code: def test_add_output_dim(self):
      for dtype in (torch.float, torch.double):
          tkwargs = {"device": self.device, "dtype": dtype}
          original_batch_shape = torch.Size([2])
          # check exception is raised when trailing batch dims do not line up
          X = torch.rand(2, 3, 2, 1, **tkwargs)
          with self.assertRaises(RuntimeError):
              add_output_dim(
                  X=X, original_batch_shape=original_batch_shape
              )
          # test no new batch dims
          X = torch.rand(2, 2, 1, **tkwargs)
          X_out, output_dim_idx = add_output_dim(
              X=X, original_batch_shape=original_batch_shape
          )
          self.assertTrue(torch.equal(X_out, X.unsqueeze(1)))
          self.assertEqual(output_dim_idx, 1)
          # test new batch dims
          X = torch.rand(3, 2, 2, 1, **tkwargs)
          X_out, output_dim_idx = add_output_dim(
              X=X, original_batch_shape=original_batch_shape
          )
          self.assertTrue(torch.equal(X_out, X.unsqueeze(2)))
          self.assertEqual(output_dim_idx, 2)
  
imports: import warnings
  import torch
  from botorch import settings
  from botorch.exceptions import InputDataError, InputDataWarning
  from botorch.models.utils import add_output_dim, check_min_max_scaling, check_no_nans, check_standardization, fantasize, gpt_posterior_settings, multioutput_to_batch_mode_transform, validate_input_scaling
  from botorch.models.utils.assorted import consolidate_duplicates, detect_duplicates
  from botorch.utils.testing import BotorchTestCase
  from gpytorch import settings
label_desc: Use image node_id 1 for calling the TestAddOutputDim obj's underlying member method code with example usage: obj.test_add_output_dim() without return types