code: def parallel_apply(cv_func, feature_list, n_jobs):
      pool = multiprocessing.Pool(n_jobs)
      manager = multiprocessing.Manager()
      result_queue = manager.Queue()
  
      for f in feature_list:
          pool.apply_async(cv_func, (f, result_queue))
  
      pool.close()
      pool.join()
  
      lofo_cv_result = [
          result_queue.get() for _ in range(len(feature_list))
      ]
      return lofo_cv_result
  
imports: import multiprocessing
  import pandas
label_desc: Use image node_id 2 for calling a global function with example usage: parallel_apply(cv_func, feature_list, n_jobs) and returns: lofo_cv_result