code: def chatgpt_generate(self, selected_inputs: List) -> List:
      # append the groundtruth function
      # actually it can be any function (maybe we can generate inputs for each llm generated code individually)
      message = f"Here is a function that we want to test:\n```\n{self.gd_code}\n```"
      str_inputs = "\n".join(
          [
              ", ".join(
                  [
                      f"'{to_raw(i)}'" if type(i) == str else str(i)
                      for i in x
                  ]
              )
              for x in selected_inputs
          ]
      )
      message += f"\nThese are some example inputs used to test the function:\n```\n{str_inputs}\n```"
      message += f"\n{random.choice(self.prompt_messages)}"
      ret = make_auto_request(
          self.client,
          message=message,
          model="gpt-3.5-turbo",
          max_tokens=256,
          response_format={"type": "text"},
      )
      return self._parse_ret(ret)
  
file_path: evalplus/evalplus/gen/chatgpt_gen.py
imports: import ast
  import random
  from typing import List
  import openai
  from openai.types.chat import ChatCompletion
  from evalplus.data.utils import to_raw
  from evalplus.gen import BaseGen
  from evalplus.gen.util import trusted_check_exec
  from evalplus.gen.util.api_request import make_auto_request
label_desc: Use image node_id 4 for calling the ChatGPTGen obj's underlying member method code with example usage: obj.chatgpt_generate(selected_inputs) and returns: self