code: def test_transformed(self):
      # When transforming the test set, we can and should use all data before that
      # In time series, the order of arguments and returns may be reversed.
      all_data = pd.concat(
          [
              *_pipeline_transform(
                  pipeline=self.pipeline_fully_trained,
                  y=self.y,
                  X=self.X,
              )
          ],
          axis=1,
      )
      # Return the y_test indices not X_test indices.
      # X_test indices are expanded indices for handling FH with gaps.
      # But if we return X_test indices, then we will get expanded test
      # indices even for univariate time series without exogenous variables
      # which would be confusing. Hence, we return y_test indices here and if
      # we want to get X_test indices, then we use self.X_test directly.
      # Refer:
      # https://github.com/sktime/sktime/issues/2598#issuecomment-1203308542
      # https://github.com/sktime/sktime/blob/4164639e1c521b112711c045d0f7e63013c1e4eb/sktime/forecasting/model_evaluation/_functions.py#L196
      return all_data.loc[self.idx[1]]
  
file_path: pycaret/pycaret/internal/pycaret_experiment/ts_supervised_experiment.py
imports: import pandas
  from pycaret.internal.pycaret_experiment.supervised_experiment import _SupervisedExperiment
  from pycaret.utils.time_series.forecasting.pipeline import _pipeline_transform
label_desc: Use image node_id 9 for calling the _TSSupervisedExperiment obj's underlying member method code with example usage: obj.test_transformed() and returns: all_data