F-test and chi-squared test for coefficient significance in sklearn generalized linear models

I have created a generalized linear model with sklearn with the following code

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3)clf_hard = GammaRegressor(alpha = 0)clf_hard.fit(X_train, y_train)

X_train and y_train are simply pandas dataframes. Now, I want to infer significance of the coefficients from this model. Especially, I want to use F-test and chi-square tests if it is possible.

My question is therefore simple.

Are there any ways to perform this in sklearn or not?If no, where can I read about the significance in such kind of models? Maybe it is possible to write custom function in python to perform these tests?