def METHOD_NAME(type_cleaned: str, stats: pd.Series, errors: str) -> None:
    """
    Describe what was done in the cleaning process
    The stats series contains the following codes in its index
        0 := the number of null values
        1 := the number of values that could not be parsed
        2 := the number of values that were transformed during cleaning
        3 := the number of values that were already in the correct format
    """
    print(f"{type_cleaned} Cleaning Report:")
    nrows = stats.sum()
    nclnd = stats.loc[2] if 2 in stats.index else 0
    if nclnd > 0:
        pclnd = round(nclnd / nrows * 100, 2)
        print(f"\t{nclnd} values cleaned ({pclnd}%)")
    nunknown = stats.loc[1] if 1 in stats.index else 0
    if nunknown > 0:
        punknown = round(nunknown / nrows * 100, 2)
        expl = "set to NaN" if errors == "coerce" else "left unchanged"
        print(f"\t{nunknown} values unable to be parsed ({punknown}%), {expl}")
    nnull = stats.loc[0] if 0 in stats.index else 0
    if errors == "coerce":
        nnull += stats.loc[1] if 1 in stats.index else 0
    pnull = round(nnull / nrows * 100, 2)
    ncorrect = nclnd + (stats.loc[3] if 3 in stats.index else 0)
    pcorrect = round(ncorrect / nrows * 100, 2)
    print(
        f"Result contains {ncorrect} ({pcorrect}%) values in the correct format "
        f"and {nnull} null values ({pnull}%)"
    )