docs: def past_stop_threshold(stop_threshold, eval_metric):
    """Return a boolean representing whether a model should be stopped.
  
    Args:
      stop_threshold: float, the threshold above which a model should stop
        training.
      eval_metric: float, the current value of the relevant metric to check.
  
    Returns:
      True if training should stop, False otherwise.
  
    Raises:
      ValueError: if either stop_threshold or eval_metric is not a number
    """
    if stop_threshold is None:
      return False
  
    if not isinstance(stop_threshold, numbers.Number):
      raise ValueError("Threshold for checking stop conditions must be a number.")
    if not isinstance(eval_metric, numbers.Number):
      raise ValueError("Eval metric being checked against stop conditions "
                       "must be a number.")
  
    if eval_metric >= stop_threshold:
      tf.logging.info(
          "Stop threshold of {} was passed with metric value {}.".format(
              stop_threshold, eval_metric))
      return True
  
    return False