def METHOD_NAME(data_loader, model, id2label=None):
    outputs = []
    progress_bar = tqdm(
        range(len(data_loader)),
        desc="Predition Iteration",
    )
    with paddle.no_grad():
        for batch in data_loader:
            input_ids, segment_ids = batch
            logits = model(input_ids)
            if id2label is not None:
                pred = paddle.argmax(logits, axis=-1).cpu().tolist()
                outputs.extend(list(map(lambda x: id2label[x], pred)))
            else:
                pred = logits.squeeze(-1).cpu().tolist()
                outputs.extend(pred)
            progress_bar.update(1)
    return outputs