Creates a criterion that optimizes a multi-label one-versus-all loss based on max-entropy, between input \(x\) and target \(y\) of size \((N, C)\).

## Arguments

- weight
(Tensor, optional): 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.