code: def test_cglb_predict() -> None:
      """
      Test that 1.) The predict method returns the same variance estimate as SGPR.
                2.) The predict method returns the same mean as SGPR for v=0.
                3.) The predict method returns a mean very similar to GPR when CG is run to low tolerance.
      """
      rng: np.random.RandomState = np.random.RandomState(999)
      train, z, xs = data(rng)
      noise = 0.2
  
      gpr = GPR(
          train, kernel=SquaredExponential(), noise_variance=noise
      )
      sgpr = SGPR(
          train,
          kernel=SquaredExponential(),
          inducing_variable=z,
          noise_variance=noise,
      )
  
      cglb = CGLB(
          train,
          kernel=SquaredExponential(),
          inducing_variable=z,
          noise_variance=noise,
      )
  
      gpr_mean, _ = gpr.predict_y(xs, full_cov=False)
      sgpr_mean, sgpr_cov = sgpr.predict_y(xs, full_cov=False)
      cglb_mean, cglb_cov = cglb.predict_y(
          xs, full_cov=False, cg_tolerance=1e6
      )  # set tolerance high so v stays at 0.
  
      assert np.allclose(sgpr_cov, cglb_cov)
      assert np.allclose(sgpr_mean, cglb_mean)
  
      cglb_mean, _ = cglb.predict_y(
          xs, full_cov=False, cg_tolerance=1e-12
      )
  
      assert np.allclose(gpr_mean, cglb_mean)
  
file_path: GPflow/tests/gpflow/models/test_cglb.py
imports: from typing import Tuple, cast
  import numpy
  import tensorflow
  from check_shapes import check_shapes
  from gpflow.base import AnyNDArray, RegressionData
  from gpflow.config import default_float
  from gpflow.kernels import SquaredExponential
  from gpflow.models import CGLB, GPR, SGPR
  from gpflow.models.cglb import NystromPreconditioner, cglb_conjugate_gradient
  from gpflow.utilities import to_default_float
label_desc: Use image node_id 5 for calling a global function with example usage: test_cglb_predict() without return types