code: def bins(self, data, labels):
      bins = []
      for feature in self.to_discretize:
          qts = np.array(np.percentile(data[:, feature], [25, 50, 75]))
          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 QuartileDiscretizer obj's underlying member method code with example usage: obj.bins(data, labels) and returns: bins