def _merge_predictions_and_labels(self, probs: np.ndarray, labels: np.ndarray) -> np.ndarray:
    """Update predictions based on defined labels when the label is not Unassigned.

    Args:
      probs: 2D matrix specifying the likelihood of each class

    Returns:
      Updated predictions based on keyword matches when available.
    """
    predictions = np.argmax(probs, axis=1)
    for label in range(1, len(Topics)):
      # Update label if (1) note was assigned based on the labeling heuristic, and (2)
      # p(Unassigned) is below the required uncertainty threshold.
      predictions[(labels == label) & (probs[:, 0] <= self._unassignedThreshold)] = label
    return predictions
Update predictions based on defined labels when the label is not Unassigned.

    Args:
      probs: 2D matrix specifying the likelihood of each class

    Returns:
      Updated predictions based on keyword matches when available.
    
sourcecode/scoring/topic_model.py