load an MLIR file as a model

Hi,
I have translated a model into MLIR as in the examples :

resnet18 = torchvision.models.resnet18(pretrained=True)
resnet18.eval()
module = torch_mlir.compile(resnet18, torch.ones(1, 3, 224, 224), output_type="torch")
print("TORCH OutputType\n", module.operation.get_asm(large_elements_limit=10))

After that, I changed the MLIR and now, I would like to do the opposite and load this MLIR as a module in my python code to continue to compile it like in examples :

backend = refbackend.RefBackendLinalgOnTensorsBackend()
compiled = backend.compile(module)
jit_module = backend.load(compiled)
predictions(resnet18.forward, jit_module.forward, img, labels)

I just need a line between the to code two load the MLIR but I couldn't find anything about it on the internet. Does anyone know how to do it?
Thanks a lot

Start from the Python examples in the issue and inspect the torch-mlir Python API for MLIR parsing or module-loading entry points. Determine whether an edited MLIR module can be passed to the shown backend.compile and backend.load flow; done means the edited file loads successfully and produces a usable jit_module.