def METHOD_NAME(lr_config, epochs, step_each_epoch):
    from . import learning_rate
    lr_config.update({'epochs': epochs, 'step_each_epoch': step_each_epoch})
    if 'name' in lr_config:
        lr_name = lr_config.pop('name')
        lr = getattr(learning_rate, lr_name)(**lr_config)
        if isinstance(lr, paddle.optimizer.lr.LRScheduler):
            return lr
        else:
            return lr()
    else:
        lr = lr_config['learning_rate']
    return lr