code: def X(self):
      X = self.dataset.drop(self.target_param, axis=1)
      if X.empty and self.fe_exogenous is None:
          return None
      else:
          # If X is not empty or empty but self.fe_exogenous is provided
          # Return X instead of None, since the index can be used to
          # generate features using self.fe_exogenous
          return X
  
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 1 for calling the _TSSupervisedExperiment obj's underlying member method code with example usage: obj.X() and returns: None, X