code: def dataset_transformed(self):
      # Use fully trained pipeline to get the requested data
      return pd.concat(
          [
              *_pipeline_transform(
                  pipeline=self.pipeline_fully_trained,
                  y=self.y,
                  X=self.X,
              )
          ],
          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 2 for calling the _TSSupervisedExperiment obj's underlying member method code with example usage: obj.dataset_transformed() and returns: pd