class_docstr: A stopping criterion which measures the agreement between sets of predictions [BV09]_.
  
  .. versionchanged:: 1.3.3
     The previous implementation, which was flawed, has been corrected.
code: def stop(
      self,
      active_learner=None,
      predictions=None,
      proba=None,
      indices_stopping=None,
  ):
      check_window_based_predictions(predictions, self.last_predictions)
  
      if self.last_predictions is None:
          self.last_predictions = predictions
          return False
      else:
          labels = np.arange(self.num_classes)
          cohens_kappa = _adapted_cohen_kappa_score(
              predictions, self.last_predictions, labels=labels
          )
  
          self.kappa_history.append(cohens_kappa)
          self.last_predictions = predictions
  
          if len(self.kappa_history) < self.window_size:
              return False
  
          self.kappa_history = self.kappa_history[-self.window_size :]
  
          if np.mean(self.kappa_history) >= self.kappa:
              return True
          else:
              return False
  
imports: import numpy
  from sklearn.metrics import cohen_kappa_score
  from small_text.stopping_criteria.base import StoppingCriterion, check_window_based_predictions
label_desc: Use image node_id 2 for calling the KappaAverage obj's underlying member method code with example usage: obj.stop(active_learner, predictions, proba, indices_stopping) and returns: False, False, True, False