Applies the Softmin function to an n-dimensional input Tensor
rescaling them so that the elements of the n-dimensional output Tensor
lie in the range `[0, 1]`

and sum to 1.
Softmin is defined as:

## Arguments

- dim
(int): A dimension along which Softmin will be computed (so every slice along dim will sum to 1).

## Shape

Input: \((*)\) where

`*`

means, any number of additional dimensionsOutput: \((*)\), same shape as the input

## Examples

```
if (torch_is_installed()) {
m <- nn_softmin(dim = 1)
input <- torch_randn(2, 2)
output <- m(input)
}
```