code: def __init__(
      self,
      model,
      space,
      objective,
      acquisition,
      evaluator,
      X_init,
      Y_init=None,
      cost=None,
      normalize_Y=True,
      model_update_interval=1,
      de_duplication=False,
  ):
      self.initial_iter = True
      self.modular_optimization = True
  
      # --- Create optimization space
      super(ModularBayesianOptimization, self).__init__(
          model=model,
          space=space,
          objective=objective,
          acquisition=acquisition,
          evaluator=evaluator,
          X_init=X_init,
          Y_init=Y_init,
          cost=cost,
          normalize_Y=normalize_Y,
          model_update_interval=model_update_interval,
          de_duplication=de_duplication,
      )
  
label_desc: Use image node_id 1 to create a new ModularBayesianOptimization object from inherited base classes: BO with example: obj = ModularBayesianOptimization(model, space, objective, acquisition, evaluator, X_init, Y_init, cost, normalize_Y, model_update_interval, de_duplication)