code: def effective_n_jobs(n_jobs=-1):
      """Determine the number of jobs that can actually run in parallel
  
      n_jobs is the is the number of workers requested by the callers.
      Passing n_jobs=-1 means requesting all available workers for instance
      matching the number of CPU cores on the worker host(s).
  
      This method should return a guesstimate of the number of workers that can
      actually perform work concurrently with the currently enabled default
      backend. The primary use case is to make it possible for the caller to know
      in how many chunks to slice the work.
  
      In general working on larger data chunks is more efficient (less
      scheduling overhead and better use of CPU cache prefetching heuristics)
      as long as all the workers have enough work to do.
  
      Warning: this function is experimental and subject to change in a future
      version of joblib.
  
      .. versionadded:: 0.10
  
      """
      backend, _ = get_active_backend()
      return backend.effective_n_jobs(n_jobs=n_jobs)
  
imports: from __future__ import division
  import os
  import sys
  from math import sqrt
  import functools
  import time
  import threading
  import itertools
  from numbers import Integral
  from contextlib import contextmanager
  from ._multiprocessing_helpers import mp
  from .format_stack import format_outer_frames
  from .logger import Logger, short_format_time
  from .my_exceptions import TransportableException, _mk_exception
  from .disk import memstr_to_bytes
  from ._parallel_backends import FallbackToBackend, MultiprocessingBackend, ThreadingBackend, SequentialBackend
  from ._compat import _basestring
  from .func_inspect import getfullargspec
  from ._parallel_backends import AutoBatchingMixin
  from ._parallel_backends import ParallelBackendBase
label_desc: Use image node_id 7 for calling a global function with example usage: effective_n_jobs(n_jobs) and returns: backend