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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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