Hi all. If I have a batch of uniform 3D grids of coordinate locations, with shape (for example), [1, 32, 32, 32, 3], what is the best way for me to split this up into multiple even chunks, so I could end up with something such as [1, 4096, 2, 2, 2, 3]? In other words, I’m splitting up that one big 32 x 32 x 32 cube where each point is an x, y, z coordinate location into 4096 smaller 2 x 2 x 2 cubes? Does a simple view operation make sense here, or would it throw off the coordinate values? I was looking into operations like torch.chunk, but they require a specific dimension to split along, which I’m not sure applies here.

My use case for this is that I have a smaller [1, 16, 16, 16, 3] cube, so I’m trying to match up points from this smaller shape into the corresponding cubes in the upsampled [1, 32, 32, 32, 3] shape (since a single coordinate point in the 16^3 shape corresponds to 8 points in the 32^3 shape).

For additional context, this is how I generate my 3D grid right now:

```
pxs = torch.linspace(-1, 1, 32)
pys = torch.linspace(-1, 1, 32)
pzs = torch.linspace(-1, 1, 32)
pxs = pxs.view(-1, 1, 1).expand(*shape).contiguous().view(size)
pys = pys.view(1, -1, 1).expand(*shape).contiguous().view(size)
pzs = pzs.view(1, 1, -1).expand(*shape).contiguous().view(size)
points = torch.stack([pxs, pys, pzs], dim=1)
grid_3d = torch.reshape(points, (32, 32, 32, 3))
```