def METHOD_NAME():
    sample_df = pd.DataFrame(
        {
            "COUNTRY": ["France", "USA"] * 4,
            "PRODUCT": (["product A"] * 2 + ["product B"] * 2) * 2,
            "YEAR": ["2019"] * 4 + ["2020"] * 4,
            "VALUE_1": [5, 10, 10, 15, 20, 20, 30, 25],
        }
    )
    step = TotalsStep(
        name="totals",
        totalDimensions=[
            TotalDimension(total_column="COUNTRY", total_rows_label="All countries"),
            TotalDimension(total_column="PRODUCT", total_rows_label="All products"),
            TotalDimension(total_column="YEAR", total_rows_label="All years"),
        ],
        aggregations=[
            Aggregation(
                columns=["VALUE_1"],
                aggfunction="sum",
                newcolumns=["VALUE_1-sum"],
            ),
            Aggregation(columns=["VALUE_1"], aggfunction="avg", newcolumns=["VALUE_1-avg"]),
        ],
    )
    real_result = execute_totals(step, sample_df)
    assert real_result[real_result["YEAR"] == "All years"].count()["YEAR"] == 9
    assert real_result[real_result["COUNTRY"] == "All countries"].count()["COUNTRY"] == 9
    assert real_result[real_result["PRODUCT"] == "All products"].count()["PRODUCT"] == 9
    # could be any column
    assert real_result.count()["YEAR"] == 27