code: def test_model(model_class, X, y, kernel, likelihood):
      if (
          model_class is SparseGPRegression
          or model_class is VariationalSparseGP
      ):
          gp = model_class(X, None, kernel, X, likelihood)
      else:
          gp = model_class(X, None, kernel, likelihood)
  
      loc, var = gp.model()
      if (
          model_class is VariationalGP
          or model_class is VariationalSparseGP
      ):
          assert_equal(loc.norm().item(), 0)
          assert_equal(var, torch.ones(var.shape[-1]).expand(var.shape))
      else:
          assert_equal(loc.norm().item(), 0)
          assert_equal(var, kernel(X).diag())
  
imports: import logging
  from collections import namedtuple
  import pytest
  import torch
  import pyro.distributions
  from pyro.contrib.gp.kernels import RBF, Cosine, Matern32, WhiteNoise
  from pyro.contrib.gp.likelihoods import Gaussian
  from pyro.contrib.gp.models import GPLVM, GPRegression, SparseGPRegression, VariationalGP, VariationalSparseGP
  from pyro.contrib.gp.util import train
  from pyro.infer.mcmc.api import MCMC
  from pyro.infer.mcmc.hmc import HMC
  from pyro.nn.module import PyroSample
  from tests.common import assert_equal
label_desc: Use image node_id 4 for calling a global function with example usage: test_model(model_class, X, y, kernel, likelihood) without return types