code: def test_lrp_skip_connection(self) -> None:
      # A custom addition module needs to be used so that relevance is
      # propagated correctly.
      class Addition_Module(nn.Module):
          def __init__(self) -> None:
              super().__init__()
  
          def forward(self, x1: Tensor, x2: Tensor) -> Tensor:
              return x1 + x2
  
      class SkipConnection(nn.Module):
          def __init__(self) -> None:
              super().__init__()
              self.linear = nn.Linear(2, 2, bias=False)
              self.linear.weight.data.fill_(5)
              self.add = Addition_Module()
  
          def forward(self, input: Tensor) -> Module:
              x = self.add(self.linear(input), input)
              return x
  
      model = SkipConnection()
      input = torch.Tensor([[2, 3]])
      model.add.rule = EpsilonRule()  # type: ignore
      lrp = LRP(model)
      relevance = lrp.attribute(input, target=1)
      assertTensorAlmostEqual(self, relevance, torch.Tensor([[10, 18]]))
  
imports: from typing import cast, Tuple
  import torch
  import torch.nn
  from captum.attr import InputXGradient, LRP
  from captum.attr._utils.lrp_rules import Alpha1_Beta0_Rule, EpsilonRule, GammaRule, IdentityRule
  from tests.helpers.basic import assertTensorAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel_ConvNet_One_Conv, BasicModel_MultiLayer, BasicModelWithReusedLinear, SimpleLRPModel
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 13 for calling the Test obj's underlying member method code with example usage: obj.test_lrp_skip_connection() and returns: unknown, x