cleaning up the parser arguments in schnetpack_qm9.py

[cod3licious]

I was wondering, couldn't the call of schnetpack_qm9 be further simplified, at least for the evaluation? Some of the arguments, like property, are general arguments, not training arguments, however, if the model was trained for a certain property it makes little sense to evaluate it on another, right?

Then in line 308 and 310, the train_args are set to args or loaded from the json file created when training the model, however at some points it seems a bit arbitrary when train_args or args are used, e.g. in line 314 when the qm9 dataset is loaded, train_args.property is used, but then below when the atomref is loaded, args.property is used instead.

I also noticed that pool_mode has disappeared from the arguments, but I assume it is still relevant to set this to 'avg' instead of 'sum' for properties like LUMO, so could that be added back in (or set automatically depending on the property)?