text: #!/usr/bin/env python3
  from typing import Any, Callable, List, Tuple, Union
  
  from torch import Tensor
  from torch.nn import Module
  
  from captum.log import log_usage
  
  from ...._utils.common import (
      _format_additional_forward_args,
      _format_input,
      _format_output,
  )
  from ...._utils.gradient import (
      apply_gradient_requirements,
      compute_layer_gradients_and_eval,
      undo_gradient_requirements,
  )
  from ...._utils.typing import TargetType
  from ..._utils.attribution import GradientAttribution, LayerAttribution
  
  
  class LayerGradientXActivation(LayerAttribution, GradientAttribution):
      r"""
      Computes element-wise product of gradient and activation for selected
      layer on given inputs.
      """
  
      def __init__(
          self,
          forward_func: Callable,
          layer: Module,
          device_ids: Union[None, List[int]] = None,
      ) -> None:
          r"""
          Args:
  
              forward_func (callable):  The forward function of the model or any
                            modification of it
              layer (torch.nn.Module): Layer for which attributions are computed.
                            Output size of attribute matches this layer's input or
                            output dimensions, depending on whether we attribute to
                            the inputs or outputs of the layer, corresponding to
                            attribution of each neuron in the input or output of
                            this layer.
              device_ids (list(int)): Device ID list, necessary only if forward_func
                            applies a DataParallel model. This allows reconstruction of
                            intermediate outputs from batched results across devices.
                            If forward_func is given as the DataParallel model itself,
                            then it is not necessary to provide this argument.
          """
          LayerAttribution.__init__(self, forward_func, layer, device_ids)
          GradientAttribution.__init__(self, forward_func)
  
      @log_usage()
      def attribute(
          self,
          inputs: Union[Tensor, Tuple[Tensor, ...]],
          target: TargetType = None,
          additional_forward_args: Any = None,
          attribute_to_layer_input: bool = False,
      ) -> Union[Tensor, Tuple[Tensor, ...]]:
          r"""
          Args:
  
              inputs (tensor or tuple of tensors):  Input for which attributions
                          are computed. If forward_func takes a single
                          tensor as input, a single input tensor should be provided.
                          If forward_func takes multiple tensors as input, a tuple
                          of the input tensors should be provided. It is assumed
                          that for all given input tensors, dimension 0 corresponds
                          to the number of examples, and if multiple input tensors
                          are provided, the examples must be aligned appropriately.
              target (int, tuple, tensor or list, optional):  Output indices for
                          which gradients are computed (for classification cases,
                          this is usually the target class).
                          If the network returns a scalar value per example,
                          no target index is necessary.
                          For general 2D outputs, targets can be either:
  
                          - a single integer or a tensor containing a single
                            integer, which is applied to all input examples
  
                          - a list of integers or a 1D tensor, with length matching
                            the number of examples in inputs (dim 0). Each integer
                            is applied as the target for the corresponding example.
  
                          For outputs with > 2 dimensions, targets can be either:
  
                          - A single tuple, which contains #output_dims - 1
                            elements. This target index is applied to all examples.
  
                          - A list of tuples with length equal to the number of
                            examples in inputs (dim 0), and each tuple containing
                            #output_dims - 1 elements. Each tuple is applied as the
                            target for the corresponding example.
  
                          Default: None
              additional_forward_args (any, optional): If the forward function
                          requires additional arguments other than the inputs for
                          which attributions should not be computed, this argument
                          can be provided. It must be either a single additional
                          argument of a Tensor or arbitrary (non-tuple) type or a
                          tuple containing multiple additional arguments including
                          tensors or any arbitrary python types. These arguments
                          are provided to forward_func in order following the
                          arguments in inputs.
                          Note that attributions are not computed with respect
                          to these arguments.
                          Default: None
              attribute_to_layer_input (bool, optional): Indicates whether to
                          compute the attribution with respect to the layer input
                          or output. If `attribute_to_layer_input` is set to True
                          then the attributions will be computed with respect to
                          layer input, otherwise it will be computed with respect
                          to layer output.
                          Default: False
  
          Returns:
              *tensor* or tuple of *tensors* of **attributions**:
              - **attributions** (*tensor* or tuple of *tensors*):
                          Product of gradient and activation for each
                          neuron in given layer output.
                          Attributions will always be the same size as the
                          output of the given layer.
                          Attributions are returned in a tuple based on whether
                          the layer inputs / outputs are contained in a tuple
                          from a forward hook. For standard modules, inputs of
                          a single tensor are usually wrapped in a tuple, while
                          outputs of a single tensor are not.
  
          Examples::
  
              >>> # ImageClassifier takes a single input tensor of images Nx3x32x32,
              >>> # and returns an Nx10 tensor of class probabilities.
              >>> # It contains an attribute conv1, which is an instance of nn.conv2d,
              >>> # and the output of this layer has dimensions Nx12x32x32.
              >>> net = ImageClassifier()
              >>> layer_ga = LayerGradientXActivation(net, net.conv1)
              >>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
              >>> # Computes layer activation x gradient for class 3.
              >>> # attribution size matches layer output, Nx12x32x32
              >>> attribution = layer_ga.attribute(input, 3)
          """
          inputs = _format_input(inputs)
          additional_forward_args = _format_additional_forward_args(
              additional_forward_args
          )
          gradient_mask = apply_gradient_requirements(inputs)
          # Returns gradient of output with respect to
          # hidden layer and hidden layer evaluated at each input.
          layer_gradients, layer_evals, is_layer_tuple = compute_layer_gradients_and_eval(
              self.forward_func,
              self.layer,
              inputs,
              target,
              additional_forward_args,
              device_ids=self.device_ids,
              attribute_to_layer_input=attribute_to_layer_input,
          )
          undo_gradient_requirements(inputs, gradient_mask)
          return _format_output(
              is_layer_tuple,
              tuple(
                  layer_gradient * layer_eval
                  for layer_gradient, layer_eval in zip(layer_gradients, layer_evals)
              ),
          )
  