code: def test_relu_linear_deeplift(self) -> None:
      model = ReLULinearModel(inplace=False)
      x1 = torch.tensor([[-10.0, 1.0, -5.0]], requires_grad=True)
      x2 = torch.tensor([[3.0, 3.0, 1.0]], requires_grad=True)
  
      inputs = (x1, x2)
      baselines = (0, 0.0001)
  
      # expected = [[[0.0, 0.0]], [[6.0, 2.0]]]
      self._deeplift_assert(model, DeepLift(model), inputs, baselines)
  
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 6 for calling the Test obj's underlying member method code with example usage: obj.test_relu_linear_deeplift() without return types