Hardware Auto-Setup for Tutorials and Examples
# What does this PR do?

Discussed with @sgugger and @LysandreJik . This PR introduces auto-setup functionality for tutorials and examples (we are sending a parallel PR to accelerate, and maybe Diffusers and Spaces shortly). This allows users to run transformers code, tutorials, and scripts on self-hosted hardware (either their own instance or a cloud instances) including on-demand allocation of the hardware itself on AWS, GCP, Azure, or Lambda Labs, and installation of dependencies. This introduces a similar level of turnkey usage and reproducibility that users only typically expect in Colab, but for any type of hardware on any cloud (we've tested on Paperspace and Coreweave as well, allocating the instance in their UI and then plugging in the IP as a static cluster). Note that Runhouse OSS is facilitating the setup (via SkyPilot) and rpc, but users don't don't need to create a Runhouse account or anything like that, this is strictly inside their own cloud accounts with their own credentials (or using their IP and ssh creds without a cloud account).

This PR is WIP and seeking feedback. A few open questions:
1. launch_auto_hardware.mdx is structured to be like a notebook, but how do I make it have the "launch in colab" etc. buttons on the top right? (I'm happy to refactor it into an ipynb if needed)
2. launch_auto_hardware.mdx shows only inference. I think it could be valuable to make it mirror the initial PyTorch parts of [the yelp fine-tuning tutorial](https://huggingface.co/docs/transformers/training), to show preprocessing, training, and inference all on remote hardware. Does that make sense?
3. We're generally using the term "remote hardware" with "auto setup". Would it be better to say "self-hosted" or something like that (to avoid confusion with hosted solutions)?
4. Should we showcase more examples? Right now we show just a few but can add more if you think that's valuable (e.g. @sgugger mentioned showing TPUs before). We can also tailor the auto-setup scripts to different examples if desired, rather than the one-size fits all approach.

Thank you for your input on this so far!!


## Before submitting
- [x] 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?
- [x] 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).
Whoops, shouldn't be on main, I'll close and reopen on a new branch.<|||||>_The documentation is not available anymore as the PR was closed or merged._