code: def _maybe_classify(self, y, k, cutoffs):
          '''Helper method for classifying continuous data.
  
          '''
  
          rows, cols = y.shape
          if cutoffs is None:
              if self.fixed:
                  mcyb = mc.Quantiles(y.flatten(), k=k)
                  yb = mcyb.yb.reshape(y.shape)
                  cutoffs = mcyb.bins
                  k = len(cutoffs)
                  return yb, cutoffs[:-1], k
              else:
                  yb = np.array([mc.Quantiles(y[:, i], k=k).yb for i in
                                 np.arange(cols)]).transpose()
                  return yb, None, k
          else:
              cutoffs = list(cutoffs) + [np.inf]
              cutoffs = np.array(cutoffs)
              yb = mc.User_Defined(y.flatten(), np.array(cutoffs)).yb.reshape(
                  y.shape)
              k = len(cutoffs)
              return yb, cutoffs[:-1], f _maybe_classify(self, y, k, cutoffs):
          '''Helper method for classifying continuous data.
  
          '''
  
          rows, cols = y.shape
          if cutoffs is None:
              if self.fixed:
                  mcyb = mc.Quantiles(y.flatten(), k=k)
                  yb = mcyb.yb.reshape(y.shape)
                  cutoffs = mcyb.bins
                  k = len(cutoffs)
                  return yb, cutoffs[:-1], k
              else:
                  yb = np.array([mc.Quantiles(y[:, i], k=k).yb for i in
                                 np.arange(cols)]).transpose()
                  return yb, None, k
          else:
              cutoffs = list(cutoffs) + [np.inf]
              cutoffs = np.array(cutoffs)
              yb = mc.User_Defined(y.flatten(), np.array(cutoffs)).yb.reshape(
                  y.shape)
              k = len(cutoffs)
              return yb, cutoffs[:-1], k
docs: Helper method for classifying continuous data.