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: Select the most competent classifier for
  the classification of the query sample x.
  The most competent classifier (dcs) or an ensemble
  with the most competent classifiers (des) is returned
  
  Parameters
  ----------
  competences : array of shape (n_samples, n_classifiers)
                The estimated competence level of each base classifier
                for test example
  
  Returns
  -------
  selected_classifiers : array containing the selected base classifiers
                         for each test sample
docstr_tok: ["Select","the","most","competent","classifier","for","the","classification","of","the","query","sample","x",".","The","most","competent","classifier","(","dcs",")","or","an","ensemble","with","the","most","competent","classifiers","(","des",")","is","returned","Parameters","--","--","--","--","--","competences",":","array","of","shape","(","n_samples",",","n_classifiers",")","The","estimated","competence","level","of","each","base","classifier","for","test","example","Returns","--","--","--","-","selected_classifiers",":","array","containing","the","selected","base","classifiers","for","each","test","sample"]
code: def select(self, competences):
      """Select the most competent classifier for
      the classification of the query sample x.
      The most competent classifier (dcs) or an ensemble
      with the most competent classifiers (des) is returned
  
      Parameters
      ----------
      competences : array of shape (n_samples, n_classifiers)
                    The estimated competence level of each base classifier
                    for test example
  
      Returns
      -------
      selected_classifiers : array containing the selected base classifiers
                             for each test sample
  
      """
      pass
  
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 5 for calling the BaseDS obj's underlying member method code with example usage: obj.select(competences) without return types