code: def test_independent_interdomain_conditional_whiten(
      whiten: bool,
  ) -> None:
      """
      This test checks the effect of the `white` flag, which changes the projection matrix `A`.
  
      The impact of the flag on the value of `A` can be easily verified by its effect on the
      predicted mean. While the predicted covariance is also a function of `A` this test does not
      inspect that value.
      """
      N, P = Data.N, Data.P
  
      Lm: AnyNDArray = np.random.randn(1, 1, 1).astype(np.float32) ** 2
      Kmm = Lm * Lm + default_jitter()
  
      Kmn = tf.ones((1, 1, N, P))
  
      Knn = tf.ones((N, P))
      f: AnyNDArray = np.random.randn(1, 1).astype(np.float32)
  
      mean, _ = independent_interdomain_conditional(
          Kmn,
          Kmm,
          Knn,
          f,
          white=whiten,
      )
  
      if whiten:
          expected_mean = (f * Kmn) / Lm
      else:
          expected_mean = (f * Kmn) / Kmm
  
      np.testing.assert_allclose(mean, expected_mean[0][0], rtol=1e-2)
  
imports: from typing import Callable, List, Sequence, Tuple, cast
  import numpy
  import pytest
  import scipy
  import tensorflow
  from _pytest.fixtures import SubRequest
  from check_shapes import ShapeChecker, check_shapes
  import gpflow
  import gpflow.inducing_variables.multioutput
  import gpflow.kernels.multioutput
  from gpflow import set_trainable
  from gpflow.base import AnyNDArray, RegressionData
  from gpflow.conditionals import sample_conditional
  from gpflow.conditionals.util import fully_correlated_conditional, fully_correlated_conditional_repeat, independent_interdomain_conditional, sample_mvn
  from gpflow.config import default_float, default_jitter
  from gpflow.inducing_variables import InducingPoints
  from gpflow.kernels import SquaredExponential
  from gpflow.likelihoods import Gaussian
  from gpflow.models import SVGP
label_desc: Use image node_id 24 for calling a global function with example usage: test_independent_interdomain_conditional_whiten(whiten) without return types