def __call__(self, questions_batch: List[List[str]], rels_batch: List[List[str]] = None) -> Dict[str, torch.tensor]:
        """Tokenize questions and relations
        texts_a and texts_b are separated by [SEP] token
        Args:
            questions_batch: list of texts,
            rels_batch: list of relations list
        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
        """
        lengths, proc_rels_batch = [], []
        for question, rels_list in zip(questions_batch, rels_batch):
            if isinstance(rels_list, list):
                rels_str = " ".join(rels_list)
            else:
                rels_str = rels_list
            encoding = self.tokenizer.encode_plus(text=question, text_pair=rels_str,
                                                  return_attention_mask=True, add_special_tokens=True,
                                                  truncation=True)
            lengths.append(len(encoding["input_ids"]))
            proc_rels_batch.append(rels_str)
        max_len = max(lengths)
        input_ids_batch, attention_mask_batch, token_type_ids_batch = [], [], []
        for question, rels_list in zip(questions_batch, proc_rels_batch):
            encoding = self.tokenizer.encode_plus(text=question, text_pair=rels_list,
                                                  truncation=True, max_length=max_len,
                                                  pad_to_max_length=True, return_attention_mask=True)
            input_ids_batch.append(encoding["input_ids"])
            attention_mask_batch.append(encoding["attention_mask"])
            if "token_type_ids" in encoding:
                token_type_ids_batch.append(encoding["token_type_ids"])
            else:
                token_type_ids_batch.append([0])
        input_features = {"input_ids": torch.LongTensor(input_ids_batch),
                          "attention_mask": torch.LongTensor(attention_mask_batch),
                          "token_type_ids": torch.LongTensor(token_type_ids_batch)}
        return input_features
Tokenize questions and relations
        texts_a and texts_b are separated by [SEP] token
        Args:
            questions_batch: list of texts,
            rels_batch: list of relations list
        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