code: def test_inference(model_class, X, y, kernel, likelihood):
      if isinstance(likelihood, MultiClass):
          latent_shape = y.shape[:-1] + (likelihood.num_classes,)
      else:
          latent_shape = y.shape[:-1]
      if model_class is VariationalSparseGP:
          gp = model_class(
              X, y, kernel, X, likelihood, latent_shape=latent_shape
          )
      else:
          gp = model_class(
              X, y, kernel, likelihood, latent_shape=latent_shape
          )
  
      train(gp, num_steps=1)
  
file_path: pyro/tests/contrib/gp/test_likelihoods.py
imports: from collections import namedtuple
  import pytest
  import torch
  from pyro.contrib.gp.kernels import RBF
  from pyro.contrib.gp.likelihoods import Binary, MultiClass, Poisson
  from pyro.contrib.gp.models import VariationalGP, VariationalSparseGP
  from pyro.contrib.gp.util import train
label_desc: Use image node_id 1 for calling a global function with example usage: test_inference(model_class, X, y, kernel, likelihood) without return types