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.
docstr: Compute the prediction of each base classifier for
  all samples in DSEL. Used to speed-up the test phase, by
  not requiring to re-classify training samples during test.
  
  Returns
  -------
  DSEL_processed_ : array of shape (n_samples, n_classifiers).
                   Each element indicates whether the base classifier
                   predicted the correct label for the corresponding
                   sample (True), otherwise (False).
  
  BKS_DSEL_ : array of shape (n_samples, n_classifiers)
             Predicted labels of each base classifier for all samples
             in DSEL.
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code: def _preprocess_dsel(self):
      """Compute the prediction of each base classifier for
      all samples in DSEL. Used to speed-up the test phase, by
      not requiring to re-classify training samples during test.
  
      Returns
      -------
      DSEL_processed_ : array of shape (n_samples, n_classifiers).
                       Each element indicates whether the base classifier
                       predicted the correct label for the corresponding
                       sample (True), otherwise (False).
  
      BKS_DSEL_ : array of shape (n_samples, n_classifiers)
                 Predicted labels of each base classifier for all samples
                 in DSEL.
      """
      BKS_dsel = self._predict_base(self.DSEL_data_)
      processed_dsel = BKS_dsel == self.DSEL_target_[:, np.newaxis]
      return processed_dsel, BKS_dsel
  
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 27 for calling the BaseDS obj's underlying member method code with example usage: obj._preprocess_dsel() and returns: processed_dsel,  BKS_dsel