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: We return ``RandomSearch`` as maximizer which samples configurations randomly from the configuration
  space and therefore neither uses the acquisition function nor the model.
docstr_tok: ["We","return","``","RandomSearch","``","as","maximizer","which","samples","configurations","randomly","from","the","configuration","space","and","therefore","neither","uses","the","acquisition","function","nor","the","model","."]
code: def get_acquisition_maximizer(scenario: Scenario) -> RandomSearch:
      """We return ``RandomSearch`` as maximizer which samples configurations randomly from the configuration
      space and therefore neither uses the acquisition function nor the model.
      """
      return RandomSearch(
          scenario.configspace,
          seed=scenario.seed,
      )
  
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 6 for calling the RandomFacade obj's underlying member method code with example usage: obj.get_acquisition_maximizer(scenario) and returns: RandomSearch