{"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/algorithm","entry":"Algorithm","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":26,"n_papers_ran":14,"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":30,"n_samples_ran":14,"n_samples_fingerprinted":0,"n_places":30,"n_places_pointer_only":13,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":14,"unverified":16},"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":"2608.02612","paper":"/paper/arxiv-2608-02612","title":"BBOWP-Bench: Evaluating LLMs on Black-Box Optimization Word Problems","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"shiralab/bbowp-bench","path":"src/bbowp/schema/bbowp.py","file_url":"https://github.com/shiralab/bbowp-bench/blob/HEAD/src/bbowp/schema/bbowp.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7e046ec2ef093a19","mcp_get_code":{"code_sha256":"7e046ec2ef093a19"}},{"arxiv_id":"2602.09574","paper":"/paper/arxiv-2602-09574","title":"Aligning Tree-Search Policies with Fixed Token Budgets in Test-Time Scaling of LLMs","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"Sora-Miyamoto/bg-mcts","path":"treesearch/src/treesearch/algos/bg_mcts.py","file_url":"https://github.com/Sora-Miyamoto/bg-mcts/blob/HEAD/treesearch/src/treesearch/algos/bg_mcts.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":"f00978caea326175","mcp_get_code":{"code_sha256":"f00978caea326175"}},{"arxiv_id":"2505.24048","paper":"/paper/neurontune-towards-self-guided-spurious-bias","title":"NeuronTune: Towards Self-Guided Spurious Bias Mitigation","date":"2025-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gtzheng/neurontune","path":"algorithms/neuron_tune.py","file_url":"https://github.com/gtzheng/neurontune/blob/HEAD/algorithms/neuron_tune.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7a4ce898b756fc69","mcp_get_code":{"code_sha256":"7a4ce898b756fc69"}},{"arxiv_id":"2505.13910","paper":"/paper/shortcutprobe-probing-prediction-shortcuts","title":"ShortcutProbe: Probing Prediction Shortcuts for Learning Robust Models","date":"2025-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gtzheng/ShortcutProbe","path":"algorithms/shortcut_probe.py","file_url":"https://github.com/gtzheng/ShortcutProbe/blob/HEAD/algorithms/shortcut_probe.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9c581ceeab1e7194","mcp_get_code":{"code_sha256":"9c581ceeab1e7194"}},{"arxiv_id":"2410.13855","paper":"/paper/diffusing-states-and-matching-scores-a-new","title":"Diffusing States and Matching Scores: A New Framework for Imitation Learning","date":"2024-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ziqian2000/smiling","path":"algorithm/diffusion.py","file_url":"https://github.com/ziqian2000/smiling/blob/HEAD/algorithm/diffusion.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"e44a493bd51f52ec","mcp_get_code":{"code_sha256":"e44a493bd51f52ec"}},{"arxiv_id":"2406.19626","paper":"/paper/safety-through-feedback-in-constrained-rl","title":"Safety through feedback in Constrained RL","date":"2024-06-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shshnkreddy/RLSF","path":"Sources/algo/prefim.py","file_url":"https://github.com/shshnkreddy/RLSF/blob/HEAD/Sources/algo/prefim.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"61bc5c3a0320b9ea","mcp_get_code":{"code_sha256":"61bc5c3a0320b9ea"}},{"arxiv_id":"2402.11338","paper":"/paper/fair-classification-with-partial-feedback-an","title":"Fair Classification with Partial Feedback: An Exploration-Based Data Collection Approach","date":"2024-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vijaykeswani/fair-classification-with-partial-feedback","path":"algorithms.py","file_url":"https://github.com/vijaykeswani/fair-classification-with-partial-feedback/blob/HEAD/algorithms.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fef8b9fd9fb75796","mcp_get_code":{"code_sha256":"fef8b9fd9fb75796"}},{"arxiv_id":"2307.12502","paper":"/paper/cross-contrastive-feature-perturbation-for","title":"Cross Contrasting Feature Perturbation for Domain Generalization","date":"2023-07-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hackmebroo/cross-contrasting-feature-perturbation-for-domain-generalization","path":"algorithms.py","file_url":"https://github.com/hackmebroo/cross-contrasting-feature-perturbation-for-domain-generalization/blob/HEAD/algorithms.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d4ab1855fefe42b4","mcp_get_code":{"code_sha256":"d4ab1855fefe42b4"}},{"arxiv_id":"2304.13774","paper":"/paper/distance-weighted-supervised-learning-for","title":"Distance Weighted Supervised Learning for Offline Interaction Data","date":"2023-04-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jhejna/dwsl","path":"research/algs/dwsl.py","file_url":"https://github.com/jhejna/dwsl/blob/HEAD/research/algs/dwsl.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0bc5a55290b00b3d","mcp_get_code":{"code_sha256":"0bc5a55290b00b3d"}},{"arxiv_id":"2303.10353","paper":"/paper/sharpness-aware-gradient-matching-for-domain","title":"Sharpness-Aware Gradient Matching for Domain Generalization","date":"2023-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wang-pengfei/sagm","path":"domainbed/algorithms/algorithms.py","file_url":"https://github.com/wang-pengfei/sagm/blob/HEAD/domainbed/algorithms/algorithms.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d67127a3c5e535a0","mcp_get_code":{"code_sha256":"d67127a3c5e535a0"}},{"arxiv_id":"2210.15836","paper":"/paper/domain-generalization-through-the-lens-of","title":"Domain Generalization through the Lens of Angular Invariance","date":"2022-10-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JinYujie99/aidgn","path":"domainbed/algorithms.py","file_url":"https://github.com/JinYujie99/aidgn/blob/HEAD/domainbed/algorithms.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"18d68e605bd8675c","mcp_get_code":{"code_sha256":"18d68e605bd8675c"}},{"arxiv_id":"2205.14546","paper":"/paper/the-missing-invariance-principle-found-the","title":"The Missing Invariance Principle Found -- the Reciprocal Twin of Invariant Risk Minimization","date":"2022-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ibm/mri","path":"custom_algorithms.py","file_url":"https://github.com/ibm/mri/blob/HEAD/custom_algorithms.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"60d5898728b2ef53","mcp_get_code":{"code_sha256":"60d5898728b2ef53"}},{"arxiv_id":"2205.12418","paper":"/paper/tiered-reinforcement-learning-pessimism-in","title":"Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret","date":"2022-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiaweihhuang/tiered-rl-experiments","path":"Tabular_MDP.py","file_url":"https://github.com/jiaweihhuang/tiered-rl-experiments/blob/HEAD/Tabular_MDP.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2e1f15ef32cf7d0a","mcp_get_code":{"code_sha256":"2e1f15ef32cf7d0a"}},{"arxiv_id":"2204.04384","paper":"/paper/the-two-dimensions-of-worst-case-training-and","title":"The Two Dimensions of Worst-case Training and the Integrated Effect for Out-of-domain Generalization","date":"2022-04-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"oodbag/w2d","path":"DomainBed/domainbed/algorithms.py","file_url":"https://github.com/oodbag/w2d/blob/HEAD/DomainBed/domainbed/algorithms.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f4f62760b15a2f01","mcp_get_code":{"code_sha256":"f4f62760b15a2f01"}},{"arxiv_id":"2203.09513","paper":"/paper/on-multi-domain-long-tailed-recognition","title":"On Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization and Beyond","date":"2022-03-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YyzHarry/multi-domain-imbalance","path":"mdlt/learning/algorithms.py","file_url":"https://github.com/YyzHarry/multi-domain-imbalance/blob/HEAD/mdlt/learning/algorithms.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bd0ab0e227b395a8","mcp_get_code":{"code_sha256":"bd0ab0e227b395a8"}},{"arxiv_id":"2201.11259","paper":"/paper/controlling-directions-orthogonal-to-a-1","title":"Controlling Directions Orthogonal to a Classifier","date":"2022-01-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"newbeeer/orthogonal_classifier","path":"style_transfer/algorithms.py","file_url":"https://github.com/newbeeer/orthogonal_classifier/blob/HEAD/style_transfer/algorithms.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"448c00adc68aba1e","mcp_get_code":{"code_sha256":"448c00adc68aba1e"}},{"arxiv_id":"2107.00052","paper":"/paper/stochastic-gradient-descent-ascent-and","title":"Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity","date":"2021-06-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hugobb/StochasticGamesOpt","path":"hamiltonian/algorithms/co.py","file_url":"https://github.com/hugobb/StochasticGamesOpt/blob/HEAD/hamiltonian/algorithms/co.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5e68062294d17a16","mcp_get_code":{"code_sha256":"5e68062294d17a16"}},{"arxiv_id":"2106.04732","paper":"/paper/adamatch-a-unified-approach-to-semi","title":"AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation","date":"2021-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zwenyu/UniSSDA","path":"algorithms/algorithms.py","file_url":"https://github.com/zwenyu/UniSSDA/blob/HEAD/algorithms/algorithms.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"39253084070f2f95","mcp_get_code":{"code_sha256":"39253084070f2f95"}},{"arxiv_id":"2103.02014","paper":"/paper/online-adversarial-attacks","title":"Online Adversarial Attacks","date":"2021-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/OnlineAttacks","path":"online_attacks/online_algorithms/stochastic_virtual.py","file_url":"https://github.com/facebookresearch/OnlineAttacks/blob/HEAD/online_attacks/online_algorithms/stochastic_virtual.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"99fa9075f7f15617","mcp_get_code":{"code_sha256":"99fa9075f7f15617"}},{"arxiv_id":"2006.05582","paper":"/paper/contrastive-multi-view-representation","title":"Contrastive Multi-View Representation Learning on Graphs","date":"2020-06-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"timothewt/AdvancedGraphClustering","path":"src/algorithms/deep/MVGRL.py","file_url":"https://github.com/timothewt/AdvancedGraphClustering/blob/HEAD/src/algorithms/deep/MVGRL.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aa8592921f0a1166","mcp_get_code":{"code_sha256":"aa8592921f0a1166"}},{"arxiv_id":"2003.04475","paper":"/paper/domain-adaptation-with-conditional","title":"Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift","date":"2020-03-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"timgaripov/asa","path":"support_alignment/core/algorithms.py","file_url":"https://github.com/timgaripov/asa/blob/HEAD/support_alignment/core/algorithms.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"abd2c75272446f0b","mcp_get_code":{"code_sha256":"abd2c75272446f0b"}},{"arxiv_id":"1901.08573","paper":"/paper/theoretically-principled-trade-off-between","title":"Theoretically Principled Trade-off between Robustness and Accuracy","date":"2019-01-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"arobey1/advbench","path":"advbench/algorithms.py","file_url":"https://github.com/arobey1/advbench/blob/HEAD/advbench/algorithms.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"afdbbd69eb450e7d","mcp_get_code":{"code_sha256":"afdbbd69eb450e7d"}},{"arxiv_id":"1807.03748","paper":"/paper/representation-learning-with-contrastive","title":"Representation Learning with Contrastive Predictive Coding","date":"2018-07-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"emadeldeen24/eval_ssl_ssc","path":"algorithms.py","file_url":"https://github.com/emadeldeen24/eval_ssl_ssc/blob/HEAD/algorithms.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f1e8c76f248d416b","mcp_get_code":{"code_sha256":"f1e8c76f248d416b"}},{"arxiv_id":"1801.01290","paper":"/paper/soft-actor-critic-off-policy-maximum-entropy","title":"Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor","date":"2018-01-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ku2482/discor.pytorch","path":"discor/algorithm/sac.py","file_url":"https://github.com/ku2482/discor.pytorch/blob/HEAD/discor/algorithm/sac.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"eaf547baf5f33e43","mcp_get_code":{"code_sha256":"eaf547baf5f33e43"}},{"arxiv_id":"1801.01290","paper":"/paper/soft-actor-critic-off-policy-maximum-entropy","title":"Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor","date":"2018-01-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"garyzyr001/rethinking-airl","path":"airl/algo/sac.py","file_url":"https://github.com/garyzyr001/rethinking-airl/blob/HEAD/airl/algo/sac.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"803d90cd603ff8c0","mcp_get_code":{"code_sha256":"803d90cd603ff8c0"}},{"arxiv_id":"1801.01290","paper":"/paper/soft-actor-critic-off-policy-maximum-entropy","title":"Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor","date":"2018-01-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ku2482/gail-airl-ppo.pytorch","path":"gail_airl_ppo/algo/sac.py","file_url":"https://github.com/ku2482/gail-airl-ppo.pytorch/blob/HEAD/gail_airl_ppo/algo/sac.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ff7a80131e64a08d","mcp_get_code":{"code_sha256":"ff7a80131e64a08d"}},{"arxiv_id":"1707.06347","paper":"/paper/proximal-policy-optimization-algorithms","title":"Proximal Policy Optimization Algorithms","date":"2017-07-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hmhuy0/SIM-RL","path":"Sources/algo/ppo.py","file_url":"https://github.com/hmhuy0/SIM-RL/blob/HEAD/Sources/algo/ppo.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d5c516ed0fa589e3","mcp_get_code":{"code_sha256":"d5c516ed0fa589e3"}},{"arxiv_id":"1707.06347","paper":"/paper/proximal-policy-optimization-algorithms","title":"Proximal Policy Optimization Algorithms","date":"2017-07-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ku2482/gail-airl-ppo.pytorch","path":"gail_airl_ppo/algo/ppo.py","file_url":"https://github.com/ku2482/gail-airl-ppo.pytorch/blob/HEAD/gail_airl_ppo/algo/ppo.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2bddf6daaed4d669","mcp_get_code":{"code_sha256":"2bddf6daaed4d669"}},{"arxiv_id":"1707.06347","paper":"/paper/proximal-policy-optimization-algorithms","title":"Proximal Policy Optimization Algorithms","date":"2017-07-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"evieq01/oodil","path":"simulated_robot/gail_airl_ppo/algo/ppo.py","file_url":"https://github.com/evieq01/oodil/blob/HEAD/simulated_robot/gail_airl_ppo/algo/ppo.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"527acc6b195e3ec9","mcp_get_code":{"code_sha256":"527acc6b195e3ec9"}},{"arxiv_id":"1703.03400","paper":"/paper/model-agnostic-meta-learning-for-fast","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","date":"2017-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mikehuisman/revisiting-learned-optimizers","path":"algorithms/maml.py","file_url":"https://github.com/mikehuisman/revisiting-learned-optimizers/blob/HEAD/algorithms/maml.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f938a07c11213649","mcp_get_code":{"code_sha256":"f938a07c11213649"}}]}