code: def _forward_layer_eval(
      forward_fn: Callable,
      inputs: Union[Tensor, Tuple[Tensor, ...]],
      layer: Module,
      additional_forward_args: Any = None,
      device_ids: Union[None, List[int]] = None,
      attribute_to_layer_input: bool = False,
  ) -> Tuple[Tuple[Tensor, ...], Literal[True, False]]:
      return _forward_layer_eval_with_neuron_grads(
          forward_fn,
          inputs,
          layer,
          additional_forward_args=additional_forward_args,
          gradient_neuron_index=None,
          device_ids=device_ids,
          attribute_to_layer_input=attribute_to_layer_input,
      )
  
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 5 for calling a global function with example usage: _forward_layer_eval(forward_fn, inputs, layer, additional_forward_args, device_ids, attribute_to_layer_input) and returns: _forward_layer_eval_with_neuron_grads