How to use the sdgym.constants.ORDINAL function in sdgym

def fit(self, data, categorical_columns=tuple(), ordinal_columns=tuple()):
        self.meta = self.get_metadata(data, categorical_columns, ordinal_columns)
        self.output_dim = 0
        for info in self.meta:
            if info['type'] in [CONTINUOUS, ORDINAL]:
                self.output_dim += 1
            else:
                self.output_dim += info['size']

def sample(self, n):
        try_mkdirs("__privbn_tmp/data")
        try_mkdirs("__privbn_tmp/log")
        try_mkdirs("__privbn_tmp/output")
        shutil.copy("privbayes/privBayes.bin", "__privbn_tmp/privBayes.bin")
        d_cols = []
        with open("__privbn_tmp/data/real.domain", "w") as f:
            for id_, info in enumerate(self.meta):
                if info['type'] in [CATEGORICAL, ORDINAL]:
                    print("D", end='', file=f)
                    counter = 0
                    for i in range(info['size']):
                        if i > 0 and i % 4 == 0:
                            counter += 1
                            print(" {", end='', file=f)
                        print("", i, end='', file=f)
                    print(" }" * counter, file=f)
                    d_cols.append(id_)
                else:
                    minn = info['min']
                    maxx = info['max']
                    d = (maxx - minn) * 0.03
                    minn = minn - d
                    maxx = maxx + d
                    print("C", minn, maxx, file=f)

def sample(self, n):
        try_mkdirs("__privbn_tmp/data")
        try_mkdirs("__privbn_tmp/log")
        try_mkdirs("__privbn_tmp/output")
        shutil.copy("privbayes/privBayes.bin", "__privbn_tmp/privBayes.bin")
        d_cols = []
        with open("__privbn_tmp/data/real.domain", "w") as f:
            for id_, info in enumerate(self.meta):
                if info['type'] in [CATEGORICAL, ORDINAL]:
                    print("D", end='', file=f)
                    counter = 0
                    for i in range(info['size']):
                        if i > 0 and i % 4 == 0:
                            counter += 1
                            print(" {", end='', file=f)
                        print("", i, end='', file=f)
                    print(" }" * counter, file=f)
                    d_cols.append(id_)
                else:
                    minn = info['min']
                    maxx = info['max']
                    d = (maxx - minn) * 0.03
                    minn = minn - d
                    maxx = maxx + d
                    print("C", minn, maxx, file=f)

def _compute_distance(train, syn, metadata, sample=300):
    mask_d = np.zeros(len(metadata['columns']))

    for id_, info in enumerate(metadata['columns']):
        if info['type'] in [CATEGORICAL, ORDINAL]:
            mask_d[id_] = 1
        else:
            mask_d[id_] = 0

    std = np.std(train, axis=0) + 1e-6

    dis_all = []
    for i in range(min(sample, len(train))):
        current = syn[i]
        distance_d = (train - current) * mask_d > 0
        distance_d = np.sum(distance_d, axis=1)

        distance_c = (train - current) * (1 - mask_d) / 2 / std
        distance_c = np.sum(distance_c ** 2, axis=1)
        distance = np.sqrt(np.min(distance_c + distance_d))
        dis_all.append(distance)

def _get_columns(metadata):
    categorical_columns = list()
    ordinal_columns = list()
    for column_idx, column in enumerate(metadata['columns']):
        if column['type'] == CATEGORICAL:
            categorical_columns.append(column_idx)
        elif column['type'] == ORDINAL:
            ordinal_columns.append(column_idx)

    return categorical_columns, ordinal_columns