y| < beta portion replaced with a quadratic function such that
     its slope is 1 at |x - y| = beta. The quadratic segment smooths
     the L1 loss near |x - y| = 0.
Note:
 Smooth L1 loss is closely related to "HuberLoss", being
 equivalent to huber(x, y) / beta (note that Smooth L1's beta
 hyper-parameter is also known as delta for Huber). This leads to
 the following differences:

 * As beta -> 0, Smooth L1 loss converges to "L1Loss", while
   "HuberLoss" converges to a constant 0 loss. When beta is 0,
   Smooth L1 loss is equivalent to L1 loss.

 * As beta -> +\infty, Smooth L1 loss converges to a constant 0
   loss, while "HuberLoss" converges to "MSELoss".

 * For Smooth L1 loss, as beta varies, the L1 segment of the loss
   has a constant slope of 1. For "HuberLoss", the slope of the L1
   segment is beta.

Parameters:
      * size_average (bool, optional) -- Deprecated (see