def METHOD_NAME():
    pivot_df = pp.pivot(
        df=multiple_metrics_df,
        index=["dttm"],
        columns=["country"],
        aggregates={
            "sum_metric": {"operator": "sum"},
            "count_metric": {"operator": "sum"},
        },
    )
    """
                   count_metric    sum_metric
    country              UK US         UK US
    dttm
    2019-01-01            1  2          5  6
    2019-01-02            3  4          7  8
    """
    cum_df = pp.cum(
        df=pivot_df,
        operator="sum",
        columns={"sum_metric": "sum_metric", "count_metric": "count_metric"},
    )
    """
               count_metric    sum_metric
    country              UK US         UK  US
    dttm
    2019-01-01            1  2          5   6
    2019-01-02            4  6         12  14
    """
    flat_df = pp.flatten(cum_df)
    """
            dttm  count_metric, UK  count_metric, US  sum_metric, UK  sum_metric, US
    0 2019-01-01                 1                 2               5               6
    1 2019-01-02                 4                 6              12              14
    """
    assert flat_df.equals(
        pd.DataFrame(
            {
                "dttm": pd.to_datetime(["2019-01-01", "2019-01-02"]),
                FLAT_COLUMN_SEPARATOR.join(["count_metric", "UK"]): [1, 4],
                FLAT_COLUMN_SEPARATOR.join(["count_metric", "US"]): [2, 6],
                FLAT_COLUMN_SEPARATOR.join(["sum_metric", "UK"]): [5, 12],
                FLAT_COLUMN_SEPARATOR.join(["sum_metric", "US"]): [6, 14],
            }
        )
    )