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: Predict the class label for each sample in X.
  
  Parameters
  ----------
  X : array of shape (n_samples, n_features)
      The input data.
  
  Returns
  -------
  predicted_labels : array of shape (n_samples)
                     Predicted class label for each sample in X.
docstr_tok: ["Predict","the","class","label","for","each","sample","in","X.","Parameters","--","--","--","--","--","X",":","array","of","shape","(","n_samples",",","n_features",")","The","input","data",".","Returns","--","--","--","-","predicted_labels",":","array","of","shape","(","n_samples",")","Predicted","class","label","for","each","sample","in","X","."]
code: def predict(self, X):
      """Predict the class label for each sample in X.
  
      Parameters
      ----------
      X : array of shape (n_samples, n_features)
          The input data.
  
      Returns
      -------
      predicted_labels : array of shape (n_samples)
                         Predicted class label for each sample in X.
      """
      X = self._check_predict(X)
      preds = np.empty(X.shape[0], dtype=np.intp)
      need_proba = self.needs_proba or self.voting == "soft"
  
      base_preds, base_probas = self._preprocess_predictions(
          X, need_proba
      )
      # predict all agree
      ind_disagreement, ind_all_agree = self._split_agreement(
          base_preds
      )
      if ind_all_agree.size:
          preds[ind_all_agree] = base_preds[ind_all_agree, 0]
      # predict with IH
      if ind_disagreement.size:
          distances, ind_ds_classifier, neighbors = self._IH_prediction(
              X, ind_disagreement, preds, is_proba=False
          )
          # Predict with DS - Check if there are still samples to be labeled.
          if ind_ds_classifier.size:
              DFP_mask = self._get_DFP_mask(neighbors)
              inds, sel_preds, sel_probas = self._prepare_indices_DS(
                  base_preds,
                  base_probas,
                  ind_disagreement,
                  ind_ds_classifier,
              )
              preds_ds = self.classify_with_ds(
                  sel_preds, sel_probas, neighbors, distances, DFP_mask
              )
              preds[inds] = preds_ds
  
      return self.classes_.take(preds)
  
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 8 for calling the BaseDS obj's underlying member method code with example usage: obj.predict(X) and returns: self