204 enum FeatureIDs {  enum
223   std::memset(Runner.getTensorUntyped(FeatureIDs::NAME), 0,                    \  in resetInputs()
244 using FeaturesListNormalizer = std::array<float, FeatureIDs::FeatureCount>;
277                            std::array<float, FeatureIDs::FeatureCount> &Largest,
289                        std::array<float, FeatureIDs::FeatureCount> &Largest,
315   std::bitset<FeatureIDs::FeatureCount> DoNotNormalize;
511   DoNotNormalize.set(FeatureIDs::mask);  in MLEvictAdvisor()
512   DoNotNormalize.set(FeatureIDs::is_free);  in MLEvictAdvisor()
513   DoNotNormalize.set(FeatureIDs::is_hint);  in MLEvictAdvisor()
514   DoNotNormalize.set(FeatureIDs::is_local);  in MLEvictAdvisor()
515   DoNotNormalize.set(FeatureIDs::min_stage);  in MLEvictAdvisor()
516   DoNotNormalize.set(FeatureIDs::max_stage);  in MLEvictAdvisor()
517   DoNotNormalize.set(FeatureIDs::progress);  in MLEvictAdvisor()
669   for (size_t FeatureIndex = 0; FeatureIndex < FeatureIDs::FeatureCount;  in tryFindEvictionCandidate()
677   *Runner->getTensor<float>(FeatureIDs::progress) =  in tryFindEvictionCandidate()
749     std::array<float, FeatureIDs::FeatureCount> &Largest, size_t Pos,  in extractFeatures()
811     Runner->getTensor<TYPE>(FeatureIDs::ID)[Pos] = static_cast<TYPE>(VAL);     \  in extractFeatures()
812     if (!DoNotNormalize.test(FeatureIDs::ID))                                  \  in extractFeatures()
813       Largest[FeatureIDs::ID] =                                                \  in extractFeatures()
814           std::max(Largest[FeatureIDs::ID], static_cast<float>(VAL));          \  in extractFeatures()
870   for (; CurrentFeature < FeatureIDs::FeatureCount; ++CurrentFeature) {  in tryFindEvictionCandidatePosition()