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()
      ndl = NeuronDeepLift(model, model.pool1)
      attr = ndl.attribute(
          inputs, neuron_selector=(0), baselines=baselines
      )
  
      ndl2 = NeuronDeepLift(model, model.lin2)
      attr2 = ndl2.attribute(
          inputs,
          neuron_selector=(0),
          baselines=baselines,
          attribute_to_neuron_input=True,
      )
      assertTensorAlmostEqual(self, attr, attr2)
  
imports: from __future__ import print_function
  from typing import Tuple, Union
  import torch
  from captum._utils.typing import TensorOrTupleOfTensorsGeneric
  from captum.attr._core.neuron.neuron_deep_lift import NeuronDeepLift, NeuronDeepLiftShap
  from tests.attr.layer.test_layer_deeplift import _create_inps_and_base_for_deeplift_neuron_layer_testing, _create_inps_and_base_for_deepliftshap_neuron_layer_testing
  from tests.helpers.basic import assertTensorAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel_ConvNet, BasicModel_ConvNet_MaxPool3d, LinearMaxPoolLinearModel, ReLULinearModel
  from torch import Tensor
label_desc: Use image node_id 11 for calling the Test obj's underlying member method code with example usage: obj.test_lin_maxpool_lin_classification() without return types