def METHOD_NAME(self):
    self.paillier_encrypt = PaillierEncrypt()
    self.paillier_encrypt.generate_key()
    # self.hetero_lr_gradient = HeteroLogisticGradient(self.paillier_encrypt)
    self.hetero_lr_gradient = hetero_lr_gradient_and_loss.Guest()
    size = 10
    self.en_wx = session.parallelize([self.paillier_encrypt.encrypt(i) for i in range(size)],
                                     partition=48,
                                     include_key=False)
    # self.en_wx = session.parallelize([self.paillier_encrypt.encrypt(i) for i in range(size)])
    self.en_sum_wx_square = session.parallelize([self.paillier_encrypt.encrypt(np.square(i)) for i in range(size)],
                                                partition=48,
                                                include_key=False)
    self.wx = np.array([i for i in range(size)])
    self.w = self.wx / np.array([1 for _ in range(size)])
    self.data_inst = session.parallelize(
        [Instance(features=np.array([1 for _ in range(size)]), label=pow(-1, i % 2)) for i in range(size)],
        partition=48, include_key=False)
    # test fore_gradient
    self.fore_gradient_local = [-0.5, 0.75, 0, 1.25, 0.5, 1.75, 1, 2.25, 1.5, 2.75]
    # test gradient
    self.gradient = [1.125, 1.125, 1.125, 1.125, 1.125, 1.125, 1.125, 1.125, 1.125, 1.125]
    self.gradient_fit_intercept = [1.125, 1.125, 1.125, 1.125, 1.125, 1.125, 1.125, 1.125, 1.125, 1.125, 1.125]
    self.loss = 4.505647