code: def MSE(y_true, y_pred, axis=None):
      """Returns the mean squared error between the two predictions
  
      Parameters
      ----------
      y_true : array of shape (n_samples, )
          Ground truth (correct) target values.
      y_pred : array of shape (n_samples, )
          Estimated target values.
  
      Returns
      -------
      float
      """
      return T.mean((y_true - y_pred) ** 2, axis=axis)
  
file_path: tensorly/tensorly/metrics/regression.py
imports: from ..None import backend
label_desc: Use image node_id 1 for calling a global function with example usage: MSE(y_true, y_pred, axis) and returns: T