code: def test_estimate_competence_batch(example_estimate_competence):
      (
          _,
          y,
          neighbors,
          _,
          dsel_processed,
          _,
      ) = example_estimate_competence
  
      expected = np.array(
          [
              [0.750, 0.666, 0.750],
              [0.800, 1.000, 0.800],
              [1.000, 0.600, 0.500],
          ]
      )
  
      mla_test = MLA()
      mla_test.n_classifiers_ = 3
      mla_test.DSEL_processed_ = dsel_processed
      distances = np.ones((3, 7))
  
      mla_test.DSEL_target_ = y
      mla_test.n_classes_ = 2
      predictions = np.array([[0, 1, 0]])
  
      competences = mla_test.estimate_competence(
          competence_region=neighbors,
          distances=distances,
          predictions=predictions,
      )
  
      assert np.allclose(competences, expected, atol=0.01)
  
file_path: DESlib/deslib/tests/dcs/test_mla.py
imports: import numpy
  import pytest
  from sklearn.linear_model import Perceptron
  from sklearn.utils.estimator_checks import check_estimator
  from deslib.dcs.mla import MLA
label_desc: Use image node_id 3 for calling a global function with example usage: test_estimate_competence_batch(example_estimate_competence) without return types