code: def _partition_estimators(n_estimators, n_jobs):
      """Private function used to partition estimators between jobs.
      See sklearn/ensemble/base.py for more information.
      """
      # Compute the number of jobs
      n_jobs = min(_get_n_jobs(n_jobs), n_estimators)
  
      # Partition estimators between jobs
      n_estimators_per_job = (n_estimators // n_jobs) * np.ones(
          n_jobs, dtype=int
      )
      n_estimators_per_job[: n_estimators % n_jobs] += 1
      starts = np.cumsum(n_estimators_per_job)
  
      return (
          n_jobs,
          n_estimators_per_job.tolist(),
          [0] + starts.tolist(),
      )
  
file_path: pyod/pyod/models/sklearn_base.py
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  import six
  from joblib.parallel import cpu_count
label_desc: Use image node_id 2 for calling a global function with example usage: _partition_estimators(n_estimators, n_jobs) and returns: n_jobs,  n_estimators_per_job,  unknown