code: def _batch_args_kwargs(
      list_of_flattened_args: List[List[Any]],
  ) -> Tuple[Tuple[Any], Dict[Any, Any]]:
      """Batch a list of flatten args and returns regular args and kwargs"""
      # Ray's flatten arg format is a list with alternating key and values
      # e.g. args=(1, 2), kwargs={"key": "val"} got turned into
      #      [None, 1, None, 2, "key", "val"]
      arg_lengths = {len(args) for args in list_of_flattened_args}
      assert (
          len(arg_lengths) == 1
      ), "All batch requests should have the same number of parameters."
      arg_length = arg_lengths.pop()
  
      batched_flattened_args = []
      for idx in range(arg_length):
          if idx % 2 == 0:
              batched_flattened_args.append(
                  list_of_flattened_args[0][idx]
              )
          else:
              batched_flattened_args.append(
                  [item[idx] for item in list_of_flattened_args]
              )
  
      return recover_args(batched_flattened_args)
  
imports: import asyncio
  import time
  from dataclasses import dataclass
  from functools import wraps
  from inspect import isasyncgenfunction, iscoroutinefunction
  from typing import Any, AsyncGenerator, Callable, Dict, Iterable, List, Optional, Tuple, Type, TypeVar, overload
  from ray._private.signature import extract_signature, flatten_args, recover_args
  from ray._private.utils import get_or_create_event_loop
  from ray.serve._private.utils import extract_self_if_method_call
  from ray.serve.exceptions import RayServeException
  from ray.util.annotations import PublicAPI
label_desc: Use image node_id 1 for calling a global function with example usage: _batch_args_kwargs(list_of_flattened_args) and returns: recover_args