{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LobbyView Package Vignette: Corporate Strategy Analysis\n",
    "## A Case Study of Microsoft's Lobbying Landscape\n",
    "\n",
    "This vignette demonstrates how to use the LobbyView Python package to analyze corporate lobbying activities through the lens of Microsoft Corporation. We'll explore how a company might evaluate its past lobbying efforts, identify key relationships with legislators, and understand where resources have been allocated.\n",
    "\n",
    "The analysis will help answer questions like:\n",
    "\n",
    "- Which issues have received the most lobbying attention?\n",
    "- Who are our key legislative contacts?\n",
    "- How has our lobbying activity changed over time?\n",
    "- What recent bills are relevant to our interests?\n",
    "\n",
    "This type of analysis would be valuable for:\n",
    "\n",
    "- Corporate government relations teams planning future strategy\n",
    "- Compliance officers reviewing lobbying activities\n",
    "- Executives making decisions about resource allocation\n",
    "- Researchers studying corporate political activity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup and Configuration\n",
    "\n",
    "First, we'll set up our environment and import the necessary packages. The LobbyView package requires an API token which should be stored in a `.env` file for security. We'll be doing our function calls to retrieve data using our initialized `lobbyview = LobbyView(LOBBYVIEW_TOKEN)` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "from dotenv import load_dotenv\n",
    "sys.path.append('../src/lobbyview/')\n",
    "from LobbyView import LobbyView\n",
    "from exceptions import InvalidPageNumberError\n",
    "\n",
    "# Load environment variables and set up LobbyView\n",
    "load_dotenv('../../.env')\n",
    "\n",
    "LOBBYVIEW_TOKEN = os.environ.get('LOBBYVIEW_TOKEN', \"NO TOKEN FOUND\")\n",
    "lobbyview = LobbyView(LOBBYVIEW_TOKEN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Starting the Analysis: Client Identification\n",
    "\n",
    "The first step in any lobbying analysis is identifying the client (company) of interest. The LobbyView API uses a UUID system for unique identification, so we need to convert from a human-readable company name to its corresponding UUID.\n",
    "\n",
    "The `clients()` method supports:\n",
    "\n",
    "- Exact UUID lookup\n",
    "- Partial name matching\n",
    "- NAICS code filtering (for identifying firms by industry)\n",
    "- Industry description search\n",
    "\n",
    "In this case, we'll search for Microsoft Corporation by name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "CLIENT_NAME = \"Microsoft Corporation\"\n",
    "client_info = lobbyview.clients(client_name=CLIENT_NAME)\n",
    "\n",
    "if not client_info.data:\n",
    "    print(f\"No data found for {CLIENT_NAME}\")\n",
    "    sys.exit(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analyzing lobbying activities for Microsoft Corporation\n"
     ]
    }
   ],
   "source": [
    "# We need to convert from the human-readable company name to its UUID for subsequent queries.\n",
    "client_uuid = client_info.data[0]['client_uuid']\n",
    "print(f\"Analyzing lobbying activities for {CLIENT_NAME}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Report Analysis\n",
    "\n",
    "With our client identified, we can now examine their lobbying reports. These reports provide the foundation for understanding a company's lobbying activities.\n",
    "\n",
    "The `reports()` method gives us access to:\n",
    "\n",
    "- Filing dates and quarters\n",
    "- Amount spent on lobbying\n",
    "- Whether it's an amendment or original filing\n",
    "- Self-filing status\n",
    "\n",
    "We'll analyze the temporal distribution of these reports to understand how Microsoft's lobbying activity has evolved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of reports found: 100\n"
     ]
    }
   ],
   "source": [
    "# Get reports for this client\n",
    "client_reports = lobbyview.reports(client_uuid=client_uuid)\n",
    "print(f\"Number of reports found: {len(client_reports.data)}\")\n",
    "\n",
    "# the returned amount is 100 because that is the default limit for the API, though we can modify the \"page\"\n",
    "# parameter to get more results from different pages (100 results per page, default returns page 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Reports by year:\n",
      "  2010: 29\n",
      "  2018: 21\n",
      "  2019: 50\n",
      "\n",
      "Reports by quarter:\n",
      "  Q1: 25\n",
      "  Q2: 25\n",
      "  Q3: 27\n",
      "  Q4: 23\n"
     ]
    }
   ],
   "source": [
    "# Analyze report years and quarters\n",
    "from collections import Counter\n",
    "\n",
    "report_years = Counter()\n",
    "report_quarters = Counter()\n",
    "for report in client_reports.data:\n",
    "    report_years[report['report_year']] += 1\n",
    "    report_quarters[report['report_quarter_code']] += 1\n",
    "\n",
    "print(\"\\nReports by year:\")\n",
    "for year, count in sorted(report_years.items()):\n",
    "    print(f\"  {year}: {count}\")\n",
    "\n",
    "print(\"\\nReports by quarter:\")\n",
    "for quarter, count in sorted(report_quarters.items()):\n",
    "    print(f\"  Q{quarter}: {count}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Issue Analysis\n",
    "\n",
    "Understanding which issues a company lobbies on is crucial for evaluating their political priorities and resource allocation. The LobbyView API provides detailed issue coding and descriptions.\n",
    "\n",
    "The `issues()` and `texts()` methods allow us to:\n",
    "\n",
    "- See what specific issues were lobbied on\n",
    "- Read detailed descriptions of the lobbying activities\n",
    "- Track issue frequency over time\n",
    "- Identify patterns in issue focus\n",
    "\n",
    "This can help companies:\n",
    "\n",
    "- Review if lobbying efforts match strategic priorities\n",
    "- Identify gaps in coverage\n",
    "- Compare against competitor focus areas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Analyzing Microsoft's lobbying issues...\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nAnalyzing Microsoft's lobbying issues...\")\n",
    "\n",
    "# not necessary - just to suppress the output of the API call when it finds no matching records\n",
    "from io import StringIO\n",
    "old_stderr = sys.stderr\n",
    "sys.stderr = StringIO()\n",
    "# below is what we actually need to analyze the issues\n",
    "# Get all issues from Microsoft's reports\n",
    "all_issues = []\n",
    "for report in client_reports.data:\n",
    "    try:\n",
    "        # use .issues() to get the issues for a report\n",
    "        issues = lobbyview.issues(report_uuid=report['report_uuid'])\n",
    "        all_issues.extend(issues.data)\n",
    "    except InvalidPageNumberError:\n",
    "        # Skip reports with no issues\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Top issues by frequency:\n",
      "  TAX: 45 occurrences\n",
      "    Sample issue text: Tax extenders...\n",
      "  TEC: 42 occurrences\n",
      "    Sample issue text: telecommuications reform; online advertising; white spaces; online competitiveness, privacy...\n",
      "  IMM: 41 occurrences\n",
      "    Sample issue text: immigration issues...\n",
      "  CPI: 31 occurrences\n",
      "    Sample issue text: Competition in the online advertising and software markets, Privacy issues, Cloud computing, China...\n",
      "  TRD: 19 occurrences\n",
      "    Sample issue text: International trade issues....\n",
      "  CPT: 16 occurrences\n",
      "    Sample issue text: patent legislation...\n",
      "  HOM: 15 occurrences\n",
      "    Sample issue text: Cyber security; state fusion centers; voluntary private sector preparedness standards;  The Protecti...\n",
      "  BUD: 14 occurrences\n",
      "    Sample issue text: Information technology spending / cloud computing...\n",
      "  INT: 12 occurrences\n",
      "    Sample issue text: Warrants and surveillance, ECPA, Cyber Security...\n",
      "  SCI: 11 occurrences\n",
      "    Sample issue text: science and technology issues\n",
      "cloud computing...\n"
     ]
    }
   ],
   "source": [
    "if not all_issues:\n",
    "    print(\"No lobbying issues found for Microsoft\")\n",
    "else:\n",
    "    # Count frequency of issue codes\n",
    "    issue_codes = Counter()\n",
    "    for issue in all_issues:\n",
    "        issue_codes[issue['issue_code']] += 1\n",
    "        \n",
    "    # Create a dictionary to store issue text descriptions\n",
    "    issue_text_cache = {}\n",
    "\n",
    "    print(\"\\nTop issues by frequency:\")\n",
    "    for code, count in issue_codes.most_common(10):\n",
    "        if code not in issue_text_cache:\n",
    "            try:\n",
    "                # Try multiple report UUIDs until we find one with text\n",
    "                for issue in all_issues:\n",
    "                    if issue['issue_code'] == code:\n",
    "                        # use .texts() to get the issue text\n",
    "                        sample_text = lobbyview.texts(issue_code=code, report_uuid=issue['report_uuid'])\n",
    "                        if sample_text.data:\n",
    "                            issue_text_cache[code] = sample_text.data[0]['issue_text']\n",
    "                            break\n",
    "                if code not in issue_text_cache:\n",
    "                    issue_text_cache[code] = \"No description available\"\n",
    "            except (InvalidPageNumberError, IndexError):\n",
    "                issue_text_cache[code] = \"No description available\"\n",
    "        \n",
    "        print(f\"  {code}: {count} occurrences\")\n",
    "        print(f\"    Sample issue text: {issue_text_cache[code][:100]}...\")  # Show first 100 chars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Legislative Network Analysis\n",
    "\n",
    "A critical aspect of lobbying is maintaining relationships with legislators. The `networks()` method helps us understand these connections.\n",
    "\n",
    "We can analyze:\n",
    "\n",
    "- Which legislators are most frequently connected to the company\n",
    "- How many bills these legislators have sponsored\n",
    "- The strength of various legislative relationships\n",
    "- Changes in network patterns over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Number of network connections: 100\n",
      "\n",
      "Top 5 legislators by bills sponsored:\n",
      "  Kevin Brady: 81\n",
      "  Max Baucus: 62\n",
      "  John Conyers, Jr.: 38\n",
      "  None: 32\n",
      "  Richard Burr: 29\n"
     ]
    }
   ],
   "source": [
    "# Analyze network data\n",
    "network_data = lobbyview.networks(client_uuid=client_uuid)\n",
    "print(f\"\\nNumber of network connections: {len(network_data.data)}\")\n",
    "\n",
    "# the returned amount is 100 because that is the default limit for the API, though we can modify the \"page\"\n",
    "# parameter to get more results from different pages (100 results per page, default returns page 1)\n",
    "\n",
    "legislator_counter = Counter()\n",
    "for network in network_data.data:\n",
    "    legislator_counter[network['legislator_id']] += network['n_bills_sponsored']\n",
    "\n",
    "def get_legislator_name(legislator_id):\n",
    "    \"\"\"\n",
    "    Get the full name of a legislator given their ID.\n",
    "\n",
    "    Args:\n",
    "        legislator_id (str): The unique ID of the legislator.\n",
    "\n",
    "    Returns:\n",
    "        str: The full name of the legislator if found, otherwise the ID.\n",
    "    \"\"\"\n",
    "    legislator_info = lobbyview.legislators(legislator_id=legislator_id)\n",
    "    if legislator_info.data:\n",
    "        return legislator_info.data[0]['legislator_full_name']\n",
    "    return legislator_id  # Return ID if name not found\n",
    "\n",
    "print(\"\\nTop 5 legislators by bills sponsored:\")\n",
    "for legislator_id, count in legislator_counter.most_common(5):\n",
    "    legislator_name = get_legislator_name(legislator_id)\n",
    "    print(f\"  {legislator_name}: {count}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recent Legislative Activity\n",
    "\n",
    "Understanding recent legislative activity helps companies stay current and evaluate the effectiveness of their lobbying efforts. We'll focus on bills since 2020 that are connected to Microsoft's network.\n",
    "\n",
    "The `bills()` method provides:\n",
    "\n",
    "- Bill status and progress\n",
    "- Sponsorship information\n",
    "- Introduction and update dates\n",
    "- Connection to specific issues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Number of Microsoft-connected bills introduced since 2020: 311\n"
     ]
    }
   ],
   "source": [
    "# Analyze recent Microsoft-connected bills\n",
    "recent_ms_bills = []\n",
    "for network in network_data.data:\n",
    "    try:\n",
    "        # Get bills sponsored by legislators connected to Microsoft since 2020\n",
    "        legislator_bills = lobbyview.bills(\n",
    "            legislator_id=network['legislator_id'],\n",
    "            min_introduced_date=\"2020-01-01\"\n",
    "        )\n",
    "        recent_ms_bills.extend(legislator_bills.data)\n",
    "    except InvalidPageNumberError:\n",
    "        # Skip legislators with no recent bills\n",
    "        continue\n",
    "\n",
    "print(f\"\\nNumber of Microsoft-connected bills introduced since 2020: {len(recent_ms_bills)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Top 5 Microsoft-connected bill sponsors since 2020:\n",
      "  John Cornyn: 105\n",
      "  Roy Blunt: 40\n",
      "  Marsha Blackburn: 32\n",
      "  Thomas R. Carper: 25\n",
      "  Sherrod Brown: 20\n"
     ]
    }
   ],
   "source": [
    "bill_sponsors = Counter()\n",
    "for bill in recent_ms_bills:\n",
    "    bill_sponsors[bill['legislator_id']] += 1\n",
    "\n",
    "print(\"\\nTop 5 Microsoft-connected bill sponsors since 2020:\")\n",
    "for legislator_id, count in bill_sponsors.most_common(5):\n",
    "    legislator_name = get_legislator_name(legislator_id)\n",
    "    print(f\"  {legislator_name}: {count}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization\n",
    "\n",
    "To make our analysis more accessible to stakeholders, we'll create visualizations of key metrics. These visualizations will help communicate:\n",
    "\n",
    "- Temporal patterns in lobbying activity\n",
    "- Key legislative relationships\n",
    "- Issue focus areas\n",
    "- Recent legislative activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: No name found for legislator ID B000657\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x2000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create visualization\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.gridspec import GridSpec\n",
    "\n",
    "plt.figure(figsize=(20, 20))\n",
    "gs = GridSpec(3, 2, figure=plt.gcf())\n",
    "\n",
    "# 1. Reports by Year\n",
    "ax1 = plt.subplot(gs[0, 0])\n",
    "years, counts = zip(*sorted(report_years.items()))\n",
    "ax1.bar(years, counts)\n",
    "ax1.set_title(f\"Lobbying Reports by Year for {CLIENT_NAME}\")\n",
    "ax1.set_xlabel(\"Year\")\n",
    "ax1.set_ylabel(\"Number of Reports\")\n",
    "\n",
    "# 2. Reports by Quarter\n",
    "ax2 = plt.subplot(gs[0, 1])\n",
    "quarters, counts = zip(*sorted(report_quarters.items()))\n",
    "ax2.bar(quarters, counts)\n",
    "ax2.set_title(f\"Lobbying Reports by Quarter for {CLIENT_NAME}\")\n",
    "ax2.set_xlabel(\"Quarter\")\n",
    "ax2.set_ylabel(\"Number of Reports\")\n",
    "\n",
    "# 3. Top Legislators by Bills Sponsored\n",
    "ax3 = plt.subplot(gs[1, 0])\n",
    "top_legislators = dict(legislator_counter.most_common(10))\n",
    "legislator_names = []\n",
    "legislator_values = []\n",
    "for leg_id, value in top_legislators.items():\n",
    "    name = get_legislator_name(leg_id)\n",
    "    if name:\n",
    "        legislator_names.append(name)\n",
    "        legislator_values.append(value)\n",
    "    else:\n",
    "        print(f\"Warning: No name found for legislator ID {leg_id}\")\n",
    "\n",
    "ax3.bar(legislator_names, legislator_values)\n",
    "ax3.set_title(f\"Top Legislators for {CLIENT_NAME} by Bills Sponsored\")\n",
    "ax3.set_xlabel(\"Legislator Name\")\n",
    "ax3.set_ylabel(\"Number of Bills Sponsored\")\n",
    "plt.setp(ax3.xaxis.get_majorticklabels(), rotation=45, ha=\"right\")\n",
    "\n",
    "# 4. Top Bill Sponsors (Recent Bills)\n",
    "if recent_ms_bills:  # Only create this plot if we have data\n",
    "    ax4 = plt.subplot(gs[1, 1])\n",
    "    top_sponsors = dict(bill_sponsors.most_common(10))\n",
    "    sponsor_names = []\n",
    "    sponsor_values = []\n",
    "    for leg_id, value in top_sponsors.items():\n",
    "        name = get_legislator_name(leg_id)\n",
    "        if name:\n",
    "            sponsor_names.append(name)\n",
    "            sponsor_values.append(value)\n",
    "        else:\n",
    "            print(f\"Warning: No name found for legislator ID {leg_id}\")\n",
    "\n",
    "    if sponsor_names:  # Only plot if we have names\n",
    "        ax4.bar(sponsor_names, sponsor_values)\n",
    "        ax4.set_title(\"Top Microsoft-Connected Bill Sponsors (Since 2020)\")\n",
    "        ax4.set_xlabel(\"Legislator Name\")\n",
    "        ax4.set_ylabel(\"Number of Bills Sponsored\")\n",
    "        plt.setp(ax4.xaxis.get_majorticklabels(), rotation=45, ha=\"right\")\n",
    "\n",
    "# Add issues plot\n",
    "ax5 = plt.subplot(gs[2, :])  # Use entire bottom row\n",
    "top_issues = dict(issue_codes.most_common(10))\n",
    "ax5.bar(top_issues.keys(), top_issues.values())\n",
    "ax5.set_title(f\"Top 10 Issues in {CLIENT_NAME}'s Lobbying Reports\")\n",
    "ax5.set_xlabel(\"Issue Code\")\n",
    "ax5.set_ylabel(\"Number of Occurrences\")\n",
    "plt.setp(ax5.xaxis.get_majorticklabels(), rotation=45, ha=\"right\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Strategic Insights\n",
    "\n",
    "Finally, we'll summarize key findings that could inform future lobbying strategy. These insights combine various aspects of our analysis to provide actionable intelligence for corporate decision-makers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Key Insights for Corporate Lobbying Strategy:\n",
      "1. Microsoft Corporation has been involved in 100 lobbying reports.\n",
      "2. The company has 100 network connections with legislators.\n",
      "3. The most active year for lobbying was 2019 with 50 reports.\n",
      "4. The most active quarter for lobbying is Q3 with 27 reports.\n",
      "5. The legislator most frequently connected to Microsoft Corporation's lobbying efforts is Kevin Brady, with 81 bills sponsored.\n",
      "6. Since 2020, 311 bills have been introduced by legislators connected to Microsoft Corporation's lobbying network.\n",
      "7. The company's most frequent lobbying issues are:\n",
      "   - TAX (45 occurrences): Tax extenders...\n",
      "   - TEC (42 occurrences): telecommuications reform; online advertising; white spaces; online competitiveness, privacy...\n",
      "   - IMM (41 occurrences): immigration issues...\n"
     ]
    }
   ],
   "source": [
    "# Corporate Strategy Insights\n",
    "print(\"\\nKey Insights for Corporate Lobbying Strategy:\")\n",
    "print(f\"1. {CLIENT_NAME} has been involved in {len(client_reports.data)} lobbying reports.\")\n",
    "print(f\"2. The company has {len(network_data.data)} network connections with legislators.\")\n",
    "most_active_year = report_years.most_common(1)[0][0]\n",
    "print(f\"3. The most active year for lobbying was {most_active_year} with {report_years[most_active_year]} reports.\")\n",
    "most_active_quarter = report_quarters.most_common(1)[0][0]\n",
    "print(f\"4. The most active quarter for lobbying is Q{most_active_quarter} with {report_quarters[most_active_quarter]} reports.\")\n",
    "if legislator_counter:\n",
    "    top_legislator_id = legislator_counter.most_common(1)[0][0]\n",
    "    top_legislator_name = get_legislator_name(top_legislator_id)\n",
    "    print(f\"5. The legislator most frequently connected to {CLIENT_NAME}'s lobbying efforts is {top_legislator_name}, with {legislator_counter[top_legislator_id]} bills sponsored.\")\n",
    "if recent_ms_bills:\n",
    "    print(f\"6. Since 2020, {len(recent_ms_bills)} bills have been introduced by legislators connected to {CLIENT_NAME}'s lobbying network.\")\n",
    "else:\n",
    "    print(f\"6. No recent bills found for legislators connected to {CLIENT_NAME}'s lobbying network.\")\n",
    "if issue_codes:\n",
    "    print(\"7. The company's most frequent lobbying issues are:\")\n",
    "    for code, count in issue_codes.most_common(3):\n",
    "        description = issue_text_cache.get(code, \"No description available\")\n",
    "        print(f\"   - {code} ({count} occurrences): {description[:100]}...\")\n",
    "else:\n",
    "    print(\"7. No lobbying issues found in the available data.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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