code: def model_etc(opt):
      etc = ExtraTreesClassifier(
          n_estimators=opt["n_estimators"],
          criterion=opt["criterion"],
          max_features=opt["max_features"],
          min_samples_split=opt["min_samples_split"],
          min_samples_leaf=opt["min_samples_leaf"],
          bootstrap=opt["bootstrap"],
      )
      scores = cross_val_score(etc, X, y, cv=3)
  
      return scores.mean()
  
file_path: Hyperactive/examples/tested_and_supported_packages/multiprocessing_example.py
imports: import numpy
  from sklearn.model_selection import cross_val_score
  from sklearn.ensemble import GradientBoostingClassifier
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.ensemble import ExtraTreesClassifier
  from xgboost import XGBClassifier
  from sklearn.datasets import load_breast_cancer
  from hyperactive import Hyperactive
label_desc: Use image node_id 1 for calling a global function with example usage: model_etc(opt) and returns: scores