code: def test_lrp_simple_attributions_all_layers(self) -> None:
      model, inputs = _get_simple_model(inplace=False)
      model.eval()
      model.linear.rule = EpsilonRule()
      model.linear2.rule = EpsilonRule()
      layers = [model.linear, model.linear2]
      lrp = LayerLRP(model, layers)
      relevance = lrp.attribute(inputs, attribute_to_layer_input=True)
      self.assertEqual(len(relevance), 2)
      assertTensorAlmostEqual(
          self, relevance[0][0], torch.tensor([18.0, 36.0, 54.0])
      )
  
imports: import torch
  import torch.nn
  from captum.attr import LayerLRP
  from captum.attr._utils.lrp_rules import Alpha1_Beta0_Rule, EpsilonRule, GammaRule
  from ...helpers.basic import assertTensorAlmostEqual, BaseTest
  from ...helpers.basic_models import BasicModel_ConvNet_One_Conv, SimpleLRPModel
label_desc: Use image node_id 10 for calling the Test obj's underlying member method code with example usage: obj.test_lrp_simple_attributions_all_layers() without return types