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 _predict_easy_samples(
      self,
      X_DS,
      distances,
      ind_disagreement,
      ind_easy,
      neighbors,
      predictions,
      is_proba,
  ):
      if ind_easy.size:
          # Accessing which samples in the original array.
          ind_knn_original_matrix = ind_disagreement[ind_easy]
  
          if is_proba:
              predictions[
                  ind_knn_original_matrix
              ] = self.roc_algorithm_.predict_proba(X_DS[ind_easy])
          else:
              y_neighbors = self.DSEL_target_[
                  neighbors[ind_easy, : self.safe_k]
              ]
              predictions_knn, _ = mode(y_neighbors, axis=1)
              predictions[
                  ind_knn_original_matrix
              ] = predictions_knn.reshape(
                  -1,
              )
          neighbors = np.delete(neighbors, ind_easy, axis=0)
          distances = np.delete(distances, ind_easy, axis=0)
      return distances, neighbors
  
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 15 for calling the BaseDS obj's underlying member method code with example usage: obj._predict_easy_samples(X_DS, distances, ind_disagreement, ind_easy, neighbors, predictions, is_proba) and returns: distances,  neighbors