How to use the ctgan.model.Sampler function in ctgan

def fit(self, train_data, categorical_columns=tuple(), ordinal_columns=tuple()):

        self.transformer = DataTransformer()
        self.transformer.fit(train_data, categorical_columns, ordinal_columns)
        train_data = self.transformer.transform(train_data)

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

        data_dim = self.transformer.output_dim
        self.cond_generator = Cond(train_data, self.transformer.output_info)

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

        discriminator = Discriminator(
            data_dim + self.cond_generator.n_opt,
            self.dis_dim
        ).to(self.device)

        optimizerG = optim.Adam(

def __init__(self, data, output_info):
        super(Sampler, self).__init__()
        self.data = data
        self.model = []
        self.n = len(data)

        st = 0
        skip = False
        for item in output_info:
            if item[1] == 'tanh':
                st += item[0]
                skip = True
            elif item[1] == 'softmax':
                if skip:
                    skip = False
                    st += item[0]
                    continue