Problem

LinearModel.get_loss() reassigned its loss argument to each submodel's returned value:

for model in self.models:
    loss = model.get_loss(loss, lr)
    if loss is not None:
        return loss

Frozen and pairtab submodels return None from get_loss() because they own no trainable loss. When such a non-trainable submodel is ordered before a trainable energy submodel, the trainable submodel is then called with loss=None, and EnerFitting.get_loss() dereferences it as a dict via loss.pop("type", ...), raising AttributeError: 'NoneType' object has no attribute 'pop' during trainer construction — even though the original loss config could have been used.

Fix

Iterate with a separate variable and pass a copy.deepcopy(loss) of the original loss configuration to each submodel, returning the first non-None result. This keeps the caller's loss config intact for the trainable submodel regardless of submodel ordering, and avoids mutating the shared config (EnerFitting.get_loss pops keys from it).

Test

source/tests/tf/test_linear_model.py covered a linear model whose submodels never hit this ordering, so the crash path was unexercised. A new lightweight TestLinearModelGetLoss constructs a LinearEnergyModel with a None-returning submodel (mimicking frozen/pairtab) ordered before a trainable submodel (mimicking EnerFitting.get_loss's loss.pop), and asserts get_loss returns the trainable loss and leaves the original config unmutated. On the current code it fails with the NoneType ... pop error; after the fix it passes.

Fix #5682

Summary by CodeRabbit

• Bug Fixes

  • Preserves the original loss configuration when evaluating multiple submodels.
  • Ensures a non-trainable submodel returning no loss does not affect later trainable submodels.

• Tests

  • Added coverage for loss handling across submodels, including unchanged input configuration and correct fallback behavior.