class_docstr: This facade configures SMAC in a multi-fidelity setting.
docstr: Returns a Hyperband intensifier instance. Budgets are supported.
  
  eta : int, defaults to 3
      Input that controls the proportion of configurations discarded in each round of Successive Halving.
  n_seeds : int, defaults to 1
      How many seeds to use for each instance.
  instance_seed_order : str, defaults to "shuffle_once"
      How to order the instance-seed pairs. Can be set to:
      * None: No shuffling at all and use the instance-seed order provided by the user.
      * "shuffle_once": Shuffle the instance-seed keys once and use the same order across all runs.
      * "shuffle": Shuffles the instance-seed keys for each bracket individually.
  incumbent_selection : str, defaults to "any_budget"
      How to select the incumbent when using budgets. Can be set to:
      * "any_budget": Incumbent is the best on any budget, i.e., the best performance regardless of budget.
      * "highest_observed_budget": Incumbent is the best in the highest budget run so far.
      refer to `runhistory.get_trials` for more details. Crucially, if true, then a
      for a given config-instance-seed, only the highest (so far executed) budget is used for
      comparison against the incumbent. Notice, that if the highest observed budget is smaller
      than the highest budget of the incumbent, the configuration will be queued again to
      be intensified again.
      * "highest_budget": Incumbent is selected only based on the absolute highest budget
      available only.
  max_incumbents : int, defaults to 10
      How many incumbents to keep track of in the case of multi-objective.
code: def get_intensifier(  # type: ignore
      scenario: Scenario,
      *,
      eta: int = 3,
      n_seeds: int = 1,
      instance_seed_order: str | None = "shuffle_once",
      max_incumbents: int = 10,
      incumbent_selection: str = "highest_observed_budget",
  ) -> Hyperband:
      """Returns a Hyperband intensifier instance. Budgets are supported.
  
      eta : int, defaults to 3
          Input that controls the proportion of configurations discarded in each round of Successive Halving.
      n_seeds : int, defaults to 1
          How many seeds to use for each instance.
      instance_seed_order : str, defaults to "shuffle_once"
          How to order the instance-seed pairs. Can be set to:
          * None: No shuffling at all and use the instance-seed order provided by the user.
          * "shuffle_once": Shuffle the instance-seed keys once and use the same order across all runs.
          * "shuffle": Shuffles the instance-seed keys for each bracket individually.
      incumbent_selection : str, defaults to "any_budget"
          How to select the incumbent when using budgets. Can be set to:
          * "any_budget": Incumbent is the best on any budget, i.e., the best performance regardless of budget.
          * "highest_observed_budget": Incumbent is the best in the highest budget run so far.
          refer to `runhistory.get_trials` for more details. Crucially, if true, then a
          for a given config-instance-seed, only the highest (so far executed) budget is used for
          comparison against the incumbent. Notice, that if the highest observed budget is smaller
          than the highest budget of the incumbent, the configuration will be queued again to
          be intensified again.
          * "highest_budget": Incumbent is selected only based on the absolute highest budget
          available only.
      max_incumbents : int, defaults to 10
          How many incumbents to keep track of in the case of multi-objective.
      """
      return Hyperband(
          scenario=scenario,
          eta=eta,
          n_seeds=n_seeds,
          instance_seed_order=instance_seed_order,
          max_incumbents=max_incumbents,
          incumbent_selection=incumbent_selection,
      )
  
file_path: SMAC3/smac/facade/multi_fidelity_facade.py
imports: from __future__ import annotations
  from ConfigSpace import Configuration
  from smac.facade.hyperparameter_optimization_facade import HyperparameterOptimizationFacade
  from smac.initial_design.random_design import RandomInitialDesign
  from smac.intensifier.hyperband import Hyperband
  from smac.scenario import Scenario
label_desc: Use image node_id 1 for calling the MultiFidelityFacade obj's underlying member method code with example usage: obj.get_intensifier(scenario) and returns: Hyperband