class_docstr: Base class for static ensembles.
  
  All static ensemble techniques should inherit from this class.
  
  Warning: This class should not be instantiated directly, use derived
  classes instead.
  
  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.
  
  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`.
  
  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
  ----------
  Kuncheva, Ludmila I. Combining pattern classifiers: methods and algorithms.
  John Wiley & Sons, 2004.
  
  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.
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code: def _encode_base_labels(self, y):
      if self.base_already_encoded_:
          return y
      else:
          return self.enc_.transform(y)
  
file_path: DESlib/deslib/static/base.py
imports: from abc import abstractmethod, ABCMeta
  import numpy
  from sklearn.base import BaseEstimator, ClassifierMixin
  from sklearn.ensemble import BaseEnsemble, BaggingClassifier
  from sklearn.preprocessing import LabelEncoder
  from sklearn.utils.validation import check_random_state
label_desc: Use image node_id 5 for calling the BaseStaticEnsemble obj's underlying member method code with example usage: obj._encode_base_labels(y) and returns: y, self