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 __init__(self, priors=None, classes=None):
      self.priors = priors
      self.classes = classes
      self.classes_ = classes
      self.class_prior_ = None
      self.class_count_ = None
      self.theta_ = None
      self.sigma_ = None
  
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 1 to create a new GaussianNB object from inherited base classes: BaseEstimator with example: obj = GaussianNB(priors, classes)