def METHOD_NAME(obj, return_metadata=False, var_name="obj"):
    metadata = dict()
    # check if the input is a dataframe
    if not isinstance(obj, pd.DataFrame):
        msg = f"{var_name} should be a pd.DataFrame"
        return ret(False, msg, None, return_metadata)
    # we now know obj is a pd.DataFrame
    index = obj.index
    if _req("is_empty", return_metadata):
        metadata["is_empty"] = len(index) < 1 or len(obj.columns) < 1
    # check that column indices are unique
    if not len(set(obj.columns)) == len(obj.columns):
        msg = "column indices must be unique"
        return ret(False, msg, None, return_metadata)
    # check that all cols are numeric
    if not np.all([is_numeric_dtype(obj[c]) for c in obj.columns]):
        msg = (
            f"{var_name} should only have numeric dtype columns, "
            f"but found dtypes {obj.dtypes}"
        )
        return ret(False, msg, None, return_metadata)
    # Check time index is ordered in time
    if not index.is_monotonic_increasing:
        msg = (
            f"The (time) index of {var_name} must be sorted monotonically increasing, "
            f"but found: {index}"
        )
        return ret(False, msg, None, return_metadata)
    # check column multiindex
    colidx = obj.columns
    msg = (
        f"column of {var_name} must be pd.MultiIndex, with two levels."
        "first level is variable name, "
        "second level are (numeric) alpha values between 0 and 1."
    )
    if not isinstance(colidx, pd.MultiIndex) or not colidx.nlevels == 2:
        return ret(False, msg, None, return_metadata)
    alphas = colidx.get_level_values(1)
    if not is_numeric_dtype(alphas):
        return ret(False, msg, None, return_metadata)
    if not (alphas <= 1).all() or not (alphas >= 0).all():
        return ret(False, msg, None, return_metadata)
    # compute more metadata, only if needed
    if _req("has_nans", return_metadata):
        metadata["has_nans"] = obj.isna().values.any()
    if _req("is_univariate", return_metadata):
        metadata["is_univariate"] = len(colidx.get_level_values(0).unique()) == 1
    return ret(True, None, metadata, return_metadata)