class_docstr: Extends LayerDeepLift and DeepLiftShap algorithms and approximates SHAP
  values for given input `layer`.
  For each input sample - baseline pair it computes DeepLift attributions
  with respect to inputs or outputs of given `layer` averages
  resulting attributions across baselines. Whether to compute the attributions
  with respect to the inputs or outputs of the layer is defined by the
  input flag `attribute_to_layer_input`.
  More details about the algorithm can be found here:
  
  http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf
  
  Note that the explanation model:
      1. Assumes that input features are independent of one another
      2. Is linear, meaning that the explanations are modeled through
          the additive composition of feature effects.
  Although, it assumes a linear model for each explanation, the overall
  model across multiple explanations can be complex and non-linear.
code: def attribute(
      self,
      inputs: Union[Tensor, Tuple[Tensor, ...]],
      baselines: Union[
          Tensor,
          Tuple[Tensor, ...],
          Callable[..., Union[Tensor, Tuple[Tensor, ...]]],
      ],
      target: TargetType = None,
      additional_forward_args: Any = None,
      return_convergence_delta: Literal[False] = False,
      attribute_to_layer_input: bool = False,
      custom_attribution_func: Union[
          None, Callable[..., Tuple[Tensor, ...]]
      ] = None,
  ) -> Union[Tensor, Tuple[Tensor, ...]]:
      ...
  
imports: import typing
  from typing import Any, Callable, Sequence, Tuple, Union, cast
  import torch
  from torch import Tensor
  from torch.nn import Module
  from .deep_lift import DeepLift, DeepLiftShap
  from captum.attr._utils.attribution import LayerAttribution
  from captum.attr._utils.common import ExpansionTypes, _call_custom_attribution_func, _compute_conv_delta_and_format_attrs, _expand_additional_forward_args, _expand_target, _format_additional_forward_args, _format_baseline, _format_callable_baseline, _format_input, _tensorize_baseline, _validate_input
  from captum.attr._utils.gradient import apply_gradient_requirements, compute_layer_gradients_and_eval, undo_gradient_requirements
  from captum.attr._utils.typing import BaselineType, Literal, TargetType, TensorOrTupleOfTensorsGeneric
label_desc: Use image node_id 2 for calling the LayerDeepLiftShap obj's underlying member method code with example usage: obj.attribute(inputs, baselines, target, additional_forward_args, return_convergence_delta, attribute_to_layer_input, custom_attribution_func) without return types