code: def test_nll():
      seed = 1
      rs = np.random.RandomState(seed)
      gp = get_gp(seed, seed)
      gp.train(np.array([[0], [1]]), np.array([0, 1]))
      n_above_1 = 0
      for i in range(1000):
          theta = np.array(
              [
                  rs.uniform(1e-10, 10),
                  rs.uniform(-10, 2),
                  rs.uniform(-10, 1),
              ]
          )  # Values from the default prior
          error = scipy.optimize.check_grad(
              lambda x: gp._nll(x)[0],
              lambda x: gp._nll(x)[1],
              theta,
              epsilon=1e-5,
          )
          if error > 0.1:
              n_above_1 += 1
      assert n_above_1 < 10
  
imports: from unittest.mock import patch
  import numpy
  import pytest
  import scipy.optimize
  import sklearn.datasets
  import sklearn.model_selection
  from ConfigSpace import CategoricalHyperparameter, ConfigurationSpace, EqualsCondition, UniformFloatHyperparameter
  from smac.model.gaussian_process.gaussian_process import GaussianProcess
  from smac.model.gaussian_process.priors import HorseshoePrior, LogNormalPrior
  from smac.utils.configspace import convert_configurations_to_array
label_desc: Use image node_id 12 for calling a global function with example usage: test_nll() without return types