code: def test_lrp_simple_attributions_all_layers_delta(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)
      inputs = torch.cat((inputs, 2 * inputs))
      relevance, delta = lrp.attribute(
          inputs,
          attribute_to_layer_input=True,
          return_convergence_delta=True,
      )
      self.assertEqual(len(relevance), len(delta))
      assertTensorAlmostEqual(
          self,
          relevance[0],
          torch.tensor([[18.0, 36.0, 54.0], [36.0, 72.0, 108.0]]),
      )
  
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 11 for calling the Test obj's underlying member method code with example usage: obj.test_lrp_simple_attributions_all_layers_delta() without return types