class_docstr: Fit a naive bayes model with a Gaussian likelihood
  
  Examples
  --------
  >>> from dask_ml import datasets
  >>> from dask_ml.naive_bayes import GaussianNB
  >>> X, y = datasets.make_classification(chunks=50)
  >>> gnb = GaussianNB()
  >>> gnb.fit(X, y)
code: def _joint_log_likelihood(self, X):
      jll = []
      for i in range(np.size(self.classes_)):
          jointi = da.log(self.class_prior_[i])
          n_ij = -0.5 * da.sum(da.log(2.0 * np.pi * self.sigma_[i, :]))
          n_ij -= 0.5 * da.sum(
              ((X - self.theta_[i, :]) ** 2) / (self.sigma_[i, :]), 1
          )
          jll.append(jointi + n_ij)
  
      joint_log_likelihood = da.stack(jll).T
      return joint_log_likelihood
  
file_path: dask-ml/dask_ml/naive_bayes.py
imports: import dask
  import dask.array
  import numpy
  from dask import delayed
  from sklearn.base import BaseEstimator
label_desc: Use image node_id 6 for calling the GaussianNB obj's underlying member method code with example usage: obj._joint_log_likelihood(X) and returns: joint_log_likelihood