code: def test_lrp_simple_tanh(self) -> None:
      class Model(nn.Module):
          def __init__(self) -> None:
              super(Model, self).__init__()
              self.linear = nn.Linear(3, 3, bias=False)
              self.linear.weight.data.fill_(0.1)
              self.tanh = torch.nn.Tanh()
              self.linear2 = nn.Linear(3, 1, bias=False)
              self.linear2.weight.data.fill_(0.1)
  
          def forward(self, x):
              return self.linear2(self.tanh(self.linear(x)))
  
      model = Model()
      inputs = torch.tensor([[1.0, 2.0, 3.0]])
      _ = model(inputs)
      lrp = LRP(model)
      relevance = lrp.attribute(inputs)
      assertTensorAlmostEqual(
          self, relevance, torch.Tensor([[0.0269, 0.0537, 0.0806]])
      )
  
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 8 for calling the Test obj's underlying member method code with example usage: obj.test_lrp_simple_tanh() and returns: self