code: def testJitMultitaskGaussianProcess(self):
      # 3x3 grid of index points in R^2 and flatten to 9x2
      index_points = np.linspace(-4.0, 4.0, 3, dtype=np.float32)
      index_points = np.stack(
          np.meshgrid(index_points, index_points), axis=-1
      )
      index_points = np.reshape(index_points, [-1, 2])
      # ==> shape = [9, 2]
  
      # Kernel with batch_shape [2, 4, 3]
      amplitude = np.array([1.0, 2.0], np.float32).reshape(
          [
              2,
              1,
          ]
      )
      length_scale = np.array([1.0, 2.0, 3.0, 4.0], np.float32).reshape(
          [
              1,
              4,
          ]
      )
      observation_noise_variance = np.float32(1e-5)
      batched_index_points = np.stack([index_points] * 4)
      # ==> shape = [4, 9, 2]
      kernel = exponentiated_quadratic.ExponentiatedQuadratic(
          amplitude, length_scale
      )
      multi_task_kernel = multitask_kernel.Independent(
          num_tasks=3, base_kernel=kernel
      )
      multitask_gp = (
          multitask_gaussian_process.MultiTaskGaussianProcess(
              multi_task_kernel,
              batched_index_points,
              observation_noise_variance=observation_noise_variance,
              validate_args=True,
          )
      )
  
      @tf.function(jit_compile=True)
      def log_prob(o):
          return multitask_gp.log_prob(o)
  
      @tf.function(jit_compile=True)
      def sample():
          return multitask_gp.sample(seed=test_util.test_seed())
  
      observations = tf.convert_to_tensor(
          np.linspace(-20.0, 20.0, 27).reshape(9, 3).astype(np.float32)
      )
      self.assertAllEqual(log_prob(observations).shape, [2, 4])
      self.assertAllEqual(sample().shape, [2, 4, 9, 3])
  
      multitask_gp = (
          multitask_gaussian_process.MultiTaskGaussianProcess(
              multi_task_kernel,
              batched_index_points,
              observation_noise_variance=None,
              validate_args=True,
          )
      )
  
      @tf.function(jit_compile=True)
      def log_prob_no_noise(o):
          return multitask_gp.log_prob(o)
  
      @tf.function(jit_compile=True)
      def sample_no_noise():
          return multitask_gp.sample(seed=test_util.test_seed())
  
      self.assertAllEqual(log_prob_no_noise(observations).shape, [2, 4])
      self.assertAllEqual(sample_no_noise().shape, [2, 4, 9, 3])
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import gaussian_process
  from tensorflow_probability.python.experimental.distributions import multitask_gaussian_process
  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 dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import tensor_util
  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 5 for calling the MultiTaskGaussianProcessTest obj's underlying member method code with example usage: obj.testJitMultitaskGaussianProcess() and returns: multitask_gp, multitask_gp, multitask_gp, multitask_gp