{"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/residual","entry":"Residual","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":24,"n_papers_ran":22,"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":31,"n_samples_ran":28,"n_samples_fingerprinted":24,"n_places":31,"n_places_pointer_only":11,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":28,"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":"2403.12580","paper":"/paper/real-iad-a-real-world-multi-view-dataset-for","title":"Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection","date":"2024-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhangzjn/ader","path":"model/realnet.py","file_url":"https://github.com/zhangzjn/ader/blob/HEAD/model/realnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9a28065e55579c87","mcp_get_code":{"code_sha256":"9a28065e55579c87"}},{"arxiv_id":"2403.00939","paper":"/paper/g3dr-generative-3d-reconstruction-in-imagenet","title":"G3DR: Generative 3D Reconstruction in ImageNet","date":"2024-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"preddy5/G3DR","path":"src/unet.py","file_url":"https://github.com/preddy5/G3DR/blob/HEAD/src/unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"df64fd9fcc993ed7","mcp_get_code":{"code_sha256":"df64fd9fcc993ed7"}},{"arxiv_id":"2401.16456","paper":"/paper/shvit-single-head-vision-transformer-with","title":"SHViT: Single-Head Vision Transformer with Memory Efficient Macro Design","date":"2024-01-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ysj9909/SHViT","path":"model/shvit.py","file_url":"https://github.com/ysj9909/SHViT/blob/HEAD/model/shvit.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"61f485bf32a93933","mcp_get_code":{"code_sha256":"61f485bf32a93933"}},{"arxiv_id":"2310.14017","paper":"/paper/contrast-everything-a-hierarchical","title":"Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series","date":"2023-10-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"emadeldeen24/TS-TCC","path":"models/TC.py","file_url":"https://github.com/emadeldeen24/TS-TCC/blob/HEAD/models/TC.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8907e91277107509","mcp_get_code":{"code_sha256":"8907e91277107509"}},{"arxiv_id":"2309.03729","paper":"/paper/phasic-content-fusing-diffusion-model-with","title":"Phasic Content Fusing Diffusion Model with Directional Distribution Consistency for Few-Shot Model Adaption","date":"2023-09-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sjtuplayer/few-shot-diffusion","path":"model/big_unet.py","file_url":"https://github.com/sjtuplayer/few-shot-diffusion/blob/HEAD/model/big_unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d2f3f77a4605ff1f","mcp_get_code":{"code_sha256":"d2f3f77a4605ff1f"}},{"arxiv_id":"2305.07027","paper":"/paper/efficientvit-memory-efficient-vision","title":"EfficientViT: Memory Efficient Vision Transformer with Cascaded Group Attention","date":"2023-05-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/cream","path":"EfficientViT/classification/model/efficientvit.py","file_url":"https://github.com/microsoft/cream/blob/HEAD/EfficientViT/classification/model/efficientvit.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a45e42ca02a983d1","mcp_get_code":{"code_sha256":"a45e42ca02a983d1"}},{"arxiv_id":"2303.08085","paper":"/paper/alias-free-convnets-fractional-shift","title":"Alias-Free Convnets: Fractional Shift Invariance via Polynomial Activations","date":"2023-03-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hmichaeli/alias_free_convnets","path":"models/convnext_afc.py","file_url":"https://github.com/hmichaeli/alias_free_convnets/blob/HEAD/models/convnext_afc.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6ba599fd0d2e6b0d","mcp_get_code":{"code_sha256":"6ba599fd0d2e6b0d"}},{"arxiv_id":"2301.03461","paper":"/paper/demt-deformable-mixer-transformer-for-multi","title":"DeMT: Deformable Mixer Transformer for Multi-Task Learning of Dense Prediction","date":"2023-01-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yangyangxu0/DeMT","path":"src/model/heads/demt_head.py","file_url":"https://github.com/yangyangxu0/DeMT/blob/HEAD/src/model/heads/demt_head.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"617fc5cd462c22cf","mcp_get_code":{"code_sha256":"617fc5cd462c22cf"}},{"arxiv_id":"2209.15571","paper":"/paper/building-normalizing-flows-with-stochastic","title":"Building Normalizing Flows with Stochastic Interpolants","date":"2022-09-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lucidrains/denoising-diffusion-pytorch","path":"denoising_diffusion_pytorch/continuous_time_gaussian_diffusion.py","file_url":"https://github.com/lucidrains/denoising-diffusion-pytorch/blob/HEAD/denoising_diffusion_pytorch/continuous_time_gaussian_diffusion.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"15688e453e256aa5","mcp_get_code":{"code_sha256":"15688e453e256aa5"}},{"arxiv_id":"2207.06405","paper":"/paper/masked-autoencoders-that-listen","title":"Masked Autoencoders that Listen","date":"2022-07-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rishikksh20/AudioMAE-pytorch","path":"audio_mae.py","file_url":"https://github.com/rishikksh20/AudioMAE-pytorch/blob/HEAD/audio_mae.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c824fe1a650a3f66","mcp_get_code":{"code_sha256":"c824fe1a650a3f66"}},{"arxiv_id":"2204.02311","paper":"/paper/palm-scaling-language-modeling-with-pathways-1","title":"PaLM: Scaling Language Modeling with Pathways","date":"2022-04-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lucidrains/CoCa-pytorch","path":"coca_pytorch/coca_pytorch.py","file_url":"https://github.com/lucidrains/CoCa-pytorch/blob/HEAD/coca_pytorch/coca_pytorch.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5fab2dfc0a4ccb06","mcp_get_code":{"code_sha256":"5fab2dfc0a4ccb06"}},{"arxiv_id":"2204.02311","paper":"/paper/palm-scaling-language-modeling-with-pathways-1","title":"PaLM: Scaling Language Modeling with Pathways","date":"2022-04-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lucidrains/PaLM-pytorch","path":"palm_pytorch/palm_pytorch.py","file_url":"https://github.com/lucidrains/PaLM-pytorch/blob/HEAD/palm_pytorch/palm_pytorch.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b7ffedd1471cf6f7","mcp_get_code":{"code_sha256":"b7ffedd1471cf6f7"}},{"arxiv_id":"2204.01697","paper":"/paper/maxvit-multi-axis-vision-transformer","title":"MaxViT: Multi-Axis Vision Transformer","date":"2022-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lucidrains/vit-pytorch","path":"vit_pytorch/max_vit.py","file_url":"https://github.com/lucidrains/vit-pytorch/blob/HEAD/vit_pytorch/max_vit.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"636a208dabc2a711","mcp_get_code":{"code_sha256":"636a208dabc2a711"}},{"arxiv_id":"2108.06693","paper":"/paper/exploring-temporal-coherence-for-more-general","title":"Exploring Temporal Coherence for More General Video Face Forgery Detection","date":"2021-08-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yinglinzheng/FTCN","path":"model/classifier/time_transformer.py","file_url":"https://github.com/yinglinzheng/FTCN/blob/HEAD/model/classifier/time_transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"780017fa8de2327d","mcp_get_code":{"code_sha256":"780017fa8de2327d"}},{"arxiv_id":"2106.05200","paper":"/paper/independent-mechanism-analysis-a-new-concept","title":"Independent mechanism analysis, a new concept?","date":"2021-06-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lgresele/independent-mechanism-analysis","path":"ima/residual.py","file_url":"https://github.com/lgresele/independent-mechanism-analysis/blob/HEAD/ima/residual.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0ee799a2b1f48c9d","mcp_get_code":{"code_sha256":"0ee799a2b1f48c9d"}},{"arxiv_id":"2105.08050","paper":"/paper/pay-attention-to-mlps","title":"Pay Attention to MLPs","date":"2021-05-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xmu-xiaoma666/External-Attention-pytorch","path":"model/mlp/g_mlp.py","file_url":"https://github.com/xmu-xiaoma666/External-Attention-pytorch/blob/HEAD/model/mlp/g_mlp.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9cceceb3671335d7","mcp_get_code":{"code_sha256":"9cceceb3671335d7"}},{"arxiv_id":"2105.08050","paper":"/paper/pay-attention-to-mlps","title":"Pay Attention to MLPs","date":"2021-05-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lucidrains/g-mlp-pytorch","path":"g_mlp_pytorch/g_mlp_pytorch.py","file_url":"https://github.com/lucidrains/g-mlp-pytorch/blob/HEAD/g_mlp_pytorch/g_mlp_pytorch.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4b858823b3eccdd7","mcp_get_code":{"code_sha256":"4b858823b3eccdd7"}},{"arxiv_id":"2105.08050","paper":"/paper/pay-attention-to-mlps","title":"Pay Attention to MLPs","date":"2021-05-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NydiaAI/g-mlp-tensorflow","path":"gmlp/gmlp.py","file_url":"https://github.com/NydiaAI/g-mlp-tensorflow/blob/HEAD/gmlp/gmlp.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f9f191324f0a26fb","mcp_get_code":{"code_sha256":"f9f191324f0a26fb"}},{"arxiv_id":"2104.13188","paper":"/paper/rethinking-bisenet-for-real-time-semantic","title":"Rethinking BiSeNet For Real-time Semantic Segmentation","date":"2021-04-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Deci-AI/super-gradients","path":"src/super_gradients/training/models/segmentation_models/stdc.py","file_url":"https://github.com/Deci-AI/super-gradients/blob/HEAD/src/super_gradients/training/models/segmentation_models/stdc.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"9593929516a67196","mcp_get_code":{"code_sha256":"9593929516a67196"}},{"arxiv_id":"2104.01136","paper":"/paper/levit-a-vision-transformer-in-convnet-s","title":"LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference","date":"2021-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/LeViT","path":"levit.py","file_url":"https://github.com/facebookresearch/LeViT/blob/HEAD/levit.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"2e3b4d9ce0862869","mcp_get_code":{"code_sha256":"2e3b4d9ce0862869"}},{"arxiv_id":"2104.01136","paper":"/paper/levit-a-vision-transformer-in-convnet-s","title":"LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference","date":"2021-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gatech-eic/vitcod","path":"Algorithm/levit/levit.py","file_url":"https://github.com/gatech-eic/vitcod/blob/HEAD/Algorithm/levit/levit.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":"37406361a9c395c4","mcp_get_code":{"code_sha256":"37406361a9c395c4"}},{"arxiv_id":"2104.00298","paper":"/paper/efficientnetv2-smaller-models-and-faster","title":"EfficientNetV2: Smaller Models and Faster Training","date":"2021-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jahongir7174/EfficientNetV2","path":"nets/nn.py","file_url":"https://github.com/jahongir7174/EfficientNetV2/blob/HEAD/nets/nn.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"acb059769b4a4327","mcp_get_code":{"code_sha256":"acb059769b4a4327"}},{"arxiv_id":"2010.11929","paper":"/paper/an-image-is-worth-16x16-words-transformers-1","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","date":"2020-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kamalkraj/Vision-Transformer","path":"model.py","file_url":"https://github.com/kamalkraj/Vision-Transformer/blob/HEAD/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"b308d5f20c384e2e","mcp_get_code":{"code_sha256":"b308d5f20c384e2e"}},{"arxiv_id":"2007.09590","paper":"/paper/awr-adaptive-weighting-regression-for-3d-hand","title":"AWR: Adaptive Weighting Regression for 3D Hand Pose Estimation","date":"2020-07-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Elody-07/AWR-Adaptive-Weighting-Regression","path":"model/hourglass.py","file_url":"https://github.com/Elody-07/AWR-Adaptive-Weighting-Regression/blob/HEAD/model/hourglass.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3c83c6ea69e1d449","mcp_get_code":{"code_sha256":"3c83c6ea69e1d449"}},{"arxiv_id":"1711.00937","paper":"/paper/neural-discrete-representation-learning","title":"Neural Discrete Representation Learning","date":"2017-11-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ks2labs/modules","path":"models/vqvae/model.py","file_url":"https://github.com/ks2labs/modules/blob/HEAD/models/vqvae/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"230f3bafb927fa9b","mcp_get_code":{"code_sha256":"230f3bafb927fa9b"}},{"arxiv_id":"1706.03762","paper":"/paper/attention-is-all-you-need","title":"Attention Is All You Need","date":"2017-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tatp22/linformer-pytorch","path":"linformer_pytorch/linformer_pytorch.py","file_url":"https://github.com/tatp22/linformer-pytorch/blob/HEAD/linformer_pytorch/linformer_pytorch.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"44deaa19ec047717","mcp_get_code":{"code_sha256":"44deaa19ec047717"}},{"arxiv_id":"1609.02907","paper":"/paper/semi-supervised-classification-with-graph","title":"Semi-Supervised Classification with Graph Convolutional Networks","date":"2016-09-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nnaakkaaii/g2-MLP","path":"gnn/src/models/networks/gcn_node.py","file_url":"https://github.com/nnaakkaaii/g2-MLP/blob/HEAD/gnn/src/models/networks/gcn_node.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1f6d95f2dbf139ad","mcp_get_code":{"code_sha256":"1f6d95f2dbf139ad"}},{"arxiv_id":"1512.03385","paper":"/paper/deep-residual-learning-for-image-recognition","title":"Deep Residual Learning for Image Recognition","date":"2015-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hongkunsun/paratranscnn","path":"model/transformer.py","file_url":"https://github.com/hongkunsun/paratranscnn/blob/HEAD/model/transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1196df45d6af449e","mcp_get_code":{"code_sha256":"1196df45d6af449e"}},{"arxiv_id":"1512.03385","paper":"/paper/deep-residual-learning-for-image-recognition","title":"Deep Residual Learning for Image Recognition","date":"2015-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Deci-AI/super-gradients","path":"src/super_gradients/modules/skip_connections.py","file_url":"https://github.com/Deci-AI/super-gradients/blob/HEAD/src/super_gradients/modules/skip_connections.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"9d84651c7de84c0b","mcp_get_code":{"code_sha256":"9d84651c7de84c0b"}},{"arxiv_id":"1512.03385","paper":"/paper/deep-residual-learning-for-image-recognition","title":"Deep Residual Learning for Image Recognition","date":"2015-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Mayurji/Image-Classification-PyTorch","path":"ResNet.py","file_url":"https://github.com/Mayurji/Image-Classification-PyTorch/blob/HEAD/ResNet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"e134f388553c4c58","mcp_get_code":{"code_sha256":"e134f388553c4c58"}},{"arxiv_id":"1512.03385","paper":"/paper/deep-residual-learning-for-image-recognition","title":"Deep Residual Learning for Image Recognition","date":"2015-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"swasun/VQ-VAE-images","path":"src/residual.py","file_url":"https://github.com/swasun/VQ-VAE-images/blob/HEAD/src/residual.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"324b2aa4437c235c","mcp_get_code":{"code_sha256":"324b2aa4437c235c"}}]}