Can we manually identify feature distribution on Naive Bayes solutions? If we want to classify some samples based on some continuous features, we basically suppose that all features are from Gaussian distribution ( Gaussian naive Bayes in scikit-learn ). If we want to use Bayes rule, is that common to identify each feature's distribution manually and then use manual method of Bayes rule to identify the target? For example first feature maybe from Gaussian distribution, second feature will be from Gamma distribution and so.
Why I ask this, is that I mostly see that in Naive Bayes solutions, people ignore the distribution of features.

Naive Bayes does not make the assumption of normality. It makes the assumption that the features are independent. In the case of a normal distribution, there is actually no point in making the independence assumption, because you can directly estimate the covariance matrix and mean vector, even when the features are correlated. You can thus directly implement Bayes' decision rule without making the independence assumption. Mathematically, this is equivalent to a classification rule known as "quadratic discriminant analysis".
On the other hand, the naive Bayes classifier can be used without any distribution assumption, namely by using kernel density estimators. I do not know whether there already is a scikit implementation, but scikit includes a krenel density estimator sklearn.neighbors.KernelDensity, so you can build your own non-parametric naive Bayes classifiers with a few lines of code.
In R, you can use klaR::NaiveBayes with the option usekernel=TRUE.
