In submission_runner.py, if we are in the self-tuning rules, the hyperparameters argument to train_once will always be None.

Then in this code snippet

 dropout_rate = None aux_dropout_rate = None if hasattr(hyperparameters, 'dropout_rate'): dropout_rate = hyperparameters.dropout_rate if hasattr(hyperparameters, 'aux_dropout_rate'): aux_dropout_rate = hyperparameters.aux_dropout_rate model_params, model_state = workload.init_model_fn( model_init_rng, dropout_rate, aux_dropout_rate) 

workload.init_model_fn will always get None for dropout_rate and aux_dropout_rate, so Dropout layers won't ever be added to the model.

Although submissions could call workload.init_model_fn again themselves to make use of its side effect of setting workload._model, this is awkward and also challenging for workloads near the memory limit since it involves superfluously reconstructing model_params again on device.