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: Check whether there is a difference in opinion among the classifiers
  in the generated_pool.
  
  Parameters
  ----------
  predictions : array of shape (n_samples, n_classifiers)
                Predictions of the base classifiers for the test examples
  
  Returns
  -------
  array of shape (classes)
      containing True if all classifiers in the generated_pool agrees
      on the same label, otherwise False.
docstr_tok: ["Check","whether","there","is","a","difference","in","opinion","among","the","classifiers","in","the","generated_pool",".","Parameters","--","--","--","--","--","predictions",":","array","of","shape","(","n_samples",",","n_classifiers",")","Predictions","of","the","base","classifiers","for","the","test","examples","Returns","--","--","--","-","array","of","shape","(","classes",")","containing","True","if","all","classifiers","in","the","generated_pool","agrees","on","the","same","label",",","otherwise","False","."]
code: def _all_classifier_agree(predictions):
      """Check whether there is a difference in opinion among the classifiers
      in the generated_pool.
  
      Parameters
      ----------
      predictions : array of shape (n_samples, n_classifiers)
                    Predictions of the base classifiers for the test examples
  
      Returns
      -------
      array of shape (classes)
          containing True if all classifiers in the generated_pool agrees
          on the same label, otherwise False.
      """
      return np.all(
          predictions == predictions[:, 0].reshape(-1, 1), axis=1
      )
  
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 30 for calling the BaseDS obj's underlying member method code with example usage: obj._all_classifier_agree(predictions) and returns: np