class_docstr: Layer-wise relevance propagation is based on a backward propagation
  mechanism applied sequentially to all layers of the model. Here, the
  model output score represents the initial relevance which is decomposed
  into values for each neuron of the underlying layers. The decomposition
  is defined by rules that are chosen for each layer, involving its weights
  and activations. Details on the model can be found in the original paper
  [https://doi.org/10.1371/journal.pone.0130140]. The implementation is
  inspired by the tutorial of the same group
  [https://doi.org/10.1016/j.dsp.2017.10.011] and the publication by
  Ancona et al. [https://openreview.net/forum?id=Sy21R9JAW].
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code: def attribute(
      self,
      inputs: TensorOrTupleOfTensorsGeneric,
      target: TargetType = None,
      additional_forward_args: Any = None,
      return_convergence_delta: Literal[False] = False,
      attribute_to_layer_input: bool = False,
      verbose: bool = False,
  ) -> Union[
      Tensor,
      Tuple[Tensor, ...],
      List[Union[Tensor, Tuple[Tensor, ...]]],
  ]:
      ...
  
file_path: captum/captum/attr/_core/layer/layer_lrp.py
imports: import typing
  from typing import Any, cast, List, Tuple, Union
  from captum._utils.common import _format_tensor_into_tuples, _reduce_list, _sort_key_list
  from captum._utils.gradient import apply_gradient_requirements, compute_gradients, undo_gradient_requirements
  from captum._utils.typing import Literal, ModuleOrModuleList, TargetType, TensorOrTupleOfTensorsGeneric
  from captum.attr._core.lrp import LRP
  from captum.attr._utils.attribution import LayerAttribution
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 2 for calling the LayerLRP obj's underlying member method code with example usage: obj.attribute(inputs, target, additional_forward_args, return_convergence_delta, attribute_to_layer_input, verbose) without return types