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:
  
  https://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.
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code: def multiplies_by_inputs(self):
      return self._multiply_by_inputs
  
imports: import typing
  from typing import Any, Callable, cast, Sequence, Tuple, Union
  import torch
  from captum._utils.common import _expand_target, _format_additional_forward_args, _format_baseline, _format_tensor_into_tuples, ExpansionTypes
  from captum._utils.gradient import compute_layer_gradients_and_eval
  from captum._utils.typing import BaselineType, Literal, TargetType, TensorOrTupleOfTensorsGeneric
  from captum.attr._core.deep_lift import DeepLift, DeepLiftShap
  from captum.attr._utils.attribution import LayerAttribution
  from captum.attr._utils.common import _call_custom_attribution_func, _compute_conv_delta_and_format_attrs, _format_callable_baseline, _tensorize_baseline, _validate_input
  from captum.log import log_usage
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 5 for calling the LayerDeepLiftShap obj's underlying member method code with example usage: obj.multiplies_by_inputs() and returns: self