code: def setup_classifiers():
      rng = np.random.RandomState(654321)
  
      # Generate a classification dataset
      X, y = make_classification(
          n_classes=2,
          n_samples=1000,
          weights=[0.2, 0.8],
          random_state=rng,
      )
      # 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
      pool_classifiers = RandomForestClassifier(
          n_estimators=10, n_jobs=-1, random_state=rng, max_depth=10
      )
      pool_classifiers.fit(X_train, y_train)
      return pool_classifiers, X_dsel, y_dsel, X_test, y_test
  
imports: import numpy
  import pytest
  from sklearn.datasets import make_classification
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import StandardScaler
  from deslib.dcs.a_posteriori import APosteriori
  from deslib.dcs.a_priori import APriori
  from deslib.dcs.lca import LCA
  from deslib.dcs.mcb import MCB
  from deslib.dcs.mla import MLA
  from deslib.dcs.ola import OLA
  from deslib.dcs.rank import Rank
  from deslib.des import DESKL
  from deslib.des.des_knn import DESKNN
  from deslib.des.des_p import DESP
  from deslib.des.knop import KNOP
  from deslib.des.knora_e import KNORAE
  from deslib.des.knora_u import KNORAU
  from deslib.des.meta_des import METADES
label_desc: Use image node_id 1 for calling a global function with example usage: setup_classifiers() and returns: pool_classifiers,  X_dsel,  y_dsel,  X_test,  y_test