code: def _relu_custom_attr_func_assert(
      self,
      attr_method: Union[NeuronDeepLift, NeuronDeepLiftShap],
      inputs: TensorOrTupleOfTensorsGeneric,
      baselines,
      expected,
  ) -> None:
      def custom_attr_func(
          multipliers: Tuple[Tensor, ...],
          inputs: Tuple[Tensor, ...],
          baselines: Union[
              None, Tuple[Union[Tensor, int, float], ...]
          ] = None,
      ) -> Tuple[Tensor, ...]:
          return tuple(multiplier * 0.0 for multiplier in multipliers)
  
      attr = attr_method.attribute(
          inputs, 0, baselines, custom_attribution_func=custom_attr_func
      )
      assertTensorAlmostEqual(self, attr[0], expected[0], 0.0)
      assertTensorAlmostEqual(self, attr[1], expected[1], 0.0)
  
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 10 for calling the Test obj's underlying member method code with example usage: obj._relu_custom_attr_func_assert(attr_method, inputs, baselines, expected) and returns: tuple