code: def test_relu_deepliftshap_with_custom_attr_func(self) -> None:
      def custom_attr_func(
          multipliers: Tuple[Tensor, ...],
          inputs: Tuple[Tensor, ...],
          baselines: Tuple[Tensor, ...],
      ) -> Tuple[Tensor, ...]:
          return tuple(multiplier * 0.0 for multiplier in multipliers)
  
      model = ReLULinearModel(inplace=True)
      x1 = torch.tensor([[-10.0, 1.0, -5.0]])
      x2 = torch.tensor([[3.0, 3.0, 1.0]])
      b1 = torch.tensor([[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]])
      b2 = torch.tensor([[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]])
      inputs = (x1, x2)
      baselines = (b1, b2)
      dls = DeepLiftShap(model)
      attr_w_func = dls.attribute(
          inputs, baselines, custom_attribution_func=custom_attr_func
      )
  
      assertTensorAlmostEqual(
          self, attr_w_func[0], [[0.0, 0.0, 0.0]], 0.0
      )
      assertTensorAlmostEqual(
          self, attr_w_func[1], [[0.0, 0.0, 0.0]], 0.0
      )
  
imports: from inspect import signature
  from typing import Callable, List, Tuple, Union
  import torch
  from captum.attr._core.deep_lift import DeepLift, DeepLiftShap
  from captum.attr._core.integrated_gradients import IntegratedGradients
  from tests.helpers.basic import assertAttributionComparision, assertTensorAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModelWithReusedModules, Conv1dSeqModel, LinearMaxPoolLinearModel, ReLUDeepLiftModel, ReLULinearModel, TanhDeepLiftModel
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 15 for calling the Test obj's underlying member method code with example usage: obj.test_relu_deepliftshap_with_custom_attr_func() and returns: tuple