code: def compute_layer_gradients_and_eval(
      forward_fn: Callable,
      layer: Module,
      inputs: Union[Tensor, Tuple[Tensor, ...]],
      model=None,
      pre_hook=None,
      target: TargetType = None,
      target_ind: TargetType = None,
      additional_forward_args: Any = None,
      gradient_neuron_index: Union[None, int, Tuple[int, ...]] = None,
      device_ids: Union[None, List[int]] = None,
      attribute_to_layer_input: bool = False,
      output_fn: Union[None, Callable] = None,
  ) -> Union[
      Tuple[Tuple[Tensor, ...], Tuple[Tensor, ...], bool],
      Tuple[
          Tuple[Tensor, ...],
          Tuple[Tensor, ...],
          Tuple[Tensor, ...],
          bool,
      ],
  ]:
      r"""
      Computes gradients of the output with respect to a given layer as well
      as the output evaluation of the layer for an arbitrary forward function
      and given input.
  
      For data parallel models, hooks are executed once per device ,so 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 inputs in a dictionary protected by a lock, analogous to the
      gather implementation for the core PyTorch DataParallel implementation.
  
      NOTE: To properly handle inplace operations, a clone of the layer output
      is stored. This structure inhibits execution of a backward hook on the last
      module for the layer output when computing the gradient with respect to
      the input, since we store an intermediate clone, as
      opposed to the true module output. If backward module hooks are necessary
      for the final module when computing input gradients, utilize
      _forward_layer_eval_with_neuron_grads instead.
  
      Args:
  
          forward_fn: forward function. This can be for example model's
                      forward function.
          layer:      Layer for which gradients / output will be evaluated.
          inputs:     Input at which gradients are evaluated,
                      will be passed to forward_fn.
          target_ind: Index of the target class for which gradients
                      must be computed (classification only).
          output_fn:  An optional function that is applied to the layer inputs or
                      outputs depending whether the `attribute_to_layer_input` is
                      set to `True` or `False`
          args:       Additional input arguments that forward function requires.
                      It takes an empty tuple (no additional arguments) if no
                      additional arguments are required
  
  
      Returns:
          2-element tuple of **gradients**, **evals**:
          - **gradients**:
              Gradients of output with respect to target layer output.
          - **evals**:
              Target layer output for given input.
      """
      with torch.autograd.set_grad_enabled(True):
          # saved_layer is a dictionary mapping device to a tuple of
          # layer evaluations on that device.
          (
              saved_layer,
              output,
              is_layer_tuple,
          ) = _forward_layer_distributed_eval(
              forward_fn,
              inputs,
              layer,
              target_ind=target_ind,
              additional_forward_args=None,
              attribute_to_layer_input=attribute_to_layer_input,
              forward_hook_with_return=True,
          )
  
          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(saved_layer.keys()), device_ids
          )
  
          all_outputs = _reduce_list(
              [
                  saved_layer[device_id]
                  if output_fn is None
                  else output_fn(saved_layer[device_id])
                  for device_id in key_list
              ]
          )
          num_tensors = len(saved_layer[next(iter(saved_layer))])
          grad_inputs = list(
              layer_tensor
              for device_id in key_list
              for layer_tensor in saved_layer[device_id]
          )
  
          # --- add for require_grad ---
          for i, grad_input in enumerate(grad_inputs):
              if not grad_input.requires_grad:
                  grad_inputs.pop(i)
                  num_tensors -= 1
  
          grad_inputs = tuple(grad_inputs)
  
          if target is None:
              num_node = len(inputs[0])
              num_feature = inputs[0].shape[1]
              grad_node = []
              import time, numpy as np
  
              total_tik = time.time()
              back_time = []
              for node_idx in range(num_node):
                  # for feature_idx in range(num_feature):
                  # --- May be using Batch to calculate parallel ---
  
                  # chosen_output = torch.cat([output[node_idx, feature_idx: feature_idx + 1], output[node_idx + num_node, feature_idx: feature_idx + 1]])
                  chosen_output = torch.cat(
                      [output[node_idx], output[node_idx + num_node]]
                  )
                  # chosen_output = output[node_idx]
                  back_tik = time.time()
                  # pre_hook.remove()
                  # jaco = torch.autograd.functional.jacobian(model, (grad_inputs, additional_forward_args[0]))
                  saved_grads = torch.autograd.grad(
                      torch.unbind(chosen_output),
                      grad_inputs,
                      retain_graph=True,
                  )
                  back_tok = time.time()
                  back_time.append(back_tok - back_tik)
                  saved_grads = [
                      saved_grads[i : i + num_tensors]
                      for i in range(0, len(saved_grads), num_tensors)
                  ]
                  if output_fn is not None:
                      saved_grads = [
                          output_fn(saved_grad)
                          for saved_grad in saved_grads
                      ]
  
                  all_grads = _reduce_list(saved_grads)
                  grad_node.append(all_grads)
                  if gradient_neuron_index is not None:
                      inp_grads = _neuron_gradients(
                          inputs,
                          saved_layer,
                          key_list,
                          gradient_neuron_index,
                      )
                      return (
                          all_grads,
                          all_outputs,
                          inp_grads,
                          is_layer_tuple,
                      )
              # print(f'#D#total time: {time.time() - total_tik}\n'
              #       f'back ward time: {np.sum(back_time)}')
              return grad_node, all_outputs, is_layer_tuple
  
          # assert output[0].numel() == 1, (
          #     "Target not provided when necessary, cannot"
          #     " take gradient with respect to multiple outputs."
          # )
  
          saved_grads = torch.autograd.grad(
              torch.unbind(output), grad_inputs
          )
          saved_grads = [
              saved_grads[i : i + num_tensors]
              for i in range(0, len(saved_grads), num_tensors)
          ]
          if output_fn is not None:
              saved_grads = [
                  output_fn(saved_grad) for saved_grad in saved_grads
              ]
  
          all_grads = _reduce_list(saved_grads)
          if gradient_neuron_index is not None:
              inp_grads = _neuron_gradients(
                  inputs, saved_layer, key_list, gradient_neuron_index
              )
              return all_grads, all_outputs, inp_grads, is_layer_tuple
      return all_grads, all_outputs, is_layer_tuple
  
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 16 for calling a global function with example usage: compute_layer_gradients_and_eval(forward_fn, layer, inputs, model, pre_hook, target, target_ind, additional_forward_args, gradient_neuron_index, device_ids, attribute_to_layer_input, output_fn) and returns: all_grads,  all_outputs,  is_layer_tuple, grad_node,  all_outputs,  is_layer_tuple, all_grads,  all_outputs,  inp_grads,  is_layer_tuple, all_grads,  all_outputs,  inp_grads,  is_layer_tuple