code: def _unfold_parallel(lists, n_jobs):
      """Internal function to unfold the results returned from the parallization
  
      Parameters
      ----------
      lists : list
          The results from the parallelization operations.
  
      n_jobs : optional (default=1)
          The number of jobs to run in parallel for both `fit` and
          `predict`. If -1, then the number of jobs is set to the
          number of cores.
  
      Returns
      -------
      result_list : list
          The list of unfolded result.
      """
      full_list = []
      for i in range(n_jobs):
          full_list.extend(lists[i])
      return full_list
  
file_path: SUOD/suod/utils/utility.py
imports: import warnings
  import numpy
  import sklearn
  from pyod.models.abod import ABOD
  from pyod.models.hbos import HBOS
  from pyod.models.iforest import IForest
  from pyod.models.knn import KNN
  from pyod.models.lof import LOF
  from pyod.models.lscp import LSCP
  from pyod.models.mcd import MCD
  from pyod.models.ocsvm import OCSVM
  from pyod.models.pca import PCA
  from scipy.special import erf
  from sklearn.preprocessing import MinMaxScaler
label_desc: Use image node_id 1 for calling a global function with example usage: _unfold_parallel(lists, n_jobs) and returns: full_list