Slow featurization when running inference for same input with multiple seeds

In `run_alphafold.py`, the call stack is `main` -> `process_fold_input` -> `predict_structure` -> `featurisation.featurise_input`, which calls `data_pipeline.process_item` repeatedly on the same inputs with different seeds. Within `WholePdbPipeline.process_structure` (the workhorse of `process_item`), the random seed is only used *once* towards the end of the method in `features.RefStructure.compute_features`. I'm wondering if the rest of the `process_structure` method could be taken outside of the for-loop, as it seems to be generating the same features anyways? Happy to work on a draft PR if this is the case. Thanks!