code: def test_RBF_eKzxKxz_gradient_notNaN() -> None:
      """
      Ensure that <K_{Z, x} K_{x, Z}>_p(x) is not NaN and correct, when
      K_{Z, Z} is zero with finite precision. See pull request #595.
      """
      kernel = krn.SquaredExponential(1, lengthscales=0.1)
      kernel.variance.assign(2.0)
  
      p = gpflow.probability_distributions.Gaussian(
          tf.constant([[10]], dtype=default_float()),
          tf.constant([[[0.1]]], dtype=default_float()),
      )
      z = iv.InducingPoints([[-10.0], [10.0]])
  
      with tf.GradientTape() as tape:
          ekz = expectation(p, (kernel, z), (kernel, z))
      grad = tape.gradient(
          ekz, kernel.lengthscales.unconstrained_variable
      )
      assert grad is not None and not np.isnan(grad)
  
imports: from typing import Any, Callable, Iterable, Mapping, Optional, Sequence
  import numpy
  import pytest
  import tensorflow
  from numpy.testing import assert_allclose
  from tensorflow import convert_to_tensor
  import gpflow
  import gpflow.inducing_variables
  import gpflow.kernels
  from gpflow import mean_functions
  from gpflow.base import AnyNDArray
  from gpflow.config import default_float
  from gpflow.expectations import expectation, quadrature_expectation
  from gpflow.probability_distributions import DiagonalGaussian, Gaussian, MarkovGaussian, ProbabilityDistribution
label_desc: Use image node_id 14 for calling a global function with example usage: test_RBF_eKzxKxz_gradient_notNaN() without return types