def _predict_one(self, document, encoding=None, return_blocks=False):
        """
        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).
        """
        # 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
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).