Fix multitask_model.pt loading (ModuleNotFoundError on import)

[RobbinBouwmeester]

Problem

multitask_model.pt was serialised when MultitaskDeepLCModel and BatchedHeads lived in a top-level module called multitask_model. That module no longer exists in the package, so any call that loads the default model (including plain deeplc.predict(...)) raised:

ModuleNotFoundError: No module named 'multitask_model'

or, with a partial shim:

AttributeError: Can't get attribute 'BatchedHeads' on <module 'multitask_model'>

Fix

deeplc/_architecture.py — Add the two missing classes:

• BatchedHeads: parallel output heads (batched linear projection + per-head dot product). Includes a proper __init__ so future checkpoints can be constructed from scratch.
• MultitaskDeepLCModel: multi-task backbone (same 4 input branches as DeepLCModel, plus a shared trunk feeding into BatchedHeads). Checkpoint-load-only for now; forward is fully implemented.

deeplc/_model_ops.py — Add _patch_legacy_multitask_module(), called inside load_model() before torch.load when the argument is a file path. It registers a sys.modules shim that maps the old multitask_model.* import paths to the new classes in deeplc._architecture. The shim is a no-op if the key is already present.

tests/test_model_ops.py — Add test_load_multitask_model_without_prior_shim to catch regressions: it explicitly removes any pre-existing shim, loads the bundled checkpoint, and asserts the output has the expected multi-head shape.

Verification

All 45 tests pass. End-to-end deeplc.predict(peptides, model=MODEL_MULTITASK) works without any user-side workaround, returning shape (n_peptides, 1020).

Performance on PXD002549 (real LC-MS/MS data, 500-peptide held-out set):

Setup	Pearson r	MAE
predict multitask (best head)	0.994	—
predict_and_calibrate multitask	0.994	0.89 min
finetune_and_predict multitask	0.994	0.98 min

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