code: def format_open_orca(
      messages: List[llama_types.ChatCompletionRequestMessage],
      **kwargs: Any,
  ) -> ChatFormatterResponse:
      system_template = "{system_message}"
      system_message = (
          "You are a helpful assistant. Please answer truthfully and write out your "
          "thinking step by step to be sure you get the right answer. If you make a mistake or encounter "
          "an error in your thinking, say so out loud and attempt to correct it. If you don't know or "
          "aren't sure about something, say so clearly. You will act as a professional logician, mathematician, "
          "and physicist. You will also act as the most appropriate type of expert to answer any particular "
          "question or solve the relevant problem; state which expert type your are, if so. Also think of "
          "any particular named expert that would be ideal to answer the relevant question or solve the "
          "relevant problem; name and act as them, if appropriate."
      )
      roles = ("User", "Assistant")
      sep = "<|end_of_turn|>\n"
      # stop_token_ids=[32000, 32001],  # "<|end_of_turn|>"
      stop_str = "User"
      system_message = system_template.format(
          system_message=system_message
      )
      _messages = _map_roles(messages, dict(zip(roles, roles)))
      _messages.append((roles[1], None))
      _prompt = _format_add_colon_space_single(
          system_message, _messages, sep
      )
      return ChatFormatterResponse(prompt=_prompt, stop=stop_str)
  
imports: from __future__ import annotations
  import os
  import json
  import ctypes
  import dataclasses
  from typing import Any, Dict, Iterator, List, Optional, Tuple, Union, Protocol
  import llama_cpp.llama
  import llama_cpp.llama_types
  import llama_cpp.llama_grammar
  from ._utils import suppress_stdout_stderr
label_desc: Use image node_id 30 for calling a global function with example usage: format_open_orca(messages) and returns: ChatFormatterResponse