class_docstr: Selects instances according to the Bayesian Active Learning by Disagreement (BALD) [HHG+11]_
  strategy.
  
  Requires that the `predict_proba()` method of the given classifier supports
  dropout sampling [GZ16]_.
  
  .. versionadded:: 1.2.0
code: def query(
      self, clf, dataset, indices_unlabeled, indices_labeled, y, n=10
  ):
      self._validate_query_input(indices_unlabeled, n)
  
      proba_dopout_sampled = clf.predict_proba(
          dataset, dropout_sampling=self.dropout_samples
      )
      bald_scores = _bald(proba_dopout_sampled)
  
      if len(indices_unlabeled) == n:
          return np.array(indices_unlabeled)
  
      indices_partitioned = np.argpartition(
          -bald_scores[indices_unlabeled], n
      )[:n]
      return np.array(
          [indices_unlabeled[i] for i in indices_partitioned]
      )
  
imports: import numpy
  from small_text.query_strategies.strategies import QueryStrategy
label_desc: Use image node_id 2 for calling the BALD obj's underlying member method code with example usage: obj.query(clf, dataset, indices_unlabeled, indices_labeled, y, n) and returns: np, np