code: def control_n_jobs(original_class):
      """Decorator for the control of 'n_jobs' parameter in estimator class. It applied
      for all estimators with and without support of parameter in original sklearn.
      In case of estimator without 'n_jobs' support, this decorator adds it.
      """
      original_init = original_class.__init__
  
      if sklearn_check_version("1.2") and hasattr(
          original_class, "_parameter_constraints"
      ):
          parameter_constraints = original_class._parameter_constraints
          if "n_jobs" not in parameter_constraints:
              parameter_constraints["n_jobs"] = [Integral, None]
  
      @wraps(original_init)
      def init_with_n_jobs(self, *args, n_jobs=None, **kwargs):
          original_init(self, *args, **kwargs)
          self.n_jobs = n_jobs
  
      # add "n_jobs" parameter to signature of wrapped init
      # if estimator doesn't originally support it
      sig = signature(original_init)
      original_params = list(sig.parameters.values())
      if "n_jobs" not in list(
          map(lambda param: param.name, original_params)
      ):
          original_params.append(
              Parameter("n_jobs", Parameter.KEYWORD_ONLY, default=None)
          )
          init_with_n_jobs.__signature__ = sig.replace(
              parameters=original_params
          )
          original_class.__init__ = init_with_n_jobs
  
      # add n_jobs to __doc__ string if needed
      if (
          hasattr(original_class, "__doc__")
          and isinstance(original_class.__doc__, str)
          and "n_jobs : int" not in original_class.__doc__
      ):
          parameters_doc_tail = "\n    Attributes"
          n_jobs_doc = """
      n_jobs : int, default=None
          The number of jobs to use in parallel for the computation.
          ``None`` means using all physical cores
          unless in a :obj:`joblib.parallel_backend` context.
          ``-1`` means using all logical cores.
          See :term:`Glossary <n_jobs>` for more details.
  """
          original_class.__doc__ = original_class.__doc__.replace(
              parameters_doc_tail, n_jobs_doc + parameters_doc_tail
          )
  
      return original_class
  
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 14 for calling a global function with example usage: control_n_jobs(original_class) and returns: original_class