[BUG] Data leakage in `TorchExperiment`

[SimonBlanke]

The TorchExperiment reuses the same LightningDataModule instance across all optimization trials. If the datamodule maintains internal state (e.g., data augmentation settings, random state, preprocessing cache), this state persists across trials. In this line the datamodule is the same for each iteration. So the lightning_module is instantiated fresh for each trial, but datamodule is reused. Here is an example of a datamodule, that would react in an undesired way:

class ImageDataModule(L.LightningDataModule):
    def __init__(self):
        super().__init__()
        self.augmentation_strength = 0.5  # Internal state

    def train_dataloader(self):
        # Augmentation strength might be modified during training
        transforms = self._get_transforms(self.augmentation_strength)
        return DataLoader(self.train_dataset, ...)

    def on_train_epoch_end(self):
        # Some datamods adjust augmentation over time
        self.augmentation_strength *= 1.1