code: def rmse(y: list, yhat: np.array) -> float:
      """
      Computes the root mean square error (RMSE)
      Parameters
      ----------
      yhat : Series or array. Reconstructed (predicted) ratings or values
      y: original true ratings or values.
      Returns:
      -------
          The root mean square error (RMSE)
      """
      rmse = sqrt(mean_squared_error(y, yhat))
      return rmse
  
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 12 for calling a global function with example usage: rmse(y, yhat) and returns: rmse