def __call__(self, texts_a: List[str], texts_b: Optional[List[str]] = None) -> List[InputFeatures]:
if texts_b is None:<EOL><INDENT>texts_b = [None] * len(texts_a)<EOL><DEDENT>examples = [InputExample(unique_id=<NUM_LIT:0>, text_a=text_a, text_b=text_b)<EOL>for text_a, text_b in zip(texts_a, texts_b)]<EOL>return convert_examples_to_features(examples, self.max_seq_length, self.tokenizer)<EOL>
Call Bert convert_examples_to_features function to 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 InputFeatures with subtokens, subtoken ids, subtoken mask, segment mask.