code: def test_use_model_list(self) -> None:
      self.assertFalse(
          use_model_list(
              datasets=self.supervised_datasets,
              botorch_model_class=SingleTaskGP,
          )
      )
      self.assertFalse(  # Batched multi-output case.
          use_model_list(
              datasets=[
                  SupervisedDataset(
                      X=self.Xs[0],
                      Y=Y,
                      feature_names=self.feature_names,
                      outcome_names=["y"],
                  )
                  for Y in self.Ys
              ],
              botorch_model_class=SingleTaskGP,
          )
      )
      # Multi-output with allow_batched_models
      self.assertFalse(
          use_model_list(
              datasets=2
              * [
                  SupervisedDataset(
                      X=self.Xs[0],
                      Y=Y,
                      feature_names=self.feature_names,
                      outcome_names=["y"],
                  )
                  for Y in self.Ys
              ],
              botorch_model_class=SingleTaskGP,
              allow_batched_models=True,
          )
      )
      self.assertTrue(
          use_model_list(
              datasets=2
              * [
                  SupervisedDataset(
                      X=self.Xs[0],
                      Y=Y,
                      feature_names=self.feature_names,
                      outcome_names=["y"],
                  )
                  for Y in self.Ys
              ],
              botorch_model_class=SingleTaskGP,
              allow_batched_models=False,
          )
      )
      self.assertTrue(
          use_model_list(
              datasets=[
                  SupervisedDataset(
                      X=self.Xs[0],
                      Y=self.Ys[0],
                      feature_names=self.feature_names,
                      outcome_names=["y"],
                  ),
                  SupervisedDataset(
                      X=self.Xs2[0],
                      Y=self.Ys2[0],
                      feature_names=self.feature_names,
                      outcome_names=["y"],
                  ),
              ],
              botorch_model_class=SingleTaskGP,
          )
      )
      self.assertTrue(
          use_model_list(
              datasets=self.supervised_datasets,
              botorch_model_class=MultiTaskGP,
          )
      )
      # Not using model list with single outcome.
      self.assertFalse(
          use_model_list(
              datasets=self.supervised_datasets,
              botorch_model_class=SaasFullyBayesianSingleTaskGP,
          )
      )
      # Using it with multiple outcomes.
      self.assertTrue(
          use_model_list(
              datasets=[
                  SupervisedDataset(
                      X=self.Xs[0],
                      Y=self.Ys[0],
                      feature_names=self.feature_names,
                      outcome_names=["y"],
                  ),
                  SupervisedDataset(
                      X=self.Xs2[0],
                      Y=self.Ys2[0],
                      feature_names=self.feature_names,
                      outcome_names=["y"],
                  ),
              ],
              botorch_model_class=SaasFullyBayesianSingleTaskGP,
          )
      )
      self.assertTrue(
          use_model_list(
              datasets=[
                  SupervisedDataset(
                      X=self.Xs[0],
                      Y=self.Ys[0].repeat(1, 2),
                      feature_names=self.feature_names,
                      outcome_names=["y1", "y2"],
                  ),
              ],
              botorch_model_class=SaasFullyBayesianSingleTaskGP,
          )
      )
  
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 8 for calling the BoTorchModelUtilsTest obj's underlying member method code with example usage: obj.test_use_model_list() without return types