How to use the ctgan.transformer.DataTransformer function in ctgan

train_data (numpy.ndarray or pandas.DataFrame):
                Training Data. It must be a 2-dimensional numpy array or a
                pandas.DataFrame.
            discrete_columns (list-like):
                List of discrete columns to be used to generate the Conditional
                Vector. If ``train_data`` is a Numpy array, this list should
                contain the integer indices of the columns. Otherwise, if it is
                a ``pandas.DataFrame``, this list should contain the column names.
            epochs (int):
                Number of training epochs. Defaults to 300.
            log_frequency (boolean):
                Whether to use log frequency of categorical levels in conditional
                sampling. Defaults to ``True``.
        """

        self.transformer = DataTransformer()
        self.transformer.fit(train_data, discrete_columns)
        train_data = self.transformer.transform(train_data)

        data_sampler = Sampler(train_data, self.transformer.output_info)

        data_dim = self.transformer.output_dimensions
        self.cond_generator = ConditionalGenerator(
            train_data,
            self.transformer.output_info,
            log_frequency
        )

        self.generator = Generator(
            self.embedding_dim + self.cond_generator.n_opt,
            self.gen_dim,
            data_dim