code: def is_converge(self):
          
  
          if len(self.list_cv_values) < self.wind:
              return
          start_idx = -self.wind
          mid_idx = -(self.wind // 2)
          old_mean = np.array(self.list_cv_values[start_idx:mid_idx]).mean()
          current_mean = np.array(self.list_cv_values[mid_idx:]).mean()
          normalize_residual_metrics = (np.abs(old_mean - current_mean) /
                                        np.abs(old_mean))
          self.converge_flag = normalize_residual_metrics < self.eps
docs: Return True if the convergence criteria is matched.