to apply to the output: "'none'" | "'mean'" | "'sum'".
        "'none'": no reduction will be applied, "'mean'": the sum of
        the output will be divided by the number of elements in the
        output, "'sum'": the output will be summed. Note:
        "size_average" and "reduce" are in the process of being
        deprecated, and in the meantime, specifying either of 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.

Return type:
      Tensor
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.