def METHOD_NAME(early_stopping_callback_kwargs=None, **train_params):
    import lightgbm as lgb
    if train_params["params"]["objective"] == "quantile":
        quantile_levels = train_params["params"].pop("quantile_levels")
        booster = QuantileBooster(quantile_levels=quantile_levels, early_stopping_callback_kwargs=early_stopping_callback_kwargs)
        return booster.fit(**train_params)
    else:
        return lgb.train(**train_params)