add test marker to run all tests with @require_bitsandbytes
Author: Titus-von-Koeller
Pull request: yes

# What does this PR do?
add test marker to run all tests with @require_bitsandbytes

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      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? Not needed IMO.


## Who can review?
@younesbelkada, do I need any documentation for this and if yes, where?


Comments:
Submitting this as a separate PR, to keep things clean/atomic.

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_28278). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

Really strange, I just committed the marker description string change that @ArthurZucker suggested, but the tests are failing with unrelated stuff. Maybe the GH Runner doesn't have the right hardware for those tests?

```
FAILED examples/pytorch/test_pytorch_examples.py::ExamplesTests::test_generation - RuntimeError: "LayerNormKernelImpl" not implemented for 'Half'
FAILED examples/pytorch/test_pytorch_examples.py::ExamplesTests::test_run_audio_classification - ValueError: FP16 Mixed precision training with AMP or APEX (`--fp16`) and FP16 half precision evaluation (`--fp16_full_eval`) can only be used on CUDA or NPU devices or certain XPU devices (with IPEX).
FAILED examples/pytorch/test_pytorch_examples.py::ExamplesTests::test_run_glue - ValueError: FP16 Mixed precision training with AMP or APEX (`--fp16`) and FP16 half precision evaluation (`--fp16_full_eval`) can only be used on CUDA or NPU devices or certain XPU devices (with IPEX).
FAILED examples/pytorch/test_pytorch_examples.py::ExamplesTests::test_run_image_classification - ValueError: FP16 Mixed precision training with AMP or APEX (`--fp16`) and FP16 half precision evaluation (`--fp16_full_eval`) can only be used on CUDA or NPU devices or certain XPU devices (with IPEX).
FAILED examples/pytorch/test_pytorch_examples.py::ExamplesTests::test_run_semantic_segmentation - ValueError: FP16 Mixed precision training with AMP or APEX (`--fp16`) and FP16 half precision evaluation (`--fp16_full_eval`) can only be used on CUDA or NPU devices or certain XPU devices (with IPEX).
FAILED examples/pytorch/test_pytorch_examples.py::ExamplesTests::test_run_speech_recognition_ctc - ValueError: FP16 Mixed precision training with AMP or APEX (`--fp16`) and FP16 half precision evaluation (`--fp16_full_eval`) can only be used on CUDA or NPU devices or certain XPU devices (with IPEX).
FAILED examples/pytorch/test_pytorch_examples.py::ExamplesTests::test_run_speech_recognition_ctc_adapter - ValueError: FP16 Mixed precision training with AMP or APEX (`--fp16`) and FP16 half precision evaluation (`--fp16_full_eval`) can only be used on CUDA or NPU devices or certain XPU devices (with IPEX).
FAILED examples/pytorch/test_pytorch_examples.py::ExamplesTests::test_run_speech_recognition_seq2seq - ValueError: FP16 Mixed precision training with AMP or APEX (`--fp16`) and FP16 half precision evaluation (`--fp16_full_eval`) can only be used on CUDA or NPU devices or certain XPU devices (with IPEX).
FAILED examples/pytorch/test_pytorch_examples.py::ExamplesTests::test_run_vit_mae_pretraining - ValueError: FP16 Mixed precision training with AMP or APEX (`--fp16`) and FP16 half precision evaluation (`--fp16_full_eval`) can only be used on CUDA or NPU devices or certain XPU devices (with IPEX).
```

Yes it's unrelated to your PR don't worry! 

rebasing on main should suffice to fix this ! 

Hmm it seems to fail on other PRs too, e.g. :https://github.com/huggingface/transformers/pull/28711

don't have good idea. i just re-triggered that job and let's see