code: def test_gaussian_mean_and_variance(
      Ntrain: int, Ntest: int, D: int
  ) -> None:
      data = rng.randn(Ntrain, D), rng.randn(Ntrain, 1)
      Xtest, _ = rng.randn(Ntest, D), rng.randn(Ntest, 1)
      kernel = Matern32() + gpflow.kernels.White()
      model_gp = gpflow.models.GPR(data, kernel=kernel)
  
      mu_f, var_f = model_gp.predict_f(Xtest)
      mu_y, var_y = model_gp.predict_y(Xtest)
  
      assert np.allclose(mu_f, mu_y)
      assert np.allclose(var_f, var_y - 1.0)
  
file_path: GPflow/tests/gpflow/models/test_model_predict.py
imports: from typing import Any, Dict, Optional, Type
  import numpy
  import pytest
  import gpflow
  from gpflow.inducing_variables import InducingPoints
  from gpflow.kernels import Kernel, Matern32
  from gpflow.models.util import InducingPointsLike
label_desc: Use image node_id 1 for calling a global function with example usage: test_gaussian_mean_and_variance(Ntrain, Ntest, D) without return types