code: def test_fantasize(self):
      for dtype in [torch.float, torch.double]:
          torch.random.manual_seed(0)
          test_x = torch.rand(2, 5, 1, device=self.device, dtype=dtype)
          sampler = IIDNormalSampler(sample_shape=torch.Size([32]))
  
          self.model.to(dtype)
          if dtype == torch.double:
              # need to clear float caches
              self.model.train()
              self.model.eval()
          _ = self.model.posterior(test_x)
          fantasy_model = self.model.fantasize(test_x, sampler=sampler)
          self.assertIsInstance(fantasy_model, HigherOrderGP)
          self.assertEqual(
              fantasy_model.train_inputs[0].shape[:2],
              torch.Size((32, 2)),
          )
  
imports: import itertools
  from unittest import mock
  import torch
  from botorch.acquisition.objective import PosteriorTransform
  from botorch.models import HigherOrderGP
  from botorch.models.higher_order_gp import FlattenedStandardize
  from botorch.models.transforms.input import Normalize
  from botorch.models.transforms.outcome import Standardize
  from botorch.optim.fit import fit_gpytorch_mll_torch
  from botorch.posteriors import GPyTorchPosterior, TransformedPosterior
  from botorch.sampling import IIDNormalSampler
  from botorch.utils.testing import BotorchTestCase
  from gpytorch.kernels import RBFKernel
  from gpytorch.likelihoods import GaussianLikelihood
  from gpytorch.mlls import ExactMarginalLogLikelihood
  from gpytorch.settings import max_cholesky_size, skip_posterior_variances
label_desc: Use image node_id 8 for calling the TestHigherOrderGP obj's underlying member method code with example usage: obj.test_fantasize() without return types