code: def test_whiten(
      Xdata: tf.Tensor,
      Xnew: tf.Tensor,
      kernel: Kernel,
      mu: tf.Tensor,
      sqrt: tf.Tensor,
  ) -> None:
      """
      Make sure that predicting using the whitened representation is the
      sameas the non-whitened one.
      """
  
      K = kernel(Xdata) + tf.eye(Nn, dtype=default_float()) * 1e-6
      L = tf.linalg.cholesky(K)
      V = tf.linalg.triangular_solve(L, mu, lower=True)
      mean1, var1 = conditional(Xnew, Xdata, kernel, mu)
      mean2, var2 = conditional(Xnew, Xdata, kernel, V, white=True)
  
      assert_allclose(mean1, mean2)
      assert_allclose(var1, var2)
  
imports: import numpy
  import pytest
  import tensorflow
  from numpy.testing import assert_allclose
  import gpflow
  from gpflow import Parameter
  from gpflow.base import AnyNDArray, MeanAndVariance
  from gpflow.conditionals import conditional
  from gpflow.config import default_float
  from gpflow.kernels import Kernel
  from gpflow.utilities.bijectors import triangular
label_desc: Use image node_id 8 for calling a global function with example usage: test_whiten(Xdata, Xnew, kernel, mu, sqrt) without return types