Creates a criterion that optimizes a multi-class classification hinge loss
(margin-based loss) between input x (a 2D mini-batch Tensor) and output y
(which is a 1D tensor of target class indices, `0 <= y <= x$size(2) - 1`

).

## Arguments

- input
tensor (N,*) where ** means, any number of additional dimensions

- target
tensor (N,*) , same shape as the input

- p
Has a default value of 1. 1 and 2 are the only supported values.

- margin
Has a default value of 1.

- weight
a manual rescaling weight given to each class. If given, it has to be a Tensor of size C. Otherwise, it is treated as if having all ones.

- reduction
(string, optional) – Specifies the reduction 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. Default: 'mean'