code: def test_gaussian_penalty(self):
      for dtype in (torch.float, torch.double):
          tkwargs = {"device": self.device, "dtype": dtype}
          init_point = torch.tensor([1.0, 1.0, 1.0], **tkwargs)
          sigma = 0.1
          gaussian_module = GaussianPenalty(
              init_point=init_point, sigma=sigma
          )
  
          # testing a batch of two points
          sample_point = torch.tensor(
              [[1.0, 2.0, 3.0], [2.0, 3.0, 4.0]], **tkwargs
          )
  
          diff_norm_squared = (
              torch.linalg.norm(
                  (sample_point - init_point), ord=2, dim=-1
              )
              ** 2
          )
          max_l2_distance = diff_norm_squared.max(dim=-1).values
          real_value = torch.exp(max_l2_distance / 2 / sigma**2)
          computed_value = gaussian_module(sample_point)
          self.assertEqual(computed_value.item(), real_value.item())
  
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 TestGaussianPenalty obj's underlying member method code with example usage: obj.test_gaussian_penalty() without return types