[MNT] Replace Black with Ruff formatting and update configuration

[Borda]

Description

Switched from Black to Ruff for code formatting to simplify tooling. Updated .pre-commit-config.yaml and pyproject.toml to reflect the removal of Black and added Ruff-specific formatting configurations. This change ensures consistency and leverages Ruff's integrated formatting capabilities.

Checklist

• Linked issues (if existing)
• Amended changelog for large changes (and added myself there as contributor)
• Added/modified tests
• Used pre-commit hooks when committing to ensure that code is compliant with hooks. Install hooks with pre-commit install.
  To run hooks independent of commit, execute pre-commit run --all-files

Make sure to have fun coding!

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
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Additional details and impacted files

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##             main    #1853   +/-   ##
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  Coverage        ?   85.01%           
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  Files           ?       62           
  Lines           ?     6166           
  Branches        ?        0           
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  Hits            ?     5242           
  Misses          ?      924           
  Partials        ?        0           

Flag	Coverage Δ
cpu	85.01% <100.00%> (?)
pytest	85.01% <100.00%> (?)

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[fkiraly]

@Borda, thanks a lot for the multiple packaging PR!

Since you are a lightning dev, we would be hugely thankful if you could look at our current design for pytorch-forecasting v2 where we are trying to:

• abstract "loader-plus-network" objects to obtain a unified interface
• use lightning feature better to enable scaling (not all pandas based)

In particular:

• example vignettes (beta) for v2 API - is there a way to avoid passing metadata directly from lightning DataModule to model? [ENH] EXPERIMENTAL: Example notebook based on the new data pipeline #1813
• more generally, API discussion issue [API] Redesign towards pytorch-forecasting 2.0 #1736 and enhancement proposal draft pytorch-forecasting and dsipts v2 API design enhancement-proposals#39 - any lightning best practice input would be much appreciated!

[Borda]

Since you are a lightning dev, we would be hugely thankful if you could look at our current design for pytorch-forecasting v2 where we are trying to:

• abstract "loader-plus-network" objects to obtain a unified interface
• use lightning feature better to enable scaling (not all pandas based)

Sure, I can have a look, just cannot guarantee how soon... :)

[fkiraly]

FYI @fnhirwa, review would be appreciated due to ruff