code: def test_vgp_vs_gpr(gpr_and_vgp: Tuple[GPR, VGP]) -> None:
      """
      With a Gaussian likelihood the Gaussian variational (VGP) model should be equivalent to the
      exact regression model (GPR) after a single nat grad step of size 1
      """
      gpr, vgp = gpr_and_vgp
      assert_gpr_vs_vgp(gpr, vgp)
  
imports: from typing import Any, Optional, Tuple
  import numpy
  import pytest
  import tensorflow
  from check_shapes import check_shapes
  import gpflow
  from gpflow import set_trainable
  from gpflow.config import default_float
  from gpflow.kernels import Kernel, SquaredExponential
  from gpflow.likelihoods import Gaussian, Likelihood
  from gpflow.models import GPR, SGPR, SVGP, VGP, BayesianModel
  from gpflow.optimizers import NaturalGradient
label_desc: Use image node_id 10 for calling a global function with example usage: test_vgp_vs_gpr(gpr_and_vgp) without return types