those two args will override "reduction". Default: "'mean'"
  * **label_smoothing** (*float**, **optional*) -- A float in
    [0.0, 1.0]. Specifies the amount of smoothing when computing
    the loss, where 0.0 means no smoothing. The targets become a
    mixture of the original ground truth and a uniform
    distribution as described in Rethinking the Inception
    Architecture for Computer Vision. Default: 0.0.

Shape:
      * Input: Shape (C), (N, C) or (N, C, d_1, d_2, ..., d_K) with K
        \geq 1 in the case of K-dimensional loss.
  * Target: If containing class indices, shape (), (N) or (N, d_1,
    d_2, ..., d_K) with K \geq 1 in the case of K-dimensional loss
    where each value should be between [0, C). If containing class
    probabilities, same shape as the input and each value should
    be between [0, 1].

  * Output: If reduction is 'none', shape (), (N) or (N, d_1, d_2,
