code: def test_construct_inputs(self):
      for dtype, infer_noise in [
          (torch.float, False),
          (torch.double, True),
      ]:
          tkwargs = {"device": self.device, "dtype": dtype}
          task_feature = 0
          datasets, (
              train_X,
              train_Y,
              train_Yvar,
          ) = gen_multi_task_dataset(
              yvar=None if infer_noise else 0.05, **tkwargs
          )
  
          model = SaasFullyBayesianMultiTaskGP(
              train_X=train_X,
              train_Y=train_Y,
              train_Yvar=train_Yvar,
              task_feature=task_feature,
          )
  
          data_dict = model.construct_inputs(
              datasets,
              task_feature=task_feature,
              rank=1,
          )
          self.assertTrue(torch.equal(data_dict["train_X"], train_X))
          self.assertTrue(torch.equal(data_dict["train_Y"], train_Y))
          self.assertAllClose(data_dict["train_Yvar"], train_Yvar)
          self.assertEqual(data_dict["task_feature"], task_feature)
          self.assertEqual(data_dict["rank"], 1)
          self.assertTrue("task_covar_prior" not in data_dict)
  
imports: import itertools
  from typing import List, Optional
  import torch
  from botorch import fit_fully_bayesian_model_nuts
  from botorch.acquisition.analytic import ExpectedImprovement, PosteriorMean, ProbabilityOfImprovement, UpperConfidenceBound
  from botorch.acquisition.monte_carlo import qExpectedImprovement, qNoisyExpectedImprovement, qProbabilityOfImprovement, qSimpleRegret, qUpperConfidenceBound
  from botorch.acquisition.multi_objective import qExpectedHypervolumeImprovement, qNoisyExpectedHypervolumeImprovement
  from botorch.models import ModelList, ModelListGP
  from botorch.models.deterministic import GenericDeterministicModel
  from botorch.models.fully_bayesian import MCMC_DIM, MIN_INFERRED_NOISE_LEVEL
  from botorch.models.fully_bayesian_multitask import MultitaskSaasPyroModel, SaasFullyBayesianMultiTaskGP
  from botorch.models.transforms.input import Normalize
  from botorch.models.transforms.outcome import Standardize
  from botorch.posteriors import GaussianMixturePosterior
  from botorch.sampling.get_sampler import get_sampler
  from botorch.sampling.normal import IIDNormalSampler
  from botorch.utils.multi_objective.box_decompositions.non_dominated import NondominatedPartitioning
  from botorch.utils.test_helpers import gen_multi_task_dataset
  from botorch.utils.testing import BotorchTestCase
  from gpytorch.kernels import MaternKernel, ScaleKernel
  from gpytorch.likelihoods import FixedNoiseGaussianLikelihood
  from gpytorch.likelihoods.gaussian_likelihood import GaussianLikelihood
  from gpytorch.means import ConstantMean
label_desc: Use image node_id 12 for calling the TestFullyBayesianMultiTaskGP obj's underlying member method code with example usage: obj.test_construct_inputs() without return types