code: def test_fantasize(self):
      for (
          iteration_fidelity,
          data_fidelities,
      ) in self.FIDELITY_TEST_PAIRS:
          n_fidelity = iteration_fidelity is not None
          if data_fidelities is not None:
              n_fidelity += len(data_fidelities)
          num_dim = 1 + n_fidelity
          for batch_shape, m, dtype, lin_trunc in itertools.product(
              (torch.Size(), torch.Size([2])),
              (1, 2),
              (torch.float, torch.double),
              (False, True),
          ):
              tkwargs = {"device": self.device, "dtype": dtype}
              model, model_kwargs = self._get_model_and_data(
                  iteration_fidelity=iteration_fidelity,
                  data_fidelities=data_fidelities,
                  batch_shape=batch_shape,
                  m=m,
                  lin_truncated=lin_trunc,
                  **tkwargs,
              )
              # fantasize
              X_f = torch.rand(
                  torch.Size(batch_shape + torch.Size([4, num_dim])),
                  **tkwargs,
              )
              sampler = SobolQMCNormalSampler(
                  sample_shape=torch.Size([3])
              )
              fm = model.fantasize(X=X_f, sampler=sampler)
              self.assertIsInstance(fm, model.__class__)
  
imports: import itertools
  import warnings
  from typing import Tuple
  import torch
  from botorch.exceptions.errors import UnsupportedError
  from botorch.exceptions.warnings import OptimizationWarning
  from botorch.fit import fit_gpytorch_mll
  from botorch.models.gp_regression_fidelity import FixedNoiseMultiFidelityGP, SingleTaskMultiFidelityGP
  from botorch.models.transforms import Normalize, Standardize
  from botorch.posteriors import GPyTorchPosterior
  from botorch.sampling import SobolQMCNormalSampler
  from botorch.utils.datasets import SupervisedDataset
  from botorch.utils.testing import _get_random_data, BotorchTestCase
  from gpytorch.kernels.scale_kernel import ScaleKernel
  from gpytorch.likelihoods import FixedNoiseGaussianLikelihood
  from gpytorch.means import ConstantMean
  from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
  from torch import Tensor
label_desc: Use image node_id 5 for calling the TestSingleTaskMultiFidelityGP obj's underlying member method code with example usage: obj.test_fantasize() without return types