class_docstr: Layer Integrated Gradients is a variant of Integrated Gradients that assigns
  an importance score to layer inputs or outputs, depending on whether we
  attribute to the former or to the latter one.
  
  Integrated Gradients is an axiomatic model interpretability algorithm that
  attributes / 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 layer inputs or outputs of the model, depending on whether
  `attribute_to_layer_input` is set to True or False, 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 layer 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
      attribute_to_layer_input (bool, optional): Indicates whether to
                  compute the attribution with respect to the layer input
                  or output. If `attribute_to_layer_input` is set to True
                  then the attributions will be computed with respect to
                  layer input, otherwise it will be computed with respect
                  to layer output.
  
                  Note that currently it is assumed that either the input
                  or the output of internal layer, 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:
      **attributions** or 2-element tuple of **attributions**, **delta**:
      - **attributions** (*Tensor* or *tuple[Tensor, ...]*):
          Integrated gradients with respect to `layer`'s inputs
          or outputs. Attributions will always be the same size and
          dimensionality as the input or output of the given layer,
          depending on whether we attribute to the inputs or outputs
          of the layer which is decided by the input flag
          `attribute_to_layer_input`.
  
          For a single layer, attributions are returned in a tuple if
          the layer inputs / outputs contain multiple tensors,
          otherwise a single tensor is returned.
  
          For multiple layers, attributions will always be
          returned as a list. Each element in this list will be
          equivalent to that of a single layer output, i.e. in the
          case that one layer, in the given layers, inputs / outputs
          multiple tensors: the corresponding output element will be
          a tuple of tensors. The ordering of the outputs will be
          the same order as the layers given in the constructor.
  
      - **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.
      >>> # It contains an attribute conv1, which is an instance of nn.conv2d,
      >>> # and the output of this layer has dimensions Nx12x32x32.
      >>> net = ImageClassifier()
      >>> lig = LayerIntegratedGradients(net, net.conv1)
      >>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
      >>> # Computes layer integrated gradients for class 3.
      >>> # attribution size matches layer output, Nx12x32x32
      >>> attribution = lig.attribute(input, target=3)
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code: def attribute(
      self,
      inputs: Union[Tensor, Tuple[Tensor, ...]],
      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,
      attribute_to_layer_input: bool = False,
  ) -> Union[
      Union[
          Tensor,
          Tuple[Tensor, ...],
          List[Union[Tensor, Tuple[Tensor, ...]]],
      ],
      Tuple[
          Union[
              Tensor,
              Tuple[Tensor, ...],
              List[Union[Tensor, Tuple[Tensor, ...]]],
          ],
          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 layer inputs or outputs of the model, depending on whether
      `attribute_to_layer_input` is set to True or False, 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 layer 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
          attribute_to_layer_input (bool, optional): Indicates whether to
                      compute the attribution with respect to the layer input
                      or output. If `attribute_to_layer_input` is set to True
                      then the attributions will be computed with respect to
                      layer input, otherwise it will be computed with respect
                      to layer output.
  
                      Note that currently it is assumed that either the input
                      or the output of internal layer, 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:
          **attributions** or 2-element tuple of **attributions**, **delta**:
          - **attributions** (*Tensor* or *tuple[Tensor, ...]*):
              Integrated gradients with respect to `layer`'s inputs
              or outputs. Attributions will always be the same size and
              dimensionality as the input or output of the given layer,
              depending on whether we attribute to the inputs or outputs
              of the layer which is decided by the input flag
              `attribute_to_layer_input`.
  
              For a single layer, attributions are returned in a tuple if
              the layer inputs / outputs contain multiple tensors,
              otherwise a single tensor is returned.
  
              For multiple layers, attributions will always be
              returned as a list. Each element in this list will be
              equivalent to that of a single layer output, i.e. in the
              case that one layer, in the given layers, inputs / outputs
              multiple tensors: the corresponding output element will be
              a tuple of tensors. The ordering of the outputs will be
              the same order as the layers given in the constructor.
  
          - **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.
          >>> # It contains an attribute conv1, which is an instance of nn.conv2d,
          >>> # and the output of this layer has dimensions Nx12x32x32.
          >>> net = ImageClassifier()
          >>> lig = LayerIntegratedGradients(net, net.conv1)
          >>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
          >>> # Computes layer integrated gradients for class 3.
          >>> # attribution size matches layer output, Nx12x32x32
          >>> attribution = lig.attribute(input, target=3)
      """
      inps, baselines = _format_input_baseline(inputs, baselines)
      _validate_input(inps, baselines, n_steps, method)
  
      baselines = _tensorize_baseline(inps, baselines)
      additional_forward_args = _format_additional_forward_args(
          additional_forward_args
      )
  
      def flatten_tuple(tup):
          return tuple(
              sum(
                  (
                      list(x) if isinstance(x, (tuple, list)) else [x]
                      for x in tup
                  ),
                  [],
              )
          )
  
      if self.device_ids is None:
          self.device_ids = getattr(
              self.forward_func, "device_ids", None
          )
  
      inputs_layer = _forward_layer_eval(
          self.forward_func,
          inps,
          self.layer,
          device_ids=self.device_ids,
          additional_forward_args=additional_forward_args,
          attribute_to_layer_input=attribute_to_layer_input,
      )
  
      # if we have one output
      if not isinstance(self.layer, list):
          inputs_layer = (inputs_layer,)
  
      num_outputs = [
          1 if isinstance(x, Tensor) else len(x) for x in inputs_layer
      ]
      num_outputs_cumsum = torch.cumsum(
          torch.IntTensor([0] + num_outputs), dim=0  # type: ignore
      )
      inputs_layer = flatten_tuple(inputs_layer)
  
      baselines_layer = _forward_layer_eval(
          self.forward_func,
          baselines,
          self.layer,
          device_ids=self.device_ids,
          additional_forward_args=additional_forward_args,
          attribute_to_layer_input=attribute_to_layer_input,
      )
      baselines_layer = flatten_tuple(baselines_layer)
  
      # inputs -> these inputs are scaled
      def gradient_func(
          forward_fn: Callable,
          inputs: Union[Tensor, Tuple[Tensor, ...]],
          target_ind: TargetType = None,
          additional_forward_args: Any = None,
      ) -> Tuple[Tensor, ...]:
          if self.device_ids is None or len(self.device_ids) == 0:
              scattered_inputs = (inputs,)
          else:
              # scatter method does not have a precise enough return type in its
              # stub, so suppress the type warning.
              scattered_inputs = scatter(  # type:ignore
                  inputs, target_gpus=self.device_ids
              )
  
          scattered_inputs_dict = {
              scattered_input[0].device: scattered_input
              for scattered_input in scattered_inputs
          }
  
          with torch.autograd.set_grad_enabled(True):
  
              def layer_forward_hook(
                  module, hook_inputs, hook_outputs=None, layer_idx=0
              ):
                  device = _extract_device(
                      module, hook_inputs, hook_outputs
                  )
                  is_layer_tuple = (
                      isinstance(hook_outputs, tuple)
                      # hook_outputs is None if attribute_to_layer_input == True
                      if hook_outputs is not None
                      else isinstance(hook_inputs, tuple)
                  )
  
                  if is_layer_tuple:
                      return scattered_inputs_dict[device][
                          num_outputs_cumsum[
                              layer_idx
                          ] : num_outputs_cumsum[layer_idx + 1]
                      ]
  
                  return scattered_inputs_dict[device][
                      num_outputs_cumsum[layer_idx]
                  ]
  
              hooks = []
              try:
                  layers = self.layer
                  if not isinstance(layers, list):
                      layers = [self.layer]
  
                  for layer_idx, layer in enumerate(layers):
                      hook = None
                      # TODO:
                      # Allow multiple attribute_to_layer_input flags for
                      # each layer, i.e. attribute_to_layer_input[layer_idx]
                      if attribute_to_layer_input:
                          hook = layer.register_forward_pre_hook(
                              functools.partial(
                                  layer_forward_hook,
                                  layer_idx=layer_idx,
                              )
                          )
                      else:
                          hook = layer.register_forward_hook(
                              functools.partial(
                                  layer_forward_hook,
                                  layer_idx=layer_idx,
                              )
                          )
  
                      hooks.append(hook)
  
                  # the inputs is an empty tuple
                  # coz it is prepended into additional_forward_args
                  output = _run_forward(
                      self.forward_func,
                      tuple(),
                      target_ind,
                      additional_forward_args,
                  )
              finally:
                  for hook in hooks:
                      if hook is not None:
                          hook.remove()
  
              assert output[0].numel() == 1, (
                  "Target not provided when necessary, cannot"
                  " take gradient with respect to multiple outputs."
              )
              # torch.unbind(forward_out) is a list of scalar tensor tuples and
              # contains batch_size * #steps elements
              grads = torch.autograd.grad(torch.unbind(output), inputs)
          return grads
  
      self.ig.gradient_func = gradient_func
      all_inputs = (
          (inps + additional_forward_args)
          if additional_forward_args is not None
          else inps
      )
  
      attributions = self.ig.attribute.__wrapped__(  # type: ignore
          self.ig,  # self
          inputs_layer,
          baselines=baselines_layer,
          target=target,
          additional_forward_args=all_inputs,
          n_steps=n_steps,
          method=method,
          internal_batch_size=internal_batch_size,
          return_convergence_delta=False,
      )
  
      # handle multiple outputs
      output: List[Tuple[Tensor, ...]] = [
          tuple(
              attributions[
                  int(num_outputs_cumsum[i]) : int(
                      num_outputs_cumsum[i + 1]
                  )
              ]
          )
          for i in range(len(num_outputs))
      ]
  
      if return_convergence_delta:
          start_point, end_point = baselines, inps
          # 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_outputs(isinstance(self.layer, list), output),
              delta,
          )
      return _format_outputs(isinstance(self.layer, list), output)
  
imports: import functools
  import warnings
  from typing import Any, Callable, List, overload, Tuple, Union
  import torch
  from captum._utils.common import _extract_device, _format_additional_forward_args, _format_outputs
  from captum._utils.gradient import _forward_layer_eval, _run_forward
  from captum._utils.typing import BaselineType, Literal, ModuleOrModuleList, TargetType
  from captum.attr._core.integrated_gradients import IntegratedGradients
  from captum.attr._utils.attribution import GradientAttribution, LayerAttribution
  from captum.attr._utils.common import _format_input_baseline, _tensorize_baseline, _validate_input
  from captum.log import log_usage
  from torch import Tensor
  from torch.nn.parallel.scatter_gather import scatter
label_desc: Use image node_id 5 for calling the LayerIntegratedGradients 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, attribute_to_layer_input) and returns: _format_outputs, tuple, grads, _format_outputs,  delta, scattered_inputs_dict, scattered_inputs_dict