docstr: Returns local and sorted random search as acquisition maximizer.
  
  Parameters
  ----------
  challengers : int, defaults to 1000
      Number of challengers.
  local_search_iterations: int, defaults to 10
      Number of local search iterations.
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code: def get_acquisition_maximizer(  # type: ignore
      scenario: Scenario,
      *,
      challengers: int = 1000,
      local_search_iterations: int = 10,
  ) -> LocalAndSortedRandomSearch:
      """Returns local and sorted random search as acquisition maximizer.
  
      Parameters
      ----------
      challengers : int, defaults to 1000
          Number of challengers.
      local_search_iterations: int, defaults to 10
          Number of local search iterations.
      """
      return LocalAndSortedRandomSearch(
          configspace=scenario.configspace,
          challengers=challengers,
          local_search_iterations=local_search_iterations,
          seed=scenario.seed,
      )
  
imports: from __future__ import annotations
  import numpy
  import sklearn.gaussian_process.kernels
  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.gaussian_process.abstract_gaussian_process import AbstractGaussianProcess
  from smac.model.gaussian_process.gaussian_process import GaussianProcess
  from smac.model.gaussian_process.kernels import ConstantKernel, HammingKernel, MaternKernel, WhiteKernel
  from smac.model.gaussian_process.mcmc_gaussian_process import MCMCGaussianProcess
  from smac.model.gaussian_process.priors import HorseshoePrior, LogNormalPrior
  from smac.multi_objective.aggregation_strategy import MeanAggregationStrategy
  from smac.random_design.probability_design import ProbabilityRandomDesign
  from smac.runhistory.encoder.encoder import RunHistoryEncoder
  from smac.scenario import Scenario
  from smac.utils.configspace import get_types
label_desc: Use image node_id 5 for calling the BlackBoxFacade obj's underlying member method code with example usage: obj.get_acquisition_maximizer(scenario) and returns: LocalAndSortedRandomSearch