code: def test_lin_maxpool_lin_classification(self) -> None:
      inputs = torch.ones(2, 4)
      baselines = torch.tensor([[1, 2, 3, 9], [4, 8, 6, 7]]).float()
  
      model = LinearMaxPoolLinearModel()
      dl = DeepLift(model)
      attrs, delta = dl.attribute(
          inputs, baselines, target=0, return_convergence_delta=True
      )
      expected = torch.Tensor(
          [[0.0, 0.0, 0.0, -8.0], [0.0, -7.0, 0.0, 0.0]]
      )
      expected_delta = torch.Tensor([0.0, 0.0])
      assertTensorAlmostEqual(self, attrs, expected, 0.0001)
      assertTensorAlmostEqual(self, delta, expected_delta, 0.0001)
  
imports: from inspect import signature
  from typing import Callable, List, Tuple, Union
  import torch
  from captum.attr._core.deep_lift import DeepLift, DeepLiftShap
  from captum.attr._core.integrated_gradients import IntegratedGradients
  from tests.helpers.basic import assertAttributionComparision, assertTensorAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModelWithReusedModules, Conv1dSeqModel, LinearMaxPoolLinearModel, ReLUDeepLiftModel, ReLULinearModel, TanhDeepLiftModel
  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_lin_maxpool_lin_classification() without return types