How to use the paddlehub.reader.batching.pad_batch_data function in paddlehub

def _pad_batch_records(self, batch_records):
        batch_token_ids = [record.token_ids for record in batch_records]
        batch_text_type_ids = [record.text_type_ids for record in batch_records]
        batch_position_ids = [record.position_ids for record in batch_records]
        batch_label_ids = [record.label_ids for record in batch_records]

        # padding
        padded_token_ids, input_mask, batch_seq_lens = pad_batch_data(
            batch_token_ids,
            pad_idx=self.pad_id,
            max_seq_len=self.max_seq_len,
            return_input_mask=True,
            return_seq_lens=True)
        padded_text_type_ids = pad_batch_data(
            batch_text_type_ids,
            max_seq_len=self.max_seq_len,
            pad_idx=self.pad_id)
        padded_position_ids = pad_batch_data(
            batch_position_ids,
            max_seq_len=self.max_seq_len,
            pad_idx=self.pad_id)
        padded_label_ids = pad_batch_data(
            batch_label_ids,
            max_seq_len=self.max_seq_len,
            pad_idx=len(self.label_map) - 1)

        return_list = [
            padded_token_ids, padded_position_ids, padded_text_type_ids,
            input_mask, padded_label_ids, batch_seq_lens
        ]

batch_token_ids = [record.token_ids for record in batch_records]
        batch_text_type_ids = [record.text_type_ids for record in batch_records]
        batch_position_ids = [record.position_ids for record in batch_records]

        # padding
        padded_token_ids, input_mask, seq_lens = pad_batch_data(
            batch_token_ids,
            pad_idx=self.pad_id,
            max_seq_len=self.max_seq_len,
            return_input_mask=True,
            return_seq_lens=True)
        padded_text_type_ids = pad_batch_data(
            batch_text_type_ids,
            pad_idx=self.pad_id,
            max_seq_len=self.max_seq_len)
        padded_position_ids = pad_batch_data(
            batch_position_ids,
            pad_idx=self.pad_id,
            max_seq_len=self.max_seq_len)

        return_list = [
            padded_token_ids, padded_text_type_ids, padded_position_ids,
            input_mask, seq_lens
        ]

        return return_list

pad_idx=self.pad_id,
            max_seq_len=self.max_seq_len,
            return_input_mask=True,
            return_seq_lens=True)
        padded_text_type_ids = pad_batch_data(
            batch_text_type_ids,
            max_seq_len=self.max_seq_len,
            pad_idx=self.pad_id)
        padded_position_ids = pad_batch_data(
            batch_position_ids,
            max_seq_len=self.max_seq_len,
            pad_idx=self.pad_id)

        if phase != "predict":
            batch_label_ids = [record.label_ids for record in batch_records]
            padded_label_ids = pad_batch_data(
                batch_label_ids,
                max_seq_len=self.max_seq_len,
                pad_idx=len(self.label_map) - 1)

            return_list = [
                padded_token_ids, padded_position_ids, padded_text_type_ids,
                input_mask, padded_label_ids, batch_seq_lens
            ]

            if self.use_task_id:
                padded_task_ids = np.ones_like(
                    padded_token_ids, dtype="int64") * self.task_id
                return_list = [
                    padded_token_ids, padded_position_ids, padded_text_type_ids,
                    input_mask, padded_task_ids, padded_label_ids,
                    batch_seq_lens

def _pad_batch_records(self, batch_records, phase=None):
        batch_token_ids = [record.token_ids for record in batch_records]
        batch_text_type_ids = [record.text_type_ids for record in batch_records]
        batch_position_ids = [record.position_ids for record in batch_records]

        padded_token_ids, input_mask = pad_batch_data(
            batch_token_ids,
            max_seq_len=self.max_seq_len,
            pad_idx=self.pad_id,
            return_input_mask=True)
        padded_text_type_ids = pad_batch_data(
            batch_text_type_ids,
            max_seq_len=self.max_seq_len,
            pad_idx=self.pad_id)
        padded_position_ids = pad_batch_data(
            batch_position_ids,
            max_seq_len=self.max_seq_len,
            pad_idx=self.pad_id)

        if phase != "predict":
            batch_labels = [record.label_id for record in batch_records]
            # the only diff with ClassifyReader: astype("float32")

def _pad_batch_records(self, batch_records, phase):
        batch_token_ids = [record.token_ids for record in batch_records]
        batch_text_type_ids = [record.text_type_ids for record in batch_records]
        batch_position_ids = [record.position_ids for record in batch_records]
        batch_unique_ids = [record.unique_id for record in batch_records]
        batch_unique_ids = np.array(batch_unique_ids).astype("int64").reshape(
            [-1, 1])

        # padding
        padded_token_ids, input_mask = pad_batch_data(
            batch_token_ids,
            pad_idx=self.pad_id,
            return_input_mask=True,
            max_seq_len=self.max_seq_len)
        padded_text_type_ids = pad_batch_data(
            batch_text_type_ids,
            pad_idx=self.pad_id,
            max_seq_len=self.max_seq_len)
        padded_position_ids = pad_batch_data(
            batch_position_ids,
            pad_idx=self.pad_id,
            max_seq_len=self.max_seq_len)

        if phase != "predict":
            batch_start_position = [
                record.start_position for record in batch_records
            ]
            batch_end_position = [
                record.end_position for record in batch_records
            ]
            batch_start_position = np.array(batch_start_position).astype(

batch_position_ids = [record.position_ids for record in batch_records]
        batch_unique_ids = [record.unique_id for record in batch_records]
        batch_unique_ids = np.array(batch_unique_ids).astype("int64").reshape(
            [-1, 1])

        # padding
        padded_token_ids, input_mask = pad_batch_data(
            batch_token_ids,
            pad_idx=self.pad_id,
            return_input_mask=True,
            max_seq_len=self.max_seq_len)
        padded_text_type_ids = pad_batch_data(
            batch_text_type_ids,
            pad_idx=self.pad_id,
            max_seq_len=self.max_seq_len)
        padded_position_ids = pad_batch_data(
            batch_position_ids,
            pad_idx=self.pad_id,
            max_seq_len=self.max_seq_len)

        if phase != "predict":
            batch_start_position = [
                record.start_position for record in batch_records
            ]
            batch_end_position = [
                record.end_position for record in batch_records
            ]
            batch_start_position = np.array(batch_start_position).astype(
                "int64").reshape([-1, 1])
            batch_end_position = np.array(batch_end_position).astype(
                "int64").reshape([-1, 1])

def _pad_batch_records(self, batch_records):
        batch_token_ids = [record.token_ids for record in batch_records]
        batch_text_type_ids = [record.text_type_ids for record in batch_records]
        batch_position_ids = [record.position_ids for record in batch_records]
        batch_label_ids = [record.label_ids for record in batch_records]

        # padding
        padded_token_ids, input_mask, batch_seq_lens = pad_batch_data(
            batch_token_ids,
            pad_idx=self.pad_id,
            max_seq_len=self.max_seq_len,
            return_input_mask=True,
            return_seq_lens=True)
        padded_text_type_ids = pad_batch_data(
            batch_text_type_ids,
            max_seq_len=self.max_seq_len,
            pad_idx=self.pad_id)
        padded_position_ids = pad_batch_data(
            batch_position_ids,
            max_seq_len=self.max_seq_len,
            pad_idx=self.pad_id)
        padded_label_ids = pad_batch_data(
            batch_label_ids,
            max_seq_len=self.max_seq_len,

batch_position_ids = [record.position_ids for record in batch_records]
        batch_unique_ids = [record.unique_id for record in batch_records]
        batch_unique_ids = np.array(batch_unique_ids).astype("int64").reshape(
            [-1, 1])

        # padding
        padded_token_ids, input_mask = pad_batch_data(
            batch_token_ids,
            pad_idx=self.pad_id,
            return_input_mask=True,
            max_seq_len=self.max_seq_len)
        padded_text_type_ids = pad_batch_data(
            batch_text_type_ids,
            pad_idx=self.pad_id,
            max_seq_len=self.max_seq_len)
        padded_position_ids = pad_batch_data(
            batch_position_ids,
            pad_idx=self.pad_id,
            max_seq_len=self.max_seq_len)

        if phase != "predict":
            batch_start_position = [
                record.start_position for record in batch_records
            ]
            batch_end_position = [
                record.end_position for record in batch_records
            ]
            batch_start_position = np.array(batch_start_position).astype(
                "int64").reshape([-1, 1])
            batch_end_position = np.array(batch_end_position).astype(
                "int64").reshape([-1, 1])

def _pad_batch_records(self, batch_records):
        batch_token_ids = [record.token_ids for record in batch_records]
        batch_text_type_ids = [record.text_type_ids for record in batch_records]
        batch_position_ids = [record.position_ids for record in batch_records]

        # padding
        padded_token_ids, input_mask, seq_lens = pad_batch_data(
            batch_token_ids,
            pad_idx=self.pad_id,
            max_seq_len=self.max_seq_len,
            return_input_mask=True,
            return_seq_lens=True)
        padded_text_type_ids = pad_batch_data(
            batch_text_type_ids,
            pad_idx=self.pad_id,
            max_seq_len=self.max_seq_len)
        padded_position_ids = pad_batch_data(
            batch_position_ids,
            pad_idx=self.pad_id,
            max_seq_len=self.max_seq_len)

        return_list = [
            padded_token_ids, padded_text_type_ids, padded_position_ids,
            input_mask, seq_lens
        ]

        return return_list