code: def _make_indices(self, Ns):
          ''' makes indices for cross validation '''
          N_new = int(Ns * self.n_splits)
  
          test = [np.full(N_new, False) for i in range(self.n_splits)]
          for i in range(self.n_splits):
              test[i][np.arange(Ns * i, Ns * (i + 1))] = True
          train = [np.logical_not(test[i]) for i in range(self.n_splits)]
  
          test = [np.arange(N_new)[test[i]] for i in range(self.n_splits)]
          train = [np.arange(N_new)[train[i]] for i in range(self.n_splits)]
  
          cv = list(zip(train, test))
          return cf _make_indices(self, Ns):
          ''' makes indices for cross validation '''
          N_new = int(Ns * self.n_splits)
  
          test = [np.full(N_new, False) for i in range(self.n_splits)]
          for i in range(self.n_splits):
              test[i][np.arange(Ns * i, Ns * (i + 1))] = True
          train = [np.logical_not(test[i]) for i in range(self.n_splits)]
  
          test = [np.arange(N_new)[test[i]] for i in range(self.n_splits)]
          train = [np.arange(N_new)[train[i]] for i in range(self.n_splits)]
  
          cv = list(zip(train, test))
          return cv
docs: makes indices for cross validation