code: def predict(self, X):
      # Check data
      X = np.atleast_2d(X)
  
      all_y_hat = [tree.predict(X) for tree in self.estimators_]
  
      # Reduce
      y_hat = sum(all_y_hat) / self.n_estimators
      y_var = np.var(all_y_hat, axis=0, ddof=1)
  
      return y_hat, y_var
  
file_path: auptimizer/src/aup/Proposer/spearmint/chooser/RandomForestEIChooser.py
imports: import numpy
  import numpy.random
  import scipy.stats
  import sklearn.ensemble
  import sklearn.ensemble.forest
  from ..None import util
  import logging
  from sklearn.externals.joblib import Parallel, delayed
label_desc: Use image node_id 1 for calling the RandomForestRegressorWithVariance obj's underlying member method code with example usage: obj.predict(X) and returns: y_hat,  y_var