How to convert BERT model to MLIR with use_tracing=False

Hi,

I am a beginner of torch-mlir.
I can execute the examples/torchscript_stablehlo_backend_tinybert.py successfully and get the MLIR output.
I found that the constant matrices are with specific encoded values in the MLIR file.
When I set the use_tracing=False, there is some errors in my output. But ResNet18 can generate without tracing.

RuntimeError: 
'Optional[Tensor]' object has no attribute or method 'size'.:
  File "/home/bshi/opt/anaconda3/envs/torch-mlir-dev/lib/python3.10/site-packages/transformers/models/bert/modeling_bert.py", line 211
            input_shape = input_ids.size()
        else:
            input_shape = inputs_embeds.size()[:-1]
                          ~~~~~~~~~~~~~~~~~~ <--- HERE
    
        seq_length = input_shape[1]

I am unfamiliar with the usage of use_tracing. So I would like to ask you for help.

Thank you!

Start with examples/torchscript_stablehlo_backend_tinybert.py and reproduce the BERT conversion with use_tracing=False. Read transformers/models/bert/modeling_bert.py around line 211 and compare the tracing and ResNet18 paths. Done means determining the cause of the Optional[Tensor] error and documenting or fixing the affected conversion behavior.