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 ``Intensifier`` as intensifier.
  
  Note
  ----
  Please use the ``HyperbandFacade`` if you want to incorporate budgets.
  
  Warning
  -------
  If you are in an algorithm configuration setting, consider increasing ``max_config_calls``.
  
  Parameters
  ----------
  max_config_calls : int, defaults to 3
      Maximum number of configuration evaluations. Basically, how many instance-seed keys should be max evaluated
      for a configuration.
  max_incumbents : int, defaults to 10
      How many incumbents to keep track of in the case of multi-objective.
docstr_tok: ["Returns","``","Intensifier","``","as","intensifier",".","Note","--","--","Please","use","the","``","HyperbandFacade","``","if","you","want","to","incorporate","budgets",".","Warning","--","--","--","-","If","you","are","in","an","algorithm","configuration","setting",",","consider","increasing","``","max_config_calls","``",".","Parameters","--","--","--","--","--","max_config_calls",":","int",",","defaults","to","3","Maximum","number","of","configuration","evaluations",".","Basically",",","how","many","instance-seed","keys","should","be","max","evaluated","for","a","configuration",".","max_incumbents",":","int",",","defaults","to","10","How","many","incumbents","to","keep","track","of","in","the","case","of","multi-objective","."]
code: def get_intensifier(
      scenario: Scenario,
      *,
      max_config_calls: int = 3,
      max_incumbents: int = 10,
  ) -> Intensifier:
      """Returns ``Intensifier`` as intensifier.
  
      Note
      ----
      Please use the ``HyperbandFacade`` if you want to incorporate budgets.
  
      Warning
      -------
      If you are in an algorithm configuration setting, consider increasing ``max_config_calls``.
  
      Parameters
      ----------
      max_config_calls : int, defaults to 3
          Maximum number of configuration evaluations. Basically, how many instance-seed keys should be max evaluated
          for a configuration.
      max_incumbents : int, defaults to 10
          How many incumbents to keep track of in the case of multi-objective.
      """
      return Intensifier(
          scenario=scenario,
          max_config_calls=max_config_calls,
          max_incumbents=max_incumbents,
      )
  
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 2 for calling the RandomFacade obj's underlying member method code with example usage: obj.get_intensifier(scenario) and returns: Intensifier