Ideally, I would like to execute something like

`t = torch.zeros([4, 3, 64, 64])`

`t[:, :, ::8, ::8].view(4, -1)`

but that produces the error

```
RuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.
```

Unfortunately, I can’t use `.reshape()`

or `.contiguous()`

because of memory consumption. This code is called too often to make a copy of the tensor each time. Instead I would like to create one big tensor and slice it each time.

Is there some way to use `.transpose()`

or something similar in combination with the above `.view()`

to achieve my goal? Is there a way to get a more detailed error message to understand which dimension exactly is the problem?

Thanks a lot in advance!