def __call__(self, texts_a: List[List[str]], texts_b: List[List[str]] = None) -> Dict[str, torch.tensor]:
        """Tokenize and create masks.

        texts_a and texts_b are separated by [SEP] token

        Args:
            texts_a: list of texts,
            texts_b: list of texts, it could be None, e.g. single sentence classification task

        Returns:
            batch of :class:`transformers.data.processors.utils.InputFeatures` with subtokens, subtoken ids, \
                subtoken mask, segment mask, or tuple of batch of InputFeatures and Batch of subtokens
        """
        input_features = []
        if texts_a and texts_b and texts_a[0] and texts_b[0]:
            input_features = self.tokenize_mc_examples(texts_a, texts_b)
        return input_features
Tokenize and create masks.

        texts_a and texts_b are separated by [SEP] token

        Args:
            texts_a: list of texts,
            texts_b: list of texts, it could be None, e.g. single sentence classification task

        Returns:
            batch of :class:`transformers.data.processors.utils.InputFeatures` with subtokens, subtoken ids,                 subtoken mask, segment mask, or tuple of batch of InputFeatures and Batch of subtokens
        
deeppavlov/models/preprocessors/torch_transformers_preprocessor.py