code: def batch_completion(
      model: str,
      # Optional OpenAI params: see https://platform.openai.com/docs/api-reference/chat/create
      messages: List = [],
      functions: Optional[List] = None,
      function_call: Optional[str] = None,
      temperature: Optional[float] = None,
      top_p: Optional[float] = None,
      n: Optional[int] = None,
      stream: Optional[bool] = None,
      stop=None,
      max_tokens: Optional[float] = None,
      presence_penalty: Optional[float] = None,
      frequency_penalty: Optional[float] = None,
      logit_bias: Optional[dict] = None,
      user: Optional[str] = None,
      deployment_id=None,
      request_timeout: Optional[int] = None,
      timeout: Optional[int] = 600,
      # Optional liteLLM function params
      **kwargs,
  ):
      """
      Batch litellm.completion function for a given model.
  
      Args:
          model (str): The model to use for generating completions.
          messages (List, optional): List of messages to use as input for generating completions. Defaults to [].
          functions (List, optional): List of functions to use as input for generating completions. Defaults to [].
          function_call (str, optional): The function call to use as input for generating completions. Defaults to "".
          temperature (float, optional): The temperature parameter for generating completions. Defaults to None.
          top_p (float, optional): The top-p parameter for generating completions. Defaults to None.
          n (int, optional): The number of completions to generate. Defaults to None.
          stream (bool, optional): Whether to stream completions or not. Defaults to None.
          stop (optional): The stop parameter for generating completions. Defaults to None.
          max_tokens (float, optional): The maximum number of tokens to generate. Defaults to None.
          presence_penalty (float, optional): The presence penalty for generating completions. Defaults to None.
          frequency_penalty (float, optional): The frequency penalty for generating completions. Defaults to None.
          logit_bias (dict, optional): The logit bias for generating completions. Defaults to {}.
          user (str, optional): The user string for generating completions. Defaults to "".
          deployment_id (optional): The deployment ID for generating completions. Defaults to None.
          request_timeout (int, optional): The request timeout for generating completions. Defaults to None.
  
      Returns:
          list: A list of completion results.
      """
      args = locals()
      batch_messages = messages
      completions = []
      model = model
      custom_llm_provider = None
      if model.split("/", 1)[0] in litellm.provider_list:
          custom_llm_provider = model.split("/", 1)[0]
          model = model.split("/", 1)[1]
      if custom_llm_provider == "vllm":
          optional_params = get_optional_params(
              functions=functions,
              function_call=function_call,
              temperature=temperature,
              top_p=top_p,
              n=n,
              stream=stream,
              stop=stop,
              max_tokens=max_tokens,
              presence_penalty=presence_penalty,
              frequency_penalty=frequency_penalty,
              logit_bias=logit_bias,
              user=user,
              # params to identify the model
              model=model,
              custom_llm_provider=custom_llm_provider,
          )
          results = vllm.batch_completions(
              model=model,
              messages=batch_messages,
              custom_prompt_dict=litellm.custom_prompt_dict,
              optional_params=optional_params,
          )
      # all non VLLM models for batch completion models
      else:
  
          def chunks(lst, n):
              """Yield successive n-sized chunks from lst."""
              for i in range(0, len(lst), n):
                  yield lst[i : i + n]
  
          with ThreadPoolExecutor(max_workers=100) as executor:
              for sub_batch in chunks(batch_messages, 100):
                  for message_list in sub_batch:
                      kwargs_modified = args.copy()
                      kwargs_modified["messages"] = message_list
                      original_kwargs = {}
                      if "kwargs" in kwargs_modified:
                          original_kwargs = kwargs_modified.pop(
                              "kwargs"
                          )
                      future = executor.submit(
                          completion,
                          **kwargs_modified,
                          **original_kwargs,
                      )
                      completions.append(future)
  
          # Retrieve the results from the futures
          results = [future.result() for future in completions]
      return results
  
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 4 for calling a global function with example usage: batch_completion(model, messages, functions, function_call, temperature, top_p, n, stream, stop, max_tokens, presence_penalty, frequency_penalty, logit_bias, user, deployment_id, request_timeout, timeout) and returns: results