code: def testMeanVarianceJit(self):
      num_tasks = 3
      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)
  
      observation_index_points = np.random.uniform(
          -1.0, 1.0, (1, 1, 7, 2)
      ).astype(np.float64)
      observations = (
          np.linspace(-20.0, 20.0, 7 * num_tasks)
          .reshape(7, num_tasks)
          .astype(np.float64)
      )
  
      index_points = np.random.uniform(-1.0, 1.0, (6, 2)).astype(
          np.float64
      )
  
      kernel = exponentiated_quadratic.ExponentiatedQuadratic(
          amplitude, length_scale
      )
      multi_task_kernel = multitask_kernel.Independent(
          num_tasks=num_tasks, base_kernel=kernel
      )
      mtgprm = mtgprm_lib.MultiTaskGaussianProcessRegressionModel(
          kernel=multi_task_kernel,
          index_points=index_points,
          observation_index_points=observation_index_points,
          observations=observations,
          observation_noise_variance=observation_noise_variance,
          validate_args=True,
      )
      # Check that Jit compiling mean and variance doesn't raise an error.
      tf.function(jit_compile=True)(mtgprm.mean)()
      tf.function(jit_compile=True)(mtgprm.variance)()
  
imports: from unittest import mock
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import gaussian_process_regression_model
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.experimental.distributions import multitask_gaussian_process_regression_model
  from tensorflow_probability.python.experimental.psd_kernels import multitask_kernel
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import test_util
  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 9 for calling the MultiTaskGaussianProcessRegressionModelTest obj's underlying member method code with example usage: obj.testMeanVarianceJit() without return types