code: def test_construct_inputs(self):
      d = 3
      for batch_shape, ncat, dtype in itertools.product(
          (torch.Size(), torch.Size([2])),
          (1, 2),
          (torch.float, torch.double),
      ):
          tkwargs = {"device": self.device, "dtype": dtype}
          X, Y = _get_random_data(
              batch_shape=batch_shape, m=1, d=d, **tkwargs
          )
          cat_dims = list(range(ncat))
          training_data = SupervisedDataset(
              X,
              Y,
              feature_names=[f"x{i}" for i in range(d)],
              outcome_names=["y"],
          )
          model_kwargs = MixedSingleTaskGP.construct_inputs(
              training_data, categorical_features=cat_dims
          )
          self.assertTrue(X.equal(model_kwargs["train_X"]))
          self.assertTrue(Y.equal(model_kwargs["train_Y"]))
          self.assertEqual(model_kwargs["cat_dims"], cat_dims)
          self.assertIsNone(model_kwargs["likelihood"])
  
      # With train_Yvar.
      training_data = SupervisedDataset(
          X,
          Y,
          Yvar=Y,
          feature_names=[f"x{i}" for i in range(d)],
          outcome_names=["y"],
      )
      model_kwargs = MixedSingleTaskGP.construct_inputs(
          training_data, categorical_features=cat_dims
      )
      self.assertTrue(X.equal(model_kwargs["train_X"]))
      self.assertTrue(Y.equal(model_kwargs["train_Y"]))
      self.assertTrue(Y.equal(model_kwargs["train_Yvar"]))
  
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 5 for calling the TestMixedSingleTaskGP obj's underlying member method code with example usage: obj.test_construct_inputs() without return types