handle_impossible_answer not working in the question answering pipeline for ROBERTa model
### Environment info
- Platform: Linux 20.04  
- Python version 3.8.5
- `transformers` version `3.5.0` and `4.3.2`

### The issue
I'm using the `pipeline("question-answering")` with QA Models downloaded [from community](https://huggingface.co/models?pipeline_tag=question-answering). I'm evaluating models on the SQUAD 2.0 dataset which doesn't always have an answer to the given question - that's what the `handle_impossible_answer` flag in the pipeline is for.  

I noticed that ROBERTa model (any ROBERTa, not just a specific model) in version 4 of `transformers` always produces an answer despite the `handle_impossible_answer` flag - even if the same model for the same example didn't produce an answer (returned "" as an answer) while using version 3 of the library.

```python
bert_model_name = 'deepset/bert-base-cased-squad2'
roberta_model_name = 'deepset/roberta-base-squad2'

bert_tokenizer = AutoTokenizer.from_pretrained(bert_model_name)
bert_model = AutoModelForQuestionAnswering.from_pretrained(bert_model_name, return_dict=True)
roberta_tokenizer = AutoTokenizer.from_pretrained(roberta_model_name)
roberta_model = AutoModelForQuestionAnswering.from_pretrained(roberta_model_name, return_dict=True)

bert_qa = pipeline('question-answering', tokenizer=bert_tokenizer, model=bert_model)
roberta_qa = pipeline('question-answering', tokenizer=roberta_tokenizer, model=roberta_model)

# Random SQUAD 2.0 example which doesn't have an answer to the question
question = 'What was the name of the only ship operating in the Indian Ocean?'
context = 'In September 1695, Captain Henry Every, an English pirate on board the Fancy, reached the Straits of Bab-el-Mandeb, where he teamed up with five other pirate captains to make an attack on the Indian fleet making the annual voyage to Mocha. The Mughal convoy included the treasure-laden Ganj-i-Sawai, reported to be the greatest in the Mughal fleet and the largest ship operational in the Indian Ocean, and its escort, the Fateh Muhammed. They were spotted passing the straits en route to Surat. The pirates gave chase and caught up with Fateh Muhammed some days later, and meeting little resistance, took some £50,000 to £60,000 worth of treasure.'

print(bert_qa(question=question, context=context, handle_impossible_answer=True))
# transformers 3.5.0: {'score': 0.999398410320282, 'start': 0, 'end': 0, 'answer': ''}
# transformers 4.3.2: {'score': 0.999398410320282, 'start': 0, 'end': 0, 'answer': ''}

print(roberta_qa(question=question, context=context, handle_impossible_answer=True))
# transformers 3.5.0: {'score': 0.979897797107697, 'start': 0, 'end': 0, 'answer': ''}
# transformers 4.3.2: {'score': 0.222181886434555, 'start': 422, 'end': 436, 'answer': 'Fateh Muhammed'}
```

### Probable issue reason
I've found out that in the `question_answering.py` file in the `pipeline` directory in version 4 of `transformers` there is a condition that provides ROBERTa models from adjusting the `p_mask` for this task. It looks simply like this: `if self.tokenizer.cls_token_id`. And since ROBERTa's `cls_token_id = 0` the condition isn't met and the `p_mask` isn't changed for the `cls_token`. This results in omitting the token while answering the question (it behaves like e.g the token was a part of a question). For example BERT's `cls_token_id = 101` so the condition is met.

### Plausible solution
Possibly the easy solution is to expand the condition to `if self.tokenizer.cls_token_id is not None`. However, there wasn't such a condition in version 3 at all so maybe it performs some crucial function in its current form that I'm not aware of...

```python
# originally the condition here was more general and looked like this
# if self.tokenizer.cls_token_id:

if self.tokenizer.cls_token_id is not None:  
    cls_index = np.nonzero(encoded_inputs["input_ids"] == self.tokenizer.cls_token_id)
    p_mask[cls_index] = 0
```
Hi! I believe this is was overlook on our part. Your change looks reasonable to me, do you want to open a PR with your proposed fix?

And thank you for opening such a detailed and well-structured issue!