code: def _partition_estimators(n_estimators, n_jobs):
      """Private function used to partition estimators between jobs."""
      # 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=np.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: catboost/contrib/python/scikit-learn/py2/sklearn/ensemble/base.py
imports: import numpy
  import numbers
  from ..base import clone
  from ..base import BaseEstimator
  from ..base import MetaEstimatorMixin
  from ..utils import _get_n_jobs, check_random_state
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