{"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":"/census/squash","entry":"squash","source":"Syntology differential census (groundwork/55, run v2_2026-09-22), per sample; not an archive number","census_date":"2026-09-22","battery_sha256":["b986f7e04d794a0d88ad4c5f32cf63ec3590b5deff0192150737bc5f1c0b4677"],"runner_sha256":["5a452d0e7c0da5b80771d1be2afe3572e253568cf5c5d59a0d08ebd663d00808"],"bucket_key":"positional (rank, kind, dtype) of each array argument; the argument name is not part of the key because the harness draws the shared array from (rank, kind) and casts it to the dtype, whatever the name","bucket_fields":["rank","kind","dtype"],"claim":"Implementations sharing this entry name were each run on one shared input fixed by the positional (rank, kind, dtype) of their array arguments (the bucket). A cluster is the set whose recorded output digest (sha256 of the output rounded to 6 decimals) is identical. Identical values to six decimals on the shared input are agreement on those inputs, not a statement about the whole domain and not a substitution claim.","n_implementations_compared":8,"n_papers":5,"n_buckets":2,"n_distinct_outputs":4,"n_class_bearing":0,"not_compared":{"not_run_on_shared_input":{"n":0,"by_error":{}},"no_array_argument_ran_on_own_fixture_arguments_only":{"n":0,"n_papers":0,"recorded_shared_digest_equals_own_fixture_digest":0},"output_not_digested_non_numeric":{"n":0,"by_type":{}}},"withdrawn_excluded":0,"code_page":"/code/squash","buckets":[{"bucket":[[2,"float","float32"]],"n_implementations_compared":5,"n_papers":4,"n_not_digested":0,"not_digested_by_type":{},"clusters":[{"output_sha":"2d134f5fe6f0d5c0","size":3,"n_papers":2,"shape":[4,8],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":0.0,"members_with_recorded_values":3,"torch_reference_conventions_with_this_digest":[],"values":[0.5419857501983643,-0.46115148067474365,0.14313212037086487,-0.207724928855896,-0.27145445346832275,-0.30982571840286255,-0.1992684155702591,0.17224721610546112,0.1500888168811798,0.2774401605129242,-0.11489298939704895,-0.4536566436290741,-0.0834609717130661,0.6716875433921814,-0.1489051729440689,0.1592445820569992,0.02374640479683876,-0.5804232954978943,0.1545451432466507,-0.1452728509902954,-0.00808085035532713,-0.42230820655822754,-0.19201156497001648,0.3518122136592865,-0.25032344460487366,0.13632100820541382,0.29731810092926025,0.3201414942741394,-0.38566863536834717,0.40090686082839966,-0.416010320186615,-0.18692439794540405],"values_recorded":32,"members":[{"code_sha256_prefix":"89d981e0b618190e","path":"capsnet.py","papers":["2311.04245","1710.09829"],"paper_pages":[{"arxiv_id":"2311.04245","page":"/paper/gpt-st-generative-pre-training-of-spatio"},{"arxiv_id":"1710.09829","page":"/paper/dynamic-routing-between-capsules"}],"arg_sig_recorded":[["x",2,"float","float32"]],"scalar_args":{"dim":"-1"},"class_bearing":false},{"code_sha256_prefix":"36782ecd204f05d4","path":"capsules.py","papers":["1710.09829"],"paper_pages":[{"arxiv_id":"1710.09829","page":"/paper/dynamic-routing-between-capsules"}],"arg_sig_recorded":[["s",2,"float","float32"]],"scalar_args":{"dim":"-1"},"class_bearing":false},{"code_sha256_prefix":"c0581450b12d51cb","path":"model/Pretrain_model/GPTST.py","papers":["2311.04245"],"paper_pages":[{"arxiv_id":"2311.04245","page":"/paper/gpt-st-generative-pre-training-of-spatio"}],"arg_sig_recorded":[["x",2,"float","float32"]],"scalar_args":{"dim":"-1"},"class_bearing":false}]},{"output_sha":"6201e41e7d1860a1","size":1,"n_papers":2,"shape":[4,8],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[0.5419856905937195,-0.46115145087242126,0.14313212037086487,-0.207724928855896,-0.27145442366600037,-0.30982568860054016,-0.1992684155702591,0.17224721610546112,0.15008880198001862,0.2774401605129242,-0.11489298939704895,-0.4536566138267517,-0.0834609642624855,0.6716875433921814,-0.1489051729440689,0.159244567155838,0.02374640479683876,-0.5804232954978943,0.1545451432466507,-0.14527283608913422,-0.00808085035532713,-0.42230817675590515,-0.19201155006885529,0.3518121838569641,-0.25032347440719604,0.136321023106575,0.29731813073158264,0.3201415240764618,-0.38566866517066956,0.40090689063072205,-0.4160103499889374,-0.18692441284656525],"values_recorded":32,"members":[{"code_sha256_prefix":"e8e375ca46107111","path":"CapsDiscriminator.py","papers":["1710.09829","1406.2661"],"paper_pages":[{"arxiv_id":"1710.09829","page":"/paper/dynamic-routing-between-capsules"},{"arxiv_id":"1406.2661","page":"/paper/generative-adversarial-networks"}],"arg_sig_recorded":[["inputs",2,"float","float32"]],"scalar_args":{"axis":"-1"},"class_bearing":false}]},{"output_sha":"bd05891f8911cd89","size":1,"n_papers":1,"shape":[4,8],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[0.5419857501983643,-0.46115151047706604,0.14313213527202606,-0.2077249437570572,-0.27145445346832275,-0.30982571840286255,-0.1992684304714203,0.17224723100662231,0.15008880198001862,0.2774401605129242,-0.11489298939704895,-0.4536566138267517,-0.0834609642624855,0.6716875433921814,-0.1489051729440689,0.159244567155838,0.02374640479683876,-0.5804232954978943,0.1545451432466507,-0.14527283608913422,-0.00808085035532713,-0.42230817675590515,-0.19201155006885529,0.3518121838569641,-0.25032344460487366,0.13632100820541382,0.29731810092926025,0.3201414942741394,-0.38566863536834717,0.40090686082839966,-0.416010320186615,-0.18692439794540405],"values_recorded":32,"members":[{"code_sha256_prefix":"4ac5c90dc72f57f2","path":"deepcaps.py","papers":["1904.09546"],"paper_pages":[{"arxiv_id":"1904.09546","page":"/paper/deepcaps-going-deeper-with-capsule-networks"}],"arg_sig_recorded":[["s",2,"float","float32"]],"scalar_args":{"dim":"-1"},"class_bearing":false}]}]},{"bucket":[[3,"float","float32"]],"n_implementations_compared":3,"n_papers":2,"n_not_digested":0,"not_digested_by_type":{},"clusters":[{"output_sha":"3f19b1a3dddce392","size":3,"n_papers":2,"shape":[2,4,8],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":5.960464477539063e-08,"members_with_recorded_values":3,"torch_reference_conventions_with_this_digest":[],"values":[-0.28596511483192444,0.5388227105140686,-0.40304917097091675,-0.08267340064048767,0.04834651201963425,-0.4593871235847473,-0.1089092344045639,-0.12203039228916168,-0.7147637009620667,0.003966865595430136,-0.28365465998649597,-0.25801193714141846,0.2795315980911255,-0.056610990315675735,0.05068330466747284,0.13596056401729584,0.6098716259002686,-0.24180294573307037,-0.02839665487408638,0.251266211271286,-0.18506906926631927,0.36477234959602356,-0.13091957569122314,0.31045979261398315,0.2832848131656647,0.27421310544013977,-0.2240077257156372,-0.09091096371412277,-0.3068942427635193,0.3683892488479614,-0.2705564498901367,0.5452003479003906,-0.26576972007751465,0.5187351107597351,-0.32803425192832947,-0.22321714460849762,0.2774065434932709,0.17506109178066254,0.012225151993334293,0.48355305194854736,-0.4253017008304596,-0.32014554738998413,-0.3618904650211334,-0.244694322347641,0.3506011962890625,-0.05130548030138016,0.13898764550685883,0.3225652575492859,-0.14192509651184082,-0.04078267514705658,0.3131537437438965,-0.41730642318725586,0.22081802785396576,0.07882529497146606,-0.29633858799934387,-0.4029157757759094,-0.32281023263931274,0.29328492283821106,0.41976475715637207,-0.14761456847190857,0.47632578015327454,0.31499814987182617,-0.32302480936050415,-0.21829627454280853],"values_recorded":64,"members":[{"code_sha256_prefix":"ac7650b0121cd6c9","path":"layers/cap_layer.py","papers":["1710.09829","1808.03749"],"paper_pages":[{"arxiv_id":"1710.09829","page":"/paper/dynamic-routing-between-capsules"},{"arxiv_id":"1808.03749","page":"/paper/neural-network-encapsulation"}],"arg_sig_recorded":[["vec",3,"float","float32"]],"scalar_args":{"manner":"'paper'"},"class_bearing":false},{"code_sha256_prefix":"43a28458e2019a51","path":"functional.py","papers":["1710.09829"],"paper_pages":[{"arxiv_id":"1710.09829","page":"/paper/dynamic-routing-between-capsules"}],"arg_sig_recorded":[["input",3,"float","float32"]],"scalar_args":{"dim":"-1","keepdim":"True"},"class_bearing":false},{"code_sha256_prefix":"5abcd8c6705f2368","path":"net.py","papers":["1710.09829"],"paper_pages":[{"arxiv_id":"1710.09829","page":"/paper/dynamic-routing-between-capsules"}],"arg_sig_recorded":[["x",3,"float","float32"]],"scalar_args":{},"class_bearing":false}]}]}]}