code: def _get_ordered_idx(self, mask_missing_values):
          
          frac_of_missing_values = mask_missing_values.mean(axis=0)
          missing_values_idx = np.nonzero(frac_of_missing_values)[0]
          if self.imputation_order == 'roman':
              ordered_idx = missing_values_idx
          elif self.imputation_order == 'arabic':
              ordered_idx = missing_values_idx[::-1]
          elif self.imputation_order == 'ascending':
              n = len(frac_of_missing_values) - len(missing_values_idx)
              ordered_idx = np.argsort(frac_of_missing_values,
                                       kind='mergesort')[n:][::-1]
          elif self.imputation_order == 'descending':
              n = len(frac_of_missing_values) - len(missing_values_idx)
              ordered_idx = np.argsort(frac_of_missing_values,
                                       kind='mergesort')[n:]
          elif self.imputation_order == 'random':
              ordered_idx = missing_values_idx
              self.random_state_.shuffle(ordered_idx)
          else:
              raise ValueError("Got an invalid imputation order: '{0}'. It must "
                               "be one of the following: 'roman', 'arabic', "
                               "'ascending', 'descending', or "
                               "'random'.".format(self.imputation_order))
          return ordered_idx
docs: Decide in what order we will update the features.
  
          As a homage to the MICE R package, we will have 4 main options of
          how to order the updates, and use a random order if anything else
          is specified.
  
          Also, this function skips features which have no missing values.
  
          Parameters
          ----------
          mask_missing_values : array-like, shape (n_samples, n_features)
              Input data's missing indicator matrix, where "n_samples" is the
              number of samples and "n_features" is the number of features.
  
          Returns
          -------
          ordered_idx : ndarray, shape (n_features,)
              The order in which to impute the features.