code: def test_lca(knne, expected):
      (
          pool_classifiers,
          X_dsel,
          y_dsel,
          X_test,
          y_test,
      ) = setup_classifiers()
  
      lca = LCA(pool_classifiers, DFP=True, knne=knne)
      lca.fit(X_dsel, y_dsel)
      assert np.isclose(lca.score(X_test, y_test), expected)
  
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 6 for calling a global function with example usage: test_lca(knne, expected) without return types