code: def get_random_search_object_callback(
      scenario_dict,
      seed,
      ta,
      ta_kwargs,
      metalearning_configurations,
      n_jobs,
      dask_client,
      multi_objective_algorithm,  # This argument will be ignored as ROAR does not yet support multi-objective optimization
      multi_objective_kwargs,
  ):
      """Random search"""
  
      if n_jobs > 1 or (
          dask_client and len(dask_client.nthreads()) > 1
      ):
          raise ValueError(
              "Please make sure to guard the code invoking Auto-sklearn by "
              "`if __name__ == '__main__'` and remove this exception."
          )
  
      scenario_dict["minR"] = len(scenario_dict["instances"])
      scenario_dict["initial_incumbent"] = "RANDOM"
      scenario = Scenario(scenario_dict)
      return ROAR(
          scenario=scenario,
          rng=seed,
          tae_runner=ta,
          tae_runner_kwargs=ta_kwargs,
          run_id=seed,
          dask_client=dask_client,
          n_jobs=n_jobs,
      )
  
file_path: auto-sklearn/examples/60_search/example_random_search.py
imports: from pprint import pprint
  import sklearn.model_selection
  import sklearn.datasets
  import sklearn.metrics
  from smac.facade.roar_facade import ROAR
  from smac.scenario.scenario import Scenario
  import autosklearn.classification
label_desc: Use image node_id 2 for calling a global function with example usage: get_random_search_object_callback(scenario_dict, seed, ta, ta_kwargs, metalearning_configurations, n_jobs, dask_client, multi_objective_algorithm, multi_objective_kwargs) and returns: ROAR