code: def smse(y_true, y_pred):
      
  
      N = y_true.shape[0]
      return ((y_true - y_pred)**2).sum() / (N * y_true.var())
docs: Standardised mean squared error.
  
      Parameters
      ----------
      y_true: ndarray
          vector of true targets
      y_pred: ndarray
          vector of predicted targets
  
      Returns
      -------
      float:
          SMSE of predictions vs truth (scalar)
  
      Example
      -------
      >>> y_true = np.random.randn(100)
      >>> smse(y_true, y_true)
      0.0
      >>> smse(y_true, np.random.randn(100)) >= 1.0
      True