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

def __init__(self, meta, side, layers, device):
        super(Classifier, self).__init__()
        self.meta = meta
        self.side = side
        self.seq = Sequential(*layers)
        self.valid = True
        if meta[-1]['name'] != 'label' or meta[-1]['type'] != CATEGORICAL or meta[-1]['size'] != 2:
            self.valid = False

        masking = np.ones((1, 1, side, side), dtype='float32')
        index = len(self.meta) - 1
        self.r = index // side
        self.c = index % side
        masking[0, 0, self.r, self.c] = 0
        self.masking = torch.from_numpy(masking).to(device)

def get_metadata(data, categorical_columns=tuple(), ordinal_columns=tuple()):
        meta = []

        df = pd.DataFrame(data)
        for index in df:
            column = df[index]

            if index in categorical_columns:
                mapper = column.value_counts().index.tolist()
                meta.append({
                    "name": index,
                    "type": CATEGORICAL,
                    "size": len(mapper),
                    "i2s": mapper
                })
            elif index in ordinal_columns:
                value_count = list(dict(column.value_counts()).items())
                value_count = sorted(value_count, key=lambda x: -x[1])
                mapper = list(map(lambda x: x[0], value_count))
                meta.append({
                    "name": index,
                    "type": ORDINAL,
                    "size": len(mapper),
                    "i2s": mapper
                })
            else:
                meta.append({
                    "name": index,

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