code: def mse(y, yhat):
      """Returns the mean squared error between y and yhat.
  
      :param y: true function values
      :param yhat: predicted function values
      :returns:
          .. math:: \\frac{1}{n} \sum_{i=1}^n \\big[(\hat{y}-y)^2\\big]
  
      Lower is better.
  
      >>> mse([0, 0], [2, 3])
      6.5
  
      """
      return float(sum([(l - p) ** 2 for l, p in zip(y, yhat)])) / len(
          y
      )
  
imports: import math
  import operator
label_desc: Use image node_id 8 for calling a global function with example usage: mse(y, yhat) and returns: unknown