class_docstr: Computes the gradient of the output of a particular neuron with
  respect to the inputs of the network.
docstr: Args:
  
      inputs (Tensor or tuple[Tensor, ...]): 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_selector (int, Callable, tuple[int], or slice):
                  Selector for neuron
                  in given layer for which attribution is desired.
                  Neuron selector can be provided as:
  
                  - a single integer, if the layer output is 2D. This integer
                    selects the appropriate neuron column in the layer input
                    or output
  
                  - a tuple of integers or slice objects. Length of this
                    tuple must be one less than the number of dimensions
                    in the input / output of the given layer (since
                    dimension 0 corresponds to number of examples).
                    The elements of the tuple can be either integers or
                    slice objects (slice object allows indexing a
                    range of neurons rather individual ones).
  
                    If any of the tuple elements is a slice object, the
                    indexed output tensor is used for attribution. Note
                    that specifying a slice of a tensor would amount to
                    computing the attribution of the sum of the specified
                    neurons, and not the individual neurons independently.
  
                  - a callable, which should
                    take the target layer as input (single tensor or tuple
                    if multiple tensors are in layer) and return a neuron or
                    aggregate of the layer's neurons for attribution.
                    For example, this function could return the
                    sum of the neurons in the layer or sum of neurons with
                    activations in a particular range. It is expected that
                    this function returns either a tensor with one element
                    or a 1D tensor with length equal to batch_size (one scalar
                    per input example)
      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[Tensor, ...]* of **attributions**:
      - **attributions** (*Tensor* or *tuple[Tensor, ...]*):
                  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))
code: def attribute(
      self,
      inputs: TensorOrTupleOfTensorsGeneric,
      neuron_selector: Union[
          int, Tuple[Union[int, slice], ...], Callable
      ],
      additional_forward_args: Any = None,
      attribute_to_neuron_input: bool = False,
  ) -> TensorOrTupleOfTensorsGeneric:
      r"""
      Args:
  
          inputs (Tensor or tuple[Tensor, ...]): 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_selector (int, Callable, tuple[int], or slice):
                      Selector for neuron
                      in given layer for which attribution is desired.
                      Neuron selector can be provided as:
  
                      - a single integer, if the layer output is 2D. This integer
                        selects the appropriate neuron column in the layer input
                        or output
  
                      - a tuple of integers or slice objects. Length of this
                        tuple must be one less than the number of dimensions
                        in the input / output of the given layer (since
                        dimension 0 corresponds to number of examples).
                        The elements of the tuple can be either integers or
                        slice objects (slice object allows indexing a
                        range of neurons rather individual ones).
  
                        If any of the tuple elements is a slice object, the
                        indexed output tensor is used for attribution. Note
                        that specifying a slice of a tensor would amount to
                        computing the attribution of the sum of the specified
                        neurons, and not the individual neurons independently.
  
                      - a callable, which should
                        take the target layer as input (single tensor or tuple
                        if multiple tensors are in layer) and return a neuron or
                        aggregate of the layer's neurons for attribution.
                        For example, this function could return the
                        sum of the neurons in the layer or sum of neurons with
                        activations in a particular range. It is expected that
                        this function returns either a tensor with one element
                        or a 1D tensor with length equal to batch_size (one scalar
                        per input example)
          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[Tensor, ...]* of **attributions**:
          - **attributions** (*Tensor* or *tuple[Tensor, ...]*):
                      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_tensor_into_tuples(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_selector=neuron_selector,
          device_ids=self.device_ids,
          attribute_to_layer_input=attribute_to_neuron_input,
      )
  
      undo_gradient_requirements(inputs, gradient_mask)
      return _format_output(is_inputs_tuple, input_grads)
  
file_path: captum/captum/attr/_core/neuron/neuron_gradient.py
imports: from typing import Any, Callable, List, Tuple, Union
  from captum._utils.common import _format_additional_forward_args, _format_output, _format_tensor_into_tuples, _is_tuple
  from captum._utils.gradient import _forward_layer_eval_with_neuron_grads, apply_gradient_requirements, undo_gradient_requirements
  from captum._utils.typing import TensorOrTupleOfTensorsGeneric
  from captum.attr._utils.attribution import GradientAttribution, NeuronAttribution
  from captum.log import log_usage
  from torch.nn import Module
label_desc: Use image node_id 2 for calling the NeuronGradient obj's underlying member method code with example usage: obj.attribute(inputs, neuron_selector, additional_forward_args, attribute_to_neuron_input) and returns: _format_output