Add probabilistic scaffold to ARModel via config-driven output_mode and ensemble_size

[Panchadip-128]

…on PR)

Describe your changes

This PR introduces a minimal probabilistic scaffold in ARModel by making probabilistic configuration fully config-driven.

Specifically, the model now reads output_mode and ensemble_size from config.training, enabling future ensemble-based probabilistic forecasting extensions while preserving existing deterministic behavior.

Additionally, a validation guard for output_mode is added and a minor typo (time_step_int_unit) is corrected to time_step_unit.

The change is intentionally lightweight and does not modify training, inference, or loss computation logic.

< Please also include relevant motivation and context. >
The probabilistic forecasting roadmap requires an architectural scaffold to support ensemble outputs without introducing breaking changes. Moving probabilistic control into the configuration layer aligns with Neural-LAM’s design philosophy and enables incremental extensions such as ensemble sampling and probabilistic losses.
< List any dependencies that are required for this change. >
No new dependencies were introduced

Issue Link

N/A — foundational scaffold for probabilistic forecasting support

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• 🐛 Bug fix (non-breaking change that fixes an issue)
• ✨ New feature (non-breaking change that adds functionality)
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• 📖 Documentation (Addition or improvements to documentation)

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[leifdenby]

this looks like the same changes as #229, can we close this @Panchadip-128 ? Please make a draft PR in future if your work isn't ready for us to look at yet :)

[Panchadip-128]

Thank you for your feedback, and apologies for any confusion caused by the duplicate PR. I appreciate your guidance and will make sure to submit draft PRs for work-in-progress in the future.

Please feel free to close this PR so we can consolidate discussion on other PR.
Sorry again for any inconvenience, and thank you for your support!

[sadamov]

closed as per discussion, work is being done here: #229