text: #!/usr/bin/env python3
  from typing import Any, Callable, List, Tuple, Union
  
  import torch
  from torch import Tensor
  from torch.nn import Module
  
  from captum.log import log_usage
  
  from ...._utils.common import _format_output
  from ...._utils.gradient import _forward_layer_eval
  from ...._utils.typing import ModuleOrModuleList
  from ..._utils.attribution import LayerAttribution
  
  
  class LayerActivation(LayerAttribution):
      r"""
      Computes activation of selected layer for given input.
      """
  
      def __init__(
          self,
          forward_func: Callable,
          layer: ModuleOrModuleList,
          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 or list(torch.nn.Module)): Layer or layers
                            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. If multiple layers are provided, attributions
                            are returned as a list, each element corresponding to the
                            activations of the corresponding 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)
  
      @log_usage()
      def attribute(
          self,
          inputs: Union[Tensor, Tuple[Tensor, ...]],
          additional_forward_args: Any = None,
          attribute_to_layer_input: bool = False,
      ) -> Union[Tensor, Tuple[Tensor, ...], List[Union[Tensor, Tuple[Tensor, ...]]]]:
          r"""
          Args:
  
              inputs (tensor or tuple of tensors):  Input for which layer
                          activation is 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.
              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.
                          Note that currently it is assumed that either the input
                          or the output of internal layer, depending on whether we
                          attribute to the input or output, is a single tensor.
                          Support for multiple tensors will be added later.
                          Default: False
  
          Returns:
              *tensor* or tuple of *tensors* or *list* of **attributions**:
              - **attributions** (*tensor* or tuple of *tensors* or *list*):
                          Activation of 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 if
                          the layer inputs / outputs contain multiple tensors,
                          otherwise a single tensor is returned.
                          If multiple layers are provided, attributions
                          are returned as a list, each element corresponding to the
                          activations of the corresponding layer.
  
  
  
          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_act = LayerActivation(net, net.conv1)
              >>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
              >>> # Computes layer activation.
              >>> # attribution is layer output, with size Nx12x32x32
              >>> attribution = layer_cond.attribute(input)
          """
          with torch.no_grad():
              layer_eval = _forward_layer_eval(
                  self.forward_func,
                  inputs,
                  self.layer,
                  additional_forward_args,
                  device_ids=self.device_ids,
                  attribute_to_layer_input=attribute_to_layer_input,
              )
          if isinstance(self.layer, Module):
              return _format_output(len(layer_eval) > 1, layer_eval)
          else:
              return [
                  _format_output(len(single_layer_eval) > 1, single_layer_eval)
                  for single_layer_eval in layer_eval
              ]
  
      @property
      def multiplies_by_inputs(self):
          return True
  