def __call__(self, input_ids: Union[List[List[int]], np.ndarray],
                 input_masks: Union[List[List[int]], np.ndarray],
                 y_masks: Union[List[List[int]], np.ndarray]) -> Tuple[List[List[List[float]]], List[List[int]]]:
        """ Predicts probas of heads and dependency ids for tokens

        Args:
            input_ids: indices of the subwords
            input_masks: mask that determines where to attend and where not to
            y_masks: mask which determines the first subword units in the the word

        Returns:
            Probas of heads and dependency ids for each token (not subtoken)

        """
        with torch.no_grad():
            head_probas, dep_ids = self.model(input_ids, input_masks, y_masks)
        return head_probas, dep_ids
 Predicts probas of heads and dependency ids for tokens

        Args:
            input_ids: indices of the subwords
            input_masks: mask that determines where to attend and where not to
            y_masks: mask which determines the first subword units in the the word

        Returns:
            Probas of heads and dependency ids for each token (not subtoken)

        
deeppavlov/models/torch_bert/torch_transformers_syntax_parser.py