code: def test_label_encoder_base():
      from sklearn.linear_model import LogisticRegression
  
      X_dsel_ex1 = np.array([[-1, 1], [-0.75, 0.5], [-1.5, 1.5]])
      y_dsel_ex1 = np.array(["cat", "dog", "plane"])
  
      x = [[-2, -2], [2, 2]]
      y = ["cat", "dog"]
      pool_classifiers = [
          LogisticRegression().fit(x, y) for _ in range(2)
      ]
  
      query = np.atleast_2d([[1, 0], [-1, -1]])
      ds_test = BaseDS(pool_classifiers, k=2)
      ds_test.fit(X_dsel_ex1, y_dsel_ex1)
      predictions = ds_test.predict(query)
  
      assert np.array_equal(predictions, np.array(["dog", "cat"]))
  
imports: import unittest.mock
  from unittest.mock import Mock, MagicMock
  import numpy
  import pytest
  from sklearn.exceptions import NotFittedError
  from sklearn.neighbors import KNeighborsClassifier
  from deslib.base import BaseDS
  from deslib.util.dfp import frienemy_pruning_preprocessed
  from .conftest import create_base_classifier
label_desc: Use image node_id 29 for calling a global function with example usage: test_label_encoder_base() without return types