code: def testCustomCholeskyFn(self):
      def test_cholesky(x):
          test_cholesky.cholesky_count += 1
          return tf.linalg.cholesky(
              tf.linalg.set_diag(x, tf.linalg.diag_part(x) + 3.0)
          )
  
      test_cholesky.cholesky_count = 0
  
      index_points = np.linspace(-4.0, 4.0, 5, dtype=np.float64)[
          ..., np.newaxis
      ]
      inducing_index_points = np.linspace(
          -4.0, 4.0, 3, dtype=np.float64
      )[..., np.newaxis]
  
      variational_inducing_observations_loc = np.zeros(
          [3], dtype=np.float64
      )
      variational_inducing_observations_scale = np.eye(
          3, dtype=np.float64
      )
  
      amplitude = np.float64(1.0)
      length_scale = np.float64(1.0)
  
      jitter = np.float64(1e-6)
      kernel = tfpk.ExponentiatedQuadratic(amplitude, length_scale)
  
      vgp = variational_gaussian_process.VariationalGaussianProcess(
          kernel=kernel,
          index_points=index_points,
          inducing_index_points=inducing_index_points,
          variational_inducing_observations_loc=(
              variational_inducing_observations_loc
          ),
          variational_inducing_observations_scale=(
              variational_inducing_observations_scale
          ),
          observation_noise_variance=1e-6,
          cholesky_fn=test_cholesky,
          jitter=jitter,
      )
      self.evaluate(vgp.get_marginal_distribution().stddev())
      # Assert that the custom cholesky function is called at least once.
      self.assertGreaterEqual(test_cholesky.cholesky_count, 1)
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import bernoulli
  from tensorflow_probability.python.distributions import categorical
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import variational_gaussian_process
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math.psd_kernels import exponentiated_quadratic
label_desc: Use image node_id 4 for calling the VariationalGaussianProcessTest obj's underlying member method code with example usage: obj.testCustomCholeskyFn() and returns: tf