code: def test_lrp_simple_attributions_batch(self) -> None:
      model, inputs = _get_simple_model()
      model.eval()
      model.linear.rule = EpsilonRule()  # type: ignore
      model.linear2.rule = EpsilonRule()  # type: ignore
      lrp = LRP(model)
      inputs = torch.cat((inputs, 3 * inputs))
      relevance, delta = lrp.attribute(
          inputs, target=0, return_convergence_delta=True
      )
      self.assertEqual(relevance.shape, inputs.shape)  # type: ignore
      self.assertEqual(delta.shape[0], inputs.shape[0])  # type: ignore
      assertTensorAlmostEqual(
          self,
          relevance,
          torch.Tensor([[18.0, 36.0, 54.0], [54.0, 108.0, 162.0]]),
      )
  
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 5 for calling the Test obj's underlying member method code with example usage: obj.test_lrp_simple_attributions_batch() without return types