code: def _predict_one(self, document, encoding=None, return_blocks=False):
          
          # blockify
          blocks = self.blockifier.blockify(document, encoding=encoding)
          # get features
          try:
              features = self.features.transform(blocks)
          except ValueError: # Can't make features, predict no content
              preds = np.zeros((len(blocks)))
          # make predictions
          else:
              if self.prob_threshold is None:
                  preds = self.model.predict(features)
              else:
                  self._positive_idx = (
                      self._positive_idx or list(self.model.classes_).index(1))
                  preds = self.model.predict_proba(features) > self.prob_threshold
                  preds = preds[:, self._positive_idx].astype(int)
  
          if return_blocks:
              return preds, blocks
          else:
              return preds
docs: Predict class (content=1 or not-content=0) of each block in an HTML
          document.
  
          Args:
              documents (str): HTML document
  
          Returns:
              ``np.ndarray``: array of binary predictions for content (1) or
              not-content (0).