code: def _recomputeRecordFromKNN(self, record):
      
      inputs = {
        "categoryIn": [None],
        "bottomUpIn": self._getStateAnomalyVector(record),
      }
  
      outputs = {"categoriesOut": numpy.zeros((1,)),
                 "bestPrototypeIndices":numpy.zeros((1,)),
                 "categoryProbabilitiesOut":numpy.zeros((1,))}
  
      # Run inference only to capture state before learning
      classifier = self.htm_prediction_model._getAnomalyClassifier()
      knn = classifier.getSelf()._knn
  
      # Only use points before record to classify and after the wait period.
      classifier_indexes = \
        numpy.array(classifier.getSelf().getParameter('categoryRecencyList'))
      valid_idx = numpy.where(
          (classifier_indexes >= self._autoDetectWaitRecords) &
          (classifier_indexes < record.ROWID)
        )[0].tolist()
  
      if len(valid_idx) == 0:
        return None
  
      classifier.setParameter('inferenceMode', True)
      classifier.setParameter('learningMode', False)
      classifier.getSelf().compute(inputs, outputs)
      classifier.setParameter('learningMode', True)
  
      classifier_distances = classifier.getSelf().getLatestDistances()
      valid_distances = classifier_distances[valid_idx]
      if valid_distances.min() <= self._classificationMaxDist:
        classifier_indexes_prev = classifier_indexes[valid_idx]
        rowID = classifier_indexes_prev[valid_distances.argmin()]
        indexID = numpy.where(classifier_indexes == rowID)[0][0]
        category = classifier.getSelf().getCategoryList()[indexID]
        return category
      return None
docs: return the classified labeling of record