code: def test_lrp_multi_inputs(self) -> None:
      model = BasicModel_MultiLayer()
      input = torch.Tensor([[1, 2, 3]])
      input = (input, 3 * input)
      lrp = LRP(model)
      attributions, delta = lrp.attribute(
          input, target=0, return_convergence_delta=True
      )
      self.assertEqual(len(input), 2)
      assertTensorAlmostEqual(
          self, attributions[0], torch.Tensor([[16, 32, 48]])
      )
      assertTensorAlmostEqual(self, delta, torch.Tensor([-104.0]))
  
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 18 for calling the Test obj's underlying member method code with example usage: obj.test_lrp_multi_inputs() without return types