrs03ASxvXN+/eY/qIzmjbZzKqlCkkwGu0ztq6ZLQ8stXx7RQgED749ykiRPhaR220tOKKWNJw6uxVvPvwCQnjx0bBPFmkX21wg4/rOQ/NF2LFiCZ1wOw9q+O/qYAGXrX3TQOvWn2N0TXwqtdZA696jTXwqtfYJuBlKyT2GeXObrquGcDLelK+Z2yOUr9cPYNWQCsQVgpo4A0rJa2Po4FXrb4aeO2jL2fRwGsfrXWGV63ONgFvq14TpcaSfV7NgXf73uOyaefE9tlic6tDK6AV+O8ooIFX7b3SwKtWXw289tFXA6/9dNbAq1Zrm4DXsBBm4/s/z1xB8UI5ES9OLLHCrV7+N5Pzltql6tG/dwXevf+IP05eRJlief1sjvre122v9bGF2J37j8VAg/XQ3ACXwSmFvab3N8/CVR548lR9/4TMmXyQLy/bk/1coYHXPvdblzSo11lneNVrzBk08KrV2Sbg5RLY9mri7DWm1kx8rVKpXzGwW2PZRa9DK3Dj9kNUaz4Qp3bOtamTgT0V45MIthxLl9rBntP6mYu18HFixZBWcrTrTpUiMbq0rPnN1vPF0wcv3n5RPj9dGAE2J/savv//n8rn/pYTaOC1j/oaeNXrrIFXvcYaeNVrbBPwPnn2Cl88PEADhAePnwv0OiRJiDixY6hfoZ7hP6PA9wy87Mu7eGo/5M+V8ZvoyXZo+Su2w+bFowS6vwfgPXXBAx8+2Zc+w4cD4sX1Rbx49p33W9x0Dbz2UV0Dr3qdNfCq11gDr3qNbQJeNvXnBjU2t9ehFQhIAUvgPf/3TYyZvgI929UV0KQbGp8S3Lr3CGWK5kF959LIljG1DFe/w0i0aVQZR/+8KO5tdGijqxjNOPjZo3FFx+bOKFc8nxxPM4ip89bJWOw84VyhiDjJWQsaSNB0wiFpQsmwOlcsgnrVSga4ntdv3qN9/6ng9TDY8aJ/54am8gOuteiv2bHn0Ck8ePQczhV+k42ck+e6gS382MGkS4safn4Q0qVu1cZ9YhnMMAde9skdPX0FPn3+Itft6emN6QvWSyu5uLFjom61EqhRsZj02922xx3nLt9AjixOYA094blE4VwYMWWpOLoxShTKaXry8vmLBya7rsWugyfx+YunnDewSyNpUWevGl7zexIlMtC4oTdSOGjg1X+ThI0CGnjDRsfARtHAq15jDbzqNbYJeOnSdfLcVenGoEMrYAvw3nv4RCyCu7Ssgca1yprsj/k4v0iB7Nh98JRY8O53myJGFszAMhrWKINkSeKjXPH8OPDHX0ib2kHMTQ4dPyeA677VBZEjR0Secm3QtnEVKau5c/8JTpy5jIFdG1td2vXbD1C9+SD06VgfmdOlRJJE8eSxOu2Yra2HRhk0WcmdNR0iRYqIRat3CFgbn3+uNX0aB7RrUk0e1RNeWY/bq10dOCZPjIHjF6BTc2fpamIEtaharhBqVy7uB3g7Na8hDnp/XbiGZTMGIEG82Bg2aYlAf/e2tUWb4ZOXoH2TagLStKBm7TytgksXySMub44OiXD91gOxECY0D524WOrsaTfMHsxL3XZh1phuYuxx8NhZ/Jo7s1hQa+BV+13WGV61+hqja+BVr7MGXvUaa+BVr7FNwMt/fGu1HmqyqlW/LD3Df1EBI8PLLGbzbuPRsVl1sRFmzF6yGdv3HcfkoR3kz15e3qjXfgQ2LBjxtW9w8WZwHd/TZEvMY+jsdu3mPbFEfvr8NWYu2igZ0lQOSVCgUnupf21cq4zAphE79v+Jqzfumv7MrC87iFiWNAS1HlofX7hyE3fuPRLLXgLw5UNLZFxL2+K6bYejUulf0aR2OXmfWewXr99i3IA28me6E1Zs1Bfu21xM9e6EZMfkicAMLCF0+cyBSJQgjlgu5y3fRuCdRiIMWi0/ef4KM0Z2EeDdffgUVs4aJK53RtCKmg57T5+/wp7DpxErZjS4jOn21WJ6rztmjOoikP61nvZraOBV+y3TwKtWX2N0DbzqddbAq15jzqA3ranV2SbgpYXstPnr5ZFwhrT+d5XzH3Zz6FC7ZD3696qAAbz8LMSLExPblo6RDCmj35h52H/0jL+uBO2bVpOuBZYQyZrXdn2nCOyW/C2XZDL5OVw9e7BkNw2XP46dK2s6dGtdC3lzZMDaLQfAUgoj+nZqIJBpCbyBrSdxgrho3n2cgDIzoV88PKWUICDgbdF9PIoVyomm/wNewvQ/tx5g2ohOsgyXxZtw/9EzEwDzNQLv0T8vSD08yxhYksEwzFyYrTV3LyQMTxnWUYD3j1MXxeHOiJ0H/kSvEXOQO1t6ZErnKHNHiRxRfkCwzGHg2PmgNTjvS/3qJSUzTXtkDbxqv0kaeNXqq4HXPvpyFg289tFaA69anW0C3jnLtuDC37cCXMnkoe018Kq9T/+J0Q3gHdClodTM5s2eAWMHtJFH6ZNd3XDn/iPMHN3V6rVYAi/huMvgGX6yojzGAF4OwuzotZv3sdRtN06du4JDG6bLXNaC5y6c0kce5zMCW894l9VSUrBwch8ZjwDdoMPIAIGXfapZpmENeFmfW6JmN0wc3B6/5vk6twG8Dx89w28FssF12VascR0q9cxv3n1AoSodsW7eMGROn8rfpVgD3qpNB6B8yQLo0JQlFsCiNTtw8uwVAV4jHj15IWVJo6YtR//ODaTcYtJsT9x/YN+PFh3Ia9XwhkNyXcNrX+V/3Nl0hlf9vdXAq15jzqCBV63ONgGv2iXo0X8UBcw3rT159hJ12g6XzCUB+MzFf6Sml08DKpQqgDdvP4D9nQnFaVMn95fh5aa0lj0nYOPCkdId5Pf9JzB6+nIB3oTx44CbwLiZK3bMGFiz5cD/6ntnIWLECFblZBY2X66MaNWgMj5+/Iybdx8GuJ7dh07ioPs5zBnXXUovXJZsDrSkITDgpRXz4AkLsXfNZD8wbr5pbdysVdjw+xG4zR0qm8m4Vk8vb0wY3E5qelnW8deFfwSorQFvo06jkS6NA3q0qY0Hj55JDXDcODEEeFdu3Cu1vcyKM2vu3GIQerevhwolC+DqLS98+Oxt349fOCBGdF/EjK6B177C/7izaeBVf2818KrXWAOveo018KrX+KeZ4eadh6jabCBO75onHQUuXrkldbq0pWYPXNaijp25ytTLOaVDYriO7yEbvSwzvD4+3AzmIlDMKFk4Fw4cO4s1c4bIprOmXcdKbSyDQNe5RQ0UK5gjQK2ZMR42ebF0fOAGsE4tnANcT8SIEdF54HTJ8jKKFMgm3SMCKmnwB7xLt+DG7QdSgtBnpCtSOSY1ZV+NBbIEgdfPdbNWmX2C+aPAbe4w+fOwyUtw5MR50/Vwgx5rlpe47ZLOFfMm9jK9R6juN3quXJthZsFyDAI7s73MZjP4XtlieTG8d3MxBrn/rxfefvKy++czWlQgcmQNvHYX/gedUAOv+hurgVe9xhp41WusgVe9xnoGMwV8fX3x4tVbycTaYljy/OUbyYyyPZdlsJuCt7e31fesiU6QfPXmndhkG5u3AlvPv4+fI07smCE20WAWu1DVjti1agJSJEsU7M8BSzY4Rvx4sYJ0rmPpBMsWkiSKj4gRfvEzF9978fKtv3FmLviCB//a1/+MJQ2VK/nAIZkG3mB/IPQJVhXQwKv+g6GBV73GGnjVa6yBV73GeoafVAFmXvcd/QtDezT9LhXQm9bU3ha9aU2tvsboGnjV66yBV73GGnjVa6yBV73GegatwHepgAZetbdFA69afTXw2kdfzqKB1z5a601ranXWwKtWXz26VuC7VUADr9pbo4FXrb4aeO2jrwZe++msgVet1hp41eqrR/8OFWBdrPvpS9KpIKg45H5O2oOxD25YBc0l3r3/iHhxYwZZmxtWc1obRwOvSnUBDbxq9dXAax99NfDaT2cNvGq11sCrVl89eggVYB9fh6QJxGI4rMPoHnHp4GI/zmOW87DFV7n6vfHHlpnS0ov/bRl0cqtVuZi/9wrkyoShPZtJJwYj2O5sytx10jrMCHaA6NC0urQNGzxhkXSOsAx2pogbJ6bMkTVDanGbM8JwQSyYN4vJjKJk7e548uyVrNvY7McNcEWqd5EOGRcPLBaXtkMnvuD9e/tuHgsXHnBwABLGt++8Yf0ZsmU8Dby2qBT6Y3QNb+g1DGoEXdIQlEJh874G3rDRMaBRNPCq1VePHkIFaDqRMW1Kf+28Qjicn9NsBV4arly/9RBThnUQSCVw0uGMbdGMiB0rhkAk31s+c4A4wrE92IgpSyUrbBhtGM5ww3o1Q9mi+aTzw407D8UxLkWyhGjTqIoA75t379G9dW0/602WJAFoHWwA9+Kp/ZA/V0Y5hu3Mtu89DmvA27G5s0k/9vkdMnGRnGMA7xdPH7x46xEWkgZrDEKvL3lX/u/HDQ289rm3GnjV66yBV73GnEEDr1qdNfCq1fenHp1GFLTwLVM0L9ZuPYB37z+hTaPKYFb05NmrmLFwg/STJfwxDh8/j+Ub9qBmxaIYMnGx2OMmS5xATBVov3vz7r8YPW252OQ6pUyGTi1qSF9ZBs0b2M+XwOh+6jLqVy+FCiXzi6EDzRcePHqOjGkd0aNtbSkjYH/g3h3qYfWm/XJ+y/oVUadqCdP9YguzMvV6Yniv5uKiZgCvtRZj1t6jScaT568wY2QXsH1awcod0K9TAzSuVdbfZ4IlDuxbTOBlmzReq2UYczSsURp37j8W3R4+fo6y9XqhduXiePD4mZ8Mb+kiebBp5x84tGGaWBRXbNQXxQrmFC0M4P39gCdevvw20Bk1qi+yZ/dFjGjfZn57fDE18NpDZUADr3qdNfCq11gDr3qNNfCq1/inncHIpFYq9SuqlC0koLp4zU7sXDkeiRLElUfsg7o1RrVyhUUjOoxlzZgaDWuUQc/hs+GYPJE4tcWIHhVpUiZDhYZ9kCV9KjStU16sc+mAtn7+cDGeaN9vqhg1sAQiRxYnZMuYBrTuJXDTsKFgnsxwP30ZsWJGFwtfAi/NLAi59/99Ji5u7ttcTL2BudZew2fj4IZpAsgGcBLW2ceXESliBNStVtL0Hq192beX/Xvdth2SjC8h++yl66Ab2pFNM0znWvtQEHjPX76BssXy+Xm7VpVi4vjGDO/2ZWNRuUl/Maj4fd9x+Pj6IlaMaDhz6bof4B3SvSlmLd6EqmULwSFZIkybtw79OzcETTIM4P0WNbzGhSVKCDRu4I3YsTXw/rR/QYTRhWvgDSMhAxlGA696jTXwqtdYA696jX/aGayVDjDTSGgkyE6Z64Y/z1yRmtRb9x6hSpP+2LlygoCuZUkDe9q26T0J+9ymIOn/SgqqNh0g2Vdmagm8GZxSoFvrWia9CZkOyRKKnbF5WFtXkeqdMaJPC5QolEsOHTB2vpQuEJYZBvDmy5kR0aNFkdeiR40i9r/Ge6zHjRY1Kr54eEgG+9fcmTB+UDsc/fM8aCVs1Ay///BJsttG8Br4PwIvQT5vjgx+1tu2cVWpuSXwEspdFm8Cs+eE8t2rJ2Lr7mP+gHdoj2b44uGJkVOXInHCeGher4LAdsseEzTw2ukbqTO89hFaA696nTXwqtdYA696jTXwqtf4p53BGljSLpgbqQZ3b4J7D5+gQsO+kqVlHeqdB4/hMqab6GUJvNzMNXXeOhzdPNOk59BJi6XbAS18Cby5s6UTmDYiX4V2UkZQs1LRIIGXIN6peQ1ULFXAVILAbGpqx6R+gNfWkobXb96jTL1eUp5AcG7QYSQOrp8mdb3cAOe6bKuMu3nXUTSqWRa0DralpIHAyy4TzHYza06Yn71ks1XgLZw/Kyo16gdPLy/sXj0Jf124poHXjt9GDbz2EVsDr3qdNfCq11gDr3qNNfCq1/inncEa8LKDQO0qxdG+STXRhVnbeHFjYf/RMwKuzJKagNfJER2aVZc/H3Q/i04DpsN9qwtix4ourzGDmymdIwZ2bWwVeJ1bDEKB3JkFes0joMyzAbys+9208yhWzBpoOi24Nbw8kRDNTDbLHljD27VVTdmcZh4s4+AagwO8tGRes+UA2AmCQB4Q8BYrmAPMjPv4+EgG+cSZvzXw2vHbqIHXPmJr4FWvswZe9Rpr4FWvsQZe9Rr/tDMYYLlp0Sgkih8HG3cewWRXN2xcOFLKDxgH/jiDzoNmwCFpQiln4KN7xrwV23D6/DXpcsCMaLhwQNl6vVG/ekm0algZp89dlfNmj+0Ogp21DC8f/RMMx/RvLV0MTp+7hvcfPyFxgrhSw2velsw8w1u/w0jUqVJcYNUIW4CXNbtJEsbDuw+fsP/oX1JjzJZi2TKlwZK1uzBxzhopv6hcuiDixI6Bh4+eo/PA6ahUuqAJeK11aUiaOD6ev3xjKmkg8JpHYMBrfpwl8C5Y6YFHT77NxzNuHKBUCR/EjqVreL/NHfhxZtXAq/5eauBVr7EGXvUaa+BVr/FPO4MBvPHixJRWXYyRfVqgRsX/LzHw9PJGztIt/XUwuH3vEVj+8M+tB8iVNZ1kW9nFodeIOdIGjNGuSVV0blFD/pvAmyd7erRqUMmkN7sfjJq2DJt3/SGvRYsaBeMHtUXC+HFQr91wf8DLsdI7pQBrg0/ucDXV6vJcW4DX/EazX267plVNNcHs+rB1zzHMXLRReuQaUbxQTrRrUk020tnSh/f49tmySS0sgNfD69u0JTPWzo4UP3LoDK997q4GXvU6a+BVr7EGXvUaa+BVr/FPO4MBvOf3L5S60zixYuCXX8L70YMbr/hY37xDgvkBL169lc4KESP8Ii8THB8/e4l4cWJJKy9bwsPDE6/ffpBNW5bzW54/bf56cE6Cuapg3TE3riVMEOebOq2dueyBj599VF2mTeNGihgODsm/7RpsWmgIDtLAGwLRQnCKBt4QiBbMUzTwBlOwEB6u+/CGUDgbT9PAa6NQ+rDgK2CLwUPHAdzIFRdDezQN/gQKzmA3hWZ1yonz2Y8e37ItmaFt+vS+aFTXG/hayfJDhQZe+9xODbzqddbAq15jzqCBV63OGnjV6vtTj84yhsPHz/mphTUXxHjMz81XdBPTYV8FNPCq1VsDr1p9jdE18KrXWQOveo018KrXWAOveo31DFqB71IBDbxqb4sGXrX6auC1j76cRQOvfbTWGV61OmvgVauvHl0r8N0qoIFX7a3RwKtWXw289tFXA6/9dNbAq1ZrDbxq9dWj/0+Bz1888Ev48IgYMcJ3rQlbd7FtmWE4YSyW9cgRIvwiNsaWwdIMuppFixr5u742tjY7c/E6yhbLK+uc6OKBe/e/bfGsk5Mv6jjrGt7v+oPznS9OlzSov0E6w6teY86ggVetzhp41er7U47O9mEExE4tnE3XT5OI7JnSoE/H+t+1Jo07j0H5EvnRsEZp0zp9fHxRum4P0K6XPX/7jp4r7c/SpXaQY46fvoxWvSbi2JZZ0l/3ew3aHTfvPg6XDy2RJd5+4IUPn7y/6XLDhfdF3Dg/ZnsyneG1z0dLA696nTXwqtdYA696jTXwqtf4p5th5cZ92HXwJGjEYAT76kaNGlmMGb7nsAa8p85dlZ7AB9ZPk/ZoWYo3w+Kp/ZA/V0a5FLYYu/vgCTKkTfFN24wFpasl8N685433H72COs2u70eJ6ouYMX4MANbAa5+PjgZe9Tpr4FWvsQZe9Rpr4FWv8U81A8GvUadRYjRB8wXG0hn9MWPBBqRNnVxMJ7btccfhE+dBx7Cte9yRMa2jZIPZrYFQNmPhBsyb2MtUIsCM8fINezBvQi98+PgJE2avEaBm5MqaVswierWrixu3H2LguAXo17kBlq/fg6fPX2PW6K5o33+qvMfIkiEV+nduaHJ6u/fwqZhT0II3pUNiPHvxBt1a1/KT4eWYCeLFRvc2tTFlrhsWrt4hznDsK+xcsQjy5cyIAWPmY9XswdLnl05tJQrlxJ7DpwWE61UrKRbJHp6eaNNrEgZ1ayzuawyukW5rE4e0h2PyRDj65wVMnL0GN+/+i9zZ0oM9hMcNbCMlFuNmrYJj8sSgG5v7qcuoX70UXrx6g8Vrd4qZBQ0++Fr7ptUQLlw40Nhh2fo9WOq2S95Pn8ZBjDyMDO+sRV/w8N9vW9Jg+eWoUskXqRx/jL68Gnjt81efBl71OmvgVa+xBl71GmvgVa/xTzXDx09fMHWeG/48cwWDuzeRaye4dRk8A9kzOYmFrmGz27xeBfyWPxt2HvgTl6/dwfr5w8Fa3yLVuwgUVitXWM6nMUXWjKnRo20dDBg7H39d+AedmjsLoM5euhmRIkXEjJFdpIyClsGJE8ZFzYpFESVKZNSqXAybdh5F7qzp5LhFq3fg1r1HMpeXt7e4qtHEonXDyogUMQIGjl+AlvUrmYCX2dsCldpj69IxcEqZDNdvP0D15oOkNCNzupRIkige3r77iDpth4EGGxF++ZoB5rF0UGNdb++RrpgyrAOKFMgujnAE09H9Wsm1zV2+DXuPnJb1cF1VmvRH3WolUb1cYTx49By9R86R91g7zHOPnDiPcsXzI0cWJ2TLmEYsh1lbnCJZQtx/+NSP3fKO/X/K+R2bVUfRgjmw9/BpLFj1uwl4v4dNa5ZfjqaNvOGURmd4f6q/NEJ5sRp4QymgDadr4LVBpDA4RNfwhoGIgQyhgVetvj/l6NZKGmgwYQ68f5y6iAWTeos+LHeo3KQ/3Le6IHas6JJFJTCvnTvUBIE7V05AogRxkKdcG4zp39oEw7OXbsHVG3f9AK+lLTAthi9cuYk79x7h4tXbAsDMchKcm3QZg+3Lxpo2qf0fe2cBVlXW/eGfga3YNcY4jh1j52CL4qDYLbZig2KhjoGFICZ2J4qKNSqOHRg4xpgTFmO3oij9f9byu/d/uSBcYl8vsvbzPc83XM7Ze5/3XPBl3XXW0k9poGO37jmGzYsmaO+lfkoDybq+8FIrZGqJTINyfnNmM8fIgR25PfLAsXP4WjNmTIf6bez59RaWtUHXstn7EE7uXMDnhYSEokLjPpGEt0TRghyB1h237z3Ejb/v4/mrN1jtuR99ulije7sm/IdC7lzZMNOpHx+un9Igwqv2x1MivGr5amYX4VXPWYRXPWNaQYRXLWcRXrV8k+XscRVe+li/flt7HPZy5xxf/4dPYdVlNIve3t/P4N6DJ/CYbo//Hj1D086jIglqdMJ77ehq/kifBqUy0INamTNl4NQDqqZAKRUkvLsPnobznPXw279Ee5/0hZcetmvdzIJTMTQjrsI7bd56hIaFczc5iipbdnTkKHL+vDkwynkpTu6cj3Rp02C8y0qEhIbCZVz/LwpvpXLFOBqtGZTmQOkbDWpXROGCebHv8Fl0a2MJip5btByCYX3acpRbhNf4P4oivMZhLsKrnrMIr3rGIrzqGYvwqmec7FbY5H2YxYuinJoRU4RXX3jpnH4j3ZA9WxYcPnkR7pMGwaJ6OVC1BEovcPt1AFdLoBGb8Lp4bMbNf+5j5exRnF975cZtdB7ozMJ77a+76NB/MgtvhvTpeD5d4dVEns/uXcTCrCu8K91HoUal0vxSbBFeXeGl4ymtYMe+E5wHXLr499qILUWSt+4+yqJPI7oIr67wvnz9DnVaDcWqOaM5/5mG3ejZqF6xNAsvMS/1Y2FttQyJ8Br3R1GE1zi8RXjVcxbhVc9YhFc9YxFe9YyT3QoXr/6N/qPcsX+jC0smPdw1eNy8L6Y0RCe8R05d5HxUkkJKZ0iZ8nPElh4gu3TtH45yBn78hCXrdqNiuWKRUhp0I7weq71x1PcyFs90QGhoGDzW7NSmNFC0tab1IM7X7dyqEecAj5m+TPvQGj08Rw97afJtNTeSUgWqViyJPp2tERj4CQ8eP48xpUFfeCnvtm7rYTydz2ZXvkYa9ACdVZdRvB+KRu8/ch4+x85HSmnQFd537wNR03ogpo7uDcu6VXHhyl9wnLIYA7vbsPDSHx70wNoMp37IlcMcC1d7c8Rc89DagWPBePfetN6e3xeOQIH88tCaad0V096NCK/6+yPCq56xCK96xiK86hknuxVIJAc7zcXJc1f52i8cWIZRzou5MkG/rs2xZusB+Ppd40oMNJ6/fIN6bexxxGsOP3DG0c3QMFRo1BtjBndGt7aWWoZPnr/CLI/NuPWvP4r/UBDhEeFIlyYNZk2w4/zcjnaToSu8j5+94ioIFOWlQZFi2pdG+kgKSUhpUBWDtwEfWGTbt6jH+bWzJw7Slh/TbIKizpNmr+ZKFANsbdDg54po1y/yQ2u6Obw0f1h4BH7930N8NA9FsNOmMcOCaZ/FVzM0UV5q0lGvdgUsXOWNPetm4IdC+fihtcrli3MNYM2gihGU80yDHpSjlA2q1NCjQ1N+oK2voytXZqBRu2pZrkahufbgkHC8eBdsYu/Pb+OBNYIqEV7jvLVEeNVzFuFVz5hWkBxetZxFeNXyTdazkzymMTND+nRp4szh3KWb/NCV7x4PLl+mGSTTVAmBBqU40Ef4FcoW46hmTOPRkxd+l8acAAAgAElEQVTIap452m5oFCkOeP9RK9s0D0nxRLdVOLTFXRtd1p2fuqu9fhuAHNmyaPOFDb1ITWSWHtqrWaVMpNN0r4+6onUbMo3/YIiJ4YfAT6A58+WOWuOYSpM9efYK2bJm5jxh3bHvSAjevDEtwSxePAz58xlK0rSPE+E1zv0R4VXPWYRXPWMRXvWMRXjVM5YV4kGA8k9z58zGD3rpDsp//e3QGa6qQDm2FMXcsdIZuXJkjccqXz6FIqeUt9u+eb1EnZcmW+vlg007DkVK1dAsUtXKjmsLU/T3yOlLGNKrNexsWyT6HmhCqdKgBKt2UhFetXw1s4vwqucswquesQivesYivOoZywpxJEDRU6qgQA9i5c+bM9LZlNLgd+kWAj585LzUmpXLIFPG9HFc4eseTs0lKGpdvnTRKBuhlIMHj54hderU3JCDGmWoGiK8qsh+nleEVy1fEV7j8KVVRHiNw1pSGtRyFuFVy1dmFwImS0CEV+2tEeFVy1eE1zh8RXiNx1mEVy1rEV61fGV2IWCyBER41d4aEV61fEV4jcNXhNd4nEV41bIW4VXLV2b/ygSorFe1iqWQzTxzjDuhkmTUopda+KocVDosm3kmFP3+O5XLGDT3sg3BePzEoEONdpBlw3AULmRaD9LF9+JFeONLLm7nSQ5v3HjF52hJaYgPtbifI8Ibd2ZxOUOENy605NhEJ0Ctdkk2B/dqlehz04TUFU23RFh0i1C1h0YdhmPi8B7c0IJaAVPpr2JFCiT6nqi0mH63NN1FVK6tfzFBIeF4KWXJEv0eayYU4VWGNtLEIrzqOYvwqmdMK4jwquUswquWr8weC4Ho2hAnJjRDhNfv8i0Mn+SBI9vmwix1Kpbk1XPGRKm/mxj7ik14Va6tv/+L14MR+Mn0mjxQV+hs2SKQNUtiEP96c4jwGoe9CK96ziK86hmL8KpnLMKrnnGyWKHTQGfUrlIWh0/9wY0OmlvWwq8O3RESGop+jm4Yb9+NG0/QoM5q1AzCvl87bkhBDRzKlijC31s7fyw3T6DmEgePX0DmTOnR1roe+nW15vq7ew764vL1f/FTmaLcNYyisCMHdgR1d5uzbBs3pCiQLyc3q2jdrA7LK0VrqfrB/QdP0dGmAQb2aBmpri11b8uZ3RwO/dpxEwcqSUbdz6hDXKtmFnzOUd9LmLPUC7fvP0KlcsUxwcGWG1XQoGun/VHtXmpwQZ3PMqRLC9fFW+B3+SbMzFKjkUVlOA3tys0jsmTOgHcBgdwZrX6tChjSuzUK5s8d7dplSxaBy8LNWL/ASfs+otrDfbs05yYUMxdu4lSM2/ceRZmPTqA1XBd54o7/YzSuUxmdWjVCuZKfWU+bE4x79z93sDOlkSkT0LVzOPLlMT0ZjwsnEd640Ir/sSK88Wdn6JkivIaSSthxEuFNGL/YzhbhjY2QfN8gAiSWJK29O//CndPmLt8Gp6Fd0MrKgiUve9bM2ha9S9fvwe8nLmDdfCfMWbYV5y7eZIGkQTI5dvoyFtfh/dvj1Zt3mLFgk7bd75otB+C62JNLepFE5sudA2VLfg+rLqNZcFs3s8C9/56wFE927MnCSx3I7GxtuOnESOclcJ80EBbVy/N67z98RPVfBmD32ul83D93H6Blz/EYNagTShcrjLy5syMoKAQ2PcdxO+M6Ncpjw/bfQVFhn81uPCetQaNL68bInzcHGtSuxA0xqI5w707NuEHG8o17ObWCWJCE2vdtgx+LFID7kq2oXqkUX2t0az96+pIbcGi6o9E6Fi2HwHlUb9SrVSHG+TStikfYtefr9Tnqhx37T+DwVndulmGKD63R9ZHwdusiwmvQD54cBBFe9W8CEV71jGkFEV61nEV41fJNNrPrpw5QO12qlTvTqR8oT3fg2Dnw3e2BjBnTccteisq2sKwN/ZQG6npGzRdcJwxAs4bVmR9FMc9dvAHvVVNBwutz3A8bF47XdkCj9rtbdh/BCe/5Ubqe6e+LcmRzZjPn9Wl47z8Jaue7edEE7b3STyuYv3I7fjt0Fj6bXfmYl6/foU6roVg4fRjq16rIwrvEZQS3LaZx5sJ19HF0xb4NLihcIE+k94B+SsP2305gw/aDfG009NfWdJyLSXh1c4J151u0Zif2HjqD2RMH8tyhoWHoOGAKtq+YwjV+RXjV/nhKhFctX83sIrzqOYvwqmcswquesQivesbJYgV9sfTcdQSrPfezJFK7XMuOjujd6ReOgI5yXoqTO+dzq1t94aXuada2YyPJIqUuTHZfC7/9S1h4T/ldBbXl1QySWBou4/pHYR2diIeGhWs7uHUdPI2jwhQd1gx96Rwzfdln8Xbqpz2mQTsHjvh2atkwyoNxJJ0k6bRf/aEvvFRFwn2pl1amEyq8uvPRvg+fvIgSRQtGlu7uNqhdtawIr+KfTBFexYD/N70Ir3rOIrzqGYvwqmcswquecbJYQV8sJ8xahf8ePcOauWP4+qkl8I59Jzg3tnTx7zlFgcYm78PYd/gsf9xP4+27D6jVYhA8ptvzR/Y0KIK778hZluDohNdtyRacOHOF0xL0R0zCq5Hrs3sXcRthXeFd6T4KNSqV5pcoB9b3wjVtFPZD4CdUa2bHqRFN6lWLIrzHfC+DWiMf3zGPc4N1hyHCq7v2H3/+Dduh02NMadCN8OoK7+wlW3Hvv8dYMG1YtO/BmQuC4e9vejm8GTMBndqHI29uyeFNFr88EniRIrwJBGjA6SK8BkBKhEMkpSERIMYwhQivWr7JZnYSy0mOPfBLwxr88NZ4l5Wg3FF64IvGi1dvUbf1Z/GiqC+JLw162Kz/KHfs3+iCVKlS8oNi3YZMR6aM6bhM2Ou3AXCY6AHLulV5vuiE9+wfN9B7xCz86mCL5pa18fjZS/j6XeMH12ISXkpVePr8tTa3WHOzKGe2asWS6NPZGoGBn3Dj73ucokCCW6tKWazz8sGitbtwbPtc5MqRNcoatGfLjiNh3bgmBtja8ENldA5JfmzCq7+2mVkqTvGgPwDoQb39R86D0kU0fxDENB+xJZYUmbZqWJ3/mKDc6SrlS+DHIt/hwlXTrdKQPTuQzTxp1+OVCK9xfv2J8KrnLMKrnjGtIMKrlrMIr1q+yWZ2Ekt6MI0qLtDo3KohRg/uzJUVNKPfSDekTWMWKeJI6Q6DneayJNO4cGAZnjx7iWETFnBFBBoU6SVpoyjsmq0HWGaXuTpGYkuvUyRWM+xsW2BIr9bRCm9YeAQ/UEe5xLMnDopSfozSACbNXs3XQsJKNYIXr9vFkWYaGdKn4/00tKjEX0dX+ozyeMe5rGChpkEP41GlBRJUqq5AlSNY/o/5cXUGTX5wdGtTLq7Hmp1aFhRBXjTDgWsGxzYfRdXpoT/KjaZBOcVLXIaj0Hd58Ck4DK8Cgk3yPUpx56Stu4AIr3HeWiK86jmL8KpnLMKrnrEIr3rGyWIFjfT9UCg/0qY14/xc3fHufSBqWg/k3NuaVcpEYfI24APSmJlFKhdG5ctoLvPMGQ1iGBYWzg+UZc2SEWnSmMV4Dgn2RLdVOLTFXfvwm+4JNBdFanNky6J9EO5TUDBHqqlyg67Ix7QQ7YdYZMyQzqBroIOiW5vSKOihM/MshrHQXSwiIoK5UHk0XZaL1gTh0SPTS2lInwGwakIpDUlbeUV4DX7LJ+hAEd4E4TPoZBFegzAl+CCJ8CYYYYwTiPCq5ZtsZo+twcNaLx9s2nEI+zfOilYwjQ2Kau1SxLh983rGXtpk1pMqDWpvhQivWr6a2UV41XMW4VXPWCK86hmL8KpnnCxW2OVzmp/8139IS3PxJ8/9ydFFqp8rwzQIiPCqvQ8ivGr5ivAahy+tIsJrHNYS4VXLWYRXLV+ZXQiYLAERXrW3RoRXLV8RXuPwFeE1HmcRXrWsRXjV8pXZFRGgigNUKsyqwefmFDGNqzfvcKWE/HlyGnxObHMa+n3qqpbNPBOKfv+doack6nGHTv6Bn0oX5WoS+kOEN1FRR5lMhFctXxFe4/AV4TUeZxFetaxFeNXyTdazU4c1kk2qcpDYg+alrmHXjq6O0l1Ndy1q69uow3AucUZVJAw5JzH3ql82LDHnNmQuKmk2d8pgTjfRH/uPBuPt56IaJjXMUgHFSoQjZzZ5aM2kboyJbkZyeNXfGElpUM+YVhDhVctZhFct32Q9u34XtcSEYajw+l2+heGTPHBk21zc+ue+CK/OTQgOCceLd0GJeVtkLh0CEuE1zttBhFc9ZxFe9YxFeNUzFuFVzzhJr/Dv3YegFrWN61TBlt1HEPD+I/p1tea2uucv3QI1b6CauBnSp+XrpKju+u0HMW5oN9gOnca1bMuWKMLfWzt/LIKCQzDLYzMOHr+AzJnSo611PZ6PynztOeiLy9f/5QYL1E64WJECcBzQAdSqd+OO3/Hg8QuU/LEQhvdvx8dTtHbkwI7Y7H2Y5+/dqRnat6gfife4mSv4QTqHfu042kznUMTZa88xvpb+3Zpra+J+/BSMeSu24bdDZ5DNPDM62NRH62Z1uVQa7e342Sv84N3ug768D5qnesVSvN7jpy/hungL/C7f5PJfjSwqw2loV66TmyVzBrwLCASlN9SvVQFDerdGwfy5+bxOA51Rp0Z5HDzmx9fXyupnNG9cC7OXbsX1v+6huWUtDO3VGlnNM/H5U9zX4vGzV3wuzTXOvhvvie4TXeuYIZ2xfttBUEk36l6nG+Gl0mRjpy9Drapl0aN9U+w/GoQ370zv7Zk6dQRKFAdyZpcIr+ndHdPbkQiv+nsiwquesQivesYivOoZJ+kVNJJIHdRIvs5duonVnvu5M1runNlg0XIoxtt3g02T2nyd1CmsbMkisLO1wZxlW3Hu4k1McLDl71HzBRKuW//6Y3j/9nj15h03RaAOZF1aN+Iuaq6LPbmSAwljvtw5EBYWxsI9tHcb1KxcGr4XriNL5owoV7IIy2uD2hVZcv979Jw7kPnu8dDWmn3/4SOq/zKAWw4XLZxfK7yaa6EObdSwQtP5bZLbGtz85z4c+rfjNInJs9dw4wm6bs3eena0ws/VymH/kXMspNuWT0ZISChseo5jHiTdlEaxfONeFk4SXhJV+75t8GORAnBfshXVK5Xi66dB5dyK/1CAeVGrheGTFnFjC0e79twcgppXDO7ZCq2b1cG1v+7inzsPUKpYYXz8FISJrqu5KQfNpblPeXJlQ5tmdZAuXVrei0Z46Z70GDYDRQrlw6wJdvwHg+Twqv3RlAivWr6a2UV41XMW4VXPWIRXPWMRXvWMk/QK0aUONOs6miO8rawsuEsYSe2WpRNxx/8xmtuO5Vq7hb7LDf2UBur2RQLmOmEAmjX8/LDZzIWbcO7iDXivmspS6XPcDxsXjtfW6u06eBoK5M/Fnc10R3T7smg5BFNG9UL9WhX5UO/9J7F1zzFsXjSBv47pWprWr44qTfth3LBuqFj2Rz6eupQ9ffEa852HRmlpfNf/Maxtx8J3t4e29fC+DS7cyUx36OfwUrR6w/aDfL0a4SUxrli2GH/dof9k/NKoBmzbNeGvqXvcyzfvtNf//OUbXLz6D569eM1RcooeU5thzbWd37ckUpML4j1tTB9ubZw9W2buLGeW+nP3OxFetT+aIrxq+YrwGocvrSLCaxzWksOrlrMIr1q+SX726CSRcmLpI3+K3Po/fAqrLqM50klpCPcePGEBo6EvvBpJ1BVDOmey+1r47V8SRSppDhK2MYM7o80vdWIVXhLxwT1ba2WaZLl1MwuOjn5JeDXX0rVNYxZYip7qdonLnTMr3CcNirI3Shmo39Yeh73ccfr8NRZ3ugb9oS+8PsfOw32pl7aVsH7DDoqQ161VAd3/J7zUVvjvOw/4wTOKKjtOWcyR8lLFCvHr6dKaYYnLiGhlXsOP/p/+2KCoPEWNNUOEV+2PpwivWr4ivMbhK8JrPM4ivGpZi/Cq5ZvkZ49OeBu0c0C75vX4434a/Ua6IXu2LDh88iLLoUX1cvz6Ju/D2Hf4LH+0T4NKidVqMYiFmD6Kp7FwlTf2HTkLkmCK8J7yu8rthzWjVa/xqF6pNEuv7vhStFYjvBq5Prt3EXdU+5LwNuk0EjZNf+aUilrNB8Fr2SSULv59lPumvzdd4b31jz8GOc3F8R3zojTeiKvw9nF0hUX18tEKb4vuTmjaoDoGdv/MfZXnPpy/dDNW4aWUDMoxvv/gCTZ5TOB8YBoivGp/PEV41fIV4TUOXxFe43EW4VXLWoRXLd8kP7tGLOkj+Nw5smLH/hOYvWQrdqx0RomiBfn6jpy6iCHj56NAvlyRWgdfvPo3+o9y58hiqlQpkTVLJnQbMh2ZMqbjMmGv3wbAYaIHLOtWxQi79tEKr8dqb3juOoLpY/uiZpUyuHD5L7wP/Ig8ObNFqbigG+Glh+mePn/NH+drhuZaKOWCIrd7fvcF5e1SOgY9WEfR1ZDQMM5xpQfd/rrtjz/+/JvlMybhTZvGDJYdR8K6cU3+I4Bq/lIKAeUmJ6bwUsS62A8FMLxfOzx4/Jz3ni1rpliFl6LDlDLRe8QsRrFy9ih+yHDJ2iA8epzC5N6jGTIAlpYRyJsr3OT2FpcNifDGhVb8j5Uc3vizM/RMSWkwlFTCjhPhTRi/2M4W4Y2NUDL/vkYSqYYtVVyg4TyqlzZNgL4mSazQqDdHYbu1tdQSCw0Lw2CnuTh57iq/duHAMjx59hLDJizA7fuP+DWK9FJ+LkVh6QEyX79rXPVBM6hywtS567DzwCl+iR7ochnfnxspdLSbHKkOLwnvkF6t0bhuFdRvY8/5qtUqlvx/4b11l8+hOegjfhqjB3XS5suSIE+avQYnzl7RnkNVHOiBOf29US5tvTb2OOI1B/Sg2JkL1/kBM5qDBqUdrF/gxMJbuXxxbSUIn2N+nPdMD8rR0E9piBLhXbsL/959wJHz037XMGbaUr4PdA30BwdxWzzTAVf/d236dYkpJWS+8xD+Y+HN2/foPMiZ86s9pjsgNBx4FRBsku9wrs8QIVUaTPLmmNimRHjV3xARXvWMaQURXrWcRXjV8k3ys2uE98rhlZySQFFaitbqDqrcQNFR3QoJut9/G/ABaczMuLyXZlBKQNq0ZtqKCrGBCg4OwZt3H5AjW5Yo6+ufS4I90W0VDm1x1z78pnsMCfrT56+QI5t5pD1pjvkUFMzXmiN7Fq5mEJdBpb8oBzhjhnRxOc3gY+mPCEpPyJs7h/bhM4NP1jvwwrVgBH403Sgqoc+fNwJpP1e8S3JDIrzGuWUivOo5i/CqZyzCq56xCK96xkl6BUMaPFD+KpXkmji8u0lc68rN+zjy2b55PZPYj6luYsa8YNz3N72UBg2vvHmBLp3CkDF90oz0ivAa550vwqueswivesYivOoZi/CqZ5ykV6CPz4+fucwlyKIbYWHh2H3wNDdgyJ83Z5K+1uS2eVN9aE1zH/LlBWy7ifAmt/dlXK9XhDeuxOJ+vAhv3JnF5wxJaYgPNcPPEeE1nJUcKQS+KQIivGpvp0R41fLVzC7Cq56zCK96xhLhVc9YhFc9Y1lBCJgkARFetbdFhFctXxFe4/ClVUR4jcNaIrxqOYvwquUrsyskQA+W+V64BqsGn7u2xTSO+V7m+rpUjiy5DOp89+Ll20iVKnSv3dRzePPlATp3lpSG5PJ+je91SoQ3vuQMP0+E13BWCTlShDch9GI/V4Q3dkZyRAII0ANkBfLlRJN61RIwS/SnGvJAHZ1JNWupwcSpXQu4Q5wpjdHTlnLJsmJFCiT6tqJr5KG7yB/XTb9KQ748UqUh0d8Y39iEIrzqb6gIr3rGtIIIr1rOIrxq+Sb72YdOmI+SPxbWdgdLTCCGCu/idbvwz52HcJ80MDGXT5S5qA7v6jljvhiFTcgisQnvp5Bwk63Dq7nuiPAIpDDdQhIx3h5JaUjIu9fwc0V4DWcV3yNFeONLLm7nifDGjVdcjxbhjSuxZHj8v3cfYsz0ZWhcpwq27D6CgPcf0a+rNfp2scb5S7dAXc2oWQR176Jx/MwVrN9+EG2a1cGvrquRLq0Z8ufJyV3Cpo7uzU0nps1dD6rfW7Rwfgzu1RqWdavwuTMXbkKh7/LgbcB7+PpdR6eWDWHVoBq2/3YCG3f8jgePX6Dkj4UwvH87rpHbccAUjBzYEZu9D/P5vTs1Q/sW9bV3iapINO44ApMde3LLXhoXrvwF10WeoI/8G9epjE6tGqFcySLYc9AXW3YfhdvEAcibKztCQkIxYsoiVCpbHD06NMWjJy8wY8FGnL14Ez+VKYp21vXQpF5VnpMaZCxasxMHj/txU4uqFUpi7JAuOHbmMvwfPONOcjQeP3sF+wkLsNJ9FJZt2IPPEfBcXN+4VTMLdLRpgKO+lzBnqRdzogYWExxsUfyHzxHgTgOdmT3VGr75z33mSWyjW/u3Q2ex99AZ/FS6KHYf9GVug3u14ooaNBavDcLjJ6ZrkzlzAM2ahiODlCVLhr91DL9kEV7DWcX3SBHe+JKL23kivHHjFdejRXjjSiwZHq+JpP7SsAaaW9ZiUV3tuZ9bBlP9XYuWQzHevhtsmtRmOtSEomzJIujSujFGTF7Enb2orFmmjOnxQ+H8sOoyCmWKf4/u7Zvi/KWb8FizE9uWT0apYoW5Mxl1OqMUCJLKciV/wMPHz1m4qeNZzcql4XvhOrJkzsiSSsLboHZFltz/Hj3HtHnrIzXAoL06Tl6Eo9vnsiD7P3zG65OAkgD7HPXjdsmHt7ojNCwcPe1ncmvgFbNHYv6K7aDcX88lv8LMzAw2PZxQocyP3E3urv8TjHRejIOebvgub05MmLUKp/2ucqe3wgXysKCTvJ48fxW3/r2P+c5DmY3/w6ew6jKa9/jsxWu07DkeowZ1QulihZE3d3YEBYXApuc4/mOiTo3y2LD9d/hdvgWfzW78BwVFhGkQ2/x5czAnkt3o1qa2yK6LPdGzoxV+rlYO+4+cw/W/7jFrGvLQmtofZonwquWrmV2EVz1nEV71jGkFEV61nEV41fL9JmaPLnWA2viSlJHIUqvccxdvYsvSiRw1bW47Fvs3zmLR1U9poPa4/Ua64dBWd+TLnZ35tOjuxPJJkVoSXmqZa9+3rZZd18HTUCB/Lm5BrDui25dFyyGYMqoX6teqyIc6zVjOIkmyTIPkkKKesyd+Tm8IDQ1jad6+YgpHQKk1sLXtWE4xoOj11qUTUaRQPpy9eAO9h8/C2nljtV3UJrmtgU3Tn5lBlab9ONqqX6940dpdXxRe88wZWWB1UxooWk6RWU3rYercVqfVUCycPoyviY5f4jICFtXL8f4psvyltfVTGu76P+Zr893tAfMsGUV4Ff90ivAqBvy/6UV41XMW4VXPWIRXPWMRXvWMk/wK0Ynl8Eke/AAYfdyuiVpS5HDv72dw78ETeEy35+vWF94d+05gzjIvnNy5QMtlottqBLwPhPukQSy8lcoVY5nWjKpWdhgzuDPa/FInVuElER/cszWaNayOd+8DUdN6IPaum8HSSoMixYdPXmSp1h0DutugdtWy/BLtkSK2Dv3a8QNluq9VLFss0nn1a1fkCDOJpO46moPiKry0Pxq6ct+gnQPzoPQOEt4NC8dBsw+NxEa3tr7wUjvn+m3tcdjLnVM2JMKr9kdThFctX83sIrzqOYvwqmcswquesQivesZJfoXohJckrF3zehhga8PXR1Hb7NmysEySuGoikCy8RQthYI+WfBzlpw52mqeNMtJrFMEtVawQxg3rFq3wtuo1HtUrlWbp1R1fijxrhJfSCrz3n2RB1IzZS7bi3n+PsWDasGjvS+DHIHS0m4zQsDCWcO9VU5EzuznnJTtOWYwzez04NUJ3vA34gFrNB2Ge8xA0sqgc6XtL1+/B5ev/YvFMB35dN6VBE+GlfN4alUrz9ym3mEqt0bo0PgR+QrVmdvzAHaUv6AtvTGuL8H7dHz0RXuPwF+FVz1mEVz1jEV71jEV41TNO8itoxJIkLHeOrJzzSuK4Y6WzNlJ65NRFDBk/nx/AonSGlCk/PwxFD2bRQ2IkmCRv9MS9ZceR6NSyAfp0scaFy7f4vEUzHFC35k/RCq/Ham947jqC6WP7omaVMrhw+S+8D/yIPDmzcTrCtaOrkeJ/j/LrRnjpAa/2zetFSjO4ePVvdBsynSOoVg2rg2r5/n7iAqqUL4Efi3zHEeCHj19guZsj+jq6wSx1Kixzc+S9N2o/guey7/s5PcLv8l8ICQ1lySVppz2MG9YV3xfMy2kJFcoUxcvXARjkNJdTJlKlSokVm37D1t1HtXnGlO9ctWJJ9OlsjcDAT7jx9z30cXRlwa1VpSzWefmAosTHts9FrhxZowiv5g+G6NamB9tO+V3FCreRvF/9CO++w0F48850354Z0gPlykcgfdoI091kDDsT4TXObRPhVc9ZhFc9YxFe9YxFeNUzTvIraIQ3e9bMePUmgK/HeVQvtG72/ykGIaFhqNCoN0dh6aEuzaCP3Cn94e87D/hjeIq2aqKlVM2Ahp1tC37YiwalNFQuX1ybSkCvUZ7q1LnrsPPAKT4mQ/p0cBnfnwWQorH6wktzFS9akHODz+9bos251eyJUhZmLNjE1RRo0ENmS1yGc86ui8dm7F47nfOLqaICzUEPfQ3sboNL1/7BuJkrcP/BU+0+SJwbWlTih+EoX5iOoUHiv9xtJPLlyQH7Xxfww280qKqDzzE/rfBSRHzS7NXMlaLlVEWByqgtXOUdZQ16QT/CS699ae0jpy/C1+8aV9Cg8fzlG9RrY48jXnOQJ1c2BIeE4cW7IBN/f5puFYnYwInwxkYocb4vwps4HGOaRYRXPWNaQR5aU8tZhFct329ido3wXjm8kiOiVEKLopW6g6ohULSSqg/QR/X6gx6+osoKFDGlQeXCnjx/hexZsyB9ujQGcQoODsGbdx+QI1uWKOvrTzB3+Ucm3w4AACAASURBVDbQmiTm0Y2IiAj+vplZ6mj3G9OGKI2ASpbRPjSRZc3x7z98RHBIKOiPA91Ba5GoR3etxOL124BI830KCsaLV2/5gTv9FIov7e1La3/p+APHgvH2898vJj2yZIlA+bLhSGrqK8JrnLeVCK96ziK86hmL8KpnLMKrnnGSX8GQBg/0sT2VKJs4vLtJXO/wSYvQo30TlC9d1CT2Y4qbMPWH1jTMKpQPRyubsCh/XJgiU909ifAa5w6J8KrnLMKrnrEIr3rGIrzqGSf5Fejj9uNnLkcpuaW5MIpQ7j54mhsa5M+bM8lfb3K5ABFetXdahFctX83sIrzqOYvwqmcswquesQivesayghAwSQIivGpviwivWr4ivMbhS6uI8BqHteTwquUswquWr8wuBEyWgAiv2lsjwquWrwivcfiK8BqPswivWtYivGr5yuwmQoBKo2Uzz4Si338X444ePnmBe/890TahMOb2qdJFWFgY0qU17CE+Op5aM798/ZZLo9FDcXEZi9YE49Fj038UrFjRMDRqEC45vHG5ucnoWElpUH+zJcKrnjGtIMKrlrMIr1q+MruBBB48fs4timdNsDO4KoGBU/Nh0XVwi+586rBGVShG2LXHys37UCBfTm74kJBBZdjowT8qORbToFJkh0/9oW06EdOx1BjDsqMjMmVIz2XVxg7pEuf86dv+4QgIDEnIpRnl3BQpIpAzRwTXcE5KQyK8xrlbIrzqOYvwqmcswquesQivesayggEEbv5zH237TsTl31dwqbDEHoYIr6ar2c7VU1GsSIEobZHju6eNOw7hwNHzWL/AKcYpqDFEwPsPsUahaRK/y7cwcOxcnN27KNYSbV9a1O/PYAR+Co/vZRn9vIwZgPz5kk4TChFe47xFRHjVcxbhVc9YhFc9YxFe9YyT1QozF27i671z/xFO+13jZhPUIa3Qd7m5NW+1CiXRvkV9PoZq4Q4eNw82TX7mjmwkvaWKFUaqlCnhNKwrypYoglWe+7B552EEvP/IDR7GDu4C8ywZ8e/dh9wEYsyQzli/7SB3EaOmFo+fvoTr4i3wu3yTxZk+6nca2pUjvFkyZ8C7gEDu/Fa/VgUM6d0aBfPn1t6fXT6nsWnHIWxZOhE+x85jvMsqpEtrhvx5cqLYDwUwdXRvPHryAjMWbMTZizfxU5miaGddj5tJ0Dj7xw3MWeaFO/6PkSuHOVe1sKxbFV0HT+XGEnQ9NNbOHxtt2sJvh8/ijz//xq8Otnx91PXNunFNbPY+zOf17tSM2dEeug6ZhqfPX/OcZUoWQeeWDaPwWDhtGAaMncNz0ShT4nuOBJcoWpC/dlkQBP//kk7YtKllOCpXCEdSCfWK8BrnV58Ir3rOIrzqGYvwqmcswquecbJagcTy2q07GNyzFbKaZ4LH6p0oV+oHTBvTB2u2HuBWub97zuaoJMmd7dDpOLXrcyey8S4ruQ1u6tSpuFPaweN+mOXhiZEDO3Lns3krtiN/3hyY7zyUUwSorTB1DGvTrA7SpUsL27aWsOk5jusBkxyGh0dg+ca9LMK0LxJdagv8Y5ECcF+yFdUrlcLw/u2194daDrdoUosllmRyxORFLOokrpkypufzbHo4oUKZH7mb3F3/JxjpvBgHPd24aUTlJv3Qv1tz/NKwBu799xRnL16HQ7/2mLNsK85dvIkJDra8VqVyxaONyhIb4rBqzmjt9TWoXZEl979HzzFt3npu7JHGzAwLVm7H4VMXWcIzZ8rAjTD0ebS1rgvv/SdRqWwxpEljhlWb97GMb1s+mfeRVB5a09ygFr+EoUolEd5k9QvFgIsV4TUAUgIPEeFNIEADT5ccXgNBxfMwEd54gpPToiegnzpAkdKpc9fjhPd8vHn3Hj/bDMESlxGwqF4Oo6cthVnq1Cxt0aU0dBrojJI/FtI2szh08g8Mm7AAvrs94P/wKQuebuvgMxeuo4+jK/ZtcOG8Vt2hv6/tv53Ahu0Htfmy1C64WdfRkTrFDZ0wHyV/LMxthWmcvXgDvYfPwtp5Y7Xtiie5rYFN05/RwrIWqv8yAEN7t0G3to0jPUCmn9JA0d41W/Zrt0fNMSgSHZ3w6rZNtmg5BFNG9UL9WhWxbe9xeO05xtFoGpo/APRbKVNb5j9v3sY9/8e4eusuC/D1Y2tEeI3wAywRXiNABiDCq56zCK96xrSCCK9aziK8avkmu9n1xfLvOw/Qqtd4HN1GndiywmnGcrwP/IhfHbqjbuthLGz0sXx0wkuCRxFYirDSoHSFRh1GYMdKZ1CbYRJeXSEkiaWUCr/9S6Jwj07E3Zd6wWezKx/rsdob/z1+jplO/bTn6gvvjn0nQA+1UZqG7qhfuyJHlDd5H+YoLA06xr5vW1T5qQT0hff5yzec+qAZVSuU5GuMTXhJyAf3bI1mDat/UXh1eVAqQ0+HmRwBpjWCgkOw56CvCK+RfipFeI0DWoRXPWcRXvWMRXjVMxbhVc84Wa2gL5YkWJSLeungcv5Y/dK1f9B18DR0tGmAa7fuaiOUt/71R5s+v+LiweVIm8aMmZEo165WDo52HfhrTQSX5Pnp81dRhJfSAajF8fEd85Azu3kk7jEJL1U8qN/GHq4TBqBG5dKRhbdoIQzs0ZJfo2oLlId8Zq/HFytJfAoKxl+3/8ParT6cR3xs+zxs2X0U+w6f5dSKmEZiC6+Lx2b+Q2Ll7FGcQnHlxm10HugcSXjv+yedHN7mzSSlIVn9MjHwYkV4DQSVgMNEeBMALw6nSoQ3DrDicagIbzygySlfJkBiSZFcktR/7z3EzAWb8F2+nHCfNEh7EoksRX5dxvXnh7Jo0EfvVZr24/zV8qWK8gNtlHO6Y/8JzJ08GHlyZcfUuevw+NkreC2bxLKsH+F9/TYAlh1H8pwDbG04F5gkkiKtMQkvPVw3YdZKbW6xZqP0IB3l/S6YNgxUwYGksVH7ERyNpVxgGn6X/0JIaCjKFP8e9NBbB5v6MM+cCZ67jnAU13f3Qly9dQf9R7lj/0YXnoPKnqWIpsZWYgsvRa2P+l7G4pkOCA0Ng8eanZFSGi5eT1pVGjKkj0DeyJkqJv2jKBFe49weEV71nEV41TOmFUR41XIW4VXLN9nNrnk4LPDjJ7726hVLcW1d3Ygr1bddsm43P6ymiebSsVSHdvG6XXwePbxGVRCcZqzA7ycu8GuUl7tg6lAu20X5qB3tJkdKaaBjKAo8zmUFP3RGgx4Qo3JgtK/K5YujT+df+HWfY35c95dSGkY5L8H3hfJpc3U1N+2u/2MMn+TBck4pChShpQg1VYegnF8a1OyB0iDKl/4B3YfN0L5O1SaG9GqNujV/AkWQBzvNxclzV/mcCweWIX26qM0lqNrE0dOXPj+0Fs31UUoDzWnVoDoofWPr7qP/n8MbzfH0x8GQcfM4ykuD8qZpD5oc3qCQcLx8F5yE36OmXaJMhNc4by0RXvWcRXjVMxbhVc9YhFc942S1giaS2qV1Yxa9LJkyRLl+qoZQrWJJljf9QZHe4JAQmGfOqP3W24AP+PQpmCsyGDpevn7Hpb8yZoi5+9jbdx9Qq8UgHNg0K1KJMt11aK4smTPCLHWqSHuiyghUnUE3WvvufSB3S8tmnjnKVuk6qMJCdLJr6HXF5zgqY5bVPDMypE8b6fRl64Px5El8Zvz652TKFIGGDSKQK6fpSq8Ir3HeJyK86jmL8KpnLMKrnrEIr3rGyWqF2Bo8XPvrLjr0n4xDW2YjX54cX50NpTNQ9YeJw7t/9b0YewNJrSyZLp+s5hHo2jkcuXOJ8Br7fWNq64nwqr8jIrzqGYvwqmcswquecbJagQSS0hc0zQ30L54+Xn/05CU3kZDxdQmI8KrlLxFetXw1s4vwqucswquesQivesYivOoZywpCwCQJiPCqvS0ivGr5ivAahy+tIsJrHNby0JpaziK8avnK7EIgEgHK4/X1u4am9atFW6khOlyU13zM9xIa1anCecRUg5gGlXlLyBDhTQi92M8V4Y2dUWIcIRHexKAY8xwivOoZS4RXPWMRXvWMZQUhoCVw/a97aN9/Eq4cXvnFWr76uKiW8VovH21L4OGTFvEh7pMGJojsb0eC8eaN6ebAxnRxadIAZUuFwzxrghAoPVmEVyle7eQivOo5i/CqZyzCq56xCK96xrKCEEiQ8Pawnwmr+tXQwaYBz/Pfo2f8/wXz504Q2aCQsCRdloxLGUfw/0xyiPAa57aI8KrnLMKrnrEIr3rGIrzqGcsKJkog4H0gZi3yxIGj53mHFcv+iOJFC2o7ux31vYQ5S71w+/4jruc7wcEWxX8owMfSa9Pmrse5SzdRtHB+DO7VGpZ1q/D3YppXP8JLJcNmLNiIsxdvct3hdtb10KReVS0x/4fPYNVlFHx3e8A8S0Z0GuiM8LBwNKpTGX27WOPN2/cYMHYOqI0wjTIlvsfYIV34oUGKDFOXN7eJA5A3V3ZQGbURUxahUtni6NGhKXyOBePdexO9OQZuK336CJQvG4bUqU2vY5wIr4E3MYGHifAmEKABp4vwGgApEQ6RHN5EgBjDFCK8avnK7CZMwGnGcvzx598Y3LMVN7VYtHYn58XOdx7KAmnTcxxLZZ0a5bFh++/wu3wLPpvduFsaSSh1V+vevinOX7rJXcy2LZ8MajgR07y6whsRAdj0cEKFMj+iW1tL3PV/gpHOi3HQ0w3f5c3J5Bat3YV7/o+5eQcN2u8qz33cee5XB1tQTrD3/pOoVLYY7526093xf8x7CQkNQ0/7mdxxbsXskZi/Yjuo/bLnkl+5YUZSzuHVvK0KFIhAz25hMEtYOrOSd6kIrxKsUSYV4VXPWYRXPWNaQYRXLWcRXrV8ZXYTJfApKBiVm/TD9LF9YdOktlYub/17n4V3/srt+O3QWe7ERoOaT9RpNRQLpw/j5hH9Rrrh0FZ35Mudnb/forsTLKqXx5DerWOcV1d4qW1x7+GzsHbeWG2DjElua2DT9Gd0btUQYWHhqNdmGGaO64/aVctqSTrPWccf45Pw0qCH2v68eZvFmDq0kQBruqlRxzlr27Hc6OP8pVvYunQiihTKx+eJ8Kp9c4rwquWrmV2EVz1nEV71jEV41TMW4VXPWFYwQQKUB9u08yjsXTdDK4AUTdUI75jpy3jX1DZYMxq0c+CIL7VDnrPMCyd3LtB+b6Lbak5lcOjXLsZ5dYV3t89pTJi1itsW6476tSuid6dmOPvHDY74Ht0+N9IDbrrCS5Hong4zkTlTBlStUBJBwSGcyqARXpp3x74TvA7tTdNaWYRX/ZtShFc9Y1pBhFc9ZxFe9YxFeNUzFuFVz1hWMEEC4eERqP7LALj9OgB1a/7EO9QVXtdFnvC9cA3eq6by9z4EfkK1ZnZcGYFSBwY7zdPm1dL3uw6ehlLFCmHskK4xzqsrvKfPX4PjlMU4s9cj2ooNo6ctRaH8uTGoZ6tIBHWF18VjM6iZx8rZozjV4sqN2+g80FkrvIEfg9DRbjK3eSYhp+uhxiAivOrflCK86hmL8BqHsQivcThLSoNaziK8avnK7CZMYNzMFbh07R+O2gZ+/IQl63ajYrlinNJw5sJ19HF0ZcGtVaUs1nn5sBAfo2hr6lSw7DgSnVo2QJ8u1rhw+RaGjJ+PRTMcWJ5jmldXeEmiG7UfgVZWFrDv24ZJ+V3+CyGhoRytrdV8EPZvdEGh7/J8UXg9VnvjqO9lLJ7pgNDQMM4l1k1poEj1w8cvsNzNEX0d3biO7zI3RxbshSuD8fCR6T3sFZe3TN68EWjZXHJ448LsWztWIrzq76gIr3rGEuFVz1iEVz1jWcFECTx5/gqzPDbj1r/+KP5DQYRHhCNdmjTaB8QWr9uFhau8eff0kBelN2haIh8/c4WjsyTKNOxsW2BIr9b83zHNe+Pve2jX7//r8JJwkyDff/A00jqv3r7Dbh9frF/gFIXelDnr+DXK4X387BWGjJvHUV4aFtXL4eS5qxzh3bb3OCgCvHvtdM41pmMp17hnRysM7G6DO/7heB8YaqJ3x8BtpQBy5ggDTNDbJcJr4D1M4GEivAkEaMDpIrwGQEqEQyTCmwgQY5hChFctX5ndhAnQx/wU6aRBKQ52o2ejQtliLIOaQQ+3vXj1FnlzZ4+SdkAPlZHcZs+aBenTpdGeY8i8+lio2gKVDcuRLQt3YOvQfzK6tmmM5pa1ohAc5DSX835183GpvFlW88zIkD6twcTvPAjBh49hBh9vygemTgVkMQ9HChMyXxFe47xjRHjVcxbhVc+YVhDhVctZhFctX5ndhAms2PQbfjt0hh9au+v/mMV2x0pn5MqRsPZdCZ2X9kHR40Uz7DmyrBkDxszh/zxx9gr2bXDhUmoJGa4eQfD/zwRDo/G4qCpVItCofpgIbzzYJfVTRHjV30ERXvWMRXjVMxbhVc9YVjBRAhSd9bt0CwEfPiJXDnPUrFwGmTKmT/BuVc27/8g5fPwUhDo1ftI+eJaQzX4LZck011+zZjiaNhbhTcj7IameK8Kr/s6J8KpnLMKrnrEIr3rGsoIQMEkCIrxqb4ukNKjlq5ldhFc9ZxFe9YxFeNUzFuFVz1hWEAImSUCEV+1tEeFVy1eE1zh8aRURXuOwlhxetZxFeNXyldmFgMEEqCXwi5dvuStaTIM6qx3zvYRGdapwmbH4DhHe+JIz7DwRXsM4JfQoifAmlGDs54vwxs4oMY4Q4U0Mil+eQ4RXLV+Z/RsjQM0gqDpCsSIFEv3K1mw5gFN+V7HCbWSMc1MntbVePti2fHKC9nDxejACP4UnaA5TOTlNGqBAfmq4bDpDhNc490KEVz1nEV71jGkFEV61nEV41fKV2b8xAmXq9cDqOWNijcLG57INFd4e9jNhVb8aOtg0iM8y2nOCQ8Px8l1QguZICiezBn8FFxbhNc67Q4RXPWcRXvWMRXjVMxbhVc9YVjAxAhQhpe5kGTOkw4Gj55E9a2aMt+8Gi+rlsWbrAdy+9wjOo3ppd00d1oKCghEREYGVm/ehQL5cyJolE1o1s0BHmwY46nsJc5Z64fb9R6hUrjgmONii+A+fI8CdBjqjX1drbgZBzSGmju6N/HlyYtGanTh43I8bV1BXtbFDuuC3Q2ex99AZ/FS6KHYf9EXJHwthcK9WqF6xlHYv/g+fwarLKG1b4wtX/sIU97XcVIJG/VoVMM6+G8wzZ+SvY9rbyk3BePK538U3PfLli0CTRuFIYeQKbCK8xnlbifCq5yzCq56xCK96xiK86hnLCiZGgCKpros9uTta+VJFsXXPUfx54zZO7lyAq7fuoqPdZG1LX2r/W62ZHZa4DOfmEy17jseoQZ1Qulhh/jooKAQ2Pcdxe+I6Ncpjw/bf4Xf5Fnw2u3ETCIoI0+jSujHy582BJvWqseye9rvKndmolu72306wOP/x59+8L+qE9nO1cqAyZNSKWDd1geT7nv9jbTe4a3/dxT93HqBUscJcsmyi62rUq1UBw/u3x793H8a4t28phzemt9iPRSPQrXOYCK+J/Rwm1nZEeBOL5JfnEeFVz1iEVz1jEV71jGUFEyOgnzrw7MUb1G9rr23m0LbvRBZO+75tWUY91njjd8/ZSJUqJQusbkrD/JXbOTLrs9mVr/Ll63eo02ooFk4fhvq1KvLxS1xGcMtfGvTAWZWm/TjS28rKIhIZ/X1RMwxr27HaaC51dqvXZhhmjuuP2lXLas99/vINLl79B89evMbB4xeQJXMGeEy3R2x7E+FV+8aUCK9avprZRXjVcxbhVc9YhFc9YxFe9YxlBRMjEF2ubFUrO0wd3YsjsN77T2L6/I04tWsBR3tbWlmge7smfBX6wjtm+jJ+faZTP+1VNmjnwBHfTi0b8vEbFo7jVsA0NBK7d90M7vCmO74k4oe93JE3V3ac/eMGRjovxtHtc7VtjikKTF3ZKJWiVLFC+PvOA6RLa8aSHdveRHjVvjFFeNXyFeE1Dl9aRYTXOKzloTW1nEV41fKV2U2QgL5YPnzyApYdHbFm7hjOpw38GIS6rYehZdPa2OR9GKd3LURW80xa4V3pPgo1KpXmr10XecL3wjV4r5rKX2tSINwnDWR51hfetwEfUKv5IMxzHoJGFpXjJLxUIaJQ/twY1LOV9rwW3Z3QtEF1DOxuw6+t8tyH85dusvDGtjcRXrVvThFetXxFeI3DV4TXeJxFeNWyFuFVy1dmN0ECJLy7fE5h6SxHBAUHw4Nyas9fxUHP2UifLg3v2MVjM9Z5+aCtdV1MduypvYpeDi6oWrEk+nS2RmDgJ9z4+x76OLqCBLdWlbJ8DuXZHts+F7lyZI0ivDRR18HTkCJFCowb1hXfF8zLKREVyhTlB9t0y5JpUi0owps+XVoW5f0bXVDouzza/dBcxX4ogOH92uHB4+eY5LYG2bJmYuE9c+F6jHtbsyUIL14a+Umur/B+yJUTaNwwzOgri/AaB7mkNKjnLBFe9YxpBRFetZxFeNXyldlNkIDmoTXN1qjqgusEO5QvXVS72ys3bqPzQGd4LZuE0sW/175++ORFTJq9Gq/eBGCArQ1XUVi8bhcWrvLmYzKkT8fpDQ0tKvHX+hFeeo0qLTjNWI5L1/7hY2j95W4jceT0Rfj6XcMyV0d+nXJz67WxxxGvOThx7gp2+/hi/QKnSERP+13DmGlLeT+0domiBZE5UwYsnunAx8W0t+CQcLx4F2yCdyiRt0TlGSLiWpcsrsdH3bMIbyLfxy9MJ8KrnrMIr3rGIrzqGYvwqmcsK5gYAU1Kw+IZDgj48JHLkukPitKePPcnNi+aEOV79PDY67cByJEtC0dqaXwKCsaLV2+5ckPqVIZ1P3v/4SOCQ0KjXV9/0Q79J6Nrm8Zoblkryn5Cw8Lw+OlL5M2dI9rOa1/am8/JYLx/b2I3x0S2U6hgOL7LlzDpFeE1zs0U4VXPWYRXPWMRXvWMRXjVM5YVTIxAbA0eqJICVVqgVIZmDat/9d2TSNODaYtm2HMUN7FGcsnhjQ+vnt3DUKSwCG982Bn7HBFe9cRFeNUzFuFVz1iEVz1jWcHECFAlg6fPX2tLhelvj1IJTp2/il8a1kCaNGYmtvvE244I75dZivAm3vtM9UwivKoJS5UG9YQ/ryA5vGpJi/Cq5SuzCwGTJSDCK8Jrsm/OOGxMhDcOsOJ5qER44wkujqeJ8MYRWBwPF+GNIzA5XAh8KwREeEV4v4X3sgiv+rsowquesUR41TMW4VXPWFZIBgQo7/eY7yU0qlMl2gfHTBHB/BXBePDw2y9LFh/2rWzCULig5PDGh52xzxHhVU9chFc9YxFe9YxFeNUzlhWSAYE9B32x1ssH25ZPTjJXe+e/ULwPNH592qQAKF26CGTKJMKbFO6VCK/6uyTCq56xCK96xiK86hnLCsmAQA/7mbCqXw0dbBokmau96x+C959EeON6w8zSpEDmzOGILTYuZcniSjZ+x4vwxo9bXM4S4Y0LrfgfKzm88WdnyJkivIZQkmOMTuDfuw8xZvoyWDeuic3eh3n93p2aoX2L+vzf1FGseqVSsGrwuWzYUd9LOHD0PFzG9YfmXCoptmH77wgJCcXw/u254sLSdbu5hm63tpbo17U5n0vR2aO+l5ExQzqeg+ryjrfvBovq5bFm6wHcvvcIzqN6aRlQjd6goGA49GvHr1EjCasuo+C72wPmWTLyfMfPXoF55ozYfdAXJX8sxA0qqlcsZVJ7n704GP89iE3bjH7rTX5Bq6ZhKFuahDdmdiK8xrmVIrzqOYvwqmdMK4jwquUswquWr8weTwJXb95BxwFT0KB2RZbc/x49x7R56+G7x4NFstuQ6Sy7nVs15BW895/ktr7eq6ZCc27jOlXQrnk9UNc0j9XeKFWsMEtuaGgYRjovxt51M1CkUD5oOq/Z2bZA+VJFsXXPUfx54zZO7lyAq7fuoqPdZG1L3w+Bn1CtmR2WuAxnIaZBAnzP/zFmTbDjrzXz9exohZ+rlcP+I+dw/a972nQHU9m7PLQWvzdnm1bhKF8uTIQ3fvgS/SwR3kRHGmVCEV71jEV41TMW4VXPWFaIBwGNtF47ulrbzcyi5RBMGdUL9WtVNEh4NecGfvyEqlZ22Lp0EsqU+NwmuFWv8bBt1wStrCxYUE/5XcUKt5H8vWcv3qB+W3vs2+CCwgXyoG3fiSyu9n3bYvtvJ+Cxxhu/e85GqlQpQV3X6rUZhpnj+qN21bJa4dWd767/Y1jbjtVGgA0RXmPsXYQ3Hm9MACK88eOm6iwRXlVk/39eEV71jEV41TMW4VXPWFaIB4HohLdZ19EY3LM1dz+LizSSlJZv2Atblk5E2RJFeDddB09Ds4Y1OEIcXec1EuSpo3uhSb1qHD2ePn8jTu1awNHellYW6N6uCc9z9o8bHC0+un2utqXwlwT6sJc78ubKbjJ7F+GNxxtThDd+0BSeJcKrEO7/phbhVc9YhFc9YxFe9YxlhXgQiE14ezm4oE6Nn9CjQ1OePbqUBk2UNDw8AuUa9DRYeB8+eQHLjo5YM3cMqlYoicCPQajbehhaNq2NTd6HcXrXQmQ1z8Trjp62FIXy58agnq20Vxmb8JrK3kV44/HGFOGNHzSFZ4nwKoQrwqsers4KksOrFrcIr1q+Mns8CcQmvJQ3e/7STcxzHoKHj59jxoJNeP8hMFIOb1yEd5fPKSyd5Yig4GB4rNmJ0+ev4qDnbKRPl4avwMVjM+cIt7Wui8mOPfm1twEfUKv5IG1+r+ZSYxNeU9g77fWPa8H48DE8nnco+Z6WJXMK5MoZjhSxPO8nD60Z5z0iwques0R41TOWCK96xiK86hnLCvEgoHlYTDeHl1IahvRqzQ+rUV7s8Eke+PvOA2RInw6VyxfDi1fv+MEw/XOji/BSSgSlRnRq+TmlwXWxp3aXBfLlgusEO5QvXVT7Gj341nmgM7yWTULp4p/zgL32HsNuH1+svpkykQAAHWFJREFUX+AU6QqpsoOv3zUsc3Xk15+/fIN6bexxxGsO8uTKZhJ7p30FhYTjZUBQPO6OnGIIgVQpUsA8kxleBQQbcrhpHpOwUsRGuSYRXvWYRXjVMxbhVc9YhFc9Y1lBIYEnz18hZ3Zzbf5sfJbSRGQXz3BAwIePXJZMf1BU9uS5P7F50QTttzr0n4yubRqjuWWt+CyLr7l32vCqzcF4+kzKksXr5iWTk6pXC0fJ4qb9KYAIr/o3owivesYivOoZi/CqZywrmDiB6B5a090ytQ2u02oopzJQVJjGi1dv4ThlMRbNsOcI89ca8dm7Zq+Sw/u17lrSWdfQihRf84pEeNXTF+FVz1iEVz1jEV71jGUFEydAaRFPn7+GRfVy0e6UUhJOnb+KXxrW4OYVpjQSsncRXlO6k6a5FxFe07wvxt6VCK9xiMtDa2o5i/Cq5SuzCwGTJSDCa7K3xmQ2JsJrMrfiq25EhNc4+EV41XIW4VXLV2YXAiZLQITXZG+NyWxMhNdkbsVX3YgIr3Hwi/Cq5SzCq5avzJ7ECNzxf4wXL9+iWsWSMe6c8nqP+V5CozpVYJY6lUleZWzXsnpLMJ6/kIfWTPLmmcimKlcIQ6mSpl2qQXJ41b9ZRHjVM6YVRHjVchbhVctXZldAgJo99On8C4oVKZDos8f2EJhmwT0HfbHWy4fLoCVkfM1r4bJk75JwyayEgDfCuSlTpoB5xtR4HRBihNXULMG1hk3bd5E9SxoEBIYgJNTEN6rmFhll1vRpUyJN6lR4+yHpvpeNAiqaRSJSRBj8MyTCq/YuifCq5SuzKyBQpl4PrJ4zJtYobHyWNlR4e9jPhFX9auhg0yA+y2jP+ZrX8se1ELz/YNolpxIE9yufTLKYxiwlgoKFscpbQYxDw8IRLpiVYU6VKgVSpkiBkFCBHFfImTIB+fKEIdZONRLhjSvaOB8vwhtnZHJCYhCgCOlR38vImCEdDhw9z7Vvx9t3g0X18qDGDbfvPYLzqF7apagOblBQMCIiIrBy8z5Qc4isWTKhVTMLdLRpgKO+lzBnqRdu33+ESuWKY4KDLYr/8DkC3GmgM/p1tcbJc1dx85/7mDq6N/LnyYlFa3bi4HE/BH78xC2Exw7pgt8OncXeQ2fwU+mi2H3QFyV/LITBvVqhesVS2r34P3wGqy6j4LvbA+ZZMiKpXovk8CbGO1nmEAJCQAh8mUDjBuH4uXbsnRlpBonwqn0nifCq5Suzf4GApruZnW0LlC9VFFv3HMWfN27j5M4F2k5p+ze6oNB3efAh8BOqNbPDEpfhyJs7O1r2HI9RgzqhdLHC/HVQUAhseo5D3y7WqFOjPDZs/x1+l2/BZ7MbMqRPC4qi0ujSujHy582BJvWqseye9rvKndsKF8iD7b+dYHH+48+/uetaz45W+LlaOew/cg7X/7oXKXWB5Pue/2PMmmDH8ybVaxHhlR9PISAEhIBaAiK8avnGZXYR3rjQkmMTjYB+6sCzF29Qv6099m1wYQFt23ciC6d937Ysox5rvPG752ykSpWSBVY3pWH+yu0cmfXZ7Mr7e/n6HTeKWDh9GOrXqsjHL3EZoa2zSw+cVWnajyO9rawsIl2T/r6ohbG17VhtNDcsLBz12gzDzHH9UbtqWa3wnvK7ihVuI/nrpHItIryJ9naWiYSAEBAC0RIQ4TWdN4YIr+nci2S1k+hyZata2WHq6F4cgfXefxLT52/EqV0L0NFuMlpaWaB7uybMSF94x0xfxq/PdOqnZdignQNHfDu1bMjHb1g4DhXLFuPvayR277oZKFIoX4zCq5HXw17uyJsrO87+cQMjnRfj6Pa52nbGSfVaRHiT1Y+cXKwQEAJfgYAI71eA/oUlRXhN514kq53oS+LDJy9g2dERa+aO4XzawI9BqNt6GFo2rY1N3odxetdCZDXPpBXele6jUKNSaf7adZEnfC9cg/eqqfy1JgXCfdJAlmd94X0b8AG1mg/CPOchaGRROU7CS1UVCuXPjUE9W2nPS6rXMmdpMB48lLJkyeoHTy5WCAgBoxL4uUY4qlWTHF6jQhfhNQXcsgcNAZLEXT6nsHSWI4KCg+FBObXnr+Kg52ykT5eGD3Px2Ix1Xj5oa10Xkx17auH1cnBB1Yol0aezNQIDP+HG3/fQx9EVJLi1qpTlcyjP9tj2uciVI2sU4aWJug6ehhQpUmDcsK74vmBeTomoUKYoP9gWXXoCRXjTp0vLoqzJLU7K10LR6jv/heL9xzB5UyoikDIFkD5tanz4FKpoBZmWCFCNWCqxFxYuZclUvSPMUqVAqpQp8SlEfl/ElXEqswhky2LYe1MeWosr3bgdLxHeuPGSoxOJgOZBL810VHXBdYIdypcuql3hyo3b6DzQGV7LJqF08e+1rx8+eRGTZq/GqzcBGGBrw1UUFq/bhYWrvPmYDOnTcXpDQ4tK/LV+hJdeo0oLTjOW49K1f/gYWn+520gcOX0Rvn7XsMzVkV9//vIN6rWxxxGvOThx7gp2+/hi/QKnSBSS4rXkyZUtke6kTCMEhIAQEAJCwPQJiPCa/j36JneoSQNYPMMBAR8+clky/UFR2pPn/sTmRROifI8eHnv9NgA5smXhSC2NT0HBePHqLVduSJ3KsO5n7z98RHBIaLTr6y/aof9kdG3TGM0ta0URXooKJ6Vr+SbfVHJRQkAICAEhIAS+QECEV94aX4VAbA0eqJICVVqgVIZmDat/lT3qLkoi7ThlMRbNsOcIsu5Iatfy1WHKBoSAEBACQkAIGJmACK+Rgctynwn8fecBnj5/rS0Vps+FUglOnb+KXxrWQJo0ZiaNLSleS8D7QISGhSGbedTIuknDTsKbE+bxu3nUbCYsPPyLn9rQH6MZM6TX5v7rrhIcHILXb98jd86s2k+C4reLb/8s+n2QMkVKUEvsuI6Y7kFc5/pWj6cgzus375A3d45oGYeHR+DZy9fImd082ve6/P5I+DtDhDfhDGUGIZBkCFBXudFTl+LI6Uu8Z8qZXjB1KP+SlRF/Ai26O3GXP90xqEdLDOzRkjv5CfP4s6VOhnOWe3Eeve7wf/gUdqPdcf/BU365dbM6+HV4d5ilTsUdGRev2w2P1Z/z+illauF0e+6gKCMqAZKxDv0noV/X5rBuXFN7AD0vMXTC/CgnXDy4HGnTmCGmeyCc/5/AkHHztL9z6b3YsqkFRti11x5w/MwV/gSRflfQmDiiB9o3r8f/Lb8/Eu+dJMKbeCxlJiFg8gRWbPoNXnuOYf2CcRwRGzBmDtci1m3jbPIXYYIbJOH9pVFNNK1fTbs788wZuZSeMI/fDSOZ6uvohgePn4MestQX3n4j3ZApY3pMG9MXT569RPv+k/Grgy3n2NPDqFSJhR4wLVfyB8xfuQO/HT6DQ1vc4xXBjN8VJI2z3JZswWrP/bxZl3H9IwnvoZN/YOz05ZE6TdJxhb7LzRHzmO5B0rh64+ySHqi2rFeVuVEt90FOc+G5+FeUK/UDNOl79PB1l9aNcMz3MoZNWMCNlOhhavn9kXj3SIQ38VjKTELA5AlQB7sm9apyUw4aPsfOY/ikRbh2dLV85JuAu0fC26NDU44y6g9hHj+w9BE7fVR+5NQlrNi0N5Lwampp6zaUmTZvPZ48e4UF04Zh9pKtuPnv/SjdD7ctn4xSxQrHb0Pf6Flv3r7Hp+BgrogzvF/7KMI7efYabvmuP2K7B98orkS5LGqMRK3sKaJO0d2BY+fg0sHl2vS9Zl1Hs/x2ad2Yu47K7+xEwQ4R3sThKLMIgSRB4HM3u978C5QG1TBu128SfPd4gCKSMuJHgIQ3Y8b0KFo4P/LnycHSUOi7PDyZMI8fU81Z+4+cg+tiz0jCe/veQ7ToMU5ba5uOXb/tIHb5nOZoJH08nM08E8YN66ZdnMoTLprhgLo1f0rYhr7Rs5t0GokhvVpHEV6KNto0qY20adOgyk8l+HcHVcGJ7R58o5gSfFmUgkNCq3kvbt1zDGu27Me+DS7auSkF4vuC+TjtQX5/JBi5dgIR3sRjKTMJAZMmQHmNZev3jPSPvuYfrUNbZiNfnhwmvX9T3hzliqZMlRIREcCRUxc5r3T7iskomD+3ME/gjYtOeDUpC7p/qJE4LFm3i8WYPmovUbRQpDxJEodJjj34QVgZUQlEJ7xXb93lT4Hoj+FHT19i6+6j6NyqIf8hEds9EMZRCVAX0K6DpyJTxgzcVTRVqpScsnDg6PlIaSP0B1umDOkxcUR3+f2RiG8kEd5EhClTCQFTJ0D/6E8b0weWdavwViXCm/h3LCQkFE06j0S3Npbo2dGKIzTCPP6cY4rwHt8xT/vApX6Elx4OchraVSK8BqKPTnj1T92x7wQmzFqFK4dX4v5/TzjK/qV7YOCyyeYwytUdNmE+p92sm+/E+f00DInwyu+PxHmbiPAmDkeZRQgkCQKUD0YPVvXp/AvvV3J41dw2alJSt1YFDOxuwzl4wjz+nKMT3ujyR53nrMOzF6+1Obx/3fbXdkx89uIN6re15yia5PBGfy8MEV5qvW43ejb+8FmGoOAQbrWum0etew/if8e/vTPfvQ/E0PHz8fFjEJbOGqGVXbpSTQ7v5d9XwMwsNV883QvbdpbaHF75/ZE47wkR3sThKLMIgSRBYPnGvdi29zhXaciQPi2XdZIqDQm7dVRNgMq80T9KObKZw+foeYyetpSjOJXLF4cwjx9fSsEJDQ3jj3upLJnPJlekSJlCW6O0j6MrsmTKyNHzL1dpGMdPws9bsQ37Dp+VKg3R3Ap6ODAiPALWtmNhZ9sC1o1qasVrk/dhlChakFu7vw14j5FTlnDZt1VzRvNMMd2D+N31b++swI9B6Gg3meuez5k8mCuL0EiZMiXy5c4O+n5Vq/4YPagTOkdTpUF+fyTee0KEN/FYykxCwOQJUA4Z5YedOHuF91q2RBGOiFFhfhnxI0DC28N+JjdS0Qz6x8u2XRP+UpjHj+u/dx/Cpue4SCdTybGZTv34tbv+j/kPNipbRqNl058xaUQPljWS5YWrvbFk3W7+HnVHXOY6AhXLFovfZr7hs6hKC33Sozv2rpvBfwi7L92KlZv3ab9FdbtdJ9hxuazY7sE3jCxOl0a/F6gqg/6glBtN9Qv6g5keVNOM8fbd0KllQ/n9ESfSsR8swhs7IzlCCHxzBOgjYco1lYYTiXNrSbBevQngIvH08B89xa4/hHnisNafhYSComYZM0Ru+U3HfQoKxqvXX+5upWZH39asxJA6X2bOmCHSR/G6VxnTPfi2aKi7mrCwcDx5/gq5c2TVRth1V5PfHwlnL8KbcIYygxAQAkJACAgBISAEhIAJExDhNeGbI1sTAkJACAgBISAEhIAQSDgBEd6EM5QZhIAQEAJCQAgIASEgBEyYgAivCd8c2ZoQEAJCQAgIASEgBIRAwgmI8CacocwgBISAEBACQkAICAEhYMIERHhN+ObI1oSAEBACQkAICAEhIAQSTkCEN+EMZQYhIASEgBAQAkJACAgBEyYgwmvCN0e2JgSEgBAQAkJACAgBIZBwAiK8CWcoMwgBISAEhIAQEAJCQAiYMAERXhO+ObI1ISAEhIAQEAJCQAgIgYQTEOFNOEOZQQgIASEgBISAEBACQsCECYjwmvDNka0JASEgBISAEBACQkAIJJyACG/CGcoMQkAICAEhIASEgBAQAiZMQITXhG+ObE0ICAEhIASEgBAQAkIg4QREeBPOUGYQAkJACAgBISAEhIAQMGECIrwmfHNka0JACAgBIRB/Apeu/YNZizyxYOpQ5MxuHv+J5EwhIASSPAER3iR/C+UChIAQEALJh0C3IdNRuEAeTB3dO9aLPnnuT9iNdsehre7Ilzt7rMeb8gFBwSGoZNkX08f2hU2T2qa8VdmbEDBJAiK8JnlbZFNCQAgIASEQHYGug6ex8E4b0ydWQN+S8H4KCkblJv1Y9FtZWcR67XKAEBACkQmI8Mo7QggIASHwjRC4cuM2XBd5YvLInth3+Czo6wa1K6Fzq4a4/+Ap3BZ74uzFm0iX1gwW1cvDcUBHZM+aGR8/BaOvoysa1amMi1f/xpkLN/h1O9sWkeTq+l/34LrYE36Xb6FAvlywblwTdt1awMwstcEEw8LCsWX3Uew6cAp3/B+zvDayqMxrbdl1BOcu3UTNKmWwacchPHj8Au2b10P39k2RO2dWLNuwB/NWbEeG9OlQomhBXnPUwI6IAKK97oL5c3GEd9ywbsyDUhzKliiCCcNt+f/jMs5cuI7lG/fi6q27yJXDHDUrl8HgXq2QzTwzYuPiOGUxKpcvjk4tG2qXnD5/I7KaZ8LA7jZa/sTzwpW/cPzMFZT8sRC6tbWEZd0qfM4gp7k45nuZuefKkZVfW+42Eu8CPmDeim04e/EGAt5/ZC4dWtRHc8tacbk8OVYIfPMERHi/+VssFygEhEByIaCJaNL1Fi2cH6WKF8ZPpX9koazf1h6VyhVngXz1NgArNu5FmRLfY4nLCAS8D0QN64GMiaKH3xfMC689x/Dg8XOsXzAOlcoVg//DZ7DqMooF1bZdE9z85z627T2O9i3qY+Lw7gYjnr1kK1Z57kO9WhVgWbcq/r79H9ZsPYDrx9bAfelWrNy8D3lyZUP75vWRKlVKzF2+DX27WMO+b1uQdI5zWYFc2bOipdXPvGbdGj/h9v1HLLb6160RXhLkTi0bIEWKFFix6TcW5mPb5yJjhnQG7ZsEdODYOSj+QwF0tGmAgA8fsXbrASycbs/CGxuXJp1G8rWOsGuvXa+H/UzkzpEVsybYReL/S8MaqFiuGI6fuYyT567izN5FyJIpA7z2HsMktzXQfJ8mamtdDz3tZ+LR0xcY0qs10qZJA78rt/Dk2Sssnulg0LXJQUIguRAQ4U0ud1quUwgIgW+egEZ4Zzj1RQvL/8/zpKjv1j3HcHzHXJY9Gp67jsB5zjqc8J6PNGapWXgnjujBQkzjQ+AnVGtmh3bW9TDJsQemzVuPTd6H4bvHA+aZM/Ixbku2YLXnfhzdNpcjsLGNF6/eom7rYVEk+dmLN3w+Ca/3/pM46Dkb6dOl4elcPDaz/O3b4MJfR5fS8KXr1ry+e800FP3+Oz7/7B830HvELLhOGIBmDavHtmX+fovuTqAcWp/NrtrjAz9+QkQEMHe5V6xcDBVeikRTNJ7GqzcBsGg5BO6TBqFJvaqILqWBouXlG/bic+hczaCIvYafQRcoBwmBZEBAhDcZ3GS5RCEgBJIHgS/lrFI0kdIQShUrrAVBUV2K4Hotm4SC+XOz8OpLYNu+Ezm6uGrOaNDDYsHBIdiydKJ2Ds16q+eMQbWKJWOFTOkKvRxcsGDaMDSoXTHK8SS8Psf8IoklRX9J2CkCHJvw6j+cFh2Pd+8DUdN64P+1d/cxVVdxHMe/3SagUK3EMGpuudwqdZTLtJrp8g90urVyPudTEj7rbKEW0XQCiU+YLh9IyVTWHy58qEQrq7WojbKWWcupc7NZijAyRSN5aN+jvxvghd/Pe1o7g/f5z3t/5+77e537x8fD93eu2THWnWO/4T0sNmlkqiyYNfa6y4O4BA28zf17DposGTPHyORRQyIGXi3mxcXr5cDnZfJwrx7Sv8+DMvCxFOn9QHe/2+J9BNqdAIG33S05N4wAAm1VoKXAO3raEgndHDL9os1HSs/75CaRiIH3medfNf2iBSteEv0MbQHQ8OsNDdEapjevzDB9t37Dq2/rmkXS96HrA3KkwFtU/LFov+t/HXjnp4+UtHHD/EqWi9WXpd+wGaZfd8bE6/2CuGjg1bYSDa/eiNTSEE3gra2rk90lX5pdcO3P1p1nvS+9PwYCCPwrQODl24AAAgi0EYGWAm/mss3y9aGf5MPteU3+1N3Q0GD6Wr0e3saBy2s/0N1FDWqLcgvk/Y++km/3F4Q/Y/07e+TNt3dJSVGedLs7yVfx1OmzMnT8wnBPrjdB/zSv/bpBAm96xkpJiO8kqxdf7TnW0dJ9R3r909LvZU7mG7J26VwZPKCPb816gbYWJCclNtnd1tfr6xvklWVv+bqMnblUune7q8nJErozrEelNe7hbS3warBNGTxVXps/UUY//VS4bs9OX7hypVayVhSaeg4fLDSmDAQQuCpA4OWbgAACCLQRgZaCnz5gpu0JT/ZPMachJMR3lF+OnzL9t7o7q8FIWxr0yX49SeBMeaUUvlsiR46eDIfZ7348JhPm5EjqoEdl0qhUOXriV1m35T3TJqE7wEHH9IWr5NDhYzJlzFBJHfiIOalh/dbdsqswO1Dg1Zo1aOtDWdp73PXOznL0xKmI5+16Htrf+kTfXvLDz8dl47a9pj2gpGi5xMZ0CFS2PkinYVz7mUcMHyg1NX+bh9bSxg8XDZx+Lnq6Q8GOD2RF1nSJi4uRvQdKZc+BUvMAWtDAq4Wq3cXqvyRz3nNy/kK19Oh+j0yYnSOzpzwrve6/V6ovXTYPttXV15tWFf3PDAMBBAi8fAcQQACBNiXgBbyDO1dL1y5Nf2hB38tes9307XpjQL/ekr9kjtTW1prAq6cjnD1XZd7WY8lyX04XvcYbxfu+kKzlheF/axtD7qIXAj2w5k3SnePctTtMr643dKdVd1zzC3bK/s/KmvTwFhV/Yq73WhpOn6mQrLwt5vgyHRrYdfdTT2loft+eR+P70mO91mbPDR9rFuQLcKW2TjZt2ysbtu0JX67HmuUvmSXJXRPFz+W3MxWyeNVWKf3miJmv93v85GnTa5uXOS3cNhFph1f7hrV/WIeeUvH6uiJzKoUOfeBwYc4m83pjy3lTR4Qf0gtyf1yDQHsQYIe3Pawy94gAAghcE9CdQe1L7XLHbRJzbYezcUvDoMdTzAkN3lmvzeE0XGqAuzUh3pwjG+3Qh8HOVf5hfvI3LvbqiQw3MqrOX5BQKBQ+MaK1udp68Ht5pbkkOalz1DufuptbXlEl8fEdzcN8jUcQFz2NIja2Q6CaW7sf/ZxbEjqFW0vUUutKSrw9vKY3Ysm1CLQHAQJve1hl7hEBBBBoRSBSD++NgFVW/SlDxi3wnVK2b0PUYdP3w6O4QHtrdae1tVG4egGnHkRhyxQEXBMg8Lq2ItSDAAII/M8Cly7XmB8wmJs2wvS6RjP07Fe/4drZsNrLq2fptja0zzcUohfWb215HwHXBQi8rq8Q9SGAAAIIIIAAAghYCRB4rfiYjAACCCCAAAIIIOC6AIHX9RWiPgQQQAABBBBAAAErAQKvFR+TEUAAAQQQQAABBFwXIPC6vkLUhwACCCCAAAIIIGAlQOC14mMyAggggAACCCCAgOsCBF7XV4j6EEAAAQQQQAABBKwECLxWfExGAAEEEEAAAQQQcF2AwOv6ClEfAggggAACCCCAgJUAgdeKj8kIIIAAAggggAACrgsQeF1fIepDAAEEEEAAAQQQsBIg8FrxMRkBBBBAAAEEEEDAdQECr+srRH0IIIAAAggggAACVgIEXis+JiOAAAIIIIAAAgi4LkDgdX2FqA8BBBBAAAEEEEDASoDAa8XHZAQQQAABBBBAAAHXBQi8rq8Q9SGAAAIIIIAAAghYCRB4rfiYjAACCCCAAAIIIOC6AIHX9RWiPgQQQAABBBBAAAErAQKvFR+TEUAAAQQQQAABBFwXIPC6vkLUhwACCCCAAAIIIGAlQOC14mMyAggggAACCCCAgOsCBF7XV4j6EEAAAQQQQAABBKwECLxWfExGAAEEEEAAAQQQcF2AwOv6ClEfAggggAACCCCAgJUAgdeKj8kIIIAAAggggAACrgsQeF1fIepDAAEEEEAAAQQQsBIg8FrxMRkBBBBAAAEEEEDAdQECr+srRH0IIIAAAggggAACVgIEXis+JiOAAAIIIIAAAgi4LkDgdX2FqA8BBBBAAAEEEEDASoDAa8XHZAQQQAABBBBAAAHXBQi8rq8Q9SGAAAIIIIAAAghYCRB4rfiYjAACCCCAAAIIIOC6AIHX9RWiPgQQQAABBBBAAAErAQKvFR+TEUAAAQQQQAABBFwXIPC6vkLUhwACCCCAAAIIIGAl8A+XPZrBGzZuGwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig.write_image(\"repo_cntrb_counts.png\")\n",
+    "Image(filename=\"repo_cntrb_counts.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>repo_gits</th>\n",
+       "      <th>repo_cntrb_counts</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>pytorch/pytorch</td>\n",
+       "      <td>194</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>108</th>\n",
+       "      <td>tensorflow/tensorflow</td>\n",
+       "      <td>77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>130</th>\n",
+       "      <td>numpy/numpy</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>107</th>\n",
+       "      <td>openxla/xla</td>\n",
+       "      <td>40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>664</th>\n",
+       "      <td>pytorch/ao</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>661</th>\n",
+       "      <td>pytorch/tutorials</td>\n",
+       "      <td>29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>350</th>\n",
+       "      <td>google/jax</td>\n",
+       "      <td>28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143</th>\n",
+       "      <td>scipy/scipy</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>pytorch/test-infra</td>\n",
+       "      <td>25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>787</th>\n",
+       "      <td>pytorch/torchtitan</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>pytorch/executorch</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>105</th>\n",
+       "      <td>llvm/llvm-project</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>663</th>\n",
+       "      <td>pytorch/benchmark</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>675</th>\n",
+       "      <td>pytorch/builder</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>790</th>\n",
+       "      <td>pytorch/torchtune</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>423</th>\n",
+       "      <td>triton-lang/triton</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>339</th>\n",
+       "      <td>tinygrad/tinygrad</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>793</th>\n",
+       "      <td>vllm-project/vllm</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>425</th>\n",
+       "      <td>pytorch/FBGEMM</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>640</th>\n",
+       "      <td>pytorch/xla</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>933</th>\n",
+       "      <td>keras-team/keras</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>686</th>\n",
+       "      <td>pytorch/pytorch.github.io</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>792</th>\n",
+       "      <td>huggingface/transformers</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>412</th>\n",
+       "      <td>Dao-AILab/flash-attention</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>390</th>\n",
+       "      <td>python/cpython</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>986</th>\n",
+       "      <td>microsoft/onnxruntime</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1060</th>\n",
+       "      <td>google-ai-edge/ai-edge-torch</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>764</th>\n",
+       "      <td>karpathy/LLM101n</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>171</th>\n",
+       "      <td>scikit-learn/scikit-learn</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>428</th>\n",
+       "      <td>networkx/networkx</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>799</th>\n",
+       "      <td>karpathy/llm.c</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>685</th>\n",
+       "      <td>pytorch/vision</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1291</th>\n",
+       "      <td>pytorch/torchchat</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>777</th>\n",
+       "      <td>pytorch/kineto</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1063</th>\n",
+       "      <td>ROCm/ROCm</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>769</th>\n",
+       "      <td>meta-llama/llama3</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>162</th>\n",
+       "      <td>scipy-conference/scipy_proceedings</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>220</th>\n",
+       "      <td>pandas-dev/pandas</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>140</th>\n",
+       "      <td>numpy/numpy.org</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1235</th>\n",
+       "      <td>pytorch-labs/gpt-fast</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2447</th>\n",
+       "      <td>intel/intel-extension-for-pytorch</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>784</th>\n",
+       "      <td>facebookresearch/HolisticTraceAnalysis</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1777</th>\n",
+       "      <td>pytorch/torchrec</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1484</th>\n",
+       "      <td>ggerganov/llama.cpp</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1517</th>\n",
+       "      <td>pytorch-labs/float8_experimental</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>109</th>\n",
+       "      <td>openxla/stablehlo</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1481</th>\n",
+       "      <td>ollama/ollama</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>144</th>\n",
+       "      <td>scientific-python/specs</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>157</th>\n",
+       "      <td>astral-sh/ruff</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   repo_gits  repo_cntrb_counts\n",
+       "92                           pytorch/pytorch                194\n",
+       "108                    tensorflow/tensorflow                 77\n",
+       "0                                        NaN                 68\n",
+       "130                              numpy/numpy                 44\n",
+       "107                              openxla/xla                 40\n",
+       "664                               pytorch/ao                 33\n",
+       "661                        pytorch/tutorials                 29\n",
+       "350                               google/jax                 28\n",
+       "143                              scipy/scipy                 27\n",
+       "99                        pytorch/test-infra                 25\n",
+       "787                       pytorch/torchtitan                 24\n",
+       "98                        pytorch/executorch                 23\n",
+       "105                        llvm/llvm-project                 23\n",
+       "663                        pytorch/benchmark                 22\n",
+       "675                          pytorch/builder                 22\n",
+       "790                        pytorch/torchtune                 22\n",
+       "423                       triton-lang/triton                 21\n",
+       "339                        tinygrad/tinygrad                 21\n",
+       "793                        vllm-project/vllm                 19\n",
+       "425                           pytorch/FBGEMM                 18\n",
+       "640                              pytorch/xla                 17\n",
+       "933                         keras-team/keras                 17\n",
+       "686                pytorch/pytorch.github.io                 15\n",
+       "792                 huggingface/transformers                 14\n",
+       "412                Dao-AILab/flash-attention                 13\n",
+       "390                           python/cpython                 13\n",
+       "986                    microsoft/onnxruntime                 12\n",
+       "1060            google-ai-edge/ai-edge-torch                 12\n",
+       "764                         karpathy/LLM101n                 12\n",
+       "171                scikit-learn/scikit-learn                 11\n",
+       "428                        networkx/networkx                 11\n",
+       "799                           karpathy/llm.c                 11\n",
+       "685                           pytorch/vision                 10\n",
+       "1291                       pytorch/torchchat                 10\n",
+       "777                           pytorch/kineto                 10\n",
+       "1063                               ROCm/ROCm                  9\n",
+       "769                        meta-llama/llama3                  9\n",
+       "162       scipy-conference/scipy_proceedings                  9\n",
+       "220                        pandas-dev/pandas                  9\n",
+       "140                          numpy/numpy.org                  9\n",
+       "1235                   pytorch-labs/gpt-fast                  8\n",
+       "2447       intel/intel-extension-for-pytorch                  8\n",
+       "784   facebookresearch/HolisticTraceAnalysis                  8\n",
+       "1777                        pytorch/torchrec                  8\n",
+       "1484                     ggerganov/llama.cpp                  8\n",
+       "1517        pytorch-labs/float8_experimental                  8\n",
+       "109                        openxla/stablehlo                  8\n",
+       "1481                           ollama/ollama                  8\n",
+       "144                  scientific-python/specs                  7\n",
+       "157                           astral-sh/ruff                  7"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Here's the list of the top-50 most mutual repos among pagerank-top contributors\n",
+    "df_for_fig[:FINAL_NUM_COMMON_REPOS]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_for_fig.to_csv('new_repos.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "After running the PageRank algorithm on AI repositories, several key observations and takeaways have been made:\n",
+    "\n",
+    "##### 1. Significance of Seed Projects\n",
+    "-  Expected Involvement: As anticipated, the repositories within the original seed projects (those identified as being part of the AI/ML ecosystem) prominently feature among the top PageRank results. This indicates that contributors to these seed projects also tend to contribute to other significant projects within the AI space.\n",
+    "- Interpretation: The inclusion of seed projects among the top-ranked repositories confirms their centrality in the AI ecosystem. These projects are influential not only because of their individual importance but also due to their role in connecting various contributors within the broader AI community.\n",
+    "##### 2. Discovery of New Influential Repositories\n",
+    "- Emerging Projects: The analysis also identified repositories that were not part of the initial seed list but have emerged as significant through mutual contributor involvement like `pytorch/ao`, `openxla/xla`, `google/jax` etc. Google's jax is one of the key repositories which we did not add in our initial seed but it is found in the process. It is becoming increasingly popular in machine learning due to its efficiency in handling complex numerical tasks.\n",
+    "- Interpretation: Discovering these repositories provides valuable insights into the evolving landscape of AI development. These projects might be on the cutting edge of innovation, attracting contributors who are deeply embedded in the AI/ML ecosystem.\n",
+    "##### 3. Understanding Community Dynamics\n",
+    "- Mutual Involvement: The analysis highlights repositories that have significant mutual involvement, meaning they share contributors with other top repositories. This mutual involvement indicates strong community dynamics and collaboration within the AI space.\n",
+    "- Interpretation: The presence of these interconnected repositories suggests that certain areas of AI development are highly collaborative, with contributors working across multiple projects. This interconnectedness can be a sign of active development, knowledge sharing, and rapid advancement within the field.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Next Steps\n",
+    "\n",
+    "Set the 'seed' of another cycle of discovery to these 50 repos, called seed_1 (the successor to seed_0). Using this new, more inclusive seed_1, build a new collaboration network and rank the contributors in the new network. We infer that there will be significant overlap between those that are most involved in the seed_0 analysis phase as those identified  in the seed_1 analysis phase. \n",
+    "\n",
+    "Re-run this analysis, producing a prospective seed_2. Repeat, eventually generating seed_n. When seed_(n-1) is sufficiently similar to seed_n, we'll conclude that the identified \"neighborhood\" of the final seed repos has converged.\n",
+    "\n",
+    "As an interesting alternate thread, consider the use of pagerank-central contributors as done in this notebook. Instead of finding repos of largest mutuality, use the Events themselves with appropriate filtering to find those projects of highest sub-population involvement, at least numerically, and compare against this [notebook](../project_discovery/repo_discovery/repo_discovery.ipynb).\n",
+    "\n",
+    "Having a set of repos we can confidently say are \"highly mutual\" at least, we can use network science to visualize the neighborhood connection over time (notebooks/graph_analysis/approaches). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}