code: def _parallel_fit(n_estimators, clfs, X, total_n_estimators, verbose):
      X = check_array(X)
      # Build estimators
      estimators = []
      rp_transformers = []
      for i in range(n_estimators):
          estimator = clone(clfs[i])
          if verbose > 1:
              print(
                  "Building estimator %d of %d for this parallel run "
                  "(total %d)..."
                  % (i + 1, n_estimators, total_n_estimators)
              )
  
          estimator.fit(X)
          estimators.append(estimator)
      return estimators
  
imports: import os
  import sys
  import time
  import warnings
  import numpy
  import scipy
  from scipy.stats import rankdata
  from sklearn.base import clone
  import joblib
  import numpy
  from scipy.stats import rankdata
  from joblib import effective_n_jobs
  from sklearn.utils import check_array
  from sklearn.utils.validation import check_is_fitted
  from sklearn.preprocessing import StandardScaler
  from joblib import effective_n_jobs
  from joblib import Parallel, delayed
  from copy import deepcopy
  import arff
  from pyod.utils.utility import score_to_label
  from joblib import load
  from pyod.models.iforest import IForest
  from pyod.models.abod import ABOD
  from pyod.models.feature_bagging import FeatureBagging
  from pyod.models.lof import LOF
  from pyod.models.cblof import CBLOF
  from pyod.models.ocsvm import OCSVM
  from pyod.models.pca import PCA
  from pyod.models.knn import KNN
  from pyod.models.hbos import HBOS
  from pyod.models.mcd import MCD
  from pyod.models.lscp import LSCP
label_desc: Use image node_id 5 for calling a global function with example usage: _parallel_fit(n_estimators, clfs, X, total_n_estimators, verbose) and returns: estimators