code: def model(opt):
      dtc = DecisionTreeClassifier(
          min_samples_split=opt["min_samples_split"],
          min_samples_leaf=opt["min_samples_leaf"],
      )
      mlp = MLPClassifier(hidden_layer_sizes=opt["hidden_layer_sizes"])
      svc = SVC(
          C=opt["C"],
          degree=opt["degree"],
          gamma="auto",
          probability=True,
      )
  
      eclf = EnsembleVoteClassifier(
          clfs=[dtc, mlp, svc],
          weights=opt["weights"],
          voting="soft",
      )
  
      scores = cross_val_score(eclf, X, y, cv=3)
  
      return scores.mean()
  
file_path: Hyperactive/examples/tested_and_supported_packages/mlxtend_example.py
imports: from sklearn.datasets import load_breast_cancer
  from sklearn.model_selection import cross_val_score
  from mlxtend.classifier import EnsembleVoteClassifier
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.neural_network import MLPClassifier
  from sklearn.svm import SVC
  from hyperactive import Hyperactive
label_desc: Use image node_id 1 for calling a global function with example usage: model(opt) and returns: scores