class_docstr: k-Nearest Oracles Union (KNORA-U).
  
  This method selects all classifiers that correctly classified at least
  one sample belonging to the region of competence of the query sample. Each
  selected classifier has a number of votes equals to the number of samples
  in the region of competence that it predicts the correct label. The votes
  obtained by all base classifiers are aggregated to obtain the final
  ensemble decision.
  
  Parameters
  ----------
   pool_classifiers : list of classifiers (Default = None)
      The generated_pool of classifiers trained for the corresponding
      classification problem. Each base classifiers should support the method
      "predict". If None, then the pool of classifiers is a bagging
      classifier.
  
  k : int (Default = 7)
      Number of neighbors used to estimate the competence of the base
      classifiers.
  
  DFP : Boolean (Default = False)
      Determines if the dynamic frienemy pruning is applied.
  
  with_IH : Boolean (Default = False)
      Whether the hardness level of the region of competence is used to
      decide between using the DS algorithm or the KNN for classification of
      a given query sample.
  
  safe_k : int (default = None)
      The size of the indecision region.
  
  IH_rate : float (default = 0.3)
      Hardness threshold. If the hardness level of the competence region is
      lower than the IH_rate the KNN classifier is used. Otherwise, the DS
      algorithm is used for classification.
  
  random_state : int, RandomState instance or None, optional (default=None)
      If int, random_state is the seed used by the random number generator;
      If RandomState instance, random_state is the random number generator;
      If None, the random number generator is the RandomState instance used
      by `np.random`.
  
  knn_classifier : {'knn', 'faiss', None} (Default = 'knn')
       The algorithm used to estimate the region of competence:
  
       - 'knn' will use :class:`KNeighborsClassifier` from sklearn
        :class:`KNNE` available on `deslib.utils.knne`
  
       - 'faiss' will use Facebook's Faiss similarity search through the
         class :class:`FaissKNNClassifier`
  
       - None, will use sklearn :class:`KNeighborsClassifier`.
  
  knn_metric : {'minkowski', 'cosine', 'mahalanobis'} (Default = 'minkowski')
      The metric used by the k-NN classifier to estimate distances.
  
      - 'minkowski' will use minkowski distance.
  
      - 'cosine' will use the cosine distance.
  
      - 'mahalanobis' will use the mahalonibis distance.
  
  knne : bool (Default=False)
      Whether to use K-Nearest Neighbor Equality (KNNE) for the region
      of competence estimation.
  
  DSEL_perc : float (Default = 0.5)
      Percentage of the input data used to fit DSEL.
      Note: This parameter is only used if the pool of classifier is None or
      unfitted.
  
  voting : {'hard', 'soft'}, default='hard'
          If 'hard', uses predicted class labels for majority rule voting.
          Else if 'soft', predicts the class label based on the argmax of
          the sums of the predicted probabilities, which is recommended for
          an ensemble of well-calibrated classifiers.
  
  n_jobs : int, default=-1
      The number of parallel jobs to run. None means 1 unless in
      a joblib.parallel_backend context. -1 means using all processors.
      Doesn’t affect fit method.
  
  References
  ----------
  Ko, Albert HR, Robert Sabourin, and Alceu Souza Britto Jr.
  "From dynamic classifier selection to dynamic ensemble
  selection." Pattern Recognition 41.5 (2008): 1718-1731.
  
  Britto, Alceu S., Robert Sabourin, and Luiz ES Oliveira.
  "Dynamic selection of classifiers—a comprehensive review."
  Pattern Recognition 47.11 (2014): 3665-3680.
  
  R. M. O. Cruz, R. Sabourin, and G. D. Cavalcanti, “Dynamic classifier
  selection: Recent advances and perspectives,”
  Information Fusion, vol. 41, pp. 195 – 216, 2018.
docstr: Select the base classifiers for the classification of the query
  sample.
  
  Each base classifier can be selected more than once. The number of
  times a base classifier is selected (votes) is equals to the number
  of samples it correctly classified in the region of competence.
  
  Parameters
  ----------
  competences : array of shape (n_samples, n_classifiers)
      Competence level estimated for each base classifier and test
      example.
  
  Returns
  -------
  selected_classifiers : array of shape (n_samples, n_classifiers)
      Boolean matrix containing True if the base classifier is selected,
      False otherwise.
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code: def select(self, competences):
      """Select the base classifiers for the classification of the query
      sample.
  
      Each base classifier can be selected more than once. The number of
      times a base classifier is selected (votes) is equals to the number
      of samples it correctly classified in the region of competence.
  
      Parameters
      ----------
      competences : array of shape (n_samples, n_classifiers)
          Competence level estimated for each base classifier and test
          example.
  
      Returns
      -------
      selected_classifiers : array of shape (n_samples, n_classifiers)
          Boolean matrix containing True if the base classifier is selected,
          False otherwise.
      """
      if competences.ndim < 2:
          competences = competences.reshape(1, -1)
  
      # Select classifier if it correctly classified at least one sample
      selected_classifiers = competences > 0
  
      # For the rows that are all False (i.e., no base classifier was
      # selected, select all classifiers (set all True)
      selected_classifiers[
          ~np.any(selected_classifiers, axis=1), :
      ] = True
  
      return selected_classifiers
  
imports: import numpy
  from deslib.des.base import BaseDES
label_desc: Use image node_id 3 for calling the KNORAU obj's underlying member method code with example usage: obj.select(competences) and returns: selected_classifiers