code: def predict_unseen(self, times, config):
          
  
          assert np.all(times > 0) and np.all(times <= self.max_num_epochs)
  
          x = np.array(config)[None, :]
  
          idx = times / self.max_num_epochs
          x = np.repeat(x, idx.shape[0], axis=0)
  
          x = np.concatenate((x, idx[:, None]), axis=1)
  
          mean, var = self.model.predict(x)
          return 1 - mean, var
docs: predict the loss of an unseen configuration
  
              Parameters:
              -----------
  
              times: numpy array
                  times where to predict the loss
              config: numpy array
                  the numerical representation of the config
  
              Returns:
              --------
              
              mean and variance prediction at input times for the given config