code: def testUnivariateLogProbWithIsMissing(self):
      index_points = tf.convert_to_tensor([[[0.0, 0.0]], [[0.5, 1.0]]])
      df = tf.convert_to_tensor(3.0)
      amplitude = tf.convert_to_tensor(1.1)
      length_scale = tf.convert_to_tensor(0.9)
  
      stp = student_t_process.StudentTProcess(
          df=df,
          kernel=psd_kernels.ExponentiatedQuadratic(
              amplitude, length_scale
          ),
          index_points=index_points,
          mean_fn=lambda x: tf.reduce_mean(x, axis=-1),
          observation_noise_variance=0.05,
          jitter=0.0,
      )
  
      x = stp.sample(3, seed=test_util.test_seed())
      lp = stp.log_prob(x)
  
      self.assertAllClose(
          lp, stp.log_prob(x, is_missing=[[False], [False]])
      )
      self.assertAllClose(
          tf.convert_to_tensor([np.zeros((3, 2)), lp]),
          stp.log_prob(x, is_missing=[[[[True]]], [[[False]]]]),
      )
      self.assertAllClose(
          tf.convert_to_tensor(
              [[lp[0, 0], 0.0], [0.0, 0.0], [0.0, lp[2, 1]]]
          ),
          stp.log_prob(
              x,
              is_missing=[
                  [[False], [True]],
                  [[True], [True]],
                  [[True], [False]],
              ],
          ),
      )
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import multivariate_student_t
  from tensorflow_probability.python.distributions import student_t_process
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
  from tensorflow_probability.python.math import psd_kernels
  from tensorflow_probability.python.math.psd_kernels.internal import test_util
label_desc: Use image node_id 9 for calling the _StudentTProcessTest obj's underlying member method code with example usage: obj.testUnivariateLogProbWithIsMissing() without return types