class_docstr: Base class for a Dynamic Ensemble Selection (DES).
  
  All dynamic ensemble selection techniques should inherit from this class.
  
  Warning: This class should not be instantiated directly, use
  derived classes instead.
code: def _mask_proba(probabilities, selected_classifiers):
      # Broadcast the selected classifiers mask
      # to cover the last axis (n_classes):
      selected_classifiers = np.expand_dims(
          selected_classifiers, axis=2
      )
      selected_classifiers = np.broadcast_to(
          selected_classifiers, probabilities.shape
      )
      masked_proba = np.ma.MaskedArray(
          probabilities, ~selected_classifiers
      )
      return masked_proba
  
file_path: DESlib/deslib/des/base.py
imports: from abc import ABCMeta
  import numpy
  from deslib.base import BaseDS
  from deslib.util.aggregation import aggregate_proba_ensemble_weighted, sum_votes_per_class, get_weighted_votes
label_desc: Use image node_id 7 for calling the BaseDES obj's underlying member method code with example usage: obj._mask_proba(probabilities, selected_classifiers) and returns: masked_proba