Is it necessary to transfer model to cpu?

[DaddyWesker]

Hello.In torch.pruning/dependency.py there is a line model.eval().cpu() . With this i cant use model RAFT (optical flow model) which i'm currently researching (it fails on

raise RuntimeError("module must have its parameters and buffers "
                                   "on device {} (device_ids[0]) but found one of "
                                   "them on device: {}".format(self.src_device_obj, t.device))

even if i'm transferrng it on cpu myself). But if i'm commenting this mentioned line model.eval().cpu() then programm passes through DG.build_dependency(model, example_inputs=[torch.randn(1, 3, 440, 1024), torch.randn(1, 3, 440, 1024)]) just fine. So, is this line model.eval().cpu() is necessary in torch_pruning? Is torch_pruning works on cpu only?

Thanks in advance.