Hello to everyone! I’m working with PyTorch 2.1. Given that `s1`

and `s2`

are two tensors of size `(self.nrep, self.npt)`

with indices ranging from 0 to `self.size-1`

, `net`

is a tensor of size `(self.size, self.size, K, 4)`

, and `conf`

is a tensor of size `(self.nrep, self.npt, self.size)`

, i would like to optmize this code avoiding the for loops:

```
net_conf = torch.zeros(size=(self.nrep, self.npt, K, 4), dtype=torch.complex128, device=f'cuda:{self.dev}')
for r in range(self.nrep):
for k in range(self.npt):
net_conf[r,k] = conf[r,k,net[s1[r,k],s2[r,k]]]
```