code: def _entropy(self, y):
      n = y.size
      return -sum(
          (np.sum(y == c) / n) * np.log2(np.sum(y == c) / n)
          for c in range(self.n_classes_)
      )
  
imports: from sklearn.tree._tree import Tree
  from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
  from sklearn.base import ClassifierMixin, RegressorMixin
  from sklearn import __version__
  from collections import namedtuple
  import pandas
  import numpy
  from collections import namedtuple
  from sklearn import __version__
  from sklearn.base import ClassifierMixin, RegressorMixin
  from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
  from sklearn.tree._tree import Tree
  import imodels.util.tree
label_desc: Use image node_id 7 for calling the CustomDecisionTreeClassifier obj's underlying member method code with example usage: obj._entropy(y) and returns: unknown