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 = LayerDeepLift(model, model.pool1)
      attrs, delta = dl.attribute(
          inputs, baselines, target=0, return_convergence_delta=True
      )
      expected = [[[-8.0]], [[-7.0]]]
      expected_delta = [0.0, 0.0]
      assertTensorAlmostEqual(
          self, cast(Tensor, attrs), expected, 0.0001, "max"
      )
      assertTensorAlmostEqual(
          self, delta, expected_delta, 0.0001, "max"
      )
  
imports: from __future__ import print_function
  from typing import cast, List, Tuple, Union
  import torch
  from captum.attr._core.layer.layer_deep_lift import LayerDeepLift, LayerDeepLiftShap
  from tests.helpers.basic import assert_delta, assertTensorAlmostEqual, assertTensorTuplesAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel_ConvNet, BasicModel_ConvNet_MaxPool3d, BasicModel_MaxPool_ReLU, BasicModel_MultiLayer, LinearMaxPoolLinearModel, ReLULinearModel
  from torch import Tensor
label_desc: Use image node_id 14 for calling the TestDeepLift obj's underlying member method code with example usage: obj.test_lin_maxpool_lin_classification() without return types