code: def test_classification_sensitivity_tpl_target_w_baseline(
      self,
  ) -> None:
      model = BasicModel_MultiLayer()
      input = torch.arange(1.0, 13.0).view(4, 3)
      baseline = torch.ones(4, 3)
      additional_forward_args = (
          torch.arange(1, 13).view(4, 3).float(),
          True,
      )
      targets: List = [(0, 1, 1), (0, 1, 1), (1, 1, 1), (0, 1, 1)]
      dl = DeepLift(model)
  
      sens1 = self.sensitivity_max_assert(
          dl.attribute,
          input,
          torch.tensor([0.01, 0.003, 0.001, 0.001]),
          additional_forward_args=additional_forward_args,
          baselines=baseline,
          target=targets,
          n_perturb_samples=10,
          perturb_func=_perturb_func,
      )
      sens2 = self.sensitivity_max_assert(
          dl.attribute,
          input,
          torch.zeros(4),
          additional_forward_args=additional_forward_args,
          baselines=baseline,
          target=targets,
          n_perturb_samples=10,
          perturb_func=_perturb_func,
          max_examples_per_batch=30,
      )
      assertTensorAlmostEqual(self, sens1, sens2)
  
imports: import typing
  from typing import Any, Callable, cast, List, Tuple, Union
  import torch
  from captum._utils.typing import BaselineType, TargetType, TensorOrTupleOfTensorsGeneric
  from captum.attr import DeepLift, GradientShap, GuidedBackprop, IntegratedGradients, Saliency
  from captum.metrics import sensitivity_max
  from captum.metrics._core.sensitivity import default_perturb_func
  from tests.helpers.basic import assertTensorAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel2, BasicModel4_MultiArgs, BasicModel_ConvNet_One_Conv, BasicModel_MultiLayer
  from torch import Tensor
label_desc: Use image node_id 9 for calling the Test obj's underlying member method code with example usage: obj.test_classification_sensitivity_tpl_target_w_baseline() without return types