feat: Add batched tensor expansion utilities for O(S) ensemble unroll…

[Debadri-das]

Describe your changes

This PR introduces a standalone batch-expanding PyTorch utility (expand_ensemble_batch and fold_ensemble_batch) to optimize tensor manipulation for Ensemble Lateral Boundary Conditions (LBCs) and probabilistic forecasting variables.

Motivation and Context: Currently, probabilistic processing of $S$ ensemble members often relies on sequential for loops. To support the upcoming model refactoring and probabilistic capabilities without introducing $O(S)$ sequential bottlenecks, these functions leverage torch.repeat_interleave() and .view() to dynamically flatten state tensors (B, ...) -> (B * S, ...). This enables optimized parallelized operations over the GPU without permanently mutating standard AR model dimensions.

Dependencies: None. Built strictly with standard PyTorch tensor primitives.

Issue Link

#49 and #62

Type of change

• 🐛 Bug fix (non-breaking change that fixes an issue)
• ✨ New feature (non-breaking change that adds functionality)
• 💥 Breaking change (fix or feature that would cause existing functionality to not work as expected)
• 📖 Documentation (Addition or improvements to documentation)

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[Sir-Sloth-The-Lazy]

Just a heads up you missed the case when S == T . Love the idea !

[Sir-Sloth-The-Lazy]

is this intentional ?

[sadamov]

linking #226 and #332 here, to remember the potential connection. this is one for @joeloskarsson to review.

[Debadri-das]

is this intentional ?
No, the code was not intentional at all, it was a flaw in the design, which I fixed now.