code: def answer_question(
      search_file_id="<SEARCH_FILE_ID>",
      fine_tuned_qa_model="<FT_QA_MODEL_ID>",
      question="Which country won the European Football championship in 2021?",
      max_len=1800,
      search_model="ada",
      max_rerank=10,
      debug=False,
      stop_sequence=["\n", "."],
      max_tokens=100,
  ):
      """
      Answer a question based on the most similar context from the search file, using your fine-tuned model.
      :param question: The question
      :param fine_tuned_qa_model: The fine tuned QA model
      :param search_file_id: The file id of the search file
      :param max_len: The maximum length of the returned context (in tokens)
      :param search_model: The search model to use
      :param max_rerank: The maximum number of reranking
      :param debug: Whether to output debug information
      :param stop_sequence: The stop sequence for Q&A model
      :param max_tokens: The maximum number of tokens to return
      :return: The answer
      """
      context = create_context(
          question,
          search_file_id,
          max_len=max_len,
          search_model=search_model,
          max_rerank=max_rerank,
      )
      if debug:
          print("Context:\n" + context)
          print("\n\n")
      try:
          # fine-tuned models requires model parameter, whereas other models require engine parameter
          model_param = (
              {"model": fine_tuned_qa_model}
              if ":" in fine_tuned_qa_model
              and fine_tuned_qa_model.split(":")[1].startswith("ft")
              else {"engine": fine_tuned_qa_model}
          )
          response = openai.Completion.create(
              prompt=f"Answer the question based on the context below\n\nText: {context}\n\n---\n\nQuestion: {question}\nAnswer:",
              temperature=0,
              max_tokens=max_tokens,
              top_p=1,
              frequency_penalty=0,
              presence_penalty=0,
              stop=stop_sequence,
              **model_param,
          )
          return response["choices"][0]["text"]
      except Exception as e:
          print(e)
          return ""
  
label_desc: Use image node_id 2 for calling a global function with example usage: answer_question(search_file_id, fine_tuned_qa_model, question, max_len, search_model, max_rerank, debug, stop_sequence, max_tokens) and returns: response, str