class_docstr: Implements gradient SHAP for a neuron in a hidden layer based on the
  implementation from SHAP's primary author. For reference, please, view:
  
  https://github.com/slundberg/shap\
  #deep-learning-example-with-gradientexplainer-tensorflowkeraspytorch-models
  
  A Unified Approach to Interpreting Model Predictions
  https://papers.nips.cc/paper\
  7062-a-unified-approach-to-interpreting-model-predictions
  
  GradientShap approximates SHAP values by computing the expectations of
  gradients by randomly sampling from the distribution of baselines/references.
  It adds white noise to each input sample `n_samples` times, selects a
  random baseline from baselines' distribution and a random point along the
  path between the baseline and the input, and computes the gradient of the
  neuron with index `neuron_selector` with respect to those selected random
  points. The final SHAP values represent the expected values of
  `gradients * (inputs - baselines)`.
  
  GradientShap makes an assumption that the input features are independent
  and that the explanation model is linear, meaning that the explanations
  are modeled through the additive composition of feature effects.
  Under those assumptions, SHAP value can be approximated as the expectation
  of gradients that are computed for randomly generated `n_samples` input
  samples after adding gaussian noise `n_samples` times to each input for
  different baselines/references.
  
  In some sense it can be viewed as an approximation of integrated gradients
  by computing the expectations of gradients for different baselines.
  
  Current implementation uses Smoothgrad from :class:`.NoiseTunnel` in order to
  randomly draw samples from the distribution of baselines, add noise to input
  samples and compute the expectation (smoothgrad).
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docstr: Args:
  
      inputs (Tensor or tuple[Tensor, ...]): Input for which SHAP attribution
                  values 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)
      baselines (Tensor, tuple[Tensor, ...], or Callable):
                  Baselines define the starting point from which expectation
                  is computed and can be provided as:
  
                  - a single tensor, if inputs is a single tensor, with
                    the first dimension equal to the number of examples
                    in the baselines' distribution. The remaining dimensions
                    must match with input tensor's dimension starting from
                    the second dimension.
  
                  - a tuple of tensors, if inputs is a tuple of tensors,
                    with the first dimension of any tensor inside the tuple
                    equal to the number of examples in the baseline's
                    distribution. The remaining dimensions must match
                    the dimensions of the corresponding input tensor
                    starting from the second dimension.
  
                  - callable function, optionally takes `inputs` as an
                    argument and either returns a single tensor
                    or a tuple of those.
  
                  It is recommended that the number of samples in the baselines'
                  tensors is larger than one.
      n_samples (int, optional): The number of randomly generated examples
                  per sample in the input batch. Random examples are
                  generated by adding gaussian random noise to each sample.
                  Default: `5` if `n_samples` is not provided.
      stdevs    (float or tuple of float, optional): The standard deviation
                  of gaussian noise with zero mean that is added to each
                  input in the batch. If `stdevs` is a single float value
                  then that same value is used for all inputs. If it is
                  a tuple, then it must have the same length as the inputs
                  tuple. In this case, each stdev value in the stdevs tuple
                  corresponds to the input with the same index in the inputs
                  tuple.
                  Default: 0.0
      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 can contain a tuple of ND tensors or
                  any arbitrary python type of any shape.
                  In case of the ND tensor the first dimension of the
                  tensor must correspond to the batch size. It will be
                  repeated for each `n_steps` for each randomly generated
                  input sample.
                  Note that the gradients 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 neuron, 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:
      **attributions** or 2-element tuple of **attributions**, **delta**:
      - **attributions** (*Tensor* or *tuple[Tensor, ...]*):
                  Attribution score computed based on GradientSHAP 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.
      >>> net = ImageClassifier()
      >>> neuron_grad_shap = NeuronGradientShap(net, net.linear2)
      >>> input = torch.randn(3, 3, 32, 32, requires_grad=True)
      >>> # choosing baselines randomly
      >>> baselines = torch.randn(20, 3, 32, 32)
      >>> # Computes gradient SHAP of first neuron in linear2 layer
      >>> # with respect to the input's of the network.
      >>> # Attribution size matches input size: 3x3x32x32
      >>> attribution = neuron_grad_shap.attribute(input, neuron_ind=0
                                                      baselines)
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code: def attribute(
      self,
      inputs: TensorOrTupleOfTensorsGeneric,
      neuron_selector: Union[
          int, Tuple[Union[int, slice], ...], Callable
      ],
      baselines: Union[
          TensorOrTupleOfTensorsGeneric,
          Callable[..., TensorOrTupleOfTensorsGeneric],
      ],
      n_samples: int = 5,
      stdevs: float = 0.0,
      additional_forward_args: Any = None,
      attribute_to_neuron_input: bool = False,
  ) -> TensorOrTupleOfTensorsGeneric:
      r"""
      Args:
  
          inputs (Tensor or tuple[Tensor, ...]): Input for which SHAP attribution
                      values 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)
          baselines (Tensor, tuple[Tensor, ...], or Callable):
                      Baselines define the starting point from which expectation
                      is computed and can be provided as:
  
                      - a single tensor, if inputs is a single tensor, with
                        the first dimension equal to the number of examples
                        in the baselines' distribution. The remaining dimensions
                        must match with input tensor's dimension starting from
                        the second dimension.
  
                      - a tuple of tensors, if inputs is a tuple of tensors,
                        with the first dimension of any tensor inside the tuple
                        equal to the number of examples in the baseline's
                        distribution. The remaining dimensions must match
                        the dimensions of the corresponding input tensor
                        starting from the second dimension.
  
                      - callable function, optionally takes `inputs` as an
                        argument and either returns a single tensor
                        or a tuple of those.
  
                      It is recommended that the number of samples in the baselines'
                      tensors is larger than one.
          n_samples (int, optional): The number of randomly generated examples
                      per sample in the input batch. Random examples are
                      generated by adding gaussian random noise to each sample.
                      Default: `5` if `n_samples` is not provided.
          stdevs    (float or tuple of float, optional): The standard deviation
                      of gaussian noise with zero mean that is added to each
                      input in the batch. If `stdevs` is a single float value
                      then that same value is used for all inputs. If it is
                      a tuple, then it must have the same length as the inputs
                      tuple. In this case, each stdev value in the stdevs tuple
                      corresponds to the input with the same index in the inputs
                      tuple.
                      Default: 0.0
          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 can contain a tuple of ND tensors or
                      any arbitrary python type of any shape.
                      In case of the ND tensor the first dimension of the
                      tensor must correspond to the batch size. It will be
                      repeated for each `n_steps` for each randomly generated
                      input sample.
                      Note that the gradients 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 neuron, 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:
          **attributions** or 2-element tuple of **attributions**, **delta**:
          - **attributions** (*Tensor* or *tuple[Tensor, ...]*):
                      Attribution score computed based on GradientSHAP 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.
          >>> net = ImageClassifier()
          >>> neuron_grad_shap = NeuronGradientShap(net, net.linear2)
          >>> input = torch.randn(3, 3, 32, 32, requires_grad=True)
          >>> # choosing baselines randomly
          >>> baselines = torch.randn(20, 3, 32, 32)
          >>> # Computes gradient SHAP of first neuron in linear2 layer
          >>> # with respect to the input's of the network.
          >>> # Attribution size matches input size: 3x3x32x32
          >>> attribution = neuron_grad_shap.attribute(input, neuron_ind=0
                                                          baselines)
  
      """
      gs = GradientShap(self.forward_func, self.multiplies_by_inputs)
      gs.gradient_func = construct_neuron_grad_fn(
          self.layer,
          neuron_selector,
          self.device_ids,
          attribute_to_neuron_input=attribute_to_neuron_input,
      )
  
      # NOTE: using __wrapped__ to not log
      return gs.attribute.__wrapped__(  # type: ignore
          gs,  # self
          inputs,
          baselines,
          n_samples=n_samples,
          stdevs=stdevs,
          additional_forward_args=additional_forward_args,
      )
  
file_path: captum/captum/attr/_core/neuron/neuron_gradient_shap.py
imports: from typing import Any, Callable, List, Tuple, Union
  from captum._utils.gradient import construct_neuron_grad_fn
  from captum._utils.typing import TensorOrTupleOfTensorsGeneric
  from captum.attr._core.gradient_shap import GradientShap
  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 NeuronGradientShap obj's underlying member method code with example usage: obj.attribute(inputs, neuron_selector, baselines, n_samples, stdevs, additional_forward_args, attribute_to_neuron_input) and returns: gs