code: def test_estimate_competence_kuncheva_ex_batch(example_kuncheva):
      # considering a batch composed of 10 samples
      query = np.ones((10, 2))
      classifier = MagicMock()
      classifier.predict.return_value = [1]
      classifier.predict_proba.return_value = None
  
      a_posteriori_test = APosteriori(
          pool_classifiers=classifier, k=example_kuncheva["k"]
      )
  
      a_posteriori_test.n_classifiers_ = 1
      a_posteriori_test.DSEL_processed_ = example_kuncheva[
          "dsel_processed"
      ]
      a_posteriori_test.DSEL_target_ = example_kuncheva["y_dependent"]
      a_posteriori_test.dsel_scores_ = example_kuncheva["dsel_scores"]
      a_posteriori_test.n_classes_ = example_kuncheva["n_classes"]
  
      # repeating the same matrix in a new axis to simulate a batch input.
      neighbors = example_kuncheva["neighbors"]
      distances = example_kuncheva["distances"]
  
      predictions = [1]
      competences = a_posteriori_test.estimate_competence(
          neighbors, distances, predictions=np.array(predictions)
      )
      assert np.allclose(competences, 0.95, atol=0.01)
  
file_path: DESlib/deslib/tests/dcs/test_a_posteriori.py
imports: from unittest.mock import MagicMock
  import numpy
  import pytest
  from sklearn.linear_model import Perceptron
  from sklearn.utils.estimator_checks import check_estimator
  from deslib.dcs.a_posteriori import APosteriori
label_desc: Use image node_id 4 for calling a global function with example usage: test_estimate_competence_kuncheva_ex_batch(example_kuncheva) without return types