code: def test_batched_multi_output_to_single_output(self):
      for dtype in (torch.float, torch.double):
          # basic test
          train_X = torch.rand(10, 2, device=self.device, dtype=dtype)
          train_Y = torch.stack(
              [
                  train_X.sum(dim=-1),
                  (train_X[:, 0] - train_X[:, 1]),
              ],
              dim=1,
          )
          batched_mo_model = SingleTaskGP(train_X, train_Y)
          batched_so_model = batched_multi_output_to_single_output(
              batched_mo_model
          )
          self.assertIsInstance(batched_so_model, SingleTaskGP)
          self.assertEqual(batched_so_model.num_outputs, 1)
          # test non-batched models
          non_batch_model = SimpleGPyTorchModel(train_X, train_Y[:, :1])
          with self.assertRaises(UnsupportedError):
              batched_multi_output_to_single_output(non_batch_model)
          gp2 = HeteroskedasticSingleTaskGP(
              train_X, train_Y, torch.ones_like(train_Y)
          )
          with self.assertRaises(NotImplementedError):
              batched_multi_output_to_single_output(gp2)
          # test custom likelihood
          gp2 = SingleTaskGP(
              train_X, train_Y, likelihood=GaussianLikelihood()
          )
          with self.assertRaises(NotImplementedError):
              batched_multi_output_to_single_output(gp2)
          # test observed noise
          train_X = torch.rand(10, 2, device=self.device, dtype=dtype)
          batched_mo_model = SingleTaskGP(
              train_X, train_Y, torch.rand_like(train_Y)
          )
          batched_so_model = batched_multi_output_to_single_output(
              batched_mo_model
          )
          self.assertIsInstance(batched_so_model, SingleTaskGP)
          self.assertIsInstance(
              batched_so_model.likelihood, FixedNoiseGaussianLikelihood
          )
          self.assertEqual(batched_so_model.num_outputs, 1)
          # test SingleTaskMultiFidelityGP
          batched_mo_model = SingleTaskMultiFidelityGP(
              train_X, train_Y, iteration_fidelity=1
          )
          batched_so_model = batched_multi_output_to_single_output(
              batched_mo_model
          )
          self.assertIsInstance(
              batched_so_model, SingleTaskMultiFidelityGP
          )
          self.assertEqual(batched_so_model.num_outputs, 1)
          # test input transform
          input_tf = Normalize(
              d=2,
              bounds=torch.tensor(
                  [[0.0, 0.0], [1.0, 1.0]],
                  device=self.device,
                  dtype=dtype,
              ),
          )
          batched_mo_model = SingleTaskGP(
              train_X, train_Y, input_transform=input_tf
          )
          batch_so_model = batched_multi_output_to_single_output(
              batched_mo_model
          )
          self.assertIsInstance(
              batch_so_model.input_transform, Normalize
          )
          self.assertTrue(
              torch.equal(
                  batch_so_model.input_transform.bounds, input_tf.bounds
              )
          )
          # test with AppendFeatures
          input_tf = AppendFeatures(
              feature_set=torch.rand(
                  2, 1, device=self.device, dtype=dtype
              )
          )
          batched_mo_model = SingleTaskGP(
              train_X, train_Y, input_transform=input_tf
          ).eval()
          batch_so_model = batched_multi_output_to_single_output(
              batched_mo_model
          )
          self.assertIsInstance(
              batch_so_model.input_transform, AppendFeatures
          )
  
          # test batched input transform
          input_tf = Normalize(
              d=2,
              bounds=torch.tensor(
                  [[-1.0, -1.0], [1.0, 1.0]],
                  device=self.device,
                  dtype=dtype,
              ),
              batch_shape=torch.Size([2]),
          )
          batched_mo_model = SingleTaskGP(
              train_X, train_Y, input_transform=input_tf
          )
          batch_so_model = batched_multi_output_to_single_output(
              batched_mo_model
          )
          self.assertIsInstance(
              batch_so_model.input_transform, Normalize
          )
          self.assertTrue(
              torch.equal(
                  batch_so_model.input_transform.bounds, input_tf.bounds
              )
          )
          # test outcome transform
          batched_mo_model = SingleTaskGP(
              train_X, train_Y, outcome_transform=Standardize(m=2)
          )
          with self.assertRaises(NotImplementedError):
              batched_multi_output_to_single_output(batched_mo_model)
  
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 4 for calling the TestConverters obj's underlying member method code with example usage: obj.test_batched_multi_output_to_single_output() without return types