code: def test_convnet_maxpool3d_classification(self) -> None:
      inputs = 100 * torch.randn(2, 1, 10, 10, 10)
      model = BasicModel_ConvNet_MaxPool3d()
  
      ndl = NeuronDeepLift(model, model.pool1)
      attr = ndl.attribute(inputs, neuron_selector=(0, 0, 0, 0))
  
      ndl2 = NeuronDeepLift(model, model.conv2)
      attr2 = ndl2.attribute(
          inputs,
          neuron_selector=(0, 0, 0, 0),
          attribute_to_neuron_input=True,
      )
  
      assertTensorAlmostEqual(self, attr.sum(), attr2.sum())
  
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 13 for calling the Test obj's underlying member method code with example usage: obj.test_convnet_maxpool3d_classification() without return types