While I tried your code, and it did not give me an error, I can say that usually the best practice to debug CUDA Runtime Errors: device-side assert like yours is to turn collab to CPU and recreate the error. It will give you a more useful traceback error.
Most of the time CUDA Runtime Errors can be the cause of some index mismatching so like you tried to train a network with 10 output nodes on a dataset with 15 labels. And the thing with this CUDA error is once you get this error once, you will recieve it for every operation you do with torch.tensors. This forces you to restart your notebook.
I suggest you restart your notebook, get a more accuracate traceback by moving to CPU, and check the rest of your code especially if you train a model on set of targets somewhere.