code: def X_test(self):
      # Use index for y_test (idx 2) to get the data
      test = self.dataset.loc[self.idx[2], :]
      X_test = test.drop(self.target_param, axis=1)
  
      if X_test.empty and self.fe_exogenous is None:
          return None
      else:
          # If X_test is not empty or empty but self.fe_exogenous is provided
          # Return X_test instead of None, since the index can be used to
          # generate features using self.fe_exogenous
          return X_test
  
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 7 for calling the _TSSupervisedExperiment obj's underlying member method code with example usage: obj.X_test() and returns: None, X_test