code: def _get_sklearn_version():  # pragma: no cover
      """Utility function to decide the version of sklearn.
      PyOD will result in different behaviors with different sklearn version
  
      Returns
      -------
      sk_learn version : int
  
      """
  
      sklearn_version = str(sklearn.__version__)
      # print(sklearn_version)
      # if int(sklearn_version.split(".")[1]) < 19 or int(
      #         sklearn_version.split(".")[1]) > 24:
      #     raise ValueError("Sklearn version error")
      # print(sklearn_version)
  
      return sklearn_version
  
imports: from __future__ import division
  from __future__ import print_function
  import numbers
  import numpy
  import sklearn
  from numpy import percentile
  from sklearn.metrics import precision_score
  from sklearn.preprocessing import StandardScaler
  from sklearn.utils import check_array
  from sklearn.utils import check_consistent_length
  from sklearn.utils import check_random_state
  from sklearn.utils import column_or_1d
  from sklearn.utils.random import sample_without_replacement
label_desc: Use image node_id 12 for calling a global function with example usage: _get_sklearn_version() and returns: sklearn_version