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: Perform classification on an array of test vectors X.
  Parameters
  ----------
  X : array-like, shape = [n_samples, n_features]
  Returns
  -------
  C : array, shape = [n_samples]
      Predicted target values for X
docstr_tok: ["Perform","classification","on","an","array","of","test","vectors","X.","Parameters","--","--","--","--","--","X",":","array-like",",","shape","=","[","n_samples",",","n_features","]","Returns","--","--","--","-","C",":","array",",","shape","=","[","n_samples","]","Predicted","target","values","for","X"]
code: def predict(self, X):
      """
      Perform classification on an array of test vectors X.
      Parameters
      ----------
      X : array-like, shape = [n_samples, n_features]
      Returns
      -------
      C : array, shape = [n_samples]
          Predicted target values for X
      """
      jll = self._joint_log_likelihood(X)
      return delayed(self.classes_)[da.argmax(jll, axis=1)]
  
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 3 for calling the GaussianNB obj's underlying member method code with example usage: obj.predict(X) and returns: delayed