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: Predicts the label of the corresponding query sample.
  Returns the predicted label.
  
  Parameters
  ----------
  predictions : array of shape (n_samples, n_classifiers)
      Predictions of the base classifiers for all test examples
  
  probabilities : array of shape (n_samples, n_classifiers, n_classes)
      Probabilities estimates of each base classifier for all test
      examples (For methods that always require probabilities from the
      base classifiers)
  
  neighbors : array of shape (n_samples, n_neighbors)
      Indices of the k nearest neighbors.
  distances : array of shape (n_samples, n_neighbors)
      Distances from the k nearest neighbors to the query
  
  DFP_mask : array of shape (n_samples, n_classifiers)
      Mask containing 1 for the selected base classifier and 0 otherwise.
  
  Returns
  -------
  predicted_label : array of shape (n_samples)
      The predicted label for each query
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code: def classify_with_ds(
      self,
      predictions,
      probabilities=None,
      neighbors=None,
      distances=None,
      DFP_mask=None,
  ):
      """Predicts the label of the corresponding query sample.
      Returns the predicted label.
  
      Parameters
      ----------
      predictions : array of shape (n_samples, n_classifiers)
          Predictions of the base classifiers for all test examples
  
      probabilities : array of shape (n_samples, n_classifiers, n_classes)
          Probabilities estimates of each base classifier for all test
          examples (For methods that always require probabilities from the
          base classifiers)
  
      neighbors : array of shape (n_samples, n_neighbors)
          Indices of the k nearest neighbors.
      distances : array of shape (n_samples, n_neighbors)
          Distances from the k nearest neighbors to the query
  
      DFP_mask : array of shape (n_samples, n_classifiers)
          Mask containing 1 for the selected base classifier and 0 otherwise.
  
      Returns
      -------
      predicted_label : array of shape (n_samples)
          The predicted label for each query
      """
      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 6 for calling the BaseDS obj's underlying member method code with example usage: obj.classify_with_ds(predictions, probabilities, neighbors, distances, DFP_mask) without return types