def METHOD_NAME():
    # sum rolling type
    post_df = pp.rolling(
        df=timeseries_df,
        columns={"y": "y"},
        rolling_type="sum",
        window=2,
        min_periods=0,
    )
    assert post_df.columns.tolist() == ["label", "y"]
    assert series_to_list(post_df["y"]) == [1.0, 3.0, 5.0, 7.0]
    # mean rolling type with alias
    post_df = pp.rolling(
        df=timeseries_df,
        rolling_type="mean",
        columns={"y": "y_mean"},
        window=10,
        min_periods=0,
    )
    assert post_df.columns.tolist() == ["label", "y", "y_mean"]
    assert series_to_list(post_df["y_mean"]) == [1.0, 1.5, 2.0, 2.5]
    # count rolling type
    post_df = pp.rolling(
        df=timeseries_df,
        rolling_type="count",
        columns={"y": "y"},
        window=10,
        min_periods=0,
    )
    assert post_df.columns.tolist() == ["label", "y"]
    assert series_to_list(post_df["y"]) == [1.0, 2.0, 3.0, 4.0]
    # quantile rolling type
    post_df = pp.rolling(
        df=timeseries_df,
        columns={"y": "q1"},
        rolling_type="quantile",
        rolling_type_options={"quantile": 0.25},
        window=10,
        min_periods=0,
    )
    assert post_df.columns.tolist() == ["label", "y", "q1"]
    assert series_to_list(post_df["q1"]) == [1.0, 1.25, 1.5, 1.75]
    # incorrect rolling type
    with pytest.raises(InvalidPostProcessingError):
        pp.rolling(
            df=timeseries_df,
            columns={"y": "y"},
            rolling_type="abc",
            window=2,
        )
    # incorrect rolling type options
    with pytest.raises(InvalidPostProcessingError):
        pp.rolling(
            df=timeseries_df,
            columns={"y": "y"},
            rolling_type="quantile",
            rolling_type_options={"abc": 123},
            window=2,
        )