Hi, thanks for the great work!

I am wondering why some hidet tensor methods (e.g., to, cuda, and cpu) do not support symbolic tensors.

class TestMode(nn.Module):
    def __init__(self):
        super().__init__()

        self.conv = nn.Linear(10, 10)

    def forward(self, x):
        z = x.unsqueeze(0).expand(4, 4, 512).to(torch.device("cuda"))
        return z

if __name__ == "__main__":
    model = TestMode()
    model = model.eval().half()
    model = model.to(device)
    hidet.torch.dynamo_config.search_space(2)
    hidet.torch.dynamo_config.use_fp16()
    model_opt = torch.compile(model, backend='hidet')

    tokens = torch.zeros(20, 10).cuda()
    model_opt(tokens)

In the above test case, the exception
NotImplementedError: hidet: Tensor.to(..., device=...) is not supported for symbolic tensors., occurred when calling tensor_to(Tensor(shape=(4, 4, 512), dtype='bool', device='cuda:0'), device(type='cuda')) is raised.

I think the operation (.to(device)) is a common operation for deep learning models as the implementation of huggingface llama

Are there any concerns or limitations regarding these operations for symbolic trace?
Look forward to your response. Thanks!