code: def compute_layer_gradients_and_eval(
      forward_fn: Callable,
      layer: ModuleOrModuleList,
      inputs: Union[Tensor, Tuple[Tensor, ...]],
      target_ind: TargetType = None,
      additional_forward_args: Any = None,
      gradient_neuron_selector: Union[
          None, int, Tuple[Union[int, slice], ...], Callable
      ] = 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, ...]],
      Tuple[Tuple[Tensor, ...], Tuple[Tensor, ...], Tuple[Tensor, ...]],
      Tuple[List[Tuple[Tensor, ...]], List[Tuple[Tensor, ...]]],
  ]:
      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:
          tuple[**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 = _forward_layer_distributed_eval(
              forward_fn,
              inputs,
              layer,
              target_ind=target_ind,
              additional_forward_args=additional_forward_args,
              attribute_to_layer_input=attribute_to_layer_input,
              forward_hook_with_return=True,
              require_layer_grads=True,
          )
          assert output[0].numel() == 1, (
              "Target not provided when necessary, cannot"
              " take gradient with respect to multiple outputs."
          )
  
          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
          )
          all_outputs: Union[
              Tuple[Tensor, ...], List[Tuple[Tensor, ...]]
          ]
          if isinstance(layer, Module):
              all_outputs = _reduce_list(
                  [
                      saved_layer[layer][device_id]
                      if output_fn is None
                      else output_fn(saved_layer[layer][device_id])
                      for device_id in key_list
                  ]
              )
          else:
              all_outputs = [
                  _reduce_list(
                      [
                          saved_layer[single_layer][device_id]
                          if output_fn is None
                          else output_fn(
                              saved_layer[single_layer][device_id]
                          )
                          for device_id in key_list
                      ]
                  )
                  for single_layer in layer
              ]
          all_layers: List[Module] = (
              [layer] if isinstance(layer, Module) else layer
          )
          grad_inputs = tuple(
              layer_tensor
              for single_layer in all_layers
              for device_id in key_list
              for layer_tensor in saved_layer[single_layer][device_id]
          )
          saved_grads = torch.autograd.grad(
              torch.unbind(output), grad_inputs
          )
  
          offset = 0
          all_grads: List[Tuple[Tensor, ...]] = []
          for single_layer in all_layers:
              num_tensors = len(
                  next(iter(saved_layer[single_layer].values()))
              )
              curr_saved_grads = [
                  saved_grads[i : i + num_tensors]
                  for i in range(
                      offset,
                      offset + len(key_list) * num_tensors,
                      num_tensors,
                  )
              ]
              offset += len(key_list) * num_tensors
              if output_fn is not None:
                  curr_saved_grads = [
                      output_fn(curr_saved_grad)
                      for curr_saved_grad in curr_saved_grads
                  ]
  
              all_grads.append(_reduce_list(curr_saved_grads))
  
          layer_grads: Union[
              Tuple[Tensor, ...], List[Tuple[Tensor, ...]]
          ]
          layer_grads = all_grads
          if isinstance(layer, Module):
              layer_grads = all_grads[0]
  
          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 (
                  cast(Tuple[Tensor, ...], layer_grads),
                  cast(Tuple[Tensor, ...], all_outputs),
                  inp_grads,
              )
      return layer_grads, all_outputs
  
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 20 for calling a global function with example usage: compute_layer_gradients_and_eval(forward_fn, layer, inputs, target_ind, additional_forward_args, gradient_neuron_selector, device_ids, attribute_to_layer_input, output_fn) and returns: layer_grads,  all_outputs, cast,  cast,  inp_grads