code: def plot_classifier_decision(ax, clf, X, mode="line", **params):
      xx, yy = make_grid(X[:, 0], X[:, 1])
  
      Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
      Z = Z.reshape(xx.shape)
      if mode == "line":
          ax.contour(xx, yy, Z, **params)
      else:
          ax.contourf(xx, yy, Z, **params)
      ax.set_xlim((np.min(X[:, 0]), np.max(X[:, 0])))
      ax.set_ylim((np.min(X[:, 1]), np.max(X[:, 0])))
  
file_path: DESlib/examples/plot_example_P2.py
imports: import matplotlib.pyplot
  import numpy
  from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier
  from sklearn.model_selection import train_test_split
  from sklearn.neural_network import MLPClassifier
  from sklearn.svm import SVC
  from sklearn.tree import DecisionTreeClassifier
  from deslib.dcs.ola import OLA
  from deslib.dcs.rank import Rank
  from deslib.des.des_p import DESP
  from deslib.des.knora_e import KNORAE
  from deslib.static import StackedClassifier
  from deslib.util.datasets import make_P2
label_desc: Use image node_id 2 for calling a global function with example usage: plot_classifier_decision(ax, clf, X, mode) without return types