code: def main():
      import json
      import os
      from argparse import ArgumentParser
  
      parser = ArgumentParser()
      parser.add_argument("--num_conversations", type=int, default=1000)
      parser.add_argument(
          "--output_dir", type=str, default="conversations"
      )
      parser.add_argument("--templates", type=str, default=None)
      args = parser.parse_args()
  
      if args.templates is not None:
          with open(args.templates, "r") as f:
              templates = json.load(f)
          template = templates["actor"]
      else:
          template = ""
      if not os.path.exists(args.output_dir):
          os.makedirs(args.output_dir)
      for conv in range(args.num_conversations):
          human_agent, bot_agent = build_agents()
          conversation = create_conversation(human_agent, bot_agent)
          with open(
              os.path.join(args.output_dir, f"conversation_{conv}.txt"),
              "w",
          ) as f:
              f.write(conversation)
  
      # convert the conversations to a single json file
      data = []
      for conv in range(args.num_conversations):
          with open(
              os.path.join(args.output_dir, f"conversation_{conv}.txt"),
              "r",
          ) as f:
              conversation = f.read()
          sub_conversations = get_sub_conversations(
              conversation, template
          )
          data.extend(sub_conversations)
      with open(
          os.path.join(args.output_dir, "actor_training_data.json"), "w"
      ) as f:
          json.dump(data, f)
  
imports: from langchain import OpenAI, LLMChain, PromptTemplate
  from langchain.chains.conversation.memory import ConversationBufferWindowMemory
  from chatllama.langchain_modules.prompt_templates import PERSON_CHATBOT_TEMPLATE, AI_CHATBOT_TEMPLATE
label_desc: Use image node_id 4 for calling a global function with example usage: main() without return types