code: def bins(self, data, labels):
      bins = []
      for feature in self.to_discretize:
          qts = np.array(
              np.percentile(
                  data[:, feature], [10, 20, 30, 40, 50, 60, 70, 80, 90]
              )
          )
          bins.append(qts)
      return bins
  
imports: import numpy
  import sklearn
  import sklearn.tree
  import scipy
  from sklearn.utils import check_random_state
  from abc import ABCMeta, abstractmethod
label_desc: Use image node_id 2 for calling the DecileDiscretizer obj's underlying member method code with example usage: obj.bins(data, labels) and returns: bins