code: def test_l1_penalty(self):
      for dtype in (torch.float, torch.double):
          init_point = torch.tensor(
              [1.0, 1.0, 1.0], device=self.device, dtype=dtype
          )
          l1_module = L1PenaltyObjective(init_point=init_point)
  
          # testing a batch of two points
          sample_point = torch.tensor(
              [[1.0, 2.0, 3.0], [2.0, 3.0, 4.0]],
              device=self.device,
              dtype=dtype,
          )
  
          real_values = torch.linalg.norm(
              (sample_point - init_point), ord=1, dim=-1
          ).unsqueeze(dim=0)
          computed_values = l1_module(sample_point)
          self.assertTrue(torch.equal(real_values, computed_values))
  
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 TestL1PenaltyObjective obj's underlying member method code with example usage: obj.test_l1_penalty() without return types