code: def train_transformed(self):
      # Use pipeline trained on training data only to get the requested data
      # In time series, the order of arguments and returns may be reversed.
      return pd.concat(
          [
              *_pipeline_transform(
                  pipeline=self.pipeline, y=self.y_train, X=self.X_train
              )
          ],
          axis=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 4 for calling the _TSSupervisedExperiment obj's underlying member method code with example usage: obj.train_transformed() and returns: pd