class_docstr: A class to mean-aggregate multi-objective costs to a single cost.
  
  Parameters
  ----------
  scenario : Scenario
  objective_weights : list[float] | None, defaults to None
      Weights for an weighted average. Must be of the same length as the number of objectives.
code: def __init__(
      self,
      scenario: Scenario,
      objective_weights: list[float] | None = None,
  ):
      super(MeanAggregationStrategy, self).__init__()
  
      if (
          objective_weights is not None
          and scenario.count_objectives() != len(objective_weights)
      ):
          raise ValueError(
              "Number of objectives and number of weights must be equal."
          )
  
      self._objective_weights = objective_weights
  
file_path: SMAC3/smac/multi_objective/aggregation_strategy.py
imports: from __future__ import annotations
  from typing import Any
  import numpy
  from smac.multi_objective.abstract_multi_objective_algorithm import AbstractMultiObjectiveAlgorithm
  from smac.scenario import Scenario
label_desc: Use image node_id 1 to create a new MeanAggregationStrategy object from inherited base classes: AbstractMultiObjectiveAlgorithm with example: obj = MeanAggregationStrategy(scenario, objective_weights)