class_docstr: Integrated Gradients is an axiomatic model interpretability algorithm that
  assigns an importance score to each input feature by approximating the
  integral of gradients of the model's output with respect to the inputs
  along the path (straight line) from given baselines / references to inputs.
  
  Baselines can be provided as input arguments to attribute method.
  To approximate the integral we can choose to use either a variant of
  Riemann sum or Gauss-Legendre quadrature rule.
  
  More details regarding the integrated gradients method can be found in the
  original paper:
  https://arxiv.org/abs/1703.01365
class_docstr_tok: ["Integrated","Gradients","is","an","axiomatic","model","interpretability","algorithm","that","assigns","an","importance","score","to","each","input","feature","by","approximating","the","integral","of","gradients","of","the","model","'s","output","with","respect","to","the","inputs","along","the","path","(","straight","line",")","from","given","baselines","\/","references","to","inputs",".","Baselines","can","be","provided","as","input","arguments","to","attribute","method",".","To","approximate","the","integral","we","can","choose","to","use","either","a","variant","of","Riemann","sum","or","Gauss-Legendre","quadrature","rule",".","More","details","regarding","the","integrated","gradients","method","can","be","found","in","the","original","paper",":","https",":","\/\/arxiv.org\/abs\/1703.01365"]
docstr: This method attributes the output of the model with given target index
  (in case it is provided, otherwise it assumes that output is a
  scalar) to the inputs of the model using the approach described above.
  
  In addition to that it also returns, if `return_convergence_delta` is
  set to True, integral approximation delta based on the completeness
  property of integrated gradients.
  
  Args:
  
      inputs (Tensor or tuple[Tensor, ...]): Input for which 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.
      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
      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, ...]*):
              Integrated gradients 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):
              The difference between the total approximated and true
              integrated gradients. This is computed using the property
              that the total sum of forward_func(inputs) -
              forward_func(baselines) must equal the total sum of the
              integrated gradient.
              Delta is calculated per example, meaning that the number of
              elements in returned delta tensor is equal to the number of
              examples in inputs.
  
  Examples::
  
      >>> # ImageClassifier takes a single input tensor of images Nx3x32x32,
      >>> # and returns an Nx10 tensor of class probabilities.
      >>> net = ImageClassifier()
      >>> ig = IntegratedGradients(net)
      >>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
      >>> # Computes integrated gradients for class 3.
      >>> attribution = ig.attribute(input, target=3)
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code: def attribute(  # type: ignore
      self,
      inputs: TensorOrTupleOfTensorsGeneric,
      baselines: BaselineType = None,
      target: TargetType = None,
      additional_forward_args: Any = None,
      n_steps: int = 50,
      method: str = "gausslegendre",
      internal_batch_size: Union[None, int] = None,
      return_convergence_delta: bool = False,
  ) -> Union[
      TensorOrTupleOfTensorsGeneric,
      Tuple[TensorOrTupleOfTensorsGeneric, Tensor],
  ]:
      r"""
      This method attributes the output of the model with given target index
      (in case it is provided, otherwise it assumes that output is a
      scalar) to the inputs of the model using the approach described above.
  
      In addition to that it also returns, if `return_convergence_delta` is
      set to True, integral approximation delta based on the completeness
      property of integrated gradients.
  
      Args:
  
          inputs (Tensor or tuple[Tensor, ...]): Input for which 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.
          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
          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, ...]*):
                  Integrated gradients 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):
                  The difference between the total approximated and true
                  integrated gradients. This is computed using the property
                  that the total sum of forward_func(inputs) -
                  forward_func(baselines) must equal the total sum of the
                  integrated gradient.
                  Delta is calculated per example, meaning that the number of
                  elements in returned delta tensor is equal to the number of
                  examples in inputs.
  
      Examples::
  
          >>> # ImageClassifier takes a single input tensor of images Nx3x32x32,
          >>> # and returns an Nx10 tensor of class probabilities.
          >>> net = ImageClassifier()
          >>> ig = IntegratedGradients(net)
          >>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
          >>> # Computes integrated gradients for class 3.
          >>> attribution = ig.attribute(input, target=3)
      """
      # Keeps track whether original input is a tuple or not before
      # converting it into a tuple.
      is_inputs_tuple = _is_tuple(inputs)
  
      inputs, baselines = _format_input_baseline(inputs, baselines)
  
      _validate_input(inputs, baselines, n_steps, method)
  
      if internal_batch_size is not None:
          num_examples = inputs[0].shape[0]
          attributions = _batch_attribution(
              self,
              num_examples,
              internal_batch_size,
              n_steps,
              inputs=inputs,
              baselines=baselines,
              target=target,
              additional_forward_args=additional_forward_args,
              method=method,
          )
      else:
          attributions = self._attribute(
              inputs=inputs,
              baselines=baselines,
              target=target,
              additional_forward_args=additional_forward_args,
              n_steps=n_steps,
              method=method,
          )
  
      if return_convergence_delta:
          start_point, end_point = baselines, inputs
          # computes approximation error based on the completeness axiom
          delta = self.compute_convergence_delta(
              attributions,
              start_point,
              end_point,
              additional_forward_args=additional_forward_args,
              target=target,
          )
          return _format_output(is_inputs_tuple, attributions), delta
      return _format_output(is_inputs_tuple, attributions)
  
file_path: captum/captum/attr/_core/integrated_gradients.py
imports: import typing
  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
  from captum._utils.typing import BaselineType, Literal, TargetType, TensorOrTupleOfTensorsGeneric
  from captum.attr._utils.approximation_methods import approximation_parameters
  from captum.attr._utils.attribution import GradientAttribution
  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
label_desc: Use image node_id 4 for calling the IntegratedGradients obj's underlying member method code with example usage: obj.attribute(inputs, baselines, target, additional_forward_args, n_steps, method, internal_batch_size, return_convergence_delta) and returns: _format_output, _format_output,  delta