code: def _forward_layer_distributed_eval(
      forward_fn: Callable,
      inputs: Union[Tensor, Tuple[Tensor, ...]],
      layer: Module,
      target_ind: TargetType = None,
      additional_forward_args: Any = None,
      attribute_to_layer_input: bool = False,
      forward_hook_with_return: Literal[False] = False,
  ) -> Tuple[Dict[device, Tuple[Tensor, ...]], Literal[True, False]]:
      ...
  
imports: import threading
  import typing
  import warnings
  from typing import Any, Callable, Dict, List, Tuple, Union, cast
  import torch
  from torch import Tensor, device
  from torch.nn import Module
  from captum.attr._utils.batching import _reduce_list, _sort_key_list
  from captum.attr._utils.common import _run_forward, _verify_select_column
  from captum.attr._utils.typing import Literal, TargetType, TensorOrTupleOfTensorsGeneric
label_desc: Use image node_id 6 for calling a global function with example usage: _forward_layer_distributed_eval(forward_fn, inputs, layer, target_ind, additional_forward_args, attribute_to_layer_input, forward_hook_with_return) without return types