Batching (TF)BertForQuestionAnswering deployment
# 🚀 Feature request

It may be already possible, but I couldn't figure out how, to call TFBertForQuestionAnswering on a batch on context-question pairs rather than just one by one.

## Motivation

I tried out the same code on a machine with 16 cores and a GPU. I could process 22 pairs/sec with the CPUs, but only 11 pairs/sec with the GPU. I assume it's not efficient due to I/O. However, I couldn't find in the example such a way to call the BERT model.

For example, when I do this:
```
input_ids = tokenizer.encode(question, context, add_special_tokens=True, max_length=512)
```

I would like to promote `question` and `context` to an array of pairs so that after calling
```
start_scores, end_scores = model({'input_ids': np.array([input_ids]), # The tokens representing our input text.
                                 'token_type_ids': np.array([segment_ids])}) # The segment IDs to differentiate question from context
```

the returned objects `start_scores` and `end_scores` should be a 2D array (or list). 
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.
