code: def testMeanShapeBroadcasts(self):
      observation_index_points = tf.Variable(
          np.random.random((10, 5)), dtype=np.float32
      )
      observations = tf.Variable(
          np.random.random((10, 3)), dtype=np.float32
      )
      index_points = tf.Variable(
          np.random.random((4, 5)), dtype=np.float32
      )
      kernel = exponentiated_quadratic.ExponentiatedQuadratic()
      multi_task_kernel = multitask_kernel.Independent(
          num_tasks=3, base_kernel=kernel
      )
      mean = tf.Variable(np.random.random((3,)), dtype=np.float32)
      gp = mtgprm_lib.MultiTaskGaussianProcessRegressionModel(
          multi_task_kernel,
          observation_index_points=observation_index_points,
          observations=observations,
          index_points=index_points,
          mean_fn=lambda _: mean,
          observation_noise_variance=np.float32(1e-2),
      )
      self.assertAllEqual(
          self.evaluate(gp.event_shape_tensor()), [4, 3]
      )
  
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 1 for calling the MultiTaskGaussianProcessRegressionModelTest obj's underlying member method code with example usage: obj.testMeanShapeBroadcasts() without return types