code: def test_penalized_acquisition_function(self):
      for dtype in (torch.float, torch.double):
          tkwargs = {"device": self.device, "dtype": dtype}
          mock_model = MockModel(
              MockPosterior(
                  mean=torch.tensor([[1.0]], **tkwargs),
                  variance=torch.tensor([[1.0]], **tkwargs),
              )
          )
          init_point = torch.tensor([0.5, 0.5, 0.5], **tkwargs)
          groups = [[0, 2], [1]]
          raw_acqf = ExpectedImprovement(model=mock_model, best_f=1.0)
          penalty = GroupLassoPenalty(
              init_point=init_point, groups=groups
          )
          lmbda = 0.1
          acqf = PenalizedAcquisitionFunction(
              raw_acqf=raw_acqf,
              penalty_func=penalty,
              regularization_parameter=lmbda,
          )
  
          sample_point = torch.tensor([[1.0, 2.0, 3.0]], **tkwargs)
          raw_value = raw_acqf(sample_point)
          penalty_value = penalty(sample_point)
          real_value = raw_value - lmbda * penalty_value
          computed_value = acqf(sample_point)
          self.assertTrue(torch.equal(real_value, computed_value))
  
          # testing X_pending for analytic raw_acqfn (EI)
          X_pending = torch.tensor([0.1, 0.2, 0.3], **tkwargs)
          with self.assertRaises(UnsupportedError):
              acqf.set_X_pending(X_pending)
  
          # testing X_pending for non-analytic raw_acqfn (EI)
          sampler = IIDNormalSampler(sample_shape=torch.Size([2]))
          raw_acqf_2 = qExpectedImprovement(
              model=mock_model, best_f=0, sampler=sampler
          )
          init_point = torch.tensor([1.0, 1.0, 1.0], **tkwargs)
          l2_module = L2Penalty(init_point=init_point)
          acqf_2 = PenalizedAcquisitionFunction(
              raw_acqf=raw_acqf_2,
              penalty_func=l2_module,
              regularization_parameter=lmbda,
          )
  
          X_pending = torch.tensor([0.1, 0.2, 0.3], **tkwargs)
          acqf_2.set_X_pending(X_pending)
          self.assertTrue(torch.equal(acqf_2.X_pending, X_pending))
  
imports: import torch
  from botorch.acquisition.analytic import ExpectedImprovement
  from botorch.acquisition.monte_carlo import qExpectedImprovement
  from botorch.acquisition.penalized import GaussianPenalty, group_lasso_regularizer, GroupLassoPenalty, L0Approximation, L0PenaltyApprox, L0PenaltyApproxObjective, L1Penalty, L1PenaltyObjective, L2Penalty, PenalizedAcquisitionFunction, PenalizedMCObjective
  from botorch.exceptions import UnsupportedError
  from botorch.sampling.normal import IIDNormalSampler
  from botorch.utils.testing import BotorchTestCase, MockModel, MockPosterior
  from torch import Tensor
label_desc: Use image node_id 1 for calling the TestPenalizedAcquisitionFunction obj's underlying member method code with example usage: obj.test_penalized_acquisition_function() without return types