code: def test_lrp_basic_attributions(self):
      model, inputs = _get_basic_config()
      logits = model(inputs)
      score, classIndex = torch.max(logits, 1)
      lrp = LayerLRP(model, model.conv1)
      relevance, delta = lrp.attribute(
          inputs, classIndex.item(), return_convergence_delta=True
      )
      assertTensorAlmostEqual(
          self,
          relevance[0],
          torch.Tensor([[[0, 4], [31, 40]], [[0, 0], [-6, -15]]]),
      )
      assertTensorAlmostEqual(self, delta, torch.Tensor([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 3 for calling the Test obj's underlying member method code with example usage: obj.test_lrp_basic_attributions() without return types