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HpqH;HHHpP0Hp HprH;HHHpP0Hp HparH;HHHpP0Hp HpprH;HHHpP0Hp{ HpWrH;HHHpP0HpJ HpqH;HHHpP0Hp HpurH;H HHpP0Hp Hp qH;H"HHpP0Hp HpKqH;H$HHpP0Hp HpZnH;H&HHpP0HpU HpqH;H(HHpP0Hp$ HpqH;H*HHpP0HpHpgTrH;H,HHpP0HpHpfrH;H.HHpP0HpHpqH;H-HHpP0Hp`HpDkyH;H,HHpP0Hp/HH'HH;Eu HĈ[]ZH 7HHH59H"Hr'H HHH5HH?H HHH5HH H HHH5HHVH kHHH5mHVH#[H 8HHH5:H#Hs(H HHH5HH@H HHH5HH H HHH5HHWH lHHH5nHWH$\H 9HHH5;H$Ht)H HHH5HHAH HHH5HHH HHH5HHXH mHHH5oHXH%]H :HHH5<H%Hu*H HHH5 HHBH HHH5HHH HHH5HHYH nHHH5pHYH&^H ;HHH5=H&Hv+H HHH5 HHCH HHH5HHH HHH5HHZH oHHH5qHZH'_H <HHH5>H'Hw,H HHH5 HHDH HHH5HHH HHH5HH^H sHHH5uH^H.fH CHHH5EH.H~ :86420.,*(&$"  HHpH_ UHSHHH HHEHpoH{ H6HHpP0H Hp=HppH;H4HHpP0Hp Hp ,nH;H6HHpP0HpHpoH;H8HHpP0HpHpoH;H:HHpP0HpyHp tH;H<HHpP0HpHHp,uH;H>HHpP0HpHpoH;H@HHpP0HpHpJpH;HBHHpP0HpHpuH;HDHHpP0HpHpnH;HFHHpP0HpSHp7rH;HHHHpP0Hp"HpnH;HJHHpP0HpHpMvH;HLHHpP0HpHpnH;HNHHpP0HpHpOvH;HPHHpP0Hp^HppH;HRHHpP0Hp-Hp*nH;HTHHpP0HpHpPrH;HVHHpP0HpHpFvH;HXHHpP0HpHpoH;HZHHpP0HpiHpmpH;H\HHpP0Hp8HpoH;H^HHpP0HpHpyH;H`HHpP0HpHp mH;HbHHpP0HpHp*nH;HaHHpP0HptHpXjrH;H`HHpP0HpCH\HH;Eu HĈ[]nH KHHH5MH6H#;H HHH5HHSH HHH5HH H HHH5HHH HHH5HjHWoH LHHH5NH7H$<H HHH5HHT H HHH5HH!H HHH5HHH HHH5HkHXpH MHHH5OH8H%=H HHH5HHU H HHH5HH"H HHH5HHH HHH5HlHYqH N HHH5PH9H&>H  HHH5HHV H HHH5HH#H HHH5HHH HHH5HmHZrH O HHH5QH:H'?H  HHH5HHW H HHH5HH$H HHH5HHH HHH5HnH^vH S HHH5UH>H.FH # HHH5%HH^ 20.,*(&$"  HHpHG ffffff.UHSHHHzHHEHp*qH{ HdHHpP0Hp!HperH{ HeHHpP0HpHprH{ HfHHpP0HpHpqH{ HgHHpP0HpHpqH{ HhHHpP0HpYHp tH{ HfHHpP0Hp'HplH{ HdHHpP0HpHHH;Eu HĈ[] H HHH5HݝH8'H HHH5HݝHH HHH5HݝHH dHHH5fHOݝHTH 1HHH53HݝHl^$H HHH5HܝH<.H HHH5ߝHܝH  HHp_H DUHSHHHZHHEHpsH{ HHHpP0H HpHpyH;HHHpP0HpHppfmH;HHHpP0HpHp?hmH;HHHpP0HpjHpdnH;HHHpP0Hp9HpnnH;HHHpP0HpHp茋yH;HHHpP0HpHptH;HHHpP0HpHp:mH;HHHpP0HpuHp#qH;HHHpP0HpDHphyH;HHHpP0HpHpnH;HHHpP0HpHptrH;HHHpP0HpHpuH;HHHpP0HpHp褜yH;HHHpP0HpOHpoH;HHHpP0HpHpRoH;HHHpP0HpHpoH;HHHpP0HpHpoH;HHHpP0HpHp_oH;HHHpP0HpZHpޭsH;HHHpP0Hp)HpayH;HHHpP0HpHp<'qH;HHHpP0HpHp軿lH;HHHpP0HpHp*qH;HHHpP0HpeHpyH;HHHpP0Hp4HpxRxH;HHHpP0HpHpbyH;HHHpP0HpHpsH;HHHpP0HpHp/tH;HHHpP0HppH HH;Eu HĈ[]#H xHHH5zٝHc֝HhH EHHH5GٝH0֝H5H HHH5ٝH՝HMVH HHH5؝H՝H#H HHH5؝H՝HH yHHH5{؝Hd՝HiH FHHH5H؝H1՝H6H HHH5؝HԝHNWH HHH5םHԝH$H HHH5םHԝHH zHHH5|םHeԝHjH GHHH5IםH2ԝH7H HHH5םHӝHOXH HHH5֝HӝH%H HHH5֝HӝHH {HHH5}֝HfӝHkH HHHH5J֝H3ӝH8H HHH5֝HӝHPYH HHH5՝HҝH&H HHH5՝HҝHH |HHH5~՝HgҝHlH IHHH5K՝H4ҝH9H HHH5՝HҝHQZH HHH5ԝHѝH'H HHH5ԝHѝHH }HHH5ԝHhѝHmH JHHH5LԝH5ѝH:H HHH5ԝHѝHR^ H HHH5ӝHНH".H HHH5ӝHНH 86420.,*(&$"  HHpH ffff.UHSHHH HHEHpw)xH{ HHHpP0HpHpyH{ HHHpP0HpHpӲsH{ HHHpP0HpMHp!yrH{ HHHpP0HpHp蟈tH{ HHHpP0HpHp-pH{ HHHpP0HpHp軃tH{ HHHpP0HpHp詌yH{ HHHpP0HpSHp'nH{ HHHpP0Hp!HpEtH{ HHHpP0HpHp3oH{ HHHpP0HpHpQoH{ HHHpP0HpHp`sH{ HHHpP0HpYHpZsH{ HHHpP0Hp'Hp+uH{ HHHpP0HpHpYuH{ HHHpP0HpHpExH{ HHHpP0HpHpPuH{ HHHpP0Hp_HpOxH{ HHHpP0Hp-Hpq2uH{ HHHpP0HpHHH;Eu HĈ[]&H HHH5ϝH˝H>H HHH5ΝH˝H H HHH5ΝH˝HXH jHHH5lΝHU˝H%ZH 7HHH59ΝH"˝Hr'H HHH5ΝHʝH?H HHH5͝HʝH H HHH5͝HʝHYH kHHH5m͝HVʝH&[H 8HHH5:͝H#ʝHs(H HHH5͝HɝH@H HHH5̝HɝH H HHH5̝HɝHZH lHHH5n̝HWɝH'\H 9HHH5;̝H$ɝHt)H HHH5̝HȝHAH HHH5˝HȝHH HHH5˝HȝH^H pHHH5r˝H[ȝH.cH @HHH5B˝H+ȝH{ $"  HHpHr f.UHSHHHHHEHplH{ H HHpP0H HpMHp nH;H HHpP0HpHpOxH;H HHpP0HpHpoH;H HHpP0HpHpNoH;H HHpP0HpHp]nH;H HHpP0HpXHp;yH;H HHpP0Hp'HpK]nH;H HHpP0HpHpZmH;H HHpP0HpHpRuH;H HHpP0HpHpx*yH;H HHpP0HpcHpG!yH;H HHpP0Hp2HpVwH;H HHpP0HpHpsrH;H HHpP0HpHpByH;H HHpP0HpHp蓓mH;H HHpP0HpnHpoH;H HHpP0Hp=HpyH;H HHpP0Hp Hp蠯yH;H HHpP0HpHp_$yH;H HHpP0HpHp1xH;H HHpP0HpyHp yH;H HHpP0HpHHp,yH;H HHpP0HpHp[0yH;H HHpP0HpHpztH;H HHpP0HpHpuH;H HHpP0HpHpHuH;H HHpP0HpSHpWuH;H HHpP0Hp"HpfuH;H HHpP0HpHpuuH;H HHpP0HpHpuH;H! 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UHAWAVAUATSHIHHoHHEHHHH{IBHHCXC`HChHCxHHǃHǃHCxW)EHEEEhighEHHKPH`HUL`BH`HDž`HtCt H{¢sH躢sEt H}諢sH8W)EEHEElowHHHTH`HUL>`BH`HDž`HtCt H{DsHˉHPϺAH,tHuHPLrE1H5pH!lLsΑsIHh[RH5tH ~tH}LhOHH5}tHPLbAHtHuHPLE1H5soHkL茑slIALeMIALhMIIALeMPIALhMIIALeMIALhM:oIlIPt H`jsLeMt6HEL9t!HX@t HxEsHI9uH}LLe+sEuEu LhMu-H}sEtH}sLhMHpL9HX@t HxÏsHI9uIPt H`螏sLeMt6HEL9t!HX@t HxysHI9uH}LLe_sEuEu LhMu-'H}9sEtH}*sLhMHpL9HX@t HxsHI9uIPt H`ҎsLeMt6HEL9t!HX@t Hx譎sHI9uH}LLe蓎sEuEuLhMu&^H}psEtH}asLhMt:HpL9t=HX@t Hx6sHI9uHhLpsEtGLDs=LI3IEu!(IEuIEu IEt H}ύsLQs fUHAVSIx贍sHHLH[A^]IH臍sL s UHAWAVAUATSHIIH?HHEHHIIq3BHIAGXAG`IGhAGpW)EHEEEhighEIOPHhHULJBHhHDžhHtCt H{踌sH谌sEt H}行sHW)EEHEElowIOTHhHUL;JBHhHDžhHtCt H{AsH9sEt H}*sHDžLW)EHEEEseedEHhHUHLLIBHhHDžhHtCt H{蹋sH豋sEt H}袋sHts H,LH IH )))ȹEAOXW)EE HEEdtypfEeHhHUH@LMKBHhHDžhHtCt H{sHsEt H}sHu$Ip@A?i7A?-HHH;EuHĘ[A\A]A^A_]2s誊sIHh7|RH5wtH ztH}Lh$HP(AHytHuHPL˄E1H5YhHzdLrs|'sIHh{RH5owtH *ztH}Lh衁HP襲AHytHuHPLHE1H5gHcLs褉sIHh1{RH5vtH 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HnHHMHHUHuHuIIFIF0INIVIF@H}Ht H}lsH]HEHtHaHlsHHH;EuLHh[A\A]A^A_]msCIH}HuH]HEHu:Lfs H}JlsH]HEHtIH]HEHtHoaHlsLfs ffff.UHAWAVAUATSHhIH~HHEпksHþHksHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]ty>Hxh>I`>IXx>IP>HW)EHE(|jsHEHH(HMHxHLxL`LhHX HMH5|sH}HU,AH5\tH(AH5wH)AHA H5sH*AHH;AHHH kHHMHHUHuHuIIFIF0INIVIF@H}Ht H}isH]HEHtH^HsisHP|HH;EuLHh[A\A]A^A_]isCIH}HuH]HEHu:Lcs H}isH]HEHtIH]HEHtH?^HhsLecs ffff.UHSHHH{HHEHU1HxCHt4H@0@EHuHUHIHb{HH;Eu3HH[]H @uH膦QH/{HH;EthsUHAVSHPHIH{HHEEHEHEH}HuH}HuHOH}HuHH}HuHH}HuH_HuL H]HEHtCt H{gsHgsHdzHH;Eu LHP[A^]gsIH]HEHtCt H{9gsH1gsLas ff.UHAVSIPgsHHBcHHH{L BHHڋHH[A^]IHfsLPas UHAVSIPfsHHbHHH{L BHًHH[A^]IHnfsL`s UHAVSHIH+yHHEA>urHH}HEHEI^IFHtACt H{fsHesH]HEHt!Ct H{esHesHEIFHxHH;Eu 1H[A^]>fsfff.UHAVSHIHkxHHEA>urHH}b HEHEI^IFHtACt H{FesH>esH]HEHt!Ct H{esHesHEIFHwHH;Eu 1H[A^]~esfff.UHAVSHIHwHHEA>urHH} HEHEI^IFHtACt H{dsH~dsH]HEHt!Ct H{^dsHVdsHEIFH%wHH;Eu 1H[A^]dsfff.UHAVSHIHvHHEA> urHH}BHEHEI^IFHtACt H{csHcsH]HEHt!Ct H{csHcsHEIFHevHH;Eu 1H[A^]csfff.UHAVSHIH+vHHEA>urHH}RHEHEI^IFHtACt H{csHbsH]HEHt!Ct H{bsHbsHEIFHuHH;Eu 1H[A^]>csfff.UHAWAVAUATSH8IIHbuHHE1LA}CHþL1}CIžL!}CIHCHK H)HtHH8IEIM H)HtHH8Mt%IGIO H)HtH%H8LuHN6LD6D0M]L06D8EO0asHEf(~f)EHbe zero!HH Hcan not HHHperator HHH Range oHHHdelta inH@(HMLuLQHH5StL}LHLLқQ`IH5TtH]HLLH衛Q/IH5TtH]HLeLHpQEt H}}`sHZsHH;ELH8[A\A]A^A_]AA)A*A*^f: L,E1MMI-`sHEHHHML(HMHU1LAH6MLu0Iw1HL!fnfpfAnfpf8@fBfnfpHyHHVHs 1HutHzH)1fDfD fD0fD@fDPfD`fDpH fHuHt,HtHf.FffH HuI9t"DHDHDHL9rIH}H*H}/_sHEu3:HEu(/HEu$HH}HtH} HEt H}T^sHXs fffff.UHAWAVAUATSH(HIHHpHHEH8H;HHHLH@H8L]sHHEEL5eM~LI@L8L\sL%eILHDž(HDž HDžHDžDž0LC`sHLHaHH~EHHHfLL8Lt H \sH[sH5MHHS[sH8s\sHoHH;EuHH([A\A]A^A_]']sHLL8Lt H d\sH>[sHfHfHH5MHHZsHH8[sHVs DUHAWAVAUATSH8IIHnHHE1LvCHþLvCIžLvCIHCHK H)HtH&H8IEIM H)HtH2H8(Mt%IGIO H)HtH9H8/HELMmLEwW.ESM0ZsHEf(ֳf)EHbe zero!HH Hcan not HHHperator HHH Range oHHHdelta inH@(HMLbQHH5KMtL}LH#LL.Q`IH5_MtH]HLLHQ/IH5sMtH]HLLH̔QEt H}YsHlHH;E7LH8[A\A]A^A_] \EZMZ^f: L,1MII߿YsHEHHHMHHMHU1LAHbMEMHSكHraHH)f.X@X@X@ X@X@X@X@H XHuHtHHXHuIH}HH}YsHEu3:HEu(/HH}Ht#H}HEu HEt H}DXsHRs fffff.UHAWAVAUATSH8IIHjHHE1LrCHþLrCIžLrCIHCHK H)HtHH8 IEIM H)HtHH8Mt%IGIO H)HtH&H8LuHHLL0M]LL8MO01WsHEf( f)EHbe zero!HH Hcan not HHHperator HHH Range oHHHdelta inH@(HMLuL蕑QHH5~ItL}LHVLLaQ`IH5ItH]HL%LH0Q/IH5ItH]HLLHQEt H} VsHhHH;ELH8[A\A]A^A_]AI)I*I*^f: L,E1MMIUsHEHHHML(HMHU1L3AH6MLuPIw11HL!fHnfpDfInfpDfHnfsfoffs ffs ffK?fHnfpDHyHHVH s 1HuuHzH)1ffDfD fD0fD@fDPfD`fDpHfHuHt,HtHf.FffH HuI9t#LHHHHHLHL9rIH}H H}TsHEu3:HEu(/HEu$HH}HtH} HEt H}SsHENs ffff.UHAWAVAUATSH8IIHrfHHE1LQnCHþLAnCIžL1nCIHCHK H)HtH(H8IEIM H)HtH4H8*Mt%IGIO H)HtH;H81HRELAMoLE'fWf.ESM0RsHEf(tf)EHbe zero!HH Hcan not HHHperator HHH Range oHHHdelta inH@(HMLQHH5DtL}LHLĽQ`IH5DtH]HLLH蛌Q/IH5EtH]HL_LHjQEt H}wQsHTdHH;E%LH8[A\A]A^A_] #\EM^f: L,1MII߿)QsHEHHHMHHMHU1LAHhMEMHSكHrWHH)X@X@X@X@ X@(X@0X@8H@XHuHtHHXHuIH}HH}PsHEu3:HEu(/HH}Ht#H}HEu HEt H}OsHvJs fffff.UHAWAVAUATSH8IIHbHHE1LjCHþLqjCIžLajCIHCHK H)HtHH8 IEIM H)HtH!H8Mt%IGIO H)HtH(H8LuHLD0M^L~D8EO0NsHEf(f)EHbe zero!HH Hcan not HHHperator HHH Range oHHHdelta inH@(HMLuLCQHH5,AtL}LHLLQ`IH5@AtH]HLLHވQ/IH5TAtH]HLLH譈QEt H}MsH`HH;ELH8[A\A]A^A_]AA)A*A*^f: L,E1MMIgMsHEHHHML(HMHU1LAHMLu;Iw1HL!fnpfpfAnpfpffBfnpfpHyHHVH0s 1HupHzH)1@pfDpfDp fDp0fDp@fDpPfDp`fDppH@fHuHt,HtpHf.FffH HuI9t#DHHDfHDHL9rIH}HH}^LsHEu3:HEu(/HEu$HH}HtH} HEt H}KsHFs ffff.UHAVSHPIH;^HHERKsHþHHKsHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H}11AHX>H5i\sHHAH5=tHiAH5wH AHH HQH5sH AHH;@HHH JHHMHHUHuHuIIFIF0INIVIF@H]HEHtH>HSIsH0\HH;Eu LHP[A^]IsIH]HEHtHj>HIsLCs UHAWAVSHXIH[HHEHsHþHHsHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H}11 AIhV>HW)EHEGsHEHHHMHHMH5YsHUL AH5-;tHAH5wHAHH HQH5܌sHAHH; @HHH AHHHMHJHUHuHuIIGIG0IOIWIG@H}Ht H}FsH]HEHtH;HFsHyYHH;EuLHX[A^A_]GsIH]HEHt7H;HSFsL@s IH}HuH]HEHuL@s H}FsH]HEHtf.UHAWAVAUATSHxIHXHHEпEsHþHEsHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H}11 AHh`S>HpT[>HxHV>I@W>I8Z>I0]>HW)EHE0\DsHEHH0HMHpHHxHPLhLpLx HX(HMH5kVsHUHhAH57tHfAH5wHAHH HQH5~sHAHH;@HHH DHHMHLHUHuHuI$ID$ID$0IL$IT$ID$@H}Ht H}YCsH]HEHtH8H8CsHVHH;EuLHx[A\A]A^A_]CsCIH}HuH]HEHu:Lt=s H}BsH]HEHtIH]HEHtH8HBsL*=s fUHAWAVAUATSHxIHeUHHEп|BsHþHrBsHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H}11AHhP>HpW>HxR>IS>IV>IY>HW)EHE0@sHEHH0HMHpHHxHPLhLpLx HX(HMH5 SsHUHh5AH5o4tH@H5)wH7AHA H5%sH^AHH;S@HHH AHHMHSHUHuHuI$ID$ID$0IL$IT$ID$@H}Ht H}@sH]HEHtH;5H?sHRHH;EuLHx[A\A]A^A_]N@sCIH}HuH]HEHu:L:s H}?sH]HEHtIH]HEHtH4HO?sL9s fUHAWAVSH8HIHRHHE1HYCIL}ؾHYCHEоHYCHEIW1H~AHEIG0@EH}HuHUHMLE7IHQHH;EuLH8[A^A_]?sffff.UHAWAVAUATSHL(IIHH5QHHEH D?AuPH3IUI $H(LHh AAuVH}H3IUI $H(L#A uSHPH3IUI $H(LAAtAuYH}H3IUI $H(L7*]AH0H3IUI $H(LAIr/pA u!H}H3IUI $H(LH/IIsCICHOHH;Eu$Hĸ[A\A]A^A_]E1A6~=sLHH5fAnfAnHrI 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.UHAVSIP1sHH-HHH{LAH8HH[A^]IH^1sL+s UHAVSIPD1sHHr-HHH{LOAHؤHH[A^]IH0sL+s UHAVSIP0sHH-HHH{LAHxHH[A^]IH0sL +s UHAWAVAUATSHMHIHLCHHEHtBHB H;BH誸H)0t0HC H;CpH脸zH)0u)@LSIILDELHIL-EHuڃH9HOII)LH?H>LHH(0(@*HrI H9s!H I9sE1HzLII((IIHHDGAHkIHH)1ff.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(]LHH9uH?HH;Eu2HĨ[A\A]A^A_]1M1M9F&-s,sIHh+RH5"tH "tH}Lh8$W)PHDž` A,sH`([)PH scalar.HHHld be a HHHmin shouH@AH"tHuHPLj&E1H5 HL,s+sIHhSRH5!tH !tH}Lh<@#W)PHDž` i+sH`()PH scalar.HHHld be a HHHmax shouH@AHH"tHuHPL%E1H5 HAL9+s IALeMIALhMIIALeMFIALhMnIIPt H`U*sLeMt6HEL9t!HX@t Hx0*sHI9uH}LLe*sEuEu LhMu-'H})sEtH})sLhMHpL9HX@t Hx)sHI9uIPt H`)sLeMt6HEL9t!HX@t Hxd)sHI9uH}LLeJ)sEuEuLhMu&^H}')sEtH})sLhMt:HpL9tEHX@t Hx(sHI9uHhLp(sEtL(sLG#s Lf.UHAWAVAUATSHMHIHl;HHEHtBHB H;BHڳH)0t0HC H;CH贳 H)0uBȅf)@L肳IILcELkHILLEHuH9HOII)LH?LHHf(0f(@HrI H9s!H I9sE1HzLIIIHHHDOAHIIH)1@fAfA\f(f_f(f_f(f]f(f]ffdfAT fA\0f(f_f(f_f(f]f(f]f\ fd0HHuMt0fAfA\f(f_f_f(f]f]ffDL9tsHzLtf(C_f(]B LHL9tCf.f(A_f(] f(A_Df(]LHH9uH<I|0DAf(f_f(f]f)HH9|H9 I)IH IH9s H IH9rqMIITHtJ<1fffff.fTff(f_f(f_f(f]f(f]f\f$HI9uM9tu)HPH@tf(A_f(] HOH9tDff.f(A_f(] f(A_Df(]LHH9uH7HH;Eu"HĨ[A\A]A^A_]1M"%s$sIHh'RH5tH tH}Lh8W)PHDž` =$sH`(W)PH scalar.HHHld be a HHHmin shouH@AHtHuHPLfE1H5HL $s#sIHhORH5tH tH}Lh<<W)PHDž` e#sH`()PH scalar.HHHld be a HHHmax shouH@AHDtHuHPLE1H5H=L5#s IALeMIALhMIIALeMFIALhMnIIPt H`Q"sLeMt6HEL9t!HX@t Hx,"sHI9uH}LLe"sEuEu LhMu-'H}!sEtH}!sLhMHpL9HX@t Hx!sHI9uIPt H`!sLeMt6HEL9t!HX@t Hx`!sHI9uH}LLeF!sEuEuLhMu&^H}#!sEtH}!sLhMt:HpL9tEHX@t Hx sHI9uHhLp sEtL sLCs LfDUHAWAVAUATSHMHIHl3HHEHt@HB H;BHD(HLHt*HC H;CDHD8AHLHuALIILwEHHHIHHYEHH rI I9LHI9E1HpL¨{L9ADD8|DA8~AA\DD8|%DA8#A\HH9uSDA8~݉A\HH9u.IIAfnff8Afnf8IHHHrHH~H)1ff.AoAo\f8H5sH}HS@I[>IS>HW)EHEsHEHHHML HXHMH5uHUL@H5ksH@H5ΉwH@HA H5IsH@HH;@HHH 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HǃHǃHǃHǃHǃHǃIHhIGHpHtH@HǃHǃLHPHðpH1p[A\A^A_]f.UHAWAVSPHIkI?t#HqH ap1AL觜OILH I?t#HqH &p1ALlOILHUI?t HqH p1AL1OwILHI?t HnqH p1ALO?ILH!I?t H6qH {p1ALOILH[A^A_]IIIHtCt H{譩pH襩pL'p ffffff.UHAWAVAUATSHIIHOHHEIE8IMpI+E0HHHHAH+HH9HEI8H8-MIL)HH LpLhwkMM)PpIKHH+AI9~LE1HI9HLt I~軚pIFIFI(pAHEHIDHM8I@L)HHHH9 CfHE1E1JH4u6M;ohCL HxH;EshL(HHxkIGhH HxH;MsHHHxDHpH nff.HpH }HHJHHJHHIJ Ht H9tHuJ HPIIIFI>H)HI9HHI9K)pIH越OH5-qH qH L&蠐HAHLqH HLDE1H5vHrLp1LMIIrKT1DLD HI9uM9IMII{KT1DLD HI9uM9E1H}HWW) HDž0@pH0(Z) HompatiblHH0H are incHH(He inputsHH Hns of thHHHdimensioHHHdcasted HHHThe broaHf@8eH H)N t H00pHpHt HxpAH}H_H}pH}HRV{ppIH耈OH5qH qH L jHnAHqH HLE1H5tHpL赖p H}螓pH}蓓pHALXMuLHALMHHt HpLXMt?H`L9t$Lh@t Hx̕pLM9uHXLL`謕p@u u&LMu6HP}p tH0hpLMTHL9gLh@t Hx5pLM9uHALXMuLHALMHHt H֔pLXMt?H`L9t$Lh@t Hx諔pLM9uHXLL`苔p@u u#LMu/gHP_p tH0JpLMt:HL9tQLh@t HxpLM9uHLpEL)pHpH)LH tfH0ēpHpHtZHx謓pH}HtNH}蚓pH}HuEL HH}Hu*HH}Hu'.HHpHuH}HuH}Ht H}CpHōp ffff.UHAWAVAUATSHhHIH賂HHEЀ{`t1H{hCHHHHHH0L5ÚMn@LLH:pHH@H0HfHnfL(pHEEIFHHIhLL0LxLLpLpHHHHHDž 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IHXHHxHu,>LL+W)EHE)EHEE1H}L]E1H}LNHEM1H]LeIMH<t%HMH;Mt;HHHMNfffff.HMH;Mt#HHHM)fLHպH}HǺHEHHEL9|H}HH;EHhu0HPHL MqHIAhHPHH0Hu HpHH0H`HHXHAHxHAHEHtH@HXLIHHHL$H$H}HMLx}oLeHEH]HtHHCHIALMu HpHH0HUHX=|oH8HH}Ht H}oH}HtHhH8߀oMtLALʀoHHH;EHHĸ[A\A]A^A_]E1IATH}Ht H}~oH}HtHhH8foA|*AGMHHIcH01AJLH8HIM?JHEH}HH@koW)EHEH}LEHE1ff.IML9IH}Ht#0IE1H}Ht H}|oH}HuMuLvo H}{oMtLAL{oLdvo fff.UHAWAVAUATSH8HHIHHHEI} HI} IW)EHEII+?tVIILH=\{oILeLeKHEI7MwI)M~LL|oMLeMwM+7E1I}1L >HHLxLLAIH}@AI9HHp H+pHW)@HDžPH@W) HDž0HDž`MtIH H`L4HLcI)LIE1E1H f.HJHHHtLH L,J4I7IHHH;Pt3HHHHHIM9r7IJ<tH@$HIM9qLLHI>H+@HH IEHIEHHtH@MIE0H$HH@H LH躭HHt#HHCHuHHPHwoIHHHHHP0HBHHDžHtWH蓹AHxoH HuGQIHHHHHP0H5H Ht H((xoH@HtI>xoH}Ht H}xoHߊHH;EuH8[A\A]A^A_]qxowoIHviNH5pH pH`L.`oH5pHL*rAHpH`HLqE1H5UHQLwo YwoIH@hNH5pH VpH`L@nW) HDž0PvoHH0(@) H5pGHxoCGAH;pH`H L1qE1H5THPLvo2H}so$voH \~HHH5^VHGSHvoLvoH )~HHH5+VHSHdvovoIHgNH5pH pH`LEmH脴MH5EpHH~rAHvoH`HLpE1H5SHOLuozuoIHgNH5pH wpH`L?lHMH5pHHqAH׊oH`HLoE1H5 SH.OL&uoHALeMHALMHSHALeMHALM"WHTHt H?toLeMt6HEL9t!Lh@t HxtoLM9uH}LLetoEu`u LMu,hH}so`tHpsoLMt>HL9Lh@t HxsoLM9uHL{soEu7LHDžMu?LHDžM LmsoLHDžMtAFt I~soLsoLHDžMJLYHt HroLeMt6HEL9t!Lh@t HxroLM9uH}LLeroEu`u LMu,dH}cro`tHpNroLMt:HL9toLh@t Hx#roLM9uHLroEtL1roLHDžMBAFI~qoLHALeMuFzHAL@MHH t H0vqoLeMt6HEL9t!Lh@t HxQqoLM9uH}LLe7qoEu`u L@Mu,dH}qo`tHppoL@Mt:HHL9tQLh@t HxpoLM9uH@LHpoE?LpoH}H37LHALeMuFzHALMHHt HBpoLeMt6HEL9t!Lh@t HxpoLM9uH}LLepoEu`u LMu,dH}oo`tHpooLMt:HL9tTLh@t HxooLM9uHLooELooH HLHLHDžML5AL)ooH HHLMtgHIFHuUILPLmoH HuDN1/HH}Hu_fHH@Hu7AHH}Hu>EHH Ht H(noH@Ht HHnnoH}Ht H}\noHho fDUHAWAVAUATSHhMMHHIHHHEHHzHdAIHHRAI9LLIH+AHL AH9HL LpM)LHM}LIEL)HHH9W)`HDžp@bmoHHH@H`HhHpW)@HDžP@$moILH@L@LHHPW) HDž0@loHUHH H(H@H0LBL+IMHHH1۸HHHHE11H1HLO$O4L9r6DHL HL8LO$O4L9tHEHL;,cHI|:I|4M94HIHLIL;|IIW)EHEkoHEHHHML(HMHHAHHAHHLtH@HLHHHHL$H$HHHHMLgoILHDžHHHիAHjoHHDžHtH讫AHjoHHHHCH~HHPHiioH}HiI/W)EHEBjoHEHHHML(HMHHAHHAHHLtH@HLHHHHL$H$HHHHMLeoILLHDžHH膪AHzioHHDžHtH_AHSioHHttHHCHubHHPH"hoH}HuQXHDžHHlH}HtBH}ho7HDžHHuH}Ht H}hoLLLLIL;mI|JI|DIM9LmHhH;pHL(HHhfDI|:LmHHH;PL(HHHffffff.LmH(H;0L(HH(DH`Hul<LIHLLIL;dH@Hu?l<LHIHLIL;@H Huk<LHIHLIL;H`HhL@HHHEHLGH H(L+ILLHHE1HLHڿHHHHH)L)W)EH)HEHHHLHHH}LILLHuH`HhH}jHHuH@HHH}OHHuHEHHHH}PHuH H(H}HHHFHHHZHJ HHHEHDH9H+:HHuHHLHEHDHHAHHAHHtH@HLH@0H$HHMLH趙LHDžHHHAHdoHHDžHtHťAHdoHHt,HHCHuHHPHcoLW)HDžHhH+`HHHH+@HHUHHVH+HHHH(H+ HHHcJIHH`HhHBFHHEHHHHNHH H(HFHH@HHHEHHBI^IN MHEHDH9H+:HHެLMLIEHDHHAHHAHHtH@HLH@0H$HHLH胗HHHDžML蹣ALboHHDžHtH蒣AHboHHt,HHCHuHHPHUaoLW)HDžHhH+`HHHH+@HHUHHVH+HHHH(H+ HHHPH`HhH9tu1L@H4HHH0HH;tHHHH!ff.LH`HhHHH)HH9rH@HHH9t}1Lfff.H4HHH0HH;tHHHH!ff.LXH@HHHHH)HH9rHELHpL9uIk1LLHDžHH;sHHHLLEHELHpHHL)HH9rH L(I9Lu1}1LH4HHH0HH;tHHHH!ff.LHH L(HLH)HH9rHELHpL)W)HDž)`HDžpH`HhHH)HHHHH+@HHHH΀}LH蟖HH`HhHQHH@HHHQHHEHHHH$HH H(H^QmHDž@Ht!H`H@H`HhH)u^1L@HHL)}HDžHHHDžHHH`H1HYoH}Ht H},YoH Ht H(YoH@Ht HHXoH`Ht HhXoMtLۙALXoMtLƙALXoHkHH;EuLHh[A\A]A^A_]&YoXoIH+JNH5BlpH tpH}LPW)HDž0AXoH(+)Hdo not aHHH shapes HHHp: InputHHHEinsum oH@ lign@$AHbnpHuHLURE1H55H2LWoWoIH>INH5UkpH spH}L+OW)HDž`TWoHH( )H5mpWHXoCWAHmpHuHLQE1H55H>1L6WoVoIH`xHNH5jpH rpH}L`SeNHAH H5lpH=lpLClpH@L H0AHkpHuH@LPE1H5d4H0L}Vo]2VoIH`GNH5ipH rpH}L`UMLDAH H5kpH kpH@L H2AHkpHuH@L(PE1H53H/LUoUoIH`GNH5(ipH kqpH}L`dLH5kpHjpLkpH@HL4AHRH6RfDUHAVSH0IHHZHHEI9tH{ H9t.IF I9tKHC I~ HZHH;EH0[A^]M9v tTHLPH{ HP IF HC HHHQI~ HP HC IF H[ H&ZHH;EtpHHuPH{ HP HC I~ HHPI~ HP IF H[ HEH}LPHEH}P Mv HYHH;E-XGoHPHPfDUHAVSH0IHHxYHHEI9tH{ H9t.IF I9tKHC I~ HKYHH;EH0[A^]M9v tTHLPH{ HP IF HC HHHQI~ HP HC IF H[ HXHH;EtpHHuPH{ HP HC I~ HHPI~ HP IF H[ HEH}LPHEH}P Mv HxXHH;E-FoHOHOfDUHAWAVAUATSHILgLoLL)HH9LM)MII6HH=HM)LHHH9H}LE II9LBMDHuI9DoHH5oH%BoH8MHHH5Z%H#!HDoLHHr IGL9`ILHL9MLCHHtHIHHHHuI@IHIHPIHPIHPIHP IHP(IHP0IHP8H@HuTHuIJ<CoIHuO LHHr IOL9)ILHL9HZHHt$Hffffff.IHHHHuHj@IHIHAIHAIHAIHA IHA(IHA0IHA8H@Hu IIIPHHAHA~fpDHPH)1fDAADAD AD0HHuHtA~fpDAADI9IHGHHHHHHDBAH%A~fpDK|0IHH)1fffff.DDDHHuMtA~fpDAADH9O4MoIHSAoL;o UHAVSH0IHHSHHEI9tH{ H9t.IF I9tKHC I~ HSHH;EH0[A^]M9v tTHLPH{ HP IF HC HHHQI~ HP HC IF H[ HFSHH;EtpHHuPH{ HP HC I~ HHPI~ HP IF H[ HEH}LPHEH}P Mv HRHH;E-x@oHJHJfDUHAVSH0IHHRHHEI9tH{ H9t.IF I9tKHC I~ HkRHH;EH0[A^]M9v tTHLPH{ HP IF HC HHHQI~ HP HC IF H[ HRHH;EtpHHuPH{ HP HC I~ HHPI~ HP IF H[ HEH}LPHEH}P Mv HQHH;E-8?oHHHHfDUHAVSH0IHHXQHHEI9tH{ H9t.IF I9tKHC I~ H+QHH;EH0[A^]M9v tTHLPH{ HP IF HC HHHQI~ HP HC IF H[ HPHH;EtpHHuPH{ HP HC I~ HHPI~ HP IF H[ HEH}LPHEH}P Mv HXPHH;E-=oHGHGUHH)HHGHHwHH]^Offfff.UHAVSHPIHOHHE=oHþHHUHuHuIIFIF0INIVIF@H]HEHtHHRoH/#HH;Eu LHP[A^]oIH]HEHtHiH oL o UHAVSHPIH"HHEoHþHoHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]z$:H5 !oH}HW<H5f,pH<H5+sH9<HH HQH5 ToHY<HH;N<HHH HHMH>HUHuHuIIFIF0INIVIF@H]HEHtHNH oH HH;Eu LHP[A^]goIH]HEHtH H oL/o UHAVSHPIHk HHE oHþHx oHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]:H5oH}H<H5*pH<H5ˑsH<HA H5QoH<HH;<HHH , HHMHHUHuHuIIFIF0INIVIF@H]HEHtHH oHvHH;Eu LHP[A^] oIH]HEHtHHT oLo fffff.UHAVSHPIH HHE" oHþH oHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH] :H5IoH}H<H5'pHH<H5ksHy<HA H5gOoH<HH;蕿<HHH HHMHHUHuHuIIFIF0INIVIF@H]HEHtHH9 oHHH;Eu LHP[A^] oIH]HEHtHPHoLvo fffff.UHAVSHPIHHHEoHþHoHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]Z:H5oH}H7<H5F%pH<H5 sH<HA H5MoH@<HH;5<HHH lHHMHHUHuHuIIFIF0INIVIF@H]HEHtH5HoHHH;Eu LHP[A^]NoIH]HEHtHHoLo fffff.UHAVSHPIHKHHEboHþHXoHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]:H5oH}H<H5"pH<H5sH<HA H5JoH<HH;պ<HHH HHMHHUHuHuIIFIF0INIVIF@H]HEHtHHyoHVHH;Eu LHP[A^]oIH]HEHtHH4oLn fffff.UHAVSHPIHHHEoHþHoHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]:H5)oH}Hw<H5 pH(<H5KsHY<HH HQH5@HoHy<HH;n<HHH HHMHޣHUHuHuIIFIF0INIVIF@H]HEHtHnHoHHH;Eu LHP[A^]oIH]HEHtH)HoLOn UHAVSHPIHHHEoHþHoHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]::H5oH}H<H5*pHȾ<H5sH<HH HQH5EoH<HH;<HHH EHHMHHUHuHuIIFIF0INIVIF@H]HEHtHHnHHH;Eu LHP[A^]'oIH]HEHtHHmnLn UHAVSHPIH+HHEBnHþH8nHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH] :H5ioH}H<H5pHh<H5sH虽<HH HQH5CoH蹾<HH;讳<HHH HHMHHUHuHuIIFIF0INIVIF@H]HEHtHHRnH/HH;Eu LHP[A^]nIH]HEHtHiH nLn UHAVSHPIHHHEnHþHnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]z:H5 oH}HW<H5jpH<H5+sH9<HH HQH5 AoHY<HH;N<HHH HHMH^HUHuHuIIFIF0INIVIF@H]HEHtHNHnH HH;Eu LHP[A^]gnIH]HEHtH HnL/n UHAVSHPIHk HHEnHþHxnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]:H5 oH}H<H5 pH訷<H5~sHٸ<HA H5>oH<HH;<HHH ,HHMHUHUHuHuIIFIF0INIVIF@H]HEHtHHnHv HH;Eu LHP[A^]nIH]HEHtHHTnLn fffff.UHAVSHPIH HHE"nHþHnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH] :H5I oH}H藻<H5pHH<H5k|sHy<HA H5gHUHuHuIIFIF0INIVIF@H]HEHtHHrnHOHH;Eu LHP[A^]nIH]HEHtHH-nLn UHAVSHPIHHHEnHþHnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]9H5)nH}Hw<H5oH(<H5KbsHY<HH HQH5@"oHy<HH;n<HHH ݆HHMHHUHuHuIIFIF0INIVIF@H]HEHtHnHnHHH;Eu LHP[A^]nIH]HEHtH)HnLOn UHAVSHPIHHHEnHþHnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]:9H5nH}H<H5.oHȘ<H5_sH<HH HQH5oH<HH;<HHH EۆHHMHHUHuHuIIFIF0INIVIF@H]HEHtHHnHHH;Eu LHP[A^]'nIH]HEHtHHmnLn UHAVSHPIH+HHEBnHþH8nHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]9H5inH}H跜<H5oHh<H5]sH虗<HA H5oH<HH;赍<HHH ؆HHMH!HUHuHuIIFIF0INIVIF@H]HEHtHHYnH6HH;Eu LHP[A^]nIH]HEHtHpHnLn fffff.UHAVSHPIHHHEnHþHnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]z9H5 nH}HW<H5noH<H5+[sH9<HA H5'oH`<HH;U<HHH ֆHHMHuHUHuHuIIFIF0INIVIF@H]HEHtHUHnHHH;Eu LHP[A^]nnIH]HEHtHHnL6n fffff.UHAVSHPIHkHHEnHþHxnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]9H5nH}H<H5oH訑<H5XsHْ<HA H5oH<HH;<HHH ,ԆHHMHUHUHuHuIIFIF0INIVIF@H]HEHtHHnHvHH;Eu LHP[A^]nIH]HEHtHHTnLn fffff.UHAVSHPIH HHE"nHþHnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]9H5InH}H藕<H5oHH<H5kVsHy<HA H5goH蠑<HH;蕆<HHH цHHMH5HUHuHuIIFIF0INIVIF@H]HEHtHH9nHHH;Eu LHP[A^]nIH]HEHtHPHnLvn fffff.UHAVSHPIHHHEnHþHnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]Z9H5nH}H7<H5NoH<H5 TsH<HA H5oH@<HH;5<HHH lφHHMHHUHuHuIIFIF0INIVIF@H]HEHtH5HnHHH;Eu LHP[A^]NnIH]HEHtHHnLn fffff.UHAVSHPIHKHHEbnHþHXnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]9H5nH}Hא<H5oH舊<H5QsH蹋<HA H5oH<HH;Ձ<HHH ͆HHMHHUHuHuIIFIF0INIVIF@H]HEHtHHynHVކHH;Eu LHP[A^]nIH]HEHtHH4nLn fffff.UHAVSHPIH݆HHEnHþHnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]9H5)nH}Hw<H5oH(<H5KOsHY<HA H5GoH耊<HH;u<HHH ʆHHMHHUHuHuIIFIF0INIVIF@H]HEHtHuHnHۆHH;Eu LHP[A^]nIH]HEHtH0HnLVn fffff.UHAVSHPIHۆHHEnHþHnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]:9H5nH}H<H5.oHȅ<H5LsH<HA H5 oH <HH;}<HHH LȆHHMHHUHuHuIIFIF0INIVIF@H]HEHtHHnHنHH;Eu LHP[A^].nIH]HEHtHлHtnLn fffff.UHAVSHPIH+نHHEBnHþH8nHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]9H5inH}H跉<H5oHh<H5JsH虄<HA H5 oH<HH;z<HHH ņHHMHHUHuHuIIFIF0INIVIF@H]HEHtH赹HYnH6׆HH;Eu LHP[A^]nIH]HEHtHpHnL薾n fffff.UHAVSHPIHֆHHEnHþHnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]z9H5 nH}HW<H5noH<H5+HsH9<HA H5'oH`<HH;Ux<HHH ÆHHMHuHUHuHuIIFIF0INIVIF@H]HEHtHUHnHԆHH;Eu LHP[A^]nnIH]HEHtHHnL6n fffff.UHAVSHPIHkԆHHEnHþHxnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]9H5nH}H<H5oH~<H5EsH<HH HQH5oH<HH;u<HHH %HHMHN HUHuHuIIFIF0INIVIF@H]HEHtHH蒿nHo҆HH;Eu LHP[A^]nIH]HEHtH詴HMnLϹn UHAVSHPIH ҆HHE"nHþHnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]9H5InH}H藂<H5oHH|<H5kCsHy}<HH HQH5`oH~<HH;s<HHH žHHMHHUHuHuIIFIF0INIVIF@H]HEHtH莲H2nHІHH;Eu LHP[A^]觽nIH]HEHtHIHnLon UHAVSHPIHφHHE¼nHþH踼nHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]Z9H5nH}H7<H5RoHy<H5 AsH{<HH HQH5oH9|<HH;.q<HHH eHHMH(HUHuHuIIFIF0INIVIF@H]HEHtH.HҺnH͆HH;Eu LHP[A^]GnIH]HEHtHH荺nLn UHAVSHPIHK͆HHEbnHþHXnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]9H5nH}H}<H5oHw<H5>sHx<HH HQH5nHy<HH;n<HHH HHMH1HUHuHuIIFIF0INIVIF@H]HEHtHέHrnHOˆHH;Eu LHP[A^]nIH]HEHtH艭H-nL诲n UHAVSHPIHʆHHEnHþHnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]9H5)nH}Hw{<H5oH(u<H5KlIH}Ht0^IH]HEHt+bIH`Hu$H}Hu0H]HEHu9L6n Hh螎nH}HtH}茎nH]HEHtHǃHknLn DUHAWAVAUATSHIH"HHEп9nHþH/nHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]њ9Iɢ9I9I蹣9HW)EHE nHEHH HML0L`LhHXHMH5 nH}HU7O<HX[9IS9IK9HC9IW)`HDžp inH`HH HpL LhHXLpHhH5 oH`HXN<H5 oH}J<H5sHK<HH HQH5nHL<HH;A<HHH HHMHiHUHuHuIIGIG0IOIWIG@H`Ht HhpnH}Ht H}^nH]HEHtH虀H=nHHH;EuLHĈ[A\A]A^A_]詋nIH}HtVIH`Hu$H}Hu>H]HEHu]L^n HhƊnH}Ht IH}HtH}覊nH]HEHtIH]HEHtHHonLn fUHAWAVAUATSHIH"HHEп9nHþH/nHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]і9Iɞ9I9I蹟9HW)EHE nHEHH HML0L`LhHXHMH5 nH}HU7K<HX[9IS9IK9HC9IW)`HDžp inH`HH HpL LhHXLpHhH5 oH`HXJ<H5 oH}F<H5 sHG<HA H5nHH<HH;=<HHH HHMHZfHUHuHuIIGIG0IOIWIG@H`Ht HhwnH}Ht H}enH]HEHtH|HDnH!HH;EuLHĈ[A\A]A^A_]谇nIH}HtVIH`Hu$H}Hu>H]HEHu]Len Hh͆nH}Ht IH}HtH}譆nH]HEHtIH]HEHtH{HvnLn UHAVSHPIH;HHERnHþHHnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]9H5ynH}HI<H5oHxC<H5 sHD<HH HQH5nHE<HH;:<HHH HHMHcHUHuHuIIFIF0INIVIF@H]HEHtHyHbnH?HH;Eu LHP[A^]ׄnIH]HEHtHyyHnL~n UHAVSHPIHۖHHEnHþHnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]芙9H5nH}HgG<H5oHA<H5;sHIB<HH HQH50nHiC<HH;^8<HHH HHMHiHUHuHuIIFIF0INIVIF@H]HEHtH^wHnHߔHH;Eu LHP[A^]wnIH]HEHtHwH轁nL?|n UHAVSHPIH{HHE蒁nHþH舁nHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]*9H5nH}HE<H5HoH><H5sH?<HA H5nHA<HH;6<HHH <HHMH}HUHuHuIIFIF0INIVIF@H]HEHtHuHnHHH;Eu LHP[A^]nIH]HEHtHtHdnLyn fffff.UHAVSHPIHHHE2nHþH(nHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]ʔ9H5YnH}HB<H5oHX<<H5{sH=<HA H5wnH><HH;3<HHH ~HHMH{HUHuHuIIFIF0INIVIF@H]HEHtHrHI}nH&HH;Eu LHP[A^]}nIH]HEHtH`rH}nLwn fffff.UHAVSHPIHHHE|nHþH|nHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]j9H5nH}HG@<H5oH9<H5sH);<HH HQH5nHI<<HH;>1<HHH u|HHMH~yHUHuHuIIFIF0INIVIF@H]HEHtH>pHznHHH;Eu LHP[A^]W{nIH]HEHtHoHznLun UHAVSHPIH[HHErznHþHhznHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH] 9H5nH}H=<H5,oH7<H5rH8<HA H5nH9<HH;.<HHH zHHMHUHUHuHuIIFIF0INIVIF@H]HEHtHmHxnHfHH;Eu LHP[A^]xnIH]HEHtHmHDxnLrn fffff.UHAVSHPIHHHExnHþHxnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]誅9H59nH}H;<H5ДoH85<H5[rHi6<HHHQH5PnH7<HH;~,<HHH wHHMH.HUHuHuIIFIF0INIVIF@H]HEHtH~kH"vnHHH;Eu LHP[A^]vnIH]HEHtH9kHunL_pn UHAVSHPIHHHEunHþHunHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]J9H5نnH}H'9<H5poH2<H5rH 4<HH HQH5nH)5<HH;*<HHH UuHHMH.HUHuHuIIFIF0INIVIF@H]HEHtHiHsnHHH;Eu LHP[A^]7tnIH]HEHtHhH}snLmn UHAVSHPIH;HHERsnHþHHsnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]9H5ynH}H6<H5oHx0<H5rH1<HA H5nH2<HH;'<HHH rHHMH5HUHuHuIIFIF0INIVIF@H]HEHtHfHiqnHFHH;Eu LHP[A^]qnIH]HEHtHfH$qnLkn fffff.UHAVSHPIHۃHHEpnHþHpnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]~9H5nH}Hg4<H5oH.<H5;rHI/<HHHQH50nHi0<HH;^%<HHH pHHMH.HUHuHuIIFIF0INIVIF@H]HEHtH^dHonH߁HH;Eu LHP[A^]wonIH]HEHtHdHnnL?in UHAWAVATSHIHtHHEؿnnHþHnnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]#|9I9HW)EHEGmnHEHHHML0HXHMH5tnH}HU/<I{9I9HW)`HDžplnH`HHHpL HXHhH5oH`L6/<H5oH+<H5*rH8,<HH HQH5nHX-<HH;M"<HHH mHHMH}HUHuHuIIGIG0IOIWIG@H`Ht HhknH}Ht H}knH]HEHtH#aHknH~HH;EuLHĀ[A\A^A_]5lnIH}Ht>lIH}Ht0^IH]HEHt+bIH`Hu$H}Hu0H]HEHu9Len Hh.knH}HtH}knH]HEHtHW`HjnL}en DUHAWAVAUATSHH@H}HHEпjnHþHjnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]]x9HPQ9HXE9I=w9I59I-9H%9IW)EHE8QinHEHH8HMHPHHXHPLhL`Lp HX(Lx0HMH5\{nH}HU+<HPw9HX9HH蕂9Hv9I腀9I}~9Iu9IW)`HDžp8hnH`HH8HpHXHHHHPHXLpLx L`(Lh0HhH58oH`HP*<H51oH&<H5rH'<HH HQH5nH(<HH;<HHH iHHMHkHUHuHuH@HHCHC0HKHSHC@H`Ht HhgnH}Ht H}ognLuHEMtL\LNgnH+zHH;EuHHĘ[A\A]A^A_]gnIH}HtVIH`Hu$H}Hu>H]HEHu]Loan HhfnH}Ht IH}HtH}fnH]HEHtIH]HEHtH[HfnLan f.UHAWAVAUATSHH@H.yHHEпEfnHþH;fnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]s9HP{9HX~9Ir9I|9Iz9H}9IW)EHE8dnHEHH8HMHPHHXHPLhL`Lp HX(Lx0HMH5vnH}HU '<HP-s9HX!{9HH~9H r9I|9Iy9I|9IW)`HDžp8dnH`HH8HpHXHHHHPHXLpLx L`(Lh0HhH5oH`HPD&<H5oH"<H58rHF#<HA H54nHm$<HH;b<HHH dHHMHRHUHuHuH@HHCHC0HKHSHC@H`Ht HhcnH}Ht H}bnLuHEMtL1XLbnHuHH;EuHHĘ[A\A]A^A_]AcnIH}HtVIH`Hu$H}Hu>H]HEHu]L\n Hh^bnH}Ht IH}HtH}>bnH]HEHtIH]HEHtHcWHbnL\n UHAVSHPIHtHHEanHþHanHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]zo9H5 snH}HW%<H5~oH<H5+rH9 <HHHQH5 nHY!<HH;N<HHH aHHMH|HUHuHuIIFIF0INIVIF@H]HEHtHNUH_nHrHH;Eu LHP[A^]g`nIH]HEHtH UH_nL/Zn UHAWAVSHxIHirHHE_nHþHv_nHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]m9HW)EHED^nHEHHHMHHMH5upnH}HU <Il9HW)pHE]nHpHHHMHHxH5{oHpLI <H5{oH<H5=rHK<HH HQH52nHk<HH;`<HHH ^HHMHzHUHuHuIIFIF0INIVIF@HpHt Hx ]nH}Ht H}\nH]HEHtH6RH\nHoHH;EuLHx[A^A_]M]nIH}Ht>lIH}Ht0^IH]HEHt+bIHpHu$H}Hu0H]HEHu9LVn HxF\nH}HtH}4\nH]HEHtHoQH\nLVn ffff.UHAWAVAUATSHH@HnHHEп[nHþH[nHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]mi9HPaq9HXUt9IMh9IEr9I=p9H5s9IW)EHE8aZnHEHH8HMHPHHXHPLhL`Lp HX(Lx0HMH5llnH}HU<HPh9HXp9HHs9Hg9Iq9Io9Ir9IW)`HDžp8YnH`HH8HpHXHHHHPHXLpLx L`(Lh0HhH5HwoH`HP<H5EwoH<H5rH<HH HQH5nH<HH;<HHH "ZHHMHuHUHuHuH@HHCHC0HKHSHC@H`Ht HhXnH}Ht H}XnLuHEMtLML^XnH;kHH;EuHHĘ[A\A]A^A_]XnIH}HtVIH`Hu$H}Hu>H]HEHu]LRn HhWnH}Ht IH}HtH}WnH]HEHtIH]HEHtHLHWnLRn f.UHAWAVAUATSHH@H>jHHEпUWnHþHKWnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]d9HPl9HXo9Ic9Im9Ik9Hn9IW)EHE8UnHEHH8HMHPHHXHPLhL`Lp HX(Lx0HMH5gnH}HU<HP=d9HX1l9HH%o9Hc9Im9I k9In9IW)`HDžp8+UnH`HH8HpHXHHHHPHXLpLx L`(Lh0HhH5roH`HPT<H5roH%<H5HrHV<HA H5DnH}<HH;r <HHH UHHMHqHUHuHuH@HHCHC0HKHSHC@H`Ht HhTnH}Ht H}TnLuHEMtLAILSnHfHH;EuHHĘ[A\A]A^A_]QTnIH}HtVIH`Hu$H}Hu>H]HEHu]LNn HhnSnH}Ht IH}HtH}NSnH]HEHtIH]HEHtHsHHSnLMn UHAVSHPIHeHHERnHþHRnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]`9H5dnH}Hg<Hoa9H5ooHHM<H5ooH<H5!rH/<HHHQH5nHO<HH;D<HHH {RHHMHoHUHuHuIIFIF0INIVIF@H]HEHtHDFHPnHcHH;Eu LHP[A^]]QnIH]HEHtHEHPnL%Kn ffff.UHAVSHPIH[cHHErPnHþHhPnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH] f9H5anH}H<H^9H5moHH<H5"moH~ <H5rH<HHHQH5nH<HH;<HHH OHHMHdrHUHuHuIIFIF0INIVIF@H]HEHtHCHhNnHEaHH;Eu LHP[A^]NnIH]HEHtHCH#NnLHn ffff.UHAVSHPIH`HHEMnHþHMnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH][9H5_nH}Hg<Ho\9H5joHHM<H5joH <H5!rH/ <HH HQH5nHO <HH;D<HHH {MHHMH$vHUHuHuIIFIF0INIVIF@H]HEHtHDAHKnH^HH;Eu LHP[A^]]LnIH]HEHtH@HKnL%Fn ffff.UHAVSHPIH[^HHErKnHþHhKnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH] a9H5\nH}H<HY9H5hoHH<H5"hoH~<H5rH <HH HQH5nH <HH;;HHH JHHMHtHUHuHuIIFIF0INIVIF@H]HEHtH>HhInHE\HH;Eu LHP[A^]InIH]HEHtH>H#InLCn ffff.UHAVSHPIH[HHEHnHþHHnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]U9H5ZnH}Hg <HoW9H5eoHHM <H5eoH<H5!rH/<HH HQH5nHO<HH;D;HHH {HHHMHqHUHuHuIIFIF0INIVIF@H]HEHtHDn ffff.UHAVSHPIHVHHECnHþHCnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]Q9H5UnH}Hg<HoR9H5`oHHM<H5`oH<H5!rH/<HA H5nHV<HH;K;HHH CHHMHvHUHuHuIIFIF0INIVIF@H]HEHtHK7HAnHTHH;Eu LHP[A^]dBnIH]HEHtH7HAnL,HHMHrHUHuHuIIFIF0INIVIF@H]HEHtHk2H=nHOHH;Eu LHP[A^]=nIH]HEHtH&2H;H5arHo;HHHQH5V~nH;HH;;HHH 9HHMHdnHUHuHuIIFIF0INIVIF@H]HEHtH-H(8nHKHH;Eu LHP[A^]8nIH]HEHtH?-H7nLe2n ffff.UHAVSHPIHJHHE7nHþH7nHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]JM9H5HnH}H';H/F9H5YToHH ;H5gToH;H5rH;HHHQH5{nH;HH;;HHH ;7HHMHqHUHuHuIIFIF0INIVIF@H]HEHtH+H5nHHHH;Eu LHP[A^]6nIH]HEHtH*Hc5nL/n ffff.UHAVSHPIHHHHE25nHþH(5nHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]B9H5YFnH}H;HC9H5QoHH;H5QoH>;H5arHo;HH HQH5VynH;HH;;HHH 4HHMHtuHUHuHuIIFIF0INIVIF@H]HEHtH(H(3nHFHH;Eu LHP[A^]3nIH]HEHtH?(H2nLe-n ffff.UHAVSHPIHEHHE2nHþH2nHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]JH9H5CnH}H';H/A9H5YOoHH ;H5gOoH;H5rH;HH HQH5vnH;HH;;HHH ;2HHMHTsHUHuHuIIFIF0INIVIF@H]HEHtH&H0nHCHH;Eu LHP[A^]1nIH]HEHtH%Hc0nL*n ffff.UHAVSHPIHCHHE20nHþH(0nHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]<9H5YAnH}H;H>9H5LoHH;H5LoH>;H5arHo;HH HQH5VtnH;HH;;HHH /HHMH4qHUHuHuIIFIF0INIVIF@H]HEHtH#H(.nHAHH;Eu LHP[A^].nIH]HEHtH?#H-nLe(n ffff.UHAVSHPIH@HHE-nHþH-nHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]JB9H5>nH}H';H/<9H5YJoHH ;H5gJoH;H5rH;HH HQH5qnH;HH;;HHH ;-HHMHtHUHuHuIIFIF0INIVIF@H]HEHtH!H+nH>HH;Eu LHP[A^],nIH]HEHtH Hc+nL%n ffff.UHAVSHPIH>HHE2+nHþH(+nHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]89H5Y;H5arHo;HA H5]onH;HH;;HHH *HHMHuHUHuHuIIFIF0INIVIF@H]HEHtHH/)nH <HH;Eu LHP[A^])nIH]HEHtHFH(nLl#n @UHAVSHPIH;HHE(nHþH(nHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]Z>9H59nH}H7;H?79H5iEoHH;H5wEoH;H5rH;HA H5lnH&;HH;;HHH R(HHMHsHUHuHuIIFIF0INIVIF@H]HEHtHH&nH9HH;Eu LHP[A^]4'nIH]HEHtHHz&nL n @UHAVSHPIH;9HHER&nHþHH&nHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]29H5y7nH}H;H49H5BoHH;H5CoH^;H5rH;HA H5}jnH;HH;;HHH %HHMHqHUHuHuIIFIF0INIVIF@H]HEHtHHO$nH,7HH;Eu LHP[A^]$nIH]HEHtHfH $nLn @UHAVSHPIH6HHE#nHþH#nHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]z89H5 5nH}HW;H_29H5@oHH=;H5@oH;H5rH;HA H5 hnHF;HH;;;HHH r#HHMHoHUHuHuIIFIF0INIVIF@H]HEHtH;H!nH4HH;Eu LHP[A^]T"nIH]HEHtHH!nLn @UHAVSHPIH[4HHEr!nHþHh!nHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH] 09H52nH}H;H/9H5>oHH;H5/>oH~;H5rH;HH HQH5enH;HH;;HHH HHMHmHUHuHuIIFIF0INIVIF@H]HEHtHHhnHE2HH;Eu LHP[A^]nIH]HEHtHH#nLn ffff.UHAVSHPIH1HHEnHþHnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]+9H50nH}Hg;Ho-9H5;oHHM;H5;oH;H5!rH/;HH HQH5cnHO;HH;D;HHH {HHMHrHUHuHuIIFIF0INIVIF@H]HEHtHDHnH/HH;Eu LHP[A^]]nIH]HEHtHHnL%n ffff.UHAVSHPIH[/HHErnHþHhnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH] 19H5-nH}H;H*9H59oHH;H5/9oH~;H5rH;HH HQH5`nH;HH;;HHH HHMHTvHUHuHuIIFIF0INIVIF@H]HEHtHHhnHE-HH;Eu LHP[A^]nIH]HEHtHH#nLn ffff.UHAVSHPIH,HHEnHþHnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]'9H5+nH}Hg;Ho(9H56oHHM;H56oH;H5!rH/;HH HQH5^nHO;HH;D;HHH {HHMHwHUHuHuIIFIF0INIVIF@H]HEHtHD HnH*HH;Eu LHP[A^]]nIH]HEHtH HnL%n ffff.UHAVSHPIH[*HHErnHþHhnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH] -9H5(nH}H;H%9H54oHH;H5/4oH~;H5rH;HH HQH5[nH;HH;;HHH HHMHzHUHuHuIIFIF0INIVIF@H]HEHtH HhnHE(HH;Eu LHP[A^]nIH]HEHtH H#nLn ffff.UHAVSHPIH'HHEnHþHnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]#9H5&nH}Hg;Ho#9H51oHHM;H51oH;H5!rH/;HH HQH5YnHO;HH;D;HHH {HHMH~HUHuHuIIFIF0INIVIF@H]HEHtHDHnH%HH;Eu LHP[A^]]nIH]HEHtHHnL% n ffff.UHAVSHPIH[%HHErnHþHhnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH] 9H5#nH}H;H 9H5/oHH;H5//oH~;H5rH;HH HQH5VnH;HH;;HHH HHMH|HUHuHuIIFIF0INIVIF@H]HEHtHHhnHE#HH;Eu LHP[A^]nIH]HEHtHH#nL n ffff.UHAVSHPIH"HHEnHþHnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]$9H5!nH}Hg;Ho9H5,oHHM;H5,oH;H5!rH/;HH HQH5TnHO;HH;D;HHH {HHMHtzHUHuHuIIFIF0INIVIF@H]HEHtHDH nH HH;Eu LHP[A^]]nIH]HEHtHH nL%n ffff.UHAVSHPIH[ HHEr nHþHh nHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH] 9H5nH}H;H9H5*oHH;H5/*oH~;H5rH;HH HQH5QnH;HH;;HHH HHMHTxHUHuHuIIFIF0INIVIF@H]HEHtHHh nHEHH;Eu LHP[A^] nIH]HEHtHH# nLn ffff.UHAVSHPIHHHE nHþH nHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH] 9H5nH}Hg;Ho9H5'oHHM;H5'oH;H5!rH/;HH HQH5OnHO;HH;D;HHH { HHMH4vHUHuHuIIFIF0INIVIF@H]HEHtHDHnHHH;Eu LHP[A^]] nIH]HEHtHHnL%n ffff.UHAVSHPIH[HHErnHþHhnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH] 9H5nH}H;H9H5%oHH;H5/%oH~;H5rH;HA H5LnH;HH;˼;HHH HHMHtHUHuHuIIFIF0INIVIF@H]HEHtHHonHLHH;Eu LHP[A^]nIH]HEHtHH*nLn @UHAVSHPIHHHEnHþHnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]9H5)nH}Hw;H9H5"oHH];H5"oH;H51rH?;HA H5-JnHf;HH;[;HHH HHMH rHUHuHuIIFIF0INIVIF@H]HEHtH[HnHHH;Eu LHP[A^]tnIH]HEHtHHnLnHY;HH;N;HHH HHMHgHUHuHuIIFIF0INIVIF@H]HEHtHNHmH HH;Eu LHP[A^]gmIH]HEHtH HmL/m UHAVSHPIHk HHEmHþHxmHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]9H5nH}H;H5_oH訴;H5{rHٵ;HA H5;nH;HH;;HHH ,HHMHeHUHuHuIIFIF0INIVIF@H]HEHtHHmHvHH;Eu LHP[A^]mIH]HEHtHHTmLm fffff.UHAVSHPIH HHE"mHþHmHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]9H5InH}H藸;H5oHH;H5kyrHy;HA H5g9nH蠴;HH;蕩;HHH HHMHcHUHuHuIIFIF0INIVIF@H]HEHtHH9mHHH;Eu LHP[A^]mIH]HEHtHPHmLvm fffff.UHAVSHPIHHHEmHþHmHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]Z 9H5nH}H7;H5oH;H5 wrH;HA H57nH@;HH;5;HHH lHHMHHUHuHuIIFIF0INIVIF@H]HEHtH5HmHHH;Eu LHP[A^]NmIH]HEHtHHmLm fffff.UHAVSHPIHKHHEbmHþHXmHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]9H5nH}H׳;H5D oH舭;H5trH蹮;HA H54nH;HH;դ;HHH HHMHHUHuHuIIFIF0INIVIF@H]HEHtHHymHVHH;Eu LHP[A^]mIH]HEHtHH4mLm fffff.UHAVSHPIHHHEmHþHmHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]8H5)mH}Hw;H5 oH(;H5KrrHY;HH HQH5@2nHy;HH;n;HHH HHMHިHUHuHuIIFIF0INIVIF@H]HEHtHnHmHHH;Eu LHP[A^]mIH]HEHtH)HmLOm UHAVSHPIHHHEmHþHmHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]:8H5mH}H;H5oHȨ;H5orH;HA H5/nH ;HH;;HHH LHHMHHUHuHuIIFIF0INIVIF@H]HEHtHHmHHH;Eu LHP[A^].mIH]HEHtHHtmLm fffff.UHAVSHPIH+HHEBmHþH8mHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]8H5imH}H跬;H8H5oHH蝬;H5oHN;H5qmrH;HAH5m-nH覨;HH;蛝;HHH HHMHˤHUHuHuIIFIF0INIVIF@H]HEHtHH?mHHH;Eu LHP[A^]mIH]HEHtHVHmL|m @UHAVSHPIHHHEmHþHmHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]j8H5mH}HG;HO8H5yoHH-;H5oHޣ;H5krH;HAH5*nH6;HH;+;HHH bHHMHHUHuHuIIFIF0INIVIF@H]HEHtH+HmHHH;Eu LHP[A^]DmIH]HEHtHHmL m @UHAVSHPIHKHHEbmHþHXmHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]8H5mH}Hק;H8H5 oHH轧;H5?oHn;H5hrH蟢;HAH5(nHƣ;HH;軘;HHH HHMHHUHuHuIIFIF0INIVIF@H]HEHtHH_mH<HH;Eu LHP[A^]mIH]HEHtHvHmLm @UHAVSHPIHHHEmHþHmHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]8H5mH}Hg;Ho8H5nHHM;H5nH;H5!frH/;HAH5&nHV;HH;K;HHH HHMHHUHuHuIIFIF0INIVIF@H]HEHtHKHmHHH;Eu LHP[A^]dmIH]HEHtHHmL,m @UHAWAVSH8IIHfHHE1LE=HþL5=IH{0OɃ w$HHc HH}LH'F9HEH5\nH}HU H}HMLHEIHEEtSH}mHHH;EtJVH}LH H}LHH}LH'HEIHiHH;EuLH8[A^A_]mHEt%H}WmIHm HIHm fduUHAVSH0HHHHEHHz0Gȃ wPH HcH )MHqHEHEa1HHZ$zD9HEH5)oH}HU"H}HMо+LHEHHEEH}%mHHH;E )MHHEHE1HHYx )MHHEHEu1HHY;> F)MH0HEHE81HH{YH4HH;Eu HH0[A^]mIEtH}$mHLm IHLm A-j@UHAVSH0HHHHEHHz0Gȃ wPH HcH )MHHEHE11HHtX$JB9HEH5'oH}HUH}HMоLHEHHEEH}mHHH;E )MH HEHE1HHWx )MHHEHEE1HHW;n v)MH`HEHE1HHKWHHH;Eu HH0[A^]mIEtH}mHLom IHLYm A-j@UHAVSH0HHkHHEHHz0Gȃ wPH HcH )MHHEHE1HHDV$@9HEH5u%oH}HUH}HMоLHEHHEEH}mHHH;E( 0)MHHEHER1HHUx )MHHEHE1HHXU;~ )MHpHEHEة1HHUHHH;Eu HH0[A^]lmIEtH}mHL?m IHL)m A-j@UHAVSH0HH;HHEHHz0Gȃ wPH HcH/ 7)MH!HEHEѨ1HHT$=9HEH5E#oH}HUH}HMоLHEHHEEH}mHrHH;E )MHHEHE"1HHeSxs {)MHeHEHE姂1HH(S; )MHHEHE1HHRHHH;Eu HH0[A^]MIMLIrKI94KDI9&1ICH)LHt!Hffff.AATHHuHDAADADADADADAD AD ADADADADADADADADHI9uDDH=L@Hp HL)IVI~ H)H9 I9t1fI0H;2HH9uHHLLIPYILEB@L=YIH2B@HHHAuDكH9HOII)LH?H>LHHH1HAH)HHtHAAXADHHuHmff.AAXADADAXDADADAXDADAD AXD AD HH9uItII HH9uIIH:SHL/<@HHHHrHI9s#H;s1HH)MH8!H01fDLATXADXAL A\0ATADD XL0XAD AL0HHuH t'LATXADXATADH(HH9HH)Mt,Hf.AXDADHHuHrgffffff.AXDADDAXDADDAXDADD AXD AD HH9uAD;D HL)HH?H>HHHN&HHHHH9HMLTI)Mt$IENH9HNIIHJI9IMLH0IBHDxHZH(LHHAH L)L8IALIYLL)HH HHxHhHHLIOHH)HKDHIDHKDHHGHpLKHID$HHM|L91H|1Hffffff.MH=HHHx 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oHHE"WHþHWHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]躡"H5IWH}HW%H5oYHHQ%H5k\HyR%HA H5gWHS%HH;H%HHH ̓oHHMHՇHUHuHuIIFIF0INIVIF@H]HEHtH蕇H9WHoHH;Eu LHP[A^]讒WIH]HEHtHPHWLvW fffff.UHAVSHPIHoHHE‘WHþH踑WHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]Z"H5WH}H7U%H5YHN%H5 \HP%HA H5WH@Q%HH;5F%HHH loHHMH%HUHuHuIIFIF0INIVIF@H]HEHtH5HُWHoHH;Eu LHP[A^]NWIH]HEHtHH蔏WLW fffff.UHAVSHPIHKoHHEbWHþHXWHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]"H5WH}HR%H5YHL%H5\HM%HA H5WHN%HH;C%HHH oHHMHuHUHuHuIIFIF0INIVIF@H]HEHtHՂHyWHVoHH;Eu LHP[A^]WIH]HEHtH萂H4WL趇W fffff.UHAVSHPIHoHHEWHþHWHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]蚝"H5)WH}HwP%H5OYH(J%H5K\HYK%HA H5GWHL%HH;uA%HHH oHHMHłHUHuHuIIFIF0INIVIF@H]HEHtHuHWHoHH;Eu LHP[A^]莋WIH]HEHtH0HԊWLVW fffff.UHAVSHPIHoHHE袊WHþH蘊WHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]:"H5ɛWH}HN%H5YHG%H5\HH%HA H5WH J%HH;?%HHH LoHHMHHUHuHuIIFIF0INIVIF@H]HEHtH~H蹈WHoHH;Eu LHP[A^].WIH]HEHtH}HtWLW fffff.UHAVSHPIH+oHHEBWHþH8WHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]ڕ"H5iWH}HK%H5YHhE%H5 \HF%HH HQH5WHG%HH;<%HHH oHHMH^HUHuHuIIFIF0INIVIF@H]HEHtH{HRWH/oHH;Eu LHP[A^]džWIH]HEHtHi{H WL菀W UHAVSHPIH˘oHHEWHþH؅WHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]z"H5 WH}HWI%H59YHC%H5+ \H9D%HH HQH5 WHYE%HH;N:%HHH oHHMHHUHuHuIIFIF0INIVIF@H]HEHtHNyHWHϖoHH;Eu LHP[A^]gWIH]HEHtH yH譃WL/~W UHAVSHPIHkoHHE肃WHþHxWHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]"H5WH}HF%H5قYH@%H5\HA%HH HQH5WHB%HH;7%HHH %oHHMHHUHuHuIIFIF0INIVIF@H]HEHtHvH蒁WHooHH;Eu LHP[A^]WIH]HEHtHvHMWL{W UHAVSHPIH oHHE"WHþHWHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]躍"H5IWH}HD%H5yYHH>%H5k\Hy?%HH HQH5`WH@%HH;5%HHH ŀoHHMHHUHuHuIIFIF0INIVIF@H]HEHtHtH2WHoHH;Eu LHP[A^]WIH]HEHtHItH~WLoyW UHAVSHPIHoHHE~WHþH~WHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]Z"H5WH}H7B%H5~YH;%H5 \H=%HA H5WH@>%HH;53%HHH l~oHHMHUHUHuHuIIFIF0INIVIF@H]HEHtH5rH|WHoHH;Eu LHP[A^]N}WIH]HEHtHqH|WLwW fffff.UHAVSHPIHKoHHEb|WHþHX|WHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]"H5WH}H?%H5{YH9%H5\H:%HA H5WH;%HH;0%HHH |oHHMHHUHuHuIIFIF0INIVIF@H]HEHtHoHyzWHVoHH;Eu LHP[A^]zWIH]HEHtHoH4zWLtW fffff.UHAVSHPIHoHHEzWHþHyWHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]蚇"H5)WH}Hw=%H5dyYH(7%H5K[HY8%HH HQH5@WHy9%HH;n.%HHH yoHHMHHUHuHuIIFIF0INIVIF@H]HEHtHnmHxWHoHH;Eu LHP[A^]xWIH]HEHtH)mHwWLOrW UHAVSHPIHoHHEwWHþHwWHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]:"H5ɈWH}H;%H5wYH4%H5[H5%HH HQH5WH7%HH;,%HHH EwoHHMH.HUHuHuIIFIF0INIVIF@H]HEHtHkHuWHoHH;Eu LHP[A^]'vWIH]HEHtHjHmuWLoW UHAVSHPIH+oHHEBuWHþH8uWHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]ډ"H5iWH}H8%H5tYHh2%H5[H3%HH HQH5WH4%HH;)%HHH toHHMH^HUHuHuIIFIF0INIVIF@H]HEHtHhHRsWH/oHH;Eu LHP[A^]sWIH]HEHtHihH sWLmW UHAVSHPIH˅oHHErWHþHrWHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]z"H5 WH}HW6%H5DrYH0%H5+[H91%HH HQH5 WHY2%HH;N'%HHH roHHMHn!HUHuHuIIFIF0INIVIF@H]HEHtHNfHpWHσoHH;Eu LHP[A^]gqWIH]HEHtH fHpWL/kW UHAVSHPIHkoHHEpWHþHxpWHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]}"H5WH}H3%H5oYH-%H5[H.%HH HQH5WH/%HH;$%HHH %poHHMHHUHuHuIIFIF0INIVIF@H]HEHtHcHnWHooHH;Eu LHP[A^]oWIH]HEHtHcHMnWLhW UHAVSHPIH oHHE"nWHþHnWHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]躂"H5IWH}H1%H5mYHH+%H5k[Hy,%HH HQH5`WH-%HH;"%HHH moHHMHHUHuHuIIFIF0INIVIF@H]HEHtHaH2lWHoHH;Eu LHP[A^]lWIH]HEHtHIaHkWLofW UHAVSHPIH~oHHEkWHþHkWHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]Zy"H5|WH}H7/%H5$kYH(%H5 [H*%HH HQH5WH9+%HH;. %HHH ekoHHMH^HUHuHuIIFIF0INIVIF@H]HEHtH._HiWH|oHH;Eu LHP[A^]GjWIH]HEHtH^HiWLdW UHAVSHPIHK|oHHEbiWHþHXiWHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]u"H5zWH}H,%H5hYH&%H5[H'%HH HQH5WH(%HH;%HHH ioHHMHHUHuHuIIFIF0INIVIF@H]HEHtH\HrgWHOzoHH;Eu LHP[A^]gWIH]HEHtH\H-gWLaW UHAVSHPIHyoHHEgWHþHfWHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]{"H5)xWH}Hw*%H5dfYH($%H5K[HY%%HH HQH5@WHy&%HH;n%HHH foHHMHHUHuHuIIFIF0INIVIF@H]HEHtHnZHeWHwoHH;Eu LHP[A^]eWIH]HEHtH)ZHdWLO_W UHAVSHPIHwoHHEdWHþHdWHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]:u"H5uWH}H(%H5dYH!%H5[H"%HH HQH5WH$%HH;%HHH EdoHHMHNHUHuHuIIFIF0INIVIF@H]HEHtHXHbWHuoHH;Eu LHP[A^]'cWIH]HEHtHWHmbWL\W UHAVSHPIH+uoHHEBbWHþH8bWHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]s"H5isWH}H%%H5aYHh%H5[H %HH HQH5WH!%HH;%HHH aoHHMH3HUHuHuIIFIF0INIVIF@H]HEHtHUHR`WH/soHH;Eu LHP[A^]`WIH]HEHtHiUH `WLZW UHAVSHPIHroHHE_WHþH_WHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]zm"H5 qWH}HW#%H5D_YH%H5+[H9%HA H5'WH`%HH;U%HHH _oHHMHPHUHuHuIIFIF0INIVIF@H]HEHtHUSH]WHpoHH;Eu LHP[A^]n^WIH]HEHtHSH]WL6XW fffff.UHAVSHPIHkpoHHE]WHþHx]WHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]j"H5nWH}H %H5\YH%H5[H%HA H5ǡWH%HH;%HHH ,]oHHMHNHUHuHuIIFIF0INIVIF@H]HEHtHPH[WHvnoHH;Eu LHP[A^]\WIH]HEHtHPHT[WLUW fffff.UHAVSHPIH noHHE"[WHþH[WHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]o"H5IlWH}H%H5ZYHH%H5k[Hy%HA H5gWH%HH;%HHH ZoHHMH%MHUHuHuIIFIF0INIVIF@H]HEHtHNH9YWHloHH;Eu LHP[A^]YWIH]HEHtHPNHXWLvSW fffff.UHAVSHPIHkoHHEXWHþHXWHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]Zi"H5iWH}H7%H5$XYH%H5 [H%HA H5WH@%HH;5 %HHH lXoHHMHuKHUHuHuIIFIF0INIVIF@H]HEHtH5LHVWHioHH;Eu LHP[A^]NWWIH]HEHtHKHVWLQW fffff.UHAVSHPIHKioHHEbVWHþHXVWHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]g"H5gWH}H%H5UYH%H5[H%HA H5WH%HH; %HHH VoHHMHIHUHuHuIIFIF0INIVIF@H]HEHtHIHyTWHVgoHH;Eu LHP[A^]TWIH]HEHtHIH4TWLNW fffff.UHAVSHPIHfoHHETWHþHSWHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]a"H5)eWH}Hw%H5nSYH(%H5K[HY%HH HQH5@WHy%HH;n%HHH SoHHMHHHUHuHuIIFIF0INIVIF@H]HEHtHnGHRWHdoHH;Eu LHP[A^]RWIH]HEHtH)GHQWLOLW UHAVSHPIHdoHHEQWHþHQWHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]:^"H5bWH}H%H5QYH%H5[H%HH HQH5WH%HH;%HHH EQoHHMHgHUHuHuIIFIF0INIVIF@H]HEHtHEHOWHboHH;Eu LHP[A^]'PWIH]HEHtHDHmOWLIW UHAVSHPIH+boHHEBOWHþH8OWHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]c"H5i`WH}H%H5NYHh %H5[H %HH HQH5WH%HH;%HHH NoHHMH.HUHuHuIIFIF0INIVIF@H]HEHtHBHRMWH/`oHH;Eu LHP[A^]MWIH]HEHtHiBH MWLGW UHAVSHPIH_oHHELWHþHLWHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]zZ"H5 ^WH}HW%H5NLYH %H5+[H9 %HH HQH5 WHY %HH;N%HHH LoHHMHHUHuHuIIFIF0INIVIF@H]HEHtHN@HJWH]oHH;Eu LHP[A^]gKWIH]HEHtH @HJWL/EW UHAVSHPIHk]oHHEJWHþHxJWHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]W"H5[WH}H %H5IYH%H5[H%HH HQH5WH %HH;$HHH %JoHHMHHUHuHuIIFIF0INIVIF@H]HEHtH=HHWHo[oHH;Eu LHP[A^]IWIH]HEHtH=HMHWLBW UHAVSHPIH [oHHE"HWHþHHWHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]\"H5IYWH}H %H5GYHH%H5k[Hy%HH HQH5`WH%HH;$HHH GoHHMH^HUHuHuIIFIF0INIVIF@H]HEHtH;H2FWHYoHH;Eu LHP[A^]FWIH]HEHtHI;HEWLo@W 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HgHH;EuHX[A\A]A^A_]IOHH}HEH9uHP HO HtHP(HO UHAWAVAUATSHXHHgHHEHgHHEHMIHIHsgHEHEHEHEH}UA$EEEMcMMADHHuHUHMP0 &Nb\MYBDCYDXHEBDH}M9}^BECEEEEEIHu胘OH `gHHH5bxgHKugH蛘O HEH9tHtHP(HgHH;Et%HP HgHH;EuHX[A\A]A^A_]苘OHH}HEH9uHP H_O HtHP(HJO fUHAWAVAUATSHXHgHHEHgHHEHMIHIH>rgHEHEHEHEH}UA$EEEMcMMADHHuHUHMP0 fLb\MYBDCYDXHEBDH}M9}^BECEEEEEIHuÖOH gHHH5vgHsgHۖO HEH9tHtHP(HNgHH;Et%HP H6gHH;EuHX[A\A]A^A_]˖OHH}HEH9uHP H蟐O HtHP(H芐O fUHAWAVAUATSHXHȨgHHEH"gHHEHMIHIH>pgHEHEHEHEH}UA$EEEMcMMADHHuHUHMP0 Jb\MYBDCYDXHEBDH}M9}^BECEEEEEIHuOH gHHH5tgHqgHO HEH9tHtHP(HgHH;Et%HP HvgHH;EuHX[A\A]A^A_] OHH}HEH9uHP HߎO HtHP(HʎO fUHAWAVAUATSHXHgHHEHbgHHEHMIHIHnngHEHEHEHEH}UA$EEEMcMMADHHuHUHMP0 Hb\MYBDCYDXHEBDH}M9}^BECEEEEEIHuCOH gHHH5"sgH pgH[O HEH9tHtHP(HΥgHH;Et%HP HgHH;EuHX[A\A]A^A_]KOHH}HEH9uHP HO HtHP(H O fUHAWAVAUATSHXHHgHHEHgHHEHMIHIHlgHEHEHEHEH}UA$EEEMcMMADHHuHUHMP0 &Gb\MYBDCYDXHEBDH}M9}^BECEEEEEIHu胑OH `gHHH5bqgHKngH蛑O HEH9tHtHP(HgHH;Et%HP HgHH;EuHX[A\A]A^A_]苑OHH}HEH9uHP H_O HtHP(HJO fUHAWAVAUATSHXHgHHEHgHHEHMIHIHkgHEHEHEHEH}UA$EEEMcMMADHHuHUHMP0 fEb\MYBDCYDXHEBDH}M9}^BECEEEEEIHuÏOH gHHH5ogHlgHۏO HEH9tHtHP(HNgHH;Et%HP H6gHH;EuHX[A\A]A^A_]ˏOHH}HEH9uHP H蟉O HtHP(H芉O fUHAWAVAUATSHXHȡgHHEH"gHHEHMIHIHvigHEHEHEHEH}UA$EEEMcMMADHHuHUHMP0 Cb\MYBDCYDXHEBDH}M9}^BECEEEEEIHuOH gHHH5mgHjgHO HEH9tHtHP(HgHH;Et%HP HvgHH;EuHX[A\A]A^A_] OHH}HEH9uHP H߇O HtHP(HʇO fUHAWAVAUATSHXHgHHEHbgHHEHMIHIHggHEHEHEHEH}UA$EEEMcMMADHHuHUHMP0 Ab\MYBDCYDXHEBDH}M9}^BECEEEEEIHuCOH gHHH5"lgH igH[O HEH9tHtHP(HΞgHH;Et%HP HgHH;EuHX[A\A]A^A_]KOHH}HEH9uHP HO HtHP(H O fUHAWAVAUATSPMLEIIIHLGOHLHIuHOL%OHLH׏HEH0HOLOHLH貏HuHF "H[A\A]A^A_]HHHHHHUHAWAVAUATSPMLEIIIHLwOHLH)AuHOLUOHLHHEH0HOL0OHLHHuHv "H[A\A]A^A_]HOHGH?H7H/H'UHAWAVAUATSH(HgHHEIIA$M|$It$IDIET$H}QHMuM]MNML9+IIEJH3HH'A$7Ml$M|$MHu3E1DHCH9ugKHtHSL9ubH{HtH{ uaM1ffffff.T A:u+HH9uvL9r1IHL9[HHnJM>LLMM'OMMHuE1uMI!IEJH1HIIIEJHE1HgHH;E蚈ODMHuE1fHCH9tHL!L9H9KHtHSL9udH{HtH{ u'Mt[1ff.T A:u5HH9u9Mt4LLMLMOLMMHutDHHTIHgHH;ELH([A\A]A^A_]HQUHAWAVAUATSH(HؙgHHEIIA$M|$It$IDIET$H}聎HMuM]MNML9+IIEJH3HH'A$7Ml$M|$MHu3E1DHCH9ugKHtHSL9ubH{HtH{ uaM1ffffff.T A:u+HH9uvL9r1IHL9[HHnJM>LLMMWOMMHuE1uMI!IEJH1HIIIEJHE1H*gHH;EʅODMHuE1fHCH9tHL!L9H9KHtHSL9udH{HtH{ u'Mt[1ff.T A:u5HH9u9Mt4LLMLMLOLMMHutDHHTIHHgHH;ELH([A\A]A^A_]H聎UHAWAVAUATSHIH7LwI)IIFHH;HHWH)HHHH9H}wYHH9HBHDH9vCӃOHH5OHOH.gHHH5PdgH`gHكOHHHEHH{OHIN,3LLH]{OID$JD3Mu HEL8L`M9t;M)I@I}LFOID$IEIII uHEL LxMHMHHL(LpHMHHHM9t'ff.I_AGt I踂OII9uMtLH[A\A]A^A_]镂OH[A\A]A^A_]1HE1 OIH蟂OL|O IMM9u2IM9t*fffff.I^AFt I~(OII9uH}HtOL|O fff.UHAWAVSHHɔgHHEHDžpHDžhƅ` fDženeDžaaffiƅgHH`OfDžW)@HDžPƅ@DžAreluƅEHH@qOfDžW) HDž0ƅ IleakyrelL!fDž)uHH OW)ƅHDžHldedreluHfDžHthresholHƅHH~OfDžW)HDžƅDžtanhƅHHu~OfDžW)HDžƅHscaledtaHfDžnhƅH H~OfDž8W)HDžƅHsigmoidHH@H}OfDžXW)HDžƅHhardsigmHDžoidH`H}OW)`ƅ`fDžxHDžpDžaeluH}H`A}OW)@fEƅ@HDžPHsoftsignHAƅIH}H@|OW) fEƅ HDž0HsoftplusH!ƅ)H}H |OfEH=6jH 荨EEE`@  % 1@=`IUamy @`t HpL}OH=ZgH5jHP}OW)`ƅ`HDžpLafDžiuHH`{OHDž #<W)@HDžPƅ@HADžIoidHH@zOHL>?HW) ƅ HDž0Dž!eluHH zOHDž?H=0jH菱 @`t Hp{OH=XgH5~jH4{OHgHH;EH[A^A_]H}{OEAH}{OE8H}z{O`/Hpa{O@#HPH{O H0/{O H{OHzOHzOHzOHzO H0zO@HPzO`HpgzOHNzOH5zOHzOHzO{HyO oH0yO@cHPyO`SZHyOlHyO`HhyO TH0OyO@HHP6yO`8?yOIu u,u8E1 uU_HxOtHxOtHxOE1 uIA t H0xO@u3IA@tHPdxO`tIA1`u Hu_Hp2xOHJA@Hffffff.`t HpwOHu IEEE`@ E1  H}CwOEfH}0wOE]H}wO`THpwO@HHPvO I<$}] HvIt$HtHFIT$AL$ED$EL$ A|$$AD$(A\$,AD$0H](HCH;AD$5IEI;EAD$6AD$8ID$@ID$pIDŽ$IDŽ$IDŽ$IDŽ$IDŽ$`IDŽ$pIDŽ$IDŽ$IDŽ$IDŽ$PIDŽ$`IDŽ$IDŽ$IDŽ$IDŽ$IDŽ$IDŽ$ IDŽ$PIDŽ$`IDŽ$IDŽ$IDŽ$IDŽ$IDŽ$IDŽ$ IDŽ$PI$`IDŽ$ IDŽ$8IDŽ$0IDŽ$0IDŽ$(IDŽ$ IDŽ$IDŽ$IDŽ$IDŽ$IDŽ$ IDŽ$IDŽ$IDŽ$IDŽ$IDŽ$IDŽ$pOM$xLExL}PINI$HI$PL%xLuXIOI$XI$`L襀INI$hI$pA|$5u HJHIKI$@I$x褷-OAL$8AD$4AD$|A|$$AD$4L'kOLu@HM8HHIH)u3I$ I$(H)oOIINH)HlI$ 02HVHHALGIIs A HHGHzHH9sH4HxH9sAH](MIIYHH~Hs 1HuoH_H)1fDLDLD$L4D$L4DDLTDDLTDdLtDdLtH HuHt,H4HfDD0 0D2 2H HuM9H](t1JJI1fffff.||I|1HH9rIINH)u/I$I$H)nOA|$6HlI$02HVHHALGIIs A HHGHzHH9sH4HxH9sAH](MIIYHH~Hs 1HuoH_H)1fDLDLD$L4D$L4DDLTDDLTDdLtDdLtH HuHt,H4HfDD0 0D2 2H HuM9H](t1JJI1fffff.||I|1HH9rA|$6t]IcD$$IMHI$I$H IMHI$I$H IMHI$I$A|$5McL$$MEBLI$`HcAH]E1IELAtCI AXBLHL9ULH)H ILHTIt1@DXDDXHH9uJ IJ4+I4KgH.bOHHuP0I$I$H9u!HP I$`IDŽ$`Hu&rHtHP(I$`IDŽ$`HtNHEI$Hu3naOH KigHHH5MAgH6>gHaO$HHuP0I$pI$H9u!HP I$ IDŽ$ Hu&rHtHP(I$ IDŽ$ HtNHEI$PHu3`OH hgHHH5@gH=gH`O|HHuP0I$0I$PH9u!HP I$IDŽ$Hu&rHtHP(I$IDŽ$HtNHEI$Hu3`OH ggHHH5?gHgH;gH^OHHuP0I$pI$H9u!HP I$ IDŽ$ Hu&rHtHP(I$ IDŽ$ HtNHEI$PHu3&^OH fgHHH5>gH:gH>^OHHuP0I$0I$PH9u!HP I$IDŽ$Hu&rHtHP(I$IDŽ$HtNHEI$Hu3~]OH [egHHH5]=gHF:gH]O4HHuP0I$I$H9u!HP I$pIDŽ$pHu&rHtHP(I$pIDŽ$pHtNHEI$Hu3\OH dgHHH5EAIFHHD[AHIr0KL0ICH)1f.DLTXDXDD TXXT D0HHuMt)AALJAXADXDM9yHAE1ID$LAt K ALAXBLHL9hLH)J4 J ILItHT1DXDDXHH9uJ4O<K K'IOK!IH9AH9H9I9E1A6 .EAIFHHDYAH`K\0Kt0ICH)1fffff.DLTXDXDD TXXT D0HHuMt,J AALLAXADXDM9aO HpALEE1ID$LAt K ALAXBLHL9mLH)J LILIKTHt1DXDDXHH9uJO,K K\%IO4K#IH9H9AH9I9E1D LE$EAIGHHDrAHK\0KL0IFH)1fDLTXDXDD TXXT D0HHuMt,JAALLAXADXDM9\OIHA1ID$HAt J4AHuAXHHH9rI)HEHLIDIKLHT1DXDDXHI9uJOK K< ILfHHZHMAHH9HLLMMEHHHGHHtH@HcwHDGIHI0I8HHLt*HHCHuHHPHm1OHHGHH0H8HHEMML HOHH H(HHHHHHHHHHwW$։O(Au u҉HUL2HRHHI9MMHHDGDO HHxH|$ DL$t$D$D$LLIIHHLtHHHUHJHMILHI9IzH9ABA;IMLIMIBH9L)I HLHtULJZ79}I9|IA;|M9|IA;|IMHHLHuMBH;}I A;LMIA8AHH9NʉDž?Džp$|xP LHHHhHXHpHtHCHHxH\$ HhH\$t$HL$L$afWLMMQLPHpHt#HHCHuHHPH5.OHu LeDž?HH`HHXHDwDTO4DtD8ÉTtÉTHHHHTHHHHHHHH|HHHHHHHHHHEHHHL H`H(HXH0HH8HH@HHDl|G$***XYLxLHLD/O۹ODD1HcHs\hHELe]H]H]fWf)EEHULH裀-H}H9t6HHLet7HP(/HxH1HHP HLeHc;HLHHLI $HEHHQHpHPHxHQtHXHPtH`HH@HHHAHE1G EtwMcu$MADDAB HHLHcHLAAAIIAMGIIslHAE$AHcHH<уNˍDH4+OHLII9+3IIGHpH4H<HHAE$~6HcI<уAN΍DH4G)OHLHH9|FA])DHHcq$HA9u@AЅHcHEHDIcHHHGHHs qHHGMtHII9s ILfHHZHMAHH9HLLMMEHHHGHHtH@HcwHDGIHI0I8HHLt*HHCHuHHPHOHHGHH0H8HHEMML HOHH H(HHHHHHHHHHwW$։O(Au u҉HUL2HRHHI9MMHHDGDO HHxH|$ DL$t$D$D$LLIIHHLtHHHUHJHMILHI9IzH9ABA;IMLIMIBH9L)I HLHtULJZ79}I9|IA;|M9|IA;|IMHHLHuMBH;}I A;LMIA8AHH9NʉDž?Džp$|xP LHHHhHXHpHtHCHHxH\$ HhH\$t$HL$L$CafWLMMLPHpHt#HHCHuHHPHOHu LeDž?HH`HHXHDwDTO4DtD8ÉTtÉTHHHHTHHHHHHHH|HHHHHHHHHHEHHHL H`H(HXH0HH8HH@HHDl|G$***XYLxLHLO۹ODD1HcHcIhHELe]H]H]fWf)EEHULHm-H}H9t6HHLet7HP(/HxH1HHP HLeHc;HLHHLI $HEHHQHpHPHxHQtHXHPtH`HH@HHHAHE1G EtwMcu$MADDAB HHLHcHLAAAIIAMGIIslHAE$AHcHH<уNˍDH4OHLII9+3IIGHpH4H<HHAE$~6HcI<уAN΍DH4OHLHH9|FA])DHHcq$HA9u@AЅHcHEHDIcHHHGHHs qHHGMtHII9s IHUHuHuIIGIG0IOIWIG@H}Ht H}1NH]HEHtHlHNHgHH;EuLH`[A\A^A_]NIH]HEHt7HHNLEN IH}HuH]HEHuLN H}NH]HEHtf.UHHHH9H9Hx]ÐUHAWAVAUATSHIIHgHHE1LHHLHHHHHSH+SHH.袋.HHHH9H9@HxHy HHHL@HHIM}1LLHL L`PMuMuPM9I}0HPhHxLe!H1ff.LLINHLje!IIH9|HhH5+eQH =eQL :OH]HHLhHLH.-EtSH}NHwgHH;EtJNLd!II}0HPILLH NL@IH-gHH;EuLHĘ[A\A]A^A_]ÿ9NIHh-H5cQH cQH}LhTW)PHDž` NH`(c)PHe input.HHHsequenceHHHptr for HHHGot nullH@AHOHuHPLE1H5fHfLNSNIHh-H5bQH bQH}LhWW)PHDž`0NH`(Pc)PHdex tensHHHt for inHHHptr inpuHHHGot nullHf@ or@"AHbQHuHPL E1H5fHfLNgNIHh-H50aQH aQH}LhcW)PHDž`0 NH`(tTc)PHut tensoHH Hfor outpHHHnullptr HHHAt: Got HHHSequenceHf@(rAHIaQHuHPLE1H5fHfLN IALeMuFzIALhMIIPt H`NLeMt6HEL9t!HX@t HxNHI9uH}LLeNEuEu LhMu-iH}NEtH}NLhMAHpL9HHX@t HxsNHI9uIALeMIALhMIIALeMFIALhMnIIPt H`NLeMt6HEL9t!HX@t HxNHI9uH}LLeNEuEu LhMu-'H}sNEtH}dNLhMHpL9HX@t Hx1NHI9uIPt H` NLeMt6HEL9t!HX@t HxNHI9uH}LLeNEuEuLhMu&^H}NEtH}NLhMt:HpL9tEHX@t HxpNHI9uHhLpUNEt)L~NLN LIEt H}'NLN UHAWAVAUATSHHfHHEHG0@ẼtuIeHcH fH H;Mt*{oHH fH H;MuHĘ[A\A]A^A_]3NNIH`8-H5t\QH aQHxL`C"H5;PHHHU{wHxHH1LH5TfHufLmN IHt HXNE1LeMuMH-IALeMt6HEL9t!HX@t HxNHI9uH}LLeNEuxu L`Mu=uH}~NxtH}lNL`MuLIAL`Mt:HhL9t5HX@t Hx-NHI9uH`LhNEu LIL1NL}N DUHAWAVAUATSH8MLHHIHHfHHEH8H:fHHHLH@H8LNLHEEL-hfIEHHI@L8LNL=$fILHDž(HDž HDžHDžDž0LNHLHHH0HNHILLNHLHHH3HANLHHcNHHHLLHHL8Lt H !NLNH5fHHNH8NHfHH;EuLH8[A\A]A^A_]bNIHHL8Lt H NLvN+HHHHHIH5 fHHNIH8NLN DUHAVSHPIHfHHENHþHNHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]:H5VQH}HgH5MXQH8H5[lSHiHA H5W,OH萧HH;腜HHH fHHMHHUHuHuIIFIF0INIVIF@H]HEHtHH)NHfHH;Eu LHP[A^]NIH]HEHtH@HNLfN fffff.UHAVSH0IHHfHHEHfHHH{ʋHhHHCPW)EE HPHEEdtypfEeH}HULHqLuHEMtAFt I~,NL$NEt H}NMtHHfHH;Eu H0[A^]NHEt H}NH[N UH]fDUHAWAVAUATSHHIIH|fHHEH{H؍HH+H1H$HHHHIGPHHH SHcHmYJtRuKuDn=np6l/`v(r!oKsn qpHH+IHfHH;EuLHĈ[A\A]A^A_]NNIHh!-H5]SQH TQH}LhyW)PHDž`07NH`(AJa)PHsequenceHH&Hutput seHH Htr for oHHHot nullpHHHEmpty: GHHHSequenceH@.AHjSQHuHPL6E1H5fHfLNNIHh-H5[RQH SQH}Lh IPH5QHPLB HuHP1LH5>fH_fLWN IPt H`NE1LeMuMHIALeMt6HEL9t!HX@t HxNHI9uH}LLeNEuEu LhMuDzH}hNEtH}YNLhMuQIALhM;HpL9BHX@t HxNHI9uIIALeMuFzIALhMIIPt H`NLeMt6HEL9t!HX@t HxNHI9uH}LLeoNEuEuLhMu&^H}LNEtH}=NLhMt:HpL9tEHX@t HxNHI9uHhLpNEtL NLlN L TTfff.UHAWAVATSH`IHWfHHEؿnNHþHdNHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]薋H5!NQH}HàIIHW)EHENHEHHHML HXHMH5ԭOHULjH5OQH;H5^cSHlHA H5Z#OH蓞HH;舓HHH fHHMHHUHuHuIIGIG0IOIWIG@H}Ht H};NH]HEHtHvHNHfHH;EuLH`[A\A^A_]NIH]HEHt7H)HNLON IH}HuH]HEHuL(N H}NH]HEHt@UHAWAVAUATSHHEfHHEHIIW)EHEHHyI?tkH%LQH 3NQ1AL-H]HKHHCH5HHPHNHfHH;E 0H(IMl$0ID$I\$ W)PHDž`H)tnH8L0IILH=HuNILXLPKH`HL0H8~LHNILXHEH@HEHHHtH@HhHPH@L<!HHHt#HHCHuHHPHNHPHt HXNLhLmM4$Mt$PM9tvI|$0HPhI|$Ht-II)LM&ANIGIGIHHEHCHEIHEHCIEHfHH;EWNNIHh-H5CQH EQH}LhsW)PHDž` NH`(Fb)PHe input.HHHsequenceHHHptr for HHHGot nullH@AHEQHuHPLE1H5EfHffL^N`NIHh-H5BQH EQH}LhW)PHDž` NH`(вb)PH tensor.HHHinput teHHHptr for HHHGot nullH@AHaOHuHPLE1H5_fHfLxNz-NIHh-H5AQH !DQH}LhW)PHDž`0NH`(*a)PHsequenceHH'Houtput sHH Hptr for HHHGot nullHHHInsert: HHHSequenceH@/AHBQHuHPLE1H5]fH~fLvNxIPtH`NLeMuEIALeMt6HEL9t!HX@t HxNHI9uH}LLeNEuEu LhMuDfH}NEtH}NLhMu=IALhM'HpL9HX@t Hx6NHI9uIPtH` NLeMuEIALeMt6HEL9t!HX@t HxNHI9uH}LLeNEuEu LhMuDmH}NEtH}NLhMuDIALhM.HpL9HX@t Hx=NHI9uIPtH`NLeMuEIALeMt6HEL9t!HX@t HxNHI9uH}LLeNEuEuLhMu:wH}NEtH}NLhMuQIALhMt?HpL9t$HX@t HxRNHI9uHhLLp2NEu{ILSNnIEucILuMu%UIEtLH}AH/ILuMt2H]L9t3MI)HèH!HèIXuH}LuNL/N LfUHAWAVAUATSH8MLHHIHHCfHHEH8HfHHHHH@H8HNLHEEL-fM}LI@L8HNL5fILHDž(HDž HDžHDžDž0LNHLHfHHNHH8NHN UHAWAVAUATSH8MLHHIHHfHHEH8HfHHHLH@H8LNLHEEL-fIEHHI@L8LwNL=fILHDž(HDž HDžHDžDž0L;NHLHHH0HNHILLNHLHH3HNLHHNHHHqLL6HHL8Lt H NL_NH56fHHNH8NH3fHH;EuLH8[A\A]A^A_]NIHHL8Lt H NLN+H*H"HHH IH5fHH8NIH8SNLN DUHAWAVAUATSPIM'IGL)HH.袋.HH9H袋.H9MwLL)LkXL9NHLM9tCHEM)IƨHIHfHǨL!HèHIƨIXuM7MgLHEHI?I_IGM9t"LL)IĨfDL!IĨHXuMtLH[A\A]A^A_]NH[A\A]A^A_]ÿNHH5OHNHfHHH5 fHԣfHNIHkNLN ffffff.UHAWAVATSH`IHfHHEؿNHþHNHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]&sH55QH}HSI{IsHW)EHENHEHHHML HXHMH5dOHULH58QH˃H5JSHHA H5 OH#HH;{HHH OfHHMH؂HUHuHuIIGIG0IOIWIG@H}Ht H}NH]HEHtHHNHfHH;EuLH`[A\A^A_]NIH]HEHt7H蹹H]NL߾N IH}HuH]HEHuL踾N H}#NH]HEHt@UHAWAVAUATSHIIHfHHE1LIMLINI+NHH.袋.HHHLkL@Ht?HlH@HHH9H9HxфcHy HH@L(IHlHH+H1LbHHNIEIEIEHEID$HEIHEID$L(I$HfHH;EuLHĸ[A\A]A^A_]ÿ*NIHh跲-H50QH #4QH}Lh褸W)PHDž`0NH`('a)PHsequenceHH&Hutput seHH Htr for oHHHot nullpHHHErase: GHHHSequenceH@.AH1QHuHPL̺E1H5ZfH{fLsNU(NIHh赱-H5/QH !3QH}Lh袷W)PHDž` ˿NH`(ub)PHe input.HHHsequenceHHHptr for HHHGot nullH@AH1QHuHPLE1H5tfHfL荿NoIPtH`NLeMuEIALeMt6HEL9t!HX@t HxNHI9uH}LLe̾NEuEu LhMuDmH}覾NEtH}藾NLhMuDIALhM.HpL9HX@t HxMNHI9uIPtH`#NLeMuEIALeMt6HEL9t!HX@t HxNHI9uH}LLeӽNEuEuLhMu:wH}谽NEtH}衽NLhMuQIALhMt?HpL9t$HX@t HxbNHI9uHhLLpBNEunILfNaIEtXH}MILuMuDILuMt8H]L9t9MI)HèfDH HèIXuH}LuͼNLON LfUHAVSHPIHfHHE袼NHþH蘼NHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]_H5NH}H}HhH5:+QHH}H5.QHyH5@SHzHA H5OH|HH;pHHH 2fHHMHyHUHuHuIIFIF0INIVIF@H]HEHtHH蟺NH|fHH;Eu LHP[A^]NIH]HEHtH趯HZNLܴN @UHAWAVAUATSHIHHfHHEH~ocHLc(MH(I|$HGcHH+Hl1LYHYHHHH0H8E11LnLx0f.LDUEL9x0`pNHH](b)EPHH5s-QNCPHHHM,HHHDžHHtCt H{NHNEtH}NAE9DH8L5W)EHEH}L1L}@LfH@HLLL@HDž@MtAFt I~VNLNND9|L8M|$MtHH0LpHII\$L9t9MI)Hè@H HèIXuI>L8H0H&LM}LҷNIFIFIHEID$HEHHEID$H(HH qfH H;MuHĸ[A\A]A^A_]N{NIHh-H5D'QH *QH}Lh0W)PHDž`@NH`(yc)PHt sequenHH(Hor outpuHH Hullptr fHHHt: Got nHHHConstrucHHHSequenceHf@0ce@2AHK'QHuHPLE1H5fHƐfL辶NPsNIHh-H5<&QH )QH}Lh-W)PHDž` NH`(a)PHore inpuHHHe 1 or mHHHMust havHf@ts@AH)QHuHPL9E1H5ǓfHfLNrIPtH`oNLeMuEIALeMt6HEL9t!HX@t Hx9NHI9uH}LLeNEuEu LhMuDmH}NEtH}NLhMuDIALhM.HpL9HX@t Hx蠴NHI9uIPtH`vNLeMuEIALeMt6HEL9t!HX@t Hx@NHI9uH}LLe&NEuEuLhMu:wH}NEtH}NLhMuQIALhMt?HpL9t$HX@t Hx赳NHI9uHhLLp蕳NEuqIL蹳NdILuMuVIEtMH}BILuMt;H]L9t= 0HH/Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@7AHQHHPL讘E1H5<|fH]xfLUN NH6NH(NݿNIHpy-H5 QH QHLpcW)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AHQHHPL˗E1H5Y{fHzwfLrNHpUNIPt H`NL Mt?H(L9t$HX@t HxŜNHI9uH LL(襜Nuu&LpMuMHvNtHaNLpMuIALpMHxL9`HX@t HxNHI9u7TIHH :IHH>IPt H`轛NL MuQIAL Mt?H(L9t$HX@t Hx~NHI9uH LL(^Nuu&LpMuFH/NtHNLpMuQIALpMt?HxL9t$HX@t HxۚNHI9uHpLLx軚NEuILܚNH}HIHpHV]ILPM+II]IEH+Ct H{;NH3NH}HIH}HIHHuXIHHt4HۙNHHu,IHHuIHHH藙NvIHHHuIHHt#HHCHuHHPHMNHHH3NHHHu IH HHHt|HHCHujHHPHNLPMuYII]IEHt(Ct H{谘NH記NHHHuIHHHuLPMtHHXL9tpMI)Hèffff.HX HèIXuHPLX7NHpHt HxNH}Ht H} NL菒N LfUHAWAVAUATSHIIHIHfHHEHEHCPHEEEEEEW)EHEMt= 0HH/Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@7AHQHHPL讆E1H5 0AHQHHPL˅E1H5YifHzefLrNHpUNIPt H`NL Mt?H(L9t$HX@t HxŊNHI9uH LL(襊Nuu&LpMuMHvNtHaNLpMuIALpMHxL9`HX@t HxNHI9u7TIHH :IHH>IPt H`轉NL MuQIAL Mt?H(L9t$HX@t Hx~NHI9uH LL(^Nuu&LpMuFH/NtHNLpMuQIALpMt?HxL9t$HX@t HxۈNHI9uHpLLx軈NEuIL܈NH}HIHpHV]ILPM+II]IEH+Ct H{;NH3NH}HIH}HIHHuXIHHt4HۇNHHu,IHHuIHHH藇NvIHHHuIHHt#HHCHuHHPHMNHHH3NHHHu IH HHHt|HHCHujHHPHNLPMuYII]IEHt(Ct H{谆NH訆NHHHuIHHHuLPMtHHXL9tpMI)Hèffff.HX HèIXuHPLX7NHpHt HxNH}Ht H} NL菀N LfUHAWAVAUATSHIIHIHfHHEHEHCPHEEEEEEW)EHEMt= 0HH/Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@7AHPHHPLtE1H5 0AHPHHPLsE1H5YWfHzSfLryNHpUvNIPt H`xNL Mt?H(L9t$HX@t HxxNHI9uH LL(xNuu&LpMuMHvxNtHaxNLpMuIALpMHxL9`HX@t HxxNHI9u7TIHH :IHH>IPt H`wNL MuQIAL Mt?H(L9t$HX@t Hx~wNHI9uH LL(^wNuu&LpMuFH/wNtHwNLpMuQIALpMt?HxL9t$HX@t HxvNHI9uHpLLxvNEuILvNH}HIHpHV]ILPM+II]IEH+Ct H{;vNH3vNH}HIH}HIHHuXIHHt4HuNHHu,IHHuIHHHuNvIHHHuIHHt#HHCHuHHPHMtNHHH3uNHHHu IH HHHt|HHCHujHHPHsNLPMuYII]IEHt(Ct H{tNHtNHHHuIHHHuLPMtHHXL9tpMI)Hèffff.HX HèIXuHPLX7tNHpHt HxtNH}Ht H} tNLnN LfUHAWAVAUATSHIIHIHfHHEHEHCPHEEEEEEW)EHEMt= 0HH/Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@7AHPHHPLbE1H5 0AHPHHPLaE1H5YEfHzAfLrgNHpUdNIPt H`fNL Mt?H(L9t$HX@t HxfNHI9uH LL(fNuu&LpMuMHvfNtHafNLpMuIALpMHxL9`HX@t HxfNHI9u7TIHH :IHH>IPt H`eNL MuQIAL Mt?H(L9t$HX@t Hx~eNHI9uH LL(^eNuu&LpMuFH/eNtHeNLpMuQIALpMt?HxL9t$HX@t HxdNHI9uHpLLxdNEuILdNH}HIHpHV]ILPM+II]IEH+Ct H{;dNH3dNH}HIH}HIHHuXIHHt4HcNHHu,IHHuIHHHcNvIHHHuIHHt#HHCHuHHPHMbNHHH3cNHHHu IH HHHt|HHCHujHHPHaNLPMuYII]IEHt(Ct H{bNHbNHHHuIHHHuLPMtHHXL9tpMI)Hèffff.HX HèIXuHPLX7bNHpHt HxbNH}Ht H} bNL\N LfUHAWAVAUATSHIIHIHtfHHEHEHCPHEEEEEEW)EHEMtLMI)HèH8 HèIXuI<$L I^,LHpL;YNID$ID$I$HPIFHXHH`IFW)PHDž`IEHpHt HxXNL8H}H14LPMtHXL9t4MI)Hèffffff.HH HèIXuHPLLX"XNHpHpwXNXNIHpI-H5PH IPHLpOW)PHDž`@WNH`(P;c)PH be >= 0HH/Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@7AHPHHPLQE1H525fHS1fLKWN H1TNH#TNVNVNIHpoH-H5PH PHLpYNW)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AHPHHPLPE1H5O4fHp0fLhVNHpKSNIPt H`UNH Ht?H(H9t$Lh@t HxUNLL9uH HH(UNuu&LpMuFHlUNtHWUNLpMuQIALpMt?HxL9t$HX@t HxUNHI9uHpLLxTNEuILUNH}HIHH :IHH3IPt H`TNL MuQIAL Mt?H(L9t$HX@t HxJTNHI9uH LL(*TNuu&LpMuFHSNtHSNLpMuQIALpMt?HxL9t$HX@t HxSNHI9uHpLLxSNEu~ILSNH}HosIHpHBIILPMII]IEHCt H{SNHRNH}HIH}HIHHuXHIHHt4HRNHHu,IHHuIHHHcRNIHHt#HHCHuHHPH%QNHHH RNHHHuO{II]IEHtvCt H{QNHQNHHHu]IHHHtNHHCHuN DUHAVSIPCNHH?fHHH{LHPtgHH[A^]IH>CNL=N UHAVSIP$CNHHR?fHHH{L/H@tgHH[A^]IHBNL`=N UHAVSIXBNHHL\H[A^]IHBNL=N UHAVSIPBNHH>fHHH{LHsgHH[A^]IH>BNLfHHH{L/HsgHH[A^]IHANL`NHI9uH}LLm>NEuEuLhMu&^H}>NEtH}>NLhMt:HpL9tEHX@t Hxq>NHI9uHhLpV>NEtL>NL8N Lfffff.UHAWAVAUATSHIIHPfHHEI_ ILLauIG0@tlLTHMIL}Hc HMIFI;FsHHIFHIu}LLeHIu^L!HMtNIDIFI;Ft&H HHIFHIu!fff.LHuHIuHOfHH;EuHĘ[A\A]A^A_]=NLL8Lt H 4NH3NH5h%fHH 3NH8@4NHeGfHH;EuHH[A\A]A^A_]4NHLL8Lt H 14NH 3NH_>HW>HH5$fHH2NHH83NHj.N fUHAVSHPIHFfHHE3NHþH3NHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H5POH5iH}H5PH5iH11HH5PHH5RHHH HQH5wNHHH; HHH D3fHHMHm&HUHuHuIIFIF0INIVIF@H]HEHtH 'H1NHDfHH;Eu LHP[A^]&2NIH]HEHtH&Hl1NL+N fDUHAVSHPIH+DfHHEB1NHþH81NHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H5 NOH3iH}H5.PH3iH11HVH5PHGH5jRHxHA H5fuNHHH;HHH 0fHHMHD'HUHuHuIIFIF0INIVIF@H]HEHtH$H8/NHBfHH;Eu LHP[A^]/NIH]HEHtHO$H.NLu)N ffff.UHAWAVAUATSHIHHHAfHHE1LsIIMf1LL/HL HHI~0DH@PA9uuHL8LxPM.MnPM9HPhHzxpL~ H1LL9,NL] HIIH9|YAA tNHILL4HL HHrH CI9I H91bfDK up?ffffff.t88fnXf~fff. AHH9u_EDED0H0HuLHM'L II~0HPILLHM.NLIEHY?fHH;E<LH[A\A]A^A_]à /AHILL膝 IL{M~IrJ I9z K gH9m 1l`fff.ׁGr1@ƃ> (8wfnXf~ 2  fA4OHI9uffHLLLI?HsPH N1AWLM-UA"HILLHLC HRHrH CI9C I H96 1HPH)HHtH (8wfnXf~ 2  f4KHI9uH0Ud HEHEH H0H}H4 HH0P0 ffHHHL5H;IXHPH _N1A~L-AHHv HHCH` HHPH!NEILHHtHCL踓 MH H 1Hr'IH LPHtHCHq#fHH0L8H@H0HPHEHEHEHUL}H]HHt HCHPH9LH(HP(H<LH1fDo bfDobfDoU`fDo%bD(-D`fDo5`D(=bD(bfo=bfo`o$fofj`fAofffAofffoAXfAfA(fA8fof8ffofr fAffAfr fof8frfff8fA$WHH9oI9 HEHELHt#HHCHuHHPHGN踩HMLIH0E1HLO H0(ILHLb HtHrH CI9ILH91HPH)HHtHMIMIrK H9J I9u1IKH)LHtHfff.A44HHuHA ALLALLAL L ALLALLALLALLHI9u&E1A  A HHs IH@蚊HL߾MIrJ H9HLH91IVH)LHt#Hffffff.,<@<HHuH:D,,TT,TT,T THI9uLHHWHHNHg1H LHHyHHrH$H~H)1fo(` T[f8f~ [f8f~LL T0[[f8f~Lf8f~T HHuHt/oL[fo`f8f~[f8f~DI9IA AF HHq IH@蹈HLMIs1LHHyHHVHtjHzH)1fL[fk[fkfpfLpD L0[fk[fkfDpfLpHHuH 1HoL[fkfp[fkfDpI9^IAG AK HHp IH@覇HLMIs17LHHyHHVHtdHzH)1ffffff.L[[LD L0[[D L0HHuHu1HL[[LI9IA [ L8A HHo IH@蛆HLMIVDHs 1HuLpI)1fH,4H4H,tHtH,tHtH,t HtHI9uHt&H4H1f.H,OHPH0zAH?OHuHPL E1H5eHeLM IALeMIALhMIIALeMFIALhMnIIPt H`MLeMt6HEL9t!HX@t HxMHI9uH}LLeMEuEu LhMu-'H}kMEtH}\MLhMHpL9HX@t Hx)MHI9uIPt H`MLeMt6HEL9t!HX@t HxMHI9uH}LLeMEuEuLhMu&^H}MEtH}MLhMt:HpL9tEHX@t HxhMHI9uHhLpMMEtLvMLM LZfHI9u>,fHHI9u,fHHI9u,HI9ueI,THI9ufUHAWAVAUATSHIHIH|eHHEEeAHHL8H@u8H] IHwHLhM1I11>E1A L(H@~] IHwIH8tMuAAAM9MOMM)MI?MIMIrK H9J I91D)IKH)HtHI4H4HHuHfDI H ILHLILHLILHLIL HL IL(HL(IL0HL0IL8HL8HI9u>LHHyHHVHHzH)1f.ffLfZfZffLfD fL0fZfZfDfLHHuHtffLfZfZffLI9upAHHA (A H@[ IHuHH8٦MIVDHsL1HAf.ZHI9uHHA pI)1ffffff.,4@4,t@t,t@t,t@tHI9uHt)H4H1ffffff.,<@<HH9uIHHA A H@Z IHtHH8軲MIVDHs 1HuCjI)1@,4f4P,tftP,tftP,tftPHI9uHt)H4HP1fffff., IHH8HH@ M:IrJ 3H9vyJ H9sp1IVH)LHtHH< HMIs1LHHyHHrHH~H)1Wfo oS`foGaoSodSfuffuff8ff8fdo\S odS0fuffuff8f\f8fdH HuHu1HoSoLSffufoR`ffufoaf8fff8fLI9}IIOMIHh܎,H59MH "PH}LhfɔW)PHDž`@MH`(,`)PHispatcheHH(HypeCallDHH Hs in MLTHHHate typeHHHr duplicHHHCheck foHf@0rAHPHuHPLE1H5}zeHveL薜MIALeMuFzIALhMIIPt H`MLeMt6HEL9t!HX@t Hx̛MHI9uH}LLe貛MEuEu LhMu- H}茛MEtH}}MLhMHpL9HX@t HxJMHI9uIIu'H eHH;EuHĨ[A\A]A^A_]蜛MMIHh行,H5MH x PH}Lhg莒H5tNHPH8%AHNHuHPL'E1H5xeHteLΚM IALeMuFzIALhMIIPt H`,MLeMt6HEL9t!HX@t HxMHI9uH}LLeMEuEuLhMu&^H}ʙMEtH}軙MLhMt:HpL9tEHX@t Hx萙MHI9uHhLpuMEtL螙MLM LKHI9uIA KHI9uIA6HKHHI9uIA SHKHHI9uIA 5fMIVDHs 1HulI)1fffff.4SW*tSW*DtSW*DtSW*DHI9uHt$H4SH1f`ffufokaf8fff8fLI9{IIMIHh {,H5iMH PH}LhfW)PHDž`@"MH`(\_)PHispatcheHH(HypeCallDHH Hs in MLTHHHate typeHHHr duplicHHHCheck foHf@0rAH*PHuHPLE1H5feHbeLƈMIALeMuFzIALhMIIPt H`!MLeMt6HEL9t!HX@t HxMHI9uH}LLeMEuEu LhMu- H}輇MEtH}譇MLhMHpL9HX@t HxzMHI9uIIu'H:eHH;EuHĨ[A\A]A^A_]̇MDMIHhx,H5.MH PH}Lhg~H5NHPH8AH/NHuHPLWE1H5deHaeLM IALeMuFzIALhMIIPt H`\MLeMt6HEL9t!HX@t Hx7MHI9uH}LLeMEuEuLhMu&^H}MEtH}MLhMt:HpL9tEHX@t HxMHI9uHhLp襅MEtL΅MLM LKHI9uIA KHI9uIA6KHHI9uIA TKHHI9uIA 7fMIMIrK H9svJ I9sm1IKH)LHtHA44HHuHMA ALLALLAL L HI9uLHHWHHNHL1H1H ooLfo-`f8fpf8fLpI9 IAA HH?IH@PPHH8XM,IrJ H9suJ H9sl1IVH)LHtHff.<HH@DMIs1LHHyHHVH s1H`HzH)1fff.f85f85LLf85Df85LD L0f85D f85L(D@LPf85D0f85L8D`LpHHuHH4Hfff.f85D3f85 3Dp pHHuHzH)1LLD L0D L0D@LPD@LPoD`oLpD`LpH HuHt.H4H@oD3o 3D0 0H HuI9IAA WLIHHz,H5&MH OH}LhfDffPHDž`@LMH`fo_fPHispatcheHH(HypeCallDHH Hs in MLTHHHate typeHHHr duplicHHHCheck foHf@0rAHOHuHPLFE1H5f*eH&eLLMHzH)1LLD L0D L0D@LPD@LPoD`oLpD`LpHHuHt.H4H@oD3o 3D0 0H HuI92IA I?IuGHD^eHH;Eu2HĨ[A\A]A^A_]1H.1HKM.KMIHh<,H5MH OH}LhgBH5NHPH8AHNHuHPLAEE1H5(eH$eLJM IALeMIALhMIIALeMFIALhMnIIPt H`JMLeMt6HEL9t!HX@t HxIMHI9uH}LLeIMEuEu LhMu-'H}IMEtH}IMLhMHpL9HX@t Hx]IMHI9uIPt H`8IMLeMt6HEL9t!HX@t HxIMHI9uH}LLeHMEuEuLhMu&^H}HMEtH}HMLhMt:HpL9tEHX@t HxHMHI9uHhLpHMEtLHMLBM LfHHI9u0fHHI9u7ˉHI9u:ˉHI9ucfUHAWAVAUATSHIIHHZeHHEH8D?AL@HH#LIH-HLMIVDHH1HAA AL軸IHH-HH@MIrJ H91HLH9"1I~H)LHtHffff.ˈHHuHˈTTTTTTHI9u~E1A &LIHHL,HH@MIVDH1H=}I)1ff.H4HxwWH*HtHWH*DHtHWH*DHtHWH*D HI9uHHH WH*XHtHyHHH WH*XDHtHiHHH WH*XDHtHYHHH WH*XD HI9H|H4H1AfHIIEAM9LINMI)MI?IM fDUHAWAVAUATSHHu,eHHEIIF0xuEII^PILΊH G,eH H;MuH@HHHĈ[A\A]A^A_]MFMIHh ,H5MH OH}LhI0H54MHDMH @MHPM訝AHMHuHPLKE1H5dHdLM IALeMuFzIALhMIIPt H`PMLeMt6HEL9t!HX@t Hx+MHI9uH}LLeMEuEuLhMu&^H}MEtH}MLhMt:HpL9tEHX@t HxMHI9uHhLpMEtLMLM LؐUHAWAVAUATSHHII։H=*eHHEL讈IDžL[HLIMLE1HeHHHeHHHffffff.Bf.zD(T :_f. *`r@ (`Lf. 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IJL@HHH9HHNHLLHL~Hw1E1 E1HHHpHHJH sf1fHHqH)f1ffffff.f82ff82Dff82Lf82\f82dfff82T fff82D f82LffHHuHt&HTHff82Rf82ffHHuffpNffI~H9tffff. IHH9IffEHEHHx H+xHtqHHL9LILeLeIHEHHpHX H)MH~fLH/LILeLHxXt"HM4hE1E11HxXuLH)HHHt"Hr8LaLe.LHxXu̾H)H}CL}LeLM7ffEHELH)tFHHL9LILmLmIDHEM)M~LLLKLMLmHU1H[MLLPM7MwPI0HLhPHPHLHIG0XHxXHLHt=HMIWDHHA1HMME1E1B<)t!KvHHHsLHJ<8H@HL.LHL@L<t=LHJ<8H@HHLLHL@LHH9gHHtJ<tDLHJ<8H@HILLHL@LLIHHM9􋽬wH 9 MH+H}Ht H}ULH}Ht H}CLH dHH;EFHHX[A\A]A^A_]HHH9HODHHuE1E1HI)HHHE1LE1ffffff.B<1t'LHHHHJLHIB|1t(LHHHH4LHIIHM9uHt+B<1t$HLHLLMLLHHH}HzLH}#LH}LLIH(c+H5?`OH `OH@L(VMHQAH`OH@HLE1H5dHdLL HALxMuLHAL(MH Ht H LLxMt9HEL9t$Lh@t HxLLM9uHxLLeL`u@u#L(Mu/gHpL@tHPjLL(Mt:H0L9tmLh@t Hx?LLM9uH(L0$LEu$H}HH}HH臾L L)LH}HtLHH}HtHH}Ht^HH}HtPHH}HtBHH}HtJHH}Hu"HH}HcHH}HQH}ZLH}HHH}DLHƽL fffff.UHAVSI`$LHHLH[A^]IHLLyL UHAVSH0IHHdHHEH dHHH{hHeHW)EHEEEaxisEHKPH}HUL螂LuMCXHEtAFt I~ULLMLEt H}>LHdHH;Eu H0[A^]LHEt H}LH萼L UHAVSI`LHHLH[A^]IHLLIL UHAVSHPIHdHHELHþHLHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]dH5LH}HH5 \OH~H5EQHHH HQH5MHHH;vHHH EdHHMHn5HUHuHuIIFIF0INIVIF@H]HEHtHH貿LHdHH;Eu LHP[A^]'LIH]HEHtHɴHmLLL UHAVSHPIH+dHHEBLHþH8LHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]JbH5iLH}H藀H5YOHh|H5CQH}HH HQH5MH~HH;sHHH dHHMH7HUHuHuIIFIF0INIVIF@H]HEHtH讲HRLH/dHH;Eu LHP[A^]ǽLIH]HEHtHiH LL菷L UHAVSHPIHdHHELHþHؼLHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]_H5 LH}H7~H5MWOHzH5+AQH9{HA H5'MH`|HH;UqHHH dHHMH55HUHuHuIIFIF0INIVIF@H]HEHtHUHLHdHH;Eu LHP[A^]nLIH]HEHtHH贺LL6L fffff.UHAWAVAUATSHMIIIHYdHHEIMwI)LHiHHHH HH+HHHHA@HH5UOH )VOH@3HH}1H{W) HDž0 ԹLH0( _) Hte scalaHHHoncatenaHHHCannot cHf@rs@H5UOH}HUH yHML3*ER [EgLxMHEL9DHX@t HxLHI9u+H5TOH TOH@'HH}1HPW) HDž0 詸LH0(c_) Hore inpuHHHe 1 or mHHHMust havHf@ts@H5TOH}HUH HML*E ELxMHEL9f.HX@t HxɷLHI9uHxfH *NL*HydHH;ELH`LI8H IE0ILHcW)EHEHxHHLtVHH9 LILLHEHL4LuHLLLuHxLHHxH#(HMHLLH+HcHhL@IHH HKHC H)HHHH;s HpHDžHfHA1HL}L$L~uHfff.HH;B0$HHIL$L~tHH1L;$WLH5tQOH QOS1H LHHH HH$HH5SOHH !SOLL Q^LH5ROH HHL*EufuoM u{HxHHEH9|@Lp@t Hx蹴LLL9uHxWfff.H}蓴LtH~LM tH0fLHxHy HH]HL`t Hp3L@Mt HPLHHL}$<tu\MHHHH;LAHEHpL,HLHH;h-LLHp{HH;hW)EHEHHx H+xHHHH9L1LIMLmLmIDHEHHpHX H)HLH誴LMILeH~>HH@0ItL@H}H@ LmLe{H}萲L vH0wLEjH}dLLxMa LLuFL`t Hp1L@t HPLHdHH;ELHĈ[A\A]A^A_]H}L H0ѱLEH}辱LLxMcME1E1L~H~UHHHH@0Hc11ffff.HH[H HL9|#MH~eHH@01ILW) HDž0LL)tbIIHI9LHH(H JH0M)ML~HLLkLLH(H 1H`.?HHC(H !HCHLtSHC HD@HJ H{0H9HuJ)JTH)HsHuLeMHǺ@HTHS HTHS HTHS HTHHS H9uHtHLHfHHS HHuHLHcxHE1I$NI@0HK(H;A0fHHs0H9v6IP)HyH)Hs.AHuqfffff.AuHAL|L|L|L|L|L|L|L|HH9uHtHLHf.L9HHuHEJL`u@HpL@HPLH HH(LLIH}y+H5FOH GOH@LEHfH5GOH GOH HL AHnGOH@H LE1H5|dHdL蕪L"H}~LH}sL H eL I t H0LHxHuKIAHxHt9HEH9t$Lh@t HxǩLLL9uHxHH]誩L`u@u LmMu=qHp聩L@tHPlLLmMuEIALmMt6HEL9t!HX@t Hx6LHI9uH}LLmLEuIL=LH}HIH HIH}HIu#u=uWLxMutH荨Lt ItHjLt ItHGLLxMuHILxMt9HEL9HX@t Hx LHI9uHxLuL`u+@u7H HH}HHp豧L@tHP蜧LH HtLIH HtgIH HtSIH}H\IH Hg&IH}H/IH}HIH}HIEu u7EuNLxMuoxH}L t I tH0蝦LEt IEtH}胦LLxMu!ILxMHEL9/fDHX@t Hx9LHI9uIH HJ IEu u7EuNLxMuhH}L t I tH0迥LEt IEtH}襥LLxMuFILxMt7HEL9t\HX@t HxiLHI9uHxLuQL`t HpHsHEsHESHAEIuIEuIEUHHHH講HHL8Lt H LHѐLH5dHH`LH8耑LHdHH;EuHH([A\A]A^A_]4LHHHL8Lt H jLHDLH蘛H萛H舛HH5dHH趏LHH8ѐLH蛋L UHAWAVAUATSHxHH}HCHK(H+HILHy0HPHHEEyDHEHEHHE11HEHMfH9HRHDH6HxHtHHHH, Hg>H0HH8HuLH@HH}Ht H}zLHύdHH;EuH@H[A\A]A^A_]Z{LzLIHh_l+H5OH OH}LhLrHPPAH>OHuHPLtE1H5XdHTdLzLzIALeMuFzIALhMIIPt H`yLLeMt6HEL9t!HX@t HxyLHI9uH}LLeyLEuEuLhMu&^H}yLEtH}yLLhMt:HpL9t=HX@t HxYyLHI9uHhLp>yLEtjLgyL`LIH}HuFMIH}Hu8?IH}Hu*1IH}Hu#IH}HuIH}Ht H}xLLQsL fUHAVSHPIHdHHExLHþHxLHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]:H5ɉLH}H<H5wMH5H5PH6HH HQH5LH8HH;-HHH ExdHHMH>7HUHuHuIIFIF0INIVIF@H]HEHtHlHvLHdHH;Eu LHP[A^]'wLIH]HEHtHkHmvLLpL UHAVSHPIH+dHHEBvLHþH8vLHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]ڃH5iLH}H9H5MHh3H5PH4HA H5LH5HH;*HHH udHHMHQHUHuHuIIFIF0INIVIF@H]HEHtHiHYtLH6dHH;Eu LHP[A^]tLIH]HEHtHpiHtLLnL fffff.UHAVSHPIHˆdHHEsLHþHsLHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]zH5 LH}HW7H5MH1H5+PH92HH HQH5 LHY3HH;N(HHH sdHHMHOHUHuHuIIFIF0INIVIF@H]HEHtHNgHqLHτdHH;Eu LHP[A^]grLIH]HEHtH gHqLL/lL UHAVSHPIHkdHHEqLHþHxqLHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H5LH}H4H5WMH.H5PH/HA H5ǵLH1HH;%HHH ,qdHHMHu`HUHuHuIIFIF0INIVIF@H]HEHtHdHoLHvdHH;Eu LHP[A^]pLIH]HEHtHdHToLLiL fffff.UHAVSHPIH dHHE"oLHþHoLHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H5ILH}H2H5MHH,H5kPHy-HH HQH5`LH.HH;#HHH ndHHMHn^HUHuHuIIFIF0INIVIF@H]HEHtHbH2mLHdHH;Eu LHP[A^]mLIH]HEHtHIbHlLLogL UHAVSHPIHdHHElLHþHlLHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]Z}H5}LH}H70H5MH)H5 PH+HA H5LH@,HH;5!HHH lldHHMHoHUHuHuIIFIF0INIVIF@H]HEHtH5`HjLH}dHH;Eu LHP[A^]NkLIH]HEHtH_HjLLeL fffff.UHAVSHPIHK}dHHEbjLHþHXjLHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]}H5{LH}H-H57MH'H5PH(HH HQH5LH)HH;HHH jdHHMHmHUHuHuIIFIF0INIVIF@H]HEHtH]HrhLHO{dHH;Eu LHP[A^]hLIH]HEHtH]H-hLLbL UHAVSHPIHzdHHEhLHþHgLHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]{H5)yLH}Hw+H5ׇMH(%H5KPHY&HA H5GLH'HH;uHHH gdHHMH}HUHuHuIIFIF0INIVIF@H]HEHtHu[HfLHxdHH;Eu LHP[A^]fLIH]HEHtH0[HeLLV`L fffff.UHAVSHPIHxdHHEeLHþHeLHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]:rH5vLH}H)H5wMH"H5PH#HH HQH5LH%HH;HHH EedHHMH{HUHuHuIIFIF0INIVIF@H]HEHtHYHcLHvdHH;Eu LHP[A^]'dLIH]HEHtHXHmcLL]L UHAVSHPIH+vdHHEBcLHþH8cLHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]oH5itLH}H&H5MHh H5PH!HA H5LH"HH;HHH bdHHMHHUHuHuIIFIF0INIVIF@H]HEHtHVHYaLH6tdHH;Eu LHP[A^]aLIH]HEHtHpVHaLL[L fffff.UHAVSHPIHsdHHE`LHþH`LHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]zuH5 rLH}HW$H5MHH5+PH9HH HQH5 LHY HH;NHHH `dHHMHHUHuHuIIFIF0INIVIF@H]HEHtHNTH^LHqdHH;Eu LHP[A^]g_LIH]HEHtH TH^LL/YL UHAVSHPIHkqdHHE^LHþHx^LHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]sH5oLH}H!H5W~MHH5PHHA H5ǢLHHH;HHH ,^dHHMHeHUHuHuIIFIF0INIVIF@H]HEHtHQH\LHvodHH;Eu LHP[A^]]LIH]HEHtHQHT\LLVL fffff.UHAVSHPIH odHHE"\LHþH\LHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]mH5ImLH}HH5{MHHH5kPHyHH HQH5`LHHH;HHH [dHHMH^HUHuHuIIFIF0INIVIF@H]HEHtHOH2ZLHmdHH;Eu LHP[A^]ZLIH]HEHtHIOHYLLoTL UHAVSHPIHldHHEYLHþHYLHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]ZkH5jLH}H7H5yMHH5 PHHA H5LH@HH;5HHH lYdHHMHHUHuHuIIFIF0INIVIF@H]HEHtH5MHWLHjdHH;Eu LHP[A^]NXLIH]HEHtHLHWLLRL fffff.UHAVSHPIHKjdHHEbWLHþHXWLHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]iH5hLH}HH57wMHH5PHHH HQH5LHHH; HHH WdHHMHHUHuHuIIFIF0INIVIF@H]HEHtHJHrULHOhdHH;Eu LHP[A^]ULIH]HEHtHJH-ULLOL UHAVSHPIHgdHHEULHþHTLHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]gH5)fLH}HwH5tMH(H5KPHYHA H5GLHHH;u HHH TdHHMHհHUHuHuIIFIF0INIVIF@H]HEHtHuHHSLHedHH;Eu LHP[A^]SLIH]HEHtH0HHRLLVML fffff.UHAVSHPIHedHHERLHþHRLHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]:iH5cLH}HH5wrMHH5PHHH HQH5LHHH;HHH ERdHHMHήHUHuHuIIFIF0INIVIF@H]HEHtHFHPLHcdHH;Eu LHP[A^]'QLIH]HEHtHEHmPLLJL UHAVSHPIH+cdHHEBPLHþH8PLHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]fH5iaLH}HH5pMHh H5PHHA H5LHHH;HHH OdHHMHHUHuHuIIFIF0INIVIF@H]HEHtHCHYNLH6adHH;Eu LHP[A^]NLIH]HEHtHpCHNLLHL fffff.UHAVSHPIH`dHHEMLHþHMLHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]zeH5 _LH}HWH5mMH H5+PH9 HH HQH5 LHY HH;NHHH MdHHMHHUHuHuIIFIF0INIVIF@H]HEHtHNAHKLH^dHH;Eu LHP[A^]gLLIH]HEHtH AHKLL/FL UHAVSHPIHk^dHHEKLHþHxKLHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]cH5\LH}HH5WkMHH5PH HA H5ǏLH HH;HHH ,KdHHMHeHUHuHuIIFIF0INIVIF@H]HEHtH>HILHv\dHH;Eu LHP[A^]JLIH]HEHtH>HTILLCL fffff.UHAVSHPIH \dHHE"ILHþHILHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]WH5IZLH}H H5hMHHH5kPHyHH HQH5`LHHH;HHH HdHHMH^HUHuHuIIFIF0INIVIF@H]HEHtHLNHxHpJ HMMKNIIIrL9J H9HHNDI)DHtHfHHHHHuI8W1ɐHHHTHTHTHTHTHTHT HT HT(HT(HT0HT0HT8HT8HT@H@L9uHxH11I?M!IpHHyH s 1HuzHwH)1ffffff.LLD L0D L0D@LPD@LPD`LpD`LpHHuHt.H HD  D H HuM9HxHI)IJ|HxH)IL$It$ HH)HHH)v%HEHpHMH` IL$It$ H9tfLpLxH)LL)1H9tQH|I\H9t&HuI|HH9u,fDHHH9uHL 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M)t[LHHH9zLJIL0L(IH8M~LLL[JML0L@HxH!HEpoJILhHf6dIHIGHxHIOIGIG HHIG0IG(IG LHL)tPIIHI9 HJIMo(Mo KDIG0H~LLHqJIMo(HHHAIGHHHIIO@IG8IG`IGXIGPLLM)tPLHHH9z LFJIMoXMoPIDIG`M~LLLJMMoXHIGhL}L I'GMLuHELhILLP0L9}BL}H]HMuh Le1H=4dH$HMLEJLhL}HEL9t,Mt0ILP(HHu'1LhHEL9uILP HHt HJHHt HJH(Ht H0JHHt HJH@HHPLXLH)IFH I|DHHQH HHHtHHtHHH;48uHDLHMHXHH 1E1I}L;` ME1I)IHX~2IIL}11@H9`t IHHHL9|L}1H0HH(HHHH@H@HLuLL)t[HHHH9_ wJILLIHM)M~LLLJMLH8HLH`H H(H@H@HHPLpLx M)t[LHHH9{ LJIL0L(IH8M~LLLKJML0L@HxH(HEp_JILhH^2dIHIGHxHIOIGIG HHIG0IG(IG LHL)tPIIHI9HJIMo(Mo KDIG0H~LLHaJIMo(HHHAIGHHHIIO@IG8IG`IGXIGPLLM)tPLHHH9~L6JIMoXMoPIDIG`M~LLLJMMoXHIGhL}L I'GMLuHELhILLP0L9}BL}H]HMuLe1H=p/dHHMLEJLhL}HEL9t,Mt0ILP(HHu'1LhHEL9uILP HHt HJHHt HJH(Ht H0JHHt HJH@HHPLXLH)IFH I|DHHQH HHHtHHtHHH;48uHDLHMHXHHHtHXH"JH}Ht H}JHHHpHDžpHLÿJIHxz)H5׊MH MHLxdHPH$H5MH ML MH}LIHHM1LH5bHbLJOJIHx)H59MH MHLxHPH$H5MH NML KMH}LM%HHM1LRH5bHbLJ7JIHx>)H5MH MHLx(HPH$H5{MH ML MH}LMYHHM1LH5EbHfbL^JJIHx)H5MH FMHLxHPH$H5݉MH ML MH}LM$HHM1LH5bHȾbLJ xJH UbHHH5WbH@bHJοHJH %bHHH5'bHbH`J랿JH bHHH5bHbH0JkJH bHHH5bHbHJ8JH bHHH5bHzbHJJH \bHHH5^bHGbHJLJH )bHHH5+bHbHdJJH bHHH5bHbH1JlMI@LJXMI@LJDMI8LJ0MI8LJHPJ HPJHPJHPJHJHoJH^JHMJHJtH)JLxMueIALxMOHL9bHX@t HxJHI9u I IEt H}JE1LMuYHIALMt?H L9t$HX@t HxnJHI9uHLL NJuu#LxMuC{H"JtH JLxMuLIALxMt:HL9tQHX@t HxJHI9uHxLJELJH}H LIIHPHIHPHIHPHIHPHIH}HIH}HtxIH}H_cIH}HJNIH}H59IH}H $IH}H IH}HIH}HIH}HIH}HIH}HIH}HIH}Hx|IH}HcgIH}HNR@>I>HJHpHDžpH.@IHpHDžpH ID72IHHH8Ht/I(,J$IHH8Ht2I( J'ILJHH4ILJHHIHHIHPHIHPHIHPHIHPHILGHu@HHuHHuH0HuHHHHP(HHtWIH}HEH9u0HP HHt/HiJHH!Hu%HHuHHHzHP(HHtIH}HEH9u,HP HHt/HJHHt/!HHHuHHt H JHPHt HXJH}Ht H}JHpHDžpHtCt H{^JHVJLJ HP(HHp<DUHAVSIX$JHHLH[A^]IHJLyJ UHAWAVAUATSHIHHbHHEHbHHH{vH*dHW)EHEEEaxisEHKPHhHUL蒐HhHDžhHtCt H{HJH@JEt H}1JHu'H bHH;EuHĈ[A\A]A^A_]JJIHh)H5_sMH sMH}LhW)PHDž`0JH`(2_)PHte valueHHHttributeHHH'axis' aHHHInvalid HHHMissing/H@&AH qMHuHPLE1H5QbHrbLjJ IALeMuFzIALhMIIPt H`JLeMt6HEL9t!HX@t HxJHI9uH}LLeJEuEuLhMu&^H}fJEtH}WJLhMt:HpL9tEHX@t Hx,JHI9uHhLpJEt)L:JLJ LIEt H}JLeJ ffff.UHAVSIXJHHLH[A^]IHJLJ UHAWAVAUATSPIHIGHCIIOHKHHLsHC(HC HCI I+t^III9)JILc LcKHC(IwMo I)MH~LLJHMLc IG@HC@IG0IO8HK8HC0HCXHCPHCHIPI+HtOLuLcHIII9w\JILkPLkHKDHCXIwHMwPI)M~LLJMLkPIG`HC`H[A\A]A^A_]LtJLjJ II<$Ht H{P JHEH8Hu II>Ht H{ JLkJ UHAWAVAUATSHHMLHHHHHbHHEH8H"bHHHLH@H8LJHEEL-WbM}LI@L8L JHEL5bILHDž(HDž HDžHDžDž0H$HHHHLMHHH LL8Lt H rJHLJH5#bHHJH8JH bHH;EuHHH[A\A]A^A_]JHLL8Lt H JHJHH5bHHPJHH8kJH5J ffff.UHAWAVAUATSPMLEIIIHLgJHLHIuH'JLEJHLHHEH0HJL JHLHHE0HJH[A\A]A^A_]H=H5H-H%HHDUHAWAVATSHPIIHbHHEH=d!JAM?MIHEL}HEEHEH$HD$HD$H=#dHMLELM"DJHEL9LNL}H]L93L}fffff.H]I|$ HtDHLP0H;]H[|H=dDHJHbHH;Eu HP[A\A^A_]PJJH bHHH5bHbHJ HHfffff.UHSPHHpdHH{PHt H{XGJH{ HtH{(H[]/JH[]fff.UHSPHH dHH{PHt H{XJH{ Ht H{(JHH[]Jff.UHAVSIpJHIvH dHH{H[A^]IHJL J DUHHGH dHH~H]UHSPHH{PHt H{XAJH{ HtH{(H[])JH[]fDUHSPHH{PHt H{XJH{ Ht H{(JHH[]JDUHHLGHWLO8HcO@HcHLWHIc4Hy LOPIcI4HO J4HHH RI4HGhH@HHGH]Jff.UHH U^H1H9NHD]DUHH d]UHH dHHG8Ht HG@H]J]f.UHSPHH dHH{8Ht H{@JHH[]Jffff.UHAWAVAUATSHIXJHH@ dHIG0HC0IG(HC(IG HC IIGHCIGIOHKHCHCHHC@HC8IG8Mg@I)tZHELuH8MILH=uXL1JIHMLi@Li8KDIIFHMHu~LLJMMn@IGPIFPLH[A\A]A^A_]HJ IH;Ht HEHx@JH}JL,J @UHAWAVAUATSPHH dHIIF0HC0IF(HC(IF HC IFHCIFINHKHCHCHHC@HC8I~@I+~8tNL{8IILH=uQJILc@Lc8KHCHIv8M~@I)M~LLJMLc@IFPHCPH[A\A]A^A_]LJ II?Ht H{@JL+J UHHHx8Ht Hx@]J]@UHSPHH{8Ht H{@aJHH[]SJUHHLGLOHw HcO(HcHHc4Hy HcO0HW8H4LH vI4HGPH@HHGH].Jf.UHH E^H1H9NHD]DUHH d]UHAWAVAUATSPIHIGHCIIOHKHHLsHC(HC HCI I+t^III9YJILc LcKHC(IwMo I)MH~LLJHMLc IG@HC@IG0IO8HK8HC0HCXHCPHCHIPI+HtOLuLcHIII9w\JILkPLkHKDHCXIwHMwPI)M~LLDJMLkPIG`HC`H[A\A]A^A_]L褽JL蚽J II<$Ht H{P;JHEH8Hu II>Ht H{ JL蛺J UHAWAVAUATSHHMLHHHHHbHHEH8HRbHHHLH@H8L7JHEEL-bM}LI@L8L=JHEL5FbILHDž(HDž HDžHDžDž0H$HHHHLMHHHPLL8Lt H 袾JH|JH5SbHH JH8+JHPbHH;EuHHH[A\A]A^A_]߾JHLL8Lt H JHJHH5ȮbHH耼JHH8蛽JHeJ ffff.UHAWAVAUATSPMLEIIIHLJHLHIIuHWJLuJHLH'HEH0H2JLPJHLHHEH0H JH[A\A]A^A_]HlHdH\HTHLHD@UHSPHHdHH{PHt H{X׼JH{ HtH{(H[]鿼JH[]fff.UHSPHHdHH{PHt H{X臼JH{ Ht H{(uJHH[]gJff.UHAVSIpTJHIvHVdHH{rH[A^]IHJL蝶J DUHHGH dHH~H]1UHSPHH{PHt H{XѻJH{ HtH{(H[]鹻JH[]fDUHSPHH{PHt H{X葻JH{ Ht H{(JHH[]qJDUHHLGHWHw8HO@HLOHH4Hy LWPIcI4HO J4HHH RI4HGhH@HHGH]BJfffff.UHH Ź^H1H9NHD]DUHHMd]UHH]dHHG8Ht HG@H]騺J]f.UHSPHH(dHH{8Ht H{@wJHH[]iJffff.UHAWAVAUATSHIXJJHHdHIG0HC0IG(HC(IG HC IIGHCIGIOHKHCHCHHC@HC8IG8Mg@I)tZHELuH8MILH=uXLJIHMLi@Li8KDIIFHMHu~LLHJMMn@IGPIFPLH[A\A]A^A_]H襶J IH;Ht HEHx@CJH}:JL輳J @UHAWAVAUATSPHHdHIIF0HC0IF(HC(IF HC IFHCIFINHKHCHCHHC@HC8I~@I+~8tNL{8IILH=uQ诸JILc@Lc8KHCHIv8M~@I)M~LL7JMLc@IFPHCPH[A\A]A^A_]L藵J II?Ht H{@9JL軲J UHHHx8Ht Hx@]J]@UHSPHH{8Ht H{@JHH[]JUHHLGHWHw HO(HH4Hy LcO0HO8J4HH vI4HGPH@HHGH]Jffff.UHH ^H1H9NHD]DUHHMd]UHAWAVAUATSPHIHCIGHHKIOIHMwIG(IG IGH{ H+{t^III9JIMg MgKIG(HsLk I)MH~LLgJHMMg HCHIGHHC@IG@HC0HK8IO8IG0IG`IGXIGPH{XH+{PtOLuMgPIII9wTEJIMoXMoPKDIG`HsPH[XH)H~LH̷JIMoXH[A\A]A^A_]L4JL*J HI<$Ht IX˵JHEH8Hu HI>Ht I 詵JH+J UHSPHHcHH{XHt H{`wJH{ HtH{(H[]_JH[]fff.UHSPHHXcHH{XHt H{`'JH{ Ht H{(JHH[]Jff.UHAVSIpJHIvHcHH{H[A^]IH軴JL=J DUHHGH cHH~H]AUHSPHH{XHt H{`qJH{ HtH{(H[]YJH[]fDUHSPHH{XHt H{`1JH{ Ht H{(JHH[]JDHLHP8HIHxHpHHLH@LcPHMcMLPPOc MyUHSLXXIcM []H@ N LLHHiJff.UHH ^H1H9NHD]DUHHc]UHHcHHG@Ht HGHH]HJ]f.UHSPHHcHH{@Ht H{HJHH[] Jffff.UHAWAVAUATSPHXJIHscI$HC8ID$8HC0ID$0HC(ID$(HC ID$ MHCID$HCHKIL$ID$ID$PID$HID$@HC@L{HI)tOHEI@LHHH=uILUJIMnHMn@IDIFPMHu~LLJMMnHLH[A\A]A^A_]LHJ II<$Ht I~HJLJLcJ ff.UHAWAVAUATSPIHPcIHHC8IF8HC0IF0HC(IF(HC IF HCIFHCHKINIFIFPIFHIF@H{HH+{@tNM~@IILH=uIGJIMfHMf@KIFPHs@H[HH)H~LHϲJIMfHH[A\A]A^A_]L7J HI?Ht I~HٰJH[J UHHHx@Ht HxH]鲰J]@UHSPHH{@Ht H{H葰JHH[]郰JUHHHHP HHHxHpL@LH(LcP0HcLIc Hy LcH8H@@J LHH]Jfff.UHH ^H1H9NHD]DUHHc]UHAWAVAUATSPHIHCIGHHKIOIHMwIG(IG IGH{ H+{t^III9良JIMg MgKIG(HsLk I)MH~LLJHMMg HCHIGHHC@IG@HC0HK8IO8IG0IG`IGXIGPH{XH+{PtOLuMgPIII9wTJIMoXMoPKDIG`HsPH[XH)H~LHlJIMoXH[A\A]A^A_]LԫJLʫJ HI<$Ht IXkJHEH8Hu HI>Ht I IJH˨J UHSPHHHcHH{XHt H{`JH{ HtH{(H[]JH[]fff.UHSPHHcHH{XHt H{`ǭJH{ Ht H{(赭JHH[]駭Jff.UHAVSIp蔭JHIvHcHH{H[A^]IH[JLݧJ DUHHGH YcHH~H]AUHSPHH{XHt H{`JH{ HtH{(H[]JH[]fDUHSPHH{XHt H{`ѬJH{ Ht H{(迬JHH[]鱬JDHLHP8HIHxHpHHLH@LXHMLPPO MyUHSLXXIcM []H@ N LLHH Jfffff.UHH ^H1H9NHD]DUHHc]UHHcHHG@Ht HGHH]J]f.UHSPHHhcHH{@Ht H{H跫JHH[]驫Jffff.UHAWAVAUATSPHX荫JIHcI$HC8ID$8HC0ID$0HC(ID$(HC ID$ MHCID$HCHKIL$ID$ID$PID$HID$@HC@L{HI)tOHEI@LHHH=uILJIMnHMn@IDIFPMHu~LL胬JMMnHLH[A\A]A^A_]LJ II<$Ht I~H艪JL聪JLJ ff.UHAWAVAUATSPIHcIHHC8IF8HC0IF0HC(IF(HC IF HCIFHCHKINIFIFPIFHIF@H{HH+{@tNM~@IILH=uIJIMfHMf@KIFPHs@H[HH)H~LHoJIMfHH[A\A]A^A_]LצJ HI?Ht I~HyJHJ UHHHx@Ht HxH]RJ]@UHSPHH{@Ht H{H1JHH[]#JUHHHHP HHHxHpL@LH(HH0I Hy LcH8H@@J LHH]鐪JfUHH E^H1H9NHD]DUHHc]ÐUHAWAVAUATSH(HebHHEIIHHAHq H):LLHHHyHHHcL$LxjHI6LL !HI6LGII6L HpHHIHhW)PHDž`M;LH=HN<L詧JHPJH`HL蒧JHX1L@IHtLP HPHHHxH9|WH*H0(JH(i_)Hger thanHH Hrank larHHHust has HHHtensor mHHHindices Hf@( 0@*HH(HWLHDžHHID$(HIL$0HIL$ HzH@I|$8HDžHIHLH1vLHhHHpHH@HHxHLHHH H}HPH(L0HbHHEH]H]H]W)EEHULH(H}H9Ht_HtHP(HHtZH5JMH}HHHMH(Et H}JHPHu%/HP HHuHHPHt HXĤJHbHH;EuHH([A\A]A^A_]0JHP֡J IEH}vJHPHIHPHIHIHPHulvIH}H9uHP HPHuLVHt9HP(HPHu3=IHPHu",IHPHuIHPHt HX覣JL(J UHAWAVAUATSHHIHHXbHHEH8HbHHHLH@H8LJHEEL50bM~LI@L8LJL%bILHDž(HDž HDžHDžDž0L誥JHLHXH3HcJHHHLL8Lt H UJH/JH5bHH辠JH8ޡJHbHH;EuHH[A\A]A^A_]蒢JHLL8Lt H ϡJH詠JHHHH5kbHH#JHH8>JHJ UHAWAVAUATSH(HEbHHEIIHHAHq H):LLHHHyHHHcL$LxJHI6LLHI6L'II6LHpHHIHhW)PHDž`M;LH=HN<L艠JHPJH`HLrJHX1L@IHtL0HPHHHxH9|WH*H0JH(b_)Hger thanHH Hrank larHHHust has HHHtensor mHHHindices Hf@( 0@*HHc(HWLHDžHHID$(HIL$0HIL$ H!H@I|$8HDžHIHLHoLHhHHpHH@HHxHLHHH H}HPH(L0HbHHEH]H]H]W)EEHULH(H}H9Ht_HtHP(HHtZH5CMH}HHjHMH(Et H}JHPHu%/HP HHuHHPHt HX褝JHbHH;EuHH([A\A]A^A_]JHP趚J IEH}VJHPHIHPHIHIHPHulvIH}H9uHP HPHuLVHt9HP(HPHu3=IHPHu",IHPHuIHPHt HX膜JLJ UHAWAVATSH`IHGbHHEؿ^JHþHTJHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]f?H5JH}H]IۨIӰHW)EHEJHEHHHML HXHMH5iMHULZ]H5Z@MH+YH5JH\ZHAH5JJH[HH;xPHHH bHHMHHUHuHuIIGIG0IOIWIG@H}Ht H}+JH]HEHtHfH JHbHH;EuLH`[A\A^A_]{JIH]HEHt7HH轙JL?J IH}HuH]HEHuLJ H}胙JH]HEHt@UHAVSHPIH;bHHERJHþHHJHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]Z<H5yJH}HZHϭH5fMHH\H5=MH^VH5OHWHH HQH5vJHXHH;MHHH ۘbHHMHHUHuHuIIFIF0INIVIF@H]HEHtH褌HHJH%bHH;Eu LHP[A^]轗JIH]HEHtH_HJL腑J ffff.UHAVSHPIHbHHEҖJHþHȖJHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]9H5JH}H'XHOH59dMHH-ZH5 ;MHSH5OHUHH HQH5JH/VHH;$KHHH [bHHMHHUHuHuIIFIF0INIVIF@H]HEHtH$HȔJHbHH;Eu LHP[A^]=JIH]HEHtH߉H胔JLJ ffff.UHAVSHPIH;bHHERJHþHHJHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]Z7H5yJH}HUHϨH5aMHHWH58MH^QH5OHRHA H5}JHSHH;HHHH bHHMHHUHuHuIIFIF0INIVIF@H]HEHtH談HOJH,bHH;Eu LHP[A^]ĒJIH]HEHtHfH JL茌J @UHAWAVAUATSH(HHIHbHHE1H藬HHH耬HHHzLzHr IM)LHHHHM4HLpPHC H+CHI9~nPSJHH0f(^f) H57MFHےJCFH L( H0HFfWf)HDžHL)HZHHLIILH=j蓐JHLHHJHLM9HHIM)LHHHLr+ID$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$H(H0fWf)HDžH 1HcIMH Ht H(衍JfWf)`f)Pf)@f)0f) HLhHH{0HCPuH(IIFPH8H IIFPH0HPHHHPHHQ0RtcL,$HL HI߉JHHDžHtZCt H{蹌JH豌JH(uGxL,$HL HI苉JHHDžHuH(6H HxH`H+XPY=]f\a=]f|HNcHEHHxHEH]H]W)MfEHULHp(1fWf)EHE0؋JHEf(^f)EHupportedHHH not supHHHata typeHHHtensor dHHHindices H@&HMMLL6(EH}?JHXHH HxH`H+X@#<]f\+<]f|HcHEHHxHEH]H]W)MfEHULH:(H}H9MLt HtHP(HP IEHXHt H`pJHHt I<$[JH8bHH;Eu)LH([A\A]A^A_]J ObH:bL`J IALeMuFzIAL`MIIHt HX$`JLeMt6HEL9t!HX@t Hx_JHI9uH}LLe_JEuxu L`Mu)aH}_JxtH}_JL`Mt:HhL9tEHX@t Hx_JHI9uH`Lhg_JEtL_JLYJ Lffffff.UHAWAVAUATSH8MIHIHHqbHHEH8HEtHtDLIL½LHIL諽HH1Et?JH8^@JHSbHH;EuHH[A\A]A^A_]AJHLL8Lt H O@JH)?JH}JHuJHH50bHH>JHH8?JH:J UHAVSI`?JHHLNH[A^]IH?JLI:J UHAVSHPIHRbHHE?JHþH?JHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]:MH5d\KH}HHNH5LHHH5}LHH5NHHH HQH5ƃJHHH;HHH +?bHHMHt HUHuHuIIFIF0INIVIF@H]HEHtH2H=JHuPbHH;Eu LHP[A^] >JIH]HEHtH2HS=JL7J ffff.UHAVSHPIH PbHHE"=JHþH=JHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]UH5YKH}HHKH5LHH}H5LH.H5QNH_HH HQH5FJHHH;tHHH H5aNHoHA H5]|JHHH;HHH 7bHHMH+HUHuHuIIFIF0INIVIF@H]HEHtH+H/6JH IbHH;Eu LHP[A^]6JIH]HEHtHF+H5JLl0J @UHAWAVAUATSPII1LPHHtTII1LLFIHILLHHI}ܦHDHs%1"H LL}s( HHHyHHrHH~H)1W( \fo \fff.AAdTTf8f~f8f~dA\ Ad0TTf8f~\f8f~d HHuHu ]1HtVAALf(`\Tfoa\f8f~Tf8f~LH9tA. HH9uILH[A\A]A^A_]fDUHAWAVATSII1LNHH!HqIHH21LHeILzHI|$ H HrI GH9HHL91HPH)HHt-Hffff.AHHH u/bHHMHHUHuHuIIFIF0INIVIF@H]HEHtH>#H-JH@bHH;Eu LHP[A^]W.JIH]HEHtH"H-JL(J UHAVSHPIH[@bHHEr-JHþHh-JHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH] ;H5>JH}HH5|LHH5NHHA H5qJHHH;HHH -bHHMHu/HUHuHuIIFIF0INIVIF@H]HEHtH H+JHf>bHH;Eu LHP[A^]+JIH]HEHtH HD+JL%J fffff.UHAVSI`$+JHHLH[A^]IH*JLy%J UHAVSH@HIH=bHHEH 'bHII~HbHIW)EHE)EHEEEaxesEH}HUHMHH]HEHtCt H{J*JHB*JEt H}3*JHtH55^H5^H}[H}HEHH9tHH9t HH9uH1H9IVPIFXHt H})JHLIII|7IIOH?HH?H!H4L-'JM9}HK|IHI)L 'JLmLeLH?H>LIII|7IIOH?HH?H!H4L&JM9}HK|IHI)L&JHhIL@MHXLMHhL gHrLKHCH(EAL4IM)LHHHE1H`LI0@LHHH`HIuAAAAI9LOIM)IBHJLH?II>MIKBIH=IIK<BLIHH9IDHDHXTXHHuHQIrZfffff.XD XT0\@XD0XXTPXD`XLpXHQH H9|((XH9~ X(XXMHhIPDHsP1Hfffff.HHH(M LH)1fDXXDXDXD XDXDXDXDHH9uHt HHfff.XHHuH9}ND)D)LL)L)HHL@fXHHuH@L@ffffff.XXDXDXD XDXDXDXDHI9uMtsHHHXHHuQL@HrD@XXDXDXD XDXDXDXDHH9uLHIH}XILL; L8L LPHXLLL)HHH LHV`(H}HtHH8 "JH4bHH;EHH[A\A]A^A_]H}L8LP?H}EAI]1L8LPMtHHIWXHWH*HMII?I>IIH|l(IIOHHD@AHZIpH)1D((\^)^)\HHuMt (^)HL)HryII(IpHHDJAH-J\IqH)1@T^T^HHuMtL^L9t+ML)Jfff.^HHuMHhHHH?II=IIH>HHL{LCHCH(IAMOIIABLH)HXAAAIM)LHHHH0HL)HLHHL1L`#1H0fHHHHH@H`HH}HHH|x(HHOHHVH! HZH)1fff.((\^)^)\HHuHHht (^)II)IMI(IPHHDNAHH\IQH)1fT^T^HHuMtH^M9Hht$LH)H D ^ HHuW)pI~PHMLULH?II=MIH>LHMBI(I(AIIIOHHHZHn1(HI~XHuHBH]HpHpHxHH?H>HHH{1D(\D( \(\(%\(-\((AYWR(YY(\YYAV(^(f8)HH9|H9L ~|HyHt!Q \^ HHH9tJ}\ffff. 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\(YIARIBHsHucI)Ҿd\YX\YX\\YXL \YXHI9uHt$HH@\YXHHuHEHL`HMHPHrxII?LH>HH(HI=II(YHIIOHHH{H<1۾H1H|'PHxH&HTfnI~X IHAuDكH9HOII)LH?H>LHHIHrII9IDI91HyH)HHt"HfDA \ALHHuHIffff.A \ALAL\ALAL\ALAL \AL HH9uH)пfnXXTXTXT f~HH9uHHHfnXf~HHuHH(H~HHD@AH(IxH)1AA\\\ATA\AT A\0\\AT A\0HHuHwH)1ffff.XT X\0XT@X\PXT`X\pXXH HuHsHt$Hfff.XX\0HHvHu((XL9~BXHh(XXH9~l)HpH)HtHXHHuHr>H)HLDXQXQXQXQXQXQXQXH HuI~XXWI*^WQ \IHAuDكH9HO^II)LH?H>LHHIHrII9IDI91HyH)HHt(HA\YATHHuH2fA\YATAT\YATAT\YATAT \YAT HH9uHH((H~HHD@AHtaIXH)1DA$Al\\YYAdAlAd Al0\\YYAd Al0HHu1Mt#A$Al\\YYAdAlH9LH4 I((HAH9HMHH)ȉHHt$HH@A$\YA)dHHuH Lr]ffff.A$\YA)dAd\YA)dAd \YA)d Ad0\YA)d0HH9|H9NI)IsLIDLIB >LIHIuHLtK MMBBLLHHI9fffff.IHHIM9H>HHH@Ht HHIHaHH;EuHH[A\A]A^A_]pIIIH@u(H5qLH LH`L@9_W)EE(HEHis requiHEHX input HEEred!EAHIH`HMLE1H5faHaLI4IIH}(H5LH LH`LEmW) HDž0 IH0(1\) Hrst outpHHHo get fiHHHfailed tH@ut!AHIH`H LE1H5aHaLI H`I HALeMuiHALeMHHAL@MHH t H0ILeMt6HEL9t!Lh@t HxILM9uH}LLeIEu`uLeMu)XH}oI`tHpZILeMt1HEL9tNLh@t Hx5ILM9uH}Le IE{LEIH@HovLHEt H}ILeMt6HEL9t!Lh@t HxILM9uH}LLeIEu`u L@Mu,dH}~I`tHpiIL@Mt:HHL9tILh@t Hx>ILM9uH@LH#IELHIHI L(HH`HuJTHH`Hu9CHH@Hu:DHH`HuHH`Ht HhIH@Ht HHIHI fffff.UHAWAVAUATSH(IIIILL)HHMaIyHL)HH9LEM9LL)HHHH=HL)HHHH9LM3HH9HBH6H9IHH5"JHIH!aHHH5CaH aHIMM)LHH9ILHUL9M]MH]I)HHItL)HLFLHIH1ITI9sItLHH9LH?L!HUHrIIAQH 1HIHu&RLLLm+HH}H<IHLEL}M)LHHIM9H}MUMM)IMKIr KDH9JHDL9HM)DHt Hf.IHHIHuI8c1I H ILHLILHLILHLIL HL IL(HL(IL0HL0IL8HL8IL@H@L9u1HE1I?M!IGHHqH s1HH}HDpH~H)1fff.AALDȐLȠAD AL0DȰLAD@ALPDLAD`ALpD HHuHH}t2IDHHLH1DD D H HuM9ulN|IM~H}HuL1IH}HEHHuI)M~LLIH}MHEH8LxHXH}Ht(IMJ4O4I|pHrL)1fDϐLϠAALDϰLAD AL0DLAD@ALPD AD`ALpHHuHIHut1H HfD AD A H HuHUI9LEIHHHHHHCLc I)It Iu H8uALW)@HDžPHiAIH@HfHIHCHK H)HLt HuFH8u@A?HDž`HHH;PiHHHHW)`HDžpMtALH=nN4LXIH`JHpHLAIHhLHy@IM)HHIH LHE1fA?t!HHH`HhH@IIH`HLHHH|HHIHH9t(HOHLH|&HLH9tfff.1HLH}IM9WQH@H`Mb1L@A<tHHH`HhL|HL9rH`Ht HhILLH@HHH)H1IILeLuHEH`HDžhH`1L2`HHѮMMINHHHQINHI@I@ LHHуHH)HMIHSLHHMIJ HE1E1H'E1HLof.MMրHt9J K4H4H9J K4H4H91HAu1f1HAt$J HILHH4H4HHH9MLMHNHLH@JJB >LIHIuHLtK MMBBLLHHI9fffff.IHHIM9H>HHH@Ht HHIHaHH;EuHH[A\A]A^A_]IIIH@(H5uLH uLH`L@9W)EE(HEHis requiHEHX input HEEred!EAHIIH`HMLxE1H5aH'aLIIIH}d(H5`tLH uLH`LEmQW) HDž0 zIH0(D\) Hrst outpHHHo get fiHHHfailed tH@ut!AH{IH`H LE1H5+aHLaLDI H`*I HALeMuiHALeMHHAL@MHH t H0qILeMt6HEL9t!Lh@t HxLILM9uH}LLe2IEu`uLeMu)XH}I`tHpILeMt1HEL9tNLh@t HxILM9uH}LeIE{LIH@HovLHEt H}ILeMt6HEL9t!Lh@t Hx^ILM9uH}LLeDIEu`u L@Mu,dH}I`tHp IL@Mt:HHL9tILh@t HxILM9uH@LHIELIH4I L(HH`HuJTHH`Hu9CHH@Hu:DHH`HuHH`Ht HhB >LIHIuHLtK MMBBLLHHI9fffff.IHHIM9H>HHH@Ht HH贿IHaHH;EuHH[A\A]A^A_] I蘿IIH@%(H5!jLH ikLH`L@9W)EE(HEHis requiHEHX input HEEred!EAHYIH`HML船E1H5aH7aL/IIIH}t(H5piLH jLH`LEmaW) HDž0 芾IH0(T\) Hrst outpHHHo get fiHHHfailed tH@ut!AHIH`H L譸E1H5;aH\aLTI H`:I HALeMuiHALeMHHAL@MHH t H0聽ILeMt6HEL9t!Lh@t Hx\ILM9uH}LLeBIEu`uLeMu)XH}I`tHp ILeMt1HEL9tNLh@t HxILM9uH}LeмIE{LIH@HovLHEt H}蓼ILeMt6HEL9t!Lh@t HxnILM9uH}LLeTIEu`u L@Mu,dH}.I`tHpIL@Mt:HHL9tILh@t HxILM9uH@LHӻIELIHDI L(HH`HuJTHH`Hu9CHH@Hu:DHH`HuHH`Ht HhLIH@Ht HH4IH趵I fffff.UHAVSIPIHHBaHHH{LaH0aHHH[A^]IHʺILLI @UHAWAVAUATSHIIHaHHE1L^HHHHHHCLc I)It Iu H8uALW)@HDžPH+IH@HHB >LIHIuHLtK MMBBLLHHI9fffff.IHHIM9H>HHH@Ht HHĴIHaHH;EuHH[A\A]A^A_]0I訴IIH@5(H51_LH `LH`L@9W)EE(HEHis requiHEHX input HEEred!EAHiIH`HML蘮E1H5&aHGaL?IIIH}脥(H5^LH ?`LH`LEmqW) HDž0 蚳IH0(d\) Hrst outpHHHo get fiHHHfailed tH@ut!AHIH`H L轭E1H5KaHlaLdI H`JI HALeMuiHALeMHHAL@MHH t H0葲ILeMt6HEL9t!Lh@t HxlILM9uH}LLeRIEu`uLeMu)XH}/I`tHpILeMt1HEL9tNLh@t HxILM9uH}LeIE{LIH@HovLHEt H}裱ILeMt6HEL9t!Lh@t Hx~ILM9uH}LLedIEu`u L@Mu,dH}>I`tHp)IL@Mt:HHL9tILh@t HxILM9uH@LHIELIHTI L(HH`HuJTHH`Hu9CHH@Hu:DHH`HuHH`Ht Hh\IH@Ht HHDIHƪI fffff.UHAVSIP$IHHRaHHH{L/VH(aHHH[A^]IHگIL\I @UHAWAVAUATSHIIHaHHE1LnHHHHHHCLc I)It Iu H8uALW)@HDžPH IH@HH~7IHCHK H)HLt HuOH8uIAW.u HDž`HHH;PHHHHW)`HDžpMtALH=N4LIH`JHpHLhIHhLHIMJHHIH LHE1fA.|1\u{!HHH`HhH@_IH`HLHHfff.H|HHIHH9t(HOHLH|&HLH9tfff.1HLH}IM9G[H@H`,l1L@A.0\u{HHH`HhLHL9rH`Ht HhάILLH@HHH)H1IILeLuHEH`HDžhH`1L8?HH׍MMwINHHHQINHI@I@ LHHуHH)HMIHSLHHMIJ HE1E1HE1HLeMMրHt9J K4H4H9J K4H4H91HAu1f1HAt$J HILHH4H4HHH9MLMHNHLH@JJB >LIHIuHLtK MMBBLLHHI9fffff.IHHIM9H>HHH@Ht HH贩IHaHH;EuHH[A\A]A^A_] I蘩IIH@%(H5!TLH cVLH`L@9W)EE(HEHis requiHEHX input HEEred!EAHYIH`HML舣E1H5aH7aL/IIIH}t(H5pSLH ULH`LEmaW) HDž0 芨IH0(T[) Hrst outpHHHo get fiHHHfailed tH@ut!AHIH`H L譢E1H5;aH\aLTI H`:I HALeMuiHALeMHHAL@MHH t H0聧ILeMt6HEL9t!Lh@t Hx\ILM9uH}LLeBIEu`uLeMu)XH}I`tHp ILeMt1HEL9tNLh@t HxILM9uH}LeЦIE{LIH@HovLHEt H}蓦ILeMt6HEL9t!Lh@t HxnILM9uH}LLeTIEu`u L@Mu,dH}.I`tHpIL@Mt:HHL9tILh@t HxILM9uH@LHӥIELIHDI L(HH`HuJTHH`Hu9CHH@Hu:DHH`HuHH`Ht HhLIH@Ht HH4IH趟I fffff.UHAVSIPIHHBaHHH{LKHaHHH[A^]IHʤILLI @UHAVSIP贤IHHaHHH{LJHaHHH[A^]IHjILI @UHAVSIPTIHHaHHH{L_JHhaHHH[A^]IH IL茞I @UHAVSIPIHH"aHHH{LIHaHHH[A^]IH誣IL,I @UHAVSIP蔣IHHŸaHHH{LIHaHHH[A^]IHJIL̝I @UHAVSHPIH aHHE"IHþHIHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]躷H5JH}HfH蟷H5LHH}fH腷H5|lKHHcfH5#OLH`H57'NHEaHH HQH5,IHebHH;ZWHHH aHHMHj=HUHuHuIIFIF0INIVIF@H]HEHtHZHIH۳aHH;Eu LHP[A^]sIIH]HEHtHH蹠IL;I UHAVSHPIH{aHHE蒠IHþH舠IHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]*H5TJH}HdHH5uLHHcHH5iKHHcH5LLH]H5$NH^HA H5IH_HH;THHH aHHMHqWHUHuHuIIFIF0INIVIF@H]HEHtHѓHuIHRaHH;Eu LHP[A^]IIH]HEHtH茓H0IL貘I f.UHAVSHPIHaHHEIHþHIHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]蚫H5ĺJH}HwaHH5LHH]aHeH5\gKHHCaH5JLHZH5"NH%\HH HQH5 IHE]HH;:RHHH qaHHMHUHUHuHuIIFIF0INIVIF@H]HEHtH:HޛIHaHH;Eu LHP[A^]SIIH]HEHtHH虛ILI UHAVSHPIH[aHHErIHþHhIHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH] H54JH}H^HH5ULHH^HկH5dKHH^H5sGLHdXH5NHYHA H5IHZHH;OHHH aHHMHhHUHuHuIIFIF0INIVIF@H]HEHtH豎HUIH2aHH;Eu LHP[A^]ʙIIH]HEHtHlHIL蒓I f.UHAVSHPIH˫aHHEIHþHؘIHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]zH5JH}HW\H_H5LHH=\HEH5IHaHH;Eu LHP[A^]賂IIH]HEHtHUwHIL{|I UHAVSHPIHaHHEҁIHþHȁIHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]jH5JH}HGEHOH5KHH-EH5H5,KKHHEH5-LH>H5NH?HA H5IHAHH;6HHH HaHHMHAHUHuHuIIFIF0INIVIF@H]HEHtHuHIHaHH;Eu LHP[A^]*IIH]HEHtHtHpILyI f.UHAVSHPIH+aHHEBIHþH8IHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]ڌH5JH}HBH迌H5%KHHBH襌H5HKHHBH5C+LH4<H5WNHe=HH HQH5LIH>HH;z3HHH ~aHHMHHUHuHuIIFIF0INIVIF@H]HEHtHzrH}IHaHH;Eu LHP[A^]}IIH]HEHtH5rH|IL[wI UHAVSHPIHaHHE|IHþH|IHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]JH5tJH}H'@H/H5KHH @HH5 FKHH?H5(LH9H5NH:HA H5IH;HH;0HHH (|aHHMHHUHuHuIIFIF0INIVIF@H]HEHtHoHzIHraHH;Eu LHP[A^] {IIH]HEHtHoHPzILtI f.UHAVSHPIH aHHE"zIHþHzIHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]躎H5JH}H=H蟇H5KHH}=H腆H5|CKHHc=H5#&LH7H57MHE8HH HQH5,IHe9HH;Z.HHH yaHHMHHUHuHuIIFIF0INIVIF@H]HEHtHZmHwIHۊaHH;Eu LHP[A^]sxIIH]HEHtHmHwIL;rI UHAVSHPIH{aHHEwIHþHwIHHCHCXHC`HCXHCHHC@HC8HC0HC(HC 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DEtL6DLD LؐUHAVSHPIH\HHEDHþHDHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]jH5DH}HGH5GHH5`IH)HH HQH5 EHIHH;>HHH u\HHMHHUHuHuIIFIF0INIVIF@H]HEHtH>ϽHDH\HH;Eu LHP[A^]WDIH]HEHtHνHDLD UHAVSHPIH[\HHErDHþHhDHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH] H5DH}HH59GH蘖H5]IHɗHA H5EHHH;HHH \HHMHHUHuHuIIFIF0INIVIF@H]HEHtH̽HDHf\HH;Eu LHP[A^]DIH]HEHtH̽HDDLD fffff.UHAVSHPIH\HHEDHþHDHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H59DH}H臚H5GH8H5[[IHiHH HQH5PEH艖HH;~HHH \HHMHHUHuHuIIFIF0INIVIF@H]HEHtH~ʽH"DH\HH;Eu LHP[A^]DIH]HEHtH9ʽHDL_D UHAVSHPIH\HHEDHþHDHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]JH5DH}H'H5GHؑH5XIH HH HQH5EH)HH;HHH U\HHMHHUHuHuIIFIF0INIVIF@H]HEHtHȽHDH\HH;Eu LHP[A^]7DIH]HEHtHǽH}DLD UHAVSHPIH;\HHERDHþHHDHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H5yDH}HǕH5&GHxH5VIH詐HA H5EHБHH;ņHHH \HHMHHUHuHuIIFIF0INIVIF@H]HEHtHŽHiDHF\HH;Eu LHP[A^]DIH]HEHtHŽH$DLD 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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;H\HH;EuHX[A\A]A^A_]fmD@UHAWAVAUATSHXIH\HHEпlDHþHlDHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]DzIHwIIH HMLHJHcH9A$I6LHcH9HMI6HƸH9tLHcH9HMHu L6HFI9t8HMHq HuHHHL)HHH1LMc1HMHIHtNHEH$H5:GH :GL :GH]HLLEWLmLH"HHIIffEHEEJ<aDHH]H]JHHEAHLmHu H?H!HZHHwH sf1fHH^H)f1f@AofAoDfAoL Ao\0Aod@ffAoTPffAoD`fAoLpfHHuHt$I|HfoWfofH HuffpNffH~H9tI ff.H1HH9uHuL)HIcH9uH;uu LmHEHEHD$ H9GHD$HEHD$HL9GHD$HEH$H58GH #9GL 8GH]HHUMLmLHۚ"EH}_DHr\HH;EE11LmM5YLHfInfpDHpHHJHLms1HHqH)1DD D0D@DPD`DpӀӐӠӰH H{Ht(HTHfffff.BH HuL9Hu t!H I)fffff.L9HIuIL}Mt)LvLIu^DLHFHFHHL~L~IEH%q\HH;EuLHx[A\A]A^A_]^D^DH}[[D HH}Ht#H}HEu HEt H}]DHdXD fff.UHAWAVAUATSHHMLHHHHHop\HHEH8H;\HHHLH@H8L\DHEEL-Ge\M}LI@L8L[DHEL5e\ILHDž(HDž HDžHDžDž0H$HHHHLM9HHHfLL8Lt H b\DH<[DH5M\HHZDH8[DHo\HH;EuHHH[A\A]A^A_]\DHLL8Lt H [DHZDHH5L\HH@ZDHH8[[DH%VD ffff.UHAWAVAUATSH8MLIHIHH;n\HHEH8H8\HHHHH@H8ZDHLHEEL-c\I]HI@L8HYDL=b\ILHDž(HDž HDžHDžDž0L~]DHLH,_HH0H0YDILK]DHLH^HHHHE0LM(LE HMHUH$HH7HHucLL8Lt H YDHXDH5xJ\HH0XDH8PYDHul\HH;EuHH8[A\A]A^A_]ZDILL8Lt H AYDHXD#HocHgcH_cHWcIH5I\HHWDIH8XDLjSD fUHAWAVAUATSHHIIHk\HHE1H{sII$IG0@ȃH kHcHH}LHL|:H}LHL!&H}LHL H}LHL@HEI$Hk\HH;EuLHĘ[A\A]A^A_]XDXDIHhI#H5^1GH 1GH}LhgOIG0HHH51GH 1GHPHH+HuHP1LRH55\H1\LWDFIPt H`JWDDžDH]HuRHqaIǰDH]Ht6HEH9t!Lh@t HxVDLL9uH}HH]VDEuEuLhMu?wH}VDEtH}VDLhMuQIǰDLhMt:HpL9t9HX@t HxmVDHI9uHhLpRVDDu LILmVD II$LPD ffff.UHAWAVAUATSHHHIIHh\HHEH{HLL+IGPHEEEW)EEHEH{HHH+HHu?HpHHHP H)HL1HJH}HOIwXIW`H}輿HHPIIPHEHD$HEHD$HEH$LELMLLDII}tYH+.GH ֯F1ALdF#H}Ht H}~TDH[g\HH;ELHĘ[A\A]A^A_]LIEHLxLh W)PHDž`M)t[MIHI9kLTDHHXHPJH`M~HLLUDLHXHHEHEHHcEHcMHHHHUIcHHH5ƒHpHH)HhHHH@HpHHHxHHHHH11HHEHL$IcL9,LPHM$W)0HDž@LXM)t\LHHH9LRDIL8L0IDH@M~LLLTDML8HHH0IH0Ht H8 RDLIDHHL4D9u1LLHSDIcHHH;IcL$HHLs1H!K dHHHHXHH`1Lffffff.LLLRDK<,HJ40LRDH`J<(K47LRDHJ<(HpJ40LRDHLxLXH9huHtZHHHHL4HHHLLLHL RDMLIuHHHHH;BBE1E1HEN,IcL9cHPHL,W)0HDž@LXI)ttHLMIHI9 LODHH8H0JH@MLH~HL-QDLH8HDH0HH0Ht H80ODHMDLeMt6HEL9t!Lh@t HxMDLM9uH}LLeLDpuPu L0Mu,dH}LDPtH`LDL0Mt:H8L9tQLh@t HxLDLM9uH0L8{LDELLDH}HLHH0Hfd=HMuIEMAFt I~ LDLLDH}Huv}HH}HufmHH0Hu$.HHPHu)3HH0Ht H8KDHPHt HXKDH}Ht H}}KDHED UHAWAVAUATSHHHIIH%^\HHEH{HLL+IGPHEEEW)EEHEH{H:HH+HHu?HeHHHP H)HL1HOJH}HIwXIW`H}<HHPIIPHEHD$HEHD$HEH$LELMLLDI}tYH#GH VF1AL;#H}Ht H}IDH\\HH;ELHĘ[A\A]A^A_]LIEHLxLh W)PHDž`M)t[MIHI9kLIDHHXHPJH`M~HLLKDLHXHHEHEHHcEHcMHHHHUIcHHH5ƒHpHH)HhHHH@HpHHHxHHHHH11HHEHL$IcL9,LPHM$W)0HDž@LXM)t\LHHH9LHDIL8L0IDH@M~LLLIDML8HHH0JIH0Ht H8GDL'IDHHL4D9u1LLH IDIcHHH;IcL$HHLs1H!K dHHHHXHH`1Lffffff.LLL`HDK<,HJ40LIHDH`J<(K47L2HDHJ<(HpJ40LHDHLxLXH9huHtZHHHHL4HHHLLLHLGDMLIuHHHHH;BBE1E1HEN,IcL9cHPHL,W)0HDž@LXI)ttHLMIHI9 L1EDHH8H0JH@MLH~HLFDLH8HDH0oHH0Ht H8DDHLDD9uHJ4HH?FDIcIIL;LIEHPH8HXDIcHHH;IcL<HHHs1HK HHHHXHH`I1LLLL=DKHfff.LL0DIIHuEIcHH@1LHHHHH@IHHHH@M$ILH)0DHIIuHHH;LLHHIcHHHH;WHHH@HE11HHELN$IcL9LPHM$W)0HDž@LXM)t[MIHI9=L1DHH8H0JH@M~HLL2DLH8HLDH0GHH0Ht H80DHDIDD9uPtGHH@HHLf.LHi.DHIIuIcHIL;LIEHPHHX/DH}HV0D/D/DIH0V!#H5 GH GHPL0@'W)HDž 0i/DH (CV)H tensor.HH Ha vectorHHHmust be HHH tensor HHHAn splitH@(AH GHPHLs)E1H5 \H" \L/DH0,D HP+D IH0HIALeMuFzIAL0MIIt H D.DLeMt6HEL9t!HX@t Hx.DHI9uH}LLe.DpuPu L0Mu,dH}-DPtH`-DL0Mt:H8L9tQHX@t Hx-DHI9uH0L8-DEL-DH}HL?II]IEHCt H{&-DH-DH}HuIH}HulsDB@IH0Hu2-IH0Ht!H8,DHPHuIHPHt HX,DH}Ht H},DL'D UHAWAVAUATSH(IHIHH5?\HHEH8H \HHHLH@H8L+DLHEEL=4\MgLI@L8L*DL-3\ILHDž(HDž HDžHDžDž0L.DHLH.0H3HoHHP.DHHH/HHH4LL8Lt H +DH)DH5\HHs)DH8*DH=\HH;EuHH([A\A]A^A_]G+DHLL8Lt H *DH^)DH4H4H4HH5\HH(DHH8)DH$D ffff.UHHHxXHt Hx`]*D]@UHSPHH{XHt H{`)DHH[])DUHAVSIx)DHH%\HHH{LHHPL0H]HH[A^]IHo)DL#D fUHAWAVAUATSHIIH<\HHEMuIEIEIEIE ffEHEEEaxisEHPHUHhHPE1HuLhHDžPtCt H{(DH(DMeEt H}(DI?ffEE HEEsplifEtHhHULLHhHDžhHtCt H{(DH(DEt H}(DHpIEIMH9tHQH)HHHs+1H&IE H:\HH;E9FI?I!IpHH{H sf1fH|HwH)f1fofoDfoL o\0od@ffoTPffoD`foLpfHHuHt,HtHf.oVfofH HuffpNffH~L9tJff.H2HH9uIu H8x0HH9uHj9\HH;EuHĘ[A\A]A^A_]&Dt&DH@Hh#H5GH GH}LhffPHDž`@&DH`foXfPHust be >HH0Hvalues mHH(Hte. All HH H attribuHHH 'split'HHHvalue inHHHInvalid Hf@8 0@:LHZGHuHPH@DžLH5x\H[H@%DtIİLL}MuKIİLHhHIIPt H`$DL}Mt6HEL9t!HX@t Hx$DHI9uH}LL}$DEuEuHhHu&^H}|$DEtH}m$DHhHt:HpH9tMLx@t HxB$DLL9uHhHp'$DLtEH@H$DI>Hu9@HIEuII>Hu#IEt H}#DI>Ht I}#DLJD fUHAVSIx#DHH\HHH{LHHPL H]HH[A^]IH_#DLD fUHAVSIxD#DHHr\HHH{LOHHPLHq]HH[A^]IH"DLqD fUHAWAVAUATSPMLEIIIHL%DHLHY'HLL%DHLH8'HEH0HC!DLa%DHLH'HE0H DH[A\A]A^A_]H~,Hv,Hn,Hf,H^,HV,fDUHAWAVAUATSPMLEIIIHL$DHLH&AuHs DL$DHLHg&HEH0H` DL$DHLHB&HEH0HM DH[A\A]A^A_]H+H+H+H+H+H+@UHAVSHPIH 4\HHE"!DHþH!DHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]*H5I2DH}Hw11HH5FH<H5_HHmHH HQH5TeDHHH;HHH \HHMHrHUHuHuIIFIF0INIVIF@H]HEHtHH&DH2\HH;Eu LHP[A^]DIH]HEHtH=HDLcD ff.UHAVSHPIH1\HHEDHþHDHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H5/DH}H11HH5FHH5HHHH HQH5bDHHH;HHH I\HHMHHUHuHuIIFIF0INIVIF@H]HEHtHHDH/\HH;Eu LHP[A^]+DIH]HEHtHHqDLD ff.UHAVSHPIH+/\HHEBDHþH8DHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]JH5i-DH}H11HH5FH\H5HHHA H5{`DHHH;HHH \HHMHHUHuHuIIFIF0INIVIF@H]HEHtHHMDH*-\HH;Eu LHP[A^]DIH]HEHtHdHDLD fUHAVSIhDHH"\HHH{LHHPL0H1\HHH[A^]IHDLD DUHAWAVSHHHIHV,\HHEIGIGIW)EHEH;qW)EHEEEaxesEH}HULuHLcEt H}DH]HtHECaeH}HuHUDHuHUHH9t9HFH9tH>HJHHHxH9tHH9uHH9H9HHH9LBI)DHt(Hfff.HH9tHXHHHuIruH9H98u(HyH98u0HyH98u8HyH98u@H H9uFfHxHHyH98tHxHHyH98tHxHHyH98tHxHH H9uHH9tHEHM9tLO#H]HEHtCt H{kDHcDH}Ht H}QDH.*\HH;Eu HH[A^A_]DIH]HEHt&Ct H{DH IEt H}DH}HuI?Hu(LhD H}DI?Ht II?HtIDL7D ffffff.UHAWAVAUATSHIHIH\)\HHE1L;1W)EHHEI}H蘿HH+HuHL0H3HHHP H)HHH諘HH}H+HsPHSXH}蘀HHpH`HUHW)@HDžPL`HhL)tiLMIILH=wHpDILHL@KHPHML~LLHDILHH@1L责HH@Ht HHDL+LkPHL8LxPM9Hx0HPhHtExu?HH~`1LLDHHIIH9|2HۅIHHx0HPILLHDI$H`Ht Hh1DH}Ht H}DH&\HH;EuLH[A\A]A^A_]DH@1DDIH@#H5FH FH`L@Ji W) ƅ $HDž0Hut is nuH)HAxes inpH!fDž1llƅ3AHFH`H L E1H5Y[Hz[LrD'DIH@#H5FH FH`L@L W) HDž00DH0(yV) H tensor.HHH vector HHHust be aHHHtensor mHHHAn axes H@'AHFH`H L E1H5_[H[LxD HALeMulHAL@MHHAL@MHH t H0DLeMt6HEL9t!Lh@t HxDLM9uH}LLeqDEu`u#L@Mu36H}HD`tHp3DL@MHHL9Lh@t HxDLM9uH t H0DLeMt6HEL9t!Lh@t HxDLM9uH}LLeDEu`u L@Mu,dH}vD`tHpaDL@Mt:HHL9tQLh@t Hx6DLM9uH@LHDEL@DH}HLHH}HHH}HHH@HuJHH@Ht;HHDH`Hu/9HH}Hu6=HH}Hu(/HH`Ht HhVDH}Ht H}DDH D fffff.UHAWAVAUATSHH!\HHEHGHGHHFHH+HIM}IEIW)EHEM)H t^HLHHH=LDILuIH]LLDH]HLIL9uLmN1E1LIL9t1@IHuiHD LHH}Ht H}W DL I>Ht4HH}HtHL H}HtH} DI>HtI~ DHD UHAWAVAUATSHHMLHHHHH\HHEH8HB[HHHLH@H8L'DHEEL-w\M}LI@L8L-DHEL56\ILHDž(HDž HDžHDžDž0H$HHHHLMHHH@LL8Lt H DHlDH5C[HHDH8DH@\HH;EuHHH[A\A]A^A_]DHLL8Lt H DHDHH5[HHpDHH8DHUD ffff.UHAWAVAUATSPMLEIIIHL DHLH9 IuH5DLe DHLH HEH0H"DL@ DHLH HuH膆H[A\A]A^A_]H_HWHOHGH?H7UHAVSIhDHH\HHH{LHHPL H!\HHH[A^]IHDL D DUHAVSIhtDHH\HHH{LHHPLH[HHH[A^]IHDLC DUHAWAVAUATSHIH\HHEпDHþHDHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]HHuHPiHX]H`QHhEHp9Hx-H%IIIW)EHEXADHEHHXHMHHHHPHPHXHPH`HPHhHP HpHP(HxHP0HX8Lp@LxHL`PHMH5DH}HUFHnH5 EHHLH5FHH5 HH.HH HQH5HDHNHH;CHHH z\HHMH'HUHuHuIEIEIE0IMIUIE@H}Ht H}DH]HEHtH0HDH\HH;EuLHĘ[A\A]A^A_]@DCIH}HuH]HEHu:L C H}xDH]HEHtIH]HEHtHHADLC ff.UHAWAVAUATSHIH\HHEп DHþHDHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]HHHPHX}H`q HheHpYHxMHEI=I5IW)EHEXaCHEHHXHMHHHHPHPHXHPH`HPHhHP HpHP(HxHP0HX8Lp@LxHL`PHMH59DH}HUfHH5EHHlH52FHH5@HHNHA H5LLCHEH`HIL}ILeHMHMHMHMLeIH'LLpHMHPLmIHMHLet)Hf.HLIL-CLLHuLeLhILmIFHHINHtLxffff.IFHHHI6HvHcH; HIHHHL,HXHRH MLmLLM)HxHPH}H}H]H:HIHHUHIHHUHIHHUHIHHUHIHHULLLEHMLqI)L}fLLLCK/HLLCLHEJ<8LLCLHEJ<8LLCLHEJ<8LLlCLHEJ<8LLVCLHEJ<8LL@CLL}LLL+CLIIMLuML}Mt"IfHLLCLIuLeMLpINLhLmHAIFHH7IN H)HINA~LMLHxAFHHH[A\A]A^A_]ff.UHAWAVAUATSHIIHO \HHE1L.IMLLx HL+xLHHMHC H+CHIHH\hL9LHIHyW)EHEHHy H+yHLIII9CHH]H]JHEHHpLh I)MHLCHLHuyH FH F#H "FH \FH FL;4"H\HH;EMLHĘ[A\A]A^A_]11HHII1fH H H HL9rW)EHEHH)tTHIIM9~CILmLmKDHEHH)H~LHHCILmHU1L҃IH}fHtwHH`ƅhHEHEHDžxHp H+pHHDžHtKHxHuHEL`HxI$HHHH}H11H)HHpLeHuƅhIuHH1b>HH@0@ȃ H HcHhHHALy I)INdMuMuPH1HqPILLLHLCLKLK&HLyHAAHHMMMgI)HLL:CLHLL)CLHLLCLHLLCLHLLCLHLLCLHLLCLHLLCLInMMLMtIHLLCLIuHIIIHpHAHpHwLHxHHHH`HvHcH;HILHHHHHvH4H4HL,LM)HqHAHxHHH6HH@HHPHHXH@H`KDHhHHKDmHpHLxI)Lffff.LLL@CK,HLL.CLHXJ< LLCLHpJ< LLCLHJ< LLCLHhJ< LLCLH`J< LLCLLPLLLCLII;LMt%Ifff.HLL`CLIuHxILIHpLLHHHpHH,HEH)HHphLHGhHHALq I)INdM}M}PH1HqPILLLHLCLKLK'HLqHAAHHMMMfI)HLL3CLHLL"CLHLLCLHLLCLHLLCLHLLCLHLLCLHLLCLInMMLMtIHLLCLIuHIIIHpHAHpHpLHxHHHH`HvHcH;HILHHHHHvH4H4HL,LM)HqHAHxHHH/Hk8HPHHHXH@H`KDHhHHKDmHpHLpI)Lfffff.LLL@CK,HLL.CLHXJ< LLCLHpJ< LLCLHJ< LLCLHhJ< LLCLH`J< LLCLLPLLLCLII;LMt%Ifff.HLL`CLIuHxILIHpLLHHHpHH, HEH)HHphLHN h HHALy I)INdMuMuPH1HqPMLLLHLCLKLK&HLyHAAHHMMMgI)HLL4CLHLL#CLHLLCLHLLCLHLLCLHLLCLHLLCLHLLCLInMMLMtIHLLCLIuHIIIHpHAHpHq LHxHHHH`HvHcH;HILHHHHHvH[H?H}HHH=+ Hc4HW)E)E)E)E)EH}AHH^LM8O4HIH@H;L9LA$L]L}LH HL,H}LHL4MIXILLMI;sIIM9ufff.I IMLKAHHH HIfH\HIDHPITI;Tr#HHHIDHff.IM9ffffff.IIM9W)E)E)E)E)EH}AHHJNMKHHIH}LUL}Hff.H;L9A$LmLH HL4LHHHMMHM M; r{I IIHKA|_HHH Hff.HTHI\HsItI;tr#HHHIDHff.IL;W)E)E)E)E)EH}AHHIMMeKtHHIH}LUL}Hfffff.H;L9fA$LmLH HL4LHHHMMHM M; ryI IIHKA|]HHH HfHTHI\HsItI;tr#HHHIDHff.IL;VW)E)E)E)E)EH}AHH>HNMKHHIH}LUL}HfH;L9HI$LmLH HL LHHHMMpM4M;4rzI IIHKA|^HHH Hf.HTHI\HsItI;tr#HHHIDHff.IL; H}Ht H}CH}Ht H}CH}Ht H}CHp[HH;EuH[A\A]A^A_]CzCIH"H5FH ܻFH0LHAHaFH0HLE1H5#[HD[LH}3H}(LEMMJ HMHL)HMHHUHHK4HFH FHL`}H!AH(FHHLE1H5O[Hp[LhCCIH`誚"H5FH lFHL`蔠HAHFHHL8E1H5Ɔ[H[LߨC蔨CIH`!"H5,FH FHL` HAH'FHHL询E1H5=[H^[LVC HHH}Ht0HH}Ht"][YWHH}Ht HH}HtH}ŸCH}HtH}谟CH}HtH}螟CHHuHHHt HsCHC ffff.UHAWAVAUATSHHIIH[HHE1HIMH I] I+]W)EHELIILH=MHݞCHENM~PMeMePM9I}0HPhHtJxuDH E1H~^1L fLLɚCL HIIH9|.H II}0HPILLHCE1LM7H@Ht HH'CH0HDž0HtCt H{CHCH`Ht HhCH}Ht H}ϛCH[HH;E>LH[A\A]A^A_]Mtb1IH9u HH9rJLCL9v4ILM9 1H9MLHH9rHIE0xtuHIE0xuhH(E1LLH(HDž(H0H0HtCt H{CHCL0HDž0E1LLHML9tH9pIHI9sKIL9uHII9rHH9>C膚CIH`"H5|FH a|FH}L`H@AHblFHuH@L觔E1H55x[HVt[LNCH}7C H@)C IALeMuFzIAL`MII@t HP蓙CLeMt6HEL9t!HX@t HxnCHI9uH}LLeTCEuEuL`Mu&^H}1CEtH}"CL`Mt:HhL9tIHX@t HxCHI9uH`LhܘCELCLMC LIH@HuzIH@HufpIH@HuU_IH}HuIH}HunuIH`HtTIH@HuIH@Ht HH'CH0HDž0Hu(H`Hu?H}Ht H}CLtC Ct H{ٗCHїCH`HtHh蹗CH}HufUHAWAVAUATSHxLxMIIH`H^[HHELj HpL+jLHIINH)HHH)vAL A<$uCMtEHCIHALGI41Lx%sHIFA<$tIMLxM4$IIOH)HHH)vL MuKsHIGMtI)MIIHH=6HI)LHHH9H]H}wtII9LBMI9v^CHH5ACHNCHa[HHH5i[HLe[H CH4L躈CIDIF~IJ<虈CN$HEJHEL}J4L{CO$MH]~H}HLCHEIMfHEIFHtHH([A\A]A^A_]CH([A\A]A^A_]E11yLFCIH)CLuC ffff.UHAWAVAUATSH8LHHIHH[HHEH8H)e[HHHLH@H8LCHEEL%^[Ml$LI@L8LCH [HHHDž(HDž HDžHDžDž0HLHHL LLL9LL8Ht H 苆CHeCH5H4HHHH9uAEW)0HDž@H*HMgIJ<CIIL0L@L8MIJ4L踂CI?t@D)HHHK!IEIUH9tEfffff.HHHHHIHH IHH9uMtL(CH]HEHtCt H{CHCH}Ht H}CH˔[HH;Eu(H[A\A]A^A_]E1IEIUH9LGC迁CIHLs"H5gFH 9hFHPL36yH5hFH hFHHEHPH1L{H5^_[H[[LwC#,CIH0r"H5dgFH gFHPL0*xH觩HgFHPHLB{DžH5^[HZ[LC藀CIH0$r"H5fFH gFHPL0,xH5gFH gFHHHPH1LzH56^[HWZ[LOC H05}CIt H(CDžLeMuRHIǰLeMt6HEL9t!HX@t HxCHI9uH}LLelCpuPu#H0HuOH}@CPtH`+CH0Hu IǰH0HH8H9L`@t Hx~CLL9u{IIǰLeMIǰH0HIIJIt Hj~CDžLeMuRH葈IǰLeMt6HEL9t!HX@t Hx~CHI9uH}LLe~CpuPu HHuE}H}}CPtH`}CHHuQIǰHHt:H H9t9L`@t Hx}CLL9uHH f}Cu HIL}CH0H<6}CH]HEH3aIt H(}CLeMt6HEL9t!HX@t Hx|CHI9uH}LLe|CpuPu H0Hu,dH}|CPtH`|CH0Ht:H8H9tRL`@t Hx\|CLL9uH0H8A|Ct9Lf|CH]HEHu1bHIH]HEHuGIH]HEHt3Ct H{{CHLIPt H`{CH}HuHH8Hu"L%vC H}{CHH8HtI}x{CLuC fUHAWAVAUATSH(IHIHH%[HHEH8HX[HHHLH@H8LzCLHEEL=[MgLI@L8LyCL-[ILHDž(HDž HDžHDžDž0Lp}CHLHH3HyCHH@}CHHH~HHH訃LL8Lt H yCHxCH5j[HHcxCH8yCH[HH;EuHH([A\A]A^A_]7zCHLL8Lt H tyCHNxCH袃H蚃H蒃HH5j[HHwCHH8xCHsC ffff.UHAWAVAUATSH(IHIHHŋ[HHEH8HhV[HHHLH@H8LMxCLHEEL=[MgLI@L8LLwCL-Y[ILHDž(HDž HDžHDžDž0L{CHLH|H3HvCHHzCHHH|HHHHLL8Lt H wCHtvCH5Kh[HHvCH8#wCHH[HH;EuHH([A\A]A^A_]wCHLL8Lt H wCHuCHBH:H2HH5g[HH`uCHH8{vCHEqC ffff.UHAVSIpvCHHr[HHH{LHHPLH1\HH[A^]IHOvCLpC fUHAVSHPIH [HHE"vCHþHvCHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]*H5ICH}Hw7H5e_FHH3H5kGHy4HA H5gCH5HH;*HHH u[HHMHUHUHuHuIIFIF0INIVIF@H]HEHtHiH9tCH[HH;Eu LHP[A^]tCIH]HEHtHPiHsCLvnC fffff.UHAWAVSH8HIIH[HHE1H肎IH@0HɃw$HHc HH}LHHEH5]FH}HUH}HM*!HEIHEEtSH}(sCH[HH;EtJVH}LH+F H}LH#H}LHgHEIH[HH;EuLH8[A^A_]QsC)'%HEtH}rCIHmC HIHmC fwUHAWAVAUATSH(IHHHHE1HHILH@HHA}QL HxHEHDžxHpW)PHDž`) HDž0H)HHHPHGHHH+@HH HDžHEILLuI9THfDL@LxMCHLffff..A HQHFHFH HuH9tH .HIAO .v0fffff.IAO .vIMHuf.vMoIGHufffff.I]HHK(H(H;0#HHHH(HIII]HuhLx(HPKHLH;LL1HDHHL}H(H;0MI]HZffff.H0oCHCC HC(HCHL{I]HpHHt HpIuHHx{HEHHK(H(H;0H H蕧H]W)EHEoCHEHHHMHHMHXH;`sRH@H@H(EHMHHW)EHEHXHxL5DHPHuЈHxLH}Ht H}\nCHLuILuHI9CHHu#HrH9tHH HqH9HQuHL9|1Lfffff.HN(V AHIIHIHHVHtf.HHHu#H~H97tHHHyH9HquHL9|1Hf.EHq8HHEMtHHu#HrH9tHfDH HqH9HQuHL9|HW)EHEHtLH}LuLpIH}HxI9tf1fIJ(HIrHtffffff.HH2Hu#IRL9tII HQH9 LQuHIH9uH H(H)t^H1fff.HHIHH9r?H H(H)HHALGIs.1HH}XCH HHHGH4I9sIH9s1gLHHVHH{H 1H"EHDMt HQ8HRItI+4HHH4HqHtfHH2Hu#HQH9 tHfDH HQH9 HIuHHL9uHWH)1LAALD L0AD AL0D@LPAD@ALPD`LpAD`ALpHHuHt(HHD ADA H HuI9tfffff.HIHH9rH Ht H(VCLPMtBHXL9t'HXHxHt HxVCHI9uHPLLXVCHxHpLHHHHRi[HH;EHH([A\A]A^A_]HMt HQ8H4RI|I+ZHpMLPt;LZH31HHxHu1HpMLPu1HDLHHLUMHX LMM@JHJHHDHHH?H!HH@HHHIuHHwpHHqH)HHGHHHH1LL0L8L@fEMA MY8L(HLEL MLE1fLIHLNHu#HrH9tHH HqH9HQuHL9|1Lfffff.HN HV(I H RI H I HHVHtfHHHu#H~H97tHHHyH9HquHL9|1Hf.EHr8HHEMtHHu#HrH9tHfDH 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HHH VHHMHHUHuHuIIFIF0INIVIF@H]HEHtH޷H>HfVHH;Eu LHP[A^]>IH]HEHtH޷HD>L> fffff.UHAVSIX$>HHLH[A^]IH>Ly> UHAWAVAUATSHIHHVHHEHVHHH{ HzVHHW)EHEEHepsilonHEHPHhHULH8 LhHDžhMtAFt I~=>L5>Et H}&>Mu7W.HVHH;EuHĈ[A\A]A^A_]>>IHhH5fAH AH}Lhr߾HPvAH@HuHPLE1H5VHVL>~x>IHhH5AH +AH}Lh޾HPAHBHuHPLE1H5'VHHVL@> IALeMIALhMIIALeMFIALhMnIIPt H`\>LeMt6HEL9t!HX@t Hx7>HI9uH}LLe>EuEu LhMu-'H}>EtH}>LhMHpL9HX@t Hx>HI9uIPt H`>LeMt6HEL9t!HX@t Hxk>HI9uH}LLeQ>EuEuLhMu&^H}.>EtH}>LhMt:HpL9tEHX@t Hx>HI9uHhLp>Et)L>LN> LIEt H}>L-> DUHAWAVAUATSHHIIHH\VHHEL H;t#HAH ޲?1A#HLHH1LIǾLHLIƾLHLIžLHLHLuHIFHXIG)W)EHEw>HEHHHM(HHXHMHU1Lq HIGHW)EHE>HEHHHMHHMHULq HIGHHHXIFLID$MtIE DžLIHHt1HLLjILjIMt Lj1HHMjIH~jIHHMHHHHH (Hى,0HH8@HHHHPLXL`HHhLpLLH LxIHIH HEEH=VXHHML1>EsHH腵HE EXMcIcHN,LHXHKH)HHHHE1fff.LIHHIsE1HHE1fD1҃8L1x@H1҃xH1x @H1҃xH1x@H1҃xHE1xAIH HuHt!HfD1҃8IHHuH`FH (:S)Hof rangeHHHdex out HHHinput inH@HHHt@H >H}Hu4;H蚵HHH>HHH}Ht H}>H}Ht H}>HVHH;EuIHHH[A\A]A^A_]Mc1ffffff.HHL9|u>IH}HtIuH}HjH}HosH >H}Ht>IH}Ht-IH}HtIH}Ht IH}HtIH}HtIH}HoIH}HZIH}HEIH}H0IH}HIH}HIH}HuqxIH}HKIHHHHtQCt H{>H8IH}HIH}HH}>H}Ht H}>L*> fUHAWAVATSH0IHHdVHHEH=QX>AL#MIHELeHEEHHEH=QXHMLELMȺ"DjjP>H HEL9LNLeH]L9 ff.LHEH;]H[|H=4QXD4>HVHH;Eu H0[A\A^A_]<>f.UHAWAVAUATSHGHcHx/;_}*HcOHHH9W ~LGHG(Ht x;O0|H[A\A]A^A_]1LELg@LWXHcIMI IDM~LG8LHLEIIHELOHIIL_PLcMEAMKHUIt!Iu@W1L]MLM(IW1L]LMMLL)ITOLL]ODW1DLAXLX ALXAXXDAXHH9uMLMA HEX X A rMM)ITK\L]KDW1LM@LXLLXXXHI9uMtA HEX A XM6WI*^DIu W1HuTjLH)K\W1d\dYX\YXH(H9uHtA\AYXM^HEXPPWQHW`HhI1IL$HAt!A ^Y X A HHH9?KLff.L^Y X L^YTXTHI9uH ILJI9AH9JI9H91A; 3LH(HFHHDSAHtPKDIJH)1T^$YXT^TY\XHHu1MtA^ YXAI9 DUHAWAVAUATSHXIH}HVHHE1LIǾLIžLIƾLHELHELHELHEL|MtNIOI HH)IuI] H)H9\H9t'1fffff.H9H;>9HH9uH$IWIO HH)HxH@ H)H9{H9t 1HH;_HH9uH)HIVIF H)H HMHAHI H)HLu-HuHt#HNHv H)HHHrH;qHH H;HbHEL@H@ L)H?I9oHEL@H@ L)HI9IHVHH;E0'>HE(Q)EH same shHHHall haveHHHand 1 shHHHInput 0 H@ apeHMLuLM0>HE(~S)EHsame shaHH Hll have HHHask) shaHHHand 7 (mHHHInput 0 Hf@(pe@*HMLuLIWIO H)HHHMH5WAH]HUHhLuLHtHHEH5KAH]HUH[LuLH7HHMH5DAH]HUHLHIHHuH5EAH]HUHLHP)>HH](cOR)EH51AAH>CAHML vP>HE(S)EHension 1HH8Hsame dimHH0Hll have HH(Hding shaHH Hnt_embedHHHnd segmeHHHedding aHHHword_embH@@HML HHEH5AH]HUH7LH H5AH ?H]HLH eHHEH5uAH]HUHyLHw /H5tAH m?H]H LHF Et H}S>H0VHH;EuLHX[A\A]A^A_]>HEu|HEunuHEucjHEuX_HEuMTHEuBIHEu7>HEu,3HEu!(HEuHEu HEt H}>H > UHAWAVAUATSHHIHHHVHHEH8HVHHHLH@H8L>HEEL5 VM~LI@L8L>L%VILHDž(HDž HDžHDžDž0L>HLHHԾH3HA>HHHؾLL8Lt H E>H>H5VHH>H8>HVHH;EuHH[A\A]A^A_]>HLL8Lt H >H>HؼHؼHH5[VHH>HH8.>H> UHAWAVAUATSHHIHH(VHHEH8H˫VHHHLH@H8L>HEEL5VM~LI@L8L>L%VILHDž(HDž HDžHDžDž0Lz>HLH(ҾH3H!>HHH־LL8Lt H %>H>H5ֽVHH>H8>HVHH;EuHH[A\A]A^A_]b>HLL8Lt H >Hy>HּHּHH5;VHH>HH8>H> UHAWAVAUATSHHIHHVHHEH8HVHHHLH@H8L>HEEL5VM~LI@L8L>L%VILHDž(HDž HDžHDžDž0LZ>HLHоH3H>HHHԾLL8Lt H >H>H5VHHn>H8>HVHH;EuHH[A\A]A^A_]B>HLL8Lt H >HY>HԼHԼHH5VHH>HH8>H> UHAWAVAUATSH8MIHIHHVHHEH8HVHHHHH@H8Hf>LHEEL%VID$HHI@L8H]>L5jVILHDž(HDž HDžHDžDž0L!>HLH;HH0H>L>HLH;HH0H>HHHCҾHHL8Lt H >Hh>H5?VHH>H8>HHHHL8Lt H >H>#H/ҼH'ҼHҼHҼHH5VHHE>HH8`>H*> fUHAWAVAUATSH8MIHIHHNVHHEH8HVHHHHH@H8H>LHEEL%VID$HHI@L8H>L5VILHDž(HDž HDžHDžDž0L>HLH?˾HH0HC>La>HLH˾HH0H>HHHϾHHL8Lt H >H>H5VHHg>H8>HVHH;EuHH8[A\A]A^A_];>HHHL8Lt H q>HK>#HϼHϼHϼHϼHH5VHH>HH8>H蚿> fUHAVSHPIHVHHE>HþH>HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH] H5>H}Hg H5AH H5>HI HAH57 ?Hp HH;ey HHH VHHMHHUHuHuIIFIF0INIVIF@H]HEHtHeH >HVHH;Eu LHP[A^]~>IH]HEHtH H>LF> fffff.UHAVSIP>HHҾVHHH{Lh HVHHH[A^]IHZ>Lܼ> @UHAVSHPIHVHHE2>HþH(>HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH] H5Y>H}H觅 H5AHX H5>H艀 HAH5w?H谁 HH;v HHH VHHMHHUHuHuIIFIF0INIVIF@H]HEHtH襵HI>H&VHH;Eu LHP[A^]>IH]HEHtH`H>L膺> fffff.UHAVSHPIHVHHEҿ>HþHȿ>HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]j H5>H}HG H5AH| H5o>H)~ HAH5?HP HH;Et HHH |VHHMH5HUHuHuIIFIF0INIVIF@H]HEHtHEH>HVHH;Eu LHP[A^]^>IH]HEHtHH褽>L&> fffff.UHAVSIX脽>HHLH[A^]IHW>Lٷ> UHAWAVAUATSHHIHVHHEHaVHII~Ac HbVHIW)EHEHDžpHDžhƅ` fDžeicDžametrƅgHHH`HMH HHHDžHHtCt H{y>Hq>`t Hp\>HuyH5;AH}}>tOH53AH}i>1ɅANPEt H}>HVHH;Eu#HĨ[A\A]A^A_]ùANPEut>>IHHyH5AH AH`LHcH0gAH@AH`H0LE1H5VHVL讻>Lf>IH`H`L訸>HVHIH5OVHVL`> I`GNH1żIIALeMuFzIALHMII0t H@虺>LeMt6HEL9t!HX@t Hxt>HI9uH}LLeZ>Eu`u LHMu,dH}4>`tHp>LHMt:HPL9tCHX@t Hx>HI9uHHLPٹ>Et-L>Eu%,LI`t Hp觹>Et H}蘹>L> fUHAWAVAUATSHHIIHLVHHEHCH(1H IƾHIIVINIF H)HHqHMII|$ID$ H)HH;w"LHLL9L/W)pHEǸ>HpHHHML8LhHxHp1H1G IL\H IFHH8LxID$HHhL@H@L@ILL\H0W)EHEH8HH}H:fLHHML`IGH LH?II=MIH>LHIAMOIIAAMwI)΃EANHPHL)HXMI1H@HhH5PAH]HWLHEt H}>HVHH;EFLH[A\A]A^A_]H5"AH]HL$LHH5(AH PCH]H#LH~gHI G1HPtYINYI|lMtLX1DT \0YXD@YXLPYXYXHIuHPHPt\0YXYX((XL9~ B YX(XXI9~xMLHf YXHHuIrED TYXYXLYXD YXHI9uHELHH;8ALHE1LHHh E1HhH}H4>AHhL]EWMOAN4LL)MI1H@YI|wIrG TYXYXLYXD YXHH9uMt HL@YXHHuALHH9fAHhL`W)EHEHDh:H}HaH%M*IGH5MI?MI=MII>MIIIOHHAMgM)ԃEAJHPHHXH)H`MI1L1LI$_1HPDYINYI|HXtML`1ffff.T \0YXD@YXLPYXYXHIuHHMHPt\0YXYX((XM9~ B YX(XXM9~uMLLfD YXHHuIrED TYXYXLYXD YXHI9uHEHHH9ALHE1H(HD$H0HD$L4$PQopWH8HML@,hLH}ELMHH(HHHhHCHHLkAALHXHHPH)H`MIHG H@1L09HhM4LH)Mun1HXfAHr/HHH0H H;(HHH91MLH)Mt$LfAXX AIHHuHWHH)H@HE1C XBXLC CLXBXLCLCLXBXLCLCL XBX CL IL9u(HPH`1AA\Ad Al0XX4XTXA4ATXXT X\0XAT A\0HHuHXt+AA\XX XTXA ATH;hff.MHH;8LLH H}Ht H}w>H}Ht H}e>HpHAEPtmH VIuAAAAI9LOHL)II?I>IIMDʃI]1HIH uAAAAI9LOIM)MI?I>MIM#IX1H}H4苭>AL]EWMOALN4LL)MI1H`HfYI|wIrG TYXYXLYXD YXHH9uMt HL@YXHHuALHH9fAL`LH)1(]Q TTQf: TQf:THH9uHtT]QQf:K<H|mD(q]Q( ^Q(^Q(^QLf$AT(YWR(YY(\YYVT),HH9|H9D)HAL)tT\QQf:HL9H)HD(\QHTQf:HTQf:HHuQLHHGHHDBAHtNI@H)1(^\Q T\ d0TT TTT\ d0HHu1MtL( \QTTLI9t)LH)H([Q@ T HHuK<IIsH9HMHL)މLHt!H([QL T) HHuH rQ(o[Qffffff. 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H} ʉ ΁u' сpfnHvO1zf.8fnXf~ǁ8D fnH,1f)HxHDfD83jfD832fEofAr fAofr fofoOffAoffAofoxOfoffDofDofEfAvfo5OfD( hOAXfDo=kOfEfD8foffofvfDvfDkfofo OfoffAvfDofDkfAXfAfof8fAofAf+Nfofifrf8f83fDfrfE8fEfDfArfArfofNfDfDfDfE(DYDYA((ȞNTA(TfoOfofffffkfof8?fvfof8?fvfkfofffffk((fo NfffffkfoOf8})P(NXXfoNfDofA8fA8flfAfDA(fAqfAqfAof8fr A(fr fo NffA(fo%OfoffA(fffr fr fA8fA8flffqfqfof8fArfArfE8+fD5(PfDD2HH)HHH;D5ooNGO/N(fH up?ffffff.t88fnXf~fff. fnYf~‰ׁGv1@ƃ> 7D8sfnAXf~f 2  f4HHH;Mf)HxHfD83*fD83RfAofr fAofr fDofofo^Ofoffofo ZOfoffofDoffvfDo=OfAD( KOAXfDoNOfAf8fDfoffofvfvffvffo5OfDffAoffvPNfEEXfAffofA8fof8fofA8fArfArfojNfDfDfDfD(DYDYA((̚N(TA(Tfo OfDofDffDofDffAofffDf(( +N(X(Xfo%ךNf8f8flfAofkfo5؜Nfffffkfo=Of8=k%PA(fr fof8A(fr fo NffA(foDOffA(fffr fr f8f8flfAfEf8fD8fDlfAfDfArfArfE8+fD-$PfDfED*HHlHHH;sO[N(fffff.H up?ffffff.t88fnXf~fff. fnYf~‰ׁGr1@ƃ> 7D8wfnAXf~f 2  f4HHH;MIHHHpL;XHEHH HH`HhHPHEHMHHH$H}H(HUHXLELM#_HHHHHHH[D=NA(D5iNOL]HpL@HhH`HHHEHEHEH0HpH$HD$(HD$ HD$HD$D$MH}HuH(HLEEH0D=NA(D5 iNOL]HH`HH0HH0H8H0H(HL9L @L`H@LL)L)L)HxL)H8HhHs)H}s5HSHH;EuAHe[A\A]A^A_]H;H}rH;HmSHH;Et;;HHN;H5CSHSH;^;HH#;H5SHSHy; 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fffff.UHAVSI`T;HHLH[A^]IH';L; UHAWAVAUATSHIIHSHHEH1SHII~ HSHIW)EHEEEaxisEIOPHhHUL辗 HhHDžhHtCt H{t;Hl;Et H}];HW)EHEEHepsilonHEIXHhHULL HhHDžhHtCt H{;H;Et H};HHSHH;EuHĈ[A\A]A^A_]B;;IHhGH5>H 8>H}Lh)4λHP8AH>HuHPLлE1H5iSHSL;~:;IHhH5~>H >H}Lh*ͻHPAHë=HuHPL[лE1H5SH SL; IALeMIALhMIIALeMFIALhMnIIPt H`;LeMt6HEL9t!HX@t Hx;HI9uH}LLe;EuEu LhMu-'H};EtH};LhMHpL9HX@t Hxw;HI9uIPt H`R;LeMt6HEL9t!HX@t Hx-;HI9uH}LLe;EuEuLhMu&^H};EtH};LhMt:HpL9tEHX@t Hx;HI9uHhLp;Et4L;L; LIEu IEt H}b;L; fff.UHAWAVAUATSHIIHHSHHE1L HþL IƾL IHL[HEL@[HEL4[HELsLI|$PHs H+sHOILLEHLLKHE1LLLa HvHEW)EHEHs H+sHH} HsHC H)HE1LuE1fDM9~LHEH;Es>HH!fDDž HEH;Es,HHHE*ff.LfDLH q IHsHC H)HHII9pW)pHpLLHn I>H>H ֟<1AJLHxHt#HHCHuHHPH ;H}Ht H};HSHH;ELH[A\A]A^A_]IILuL}HDžhW) HDž0M)tQMIM9eL;HH(H JH0M~HLL;LH(H H^ HH Ht H(;Ht/HtHh1H1H1HLpILpHyDHHH t0H>t)H"HHHH!H HHH!LAIHpHHxHt/HCHCHHCHuHHPH;1HLHLhHDž`W) HDž0LuH]L)tQIIM9H;IL(L KH0H~LLHM;IL(H H ] HH HLLt H(@;HtHGrH`1E1E1LpILpMyLH t,LH>ttH"JHH!H JHH!LAILpHxHLt/HCHCHHCHuHHPHk;1L`HH HEH(HEH0HEH8HEH@IcHMHHHhHPH`HXHEH=JUH HML 1S;IMtMt IELLPHt#HHCHuHHPH;HHHt HtHPHH$HHCHHHPH8;HxH;H \; 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LHP[A^].;IH]HEHtHHt;L; fffff.UHAVSHPIH+.SHHEB;HþH8;HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H}1H(H5V,;HHH5<HVH5.;HHAH5u_;HHH;HHH SHHMH:HUHuHuIIFIF0INIVIF@H]HEHtHHG;H$,SHH;Eu LHP[A^];IH]HEHtH^H;L; fff.UHAVSHPIH+SHHE;HþH;HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]j&H5);H}HGH5<HH5},;H)HAH5];HPHH;EHHH |SHHMH<HUHuHuIIFIF0INIVIF@H]HEHtHE H;H)SHH;Eu LHP[A^]^;IH]HEHtH H;L&; fffff.UHAVSHPIH[)SHHEr;HþHh;HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH] $H5';H}HH5<HH5*;HHAH5Z;HHH;HHH SHHMH=HUHuHuIIFIF0INIVIF@H]HEHtH H;Hf'SHH;Eu LHP[A^];IH]HEHtH HD;L; fffff.UHAVSHPIH&SHHE;HþH;HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]!H59%;H}HH56<H8H5';HiHAH5WX;HHH;HHH SHHMH;HUHuHuIIFIF0INIVIF@H]HEHtHH);H%SHH;Eu LHP[A^];IH]HEHtH@H;Lf ; fffff.UHAVSHPIH$SHHE;HþH;HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]JH5";H}H'H5<HH5]%;H HAH5U;H0HH;%HHH \SHHMH9HUHuHuIIFIF0INIVIF@H]HEHtH%H;H"SHH;Eu LHP[A^]>;IH]HEHtHH;L ; fffff.UHAVSHPIH;"SHHER;HþHH;HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H5y ;H}HH5=HxH5";HHAH5S;HHH;HHH SHHMH7HUHuHuIIFIF0INIVIF@H]HEHtHHi ;HF SHH;Eu LHP[A^] ;IH]HEHtHH$ ;L; fffff.UHAWAVAUATSHIIHSHHE1L' HHCHK H)H L`-BH1LHHHH1L8 ILcH锺HL莰LHHIHBSHH;EuLHĈ[A\A]A^A_] ;I ;IHhH5)>H Q)>H}LhQHP4AH)>HuHPLjE1H5RHRL ;~ ;IHhVH5(>H (>H}LhRCHPG4AH9)>HuHPLE1H5xRHRL ; IALeMIALhMIIALeMFIALhMnIIPt H` ;LeMt6HEL9t!HX@t Hx ;HI9uH}LLen ;EuEu LhMu-'H}H ;EtH}9 ;LhMHpL9HX@t Hx ;HI9uIPt H` ;LeMt6HEL9t!HX@t Hx ;HI9uH}LLe ;EuEuLhMu&^H} ;EtH}p ;LhMt:HpL9tEHX@t HxE ;HI9uHhLp* ;EtLS ;L; LfUHAWAVAUATSHHIH@HSHHE1H# HHHHLx I)I H0I\$PH;Y6MIIW)EHE ;HEJ HMH@H@H@HHMHHLQIHIVE1L8I|$XHEHAIEAIu 1MuQjIPM)L\H1LHHII\HHI42I4HLLI9uMtJDHH LHI H HU1H0 HHHяIHvH8HyXH}t LH" LH H@HH}Ht H};HSHH;EuHHĨ[A\A]A^A_];;IHhH5#>H $>H}Lh^vHPz/AH%>HuHPLE1H5RHRL;y;IHhH5H#>H @$>H}Lh_HP.AH$>HuHPLE1H5(RHIRLA;^IALeMIALhMIIALeMFIALhMnIIPt H`Z;LeMt6HEL9t!HX@t Hx5;HI9uH}LLe;EuEu LhMu-'H};EtH};LhMHpL9HX@t Hx;HI9uIPt H`;LeMt6HEL9t!HX@t Hxi;HI9uH}LLeO;EuEuLhMu&^H},;EtH};LhMt:HpL9t=HX@t Hx;HI9uHhLp;Et@L;6LIH}Hu#IH}HuIH}Ht H};L: fff.UHAWAVAUATSHIIHH8SHHE1L HþL IƾL HL H0LxMgPHpL{LMvE1HHLLL @H;t&H>H A;1A|HIyL`LhL8HHpHA H+AH I& 7HMI$H@H9Hhr1HHy W)`HDžpH`LH`,I>Hxt#H>H ~;1ALIHhH+`HHW)@HDžPHHLHI)LHIH90 Lv;HHHHH@JHPMH@~eHHHL;HHLHHMtBHxH n >L'?HHH@1HHHxHEH)HHHwH)H@HUH t H HHHW) HDž0LLM)tsLHH9LN;HH(HHH(H HH0M~ALHL;HHLH(Mt!HxgH(HH1HHHxHhH+`HHEH)HHH)vH HU s HH(IW)HDžHChLkpI)t~HXLHL9IL>:HHHHJHMHP~BHHXL;HLHMt!LpgHHP1HHLpHhH+`HHEH)HHH)vHHU~ s HHW)HDžI$HHEH`HHHEHMLLHU1L膧HH| Ld$H@H$D$HH`LL LHh/0I>H8H>H 5;1ALpHHt H:HHt Hl:HHtHPH8Q:H HtHHH86:H@HtH@H8:H`Ht Hh:HSHH;ELH[A\A]A^A_]IH1HF HHˠHpL0MLxtsHCHs HH)IUI} H)H9H9t1H<0H;<2HH9uL蹄HH9ptI}m HHpHH:LHGHhH`HXL H@H`HH HH(Ml$X7HH(IHHH5>H ";H}O HHu1 W)HDž f:H(O)Hst matchHHHhape musHHHnd sum sHHHoutput aH@H5>HHHHL5L}M11H0HxHH8HRHT$8L$0Ht$(HpHL$ HD$Ld$H\$L,$HhHXL L(LH`3IHHpwH:,H: H:L}Mt6HEL9t!HX@t Hx:HI9uH}LL}~:Et H}o:EH}\:HH:P:IH`H5>H >H}L`H@!AH>HuH@LqE1H5RH RL:~:IH`]H5>H 2>H}L`JH@N!AHz>HuH@LE1H5RHRL: H@~:H p:Hb:IALeMuFzIAL`MII@t HP:LeMt6HEL9t!HX@t Hx:HI9uH}LLe:EuEu L`Mu-[H}g:EtH}X:L`M3HhL9>HX@t Hx%:HI9uIALeMuFzIAL`MII@t HP:LeMt6HEL9t!HX@t Hx:HI9uH}LLe:EuEuL`Mu&^H}j:EtH}[:L`Mt:HhL9tIHX@t Hx0:HI9uH`Lh:EL::L: LqIu u:uTL}MunH:t ItH:t ItHr:L}MuCIL}Mt7HEL9tefDHX@t Hx9:HI9uH}L}$:Et H}:EH}:HHLd_IHH{IH H~IH@HIIIHCt H{q:Hi:HHHM:HHH1:HHIHHHIH HH(IH@HHH:H`H%)IHHX7IHHt(BIHHHHHHtHPH8A:H HtHHH8&:H@HtWH@H8 :H`HuHxIIIHt(Ct H{:H:H`HuAIH`Ht2Hh$IIIHtCt H{:H:L: ffffff.UHAWAVAUATSHLIH(H%SHHE1H H HPHHH H)H HZx'H1HHHCW)EHEM~HI^PL)tIIILH=H:ILeLeKHEH~LLHT:ILeHHXMfHHHHhLELHpHh1H ΂ IL+A<$t 1H01H8E11H@%IF0H8IF`H@MvxHEH0LhHIyHLH HIHL$HD$H\$L$$LLH8LL0L@ 2H(HHhHt Hpp:H}Ht H}^:H;SHH;EuH(H[A\A]A^A_]:>:IHhH5 >H >H}LhHPAH >HuHPL_E1H5RHRL::IHhHH5 >H 5>H}Lh5HP9AH+ >HuHPLE1H5jRHRL: H}l: IALeMIALhMIIALeMFIALhMnIIPt H`:LeMt6HEL9t!HX@t Hxo:HI9uH}LLeU:EuEu LhMu-'H}/:EtH} :LhMHpL9HX@t Hx:HI9uIPt H`:LeMt6HEL9t!HX@t Hx:HI9uH}LLe:EuEuLhMu&^H}f:EtH}W:LhMt:HpL9tEHX@t Hx,:HI9uHhLp:Et`L::L: LIH}Hu4;IH}Hu&-IHhHt Hp:H}Ht H}:L.: fDUHSPHHP1HH[]fUHSPH~iHvPHH[]UHAWAVAUATSHIIHHHRHHE1L IIEIM H)H  HXf!H1HHHqIE)0IOPHYLqWLpHX)EHE :HEHH HM(0HXLpHMHU1Lz IMeI_PLsILgHLHLHR.HHHH}Ht H}:HRHH;EuHHHĨ[A\A]A^A_]::IHhH5>H >H}LhxHP|AHQ>HuHPLE1H5RHRL:{:IHhH5J>H y >H}LhHPAH>HuHPLE1H5*RHKRLC:BIALeMIALhMIIALeMFIALhMnIIPt H`\:LeMt6HEL9t!HX@t Hx7:HI9uH}LLe:EuEu LhMu-'H}:EtH}:LhMHpL9HX@t Hx:HI9uIPt H`:LeMt6HEL9t!HX@t Hxk:HI9uH}LLeQ:EuEuLhMu&^H}.:EtH}:LhMt:HpL9t=HX@t Hx:HI9uHhLp:Et$L:LIH}Ht H}:L2: f.UHSPHHhTHHHt Hq:t H\:HHt HD:HHt H,:H{hHtH{pH[]:H[]ÐUHSPHHXhTHHHt H:t H:HHt H:HHt H:H{hHt H{p:HH[]|:UHH-iTHHGPHt HGXH]X:]f.UHSPHHhTHH{PHt H{X':HH[]:ffff.UHAVSIP:HH2RHHH{L HfTHH[A^]IH:L@: UHAVSI`:HHLH[A^]IHw:L: UHAWAVAUATSHIIH/RHHEHRHI$I|$_ H0fTI$W)EEHEIchannelsLmEM|$PHhHULL HhHDžhHtCt H{:H:Et H}:HI?3W)EHEEHels_lastHELmEIXHhHULLq HhHDžhHtCt H{':H:Et H}:HtHRHH;EuHĈ[A\A]A^A_]v::IHh{H5j>H >H}LhhۺHPl AH>HuHPL޺E1H5RHRL:Hk:IHhH5>H >H}LhںW)PƅP*HDž`Hel countH^Hchannel HYHinvalid HQƅfAH5>HuHPLBݺE1H5RHRL:~:IHh.H5>H U>H}LhںHP AH>HuHPLܺE1H5PRHqRLi: IALeMIALhMIIALhMIIALeMIALhM:oIlIPt H`b:LeMt6HEL9t!HX@t Hx=:HI9uH}LLe#:EuEu LhMu-H}:EtH}:LhMHpL9HX@t Hx:HI9uIPt H`:LeMt6HEL9t!HX@t Hxq:HI9uH}LLeW:EuEu LhMu-'H}1:EtH}":LhMHpL9HX@t Hx:HI9uIPt H`:LeMt6HEL9t!HX@t Hx:HI9uH}LLe:EuEuLhMu&^H}h:EtH}Y:LhMt:HpL9tEHX@t Hx.:HI9uHhLp:Et4L<:L: LIEu IEt H}:L\: @UHAVSI:HHLH[A^]IH:L: UHAWAVAUATSHHIHORHHEHRHI$I|$ H_TI$I|$PH'8I$H}HH]HEHu'HRHH;EuHĘ[A\A]A^A_]}:Ct H{:H::IHhkH5Z=H =H}Lh)XպHP\LH=HuHPL׺DžLH5RHRL:IǰLLmMuKIǰLHhHI)IPt H`:LmMt6HEL9t!HX@t Hx:HI9uH}LLm:EuEuHhHu&bH}:EtH}z:HhHt>HpH9Lh@t HxK:LL9uHhHp0:LuMI$HulA$uyI$HI$HI|$hHL:I$HtHII$HtI$:A$tI$:I$HzI$k:I$HmI$M:I|$hHtI|$p9:IL: fffff.UHAVSI:HHBRHHH{L HHPL0H\THH[A^]IH:LA: fUHAWAVAUATSHHIHoRHHEH蠃 LH:Mt$H踃 H\LHLאA|$uID$8I+D$0Hu'HRHH;EuHĘ[A\A]A^A_]:":IHhH5=H C=H}Lh8ѺW)PHDž`@:H`(O)PHum_dims.HH0Hwith X nHH(Hpatible HH H not comHHH_dims isHHHhape numHHHkernel_sH@8LH=HuHPLӺDžLH58RHYRLQ: IǰLLmMuKIǰLHhHIIPt H`:LmMt6HEL9t!HX@t Hx:HI9uH}LLmf:EuEuHhHu&bH}C:EtH}4:HhHt>HpH9Lh@t Hx:LL9uHhHp:Lu=I|$xHuEI|$`HuRI|$HHu\I|$0HufA$L0: L:I|$xHtI$:I|$`HtI|$hq:I|$HHtI|$P]:I|$0HtI|$8I:A$tHBIA$tI|$$:L: fffff.UHAVSI:HH2RHHH{L} HHPL HqXTHH[A^]IH:L1: fUHAVSI:HHRHHH{L| HHPLHXTHH[A^]IH?:L: fUHAVSI$:HHRRHHH{L/| HHPL@H1XTHH[A^]IH:LQ: fUHAVSIh:HHLH[A^]IH:L : UHAWAVAUATSHHIH?RHHEHRHIEI}p{ HWTIEMuPIE`IEXIEPHEHEE fEesEscalEHhHUHL> HhHDžhHtCt H{:H:Et H}:HIEPIMXH)H xH8uHu!(HIEt H} :I>Ht I}X:L{: UHAVSHPIHRHHE:HþH:HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]jH5:H}HGH5A=HH5o:H)HAH5;HPHH;EHHH |RHHMHHUHuHuIIFIF0INIVIF@H]HEHtHE³H:HRHH;Eu LHP[A^]^:IH]HEHtH³H:L&: fffff.UHAVSHPIH[RHHEr:HþHh:HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH] H5:H}HH5=H蘉H5:HɊHAH5;HHH;HHH RHHMH%%HUHuHuIIFIF0INIVIF@H]HEHtH忳H:HfRHH;Eu LHP[A^]:IH]HEHtH蠿HD:L: fffff.UHAVSHPIHRHHE:HþH:HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃƃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H59:H}H臍H5=H8H5:HiHAH5W;H萉HH;~HHH RHHMH58HUHuHuIIFIF0INIVIF@H]HEHtH腽H):HRHH;Eu 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H@ [A\A^A_]IM&I^Ht"H.,IH*4L%4 IL$4 fffff.UHAVSIHwMII^HtH.,H/*4L[A^]#*4UHV2Ot H;2O]H=B2O;*4tHLHH2O2OH=2O*4H1O]UHAWAVATSIL%MM&0)4HW)C )C)H),I^HLHI{(uHC H&,C(1qT,HC H@ [A\A^A_]IM&I^Ht"Hr-,IH)4L#4 IL#4 fffff.UH>1Ot H#1O]H=*1O )4tHLHH0O0OH=0O(4H0O]UHAWAVATSIL%MM&0v(4HW)C )C)H(,I^HzLHI{(uHC H%,C(1AS,HC H@ [A\A^A_]IM&I^Ht"HB,,IH'4Lc"4 ILV"4 fffff.UH&0Ot H 0O]H=0O'4tHLHH/O/O H=/O'4H/O]UHAWAVATSIL%sMM&0F'4HW)C )C)H',I^H:LHI{(uHC HO$,C(1R,HC H@ [A\A^A_]IM&I^Ht"H+,IH&4L3!4 IL&!4 fffff.UH/Ot H.O]H=.O&4tHBLHH.O.OH=.O&4H.O]UHAWAVATSIL%CMM&0&4HW)C )C)HU&,I^HLHI{(uHC H#,C(1P,HC H@ [A\A^A_]IM&I^Ht"H),IH%4L 4 IL4 fffff.UH-Ot H-O]H=-O{%4tHLHH-O-OH=-OU%4H-O]UHAWAVATSIL%MM&0$4HW)C )C)H%%,I^HLHI{(uHC H!,C(1O,HC H@ [A\A^A_]IM&I^Ht"H(,IHQ$4L4 IL4 fffff.UH,Ot H,O]H=,OK$4tH LHH,O,OH=,O%$4H,O]UHAWAVATSIL%MM&0#4HW)C )C)H#,I^HLHI{(uHC H ,C(1N,HC H@ [A\A^A_]IM&I^Ht"H',IH!#4L4 IL4 fffff.UH+Ot H+O]H=+O#4tHLHH+O+OH=+O"4Hj+O]UHAWAVATSIL%MM&0"4HW)C )C)H",I^HLHI{(uHC H,C(1QM,HC H@ [A\A^A_]IM&I^Ht"HR&,IH!4Ls4 ILf4 fffff.UH*Ot H*O]H=*O!4tHLHHo*Om*OH=n*O!4HR*O]UHAWAVATSIL%MM&0V!4HW)C )C)H!,I^HLHI{(uHC H_,C(1!L,HC H@ [A\A^A_]IM&I^Ht"H"%,IH 4LC4 IL64 fffff.UH)Ot H{)O]H=)O 4tHLHHW)OU)OH=V)O 4H:)O]UHAWAVATSIL%SMM&0& 4HW)C )C)He ,I^H2LHI{(uHC H/,C(1J,HC H@ [A\A^A_]IM&I^Ht"H#,IH4L4 IL4 fffff.UH~(Ot Hc(O]H=j(O4tH:LHH?(O=(O H=>(Oe4H"(O]UHAWAVATSIL%#MM&04HW)C )C)H5,I^H"LHI{(uHC H,C(1I,HC H@ [A\A^A_]IM&I^Ht"H",IHa4L4 IL4 fffff.UHf'Ot HK'O]H=R'O[4tH*LHH''O%'O H=&'O54H 'O]UHAWAVATSIL%MM&04HW)C )C)H,I^HLHI{(uHC H,C(1H,HC H@ [A\A^A_]IM&I^Ht"H!,IH14L4 IL4 fffff.UHN&Ot H3&O]H=:&O+4tHLHH&O &O H=&O4H%O]UHAWAVATSIL%÷MM&04HW)C )C)H,I^H LHI{(uHC H,C(1aG,HC H@ [A\A^A_]IM&I^Ht"Hb ,IH4L4 ILv4 fffff.UH6%Ot H%O]H="%O4tHLHH$O$O H=$O4H$O]UHAWAVATSIL%MM&0f4HW)C )C)H,I^HJ LHI{(uHC Ho,C(11F,HC H@ [A\A^A_]IM&I^Ht"H2,IH4LS4 ILF4 fffff.UH$Ot H$O]H= $O4tHRLHH#O#OH=#O4H#O]UHAWAVATSIL%MM&064HW)C )C)Hu,I^H LHI{(uHC H?,C(1QI,HC H@ [A\A^A_]IM&I^Ht"H,IH4L#4 IL4 fffff.UHAVSIHMII^HtH,HO4L[A^]C4UHAWAVATSIL%۴MM&0&4HW)C )C)He,I^Hj LHI{(uHC H/,C(1AH,HC H@ [A\A^A_]IM&I^Ht"H,IH4L4 IL4 fffff.UHAWAVATSIL% MM&0V4HW)C )C)H,I^H LHI{(uHC H_,C(1qG,HC H@ [A\A^A_]IM&I^Ht"H",IH4LC4 IL64 fffff.UHAWAVATSIL%;MM&04HW)C )C)H,I^H LHI{(uHC H,C(1F,HC H@ [A\A^A_]IM&I^Ht"HR,IH4Ls4 ILf4 fffff.UHAWAVATSIL%kMM&04HW)C )C)H,I^H LHI{(uHC H,C(1E,HC H@ [A\A^A_]IM&I^Ht"H,IH!4L4 IL4 fffff.UHAWAVATSIL%MM&04HW)C )C)H%,I^HRLHI{(uHC H,C(1E,HC H@ [A\A^A_]IM&I^Ht"H,IHQ4L4 IL4 fffff.UHAWAVATSIL%˰MM&04HW)C )C)HU,I^HzLHI{(uHC H,C(11D,HC H@ [A\A^A_]IM&I^Ht"H,IH4L4 IL4 fffff.UHAWAVATSIL%MM&0F4HW)C )C)H,I^HLHI{(uHC HO,C(1aC,HC H@ [A\A^A_]IM&I^Ht"H,IH4L34 IL&4 fffff.UHAWAVATSIL%+MM&0v4HW)C )C)H,I^HLHI{(uHC H,C(1B,HC H@ [A\A^A_]IM&I^Ht"HB,IH4Lc 4 ILV 4 fffff.UHAWAVATSIL%[MM&04HW)C )C)H,I^HLHI{(uHC H,C(1A,HC H@ [A\A^A_]IM&I^Ht"Hr,IH4L 4 IL 4 fffff.UHAWAVATSIL%MM&04HW)C )C)H,I^HRLHI{(uHC H,C(1@,HC H@ [A\A^A_]IM&I^Ht"H,IHA4L 4 IL 4 fffff.UHAWAVATSIL%MM&04HW)C )C)HE,I^H*LHI{(uHC H,C(1!@,HC H@ [A\A^A_]IM&I^Ht"H,IHq4L 4 IL 4 fffff.UHAWAVATSIL%MM&064HW)C )C)Hu,I^HRLHI{(uHC H? ,C(1Q?,HC H@ [A\A^A_]IM&I^Ht"H,IH4L# 4 IL 4 fffff.UHAWAVATSIL%+MM'0f4HW)C )C)H,I_H" LHIHK[A\A^A_]IM'I_Ht"H[,IH4L| 4 ILo 4 UHAWAVAUATSHIH!LHHEA(uIG L ,AG(1<,IG H@ BOH#OH=OP(IMA(uI_ KH{Hu;+L ,AG(1,<,HI_ KH{Hu1q@,HH{H LHH;EudLHĘ[A\A]A^A_] ,H=O4SH=vOaH=KH5cOH 4H=`O 4#4 4IHh(H5ys7H s7H}LhHKHHHzt7HPHHAHFt7HuHPLE1H5.KHOKLG 4 IALeMuFzIALhMIIPt H` 4LeMt6HEL9t!HX@t Hx 4HI9uH}LLef 4EuEuLhMu&^H}C 4EtH}4 4LhMt:HpL9tEHX@t Hx 4HI9uHhLp 4Et&L 4Lc4 LIH=O 4LE4 ffff.UHAVSIH_MII^HtH,H 4L[A^]s 4UH] fDUH]DUHHK]UHHUL]UHAWAVAUATSHIHHHLHHEH8HKHHHLH@H8Lp 4HEEL=LMgLI@L8Lv 4L-LILHDž(HDž HDžHDžDž0HH7 4HHHL 4HLHHHHLL8Lt H 4H4H5KHH@4H8` 4HLHH;EuHH[A\A]A^A_] 4HLL8Lt H Q 4H+4HHwHH5KHH4HH84H4 fUHSPHHtHsH6HH[]4H[]ÐUH4HHH@H@H]fUHAWAVATSIL%KMM'04HW)C )C)H,I_HZLHIHK[A\A^A_]IM'I_Ht"H{ ,IH4L4 IL4 UHAWAVAUATSHIHLHHEA(uIG L,AG(15,IG H@  OH OH= OP(IMA(uI_ KH{Hu;+L,AG(1L5,HI_ KH{Hu19,HH{HLHH;EudLHĘ[A\A]A^A_],H=2 O34SH= OqH=RKH5 OH,}4H= O4C44IHhHH5l7H m7H}Lh5HKH@HHHm7HPHHAHbm7HuHPLE1H5JKHkKLc4 IALeMuFzIALhMIIPt H`4LeMt6HEL9t!HX@t Hx4HI9uH}LLe4EuEuLhMu&^H}_4EtH}P4LhMt:HpL9tEHX@t Hx%4HI9uHhLp 4Et&L34L3 LIH= O4La3 fUHHK]UHHL]UHSPHHtHsH覰HH[]4H[]ÐUHz4HHH@H@H]fUHAWAVATSIL%MM'064HW)C )C)Hu,I_HKHIHK[A\A^A_]IM'I_Ht"H+,IH4LL3 IL?3 UHAWAVAUATSHIHrLHHEA(uIG L,AG(1g1,IG H@ OHOH=OP(IMA(uI_ KH{Hu;+L[,AG(10,HI_ KH{Hu1A5,HH{HLHH;EudLHĘ[A\A]A^A_],H=:O4SH=OH=KH5OHx4H=O44k4IHhH5Ih7H Hj7H}LhHKH@HHHFi7HPHHAHi7HuHPLlE1H5KHKL4 IALeMuFzIALhMIIPt H`q4LeMt6HEL9t!HX@t HxL4HI9uH}LLe24EuEuLhMu&^H}4EtH}4LhMt:HpL9tEHX@t Hx4HI9uHhLp4Et&L4L/3 LIH=(O4L3 fUHH5K]UHH L]UHSPHHtHsHVHH[]44H[]ÐUH*4HHH@H@H]fUHAWAVATSIL%MM'03HW)C )C)H%,I_HKHIHK[A\A^A_]IM'I_Ht"H,IHz3L3 IL3 UHAWAVAUATSHIH"LHHEA(uIG Lv+AG(1-,IG H@ ROH3OH=,OP(IMA(uI_ KH{Hu;+L +AG(1,,HI_ KH{Hu10,HH{HcLHH;EudLHĘ[A\A]A^A_]q,H=O3SH=OH=zKH5sOHtA3H=pO_333IHhH5c7H f7H}LhH6KH@HHHd7HPHHyAHd7HuHPLE1H5KHKL3 IALeMuFzIALhMIIPt H`!3LeMt6HEL9t!HX@t Hx3HI9uH}LLe3EuEuLhMu&^H}3EtH}3LhMt:HpL9tEHX@t Hx3HI9uHhLpj3Et&L3L3 LIH=O{3L3 fUHHK]UHH= L]UHSPHHtHsHHH[]3H[]ÐUH3HHH@H@H]fUHAWAVATSIL%[MM'03HW)C )C)H+I_HrKHIHK[A\A^A_]IM'I_Ht"H+IH*3L3 IL3 UHAWAVAUATSHIH LHHEA(uIG L&+AG(1(,IG H@ rNHSNH=LNP(IMA(uI_ KH{Hu;+L+AG(1\(,HI_ KH{Hu1,,HH{H LHH;EudLHĘ[A\A]A^A_]! ,H=NC3SH=NH=KH5NH3L3 fff.UHAWAVATSIL%MM'03HW)C )C)H+I_HKHIHK[A\A^A_]IM'I_Ht"H+IHj3L3 IL3 UHAWAVAUATSHIHKHHEЊNHNH=NP(IMA(uI_ KH{Hu;+L.+AG(1_,HI_ KH{Hu1,HH{HKHH;EudLHĘ[A\A]A^A_]+H=UN3SH=1N4H=KH5NHWd3H=N33>3IHhH5G7H M7H}LhسHAKH@HHHH7HPHHAHL7HuHPL?۳E1H5;KHKL3 IALeMuFzIALhMIIPt H`D3LeMt6HEL9t!HX@t Hx3HI9uH}LLe3EuEuLhMu&^H}3EtH}3LhMt:HpL9tEHX@t Hx3HI9uHhLp3Et&L3L3 LIH=CN3L3 fff.UHAWAVATSIL%s{MM'063HW)C )C)Hu+I_HKHIHK[A\A^A_]IM'I_Ht"H++IH3LL3 IL?3 UHAWAVAUATSHIHrKHHEЊuNHVNH=ONP(IMA(uI_ KH{Hu;+L+AG(1 ,HI_ KH{Hu1t,HH{HKHH;EudLHĘ[A\A]A^A_]+H=N3SH=NĸH= KH5NHT3H=N3&33IHh+H5|C7H [J7H}LhճHKH@HHHyD7HPHHAHI7HuHPL׳E1H5-KHNKLF3 IALeMuFzIALhMIIPt H`3LeMt6HEL9t!HX@t Hx3HI9uH}LLee3EuEuLhMu&^H}B3EtH}33LhMt:HpL9tEHX@t Hx3HI9uHhLp3Et&L3Lb3 LIH=N3LD3 fff.UHAWAVATSIL%wMM'03HW)C )C)H+I_HKHIHK[A\A^A_]IM'I_Ht"H+IH*3L3 IL3 UHAWAVAUATSHIHKHHEЊNHNH=NP(IMA(uI_ KH{Hu;+L+AG(1,HI_ KH{Hu1 ,HH{HFKHH;EudLHĘ[A\A]A^A_]T+H=]Nv3SH=9N脷H=KH5&NHoP$3H=#NB333IHhH5?7H 4G7H}LhxѳHAKH@HHH@7HPHH\AHzE7HuHPLӳE1H5KHKL3 IALeMuFzIALhMIIPt H`3LeMt6HEL9t!HX@t Hx3HI9uH}LLe3EuEuLhMu&^H}3EtH}3LhMt:HpL9tEHX@t Hxh3HI9uHhLpM3Et&Lv3L3 LIH=KN^3L3 fff.UHAWAVATSIL%3tMM'03HW)C )C)H5+I_HbKHIHK[A\A^A_]IM'I_Ht"H+IH3L 3 IL3 UHAWAVAUATSHIH2KHHEЊMNH.NH='NP(IMA(uI_ KH{Hu;+LN+AG(1,HI_ KH{Hu14 ,HH{HKHH;EudLHĘ[A\A]A^A_]+H=N3SH=N贲H=KH5nNHL3H=kN33^3IHhH5<<7H C7H}LhͳHKH@HHH9=7HPHHAHA7HuHPL_гE1H5KHKL3 IALeMuFzIALhMIIPt H`d3LeMt6HEL9t!HX@t Hx?3HI9uH}LLe%3EuEuLhMu&^H}3EtH}3LhMt:HpL9tEHX@t Hx3HI9uHhLp3Et&L3L"3 LIH=N3L3 fff.UHAWAVATSIL%pMM'0V3HW)C )C)H+I_HKHIHK[A\A^A_]IM'I_Ht"HK+IH3Ll3 IL_3 UHAWAVAUATSHIHKHHEЊNHNH=NP(IMA(uI_ KH{Hu;+L+AG(1+HI_ KH{Hu1,HH{HKHH;EudLHĘ[A\A]A^A_]+H=uN63SH=QN货H=5KH5>NH/I3H=;N3F33IHhKH587H @7H}Lh8ʳHKH@HHH97HPHHAH:>7HuHPL̳E1H5MKHnKLf3 IALeMuFzIALhMIIPt H`3LeMt6HEL9t!HX@t 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KH{Hu1+HH{H&KHH;EudLHĘ[A\A]A^A_]4+H=NV3SH=yN$H=}KH5fNHO>3H=cN"3f33IHhkH5-7H E77H}LhXH9KH@HHH.7HPHH3L3 fff.UHAWAVATSIL%bMM'03HW)C )C)H+I_H KHIHK[A\A^A_]IM'I_Ht"H+IHj3L3 IL߿3 UHAWAVAUATSHIHKHHEЊNHNH=NP(IMA(uI_ KH{Hu;+L.+AG(1_+HI_ KH{Hu1+HH{HKHH;EudLHĘ[A\A]A^A_]+H=%N3SH=NԛH=KH5NH:d3H=N33>3IHh˵H5*7H (47H}Lh踻HIKH@HHH+7HPHH蜸AH/7HuHPL?E1H5͡KHKL3 IALeMuFzIALhMIIPt H`D3LeMt6HEL9t!HX@t Hx3HI9uH}LLe3EuEuLhMu&^H}3EtH}3LhMt:HpL9tEHX@t Hx3HI9uHhLp3Et&L3L3 LIH=N3L3 fff.UHAWAVATSIL%s^MM'063HW)C )C)Hu+I_HZKHIHK[A\A^A_]IM'I_Ht"H++IH3LL3 IL?3 UHAWAVAUATSHIHrKHHEЊENH&NH=NP(IMA(uI_ KH{Hu;+L莾+AG(1+HI_ KH{Hu1t+HH{HKHH;EudLHĘ[A\A]A^A_]+H=N3SH=yNdH=KH5fNH73H=cN3&33IHh+H5|&7H 07H}LhHɩKHHH}'7HPHHAH,7HuHPL裺E1H51KHRKLJ3 IALeMuFzIALhMIIPt H`訿3LeMt6HEL9t!HX@t Hx胿3HI9uH}LLei3EuEuLhMu&^H}F3EtH}73LhMt:HpL9tEHX@t Hx 3HI9uHhLp3Et&L3Lf3 LIH=N3LH3 UHAWAVATSIL%ZMM'0覾3HW)C )C)H+I_H²KHIHK[A\A^A_]IM'I_Ht"H+IH:3L輸3 IL诸3 UHAWAVAUATSHIHKHHEЊNHnNH=gNP(IMA(uI_ KH{Hu;+L+AG(1/+HI_ KH{Hu1+HH{HVKHH;EudLHĘ[A\A]A^A_]d+H=N膽3SH=N脠H=MKH5NH343H=NR3薽33IHh蛮H5"7H -7H}Lh舴H!KHHH#7HPHHpAH(7HuHPLE1H5KH–KL躼3 IALeMuFzIALhMIIPt H`3LeMt6HEL9t!HX@t Hx3HI9uH}LLeٻ3EuEuLhMu&^H}趻3EtH}觻3LhMt:HpL9tEHX@t Hx|3HI9uHhLpa3Et&L芻3Lֵ3 LIH=׿Nr3L踵3 UHAWAVATSIL%SWMM'03HW)C )C)HU+I_H*KHIHK[A\A^A_]IM'I_Ht"H +IH誺3L,3 IL3 UHAWAVAUATSHIHRKHHEЊNHNH=NP(IMA(uI_ KH{Hu;+Ln+AG(1+HI_ KH{Hu1T+HH{HKHH;EudLHĘ[A\A]A^A_]+H=mN3SH=IN$H=KH56NH/褹3H=3N¹33~3IHh H5\7H *7H}LhHiKHHH] 7HPHHAH$7HuHPL胳E1H5KH2KL*3 IALeMuFzIALhMIIPt H`舸3LeMt6HEL9t!HX@t Hxc3HI9uH}LLeI3EuEuLhMu&^H}&3EtH}3LhMt:HpL9tEHX@t Hx3HI9uHhLpѷ3Et&L3LF3 LIH=_N3L(3 UHAWAVATSIL%KTMM'0膷3HW)C )C)Hŷ+I_H KHIHK[A\A^A_]IM'I_Ht"H{+IH3L蜱3 IL菱3 UHAWAVAUATSHIHKHHEЊNHNH=߼NP(IMA(uI_ KH{Hu;+L޳+AG(1+HI_ KH{Hu1+HH{H6KHH;EudLHĘ[A\A]A^A_]D+H=]Nf3SH=9NıH=KH5&NH_,3H=#N23v33IHh{H57H '7H}LhhH9KHHH7HPHHPAHn!7HuHPLE1H5KHKL蚵3 IALeMuFzIALhMIIPt H`3LeMt6HEL9t!HX@t HxӴ3HI9uH}LLe蹴3EuEuLhMu&^H}薴3EtH}致3LhMt:HpL9tEHX@t Hx\3HI9uHhLpA3Et&Lj3L趮3 LIH=ONR3L蘮3 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E1A1H`E11HhHXHHHHPH1HH4t~HpUL;HH51H ot-HHLHHx2j 8H5/-'H s-HxGm A2H 0Lm H 0LxLl IW)HDžH)HDžHv1-II;IDLMHXȃ H THcHHxLHhLpIHxH>" H0H 01A LA$L@u IID$HhMd$HH+-MIILz--ILILLLxLHL.-C,HHLF t H --I?tXE1H0H 0AL A1H`E11HhHXP^IHHH*-H/\*- HsHEsHESH.*-u fDžHHDžH13*-HHHHHHHL`H@ t HHHIHHhHaW)HDžH`HHLHpLPXHHHDžCt H{L+-L0H=+-HHDžtHHt HHH +-ILH+-H`HH@PLHL8LHHHHT$H $LHpH0II?HD0H |01ALH8E Hc*-I?u\E11H`E1H`HHLHP@I?H0H 01AL I?tE1H0H 0AL A1H`E1HXu]nLLHH`IIL-aL8L`HXIE1tHX)-HXHHDžHt,C(t H{8+)-t H{)-H)-HXEt?LxMt H`AH;EHH;E LH[A\A]A^A_]MHxt_H0H 01A HxLHhLpICHxH>H0H 01A HxLHhLpI2HxH>H;0H z01AF dHxLHhLpI!HxH>lH0H )01A HxLHhLpIHxH>H0H 01A HxLHhLpI/HxH>HH0H 01AS qHxLHhLpIHxH>yH0H 601A HxLHhLpI}HxH>(H0H 01A HxLHhLpIHxH>HU0H 01A` ~HxLHhLpIHxH>H0H C01A -HpAu;HLHIr%HHHGHH4@Hp$-HL}I$-HpHHHHDpIGHXHxHcA`H9HIhLqHLDHLpI6L"-IIHu[HxLHhLpIHxH>IH0H 01A HxLHhLpIHxH>Hv0H 01A LPL0H/EHHHLH@H0LL#-HEEHa+ELkLH@H0L"-L5$+EILHDž HDžHDžHDžDž(H5c0H)'HXHl!-HL7,HHxz] t H"-LH0Lt HW"-L5!-H5 EHH -L!-LP~`t/Hxl` X` HxHHxH>qLPHXHGHLc(W)HDžMHxtoHXEETN$L|!-HHNH 0H L01A  6-H-Hh- HZ-  IHxHHHCt H{-H-MHxtSH-EL -LXLcsMH8Lc IAM<$Mt My!LH LH HtHte!nfffff.Ht[DHHtOHH tQ!H DELIs9!fIIr0DHHu@DELIIHALHHXLchMLx H@H@HPIADM7Mt My LH LH HtHtv ffffff.HtkDHHt_HH tQ H DELIs9 fH@p;pt:Vffffff.DHHu@DELH@p;puHP -HP0H@HH ^XHcL4IIHXLx0MuL=0HD`AL0Dž@H50HH@HXHF t H -LX{H@HH XAHXLx0MWYHPHDžHDžHHLH8'I}tIE1H0H 0AL HHDžHIEHHXHDžDHHL1I}E1H@0H 0ALE HHDžHtJ -t2Mt1AGt I7 -L/ -H EHH;Et -M}HEHH;EuLHĨ[A\A]A^A_]IELMI LH MtH+EHtELMDDHW)pHDžHHs"pHqIHL0u;_LiILIR -LILILpHxHqH`LIL -H`LL0AptHHcHHH(H FHpHXHxHuJHELpLeIw IcWHXtpAHXyApHHMwxHXH`H)HHH HLL@1IHH@H}HHLLP0HH;HrLpVHxFHEHXHEHuILeLeLeIw IcWHXoAHXxApHHH`MwxHXH`H)HHH# HLL@1IHH@H}HHLLP0HH;HrLeIH50H R0HzH(H15W)HDž -H(@)Hbe INT64HHHd to be HHH expecteHHHIndiciesH@H5c0H@H(HerHhH@@ @(LM H5f0H 50H]H(H1W)HDž q-H(@)Hbe INT64HHHd to be HHH expecteHHHIndiciesH@H5F0H@H(HHqHhH@? @(LM DLMLH tI DDHLILHPJ-cH1-(WH8-LMP HL91 HX@t Hx-HI9uH HP-H-(H8-LM HL9 HX@t Hx`-HI9uH AGW)HDžP.-HH(UA)H50BH-CBHhH = - AGmW)HDžP-HH(ٓA)H5k0BH<-CBHhH == H`HBH9 H W)@HDžPH@HxH@HDHHHP|.HH HHH HHHHTHMHHكII)MLqHHPHHHXE1L811Ir]ff.H4H|I4HI|HHtItHHTITHHI9uH@11IsHtHXfHH@HPs11Mt$HXfH@uqIHH9un@I H`MAH`HH9IINIHH9tH50H y0H H(H1\W)HDž@,H(_A)Hxpected.HH.Hatch expHH(Hes not mHH H size doHHHraw dataHHHndicies HHHSparse IH@6H50H@H(HB+HhH@6 @(LMINH`HH9yH5G0H 0H> H(H1W)HDž@R,H(A)Hxpected.HH.Hatch expHH(Hes not mHH H size doHHHraw dataHHHndicies HHHSparse IH@6H5_0H@H(H)HhH@r5 @R^(jLMrMvLMvHP1,MH,(AH8,LMt;HL9t HX@t Hx,HI9uLL,t H,t H,HXHt H`v,LhHDžhH}I9H HP(MH@IELHL;5jDtAuwfA~,HHHHpHxHPHW)pHDžH@H1HHDžHOOIFIF11L,HIFHpHxINIW)pHDžHHDžHHP#,H ,(H8,LMt;HL9t HX@t Hx,HI9uaLL,t H,t H,HXHt H`h,LhHDžhH}I9HP MH@ 5,HIHHHIw6,I]H0H m01A#L ppH,HHDžH``Hp,T,IHX: H540H 0HLX $ƅ(Hb0H@H(HH@1LH5LDHmDLe,,H DHHH5DHDH2,,H DHHH5DHDH,^,H DHHH5DHDH,.,H dDHHH5fDHODH, W,IH@ H50H |0HL@H(AH0HH(LrE1H5DH!DL,u,IH@[ H5U0H 0HL@EH(IAHH0HH(LE1H5wDHDL,E,IH( H50H j0HL(HAH0HHL`E1H5DHDL,c,IH(I H5C0H 0HL(3H7AHV0HHLE1H5eDHDL~,It H ,LMuQIALMt?HL9t$HX@t Hx,HI9uHLL,uu&L(MuFH,tHj,L(MuQIAL(Mt?H0L9t$HX@t Hx+,HI9uH(LL0 ,EuJ IL,,H@H; | It H,LMuQIALMt?HL9t$HX@t Hx,HI9uHLLj,uu&L(MuFH;,tH&,L(MuQIAL(Mt?H0L9t$HX@t Hx,HI9uH(LL0,Eu/ IL,H@H m I(t H8,LMuQIALMt?HL9t$HX@t HxF,HI9uHLL&,uu&L@MuFH,tH,L@MuQIAL@Mt?HHL9t$HX@t Hx,HI9uH@LLH,Eu IL,HXH I(t H8A,LMuQIALMt?HL9t$HX@t Hx,HI9uHLL,uu&L@MuFH,tH,L@MuQIAL@Mt?HHL9t$HX@t Hx_,HI9uH@LLH?,EuIL`,HXHVzI@t HP,E1LMuYHIALMt?HL9t$HX@t Hx,HI9uHLL,uu#LXMuC{HS,tH>,LXMuLIALXMt:H`L9t8HX@t Hx,HI9uHXL`,Eu LIL,I@u/(LM%HP~,t^I@u+up(LM(HP0,t7IiH ,(]3ItH,(t=I((H8,LMuEI(IH8,LMui_ILMHL9fffff.HX@t Hx9,HI9u[ILMHL9.ffff.HX@t Hx,HI9uoI&XIx;kI$I}D7HI@u/(LM%_HP,t^I@u+up(LMmHP,t7IiH,(]3ItHu,(t=I((H8N,LMuEI(IH8!,LMILMt?HL9tvDHX@t Hx,HI9uHL,t H,7H,HXH'.LILMtIHL9|ff.HX@t Hx9,HI9uHL,t H ,H,HXHLidImIH}HpH9^II]IEHCtH{w,IHj,pgnIpRYWSII]IEHtBCt H{,H,)I^H,Lv, IE1HHDžHIH@HtFHH,HXHu:DIH@HtRHHv,HXHuFPIHXHt H`M,H}HEH9t8Ht9HP(1IHXHt H`,H}HpH9uHP E1pt H,HHDžHt,Mt'AGt I,L,L3, IL$, fff.UHAWAVAUATSH8MLHHHHH/DHHEH8HDHHHLH@H8L,HEEL-DM}LI@L8L,L5DILHDž(HDž HDžHDžDž0HHHHLMLHHHLL8Lt H *,H,H5DHH,H8,HDHH;EuHH8[A\A]A^A_]g,HLL8Lt H ,H~,HH5PDHH,HH8#,H, DUHAWAVAUATSH(H(DHHEIIIAP>H5 HcHA]uMce(J<H9A]Mce@K<$H9A]Mce@K<$H9pA]Mce@J<H9A]McJ<H9%A]McJ<H9~A]Mce@K<$H9A]Mce@A]Mce@A]McepJ<H9|EH50H]HUHLH EH},H~DHH;EA]ZMce@~A]`Mce@K<$H9A]^McJ<H98IQKII IuoII93IIDIYDRIIDIuoIHzH9II,I?IHt ,A]tIuH11HJHRMLLM蠑HIDHH;EHzH9IIG,I?IHt +,A]tIuH11HJHRMLLM脍HDHH;EIVHzH9II,I?IHt ,A]tIuH11HJHRMLLMGHPDHH;EHzH9IIM,I?IHt 1,A]tIuH11HJHRMLLM蚞HDHH;EO\HzH9II,I?IHt ,A]tIuH11HJHRMLLMHVDHH;EHzH9IIT,I?IHt 8,A]tIuH11HJHRMLLMHDHH;EVcHzH9II,I?IHt ,A]tIuH11HJHRMLLM{H]DHH;ELbL!L^,I?IHt B,A]tIuH11HJHRMLLM{HDHH;E`mLbL!L,I?IHt ,A]tIuH11HJHRMLLM蒇HkDHH;EHzH9IIh,I?IHt L,A]tIuH11HJHRMLLMuHDHH;EjwLbL!L,I?IHt ,A]tIuH11HJHRMLLM茞HuDHH;EHzH9IIs,I?IHt W,A]tIuH11HJHRMLLM蠡HDHH;EtyHzH9II,I?IHt ,A]tIuH11HJHRMLLM~HDHH;EuLH([A\A]A^A_],HEt H}j,H, O0Fp\} }}@UHAVSH HDHHEIEHEHEȃwHH vAHHfHnEHuL=AH]HEHtCt H{,H,HnDHH;Eu LH [A^],IH]HEHtCt H{M,HE,L, ffffff.UHAWAVAUATSHIHHHDHHEH8HDHHHLH@H8Lp,HEEL5DM~LI@L8Lv,L%DILHDž(HDž HDžHDžDž03H,L+,HLH۬HHHLL8Lt H ,H,H5DHHN,H8n,HDHH;EuHH[A\A]A^A_]",HLL8Lt H _,H9,HHHH5DHH,HH8,H, UHAWAVAUATSHIIIHDHHEEAtOEdH}k$MH]Hu1$HH]IKHH;=ڱDto,tEH5z0HHHUHxLH3 HVHX6,HDHH;EJ,He%LKDMHEHu 1$HEDHLHHǀMcoMLI_ IAffffff.L#Mt MyMI LH MtHtfoffffff.Ht[ELMtOLH tI DELMsfHIr0DLMu@DELHIHAHDž(HDž H H(LLH6I<$t H30H 901AvL; TI$HDžHHLI<$tlHݣ0H 01AyL H HDž Ht,H}Pk$HDHH;ELH[A\A]A^A_]I$HHHH L LzpMtJ1ff.A|t-rp;rtuL,A7HHBxNJpHcHHI9uHHLL;5rDu'HHH5GH[%L3HHcrp1L,AIL IHLjpMtD1@A<t.rp;rtuL[,AuHHBxNJpHcHHIuMHHHH;Du'HHH5GH%HHHcrpH1H),u_H^MvHLMIcHpHI@xHHHHƉHI1HH[LLMIcHpHHI@xHHHHƉH1HLIMc1AuA;u H)1H8A\=AHxA\=A\HxA\=A\HxA\=A\HH H9uHt%I1f.H4A\5AHH9uLMc1AuA;ut}H)1H8A<ȬE1H5̫DHDL, ,IH0* H5$0H 0HHL0ŬƅHR0HHHHH1LǬH5ҪH6ҪHH5DHHd,HH8,HI, UHHt],]ffffff.UHAWAVAUATSH8MIHIHHNDHHEH8HDHHHHH@H8H,LHEEL%DID$HHI@L8H,L5DILHDž(HDž HDžHDžDž0L,HLH?ˬHH0H1,La,HLHˬHH0H,HHHϬHHL8Lt H ,H,H5DHHg,H8,HDHH;EuHH8[A\A]A^A_];,HHHL8Lt H q,HK,#HϪHϪHϪHϪHH5DHH,HH8,H蚿, fUHAWAVSHHDHHELIII)H)I9u-IȿIHDHH;EH50H 0H`6HOH}1HŬW)@HDžP0j,HP($A)@Hze mismaHH Huffer siHHHnation bHHHnd destiHHHsource aH@(tchH50H}HUH@HMȾL EuK@uTEu`L}MtwHEL9tbfHX@t Hx,HI9uH};H}r,@tHP],EtH}N,L}Mu LL}7,Eu*`u3HDHH;Eu?LHĨ[A^A_]H},`tHp,HDHH;Etj,IEu@u4EuKL}MubH},@t I@tHP,Et IEtH}h,L}MuIIL}Mt=HEL9tufff.HX@t Hx),HI9uH}L},Eu`uL臼, H},`tHp,L_, LfUHAWAVAUATSH(IHIHHDHHEH8H(DHHHLH@H8L ,HEEL=]DIGHHI@L8L ,L%DILHDž(HDž HDžHDžDž0L,HLH~ŬHsHEsHESHYŬAEIuIEuIEUH2ŬHHHɬHHL8Lt H 7,H,H5DHH蠾,H8,HDHH;EuHH([A\A]A^A_]t,HHHL8Lt H 調,H脾,HɪHɪHɪHH5>DHH,HH8,H۹, UHAWAVSHHDHHELIII)H)I9u-IȿIHDHH;EH50H 0H`6HH}1HQW)@HDžP0誾,HP(dA)@Hze mismaHH Huffer siHHHnation bHHHnd destiHHHsource aH@(tchH5ʒ0H}HUH@VHMȾL EuK@uTEu`L}MtwHEL9tbfHX@t Hxɽ,HI9uH};H}貽,@tHP蝽,EtH}莽,L}Mu LL}w,Eu*`u3HEDHH;Eu?LHĨ[A^A_]H}>,`tHp),HDHH;Et誽,IEu@u4EuKL}MubH},@t I@tHP¼,Et IEtH}証,L}MuIIL}Mt=HEL9tufff.HX@t Hxi,HI9uH}L}T,Eu`uLǶ, H}2,`tHp,L蟶, LfUHAWAVSHHDHHEIHH)LH)H9u*IHDHH;EH50H 0H`6HRH}1HW)@HDžP0m,HP('A)@Hze mismaHH Huffer siHHHnation bHHHnd destiHHHsource aH@(tchH50H}HUH@HMȾL EuN@uWEucL}MtzHEL9tefff.HX@t Hx艺,HI9uH};H}r,@tHP],EtH}N,L}Mu LL}7,Eu*`u3HDHH;Eu?LHĨ[A^A_]H},`tHp,HDHH;Etj,IEu@u4EuKL}MubH}襹,@t I@tHP肹,Et IEtH}h,L}MuIIL}Mt=HEL9tufff.HX@t Hx),HI9uH}L},Eu`uL至, H},`tHpݸ,L_, LfUHAWAVSHHDHHEIHH)LH)H9u*IHfDHH;EH5C0H }0H`6HH}1HԸW)@HDžP0-,HP(A)@Hze mismaHH Huffer siHHHnation bHHHnd destiHHHsource aH@(tchH5M0H}HUH@HMȾLs EuN@uWEucL}MtzHEL9tefff.HX@t HxI,HI9uH};H}2,@tHP,EtH},L}Mu LL},Eu*`u3HDHH;Eu?LHĨ[A^A_]H}辶,`tHp詶,HDHH;Et*,IEu@u4EuKL}MubH}e,@t I@tHPB,Et IEtH}(,L}MuIIL}Mt=HEL9tufff.HX@t Hx,HI9uH}L}Ե,Eu`uLG, H}貵,`tHp蝵,L, LfUHAWAVSHHYDHHELIII)H)I9u-IȿQIH#DHH;EH50H :0H`6HH}1H葵W)@HDžP0,HP(A)@Hze mismaHH Huffer siHHHnation bHHHnd destiHHHsource aH@(tchH5 0H}HUH@HMȾL0 EuK@uTEu`L}MtwHEL9tbfHX@t Hx ,HI9uH};H},@tHPݳ,EtH}γ,L}Mu LL}跳,Eu*`u3HDHH;Eu?LHĨ[A^A_]H}~,`tHpi,HFDHH;Et,IEu@u4EuKL}MubH}%,@t I@tHP,Et IEtH},L}MuIIL}Mt=HEL9tufff.HX@t Hx該,HI9uH}L}蔲,Eu`uL, H}r,`tHp],L߬, LfUHAWAVSHHDHHELIII)H)I9u-Iȿ IHDHH;EH50H 0H`6HH}1HQW)@HDžP0誱,HP(dA)@Hze mismaHH Huffer siHHHnation bHHHnd destiHHHsource aH@(tchH5ʅ0H}HUH@VHMȾL EuK@uTEu`L}MtwHEL9tbfHX@t Hxɰ,HI9uH};H}貰,@tHP蝰,EtH}莰,L}Mu LL}w,Eu*`u3HEDHH;Eu?LHĨ[A^A_]H}>,`tHp),HDHH;Et誰,IEu@u4EuKL}MubH},@t I@tHP¯,Et IEtH}訯,L}MuIIL}Mt=HEL9tufff.HX@t Hxi,HI9uH}L}T,Eu`uLǩ, H}2,`tHp,L蟩, LfUHAWAVSHHDHHELIII)H)I9u-IȿIHDHH;EH50H 0H`6HOH}1HW)@HDžP0j,HP($A)@Hze mismaHH Huffer siHHHnation bHHHnd destiHHHsource aH@(tchH50H}HUH@HMȾL EuK@uTEu`L}MtwHEL9tbfHX@t Hx艭,HI9uH};H}r,@tHP],EtH}N,L}Mu LL}7,Eu*`u3HDHH;Eu?LHĨ[A^A_]H},`tHp,HƿDHH;Etj,IEu@u4EuKL}MubH}襬,@t I@tHP肬,Et IEtH}h,L}MuIIL}Mt=HEL9tufff.HX@t Hx),HI9uH}L},Eu`uL臦, H},`tHpݫ,L_, LfUHAWAVSHHDHHELIII)H)I9u-IȿIHcDHH;EH5@0H z0H`6HH}1HѫW)@HDžP0*,HP(A)@Hze mismaHH Huffer siHHHnation bHHHnd destiHHHsource aH@(tchH5J0H}HUH@HMȾLp EuK@uTEu`L}MtwHEL9tbfHX@t HxI,HI9uH};H}2,@tHP,EtH},L}Mu LL},Eu*`u3HżDHH;Eu?LHĨ[A^A_]H}辩,`tHp詩,HDHH;Et*,IEu@u4EuKL}MubH}e,@t I@tHPB,Et IEtH}(,L}MuIIL}Mt=HEL9tufff.HX@t Hx,HI9uH}L}Ԩ,Eu`uLG, H}貨,`tHp蝨,L, LfUHAWAVSHHYDHHELIII)H)I9u-IȿQIH#DHH;EH5}0H :}0H`6HϸH}1H葨W)@HDžP0,HP(A)@Hze mismaHH Huffer siHHHnation 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HHHConstraiH@5HHHHJH5D.HELu/HhH=IFHLt HP+LMHL9HX@t Hx+HI9uHHP H`t H+t H +H=:F +?FH=0F+W)ƅHDžHInverseH)HDžPb+HH( @)H5/IH+CIH`HHKH5+H`8H[D$D$$H+H /L+1E1HD$D$$H+H /L+1E1H H5+HKELB/HGH hEHHHHHH=FHJHH9t"Ht#HP(H`达u'HP H`衾t H+t H+H=0F+5FH=&F+W)HDžƅ DžTrilfDžu)pHDžPF+HH(@)pH5c/IH+CIH`HHp/H5+H`H?HDžH5/HK/LH貲D$D$$H+H i/L+1E1HID$D$$HY/H T/L-/AH D$D$$Hd+H \/LT+1E1HH5@+H۱EL/ HH `EHHHHHXH=)FH HHH9t"Ht#HP(H`6pu'HP H`pt H@+t H++H=Fg+F4H=FG+ W)PƅPHDž`HBiasSoftHQDžYmax)0HDž@P+HH@(]@)0H5ӭ/IH@+CIH`HPH0+ 蟳H5F+H`茤H诘HDžH5//H5/LH"HDžH5O/HW/LHD$D$$H/H u/L+1E1HD$D$$H./H 1/L+E1HPD$D$$H+H /L+1E1HH5+HEL/HH fCHHpH CHxHpHHH茞H=mFH=EHH9t"Ht#HP(H`j0u'HP H`M0t H@t+Pt H`_+H=F+ HrrLL0+H`۸ t H0+H=F1+FH=F+{W)ƅHDžHnsorFillHHGivenTenHƅ)pHDžPr+HH(@)pH5/IH+CIH`HHp[H`zD$D$$H,}.H /L4+1AHD$D$$H+H ׸/L+1E1H%H5+HEL^/HcH5T0HH0E1H蕦H5|.HH0E1HwH5p/HH0E1HYH5a/HH0E1H;H EHpHpHHH脛H=FH5BHH9teHtfHP(H`bpu`jH+H`?dHb+ T[HP H`pt H)+t H+H=FP+FH=F0+W)PHDž`ƅP DžQScalfDžUe)0HDž@P+HH@(N@)0H5ħ/IH1+CIH`HPH0萭H`诒D$D$$Hq+H u/Li+1E1HD$D$$HB+H 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>HHH5=H=H%%IH7H57*H P*H L !HHH5P*H P*H|AHP*H HLE1H5:=H[=LS% It H%HXHuQIAHXHt?H`H9t$Lh@t Hx%LL9uHXHH`%@u u&HHuFHPW% tH0B%HHuQIAHHt?HH9t$Lh@t Hx%LL9uHHH%EuIL%H]HHII]IEHCt H{%H%II]IEHJCt H{T%HL%H]H3PI tH0%uIH%H]HIH H]HHIHHuNX{IHHu;EHIHHu",IHHuIHHt HA%H HtH()%H]Hu4IH]Ht&@L3Ct H{ %H%LMuH}HEHt %IHHL<% @UHAWAVAUATSH8Hx>HHEIIAEMeIuIDIEUH}$HMwMH}hMVM]IL9r 1HIIIJHPHHDAEMMMeMML}LMfDHCH9tL9r1IHL9KHtHSL9uWu)1ffffff.T A: u6HH9uXH{ LLM%LMMH}L}/HHcqHCH9tL9r1IHL9uUCtHCHu"ffffff.HHHuMI!IJHLE0%HIILL%HC(HEHCHIGHxH*HHH H*XHuLHDH MxI*AO t Y.H*XAO uK 6IrIV1LH ^f:  ZZ8(\H,HH1H,.HCH9HCLvMwIFLHut'L9r 1HIHIHHtHH gH!IHHuIGIWHI_HHH<H@INLL91IH DM3MLEL}LMDHCH9t L!L9/KHtHSL9uQu#1fT A: u6HH9uH{ LLM^%MLMLEH}L}toHHsfffff.HCH9t L!L9Ct HCHu (HtHHuaH!HIHIGH>HH;EuH(HH8[A\A]A^A_]%IA$tH{ %IH%LS% HUHAWAVAUATSH(HHIHHq>HHEH8H=HHHLH@H8L%HEEL=I=MgLI@L8L%L- =ILHDž(HDž HDžHDžDž0L%HLHqHsHEsHESHLHH}%HHH+HHHLL8Lt H 7%H%H5=HH%H8%H>HH;EuHH([A\A]A^A_]t%HLL8Lt H %H%HHHHH5E=HH%HH8%H% f.UHAWAVAUATSHH>HHEIIAIL9t I7IWIIIMgL9MMDHHEȀx8Hp LyHtHI9uBII;sHMHIHI9uLHu HI9uII`I HIML9MIH0MID$H8L;0tjf.HI;Et=I I;(tHI HI9unLHHI9uWHCIH98HuA@I I;(tH HI HI9uLHHI9uAƆfAdžH=HH;EuHĨ[A\A]A^A_]%$%IH`H5"**H C*HxL` W)@HDžP %HP(n:)@Ht exist.HHH set musHHHInput toH@AHC*HxH@LE1H5x=H=L% IALeMuFzIAL`MII@t HP%LeMt6HEL9t!HX@t Hx%HI9uH}LLe%Euxu L`Mu)aH}%xtH}x%L`Mt:HhL9tEHX@t HxM%HI9uH`Lh2%EtL[%L% Lf.UHSPHH0H9tHHVHƃHfǃH[]DUHAWAVSPIHIL HKH{0Hu1HH{0LAƆH[A^A_]fff.UHAWAVATSIH;?IIXHt.fff.L;Ct H{ *%H"%LMuIHIdžHHt%IpƓIHHt,f.L;Ct H{ %H%LMuI8Idž8Ht%M Mt`fDM<$I|$8HtfHt%HHuI|$(ID$(HtT%AD$t I|$ B%L:%MMuIIdžHt%IHt0fffff.L;Ct H{ %H%LMuIIdžHt%IHt(fDL;Ct H{ %H%LMuIIdžHtn%IHt(fDL;Ct H{ J%HB%LMuIIdžHt%IxHtJfDL#L{(HC(MtIL%Ct H{ %H%LMuIhIdžhHt%IPHt IX%I0Ht I8|%IHt I d%IHt IL%IHt I4%IHt I%IIH9t`HtHP(M`MtlIhL9tQf.LcHCH[MtLL%I9uI`HP M`MuLMh%MHMtFIPL9t+fH{HCH[HtHPI9uIHLMP4%I@HtHHCHI Htf.H%HHuIIdžHt%IHt*L;Ct H{ %H%LMuIIdžHt~%IL[A\A^A_]'HHPHW%I HMXUH]fDUHSPHHH[]%@UHAWAVAUATSHHHIH=HHEH5B<*LH]LM9ffffff.M&LH5<*IIL$ HqHEqHEQH!HH5;* I $HqHEqHEQHHH5i`&IM9eH5};*LLeI$I$HLpL9HMslHtXL}@HHt%HHEI$HHLP0t HL9rI$H@H9Lmu$,H;u HL9rLH9Lm LuHEfH]HLH5:*KHs HEsHESHHH5z:*K Hs!HEs0HES(HHH5=&wLH]LM9fff.M<$A8LutaIG HpHEpHEPLHH59*IHqHEqHEQHLH5&IM9oLmLH5v9*HEHLL9LuL#A|$8tdID$ HpHEpHEPLMHH59*9I $9Hq@HEqHEQHLH5&HI9pLH5s]&H]L9HLL9LeL}tkH}tD@HHt%HHEI$HHLP0tlHI9uH;uSHI9uLfffff.HHMH9@:fffff.HLeHHMH9HHHMH9H57* LM$0M$8M9M@M/LH5u7*IM HqHEqHEQHHH5>7*~IMHqHEqHEQHWHH5[&CIM9fH_=HH;EuHEHH[A\A]A^A_]%h%H E=HHH5G=H0=H%:%H =HHH5=H=HR% H8H0UHAWAVAUATSH8MLEIIHuIH=HHEп%H1E1HLLLM6I<$I$Ht HPI$LHHUI?t H*H *1ALRII4$ƆEHEEHUL?I?t@H:*H N*1A LH=HH;Eu)LH8[A\A]A^A_]IH=HH;EtO%IIIHtCtH{%IH%L % UH]v5fDUHAWAVAUATSHIIH/=HHEHc HH)HprjHt HKHD>I1EL H7HD1IM11E1M11H`E11uj1H`1E1E111tQH*I$A^$f:  B8(\H,HH1H,.HC|LLAHIDH8IID$HHhI$HI$H@LfALMID$XHt*IcL$P0M9},QAT$PLlaffff.MAt$T fDA;t$TuHh;!ID$X0Ɖ0I|$HfIID$XIcL$PQAT$PLlLHL.I>$IMA$Mt$It$IDIET$HXHLHHHLxLoItHH9r1HHH LH!HHHHL8MA$MD$Mt$IMLHHPIGH9tH9r1HHH9AAOHtIWL9ufu01fffff.ATA:uEHH9uI ILLM%MHPLHLM?MSDI'M5LHHPHXIGH9t L!H9kAOHtIWL9uYu 1ATA:u>HH9uEI HLLM%MHPLHHHXtM?MjLLI$x{ :H@L0`LLH5*H *YE1HXHLM0HH5(0*ܥHHHqHEqHEQHۥ7HH5>*ۥHH5b*ۥHX萐LM4HL9HX@t Hx%HI9uHLfIGH9tH9r1HHH9AGtIGHM?MuqHuLf.IGH9tL!H9u?AGt&IGHKM?Muffffff.Hu#HXHIH0%IMILL%I^(HIFIHHHxH*fHHH H*XHLHHH Hx.H*Ht2Y.w)HXH*XHuH ?HrHW1HL^f: (=<8\H,H HH1H,.HCH9HCHI$HGHt HH9Lxr1HHHHH!LxIHHHHt HINDH@HIL2HIHt0H@HOHtH9r 1HHH!HHHL0LI$LIL;8jLL%t H%Lt H%HIG(LxLIL;8q`PAWH*I$A^$0f: :8(\H,HH1H,.HCHXAHIDH8IID$HHxI$HI$H`L ALTLgLHLI}IEID$XHtIcL$P09}QAT$PH\RfAt$TA;t$TuHx!ID$X0Ɖ0HxH8 HID$XIcL$PQAT$PH\LLHI} LIEHLAI^IvHDIEVHXa٤HM$MHML$MtIL9r1HIIMI!HHJHjL(M^A%MFI^M#MLhfIEH9tL9r1IHL9AMHtIUL9uXu(1fffff.AT : u8HH9uI} HLMN%MHLhaMmM`ff.IEH9tL9r1IHL9aAEtIEHMmMu=HuDMMLhL@IEH9t L!L9AMHtIUL9uJu1AT : u8HH9uI} HLMA%ML@HLhtQMmMx{IEH9t L!L9fAEtIEHtMmMuFHuLI$x{ L H=@L0rHHH5 *H *E1HXHLL8HH5|*GӥHHHqHEqHEQHӥ7HH5q*ӥHH5*ҥHXÇLMHL9HX@t Hx%HI9uHf0%ILHHL%HIE(HIEIEHHHxH*fHHH H*XLHDH Mx'I*Ht+Y.fH*XHuK $IrIT$1LL^f: (<48\H,H HH1H,.HCH9HCHMID$Lt LM9Hr1LIILI!HHHJHtHIML SH`HIEL*JIEHL t1H@IL$LtL9r 1IHH!HHHL(LI$H%ILI^HsHEsHESHXVӤIFIHXLLuLk%LcLIL;8ILL:%t H%%t H%HIu(*LpIcELH) NHEtM<O|5AAWH*HHh^f:  18(\H,HH1H,.HCHAHHHLM|IDHE1LxC\'IHHMtLXL)|LLLHX& I}IE@%ILHXIIcDHH)LAD HI El IMC\,W)HDžHsLuESLLsHI!LH%ILILHLKt,LH%AHHe.HL0HtH{}H%Lxt H%HX}IL9LpcIcMLH)1 Lr HL HLLt LLI$`I$`M$hLH)HH9Hs H)輇L<ff.H(H%M9t[I^IFMvHuE11E1MAMAH`4M$hHpHc HH WHH)pHH3  HHxHHHD3HHH1HD<N48Ip HM$x%IJ;HHc LH)HL r&DHLt IIB:ffff.1L 1I$AD$XID$PID$HID$@ID$8ID$0ID$(ID$ ID$ID$ID$ID$\I$IDŽ$IDŽ$IDŽ$IDŽ$IDŽ$IDŽ$IDŽ$IDŽ$IDŽ$IDŽ$IDŽ$IDŽ$IDŽ$IDŽ$IDŽ$ID$xID$pID$hI$I$IDŽ$IDŽ$I$LH IDŽ$(IDŽ$ I$IDŽ$`IDŽ$XIDŽ$PIDŽ$HIDŽ$@IDŽ$8IDŽ$0ADŽ$h?HI$pIDŽ$IDŽ$IDŽ$IDŽ$xADŽ$?IDŽ$IDŽ$IDŽ$LLLLHI} IEI$HpHcHH)11Ƀ rHT Ht H9 HL4L$MtLкL%LA$HHH9xQHc HH)L AH HH4 D4IMHL| 1EJL K Hc4 LH)LD E1AArt AHt LLD3HpHc0HH)? rHt0 Ht HpD0E9 A1rL Ht IMB4I$`H4LLI}tIEHI9HpHc HH)r@Ht H؋H1I$LL1I}t#H*H -*1AL誳IEI$M$L9HLLLXHHXHp L*PHtHI9uRf.HHH;tHXHHHI9uLLHI9uHHH`H HHHLL9HLLLIGHM9ttHI;EtMHH H;(t HH HI9uzf.LHHI9uZHCIH9HuDHH H;(tH HH HI9uLHYHI9uHpHcHH) r,HDHLHHNjH1LHH5)H )HX[H5H 1HW)HDž@M%H(:)Ht model.HH0HRT formaHH(Hnvalid OHH Hssing. IHHHor is miHHHzer tensHHHInitialiH@8H5N*HH H!HLq LM& HL9 f.HX@t Hx)%HI9u HA)H U)1A]LMϯ H%jHѽ% ^H0踽%LMRs 1H0L-I}t#H)H )1ALAb IEH==HH;EZ ݽ%H5l)H )HXkHH 1H訽W)HDž@%H(;8)Ht model.HH7HT formatHH0Hvalid ORHH(Hsing. InHH Hr is misHHHer tensoHHHnitializHHHSparse IH@?H5Z*HH HHL~ LM H)H 0)1AmL譭H)H )1AoL荭H TH=HH;E HH5)H )HXH6H 1HW)HDž0N%LH(~:)Hmat modeHH H ORT forHHH InvalidHHHmissing.HHHNode is Hf@(l.@*H59*HH HHLt LM1H5)H )HXHH 1HºLW)HDž0%H(8)Ht model.HH%Hformat mHH Hlid ORT HHHng. InvaHHHis missiHHHNodeArg H@-H5*HH H)HLF LMHK)H _)1ALܪHXnH=HH;EH)H )1AL虪H5)H )HXeH HX1 LW)HDž r%H(,8)Ht of ranHHHex is ouHHHNode indHf@ge@H5*HH H'HL* 6LMBH蜷%vH胷% jH0j%LM*HL9 fffff.HX@t Hx)%HI9uH%H% tH0޶%LMHL9HX@t Hx諶%HI9uVH蒶%=Hy% 1H0`%LM HL9HX@t Hx-%HI9uH5E)H Y)HXHH 1H聶W)HDž0ڵ%H(8)Ht model.HH&H format HH Halid ORTHHHing. InvHHH is missHHHNodeEdgeH@.H5B*HH HHL  LM H5)H %)HXHH 1HMW)HDž 覴%H(`8)Ht of ranHHHex is ouHHHNode indHf@ge@H5j*HH H5!HLR^ jLMrH)H )1AL蒥H譳%H蔳% H0{%LMHL9~HX@t HxH%HI9uHZH(% H% H0%LMHL9HX@t Hxò%HI9uqH譲%H蔲% H0{%LMt?HL9t$HX@t HxP%HI9uHLL0%xt H%Xt Hh%H=HH;ELHĨ[A\A]A^A_]LLα%xt H蹱%Xt Hh褱%LШL蔱%Hq=HH;Et3+Iu#u= uWLMu{H?%t ItH% t I tH0%LMuZILMGHL9ofHX@t Hx詰%HI9uIu#u= uWLMu{Ha%t ItH>% t I tH0%LMu|ILMiHL9ff.HX@t Hxɯ%HI9uIu#u= uWLMutH聯%t ItH^% t I tH0;%LMuOILMt@HL9tgfDHX@t Hx%HI9uHLޮ%xt Hɮ%XHh谮%LIMuIEMAFt I~y%Lq%II]IEHCt H{E%H2Iu#u= uWLMu{H%t ItH߭% t I tH0輭%LMuILM HL92fff.HX@t Hxi%HI9uIu#u= uWLMu{H!%t ItH% t I tH0۬%LMu<ILM)HL9Qff.HX@t Hx艬%HI9uII]IEHCt H{S%HK%qIu#u= uWLMu{H %t ItH% t I tH0ū%LMu&ILMHL9;DHX@t Hxy%HI9uIu#u= uWLMutH,%t ItH % t I tH0%LMuJILMt;HL9tgHX@t Hx詪%HI9uHL莪%xt Hy%XHh`%L% LII]IEHnCt H{(%H %L袤% ILMu:w(ILMlIHXmcLMt?HL9tfDHX@t Hx詩%HI9uHL莩%t Hy%H`%LIL+ItAI} 9%6It H%LH'^IL%RIL%=IL%ILLʨ%MLI% HIHX]L(% IHX!bLMtCHL9tkfHX@t HxY%HI9uHL>%t H)%tWH%L薢% LIL脢% H2IAt'I~ ڧ%IH -@LK% IL货%L6% 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InvHH HinitialiHHHata for HHHstring dHHHMissing H@>H5Ou(H}HUH0]2HMLME 0E!LuM*H5s(H t(HPHR#H}1HW)0HDž@@m$HH@(8)0H5t(;H$C;H5t(H}HUH0z1HMLLE0ELuMH}$0H@$EH}$LuMHEL9fHX@t Hxi$HI9uH}S$0]H@:$EQH}'$LuMt>HEL9t)fHX@t Hx$HI9uLLu$pu.Pu7H#<HH;EuCLH[A\A]A^A_]H}$PtH`$Hh#<HH;Et $JJ IEu%0u~EL}MH}2$0tNIEu%0u`EL}MH}$0t0I0tH@$Ev-I0tH@$Et1IEGH}$L}Mu9YIEeH}g$L}MuR5IL}M%HEL9HHX@t Hx)$HI9uIL}MHEL9 HX@t Hx$HI9uIEu0u4EuKL}MubH}$0t I0tH@$Et IEtH}p$L}MuAIL}Mt5HEL9t\@HX@t Hx9$HI9uH}L}$$puPu/L$ H} $PtLIPtH` $LY$ UHAWAVAUATSH(IIH <HHEANW)EHEL$II4Is9DELeMuNC,IH;;t\fLHHH8 $ILeHH]LmLLL$C,IH;D;u $HMHHHMHUHPHW)EHEIH<HH;EtQ^HCHC1H $HEHCHEHMHKHW)EHEHK<HH;EuH([A\A]A^A_] $H} $HEt H}/ $H$ HaUHAWAVAUATSH(IIH<HHEANW)EHEL$II4Is9DELeMuNC,IH;;t\fLHHH $ILeHH]LmLLL $C,IH;;uH $HMHHHMHUHPHW)EHEIH<HH;EtQ^HCHC1H$HEHCHEHMHKHW)EHEH<HH;EuH([A\A]A^A_]0 $H}$HEt H} $H$ HUHAWAVAUATSH(IH5<HHEANIIW)EHEI1Is9DELeMuKC,IH;H;tYfI]HH $ILeHH]LmLLLx $C,IH;;u $HMHHHMHUHPHW)EHEIHG<HH;EtQ^HCHC1HS$HEHCHEHMHKHW)EHEH<HH;EuH([A\A]A^A_] $H}@$HEt H}$H\$ H fff.UHAWAVAUATSHHIIH|<HHEH}̝Ic$LH) r{HEtMtmLME>MLmMu1;/ILmMLLLL I?Hi(H 8k(1ALH5h(H k(HHhH1[W)HDžP$H(8)Ht model.HH8HRT formaHH0Hnvalid OHH(Hlizer. IHH He initiaHHHor sparsHHHvalues fHHHMissing H@@H5j(H}HhHYHMLAEuaujhuvLPMYHXL9:HX@t Hx$HI9uHPH}|$tHg$htHxR$LPMuIIHIc$LH) HEtMME>MHUHu1,HHUMLLLI?Hf(H h(1ALiOHAHhH5uf(H h(H(HhH1W)HDžP<$HH(8)H5h(DH$CDH5@h(H}HhH֓HML?EhLPM-HXL9fff.HX@t Hxi$HI9uH5Ne(H vg(HHhH1IH5!h(Hơ'L =h(HI H5g(H}HhH*HML>EuWu`hulLPM6HXL9DHX@t Hxy$HI9uH}c$tHN$htHx9$LPMuH}$]H$hQHx$LPMEmIIc$LH) HATH I0Lt0 0H0LlzI%HHuHH}#Lc]M9HLHLUK HHLJI JHtLIt4 H9vLITItH9H?L!HHzHHrH 1HLHXH5b(H d(HCHhH1IH5e(H'L e(HIc H5.c(H}HhHeHML;Eu u)hu5LPMuAzH}$tH$htHx$LPMt;HXL9t HX@t Hx$HI9uLLXk$8t HHV$t H(A$H}蜘H<HH;ELH[A\A]A^A_]M9IuI;uuH}#uHEHc΍VUHIM9upI|tKDpLFI)1DאLנDАLРDװLDаLDLDLD D HIuHLHt5JIDIHITH1D: :D8 8H HuHI9HM4H HIDL)IDHtHIHIHHuI8r^1IHITHTITHTITHTIT HT IT(HT(IT0HT0IT8HT8IT@H@L9uAD]HEL9t H}LHLI#IEu#u:huTLPMux}H}#t ItH#ht IhtHx#LPMuILPM HXL9&fff.HX@t HxY#HI9uIEu#u:huTLPMuxH}#t ItH#ht IhtHx#LPMu3ILPM HXL9<fHX@t Hxy#HI9uIEu#u:huTLPMuxH}2#t ItH#ht IhtHx#LPMu]ILPMJHXL9ffff.HX@t Hx#HI9u<IIIH)Ct H{b#HZ# IEu#u:huTLPMuqH}#t ItH#ht IhtHx#LPMuIILPMt:HXL9tZHX@t Hx#HI9uHPLX~#8t HHi#tH(T# LIH}裒L# fUHAWAVAUATSH8MLHHIHH <HHEH8Hv;HHHHH@H8H[#LHEEL-<M}LI@L8HZ#L5g<ILHDž(HDž HDžHDžDž0L#HLHHHHHLd/HHH^LL8Lt H #H#H5a;HH#H89#H^ <HH;EuHH8[A\A]A^A_]#HLL8Lt H *#H# HXHH5;HH#HH8#Hk# UHAWAVAUATSH8MLHHIHH <HHEH8H&;HHHHH@H8H #LHEEL-T;M}LI@L8H #L5;ILHDž(HDž HDžHDžDž0L#HLH|HHHHL-HHHLL8Lt H `#H:#H5;HH#H8#H <HH;EuHH8[A\A]A^A_]#HLL8Lt H #H# HHH5~;HH6#HH8Q#H# UHAWAVAUATSHMMIIHH(H?<HHELlHc HHHH)HHtH HtLG#Hc HH)HHtH HtL$Hc HH) rt@Htk AFAVAɃwZH"Hc HHc HH)W rHL Ht AFAANAdžL(I$H<HH;ELH[A\A]A^A_]Hc HH)HL HHˋ HH AFIHu1HIH5X(E%H(HLLLEMIKI<$HyT(H X(1A L='HcHH) GHDH6HË D< W)PHDž`I$DPLQEDOHc HH)HL HHD;I AFIHL(u1HILLsI<$HvS(H W(1AL:$HcHH) HDHIL$E<$C4HML$E0 ELuM- 1 AFI1H5oJ(H N(HPHH}1HW)0HDž@@:#L(H@(8)0Ht model.HH(HRT formaHH Hnvalid OHHHibute. IHHHats attrHHHNull floH@0H5M(H}HUH0KHMLn#E30<EHLuMQ H5II(H L(HPHH}1HW)0HDž@0#L(H@(O6)0Ht model.HH&H format HH Halid ORTHHHute. InvHHHs attribHHHNull intH@.H5L(H}HUH0HMLH"E|0ELuMH5#H(H K(HP'HH}1HW)0HDž@@#L(H@(!N6)0Hat modelHH(HORT formHH HInvalid HHHribute. HHHsors attHHHNull tenHf@0.H5iL(H}HUH0f;HML !E0ELuMH}#0H@#EH}#LuM`HEL9GHX@t Hx#HI9uH}#0H@x#EzH}e#LuMHEL9HX@t Hx8#HI9u#H}"#0=H@ #E1H}#LuMHEL9iHX@t Hx#HI9uH}#0H@#EH}#LuMHEL9HX@t HxZ#HI9uEH}D#0H@+#EH}#LuMHEL9HX@t Hx#HI9uH}#0{H@#EoH}#LuM5HEL9HX@t Hx|#HI9ujH}i#07H@P#E+H}=#LuMHEL9HX@t Hx#HI9uH}I$IGII!LI#LIL`ILPLXHt LLi#C<ANIH;۾;tf|#H`HHHPHXHPHW)PHDž`IM9LAvAA;vuLx#A4$IF Hc΍VAVIM9uM9LAv(IA;v,uLt#A4$IF0Hc΍VAV(HIM9uJHCHC1H#H`HCHPHXHKHW)PHDž`H55B(H E(HP!HH}1HW)0HDž@@#HL(H@(8)0H5F(;H#C;H5'E(H}HUH0vHMLpE90BENLuMWH5KA(H D(HP+HH}1HW)0HDž@@#HL(H@(&8)0H5F(;H#C;H5Z8(H}HUH0BHMLE0ELuMHa@(H D(1A,LM" H}@#0H@'#EH}#LuMHEL9HX@t Hx#HI9uH}#09H@#E-H}#LuMt5HEL9t HX@t Hx#HI9ukLLug#pt H}U#P H`<#H;HH;E#H HH>JtHHt3H9LITHtH9HJHL)HÉHtHAIHHuHb1AATTATTAT T ATTATTATTATTIT H L9uH HH>JtHHt3 H9LITHtH9HJHDL)HÉHtHIHIHHuH81IHITHTITHTITHTIT HT IT(HT(IT0HT0IT8HT8IT@H@L9u#HL!HHrHHDrAHs_1MLH?L!HHrHHDrAH 1MLPH|tKDpIvH)1DLDLDLDLDLDLD D H HuMLt@JIDH HHHTI1D2 2D0 0H IuHI9MIEkH}\#L}McIEH}4#L}MtIEH} #L}MIL}MHEL9HX@t Hx#HI9u{IL}MxHEL9HX@t Hx#HI9u;IL}M8HEL9HHX@t HxI#HI9uIL}MHEL9HX@t Hx #HI9uIL}MHEL9HX@t Hx#HI9u~IL}MtHEL9f.HX@t Hx#HI9u>IL}Mt?HEL9tSfffff.HX@t HxI#HI9uH}L}4#ptH}"#PuLIPt H`#L|# ZUHAWAVAUATSH(IIH;HHEANW)EHEL#II4Is9DELeMuNC,IH;;t\fLHHH8#ILeHH]LmLLL#C,IH;D;u#HMHHHMHUHPHW)EHEIH;HH;EtQ^HCHC1H#HEHCHEHMHKHW)EHEHK;HH;EuH([A\A]A^A_]#H}#HEt H}/#H# HaڡUHAWAVAUATSH(IIH;HHEANW)EHEL#II4Is9DELeMuNC,IH;;t\fLHHH#ILeHH]LmLLL#C,IH;;uH#HMHHHMHUHPHW)EHEIH;HH;EtQ^HCHC1H#HEHCHEHMHKHW)EHEH;HH;EuH([A\A]A^A_]0#H}#HEt H}#H# HءUHAWAVAUATSH(IIH2;HHEANW)EHEL#II4Is9DELeMuNC,IH;@;t\fLHHH#ILeHH]LmLLLm#C,IH;;u#HMHHHMHUHPHW)EHEIH<;HH;EtQ^HCHC1HH#HEHCHEHMHKHW)EHEH;HH;EuH([A\A]A^A_]#H}5#HEt H}#HQ# HסUHAWAVSPAH.u z {PH{HwHCHD{ HC0A)D{(AAHH+K8L9Es)HLnEHC0L)HC0Iu"L)HC0MIIWIЃIs HuKdH)1ffffff.Hs0DHs0DHs0DHs0DHH9uHHtHHS0 HHuHK0HC8HH)HwHEHK0HC8HQHS0AL{ I)L{(H)HsHTHC8AHH DH H0HC8C@CDf9fBfCDH[A^A_]f.UHAWAVSPIHHCHt(HcK09}#QSH\fs ;s uH[HC0Ɖ0L;Mt!IpH;L芤 v#INHHIIVHPHIFIFIHKHcSrsHDFHCHC1H##IFHCIINHKHIFIFIH[A^A_]H5ԩ;L觫4Hӡf.UHu]H]#ff.UHAWAVAUATSPMIHIIL[#LLH 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HID$8AD$@AD$DffGfAL$DLDLD%HMIH13HH;Eu-HX[A\A]A^A_]ID$HID$0I;D$8)訦HH}HuX_HH}HuFMHM7IMt9AFt I~ΥLHEtH}HH}Ht H}襥H' ffffff.UHAWAVAUATSH(HHIHHA3HHEH8H3HHHLH@H8LɤHEEL=3MgLI@L8LϣL-ܬ3ILHDž(HDž HDžHDžDž0L蓧HLHAHsHEsHESHHHMHHHHHH赭LL8Lt H HH53HHpH8萣H3HH;EuHH([A\A]A^A_]DHLL8Lt H 聣H[H语H觭H蟭HH53HH͡HH8H貝 f.HtUHH]fff.UHAWAVATSHIHIH3HHELOXHc HHHH)HHtH HtLfHc HH)HHtH HtLHc HH) rf@Ht]HD#AOIIW(Hu1HIW(LLI>H* H + 1AL“yIOuHuYHAHLH5Y* H 7+ HXHeH}1H'IWH5!+ H aH@H5* H}HUH@HML(EuC@uLEuXL}MtoHEL9tZHX@t Hx HI9uH};H}@tHPݠEtH}ΠL}Mu LL}跠xt H}襠Xt,Hh萠Hm3HH;Et 1IIHM3HH;EuLHĠ[A\A^A_]ޠIM>IMAGt I"LIEu@u4EuKLuMubH}@t I@tHPȟEt IEtH}讟LuMu?ILuMt3HEL9t[fHX@t HxyHI9uH}Ludxt H}RXt Hh=L这 LfUHAWAVAUATSH(IIH3HHEANW)EHELߡII+Is6DELeMuKC,I^H;{3tYfLHHH蛞ILeHH]LmLLL0C,I^H;z3u^HMHHHMHUHPHW)EHEIFH3HH;EtQ^HCHC1HHEHCHEHMHKHW)EHEH3HH;EuH([A\A]A^A_]IH}HEt H}蘝H Hʧf.UHAWAVAUATSH(IIHB3HHEANW)EHEL/II+Is6DELeMuKC,I^ H;Sy3tYfLHHHILeHH]LmLLL耞C,I^ H;x3u讜HMHHHMHUHPHW)EHEIF HU3HH;EtQ^HCHC1HaHEHCHEHMHKHW)EHEH3HH;EuH([A\A]A^A_]虜H}NHEt H}Hj Hf.UHAWAVAUATSH(HHIHH3HHEH8H$y3HHHLH@H8L HEEL=Y3MgLI@L8LL-3ILHDž(HDž HDžHDžDž0LӝHLH聟HsHEsHESH\HH荝HHH;HHHLL8Lt H GH!H53HH谘H8ЙH3HH;EuHH([A\A]A^A_]脚HLL8Lt H H蛘HH磙HߣHH5U3HH HH8(H f.UHAWAVAUATSHIHIH3HHEHc HH HH)HHtH HtL,2Hc HH)PHtzt?Ƀ j@H]HD;IA~( I^ Ƀ @HHD;IA~(I^ Ƀ @HHD;IA~(+I^ @v H H.% HEH5# H # H0HUHH5LH0t H@躗H3HH;E H[A\A]A^A_]H5* H " H0H6H}1HW)HDž @QH (Y0)Hmat modeHH(H ORT forHH H InvalidHHHpe info.HHHuence tyHHHNull seqHf@0l.@2H5! H}HUHLHMLEpyELhMH5 H l! H0HH}1HЖW)HDž 0)H (3-)Ht model.HH%Hformat mHH Hlid ORT HHHfo. InvaHHH type inHHHNull mapH@-H5 ! H}HUHHMLdEELhMH5 H J H0HH}1H讕W)HDž @H (qW0)Ht model.HH(HRT formaHH Hnvalid OHHH info. IHHHsor typeHHHNull tenH@0H5\ H}HUHX/HMLBE%.E:LhM?H}H E{H}LhM:HpL9HX@t Hx蹓HI9uH}裓-H 芓E!H}wLhMHpL9ff.HX@t Hx9HI9unH}&H EH}LhMtEHpL9t*fDHX@t HxɒHI9uHhLLp詒PH`萒0LʏAF(1HI^ IcLH) rmHADHt_IE7MtTKHSHu1HHSLLI<$ H H  1AL軃 H5x H  H0H脢H}1HFW)HDž P蟑HH (&u0)H5? DH)CDH5 H}HUHHMLEELhM LHAF(1HI^ IcLH)1ɃrHADHtA CƒSK IcLH)roHALHtaIE7MtVCHSHu1HHSLLI<$ H H P 1AL} H5 H - H0H͠H}1H菐W)HDž @H ("-)Ht model.HH7HT formatHH0Hvalid ORHH(HType. InHH Hfbs::MapHHHinfo in HHHue type HHHNull valH@?H5 H}HUHHMLE1:EFLhMOL9AF(1HI^ IcLH)1ɃrHADHtA CƒSK IcLH)+HALHIE/MCHCHHu1HHCIcELH) HADHIA]HHxAtH譀ADH HwILMtHDHH]E>MH0HHC(HtHcK 09}QS LlGs$;s$uH*HC(0Ɖ0HH8聶IHC(HcK QS LlIcLH)H r/ALHt0IA HtLg/IcLH)H Ƀ>ADH/IAIIc HH)HtH0t7҃LHL<A}(҃ r]LHtSHȋHtID;W)0HDž@ID0H1HEH0H5 H V 蹜1HH0tW)HDž@͋H]L}H(_o0)Ht model.HH/HT formatHH(Hvalid ORHH Hname. InHHHwith no HHHm value HHHdim_paraH@7LH5Y HHLLEuLhME1A}(tL*1AE(M} I$H]I$H0IL;H}xHflH MLhMtCHpL9t$HX@t Hx"HI9uHhLLpH]Pu0u%I<$3H`Љ0tH@軉I<$IHH!H蚉HHH@HHH0L8HHHL HB8A}(uI] H;xe3t'unfvLz/AE(H6IE I H@HHH0H8HPHW)0HDž@IEHCHC1H貆H@HCH0H8HKHW)0HDž@H}[3H BE'H}/LhMHpL9HX@t HxHI9uHhH}߇H ƇEH}資LhMHpL9HX@t Hx耇HI9uHhI$I$I$LLp@Pt H`+0t H@I<$uhH H  1ALx-LLp؆Pt H`Æ0t H@讆I<$tCI$Hw3HH;EI$eI$uH H _ 1ALxH5 H 9 H0HޖH}1H蠆W)HDž @H (-)Hrmat modHH(Hd ORT foHH H. InvaliHHHmensionsHHHry in diHHHNull entH@0el.H5P H}HUH7%HML1Euu&Eu2LhMu;xH}!tH EtH}LhMt?HpL9t$HX@t Hx҄HI9uHhLLp貄Pu0u!I<$u10H`舄0tH@sI<$H H  1ALvI<$H H ? 1ALubH H  1ALuH})I<$uII$I$HCt H{ƒHIEu u7EuNLhMuoIH}~t ItH [Et IEtH}ALhMuILhMHpL9HX@t HxHI9uII$I$H Ct H{迂H跂L9} IEu,ELhM:H}otUIEu(ugELhMH}*t4IxH El-ItH ߁Et4IE=H}LhMu?rIE[H}蚁LhMueKILhM8HpL9Kf.HX@t HxIHI9uILhMHpL9ffff.HX@t HxHI9uIEu,EFLhMyH}讀tIEu,E!LhM-H}btIEu,ELhMH}t`I%H E]IIH E=\ImH Ea_IEH}}LhMun.IEH}VLhMIEH}+LhMILhMHpL9HX@t Hx~HI9u~ILhM~HpL9HX@t Hx~HI9u>ILhMtBHpL9tYHX@t HxY~HI9uHhLp>~Pt*H`)~0u"ZLHRI0=H@}Ltx I0 IEu#u:uTLhMuqH}}t ItH{}t ItH X}LhMuLILhMt=HpL9t]HX@t Hx}HI9uHhLp|Pt H`|0tH@|LIH}"LCw ff.UHAWAVAUATSHIIIHl3HHEMtLI|$zMI\$Ht"L3Ct H{ J|HB|LMuID$IT$HM(HJЃH1HH5 H  H@ H蟌H}1Ha|W) HDž0@{H0(>0) Ht model.HH0HRT formaHH(Hnvalid OHH Hports. IHHHopset imHHHst have HHHModel muH@8H5 H}HUH HMLEuQ uZEufLxMHEL9DHX@t HxzHI9uH}z tH0zEtH}zLxMuH)1ffff.I4$HI4$HDI4$HDI4$HDHH9uHt#Hfff.I$HHHuID$AH*A^D$ f:  6-(\H,HH1H,.HCL`AHHHE1LLffff.Ct>I7I H7I<6IcT?HH)LAT|ALHmL,K.ATHHLHMHIMGd/W)@HDžPIHsED@LAEudfffff.C&@tfHHbI\$HH!H~xILPHH@LHKt/LLzC&@uLLHHu&1HAH@H 6utzHJcL8HH)LALr$AD HtHLI\fff.1LH@T@IL9HJcL8HH)LALrAD Ht HLI\1W)EHELHuEEtH}w@wHPvIL9sLIH3HH;ETH5NH H@(HZH}1HwW) HDž0@uvLH0(Z0) Ht model.HH.H format HH(Halid ORTHH Hull. InvHHHain is nHHHport domHHHopset imH@6H5 H}HUH HML蛰EuU u^EujLxMHEL9fHX@t HxiuHI9u{H}Su tH0>uEtH}/uLxMuRH5H H@%HH}1HuW) HDž00tLH0(X0) Hrmat modHH Hd ORT foHHH. InvaliHHH is nullHHHopset idH@(el.H5_H}HUH HMLE ELxMt=HEL9t(@HX@t HxsHI9uHxLLus`u.@u:H3HH;EuFLH[A\A]A^A_]Hps@tHPtsHQ3HH;EtsH}Vs *H0=sEH}*sLxMIIEu u7EuNLxMuo+H}r t I tH0rEt IEtH}rLxMuILxMHEL9fHX@t HxYrHI9u{IEu u7EuNLxMuobH}r t I tH0qEt IEtH}qLxMu ILxMHEL9 HX@t HxqHI9uIEu u7EuNLxMuhH}Zq t I tH07qEt IEtH}qLxMuNILxMt?HEL9tVff.HX@t HxpHI9uHxL}p`tDHpp@u8BLIEtH}p@u HzI@t HP_pLj f | UHHH5B(s]1UHAWAVAUATSH(IHIHHՂ3HHEH8HxM3HHHLH@H8L]oLHEEL=w3MgLI@L8L\nL-iw3ILHDž(HDž HDžHDžDž0L rHLHs3HmHHqHHHsHHHYxLL8Lt H nHmH5\_3HHmH84nHY3HH;EuHH([A\A]A^A_]nHLL8Lt H %nHlHSxHKxHCxHH5^3HHqlHH8mHVh fffff.UHAWAVAUATSPIHL3HCL)HL9sjLH>upLcM)J<mIKDMMM~LLLIoLL+L{HCMtLH[A\A]A^A_]+mH[A\A]A^A_]ÿ*mHH5eHrjHu3HHH5M3HpI3H0mIHmLSg ff.UHAWAVAUATSPHH~HLvIL9uL%xu3I)LHHHu%M0LmGtH{HwHCHID{ HC0A)D{(AAHH+K8L9s'HL,HC0L)HC0Iu%L)HC0MIIWIЃIsHuKd@H)1ff.Hs0DHs0DHs0DHs0DHH9uHHtHHS0 HHuC +C0C(D)HMCFHHu쵗1CFDs D+s0Ds(H9HDH[A\A]A^A_]鰹UHAWAVATSAAHCFDs D+s0Ds(1HD෗Eu {PH{HHC0H;C8uH菶HC0HHHK0DxL{ HK0I)L{(HC8H)HsHVHC8DHH HHC8C@CDffGfKDHD[A\A^A_]HCHHC0H;C8_kfDUHAWAVATSIAHI9u {PrH{HwHCHDc HC0A)Dc(AAHH+K8L9s$HL葵HC0L)HC0IuL)HC0MItIT$IЃIs HuF_H)1f.Hs0DHs0DHs0DHs0DHH9uHHtHHS0 HHuHK0HC8HH)HwH迴HK0HC8HQHS0LyL{ I)L{(H)HsH芴HC8AHH DH H0HC8C@CDf9fBfCD[A\A^A_]fUHAWAVAUATSHIIIuMeI)MIIGHH> H?MuI)LHHH9w`II9LBMLEHuI9vKgHH5HeH$p3HHH5FH3HD3HgLEHuIJ<ogHHuLEN4ABN|M~HLIhLI]M}MuHtHH[A\A]A^A_] gH[A\A]A^A_]E11L5dIHgLda fff.UHAWAVAUATSH(IIHy3HHEANW)EHELiII+Is6DELeMuKC,I^H;B3tYfLHHH;fILeHH]LmLLLgC,I^H;JB3ueHMHHHMHUHPHW)EHEIFHx3HH;EtQ^HCHC1HcHEHCHEHMHKHW)EHEHTx3HH;EuH([A\A]A^A_]eH}bHEt H}8eH_ Hjof.UHAWAVAUATSH(IHIHHw3HHEH8HxB3HHHLH@H8L]dHEEL=l3IGHHI@L8L\cL%il3ILHDž(HDž HDžHDžDž0L gHLHhHsHEsHESHhAEIuIEuIEUHhHHHHHtDž(HH@苄/@tHP)/ELyHqHxuPZHxuBLHxu4>HBH=Hxu&HxuHxt H.EL.H) H7HH87.xuGQHXuNXHHK`HH(,H8-xt H#.Xt Hh.H( H@8 BB UHAWAVAUATSHhMLHHIHxHc@3HHEH0L=n53I_@HLH 3HH@H0HfHnfL,LHEEIGHHIhL0HHL+L543ILHDž HDžHDžHDžDž(LL/LLH31LxHH]/LHH1HH@/LHH0HH#/LHH0HH/LHH0LHy5HHL0HHLt H+H*H5_3HHm*H0?+Hd>3HH;EuLHh[A\A]A^A_]+HSHeHHHL0HHLt H+H)H53HH)H0*Hd% fff.UHAWAVAUATSHXIIHHH=3HHEH0L-23I]@HLH3HH@H0HfHnfL)LHEEIEHHIhL0HHL(L%13ILHDž HDžHDžHDžDž(LHH,LHHJ.AIvIEvIEVL(.HHY,LHH .HH2HHL0HHLt H)H'H53HH'H0(H;3HH;EuHHX[A\A]A^A_]>)HSHeHHHL0HHLt H\(H6'H53HH'H0'H" UHAWAVAUATSHXLHIIHH:3HHEH0L-/3I]@HLHL3HH@H0HfHnfL:'HEEIEHHIhL0HHL2&L=?/3ILHDž HDžHDžHDžDž(HL)HLH+LAIvIEvIEVHt+HH)HHHS+HHH)HHH/+LH/HHL0HHLt H-&H%H53HH$H0%H83HH;EuLHX[A\A]A^A_]j&HSHeHHHL0HHLt H%Hb$H513HH?$H0%H UHAWAVAUATSHXLLHIIHH73HHEH0L5,3M~@LHHu3HH@H0HfHnfHc$HEEIFHHIhL0LLH[#L=h,3ILHDž HDžHDžHDžDž(HL'HLH(LA$It$IEt$IET$H(HH&HHHx(HHwHEwHEWHL(HH}&HHH+(LH,HHL0HHLt H)#H"H53HH!H0"H53HH;EuLHX[A\A]A^A_]f#HSHeHHHL0HHLt H"H^!H5-3HH;!H0 "H ffffff.UHAWAVAUATSHXIIHHH43HHEH0L-)3I]@HLHs2HH@H0HfHnfLa!LHEEIEHHIhL0HHLR L%_)3ILHDž HDžHDžHDžDž(LHH$LHH%AIvIEvIEVL%HH#LHH{%HH8*HHL0HHLt Hu HOH53HH,H0H#33HH;EuHHX[A\A]A^A_] HSHeHHHL0HHLt HHH5u3HHH0UH UHAWAVAUATSHXIIHHHA23HHEH0L-L'3I]@HLH2HH@H0HfHnfLLHEEIEHHIhL0HHLL%&3ILHDž HDžHDžHDžDž(LHHX!LHH #AIvIEvIEVL"HH!LHH"HH'HHL0HHLt HHH5n3HH|H0NHs03HH;EuHHX[A\A]A^A_]HSHeHHHL0HHLt HHH5 3HHH0Ho UHAWAVAUATSHXIIHHH/3HHEH0L-$3I]@HLH2HH@H0HfHnfLLHEEIEHHIhL0HHLL%#3ILHDž HDžHDžHDžDž(LHHLHHZ AIvIEvIEVL8 HHiLHH HH$HHL0HHLt HHH5 3HHH0H-3HH;EuHHX[A\A]A^A_]NHSHeHHHL0HHLt HlHFH5 3HH#H0H UHAWAVAUATSHXIIHHH,3HHEH0L-!3I]@HLHc2HH@H0HfHnfLQLHEEIEHHIhL0HHLBL%O!3ILHDž HDžHDžHDžDž(LHHLHHAIvIEvIEVLHHLHHkHH("HHL0HHLt HeH?H5 3HHH0H+3HH;EuHHX[A\A]A^A_]HSHeHHHL0HHLt HHH5e3HHsH0EH UHAWAVAUATSHhMLHHIHxH#*3HHEH0L%.3I\$@HLH2HH@H0HfHnfLLHEEID$HHIhL0HHLL=3ILHDž HDžHDžHDžDž(LL?LLHLxH0LHH LHHHHLHHHHLHHHEHHLHHeH]HLHHKLH HHL0HHLt HIH#H53HHH0H'3HH;EuLHh[A\A]A^A_]HSHeHHHL0HHLt HH~H5M3HH[H0-H ffffff.UHAWAVAUATSHhMLHHIHxH'3HHEH0L%3I\$@HLH2HH@H0HfHnfLrLHEEID$HHIhL0HHLbL=o3ILHDž HDžHDžHDžDž(LLLLHLxH0LHHLHHHHLHHHHLHHbHEHHLHHEH]HyLHH+LHHHL0HHLt H)HH53HHH0H$3HH;EuLHh[A\A]A^A_]fHSHeHHHL0HHLt HH^H5-3HH;H0 H  ffffff.UHAWAVAUATSHXMHIHHH#3HHEH0L=3I_@HLHl2HH@H0HfHnfLZLHEEIGHHIhL0HHLKL-X3ILHDž HDžHDžHDžDž(LHHLHHAIvIEvIEVLHHLHHtH0LYHH HHL0HHLt H]H7H53HHH0H "3HH;EuHHX[A\A]A^A_]ISIeIHHL0HHLt HH H5]2HHk H0=L  ffffff.UHAWAVSH(IIH6!3HHEAF9AȃH HcHIF(HXHHDMcv MIfH;LHIuIFXHXHHDMcvPMIfDH;LEHIu~IFpHXHHDMcvhMtfIffff.H;L5 HIu>IF@HXHHDMcv8Mt&Iffff.H;LHIuH 3HH;Eu H([A^A_] ( HH5HqH LcL BH}BAHuH[ H 3HHHC HCHCE1H52H<2H ( HIVxAEH5H L H}LE AHuH H 3HHHC HCHCE1H5s2H2HD  I1I,IEu IEt H} Eu LD H L2 f?UHAWAVAUATSHhMLHHIHxH33HHEH0L=>3I_@HLH2HH@H0HfHnfL LHEEIGHHIhL0HHL L53ILHDž HDžHDžHDžDž(LLQ LLHLxHH- LHHHH LHHHH LHHHH LHHLHIHHL0HHLt H H`H5/2HH=H0 H43HH;EuLHh[A\A]A^A_] HSHeHHHL0HHLt HHH52HHH0jH4 fff.UHAWAVATSH IHHd3HHECLs(LL4A~HcCHhIN L9~$HK 1ff.H<HH9|Cu*MH3HH;EH [A\A^A_]Ls0LLAAFt3PH3HH;EuDLHLH [A\A^A_]Hm3HH;EuLHLH [A\A^A_]j)(oIIH5-H pH} AHuLH3HIIG IGIG1H5m2H2L> (HH5H:H LL #H}38AHuH<Hu3HHHC HCHCE1H52H2Hr(jIIH5*H :H}:HuLH3HIIG IGIG1H5h2H2L9(IIH5H ҘH}<HuLCH|3HIIG IGIG1H52H%2Lz(rIIH50H ØH}GHuLH3HIIG IGIG1H5p2H2LA(HIH5H H}AAAHuHJH3HHHC HCHCE1H52H+2H(xHIH5H H}sCAHuHH3HHHC HCHCE1H5t2H2HEIHIHIIEuIPIEu>EIEuT[IZIUIHPIEu7>IEu,3IEt H}sEt(HIIEt H}RtL|L UHAWAVSH(IH 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A:u0HH9u$H{ LLoH}LEHuHHiQ@HCH9tL9r1IHH9.CtHCHuff.HHHuDMMLELUDHCH9t L!H9KHtHSL9uOu#1fT A:u4HH9uH{ LLnLUH}LEHuHHu@HCH9tL!H9u,CtHCHuHHHuLÿ(lIMILLjHEID$I$HCHxH*HHH H*XLHDH Mx.I*K t2Y.w)HuH HHH*XK uK 6IrIV1LH ^f:  ,(\H,HH1H,.HCH9HCHcLsIFLHut%L9r 1HIHH HHtHI $RH!H HHuHCHSI$LcHI$Ht)H@INLtL9r 1IHH!HHL HCH}2HH;EuH8[A\A]A^A_]bkIAtI|$ jILjL.e Htfffff.UHAWAVAUATSHhMLHHIHxH3}2HHEH0L=>r2I_@HLHG2HH@H0HfHnfLiLHEEIGHHIhL0HHLhL5q2ILHDž HDžHDžHDžDž(LLQlLLHnLxHH-lLHHmHHlLHHmHHkLHHmHHkLHHmLHIrHHL0HHLt HhH`gH5/Y2HH=gH0hH4{2HH;EuLHh[A\A]A^A_]hHSHeHHHL0HHLt HgHfH5X2HHfH0jgH4b fff.UHAWAVAUATSHXLLHIIHHCz2HHEH0L5No2M~@LHHD2HH@H0HfHnfHfHEEIFHHIhL0LLHeL=n2ILHDž HDžHDžHDžDž(HLhiHLHkLA$It$IEt$IET$HjHHiHHHjHHwHEwHEWHjHHhHHH{jLHIHKHH5RIt HtKEuTIHKHHSpIxIH`KLE IH`HIH]HNfff.L3Ct H{ JHJLMuRPIt(I} J;IAI} JvILJHHu!AHTIHHt(fDL3Ct H{ JJHBJLMuHHDžHu ILJHHt0fffff.L3Ct H{ IHILMuHHDžHt/IHHu6VIHHuHSIHHt.fff.L3Ct H{ jIHbILMuHHDžHt>IH`Ht(fDL3Ct H{ IHILMuHPHDžPHtHEt H}HH]Ht,f.L3Ct H{ HHHLMuH}HEHtHLC 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He;H?:H5,2HH:H0:HN2HH;EuHHX[A\A]A^A_];HSHeHHHL0HHLt H:H9H5e+2HHs9H0E:H5 UHAWAVAUATSHXLLHIIHH#M2HHEH0L5.B2M~@LHH2HH@H0HfHnfH9HEEIFHHIhL0LLH8L=A2ILHDž HDžHDžHDžDž(HLH<HLH=LA$It$IEt$IET$H=HH;HHH=HHwHEwHEWH|=HH;HHH[=LHBHHL0HHLt HY8H37H5)2HH7H07HK2HH;EuLHX[A\A]A^A_]8HSHeHHHL0HHLt H7H6H5](2HHk6H0=7H2 ffffff.UHAWAVAUATSHXIIHHH!J2HHEH0L-,?2I]@HLH2HH@H0HfHnfL6LHEEIEHHIhL0HHL5L%>2ILHDž HDžHDžHDžDž(LHH89LHH:AIvIEvIEVL:HH8LHH:HHh?HHL0HHLt H5H4H5N&2HH\4H0.5HSH2HH;EuHHX[A\A]A^A_]5HSHeHHHL0HHLt H4H3H5%2HH3H04HO/ UHAWAVAUATSHIHG2HHEHH H HcO8HH0L W)P)@Dž`?HG@HXHHDHHHLhAEM}IuIDIEUH};IHHHHLgI{LI9r 1LHHH@HHHHxAE[MEM}IH8L(YM@HCL9tH9r1HHH9!KHtHSL9uau11ffffff.T A:u@HH9uIH{ LL4L(LH8tHHY@HCL9tH9r1HHH9qCt)HCH+HHuOffffff.HuLL!H@HHDIH8L(MH@HCL9t L!H9KHtHSL9u\u+1fT A:uAHH9uWH{ LLY3L(HH8#HHhFfDHCL9tL!H9u/CtHCHHHuHuIL8I(!1HHIHL&/H8ID$I$HXHxWH*"f.HHH WH*XLHHH Hx"H*`t&Y.@H*X`uH 6HrHV1H DL8^f: (,\H,H HH1H,.HCH9HCH@KHHHFHt0I9r1LHIH@JHt HI $gI!H@JHu@HPI$LPHPJI$Ht-H@HNHtH9r 1HHH!HH@L HXHHH;HPHL t-ff.L3Ct H{ 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IH&HPHuI@HPHIHPHt"L;Ct H{ 1&H)&LMuH@HDž@Ht&H]H|IH]HukItI|$ %IL%HPHIHPHH/IH]Ht(fDL;Ct H{ j%Hb%LMuH}HEH@UHAWAVAUATSHIH82HHEHL0H,2Lc@LLH`2HH@H0HfHnfLLc$HEEHCH(HHhH H0LLFMwHt$HH@H#.AIIDH5To%HHtDž HH@胼L0@HffpHDžHp1rptHHx HqHXDžHDžHDžHDžHDžDž?ƅl HHDžHDžffPHDžHDž`AtMwMgMuDMK\&L-f3Lr$Hu HIuL)Ht{HH81LHM Pu fDžPH`HDžXHP1d HHH`H8H@HXHPHHPK HHDPLL0HHt"L3Ct H{ !H!LMuHHDžHt!pL0H t Hk!H(HH0LL;SH52HH L H!H32HH;EuLHĸ[A\A]A^A_]H`  H HHQ!( IH5'H ;HL%HLHU2HIEIE IEIE1H51H1L (J IH5H HLVHLH2HIEIE IEIE1H5E1H~1L !I%IZIt HuLpipIt HaPLCH)IPIHHL3Ct H{ HLMuIpIL0IIL L0ILPH5L2HHrLIPt H`xHHt%L3Ct H{ JHBLMuHHDžHtpt H H(HH H0H0HHOH5p2HHH0hHL& HHH"?ffff.UHAWAVAUATSHXIIHHH!02HHEH0L-,%2I]@HLH1HH@H0HfHnfLLHEEIEHHIhL0HHLL%$2ILHDž HDžHDžHDžDž(LHH8LHH AIvIEvIEVL HHLHH HHh%HHL0HHLt HHH5N 2HH\H0.HS.2HH;EuHHX[A\A]A^A_]HSHeHHHL0HHLt HHH5 2HHH0HO UHAVSHIH-2HHEH]ȹHW)pH]HEHEHEEH5EE)EHpHpñHpLHp膮H,2HH;EuHĀ[A^]HHn HHpGHU ffff.UHAWAVAUATSHXIIHHHq,2HHEH0L-|!2I]@HLH1HH@H0HfHnfLLHEEIEHHIhL0HHLL% 2ILHDž HDžHDžHDžDž(LHHLHH:AIvIEvIEVLHHILHHHH!HHL0HHLt HHH52HHH0~H*2HH;EuHHX[A\A]A^A_].HSHeHHHL0HHLt HLH&H52HHH0H UHAVSHH2HHH HHhHH@HCH{HH5u2HLHL[A^]jfDUHAWAVSHhHHy)2HHEEHEHEHEHEE?E{ HEHEHEHEHuHsEt H} H]Ht-ff.L3Ct H{ HLMuH}HEHtH(2HH;Eu Hh[A^A_]JIEt H}H]Ht/ffff.L;Ct H{ zHrLMuH}HEHtTL fffff.UHAWAVAUATSHXIIHHH'2HHEH0L52I^@HLHs1HH@H0HfHnfLaLHEEIFHHIhL0HHLRL=_2ILHDž HDžHDžHDžDž(LHHHLHHLHHHHHA$It$IEt$IET$HeLH&HHL0HHLt HcH=H5 2HHH0H&2HH;EuLHX[A\A]A^A_]HSHeHHHL0HHLt HHH5g2HHuH0GH  fUHAVSHJ2HHHOHHhHH@HH_LH4DH52HHL[A^]DUHAVSHH@HH 2HQHLHQhHH@HLH|CH5q2HHL[A^]mfUHAVSHHw2HHH LHHhHH@HCH{eCH52HH8LH[A^]Jfffff.UHAVSH 2HHHOHHhHH@HH_LHBH52HHLH[A^]ffff.UHAVSHH@HH 2HQHLHQhHH@HLH|xBH5!2HHKL!H[A^]]UHAWAVSPIHIIFIFI @HHCHtHCH;C uHHPHu1Mt5IGI;G uILPHtHuZHCH;C t $fHt=E1HCH;C uHHPHL HCH;C lHHPPeH[A^A_]HAt I~h HH  Hff.UHAWAVAUATSHH_HtHFHEHvHuDs Au HHuHEDs AtL{( DDHK LmM9MGMt3AHMt Lc0LaH}LLUHMty KL9}rDMt5AtLc0HILHuLtx3fff.L9}v!HHHJ1H[A\A]A^A_]øꐐUHSHH 2HHEW)HDžƅDžATenƅ)ƅ fDžneDžAffiƅ)) 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H^IHiGANAdžIMgL9t)M~8fLHHHI9uL[A\A^A_]HL{xH9 UHAWAVATSIHIoANIH;=1t HIHFANAdž IMgL9M~PDIF`Ht'IcNX09}-QAVXHDUffffff.Av\f.A;v\uLIF`0Ɖ0I?'IN`IcVXrAvXHDHHHI9oL[A\A^A_]HLVwH fff.UHAWAVATSIHInANIH;=1t H~IHEANAdž IMgL9M~hDIFxHt'IcNp09}-QAVpHDUffffff.Avtf.A;vtuLpIFx0Ɖ0I?%INxIcVprAvpHDHH[HI9oL[A\A^A_]HL&vH fff.UHSPHH HH[]ÐUHAWAVATSAIIHTmKHH;=J1t&L,KHH;=.1tL HLDHL DKDH[A\A^A_]IHXuL fffff.UHAWAVSH8HHI2HHEu܉U؉9/9}+1IH"2HH;EuH8[A^A_](/IHEH$H5XH ZL `H}LEHwHuLuH1HIIG IGIG1H51H1L I$IEuuL  H}ttLL fUHAWAVAUATSHIH"2HHEI$PI $H W)EEHEHn_inputsHUHnum_scanHUIEHuLHEt H}HhMI)I$LP H`L)HXW)@) )HDžPƅHDžHput_axesHHDž0Hscan_inpHƅI$HLHLhLxLh0Lcx(W)EHEME'J<HH]H]KDN LMJ HHHLHCH9sHLM9/I?I!IpHHWH sH1H1E1E11H@HLxHrH)1f.ADALLAD AL0D L0AD@ALPD@LPAD`ALpD`LpHHuHt8H4Hffffff.AD5AL5D3 3H HuL9JKtODI)DHtHHH HHHuI8rc1DH<H< H|H| H|H| H|H| H| H| H|(H| (H|0H| 0H|8H| 8H|@H@H9uHL)HLtLuH@HLxt'HHMMW)@HDžPH@LHLPLht HOHHH+@HH;h(6HHH+@IHHpH|?H$H5SHtLH}HpLh迍HuLYHz1HIIF IFIF1H51H1L#HHHhHEH@HMXW)ƅ 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L@HLcHW)HDžfDžTƅ\LHHW)HDž@%H())Hric tensHH(H to numeHH Hut typesHHHand outpHHHn input HHHConstraiH@0ors.@4HHHHDH5BHH5H  HCH55@H?LH4't H$LMtFHL9t+HX@t Hxi$HI9uHLLI$t H4$H@I9t HtHP(HP HPH6/HH;EuLH[A\A^A_]w$ILMuOIH@I9Iut~It H#LMtEHL9HX@t HxY#HI9uHL>#t H)#H@I9uHP HtHP(HPL LUHAWAVATSHIH5/HHEHPHȐH_V1H HH(L L@HL`HW)HDžfDžTƅHHHW)HDž@6"H(`))Hric tensHH(H to numeHH Hut typesHHHand outpHHHn input HHHConstraiH@0ors.@4HHHHNAH5?H_H5"H HH5JHLH#t H;!LMtFHL9t+HX@t Hx !HI9uHLL t H H@I9t HtHP(HP HPmH3/HH;EuLH[A\A^A_]!ILMuOIH@I9Iut~It H/ LMtEHL9HX@t HxHI9uHLt HH@I9uHP HtHP(HPbL  LUHAWAVSHIHV2/HHEH`HŐHS1H0HH8L0LPHL;]H5z1HML1LHOH5=HH5H+ HNH5@WHJLH?!HPI9t HtHP(HP H`THm1/HH;EuLHĸ[A^A_]HHPI9uHP HtHP(H`H fDUHAWAVSHIH0/HHEH`H ĐHQ1H0H_H8L0LPHL[H50H K1LHNH5;HjH5-H HH5dHLHHPI9t HtHP(HP H`H 0/HH;EuLHĸ[A^A_]HHPI9uHP HtHP(H`H^ fDUHAWAVSH(IH//HHEH`HD$D$$H.H L.1E1HG5D$D$$HH L.AH5D$D$$HH LR.AH4D$D$$H%.H 3L.1E1H@9HW)HDž fDžTƅ#BHHW)HDž@]H(+)Htensors.HH0Hnumeric HH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@8HHHHn:H /HH0H E.H8H0HPHH5H5OHFH5 H HH5HLHHPH9tcHtHP(t H LMtWHL9t<HX@t HxHI9uHHP uLLt H H`K Hd,/HH;EuLH([A^A_]IugqILMunIIIHPH9tHtHP(uHP t HLMtHHL9tgfffff.HX@t HxHI9uHLt H yH`1 L LfUHAWAVSHIH&+/HHEH`HPHI1H0L0LPHLVH L1HHH HHrRH5vHH5fH H'H5H#LHH H9t$HtHP(HPI9tHt HP(HP HPI9uHP H` H*/HH;EuLH[A^A_]IHPI9t fDUHAVSHIHx&/HHEH`H袹D$D$$H%H ,L%1E1H),D$D$$H%H Ls%1E1H0H5_%Hr@1LHCH ?/HH0H u.H8H0HPHHeH5iHvH59HHH5AHLHHPH9t HtHP(HP H`H%/HH;EuLH[A^]I"IHPH9uHP HtHP(H`Lg  ffffff.UHAVSHIH$/HHEH`H·D$D$$H#H L#1E1HI*D$D$$H#H L#1E1H.H5#H>1LHAH _/HH0H .H8H0HPHHH5sHH5YHHH5 bHLH HPH9t HtHP(HP H` H9#/HH;EuLH[A^]I"IHPH9uHP HtHP(H`L  ffffff.UHAVSHIH"/HHEH`HⵐD$D$$H H ZL!1E1Hi(D$D$$HH L!E1H-(D$D$$HH Lw!AH'D$D$$HH!H L8!1E1He,H5$!Hg<1LH?HDž(H5_HL(HHDž H5\`HL HiDž?H5uHLH Dž?H5HLHH C1H0H0HPHHH5^HH5^H HH5HLHHPH9t HtHP(HP H`%H> /HH;EuLH[A^] I6I1I,I'I"IHPH9uHP HtHP(H`Lx UHAWAVAUATSHAIH/HHEHcI$PH9I$LHPHI$LHPH(t H@H(u H@ @uqulH@ @tbMcI$LPL9vPI$LLPHt>I$LLPH(tH@H(u H@ @uu H@ @u&H/HH;E Hĸ[A\A]A^A_]I$LHPx(uH@ HpHtHH12HpHuH532H}课I$LLPx(uH@ HpHtH12HpHuH532Hpj}z }p H@ڼHμ]LXu>HhHHc`09Q`H\{(D`E~)HhHH;bHIuDž`]?HEHHLIHhLc(Hc`L@H8I)HP~5E~0HcPAHHJHHLH=HPH9teHtHP(t HLMtYHL9t>fHX@t HxYHI9uHHP uLL(t H H`܏H.HH;EuLH([A^A_]wIugqILMunIIIHPH9tHtHP(uHP t HbLMtHHL9tgfffff.HX@t Hx)HI9uHLt H H`ۏLo LfUHAVSHIH.HHEH`HҍD$D$$HH \L1E1HYD$D$$HH dL1E1HH5H1L HH o.HH0H .H8H0HPHH蕿H5HH5ilH H*H5H&LHHPH9t HtHP(HP H`0ڏHI.HH;EuLH[A^]I"IHPH9uHP HtHP(H`ُL ffffff.UHAVSHIH.HHEH`HD$D$$HH |L1E1HyD$D$$HH ɳL1E1HH5H*1L)H.H .HH0H .H8H0HPHH赽H5HƾH5jH' HJH5<AHFLH;HPH9t HtHP(HP H`P؏Hi.HH;EuLH[A^]I"IHPH9uHP HtHP(H`׏L ffffff.UHAVSHIH.HHEH`HD$D$$H$H L1E1HD$D$$HH 0L1E1HH5Hb1LIHNH .HH0H .H8H0HPHHջH5HH5hHG HjH5\cHfLH[HPH9t HtHP(HP H`p֏H.HH;EuLH[A^]I"IHPH9uHP HtHP(H`֏L ffffff.UHAVSHIH.HHEH`H2D$D$$HDH L<1E1HD$D$$HH L1E1H0H5H1LiHnH .HH0H .H8H0HPHHH5CHH5fHg H芲H5|H膼LH{HPH9t HtHP(HP H`ԏH.HH;EuLH[A^]>I"IHPH9uHP HtHP(H`9ԏL ffffff.UHAVSHIH(.HHEH`HRD$D$$HdH L\1E1HD$D$$H5H L#1E1HPH5H1LHH .HH0H %.H8H0HPHHH5iH&H5dH臼 H誰H5H覺LHHPH9t HtHP(HP H`ҏH.HH;EuLH[A^]^I"IHPH9uHP HtHP(H`YҏL ffffff.UHAWAVSH(IHF.HHEH`HpD$D$$HH Lz1E1HD$D$$HSH {LA1E1HnHW)HDž fDžTƅQHHזW)HDž@H(+)Htensors.HH0Hnumeric HH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@8HHHHH =.HH0H s.H8H0HPHHcH59HtH57bHչ HH5HLHޏHPH9taHtHP(t H9LMtUHL9t:DHX@t Hx HI9uHHP uLLt H H`{ϏH.HH;EuLH([A^A_]'IugqILMunIIIHPH9tHtHP(uHP t HLMtHHL9tgfffff.HX@t HxHI9uHLt H H`aΏL LfUHAWAVSH(IHV.HHEH`H耀D$D$$HH L1E1HD$D$$HcH LQ1E1H~HW)HDž fDžTƅaHH"ӖW)HDž@H(+)Htensors.HH0Hnumeric HH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@8HHHHH M.HH0H .H8H0HPHHsH5H脲H5G^H HH5HLHڏHPH9taHtHP(t HILMtUHL9t:DHX@t HxHI9uHHP uLLt H H`ˏH.HH;EuLH([A^A_]7IugqILMunIIIHPH9tHtHP(uHP t H"LMtHHL9tgfffff.HX@t HxHI9uHLt H H`qʏL/ LfUHAVSHIHh.HHEH`H|D$D$$HͥH ȥL71E1HD$D$$HH L9E1HD$D$$HH LE1HD$D$$HH LPE1HeD$D$$HvH wL9E1H)D$D$$H]H cLJPE1HD$D$$HNH OL8E1HD$D$$H6H <LcE1HuD$D$$HH !L'1E1HH5FH1L%H*H5`OHt1LKHH5ϞHb1LqHH 1H0H0HPHHH5-H萭H5SYH HH5x HLH֏HPH9t HtHP(HP H`ǏH3.HH;EuLH[A^]IHPH9uHP Ht HP(IH`ƏL fUHAVSHIH.HHEH`HxD$D$$H H L1E1HiD$D$$HH pLME1H-D$D$$HH ߣLAHD$D$$H"H LMAHD$D$$H H La1E1H&H5H1L?HdH5LH1LRHBH5 H1LeH H Q 1H0H0HPHH蹩H58)HʪH5VH+ HNH5@ HJLH?ӏHPH9t HtHP(HP H`TďHm.HH;EuLH[A^]IHPH9uHP Ht HP(IH`ÏL UHAVSHIH.HHEH`H"vHDž(H5HHL(HHDž H5)HL H踸D$D$$H_H L1E1HOD$D$$HUH LpJE1HD$D$$HH +L]1E1HH5IH|0LDHH5IH0L/HH .HH0H =.H8H0HPHH-H5H>H5TH蟫 HŸH5$ H辩LHЏHPH9t HtHP(HP H`H.HH;EuLH[A^]v$IHPH9uHP Ht HP(IH`oL- DUHAVSHIHh.HHEH`HsD$D$$HH L1E1HD$D$$HsH Lc1E1HH5OH0LɶHH /.HH0H e.H8H0HPHHUH5)HfH5)RHǩ HH5ܕQ HLHΏHPH9t HtHP(HP H`H .HH;EuLH[A^]I"IHPH9uHP HtHP(H`虿LW ffffff.UHAVSHIH.HHEH`HqD$D$$HH <L1E1H9D$D$$HH L1E1HH5oH0LHH 1H0H0HPHH臣H5 H蘤H5[PH HH5 HLH ͏HPH9t HtHP(HP H`"H;.HH;EuLH[A^]I"IHPH9uHP HtHP(H`˽L UHAWAVAUATSH IHHH.HHEffpfPHDžƅPHDž`Hconst_zeHQfDžYroƅ[}IHPLffPƅPHConstantHDž`HQƅYf0Mƅ0 IHDž@Dž1valufDž5eH1zHH0HqHp7 HHpH;v HHpHo< fHnfpDf +LpLxL}HPHXHMHEH`HEffPLmfEHEH]fMHDž`HxH;H@H@H(pHMHHfpHEIMHH(IMIUHP HHIEIEIEH@8H@0H@@foMH0HMHH@fAEIE(H@XH@PH@HoE@HHMHHXIE@IE8IE0Hx`HpHpHp Hp= H= H5;0t H@Pt H`ffPHDž`ƅPHconst_onHQfDžYeqIHPLyffƅHDž HConstantHƅf0Mƅ0 IHDž@Dž1valufDž5eHwHH0HbHp3 HpH3s HHpHg9 fHnfpDf +LpLxL}HHHMHEH HEfffEHEH]fMHDž HxH;H@H@H(pHMHHfpHEIMHH(IMIUHP HHIEIEIEH@8H@0H@@foMH0HMHH@fAEIE(H@XH@PH@HoE@HHMHHXIE@IE8IE0Hx`HpHpHpHp: H: H180t H@Pt H`ffPHDž`ƅPDžQaxesƅUvIHPL~ffƅHDžHConstantHƅf0Mƅ0 IHDž@Dž1valufDž5eHuHH0HgHp0 HpH8p HHpHl6 fHnfpDf +LpLxL}HHHMHEHHEfffEHEH]fMHDžHxH;H@H@H(pHMHHfpHEIMHH(IMIUHP HHIEIEIEH@8H@0H@@foMH0HMHH@fAEIE(H@XH@PH@HoE@HHMHHXIE@IE8IE0Hx`HpHp|HpHp7 H7 H650t H@Pt H`ffƅHDžHd_targetHHexpandedHƅcIHLkLMffHDžƅHUnsqueezHHDžHDžxƅp fDžuetDžqtargƅwIfDžeHDžHDžHDžƅDžaxesƅ0葿HIHpH薽HLcL能HH0LLLHHHHLHHffHDžHHHHDžHDžHDžHxH;>H@H@H(HHHfHDžIMHH(IMIUHP HHIEIEIEH@8H@0H@@foH0HHH@fAEIE(H@XH@PH@Ho@HHHHXIE@IE8IE0Hx`HtTH葽ptHH|u$ fHnfpDf |+LLLHHpHxHHHHffpLLLHfHDžHxH; H@H@H(HHHfHDžHNHH(HHVHP HHHFHFHH@8H@0H@@foH0HHH@fFHF(H@XH@PH@Ho@HHHHXHF@HF8HF0Hx`LHnore_ind$HpHjLHnore_indHH.% Hp"% 0ZPfpt H}2ffHDžƅDžmaskƅIHL MffPIƅP HDž`DžQEquafpfDžUlƅp*fHet_int64H~H_target_HyHexpandedHqƅHDžHDžƅ$HHconst_igHfDžexƅ0HIHpH HLcL HLLLHPHXHHH0H`HffPHDž`HHHHDžHDžHDžHxH;H@H@H(HHHfHDžIMHH(IMIUHP HHIEIEIEH@8H@0H@@foH0HHH@fAEIE(H@XH@PH@Ho@HHHHXIE@IE8IE0Hx`H[HpHu~H@PH`ҪpHpHfHjpvt Hrffƅ"HDžHm_targetHHtransforHfDžs+IHL3Mff0Iƅ0 fpfHDž@ƅpDžqmaskƅuDž1WherfDž5eHDžHDž 虩HHet_typedHHHro_targeHHHconst_zeH@fok+ffHDžƅHd_targetHHexpandedHƅHILHpL HI\$HHI\$0HLLLLH0H8HHHHH@Hff0HDž@LHHHDžHDžHDžHxH;SH@H@H(HHHfHDžIMHH(IMIUHP HHIEIEIEH@8H@0H@@foH0HHH@fAEIE(H@XH@PH@Ho@HHHHXIE@IE8IE0Hx`H/tiHt]HptQH̦uEOHpHbHuuput HrffPƅP(HDž`Hther_eleHYHinput_gaHQDžamentƅe#IHPL+HDž ƅHElementsHMHGatherElHƅffpfEƅp DžqinpufDžutE"HEHEHm_targetHEIHtransforHEfEs0dILHpLiHuI^HYMI0ff0HDž@ƅ0Dž1axisƅ5HDžHpH0HH@H@H(pHMHHfpHEHMHHNHH(HHVHP HHHFHFHH@8H@0H@@foMH0HMHH@fAD$ID$(H@XH@PH@HoE@HHMHHXID$@ID$8ID$0Hx`ZH@ğEH}豟pH}蛟P HpHpj[HpHp" H H0t H@1Pt H`ffHDž Hfo(fHransformHHIment_traLxHther_eleHHHinput_gaH@諞IHL賜MffIƅ fpfƅpDžqmaskƅuHDžHDžƅ 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LMtQHL9t6HX@t Hx HI9uHHP uLLx t H c H`H4&HH;EuLH([A^A_] ILMu|IuS]IIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hxy HI9uHL^ t H I H`L LfUHAWAVSH(IH&HHEH`H D$D$$HH L*1E1H"D$D$$H-H dL'1E1H'HW)HDž fDžTƅcHHW)HDž0; H(El")Hry type.HHHrbitraryHHH be of aHHHnsor canHHHInput teH@&HHHHh(H5&H(LH9H (H0H0HPHHH5H0H5H HH5`HLH HPH9t]HtHP(t HLMtQHL9t6HX@t HxHI9uHHP uLLt H H`;HT&HH;EuLH([A^A_]ILMu|IuS]IIIHPH9tHtHP(uHP t HLMtHHL9tgfffff.HX@t HxHI9uHL~t H iH`!L LfUHAWAVSH(IH&HHEH`H@H5HDAH?D$D$$HH L)1AHD$D$$HH L1E1H#HW)HDž fDžTƅ_HHW)HDž07H(")Hnsor typHH Ho any teHHH types tHHHn outputHHHConstraiHf@(e.@*HHHH^$H (H0H0HPHH7H5-HHH5 H HH5HLHHPH9teHtHP(t H LMtYHL9t>fHX@t HxHI9uHHP uLLt H H`KHd&HH;EuLH([A^A_]IugqILMunIIIHPH9tHtHP(uHP t HLMtHHL9tgfffff.HX@t HxHI9uHLt H yH`1L  LfUHAWAVSH(IH&&HHEH`HPD$D$$HbH LZ1E1HD$D$$H+H ˜LfAHD$D$$H H L1AH HW)HDž fDžTƅ[HHW)HDž@)H(")Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH: HDžH5|HưLHmH (H0H0HPHHH5HH5HX H{H5HHwLHlHPH9tdHtHP(t H LMtXHL9t=HX@t Hx HI9uHHP uLLX t H C H`H&HH;EuLH([A^A_] IuuIugqILMunIIIHPH9tHtHP(uHP t H LMt:HL9tYHX@t HxY HI9uHL> t H ) H`L  LfUHAWAVSH(IH&HHEH`HD$D$$HH L 1E1HD$D$$HH L,E1HKD$D$$HH TLE1HD$D$$HH aLAHD$D$$HɮH ȮLuAHD$D$$HH hL1E1HHW)HDž fDžTƅVHHW)HDž@% H(")Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH6H50Hx(LaHT,H E|(H0H0HPHHH5HH5H_ HH5_H~LHsHPH9tkHtHP(t H LMt_HL9tDffffff.HX@t Hx HI9uHHP uLLX t H C H`H &HH;EuLH([A^A_] ILMutIuKUIIHPH9tHtHP(uHP t H LMt@HL9tKfDHX@t Hxi HI9uHLN tH 9 LIH`L  ffff.UHAWAVSH(IH &HHEH`HH5[HYE1HD$D$$HH ަL 1E1HiD$D$$HdH hL 1E1HHW)HDž fDžTƅRHHW)HDž@ H(W")Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH x(H0H0HPHHH5HH5|HY H|H5HxLHmHPH9teHtHP(t H LMtYHL9t>fHX@t Hx HI9uHHP uLLX t H C H`H &HH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t HxY HI9uHL> t H ) H`L  LfUHAWAVSH(IH&HHEH`HHXHDž(H5H L(HD$D$$HwH |L1E1HR D$D$$HaH LE1H D$D$$H/H L`E1H D$D$$H6H L$1E1HQHW)HDž fDžTƅHHW)HDž@n H(Z )Hsor typeHH(H any tenHH Han be ofHHH types cHHHd outputHHHInput anHf@0.HHHHH5Ho(LH#H t(H0H0HPHHBH5HSH5xH HH5 'HLHHPH9t`HtHP(t H LMtTHL9t9@HX@t Hx HI9uHHP uLL t H H`[Ht&HH;EuLH([A^A_] ILMIIuS]IIIHPH9tHtHP(uHP t H LMtE1HD$D$$HH L% 1E1HRHW)HDž fDžTƅ4HHҍW)HDž@o H(j")Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH ](H0H0HPHHYH56HjH5-^H˵ HH5#oHLHچHPH9tgHtHP(t H/ LMt[HL9t@ff.HX@t Hx HI9uHHP uLL t H H`kˆH%HH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hx HI9uHL t H H`QʆL  LfUHAWAVSH(IHF%HHEH`Hp|H5vHVAHoD$D$$Ha H JLY 1E1HD$D$$H2 H L 1E1HMHW)HDž fDžTƅ0HH΍W)HDž@j H(f")Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH{H ,Z(H0H0HPHHTH5HeH5(ZHƱ HH5kHLHֆHPH9tbHtHP(t H* LMtVHL9t;fDHX@t Hx HI9uHHP uLL t H H`kdžH%HH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hx HI9uHL t H H`QƆL  LfUHAWAVSHHIHF%HHEH`HpxH5vHVAHoW)HDž ƅDžDCRH5CHLHD$D$$H H L 1E1HD$D$$H H L 1E1HHW)HDžfDžTƅ+HHʍW)HDž@" H(|b")Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH3H dV(H0H0HPHH H5kHH5UH~ H衡H5fxH蝫LH҆HPH9tjHtHP(t H LMt^HL9tCfffff.HX@t Hx HI9uHHP uLLx t Hc t H N H`ÆH%HH;EuLHH[A^A_] IILMIIIIHPH9tHtHP(uHP t H} LMtCHL9twfHX@t HxI HI9uHL. t H t H  H`Lz  Lffff.UHAWAVSH(IH%HHEH`HsD$D$$H H 7L 1E1HWD$D$$H H !L<E1HD$D$$Hw H cLe 1E1HHW)HDž fDžTƅU'HH6ƍW)HDž@ H( ^")Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH5HbI(LHH OR(H0H0HPHHwH5=H舥H5KQH H H5AbHLH͆HPH9teHtHP(t HM LMtYHL9t>fHX@t Hx HI9uHHP uLL t H H`苾H%HH;EuLH([A^A_]7 ILMu|IuS]IIIHPH9tHtHP(uHP t H" LMtHHL9tgfffff.HX@t Hx HI9uHL t H H`qL/  LfUHAWAVSHHIHf%HHEH`HoHW)ƅHDž HnearestHH5uHLHD$D$$HI H LC 1E1HD$D$$H$H Lm,E1HD$D$$H H L 1E1HHW)HDžfDžTƅ~HHW)HDž@ H(")Hr types.HH/Hl tensorHH(HY' to alHH Houtput 'HHH'X' and HHHn input HHHConstraiH@7HHHH)H ZN(H0H0HPHHH5/HH5LHt H藘H5] H蓢LHɆHPH9t`HtHP(t H LMtTHL9t9@HX@t Hx HI9uHHP uLLx t Hc t H N H`H%HH;EuLHH[A^A_] IILMIIIIHPH9tHtHP(uHP t H} LMtCHL9twfHX@t HxI HI9uHL. t H t H  H`輸Lz  Lffff.UHAWAVSHIH%HHEH`HjW)ƅHDž HnearestHH5HTLH1Dž @H5ɂHL HHDžH5H~LHW)ƅHDžHhalf_pixHfDželƅH5pHjLHyW)ƅ$HDžHefer_floHHround_prHfDžorƅH5HWLH DžH5HSLHD$D$$H= H ۃL1E1HD$D$$HH L=AHuD$D$$HH YL"&AH6D$D$$HH Lq&AHD$D$$HQ H L1E1HnHW)HDžƅfDžT1ƅ*HH W)pHDž@ H(")pHr types.HH/Hl tensorHH(HY' to alHH Houtput 'HHH'X' and HHHn input HHHConstraiH@7HHHpHH5 <H?>(LHH $H(H0H0HPHHLH55}H]H5 FH辝 HH5WHݛLH†HPH9tjHtHP(pt H" LMt^HL9tCfffff.HX@t Hx HI9uHHP puLL踿 uTu`ult H 舿 H`@HY%HH;EuRLHĘ[A^A_]HO tH: tH% u諿 ILMI!xIxI IIIpuKUIIHPH9tHtHP(puHP pt H1 LMtGHL9fHX@t Hx HI9uHL޽ u=u\uht H 讽 H`fL$  H茽 tLItHd tHO uUHAWAVSH(IH%HHEH`H0cD$D$$HB H L: 1E1HD$D$$H H L 1E1H.HW)HDž fDžTƅHHҵW)HDž@K H(M")Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH\H %HH0H 3%H8H0HPHH#H5NbH4H5@H蕘 H踌H5QH贖LH詽HPH9taHtHP(t H LMtUHL9t:DHX@t Hxɺ HI9uHHP uLL蘺 t H 胺 H`;HT%HH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t Hҹ LMtHHL9tgfffff.HX@t Hx虹 HI9uHL~ t H i H`!L߳  LfUHAWAVSH(IH%HHEH`H@_H5HRE1HBD$D$$H4 H ӼL, 1E1HD$D$$HH RLE1HmD$D$$H H gL 1E1HHW)HDž fDžTƅgHH舱W)HDž@ H([I")Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH5LH4(LH0H !?(H0H0HPHHɏH5QHڐH5<H; H^H5MHZLHOHPH9tgHtHP(t H蟶 LMt[HL9t@ff.HX@t Hxi HI9uHHP uLL8 t H # H`۩H%HH;EuLH([A^A_]臶 ILMu|IuS]IIIHPH9tHtHP(uHP t Hr LMtHHL9tgfffff.HX@t Hx9 HI9uHL t H H`L  LfUHAWAVAUATSHIH%HHEHPHZHDžH5HOLH蟝D$D$$H~H PLT1E1H6D$D$$HhH QLW/E1HD$D$$HeEH xL~E1HD$D$$H H ]Lp~1E1H5HW)HDžƅfDžT1ƅQHHҬL(W)HDž0D H(. )Ic types.L`I numericLhI to onlyLpIns inputLxHConstraiH@'HHHHqHW)HDžƅfDžT2ƅmHHW)HDž0g H(Q )L`LhLpLxIConstraiL0@'HHHHHW)`HDžpƅ`fDžaT3ƅcL(HHH2W)0HDž@ 諱 H@(ő!)0Hor type.HHH tensor HHHn to anyHHL0@H`HHH0HH 9(H H H@HHɉH5^HڊH56H; H^H5G HZLHOH@H9tgHtHP(0t H@蟰 LHMt[HPL9t@ff.HX@t Hxi HI9uHHHP 0uLLP8 `uGuSLMtpHL9tUfHX@t Hx HI9uH2Hpܯ tHǯ LMuLL誯 uIuULMtrHL9tW@HX@t Hxi HI9uH2HL tH7 LMuLL t H HP轢H%HH;EuLH[A\A]A^A_]e I0ILHM,I.LILMQIILMI`IBTIIIH@H9tHtHP(0uHP 0t H@ LHMt?HPL9t$HX@t Hx薭 HI9uHHLLPv `uu#LMu/lHpJ tH5 LMt?HL9t$HX@t Hx HI9uHLL uu#LMu/lH辬 tH詬 LMt?HL9t^DHX@t Hxy HI9uHL^ t HI HPL迦  LfUHAVSHIH%HHEH`H"RD$D$$H2 H - L1E1HD$D$$H H L&1E1H H5zH )(LoH^H5&H)(LoHH @LE1H衷D$D$$H H @L 1E1HHW)HDžfDžTƅvHH輗W)HDž@5 H(o )Hsor typeHH(H any tenHH Han be ofHHH types cHHHd outputHHHInput anHf@0.HHHHRH ((H0H0HPHH+vH5aHL 1E1H|D$D$$H֬ H 6>LƬ 1E1HD$D$$HRH >L~AH贷D$D$$H?H ?L?AHuD$D$$HťH F@LAH6HW)HDž fDžTƅqHHڒW)HDž0S H( s")Hor type.HHHany tensHHHn be of HHHInput caH@ HHHH莸H ?$(H0H0HPHHgqH55HxrH5;Hu HiH51/ HsLH횆HPH9teHtHP(t H= LMtYHL9t>fHX@t Hx HI9uHHP uLLؗ t H × H`{H%HH;EuLH([A^A_]' Iu_iILMufIIHPH9tHtHP(uHP t H LMt@HL9tKfDHX@t Hx HI9uHLΖ tH 蹖 LIH`gL%  ffff.UHAWAVSH(IHV%HHEH`HHiH5PHl HaH5F& H kLHHPH9tjHtHP(t HR LMt^HL9tCfffff.HX@t Hx HI9uHHP uLL t Hӎ t H 辎 H`vH%HH;EuLHH[A^A_]" Iut~ILMu{IIIHPH9tHtHP(uHP t H LMtFHL9tzfff.HX@t Hxɍ HI9uHL讍 t H虍 t H 脍 H`I1LPH*I1LPH(u!H@ @H@H(tH@ @I1LPx(H@ LhMuL-(E} D}W)EHEEEaxisEIHuLHt x1Et H}u D}̉]D9uA9ly AD}DIE(HcHDx( @ EW)EHEILPHLxILPHILPHH}HHuHUH}H}Ht H}Ct L}H]ILP L)HH9HEHEHUH9HH)HHOH81HHx(LhMeYILP 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(x IH58GHH}ZAHuL H$HIIF IFIFE1H5s$H|$LL r( IH5FHH}&AHuL_ H$HIIF IFIFE1H5$H$L  H'HEuEuH}HukrH}[ EtL H}HuJQHHEt H}, Et5LU H  HH}HuHH}Ht H} Ht  fff.UHH h"H1H9NHD]DUHH}w']UHAVSHIHx$HHEH`H袐H5HAHD$D$$H H RL&1E1HD$D$$Hd H 2L)e1E1HH5Hv'L] HH5dHw'Ly HH tz'H0H0HPHH4H5dHEH5oH HɺH5PWHLHHPH9t HtHP(HP H`܅H$HH;EuLH[A^]} I"IHPH9uHP HtHP(H`x܅L6  fffff.UHAWAVSH(IHf$HHEH`H萎D$D$$H<H L 1E1HD$D$$HYH dLRLE1HD$D$$HKH ML% 1E1HRHW)HDž fDžTƅտHHW)HDž@o H(x!)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH !x'H0H0HPHHYH5HjH5-lHHH5uHLHHPH9tgHtHP(t H/ LMt[HL9t@ff.HX@t Hx HI9uHHP uLL t H H`kمH$HH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hx HI9uHL t H H`Q؅L  LfUHAWAVSH(IHF$HHEH`HpD$D$$HH lLz 1E1HD$D$$H9H xL1E1HnHW)HDž fDžTƅHH݌W)HDž0 H(F!)Hry type.HHHrbitraryHHH be of aHHHnsor canHHHInput teH@&HHHHH5Hp'LHH t'H0H0HPHHoH5SH耼H5ChHHH5HLHHPH9t]HtHP(t HE LMtQHL9t6HX@t Hx HI9uHHP uLL t H H`ՅH$HH;EuLH([A^A_]7 ILMu|IuS]IIIHPH9tHtHP(uHP t H" LMtHHL9tgfffff.HX@t Hx HI9uHL t H H`qԅL/  LfUHAWAVSH(IHf$HHEH`H萆D$D$$H<H L 1E1HD$D$$HH ԎL 1E1HHW)HDž fDžTƅHH2ٌW)HDž0 H(B!)Hry type.HHHrbitraryHHH be of aHHHnsor canHHHInput teH@&HHHHH5 Hl'LHH Wq'H0H0HPHH菷H5~H蠸H5cdHH$H5 H LHHPH9t]HtHP(t He LMtQHL9t6HX@t Hx9 HI9uHHP uLL t H H`хH$HH;EuLH([A^A_]W ILMu|IuS]IIIHPH9tHtHP(uHP t HB LMtHHL9tgfffff.HX@t Hx HI9uHL t H H`ЅLO  LfUHAWAVSH(IH$HHEH`H谂H5HAH诿D$D$$H H L 1AHD$D$$HH L] 1E1HHW)HDž fDžTƅ HH.ՌW)HDž0 H(q!)Hnsor typHH Ho any teHHH types tHHHn outputHHHConstraiHf@(e.@*HHHHH m'H0H0HPHH觳H5vH踴H5{`H HfHX@t HxI HI9uHHP uLL t H  H`ͅH$HH;EuLH([A^A_]g IugqILMunIIIHPH9tHtHP(uHP t HR LMtHHL9tgfffff.HX@t Hx HI9uHL t H H`̅L_  LfUHAWAVSH(IH$HHEH`H~D$D$$H H L 1E1HGD$D$$HH [L 1AHHW)HDž fDžTƅ>HH_ьW)HDž@ H(2i!)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHHDžH5+HuLHH5WHއE1H.H ?j'H0H0HPHHwH5)H舰H5K\H H H5HLH؅HPH9teHtHP(t HM LMtYHL9t>fHX@t Hx HI9uHHP uLL t H H`ɅH$HH;EuLH([A^A_]7 IuuILMu|IuS]IIIHPH9tHtHP(uHP t H LMt:HL9tYHX@t Hx HI9uHL t H H`qȅL/  LfUHAWAVSH(IHf$HHEH`HzD$D$$H<H :L 1E1HD$D$$HmH (LE1HD$D$$H2H LE1HD$D$$HBH LDAH`D$D$$HYH XLAH!D$D$$H} H Lk 1E1HHW)HDž fDžTƅHH<̌W)HDž@ H(d!)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH5H_'LHH e'H0H0HPHH}H5qH莫H5QWH HH5HLHԅHPH9tkHtHP(t HS LMt_HL9tDffffff.HX@t Hx HI9uHHP uLL t H H`ąH$HH;EuLH([A^A_]7 ILMutIuKUIIHPH9tHtHP(uHP t H* LMt@HL9tKfDHX@t Hx HI9uHL tH LIH`wÅL5  ffff.UHAWAVSH(IHf$HHEH`HuH5HE1H蒲D$D$$HH n}L| 1E1HD$D$$HH LC 1E1HpHW)HDž fDžTƅHHȌW)HDž@ H(_!)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH ?b'H0H0HPHHwH56H舧H5KSHH H5{HLHυHPH9teHtHP(t HM LMtYHL9t>fHX@t Hx HI9uHHP uLL t H H`H$HH;EuLH([A^A_]7 IugqILMunIIIHPH9tHtHP(uHP t H" LMtHHL9tgfffff.HX@t Hx HI9uHL t H H`qL/  LfUHAWAVSH(IHf$HHEH`HqD$D$$H<H AL 1E1HD$D$$H&H 'LR/E1HD$D$$H}H }L% E1HD$D$$H H L 1E1HHW)HDž fDžTƅ虢HHÌW)HDž@3 H(]1)Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHHKH l^'H0H0HPHH$H5 H5H5NH薦 H蹚H5@wpH赤LH˅HPH9tbHtHP(t H LMtVHL9t;fDHX@t Hx HI9uHHP uLL t H H`;HT$HH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hx HI9uHL~ t H i H`!L  LfUHAWAVSH(IH$HHEH`H@mHDž(H5HTzL(HD$D$$HH L 1E1HD$D$$HH fzL?E1H^D$D$$HwyH zL E1H"D$D$$H~ H zLl 1E1HHW)HDž fDžTƅHH=W)HDž@ H(,)Hsor typeHH(H any tenHH Han be ofHHH types cHHHd outputHHHInput anHf@0.HHHHH5͓HR'LHH RZ'H0H0HPHH芝H5jH蛞H5^JH HH5rHLHDžHPH9thHtHP(t H` LMt\HL9tAfff.HX@t Hx) HI9uHHP uLL t H H`蛷H$HH;EuLH([A^A_]G ILMu|IuS]IIIHPH9tHtHP(uHP t H2 LMtHHL9tgfffff.HX@t Hx HI9uHL t H H`聶L?  LfUHAWAVSH(IHv$HHEH`HhHDž(H5rHhwL(HcD$D$$HH $L} 1E1HD$D$$H H ywLE1HD$D$$H H wL 1E1H5HW)HDž fDžTƅ踙HHٺW)HDž@R H(|()Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHHjH5dHN'LHH iV'H0H0HPHH!H5VaH2H5EH蓝 H趑H5=nHH貛LH…HPH9t_HtHP(t H LMtSHL9t8HX@t Hxɿ HI9uHHP uLL蘿 t H 胿 H`;HT$HH;EuLH([A^A_] ILMu|IuS]IIIHPH9tHtHP(uHP t HҾ LMtHHL9tgfffff.HX@t Hx虾 HI9uHL~ t H i H`!L߸  LfUHAWAVSH(IH$HHEH`H@dHDž(H5HsL(HD$D$$HH L 1E1HD$D$$HH sL?E1H^D$D$$H H tL 1E1HHW)HDž fDžTƅXHHyW)HDž@ H($)Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHH H5HfHX@t Hxi HI9uHHP uLL8 t H # H`۪H$HH;EuLH([A^A_]臷 IugqILMunIIIHPH9tHtHP(uHP t Hr LMtHHL9tgfffff.HX@t Hx9 HI9uHL t H H`L  LfUHAWAVSH(IH$HHEH`H[H5ZHnAHߘD$D$$HkH unL 1E1HFD$D$$HSnH jmL 1E1HHW)HDž fDžTƅ@HHaW)HDž@ڴ H(4F!)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH K'H0H0HPHHČH5HՍH59H6 HYH5aTHULHJHPH9tbHtHP(t H蚳 LMtVHL9t;fDHX@t Hxi HI9uHHP uLL8 t H # H`ۦH$HH;EuLH([A^A_]至 IugqILMunIIIHPH9tHtHP(uHP t Hr LMtHHL9tgfffff.HX@t Hx9 HI9uHL t H H`L  LfUHAWAVSH(IH$HHEH`HWH5HjAHߔD$D$$H H jL 1E1HFD$D$$H H jL 1E1HHW)HDž fDžTƅ@HHaW)HDž@ڰ H(4B!)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH H'H0H0HPHHĈH5HՉH55H6HYH5]HULHJHPH9tbHtHP(t H蚯 LMtVHL9t;fDHX@t Hxi HI9uHHP uLL8 t H # H`ۢH$HH;EuLH([A^A_]臯 IugqILMunIIIHPH9tHtHP(uHP t Hr LMtHHL9tgfffff.HX@t Hx9 HI9uHL t H H`L  LfUHAWAVSHHIH$HHEH`HSH5HfAHߐW)HDž ƅDžDCRH5HkgLH'D$D$$H H rfL 1E1HD$D$$HZ H VgLH 1E1HuHW)HDžfDžTƅHHW)HDž@蒬 H(=!)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH DD'H0H0HPHH|H5ۀH荅H5P1H H}H5YH LHHPH9tjHtHP(t HR LMt^HL9tCfffff.HX@t Hx HI9uHHP uLL t HӪ t H 辪 H`vH$HH;EuLHH[A^A_]" IILMIIIIHPH9tHtHP(uHP t H LMtCHL9twfHX@t Hx蹩 HI9uHL螩 t H艩 t H t H`,L  Lffff.UHAWAVSH(IH$HHEH`H@OD$D$$HR H cLJ 1E1HD$D$$HcH cL E1HD$D$$H H cLպ 1E1HHW)HDž fDžTƅ腀HH覡W)HDž@ H(y9!)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH0H5j Hr5'L cHNH /@'H0H0HPHHH5HH5,HYH|xH5U/HxLHmHPH9teHtHP(t H车 LMtYHL9t>fHX@t Hx艦 HI9uHHP uLLX t H C H`H$HH;EuLH([A^A_]触 ILMu|IuS]IIIHPH9tHtHP(uHP t H蒥 LMtHHL9tgfffff.HX@t HxY HI9uHL> t H ) H`ᘅL蟟  LfUHAWAVSHIHַ$HHEH`HKW)ƅHDž HnearestHH5H`LHaDž @H5bHaL HEHDžH5bHbLHHW)ƅHDžHhalf_pixHfDželƅH5aHILH詘W)ƅ$HDžHefer_floHHround_prHfDžorƅH5aHbLH9DžH5`HcLHD$D$$Hm H dL 1E1HD$D$$HcH cLE1H註D$D$$H `H dLUE1HlD$D$$HeH eLAH-D$D$$H H DfL 1E1H褿HW)HDžƅfDžT1ƅ zHHAW)pHDž@躡 H(T!)pHr types.HH/Hl tensorHH(HY' to alHH Houtput 'HHH'X' and HHHn input HHHConstraiH@7HHHpHH5AH/'L$HH;EuLHİ[A^]Ӊ HH`|H调  UHAVSHIH$HHEH`H/H5x5Ht5AHlD$D$$H H 5L 1E1HxD$D$$HԚ H 5L1E1HH5I HQ'LP H-H5cH'LP H H #'H0H0HPHH`H5HaH5x HeH9YH55H5cLH*HPH9t HtHP(HP H`?{HX$HH;EuLH[A^] I"IHPH9uHP HtHP(H`zL要  fffff.UHAVSHIHؙ$HHEH`H-H5ߗHOE1HjD$D$$H`6H `6L 1AHhD$D$$HM6H T6L 1E1HߣH5 H'L6OHH5!H_H5 HOcHrWH53HnaLHcH`yH$HH;EuLHİ[A^]E HH`dyH"  f.UHAWAVSH(IHV$HHEH`H+H5]HwNAHhD$D$$H4H 4Li 1AHD$D$$H4H 4L- 1E1HZHW)HDž fDžTƅ\HH}W)HDž0w H(AG!)Hnsor typHH Ho any teHHH types tHHHn outputHHHConstraiHf@(e.@*HHHH螣H ? 'H0H0HPHHw\H5mH]H5K H`H UH51H_LHHPH9teHtHP(t HM LMtYHL9t>fHX@t Hx HI9uHHP uLL t H ӂ H`vH$HH;EuLH([A^A_]7 IugqILMunIIIHPH9tHtHP(uHP t H" LMtHHL9tgfffff.HX@t Hx HI9uHL΁ t H 蹁 H`quL/|  LfUHAVSHIHh$HHEH`H'D$D$$H H R1L 1E1HD$D$$Hm[H QJLc AHڙD$D$$HPJH 0L$ 1AHNH5 H'L!EH茱H5H JE1HcH5ZHJE1HcH5XH!ZH5H]HQH5,. H[LH薂H`sH$HH;EuLHİ[A^]x HH`sHUz  ffff.UHAVSHIH$HHEH`H%H57IH9IAHbW)@ƅ@HDžPHconstantHAƅIH5wHnJL@HsDž<H50HpJL<HmD$D$$HrH L 1E1H薗D$D$$H H PL 1E1H H5̐ H_'LFhHKH5,HXH5H}[HOH5',L HYLH葀@t HP} H`qHɐ$HH;EuLH[A^]^~ H@uHH@t HP} H`UqHx  ff.UHAVSHIHH$HHEH`Hr#H56HHE1Ht`H5dHdE1HV`D$D$$HpH 2+L@ 1E1H轕D$D$$H-+H /+L 1E1H4H5 H 'LmfHrH55HCVH5HYHMH5N*i HWLH~H`oH$HH;EuLHİ[A^]| HH`oHwv  ffffff.UHAVSHIH$HHEH`H!D$D$$H H 96L܍ 1E1HYD$D$$H GH GL E1HD$D$$H#H GLg E1HD$D$$H= H FL+ 1E1HXH5 H 'L+?H薫H5 H 'LFHtH U'H0H0HPHH SH5YHTH5HWHKH5)( HULH|HPH9t HtHP(HP H`mH$HH;EuLH[A^]Vz IHPH9uHP Ht HP(IH`QmLt  UHAVSHIHH$HHEH`HrHRH5EHEAHd\H5EHEAHC\W)@ƅ@HDžPHnearestHAH5HEL@HmD$D$$H H ELފ 1E1H[D$D$$H H EL 1E1HҕH5 Ht'LEHH5eHQH5HBUHeIH5% HaSLHVz@t HPw H`ukH$HH;EuLH[A^]#x HH@t HPpw H`(kHq  fffff.UHAWAVSHHIH$HHEH`H@H5{4HDAH?ZW)ƅHDž HnearestHH5 ~H?ELHkD$D$$H H ~7Lڈ 1E1HWD$D$$H H n:L 1E1HΓHW)HDžfDžTƅQNHHroW)HDž@u H(E!)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH 'H0H0HPHHMH5cHNH5HGRHjFH5" HfPLH[wHPH9tcHtHP(t Ht LMtWHL9t<HX@t Hxyt HI9uHHP uLLHt t H3t t H t H`gH$HH;EuLHH[A^A_]t IILMIIIIHPH9tHtHP(uHP t HMs LMtCHL9twfHX@t Hxs HI9uHLr t Hr t H r H`fLJm  Lffff.UHAWAVSHHIHv$HHEH`HW)ƅHDž HnearestHH5yH@LHgD$D$$Ha H 2L[ 1E1H؊D$D$$HHH H]Lz 1E1HޅHW)HDž fDžTƅa@HHaW)HDž@g H(U )Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH H -'H0H0HPHH?H55H@H5HWDHz8H5 HvBLHkiHPH9tcHtHP(t Hf LMtWHL9t<HX@t Hxf HI9uHHP uLLXf t H Cf H`YHy$HH;EuLH([A^A_]f IugqILMunIIIHPH9tHtHP(uHP t He LMtHHL9tgfffff.HX@t HxYe HI9uHL>e t H )e H`XL_  LfUHAWAVSH(IHw$HHEH`H D$D$$HXH XL w 1E1H}D$D$$H6H XL,3E1HK}D$D$$HXH TL2E1H}D$D$$HXH XL2AH|D$D$$HH a4Lu2AH|D$D$$Hu H hXLu 1E1HHW)HDž fDžTƅ;HH\W)HDž@%c H( )Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH6H501H&LaWHTH 'H0H0HPHH:H50H;H5H_? 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constp_providerSyncProviders/Users/runner/work/1/s/onnxruntime/core/session/abi_session_options.ccOrtSessionOptions &OrtSessionOptions::operator=(const OrtSessionOptions &)execution_mode is not validgraph_optimization_level is not validWARNING: Since openmp is enabled in this build, this API cannot be used to configure intra op num threads. Please use the openmp environment variables to control the number of threads. Result buffer is not large enoughADescription/Users/runner/work/1/s/onnxruntime/core/session/custom_ops.ccCreateCustomRegistryonnxruntime::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.inter-opMemcpyFromHostXinputTYoutputConstrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.MemcpyToHostInput expected to have type but instead is null expected to have tensor typeElement type of input unknownOutput expected to have sequence typeTrying to register schema with name (domain: version: ) from file line , but it is already registered from file , but it its domain is not known by the checker., but it 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 trueintra-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: onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions &, const onnxruntime::Environment &, std::istream &)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.Received nullptr for graph transformerRegisterGraphTransformerGraph transformers must be registered before the session is initialized.AddCustomOpDomainsReceived nullptr for custom registry2SaveToOrtFormatLoadThis 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.Not satisfied: fbs::VerifyInferenceSessionBuffer(verifier) Satisfied, but should not be: nullptr == fbs_session Satisfied, but should not be: fbs_ort_model_version == nullptr Not satisfied: IsOrtModelVersionSupported(fbs_ort_model_version->str()) The ORT format model version [] is not supported this build Satisfied, but should not be: nullptr == fbs_model Satisfied, but should not be: nullptr == fbs_sess_state InitializeInitializing 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. The generated model may contain hardware and execution provider specific optimizations, and should only be used in the same environment the model was optimized for.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.ValidateInputs is not expected to be of type sparse tensor. is not expected to be of type tensor sequence.Output 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.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: virtual void onnxruntime::InferenceSession::AddPredefinedTransformers(onnxruntime::GraphTransformerManager &, onnxruntime::TransformerLevel, const std::vector &)graph_optimization_level <= TransformerLevel::MaxLevelExceeded max transformer level. Current level is set to WaitForNotificationtimeout_in_ms >0 is not supportedonnxruntime::SessionIOBinding::SessionIOBinding(onnxruntime::InferenceSession *)session->NewIOBinding(&binding_).IsOK()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 stringbooleandiscardednumberexcessive 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 rangeCUDA execution provider is not enabled.1.6.0FlatBuffers 1.12.0allocator::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]Ret onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper::Get() [Ret = OrtStatus *, UnsupportedPolicy = c_api_internal::UnsupportedReturnFailStatus]called_ < 2void 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_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 /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::vector &)Unsupported levelACLExecutionProviderArmNNExecutionProviderstd::vector > onnxruntime::optimizer_utils::GenerateTransformers(onnxruntime::TransformerLevel, gsl::span, const onnxruntime::IExecutionProvider &, const std::vector &)Unsupported level Not satisfied: tensor_proto.data_type() != ONNX_NAMESPACE::TensorProto_DataType_UNDEFINED && tensor_proto.data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRING /Users/runner/work/1/s/onnxruntime/core/optimizer/initializer.ccReadExternalRawDataSatisfied, but should not be: model_path.IsEmpty() model_path must not be empty. Ensure that a path is provided when the model is created or loaded.Not satisfied: external_data_length == 0 || external_data_length == actual_tensor_data_length TensorProto external data size mismatch. Computed size: , external_data.length: /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_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_Info/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.ccApplyImplTensorrtExecutionProvidervoid 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.hRecurseForEachWithIndexauto 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 EluHardSigmoidLeakyReluSoftplusReluSigmoidSoftsignTanhThresholdedReluSeluParametricSoftplusScaledTanhunknown 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]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::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]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.hBVIfthen_branch/Users/runner/work/1/s/onnxruntime/core/providers/cpu/controlflow/if.cconnxruntime::If::If(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::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.onnxruntime::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::Loop(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 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) constonnxruntime::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 /Users/runner/work/1/s/onnxruntime/core/providers/cpu/controlflow/scan_8.cconnxruntime::Scan<8>::Scan(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.cconnxruntime::Scan<9>::Scan(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 &, int64_t)num_entries < 0 || gsl::narrow_cast(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.ccRegisterOnnxOperatorKernelsRegisterOnnxMLOperatorKernelsGetKernelRegistryvirtual std::shared_ptr onnxruntime::CPUExecutionProvider::GetKernelRegistry() constRegisterCPUKernelsCpuConstantOfShape/Users/runner/work/1/s/onnxruntime/core/providers/cpu/generator/constant_of_shape.ccvoid onnxruntime::ConstantOfShapeBase::SetValueFromTensorProto(const onnx::TensorProto &)utils::HasDataType(t_proto)TensorProto::DataType_IsValid(t_proto.data_type())unpack_status.IsOK()Value attribute unpacking failed:Unsupported value attribute datatype: onnxruntime::ConstantOfShapeBase::ConstantOfShapeBase(const onnxruntime::OpKernelInfo &)t_proto.dims_size() == 1t_proto.dims()[0] == 1onnxruntime::common::Status onnxruntime::ConstantOfShapeBase::PrepareCompute(onnxruntime::OpKernelContext *, onnxruntime::Tensor **) constinput_shape.NumDimensions() > 0virtual onnxruntime::common::Status onnxruntime::ConstantOfShape::Compute(onnxruntime::OpKernelContext *) constUnsupported value attribute datatype with sizeof=: 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::UNDEFINEDonnxruntime::common::Status onnxruntime::RandomNormalCompute(float, float, std::default_random_engine &, TensorProto::DataType, onnxruntime::Tensor &)onnxruntime::common::Status onnxruntime::RandomUniformCompute(float, float, std::default_random_engine &, TensorProto::DataType, onnxruntime::Tensor &)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_void onnxruntime::utils::MLTypeCallDispatcher::Invoke(Args &&...) const [Fn = ComputeImpl, Types = , Args = ]/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().NumDimensions() == 0max->Shape().NumDimensions() == 0void 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 &)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)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]SubMulDivAbsNegFloorCeilReciprocalSqrtT1PowExpLogSumMaxMinLessGreaterEqualMeanBitShiftErfNotAndOrXorUnsupported X type: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.ccvirtual onnxruntime::common::Status onnxruntime::Sum_6::Compute(onnxruntime::OpKernelContext *) const [T = float]input_count >= 1data_1.Shape() == shapedata_n.Shape() == shapevirtual onnxruntime::common::Status onnxruntime::Min_6::Compute(onnxruntime::OpKernelContext *) const [T = float]inputCount >= 1virtual 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]SinCosTanAsinAcosAtanSinhCoshAsinhAcoshAtanhPReluExpandModvirtual onnxruntime::common::Status onnxruntime::Mod::Compute(onnxruntime::OpKernelContext *) constfmod_/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::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]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]onnxruntime::Tensor *onnxruntime::OpKernelContext::Output(int)p_ml_valuevoid 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(int64_t, int64_t)axis == 1 || axis == largestAttempting to broadcast an axis by a dimension other than 1. by void onnxruntime::BroadcastIterator::Append(int64_t, int64_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_ == 0onnxruntime::Mod::Mod(const onnxruntime::OpKernelInfo &)(fmod == 0) || (fmod == 1)onnxruntime::OutputBroadcaster::OutputBroadcaster(size_t, onnxruntime::Tensor &, int64_t, int64_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: Ret onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper >::Get() [Ret = onnxruntime::common::Status, UnsupportedPolicy = onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy]Ret onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy::operator()(int32_t) const [Ret = onnxruntime::common::Status]Unsupported data type: void onnxruntime::utils::MLTypeCallDispatcher::Invoke(Args &&...) const [Fn = mod_internal::CallModImpl, Types = , Args = ]helper.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()/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_ >= 0/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 = float]c_shape != nullptr/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.ccNot satisfied: left_num_dims >= 1 && right_num_dims >= 1 /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/matmul_helper.hNot satisfied: K_ == right_shape[right_num_dims - 2] || transb && K_ == right_shape[right_num_dims - 1] Not satisfied: left_padded_dims_[idx_dim] == 1 left operand cannot broadcast on dim Not satisfied: right_padded_dims_[idx_dim] == 1 right operand cannot broadcast on dim Not satisfied: K_ == right_shape[transb ? right_num_dims - 1 : right_num_dims - 2] Not satisfied: num_dims_with_pad == num_output_dims Not satisfied: M_ == 1 && N_ == 1 Not satisfied: num_dims_with_pad - 1 == num_output_dims Not satisfied: num_dims_with_pad - 2 == num_output_dims Not satisfied: K_ == right_shape[0] MatMulInteger/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 1IsScalarOr1ElementVector(b_zero_point)MatmulInteger : input2 zero point must be a scalar or 1D tensor of size 1QLinearMatMul/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 1IsScalarOr1ElementVector(b_offset)QLinearMatmul : weight zero point must be a scalar or 1D tensor of size 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 1IsScalarOr1ElementVector(b_scale)QLinearMatmul : weight scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_scale)QLinearMatmul : result scale must be a scalar or 1D tensor of size 1RoundSignvoid onnxruntime::utils::MLTypeCallDispatcher::Invoke(Args &&...) const [Fn = CallSignImpl, Types = , Args = ]SoftmaxLogSoftmax/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/softmax.ccComputeImplOpset13SoftmaxCPU inputs N, D and N * D must be < /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/top_k.cconnxruntime::TopK<9, float>::TopK(const onnxruntime::OpKernelInfo &) [OpSet = 9, T = float]op_kernel_info.GetAttr("k", &k_temp).IsOK()k_temp > 0op_kernel_info.GetAttr("axis", &axis_temp).IsOK()input count mismatch, expected 1 input - the tensor to be processedonnxruntime::TopK<10, float>::TopK(const onnxruntime::OpKernelInfo &) [OpSet = 10, T = float]input count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valueTopKvoid onnxruntime::TopkOpset11ConstructorCommon(const onnxruntime::OpKernelInfo &, int &, bool &, bool &)largestop_kernel_info.GetAttr("largest", &largest_temp).IsOK()sortedop_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()k argument [] should not be greater than specified axis dim value [output count mismatch, expected 2 outputs to be present for TopK operatorArrayFeatureExtractorInvalid 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]Scaleroffset/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 > > > >]MaxUnpoolNot satisfied: X_shape.NumDimensions() >= 3 /Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/Unpool.ccNot satisfied: I_shape == X_shape Index tensor shape should be same as that of the input data tensor to unpool.Not satisfied: tensor_shape->Shape().GetDims().size() == 1 Not satisfied: given_shape.Size() >= shape.Size() output_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]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: ComputePadAndOutputShapeDilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER.ComputePad: pad type not supported.kernel_shapestridesdilationsgroupT3ConvInteger/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)ConvTransposeoutput_paddingoutput_shape/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/conv_transpose.ccDoConvTransposegroup 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, trainable_dropout = false]!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, trainable_dropout = false]float onnxruntime::(anonymous namespace)::GetRatioOrDefault(const onnxruntime::Tensor *) [T2 = double]virtual onnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext *) const [T1 = double, T2 = float, trainable_dropout = false]virtual onnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext *) const [T1 = double, T2 = double, trainable_dropout = false]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_ == 2input 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_ > 0.0fNot satisfied: input_dims >= 3 /Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/pool.ccUnsupported pooling size.Not satisfied: pooling_dims == pool_attrs_.kernel_shape.size() 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()Not satisfied: x_shape.NumDimensions() >= 3 Unsupported pooling size : T4QLinearConv/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/qlinearconv.ccvirtual 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_tensorNot satisfied: boxes_shape.NumDimensions() == 3 PrepareComputeNot satisfied: scores_shape.NumDimensions() == 3 Not satisfied: boxes_dims[0] == scores_dims[0] Not satisfied: boxes_dims[1] == scores_dims[2] Not satisfied: boxes_dims[2] == 4 Not satisfied: (iou_threshold >= 0 && iou_threshold <= 1.f) GetThresholdsFromInputsNot satisfied: ret.IsOK() 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 /Users/runner/work/1/s/onnxruntime/core/providers/cpu/reduction/reduction_ops.ccstatic onnxruntime::Tensor onnxruntime::ReduceSum::Impl(const onnxruntime::Tensor &, const std::vector &, onnxruntime::AllocatorPtr, concurrency::ThreadPool *, bool, const onnxruntime::TensorShape *) [T = float]keep_dimsCan't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:static onnxruntime::Tensor onnxruntime::ReduceSum::Impl(const onnxruntime::Tensor &, const std::vector &, onnxruntime::AllocatorPtr, concurrency::ThreadPool *, bool, const onnxruntime::TensorShape *) [T = int]static onnxruntime::Tensor onnxruntime::ReduceSum::Impl(const onnxruntime::Tensor &, const std::vector &, onnxruntime::AllocatorPtr, concurrency::ThreadPool *, bool, const onnxruntime::TensorShape *) [T = double]static onnxruntime::Tensor onnxruntime::ReduceSum::Impl(const onnxruntime::Tensor &, const std::vector &, onnxruntime::AllocatorPtr, concurrency::ThreadPool *, bool, const onnxruntime::TensorShape *) [T = long long]ReduceL1ReduceL2ReduceLogSumReduceLogSumExpReduceMaxReduceMeanReduceMinReduceProdReduceSumReduceSumSquareArgMaxArgMinbool onnxruntime::SetupForReduce(const onnxruntime::Tensor *, const std::vector &, std::vector &, onnxruntime::TensorShape &, std::vector &, bool &, const onnxruntime::TensorShape *)input_tensor_ptr->Shape().Size() == input_shape_override->Size()The input shape override's size does not match the input tensor's shape sizevoid onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = float, AGG = onnxruntime::ReduceAggregatorL1]keepdims_/Users/runner/work/1/s/onnxruntime/core/providers/cpu/reduction/reduction_ops.hvoid onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = float, AGG = onnxruntime::ReduceAggregatorL1]count == 1last_results.last_loop_red_size > 0last_results.last_loop_size > 0last_results.projected_index.size() > 0void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = int, AGG = onnxruntime::ReduceAggregatorL1]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = int, AGG = onnxruntime::ReduceAggregatorL1]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = float, AGG = onnxruntime::ReduceAggregatorL2]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = float, AGG = onnxruntime::ReduceAggregatorL2]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = int, AGG = onnxruntime::ReduceAggregatorL2]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = int, AGG = onnxruntime::ReduceAggregatorL2]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = float, AGG = onnxruntime::ReduceAggregatorLogSum]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = float, AGG = onnxruntime::ReduceAggregatorLogSum]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = int, AGG = onnxruntime::ReduceAggregatorLogSum]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = int, AGG = onnxruntime::ReduceAggregatorLogSum]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = float, AGG = onnxruntime::ReduceAggregatorLogSumExp]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = float, AGG = onnxruntime::ReduceAggregatorLogSumExp]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = int, AGG = onnxruntime::ReduceAggregatorLogSumExp]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = int, AGG = onnxruntime::ReduceAggregatorLogSumExp]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = float, AGG = onnxruntime::ReduceAggregatorMax]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = float, AGG = onnxruntime::ReduceAggregatorMax]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = int, AGG = onnxruntime::ReduceAggregatorMax]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = int, AGG = onnxruntime::ReduceAggregatorMax]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = long long, AGG = onnxruntime::ReduceAggregatorMax]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = long long, AGG = onnxruntime::ReduceAggregatorMax]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = signed char, AGG = onnxruntime::ReduceAggregatorMax]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = signed char, AGG = onnxruntime::ReduceAggregatorMax]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = unsigned char, AGG = onnxruntime::ReduceAggregatorMax]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = unsigned char, AGG = onnxruntime::ReduceAggregatorMax]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = float, AGG = onnxruntime::ReduceAggregatorMean]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = float, AGG = onnxruntime::ReduceAggregatorMean]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = int, AGG = onnxruntime::ReduceAggregatorMean]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = int, AGG = onnxruntime::ReduceAggregatorMean]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = float, AGG = onnxruntime::ReduceAggregatorMin]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = float, AGG = onnxruntime::ReduceAggregatorMin]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = int, AGG = onnxruntime::ReduceAggregatorMin]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = int, AGG = onnxruntime::ReduceAggregatorMin]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = long long, AGG = onnxruntime::ReduceAggregatorMin]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = long long, AGG = onnxruntime::ReduceAggregatorMin]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = signed char, AGG = onnxruntime::ReduceAggregatorMin]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = signed char, AGG = onnxruntime::ReduceAggregatorMin]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = unsigned char, AGG = onnxruntime::ReduceAggregatorMin]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = unsigned char, AGG = onnxruntime::ReduceAggregatorMin]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = float, AGG = onnxruntime::ReduceAggregatorProd]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = float, AGG = onnxruntime::ReduceAggregatorProd]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = int, AGG = onnxruntime::ReduceAggregatorProd]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = int, AGG = onnxruntime::ReduceAggregatorProd]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = long long, AGG = onnxruntime::ReduceAggregatorProd]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = long long, AGG = onnxruntime::ReduceAggregatorProd]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = float, AGG = onnxruntime::ReduceAggregatorSumSquare]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = float, AGG = onnxruntime::ReduceAggregatorSumSquare]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = int, AGG = onnxruntime::ReduceAggregatorSumSquare]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = int, AGG = onnxruntime::ReduceAggregatorSumSquare]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = double, AGG = onnxruntime::ReduceAggregatorSumSquare]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = double, AGG = onnxruntime::ReduceAggregatorSumSquare]onnxruntime::ReduceKernelBase::ReduceKernelBase(const onnxruntime::OpKernelInfo &, optional) [allow_multi_axes = false]info.GetAttr("keepdims", &keepdims).IsOK()noop_with_empty_axesselect_last_indexvoid onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = float, AGG = onnxruntime::ReduceAggregatorArgMaxLastIndex]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = float, AGG = onnxruntime::ReduceAggregatorArgMaxLastIndex]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = float, AGG = onnxruntime::ReduceAggregatorArgMax]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = float, AGG = onnxruntime::ReduceAggregatorArgMax]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = int, AGG = onnxruntime::ReduceAggregatorArgMaxLastIndex]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = int, AGG = onnxruntime::ReduceAggregatorArgMaxLastIndex]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = int, AGG = onnxruntime::ReduceAggregatorArgMax]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = int, AGG = onnxruntime::ReduceAggregatorArgMax]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = double, AGG = onnxruntime::ReduceAggregatorArgMaxLastIndex]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = double, AGG = onnxruntime::ReduceAggregatorArgMaxLastIndex]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = double, AGG = onnxruntime::ReduceAggregatorArgMax]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = double, AGG = onnxruntime::ReduceAggregatorArgMax]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = float, AGG = onnxruntime::ReduceAggregatorArgMinLastIndex]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = float, AGG = onnxruntime::ReduceAggregatorArgMinLastIndex]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = float, AGG = onnxruntime::ReduceAggregatorArgMin]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = float, AGG = onnxruntime::ReduceAggregatorArgMin]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = int, AGG = onnxruntime::ReduceAggregatorArgMinLastIndex]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = int, AGG = onnxruntime::ReduceAggregatorArgMinLastIndex]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = int, AGG = onnxruntime::ReduceAggregatorArgMin]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = int, AGG = onnxruntime::ReduceAggregatorArgMin]onnxruntime::ReduceKernelBase::ReduceKernelBase(const onnxruntime::OpKernelInfo &, optional) [allow_multi_axes = true]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = float, AGG = onnxruntime::ReduceAggregatorSum]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = float, AGG = onnxruntime::ReduceAggregatorSum]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = int, AGG = onnxruntime::ReduceAggregatorSum]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = int, AGG = onnxruntime::ReduceAggregatorSum]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = double, AGG = onnxruntime::ReduceAggregatorSum]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = double, AGG = onnxruntime::ReduceAggregatorSum]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = long long, AGG = onnxruntime::ReduceAggregatorSum]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = long long, AGG = onnxruntime::ReduceAggregatorSum]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_) * 2forwardreversebidirectional/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.ccvirtual 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 {/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 Dump matrix: 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, onnxruntime::AllocatorPtr, 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, onnxruntime::AllocatorPtr, 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 1.0freluaffineleakyreluthresholdedreluscaledtanhhardsigmoidelusoftsignsoftplusonnxruntime::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 *) const/Users/runner/work/1/s/onnxruntime/core/providers/cpu//tensor/concat.hSSequenceLength/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 *) constSequenceInsertCreateCopyAndAppendCpuTensorvirtual 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.SplitToSequencevirtual onnxruntime::common::Status onnxruntime::SplitToSequence::Compute(onnxruntime::OpKernelContext *) constSplitToSequence operator does not support split_size_sum () != split_dim_size (int64_t onnxruntime::GetSeqIdx(const onnxruntime::Tensor &)const T *onnxruntime::OpKernelContext::Input(int) const [T = onnxruntime::TensorSeq]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 &)void onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape &)shape_.Size() == new_shape.Size()Tensor size () != new size (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::Cast::Cast(const onnxruntime::OpKernelInfo &)void onnxruntime::utils::MLTypeCallDispatcher::Invoke(Args &&...) const [Fn = SrcDispatcher, Types = , Args = ]helper.called_ < 2void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invoke(Args &&...) const [TCarried = float, Fn = Cast::Dispatcher, Types = , Args = ]void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invoke(Args &&...) const [TCarried = double, Fn = Cast::Dispatcher, Types = , Args = ]void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invoke(Args &&...) const [TCarried = signed char, Fn = Cast::Dispatcher, Types = , Args = ]void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invoke(Args &&...) const [TCarried = unsigned char, Fn = Cast::Dispatcher, Types = , Args = ]void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invoke(Args &&...) const [TCarried = short, Fn = Cast::Dispatcher, Types = , Args = ]void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invoke(Args &&...) const [TCarried = unsigned short, Fn = Cast::Dispatcher, Types = , Args = ]void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invoke(Args &&...) const [TCarried = int, Fn = Cast::Dispatcher, Types = , Args = ]void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invoke(Args &&...) const [TCarried = unsigned int, Fn = Cast::Dispatcher, Types = , Args = ]void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invoke(Args &&...) const [TCarried = long long, Fn = Cast::Dispatcher, Types = , Args = ]void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invoke(Args &&...) const [TCarried = unsigned long long, Fn = Cast::Dispatcher, Types = , Args = ]void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invoke(Args &&...) const [TCarried = bool, Fn = Cast::Dispatcher, Types = , Args = ]void onnxruntime::utils::MLTypeCallDispatcher::Invoke(Args &&...) const [Fn = StringDispatcher, Types = , Args = ]NaN-INFINFgsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = std::__1::basic_string]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = double]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = onnxruntime::MLFloat16]void onnxruntime::(anonymous namespace)::CastFromStringData(const onnxruntime::Tensor &, onnxruntime::Tensor &, const onnxruntime::TensorShape &) [DstType = onnxruntime::MLFloat16]Unsupported type in cast op: from String to gsl::span onnxruntime::Tensor::DataAsSpan() const [T = signed char]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = unsigned char]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = short]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = unsigned short]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = unsigned int]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = long long]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = unsigned long long]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = bool]void onnxruntime::(anonymous namespace)::CastFromStringData(const onnxruntime::Tensor &, onnxruntime::Tensor &, const onnxruntime::TensorShape &) [DstType = bool]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()ConcatNot satisfied: input_count >= 1 /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/concat.ccPrepareForComputeNot satisfied: inputs_0_rank > 0 onnxruntime::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: Not satisfied: dim_value == inputs_0_dims[axis_index] Non concat axis dimensions must match: Axis has mismatched dimensions of Not satisfied: data_n.DataType() == p.output_tensor->DataType() /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/concat.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 != nullptrEyeLike/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/eye_like.ccvirtual onnxruntime::common::Status onnxruntime::EyeLike::Compute(onnxruntime::OpKernelContext *) constT1 != nullptrUnsupported 'dtype' value: dtypeEyeLike : Input tensor dimension is not 2Gather/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/gather.cc/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/gather.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 void *onnxruntime::Tensor::DataRaw(onnxruntime::MLDataType) consttype == dtype_Tensor type mismatch.void *onnxruntime::Tensor::MutableDataRaw(onnxruntime::MLDataType)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_negativevirtual onnxruntime::common::Status onnxruntime::IsInf::Compute(onnxruntime::OpKernelContext *) constData type X must be float or double, but instead got IsNaNNull 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/pad.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 shapeReshape/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/reshape.hvirtual onnxruntime::common::Status onnxruntime::Reshape::Compute(onnxruntime::OpKernelContext *) constshapeTensor->Shape().NumDimensions() == 1/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/reshape_helper.honnxruntime::ReshapeHelper::ReshapeHelper(const onnxruntime::TensorShape &, std::vector &)requested_shape[i] >= -1A dimension cannot be less than -1, got 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) == 0The input tensor cannot be reshaped to the requested shape. Input shape:, requested shape:gsl::narrow_cast(input_shape.Size()) == sizeonnxruntime::Reshape_1::Reshape_1(const onnxruntime::OpKernelInfo &)Resize/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/upsample.honnxruntime::UpsampleBase::UpsampleBase(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 *) constbatch_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 gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = bool]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = double]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned char]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = short]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned short]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned long long]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]ScatterScatterElementsIndices 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=/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/scatter.ccvirtual onnxruntime::common::Status onnxruntime::Scatter::Compute(onnxruntime::OpKernelContext *) constUnknown tensor type of onnxruntime::Scatter::Scatter(const onnxruntime::OpKernelInfo &)/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/scatter_nd.cconnxruntime::common::Status onnxruntime::ScatterNDBase::PrepareForCompute(onnxruntime::OpKernelContext *, onnxruntime::ScatterNDBase::Prepare &) const [Tind = long long]input_tensor != nullptrindice_tensor != nullptrupdate_tensor != nullptrinput 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: invalid indice found, indice = ScatterNDSizeSliceTind'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.ccstatic void onnxruntime::SliceBase::FillVectorsFromInput(const onnxruntime::Tensor &, const onnxruntime::Tensor &, const onnxruntime::Tensor *, const onnxruntime::Tensor *, std::vector &, std::vector &, std::vector &, std::vector &)start_tensor.Shape().NumDimensions() == 1ends_tensor.Shape().NumDimensions() == 1start_tensor.Shape() == ends_tensor.Shape()nullptr == axes_tensor || start_tensor.Shape() == axes_tensor->Shape()nullptr == steps_tensor || start_tensor.Shape() == steps_tensor->Shape()Data type for starts and ends inputs' need to be int32_t or int64_t, but instead got onnxruntime::common::Status onnxruntime::SliceBase::Compute(onnxruntime::OpKernelContext *) constinput_tensor_ptr != nullptrstarts/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 = 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 *) constTile doesn't have an implementation yet for the type: 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 *) constTransposevoid 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.void 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)void onnxruntime::TypedDoTransposeEltWise(int64_t, const std::vector &, size_t, const std::vector &, const uint8_t *, uint8_t *) [T = unsigned int]void onnxruntime::TypedDoTransposeEltWise(int64_t, const std::vector &, size_t, const std::vector &, const uint8_t *, uint8_t *) [T = unsigned short]void onnxruntime::TypedDoTransposeEltWise(int64_t, const std::vector &, size_t, const std::vector &, const uint8_t *, uint8_t *) [T = unsigned char]UniqueUnsupported tensor type of gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = long long]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = signed char]gsl::span onnxruntime::Tensor::DataAsSpan() const [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[Attentionnum_heads/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/bert/attention.cconnxruntime::contrib::AttentionBase::AttentionBase(const onnxruntime::OpKernelInfo &)info.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0unidirectionalInput 'input' is expected to have 3 dimensions, got Input 0 dimension 2 should be divisiable by value of the num_heads attribute.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' dimension 0 shall have length of batch_size or 2 * batch_sizeInputs 'mask_index' with raw attention mask shall have shape batch_size x (past_sequence_length + sequence_length)Inputs 'mask_index' of 3d shall have shape batch_size x sequence_length x (past_sequence_length + sequence_length)Input 'mask_index' is expected to have 1, 2 or 3 dimensions, got onnxruntime::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 given/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 (Inversevoid onnxruntime::utils::MLTypeCallDispatcher::Invoke(Args &&...) const [Fn = ComputeImpl, Types = , Args = ]LayerNormalizationSimplifiedLayerNormalization/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 == 0ReorderInputReorderOutput/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/nchwc_ops.ccvirtual onnxruntime::common::Status onnxruntime::contrib::ReorderInput::Compute(onnxruntime::OpKernelContext *) constX_shape.NumDimensions() == 4(X_shape[1] % MlasNchwcGetBlockSize()) == 0virtual onnxruntime::common::Status onnxruntime::contrib::ReorderOutput::Compute(onnxruntime::OpKernelContext *) constX_rank == 4channels_ <= X_shape[1]virtual onnxruntime::common::Status onnxruntime::contrib::NchwcConv::Compute(onnxruntime::OpKernelContext *) const(static_cast(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)Unsupported convolution size.Not satisfied: Y->Shape() == sum_shape onnxruntime::common::Status onnxruntime::contrib::NchwcPoolBase::NchwcPool(onnxruntime::OpKernelContext *, MLAS_POOLING_KIND) constvirtual onnxruntime::common::Status onnxruntime::contrib::NchwcUpsample::Compute(onnxruntime::OpKernelContext *) const/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/nchwc_ops.honnxruntime::contrib::ReorderOutput::ReorderOutput(const onnxruntime::OpKernelInfo &)info.GetAttr("channels", &channels_).IsOK()channels_ > 0invalid channel countinfo.GetAttr("channels_last", &channels_last_).IsOK()onnxruntime::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] >= 1QLinearAddQLinearMul/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_global_average_pool.ccvirtual onnxruntime::common::Status onnxruntime::contrib::QLinearGlobalAveragePool::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(tensor_x_zero_point)input x_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_y_zero_point)input y_zero_point must be a scalar or 1D tensor of size 1 if givenNot satisfied: x_shape.size() >= 3 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 = signed char]IsScalarOr1ElementVector(tensor_x_scale)QLinearLeakyRelu : input X_scale must be a scalar or 1D tensor of size 1tensor_x_zero_point == nullptr || IsScalarOr1ElementVector(tensor_x_zero_point)QLinearLeakyRelu : input X_zero_point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_y_scale)QLinearLeakyRelu : input Y_scale must be a scalar or 1D tensor of size 1tensor_y_zero_point == nullptr || IsScalarOr1ElementVector(tensor_y_zero_point)QLinearLeakyRelu : 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 = unsigned char]QAttention/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/attention_quant.ccNot satisfied: IsScalarOr1ElementVector(input_scale_tensor) Not satisfied: IsScalarOr1ElementVector(weight_scale_tensor) Not satisfied: IsScalarOr1ElementVector(i_zp_tensor) Not satisfied: IsScalarOr1ElementVector(w_zp_tensor) W_zero_point must have shape {} for per-tensor/layer quantization or shape {, 4*} for per-channel quantization. Actual:R_zero_pointW_scaleR_scaleDynamicQuantizeLSTM : InputWeight zero point must be zeroWeight point must be constantRecurrentDynamicQuantizeLSTM/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/dynamic_quantize_matmul.ccComputeCommonvirtual onnxruntime::common::Status onnxruntime::contrib::DynamicQuantizeMatMul::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(b_scale_tensor)DynamicQuantizeMatMul : input B scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.IsScalarOr1ElementVector(b_zero_point_tensor)DynamicQuantizeMatMul : input B zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.virtual onnxruntime::common::Status onnxruntime::contrib::MatMulIntegerToFloat::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(a_scale_tensor)MatMulIntegerToFloat : input A scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.MatMulIntegerToFloat : input B scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.IsScalarOr1ElementVector(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.MatMulIntegerToFloat : input B zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.DynamicQuantizeMatMulMatMulIntegerToFloatSampleOpSkipLayerNormalization/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] allowedTrilu/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/trilu.ccvirtual onnxruntime::common::Status onnxruntime::contrib::Trilu::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(k)upper/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/trilu.honnxruntime::contrib::Trilu::Trilu(const onnxruntime::OpKernelInfo &)info.GetAttr("upper", &temp).IsOK()Conv 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.ccvoid onnxruntime::math::Gemv(CBLAS_TRANSPOSE, int, int, float, const T *, const T *, float, T *, Provider *) [T = float, Provider = onnxruntime::CPUMathUtil]Gemv 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 1AllocateAllocateStaticallyPreExistingReuseAllocateOutputShareNotSetAllocation Plan: (ort_value_idx) output_name : , use fence when asyncIndex out-of-range! Execution Plan: /Users/runner/work/1/s/onnxruntime/core/framework/allocation_planner.ccstd::ostream &onnxruntime::operator<<(std::ostream &, std::pair)nullptr != node[Free ml-values: 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()ComputeReusePlanGeneratePlanForWeightsOrtMemoryInfo 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 /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: memory limit: arena_extend_strategy Creating bins of max chunk size onnxruntime::BFCArena::BFCArena(std::unique_ptr, size_t, onnxruntime::ArenaExtendStrategy, 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 virtual void *onnxruntime::BFCArena::Reserve(size_t)reserved_chunks_.find(ptr) == reserved_chunks_.end()size_t onnxruntime::BFCArena::RequestedSize(const void *)h != kInvalidChunkHandlesize_t onnxruntime::BFCArena::AllocatedSize(const void *)AllocateRawInternaltried to allocate 0 bytesExtending BFCArena for . bin_num: 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)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)0 == memory_size % kMinAllocationSizeRegionForCould not find Region for int onnxruntime::BFCArena::AllocationRegion::IndexFor(const void *) constp_int >= base_intp_int < base_int + memory_size_ Size: | Requested Size: | in_use: , prev: , next: Limit: InUse: TotalAllocated: MaxInUse: NumAllocs: MaxAllocSize: /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 &) constbool onnxruntime::NonTensorTypeBase::IsOpaqueCompatible(const onnx::TypeProto &) constthisProto->value_case() == TypeProto::ValueCase::kOpaqueTypevirtual void onnxruntime::NonTensorTypeBase::FromDataContainer(const void *, size_t, OrtValue &) constNot implementedvirtual void onnxruntime::NonTensorTypeBase::ToDataContainer(const OrtValue &, size_t, void *) const(null)tensor(float)tensor(bool)tensor(double)tensor(string)tensor(int8)tensor(uint8)tensor(int16)tensor(uint16)tensor(int32)tensor(uint32)tensor(int64)tensor(uint64)tensor(float16)tensor(bfloat16)tensor type sparse tensor 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/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(int, const onnxruntime::TensorShape *, OrtValue *&, 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: 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)/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_) == 0InlineNodesPartitionOrtFormatModelImplonnxruntime::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.Op 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:72:11), TraverseFn = (lambda at /Users/runner/work/1/s/onnxruntime/core/framework/kernel_registry.cc:73:11)]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:97:9), TraverseFn = (lambda at /Users/runner/work/1/s/onnxruntime/core/framework/kernel_registry.cc:100:9)]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::GraphNodes &, 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 implementedElementTypeFromProto:tensor type 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_onnxruntime::SparseTensor *OrtValue::GetMutable()/Users/runner/work/1/s/onnxruntime/core/framework/op_kernel_info.ccconst OrtMemoryInfo &onnxruntime::OpKernelInfo::GetMemoryInfo(int, OrtMemType) constCould not find OrtValue with name ''No attribute with name: is defined.Requested attribute: is expected to have type: but is of type: /Users/runner/work/1/s/include/onnxruntime/core/framework/op_node_proto_helper.hconst onnx::AttributeProto *onnxruntime::OpNodeProtoHelper::GetAttribute(const std::string &) const [Impl_t = onnxruntime::ProtoHelperNodeContext]attr != nullptrconst onnx::AttributeProto *onnxruntime::OpNodeProtoHelper::GetAttribute(const std::string &) const [Impl_t = onnx::InferenceContext]Attibute name and type don't matchNo attribute with this name is defined./Users/runner/work/1/s/onnxruntime/core/framework/op_node_proto_helper.cconnxruntime::common::Status onnxruntime::OpNodeProtoHelper::GetAttrs(const std::string &, gsl::span) const [Impl_t = onnxruntime::ProtoHelperNodeContext]values.size() == static_cast(attr->floats_size())onnxruntime::common::Status onnxruntime::OpNodeProtoHelper::GetAttrs(const std::string &, gsl::span) const [Impl_t = onnx::InferenceContext]onnxruntime::common::Status onnxruntime::OpNodeProtoHelper::GetAttrs(const std::string &, gsl::span) const [Impl_t = onnxruntime::ProtoHelperNodeContext]values.size() == static_cast(attr->ints_size())onnxruntime::common::Status onnxruntime::OpNodeProtoHelper::GetAttrs(const std::string &, gsl::span) const [Impl_t = onnx::InferenceContext]onnxruntime::common::Status onnxruntime::OpNodeProtoHelper::GetAttrs(const std::string &, gsl::span) const [Impl_t = onnxruntime::ProtoHelperNodeContext]values.size() == static_cast(attr->strings_size())onnxruntime::common::Status onnxruntime::OpNodeProtoHelper::GetAttrs(const std::string &, gsl::span) const [Impl_t = onnx::InferenceContext]onnxruntime::common::Status onnxruntime::OpNodeProtoHelper::GetAttrs(const std::string &, gsl::span) const [Impl_t = onnxruntime::ProtoHelperNodeContext]values.size() == static_cast(attr->tensors_size())onnxruntime::common::Status onnxruntime::OpNodeProtoHelper::GetAttrs(const std::string &, gsl::span) const [Impl_t = onnx::InferenceContext]onnxruntime::common::Status onnxruntime::OpNodeProtoHelper::GetAttrs(const std::string &, gsl::span) const [Impl_t = onnxruntime::ProtoHelperNodeContext]values.size() == static_cast(attr->graphs_size())onnxruntime::common::Status onnxruntime::OpNodeProtoHelper::GetAttrs(const std::string &, gsl::span) const [Impl_t = onnx::InferenceContext]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]onnxruntime::OrtValueTensorSlicer::Iterator::const_reference onnxruntime::OrtValueTensorSlicer::Iterator::operator*() const [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]onnxruntime::OrtValueTensorSlicer::Iterator::const_reference onnxruntime::OrtValueTensorSlicer::Iterator::operator*() const [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.OrtSessionOptionsAppendExecutionProvider_Dnnl: Failed to load shared libraryOrtSessionOptionsAppendExecutionProvider_Tensorrt: Failed to load shared librarySessionOptionsAppendExecutionProvider_OpenVINO: Failed to load shared libraryOrtSessionOptionsAppendExecutionProvider_OpenVINO: Failed to load shared libraryNOTSETVALIDSAME_UPPERSAME_LOWER/Users/runner/work/1/s/onnxruntime/core/providers/common.honnxruntime::AutoPadType onnxruntime::StringToAutoPadType(const std::string &)unordered_map::at: key not found/Users/runner/work/1/s/include/onnxruntime/core/framework/op_kernel.hconst T *onnxruntime::OpKernelContext::Input(int) const [T = onnxruntime::Tensor]Missing Input: libonnxruntime_providers_dnnl.dyliblibonnxruntime_providers_openvino.dyliblibonnxruntime_providers_tensorrt.dylib/Users/runner/work/1/s/onnxruntime/core/framework/provider_bridge_ort.ccGetEnsure/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_options.ccAddConfigEntrySession Config with key [] already exists with value []. It will be overwritten/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?PrepackConstantInitializedTensorsUsing 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 Failed to find output name in the mapping: 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.CreateSubgraphSessionStateSatisfied, but should not be: nullptr == fbs_kcis Satisfied, but should not be: nullptr == node_indices Kernel create info node indices are null. Invalid ORT format model.Satisfied, but should not be: nullptr == kernel_def_hashes Not satisfied: node_indices->size() == kernel_def_hashes->size() Size mismatch for kernel create info node indexes and hashes. Invalid ORT format model.Satisfied, but should not be: compiled_kernel_hashes.empty() Can't find node with index . Invalid ORT format model.Satisfied, but should not be: hash_info == compiled_kernel_hashes.cend() Unable to find compiled kernel hash for node ''.Satisfied, but should not be: nullptr == fbs_sub_graph_session_states Satisfied, but should not be: nullptr == fbs_sub_graph_ss Subgraph SessionState entry for is missing. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_sub_session_state 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_kernelCould 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 OrtMemoryInfo &, 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()[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 (DeserializeTensorProtoInternal error. The preallocated buffer is too small. Requires , Got Failed to copy tensor to invalid ort_value_index:/Users/runner/work/1/s/onnxruntime/core/framework/simple_tensor_allocator.ccGetPreallocatedBufferFailed to get allocator for initializer '', location: /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)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 lengthchecksum/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 overflowstring tensor can not have raw data/Users/runner/work/1/s/onnxruntime/core/framework/tensorprotoutils.ccTensorProtoToMLValuetensor can't contain negative dimsThe buffer planner is not consistent with tensor buffer size, expected Initialized tensor with unexpected type: ConstantNodeProtoToTensorProtocommon::Status onnxruntime::utils::ConstantNodeProtoToTensorProto(const onnx::NodeProto &, onnx::TensorProto &)Unsupported attribute value type of in 'Constant' node 'SparseTensorProtoToDenseTensorProtocommon::Status onnxruntime::utils::SparseTensorProtoToDenseTensorProto(const onnx::SparseTensorProto &, onnx::TensorProto &)BUG! Report to onnxruntime team.common::Status onnxruntime::utils::DenseTensorToSparseTensorProto(const onnx::TensorProto &, onnx::SparseTensorProto &)HasDataType(dense_proto)DenseTensorToSparseTensorProtoUnsupported type: GetFileContentUnsupported sparse tensor data type of UnpackTensor: the pre-allocated size does not match the raw data size, expected UnpackTensorWithRawDataNot satisfied: source_bytes.size_bytes() == destination.size_bytes() /Users/runner/work/1/s/onnxruntime/core/framework/endian_utils.hReadLittleEndianRet onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper::Get() [Ret = onnxruntime::common::Status, UnsupportedPolicy = onnxruntime::utils::UnsupportedSparseDataType]Not satisfied: indices.data_type() == ONNX_NAMESPACE ::TensorProto_DataType_INT64 CopySparseDataNot satisfied: indices.raw_data().size() == (elements * sizeof(int64_t)) 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 int]DnnlExecutionProviderNupharExecutionProviderVitisAIExecutionProviderOpenVINOExecutionProviderNnapiExecutionProviderRknpuExecutionProviderInternalTestingExecutionProvider/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 multiplication/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, hidden_size), hidden_size = num_heads * head_sizeweight2D input tensor with shape (hidden_size, 3 * hidden_size)bias1D input tensor with shape (3 * hidden_size)mask_indexAttention mask with shape (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 typesinput_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, which means a per-tensor/layer quantization.Attention 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, which means a per-tensor/layer quantization.3D 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 lengthmaskAttention 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_ndimnormalizedonesidedinput_0input_1CWPadsConstrain input and output types to float tensorsactivation_paramsInput 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.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.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.Incompatible dimensionsInputs 0 shall be 3 dimensionsInputs 4 shall be 5 dimensionsInputs 0 shall be 2 dimensionsword_embedding should have 2 dimensions and dimension size is known.Negative 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:/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.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_scaleInput scale. It's a scalar, which means a per-tensor/layer quantization.data_zero_pointInput zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.reduced_scalereduced_zero_pointOutput 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.X_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. 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_pointConstrain input and output types to 8 bit signed and unsigned tensors.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 &)onnxruntime::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() != nullptrvoid 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 [Not satisfied: it != attr_to_subgraph_map_.cend() ] does not have the graph for key LoadFromOrtFormatSatisfied, but should not be: nullptr == fbs_attr Not satisfied: subgraph Serialization error. Graph attribute was serialized without Graph instanceSatisfied, but should not be: nullptr == fbs_input_arg_counts Satisfied, but should not be: fbs_node_edges.node_index() != index_ LoadEdgesFromOrtFormatinput 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=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 () 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!foundNot satisfied: name_to_initializer_it != name_to_initial_tensor_.end() ReplaceInitializedTensorFailed to find existing initializer with name Not satisfied: dims_eq() Not satisfied: old_initializer.data_type() == new_initializer.data_type() onnxruntime::common::Status onnxruntime::Graph::ReplaceInitializedTensor(const onnx::TensorProto &)existing_entry != mutable_initializers.pointer_end()void onnxruntime::Graph::AddValueInfo(const onnxruntime::NodeArg *)info->Name() != new_value_info->Name()_token_NoOpbool onnxruntime::Graph::RemoveNode(onnxruntime::NodeIndex)node->GetOutputEdgesCount() == 0Can't remove node as it still has output edges.onnx::GraphProto onnxruntime::Graph::ToGraphProto() constvoid 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 &)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()Inputs: : Nodes: : ) -> Outputs: Satisfied, but should not be: nullptr == fbs_tensor Duplicate initializer (dense or ConstantNode): 'Satisfied, but should not be: nullptr == fbs_sparse_tensor Satisfied, but should not be: nullptr == fbs_value_info Satisfied, but should not be: nullptr == fbs_node Satisfied, but should not be: node->Index() >= fbs_graph.max_node_index() Satisfied, but should not be: nullptr == fbs_node_edge Satisfied, but should not be: fbs_node_edge->node_index() >= fbs_graph.max_node_index() 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:Satisfied, but should not be: nullptr == fbs_node_arg_names Satisfied, but should not be: nullptr == node_arg_name Satisfied, but should not be: nullptr == node_arg LoadNodeArgsFromOrtFormat: 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:/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()[TypeInferenceError] No Graph instance was found for attribute in node Graph attribute inferencing failed: ) Op () This is an invalid model. Error in Node:Satisfied, but should not be: nullptr == fbs_node_arg_name /Users/runner/work/1/s/onnxruntime/core/graph/graph_flatbuffers_utils.ccSaveInitializerOrtFormatSaveSparseInitializerOrtFormatSaveAttributeOrtFormatSatisfied, but should not be: nullptr == graph SaveAttributeOrtFormat: Unsupported attribute type: Satisfied, but should not be: nullptr == fbs_dims LoadInitializerOrtFormatSatisfied, but should not be: nullptr == fbs_str_data Satisfied, but should not be: nullptr == fbs_raw_data Missing raw data for initializer. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_values_tensor LoadSparseInitializerOrtFormatSatisfied, but should not be: values_tensor->name().empty() Missing name for SparseTensor initializer. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_indicies_tensor Missing indicies for sparse initializer: Invalid ORT format model.Missing dims for sparse initializer: Satisfied, but should not be: nullptr == fbs_str LoadAttributeOrtFormatSatisfied, but should not be: nullptr == fbs_graph Empty graph proto from deserialization of ORT format modelSatisfied, but should not be: nullptr == fbs_floats Satisfied, but should not be: nullptr == fbs_ints Satisfied, but should not be: nullptr == fbs_strings Null string in strings attribute. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_tensors 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::GetNodeInputIndexFromInputName(const onnxruntime::Node &, const std::string &)itr != node.InputDefs().end()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.Invalid istream object.Null model_proto ptr.No graph was found in the protobuf.Protobuf parsing failed.LoadFromBytes less than 0. is less than 0.SaveProtobuf serialization failed.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.ccRegisterOpSetRegisterOpSchemaInternalknown by the checker., but it its version is higherthan the operator set version [...truncated...] ERROR LOG MSG NOTIFICATION: Failure to successfully parse the message/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 &)onnxruntimeSystem [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 &)..onnxruntime::Path &onnxruntime::Path::Concat(const onnxruntime::PathString &)first_separator == value.end()Cannot concatenate with a string containing a path separator. String: Not satisfied: src.GetRootPathString() == dst.GetRootPathString() RelativePathPaths must have the same root to compute a relative path. src root: , dst root: Not satisfied: has_trailing_separator ParsePathRootFailed to parse path root: /Users/runner/work/1/s/onnxruntime/core/common/profiler.cc::onnxruntime::TimePoint onnxruntime::profiling::Profiler::StartTime() constenabled_void onnxruntime::profiling::Profiler::Initialize(const logging::Logger *)session_logger != nullptrvoid onnxruntime::profiling::Profiler::StartProfiling(const logging::Logger *)custom_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 ERROR/Users/runner/work/1/s/onnxruntime/core/common/threadpool.ccvoid onnxruntime::concurrency::ThreadPool::ParallelFor(std::ptrdiff_t, const onnxruntime::TensorOpCost &, const std::function &)n >= 0static void onnxruntime::concurrency::ThreadPool::TryParallelFor(concurrency::ThreadPool *, std::ptrdiff_t, const onnxruntime::TensorOpCost &, const std::function &)total >= 0;unknown;unknown;0;0;;/Users/runner/work/1/s/onnxruntime/core/platform/env.ccvoid gsl::fail_fast_assert_handler(const char *const, const char *const, const char *const, int)/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: Not satisfied: file_path ReadFileIntoBufferNot satisfied: offset >= 0 Not satisfied: length <= buffer.size() openlseekreadReadFileIntoBuffer - unexpected end of file. File: , offset: , length: MapFileIntoMemorymmapUnmapFilemunmap failed. error code: Not satisfied: result == 0 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.packing unavailablebad 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.Satisfied, but should not be: !value_info_proto.name().empty() LoadValueInfoOrtFormatNull type info for Satisfied, but should not be: nullptr == fbs_op_set_ids LoadOpsetImportOrtFormatSatisfied, but should not be: nullptr == fbs_op_set_id Satisfied, but should not be: nullptr == fbs_domain SaveTypeInfoOrtFormatWe do not support type [] for nowSaveSequenceTypeOrtFormatSaveMapTypeOrtFormatSatisfied, but should not be: nullptr == fbs_tensor_type LoadTypeInfoOrtFormatSatisfied, but should not be: nullptr == fbs_sequence_type Satisfied, but should not be: nullptr == fbs_map_type Type: is not supported currentlyLoadTensorTypeAndShapeOrtFormatSatisfied, but should not be: nullptr == fbs_dim LoadTensorShapeOrtFormatSatisfied, but should not be: nullptr == fbs_dim_param LoadTensorDimensionOrtFormatSatisfied, but should not be: nullptr == fbs_type_info LoadSequenceTypeOrtFormatNull value type info in fbs::SequenceType. Invalid ORT format model.LoadMapTypeOrtFormatNONEField '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.location) 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: sequenceSequence ( Structure name: , elem_type: ) is not have a valid element type.setting key_type field (map name: ) to invalid TensorProto key_type is not allowedMap (name: ) should not contain more than one keys field.valuesLength of map keys and map values are not the same (map name: Sparse 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: is not output of any previous nodes.Bad node spec: ' has been used as output names multiple times.functionsince_version' has been used multiple times.function () should not have duplicate outputs specified.) should not have duplicate attributes specified.Nodes in a function must be topologically sorted, however input ' is neither output of any previous nodes nor input of the function.Function must be in single static assignment (SSA) form, however 'Unable to open model file:. Please check if it is a valid file.Unable to parse model from file:. Please check if it is a valid protobuf file of model.\/ ==> Context: DynamicSliceImageScalerScalerLoop '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 tensor(seq(map(sparse_tensor(seqmapopaquesparse_tensortensorFunc_Input for function node Output for function node 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.ZConstrain input and output types to integer tensors.Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).LessOrEqualGreaterOrEqualConstrains 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.CeluValue of alpha.Value of beta.The 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.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 onlyConstrain 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.InputsOperandsOutputinputs 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.ccfmodWhether 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.Constrain input and output types to all 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.targetignore_indexreductionnonelossInput 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 typesNegativeLogLikelihoodLossscoreslog_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).SoftmaxCrossEntropyLossInput 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 retrieveType 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. 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. 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./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 attributeInput 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 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 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. 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. 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.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 > 0(Optional) Seed to the random generator, if not specified we will auto generate one.ratioThe 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.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.Constrain input 'ratio' types to float tensors.Constrain output 'mask' types to boolean tensors./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.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.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.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.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).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).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.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.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.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 but has rank expected to have rank >Dimension mismatch in unification between /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).keepdimsKeep 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}First input tensor must have rank 3/Users/runner/work/1/s/cmake/external/onnx/onnx/defs/rnn/defs.ccCouple the input and forget gates if 1.A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, 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]`.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. 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())Attributes:Inputs: (no explicit description available)Outputs:(no documentation yet)Defined at (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.ccOptional 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.(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' 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).'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)]Invalid value of attribute 'axis'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=Unable to save inferred model to the target path: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.input is out of boundsGraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. does not contain a graph.output /Users/runner/work/1/b/Release/external/onnx/onnx/onnx-ml.pb.ccsize_t to int conversion/Users/runner/work/1/b/Release/external/onnx/onnx/onnx-operators-ml.pb.cc/Users/runner/work/1/b/Release/external/onnx/onnx/onnx-data.pb.cc/Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/arena.ccCHECK failed: (min_bytes) <= (std::numeric_limits::max() - kBlockHeaderSize): /Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/extension_set.ccCHECK failed: (type) != (WireFormatLite::TYPE_ENUM): CHECK failed: (type) != (WireFormatLite::TYPE_MESSAGE): CHECK failed: (type) != (WireFormatLite::TYPE_GROUP): CHECK failed: (type) == (WireFormatLite::TYPE_ENUM): CHECK failed: type == WireFormatLite::TYPE_MESSAGE || type == WireFormatLite::TYPE_GROUP: Don't lookup extension types if they aren't present (1). Don't lookup extension types if they aren't present (2). CHECK failed: extension != NULL: Index out-of-bounds (field is empty).Extension not found.Non-primitive types can't be packed.Can't get here.Invalid message set extension.Multiple extension registrations for type "", field number can't reach here./Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/extension_set_inl.h/Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/generated_message_util.ccNot implemented field number CHECK failed: (scc->visit_status.load(std::memory_order_relaxed)) == (SCCInfoBase::kRunning): with type /Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/io/coded_stream.ccA protocol message was rejected because it was too big (more than bytes). To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in third_party/protobuf/src/google/protobuf/io/coded_stream.h.CHECK failed: (buffer_size) >= (0): /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_: Can't BackUp() after failed Next()./Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream_impl_lite.ccCHECK failed: (last_returned_size_) > (0): BackUp() can only be called after a successful Next().CHECK failed: (count) <= (last_returned_size_): CHECK failed: (count) >= (0): CHECK failed: target_ != NULL: Cannot allocate buffer larger than kint32max for StringOutputStream.CHECK failed: (count) <= (target_->size()): 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_): MessageLite at 0x/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: CHECK failed: (byte_size_before_serialization) == (byte_size_after_serialization): was modified concurrently during serialization.CHECK failed: (bytes_produced_by_serialization) == (byte_size_before_serialization): Byte size calculation and serialization were inconsistent. This may indicate a bug in protocol buffers or it may be caused by concurrent modification of This shouldn't be called if all the sizes are equal.parsing/Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/parse_context.hCan't happen/Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): /Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/stubs/common.ccThis program requires version of the Protocol Buffer runtime library, but the installed version is . Please update your library. If you compiled the program yourself, make sure that your headers are from the same version of Protocol Buffers as your link-time library. (Version verification failed in "".)This program was compiled against version of the Protocol Buffer runtime library, which is not compatible with the installed version (). Contact the program author for an update. If you compiled the program yourself, make sure that your headers are from the same version of Protocol Buffers as your link-time library. (Version verification failed in "%d.%d.%dINFOWARNINGERRORFATAL[libprotobuf %s %s:%d] %s %c%d%u%ld%lu%g%p%lld%llu/Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/stubs/int128.ccDivision or mod by zero: dividend.hi=, lo=/Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/stubs/stringpiece.ccsize too big: details: /Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/stubs/stringprintf.ccCHECK failed: (v.size()) <= (kStringPrintfVectorMaxArgs): StringPrintfVector currently only supports up to arguments. Feel free to add support for more if you need it./Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/stubs/strutil.ccCHECK failed: dest: \x%02x\%03oCHECK failed: i >= 0: FastHexToBuffer() wants non-negative integers, not %.*gCHECK failed: value != nullptr: nullptr output boolean given.tyesyfalsefnon0123456789abcdefCHECK failed: s != nullptr: This can't happen; base64 decoder state = Logic problem? szsrc = %.1fCHECK failed: (temp[0]) == ('1'): CHECK failed: (temp[size - 1]) == ('5'): CHECK failed: (size) <= (6): CHECK failed: result != nullptr: /Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/wire_format_lite.ccCHECK failed: (value.size()) <= (kint32max): '%s'String field contains invalid UTF-8 data when a protocol buffer. Use the 'bytes' type if you intend to send raw bytes. serializing/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: hello, world|%s%d(%p)|| flag=%#x/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 step(?,?)(%d,?)(%d,%d)Bad args: nsubmatch=Unexpected opcode in short circuit: in ComputeFirstByte in Prog::Fanout()/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:]alt -> %d | %daltmatch -> %d | %dbyte%s [%02x-%02x] -> %d/icapture %d -> %demptywidth %#x -> %dmatch! %dnop -> %dfail[%02x-%02x] -> %d %d. %s %d+ %s /Users/runner/work/1/s/cmake/external/re2/re2/re2.ccUnknown encoding Error parsing '': Error compiling '\x00Invalid RE2: RE2: invalid startpos, endpos pair. [startpos: endpos: text size: DFA out of memory: size bytemap range list count SearchDFA inconsistencySearchOnePass inconsistencySearchBitState inconsistencySearchNFA inconsistencyRewrite schema error: '\' not allowed at end.Rewrite schema error: '\' must be followed by a digit or '\'.Rewrite schema requests %d matches, but the regexp only has %d parenthesized subexpressions.requested group in regexp invalid rewrite pattern: Error 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.NamedCapturesWalker::ShortVisit calledCaptureNamesWalker::ShortVisit calledWalk NULL/Users/runner/work/1/s/cmake/external/re2/re2/simplify.ccSimplify failed on Case 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 write mode nsync_mu not held in some 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()'//cMLIA2:-#0W)'//cML1 =u 7% &2g ;#  3C x)'ppxiF R})'OրN!! *#;3)'LL]T3)'LL]T3UA /k4(\c}}<[ ! iq.,",}}Ș     IAs j~ u*5_ jIAs j~ u*5_ j7 B]; GzLwրNv X+ clM XրNv X+ clM X '$3{D_րN ':  ]] eW i s g x Fm  Kl w Jj   I b o! ~ $  > A &* 5I \O #    yIAC O IAC O )'9)'9# b)'bb OlF R})' #55E] P\8K@IN0"Nf%Q% K #pN88 O_ÀA""3DiI4= [w])'94""3J;yIAOM ofRn)'9ÀA== QdlրNo/9M)'!!=/#kk w8 # <tG?  0IA/ }Ia_;րNZ()'''?=5'c}€4$3< F`j},>)'v5g;  y B  Q - 8p RO 3l Ryt   6 & -S     [ p| }ꀀhMjCE'F:Fv ')'* 4InB!4"/K4-?4Ah$B@=450(p:"vK{Se"[Kk"V K`TPxxI!Vw"#K("Ki{"Kt450(p:450(p:450(p:450(p:450(p:„%\-ZA^ X\G6t , i h$&: G^ ZD oN   R ;_$- ;   M t 2  -  1 F $ v   %    N7  ',$\p2}IAC NO 7 AIAC O )'9)'9)'gL[րNK a @ YvNcVF R}^^Ef ! 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M^d h  f  = ; J' 3& Y v6 +* d *   & Q6  &  K 6 S   :   3 E  ^ {m # . 3    # ( 73 j| # 3   #. u=3& p)';;'bDN C 񃀀Ni ) W m , / "Jl0Y  3 %Mr~    W U F9 E& k 6 +D !~ >  / & g6  &  e 67 m 3  #:)'+3%+ ! # /# i 3  #%4 Y @#> c & r3   #  3- ( : F # i x 3a )';;'bD)';;'bD)';;'bD)';;'bD)';;'bDN C 񃀀LI " P M , / HZ09   M^^ b  `  7  5 A! -& S p6 +$ ^ $   & K6  &  E 6 } M   } :   3 u E { B } K #0   >3S s }  #  3S  x #] @ E O3  = #"  3  )';;'bDN C 񃀀LK " P O , / HZ0;   M^` d  b  9  7 C# /& U r6 +& ` &   & M6  &  G 6  O   p:vtx3rex5 n~5p >|## 1 3Ff pr  # s 3F  k  #P 3 8 B 3z u  0 # 3 )';;'bD)';;'bD)';;'bD)';;'bD)';;'bDN C 񃀀L#" P  , /1HZ0  p M^     ~ ( &  60 + d   #% l 1&Q 6[  I &. 7  6t p  :3  # C 3   3  # = L 3   # 3   # C R 3; )';;'bDN C 񃀀L9 "  P { = ,} / HZ0)   M^N R  P  ' % 4 & C `6 + N   & ;6 q &  5 6 m = {   M:SQU3OU KfM Y #  3#C MO ^ j #h P 3#  H #-    3W R d p # 3 )';;'bD)';;'bD)';;'bD)';;'bD)';;'bDN C 񃀀L9 "  P { = ,} / HZ0)   M^N R  P  ' % 4 & C `6 + N   & ;6 q &  5 6 m = {   / :5 3 7 31 7  -  $/ ;# u 3 % / 1@ e L#J o 2 ~3  * #  39 4F R#u 3m )';;'bDN C 񃀀L9 "  P { = ,} / HZ0)   M^N R  P  ' % 4 & C `6 + N   & ;6 q &  5 6 m = {   :3U ) # * 3     # $ 3 3 f x # 3   # * q 9 3" l )';;'bDN C 񃀀L; " P } ? , / HZ0+   M^P T  R  ) ' 6 & E b6 + P    & =6 s &  7 6 o ? }   M :S Q U /O U  K  EM  Y#  3# C M O^ j#h P3#  H#- 3WRd p# 3)';;'bD)';;'bD)';;'bD)';;'bD)';;'bD)';;'bD)';;'bDN C 񃀀L9 "  P { = ,} / HZ0)   M^N R  P  ' % 4 & C `6 + N   & ;6 q &  5 6 m = {   :   /   Z  c #H  7 V3k   # 3k ) 5#uX ]g3 U#: "3)';;'bDN C 񃀀L9 "  P { = ,} / HZ0)   M^N R  P  ' % 4 & C `6 + N   & ;6 q &  5 6 m = {   :   /   Z  c #H  7 V3k   # 3k ) 5#uX ]g3 U#: "3)';;'bD)';;'bD)';;'bD)';;'bD)';;'bD)';;'bDN C 񃀀L@ " P D , / HZ00  M^U Y  W  . , ; $& J g6 + U    & B6 x &  < 6 t D  :   3  u I  R #7  & E3Z z  #  3Z   $#d G L V3  D #)   3 )';;'bDN C 񃀀L9 "  P { = ,} / HZ0)   M^N R  P  ' % 4 & C `6 + N   & ;6 q &  5 6 m = {   t:zx|3v|U r9t B#' 5 3Jj tv  # w 3J  o  #T 7 < F 3~ y  4 #  3 )';;'bD)';;'bD)';;'bD)';;'bD)';;'bDN C 񃀀L9 "  P { = ,} / HZ0)   M^N R  P  ' % 4 & C `6 + N   & ;6 q &  5 6 m = {   / :5 3 7 31 7  -  $/ ;# u 3 % / 1@ e L#J o 2 ~3  * #  39 4F R#u 3m )';;'bDN C 񃀀L9 "  P { = ,} / HZ0)   M^N R  P  ' % 4 & C `6 + N   & ;6 q &  5 6 m = {   :3U ) # * 3     # $ 3 3 f x # 3   # * q 9 3" l )';;'bDN C 񃀀L; " P } ? , / HZ0+   M^P T  R  ) ' 6 & E b6 + P    & =6 s &  7 6 o ? }   M :S Q U /O U  K  EM  Y#  3# C M O^ j#h P3#  H#- 3WRd p# 3)';;'bD)';;'bD)';;'bD)';;'bD)';;'bD)';;'bD)';;'bDN C 񃀀L9 "  P { = ,} / HZ0)   M^N R  P  ' % 4 & C `6 + N   & ;6 q &  5 6 m = {   :   /   Z  c #H  7 V3k   # 3k ) 5#uX ]g3 U#: "3)';;'bDN C 񃀀L9 "  P { = ,} / HZ0)   M^N R  P  ' % 4 & C `6 + N   & ;6 q &  5 6 m = {   :   /   J  S #8  ' F3[ {  # 3[  %#eH MW3 E#* 3 )';;'bD)';;'bD)';;'bD)';;'bD)';;'bD)';;'bDN C 񃀀L9 "  P { = ,} / HZ0)   M^N R  P  ' % 4 & C `6 + N   & ;6 q &  5 6 m = {   x :~ |  3z u E v = x F #+   93N n x z #  { 3N   s #X ; @ J3 } 8 #   3 )';;'bDN C 񃀀L9 "  P { = ,} / HZ0)   M^N R  P  ' % 4 & C `6 + N   & ;6 q &  5 6 m = {   1:759339 /;1 = #w  3' 13 B g N #L q 4 3  , #  3; 6 H T # w 3o )';;'bDN C 񃀀L; " P } ? , / HZ0+   M^P T  R  ) ' 6 & E b6 + P    & =6 s &  7 6 o ? }   ' :- + / /) /  %  ' 3# m 3  ' )8 ]D#Bg *v3 "# 31,> J#m |3e)';;'bD)';;'bD)';;'bD)';;'bD)';;'bD)';;'bD)';;'bD)';;'bD)';;'bD)';;'bD)';;'bD)';;'bD)';;'bD)';;'bD)';;'bD)';;'bD)';;'bD)';;'bD)';;'bDN C 񃀀LH " P L , / HZ08   M^] a  _  6 4 C  ,& R o6 +# ] #   & J6  &  D 6 | L   :   3 E  ^ {i # * 3    #$ 33 fx # 3  #* q93" l)';;'bDN C 񃀀L< " P ~ @ , / HZ0,   M^Q U  S  * ( 7 & F c6 + Q    & >6 t &  8 6 p @ ~   :3   # Z 3  % J 1#/ T  c3   #  3  + 7 # Z i 3R )';;'bDN C 񃀀Ni ) W m , / "Ln0Y  3 'Mt~    W U D9 E& k 6 +D !~ >  / & g6  &  e 67 m 3  ; :A ? C 3= UC " 9 k ; G#  3 1 ; =L qX#V{ >3  6#  3E @R ^# 3y)';;'bD)';;'bD)';;'bD)';;'bD)';;'bD)';;'bD)';;'bD Cf qCn | lY 0V" K=g %s& =-񃀀N%) W  , /3"Jl0  r %Mr     x * &  62 + ` } %! n -&S 6]  K &0 7  6v r 񃀀L9 "  P { = ,} / HZ0)   M^N R  P ' % 7 & C `6 + N   & ;6 q &  5 6 m = {   :   /   _  h #M  < [3p   # 3p . :#z] bl3 Z#? '3" f :l j n /h n  d + Kf 4 r#   '3< \ f hw # i3<  a#F) .83pk} &#  3)';;'bD Cf qCn | 񃀀L9 "  P { = ,} / HZ0)   M^N R  P ' % 7 & C `6 + N   & ;6 q &  5 6 m = {  񃀀L9 "  P { = ,} / HZ0)   M^N R  P ' % 7 & C `6 + N   & ;6 q &  5 6 m = {   :   /   e  n #S  B a3v   # 3v "4 @#c hr3 `#E -3( O :U S W /Q W  M  4O  [#  3% E O Q` l#j R3%  J#/ !3YTf r# 3)';;'bD)';;'bD)';;'bD Cf qCn | 񃀀N.) 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5ExecuteSubgraphERKNS_12SessionStateERKNS_19FeedsFetchesManagerERKNSt3__16vectorI8OrtValueNS7_9allocatorIS9_EEEERSC_RKNS7_13unordered_mapImNS7_8functionIFNS_6common6StatusERKNS_11TensorShapeERK13OrtMemoryInfoRS9_RbEEENS7_4hashImEENS7_8equal_toImEENSA_INS7_4pairIKmST_EEEEEE13ExecutionModeRKbRKNS_7logging6LoggerE 2EPKNS_12DataTypeImplE 1EPKNS_12DataTypeImplE IsOpaqueTypeEPKNS_12DataTypeImplEPKcS5_ UnpackTensorI DefaultAllocEm ExecuteGraphERKNS_12SessionStateERNS_19FeedsFetchesManagerERKNSt3__16vectorI8OrtValueNS6_9allocatorIS8_EEEERSB_13ExecutionModeRKbRKNS_7logging6LoggerEb nTensorTypeINSt3__1Ο deIndexInfoC 3mapI 6vectorINS1_3mapI NS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE x S8_NS1_4lessIS8_EENS6_INS1_4pairIKS8_S8_EEEEEEE6DeleteEPv xNS1_4lessIS8_EENS6_INS1_4pairIKS8_xEEEEEEE6DeleteEPv fNS1_4lessIS8_EENS6_INS1_4pairIKS8_fEEEEEEE6DeleteEPv˦ dNS1_4lessIS8_EENS6_INS1_4pairIKS8_dEEEEEEE6DeleteEPv٦ 3NonTensorTypeINSt3__1 9PriorityNodeCompare9IsHighPriEPKNS_4NodeEE12high_pri_ops3mapI 6vectorINS1_3mapI NS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE x S8_NS1_4lessIS8_EENS6_INS1_4pairIKS8_S8_EEEEEEE13GetCreateFuncEvENUlvE_8__invokeEv xNS1_4lessIS8_EENS6_INS1_4pairIKS8_xEEEEEEE13GetCreateFuncEvENUlvE_8__invokeEvĦ fNS1_4lessIS8_EENS6_INS1_4pairIKS8_fEEEEEEE13GetCreateFuncEvENUlvE_8__invokeEvҦ dNS1_4lessIS8_EENS6_INS1_4pairIKS8_dEEEEEEE13GetCreateFuncEvENUlvE_8__invokeEv ЁNS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS1_4lessIxEENS6_INS1_4pairIKxS8_EEEEEEE6DeleteEPv xNS1_4lessIxEENS1_9allocatorINS1_4pairIKxxEEEEEEE6DeleteEPv fNS1_4lessIxEENS1_9allocatorINS1_4pairIKxfEEEEEEE6DeleteEPvˬ dNS1_4lessIxEENS1_9allocatorINS1_4pairIKxdEEEEEEE6DeleteEPv٬ NS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS1_4lessIxEENS6_INS1_4pairIKxS8_EEEEEEE13GetCreateFuncEvENUlvE_8__invokeEv xNS1_4lessIxEENS1_9allocatorINS1_4pairIKxxEEEEEEE13GetCreateFuncEvENUlvE_8__invokeEvĬ fNS1_4lessIxEENS1_9allocatorINS1_4pairIKxfEEEEEEE13GetCreateFuncEvENUlvE_8__invokeEvҬ dNS1_4lessIxEENS1_9allocatorINS1_4pairIKxdEEEEEEE13GetCreateFuncEvENUlvE_8__invokeEv нNS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEfNS1_4lessIS9_EENS7_INS1_4pairIKS9_fEEEEEENS7_ISG_EEEEE6DeleteEPv xfNS1_4lessIxEENS1_9allocatorINS1_4pairIKxfEEEEEENS6_ISB_EEEEE6DeleteEPvǰ NS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEfNS1_4lessIS9_EENS7_INS1_4pairIKS9_fEEEEEENS7_ISG_EEEEE13GetCreateFuncEvENUlvE_8__invokeEv xfNS1_4lessIxEENS1_9allocatorINS1_4pairIKxfEEEEEENS6_ISB_EEEEE13GetCreateFuncEvENUlvE_8__invokeEvΰ ейoOrtStatusERKNS_6common6StatusE ensorShapeC ErrorCodeEPK9OrtStatusƲ ValueTypeEPK8OrtValueP8ONNXType ErrorMessageEPK9OrtStatus MapValueTypeEPK14OrtMapTypeInfoPP11OrtTypeInfo D 15LoadExternalLibERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPPv 2Ev 1Ev 0Ev C2ERKNS_18OrtValueNameIdxMapERKNS_13NodeIndexInfoERKNSt3__16vectorIiNS7_9allocatorIiEEEE Dŷ 34GetMutableNodeInputOrOutputMLValueEi۸ 28GetOrCreateNodeOutputMLValueEiPKNS_11TensorShapeERP8OrtValuem 1ȹ 4InitERKNSt3__16vectorIiNS1_9allocatorIiEEEERKNS2_I8OrtValueNS3_IS8_EEEERKNS1_13unordered_mapIiS8_NS1_4hashIiEENS1_8equal_toIiEENS3_INS1_4pairIKiS8_EEEEEESC_ź 2Evܷ 1Ev 0Ev 2 1 8IsOutputEi̺ 7GetNodeInputOrOutputMLValueEi͸ 2GetNodeIdxToMLValueIdxEiԸ 9TryGetInferredShapeEiRNS_11TensorShapeE 2GetAllocatorERK13OrtMemoryInfo 4ReleaseMLValueEi 8ReleaseMLValueImplEi 0GetOutputsERNSt3__16vectorI8OrtValueNS1_9allocatorIS3_EEEEӺ C D 3 4 1 27 9TraceFreeEi 2ERKNSt3__16vectorIiNS1_9allocatorIiEEEERKNS2_I8OrtValueNS3_IS8_EEEES7_SC_RKNS1_13unordered_mapImNS1_8functionIFNS_6common6StatusERKNS_11TensorShapeERK13OrtMemoryInfoRS8_RbEEENS1_4hashImEENS1_8equal_toImEENS3_INS1_4pairIKmSQ_EEEEEERKNS_12SessionStateE 1ERKNSt3__16vectorIiNS1_9allocatorIiEEEERKNS2_I8OrtValueNS3_IS8_EEEES7_SC_RKNS1_13unordered_mapImNS1_8functionIFNS_6common6StatusERKNS_11TensorShapeERK13OrtMemoryInfoRS8_RbEEENS1_4hashImEENS1_8equal_toImEENS3_INS1_4pairIKmSQ_EEEEEERKNS_12SessionStateE в2Ev 1Ev 0Ev 0CopyTensorERKNS_6TensorERS1_ 6Ge 9TryGetInferredShapeEiRNS_11TensorShapeE 4AllocateMLValueTensorSelfOwnBufferER8OrtValueiPKNS_12DataTypeImplERK13OrtMemoryInfoRKNS_11TensorShapeEb 8AllocateMLValueTensorPreAllocateBufferER8OrtValueiPKNS_12DataTypeImplERK13OrtMemoryInfoRKNS_11TensorShapeEb 0AllocateMLValueTensorSelfOwnBufferHelperER8OrtValueiPKNS_12DataTypeImplERK13OrtMemoryInfoRKNS_11TensorShapeEb 1AllocateTensorWithPreAllocateBufferHelperER8OrtValuePvPKNS_12DataTypeImplERK13OrtMemoryInfoRKNS_11TensorShapeE 7GetAllocationPlanEi 3TraceAllocateEim 8ReleaseMLValueImplEi AllocateAsPerAllocationPlanER8OrtValueiPKNS_11TensorShapeEm CreateNodeOutputMLValueImplER8OrtValueiPKNS_11TensorShapeEm tAllocatorImplERK13OrtMemoryInfo 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а6CreateERKNSt3__16vectorINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS6_IS8_EEEESC_RKNS_18OrtValueNameIdxMapERNS1_10unique_ptrIS0_NS1_14default_deleteIS0_EEEE C 19SetDeviceCopyChecksENS_15DeviceCopyCheckES1_ л2EONS_16FeedsFetchesInfoE 1EONS_16FeedsFetchesInfoE S9_ ONS_15NodeComputeInfoE 2 9PartitionERNS_5GraphEbRNS_11FuncManagerENS0_4ModeEPNSt3__113unordered_mapINS6_12basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEEyNS6_4hashISD_EENS6_8equal_toISD_EENSB_INS6_4pairIKSD_yEEEEEE 4PartitionOnnxFormatModelERNS_5GraphEbRNS_11FuncManagerERNS_14KernelRegistryENS0_4ModeERi 3PartitionOrtFormatModelERNS_5GraphERNS_11FuncManagerERNS_14KernelRegistryERNSt3__113unordered_mapINS7_12basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEEyNS7_4hashISE_EENS7_8equal_toISE_EENSC_INS7_4pairIKSE_yEEEEEERi 7SetNameE 9SetDomainE 8ProviderE 1 5AliasE RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE PKc RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE PKc ВRKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE PKc 4TypeConstraintE 0MayInplaceE RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE PKc RKNS1_6vectorIPKNS_12DataTypeImplENS5_ISD_EEEE PKNS_12DataTypeImplE RKNSt3__16vectorIPKNS_12DataTypeImplENS3_9allocatorIS7_EEEE PKNS_12DataTypeImplE МRKNSt3__16vectorINS1_4pairIiiEENS1_9allocatorIS4_EEEE ii RKNSt3__16vectorINS1_4pairIiiEENS1_9allocatorIS4_EEEE ii 15VerifyKernelDefERKNS_4NodeERKNS_9KernelDefERNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEE 8RegisterE 5TryCreateKernelERKNS_4NodeERKNS_18IExecutionProviderERKNSt3__113unordered_mapIi8OrtValueNS7_4hashIiEENS7_8equal_toIiEENS7_9allocatorINS7_4pairIKiS9_EEEEEERKNS_18OrtValueNameIdxMapERKNS_11FuncManagerERKNS_19DataTransferManagerERNS7_10unique_ptrINS_8OpKernelENS7_14default_deleteISW_EEEE 3TryFindKernelERKNS_4NodeERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEE PPKNS_16KernelCreateInfoE yPPKNS_16KernelCreateInfoE RNS_16KernelDefBuilderERKNSt3__18functionIFPNS_8OpKernelERKNS_12OpKernelInfoEEEE ONS_16KernelCreateInfoE 12CreateKernelERKNS_4NodeERKNS_18IExecutionProviderERKNS_12SessionStateERKNS_16KernelCreateInfoE 20SearchKernelRegistryERKNS_4NodeE 1 22RegisterKernelRegistryENSt3__110shared_ptrINS_14KernelRegistryEEE 5RegisterKernelsERKNS_18ExecutionProvidersE 9HasImplementationOfERKS0_RKNS_4NodeERKNSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEE PPKNS_16KernelCreateInfoE yPPKNS_16KernelCreateInfoE Get SetRandomSeedEx MLDataTypeERKNS_7NodeArgE RandomSeedEv 6x86_32EPKvijPv 7x86_128EPKvijPv 2ERKNS 1ERKNS _1 t3__16vectorIPKNS_4NodeENS1_9allocatorIS5_EEEERKNS_18OrtValueNameIdxMapE 1GraphViewerERKNS_18OrtValueNameIdxMapE 0GraphNodesERKNS_18OrtValueNameIdxMapE _1 t3__16vectorIPKNS_4NodeENS1_9allocatorIS5_EEEERKNS_18OrtValueNameIdxMapE 1GraphViewerERKNS_18OrtValueNameIdxMapE 0GraphNodesERKNS_18OrtValueNameIdxMapE C 13FromTypeProtoEPKN4onnx9TypeProtoEPPS_ 5CloneEPPS_ 2E25ONNXTensorElementDataTypeP11OrtTypeInfo 1E25ONNXTensorElementDataTypeP11OrtTypeInfo C 13FromTypeProtoEPKN4onnx9TypeProtoEPPS_ 5CloneEPPS_ 2EP11OrtTypeInfo 1EP11OrtTypeInfo C D 1 20ElementTypeFromProtoEi 5CloneEPPS_ 2E8ONNXType 1E8ONNXType P P 25OrtTensorTypeAndShapeInfo 1 25OrtTensorTypeAndShapeInfo 1 4OrtMapTypeInfo 9OrtSequenceTypeInfo 4OrtMapTypeInfo 9OrtSequenceTypeInfo 2Ev 1Ev 2FromOrtValueERK8OrtValuePPS_ 3FromTypeProtoEPKN4onnx9TypeProtoEPPS_ C 6OutputEi 1 24GetOrCreateOutputMLValueEi 2EPNS_15IExecutionFrameEPKNS_8OpKernelEPNS_11concurrency10ThreadPoolERKNS_7logging6LoggerE 1EPNS_15IExecutionFrameEPKNS_8OpKernelEPNS_11concurrency10ThreadPoolERKNS_7logging6LoggerE RK mRKNS_11TensorShapeE NS St16initializer_listIxE _11TensorShapeE t3__16vectorIxNS1_9allocatorIxEEEE 3OutputMLValueEiRKNS_11TensorShapeEm 6GetOutputMLValueEi 2 1 9InputTypeEi 4 5TryGetInferredOutputShapeEiRNS_11TensorShapeE 1GetTempSpaceAllocatorEPNSt3__110shared_ptrINS_10IAllocatorEEE 3GetImplicitInputMLValueEi TryGetInferredInputShapeEiRNS_11TensorShapeE GetImplicitInputArgIndexEi 6GetInputArgIndexEi 7 0 8ImplicitInputFenceEi 1 2GetNodeIndexEv 5GetInputMLValueEi GetOutputArgIndexEi NumVariadicInputsEm OutputTypeEi InputFenceEi OutputFenceEi GetOpDomainEv 2ERK 1ERK NS_4NodeERKNS_9KernelDefERKNS_18IExecutionProviderERKNSt3__113unordered_mapIi8OrtValueNSA_4hashIiEENSA_8equal_toIiEENSA_9allocatorINSA_4pairIKiSC_EEEEEERKNS_18OrtValueNameIdxMapERKNS_11FuncManagerERKNS_19DataTransferManagerE S0_ NS_4NodeERKNS_9KernelDefERKNS_18IExecutionProviderERKNSt3__113unordered_mapIi8OrtValueNSA_4hashIiEENSA_8equal_toIiEENSA_9allocatorINSA_4pairIKiSC_EEEEEERKNS_18OrtValueNameIdxMapERKNS_11FuncManagerERKNS_19DataTransferManagerE S0_ 1 2 4nodeEv 3Get 2Get 9TryGetConstantInputEiPPKNS_6TensorE MemoryInfoEi10OrtMemType FusedFuncsERPNS_15NodeComputeInfoE AllocatorEi10OrtMemType KernelDefEv 0GetExecutionProviderEv 2GetDataTransferManagerEv S_22ProtoHelperNodeContextEEC 4onnx16InferenceContextEEC 2EPKS1_ 1EPKS1_ S_22ProtoHelperNodeContextEE 4onnx16InferenceContextEE 1 2 7GetAttrI 8GetAttrsIӗ 8GetAttrsStringRefsERKNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEERNS3_6vectorINS3_17reference_wrapperISA_EENS7_ISE_EEEE 5TryGetAttributeERKNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEE 3Get݆ 4Get 2Get 8GetPrimitiveAttrElementCountEN4onnx28AttributeProto_AttributeTypeERKNSt3__112basic_stringIcNS5_11char_traitsIcEENS5_9allocatorIcEEEEƅ 1HasPrimitiveAttributeEN4onnx28AttributeProto_AttributeTypeERKNSt3__112basic_stringIcNS5_11char_traitsIcEENS5_9allocatorIcEEEEֆ 2get߅ 3get AttributeERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEφ NumInputsEv InputTypeEmȈ InputCountEv OutputTypeEmψ OutputCountEv AttrsAsSpanI̤ NumOutputsEvه OutputTypeEmֈ InputTypeEm AttributeERKNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEE݈ 2EPKS2_ 1EPKS2_ Ѓ1 2͋ 7GetAttrIԐ 8GetAttrsIҙ 8GetAttrsStringRefsERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEERNS4_6vectorINS4_17reference_wrapperISB_EENS8_ISF_EEEE 5TryGetAttributeERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEEƋ 3Get 4GetOutputCountEv 2Get 8GetPrimitiveAttrElementCountENS1_28AttributeProto_AttributeTypeERKNSt3__112basic_stringIcNS5_11char_traitsIcEENS5_9allocatorIcEEEE֍ 1HasPrimitiveAttributeENS1_28AttributeProto_AttributeTypeERKNSt3__112basic_stringIcNS5_11char_traitsIcEENS5_9allocatorIcEEEEݍ InputCountEv OutputTypeEm InputTypeEm AttributeERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEE ЊfEENS_6common6StatusERKNSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEEPT_͐ xEENS_6common6StatusERKNSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEEPT_ N fEENS_6common6StatusERKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEEPT_ xEENS_6common6StatusERKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEEPT_ NS St3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEEEENS_6common6StatusERKSA_PT_ 4onnx1 t3__112basic_stringIcNS5_11char_traitsIcEENS5_9allocatorIcEEEEEENS_6common6StatusERKSB_PT_ 1_1 1TensorProtoEEENS_6common6StatusERKNSt3__112basic_stringIcNS8_11char_traitsIcEENS8_9allocatorIcEEEEPT_ 0GraphProtoEEENS_6common6StatusERKNSt3__112basic_stringIcNS8_11char_traitsIcEENS8_9allocatorIcEEEEPT_ŗ 1TensorProtoEEENS_6common6StatusERKNSt3__112basic_stringIcNS8_11char_traitsIcEENS8_9allocatorIcEEEEPT_ 0GraphProtoEEENS_6common6StatusERKNSt3__112basic_stringIcNS8_11char_traitsIcEENS8_9allocatorIcEEEEPT_̗ fEENS_6common6StatusERKNSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEE xEENS_6common6StatusERKNSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEEқ N֜ RNS6_6vectorIT_NSA_ISG_EEEEę N3gsl4spanIT_EE˙ fEENS_6common6StatusERKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEE xEENS_6common6StatusERKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEE NS RNS7_6vectorIT_NSB_ISH_EEEEě N3gsl4spanIT_EE˛ RNS6_6vectorIT_NSA_ISG_EEEE N3gsl4spanIT_EE RNS7_6vectorIT_NSB_ISH_EEEEȜ N3gsl4spanIT_EEϜ St3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEEEENS_6common6StatusERKSA_ 4onnx1 RNS4_6vectorIT_NS8_ISG_EEEE N3gsl4spanIT_EE t3__112basic_stringIcNS5_11char_traitsIcEENS5_9allocatorIcEEEEEENS_6common6StatusERKSB_ 1_1 RNS5_6vectorIT_NS9_ISH_EEEE N3gsl4spanIT_EE 1TensorProtoEEENS_6common6StatusERKNSt3__112basic_stringIcNS8_11char_traitsIcEENS8_9allocatorIcEEEE 0GraphProtoEEENS_6common6StatusERKNSt3__112basic_stringIcNS8_11char_traitsIcEENS8_9allocatorIcEEEEȣ RNS8_6vectorIT_NSC_ISI_EEEE N3gsl4spanIT_EE 1TensorProtoEEENS_6common6StatusERKNSt3__112basic_stringIcNS8_11char_traitsIcEENS8_9allocatorIcEEEE 0GraphProtoEEENS_6common6StatusERKNSt3__112basic_stringIcNS8_11char_traitsIcEENS8_9allocatorIcEEEE RNS8_6vectorIT_NSC_ISI_EEEE N3gsl4spanIT_EE RNS8_6vectorIT_NSC_ISI_EEEE N3gsl4spanIT_EE RNS8_6vectorIT_NSC_ISI_EEEE N3gsl4spanIT_EEŤ ЃfEENS_6common6StatusERKNSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEERN3gsl4spanIKT_EE xEENS_6common6StatusERKNSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEERN3gsl4spanIKT_EE CЦ 1 9TraceFreeEi 2ERKNS_17ExecutionPlanBaseEb 1ERKNS_17ExecutionPlanBaseEb 5TraceAllocationEim 6GeneratePatternsEPNS_18MemoryPatternGroupE 8OrtValueE K8OrtValueEԫ 6CreateERS1_xx¨ Cɨ 8Iterator 1ERS1_xx 2ERS1_xxͫ C p deEv 2ERS1_mmxNS3_9DirectionE 1ERS1_mmxNS3_9DirectionE 8OrtValueE K8OrtValueE 8Iterator 5beginEv 3endEv 6rbeginEv 4rendEvƫ eqERKS3_ު neERKS3_ deEv 18MaterializeMLValueEv pE LEl v i ІИЙ6CreateERS2_xx C 8Iterator 1ERS2_xx 2ERS2_xx C p deEv 2ERS2_mmxNS4_9DirectionE 1ERS2_mmxNS4_9DirectionE л8Iterator 5beginEvʮ 3endEvѮ 6rbeginEvخ 4rendEv߮ eqERKS4_ neERKS4_ deEv 18MaterializeMLValueEvî pE LEl v i пC 7ExecuteERKNS_12SessionStateERKNSt3__16vectorIiNS4_9allocatorIiEEEERKNS5_I8OrtValueNS6_ISB_EEEESA_RSD_RKNS4_13unordered_mapImNS4_8functionIFNS_6common6StatusERKNS_11TensorShapeERK13OrtMemoryInfoRSB_RbEEENS4_4hashImEENS4_8equal_toImEENS6_INS4_4pairIKmSU_EEEEEERKNS_7logging6LoggerE߱ 1 2ERKNS_12SessionStateERKbѱ 1ERKNS_12SessionStateERKbر 1EnqueueNodeEmRKNS_12SessionStateERKNS_7logging6LoggerE߲ 2RunNodeAsyncEmRKNS_12SessionStateERKNS_7logging6LoggerE TensorrtEi OpenVINOEPK26OrtOpenVINOProviderOptions ЄSetRunLogVerbosityLevelEP13OrtRunOptionsi GetRunLogVerbosityLevelEPK13OrtRunOptionsPi SetRunLogSeverityLevelEP13OrtRunOptionsi˵ GetRunLogSeverityLevelEPK13OrtRunOptionsPi SetRunTagEP13OrtRunOptionsPKc GetRunTagEPK13OrtRunOptionsPPKc 7TryGetConfigEntryERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERS7_ 8GetConfigOrDefaultERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES9_ ConfigEntryEPKcS2_ɸ InitializerEPKcPK8OrtValueи 1 2 3 5 3CreateKernelsERKNS_21KernelRegistryManagerE 7LoadFromOrtFormatERKNS_12experimental3fbs12SessionStateERKNS_21KernelRegistryManagerE SetupAllocatorsEv CreateGraphInfoEv 1 2 2GetAllocatorEټ 6Get 9GetInputNodeInfoMapEv 7GetOutputNodeInfoERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERNS1_6vectorINS0_8NodeInfoENS5_ISB_EEEE 5SaveToOrtFormatERN11flatbuffers17FlatBufferBuilderERNS1_6OffsetINS_12experimental3fbs12SessionStateEEE RK13OrtMemoryInfo 9OrtDevice 4 0 9AddInputNameToNodeInfoMappingERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERKNS0_8NodeInfoE 3 6CreateSubgraphSessionStateEv PopulateKernelCreateInfoERNS_21KernelRegistryManagerEb ResolveMemoryPatternFlagEv FinalizeSessionStateImplERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERNS_21KernelRegistryManagerEPKNS_4NodeERKNS_14SessionOptionsEbRNS1_13unordered_mapIS7_mNS1_4hashIS7_EENS1_8equal_toIS7_EENS5_INS1_4pairIS8_mEEEEEE 3Get 1Get 9 2GetEnableMemoryPatternEv 0Get NodeKernelCreateInfoEm SubgraphSessionStateEmRKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE ExecutionPlanEv InputNodeInfoERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERNS1_6vectorINS0_8NodeInfoENS5_ISB_EEEE NodeIndexInfoEv AddInitializedTensorEiRK8OrtValuePKNS_11OrtCallbackEb FinalizeSessionStateERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERNS_21KernelRegistryManagerERKNS_14SessionOptionsEPKNS_12experimental3fbs12SessionStateEbb InitializedTensorsEv MemoryPatternGroupERKNSt3__16vectorINS1_17reference_wrapperIKNS_11TensorShapeEEENS1_9allocatorIS6_EEEERKNS2_IiNS7_IiEEEERNS1_13unordered_mapIiS4_NS1_4hashIiEENS1_8equal_toIiEENS7_INS1_4pairIKiS4_EEEEEE GetConstantInitializedTensorsEv UpdateMemoryPatternGroupCacheERKNSt3__16vectorINS1_17reference_wrapperIKNS_11TensorShapeEEENS1_9allocatorIS6_EEEENS1_10unique_ptrINS_18MemoryPatternGroupENS1_14default_deleteISD_EEEE 2CleanInitializedTensorsFromGraphEv 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1ʣ43dbg_count_check_0dbg_count_check_7dbg_count_check_InstanceNormalization_Onnx_ver8dbg_count_check_DynamicQuantizeLinear_Onnx_ver11E59dbg_count_check_܃convTransposeWithDynamicPadsShapeInferenceERNS_16InferenceContextE dbg_count_check_BertSchemasEvρ NhwcSchemasEv NchwcPoolOpSchemaGeneratorERN4onnx8OpSchemaÊ convPoolShapeInferenceNhwcERN4onnx16InferenceContextEbbii C D 2ERKNS_5GraphERKȃ 1ERKNS_5GraphERK NS_15IndexedSubGraphERKNS_7logging6LoggerE mRKN4onnx13FunctionProtoERKNS_7logging6LoggerE NS_15IndexedSubGraphERKNS_7logging6LoggerE mRKN4onnx13FunctionProtoERKNS_7logging6LoggerE 2Ev 1Evƅ 0Evͅ 8OpSchemaEv 4BodyEv C D 2ERKNS_5GraphERKNS_15IndexedSubGraphERKNS_7logging6LoggerE 1ERKNS_5GraphERKNS_15IndexedSubGraphERKNS_7logging6LoggerE 2Ev 1Ev 0Ev C 8SetShapeERKN4onnx16TensorShapeProtoE 1 7SetTypeE 2E 1E RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKN4onnx9TypeProtoE ON4onnx14ValueInfoProtoE RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKN4onnx9TypeProtoE ON4onnx14ValueInfoProtoE 4 11TypeAsProtoEv 5ShapeEv 22HasTensorOrScalarShapeEv 6ExistsEv NameEv TypeEv а0ClearShapeEv 8UpdateTypeAndShapeERK N4onnx9TypeProtoEbbRKNS_7logging6LoggerE S0_bbRKNS_7logging6LoggerE PKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE RKN4onnx9TypeProtoE 7EdgeEndC 1 2Ӣ 4InitERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES9_S9_RKNS1_6vectorIPNS_7NodeArgENS5_ISC_EEEESG_PKNS1_13unordered_mapIS7_N4onnx14AttributeProtoENS1_4hashIS7_EENS1_8equal_toIS7_EENS5_INS1_4pairIS8_SJ_EEEEEES9_ 2ERKS0_ӏ 1ERKS0_ iiߏ ii 7 1 5 2AddAttributeERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEá 4C 8MutableDefinitionsEvإ 9UpdateInputArgCountEv NodeConstIterator LoadFromOrtFormatERKNS_12experimental3fbs4NodeERĠ C ppEv mmEv 2ENSt3__121__tree_const_iteratorINS0_7EdgeEndEPNS2_11__tree_nodeIS4_PvEElEE 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learAttributeERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 1ER 2ERը S0_RKNS_4NodeERN4onnx10GraphProtoE KNS_5ModelE вжкfNS5_IfEEEE xNS5_IxEEEE S7_NS5_IS7_EEEE N4onnx1 1TensorProtoENS5_ISC_EEEE 0GraphProtoENS5_ISC_EEEE 7SparseTensorProtoENS5_ISC_EEEE KNS_5ModelE S0_RKNS_4NodeERN4onnx10GraphProtoE PN4onnx10GraphProtoERKNSt3__113unordered_mapINS7_12basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEEiNS7_4hashISE_EENS7_8equal_toISE_EENSC_INS7_4pairIKSE_iEEEEEExNS7_10shared_ptrINS_30IOnnxRuntimeOpSchemaCollectionEEE RKNSt3__113unordered_mapINS4_12basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEEiNS4_4hashISB_EENS4_8equal_toISB_EENS9_INS4_4pairIKSB_iEEEEEEPS0_PKNS_4NodeERKNS_7logging6LoggerE RKNS_7logging6LoggerE PS0_PKNS_4NodeERKNS_7logging6LoggerE 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LoadSparseInitializerOrtFormatERKNS0_3fbs12SparseTensorERN4onnx17SparseTensorProtoE ੍Save Load AttributeOrtFormatERN11flatbuffers17FlatBufferBuilderERKN4onnx14AttributeProtoERNS2_6OffsetINS0_3fbs9AttributeEEEPKNS_5GraphE ValueInfoOrtFormatERN11flatbuffers17FlatBufferBuilderERKN4onnx14ValueInfoProtoERNS2_6OffsetINS0_3fbs9ValueInfoEEE InitializerOrtFormatERKNS0_3fbs6TensorERN4onnx11TensorProtoE OpsetImportOrtFormatEPKN11flatbuffers6VectorINS2_6OffsetINS0_3fbs13OperatorSetIdEEEEERNSt3__113unordered_mapINSB_12basic_stringIcNSB_11char_traitsIcEENSB_9allocatorIcEEEEiNSB_4hashISI_EENSB_8equal_toISI_EENSG_INSB_4pairIKSI_iEEEEEEЕ ݍAttributeOrtFormatERKNS0_3fbs9AttributeERN4onnx14AttributeProtoERNSt3__110unique_ptrINS_5GraphENS9_14default_deleteISB_EEEERSB_RNS_4NodeERKNS_7logging6LoggerE ValueInfoOrtFormatERKNS0_3fbs9ValueInfoERN4onnx14ValueInfoProtoEɕ  8internal 5Arena21796lsERNSt3__113basic_ostreamIcNS1_11char_traitsIcEEEE4utilݼ30WebSafeBase64EscapeWithPaddingENS0_11StringPieceEPNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE2 9ArenaImpl136IsNullILi8byteswapILi1 45RepeatedPrimitiveDefaults16default_instanceEv0369arena_destruct_objectINS InitializeEnumStringsEPKNS1_9EnumEntryEPKimPNS1_21ExplicitlyConstructedINSt3__112basic_stringIcNS8_11char_traitsIcEENS8_9allocatorIcEEEEEEUnknownGroupLiteParseEPNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEPKcPNS1_12ParseContextEt3__1 0_11_12basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEEEEvPv 3mapIiNS1_12ExtensionSet9ExtensionENS3_4lessIiEENS3_9allocatorINS3_4pairIKiS6_EEEEEEEEvPv3 1 2 8FindPathER 0GetNodeInputIndexFromInputNameERKNS_4NodeERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEE 3IsSupportedOptypeVersionAndDomainERKNS_4NodeE 2RemoveNodesWithOneOutputBottomUpERNS_5GraphERKNS_4NodeE 6 7 8 9IsSupportedProviderERKNS_4NodeERKNSt3__113unordered_setINS4_12basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEENS4_4hashISB_EENS4_8equal_toISB_EENS9_ISB_EEEE 2 3 0RemoveNodeERNS_5GraphERNS_4NodeE 4AddInitializerERNS_5GraphERKN4onnx11TensorProtoE GetNode ReplaceNodeInputERNS_4NodeEiRNS_7NodeArgE FirstChildByTypeERKNS_4NodeERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEE InputNameERKNS_4NodeEi AttributeERKNS_4NodeERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEE GetNodeOutputNameERKNS_4NodeEi NodeArgIsConstantERKNS_5GraphERKNS_7NodeArgE FirstParentByTypeERKNS_4NodeERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEE RKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEERKSt16initializer_listIiESC_ PKcRKSt16initializer_listIiES5_ 1 6Replace 9CanReplaceNodeWithInitializerERKNS_5GraphERKNS_4NodeERKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEERKNS_7logging6LoggerE 2GetConstantInitializerERKNS_5GraphERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEEb 4AllNodeInputsAreConstantERKNS_5GraphERKNS_4NodeERNSt3__113unordered_mapINS7_12basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEEPKN4onnx11TensorProtoENS7_4hashISE_EENS7_8equal_toISE_EENSC_INS7_4pairIKSE_SI_EEEEEERKNS7_13unordered_setISE_SK_SM_NSC_ISE_EEEE MatchesOpSinceVersionERKNS_4NodeERK RemoveNodeOutputEdgesERNS_5GraphERNS_4NodeE IsConstantInitializerERKNS_5GraphERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEEb St16initializer_listIiE NSt3__16vectorIiNS4_9allocatorIiEEEE MatchesOpSetDomainERKNS_4NodeERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEE FinalizeNodeFusionERNS_5GraphER НIs AddNodeInputERNS_4NodeEiRNS_7NodeArgE GetInput OutputUsedERKNS_4NodeEi GraphInputERKNS_5GraphEPKNS_7NodeArgE C IsInitializerERKNS_5GraphERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEEb anRemoveNodeERKNS_5GraphERKNS_4NodeERKNS_7logging6LoggerE reateNodeArgERNS_5GraphERKNS_7NodeArgE ПгNodeWithInitializerERNS_5GraphERNS_4NodeERNS_7NodeArgE DownstreamNodeInputERNS_5GraphERNS_4NodeEiS4_i ȏڏߏNS_4NodeES4_ KNSt3__16vectorINS3_17reference_wrapperINS_4NodeEEENS3_9allocatorIS7_EEEERS6_ EdgeERKNS_4NodeEi NodeERKNS_4NodeEi KNS_4NodeEbRKNSt3__16vectorINS0_14EdgeEndToMatchENS4_9allocatorIS6_EEEERNS5_IPKNS1_7EdgeEndENS7_ISE_EEEERKNS_7logging6LoggerE NS_5GraphERKNS_4NodeEbRKNSt3__16vectorINS0_14EdgeEndToMatchENS6_9allocatorIS8_EEEERNS7_INS6_17reference_wrapperIS3_EENS9_ISF_EEEERKNS_7logging6LoggerE ॐϐ2ERKNS_5GraphE 1ERKNS_5GraphE PKNS_15IndexedSubGraphE RKNS_15IndexedSubGraphE ЮPKNS_15IndexedSubGraphE RKNS_15IndexedSubGraphE ஑7GetNodeEm 4NameEv 1 2 9GetInputsEv 30GetInputsIncludingInitializersEv 5NodesEvԆ 1DescriptionEv 0 2 3NumberOfNodesEvۆ о0GetInitializedTensorERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERPKN4onnx11TensorProtoE 2CanOverrideInitializerEv 6GetNodesInTopologicalOrderENS_14ExecutionOrderE 4GetAllInitializedTensorsEv 1IsConstantInitializerERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEb пGet IsSubgraphEv OutputsEv NodeArgERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE Get MaxNodeIndexEv ValueInfoEv͆ RootNodesEv Ƒˑˑˑ̑C 1Δ 9 7ToProtoEv 4 2EŇ 1E RKN ON4onnx10ModelProtoERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEEPKNS4_4listINS4_10shared_ptrINS_30IOnnxRuntimeOpSchemaCollectionEEENS8_ISG_EEEERKNS_7logging6LoggerE v St3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEbRKNS1_13unordered_mapIS7_S7_NS1_4hashIS7_EENS1_8equal_toIS7_EENS5_INS1_4pairIS8_S7_EEEEEES9_RKNS1_4listINS1_10shared_ptrINS_30IOnnxRuntimeOpSchemaCollectionEEENS5_ISO_EEEERKNSA_IS7_iSC_SE_NS5_INSF_IS8_iEEEEEERKNS1_6vectorIN4onnx13FunctionProtoENS5_IS10_EEEERKNS_7logging6LoggerE 4onnx10ModelProtoERKNSt3__112basic_stringIcNS5_11char_traitsIcEENS5_9allocatorIcEEEEPKNS5_4listINS5_10shared_ptrINS_30IOnnxRuntimeOpSchemaCollectionEEENS9_ISH_EEEERKNS_7logging6LoggerE 9؍ 1 6DomainEv 8MetaDataEv IrVersionEv DocStringEv MainGraphEv RKNڏ ON4onnx10ModelProtoERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEEPKNS4_4listINS4_10shared_ptrINS_30IOnnxRuntimeOpSchemaCollectionEEENS8_ISG_EEEERKNS_7logging6LoggerE v St3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEbRKNS1_13unordered_mapIS7_S7_NS1_4hashIS7_EENS1_8equal_toIS7_EENS5_INS1_4pairIS8_S7_EEEEEES9_RKNS1_4listINS1_10shared_ptrINS_30IOnnxRuntimeOpSchemaCollectionEEENS5_ISO_EEEERKNSA_IS7_iSC_SE_NS5_INSF_IS8_iEEEEEERKNS1_6vectorIN4onnx13FunctionProtoENS5_IS10_EEEERKNS_7logging6LoggerE 4onnx10ModelProtoERKNSt3__112basic_stringIcNS5_11char_traitsIcEENS5_9allocatorIcEEEEPKNS5_4listINS5_10shared_ptrINS_30IOnnxRuntimeOpSchemaCollectionEEENS9_ISH_EEEERKNS_7logging6LoggerE Ё2 5 ProducerNameEvǔ ModelVersionEv ж5Set 8SetProducerVersionERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 2SetDocStringERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 3LoadFromBytesEiPvR 7LoadFromOrtFormatERKNS_12experimental3fbs5ModelERKNS_7logging6LoggerERNSt3__110unique_ptrIS0_NSA_14default_deleteIS0_EEEE ProducerNameERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE ModelVersionEx ඒProducerVersionEv SaveToOrtFormatERN11flatbuffers17FlatBufferBuilderERNS1_6OffsetINS_12experimental3fbs5ModelEEE зSetDomainERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE MainGraphEv ිиฒLoadEϚ SaveERS0_Ƣ R ON4onnx10ModelProtoER iR NSt3__113basic_istreamIcNS1_11char_traitsIcEEEEPN4onnx10ModelProtoEě KN˛ 4onnx10ModelProtoER St3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERN NSt3__110shared_ptrIS0_EEPKNS5_4listINS6_INS_30IOnnxRuntimeOpSchemaCollectionEEENS5_9allocatorISB_EEEERKNS_7logging6LoggerEޞ KNSt3__112basic_stringIcNS5_11char_traitsIcEENS5_9allocatorIcEEEERNS5_10shared_ptrIS0_EEPKNS5_4listINSE_INS_30IOnnxRuntimeOpSchemaCollectionEEENS9_ISJ_EEEERKNS_7logging6LoggerE དྷNSt3__110shared_ptrIS0_EEPKNS4_4listINS5_INS_30IOnnxRuntimeOpSchemaCollectionEEENS4_9allocatorISA_EEEERKNS_7logging6LoggerE KNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEERNS4_10shared_ptrIS0_EEPKNS4_4listINSD_INS_30IOnnxRuntimeOpSchemaCollectionEEENS8_ISI_EEEERKNS_7logging6LoggerE ɒʒ4onnx10ModelProtoE S1_10shared_ptrIS0_EEPKNS1_4listINSA_INS_30IOnnxRuntimeOpSchemaCollectionEEENS5_ISF_EEEERKNS_7logging6LoggerE ӒےRKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE i Nդ KNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEERNS2_10shared_ptrIS0_EEPKNS2_4listINSB_INS_30IOnnxRuntimeOpSchemaCollectionEEENS6_ISG_EEEERKNS_7logging6LoggerE 4onnx10ModelProtoE St3__110shared_ptrIS0_EEPKNS2_4listINS3_INS_30IOnnxRuntimeOpSchemaCollectionEEENS2_9allocatorIS8_EEEERKNS_7logging6LoggerE N KNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERNS1_10shared_ptrIS0_EEPKNS1_4listINSA_INS_30IOnnxRuntimeOpSchemaCollectionEEENS5_ISF_EEEERKNS_7logging6LoggerEߨ 4onnx10ModelProtoEѨ St3__110shared_ptrIS0_EEPKNS1_4listINS2_INS_30IOnnxRuntimeOpSchemaCollectionEEENS1_9allocatorIS7_EEEERKNS_7logging6LoggerEب 35SetBaselineAndOpsetVersionForDomainERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEii 1ѫ 24RegisterOpSchemaInternalEON4onnx8OpSchemaE ē22GetLatestOpsetVersionsEbʫ 19GetSchemaAndHistoryERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEiS9_PPKN4onnx8OpSchemaEPi ϓ3RegisterOpSetERNSt3__16vectorIN4onnx8OpSchemaENS1_9allocatorIS4_EEEERKNS1_12basic_stringIcNS1_11char_traitsIcEENS5_IcEEEEii 6RegisterOpSchemaEON4onnx8OpSchemaE ѓӓӓ34GetDomainToVersionMapForRegistriesERNSt3__113unordered_mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEiNS1_4hashIS8_EENS1_8equal_toIS8_EENS6_INS1_4pairIKS8_iEEEEEEbկ 2ܯ 19GetSchemaAndHistoryERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEiS9_PPKN4onnx8OpSchemaEPi 8GetLastReleasedOpsetVersionsEb 2GetLatestOpsetVersionsEb 2Evð 1Evʰ DenormalAsZeroEb TimeSpecToZeroEP8timespec 7Capture 1 8Category13 D CapturePrintfEPKcz ProcessPrintfEPKcP13__va_list_tag 2Ev 1Ev 4LoggingManagerղ 1¹ 2GetProcessIdEv 9GetEpochsEv C 1 D޷ 26LogFatalAndCreateExceptionEPKcRKNS_12CodeLocationES3_z 2ENSt3__110unique_ptrINS0_5ISinkENS2_14default_deleteIS4_EEEENS0_8SeverityEbNS1_12InstanceTypeEPKNS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEi 1ENSt3__110unique_ptrINS0_5ISinkENS2_14default_deleteIS4_EEEENS0_8SeverityEbNS1_12InstanceTypeEPKNS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEi׷ 9CreateDefaultLoggerERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEз 2CreateLoggerERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE 7s_default_logger_E2Ev 1Ev NS0_8SeverityEbi 3LogERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEERKNS0_7CaptureE 16SendProfileEventERNS_9profiling11EventRecordE GetThreadIdEv OStreamSink8SendImplERKNSt3__16chrono10time_pointINS3_12system_clockENS3_8durationIxNS2_5ratioILl1ELl1000000EEEEEEERKNS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEERKNS0_7CaptureE 5ParseERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 9NormalizeEv 6½ RS0_ ܕ1ļ 7IsEmptyEv 2ToPathStringEv 7GetRootPathStringEv 0 IsAbsoluteEv ParentPathEv AppendERKS0_ ConcatERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 1Ǿ D 2 4StartProfiling 0InitializeEPKNS_7logging6LoggerE 2EndProfilingEv IcEEvRKNSt3__112basic_stringIT_NS3_11char_traitsIS5_EENS3_9allocatorIS5_EEEE EPKNS_7logging6LoggerE –2Ev 1Ev ƖȖɖ͖1EndTimeAndRecordEventENS0_13EventCategoryERKNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEERKNS3_6chrono10time_pointINSC_12steady_clockENSC_8durationIxNS3_5ratioILl1ELl1000000000EEEEEEERKSt16initializer_listINS3_4pairIS9_S9_EEEb 2global_max_num_events_EіݖC 11EmptyStringEv 2ENS0_14StatusCategoryEi 1ENS0_14StatusCategoryEi RKNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEE PKc RKNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEE PKc Џ8 4CodeEv 12ErrorMessageEv CategoryEv ToStringEv C D 35ParallelForFixedBlockSizeSchedulingEllRKNSt3__18functionIFvllEEE 1 8ScheduleENSt3__18functionIFvvEEE 2EPNS_3EnvERKNS_13ThreadOptionsEPKcib 1EPNS_3EnvERKNS_13ThreadOptionsEPKcib 2Ev 1Ev 9DegreeOfParallelismEPKS1_ 3RunInParallelENSt3__18functionIFvjEEEj 7S 5ParallelSection 1ParallelForEl 4TryParallelForEPS1_lRKNS_12TensorOpCostERKNSt3__18functionIFvllEEE impleParallelForElRKNSt3__18functionIFvlEEE houldParallelizeEPKS1_ C D 3ps_MUlPNS0_25ThreadPoolParallelSectionEE_8__invokeES4_ 2EPS1_ 1EPS1_ ગ2Ev 1Ev WN11onnxruntime11concurrency10ThreadPool15ParallelSection24current_parallel_sectionE SӍIV૗21ShouldParallelizeLoopEll 1 5CurrentThreadIdEv 0NumThreadsEv RKNS_12TensorOpCostERKNSt3__18functionIFvllEEE dRKNSt3__18functionIFvllEEE ƗЗconcurrency15ThreadPoolTemplINS_3EnvEE CPUMathUtil8InstanceEvE1p23AllocateParallelSectionEvENUlPNS0_25ThreadPoolParallelSectionEE_8__invokeES5_ 12GetPerThreadEvE11per_thread_C2Ev 7DefaultEv ˘gsl24fail_fast_assert_handlerEPKcS1_S1_i re23MLAS_NCHWC_GROUPED_CONV_ALGORITHM13FilterSetSizeEȞOrt6GlobalIvE4api_Eה̘C2Ev 7DefaultEv ՘՘ؘ֘15timed_wait_implERNSt3__111unique_lockINS_8OrtMutexEEENS1_6chrono10time_pointINS6_12system_clockENS6_8durationIxNS1_5ratioILl1ELl1000000000EEEEEEE 4waitERNSt3__111unique_lockINS_8OrtMutexEEE 2 1 1 2DisableTelemetryEventsEv 3LogSessionCreationStartEv 5LogExecutionProviderEventEP5_LUID EnableTelemetryEventsEv SetLanguageProjectionEj 4Log 7LogEvaluationStopEv 8Log 5LogRuntimeErrorEjRKNS_6common6StatusEPKcS6_j ProcessInfoEv RuntimePerfEjjx EvaluationStartEv SessionCreationEjxRKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES9_S9_RKNS1_13unordered_mapIS7_iNS1_4hashIS7_EENS1_8equal_toIS7_EENS5_INS1_4pairIS8_iEEEEEES9_RKNSA_IS7_S7_SC_SE_NS5_INSF_IS8_S7_EEEEEES9_RKNS1_6vectorIS7_NS5_IS7_EEEEb 2Ev 1Ev 1MlasGe 4MlasQLinearSafePaddingElementCountmm 2MlasQLinearGlobalAveragePoolN tPreferredBufferAlignmentv mmU8X8CopyPackBProcessSsePsDv2_xS0_S0_PS0_ ExecuteThreadedPFvPviES_iPN11onnxruntime11concurrency10ThreadPoolE SgemmTransposeAPfPKfmmm ComputeLogisticPKfPfm DgemmTransposeAPdPKdmmm SgemmMultiplyBetaPfmmmf GemmU8X8 ReorderOutputN NchwcGetBlockSizev FindMinMaxElementPKfPfS1_m DgemmMultiplyBetaPdmmmd Sgemm ComputeSoftmaxPKfPfmmbPN11onnxruntime11concurrency10ThreadPoolE QuantizeLinearI Dgemm CopyPackBPfPKfmmm Operation15CBLAS_TRANSPOSES_mmmfPKfmS1_mfPfm ЕSgemmTransposePackBPfPKfmmm ReorderFilterOIHWBoPKxPKfPf DgemmTransposePackBPdPKdmmm ചSgemmPackedOperation15CBLAS_TRANSPOSEmmmmfPKfmPKvmfPfm GemmU8X8CopyPack ԚSgemm GemmPackBSizemm Conv Nchwc ThreadedPvi ScheduleP21MLAS_SGEMM_WORK_BLOCKPN11onnxruntime11concurrency10ThreadPoolE Gemm ConvPK20MLAS_CONV_PARAMETERSPKfS3_S3_PfS4_PN11onnxruntime11concurrency10ThreadPoolE Pool17MLAS_POOLING_KINDmPKxS1_S1_S1_S1_PKfPfPN11onnxruntime11concurrency10ThreadPoolE 15CBLAS_TRANSPOSE mmmPKhmh S_mmm mmmfPKfmPKvfPfmPN11onnxruntime11concurrency10ThreadPoolE fPKfmS1_mfPfmPN11onnxruntime11concurrency10ThreadPoolE dPKdmS1_mdPdmPN11onnxruntime11concurrency10ThreadPoolE b GemmPackB TransposePKhPhmm Nchwc 15CBLAS_TRANSPOSEmmPKfmPv mmPKhmbPv GemmU8X8ScaleSumBufferPiPKimi Co DgemmOperationThreadedPvi ASsePsPKhmmmPi BSsePsPKhmmmPib KernelSsePKsS0_PimmPKiS3_ib OperationI2 5MLAS_GEMM_U8X8_KERNEL_SSEEvPK25MLAS_GEMM_U8X8_WORK_BLOCK 6MLAS_GEMM_U8 ࣛS8_KERNEL_AVX2EvPK25MLAS_GEMM_U8X8_WORK_BLOCK U8_KERNEL_AVX2EvPK25MLAS_GEMM_U8X8_WORK_BLOCK ћS8_KERNEL_AVX2EvPK25MLAS_GEMM_U8X8_WORK_BLOCK U8_KERNEL_AVX2EvPK25MLAS_GEMM_U8X8_WORK_BLOCK GemmU8X8 PoolGlobalKernelI20MLAS_ RequantizeOutputPKiPhS0_mmPKfbh ThreadedPvi ScheduleP25MLAS_GEMM_U8X8_WORK_BLOCKPN11onnxruntime11concurrency10ThreadPoolE S0_mhbPimPN11onnxruntime11concurrency10ThreadPoolEPK27MLAS_QGEMM_OUTPUT_PROCESSOR PKvhbPimPN11onnxruntime11concurrency10ThreadPoolEPK27MLAS_QGEMM_OUTPUT_PROCESSOR Co ActivationPK15MLAS_ACTIVATIONPfPKfmmm QLinear€ nvIm2ColPK20MLAS_CONV_PARAMETERSPKfPfmmmm mputeE nv mputeTanhPKfPfm Vol2ColPK20MLAS_CONV_PARAMETERSPKfPfmmmm PrepareP20MLAS_CONV_PARAMETERSmmmmPKxS2_S2_S2_S2_S2_mPK15MLAS_ACTIVATIONPmPN11onnxruntime11concurrency10ThreadPoolE ๜OperationPK20MLAS_CONV_PARAMETERSPKfS3_S3_PfS4_mm DepthwiseI ͜ConvOperationThreadedPvi ReorderFilterOIHWBiBoPKxPKfPf NchwcPrepareWorkBlockP21MLAS_NCHWC_WORK_BLOCKPKxS2_S2_S2_S2_S2_ ҜnvGemmDirectThreadedPvi mputeSoftmaxThreadedPvi ӜלaEvPKhhPKT_S2_Pimmm hEvPKhhPKT_S2_Pimmm Pool ReorderInputPKxPKfPf 1DKernelI20MLAS_ 2DKernelI20MLAS_ 3DKernelI20MLAS_ 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84Prog96312lsERNSt3__113basic_ostreamIcNS0_11char_traitsIcEEEERKNS_11StringPieceE7BitStateCompilerՃfullruneEPKciC94PushEiPKci6SearchERKNS_11StringPieceES3_bbPS1_i2EPNS_4ProgE1EPNS_4ProgEGrowStackEvTrySearchEiPKc࢒ 169ŝ30TEST_dfa_should_bail_when_slowEb4CD87FlattenEv408PossibleMatchRangeEPNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES8_i6ComputeFirstByteEv31DumpByteMapEvSearchBitStateERKNS_11StringPieceES3_NS0_6AnchorENS0_9MatchKindEPS1_iBuildEntireDFAENS0_9MatchKindERKNSt3__18functionIFvPKibEEEDumpUnanchoredEvComputeByteMapEvMarkSuccessorsEPNS_11SparseArrayIiEES3_PNSt3__16vectorINS5_IiNS4_9allocatorIiEEEENS6_IS8_EEEEPNS_10SparseSetTIvEEPS8_ШPatchList͂ApplyFoldEPKNS_8CaseFoldEiCharClass2MkEj56AppendEPNS_4Prog4InstES0_S0_΃଒DerefEPNS_4Prog4InstES0_PatchEPNS_4Prog4InstES0_jǃ୒ 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Domain_ver7_12_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Div_kOnnxDomain_ver7_12_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Equal_kOnnxDomain_ver11_12_boolEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Equal_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Equal_kOnnxDomain_ver13_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Equal_kOnnxDomain_ver7_10_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Expand_kOnnxDomain_ver13_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Expand_kOnnxDomain_ver13_int8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Expand_kOnnxDomain_ver13_stringEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Expand_kOnnxDomain_ver8_12_boolEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Floor_kOnnxDomain_ver6_12_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Greater_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_IsNaN_kOnnxDomain_ver9_12_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Less_kOnnxDomain_ver9_12_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_MatMul_kOnnxDomain_ver13_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_MatMul_kOnnxDomain_ver1_8_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Mul_kOnnxDomain_ver7_12_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Mul_kOnnxDomain_ver7_12_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Neg_kOnnxDomain_ver6_12_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Neg_kOnnxDomain_ver6_12_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_NonZero_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_QLinearMatMul_kOnnxDomain_ver10EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_SequenceEmpty_kOnnxDomain_ver11EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_SequenceErase_kOnnxDomain_ver11EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Softmax_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Sqrt_kOnnxDomain_ver6_12_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Sub_kOnnxDomain_ver7_12_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Sub_kOnnxDomain_ver7_12_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Abs_kOnnxDomain_ver6_12_uint16_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Abs_kOnnxDomain_ver6_12_uint32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Abs_kOnnxDomain_ver6_12_uint64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_ArgMax_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_ArgMax_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_ArgMin_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_ArgMin_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_AveragePool_kOnnxDomain_ver10_10EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_ConstantOfShape_kOnnxDomain_ver9EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_CumSum_kOnnxDomain_ver11_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_CumSum_kOnnxDomain_ver11_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_DepthToSpace_kOnnxDomain_ver1_10EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Equal_kOnnxDomain_ver11_12_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Equal_kOnnxDomain_ver7_10_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Expand_kOnnxDomain_ver13_int16_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Expand_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Expand_kOnnxDomain_ver13_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Expand_kOnnxDomain_ver13_uint8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Expand_kOnnxDomain_ver8_12_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_GatherElements_kOnnxDomain_ver13EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Greater_kOnnxDomain_ver13_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Greater_kOnnxDomain_ver7_8_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Less_kOnnxDomain_ver9_12_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Less_kOnnxDomain_ver9_12_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_LpNormalization_kOnnxDomain_ver1EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_MatMul_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_MatMul_kOnnxDomain_ver13_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_MatMul_kOnnxDomain_ver1_8_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_MatMul_kOnnxDomain_ver9_12_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_NonZero_kOnnxDomain_ver9_12_boolEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_ReduceL1_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_ReduceL2_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Resize_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Resize_kOnnxDomain_ver13_uint8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_RoiAlign_kOnnxDomain_ver10_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_SequenceInsert_kOnnxDomain_ver11EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_SequenceLength_kOnnxDomain_ver11EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Softmax_kOnnxDomain_ver13_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_SpaceToDepth_kOnnxDomain_ver1_12EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_TfIdfVectorizer_kOnnxDomain_ver9EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_ArgMax_kOnnxDomain_ver11_12_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_ArgMin_kOnnxDomain_ver11_12_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_ConvTranspose_kOnnxDomain_ver1_10EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_DepthToSpace_kOnnxDomain_ver11_12EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Equal_kOnnxDomain_ver11_12_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Equal_kOnnxDomain_ver7_10_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Equal_kOnnxDomain_ver7_10_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Expand_kOnnxDomain_ver13_uint16_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Expand_kOnnxDomain_ver13_uint32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Expand_kOnnxDomain_ver13_uint64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Expand_kOnnxDomain_ver8_12_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Expand_kOnnxDomain_ver8_12_int8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Expand_kOnnxDomain_ver8_12_stringEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Greater_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Greater_kOnnxDomain_ver13_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Greater_kOnnxDomain_ver7_8_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Greater_kOnnxDomain_ver9_12_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_IsNaN_kOnnxDomain_ver13_MLFloat16EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_MatMul_kOnnxDomain_ver9_12_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_NonZero_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_NonZero_kOnnxDomain_ver13_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_NonZero_kOnnxDomain_ver13_uint8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_NonZero_kOnnxDomain_ver9_12_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_RandomNormalLike_kOnnxDomain_ver1EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Resize_kOnnxDomain_ver10_10_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Resize_kOnnxDomain_ver11_12_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_ReverseSequence_kOnnxDomain_ver10EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_RoiAlign_kOnnxDomain_ver10_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Round_kOnnxDomain_ver11_MLFloat16EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_ScatterElements_kOnnxDomain_ver13EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Softmax_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_SplitToSequence_kOnnxDomain_ver11EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_ThresholdedRelu_kOnnxDomain_ver10EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Upsample_kOnnxDomain_ver7_8_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Upsample_kOnnxDomain_ver9_9_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ArgMax_kOnnxDomain_ver11_12_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ArgMax_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ArgMin_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_BitShift_kOnnxDomain_ver11_uint8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_Equal_kOnnxDomain_ver11_12_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_Equal_kOnnxDomain_ver11_12_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_Expand_kOnnxDomain_ver13_MLFloat16EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_Expand_kOnnxDomain_ver8_12_int16_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_Expand_kOnnxDomain_ver8_12_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_Expand_kOnnxDomain_ver8_12_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_Expand_kOnnxDomain_ver8_12_uint8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_GlobalAveragePool_kOnnxDomain_ver1EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_Greater_kOnnxDomain_ver9_12_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56k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v__ZN4onnx11GetOpSchemaINS_13Neg_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Neg_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Not_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Pad_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Pad_Onnx_ver2EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Pow_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Pow_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13RNN_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13RNN_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Sin_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Sub_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Sub_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Sub_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Sum_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Sum_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Sum_Onnx_ver8EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Tan_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Xor_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Xor_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Abs_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Acos_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Add_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Asin_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Atan_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Ceil_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Ceil_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Clip_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Clip_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Conv_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Cosh_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Det_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Div_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Erf_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Exp_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14LRN_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14LSTM_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14LSTM_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Less_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Less_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Less_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Log_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Loop_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Max_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Max_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Mean_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Mean_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Mean_Onnx_ver8EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Min_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Min_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Mod_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Mod_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Mul_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Neg_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Pow_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Pow_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Relu_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Relu_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Scan_Onnx_ver8EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Scan_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Selu_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Selu_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Sign_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Sinh_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Sqrt_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Sqrt_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Sub_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Sum_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Tanh_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Tanh_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14TopK_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Acosh_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Asinh_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Atanh_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Cast_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Ceil_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Celu_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Clip_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Clip_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Clip_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Conv_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Equal_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Equal_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Floor_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Floor_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Gemm_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Gemm_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Less_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Loop_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Loop_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Mean_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15PRelu_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15PRelu_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15PRelu_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15PRelu_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Relu_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Scan_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Sign_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Size_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Slice_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Split_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Split_Onnx_ver2EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Sqrt_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Tanh_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Tile_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15TopK_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15TopK_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16ArgMax_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16ArgMin_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Concat_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Concat_Onnx_ver4EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Equal_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Equal_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Expand_Onnx_ver8EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Floor_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Gather_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16IsNaN_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16LpPool_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16LpPool_Onnx_ver2EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16OneHot_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Range_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Round_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Shrink_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17ArgMax_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17ArgMax_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17ArgMax_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17ArgMin_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17ArgMin_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17ArgMin_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17CumSum_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Dropout_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Dropout_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Dropout_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Einsum_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Expand_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17EyeLike_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Greater_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Greater_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Greater_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Hardmax_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17LpPool_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17MatMul_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17MaxPool_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17MaxPool_Onnx_ver8EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Reshape_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Scatter_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Sigmoid_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Sigmoid_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Softmax_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Squeeze_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Compress_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Greater_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Hardmax_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Hardmax_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Identity_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18MaxPool_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18MaxPool_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18MaxPool_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18NonZero_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18ReduceL1_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18ReduceL2_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Reshape_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Scaler_OnnxML_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Sigmoid_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Softmax_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Softmax_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Softplus_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Softsign_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18ZipMap_OnnxML_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19BitShift_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19CastMap_OnnxML_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19Identity_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19Imputer_OnnxML_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19LeakyRelu_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19LeakyRelu_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19MaxUnpool_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19ReduceL1_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19ReduceL1_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19ReduceL2_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19ReduceL2_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19ReduceMax_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19ReduceMin_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19ReduceSum_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19Unsqueeze_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20LogSoftmax_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20MaxRoiPool_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20MaxUnpool_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaIN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