def METHOD_NAME(self, dy_model, config):
    lr = config.get("hyper_parameters.optimizer.learning_rate", 0.001)
    if int(config.get('stage', 0)) == 1:
        optimizer1 = paddle.optimizer.Adam(
            learning_rate=lr, parameters=dy_model.parameters())
        optimizer2 = None
    else:
        from optimizer import SimpleGrda
        params = [p for n, p in dy_model.named_parameters() if n != 'mask']
        mask_params = [
            p for n, p in dy_model.named_parameters() if n == 'mask'
        ]
        optimizer1 = paddle.optimizer.Adam(
            learning_rate=lr, parameters=params)
        optimizer2 = SimpleGrda(mask_params, 1,
                                config['hyper_parameters.grad_c'],
                                config['hyper_parameters.grad_mu'])
    return optimizer1, optimizer2