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 _prepare_indices_DS(
      self,
      base_predictions,
      base_probabilities,
      ind_disagreement,
      ind_ds_classifier,
  ):
      # Get the real indices_ of the samples that will be classified
      # using a DS algorithm.
      ind_ds_original_matrix = ind_disagreement[ind_ds_classifier]
      if base_probabilities is not None:
          selected_probas = base_probabilities[ind_ds_original_matrix]
      else:
          selected_probas = None
      selected_preds = base_predictions[ind_ds_original_matrix]
      return ind_ds_original_matrix, selected_preds, selected_probas
  
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 16 for calling the BaseDS obj's underlying member method code with example usage: obj._prepare_indices_DS(base_predictions, base_probabilities, ind_disagreement, ind_ds_classifier) and returns: ind_ds_original_matrix,  selected_preds,  selected_probas