Register ModelOutput class with PyTorch's pytree
Author: wconstab
Pull request: yes

Summary:
PyTree lets users flatten arbitrarily complex py data structures (e.g. ModelOutput) and operate over a flat list of their constituent parts.

Users could opt to register HF's ModelOutput with pytree manually and then use it, but many users would not know to do this or would be inconvenienced by it.

A common use case is to operate over all the tensors in a model's output.

Test Plan:
I've added a unit test but haven't run it locally (yet as it depends on PT and TF) - would check if CI runs it first.

Reviewers:

Subscribers:

Tasks:

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# What does this PR do?

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Fixes # (issue)


## 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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Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
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- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

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- ray/raytune: @richardliaw, @amogkam

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HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

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- PyTorch: @sgugger
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Comments:
Was superseded by #25358 as discussed there.