code: def testCompositeTensorOrPytree(self):
      index_points = np.random.uniform(-1.0, 1.0, 10)[..., np.newaxis]
      gp = gaussian_process.GaussianProcess(
          kernel=psd_kernels.ExponentiatedQuadratic(),
          index_points=index_points,
      )
  
      flat = tf.nest.flatten(gp, expand_composites=True)
      unflat = tf.nest.pack_sequence_as(
          gp, flat, expand_composites=True
      )
      self.assertIsInstance(unflat, gaussian_process.GaussianProcess)
  
      x = self.evaluate(gp.sample(3, seed=test_util.test_seed()))
      actual = self.evaluate(gp.log_prob(x))
  
      self.assertAllClose(self.evaluate(unflat.log_prob(x)), actual)
  
      @tf.function
      def call_log_prob(d):
          return d.log_prob(x)
  
      self.assertAllClose(actual, call_log_prob(gp))
      self.assertAllClose(actual, call_log_prob(unflat))
  
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 13 for calling the _GaussianProcessTest obj's underlying member method code with example usage: obj.testCompositeTensorOrPytree() and returns: d