def METHOD_NAME():
    df = pd.DataFrame({"col1": [1, 2], "col2": [3, 4], "target": [5, 6]})
    df["time_col"] = pd.date_range(start="1/1/2010", freq="M", periods=1)[0]
    model_args = {
        "order_by": "time_col",
        "group_by": ["col1", "col2"],
        "target": "target",
        "hierarchy": ["col1", "col2"],
    }
    training_df, hier_df, hier_dict = get_hierarchy_from_df(df, model_args)
    forecast_df = pd.DataFrame(
        {"ARIMA": [15, 8, 7, 8, 7]}, index=["total", "total/1", "total/2", "total/1/3", "total/2/4"]
    )
    forecast_df["ds"] = pd.date_range(start="1/3/2010", freq="M", periods=1)[0]
    forecast_df.index.name = "unique_id"
    results_df = reconcile_forecasts(training_df, forecast_df, hier_df, hier_dict)
    # Check we keep the hierarchically reconciled results, not the original forecast
    assert "ARIMA/BottomUp" in results_df.columns
    assert "ARIMA" not in results_df.columns