{"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/torch2np","entry":"torch2np","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":5,"n_papers_ran":1,"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":5,"n_samples_ran":1,"n_samples_fingerprinted":1,"n_places":5,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":0,"unverified":4},"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":"2507.15037","paper":null,"title":"arXiv:2507.15037","date":null,"month_inferred_from_arxiv_id":"2025-07","title_source":null,"repo":"Jerome-Young/OmniVTON","path":"src/methods/vton.py","file_url":"https://github.com/Jerome-Young/OmniVTON/blob/HEAD/src/methods/vton.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4f4551c2ebb9f1bd","mcp_get_code":{"code_sha256":"4f4551c2ebb9f1bd"}},{"arxiv_id":"2306.00488","paper":"/paper/reconstructing-graph-diffusion-history-from-a","title":"Reconstructing Graph Diffusion History from a Single Snapshot","date":"2023-06-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"q-rz/kdd23-ditto","path":"inc/utils.py","file_url":"https://github.com/q-rz/kdd23-ditto/blob/HEAD/inc/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0148fb4f4b21f606","mcp_get_code":{"code_sha256":"0148fb4f4b21f606"}},{"arxiv_id":"2211.02578","paper":"/paper/data-models-for-dataset-drift-controls-in","title":"Data Models for Dataset Drift Controls in Machine Learning With Optical Images","date":"2022-11-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aiaudit-org/raw2logit","path":"utils/base.py","file_url":"https://github.com/aiaudit-org/raw2logit/blob/HEAD/utils/base.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"247a4dfe7a43e089","mcp_get_code":{"code_sha256":"247a4dfe7a43e089"}},{"arxiv_id":"2006.14606","paper":"/paper/global-convergence-and-induced-kernels-of","title":"Global Convergence and Generalization Bound of Gradient-Based Meta-Learning with Deep Neural Nets","date":"2020-06-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AI-secure/Meta-Neural-Kernel","path":"maml_utils.py","file_url":"https://github.com/AI-secure/Meta-Neural-Kernel/blob/HEAD/maml_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f482f7091be9fc72","mcp_get_code":{"code_sha256":"f482f7091be9fc72"}},{"arxiv_id":"Li_Self-Supervised_Blind_Motion_Deblurring_With_Deep_Expectation_Maximization_CVPR_2023_paper","paper":null,"title":"arXiv:Li_Self-Supervised_Blind_Motion_Deblurring_With_Deep_Expectation_Maximization_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Chilie/Deblur_MCEM","path":"utils/imtools.py","file_url":"https://github.com/Chilie/Deblur_MCEM/blob/HEAD/utils/imtools.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":"7dbf5db16ef051fe","mcp_get_code":{"code_sha256":"7dbf5db16ef051fe"}}]}