code: def test_lrp_simple_attributions(self):
      model, inputs = _get_simple_model(inplace=False)
      model.eval()
      model.linear.rule = EpsilonRule()
      model.linear2.rule = EpsilonRule()
      lrp_upper = LayerLRP(model, model.linear2)
      relevance_upper, delta = lrp_upper.attribute(
          inputs,
          attribute_to_layer_input=True,
          return_convergence_delta=True,
      )
      lrp_lower = LayerLRP(model, model.linear)
      relevance_lower = lrp_lower.attribute(inputs)
      assertTensorAlmostEqual(
          self, relevance_lower[0], relevance_upper[0]
      )
      self.assertEqual(delta.item(), 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 4 for calling the Test obj's underlying member method code with example usage: obj.test_lrp_simple_attributions() without return types