code: def compute_layer_gradients_and_eval(
      forward_fn: Callable,
      layer: Module,
      inputs: Union[Tensor, Tuple[Tensor, ...]],
      target_ind: TargetType = None,
      additional_forward_args: Any = None,
      *,
      gradient_neuron_index: Union[int, Tuple[int, ...]],
      device_ids: Union[None, List[int]] = None,
      attribute_to_layer_input: bool = False,
      output_fn: Union[None, Callable] = None,
  ) -> Tuple[
      Tuple[Tensor, ...],
      Tuple[Tensor, ...],
      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 14 for calling a global function with example usage: compute_layer_gradients_and_eval(forward_fn, layer, inputs, target_ind, additional_forward_args) without return types