code: def test_construct_inputs(self):
      for batch_shape, dtype in itertools.product(
          (torch.Size(), torch.Size([2])), (torch.float, torch.double)
      ):
          tkwargs = {"device": self.device, "dtype": dtype}
          model, model_kwargs = self._get_model_and_data(
              batch_shape=batch_shape, m=1, **tkwargs
          )
          X = model_kwargs["train_X"]
          Y = model_kwargs["train_Y"]
          Yvar = model_kwargs["train_Yvar"]
          training_data = SupervisedDataset(
              X,
              Y,
              Yvar=Yvar,
              feature_names=[f"x{i}" for i in range(X.shape[-1])],
              outcome_names=["y"],
          )
          data_dict = model.construct_inputs(training_data)
          self.assertTrue(X.equal(data_dict["train_X"]))
          self.assertTrue(Y.equal(data_dict["train_Y"]))
          self.assertTrue(Yvar.equal(data_dict["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.gp_regression import FixedNoiseGP, HeteroskedasticSingleTaskGP, SingleTaskGP
  from botorch.models.transforms import Normalize, Standardize
  from botorch.models.transforms.input import InputStandardize
  from botorch.posteriors import GPyTorchPosterior
  from botorch.sampling import SobolQMCNormalSampler
  from botorch.utils.datasets import SupervisedDataset
  from botorch.utils.sampling import manual_seed
  from botorch.utils.test_helpers import get_pvar_expected
  from botorch.utils.testing import _get_random_data, BotorchTestCase
  from gpytorch.kernels import MaternKernel, RBFKernel, ScaleKernel
  from gpytorch.likelihoods import _GaussianLikelihoodBase, FixedNoiseGaussianLikelihood, GaussianLikelihood, HeteroskedasticNoise
  from gpytorch.means import ConstantMean, ZeroMean
  from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
  from gpytorch.mlls.noise_model_added_loss_term import NoiseModelAddedLossTerm
  from gpytorch.priors import GammaPrior
label_desc: Use image node_id 4 for calling the TestFixedNoiseGP obj's underlying member method code with example usage: obj.test_construct_inputs() without return types