code: def _get_data_and_model(
      self,
      task_rank: Optional[int] = None,
      output_tasks: Optional[List[int]] = None,
      infer_noise: bool = False,
      **tkwargs,
  ):
      with torch.random.fork_rng():
          torch.manual_seed(0)
          train_X = torch.rand(10, 4, **tkwargs)
          task_indices = torch.cat(
              [
                  torch.zeros(5, 1, **tkwargs),
                  torch.ones(5, 1, **tkwargs),
              ],
              dim=0,
          )
          self.num_tasks = 2
          train_X = torch.cat([train_X, task_indices], dim=1)
          train_Y = torch.sin(train_X[:, :1])
          train_Yvar = 0.5 * torch.arange(10, **tkwargs).unsqueeze(-1)
          model = SaasFullyBayesianMultiTaskGP(
              train_X=train_X,
              train_Y=train_Y,
              train_Yvar=None if infer_noise else train_Yvar,
              task_feature=4,
              output_tasks=output_tasks,
              rank=task_rank,
          )
      return train_X, train_Y, train_Yvar, model
  
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 1 for calling the TestFullyBayesianMultiTaskGP obj's underlying member method code with example usage: obj._get_data_and_model(task_rank, output_tasks, infer_noise) and returns: train_X,  train_Y,  train_Yvar,  model