code: def test_relu_deepliftshap_baselines_as_func(self) -> None:
      model = ReLULinearModel(inplace=True)
      x1 = torch.tensor([[-10.0, 1.0, -5.0]])
      x2 = torch.tensor([[3.0, 3.0, 1.0]])
  
      def gen_baselines() -> Tuple[Tensor, ...]:
          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]])
          return (b1, b2)
  
      def gen_baselines_scalar() -> Tuple[float, ...]:
          return (0.0, 0.0001)
  
      def gen_baselines_with_inputs(
          inputs: Tuple[Tensor, ...]
      ) -> Tuple[Tensor, ...]:
          b1 = torch.cat([inputs[0], inputs[0] - 10])
          b2 = torch.cat([inputs[1], inputs[1] - 10])
          return (b1, b2)
  
      def gen_baselines_returns_array() -> (
          Tuple[List[List[float]], ...]
      ):
          b1 = [[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]]
          b2 = [[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]]
          return (b1, b2)
  
      inputs = (x1, x2)
  
      dl_shap = DeepLiftShap(model)
      self._deeplift_assert(model, dl_shap, inputs, gen_baselines)
      self._deeplift_assert(
          model, dl_shap, inputs, gen_baselines_with_inputs
      )
      with self.assertRaises(AssertionError):
          self._deeplift_assert(
              model,
              DeepLiftShap(model),
              inputs,
              gen_baselines_returns_array,
          )
      with self.assertRaises(AssertionError):
          self._deeplift_assert(
              model, dl_shap, inputs, gen_baselines_scalar
          )
  
      baselines = gen_baselines()
      attributions = dl_shap.attribute(inputs, baselines)
      attributions_with_func = dl_shap.attribute(inputs, gen_baselines)
      assertTensorAlmostEqual(
          self, attributions[0], attributions_with_func[0]
      )
      assertTensorAlmostEqual(
          self, attributions[1], attributions_with_func[1]
      )
  
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 14 for calling the Test obj's underlying member method code with example usage: obj.test_relu_deepliftshap_baselines_as_func() and returns: b1,  b2, int,  int, b1,  b2, b1,  b2