code: def batch_completion_models(*args, **kwargs):
      """
      Send a request to multiple language models concurrently and return the response
      as soon as one of the models responds.
  
      Args:
          *args: Variable-length positional arguments passed to the completion function.
          **kwargs: Additional keyword arguments:
              - models (str or list of str): The language models to send requests to.
              - Other keyword arguments to be passed to the completion function.
  
      Returns:
          str or None: The response from one of the language models, or None if no response is received.
  
      Note:
          This function utilizes a ThreadPoolExecutor to parallelize requests to multiple models.
          It sends requests concurrently and returns the response from the first model that responds.
      """
      import concurrent
  
      if "model" in kwargs:
          kwargs.pop("model")
      if "models" in kwargs:
          models = kwargs["models"]
          kwargs.pop("models")
          futures = {}
          with concurrent.futures.ThreadPoolExecutor(
              max_workers=len(models)
          ) as executor:
              for model in models:
                  futures[model] = executor.submit(
                      completion, *args, model=model, **kwargs
                  )
  
              for model, future in sorted(
                  futures.items(), key=lambda x: models.index(x[0])
              ):
                  if future.result() is not None:
                      return future.result()
      elif "deployments" in kwargs:
          deployments = kwargs["deployments"]
          kwargs.pop("deployments")
          kwargs.pop("model_list")
          nested_kwargs = kwargs.pop("kwargs", {})
          futures = {}
          with concurrent.futures.ThreadPoolExecutor(
              max_workers=len(deployments)
          ) as executor:
              for deployment in deployments:
                  for key in kwargs.keys():
                      if (
                          key not in deployment
                      ):  # don't override deployment values e.g. model name, api base, etc.
                          deployment[key] = kwargs[key]
                  kwargs = {**deployment, **nested_kwargs}
                  futures[deployment["model"]] = executor.submit(
                      completion, **kwargs
                  )
  
              while futures:
                  # wait for the first returned future
                  print_verbose("\n\n waiting for next result\n\n")
                  done, _ = concurrent.futures.wait(
                      futures.values(),
                      return_when=concurrent.futures.FIRST_COMPLETED,
                  )
                  print_verbose(f"done list\n{done}")
                  for future in done:
                      try:
                          result = future.result()
                          return result
                      except Exception as e:
                          # if model 1 fails, continue with response from model 2, model3
                          print_verbose(
                              f"\n\ngot an exception, ignoring, removing from futures"
                          )
                          print_verbose(futures)
                          new_futures = {}
                          for key, value in futures.items():
                              if future == value:
                                  print_verbose(f"removing key{key}")
                                  continue
                              else:
                                  new_futures[key] = value
                          futures = new_futures
                          print_verbose(f"new futures{futures}")
                          continue
  
                  print_verbose("\n\ndone looping through futures\n\n")
                  print_verbose(futures)
  
      return None
  
imports: import os, openai, sys, json, inspect, uuid, datetime, threading
  from typing import Any, Literal, Union
  from functools import partial
  import dotenv, traceback, random, asyncio, time, contextvars
  from copy import deepcopy
  import httpx
  import litellm
  from litellm import client, exception_type, get_optional_params, get_litellm_params, Logging
  from litellm.utils import get_secret, CustomStreamWrapper, read_config_args, completion_with_fallbacks, get_llm_provider, get_api_key, mock_completion_streaming_obj, convert_to_model_response_object, token_counter, Usage, get_optional_params_embeddings, get_optional_params_image_gen
  from .llms import anthropic, together_ai, ai21, sagemaker, bedrock, huggingface_restapi, replicate, aleph_alpha, nlp_cloud, baseten, vllm, ollama, ollama_chat, cloudflare, cohere, petals, oobabooga, openrouter, palm, gemini, vertex_ai, maritalk
  from .llms.openai import OpenAIChatCompletion, OpenAITextCompletion
  from .llms.azure import AzureChatCompletion
  from .llms.huggingface_restapi import Huggingface
  from .llms.prompt_templates.factory import prompt_factory, custom_prompt, function_call_prompt
  import tiktoken
  from concurrent.futures import ThreadPoolExecutor
  from typing import Callable, List, Optional, Dict, Union, Mapping
  from .caching import enable_cache, disable_cache, update_cache
  from litellm.utils import get_secret, CustomStreamWrapper, TextCompletionStreamWrapper, ModelResponse, TextCompletionResponse, TextChoices, EmbeddingResponse, read_config_args, Choices, Message
label_desc: Use image node_id 5 for calling a global function with example usage: batch_completion_models() and returns: None, future, result