class_docstr: Extends NeuronAttribution and uses LayerDeepLiftShap algorithms and
  approximates SHAP values for given input `layer` and `neuron_selector`.
  For each input sample - baseline pair it computes DeepLift attributions
  with respect to inputs or outputs of given `layer` and `neuron_selector`
  averages resulting attributions across baselines. Whether to compute the
  attributions with respect to the inputs or outputs of the layer is defined
  by the input flag `attribute_to_layer_input`.
  More details about the algorithm can be found here:
  
  https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf
  
  Note that the explanation model:
      1. Assumes that input features are independent of one another
      2. Is linear, meaning that the explanations are modeled through
          the additive composition of feature effects.
  
  Although, it assumes a linear model for each explanation, the overall
  model across multiple explanations can be complex and non-linear.
class_docstr_tok: ["Extends","NeuronAttribution","and","uses","LayerDeepLiftShap","algorithms","and","approximates","SHAP","values","for","given","input","`","layer","`","and","`","neuron_selector","`",".","For","each","input","sample","-","baseline","pair","it","computes","DeepLift","attributions","with","respect","to","inputs","or","outputs","of","given","`","layer","`","and","`","neuron_selector","`","averages","resulting","attributions","across","baselines",".","Whether","to","compute","the","attributions","with","respect","to","the","inputs","or","outputs","of","the","layer","is","defined","by","the","input","flag","`","attribute_to_layer_input","`",".","More","details","about","the","algorithm","can","be","found","here",":","https",":","\/\/papers.nips.cc\/paper\/7062-a-unified-approach-to-interpreting-model-predictions.pdf","Note","that","the","explanation","model",":","1",".","Assumes","that","input","features","are","independent","of","one","another","2",".","Is","linear",",","meaning","that","the","explanations","are","modeled","through","the","additive","composition","of","feature","effects",".","Although",",","it","assumes","a","linear","model","for","each","explanation",",","the","overall","model","across","multiple","explanations","can","be","complex","and","non-linear","."]
docstr: Args:
  
      inputs (Tensor or tuple[Tensor, ...]): Input for which layer
                  attributions are computed. If model takes a
                  single tensor as input, a single input tensor should be
                  provided. If model 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 (aka batch size),
                  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 reference samples that are compared with
                  the inputs. In order to assign attribution scores DeepLift
                  computes the differences between the inputs/outputs and
                  corresponding references. Baselines 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.
      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 model 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 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
      custom_attribution_func (Callable, optional): A custom function for
                  computing final attribution scores. This function can take
                  at least one and at most three arguments with the
                  following signature:
  
                  - custom_attribution_func(multipliers)
                  - custom_attribution_func(multipliers, inputs)
                  - custom_attribution_func(multipliers, inputs, baselines)
  
                  In case this function is not provided, we use the default
                  logic defined as: multipliers * (inputs - baselines)
                  It is assumed that all input arguments, `multipliers`,
                  `inputs` and `baselines` are provided in tuples of same
                  length. `custom_attribution_func` returns a tuple of
                  attribution tensors that have the same length as the
                  `inputs`.
                  Default: None
  
  Returns:
      **attributions** or 2-element tuple of **attributions**, **delta**:
      - **attributions** (*Tensor* or *tuple[Tensor, ...]*):
                  Computes attributions using Deeplift's rescale rule for
                  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.
      >>> net = ImageClassifier()
      >>> # creates an instance of LayerDeepLift to interpret target
      >>> # class 1 with respect to conv4 layer.
      >>> dl = NeuronDeepLiftShap(net, net.conv4)
      >>> input = torch.randn(1, 3, 32, 32, requires_grad=True)
      >>> # Computes deeplift attribution scores for conv4 layer and neuron
      >>> # index (4,1,2).
      >>> attribution = dl.attribute(input, (4,1,2))
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code: def attribute(
      self,
      inputs: TensorOrTupleOfTensorsGeneric,
      neuron_selector: Union[
          int, Tuple[Union[int, slice], ...], Callable
      ],
      baselines: Union[
          TensorOrTupleOfTensorsGeneric,
          Callable[..., TensorOrTupleOfTensorsGeneric],
      ],
      additional_forward_args: Any = None,
      attribute_to_neuron_input: bool = False,
      custom_attribution_func: Union[
          None, Callable[..., Tuple[Tensor, ...]]
      ] = None,
  ) -> TensorOrTupleOfTensorsGeneric:
      r"""
      Args:
  
          inputs (Tensor or tuple[Tensor, ...]): Input for which layer
                      attributions are computed. If model takes a
                      single tensor as input, a single input tensor should be
                      provided. If model 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 (aka batch size),
                      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 reference samples that are compared with
                      the inputs. In order to assign attribution scores DeepLift
                      computes the differences between the inputs/outputs and
                      corresponding references. Baselines 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.
          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 model 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 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
          custom_attribution_func (Callable, optional): A custom function for
                      computing final attribution scores. This function can take
                      at least one and at most three arguments with the
                      following signature:
  
                      - custom_attribution_func(multipliers)
                      - custom_attribution_func(multipliers, inputs)
                      - custom_attribution_func(multipliers, inputs, baselines)
  
                      In case this function is not provided, we use the default
                      logic defined as: multipliers * (inputs - baselines)
                      It is assumed that all input arguments, `multipliers`,
                      `inputs` and `baselines` are provided in tuples of same
                      length. `custom_attribution_func` returns a tuple of
                      attribution tensors that have the same length as the
                      `inputs`.
                      Default: None
  
      Returns:
          **attributions** or 2-element tuple of **attributions**, **delta**:
          - **attributions** (*Tensor* or *tuple[Tensor, ...]*):
                      Computes attributions using Deeplift's rescale rule for
                      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.
          >>> net = ImageClassifier()
          >>> # creates an instance of LayerDeepLift to interpret target
          >>> # class 1 with respect to conv4 layer.
          >>> dl = NeuronDeepLiftShap(net, net.conv4)
          >>> input = torch.randn(1, 3, 32, 32, requires_grad=True)
          >>> # Computes deeplift attribution scores for conv4 layer and neuron
          >>> # index (4,1,2).
          >>> attribution = dl.attribute(input, (4,1,2))
      """
  
      dl = DeepLiftShap(
          cast(Module, self.forward_func), self.multiplies_by_inputs
      )
      dl.gradient_func = construct_neuron_grad_fn(
          self.layer,
          neuron_selector,
          attribute_to_neuron_input=attribute_to_neuron_input,
      )
  
      # NOTE: using __wrapped__ to not log
      return dl.attribute.__wrapped__(  # type: ignore
          dl,  # self
          inputs,
          baselines,
          additional_forward_args=additional_forward_args,
          custom_attribution_func=custom_attribution_func,
      )
  
file_path: captum/captum/attr/_core/neuron/neuron_deep_lift.py
imports: from typing import Any, Callable, cast, Tuple, Union
  from captum._utils.gradient import construct_neuron_grad_fn
  from captum._utils.typing import BaselineType, TensorOrTupleOfTensorsGeneric
  from captum.attr._core.deep_lift import DeepLift, DeepLiftShap
  from captum.attr._utils.attribution import GradientAttribution, NeuronAttribution
  from captum.log import log_usage
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 2 for calling the NeuronDeepLiftShap obj's underlying member method code with example usage: obj.attribute(inputs, neuron_selector, baselines, additional_forward_args, attribute_to_neuron_input, custom_attribution_func) and returns: dl