Modified use of crowding distance calculation to work with new implem…

…entation

use_cases/eluc/prescriptors/nsga2/torch_prescriptor.py

@@ -145,9 +145,7 @@ def _select_parents(self, candidates: list[Candidate], n_parents: int) -> list[C
         for front in fronts:
             # Compute crowding distance here even though it's technically not necessary now
             # so that later we can sort by distance
-            crowding_distance = nsga2_utils.calculate_crowding_distance(front)
-            for candidate, distance in zip(front, crowding_distance):
-                candidate.distance = distance
+            nsga2_utils.calculate_crowding_distance(front)
             if len(parents) + len(front) > n_parents:  # If adding this front exceeds num_parents
                 front = sorted(front, key=lambda candidate: candidate.distance, reverse=True)
                 parents += front[:n_parents - len(parents)]