code: def test_likelihood(self):
      rs = np.random.RandomState(1)
      X, Y, n_dims = get_cont_data(rs)
      model = get_gp(n_dims, rs)
      self.assertIsInstance(model.likelihood, GaussianLikelihood)
      for prior in model.likelihood.named_priors():
          self.assertIsInstance(prior[1].noise_prior, HorseshoePrior)
  
      for (
          constraint_name,
          constraint,
      ) in model.likelihood.named_constraints():
          self.assertIsInstance(constraint, Interval)
          np.testing.assert_almost_equal(
              constraint.lower_bound.numpy(),
              torch.tensor(np.exp(-25)).numpy(),
          )
          np.testing.assert_almost_equal(
              constraint.upper_bound.numpy(),
              torch.tensor(np.exp(2)).numpy(),
          )
  
      self.assertEqual(
          torch.tensor([0.0]), model.likelihood.raw_noise.data
      )
      noise_level = 1e-3
      model = get_gp(n_dims, rs, noise=1e-3)
      self.assertEqual(
          torch.tensor([noise_level]), model.likelihood.raw_noise.data
      )
  
imports: import unittest.mock
  import numpy
  import pyro
  import torch
  from botorch.models.kernels.categorical import CategoricalKernel
  from gpytorch.constraints.constraints import Interval
  from gpytorch.kernels import MaternKernel, ProductKernel, ScaleKernel
  from gpytorch.likelihoods.gaussian_likelihood import GaussianLikelihood
  from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
  from gpytorch.models.exact_gp import ExactGP
  from gpytorch.priors import HorseshoePrior, LogNormalPrior, UniformPrior
  from gpytorch.utils.errors import NotPSDError
  from smac.configspace import CategoricalHyperparameter, ConfigurationSpace, UniformFloatHyperparameter
  from smac.model.gaussian_process.gpytorch_gaussian_process import GPyTorchGaussianProcess
  from ._test_gp import TestGP, get_cat_data, get_cont_data
label_desc: Use image node_id 2 for calling the TestGPGPyTorch obj's underlying member method code with example usage: obj.test_likelihood() without return types