code: def test_estimate_competence_diff_target(
      index, example_estimate_competence, create_pool_classifiers
  ):
      (
          _,
          y,
          neighbors,
          distances,
          dsel_processed,
          _,
      ) = example_estimate_competence
  
      lca_test = LCA(create_pool_classifiers)
      lca_test.DSEL_processed_ = dsel_processed
      lca_test.DSEL_target_ = np.ones(15, dtype=int) * 3
  
      neighbors = neighbors[index, :].reshape(1, -1)
      distances = distances[index, :].reshape(1, -1)
  
      query = np.atleast_2d([1, 1])
      expected = [0.0, 0.0, 0.0]
  
      predictions = np.array([[0, 1, 0]])
      competences = lca_test.estimate_competence(
          neighbors,
          distances=distances,
          predictions=np.array(predictions),
      )
  
      assert np.isclose(competences, expected).all()
  
file_path: DESlib/deslib/tests/dcs/test_lca.py
imports: import numpy
  import pytest
  from sklearn.linear_model import Perceptron
  from sklearn.utils.estimator_checks import check_estimator
  from deslib.dcs.lca import LCA
label_desc: Use image node_id 3 for calling a global function with example usage: test_estimate_competence_diff_target(index, example_estimate_competence, create_pool_classifiers) without return types