viewing a tensor as a new dtype with a different number of bytes per element is not supported.

[Levishery]

My PyTorch version is 1.10.0.
On line 143 of the hilbert.py file, the code
locs_uint8 = locs.long().view(torch.uint8).reshape((-1, num_dims, 8)).flip(-1)
will report an error:
viewing a tensor as a new dtype with a different number of bytes per element is not supported.
I understand that switching to PyTorch 1.12.0 can solve this problem, but my CUDA version doesn't support me doing so. May I ask if there is an alternative solution for this line of code?

[Levishery]

Is torch.view(torch.uint8) do the same function as numpy.view(numpy.uint8)?
Is changing the code to

    locs = locs.long()
    locs_uint8 = torch.tensor(np.asarray(locs).view(np.uint8))
    locs_uint8 = locs_uint8.reshape((-1, num_dims, 8)).flip(-1)

ok?

[Gofinge]

I think it would harm the performance with change tensor in CUDA to array in CPU. How about ask ChatGPT about the solution?