code: def test_fantasy_call(
      self,
      data_batch_shape=None,
      inducing_batch_shape=None,
      model_batch_shape=None,
      expected_batch_shape=None,
      constant_mean=True,
  ):
      # Batch shapes
      model_batch_shape = (
          model_batch_shape
          if model_batch_shape is not None
          else self.batch_shape
      )
      data_batch_shape = (
          data_batch_shape
          if data_batch_shape is not None
          else self.batch_shape
      )
      inducing_batch_shape = (
          inducing_batch_shape
          if inducing_batch_shape is not None
          else self.batch_shape
      )
      expected_batch_shape = (
          expected_batch_shape
          if expected_batch_shape is not None
          else self.batch_shape
      )
  
      num_inducing = 16
      num_fant = 10
      # Make model and likelihood
      model, likelihood = self._make_model_and_likelihood(
          batch_shape=model_batch_shape,
          inducing_batch_shape=inducing_batch_shape,
          distribution_cls=self.distribution_cls,
          strategy_cls=self.strategy_cls,
          constant_mean=constant_mean,
          num_inducing=num_inducing,
      )
  
      # we iterate through the covar and mean module possible settings
      covar_mean_options = [
          {"covar_module": None, "mean_module": None},
          {
              "covar_module": gpytorch.kernels.MaternKernel(),
              "mean_module": gpytorch.means.ZeroMean(),
          },
      ]
      for cm_dict in covar_mean_options:
          fant_model = self._fantasy_iter(
              model,
              likelihood,
              data_batch_shape,
              self.cuda,
              num_fant=num_fant,
              **cm_dict,
          )
          self.assertTrue(
              isinstance(fant_model, gpytorch.models.ExactGP)
          )
  
          # we check to ensure setting the covar_module and mean_modules are okay
          if cm_dict["covar_module"] is None:
              self.assertEqual(
                  type(fant_model.covar_module),
                  type(model.covar_module),
              )
          else:
              self.assertNotEqual(
                  type(fant_model.covar_module),
                  type(model.covar_module),
              )
          if cm_dict["mean_module"] is None:
              self.assertEqual(
                  type(fant_model.mean_module), type(model.mean_module)
              )
          else:
              self.assertNotEqual(
                  type(fant_model.mean_module), type(model.mean_module)
              )
  
          # now we check to ensure the shapes of the fantasy strategy are correct
          self.assertTrue(fant_model.prediction_strategy is not None)
          for (
              key
          ) in fant_model.prediction_strategy._memoize_cache.keys():
              if key[0] == "mean_cache":
                  break
          mean_cache = fant_model.prediction_strategy._memoize_cache[
              key
          ]
          self.assertEqual(
              mean_cache.shape,
              torch.Size(
                  [*expected_batch_shape, num_inducing + num_fant]
              ),
          )
  
      # we remove the mean_module and covar_module and check for errors
      del model.mean_module
      with self.assertRaises(ModuleNotFoundError):
          self._fantasy_iter(
              model,
              likelihood,
              data_batch_shape,
              self.cuda,
              num_fant=num_fant,
          )
  
      model.mean_module = gpytorch.means.ZeroMean()
      del model.covar_module
      with self.assertRaises(ModuleNotFoundError):
          self._fantasy_iter(
              model,
              likelihood,
              data_batch_shape,
              self.cuda,
              num_fant=num_fant,
          )
  
      # finally we check to ensure failure for a non-gaussian likelihood
      with self.assertRaises(NotImplementedError):
          self._fantasy_iter(
              model,
              gpytorch.likelihoods.BernoulliLikelihood(),
              data_batch_shape,
              self.cuda,
              num_fant=num_fant,
          )
  
imports: from abc import abstractproperty
  from unittest.mock import MagicMock, patch
  import linear_operator
  import torch
  import gpytorch
  from .base_test_case import BaseTestCase
label_desc: Use image node_id 20 for calling the VariationalTestCase obj's underlying member method code with example usage: obj.test_fantasy_call(data_batch_shape, inducing_batch_shape, model_batch_shape, expected_batch_shape, constant_mean) without return types