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_predict(self, X):
      check_is_fitted(
          self, ["DSEL_processed_", "DSEL_data_", "DSEL_target_"]
      )
      X = check_array(X)
      if self.n_features_ != X.shape[1]:
          raise ValueError(
              "Number of features of the model must "
              "match the input. Model n_features is {0} and "
              "input n_features is {1}."
              "".format(self.n_features_, X.shape[1])
          )
      return X
  
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 9 for calling the BaseDS obj's underlying member method code with example usage: obj._check_predict(X) and returns: X