code: def setUp(self) -> None:
      self.dtype = torch.float
      (
          self.Xs,
          self.Ys,
          self.Yvars,
          _,
          _,
          self.feature_names,
          self.metric_names,
      ) = get_torch_test_data(dtype=self.dtype)
      self.Xs2, self.Ys2, self.Yvars2, _, _, _, _ = get_torch_test_data(
          dtype=self.dtype, offset=1.0  # Making this data different.
      )
      self.fixed_noise_datasets = [
          SupervisedDataset(
              X=X,
              Y=Y,
              Yvar=Yvar,
              feature_names=self.feature_names,
              outcome_names=[mn],
          )
          for X, Y, Yvar, mn in zip(
              self.Xs, self.Ys, self.Yvars, self.metric_names
          )
      ]
      self.supervised_datasets = [
          SupervisedDataset(
              X=X,
              Y=Y,
              feature_names=self.feature_names,
              outcome_names=[mn],
          )
          for X, Y, mn in zip(self.Xs, self.Ys, self.metric_names)
      ]
      self.none_Yvars = [torch.tensor([[np.nan], [np.nan]])]
      self.task_features = []
      self.objective_thresholds = torch.tensor([0.5, 1.5])
  
imports: import warnings
  from typing import OrderedDict
  import numpy
  import torch
  from ax.core.search_space import SearchSpaceDigest
  from ax.exceptions.core import AxError, AxWarning, UnsupportedError
  from ax.models.torch.botorch_modular.utils import _get_shared_rows, _tensor_difference, check_metric_dataset_match, choose_botorch_acqf_class, choose_model_class, construct_acquisition_and_optimizer_options, convert_to_block_design, get_subset_datasets, subset_state_dict, use_model_list
  from ax.models.torch.utils import _to_inequality_constraints
  from ax.utils.common.constants import Keys
  from ax.utils.common.testutils import TestCase
  from ax.utils.common.typeutils import checked_cast, not_none
  from ax.utils.testing.torch_stubs import get_torch_test_data
  from botorch.acquisition import qLogNoisyExpectedImprovement
  from botorch.acquisition.multi_objective.logei import qLogNoisyExpectedHypervolumeImprovement
  from botorch.models.fully_bayesian import SaasFullyBayesianSingleTaskGP
  from botorch.models.gp_regression import SingleTaskGP
  from botorch.models.gp_regression_fidelity import SingleTaskMultiFidelityGP
  from botorch.models.gp_regression_mixed import MixedSingleTaskGP
  from botorch.models.model_list_gp_regression import ModelListGP
  from botorch.models.multitask import MultiTaskGP
  from botorch.utils.datasets import SupervisedDataset
label_desc: Use image node_id 1 for calling the BoTorchModelUtilsTest obj's underlying member method code with example usage: obj.setUp() without return types