code: def mse(y: list, yhat: np.array) -> float:
      """
      Computes the mean square error (MSE)
      Parameters
      ----------
      yhat : Series or array. Reconstructed (predicted) ratings or interaction values.
      y: original true ratings or interaction values.
      Returns:
      -------
          The mean square error (MSE)
      """
      mse = mean_squared_error(y, yhat)
      return mse
  
imports: import random
  from itertools import product
  from math import sqrt
  from typing import List
  import matplotlib.pyplot
  import numpy
  import pandas
  import scipy.sparse
  from sklearn.metrics import confusion_matrix, mean_squared_error
  from sklearn.metrics.pairwise import cosine_similarity
  import warnings
label_desc: Use image node_id 11 for calling a global function with example usage: mse(y, yhat) and returns: mse