class_docstr: Implements gradient SHAP based on the implementation from SHAP's primary
  author. For reference, please view the original
  `implementation
  <https://github.com/slundberg/shap#deep-learning-example-with-gradientexplainer-tensorflowkeraspytorch-models>`_
  and the paper: `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 outputs
  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.
      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
      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 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
      return_convergence_delta (bool, optional): Indicates whether to return
                  convergence delta or not. If `return_convergence_delta`
                  is set to True convergence delta will be returned in
                  a tuple following attributions.
                  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.
      - **delta** (*Tensor*, returned if return_convergence_delta=True):
                  This is computed using the property that the total
                  sum of forward_func(inputs) - forward_func(baselines)
                  must be very close to the total sum of the attributions
                  based on GradientSHAP.
                  Delta is calculated for each example in the input after adding
                  `n_samples` times gaussian noise to each of them. Therefore,
                  the dimensionality of the deltas tensor is equal to the
                  `number of examples in the input` * `n_samples`
                  The deltas are ordered by each input example and `n_samples`
                  noisy samples generated for it.
  
  Examples::
  
      >>> # ImageClassifier takes a single input tensor of images Nx3x32x32,
      >>> # and returns an Nx10 tensor of class probabilities.
      >>> net = ImageClassifier()
      >>> gradient_shap = GradientShap(net)
      >>> input = torch.randn(3, 3, 32, 32, requires_grad=True)
      >>> # choosing baselines randomly
      >>> baselines = torch.randn(20, 3, 32, 32)
      >>> # Computes gradient shap for the input
      >>> # Attribution size matches input size: 3x3x32x32
      >>> attribution = gradient_shap.attribute(input, baselines,
                                                          target=5)
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code: def attribute(
      self,
      inputs: TensorOrTupleOfTensorsGeneric,
      baselines: Union[
          TensorOrTupleOfTensorsGeneric,
          Callable[..., TensorOrTupleOfTensorsGeneric],
      ],
      n_samples: int = 5,
      stdevs: Union[float, Tuple[float, ...]] = 0.0,
      target: TargetType = None,
      additional_forward_args: Any = None,
      return_convergence_delta: bool = False,
  ) -> Union[
      TensorOrTupleOfTensorsGeneric,
      Tuple[TensorOrTupleOfTensorsGeneric, Tensor],
  ]:
      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.
          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
          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 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
          return_convergence_delta (bool, optional): Indicates whether to return
                      convergence delta or not. If `return_convergence_delta`
                      is set to True convergence delta will be returned in
                      a tuple following attributions.
                      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.
          - **delta** (*Tensor*, returned if return_convergence_delta=True):
                      This is computed using the property that the total
                      sum of forward_func(inputs) - forward_func(baselines)
                      must be very close to the total sum of the attributions
                      based on GradientSHAP.
                      Delta is calculated for each example in the input after adding
                      `n_samples` times gaussian noise to each of them. Therefore,
                      the dimensionality of the deltas tensor is equal to the
                      `number of examples in the input` * `n_samples`
                      The deltas are ordered by each input example and `n_samples`
                      noisy samples generated for it.
  
      Examples::
  
          >>> # ImageClassifier takes a single input tensor of images Nx3x32x32,
          >>> # and returns an Nx10 tensor of class probabilities.
          >>> net = ImageClassifier()
          >>> gradient_shap = GradientShap(net)
          >>> input = torch.randn(3, 3, 32, 32, requires_grad=True)
          >>> # choosing baselines randomly
          >>> baselines = torch.randn(20, 3, 32, 32)
          >>> # Computes gradient shap for the input
          >>> # Attribution size matches input size: 3x3x32x32
          >>> attribution = gradient_shap.attribute(input, baselines,
                                                              target=5)
  
      """
      # since `baselines` is a distribution, we can generate it using a function
      # rather than passing it as an input argument
      baselines = _format_callable_baseline(baselines, inputs)
      assert isinstance(baselines[0], torch.Tensor), (
          "Baselines distribution has to be provided in a form "
          "of a torch.Tensor {}.".format(baselines[0])
      )
  
      input_min_baseline_x_grad = InputBaselineXGradient(
          self.forward_func, self.multiplies_by_inputs
      )
      input_min_baseline_x_grad.gradient_func = self.gradient_func
  
      nt = NoiseTunnel(input_min_baseline_x_grad)
  
      # NOTE: using attribute.__wrapped__ to not log
      attributions = nt.attribute.__wrapped__(
          nt,  # self
          inputs,
          nt_type="smoothgrad",
          nt_samples=n_samples,
          stdevs=stdevs,
          draw_baseline_from_distrib=True,
          baselines=baselines,
          target=target,
          additional_forward_args=additional_forward_args,
          return_convergence_delta=return_convergence_delta,
      )
  
      return attributions
  
imports: import typing
  from typing import Any, Callable, Tuple, Union
  import numpy
  import torch
  from captum._utils.common import _is_tuple
  from captum._utils.typing import BaselineType, Literal, TargetType, Tensor, TensorOrTupleOfTensorsGeneric
  from captum.attr._core.noise_tunnel import NoiseTunnel
  from captum.attr._utils.attribution import GradientAttribution
  from captum.attr._utils.common import _compute_conv_delta_and_format_attrs, _format_callable_baseline, _format_input_baseline
  from captum.log import log_usage
label_desc: Use image node_id 4 for calling the GradientShap obj's underlying member method code with example usage: obj.attribute(inputs, baselines, n_samples, stdevs, target, additional_forward_args, return_convergence_delta) and returns: attributions