docstr: Returns a log scaled runhistory encoder. That means that costs are log scaled before
  training the surrogate model.
docstr_tok: ["Returns","a","log","scaled","runhistory","encoder",".","That","means","that","costs","are","log","scaled","before","training","the","surrogate","model","."]
code: def get_runhistory_encoder(  # type: ignore
      scenario: Scenario,
  ) -> RunHistoryLogScaledEncoder:
      """Returns a log scaled runhistory encoder. That means that costs are log scaled before
      training the surrogate model.
      """
      return RunHistoryLogScaledEncoder(scenario)
  
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 8 for calling the HyperparameterOptimizationFacade obj's underlying member method code with example usage: obj.get_runhistory_encoder(scenario) and returns: RunHistoryLogScaledEncoder