Hi,

The coordinate convention for the index in the grid sample is a bit surprising to me. Indeed, it seems to correspond to the standard tensor indexing coordinate but in reverse order.

Here is a code sample to observe what I am saying:

```
import torch
a = torch.rand(1, 1, 4, 3)
b = torch.nn.functional.grid_sample(a, torch.tensor([[[[1, -1]]]]).type(torch.FloatTensor), align_corners=True)
#b corresponds to top right corner, i.e. j,i coordinates
print(a[0, 0, 0, 2], b)
c = torch.rand(1, 1, 4, 3, 2)
d = torch.nn.functional.grid_sample(c, torch.tensor([[[[[1, 1, -1]]]]]).type(torch.FloatTensor), align_corners=True)
#Again, here the convention is k, j, i
print(c[0, 0, 0, 2, 1], d)
```

I was wondering if there were any reasoning behind this convention as it seems counter-intuitive to me.

Thank you for your help,

Samuel