code: def _get_n_jobs(n_jobs):
      """Get number of jobs for the computation.
      See sklearn/utils/__init__.py for more information.
  
      This function reimplements the logic of joblib to determine the actual
      number of jobs depending on the cpu count. If -1 all CPUs are used.
      If 1 is given, no parallel computing code is used at all, which is useful
      for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used.
      Thus for n_jobs = -2, all CPUs but one are used.
      Parameters
      ----------
      n_jobs : int
          Number of jobs stated in joblib convention.
      Returns
      -------
      n_jobs : int
          The actual number of jobs as positive integer.
      """
      if n_jobs < 0:
          return max(cpu_count() + 1 + n_jobs, 1)
      elif n_jobs == 0:
          raise ValueError("Parameter n_jobs == 0 has no meaning.")
      else:
          return n_jobs
  
imports: import numpy
  from joblib.parallel import cpu_count
label_desc: Use image node_id 1 for calling a global function with example usage: _get_n_jobs(n_jobs) and returns: max, n_jobs