code: def _forward_layer_eval_with_neuron_grads(
      forward_fn: Callable,
      inputs: Union[Tensor, Tuple[Tensor, ...]],
      layer: List[Module],
      additional_forward_args: Any = None,
      gradient_neuron_selector: None = None,
      grad_enabled: bool = False,
      device_ids: Union[None, List[int]] = None,
      attribute_to_layer_input: bool = False,
  ) -> List[Tuple[Tensor, ...]]:
      ...
  
imports: import threading
  import typing
  import warnings
  from collections import defaultdict
  from typing import Any, Callable, cast, Dict, List, Optional, Sequence, Tuple, Union
  import torch
  from captum._utils.common import _reduce_list, _run_forward, _sort_key_list, _verify_select_neuron
  from captum._utils.sample_gradient import SampleGradientWrapper
  from captum._utils.typing import Literal, ModuleOrModuleList, TargetType, TensorOrTupleOfTensorsGeneric
  from torch import device, Tensor
  from torch.nn import Module
label_desc: Use image node_id 15 for calling a global function with example usage: _forward_layer_eval_with_neuron_grads(forward_fn, inputs, layer, additional_forward_args, gradient_neuron_selector, grad_enabled, device_ids, attribute_to_layer_input) without return types