code: def _iterative_mean(iter, current_mean, x):
      """
      Iteratively calculates mean using
      http://www.heikohoffmann.de/htmlthesis/node134.html
      :param iter: non-negative integer, iteration
      :param current_mean: numpy array, current value of mean
      :param x: numpy array, new value to be added to mean
      :return: numpy array, updated mean
      """
      return current_mean + ((x - current_mean) / (iter + 1))
  
file_path: treeinterpreter/treeinterpreter/treeinterpreter.py
imports: import numpy
  import sklearn
  from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, RandomForestRegressor, ExtraTreesRegressor
  from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier, _tree
  from distutils.version import LooseVersion
label_desc: Use image node_id 3 for calling a global function with example usage: _iterative_mean(iter, current_mean, x) and returns: unknown