def METHOD_NAME(sample_weight):
    """
    Ensure
    (1) Without sample_weight, AutoGluon's custom rmse produces the same result as sklearn's rmse
    (2) With sample_weight, computed wrmse is as expected
    """
    y_true = np.array([0, 0, 1, 1, 1, 1, 0])
    y_pred = np.array([0.13, 0.09, 0.78, 0.43, 0.8, 0.91, 0.32])
    expected_rmse = sklearn.metrics.mean_squared_error(y_true, y_pred, squared=False, sample_weight=sample_weight)
    kwargs = {"y_true": y_true, "y_pred": y_pred}
    if sample_weight is not None:
        kwargs["sample_weight"] = sample_weight
    computed_rmse = rmse_func(**kwargs)
    assert np.isclose(computed_rmse, expected_rmse)