code: def test_L0PenaltyApprox(self):
      for dtype in (torch.float, torch.double):
          tkwargs = {"device": self.device, "dtype": dtype}
          target_point = torch.zeros(2, **tkwargs)
  
          # test init
          l0_acqf = L0PenaltyApprox(
              target_point=target_point, **tkwargs
          )
          self.assertTrue(
              torch.equal(l0_acqf.target_point, target_point)
          )
          self.assertAllClose(
              l0_acqf.a.data, torch.tensor(1.0, **tkwargs)
          )
  
          # check two-dim input tensors X
          self.assertTrue(
              torch.equal(
                  l0_acqf(torch.zeros(3, 2, **tkwargs)).data,
                  torch.tensor(0, **tkwargs),
              )
          )
          # check "batch_shape x q x dim" input tensors X
          batch_shape = 16
          self.assertTrue(
              torch.equal(
                  l0_acqf(
                      torch.zeros(batch_shape, 3, 2, **tkwargs)
                  ).data,
                  torch.zeros(batch_shape, **tkwargs),
              )
          )
  
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 3 for calling the TestL0Approximation obj's underlying member method code with example usage: obj.test_L0PenaltyApprox() without return types