class_docstr: A stopping criterion which stops as soon as the average overall uncertainty falls
  below a given threshold [ZWH08]_.
  
  As a measure of uncertainty, normalized prediction entropy is used. In order to reproduce
  the original implementation pass the unlabeled set `indices_stopping` to `stop()` method.
  
  .. versionadded:: 1.1.0
code: def stop(
      self,
      active_learner=None,
      predictions=None,
      proba=None,
      indices_stopping=None,
  ):
      if indices_stopping is None:
          raise ValueError("indices_stopping must not be None")
  
      prediction_entropy = np.apply_along_axis(
          lambda x: entropy(x), 1, proba[indices_stopping]
      )
      normalized_prediction_entropy = prediction_entropy / np.log(
          self.num_classes
      )
      normalized_prediction_entropy = np.mean(
          normalized_prediction_entropy
      )
  
      if normalized_prediction_entropy < self.threshold:
          return True
  
      return False
  
imports: import numpy
  from scipy.stats import entropy
  from small_text.stopping_criteria.base import StoppingCriterion
label_desc: Use image node_id 2 for calling the OverallUncertainty obj's underlying member method code with example usage: obj.stop(active_learner, predictions, proba, indices_stopping) and returns: False, True