code: def mean_squared_error(true, pred):
    
    result = tf.reduce_sum(
        tf.squared_difference(true, pred)) / tf.to_float(tf.size(pred))
    return result
docs: L2 distance between tensors true and pred.
  
    Args:
      true: the ground truth image.
      pred: the predicted image.
    Returns:
      mean squared error between ground truth and predicted image.