Added DagshubCallback
Author: jinensetpal
Pull request: yes

# What does this PR do?

Adds a Trainer Integration with [DagsHub](https://dagshub.com/). It extends the MLFlow integration that already currently exists to also track and push artifacts using DVC, into DagsHub repositories. The idea is to allow users to integrate an MLOps stack to their current setups with minimal effort.

Here's a colab notebook with a sample integration: https://colab.research.google.com/drive/1KEeQYp3jD8kmkMMhGKR1R1C6cEjnbJLZ

## 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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- [ ] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

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Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts and @NielsRogge
- speech models: @sanchit-gandhi
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Library:

- flax: @sanchit-gandhi
- generate: @gante
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- tokenizers: @ArthurZucker
- trainer: @sgugger

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- PyTorch: @sgugger
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Comments:
_The documentation is not available anymore as the PR was closed or merged._

Thank you so much for the lightning review, @sgugger!

I've made the relevant changes. I also added a check within `get_available_reporting_integrations` to ensure available integrations are reported accurately. 

Since `DagsHubCallback` subclasses `MLFlowCallback`, it's still separate. If both integrations are requested, it does not execute twice. This is what the `get_available_reporting_integrations` looks like when moving packages around:

![2023-02-01-095908_1920x606_scrot](https://user-images.githubusercontent.com/52078103/216080956-e57170a6-9a77-49e2-adff-e36e3b885909.png)

Hope this was helpful! :)

Thanks again for your contribution!

Thank you for your rapid responses!! Have an awesome rest of day 🙂