code: def test_forward_with_empty_latent_shape(
      model_class, X, y, kernel, likelihood
  ):
      # regression models don't use latent_shape, no need for test
      if (
          model_class is GPRegression
          or model_class is SparseGPRegression
      ):
          return
      elif model_class is VariationalGP:
          gp = model_class(
              X, y, kernel, likelihood, latent_shape=torch.Size([])
          )
      else:  # model_class is VariationalSparseGP
          gp = model_class(
              X, y, kernel, X, likelihood, latent_shape=torch.Size([])
          )
  
      # test shape
      Xnew = torch.tensor([[2.0, 3.0, 1.0]])
      loc0, cov0 = gp(Xnew, full_cov=True)
      loc1, var1 = gp(Xnew, full_cov=False)
      assert loc0.shape[-1] == Xnew.shape[0]
      assert cov0.shape[-1] == cov0.shape[-2]
      assert cov0.shape[-1] == Xnew.shape[0]
      # test latent shape
      assert loc0.shape[:-1] == torch.Size([])
      assert cov0.shape[:-2] == torch.Size([])
      assert_equal(loc0, loc1)
      assert_equal(cov0.diag(), var1)
  
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 6 for calling a global function with example usage: test_forward_with_empty_latent_shape(model_class, X, y, kernel, likelihood) without return types