code: def _unpack_kwargs(
      flat_args: Tuple[Any, ...], kwarg_keys: Tuple[str, ...]
  ) -> Tuple[Tuple[Any, ...], Dict[str, Any]]:
      """See _pack_kwargs."""
      assert len(kwarg_keys) <= len(
          flat_args
      ), f"too many keys {len(kwarg_keys)} vs. {len(flat_args)}"
      if len(kwarg_keys) == 0:
          return flat_args, {}
      args = flat_args[: -len(kwarg_keys)]
      kwargs = dict(zip(kwarg_keys, flat_args[-len(kwarg_keys) :]))
      return args, kwargs
  
imports: import dataclasses
  import traceback
  from typing import Any, Callable, Container, Dict, List, Optional, OrderedDict, Tuple, TypeVar, overload
  import torch
  import torch.distributed
  from torch import nn
  from torch.nn.parallel._functions import _get_stream
  from torch.nn.parallel.scatter_gather import _is_namedtuple
  from torch.nn.utils.rnn import PackedSequence
label_desc: Use image node_id 3 for calling a global function with example usage: _unpack_kwargs(flat_args, kwarg_keys) and returns: args,  kwargs, flat_args,  dict