code: def test_model_list_to_batched(self):
      for dtype in (torch.float, torch.double):
          # basic test
          train_X = torch.rand(10, 2, device=self.device, dtype=dtype)
          train_Y1 = train_X.sum(dim=-1, keepdim=True)
          train_Y2 = (train_X[:, 0] - train_X[:, 1]).unsqueeze(-1)
          gp1 = SingleTaskGP(train_X, train_Y1)
          gp2 = SingleTaskGP(train_X, train_Y2)
          list_gp = ModelListGP(gp1, gp2)
          batch_gp = model_list_to_batched(list_gp)
          self.assertIsInstance(batch_gp, SingleTaskGP)
          self.assertIsInstance(batch_gp.likelihood, GaussianLikelihood)
          # test degenerate (single model)
          batch_gp = model_list_to_batched(ModelListGP(gp1))
          self.assertEqual(batch_gp._num_outputs, 1)
          # test mixing different likelihoods
          gp2 = SingleTaskGP(
              train_X, train_Y1, torch.ones_like(train_Y1)
          )
          with self.assertRaises(UnsupportedError):
              model_list_to_batched(ModelListGP(gp1, gp2))
          # test non-batched models
          gp1_ = SimpleGPyTorchModel(train_X, train_Y1)
          gp2_ = SimpleGPyTorchModel(train_X, train_Y2)
          with self.assertRaises(UnsupportedError):
              model_list_to_batched(ModelListGP(gp1_, gp2_))
          # test list of multi-output models
          train_Y = torch.cat([train_Y1, train_Y2], dim=-1)
          gp2 = SingleTaskGP(train_X, train_Y)
          with self.assertRaises(UnsupportedError):
              model_list_to_batched(ModelListGP(gp1, gp2))
          # test different training inputs
          gp2 = SingleTaskGP(2 * train_X, train_Y2)
          with self.assertRaises(UnsupportedError):
              model_list_to_batched(ModelListGP(gp1, gp2))
          # check scalar agreement
          gp2 = SingleTaskGP(train_X, train_Y2)
          gp2.likelihood.noise_covar.noise_prior.rate.fill_(1.0)
          with self.assertRaises(UnsupportedError):
              model_list_to_batched(ModelListGP(gp1, gp2))
          # check tensor shape agreement
          gp2 = SingleTaskGP(train_X, train_Y2)
          gp2.covar_module.raw_outputscale = torch.nn.Parameter(
              torch.tensor([0.0], device=self.device, dtype=dtype)
          )
          with self.assertRaises(UnsupportedError):
              model_list_to_batched(ModelListGP(gp1, gp2))
          # test HeteroskedasticSingleTaskGP
          gp2 = HeteroskedasticSingleTaskGP(
              train_X, train_Y1, torch.ones_like(train_Y1)
          )
          with self.assertRaises(NotImplementedError):
              model_list_to_batched(ModelListGP(gp2))
          # test custom likelihood
          gp2 = SingleTaskGP(
              train_X, train_Y2, likelihood=GaussianLikelihood()
          )
          with self.assertRaises(NotImplementedError):
              model_list_to_batched(ModelListGP(gp2))
          # test non-default kernel
          gp1 = SingleTaskGP(
              train_X, train_Y1, covar_module=RBFKernel()
          )
          gp2 = SingleTaskGP(
              train_X, train_Y2, covar_module=RBFKernel()
          )
          list_gp = ModelListGP(gp1, gp2)
          batch_gp = model_list_to_batched(list_gp)
          self.assertEqual(type(batch_gp.covar_module), RBFKernel)
          # test error when component GPs have different kernel types
          gp1 = SingleTaskGP(
              train_X, train_Y1, covar_module=RBFKernel()
          )
          gp2 = SingleTaskGP(train_X, train_Y2)
          list_gp = ModelListGP(gp1, gp2)
          with self.assertRaises(UnsupportedError):
              model_list_to_batched(list_gp)
          # test observed noise
          train_X = torch.rand(10, 2, device=self.device, dtype=dtype)
          train_Y1 = train_X.sum(dim=-1, keepdim=True)
          train_Y2 = (train_X[:, 0] - train_X[:, 1]).unsqueeze(-1)
          gp1_ = SingleTaskGP(
              train_X, train_Y1, torch.rand_like(train_Y1)
          )
          gp2_ = SingleTaskGP(
              train_X, train_Y2, torch.rand_like(train_Y2)
          )
          list_gp = ModelListGP(gp1_, gp2_)
          batch_gp = model_list_to_batched(list_gp)
          self.assertIsInstance(
              batch_gp.likelihood, FixedNoiseGaussianLikelihood
          )
          # test SingleTaskMultiFidelityGP
          gp1_ = SingleTaskMultiFidelityGP(
              train_X, train_Y1, iteration_fidelity=1
          )
          gp2_ = SingleTaskMultiFidelityGP(
              train_X, train_Y2, iteration_fidelity=1
          )
          list_gp = ModelListGP(gp1_, gp2_)
          batch_gp = model_list_to_batched(list_gp)
          gp2_ = SingleTaskMultiFidelityGP(
              train_X, train_Y2, iteration_fidelity=2
          )
          list_gp = ModelListGP(gp1_, gp2_)
          with self.assertRaises(UnsupportedError):
              model_list_to_batched(list_gp)
          # test input transform
          input_tf = Normalize(
              d=2,
              bounds=torch.tensor(
                  [[0.0, 0.0], [1.0, 1.0]],
                  device=self.device,
                  dtype=dtype,
              ),
          )
          gp1_ = SingleTaskGP(
              train_X, train_Y1, input_transform=input_tf
          )
          gp2_ = SingleTaskGP(
              train_X, train_Y2, input_transform=input_tf
          )
          list_gp = ModelListGP(gp1_, gp2_)
          batch_gp = model_list_to_batched(list_gp)
          self.assertIsInstance(batch_gp.input_transform, Normalize)
          self.assertTrue(
              torch.equal(
                  batch_gp.input_transform.bounds, input_tf.bounds
              )
          )
          # test with AppendFeatures
          input_tf3 = AppendFeatures(
              feature_set=torch.rand(
                  2, 1, device=self.device, dtype=dtype
              )
          )
          gp1_ = SingleTaskGP(
              train_X, train_Y1, input_transform=input_tf3
          )
          gp2_ = SingleTaskGP(
              train_X, train_Y2, input_transform=input_tf3
          )
          list_gp = ModelListGP(gp1_, gp2_).eval()
          batch_gp = model_list_to_batched(list_gp)
          self.assertIsInstance(batch_gp, SingleTaskGP)
          self.assertIsInstance(
              batch_gp.input_transform, AppendFeatures
          )
          # test different input transforms
          input_tf2 = Normalize(
              d=2,
              bounds=torch.tensor(
                  [[-1.0, -1.0], [1.0, 1.0]],
                  device=self.device,
                  dtype=dtype,
              ),
          )
          gp1_ = SingleTaskGP(
              train_X, train_Y1, input_transform=input_tf
          )
          gp2_ = SingleTaskGP(
              train_X, train_Y2, input_transform=input_tf2
          )
          list_gp = ModelListGP(gp1_, gp2_)
          with self.assertRaisesRegex(
              UnsupportedError, "have the same"
          ):
              model_list_to_batched(list_gp)
  
          # test batched input transform
          input_tf2 = Normalize(
              d=2,
              bounds=torch.tensor(
                  [[-1.0, -1.0], [1.0, 1.0]],
                  device=self.device,
                  dtype=dtype,
              ),
              batch_shape=torch.Size([3]),
          )
          gp1_ = SingleTaskGP(
              train_X, train_Y1, input_transform=input_tf2
          )
          gp2_ = SingleTaskGP(
              train_X, train_Y2, input_transform=input_tf2
          )
          list_gp = ModelListGP(gp1_, gp2_)
          with self.assertRaises(UnsupportedError):
              model_list_to_batched(list_gp)
  
          # test outcome transform
          octf = Standardize(m=1)
          gp1_ = SingleTaskGP(train_X, train_Y1, outcome_transform=octf)
          gp2_ = SingleTaskGP(train_X, train_Y2, outcome_transform=octf)
          list_gp = ModelListGP(gp1_, gp2_)
          with self.assertRaises(UnsupportedError):
              model_list_to_batched(list_gp)
  
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 2 for calling the TestConverters obj's underlying member method code with example usage: obj.test_model_list_to_batched() without return types