can u please explain me the sole purpose of the below code
def cleanStdSheetVars(dt, nameVar):
    drop_vars = [
        "QUESTIONS_FOR_DON",
        "NOTES",
        "DON_NOTES",
        "COUNT",
        "QUESTIONS_NEED_VALIDATION",
    ]
    drop_vars = [ik for ik in dt.columns if ik in drop_vars]
    dt = dt.drop(drop_vars, axis=1)

    set_na_blank = [
        "Notes",
        "Broker_String",
        "Broker",
        "Carrier_String",
        "To_Do",
        "Carrier",
        "Med_Funding_Restrict",
        "Not_Offer",
        "Single_Welf_LOB",
        "Parent",
        "Child",
        "States",
        "Not_Broker_Notes",
        "LOBS_Offered_Not_Read_In",
        "Reviewed",
        "Broker_HQ_State",
        "Broker_HQ_Zip",
        "Broker_HQ_Address",
    ]
    set_na_blank = list(
        set(set_na_blank + [x.upper() for x in set_na_blank]).intersection(
            set(dt.columns)
        )
    )
    for v in set_na_blank:
        dt[v] = dt[v].astype(str).replace("nan", "")

    set_na_zero = [
        "EIN",
        "Exact",
        "Not_Medical",
        "TPA_ONLY",
        "BLUE",
        "HMO_ONLY",
        "Not_Welfare_Broker",
    ]
    set_na_zero = list(
        set(set_na_zero + [x.upper() for x in set_na_zero]).intersection(
            set(dt.columns)
        )
    )

    for v in set_na_zero:
        dt[v] = pd.to_numeric(dt[v], errors="coerce").fillna(0)

    drop_random = list(
        set(dt.columns) - set(set_na_blank) - set(set_na_zero) - set(["ORDER"])
    )
    if drop_random:
        dt = dt.drop(columns=drop_random)

    dt["DROP"] = dt[nameVar].apply(lambda x: 1 if pd.isna(x) or x == "" else 0)
    print(f"\nDrop b/c {nameVar} is NA/blank: {dt['DROP'].sum()}")
    dt = dt[dt["DROP"] == 0]

    if "Carrier_String" in dt.columns:
        dt["DROP"] = dt["Carrier_String"].apply(
            lambda x: 1 if pd.isna(x) or x == "" else 0
        )
    if "BROKER_STRING" in dt.columns:
        dt["DROP"] = dt["BROKER_STRING"].apply(
            lambda x: 1 if pd.isna(x) or x == "" else 0
        )
    print(f"\nDrop b/c STRING is NA/blank: {dt['DROP'].sum()}")
    dt = dt[dt["DROP"] == 0]
    if nameVar.upper() == "CARRIER":
        vars = ["NEVER_LF", "LF_ONLY_IF_MED", "SI_ONLY", "FI_ONLY"]
        for v in vars:
            dt[v] = np.where(dt["MED_FUNDING_RESTRICT"] == v, 1, 0)
            dt[v] = np.where(dt["MED_FUNDING_RESTRICT"].str.contains(v), 1, dt[v])
            print(f"\nTotal carrier strings that are {v} {dt[v].sum()}")
        if "SINGLE_WELF_LOB" not in dt.columns:
            raise ValueError("SINGLE_WELF_LOB field is missing")
        singleLobs = [
            "MEDICAL_ALWAYS",
            "DENTAL_ONLY",
            "PBM_ONLY",
            "LIFE_ONLY",
            "VISION_ONLY",
            "ADD_TRAVEL_ONLY",
            "STOPLOSS_ONLY",
            "FSA_ONLY",
            "INJ_ONLY",
            "LEGAL_ONLY",
            "EAP_ONLY",
            "LTC_ONLY",
            "WELLNESS_ONLY",
            "MASSAGE_ONLY",
            "RETIREE_ONLY",
            "DISAB_ONLY",
            "LIFE_DISAB_ONLY",
            "DENTAL_VISION_ONLY",
            "SERVICES_ONLY",
            "BROKER_NOT_CARRIER",
            "MOSTLY_VOL",
        ]
        for v in singleLobs:
            dt[v] = np.where(dt["SINGLE_WELF_LOB"] == v, 1, 0)
        if "NOT_OFFER" not in dt.columns:
            raise ValueError("NOT_OFFER missing from carrier std map")
        notLobs = [
            "PBM",
            "DENTAL",
            "VISION",
            "EAP",
            "LIFE",
            "STOPLOSS",
            "LTD",
            "STD",
            "ADD_TRAVEL",
            "LEGAL",
            "CI",
            "HOSPITAL",
            "ACCIDENT",
            "LTC",
            "WELLNESS",
            "WELFARE",
        ]
        for v in notLobs:
            notVar = "NOT_" + v
            dt[notVar] = np.where(dt["NOT_OFFER"].str.contains(v), 1, 0)
            if v in ["CI", "HOSPITAL", "LEGAL", "ACCIDENT"]:
                dt[notVar] = np.where(
                    dt["NOT_OFFER"].str.contains("VOL"), 1, dt[notVar]
                )
        tot = dt[notVar].sum()
        if tot == 0:
            print(f"\nNO car strings that are {v}")
    if "STATES" in dt.columns:
        dt["STATE_EXCLUSIVE"] = np.where(dt["STATES"].str.len() == 2, dt["STATES"], "")
    if nameVar.upper() == "BROKER":
        dropVars = [
            "COUNT",
            "QUESTIONS_NEED_VALIDATION",
            "REVIEWED",
            "NOTES",
            "NEW",
            "DROP",
        ]
        dt.drop(columns=list(set(dropVars).intersection(dt.columns)), inplace=True)
    dt["ORDER"] = pd.to_numeric(dt["ORDER"], errors="coerce")
    dt["ORDER"].fillna(500, inplace=True)
    dt = dt.sort_values(by=["ORDER"])
    na = dt.columns[dt.isna().any()].tolist()
    if len(na) > 0:
        print(na)
        raise ValueError("- have NAs @54")
    return dt


this is in regards to form 5500 also please explain me the column names that are mentioned