code: def load_dataset(rng):
      # Generate a classification dataset
      weights = [0.1, 0.2, 0.7]
      X, y = make_classification(
          n_classes=3,
          n_samples=2000,
          n_informative=3,
          random_state=rng,
          weights=weights,
      )
  
      # split the data into training and test data
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, test_size=0.33, random_state=rng
      )
      # Scale the variables to have 0 mean and unit variance
      scalar = StandardScaler()
      X_train = scalar.fit_transform(X_train)
      X_test = scalar.transform(X_test)
      # Split the data into training and DSEL for DS techniques
      X_train, X_dsel, y_train, y_dsel = train_test_split(
          X_train, y_train, test_size=0.5, random_state=rng
      )
      # Considering a pool composed of 10 base classifiers
      # Calibrating Perceptrons to estimate probabilities
      return X_dsel, X_test, X_train, y_dsel, y_test, y_train
  
file_path: DESlib/deslib/tests/test_des_integration_multiclass.py
imports: import numpy
  from sklearn.datasets import make_classification
  from sklearn.ensemble import AdaBoostClassifier
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import StandardScaler
  from deslib.dcs.a_priori import APriori
  from deslib.dcs.mcb import MCB
  from deslib.des.des_mi import DESMI
  from deslib.des.des_p import DESP
  from deslib.des.knop import KNOP
  from deslib.des.meta_des import METADES
label_desc: Use image node_id 2 for calling a global function with example usage: load_dataset(rng) and returns: X_dsel,  X_test,  X_train,  y_dsel,  y_test,  y_train