是否有方法可以在ABESS库内部直接禁用标准化处理？

[DO891]

查询了x_train第三十个特征数据不是全部相同的，虽然删除该特征是可以的。但是我想不删除，有什么办法？
代码如下：

import abess
from abess import LinearRegression
model = LinearRegression()
model.fit(x_train,y_train)

OverflowError                             Traceback (most recent call last)
Cell In[56], line 5
      2 from abess import LinearRegression
      4 model = LinearRegression()
----> 5 model.fit(x_train,y_train)

File ~\AppData\Local\Programs\Python\Python312\Lib\site-packages\abess\bess_base.py:610, in bess_base.fit(self, X, y, is_normal, sample_weight, cv_fold_id, sparse_matrix, beta_low, beta_high)
    608     result = [np.zeros((p, M)), np.zeros(M), 0, 0, 0]
    609 else:
--> 610     result = pywrap_GLM(
    611         X, y, sample_weight, n, p, normalize, algorithm_type_int,
    612         model_type_int,
    613         self.max_iter, self.exchange_num, path_type_int,
    614         self.is_warm_start, eval_type_int, self.ic_coef, self.cv,
    615         g_index,
    616         support_sizes, alphas, cv_fold_id, new_s_min, new_s_max,
    617         new_lambda_min, new_lambda_max, n_lambda, self.screening_size,
    618         always_select_list, self.primary_model_fit_max_iter,
    619         self.primary_model_fit_epsilon, early_stop,
    620         self.approximate_Newton, self.thread, self.covariance_update,
    621         sparse_matrix, self.splicing_type, self.important_search,
    622         A_init_list, self.fit_intercept, beta_low, beta_high)
    624 self.coef_ = result[0].squeeze()
    625 self.intercept_ = result[1].squeeze()

OverflowError: The variable 30 is constant. Please drop this variable or disable the normalization.

[Mamba413]

@DO891 考虑将代码改成：

import abess
from abess import LinearRegression
model = LinearRegression()

## fit 方法中增加一个 `is_normal=True` 的选项
model.fit(x_train,y_train, is_normal=False)