{"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/is-howmany","entry":"is_howmany","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":5,"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":1,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":1,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":0},"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":"2105.08913","paper":"/paper/multiple-meta-model-quantifying-for-medical","title":"Multiple Meta-model Quantifying for Medical Visual Question Answering","date":"2021-05-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aioz-ai/MICCAI19-MedVQA","path":"dataset_RAD.py","file_url":"https://github.com/aioz-ai/MICCAI19-MedVQA/blob/HEAD/dataset_RAD.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ac9f54e37ab206e4","mcp_get_code":{"code_sha256":"ac9f54e37ab206e4"}},{"arxiv_id":"2006.14744","paper":"/paper/graph-optimal-transport-for-cross-domain","title":"Graph Optimal Transport for Cross-Domain Alignment","date":"2020-06-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LiqunChen0606/Graph-Optimal-Transport","path":"BAN_vqa/dataset.py","file_url":"https://github.com/LiqunChen0606/Graph-Optimal-Transport/blob/HEAD/BAN_vqa/dataset.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ac9f54e37ab206e4","mcp_get_code":{"code_sha256":"ac9f54e37ab206e4"}},{"arxiv_id":"2003.10286","paper":"/paper/pathvqa-30000-questions-for-medical-visual","title":"PathVQA: 30000+ Questions for Medical Visual Question Answering","date":"2020-03-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"KaveeshaSIlva/PathVQA","path":"baselines/method2/dataset.py","file_url":"https://github.com/KaveeshaSIlva/PathVQA/blob/HEAD/baselines/method2/dataset.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac9f54e37ab206e4","mcp_get_code":{"code_sha256":"ac9f54e37ab206e4"}},{"arxiv_id":"1903.12314","paper":"/paper/relation-aware-graph-attention-network-for","title":"Relation-Aware Graph Attention Network for Visual Question Answering","date":"2019-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"linjieli222/VQA_ReGAT","path":"dataset.py","file_url":"https://github.com/linjieli222/VQA_ReGAT/blob/HEAD/dataset.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"ac9f54e37ab206e4","mcp_get_code":{"code_sha256":"ac9f54e37ab206e4"}},{"arxiv_id":"1805.07932","paper":"/paper/bilinear-attention-networks","title":"Bilinear Attention Networks","date":"2018-05-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jnhwkim/ban-vqa","path":"dataset.py","file_url":"https://github.com/jnhwkim/ban-vqa/blob/HEAD/dataset.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ac9f54e37ab206e4","mcp_get_code":{"code_sha256":"ac9f54e37ab206e4"}}]}