code: def run_with_n_jobs(method):
      """Decorator for running of methods containing oneDAL kernels with 'n_jobs'"""
  
      @wraps(method)
      def method_wrapper(self, *args, **kwargs):
          # threading parallel backend branch
          if not isinstance(
              threading.current_thread(), threading._MainThread
          ):
              warn(
                  "'Threading' parallel backend is not supported by "
                  "Intel(R) Extension for Scikit-learn*. "
                  "Falling back to usage of all available threads."
              )
              result = method(self, *args, **kwargs)
              return result
          # multiprocess parallel backends branch
          cl = self.__class__
          method_name = ".".join(
              [cl.__module__, cl.__name__, method.__name__]
          )
          # search for specified n_jobs
          n_jobs = self.n_jobs
          n_cpus = cpu_count()
          # receive n_threads limitation from upper parallelism context
          # using `threadpoolctl.ThreadpoolController`
          n_threads = get_suggested_n_threads(n_cpus)
          # get real `n_jobs` number of threads for oneDAL
          # using sklearn rules and `n_threads` from upper parallelism context
          if n_jobs is None or n_jobs == 0:
              if n_threads is None:
                  # default branch with no setting for n_jobs
                  return method(self, *args, **kwargs)
              else:
                  n_jobs = n_threads
          elif n_jobs < 0:
              if n_threads is None:
                  n_jobs = max(1, n_cpus + n_jobs + 1)
              else:
                  n_jobs = max(1, n_threads + n_jobs + 1)
          # branch with set n_jobs
          old_n_threads = get_n_threads()
          if n_jobs != old_n_threads:
              logger = logging.getLogger("sklearnex")
              logger.debug(
                  f"{method_name}: setting {n_jobs} threads (previous - {old_n_threads})"
              )
              set_n_threads(n_jobs)
          result = method(self, *args, **kwargs)
          if n_jobs != old_n_threads:
              set_n_threads(old_n_threads)
          return result
  
      return method_wrapper
  
imports: import os
  import sys
  import threading
  import warnings
  from functools import wraps
  from inspect import Parameter, signature
  from multiprocessing import cpu_count
  from numbers import Integral
  from typing import Any, Callable, Tuple
  from warnings import warn
  import numpy
  import threadpoolctl
  from numpy.lib.recfunctions import require_fields
  from sklearn import __version__
  from daal4py import _get__daal_link_version__
  from daal4py import daalinit
  from daal4py import num_threads
  import logging
label_desc: Use image node_id 16 for calling a global function with example usage: run_with_n_jobs(method) and returns: method_wrapper, result, result, method