code: def process_api_requests(
      lc_model,
      requests: Optional[List[Union[Any, Dict[str, Any]]]] = None,
      max_workers: int = 10,
      callback_handlers: Optional[List[BaseCallbackHandler]] = None,
      convert_chat_responses: bool = False,
  ):
      """
      Processes API requests in parallel.
      """
  
      # initialize trackers
      retry_queue = queue.Queue()
      status_tracker = (
          StatusTracker()
      )  # single instance to track a collection of variables
      next_request = None  # variable to hold the next request to call
  
      results: list[tuple[int, str]] = []
      errors: dict = {}
      requests_iter = enumerate(requests)
      with ThreadPoolExecutor(max_workers=max_workers) as executor:
          while True:
              # get next request (if one is not already waiting for capacity)
              if not retry_queue.empty():
                  next_request = retry_queue.get_nowait()
                  _logger.warning(
                      f"Retrying request {next_request.index}: {next_request}"
                  )
              elif req := next(requests_iter, None):
                  # get new request
                  index, request_json = req
                  next_request = APIRequest(
                      index=index,
                      lc_model=lc_model,
                      request_json=request_json,
                      results=results,
                      errors=errors,
                      convert_chat_responses=convert_chat_responses,
                  )
                  status_tracker.start_task()
              else:
                  next_request = None
  
              # if enough capacity available, call API
              if next_request:
                  # call API
                  executor.submit(
                      next_request.call_api,
                      status_tracker=status_tracker,
                      callback_handlers=callback_handlers,
                  )
  
              # if all tasks are finished, break
              # check next_request to avoid terminating the process
              # before extra requests need to be processed
              if (
                  status_tracker.num_tasks_in_progress == 0
                  and next_request is None
              ):
                  break
  
              time.sleep(0.001)  # avoid busy waiting
  
          # after finishing, log final status
          if status_tracker.num_tasks_failed > 0:
              raise mlflow.MlflowException(
                  f"{status_tracker.num_tasks_failed} tasks failed. Errors: {errors}"
              )
  
          return [res for _, res in sorted(results)]
  
imports: from __future__ import annotations
  import json
  import logging
  import queue
  import threading
  import time
  import traceback
  from concurrent.futures import ThreadPoolExecutor
  from dataclasses import dataclass
  from typing import Any, Dict, List, Literal, Optional, Set, Union
  import langchain.chains
  import pydantic
  from langchain.callbacks.base import BaseCallbackHandler
  from langchain.schema import AgentAction, AIMessage, HumanMessage, SystemMessage
  from langchain.schema import ChatMessage
  from packaging.version import Version
  import mlflow
  from mlflow.exceptions import MlflowException
label_desc: Use image node_id 1 for calling a global function with example usage: process_api_requests(lc_model, requests, max_workers, callback_handlers, convert_chat_responses) and returns: unknown