code: def test_gradient(self):
      x_obs = normal.Normal(0.0, 1.0).sample(
          (10, 6), seed=test_util.test_seed()
      )
      y_obs = tf.reduce_sum(x_obs, axis=-1)
  
      def loss(length_scales):
          kernel = psd_kernels.MaternFiveHalves(
              amplitude=tf.math.sqrt(1e-2)
          )
          kernel = psd_kernels.FeatureScaled(
              kernel, scale_diag=tf.math.sqrt(length_scales)
          )
          return gaussian_process.GaussianProcess(
              kernel,
              index_points=x_obs,
              observation_noise_variance=1.0,
          ).log_prob(y_obs)
  
      lscales = tf.convert_to_tensor(
          [
              11.67385626,
              0.21246016,
              0.0215677,
              0.08823962,
              0.22416186,
              0.06885594,
          ]
      )
  
      def _grad(lscales):
          return gradient.value_and_gradient(loss, lscales)[1]
  
      self.assertAllClose(
          _grad(lscales),
          tf.function(_grad, jit_compile=True)(lscales),
          atol=0.01,
      )
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import gaussian_process
  from tensorflow_probability.python.distributions import gaussian_process_regression_model
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import normal
  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 1 for calling the GaussianProcessStaticTest obj's underlying member method code with example usage: obj.test_gradient() and returns: gaussian_process, gradient