code: def test_relu_deepliftshap_with_hypothetical_contrib_func(
      self,
  ) -> None:
      model = Conv1dSeqModel()
      rand_seq_data = torch.abs(torch.randn(2, 4, 1000))
      rand_seq_ref = torch.abs(torch.randn(3, 4, 1000))
      dls = DeepLiftShap(model)
      attr = dls.attribute(
          rand_seq_data,
          rand_seq_ref,
          custom_attribution_func=_hypothetical_contrib_func,
          target=(0, 0),
      )
      self.assertEqual(attr.shape, rand_seq_data.shape)
  
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 16 for calling the Test obj's underlying member method code with example usage: obj.test_relu_deepliftshap_with_hypothetical_contrib_func() without return types