docs(LightningModule): update docs for .training mode in loops

[adosar]

Related to #18951 (comment).

Update the pseudocode of validation loop according to #18951:

When the validation loop ends, and before switching to training, it restores the .training mode on all submodules to what it was before.

and add a corresponding note to {validation,test,predict}_step since they exhibit this behavior as can be seen in the following snippet:

from lightning.pytorch.demos.boring_classes import BoringModel
import lightning as L
import warnings

warnings.filterwarnings('ignore')

trainer = L.Trainer(max_epochs=1)
loop = trainer.test

litmodel = BoringModel()
litmodel.train()
print('Before loop', litmodel.training)
loop(litmodel)
print('After loop', litmodel.training)

litmodel = BoringModel()
litmodel.eval()
print('Before loop', litmodel.training)
loop(litmodel)
print('After loop', litmodel.training)

GPU available: False, used: False
TPU available: False, using: 0 TPU cores
HPU available: False, using: 0 HPUs
Before loop True
Testing DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 64/64 [00:00<00:00, 1299.38it/s]
After loop True
Before loop False
Testing DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 64/64 [00:00<00:00, 1332.01it/s]
After loop False

@awaelchli Can you please confirm that this is the intended (default) behavior of the loops?

Additional changes:

• Fix incorrect comment in lightning_module.rst that trainer.test(model) loads the best weights. According to the docs, If ckpt_path=None and the model instance was passed, use the current weights.

What does this PR do?

Fixes #<issue_number>

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📚 Documentation preview 📚: https://pytorch-lightning--20716.org.readthedocs.build/en/20716/

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[adosar]

cc @Borda

[SkafteNicki]

for completeness can we add a inverse note to the training step, e.g. that when train_step is called, the model has NOT been put into train mode and that it is something the user should be aware of

will re-review