code: def register_chat_format(name: str):
      def decorator(f: ChatFormatter):
          def basic_create_chat_completion(
              *,
              llama: llama.Llama,
              messages: List[llama_types.ChatCompletionRequestMessage],
              functions: Optional[
                  List[llama_types.ChatCompletionFunction]
              ] = None,
              function_call: Optional[
                  llama_types.ChatCompletionRequestFunctionCall
              ] = None,
              tools: Optional[
                  List[llama_types.ChatCompletionTool]
              ] = None,
              tool_choice: Optional[
                  llama_types.ChatCompletionToolChoiceOption
              ] = None,
              temperature: float = 0.2,
              top_p: float = 0.95,
              top_k: int = 40,
              min_p: float = 0.05,
              typical_p: float = 1.0,
              stream: bool = False,
              stop: Optional[Union[str, List[str]]] = [],
              seed: Optional[int] = None,
              response_format: Optional[
                  llama_types.ChatCompletionRequestResponseFormat
              ] = None,
              max_tokens: Optional[int] = None,
              presence_penalty: float = 0.0,
              frequency_penalty: float = 0.0,
              repeat_penalty: float = 1.1,
              tfs_z: float = 1.0,
              mirostat_mode: int = 0,
              mirostat_tau: float = 5.0,
              mirostat_eta: float = 0.1,
              model: Optional[str] = None,
              logits_processor: Optional[
                  llama.LogitsProcessorList
              ] = None,
              grammar: Optional[llama.LlamaGrammar] = None,
              logit_bias: Optional[Dict[str, float]] = None,
              **kwargs,  # type: ignore
          ) -> Union[
              llama_types.CreateChatCompletionResponse,
              Iterator[llama_types.CreateChatCompletionStreamResponse],
          ]:
              result = f(
                  messages=messages,
                  functions=functions,
                  function_call=function_call,
              )
              prompt = result.prompt
              if result.stop is not None:
                  stop = (
                      []
                      if stop is None
                      else [stop]
                      if isinstance(stop, str)
                      else stop
                  )
                  rstop = (
                      result.stop
                      if isinstance(result.stop, list)
                      else [result.stop]
                  )
                  stop = stop + rstop
  
              if (
                  response_format is not None
                  and response_format["type"] == "json_object"
              ):
                  grammar = llama_grammar.LlamaGrammar.from_string(
                      llama_grammar.JSON_GBNF
                  )
  
              completion_or_chunks = llama.create_completion(
                  prompt=prompt,
                  temperature=temperature,
                  top_p=top_p,
                  top_k=top_k,
                  min_p=min_p,
                  typical_p=typical_p,
                  stream=stream,
                  stop=stop,
                  seed=seed,
                  max_tokens=max_tokens,
                  presence_penalty=presence_penalty,
                  frequency_penalty=frequency_penalty,
                  repeat_penalty=repeat_penalty,
                  tfs_z=tfs_z,
                  mirostat_mode=mirostat_mode,
                  mirostat_tau=mirostat_tau,
                  mirostat_eta=mirostat_eta,
                  model=model,
                  logits_processor=logits_processor,
                  grammar=grammar,
                  logit_bias=logit_bias,
              )
              return _convert_completion_to_chat(
                  completion_or_chunks, stream=stream
              )
  
          register_chat_completion_handler(name)(
              basic_create_chat_completion
          )
          return f
  
      return decorator
  
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 15 for calling a global function with example usage: register_chat_format(name) and returns: decorator, f, _convert_completion_to_chat