text: #!/usr/bin/env python3
  from typing import Any, Callable, List, Tuple, Union
  
  from torch.nn import Module
  
  from ...._utils.common import _format_additional_forward_args, _format_input, _is_tuple
  from ...._utils.typing import TensorOrTupleOfTensorsGeneric
  from ..._utils.attribution import GradientAttribution, NeuronAttribution
  from ..._utils.common import _format_attributions
  from ..._utils.gradient import (
      _forward_layer_eval_with_neuron_grads,
      apply_gradient_requirements,
      undo_gradient_requirements,
  )
  
  
  class NeuronGradient(NeuronAttribution, GradientAttribution):
      r"""
      Computes the gradient of the output of a particular neuron with
      respect to the inputs of the network.
      """
  
      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.
                            Currently, it is assumed that the inputs or the outputs
                            of the layer, depending on which one is used for
                            attribution, can only be a single tensor.
              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.
          """
          NeuronAttribution.__init__(self, forward_func, layer, device_ids)
          GradientAttribution.__init__(self, forward_func)
  
      def attribute(
          self,
          inputs: TensorOrTupleOfTensorsGeneric,
          neuron_index: Union[int, Tuple[int, ...]],
          additional_forward_args: Any = None,
          attribute_to_neuron_input: bool = False,
      ) -> TensorOrTupleOfTensorsGeneric:
          r"""
          Args:
  
              inputs (tensor or tuple of tensors):  Input for which neuron
                          gradients 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.
              neuron_index (int or tuple): Index of neuron in output of given
                              layer for which attribution is desired. Length of
                              this tuple must be one less than the number of
                              dimensions in the output of the given layer (since
                              dimension 0 corresponds to number of examples).
                              An integer may be provided instead of a tuple of
                              length 1.
              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_neuron_input (bool, optional): Indicates whether to
                          compute the attributions with respect to the neuron input
                          or output. If `attribute_to_neuron_input` is set to True
                          then the attributions will be computed with respect to
                          neuron's inputs, otherwise it will be computed with respect
                          to neuron's outputs.
                          Note that currently it is assumed that either the input
                          or the output of internal neurons, 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* of **attributions**:
              - **attributions** (*tensor* or tuple of *tensors*):
                          Gradients of particular neuron with respect to each input
                          feature. Attributions will always be the same size as the
                          provided inputs, with each value providing the attribution
                          of the corresponding input index.
                          If a single tensor is provided as inputs, a single tensor is
                          returned. If a tuple is provided for inputs, a tuple of
                          corresponding sized tensors is returned.
  
          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()
              >>> neuron_ig = NeuronGradient(net, net.conv1)
              >>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
              >>> # To compute neuron attribution, we need to provide the neuron
              >>> # index for which attribution is desired. Since the layer output
              >>> # is Nx12x32x32, we need a tuple in the form (0..11,0..31,0..31)
              >>> # which indexes a particular neuron in the layer output.
              >>> # For this example, we choose the index (4,1,2).
              >>> # Computes neuron gradient for neuron with
              >>> # index (4,1,2).
              >>> attribution = neuron_ig.attribute(input, (4,1,2))
          """
          is_inputs_tuple = _is_tuple(inputs)
          inputs = _format_input(inputs)
          additional_forward_args = _format_additional_forward_args(
              additional_forward_args
          )
          gradient_mask = apply_gradient_requirements(inputs)
  
          _, input_grads, _ = _forward_layer_eval_with_neuron_grads(
              self.forward_func,
              inputs,
              self.layer,
              additional_forward_args,
              gradient_neuron_index=neuron_index,
              device_ids=self.device_ids,
              attribute_to_layer_input=attribute_to_neuron_input,
          )
  
          undo_gradient_requirements(inputs, gradient_mask)
          return _format_attributions(is_inputs_tuple, input_grads)
  