code: def test_convnet_maxpool2d_classification(self) -> None:
      inputs = 100 * torch.randn(2, 1, 10, 10)
  
      model = BasicModel_ConvNet()
      model.eval()
  
      dl = LayerDeepLift(model, model.pool1)
      dl2 = LayerDeepLift(model, model.conv2)
  
      attr = dl.attribute(inputs, target=0)
      attr2 = dl2.attribute(
          inputs, target=0, attribute_to_layer_input=True
      )
  
      self.assertTrue(
          cast(Tensor, attr).sum() == cast(Tensor, attr2).sum()
      )
  
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 15 for calling the TestDeepLift obj's underlying member method code with example usage: obj.test_convnet_maxpool2d_classification() without return types