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)
docstr: Return probability estimates for the test vector X.
  Parameters
  ----------
  X : array-like, shape = [n_samples, n_features]
  Returns
  -------
  C : array-like, shape = [n_samples, n_classes]
      Returns the probability of the samples for each class in
      the model. The columns correspond to the classes in sorted
      order, as they appear in the attribute `classes_`.
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code: def predict_proba(self, X):
      """
      Return probability estimates for the test vector X.
      Parameters
      ----------
      X : array-like, shape = [n_samples, n_features]
      Returns
      -------
      C : array-like, shape = [n_samples, n_classes]
          Returns the probability of the samples for each class in
          the model. The columns correspond to the classes in sorted
          order, as they appear in the attribute `classes_`.
      """
      return da.exp(self.predict_log_proba(X))
  
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 5 for calling the GaussianNB obj's underlying member method code with example usage: obj.predict_proba(X) and returns: da