How to trace the BertForQuestionAnswering
I followed the example [here](https://huggingface.co/transformers/model_doc/bert.html#bertforquestionanswering) and want to convert BertForQuestionAnswering to TorchScript. 
Here is my code
```
from transformers import BertTokenizer, BertForQuestionAnswering
import torch

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForQuestionAnswering.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad', torchscript=True)

question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
input_ids = tokenizer.encode(question, text)
token_type_ids = [0 if i <= input_ids.index(102) else 1 for i in range(len(input_ids))]

input_tensor = torch.tensor([input_ids])
token_type_ids_tensor = torch.tensor([token_type_ids])

# The way I traced the model could be wrong here
traced_model = torch.jit.trace(model, (input_tensor, token_type_ids_tensor))
traced_model.eval()
start_scores, end_scores = traced_model(input_tensor, token_type_ids_tensor)

all_tokens = tokenizer.convert_ids_to_tokens(input_ids)
answer = ' '.join(all_tokens[torch.argmax(start_scores) : torch.argmax(end_scores)+1])

print(answer)
```
The answer I got is `jim henson was a nice puppet [SEP]`
Do you have any idea how to make it correct?
Thanks
I found a workaround solution, just to pass the complete inputs including `input_ids, token_type_ids and attention_mask` to the trace method and invoke forward along with those 3 inputs. 
But it would be great to know how I can just pass in input_ids and token_type_ids<|||||>This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
