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
  from ConfigSpace import Configuration
  from smac.acquisition.function.expected_improvement import EI
  from smac.acquisition.maximizer.local_and_random_search import LocalAndSortedRandomSearch
  from smac.facade.abstract_facade import AbstractFacade
  from smac.initial_design.sobol_design import SobolInitialDesign
  from smac.intensifier.intensifier import Intensifier
  from smac.model.random_forest.random_forest import RandomForest
  from smac.multi_objective.aggregation_strategy import MeanAggregationStrategy
  from smac.random_design.probability_design import ProbabilityRandomDesign
  from smac.runhistory.encoder.log_scaled_encoder import RunHistoryLogScaledEncoder
  from smac.scenario import Scenario
label_desc: Use image node_id 7 for calling the HyperparameterOptimizationFacade obj's underlying member method code with example usage: obj.get_multi_objective_algorithm(scenario) and returns: MeanAggregationStrategy