class_docstr: Base class for a dynamic classifier selection (dcs) and
     dynamic ensemble selection (des) methods.
  
  All DCS and DES techniques should inherit from this class.
  
  Warning: This class should not be used directly.
  Use derived classes instead.
code: def _check_label_encoder(self):
      # Check if base classifiers are not using LabelEncoder (the case for
      # scikit-learn's ensembles):
      if isinstance(self.pool_classifiers_, BaseEnsemble):
          if np.array_equal(
              self.pool_classifiers_.classes_,
              self.pool_classifiers_[0].classes_,
          ):
              self.base_already_encoded_ = False
          else:
              self.base_already_encoded_ = True
      else:
          self.base_already_encoded_ = False
  
imports: import functools
  import math
  import warnings
  from abc import abstractmethod, ABCMeta
  import numpy
  from scipy.stats import mode
  from sklearn.base import BaseEstimator, ClassifierMixin
  from sklearn.ensemble import BaseEnsemble, BaggingClassifier
  from sklearn.model_selection import train_test_split
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.preprocessing import LabelEncoder, normalize
  from sklearn.utils.validation import check_X_y, check_is_fitted, check_array, check_random_state
  from deslib.util import KNNE
  from deslib.util import faiss_knn_wrapper
  from deslib.util.dfp import frienemy_pruning_preprocessed
  from deslib.util.instance_hardness import hardness_region_competence
label_desc: Use image node_id 19 for calling the BaseDS obj's underlying member method code with example usage: obj._check_label_encoder() without return types