code: def test_gradient_non_nan(self):
      x_obs = (
          np.linspace(1.0, 10.0, num=30)
          .reshape([5, 2, 3])
          .astype(np.float32)
      )
      y_obs = tf.reduce_sum(x_obs, axis=(-1, -2))
      x_obs = tf.concat(
          [x_obs, np.full([3, 2, 3], np.nan, dtype=np.float32)], axis=0
      )
      y_obs = tf.concat(
          [y_obs, np.full([3], np.nan, dtype=np.float32)], axis=0
      )
      is_missing = np.array([False] * 5 + [True] * 3)
  
      def loss(x, y, l, o):
          kernel = psd_kernels.ExponentiatedQuadratic(
              amplitude=1e-2, feature_ndims=2
          )
          kernel = psd_kernels.FeatureScaled(
              kernel, scale_diag=tf.math.sqrt(l)
          )
          return student_t_process.StudentTProcess(
              df=4.0,
              kernel=kernel,
              index_points=x,
              observation_noise_variance=o,
          ).log_prob(y, is_missing=is_missing)
  
      observation_noise_variance = tf.constant(1e-3)
      lscales = tf.convert_to_tensor(
          [
              [11.67385626, 1.21246016, 1.5215677],
              [1.08823962, 1.22416186, 1.16885594],
          ]
      )
  
      value, grads = gradient.value_and_gradient(
          loss, [x_obs, y_obs, lscales, observation_noise_variance]
      )
      self.assertAllNotNan(value)
      for g in grads:
          self.assertAllNotNan(g)
  
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 10 for calling the _StudentTProcessTest obj's underlying member method code with example usage: obj.test_gradient_non_nan() and returns: student_t_process