code: def test_L0Approximation(self):
      for dtype in (torch.float, torch.double):
          tkwargs = {"device": self.device, "dtype": dtype}
          target_point = torch.zeros(2, **tkwargs)
  
          # test init
          l0 = L0Approximation(target_point=target_point, **tkwargs)
          self.assertTrue(torch.equal(l0.target_point, target_point))
          self.assertAllClose(l0.a.data, torch.tensor(1.0, **tkwargs))
  
          # verify L0 norm
          self.assertTrue(
              torch.equal(
                  l0(torch.zeros(2, **tkwargs)).data,
                  torch.tensor([0], **tkwargs),
              )
          )
          # check two-dim input tensors X
          self.assertTrue(
              torch.equal(
                  l0(torch.zeros(3, 2, **tkwargs)).data,
                  torch.zeros(3, 1, **tkwargs),
              )
          )
  
          # test raise when X and target_point have mismatched shape
          with self.assertRaises(ValueError):
              l0(torch.zeros(3, **tkwargs))
  
          # test init with different a
          l0 = L0Approximation(
              target_point=target_point, a=2.0, **tkwargs
          )
          self.assertAllClose(l0.a.data, torch.tensor(2.0, **tkwargs))
          self.assertAllClose(
              l0(torch.ones(2, **tkwargs)).data,
              torch.tensor([0.2350], **tkwargs),
              rtol=1e-04,
          )
  
          # reset a
          l0.a.data.fill_(0.5)
          self.assertTrue(
              torch.equal(l0.a.data, torch.tensor(0.5, **tkwargs))
          )
          self.assertAllClose(
              l0(torch.ones(2, **tkwargs)).data,
              torch.tensor([1.7293], **tkwargs),
              rtol=1e-04,
          )
  
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 TestL0Approximation obj's underlying member method code with example usage: obj.test_L0Approximation() without return types