code: def _forward_layer_eval_with_neuron_grads(
      forward_fn: Callable,
      inputs: Union[Tensor, Tuple[Tensor, ...]],
      layer: ModuleOrModuleList,
      additional_forward_args: Any = None,
      gradient_neuron_selector: Union[
          None, int, Tuple[Union[int, slice], ...], Callable
      ] = None,
      grad_enabled: bool = False,
      device_ids: Union[None, List[int]] = None,
      attribute_to_layer_input: bool = False,
  ) -> Union[
      Tuple[Tuple[Tensor, ...], Tuple[Tensor, ...]],
      Tuple[Tensor, ...],
      List[Tuple[Tensor, ...]],
  ]:
      """
      This method computes forward evaluation for a particular layer using a
      forward hook. If a gradient_neuron_selector is provided, then gradients with
      respect to that neuron in the layer output are also returned.
  
      These functionalities are combined due to the behavior of DataParallel models
      with hooks, in which hooks are executed once per device. We need to internally
      combine the separated tensors from devices by concatenating based on device_ids.
      Any necessary gradients must be taken with respect to each independent batched
      tensor, so the gradients are computed and combined appropriately.
  
      More information regarding the behavior of forward hooks with DataParallel models
      can be found in the PyTorch data parallel documentation. We maintain the separate
      evals in a dictionary protected by a lock, analogous to the gather implementation
      for the core PyTorch DataParallel implementation.
      """
      grad_enabled = (
          True if gradient_neuron_selector is not None else grad_enabled
      )
  
      with torch.autograd.set_grad_enabled(grad_enabled):
          saved_layer = _forward_layer_distributed_eval(
              forward_fn,
              inputs,
              layer,
              additional_forward_args=additional_forward_args,
              attribute_to_layer_input=attribute_to_layer_input,
          )
      device_ids = _extract_device_ids(
          forward_fn, saved_layer, device_ids
      )
      # Identifies correct device ordering based on device ids.
      # key_list is a list of devices in appropriate ordering for concatenation.
      # If only one key exists (standard model), key list simply has one element.
      key_list = _sort_key_list(
          list(next(iter(saved_layer.values())).keys()), device_ids
      )
      if gradient_neuron_selector is not None:
          assert isinstance(
              layer, Module
          ), "Cannot compute neuron gradients for multiple layers simultaneously!"
          inp_grads = _neuron_gradients(
              inputs,
              saved_layer[layer],
              key_list,
              gradient_neuron_selector,
          )
          return (
              _gather_distributed_tensors(
                  saved_layer[layer], key_list=key_list
              ),
              inp_grads,
          )
      else:
          if isinstance(layer, Module):
              return _gather_distributed_tensors(
                  saved_layer[layer], key_list=key_list
              )
          else:
              return [
                  _gather_distributed_tensors(
                      saved_layer[curr_layer], key_list=key_list
                  )
                  for curr_layer in layer
              ]
  
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 16 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) and returns: _gather_distributed_tensors,  inp_grads, _gather_distributed_tensors, unknown