code: def testEmptyDataMatchesStPPrior(self):
      df = np.float64(3.5)
      amp = np.float64(0.5)
      len_scale = np.float64(0.2)
      index_points = np.random.uniform(-1.0, 1.0, (10, 1)).astype(
          np.float64
      )
  
      # k_xx - k_xn @ (k_nn + sigma^2) @ k_nx + sigma^2
      mean_fn = lambda x: x[:, 0] ** 2
  
      kernel = exponentiated_quadratic.ExponentiatedQuadratic(
          amp, len_scale
      )
      stp = student_t_process.StudentTProcess(
          df, kernel, index_points, mean_fn=mean_fn, validate_args=True
      )
  
      dist_nones = stprm.StudentTProcessRegressionModel(
          df,
          kernel=kernel,
          index_points=index_points,
          mean_fn=mean_fn,
          validate_args=True,
      )
  
      dist_zero_shapes = stprm.StudentTProcessRegressionModel(
          df,
          kernel=kernel,
          index_points=index_points,
          observation_index_points=tf.ones([0, 1], tf.float64),
          observations=tf.ones([0], tf.float64),
          mean_fn=mean_fn,
          validate_args=True,
      )
  
      for dist in [dist_nones, dist_zero_shapes]:
          self.assertAllClose(
              self.evaluate(stp.mean()), self.evaluate(dist.mean())
          )
          self.assertAllClose(
              self.evaluate(stp.covariance()),
              self.evaluate(dist.covariance()),
          )
          self.assertAllClose(
              self.evaluate(stp.variance()),
              self.evaluate(dist.variance()),
          )
  
          observations = np.random.uniform(-1.0, 1.0, 10).astype(
              np.float64
          )
          self.assertAllClose(
              self.evaluate(stp.log_prob(observations)),
              self.evaluate(dist.log_prob(observations)),
          )
  
imports: from unittest import mock
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import gaussian_process_regression_model
  from tensorflow_probability.python.distributions import student_t_process
  from tensorflow_probability.python.distributions import student_t_process_regression_model
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math.psd_kernels import exp_sin_squared
  from tensorflow_probability.python.math.psd_kernels import exponentiated_quadratic
  from tensorflow_probability.python.math.psd_kernels.internal import test_util
label_desc: Use image node_id 6 for calling the StudentTProcessRegressionModelTest obj's underlying member method code with example usage: obj.testEmptyDataMatchesStPPrior() without return types