code: def test_fantasize(self):
      d = 3
      for batch_shape, m, ncat, dtype, observed_noise in (
          (torch.Size(), 2, 1, torch.float, True),
          (torch.Size([2]), 1, 2, torch.double, False),
      ):
          tkwargs = {"device": self.device, "dtype": dtype}
          train_X, train_Y = _get_random_data(
              batch_shape=batch_shape, m=m, d=d, **tkwargs
          )
          train_Yvar = (
              torch.full_like(train_Y, 0.1) if observed_noise else None
          )
          cat_dims = list(range(ncat))
          model = MixedSingleTaskGP(
              train_X=train_X,
              train_Y=train_Y,
              cat_dims=cat_dims,
              train_Yvar=train_Yvar,
          )
  
          # fantasize
          X_f = torch.rand(
              torch.Size(batch_shape + torch.Size([4, d])), **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
  import torch
  from botorch.exceptions.warnings import OptimizationWarning
  from botorch.fit import fit_gpytorch_mll
  from botorch.models.converter import batched_to_model_list
  from botorch.models.gp_regression_mixed import MixedSingleTaskGP
  from botorch.models.kernels.categorical import CategoricalKernel
  from botorch.models.transforms import Normalize
  from botorch.posteriors import GPyTorchPosterior
  from botorch.sampling import SobolQMCNormalSampler
  from botorch.utils.datasets import SupervisedDataset
  from botorch.utils.test_helpers import get_pvar_expected
  from botorch.utils.testing import _get_random_data, BotorchTestCase
  from gpytorch.kernels.kernel import AdditiveKernel, ProductKernel
  from gpytorch.kernels.matern_kernel import MaternKernel
  from gpytorch.kernels.scale_kernel import ScaleKernel
  from gpytorch.likelihoods import FixedNoiseGaussianLikelihood
  from gpytorch.likelihoods.gaussian_likelihood import GaussianLikelihood
  from gpytorch.means import ConstantMean
  from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
label_desc: Use image node_id 3 for calling the TestMixedSingleTaskGP obj's underlying member method code with example usage: obj.test_fantasize() without return types