docs: def return_labels_numpy(self, original=False):
          """
          Returns a 2d numpy array of labels
  
          Parameters
          ----------
          original : if True, will return original labels, if False, will return transformed labels (as defined by
          label_dict), default value: False
  
          Returns
          -------
          A numpy array of labels, each row corresponds to a single datapoint
          """
          if self._prepopulated is False:
              raise errors.EmptyDatabase(self.dbpath)
          else:
              engine = create_engine('sqlite:////' + self.dbpath)
              trainset.Base.metadata.create_all(engine)
              session_cl = sessionmaker(bind=engine)
              session = session_cl()
              tmp_object = session.query(trainset.TrainSet).get(1)
  
              columns_amt = len(tmp_object.labels['original'])
              return_array = np.zeros([self.points_amt, columns_amt])
              for i in enumerate(session.query(trainset.TrainSet).order_by(trainset.TrainSet.id)):
                  if original is False:
                      return_array[i[0], :] = i[1].labels['transformed']
                  else:
                      return_array[i[0], :] = i[1].labels['original']
              session.close()
              return return_array