docstr: 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 attributions 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 the
                  layer input, otherwise it will be computed with respect
                  to layer output.
                  Note that currently it is assumed that either the input
                  or the outputs of internal layers, depending on whether we
                  attribute to the input or output, are single tensors.
                  Support for multiple tensors will be added later.
                  Default: False
      relu_attributions (bool, optional): Indicates whether to
                  apply a ReLU operation on the final attribution,
                  returning only non-negative attributions. Setting this
                  flag to True matches the original GradCAM algorithm,
                  otherwise, by default, both positive and negative
                  attributions are returned.
                  Default: False
  
  Returns:
      *tensor* or tuple of *tensors* of **attributions**:
      - **attributions** (*tensor* or tuple of *tensors*):
                  Attributions based on GradCAM method.
                  Attributions will be the same size as the
                  output of the given layer, except for dimension 2,
                  which will be 1 due to summing over channels.
                  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 a layer conv4, which is an instance of nn.conv2d,
      # >>> # and the output of this layer has dimensions Nx50x8x8.
      # >>> # It is the last convolution layer, which is the recommended
      # >>> # use case for GradCAM.
      # >>> net = ImageClassifier()
      # >>> layer_gc = LayerGradCam(net, net.conv4)
      # >>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
      # >>> # Computes layer GradCAM for class 3.
      # >>> # attribution size matches layer output except for dimension
      # >>> # 1, so dimensions of attr would be Nx1x8x8.
      # >>> attr = layer_gc.attribute(input, 3)
      # >>> # GradCAM attributions are often upsampled and viewed as a
      # >>> # mask to the input, since the convolutional layer output
      # >>> # spatially matches the original input image.
      # >>> # This can be done with LayerAttribution's interpolate method.
      # >>> upsampled_attr = LayerAttribution.interpolate(attr, (32, 32))
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code: def attribute(
      self,
      inputs: Union[Tensor, Tuple[Tensor, ...]],
      target: TargetType = None,
      additional_forward_args: Any = None,
      attribute_to_layer_input: bool = False,
      relu_attributions: 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 attributions 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 the
                      layer input, otherwise it will be computed with respect
                      to layer output.
                      Note that currently it is assumed that either the input
                      or the outputs of internal layers, depending on whether we
                      attribute to the input or output, are single tensors.
                      Support for multiple tensors will be added later.
                      Default: False
          relu_attributions (bool, optional): Indicates whether to
                      apply a ReLU operation on the final attribution,
                      returning only non-negative attributions. Setting this
                      flag to True matches the original GradCAM algorithm,
                      otherwise, by default, both positive and negative
                      attributions are returned.
                      Default: False
  
      Returns:
          *tensor* or tuple of *tensors* of **attributions**:
          - **attributions** (*tensor* or tuple of *tensors*):
                      Attributions based on GradCAM method.
                      Attributions will be the same size as the
                      output of the given layer, except for dimension 2,
                      which will be 1 due to summing over channels.
                      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 a layer conv4, which is an instance of nn.conv2d,
          # >>> # and the output of this layer has dimensions Nx50x8x8.
          # >>> # It is the last convolution layer, which is the recommended
          # >>> # use case for GradCAM.
          # >>> net = ImageClassifier()
          # >>> layer_gc = LayerGradCam(net, net.conv4)
          # >>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
          # >>> # Computes layer GradCAM for class 3.
          # >>> # attribution size matches layer output except for dimension
          # >>> # 1, so dimensions of attr would be Nx1x8x8.
          # >>> attr = layer_gc.attribute(input, 3)
          # >>> # GradCAM attributions are often upsampled and viewed as a
          # >>> # mask to the input, since the convolutional layer output
          # >>> # spatially matches the original input image.
          # >>> # This can be done with LayerAttribution's interpolate method.
          # >>> upsampled_attr = LayerAttribution.interpolate(attr, (32, 32))
      """
      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)
  
      # Gradient Calculation end
  
      # Addition: shape from PyG to General PyTorch
      layer_gradients = tuple(
          layer_grad.transpose(0, 1).unsqueeze(0)
          for layer_grad in layer_gradients
      )
  
      layer_evals = tuple(
          layer_eval.transpose(0, 1).unsqueeze(0)
          for layer_eval in layer_evals
      )
      # end
  
      summed_grads = tuple(
          torch.mean(
              layer_grad,
              dim=tuple(x for x in range(2, len(layer_grad.shape))),
              keepdim=True,
          )
          for layer_grad in layer_gradients
      )
  
      scaled_acts = tuple(
          torch.sum(summed_grad * layer_eval, dim=1, keepdim=True)
          for summed_grad, layer_eval in zip(summed_grads, layer_evals)
      )
      if relu_attributions:
          scaled_acts = tuple(
              F.relu(scaled_act) for scaled_act in scaled_acts
          )
  
      # what I add: shape from General PyTorch to PyG
  
      scaled_acts = tuple(
          scaled_act.squeeze(0).transpose(0, 1)
          for scaled_act in scaled_acts
      )
  
      # end
  
      return _format_attributions(is_layer_tuple, scaled_acts)
  
imports: from typing import Any, Callable, List, Tuple, Union, Dict
  import captum.attr
  import torch
  import torch.nn
  import torch.nn.functional
  from captum.attr._utils.common import _format_additional_forward_args, _format_attributions, _format_input
  from captum.attr._utils.gradient import apply_gradient_requirements, compute_layer_gradients_and_eval, undo_gradient_requirements
  from captum.attr._utils.typing import TargetType
  from torch import Tensor
  from torch.nn import Module
  from torch_geometric.utils.loop import add_remaining_self_loops
  from .base_explainer import WalkBase
  from ..models.utils import subgraph, normalize
label_desc: Use image node_id 2 for calling the GraphLayerGradCam obj's underlying member method code with example usage: obj.attribute(inputs, target, additional_forward_args, attribute_to_layer_input, relu_attributions) and returns: _format_attributions