class_docstr: Approximates the integral of gradients for a particular neuron
  along the path from a baseline input to the given input.
  If no baseline is provided, the default baseline is the zero tensor.
  More details regarding the integrated gradient method can be found in the
  original paper here:
  https://arxiv.org/abs/1703.01365
  
  Note that this method is equivalent to applying integrated gradients
  where the output is the output of the identified neuron.
docstr: Args:
  
      inputs (Tensor or tuple[Tensor, ...]): Input for which neuron integrated
                  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)
      baselines (scalar, Tensor, tuple of scalar, or Tensor, optional):
                  Baselines define the starting point from which integral
                  is computed.
                  Baselines can be provided as:
  
                  - a single tensor, if inputs is a single tensor, with
                    exactly the same dimensions as inputs or the first
                    dimension is one and the remaining dimensions match
                    with inputs.
  
                  - a single scalar, if inputs is a single tensor, which will
                    be broadcasted for each input value in input tensor.
  
                  - a tuple of tensors or scalars, the baseline corresponding
                    to each tensor in the inputs' tuple can be:
  
                    - either a tensor with matching dimensions to
                      corresponding tensor in the inputs' tuple
                      or the first dimension is one and the remaining
                      dimensions match with the corresponding
                      input tensor.
  
                    - or a scalar, corresponding to a tensor in the
                      inputs' tuple. This scalar value is broadcasted
                      for corresponding input tensor.
  
                  In the cases when `baselines` is not provided, we internally
                  use zero scalar corresponding to each input tensor.
  
                  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 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.
                  For a tensor, the first dimension of the tensor must
                  correspond to the number of examples. It will be
                  repeated for each of `n_steps` along the integrated
                  path. For all other types, the given argument is used
                  for all forward evaluations.
                  Note that attributions are not computed with respect
                  to these arguments.
                  Default: None
      n_steps (int, optional): The number of steps used by the approximation
                  method. Default: 50.
      method (str, optional): Method for approximating the integral,
                  one of `riemann_right`, `riemann_left`, `riemann_middle`,
                  `riemann_trapezoid` or `gausslegendre`.
                  Default: `gausslegendre` if no method is provided.
      internal_batch_size (int, optional): Divides total #steps * #examples
                  data points into chunks of size at most internal_batch_size,
                  which are computed (forward / backward passes)
                  sequentially. internal_batch_size must be at least equal to
                  #examples.
                  For DataParallel models, each batch is split among the
                  available devices, so evaluations on each available
                  device contain internal_batch_size / num_devices examples.
                  If internal_batch_size is None, then all evaluations are
                  processed in one batch.
                  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:
      *Tensor* or *tuple[Tensor, ...]* of **attributions**:
      - **attributions** (*Tensor* or *tuple[Tensor, ...]*):
                  Integrated gradients 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.
      >>> # 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 = NeuronIntegratedGradients(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 integrated gradients for neuron with
      >>> # index (4,1,2).
      >>> attribution = neuron_ig.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[None, Tensor, Tuple[Tensor, ...]] = None,
      additional_forward_args: Any = None,
      n_steps: int = 50,
      method: str = "gausslegendre",
      internal_batch_size: Union[None, int] = None,
      attribute_to_neuron_input: bool = False,
  ) -> TensorOrTupleOfTensorsGeneric:
      r"""
      Args:
  
          inputs (Tensor or tuple[Tensor, ...]): Input for which neuron integrated
                      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)
          baselines (scalar, Tensor, tuple of scalar, or Tensor, optional):
                      Baselines define the starting point from which integral
                      is computed.
                      Baselines can be provided as:
  
                      - a single tensor, if inputs is a single tensor, with
                        exactly the same dimensions as inputs or the first
                        dimension is one and the remaining dimensions match
                        with inputs.
  
                      - a single scalar, if inputs is a single tensor, which will
                        be broadcasted for each input value in input tensor.
  
                      - a tuple of tensors or scalars, the baseline corresponding
                        to each tensor in the inputs' tuple can be:
  
                        - either a tensor with matching dimensions to
                          corresponding tensor in the inputs' tuple
                          or the first dimension is one and the remaining
                          dimensions match with the corresponding
                          input tensor.
  
                        - or a scalar, corresponding to a tensor in the
                          inputs' tuple. This scalar value is broadcasted
                          for corresponding input tensor.
  
                      In the cases when `baselines` is not provided, we internally
                      use zero scalar corresponding to each input tensor.
  
                      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 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.
                      For a tensor, the first dimension of the tensor must
                      correspond to the number of examples. It will be
                      repeated for each of `n_steps` along the integrated
                      path. For all other types, the given argument is used
                      for all forward evaluations.
                      Note that attributions are not computed with respect
                      to these arguments.
                      Default: None
          n_steps (int, optional): The number of steps used by the approximation
                      method. Default: 50.
          method (str, optional): Method for approximating the integral,
                      one of `riemann_right`, `riemann_left`, `riemann_middle`,
                      `riemann_trapezoid` or `gausslegendre`.
                      Default: `gausslegendre` if no method is provided.
          internal_batch_size (int, optional): Divides total #steps * #examples
                      data points into chunks of size at most internal_batch_size,
                      which are computed (forward / backward passes)
                      sequentially. internal_batch_size must be at least equal to
                      #examples.
                      For DataParallel models, each batch is split among the
                      available devices, so evaluations on each available
                      device contain internal_batch_size / num_devices examples.
                      If internal_batch_size is None, then all evaluations are
                      processed in one batch.
                      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:
          *Tensor* or *tuple[Tensor, ...]* of **attributions**:
          - **attributions** (*Tensor* or *tuple[Tensor, ...]*):
                      Integrated gradients 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.
          >>> # 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 = NeuronIntegratedGradients(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 integrated gradients for neuron with
          >>> # index (4,1,2).
          >>> attribution = neuron_ig.attribute(input, (4,1,2))
      """
      ig = IntegratedGradients(
          self.forward_func, self.multiplies_by_inputs
      )
      ig.gradient_func = construct_neuron_grad_fn(
          self.layer,
          neuron_selector,
          self.device_ids,
          attribute_to_neuron_input,
      )
      # NOTE: using __wrapped__ to not log
      # Return only attributions and not delta
      return ig.attribute.__wrapped__(  # type: ignore
          ig,  # self
          inputs,
          baselines,
          additional_forward_args=additional_forward_args,
          n_steps=n_steps,
          method=method,
          internal_batch_size=internal_batch_size,
      )
  
file_path: captum/captum/attr/_core/neuron/neuron_integrated_gradients.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.integrated_gradients import IntegratedGradients
  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 NeuronIntegratedGradients obj's underlying member method code with example usage: obj.attribute(inputs, neuron_selector, baselines, additional_forward_args, n_steps, method, internal_batch_size, attribute_to_neuron_input) and returns: ig