code: def test_predict():
      seed = 1
      rs = np.random.RandomState(seed)
      X = rs.rand(20, 10)
      Y = rs.rand(10, 1)
      model = get_gp(10, seed)
      model.train(X[:10], Y[:10])
      m_hat, v_hat = model.predict(X[10:])
      assert m_hat.shape == (10, 1)
      assert v_hat.shape == (10, 1)
  
file_path: SMAC3/tests/test_model/test_gp_mcmc.py
imports: from unittest.mock import patch
  import numpy
  import pytest
  import sklearn.datasets
  import sklearn.model_selection
  from ConfigSpace import ConfigurationSpace, UniformFloatHyperparameter
  from smac.model.gaussian_process.mcmc_gaussian_process import MCMCGaussianProcess
  from smac.model.gaussian_process.priors import HorseshoePrior, LogNormalPrior
label_desc: Use image node_id 5 for calling a global function with example usage: test_predict() without return types