[`T5`] Adding model_parallel = False to `T5ForQuestionAnswering` and `MT5ForQuestionAnswering`
# What does this PR do?

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Fixes https://github.com/huggingface/transformers/issues/24682 by adding `self.model_parallel = False`


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
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      Pull Request section?
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      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

@ArthurZucker and @younesbelkada

Is there a good way to add a test for this? I wasn't sure where a test like this would be added. <|||||>> Is there a good way to add a test for this? I wasn't sure where a test like this would be added.

No need to add a test for this. We have `test_model_parallelization` which tests model parallelization (the opposite).
As we are dealing with some  deprecated thing, it doesn't worth much time on it.

<|||||>Thanks! Would be nice if you can check this change works (i.e. fix the issue you opened in #24682) 🙏 <|||||>_The documentation is not available anymore as the PR was closed or merged._<|||||>> Thanks! Would be nice if you can check this change works

Definitely! I just ran the code locally and it works. <|||||>Yes. It seems a bit strange indeed. So far all `is_parallelizable` is set at the `XXXPreTrainedModel` level.

I think it's fine as the only usage of `is_parallelizable` is here

```python
        if hasattr(model, "is_parallelizable") and model.is_parallelizable and model.model_parallel:
            self.is_model_parallel = True
        else:
            self.is_model_parallel = False
```
There shouldn't bee too much confusion between `is_parallelizable = True` and `always model_parallel = False` for just this single (and new) model class.