def METHOD_NAME(self, text, schema):
    def to_tensor(x):
        return paddle.to_tensor(x, dtype="int64")
    ssi = schema_to_ssi(schema=schema)
    text = [ssi + x for x in text]
    inputs = self.tokenizer(
        text, return_token_type_ids=False, return_attention_mask=True, max_seq_len=self.max_source_length
    )
    inputs = {
        "input_ids": to_tensor(Pad(pad_val=self.tokenizer.pad_token_id)(inputs["input_ids"])),
        "attention_mask": to_tensor(Pad(pad_val=0)(inputs["attention_mask"])),
    }
    pred, _ = self.model.generate(
        input_ids=inputs["input_ids"],
        attention_mask=inputs["attention_mask"],
        max_length=self.max_target_length,
    )
    pred = self.tokenizer.batch_decode(pred.numpy())
    return [post_processing(x) for x in pred]