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fff.UHAWAVSPII^8Ht;f.L;C(t H{8lPCt H{ ]PHUPLMuI~(IF(Ht7PAFu H[A^A_]I~H[A^A_]P@UHAWAVAUATSH(IHHHHHEH8HHHHLH@H8LHOHEEL5M~LI@L8LZNL%ILHDž(HDž HDžHDžDž0HHHQHHHJI]HQHHH,HHHLL8Lt H NHMH5HH%MH8-NHHH;EuHH([A\A]A^A_]NHLL8Lt H $NHMHHHH5&HHLH8MHHH8{MHUHAWAVAUATSPHIKMt$HsLKM|$0Hs0LLuKMl$HHsHLKMt$`Hs`LKHCxID$xLHHHH[A\A]A^A_]HAu+AEuAAuVHEunA$HHJ I|$pLAEt HAEtI|$XLAt HAtI|$@LHEt HHEtI|$(LA$t HA$tI|$~LHI fDUHAWAVAUATSHhLIH}dH51H cHXLEsW)EE HEHoverflowHEHInteger HEEHXHM1LH54ҘHLL IEt H}KL}Mt6HEL9t!HX@t HxKHI9uH}LL}pKxt H}^KXt HhIKE1LmMuUH%IALmMt>HEL9tLeMt6HEL9t!HX@t Hx>HI9uH}LLe>EuEuLhMu&^H}|>EtH}m>LhMt:HpL9tEHX@t HxB>HI9uHhLp'>EtLJ>LZ; Lffff.UHAWAVAUATSHHHHEHHOH)HH9vFu=Tu3HVH9sNLu DH 3H H;Mt=1H H H;MuHĈ[A\A]A^A_]ÿm=IHhdH5H oH}LhFuW)PHDž`0=H`(h)PHts valueHHHy for itHHHype entrHHHissing tHHHMap is mH@'AHHuHPLE1H5˜HJL< IALeMuFzIALhMIIPt H`"<LeMt6HEL9t!HX@t Hx;HI9uH}LLe;EuEuLhMu&^H};EtH};LhMt:HpL9tEHX@t Hx;HI9uHhLpk;EtL;L8 LؐUHAWAVAUATSHHHҟHHE趁PH;Cu*HH H H;MuHĘ[A\A]A^A_]T;;IH`dH5상H HxL`0BPH@HHHELHH@HULH_AHHxLLE1H5H"L: IALeMuFzIAL`MIIHt HX9LeMt6HEL9t!HX@t Hx9HI9uH}LLe9Euxu L`Mu)aH}9xtH}9L`Mt:HhL9tEHX@t HxX9HI9uH`Lh=9EtL`9Lp6 LUHAWAVAUATSHIHHHEH8HݛHHHLH@H8Lf8HEEH"LkLH@H8Lx7L5ݜILHDž(HDž HDžHDžDž0LLaLH8Lt H 8L6H50HH6H87HHH;EuLH[A\A]A^A_] 8ILH8Lt H 7Lw6IH5HH 6H87LMIH86L6fDUHAWAVAUATSHHśHHEHHOH)HH9vFu=Tu3HVH9sNLu DH sH H;Mt#71H \H H;MuHĈ[A\A]A^A_]ÿ6IHhHdH5H <H}LhFW)PHDž`0P6H`({)PHts valueHHHy for itHHHype entrHHHissing tHHHMap is mH@'AHQHuHPLE1H5!HL5 IALeMuFzIALhMIIPt H`b5LeMt6HEL9t!HX@t Hx=5HI9uH}LLe#5EuEuLhMu&^H}5EtH}4LhMt:HpL9tEHX@t Hx4HI9uHhLp4EtL4L1 LؐUHAWAVAUATSHHHHHEpPH;Cu*HH H H;MuHĘ[A\A]A^A_]4B4IH`dH5,}H LHxL`0GBpPH@HHEHELHH@HULHAH醁HxLLE1H5HbL3 IALeMuFzIAL`MIIHt HX:3LeMt6HEL9t!HX@t Hx3HI9uH}LLe2Euxu L`Mu)aH}2xtH}2L`Mt:HhL9tEHX@t Hx2HI9uH`Lh}2EtL2L/ LUHAWAVAUATSPIM.IFL)HHHH9H H9I^IM)HLupLcM)J<v%IKDMMM~LLLI&LL+L{HCMtLH[A\A]A^A_]%H[A\A]A^A_]ÿ%HH5mH"HmHHH5H0H%IH$L" ff.UHAWAVAUATSHIIIuMeI)MIIGHH> 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HHH苷HrHuLH譏DžH5vHEhLqtqIHHUH5rH rH}LH'|H HHrHuLHDžH5NvHgL+q H}8q IưLHMOITIưL`MIIưL`MIIưL`M?aIfI t H0qL}Mt6HEL9t!HX@t HxqHI9uH}LL}qEuEuLHMu&^H}qEtH}qLHMt:HPL9tXHX@t Hx|qHI9uHHLPaqL|qH`H:LIHt HXqL}Mt6HEL9t!HX@t HxqHI9uH}LL}qEuEu L`Mu-H}qEtH}qL`MHhL9HX@t HxsqHI9u|IHt HXNqL}Mt6HEL9t!HX@t Hx)qHI9uH}LL}qEuEu L`Mu-H}qEtH}qL`MHhL9HX@t HxqHI9uIHt HXqL}Mt6HEL9t!HX@t Hx]qHI9uH}LL}CqEuEu L`Mu-OH}qEtH}qL`M'HhL9DHX@t HxqHI9uIưL`MIIHt HXqL}Mt6HEL9t!HX@t HxiqHI9uH}LL}OqEuEuL`Mu&^H},qEtH}qL`Mt:HhL9t[HX@t HxqHI9uH`LhqLqHH8HLIH}Ht,H}qH`Hu#IEt H}mqH`HtdHhUqHH8HuXfIHH8HuDRIEu!(IEuIEu IEt H}qHH8HtHHx@qHL(HxH@HtqLq fDUHAWAVAUATSH(IHHH$NHHEH8H_LHHHLH@H8LqHEEL5MM~LI@L8LqL%_MILHDž(HDž HDžHDžDž0HHHHqHHHAuHHqHHH褗LL8Lt H XqH8qH5oLHHqH8qHLHH;EuHH([A\A]A^A_]_qHLL8Lt H qHqH讍H覍HH5KHH8qH8@qHxHH8)qHaUHAVSIPfqHHLH[A^]IH9qLyq UHAWAVAUATSHIHHKHHEL-ɉL+H{DH6ljHCC HC(HC8HHCHHC@HC8W)EHEEEhighEHHKH`HULFDH`HDž`HtCt H{NqHFqEt H}7qHHW)EEHEElowHHHH`HULDH`HDž`HtCt H{qHqEt H}qHXDžDW)EHEEEseedEH`HUHDL@DH`HDž`HtCt H{HqH@qEt H}1qHtsq H,DH IH )))ȹEHHW)EE HEEdtypfEeH`HUH8LDH`HDž`HtCt H{qHqEt H}uqH8H{0lH[0;W)`)EE HDžpHEEshapfEeHHHUH`LxDHHHDžHHtCt H{qHqEt H}qHtW)EHEL`HhL)IILH=HzqILuLuO,LmMH~LLHqILuHH;Ht3HHx@qHCHCHL}LuLmHLx8Lp@LhHH`Ht HhqHlGHH;Eu0H[A\A]A^A_]E1E1E1HH;HdqqIH`5UH5rH rH}L`t~H?rH LHH LH{HrHuLL-DžH5\hHYL9q>qIH`菏UH52rH rH}L`b}HHH}HrHuLH蟀DžH5gH7YLqfqIH`UH5rH grH}L`cn}HHHHrHuLHDžH5@gHXLq"qIH`sUH5rH rH}L`q|HHHaHrHuLHDžH5fHXLqJqIHHUH5rH KrH}LHwR|H HӦHrHuLH~DžH5$fHWLq H}q IưLHMOITIưL`MIIưL`MIIưL`M?aIfI t H0qL}Mt6HEL9t!HX@t HxqHI9uH}LL}qEuEuLHMu&^H}qEtH}}qLHMt:HPL9tXHX@t HxRqHI9uHHLP7qLRqH`H:LIHt HXqL}Mt6HEL9t!HX@t HxqHI9uH}LL}qEuEu L`Mu-H}qEtH}|qL`MHhL9HX@t HxIqHI9u|IHt HX$qL}Mt6HEL9t!HX@t HxqHI9uH}LL}qEuEu L`Mu-H}qEtH}qL`MHhL9HX@t Hx}qHI9uIHt HXXqL}Mt6HEL9t!HX@t Hx3qHI9uH}LL}qEuEu L`Mu-OH}qEtH}qL`M'HhL9DHX@t HxqHI9uIưL`MIIHt HXdqL}Mt6HEL9t!HX@t Hx?qHI9uH}LL}%qEuEuL`Mu&^H}qEtH}qL`Mt:HhL9t[HX@t HxqHI9uH`LhqLqHH8HLIH}Ht,H}fqH`Hu#IEt H}CqH`HtdHh+qHH8HuXfIHH8HuDRIEu!(IEuIEu IEt H}qHH8HtHHx@qHL(HxH@HtqLq fff.UHAVSI8vqHHLH[A^]IHIqLq UHAWAVAUATSHIIH=HHEL-ѻM/I襶DH渉IAGAG IG(AG0W)pHEƅpDžqmeanƅuIOHXHpLgDHXHDžXHtCt H{oqHgqpt H}UqH6W)pƅp HEDžqscalfDžueIOHXHpLDHXHDžXHtCt H{qHqpt H}qHGDž<W)pHEƅpDžqseedƅuHXHpHDHXHDžXHtCt H{FqH>qpt H},qHtnq H,qH@H,HFrHpH H!tDž,H5P[HLH )q$qH HX{UH5rH rHpLXOpHrHEL@HuLH,HrHpH LosDž,H5ZHLH wqrIư,HXHIIư,HXHIIư,HXHLII@t HPqLeMt6HEL9t!HX@t HxoqHI9uH}LLeUqEupu#HXHu0H},qptH}qHXHH`H9L`@t HxqLL9uI@t HPqLeMt6HEL9t!HX@t HxqHI9uH}LLeqEupu#HXHu00H}ZqptH}HqHXHH`H9 L`@t HxqLL9uI@t HPqLeMt6HEL9t!HX@t HxqHI9uH}LLeqEupu HXHu)aH}qptH}yqHXHt:H`H9tEL`@t HxNqLL9uHXH`3q,tWH NqIHI?Ipu*1Ipu#IpuIpt H}qM/IIGHtqLq fUHAVSI8qHHLH[A^]IHyqLq UHAWAVAUATSHIIH4HHEL-M/IխDHְIAGAG IG(AG0W)pHEƅpDžqhighƅuIOHXHpLDHXHDžXHtCt H{qHqpt H}qH-W)pƅpHEDžqlowIOHXHpLDHXHDžXHtCt H{qHqpt H}qHGDž<W)pHEƅpDžqseedƅuHXHpHH]HEHu]LQq HhqH}Ht IH}HtH}۬qH]HEHtIH]HEHtHNEH褬qLq fff.UHAWAVAUATSHHXHHHEп(gqHþ H]qHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]0@I(@I@IX@HP@IW)EHE(讪qHEHH(HML8L`LhHXLp HM @I@I@I@I)@HW)`HDžp(AqH`HH(HpL8L`LhLpHX HhH5qH}HUH`ͳCH5rHޭCH5uHCHA H5qH6CHH;CHHH ΉHMH?HUHuHuHXHHCHC0HKHSHC@H`Ht HhGqH}Ht H}5qLuHEMtLELqH HH;EuHHĈ[A\A]A^A_]JqIH`Hu$H}Hu>H]HEHu]Lq Hh諨qH}Ht IH}HtH}苨qH]HEHtIH]HEHtHEHTqL蔥q fff.UHAWAVATSH@HIH HHEH]1HEIľHEIǾHEHEIL$IT$ H)HtHH9IOIW H)HtHH9Ht%HHHP H)HtHH9ID$0@EHuHULMLLM`H HH;EIHrHEH]HuHL59.LHvTpIHrHEH]HuHL8.LHvT7HH rHMH]HuHH8.LH^vTEt H}}qH HH;EuLH@[A\A^A_]踦qHEuHEu HEt H}0qHpq UHHH ҈HHxH@Ht]q]fDUHSPHHHH{HCHtťqHH[]鷥qfUHAVSI覥qHHRHH{L#DH솉HH[A^]IHdqL褢q fff.UHAVSIFqHHHH{LÂDHHH[A^]IHqLDq fff.UHAWAVAUATSHHMMIHIHi HHEЋEHEHEH}HuH}HuHLMM'H}HuHLMMH}HuHLMMuH}HuHLMMH}uuHUH} H]Htc qIHILHHH qH]M>HEHt8Ct H{ѣqHɣqHdHH;Et!1IHEHCHH;EuLHH[A\A]A^A_]qILvqH]HEHu 5IH]HEHtCt H{6qH.qLnq fDUHAVSHIHHHEA>uuHM H}HEHEI^IFHtACt H{ŢqH轢qH]HEHt!Ct H{蝢qH蕢qHEIFH"HH;Eu 1H[A^]ǢqfUHAVSHIHHHEA>uuHM H}HEHEI^IFHtACt H{qHqH]HEHt!Ct H{ݡqHաqHEIFHbHH;Eu 1H[A^]qfUHAVSHIH+HHEA>uuHM H}/HEHEI^IFHtACt H{EqH=qH]HEHt!Ct H{qHqHEIFHHH;Eu 1H[A^]GqfUHAVSHIHkHHEA> uuHM H} HEHEI^IFHtACt H{腠qH}qH]HEHt!Ct H{]qHUqHEIFHHH;Eu 1H[A^]臠qfUHAVSHIHHHEA>uuHM H} HEHEI^IFHtACt H{şqH轟qH]HEHt!Ct H{蝟qH蕟qHEIFH"HH;Eu 1H[A^]ǟqfUHAWAVAUATSH8MMIH}HHHEHLD0MLD E0qHEf(<f)EHbe zero!HH Hcan not HHHperator HHH Range oHHHdelta inH@(HMH]HTnTEH}AA)A*A*^f: L,E1MMIEqHEHHHML(HMHU1LڀDHM*Iw1 HL!fnfpfAnfpf8@3fBfnfpHyHHVHs 1HunHzH)1ffDfD fD0fD@fDPfD`fDpH fHuHt,HtHf.FffH HuI9t"DHDHDHL9rH]HH}Ht H}qH[HH;EuHH8[A\A]A^A_]qHEtH}HH}Ht H}uqH赙q ffff.UHAWAVAUATSH8MMIH}HHHEHHLL0MLL M0qHEf(;f)EHbe zero!HH Hcan not HHHperator HHH Range oHHHdelta inH@(HMH]HSkTEH}AI)I*I*^f: L,E1MMIDqHEHHHML(HMHU1L}DH MJIw1+HL!fHnfpDfInfpDfHnfsfoffs ffs ffK$fHnfpDHyHHVH s 1HuoHzH)1fDfD fD0fD@fDPfD`fDpHfHuHt,HtHf.FffH HuI9t#LHHHHHLHL9rH]HH}Ht H}蟙qH:HH;EuHH8[A\A]A^A_]ؙqHEtH}HH}Ht H}TqH蔖q fff.UHAWAVATSH0LMIIHHHEH_ELNHHE4W.E0ɘqHEf(f)EHbe zero!HH Hcan not HHHperator HHH Range oHHHdelta inH@(HMLhTE/H}! C\EZMZ^f: L,1MIIܿ qHEHHHMHHMHU1LzDH'MEMHSكHrfHH)ffffff.X@X@X@ X@X@X@X@H XHuHtHHXHuIH}Ht H}qHHH;EuLH0[A\A^A_]OqHEtH}HH}Ht H}˖qH q UHAWAVATSH0LMIIH>HHEHELHHEfWf.E0GqHEf(f)EHbe zero!HH Hcan not HHHperator HHH Range oHHHdelta inH@(HMLeTEH} \EM^f: L,1MIIܿ萕qHEHHHMHHMHU1L%xDHMEMHSكHr\HH)DX@X@X@X@ X@(X@0X@8H@XHuHtHHXHuIH}Ht H}褔qH?HH;EuLH0[A\A^A_]ߔqHEtH}HH}Ht H}[qH蛑q UHAWAVAUATSH8MMIH}HHHEHLD0MLlD E0qHEf()f)EHbe zero!HH Hcan not HHHperator HHH Range oHHHdelta inH@(HMH]HAcTEH}AA)A*A*^f: L,E1MMI/qHEHHHML(HMHU1LuDHMEIw1&HL!fnpfpfAnpfpf'fBfnpfpHyHHVH0s 1HuzHzH)1fffff.pfDpfDp fDp0fDp@fDpPfDp`fDppH@fHuHt,HtpHf.FffH HuI9t#DHHDfHDHL9rH]HH}Ht H}菑qH*HH;EuHH8[A\A]A^A_]ȑqHEtH}HH}Ht H}DqH脎q fff.UHAVSHPIHHHE(qHþ H qHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H}11蕝CH;@H5qHH[CH5(rH\CH5buH荔CHH HQH5tqH譕CHH;rCHHH QHMHHUHuHuIIFIF0INIVIF@H]HEHtHfEH輎qHWHH;Eu LHP[A^]qIH]HEHtH!EHwqL跋q ffffff.UHAWAVSHxIHHHE(BqHþ H8qHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H}11ÚCI@HW)EHE虌qHEHHHMHHMĻ@HW)pHE_qHpHHHMHHxH5IqHUHpLCH5܊rHCH5uHACHH HQH5(qHaCHH;&CHHH HMH:HUHuHuIIGIG0IOIWIG@HpHt HxyqH}Ht H}gqH]HEHtHEHFqHHH;EuLHx[A^A_]胋qIH}Ht0^IH]HEHt+bIHpHu$H}Hu0H]HEHu9Lq Hx辊qH}HtH}謊qH]HEHtH5EH苊qLˇq UHAWAVAUATSHHPHHHEп(WqHþ HMqHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H}11ؖCHH @HX@I8@I@H(@I`@IW)EHE0~qHEHH0HMHXHLhLpHXL` Lx(HM获@HX肽@I躹@Ir@I誼@I@HW)`HDžp0qH`HH0HpHXHLhL`LpLx HX(HhH5ƔqHUH`HHxCH5UrH艋CH5uH躌CHH HQH5qHڍCHH;蟂CHHH ~HMHHUHuHuHPHHCHC0HKHSHC@H`Ht HhqH}Ht H}نqLuHEMtLbEL踆qHSHH;EuHHĘ[A\A]A^A_]qrIH`Hu$H}Hu>H]HEHu]L裃q HhMqH}Ht IH}HtH}-qH]HEHtIH]HEHtH蠟EHqL6q fffff.UHAWAVAUATSHHPH^HHEп(跅qHþ H譅qHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H}118CHHl@HX`@I蘵@IP@H舸@I@IW)EHE0ރqHEHH0HMHXHLhLpHXL` Lx(HM@HX@I@Iҵ@I @IB@HW)`HDžp0ZqH`HH0HpHXHLhL`LpLx HX(HhH5&qHUH`HH،CH5rHCH5uHCHA H5qHACHH;~CHHH 姉HMH HUHuHuHPHHCHC0HKHSHC@H`Ht HhRqH}Ht H}@qLuHEMtLɛELqHHH;EuHHĘ[A\A]A^A_]UqrIH`Hu$H}Hu>H]HEHu]L q Hh贁qH}Ht IH}HtH}蔁qH]HEHtIH]HEHtHEH]qL~q DUHAWAVSH8HIHHHE1HEEIL}ؾH1EHEоH EHEIW1HcDHEIG0@EH}HuHUHMLEg IHYHH;EuLH8[A^A_]qffff.UHHH BcHHxH@Ht]fq]fDUHSPHHcHH{HCHt5qHH[]'qfUHAVSIqHH{LMHbHHHL]DHbHH[A^]IHqL}q ffff.UHAWAVAUATSHH5HHEW)EEIIHEEminMgHhHUL'|DHhHDžhHtCt H{/qH'qA$Et H}qW)EHEEEmaxHhHULL{DHhHDžhHtCt H{~qH~qAEt H}~qAA.Gr'H+HH;EuHĈ[A\A]A^A_]~qw~qIHh,UH5}rH W}rH}LhHPHEAH}rHuLH'E1H5]HL:~q IALhMIIPt H`}qLeMt6HEL9t!HX@t Hx}qHI9uH}LLep}qEuEuLhMu&^H}M}qEtH}>}qLhMt:HpL9tEHX@t Hx}qHI9uHhLp|qEt4L}qL+zq LIEu IEt H}|qLyq UHHH b_HHxH@Ht]|q]fDUHSPHH0_HH{HCHtU|qHH[]G|qfUHAWAVAUATSPIIH}1L>EHLk1LL^DIHOILGEl$Ed$L2HI~GHuڃH9HOII)LH?H>LHH5fAnfAnHrI H9s!H I9sE1HzLIIfofoIIHHDGAHIHH)1fDA$(_(]$Ad(_(]dHHuMtA$_]L9tjHzLtfoC_fo]BLHL9t:foA_fo]foA_Tfo]\HH9uH<I|;fAnfAnHA(_(])HH9|H9RfAnfAnI)ILI I9sLI L9MIfofoIHIIENAHHM\HtIIL)1ff.Ad(_(]dA$(_(]$HHuMtHA$_]M9twLlj)HPH@tfoA_fo]HOH9tCf.foA_fo]foA_Tfo]\HH9uHEHH[A\A]A^A_]1M1M=Jffff.UHAVSIvxqHH"[HH{LUDHZHH[A^]IH4xqLtuq fff.UHAVSIxqHHZHH{LUDHYHH[A^]IHwqLuq fff.UHAVSIwqHHbZHH{L3UDH\YHH[A^]IHtwqLtq fff.UHAWAVATSHPMIIHHۈHHE؋EHEtm  uH3I$IMH}V$HEH}5 HrۈHH;EHP[A\A^A_]H3I$IMH}HEЋEȃ vH3I$IMH}HEЋEȃYH3I$IMH}HEЋEȃLHH(0(@,HrI H9s!H I9sE1HzLII((IIHHDGAHmIHH)1ffff.A(_(]AT(_(]THHuMtA_] L9tfHzLt(C_(]B LHL9t8(A_(] (A_D(]LHH9uH<I|/((HA(_(])HH9|H9>I)ILI I9sLI L9MI((IHIIEOAHHM\HtIIL)1AT(_(]TA(_(]HHuMtHA_] M9tsLlj)HPH@t(A_(] HOH9tAfff.(A_(] (A_D(]LHH9uHֈHH;Eu2HĨ[A\A]A^A_]1M1M9F"rqqqIHhkUH5-qrH iqrH}Lh\ W)PHDž` sqqH`(ׅ)PH scalar.HHHld be a HHHmin shouH@AHqrHuHPL*E1H5`HL=qqpqIHhUH5UprH prH}Lh` W)PHDž` pqH`(ׅ)PH scalar.HHHld be a HHHmax shouH@AHprHuHPLRE1H5HLepq IALeMIALhMIIALeMFIALhMnIIPt H`oqLeMt6HEL9t!HX@t HxboqHI9uH}LLeHoqEuEu LhMu-'H}"oqEtH}oqLhMHpL9HX@t HxnqHI9uIPt H`nqLeMt6HEL9t!HX@t HxnqHI9uH}LLe|nqEuEuLhMu&^H}YnqEtH}JnqLhMt:HpL9tEHX@t HxnqHI9uHhLpnqEtL'nqL7kq Lf.UHAWAVAUATSHMIHH\҈HHEHtpHBHJ H)HtHH8HM)0tGIFIN H)HtHH8L趝ۄM)0u$f)@H脝IHHGL轸HILGHuH9HOII)LH?LHHf(0f(@HrI H9s!H I9sE1HzLIIIHHHDOAHIIH)1fDfAfA\f(f_f(f_f(f]f(f]ffdfAT fA\0f(f_f(f_f(f]f(f]f\ fd0HHuMt0fAfA\f(f_f_f(f]f]ffDL9tsHzLtf(C_f(]B LHL9tCf.f(A_f(] f(A_Df(]LHH9uH<I|0DAf(f_f(f]f)HH9|H9 I)IH IH9s H IH9rqMIITHtJ<1fffff.fTff(f_f(f_f(f]f(f]f\f$HI9uM9tu)HPH@tf(A_f(] HOH9tDff.f(A_f(] f(A_Df(]LHH9uH`ΈHH;Eu"HĨ[A\A]A^A_]1MiqiqIHh7UH5hrH jrH}Lh\W)PHDž` ?iqH`(υ)PH scalar.HHHld be a HHHmin shouH@AHlirHuHPLE1H5,HL iqhqIHh_UH5!hrH DirH}Lh`W)PHDž` ghqH`(΅)PH scalar.HHHld be a HHHmax shouH@AHhrHuHPLE1H5THL1hq IALeMIALhMIIALeMFIALhMnIIPt H`SgqLeMt6HEL9t!HX@t Hx.gqHI9uH}LLegqEuEu LhMu-'H}fqEtH}fqLhMHpL9HX@t HxfqHI9uIPt H`fqLeMt6HEL9t!HX@t HxbfqHI9uH}LLeHfqEuEuLhMu&^H}%fqEtH}fqLhMt:HpL9tEHX@t HxeqHI9uHhLpeqEtLeqLcq LfDUHAWAVAUATSHLIIH,ʈHHEHtgHBHJ H)HtHH8HD Mt:IFIN H)HtHJH8@LND8 AMuAL6IILGHOIHHGHH rI I9LHI9E1HpL¨L9f.ADD8|DA8~A\A\DD8|$DA8"A\HH9uUDA8~މA\HH9u1IIAfnff8Afnf8IHHHrHH~H)1ff.AoAo\f8H84LD8 E1MuALIILGHIHHGHH rI I9LHI9E1HpL¨xL9f.ADD8rDA8rA\A\DD8r$DA8s"A\HH9uIDA8rމA\HH9u%IIAfnff8Afnf8IHHHrHH~H)1ff.AoAo\ffffATA\AoT Ao\0ffffAT A\0H@HuHt+AoAo\ffffATA\L9tHE1MuHL"IILGL;IILGHƒHu1H{H)1Iw@I6}@HW)EHEHqHEHHHML HXHMH5xsHULPCH5LrHNLCH5TuuHMCHH HQH5fqHNCHH;dCCHHH CmHMHHUHuHuIIGIG0IOIWIG@H}Ht H}GqH]HEHtHFaEHGqH7HH;EuLH`[A\A^A_]GqIH]HEHt7H`EHOGqLDq IH}HuH]HEHuLhDq H}GqH]HEHt@UHAWAVATSH`IHHHEؿ(FqHþ HFqHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]z@H5iRqH}H6RCIs@Iy@HW)EHE$EqHEHHHML HXHMH5,usHULMMCH5lIrHHCH5quHJCHH HQH5qH/KCHH;?CHHH iHMHhCHUHuHuIIGIG0IOIWIG@H}Ht H}MDqH]HEHtH]EH,DqHǨHH;EuLH`[A\A^A_]gDqIH]HEHt7H]EHCqLAq IH}HuH]HEHuL@q H}CqH]HEHt@UHAWAVATSH`IHHHEؿ(pCqHþ HfCqHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]yp@H5NqH}HNCI^p@IVv@HW)EHEAqHEHHHML HXHMH5qsHULICH5ErHnECH5tnuHFCHH HQH5qHGCHH;qHEHHHML HXHMH5LnsHULmFCH5BrHACH5kuH/CCHH HQH5qHODCHH;9CHHH bHMHhHUHuHuIIGIG0IOIWIG@H}Ht H}m=qH]HEHtHVEHL=qH硈HH;EuLH`[A\A^A_]=qIH]HEHt7HVEHCH5guH?CHAH5~qH@CHH;5CHHH _HMHHUHuHuIIGIG0IOIWIG@H}Ht H}:qH]HEHtHSEH9qH~HH;EuLH`[A\A^A_]:qIH]HEHt7H@SEH9qL6q IH}HuH]HEHuL6q H}\9qH]HEHtff.UHAWAVATSH`IHǝHHEؿ( 9qHþ H9qHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]l@H5DqH}HvDCIf@Il@HW)EHEd7qHEHHHML HXHMH5lgsHUL?CH5;rH;CH5$duHO3qIH]HEHt7H`LEH2qL/q IH}HuH]HEHuL/q H}|2qH]HEHtff.UHAWAVATSH`IH疈HHEؿ(@2qHþ H62qHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]Ie@H5=qH}H=CI._@I&e@HW)EHE0qHEHHHML HXHMH5`sHUL8CH54rH>4CH5D]uHo5CHAH5]tqH6CHH;[+CHHH :UHMH_HUHuHuIIGIG0IOIWIG@H}Ht H}/qH]HEHtH=IEH/qH.HH;EuLH`[A\A^A_]/qIH]HEHt7HHEHF/qL,q IH}HuH]HEHuL_,q H} /qH]HEHtff.UHAVSI .qHHLH[A^]IH.qL+q UHAWAVATSH@HIH4HHEL%FM&I~ DHsIIFIFHEW)EHEEHexclusivHEfEeH}HUHMH-,DEt H} .qH}uHEHwINHW)EHEEHEHreverseHEH}HUHMH+DHEHEH]HEHCt H{-qH-qH]HEHtCt H{c-qH[-qEt H}L-qH]HtLHECt H{,-qH$-qHHH;EuCH@[A\A^A_]HEEuHEHwIFHEH|HH;Et,-qIEt H},qH]HEHt&Ct H{,qH IEt H},qM&I~IFHtj,qL)q fUHHH HHxH@Ht]6,q]fDUHSPHHHH{HCHt,qHH[]+qfUHAWAVAUATSHIH`IHhHHE1LnEHHC H+CHp LnEIW)EHEH{ HH+{t[HHHH9 Z+qILeLeIHEHHpHX H)H~LH,qILeHU1L DHH}ƭGHHpHHEHHMLHHHw HEHLcHH@JHW)PHDž`HHx H+xHHHH9 R*qILXLPIH`HHpHX H)H~LH+qLHHXKHP蹬GHxW)0HDž@LPHXL)t\IIHI9' H)qIL8L0KDH@H~LLH*qIL8W)HDž HH9LpL-)qHHHH H5҂HL*qHH`HxLXHHYHxHtHHxH$L0LHHHH HHHHW)HDžLpLV(qHHLN4LHL5(qLLHJW)HDžL'qILKHLL'qHHKHxH$HHL0LHH Ly'qHHt Ha'qH}Lpfffff.H`HXW)HDžL"'qIHLxHHIDLHM,LLL&qLLLKW)HDžL&qILHIHLL&qHLhO<W)HDžLZ&qHHHLu^H"HHH!H Hy9HH9w2HHy1I)r17DHHH!HxHHy 1IsE1H L`HHJ\IHH MtHǾfHELMLH t虾I DALMs聾MGxDMLMfffff.DHH L`H.fLMjDALMHHBH HNH+HHLHN(H;u)HLHDH HA(HIH HA0HnHH;EuH[A\A]A^A_]< q qIHP腷TH5SsH rHhLPHHAH+HH0L(H~rHEH\qHEHurHEH8HuH0HMLEL(HEAHrHhLH9E1H5oH؀LL q qIHP袶TH5"RsH *rHhLP HHAH+HH0H HAH+AHH(HrHEH[qHEHrHEH8HuH0HMLEL(HOAHWrHhLHCE1H5yHLVqqIHP謵TH5,QsH 4 rHhLPHHAH+HH0L(H rHEHZqHEH rHEH8HuH0HMLEL(HlAH rHhLH`E1H5H~Lsq IALPMILIALPMILIALPMnII8t HHqLmMt6HEL9t!HX@t HxwqHI9uH}LLm]qEuhu#LPMu36H}4qhtHxqLPMHXL9 HX@t HxqHI9uI8t HHqLmMt6HEL9t!HX@t HxqHI9uH}LLmqEuhu LPMu,dH}bqhtHxMqLPMt:HXL9t?HX@t Hx"qHI9uHPLXqELLI8t HHqLmMt6HEL9t!HX@t HxqHI9uH}LLmqEuhu LPMu,dH}tqhtHx_qLPMt:HXL9tEHX@t Hx4qHI9uHPLXqEtL<qLLq Lffffff.UHAWAVAUATSH8LLHHIHHShHHEH8HfHHHLH@H8LqHHEEL-gMuLI@L8L"qL=gILHDž(HDž HDžHDžDž0I$HvqHHHHH0HqHHH<qHHHޫHHHqHHH身HH0H$qHHHnLL8Lt H "qHqH59fHHqH8qHfHH;EuHH8[A\A]A^A_])qHLL8Lt H qH|q+HxHpHhH`HXHH5eHHpH8qH*HH8qHUHAVSI qHHLH[A^]IHqL)p UHAWAVATSH@HIHdeHHEL%vM&I~JCHIIFIFHEW)EHEEHexclusivHEfEeH}HUHMH]CEt H}<qH}uHEHwINHW)EHEEHEHreverseHEH}HUHMHCHEHEH]HEHCt H{pHpH]HEHtCt H{pHpEt H}|pH]HtLHECt H{\pHTpHcHH;EuCH@[A\A^A_]HEEuHEHwIFHEHcHH;Et\pIEt H}pH]HEHt&Ct H{pH IEt H}pM&I~IFHtpLp fUHHH BHHxH@Ht]fp]fDUHSPHHHH{HCHt5pHH[]'pfUHAWAVAUATSHIH`IHbHHE1LAEHHC H+CHp L@EIW)EHEH{ HH+{t[HHHH9 pILeLeIHEHHpHX H)H~LHpILeHU1LCHH}GHHpHHEHHMLHHHw HEHLcHH@JHW)PHDž`HHx H+xHHHH9 pILXLPIH`HHpHX H)H~LHpLHHXKHP~GHxW)0HDž@LPHXL)t\IIHI9' HpIL8L0KDH@H~LLHpIL8W)HDž HH9LpL]pHHHH H5äHLpHH`HxLXHHYHxHtHHxH$L0LHHHH HHHHW)HDžLpLpHHLN4LHLepLLHJW)HDžL)pILKHLLpHHKHxH$HHL0LHH LpHHt HpH}Lpfffff.H`HXW)HDžLRpIHLxHHIDLHM,LLLpLLLKW)HDžLpILHIHLLpHLhO<W)HDžLpHHHLp IHEH8HuHEH8HuLp H{XpHEH8HtH{@pLp fUHAWAVAUATSHHDHHEMIHFHH+HHHI)IL9HOH HWH)HH9M)IL9HLAHIffff.HJ\Mt HyEMI HH Mt"H$ffffff.HELMwLH tI DHLsґfH L`HYHHHLHH tIHH=u^H#HHH!H Hy9HH9w2HHy1I)r17DHHH!HxHHy=1Is1E1H L`HHJ\IHH MtHfHELMLH tɐI DALMs豐MGxDMLMfffff.DHH L`H^fLMjDALMHHBH HNH+HHLHN(H;u)HLHDH HA(HIH HA0H@HH;EuH[A\A]A^A_]lppIHP赉TH55%sH qHhLPxHHAH+HH0L(HqHEH/qHEHqHEH8HuH0HMLEL(HuAHDqHhLHizE1H5aHSL|p7pIHP҈TH5R$sH qHhLPu^H"HHH!H Hy9HH9w2HHy1I)r17DHHH!HxHHy]f1IsQfE1H L`HHJ\IHH MtHffHELMLH teI DALMseMGxDMLMfffff.DHH L`H~efLMjDALMHHBH HNH+HHLHN(H;u)HLHDH HA(HIH HA0HHH;EuH[A\A]A^A_]茱p:pIHP^TH5UrH qHhLP?MHHAH+HH0L(HζqHEH"qHEHŶqHEH8HuH0HMLEL(H蕬AHdqHhLHOE1H56H((L蜰pWpIHP]TH5rrH &qHhLP\LHHAH+HH0H HAH+AHH(HصqHEH,qHEHqHEH8HuH0HMLEL(H蟫AHqHhLHNE1H55H2'L覯papIHP\TH5|rH 0qHhLPfKHHAH+HH0L(HqHEHIqHEHFqHEH8HuH0HMLEL(H輪AHqHhLHME1H54HO&Lîp IALPMILIALPMILIALPMnII8t HHpLmMt6HEL9t!HX@t HxǭpHI9uH}LLm譭pEuhu#LPMu36H}脭phtHxopLPMHXL9 HX@t HxHEH;EtHHHE LH}PLIIL;@|HU1H8_CH6IE~YMcHHHHHE1Lh1ffff.DH(H4L:AHLHL9|H}Ht H}qypL0IH݇HH;EypXypIHh&TH5ނqH qH}Lh`HPH?AHbqHuLHE1H5>HLyp[HALhMHHPt H`xpLeMt6HEL9t!Lh@t HxhxpLM9uH}LLeNxpEuEuLhMu&^H}+xpEtH}xpLhMt:HpL9t=Lh@t HxwpLM9uHhLpwpEt\LwpRLHH}Hu8?HH}Hu*1HH}Hu#HH}HuHH}Ht H}uwpHtp ffff.UHAWAVSHxHHۇHHEHc7HtzW)EHEH}HOHML]L]MI?I>MMIIIIOHHHxHs91H("|HIۇHH;EHwH)1fffff.)D)DD )D D0)D0D@)D@DP)DPD`)D`Dp)DpH HuHt$HHfff.)HHuMM)IJJH9sJJH9MIIPHHDpAHs1M}J|pJTpIvH)1DLDLDLDLDLDLD D H HuMt.IIIDID D H IuM9MDD)ICL)HtHB4B4IHuHraM)JDJL1ff.tttttttttttttt44HI9uH}HuLH}UupEW*HuHMH9HHNHtu7HHspHtHt!I$I$M|$Mt$[A\A^A_]ÿErpHH rpH5ՇHԇHZrpfffff.UHAWAVAUATSPIIIt$IT$IFIFIMnI\$IF IFHt;~1HH>wL<LrpMt HIEIELmI^ Mn(I\$IF0IF(Ht;~1HH>yL<LrpMt HIEIEI^0AF8fAF<I$It$IT$I9vuI9VtLIvIVIHII?I>IMIHIIOHHH~Hs 1HulHOH)1()(D)D(D )D (D0)D0(D@)D@(DP)DP(D`)D`(Dp)DpH HuHt$HHfff.(3)0HHuIM)IJ H4H9sH J4H9MIIHHHD^AHs1MN|pJ|pIKH)1ADALDLADALDLADALDLADA D H HuMt6IIILIff.D  D H IuM9MĉD)HJL)HtH@BHnpHtjp fff.UHAWAVSHXIHчHHEI~/L}L}H}HuHU"fA~G8I_I9_0uMG(_AG8I_I9_0tI(mpH~4HH>L4LnpIMt MMG( IG(E1I_0IIIwH$LMHzE$AGL4LtmpMt HIGI_ IGHs1dAAHL)Hxfofo foHfofGfH HuMtfff.HH9uH~,IGIO(HDHcTt<|4HHAG=HχHH;Eu!HX[A^A_]IGI_ WkpkpHHjpH5E·H͇HkpH hp @UHAWAVAUATSPHBLpIFHLuLH?II>ML'ID$Ml$HHEI4$L@LHT $IHLHJH<@(HDBTBTX8T$HTXXXHH9|HHHHEH9NLMyMtPMAT IIIAwIGHHIWWI| MMIIQEGAHHHH4[MOM)MIA(MސCTIs!LmMuhfffff.LLm@\TXDTX\1TXD9HTXHI9uMt.HLILffff.TXHHu_II(M96] H9)I)Ht:HIHJ<H(fTXHHHuIrhHMH)LBMLHHZIHIHI(TX TXTXB TXHHuMIILm I=MHEHI4$L@LHFT HLHJH<@( Hffffff.B\TBdTX\8TlHTXXXHH9|HHHHEH9(AFINHC((H` WHHHLRLH)(" Lffffff.TX TXBTX HTXHH9uHt1HIHH( TXHHuI [AFINH(H H9)I)HtMLHHPMIr1HIHIL H H9JL91M]I)LHt6Hp43HcHhH4HfDHHuIHXHpHHhH8Hfff.t4TTttTTttTT 4t HI9uMLHHAHHDsAHHhHXHp0HHH(H4IFH)1fffff.ADALT\ATA\DLADA TATAD HHuMt/HLAA\\AALI9H`HXM"@LL0HPN4.ILhM]M9MMIM)DLHtMHXHpHIH`HHLfff.(  )HHuI HXrHpHIH(Hffffff.D( L)D(LL)DD(L L)D (L0 )D0HL9|I9L M)I)IL@HLIIIK HHHH0HHH9s-LLHPH`HI9v L@4MIID$HHHHOHpHIIHPIH`JI)IIIAqHL)J 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IEHIEHHtH@MIE0H$HH@H LHjHHt#HHCHuHHPH=mHIH`IHHHL`HP0HeHHDžHtlH^DHļmH Hu\fIH`IHHHL`HHP0H<H Ht H(PmH@HtI>HL9Lh@t HxٷmLM9uHL辷mEu7LHDžMu?LHDžML誷mLHDžMtAFt I~SmLKmLHDžM9LYHt H mLeMt6HEL9t!Lh@t HxmLM9uH}LLe̶mEu`u LMu,dH}覶m`tHp葶mLMt:HL9toLh@t HxfmLM9uHLKmEtLnmLHDžM1AFI~mLHALeMuFzHAL@MHH t H0蹵mLeMt6HEL9t!Lh@t Hx蔵mLM9uH}LLezmEu`u L@Mu,dH}Tm`tHp?mL@Mt:HHL9tQLh@t HxmLM9uH@LHmE.LmH}H"&LHALMHHt H薴mLeMt6HEL9t!Lh@t HxqmLM9uH}LLeWmEu`u LMu,dH}1m`tHpmLMt:HL9tTLh@t HxmLM9uHLֳmELmH HLHLHDžMLDL}mH HHLMtgHIFHuUILPL:mH HuDN1/HH}Hu_fHH@Hu7AHH}Hu>EHH Ht H(ڲmH@Ht HH²mH}Ht H}谲mHm UHAWAVAUATSHMMHHIHHHEHHzH$5DIHH5DI9LLIH4DHL4DH9HL8LpM)LHM,$LID$L)HHH9TW)`HDžp@賱mILH@L`LhHpW)@HDžP@umHHH@H@HHHPW) HDž0@7mHUHH H(H@H0LBL+IMHHHh1۸HHxHpHE11H1HLO DH褭mHHDžHtH DH}mHHttHHCHubHHPHRmH}HuQXHDžHHVH}HtBH}m7HDžHHuH}Ht H}mLLLLIL;EI|JI|DIM9LeHhH;pHL HHhfDI|:LeHHH;PL HHHffffff.LeH(H;0L HH(DH`Hu蠸>LIHLLIL;DH@Hu_>LHxIHxLIL;@H Hu>LHpIHpLIL;L`HhH@HHHEHLGH H(L+ILNH1HHHLHHpHxHH)H)L)W)E)HEHDžHHHLHHHHHkLLLHH`HhHyAHH@HHHXAHHEHHHHIHH H(HAHHBHHLvHN HIEHDH9H+:HH諳HHLLH)tCI6H11@H H<uHHHH9rDH9MHHH9rHML9t-HHpHP H}HDHLI9uH]?HLIH]I)I1H H H HL9rW)HDžL}LH)t\IILH=lmILLKHI)ML~LHL譩mMLHHAHHHt HmHH HHHAHHAHHtH@HLH@0H$HHLLyHHHDžHHthHDHBmHHDžHtHDHmHHtmH@HHH"mH`HHhmHHCHmHHIHHu)3ILHHuIHHt H蔇mLHHt HymHHHHt HSmH}Hu_H HH@HH`HHHHtHCHmL5m H}mH HtUHAVSH0IHHXHHEI9tH{ H9t.IF I9tKHC I~ H+HH;EH0[A^]M9v tTHLPH{ HP IF HC HHHQI~ HP HC IF H[ HHH;EtpHHuPH{ HP HC I~ HHPI~ HP IF H[ HEH}LPHEH}P Mv HXHH;E-mH+H+fDUHAVSH0IHHHHEI9tH{ H9t.IF I9tKHC I~ HHH;EH0[A^]M9v tTHLPH{ HP IF HC HHHQI~ HP HC IF H[ HHH;EtpHHuPH{ HP HC I~ HHPI~ HP IF H[ HEH}LPHEH}P Mv HHH;E-ĄmHN*HF*fDUHAVSH0IHHHHEI9tH{ H9t.IF I9tKHC I~ HHH;EH0[A^]M9v tTHLPH{ HP IF HC HHHQI~ HP HC IF H[ HFHH;EtpHHuPH{ HP HC I~ HHPI~ HP IF H[ HEH}LPHEH}P Mv HHH;E-脃mH)H)fDUHAWAVAUATSH8LLHHIHHsHHEH8HHHHLH@H8L7mHHEEL-MuLI@L8LBmL=ILHDž(HDž HDžHDžDž0I$H薄mHHH8+HHHrmHHH+HHDHHH;mHHH*HH0HYmHHH0LL8Lt H EmH%mH5\HHmH8ȀmHHH;EuHH8[A\A]A^A_]LmHLL8Lt H 迀mHm+H&H&H&H&H{&HH5HH mH8mHM&HH8mH6&fDUHAWAVAUATSHILgLoLL)HH9LM)MII6HH=HM)LHHH9H}LE II9LBMDHuI9mHH5DmH}mHHHH5HHmLHHr IGL9`ILHL9MLCHHtHIHHHHuI@IHIHPIHPIHPIHP IHP(IHP0IHP8H@HuTHuIJ<~mIHuO LHHr IOL9)ILHL9HZHHt$Hffffff.IHHHHuHj@IHIHAIHAIHAIHA IHA(IHA0IHA8H@Hu IIIPHHAHA~fpDHPH)1fDAADAD AD0HHuHtA~fpDAADI9IHGHHHHHHDBAH%A~fpDK|0IHH)1fffff.DDDHHuMtA~fpDAADH9O4Mm fffff.UHAVSHPIHHHE(AmHþ H AmHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]n<H5LmH}HjL?H5fnHkC?H5qlqHD?HAH5mHE?HH;:?HHH gdHMHHUHuHuIIFIF0INIVIF@H]HEHtH|XAH>mHmHH;Eu LHP[A^]?mIH]HEHtH7XAH>mL;m DUHAVSHPIH HHE(d>mHþ HZ>mHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]-r<H5ImH}HI?H5cnH@?H5iqHA?HAH5ڀmHC?HH;7?HHH aHMHHUHuHuIIFIF0INIVIF@H]HEHtHUAH"?H5gqH?H5'YnH5?H5^qH7?HH HQH5vmH<8?HH;-?HHH VHMHHUHuHuIIFIF0INIVIF@H]HEHtHJAHK1mH敄HH;Eu LHP[A^]1mIH]HEHtHJAH1mLF.m fffff.UHAVSHPIH{HHE(0mHþ H0mHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]]<H5]HMHHUHuHuIIFIF0INIVIF@H]HEHtH\2AHmHM}HH;Eu LHP[A^]mIH]HEHtH2AHmmLm DUHAVSHPIH|HHE(DmHþ H:mHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]MK<H5#mH}H#?H5=nH?H5CqH?HAH5ZmH?HH;?HHH ;HMH\HUHuHuIIFIF0INIVIF@H]HEHtH/AHmHzHH;Eu LHP[A^]AmIH]HEHtHg/AHmLm DUHAVSHPIH;zHHE(mHþ HmHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]]C<H5!mH}H ?H5+;nH?H5@qH?HH HQH5XmH<?HH;?HHH 8HMHHUHuHuIIFIF0INIVIF@H]HEHtH,AHKmHwHH;Eu LHP[A^]mIH]HEHtH,AHmLFm fffff.UHAVSHPIH{wHHE(mHþ HmHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]F<H5]mH}H*?H5k8nH+?H51>qH\?HH HQH5CUmH|?HH;A ?HHH 6HMHHUHuHuIIFIF0INIVIF@H]HEHtH5*AHmH&uHH;Eu LHP[A^]mIH]HEHtH)AHFmL m fffff.UHAVSHPIHtHHE(mHþ H mHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]=<H5mH}Hj?H55nHk?H5q;qH?HH HQH5RmH?HH; ?HHH `3HMHHUHuHuIIFIF0INIVIF@H]HEHtHu'AH mHfrHH;Eu LHP[A^] mIH]HEHtH0'AH mL m fffff.UHAVSHPIHqHHE(T mHþ HJ mHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]]@<H5mH}H?H52nH?H58qH?HH HQH5OmH?HH;?HHH 0HMHHUHuHuIIFIF0INIVIF@H]HEHtH$AH mHoHH;Eu LHP[A^]J mIH]HEHtHp$AH mLm fffff.UHAVSHPIH;oHHE( mHþ H mHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]]8<H5mH}H?H5+0nH ?H55qH?HH HQH5MmH<?HH;?HHH -HMHHUHuHuIIFIF0INIVIF@H]HEHtH!AHKmHlHH;Eu LHP[A^]mIH]HEHtH!AHmLFm fffff.UHAVSHPIH{lHHE(mHþ HmHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H];<H5]mH}H*?H5k-nH+ ?H513qH\ ?HH HQH5CJmH| ?HH;A?HHH +HMHHUHuHuIIFIF0INIVIF@H]HEHtH5AHmH&jHH;Eu LHP[A^]mIH]HEHtHAHFmLm fffff.UHAVSHPIHiHHE(mHþ H mHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]2<H5mH}Hj?H5*nHk?H5q0qH?HH HQH5GmH ?HH;>HHH `(HMH5HUHuHuIIFIF0INIVIF@H]HEHtHuAHmHfgHH;Eu LHP[A^] mIH]HEHtH0AHmLl fffff.UHAVSHPIHfHHE(TmHþ HJmHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]]5<H5 mH}H ?H5'nH?H5-qH?HH HQH5DmH?HH;>HHH %HMHHUHuHuIIFIF0INIVIF@H]HEHtHAH mHdHH;Eu LHP[A^]JmIH]HEHtHpAHlLl fffff.UHAVSHPIH;dHHE(lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]]-<H5 mH}H ?H5+%nH?H5*qH?HAH5 BmHC?HH;>HHH "HMH|HUHuHuIIFIF0INIVIF@H]HEHtHAHRlHaHH;Eu LHP[A^]lIH]HEHtHAH lLMl DUHAVSHPIHaHHE(lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]0<H5mmH}H:?H5{"nH;>H5A(qHl?HAH5Z?mH?HH;X>HHH 7 HMH,HUHuHuIIFIF0INIVIF@H]HEHtHLAHlH=_HH;Eu LHP[A^]lIH]HEHtHAH]lLl DUHAVSHPIH^HHE(4lHþ H*lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]='<H5mH}H?H5nH>H5%qH>HAH5HH;>HHH HMHHUHuHuIIFIF0INIVIF@H]HEHtHAHlH\HH;Eu LHP[A^]1lIH]HEHtHWAHlLl DUHAVSHPIH+\HHE(lHþ HzlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]*<H5 mH}H?H5nH>H5"qH >HAH59mH3>HH;>HHH HMHHUHuHuIIFIF0INIVIF@H]HEHtHAHBlHYHH;Eu LHP[A^]lIH]HEHtHAHlL=l DUHAVSHPIH{YHHE(lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]"<H5]mH}H*?H5onH+>H51 qH\>HH HQH5C7mH|>HH;A>HHH HMH5HUHuHuIIFIF0INIVIF@H]HEHtH5 AHlH&WHH;Eu LHP[A^]lIH]HEHtH AHFlLl fffff.UHAVSHPIHVHHE(lHþ H lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]%<H5lH}Hj>H5nHk>H5qqH>HH HQH54mH>HH;>HHH `HMH5HUHuHuIIFIF0INIVIF@H]HEHtHu AHlHfTHH;Eu LHP[A^] lIH]HEHtH0 AHlLl fffff.UHAVSHPIHSHHE(TlHþ HJlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]]<H5lH}H>H5nH>H5qH>HH HQH51mH>HH;>HHH HMHHUHuHuIIFIF0INIVIF@H]HEHtHAH lHQHH;Eu LHP[A^]JlIH]HEHtHpAHlLl fffff.UHAVSHPIH;QHHE(lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]<H5lH}H>H5/nH>H5qH>HH HQH5/mH<>HH;>HHH HMHHUHuHuIIFIF0INIVIF@H]HEHtHAHKlHNHH;Eu LHP[A^]lIH]HEHtHAHlLFl fffff.UHAVSHPIH{NHHE(lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]<H5]lH}H*>H5onH+>H51qH\>HH HQH5C,mH|>HH;A>HHH HMH HUHuHuIIFIF0INIVIF@H]HEHtH5AHlH&LHH;Eu LHP[A^]lIH]HEHtHAHFlLl fffff.UHAVSHPIHKHHE(lHþ H lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]<H5lH}Hj>H5 nHk>H5qqH>HH HQH5)mH>HH;>HHH ` HMHeHUHuHuIIFIF0INIVIF@H]HEHtHu@HlHfIHH;Eu LHP[A^] lIH]HEHtH0@HlLl fffff.UHAVSHPIHHHHE(TlHþ HJlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]<H5lH}H>H5 nH>H5qH>HH HQH5&mH>HH;>HHH HMHU"HUHuHuIIFIF0INIVIF@H]HEHtH@H lHFHH;Eu LHP[A^]JlIH]HEHtHp@HlLl fffff.UHAVSHPIH;FHHE(lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]<H5lH}H>H5/nH>H5 qH>HH HQH5$mH<>HH;>HHH HMHU+HUHuHuIIFIF0INIVIF@H]HEHtH@HKlHCHH;Eu LHP[A^]lIH]HEHtH@HlLFl fffff.UHAVSHPIH{CHHE(lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]]<H5]lH}H*>H5onH+>H51 qH\>HH HQH5C!mH|>HH;A>HHH HMHu4HUHuHuIIFIF0INIVIF@H]HEHtH5@HlH&AHH;Eu LHP[A^]lIH]HEHtH@HFlLl fffff.UHAVSHPIH@HHE(lHþ H lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]]<H5lH}Hj>H5nHk>H5qqH>HH HQH5mH>HH;>HHH `HMHu=HUHuHuIIFIF0INIVIF@H]HEHtHu@HlHf>HH;Eu LHP[A^] lIH]HEHtH0@HlLl fffff.UHAVSHPIH=HHE(TlHþ HJlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]<H5lH}H>H5mH>H5qH>HA H5mH>HH;>HHH HMHFHUHuHuIIFIF0INIVIF@H]HEHtH@HlH;HH;Eu LHP[A^]QlIH]HEHtHw@HlL l DUHAVSHPIHK;HHE(lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]m <H5-lH}H>H5?mH>H5qH,>HA H5mHS>HH;>HHH HMH,DHUHuHuIIFIF0INIVIF@H]HEHtH @HblH8HH;Eu LHP[A^]lIH]HEHtH@HlL]l DUHAVSHPIH8HHE(lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]<H5}lH}HJ>H5mHK>H5QpH|>HA H5jmH>HH;h>HHH GHMHAHUHuHuIIFIF0INIVIF@H]HEHtH\@HlHM6HH;Eu LHP[A^]lIH]HEHtH@HmlLl DUHAVSHPIH5HHE(DlHþ H:lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]<H5lH}H>H5mH>H5pH>HA H5mH>HH;>HHH HMHL?HUHuHuIIFIF0INIVIF@H]HEHtH@HlH3HH;Eu LHP[A^]AlIH]HEHtHg@HlLl DUHAVSHPIH;3HHE(lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H];H5lH}H>H5/mH>H5pH>HA H5 mHC>HH;>HHH HMH<HUHuHuIIFIF0INIVIF@H]HEHtH@HRlH0HH;Eu LHP[A^]lIH]HEHtH@H lLMl DUHAVSHPIH0HHE(lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H];H5mlH}H:>H5mH;>H5ApHl>HA H5ZmH>HH;X>HHH 7HMHl:HUHuHuIIFIF0INIVIF@H]HEHtHL@HlH=.HH;Eu LHP[A^]lIH]HEHtH@H]lLl DUHAVSHPIH-HHE(4lHþ H*lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H];H5lH}H>H5mH>H5pH>HA H5 mH>HH;>HHH HMH7HUHuHuIIFIF0INIVIF@H]HEHtH@HlH+HH;Eu LHP[A^]1lIH]HEHtHW@HlLl DUHAVSHPIH++HHE(lHþ HzlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] ;H5 lH}H>H5mH>H5pH >HA H5mH3>HH;>HHH HMH5HUHuHuIIFIF0INIVIF@H]HEHtH@HBlH(HH;Eu LHP[A^]lIH]HEHtH@HlL=l DUHAVSHPIH{(HHE(lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]];H5]lH}H*>H5omH+>H51pH\>HA H5JmH>HH;H>HHH 'HMH3HUHuHuIIFIF0INIVIF@H]HEHtH<@HlH-&HH;Eu LHP[A^]lIH]HEHtH@HMlL荾l DUHAVSHPIH%HHE($lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]m;H5lH}Hz>H5mH{>H5pH>HA H5mH>HH;蘺>HHH wHMH0HUHuHuIIFIF0INIVIF@H]HEHtH@HlH}#HH;Eu LHP[A^]!lIH]HEHtHG@H蝾lLݻl DUHAVSHPIH#HHE(tlHþ HjlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]=;H5lH}H>H5mH>H5pH>HH HQH5mH>HH;>HHH HMH5.HUHuHuIIFIF0INIVIF@H]HEHtH@H+lH HH;Eu LHP[A^]jlIH]HEHtH@HlL&l fffff.UHAVSHPIH[ HHE(贻lHþ H誻lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]};H5=lH}H >H5SmH >H5pH<>HH HQH5#lH\>HH;!>HHH ߄HMH<HUHuHuIIFIF0INIVIF@H]HEHtH@HklHHH;Eu LHP[A^]誹lIH]HEHtH@H&lLfl fffff.UHAVSHPIHHHE(lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H];H5}lH}HJ>H5mHK>H5QpH|>HH HQH5clH蜽>HH;a>HHH @܄HMHEKHUHuHuIIFIF0INIVIF@H]HEHtHU@H諶lHFHH;Eu LHP[A^]lIH]HEHtH@HflL観l fffff.UHAVSHPIHHHE(4lHþ H*lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]=;H5lH}H>H5mH苸>H5pH輹>HH HQH5lHܺ>HH;衯>HHH لHMHeTHUHuHuIIFIF0INIVIF@H]HEHtH@HlHHH;Eu LHP[A^]*lIH]HEHtHP@H観lLl fffff.UHAVSHPIHHHE(tlHþ HjlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]};H5lH}Hʾ>H5mH˵>H5pH>HH HQH5lH>HH;>HHH քHMHbHUHuHuIIFIF0INIVIF@H]HEHtH@H+lHHH;Eu LHP[A^]jlIH]HEHtH@HlL&l fffff.UHAVSHPIH[HHE(贰lHþ H誰lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]};H5=lH}H >H5SmH >H5pH<>HA H5*lHc>HH;(>HHH ԄHMHkHUHuHuIIFIF0INIVIF@H]HEHtH@HrlH HH;Eu LHP[A^]豮lIH]HEHtH@H-lLml DUHAVSHPIHHHE(lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H];H5lH}HZ>H5mH[>H5apH茱>HA H5zlH賲>HH;x>HHH WфHMH|iHUHuHuIIFIF0INIVIF@H]HEHtHl@H«lH]HH;Eu LHP[A^]lIH]HEHtH'@H}lL轨l DUHAVSHPIHHHE(TlHþ HJlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]];H5ݶlH}H誶>H5mH諭>H5pHܮ>HA H5lH>HH;Ȥ>HHH ΄HMH gHUHuHuIIFIF0INIVIF@H]HEHtH@HlH HH;Eu LHP[A^]QlIH]HEHtHw@HͨlL l DUHAVSHPIHK HHE(褨lHþ H蚨lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H];H5-lH}H>H5CmH>H5pH,>HA H5lHS>HH;>HHH ˄HMHdHUHuHuIIFIF0INIVIF@H]HEHtH @HblH HH;Eu LHP[A^]衦lIH]HEHtHǿ@HlL]l DUHAVSHPIH HHE(lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H];H5}lH}HJ>H5mHK>H5QpH|>HA H5jlH裪>HH;h>HHH GɄHMH,bHUHuHuIIFIF0INIVIF@H]HEHtH\@H貣lHMHH;Eu LHP[A^]lIH]HEHtH@HmlL譠l DUHAVSHPIHHHE(DlHþ H:lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] ;H5ͮlH}H蚮>H5mH蛥>H5pH̦>HH HQH5lH>HH;豜>HHH ƄHMH_HUHuHuIIFIF0INIVIF@H]HEHtH襺@HlHHH;Eu LHP[A^]:lIH]HEHtH`@H趠lLl fffff.UHAVSHPIH+HHE(脠lHþ HzlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]M;H5 lH}Hګ>H5'mHۢ>H5pH >HA H5lH3>HH;>HHH ÄHMHnHUHuHuIIFIF0INIVIF@H]HEHtH@HBlHHH;Eu LHP[A^]聞lIH]HEHtH觷@HlL=l DUHAVSHPIH{HHE(ԝlHþ HʝlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H];H5]lH}H*>H5}mH+>H51pH\>HH HQH5ClH|>HH;A>HHH HMHkHUHuHuIIFIF0INIVIF@H]HEHtH5@H苛lH&HH;Eu LHP[A^]ʛlIH]HEHtH@HFlL膘l fffff.UHAVSHPIHHHE(lHþ H lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H];H5lH}Hj>H5mHk>H5qpH蜞>HA H5lHß>HH;舔>HHH gHMH zHUHuHuIIFIF0INIVIF@H]HEHtH|@HҘlHmHH;Eu LHP[A^]lIH]HEHtH7@H荘lL͕l DUHAVSHPIH HHE(dlHþ HZlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]-;H5lH}H躣>H5mH軚>H5pH>HH HQH5lH >HH;ё>HHH HMHwHUHuHuIIFIF0INIVIF@H]HEHtHů@HlHHH;Eu LHP[A^]ZlIH]HEHtH耯@H֕lLl fffff.UHAVSHPIHKHHE(褕lHþ H蚕lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]m;H5-lH}H>H5RmH>H5pH,>HH HQH5lHL>HH;>HHH HMHuHUHuHuIIFIF0INIVIF@H]HEHtH@H[lHHH;Eu LHP[A^]蚓lIH]HEHtH@HlLVl fffff.UHAVSHPIHHHE(lHþ HڒlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H];H5mlH}H:>H5mH;>H5ApHl>HA H5ZlH蓗>HH;X>HHH 7HMHHUHuHuIIFIF0INIVIF@H]HEHtHL@H袐lH=HH;Eu LHP[A^]lIH]HEHtH@H]lL蝍l DUHAVSHPIHHHE(4lHþ H*lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H];H5lH}H芛>H5mH苒>H5pH輓>HA H5lH>HH;訉>HHH HMH<HUHuHuIIFIF0INIVIF@H]HEHtH蜧@HlHHH;Eu LHP[A^]1lIH]HEHtHW@H譍lLl DUHAVSHPIH+HHE(脍lHþ HzlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]M;H5 lH}Hژ>H5=mHۏ>H5pH >HH HQH5lH,>HH;>HHH аHMHŌHUHuHuIIFIF0INIVIF@H]HEHtH@H;lHHH;Eu LHP[A^]zlIH]HEHtH蠤@HlL6l fffff.UHAVSHPIHkHHE(ĊlHþ H躊lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]荾;H5MlH}H>H5}mH>H5!pHL>HH HQH53lHl>HH;1>HHH HMHHUHuHuIIFIF0INIVIF@H]HEHtH%@H{lHHH;Eu LHP[A^]躈lIH]HEHtH@H6lLvl fffff.UHAVSHPIHHHE(lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]͵;H5lH}HZ>H5mH[>H5apH茋>HA H5zlH賌>HH;x>HHH WHMH̟HUHuHuIIFIF0INIVIF@H]HEHtHl@H…lH]HH;Eu LHP[A^]lIH]HEHtH'@H}lL轂l DUHAVSHPIHHHE(TlHþ HJlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H];H5ݐlH}H誐>H5 mH談>H5pH܈>HA H5lH>HH;~>HHH HMH\HUHuHuIIFIF0INIVIF@H]HEHtH輜@HlHHH;Eu LHP[A^]QlIH]HEHtHw@H͂lL l DUHAVSHPIHKHHE(褂lHþ H蚂lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H52lH4IH EIH}>H5\mH>H5pH&>HH HQH5 lHF>HH; |>HHH ꥄHMHߚHUHuHuIIFIF0INIVIF@H]HEHtH@HUlHHH;Eu LHP[A^]蔀lIH]HEHtH躙@HlLP}l UHAVSHPIHHHE(lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5rlHFH FH}$>H5mHFH FH>H5mH>H5pHI>HH HQH50lHi>HH;.y>HHH HMHbHUHuHuIIFIF0INIVIF@H]HEHtH"@Hx}lHHH;Eu LHP[A^]}lIH]HEHtHݖ@H3}lLszl ff.UHAVSHPIHHHE(}lHþ H|lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5lHCH CH}D>H5mHCH CH'>H5mH8>H5>pHi>HA H5WlH萁>HH;Uv>HHH 4HMHHUHuHuIIFIF0INIVIF@H]HEHtHI@HzlH:߃HH;Eu LHP[A^]zlIH]HEHtH@HZzlLwl fUHAVSHPIHރHHE(4zlHþ H*zlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H];H5lH}H芅>H5mH|>H5pH}>HH HQH5lH~>HH;s>HHH HMHHUHuHuIIFIF0INIVIF@H]HEHtH蕑@HwlH܃HH;Eu LHP[A^]*xlIH]HEHtHP@HwlLtl fffff.UHAVSHPIH܃HHE(twlHþ HjwlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]=;H5lH}Hʂ>H59mHy>H5ѢpHz>HH HQH5lH|>HH;p>HHH HMHUHUHuHuIIFIF0INIVIF@H]HEHtHՎ@H+ulHكHH;Eu LHP[A^]julIH]HEHtH萎@HtlL&rl fffff.UHAVSHPIH[كHHE(tlHþ HtlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]};H5=lH}H >H5ymH w>H5pHHA H5*lHcy>HH;(n>HHH HMHLHUHuHuIIFIF0INIVIF@H]HEHtH@HrrlH ׃HH;Eu LHP[A^]rlIH]HEHtH׋@H-rlLmol DUHAVSHPIHփHHE(rlHþ HqlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]ͥ;H5}lH}HZ}>H5ɗmH[t>H5apHu>HA H5zlHv>HH;xk>HHH WHMHܫHUHuHuIIFIF0INIVIF@H]HEHtHl@HolH]ԃHH;Eu LHP[A^]plIH]HEHtH'@H}olLll DUHAVSHPIHӃHHE(TolHþ HJolHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H];H5zlH}Hz>H5mHq>H5pHr>HH HQH5ñlHs>HH;h>HHH HMHeHUHuHuIIFIF0INIVIF@H]HEHtH赆@H mlHуHH;Eu LHP[A^]JmlIH]HEHtHp@HllLjl fffff.UHAVSHPIH;уHHE(llHþ HllHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]];H5xlH}Hw>H5]mHn>H5pHp>HH HQH5lHHH;f>HHH HMHHUHuHuIIFIF0INIVIF@H]HEHtH@HKjlH΃HH;Eu LHP[A^]jlIH]HEHtH调@HjlLFgl fffff.UHAVSHPIH{΃HHE(ilHþ HilHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]蝗;H5]ulH}H*u>H5mH+l>H51pH\m>HA H5JlHn>HH;Hc>HHH 'HMHHUHuHuIIFIF0INIVIF@H]HEHtH<@HglH-̃HH;Eu LHP[A^]glIH]HEHtH@HMglLdl DUHAVSHPIH˃HHE($glHþ HglHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H];H5rlH}Hzr>H5mH{i>H5pHj>HA H5lHk>HH;`>HHH wHMHHUHuHuIIFIF0INIVIF@H]HEHtH~@HdlH}ɃHH;Eu LHP[A^]!elIH]HEHtHG~@HdlLal DUHAVSHPIHɃHHE(tdlHþ HjdlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]=;H5olH}Ho>H5AmHf>H5яpHg>HHHQH5lHi>HH;]>HHH HMH5HUHuHuIIFIF0INIVIF@H]HEHtH{@H+blHƃHH;Eu LHP[A^]jblIH]HEHtH{@HalL&_l fffff.UHAVSHPIH[ƃHHE(alHþ HalHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]};H5=mlH}H m>H5mH d>H5pHHHHQH5#lH\f>HH;![>HHH HMH%HUHuHuIIFIF0INIVIF@H]HEHtHy@Hk_lHăHH;Eu LHP[A^]_lIH]HEHtHx@H&_lLf\l fffff.UHAVSHPIHÃHHE(^lHþ H^lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]轌;H5}jlH}HJj>H5mHKa>H5QpH|b>HH HQH5clHc>HH;aX>HHH @HMHHUHuHuIIFIF0INIVIF@H]HEHtHUv@H\lHFHH;Eu LHP[A^]\lIH]HEHtHv@Hf\lLYl fffff.UHAVSHPIHHHE(4\lHþ H*\lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H];H5glH}Hg>H5mH^>H5pH_>HH HQH5lH`>HH;U>HHH HMH HUHuHuIIFIF0INIVIF@H]HEHtHs@HYlHHH;Eu LHP[A^]*ZlIH]HEHtHPs@HYlLVl fffff.UHAVSHPIHHHE(tYlHþ HjYlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]=;H5dlH}Hd>H5AmH[>H5фpH\>HA H5lH#^>HH;R>HHH |HMH<HUHuHuIIFIF0INIVIF@H]HEHtHp@H2WlHͻHH;Eu LHP[A^]qWlIH]HEHtHp@HVlL-Tl DUHAVSHPIHkHHE(VlHþ HVlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]荊;H5MblH}Hb>H5|mHY>H5!pHLZ>HA H5:lHs[>HH;8P>HHH zHMHHUHuHuIIFIF0INIVIF@H]HEHtH,n@HTlHHH;Eu LHP[A^]TlIH]HEHtHm@H=TlL}Ql DUHAVSHPIHHHE(TlHþ H TlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]݁;H5_lH}Hj_>H5ymHkV>H5qpHW>HHHQH5lHX>HH;M>HHH `wHMHHUHuHuIIFIF0INIVIF@H]HEHtHuk@HQlHfHH;Eu LHP[A^] RlIH]HEHtH0k@HQlLNl fffff.UHAVSHPIHHHE(TQlHþ HJQlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5\lHtH H}Y>H5wmHS>H5|pHT>HH HQH5lHU>HH;J>HHH tHMHHUHuHuIIFIF0INIVIF@H]HEHtHh@HOlHHH;Eu LHP[A^]DOlIH]HEHtHjh@HNlLLl UHAVSHPIH;HHE(NlHþ HNlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5"ZlHH H}V>H5_tmHP>H5ypHR>HH HQH5lH6S>HH;G>HHH qHMH?HUHuHuIIFIF0INIVIF@H]HEHtHe@HELlHఃHH;Eu LHP[A^]LlIH]HEHtHe@HLlL@Il UHAVSHPIH{HHE(KlHþ HKlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5bWlH H 5H}T>H5qmH%N>H5+wpHVO>HA H5DlH}P>HH;BE>HHH !oHMHHUHuHuIIFIF0INIVIF@H]HEHtH6c@HIlH'HH;Eu LHP[A^]IlIH]HEHtHb@HGIlLFl ffffff.UHAVSHPIHHHE(IlHþ H IlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]v;H5TlH}HjT>H5nmHkK>H5qtpHL>HHHQH5lHM>HH;B>HHH `lHMH HUHuHuIIFIF0INIVIF@H]HEHtHu`@HFlHfHH;Eu LHP[A^] GlIH]HEHtH0`@HFlLCl fffff.UHAVSHPIHHHE(TFlHþ HJFlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5QlH H H}N>H5#lmHH>H5qpHI>HH HQH5lHJ>HH;?>HHH iHMH HUHuHuIIFIF0INIVIF@H]HEHtH]@HDlHHH;Eu LHP[A^]DDlIH]HEHtHj]@HClLAl UHAVSHPIH;HHE(ClHþ HClHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5"OlH, H U H}K>H5cimHE>H5npHG>HH HQH5lH6H>HH;<>HHH fHMH/HUHuHuIIFIF0INIVIF@H]HEHtHZ@HEAlHृHH;Eu LHP[A^]AlIH]HEHtHZ@HAlL@>l UHAVSHPIH{HHE(@lHþ H@lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5bLlHlH H}I>H5fmH%C>H5+lpHVD>HA H5DlH}E>HH;B:>HHH !dHMHHUHuHuIIFIF0INIVIF@H]HEHtH6X@H>lH'HH;Eu LHP[A^]>lIH]HEHtHW@HG>lL;l ffffff.UHAVSHPIHHHE(>lHþ H >lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]k;H5IlH}HjI>Hl;H5cmHHPI>H5cmHQ@>H5WipHA>HHHQH5ilHB>HH;g7>HHH FaHMH[HUHuHuIIFIF0INIVIF@H]HEHtH[U@H;lHLHH;Eu LHP[A^];lIH]HEHtHU@Hl;lL8l @UHAVSHPIH럃HHE(D;lHþ H:;lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] o;H5FlH}HF>Hi;H5`mHHF>H5amH=>H5fpH>>HHHQH5}lH?>HH;4>HHH v^HMHHUHuHuIIFIF0INIVIF@H]HEHtHR@H8lH|HH;Eu LHP[A^] 9lIH]HEHtHFR@H8lL5l @UHAVSHPIHHHE(t8lHþ Hj8lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]=f;H5ClH}HC>Hf;H5.^mHHC>H53^mH:>H5cpH;>HH HQH5zlH=>HH;1>HHH [HMH HUHuHuIIFIF0INIVIF@H]HEHtHO@H6lHHH;Eu LHP[A^]P6lIH]HEHtHvO@H5lL 3l @UHAVSHPIHKHHE(5lHþ H5lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]mi;H5-AlH}H@>Hd;H5^[mHH@>H5c[mH7>H5`pH9>HH HQH5wlH2:>HH;.>HHH XHMH[HUHuHuIIFIF0INIVIF@H]HEHtHL@HA3lHܗHH;Eu LHP[A^]3lIH]HEHtHL@H2lL<0l @UHAVSHPIH{HHE(2lHþ H2lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]_;H5]>lH}H*>>HBa;H5XmHH>>H5XmH5>H5^pHB6>HH HQH5)ulHb7>HH;',>HHH VHMHHUHuHuIIFIF0INIVIF@H]HEHtHJ@Hq0lH HH;Eu LHP[A^]0lIH]HEHtHI@H,0lLl-l @UHAVSHPIHHHE(0lHþ H/lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] c;H5;lH}HZ;>Hr^;H5UmHH@;>H5UmHA2>H5G[pHr3>HH HQH5YrlH4>HH;W)>HHH 6SHMH HUHuHuIIFIF0INIVIF@H]HEHtHKG@H-lH<HH;Eu LHP[A^]-lIH]HEHtHG@H\-lL*l @UHAVSHPIHۑHHE(4-lHþ H*-lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC 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$Ht:IGJLfff.HQH9A HMHMH HuL9tH;F }SHEHEufHCHC1Hu`HEHMHKHCHC H(Hu`HIM9NHEHH wH H;MuH8[A\A]A^A_]mw`HUHAVSI(v`HHLH[A^]IHv`L t` UHAWAVAUATSHHIH?wHHEL=QYxM}I}$T3H%xIEIE IEIEW)EE HEHcabularyHEHint64_voHEMuEHhHUHL|3HhHDžhHtCt H{u`Hu`Et H}u`Hu'HkwHH;EuHĨ[A\A]A^A_] v`u`H8HhN#DH5raH aH}LhHPH<<LHDaHuH8HZDžLH5wHH8bu` H@LHhHH@H@Pt H`t`LeMt6HEL9t!HX@t Hxt`HI9uH}LLet`EuEuHhHu&^H}dt`EtH}Ut`HhHt:HpH9tML`@t Hx*t`LL9uHhHpt`Lt1H8*t`I>Hu%,HH@Et H}s`I>Ht I}s`M}I}IEHts`H@p` fffff.UHSPHH(xHH{Ht H{is`H$VxHH{HCHHt[]Cs`[]fUHSPHHصxHH{Ht H{s`HUxHH{HCHtr`HH[]r`ffff.UHAWAVAUATSH(IIIH_wHHE1LHEIOI+OIHHMHEHEHEHU1LY3H轹MGMWM)IMMMt\I1fILLfDHNH9V HMHMH1HuWL9t H;S |C(HHL9rIIGHHq`IHlwHH;EuLH([A\A]A^A_] r`fff.UHAVSI(q`HHLH[A^]IHiq`Ln` UHAWAVAUATSHHIHwHHEL=SxM}I}N3H%xIEIE IEIEW)EE HEHcabularyHEHint64_voHEMuEHhHUHL4w3HhHDžhHtCt H{p`Hp`Et H}up`Hu'H wHH;EuHĨ[A\A]A^A_]p`Wp`H8HhDH5aH NaH}Lh[ HPH6LHaHuH8HDžLH5)wHH8p` H@LHhHH@H@Pt H`fo`LeMt6HEL9t!HX@t HxAo`HI9uH}LLe'o`EuEuHhHu&^H}o`EtH}n`HhHt:HpH9tML`@t 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UHAWAVATSHIHtwHHEؿ(9`Hþ H9`HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]i/Il/HW)EHE,8`HEHHHML8HXHMH5d_aH}HUT@2Ih/Itl/HW)`HDžp7`H`HHHpL HXHhH5gbH`L?2HHHQH5aHi;2H53=dH<2HHHQH5{`H=2HH;22HHH ^\xHMHC(HUHuHuIIFIF0INIVIF@H`Ht Hh6`H}Ht H}6`H]HEHtHIP4H6`H:wHH;EuLHĀ[A\A^A_]6`IH}HtN|IH}Ht@nIH}Ht0^IH]HEHt+bIH`Hu$H}Hu0H]HEHu9LL3` Hh5`H}HtH}5`H]HEHtHmO4H5`L3` ff.UHAWAVAUATSHIHuI1Lx4HHII1LL+3IHC0xIF0@L}HGIL6HLIL6HHMLqHxHH[LLI"LHH@(HDHIIIHuuH bvL}HJZHLo6ILD@ILY6MH}&N4Lo`@Hw@HH LNIIH9w 1HHIHG8JHH8HItNfffff.HGH9uH9Ou#~fff.H9r1HHL9u^H?HuT@HGH9t HL!L9u4H$`L!` ff.UHAVSH0HIH(wHHEI~@H5ܬak"`I~XH5a["`W)EEHEHlt_floatHEHdefault_HEEI8H}HUHL 3H]HEHtCt H{#`H#`AEt H}#`HrwHH;Eu H0[A^]$`HEt H}#`H ` ffffff.UHAWAVSHxIHwHHE(r#`Hþ Hh#`HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H];Q/HW)EHE!`HEHHHMHHMH5IaH}HU*2I-V/HW)pHE!`HpHHHMHHxH5QbHpL)2H5aH=%2H5'dHn&2HAH5\e`H'2HH;Z2HHH 9FxHMH_HUHuHuIIFIF0INIVIF@HpHt Hx `H}Ht H} `H]HEHtH$:4Hz 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H芠SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]"H5SH}H%H5¥UH%H5WH%HH HQH5SH<%HH;%HHH kHMHEHUHuHuIIFIF0INIVIF@H]HEHtH'HKSHkHH;Eu LHP[A^]芞SIH]HEHtH谷'HSLFS fffff.UHAVSHPIH{kHHE(ԝSHþ HʝSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]"H5]SH}H*%H5UH+%H51WH\%HA H5JSH胢%HH;H%HHH 'kHMHLHUHuHuIIFIF0INIVIF@H]HEHtH<'H蒛SH-kHH;Eu LHP[A^]ћSIH]HEHtH'HMSL荘S DUHAVSHPIHjHHE($SHþ HSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]-"H5SH}Hz%H5RUH{%H5WH謞%HA H5SHӟ%HH;蘔%HHH wkHMH\HUHuHuIIFIF0INIVIF@H]HEHtH茲'HSH}jHH;Eu LHP[A^]!SIH]HEHtHG'H蝘SLݕS DUHAVSHPIHjHHE(tSHþ HjSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]="H5SH}Hʣ%H5UH˚%H5WH%HH HQH5SH%HH;%HHH kHMHe}HUHuHuIIFIF0INIVIF@H]HEHtHկ'H+SHjHH;Eu LHP[A^]jSIH]HEHtH萯'HSL&S fffff.UHAVSHPIH[jHHE(贕SHþ H誕SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]"H5=SH}H %H5UH %H5WH<%HH HQH5#SH\%HH;!%HHH kHMHʼnHUHuHuIIFIF0INIVIF@H]HEHtH'HkSHjHH;Eu LHP[A^]誓SIH]HEHtHЬ'H&SLfS fffff.UHAVSHPIHjHHE(SHþ HSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]"H5}SH}HJ%H5+UHK%H5QWH|%HH HQH5cSH蜗%HH;a%HHH @kHMH՘HUHuHuIIFIF0INIVIF@H]HEHtHU'H諐SHFjHH;Eu LHP[A^]SIH]HEHtH'HfSL覍S fffff.UHAVSHPIHjHHE(4SHþ H*SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]="H5SH}H芛%H5kUH苒%H5WH輓%HH HQH5SHܔ%HH;衉%HHH kHMHՖHUHuHuIIFIF0INIVIF@H]HEHtH蕧'HSHjHH;Eu LHP[A^]*SIH]HEHtHP'H覍SLS fffff.UHAVSHPIHjHHE(tSHþ HjSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]="H5SH}Hʘ%H5UHˏ%H5ѸWH%HA H5SH#%HH;%HHH ǰkHMHܔHUHuHuIIFIF0INIVIF@H]HEHtHܤ'H2SHjHH;Eu LHP[A^]qSIH]HEHtH藤'HSL-S DUHAVSHPIHkjHHE(ĊSHþ H躊SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]ͷ"H5MSH}H%H5UH%H5!WHL%HA H5:SHs%HH;8%HHH kHMHHUHuHuIIFIF0INIVIF@H]HEHtH,'H肈SHjHH;Eu LHP[A^]SIH]HEHtH'H=SL}S DUHAVSHPIHjHHE(SHþ H SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]ݵ"H5SH}Hj%H5TUHk%H5qWH蜋%HH HQH5SH輌%HH;聁%HHH `kHMHHUHuHuIIFIF0INIVIF@H]HEHtHu'H˅SHfjHH;Eu LHP[A^] SIH]HEHtH0'H膅SLƂS fffff.UHAVSHPIHjHHE(TSHþ HJSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]]"H5ݐSH}H誐%H5UH談%H5WH܈%HH HQH5SH%HH;~%HHH kHMHHUHuHuIIFIF0INIVIF@H]HEHtH赜'H SHjHH;Eu LHP[A^]JSIH]HEHtHp'HƂSLS fffff.UHAVSHPIH;jHHE(蔂SHþ H节SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]]"H5SH}H%H5ԇUH%H5WH%HH HQH5SH<%HH;|%HHH kHMHEHUHuHuIIFIF0INIVIF@H]HEHtH'HKSHjHH;Eu LHP[A^]芀SIH]HEHtH谙'HSLF}S fffff.UHAVSHPIH{jHHE(SHþ HSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]ݬ"H5]SH}H*%H5UH+%H51WH\%HH HQH5CSH|%HH;Ay%HHH kHMHEHUHuHuIIFIF0INIVIF@H]HEHtH5'H}SH&jHH;Eu LHP[A^]}SIH]HEHtH'HF}SLzS fffff.UHAVSHPIHjHHE(}SHþ H }SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]ݪ"H5SH}Hj%H5TUHk%H5qWH蜀%HA H5SHÁ%HH;v%HHH gkHMHLHUHuHuIIFIF0INIVIF@H]HEHtH|'HzSHmjHH;Eu LHP[A^]{SIH]HEHtH7'HzSLwS DUHAVSHPIH jHHE(dzSHþ HZzSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]m"H5SH}H躅%H5UH|%H5WH}%HA H5ڼSH%HH;s%HHH kHMH\HUHuHuIIFIF0INIVIF@H]HEHtH̑'H"xSHjHH;Eu LHP[A^]axSIH]HEHtH臑'HwSLuS DUHAVSHPIH[jHHE(wSHþ HwSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]}"H5=SH}H %H5}UH z%H5WH<{%HH HQH5#SH\|%HH;!q%HHH kHMHeHUHuHuIIFIF0INIVIF@H]HEHtH'HkuSHjHH;Eu LHP[A^]uSIH]HEHtHЎ'H&uSLfrS fffff.UHAVSHPIHjHHE(tSHþ HtSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]"H5}SH}HJ%H5AzUHKw%H5QWH|x%HH HQH5cSHy%HH;an%HHH @kHMHUHUHuHuIIFIF0INIVIF@H]HEHtHU'HrSHFjHH;Eu LHP[A^]rSIH]HEHtH'HfrSLoS fffff.UHAVSHPIHjHHE(4rSHþ H*rSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]"H5}SH}H}%H5wUHt%H5WHu%HH HQH5SHv%HH;k%HHH kHMHHUHuHuIIFIF0INIVIF@H]HEHtH蕉'HoSHjHH;Eu LHP[A^]*pSIH]HEHtHP'HoSLlS fffff.UHAVSHPIHjHHE(toSHþ HjoSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]}"H5zSH}Hz%H5tUHq%H5њWHr%HH HQH5SHt%HH;h%HHH kHMHHUHuHuIIFIF0INIVIF@H]HEHtHՆ'H+mSHjHH;Eu LHP[A^]jmSIH]HEHtH萆'HlSL&jS fffff.UHAVSHPIH[jHHE(lSHþ HlSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]}"H5=xSH}H x%H5rUH o%H5WHS fffff.UHAVSHPIHjHHE(@SHþ H@SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]t"H5mLSH}H:L%H5AFUH;C%H5AlWHlD%HH HQH5SSHE%HH;Q:%HHH 0dkHMHEgHUHuHuIIFIF0INIVIF@H]HEHtHEX'H>SH6jHH;Eu LHP[A^]>SIH]HEHtHX'HV>SL;S fffff.UHAVSHPIHˢjHHE($>SHþ H>SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]-n"H5ISH}HzI%H5CUH{@%H5iWHA%HH HQH5SHB%HH;7%HHH pakHMHEeHUHuHuIIFIF0INIVIF@H]HEHtHU'H;SHvjHH;Eu LHP[A^]%HH HQH5}SH @%HH;4%HHH ^kHMHuHUHuHuIIFIF0INIVIF@H]HEHtHR'H9SHjHH;Eu LHP[A^]Z9SIH]HEHtHR'H8SL6S fffff.UHAVSHPIHKjHHE(8SHþ H8SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]mf"H5-DSH}HC%H5>UH:%H5dWH,<%HA H5{SHS=%HH;2%HHH [kHMHHUHuHuIIFIF0INIVIF@H]HEHtH P'Hb6SHjHH;Eu LHP[A^]6SIH]HEHtHO'H6SL]3S DUHAVSHPIHjHHE(5SHþ H5SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]b"H5}ASH}HJA%H5Q;UHK8%H5QaWH|9%HA H5jxSH:%HH;h/%HHH GYkHMHHUHuHuIIFIF0INIVIF@H]HEHtH\M'H3SHMjHH;Eu LHP[A^]3SIH]HEHtHM'Hm3SL0S DUHAVSHPIHjHHE(D3SHþ H:3SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]Mf"H5>SH}H>%H58UH5%H5^WH6%HA H5uSH7%HH;,%HHH VkHMHHUHuHuIIFIF0INIVIF@H]HEHtHJ'H1SHjHH;Eu LHP[A^]A1SIH]HEHtHgJ'H0SL-S DUHAVSHPIH;jHHE(0SHþ H0SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]]d"H5kHMHHUHuHuIIFIF0INIVIF@H]HEHtH<2'HSH-}jHH;Eu LHP[A^]SIH]HEHtH1'HMSLS DUHAVSHPIH|jHHE($SHþ HSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]K"H5#SH}Hz#%H5UH{%H5CWH%HH HQH5ZSH%HH;%HHH p;kHMHHUHuHuIIFIF0INIVIF@H]HEHtH/'HSHvzjHH;Eu LHP[A^]SIH]HEHtH@/'HSLS fffff.UHAVSHPIH zjHHE(dSHþ HZSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]-I"H5 SH}H %H5UH%H5@WH%HH HQH5WSH %HH;%HHH 8kHMHHUHuHuIIFIF0INIVIF@H]HEHtH,'HSHwjHH;Eu LHP[A^]ZSIH]HEHtH,'HSLS fffff.UHAVSHPIHKwjHHE(SHþ HSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]mF"H5-SH}H%H5 UH%H5>WH,%HA H5USHS%HH; %HHH 5kHMHHUHuHuIIFIF0INIVIF@H]HEHtH *'HbSHtjHH;Eu LHP[A^]SIH]HEHtH)'HSL] S DUHAVSHPIHtjHHE(SHþ HSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]="H5}SH}HJ%H5fUHK%H5Q;WH|%HH HQH5cRSH%HH;a %HHH @3kHMHHUHuHuIIFIF0INIVIF@H]HEHtHU''H SHFrjHH;Eu LHP[A^] SIH]HEHtH''Hf SL S fffff.UHAVSHPIHqjHHE(4 SHþ H* SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]=:"H5SH}H%H5UH%H58WH%HH HQH5OSH%HH;%HHH 0kHMH5HUHuHuIIFIF0INIVIF@H]HEHtH$'H SHojHH;Eu LHP[A^]* SIH]HEHtHP$'H SLS fffff.UHAVSHPIHojHHE(t SHþ Hj SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]}="H5SH}H%H5UH %H55WH %HH HQH5LSH%HH;%HHH -kHMH$HUHuHuIIFIF0INIVIF@H]HEHtH!'H+SHljHH;Eu LHP[A^]jSIH]HEHtH!'HSL&S fffff.UHAVSHPIH[ljHHE(SHþ HSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]};"H5=SH}H %H5& UH %H53WH< %HH HQH5#JSH\ %HH;!%HHH +kHMH%CHUHuHuIIFIF0INIVIF@H]HEHtH'HkSHjjHH;Eu LHP[A^]SIH]HEHtH'H&SLfS fffff.UHAVSHPIHijHHE(SHþ HSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]2"H5}SH}HJ%H5f UHK%H5Q0WH|%HH HQH5cGSH %HH;a$HHH @(kHMH%mHUHuHuIIFIF0INIVIF@H]HEHtHU'HSHFgjHH;Eu LHP[A^]SIH]HEHtH'HfSLR fffff.UHAVSHPIHfjHHE(4SHþ H*SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]=/"H5 SH}H %H5UH%H5-WH%HH HQH5DSH%HH;$HHH %kHMH%kHUHuHuIIFIF0INIVIF@H]HEHtH'HRHdjHH;Eu LHP[A^]*SIH]HEHtHP'HRLR fffff.UHAVSHPIHdjHHE(tRHþ HjRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]}2"H5 SH}H %H5UH%H5*WH%HH HQH5ASH%HH;$HHH "kHMH%iHUHuHuIIFIF0INIVIF@H]HEHtH'H+RHajHH;Eu LHP[A^]jRIH]HEHtH'HRL&R fffff.UHAVSHPIH[ajHHE(RHþ HRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]}0"H5=SH}H %H5&UH $H5(WH<%HH HQH5#?SH\%HH;!$HHH kHMH%gHUHuHuIIFIF0INIVIF@H]HEHtH'HkRH_jHH;Eu LHP[A^]RIH]HEHtH'H&RLfR fffff.UHAVSHPIH^jHHE(RHþ HRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]'"H5}SH}HJ%H5fTHK$H5Q%WH|$HH HQH5cjHH;Eu LHP[A^]RIH]HEHtH'&H}RLR DUHAVSHPIH=jHHE(TRHþ HJRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]"H5RH}H$H5TH$H5WH$HH HQH5SH$HH;$HHH jHMHHUHuHuIIFIF0INIVIF@H]HEHtH&H RH;jHH;Eu LHP[A^]JRIH]HEHtHp&HRLR fffff.UHAVSHPIH;;jHHE(RHþ HRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]"H5RH}H$H5TH$H5WH$HH HQH5SH<$HH;$HHH jHMHHUHuHuIIFIF0INIVIF@H]HEHtH&HKRH8jHH;Eu LHP[A^]RIH]HEHtH&HRLFR fffff.UHAVSHPIH{8jHHE(RHþ HRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]"H5]RH}H*$H5PTH+$H51VH\$HH HQH5CSH|$HH;A$HHH jHMHHUHuHuIIFIF0INIVIF@H]HEHtH5&HRH&6jHH;Eu LHP[A^]RIH]HEHtH&HFRLR fffff.UHAVSHPIH5jHHE(RHþ H RHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]!H5RH}Hj$H5THk$H5qVH$HH HQH5SH$HH;$HHH `jHMHHUHuHuIIFIF0INIVIF@H]HEHtHu&HRHf3jHH;Eu LHP[A^] RIH]HEHtH0&HRLR fffff.UHAVSHPIH2jHHE(TRHþ HJRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]]!H5RH}H$H5TH$H5VH$HH HQH5SH$HH;$HHH jHMHHUHuHuIIFIF0INIVIF@H]HEHtH&H RH0jHH;Eu LHP[A^]JRIH]HEHtHp&HRLR fffff.UHAVSHPIH;0jHHE(RHþ HRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]!H5RH}H$H5TH$H5VH$HH HQH5SH<$HH;$HHH jHMHHUHuHuIIFIF0INIVIF@H]HEHtH&HKRH-jHH;Eu LHP[A^]RIH]HEHtH&HRLFR fffff.UHAVSHPIH{-jHHE(RHþ HRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]!H5]RH}H*$H5PTH+$H51VH\$HA H5J SH$HH;H$HHH 'jHMHHUHuHuIIFIF0INIVIF@H]HEHtH<&HRH-+jHH;Eu LHP[A^]RIH]HEHtH&HMRLR DUHAVSHPIH*jHHE($RHþ HRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]-!H5RH}Hz$H5TH{$H5VH$HA H5SH$HH;蘿$HHH wjHMH HUHuHuIIFIF0INIVIF@H]HEHtH&HRH}(jHH;Eu LHP[A^]!RIH]HEHtHG&HRLR DUHAVSHPIH(jHHE(tRHþ HjRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]}!H5RH}H$H5TH$H5VH$HA H5SH#$HH;$HHH jHMHHUHuHuIIFIF0INIVIF@H]HEHtH&H2RH%jHH;Eu LHP[A^]qRIH]HEHtH&HRL-R DUHAVSHPIHk%jHHE(RHþ HRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]!H5MRH}H$H5KTH$H5!VHL$HH HQH53SHl$HH;1$HHH jHMH%HUHuHuIIFIF0INIVIF@H]HEHtH%&H{RH#jHH;Eu LHP[A^]躾RIH]HEHtH&H6RLvR fffff.UHAVSHPIH"jHHE(RHþ HRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] !H5RH}HZ$H5TH[$H5aVH$HH HQH5sSH$HH;q$HHH PjHMH%HUHuHuIIFIF0INIVIF@H]HEHtHe&H軻RHV jHH;Eu LHP[A^]RIH]HEHtH &HvRL趸R fffff.UHAVSHPIHjHHE(DRHþ H:RHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]M!H5RH}H$H5TH蛽$H5VH̾$HH HQH5RH$HH;豴$HHH jHMH%HUHuHuIIFIF0INIVIF@H]HEHtH&HRHjHH;Eu LHP[A^]:RIH]HEHtH`&H趸RLR fffff.UHAVSHPIH+jHHE(脸RHþ HzRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]M!H5 RH}H$H5 THۺ$H5VH $HH HQH5RH,$HH;$HHH jHMH%HUHuHuIIFIF0INIVIF@H]HEHtH&H;RHjHH;Eu LHP[A^]zRIH]HEHtH&HRL6R fffff.UHAVSHPIHkjHHE(ĵRHþ H躵RHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]!H5MRH}H$H5KTH$H5!VHL$HH HQH53RHl$HH;1$HHH jHMH%HUHuHuIIFIF0INIVIF@H]HEHtH%&H{RHjHH;Eu LHP[A^]躳RIH]HEHtH&H6RLvR fffff.UHAVSHPIHjHHE(RHþ HRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] !H5RH}HZ$H5TH[$H5aVH茶$HH HQH5sRH謷$HH;q$HHH PjHMH%HUHuHuIIFIF0INIVIF@H]HEHtHe&H軰RHVjHH;Eu LHP[A^]RIH]HEHtH &HvRL趭R fffff.UHAVSHPIHjHHE(DRHþ H:RHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]M!H5ͻRH}H蚻$H5˵TH蛲$H5VH̳$HH HQH5RH$HH;豩$HHH jHMH%HUHuHuIIFIF0INIVIF@H]HEHtH&HRHjHH;Eu LHP[A^]:RIH]HEHtH`&H趭RLR fffff.UHAVSHPIH+jHHE(脭RHþ HzRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]M!H5 RH}Hڸ$H5 THۯ$H5VH $HH HQH5RH,$HH;$HHH jHMH%HUHuHuIIFIF0INIVIF@H]HEHtH&H;RHjHH;Eu LHP[A^]zRIH]HEHtH&HRL6R fffff.UHAVSHPIHkjHHE(ĪRHþ H躪RHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]!H5MRH}H$H5KTH$H5!VHL$HA H5:RHs$HH;8$HHH jHMH,HUHuHuIIFIF0INIVIF@H]HEHtH,&H肨RH jHH;Eu LHP[A^]RIH]HEHtH&H=RL}R DUHAVSHPIH jHHE(RHþ H RHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]!H5RH}Hj$H5THk$H5qVH蜫$HA H5RHì$HH;舡$HHH gjHMH<HUHuHuIIFIF0INIVIF@H]HEHtH|&HҥRHm jHH;Eu LHP[A^]RIH]HEHtH7&H荥RL͢R DUHAVSHPIH jHHE(dRHþ HZRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]m!H5RH}H躰$H5TH軧$H5VH$HA H5RH$HH;؞$HHH jHMHLHUHuHuIIFIF0INIVIF@H]HEHtH̼&H"RHjHH;Eu LHP[A^]aRIH]HEHtH臼&HݢRLR DUHAVSHPIH[jHHE(财RHþ H誢RHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]}!H5=RH}H $H5;TH $H5VH<$HA H5*RHc$HH;($HHH jHMH\HUHuHuIIFIF0INIVIF@H]HEHtH&HrRH jHH;Eu LHP[A^]豠RIH]HEHtH׹&H-RLmR DUHAVSHPIHjHHE(RHþ HRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]!H5RH}HZ$H5TH[$H5aVH茣$HH HQH5sRH謤$HH;q$HHH PjHMHeHUHuHuIIFIF0INIVIF@H]HEHtHe&H軝RHVjHH;Eu LHP[A^]RIH]HEHtH &HvRL趚R fffff.UHAVSHPIHjHHE(DRHþ H:RHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]M!H5ͨRH}H蚨$H5բTH蛟$H5VH̠$HH HQH5RH$HH;豖$HHH jHMHեHUHuHuIIFIF0INIVIF@H]HEHtH襴&HRHiHH;Eu LHP[A^]:RIH]HEHtH`&H趚RLR fffff.UHAVSHPIH+iHHE(脚RHþ HzRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]M!H5 RH}Hڥ$H5THۜ$H5VH $HH HQH5RH,$HH;$HHH нjHMHHUHuHuIIFIF0INIVIF@H]HEHtH&H;RHiHH;Eu LHP[A^]zRIH]HEHtH蠱&HRL6R fffff.UHAVSHPIHkiHHE(ėRHþ H躗RHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]!H5MRH}H$H5UTH$H5!VHL$HH HQH53RHl$HH;1$HHH jHMHHUHuHuIIFIF0INIVIF@H]HEHtH%&H{RHiHH;Eu LHP[A^]躕RIH]HEHtH&H6RLvR fffff.UHAVSHPIHiHHE(RHþ HRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] !H5RH}HZ$H5TH[$H5aVH茘$HH HQH5sRH謙$HH;q$HHH PjHMHHUHuHuIIFIF0INIVIF@H]HEHtHe&H軒RHViHH;Eu LHP[A^]RIH]HEHtH &HvRL趏R fffff.UHAVSHPIHiHHE(DRHþ H:RHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] !H5͝RH}H蚝$H5՗TH蛔$H5VH̕$HH HQH5RH$HH;豋$HHH jHMHHUHuHuIIFIF0INIVIF@H]HEHtH襩&HRHiHH;Eu LHP[A^]:RIH]HEHtH`&H趏RLR fffff.UHAVSHPIH+iHHE(脏RHþ HzRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]M!H5 RH}Hښ$H5THۑ$H5VH $HA H5RH3$HH;$HHH ײjHMHHUHuHuIIFIF0INIVIF@H]HEHtH&HBRHiHH;Eu LHP[A^]聍RIH]HEHtH触&HRL=R DUHAVSHPIH{iHHE(ԌRHþ HʌRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]ݹ!H5]RH}H*$H5eTH+$H51VH\$HA H5JRH胑$HH;H$HHH 'jHMHHUHuHuIIFIF0INIVIF@H]HEHtH<&H蒊RH-iHH;Eu LHP[A^]ъRIH]HEHtH&HMRL荇R DUHAVSHPIHiHHE($RHþ HRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]!H5RH}Hz$H5TH{$H5VH謍$HA H5RHӎ$HH;蘃$HHH wjHMHHUHuHuIIFIF0INIVIF@H]HEHtH茡&HRH}iHH;Eu LHP[A^]!RIH]HEHtHG&H蝇RL݄R DUHAVSHPIHiHHE(tRHþ HjRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]=!H5RH}Hʒ$H5THˉ$H5ѲVH$HH HQH5RH$HH;$HHH jHMHŴHUHuHuIIFIF0INIVIF@H]HEHtH՞&H+RHiHH;Eu LHP[A^]jRIH]HEHtH萞&HRL&R fffff.UHAVSHPIH[iHHE(贄RHþ H誄RHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]轱!H5=RH}H $H5UTH $H5VH<$HH HQH5#RH\$HH;!~$HHH jHMHEHUHuHuIIFIF0INIVIF@H]HEHtH&HkRHiHH;Eu LHP[A^]誂RIH]HEHtHЛ&H&RLfR fffff.UHAVSHPIHiHHE(RHþ HRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]软!H5}RH}HJ$H5THK$H5QVH|$HH HQH5cRH蜆$HH;a{$HHH @jHMHHUHuHuIIFIF0INIVIF@H]HEHtHU&HRHFiHH;Eu LHP[A^]RIH]HEHtH&HfRL|R fffff.UHAVSHPIHiHHE(4RHþ H*RHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]=!H5RH}H芊$H5ՄTH苁$H5VH輂$HH HQH5RH܃$HH;x$HHH jHMHHUHuHuIIFIF0INIVIF@H]HEHtH蕖&H|RHiHH;Eu LHP[A^]*}RIH]HEHtHP&H|RLyR fffff.UHAVSHPIHiHHE(t|RHþ Hj|RHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]=!H5RH}Hʇ$H5TH~$H5ѧVH$HH HQH5RH$HH;u$HHH jHMHHUHuHuIIFIF0INIVIF@H]HEHtHՓ&H+zRHiHH;Eu LHP[A^]jzRIH]HEHtH萓&HyRL&wR fffff.UHAVSHPIH[iHHE(yRHþ HyRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]}!H5=RH}H $H5UTH |$H5VH<}$HA H5*RHc~$HH;(s$HHH jHMH,HUHuHuIIFIF0INIVIF@H]HEHtH&HrwRH iHH;Eu LHP[A^]wRIH]HEHtHא&H-wRLmtR DUHAVSHPIHiHHE(wRHþ HvRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] !H5RH}HZ$H5|TH[y$H5aVHz$HA H5zRH{$HH;xp$HHH WjHMH<HUHuHuIIFIF0INIVIF@H]HEHtHl&HtRH]iHH;Eu LHP[A^]uRIH]HEHtH'&H}tRLqR DUHAVSHPIHiHHE(TtRHþ HJtRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]!H5RH}H$H5yTHv$H5VHw$HA H5ʶRHy$HH;m$HHH jHMHLHUHuHuIIFIF0INIVIF@H]HEHtH輋&HrRHiHH;Eu LHP[A^]QrRIH]HEHtHw&HqRL oR DUHAVSHPIHKiHHE(qRHþ HqRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]m!H5-}RH}H|$H5LwTHs$H5VH,u$HH HQH5RHLv$HH;k$HHH jHMHUHUHuHuIIFIF0INIVIF@H]HEHtH&H[oRHiHH;Eu LHP[A^]oRIH]HEHtH&HoRLVlR fffff.UHAVSHPIHiHHE(nRHþ HnRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]!H5mzRH}H:z$H5tTH;q$H5AVHlr$HH HQH5SRHs$HH;Qh$HHH 0jHMHU HUHuHuIIFIF0INIVIF@H]HEHtHE&HlRH6iHH;Eu LHP[A^]lRIH]HEHtH&HVlRLiR fffff.UHAVSHPIHiHHE($lRHþ HlRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]!H5wRH}Hzw$H5qTH{n$H5VHo$HH HQH5RHp$HH;e$HHH pjHMH#HUHuHuIIFIF0INIVIF@H]HEHtH腃&HiRHviHH;Eu LHP[A^]jRIH]HEHtH@&HiRLfR fffff.UHAVSHPIH iHHE(diRHþ HZiRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]m!H5tRH}Ht$H5 oTHk$H5VHl$HH HQH5ӫRH n$HH;b$HHH jHMH!HUHuHuIIFIF0INIVIF@H]HEHtHŀ&HgRHiHH;Eu LHP[A^]ZgRIH]HEHtH耀&HfRLdR fffff.UHAVSHPIHKiHHE(fRHþ HfRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]m!H5-rRH}Hq$H5LlTHh$H5VH,j$HH HQH5RHLk$HH;`$HHH jHMHHUHuHuIIFIF0INIVIF@H]HEHtH~&H[dRHiHH;Eu LHP[A^]dRIH]HEHtH}&HdRLVaR fffff.UHAVSHPIHiHHE(cRHþ HcRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]譑!H5moRH}H:o$H5iTH;f$H5AVHlg$HA H5ZRHh$HH;X]$HHH 7jHMH<6HUHuHuIIFIF0INIVIF@H]HEHtHL{&HaRH=iHH;Eu LHP[A^]aRIH]HEHtH{&H]aRL^R DUHAVSHPIHiHHE(4aRHþ H*aRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]=!H5lRH}Hl$H5fTHc$H5VHd$HA H5RHe$HH;Z$HHH jHMHL4HUHuHuIIFIF0INIVIF@H]HEHtHx&H^RHiHH;Eu LHP[A^]1_RIH]HEHtHWx&H^RL[R DUHAVSHPIH+iHHE(^RHþ Hz^RHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]M!H5 jRH}Hi$H5,dTH`$H5VH b$HA H5RH3c$HH;W$HHH ׁjHMH\2HUHuHuIIFIF0INIVIF@H]HEHtHu&HB\RHiHH;Eu LHP[A^]\RIH]HEHtHu&H[RL=YR DUHAWAVAUATSHHuiHHEHH~@HpHG8H;G0(HGiHH;EuHĈ[A\A]A^A_][R[RIHh. 6H5bTH cTH}LhHPH"AHcTHuLHCE1H5yiHLV[R[RIHh6H55bTH bTH}LhHPH!AHbTHuLHE1H5iH`LZR}ZRIHh-6H5aTH bTH}LhHPH!AHEbTHuLHBE1H5xiHLUZR IALhMIIALhMIIALhM:oIlIPt H`YRLmMt6HEL9t!HX@t Hx_YRHI9uH}LLmEYREuEu LhMu-H}YREtH}YRLhMHpL9HX@t HxXRHI9uIPt H`XRLmMt6HEL9t!HX@t HxXRHI9uH}LLmyXREuEu LhMu-'H}SXREtH}DXRLhMHpL9HX@t HxXRHI9uIPt H`WRLmMt6HEL9t!HX@t HxWRHI9uH}LLmWREuEuLhMu&^H}WREtH}{WRLhMt:HpL9tEHX@t HxPWRHI9uHhLp5WREtLXWRLhTR Lff.UHUHAWAVAUATSHVRIH}6H5^TH bTHXLEW)EHEE&HoverloadHEHmust be HEEed.AH5;XHXHMLE1H5iHLVR IALeMIIEt H}VRLeMt6HEL9t!HX@t HxURHI9uH}LLeURxuXuLeMu)_H}URXtHhURLeMt8HEL9tFHX@t HxkURHI9uH}LeVUREtLyURLRR Lfff.UH'UHUHAWAVAUATSHH`IHiHHEHW)EHEHHHLoI)tMMMII9LTRILuLuKHEM~,LHLURLLHMLeE11L`LL`MLeHL)HHHA|1HHHH HfDH I ILHMMWM9t)IrL)HHPHsLHuLibHzH)ǸLfHcHHcNHHcNHHcNHH HuHtHff.HcHHHuM)MIIHLHIcH4IqHIIIQPIc HH&%HƊIHpIH(LLHDR1H[A\A]A^A_]ffff.UHAWAVAUATSHxLMLAHpHxIHiHHE1&IH]W)EHEI} I+}tNHHHH=*BRILuLuIHEIuI] H)H~LH DRILuW)EHEEH]LHDRHH[]>RUHSPHH{Ht H{>RHn!jHH{HCHHt[]>R[]fff.UHSPHH{Ht H{c>RH!jHH{HCHtC>RHH[]5>RUHSPHH{Ht H{>RH jHH{HCHHt[]=R[]fff.UHSPHH{Ht H{=RH~ jHH{HCHt=RHH[]=RUHAWAVSHIHiHHE0r=RHL= jL;H{L%HHEHEHULHjHHiHH;EuHH[A^A_]a=RL;IH{HCHt cWWWTUVXHpHLW)`)P)@)0) ))))HDžLeH]L)tcLIILH=dHP:RILLKHHL~LLH;RILMEHHpLHLHHt H9RHHHt HP9RH Ht H(9RHHt H|9RHHt%Hd9RHpHu#HLHpHt Hx39RH}Ht H}!9RH}Ht H}9RHiHH;EuHH[A\A]A^A_]H9RHB6R HHpH\HHpHtqHHHHuHHHt H8RHHHuXH HudHHupHHu|HpHH}HH}HHh5R HP8RH HtH(7RHHtH7RHHtH7RHpHxRHHpHc=HHpHN(HHpH9HHpH$HxZ7RH}HH}D7RH}HH}.7RHn4R fDUHAWAVAUATSHHiHHELL0H HH8IW)EHEIEMe I)tTH(MILH=L6RILuLuKHEMH(~LL7RMLuHcIL}ILH}(IH LHNL)tH8HHRH)H9LNH8Ҹ(H&HHH2HH?HH>HHATcHH=HHAOT zcHHHOHHwHH^H) (9cfA\A$TXADTXAL TXTXHHuH_HtA(cTXATX((XH9~A T cX(XXH9HPH)HHtH( cATXHHuHH)IL ( RcfQYTXTXQTXTXHHufMMI}HL)H9uTMEI] HL)H9uAL)t1DI H;u+HH9uL)tB1fDII; u HH9u%H8LI}HI}pLIE8IMHI+E0HHHXIMPH`LhLpLxH H*@H@HHHH@WH*PHujHEHXHELuLuH@H0LL>5H}I9tpHHP(H}H WAH}HAT}cHHHPHs'Hu~HP H}HH)Ⱥ( -cffff.AA\TXTXATTXAD TXHH9uHt/IH( cffff.TXHHuAH}Ht H}1RH^iHH;Eu*H[A\A]A^A_]ÿ Hr1RH}.R HH}I9uHP H}HtmHuH}Hu_H.R HP(H}HtDHH}Ht6HH}Ht(HH}HtHH}Ht HH}HtH}0RH&.R fffff.UH]f.UH]0RfDUHSPH0RH jHHKHHH[]@UHHjHHGHF]UH]f.UH]D0RfDUHAWAVAUATSHHLH2LWIBHEHHpLHHMHHuL)MB HMHy0HY8H}H9IzHHMLIXHqxIHuLLMM,IrHuLyPLcHMI)HIHM(cWLuAuOHMH LHuH4W1fff.(WWTUVXLH9|LuMIHMNffffff.II41(WWTUVXLH9|INI4E1ffff.B(WWTUVXMI9|II9zMC HH;U}(LmHIHL;]pffffff.HpHMHI`HUH)HH9}$Ll1IHL;],ff.1IHL;]n KjL;H{L %HHEHEHUL HjHHߑiHH;EuHH[A^A_]-RL;IH{HCHt -RIH,RL8*R UHSPHH{Ht H{,RHjHH{HCHHt[],R[]fff.UHSPHH{Ht H{,RH>jHH{HCHtc,RHH[]U,RUHSPHF(H1HHHHH[]ÐUHAWAVAUATSHHHiHHEW)EIIHE)EHE)pHEHHD$(HHD$ HHD$HpHD$HEH$D$LELMQ1Ln&IHU1L.%Iǀ} uLLHH(HLԣHL LщHpHLW)`)P)@)0) ))))HDžLeH]L)tcLIILH=dH*RILLKHHL~LLH+RILMEHHpLHLHHt H*RHHHt HP)RH Ht H()RHHt H)RHHt%H)RHpHu#HL<HpHt Hxr)RH}Ht H}`)RH}Ht H}N)RHiHH;EuHH[A\A]A^A_])RH&R HHpH\HHpHtqHHHHuHHHt H(RHHHuXH HudHHupHHu|HpHH}HH}HH%R HPQ(RH HtH(9(RHHtH!(RHHtH (RHpHxRHHpHc=HHpHN(HHpH9HHpH$Hx'RH}HH}'RH}HH}m'RH$R DUHAWAVAUATSHHiHHELL0H HH8IffEHEIEMe I)tTH(MILH="L&RILuLuKHEMH(~LL5(RMLuHBILgILH}T(IH LHNL)tH8HHRH)H9]LH8(HHHHHH?HH>HHAof8HH=HHAoOf8HHHOHHwHH^H) AoTAoAodAol f8ff8ff8ff8fHHuH_HtAoAof8ff8ffofofH9~Ao f8ff8f8f~HH)HIIff:IPHHDKAHI|0IQH)f1ffff.oTo\odo,f8ff8ff8ff8fHHuMt"HAoAo\f8ff8fffpNff8f~L9LH)I ff.1LHHuMMI}HL)H9uYMEI] HL)H9uFL)t1f.I H;u+HH9uL)tB1fDII; u HH9u%H8LlI}H8I}p-LnIE8IMHI+E0HHHXIMPH`LhLpLxH H*@H@HHHH@WH*fօPHjHEHXHELuLuH@H0LL.5H}I9t`HbHP(H}HX\1AH}HAEALHHpHs"HP H}HHHfnHYHHWH*HZH)fɿAoAo\Aod Aol0f8ff8ff8ff8fHHuHtAoAo\f8ff8fffpNff8f~H9t,HH)I ffff.1LHHuAH}Ht H}%!RHiHH;EuQH[A\A]A^A_]ÿ H1M|fɿH)C!RH}R HH}I9uHP H}HtmHuH}Hu_HR 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HHLPMtIHIFHu7ILPLNK{(t*5ILPLNK{5{(HH8HCH@HtH@HcCHcKHHcs HHH81WUHHL@Mt#HIFHuILPLNKHH(HCH0HtH@HcCHcKHHcs$HH H(1WHHH0Ht#HHCHuHHPHMKH)bHH;Eu H[A^]NKIH0HIH@HIHPHIH`HuyIHpHueIH]HuTuIH]HuFgIH]Hu8YIH]Hu*KIH]Hu=IH]Hu/IH]Ht#HHCHuHHPH~LKLJK fUH]fDUHAWAVAUATSHLLHH H(IHbHHEЃ}H ]8IcD$$HH0HcE@H8E1( IItHKHHHMHHPHUHMH@H@HH5PdOH}>KHHHMHHPHUHMuH+u@H@H@HHX@KXtHhH` HYH}>KHHHMHHPHUHMH@H@HH5)NH}=KLpH@H@HEuEXuNEuZEucpuluuHFbHH;Eu}H[A\A]A^A_]H}?KXtHhk?KEtH}\?KEtH}M?KptH};?KtL.?KHɣbHH;Ety?KHEu,XuCEuZEunpH.KXt HXtHh>KEt HEtH}>KEt HEtH}>KptHp{H}`>KH;K UHAWAVAUATSHHբbHHEHH)HH9w*HbHH;EuHHĈ[A\A]A^A_]S>K>KIHh.H5NMH >bMH}Lh HPHAHSMHuLHE1H5bHPL=K IALhMIIPt H`9=KLmMt6HEL9t!HX@t Hx=KHI9uH}LLmAE$~6HcI<уAN΍DH40KHLHH9|FA])DHHcq$HA9u@AЅHcHEHDIcHHHGHHs tHHGMtHII9s ILfHHZHMƉHH9HLHHHGHHtH@HcwHHDCHH0H8LMHt+HIEHuIELPL%KHHGHH0H8HH HOHH H(HHHHHHHHHHLmDWW$A҉O(u u҉IUIuHXH`H9HHHDGHDO HHxH|$ DL$DT$D$D$LLIIHLLtHHIMIELHI9IrH9ABA;IMLIMIBH9L)I HLHtULJZ>9}I9|IA;|M9|IA;|IMHHLHuMBH;}I A;LMIA0ALAM9NȉDž?DžAE$|A]Lz |AU MIIIxHD$(H|$t$HL$L$HD$ LMMDž?HHpHHhEuDdAM4DtDA}8ÉdtÉdLHHHdHHHHHHHH|HHHHHHHHHHEH HH(HEH0HpH8HhH@HHHHHPHE HX|AE$***XYMxLHL#K۹ODD1HcHGdHELe]H]H]fWf)EEHULH-.H}H9t+HHt,HP($IxH1UHP HHc;HLHHLHEH<HMH HPHxHQHHPtHhHQtHpHHIHHH@HE1AfG EMcp$MADDAB HHLHcHLAAAiIAMGIIyLHfDA@$$AHcHH<уAN͍DH4D KHLII9BIIGHxH4H<LI|H9sLHHFHH_Hs1AHDAHHhH DHHpHHCH)1ADDLDLDLDLDLDLDf Df H HuHtNDAAIcHHHHcHHH H1Df Df H HuI9LHt:MMH1fff.ALALHLHL9rfII94_I1AA<u>A@$~6HcI<уAN΍DH4KHLHH9|FAX)DHHcq$HA9u@AЅHcHEHDIcHHHGHHs tHHGMtHII9s IA9uH[A^A_]fff.UHH z^H1H9NHD]DUHHc]UHAWAVAUATSHHwbHHEDIIL?LoMtIEMPLXHX1L()t|IHXLPMtIEHcHUL}LmHMHMH`HpHEHpLxLmHMt IEH}H9t$Ht%HP(HDž`HE1 HP H`HDž`I $I$HɉTtHMI|$0HHHuP0M|$0I\$I|$0ID$0H9tGHtHP(HpH}HtBH9t]I?HEH`HDž`HudHP HpH}HuIH`HDž`Hu+JIHHHPHH`HDž`Ht!HHEH}HHHuP0HH}H9t?HtHP(Mt:HIEHu(IELPLKI$Mu/HP MuI$MtPtTHLoKHxtbHH;EuIHLHĈ[A\A]A^A_] KKH sbHHH5rbH9HK.KH sbHHH5rbH HK H7HMt$HIEHuIELPLKHY K HHUHSPHcHH_HtHHCHtH[]HHPHH[] KUHAVSHH7cHLsMt#HIFHuILPLr KH[A^]bK@UHSPHWKH cHHKHHHKHHHtHAH[]fUHHcHHGHFHGHFHtH@]fDUHSPH_HtHHCHtH[]HHPHH[] KUHAVSII^Ht#HHCHuHHPH| KL[A^]l Kfffff.UHH6HHH@]ff.UHH uv^H1H9NHD]DUHHc]UH]f.UH] KfDUHSPH KH cHHKHSHHHPH[]fff.UHHcHHGHOHNHF]UH]f.UH]t KfDUHAWAVSPID:D9}"fI~Av1A9uH[A^A_]fff.UHH v^H1H9NHD]DUHH}c]ÐUHAVSHPIH{pbHHE( KHþ H KHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]=H5[0MH}HH5k/MH+H517OH\HA H5JNKHHH;HHHH '/cHMHHUHuHuIIFIF0INIVIF@H]HEHtH<#H KH-nbHH;Eu LHP[A^] KIH]HEHtH"HM KLK DUHAWAVAUATSHIIIHmbHHE1L;HHL0L8W)`HDžpHCH+CHH.袋.HH`F5LsL{M9t]L`H]fffff.LuHhH;psL0HhIXM9ufDLHBIXM9uW)EHEIH@H`LELH0xH@HH8taHHDž@H}Ht H}KH`Ht HhKHflbHH;EHHĨ[A\A]A^A_]H}tPHUHL.L@HDž@MtAFt I~eKL]KH}HdhHHDž@H}H@DK0KIH`˴.H5O,MH ,MH}L`8W)@HDžP KHP(?\)@He input.HHHsequenceHHHptr for HHHGot nullH@AHLHuH@L|E1H5bH~LKZHALeMuFzHAL`MHH@t HPKLeMt6HEL9t!Lh@t HxKLM9uH}LLeKEuEuL`Mu&^H}KEtH}KL`Mt:HhL9t=Lh@t HxTKLM9uH`Lh9KEtJL\K@LHL@HDž@Mu+H}HuQH`Ht HhKH*K AFt I~KLKH}Ht HH}HtH}KH`HuHH`HuHH`Huf.UHHH "bHHxH@Ht]FK]fDUHSPHHbHH{HCHtKHH[]KfUHAWAVSPI KHL=bL;H{LpHHLL2H]cHHH[A^A_]L;IH{HCHt KIHyKLK UHAVSHPIHgbHHE(TKHþ HJKHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]轓H5'MH}H HB6H5KHHH5'MHH5.OHHA H5EKHHH;HHH &cHMHRHUHuHuIIFIF0INIVIF@H]HEHtHHKHebHH;Eu LHP[A^]7KIH]HEHtH]HKLJ ff.UHAWAVAUATSHIIHebHHE1L螖HHtpHl^1LeH<HHKH+KHH.袋.HHIHdbHH;EuLHĈ[A\A]A^A_]TKKIHh蝭.H5%MH D&MH}Lh W)PHDž` JH`(8\)PHe input.HHHsequenceHHHptr for HHHGot nullH@AH LHuHPLNE1H5bHvLaJJIHh跬.H5%MH ^%MH}Lh#$W)PHDž`0JH`(8\)PHt tensorHH%Houtput tHH Hptr for HHHGot nullHHHLength: HHHSequenceH@-AH#%MHuHPLLE1H5bHuL_J IALeMIALhMIIALeMFIALhMnIIPt H`JLeMt6HEL9t!HX@t Hx\JHI9uH}LLeBJEuEu LhMu-'H}JEtH} JLhMHpL9HX@t HxJHI9uIPt H`JLeMt6HEL9t!HX@t HxJHI9uH}LLevJEuEuLhMu&^H}SJEtH}DJLhMt:HpL9tEHX@t HxJHI9uHhLpJEtL!JL1J L@UHAWAVATSH`IHg`bHHEؿ(JHþ HJHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H])H5G MH}HHH5.KHHlI(I.HW)EHEJHEHHHML HXHMH5KHULH5 MHH5&OHHA H5=KHHH;HHH cHMHūHUHuHuIIGIG0IOIWIG@H}Ht H}JH]HEHtHHJH]bHH;EuLH`[A\A^A_]4JIH]HEHt7HVHJLJ IH}HuH]HEHuLJ H}rJH]HEHt룐UHAWAVAUATSHIIH\bHHE1L^HH L5;HH$H8HSH+SHH.袋.HHHH9H9@HxHy HH8L(HH4IM}1LL\HLL L`PMuMuPM9#I}0HPhHxLz!H1ff.LLaJHLy!IIH9|H0HnMH`HyMH@HJIH]HEHt7H`HJLJ IH}HuH]HEHuLJ H}|JH]HEHtff.UHAWAVAUATSHHEbHHEHIIW)EHEHHI?tkH8MH b M1ALێ.H]HKHHCH5HHPHJH`EbHH;E 0H(IMl$0ID$I\$ W)PHDž`H)tnH8L0IILH=HWJILXLPKH`HL0H8~LHJILXHEH@HEHHHtH@HhHPH@L 3!HHHt#HHCHuHHPHJHPHt HXJLhLmM4$Mt$PM9tvI|$0HPhI|$HtI6HW)EHEJHEHHHML HXHMH5KHULH5LHNH5TNHHA H5mKHHH;kHHH JbHMHoHUHuHuIIGIG0IOIWIG@H}Ht H}JH]HEHtHMHJH>2bHH;EuLH`[A\A^A_]JIH]HEHt7HHVJLJ IH}HuH]HEHuLoJ H}JH]HEHtff.UHAWAVAUATSHIIH1bHHE1LbIMLINI+NHH.袋.HH8LkL0Ht?HH0HHH9H9HxфvHy HH0LI%HH+HN1LH;H萇IM'I6L[W)pHEHpLH8~v1fff.H;0tSLHH(HLHp#L(HDž(MtAEt I}ZJLRJHH98M|$MMl$MII\$L9L MI)Hèffffff.H(!HèIXuL I}HKLHXHVLH@HhKHEHpHXH0H@L8LMHȁLHC-ptxH}_JH.bHH;EtoJMI#LM>6JIEIEIEHpID$HxIHEID$LI$H.bHH;EuLH[A\A]A^A_]ÿJIHXtw.H5LH LHpLXeW)@HDžP0yJHP(\)@HsequenceHH&Hutput seHH Htr for oHHHot nullpHHHErase: GHHHSequenceH@.AHLHpH@LhE1H59ObH@LJJIHXlv.H5LH LHpLXdW)@HDžP qJHP(\)@He input.HHHsequenceHHHptr for HHHGot nullH@AHLHpH@LgE1H5MNbH?L*JI@tHPJLeMuEIALeMt6HEL9t!HX@t HxJHI9uH}LLeoJEupu#LXMuGvH}FJptH}4JLXMuJIALXM4H`L9HX@t HxJHI9u I@tHPJLeMuEIALeMt6HEL9t!HX@t HxJHI9uH}LLepJEupu LXMu=zH}JJptH}8JLXMuQIALXMt?H`L9t$HX@t HxJHI9uHXLL`JEuILJyIptmH}bILpMuVILpMtGHxL9tEMI)Hèfff.H"!HèIXuHpLxIJLJ Lfff.UHAVSHPIH)bHHE(JHþ H JHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]}MH5JH}HHbUH5LHHH5@LHQH5WNHHA H5pKHHH;nHHH MbHMHvHUHuHuIIFIF0INIVIF@H]HEHtHbHJHS'bHH;Eu LHP[A^]JIH]HEHtHHsJL賿J ff.UHAWAVAUATSHIHH&bHHEH HLc(MH(I|$HH+Hq1L踰H^Hg}HMH0H8E11LLx0ffffff.LDEL9x0`JHH](z-^)EPHH5LJCPHHHM-HHHDžHHtCt H{ JHJEtH}JAE9DH8LPW)EHEH}L1L}@LH@HLL1L@HDž@MtAFt I~hJL`JD9|L8M|$MtHH0LpHII\$L9t9MI)Hè@H!HèIXuI>L8H0H&LM}LJIFIFIHEID$HEHHEID$H(HH A$bH H;MuHĸ[A\A]A^A_]߿J荿JIHh(m.H5LH SLH}Lh2[W)PHDž`@0JH`('_)PHt sequenHH(Hor outpuHH Hullptr fHHHt: Got nHHHConstrucHHHSequenceHf@0ce@2AHLHuHPL]E1H5DbHV6LʾJP腾JIHh l.H5LH KLH}Lh/ZW)PHDž` (JH`(P\)PHore inpuHHHe 1 or mHHHMust havHf@ts@AH7LHuHPL\E1H5DbHx5LJrIPtH`聽JLeMuEIALeMt6HEL9t!HX@t HxKJHI9uH}LLe1JEuEu LhMuDmH} JEtH}JLhMuDIALhM.HpL9HX@t Hx貼JHI9uIPtH`舼JLeMuEIALeMt6HEL9t!HX@t HxRJHI9uH}LLe8JEuEuLhMu:wH}JEtH}JLhMuQIALhMt?HpL9t$HX@t HxǻJHI9uHhLLp觻JEuqILŻJdILuMuVIEtMH}BILuMt;H]L9tJHEHH(HML8L`LhHXLp HMYIQIIIHW)`HDžp(ѸJH`HH(HpL8L`LhLpHX HhH5JH}HUH`]HeJH5LHHIIHW)@HDžP;JH@HHHPL0HXHHH5#KH@LXH5LHH5NHHA H5JHAHH;HHH bHMHkHUHuHuH8HHCHC0HKHSHC@H@Ht HHRJH`Ht Hh:JH}Ht H}(JLuHEMtLLJHbHH;EuHHĨ[A\A]A^A_]=JIH`Ht?yIH`Ht.hIH`HtWIH@Hu0H`HubHH;EuLH@[A\A^A_]޲JHIH謯J HEtH}PJIH良J :Qh@UHAWAVAUATSHIHpIIHbHHEHEID$HEEEEEEW)EHEMt5I@ I;@uDLkHHi L}H]HEHxpHEHx1E11WHuLmL}H]HUI9Hxt)Lffffff.H8 HH9u11LIVHuHt$0HuHt$(HuHt$ HuHt$HuHt$HuHt$HuH4$DLMLL/WI}t_HLH FL1ALZ^.H}HtHxH8`JHbHH;E LH[A\A]A^A_]H8L0L@IEM~LMf W)pHEM)tXMIHI9 LޯJHHxHpJHEM~HLL,JLHxW)PHDž`LLHXHEHL`_11H@HH9uEtE fff.HEHHcH9LcHEHpL<W)@LHpH@jHI}tlA1ALHLH yL\.HHHHHCHrHHPH胭JEa(IEMd$0HpLxW)HDžI)tfHLHHH9LJILLIHMH~LLcJMLH@HHHHHtH@LLLHH!HHt#HHCHuHHPHbJHHt HFJLHËEHHcUIHXHL$HcE9Hu!HHcHHLrJHhHL4HHHBHHHHPsE1H)HhHHHRHHHHKvHIIHH HH(E1HLL覭JI<HJ4 L菭JH(H<H J4 LqJHH<HJ4 LSJILLL9uHPHLhHHtNHIL= 0HH/Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@7AHLHHPLnEE1H5,bH L聦J JH膣JHxJݿJIHpS.H5LH ULHLp#BW)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AHLHHPLDE1H5+bH*L螥JHp襢JIPt H`"JL Mt?H(L9t$HX@t HxJHI9uH LL(פJuu&LpMuMH訤JtH蓤JLpMuIALpMHxL9`HX@t HxIJHI9u7TIHH :IHH>IPt H`JL MuQIAL Mt?H(L9t$HX@t Hx谣JHI9uH LL(萣Juu&LpMuFHaJtHLJLpMuQIALpMt?HxL9t$HX@t Hx JHI9uHpLLxJEuILJH}HIHpHV]ILPM+II]IEH+Ct H{mJHeJH}HIH}HIHHuXIHHt4H JHHu,IHHuIHHHɡJvIHHHuIHHt#HHCHuHHPH腠JHHHeJHHHu IH HHHt|HHCHujHHPHJLPMuYII]IEHt(Ct H{JHڠJHHHuIHHHuLPMtHHXL9tpMI)Hèffff.H HèIXuHPLXiJHpHt HxQJH}Ht H}?JLJ LfUHAWAVAUATSHIHpIIHbHHEHEID$HEEEEEEW)EHEMt5I@ I;@uDL'YHHi L}H]HEHxpHEHx1E11WHuL/[L}H]HUI9Hxt)Lffffff.H8 HH9u11LIVHuHt$0HuHt$(HuHt$ HuHt$HuHt$HuHt$HuH4$DLMLLOEI}t_HLH fL1ALzL.H}HtHxH8耞JHbHH;E LH[A\A]A^A_]H8L0L@IEM~LMf W)pHEM)tXMIHI9 LJHHxHpJHEM~HLLLJLHxW)PHDž`LL HXHEHL`_11H@HH9uEtE fff.HEHHcH9LcHEHpL<W)@LHpH@芉HI}tlA1ALH LH LJ.HHHHHCHrHHPH裛JEa(IEMd$0HpLxW)HDžI)tfHLHHH9L= 0HH/Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@7AHLHHPL3E1H5bH- L衔J @JH覑JH蘑Jݿ>JIHpA.H58LH \LHLpC0W)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AHLHHPL2E1H5bHJ L输JHpŐJIPt H`BJL Mt?H(L9t$HX@t HxJHI9uH LL(Juu&LpMuMHȒJtH賒JLpMuIALpMHxL9`HX@t HxiJHI9u7TIHH :IHH>IPt H`JL MuQIAL Mt?H(L9t$HX@t HxБJHI9uH LL(谑Juu&LpMuFH聑JtHlJLpMuQIALpMt?HxL9t$HX@t Hx-JHI9uHpLLx JEuIL(JH}HIHpHV]ILPM+II]IEH+Ct H{荐JH腐JH}HIH}HIHHuXIHHt4H-JHHu,IHHuIHHHJvIHHHuIHHt#HHCHuHHPH襎JHHH腏JHHHu IH HHHt|HHCHujHHPH:JLPMuYII]IEHt(Ct H{JHJHHHuIHHHuLPMtHHXL9tpMI)Hèffff.H8 HèIXuHPLX艎JHpHt HxqJH}Ht H}_JL蟋J LfUHAWAVAUATSHIHpIIHaHHEHEID$HEEEEEEW)EHEMt5I@ I;@uDLGGHHi L}H]HEHxpHEHx1E11WHuLOIL}H]HUI9Hxt)Lffffff.H8 HH9u11LIVHuHt$0HuHt$(HuHt$ HuHt$HuHt$HuHt$HuH4$DLMLLo3I}t_HLH L1AL:.H}HtHxH8蠌JH;aHH;E LH[A\A]A^A_]H8L0L@IEM~LMf W)pHEM)tXMIHI9 LJHHxHpJHEM~HLLlJLHxW)PHDž`LLZHXHEHL`_11H@HH9uEtE fff.HEHHcH9LcHEHpL<W)@LHpH@wHI}tlA1ALH*LH L8.HHHHHCHrHHPHÉJEa(IEMd$0HpLxW)HDžI)tfHLHHH9L\JILLIHMH~LL裋JMLH@HHHHHtH@LLLHH HHt#HHCHuHHPH袈JHHt H膉JLPHËEHHcUIHXHL$HcE9Hu!HHcHHL貊JHhHL4HHHBHHHHPsE1H)HhHHHRHHHHKvHIIHH HH(E1HLLJI<HJ4 LωJH(H<H J4 L豉JHH<HJ4 L蓉JILLL9uHPHLhHHtNHIL= 0HH/Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@7AHѮLHHPL!E1H5bHMLJ `JHJHJݿ^JIHp/.H5XLH /LHLpcW)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AHݭLHHPL E1H5bHjLށJHp~JIPt H`bJL Mt?H(L9t$HX@t Hx7JHI9uH LL(Juu&LpMuMHJtHӀJLpMuIALpMHxL9`HX@t Hx艀JHI9u7TIHH :IHH>IPt H`/JL MuQIAL Mt?H(L9t$HX@t HxJHI9uH LL(Juu&LpMuFHJtHJLpMuQIALpMt?HxL9t$HX@t HxMJHI9uHpLLx-JEuILHJH}HIHpHV]ILPM+II]IEH+Ct H{~JH~JH}HIH}HIHHuXIHHt4HM~JHHu,IHHuIHHH ~JvIHHHuIHHt#HHCHuHHPH|JHHH}JHHHu IH HHHt|HHCHujHHPHZ|JLPMuYII]IEHt(Ct H{"}JH}JHHHuIHHHuLPMtHHXL9tpMI)Hèffff.HX HèIXuHPLX|JHpHt Hx|JH}Ht H}|JLyJ LfUHAWAVAUATSHIHpIIHaHHEHEID$HEEEEEEW)EHEMt5I@ I;@uDLg5HHi L}H]HEHxpHEHx1E11WHuLo7L}H]HUI9Hxt)Lffffff.H8 HH9u11LIVHuHt$0HuHt$(HuHt$ HuHt$HuHt$HuHt$HuH4$DLMLL!I}t_HLH L1AL(.H}HtHxH8zJH[aHH;E LH[A\A]A^A_]H8L0L@IEM~LMf W)pHEM)tXMIHI9 L>zJHHxHpJHEM~HLL{JLHxW)PHDž`LLzHXHEHL`_11H@HH9uEtE fff.HEHHcH9LcHEHpL<W)@LHpH@eHI}tlA1ALHJLH يL&.HHHHHCHrHHPHwJEa(IEMd$0HpLxW)HDžI)tfHLHHH9L|xJILLIHMH~LLyJMLH@HHHHHtH@LLLHH/ HHt#HHCHuHHPHvJHHt HwJLpHËEHHcUIHXHL$HcE9Hu!HHcHHLxJHhLN4HHHBHHHHPsE1H)HhHHHRHHHHKvHLHH HH(IE1HLLxJI<HJ4 LwJI<H(J4 LwJHH<HJ4 LwJILH L9uHPHLhHHtQHIL= 0HH/Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@7AHLHHPLE1H5aHmLpJ pJHmJHmJݿ~pJIHp.H5xLH LHLp W)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AHLHHPLE1H5!aHLoJHpmJIPt H`oJL Mt?H(L9t$HX@t HxWoJHI9uH LL(7oJuu&LpMuMHoJtHnJLpMuIALpMHxL9`HX@t HxnJHI9u7TIHH :IHH>IPt H`OnJL MuQIAL Mt?H(L9t$HX@t HxnJHI9uH LL(mJuu&LpMuFHmJtHmJLpMuQIALpMt?HxL9t$HX@t HxmmJHI9uHpLLxMmJEuILhmJH}HIHpHV]ILPM+II]IEH+Ct H{lJHlJH}HIH}HIHHuXIHHt4HmlJHHu,IHHuIHHH)lJvIHHHuIHHt#HHCHuHHPHjJHHHkJHHHu IH7 HHHt|HHCHujHHPHzjJLPMuYII]IEHt(Ct H{BkJH:kJHHHuIHHHuLPMtHHXL9tpMI)Hèffff.Hx HèIXuHPLXjJHpHt HxjJH}Ht H}jJLgJ LfUHAWAVAUATSHIHHIHaHHEHEHCHEEEEEEW)EHEMt5I@ I;@uDL#HH L}HEHLulLu1HE11SHuL%L}HULuI9t(LH8HH9uH1 1LIT$HuHt$0HuHt$(HuHt$ HuHt$HuHt$HuHt$HuH4$DLMLHI}tXH;LH zL1AL.H}HtI>hJHaHH;E8LH[A\A]A^A_]L@H8LHIEM|$Mt$ W)pHEM)Lt_MIHI9 LghJHHxHpJHEML~HLLiJLHxW)PHDž`LzH0HEH11HhLpHH9uEtEfHEHHcH9?LcHEHpL<W)@LHH@SH(I}tlA1ALHzLH yL(.HHHHHCHHHPHfJEIEMl$0HpLxW)HDžI)tfHxLHHH9!LfJILLIHMHx~LLgJMLH@HHHHHtH@LLLHH_ HHt#HHCHuHHPHdJHHLt HeJLyqIŋULcuMHhH@H0LLpMI)HèHX HèIXuI<$Lp I^,LHL;aJID$ID$I$HPIFHXHH`IFW)PHDž`IEHpHt Hx(aJLHH}H!$LPMtHXL9t4MI)Hèffffff.Hh HèIXuHPLLX`JHpHpw`J`JIHpC.H5LH ߏLHLpW)PHDž`@H`JH`(0^)PH be >= 0HH/Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@7AHLHHPLE1H5aHcL_J H\JH\JZ_Jt_JIHp .H5nLH LHLpyW)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AHLHHPLE1H5aHL^JHp[JIPt H`x^JH Ht?H(H9t$Lh@t HxM^JLL9uH HH(-^Juu&LpMuFH]JtH]JLpMuQIALpMt?HxL9t$HX@t Hx]JHI9uHpLLx]JEuIL]JH}HIHH :IHH3IPt H`]JL MuQIAL Mt?H(L9t$HX@t Hx\JHI9uH LL(\Juu&LpMuFH\JtHx\JLpMuQIALpMt?HxL9t$HX@t Hx9\JHI9uHpLLx\JEu~IL4\JH}HosIHpHBIILPMII]IEHCt H{[JH[JH}HIH}HIHHuXHIHHt4H9[JHHu,IHHuIHHHZJIHHt#HHCHuHHPHYJHHHZJHHHuO{II]IEHtvCt H{gZJH_ZJHHHu]IHHHtNHHCHu NDpHDžxAu@Hq IHRvHHSM~hI1E1AAH IHuHH||MnIs\10AHDžxAA|LHHyHHVH1H/!LHHyHHrHH~H)1ffo [ff.f81f81\fvfvff\f81Tf81\ fvfvffT \0HHuHt4f81f81Lffvfvfo^[ffLI9t1fnN[fffff.< fouff~ HI9uILxA rA H[ IH]ffufPLPI9LxDpILxAAfAmHg IHHgHHyMI rJ #H9HLH91I|$H)LHtH HHuH/ T TT TT THI9uAILxA LHHyHHVH`s 1Hu}HzH)1fffff.3L30L0D3 L30D0 L00D3@L3PD0@L0PoD3`oL3pD0`L0pHHuHt.HHoD3o 3D0 0H HuI9LxDpILxA AH耧 IHaeHHR~M%IrJ #H91J `H9$1IT$H)LHt4H< tf?fH( HD8DpHDžxA u@H IHۙHHdMIsb1~E1AAHã IH脙HH%hM.I1dLHHyHHrH$H~H)1ffo [fo[ffff.oodfvffvff8f~f8f~do\ od0fvffvff8f~\f8f~d HHuHiLHHyHHVH HzH)1fff.L[[LD L0[[D L0HHuHc1HooLffvfo[ffvfo[f8f~ff8f~LI9HDžxAAHǡ IH舗HH詤M]Is1@LHHyHHVH s1HHzH)1fff.f8%f8%LLf8%Df8%LD L0f8%D f8%L(D@LPf8%D0f8%L8D`LpHHuHH4Hfff.f8%D3f8% 3Dp pHHuR1HaL[[LI9aILxA 5A  HD IHHHƩM}aIs1`aLHHyHHVH s1H=aHzH)1ff8%f8%LLf8%Df8%LD L0f8%D f8%L(D@LPf8%D0f8%L8D`LpHHuH`H4Hfff.f8%D3f8% 3Dp pHHuu`H2 HHI9uILxA b HI9uDpILxAz fHHI9uILxA fHHI9uILxA<HI9ueHcHHI9uILxA`A H IHړHHfMIVDHs 1HuS~I)1@W*W*DDW*DDW*D fDHI9uHt-H4H1fffff.W*fHH9uILxA A DH1 IHHH裛MIrJ H9J H91IT$H)LHtH@< 28wfnXf~f. 2  f4HHI9uLxDpILxAAH IHIHHME1Lff.C4LJufHCHC1H JHHCHHHKHIHM9|LxILxHpH(HD8 7DpHDžxA uWH IH4HHQM$IVDH1HE1AA^H茑 IHM4HHUM IVDHx1HA ILxAI)1ffff.H<H|DH|DH|DHI9uHt)H4H1ffffff.H<HH9uHDžxAAH舐 IHI3HHjM~I1QAILxAA H$ IH2HHXMIVDH1H:_I)1@WH*WH*DDWH*DDWH*Df~D HI9uHt*H4H1f.WH*f~HH9uILxA A H0 IH1HH貘MIrJ H9J H9u1IT$H)LHtHH Affffff.8wfnXf~f. 2  f4HHI9sLxDpILxA"A"A ILxAuLHHyHHVHHzH)1foe[Do oTf8f: 0f8f:T0oL oT0f8f:L0f8f:T0HHu1Ht,ooLfo[f8f:0f8f:L0I9LxDpILxA AH IH%HHYM~DIu/1HAILxA[I)1WH*f~Ɖ>f4HWH*Df~Ɖ>ftHHI9uHt#WH*f~‰2fHLxDpILxAH蹁 IHz$IH{ HM{E1LDK4LrIufHCHC1HIHHCHHHKHIHM9|LxILxHp޼H( HD8DHDžA uCEH跀 IH(+IH@MIsu1fE1AAH` IH*HHBM{IVDH1H6WLHHyHHrHH~H)1f( [fo[AAdTTf8f~f8f~dA\ Ad0TTf8f~\f8f~d HHuHtAAALf([Tfo[f8f~Tf8f~LI9t%ffA.ӈHI9uHDžAEAdH~ IH?)HHvM~IY1@AILAA Hj~ IH(HHLHHyHHVHHzH)1fDZZLLZDZLD L0HHuHi>M)13?[Hf (\H,H1H,.HFH L(\H,H1H,.HFH|L(\H,H1H,.HFH|L (\H,H1H,.HFH|HI9]MtTH4H1f>[Hfff. (\H,H1H,.HFHHI9uILA AH { IH{%HHeM"Is1=LHHyHHVHHzH)1fff.L[fk[fkfpfLpD L0[fk[fkfDpfLpHHuHT<1H 1H!<L[[LI9;DILAw^1H;ZZLLI9;ILA A xHy IH#IHwM~ Iso1;A ILA,AH,y IH#HH~jMIe1C;LHHyHHDBAHIxH)1A H,H,fnf:"(H,f:"H,ALf:"H,H,fnf:"(H,f:"H,f:"TAL H,H,fnf:"(H,f:"H,AL0f:"H,H,fnf:"(H,f:"H,f:"D T0HHM9LHHyHHVH HzH)1fff.L[f8+[f8+fpfLpD L0[f8+[f8+fDpfLpHHuHU91H49oL[fkfp[fkfDpI98ILAuAHv IH!HH TMIrJ H9HLH91IT$H)LHtH,<@<HHuH$D,,TT,TT,T THI9uAILALHHyHHrHlH~H)1fo;[ff. T[f8f~ [f8f~LL T0[[f8f~Lf8f~T HHu1MG7A H,H,fnf:"(H,f:"H,ALf:"H,H,fnf:"(H,f:"H,f:"TI96DILA,S1H6oL[f8+fp[f8+fDpI9}6ILAwAAHt IHuHHXMIrJ H9HLH91IT$H)LHtHD,<@<HHuH2D,,TT,TT,T THI9uAILA hLHHyHHrHzH~H)1fo[ff. 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Sfn,Ȉ LSfn,ȈLHI9uLDILA"IHHH}KHHI9uILxA|MKW*f~HI9uKHHI9uILxA 4KHI9uDpILxA_ffffff.UHAWAVAUATSHHHb`HHE.z0( ZT. %Zr) /\.H5dKKH5TKKZHQKKLL8IADEMcMfIL&IILL$IHJKLL8TIM~ MD9=M忈IIHX,H5IKH JKHpLXiW)@HDžP0IHP(])@Hrintf().HHHth snpriHHHvalue wiHHHo write HHHFailed tH@&8HJKHpH@LDž8H5J`HL'IH5IKHIH`}`HH;Eu@HĨ[A\A]A^A_]E1HLLIMtLIH }`HH;EtI~IIHX,H5HKH 2IKHpLX\胴HIKHH@HHHhAHIKHpLH E1H5B`HLI IALXM:I;Iư8LM[Iư8LXMII@t HPLILMt?HL9t$HX@t Hx!IHI9uHLLIupu#LXMu/gHIptHILXMt:H`L9tIHX@t HxIHI9uHXL`zIE9LILI LI@t HPBILMt?HL9t$HX@t HxIHI9uHLLIupu#LXMu/gHIptHILXMt:H`L9tAHX@t HxIHI9uHXL`pI8t'LILILI IMMtL2ILxI UHAVSI&IHHLH[A^]IHIL9I UHAWAVAUATSHHHry`HHEDHXH`JILh4IILpeKILxCILuJHEAHHExGHEEHEFHEKHELHEKDHEEHEHCHCHpIHHXHLxL`LhLp E@(E@8E@HE@XHMHHhfHnfpDf6\CHJx`HH;EuHHĈ[A\A]A^A_]IUHAWAVAUATSHHHx`HHEfCHXH`AILhGILpuBILxfHILuIHEIHEFHEEHEvDHE-EHEdJHEKHECHEHCHCHpIHHXHLxL`LhLp E@(E@8E@HE@XHMHHhfHnfpDfƀ\CHv`HH;EuHHĈ[A\A]A^A_]uIUHAWAVSH(Hv`HHEHcHuHL=L5LHLIHcHuHLHLIHcHuHLHLIHcHuHLHLIHu`HH;Eu H([A^A_]IUHAVSHPIHu`HHE($IHþ HIHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]荙H5IH}HH?H56JHH`H5DBKHaH5gHsLHJ<8H@HLtIHL@L<t=LHJ<8H@HHL1IHL@LHH9gHHtJ<tDLHJ<8H@HILIHL@LLIHHM9􋽬wH IFIH+H}Ht H}IH}Ht H}IH`g`HH;EFHHX[A\A]A^A_]HHH9HODHHuE1E1HI)HHHE1LE1ffffff.B<1t'LHHHHIHIB|1t(LHHHH4`IHIIHM9uHt+B<1t$HLHLLMLIHHH}HIH}HH}HXIIH(,H54KH R4KH@L(V]HHAH4KH@LHE1H58`HxLI HAL(MH Ht H ILxMt9HEL9t$Lh@t HxbILM9uHxLLeEI`u@u#L(Mu/gHpI@tHPIL(Mt:H0L9tmLh@t HxHLM9uH(L0HEu$H}HH}HHH LHH}HtLHH}HtHH}Ht^HH}HtPHH}HtBHH}HtJHH}Hu"HH}HcHH}HQH}HH}HHH}HHH fDUHHH `HHxH@Ht]H]fDUHSPHHP`HH{HCHtuHHH[]gHfUHAVSI VHHHLH[A^]IH)HLiH UHAWAVATSH0HIHb`HHEL%`M'IH aIW)EHEEEaxisEIOH}HUHH]HAGHEtCt H{HHHEt H}pHH b`HH;Eu H0[A\A^A_]HIEt H}'KH ~'KH0'HH}1H0HnIHHJHH]HuHHHrLH+EoExLhMHpL9sffff.HX@t HxkHHI9uHhBH tJLP+HV`HH;ECHXI>HID$0HH`IcW)EHEHHLtOHH9 H`HHHHEHL4LuHHHLuLLHHXHDtHMHAL5IcLhHxHHHL,M IMIE H)HHH H;( HDžH{HpA1L$LA~uCffff.HH;B0IML$LA~tH1L;$LIܺWH0HH5`$KH $K1LLH[HHI$H kIHH %KHH %KHMH LHMHD$HEHD$HH$H]HLHHLLM #HHp+EujusHhHLHpH9tqfffff.Lp@t HxKHLL9uHhBfff.H}%HtHHHhHLuHHpHPHt H`H0Lt H@H$<tuUMHHHH;(HxHpAHELHH}H H}(HH}HIELhMt@HpL9fHX@t HxHHI9uHhLpHPt H`H0tkH@HH}Hu_fH}rHiHYHLhMYLILhM?zIH}Ht H}HLUH ffff.UHAWAVAUATSH8HC`HHEIIII|$ID$H9L9IFHH)IIHOH9xJHH)MIIIMJIv KIDH9]JDH9OHHAH9HGIIIDHtHؐH1H3HHHuI81H4H4HtHtHtHtHtHtHt Ht Ht(Ht(Ht0Ht0Ht8Ht8Ht@H@H9rM,$L)HHHH=IIT$L)HHHLH9wHH9HBHHEHUHt@L9cH<>H*IIIFID$HA`HH;E1LL)HHEH HMHMHHEH}LL}I4$LH)LH)H}H~Hw JHH(H4HTL)H]xB.UHH e(\H1H9NHD]DUHH a]UHAVSHPIH`HHE(HHþ HHHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5HH}HZH5IH[H5aLH茻HH HQH5sHH謼HH;qHHH P`HMH%?HUHuHuIIFIF0INIVIF@H]HEHtHeH軵HHV`HH;Eu LHP[A^]HIH]HEHtH HvHL趲H fffff.UHAVSHPIH`HHE(DHHþ H:HHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] H5HH}HH5IH蛷H5LH̸HA H5HHHH;踮HHH `HMHlYHUHuHuIIFIF0INIVIF@H]HEHtHHHH`HH;Eu LHP[A^]AHIH]HEHtHgH轲HLH DUHAVSHPIH;`HHE(蔲HHþ H芲HHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]]H5HH}HH5$IHH5LHHH HQH5HH<HH;HHH `HMHWHUHuHuIIFIF0INIVIF@H]HEHtHHKHH`HH;Eu LHP[A^]芰HIH]HEHtHHHLFH fffff.UHAVSHPIH{`HHE(ԯHHþ HʯHHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5]HH}H*H5dIH+H51LH\HA H5JHH胴HH;HHHH '`HMHLiHUHuHuIIFIF0INIVIF@H]HEHtH<H蒭HH-`HH;Eu LHP[A^]ѭHIH]HEHtHHMHL荪H DUHAVSHPIH`HHE($HHþ HHHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]-H5HH}HzH5IH{H5LH謰HH HQH5HH̱HH;葦HHH p`HMHfHUHuHuIIFIF0INIVIF@H]HEHtHH۪HHv`HH;Eu LHP[A^]HIH]HEHtH@H薪HL֧H fffff.UHAVSHPIH `HHE(dHHþ HZHHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]mH5HH}H躵H5IH軬H5LHHA H5HHHH;أHHH `HMHxHUHuHuIIFIF0INIVIF@H]HEHtHH"HH `HH;Eu LHP[A^]aHIH]HEHtHHݧHLH DUHAVSHPIH[ `HHE(货HHþ H誧HHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5=HH}H H5DIH H5LH<HH HQH5#HH\HH;!HHH `HMHvHUHuHuIIFIF0INIVIF@H]HEHtHHkHH `HH;Eu LHP[A^]誥HIH]HEHtHоH&HLfH fffff.UHAVSHPIH `HHE(HHþ HHHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]=H5}HH}HJH5IHKH5QLH|HA H5jHH裩HH;hHHH G`HMHHUHuHuIIFIF0INIVIF@H]HEHtH\H貢HHM`HH;Eu LHP[A^]HIH]HEHtHHmHL譟H DUHAVSHPIH`HHE(DHHþ H:HHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]MH5ͭHH}H蚭H5IH蛤H5LH̥HH HQH5HHHH;豛HHH `HMHeHUHuHuIIFIF0INIVIF@H]HEHtH襹HHH`HH;Eu LHP[A^]:HIH]HEHtH`H趟HLH fffff.UHAVSHPIH+`HHE(脟HHþ HzHHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5 HH}HڪH5IHۡH5LH HA H5HH3HH;HHH `HMHHUHuHuIIFIF0INIVIF@H]HEHtHHBHH`HH;Eu LHP[A^]聝HIH]HEHtH觶HHL=H DUHAVSHPIH{`HHE(ԜHHþ HʜHHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5]HH}H*H5dIH+H51LH\HH HQH5CHH|HH;AHHH `HMH5HUHuHuIIFIF0INIVIF@H]HEHtH5H苚HH&_HH;Eu LHP[A^]ʚHIH]HEHtHHFHL膗H fffff.UHAVSHPIH_HHE(HHþ H HHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5HH}HjH5IHkH5qLH蜝HA H5HHÞHH;舓HHH g`HMHlHUHuHuIIFIF0INIVIF@H]HEHtH|HҗHHm_HH;Eu LHP[A^]HIH]HEHtH7H荗HL͔H DUHAVSHPIH _HHE(dHHþ HZHHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]-H5HH}H躢H5IH軙H5LHHH HQH5HH HH;ѐHHH `HMHHUHuHuIIFIF0INIVIF@H]HEHtHŮHHH_HH;Eu LHP[A^]ZHIH]HEHtH耮H֔HLH fffff.UHAVSHPIHK_HHE(褔HHþ H蚔HHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]mH5-HH}HH54IHH5LH,HA H5HHSHH;HHH `HMHHUHuHuIIFIF0INIVIF@H]HEHtH HbHH_HH;Eu LHP[A^]衒HIH]HEHtHǫHHL]H DUHAVSHPIH_HHE(HHþ HHHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]}H5}HH}HJH5IHKH5QLH|HH HQH5cHH蜖HH;aHHH @`HMHŵHUHuHuIIFIF0INIVIF@H]HEHtHUH諏HHF_HH;Eu LHP[A^]HIH]HEHtHHfHL覌H fffff.UHAVSHPIH_HHE(4HHþ H*HHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5HH}H芚H5ķIH苑H5LH輒HA H5HHHH;計HHH `HMHHUHuHuIIFIF0INIVIF@H]HEHtH蜦HHH_HH;Eu LHP[A^]1HIH]HEHtHWH譌HLH DUHAVSHPIH+_HHE(脌HHþ HzHHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] H5 HH}HڗH5IHێH5LH HH HQH5HH,HH;HHH Я`HMHHUHuHuIIFIF0INIVIF@H]HEHtHH;HH_HH;Eu LHP[A^]zHIH]HEHtH蠣HHL6H fffff.UHAVSHPIHk_HHE(ĉHHþ H躉HHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]MH5MHH}HH5TIHH5!LHLHA H5:HHsHH;8HHH `HMHHUHuHuIIFIF0INIVIF@H]HEHtH,H肇HH_HH;Eu LHP[A^]HIH]HEHtHH=HL}H DUHAVSHPIH_HHE(HHþ H HHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]]H5HH}HjH5IHkH5qLH蜊HH HQH5HH輋HH;聀HHH ``HMHUHUHuHuIIFIF0INIVIF@H]HEHtHuH˄HHf_HH;Eu LHP[A^] HIH]HEHtH0H膄HLƁH fffff.UHAVSHPIH_HHE(THHþ HJHHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]蝹H5ݏHH}H誏H5IH諆H5LH܇HA H5HHHH;}HHH `HMHHUHuHuIIFIF0INIVIF@H]HEHtH輛HHH_HH;Eu LHP[A^]QHIH]HEHtHwH́HL H DUHAVSHPIHK_HHE(褁HHþ H蚁HHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]-H5-HH}HH54IHH5LH,HH HQH5HHLHH;{HHH `HMH5HUHuHuIIFIF0INIVIF@H]HEHtHH[HH_HH;Eu LHP[A^]HIH]HEHtHHHLV|H fffff.UHAVSHPIH_HHE(~HHþ H~HHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]mH5mHH}H:H5tIH;H5ALHlHA H5ZHH蓃HH;XxHHH 7`HMH<HUHuHuIIFIF0INIVIF@H]HEHtHLH|HH=_HH;Eu LHP[A^]|HIH]HEHtHH]|HLyH DUHAVSHPIH_HHE(4|HHþ H*|HHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]=H5HH}H芇H5ĤIH~H5LHHH HQH5HH܀HH;uHHH `HMHHUHuHuIIFIF0INIVIF@H]HEHtH蕓HyHH_HH;Eu LHP[A^]*zHIH]HEHtHPHyHLvH fffff.UHAVSHPIH_HHE(tyHHþ HjyHHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]}H5HH}HʄH5IH{H5ѤLH|HA H5HH#~HH;rHHH ǜ`HMHHUHuHuIIFIF0INIVIF@H]HEHtHܐH2wHH_HH;Eu LHP[A^]qwHIH]HEHtH藐HvHL-tH DUHAVSIvHHHY`HH{LSTH[aHH[A^]IHvHLsH fff.UHHH 2Y`HHxH@Ht]VvH]fDUHSPHHY`HH{HCHt%vHHH[]vHfUHAWAVAUATSH8HHH_HHE1HILHxID$HM|$ HHøIL8HI~\IfWf)`HDžpMJ<IIHI9 LuHNHhH`J HpMKNIIIrL9J H9HHNDI)DHtHHHHHHuI8Y1ɐHHHTHTHTHTHTHTHT HT HT(HT(HT0HT0HT8HT8HT@H@L9uHhH11I?M!IpHHyH s 1Hu|HwH)1ffffff.LLD L0D L0D@LPD@LPfD`LpfD`LpHHuHt.H HfDfD  fD H HuM92HhHI)IJ|HhH)IL$It$ HH)HHH)v%HEH`HMH IL$It$ H9tdL`LhH)LL)1fDH9tQH|I\H9t&HuI|HH9u,fDHHH9uHL L`fWf)@HDžPLL)t^LIIHI9CrHILHL@KHPHI)M~LLsHMLHH@1HTH0L+H8IH`HL!I)MIM9LILH0HyHHHILALxqHHH(LbqHHH LLqHILHMLLL'IžLMKDHHKTHHHHIt uIIIf.HCHI|ItHHHHHHIHIII_pffff.HHYHHHDžHx HHH@HIL0IHK|IfWf)HDžMHtJI9hMN<LoHHJHHLoHHMLKDHHHHHxQk+HcLHHIIHLpHHEPoHH nTaHHHHHHHH0HHH(HH H HH(H8HH0HxHH8HHH@HHHHHEfHnfb'Zf\/Zf|W)MfEHUH]LLHIz+H}H91H.HP(#ME1fff.HIHHxL0L9}.L(L ESA)IKAH9uH1MuZi1efff.M)H1HHIjHH(HDž(Ht"jHH@Ht HHjHH`HtHH8iHH_HH;EHH8[A\A]A^A_]fWf)EE(HEHexpand sHEHinvalid HEEhapeEHMHHE9+EYH}KiHH`fHH@fH HfH IEH}iHH`HIH}H9HuvHHHHDžHH HDž HH(HDž(HH@HHP(HHt`IHHpHIHHX0IHH@IH`HIH@HelIH HDž H# IH(HDž(H IH@HIH`HHhIH@HIH@HIH}I99HP HH8IHH#HfHHHDžHfHH HDž HfHH(HDž(H fHH@Ht HHfHH`HtHH8efHLcH ffff.UHAWAVAUATSH(IIIIMISMcLH)HL9LML]IHUHH)HLHH=HI)LHHH9II9LBMI9eHHH5CHHcHH_HHH5_H_HeHIM)LHHHML9ILI)LLIIFL9vKL"M9IFINH0IHHxINLL9}.IN IV(L LESA)IKAH9uH1MuZi1efff.M)H1HHIaHH[A^]IHYHLVH fff.UHAVSIvYHHH"<`HH{L6H?aHH[A^]IH4YHLtVH fff.UHHH ;`HHxH@Ht]XH]fDUHSPHH;`HH{HCHtXHHH[]XHfUHAWAVAUATSH8HHH+_HHE1H蚛IL߈HxID$HM|$ HHcIL}HI~IfWf)`HDžpMJ<IIHI9 WHNHhH`J HpMKNIIIrL9J H9HHNDI)DHtHHHHHHuI8Y1ɐHHHTHTHTHTHTHTHT HT HT(HT(HT0HT0HT8HT8HT@H@L9uHhH11I?M!IpHHyH s 1Hu|HwH)1ffffff.LLD L0D L0D@LPD@LPfD`LpfD`LpHHuHt.H HfDfD  fD H HuM92HhHI)IJ|HhH)IL$It$ HH)HHH)v%HEH`HMHNIL$It$ H9tdL`LhH)LL)1fDH9tQH|I\H9t&HuI|HH9u,fDHHH9uHL L`fWf)@HDžPLL)t^LIIHI9THILHL@KHPHI)M~LL+VHMLHH@1HH7H@L+H8IH`HL!I)MIM9LILH0HyHHHILALTHHH(LTHHH LSHILHMLLL)IžLMKDHHKTHHHHIt uIIIf.HCHI|ItHHHHHHIHIII_rffff.HHYHHHDžHxH HHHH@HIL0IHK|IfWf)HDžMHtJI9vMN<L>RHHJHHL'RHHMLKDHHHHHxM+HcLHHIIHLpHHEPQHH t8aHHHHHHHH0HHH(HH H HH(H8HH0HxHH8HHH@HHHHHEfHnfbYf\Yf|W)MfEHUH]LLH\+H}H9/H,HP(!ME1f.HIHHxL0L9}.L(L ESA)IKAH9uH1MuZi1efff.M)H1HHIL"M9IFINH0IHHxINLL9}.IN 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HxID$HhHpIL$ID$ID$HID$@ID$8M)tSLHHH9LTGIMt$@Mt$8IID$HM~LLLGMMt$@HPID$PLeLHI'DML(L}~THEI$LLP0L9}1LeH]HMu1HLHU /L(LeHEL9tMt"I$LP(HHu"I$LP HHt HKGHHLIt H"GH HH@LHLH)IFH |DHHQH HHHtHHtHHH;48uHDLHHPHHHPH0HHY HH1HPI)IH0~GML L0HhH+HEpGIH_IHxIGHhHpIOIGIG HIG0IG(IG LHL)tPIIHI9kHGIMo(Mo KDIG0H~LLH[GIMo(HHHAIGHHHIIO@IG8IG`IGXIGPLLM)tPLHHH9LlGIMoXMoPIDIG`M~LLLGMMoXHIGhL}LHI'KMLHEL(ILHuP0L9}8L}H]HMu1HLHU5 LL(L}HEL9t,Mt0ILP(HHu'1L(HEL9uILP HHt HKGHHt H3GHHt H GHHt HGH HH@LHLH)IFH I|DHHQH HHHtHHtHHH;48uHDLHHPHHHPH0HHLIHtHHHFGHxHLÿ<GIHh׭*H5:IH 4;IHLhAH;IH@HIAHFHXHDžXH1CIHXHDžXH ID72IHH8Ht/I(F$IHH8Ht2I(eF'ILVFHH4IL4FHHIHHIH@HIH@HIH@HIH@HILFHHILrFHHDIHH?IHH'IHHIHHIH@HIH@HIH@HIH@HIHHx,IHH`IHHIHHIHH/IHHIH HIH HsIHH,IHHIHH|HIHHd0IH}HEH9uHHP HHtGHMFHHt;H5FHH-HuIHHuHHuHHH FHHHP(HHteIH}HEH9u_HP HHt^HFHHtRHvFH HtFH(^FHHu>GHu@HHuHHuH HuHHHHP(HHtWIH}HEH9u0HP HHt/HFHH!Hu%HHuHHHzHP(HHtIH}HEH9u,HP HHt/HAFHHt/!HHHuHHt HFH@Ht HHFHxHt H}FHXHDžXHtCt H{FHFLF HP(HHm9UHHH 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IFHCIFINHKHCHCHHC@HC8I~@I+~8tNL{8IILH=uQFILc@Lc8KHCHIv8M~@I)M~LLMFMLc@IFPHCPH[A\A]A^A_]LF II?Ht H{@FLF UHHHx8Ht Hx@]dF]@UHSPHH{8Ht H{@CFHH[]5FUHHLGHWHw HO(HH4Hy LcO0HO8J4HH vI4HGPH@HHGH]Fffff.UHH ]ZH1H9NHD]DUHH _]UHAWAVAUATSPHIHCIGHHKIOIHMwIG(IG IGH{ H+{t^III9;FIMg MgKIG(HsLk I)MH~LL}FHMMg HCHIGHHC@IG@HC0HK8IO8IG0IG`IGXIGPH{XH+{PtOLuMgPIII9wTFIMoXMoPKDIG`HsPH[XH)H~LHFIMoXH[A\A]A^A_]LFLF HI<$Ht IXFHEH8Hu HI>Ht I FH;F UHSPHHh_HH{XHt H{`FH{ HtH{(H[]FH[]fff.UHSPHH_HH{XHt H{`yFH{ Ht H{(gFHH[]YFff.UHAVSIpFFHIvH_HH{H[A^]IH FLMF DUHHGH y_HH~H]AUHSPHH{XHt H{`FH{ HtH{(H[]FH[]fDUHSPHH{XHt H{`FH{ Ht H{(qFHH[]cFDHLHP8HIHxHpHHLH@LcPHMcMLPPOc MyUHSLXXIcM []H@ N LLHHFff.UHH uZZH1H9NHD]DUHH_]UHH_HHG@Ht HGHH]F]f.UHSPHH_HH{@Ht H{HiFHH[][Fffff.UHAWAVAUATSPHX?FIH3_I$HC8ID$8HC0ID$0HC(ID$(HC ID$ MHCID$HCHKIL$ID$ID$PID$HID$@HC@L{HI)tOHEI@LHHH=uILFIMnHMn@IDIFPMHu~LLFMMnHLH[A\A]A^A_]LF II<$Ht I~H;FL3FLsF ff.UHAWAVAUATSPIH_IHHC8IF8HC0IF0HC(IF(HC IF HCIFHCHKINIFIFPIFHIF@H{HH+{@tNM~@IILH=uIFIMfHMf@KIFPHs@H[HH)H~LHFIMfHH[A\A]A^A_]LF HI?Ht I~H+FHkF UHHHx@Ht HxH]F]@UHSPHH{@Ht H{HFHH[]FUHHHHP HHHxHpL@LH(LcP0HcLIc Hy LcH8H@@J LHH]Ffff.UHH WZH1H9NHD]DUHH_]UHAWAVAUATSPHIHCIGHHKIOIHMwIG(IG IGH{ H+{t^III9FIMg MgKIG(HsLk I)MH~LLFHMMg HCHIGHHC@IG@HC0HK8IO8IG0IG`IGXIGPH{XH+{PtOLuMgPIII9wT7FIMoXMoPKDIG`HsPH[XH)H~LHFIMoXH[A\A]A^A_]LDFL:F HI<$Ht IXFHEH8Hu HI>Ht I FHF UHSPHH_HH{XHt H{`iFH{ HtH{(H[]QFH[]fff.UHSPHH_HH{XHt H{`FH{ Ht H{(FHH[]Fff.UHAVSIpFHIvHV_HH{H[A^]IHFLF DUHHGH _HH~H]AUHSPHH{XHt H{`cFH{ HtH{(H[]KFH[]fDUHSPHH{XHt H{`#FH{ Ht H{(FHH[]FDHLHP8HIHxHpHHLH@LXHMLPPO MyUHSLXXIcM []H@ N LLHH"Ffffff.UHH UZH1H9NHD]DUHHM_]UHH]_HHG@Ht HGHH]:F]f.UHSPHH(_HH{@Ht H{H FHH[]Fffff.UHAWAVAUATSPHXFIHӽ_I$HC8ID$8HC0ID$0HC(ID$(HC ID$ MHCID$HCHKIL$ID$ID$PID$HID$@HC@L{HI)tOHEI@LHHH=uILGFIMnHMn@IDIFPMHu~LLFMMnHLH[A\A]A^A_]LXF II<$Ht I~HFLFLF ff.UHAWAVAUATSPIH_IHHC8IF8HC0IF0HC(IF(HC IF HCIFHCHKINIFIFPIFHIF@H{HH+{@tNM~@IILH=uI9FIMfHMf@KIFPHs@H[HH)H~LHFIMfHH[A\A]A^A_]LGF HI?Ht I~H˿FH F UHHHx@Ht HxH]餿F]@UHSPHH{@Ht H{H胿FHH[]uFUHHHHP HHHxHpL@LH(HH0I Hy LcH8H@@J LHH]FfUHH RZH1H9NHD]DUHH]_]ÐUHAWAVAUATSH(Hu#^HHEIIHHAHq H):LLHHHyHHHcL$Lx*CHI6LLFHI6LCII6LEHpHHIHhW)PHDž`M;LH= HN<LFHPJH`HLFHX1L@IHtL`EHPHHHxH9|WH*H0zFH(SX)Hger thanHH Hrank larHHHust has HHHtensor mHHHindices Hf@( 0@*HHÌ)HWLHDžHHID$(HIL$0HIL$ H05H@I|$8HDžHIHLHaLHhHHpHH@HHxHLHHH HPH(H}L0H _HEH]H]H]W)EEHULH)H}H9HtiHtHP(HHtdHHHELuHuHHL3OHL.)Et H}MFHPHu%/HP HHuHHPHt HXFH^HH;EuHH([A\A]A^A_]FFHP@F IEH}ºFHPHIHIHPHulvIH}H9uHP HPHuLVHt9HP(HPHu3=IHPHu",IHPHuIHPHt HX FLJF fUHAWAVATSHIII?IGHH)HL9sxHt"IƹFIGIGI1LH=u@HHHHH9HML9IH9L¶FMWMI)IM9LIBHpHrHSH9HH9HL@HHtHf.H HHHHuI@H HH HNH HNH HNH HN H HN(H HN0H HN8H@HuIJ<蔸FIGIJ IOHLIrHKH9J4HLH9vyLBHHtHH3H1HHHuI@H3H1H3HqH3HqH3HqH3Hq H3Hq(H3Hq0H3Hq8H@Hu}LHH~HHJH`~fpDHyH)1DD D0HHuHt~fpDDL9IJIIIPHHDNAH~fpDIQH)1ff.DD D0HHuMt~fpDDL9IM97M)LLIHCL9vKLLH9LHHFHHJHT~fpDHAH)1fffff.AADAD AD0HHuHt~fpDAADI9t|I LH)HrHHtHؐH;H9HHHuHrIHHHHAHHAHHAHHA HHA(HHA0HHA8H@HuKJI[A\A^A_]H LH)LBHH+1HJ4L)L@HH$>1M7F1Hfff.UHAWAVAUATSH(H^HHEIIHHAHq H):LLHHHyHHHcL$Lx:9HI6LL)<HI6L9II6L <HpHHIHhW)PHDž`M;LH= HN<L FHPJH`HLFHX1L@IHtLp;HPHHHxH9|WH*H0芳FH(IX)Hger thanHH Hrank larHHHust has HHHtensor mHHHindices Hf@( 0@*HHӂ)HWLHDžHHID$(HIL$0HIL$ H@H@I|$8HDžHIHLHqLHhHHpHH@HHxHLHHH HPH(H}L0H_HEH]H]H]W)EEHULH躽)H}H9HtiHtHP(HHtdHHHELuHuHHLCEHL>)Et H}]FHPHu%/HP HHuHHPHt HX FH^HH;EuHH([A\A]A^A_]VFHPPF IEH}ҰFHPHIHIHPHulvIH}H9uHP HPHuLVHt9HP(HPHu3=IHPHu",IHPHuIHPHt HXFLZF fUHAWAVATSH`IH^HHEؿ(FHþ HFHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]Y8H5yFH}H趶IIHW)EHE4FHEHHHML HXHMH5(IHUL]H5THHH5FHHAH5 FHFHH; HHH ^HMH/HUHuHuIIGIG0IOIWIG@H}Ht H}dFH]HEHtHHCFH^HH;EuLH`[A\A^A_]~FIH]HEHt7HHFL6F IH}HuH]HEHuLF H}輬FH]HEHtff.UHAVSHPIH+^HHE(脬FHþ HzFHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]4H5 FH}HJHrH5IHHH5'HHH5JHHH HQH5FHHH;ץHHH ^HMH[HUHuHuIIFIF0INIVIF@H]HEHtHH!FH^HH;Eu LHP[A^]`FIH]HEHtHHܩFLF @UHAVSHPIH[^HHE(贩FHþ H誩FHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]2H5=FH}HzHH5*IHHH5WHHH5JH"HH HQH5 FHBHH;HHH ^HMHHUHuHuIIFIF0INIVIF@H]HEHtHHQFH ^HH;Eu LHP[A^]萧FIH]HEHtHH FLLF @UHAVSHPIH ^HHE(FHþ HڦFHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]M/H5mFH}H読HH5ZIHH H5HH!H5'JHRHA H5@FHyHH;>HHH ^HMHBHUHuHuIIFIF0INIVIF@H]HEHtH2H舤FH# ^HH;Eu LHP[A^]ǤFIH]HEHtHHCFL胡F ff.UHAWAVAUATSH(HHIH^HHE1HHHHHHHzLzHr IM)LHHHHM4HLpHC H+CHI9~nP腣FHH0f(Y7Zf) H5HFHѤFCFH Ls) H0HFfWf)HDžHL)HZHHLIILH=jŢFHLHHJHLM9HHIM)LHHHLr+ID$HIDH9IIIM9HML)Ht"Hfff.IHIHHuH8{1IHITHTITHTITHTIT HT IT(HT(IT0HT0IT8HT8IT@H@L9uL1LM HI?I!I@HHzH s1MHHGH)1MAALLAD AL0D L0AD@ALPD@LPfAD`ALpfD`LpHHuHt8HHfffff.fADA fD H HuL9IJtHILsHK HL]HHH LI$H(H0fWf)HDžH 1H賂IMH Ht H(ӟFfWf)`f)Pf)@f)0f) HLhHH{0HCPuH(IIFPH8H IIFPH0HPHHHHHQ0RtcL,$HL HI#HHDžHtZCt H{FHFH(uGxL,$HL HIHHDžHuH(6H HxH`H+XP EXf\EXf|H_HEHHxHEH]H]W)MfEHULH)1fWf)EHE0 FHEf(4Zf)EHupportedHHH not supHHHata typeHHHtensor dHHHindices H@&HMMLLVm)EH}qFHXHH HxH`H+X@CXf\CXf|HH_HEHHxHEH]H]W)MfEHULHڨ)H}H9MLt HtHP(HP IEHXHt H`袜FHHt I<$荜FH(^HH;Eu)LH([A\A]A^A_]J OH[A^]IHYFL虉F UH]f.UH]4FfDUHSPH'FH 6_HHKHHH[]@UHH_HHGHF]UH]f.UH]ċFfDUHAWAVAUATSHH9(HwHVHUHVHUHVHHUHV HUHV@HR8HUHcHHEL$IHHH}H?HUHHULM|HH^(L LI LIHU1E1ff.NcH5HH_H5eJH萁HH HQH5wFH谂HH;uwHHH T^HMH9HUHuHuIIFIF0INIVIF@H]HEHtHiH{FHZ]HH;Eu LHP[A^]{FIH]HEHtH$Hz{FLxF fUHAVSHPIH]HHE(T{FHþ HJ{FHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5݆FH}H11H~H5PHH}H5JH~HH HQH5FHHH;tHHH ^HMHIHUHuHuIIFIF0INIVIF@H]HEHtH詒HxFH]HH;Eu LHP[A^]>yFIH]HEHtHdHxFLuF fUHAVSHPIH;]HHE(xFHþ HxFHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5MKGH}HZ11H辅H5HHzH5JH|HAH5FH7}HH;qHHH ۛ^HMHHUHuHuIIFIF0INIVIF@H]HEHtHHFvFH]HH;Eu LHP[A^]vFIH]HEHtH諏HvFLAsF fUHAVSIuFHHX^HH{LcSHt_HH[A^]IHuFLrF fff.UHHH BX^HHxH@Ht]fuF]fDUHSPHHX^HH{HCHt5uFHH[]'uFfUHAWAVAUATSHIIH]HHE1L~bH6~H9L1L IMI|$RHH+H~1LfcHt H01I6HHsW)EHxHUL`HxW)EHEM~IFHI9L ;ff.HHPHsFH}Hffff.Mo0W)EHEI I+tNHHHH=sFILuLuIHEIwI_ H)H~LHuFILuHEHHEHHtH@LLHUHHHtHHCH H}Ht H} sFLH HpI7IwPHjtFH}H;}sLHEXffff.H}L贞L<IXL9HLaMLyLqHYL9tMI)HèHHèIXuI?K1L\HHRII1LLLTILk0HLILLLILM9MHC0xubHFIHg}IL|HtfHH@ffffff.LLoFIIHu4LqL4HIELPHLHHrFHLSHLHHXPHHHqFLM&pFIGIGIHHEHAHEIHEHAHxHDžxHLtCt H{pFHpFH]Ht#HHCHuHHPH_oFIH]HH;EuLH[A\A]A^A_]pFH}mF*pFIH}*H5HH HH LE@5 H]H6AH:zGH LHE1H5]H|LoF;oFIH}I*H54HH xHH LEG W)EHE WoFHE(W)EH allocatHHHo get anHHHUnable tHf@or@H HM1LH5O]HL,oFznFIH}*H5sHH HH LE H]Hy5AHxGH LH E1H5]HWfffff.AT. HH9uHHuKFII!IXHHDNAHIYH)1(W( WfoWf.dTTf8f~7f8f~d7\ d0TTf8f~\7f8f~d7 HHuMtBL(W(WTfoWf8f~7Tf8f~L7L9LJ HH)1W@ .HHuW1H`II!IXHHDNAHIYH)1(]W( fWfonWfffff.dTTf8f~7f8f~d7\ d0TTf8f~\7f8f~d7 HHu1M1MtBL(W(WTfoWf8f~7Tf8f~L7L9t-LJ HH)1Y@.HHuH[A\A]A^A_]UHAWAVAUATSPDAIIMl$LHEL蝋EtHtDL yILaLyHILJHH1EtFH]HH;Eu LHP[A^]P>FIH]HEHtHvWH=FL ;F @UHAVSHPIHK]HHE(=FHþ H=FHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]mkH5ycGH}HHHlH5.lHHHHH5{HH?H5hJHAHA H5FH9BHH;6HHH `^HMH HUHuHuIIFIF0INIVIF@H]HEHtHTHH;FH]HH;Eu LHP[A^];FIH]HEHtHTH;FLC8F ff.UHAVSHPIH{]HHE(:FHþ H:FHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]pH5`GH}H*FHBiH5^iHHHFH5 yHH=H5fJHB>HA H50}FHi?HH;.4HHH ^^HMHHUHuHuIIFIF0INIVIF@H]HEHtH"RHx8FH]HH;Eu LHP[A^]8FIH]HEHtHQH38FLs5F ff.UHAWAVAUATSPII1L"{HHtTII1LLIH+eIL蠺LzHI}茺HDHs%1"H wHL ) HHHyHHrHH~H)1W( WfoWfff.AAdTTf8f~f8f~dA\ Ad0TTf8f~\f8f~d HHuHu ]1HtVAALf( WTfo!Wf8f~Tf8f~LH9tA. HH9uILH[A\A]A^A_]fDUHAWAVATSII1LUyHH!HIHH1LHILxHI|$轸H HrI GH9HHL91HPH)HHt-Hffff.ALIII|7IIOH?HH?H!H4Lo'FM9}HK|IHI)LN'FLmLeLH?H>LIII|7IIOH?HH?H!H4L&FM9}HK|IHI)L&FHhIL@MHXLMHhL gHrLKHCH(EAL4IM)LHHHE1H`LI0@LHHH`HIuAAAAI9LOIM)IBHJLH?II>MIKBIH=IIK<BLIHH9IDHDHXTXHHuHQIrZfffff.XD XT0\@XD0XXTPXD`XLpXHQH H9|((XH9~ X(XXMHhIPDHsP1Hfffff.HHH(M LH)1fDXXDXDXD XDXDXDXDHH9uHt HHfff.XHHuH9}ND)D)LL)L)HHL@fXHHuH@L@ffffff.XXDXDXD XDXDXDXDHI9uMtsHHHXHHuQL@HrD@XXDXDXD XDXDXDXDHH9uLHIH}XILL; L8L LPHXLLL)HHH bHHv(H}HtHH8L"FH]HH;EHH[A\A]A^A_]H}L8LP?H}EAI]1L8LPMtHHIWXHWH*HMII?I>IIH|l(IIOHHD@AHZIpH)1D((\^)^)\HHuMt (^)HL)HryII(IpHHDJAH-J\IqH)1@T^T^HHuMtL^L9t+ML)Jfff.^HHuMHhHHH?II=IIH>HHL{LCHCH(IAMOIIABLH)HXAAAIM)LHHHH0HL)HLHHL1L`#1H0fHHHHH@H`HH}HHH|x(HHOHHVH! HZH)1fff.((\^)^)\HHuHHht (^)II)IMI(IPHHDNAHH\IQH)1fT^T^HHuMtH^M9Hht$LH)H D ^ HHuW)pI~HMLULH?II=MIH>LHMBI(I(AIIIOHHHZHn1(HI~HuHBH]HpXHpHxHH?H>HHH{1D(WD( W(W(%nW(-WW((AYWR(YY(\YYAV(^(f8)HH9|H9L ~|HyHt!Q W^ HHH9tJWffff. Q(^LQ(^THH9uMHhHCH0M]0HHH`LQ0IE HH1LpfnIAzIRH HJ m E1MHhL1U1MHh+KfILOlH(HH@HELHMLXfnfnPIrKI9KTI91AI~H)LHt$H\YHHuHHhDRf\YT\YTT\YTT \YT HI9uLLHfofoHZHHDoAH,MMI)E1f.CdCl\\YYCdClCdC,\\YYCdC,IIuMHhLt)C$Cl\\YYHXB$BlI9֋hDN,0IAfnfnPIVI9IMHL)Ht&Hf.B\YB)IHuH Dr]fffff.B\YC)TBT\YC)TBT \YC)TBT0\YC)IM9|I9fnfnPI)IIH`JHXJH9sH8JKH9LXMIfofoIPHHDpAH6IVH)JHHJ<1$l\\YYdld l0\\YY$lHHuMHht%LA$Al\\YYA$AlM9Mff.D)H0L)Ht'HB\YBIHuHrmfffff.B\YBBT\YBTBT\YBTBT \YBT IL9uH(HHIIHHHHH; L81M1HHh(NfoMHhSHFHM]0HHH`LQ0MM 1L8E1ML0HH#1MfILK|H(HH@HEPI@uAAAAI9LOHXH`JLHMfnPIr!JHXH9JH91LHMIIt$H)LHtH@ \ HHuHLMLH# \ L\LL\LL \L HI9uHHL0LHfoHzHHD~AHCMOI)E1CTC\\\CTC\CTC\\CTCIIuML0HHt#BB\\\HXBB\I9L8fDN< IfnPIT$I9IMHL)HtH@B \B) IHuH rG@B \C)LBL\C)LBL \C)LBL0\C) IM9|HhI9fnPI)ILhH`JHXJH9sKDJH9HHMIfoIVHHD`AHL0IT$H)J4K<1f\\\T\T \0\\\HHuMt)LHH\\\HX\M9HhL0Mfff.HhD)HL)HtHB \B IHuHHhrZff.B \B BL\BLBL\BLBL \BL IL9uH(HHIIHHIH; 4LL)1WL8LPfDHHI X HAHI X HH9uHhMhNLH)1Wf.HHIXHAHIXHH9uHhH_E1M 1MQZ(XL9LH)fDfnXXDXDXD f~HH9uHt(HHfff.fnXf~HHuHh HsH)1(XL XD0XL@XDPXL`XDpXXH HuHwHt$Hfff.X XD0HHvHuXL9~BXHh(XXI9~D)IrH)Ht HHDXHHuHs LHHrCLH)HTXBXBXBXBXBXBXBXH HuWI*^WWMmI( (\YI(a\YIIIOHHDBAH IxH)1ffff.(l (t0\YX(L@\YX(dP\YX\YXHHuHSMt(,\(t0\YXYX((XL9~B($\YXHh(XXI9VIrH)LHt&Hf.\YXHHuHI)HL DYa\YX\YXY\YX \YXHIu\(YIARIBHsHucI)Ҿd\YX\YX\\YXL \YXHI9uHt$HH@\YXHHuHEHL`HMHPHrxII?LH>HH(HI=II(YHIIOHHH{H<1۾H1H|'PHxH&HTfnI~ IHAuDكH9HOII)LH?H>LHHIHrII9IDI91HyH)HHt"HfDA \ALHHuHIffff.A \ALAL\ALAL\ALAL \AL HH9uH)пfnXXTXTXT f~HH9uHHHfnXf~HHuHH(H~HHD@AH(IxH)1AA\\\ATA\AT A\0\\AT A\0HHuHwH)1ffff.XT X\0XT@X\PXT`X\pXXH HuHsHt$Hfff.XX\0HHvHu((XL9~BXHh(XXH9~l)HpH)HtHXHHuHr>H)HLDXQXQXQXQXQXQXQXH HuI~XWI*^WQ WIHAuDكH9HO^II)LH?H>LHHIHrII9IDI91HyH)HHt(HA\YATHHuH2fA\YATAT\YATAT\YATAT \YAT HH9uHH((H~HHD@AHtaIXH)1DA$Al\\YYAdAlAd Al0\\YYAd Al0HHu1Mt#A$Al\\YYAdAlH9LH4 I((HAH9HMHH)ȉHHt$HH@A$\YA)dHHuH Lr]ffff.A$\YA)dAd\YA)dAd \YA)d Ad0\YA)d0HH9|H9NI)IsLIDLIFHHH;HHH ^HMHHUHuHuIIFIF0INIVIF@H]HEHtHHEH^]HH;Eu LHP[A^]:EIH]HEHtH`HELE fffff.UHAVSHPIH+^]HHE(EHþ HzEHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]M*H5 FH}HH5^8HHH5$JH HH HQH5;FH,HH;HHH ^HMH%*HUHuHuIIFIF0INIVIF@H]HEHtHH;EH[]HH;Eu LHP[A^]zEIH]HEHtHHEL6E fffff.UHAVSHPIHk[]HHE(EHþ HEHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]#H5MFH}HH55HHH5!"JHLHH HQH539FHlHH;1HHH ^HMH2HUHuHuIIFIF0INIVIF@H]HEHtH%H{EHY]HH;Eu LHP[A^]EIH]HEHtH H6ELvE fffff.UHAVSHPIHX]HHE(EHþ HEHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] 'H5EH}HZH52HH[H5aJHHH HQH5s6FHHH;qHHH P^HMHe;HUHuHuIIFIF0INIVIF@H]HEHtHe HEHVV]HH;Eu LHP[A^]EIH]HEHtH HvELE fffff.UHAVSHPIHU]HHE(DEHþ H:EHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] H5EH}HH50HHH5JHHH HQH53FHHH;HHH ^HMHDHUHuHuIIFIF0INIVIF@H]HEHtHHEHS]HH;Eu LHP[A^]:EIH]HEHtH`HELE fffff.UHAVSHPIH+S]HHE(EHþ HzEHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] H5 EH}HH5^-HHH5JH HA H50FH3HH;HHH ^HMHLHUHuHuIIFIF0INIVIF@H]HEHtHHBEHP]HH;Eu LHP[A^]EIH]HEHtHHEL=E DUHAVSHPIH{P]HHE(EHþ HEHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5]EH}H*H5*HH+H51JH\HA H5J.FHHH;HHHH '^HMH|JHUHuHuIIFIF0INIVIF@H]HEHtH<HEH-N]HH;Eu LHP[A^]EIH]HEHtHHMELE DUHAVSHPIHM]HHE($EHþ HEHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]-H5EH}HzH5'HH{H5JHHA H5+FHHH;HHH w ^HMH,HHUHuHuIIFIF0INIVIF@H]HEHtHHEH}K]HH;Eu LHP[A^]!EIH]HEHtHGHELE DUHAVSHPIHK]HHE(tEHþ HjEHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]}H5EH}HH5N%HHH5JHHA H5(FH#HH;HHH ^HMHEHUHuHuIIFIF0INIVIF@H]HEHtHH2EHH]HH;Eu LHP[A^]qEIH]HEHtHHEL-E DUHAVSHPIHkH]HHE(EHþ HEHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5MEH}HH5"HHH5!JHLHA H5:&FHsHH;8HHH ^HMHCHUHuHuIIFIF0INIVIF@H]HEHtH,HEHF]HH;Eu LHP[A^]EIH]HEHtHH=EL}E DUHAVSI&EHH]HH{L裾H^HH[A^]IHEL$E fff.UHHH ]HHxH@Ht]E]fDUHSPHHP]HH{HCHtuEHH[]gEfUHAWAVAUATSHIIHD]HHE1LN#HHHHHHCLc I)It Iu H8uALW)@HDžPHbIH@HfHn IHCHK H)HLt HuFH8u@A?HDž`HHH;PiHHHHW)`HDžpMtALH=nN4LEH`JHpHLEHhLHaIM)HHIH LHE1fA?t!HHH`HhH@IH`HLHHH|HHIHH9t(HOHLH|&HLH9tfff.1HLH}IM9WQH@H`Brb1L@A<tHHH`HhLHL9rH`Ht HhELLH@HHH)H1IILeLuHEH`HDžhH`1LBHHbMMINHHHQINHI@I@ LHHуHH)HMIHSLHHMIJ HE1E1H'E1HLof.MMրHt9J K4H4H9J K4H4H91HAu1f1HAt$J HILHH4H4HHH9MLMHNHLH@JJB >LIHIuHLtK MMBBLLHHI9fffff.IHHIM9H>HHH@Ht HHfEH?]HH;EuHH[A\A]A^A_]EJEIH@)H5HH HH`L@9OvW)EE(HEHis requiHEHX input HEEred!EAHEH`HMLxE1H5_]HgQLEEIH}4)H5HH 4HH`LEmuW) HDž0 B >LIHIuHLtK MMBBLLHHI9fffff.IHHIM9H>HHH@Ht HHEH1+]HH;EuHH[A\A]A^A_]EzEIH@t)H5HH HH`L@9bW)EE(HEHis requiHEHX input HEEred!EAHEH`HMLdE1H5.L]H=L EEIH}ds)H5 HH HH`LEmaW) HDž0 lEH0(V) Hrst outpHHHo get fiHHHfailed tH@ut!AH?EH`H LdE1H5SK]HELM9uH}LLe$EEu`uLeMu)XH}E`tHpELeMt1HEL9tNLh@t HxELM9uH}LeEE{LEH@HovLHEt H}uELeMt6HEL9t!Lh@t HxPELM9uH}LLe6EEu`u L@Mu,dH}E`tHpEL@Mt:HHL9tILh@t HxELM9uH@LHEELEHE L(HH`HuJTHH`Hu9CHH@Hu:DHH`HuHH`Ht Hh.EH@Ht HHEHVE fffff.UHAVSIEHH]HH{LsH^HH[A^]IHELE fff.UHHH R]HHxH@Ht]vE]fDUHSPHH ]HH{HCHtEEHH[]7EfUHAWAVAUATSHIIH%]HHE1LHHHHHHCLc I)It Iu H8uALW)@HDžPHiCIH@H6H9IHCHK H)HLt HuFH8u@A?HDž`HHH;PiHHHHW)`HDžpMtALH=nN4LEH`JHpHLӿEHhLHyBIM)HHIH LHE1fA?t!HHH`HhH@yIH`HLHHfDH|HHIHH9t(HOHLH|&HLH9tfff.1HLH}IM9WQH@H`Sb1L@A<tHHH`HhLHL9rH`Ht HhZELLH@HHH)H1IILeLuHEH`HDžhH`1LHHCMMINHHHQINHI@I@ LHHуHH)HMIHSLHHMIJ HE1E1H'E1HLof.MMրHt9J K4H4H9J K4H4H91HAu1f1HAt$J HILHH4H4HHH9MLMHNHLH@JJB >LIHIuHLtK MMBBLLHHI9fffff.IHHIM9H>HHH@Ht HH6EH]HH;EuHH[A\A]A^A_]lEEIH@h)H5qGH GH`L@9WW)EE(HEHis requiHEHX input HEEred!EAHEH`HMLYE1H5@]H72L諺EfEIH}h)H5GH GH`LEmqVW) HDž0 EH0($V) Hrst outpHHHo get fiHHHfailed tH@ut!AHEH`H LXE1H5?]H\1LйE H`ڶE HALeMuiHALeMHHAL@MHH t H0ELeMt6HEL9t!Lh@t Hx޸ELM9uH}LLeĸEEu`uLeMu)XH}衸E`tHp茸ELeMt1HEL9tNLh@t HxgELM9uH}LeREE{LqEH@HovLHEt H}ELeMt6HEL9t!Lh@t HxELM9uH}LLeַEEu`u L@Mu,dH}谷E`tHp蛷EL@Mt:HHL9tILh@t HxpELM9uH@LHUEELtEH脴E L(HH`HuJTHH`Hu9CHH@Hu:DHH`HuHH`Ht HhζEH@Ht HH趶EHE fffff.UHAVSI薶EHHB]HH{LH^HH[A^]IHTEL蔳E fff.UHHH ]HHxH@Ht]E]fDUHSPHH]HH{HCHtEHH[]׵EfUHAWAVAUATSHIIHO]HHE1LHHHHHHCLc I)It Iu H8uALW)@HDžPH 8IH@H֊HIHCHK H)HLt HuFH8u@I?HDž`HHH;PiHHHHW)`HDžpMtALH=nN4L芴EH`JHpHLsEHhLH7IM)HHIH LHE1fI?t!HHH`HhH@IH`HLHHfDH|HHIHH9t(HOHLH|&HLH9tfff.1HLH}IM9WQH@H`Gb1L@I<tHHH`HhLLHL9rH`Ht HhELLH@HHH)H1IILeLuHEH`HDžhH`1L貙HHa8MMINHHHQINHI@I@ LHHуHH)HMIHSLHHMIJ HE1E1H'E1HLof.MMրHt9J K4H4H9J K4H4H91HAu1f1HAt$J HILHH4H4HHH9MLMHNHLH@JJB >LIHIuHLtK MMBBLLHHI9fffff.IHHIM9H>HHH@Ht HH֯EHq]HH;EuHH[A\A]A^A_] E躯EIH@U])H5GH GH`L@9KW)EE(HEHis requiHEHX input HEEred!EAHOEH`HML8NE1H5n5]H&LKEEIH}\)H5`GH GH`LEmKW) HDž0 謮EH0(V) Hrst outpHHHo get fiHHHfailed tH@ut!AHEH`H L]ME1H54]H%LpE H`zE HALeMuiHALeMHHAL@MHH t H0裭ELeMt6HEL9t!Lh@t Hx~ELM9uH}LLedEEu`uLeMu)XH}AE`tHp,ELeMt1HEL9tNLh@t HxELM9uH}LeEE{LEH@HovLHEt H}赬ELeMt6HEL9t!Lh@t Hx萬ELM9uH}LLevEEu`u L@Mu,dH}PE`tHp;EL@Mt:HHL9tILh@t HxELM9uH@LHEELEH$E L(HH`HuJTHH`Hu9CHH@Hu:DHH`HuHH`Ht HhnEH@Ht HHVEH薨E fffff.UHAVSI6EHH]HH{L賈H^HH[A^]IHEL4E fff.UHHH ]HHxH@Ht]鶪E]fDUHSPHH`]HH{HCHt腪EHH[]wEfUHAWAVAUATSHIIH]HHE1L^HHHHHHCLc I)It Iu H8uALW)@HDžPH,IH@HvHIHCHK H)HLt HuOH8uIAW.u HDž`HHH;PHHHHW)`HDžpMtALH=N4L!EH`JHpHL EHhLH+IMJHHIH LHE1fA.Wu{!HHH`HhH@IH`HLHHfff.H|HHIHH9t(HOHLH|&HLH9tfff.1HLH}IM9G[H@H`2<l1L@A.Wu{HHH`HhLHL9rH`Ht HhpELLH@HHH)H1IILeLuHEH`HDžhH`1L(HH,MMwINHHHQINHI@I@ LHHуHH)HMIHSLHHMIJ HE1E1HE1HLeMMրHt9J K4H4H9J K4H4H91HAu1f1HAt$J HILHH4H4HHH9MLMHNHLH@JJB >LIHIuHLtK MMBBLLHHI9fffff.IHHIM9H>HHH@Ht HHVEH]HH;EuHH[A\A]A^A_]茤E:EIH@Q)H5GH GH`L@9?@W)EE(HEHis requiHEHX input HEEred!EAHϴEH`HMLBE1H5)]HWLˣE膣EIH}$Q)H5GH "GH`LEm?W) HDž0 ,EH0(DV) Hrst outpHHHo get fiHHHfailed tH@ut!AHEH`H LAE1H5)]H|LE H`E HALeMuiHALeMHHAL@MHH t H0#ELeMt6HEL9t!Lh@t HxELM9uH}LLeEEu`uLeMu)XH}E`tHp謡ELeMt1HEL9tNLh@t Hx臡ELM9uH}LerEE{L葡EH@HovLHEt H}5ELeMt6HEL9t!Lh@t HxELM9uH}LLeEEu`u L@Mu,dH}РE`tHp軠EL@Mt:HHL9tILh@t Hx萠ELM9uH@LHuEEL蔠EH褝E L(HH`HuJTHH`Hu9CHH@Hu:DHH`HuHH`Ht HhEH@Ht HH֟EHE fffff.UHAVSI趟EHHb]HH{L3}H^HH[A^]IHtEL贜E fff.UHAVSIVEHH]HH{L|H^HH[A^]IHELTE fff.UHAVSIEHH]HH{Ls|H^HH[A^]IH贞ELE fff.UHAVSI薞EHHB]HH{L|H^HH[A^]IHTEL蔛E fff.UHAVSI6EHH]HH{L{H^HH[A^]IHEL4E fff.UHAVSHPIHk]HHE(ĝEHþ H躝EHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5FH}HHH5NGHHHH5qGHHH56GHH5IHHH HQH5EH8HH;HHH ]HMHBHUHuHuIIFIF0INIVIF@H]HEHtHHGEH\HH;Eu LHP[A^]膛EIH]HEHtH謴HELBE f.UHAVSHPIH{\HHE(ԚEHþ HʚEHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5FH}H*HH5^GHHHH5nGHHH5FGHH5IH(HA H5EHOHH;HHH ]HMH\HUHuHuIIFIF0INIVIF@H]HEHtHH^EH\HH;Eu LHP[A^]蝘EIH]HEHtHñHELYE UHAVSHPIH\HHE(EHþ HEHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5ɽFH}HJHH5~GHH0HH5kGHHH5fGHH5IHHHH HQH5/EHhHH;-HHH ]HMHQZHUHuHuIIFIF0INIVIF@H]HEHtH!HwEH\HH;Eu LHP[A^]趕EIH]HEHtHܮH2ELrE f.UHAVSHPIH\HHE(EHþ HEHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5ٺFH}HZHH5GHH@HH5hGHH&H5vGH'H5-IHXHA H5FEHHH;DHHH #]HMHnHUHuHuIIFIF0INIVIF@H]HEHtH8H莒EH)\HH;Eu LHP[A^]͒EIH]HEHtHHIEL艏E UHAVSHPIH\HHE($EHþ HEHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]-H5FH}HzHH5GHH`H8H5eGHHFH5GHGH5MIHxHH HQH5_EH蘖HH;]HHH <]HMHqkHUHuHuIIFIF0INIVIF@H]HEHtHQH觏EHB\HH;Eu LHP[A^]EIH]HEHtH HbEL袌E f.UHAVSHPIH\HHE(4EHþ H*EHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]=H5 FH}H芚H"H5GHHpHHH5 cGHHVH5GHWH5]IH舒HA H5vEH诓HH;tHHH S]HMHHuHUHuHuIIFIF0INIVIF@H]HEHtHhH辌EHY\HH;Eu LHP[A^]EIH]HEHtH#HyEL蹉E UHAVSHPIH\HHE(TEHþ HJEHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5)FH}H誗HBH5޺GHH萗HhH5-`GHHvH5GHwH5}IH訏HH HQH5EHȐHH;荅HHH l]HMHrHUHuHuIIFIF0INIVIF@H]HEHtH聣H׉EHr\HH;Eu LHP[A^]EIH]HEHtH<H蒉EL҆E f.UHAVSHPIH \HHE(dEHþ HZEHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]-H59FH}H躔HRH5GHH蠔HxH5=]GHH膔H5GH臋H5IH踌HA H5EHߍHH;褂HHH 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MBLHIJHH)HDЃJ HLLHLHHHHT A<|A||A||A| K\0HyL)IHI HL4LE1CDC BDBLCDCL BDB IHuHt*MHIT HLCCLHI9LLHI̐D)HL)HtHCCIHuHHrBIL fffff.CBTCTBTCTBTCT BIL9uLI܃tNH1Lfffff.H9HH9H;y|1HH+qHH L9rL HqHHHIL;HWHH;EuHX[A\A]A^A_]"i@@UHAWAVAUATSHHXH>WHHEп(h@Hþ Hh@HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]`I蘕I萛IHHIW)EHE(f@HEHH(HML8L`LhHXLp HM9I1IIIYHW)`HDžp(qf@H`HH(HpL8L`LhLpHX HhH5Hs@H}HUH`oH5BHjH5DH?kHH HQH5&@H_lHH;$aHHH XHMH(GHUHuHuHXHHCHC0HKHSHC@H`Ht Hhpe@H}Ht H}^e@LuHEMtL~L=e@HWHH;EuHHĈ[A\A]A^A_]se@IH`Hu$H}Hu>H]HEHu]L*b@ Hhd@H}Ht IH}HtH}d@H]HEHtIH]HEHtH'~H}d@La@ DUHAWAVAUATSHHXHWHHEп(Gd@Hþ H=d@HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]IHI@IHpIW)EHE(b@HEHH(HML8L`LhHXLp HMII虑I葔I HW)`HDžp(!b@H`HH(HpL8L`LhLpHX HhH5n@H}HUH`kH5BHeH5ĎDHfHH HQH5֥@HhHH;\HHH XHMHIHUHuHuHXHHCHC0HKHSHC@H`Ht Hh a@H}Ht H}a@LuHEMtLzL`@HWHH;EuHHĈ[A\A]A^A_]#a@IH`Hu$H}Hu>H]HEHu]L]@ Hh`@H}Ht IH}HtH}d`@H]HEHtIH]HEHtHyH-`@Lm]@ DUHAWAVAUATSHHXHWHHEп(_@Hþ H_@HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]III訐H IW)EHE(>^@HEHH(HML8L`LhHXLp HM虌I葒IIIAI蹎HW)`HDžp(]@H`HH(HpL8L`LhLpHX HhH5j@H}HUH`]gH5OBHnaH5tDHbHA H5@HcHH;XHHH jXHMHOEHUHuHuHXHHCHC0HKHSHC@H`Ht Hh\@H}Ht H}\@LuHEMtLNvL\@H?WHH;EuHHĈ[A\A]A^A_]\@IH`Hu$H}Hu>H]HEHu]LY@ Hh;\@H}Ht IH}HtH}\@H]HEHtIH]HEHtHuH[@L$Y@ fff.UHAWAVAUATSHHUWHHEMMAIHhDmI_I+HH`H>H3IIH HXLHHcH9`A$I6LHcH9EHMI6HƸH9tLHcH9HMHu L6HFI9t>H`Hq HxH.HHL)HHH1LMc1HXHIHtlHzBHEHBHEHBHEHEH$H]HuHMLMHLL`CLhLH *#[HHIIffEHEEbJ<Z@HH]H]JHHEAIHLhHu [H?H!HZHHwH sf1fHH^H)f1ffffff.AofAoDfAoL Ao\0Aod@ffAoTPffAoD`fAoLpfHHuHt$I|HfoWfofH HuffpNffH~H9tI ff.H1HH9uHxL)HIcH9uH;Xu LheHpHBHEHBHEHBHEHBHEHBHEHxHD$ HEHD$HpHD$HEHD$HEH$H]HuHMLMHH`MCLhLH'#EH}W@HaWHH;EE11LhMBfLHfInfpDHpHHJHLhs1HHqH)1ffff.DD D0D@DPD`DpӀӐӠӰH H{Ht(HTHfffff.BH HuL9Hu t!H I)fffff.L9HIuIL}Mt)LvLIGV@LHFHFHHL~L~IEHWHH;EuLHĨ[A\A]A^A_]PV@U@H}HS@ HH}Ht#H}HEu HEt H}U@HR@ fUHAWAVAUATSHHIIHWHHE1H苘II$IG0@ȃH HcHHpLHLZHpLHL2CHpLHLk!,HpLHL HpLHL+HpI$H[WHH;EuLHĨ[A\A]A^A_]T@T@IHX?$H5hBH BHpLXrIG0H8HBHEHBHEH@HuH8HMHCHp1LH&H5_WH˾L@A@IH0#H5ӲBH FBHPL0ݿW)HDž 0@@H (T)H tensor.HH Ha vectorHHHmust be HHH tensor HHHAn splitH@(AH|BHPHLG߿E1H5}WH淾LZ@@ HPd=@ HALeMuFzHAL0MHHt H ?@LeMt6HEL9t!Lh@t Hx?@LM9uH}LLeq?@puPu L0Mu,dH}H?@PtH`3?@L0Mt:H8L9tQLh@t Hx?@LM9uH0L8>@EL ?@H}HLHH0HdbHMuIEMAFt I~>@Ly>@H}Huv}HH}HufmHH0Hu$.HHPHu)3HH0Ht H8>@HPHt HX>@H}Ht H}=@H1;@ fUHAWAVAUATSHHHIIHUWHHEH{LL+IGHEEEEW)EHEHHwHt2HHHP H)HL1HbJH}HҫIwIW H}蟹HHPIIHEHD$HEHD$HEH$LELMLLDI}tYHBH sNB1AL#H}Ht H}<@H.WHH;ELHĘ[A\A]A^A_]LIEHLxLh W)PHDž`M)t[MIHI9^L<@HHXHPJH`M~HLLg=@LHXHmaHEHEHHcEHcMHHHHUIcHHH(ƒHpHH)HhHHH@HpHHHxHHHHH11Hf.HEHL,IcL9LPHM,W)0HDž@LXM)t[LHHH9L:@IL8L0IH@M~LLL;@ML8HHH0IH0Ht H8:@L迿HDHHL4D9u2HLHd;@IcHHH;IcL$HHHs1HK dHHHHXHH`I1LLLL:@K<,HJ40L:@H`J<(K47L:@HJ<(HpJ40Lz:@HLxLXH9huHtZHHHHL4HHHLLLHL:@MLIuHHHHH;RBE1E1HEN,IcL9cHPHL,W)0HDž@LXI)ttHLMIHI9 L7@HH8H0JH@MLH~HL9@LH8HDH0/HH0Ht H8R7@HDD9uHJ4HH8@IcIIL;LIEHPHEHX6@H}H59'7@6@H04@6@IH0Z#H5BH BHPL0ҿW)HDž 0_6@H (T)H tensor.HH Ha vectorHHHmust be HHH tensor HHHAn splitH@(AH,BHPHLԿE1H5-WHL 6@ HP3@ HALeMuFzHAL0MHHt H `5@LeMt6HEL9t!Lh@t Hx;5@LM9uH}LLe!5@puPu L0Mu,dH}4@PtH`4@L0Mt:H8L9tQLh@t Hx4@LM9uH0L84@EL4@H}HLHH0HdbHMuIEMAFt I~14@L)4@H}Huv}HH}HufmHH0Hu$.HHPHu)3HH0Ht H83@HPHt HX3@H}Ht H}3@H0@ fUHAWAVAUATSHHHIIHWHHEH{ELL+IGHEEEEW)EHEHH'vHt2HHHP H)HL1HXJH}H股IwIW H}OHHPIIHEHD$HEHD$HEH$LELMLLDI}tYH^BH #DB1AL7#H}Ht H}C2@HޖWHH;ELHĈ[A\A]A^A_]LIEHLxLh W)PHDž`M)t[MIHI9L1@HHXHPJH`M~HLL3@LHXHMHEHEHHcEHcMHHHUIcHHH[ƒHpHH)HxHIHHHH HHHH1E1fLHEHL,IcL9eLPHM,W)0HDž@LXM)t[LHHH9Lc0@IL8L0IH@M~LLL1@ML8HHH0IH0Ht H8/@LᖿILDHN48D9u;LLH-1@McMHH;fffff.IcHHs,E1LH[ffffff.HH @HH HhHHpME1fffff.LLHHo0@HJ40J<+HX0@HJ40HpJ<(H:0@HJ40HJ<(H0@ILLhL9xyLHttLMMMLMLHfffff.LLL/@MLHuHMHH;)iHLMHH;CE1E1HEN,IcL9cHPHL,W)0HDž@LXI)ttHLMIHI9 LS-@HH8H0JH@MLH~HL.@LH8HDH0HH0Ht H8,@H̓DD9uHJ4!HH%.@IcIIL;LIEHPHHX^,@H}H ,@6,@H0)@?,@IH0#H5BH BHPL0DȿW)HDž 0+@H (T)H tensor.HH Ha vectorHHHmust be HHH tensor HHHAn splitH@(AHBHPHLwʿE1H5WHL+@ HP(@ HALeMuFzHAL0MHHt H *@LeMt6HEL9t!Lh@t Hx*@LM9uH}LLe*@puPu L0Mu,dH}x*@PtH`c*@L0Mt:H8L9tQLh@t Hx8*@LM9uH0L8*@EL<*@H}HLHH0HdbHMuIEMAFt I~)@L)@H}Huv}HH}HufmHH0Hu$.HHPHu)3HH0Ht H8K)@HPHt HX3)@H}Ht H}!)@Ha&@ fUHAWAVAUATSHhHHIIHWHHEH{LL+IGHEEEEW)EHEHHkHt2HHHP H)HL1HNJH}HIwIW H}ϤHHPIIHEHD$HEHD$HEH$LELMLLD,I}tYHޙBH 9B1AL#H}Ht H}'@H^WHH;E8LHh[A\A]A^A_]LIEHLxHX W)PHDž`L)t\IIHI9HL'@ILXLPKDH`H~LLH(@ILXH|9HEL6HEHHcEHcUHHHЋMIcHHHfHHH@H1W1HHEH HcH9LPHHI )0HDž@LXM)HtiLHHH9L &@IL8L0IH@MLH~LLLJ'@ML8HH0gHH0Ht H8%@H41ILDHH@HLHH0Hvalues mHH(Hte. All HH H attribuHHH 'split'HHHvalue inHHHInvalid Hf@8 0@:LHjBHuHPH@!DžLH5PWHH@)@tIİLL}MuKIİLHhHIIPt H`@L}Mt6HEL9t!HX@t Hx[@HI9uH}LL}A@EuEuHhHu&^H}@EtH}@HhHt:HpH9tMLx@t Hx@LL9uHhHp@LtEH@@I>Hu9@HIEuII>Hu#IEt H}{@I>Ht I}j@L@ fUHAWAVSPI8S@HL=WL;H{LHHL!Hr+YHHH[A^A_]L;IH{HCHt @IH@L@ fDUHAWAVSPI8@HL=oWL;H{L@HHLH*YHHH[A^A_]L;IH{HCHt [@IHN@L@ fDUHAWAVAUATSH8LLHIIHH{WHHEH8HyWHHHLH@H8Lk@HHEEL- {WIEHHI@L8Lo@L5zWILHDž(HDž HDžHDžDž0I$H@HHHeHL6HHH@HHH2HH0H@HHHV@HHHHE0HT@HHHĿHHL8Lt H ]@H=@H5tyWHH@H8@HyWHH;EuHH8[A\A]A^A_]d@HHHL8Lt H @H@3H謺H褺H蜺H蔺H茺H脺HH5xWHH@H8@HVHH8@H?ffffff.UHAWAVAUATSHXLLHHIHHxWHHEH8HvWHHHLH@H8Lg@HHEEL5xWM~LI@L8Lr@L-wWILHDž(HDž HDžHDžDž0I$H@HHHhHE0HM(HU HuLMHD$HL$HT$H4$HHHHL)HHHLL8Lt H @Hw@H5vWHH@H8@HwWHH;EuHHX[A\A]A^A_]@HLL8Lt H @H@ HHH5vWHH@H8@H迷HH8p@H訷UHAWAVAUATSPLMMIIIH6\@I]H @LHH谺LL腠IH@LHH茺HEЋ0L@HEHH@LHHaHEH0L@HE HH@LHH5HE(H0L@H[A\A]A^A_]H迶H跶H诶H觶H蟶H藶H菶H臶Hffffff.UHAWAVAUATSH(HHIHHtWHHEH8H,sWHHHLH@H8L@HHEEL%jtWMl$LI@L8L@L=$tWILHDž(HDž HDžHDžDž0IH@HHH趸HH0HЪHHH@HHH|HHHFLL8Lt H @H @H5sWHHu @H8}@HfsWHH;EuHH([A\A]A^A_]@HLL8Lt H t@HT @HPHHH@HH5nrWHH @H8 @HHH8 @HUHAVSHPIHrWHHE( @Hþ H @HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]MH5m@H}H11HH5BH/H559DH`HH HQH5GP@HHH;EHHH $1XHMHHUHuHuIIFIF0INIVIF@H]HEHtH9%H @H*pWHH;Eu LHP[A^] @IH]HEHtH$HJ @L@ fUHAVSHPIHoWHHE($ @Hþ H @HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]荓H5@H}H11HNH5.BHo H5u6DHHH HQH5M@HHH;HHH d.XHMH HUHuHuIIFIF0INIVIF@H]HEHtHy"H@HjmWHH;Eu LHP[A^] @IH]HEHtH4"H@L@ fUHAVSHPIH mWHHE(d@Hþ HZ@HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]͐H5@H}H*11HH5n}BH H53DH HA H5J@H HH;HHH +XHMHHUHuHuIIFIF0INIVIF@H]HEHtHH@HjWHH;Eu LHP[A^]U@IH]HEHtH{H@L@ fUHAWAVSPI(@HL=_WL;H{L0HHLQHjYHHH[A^A_]L;IH{HCHt K@IH>@L~@ fDUHAWAVSHHHIHiWHHEIGIGIW)EHEH;qW)EHEEEaxesEH}HULuHL# Et H}@H]HtHECaeH}HuHU@HuHUHH9t9HFH9tH>HJHHHxH9tHH9uHH9H9HHH9LBI)DHt(Hfff.HH9tHXHHHuIruH9H98u(HyH98u0HyH98u8HyH98u@H H9uFfHxHHyH98tHxHHyH98tHxHHyH98tHxHH H9uHH9tHEHM9tL`H]HEHtCt H{ @H@H}Ht H}@HgWHH;Eu HH[A^A_]3@IH]HEHt&Ct H{@H IEt H}@H}HuI?Hu(L? H}u@I?Ht II?HtIW@L? ffffff.UHSPHH{Ht H{#@HWHH{HCHHt[]@[]fff.UHSPHH{Ht H{@HWHH{HCHt@HH[]@UHAWAVAUATSHIHIHfWHHE1LDHW)EHEI}<HH+HuHLHDH7HHHP H)HHH&HH}HoHsHSH}l}HHpH`HUHzW)@HDžPL`HhL)tiLMIILH={Hv@ILHL@KHPHML~LLH@ILHH@1LHH@Ht HH?L+LkPHL8LxPM9Hx0HPhHtIxuCH莂H~d1LL?HiHIIH9|2HKIHHx0HPILLH@I$H`Ht Hh3?H}Ht H}!?HcWHH;EuLH[A\A]A^A_]W?H@Q??IH@菬#H5uBH uBH`L@MW) ƅ $HDž0Hut is nuH)HAxes inpH!fDž1llƅ3AHuBH`H L[E1H5WHuLn?)?IH@ī#H5tBH tBH`L@O.W) HDž00?H0(!CQ) H tensor.HHH vector HHHust be aHHHtensor mHHHAn axes H@'AHtBH`H LaE1H5WHuLt? HALeMulHAL@MHHAL@MHH t H0?LeMt6HEL9t!Lh@t Hx?LM9uH}LLes?Eu`u#L@Mu36H}J?`tHp5?L@MHHL9Lh@t Hx?LM9uH t H0?LeMt6HEL9t!Lh@t Hx?LM9uH}LLe?Eu`u L@Mu,dH}x?`tHpc?L@Mt:HHL9tQLh@t Hx8?LM9uH@LH?EL?huHu#L0Mu/gHx?HtHX?L0Mt:H8L9thLh@t Hx?LM9uH0L8?ELtL?H}Ht iH}Hu`I>HuiH? LHH}Ht H}c?LI>Ht4HH}HtHLH}HtH}&?I>HtI~?HU? ffff.UHAWAVAUATSH8LLHIIHHgXWHHEH8HVWHHHLH@H8L+?HHEEL-WWIEHHI@L8L/?L5WWILHDž(HDž HDžHDžDž0I$H?HHH%I7H?HHHP?HHHHH0Hn?HHH?HHH踛HuH舁HHHrHHL8Lt H ?H?H56VWHH?H8?HVWHH;EuHH8[A\A]A^A_]&?HHHL8Lt H ?Hr?3HnHfH^HVHNHFHH5tUWHH?H8?HHH8?HUHAWAVSPI(?HL=WL;H{LHHLHYHHH[A^A_]L;IH{HCHt ?IH?L? fDUHAWAVSPI(s?HL=WL;H{LHHLH*YHHH[A^A_]L;IH{HCHt ?IH?L>? fDUHAWAVAUATSHIHrTWHHEп(?Hþ H?HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]HH#HPHX!H`Hh"HpL Hx!I#I$IHW)EHEX?HEHHXHMHHHHPHPHXHPH`HPHhHP HpHP(HxHP0Lh8Lp@LxHHXPHMH5]?H}HUH!H5AHHH5eBHH5DHAHH HQH5(1@HaHH;&HHH XHMH -HUHuHuI$ID$ID$0IL$IT$ID$@H}Ht H}y?H]HEHtHHX?HPWHH;EuLHĘ[A\A]A^A_]?CIH}HuH]HEHu:LO? H}?H]HEHtIH]HEHtHoH?L? ffff.UHAWAVAUATSHIH2PWHHEп(?Hþ H?HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]THHHHP|HXH`dHhXHp HxIIp IHW)EHEX?HEHHXHMHHHHPHPHXHPH`HPHhHP HpHP(HxHP0Lh8Lp@LxHHXPHMH5?H}HUYHH5MAHHH5XaBHH5DHHA H5,@H(HH;HHH XHMH1)HUHuHuI$ID$ID$0IL$IT$ID$@H}Ht H}@?H]HEHtHH?HLWHH;EuLHĘ[A\A]A^A_]U?CIH}HuH]HEHu:L? 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HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H}11H-H5M>HH苄H5lAHH5"BHMHA H5;>HtHH;9wHHH VHMH HUHuHuIIFIF0INIVIF@H]HEHtH-H{>HUHH;Eu LHP[A^]{>IH]HEHtHH>{>L~x> fDUHAWAVAUATSHIIHIHUHHE1LHH@W)`HDžpI}XHH+Hu[LƽHHHC H+CHH4HHHIH JH`H H3HSH`LLL`LhLL)HHHY H+YHW)@HDžPHLHH=H<Hy>IL@M4LPLHy>LHLM9t}M)I<$ILQHL@LHLL)HH9I<IIM9uLI9Lu"\E1E11M9uI9L?HHIHpL)HHLffff.H:tHHuqffffff.HHHHHuMH AHHK!tH AHHfK!OLH}@H:t*Hzt4Hzt?HztJH H9uSf.H1HH2HzuH1HHrHzuH1HHrHzuH1HHrH H9uW) HDž0LL)tUIILH=w>HH(H JH0M)M~HLLx>LH(H 1HZIEHHHIEHH Ht H(w>H@Ht HHv>H`Ht Hhv>HkUHH;EuHH[A\A]A^A_]w>v>IH`O$"H5@H @H}L`)H@H==AH?HuLHdE1H5UHLwv>cH@~s>R!v>IH@#"H5G@H @H}L@0)W) ƅ $HDž0Hut is nuH)HAxes inpH!fDž1llƅ3AH@HuH LE1H5UH-Lu>\u>IH@""H5@H @H}L@3dW) HDž0@t>H0(R) H tensor.HH(Hor a 1-DHH H scalar HHHust be aHHHtensor mHHHAn axes H@0AH@HuH LE1H5UH+Lt>Zt>IH!"H5@H @H}LVbHH:AH@HuLH E1H5@UHLt> H 'q> HALM5H2HALeMHAL@MHHAL@MHHt H&s>LeMt6HEL9t!Lh@t Hxs>LM9uH}LLer>EuEuLMu&^H}r>EtH}r>LMt:HL9tqLh@t Hxr>LM9uHLor>EtLr>H HtBH H2H@HLH t H0r>LeMt6HEL9t!Lh@t Hxq>LM9uH}LLeq>EuEu L@Mu-'H}q>EtH}q>L@MHHL9Lh@t Hxhq>LM9uH t H0Cq>LeMt6HEL9t!Lh@t Hxq>LM9uH}LLeq>EuEuL@Mu&^H}p>EtH}p>L@Mt:HHL9tTLh@t Hxp>LM9uH@LHp>ELp>H`HLHAL`MHH@t HP&p>LeMt6HEL9t!Lh@t Hxp>LM9uH}LLeo>EuEuL`Mu&^H}o>EtH}o>L`Mt:HhL9tILh@t Hxo>LM9uH`Lhoo>ELo>Hl> LHH H+HH@HHH HH(o>H@HufpHH`HumwHH`Hu\fHH@Hu3=HH`Hu:DHH`Hu)3HH@Ht HHn>H`Ht Hhmn>Hk> DUHAWAVAUATSHIHUHHEW)EHHMI>tDHo@H F?1A]L"HUHH;ELH[A\A]A^A_]ILeHEL8LxPM,$Ml$PM9t{I|$0HPhHtAxu;ILjH~T1LLk>HLJIIH9|*I|$1HI|$0HPHLLHn>IHUHH;E4m>IIIHtCt H{l>Hl>L6j> fffff.UHSPHH{Ht H{l>H~OVHH{HCHHt[]l>[]fff.UHSPHH{Ht H{sl>H.OVHH{HCHtSl>HH[]El>UHAWAVSPI(3l>HL=NVL;H{LIHHLQH"WHHH[A^A_]L;IH{HCHt k>IHk>Lh> fDUHAWAVAUATSHHIH/UHHEID$ID$I$H;guvW)EHEEEaxesEHhHUHLqHhHDžhHtCt H{k>Hj>Et H}j>Hu'HUHH;EuHĘ[A\A]A^A_]"k>j>IHhk"H5@H @H}LhW)PHDž`0sj>H`(jR)PHte valueHHHttributeHHH'axes' aHHHInvalid HHHMissing/H@&LHp@HuHPL DžLH58UHLj>iIǰLLmMuKIǰLHhHIIPt H`li>LmMt6HEL9t!HX@t HxGi>HI9uH}LLm-i>EuEuHhHu&^H} i>EtH}h>HhHt:HpH9tJLh@t Hxh>LL9uHhHph>Lt8Lh>I<$Hu08HIEtH}h>I<$HuII<$Ht I|$ah>Le> fUHAWAVSPI(Ch>HL=JVL;H{LEHHLaH2~WHHH[A^A_]L;IH{HCHt g>IHg>Le> fDUHAWAVSPI(g>HL=_JVL;H{L0EHHLH}WHHH[A^A_]L;IH{HCHt Kg>IH>g>L~d> fDUHAVSHPIHUHHE(g>Hþ H g>HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]ݔ H5r>H}HjrH5 @HkiH5qBHjHHHQH5>HkHH;`HHH `VHMHeHUHuHuIIFIF0INIVIF@H]HEHtHu~Hd>HfUHH;Eu LHP[A^] e>IH]HEHtH0~Hd>La> fffff.UHAVSHPIHUHHE(Td>Hþ HJd>HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]] H5o>H}HoH5K@HfH5BHgHHHQH5æ>HhHH;]HHH VHMHHUHuHuIIFIF0INIVIF@H]HEHtH{H b>HUHH;Eu LHP[A^]Jb>IH]HEHtHp{Ha>L_> fffff.UHAVSHPIH;UHHE(a>Hþ Ha>HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]] H5m>H}HlH5@HcH5BHeHHHQH5>HHUHH;Eu LHP[A^]_>IH]HEHtHxH_>LF\> fffff.UHAVSHPIH{UHHE(^>Hþ H^>HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]蝌 H5]j>H}H*jH5@H+aH51BH\bHH HQH5C>H|cHH;AXHHH VHMH5HUHuHuIIFIF0INIVIF@H]HEHtH5vH\>H&UHH;Eu LHP[A^]\>IH]HEHtHuHF\>LY> fffff.UHAVSHPIHUHHE(\>Hþ H \>HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] H5g>H}HjgH5 @Hk^H5qBH_HH HQH5>H`HH;UHHH `VHMHEHUHuHuIIFIF0INIVIF@H]HEHtHusHY>HfUHH;Eu LHP[A^] Z>IH]HEHtH0sHY>LV> fffff.UHAVSHPIHUHHE(TY>Hþ HJY>HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] H5d>H}HdH5K@H[H5BH\HH HQH5Û>H]HH;RHHH |VHMHUHUHuHuIIFIF0INIVIF@H]HEHtHpH W>HUHH;Eu LHP[A^]JW>IH]HEHtHppHV>LT> fffff.UHAVSHPIH;UHHE迈V>HH}L[KH{HunH'9VHHHHu3H mWHHUHH;Eu HHP[A^]V>IH{XHt H{`V>H{@HtH{HV>IHU>L3S> ff.UHSPHH8VHH{HCHtU>H{XHt H{`U>H{@HtH{HH[]U>H[]fDUHSPHH08VHH{HCHtUU>H{XHt H{`CU>H{@Ht H{H1U>HH[]#U>DUHAWAVAUATSH(IIHUHHE1HHIMI_ I+_W)`HDžptCIILH=HT>H`N4LpHHT>LhW)@HDžPA}qIEXH@HEHL.2HH+HunME@IGIO H)t8IH`H1fWH*YH,HHH9rL`LLHHAu|HHɖHAH賖IA}pJMME@IGIO H)t:IH`H1ff.WH*YH,HHH9rL`LLHHAH@Ht HHR>H`Ht HhR>HzUHH;EvLH([A\A]A^A_]A}rqAuxH赕IM?I~PHH.H@HHH)HHH)H@njH@LMg M+gW) HDž0tCLHHH>.ILR>H HH0HLQ>H(IEHHHH{~HtvMH HHʰIGIO H)H H`H1DWH*YH,HHH9r11MSI^HH>L`LvIHHLLHmR>IG I+GHhH+`H9IH`H HL L L`HLHHH Ht H(dP>LH@HJQH@HHMw M+wLHH)HHH)vH@蒊 s HHHMIL@IAMGIv'IIGI I I9stIK H9sg1Kfffff.A?HL9r>s HHHHL|HHHHP>LHHAHHwHs1HIQpKDpH^H)1W( cODDLLDDLLDDLLDL H HuHt>J7HW( OLffffff.D LH HuI9N>^N>IH`!H5@H @H}L`fH@HAHhX?HuLHE1H5DUHżL!N>H`(K>M>IH f!H5@@H @H}L W)HDž@nM>H(Q)Hs input.HH.Hided as HH(H be provHH Hzes mustHHHes or siHHH of scalHHHOnly oneH@6AHY@HuHLE1H5#UHļLM>L>IH V!H50@H r@H}L W)HDž ^L>H(VR)Ht index.HHHroi inpuHHHInvalid H@AHn@HuHLE1H5KUHüL(L>K>IH~!H5X@H @H}L W)HDž@K>H(O)Has inputHH(Hrovided HH HUST be pHHH sizes MHHHcales orHHHEither sHf@0.AH@HuHLE1H5GUH¼L$K>H +H>J>IHi!H5C@H @H}LW)HDžPqJ>HH(&O)H53@DHK>CDAH@HuHL:E1H5pUHLMJ> J>IH!H5}@H @H}LW)HDž@I>H(CQ)Hs input.HH.Hided as HH(H be provHH Hzes mustHHHes or siHHH of scalHHHOnly oneH@6AH@HuHL*E1H5`UHL=I> IALeMIALM[I\IALeMFIALMu I!It H_H>LeMt6HEL9t!HX@t Hx:H>HI9uH}LLe H>EuEu LMu-H}G>EtH}G>LM|HL9HX@t HxG>HI9u9It HG>LeMt6HEL9t!HX@t HxnG>HI9uH}LLeTG>EuEu LMu-H}.G>EtH}G>LMHL9HX@t HxF>HI9umIALeMIALMMINIALeM~IAL MIIALeM@IAL MhIIt HF>LeMt6HEL9t!HX@t HxE>HI9uH}LLeE>EuEuLMu&^H}E>EtH}E>LMt:HL9tTHX@t HxzE>HI9uHL_E>EL~E>H H&LIt HE>LeMt6HEL9t!HX@t HxD>HI9uH}LLeD>EuEu L Mu-'H}D>EtH}D>L MH(L9HX@t HxuD>HI9uIt HPD>LeMt6HEL9t!HX@t Hx+D>HI9uH}LLeD>EuEuL Mu&^H}C>EtH}C>L Mt:H(L9tTHX@t HxC>HI9uH L(C>E}LC>H@HqxLIAL`MII@t HP3C>LeMt6HEL9t!HX@t HxC>HI9uH}LLeB>EuEuL`Mu&^H}B>EtH}B>L`Mt:HhL9tIHX@t HxB>HI9uH`Lh|B>ELB>L?> LD@>H@HuIH@Ht HHA>H`Ht HhA>L#?> ff.UHAWAVAUATSHMLH0HIIH;UHHE1H誄IMU IEI+EIN I+NH9 1HhHL!'H8HXHHRLMVM~ M)LHHLH@L)HH9;H0HAH+L9>MMI}L)fMIH11Du II;t1HH9r)AD$qLmIH8資ED$tAT$sAD$8At$M~PHLH@>LIHӣUHH;E:LH[A\A]A^A_]ILHLθHpHxH H5IHIBHhLMRHLMIHIx A|$tH@H q@HDȾI IHpIBHxLMRHIqHH@IHHHM,W)HHHhu*HH>H~@H @1AI!LM+wLHLHLǸHHhHHpHHxM:IHP@AHLDcsC0XC8dDk4LijIH8ZHKHL$(HD$ Lt$H0HD$Dl$D$$HHhLHxLPLp@HXdJJH0HtUHHHHH@H0H;>HEEL=UMoLI@L0H;>L5tUILHDž HDžHDžHDžDž(H5@H~ 1A|$tH @H5l@HDHTHHHHHA !t HZ;>LL0Lt H0;>H:>H5KUHH9>H0:>LHUHH;EE;>HHHHHILDzsB8HDHPH@ HDPXLgIH8ˁIL$LHHA|LHhHRAIMBLHHpHxL1HT$0HL$(HHL$ HD$Lt$AljD$H0H$HpHxHHH@IMPX'LILMQHIFH;ILPLf8>HUHH;E*_I(_IHpIBHxIBMBI IQLIHH@IHHIHPHHXIM,W)HHHh8%HH>t#HE@H @1AIm!HHHHHHPHp ILDsHDHHHDHDG8,(dLgeIH8XIL$LHHA6HhHR(H0HĚUHHHHH@H0HM7>HEEL= UMoLI@L0H_6>L5ěUILHDž HDžHDžHDžDž(H5<@H[1A|$tH ӏ@H5@HDHTH-HHHH!K1HT$@HL$8HHL$0HD$(Lt$ AljD$H0HD$Ll$H$HpHxLXL@HPHH,(d156>IH!H5@H 2@HLS:ҽHHAH<@?HLHԽE1H5UH~L5>5>IHH!H5"@H @HLUѽW)HDž0M5>H(5R)Hbe same.HH'Hshould bHH H tensor HHHd outputHHHinput anHHHRank of H@/AH@HHLӽE1H5 UHvL4> HALMHALM%H"HALM H Ht H 4>LMt?HL9t$HX@t Hx3>HI9uHLL3>uu#LMu/gH3>tH}3>LMt:HL9tMHX@t HxR3>HI9uHL73>ELV3>Hb0> LHt H2>LMt?HL9t$HX@t Hx2>HI9uHLL2>uu#LMu/gH2>tHk2>LMt:HL9tMHX@t Hx@2>HI9uHL%2>EnLD2>HP/> LHu/3HBHt{H1>mdHHHM7IMAFt I~k1>Lc1>HHHLL0Lt H1>H0>H58UHH/>H00>H..> HHHt#HHCHuHHPH/>H-> fUHAWAVAUATSHIIHUHHEHEM$MT$M)trIMH1O1fff.IMHt#WI*WH*^ffff.(MuHHHuHHL9rHUHH;EuHH[A\A]A^A_]u{0>/>IH8g!H5@H @HPL8P˽I$HIH n@HMH @HMH ̾@HMH ;@HMH վ@HMH\$HMHL$H$H HuHUHMLELMHAH@HPLHνE1H5EUHL"/> IAL8MII t H0.>LmMt6HEL9t!HX@t Hxr.>HI9uH}LLmX.>puPu L8Mu,dH}/.>PtH`.>L8Mt:H@L9tEHX@t Hx->HI9uH8L@->EtL->L+> Lf.UHAWAVAUATSH(IL]0DUH*UHHEHHHL1LiM)LHI8I@H)HH9ML`HLf~pHffEHEHHH=HLL,>LxHELN$LeHL,>LeffEHEL,>LILmOdLeLH,>LeIGHHhHH<],>HMIJ4HI,>LH?Lt@)HHHJ!HIDH LLL`|?IHH LHhfDHHI>ITLHM LeHLHHHL9I?ME1txL<Hz@H @H@H @EHEʾL1 HUHH;ELH([A\A]A^A_]L]0HI?WI*WH*IWI*I$ ,MAI$1fn O.@U8HHHLL9|IHI?fnp@HHLLuHy1HfnfB~IHHI9Le:DL]0HI?WI*WH*IWI*fnI$ ,Af.wIEHHWH*.vAHH1fn O.@U8IMLL9|I@I?fnp@HHLLeHuHy1HfnfB~IHHI9HxHH@H ,@EHEʾHHHHPLXHEH`LhLpxIHE0HEHE8HEHZH)HHr L8ff0HDž@MtHM98N4LY(>H0NL8HfLxfHDž Mt=M9N4L'>HNLHHH01LHHHLHHLML0HLpEAMM)HLHE1E1fnpfff.MK Iu>1MH fox HfnHfB~IIM9|K1fHHfox Hfnf~DHTHfox Hfnf~HI9uIMkffff.IM94 HL8ff0HDž@MtHM95N4L:&>H0NL8HfLxfHDž MtDM9N4L%>HNLHLxffHDžMt=M9N4Lr%>HNLHHH01OJHHH7JHHHJHHHHH( H0HLHHL`HH@HE11fnpfff.MHHHXHHHЉكHH)E1Hfff.OLIHuBE1HN fox HfBnHfB~IIM9|fHHNE1ffffff.N4Mfox HfBnfC~DJtLfox HfnfC~IL9uMHHHJIM9$HHH; E(dIfn.OPJfn@.O82fn@.sO fn@ .[OIHHIFHHIFIVH H4HHHHHHILHAHHJHqDHHHhHpHFH`H)H@LHHHHHLJHHHHHBHIA H LHHAH(IAH01HPH8LXHHPHHHHH0HHHHL0H(HL8L LX1HHHHHHHHHHHHL$LHLLE1E1HpZDLHILHIHvCH HLHH4>I4H9K HH4>I4H91Ht%J4A4HHHL4L HL->LML3H01E1HHL2HHH9| HL8ff0HDž@MtHM9N4L>H0NL8HfLxfHDž MtDM9sN4L@>HNLHLxffHDžMtHM9N4L>HNLHfLxfHDžMt=M9N4Lx>HNLHHH01U@HHH=@HHH%@HHH @HHHHhHLH0HHHHHHH@H@HHH@HE11fnpfffff.HH`KL@IXH7HHHHMXEAMM)1fMHHH4H1HDL,IIuCE1MN,fox HfBnHfB~IHH9|fDHN4E1fN$Mfox HfBnfC~DJTLfox HfnfC~IM9uMHM\HH97HHH;H`HH;hLHHxHHLu;EHHxHHLt H>HHt Hs>H0Ht H8[>LS>H}Ht H}A>H}HHxt H}(>H|UHH;EHHHxHHt H>HH?FE1L蘚IMHfnpHMH0M?IHH LHhH@HHE1foMx HfBnHfB~LHHfffff.HtI$L+4LFLDIIO;/|&HDL2LIHIM9wLtIM9dHxHLHLHxHHIQDɃHs1LHI)1LfoMx HfBnf~foMx HfBnf~DfoMx HfBnf~DfoMx HfBnf~D HI9uHt*HHfoMx HfBnf~HHuHxHHt H>L0MH8L9t'HXHxHt HxU>HI9uH0LL8>H}> H0>Hq>H0c>HU>HG>H09>H+>H>H>IHHhoIHHhIHH\IH0HTIHHtH(>L0MdKIHHIHHIH0HYIHHXIH0H,I$IHHtz'IHHt]He>HHtQHM>H0HCIHHt H >HHuHHuH0HH8}IH}HIH}HIL0MtGH8L9tDf.HXHxHt Hx>HI9uH0L8m>LLILN>H}Ht H}<>H}Ht H}*>Lj> 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bHH H tensor HHHd outputHHHinput anHHHRank of H@/AH@HHL'E1H5UHfLڈ= HALMHALM%H"HALM H Ht H=LMt?HL9t$HX@t Hx·=HI9uHLL讇=uu#LMu/gH肇=tHm=LMt:HL9tMHX@t HxB=HI9uHL'=ELF=HR= LHt H=LMt?HL9t$HX@t Hx輆=HI9uHLL蜆=uu#LMu/gHp=tH[=LMt:HL9tMHX@t Hx0=HI9uHL=EnL4=H@= LHu/3HBHt{H覅=mdHHHM7IMAFt I~[=LS=HHHLL0Lt H =H=H5(THH范=H0蔄=H= HHHt#HHCHuHHPH袃=Hځ= fUHAWAVAUATSH(MHHE0DEH=TH?H}HHH LL1LiM)MII7IH)HI9M(HhHf~pHffEHELH=HL˃=HELL$LeHL跃=LeffEHEL蔃=LILLmMdLeLHxq=LeHCHH`HH<H=IMIJ4HH1=LHI?t@D)HHHJ!HHIDHx LfnpLh|:IHH LH`HHI>ITLHM LmHxHHHHD,H7M1ۄ4E1LkHY@H x@H@H @EHEʾHH U HTHH;EHH([A\A]A^A_]LU0HH7WI*WH*I$WI*fnIE H,Af.wHHHHWH*.v HI$1fn ,O.@U8HH LL9|I@HH7D@HuHHHH B IHHHI9H@H @EHEʾHHHHPLLXHEH`HHhLpxIHE0HEHE8HEHWH&Hb HL8ff0HDž@MtHM9N4L =H0NI4H9sz1Ht%J4A4HHHHHt Hho=HHt HPo=H0Ht H88o=H,o=H}Ht H}o=H}HH}o=HTHH;E Ko=HHHHt Hn=HHAHE1LwHHHfnpD,HMH0LM'IH LHH`H IHHE1DMx HB B LLHffff.LLIuN+4IAHDLHI; |-HDL6MIHII9HyNtII9H_HHHHHHHCڃHs 1HusH)1DMx HBDMx HBDDMx HBDDMx HBD HH9uHt%HHDMx HB HHuHHHt Hl=L0MKH8L9t.HXHxHt Hxal=HI9uH0HLL8H}i= H0i=H{i=H0mi=H_i=HQi=H0Ci=H5i=H'i=Hi=IHHelIHHeIHHYIH0HQIHHqH2k=L0MaHIHHIHHIH0HcIHHUIH0H6.IHHtz'IHHt]Hrj=HHtQHZj=H0HCIHHt H-j=HHuHHuH0HH8zIH}HIH}HIL0MtTH8L9t:HXHxHt Hxi=HI9uH0L8}i=LIHai=H}Ht H}Oi=H}Ht H}=i=L}f= DUHAWAVAUATSHLHEH=TH?H}HuHUHMLxHpohW)PHDž`HPLBW)0HDž@H0HBHpHxyII t8HHJHt+HP(HHu%1ILHHHtHt HHPMt#HIGHuILPLH=HHu"H0Hu.HPHu:LF= HhI=H0HtH8PI=HPHtHX8I=LxF= HUHAWAVAUATSHIHuHHC HB . aNuIzGHKIcH H<rHSHHuH61JHHHH;<|@HCMcHJH)HKHSH H1H+2E1H1Huffffff.HS HB HS(WI*Hs8WH*WJ*HB$H L,{0t6AW.w.HCHJHWH*.Afff.E1HC H1 =HH}L3H{Hu.pH UHHHHuHeWVHHTHH;Eu HHP[A^]G>=IH{XHt H{`==H{@HtH{H==IH==L:= ff.UHSPHHP UHH{HCHtu==H{XHt H{`c==H{@HtH{HH[]K==H[]fDUHSPHHUHH{HCHt==H{XHt H{`==H{@Ht H{H<=HH[]<=DUHAWAVAUATSH(IIH_THHE1HHIMI_ I+_W)`HDžptCIILH=Hj<=H`N4LpHHS<=LhW)@HDžPA}qIEXH@HEHLHH+HunME@IGIO H)t8IH`H1fWH*YH,HHH9rL`LLHHAu|HH~HAHs~IA}pJMME@IGIO H)t:IH`H1ff.WH*YH,HHH9rL`LLHHAH@Ht HH:=H`Ht Hh:=H:THH;EvLH([A\A]A^A_]A}rqAuxHu}IM?I~HH.H@HHH)HHH)H@tH@LMg M+gW) HDž0tCLHHH>.IL9=H HH0HL9=H(IEHHHH{>HtvMH HH芘IGIO H)H H`H1DWH*YH,HHH9r11MSI^H讻H>L`Lf^IH苻HLLH-:=IG I+GHhH+`H9IH`H HLL L`HLHHH Ht H($8=LH@HJQH@HHMw M+wLHH)HHH)vH@Rr s HHHMIL@IAMGIv'IIGI I I9stIK H9sg1Kfffff.A?HL9r>s HHHHLxdHHHH8=LHHAHHwHs1HIQpKDpH^H)1W( #NDDLLDDLLDDLLDL H HuHt>J7HW( NLffffff.D LH HuI9p6=6=IH` H5?H ?H}L`&ҼH@HAH(@>HuLHԼE1H5THmL5=H`2=5=IH & H5?H ??H}L ѼW)HDž@.5=H(P)Hs input.HH.Hided as HH(H be provHH Hzes mustHHHes or siHHH of scalHHHOnly oneH@6AH?HuHLӼE1H5THLL4={4=IH  H5?H /?H}L мW)HDž 4=H(Q)Ht index.HHHroi inpuHHHInvalid H@AH.?HuHLҼE1H5 THtL3=3=IH> H5?H W?H}L ϼW)HDž@F3=H(xN)Has inputHH(Hrovided HH HUST be pHHH sizes MHHHcales orHHHEither sHf@0.AHN?HuHLѼE1H5THpL2=H /=2=IH) H5?H B?H}LμW)HDžP12=HH(N)H5?DH3=CDAH?HuHLмE1H50THL 2= 1=IHc H5=?H |?H}LͼW)HDž@k1=H(P)Hs input.HH.Hided as HH(H be provHH Hzes mustHHHes or siHHH of scalHHHOnly oneH@6AHV?HuHLϼE1H5 THL0= IALeMIALM[I\IALeMFIALMu I!It H0=LeMt6HEL9t!HX@t Hx/=HI9uH}LLe/=EuEu LMu-H}/=EtH}/=LM|HL9HX@t Hxx/=HI9u9It HS/=LeMt6HEL9t!HX@t Hx./=HI9uH}LLe/=EuEu LMu-H}.=EtH}.=LMHL9HX@t Hx.=HI9umIALeMIALMMINIALeM~IAL MIIALeM@IAL MhIIt H-=LeMt6HEL9t!HX@t Hx-=HI9uH}LLe-=EuEuLMu&^H}t-=EtH}e-=LMt:HL9tTHX@t Hx:-=HI9uHL-=EL>-=H H&LIt H,=LeMt6HEL9t!HX@t Hx,=HI9uH}LLe,=EuEu L Mu-'H}w,=EtH}h,=L MH(L9HX@t Hx5,=HI9uIt H,=LeMt6HEL9t!HX@t Hx+=HI9uH}LLe+=EuEuL Mu&^H}+=EtH}+=L Mt:H(L9tTHX@t Hxt+=HI9uH L(Y+=E}Lx+=H@HqxLIAL`MII@t HP*=LeMt6HEL9t!HX@t Hx*=HI9uH}LLe*=EuEuL`Mu&^H}*=EtH}*=L`Mt:HhL9tIHX@t HxW*=HI9uH`Lh<*=EL[*=Lk'= LD@>MMI}L)fMIH11Du II;t1HH9r)AD$qLIH8#ED$tAT$sAD$8At$M~P}HLH(=LIHTHH;E:LH[A\A]A^A_]ILHLθHpHxH H5IHIBHhLMRHLMIHIx A|$tHP?H 1?HDȾI IHpIBHxLMRHIqHH@IHHHM,W)HHHh5HH>H>?H ?1AIf LM+wLHLHLǸHHhHHpHHxM:IHP@AHLDcsC0XC8dDk4L)RIH8lHKHL$(HD$ Lt$H0HD$Dl$D$$HHhLHxLPLp@HXdG2JH0H4THHHHH@H0H#=HEEL=yTMoLI@L0H"=L54TILHDž HDžHDžHDžDž(H5|?H~̼1A|$tH C|?H5,|?HDHTH̼HHnҼHHt H#=LL0Lt H"=H!=H5 THHo!=H0w"=LHYTHH;E#=HHHHHILDzsB8HDHPH@ HDPXLIH8;IL$LHHA|LHhHRAIMBLHHpHxL1HT$0HL$(HHL$ HD$Lt$AljD$H0H$HpHxHHH@IMPXY!LILMQHIFH;ILPL& =HTHH;E*_I(_IHpIBHxIBMBI IQLIHH@IHHIHPHHXIM,W)HHHh HH>t#H?H H?1AI- HHHHHHPHp ILDsHDHHHDHDG8,(dL规IH8ȆIL$LHHA6HhHR(H0HTHHHHH@H0H =HEEL=ɃTMoLI@L0H=L5TILHDž HDžHDžHDžDž(H5v?Hȼ1A|$tH w?H5|w?HDHTHǼHHͼHHQK1HT$@HL$8HHL$0HD$(Lt$ AljD$H0HD$Ll$H$HpHxLXL@HPHH,(d+=IH H5j?H 5?HLSHH{AH'>HLH蟼E1H5գTH>L=m=IH H5?H ?HLUrW)HDž0 =H(Q)Hbe same.HH'Hshould bHH H tensor HHHd outputHHHinput anHHHRank of H@/AH?HHL藻E1H5͢TH6L= HALMHALM%H"HALM H Ht H=LMt?HL9t$HX@t Hx=HI9uHLL~=uu#LMu/gHR=tH==LMt:HL9tMHX@t Hx=HI9uHL=EL=H"= LHt H=LMt?HL9t$HX@t Hx=HI9uHLLl=uu#LMu/gH@=tH+=LMt:HL9tMHX@t Hx=HI9uHL=EnL=H= LHu/3HBHt{Hv=mdHHHM7IMAFt I~+=L#=HHHLL0Lt H=H=H5|THH\=H0d=H= HHHt#HHCHuHHPHr=H= fUHAWAVAUATSH(LIIILU0DEH|THHUM]MTHLaI)LHH;HsH)HH9<MkL`H@HHxLf~hffEHEHHH=LpL=LLPHELN4LuHILm=LuffEHELLC=IL}O4LuLH+=LuffpHEMt$LHXIJ<=IHpLuLxMIJ4HH=HLH?Lt@)HHHJ!IDLI fnhLL@|BIHH LHXfHHI;ITLHM LEILLPHHHP,ЉI>E1rH`kH?H ?H$?H M?EHEʾLH&zTHH;ELH([A\A]A^A_]@HM0LHI>WI*WH*IWI*fnI ,f.wHxHHHWH*.vAHH1fn WN.@U8HxH H H9|HHI>@HLLEu Hy1ALC WI*WH*IWI*I ,MI1fn uN.@U8HHxHHH9|HHLI>@HLLEu Hy1AD CHPHH9IHH4>HH9sw1HAt%H4H3H4GA1HA1HHH9 HxD2Dqq2DqDqHI9uf.LHHH4HXE1f.BofofhCDSf`CLSBoDfof`fhCDSC SI HuHht2IHoJWHfof`fhADALH@HI9I@IMLM9NHHHIHHLIH`HHxHH9HH0HH8HHHHH H(HHHH;LpLpIMCFH@1HE1E1E1HHLHHJ 7JT7H)HHH)LvLSHLKH4s HJD7H(L)IILHIL;nffHDžLL =HHL4LHLm =LMH1E1Hf.HL2HHH9|L9ffHDž MtYI9mN4L=HNHLpIHt Ht=HHt H\=HH0H@=E1HHfnh,HHIHHH LHHXH IH HE1HxMxCD5CH=LM.|IHH3IHHEIHHu{/'IHHuFPIHHt)`IHHt H:=HHu9HHt H=HHH=H=HHuIH}HIH}HILMtSHL9t9fDHXHxHt Hxx=HI9uHL]=LIHA=H}Ht H}/=H}Ht H}=L]< DUHAWAVAUATSHLHEH=xdTH?H}HuHUHMLxHpohW)PHDž`HPLٻW)0HDž@H0HٻHpHxyܲII t8HHJHt+HP(HHu%1ILHHHtHt HHPMt#HIGHuILPL<HHu"H0Hu.HPHu:L< HH<H0HtH80<HPHtHX<LX< HUHAWAVAUATSHIHuHHC HB . 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HC@@HKH JH9|HHHUEuHy1HSHJHHEHJIHCHJI9H[A\A]A^A_]UH]f.UH]<fDUHAVSI<HIvHUHH{a<H[A^]DUHHGH iUHH~H]0<fff.UH]f.UH]<fDUHHH6]?ffffff.UHH PH1H9NHD]DUHH=U]UHAVSHG0HHLHWHGHHHHWHw L LLMHw(HW8HILLLE1Wffffff.Hw1DHG@8tgHGHHB .wEHGHHWH*.w.HGPH .wHG HHWH*.vfDHGXHG`HwhHJH6LI4A41H_pHHHE4HGxHJIGHAHH HHB(YW*YHHYWA*YXHHBYWA*YW*XYYX,ILˆHHG8HH9HG0HII9t[A^]ffffff.UH]f.UH]<fDUHAVSI<HIvHUHH{Q<H[A^]DUHHGH UHH~H] <fff.UH]f.UH]t<fDUHHH6]?ffffff.UHH PH1H9NHD]DUHHU]UHAWAVAUATSHG8HHILHWH_HHHHWHw HG(HHHHIHUHw0HG@HHWHHILLLLE1WfHE1@HE1@HOP9HOXH B.wqHOH HWH*.wZHO`H B.wHHO H HWH*.w1HOhH B.wHO(H HWH*.vHOp1HOxHH J4H NM<2HHN4K>HEHHLN MHHN$LJ 6HMLHHJILO$2MILIHH FHH F,HH FHEHM D*HH F<H]HEHtHH<H]Ht#HHCHuHHPH<H&THH;EuLHh[A^A_].<IH]HEHuH]HEHuEH]HucH%H<H]HEHtIH]HEHtHHT<H]Hu/IH]Ht#HHCHuHHPH<LZ< fUHAWAVAUATSHL0L H(AHH8Hs%THHEE11HIƾD)HLHHL{LI~0HPHHtH{0HPI1HIIFPHMt IEIEP1HHILLH0HMH HH9HNHEW)@HDžPLuH]L)tUIILH=Hֿ<ILHL@KHPH~LLH!<ILHH@H8HH( H@Ht HH[<LuHH{0HPHLH{BHH HKPHHHHAHH@HHHHDž`HDžXHDžPHMHhHDžpHxHDžHHH@H0ԳH}Ht H}p<H}Ht H}^<HpHt HxF<HXHt H`.<H0Ht H8<HHt H <HHt H<H"THH;EuHĸ[A\A]A^A_]<H@< IH@HuCIH@Ht2HH$IH8HHHtHH]<H}Hu[H}HudHpHumHXHuyH0HHHHHLC< H}<H}HtH}޼<HpHtHxƼ<HXHtH`讼<H0H{H8蒼<HHoH v<HHcHZ<L蚹< fUHAWAVAUATSHHH THHEHH8IILMl$LH{0HPHI|$0HPHHHCPHI$ID$PHHHHL-H0HMH HH9HNHEW)@HDžPLeH]L)tVIILH= H]<ILHL@KDHPH~LLH觼<ILHH@1LȝHH@Ht HH<LuH{0HPHLHH=HH HKPHHHHHH@HHHHDž`HDžXHDžPHEHhHDžpHxHDžHHDžH@H8dH}Ht H}<H}Ht H}<HpHt Hxֹ<HXHt H`边<H0Ht H8覹<HHt H 莹<HHt Hv<HTHH;EuHĨ[A\A]A^A_]诹<H@詶< HH@Hu+&HH@HtHH<H}HtgHH}Hu[H}HudHpHumHXHuyH0HHHHHH< H}蘸<H}HtH}膸<HpHtHxn<HXHtH`V<H0H{H8:<HHoH <HHcH<HB< f.UHAWAVAUATSPH1HxPHHxHHY(3LX@HpHLwK<9HLA0LH0LO<MH r#I2LLH9JLI91HNH)HHtHf.AHHuHrQMKLKTffff.\\\\\\HH9u H~<H[A\A]A^A_]HHHHHHDbAH`s1M|KLpK I\pMl$I)1fDLDLDLDLDLDLD D HIuMt.K ILK\IfD D H IuH9UHAWAVAUATSE1HPLgHM(L HHwLJ(HJ0LKLHLG@LvO,I r#K "LLI9K LH91M\$I)LHtHD83t1AL5HHuIMKLK|8D1t81ۈ\78D1u6\78D1t81ۈ\781u67HI9u=\78D1t1ۈ\78D1uȈ\781t1ۈ7HI9uLELHAfAnff8fnf8H~IIEXAHL}L]HMI|0K :M| 0IM)1fAoTAo\ftftffT\AoTAoftftffTH@IuH}t)o o\ ftftffAT A\ I9L}LE:[A\A]A^A_]1H}uffff.UHAWAVAUATSHPLWHMHw HHWLh(Lx0LLG0LHG@LbEAILEHuHEu 1MuwM)IDI6ItKMD1Lffff.8L>uEL8DL88 >t1fDE1DL88 >uA8Iو8HI9uMt%MLmLeA8L=u ML}A?1A<[A\A]A^A_]f.UHSPHHH[]UHAWAVAUATSHIHIHTHHEC. 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 UHH %H1H9NHD]DUHH$]UHAWAVAUATSHHHIHH8#!HHEH0L%"!M|$@LLHI!!HH@H0HfHnfL HEEID$HHIhL0LLL L5K"!ILHDž HDžHDžHDžDž(LL7 LLHfLH HHHfLHlHHL0HHLt H, H H53!!HH H0诼 H!!HH;EuLHH[A\A]A^A_]3 HSHeHHHL0HHLt H臼 Hg H5 !HHD H0 H蘹  UH]f.UH]4 fDUH, H ##H]fDUHH #H]UH]f.UH] fDUHAWAVSH(HHi !HHEH1HPIMt5H1HP(H = !H H;MuHLH([A^A_]_Qػ (膻 HH5I HH}AHuH HP#HHC HCHCE1H5#HDHX  IIEt H} EtH L$  fff.UHH H1H9NHD]DUHH #]UH]f.UH]鄺 fDUH| H #H]fDUHH#H]UH]f.UH]4 fDUHAVSHH1HQ(IA~(uI^ *L;AF(I~@uPHgHI^ C KH{Ht [A^]$H{@u(HAfHH{[A^]@uHH?@u HH?11ff.UHH H1H9NHD]DUHH-#]UH]f.UH]$ fDUH H #H]fDUHH#H]UH]f.UH]Ը fDUHAWAVAUATSHhIHU!HHEI1LPIM I1LP(HÃ{(u|Lc AL$I\$HI|$@HgHI\${(H:C(H{@gHKeHC A~(t_H x$ZH9C(H{@WHeILc AL$I\$H[{({HC A~(uIN I HH ILPHtJI1LPHt:I1LPH(ufH@ @tH@H(tu H@ @u#H!HH;EHh[A\A]A^A_]A~(IF LpMuL5aw$EfDeffEHEEEaxisEIHuLHt x1Et H}賶 De]D9QA9HyADeILPHu{IHffEEHEHkeepdimsHMEHuLHEt H}4 H LHv$LpMILPHHILPHILPH(uH@ @H@H(tH@ @ILPHtp@tjffEHE{H}H轨HuHUHMH}8HEaOAHxI1LP(HÃ{(uL{ AOI_Hu[;H6C(H{@yHMbIL{ AOI_HuI@qHcHI_{(uL{ AOMgMu[;H86C(H{@HjaIL{ AOMgMuI@H`IMgMt]LLHcEIL$ H\C(LxMtH9C(Lk HW!HH;Es AFNM|$1fMH9tJID$ HtIcL$;}QAT$H|I?~_LH#aHIF HtAFHHcH9|u(HC C(H!HH;EWH{ Hs@ H-C(Ho!HH;E@*HH?_HC A~(@HH?@ HH?H}H{HuHUHMH}}H}Ht H}2 LmH}I9}HcEIN HDx(uqL` LeILPx(H@ H@HuH3r$xH}HEH9HHH)HHH1HHHC1^HC A~(^11Hq$H@HqeH}HLHHHHHH H1HUI9( IH5 H Le H}HMLMAHuL脮 H#IIF IFIFE1H5x#HLݰ :I?I!IXHHrH sf1fHutH^H)f1fofoDfoL o\0od@ffoTPffoD`foLpfHHuHt"HTHoRfofH HuffpNffH~L9tJ HHH9uHUI9HH9t$HOH9t'HHpHH;Qu HH9u HH9uHHCI}@ 5@uBHH?y@u7HH?@u,HH?@u!HH?1<11Z1@u HH61(̮ IH5 H: H}AHuLE H#IIF IFIFE1H5av#HL螮  (\ IH5ֱ H0[LR[H}HMLMmAHuLƫ H#IIF IFIFE1H5u#H L |(ڭ IH5T H H}*AHuLS H#IIF IFIFE1H5ou#HL謭 (g IH5 HK H}sAHuL H1#IIF IFIFE1H5t#H%L9 HEt H}Ԭ Eu"HEt HEuHLլ H}HH'HEuHHEt H}l EtsL菬 H蟩  0HH}Hu%HH}HtH}0 H}Hu1HH}Ht#H}HEu HEt H} H4  fff.UHH H1H9NHD]DUHH#]UHAWAVAUATSHhMLHHIHxH!HHEH0L=!I_@HLH%!HH@H0HfHnfL迪 LHEEIGHHIhL0HHL輩 L5!!ILHDž HDžHDžHDžDž(LL LLHSLxHH LHHSH0L HH軬 LHHaSH0L辨 LH!YHHL0HHLt H H蠨 H5 !HH} H0C H,!HH;EuLHh[A\A]A^A_]ǩ HSHeHHHL0HHLt H H H5" !HHا H0螨 H,  @UHAWAVAUATSHhMLHHIHxHC !HHEH0L= !I_@HLHU !HH@H0HfHnfL LHEEIGHHIhL0HHL L5Q !ILHDž HDžHDžHDžDž(LL= LLHPLxHH LHHPHH0L? HH LHHPHH0L LHOVHHL0HHLt H HΥ H5 !HH諥 H0q HZ !HH;EuLHh[A\A]A^A_] HSHeHHHL0HHLt HI H) H5P !HH H0̥ HZ  fUH]f.UH] fDUH H c#H]fDUHHM#H]UH]f.UH]餥 fDUHAWAVAUATSHHIH% !HHEI1LPHx(uH@ H@HtHe$H@HuHf$x(uH@ He$Dx I1LP(HÃ{(uHC *H&C(H{@HRHC HDx ILPHI1LPHI1LPH(tH@H(uH@ @uH@ @|IHW)EHEEEaxisEHuLHEt H}- HEIHW)EEHEHnew_axisHMEHuLHEt H}գ Ht DE1I1LPx(uH@ H@HtHOd$H@HuHd$x(uH@ L`MtHc$L`MuL%c$AOAD$1AE)ЉEuM99yDEI1LP(HÃ{(uLs ANMnMu[;H$C(H{@HOILs ANMnMuI~@H0OIMn}Mu1fIM Ht'AE;}AEEH9u,\ffffff.I>hNLH PEH9t2HEIU H|H9 )ID$ HcHtCHcEH9H[rH!HH;EuZHH[A\A]A^A_]@u HH?1@uHH?@uHH?11 (菡 IH5R HH}AHuL HY#IIF IFIFE1H5$i#HMLa  ( IHEHD$HH$H5 HLo H}HMLMAHuLu HƷ#IIF IFIFE1H5h#HLΠ h(艠 IH5 HLH}AHuL HS#IIF IFIFE1H5h#HGL[ ( IH5 H~H}ְAHuL菝 H#IIF IFIFE1H5g#HL HGHBH=HEu&-HEu"HEuHHEt H}N Et/Lq H聜  HEu HEt H} HZ  fUHH H1H9NHD]DUHH#]UHAWAVAUATSHhMLHHIHxHC!HHEH0L=!I_@HLHU!HH@H0HfHnfL LHEEIGHHIhL0HHL L%Q!ILHDž HDžHDžHDžDž(LL= LLHFLxHH LHHFH0L HH LHHFH0L H]H LHHfFHE0Lƛ LH)LHHL0HHLt HȜ H訛 H5!HH腛 H0K H4!HH;EuLHh[A\A]A^A_]Ϝ HSHeHHHL0HHLt H# H H5*!HH H0覛 H4  UHAVSHIHh!HHEH`HB‚H5HAHAD$D$$H H L 1E1HX;D$D$$Hh H L_ 1E1H?H5{ H#LH SH5) H_#LHRH #H0H0HPHHH5 HH5 Hf H H5nhHLHjHPH9t HtHP(HP H`OH HH;EuLH[A^]y I"IHPH9uHP HtHP(H`L&  fffff.UHAWAVSH(IHV HHEH`H0HDž(H5 HL(HcD$D$$Ho, H L^ 1E1H:9D$D$$H H L E1H8D$D$$HH L 1E1Hu=HW)HDž fDžTƅxnHH6W)HDž@t H(<)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH>H #H0H0HPHH| H5 H H5 H^HH5fH}LHbHPH9tjHtHP(t H4 LMt^HL9tCfffff.HX@t Hx HI9uHHP uLLʖ t H 赖 H`HD HH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hx˕ HI9uHL谕 t H 蛕 H`Lϒ  LfUHAWAVSH(IH HHEH`HໂD$D$$HL( H L; 1E1H5D$D$$Ho H LN 1E1H9HW)HDž fDžTƅjHH2W)HDž0荔 H()Hry type.HHHrbitraryHHH be of aHHHnsor canHHHInput teH@&HHHH:H5d H#L HKH #H0H0HPHHH5 H H5 Hq HH5yMH LHuHPH9t]HtHP(t HG LMtQHL9t6HX@t Hx HI9uHHP uLL t H Ւ H`Hd HH;EuLH([A^A_] ILMu|IuS]IIIHPH9tHtHP(uHP t H$ LMtHHL9tgfffff.HX@t Hx HI9uHLБ t H 軑 H`L  LfUHAWAVSH(IH& HHEH`HD$D$$Hl$ H L[ 1E1H71D$D$$Hq]H $ Ln 1E1H5HW)HDž fDžTƅfHH.W)HDž0譐 H(Ր)Hry type.HHHrbitraryHHH be of aHHHnsor canHHHInput teH@&HHHH6H5 H#LRHHH G#H0H0HPHHH5 HH5ּ H HH5tHLH HPH9t]HtHP(t Hg LMtQHL9t6HX@t Hx; HI9uHHP uLL t H H`H HH;EuLH([A^A_]# ILMu|IuS]IIIHPH9tHtHP(uHP t HD LMtHHL9tgfffff.HX@t Hx HI9uHL t H ۍ H`L  LfUHAWAVSH(IHF HHEH`H H5: HAH D$D$$H[H [LZ 1AH3-D$D$$HHH OL 1E1H1HW)HDž fDžTƅbHH*W)HDž0詌 H(#)Hnsor typHH Ho any teHHH types tHHHn outputHHHConstraiHf@(e.@*HHHH2H #H0H0HPHHH5 HH5 H HH5HLH HPH9teHtHP(t H LMtYHL9t>fHX@t HxK HI9uHHP uLL t H  H` H HH;EuLH([A^A_]3 IugqILMunIIIHPH9tHtHP(uHP t HT LMtHHL9tgfffff.HX@t Hx HI9uHL t H H`L  LfUHAWAVSH(IHV HHEH`H0D$D$$H H L 1E1Hg)D$D$$H H LH AH()D$D$$Hb H lL 1AH-HW)HDž fDžTƅ^HH&W)HDž@蛈 H(c)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH.HDžH5U5 HLH H #H0H0HPHHvH5x HH5 HX HH5`]HwLH\HPH9tdHtHP(t H. LMtXHL9t=HX@t Hx HI9uHHP uLLʆ t H 赆 H`HD HH;EuLH([A^A_] IuuIugqILMunIIIHPH9tHtHP(uHP t H LMt:HL9tYHX@t Hx˅ HI9uHL谅 t H 蛅 H`Lς  LfUHAWAVSH(IH HHEH`HૂD$D$$HL H J L; 1E1H%D$D$$H H 8 L E1H$D$D$$HB H L E1H$D$D$$HR H !L AH`$D$D$$HH LS AH!$D$D$$H1 H  L 1E1H(HW)HDž fDžTƅYHH!W)HDž@藃 H(_)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH)H5 H#L H:H #H0H0HPHH}H5 HH5 H_ HH5gwH~LHcHPH9tkHtHP(t H5 LMt_HL9tDffffff.HX@t Hx HI9uHHP uLLʁ t H 赁 H`HD HH;EuLH([A^A_] ILMutIuKUIIHPH9tHtHP(uHP t H LMt@HL9tKfDHX@t Hxۀ HI9uHL tH 諀 LIH`L}  ffff.UHAWAVSH(IH HHEH`HংH5HE1HD$D$$H. H L 1E1HD$D$$H$H (L 1E1Hp$HW)HDž fDžTƅsUHHW)HDž@o H(7)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH%H #H0H0HPHHwH5 HH5 HY HH5aHxLH]HPH9teHtHP(t H/~ LMtYHL9t>fHX@t Hx} HI9uHHP uLL} t H } H`HD HH;EuLH([A^A_]} IugqILMunIIIHPH9tHtHP(uHP t H} LMtHHL9tgfffff.HX@t Hx| HI9uHL| t H | H`Ly  LfUHAWAVSH(IH HHEH`HࢂHxHDž(H5) HL(H D$D$$H H  L 1E1HD$D$$H H L E1HD$D$$HH pL E1HjD$D$$Hz H jLU 1E1HHW)HDž fDžTƅ4yHHW)HDž@z H(x)Hsor typeHH(H any tenHH Han be ofHHH types cHHHd outputHHHInput anHf@0.HHHH!H5c H=#LV H92H #H0H0HPHHH5 HH5 H HH5?HLHHPH9t`HtHP(t Hy LMtTHL9t9@HX@t Hx[y HI9uHHP uLL*y t H y H`H HH;EuLH([A^A_]Cy ILMIIuS]IIIHPH9tHtHP(uHP t HXx LMtfHX@t HxR HI9uHHP uLLZR t H ER H`KHԶ HH;EuLH([A^A_]sR ILMu|IuS]IIIHPH9tHtHP(uHP t HQ LMtHHL9tgfffff.HX@t Hx[Q HI9uHL@Q t H +Q H`1L_N  LfUHAWAVSHHIH HHEH`HpwHW)ƅHDž HnearestHH5 HhLHD$D$$Ha H Lt] 1E1HPD$D$$H H UL`b E1HD$D$$H"a H [L\ 1E1HHW)HDžfDžTƅMHHW)HDž@O H(ry)Hr types.HH/Hl tensorHH(HY' to alHH Houtput 'HHH'X' and HHHn input HHHConstraiH@7HHHHH #H0H0HPHHH5S HH5{ Ht H跿H5|'HLHx́HPH9t`HtHP(t HJN LMtTHL9t9@HX@t HxN HI9uHHP uLLM t HM t H M H`ƻHO HH;EuLHH[A^A_]M IILMIIIIHPH9tHtHP(uHP t HL LMtCHL9twfHX@t HxL HI9uHLL t HL t H vL H`|LI  Lffff.UHAWAVSHIHְ HHEH`HrW)ƅHDž HnearestHH5a HLHADž @H5 H^L H5HDžH5l H>LHhW)ƅHDžHhalf_pixHfDželƅH52 HLHW)ƅ$HDžHefer_floHHround_prHfDžorƅH5o HLHDžH5 HLH D$D$$H[ H Lq 1E1HDD$D$$HtH qLKz AHD$D$$H H L\ AHD$D$$HmH lL[ AHD$D$$HZ H Lp 1E1HHW)HDžƅfDžT1ƅHH W)pHDž@H H(r)pHr types.HH/Hl tensorHH(HY' to alHH Houtput 'HHH'X' and HHHn input HHHConstraiH@7HHHpH%H5x H#LHCH t#H0H0HPHHܼH5 HH5u H HH5ƤHݾLHŁHPH9tjHtHP(pt HG LMt^HL9tCfffff.HX@t Hx[G HI9uHHP puLL*G uTu`ult H F H`H HH;EuRLHĘ[A^A_]HF tHF tHF uF ILMI!xIxI IIIpuKUIIHPH9tHtHP(puHP pt HE LMtGHL9fHX@t HxkE HI9uHLPE u=u\uht H E H`<B  HD tLItHD tHD uUHAWAVATSH@IH4 HHEHPHkD$D$$HU H X L 1E1HED$D$$HUU H L` 1E1HIW)HDžfDžVƅHHkHHHHHH{LMtBHL9t'HX@t HxkC HI9uHLLKC W)HDžP8C HH(o)H5pBHD CBHHHLH #H H #H(H H@HH|H5N H荸H5o H^H衳H5fH}LHbH@H9tjHtHP(t H4B LMt^HL9tCfffff.HX@t HxA HI9uHHP uLLA t HA HP軯HD HH;EuLH@[A\A^A_]A IILMTILMtSHL9t$HX@t Hx&A HI9uHLLA LMuILMHL9@HX@t Hx@ HI9uIIIH@H9tHtHP(uHP t HQ@ LMtEHL9tdff.HX@t Hx@ HI9uHL@ t H? HPL=  LfUHAWAVSH(IHV HHEH`H0fH5 H6E1H2D$D$$HP H LmL 1E1HID$D$$HVH Lf E1H D$D$$HP H 7LK 1E1HHW)HDž fDžTƅ H(K>)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH5>e H4#LkHH #H0H0HPHHiH5mp HzH5j HK H莮H5S HjLHOHPH9tgHtHP(t H!= LMt[HL9t@ff.HX@t Hx< HI9uHHP uLL< t H < H`諪H4 HH;EuLH([A^A_]< ILMu|IuS]IIIHPH9tHtHP(uHP t H; LMtHHL9tgfffff.HX@t Hx; HI9uHL; t H ; H`葩L8  LfUHAWAVAUATSHIH HHEHPHaHDžH5 HLHD$D$$H2 H LFb 1E1HD$D$$H8H Lj E1HD$D$$Hq H HL  E1H^D$D$$HnK H -L 1E1HHW)HDžƅfDžT1ƅQHH׈L(W)HDž09 H()Ic types.L`I numericLhI to onlyLpIns inputLxHConstraiH@'HHHHHW)HDžƅfDžT2ƅmHHֈW)HDž08 H(A~)L`LhLpLxIConstraiL0@'HHHH\HW)`HDžpƅ`fDžaT3ƅc6L(HHHBֈW)0HDž@ -8 H@()0Hor type.HHH tensor HHHn to anyHHL0@H`HHH0HH #H H H@HHiH5z HzH5d HK H莨H5S HjLHOH@H9tgHtHP(0t H@!7 LHMt[HPL9t@ff.HX@t Hx6 HI9uHHHP 0uLLP6 `uGuSLMtpHL9tUfHX@t Hx{6 HI9uH2Hp^6 tHI6 LMuLL,6 uIuULMtrHL9tW@HX@t Hx5 HI9uH2H5 tH5 LMuLL5 t H5 HP荣H HH;EuLH[A\A]A^A_]5 I0ILHM,I.LILMQIILMI`IBTIIIH@H9tHtHP(0uHP 0t H@C4 LHMt?HPL9t$HX@t Hx4 HI9uHHLLP3 `uu#LMu/lHp3 tH3 LMt?HL9t$HX@t Hx3 HI9uHLLl3 uu#LMu/lH@3 tH+3 LMt?HL9t^DHX@t Hx2 HI9uHL2 t H2 HPѠL/  LfUHAVSHIH8 HHEH`HYD$D$$HC H C LY 1E1HID$D$$HWC H RC LPb 1E1HH5lY Hjw#LÿHH5b Hhw#LɿHH #H0H0HPHHuH5q H膧H5^ HW H蚢H5_ HvLH[HPH9t HtHP(HP H`@Hɕ HH;EuLH[A^]j1 I"IHPH9uHP HtHP(H`鞁L.  ffffff.UHAVSHIHH HHEH`H"WD$D$$HA H A LW 1E1HYD$D$$HgA H bA L`` 1E1HHDž(H5ro HOL(HHDž H5o HіL H趴H5"W HHu#LyHH5_ H6u#LHH À#H0H0HPHH+H5n H H L: E1HD$D$$H> H Lz: 1E1HH5 Hr#LŕHDHW)HDž fDžTƅ7+HHʈW)HDž@, H(,)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH ~#H0H0HPHHH5e HH5Y Hͤ HH5Ոb HLHѩHPH9tiHtHP(t H+ LMt]HL9tBffff.HX@t Hxk+ HI9uHHP uLL:+ t H %+ H`+H HH;EuLH([A^A_]S+ IugqILMunIIIHPH9tHtHP(uHP t Ht* LMtHHL9tgfffff.HX@t Hx;* HI9uHL * t H * H`L?'  LfUHAWAVSH(IHv HHEH`HPPD$D$$H: H : L6 1E1HD$D$$H: H : Lh; 1E1HHW)HDž fDžTƅHHLjW)HDž ( H(eb)Hr types.HHH tensor HHHn to allHHHConstraiH@HHHHVH z#H0H0HPHH/H5h H@H5VU H HTH5 H0LHHPH9t]HtHP(t H' LMtQHL9t6HX@t Hx' HI9uHHP uLL' t H u' H`{H HH;EuLH([A^A_]' IugqILMunIIIHPH9tHtHP(uHP t H& LMtHHL9tgfffff.HX@t Hx& HI9uHLp& t H [& H`aL#  LfUHAWAVSH8IHƊ HHEH`HLHDž(H5Ӄ HL(HӪHDž H5 HL H親D$D$$H6 H ЎL2 1E1H}D$D$$Hph H L^7 E1HAD$D$$HO6 H ƎL,2 1E1HHW)HDžfDžTƅ #HHˆW)HDž@$ H(Oj)Hsor typeHH(H any tenHH Han be ofHHH types cHHHd outputHHHInput anHf@0.HHHHH v#H0H0HPHH˘H5 HܙH5P H譜 HH5 H̚LH象HPH9tiHtHP(t H# LMt]HL9tBffff.HX@t HxK# HI9uHHP uLL# t H# H` H HH;EuLH8[A^A_]3# IuoyILMuvIIIIHPH9tHtHP(uHP t HL" LMt@HL9t_fDHX@t Hx" HI9uHL" t H! 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Lg1E1HD$D$$Hߪ H  LhlE1HKD$D$$H H L6g1E1HHW)HDž fDžTƅ/HHW)HDž@YH(Y)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH Ѳ"H0H0HPHHH5` HH5 H HH5 HLHրHPH9tgHtHP(t HXLMt[HL9t@ff.HX@t HxKXHI9uHHP uLLXt H XH` ƀHHH;EuLH([A^A_]3XIugqILMunIIIHPH9tHtHP(uHP t HTWLMtHHL9tgfffff.HX@t HxWHI9uHLWt H VH`ĀLT LfUHAWAVSH(IHVHHEH`H0}D$D$$H H Lc1E1HgD$D$$H H LHhE1H+D$D$$H H Lc1E1HHW)HDž fDžTƅSHHW)HDž@UH(iU)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH 1"H0H0HPHHH5@ HH5Ё HHH5 HLHҀHPH9tgHtHP(t HaTLMt[HL9t@ff.HX@t Hx+THI9uHHP uLLSt H SH`HtHH;EuLH([A^A_]TIugqILMunIIIHPH9tHtHP(uHP t H4SLMtHHL9tgfffff.HX@t HxRHI9uHLRt H RH`LO LfUHAWAVSH(IH6HHEH`HyD$D$$H| H Lk_1E1HGD$D$$H H L~y 1E1HHW)HDž fDžTƅPHHW)HDž0QH(Q)Hry type.HHHrbitraryHHH be of aHHHnsor canHHHInput teH@&HHHHH5x H:"L H& H ǫ"H0H0HPHHH5i HH5} HHH5 HLH΀HPH9t]HtHP(t HwPLMtQHL9t6HX@t HxKPHI9uHHP uLLPt H PH` HHH;EuLH([A^A_]3PILMu|IuS]IIIHPH9tHtHP(uHP t HTOLMtHHL9tgfffff.HX@t HxOHI9uHLOt H NH`LL LfUHAWAVSH(IHVHHEH`H0uD$D$$H H L[1E1HgD$D$$H H T Lu 1E1HHW)HDž fDžTƅ1LHHW)HDž0MH(N)Hry type.HHHrbitraryHHH be of aHHHnsor canHHHInput teH@&HHHH(H5t Hb"L HFH g"H0H0HPHHH5 HH5z HHH5+ HLHʀHPH9t]HtHP(t HLLMtQHL9t6HX@t HxkLHI9uHHP uLL:Lt H %LH`+HHH;EuLH([A^A_]SLILMu|IuS]IIIHPH9tHtHP(uHP t HtKLMtHHL9tgfffff.HX@t Hx;KHI9uHL Kt H KH`L?H LfUHAWAVSH(IHvHHEH`HPqH5& H4 AHOD$D$$H H LW1AHcD$D$$Hx H  LNW1E1HHW)HDž fDžTƅ-HHHW)HDž0IH(1)Hnsor typHH Ho any teHHH types tHHHn outputHHHConstraiHf@(e.@*HHHHH "H0H0HPHHH5} HH5v H HH5C HLHƀHPH9teHtHP(t HHLMtYHL9t>fHX@t Hx{HHI9uHHP uLLJHt H 5HH`;HĬHH;EuLH([A^A_]cHIugqILMunIIIHPH9tHtHP(uHP t HGLMtHHL9tgfffff.HX@t HxKGHI9uHL0Gt H GH`!LOD LfUHAWAVSH(IHHHEH`H`mD$D$$HWH LS1E1HD$D$$Hѕ H ۿ LS1AH HW)HDž fDžTƅ^DHHW)HDž@ FH(E)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH9HDžH5 H LH,H5 H^ E1HH O"H0H0HPHHǹH5ɵ HغH5q H詽 HH5 HȻLH€HPH9teHtHP(t HDLMtYHL9t>fHX@t HxKDHI9uHHP uLLDt H DH` HHH;EuLH([A^A_]3DIuuILMu|IuS]IIIHPH9tHtHP(uHP t HFCLMt:HL9tYHX@t HxCHI9uHLCt H BH`L@ LfUHAWAVSH(IHVHHEH`H0iD$D$$H H LO1E1HgD$D$$H H LZ E1H+D$D$$H H d L E1HD$D$$H H q L AHD$D$$Hټ H ؼ L AHqD$D$$HRH X L\N1E1HHW)HDž fDžTƅ;?HHއW)HDž@@H(@)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH5^ HX"LQ H4H ՜"H0H0HPHHʹH5 H޵H5l H诸 HH5 HζLH賽HPH9tkHtHP(t H?LMt_HL9tDffffff.HX@t HxK?HI9uHHP uLL?t H ?H` HHH;EuLH([A^A_]3?ILMutIuKUIIHPH9tHtHP(uHP t H\>LMt@HL9tKfDHX@t Hx+>HI9uHL>tH =LIH`L%; ffff.UHAWAVSH(IHVHHEH`H0dH5k Hi E1H2D$D$$H~ H LmJ1E1HID$D$$Ht H x L4J1E1HHW)HDž fDžTƅ;HHڇW)HDž@<H(<)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH O"H0H0HPHHǰH5 HرH5h H詴HH5 HȲLH譹HPH9teHtHP(t H;LMtYHL9t>fHX@t HxK;HI9uHHP uLL;t H ;H` HHH;EuLH([A^A_]3;IugqILMunIIIHPH9tHtHP(uHP t HT:LMtHHL9tgfffff.HX@t Hx:HI9uHL:t H 9H`L7 LfUHAWAVSH(IHVHHEH`H0`D$D$$H H LF1E1HgD$D$$H H LHKE1H+D$D$$Ht H u LFE1HD$D$$HIH LE1E1HfHW)HDž fDžTƅ6HHzևW)HDž@e8H( )Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHHH |"H0H0HPHHtH5_ H腭H5d HV H虨H5 HuLHZHPH9tbHtHP(t H,7LMtVHL9t;fDHX@t Hx6HI9uHHP uLL6t H 6H`軤HDHH;EuLH([A^A_]6IugqILMunIIIHPH9tHtHP(uHP t H6LMtHHL9tgfffff.HX@t Hx5HI9uHL5t H 5H`衣L2 LfUHAWAVSH(IHHHEH`H[HDž(H5 HԱ L(HD$D$$H H ( LB1E1HD$D$$H H Lݝ E1HD$D$$H H x LAE1HrD$D$$HEH r L]A1E1HHW)HDž fDžTƅ<2HHчW)HDž@3H(y)Hsor typeHH(H any tenHH Han be ofHHH types cHHHd outputHHHInput anHf@0.HHHH#H5k Hu"L^ HAH b"H0H0HPHHڧH5 HH5` H輫 HH5& rH۩LHHPH9thHtHP(t H2LMt\HL9tAfff.HX@t Hx[2HI9uHHP uLL*2t H 2H`HHH;EuLH([A^A_]C2ILMu|IuS]IIIHPH9tHtHP(uHP t Hd1LMtHHL9tgfffff.HX@t Hx+1HI9uHL1t H 0H`L/. LfUHAWAVSH(IHfHHEH`H@WHDž(H5 H L(HsD$D$$H H Ln=1E1HJD$D$$Hm H L= E1HD$D$$HAH z L<1E1HHW)HDž fDžTƅ-HH͇W)HDž@/H(,)Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHHH5 H"L HH y"H0H0HPHHqH5If H肤H5[ HS H薟H5 HrLHWHPH9t_HtHP(t H).LMtSHL9t8HX@t Hx-HI9uHHP uLL-t H -H`軛HDHH;EuLH([A^A_]-ILMu|IuS]IIIHPH9tHtHP(uHP t H-LMtHHL9tgfffff.HX@t Hx,HI9uHL,t H ,H`衚L) LfUHAWAVSH(IHHHEH`HRHDž(H5 H L(HD$D$$H H ( L91E1HD$D$$H H r Lݔ E1HD$D$$H<H  L81E1H%HW)HDž fDžTƅx)HH9ɇW)HDž@$+H()Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHHZH5 H̀"L HxH "H0H0HPHHH5Dc H"H58W H H6H5] KHLHHPH9t_HtHP(t H)LMtSHL9t8HX@t Hx)HI9uHHP uLLj)t H U)H`[HHH;EuLH([A^A_])ILMu|IuS]IIIHPH9tHtHP(uHP t H(LMtHHL9tgfffff.HX@t Hxk(HI9uHLP(t H ;(H`ALo% LfUHAWAVSH(IHHHEH`HNH5 H E1H肦D$D$$Hκ H L41E1HD$D$$H H L41E1HHW)HDž fDžTƅc%HH$ŇW)HDž@'H(&)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH>H "H0H0HPHHH5 H(H5>S H HfHX@t Hx%HI9uHHP uLLj%t H U%H`[HHH;EuLH([A^A_]%IugqILMunIIIHPH9tHtHP(uHP t H$LMtHHL9tgfffff.HX@t Hxk$HI9uHLP$t H ;$H`ALo! LfUHAWAVSH(IHHHEH`HJH5 H AHD$D$$H˶ H L01E1HD$D$$Hӥ H L01E1H HW)HDž fDžTƅ`!HH!W)HDž@ #H(")Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH;H "H0H0HPHHH5 H%H5;O H H9H5` HLHHPH9tbHtHP(t H!LMtVHL9t;fDHX@t Hx!HI9uHHP uLLj!t H U!H`[HHH;EuLH([A^A_]!IugqILMunIIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hxk HI9uHLP t H ; H`ALo LfUHAWAVSH(IHHHEH`HFH5 HF AHD$D$$H0H : L,1E1H薿D$D$$H0H v L,1E1H HW)HDž fDžTƅ`HH!W)HDž@ H()Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH;H "H0H0HPHHH5 H%H5;K HH9H5` /HLHHPH9tbHtHP(t HLMtVHL9t;fDHX@t HxHI9uHHP uLLjt H UH`[HHH;EuLH([A^A_]IugqILMunIIIHPH9tHtHP(uHP t HLMtHHL9tgfffff.HX@t HxkHI9uHLPt H ;H`ALo LfUHAWAVSHHIHHHEH`HBH5 HF AHW)HDž ƅDžDCRH5 H LHD$D$$H,H Lr(1E1HND$D$$H^,H ֞ L9(1E1HſHW)HDžfDžTƅHHٸW)HDž@H()Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH T{"H0H0HPHH̎H5{ HݏH5F H讒 HH5 H͐LH貗HPH9tjHtHP(t HLMt^HL9tCfffff.HX@t HxKHI9uHHP uLLt Ht H H`H}HH;EuLHH[A^A_]IILMIIIIHPH9tHtHP(uHP t HLMtCHL9twfHX@t HxHI9uHLt Ht H H`謅L Lffff.UHAWAVSH(IH|HHEH`H=D$D$$HX(H ' L;$1E1HD$D$$H H  LN> E1H۶D$D$$H'H S L#1E1HRHW)HDž fDžTƅHHfW)HDž@QH()Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH耼H5 = Hl"L HH ?w"H0H0HPHH7H5 HHH5^B HH\H5 H8LHHPH9teHtHP(t HLMtYHL9t>fHX@t HxHI9uHHP uLLt H uH`{HyHH;EuLH([A^A_]ILMu|IuS]IIIHPH9tHtHP(uHP t HLMtHHL9tgfffff.HX@t HxHI9uHLpt H [H`aL LfUHAWAVSHIHwHHEH`H9W)ƅHDž HnearestHH5Q H LH1Dž @H5{h HN L H%HDžH5\h H. LHXW)ƅHDžHhalf_pixHfDželƅH5"g Hɷ LHyW)ƅ$HDžHefer_floHHround_prHfDžorƅH5_g H LH DžH5rf H LHD$D$$Hs"H L8 1E1H4D$D$$Hd H a L;A E1HD$D$$He H  L#E1H輰D$D$$Hc H b L"AH}D$D$$H!H ĝ L7 1E1HHW)HDžƅfDžT1ƅ@HHW)pHDž@H(9)pHr types.HH/Hl tensorHH(HY' to alHH Houtput 'HHH'X' and HHHn input HHHConstraiH@7HHHpHH5w? He"L H9H Zq"H0H0HPHH҃H5b HH5; H贇 HH5 @HӅLH踌HPH9t`HtHP(pt HLMtTHL9t9@HX@t Hx[HI9uHHP puLL*uTu`ult H H`|HrHH;EuRLHĘ[A^A_]H tH tH u ILMI!xIxI IIIpuKUIIHPH9tHtHP(puHP pt H LMtGHL9fHX@t Hxk HI9uHLP u=u\uht H H`&zLT  H tLItH tH uUHAWAVSH(IH6pHHEH`H2D$D$$HH Z Lk1E1HGD$D$$HWH Ϙ L21E1H辯HW)HDž fDžTƅHHҨW)HDž@ H( )Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH %!H0H H8H0HPHH~H5G HH56 H虂 HzH5 ]H踀LH蝇HPH9teHtHP(t Ho LMtYHL9t>fHX@t Hx; HI9uHHP uLL t H H`vHmHH;EuLH([A^A_]# IugqILMunIIIHPH9tHtHP(uHP t HDLMtHHL9tgfffff.HX@t Hx HI9uHLt H H`uL LfUHAWAVSH(IHFlHHEH`H .D$D$$HH j L{1E1HWD$D$$HgH ߔ LB1E1HΫHW)HDž fDžTƅ!HH⤇W)HDž@H()Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH 5!H0H 'H8H0HPHHzH5C H{H52 H~HvH5} jH|LH譃HPH9teHtHP(t HLMtYHL9t>fHX@t HxKHI9uHHP uLLt H H` sHiHH;EuLH([A^A_]3IugqILMunIIIHPH9tHtHP(uHP t HTLMtHHL9tgfffff.HX@t HxHI9uHLt H H`qL LfUHAVSHIHXhHHEH`H2*D$D$$HH L* 1E1HiD$D$$HwH rLp3 1E1HH5* HY"L HH5:3 HY"L HH e"H0H0HPHHwH5B HxH5/ Hw{ HsH5y HyLH{HPH9t HtHP(HP H``pHfHH;EuLH[A^]I"IHPH9uHP HtHP(H` pL7 ffffff.UHAWAVSH(IHffHHEH`H@(D$D$$HH L1E1HwD$D$$HH LX1E1HHW)HDž fDžTƅAHHW)HDž H(U:)Hr types.HHH tensor HHHn to allHHHConstraiH@HHHHFH c"H0H0HPHHuH5@ H0vH5F- Hy HDqH5kw H wLH~HPH9t]HtHP(t HLMtQHL9t6HX@t HxHI9uHHP uLLzt H eH`kmHcHH;EuLH([A^A_]IugqILMunIIIHPH9tHtHP(uHP t HLMtHHL9tgfffff.HX@t Hx{HI9uHL`t H KH`QlL LfUHAWAVSH(IHbHHEH`H$HDž(H5; H L(HÂD$D$$Hϐ H ؠ L 1E1H蚝D$D$$H H L{E1H^D$D$$HnH y LI 1E1HաHW)HDž fDžTƅ(HH隇W)HDž@H(|)Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHH H _"H0H0HPHHpH5j8 HqH5 ) Ht HmH5/s MHrLHyHPH9taHtHP(t HLMtUHL9t:DHX@t HxkHI9uHHP uLL:t H %H`+iH_HH;EuLH([A^A_]SIugqILMunIIIHPH9tHtHP(uHP t HtLMtHHL9tgfffff.HX@t Hx;HI9uHL t H H`hL? LfUHAWAVSHHIHv^HHEH`HP W)ƅHDž HconstantHƅH5 H LHډD$D$$Hf H LU1E1H1D$D$$HK H g L E1HD$D$$H H LAH趘D$D$$H H L1E1H-HW)HDžfDžTƅ谝HHAW)HDž@,H(`)Hric typeHH(Hnly numeHH Hput to oHHH and outHHHns inputHHHConstraiHf@0s.@2HHHHcH ["H0H0HPHHXHH;EuLHİ[A^]HH`aH UHAVSHIHWHHEH`HH5i Hi AHqD$D$$HH Ri LH 1E1HؒD$D$$HH 2i L" 1E1HOH5 HI"L H荪H5" H/J"L< HkH W"H0H0HPHHgH5c" HhH5+ HjH)cH5Pi _ HiLHoHPH9t HtHP(HP H`_HXVHH;EuLH[A^]I"IHPH9uHP HtHP(H`x_L fffff.UHAVSHIHUHHEH`HH5 H2 E1HoD$D$$Hi H i L1AHȐD$D$$Hi H i L1E1H?H5HH"LƂ H}H5d$ HNfH5d HiHbaH5g x H>gLH#nH`'^HTHH;EuLHİ[A^]QHH`]H" f.UHAWAVSH(IHVTHHEH`H0H5 H AH/nD$D$$Hkh H kh Lj1AHCD$D$$HXh H _h L.1E1H躓HW)HDž fDžTƅ HHΌW)HDž0H()Hnsor typHH Ho any teHHH types tHHHn outputHHHConstraiHf@(e.@*HHHHH _S"H0H0HPHHbH5! HcH5 HfH^H5#e HdLHkHPH9teHtHP(t HLMtYHL9t>fHX@t Hx[HI9uHHP uLL*t H H`QHH;EuLH([A^A_]CIugqILMunIIIHPH9tHtHP(uHP t HdLMtHHL9tgfffff.HX@t Hx+HI9uHLt H H`ZL/ LfUHAVSHIHhPHHEH`HBD$D$$HH d L1E1HyD$D$$H^ H } LdAH:D$D$$H} H ~d L%1AH讏H5H(C"Lx HH5B H} E1HniH5y] H} E1HPiH5r[ H`H5 HRcH[H5a HqaLHVhH`ZXHNHH;EuLHİ[A^]HH`'XHU ffff.UHAVSHIHNHHEH`HbH5| H| AHahW)@ƅ@HDžPHconstantHAƅIH5 H} L@HyDž<H50 H~ L<HsD$D$$H+| H m L1E1HD$D$$HH Ē L1E1HmH5H@"Lo H諠H5, H|^H5 HMaHYH5_  Hl_LHQf@t HP:H`@VHLHH;EuLH[A^]jH@uHH@t HPH`UH ff.UHAVSHIHHLHHEH`H"H58 H{| E1H$fH5,w H5w E1HfD$D$$HRz H ^ LA1E1HD$D$$H^ H ^ L1E1H蔋H5H>?"L%n HҞH5)7 H\H5 Ht_HWH5] 9 H]LHxdH`|THKHH;EuLHİ[A^]HH`ITHw ffffff.UHAVSHIHJHHEH`H D$D$$HH i L1E1H蹅D$D$$Hz H z LE1H}D$D$$H̒ H z LhE1HAD$D$$HQH vz L,1E1H踉H5Hz="Lr HH5B Hp="LZz HԜH uJ"H0H0HPHHmYH5] H~ZH5 HO]HUH5[ p Hn[LHSbHPH9t HtHP(HP H`8RHHHH;EuLH[A^]bIHPH9uHP Ht HP(IH`QL UHAVSHIHHHHHEH`H" HXH59y H>y AHbH5py Hvy AHaW)@ƅ@HDžPHnearestHAH5 Huy L@HdsD$D$$HH py L1E1H軂D$D$$HH My L1E1H2H5H;"LIy HpH5k HAXH5W H[HUSH5|Y H1YLH`@t HPH`PHFHH;EuLH[A^]/HH@t HPH`OH fffff.UHAWAVSHHIHFHHEH`HH5 6 H|x AH_W)ƅHDž HnearestHH5 Hx LH`qD$D$$HH k L1E1H跀D$D$$HH m L1E1H.HW)HDžfDžTƅHHB~W)HDž@-H()Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH\H E"H0H0HPHH5TH5h HFUH5\ HXHZPH5V H6VLH]HPH9tcHtHP(t HLMtWHL9t<HX@t HxHI9uHHP uLLt Hut H `H`fLHBHH;EuLHH[A^A_]IILMIIIIHPH9tHtHP(uHP t HLMtCHL9twfHX@t Hx[HI9uHL@t H+t H H`KLJ Lffff.UHAWAVSHHIHvAHHEH`HPW)ƅHDž HnearestHH5 HPt LHlD$D$$HwH f L\1E1H8|D$D$$H0 H =s LHE1H{D$D$$H H Ci L1E1HsHW)HDžfDžTƅHHyW)HDž@rH(Z)Hr types.HH/Hl tensorHH(HY' to alHH Houtput 'HHH'X' and HHHn input HHHConstraiH@7HHHH衁H A"H0H0HPHHzOH5;d HPH5 H\S HKH5Q H{QLH`XHPH9thHtHP(t H2LMt\HL9tAfff.HX@t HxHI9uHHP uLLt Ht H H`GH/>HH;EuLHH[A^A_]Iut~ILMu{IIIHPH9tHtHP(uHP t HLMtFHL9tzfff.HX@t HxHI9uHLt H{t H fH`lFL Lffff.UHAWAVSHHIH<HHEH`HW)ƅHDž HnearestHH5Q~ Ho LH1hD$D$$HH a L1E1HwD$D$$H, H o LE1HLwD$D$$HZH d L71E1H{HW)HDžfDžTƅHHtW)HDž@H()Hr types.HH/Hl tensorHH(HY' to alHH Houtput 'HHH'X' and HHHn input HHHConstraiH@7HHHH|H R="H0H0HPHHJH5) HKH5 HN HFH5M  HLLHSHPH9thHtHP(t HLMt\HL9tAfff.HX@t HxKHI9uHHP uLLt Ht H H`BH9HH;EuLHH[A^A_]Iut~ILMu{IIIHPH9tHtHP(uHP t H2LMtFHL9tzfff.HX@t HxHI9uHLt Ht H H`AL Lffff.UHAWAVSH(IH8HHEH`HD$D$$H\f H Zf LK1E1H'sH5f Hl E1HQH5= Hvl AHQH5ff Hl AHwQD$D$$HH f L1E1H>wHW)HDž fDžTƅHHRpW)HDž@=H()Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHlxH M9"H0H0HPHHEFH5: HVGH5l H'JHjBH5H HFHLH+OHPH9tcHtHP(t HLMtWHL9t<HX@t HxHI9uHHP uLLt H H`>H5HH;EuLH([A^A_]IugqILMunIIIHPH9tHtHP(uHP t HLMtHHL9tgfffff.HX@t HxHI9uHLt H kH`q=L LfUHAWAVSH(IH3HHEH`HD$D$$Hb H b L 1E1HnD$D$$H9 H b L7 E1HnD$D$$Hb H H L7 E1HonD$D$$H"b H b Lb7 AH0nD$D$$HYI H g L#7 AHmD$D$$HH a L1E1HhrHW)HDž fDžTƅHH|kW)HDž@gH(/)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHsH55 H%"L` H贄H 4"H0H0HPHHMAH55 H^BH5t H/E Hr=H5C HNCLH3JHPH9tkHtHP(t HLMt_HL9tDffffff.HX@t HxHI9uHHP uLLt H H`9H0HH;EuLH([A^A_]ILMutIuKUIIHPH9tHtHP(uHP t HLMt@HL9tKfDHX@t HxHI9uHLtH {LIH`w8L ffff.UHAWAVSH(IH.HHEH`HHDž(H5{w H6d L(HND$D$$H\ H l L1E1HiD$D$$Hl H 2d L2 E1H~iD$D$$HE H HG LiE1HBiD$D$$HRH BG L-1E1HmHW)HDž fDžTƅ HHfW)HDž@H(P)Hsor typeHH(H any tenHH Han be ofHHH types cHHHd outputHHHInput anHf@0.HHHHnH5;1 H "L.\ HH 0"H0H0HPHH H>LHEHPH9thHtHP(t HbLMt\HL9tAfff.HX@t Hx+HI9uHHP uLLt H H`4Ht+HH;EuLH([A^A_]ILMu|IuS]IIIHPH9tHtHP(uHP t H4LMtHHL9tgfffff.HX@t HxHI9uHLt H H`3L LfUHAWAVSH(IH6*HHEH`HH55 HG AHDD$D$$HgH G LJ1E1H&eD$D$$H6H H L1E1HiHW)HDž fDžTƅHHbW)HDž@H(d)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHjH ,-"H0H0HPHH8H5S1 H9H5 H<H4H5: H:LHAHPH9tbHtHP(t H\LMtVHL9t;fDHX@t Hx+HI9uHHP uLLt H H`0Ht'HH;EuLH([A^A_]IugqILMunIIIHPH9tHtHP(uHP t H4LMtHHL9tgfffff.HX@t HxHI9uHLt H H`/L LfUHAWAVSH(IH6&HHEH`HHDž(H5n HC\ L(HCFD$D$$HOT H Xd L>1E1HaD$D$$H=d H >\ L * E1H`D$D$$HH J@ L1E1HUeHW)HDž fDžTƅ訾HHi^W)HDž@TH(j)Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHHfH5( H"LS HwH I)"H0H0HPHHA4H5 HR5H5h H#8Hf0H56 o HB6LH'=HPH9t_HtHP(t HLMtSHL9t8HX@t Hx˾HI9uHHP uLL蚾t H 腾H`,H#HH;EuLH([A^A_]賾ILMu|IuS]IIIHPH9tHtHP(uHP t HԽLMtHHL9tgfffff.HX@t Hx蛽HI9uHL耽t H kH`q+L蟺 LfUHAWAVSH(IH!HHEH`HH5P HX E1H;D$D$$HO H !> L1E1H\D$D$$H> H > L1E1H@aHW)HDž fDžTƅ蓺HHTZW)HDž@?H()Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHnbH %"H0H0HPHHG0H5'2 HX1H5n H)4Hl,H52 HH2LH-9HPH9teHtHP(t HLMtYHL9t>fHX@t Hx˺HI9uHHP uLL蚺t H 腺H`(HHH;EuLH([A^A_]賺IugqILMunIIIHPH9tHtHP(uHP t HԹLMtHHL9tgfffff.HX@t Hx蛹HI9uHL耹t H kH`q'L蟶 LfUHAWAVSH(IHHHEH`H߀H5L H HU-H5k H&0Hi(H5. HE.LH*5HPH9tbHtHP(t HLMtVHL9t;fDHX@t Hx˶HI9uHHP uLL蚶t H 腶H`$HHH;EuLH([A^A_]賶IugqILMunIIIHPH9tHtHP(uHP t HԵLMtHHL9tgfffff.HX@t Hx蛵HI9uHL耵t H kH`q#L蟲 LfUHAWAVAUATSHIHHHEHPHۀHDžH5wb HsQ LH9D$D$$HX H Q L&1E1HTD$D$$HS H S L E1HzTD$D$$H H (T L E1H>TD$D$$HNH U L 1E1HXHW)HDžƅfDžT1ƅ1YHHQL(W)HDž0観H()Ic types.L`I numericLhI to onlyLpIns inputLxHConstraiH@'HHHHYHW)HDžƅfDžT2ƅMXHHPW)HDž0ɲH(!)L`LhLpLxIConstraiL0@'HHHHtH)LMuLL uIuULMtrHL9tW@HX@t Hx˯HI9uH2H讯tH虯LMuLL|t HgHPmHHH;EuLH[A\A]A^A_]葯I0ILHM,I.LILMQIILMI`IBTIIIH@H9tHtHP(0uHP 0t H@#LHMt?HPL9t$HX@t HxHI9uHHLLPح`uu#LMu/lHp謭tH藭LMt?HL9t$HX@t HxlHI9uHLLLuu#LMu/lH tH LMt?HL9t^DHX@t Hx۬HI9uHLt H諬HPLߩ LfUHAWAVSHIHHHEH`HҀH5Y H N E1H*D$D$$HJH GO L-1E1H LD$D$$HN H N L@E1HKD$D$$HݼH N L1E1HDPHW)@HDžPfDž@TƅB藩H(HXIW)HDž @CH ( )Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5H@H(HHrQH5H"L+N HbH5T Ha H5w H2# HuH5! HQ!LH6(t H L(MtHH0L9t-fHX@t HxHI9uH(LL0˩@t HP趩H`HEHH;EuLH[A^A_]IL(Mu9sI@upzIuIt H HL(MtfHX@t HxHI9uHHP uLLt H եH`Hd HH;EuLH([A^A_]IuuILMu|IuS]IIIHPH9tHtHP(uHP t HLMt:HL9tYHX@t HxHI9uHLФt H 軤H`L LfUHAVSHIH( HHEH`HˀH5; H:H AH#W)ƅHDž HconstantHƅH5J H8 LHk4Dž H54 HbI L H_.D$D$$H6 H K L1E1HCD$D$$HH dM L1E1H HH5mHW!L* HK[H l"H0H0HPHHH5@ HH5 HH H50 HLH HPH9tKHtHP(t H 蜢H`H+HH;Eu LH[A^]HP u転IuISINIu6@IHPH9uHP u HtHP(t H H`L. fDUHAWAVSH(IHfHHEH`H@ȀD$D$$H4 H D L1E1HwAD$D$$HD H / LXE1H;AD$D$$HKH  L&1E1HEHW)HDž fDžTƅHH>W)HDž@豠H(YK)Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHHFH "H0H0HPHHH5G HH5 H HH5  HLHHPH9t^HtHP(t HxLMtRHL9t7fHX@t HxKHI9uHHP uLLt H H` HHH;EuLH([A^A_]3IugqILMunIIIHPH9tHtHP(uHP t HTLMtHHL9tgfffff.HX@t HxHI9uHLt H H` L LfUH]f.UH]鴝fDUH謝H !H]fDUHH!H]UH]f.UH]dfDUHAVSH HHHEHW)EHEEfEtoEHu11HEt H}HHPHtuH1HPHteH1HPH(tDH@H(u H@ @u3u.H@ @t$H1HP(IH1HPHLHHH;Eu H [A^]ŜHEt H}SH蓙 ff.UHH H1H9NHD]DUHH!]UHAWAVATSH AIIHHHHEHHxDD0(ěIH5 H[ LLG H}LzHuL4H!IIF 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tHxPuE1MH L;)UM}ff.LLUHIL$HAPHt HyXЅtZMt%ID$H@@H H;tE%_ EdLLIH}LHEID$Lh@;ID$H@pHpHL9t I;t$tLLLHHH L;)IH*DLLuIID$HxPtH= ZID$HxXPPtZMt%ID$H@@H H;tE%_ EdLL%IH}LHEID$Lh@;ID$H@pHpHL9t I;t$tLLLIMH L;)tMMfffff.UԉсDщUԀ}Let1LH}FHLIHtSMtNH{HGPHtAIwH;FPu7HGXHNXH9tHW`Ht!HH҅tH{IwHhHh LH}HlHEMt$ID$LEHEID$L}LE1Aŋ]ԉ%FtAI|$YB ADA$‹u) !A$tfA$) !A$uLHHtKfff.LHILHIHC@ H{HLMuH([A\A]A^A_]fUH1ɸu]ËHt=r3H=u ] ƒ t tHH0]fH=~ ]i fUH1ɸu]Ëuu6H= ]- ጁt tHH0]H=M ] UHAWAVAUATSHHLMLEHMIIIAt v,H(EMu0H=g  wHEMVHLAօEH]E1ELeLufDtIIDž@HEIHMXM`HEIhADž(APEľLiAA t>IIHHSIH7H]IGAuyGIIPHHIHH]IGAu;ff.ASƉ)сE)΃A7u!ffff.AK)ʃAuLutA(u#@LHA(vIHEE1E11fffff.LHuHUHMÅA(1E'ACu,iE'ACuAT$DAtA"uD DAA(tXAP;EuLIHuIGADž(HED`E'A]A(uDM1A(u5>E'E1A(u"+ffff.A(t DAA(uELeLuH]tMt)LAօ@LLHmMuױ1 UԻ1ɋMEȉML^EHH[A\A]A^A_]fff.UHHH!LLHH$tH=H]H]fUHAWAVAUATSPIAFTt#HL8HXLHH[A\A]A^A_]Mf0LXANTL-LuA~u HIF L8HXLsIUIMLH~HLHHuLZUHAWAVATSHLs0LSTHHu{u H HK H9HqHHH~VCPL{XMt=Mg0L3uLL_LWHL|L 1HhKPL[A\A^A_]uDUHH`]fUHAVSIaH ZHHIHHH~ L[A^][A^]UHAWAVAUATSPIIAWTHHuAu H IO H9HqHHHsAGTIhZHt7DHXIhH`IGhC H{(L Ih#HuI`IMuKff.LXMt3I\$I`LILk0L{PuHLI0H51LMtI~`LIF`IGXH[A\A]A^A_]ÐUHAWAVAUATSHhIIIHpHHEMHULL5IINHHHE+mrIL}H}HuEEI\$0HAL$TLLufA|$uYHUHqoHH;EpILLHh[A\A]A^A_]wAYH:oHH;E'4ID$ LxH]L0LhHHILL?I|$hHuID$hHxHHuLL ELHLHmH}TLLLAƃ}xAh%@h%Bh%Dh%Fh%Hh%Jh%Lh%Nh%Ph%Rh%Th%Vh%Xh%Zh%\h%^h%`h%bh%dh%fh%hh%jh%lh%nh%ph%rh%th%vh%xh%zh%|h%~h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%h%i%i%i%i%i% i% i%i%i%i%i%i%i%i%i%i% i%"i%$i%&i%(i%*i%,i%.i%0i%2i%4i%6i%8i%:i%i%@iLA^AS%1^hh<hhh@hhhhDhrhhxhnhxdhZhPhFh@h04hn*h hRh hhh-hhhhh5hhhh2h_hhvh$lhCbhdXhNhDh:h0hI&hxhhh h-h\hhhhh h)hGhhhh|h%rh[hhu^hThJh@h6h,h"hhhFh&h6hIhbhthhhhhhhhhxhnhdh*Zh8PhIFhYh 4h *h h!h;! hP!hl!h!h!h!h!h!h!h!h"h"h"h0"hE"hT"vhc"lht"bh"Xh"Nh"Dh":h"0h"&h"h"h#h#h##h0#h>#hP#h`#hp#OrtGetApiBaseOrtSessionOptionsAppendExecutionProvider_CPU/Users/runner/work/1/s/onnxruntime/core/session/IOBinding.ccBindInputp_providerexecution_mode is not validgraph_optimization_level is not validResult buffer is not large enoughInputT/Users/runner/work/1/s/onnxruntime/core/session/custom_ops.cccommon::Status onnxruntime::CreateCustomRegistry(const std::vector &, std::shared_ptr &)type_id_counter == 1There must be one (and only one) dynamic typed input to the custom op. Its type info at runtime will be used to infer the type info of this dynamic typed output which is required for the success of the model loading step. More than one dynamic typed inputs are currently not supported as differing types at runtime means the output type cannot be inferred without which model loading cannot proceed.OutputCreateCustomRegistryonnxruntime::CustomOpKernel::CustomOpKernel(const onnxruntime::OpKernelInfo &, const OrtCustomOp &)Unsupported version '' in custom op 'ComputeAsyncNo requested allocator availableEnv is nullOrtMemoryInfo is nullAllocator with this OrtMemoryInfo is already registered.Received invalid value for arena extend strategy. Valid values can be either 0, 1 or -1.intra-opinter-opMemcpyFromHostXinputYoutputConstrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.MemcpyToHosttensor(uint8)tensor(uint16)tensor(uint32)tensor(uint64)tensor(int8)tensor(int16)tensor(int32)tensor(int64)tensor(bfloat16)tensor(float16)tensor(float)tensor(double)tensor(string)tensor(bool)Input expected to have type but instead is null expected to have tensor typeElement type of input unknownOutput expected to have sequence typeMismatch between source and target type. Source=Trying to register schema with name (domain: version: ) from file line , but it is already registered from file , but its domain is not known by the checker., but its version is not in the inclusive range [] (usually, this means you bumped the operator version but forgot to update the version range in DomainToVersionRange in onnx/defs/schema.h).Schema error: com.microsoftcom.microsoft.nchwc/Users/runner/work/1/s/onnxruntime/core/session/inference_session.ccvoid onnxruntime::InferenceSession::ConstructorCommon(const onnxruntime::SessionOptions &, const onnxruntime::Environment &)status.IsOK()Could not finalize session options while constructing the inference session. Error Message: graph_transformation_mgr_.SetSteps(session_options_.max_num_graph_transformation_steps).IsOK()ConstructorCommonCreating and using per session threadpools since use_per_session_threads_ is true-session--intra-op-inter-opFailed to create the inter-op thread pool for the parallel executor, setting ExecutionMode to SEQUENTIALUsing global/env threadpools since use_per_session_threads_ is falsesession_env.EnvCreatedWithGlobalThreadPools()When the session is not configured to use per session threadpools, the env must be created with the the CreateEnvWithGlobalThreadPools API.onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions &, const onnxruntime::Environment &, const std::string &)Given model could not be parsed while creating inference session. Error message: st.IsOK()Could not parse model successfully while constructing the inference sessiononnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions &, const onnxruntime::Environment &, const void *, int)result~InferenceSessionUnknown error during EndProfiling()Received nullptr for exec providerRegisterExecutionProviderExecution providers must be registered before the session is initialized. Execution providers must be registered before the session is initialized.DmlExecutionProviderHaving memory pattern enabled is not supported while using the DML Execution Provider. So disabling it for this session since it uses the DML Execution Provider.Parallel execution mode does not support the DML Execution Provider. So making the execution mode sequential for this session since it uses the DML Execution Provider.CUDAExecutionProviderParallel execution mode does not support the CUDA Execution Provider. So making the execution mode sequential for this session since it uses the CUDA Execution Provider.TensorrtExecutionProviderAddCustomOpDomainsReceived nullptr for custom registry4SaveToOrtFormatLoadThis session already contains a loaded model.Unknown exception in Load()Encountered unknown exception in Load()ORTModelProto corresponding to the model to be loaded has already been parsed. Invoke Load().ModelProto corresponding to the model to be loaded has not been parsed yet. This API should be called in conjunction with a ctor that takes a model abstraction.TransformGraphCould not find an implementation for the node ( (Node placementsAll nodes have been placed on []., Provider: [: [PartitionOrtFormatModelLoadOrtModelThis session has already been initialized.ORT model verification failed.InferenceSession is null. Invalid ORT format model.Serialized version info is null. Invalid ORT format model.The ORT format model version [] is not supported this build Missing Model. Invalid ORT format model.SessionState is null. Invalid ORT format model.The provided PrePackedWeightsContainer instance to be added to the session is nullInitializeInitializing session.Model was not loadedModel was not loaded.Session has already been initialized.Adding default CPU execution provider.This session will use the allocator registered with the environment.Unable to serialize model as it contains compiled nodes. Please disable any execution providers which generate compiled nodes.Serializing optimized model with Graph Optimization level greater than ORT_ENABLE_EXTENDED and the NchwcTransformer enabled. The generated model may contain hardware specific optimizations, and should only be used in the same environment the model was optimized in.Session successfully initialized.Invalid rank for input: Got: Expected: Please fix either the inputs or the model.Got invalid dimensions for input: for the following indices index: Size mismatch: feed_names has elements, but feeds has elements.Invalid Feed Input Name:Input with name: is not expected to be of type tensor.tensorValidateInputs is not expected to be of type sparse tensor.sparse_tensor is not expected to be of type tensor sequence.seqOutput vector pointer is NULLAt least one output should be requested.Output vector incorrectly sized: output_names.size(): p_fetches->size(): Invalid Output Name:RunSession was not initializedSession not initialized.Running with tag: Encountered unknown exception in Run()GetModelMetadataGetModelInputsGetOverridableInitializersGetModelOutputsNewIOBindingEndProfilingProfiler is disabled.Could not write a profile because no model was loaded.cpugpuUnsupported device specified in the memory arena shrink list: Unsupported device id in the memory arena shrink list: Did not find an arena based allocator registered for device-id combination in the memory arena shrink list: The registered allocator for device-id combination is not an arena based allocator: ShrinkMemoryArenasUnable to shrink arena: error message: const logging::Logger &onnxruntime::InferenceSession::CreateLoggerForRun(const onnxruntime::RunOptions &, std::unique_ptr &)run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: void onnxruntime::InferenceSession::InitLogger(logging::LoggingManager *)session_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: The only supported values for the environment variable are '0' and '1'. The environment variable contained the value: FinalizeSessionOptionsReading the provided model for the ORT configonnxruntime::common::Status onnxruntime::FinalizeSessionOptions(const onnxruntime::SessionOptions &, const onnx::ModelProto &, bool, onnxruntime::SessionOptions &)is_model_proto_parsedError during EndProfiling(): Provider has already been registered./Users/runner/work/1/s/onnxruntime/core/framework/execution_providers.hAddORTMException during loading: Exception during initialization: Unexpected input data type. Actual: ()) , expected: ())/Users/runner/work/1/s/onnxruntime/core/framework/feeds_fetches_manager.hFeedsFetchesInfoonnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(const std::vector &, const std::vector &, const onnxruntime::OrtValueNameIdxMap &)Flush-to-zero and denormal-as-zero are onoffwbrbLoad model from failed:Failed to load model because protobuf parsing failed.LoadOrtModelBytes failed. Only / bytes were able to be read._.json%Y-%m-%d_%H-%M-%SThe Model Proto has already been checked for the ORT config json.ort_config/Users/runner/work/1/s/onnxruntime/core/session/inference_session_utils.ccParseOrtConfigJsonInModelProtoFound session/run/environment configuration in the model file to be used while running the modelORT config json from the model: ParseSessionOptionsFromModelProtoDid not find session options in the model file to be used while running the modelsession_optionsintra_op_num_threadsinter_op_num_threadsexecution_modegraph_optimization_levelgraph_optimization_level option in the model file must be an integerenable_profilingIgnoring unsupported session option in ORT config: Json stored in the `ort_config` key cannot be parsed. Error message: SetIntraOpNumThreadsUnsupported value for intra_op_num_threads: Setting intra_op_num_threads to SetInterOpNumThreadsUnsupported value for inter_op_num_threads: Setting inter_op_num_threads to SetExecutionModeUnsupported execution_mode value in ORT config: Setting execution_mode to Sequential modeParallel modeSetGraphOptimizationLevelSetting graph_optimization_level to ORT_DISABLE_ALLSetting graph_optimization_level to ORT_ENABLE_BASICSetting graph_optimization_level to ORT_ENABLE_EXTENDEDSetting graph_optimization_level to ORT_ENABLE_ALLUnsupported graph_optimization_level value in ORT config: SetEnableProfilingUnsupported value for enable_profiling option: Setting enable_profiling to true[json.exception.invalid BOM; must be 0xEF 0xBB 0xBF if givennullinvalid literalinvalid string: missing closing quoteinvalid string: '\u' must be followed by 4 hex digitsinvalid string: surrogate U+DC00..U+DFFF must be followed by U+DC00..U+DFFFinvalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFFinvalid string: forbidden character after backslashinvalid string: control character U+0000 (NUL) must be escaped to \u0000invalid string: control character U+0001 (SOH) must be escaped to \u0001invalid string: control character U+0002 (STX) must be escaped to \u0002invalid string: control character U+0003 (ETX) must be escaped to \u0003invalid string: control character U+0004 (EOT) must be escaped to \u0004invalid string: control character U+0005 (ENQ) must be escaped to \u0005invalid string: control character U+0006 (ACK) must be escaped to \u0006invalid string: control character U+0007 (BEL) must be escaped to \u0007invalid string: control character U+0008 (BS) must be escaped to \u0008 or \binvalid string: control character U+0009 (HT) must be escaped to \u0009 or \tinvalid string: control character U+000A (LF) must be escaped to \u000A or \ninvalid string: control character U+000B (VT) must be escaped to \u000Binvalid string: control character U+000C (FF) must be escaped to \u000C or \finvalid string: control character U+000D (CR) must be escaped to \u000D or \rinvalid string: control character U+000E (SO) must be escaped to \u000Einvalid string: control character U+000F (SI) must be escaped to \u000Finvalid string: control character U+0010 (DLE) must be escaped to \u0010invalid string: control character U+0011 (DC1) must be escaped to \u0011invalid string: control character U+0012 (DC2) must be escaped to \u0012invalid string: control character U+0013 (DC3) must be escaped to \u0013invalid string: control character U+0014 (DC4) must be escaped to \u0014invalid string: control character U+0015 (NAK) must be escaped to \u0015invalid string: control character U+0016 (SYN) must be escaped to \u0016invalid string: control character U+0017 (ETB) must be escaped to \u0017invalid string: control character U+0018 (CAN) must be escaped to \u0018invalid string: control character U+0019 (EM) must be escaped to \u0019invalid string: control character U+001A (SUB) must be escaped to \u001Ainvalid string: control character U+001B (ESC) must be escaped to \u001Binvalid string: control character U+001C (FS) must be escaped to \u001Cinvalid string: control character U+001D (GS) must be escaped to \u001Dinvalid string: control character U+001E (RS) must be escaped to \u001Einvalid string: control character U+001F (US) must be escaped to \u001Finvalid string: ill-formed UTF-8 byteinvalid number; expected digit after '-'invalid number; expected digit after '.'invalid number; expected '+', '-', or digit after exponentinvalid number; expected digit after exponent signnumber overflow parsing 'arrayobjectexcessive object size: cannot use erase() with booleandiscardednumberexcessive array size: parse error at line , column while parsing - ; last read: 'unexpected ; expected true literalfalse literalnull literalstring literalnumber literal'[''{'']''}'':'','end of input'[', '{', or a literalunknown tokenkey '' not foundcannot use at() with map::at: key not foundtype must be number, but is tried creating tensor with negative value in shapesize overflownot enough space: expected , got type is not supported in this functionRegisterCustomOpsLibrary: Failed to load libraryRegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in libraryinput name cannot be emptyCPUExecutionProvideroutput name cannot be emptylengths allocation failedstring buffer allocation failedOutput buffer allocation failedinput array doesn't equal tensor sizeelement index is out of boundsshape is invalidoffsets buffer is not equal to tensor sizeoutput buffer is too smallbuffer size is too small for stringinput array is too shortopaque(,)/Users/runner/work/1/s/onnxruntime/core/session/onnxruntime_c_api.ccOrtStatusPtr OrtApis::CreateOpaqueValue(const char *, const char *, const void *, size_t, OrtValue **)ml_type != nullptrSpecified domain and type names combination does not refer to a registered opaque typenon_tensor_base != nullptrOrtStatusPtr OrtApis::GetOpaqueValue(const char *, const char *, const OrtValue *, void *, size_t)this API does not support stringslocation dimensions do not match shape sizeinvalid location rangeROCM execution provider is not enabled.max_memarena_extend_strategyinitial_chunk_size_bytesmax_dead_bytes_per_chunkinitial_growth_chunk_size_bytesInvalid key found: The given version [%u] is not supported, only version 1 to %u is supported in this build. 1.8.1allocator::allocate(size_t n) 'n' exceeds maximum supported sizeORT_LOAD_CONFIG_FROM_MODEL1/Users/runner/work/1/s/include/onnxruntime/core/framework/ml_value.honnxruntime::Tensor *OrtValue::GetMutable()IsTensor()Trying to get a Tensor, but got: const onnxruntime::Tensor &OrtValue::Get() constout of index/Users/runner/work/1/s/onnxruntime/core/common/safeint.hstatic void SafeIntExceptionHandler::SafeIntOnOverflow()internal errorindex out of rangeconst onnxruntime::TensorSeq &OrtValue::Get() constIsTensorSequence()Trying to get a TensorSeq, but got: Input is not of one of the supported sequence types.Input is not of type sequence or map./Users/runner/work/1/s/include/onnxruntime/core/framework/data_types_internal.hstatic bool onnxruntime::utils::ContainerChecker::IsContainerOfType, float, std::__1::less >, std::__1::allocator, float> > >, std::__1::allocator, float, std::__1::less >, std::__1::allocator, float> > > > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::vector, float, std::__1::less >, std::__1::allocator, float> > >, std::__1::allocator, float, std::__1::less >, std::__1::allocator, float> > > > >]++index < c.size()static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, float, std::__1::less >, std::__1::allocator, float> > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::map, float, std::__1::less >, std::__1::allocator, float> > >]const T &OrtValue::Get() const [T = std::__1::vector, float, std::__1::less >, std::__1::allocator, float> > >, std::__1::allocator, float, std::__1::less >, std::__1::allocator, float> > > > >]onnxruntime::DataTypeImpl::GetType() == type_ != static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, std::__1::allocator > >, std::__1::allocator, std::__1::allocator > > > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::vector, std::__1::allocator > >, std::__1::allocator, std::__1::allocator > > > >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, std::__1::allocator > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::map, std::__1::allocator > >]const T &OrtValue::Get() const [T = std::__1::vector, std::__1::allocator > >, std::__1::allocator, std::__1::allocator > > > >]/Users/runner/work/1/s/onnxruntime/core/framework/TensorSeq.hconst onnxruntime::Tensor &onnxruntime::TensorSeq::Get(size_t) consti < tensors_.size()Input is not of one of the supported map types.static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, std::__1::basic_string, std::__1::less >, std::__1::allocator, std::__1::basic_string > > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::map, std::__1::basic_string, std::__1::less >, std::__1::allocator, std::__1::basic_string > > >]Invalid index requested for map type.const T &OrtValue::Get() const [T = std::__1::map, std::__1::basic_string, std::__1::less >, std::__1::allocator, std::__1::basic_string > > >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, long long, std::__1::less >, std::__1::allocator, long long> > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::map, long long, std::__1::less >, std::__1::allocator, long long> > >]const T &OrtValue::Get() const [T = std::__1::map, long long, std::__1::less >, std::__1::allocator, long long> > >]const T &OrtValue::Get() const [T = std::__1::map, float, std::__1::less >, std::__1::allocator, float> > >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, double, std::__1::less >, std::__1::allocator, double> > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::map, double, std::__1::less >, std::__1::allocator, double> > >]const T &OrtValue::Get() const [T = std::__1::map, double, std::__1::less >, std::__1::allocator, double> > >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, std::__1::less, std::__1::allocator > > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::map, std::__1::less, std::__1::allocator > > >]const T &OrtValue::Get() const [T = std::__1::map, std::__1::less, std::__1::allocator > > >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, std::__1::allocator > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::map, std::__1::allocator > >]const T &OrtValue::Get() const [T = std::__1::map, std::__1::allocator > >]const T &OrtValue::Get() const [T = std::__1::map, std::__1::allocator > >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, std::__1::allocator > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::map, std::__1::allocator > >]const T &OrtValue::Get() const [T = std::__1::map, std::__1::allocator > >]Number of values should be at least 1.For map type num_values MUST be 2Either the key tensor or the value tensor has NumDimensions > 1Key and value tensors have unequal number of elements.Key type is not supported yet.OrtStatus *OrtCreateValueImplMapHelper(const onnxruntime::Tensor &, const onnxruntime::Tensor &, OrtValue **) [KeyType = std::__1::basic_string]value_type != nullptrTensor must always contain primitive types. Found: OrtStatus *OrtCreateMapMLValue(const onnxruntime::Tensor &, const onnxruntime::Tensor &, OrtValue **) [KeyType = std::__1::basic_string, ValueType = std::__1::basic_string]len >= 0 && static_cast(len) < std::numeric_limits::max()OrtStatus *OrtCreateMapMLValue(const onnxruntime::Tensor &, const onnxruntime::Tensor &, OrtValue **) [KeyType = std::__1::basic_string, ValueType = long long]/Users/runner/work/1/s/include/onnxruntime/core/framework/tensor.hconst T *onnxruntime::Tensor::Data() const [T = long long]utils::IsPrimitiveDataType(dtype_)Tensor type mismatch. T !=OrtStatus *OrtCreateMapMLValue(const onnxruntime::Tensor &, const onnxruntime::Tensor &, OrtValue **) [KeyType = std::__1::basic_string, ValueType = float]const T *onnxruntime::Tensor::Data() const [T = float]OrtStatus *OrtCreateMapMLValue(const onnxruntime::Tensor &, const onnxruntime::Tensor &, OrtValue **) [KeyType = std::__1::basic_string, ValueType = double]const T *onnxruntime::Tensor::Data() const [T = double]OrtStatus *OrtCreateValueImplMapHelper(const onnxruntime::Tensor &, const onnxruntime::Tensor &, OrtValue **) [KeyType = long long]OrtStatus *OrtCreateMapMLValue(const onnxruntime::Tensor &, const onnxruntime::Tensor &, OrtValue **) [KeyType = long long, ValueType = std::__1::basic_string]OrtStatus *OrtCreateMapMLValue(const onnxruntime::Tensor &, const onnxruntime::Tensor &, OrtValue **) [KeyType = long long, ValueType = long long]OrtStatus *OrtCreateMapMLValue(const onnxruntime::Tensor &, const onnxruntime::Tensor &, OrtValue **) [KeyType = long long, ValueType = float]OrtStatus *OrtCreateMapMLValue(const onnxruntime::Tensor &, const onnxruntime::Tensor &, OrtValue **) [KeyType = long long, ValueType = double]Each element of the sequence should be either tensor or map.At least one element in the sequence is of a type different from others.Unsupported input typeOrtStatus *OrtCreateValueImplSeqHelper(const OrtValue *const *, size_t, OrtValue **)in[idx]->IsTensor()Expecting all elements to be tensors. Got: Sequences must have tensors of the same data type. There was at least one tensor in the input that was different.Encountered nullptr.const T *onnxruntime::Tensor::Data() const [T = bool]T *onnxruntime::Tensor::MutableData() [T = bool]T *onnxruntime::Tensor::MutableData() [T = float]T *onnxruntime::Tensor::MutableData() [T = double]const T *onnxruntime::Tensor::Data() const [T = onnxruntime::MLFloat16]T *onnxruntime::Tensor::MutableData() [T = onnxruntime::MLFloat16]const T *onnxruntime::Tensor::Data() const [T = onnxruntime::BFloat16]T *onnxruntime::Tensor::MutableData() [T = onnxruntime::BFloat16]const T *onnxruntime::Tensor::Data() const [T = signed char]T *onnxruntime::Tensor::MutableData() [T = signed char]const T *onnxruntime::Tensor::Data() const [T = unsigned char]T *onnxruntime::Tensor::MutableData() [T = unsigned char]const T *onnxruntime::Tensor::Data() const [T = short]T *onnxruntime::Tensor::MutableData() [T = short]const T *onnxruntime::Tensor::Data() const [T = unsigned short]T *onnxruntime::Tensor::MutableData() [T = unsigned short]const T *onnxruntime::Tensor::Data() const [T = int]T *onnxruntime::Tensor::MutableData() [T = int]const T *onnxruntime::Tensor::Data() const [T = unsigned int]T *onnxruntime::Tensor::MutableData() [T = unsigned int]T *onnxruntime::Tensor::MutableData() [T = long long]const T *onnxruntime::Tensor::Data() const [T = unsigned long long]T *onnxruntime::Tensor::MutableData() [T = unsigned long long]void onnxruntime::TensorSeq::SetType(onnxruntime::MLDataType)elem_type_ != nullptr was false. Stacktrace: :/\T *onnxruntime::Tensor::MutableData() [T = std::__1::basic_string]const T *onnxruntime::Tensor::Data() const [T = std::__1::basic_string]/Users/runner/work/1/s/onnxruntime/core/session/ort_env.ccstatic void OrtEnv::Release(OrtEnv *)env_ptr == p_instance_/Users/runner/work/1/s/onnxruntime/core/optimizer/attention_fusion_helper.hbool onnxruntime::AttentionFusionHelper::CheckSliceParameters(const onnxruntime::Graph &, const onnxruntime::Node &, const std::vector &, const std::vector &, const logging::Logger &)input_indices.size() == expected_values.size() && input_indices.size() > 0CheckSliceParametersSlice does not have enough number of inputsSlice ends is less than INT_MAXSlice parameter is not expected. Input index:Expected value:MatchGemmSubgraphStart MatchGemmSubgraphFaild to match gemm pathInput of reshape_before_gemm is not the input of subgraphOutput edge count not expected for nodes in gemm pathGemm does not have 3 inputsGemm bias is not constantGemm bias shape not expectedCheckSliceParameters return falseconcat first input value is not -1Faild to match concat node for Gather pathsconcat_after_gather does not have expected number of inputs or output edgesconcat_after_gather input 2 does not have expected valueFaild to match gemm gather pathOutput edge count not expected for nodes in gemm gather pathunsqueeze_after_gather axes value not expectedgather axis value not expectedgather input 1 value is not expectedPass MatchGemmSubgraphValidateGemmInitializerStart ValidateGemmInitializerGemm bias is not constant initializerGemm bias shape is not expectedGemm weight is not constant initializerGemm weight shape is not expectedPass ValidateGemmInitializerValidateUnidirMaskunidir mask is not constantunidir mask shape not expectedThis optimizer does not support external data for unidirectional mask right nowMask is neither unidirectional nor all onesExpect mask data type is uint8 or floatMatchUnidirMaskSubgraphStart MatchUnidirMaskSubgraphFaild to match the path (Div-->Where-->Add) for unidirectional maskFaild to match path 1 for unidirectional maskOutput edge count not expected for nodes in path 1 of unidirectional maskDiv and Shape1 does not have edgeCheckSliceParameters returns false for last_sliceCheckSliceParameters returns false for mask_sliceValidateUnidirMask returns false for mask_sliceCheckSliceParameters returns false for slice1Faild to match path 2 for unidirectional maskOutput edge count not expected for unsqueeze2 of unidirectional maskFaild to match path 3 for unidirectional maskOutput edge count not expected for unsqueeze3 of unidirectional maskFaild to match path 4 for unidirectional maskDiv and Shape does not have edgeOutput edge count not expected for squeeze_2/slices2/shape2 of unidirectional maskCheckSliceParameters return false for slice2Pass MatchUnidirMaskSubgraphMatchInputMaskSubgraphStart MatchInputMaskSubgraphFailed to find Softmax nodeOutput edge count not expected for SoftmaxFailed to find path for maskOutput edge count not expected for mask nodesSoftmax attribute axis is expected to be 3mask_unsqueeze_1 axes not matched. Expect: 1mask_unsqueeze_2 axes not matched. Expect: 2mask_sub const input not matchedmask_mul const input not matchedPass MatchInputMaskSubgraphStart MatchInputMaskSubgraphDistilBertFailed to find mask pathwhere const not matched.Failed to find shape pathequal const not matched.Failed to find reshape shape path 1Failed to find reshape shape path 2gather indices not matched.Pass MatchInputMaskSubgraphDistilBertMatchPastSubgraphStart MatchPastSubgraphFailed to find path for past_kFailed to find path for present_kFailed to find path for present_v and past_vFailed to match v_concatpast_k_transpose perm attribute not matchedpresent_k_transpose perm attribute not matchedpresent_k_unsqueeze axes value not expectedpresent_v_unsqueeze axes value not expectedpast_v_gather indices != 1past_k_gather indices != 0past_v_gather and past_k_gather does not have same past inputOutput edge count not expected for nodes in past subgraphPass MatchPastSubgraphCheckDistilBertReshapeShapeCheckNodesInPathVStart CheckNodesInPathVOutput edge count not expected for nodes in path vFailed in match Transpose attribute perm. Expected: 0, 2, 1, 3Failed in match v_transpose attribute perm. Expected: 0, 2, 1, 3hidden_size != num_heads * head_sizev_reshape initializer value is not expectedPass CheckNodesInPathVreshape initializer value is not expectedCheckNodesInPathQStart CheckNodesInPathQq_reshape const not matchedqk_div const not matched.q_transpose perm attribute not matchedPass CheckNodesInPathQCheckNodesInPathKStart CheckNodesInPathKk_transpose perm attribute not matchedk_reshape const not matchedPass CheckNodesInPathKFuseGptAttentionStart FuseGptAttentionFaild to find path to qkv_matmulFaild to find path v to SplitCheckNodesInPathV return falseMatchInputMaskSubgraph returns falseMatchUnidirMaskSubgraph returns NULLFailed to find path for qq and v are not from same Split nodeCheckNodesInPathQ returns falseFailed to find path for kk and v are not from same Split nodeCheckNodesInPathK returns falseMatchPastSubgraph returns falseFused an attention node for GPT./Users/runner/work/1/s/onnxruntime/core/optimizer/attention_fusion.ccshape of layer norm bias tensor not expectedTotal fused Attention node count: FuseSubGraphFaild to find path vOutput edge count not expected for Add or MatMul in path vFailed in match v_matmul and v_add input shapeFailed in match input mask subgraphFuseSubGraphQKFused an attention node.FuseSubGraphQKImplq root should be layer normalizationq_matmul and q_add shape not matchedk root is not layer normk_matmul and k_add shape not matchedFailed to load Q, K and V weights, or data type is not float or float16.Failed to load Q, K and V bias tensors, or data type is not float or float16.Failed to convert mask to int32ConvertMaskToInt32Mask shape is unknown or not 2D, or data type unknownMask data type is not int32 or int64 or float32qkv_weightsqkv_biasFuseSubGraphQKDistilBert/Users/runner/work/1/s/onnxruntime/core/optimizer/bias_dropout_fusion.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/bias_gelu_fusion.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/bias_softmax_fusion.ccfused into softmax(input + bias)/Users/runner/work/1/s/onnxruntime/core/optimizer/common_subexpression_elimination.ccvirtual onnxruntime::common::Status onnxruntime::CommonSubexpressionElimination::ApplyImpl(onnxruntime::Graph &, bool &, int, const logging::Logger &) constrepresentative.output_index != kInvalidOutputIndexNot eliminating output of node ] because it's the graph's output./Users/runner/work/1/s/onnxruntime/core/optimizer/constant_folding.ccCould not find a CPU kernel and hence can't constant fold node 'virtual onnxruntime::common::Status onnxruntime::ConstantFolding::ApplyImpl(onnxruntime::Graph &, bool &, int, const logging::Logger &) constfetches.size() == node->OutputDefs().size()Unsupported output type of . Can't constant fold /Users/runner/work/1/s/onnxruntime/core/optimizer/conv_activation_fusion.ccfused Conv with activation auto onnxruntime::(anonymous namespace)::GetClipConstantMinMax(const onnxruntime::Graph &, const onnxruntime::Node &, float &, float &)::(anonymous class)::operator()(const onnxruntime::Node &, size_t, float &) constUnexpected data type for Clip input of /Users/runner/work/1/s/onnxruntime/core/optimizer/conv_add_fusion.ccvirtual onnxruntime::common::Status onnxruntime::ConvAddFusion::Apply(onnxruntime::Graph &, onnxruntime::Node &, onnxruntime::RewriteRule::RewriteRuleEffect &, const logging::Logger &) constadd_B_tensor_protoConvAddFusion_B_ConvAddFusion_Add_B_/Users/runner/work/1/s/onnxruntime/core/optimizer/conv_bn_fusion.ccvirtual onnxruntime::common::Status onnxruntime::ConvBNFusion::Apply(onnxruntime::Graph &, onnxruntime::Node &, onnxruntime::RewriteRule::RewriteRuleEffect &, const logging::Logger &) constbn_scale_tensor_protobn_B_tensor_protobn_mean_tensor_protobn_var_tensor_protoconv_W_tensor_protoconv_B_tensor_protoConvBnFusion_W_ConvBnFusion_BN_B_/Users/runner/work/1/s/onnxruntime/core/optimizer/initializer.honnxruntime::Initializer::Initializer(const onnx::TensorProto &, const onnxruntime::Path &)size_ == sizeInitializerunsupported data type: ReadExternalRawData() failed: ToProtodata type is not supported/Users/runner/work/1/s/onnxruntime/core/optimizer/conv_mul_fusion.ccvirtual onnxruntime::common::Status onnxruntime::ConvMulFusion::Apply(onnxruntime::Graph &, onnxruntime::Node &, onnxruntime::RewriteRule::RewriteRuleEffect &, const logging::Logger &) constmul_B_tensor_protoConvMulFusion_W_ConvMulFusion_Mul_B_/Users/runner/work/1/s/onnxruntime/core/optimizer/dynamic_quantize_matmul_fusion.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/embed_layer_norm_fusion.ccFuseSubGraphDistilBertWord embedding shape not expected.Failed to match position embedding subgraph.Failed to get position embedding weights.Position embedding shape is not expected.Input id is not valid. Gamma should be of shape (hidden_size). Beta should be of shape (hidden_size). MatchPositionEmbeddingSubgraphsFromGatherOutput edge count not expected for nodes in path1.The first input of Range should be a constant with value 0.The third input of Range should be a constant with value 1.Second input of Gather should be a constant with value 1. Failed to match Shape node. The parent of shape nodes are expected to be input_ids.Optional position subgraph nodes number of outputs unexpected.Optional position subgraph nodes Where node is expected to be the parent of Reshape.Failed to match position subgraph.MatchInputToConcatSubgraphFailed to find path 1 of position shape.Output edge count not expected for nodes in path 1 of position shape.Second input of Gather in path 1 of position shape should be a constant with value 0.Failed to find path 2 of position shape.Output edge count not expected for nodes in path 2 of position shape.Gather node in path 2 is not linked to another subgraph.Second input of Gather in path 2 of position shape should be a constant with value 1.The parent of two shape nodes are expected to be input_ids.two paths share the same shapeCheckInputInput shape is unknown or not 2D, or data type unknownInput data type is not int32 or int64_Int32_CastInput is expected to have dim value in all dimensions.Failed to get initializer tensor.Position embedding shape not matched.Position embedding data type shall be float or float16.Segment id is not valid. Input_ids and segment id should have the same shape. /Users/runner/work/1/s/onnxruntime/core/optimizer/fast_gelu_fusion.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/free_dim_override_transformer.cconnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformer(gsl::span)Invalid free dimension override.Conflicting free dimension overrides.The model has input '' with a fixed dimension size which does not equal the specified override of /Users/runner/work/1/s/onnxruntime/core/optimizer/gelu_approximation.ccTotal Gelu Approximation (FastGelu) node count: /Users/runner/work/1/s/onnxruntime/core/optimizer/gelu_fusion.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/gemm_activation_fusion.ccfused Gemm _transformed/Users/runner/work/1/s/onnxruntime/core/optimizer/graph_transformer.ccApply/Users/runner/work/1/s/onnxruntime/core/optimizer/graph_transformer_mgr.ccApplyTransformersThis transformer is already registered Level_RuleBasedTransformer/Users/runner/work/1/s/onnxruntime/core/optimizer/graph_transformer_utils.ccstd::vector > onnxruntime::optimizer_utils::GenerateRewriteRules(onnxruntime::TransformerLevel, const std::unordered_set &)Unsupported levelstd::vector > onnxruntime::optimizer_utils::GenerateTransformers(onnxruntime::TransformerLevel, const onnxruntime::SessionOptions &, const onnxruntime::IExecutionProvider &, const std::unordered_set &)Unsupported level YieldOp/Users/runner/work/1/s/onnxruntime/core/optimizer/initializer.ccReadExternalRawDataExternal data type must not be UNDEFINED or STRING.model_path must not be empty. Ensure that a path is provided when the model is created or loaded.TensorProto external data size mismatch. Computed size: InsertedCast_/Users/runner/work/1/s/onnxruntime/core/optimizer/insert_cast_transformer.ccvirtual onnxruntime::common::Status onnxruntime::InsertCastTransformer::ApplyImpl(onnxruntime::Graph &, bool &, int, const logging::Logger &) constdtype_attribute->second.has_i()InsertCastTransformer works on the assumption that `dtype` attribute holds an integer./Users/runner/work/1/s/onnxruntime/core/optimizer/layer_norm_fusion.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/matmul_add_fusion.ccfused Matmul and Add /Users/runner/work/1/s/onnxruntime/core/optimizer/matmul_integer_to_float.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/matmul_scale_fusion.cc_FusedMatMulAndScalestd::vector onnxruntime::(anonymous namespace)::GetInputNodeMerges(onnxruntime::Graph &, onnxruntime::Node &, const std::unordered_set &)input_node.InputDefs().size() == 2 && scale_and_index.value().second < 2optional > onnxruntime::(anonymous namespace)::GetScaleFromNode(const onnxruntime::Graph &, const onnxruntime::Node &, const std::unordered_set &)div_inputs.size() == 2mul_inputs.size() == 2optional onnxruntime::(anonymous namespace)::GetScalarConstantInitializer(const onnxruntime::Graph &, const onnxruntime::NodeArg &)Constant initializer NodeArg shape should not be null. NodeArg: GetScalarConstantInitializerstd::vector onnxruntime::(anonymous namespace)::GetOutputNodeMerges(onnxruntime::Graph &, onnxruntime::Node &, const std::unordered_set &)output_node.OutputDefs().size() == 1/Users/runner/work/1/s/onnxruntime/core/optimizer/matmul_transpose_fusion.ccbool onnxruntime::GetTransposePerms(const onnxruntime::Node &, std::vector &)transpose_node.InputDefs().size() == 1onnxruntime::Node *onnxruntime::ReorderCastAndTranspose(onnxruntime::Graph &, onnxruntime::Node *, std::unordered_map &, std::deque &)cast != nullptrCreated a new Transpose node to interchange Cast and Transpose nodessize_t onnxruntime::UpdateConsumerCount(onnxruntime::Graph &, onnxruntime::NodeArg *, std::unordered_map &)!node_consumers.empty()_nchwc_bn_nchwc/Users/runner/work/1/s/onnxruntime/core/optimizer/nchwc_transformer.cc_nhwc/Users/runner/work/1/s/onnxruntime/core/optimizer/nhwc_transformer.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/optimizer_execution_frame.cconnxruntime::OptimizerExecutionFrame::Info::Info(const std::vector &, const onnxruntime::InitializedTensorSet &, const onnxruntime::Path &, const onnxruntime::IExecutionProvider &)allocator_ptr_InfoQLinear855dd0fa-cd7b-4b10-ae5a-df64cabfe1f835b188f7-c464-43e3-8692-97ac832bb14a/Users/runner/work/1/s/onnxruntime/core/optimizer/qdq_transformer/qdq_propagation.ccvoid onnxruntime::SwapAdjacentNodes(onnxruntime::Graph &, onnxruntime::Node &, onnxruntime::Node &)optimizer_utils::CheckOutputEdges(graph, up_node, 1)up_node should have only one Edge that points to down_node and its output is not graph outputedge_it->GetDstArgIndex() == 0 && edge_it->GetSrcArgIndex() == 0 && edge_it->GetNode().Index() == down_node.Index()up_node should be parent of down_node and NodeArg slots of the edge between up_node and down_node should be (0, 0)./Users/runner/work/1/s/onnxruntime/core/optimizer/qdq_transformer/qdq_s8_to_u8.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/qdq_transformer/qdq_transformer.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/relu_clip_fusion.ccvirtual onnxruntime::common::Status onnxruntime::FuseReluClip::Apply(onnxruntime::Graph &, onnxruntime::Node &, onnxruntime::RewriteRule::RewriteRuleEffect &, const logging::Logger &) constUnexpected data type for Clip 'min' input of FuseReluClip__min_zero_constantonnxruntime::Initializer::Initializer(onnx::TensorProto_DataType, const std::string &, const std::vector &)data type is not supported./Users/runner/work/1/s/onnxruntime/core/optimizer/reshape_fusion.ccFused reshape node: Total fused reshape node count: Fuse_SubgraphCannot replace concat node with initializer:/Users/runner/work/1/s/onnxruntime/core/optimizer/rule_based_graph_transformer.ccApplyRulesOnNode/Users/runner/work/1/s/onnxruntime/core/optimizer/skip_layer_norm_fusion.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/transformer_memcpy.ccApplyImplvoid onnxruntime::TransformerMemcpyImpl::ProcessDefs(onnxruntime::Node &, const onnxruntime::KernelRegistryManager &, onnxruntime::InitializedTensorSet &)Execution type '' doesn't support memcpy bool onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager &, const onnxruntime::InitializedTensorSet &)/Users/runner/work/1/s/include/onnxruntime/core/optimizer/graph_transformer.hRecurseauto onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager &, const onnxruntime::InitializedTensorSet &)::(anonymous class)::operator()(const onnxruntime::NodeArg &, size_t) constdup_replacements.find(&arg) == dup_replacements.end()UnsqueezeElimination_/Users/runner/work/1/s/onnxruntime/core/optimizer/unsqueeze_elimination.ccUnsqueezeElimination cannot remove node MIGraphXExecutionProviderROCMExecutionProviderOpenVINOExecutionProviderDnnlExecutionProviderNupharExecutionProviderVitisAIExecutionProviderNnapiExecutionProviderArmNNExecutionProviderACLExecutionProviderRknpuExecutionProviderCeluEluHardSigmoidLeakyReluSoftplusReluSigmoidSoftsignTanhThresholdedReluSeluParametricSoftplusScaledTanhunknown kernel typeonnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Elu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Elu]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::HardSigmoid]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::HardSigmoid]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::LeakyRelu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::LeakyRelu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Relu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Relu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Relu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Relu]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Selu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Selu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Sigmoid]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Sigmoid]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Softplus]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Softsign]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Tanh]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Tanh]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Celu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Celu]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::ThresholdedRelu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::ThresholdedRelu]alphaNo attribute with name:''is defined.Attribute name and type don't match for '/Users/runner/work/1/s/onnxruntime/core/providers/cpu/activation/activations.hInitgamma/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/activations.hBVIf/Users/runner/work/1/s/onnxruntime/core/providers/cpu/controlflow/if.cconnxruntime::If::Info::Info(const onnxruntime::Node &, const onnxruntime::GraphViewer &)num_subgraph_outputs == static_cast(num_outputs)'If' node has outputs which doesn't match the subgraph's outputs.then_branchvoid onnxruntime::If::Init(const onnxruntime::OpKernelInfo &)info.GetAttr("then_branch", &proto).IsOK()else_branchinfo.GetAttr("else_branch", &proto).IsOK()virtual common::Status onnxruntime::If::SetupSubgraphExecutionInfo(const onnxruntime::SessionState &, const std::string &, const onnxruntime::SessionState &)info == nullptrSetupSubgraphExecutionInfo should only be called once for each subgraph.SetupSubgraphExecutionInfovirtual onnxruntime::common::Status onnxruntime::If::Compute(onnxruntime::OpKernelContext *) constthen_feeds_fetches_manager_ && else_feeds_fetches_manager_CreateFeedsFetchesManager must be called prior to execution of graph.session_stateSubgraph SessionState was not found for '' attribute.ComputeFailed to create output tensor for Executeonnxruntime::TensorSeq *OrtValue::GetMutable()Failed to create output tensor for If output ILoop/Users/runner/work/1/s/onnxruntime/core/providers/cpu/controlflow/loop.cconnxruntime::Loop::Info::Info(const onnxruntime::Node &, const onnxruntime::GraphViewer &)static_cast(num_subgraph_inputs) == subgraph_inputs.size()Graph in 'body' attribute of Loop should have inputs. Found:num_subgraph_outputs - 1 == num_outputs'Loop' node has outputs so the subgraph requires but has void onnxruntime::Loop::Init(const onnxruntime::OpKernelInfo &)info.GetAttr("body", &proto).IsOK()virtual common::Status onnxruntime::Loop::SetupSubgraphExecutionInfo(const onnxruntime::SessionState &, const std::string &, const onnxruntime::SessionState &)info_ == nullptrvirtual onnxruntime::common::Status onnxruntime::Loop::Compute(onnxruntime::OpKernelContext *) constfeeds_fetches_manager_'Loop' input 'M' should be a scalar tensor. Got shape of 'Loop' input 'cond' should be a scalar tensor. Got shape of Loop subgraph input 0 has unknown shape: Loop subgraph input 1 has unknown shape: void onnxruntime::LoopImpl::SaveOutputsAndUpdateFeeds(const std::vector &, std::vector &)last_outputs[j + 1].IsTensor()ConcatenateLoopOutputLoop had zero iterations and the shape of subgraph output was not found. Defaulting to a rank 1 shape of {0}.Inconsistent shape in loop output for output. Expected: Got:auto onnxruntime::LoopImpl::Execute(const onnxruntime::FeedsFetchesManager &)::(anonymous class)::operator()(const OrtValue &, int) const/Users/runner/work/1/s/onnxruntime/core/providers/cpu/controlflow/scan_8.ccvoid onnxruntime::Scan<8>::Init(const onnxruntime::OpKernelInfo &) [OpSet = 8]directionsvirtual onnxruntime::common::Status onnxruntime::Scan<8>::SetupSubgraphExecutionInfo(const onnxruntime::SessionState &, const std::string &, const onnxruntime::SessionState &) [OpSet = 8]virtual onnxruntime::common::Status onnxruntime::Scan<8>::Compute(onnxruntime::OpKernelContext *) const [OpSet = 8]Scan inputs have inconsistent batch size. Previous value was but has batch size of sequence_lens length of did not match batch size of Invalid entries in sequence_lens. Max sequence length was onnxruntime::common::Status onnxruntime::Scan8Impl::CreateLoopStateVariables(std::vector > &)p_mlvalueScan<8> spec does not support transpose of output. This should never be called./Users/runner/work/1/s/onnxruntime/core/framework/ort_value_tensor_slicer.htypename std::enable_if::value, reference>::type onnxruntime::OrtValueTensorSlicer::Iterator::operator*() [T = const OrtValue]position_ >= 0 && position_ < sequence_length_/Users/runner/work/1/s/onnxruntime/core/providers/cpu/controlflow/scan_9.ccvoid onnxruntime::Scan<9>::Init(const onnxruntime::OpKernelInfo &) [OpSet = 9]num_scan_inputsinfo.GetAttr("num_scan_inputs", &num_scan_inputs_).IsOK()scan_input_directionsscan_output_directionsscan_input_axesgsl::narrow_cast(input_axes_.size()) == num_scan_inputs_Number of entries in 'scan_input_axes' was but expected scan_output_axesgsl::narrow_cast(output_axes_.size()) == num_scan_outputsNumber of entries in 'scan_output_axes' was virtual onnxruntime::common::Status onnxruntime::Scan<9>::SetupSubgraphExecutionInfo(const onnxruntime::SessionState &, const std::string &, const onnxruntime::SessionState &) [OpSet = 9]virtual onnxruntime::common::Status onnxruntime::Scan<9>::Compute(onnxruntime::OpKernelContext *) const [OpSet = 9]feeds_fetches_manager_ && info_Invalid scan input: Expected dimensions or more but input had shape of Scan inputs have inconsistent sequence lengths. Previous value was but input '' dimension has length of Invalid value in scan_input_axes for input of . Input tensor rank was ValidateInputSetupInputsSubgraph in 'body' produces outputs but Scan expects AllocateOutputTensorsCreateLoopStateVariablesonnxruntime::common::Status onnxruntime::ScanImpl::CreateLoopStateVariables(std::vector &)output_mlvalueOutput OrtValue has not been created for loop state variable output Invalid value in scan_output_axes for output . Output tensor rank was onnxruntime::common::Status onnxruntime::ScanImpl::TransposeOutput()TransposeOutputScanint64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)axis >= -tensor_rank && axis <= tensor_rank - 1axis is not in valid range [-/Users/runner/work/1/s/onnxruntime/core/providers/cpu/controlflow/scan_utils.hconst OrtValue &onnxruntime::scan::detail::OutputIterator::GetOutput() constfinal_output_mlvalue_/Users/runner/work/1/s/onnxruntime/core/providers/cpu/controlflow/scan_utils.cconnxruntime::scan::detail::Info::Info(const onnxruntime::Node &, const onnxruntime::GraphViewer &, int, bool)num_variadic_inputs == num_subgraph_inputsThe subgraph in 'body' requires inputs but Scan was only given void onnxruntime::scan::detail::ReadDirections(const onnxruntime::OpKernelInfo &, const std::string &, std::vector &, size_t)directions.size() == num_entriesNumber of entries in '' was validInvalid values in ''. 0 == forward. 1 == reverse.Subgraph must have the shape set for all outputs but did not.CreateFeedsFetchesManagerIterateSequencevoid onnxruntime::scan::detail::LoopStateVariable::Next()iteration_num_ < sequence_len_Misuse of LoopStateVariable. Attempt to move beyond end of sequenceFailed to create output tensor for output #AllocateFinalBufferonnxruntime::common::Status onnxruntime::scan::detail::OutputIterator::AllocateFinalOutput(const onnxruntime::TensorShape &)!is_concrete_shape_AllocateFinalOutputOrtValue &onnxruntime::scan::detail::OutputIterator::operator*()cur_iteration_ < num_iterations_is_concrete_shape_Expected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.onnxruntime::scan::detail::OutputIterator &onnxruntime::scan::detail::OutputIterator::operator++()Expected AllocateFinalOutput to have been called to before we increment the iteratorMismatch between expected shape and shape from first output is not compatible with /Users/runner/work/1/s/onnxruntime/core/providers/cpu/cpu_execution_provider.ccRegisterOnnxOperatorKernelsRegisterOnnxMLOperatorKernelsRegisterCPUKernelsGetKernelRegistryvirtual std::shared_ptr onnxruntime::CPUExecutionProvider::GetKernelRegistry() constCpuConstantOfShape/Users/runner/work/1/s/onnxruntime/core/providers/cpu/generator/constant_of_shape_base.honnxruntime::ConstantOfShapeBase >::ConstantOfShapeBase(const onnxruntime::OpKernelInfo &) [EnabledOutputTypeList = onnxruntime::TypeList]t_proto_p->dims_size() == 1t_proto_p->dims()[0] == 1void onnxruntime::ConstantOfShapeBase >::SetValueFromTensorProto(const onnx::TensorProto &) [EnabledOutputTypeList = onnxruntime::TypeList]utils::HasDataType(t_proto)ONNX_NAMESPACE::TensorProto::DataType_IsValid(t_proto.data_type())!utils::HasExternalData(t_proto)Tensor proto with external data for value attribute is not supported.SetValueFromTensorProtoUnsupported value attribute datatype: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/generator/constant_of_shape.ccUnsupported output datatype with size: PrepareComputeMust have a valid input shape.RandomNormalRandomUniformRandomNormalLikeRandomUniformLikeMultinomial/Users/runner/work/1/s/onnxruntime/core/providers/cpu/generator/random.ccCould not infer data type from input tensor with data type Empty dimensions for input tensorbatch_size is < 1num_classes is < 1num_samples is < 1Invalid data type of /Users/runner/work/1/s/onnxruntime/core/providers/cpu/generator/random.honnxruntime::RandomNormal::RandomNormal(const onnxruntime::OpKernelInfo &)info.GetAttr("mean", &mean_).IsOK()scaleinfo.GetAttr("scale", &scale_).IsOK()info.GetAttr("dtype", &dtype).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDInvalid dtype of info.GetAttrs("shape", shape).IsOK()onnxruntime::RandomUniform::RandomUniform(const onnxruntime::OpKernelInfo &)info.GetAttr("high", &high_).IsOK()lowinfo.GetAttr("low", &low_).IsOK()onnxruntime::RandomNormalLike::RandomNormalLike(const onnxruntime::OpKernelInfo &)onnxruntime::RandomUniformLike::RandomUniformLike(const onnxruntime::OpKernelInfo &)sample_sizeonnxruntime::Multinomial::Multinomial(const onnxruntime::OpKernelInfo &)info.GetAttr("sample_size", &num_samples_).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDOutput type not supported in this build: MultinomialComputeRangestart in Range operator should be scalar like tensor, yet got shape:limit in Range operator should be scalar like tensor, yet got shape:delta in Range operator should be scalar like tensor, yet got shape:delta in Range operator can not be zero!Clipminmax/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/clip.honnxruntime::clip_internal::Clip_6Base::Clip_6Base(const onnxruntime::OpKernelInfo &) [T = float]min_ <= max_/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/clip.ccvoid onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, onnxruntime::Tensor *) const [T = float]min->Shape().IsScalar()max->Shape().IsScalar()void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, onnxruntime::Tensor *) const [T = double]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, onnxruntime::Tensor *) const [T = signed char]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, onnxruntime::Tensor *) const [T = unsigned char]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, onnxruntime::Tensor *) const [T = long long]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, onnxruntime::Tensor *) const [T = unsigned long long]CumSumexclusive/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/cumsum.ccvirtual onnxruntime::common::Status onnxruntime::CumSum::Compute(onnxruntime::OpKernelContext *) const [T = float]void onnxruntime::WritableSliceIterator::Init(const std::vector &, gsl::span, gsl::span) [T = float]dims.size() == starts.size()dims.size()=starts.size()=dims.size() == extents_.size()extents.size()=dims.size() == steps.size()steps.size()=virtual onnxruntime::common::Status onnxruntime::CumSum::Compute(onnxruntime::OpKernelContext *) const [T = double]void onnxruntime::WritableSliceIterator::Init(const std::vector &, gsl::span, gsl::span) [T = double]virtual onnxruntime::common::Status onnxruntime::CumSum::Compute(onnxruntime::OpKernelContext *) const [T = int]void onnxruntime::WritableSliceIterator::Init(const std::vector &, gsl::span, gsl::span) [T = int]virtual onnxruntime::common::Status onnxruntime::CumSum::Compute(onnxruntime::OpKernelContext *) const [T = long long]void onnxruntime::WritableSliceIterator::Init(const std::vector &, gsl::span, gsl::span) [T = long long]Det/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/det.ccvirtual onnxruntime::common::Status onnxruntime::Det::Compute(onnxruntime::OpKernelContext *) const [T = float]X != nullptrInput tensor should have a rank of at least 2Matrix dimensions are not equal. Square matrix is expectedEinsumThere was a problem acquiring temporary memory allocator in Einsum op/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/einsum.ccDeviceComputeEinsum op: An implementation for the input type is not supported yet/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/einsum.honnxruntime::Einsum::Einsum(const onnxruntime::OpKernelInfo &)info.GetAttr("equation", &equation_).IsOK()->/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_auxiliary_ops.ccstd::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor &, const std::vector &, const onnxruntime::Tensor &, const std::vector &, onnxruntime::AllocatorPtr, concurrency::ThreadPool *, void *, const DeviceHelpers::MatMul &) [T = float]input_1.DataType() == input_2.DataType()input_shape_1_override.size() == 3 && input_shape_2_override.size() == 3input_shape_1_override[0] == input_shape_2_override[0]input_shape_1_override[2] == input_shape_2_override[1]Einsum op: Exception during MatMul operation: std::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor &, const std::vector &, const onnxruntime::Tensor &, const std::vector &, onnxruntime::AllocatorPtr, concurrency::ThreadPool *, void *, const DeviceHelpers::MatMul &) [T = int]std::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor &, const std::vector &, const onnxruntime::Tensor &, const std::vector &, onnxruntime::AllocatorPtr, concurrency::ThreadPool *, void *, const DeviceHelpers::MatMul &) [T = double]std::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor &, const std::vector &, const onnxruntime::Tensor &, const std::vector &, onnxruntime::AllocatorPtr, concurrency::ThreadPool *, void *, const DeviceHelpers::MatMul &) [T = long long]onnxruntime::common::Status onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopy(const onnxruntime::Tensor &, onnxruntime::Tensor &, void *)output.SizeInBytes() == input.SizeInBytes()Einsum op: The candidate output does not match the actual output's shapestd::unique_ptr onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::Diagonal(const onnxruntime::Tensor &, int64_t, int64_t, onnxruntime::AllocatorPtr, void *)rank >= 2 && dim_1 != dim_2 && input_dims[dim_1] == input_dims[dim_2]Cannot parse the diagonal elements along dims for input shape bool onnxruntime::EinsumOp::IsTransposeRequired(size_t, const std::vector &)input_rank == permutation.size()std::unique_ptr onnxruntime::EinsumOp::Transpose(const onnxruntime::Tensor &, const std::vector &, const std::vector &, onnxruntime::AllocatorPtr, void *, const DeviceHelpers::Transpose &)Length of permutation must match the rank of the input to be permutatedEinsum op: Transpose failed: std::unique_ptr onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDims(const onnxruntime::Tensor &, bool, onnxruntime::AllocatorPtr)input_dims[rank - 2] == input_dims[rank - 1]The innermost dims should have the same dim value to parse the diagonal elementsEinsum op: Unsupported data type for Diagonal /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_compute_preprocessor.ccNumber of subscripts in the input equation does not match number of input tensorsFound a '.' not part of an ellipsis in input: Einsum subscripts string contains too many subscript labels when compared to the rank of the inputEllipsis must indicate a fixed number of dimensions across all inputsFound '.' not part of an ellipsis in input: The only subscript labels allowed are lower-cased letters (a-z) and upper-cased letters (A-Z)Einsum operands could not be broadcast together. Please check input shapes/equation provided.Input shape of operand is incompatible in the dimension . The shape is: Another operand has a dim value of in the same dimensionEinsum subscripts string contains too many subscript labels when compared to the rank of the input Einsum subscripts does not contain enough subscript labels and there is no ellipsis for input onnxruntime::common::Status onnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDims()num_broadcasted_indices < num_of_ellipsis_dims_dim_iter == rank...Inputs have ellipses in them but the provided output subscript does not contain an ellipsisFound a '.' not part of an ellipsis in the output subscript providedFound '.' not part of an ellipsis in the output subscript providedRank of the input must match number of subscript labels corresponding to the input/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_typed_compute_processor.ccstd::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const std::vector &, bool) [T = float]left.Shape().Size() == left_shape_override.Size()The override dims are not compatible with given tensor's shape. Left shape: Left shape override: right.Shape().Size() == right_shape_override.Size()Right shape: Right shape override: left_rank == right_rankRanks of pair-wise operands must be equal. Right shape: left_dim == right_dimEinsum op: Input dimensions must be equal along an axis to be reduced across all inputsvoid onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor &, const std::vector &) [T = float]candidate_output.Shape().Size() == output_shape.Size()Einsum op: The candidate output cannot be reshaped into the op's outputcandidate_output_dims[iter] == 1Not all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: Einsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const std::vector &, bool) [T = int]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor &, const std::vector &) [T = int]std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const std::vector &, bool) [T = double]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor &, const std::vector &) [T = double]std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const std::vector &, bool) [T = long long]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor &, const std::vector &) [T = long long]SubMulDivAbsNegFloorCeilReciprocalSqrtPowT1ExpLogSumMaxMinLessGreaterEqualLessOrEqualGreaterOrEqualMeanBitShiftErfNotAndOrXorUnsupported X type: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.ccvirtual onnxruntime::common::Status onnxruntime::Min_6::Compute(onnxruntime::OpKernelContext *) const [T = float]inputCount >= 1Must have 1 or more inputsdata_n.Shape() == shapevirtual onnxruntime::common::Status onnxruntime::Max_6::Compute(onnxruntime::OpKernelContext *) const [T = float]virtual onnxruntime::common::Status onnxruntime::Mean_6::Compute(onnxruntime::OpKernelContext *) const [T = float]data_1.Shape() == shapeSinCosTanAsinAcosAtanSinhCoshAsinhAcoshAtanhPReluExpandMod/Users/runner/work/1/s/onnxruntime/core/providers/cpu/element_wise_ranged_transform.hElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Abs]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Abs]input_size < std::numeric_limits::max()onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Abs]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Abs]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Abs]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Abs]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Abs]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Abs]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Abs]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Abs]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Abs]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Neg]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Neg]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Neg]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Neg]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Neg]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Floor]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Floor]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Ceil]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Ceil]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Reciprocal]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Reciprocal]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Reciprocal]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Reciprocal]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Sqrt]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Sqrt]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Sqrt]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Sqrt]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Exp]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Exp]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Exp]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Exp]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Log]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Log]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Log]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Log]virtual onnxruntime::common::Status onnxruntime::Sum_6::Compute(onnxruntime::OpKernelContext *) const [T = float]input_count >= 1virtual onnxruntime::common::Status onnxruntime::Sum_6::Compute(onnxruntime::OpKernelContext *) const [T = double]directiononnxruntime::BitShift::BitShift(const onnxruntime::OpKernelInfo &) [T = unsigned char]LEFTRIGHTInvalid direction value of ''. Valid values are 'LEFT' or 'RIGHT'.auto onnxruntime::BitShift::Compute(onnxruntime::OpKernelContext *)::(anonymous class)::operator()(onnxruntime::BroadcastHelper &) const [T = unsigned char]cur1 == end1cur_out == end_outonnxruntime::BitShift::BitShift(const onnxruntime::OpKernelInfo &) [T = unsigned int]auto onnxruntime::BitShift::Compute(onnxruntime::OpKernelContext *)::(anonymous class)::operator()(onnxruntime::BroadcastHelper &) const [T = unsigned int]onnxruntime::BitShift::BitShift(const onnxruntime::OpKernelInfo &) [T = unsigned long long]auto onnxruntime::BitShift::Compute(onnxruntime::OpKernelContext *)::(anonymous class)::operator()(onnxruntime::BroadcastHelper &) const [T = unsigned long long]void onnxruntime::UntypedExpand(onnxruntime::OpKernelContext &, const onnxruntime::ProcessBroadcastSpanFuncs &)shape_data_tensor.Shape().GetDims().size() == 1/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.honnxruntime::Broadcaster::Broadcaster(const std::vector &, const std::vector &)largest <= 1Can broadcast 0 by 0 or 1. is invalid.void onnxruntime::BroadcastIterator::Init(ptrdiff_t, ptrdiff_t)axis == 1 || axis == largestAttempting to broadcast an axis by a dimension other than 1. by void onnxruntime::BroadcastIterator::Append(ptrdiff_t, ptrdiff_t)void onnxruntime::ExpandBroadcastLooper(onnxruntime::BroadcastHelper &, const onnxruntime::ProcessBroadcastSpanFuncs &)!helper.HaveTwoTensorInputs()ExpandBroadcastLooper should only have a shape for the second input.void onnxruntime::InputBroadcaster::AdvanceBy(size_t)offset % span_size_ == 0fmodonnxruntime::Mod::Mod(const onnxruntime::OpKernelInfo &)(fmod == 0) || (fmod == 1)onnxruntime::OutputBroadcaster::OutputBroadcaster(size_t, onnxruntime::Tensor &, ptrdiff_t, ptrdiff_t)start_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= lenInvalid start/ending offset [) for tensor of length:start_offset % span_size == 0 && real_end % span_size == 0Broadcast Output range [) are not at boundary of span with size:onnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext &)Unsupported Y type: void onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy::operator()(int32_t, Ret &) const [Ret = onnxruntime::common::Status]Unsupported data type: void onnxruntime::mod_internal::CallModImpl::operator()(bool, onnxruntime::OpKernelContext *) const [T = float, Enable = void]void onnxruntime::mod_internal::CallModImpl::operator()(bool, onnxruntime::OpKernelContext *) const [T = double, Enable = void]void onnxruntime::mod_internal::CallModImpl::operator()(bool, onnxruntime::OpKernelContext *) constvoid onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::CheckCalledOnce()called_ == 1void onnxruntime::BroadcastLooper(TBroadcastHelper &, const onnxruntime::ProcessBroadcastSpanFuncs &) [TBroadcastHelper = onnxruntime::BroadcastHelper]helper.HaveTwoTensorInputs()GemmtransA/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/gemm.honnxruntime::Gemm::Gemm(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttr("transA", &temp).IsOK()transBinfo.GetAttr("transB", &temp).IsOK()onnxruntime::Gemm::Gemm(const onnxruntime::OpKernelInfo &) [T = double]/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/gemm.ccvoid onnxruntime::GemmBroadcastBias(int64_t, int64_t, float, const T *, const onnxruntime::TensorShape *, T *) [T = double]c_shape != nullptr/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/gemm_helper.honnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &)left.NumDimensions() == 2 || left.NumDimensions() == 1right.NumDimensions() == 2GEMM: Dimension mismatch, W: K: N:Gemm: Invalid bias shape for broadcastM_ >= 0 && K_ > 0 && N_ >= 0void onnxruntime::GemmBroadcastBias(int64_t, int64_t, float, const T *, const onnxruntime::TensorShape *, T *) [T = float]/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/hardmax.ccHardmax inputs N, D and N * D must be < . N=, D=HardmaxMatMul/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/matmul.cc/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/matmul_helper.hleft_num_dims and right_num_dims must be >= 1MatMul dimension mismatchleft operand cannot broadcast on dim right operand cannot broadcast on dim num_dims_with_pad != num_output_dimsM_ == 1 && N_ == 1 was falsenum_dims_with_pad - 1 != num_output_dimsnum_dims_with_pad - 2 != num_output_dimsMatMulInteger/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/matmul_integer.ccvirtual onnxruntime::common::Status onnxruntime::MatMulInteger::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(a_zero_point)MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1IsBQuantParamSupported(b_zero_point->Shape(), b ? b->Shape() : b_shape_)QLinearMatMul/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/quantize_linear_matmul.ccvirtual onnxruntime::common::Status onnxruntime::QLinearMatMul::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(a_offset)QLinearMatmul : input zero point must be a scalar or 1D tensor of size 1IsBQuantParamSupported(b_offset->Shape(), b ? b->Shape() : b_shape_)QLinearMatmul : weight zero point must be a scalar, 1D tensor of size 1, or last to second dimension is 1IsScalarOr1ElementVector(y_offset)QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_scale)QLinearMatmul : input scale must be a scalar or 1D tensor of size 1IsBQuantParamSupported(b_scale->Shape(), b ? b->Shape() : b_shape_)QLinearMatmul : weight scale must be a scalar, 1D tensor of size 1, or last to second dimension is 1IsScalarOr1ElementVector(y_scale)QLinearMatmul : result scale must be a scalar or 1D tensor of size 1Per-column quantization parameter of batched matrix should have same dimension as the matrix,and its size by K should be equal to the matrix's size.RoundSignSoftmaxLogSoftmax/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/softmax.ccComputeImplOpset13SoftmaxCPU inputs N, D and N * D must be < TopK/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/top_k.ccvoid onnxruntime::TopkOpset9ConstructorCommon(const onnxruntime::OpKernelInfo &, int &, unsigned int &)op_kernel_info.GetAttr("k", &k_temp).IsOK()k_temp > 0op_kernel_info.GetAttr("axis", &axis_temp).IsOK()void onnxruntime::TopkOpset10ConstructorCommon(const onnxruntime::OpKernelInfo &, int &)void onnxruntime::TopkOpset11ConstructorCommon(const onnxruntime::OpKernelInfo &, int &, bool &, bool &)largestop_kernel_info.GetAttr("largest", &largest_temp).IsOK()op_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()input count mismatch, expected 1 input - the tensor to be processedk argument [] should not be greater than specified axis dim value [output count mismatch, expected 2 outputs to be present for TopK operatorinput count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valueArrayFeatureExtractorInvalid argument: X input has empty dimensions.Invalid Y argument: num_indices = 0Invalid Y argument: index is out of range: Y[] () >=BinarizerthresholdInput data with index: is NaNCastMapInvalid input type of value: Expected std::map or std::mapUnexpected CAST_TO value of cast_to/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/cast_map.honnxruntime::ml::CastMap::CastMap(const onnxruntime::OpKernelInfo &)info.GetAttr("cast_to", &attr).IsOK()info.GetAttr("map_form", &attr).IsOK()max_mapinfo.GetAttr("max_map", &max_map_).IsOK()map_form_ != PACK_MAP::SPARSE || max_map_ > 0TO_FLOATTO_STRINGTO_INT64/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/ml_common.honnxruntime::ml::CAST_TO onnxruntime::ml::MakeCast(const std::string &)Invalid CAST_TO value of Expected TO_FLOAT, TO_STRING or TO_INT64DENSESPARSEonnxruntime::ml::PACK_MAP onnxruntime::ml::MakePack(const std::string &)Invalid PACK_MAP value of Expected DENSE or SPARSE/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/cast_map.cconnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext &, TTo) const [TFrom = float, TTo = float]cur_input == end_input || cur_input->first >= 0Negative index values are not permitted. First entry in map has index value of const T *onnxruntime::OpKernelContext::Input(int) const [T = std::__1::map, std::__1::allocator > >]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext &, TTo) const [TFrom = std::__1::basic_string, TTo = float]const T *onnxruntime::OpKernelContext::Input(int) const [T = std::__1::map, std::__1::less, std::__1::allocator > > >]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext &, TTo) const [TFrom = float, TTo = long long]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext &, TTo) const [TFrom = std::__1::basic_string, TTo = long long]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext &, TTo) const [TFrom = float, TTo = std::__1::basic_string]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext &, TTo) const [TFrom = std::__1::basic_string, TTo = std::__1::basic_string]CategoryMapperInput of string must have output of int64Input of int64 must have output of string cats_strings/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/category_mapper.honnxruntime::ml::CategoryMapper::CategoryMapper(const onnxruntime::OpKernelInfo &)info.GetAttrs("cats_strings", string_categories).IsOK()cats_int64sinfo.GetAttrs("cats_int64s", int_categories).IsOK()default_stringinfo.GetAttr("default_string", &default_string_).IsOK()default_int64info.GetAttr("default_int64", &default_int_).IsOK()num_entries == int_categories.size()DictVectorizerstring_vocabulary/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/dictvectorizer.honnxruntime::ml::DictVectorizerOp, long long>::DictVectorizerOp(const onnxruntime::OpKernelInfo &) [AttrType = std::__1::basic_string, TargetType = long long]info.GetAttrs(std::is_same::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()const T *onnxruntime::OpKernelContext::Input(int) const [T = std::__1::map, long long, std::__1::less >, std::__1::allocator, long long> > >]onnxruntime::ml::DictVectorizerOp, float>::DictVectorizerOp(const onnxruntime::OpKernelInfo &) [AttrType = std::__1::basic_string, TargetType = float]const T *onnxruntime::OpKernelContext::Input(int) const [T = std::__1::map, float, std::__1::less >, std::__1::allocator, float> > >]onnxruntime::ml::DictVectorizerOp, double>::DictVectorizerOp(const onnxruntime::OpKernelInfo &) [AttrType = std::__1::basic_string, TargetType = double]const T *onnxruntime::OpKernelContext::Input(int) const [T = std::__1::map, double, std::__1::less >, std::__1::allocator, double> > >]int64_vocabularyonnxruntime::ml::DictVectorizerOp >::DictVectorizerOp(const onnxruntime::OpKernelInfo &) [AttrType = long long, TargetType = std::__1::basic_string]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo &) [AttrType = long long, TargetType = float]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo &) [AttrType = long long, TargetType = double]const T *onnxruntime::OpKernelContext::Input(int) const [T = std::__1::map, std::__1::allocator > >]FeatureVectorizer/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/feature_vectorizer.ccvirtual onnxruntime::common::Status onnxruntime::ml::FeatureVectorizer::Compute(onnxruntime::OpKernelContext *) constinput_count >= 0 && static_cast(input_count) == input_dimensions_.size()Number of inputs () does not match number of inputdimensions values (Invalid input type:inputdimensions/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/feature_vectorizer.honnxruntime::ml::FeatureVectorizer::FeatureVectorizer(const onnxruntime::OpKernelInfo &)status.IsOK() && !input_dimensions_.empty()Imputerimputed_value_floatsimputed_value_int64sreplaced_value_float/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/imputer.cconnxruntime::ml::ImputerOp::ImputerOp(const onnxruntime::OpKernelInfo &)Expected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedreplaced_value_int64Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedimputed_values_float_.empty() ^ imputed_values_int64_.empty()Must provide imputed_values_float_ or imputed_values_int64_ but not both.virtual common::Status onnxruntime::ml::ImputerOp::Compute(onnxruntime::OpKernelContext *) constInvalid typeEmpty value of imputed values.Empty input dimensions.LabelEncoderInput of tensor(string) must have output of tensor(int64)Input of tensor(int64) must have output of tensor(string)keys_floatsvalues_stringskeys_stringsvalues_floatsdefault_floatkeys_int64svalues_int64sclasses_strings/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/label_encoder.honnxruntime::ml::LabelEncoder::LabelEncoder(const onnxruntime::OpKernelInfo &)info.GetAttrs("classes_strings", string_classes).IsOK()onnxruntime::ml::LabelEncoder_2 >::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = float, TValue = std::__1::basic_string]info.GetAttrs(_key_field_name, keys).IsOK()info.GetAttrs(_value_field_name, values).IsOK()num_keys == num_valuesThe attribtues in LabelEncoder (name: ) must have the same length. However, the number of key is and the number of values is onnxruntime::ml::LabelEncoder_2, float>::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = std::__1::basic_string, TValue = float]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = long long, TValue = float]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = float, TValue = long long]onnxruntime::ml::LabelEncoder_2 >::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = long long, TValue = std::__1::basic_string]onnxruntime::ml::LabelEncoder_2, long long>::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = std::__1::basic_string, TValue = long long]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = long long, TValue = long long]LinearClassifiermulti_class/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/linearclassifier.cconnxruntime::ml::LinearClassifier::LinearClassifier(const onnxruntime::OpKernelInfo &)void onnxruntime::ml::LinearClassifier::ComputeImpl(const gsl::span, int64_t, int64_t, int64_t, const std::vector &, const std::vector &, onnxruntime::Tensor &, onnxruntime::Tensor &, onnxruntime::ml::POST_EVAL_TRANSFORM, bool, concurrency::ThreadPool *) constscores_output_data.length() >= scores_output_sizeScores output is incorrect size. Expected: Found:Input shape needs to be at least a single dimension.Unsupported input element type of void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor &, gsl::span &) [SrcType = int]shape_size == out.length()void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor &, gsl::span &) [SrcType = long long]void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor &, gsl::span &) [SrcType = double]LinearRegressorinterceptstargets/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/linearregressor.cconnxruntime::ml::LinearRegressor::LinearRegressor(const onnxruntime::OpKernelInfo &)info.GetAttr("targets", &num_targets_).IsOK()Input shape had more than 2 dimension. Dims=Normalizer/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/normalizer.honnxruntime::ml::Normalizer::Normalizer(const onnxruntime::OpKernelInfo &)info.GetAttr("norm", &norm).IsOK()MAXL1L2onnxruntime::ml::NORMALIZE onnxruntime::ml::MakeNormalize(const std::string &)Invalid normalize value of Rank of input to Normalized must be less than 2. Got Unexpected NORMALIZE value of OneHotEncoderUnknown Category and zeros = 0.zeros/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/onehotencoder.cconnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo &) [T = long long]tmp_cats_int64s.empty() || tmp_cats_strings.empty()One and only one of the 'cats_*' attributes must be definednum_categories_ > 0onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo &) [T = float]onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo &) [T = double]onnxruntime::ml::OneHotEncoderOp >::OneHotEncoderOp(const onnxruntime::OpKernelInfo &) [T = std::__1::basic_string]Scaler/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/scaler.cconnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo &) [T = float]!scale_.empty()scale_.size() == offset_.size()Scale size: () != (Invalid argument: input has empty dimensions.Either both scale and offset can be of feature size () or 1onnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo &) [T = double]onnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo &) [T = long long]onnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo &) [T = int]SVMClassifiervectors_per_classprob_aprob_b/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/svmclassifier.cconnxruntime::ml::SVMClassifier::SVMClassifier(const onnxruntime::OpKernelInfo &)proba_.size() == probb_.size()classlabels_intsinfo.GetAttrs("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs("classlabels_ints", classlabels_ints_).IsOK()classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0coefficients_.size() > 0Unsupported data type of void onnxruntime::ml::batched_update_scores_inplace(gsl::span, int64_t, int64_t, onnxruntime::ml::POST_EVAL_TRANSFORM, int, bool, concurrency::ThreadPool *) [T = float]scores.size() == static_cast(expected_num_scores)Unexpected value for 'add_second_class' of SVMRegressorn_supportssupport_vectorspost_transformrho/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/svmregressor.cconnxruntime::ml::SVMRegressor::SVMRegressor(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttrs("rho", rho_).IsOK()coefficientsinfo.GetAttrs("coefficients", coefficients_).IsOK()!coefficients_.empty()one_classkernel_typeLINEARkernel_params/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/svmclassifier.honnxruntime::ml::SVMCommon::SVMCommon(const onnxruntime::OpKernelInfo &)info.GetAttrs("kernel_params", kernel_params).IsOK()POLYRBFLOGISTICSOFTMAXSOFTMAX_ZEROvirtual onnxruntime::common::Status onnxruntime::ml::SVMRegressor::Compute(onnxruntime::OpKernelContext *) const [T = float]num_features == feature_count_Unexpected mode:TreeEnsembleClassifierclass_idsclass_nodeidsclass_treeidsclass_weightsX dims is empty.computevoid onnxruntime::ml::detail::TreeEnsembleCommonClassifier::compute(onnxruntime::OpKernelContext *, const onnxruntime::Tensor *, onnxruntime::Tensor *, onnxruntime::Tensor *) const [ITYPE = float, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(std::vector > &, OTYPE *, int, int64_t *) const [ITYPE = float, OTYPE = float]predictions.size() == 2int64_t onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binary(int &, const std::vector > &) const [ITYPE = float, OTYPE = float]classes.size() == 2 || classes.size() == 1void onnxruntime::ml::detail::TreeEnsembleCommonClassifier::compute(onnxruntime::OpKernelContext *, const onnxruntime::Tensor *, onnxruntime::Tensor *, onnxruntime::Tensor *) const [ITYPE = double, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(std::vector > &, OTYPE *, int, int64_t *) const [ITYPE = double, OTYPE = float]int64_t onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binary(int &, const std::vector > &) const [ITYPE = double, OTYPE = float]onnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommon(int, int, const std::string &, const std::vector &, int64_t, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::string &, const std::vector &, const std::vector &, const std::vector &, const std::vector &) [ITYPE = long long, OTYPE = float]void onnxruntime::ml::detail::TreeEnsembleCommonClassifier::compute(onnxruntime::OpKernelContext *, const onnxruntime::Tensor *, onnxruntime::Tensor *, onnxruntime::Tensor *) const [ITYPE = long long, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(std::vector > &, const TreeNodeElement &) const [ITYPE = long long, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(std::vector > &, OTYPE *, int, int64_t *) const [ITYPE = long long, OTYPE = float]int64_t onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binary(int &, const std::vector > &) const [ITYPE = long long, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(std::vector > &, const std::vector > &) const [ITYPE = long long, OTYPE = float]onnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommon(int, int, const std::string &, const std::vector &, int64_t, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::string &, const std::vector &, const std::vector &, const std::vector &, const std::vector &) [ITYPE = int, OTYPE = float]void onnxruntime::ml::detail::TreeEnsembleCommonClassifier::compute(onnxruntime::OpKernelContext *, const onnxruntime::Tensor *, onnxruntime::Tensor *, onnxruntime::Tensor *) const [ITYPE = int, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(std::vector > &, const TreeNodeElement &) const [ITYPE = int, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(std::vector > &, OTYPE *, int, int64_t *) const [ITYPE = int, OTYPE = float]int64_t onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binary(int &, const std::vector > &) const [ITYPE = int, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(std::vector > &, const std::vector > &) const [ITYPE = int, OTYPE = float]TreeEnsembleRegressoraggregate_functionSUMbase_valuesn_targetsnodes_falsenodeidsnodes_featureidsnodes_hitratesnodes_missing_value_tracks_truenodes_modesnodes_nodeidsnodes_treeidsnodes_truenodeidsnodes_valuestarget_idstarget_nodeidstarget_treeidstarget_weights/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/tree_ensemble_common.honnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommon(int, int, const std::string &, const std::vector &, int64_t, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::string &, const std::vector &, const std::vector &, const std::vector &, const std::vector &) [ITYPE = float, OTYPE = float]n_targets_or_classes > 0nodes_falsenodeids.size() == nodes_featureids.size()nodes_falsenodeids.size() == nodes_modes.size()nodes_falsenodeids.size() == nodes_nodeids.size()nodes_falsenodeids.size() == nodes_treeids.size()nodes_falsenodeids.size() == nodes_truenodeids.size()nodes_falsenodeids.size() == nodes_values.size()target_class_ids.size() == target_class_nodeids.size()target_class_ids.size() == target_class_treeids.size() in tree is already there.Unable to find node (truenode).One falsenode is pointing either to itself, either to another tree. (falsenode). (weights).AVERAGEMINBRANCH_LEQLEAFBRANCH_LTBRANCH_GTEBRANCH_GTBRANCH_EQvoid onnxruntime::ml::detail::TreeEnsembleCommon::compute(onnxruntime::OpKernelContext *, const onnxruntime::Tensor *, onnxruntime::Tensor *, onnxruntime::Tensor *) const [ITYPE = float, OTYPE = float]/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/tree_ensemble_aggregator.hvoid onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(std::vector > &, const TreeNodeElement &) const [ITYPE = float, OTYPE = float]it->i < (int64_t)predictions.size()void onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScores(std::vector > &, OTYPE *, int, int64_t *) const [ITYPE = float, OTYPE = float]this->base_values_.size() == predictions.size()void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(std::vector > &, const std::vector > &) const [ITYPE = float, OTYPE = float]predictions.size() == predictions2.size()void onnxruntime::ml::detail::TreeAggregator::FinalizeScores(std::vector > &, OTYPE *, int, int64_t *) const [ITYPE = float, OTYPE = float]predictions.size() == (size_t)n_targets_or_classes_void onnxruntime::ml::detail::TreeAggregatorMin::MergePrediction(std::vector > &, const std::vector > &) const [ITYPE = float, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorMax::MergePrediction(std::vector > &, const std::vector > &) const [ITYPE = float, OTYPE = float]onnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommon(int, int, const std::string &, const std::vector &, int64_t, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::string &, const std::vector &, const std::vector &, const std::vector &, const std::vector &) [ITYPE = double, OTYPE = float]void onnxruntime::ml::detail::TreeEnsembleCommon::compute(onnxruntime::OpKernelContext *, const onnxruntime::Tensor *, onnxruntime::Tensor *, onnxruntime::Tensor *) const [ITYPE = double, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(std::vector > &, const TreeNodeElement &) const [ITYPE = double, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScores(std::vector > &, OTYPE *, int, int64_t *) const [ITYPE = double, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(std::vector > &, const std::vector > &) const [ITYPE = double, OTYPE = float]void onnxruntime::ml::detail::TreeAggregator::FinalizeScores(std::vector > &, OTYPE *, int, int64_t *) const [ITYPE = double, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorMin::MergePrediction(std::vector > &, const std::vector > &) const [ITYPE = double, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorMax::MergePrediction(std::vector > &, const std::vector > &) const [ITYPE = double, OTYPE = float]ZipMapclasslabels_int64sclasslabels_strings/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/zipmap.cconnxruntime::ml::ZipMapOp::ZipMapOp(const onnxruntime::OpKernelInfo &)classlabels_strings_.empty() ^ classlabels_int64s_.empty()Must provide classlabels_strings or classlabels_int64s but not both.Zipmap does not support empty dim countZipmap only supports 1D or 2D input tensorsInput features_per_batch[] != number of classlabels[T *OrtValue::GetMutable() [T = std::__1::vector, float, std::__1::less >, std::__1::allocator, float> > >, std::__1::allocator, float, std::__1::less >, std::__1::allocator, float> > > > >]T *OrtValue::GetMutable() [T = std::__1::vector, std::__1::allocator > >, std::__1::allocator, std::__1::allocator > > > >]MaxUnpool/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/Unpool.ccIndex tensor shape should be same as that of the input data tensor to unpool.Shape must be 1 dimensional as it's tensor data of a shapeoutput_shape is smaller than minimum required. output_shape: inferred output shape:/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/unpool.honnxruntime::MaxUnpool::MaxUnpool(const onnxruntime::OpKernelInfo &)info.GetAttrs("kernel_shape", kernel_shape_).IsOK()kernel_shape_[dim] > 0pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]strides_.size() == kernel_shape_.size()BatchNormalizationspatial/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/batch_norm.honnxruntime::BatchNorm::BatchNorm(const onnxruntime::OpKernelInfo &) [T = float]!is_train_Invalid input X: The rank of input X must be atleast 2. Got rank: Invalid input scale: NumDimensions() != Invalid input scale: 0th dimension != Invalid input scale: dimension != Invalid input B: NumDimensions() != Invalid input B: 0th dimension != Invalid input B: Invalid input mean: NumDimensions() != Invalid input mean: 0th dimension != Invalid input mean: Invalid input var: NumDimensions() != Invalid input var: 0th dimension != Invalid input var: onnxruntime::BatchNorm::BatchNorm(const onnxruntime::OpKernelInfo &) [T = double]/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/conv.ccConvX num_dims does not match W num_dims. X: W: Input channels C is not equal to kernel channels * group. C: kernel channels: group: Output channels M is not divisible by group. M: kernel_shape num_dims is not compatible with W num_dims. kernel_shape: kernel_shape is not compatible with W shape.Not enough elements in strides. Expected: Not enough elements in kernel shape. Expected: Not enough elements in dilations. Expected: Not enough elements in pads. Expected: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/conv_attributes.hInferOutputShapeInvalid input shape: /Users/runner/work/1/s/onnxruntime/core/providers/common.hComputePadAndOutputShapeDilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER.ComputePad: pad type not supported.kernel_shapestridesonnxruntime::ConvAttributes::ConvAttributes(const onnxruntime::OpKernelInfo &)auto_pad == AutoPadType::NOTSETA Conv/ConvTranspose node has both 'auto_pad' and 'pads' attributesdilationsgroupNOTSETVALIDSAME_UPPERSAME_LOWERonnxruntime::AutoPadType onnxruntime::StringToAutoPadType(const std::string &)T3ConvInteger/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/conv_integer.ccvirtual onnxruntime::common::Status onnxruntime::ConvInteger::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(X_Zero_Point)IsScalarOr1ElementVector(W_Zero_Point)ConvTranspose/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/conv_transpose.ccDoConvTransposeoutput_paddingoutput_shapegroup count is <= 0filter number not equal to input channel number. filter_number: num_input_channels: Input channels is not divisible by group./Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/conv_transpose_attributes.hvoid onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShape(onnxruntime::TensorShape, int64_t, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const int64_t, std::vector *, std::vector *) constdim_size > 0void onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShape(const int64_t, const int64_t, const int64_t, const int64_t, const int64_t, onnxruntime::AutoPadType, int64_t *, int64_t *, int64_t *) const*out_size >= 0/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/dropout_op.hvirtual onnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext *) const [T1 = float, T2 = float]!mask || mask->Shape() == X_shapefloat onnxruntime::(anonymous namespace)::GetRatioOrDefault(const onnxruntime::Tensor *) [T2 = float]ratio_tensor->Shape().Size() == 10.0f <= ratio_value && ratio_value < 1.0fvirtual onnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext *) const [T1 = float, T2 = double]float onnxruntime::(anonymous namespace)::GetRatioOrDefault(const onnxruntime::Tensor *) [T2 = double]virtual onnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext *) const [T1 = double, T2 = float]virtual onnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext *) const [T1 = double, T2 = double]Flatten/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/flatten.honnxruntime::Flatten::Flatten(const onnxruntime::OpKernelInfo &)virtual onnxruntime::common::Status onnxruntime::Flatten::Compute(onnxruntime::OpKernelContext *) constgsl::narrow_cast(X_shape.NumDimensions()) >= axisInstanceNormalization/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/instance_norm.ccepsilon/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/instance_norm.honnxruntime::InstanceNorm::InstanceNorm(const onnxruntime::OpKernelInfo &) [T = float]op_kernel_info.GetAttr("epsilon", &epsilon_).IsOK()Invalid input data: number of dimensions is less than 3: Invalid input scale: number of dimensions is not 1: Mismatch between input data and scale: size of scale != input channel count vs. Invalid input B: number of dimensions is not 1: Mismatch between input data and B: size of B != input channel count LpNormalization/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/lp_norm.honnxruntime::LpNorm::LpNorm(const onnxruntime::OpKernelInfo &) [T = float]op_kernel_info.GetAttr("axis", &axis_).IsOK()op_kernel_info.GetAttr("p", &p_).IsOK()p_ == 1 || p_ == 2onnxruntime::LpNorm::LpNorm(const onnxruntime::OpKernelInfo &) [T = double]input count mismatch/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/lrn.ccvirtual onnxruntime::common::Status onnxruntime::LRN::Compute(onnxruntime::OpKernelContext *) const [T = float]X->Shape().NumDimensions() == 4LRN/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/lrn.honnxruntime::LRN::LRN(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttr("size", &size).IsOK()size_ > 0size_ % 2 == 1info.GetAttr("alpha", &alpha_).IsOK()alpha_ > 0.0finfo.GetAttr("beta", &beta_).IsOK()beta_ > 0.0f/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/pool.ccInput dimension cannot be less than 3.Unsupported pooling size.kernel_shape num_dims is not compatible with X num_dims.AveragePoolMaxPoolLpPoolGlobalLpPoolGlobalAveragePoolGlobalMaxPool/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/pool_attributes.hstd::vector onnxruntime::PoolAttributes::SetOutputSize(const onnxruntime::TensorShape &, int64_t, std::vector *) constinput_shape.Size() > 0 || input_shape[0] == 0Invalid input shape. Only N can be zero. Got:void onnxruntime::PoolAttributes::InferOutputSize(const std::vector &, std::vector *, std::vector *) constinput_dims.size() >= 2void onnxruntime::PoolAttributes::ComputeSizePadDilations(const int64_t, const int64_t, const int64_t, int64_t *, int64_t *, int64_t, int64_t *) constonnxruntime::PoolAttributes::PoolAttributes(const OpNodeProtoHelper &, const std::string &, int)info.GetAttrs("kernel_shape", kernel_shape).IsOK()info.GetAttr("auto_pad", &auto_padding).IsOK()ceil_modecount_include_padinfo.GetAttr("count_include_pad", &temp).IsOK()storage_orderinfo.GetAttr("storage_order", &storage_order).IsOK()kernel_shape[dim] > 0pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]strides.size() == kernel_shape.size()dilations.size() == kernel_shape.size()/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/pool_base.hvoid onnxruntime::PoolProcessContext::init(const onnxruntime::OpKernelInfo &)info.GetAttr("p", &p_).IsOK()Unsupported pooling size : T4QLinearConv/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/qlinearconv.ccvirtual onnxruntime::common::Status onnxruntime::QLinearConv::UseSharedPrePackedBuffers(std::vector &, int, bool &)prepacked_buffers[0].get() == nullptrvirtual onnxruntime::common::Status onnxruntime::QLinearConv::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(X_zero_point)QLinearConv : input zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_zero_point)QLinearConv : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(X_scale)QLinearConv : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_scale)QLinearConv : result scale must be a scalar or 1D tensor of size 1channels_lastMaxRoiPool/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/roi_pool.ccvirtual onnxruntime::common::Status onnxruntime::RoiPool::Compute(onnxruntime::OpKernelContext *) const [T = float]R->Shape()[1] == 5roi_batch_id >= 0roi_batch_id < batch_sizepooled_shape/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/roi_pool.honnxruntime::RoiPool::RoiPool(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttrs("pooled_shape", pooled_shape).IsOK()pooled_shape.size() == 2pooled_height_ > 0pooled_width_ > 0spatial_scaleinfo.GetAttr("spatial_scale", &spatial_scale_).IsOK()spatial_scale_ > 0Shrink/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/shrink.honnxruntime::Shrink::Shrink(const onnxruntime::OpKernelInfo &)op_kernel_info.GetAttr("bias", &bias_temp).IsOK()lambdop_kernel_info.GetAttr("lambd", &lambd_temp).IsOK()StringNormalizer/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/string_normalizer.cconnxruntime::StringNormalizer::StringNormalizer(const onnxruntime::OpKernelInfo &)LOWERUPPERlocalestopwords!sw.empty()wstr != wconv_errorSingle dimension value must be greater than 0Input dimensions are either[C > 0] or [1][C > 0] allowedInput contains invalid utf8 chars at: auto onnxruntime::string_normalizer::Locale::Locale(const std::string &)::(anonymous class)::operator()() constFailed to construct locale with name::Please, install necessary language-pack-XX and configure localeswstring_convert: from_bytes errorwstring_convert: to_bytes errorTfIdfVectorizer/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/tfidfvectorizer.cconnxruntime::TfIdfVectorizer::TfIdfVectorizer(const onnxruntime::OpKernelInfo &)TFIDFTFIDFimpl_->weighting_criteria_ != kNonemode: is unrecognized, acceptable values are TF,IDF,TFIDFmin_gram_lengthimpl_->min_gram_length_ > 0Required min_gram_length must be positive: max_gram_lengthimpl_->max_gram_length_ >= impl_->min_gram_length_min_gram_length >= max_gram_length required: >= max_skip_countimpl_->max_skip_count_ >= 0max_skip_count must be non-negative: ngram_countsstatus.IsOK() && !impl_->ngram_counts_.empty()size_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size()min_gram_length must be inbounds of ngram_counts: <= size_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size()max_gram_length must be inbounds of ngram_counts: ngram_indexesstatus.IsOK() && !impl_->ngram_indexes_.empty()std::all_of(impl_->ngram_indexes_.cbegin(), impl_->ngram_indexes_.cend(), [](int64_t i) { return i >= 0; })weightsimpl_->weights_.size() == impl_->ngram_indexes_.size()Got weights of size: but ngram_indexes size: must be of equal sizepool_strings!pool_strings.empty()pool_int64sstatus.IsOK() && !pool_int64s.empty()end_idx >= start_idx && end_idx <= total_itemsn-gram counts out of bounds for -grams(items % ngram_size == 0)Number of items must compose whole Input shape must have either [C] or [B,C] dimensions with B > 0.size_t onnxruntime::ngram_details::PopulateGrams(ForwardIter, size_t, size_t, size_t, Map &) [K = long long, ForwardIter = const long long *, Map = std::__1::unordered_map, std::__1::default_delete > >, std::__1::hash, std::__1::equal_to, std::__1::allocator, std::__1::default_delete > > > > >]p.first->second->id_ == 0Duplicate ngram detected, size: id: size_t onnxruntime::ngram_details::PopulateGrams(ForwardIter, size_t, size_t, size_t, Map &) [K = std::__1::basic_string, ForwardIter = std::__1::__wrap_iter > *>, Map = std::__1::unordered_map >, std::__1::unique_ptr, std::__1::default_delete > >, std::__1::hash >, std::__1::equal_to >, std::__1::allocator >, std::__1::unique_ptr, std::__1::default_delete > > > > >]NonMaxSuppression/Users/runner/work/1/s/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.ccstatic onnxruntime::common::Status onnxruntime::NonMaxSuppressionBase::PrepareCompute(onnxruntime::OpKernelContext *, onnxruntime::PrepareContext &)boxes_tensorscores_tensorboxes must be a 3D tensor.scores must be a 3D tensor.boxes and scores should have same num_batches.boxes and scores should have same spatial_dimension.The most inner dimension in boxes must have 4 data.GetThresholdsFromInputsiou_threshold must be in range [0, 1].virtual onnxruntime::common::Status onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext *) constoutput != nullptrcenter_point_box/Users/runner/work/1/s/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.honnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBase(const onnxruntime::OpKernelInfo &)0 == center_point_box_ || 1 == center_point_box_RoiAlignNull input X ptrNull rois_ptrNull batch_indices_ptrNumber of dimensions for batch indices should be exactly 1Number of dimensions for rois should be exactly Second dimension for rois should be exactly First dimension (num_rois) of batch_indices and rois don't matchavg/Users/runner/work/1/s/onnxruntime/core/providers/cpu/object_detection/roialign.honnxruntime::RoiAlignBase::RoiAlignBase(const onnxruntime::OpKernelInfo &)Invalid mode of value specified. It should be either avg or maxoutput_heightoutput_widthsampling_ratiosampling_ratio_ >= 0Sampling ratio should be >=0, but it was ReduceL1ReduceL2ReduceLogSumReduceLogSumExpReduceMaxReduceMeanReduceMinReduceProdReduceSumReduceSumSquareArgMaxArgMin/Users/runner/work/1/s/onnxruntime/core/providers/cpu/reduction/reduction_ops.ccvoid onnxruntime::ResultsNoTransposePrepareForReduce::ValidateNotEmpty()last_loop_red_size > 0last_loop_size > 0projected_index.size() > 0void onnxruntime::ValidateNoTransposeReduce(int64_t)count == 1void onnxruntime::ValidateCommonFastReduce(const onnxruntime::Tensor *)keepdims/Users/runner/work/1/s/onnxruntime/core/providers/cpu/reduction/reduction_ops.honnxruntime::ReduceKernelBase::ReduceKernelBase(const onnxruntime::OpKernelInfo &, optional) [allow_multi_axes = false]info.GetAttr("keepdims", &keepdims).IsOK()noop_with_empty_axesselect_last_indexvoid onnxruntime::ValidateFastReduceKR(const std::vector &, const onnxruntime::Tensor &)fast_shape.size() == 2fast_shape[0] == output.Shape().Size()void onnxruntime::ValidateFastReduceRK(const std::vector &, const onnxruntime::Tensor &)fast_shape[1] == output.Shape().Size()void onnxruntime::ValidateFastReduceKRK(const std::vector &, const onnxruntime::Tensor &)fast_shape.size() == 3fast_shape[0] * fast_shape[2] == output.Shape().Size()void onnxruntime::ValidateMustBeOverloaded()void onnxruntime::ValidateKeepDims(const onnxruntime::TensorShape &, int64_t)Can't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:onnxruntime::ReduceKernelBase::ReduceKernelBase(const onnxruntime::OpKernelInfo &, optional) [allow_multi_axes = true]GRU/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.ccvirtual onnxruntime::common::Status onnxruntime::DeepCpuGruOp::Compute(onnxruntime::OpKernelContext *) constInvalid data type for GRU operator of /Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.honnxruntime::DeepCpuGruOp::DeepCpuGruOp(const onnxruntime::OpKernelInfo &)info.GetAttr("direction", &direction).IsOK()linear_before_resetinfo.GetAttr("linear_before_reset", &int64_value).IsOK()hidden_sizeinfo.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0activationsactivation_alphaactivation_betaclip_ > 0.fsigmoidtanhactivation_func_names.size() == static_cast(num_directions_) * 2layoutlayout_ == 0Batchwise recurrent operations (layout == 1) are not supported. If you need support create a github issue with justification.forwardreversebidirectional/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/rnn_helpers.honnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const std::string &)Invalid 'direction' argument of ''. Must be one of 'forward', 'reverse', or 'bidirectional'.ComputeImplgsl::span onnxruntime::Tensor::DataAsSpan() const [T = float]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = float]void onnxruntime::rnn::detail::ComputeGemm(const int, const int, const int, const float, TSpanAIter, TSpanAIter, const int, TSpanBIter, TSpanBIter, const int, const float, TSpanCIter, TSpanCIter, const int, concurrency::ThreadPool *) [TSpanAIter = const float *, TSpanBIter = const float *, TSpanCIter = float *]lda >= K && ldb >= K && ldc >= NA + (M * lda - (lda - K)) <= A_endB + (N * ldb - (ldb - K)) <= B_endC + (M * ldc - (ldc - N)) <= C_endconst T *onnxruntime::rnn::detail::SafeRawConstPointer(typename gsl::span::const_iterator, typename gsl::span::const_iterator, size_t) [T = float]cur + size <= endT *onnxruntime::rnn::detail::SafeRawPointer(typename gsl::span, size_t, size_t) [T = float]offset + size <= size_t(span.size())T *onnxruntime::rnn::detail::SafeRawPointer(typename gsl::span::iterator, typename gsl::span::iterator, size_t) [T = float]void onnxruntime::rnn::detail::ComputeGemm(const int, const int, const int, const float, TSpanAIter, TSpanAIter, const int, TSpanBIter, TSpanBIter, const int, const float, TSpanCIter, TSpanCIter, const int, concurrency::ThreadPool *) [TSpanAIter = float *, TSpanBIter = const float *, TSpanCIter = float *]LSTM/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.ccPrePackvirtual onnxruntime::common::Status onnxruntime::DeepCpuLstmOp::Compute(onnxruntime::OpKernelContext *) constInvalid data type for LSTM operator of /Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/lstm_base.honnxruntime::LSTMBase::LSTMBase(const onnxruntime::OpKernelInfo &)input_forgetactivation_func_names.size() == static_cast(num_directions_) * 3/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/lstm_base.ccRNN/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/rnn.ccAffine/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/rnn.honnxruntime::RNN::RNN(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttr("direction", &direction_).IsOK()allowed_directions.find(direction_) != allowed_directions.end()info.GetAttrs("activations", activations_).IsOK()info.GetAttr("hidden_size", &hidden_size_).IsOK()activations_.size() == static_cast(num_directions)allowed_activations.find(activations_[direction]) != allowed_activations.end()RNN op: Invalid activation attribute - Input X must have 3 dimensions only. Actual:Input W must have shape {*}. Actual:Input R must have shape {Input B must have shape {Input sequence_lens must have shape {Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length=Input initial_h must have shape {affinereluleakyreluthresholdedreluscaledtanhhardsigmoidelusoftsignsoftplus/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/rnn_helpers.ccstd::string onnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCount(const std::string &, std::vector::const_iterator &, const std::vector::const_iterator &, std::vector::const_iterator &, const std::vector::const_iterator &, float &, float &)Expecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got void onnxruntime::rnn::detail::ComputeGemm(const int, const int, const int, const float, const float *, const float *, const GemmWeights &, const float, float *, float *, const int, uint8_t *, int32_t *, concurrency::ThreadPool *)A + (M * K) <= A_endvoid onnxruntime::rnn::detail::ComputeGemm(const int, const int, const int, const float, const float *, const float *, const GemmWeights &, const float, float *, float *, const int, uint8_t *, int32_t *, concurrency::ThreadPool *)weights.quant_para_alpha == 1.0f && (beta == 0.0f || beta == 1.0f)Quantized GEMM only support alpha equal to 1.0f and beta equal to 0.0f or 1.0fonnxruntime::rnn::detail::deepcpu::ActivationFuncPtr onnxruntime::rnn::detail::deepcpu::ActivationFuncByName(const std::string &)Invalid activation function of onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncPtr onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByName(const std::string &)Invalid LSTM merge activation function of onnxruntime::rnn::detail::deepcpu::GruResetGateFuncPtr onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByName(const std::string &)Invalid GRU reset gate activation function: onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncPtr onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByName(const std::string &)Invalid GRU hidden gate activation function: rows[const T *onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span, size_t, size_t) [T = const float]const T *onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span, size_t, size_t) [T = float]ConcatFromSequence/Users/runner/work/1/s/onnxruntime/core/providers/cpu/sequence/concat_from_sequence.ccvirtual onnxruntime::common::Status onnxruntime::ConcatFromSequence::Compute(onnxruntime::OpKernelContext *) constSSequenceLength/Users/runner/work/1/s/onnxruntime/core/providers/cpu/sequence/sequence_ops.ccvirtual onnxruntime::common::Status onnxruntime::SequenceLength::Compute(onnxruntime::OpKernelContext *) constY != nullptrSequenceAtvirtual onnxruntime::common::Status onnxruntime::SequenceAt::Compute(onnxruntime::OpKernelContext *) constI != nullptrInvalid sequence index () specified for sequence of size (SequenceEmptyvirtual onnxruntime::common::Status onnxruntime::SequenceEmpty::Compute(onnxruntime::OpKernelContext *) constUnsupported 'dtype' value: SequenceInsertCreateCopyAndAppendCpuTensorvirtual onnxruntime::common::Status onnxruntime::SequenceInsert::Compute(onnxruntime::OpKernelContext *) constS != nullptrData type of the input tensor MUST be same as that of the input sequence. Sequence data type (), input tensor data type (SequenceErasevirtual onnxruntime::common::Status onnxruntime::SequenceErase::Compute(onnxruntime::OpKernelContext *) constSequenceConstructvirtual onnxruntime::common::Status onnxruntime::SequenceConstruct::Compute(onnxruntime::OpKernelContext *) constnum_inputs >= 1Violation of the requirment that all input tensors must have the same data type.SplitToSequenceSplitToSequence operator does not support split_size_sum () != split_dim_size (int64_t onnxruntime::GetSeqIdx(const onnxruntime::Tensor &)onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &, const onnxruntime::Tensor *) const [T = float]split_scalar > 0std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value >= 0; })int64_t onnxruntime::GetScalarSplitInput(const onnxruntime::Tensor &)Invalid data type for split tensor void onnxruntime::GetSplitSizesInput(const onnxruntime::Tensor &, std::vector &)onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &, const onnxruntime::Tensor *) const [T = double]onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &, const onnxruntime::Tensor *) const [T = int]onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &, const onnxruntime::Tensor *) const [T = long long]onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &, const onnxruntime::Tensor *) const [T = std::__1::basic_string]T2Cast/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/cast_op.cconnxruntime::(anonymous namespace)::Cast::Cast(const onnxruntime::OpKernelInfo &)NaN-INFINF%.8gtypename std::enable_if::value, void>::type onnxruntime::(anonymous namespace)::CastToString(const SrcType &, std::string &) [SrcType = float]snprintf_result > 0snprintf() failed with return value: snprintf_result > 0 && gsl::narrow_cast(snprintf_result) == buffer_span.size() - 1typename std::enable_if::value, void>::type onnxruntime::(anonymous namespace)::CastToString(const SrcType &, std::string &) [SrcType = double]Compress/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/compress.ccvirtual onnxruntime::common::Status onnxruntime::Compress::Compute(onnxruntime::OpKernelContext *) constaxes_right_stride >= 0 && static_cast(axes_right_stride) < std::numeric_limits::max()Concat/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/concat.ccPrepareForComputeCannot concatenate scalarsonnxruntime::common::Status onnxruntime::ConcatBase::PrepareForCompute(onnxruntime::OpKernelContext *, const std::vector &, onnxruntime::Prepare &) constinputs_n_rank == inputs_0_rankRanks of input data are different, cannot concatenate them. expected rank: got: Non concat axis dimensions must match: Axis has mismatched dimensions of Data type mismatch/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/concatbase.honnxruntime::ConcatBase::ConcatBase(const onnxruntime::OpKernelInfo &, bool)DynamicQuantizeLinear/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/dynamicquantizelinear.ccvirtual onnxruntime::common::Status onnxruntime::DynamicQuantizeLinear::Compute(onnxruntime::OpKernelContext *) const [T = unsigned char]x_ptr != nullptrEyeLikedtypeGather/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/gather.cc/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/gatherbase.honnxruntime::GatherBase::GatherBase(const onnxruntime::OpKernelInfo &)info.GetAttr("axis", &axis_).IsOK()indices element out of data bounds, idx= must be within the inclusive range [GatherElementsGatherElements op: Rank of input 'data' needs to be equal to rank of input 'indices'GatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: GatherElements op: Data type of input 'data' should match the data type of the output/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/gather_elements.honnxruntime::GatherElements::GatherElements(const onnxruntime::OpKernelInfo &)/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/gather_elements.ccauto onnxruntime::core_impl(const onnxruntime::Tensor *, const onnxruntime::Tensor *, onnxruntime::Tensor *, int64_t, concurrency::ThreadPool *)::(anonymous class)::operator()(ptrdiff_t) constGatherElements op: Value in indices must be within bounds [ , ]. Actual value is invalid index found, index = GatherND/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/gather_nd.ccvirtual onnxruntime::common::Status onnxruntime::GatherND::Compute(onnxruntime::OpKernelContext *) constinput_tensor != nullptr && indices_tensor != nullptrlast dimension of indices must not be larger than rank of input tensorbatch_dimsDropoutIdentity/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/identity_op.hvirtual onnxruntime::common::Status onnxruntime::IdentityOp::Compute(onnxruntime::OpKernelContext *) const [is_dropout = true]const T *onnxruntime::OpKernelContext::Input(int) const [T = onnxruntime::TensorSeq]virtual onnxruntime::common::Status onnxruntime::IdentityOp::Compute(onnxruntime::OpKernelContext *) const [is_dropout = false]IsInfdetect_positive/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/isinf.cconnxruntime::IsInf::IsInf(const onnxruntime::OpKernelInfo &)detect_negativeIsNaNNull input ptrMeanVarianceNormalizationInput is expected to have four dimensions corresponding to [N,C,H,W]NonZero/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/nonzero_op.ccvirtual onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext *) const [T = bool]virtual onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext *) const [T = unsigned char]virtual onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext *) const [T = int]virtual onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext *) const [T = long long]virtual onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext *) const [T = float]OneHotInvalid argument for depth; it's not a scalar.Invalid argument for values; either it's rank is more than 1 or it has more than 2 elements/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/onehot.ccDepth is negative.PadCannot use 'reflect' mode to pad dimension with a value of 0. Input shape:Unexpected mode of /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/pad.ccvirtual onnxruntime::common::Status onnxruntime::Pad::Compute(onnxruntime::OpKernelContext *) constpads_tensor.IsDataType()pads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)Pads tensor should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]pads_size == 2 * data_rankPads tensor size should be equal to twice the input dimension count value_tensor->DataType() == data_type && value_tensor->Shape().Size() == 1Value tensor should be a 1D tensor of size 1 with the same type as that of the input tensorUnsupported input data type of valueconstantreflectedge/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/padbase.honnxruntime::PadBase::PadBase(const onnxruntime::OpKernelInfo &)onnxruntime::PadValue onnxruntime::PadValueFromFloat(float, onnxruntime::MLDataType)onnxruntime::common::Status onnxruntime::PadImpl(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &, const onnxruntime::Mode &, T) [T = unsigned int]data_rank > 0data_rank * 2 == pads.size()PadInputWithDimValueOfZero/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/utils.honnxruntime::SliceSkips::SliceSkips(const onnxruntime::TensorShape &, gsl::span, gsl::span)dims.size() == extents.size() && dims.size() >= steps.size()void onnxruntime::SliceIteratorBase::Init(const std::vector &, gsl::span, gsl::span)dims.size() == starts.size() && dims.size() == extents_.size() && dims.size() >= steps.size()onnxruntime::common::Status onnxruntime::PadImpl(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &, const onnxruntime::Mode &, T) [T = unsigned long long]onnxruntime::common::Status onnxruntime::PadImpl(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &, const onnxruntime::Mode &, T) [T = unsigned char]DequantizeLinearQuantizeLinear/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/quantize_linear.ccvoid onnxruntime::PrepareForQDQ(const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const onnxruntime::Tensor *, int64_t, int64_t &, int64_t &, int64_t &)zero_point_ptr == nullptr || IsScalarOr1ElementVector(zero_point_ptr)scale.Shape().NumDimensions() == 1 && scale.Shape()[0] == broadcast_dimscale must be 1D tensor with size zero_point_ptr == nullptr || (zero_point_ptr->Shape().NumDimensions() == 1 && zero_point_ptr->Shape()[0] == broadcast_dim)x_zero_point must be null or 1D tensor with size virtual onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext *) const [T = int]zero_point == nullptr || std::all_of(zero_point, zero_point + x_zero_point->Shape().Size(), [](int32_t zp) { return zp == 0; })DequantizeLinear with type int32 should have no zero point or all zero points should be 0Reshapeshape/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/reshape.honnxruntime::Reshape_1::Reshape_1(const onnxruntime::OpKernelInfo &)/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/reshape_helper.honnxruntime::ReshapeHelper::ReshapeHelper(const onnxruntime::TensorShape &, std::vector &, bool)requested_shape[i] >= -1A dimension cannot be less than -1, got !allow_zeroThe input tensor cannot be reshaped to the requested shape. Input shape:, requested shape:unknown_dim == -1i < input_shape.NumDimensions()The dimension with value zero exceeds the dimension size of the input tensor.size != 0 && (input_shape.Size() % size) == 0gsl::narrow_cast(input_shape.Size()) == sizeallowzerovirtual onnxruntime::common::Status onnxruntime::Reshape::Compute(onnxruntime::OpKernelContext *) constshapeTensor->Shape().NumDimensions() == 1Resize/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/upsample.honnxruntime::UpsampleBase::UpsampleBase(const onnxruntime::OpKernelInfo &)info.GetAttr("mode", &mode).IsOK()scalesinfo.GetAttrs("scales", scales_).IsOK()extrapolation_valuecoordinate_transformation_modehalf_pixelasymmetricUpsampleBase`tf_half_pixel_for_nn` is deprecated since opset 13, yet this opset model uses the deprecated attributenearest_moderound_prefer_floorcubic_coeff_aexclude_outsideexclude_outside can be set to 1 only when mode is CUBIC. Current mode is set to nearestlinearcubiconnxruntime::UpsampleMode onnxruntime::UpsampleBase::StringToUpsampleMode(const std::string &)mode attribute is . It can only be (default) or or void onnxruntime::UpsampleBase::ScalesValidation(const std::vector &, const onnxruntime::UpsampleMode) constscale >= 1scale > 0scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1) || scales.size() == 3 || (scales.size() == 5 && scales[0] == 1 && scales[1] == 1)'Linear' mode only support 2-D inputs or 3-D inputs ('Bilinear', 'Trilinear') or 4-D inputs or 5-D inputs with the corresponding outermost 2 scale values being 1 in the Resize operatorUpsample operatorscales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1)'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the pytorch_half_pixeltf_half_pixel_for_nnalign_cornerstf_crop_and_resizeonnxruntime::ResizeCoordinateTransformationMode onnxruntime::UpsampleBase::StringToCoordinateTransformationMode(const std::string &)coordinate_transform_mode:[] is not supportted!round_prefer_ceilfloorceilonnxruntime::ResizeNearestMode onnxruntime::UpsampleBase::StringToNearestMode(const std::string &)nearest_mode:[] is not supported!void onnxruntime::UpsampleBase::ParseScalesData(const onnxruntime::Tensor *, std::vector &) constscales_size > 0ReverseSequencesequence_lens shape must be {batch_size}. Got:. batch_size=/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/reverse_sequence.ccvirtual onnxruntime::common::Status onnxruntime::ReverseSequenceOp::Compute(onnxruntime::OpKernelContext *) constUnknown tensor type of batch_axis/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/reverse_sequence.honnxruntime::ReverseSequenceOp::ReverseSequenceOp(const onnxruntime::OpKernelInfo &)info.GetAttr("batch_axis", &batch_axis).IsOK()time_axisinfo.GetAttr("time_axis", &time_axis).IsOK()batch_axis < 2Invalid batch_axis of . Must be 0 or 1time_axis < 2Invalid time_axis of batch_axis != time_axistime_axis and batch_axis must have different values but both are Invalid sequence length: . Value must be in range [0,gsl::span onnxruntime::Tensor::DataAsSpan() const [T = bool]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = bool]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = double]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = double]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = unsigned char]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned char]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = unsigned short]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned short]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = short]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = short]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = int]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = unsigned int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned int]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = unsigned long long]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned long long]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = onnxruntime::MLFloat16]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = onnxruntime::MLFloat16]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = onnxruntime::BFloat16]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = onnxruntime::BFloat16]ScatterScatterElements/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/scatter.cconnxruntime::Scatter > >::Scatter(const onnxruntime::OpKernelInfo &) [EnabledDataTypes = onnxruntime::TypeList >]Indices vs updates dimensions differs at position= vs Indices must have the same rank as Input. Indices rank=. Input rank=Indices dim= at pos= is greater than input dim=ScatterNDinput tensor and indices tensor must has rank larger than 0. input shape: , indices shape: updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. updates shape: , data shape: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/scatter_nd.ccinvalid indice found, indice = SizeSliceTind'axes' has an axis outside of the tensor dimension count'axes' has duplicates'step' value cannot be 0/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/slice.ccFillVectorsFromInputStarts must be a 1-D arrayEnds must be a 1-D arrayStarts and ends shape mismatchStarts and axes shape mismatchStarts and steps shape mismatchData type for starts and ends inputs' is not supported in this build. Got starts/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/slice.honnxruntime::SliceBase::SliceBase(const onnxruntime::OpKernelInfo &, bool)has_starts && has_ends && attr_starts_.size() == attr_ends_.size()!has_axes || attr_axes_.size() == attr_starts_.size()Invalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributesauto onnxruntime::SliceImpl(onnxruntime::OpKernelContext *, const onnxruntime::Tensor &, SliceOp::PrepareForComputeMetadata &)::(anonymous class)::operator()(SliceIterator &) constoutput == output_endvoid *onnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStep(void *)Unexpected element size of SpaceToDepthDepthToSpace/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/space_depth_ops.ccvirtual onnxruntime::common::Status onnxruntime::SpaceToDepth::Compute(onnxruntime::OpKernelContext *) const [T = float]input_shape.NumDimensions() == 4input_height % this->blocksize_ == 0input_width % this->blocksize_ == 0virtual onnxruntime::common::Status onnxruntime::DepthToSpace::Compute(onnxruntime::OpKernelContext *) const [T = float]input.Shape().NumDimensions() == 4input_depth % (blocksize_ * blocksize_) == 0blocksize/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/space_depth_ops.honnxruntime::SpaceDepthBase::SpaceDepthBase(const onnxruntime::OpKernelInfo &)info.GetAttr("blocksize", &blocksize_).IsOK()CRDDCRonnxruntime::DepthToSpace::DepthToSpace(const onnxruntime::OpKernelInfo &) [T = float]SplitInput cannot be split evenly on selected axis. Input shape= Axis= NumOutputs=Cannot split using values in 'split' attribute. Axis= Input shape= Num entries in 'split' (must equal number of outputs) was Sum of sizes in 'split' (must equal size of selected axis) was /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/split.ccvirtual onnxruntime::common::Status onnxruntime::Split::Compute(onnxruntime::OpKernelContext *) constSplit operator does not support yetsplit/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/split.honnxruntime::SplitBase::SplitBase(const onnxruntime::OpKernelInfo &)std::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value >= 0; })onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &) const [T = float]split_tensor->Shape().NumDimensions() == 1onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &) const [T = int]onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &) const [T = long long]onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &) const [T = unsigned char]onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &) const [T = std::__1::basic_string]Squeeze/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/squeeze.hvirtual onnxruntime::common::Status onnxruntime::Squeeze::Compute(onnxruntime::OpKernelContext *) constaxes_tensor != nullptraxes_tensor->Shape().NumDimensions() == 1static std::vector onnxruntime::SqueezeBase::ComputeOutputShape(const onnxruntime::TensorShape &, const std::vector &)input_shape[i] == 1Dimension of input must be 1 instead of . shape=TileInput count of Tile OP mismatch, the first one is emptyInput count of Tile OP mismatch, the second one is emptythe tensor to be tiled using Tile OP must be atleast 1 dimensional'repeat' input tensor must be 1 dimensional'repeat' input tensor must have the same length as the 'input' tensor/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/tile.ccvirtual onnxruntime::common::Status onnxruntime::Tile::Compute(onnxruntime::OpKernelContext *) const!input_tensor.IsDataType()Tile doesn't have an implementation yet for the type: Transpose of element size not supported in this build. Size=Mismatched data types between input and output Tensors. /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/transpose.ccvirtual onnxruntime::common::Status onnxruntime::Transpose::Compute(onnxruntime::OpKernelContext *) constinput_tensor_ptr != nullptrTransposevoid onnxruntime::DoTransposeEltWise(int64_t, const std::vector &, size_t, const std::vector &, const std::string *, std::string *)num_axes > 0(local_source >= source) && (local_source < source + num_blocks)void onnxruntime::IncrementIndexAndComputeOffsetSetup(onnxruntime::MultiIndex &, size_t, const std::vector &, const std::vector &, size_t)naxes > 0Method IncrementIndexAndComputeOffset assumes this value is strictly positive.void onnxruntime::DoTransposeImpl(int64_t, const std::vector &, size_t, size_t, const std::vector &, const std::string *, std::string *)(local_source >= source) && (local_source < source + num_blocks * num_elts_in_block)void onnxruntime::DoTransposeImpl(int64_t, const std::vector &, size_t, size_t, const std::vector &, const uint8_t *, uint8_t *, size_t)(local_source >= source) && (local_source < source + num_blocks * blocksize)[ perm: does not align with rank of input data: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/transpose.honnxruntime::TransposeBase::TransposeBase(const onnxruntime::OpKernelInfo &)v >= 0 && static_cast(v) <= std::numeric_limits::max()Attribute perm of Transpose has an invalid value. Value is outside range. is repeated.bool onnxruntime::TypedDoTransposeEltWise(int64_t, const std::vector &, size_t, const std::vector &, const uint8_t *, uint8_t *) [T = unsigned long long](local_source >= source) && (local_source < source + sizeof(T) * num_blocks)bool onnxruntime::TypedDoTransposeEltWise(int64_t, const std::vector &, size_t, const std::vector &, const uint8_t *, uint8_t *) [T = unsigned int]bool onnxruntime::TypedDoTransposeEltWise(int64_t, const std::vector &, size_t, const std::vector &, const uint8_t *, uint8_t *) [T = unsigned short]bool onnxruntime::TypedDoTransposeEltWise(int64_t, const std::vector &, size_t, const std::vector &, const uint8_t *, uint8_t *) [T = unsigned char]Trilu/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/trilu.ccvirtual onnxruntime::common::Status onnxruntime::Trilu::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(k)upper/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/trilu.honnxruntime::Trilu::Trilu(const onnxruntime::OpKernelInfo &)info.GetAttr("upper", &temp).IsOK()UniqueUnsupported tensor type of sortedgsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = long long]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = signed char]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = signed char]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = std::__1::basic_string]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = std::__1::basic_string]Unsqueeze/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/unsqueeze.cconnxruntime::common::Status onnxruntime::UnsqueezeBase::PrepareCompute(onnxruntime::OpKernelContext *, onnxruntime::UnsqueezeBase::Prepare &) constaxes_tensor->Shape().NumDimensions() == 0 || axes_tensor->Shape().NumDimensions() == 1'axes' has an out of range axis'axes' has a duplicate axisnullptr != p.output_tensor/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/unsqueeze.honnxruntime::UnsqueezeBase::UnsqueezeBase(const onnxruntime::OpKernelInfo &)info.GetAttrs("axes", axes_).IsOK()Upsample/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/upsample.ccvirtual onnxruntime::common::Status onnxruntime::Upsample::Compute(onnxruntime::OpKernelContext *) const [T = float]roi_input_idx_ > 0sizes == nullptrsizes != nullptr && sizes->Shape().Size() != 0X->Shape().GetDims().size() == output_dims.size()Resize: input tensor's rank does not match the output tensor's rank.onnxruntime::common::Status onnxruntime::Upsample::BaseCompute(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &, const std::vector &) const [T = float]output_dims.size() == dims.size()Resize: input tensor's dimension does not match the scales.Upsample: input tensor's dimension does not match the scales.Resize: size of roi array should be 2 * N where N is the rank of input tensor X.BaseComputeResize: unexpected modeUpsample: unexpected modeResize: input/output value is nullptrUpsample: input/output value is nullptrResize: input/output value's dimension mismatchUpsample: input/output value's dimension mismatchResize: input shape needs to be at least a single dimensionUpsample: input shape needs to be at least a single dimension.void onnxruntime::UpsampleBase::ParseScalesDataFromOutputSize(const std::vector &, const std::vector &, std::vector &) constoutput_dims[i] == 0Input dim is zero but required output dim is non-zero. Cannot scale 0 by any factor to generate a non-zero value. Dimension: Input dim value: Output dim value: virtual onnxruntime::common::Status onnxruntime::Upsample::Compute(onnxruntime::OpKernelContext *) const [T = int]onnxruntime::common::Status onnxruntime::Upsample::BaseCompute(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &, const std::vector &) const [T = int]virtual onnxruntime::common::Status onnxruntime::Upsample::Compute(onnxruntime::OpKernelContext *) const [T = unsigned char]onnxruntime::common::Status onnxruntime::Upsample::BaseCompute(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &, const std::vector &) const [T = unsigned char]WhereGeluonnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::ParametricSoftplus]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::ParametricSoftplus]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::ScaledTanh]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::ScaledTanh]/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/attnlstm/bahdanau_attention.cconnxruntime::contrib::BahdanauAttention::BahdanauAttention(onnxruntime::AllocatorPtr, const logging::Logger &, int, int, int, int, int, bool, concurrency::ThreadPool *) [T = float]!normalize_virtual void onnxruntime::contrib::BahdanauAttention::PrepareMemory(const gsl::span &, const gsl::span &) [T = float]mem_steps <= max_memory_steps_ && mem_steps > 0Real memory steps is not in (0, AttnLSTM/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.ccvirtual onnxruntime::common::Status onnxruntime::contrib::DeepCpuAttnLstmOp::Compute(onnxruntime::OpKernelContext *) constAttention mechanism memory shape error! Expected: {}, actural: Attention mechanism memory sequence lengths must have shape {Attention mechanism memory sequence lengths value must in (0, ], while found!Attention memory layer weight shape error! Expected:{, am_attn_size}, Got:Attention query layer weight shape error! Expected:{}, Got: Attention v weight shape error! Expected:{}. Got: Attention layer weight shape error! Expected: {, aw_attn_size}. Got:Input initial_c must have shape {Input P must have shape {/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.honnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOp(const onnxruntime::OpKernelInfo &)static_cast(activation_func_names.size()) == num_directions_ * 3 [seqno= row[AttentionInput 'input' is expected to have 3 dimensions, got Input 'weights' is expected to have 2 dimensions, got Input 1 dimension 0 should have same length as dimension 2 of input 0Input 'bias' is expected to have 1 dimension, got Input 'bias' dimension 0 should have same length as dimension 1 of input 'weights'Input 'past' is expected to have 5 dimension, got Inputs 'past' dimension 1 shall have same length as dimension 0 of input 0Inputs 'past' dimension 2 shall have length of num_headsInputs 'past' dimension 2 shall have length of Inputs 'mask_index' with 1D data shall have length of batch_size or 2 * batch_sizeInputs 'mask_index' with 2D data shall have shape batch_size x (past_sequence_length + sequence_length)Inputs 'mask_index' with 3D data shall have shape batch_size x sequence_length x (past_sequence_length + sequence_length)Inputs 'mask_index' with 4D data shall have shape batch_size x 1 x max_sequence_length x max_sequence_length)Inputs 'mask_index' with 4D data shall have is_unidirectional_ set to falseInput 'mask_index' is expected to have 1, 2, 3 or 4 dimensions, got /Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/bert/attention.cconnxruntime::Tensor *onnxruntime::contrib::AttentionBase::GetPresent(onnxruntime::OpKernelContext *, const onnxruntime::Tensor *, int, int, int, int &) constExpect to have present state output when past state input is givennum_heads/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/bert/attention_base.honnxruntime::contrib::AttentionBase::AttentionBase(const onnxruntime::OpKernelInfo &)info.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0unidirectional/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/bert/attention_cpu_base.hApplyAttention/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/bert/bias_gelu.ccvirtual onnxruntime::common::Status onnxruntime::contrib::BiasGelu::Compute(onnxruntime::OpKernelContext *) const [T = float, use_approximation = false]use_approximationBiasGeluInput 0 is expected to have 1 or more dimensions, got Input 1 is expected to have 1 dimensions, got Input 1 dimension 0 should have same length as the last dimension of input 0EmbedLayerNormalization/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/bert/embed_layer_norm.cconnxruntime::contrib::EmbedLayerNorm::EmbedLayerNorm(const onnxruntime::OpKernelInfo &) [T = float]input_ids is expected to have 2 dimensions, got word_embedding is expected to have 2 dimensions, got position_embedding is expected to have 2 dimensions, got segment_embedding is expected to have 2 dimensions, got word_embedding and position_embedding shall have same dimension 1beta is expected to have 1 dimensions, got beta is expected to have size of gamma is expected to have 1 dimensions, got gamma is expected to have size of FastGeluCDistmetric/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/cdist.honnxruntime::contrib::CDist::CDist(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttr("metric", &metric).IsOK()sqeuclideaneuclideanThe first input of CDist kernel has wrong shape: The second input of CDist kernel has wrong shape: Input shape dimensions mismatch:onnxruntime::contrib::CDist::CDist(const onnxruntime::OpKernelInfo &) [T = double]ConvTransposeWithDynamicPads/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/cpu_contrib_kernels.ccRegisterNchwcKernelsRegisterQuantizationKernelsRegisterCpuContribKernelsCropborder/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/crop.hAttribute border needs to be specified with four border elements, got Input is expected to have four dimensions corresponding to [N,C,H,W], got input dimensions insteadInput's height () needs to be greater than or equal to the topBorder () + bottomBorder (Input's width () needs to be greater than or equal to the leftBorder () + rightBorder () + scale_[0] () + scale_[1] (CropAndResizebilinear/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/crop_and_resize.honnxruntime::contrib::CropAndResize::CropAndResize(const onnxruntime::OpKernelInfo &) [T = float] specified. It should be either bilinear or nearestNull crop_size_ptrNumber of dimensions for crop size should be exactly 1/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/element_wise_ops.honnxruntime::contrib::Affine::Affine(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttr("alpha", &alpha_).IsOK()info.GetAttr("beta", &beta_).IsOK()onnxruntime::contrib::Scale::Scale(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttr("scale", &scale_).IsOK()axisExpandDims/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/expand_dims.hvirtual onnxruntime::common::Status onnxruntime::contrib::ExpandDims::Compute(onnxruntime::OpKernelContext *) constaxis_tensor->Shape().IsScalar()axis <= X_NumDims && axis >= -X_NumDimsAxis must be within range [ Axis is unimplemented activation: activation_params count mismatchFusedConv/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/fused_conv.cconnxruntime::contrib::FusedConvFloat::FusedConvFloat(const onnxruntime::OpKernelInfo &)FusedGemmactivationactivation_/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/fused_gemm.cconnxruntime::contrib::FusedGemm::FusedGemm(const onnxruntime::OpKernelInfo &) [T = float]TransposeMatMulFusedMatMul/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/image_scaler.honnxruntime::contrib::ImageScaler::ImageScaler(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttrs("bias", bias_).IsOK()Bias size () does not match the number of channels (InverseLayerNormalizationSimplifiedLayerNormalization/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/layer_norm.cconnxruntime::contrib::LayerNorm::LayerNorm(const onnxruntime::OpKernelInfo &) [T = float, simplified = false]op_kernel_info.GetAttr("axis", &axis_).IsOK()onnxruntime::contrib::LayerNorm::LayerNorm(const onnxruntime::OpKernelInfo &) [T = float, simplified = true]onnxruntime::contrib::LayerNorm::LayerNorm(const onnxruntime::OpKernelInfo &) [T = double, simplified = false]onnxruntime::contrib::LayerNorm::LayerNorm(const onnxruntime::OpKernelInfo &) [T = double, simplified = true]MatMulInteger16/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/matmul_integer16.ccvirtual onnxruntime::common::Status onnxruntime::contrib::MatMulInteger16::Compute(onnxruntime::OpKernelContext *) const [T1 = short, T2 = short, T3 = int]A != nullptr && B != nullptrMaxpoolWithMask/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/maxpool_with_mask.hacross_channels/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/mean_variance_normalization.honnxruntime::MeanVarianceNormalization_0::MeanVarianceNormalization_0(const onnxruntime::OpKernelInfo &, bool) [T = float]info.GetAttr("across_channels", &across_channels_).IsOK()normalize_varianceinfo.GetAttr("normalize_variance", &normalize_variance_).IsOK()MurmurHash3/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/murmur_hash3.ccvirtual onnxruntime::common::Status onnxruntime::contrib::MurmurHash3::Compute(onnxruntime::OpKernelContext *) constkeyssizeof(uint32_t) == output_element_bytesinput_num_bytes % 4 == 0/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/nchwc_ops.ccvirtual onnxruntime::common::Status onnxruntime::contrib::ReorderInput::Compute(onnxruntime::OpKernelContext *) constX_rank == 4(channels % 4) == 0virtual onnxruntime::common::Status onnxruntime::contrib::ReorderOutput::Compute(onnxruntime::OpKernelContext *) constchannels_ <= X_shape[1]virtual onnxruntime::common::Status onnxruntime::contrib::NchwcConv::Compute(onnxruntime::OpKernelContext *) constX_shape.NumDimensions() == 4(static_cast(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)Unsupported convolution size.output and sum shape must matchonnxruntime::common::Status onnxruntime::contrib::NchwcPoolBase::NchwcPool(onnxruntime::OpKernelContext *, MLAS_POOLING_KIND) const(X_shape[1] % MlasNchwcGetBlockSize()) == 0virtual onnxruntime::common::Status onnxruntime::contrib::NchwcUpsample::Compute(onnxruntime::OpKernelContext *) constX_shape.size() == 4ReorderInputReorderOutput/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/nchwc_ops.honnxruntime::contrib::ReorderInput::ReorderInput(const onnxruntime::OpKernelInfo &)info.GetAttr("channels_last", &channels_last_).IsOK()onnxruntime::contrib::ReorderOutput::ReorderOutput(const onnxruntime::OpKernelInfo &)info.GetAttr("channels", &channels_).IsOK()channels_ > 0invalid channel countonnxruntime::contrib::NchwcConv::NchwcConv(const onnxruntime::OpKernelInfo &)GetFusedActivationAttr(info, activation_).IsOK()onnxruntime::contrib::NchwcPoolBase::NchwcPoolBase(const onnxruntime::OpKernelInfo &)pool_attrs_.kernel_shape.size() == 2onnxruntime::contrib::NchwcUpsample::NchwcUpsample(const onnxruntime::OpKernelInfo &)info.GetAttrs("scales", scales_).IsOK()scales_.size() == 4scales_[0] == 1 && scales_[1] == 1 && scales_[2] >= 1 && scales_[3] >= 1info.GetAttr("coordinate_transformation_mode", &transformation_mode).IsOK()Unsupported transformation mode '' for NCHWc Upsampletransformation_mode_ == TransformationMode::ASYMMETRICUnsupported mode 'QLinearAddQLinearMul/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/qlinear_binary_op.ccvoid onnxruntime::contrib::(anonymous namespace)::QLinearImpl(onnxruntime::OpKernelContext &, double, const onnxruntime::ProcessBroadcastSpanFuncs &) [T = signed char]IsScalarOr1ElementVector(tensor_a_scale)MatmulInteger : input1 A_scale must be a scalar or 1D tensor of size 1tensor_a_zero_point == nullptr || IsScalarOr1ElementVector(tensor_a_zero_point)MatmulInteger : input1 A_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_b_scale)MatmulInteger : input1 B_scale must be a scalar or 1D tensor of size 1tensor_b_zero_point == nullptr || IsScalarOr1ElementVector(tensor_b_zero_point)MatmulInteger : input1 B_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_c_scale)MatmulInteger : input1 C_scale must be a scalar or 1D tensor of size 1tensor_c_zero_point == nullptr || IsScalarOr1ElementVector(tensor_c_zero_point)MatmulInteger : input1 C_zero_point must be a scalar or 1D tensor of size 1 if givenvoid onnxruntime::BroadcastLooper(TBroadcastHelper &, const onnxruntime::ProcessBroadcastSpanFuncs &) [TBroadcastHelper = onnxruntime::contrib::(anonymous namespace)::QLinearBroadcastHelper]void onnxruntime::contrib::(anonymous namespace)::QLinearImpl(onnxruntime::OpKernelContext &, double, const onnxruntime::ProcessBroadcastSpanFuncs &) [T = unsigned char]/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/qlinear_concat.cconnxruntime::contrib::QLinearConcat::QLinearConcat(const onnxruntime::OpKernelInfo &)input_def_count >= 8 && (input_def_count - 2) % 3 == 0At least two inputs are needed, and each input must be (tensor, scale, zero_point) tuple!tensor_x_scale->IsDataType()Input scale is not float for input def @tensor_x_zero_point->GetElementType() == tensor_y_zero_point->GetElementType()Wrong input type encountered for zero point input def @virtual onnxruntime::common::Status onnxruntime::contrib::QLinearConcat::Compute(onnxruntime::OpKernelContext *) constinput_count_x3 >= 6 && input_count_x3 % 3 == 0Input scale is not float for quantized input @Wrong input type encountered for zero point of quantized input @QLinearConcat/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/qlinear_global_average_pool.ccvirtual onnxruntime::common::Status onnxruntime::contrib::QLinearGlobalAveragePool::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(tensor_x_zero_point)IsScalarOr1ElementVector(tensor_y_zero_point)QLinearGlobalAveragePoolQLinearLeakyReluQLinearSigmoid/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/qlinear_lookup_table.ccvoid onnxruntime::contrib::QlinearBuildLookupTable(uint8_t *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::contrib::LookupTableArrayTransformer &) [T = unsigned char]QlinearBuildLookupTable : input X_scale must be a scalar or 1D tensor of size 1QlinearBuildLookupTable : input X_zero_point must be a scalar or 1D tensor of size 1QlinearBuildLookupTable : input Y_scale must be a scalar or 1D tensor of size 1QlinearBuildLookupTable : input Y_zero_point must be a scalar or 1D tensor of size 1void onnxruntime::contrib::QlinearBuildLookupTable(uint8_t *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::contrib::LookupTableArrayTransformer &) [T = signed char]/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/qlinear_pool.ccvirtual onnxruntime::common::Status onnxruntime::contrib::QLinearAveragePool::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(tensor_x_scale)tensor_x_zero_point == nullptr || IsScalarOr1ElementVector(tensor_x_zero_point)input x_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_y_scale)tensor_y_zero_point == nullptr || IsScalarOr1ElementVector(tensor_y_zero_point)input y_zero_point must be a scalar or 1D tensor of size 1 if givenUnsupported 'dtype' in QLinear Pooling:QLinear Pooling unsupported pooling size!QLinearAveragePoolQAttention/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/attention_quant.ccinput scale must be a scalar or 1D tensor of size 1input zero point must be a scalar or 1D tensor of size 1./Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/dynamic_quantize_lstm.ccW_zero_point must have shape {} for per-tensor/layer quantization or shape {, 4*} for per-channel quantization. Actual:R_zero_pointW_scaleR_scaleDynamicQuantizeLSTM : Weight zero point must be zeroWeight point must be constantRecurrentDynamicQuantizeLSTM/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/dynamic_quantize_matmul.ccComputeCommononnxruntime::common::Status onnxruntime::contrib::MatMulIntegerToFloatBase::ComputeCommon(onnxruntime::OpKernelContext *, const uint8_t *, const onnxruntime::TensorShape &, float, uint8_t, const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *) constIsBQuantParamSupported(b_zp_tensor->Shape(), b_tensor ? b_tensor->Shape() : b_shape_)virtual onnxruntime::common::Status onnxruntime::contrib::MatMulIntegerToFloat::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(a_zero_point_tensor)MatMulIntegerToFloat : input a zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.DynamicQuantizeMatMulMatMulIntegerToFloat/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/nhwc_max_pool.ccvirtual onnxruntime::common::Status onnxruntime::contrib::NhwcMaxPool::Compute(onnxruntime::OpKernelContext *) constinput_shape.Size() > 0 || N == 0NhwcMaxPoolSampleOpSkipLayerNormalization/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/skip_layer_norm.cconnxruntime::contrib::SkipLayerNorm::SkipLayerNorm(const onnxruntime::OpKernelInfo &) [T = float]epsilon_ >= 0input is expected to have 3 dimensions, got gamma is expected to have 1 dimension, got beta is expected to have 1 dimension, got bias is expected to have 1 dimension, got onnxruntime::contrib::SkipLayerNorm::SkipLayerNorm(const onnxruntime::OpKernelInfo &) [T = double]Tokenizer/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/tokenizer.cconnxruntime::contrib::Tokenizer::Tokenizer(const onnxruntime::OpKernelInfo &)pad_valuemincharnummincharnum_ > 0separatorsEither one of the separators OR tokenexp attributes required but none is set!tokenexp.empty()!separators.empty()!char_tokenezation_ || mincharnum_ < 2Can not digest separators: Can not digest tokenexp: Input string contains invalid utf8 chars: Match contains invalid utf8 chars: tensor(string) expected as inputInput dimensions are either [C] or [N][C] allowedConv filter size does not match embedding_size attribute. embedding_size attribute: conv filter size: Conv kernal size 1 does not match conv_window_size attribute . conv_window_size attribute: conv kernal size 1: Char embedding size does not match char_embedding_size attribute. char_embedding_size attribute: Char embedding size: Char embedding size does not match conv kernal size 2. Conv kernal size 2 : /Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/word_conv_embedding.ccWordConvEmbeddingembedding_sizeconv_window_sizechar_embedding_size/Users/runner/work/1/s/onnxruntime/core/util/math_cpu.ccGemv found an unexpected CBLAS_TRANSPOSE input ofvoid onnxruntime::math::Gemv(CBLAS_TRANSPOSE, int, int, float, const T *, const T *, float, T *, Provider *) [T = double, Provider = onnxruntime::CPUMathUtil]bool onnxruntime::math::NextPosition(int64_t, const int64_t *, int64_t *)dims[d_i] < d_maxReceived null OrtThreadingOptionsReceived invalid value for allow_spinning. Valid values are 0 or 1/Users/runner/work/1/s/onnxruntime/core/framework/allocation_planner.cc[CreatePlanCan not find the node onnxruntime::common::Status onnxruntime::PlannerImpl::ComputeUseCounts()p_kernel_defShould not have entry in kernel create info with nullptr for kernel_defCan not find the execution provider ComputeUseCountsallocatoronnxruntime::OrtValueIndex onnxruntime::PlannerImpl::Index(const onnxruntime::OrtValueName &)void onnxruntime::PlannerImpl::ProcessDef(onnxruntime::OrtValueIndex, const onnxruntime::NodeArg *)id >= 0 && static_cast(id) < ort_value_info_.size()int &onnxruntime::PlannerImpl::UseCount(onnxruntime::OrtValueIndex)n >= 0 && static_cast(n) < ort_value_info_.size()const onnxruntime::KernelCreateInfo &onnxruntime::GetKernelCreateInfo(const std::unordered_map > &, onnxruntime::NodeIndex)entry != kernel_create_info_map.cend()SessionState should have saved the KernelCreateInfo prior to this running. NodeIndex:onnxruntime::AllocPlanPerValue &onnxruntime::PlannerImpl::AllocPlan(onnxruntime::OrtValueIndex)n >= 0 && static_cast(n) < plan_.allocation_plan.size()ComputeReusePlanonnxruntime::common::Status onnxruntime::PlannerImpl::ComputeReusePlan()!IsNonTensor(*node_output)GeneratePlanForWeightsOrtMemoryInfo onnxruntime::PlannerImpl::GetLocationForNodeInput(size_t, const onnxruntime::Node &)void onnxruntime::PlannerImpl::Reuse(onnxruntime::OrtValueIndex, onnxruntime::OrtValueIndex, onnxruntime::AllocKind)reused != reused_for/Users/runner/work/1/s/onnxruntime/core/framework/sequential_execution_plan.hvoid onnxruntime::AllocPlanPerValue::ProgramCounter::AddStart(size_t)starts_.size() == ends_.size()starts_.empty() || start > ends_.back()static size_t onnxruntime::PlannerImpl::GetElementSize(const onnx::DataType &)nullptr != tensor_type_baseonnxruntime::OrtValueIndex &onnxruntime::PlannerImpl::Buffer(onnxruntime::OrtValueIndex)void onnxruntime::AllocPlanPerValue::ProgramCounter::AddEnd(size_t)starts_.size() == ends_.size() + 1end >= starts_.back()void onnxruntime::PlannerImpl::GenerateDeallocationPlan()AllocPlan(ml_value_idx).program_counter.Ends().back() == program_countervoid onnxruntime::PlannerImpl::VerifyMemoryTimeSchedule()entry.program_counter.HasValidEntries()Invalid program_counter entries at index CudaCudaPinnedSpecified device is not supported./Users/runner/work/1/s/onnxruntime/core/framework/allocator.ccoperator()/Users/runner/work/1/s/include/onnxruntime/core/common/logging/logging.hstatic const onnxruntime::logging::Logger &onnxruntime::logging::LoggingManager::DefaultLogger()OrtMemoryInfo:[name: id: OrtMemType: OrtAllocatorType:]Device:[DeviceType: MemoryType: DeviceId:/Users/runner/work/1/s/onnxruntime/core/framework/allocatormgr.ccCreateAllocatorReceived invalid value of arena_extend_strategy void onnxruntime::AllocatorManager::InsertAllocator(onnxruntime::AllocatorPtr)/Users/runner/work/1/s/onnxruntime/core/framework/bfc_arena.ccBFCArenaCreating BFCArena for with following configs: initial_chunk_size_bytes: max_dead_bytes_per_chunk: initial_growth_chunk_size_bytes: memory limit: arena_extend_strategy: Creating bins of max chunk size onnxruntime::BFCArena::BFCArena(std::unique_ptr, size_t, onnxruntime::ArenaExtendStrategy, int, int, int)BinForSize(bin_size) == BinFromIndex(b)BinForSize(bin_size + 255) == BinFromIndex(b)BinForSize(bin_size * 2 - 1) == BinFromIndex(b)BinForSize(bin_size * 2) != BinFromIndex(b)BFCArena::Chunk *onnxruntime::BFCArena::ChunkFromHandle(onnxruntime::BFCArena::ChunkHandle)h < chunks_.size()Available memory of is smaller than requested bytes of Failed to allocate memory for requested buffer of size ExtendExtended allocation by bytes.Total allocated bytes: Allocated memory at ReserveReserving memory in BFCArena for size: virtual void *onnxruntime::BFCArena::Reserve(size_t)reserved_chunks_.find(ptr) == reserved_chunks_.end()h != kInvalidChunkHandleAllocateRawInternaltried to allocate 0 bytesExtending BFCArena for . bin_num: (requested) num_bytes: (actual) rounded_bytes:Failed to find a free memory block despite calling Extend. rounded_bytes=BFC Arena ran out of memory trying to allocate . Current allocation summary follows.void *onnxruntime::BFCArena::AllocateRawInternal(size_t, bool)void *onnxruntime::BFCArena::FindChunkPtr(onnxruntime::BFCArena::BinNum, size_t, size_t)!chunk->in_use()void onnxruntime::BFCArena::SplitChunk(BFCArena::ChunkHandle, size_t)!c->in_use() && (c->bin_num == kInvalidBinNum) BFC Arena shrunk by bytes. The total allocated bytes is now void onnxruntime::BFCArena::DeallocateRawInternal(void *)void onnxruntime::BFCArena::Merge(BFCArena::ChunkHandle, BFCArena::ChunkHandle)!c1->in_use() && !c2->in_use()c2->prev == h1void onnxruntime::BFCArena::InsertFreeChunkIntoBin(BFCArena::ChunkHandle)void onnxruntime::BFCArena::RemoveFreeChunkIterFromBin(BFCArena::Bin::FreeChunkSet *, const BFCArena::Bin::FreeChunkSet::iterator &)!c->in_use() && (c->bin_num != kInvalidBinNum)void onnxruntime::BFCArena::RemoveFreeChunkFromBin(BFCArena::ChunkHandle)BinFromIndex(c->bin_num)->free_chunks.erase(h) > 0void onnxruntime::BFCArena::FreeAndMaybeCoalesce(BFCArena::ChunkHandle)c->in_use() && (c->bin_num == kInvalidBinNum)std::array onnxruntime::BFCArena::get_bin_debug_info()bin->free_chunks.count(h) == 1c->bin_num == bin_numDumpMemoryLogAllocator:Bin size: Chunks in_use/total (if not zero). Allocated bytes in_use/total. Requested bytes.void onnxruntime::BFCArena::DumpMemoryLog(size_t)b->free_chunks.size() == bin_info.total_chunks_in_bin - bin_info.total_chunks_in_use: Chunks . Bytes . Requested Diff between in-use and requested bytes is Bin for bytes has max bytes of , Chunk State: Overall chunks summary: Chunk Free at of size Summary of in-use chunks by size: chunks of size . Total Sum Total of in-use chunks: Stats: auto onnxruntime::BFCArena::Extend(size_t)::(anonymous class)::operator()(const size_t) constIncorrect arena extend strategy./Users/runner/work/1/s/onnxruntime/core/framework/bfc_arena.honnxruntime::BFCArena::AllocationRegion::AllocationRegion(void *, size_t, int64_t)0 == memory_size % kMinAllocationSizeRegionForCould not find Region for int onnxruntime::BFCArena::AllocationRegion::IndexFor(const void *) constp_int >= base_intp_int < base_int + memory_size_void onnxruntime::BFCArena::RegionManager::RemoveAllocationRegion(void *)entry != regions_.end()Could not find Region for: Size: | Requested Size: | in_use: , prev: , next: Limit: InUse: TotalAllocated: MaxInUse: NumAllocs: NumReserves: NumArenaExtensions: NumArenaShrinkages: MaxAllocSize: /Users/runner/work/1/s/onnxruntime/core/framework/config_options.ccAddConfigEntryConfig with key [] already exists with value []. It will be overwritten/Users/runner/work/1/s/onnxruntime/core/framework/data_transfer.ccvirtual common::Status onnxruntime::CPUDataTransfer::CopyTensor(const onnxruntime::Tensor &, onnxruntime::Tensor &, int) constsrc.SizeInBytes() == dst.SizeInBytes()data_transfer registered is nullptr.Tensor size mismatchThere's no data transfer registered for copying tensors from to /Users/runner/work/1/s/onnxruntime/core/framework/data_transfer_manager.ccCopyTensors/Users/runner/work/1/s/onnxruntime/core/framework/data_types.ccbool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Map &, const onnx::TypeProto_Map &)bool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Sequence &, const onnx::TypeProto_Sequence &)virtual bool onnxruntime::TensorTypeBase::IsCompatible(const onnx::TypeProto &) constthisProto->value_case() == TypeProto::ValueCase::kTensorTypeutils::HasElemType(thisProto->tensor_type())virtual bool onnxruntime::SparseTensorTypeBase::IsCompatible(const onnx::TypeProto &) constthisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypeutils::HasElemType(thisProto->sparse_tensor_type())virtual bool onnxruntime::SequenceTensorTypeBase::IsCompatible(const onnx::TypeProto &) constthisProto->value_case() == TypeProto::ValueCase::kSequenceTypeutils::HasElemType(thisProto->sequence_type())bool onnxruntime::NonTensorTypeBase::IsMapCompatible(const onnx::TypeProto &) constthisProto->value_case() == TypeProto::ValueCase::kMapTypeutils::HasKeyType(thisProto->map_type())bool onnxruntime::NonTensorTypeBase::IsSequenceCompatible(const onnx::TypeProto &) constvirtual void onnxruntime::NonTensorTypeBase::FromDataContainer(const void *, size_t, OrtValue &) constNot implementedvirtual void onnxruntime::NonTensorTypeBase::ToDataContainer(const OrtValue &, size_t, void *) const(null)floatbooldoublestringint8uint8int16uint16int32uint32int64uint64float16bfloat16tensor type MLDataType for: is not currently registered or supportedonnxruntime::utils::ContainerChecker::ContainerChecker(onnxruntime::MLDataType)void onnxruntime::data_types_internal::DataTypeRegistry::RegisterDataType(onnxruntime::MLDataType)proto != nullptrp.secondWe do not expect duplicate registration of types for: GetElementType is not implemented/Users/runner/work/1/s/include/onnxruntime/core/framework/data_types.hstatic void onnxruntime::data_types_internal::SetMapTypes, std::__1::basic_string >::Set(onnx::TypeProto &) [K = std::__1::basic_string, V = std::__1::basic_string]value_proto != nullptr expected to be a registered ONNX typestatic void onnxruntime::data_types_internal::SetMapTypes, long long>::Set(onnx::TypeProto &) [K = std::__1::basic_string, V = long long]static void onnxruntime::data_types_internal::SetMapTypes, float>::Set(onnx::TypeProto &) [K = std::__1::basic_string, V = float]static void onnxruntime::data_types_internal::SetMapTypes, double>::Set(onnx::TypeProto &) [K = std::__1::basic_string, V = double]static void onnxruntime::data_types_internal::SetMapTypes >::Set(onnx::TypeProto &) [K = long long, V = std::__1::basic_string]static void onnxruntime::data_types_internal::SetMapTypes::Set(onnx::TypeProto &) [K = long long, V = long long]static void onnxruntime::data_types_internal::SetMapTypes::Set(onnx::TypeProto &) [K = long long, V = float]static void onnxruntime::data_types_internal::SetMapTypes::Set(onnx::TypeProto &) [K = long long, V = double]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = float]elem_proto != nullptrstatic void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = double]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = signed char]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = unsigned char]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = short]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = unsigned short]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = int]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = unsigned int]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = long long]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = unsigned long long]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = bool]static void onnxruntime::data_types_internal::SetSequenceType >::Set(onnx::TypeProto &) [T = std::__1::basic_string]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = onnxruntime::MLFloat16]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = onnxruntime::BFloat16]static void onnxruntime::data_types_internal::SetSequenceType, float, std::__1::less >, std::__1::allocator, float> > > >::Set(onnx::TypeProto &) [T = std::__1::map, float, std::__1::less >, std::__1::allocator, float> > >]static void onnxruntime::data_types_internal::SetSequenceType, std::__1::allocator > > >::Set(onnx::TypeProto &) [T = std::__1::map, std::__1::allocator > >]/Users/runner/work/1/s/onnxruntime/core/framework/endian_utils.ccCopyLittleEndiansource and destination buffer size mismatch/Users/runner/work/1/s/onnxruntime/core/framework/ex_lib_loader.cc~ExLibLoaderUnloading DSO Failed to unload DSO: A dso with name has already been loaded.LoadExternalLibCaught exception while destructing CustomOpsLoader with message: Caught exception while loading custom ops with message: /Users/runner/work/1/s/onnxruntime/core/framework/execution_frame.cconnxruntime::IExecutionFrame::IExecutionFrame(const onnxruntime::OrtValueNameIdxMap &, const onnxruntime::NodeIndexInfo &, const std::vector &)node_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx()node_index_info and ort_value_idx_map are out of sync and cannot be usedonnxruntime::common::Status onnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValue(const int, int, const onnxruntime::TensorShape *, OrtValue *&, const onnxruntime::Node &, size_t)shape && tensor.Shape() == *shapeOrtValue shape verification failed. Current shape: Requested shape:invalid index void onnxruntime::IExecutionFrame::Init(const std::vector &, const std::vector &, const std::unordered_map &, const std::vector &)feeds.size() == feed_mlvalue_idxs.size()fetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size()Fetches vector passed to GetOutputs contains entries which doesn't match the number of fetches the frame was initialized with of onnxruntime::ExecutionFrame::ExecutionFrame(const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::unordered_map &, const onnxruntime::SessionState &)buffers_.find(location) == buffers_.end()static_activation_memory_sizes_in_byte_.find(location.name) == static_activation_memory_sizes_in_byte_.end()Memory type should only appear once.ExecutionFrameAllocation of memory pattern buffer for returned nullptrTrying to allocate memory for unused optional inputs/outputsTensor shape cannot contain any negative valueonnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper(OrtValue &, int, onnxruntime::MLDataType, const OrtMemoryInfo &, const onnxruntime::TensorShape &, bool)ort_value.Fence() == nullptrAllocateMLValueTensorSelfOwnBufferHelperFor ort_value with index: , block in memory pattern size is: but the actually size is: , fall back to default allocation behaviorShape mismatch attempting to re-use buffer. . Validate usage of dim_value (values should be > 0) and dim_param (all values with the same string should equate to the same size) in shapes in the model.AllocateMLValueTensorPreAllocateBufferonnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlan(OrtValue &, int, const onnxruntime::TensorShape *, size_t)ort_value_index >= 0 && static_cast(ort_value_index) < alloc_plan.size()Tried to allocate without valid type information, ort_value index=We don't expect custom allocators for non-tensor types, so a shape is mandatory here.AllocateAsPerAllocationPlanInvalid allocation kind: VerifyOutputSizesExpected shape from model of does not match actual shape of for output ReleaseMLValueImplconst onnxruntime::AllocPlanPerValue &onnxruntime::ExecutionFrame::GetAllocationPlan(int)ort_value_idx >= 0 && static_cast(ort_value_idx) < alloc_plan.size()TraceAllocateTraceAllocation for ort_value_idx= size= failed: void onnxruntime::ExecutionFrame::TraceFree(int)TraceFreeTraceFree for ort_value_idx=Memory pattern planner is not enabled on this execution framework./Users/runner/work/1/s/onnxruntime/core/framework/execution_frame.hconst OrtValue &onnxruntime::IExecutionFrame::GetMLValue(int) constort_value_index >= 0 && static_cast(ort_value_index) < all_values_size_int onnxruntime::NodeIndexInfo::GetMLValueIndex(int) constoffset >= 0 && static_cast(offset) < node_values_size_ failed. Error:onnxruntime::common::Status onnxruntime::AllocateSparseTensor(OrtValue &, const onnxruntime::DataTypeImpl &, onnxruntime::AllocatorPtr, const onnxruntime::TensorShape &, size_t, bool, const onnxruntime::SessionState &)mlvalue.Fence() == nullptr/Users/runner/work/1/s/onnxruntime/core/framework/execution_provider.ccvoid onnxruntime::IExecutionProvider::InsertAllocator(onnxruntime::AllocatorPtr)TryInsertAllocatorduplicated allocator: IExecutionProvider::Compile with fused Node is not implemented by IExecutionProvider::Compile with fused Node and dll path is not implemented by IExecutionProvider::Compile with FusedNodeAndGraph is not implemented by virtual int onnxruntime::IExecutionProvider::GenerateMetaDefId(const onnxruntime::GraphViewer &, uint64_t &) constmetadef_id_generator_IExecutionProvider constructor must be called with true for use_metadef_id_creator/Users/runner/work/1/s/onnxruntime/core/framework/fallback_cpu_capability.ccstd::unordered_set onnxruntime::GetCpuPreferredNodes(const onnxruntime::GraphViewer &, const std::string &, const std::vector &, const std::vector &)kernel_info != nullptrGetCpuPreferredNodesORT optimization- Force fallback to CPU execution for node: because the CPU execution path is deemed faster than overhead involved with execution on other EPs capable of executing this nodeForEachWithIndexCandidate for fallback CPU execution: /Users/runner/work/1/s/onnxruntime/core/framework/feeds_fetches_manager.ccMapNamesToMLValueIdxsError mapping feeds: Error mapping output names: void onnxruntime::FeedsFetchesManager::SetDeviceCopyChecks(onnxruntime::DeviceCopyCheck, onnxruntime::DeviceCopyCheck)input_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::Unknownstatic void SafeIntExceptionHandler::SafeIntOnDivZero()func info for node: already exist.Can't use func with null ptr not found./Users/runner/work/1/s/onnxruntime/core/framework/fuse_nodes_funcs.ccGetFuncs/Users/runner/work/1/s/onnxruntime/core/framework/graph_partitioner.ccPartitionOnnxFormatModelNo provider specified.Partitiononnxruntime::common::Status onnxruntime::GraphPartitioner::Partition(onnxruntime::Graph &, bool, onnxruntime::FuncManager &, onnxruntime::GraphPartitioner::Mode, std::unordered_map *) constcompiled_kernel_hashes != nullptrPartitionOnnxFormatModelImplonnxruntime::common::Status onnxruntime::PartitionOnnxFormatModelImpl(onnxruntime::Graph &, bool, onnxruntime::FuncManager &, onnxruntime::KernelRegistryManager &, onnxruntime::KernelRegistry &, onnxruntime::IExecutionProvider &, GraphPartitioner::Mode, int &)fusion_style == IExecutionProvider::FusionStyle::FunctionMust use Function based fusion when exporting compiled nodes to dll. did not return correct number of compiled functionsonnxruntime::Node *onnxruntime::PlaceNode(onnxruntime::Graph &, const onnxruntime::IndexedSubGraph &, const onnxruntime::KernelRegistryManager &, const std::string &, IExecutionProvider::FusionStyle, GraphPartitioner::Mode, int &)1 == capability.nodes.size()/Users/runner/work/1/s/onnxruntime/core/framework/func_kernel.honnxruntime::FunctionKernel::FunctionKernel(const onnxruntime::OpKernelInfo &)compute_info_->create_state_func(&context, &func_state_) == 0InlineNodesPartitionOrtFormatModelImplEP: has Compile error: single_node_compute_func should have 1 elementsonnxruntime::common::Status onnxruntime::PartitionOrtFormatModelImpl(onnxruntime::Graph &, onnxruntime::FuncManager &, onnxruntime::KernelRegistryManager &, onnxruntime::KernelRegistry &, onnxruntime::IExecutionProvider &, std::unordered_map &, int &)Existing entry in compiled kernel hashes for . Execution Provider must generate unique names across the entire model./Users/runner/work/1/s/onnxruntime/core/framework/kernel_def_builder.cconnxruntime::KernelDefBuilder &onnxruntime::KernelDefBuilder::VariadicAlias(int, int)input_offset >= 0 && output_offset >= 0Op with name ( and type ( Version mismatch. node_version: kernel start version: kernel_end_version: Found kernel for Op with name ( in the supported version range (node_version: However the types are incompatible. This op has been implemented only for the following types (), but the node in the model has the following type (/Users/runner/work/1/s/onnxruntime/core/framework/kernel_registry.ccTryCreateKernel kernel not found in No matching hash for kernel is not supported in Encountered following errors: (Kernel not foundkernel def can't be NULLFailed to add kernel for : Conflicting with a registered kernel with op versions.void onnxruntime::(anonymous namespace)::TraverseFormalParametersWithTypeProto(const onnxruntime::Node &, ParamFilterFn, TraverseFn) [ParamFilterFn = (lambda at /Users/runner/work/1/s/onnxruntime/core/framework/kernel_registry.cc:75:11), TraverseFn = (lambda at /Users/runner/work/1/s/onnxruntime/core/framework/kernel_registry.cc:76:11)]len <= op_schema.inputs().size()void onnxruntime::(anonymous namespace)::TraverseFormalParametersWithTypeProto(const onnxruntime::Node &, ParamFilterFn, TraverseFn) [ParamFilterFn = (lambda at /Users/runner/work/1/s/onnxruntime/core/framework/kernel_registry.cc:100:9), TraverseFn = (lambda at /Users/runner/work/1/s/onnxruntime/core/framework/kernel_registry.cc:103:9)]found duplicated provider in KernelRegistryManager (node ). /Users/runner/work/1/s/onnxruntime/core/framework/mldata_type_utils.cconnxruntime::MLDataType onnxruntime::utils::GetMLDataType(const onnxruntime::NodeArg &)nullptr != type_proto/Users/runner/work/1/s/onnxruntime/core/framework/node_index_info.ccauto onnxruntime::NodeIndexInfo::Init(const onnxruntime::ConstGraphNodes &, onnxruntime::NodeIndex, const onnxruntime::OrtValueNameIdxMap &)::(anonymous class)::operator()(const onnxruntime::NodeArg &, bool) constauto onnxruntime::NodeIndexInfo::Init(const onnxruntime::ValidNodes > > &, onnxruntime::NodeIndex, const onnxruntime::OrtValueNameIdxMap &)::(anonymous class)::operator()(const onnxruntime::NodeArg &, bool) consttype_proto is not of type map!type_proto is not of type sequence!OrtValue is TensorSequence type but has no element Tensor DataType.Tensor types should have been handled alreadynot implemented is not supportedconst onnxruntime::SparseTensor &OrtValue::Get() constIsSparseTensor()Trying to get a SparseTensor, but got: /Users/runner/work/1/s/onnxruntime/core/framework/op_kernel.cconnxruntime::OpKernelContext::OpKernelContext(onnxruntime::IExecutionFrame *, const onnxruntime::OpKernel *, concurrency::ThreadPool *, const logging::Logger &)frame != nullptrkernel != nullptrOrtValue *onnxruntime::OpKernelContext::OutputMLValue(int, const onnxruntime::TensorShape &, size_t)int onnxruntime::OpKernelContext::NumVariadicInputs(size_t) constarg_num < arg_counts.size()Invalid arg_num of . Num args is TempSpace allocator not foundOrtValue *onnxruntime::OpKernelContext::GetOrCreateOutputMLValue(int)/Users/runner/work/1/s/onnxruntime/core/framework/node_index_info.hint onnxruntime::NodeIndexInfo::GetNodeOffset(onnxruntime::NodeIndex) constnode_offsets_index < node_offsets_size_/Users/runner/work/1/s/onnxruntime/core/framework/op_kernel_info.ccconst OrtMemoryInfo &onnxruntime::OpKernelInfo::GetMemoryInfo(int, OrtMemType) constNo attribute with name: is defined.Requested attribute: is expected to have type: but is of type: Attibute name and type don't matchNo attribute with this name is defined.Attribute: expected to be of type: /Users/runner/work/1/s/onnxruntime/core/framework/mem_pattern_planner.hvoid onnxruntime::MemPatternPlanner::TraceAllocation(int, size_t)!using_counters_current <= buffer_size_/Users/runner/work/1/s/onnxruntime/core/framework/ort_value_tensor_slicer.ccstatic OrtValueTensorSlicer onnxruntime::OrtValueTensorSlicer::Create(T &, int64_t, int64_t) [T = OrtValue]ort_value.IsTensor()Can't slice a non-tensor OrtValue. Type was ort_value.IsAllocated()gsl::narrow_cast(tensor_shape.NumDimensions()) >= slice_dimensionInsufficient dimensions to slice on . Shape:dim0_offset < dim0_sizeInvalid dim0_offset of . Dimension 0 is onnxruntime::OrtValueTensorSlicer::Iterator::Iterator(T &, size_t, size_t, int64_t, onnxruntime::OrtValueTensorSlicer::Iterator::Direction) [T = OrtValue]typename std::enable_if::value, reference>::type onnxruntime::OrtValueTensorSlicer::Iterator::operator*() [T = OrtValue]static OrtValueTensorSlicer onnxruntime::OrtValueTensorSlicer::Create(T &, int64_t, int64_t) [T = const OrtValue]onnxruntime::OrtValueTensorSlicer::Iterator::Iterator(T &, size_t, size_t, int64_t, onnxruntime::OrtValueTensorSlicer::Iterator::Direction) [T = const OrtValue]Multiple errors were found./Users/runner/work/1/s/onnxruntime/core/framework/parallel_executor.ccRunNodeAsyncBegin executionExiting due to terminate flag being set to true.onnxruntime::common::Status onnxruntime::ParallelExecutor::RunNodeAsync(size_t, const onnxruntime::SessionState &, const logging::Logger &)Got nullptr from GetKernel for node: _fence_beforeNon-zero status code returned while running node. Name:'' Status Message: _kernel_time_fence_after/Users/runner/work/1/s/onnxruntime/core/framework/op_kernel_context_internal.honnxruntime::OpKernelContextInternal::OpKernelContextInternal(const onnxruntime::SessionState &, onnxruntime::IExecutionFrame &, const onnxruntime::OpKernel &, const logging::Logger &, const bool &)entry != nullptrAll implicit inputs should have OrtValue instances by now. does not.Exception running nodes starting at '. Unknown exception was caught by catch-all handler./Users/runner/work/1/s/onnxruntime/core/framework/prepacked_weights.ccuint64_t onnxruntime::PrePackedWeights::GetHash() constbuffers_.size() == buffer_sizes_.size()/Users/runner/work/1/s/onnxruntime/core/framework/prepacked_weights_container.cconnxruntime::AllocatorPtr onnxruntime::PrepackedWeightsContainer::GetOrCreateAllocator(const std::string &)Unsupported device allocator in the context of pre-packed weights caching: libonnxruntime_providers_cuda.dyliblibonnxruntime_providers_dnnl.dyliblibonnxruntime_providers_openvino.dyliblibonnxruntime_providers_tensorrt.dylib/Users/runner/work/1/s/onnxruntime/core/framework/provider_bridge_ort.ccSessionOptionsAppendExecutionProvider_Tensorrt: Failed to load shared librarySessionOptionsAppendExecutionProvider_OpenVINO: Failed to load shared libraryCUDA execution provider is either not enabled or not available.OrtSessionOptionsAppendExecutionProvider_Cuda: Failed to load shared libraryEnsureGetunordered_map::at: key not found/Users/runner/work/1/s/include/onnxruntime/core/framework/op_kernel_context.hconst T *onnxruntime::OpKernelContext::Input(int) const [T = onnxruntime::Tensor]Missing Input: const T &onnxruntime::OpKernelContext::RequiredInput(int) const [T = onnxruntime::Tensor]input_ptrRequired input at index is not present.onnxruntime::Tensor *onnxruntime::OpKernelContext::Output(int)p_ml_valueonnxruntime::Tensor &onnxruntime::OpKernelContext::RequiredOutput(int, const onnxruntime::TensorShape &)output_ptrRequired output at index gsl::span onnxruntime::Tensor::DataAsSpan() const [T = long long]void *onnxruntime::Tensor::MutableDataRaw(onnxruntime::MLDataType)type == dtype_Tensor type mismatch.const void *onnxruntime::Tensor::DataRaw(onnxruntime::MLDataType) constvoid onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape &)shape_.Size() == new_shape.Size()Tensor size () != new size (/Users/runner/work/1/s/onnxruntime/core/framework/sequential_executor.cc[Memory] ExecutionFrame statically allocates [Memory] ExecutionFrame dynamically allocates ReleaseNodeMLValues/Users/runner/work/1/s/onnxruntime/core/framework/session_state.ccSetupAllocatorsAllocator already registered for . Ignoring allocator from CreateGraphInfoSaveMLValueNameIndexMappingDone saving OrtValue mappings.PopulateKernelCreateInfoconst onnxruntime::KernelCreateInfo &onnxruntime::SessionState::GetNodeKernelCreateInfo(onnxruntime::NodeIndex) constentry != kernel_create_info_map_.cend()duplicated ort_value index:. Do you have duplicated calls to SessionState::AddInitializedTensor function?Using an input in multiple nodes on different devices is not supported currently. Input: is used by node ) and node Failed to find input name in the mapping: void onnxruntime::SessionState::AddOutputNameToNodeInfoMapping(const std::string &, const onnxruntime::SessionState::NodeInfo &)output_names_to_nodeinfo.empty()Only one node should produce an output. Existing entry for void onnxruntime::SessionState::AddSubgraphSessionState(onnxruntime::NodeIndex, const std::string &, std::unique_ptr)existing_entries.find(attribute_name) == existing_entries.cend()Entry exists in node for attribute const onnxruntime::NodeIndexInfo &onnxruntime::SessionState::GetNodeIndexInfo() constnode_index_info_SetGraphAndCreateKernels must be called prior to GetExecutionInfo.void onnxruntime::SessionState::UpdateToBeExecutedNodes(const std::vector &)onnxruntime::common::Status onnxruntime::SessionState::CreateSubgraphSessionState()subgraphMain Graph instance should have populated all subgraphs when being resolved.CreateSubgraphSessionStateKernel create info is null. Invalid ORT format model.Kernel create info node indices are null. Invalid ORT format model.Kernel create info hashes are null. Invalid ORT format model.Size mismatch for kernel create info node indexes and hashes. Invalid ORT format model.Can't find node with index . Invalid ORT format model.Unable to find compiled kernel hash for node ''.SessionState for subgraphs is null. Invalid ORT format model.Subgraph SessionState entry for is missing. Invalid ORT format model.Subgraph SessionState for is null. Invalid ORT format model.FinalizeSessionStateFinalizeSessionStateImplonnxruntime::common::Status onnxruntime::SessionState::FinalizeSessionStateImpl(const std::basic_string &, onnxruntime::KernelRegistryManager &, const onnxruntime::Node *, const onnxruntime::SessionOptions &, bool, std::unordered_map &)entry != node_to_subgraph_ss.second.cend()Missing session state for subgraph. Node:'' OpType: Index: Attribute:p_op_kernelauto onnxruntime::SessionState::PrepackConstantInitializedTensors(std::unordered_map &, const std::unordered_map &)::(anonymous class)::operator()(bool) constallocator_for_caching.get() != nullptrweights_to_be_filled_in.buffers_.size() > 0The kernel corresponding to the node doesn't have an implementation that can cache computed pre-packed weights!op_type.empty()Using cached version of pre-packed weight for constant initializer: used in the node: which is of op type: Unable to write the provided PrePackedWeights instance into the containerCould not find OrtValue with name ''+KernelUseSharedPrePackedBuffers doesn't have an implementation that can consume provided pre-packed weightsCould not find OrtValue with idx 'GetSubGraphSessionStatesOrtFormat/Users/runner/work/1/s/onnxruntime/core/framework/session_state_utils.ccSaveInitializedTensorsSaving initialized tensors.common::Status onnxruntime::session_state_utils::SaveInitializedTensors(const onnxruntime::Env &, const std::basic_string &, const onnxruntime::GraphViewer &, const onnxruntime::AllocatorPtr &, const onnxruntime::OrtValueNameIdxMap &, const std::vector &, onnxruntime::ITensorAllocator &, const std::function &, const logging::Logger &, const onnxruntime::DataTransferManager &, const onnxruntime::ExecutionPlanBase &, const onnxruntime::SessionOptions &)ort_value_name_idx_map.MaxIdx() > -1entry != initialized_tensors_to_allocate.end() && entry->second->data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRING[Memory] SessionStateInitializer statically allocates bytes for Using user supplied initializer with name (Deserialize tensor failed.Done saving initialized tensorsSaveInputOutputNamesToNodeMappingSubgraph input with name is not used by any node.Cannot use user supplied initializer with name: () because the ORT planned memory location device ) is different from what is supplied (DeserializeTensorProto() takes either pre-allocated buffer or an allocator!Internal error. The preallocated buffer is too small. Requires DeserializeTensorProtoFailed to copy tensor to /Users/runner/work/1/s/onnxruntime/core/framework/tensor.cconnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape &, void *, const OrtMemoryInfo &, ptrdiff_t)p_type != nullptronnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape &, std::shared_ptr)onnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape &, void *, std::shared_ptr, ptrdiff_t)size_t onnxruntime::Tensor::SizeInBytes() constvoid onnxruntime::Tensor::Init(onnxruntime::MLDataType, const onnxruntime::TensorShape &, void *, onnxruntime::AllocatorPtr, ptrdiff_t)dtype_ != nullptrTensor is expected to contain one of the primitive data types. Got: /Users/runner/work/1/s/onnxruntime/core/framework/tensor_allocator_with_mem_pattern.hFinalizePlanFailed to get allocator for location: duplicated locationMem pattern for initializer is not foundWeight buffer for initializer '' is not foundGet preallocated buffer for initializer '' failedTracemodel format error! Need a value for the external data infoparsing checksum/Users/runner/work/1/s/onnxruntime/core/framework/tensor_shape.ccint64_t onnxruntime::TensorShape::SizeToDimension(size_t) constdimension <= num_dimsInvalid dimension of for SizeFromDimension. Tensor has dimensions.int64_t onnxruntime::TensorShape::SizeFromDimension(size_t) constonnxruntime::TensorShape onnxruntime::TensorShape::Slice(size_t, size_t) constdimstart <= dimend && dimend <= size(){}/Users/runner/work/1/s/onnxruntime/core/framework/tensor_type_and_shape.ccOrtStatusPtr OrtApis::GetTensorTypeAndShape(const OrtValue *, OrtTensorTypeAndShapeInfo **)type != nullptrInvalid TensorProtocorrupted protobuf data: tensor shape size() does not match the data size() in protoUnpackTensor: the pre-allocate size does not match the size in protodata overflowTensorProtoToTensor() tensor shape mismatch!TensorProto type can not be writen into Tensor type /Users/runner/work/1/s/onnxruntime/core/framework/tensorprotoutils.ccTensorProtoToTensorstring tensor can not have raw datatensor can't contain negative dimsInitialized tensor with unexpected type: TensorProtoToMLValue() must take a pre-allocated MemBuffer!string tensor can not use pre-allocated bufferThe preallocated buffer is too small. Requires , Got ConstantNodeProtoToTensorProtocommon::Status onnxruntime::utils::ConstantNodeProtoToTensorProto(const onnx::NodeProto &, const onnxruntime::Path &, onnx::TensorProto &)Unsupported attribute value type of in 'Constant' node 'SparseTensorProtoToDenseTensorProto BUG! Report to onnxruntime team. element_size of: is not supported. type: common::Status onnxruntime::utils::DenseTensorToSparseTensorProto(const onnx::TensorProto &, const onnxruntime::Path &, onnx::SparseTensorProto &)HasDataType(dense_proto)DenseTensorToSparseTensorProto data_type: UnpackInitializerDataUnsupported type: UnpackTensorWithExternalDataImplnullptr == p_dataGetExternalDataInfoTensor does not have external data to read from.External data type cannot be UNDEFINED or STRING.TensorProto external data size mismatch. Computed size: , external_data.length: GetFileContentUnsupported sparse tensor data type of ReadExternalDataForTensorUnpackTensor: the pre-allocated size does not match the raw data size, expected CopySparseDataIndicies expected to be INT64Sparse Indicies raw data size does not match expected.Invalid SparseTensor indices. Should either have raw or int64 dataonnxruntime::common::Status onnxruntime::utils::CopySparseData(size_t, const onnx::TensorProto &, gsl::span, std::function) [T = unsigned char]rank == dims.size() && rank > 0cur_index == &*indices_data.cend()Invalid SparseTensor indices. Should be rank 0 or 1. Got:onnxruntime::common::Status onnxruntime::utils::CopySparseData(size_t, const onnx::TensorProto &, gsl::span, std::function) [T = unsigned short]onnxruntime::common::Status onnxruntime::utils::CopySparseData(size_t, const onnx::TensorProto &, gsl::span, std::function) [T = unsigned int]onnxruntime::common::Status onnxruntime::utils::CopySparseData(size_t, const onnx::TensorProto &, gsl::span, std::function) [T = unsigned long long]CoreMLExecutionProviderInternalTestingExecutionProvider/Users/runner/work/1/s/onnxruntime/core/framework/utils.ccconst OrtMemoryInfo &onnxruntime::utils::FindMemoryInfoForValue(const onnxruntime::SessionState &, const std::string &)exec_plan_ptrInitializeFeedFetchCopyInfoCopyOneInputAcrossDevicesExecuteGraphFindMemoryInfoForValueconst OrtMemoryInfo &onnxruntime::utils::FindMemoryInfoForValue(const onnxruntime::OrtValueNameIdxMap &, const onnxruntime::SequentialExecutionPlan &, const std::string &)CalculateStaticCopyInfoForFeedsbool onnxruntime::utils::FinalizeCopyInfoForFeeds(const std::vector &, std::vector &)feed_locations.size() == copy_info.size()bool onnxruntime::utils::FinalizeCopyInfoForFetches(const std::vector &, std::vector &)fetch_alloc_info.size() == copy_info.size()CalculateStaticCopyInfoForFeedonnxruntime::common::Status onnxruntime::utils::BatchOrCopyMLValue(const onnxruntime::SessionState &, const onnxruntime::MLValueCopyInfo &, const OrtValue &, OrtValue &, std::vector *)allocator != nullptrFailed to find allocator for device BatchOrCopyMLValueExecuteGraphImplOnly one thread was configured for parallel execution. Hence will use sequential execution.common::Status onnxruntime::utils::CopyInputsAcrossDevices(const onnxruntime::SessionState &, const std::vector &, std::vector &, const std::vector &)copy_info.size() == num_feedsCopyInputsAcrossDevicesCopyOutputsAcrossDevicesNumber of neurons in the hidden layer.Constrain seq_lens to integral tensors.The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]` QWThe weight tensor of the query layer in the attention mechanism. Should be of shape `[num_directions, am_query_depth(hidden_size of lstm), am_attn_size]` MWThe weight tensor of the memory layer in the attention mechanism. Should be of shape `[num_directions, memory_depth, am_attn_size]` The attention_v tensor in the attention mechanism. Should be of shape `[num_directions, am_attn_size]` The sequence of the memory (input) for attention mechanism. Should be of `[batch_size, max_memory_step, memory_depth]` memory_seq_lensThe sequence length of the input memory for the attention mechanism. Should be of `[batch_size]` AWThe weights of attention layer in the attention wrapper. If exists, should be of shape `[num_directions, memory_depth+hidden_size, aw_attn_size]. Please note that attention mechanism context depth is also memory_depth in the attention mechanism.` A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`. 'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64Pads has incorrect number of valuesInput tensors of wrong rank (0).Incompatible dimensions for matrix multiplicationInputs 0 shall be 3 dimensionsInvalid bias shapeInputs 4 shall be 5 dimensions/Users/runner/work/1/s/onnxruntime/core/graph/contrib_ops/contrib_defs.ccNumber of attention headsWhether every token can only attend to previous tokens. Default value is 0.3D input tensor with shape (batch_size, sequence_length, input_hidden_size)weight2D input tensor with shape (input_hidden_size, 3 * hidden_size), where hidden_size = num_heads * head_sizebias1D input tensor with shape (3 * hidden_size)mask_indexAttention mask with shape (batch_size, 1, max_sequence_length, max_sequence_length), (batch_size, past_sequence_length + sequence_length)or (batch_size, sequence_length, past_sequence_length + sequence_length), or index with shape (batch_size) or (2 * batch_size).Mpastpast state for key and value with shape (2, batch_size, num_heads, past_sequence_length, head_size).3D output tensor with shape (batch_size, append_length, hidden_size)presentpresent state for key and value with shape (2, batch_size, num_heads, past_sequence_length + sequence_length, head_size)Constrain input and output types to float tensors.Constrain mask index to integer types2D input tensor with shape (input_hidden_size, 3 * hidden_size), hidden_size = num_heads * head_sizeinput_scalescale of quantized input tensor. It's a scalar, which means a per-tensor/layer quantization.weight_scalescale of weight scale. It's a scalar or a 1D tensor, which means a per-tensor/per-column quantization.Its size should be 3 * hidden_size if it is per-column quantizationAttention mask index with shape (batch_size)input_zero_pointzero point of quantized input tensor.It's a scalar, which means a per-tensor/layer quantization.weight_zero_pointzero point of quantized weight tensor. It's a scalar or a 1D tensor, which means a per-tensor/per-column quantization.Its size should be 3 * hidden_size if it is per-column quantization3D output tensor with shape (batch_size, sequence_length, hidden_size)Constrain input and output types to int8 tensors.windowOne sided attention windows length W, or half of total window length3D input tensor with shape (batch_size, sequence_length, hidden_size), hidden_size = num_heads * head_size2D input tensor with shape (hidden_size, 3 * hidden_size)maskAttention mask with shape (batch_size, sequence_length)global_weightglobal_biasglobalGlobal attention flags with shape (batch_size, sequence_length)GConstrain to integer typesThe epsilon value to use to avoid division by zero.input_ids2D words IDs with shape (batch_size, sequence_length)segment_ids2D segment IDs with shape (batch_size, sequence_length)word_embedding2D with shape (,hidden_size)position_embedding2D with shape (, hidden_size)segment_embedding1D gamma tensor for layer normalization with shape (hidden_size)beta1D beta tensor for layer normalization with shape (hidden_size)2D attention mask with shape (batch_size, sequence_length)1D mask_index tensor with shape (batch_size)Constrain input and output integer tensors typesConstrain input and output float tensors types.input tensorbias tensoroutput tensorConstrain input and output types to float or half tensors.3D input tensor with shape (batch_size, sequence_length, hidden_size)skip3D skip tensor with shape (batch_size, sequence_length, hidden_size)1D input tensor with shape (hidden_size)1D skip tensor with shape (hidden_size1D bias tensor with shape (hidden_sizemeanSaved mean used during training to speed up gradient computationUinv_std_varSaved inverse standard variance used during training to speed up gradient computation.Constrain mean and inv_std_var to float tensors.Value of alphaValue of beta1D input tensor1D output tensorBias applied to each channel, same size as C.The scale to apply.Input tensor of shape [N,C,H,W]Result, has same shape and type as inputA 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).A 1-D values of (height, width).Result, has same type as input, with H and W dimensions reduced.Threshold valueInput tensorOutput tensordataTensor of data to extract slices from.1-D tensor of starting indices of corresponding axis in `axes`ends1-D tensor of ending indices (exclusive) of corresponding axis in axesaxes1-D tensor of axes that `starts` and `ends` apply to.Sliced data tensor.Constrain indices to integer typesThe shape of filled tensorThe filled tensorinput_as_shapeextra_shapeInput data to be scaledOutput data after scalingdrop_statesBool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.hidden_prevThe previous GRU hidden state.gatesUnactivated gate outputs from forget, update, and output gates, pre-activation.seq_lengthsArray of sequence lengths. len(seq_lengths) should equal batch size N.The timestep for this operation.hiddenThe new GRU hidden state calculated by this op.If 1, mean and variance are computed across channels. Default is 0.If 0, normalize the mean only. Default is 1.Scaling valueThe scaled hyperbolic tangent values of the input tensor computed element-wiseauto_padpadsConstrain input0 and output types to float tensorssignal_ndimnormalizedonesidedAinput_0input_1CWPadsConstrain input and output types to float tensorsactivation_paramsZInput tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.Input tensor C. The shape of C should be unidirectional broadcastable to (M, N).Output tensor of shape (M, N).Constrain input and output types to float/int tensors.Whether A should be transposedWhether B should be transposedScalar multiplier for the product of input tensors A * B.Scalar multiplier for input tensor C.activation_gammaSpecified axis to insert a dimensionStrings to tokenizeTokenized stringsInput/Output is a string tensormarkBoolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03).The string used to pad output tensors when the tokens extracted doesn't match the maximum number of tokens found. If start/end markers are needed, padding will appear outside the markers.tokenexpAn optional string. Token's regular expression in basic POSIX format (pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap09.html#tag_09_03). If set, tokenizer may produce tokens matching the specified pattern. Note that one and only of 'tokenexp' and 'separators' should be set.an optional list of strings attribute that contains a list of separators - regular expressions to match separators Two consecutive segments in X connected by a separator would be divided into two tokens. For example, if the input is "Hello World!" and this attribute contains only one space character, the corresponding output would be ["Hello", "World!"]. To achieve character-level tokenization, one should set the 'separators' to [""], which contains an empty string.Minimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignoredN-dimensional matrix AN-dimensional matrix BMatrix multiply results from A * BConstrain input A data types as 16-bit integer tensorConstrain input B data types as 16-bit integer tensorConstrain output Y data types as 32-bit integer tensor.T3 must be tensor(uint32) when both T1 and T2 are tensor(uint16),or must be tensor(int32) when either T1 or T2 is tensor(int16).Scalar multiplier for the product of the input tensors.Whether A should be transposed on the last two dimensions before doing multiplicationWhether B should be transposed on the last two dimensions before doing multiplicationMatrix multiply resultsAn input tensor to hash.32-bit hash value.Constrain input type to unsigned or signed 32-bit integer tensor, or string tensor. It should be utf-8 encoded if using unicode.Constrain output type to unsigned and signed 32-bit integer tensor.seedSeed for the hashing algorithm, unsigned 32-bit integer, default to 0.positiveIf value is 1, output type is uint32_t, else int32_t. Default value is 1.Tensor of rank r >= 1.indicesTensor of rank q >= 1.Tensor of rank q-1+r-indices[-1].Constrain indice type to int32 or int64Integer representing the embedding vector size for each word.If not provide, use the fileter size of conv weightThis operator applies convolution to word from left to right with window equal to conv_window_size and stride to 1.Take word 'example' for example, with conv_window_size equal to 2, conv is applied to [ex],[xa], [am], [mp]...If not provide, use the first dimension of conv kernal shape.Integer representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector.SequenceSpecify batchs of sequence words to embeddingSpecify weights of convSpecify bias of convSpecify embedding vector of charConstrain to tensor(int32).Constrain to tensor(float).modeThree modes: `constant`(default) - pads with a given constant value, `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis, `edge` - pads with the edge values of arrayInput tensor.Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]. `pads` format (1D example) should be as follow [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.(Optional) A scalar or rank 1 tensor containing a single value to be filled if the mode chosen is `constant` (by default it is 0.0).Tensor after padding.xA 1-D input tensor that is to be processed.A 1-D tensor of the same type as 'x' containing all the unique values in 'x' sorted in the same order that they occur in the input 'x'idxA 1-D INT64 tensor of the same size as 'x' containing the indices for each value in 'x' in the output 'uniques'countsA 1-D INT64 tensor containing the the count of each element of 'uniques' in the input 'x'Input can be of any tensor type.The distance metric to use. If a string, the distance function can be "braycurtis", "canberra", "chebyshev", "cityblock", "correlation", "cosine", "dice", "euclidean", "hamming", "jaccard", "jensenshannon", "kulsinski", "mahalanobis", "matching", "minkowski", "rogerstanimoto", "russellrao", "seuclidean", "sokalmichener", "sokalsneath", "sqeuclidean", "wminkowski", "yule".2D matrix with shape (M,N)2D matrix with shape (K,N)A 2D Matrix that represents the distance between each pair of the two collections of inputs.Constrains input to only numeric types.The pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'.Value used for extrapolation, when applicable. Default is 0.0f. Input data tensor from the previous operator; 4-D feature map of shape (N, C, H, W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.roisRoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[y1, x1, y2, x2], ...]. The RoIs' coordinates are normalized in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.batch_indices1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.crop_size1-D tensor of 2 elements: [crop_height, crop_width]. All cropped image patches are resized to this size. Both crop_height and crop_width need to be positive.RoI pooled output, 4-D tensor of shape (num_rois, C, crop_height, crop_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1].Constrain types to float tensors.Constrain types to int tensors.The first normalization dimension: normalization will be performed along dimensions axis : rank(inputs).stash_typetype used for stash mean/inv_std_varInput data tensor from the previous layer.Scale tensor.Bias tensor.Output data tensor.InvStdDevSaved inverse standard deviation used during training to speed up gradient computation.Constrain input types and output Y type to float tensors.Type of Mean and InvStdDev tensors.Constrain input and output types (except mean and inv_std_var) to float tensors.Constrain mean and inv_std_var to be float tensors.The input data as Tensor.The output.The normal input data.The bias input data that is a 1D tensor.Input tensor. Every matrix in the batch must be invertible.Output tensor of the same type and shape as the input tensor.The embedding matrix of size N x M. 'N' is equal to the maximum possible index + 1, and 'M' is equal to the embedding sizeLong tensor containing the indices to extract from embedding matrix.padding_idxA 0-D scalar tensor. If specified, the entries at `padding_idx` do not contribute to the gradient; therefore, the embedding vector at `padding_idx` is not updated during training, i.e. it remains as a fixed pad.scale_grad_by_freqA 0-D bool tensor. If given, this will scale gradients by the inverse of frequency of the indices (words) in the mini-batch. Default is ``False``Output tensor of the same type as the input tensor. Shape of the output is * x M, where '*' is the shape of input indices, and 'M' is the embedding size.Constrain input and output types to all numeric tensors.Boolean. Indicates whether upper or lower part of matrix is retained. Default is true.Input tensor of rank 2 or higher.kA 0-D tensor containing a single value corresponding to the number diagonals above or the main diagonal to exclude or include.Default value is 0 if it's not specified.Constrain input and output types to all numeric tensors and bool tensors.softmax_axisapply softmax to elements for dimensions softmax_axis or higherbroadcast_axisbroadcast bias across input for dimensions broadcast_axis to softmax_axis-1The bias (or mask) as Tensor.(Optional) Seed to the random generator, if not specified we will auto generate one.The bias input, a vector with the same shape as last dim of dataresidualThe residual input, must have the same shape as dataratioThe ratio of random dropout, with value in [0, 1). If this input was not set, or if it was set to 0, the output would be a simple copy of the input. If it's non-zero, output will be a random dropout of input, which is typically the case during training.training_modeIf set to true then it indicates dropout is being used for training. It is an optional value hence unless specified explicitly, it is false. If it is false, ratio is ignored and the operation mimics inference mode where nothing will be dropped from the input data and if mask is requested as output it will contain all ones.The output mask of dropout.Constrain input 'ratio' types to float tensors.Constrain output 'mask' types to boolean tensors.isinf_onlyIf true, check only for Inf, -Inf.isnan_onlyIf true, check only for NaN.Constrain the output to a boolean tensor.Input tensors to check.The output scalar. Its value is true if all input tensors are finite. Otherwise, the output value would be false.Incompatible dimensionsInputs 0 shall be 2 dimensionsword_embedding should have 2 dimensions and dimension size is known.X_biasNegative values are not allowed in a shape specificationAttribute should specify a shapeInput's shape must be 4-D'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border)'Scale' must contain exactly 2 values - (height, width)) needs to be greater than or equal to the top_border () + bottom_border () needs to be greater than or equal to the left_border () + right_border () + scale[0] () + scale[1] (First input does not have rank 2Second input does not have rank 2Input axis is invalid: inputs are expected to have tensor type and output type should not be null.both data and indices tensor need to have rank larger than zero.last dimension of indices must not be larger and rank of data tensor'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64first input tensor has wrong dimensionrois input tensor has wrong dimensionbatch_indices shape input tensor has wrong dimensioncrop_size shape input tensor has wrong dimensionInput rank must be >= 2.The inner-most 2 dimensions must have the same size (mat_w: != mat_h: expected to have rank > but has rank Dimension mismatch in unification between auto onnxruntime::contrib::RegisterContribSchemas()::(anonymous class)::operator()(onnx::InferenceContext &) const!(isinf_only && isnan_only)Both attributes isinf_only and isnan_only cannot be set. Unset both to check for both conditions./Users/runner/work/1/s/onnxruntime/core/graph/contrib_ops/nchwc_schema_defs.ccchannelstensor rank too smallinvalid scales dimensioninvalid scales value/Users/runner/work/1/s/onnxruntime/core/graph/contrib_ops/nhwc_schema_defs.ccInput data tensor from the previous operator; According to channels_last, dimensions for image case are (N x C x H x W), or (N x H x W x C) where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), or (N x D1 X D2 ... Dn x C) where N is the batch size.Scale of quantized input 'X'. It must be a scalar.Zero point tensor for input 'X'. It must be a scalar.Scale of quantized output 'Y'. It must be a scalar.Zero point tensor for output 'Y'. It must be a scalar.Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. with the N and C value keep it value, while the otherdimensions are all 1.Constrain input and output types to singed/unsigned int8 tensors.Works on NHWC layout or not? Default not.Input scale. It's a scalar, which means a per-tensor/layer quantization.Input zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Output zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Constrain input and output types to 8 bit tensors.Output tensor must have at least 2 dimensionsInput data type does not match the expected data typeInput data type does not match the expected data type. Current data type is Scale and Zero-point must be a scalarScale and Zero-point must be of rank 1Scale and Zero-point must be of rank 1 and the number of elements should be equal to the number of rows of the corresponding input./Users/runner/work/1/s/onnxruntime/core/graph/contrib_ops/quantization_defs.ccThe axis along which same quantization parameters are applied. It's optional.If it's not specified, it means per-tensor quantization and input 'x_scale' and 'x_zero_point' must be scalars.If it's specified, it means per 'axis' quantization and input 'x_scale' and 'x_zero_point' must be 1-D tensors.Scale for doing quantization to get 'y'. It could be a scalar or a 1-D tensor,which means a per-tensor or per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.Zero point for doing quantization to get 'y'. It could be a scalar or a 1-D tensor, which means a per-tensoror per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.Constrain 'x', 'y_scale' to float tensors.Constrain 'y_zero_point' and 'y' to 8-bit integer tensors.N-D quantized Input tensor to be de-quantized.Scale for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.Zero point for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.Constrain 'x' and 'x_zero_point' to 8-bit integer tensors.Constrain 'y', 'x_scale' to float tensors.Constrain output data type to 32-bit integer tensor.T2 must be tensor(uint32) when T1 is tensor(uint8),or must be tensor(int32) when T1 is tensor(int8).A_zero_pointInput A zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.B_zero_pointInput B zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Constrain output to 32 bit tensorConstrain input types to 8 bit signed and unsigned tensors.Constrain output types to 32 bit tensors.Scale of quantized input 'B'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'.1D input tensor, whose dimension is same as B's last dimensionConstrain input A, b_scale and output Y data type as float tensor.Scale of quantized input 'A'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'A'.Zero point tensor for input 'A'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'A'.Constrain input a_scale, b_scale and output Y data type as float tensor.additionC = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_pointmultiplicationC = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_pointdata_scaledata_zero_pointreduced_scalereduced_zero_pointX_scaleInput X's scale. It's a scalar, which means a per-tensor/layer quantization.X_zero_pointInput X's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Y_scaleOutput Y's scale. It's a scalar, which means a per-tensor/layer quantization.Y_zero_pointOutput Y's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, input_size, 4*hidden_size]`.The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, hidden_size, 4*hidden_size]`.W's scale. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size.W's zero point. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size.R's scale. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size.R's zero point. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size.Constrain weights types to 8 bit tensors.Y's scale.Y's zero point.T8List of tensors/scale/zero_point for concatenationTVConstrain input and output types to 8 bit signed and unsigned tensors.Constrain scale types to any float tensor type.Sequence of (Tensor, Scale, ZeroPoint) tuples. The type is sequence of (T8, TF, T8). Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support). {additionalDocumentation} {additionalDocumentation}A_scaleInput A's scale. It's a scalar, which means a per-tensor/layer quantization.B_scaleInput B's scale. It's a scalar, which means a per-tensor/layer quantization.C_scaleC_zero_pointaxis must be in [-rank, rank)Constrain input and output types.Tensor(scalar, or dims=[1]). First entry in the range.Tensor(scalar, or dims=[1]). Upper limit of sequence, exclusive.Tensor(scalar, or dims=[1]). Number that increments start. Defaults to 1.1-D Tensor of the range.Unsupported type:Can not get shape initializer data!Unsupported non-raw-data data type!inout/Users/runner/work/1/s/onnxruntime/core/graph/function.cconnxruntime::FunctionImpl::FunctionImpl(const onnxruntime::Graph &, const onnxruntime::IndexedSubGraph &, const logging::Logger &)_dummyonnxruntime::FunctionImpl::FunctionImpl(const onnxruntime::Graph &, const onnxruntime::NodeIndex &, const onnx::FunctionProto &, const logging::Logger &)Resolve subgraph failed:std::unique_ptr onnxruntime::CreateSchema(const onnxruntime::Graph &, const onnxruntime::IndexedSubGraph &)input_arg->Type() != nullptrstd::unordered_map onnxruntime::CreateOpsetImportsForFunction(const onnx::FunctionProto &, const std::unordered_map &)(result.first->second == opset_version)ONNX model does not support multiple opset versions for a domain. Model imports opset version for domain and function is trying to import opset version for the same domainvoid onnxruntime::update_subgraphs_within_function_body(onnx::GraphProto &, const onnxruntime::Graph &, const onnx::NodeProto &, const std::unordered_map &, const std::unordered_map &)A node with a function body within a subgraph within another function body is currently not supported in ORT/Users/runner/work/1/s/onnxruntime/core/graph/function_impl.hvirtual const onnxruntime::Graph &onnxruntime::ViewerFunctionImpl::Body() constType mismatch. Current= Input=Tensor element type mismatch. /Users/runner/work/1/s/onnxruntime/core/graph/graph.ccUpdateTypeAndShapeSparseTensor element type mismatch. Serialization of fused function body is not currently supported, Node [] op_type [] does not have the graph for key LoadFromOrtFormatfbs_attr cannot be nullSerialization error. Graph attribute was serialized without Graph instanceNode::LoadFromOrtFormat, input_arg_counts is missingLoadEdgesFromOrtFormatinput index: is not the same as this node's index:ai.onnxThis is an invalid model. The sum of input arg count is not equal to size of input defs in node (onnxruntime::Graph::Graph(const onnxruntime::Model &, onnx::GraphProto *, const std::unordered_map &, onnxruntime::Version, onnxruntime::IOnnxRuntimeOpSchemaCollectionPtr, onnxruntime::Graph *, const onnxruntime::Node *, const logging::Logger &)graph_proto != nullptrConstantDuplicate constant node sparse initializer name: '' Model is invalid.utils::HasName(sparse_tensor)Duplicate sparse_tensor_initializer: 'GraphDuplicate initializer (dense, sparse or ConstantNode): '' the model will use the latest encountered initializer. Please, fix your model.Initializer appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.void onnxruntime::Graph::InitializeStateFromModelFileGraphProto()graph_inputs_excluding_initializers_.empty() && graph_inputs_including_initializers_.empty() && value_info_.empty() && graph_outputs_.empty()node_argGraph ctor should have created NodeArg for initializer. Missing:This is an invalid model. Graph output () does not exist in the graph.This is an invalid model. Error: two nodes with same node name ().This is an invalid model. Error: Duplicate definition of name (SetOuterScopeNodeArgsvoid onnxruntime::Graph::AddEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int)void onnxruntime::Graph::RemoveEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int)BuildConnectionsThis is an invalid model. At top level graph without matching NodeArg that subgraph consumes. Name= Graph may not conform to the ONNX spec and contain initializers that are not graph inputs.This is an invalid model. Failed to find NodeArg in all parent graphs. Name=Invalid model. Node input '' is not a graph input, initializer, or output of a previous node.void onnxruntime::Graph::KahnsTopologicalSort(const std::function &, const std::function &) constSome nodes are not included in the topological sort, graph have a cycle.This is an invalid model. Error: the graph is not acyclic.Size mismatch validating subgraph inputs. Got inputs but subgraph has inputs and requires inputs. Either provide all subgraph inputs, or just the required inputs.Node:InferAndVerifySubgraphTypesonnxruntime::common::Status onnxruntime::Graph::UpdateShapeInference(onnxruntime::Node &)node.GetAttributeNameToMutableSubgraphMap().empty()UpdateTypeShapeInference is not intended to be used with control flow nodes containing subgraphsThis is an invalid model. Node () input arg () does not have type information set by parent node.This is an invalid model. Type Error: Type '' of input parameter () of operator () in node () is invalid.Type Error: Type parameter () of Optype () bound to different types ( and in node (InferAndVerifyTypeMatchNode () output arg () type inference failedType Error: Type () of output arg () of node () does not match expected type (This is an invalid model. Model input () does not have type information.Type Error: Data in initializer '' has element type but usage of initializer in graph expects Type Error: Shape of initializer does not match. VerifyNodeAndOpMatchFatal error: is not a registered function/op) attribute () is required but not specified.Error: Duplicate definition-site for (InitInputsInitializersOutputsPerformTypeAndShapeInferencingForThisAndAllSubgraphsResolveonnxruntime::common::Status onnxruntime::Graph::Resolve(const onnxruntime::Graph::ResolveOptions &)outer_scope_node_args_consumed.empty()Shouldn't be possible to have NodeArgs that haven't been handled already.void onnxruntime::Graph::AddInitializedTensor(const onnx::TensorProto &)existing->second == &tensorAddInitializedTensor already has tensor with name but different TensorProto.void onnxruntime::Graph::RemoveInitializedTensor(const std::string &)sparse_tensor_names_.count(tensor_name) == 0!found_token_NoOpbool onnxruntime::Graph::RemoveNode(onnxruntime::NodeIndex)node->GetOutputEdgesCount() == 0Can't remove node as it still has output edges.onnx::GraphProto onnxruntime::Graph::ToGraphProto() constlocationoffsetlengthvoid onnxruntime::Graph::ToGraphProtoInternal(onnx::GraphProto &) constOuter scope node arg name ''was added but does not exist. CleanUnusedInitializersRemoving initializer ''. It is not used by any node and should be removed from the model.'. It is no longer used by any node. must be either specified in graph inputs or graph initializers.gsl::not_null onnxruntime::Graph::AllocateNode()nodes_.size() < static_cast(std::numeric_limits::max())onnxruntime::Node &onnxruntime::Graph::CreateFusedSubGraphNode(const onnxruntime::IndexedSubGraph &, const std::string &)nullptr != func_meta_defvoid onnxruntime::Graph::FinalizeFuseSubGraph(const onnxruntime::IndexedSubGraph &, onnxruntime::Node &)dst_implicit_input_idx < (int)node->ImplicitInputDefs().size()Node 's number of inputs is different from function body graph's number of input.onnxruntime::common::Status onnxruntime::Graph::InlineFunction(onnxruntime::Node &)node.MutableOutputDefs().size() == subgraph.GetOutputs().size()'s number of outputs is different from function body graph's number of outputs.InlineFunctionvoid onnxruntime::Graph::SetInputs(const std::vector &)input->Exists() : : Initializer tensor is missing. Invalid ORT format model.Duplicate initializer (dense or ConstantNode): 'Sparse Initializer tensor is missing. Invalid ORT format model.NodeArg is missing. Invalid ORT format model.Node is missing. Invalid ORT format model.Node index is out of rangeNodeEdge is missing. Invalid ORT format model.MergeShapeInfoError merging shape info for output. '' source: target:. Falling back to lenient merge.[ShapeInferenceError] Mismatch between number of source and target dimensions. Source= Target=Can't merge shape info. Both source and target dimension have values but they differ. Source= Dimension=Output:fbs_node_arg_names cannot be nullnode_arg_name cannot be nullLoadNodeArgsFromOrtFormat: Node [], could not find NodeArg ALLOW_RELEASED_ONNX_OPSET_ONLY/Users/runner/work/1/s/onnxruntime/core/graph/model_load_utils.hbool onnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySet() are '0' and '1'. The environment variable contained the value: ai.onnx.mlai.onnx.training/Users/runner/work/1/s/include/onnxruntime/core/graph/graph.honnxruntime::Node *onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constnode_index < nodes_.size()Validating no unexpected access using an invalid node_index. Got: Max:[TypeInferenceError] No Graph instance was found for attribute in node Graph attribute inferencing failed: ) Op () This is an invalid model. Error in Node:NodeArg Name is missing. Invalid ORT format model./Users/runner/work/1/s/include/onnxruntime/core/common/const_pointer_container.hconst onnxruntime::ConstPointerContainer::T *onnxruntime::ConstPointerContainer > >::at(size_t) const [Container = std::__1::vector >]index < data_.size()/Users/runner/work/1/s/onnxruntime/core/graph/graph_flatbuffers_utils.ccSaveInitializerOrtFormatSaveSparseInitializerOrtFormatSaveAttributeOrtFormatGraph attribute value was null. Invalid ORT format model.SaveAttributeOrtFormat: Unsupported attribute type: LoadInitializerOrtFormatMissing dimensions for initializer. Invalid ORT format model.Missing string data for initializer. Invalid ORT format model.Missing raw data for initializer. Invalid ORT format model.LoadSparseInitializerOrtFormatMissing values for sparse initializer. Invalid ORT format model.Missing name for SparseTensor initializer. Invalid ORT format model.Missing indicies for sparse initializer: Invalid ORT format model.Missing dims for sparse initializer: LoadAttributeOrtFormatNull string attribute. Invalid ORT format model.Null tensor attribute. Invalid ORT format model.Null graph attribute. Invalid ORT format model.Empty graph proto from deserialization of ORT format modelNull floats attribute. Invalid ORT format model.Null ints attribute. Invalid ORT format model.Null strings attribute. Invalid ORT format model.Null string in strings attribute. Invalid ORT format model.Null tensors attribute. Invalid ORT format model.Null tensor in tensors attribute. Invalid ORT format model.UNDEFINEDFLOATINTSTRINGTENSORGRAPHFLOATSINTSSTRINGSTENSORSGRAPHSSPARSE_TENSORSPARSE_TENSORS/Users/runner/work/1/s/onnxruntime/core/graph/graph_utils.ccint onnxruntime::graph_utils::GetIndexFromName(const onnxruntime::Node &, const std::string &, bool)itr != node_args.end()Attempting to get index by a name which does not exist:for node: const std::string &onnxruntime::graph_utils::GetNodeInputName(const onnxruntime::Node &, int)index >= 0 && static_cast(index) < inputs.size()const std::string &onnxruntime::graph_utils::GetNodeOutputName(const onnxruntime::Node &, int)index >= 0 && static_cast(index) < outputs.size()bool onnxruntime::graph_utils::RemoveNode(onnxruntime::Graph &, onnxruntime::Node &)Should be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.onnxruntime::NodeArg &onnxruntime::graph_utils::AddInitializer(onnxruntime::Graph &, const onnx::TensorProto &)!graph.GetInitializedTensor(new_initializer.name(), existing)Initializer with same name exists. Name:void onnxruntime::graph_utils::ReplaceNodeInput(onnxruntime::Node &, int, onnxruntime::NodeArg &)Invalid input index for node . Index: ExplicitInputs: ImplicitInputs:void onnxruntime::graph_utils::AddNodeInput(onnxruntime::Node &, int, onnxruntime::NodeArg &)num_explicit_inputs == static_cast(target_input_idx)FindPathFailed since multiple edges matched:CanUpdateImplicitInputNameInSubgraphs Implicit input name cannot be safely updated to in one of the subgraphs.bool onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutput(onnxruntime::Graph &, onnxruntime::Node &)std::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })void onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraph(onnxruntime::Node &, const std::string &, const std::string &)std::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0/Users/runner/work/1/s/onnxruntime/core/graph/graph_viewer.cconnxruntime::GraphViewer::GraphViewer(const onnxruntime::Graph &, const onnxruntime::IndexedSubGraph *)graph_->GetNode(idx) != nullptrnodeargMismatch between Graph and IndexedSubGraph. Input not found:Mismatch between Graph and IndexedSubGraph. Output not found:nodeMismatch between Graph and IndexedSubGraph. Node not found: const std::vector &onnxruntime::GraphViewer::GetNodesInTopologicalOrder(onnxruntime::ExecutionOrder) constconst std::vector &onnxruntime::GraphViewer::GetRootNodes() constfilter_info_ == nullptrShape/Users/runner/work/1/s/onnxruntime/core/graph/model.cconnxruntime::Model::Model(onnx::ModelProto &&, const onnxruntime::PathString &, const onnxruntime::IOnnxRuntimeOpSchemaRegistryList *, const logging::Logger &)Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported.ModelONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'. Please upgrade your model to opset 7 or higher. For now, this opset model may run depending upon legacy support of some older opset version operators.No graph was found in the protobuf.Protobuf parsing failed. less than 0. is less than 0.SaveProtobuf serialization failed.Graph is null. Invalid ORT format model.void onnxruntime::model_load_utils::ValidateOpsetForDomain(const std::unordered_map &, const logging::Logger &, bool, const std::string &, int)ONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions. Opset is under development and support for this is limited. The operator schemas and or other functionality may change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain is till opset ValidateOpsetForDomain is under development and support for this is limited. The operator schemas and or other functionality could possibly change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain Failed to load model with error: Load model failed. File doesn't exist failedsystem error number SaveModelDomain already set in registry/Users/runner/work/1/s/onnxruntime/core/graph/schema_registry.ccRegisterOpSetRegisterOpSchemaInternal, but it its domain is notknown by the checker., but it its version is higherthan the operator set version onnxruntime/Users/runner/work/1/s/onnxruntime/core/common/logging/logging.cconnxruntime::logging::LoggingManager::LoggingManager(std::unique_ptr, onnxruntime::logging::Severity, bool, const onnxruntime::logging::LoggingManager::InstanceType, const std::string *, int)default_logger_id must be provided if instance_type is InstanceType::DefaultOnly one instance of LoggingManager created with InstanceType::Default can exist at any point in time.void onnxruntime::logging::LoggingManager::CreateDefaultLogger(const std::string &) [VIWEFampm is not a valid date is not a valid year is not a valid day/Users/runner/work/1/s/onnxruntime/core/common/path.ccParsestatic onnxruntime::Path onnxruntime::Path::Parse(const onnxruntime::PathString &)..ParsePathRootFailed to parse path root: /Users/runner/work/1/s/onnxruntime/core/common/profiler.cc::onnxruntime::TimePoint onnxruntime::profiling::Profiler::StartTime() constvoid onnxruntime::profiling::Profiler::Initialize(const logging::Logger *)session_logger != nullptrEndTimeAndRecordEventMaximum number of events reached, could not record profile event.Writing profiler data to file [ {"cat" : "","pid" :"tid" :"dur" :"ts" :"ph" : "X","name" :""args" : {" : " : "} }, ] SessionNodew/Users/runner/work/1/s/onnxruntime/core/common/status.cconnxruntime::common::Status::Status(onnxruntime::common::StatusCategory, int, const std::string &)code != static_cast(common::OK)onnxruntime::common::Status::Status(onnxruntime::common::StatusCategory, int, const char *)SystemError[ONNXRuntimeError]SUCCESSFAILINVALID_ARGUMENTNO_SUCHFILENO_MODELENGINE_ERRORRUNTIME_EXCEPTIONINVALID_PROTOBUFMODEL_LOADEDNOT_IMPLEMENTEDINVALID_GRAPHEP_FAILGENERAL ERRORunnamed_thread_pool/Users/runner/work/1/s/onnxruntime/core/common/threadpool.ccstd::string onnxruntime::concurrency::ThreadPoolProfiler::Stop()enabled_{"main_thread": {"thread_pool_name": "", }, "sub_threads": {}}void onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEnd(onnxruntime::concurrency::ThreadPoolProfiler::ThreadPoolEvent)!points_.empty()void onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEndAndStart(onnxruntime::concurrency::ThreadPoolProfiler::ThreadPoolEvent)std::string onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::Reset()points_.empty()"thread_id": "", "block_size": [], "core": "": DistributionDistributionEnqueueWaitWaitRevoke": {"num_run": "core": onnxruntime::concurrency::ThreadPool::ParallelSection::ParallelSection(onnxruntime::concurrency::ThreadPool *)!current_parallel_sectionvoid onnxruntime::concurrency::ThreadPool::ParallelFor(std::ptrdiff_t, const onnxruntime::TensorOpCost &, const std::function &)n >= 0/Users/runner/work/1/s/onnxruntime/core/platform/posix/env.cconnxruntime::(anonymous namespace)::PosixThread::PosixThread(const char *, int, unsigned int (*)(int, Eigen::ThreadPoolInterface *), Eigen::ThreadPoolInterface *, const onnxruntime::ThreadOptions &)CleanUpFailed to close file descriptor - error code: Invalid fd was supplied: fstat file "" failed: ReadFileIntoBufferfile_path == nullptroffset < 0length > buffer.size()openlseekreadReadFileIntoBuffer - unexpected end of file. File: , offset: , length: MapFileIntoMemorymmapUnmapFilemunmap failed. error code: DeleteFolderDeleteFolder(): nftw() failed with error: nftw_removeremove() failed. Error code: , path: closerealpathFailed to load library with error: Got null library handleFailed to unload library with error: Failed to get symbol lib.so.bad dimensionsElementSize must be power of 2 and less or equal than 16!QLinearGlobalAveragePool ImageSize too large!QLinearGlobalAveragePool parameter out of computation range!/Users/runner/work/1/s/onnxruntime/core/flatbuffers/flatbuffers_utils.ccSaveValueInfoOrtFormatSaveValueInfoOrtFormat: value_info_proto for is missing type info.LoadValueInfoOrtFormatNull type info for LoadOpsetImportOrtFormatModel must have opset imports. Invalid ORT format model.opset id is null. Invalid ORT format model.opset import domain is null. Invalid ORT format model.SaveTypeInfoOrtFormatWe do not support type [] for nowSaveSequenceTypeOrtFormatSaveMapTypeOrtFormatLoadTypeInfoOrtFormatNull tensor type info. Invalid ORT format model.Null sequence type info. Invalid ORT format model.Null map type info. Invalid ORT format model.Type: is not supported currentlyLoadTensorTypeAndShapeOrtFormatLoadTensorShapeOrtFormatNull entry in dimensions. Invalid ORT format model.LoadTensorDimensionOrtFormatdim_param value with no name. Invalid ORT format model.LoadSequenceTypeOrtFormatNull value type info in fbs::SequenceType. Invalid ORT format model.LoadMapTypeOrtFormatNull value type info in fbs::MapType. Invalid ORT format model.NONEField 'name' of value_info is required to be non-empty.type is required but missing.elem_typekey_typevalue_typeUnrecognized type value case (value_info name: ): data_typesetting data_type field (tensor name: ) to UNDEFINED is not allowedfloat_dataint32_datastring_dataint64_dataraw_datadouble_datauint64_dataData of TensorProto ( tensor name: ) is stored externally and should not have data field.) should be stored in , but it doesn't exist or is not accessible.TensorProto ( tensor name: ) is stored externally but doesn't have a location.TensorProto (tensor name: ) is 0-element but contains data!) should contain one and only one value field.STRING data (tensor name: ) should not be stored in raw_data fieldvalues of data_type '' should be stored in field '' instead of 'Unrecognized data_type (tensor name: valuesSparse tensor indices () has values, but NNZ is Sparse tensor () index value at position [] out of range [0, ] not in sorted order.) first dimension size does not equal NNZ.) second dimension size does not match rank of tensor.] out of range.] not in lexicographic sorted order.sparse_tensor_protoSparse tensor values () must have rank 1.) must have a dense-rank > 0) dimensions are not positive.) must have INT64 type.) must have rank 1 or 2.) has no index values.attrtype field and data field mismatch in attribute Attribute (name: ) should not contain more than one value field.) should refer to attribute in parent node.op_typeNodeProto (name: , type: ) has zero input and zero output.Warning: Checker does not support models with experimental ops: No opset import for domain 'No Op registered for with domain_version of Op registered for is deprecated in domain_version of graphGraph must be in single static assignment (SSA) form, however '' has been used as graph input names multiple times.init initializer name is not unique in initializer but not in graph input sparse initializer name is not unique across initializers and sparse_initializersNodes in a graph must be topologically sorted, however input '' of node: name: OpType: is not output of any previous nodes.' has been used as output names multiple times. imports opset version where as the model imports opset version DynamicSliceImageScalerScaleBad node spec for node. Name: ==> Context: Loop 'body' subgraph outputs should all be tensors or sequences but output Loop 'body' subgraph scan outputs should all be tensors but output /Users/runner/work/1/s/cmake/external/onnx/onnx/defs/controlflow/defs.ccFinal N loop carried dependency values then K scan_outputs. Scan outputs must be Tensors.An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].Mismatched type: source= target=Mismatched tensor element type:source sequence type missing element type.target sequence type missing element type.Scan input was not a tensor.bodyGraph attribute inferencing returned type information for outputs. Expected Scan 'body' subgraph outputs should all be tensors but output was not axis value is invalid for a tensor of rank Number of scan input axes specified () is not equal to number of scan inputs (Number of scan output axes specified () is not equal to number of scan outputs (sequence_lensOptional tensor specifying lengths of the sequences in a batch. If this input is not specified, all sequences are assumed to be of the maximum sequence length (the dimension of the sequence axis of the scan_input tensors).initial_state_and_scan_inputsInitial values of the loop's N state variables followed by M scan_inputsfinal_state_and_scan_outputsFinal values of the loop's N state variables followed by K scan_outputsThe graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.An attribute specifying the number of scan_inputs M. An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.Int64 tensor/Users/runner/work/1/s/cmake/external/onnx/onnx/defs/controlflow/old.ccLoop 'body' subgraph outputs should all be tensors but output was A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.condA boolean termination condition. Optional. Pass empty string to skip.v_initialThe initial values of any loop-carried dependencies (values that change across loop iterations)v_final_and_scan_outputsFinal N loop carried dependency values then K scan_outputsThe graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.tensor of int64, which should be a scalar.tensor of bool, which should be a scalar.An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input.An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output.then_branch and else_branch produce different number of outputs. If node has but subgraphs produce Mismatched type for output then= else=Mismatched tensor element type for output Condition for the ifoutputsValues that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same shape and same data type.Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.Only boolValues that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.Input type was nullInput was expected to have either tensor or sequence type. Got Input was expected to have tensor type. Got Element type of input was unknownInput element type of does not match existing output type of Output was expected to have tensor type. Got Input was expected to have sequence type. Got Invalid data type tensor(seq(map(sparse_tensor(Unsuported type proto value case.Invalid tensor data type mapopaqueDataTypeUtils::FromDataTypeString - Received invalid data type string The value for the elements of the output tensor.sparse_valueThe value for the elements of the output tensor in sparse format.value_intThe value for the sole element for the scalar, int64, output tensor.value_intsThe values for the elements for the 1D, int64, output tensor.value_floatThe value for the sole element for the scalar, float32, output tensor.value_floatsThe values for the elements for the 1D, float32, output tensor.value_stringThe value for the sole element for the scalar, UTF-8 string, output tensor.value_stringsThe values for the elements for the 1D, UTF-8 string, output tensor.Output tensor containing the same value of the provided tensor./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/generator/defs.cc(Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float321D tensor. The shape of the expected output tensor. If empty tensor is given, the output would be a scalar. All values must be >= 0.Output tensor of shape specified by 'input'.If attribute 'value' is specified, the value and datatype of the output tensor is taken from 'value'.If attribute 'value' is not specified, the value in the output defaults to 0, and the datatype defaults to float32.Constrain input types.Constrain output types to be numerics.(Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal, and k < 0 populates a lower diagonal.(Optional) The data type for the elements of the output tensor. If not specified,the data type of the input tensor T1 is used. If input tensor T1 is also notspecified, then type defaults to 'float'.2D input tensor to copy shape, and optionally, type information from.Output tensor, same shape as input tensor T1.Constrain input types. Strings and complex are not supported.Constrain output types. Strings and complex are not supported.Lower boundary of the output values.highUpper boundary of the output values.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.The shape of the output tensor.Output tensor of random values drawn from uniform distributionThe mean of the normal distribution.The standard deviation of the normal distribution.The data type for the elements of the output tensor. Default is TensorProto::FLOAT.Output tensor of random values drawn from normal distribution(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.Input tensor to copy shape and optionally type information from.Number of times to sample.(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.Input tensor with shape [batch_size, class_size], where class_size is the number of all possible outcomes. Each value along the axis zero represents the unnormalized log-probability of each corresponding outcome in a batch.Output tensor with shape [batch_size, sample_size], where sample_size is the number of times to sample. Each value along the axis zero represents the outcome of the corresponding sample in a batch.Constrain output types to integral tensors.loop_body_attributeiprevcond_outdeltacurrentrangelimitstartScalar. First entry for the range of output values.Scalar. Exclusive upper limit for the range of output values.Scalar. Value to step by.A 1-D tensor with same type as the inputs containing generated range of values.Constrain input types to common numeric type tensors.One and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.Attribute 'value_int' expect an integer.Attribute 'value_ints' expect a list of integers.Attribute 'value_float' expect a float.Attribute 'value_floats' expect a list of floats.Attribute 'value_string' expect a string.Attribute 'value_strings' expect a list of strings.TypeAndShapeInferenceFunction implementation incomplete: this line should never be reached.Shape input must be a one-dimensional tensor.Invalid shape value: Attribute expected to have tensor typeAttribute expected to have a one-dim tensorInput tensor must be 2-dimensionalOutput type must be int32 or int64Input tensor must have rank 2All inputs to 'Range' op must be of the same typeInput to 'Range' op should be scalars (Tensor with only one element and shape empty)/Users/runner/work/1/s/cmake/external/onnx/onnx/defs/generator/old.ccAttribute 'value' of Constant node must exist with 'Tensor' data.Only one of the attributes 'value' or 'sparse_value' must be specified for a Constant node.One of the attributes 'value' or 'sparse_value' must be specified for a Constant node./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/logical/defs.ccConstrains input/output to boolean tensors.First operand, input to be shifted.Second operand, amounts of shift.Constrain input and output types to integer tensors.Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).Constrains input types to all numeric tensors.Constrains output to boolean tensor./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/logical/old.ccConstrains input to boolean tensor.Constrains input to float tensors.Constrains input to integral tensors.First input operand for the logical operator.Second input operand for the logical operator.Result tensor.broadcastEnable broadcastingIf set, defines the broadcast dimensions.Left input tensor for the logical operator.Right input tensor for the logical operator./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/math/defs.ccCoefficient of leakage.Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717).Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946).Coefficient of ELU.The Alpha value in Celu formula which control the shape of the unit. The default value is 1.0.Constrain input and output types to float32 tensors.Value of alpha.Value of beta.HardSwishThe softsign (x/(1+|x|)) values of the input tensor computed element-wiseDimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Whether to return the top-K largest or smallest elements.Whether to return the elements in sorted order.The sine of the input tensor computed element-wiseThe cosine of the input tensor computed element-wiseThe tangent of the input tensor computed element-wiseThe arcsine of the input tensor computed element-wiseThe arccosine of the input tensor computed element-wiseThe arctangent of the input tensor computed element-wiseThe hyperbolic sine values of the input tensor computed element-wiseThe hyperbolic cosine values of the input tensor computed element-wiseThe hyperbolic arcsine values of the input tensor computed element-wiseThe hyperbolic arccosine values of the input tensor computed element-wiseThe hyperbolic arctangent values of the input tensor computed element-wiseaN-dimensional quantized matrix aa_scalescale of quantized input aa_zero_pointzero point of quantized input abN-dimensional quantized matrix bb_scalescale of quantized input bb_zero_pointzero point of quantized input by_scalescale of quantized output yy_zero_pointzero point of quantized output yQuantized matrix multiply results from a * bConstrain input a and its zero point data type to 8-bit integer tensor.Constrain input b and its zero point data type to 8-bit integer tensor.Constrain output y and its zero point data type to 8-bit integer tensor.Zero point tensor for input 'A'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-row quantization. If it's a 1-D tensor, its number of elements should be equal to the number of rows of input 'A'.Zero point tensor for input 'B'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'.Constrain input A data type to 8-bit integer tensor.Constrain input B data type to 8-bit integer tensor.Constrain output Y data type as 32-bit integer tensor.Constrain input and output types to floating-point tensors.Number of input tensors does not match the operands in the equation.Ellipsis represents incompatible dimensions.Rank of input does not match the equation indices.equationEinsum expression string.InputsOperandsinputs are expected to have tensor type.input and zero_point pair is expected to have be same type.input and zero_point pair is expected to have same type./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/math/old.ccWhether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatmentDividend tensorDivisor tensorRemainder tensorConstrain input and output types to high-precision numeric tensors.Constrain input and output types to signed numeric tensors.The exponential of the input tensor computed element-wiseThe natural log of the input tensor computed element-wiseThe hyperbolic tangent values of the input tensor computed element-wiseFirst operand, base of the exponent.Second operand, power of the exponent.Output tensor.Constrain input X and output types to float/int tensors.Constrain input Y types to float/int tensors.sumInput tensor whose elements to be clippedMinimum value, under which element is replaced by min. It must be a scalar(tensor of empty shape).Maximum value, above which element is replaced by max. It must be a scalar(tensor of empty shape).Output tensor with clipped input elementsOptional input tensor C. If not specified, the computation is done as if C is a scalar 0. The shape of C should be unidirectional broadcastable to (M, N).A 1-D tensor indicates the shape you want to expand to, following the broadcast ruleThe sign of the input tensor computed element-wise. It has the same shape and type of the input.The error function of the input tensor computed element-wise. It has the same shape and type of the input.If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.If set to 1 will perform the sums in reverse direction.An input tensor that is to be processed.A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back.Output tensor of the same type as 'x' with cumulative sums of the x's elementsaxis tensor can be int32 or int64 onlytargetignore_indexreductionnonelossconst_zero_floatconst_zero_castedconst_one_floatconst_one_castedInput tensor of shape (N, C) or (N, C, d1, d2, ..., dk).Target tensor of shape (N) or (N, d1, d2, ..., dk). Target element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the target values should either be in the range [0, C) or have the value ignore_index.Optional rescaling weight tensor. If given, it has to be a tensor of size C. Otherwise, it is treated as if having all ones.The negative log likelihood lossType of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights.Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.Constrain input, weight, and output types to floating-point tensors.Constrain target to integer typesNegativeLogLikelihoodLossType of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 'mean': the sum of the output will be divided by the number of elements in the output.scoreslog_problabelsThe predicted outputs with shape [batch_size, class_size], or [batch_size, class_size, D1, D2 , ..., Dk], where K is the number of dimensions.The ground truth output tensor, with shape [batch_size], or [batch_size, D1, D2, ..., Dk], where K is the number of dimensions. Labels element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the label values should either be in the range [0, C) or have the value ignore_index.A manual rescaling weight given to each class. If given, it has to be a 1D Tensor assigning weight to each of the classes. Otherwise, it is treated as if having all ones.Weighted loss float Tensor. If reduction is 'none', this has the shape of [batch_size], or [batch_size, D1, D2, ..., Dk] in case of K-dimensional loss. Otherwise, it is a scalar.Log probability tensor. If the output of softmax is prob, its value is log(prob).SoftmaxCrossEntropyLoss If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of element size 1 (including a scalar tensor and any tensor with rank equal to or smaller than the first tensor), or having its shape as a contiguous subset of the first tensor's shape. The starting of the mutually equal shape is specified by the argument "axis", and if it is not set, suffix matching is assumed. 1-dim expansion doesn't work yet. For example, the following tensor shapes are supported (with broadcast=1): shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 Attribute `broadcast=1` needs to be passed to enable broadcasting. Input tensor of any shape, base of the exponent.Input tensor of any shape broadcastable to X shape, the exponent component.Pass 1 to enable broadcastingIf set, defines the broadcast dimensions. See doc for details.Output tensor (same size as X)consumed_inputslegacy optimization attribute.Coefficient of leakage default to 0.01.Coefficient of SELU default to 1.6732.Coefficient of SELU default to 1.0507.Coefficient of ELU default to 1.0.1-D input tensorslopeSlope tensor. If `Slope` is of size 1, the value is sharedacross different channelsSlope tensor. The shape of slope can be smaller then first input X; if so, its shape must be unidirectional broadcastable to XValue of alpha default to 0.2Value of beta default to 0.5data_0List of tensors for Max.Output tensor. Same dimension as inputs.List of tensors for MinList of tensors for Sum.List of tensors for Mean.Minimum value, under which element is replaced by minMaximum value, above which element is replaced by maxInput tensor AInput tensor BInput tensor C, can be inplace.Whether C should be broadcastedScalar multiplier for the product of input tensors A * B, the default value is 1.0.Scalar multiplier for input tensor C, the default value is 1.0.Input tensor CTensor of shape [a_1, a_2, ..., a_n, r]ValuesTensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing top K values from the input tensorIndicesTensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing the corresponding input tensor indices for the top K values.Constrain index tensor to int64Number of top elements to retrieveDimension on which to do the sort.KA 1-D tensor containing a single positive value corresponding to the number of top elements to retrieve Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) where the function `f(x) = x^exponent`, is applied to the data tensor elementwise. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).First operand.Second operand.Result, has same element type as two inputsDescribes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).The input tensor that's coerced into a 2D matrix of size (NxD) as described above.The output values with the same shape as input tensor (the original size without coercion).'axis' must be in []. Its actual value is: List of tensors for 'shape' input must be 1D tensor of type INT64Target rank must be 1 less than the input rank.Input and target dimension value mismatch.Weight rank must be 1.Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_sizeFirst operand, should share the type with the second operand.Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.Result, has same dimensions and type as AInvalid value for attribute axisInvalid value for attribute kK input must be a one-dimensional tensor of size 1.K input must be of type int64.Axis has less than the requested k elements.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = input_shape[i] * strides[i]` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/nn/defs.ccInput tensor must have at least 2 dimensionsRoIs tensor must have 2 dimensionsAttribute pooled_shape has incorrect lengthAttribute pooled_shape must be specifiedx_scaleScale tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.x_zero_pointZero point tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.w_scaleScale tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).w_zero_pointZero point tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).Scale tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.Zero point tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.Optional 1D bias to be added to the convolution, has size of M. Bias must be quantized using scale = x_scale * w_scale and zero_point = 0Constrain input type to 8-bit integer tensor.Constrain filter type to 8-bit integer tensor.Constrain output type to 8-bit integer tensor.Constrain bias type to 32-bit integer tensor.The shape of the convolution kernel. If not present, should be inferred from input 'w'.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.number of groups input channels and output channels are divided into. default is 1.Zero point tensor for input 'x'. It's optional and default value is 0. It's a scalar, which means a per-tensor/layer quantization.Zero point tensor for input 'w'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M)Constrain input x and its zero point data type to 8-bit integer tensor.Constrain input w and its zero point data type to 8-bit integer tensor.Constrain output y data type to 32-bit integer tensor.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.The pads attribute cannot be used simultaneously with auto_pad attributeIf set to true, it indicates BatchNormalization is being used for training, and outputs 1, 2, 3, and 4 would be populated.input_meaninput_varrunning_meanrunning_varThe running variance after the BatchNormalization operator. This op uses the population size (N) for calculating variance, and not the sample size N-1.Constrain mean and variance types to float tensors. It allows all float type for U.The output tensor of the same shape as input.Input matrixMatrix after normalizationThe axis on which to apply normalization, -1 mean last axis.The order of the normalization, only 1 or 2 are supported.The lambd value for the Shrink formulation. Default is 0.5.The bias value added to output. Default is 0.Input for n-gram extractionNgram resultsInput is ether string UTF-8 or int32/int641-D tensor of floatsMaximum n-gram length. If this value is 3, 3-grams will be used to generate the output.Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.Maximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=1List of strings n-grams learned from the training set. Either this or pool_int64s attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.List of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.The starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity.list of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor.list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts.The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)UTF-8 strings to normalizeUTF-8 Normalized stringsstring enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"Boolean. Whether the identification of stop words in X is case-sensitive. Default is falseList of stop words. If not set, no word would be removed from X.Environment dependent string that denotes the locale according to which output strings needs to be upper/lowercased.Default en_US or platform specific equivalent as decided by the implementation. Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, There are five required inputs 'X', 'scale', 'B', 'input_mean' and 'input_var'. Note that 'input_mean' and 'input_var' are expected to be the estimated statistics in inference mode (training_mode=False, default), and the running statistics in training mode (training_mode=True). There are multiple cases for the number of outputs, which we list below: Output case #1: Y, running_mean, running_var (training_mode=True) Output case #2: Y (training_mode=False) When training_mode=False, extra outputs are invalid. The outputs are updated as follows when training_mode=True: ``` running_mean = input_mean * momentum + current_mean * (1 - momentum) running_var = input_var * momentum + current_var * (1 - momentum) Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B where: current_mean = ReduceMean(X, axis=all_except_channel_index) current_var = ReduceVar(X, axis=all_except_channel_index) Notice that ReduceVar refers to the population variance, and it equals to sum(sqrd(x_i - x_avg)) / N where N is the population size (this formula does not use sample size N - 1). ``` When training_mode=False: ``` Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B ``` For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C * D1 * D2 * ... * Dn) before a BatchNormalization Op. Constrain input and output types to float and 8 bit tensors.ROI pool output shape (height, width).Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.RoIs (Regions of Interest) to pool over. Should be a 2-D tensor of shape (num_rois, 5) given as [[batch_id, x1, y1, x2, y2], ...].RoI pooled output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]).dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis.weight and zero_point pair is expected to have same type.Additional elements added to the side with higher coordinate indices in the output. Each padding value in "output_padding" must be less than the corresponding stride/dilation dimension. By default, this attribute is a zero vector. Note that this attribute doesn't directly affect the computed output values. It only controls the selection of the computed values, so changing this attribute only adds or removes output elements. If "output_shape" is explicitly provided, "output_padding" does not contribute additional size to "output_shape" but participates in the computation of the needed padding amount. This is also called adjs or adjustment in some frameworks.Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.This number of op outputs should be 3 when Training_mode = True, but it is not.This number of op outputs should be 1 when Training_mode = False, but it is not.ngram_indexes must be non-empty with no negative valuesInput tensor must have rank 1 or 2Input shape must have either [C] or [1,C] dimensions where C > 0The ratio of random dropout, with value in [0, 1). If this input was not set, or if it was set to 0, the output would be a simple copy of the input. If it's non-zero, output will be a random dropout of the scaled input, which is typically the case during training. It is an optional value, if not specified it will default to 0.5.The output mask./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/nn/old.ccA tensor of rank >= axis.A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). sizeThe number of channels to sum overScaling parameter.The exponent.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].Output tensor, which has the shape and type as input tensorConstrain input and output types to float tensors.A list of integers, along which to reduce. The default is to caculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.Input tensor must have atleast 2 dimensionsAttribute dilations has incorrect sizeAttribute strides has incorrect sizeAttribute kernel_shape has incorrect sizeAttribute kernel_shape must be specifiedAttribute pads has incorrect sizeSecond input tensor has wrong dimensionWhether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.The storage order of the tensor. 0 is row major, and 1 is column major.Indices tensor from max pooling across the input tensor. The dimensions of indices are the same as output tensor. The values in indices of are the indices of the selected values during pooling. The indices are computed as flatten 1-D tensor, and the indices do not consider padding. So the values in indices are in [0, N x C x D1 x ... x Dn).Dilation value along each spatial axis of filter.Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.MaxUnpool op must have either two or three inputs.Input tensor X must have atleast 2 dimensions.Attribute pads has incorrect size.Attribute strides has incorrect size.Attribute kernel_shape has incorrect size.Attribute kernel_shape must be specified.'output_shape' must be rank 1 tensor.'output_shape' must have same number of elements as the shape of input tensor X.The size of the kernel along each axis.Stride along each spatial axis.Input data tensor that has to be unpooled. This tensor is typically the first output of the MaxPool op.Dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non-image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].Input data tensor containing the indices corresponding to elements in the first input tensor X.This tensor is typically the second output of the MaxPool op.Dimensions must be the same as input tensor X. The indices are linear, i.e. computed considering the tensor as flattened 1-D tensor, assuming row-major storage. Also, the linear indices should not consider padding. So the values in indices are in the range [0, N x C x D1 x ... x Dn).The shape of the output can be explicitly set which will cause pads values to be auto generated. If 'output_shape' is specified, 'pads' values are ignored.Output data tensor that contains the result of the unpooling.Padding for the beginning and ending along each axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding. DEPRECATION NOTE: auto_pad is only intended to support legacy uses, and for framework authors, one is explicitly encouraged to use explicit padding specified in the pads attribute.Stride along each axis.pp value of the Lp norm used to pool over the input data, default is 2.0.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimension are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.Output data tensor from pooling across the input tensor. Dimensions will be N x C x 1 x 1If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.is_testIf set to nonzero, run spatial batch normalization in test mode, default is 0.The epsilon value to use to avoid division by zero, default is 1e-5f.momentumFactor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.The input 4-dimensional tensor of shape NCHW.The scale as a 1-dimensional tensor of size C to be applied to the output.The bias as a 1-dimensional tensor of size C to be applied to the output.The running mean (training) or the estimated mean (testing) as a 1-dimensional tensor of size C.varThe running variance (training) or the estimated variance (testing) as a 1-dimensional tensor of size C.The output 4-dimensional tensor of the same shape as X.The running mean after the BatchNormalization operator. Must be in-place with the input mean. Should not be used for testing.The running variance after the BatchNormalization operator. Must be in-place with the input var. Should not be used for testing.saved_meanSaved mean used during training to speed up gradient computation. Should not be used for testing.saved_varSaved variance used during training to speed up gradient computation. Should not be used for testing.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).Input data tensor from the previous operator; dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size, C is the number of channels. Statistics are computed for every channel of C over N and D1 to Dn dimensions. For image data, input dimensions become (N x C x H x W). The op also accepts single dimension input of size N in which case C is assumed to be 1Scale tensor of shape (C).Bias tensor of shape (C).running (training) or estimated (testing) mean tensor of shape (C).running (training) or estimated (testing) variance tensor of shape (C).The output tensor of the same shape as XThe running mean after the BatchNormalization operator.The running variance after the BatchNormalization operator.Saved mean used during training to speed up gradient computation.Saved variance used during training to speed up gradient computation.The input 1-dimensional scale tensor of size C.The input 1-dimensional bias tensor of size C.The output 4-dimensional tensor of the same shape as input.(float, default 0.5) the ratio of random dropout(int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X.The output mask. If is_test is nonzero, this output is not filled.The ratio of random dropoutConstrain output mask types to boolean tensors.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.The output tensor of the same shape as X.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). If true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch.If spatial is true, the dimension of scale is (C). If spatial is false, the dimensions of scale are (C x D1 x ... x Dn)If spatial is true, the dimension of bias is (C). If spatial is false, the dimensions of bias are (C x D1 x ... x Dn)If spatial is true, the dimension of the running mean (training) or the estimated mean (testing) is (C). If spatial is false, the dimensions of the running mean (training) or the estimated mean (testing) are (C x D1 x ... x Dn).If spatial is true, the dimension of the running variance(training) or the estimated variance (testing) is (C). If spatial is false, the dimensions of the running variance(training) or the estimated variance (testing) are (C x D1 x ... x Dn). Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, there are multiple cases for the number of outputs, which we list below: Output case #1: Y, mean, var, saved_mean, saved_var (training mode) Output case #2: Y (test mode) For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C*D1*D2 ..*Dn) before a BatchNormalization Op. Ratio of Dropout must be a scalar.training_mode of Dropout must be a scalar.Invalid value() for attribute 'axis'Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is usedStride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.Whether to use ceil or floor (default) to compute the output shape.p value of the Lp norm used to pool over the input data.Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. X.shape[1] == (W.shape[1] * group) == C (assuming zero based indices for the shape array). Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL. Optional 1D bias to be added to the convolution, has size of M.Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.The shape of the convolution kernel. If not present, should be inferred from input W.dilation value along each spatial axis of the filter.number of groups input channels and output channels are divided into.Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn)The weight tensor that will be used in the convolutions; has size (C x M/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the weight shape will be (C x M/group x k1 x k2 x ... x kn), where (k1 x k2 x ... x kn) is the dimension of the kernel. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, pad lengths and group count. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate padsThe zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f. default 1; Pooled output Y's height.default 1; Pooled output Y's width.Number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly sampling_ratio x sampling_ratio grid points are used. If == 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default is 0.The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.RoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[x1, y1, x2, y2], ...]. The RoIs' coordinates are in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.RoI pooled output, 4-D tensor of shape (num_rois, C, output_height, output_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1]./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/object_detection/defs.ccboxesAn input tensor with shape [num_batches, spatial_dimension, 4]. The single box data format is indicated by center_point_box.An input tensor with shape [num_batches, num_classes, spatial_dimension]max_output_boxes_per_classInteger representing the maximum number of boxes to be selected per batch per class. It is a scalar. Default to 0, which means no output.iou_thresholdFloat representing the threshold for deciding whether boxes overlap too much with respect to IOU. It is scalar. Value range [0, 1]. Default to 0.score_thresholdFloat representing the threshold for deciding when to remove boxes based on score. It is a scalar.selected_indicesselected indices from the boxes tensor. [num_selected_indices, 3], the selected index format is [batch_index, class_index, box_index].Integer indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models. expected to have rank /Users/runner/work/1/s/cmake/external/onnx/onnx/defs/object_detection/old.ccScale for doing quantization to get 'y'. It can be a scalar, which means per-tensor/layer quantization, or a 1-D Tensor for per-axis quantization.Zero point for doing quantization to get 'y'. It can be a scalar, which means a per-tensor/layer quantization, or a 1-D tensor for per-axis quantization. Default value is uint8 typed 0 if it's not specified.(Optional) The axis of the quantization dimension of the input tensor. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input)/Users/runner/work/1/s/cmake/external/onnx/onnx/defs/quantization/defs.ccScale for input 'x'. It can be a scalar, which means a per-tensor/layer dequantization, or a 1-D tensor for per-axis dequantization.Zero point for input 'x'. It can be a scalar, which means a per-tensor/layer dequantization, or a 1-D tensor for per-axis dequantization. It's optional. 0 is the default value when it's not specified.(Optional) The axis of the dequantizing dimension of the input tensor. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input)Quantized output tensorOutput scale. It's a scalar, which means a per-tensor/layer quantization.Output zero point. It's a scalar, which means a per-tensor/layer quantization.Constrain 'x' to float tensor.Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor.N-D full precision Input tensor to be quantized.Scale for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization.Zero point for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization. Default value is uint8 typed 0 if it's not specified.N-D quantized output tensor. It has same shape as input 'x'.Constrain 'x' to float or int32 tensor.Constrain 'y_zero_point' and 'y' to 8-bit integer tensor./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/quantization/old.ccN-D quantized input tensor to be de-quantized.Scale for input 'x'. It's a scalar, which means a per-tensor/layer quantization.Zero point for input 'x'. It's a scalar, which means a per-tensor/layer quantization. It's optional. 0 is the default value when it's not specified.N-D full precision output tensor. It has same shape as input 'x'.Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/reduction/defs.ccDefines behaviour if 'axes' is empty. Default behaviour with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor.Optional input list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor if 'noop_with_empty_axes' is false, else act as an Identity op when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).axes as an input and attribute cannot be specified at the same time./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/reduction/old.ccA list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).Keep the reduced dimension or not, default 1 mean keep reduced dimension.reducedReduced output tensor.Constrain input and output types to high-precision and 8 bit numeric tensors.axis must be in [-rank, rank-1]. input rank was The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).Reduced output tensor with integer data type.A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.The axis in which to compute the arg indices. Computes the indices of the {name} elements of the input tensor's element along the provided axis. The resulting tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulting tensor have the reduced dimension pruned. The type of the output tensor is integer.{name}/Users/runner/work/1/s/cmake/external/onnx/onnx/defs/rnn/defs.ccThe shape format of inputs X, initial_h, initial_c and outputs Y, Y_h, Y_c. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [num_directions, batch_size, hidden_size].The shape format of inputs X, initial_h and outputs Y, Y_h. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = [batch_size, num_directions, hidden_size].A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`.RThe recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`.The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0/Users/runner/work/1/s/cmake/external/onnx/onnx/defs/rnn/old.ccoutput_sequenceOne (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if not specified.The weight tensor for input gate. Concatenation of `Wi` and `WBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, input_size]`.The recurrence weight tensor. Concatenation of `Ri` and `RBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, hidden_size]`.The bias tensor for input gate. Concatenation of `[Wbi, Rbi]` and `[WBbi, RBbi]` (if bidirectional). The tensor has shape `[num_directions, 2*hidden_size]`. Optional: If not specified - assumed to be 0.When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.Couple the input and forget gates if 1, default 0.The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, 4*hidden_size, input_size]`.The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 4*hidden_size, hidden_size]`.The bias tensor for input gate. Concatenation of `[Wb[iofc], Rb[iofc]]`, and `[WBb[iofc], RBb[iofc]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 8*hidden_size]`. Optional: If not specified - assumed to be 0.initial_cOptional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.PThe weight tensor for peepholes. Concatenation of `P[iof]` and `PB[iof]` (if bidirectional) along dimension 0. It has shape `[num_directions, 3*hidde_size]`. Optional: If not specified - assumed to be 0.Y_cThe last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`.First input tensor must have rank 3Couple the input and forget gates if 1. Computes an one-layer simple RNN. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `t` - time step (t-1 means previous time step) `Wi` - W parameter weight matrix for input gate `Ri` - R recurrence weight matrix for input gate `Wbi` - W parameter bias vector for input gate `Rbi` - R parameter bias vector for input gate `WBi` - W parameter weight matrix for backward input gate `RBi` - R recurrence weight matrix for backward input gate `WBbi` - WR bias vectors for backward input gate `RBbi` - RR bias vectors for backward input gate `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Tanh): - Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi) This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. Computes an one-layer GRU. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `z` - update gate `r` - reset gate `h` - hidden gate `t` - time step (t-1 means previous time step) `W[zrh]` - W parameter weight matrix for update, reset, and hidden gates `R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates `Wb[zrh]` - W bias vectors for update, reset, and hidden gates `Rb[zrh]` - R bias vectors for update, reset, and hidden gates `WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates `RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates `WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates `RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh): - zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz) - rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr) - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0 - ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0 - Ht = (1 - zt) (.) ht + zt (.) Ht-1 Computes an one-layer LSTM. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `o` - output gate `f` - forget gate `c` - cell gate `t` - time step (t-1 means previous time step) `W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates `R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates `Wb[iofc]` - W bias vectors for input, output, forget, and cell gates `Rb[iofc]` - R bias vectors for input, output, forget, and cell gates `P[iof]` - P peephole weight vector for input, output, and forget gates `WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates `RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates `WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates `RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates `PB[iof]` - P peephole weight vector for backward input, output, and forget gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh, h=Tanh): - it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi) - ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf) - ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc) - Ct = ft (.) Ct-1 + it (.) ct - ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo) - Ht = ot (.) h(Ct) Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.Number of neurons in the hidden layerOptional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM.The sequence output for the hidden is optional if 0. Default 0.clipCell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.initial_hOptional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. It is optional if `output_sequence` is 0.Y_hThe last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.Constrain seq_lens to integer tensor.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. typestr: , has unsupported type: has inconsistent type has unsupported type Operator '' has been deprecated since version ) has input size not in range [min=, max= not in allowed input sizes.) has output size has output size not in allowed output sizes.) has more inputs () than declared () in op definition.)'s input is marked single but has an empty string in the graph) has more outputs ()'s output Attribute '' appeared multiple times.Unrecognized attribute: for operator Mismatched attribute type in '' is expected to have field 't'' is expected to have field 'sparse_tensor'' is expected to have field 'g'' is expected to have field 'floats'' is expected to have field 'ints'' is expected to have field 'strings'' is expected to have field 'tensors'' is expected to have field 'graphs' has unknown expected typeRequired attribute '' is missing.ONNX Schema : failed validating the check: (inputs_.size() - 1) == i(outputs_.size() - 1) == i!(it.GetName().empty())(Optional) The data type of the tensors in the output sequence. The default type is 'float'.Empty sequence./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/sequence/defs.ccTensors.Sequence enclosing the input tensors.input_sequenceInput sequence.Input tensor to be inserted into the input sequence.positionPosition in the sequence where the new tensor is inserted. It is optional and default is to insert to the back of the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).Output sequence that contains the inserted tensor at given position.Constrain position to integral tensor. It must be a scalar(tensor of empty shape).Position of the tensor in the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).Output tensor at the specified position in the input sequence.Output sequence that has the tensor at the specified position removed.Length of input sequence. It must be a scalar(tensor of empty shape).Constrain output to integral tensor. It must be a scalar(tensor of empty shape).Length of each output. It can be either a scalar(tensor of empty shape), or a 1-D tensor. All values must be >= 0. One or more outputs forming a sequence of tensors after splittingConstrain split size to integral tensor.Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1].Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored.Which axis to concat on. Accepted range in `[-r, r - 1]`, where `r` is the rank of input tensors. When `new_axis` is 1, accepted range is `[-r - 1, r]`. new_axisInsert and concatenate on a new axis or not, default 0 means do not insert new axis.Sequence of tensors for concatenationAttribute dtype should be of integer type and specify a type.SequenceConstruct is expected to have at least 1 input.Input type for input at index is null. Type info is expected.Element type of inputs are expected to be the same.Input Sequence and Tensor are expected to have type info. Current type is null.Input Sequence and Tensor are expected to have the same elem type. Sequence= Tensor=Input type for input at index 0 is null. Type info is expected.Only supports `int32_t` or `int64_t` inputs for splitInput 'split' can not be empty.Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size= sum of split values=new_axis must be either 0 or 1Invalid value of attribute 'axis'. Accepted range=[], Value=/Users/runner/work/1/s/cmake/external/onnx/onnx/defs/tensor/defs.cc(Optional) By default, when any value in the 'shape' input is equal to zero the corresponding dimension value is copied from the input tensor dynamically. allowzero=1 indicates that if any value in the 'shape' input is set to zero, the zero value is honored, similar to NumPy.Optional length of each output. Values should be >= 0.Sum of the values must be equal to the dim value at 'axis' specified.The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. One of 'scales' and 'sizes' MUST be specified and it is an error if both are specified. If 'sizes' is needed, the user can use an empty string as the name of 'scales' in this operator's input list.Constrain input and output types to all tensor and sequence types.(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length along the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices).Input tensor containing indices. Any entries in the 'indices' input tensor with values outside the range [-depth, depth-1] will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.Scalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [-depth, depth-1]. In case 'depth' is of non-integer type, it will be casted to int64 before use.(Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false.(Optional) Whether map negative infinity to true. Default to 1 so that negative infinity induces true. Set this attribute to 0 if negative infinity should be mapped to false.When True (nonzero), yield X, otherwise yield Yvalues selected at indices where condition is Truevalues selected at indices where condition is FalseTensor of shape equal to the broadcasted shape of condition, X, and Y.Constrain to boolean tensors.(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.Tensor of rank r >= 2.Tensor specifying lengths of the sequences in a batch. It has shape `[batch_size]`.Tensor with same shape of input.(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).(Optional) The dimension to apply unique. If not specified, the unique elements of the flattened input are returned. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).A N-D input tensor that is to be processed.A tensor of the same type as 'X' containing all the unique values or subtensors sliced along a provided 'axis' in 'X', either sorted or maintained in the same order they occur in input 'X'A 1-D INT64 tensor containing indices of 'Y' elements' first occurance in 'X'. When 'axis' is provided, it contains indices to subtensors in input 'X' on the 'axis'. When 'axis' is not provided, it contains indices to values in the flattened input tensor. inverse_indicesA 1-D INT64 tensor containing, for elements of 'X', its corresponding indices in 'Y'. When 'axis' is provided, it contains indices to subtensors in output 'Y' on the 'axis'. When 'axis' is not provided, it contains indices to values in output 'Y'. A 1-D INT64 tensor containing the count of each element of 'Y' in input 'X'(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0, empty string or False).A 0-D tensor containing a single value corresponding to the number diagonals above or below the main diagonal to exclude or include. Default value is 0 if it's not specified. This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_crop_and_resize",
x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).Target shape may not have multiple -1 dimensions.Invalid position of 0.Invalid Target shape product of 0. Product cannot be 0 in combination with -1'axis' must be in [-rank(indices), rank(indices)-1]'input' must have rank >= 2'sequence_lens' must have rank of 1toThe data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProtoInput tensor to be cast.Output tensor with the same shape as input with type specified by the 'to' argumentConstrain input types. Casting from complex is not supported.Constrain output types. Casting to complex is not supported./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/tensor/old.ccAn input tensor.Specified shape for output.reshapedReshaped data.Shape of the input tensorConstrain output to int64 tensor.Total number of elements of the input tensorConstrain output to int64 tensor, which should be a scalar though.Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..inputsList of tensors for concatenationconcat_resultConcatenated tensorThe tensor to splitOne or more outputs forming list of tensors after splittingWhich axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).length of each output. Values should be >= 0.1-D tensor of ending indices (exclusive) of corresponding axis in `axes`1-D tensor of axes that `starts` and `ends` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).steps1-D tensor of slice step of corresponding axis in `axes`. Negative value means slicing backward. 'steps' cannot be 0. Defaults to 1.permA list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.transposedTransposed output.updatesTensor of rank q + r - indices_shape[-1] - 1.Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of rank r >=1 (same rank and shape as indices)Tensor of rank r >= 1 (same rank as input).Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of rank q + (r - 1).Tensor of int32/int64 indices, with the same rank r as the input. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of the same shape as indices.List of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensors with at least max(dims) dimensions.squeezedReshaped tensor with same data as input.List of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded).Original tensorexpandedBlocks of [blocksize, blocksize] are moved.Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.Output tensor of [N, C * blocksize * blocksize, H/blocksize, W/blocksize].DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize].Input tensor of any shape.repeats1D int64 tensor of the same length as input's dimension number, includes numbers of repeated copies along input's dimensions.Output tensor of the same dimension and type as tensor input. output_dim[i] = input_dim[i] * repeats[i]Constrain repeat's type to int64 tensors.Three interpolation modes: nearest (default), linear and cubic. The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if "mode" is "cubic".If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0.Four modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.N-D tensorroi1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize"The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified. If 'size' is needed, the user can use an empty string as the name of 'scales' in this operator's input list.sizesThe size of the output tensor. The number of elements of 'sizes' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified.N-D tensor after resizingConstrain roi type to float or double.Tensor to copy input into.Constrain input types to float tensors.Constrain output types to boolean tensors.The number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]Tensor of rank q >= 1. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Supported modes: `constant`(default), `reflect`, `edge`Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank]. `pads` format should be: [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `i` and xi_end, the number of pad values added at the end of axis `i`.constant_value(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0).Constrain input types. Casting from strings and complex are not supported.Constrain output types. Casting to strings and complex are not supported.Which axis to concat on. Default value is 1.Constrain output types to float tensors.Which axis to concat onOptional list of output lengths (see also arg 'split')outputs...Which axis to split onlength of each outputpaddingsList of integers indicate the padding element count at the beginning and end of each axis, for 2D it is the number of pixel. `paddings` rank should be double of the input's rank. `paddings` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.Three modes: constant(default), reflect, edgeOne float, indicates the value to be filled, default is 0New shapetilesNumber of repeated copies to make of the input tensor.Axis along which to repeat.Output tensor of same shape and type as input.Constrain tiles and axis's type to int64 tensors.width_scaleThe scale along width dimension. It takes value greater than or equal to 1.height_scaleThe scale along height dimension. It takes value greater than or equal to 1.Two interpolation modes: nearest(default), bilinear4-D tensor, [N,C,H,W]4-D tensor after resizing, [N,C,H,W]Constrain output types to bool, int32, int64, float16, float, double tensors.The scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'.Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'.Axes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1].Starting indices of corresponding axis in `axes`Ending indices (exclusive) of corresponding axis in axes`1-D tensor of slice step of corresponding axis in `axes`. Default to 1. Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Tensor of int32/int64 indices, of r >= 1 (same rank as input).Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds. It is an error if any of the index values are out of bounds.List of non-negative integers, indicate the dimensions to squeeze.List of non-negative integers, indicate the dimensions to be inserted(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor.Input tensor containing indices. The values must be non-negative integers. Any entries in the 'indices' input tensor with values outside the range [0, depth) will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.depthScalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [0, depth). In case 'depth' is of non-integer type, it will be casted to int64 before use.Rank 1 tensor containing exactly two elements, in the format [off_value, on_value], where 'on_value' is the value used for filling locations specified in 'indices' input tensor, and 'off_value' is the value used for filling locations other than those specified in 'indices' input tensor. Tensor of rank one greater than input tensor 'indices', i.e. rank(output) = rank(indices) + 1. The data type for the elements of the output tensor is the same as the type of input 'values' is used.(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.conditionRank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length alone the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1.Constrains to boolean tensors.Which axis to split on. List of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D it is the number of pixels. `pads` rank should be double of the input's rank. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.One float, indicates the value to be filled. This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_half_pixel_for_nn",
x_original = (x_resized + 0.5) / scale,
if coordinate_transformation_mode is "tf_crop_and_resize",
x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).Value of attribute not specified should be of integer type and specify a type. does not specify a valid type.Target shape may not have multiple -1 dimensionsInvalid position of 0Invalid dimension value: Invalid Target shape product of 0Dimension could not be inferred: incompatible shapesRequired attribute axis is missingaxis must be in [-rank, rank-1].All inputs to Concat must have same rankInvalid value of attribute 'axis'. Rank= Value=Mismatch between number of splits () and outputs (Mismatch between the sum of 'split' () and the split dimension of the input (The input is not evenly splittableSlice op must have either three, four or five inputs.Incorrect or missing input value for starts and endsInput axes has incorrect lengthInput steps has incorrect lengthInput axes has invalid data'step' cannot be 0Only supports `int32_t` or `int64_t` inputs for starts/ends/axes/stepsInvalid attribute perm {}, input shape = {data tensor must have rank >= 1axis must be in [-r, r-1]'axes' attribute must not contain any duplicatesvalues in 'axes' are beyond the bounds of the computed output shapeBlocksize must be positiveInput tensor must be 4-dimensional'Repeats' input must be 1D tensor of type int64'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions.Both `data` and `indices` input tensors in GatherND op need to have rank larger than 0.Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensor'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64rank must be greater than axisRanks inferred () is not equal to the existing rank value (Number of elements of attribute 'scales' must be same as rank of input 'X'Attribute 'scales' must have floats type.Attribute 'scales' is required.Incorrect or missing attribute value for starts and endsAttribute axes has incorrect lengthOneHot node must have three inputs.Input 'depth' must be a scalar or rank 1 tensor.Input 'depth' must have exactly one element.Input 'values' must be rank 1 tensor.Input 'values' must have exactly two elements.Indices tensor must have rank >= 1'axis' must be in [-rank(indices)-1, rank(indices)]Attribute value for pads is requiredAttribute pads has incorrect lengthDimension value inferred () is not equal to the existing dim value (Number of elements of input 'sizes' must be same as rank of input 'X'Input 'sizes' must have int64 element type.Number of elements of input 'scales' must be same as rank of input 'X'Input 'scales' must have float element type.Data to be selectedThe indices, based on 0 as the first index of any dimension.Selected output data as an arrayThe input must be a tensor of a numeric type or string. The output will be of the same tensor type./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/traditionalml/defs.ccData to be binarizedBinarized output dataThe input must be a tensor of a numeric type. The output will be of the same tensor type.Values greater than this are mapped to 1, others to 0.The input map that is to be cast to a tensorA tensor representing the same data as the input map, ordered by their keysmap(int64, string)map(int64, float)The input must be an integer map to either string or float.The output is a 1-D tensor of string, float, or integer.A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.map_formIndicates whether to only output as many values as are in the input (dense), or position the input based on using the key of the map as the index of the output (sparse).
One of 'DENSE', 'SPARSE'.If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.Input dataOutput data. If strings are input, the output values are integers, and vice versa.The input must be a tensor of strings or integers, either [N,C] or [C].The output is a tensor of strings or integers. Its shape will be the same as the input shape.The strings of the map. This sequence must be the same length as the 'cats_int64s' sequenceThe integers of the map. This sequence must be the same length as the 'cats_strings' sequence.A string to use when an input integer value is not found in the map.
One and only one of the 'default_*' attributes must be defined.An integer to use when an input string value is not found in the map.
One and only one of the 'default_*' attributes must be defined.A dictionary.A 1-D tensor holding values from the input dictionary.map(string, int64)map(int64, double)map(string, float)map(string, double)The input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same.The output will be a tensor of the value type of the input map. It's shape will be [1,C], where C is the length of the input dictionary.A string vocabulary array.
One and only one of the vocabularies must be defined.An integer vocabulary array.
One and only one of the vocabularies must be defined.An ordered collection of tensors, all with the same element type.The output array, elements ordered as the inputs.The input type must be a tensor of a numeric type.The size of each input in the input listData to be processed.Imputed output dataThe input type must be a tensor of a numeric type, either [N,C] or [C]. The output type will be of the same tensor type and shape.Value(s) to change toA value that needs replacing.Value(s) to change to.Input data. It can be either tensor or scalar.Output data.The input type is a tensor of any shape.Output type is determined by the specified 'values_*' attribute.A list of strings. One and only one of 'keys_*'s should be set.A list of ints.A list of floats.A list of strings. One and only one of 'value_*'s should be set.A string.An integer.A float.Data to be classified.Classification outputs (one class per example).Classification scores ([N,E] - one score for each class and exampleThe input must be a tensor of a numeric type, and of of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]The output will be a tensor of strings or integers.A collection of weights of the model(s).A collection of intercepts.Indicates whether to do OvR or multinomial (0=OvR is the default).Class labels when using string labels. One and only one 'classlabels' attribute must be defined.Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.Indicates the transform to apply to the scores vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Data to be regressed.Regression outputs (one per target, per example).The input must be a tensor of a numeric type.Indicates the transform to apply to the regression output vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Weights of the model(s).Weights of the intercepts, if used.The total number of regression targets, 1 if not defined.Data to be encoded, a tensor of shape [N,C] or [C]Encoded output datanormOne of 'MAX,' 'L1,' 'L2'Data to be encoded.Encoded output data, having one more dimension than X.List of categories, ints.
One and only one of the 'cats_*' attributes must be defined.List of categories, strings.
One and only one of the 'cats_*' attributes must be defined.If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.Data to be scaled.Scaled output data.First, offset by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.Second, multiply by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.
Must be same length as 'offset'Class scores (one per class per example), if prob_a and prob_b are provided they are probabilities for each class, otherwise they are raw scores.The input must be a tensor of a numeric type, either [C] or [N,C].The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used. Its size will match the bactch size of the input.The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.First set of probability coefficients.Second set of probability coefficients. This array must be same size as prob_a.
If these are provided then output Z are probability estimates, otherwise they are raw scores.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Class labels if using string labels.
One and only one of the 'classlabels_*' attributes must be defined.Class labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined.Regression outputs (one score per target per example).The input type must be a tensor of a numeric type, either [C] or [N,C].Chosen support vectorsFlag indicating whether the regression is a one-class SVM or not.Support vector coefficients.The number of support vectors.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Input of shape [N,F]N, Top class for each pointThe class score for each class, for each point, a tensor of shape [N,E].The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used.Tree id for each node.Node id for each node. Ids may restart at zero for each tree, but it not required to.Feature id for each node.Thresholds to do the splitting on for each node.Popularity of each node, used for performance and may be omitted.The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.
One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'Child node if expression is true.Child node if expression is false.For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.
This attribute may be left undefined, and the defalt value is false (0) for all nodes.The id of the tree that this node is in.node id that this weight is for.The index of the class list that each weight is for.The weight for the class in class_id.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)N classesNode id for each node. Node ids must restart at zero for each tree and increase sequentially.Child node if expression is trueChild node if expression is falseFor each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.
This attribute may be left undefined and the defalt value is false (0) for all nodes.The id of the tree that each node is in.The node id of each weightThe index of the target that each weight is forThe weight for each targetThe total number of targets.Defines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'The input valuesThe output mapseq(map(string, float))seq(map(int64, float))The output will be a sequence of string or integer maps to float.The keys when using string keys.
One and only one of the 'classlabels_*' attributes must be defined.The keys when using int keys.
One and only one of the 'classlabels_*' attributes must be defined.Label encoder has only one input.Label encoder has only one output.Only one of keys_*'s can be set in label encoder.Input type is not string tensor but key_strings is setInput type is not int64 tensor but keys_int64s is setInput type is not float tensor but keys_floats is setOnly one of values_*'s can be set in label encoder.Input's shape should be 1D or 2DInput data.The input type must be a tensor of integers or strings, of any shape.The output type will be a tensor of strings or integers, and will have the same shape as the input.A list of labels./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/traditionalml/old.ccInferred elem type differs from existing elem type: () vs (Inferred shape and existing shape differ in rank: (Inferred shape and existing shape differ in dimension : (type case mismatch. existing= inferred=type case unsupported. existing=Graph has inputs but were provided, node name: (op_type:Warning: Shape inference does not support models with experimental operators: Warning: Unsupported operator . No schema registered for this operator.ONNX models don't support multiple opset version imports for a domain. Function input is out of boundsGraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. does not contain a graph.output /Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/arena.ccCHECK failed: (min_bytes) <= (std::numeric_limits::max() - SerialArena::kBlockHeaderSize): /Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream.ccThis ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug./Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream_impl.ccclose() failed: CHECK failed: !is_closed_: /Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream_impl_lite.ccCHECK failed: (count) >= (0): CHECK failed: backup_bytes_ == 0 && buffer_.get() != NULL: BackUp() can only be called after Next().CHECK failed: (count) <= (buffer_used_): Can't back up over more bytes than were returned by the last call to Next(). Parameter to BackUp() can't be negative.CHECK failed: (backup_bytes_) == (0): CHECK failed: (buffer_used_) == (buffer_size_): /Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/message_lite.ccparse exceeded maximum protobuf size of 2GB: Can't message of type "" because it is missing required fields: /Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/parse_context.hCan't happenINFOWARNINGERRORFATAL[libprotobuf %s %s:%d] %s %lu\x%02xtyfalse/Users/runner/work/1/s/cmake/external/re2/re2/bitstate.ccGrowStack() failed: njob_ = job_.size() = Unexpected opcode: arg Bad arg in kInstAltMatch: Bad arg in kInstCapture: /Users/runner/work/1/s/cmake/external/re2/re2/compile.ccshould never happenCompiler::Copy called!No ranges in char classMissing case in Compiler: |/Users/runner/work/1/s/cmake/external/re2/re2/dfa.ccDeadState in RunStateOnByteNULL state in RunStateOnByteUnexpected special state in RunStateOnByteStateSaver failed to restore state.context does not contain textFailed to analyze start state.RunStateOnByteUnlocked failed after ResetCacheRunStateOnByteUnlocked failed after Reset/Users/runner/work/1/s/cmake/external/re2/re2/nfa.ccunhandled in AddToThreadqUnhandled in stepBad args: nsubmatch=Unexpected opcode in short circuit: in ComputeFirstByte/Users/runner/work/1/s/cmake/external/re2/re2/onepass.ccCannot use SearchOnePass for unanchored matches.unhandled opcode: /Users/runner/work/1/s/cmake/external/re2/re2/parse.ccRepetitionWalker::ShortVisit calledConcat of unknown round: RE2: unexpected op: Bad call to ParseState::ParsePerlFlagsAddFoldedRange recurses too much.AnyBad hex digit \d\D\s\S\w\W[:alnum:][:^alnum:][:alpha:][:^alpha:][:ascii:][:^ascii:][:blank:][:^blank:][:cntrl:][:^cntrl:][:digit:][:^digit:][:graph:][:^graph:][:lower:][:^lower:][:print:][:^print:][:punct:][:^punct:][:space:][:^space:][:upper:][:^upper:][:word:][:^word:][:xdigit:][:^xdigit:]/Users/runner/work/1/s/cmake/external/re2/re2/re2.ccUnknown encoding Error parsing '': Error compiling 'Invalid RE2: RE2: invalid startpos, endpos pair. [startpos: endpos: text size: DFA out of memory: size bytemap range list count SearchDFA inconsistencySearchOnePass inconsistencySearchBitState inconsistencySearchNFA inconsistencyError reverse compiling '/Users/runner/work/1/s/cmake/external/re2/re2/regexp.ccRegexp not destroyed.Bad reference count Unexpected op in Regexp::Equal: no errorunexpected errorinvalid escape sequenceinvalid character classinvalid character class rangemissing ]missing )trailing \no argument for repetition operatorinvalid repetition sizebad repetition operatorinvalid perl operatorinvalid UTF-8invalid named capture groupNumCapturesWalker::ShortVisit called/Users/runner/work/1/s/cmake/external/re2/re2/walker-inl.hStack not empty.Walk NULL/Users/runner/work/1/s/cmake/external/re2/re2/simplify.ccCase not handled in ComputeSimple: CoalesceWalker::ShortVisit calledDoCoalesce failed: r1->op() is DoCoalesce failed: r2->op() is SimplifyWalker::ShortVisit calledSimplify case not handled: Malformed repeat [truncated](?:/Users/runner/work/1/s/cmake/external/re2/re2/tostring.cckRegexpCapture cap() == 0?P<>[^\x00-\x{10ffff}](?:)Bad final char: ?{%d,}{%d}{%d,%d}\C^$(?-m:^)(?-m:$)\z\b\B(?HaveMatch:%d)(){}[]*+?|.^$\[]^-\\\r\t\n\f\x{%x}AdlamAhomAnatolian_HieroglyphsArabicArmenianAvestanBalineseBamumBassa_VahBatakBengaliBhaiksukiBopomofoBrahmiBrailleBugineseBuhidCanadian_AboriginalCarianCaucasian_AlbanianCcCfChakmaChamCherokeeCoCommonCopticCsCuneiformCypriotCyrillicDeseretDevanagariDograDuployanEgyptian_HieroglyphsElbasanEthiopicGeorgianGlagoliticGothicGranthaGreekGujaratiGunjala_GondiGurmukhiHanHangulHanifi_RohingyaHanunooHatranHebrewHiraganaImperial_AramaicInheritedInscriptional_PahlaviInscriptional_ParthianJavaneseKaithiKannadaKatakanaKayah_LiKharoshthiKhmerKhojkiKhudawadiLLaoLatinLepchaLimbuLinear_ALinear_BLisuLlLmLoLtLuLycianLydianMahajaniMakasarMalayalamMandaicManichaeanMarchenMasaram_GondiMcMeMedefaidrinMeetei_MayekMende_KikakuiMeroitic_CursiveMeroitic_HieroglyphsMiaoMnModiMongolianMroMultaniMyanmarNNabataeanNdNew_Tai_LueNewaNkoNlNoNushuOghamOl_ChikiOld_HungarianOld_ItalicOld_North_ArabianOld_PermicOld_PersianOld_SogdianOld_South_ArabianOld_TurkicOriyaOsageOsmanyaPahawh_HmongPalmyrenePau_Cin_HauPcPdPePfPhags_PaPhoenicianPiPoPsPsalter_PahlaviRejangRunicSamaritanSaurashtraScSharadaShavianSiddhamSignWritingSinhalaSkSmSoSogdianSora_SompengSoyomboSundaneseSyloti_NagriSyriacTagalogTagbanwaTai_LeTai_ThamTai_VietTakriTamilTangutTeluguThaanaThaiTibetanTifinaghTirhutaUgariticVaiWarang_CitiYiZanabazar_SquareZlZpZsmu held in reader and writer mode simultaneously on entry to nsync_cv_wait_with_deadline() mu not held on entry to nsync_cv_wait_with_deadline() checking a waiter condition while unlocked attempt to nsync_mu_unlock() an nsync_mu held in read mode attempt to nsync_mu_unlock() an nsync_mu not held in write mode attempt to nsync_mu_runlock() an nsync_mu held in write mode attempt to nsync_mu_runlock() an nsync_mu not held in read mode nsync_mu not held in some mode when calling nsync_mu_wait_with_deadline() nsync_mu_wait woke but condition not true panic: unique_lock::lock: references null mutexunique_lock::lock: already lockedunique_lock::unlock: not locked}uWWxl8.l l(1 =F R})'OUA 3o8(`g}}hdi[ ! iq.,",}}hh   IAs j~ u*5_ jIAs j~ u*5_ j8 C]; GzLwրNv X+ clM XրNv X+ clM X 3 3 '$3{D_րN ''d ;1 '}u * ! w/ ;a }u * ! w/ ;a ߇\\dY\e<} G EsavA teT? 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EvE4$_12NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_18Squeeze_Onnx_ver11EEENS2_8OpSchemaEvE4$_13NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEvE4$_13NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_20Unsqueeze_Onnx_ver11EEENS2_8OpSchemaEvE4$_14NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEvE4$_14NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_22SpaceToDepth_Onnx_ver1EEENS2_8OpSchemaEvE4$_15NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvE4$_15NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_23DepthToSpace_Onnx_ver11EEENS2_8OpSchemaEvE4$_16NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvE4$_16NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_14Tile_Onnx_ver6EEENS2_8OpSchemaEvE4$_17NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEvE4$_17NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_17Resize_Onnx_ver11EEENS2_8OpSchemaEvE4$_18NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEvE4$_18NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_15IsNaN_Onnx_ver9EEENS2_8OpSchemaEvE4$_19NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_8OpSchemaEvE4$_19NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_17NonZero_Onnx_ver9EEENS2_8OpSchemaEvE4$_20NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEvE4$_20NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_19GatherND_Onnx_ver12EEENS2_8OpSchemaEvE4$_21NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver12EEENS_8OpSchemaEvE4$_21NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_14Pad_Onnx_ver11EEENS2_8OpSchemaEvE4$_22NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEvE4$_22NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_14Cast_Onnx_ver6EEENS2_8OpSchemaEvE4$_23NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver6EEENS_8OpSchemaEvE4$_23NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_16Concat_Onnx_ver4EEENS2_8OpSchemaEvE4$_24NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_16Concat_Onnx_ver4EEENS_8OpSchemaEvE4$_24NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_14Tile_Onnx_ver1EEENS2_8OpSchemaEvE4$_25NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver1EEENS_8OpSchemaEvE4$_25NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_18Upsample_Onnx_ver7EEENS2_8OpSchemaEvE4$_26NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver7EEENS_8OpSchemaEvE4$_26NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_18Upsample_Onnx_ver9EEENS2_8OpSchemaEvE4$_27NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver9EEENS_8OpSchemaEvE4$_27NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_17Resize_Onnx_ver10EEENS2_8OpSchemaEvE4$_28NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver10EEENS_8OpSchemaEvE4$_28NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_15Slice_Onnx_ver1EEENS2_8OpSchemaEvE4$_29NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_15Slice_Onnx_ver1EEENS_8OpSchemaEvE4$_29NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_16Slice_Onnx_ver10EEENS2_8OpSchemaEvE4$_30NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver10EEENS_8OpSchemaEvE4$_30NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_17Scatter_Onnx_ver9EEENS2_8OpSchemaEvE4$_31NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_17Scatter_Onnx_ver9EEENS_8OpSchemaEvE4$_31NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_22DepthToSpace_Onnx_ver1EEENS2_8OpSchemaEvE4$_32NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_22DepthToSpace_Onnx_ver1EEENS_8OpSchemaEvE4$_32NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_16Gather_Onnx_ver1EEENS2_8OpSchemaEvE4$_33NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_16Gather_Onnx_ver1EEENS_8OpSchemaEvE4$_33NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_17Squeeze_Onnx_ver1EEENS2_8OpSchemaEvE4$_34NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_17Squeeze_Onnx_ver1EEENS_8OpSchemaEvE4$_34NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_19Unsqueeze_Onnx_ver1EEENS2_8OpSchemaEvE4$_35NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_19Unsqueeze_Onnx_ver1EEENS_8OpSchemaEvE4$_35NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_16OneHot_Onnx_ver9EEENS2_8OpSchemaEvE4$_36NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_16One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ntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Less_kOnnxDomain_ver9_12_int64_tEEENS_16KernelCreateInfoEvEN5$_1208__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime4LessIxED1Ev__ZN11onnxruntime4LessIxED0Ev__ZNK11onnxruntime4LessIxE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime4LessIxE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime4LessIxE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime4LessIxE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Less_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEvEN5$_1218__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_Less_kOnnxDomain_ver13_doubleEEENS_16KernelCreateInfoEvEN5$_1228__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Less_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEvEN5$_1238__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Less_kOnnxDomain_ver13_int64_tEEENS_16KernelCreateInfoEvEN5$_1248__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Greater_kOnnxDomain_ver7_8_floatEEENS_16KernelCreateInfoEvEN5$_1258__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime7GreaterIfED1Ev__ZN11onnxruntime7GreaterIfED0Ev__ZNK11onnxruntime7GreaterIfE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime7GreaterIfE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime7GreaterIfE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime7GreaterIfE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Greater_kOnnxDomain_ver7_8_doubleEEENS_16KernelCreateInfoEvEN5$_1268__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime7GreaterIdED1Ev__ZN11onnxruntime7GreaterIdED0Ev__ZNK11onnxruntime7GreaterIdE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime7GreaterIdE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime7GreaterIdE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime7GreaterIdE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Greater_kOnnxDomain_ver9_12_floatEEENS_16KernelCreateInfoEvEN5$_1278__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_Greater_kOnnxDomain_ver9_12_doubleEEENS_16KernelCreateInfoEvEN5$_1288__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_Greater_kOnnxDomain_ver9_12_int32_tEEENS_16KernelCreateInfoEvEN5$_1298__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime7GreaterIiED1Ev__ZN11onnxruntime7GreaterIiED0Ev__ZNK11onnxruntime7GreaterIiE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime7GreaterIiE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime7GreaterIiE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime7GreaterIiE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_Greater_kOnnxDomain_ver9_12_int64_tEEENS_16KernelCreateInfoEvEN5$_1308__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime7GreaterIxED1Ev__ZN11onnxruntime7GreaterIxED0Ev__ZNK11onnxruntime7GreaterIxE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime7GreaterIxE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime7GreaterIxE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime7GreaterIxE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Greater_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEvEN5$_1318__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Greater_kOnnxDomain_ver13_doubleEEENS_16KernelCreateInfoEvEN5$_1328__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Greater_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEvEN5$_1338__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Greater_kOnnxDomain_ver13_int64_tEEENS_16KernelCreateInfoEvEN5$_1348__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Equal_kOnnxDomain_ver7_10_boolEEENS_16KernelCreateInfoEvEN5$_1358__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime5EqualIbED1Ev__ZN11onnxruntime5EqualIbED0Ev__ZNK11onnxruntime5EqualIbE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime5EqualIbE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime5EqualIbE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime5EqualIbE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Equal_kOnnxDomain_ver7_10_int32_tEEENS_16KernelCreateInfoEvEN5$_1368__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime5EqualIiED1Ev__ZN11onnxruntime5EqualIiED0Ev__ZNK11onnxruntime5EqualIiE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime5EqualIiE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime5EqualIiE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime5EqualIiE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Equal_kOnnxDomain_ver7_10_int64_tEEENS_16KernelCreateInfoEvEN5$_1378__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime5EqualIxED1Ev__ZN11onnxruntime5EqualIxED0Ev__ZNK11onnxruntime5EqualIxE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime5EqualIxE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime5EqualIxE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime5EqualIxE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Equal_kOnnxDomain_ver7_10_floatEEENS_16KernelCreateInfoEvEN5$_1388__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime5EqualIfED1Ev__ZN11onnxruntime5EqualIfED0Ev__ZNK11onnxruntime5EqualIfE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime5EqualIfE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime5EqualIfE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime5EqualIfE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Equal_kOnnxDomain_ver7_10_doubleEEENS_16KernelCreateInfoEvEN5$_1398__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime5EqualIdED1Ev__ZN11onnxruntime5EqualIdED0Ev__ZNK11onnxruntime5EqualIdE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime5EqualIdE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime5EqualIdE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime5EqualIdE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Equal_kOnnxDomain_ver11_12_boolEEENS_16KernelCreateInfoEvEN5$_1408__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_Equal_kOnnxDomain_ver11_12_int32_tEEENS_16KernelCreateInfoEvEN5$_1418__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_Equal_kOnnxDomain_ver11_12_int64_tEEENS_16KernelCreateInfoEvEN5$_1428__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Equal_kOnnxDomain_ver11_12_floatEEENS_16KernelCreateInfoEvEN5$_1438__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Equal_kOnnxDomain_ver11_12_doubleEEENS_16KernelCreateInfoEvEN5$_1448__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Equal_kOnnxDomain_ver13_boolEEENS_16KernelCreateInfoEvEN5$_1458__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Equal_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEvEN5$_1468__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Equal_kOnnxDomain_ver13_int64_tEEENS_16KernelCreateInfoEvEN5$_1478__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_Equal_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEvEN5$_1488__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Equal_kOnnxDomain_ver13_doubleEEENS_16KernelCreateInfoEvEN5$_1498__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_LessOrEqual_kOnnxDomain_ver12_floatEEENS_16KernelCreateInfoEvEN5$_1508__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime11LessOrEqualIfED1Ev__ZN11onnxruntime11LessOrEqualIfED0Ev__ZNK11onnxruntime11LessOrEqualIfE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime11LessOrEqualIfE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime11LessOrEqualIfE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime11LessOrEqualIfE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_LessOrEqual_kOnnxDomain_ver12_doubleEEENS_16KernelCreateInfoEvEN5$_1518__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime11LessOrEqualIdED1Ev__ZN11onnxruntime11LessOrEqualIdED0Ev__ZNK11onnxruntime11LessOrEqualIdE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime11LessOrEqualIdE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime11LessOrEqualIdE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime11LessOrEqualIdE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_LessOrEqual_kOnnxDomain_ver12_int32_tEEENS_16KernelCreateInfoEvEN5$_1528__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime11LessOrEqualIiED1Ev__ZN11onnxruntime11LessOrEqualIiED0Ev__ZNK11onnxruntime11LessOrEqualIiE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime11LessOrEqualIiE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime11LessOrEqualIiE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime11LessOrEqualIiE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_LessOrEqual_kOnnxDomain_ver12_int64_tEEENS_16KernelCreateInfoEvEN5$_1538__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime11LessOrEqualIxED1Ev__ZN11onnxruntime11LessOrEqualIxED0Ev__ZNK11onnxruntime11LessOrEqualIxE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime11LessOrEqualIxE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime11LessOrEqualIxE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime11LessOrEqualIxE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_GreaterOrEqual_kOnnxDomain_ver12_floatEEENS_16KernelCreateInfoEvEN5$_1548__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime14GreaterOrEqualIfED1Ev__ZN11onnxruntime14GreaterOrEqualIfED0Ev__ZNK11onnxruntime14GreaterOrEqualIfE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime14GreaterOrEqualIfE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime14GreaterOrEqualIfE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime14GreaterOrEqualIfE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_61kCpuExecutionProvider_GreaterOrEqual_kOnnxDomain_ver12_doubleEEENS_16KernelCreateInfoEvEN5$_1558__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime14GreaterOrEqualIdED1Ev__ZN11onnxruntime14GreaterOrEqualIdED0Ev__ZNK11onnxruntime14GreaterOrEqualIdE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime14GreaterOrEqualIdE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime14GreaterOrEqualIdE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime14GreaterOrEqualIdE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_62kCpuExecutionProvider_GreaterOrEqual_kOnnxDomain_ver12_int32_tEEENS_16KernelCreateInfoEvEN5$_1568__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime14GreaterOrEqualIiED1Ev__ZN11onnxruntime14GreaterOrEqualIiED0Ev__ZNK11onnxruntime14GreaterOrEqualIiE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime14GreaterOrEqualIiE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime14GreaterOrEqualIiE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime14GreaterOrEqualIiE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_62kCpuExecutionProvider_GreaterOrEqual_kOnnxDomain_ver12_int64_tEEENS_16KernelCreateInfoEvEN5$_1578__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime14GreaterOrEqualIxED1Ev__ZN11onnxruntime14GreaterOrEqualIxED0Ev__ZNK11onnxruntime14GreaterOrEqualIxE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime14GreaterOrEqualIxE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime14GreaterOrEqualIxE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime14GreaterOrEqualIxE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_Mean_kOnnxDomain_ver6_7_floatEEENS_16KernelCreateInfoEvEN5$_1588__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime6Mean_6IfED1Ev__ZN11onnxruntime6Mean_6IfED0Ev__ZZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Mean_kOnnxDomain_ver8_12_floatEEENS_16KernelCreateInfoEvEN5$_1598__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime6Mean_8IfED1Ev__ZN11onnxruntime6Mean_8IfED0Ev__ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Mean_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEvEN5$_1608__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_BitShift_kOnnxDomain_ver11_uint8_tEEENS_16KernelCreateInfoEvEN5$_1618__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime8BitShiftIhEC2ERKNS_12OpKernelInfoE__ZN11onnxruntime8BitShiftIhED1Ev__ZN11onnxruntime8BitShiftIhED0Ev__ZNK11onnxruntime8BitShiftIhE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime8BitShiftIhE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime8BitShiftIhE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime8BitShiftIhE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZNK11onnxruntime8BitShiftIhE7ComputeEPNS_15OpKernelContextEENKUlRNS_15BroadcastHelperEE1_clES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_BitShift_kOnnxDomain_ver11_uint32_tEEENS_16KernelCreateInfoEvEN5$_1628__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime8BitShiftIjEC2ERKNS_12OpKernelInfoE__ZN11onnxruntime8BitShiftIjED1Ev__ZN11onnxruntime8BitShiftIjED0Ev__ZNK11onnxruntime8BitShiftIjE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime8BitShiftIjE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime8BitShiftIjE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime8BitShiftIjE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZNK11onnxruntime8BitShiftIjE7ComputeEPNS_15OpKernelContextEENKUlRNS_15BroadcastHelperEE1_clES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_BitShift_kOnnxDomain_ver11_uint64_tEEENS_16KernelCreateInfoEvEN5$_1638__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime8BitShiftIyEC2ERKNS_12OpKernelInfoE__ZN11onnxruntime8BitShiftIyED1Ev__ZN11onnxruntime8BitShiftIyED0Ev__ZNK11onnxruntime8BitShiftIyE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime8BitShiftIyE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime8BitShiftIyE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime8BitShiftIyE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZNK11onnxruntime8BitShiftIyE7ComputeEPNS_15OpKernelContextEENKUlRNS_15BroadcastHelperEE1_clES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_Erf_kOnnxDomain_ver9_12_floatEEENS_16KernelCreateInfoEvEN5$_1648__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime3ErfIfED1Ev__ZN11onnxruntime3ErfIfED0Ev__ZZN11onnxruntime21BuildKernelCreateInfoINS_49kCpuExecutionProvider_Erf_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEvEN5$_1658__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_42kCpuExecutionProvider_Not_kOnnxDomain_ver1EEENS_16KernelCreateInfoEvEN5$_1668__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_42kCpuExecutionProvider_And_kOnnxDomain_ver7EEENS_16KernelCreateInfoEvEN5$_1678__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_41kCpuExecutionProvider_Or_kOnnxDomain_ver7EEENS_16KernelCreateInfoEvEN5$_1688__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_42kCpuExecutionProvider_Xor_kOnnxDomain_ver7EEENS_16KernelCreateInfoEvEN5$_1698__invokeERKNS_12OpKernelInfoE__ZZNK11onnxruntime3And7ComputeEPNS_15OpKernelContextEEN5$_1708__invokeERNS_15BroadcastHelperE__ZZNK11onnxruntime3And7ComputeEPNS_15OpKernelContextEEN5$_1718__invokeERNS_15BroadcastHelperE__ZZNK11onnxruntime3And7ComputeEPNS_15OpKernelContextEEN5$_1728__invokeERNS_15BroadcastHelperE__ZZNK11onnxruntime2Or7ComputeEPNS_15OpKernelContextEEN5$_1738__invokeERNS_15BroadcastHelperE__ZZNK11onnxruntime2Or7ComputeEPNS_15OpKernelContextEEN5$_1748__invokeERNS_15BroadcastHelperE__ZZNK11onnxruntime2Or7ComputeEPNS_15OpKernelContextEEN5$_1758__invokeERNS_15BroadcastHelperE__ZZNK11onnxruntime3Xor7ComputeEPNS_15OpKernelContextEEN5$_1768__invokeERNS_15BroadcastHelperE__ZZNK11onnxruntime3Xor7ComputeEPNS_15OpKernelContextEEN5$_1778__invokeERNS_15BroadcastHelperE__ZZNK11onnxruntime3Xor7ComputeEPNS_15OpKernelContextEEN5$_1788__invokeERNS_15BroadcastHelperE__ZZNK11onnxruntime6Mean_8IfE7ComputeEPNS_15OpKernelContextEEN5$_1808__invokeERNS_15BroadcastHelperE__ZZNK11onnxruntime6Mean_8IfE7ComputeEPNS_15OpKernelContextEEN5$_1818__invokeERNS_15BroadcastHelperE__ZZNK11onnxruntime6Mean_8IfE7ComputeEPNS_15OpKernelContextEEN5$_1828__invokeERNS_15BroadcastHelperE__ZZNK11onnxruntime6Mean_8IfE7ComputeEPNS_15OpKernelContextEEN5$_1798__invokeERKNS_15TensorAllocatorERKNS_11TensorShapeE__ZZN11onnxruntime21BuildKernelCreateInfoINS_48kCpuExecutionProvider_Sin_kOnnxDomain_ver7_floatEEENS_16KernelCreateInfoEvEN5$_1838__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime3SinIfED1Ev__ZN11onnxruntime3SinIfED0Ev__ZNK11onnxruntime3SinIfE7ComputeEPNS_15OpKernelContextE__ZN5Eigen8internal13psincos_floatILb1EDv4_fEET0_RKS3___ZZN11onnxruntime21BuildKernelCreateInfoINS_49kCpuExecutionProvider_Sin_kOnnxDomain_ver7_doubleEEENS_16KernelCreateInfoEvEN5$_1848__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime3SinIdED1Ev__ZN11onnxruntime3SinIdED0Ev__ZNK11onnxruntime3SinIdE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Block1523.LProcessCountM5.LProcessNextColumnLoop2xN1934.LComputeBlockBy4Loop1936.LProcessRemainingBlocks1936.LComputeBlockBy1Loop1936.LOutputBlock1936.LMultiplyAlpha2xNBlock1934.LStore2xNBlock1934.LProcessRemainingCountN1934.LComputeBlockBy4Loop2193.LProcessRemainingBlocks2193.LComputeBlockBy1Loop2193.LOutputBlock2193.LMultiplyAlpha1xNBlock1934.LStore1xNBlock1934.LOutputMasked2xNBlock1934.LMultiplyAlphaMasked2xNBlock1934.LStoreMasked2xNBlock1934.LOutputMasked1xNBlock1934.LMultiplyAlphaMasked1xNBlock1934.LStoreMasked1xNBlock1934.LNchw.4.ProcessOutputCount.LNchw.4.ProcessNextOutputCountBy3.LNchw.4.3.ProcessNextRow.LNchw.4.3.ProcessNextColumn.LNchw.4.3.SkipOverPadding.LNchw.4.3.HandlePostProcessing.LNchw.4.ProcessRemainingOutputCount.LNchw.4.2.ProcessNextRow.LNchw.4.2.ProcessNextColumn.LNchw.4.2.SkipOverPadding.LNchw.4.2.HandlePostProcessing.LNchw.4.ProcessOutputCountRightPadAndRemaining.LNchw.ProcessFilterCount3.LNchw.3.ProcessOutputCount.LNchw.3.ProcessNextOutputCountBy3.LNchw.3.3.ProcessNextRow.LNchw.3.3.ProcessNextColumn.LNchw.3.3.SkipOverPadding.LNchw.3.3.HandlePostProcessing.LNchw.3.ProcessRemainingOutputCount.LNchw.3.2.ProcessNextRow.LNchw.3.2.ProcessNextColumn.LNchw.3.2.SkipOverPadding.LNchw.3.2.HandlePostProcessing.LNchw.3.ProcessOutputCountRightPadAndRemaining.LNchw.ProcessFilterCountLessThan3.LNchw.2.ProcessOutputCount.LNchw.2.ProcessNextOutputCountBy3.LNchw.2.3.ProcessNextRow.LNchw.2.3.ProcessNextColumn.LNchw.2.3.SkipOverPadding.LNchw.2.3.HandlePostProcessing.LNchw.2.ProcessRemainingOutputCount.LNchw.2.2.ProcessNextRow.LNchw.2.2.ProcessNextColumn.LNchw.2.2.SkipOverPadding.LNchw.2.2.HandlePostProcessing.LNchw.2.ProcessOutputCountRightPadAndRemaining.LNchw.ProcessFilterCount1.LNchw.1.ProcessOutputCount.LNchw.1.ProcessNextOutputCountBy3.LNchw.1.3.ProcessNextRow.LNchw.1.3.ProcessNextColumn.LNchw.1.3.SkipOverPadding.LNchw.1.3.HandlePostProcessing.LNchw.1.ProcessRemainingOutputCount.LNchw.1.2.ProcessNextRow.LNchw.1.2.ProcessNextColumn.LNchw.1.2.SkipOverPadding.LNchw.1.2.HandlePostProcessing.LNchw.1.ProcessOutputCountRightPadAndRemaining.LNchw.ExitKernelMlasConvNchwFloatSingleFma3Filter1.LNchw.1.1.ProcessNextRow.LNchw.1.1.ProcessNextColumn.LNchw.1.1.SkipOverPadding.LNchw.1.1.HandlePostProcessingMlasConvNchwFloatSingleFma3Filter2.LNchw.2.1.ProcessNextRow.LNchw.2.1.ProcessNextColumn.LNchw.2.1.SkipOverPadding.LNchw.2.1.HandlePostProcessingMlasConvNchwFloatSingleFma3Filter3.LNchw.3.1.ProcessNextRow.LNchw.3.1.ProcessNextColumn.LNchw.3.1.SkipOverPadding.LNchw.3.1.HandlePostProcessingMlasConvNchwFloatSingleFma3Filter4.LNchw.4.1.ProcessNextRow.LNchw.4.1.ProcessNextColumn.LNchw.4.1.SkipOverPadding.LNchw.4.1.HandlePostProcessing.LNchwc.4.ProcessOutputCount.LNchwc.4.ProcessNextOutputCountBy3.LNchwc.4.3.ProcessNextRow.LNchwc.4.3.ProcessNextColumn.LNchwc.4.3.SkipOverPadding.LNchwc.4.3.HandlePostProcessing.LNchwc.4.ProcessRemainingOutputCount.LNchwc.4.2.ProcessNextRow.LNchwc.4.2.ProcessNextColumn.LNchwc.4.2.SkipOverPadding.LNchwc.4.2.HandlePostProcessing.LNchwc.4.ProcessOutputCountRightPadAndRemaining.LNchwc.ProcessFilterCount3.LNchwc.3.ProcessOutputCount.LNchwc.3.ProcessNextOutputCountBy3.LNchwc.3.3.ProcessNextRow.LNchwc.3.3.ProcessNextColumn.LNchwc.3.3.SkipOverPadding.LNchwc.3.3.HandlePostProcessing.LNchwc.3.ProcessRemainingOutputCount.LNchwc.3.2.ProcessNextRow.LNchwc.3.2.ProcessNextColumn.LNchwc.3.2.SkipOverPadding.LNchwc.3.2.HandlePostProcessing.LNchwc.3.ProcessOutputCountRightPadAndRemaining.LNchwc.ProcessFilterCountLessThan3.LNchwc.2.ProcessOutputCount.LNchwc.2.ProcessNextOutputCountBy3.LNchwc.2.3.ProcessNextRow.LNchwc.2.3.ProcessNextColumn.LNchwc.2.3.SkipOverPadding.LNchwc.2.3.HandlePostProcessing.LNchwc.2.ProcessRemainingOutputCount.LNchwc.2.2.ProcessNextRow.LNchwc.2.2.ProcessNextColumn.LNchwc.2.2.SkipOverPadding.LNchwc.2.2.HandlePostProcessing.LNchwc.2.ProcessOutputCountRightPadAndRemaining.LNchwc.ProcessFilterCount1.LNchwc.1.ProcessOutputCount.LNchwc.1.ProcessNextOutputCountBy3.LNchwc.1.3.ProcessNextRow.LNchwc.1.3.ProcessNextColumn.LNchwc.1.3.SkipOverPadding.LNchwc.1.3.HandlePostProcessing.LNchwc.1.ProcessRemainingOutputCount.LNchwc.1.2.ProcessNextRow.LNchwc.1.2.ProcessNextColumn.LNchwc.1.2.SkipOverPadding.LNchwc.1.2.HandlePostProcessing.LNchwc.1.ProcessOutputCountRightPadAndRemaining.LNchwc.ExitKernelMlasConvNchwcFloatSingleFma3Filter1.LNchwc.1.1.ProcessNextRow.LNchwc.1.1.ProcessNextColumn.LNchwc.1.1.SkipOverPadding.LNchwc.1.1.HandlePostProcessingMlasConvNchwcFloatSingleFma3Filter2.LNchwc.2.1.ProcessNextRow.LNchwc.2.1.ProcessNextColumn.LNchwc.2.1.SkipOverPadding.LNchwc.2.1.HandlePostProcessingMlasConvNchwcFloatSingleFma3Filter3.LNchwc.3.1.ProcessNextRow.LNchwc.3.1.ProcessNextColumn.LNchwc.3.1.SkipOverPadding.LNchwc.3.1.HandlePostProcessingMlasConvNchwcFloatSingleFma3Filter4.LNchwc.4.1.ProcessNextRow.LNchwc.4.1.ProcessNextColumn.LNchwc.4.1.SkipOverPadding.LNchwc.4.1.HandlePostProcessing.LDepthwise.1.ProcessOutputCount.LDepthwise.1.ProcessNextOutputCountBy3.LDepthwise.1.3.ProcessNextRow.LDepthwise.1.3.ProcessN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