def get_counts(self, docs: List[str], doc_ids: List[Any]) \
            -> Generator[Tuple[KeysView, ValuesView, List[int]], Any, None]:
        """Get term counts for a list of documents.

        Args:
            docs: a list of input documents
            doc_ids: a list of document ids corresponding to input documents

        Yields:
            a tuple of term hashes, count values and column ids

        Returns:
            None

        """
        logger.debug("Tokenizing batch...")
        batch_ngrams = list(self.tokenizer(docs))
        logger.debug("Counting hash...")
        doc_id = iter(doc_ids)
        for ngrams in batch_ngrams:
            counts = Counter([hash_(gram, self.hash_size) for gram in ngrams])
            hashes = counts.keys()
            values = counts.values()
            _id = self.doc_index[next(doc_id)]
            if values:
                col_id = [_id] * len(values)
            else:
                col_id = []
            yield hashes, values, col_id
Get term counts for a list of documents.

        Args:
            docs: a list of input documents
            doc_ids: a list of document ids corresponding to input documents

        Yields:
            a tuple of term hashes, count values and column ids

        Returns:
            None

        
deeppavlov/models/vectorizers/hashing_tfidf_vectorizer.py