code: def _fantasy_iter(
      self,
      model,
      likelihood,
      batch_shape=torch.Size([]),
      cuda=False,
      num_fant=10,
      covar_module=None,
      mean_module=None,
  ):
      model.likelihood = likelihood
      val_x = torch.randn(*batch_shape, num_fant, 2).clamp(-2.5, 2.5)
      val_y = torch.linspace(-1, 1, num_fant)
      val_y = val_y.view(num_fant, *([1] * (len(self.event_shape) - 1)))
      val_y = val_y.expand(
          *batch_shape, num_fant, *self.event_shape[1:]
      )
      if cuda:
          model = model.cuda()
          val_x = val_x.cuda()
          val_y = val_y.cuda()
      updated_model = model.get_fantasy_model(
          val_x,
          val_y,
          covar_module=covar_module,
          mean_module=mean_module,
      )
      return updated_model
  
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 4 for calling the VariationalTestCase obj's underlying member method code with example usage: obj._fantasy_iter(model, likelihood, batch_shape, cuda, num_fant, covar_module, mean_module) and returns: updated_model