code: def testGPPosteriorPredictive(self):
      amplitude = np.float64(0.5)
      length_scale = np.float64(2.0)
      jitter = np.float64(1e-4)
      observation_noise_variance = np.float64(3e-3)
      kernel = psd_kernels.ExponentiatedQuadratic(
          amplitude, length_scale
      )
  
      index_points = np.random.uniform(-1.0, 1.0, 10)[..., np.newaxis]
  
      gp = gaussian_process.GaussianProcess(
          kernel,
          index_points,
          observation_noise_variance=observation_noise_variance,
          jitter=jitter,
          validate_args=True,
      )
  
      predictive_index_points = np.random.uniform(1.0, 2.0, 10)[
          ..., np.newaxis
      ]
      observations = np.linspace(1.0, 10.0, 10)
  
      expected_gprm = gprm.GaussianProcessRegressionModel(
          kernel=kernel,
          observation_index_points=index_points,
          observations=observations,
          observation_noise_variance=observation_noise_variance,
          jitter=jitter,
          index_points=predictive_index_points,
          validate_args=True,
      )
  
      actual_gprm = gp.posterior_predictive(
          predictive_index_points=predictive_index_points,
          observations=observations,
      )
  
      samples = self.evaluate(
          actual_gprm.sample(10, seed=test_util.test_seed())
      )
  
      self.assertAllClose(
          self.evaluate(expected_gprm.mean()),
          self.evaluate(actual_gprm.mean()),
      )
  
      self.assertAllClose(
          self.evaluate(expected_gprm.covariance()),
          self.evaluate(actual_gprm.covariance()),
      )
  
      self.assertAllClose(
          self.evaluate(expected_gprm.log_prob(samples)),
          self.evaluate(actual_gprm.log_prob(samples)),
      )
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import gaussian_process
  from tensorflow_probability.python.distributions import gaussian_process_regression_model
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.math import psd_kernels
  from tensorflow_probability.python.math.psd_kernels.internal import test_util
label_desc: Use image node_id 9 for calling the _GaussianProcessTest obj's underlying member method code with example usage: obj.testGPPosteriorPredictive() without return types