class_docstr: Facade to use Random Online Aggressive Racing (ROAR).
  
  *Aggressive Racing:*
  When we have a new configuration θ, we want to compare it to the current best
  configuration, the incumbent θ*. ROAR uses the 'racing' approach, where we run few times for unpromising θ and many
  times for promising configurations. Once we are confident enough that θ is better than θ*, we update the
  incumbent θ* ⟵ θ. `Aggressive` means rejecting low-performing configurations very early, often after a single run.
  This together is called `aggressive racing`.
  
  *ROAR Loop:*
  The main ROAR loop looks as follows:
  
  1. Select a configuration θ uniformly at random.
  2. Compare θ to incumbent θ* online (one θ at a time):
    - Reject/accept θ with `aggressive racing`
  
  *Setup:*
  Uses a random model and random search for the optimization of the acquisition function.
  
  Note
  ----
  The surrogate model and the acquisition function is not used during the optimization and therefore replaced
  by dummies.
docstr: Returns the mean aggregation strategy for the multi-objective algorithm.
  
  Parameters
  ----------
  scenario : Scenario
  objective_weights : list[float] | None, defaults to None
      Weights for averaging the objectives in a weighted manner. Must be of the same length as the number of
      objectives.
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code: def get_multi_objective_algorithm(  # type: ignore
      scenario: Scenario,
      *,
      objective_weights: list[float] | None = None,
  ) -> MeanAggregationStrategy:
      """Returns the mean aggregation strategy for the multi-objective algorithm.
  
      Parameters
      ----------
      scenario : Scenario
      objective_weights : list[float] | None, defaults to None
          Weights for averaging the objectives in a weighted manner. Must be of the same length as the number of
          objectives.
      """
      return MeanAggregationStrategy(
          scenario=scenario,
          objective_weights=objective_weights,
      )
  
imports: from __future__ import annotations
  import numpy
  from ConfigSpace import Configuration
  from smac.acquisition.function.abstract_acquisition_function import AbstractAcquisitionFunction
  from smac.acquisition.maximizer.random_search import RandomSearch
  from smac.facade.abstract_facade import AbstractFacade
  from smac.initial_design.default_design import DefaultInitialDesign
  from smac.intensifier.intensifier import Intensifier
  from smac.model.random_model import RandomModel
  from smac.multi_objective.aggregation_strategy import MeanAggregationStrategy
  from smac.random_design import AbstractRandomDesign
  from smac.runhistory.encoder.encoder import RunHistoryEncoder
  from smac.scenario import Scenario
label_desc: Use image node_id 7 for calling the RandomFacade obj's underlying member method code with example usage: obj.get_multi_objective_algorithm(scenario) and returns: MeanAggregationStrategy