class_docstr: Computes conductance with respect to particular hidden neuron. The
  returned output is in the shape of the input, showing the attribution
  / conductance of each input feature to the selected hidden layer neuron.
  The details of the approach can be found here:
  https://arxiv.org/abs/1805.12233
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docstr: Args:
  
      inputs (Tensor or tuple[Tensor, ...]): Input for which neuron
                  conductance is 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. 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).
                    This can be used as long as the layer input / output
                    is a single tensor.
  
                  - a callable, which should
                    take the target layer as input (single tensor or tuple
                    if multiple tensors are in layer) and return a selected
                    neuron - output shape should be 1D with length equal to
                    batch_size (one scalar per input example)
  
                    NOTE: Callables applicable for neuron conductance are
                    less general than those of other methods and should
                    NOT aggregate values of the layer, only return a specific
                    output. This option should only be used in cases where the
                    layer input / output is a tuple of tensors, where the other
                    options would not suffice. This limitation is necessary since
                    neuron conductance, unlike other neuron methods, also utilizes
                    the gradient of output with respect to the intermedite neuron,
                    which cannot be computed for aggregations of multiple
                    intemediate neurons.
      baselines (scalar, Tensor, tuple of scalar, or Tensor, optional):
                  Baselines define the starting point from which integral
                  is computed and 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
      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 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, ...]*):
                  Conductance 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_cond = NeuronConductance(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.
      >>> # Computes neuron conductance for neuron with
      >>> # index (4,1,2).
      >>> attribution = neuron_cond.attribute(input, (4,1,2))
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code: def attribute(
      self,
      inputs: TensorOrTupleOfTensorsGeneric,
      neuron_selector: Union[int, Tuple[int, ...], Callable],
      baselines: BaselineType = None,
      target: TargetType = None,
      additional_forward_args: Any = None,
      n_steps: int = 50,
      method: str = "riemann_trapezoid",
      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
                      conductance is 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. 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).
                        This can be used as long as the layer input / output
                        is a single tensor.
  
                      - a callable, which should
                        take the target layer as input (single tensor or tuple
                        if multiple tensors are in layer) and return a selected
                        neuron - output shape should be 1D with length equal to
                        batch_size (one scalar per input example)
  
                        NOTE: Callables applicable for neuron conductance are
                        less general than those of other methods and should
                        NOT aggregate values of the layer, only return a specific
                        output. This option should only be used in cases where the
                        layer input / output is a tuple of tensors, where the other
                        options would not suffice. This limitation is necessary since
                        neuron conductance, unlike other neuron methods, also utilizes
                        the gradient of output with respect to the intermedite neuron,
                        which cannot be computed for aggregations of multiple
                        intemediate neurons.
          baselines (scalar, Tensor, tuple of scalar, or Tensor, optional):
                      Baselines define the starting point from which integral
                      is computed and 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
          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 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, ...]*):
                      Conductance 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_cond = NeuronConductance(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.
          >>> # Computes neuron conductance for neuron with
          >>> # index (4,1,2).
          >>> attribution = neuron_cond.attribute(input, (4,1,2))
      """
      if callable(neuron_selector):
          warnings.warn(
              "The neuron_selector provided is a callable. Please ensure that this"
              " function only selects neurons from the given layer; aggregating"
              " or performing other operations on the tensor may lead to inaccurate"
              " results."
          )
      is_inputs_tuple = _is_tuple(inputs)
  
      inputs, baselines = _format_input_baseline(inputs, baselines)
      _validate_input(inputs, baselines, n_steps, method)
  
      num_examples = inputs[0].shape[0]
  
      if internal_batch_size is not None:
          num_examples = inputs[0].shape[0]
          attrs = _batch_attribution(
              self,
              num_examples,
              internal_batch_size,
              n_steps,
              inputs=inputs,
              baselines=baselines,
              neuron_selector=neuron_selector,
              target=target,
              additional_forward_args=additional_forward_args,
              method=method,
              attribute_to_neuron_input=attribute_to_neuron_input,
          )
      else:
          attrs = self._attribute(
              inputs=inputs,
              neuron_selector=neuron_selector,
              baselines=baselines,
              target=target,
              additional_forward_args=additional_forward_args,
              n_steps=n_steps,
              method=method,
              attribute_to_neuron_input=attribute_to_neuron_input,
          )
      return _format_output(is_inputs_tuple, attrs)
  
file_path: captum/captum/attr/_core/neuron/neuron_conductance.py
imports: import warnings
  from typing import Any, Callable, List, Tuple, Union
  import torch
  from captum._utils.common import _expand_additional_forward_args, _expand_target, _format_additional_forward_args, _format_output, _is_tuple, _verify_select_neuron
  from captum._utils.gradient import compute_layer_gradients_and_eval
  from captum._utils.typing import BaselineType, TargetType, TensorOrTupleOfTensorsGeneric
  from captum.attr._utils.approximation_methods import approximation_parameters
  from captum.attr._utils.attribution import GradientAttribution, NeuronAttribution
  from captum.attr._utils.batching import _batch_attribution
  from captum.attr._utils.common import _format_input_baseline, _reshape_and_sum, _validate_input
  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 NeuronConductance obj's underlying member method code with example usage: obj.attribute(inputs, neuron_selector, baselines, target, additional_forward_args, n_steps, method, internal_batch_size, attribute_to_neuron_input) and returns: _format_output