{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/clip-gradients","entry":"clip_gradients","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":31,"n_papers_ran":28,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":4,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":31,"n_places_pointer_only":4,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":3},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2504.12576","paper":"/paper/cm3ae-a-unified-rgb-frame-and-event-voxel","title":"CM3AE: A Unified RGB Frame and Event-Voxel/-Frame Pre-training Framework","date":"2025-04-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"event-ahu/cm3ae","path":"utils.py","file_url":"https://github.com/event-ahu/cm3ae/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2b5489053b222e00","mcp_get_code":{"code_sha256":"2b5489053b222e00"}},{"arxiv_id":"2410.17980","paper":"/paper/stick-breaking-attention","title":"Scaling Stick-Breaking Attention: An Efficient Implementation and In-depth Study","date":"2024-10-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ibm/dolomite-engine","path":"lm_engine/kernels/torch_utils.py","file_url":"https://github.com/ibm/dolomite-engine/blob/HEAD/lm_engine/kernels/torch_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"be3f8284e0ad756c","mcp_get_code":{"code_sha256":"be3f8284e0ad756c"}},{"arxiv_id":"2410.09890","paper":"/paper/large-scale-3d-medical-image-pre-training","title":"Large-Scale 3D Medical Image Pre-training with Geometric Context Priors","date":"2024-10-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"luffy03/large-scale-medical","path":"VoComni/models/MiT_utils.py","file_url":"https://github.com/luffy03/large-scale-medical/blob/HEAD/VoComni/models/MiT_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"2b5489053b222e00","mcp_get_code":{"code_sha256":"2b5489053b222e00"}},{"arxiv_id":"2407.17486","paper":"/paper/learning-from-memory-non-parametric-memory","title":"Learning from Memory: Non-Parametric Memory Augmented Self-Supervised Learning of Visual Features","date":"2024-07-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sthalles/MaSSL","path":"utils.py","file_url":"https://github.com/sthalles/MaSSL/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2b5489053b222e00","mcp_get_code":{"code_sha256":"2b5489053b222e00"}},{"arxiv_id":"2406.07487","paper":"/paper/glad-towards-better-reconstruction-with","title":"GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection","date":"2024-06-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hyao1/glad","path":"dino/utils.py","file_url":"https://github.com/hyao1/glad/blob/HEAD/dino/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2b5489053b222e00","mcp_get_code":{"code_sha256":"2b5489053b222e00"}},{"arxiv_id":"2406.05308","paper":"/paper/weakly-supervised-set-consistency-learning","title":"Weakly Supervised Set-Consistency Learning Improves Morphological Profiling of Single-Cell Images","date":"2024-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Genentech/set-dino","path":"utils.py","file_url":"https://github.com/Genentech/set-dino/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"2b5489053b222e00","mcp_get_code":{"code_sha256":"2b5489053b222e00"}},{"arxiv_id":"2405.16567","paper":"/paper/automatic-jailbreaking-of-the-text-to-image","title":"Automatic Jailbreaking of the Text-to-Image Generative AI Systems","date":"2024-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Kim-Minseon/APGP","path":"dino_utils.py","file_url":"https://github.com/Kim-Minseon/APGP/blob/HEAD/dino_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2b5489053b222e00","mcp_get_code":{"code_sha256":"2b5489053b222e00"}},{"arxiv_id":"2404.03392","paper":"/paper/two-tricks-to-improve-unsupervised","title":"Boosting Unsupervised Segmentation Learning","date":"2024-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alperensari/segmentation-tricks","path":"utils.py","file_url":"https://github.com/alperensari/segmentation-tricks/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2b5489053b222e00","mcp_get_code":{"code_sha256":"2b5489053b222e00"}},{"arxiv_id":"2403.07700","paper":"/paper/cuvler-enhanced-unsupervised-object","title":"CuVLER: Enhanced Unsupervised Object Discoveries through Exhaustive Self-Supervised Transformers","date":"2024-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shahaf-arica/CuVLER","path":"dino/utils.py","file_url":"https://github.com/shahaf-arica/CuVLER/blob/HEAD/dino/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2b5489053b222e00","mcp_get_code":{"code_sha256":"2b5489053b222e00"}},{"arxiv_id":"2403.01482","paper":"/paper/eagle-eigen-aggregation-learning-for-object","title":"EAGLE: Eigen Aggregation Learning for Object-Centric Unsupervised Semantic Segmentation","date":"2024-03-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MICV-yonsei/EAGLE","path":"src_EAGLE/dino/utils.py","file_url":"https://github.com/MICV-yonsei/EAGLE/blob/HEAD/src_EAGLE/dino/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2b5489053b222e00","mcp_get_code":{"code_sha256":"2b5489053b222e00"}},{"arxiv_id":"2401.12233","paper":"/paper/memorization-in-self-supervised-learning","title":"Memorization in Self-Supervised Learning Improves Downstream Generalization","date":"2024-01-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sprintml/memorizationssl","path":"models/DINO/utils.py","file_url":"https://github.com/sprintml/memorizationssl/blob/HEAD/models/DINO/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2b5489053b222e00","mcp_get_code":{"code_sha256":"2b5489053b222e00"}},{"arxiv_id":"2311.16432","paper":"/paper/text-driven-image-editing-via-learnable","title":"Text-Driven Image Editing via Learnable Regions","date":"2023-11-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yuanze-lin/Learnable_Regions","path":"models/dino_utils.py","file_url":"https://github.com/yuanze-lin/Learnable_Regions/blob/HEAD/models/dino_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2b5489053b222e00","mcp_get_code":{"code_sha256":"2b5489053b222e00"}},{"arxiv_id":"2310.17874","paper":"/paper/smooseg-smoothness-prior-for-unsupervised-1","title":"SmooSeg: Smoothness Prior for Unsupervised Semantic Segmentation","date":"2023-10-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mc-lan/smooseg","path":"models/dino/utils.py","file_url":"https://github.com/mc-lan/smooseg/blob/HEAD/models/dino/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2b5489053b222e00","mcp_get_code":{"code_sha256":"2b5489053b222e00"}},{"arxiv_id":"2309.05300","paper":"/paper/decur-decoupling-common-unique","title":"Decoupling Common and Unique Representations for Multimodal Self-supervised Learning","date":"2023-09-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhu-xlab/dino-mm","path":"models/dino/utils.py","file_url":"https://github.com/zhu-xlab/dino-mm/blob/HEAD/models/dino/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"2b5489053b222e00","mcp_get_code":{"code_sha256":"2b5489053b222e00"}},{"arxiv_id":"2308.01948","paper":"/paper/a-multidimensional-analysis-of-social-biases","title":"A Multidimensional Analysis of Social Biases in Vision Transformers","date":"2023-08-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jannik-brinkmann/social-biases-in-vision-transformers","path":"src/embedding_extractors/models/dino/utils.py","file_url":"https://github.com/jannik-brinkmann/social-biases-in-vision-transformers/blob/HEAD/src/embedding_extractors/models/dino/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2b5489053b222e00","mcp_get_code":{"code_sha256":"2b5489053b222e00"}},{"arxiv_id":"2305.11435","paper":"/paper/syllable-discovery-and-cross-lingual","title":"Syllable Discovery and Cross-Lingual Generalization in a Visually Grounded, Self-Supervised Speech Model","date":"2023-05-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jasonppy/syllable-discovery","path":"models/vit_utils.py","file_url":"https://github.com/jasonppy/syllable-discovery/blob/HEAD/models/vit_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"2b5489053b222e00","mcp_get_code":{"code_sha256":"2b5489053b222e00"}},{"arxiv_id":"2303.16947","paper":"/paper/de-coupling-and-de-positioning-dense-self","title":"De-coupling and De-positioning Dense Self-supervised Learning","date":"2023-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ztt1024/densessl","path":"utils.py","file_url":"https://github.com/ztt1024/densessl/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2b5489053b222e00","mcp_get_code":{"code_sha256":"2b5489053b222e00"}},{"arxiv_id":"2303.13245","paper":"/paper/croc-cross-view-online-clustering-for-dense","title":"CrOC: Cross-View Online Clustering for Dense Visual Representation Learning","date":"2023-03-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stegmuel/CrOC","path":"utils/utils.py","file_url":"https://github.com/stegmuel/CrOC/blob/HEAD/utils/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2b5489053b222e00","mcp_get_code":{"code_sha256":"2b5489053b222e00"}},{"arxiv_id":"2303.09165","paper":"/paper/a-new-benchmark-on-the-utility-of-synthetic","title":"A New Benchmark: On the Utility of Synthetic Data with Blender for Bare Supervised Learning and Downstream Domain Adaptation","date":"2023-03-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huitangtang/on_the_utility_of_synthetic_data","path":"supervised_learning/utils.py","file_url":"https://github.com/huitangtang/on_the_utility_of_synthetic_data/blob/HEAD/supervised_learning/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2b5489053b222e00","mcp_get_code":{"code_sha256":"2b5489053b222e00"}},{"arxiv_id":"2209.09341","paper":"/paper/a-simple-and-powerful-global-optimization-for","title":"A Simple and Powerful Global Optimization for Unsupervised Video Object Segmentation","date":"2022-09-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ponimatkin/ssl-vos","path":"dino_utils.py","file_url":"https://github.com/ponimatkin/ssl-vos/blob/HEAD/dino_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2b5489053b222e00","mcp_get_code":{"code_sha256":"2b5489053b222e00"}},{"arxiv_id":"2209.03917","paper":"/paper/exploring-target-representations-for-masked","title":"Exploring Target Representations for Masked Autoencoders","date":"2022-09-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liuxingbin/dbot","path":"evaluation/utils.py","file_url":"https://github.com/liuxingbin/dbot/blob/HEAD/evaluation/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"2b5489053b222e00","mcp_get_code":{"code_sha256":"2b5489053b222e00"}},{"arxiv_id":"2203.14415","paper":"/paper/mugs-a-multi-granular-self-supervised","title":"Mugs: A Multi-Granular Self-Supervised Learning Framework","date":"2022-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sail-sg/mugs","path":"src/optimizer.py","file_url":"https://github.com/sail-sg/mugs/blob/HEAD/src/optimizer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"3adab9891c1273b6","mcp_get_code":{"code_sha256":"3adab9891c1273b6"}},{"arxiv_id":"2203.08414","paper":"/paper/unsupervised-semantic-segmentation-by-2","title":"Unsupervised Semantic Segmentation by Distilling Feature Correspondences","date":"2022-03-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"leggedrobotics/self_supervised_segmentation","path":"stego/backbones/dino/utils.py","file_url":"https://github.com/leggedrobotics/self_supervised_segmentation/blob/HEAD/stego/backbones/dino/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2b5489053b222e00","mcp_get_code":{"code_sha256":"2b5489053b222e00"}},{"arxiv_id":"2202.11929","paper":"/paper/word-segmentation-on-discovered-phone-units","title":"Word Segmentation on Discovered Phone Units with Dynamic Programming and Self-Supervised Scoring","date":"2022-02-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jasonppy/word-discovery","path":"models/vit_utils.py","file_url":"https://github.com/jasonppy/word-discovery/blob/HEAD/models/vit_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"2b5489053b222e00","mcp_get_code":{"code_sha256":"2b5489053b222e00"}},{"arxiv_id":"2111.12958","paper":"/paper/self-distilled-self-supervised-representation","title":"Self-Distilled Self-Supervised Representation Learning","date":"2021-11-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hagiss/sdssl","path":"utils.py","file_url":"https://github.com/hagiss/sdssl/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2b5489053b222e00","mcp_get_code":{"code_sha256":"2b5489053b222e00"}},{"arxiv_id":"2109.13228","paper":"/paper/pass-an-imagenet-replacement-for-self","title":"PASS: An ImageNet replacement for self-supervised pretraining without humans","date":"2021-09-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/dino","path":"utils.py","file_url":"https://github.com/facebookresearch/dino/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"2b5489053b222e00","mcp_get_code":{"code_sha256":"2b5489053b222e00"}},{"arxiv_id":"2106.09681","paper":"/paper/xcit-cross-covariance-image-transformers","title":"XCiT: Cross-Covariance Image Transformers","date":"2021-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"adrienangeli/dino","path":"utils.py","file_url":"https://github.com/adrienangeli/dino/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"2b5489053b222e00","mcp_get_code":{"code_sha256":"2b5489053b222e00"}},{"arxiv_id":"1807.09190","paper":"/paper/premvos-proposal-generation-refinement-and","title":"PReMVOS: Proposal-generation, Refinement and Merging for Video Object Segmentation","date":"2018-07-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AyaLotfy/PReMVOS-trial","path":"code/ReID_net/Util.py","file_url":"https://github.com/AyaLotfy/PReMVOS-trial/blob/HEAD/code/ReID_net/Util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fe9c62a98e81d5ca","mcp_get_code":{"code_sha256":"fe9c62a98e81d5ca"}},{"arxiv_id":"Zhou_Towards_Effective_Foundation_Model_Adaptation_for_Extreme_Cross-Domain_Few-Shot_Learning_ICCV_2025_paper","paper":null,"title":"arXiv:Zhou_Towards_Effective_Foundation_Model_Adaptation_for_Extreme_Cross-Domain_Few-Shot_Learning_ICCV_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"NWPUZhoufei/FMA","path":"dino_utils.py","file_url":"https://github.com/NWPUZhoufei/FMA/blob/HEAD/dino_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"2b5489053b222e00","mcp_get_code":{"code_sha256":"2b5489053b222e00"}},{"arxiv_id":"Sun_Pixel-level_Semantic_Correspondence_through_Layout-aware_Representation_Learning_and_Multi-scale_Matching_CVPR_2024_paper","paper":null,"title":"arXiv:Sun_Pixel-level_Semantic_Correspondence_through_Layout-aware_Representation_Learning_and_Multi-scale_Matching_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"YXSUNMADMAX/LPMFlow","path":"models/feature_backbones/utils.py","file_url":"https://github.com/YXSUNMADMAX/LPMFlow/blob/HEAD/models/feature_backbones/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"2b5489053b222e00","mcp_get_code":{"code_sha256":"2b5489053b222e00"}},{"arxiv_id":"136810551","paper":null,"title":"arXiv:136810551","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"YtongXie/UniMiSS-code","path":"UniMiSS/utils.py","file_url":"https://github.com/YtongXie/UniMiSS-code/blob/HEAD/UniMiSS/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2b5489053b222e00","mcp_get_code":{"code_sha256":"2b5489053b222e00"}}]}