code: def test_multioutput_to_batch_mode_transform(self):
      for dtype in (torch.float, torch.double):
          tkwargs = {"device": self.device, "dtype": dtype}
          n = 3
          num_outputs = 2
          train_X = torch.rand(n, 1, **tkwargs)
          train_Y = torch.rand(n, num_outputs, **tkwargs)
          train_Yvar = torch.rand(n, num_outputs, **tkwargs)
          X_out, Y_out, Yvar_out = multioutput_to_batch_mode_transform(
              train_X=train_X,
              train_Y=train_Y,
              num_outputs=num_outputs,
              train_Yvar=train_Yvar,
          )
          expected_X_out = train_X.unsqueeze(0).expand(
              num_outputs, -1, 1
          )
          self.assertTrue(torch.equal(X_out, expected_X_out))
          self.assertTrue(torch.equal(Y_out, train_Y.transpose(0, 1)))
          self.assertTrue(
              torch.equal(Yvar_out, train_Yvar.transpose(0, 1))
          )
  
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 TestMultiOutputToBatchModeTransform obj's underlying member method code with example usage: obj.test_multioutput_to_batch_mode_transform() without return types