code: def test_predict(self):
      rs = np.random.RandomState(1)
      # cont
      X, Y, n_dims = get_cont_data(rs)
      # cat
      X, Y, cat_dims, cont_dims = get_cat_data(rs)
  
      for model in (
          get_gp(n_dims, rs),
          get_mixed_gp(cat_dims, cont_dims, rs),
      ):
          model.train(X[:10], Y[:10])
          m_hat, v_hat = model.predict(X[10:])
          self.assertEqual(m_hat.shape, (10, 1))
          self.assertEqual(v_hat.shape, (10, 1))
  
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 3 for calling the TestGPGPyTorch obj's underlying member method code with example usage: obj.test_predict() without return types