text: #!/usr/bin/env python3
  from typing import Any, Callable, List, Tuple, Union
  
  import torch
  from torch import Tensor
  from torch.nn import Module
  
  from captum.log import log_usage
  
  from ...._utils.common import (
      _expand_additional_forward_args,
      _expand_target,
      _format_additional_forward_args,
      _format_output,
  )
  from ...._utils.gradient import compute_layer_gradients_and_eval
  from ...._utils.typing import BaselineType, TargetType
  from ..._utils.approximation_methods import approximation_parameters
  from ..._utils.attribution import GradientAttribution, LayerAttribution
  from ..._utils.batching import _batch_attribution
  from ..._utils.common import _format_input_baseline, _reshape_and_sum, _validate_input
  
  
  class InternalInfluence(LayerAttribution, GradientAttribution):
      r"""
      Computes internal influence by approximating the integral of gradients
      for a particular layer 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 on this approach can be found here:
      https://arxiv.org/pdf/1802.03788.pdf
  
      Note that this method is similar to applying integrated gradients and
      taking the layer as input, integrating the gradient of the layer with
      respect to the output.
      """
  
      def __init__(
          self,
          forward_func: Callable,
          layer: Module,
          device_ids: Union[None, List[int]] = None,
      ) -> None:
          r"""
          Args:
  
              forward_func (callable):  The forward function of the model or any
                            modification of it
              layer (torch.nn.Module): Layer for which attributions are computed.
                            Output size of attribute matches this layer's input or
                            output dimensions, depending on whether we attribute to
                            the inputs or outputs of the layer, corresponding to
                            attribution of each neuron in the input or output of
                            this layer.
              device_ids (list(int)): Device ID list, necessary only if forward_func
                            applies a DataParallel model. This allows reconstruction of
                            intermediate outputs from batched results across devices.
                            If forward_func is given as the DataParallel model itself,
                            then it is not necessary to provide this argument.
          """
          LayerAttribution.__init__(self, forward_func, layer, device_ids)
          GradientAttribution.__init__(self, forward_func)
  
      @log_usage()
      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,
          attribute_to_layer_input: bool = False,
      ) -> Union[Tensor, Tuple[Tensor, ...]]:
          r"""
          Args:
  
              inputs (tensor or tuple of tensors):  Input for which internal
                          influence 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.
              baselines scalar, tensor, tuple of scalars or tensors, optional):
                          Baselines define a 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 (string, 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_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 inputs, otherwise it will be computed with respect
                          to layer outputs.
                          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:
              *tensor* or tuple of *tensors* of **attributions**:
              - **attributions** (*tensor* or tuple of *tensors*):
                          Internal influence of each neuron in given
                          layer output. Attributions will always be the same size
                          as the output or input of the given layer depending on
                          whether `attribute_to_layer_input` is set to `False` or
                          `True`respectively.
                          Attributions are returned in a tuple based on whether
                          the layer inputs / outputs are contained in a tuple
                          from a forward hook. For standard modules, inputs of
                          a single tensor are usually wrapped in a tuple, while
                          outputs of a single tensor are not.
  
          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()
              >>> layer_int_inf = InternalInfluence(net, net.conv1)
              >>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
              >>> # Computes layer internal influence.
              >>> # attribution size matches layer output, Nx12x32x32
              >>> attribution = layer_int_inf.attribute(input)
          """
          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]
              attrs = _batch_attribution(
                  self,
                  num_examples,
                  internal_batch_size,
                  n_steps,
                  inputs=inputs,
                  baselines=baselines,
                  target=target,
                  additional_forward_args=additional_forward_args,
                  method=method,
                  attribute_to_layer_input=attribute_to_layer_input,
              )
          else:
              attrs = self._attribute(
                  inputs=inputs,
                  baselines=baselines,
                  target=target,
                  additional_forward_args=additional_forward_args,
                  n_steps=n_steps,
                  method=method,
                  attribute_to_layer_input=attribute_to_layer_input,
              )
  
          return attrs
  
      def _attribute(
          self,
          inputs: Tuple[Tensor, ...],
          baselines: Tuple[Union[Tensor, int, float], ...],
          target: TargetType = None,
          additional_forward_args: Any = None,
          n_steps: int = 50,
          method: str = "gausslegendre",
          attribute_to_layer_input: bool = False,
          step_sizes_and_alphas: Union[None, Tuple[List[float], List[float]]] = None,
      ) -> Union[Tensor, Tuple[Tensor, ...]]:
          if step_sizes_and_alphas is None:
              # retrieve step size and scaling factor for specified approximation method
              step_sizes_func, alphas_func = approximation_parameters(method)
              step_sizes, alphas = step_sizes_func(n_steps), alphas_func(n_steps)
          else:
              step_sizes, alphas = step_sizes_and_alphas
  
          # Compute scaled inputs from baseline to final input.
          scaled_features_tpl = tuple(
              torch.cat(
                  [baseline + alpha * (input - baseline) for alpha in alphas], dim=0
              ).requires_grad_()
              for input, baseline in zip(inputs, baselines)
          )
  
          additional_forward_args = _format_additional_forward_args(
              additional_forward_args
          )
          # apply number of steps to additional forward args
          # currently, number of steps is applied only to additional forward arguments
          # that are nd-tensors. It is assumed that the first dimension is
          # the number of batches.
          # dim -> (bsz * #steps x additional_forward_args[0].shape[1:], ...)
          input_additional_args = (
              _expand_additional_forward_args(additional_forward_args, n_steps)
              if additional_forward_args is not None
              else None
          )
          expanded_target = _expand_target(target, n_steps)
  
          # Returns gradient of output with respect to hidden layer.
          layer_gradients, _, is_layer_tuple = compute_layer_gradients_and_eval(
              forward_fn=self.forward_func,
              layer=self.layer,
              inputs=scaled_features_tpl,
              target_ind=expanded_target,
              additional_forward_args=input_additional_args,
              device_ids=self.device_ids,
              attribute_to_layer_input=attribute_to_layer_input,
          )
          # flattening grads so that we can multiply it with step-size
          # calling contiguous to avoid `memory whole` problems
          scaled_grads = tuple(
              layer_grad.contiguous().view(n_steps, -1)
              * torch.tensor(step_sizes).view(n_steps, 1).to(layer_grad.device)
              for layer_grad in layer_gradients
          )
  
          # aggregates across all steps for each tensor in the input tuple
          attrs = tuple(
              _reshape_and_sum(
                  scaled_grad, n_steps, inputs[0].shape[0], layer_grad.shape[1:]
              )
              for scaled_grad, layer_grad in zip(scaled_grads, layer_gradients)
          )
          return _format_output(is_layer_tuple, attrs)
  