code: def test_batched_to_model_list(self):
      for dtype in (torch.float, torch.double):
          # test SingleTaskGP
          train_X = torch.rand(10, 2, device=self.device, dtype=dtype)
          train_Y1 = train_X.sum(dim=-1)
          train_Y2 = train_X[:, 0] - train_X[:, 1]
          train_Y = torch.stack([train_Y1, train_Y2], dim=-1)
          batch_gp = SingleTaskGP(train_X, train_Y)
          list_gp = batched_to_model_list(batch_gp)
          self.assertIsInstance(list_gp, ModelListGP)
          self.assertIsInstance(
              list_gp.models[0].likelihood, GaussianLikelihood
          )
          # test observed noise
          batch_gp = SingleTaskGP(
              train_X, train_Y, torch.rand_like(train_Y)
          )
          list_gp = batched_to_model_list(batch_gp)
          self.assertIsInstance(list_gp, ModelListGP)
          self.assertIsInstance(
              list_gp.models[0].likelihood, FixedNoiseGaussianLikelihood
          )
          # test SingleTaskMultiFidelityGP
          for lin_trunc in (False, True):
              batch_gp = SingleTaskMultiFidelityGP(
                  train_X,
                  train_Y,
                  iteration_fidelity=1,
                  linear_truncated=lin_trunc,
              )
              list_gp = batched_to_model_list(batch_gp)
              self.assertIsInstance(list_gp, ModelListGP)
          # test HeteroskedasticSingleTaskGP
          batch_gp = HeteroskedasticSingleTaskGP(
              train_X, train_Y, torch.rand_like(train_Y)
          )
          with self.assertRaises(NotImplementedError):
              batched_to_model_list(batch_gp)
          # test with transforms
          input_tf = Normalize(
              d=2,
              bounds=torch.tensor(
                  [[0.0, 0.0], [1.0, 1.0]],
                  device=self.device,
                  dtype=dtype,
              ),
          )
          octf = Standardize(m=2)
          batch_gp = SingleTaskGP(
              train_X,
              train_Y,
              outcome_transform=octf,
              input_transform=input_tf,
          )
          list_gp = batched_to_model_list(batch_gp)
          for i, m in enumerate(list_gp.models):
              self.assertIsInstance(m.input_transform, Normalize)
              self.assertTrue(
                  torch.equal(m.input_transform.bounds, input_tf.bounds)
              )
              self.assertIsInstance(m.outcome_transform, Standardize)
              self.assertEqual(m.outcome_transform._m, 1)
              expected_octf = octf.subset_output(idcs=[i])
              for attr_name in ["means", "stdvs", "_stdvs_sq"]:
                  self.assertTrue(
                      torch.equal(
                          m.outcome_transform.__getattr__(attr_name),
                          expected_octf.__getattr__(attr_name),
                      )
                  )
          # test with AppendFeatures
          input_tf = AppendFeatures(
              feature_set=torch.rand(
                  2, 1, device=self.device, dtype=dtype
              )
          )
          batch_gp = SingleTaskGP(
              train_X,
              train_Y,
              outcome_transform=octf,
              input_transform=input_tf,
          ).eval()
          list_gp = batched_to_model_list(batch_gp)
          self.assertIsInstance(list_gp, ModelListGP)
          self.assertIsInstance(
              list_gp.models[0].input_transform, AppendFeatures
          )
  
file_path: botorch/test/models/test_converter.py
imports: import torch
  from botorch.exceptions import UnsupportedError
  from botorch.models import HeteroskedasticSingleTaskGP, ModelListGP, SingleTaskGP, SingleTaskMultiFidelityGP
  from botorch.models.converter import batched_multi_output_to_single_output, batched_to_model_list, model_list_to_batched
  from botorch.models.transforms.input import AppendFeatures, Normalize
  from botorch.models.transforms.outcome import Standardize
  from botorch.utils.test_helpers import SimpleGPyTorchModel
  from botorch.utils.testing import BotorchTestCase
  from gpytorch.kernels import RBFKernel
  from gpytorch.likelihoods import GaussianLikelihood
  from gpytorch.likelihoods.gaussian_likelihood import FixedNoiseGaussianLikelihood
label_desc: Use image node_id 1 for calling the TestConverters obj's underlying member method code with example usage: obj.test_batched_to_model_list() without return types