ONNX export broken for QA models
# 🐛 Bug

## Information

Model I am using (Bert, XLNet ...): bert-base-uncased

Language I am using the model on (English, Chinese ...): -

The problem arises when using:
* [x] the official example scripts: convert_graph_to_onnx.py
* [x] my own modified scripts: any script attempting to do a regular torch.onnx.export on a `PreTrainedModel`

The tasks I am working on is:
* [x] an official GLUE/SQUaD task: SQuAD
* [ ] my own task or dataset: (give details below)

## To reproduce

Steps to reproduce the behavior:

1. Run:
`src/transformers/convert_graph_to_onnx.py --model bert-base-uncased --framework pt --pipeline question-answering /tmp/test_hf_onnx/test_hf.onnx`

2. Observe in console:
```
Error while converting the model: Only tuples, lists and Variables supported as JIT inputs/outputs. Dictionaries and strings are also accepted but their usage is not recommended. But got unsupported type QuestionAnsweringModelOutput
```

## Expected behavior

Successful export of the model to `/tmp/test_hf_onnx/test_hf.onnx`

## Environment info
<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.
     Don't forget to fill out the missing fields in that output! -->
     
- `transformers` version: current master ce374ba87767d551f720242d5e64bfa976531079
- Platform: Ubuntu 18.04
- Python version: 3.7
- PyTorch version (GPU?): 1.5.0
- Tensorflow version (GPU?): -
- Using GPU in script?: -
- Using distributed or parallel set-up in script?: -

Might I also add that breaking regular PyTorch ONNX export like that with custom model output wrappers is IMO a Really Bad Idea (TM). Your `PreTrainedModel`'s are still `torch.nn.Module`'s, and as such should be exportable using standard `torch.onnx.export` APIs. 

To make matters worse, the proposed `convert_pytorch` API that I discovered in `src/transformers/convert_graph_to_onnx.py` does not work with general argument-forwarding `forward(*args, **kwargs)` wrapper over the specific HF Transformers models. The `convert_pytorch` path's `__code__` shenanigans in their current way fail when exporting the following kinds of models:
```python
class BertWithWrappedForward(BertForQuestionAnswering):
    def forward(*args, **kwargs):
        # pre-forward actions here
        super().forward(*args, **kwargs)
```

The default `torch.onnx.export` path might have worked here, and it would be good to see some kind of support for such models in the `convert_pytorch` API, or at least a fallback scenario.<|||||>@mfuntowicz @julien-c @LysandreJik <|||||>Just wanted to add that `convert_graph_to_onnx.py` is also broken for text-generation with GPT2 for me.

Running `python src/transformers/convert_graph_to_onnx.py --pipeline text-generation --model gpt2 --framework pt output/model.onnx` returns
```
Error while converting the model: Only tuples, lists and Variables supported as JIT inputs/outputs. 
Dictionaries and strings are also accepted but their usage is not recommended. But got unsupported type CausalLMOutputWithPast
```

Although the type is different (now `CausalLMOutputWithPast`), this seems to be the same error happening.<|||||>To help with resolving the issue, this is the merge that seems to be causing the problems here: [5438](https://github.com/huggingface/transformers/pull/5438)

Also tried running the previous version before this release (`pip install -Iv transformers=3.0.1`), and now my model is properly converted to onnx.<|||||>Hi! This was an unseen error that appeared when we made the switch from tuples to namedtuples. The fix was to specify to pipelines to continue using tuples instead of namedtuples!

#6061 should have fixed it, thanks for letting us know!