Hello everyone,

I’m trying to identify a sub-window in `test`

such that the sub-window yields the maximum sum of the averages of all other subwindows. (Averages defined with respect to each subwindow, sum representing along dimension 2 (the number of features), and max across dimension 1, the original sequence length).

Here’s my code:

```
import torch
test = torch.randn(3, 2307, 2)
window_len = 50
unfolded = test.unfold(len(test.shape) - 2, window_len, 1)
_, indices = torch.max(torch.sum(torch.mean(unfolded, dim = -1), dim = 2), dim = 1)
## Should match this result: Size: torch.Size([3, 50, 2])
torch.stack([
unfolded[0, :, :, :][indices[0], :, :].T,
unfolded[1, :, :, :][indices[1], :, :].T,
unfolded[2, :, :, :][indices[2], :, :].T
])
```

Is there a more efficient and straightforward way to get this solution using PyTorch’s functions?

Thanks,

Wilson