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
docstr: Parameters
  ----------
  dropout_samples : int
      For every instance in the dataset, `dropout_samples`-many predictions will be used
      to obtain uncertainty estimates.
docstr_tok: ["Parameters","--","--","--","--","--","dropout_samples",":","int","For","every","instance","in","the","dataset",",","`","dropout_samples","`","-many","predictions","will","be","used","to","obtain","uncertainty","estimates","."]
code: def __init__(self, dropout_samples=10):
      """
      Parameters
      ----------
      dropout_samples : int
          For every instance in the dataset, `dropout_samples`-many predictions will be used
          to obtain uncertainty estimates.
      """
      self.dropout_samples = dropout_samples
  
imports: import numpy
  from small_text.query_strategies.strategies import QueryStrategy
label_desc: Use image node_id 1 to create a new BALD object from inherited base classes: QueryStrategy with example: obj = BALD(dropout_samples)