How to use the sdgym.utils.data.utils.CONTINUOUS function in sdgym

df = df.apply(lambda x: x.str.strip(' \t.'))
    df.drop(['url', ' timedelta'], axis=1, inplace=True)

    meta = []
    for col_name in df.columns:
        if "is_" in col_name:
            meta.append({
                "name": col_name,
                "type": CATEGORICAL,
                "size": 2,
                "i2s": ['0', '1']
            })
        else:
            meta.append({
                "name": "label" if col_name.strip() == "shares" else col_name.strip(),
                "type": CONTINUOUS,
                "min": np.min(df[col_name].values.astype('float')),
                "max": np.max(df[col_name].values.astype('float'))
            })

    tdata = df.values.astype('float32')

    np.random.seed(0)
    np.random.shuffle(tdata)

    t_train = tdata[:-8000]
    t_test = tdata[-8000:]

    name = "news"
    with open("{}/{}.json".format(output_dir, name), 'w') as f:
        json.dump(meta, f, sort_keys=True, indent=4, separators=(',', ': '))
    np.savez("{}/{}.npz".format(output_dir, name), train=t_train, test=t_test)

df = pd.read_csv("data/raw/adult/adult.data", dtype='str', header=-1)
    df = df.apply(lambda x: x.str.strip(' \t.'))

    col_type = [
        ("age", CONTINUOUS),
        ("workclass", CATEGORICAL),
        ("fnlwgt", CONTINUOUS),
        ("education", ORDINAL, ["Preschool", "1st-4th", "5th-6th", "7th-8th", "9th", "10th", "11th", "12th", "HS-grad", "Prof-school", "Assoc-voc", "Assoc-acdm", "Some-college", "Bachelors", "Masters", "Doctorate"]),
        ("education-num", CONTINUOUS),
        ("marital-status", CATEGORICAL),
        ("occupation", CATEGORICAL),
        ("relationship", CATEGORICAL),
        ("race", CATEGORICAL),
        ("sex", CATEGORICAL),
        ("capital-gain", CONTINUOUS),
        ("capital-loss", CONTINUOUS),
        ("hours-per-week", CONTINUOUS),
        ("native-country", CATEGORICAL),
        ("label", CATEGORICAL)
    ]

    meta = []
    for id_, info in enumerate(col_type):
        if info[1] == CONTINUOUS:
            meta.append({
                "name": info[0],
                "type": info[1],
                "min": np.min(df.iloc[:, id_].values.astype('float')),
                "max": np.max(df.iloc[:, id_].values.astype('float'))
            })
        else:
            if info[1] == CATEGORICAL:

df.drop([19], axis=1, inplace=True)

    col_type = [
        ("duration", CONTINUOUS),
        ("protocol_type", CATEGORICAL),
        ("service", CATEGORICAL),
        ("flag", CATEGORICAL),
        ("src_bytes", CONTINUOUS),
        ("dst_bytes", CONTINUOUS),
        ("land", CATEGORICAL),
        ("wrong_fragment", ORDINAL, ['0', '1', '2', '3']),
        ("urgent", ORDINAL, ['0', '1', '2', '3']),
        ("hot", CONTINUOUS),
        ("num_failed_logins", ORDINAL, ['0', '1', '2', '3', '4', '5']),
        ("logged_in", CATEGORICAL),
        ("num_compromised", CONTINUOUS),
        ("root_shell", CATEGORICAL),
        ("su_attempted", ORDINAL, ['0', '1', '2', '3']),
        ("num_root", CONTINUOUS),
        ("num_file_creations", CONTINUOUS),
        ("num_shells", ORDINAL, ['0', '1', '2']),
        ("num_access_files", ORDINAL, ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']),
        # ("num_outbound_cmds", CONTINUOUS), # all zero, removed
        ("is_host_login", CATEGORICAL),
        ("is_guest_login", CATEGORICAL),
        ("count", CONTINUOUS),
        ("srv_count", CONTINUOUS),
        ("serror_rate", CONTINUOUS),
        ("srv_serror_rate", CONTINUOUS),
        ("rerror_rate", CONTINUOUS),
        ("srv_rerror_rate", CONTINUOUS),
        ("same_srv_rate", CONTINUOUS),

pass

    try:
        os.mkdir(temp_dir)
    except:
        pass

    df = pd.read_csv("data/raw/creditcard.csv")
    df.drop(columns=['Time'], inplace=True)
    values = df.values

    meta = []
    for i in range(28):
        meta.append({
            "name": "V%d" % i,
            "type": CONTINUOUS,
            "min": np.min(values[:, i]),
            "max": np.max(values[:, i])
        })
    meta.append({
        "name": "Amount",
        "type": CONTINUOUS,
        "min": np.min(values[:, 28]),
        "max": np.max(values[:, 28])
    })
    meta.append({
        "name": "label",
        "type": CATEGORICAL,
        "size": 2,
        "i2s": ["0", "1"]
    })

try:
        os.mkdir(output_dir)
    except:
        pass

    try:
        os.mkdir(temp_dir)
    except:
        pass

    df = pd.read_csv("data/raw/covtype/covtype.data", dtype='str', header=-1)

    col_type = [
        ("Elevation", CONTINUOUS),
        ("Aspect", CONTINUOUS),
        ("Slope", CONTINUOUS),
        ("Horizontal_Distance_To_Hydrology", CONTINUOUS),
        ("Vertical_Distance_To_Hydrology", CONTINUOUS),
        ("Horizontal_Distance_To_Roadways", CONTINUOUS),
        ("Hillshade_9am", CONTINUOUS),
        ("Hillshade_Noon", CONTINUOUS),
        ("Hillshade_3pm", CONTINUOUS),
        ("Horizontal_Distance_To_Fire_Points", CONTINUOUS)
    ] + [
        ("Wilderness_Area_{}".format(i), CATEGORICAL) for i in range(4)
    ] + [
        ("Soil_Type{}".format(i), CATEGORICAL) for i in range(40)
    ] + [
        ("label", CATEGORICAL)
    ]

    meta = []

("detailed industry recode", CATEGORICAL),
        ("detailed occupation recode", CATEGORICAL),
        ("education", CATEGORICAL),
        ("wage per hour", CONTINUOUS),
        ("enroll in edu inst last wk", CATEGORICAL),
        ("marital stat", CATEGORICAL),
        ("major industry code", CATEGORICAL),
        ("major occupation code", CATEGORICAL),
        ("race", CATEGORICAL),
        ("hispanic origin", CATEGORICAL),
        ("sex", CATEGORICAL),
        ("member of a labor union", CATEGORICAL),
        ("reason for unemployment", CATEGORICAL),
        ("full or part time employment stat", CATEGORICAL),
        ("capital gains", CONTINUOUS),
        ("capital losses", CONTINUOUS),
        ("dividends from stocks", CONTINUOUS),
        ("tax filer stat", CATEGORICAL),
        ("region of previous residence", CATEGORICAL),
        ("state of previous residence", CATEGORICAL),
        ("detailed household and family stat", CATEGORICAL),
        ("detailed household summary in household", CATEGORICAL),
        ("migration code-change in msa", CATEGORICAL),
        ("migration code-change in reg", CATEGORICAL),
        ("migration code-move within reg", CATEGORICAL),
        ("live in this house 1 year ago", CATEGORICAL),
        ("migration prev res in sunbelt", CATEGORICAL),
        ("num persons worked for employer", CONTINUOUS),
        ("family members under 18", CATEGORICAL),
        ("country of birth father", CATEGORICAL),
        ("country of birth mother", CATEGORICAL),
        ("country of birth self", CATEGORICAL),