code: def testMeanVarianceAndCovariancePrecomputed(self):
      amplitude = np.array([1.0, 2.0], np.float64).reshape([2, 1])
      length_scale = np.array([0.1, 0.2, 0.3], np.float64).reshape(
          [1, 3]
      )
      observation_noise_variance = np.array([1e-9], np.float64)
      df = np.float64(3.0)
  
      observation_index_points = np.random.uniform(
          -1.0, 1.0, (1, 1, 7, 2)
      ).astype(np.float64)
      observations = np.random.uniform(-1.0, 1.0, (1, 1, 7)).astype(
          np.float64
      )
  
      index_points = np.random.uniform(-1.0, 1.0, (6, 2)).astype(
          np.float64
      )
  
      kernel = exponentiated_quadratic.ExponentiatedQuadratic(
          amplitude, length_scale
      )
      dist = stprm.StudentTProcessRegressionModel(
          df=df,
          kernel=kernel,
          index_points=index_points,
          observation_index_points=observation_index_points,
          observations=observations,
          observation_noise_variance=observation_noise_variance,
          validate_args=True,
      )
  
      precomputed_dist = stprm.StudentTProcessRegressionModel.precompute_regression_model(
          df=df,
          kernel=kernel,
          index_points=index_points,
          observation_index_points=observation_index_points,
          observations=observations,
          observation_noise_variance=observation_noise_variance,
          validate_args=True,
      )
  
      self.assertAllClose(
          self.evaluate(precomputed_dist.covariance()),
          self.evaluate(dist.covariance()),
      )
      self.assertAllClose(
          self.evaluate(precomputed_dist.variance()),
          self.evaluate(dist.variance()),
      )
      self.assertAllClose(
          self.evaluate(precomputed_dist.mean()),
          self.evaluate(dist.mean()),
      )
  
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 4 for calling the StudentTProcessRegressionModelTest obj's underlying member method code with example usage: obj.testMeanVarianceAndCovariancePrecomputed() without return types