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H|H1IHH/H1H1III/I1ML\IJLH+HLM< J MMIMIH\HLLLL$MHLILIIII+MIHH@H)H]HHUHH@Isofff.HH]H@HUH@MLHLvHVLLLFLHHILNMLHIMLUH^LHMIMIIMHIIM1ILILH+IIHH^L^LHMMLHIMH+IHUH1I1Hi-8ߝLLH/I1I1LLH/L1HHH/H1HsoHLHM1LLH/M1I1LLH/L1HHH1HHH/H1H1HHH/H1H[A\A]A^A_]HrDHJHO@/;jHtHH<>tHH ύ HHWePIHH1HH/H1HfUHAWAVSPII(Ht)L;Ct H{ HLMuIIdžHt윅IHt7fDL;C(t H{8ȜCt H{ 蹜H豜LMuIIdžHt荜MMtJIL9t/ff.HX@t HxWHI9uILM7AFHuAF0u"AFu+H[A^A_]I~XAF0tI~@AFtI~ H[A^A_]䛅f.UHAWAVATSIIIMfI)IID$HH=HI^H)HHHH9wYHH9HBHH9vCwHH5"HјHOHHH5V"HHwHH<%JLI7IJ4JDM>IvL9H^L)Ht-Hffff.HNHFHvHJHHuH8fHNHFHJHNHFHJHNHFHJHNHFHJHNHFHJHNHFHJHNHFHJHNHFHvHJHI9oM>I^LIIFMFL9t#H{HCH[HtHPI9uMtL[A\A^A_]鵙[A\A^A_]11LIHL fff.UHAWAVAUATSHHIHHWHHEH8HHHHLH@H8L踘HEEL5LM~LI@L8LЗL%LILHDž(HDž HDžHDžDž0L@HLHHH#HHHHHHeLL8Lt H %H H5H2HH蠖H8託HVHH;EuHH[A\A]A^A_],HLL8Lt H 蟗H腖HHHH51HHHH8H躑 fUHAWAVAUATSPHIiMt$HsLXM|$0Hs0LLuCMl$HHsHL2Mt$`Hs`L!HCxID$xLHHHH[A\A]A^A_]HAu+AEuAAuVHEunA$H I|$p耖AEt HAEtI|$XcAt HAtI|$@HHEt HHEtI|$('A$t HA$tI|$ Hn fDUHAWAVAUATSHIH}LdH5H HXLEW)EE HEHoverflowHEHInteger HEEHXHM1LkH5HL蝕 IEt H};L}Mt6HEL9t!HX@t HxHI9uH}LL}xt H}ꔅXt HhՔE1LmMuUHEIALmMt>HEL9tHI9uH}LLe$EuEuLhMu&^H}EtH}LhMt:HpL9tEHX@t HxNJHI9uHhLp謊EtLϊL LfDUHAWAVAUATSH(IIHHHIHHEH8HHHHLH@H8LʼnLHEEL==MgLI@L8LֈL-=ILHDž(HDž HDžHDžDž0H3HPL7HLHHH0HPHHHcLL8Lt H #H H5F#HH螇H8覈HGHH;EuHH([A\A]A^A_]*HLL8Lt H 蝈H胇H HHHH5"HHHH8H谂 UHAWAVAUATSHHFHHEHHGH)HH9v7 u.HH9sOHFHH;Eu:HĈ[A\A]A^A_]THmFHH;Eu1HĈ[A\A]A^A_]資IHhr>dH5 ՅH ݅H}LhW)PHDž`0VH`(')PH elementHH&Hor its eHH H entry fHHHing typeHHH is missHHHSequenceH@.AH_؅HuHPLE1H5 H&L IALeMuFzIALhMIIPt H`ZLeMt6HEL9t!HX@t Hx5HI9uH}LLeEuEuLhMu&^H}EtH}酅LhMt:HpL9tEHX@t Hx辅HI9uHhLp装EtLƅL Lffff.UHAWAVAUATSHHDHHEHHOH)HH9vFu=Tu3HVH9sNLu DH CH H;Mt_1H CH H;MuHĈ[A\A]A^A_]ÿ鄅IHh;dH5C҅H ݅H}LhW)PHDž`0茄H`(%)PHts valueHHHy for itHHHype entrHHHissing tHHHMap is mH@'AHՅHuHPL E1H5IHjL: IALeMuFzIALhMIIPt H`螃LeMt6HEL9t!HX@t HxyHI9uH}LLe_EuEuLhMu&^H}<EtH}-LhMt:HpL9tEHX@t HxHI9uHhLp炅EtL L>} LؐUHAWAVAUATSHHHRAHHEcPH;Cu*HH 6AH H;MuHĘ[A\A]A^A_]Ђ~IHh=9dH5~ͅH ܅H}Lh)jbPHHHHׅHPHHH}AHIׅHuHPLE1H5.HOL IALhMIIPtH`蔁LeMuEIALeMt6HEL9t!HX@t Hx^HI9uH}LLeDEuEuLhMu&^H}!EtH}LhMt:HpL9tEHX@t Hx瀅HI9uHhLp̀EtLL#{ LfDUHAWAVAUATSHIH2?HHEH8H5HHHLH@H8LHEEH 4LkLH@H8L L53ILHDž(HDž HDžHDžDž0LLLH8Lt H L~H5HH ~H8(H>HH;EuLH[A\A]A^A_]ILH8Lt H L ~IH5AHH}IH8~LNy fDUHAWAVAUATSHHe=HHEHHOH)HH9vFu=Tu3HVH9sNLu DH =H H;Mt~1H <H H;MuHĈ[A\A]A^A_]ÿI~IHh5dH5˅H مH}Lh5W)PHDž`0}H`(h)PHts valueHHHy for itHHHype entrHHHissing tHHHMap is mH@'AHυHuHPLkE1H5HL} IALeMuFzIALhMIIPt H`|LeMt6HEL9t!HX@t Hx|HI9uH}LLe|EuEuLhMu&^H}|EtH}|LhMt:HpL9tEHX@t Hxb|HI9uHhLpG|EtLj|Lv LؐUHAWAVAUATSHHH:HHE&EPH;Cu*HH :H H;MuHĘ[A\A]A^A_]0|{IHh2dH5ƅH مH}Lh)DPHHHHЅHPHHHAHЅHuHPLPE1H5HL{ IALhMIIPtH`zLeMuEIALeMt6HEL9t!HX@t HxzHI9uH}LLezEuEuLhMu&^H}zEtH}rzLhMt:HpL9tEHX@t HxGzHI9uHhLp,zEtLOzLt LfDUHAWAVAUATSPIM.IFL)HHHH9H H9I^IM)HLPH;Cu*HH 2H H;MuHĘ[A\A]A^A_]0tsIHh*dH5޾H ՅH}Lh)ʿ=PHHHHȅHPHHHAHȅHuHPLPE1H5HLs IALhMIIPtH`rLeMuEIALeMt6HEL9t!HX@t HxrHI9uH}LLerEuEuLhMu&^H}rEtH}rrLhMt:HpL9tEHX@t HxGrHI9uHhLp,rEtLOrLl 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H}.wH]HEHtHk H wHHH;EuLHh[A\A]A^A_]FwCIH}HuH]HEHu:L+w H}wH]HEHtIH]HEHtHH}wLw fUHAWAVAUATSHhIHHHEпJwHþH@wHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]BHx腇BI}BIu~BHmBIW)EHE(wHEHH(HMHxHLxL`HXLp HMH5awH}HU0QEH52xHKEH5J|H"MEHA H5xHINEHH;.CEHHH {HHMHHUHuHuIEIEIE0IMIUIE@H}Ht H}wH]HEHtH HwHLHH;EuLHh[A\A]A^A_]wCIH}HuH]HEHu:Lw H}TwH]HEHtIH]HEHtH{HwLw fUHSHHHHHEHU1HGHt4H@0@EHuHUH HbHH;Eu3HH[]H ]yHUH/HH;EtwUHAVSIPrwHHtHHH{L7FHHH[A^]IH,wLw UHAVSIPwHH"tHHH{L7FHXHH[A^]IHwL0w UHAWAVSHHHIHFHHEEHEHEH}Hu]H}HuH H}HuHH}HuHmH}HuHH}uuHUH} H]Htc wIHILHHH)wH]M>HEHt8Ct H{wHwHeHH;Et!-IHEHDHH;EuLHH[A^A_]wILwwH]HEHu 5IH]HEHtCt H{7wH/wLw ff.UHAVSHIHHHEA>urHH}HEHEI^IFHtACt H{wHwH]HEHt!Ct H{wHwHEIFH%HH;Eu 1H[A^]wfff.UHAVSHIH뉐HHEA>urHH}b HEHEI^IFHtACt H{wHwH]HEHt!Ct H{wHwHEIFHeHH;Eu 1H[A^]wfff.UHAVSHIH+HHEA>urHH} HEHEI^IFHtACt H{DwH urHH}BHEHEI^IFHtACt H{wH|wH]HEHt!Ct H{\wHTwHEIFH凐HH;Eu 1H[A^]wfff.UHAVSHIHHHEA>urHH}RHEHEI^IFHtACt H{wHwH]HEHt!Ct H{wHwHEIFH%HH;Eu 1H[A^]wfff.UHAWAVAUATSH8IIH←HHE1L1GHþL!GIžLGIHCHK H)HtHH8IEIM H)HtHH8Mt%IGIO H)HtH%H8LuH~LtD0M]L`D8EO0`wHEf(#f)EHbe zero!HH Hcan not HHHperator HHH Range oHHHdelta inH@(HMLuLUHH5xL}LHLLU`IH5xH]HLLHU/IH5;xH]HLeLHpUEt H};wHڄHH;ELH8[A\A]A^A_]AA)A*A*^f: L,E1MMIwHEHHHML(HMHU1LFHMLu0Iw1HL!fnfpfAnfpf8@٢fBfnfpHyHHVHs 1HutHzH)1fDfD fD0fD@fDPfD`fDpH fHuHt,HtHf.FffH HuI9t"DHDHDHL9rIH}H*H}wHEu3:HEu(/HEu$HH}HtH} HEt H}wHvw fffff.UHAWAVAUATSH(HIHHxHHEH8H{GHHHLH@H8L8wHHEEL5IwM~LI@L8LIwL% wILHDž(HDž HDžHDžDž0LwHLH!HHIHHH LL8Lt H wHwH5\HH#wH8+wHHH;EuHH([A\A]A^A_]wHLL8Lt H "wHwHHHH50\HHwHH8wH=w DUHAWAVAUATSH8IIHRHHE1L衝GHþL葝GIžL聝GIHCHK H)HtH&H8IEIM H)HtH2H8(Mt%IGIO H)HtH9H8/HELџMmLE跟W.ESM0wHEf(&f)EHbe zero!HH Hcan not HHHperator HHH Range oHHHdelta inH@(HMLbUHH5xL}LH#LL.U`IH5xH]HLLHU/IH5)xH]HLLH̿UEt H}藿wH6~HH;E7LH8[A\A]A^A_] \EZMZ^f: L,1MII߿AwHEHHHMHHMHU1LFH蒷MEMHSكHraHH)f.X@X@X@ X@X@X@X@H XHuHtHHXHuIH}HH}觾wHEu3:HEu(/HH}Ht#H}HEu HEt H}wHfw fffff.UHAWAVAUATSH8IIHr|HHE1LGHþL豙GIžL衙GIHCHK H)HtHH8 IEIM H)HtHH8Mt%IGIO H)HtH&H8LuHNHLCL0M]L/L8MO0wHEf(]f)EHbe zero!HH Hcan not HHHperator HHH Range oHHHdelta inH@(HMLuL蕼UHH54xL}LHVLLaU`IH5HxH]HL%LH0U/IH5\xH]HLLHUEt H}ʻwHizHH;ELH8[A\A]A^A_]AI)I*I*^f: L,E1MMIzwHEHHHML(HMHU1L3 FH+MLuPIw11HL!fHnfpDfInfpDfHnfsfoffs ffs ffK?fHnfpDHyHHVH s 1HuuHzH)1ffDfD fD0fD@fDPfD`fDpHfHuHt,HtHf.FffH HuI9t#LHHHHHLHL9rIH}H H}&wHEu3:HEu(/HEu$HH}HtH} HEt H}聹wHw ffff.UHAWAVAUATSH8IIHwHHE1LAGHþL1GIžL!GIHCHK H)HtH(H8IEIM H)HtH4H8*Mt%IGIO H)HtH;H81H蒚EL聚MoLEgfWf.ESM0VwHEf(f)EHbe zero!HH Hcan not HHHperator HHH Range oHHHdelta inH@(HMLUHH5xL}LHLL̷U`IH5xH]HLLH蛷U/IH5ǯxH]HL_LHjUEt H}5wHuHH;E%LH8[A\A]A^A_] "\EM^f: L,1MII߿wHEHHHMHHMHU1LFH蘳MEMHSكHrWHH)X@X@X@X@ X@(X@0X@8H@XHuHtHHXHuIH}HH}WwHEu3:HEu(/HH}Ht#H}HEu HEt H}貵wHw fffff.UHAWAVAUATSH8IIH"tHHE1LqGHþLaGIžLQGIHCHK H)HtHH8 IEIM H)HtH!H8Mt%IGIO H)HtH(H8LuHLD0M^LD8EO0蝴wHEf( f)EHbe zero!HH Hcan not HHHperator HHH Range oHHHdelta inH@(HMLuLCUHH5xL}LHLLU`IH5xH]HLLH޳U/IH5 xH]HLLH譳UEt H}xwHrHH;ELH8[A\A]A^A_]AA)A*A*^f: L,E1MMI%wHEHHHML(HMHU1LFHMLu;Iw1HL!fnpfpfAnpfpfڎfBfnpfpHyHHVH0s 1HupHzH)1@pfDpfDp fDp0fDp@fDpPfDp`fDppH@fHuHt,HtpHf.FffH HuI9t#DHHDfHDHL9rIH}HH}wHEu3:HEu(/HEu$HH}HtH} HEt H}AwH襫w ffff.UHAVSHPIHoHHEwHþHwHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]H}117EHG^BH5wHH5EH5UxH+EH5*|HG,EHH HQH54wHg-EHH;L"EHHH ZHHMH HUHuHuIIFIF0INIVIF@H]HEHtHHwHcHH;EuLHx[A\A]A^A_]wCIH}HuH]HEHu:Lw H}臤wH]HEHtIH]HEHtHHPwL贞w fff.UHAWAVSH8HIHbHHE1HGIL}ؾHGHEоHGHEIW1HEHEIG0@EH}HuHUHMLEIHIbHH;EuLH8[A^A_]wffff.UHAVSIXrwHH{PLMHvIHHHHL FHEHHH[A^]IHwL}w DUHAWAVAUATSHHaHHEW)EEIIHEEminMgHhHUL$FHhHDžhHtCt H{苢wH胢wA$Et H}lwW)EHEEEmaxHhHULL#FHhHDžhHtCt H{wHwAEt H}wAA.Gr'H`HH;EuHĈ[A\A]A^A_]%wӡwIHhXVH5xH xH}LhHP AHxHuHPLfE1H5%H!L蕡w IALeMuFzIALhMIIPt H`wLeMt6HEL9t!HX@t HxԠwHI9uH}LLe躠wEuEuLhMu&^H}藠wEtH}舠wLhMt:HpL9tEHX@t Hx]wHI9uHhLpBwEt4LewL虚w LIEu IEt H} wLmw DUHAWAVAUATSPIIH}1L{GHLk1LLEIH~ILdIEl$PEd$TL"HI~FIHuڃH9HOII)LH?H>LHH5fAnfAnHrI H9s!H I9sE1HzLIIfofoIIHHDGAHIHH)1fDA$(_(]$Ad(_(]dHHuMtA$_]L9tjHzLtfoC_fo]BLHL9t:foA_fo]foA_Tfo]\HH9uH<I|;fAnfAnHA(_(])HH9|H9RfAnfAnI)ILI I9sLI L9MIfofoIHIIENAHHM\HtIIL)1ff.Ad(_(]dA$(_(]$HHuMtHA$_]M9twLlj)HPH@tfoA_fo]HOH9tCf.foA_fo]foA_Tfo]\HH9uHEHH[A\A]A^A_]1M1M=Jffff.UHAVSIP2wHHBBHHH{LFHHH[A^]IHwLPw UHAVSIPқwHHAHHH{L_FHxHH[A^]IH茛wLw UHAVSIPrwHHAHHH{LFHHH[A^]IH,wL萕w UHAWAVATSHPMIIHHYHHE؋EHEtm  uH3I$IMH}#HEH} H2YHH;EHP[A\A^A_]H3I$IMH}HEЋEȃ vH3I$IMH}lHEЋEȃYH3I$IMH}FHEЋEȃLHH(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(]LHH9uHTHH;Eu2HĨ[A\A]A^A_]1M1M9Fw輕wIHh{LVH5 xH \xH}Lh8W)PHDž` _wH`()PH scalar.HHHld be a HHHmin shouH@AHxHuHPLE1H58HYL)wwIHhKVH5HxH xH}Lh<W)PHDž` 臔wH`(#)PH scalar.HHHld be a HHHmax shouH@AHݎxHuHPL"E1H5`HLQw IALeMIALhMIIALeMFIALhMnIIPt H`swLeMt6HEL9t!HX@t HxNwHI9uH}LLe4wEuEu LhMu-'H}wEtH}wLhMHpL9HX@t Hx̒wHI9uIPt H`角wLeMt6HEL9t!HX@t Hx肒wHI9uH}LLehwEuEuLhMu&^H}EwEtH}6wLhMt:HpL9tEHX@t Hx wHI9uHhLpwEtLwLGw Lf.UHAWAVAUATSHMHIHLPHHEHtBHB H;BHzsH)0t0HC H;CHTsFH)0umf)@L"sIILӷILHIL輷IHuH9HOII)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$HI9uM9tu)HPH@tf(A_f(] HOH9tDff.f(A_f(] f(A_Df(]LHH9uHLHH;Eu"HĨ[A\A]A^A_]1M w踍wIHhwDVH5xH SxH}Lh8W)PHDž` [wH`()PH scalar.HHHld be a HHHmin shouH@AHxHuHPLE1H54HU L%wwIHhCVH5DxH {xH}Lh<W)PHDž` 背wH`()PH scalar.HHHld be a HHHmax shouH@AHنxHuHPLE1H5\H} LMw IALeMIALhMIIALeMFIALhMnIIPt H`owLeMt6HEL9t!HX@t HxJwHI9uH}LLe0wEuEu LhMu-'H} wEtH}wLhMHpL9HX@t HxȊwHI9uIPt H`裊wLeMt6HEL9t!HX@t Hx~wHI9uH}LLedwEuEuLhMu&^H}AwEtH}2wLhMt:HpL9tEHX@t HxwHI9uHhLpwEtLwLCw LfDUHAWAVAUATSHMHIHLHHHEHt@HB H;BH芘D(HLHt*HC H;CDHeD8AHLHuALFIILIHHHHIHHɯIHH rI I9LHI9E1HpL¨{L9ADD8|DA8~AA\DD8|%DA8#A\HH9uSDA8~݉A\HH9u.IIAfnff8Afnf8IHHHrHH~H)1ff.AoAo\f8UL(L3UIIL褢IḺIIL荢IHƒHu1HI]MuH)1fIYwH`()PHa scalarHH Hator on HHHSum operHHHpply CumHHHCannot aH@(HPLXUPH`IH}HkoHxPt=HxH$L0L1HHH#AE1W)HDžLpL*XwILLKHLL XwHHIW)HDžLWwILKHLLWwHHMw IHEH8HuHEH8HuLi;w H{X@wHEH8HtH{@@wLA;w fUHAWAVAUATSHHUHHEMIHFH0H+HHHI)IL9HOH8HWH)HH9M)IL9HL(AH Iffff.HJ\Mt HyMI HH Mt"Hffffff.HELMwLH t詩I DHLs蒩fH8L`HYHHHLHH tIHH>u^H"HHH!H Hy9HH9w2HHy1I)r17DHHH!HxHHy1IsE1H8L`H0HJ\IHH MtH跨fHELMLH t艨I DALMsqMGxDMLMfffff.DHH8L`HfLMjDALMH0HBH8HNH+HHLHN(H; u)HL(HDH8HA(HIH8HA0HHH;EuH[A\A]A^A_]=wf=wIHh%UH5yH sH8L>H8ALeMH8ALhMH8LH8ALeM'XH8ALhM|H8H8Pt H`9wLeMt6HEL9t!HX@t Hx9wHI9uH}LLe9wEuEuLhMu&^H}9wEtH}9wLhMt:HpL9t?HX@t Hxc9wHI9uHhLpH9wELLH8Pt H`9wLeMt6HEL9t!HX@t Hx8wHI9uH}LLe8wEuEuLhMu&^H}8wEtH}8wLhMt:HpL9t?HX@t Hxz8wHI9uHhLp_8wELLH8Pt H`-8wLeMt6HEL9t!HX@t Hx8wHI9uH}LLe7wEuEuLhMu&^H}7wEtH}7wLhMt:HpL9tIHX@t Hx7wHI9uHhLpv7wEtL7wH81w Lfff.UHAWAVAUATSH8MLIHIHHHHEH8HHHHHH@H8Hw6wLHEEL-IEHHI@L8H5wL=DILHDž(HDž HDžHDžDž0L8wHLHYHH0H4wL8wHLH)HH8wHHHHH0H4wHHH̓HHL8Lt H 5wHk4wH5ϏHH4wH85wHHH;EuHH8[A\A]A^A_]5wHHHL8Lt H 4wH3w+HhH`HXHPHHHH5ΏHHF3wHH8I4wH.w UHAWAVAUATSH8MLIHIHHHHEH8HHHHHH@H8H3wLHEEL-IEHHI@L8H2wL=ILHDž(HDž HDžHDžDž0L16wHLH虌HH0H%2wL6wHLHiHH5wHHHHHH0H1wHHH HHL8Lt H 2wH1wH5̏HH@1wH8H2wH5HH;EuHH8[A\A]A^A_]2wHHHL8Lt H 82wH1w+H訏H蠏H蘏H萏H舏HH5.̏HH0wHH81wH;,w UHAWAVAUATSH8MLIHIHH7HHEH8H:HHHHH@H8H0wLHEEL-IEHHI@L8H0wL=ILHDž(HDž HDžHDžDž0Lq3wHLHىHH0He/wLA3wHLH詉HH 3wHHH舉HH0H/wHHHLHHL8Lt H 0wH.wH5(ʏHH.wH8/wHuHH;EuHH8[A\A]A^A_] 0wHHHL8Lt H x/wH^.w+HHH،HЌHȌHH5nɏHH-wHH8.wH{)w UHAVSI`/wHHLH[A^]IH.wL9)w UHAVSH@HIHXHHEHԏHII~JEH+яHIIFXIFPHEW)EHEEHexclusivHEfEeH}HUHMHٱEEt H}$.wH}uHEHwINPHW)EHEEHEHreverseHEH}HUHMHuEHEHEH]HEHCt H{-wH-wH]HEHtCt H{{-wHs-wEt H}d-wH]HtHHECt H{D-wH<-wHHH;Eu?H@[A^]HEEuHEHwIFXHEHHH;EtH-wIEt H},wH]HEHt&Ct H{,wH IEt H},wL'w ff.UHAWAVAUATSHIH`IHHHE1LWGHHC H+CHp L2GIW)EHEH{ HH+{t[HHHH9 +wILeLeIHEHHpHX H)H~LHA-wILeHU1L}EHH}FRIHHpHHEHHMLHH譹Hw HEHLcHH@JHW)PHDž`HHx H+xHHHH9 *wILXLPIH`HHpHX H)H~LH0,wLHHXKHP9QIHxW)0HDž@LPHXL)t\IIHI9( H>*wIL8L0KDH@H~LLH+wIL8W)HDž HH9LpL)wHHHH H5HL0+wHH`HxXLXHHYHxPHtHHxH$L0LHHHH HHHHW)HDžLpL(wHHLN4LHL(wLLHJW)HDžL(wILKHLL{(wHHKHxH$HHL0LHH L(wHHt H'wH}Lpfffff.H`HXPW)HDžL'wIHLxHHIDLHM,LLL'wLLLKW)HDžLL'wILHIHLL+'wHLhO<W)HDžL&wHHHLsH8L>H8ALeMH8ALhMH8LH8ALeM'XH8ALhM|H8H8Pt H`wLeMt6HEL9t!HX@t HxwHI9uH}LLewEuEuLhMu&^H}wEtH}~wLhMt:HpL9t?HX@t HxSwHI9uHhLp8wELLH8Pt H`wLeMt6HEL9t!HX@t HxwHI9uH}LLewEuEuLhMu&^H}wEtH}wLhMt:HpL9t?HX@t HxjwHI9uHhLpOwELLH8Pt H`wLeMt6HEL9t!HX@t HxwHI9uH}LLewEuEuLhMu&^H}wEtH}wLhMt:HpL9tIHX@t HxwHI9uHhLpfwEtLwH8v Lfff.UHAVSI`2wHHLH[A^]IHwLiv UHAVSH@HIHHHEHHII~zmEHkHIIFXIFPHEW)EHEEHexclusivHEfEeH}HUHMH EEt H}TwH}uHEHwINPHW)EHEEHEHreverseHEH}HUHMH襆EHEHEH]HEHCt H{wHwH]HEHtCt H{wHwEt H}wH]HtHHECt H{twHlwH HH;Eu?H@[A^]HEEuHEHwIFXHEHHH;EtxwIEt H}wH]HEHt&Ct H{wH IEt H}wL3v ff.UHAWAVAUATSHIH`IH8HHE1LFHHC H+CHp LbFIW)EHEH{ HH+{t[HHHH9 &wILeLeIHEHHpHX H)H~LHqwILeHU1LREHH}v'IHHpHHEHHMLHHݎHw HEHLcHH@JHW)PHDž`HHx H+xHHHH9 wILXLPIH`HHpHX H)H~LH`wLHHXKHPi&IHxW)0HDž@LPHXL)t\IIHI9( HnvIL8L0KDH@H~LLHwIL8W)HDž HH9LpLvHHHH H53HL`wHH`HxXLXHHYHxPHtHHxH$L0LHHHH HHHHW)HDžLpL"vHHLN4LHLvLLHJW)HDžLvILKHLLvHHKHxH$HHL0LHH LEvHHt H-vH}Lpfffff.H`HXPW)HDžLvIHLxHHIDLHM,LLLvLLLKW)HDžL|vILHIHLL[vHLhO<W)HDžL&vHHHLu^H"HHH!H Hy9HH9w2HHy1I)r17DHHH!HxHHy=K1Is1KE1H8L`H0HJ\IHH MtHJfHELMLH tJI DALMsJMGxDMLMfffff.DHH8L`H^JfLMjDALMH0HBH8HNH+HHLHN(H; u)HL(HDH8HA(HIH8HA0H^HH;EuH[A\A]A^A_]vvIHheUH5RyH wH}Lh+H0HAH+HHHL@H5XwH 4wLWwHPHHL@çAH wHuHPL-E1H54cHU_L%vvIHh蟕UH5yH wH}Lh*H0HAH+HHHH8HAH+AHH@H5wH 3wLwHPHHL@誩AHiwHuHPL-E1H5[bH|^LLvvIHhƔUH5yH wH}Lh)H0HAH+HHHL@H5wH 3wLwHPHHL@褫AHwHuHPLW,E1H5aH]Lv H8ALeMH8ALhM>sH8L>H8ALeMH8ALhMH8LH8ALeM'XH8ALhM|H8H8Pt H`?vLeMt6HEL9t!HX@t HxvHI9uH}LLevEuEuLhMu&^H}vEtH}vLhMt:HpL9t?HX@t HxvHI9uHhLpvELLH8Pt H`VvLeMt6HEL9t!HX@t Hx1vHI9uH}LLevEuEuLhMu&^H}vEtH}vLhMt:HpL9t?HX@t HxvHI9uHhLpvELLH8Pt H`mvLeMt6HEL9t!HX@t HxHvHI9uH}LLe.vEuEuLhMu&^H} vEtH}vLhMt:HpL9tIHX@t HxvHI9uHhLpvEtLvH8 v Lfff.UHAVSI`vHHLH[A^]IHUvLv UHAVSH@HIHؗHHEHJHII~BEH{HIIFXIFPHEW)EHEEHexclusivHEfEeH}HUHMHY\EEt H}vH}uHEHwINPHW)EHEEHEHreverseHEH}HUHMH[EHEHEH]HEHCt H{#vHvH]HEHtCt H{vHvEt H}vH]HtHHECt H{vHvH[HH;Eu?H@[A^]HEEuHEHwIFXHEHHH;EtvIEt H}VvH]HEHt&Ct H{6vH IEt H}vLv ff.UHAWAVAUATSHIH`IHHHE1LײFHHC H+CHp L貲FIW)EHEH{ HH+{t[HHHH9 vvILeLeIHEHHpHX H)H~LHvILeHU1L(EHH}HHHpHHEHHMLHH-dHw HEHLcHH@JHW)PHDž`HHx H+xHHHH9 nvILXLPIH`HHpHX H)H~LHvLHHXKHPHHxW)0HDž@LPHXL)t\IIHI9( HvIL8L0KDH@H~LLHvIL8W)HDž HH9LpLIvHHHH H5ĉHLvHH`HxXLXHHYHxPHtHHxH$L0LHHHH HHHHW)HDžLpLrvHHLN4LHLQvLLHJW)HDžLvILKHLLvHHKHxH$HHL0LHH LvHHt H}vH}Lpfffff.H`HXPW)HDžL>vIHLxHHIDLHM,LLLvLLLKW)HDžLvILHIHLLvHLhO<W)HDžLvvHHHLvIH0UH5wH wHPL0'HHtHPH1LH5 PH.LLv(HPvHv H0v It H lvE1LmMuMH(IALmMt6HEL9t!HX@t Hx+vHI9uH}LLmvpuPu L0Mu@xH}vPtH`vL0MuLIAL0Mt:H8L9t5HX@t HxvHI9uH0L8yvEu /LILvHHDžHu#@IHHDžH"Ct H{vHvH}H IHH HIHHIHHIHHxILLvHH9@IHH!(IHH!kIH0HkIP*H`IHHIHPHIH}HIHHvIHhHvHRIHFIHHtOIHHbvILLNvIHH6vHHuOH0Hu[HPHt 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A:u5HH9u9Mt4LLMLMTPLMMHutDHHTIHHiHH;ELH([A\A]A^A_]HRUHAWAVAUATSHIH7LwI)IIFHH;HHWH)HHHH9H}wYHH9HBHDH9vCPHH5>QHkPH.iHHH5ziHyviHPHHHEHHPHIN,3LLH]PID$JD3Mu HEL8L`M9t;M)I@I}LPID$IEIII uHEL LxMHMHHL(LpHMHHHM9t'ff.I_AGt IPII9uMtLH[A\A]A^A_]PH[A\A]A^A_]1HE1 PIHPL P IMM9u2IM9t*fffff.I^AFt I~fPII9uH}HtPPLP fff.UHAWAVSHHɰiHHEHDžpHDžhƅ` fDženeDžaaffiƅgHH` PfDžW)@HDžPƅ@DžAreluƅEHH@PfDžW) HDž0ƅ IleakyrelL!fDž)uHH oPW)ƅHDžHldedreluHfDžHthresholHƅHHPfDžW)HDžƅDžtanhƅHHPfDžW)HDžƅHscaledtaHfDžnhƅH HlPfDž8W)HDžƅHsigmoidHH@H#PfDžXW)HDžƅHhardsigmHDžoidH`HPW)`ƅ`fDžxHDžpDžaeluH}H`PW)@fEƅ@HDžPHsoftsignHAƅIH}H@GPW) fEƅ HDž0HsoftplusH!ƅ)H}H PfEH=lH -EEE`@  % 1@=`IUamy @`t HpPH=ypiH5lHPW)`ƅ`HDžpLafDžiuHH`iPHDž #<W)@HDžPƅ@HADžIoidHH@PHL>?HW) ƅ HDž0Dž!eluHH PHDž?H=lH/ @`t Hp#PH=oiH5slHT'PHiHH;EH[A^A_]H}PEAH}PE8H}P`/HpP@#HPP H0mP HTPH;PH"PH PHP H0P@HPP`HpPHPHsPHZPHAP{H(P oH0P@cHPP`SZHPlHP`HP TH0P@HHPtP`8?PIu u,u8E1 uU_H%PtHPtHPE1 uIA t H0P@u3IA@tHPP`tIA1`u Hu_HppPHJA@Hffffff.`t Hp3PHu IEEE`@ E1  H}PEfH}nPE]H}[P`THpBP@HHP)P I<$}] HvIt$HtHFIT$AL$ED$EL$ A|$$AD$(A\$,AD$0H](HCH;AD$5IEI;EAD$6AD$8ID$@ID$pIDŽ$IDŽ$IDŽ$IDŽ$IDŽ$`IDŽ$pIDŽ$IDŽ$IDŽ$IDŽ$PIDŽ$`IDŽ$IDŽ$IDŽ$IDŽ$IDŽ$IDŽ$ IDŽ$PIDŽ$`IDŽ$IDŽ$IDŽ$IDŽ$IDŽ$IDŽ$ IDŽ$PI$`IDŽ$ IDŽ$8IDŽ$0IDŽ$0IDŽ$(IDŽ$ IDŽ$IDŽ$IDŽ$IDŽ$IDŽ$ IDŽ$IDŽ$IDŽ$IDŽ$IDŽ$IDŽ$PM$xLExL}PINI$HI$PL%xLuXIOI$XI$`L襀INI$hI$pA|$5u HJHIKI$@I$x.OAL$8AD$4AD$|A|$$AD$4L;PLu@HM8HHIH)u3I$ I$(H)PIINH)HlI$ 02HVHHALGIIs A HHGHzHH9sH4HxH9sAH](MIIYHH~Hs 1HuoH_H)1fDLDLD$L4D$L4DDLTDDLTDdLtDdLtH HuHt,H4HfDD0 0D2 2H HuM9H](t1JJI1fffff.||I|1HH9rIINH)u/I$I$H)MPA|$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$`HcAH]E1IELAtCI AXBLHL9ULH)H ILHTIt1@DXDDXHH9uJ IJ4+I4KUiHfPHHuP0I$I$H9u!HP I$`IDŽ$`Hu&rHtHP(I$`IDŽ$`HtNHEI$Hu3PH KiHHH5WiHTiHP$HHuP0I$pI$H9u!HP I$ IDŽ$ Hu&rHtHP(I$ IDŽ$ HtNHEI$PHu3PH iHHH5EWiHSiHP|HHuP0I$0I$PH9u!HP I$IDŽ$Hu&rHtHP(I$IDŽ$HtNHEI$Hu3\PH iHHH5ViHFSiHnPHHuP0I$I$H9u!HP I$IDŽ$Hu&rHtHP(I$IDŽ$HtNHEI$Hu3PH SiHHH5UiHRiHP,HHuP0I$I$H9u!HP I$`IDŽ$`Hu&rHtHP(I$`IDŽ$`HtNHEI$Hu3 PH iHHH5MUiHQiHPHHuP0I$pI$H9u!HP I$ IDŽ$ Hu&rHtHP(I$ IDŽ$ HtNHEI$PHu3dPH iHHH5TiHNQiHvPHHuP0I$0I$PH9u!HP I$IDŽ$Hu&rHtHP(I$IDŽ$HtNHEI$Hu3PH [iHHH5SiHPiHP4HHuP0I$I$H9u!HP I$pIDŽ$pHu&rHtHP(I$pIDŽ$pHtNHEI$Hu3PH iHHH5USiHOiH&PHHuP0I$I$H9u!HP I$0IDŽ$0Hu&rHtHP(I$0IDŽ$0HtNHEI$`Hu3lPH iHHH5RiHVOiH~PHHuP0I$@I$`H9u!HP I$IDŽ$Hu&rHtHP(I$IDŽ$HtNHEI$Hu3PH ciHHH5RiHNiHP<HHuP0I$I$H9u!HP I$IDŽ$Hu&rHtHP(I$IDŽ$HtNHEI$Hu3PH ~iHHH5]QiHNiH.PHHuP0I$I$H9uHP ID$@ID$@Hu fHtHP(ID$@ID$@HtHHEI|$pHu0PH "~iHHH5PiHmMiHP HHuP0ID$PI|$pH9uHP I\$Hu8HtHP(I\$Ht#HHCHuHHPHPLFP HV'HN'HF'H>'H6'H.'H&'H'H'H'H'H&H&H&H&H&fffff.UHSPHHx.OȉK8C4C| {$C4H[]fUHAVSHHiHHEHHHEHCHEHtH@Hcs$HHH}عW蜿HHLuMt#HIFHuILPLvPHHEHCHEHtH@Hcs$H0H}ȹW-HHLuMt#HIFHuILPLPHHEHCHEHtH@HcCHcs$HHH}1W蹾H H(LuMt#HIFHuILPLPHHEHCHEHtH@HcCHcs$HHpH}1WEHHLuMt#HIFHuILPLPHHEHCHEHtH@HcCHcs$HHH}WνHHLuMt#HIFHuILPLPHHEHCHEHtH@C$HcKHHcsHHHS@H}1WQHHLuMtHIFH{5HHxHCHEHtH@Hcs$H Hx1WH`HhLuMt#HIFHuILPLPHHhHCHpHtH@Hcs$H`Hh1WmHpHxLpMt#HIFHuILPLDPHHXHCH`HtH@Hcs$HHX1WHHL`Mt#HIFHuILPLPHHHHCHPHtH@Hcs$HHH1W}HHLPMtIHIFHu7ILPLTP{(t*5ILPL8P{5{(HH8HCH@HtH@HcCHcKHHcs HHH81WźHHL@Mt#HIFHuILPLPHH(HCH0HtH@HcCHcKHHcs$HH H(1W=HHH0Ht#HHCHuHHPHPHiHH;Eu H[A^]IPIH0HIH@HIHPHIH`HuyIHpHueIH]HuTuIH]HuFgIH]Hu8YIH]Hu*KIH]Hu=IH]Hu/IH]Ht#HHCHuHHPHPLZP fUHAWAVSPIIHHNH)I I(H).PIINH))H8I02H#HHALGIIr/HHGHzHH9H4HxH9A1@||I|1HH9rHI 02HtqHHALGIIr/HHGHzHH9H4HxH9A1f.||I|1HH9rIINH)IIH)toPH[A^A_]MIIqHH_HsB1HMIIqHH{H1H,SHsH)1ffffff.DLDLD$L4D$L4DDLTDDLTDdLtDdLtH HuHt,H4HfDD0 0D2 2H HuM9JJIHwH)1fDLDLD$L4D$L4DDLTDDLTDdLtDdLtH HuHt,H4HfDD0 0D2 2H HuM9JJIff.UHHcG$HHHHH HHHHH HHHH]UHAWAVAUATSLcO$MEBLH`LcAE1ID$LAtCK AXBLHL9SLH)J ILHTIt1fDXDDXHH9uJ KK4 I4KH9@H9AH9I9E1DF >EAIFHHD[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*HHCHuHHPH跡PHHGHH0H8HHEMML 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$L$cfWLMMLPHpHt#HHCHuHHPHPHu LeDž?HH`HHXHDwDTO4DtD8ÉTtÉTHHHHTHHHHHHHH|HHHHHHHHHHEHHHL H`H(HXH0HH8HH@HHDl|G$***XYLxLHLLP۹ODD1HcHÖjHELe]H]H]fWf)EEHULHS.H}H9t6HHLet7HP(/HxH1HHP HLeHc;HLHHLI $HEHHQHpHPHxHQtHXHPtH`HH@HHHAHE1G EtwMcu$MADDAB HHLHcHLAAAIIAMGIIslHAE$AHcHH<уNˍDH4PHLII9+3IIGHpH4H<HHAE$~6HcI<уAN΍DH4腙PHLHH9|FA])DHHcq$HA9u@AЅHcHEHDIcHHHGHHs qHHGMtHII9s ILfHHZHMAHH9HLLMMEHHHGHHtH@HcwHDGIHI0I8HHLt*HHCHuHHPH'PHHGHH0H8HHEMML HOHH H(HHHHHHHHHHwW$։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$L$c~cfWLMMaLPHpHt#HHCHuHHPHPHu LeDž?HH`HHXHDwDTO4DtD8ÉTtÉTHHHHTHHHHHHHH|HHHHHHHHHHEHHHL H`H(HXH0HH8HH@HHDl|G$***XYLxLHL輋P۹ODD1HcHjHELe]H]H]fWf)EEHULHç.H}H9t6HHLet7HP(/HxH1HHP HLeHc;HLHHLI $HEHHQHpHPHxHQtHXHPtH`HH@HHHAHE1G EtwMcu$MADDAB HHLHcHLAAAIIAMGIIslHAE$AHcHH<уNˍDH41PHLII9+3IIGHpH4H<HHAE$~6HcI<уAN΍DH4PHLHH9|FA])DHHcq$HA9u@AЅHcHEHDIcHHHGHHs qHHGMtHII9s I"H]Ht#HHCHuHHPHFPLQBP fUHAWAVAUATSHIIH_iHHE1LIMcL# HH0M7H{0HPhI9I?KNHhH{0;NHPH5-RH RL PH]HhLPH5 LH0G.Et H}FPHiHH;ELH[A\A]A^A_]HL" IOI+OHH.袋.HHHLsL@Ht?HH@HHH9H9HxфqHy HH@I|$HHH+H1L"HHHHW)EHEH}L HLHHH@HLE1ff.I9H8LLHML8HDž8MtAFt I~dEPL\EPLL7:LH0HLHMjH0HDž0HtcCt H{EPHEPJf.LL9H(HLHMH(HDž(HuIHHH@L9HH9LuDH HMLLH HDž HtCt H{UDPHMDPI7HH!LcML{IIH[L9|MI)HèfHH:"HèIXuI?LUH5kRH }RL PH]H@LHH# LHC.II)LM&CPIGIGIHHEHCHEIHEHCIEHiHH;EWCPGCPIHh.H5QRH |RH}Lh3W)PHDž` BPH`(b)PHe input.HHHsequenceHHHptr for HHHGot nullH@AH]RHuHPLwE1H5hHhLBP`aBPIHh .H5kRH RH}LhMW)PHDž` BPH`(0d)PH tensor.HHHinput teHHHptr for HHHGot nullH@AH&FQHuHPL葐E1H5hHhLAPz{APIHh:.H5RH RH}LhgW)PHDž`0APH`(Zke)PHsequenceHH'Houtput sHH Hptr for HHHGot nullHHHInsert: HHHSequenceH@/AHRHuHPL菏E1H5hHhL@PxIPtH`S@PLeMuEIALeMt6HEL9t!HX@t Hx@PHI9uH}LLe@PEuEu LhMuDfH}?PEtH}?PLhMu=IALhM'HpL9HX@t Hx?PHI9uIPtH`Z?PLeMuEIALeMt6HEL9t!HX@t Hx$?PHI9uH}LLe ?PEuEu LhMuDmH}>PEtH}>PLhMuDIALhM.HpL9HX@t Hx>PHI9uIPtH`a>PLeMuEIALeMt6HEL9t!HX@t Hx+>PHI9uH}LLe>PEuEuLhMu:wH}=PEtH}=PLhMuQIALhMt?HpL9t$HX@t Hx=PHI9uHhLLp=PEu{IL=PnIEucILuMu%UIEtLH}AH迚ILuMt2H]L9t3MI)HèHX3"HèIXuH}LuPHLHHHHHLċHHH讙LL8Lt H n;PHT:PH5hHH9PH8:PHhHH;EuHH8[A\A]A^A_]u;PHLL8Lt H :PH9P HXHH5hHHV9PHH8Y:PH 5P UHAWAVAUATSH8MLHHIHHhHHEH8H hHHHLH@H8L9PLHEEL-hIEHHI@L8L8PL=hILHDž(HDž HDžHDžDž0LA3PH]HEHt룐UHAWAVAUATSHIIHhHHE1LIML INI+NHH.袋.HHHLkL@Ht?HH@HHH9H9HxфcHy HH@L(IH1HH+H1L=HH,IMI6LqW)EHEH}LHH~u1fffff.H;@tPLH&H8HLHML8HDž8MtAEt I}1PL1PHH9HM|$MMl$MII\$L9L0MI)HèfHx'"HèIXuL0I}tH5RH RL OyPH]H@LHHRLH0.EtrH}0PHghHH;Eti1PMI#LM>0PIEIEIEHEID$HEIHEID$L(I$HhHH;EuLHĸ[A\A]A^A_]ÿH0PIHh.H5RRH RH}Lh4|W)PHDž`0/PH`(b)PHsequenceHH&Hutput seHH Htr for oHHHot nullpHHHErase: GHHHSequenceH@.AH_RHuHPL\~E1H5hHhL/PUF/PIHh.H5PRH RH}Lh2{W)PHDž` .PH`(շb)PHe input.HHHsequenceHHHptr for HHHGot nullH@AH\RHuHPLv}E1H5hHծhL.PoIPtH`:.PLeMuEIALeMt6HEL9t!HX@t Hx.PHI9uH}LLe-PEuEu LhMuDmH}-PEtH}-PLhMuDIALhM.HpL9HX@t Hxk-PHI9uIPtH`A-PLeMuEIALeMt6HEL9t!HX@t Hx -PHI9uH}LLe,PEuEuLhMu:wH},PEtH},PLhMuQIALhMt?HpL9t$HX@t Hx,PHI9uHhLLp`,PEunIL~,PaIEtXH}MILuMuDILuMt8H]L9t9MI)HèfDHH""HèIXuH}Lu+PLO&P LfUHAVSHPIHkhHHE+PHþH+PHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]qH5D9PH}HHzH5fRHHH5 RH軥H5THHA H5pPHHH;HHH whHHMHHyHUHuHuIIFIF0INIVIF@H]HEHtH_H)PH)hHH;Eu LHP[A^])PIH]HEHtH_HE)PL#P UHAWAVAUATSHIHHhHHEH~ߕHLc(MH(I|$H跕HH+Hl1LËHYH貧HHH0H8E11L Lx0f.LDu EL9x0`^(PHH](d)EPHH5R)PCPHHHM4(.HHHDžHHtCt H{'PH'PEtH}'PAE9DH8LgW)EHEH}L1L}@L H@HLLL@HDž@MtAFt I~D'PL<'PD9|L8M|$MtHH0LpHII\$L9t9MI)Hè@H8"HèIXuI>L8H0H&LM}L&PIFIFIHEID$HEHHEID$H(HH !hH H;MuHĸ[A\A]A^A_]&Pi&PIHh(.H5sRH #RH}Lh0UrW)PHDž`@ &PH`(e)PHt sequenHH(Hor outpuHH Hullptr fHHHt: Got nHHHConstrucHHHSequenceHf@0ce@2AHzRHuHPLwtE1H5hH֥hL%PPa%PIHh .H5kRH RH}Lh-MqW)PHDž` %PH`(Pb)PHore inpuHHHe 1 or mHHHMust havHf@ts@AHRHuHPLsE1H5רhHhL$PrIPtH`]$PLeMuEIALeMt6HEL9t!HX@t Hx'$PHI9uH}LLe $PEuEu LhMuDmH}#PEtH}#PLhMuDIALhM.HpL9HX@t Hx#PHI9uIPtH`d#PLeMuEIALeMt6HEL9t!HX@t Hx.#PHI9uH}LLe#PEuEuLhMu:wH}"PEtH}"PLhMuQIALhMt?HpL9t$HX@t Hx"PHI9uHhLLp"PEuqIL"PdILuMuVIEtMH}BILuMt;H]L9tPH]HEHt+^IH`Hu$H}HuH]HEHu5LfP HhPH}HtIH]HEHtHTHPL!P fUHAWAVATSH0IIH4hHHEHhHII&H?jIfHnfsAGPIG`ffEHEEEaxisEH}HUHML軠H]E1HuLeHEtCt H{PHPMgPEt H}PffEEHEHkeepdimsHEEH}HUHML<H]Hu1LuHEt3Ct H{pPHhPMwXEu#AHEuMwXEt H}9PHhHH;Eu H0[A\A^A_]wPHEu HEt H}PH^P fDUH]6fDUHAWAVAUATSHHIIH\hHHE1HIǾHI$IO0IɃ HHc HH}LHLIZH}LHLIJCH}LHLIn&,H}LHLIW8H}LHLI@HEI$HhHH;EuLHĘ[A\A]A^A_](PPIHh.H5RH sRH}LhofIG0HHH5RH RHPHHZHuHP1LQiH5hHhLPHIPt H`PDžDH]HuRHwIǰDH]Ht6HEH9t!Lh@t HxPLL9uH}HH]PEuEuLhMu?wH}PEtH}PLhMuQIǰDLhMt:HpL9t9HX@t Hx;PHI9uHhLp PDu LIL5P II$LTP {UHAWAVAUATSHIIHIH9hHHEHEHCPHEEEEEEW)EHEMt= 0HH/Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@7AH֔RHHPL[E1H5hH=hL P PH PH Pݿ PIHpi.H5RH RHLpXW)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AHRHHPLZE1H59hHZhL* PHp% PIPt H` PL Mt?H(L9t$HX@t Hx PHI9uH LL(c Puu&LpMuMH4 PtH PLpMuIALpMHxL9`HX@t Hx PHI9u7TIHH :IHH>IPt H`{ PL MuQIAL Mt?H(L9t$HX@t Hx< PHI9uH LL( Puu&LpMuFH PtH PLpMuQIALpMt?HxL9t$HX@t Hx PHI9uHpLLxy PEuIL PH}HIHpHV]ILPM+II]IEH+Ct H{PHPH}HIH}HIHHuXIHHt4HPHHu,IHHuIHHHUPvIHHHuIHHt#HHCHuHHPHPHHHPHHHu IH!HHHt|HHCHujHHPHPLPMuYII]IEHt(Ct H{nPHfPHHHuIHHHuLPMtHHXL9tpMI)Hèffff.HX!HèIXuHPLXPHpHt HxPH}Ht H}PL/P LfUHAWAVAUATSHIIHIH9hHHEHEHCPHEEEEEEW)EHEMt= 0HH/Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@7AHւRHHPLIE1H5hH={hL O OHOHOݿOIHpi.H5{RH RHLpFW)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AHRHHPLHE1H59~hHZzhL*OHp%OIPt H`OL Mt?H(L9t$HX@t HxOHI9uH LL(cOuu&LpMuMH4OtHOLpMuIALpMHxL9`HX@t HxOHI9u7TIHH :IHH>IPt H`{OL MuQIAL Mt?H(L9t$HX@t Hx= 0HH/Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@7AHpRHHPL7E1H5mhH=ihL O OHOHOݿOIHpi.H5iRH rRHLp4W)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AHoRHHPL6E1H59lhHZhhL*OHp%OIPt H`OL Mt?H(L9t$HX@t HxOHI9uH LL(cOuu&LpMuMH4OtHOLpMuIALpMHxL9`HX@t HxOHI9u7TIHH :IHH>IPt H`{OL MuQIAL Mt?H(L9t$HX@t Hx= 0HH/Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@7AH^RHHPL%E1H5[hH=WhL O OHOHOݿOIHpi.H5WRH haRHLp"W)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AH]RHHPL$E1H59ZhHZVhL*OHp%OIPt H`OL Mt?H(L9t$HX@t HxOHI9uH LL(cOuu&LpMuMH4OtHOLpMuIALpMHxL9`HX@t HxOHI9u7TIHH :IHH>IPt H`{OL MuQIAL Mt?H(L9t$HX@t HxLMI)HèH8!HèIXuI<$L I^,LHpL;OID$ID$I$HPIFHXHH`IFW)PHDž`IEHpHt HxTOL8H}H14LPMtHXL9t4MI)Hèffffff.HH!HèIXuHPLLXOHpHpw&OOIHp}.H5GRH HRRHLpW)PHDž`@tOH`(*d)PH be >= 0HH/Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@7AHMRHHPLE1H5JhH3FhLO HOHOOOIHp_|.H5FRH QRHLpW)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AHLRHHPLE1H5/IhHPEhL OHpOIPt H`OH Ht?H(H9t$Lh@t HxyOLL9uH HH(YOuu&LpMuFH*OtHOLpMuQIALpMt?HxL9t$HX@t HxOHI9uHpLLxOEuILOH}HIHH :IHH3IPt H`GOL MuQIAL Mt?H(L9t$HX@t HxOHI9uH LL(Ouu&LpMuFHOtHOLpMuQIALpMt?HxL9t$HX@t HxeOHI9uHpLLxEOEu~IL`OH}HosIHpHBIILPMII]IEHCt H{OHOH}HIH}HIHHuXHIHHt4HeOHHu,IHHuIHHH!OIHHt#HHCHuHHPHOHHHOHHHuO{II]IEHtvCt H{OHOHHHu]IHHHtNHHCHuRH 2>RL OH]HULEHLeLH覷-EH}mOH vhHH;E贷OKHOH)1f 0@P`pDހDސDޠDްDDDDH HuHtLILHfAH HuL9L)JHff.LHHHE1E1MHHHzHHNHtB~fpDHyH)1ADADAD AD0HHu1Ht~fpDADADH9uiLuMt#MgL躵OIGIGIM/MwMwLeI$H+thHH;ELHX[A\A]A^A_]ILH)HSHH7OH}蔲O HH}HtH} HEt H}OHxO UHAWAVAUATSH8MLHHIHHwshHHEH8Hz8hHHHLH@H8L7OLHEEL-HhhIEHHI@L8LAOL=hhILHDž(HDž HDžHDžDž0L豶OHLH HH0H跲OHILLpOHLH H3H}OLHH9OHHH LLHHL8Lt H ?OL)OH5fMhHH辱OH8ƲOHqhHH;EuLH8[A\A]A^A_]JOIHHL8Lt H 趲OL蠱O+H*H"HHH IH5LhHHOIH8 OL转O DUHAVSIPBOHHRXhHHH{LH iHH[A^]IHOL`O UHAVSIPOHHWhHHH{LoHiHH[A^]IH蜱OLO UHAVSIX肱OHHL\H[A^]IHUOL蹫O UHAVSIPBOHHRWhHHH{LHiHH[A^]IHOL`O UHAVSIPOHHVhHHH{LoHiHH[A^]IH蜰OLO UHAVSIP肰OHHVhHHH{LHiHH[A^]IH DxHDžAuDHѾ!IH"HH胐MI1E1A AqHv!IHljHHHMI rJ #H9HLH91I|$H)LHtH HHuH T TT TT THI9uAILALHHyHHrHH~H)1ffo bff.f81f81\fvfvff\f81Tf81\ fvfvffT \0HHuHt4f81f81Lffvfvfo^bffLI9t1fn>bfffff.< fouff~ HI9uHDžAA Hn!IH过HH耒M"IVDHs1HI)1~Bef.<fouVff Ѐ|fotRfL|fouVffL|fotRfLHI9uXff.f Ѐ|fouffL|fotfL|fouffLHI9SHt9HH1~ueffff.<fouff HH9uILAA1H!IHLHH͸MVIs1VLHHyHHVH0sB1HzVLHHyHHVH`1H=fHzH)1fffff.f803f80L3pLpf80D3f80L3Dp Lp0f80D3 f80L3(Dp@LpPf80D30f80L38Dp`LppH@HuHUHHf80D3f80 3Dp pHHuU1HHzH)13L30L0D3 L30D0 L00D3@L3PD0@L0PoD3`oL3pD0`L0pHHuHt.HHoD3o 3D0 0H HuI9LDxILAA BH!IHHHrM~ Is&1TA ILA LHHyHHVHs1HLTHzH)1f.f813f81L3Lf81D3f81L3 D L0f81D3f81L3D@LPf81D3f81L3D`LpH HuHSHHf81D3f81 3D HHuSA &Ht!IHłHHMSIs1nSLHHyHHVH s1HKSHzH)1ff823f82L3Lf82D3f82L3D L0f82D3f82L3 D@LPf82D3 f82L3D`LpHHuHRHHf82D3f82 3D HHuRA&HD!IH蕁HH薠MI rJ #H9HLH91I|$H)LHtHD HHuH/ T TT TT THI9uAILAQLHHyHHVH`s 1Hu}HzH)1fffff.3L30L0D3 L30D0 L00D3@L3PD0@L0PoD3`oL3pD0`L0pHHuHt.HHoD3o 3D0 0H HuI9LDxILAVArH`!IHHH袸M~ Is&1PAILAL!LHHyHHVHs1HmPHzH)1f.f813f81L3Lf81D3f81L3 D L0f81D3f81L3D@LPf81D3f81L3D`LpH HuHOHHf81D3f81 3D HHuOAH!IHe~HHfMTOIs17OLHHyHHVH0s1HOHzH)1ff803f80L3pLpf80D3f80L3Dp Lp0f80D3 f80L3(Dp@LpPf80D30f80L38Dp`LppH@HuHNHHf80D3f80 3Dp pHHu\NA&H!IH5}HHM|NIs1_NLHHyHHVH s1HMIrJ H9HLH9s1IT$H)LHt!Hffff.,<@<HHuH D,,TT,TT,T THI9u AILA LHHyHHrHYH~H)1f( rbforbAAdTTf8f~f8f~dA\ Ad0TTf8f~\f8f~d HHuHtAAALf(YrbTfoZrbf8f~Tf8f~LI9t%ffA.ӈHI9uHDžAEA HN!IHo`HH`~MLIs1DLHHyHHVHHzH)1ZZLLZDZLD L0HHuHDLHHyHHrHeH~H)1foqbfffff. T[f8f~ [f8f~LL T0[[f8f~Lf8f~T HHu1H%a1HCZZLLI9CILAOAH豦!IH^HH胤MIs1pCLHHyHHVHtrHzH)1L[f8+[f8+fpfLpD L0[f8+[f8+fDpfLpHHuHuB1HBoL[f8+fp[f8+fDpI9BILA A @H艥!IH]HHM>IVEAHs1MM)1dbHf. (\H,H1H,.HFH L(\H,H1H,.HFH|L(\H,H1H,.HFH|L (\H,H1H,.HFH|HI9]MtTH4H1bHfff. (\H,H1H,.HFHHI9uILAAH!IH\HHLMIs1@LHHyHHVHtgHzH)1L[fk[fkfpfLpD L0[fk[fkfDpfLpHHuHum@1Hb@oL[fkfp[fkfDpI9(@ILAAHߢ!IH[HHMIVDHs 1HuMqI)1f.H,4H4H,tHtH,tHtH,t HtHI9uHt&H4H1f.H, ;f8wfnXf~f. 2  f4HHI9uLDILA5AucH3!IHTQIH54HM~'ffff.fAnHsHIIuILHpH(HD8 jD0HDž8A uGEH艘!IHSIHeMI1E1AAH.!IH_SHHM~CIVDH1H$HA ILAAIL8A1LHHyHHrHH~H)1ff( #abfo+abff.fAfAdffTffTf8f:f8f:dfA\ fAd0ffTffTf8f:\f8f:dHHuHtLfAfALfff(q`bfTffop`bf8f:fTf8f:LI9t'ff.fA.ӈHI9uHDž8AEAH=!IHnQHHgMfI 1O3I)1,4@4,t@t,t@t,t@tHI9uHt)H4H1ffffff.,<@<HH9uL8D0IL8AA Hg!IHPHHM~$IVDH1HA IL8A @LHHyHHVHHzH)1ffLfZfZffLfD fL0fZfZfDfLHHuH1I)1DH,4Ӊ4H,ttH,ttH,tt HI9uHt)H4H1fffff.H,<։ Affffff.8wfnXf~f. 2  f4HHI9sL8D0IL8AAH!IHLEIH%IM~1LLA~ f.zDfofPbf.cr>~cLf.H5Qffff.LH5QHHQffoeOADE!IcLrIHLtbOILLrbOLLHrQfodOMLH` LD9t-S H5.Q`OHIL96E1HL_OMtLaOLHIL9L8IL8H0H( HD8DxHDžA H!IHvHHwUM" I rJ #H9zHLH9k1IT$H)LHtHfD< HHuH fD< | D| D| DHI9u E1A A ; H9!IHuHHK]MIVDHs+1HuA IL8AGI)1f4W*tW*DtW*DtW*fDHI9uHt$HH14W*fHH9uILAD A  H.!IHtHHMIrJ #H91J H9$1IT$H)LHtHސ< ?ffff.8wfnXf~f. 2  f4HHI9sLDxILA An (A ILAu LH1fDoIbfDo=cfo=Eb( FbfoGbDf81Df81 D[D[A((@Eb(TA(TfDo:cfEofDffDffEofDffAoffD(DX(Xf8fD8fDlfofAkfffffkfo%]cf8%DcA(fr fofA8A(fr fo @GbffA(fAfA(fAffr fr f8f8flfEfAfD8f8fAlfffArfArfE8+fD-ɼcfDfDD,PHH9I9 1Ht2ooLfftfoCbfftfLI9HDžAAbH|!IHfkHHWNM`IrJ #H9J H91IT$H)LHt#H< W*f~HHuHfffff. 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LHP[A^] LIH]HEHtH6HLLK UHAVSHPIHdHHEpLHþHfLHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]EH5 LH}H11H(H5ЙNHyzH5zyPH{HA H5ELH|HH;qHHH 5dHHMHHUHuHuIIFIF0INIVIF@H]HEHtH4HHKHdHH;Eu LHP[A^]KIH]HEHtHa4HKLgK ffffff.UHAVSIPKHHdHHH{LogHdHHH[A^]IHKLK @UHAWAVAUATSHIH0H dHHE1LZHH;II1L8LLOILs0HLILHLLIL@M9~HC0xuJHgIHH訾IL}#HtMHH@LLKIIHu*LM#HILPHLH@HKH8LGNHtHHXPHHHTKH0HH ӺdH H;MuHĨ[A\A]A^A_]mKKIHhڲ*H5ۖNH NH}LhHHP{AHCMHuHPLJE1H5dH |dLK IALeMuFzIALhMIIPt H`AKLeMt6HEL9t!HX@t HxKHI9uH}LLeKEuEuLhMu&^H}KEtH}KLhMt:HpL9tEHX@t HxKHI9uHhLpKEtLKLK L@UHAWAVAUATSHHHdHHEHuH9s0u.HHCPH ӸdH H;MuHĘ[A\A]A^A_]mKKIH`ڰ*H5jLH NHxL`FH0H5ؕNH kLHHHUIAHNHxHHLHE1H5}dHydLK IALeMuFzIAL`MIIHt HX"KLeMt6HEL9t!HX@t HxKHI9uH}LLeKEuxu L`Mu)aH}KxtH}KL`Mt:HhL9tEHX@t HxKHI9uH`LheKEtLKLK Lffffff.UHAWAVAUATSHHH¶dHHEHuH9s0u.HHCPH dH H;MuHĘ[A\A]A^A_]=KKIH`誮*H5hLH NHxL`CH0H5NH YiLHHHUIoAHuNHxHHL_FE1H5{dHwdLK IALeMuFzIAL`MIIHt HXKLeMt6HEL9t!HX@t HxKHI9uH}LLeKEuxu L`Mu)aH}KxtH}{KL`Mt:HhL9tEHX@t HxPKHI9uH`Lh5KEtLXKLK Lffffff.UHAWAVAUATSH8MIHIHH~dHHEH8HydHHHHH@H8H>KLHEEL%OdID$HHI@L8HGKL5 dILHDž(HDž HDžHDžDž0LKHLHNHH0HiPLKHLHMHH0H9PHHHRHHL8Lt H lKHRKH5dHHKH8KHܲdHH;EuHH8[A\A]A^A_]sKHHHL8Lt H KHK#HOQHGQH?QH7QHH5ݍdHH5KHH88KHK fUHAVSIPrKHHdHHH{L\HdHHH[A^]IH(KLK @UHAVSIPKHH"dHHH{L\H0dHHH[A^]IHKL,K @UHAWAVAUATSHIH8H;dHHE1LHH II1LLJDILk0HLILHLLIL@M9HC0xuQH螺IHH߳ILHtUHH@LL)KIIHu+L}HIELPHLH@H0KH8HH 5dH H;MuHĨ[A\A]A^A_]K}KIHh<*H5=NH NH}Lhi=HP}pAHLHuHPL@E1H5NudHoqdL?K IALeMuFzIALhMIIPt H`KLeMt6HEL9t!HX@t Hx~KHI9uH}LLedKEuEuLhMu&^H}AKEtH}2KLhMt:HpL9tEHX@t HxKHI9uHhLpKEtLKLCK LfDUHAVSIPKHHҕdHHH{LOYHdHHH[A^]IHxKLK @UHAWAVSHxIHdHHENKHþHDKHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]蕜I荤HW)EHEKHEHHHML8HXHMRIJHW)pHEKHpHHHML8HXHxH5MH}HUHp`pHH5yNHHVsH5NHhH5gPHiHA H53LHkHH;_HHH sdHHMHHUHuHuIIFIF0INIVIF@HpHt HxKH}Ht H}}KH]HEHtH"H\KHdHH;EuLHx[A^A_]KIH}Ht0^IH]HEHt+bIHpHu$H}Hu0H]HEHu9LPK HxKH}HtH}KH]HEHtH"HKLK ffffff.UHAWAVAUATSHIHHdHHEHdHHH{UHfH(^CPW)pƅpHEIpositiveLxHdetect_pHqHKPEH}HpLnpt H}KH}W)pƅpHEInegativeLxHdetect_nHqHXEHXHpLH nHXHDžXH]HEHCt H{5KH-KHXHDžXHtCt H{KHKpt H}KH}HdHH;Eu(HĘ[A\A]A^A_]HDžXpuKKIHXr*H5NH φNHpLX25W)@HDžP0SKHP(]a)@LxH detect_HHHo obtainHHHFailed tH@ AH3LHpH@L7E1H5%mdHFidLKKIHX萟*H5NH NHpLX44W)@HDžP0qKHP(=\a)@LxH detect_HHHo obtainHHHFailed tH@ AHQKHpH@L7E1H5CldHdhdL4K IALeMIALXMIIALeMLIALXMzII@t HPVKLeMt6HEL9t!HX@t Hx1KHI9uH}LLeKEupu#LXMu00H}KptH}KLXMH`L9HX@t HxKHI9uI@t HPKLeMt6HEL9t!HX@t Hx_KHI9uH}LLeEKEupu LXMu)aH}KptH} KLXMt:H`L9tNHX@t HxKHI9uHXL`KEt5LKH]HEHu-TLIpt H}KH]HEHt)Ct H{mKHIpt H}SKLK ffffff.UH]fDUHAWAVAUATSH(HIIHdHHE1HIIT$1H6HIL$0IM}PMmXMHEu(E1MA1MH}LHHEȋM u%E1MA1MH}LHHEH}-IHdHH;EuLH([A\A]A^A_]KUHAVSI`2KHHLH[A^]IHKLiK UHAWAVAUATSPDAIIMl$L HELEtHtDLILa LHILJ HH1EtLIII|7IIOH?HH?H!H4L軿KM9}HK|IHI)L蚿KLmLeLH?H>LIII|7IIOH?HH?H!H4L@KM9}HK|IHI)LKHhIL@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 XNH&)H}HtHH8蘺KH7ydHH;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$LH)H D ^ HHuW)pI~PHMLULH?II=MIH>LHMBI(I(AIIIOHHHZHn1(HI~XHuHBH]HpHpHxHH?H>HHH{1D(^D( ^(Š^(%^(-w^((AYWR(YY(\YYAV(^(f8)HH9|H9L ~|HyHt!Q ^^ HHH9tJ^ffff. Q(^LQ(^THH9uMHhHCH0M]0HHH`LQ0IE HH1LpfnIAzIRH HJ m E1MHhL1U1MHh+KfILOlH(HH@HELHMLXfnfnPIrKI9KTI91AI~H)LHt$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$\YA)dAd\YA)dAd \YA)d Ad0\YA)d0HH9|H9NI)IsLIDLIdHHMHHUHuHuIIFIF0INIVIF@H]HEHtHȿHmKH QdHH;Eu LHP[A^]謒KIH]HEHtHȿH(KL茌K @UHAVSHPIHPdHHEKHþHKHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]GCH5KH}HH5.NH H5 PHF HH HQH53KHfHH;KHHH ;dHHMH{(HUHuHuIIFIF0INIVIF@H]HEHtH;ƿHݏKH|NdHH;Eu LHP[A^]KIH]HEHtHſH蘏KLK @UHAVSHPIHNdHHEpKHþHfKHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH];H5KH}H$H5 ,NH H5PH HH HQH5KH HH;HHH :9dHHMH0HUHuHuIIFIF0INIVIF@H]HEHtHÿHMKHKdHH;Eu LHP[A^]茍KIH]HEHtHfÿHKLlK @UHAVSHPIHKdHHEKHþH֌KHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]'AH5dKH}HH5y)NHH5PH&HH HQH5KHF HH;+HHH 6dHHMH;9HUHuHuIIFIF0INIVIF@H]HEHtHH轊KH\IdHH;Eu LHP[A^]KIH]HEHtHHxKL܄K @UHAVSHPIHHdHHEPKHþHFKHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]7H5ԗKH}HH5&NHeH5fPHHH HQH5KHHH;HHH 4dHHMHAHUHuHuIIFIF0INIVIF@H]HEHtH苾H-KHFdHH;Eu LHP[A^]lKIH]HEHtHFHKLLK @UHAVSHPIHkFdHHEKHþH趇KHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]6H5DKH}Ht H5Y$NHH5PHHA H5KH-HH;HHH 1dHHMH"JHUHuHuIIFIF0INIVIF@H]HEHtHH褅KHCDdHH;Eu LHP[A^]KIH]HEHtH轻H_KLK ff.UHAVSHPIHCdHHE0KHþH&KHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]w6H5KH}H H5!NHEH5FOHvHA H5jKHHH;HHH /dHHMHGHUHuHuIIFIF0INIVIF@H]HEHtHrHKHAdHH;Eu LHP[A^]SKIH]HEHtH-HςKL3}K ff.UHAVSHPIHKAdHHE蠂KHþH薂KHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH].H5$KH}HTH59NHH5OHHA H5KH HH;HHH q,dHHMHEHUHuHuIIFIF0INIVIF@H]HEHtH⶿H脀KH#?dHH;Eu LHP[A^]ÀKIH]HEHtH蝶H?KLzK ff.UHAVSHPIH>dHHEKHþHKHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]W4H5KH}HH5NH%H5&OHVHA H5JKH}HH;bHHH )dHHMHCHUHuHuIIFIF0INIVIF@H]HEHtHRH}KHB >LIHIuHLtK MMBBLLHHI9fffff.IHHIM9H>HHH@Ht HHtKHQ3dHH;EuHH[A\A]A^A_]tKtKIH@U+*H5NH NH`L@9W)EE(HEHis requiHEHX input HEEred!EAHKH`HMLE1H56cHWcL'tKsKIH}**H5NH NH`LEmѿW) HDž0 sKH0(^) Hrst outpHHHo get fiHHHfailed tH@ut!AH#KH`H LE1H5[cH|cLLsK H`JpK HALeMuiHALeMHHAL@MHH t H0rKLeMt6HEL9t!Lh@t HxZrKLM9uH}LLe@rKEu`uLeMu)XH}rK`tHprKLeMt1HEL9tNLh@t HxqKLM9uH}LeqKE{LqKH@HovLHEt H}qKLeMt6HEL9t!Lh@t HxlqKLM9uH}LLeRqKEu`u L@Mu,dH},qK`tHpqKL@Mt:HHL9tILh@t HxpKLM9uH@LHpKELpKH$kK L(HH`HuJTHH`Hu9CHH@Hu:DHH`HuHH`Ht HhJpKH@Ht HH2pKHjK fffff.UHAWAVAUATSH(IIIILL)HHMaIyHL)HH9LEM9LL)HHHH=HL)HHHH9LM3HH9HBH6H9toKHH5KHlKH#dHHH5ScHcHtoKMM)LHH9ILHUL9M]MH]I)HHItL)HLFLHIH1ITI9sItLHH9LH?L!HUHrIIAQH 1HIHu&RLLLm+HH}H<HnKHLEL}M)LHHIM9H}MUMM)IMKIr KDH9JHDL9HM)DHt Hf.IHHIHuI8c1I H ILHLILHLILHLIL HL IL(HL(IL0HL0IL8HL8IL@H@L9u1HE1I?M!IGHHqH s1HH}HDpH~H)1fff.AALDȐLȠAD AL0DȰLAD@ALPDLAD`ALpD HHuHH}t2IDHHLH1DD D H HuM9ulN|IM~H}HuLmKH}HEHHuI)M~LLmKH}MHEH8LxHXH}HtkKMJ4O4I|pHrL)1fDϐLϠAALDϰLAD AL0DLAD@ALPD AD`ALpHHuHIHut1H HfD AD A H HuHUI9LEIB >LIHIuHLtK MMBBLLHHI9fffff.IHHIM9H>HHH@Ht HHRaKHdHH;EuHH[A\A]A^A_]aK6aKIH@*H5"MH MH`L@9W)EE(HEHis requiHEHX input HEEred!EAHsKH`HML蘯E1H5cHcL`K`KIH}D*H5qMH .MH`LEmqW) HDž0 (`KH0(T]) Hrst outpHHHo get fiHHHfailed tH@ut!AHrKH`H L轮E1H5cHcL_K H`\K HALeMuiHALeMHHAL@MHH t H0_KLeMt6HEL9t!Lh@t Hx^KLM9uH}LLe^KEu`uLeMu)XH}^K`tHp^KLeMt1HEL9tNLh@t Hx^KLM9uH}Len^KE{L^KH@HovLHEt H}1^KLeMt6HEL9t!Lh@t Hx ^KLM9uH}LLe]KEu`u L@Mu,dH}]K`tHp]KL@Mt:HHL9tILh@t Hx]KLM9uH@LHq]KEL]KHWK L(HH`HuJTHH`Hu9CHH@Hu:DHH`HuHH`Ht Hh\KH@Ht HH\KH6WK fffff.UHAVSIP\KHHdHHH{L?HdHHH[A^]IHh\KLVK @UHAWAVAUATSHIIHdHHE1L.8HHHHHHCLc I)It Iu H8uALW)@HDžPHyIH@HH螄IHCHK H)HLt HuFH8u@A?HDž`HHH;PiHHHHW)`HDžpMtALH=nN4L[KH`JHpHLZKHhLH艁IM)HHIH LHE1fA?t!HHH`HhH@YIH`HLHHfDH|HHIHH9t(HOHLH|&HLH9tfff.1HLH}IM9WQH@H`9b1L@A<tHHH`HhLHL9rH`Ht HhYKLLH@HHH)H1IILeLuHEH`HDžhH`1L2HHMMINHHHQINHI@I@ LHHуHH)HMIHSLHHMIJ HE1E1H'E1HLof.MMրHt9J K4H4H9J K4H4H91HAu1f1HAt$J HILHH4H4HHH9MLMHNHLH@JJB >LIHIuHLtK MMBBLLHHI9fffff.IHHIM9H>HHH@Ht HHbVKHdHH;EuHH[A\A]A^A_]VKFVKIH@ *H52MH zMH`L@9/W)EE(HEHis requiHEHX input HEEred!EAHhKH`HML訤E1H5cHcLUKUKIH}T *H5MH MH`LEm聡W) HDž0 8UKH0(d]) Hrst outpHHHo get fiHHHfailed tH@ut!AHgKH`H LͣE1H5 cH,cLTK H`QK HALeMuiHALeMHHAL@MHH t H0/TKLeMt6HEL9t!Lh@t Hx TKLM9uH}LLeSKEu`uLeMu)XH}SK`tHpSKLeMt1HEL9tNLh@t HxSKLM9uH}Le~SKE{LSKH@HovLHEt H}ASKLeMt6HEL9t!Lh@t HxSKLM9uH}LLeSKEu`u L@Mu,dH}RK`tHpRKL@Mt:HHL9tILh@t HxRKLM9uH@LHRKELRKHLK L(HH`HuJTHH`Hu9CHH@Hu:DHH`HuHH`Ht HhQKH@Ht HHQKHFLK fffff.UHAVSIPQKHHcHHH{LOHcHHH[A^]IHxQKLKK @UHAWAVAUATSHIIHdHHE1L>-HHHHHHCLc I)It Iu H8uALW)@HDžPHwIH@HH)IHCHK H)HLt HuFH8u@I?HDž`HHH;PiHHHHW)`HDžpMtALH=nN4L&PKH`JHpHLPKHhLHvIM)HHIH LHE1fI?t!HHH`HhH@iIH`HLHHfDH|HHIHH9t(HOHLH|&HLH9tfff.1HLH}IM9WQH@H`.b1L@I<tHHH`HhLHL9rH`Ht HhNKLLH@HHH)H1IILeLuHEH`HDžhH`1LBHHMMINHHHQINHI@I@ LHHуHH)HMIHSLHHMIJ HE1E1H'E1HLof.MMրHt9J K4H4H9J K4H4H91HAu1f1HAt$J HILHH4H4HHH9MLMHNHLH@JJB >LIHIuHLtK MMBBLLHHI9fffff.IHHIM9H>HHH@Ht HHrKKH dHH;EuHH[A\A]A^A_]KKVKKIH@*H5BMH MH`L@9?W)EE(HEHis requiHEHX input HEEred!EAH]KH`HML踙E1H5cHcLJKJKIH}d*H5MH PMH`LEm葖W) HDž0 HJKH0(t]) Hrst outpHHHo get fiHHHfailed tH@ut!AH\KH`H LݘE1H5cHB >LIHIuHLtK MMBBLLHHI9fffff.IHHIM9H>HHH@Ht HHb@KHcHH;EuHH[A\A]A^A_]@KF@KIH@)H52MH tMH`L@9/W)EE(HEHis requiHEHX input HEEred!EAHRKH`HML討E1H5cHcL?K?KIH}T)H5MH MH`LEm聋W) HDž0 8?KH0(d]) Hrst outpHHHo get fiHHHfailed tH@ut!AHQKH`H L͍E1H5 cH,cL>K H`;K HALeMuiHALeMHHAL@MHH t H0/>KLeMt6HEL9t!Lh@t Hx >KLM9uH}LLe=KEu`uLeMu)XH}=K`tHp=KLeMt1HEL9tNLh@t Hx=KLM9uH}Le~=KE{L=KH@HovLHEt H}A=KLeMt6HEL9t!Lh@t Hx=KLM9uH}LLe=KEu`u L@Mu,dH}zHUHuHuIIFIF0INIVIF@H]HEHtHZH@$KHcHH;Eu LHP[A^]$KIH]HEHtHYZH#KL_K UHAVSHPIH{cHHE#KHþH#KHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]H5FLH}H脨HH5MHHjHH5jLHHPH5MH豝H5OHHH HQH5hKHHH;HHH fcHHMHwHUHuHuIIFIF0INIVIF@H]HEHtHWHy!KHcHH;Eu LHP[A^]!KIH]HEHtHWH4!KLK UHAVSHPIHcHHE!KHþH!KHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]WH51DLH}HĥH<H5MHH誥H"H5LHH营H5AMHH5OH"HA H5fKHIHH;.HHH cHHMHHUHuHuIIFIF0INIVIF@H]HEHtHUHKH_cHH;Eu LHP[A^]KIH]HEHtHTH{KLK UHAVSHPIHcHHEPKHþHFKHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]H5qALH}HH|H5AMHHHbH5LHHТH5MH1H52OHbHH HQH5OcKH肚HH;gHHH cHHMHHUHuHuIIFIF0INIVIF@H]HEHtHWRHKHcHH;Eu LHP[A^]8KIH]HEHtHRHKLK UHAVSHPIH;cHHEKHþHKHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]H5>LH}HDHH5MHH*HH5*LHHH5MHqH5rOH袖HA H5`KHɗHH;讌HHH -cHHMHHUHuHuIIFIF0INIVIF@H]HEHtHOH@KHcHH;Eu LHP[A^]KIH]HEHtHYOHKL_K UHAVSHPIH{cHHEKHþHKHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]H5;LH}H脝HH5MHHjHH5jLHHPH5MH豒H5OHHH HQH5]KHHH;HHH fcHHMHHUHuHuIIFIF0INIVIF@H]HEHtHLHyKHcHH;Eu LHP[A^]KIH]HEHtHLH4KLK UHAVSHPIHcHHEKHþHKHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]WH519LH}HĚH<H5MHH誚H"H5LHH萚H5AMHH5OH"HA H5[KHIHH;.HHH cHHMHHUHuHuIIFIF0INIVIF@H]HEHtHJHKH_cHH;Eu LHP[A^]KIH]HEHtHIH{KL K UHAVSHPIHcHHEPKHþHFKHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]H5q6LH}HH|H5AMHHHbH5LHHЗH5MH1H52OHbHH HQH5OXKH肏HH;gHHH cHHMH7HUHuHuIIFIF0INIVIF@H]HEHtHWGHKHcHH;Eu LHP[A^]8KIH]HEHtHGHKL K UHAVSHPIH;cHHEKHþHKHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]׽H53LH}HDH輽H5MHH*H袽H5*LHHH5MHqH5rOH袋HA H5UKHɌHH;讁HHH -cHHMHHUHuHuIIFIF0INIVIF@H]HEHtHDH@KHcHH;Eu LHP[A^]KIH]HEHtHYDH KL_K UHAVSHPIH{cHHE KHþH KHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]H50LH}H脒HH5MHHjHH5jLHHPH5MH豇H5OHHH HQH5RKHHH;~HHH fcHHMHHUHuHuIIFIF0INIVIF@H]HEHtHAHy KHcHH;Eu LHP[A^] KIH]HEHtHAH4 KLK UHAVSHPIHcHHE KHþH KHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]WH51.LH}HďH<H5MHH誏H"H5LHH萏H5AMHH5OH"HA H5PKHIHH;.|HHH cHHMHHUHuHuIIFIF0INIVIF@H]HEHtH?HKH_cHH;Eu LHP[A^]KIH]HEHtH>H{KLK UHAVSHPIHcHHEPKHþHFKHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]藼H5q+LH}HH|H5AMHHHbH5LHHЌH5MH1H52OHbHH HQH5OMKH肄HH;gyHHH cHHMH7HUHuHuIIFIF0INIVIF@H]HEHtHWHH[A\A]A^A_]H MHG(HpcHH;EtJAwH)1Affffff.L$LdLdLdLd Ld(Ld0Ld8HH9uHt H Hfff.L!HHuHHIHHHLEHW)PHDž`H H(H)taHIILH=HJILXLPKDH`HH~LHJILXHP1LTCIHPHHt HXLJI~HHdHLHIW)HDžHL~ME1L@HHx(HH;u6LH[ff.HHPHH;sHHHIHM9|&LHP'IHM9pHH&HELeHHEHHuHHHHMHhHMH`HMHUHXHPHpLxHuHEH}HEH@HPHHH@H8HHHHt$HsJH HuSHH HHH>H;~|!HH+VHH L9r HVILML;`1W)p)E)E)E)E)EH)HHDžpHHxHLHMHHHUHhHt,HEHhHLHMHTHUHHHMH`WYhtPh1LpL1ff.H9HH9H;y|HHH H9rLH;`|HcHH;EuH[A\A]A^A_]EJff.UHAVSIXJHHLH[A^]IHJL J UHAVSIXJHHLH[A^]IHeJLJ UHAVSH0HIHcHHEHZcHII~BH~cHIIFPW)EHEEEaxisEH}HUHMH\H]HEHtCt H{JHJEuHt'HHcHH;Eu/H0[A^]H}JHuHEIFPHcHH;EtJHEt H}SJHJ ffffff.UHAWAVAUATSHIIHHcHHE1L HLIƾLIIFIN H)HtH:H80IE I+EH'LHQHHH;#HL觰HHKHW) HDž0Mt$PHHPHHHL`HX H HH9tH LH6L)HHLH|HH H4H HP;HHHHS؃H1AHH gwMH wMH(H;HwMH $K1AHE)HޔcHH;Eu>HH[A\A]A^A_]H wMHw(HcHH;EtLJAwH)1Affffff.L$LdLdLdLd Ld(Ld0Ld8HH9uHt H Hfff.L!HHuHHIHHHLuHW)PHDž`H H(H)taHIILH=HJILXLPKDH`HH~LHEJILXHP1L&IHPHHt HX|JI~)HHDHLHIW)HDžHLfMHWH*E1WLf..v7XPHH;r'LHPWCHH;tHHIHM9|#LHfWDIHM9pHH- HELeHHEHHuHHHHMHhHMH`HMHUHXHPHpLxHuHEH}HEH@HPHHH@H8HHHHt$HJH Hu3HH HH(OJHcHH;E EHPJ HHHHHHHHHtrHHPHu\HHPHHXJH Huz*HHL1HMtjAFt I~oJLPHH Hu:DHHHuHHHt H-JH Ht H(JHyJ UHAWAVAUATSHIIIuMeI)MIIGHH> H?MuI)LHHH9w`II9LBMLEHuI9vKJHH5BKHJHcHHH5vWcHRcHJLEHuIJ<=JHHuLEN4ABN|M~HLIJLI]M}MuHtHH[A\A]A^A_]JH[A\A]A^A_]E11LJIHJLJ 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H(H)taHIILH=HJILXLPKDH`HH~LHeJILXHP1LIHPHHt HX蜱JI~IHHdHLHIW)HDžHLCMHWH*E1WLf..v7XPHH;r'LHPWCHH;tHHIHM9|#LHDWDIHM9pHH=rHELeHHEHHuHHHHMHhHMH`HMHUHXHPHpLxHuHEH}HEH@HPHHH@H8HHHHt$H裯JH Hu3HH HH(oJHncHH;E EHP褬J HHHHHHHHHtrHHPHu\HHPHHXǮJH Huz*HHL1HMtjAFt I~菮JLPHH Hu:DHHHuHHHt HMJH Ht H(5JH虨J UHAWAVATSHHlcHHEHHHHHH(HHH@H HH0HGH8HDžPHDžHHDž@HEHEHEHEHDžxHDžpHHPHMHHHHMHH@HMHH8HMHH0HMHH HP(HUHMHHHPH@HPHHH@HDžhLPL`MtrIƃ?@)»?)ÿHL9EڍK@1HHE1@HEIELH?LJHKNANHEUMLHLIHXHtxHƒ?@)ƻ?)ÿHH9EލK@1HHE1@HEIEHH?茬JHKNANHpx|L@MM~ 1L@LH5HI9uHfjcHH;EuH[A\A^A_]JUHAWAVSH(IIH&jcHHEIG`AOhAwlLH?HIHLH)HH׉HIGHHMI)MOpAOxEG|LLH?LIIHH)HHDHH*ILIwPH)I.IIHEHJH0H}JIK vHufHTHD1H蹨JHEHCHEHMHKHHicHH;Eu H([A^A_]輪JHf.UHAVSIXBJHHLH[A^]IHJLyJ UHAVSIXJHHLH[A^]IHթJL9J UHAVSH0HIHXhcHHEHOcHII~JHOcHIIFPW)EHEEEaxisEH}HUHMH,H]HEHtCt H{,JH$JEuHt'HgcHH;Eu/H0[A^]H}JHuHEIFPHgcHH;Et5JHEt H}èJH'J ffffff.UHAWAVAUATSHIIHH,gcHHE1L{HLgIƾLWIIFIN H)HtH:H80IE I+EH'LHHHH;#HLHHKHW) HDž0Mt$PHHPHHHL`HX H HH9tH LHqL)HHLHUHH H4H HP諫HHHHS؃H1AHH GMH GMHM(H;H6HMH 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FEuEuLhMu&^H} FEtH} FLhMt:HpL9tEHX@t Hx FHI9uHhLp{ FEtL FLF LDUHAWAVAUATSHIHH^HHEI}@HHKH0HKH9GH@HHIE8MeLA}tFHtYI]HL<@LHfffff.LH FHIIuDMtIuHHL$!FLHMMeIEPIMIUHH)HHLHLIMHtEIu I}`f.H\HH\H;\uHDI]H\HIMHuH@HLHHKH0HH9(LH HHHKH0HH9rH9u'Hd^HH;EuHĘ[A\A]A^A_]FFIHhk$H5HH HH}LhIjHP謝AH HHuHPL?mE1H5}^H^LnF IALeMuFzIALhMIIPt H`FLeMt6HEL9t!HX@t HxFHI9uH}LLeFEuEuLhMu&^H}pFEtH}aFLhMt:HpL9tEHX@t Hx6FHI9uHhLpFEtL>FLrF LDUHAWAVAUATSHIHH^HHEI}@HHKH0HKH9GH@HHIE8MeLA}tFHtYI]HL<@LHfffff.LHFHIIuDMtIuHHLFLHMMeIEPIMIUHH)HHLHLIMHtEIu I}`f.H\HH\H;\uHDI]H\HIMHuH@HLHHKH0HH9(LH HHHKH0HH9rH9u'H^HH;EuHĘ[A\A]A^A_]FLFIHh $H5wHH HH}LhI8gHPLAHHHuHPLiE1H5^H>^LF IALeMuFzIALhMIIPt H`rFLeMt6HEL9t!HX@t HxMFHI9uH}LLe3FEuEuLhMu&^H}FEtH}FLhMt:HpL9tEHX@t HxFHI9uHhLpFEtLFLF LؐUHAWAVAUATSHH(^HHEHaHuH辣L=Ϝ^L5ŹLHLjFIHHoaH\aHIaL-BaFH1aHHH 6aL HXH aLLLFHaHuHLHLF\ITHHaHaHaL-aFHaHHH aL HXH aLLLiFH^HH;EuH[A\A]A^A_]FUHAVSHPIH^HHEFHþHFHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]GH5%FH}H贜H5HHH5JHFHH HQH53]FHfHH;KHHH ^HHMHHUHuHuIIFIF0INIVIF@H]HEHtH;LHFH|^HH;Eu LHP[A^]FIH]HEHtHKHFLF @UHAVSHPIH^HHEpFHþHfFHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]H5"FH}H$H5=HH腏H5JH趐HA H5ZFHݑHH;†HHH A^HHMHHUHuHuIIFIF0INIVIF@H]HEHtHIHTFH^HH;Eu LHP[A^]FIH]HEHtHmIHFLs F ff.UHAVSHPIH^HHEFHþHFHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]'H5d FH}H蔗H5HHH5JH&HH HQH5XFHFHH;+HHH ^HHMH[HUHuHuIIFIF0INIVIF@H]HEHtHGHFH\^HH;Eu LHP[A^]FIH]HEHtHFHxFL F @UHAVSHPIH^HHEPFHþHFFHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]藽H5FH}HH5*HHeH5fJH薋HH HQH5UFH趌HH;蛁HHH ^HHMHHUHuHuIIFIF0INIVIF@H]HEHtHDH-FH^HH;Eu LHP[A^]lFIH]HEHtHFDH FLLF @UHAVSHPIHk^HHE FHþH FHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]H5DFH}HtH5HHՇH5ֆJHHA H5RFH-HH;HHH ^HHMHHUHuHuIIFIF0INIVIF@H]HEHtHBH FHC^HH;Eu LHP[A^] FIH]HEHtHAH_ FLF ff.UHAWAVAUATSHIHHH^HHE1LHHHsHC H)H L~HHxPLHHHHVLvLHHHL.HvHHHHLhHpHxHEHhH0HDž8H01L^`HHL`PLHIILHIIHHhLpHHxL}LeLuLefHnfsE(ZE(ZEHLH0L8L@LHHPLXL`H0HHhHHH(HHH ^H H;Mt5EH GHH $H^HH;EuHH[A\A]A^A_]\ F FIH0ɿ$H5HI|HHIDH1D D H HuHH9HLLpHffff.)HH)HtK4KLHLHHLHH H@HHHux~GH1HHAHHAH;A|/HAL+IL+)HH0H9|fHLILiLLHHH;MIHMrI MIIcLHDxIJHAIOH)HLHH1HLLLuMbILHHLcxAD$IJHHHL)H1HLpHL@`HHP0MaJLpKtpH1fffff.DDDDDDDDDDDDDDH HuJOHt*ADADD AD D0AD0LHHIL9HLBCIM9~IM)xIKD=HHHO$9J HH H9sEJ/HJ H9s4Lp{ff.1JOH7LhLHH`HHHHpHs1HHHH|pHHhLdpHNH)1ADALDLADALDLADALDLADA D H HuHtVHHHH HhHHHH޿fD8 8D9 9H HuH`I9HLpI@D)HL)HtHB4C4IHuHrqHJDHJLfBtBtBtBtBtBtBtBtBtBtBtBtBtBtB4B4IL9ux~@H1HHpHHpH;p|/H@L+HL+(HH0L9|fHLHLhHHHH;M*I HHHH`LL^0HHIHMzEAMM)LJ<HLHMI1HfDHIs 1MuHvHJ1ff.C40C4B4C4B4C44HHI9uMt0ILHHLffff.C40HHux~CH1HqHHqH;q|!HAL+IL+)HH0H;|LILiHHHHHHH;! MBLHIJHH)HDЃJ HLLHLHHHHT A<|A||A||A| K\0HyL)IHI HL4LE1CDC BDBLCDCL BDB IHuHt*MHIT HLCCLHI9LLHI̐D)HL)HtHCCIHuHHrBIL fffff.CBTCTBTCTBTCT BIL9uLI܃tNH1Lfffff.H9HH9H;y|1HH+qHH L9rL HqHHHIL;Hf^HH;EuHX[A\A]A^A_]nE@UHAWAVAUATSHhIHf^HHEпEHþHEHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]1UHx%TI\IYH WIW)EHE(WEHEHH(HMHxHLxL`HXLp HMH5EH}HU&H5rHH!H5 JH"HH HQH5EH#HH;HHH FQ^HHMHLHUHuHuIEIEIE0IMIUIE@H}Ht H}gEH]HEHtH۹HFEHc^HH;EuLHh[A\A]A^A_]ECIH}HuH]HEHu:LdE H}EH]HEHtIH]HEHtH۹H趤ELE fUHAWAVAUATSHhIH5c^HHEп芤EHþH耤EHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]QHxPIXIUHSIW)EHE(EHEHH(HMHxHLxL`HXLp HMH5EH}HUp#H5IoHH1H52JHbHH HQH5OEH HH;gHHH M^HHMHNHUHuHuIEIEIE0IMIUIE@H}Ht H}EH]HEHtHDعHEH`^HH;EuLHh[A\A]A^A_]ECIH}HuH]HEHu:LE H}荡EH]HEHtIH]HEHtH׹HVEL躛E fUHAWAVAUATSHhIH_^HHEп*EHþH EHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]qNHxeMI]UIURHMPIW)EHE(藟EHEHH(HMHxHLxL`HXLp HMH5AEH}HU H5kHHH5JHHA H5EH)HH;HHH J^HHMHKHUHuHuIEIEIE0IMIUIE@H}Ht H}讞EH]HEHtHԹH荞EH,]^HH;EuLHh[A\A]A^A_]ƞECIH}HuH]HEHu:L諘E H}4EH]HEHtIH]HEHtH[ԹHELaE fUHAWAVAUATSHxHx\^HHEMMAIH}DmI_I+HHuH>H3LIIH HMLHHcH9A$I6LHcH9HMI6HƸH9tLHcH9HMHu L6HFI9t8HMHq HuHHHL)HHH1LMc1HMHIHtNHEH$H5niHH iHL iHH]HLLEWLmLH讜#HHIIffEHEEJ<WEHH]H]JHHEAHLmHu H?H!HZHHwH sf1fHH^H)f1f@AofAoDfAoL Ao\0Aod@ffAoTPffAoD`fAoLpfHHuHt$I|HfoWfofH HuffpNffH~H9tI ff.H1HH9uHuL)HIcH9uH;uu LmHEHEHD$ HThHHD$HEHD$HhHHD$HEH$H5gHH gHL gHH]HHUMLmLH蛚#EH}bEHY^HH;EE11LmM5YLHfInfpDHpHHJHLms1HHqH)1DD D0D@DPD`DpӀӐӠӰH H{Ht(HTHfffff.BH HuL9Hu t!H I)fffff.L9HIuIL}Mt)LvLIELHFHFHHL~L~IEHeW^HH;EuLHx[A\A]A^A_]E莘EH}E HH}Ht#H}HEu HEt H}`EHĒE fff.UHAWAVAUATSHHMLHHHHHV^HHEH8H^HHHLH@H8LoEHEEL-K^M}LI@L8L臖EHEL5FK^ILHDž(HDž HDžHDžDž0H$HHHHLMDHHH LL8Lt H EHƕEH51^HH[EH8cEHPU^HH;EuHHH[A\A]A^A_]EHLL8Lt H ZEH@EHH5x0^HHДEHH8ӕEH腐E ffff.UHAWAVAUATSH8MLIHIHH{T^HHEH8H~^HHHHH@H8>EHLHEEL-HI^I]HI@L8HDEL=I^ILHDž(HDž HDžHDžDž0L贗EHLHHH0H躓EIL聗EHLHHHVHHE0LM(LE HMHUH$HHBHHLL8Lt H EEH+EH5h.^HHEH8ȓEHR^HH;EuHH8[A\A]A^A_]LEILL8Lt H 迓EH襒E#H/H'HHIH5-^HHEIH8ELʍE fUHAWAVAUATSHHIIHQ^HHE1H+oII$IG0@ȃH HcHH}LHLNH}LHLx:H}LHL!&H}LHL H}LHL\,HEI$H-Q^HH;EuLHĘ[A\A]A^A_]ĒErEIHh1I$H5`HH @`HH}Lhl^IG0HHH5`HH `HHPHH5HuHP1LH5.^HO^LEHIPt H`贑EDžDH]HuRHIǰDH]Ht6HEH9t!Lh@t HxgELL9uH}HH]MEEuEuLhMu?wH}*EEtH}ELhMuQIǰDLhMt:HpL9t9HX@t HxאEHI9uHhLp輐EDu LILѐE II$LE UHAWAVAUATSHHHIIHN^HHEH{HLL+IGPHEEEW)EEHEH{HHH+HHu?HkHHHP H)HL1HhJH}HIwXIW`H}|HHPIIPHEHD$HEHD$HEH$LELMLLD9I}tYH\HH G1ALF$H}Ht H}EHM^HH;ELHĘ[A\A]A^A_]LIEHLxLh W)PHDž`M)t[MIHI9kLuEHHXHPJH`M~HLLƏELHXH mHEHEHHcEHcMHHHHUIcHHH5ƒHpHH)HhHHH@HpHHHxHHHHH11HHEHL$IcL9,LPHM$W)0HDž@LXM)t\LHHH9LEIL8L0IDH@M~LLLFEML8HHH0zIH0Ht H8yELIDHHL4D9u1LLHōEIcHHH;IcL$HHLs1H!K dHHHHXHH`1Lffffff.LLLEK<,HJ40LEH`J<(K47LEHJ<(HpJ40ĽEHLxLXH9huHtZHHHHL4HHHLLLHLXEMLIuHHHHH;BBE1E1HEN,IcL9cHPHL,W)0HDž@LXI)ttHLMIHI9 LEHH8H0JH@MLH~HLeELH8HDH0HH0Ht H8螉EHDD9uHJ4HHEIcIIL;LIEHPH8HX*EH}H(,sEEH0\E EIH0?$H5VHH WXHHPL0W)HDž 0諈EH (Z)H tensor.HH Ha vectorHHHmust be HHH tensor HHHAn splitH@(AHCXHHPHL'E1H5e ^H^LVE HPTE HALeMuFzHAL0MHHt H 謇ELeMt6HEL9t!Lh@t Hx臇ELM9uH}LLemEpuPu L0Mu,dH}DEPtH`/EL0Mt:H8L9tQLh@t HxELM9uH0L8EELEH}HLHH0Hfd=HMuIEMAFt I~{ELsEH}Huv}HH}HufmHH0Hu$.HHPHu)3HH0Ht H8EHPHt HXEH}Ht H}EHOE UHAWAVAUATSHHHIIHUD^HHEH{HqLL+IGPHEEEW)EEHEH{H*HH+HHu?H/aHHHP H)HL1H?^JH}HIwXIW`H}HHPIIPHEHD$HEHD$HEH$LELMLLDI}tYHRRHH G1AL;$H}Ht H}lEH C^HH;ELHĘ[A\A]A^A_]LIEHLxLh W)PHDž`M)t[MIHI9kLEHHXHPJH`M~HLLFELHXH蚬HEHEHHcEHcMHHHHUIcHHH5ƒHpHH)HhHHH@HpHHHxHHHHH11HHEHL$IcL9,LPHM$W)0HDž@LXM)t\LHHH9LvEIL8L0IDH@M~LLLƃEML8HHH0IH0Ht H8ELIDHHL4D9u1LLHEEIcHHH;IcL$HHLs1H!K dHHHHXHH`1Lffffff.LLL蘂EK<,HJ40L聂EH`J<(K47LjEHJ<(HpJ40LLEHLxLXH9huHtZHHHHL4HHHLLLHL؁EMLIuHHHHH;BBE1E1HEN,IcL9cHPHL,W)0HDž@LXI)ttHLMIHI9 LEHH8H0JH@MLH~HLELH8HDH0HH0Ht H8EH,DD9uHJ4HHwEIcIIL;LIEHPH8HX~EH}H(,~E~EH0{E~EIH0J5$H5LHH NHHPL0tW)HDž 0+~EH (Z)H tensor.HH Ha vectorHHHmust be HHH tensor HHHAn splitH@(AHMHHPHLE1H5^H]L}E HPzE HALeMuFzHAL0MHHt H ,}ELeMt6HEL9t!Lh@t Hx}ELM9uH}LLe|EpuPu L0Mu,dH}|EPtH`|EL0Mt:H8L9tQLh@t Hx|ELM9uH0L8i|EEL|EH}HLHH0Hfd=HMuIEMAFt I~{EL{EH}Huv}HH}HufmHH0Hu$.HHPHu)3HH0Ht H8{EHPHt HX}{EH}Ht H}k{EHuE UHAWAVAUATSHHHIIH9^HHEH{HLL+IGPHEEEW)EEHEH{HHH+HHu?HVHHHP H)HL1HSJH}HIwXIW`H}|HHPIIPHEHD$HEHD$HEH$LELMLLD9I}tYHGHH G1AL1$H}Ht H}yEH8^HH;ELHĘ[A\A]A^A_]LIEHLxLh W)PHDž`M)t[MIHI9[LuyEHHXHPJH`M~HLLzELHXHZRHEHEHHcEHcMHHHHUIcHHH%ƒHpHH)HhHHH@HpHHHxHHHHH11HHEHL,IcL9LPHM,W)0HDž@LXM)t[LHHH9LwEIL8L0IH@M~LLLGyEML8HHH0{IH0Ht H8zwELXHDHHL4D9u2HLHxEIcHHH;IcL<HHHs1HK HHHHXHH`I1LLLL(xEKJH}H_IwXIW`H}\HHPIIPHEHD$HEHD$HEH$LELMLLDI}tYH2HH eG1AL$H}Ht H}dEHk#^HH;E5LHh[A\A]A^A_]LIEHLxHX W)PHDž`L)t\IIHI9HUdEILXLPKDH`H~LLHeEILXH,HEL3HEHHcEHcUHHHЋMIcHHHcHHH@H1W1HHEH HcH9LPHHI )0HDž@LXM)HtiLHHH9LcEIL8L0IH@MLH~LLLYdEML8HH0蔴HH0Ht H8bEHa$ILDHH@HLHfff.LLy`EIIHuEIcHH@1LHHHHH@IHHHH@M$ILH_EHIIuHHH;LLHHIcHHHH;WHHH@HE11HHELN$IcL9LPHM$W)0HDž@LXM)t[MIHI9=L`EHH8H0JH@M~HLLbELH8HLDH0GHH0Ht H8F`EH"IDD9uPtGHH@HHLf.LH9^EHIIuIcHIL;LIEHPHHX_EH}H_Em_E_EIH0F$H5-HH 41HHPL0pW)HDž 0'_EH (Z)H tensor.HH Ha vectorHHHmust be HHH tensor HHHAn splitH@(AH.HHPHL裭E1H5]H]L^EH0[E HP[E IH0HIALeMuFzIAL0MIIt H ^ELeMt6HEL9t!HX@t Hx]EHI9uH}LLe]EpuPu L0Mu,dH}]EPtH`]EL0Mt:H8L9tQHX@t HxZ]EHI9uH0L8?]EEL^]EH}HL?II]IEHCt H{\EH\EH}HuIH}HulsDB@IH0Hu2-IH0Ht!H8\EHPHuIHPHt HX]\EH}Ht H}K\ELVE UHAWAVAUATSH(IHIHH^HHEH8H]HHHLH@H8Lu[ELHEEL=^MgLI@L8LZEL-I^ILHDž(HDž HDžHDžDž0L]EHLH^H3H诶HH]EHHH.HHHLL8Lt H ZEHYEH5]HHCYEH8KZEH8^HH;EuHH([A\A]A^A_]ZEHLL8Lt H BZEH(YEH買H誷H袷HH5H]HHXEHH8YEHUTE ffff.UHHHxXHt Hx`]YE]@UHSPHH{XHt H{`YEHH[]YEUHAVSIxYEHH]HHH{LHHPL0HA_HH[A^]IH-YELSE fUHAWAVAUATSHIIH^HHEMuIEIEIEIE ffEHEEEaxisEHPHUHh=HPE1HuLhHDžPtCt H{eXEH]XEMeEt H}JXEI?ffEE HEEsplifEtHhHULLHhHDžhHtCt H{WEHWEEt H}WEHpIEIMH9tHQH)HHHs+1H&IE H'^HH;E9FI?I!IpHH{H sf1fH|HwH)f1fofoDfoL o\0od@ffoTPffoD`foLpfHHuHt,HtHf.oVfofH HuffpNffH~L9tJff.H2HH9uIu H8x0HH9uH^HH;EuHĘ[A\A]A^A_]VE2VEH@Hh $H5$HH $HH}LhffPHDž`@UEH`foPZfPHust be >HH0Hvalues mHH(Hte. All HH H attribuHHH 'split'HHHvalue inHHHInvalid Hf@8 0@:LHQ$HHuHPH@!DžLH5X]Hy]H@EUEtIİLL}MuKIİLHhHIIPt H`TEL}Mt6HEL9t!HX@t HxwTEHI9uH}LL}]TEEuEuHhHu&^H}:TEEtH}+TEHhHt:HpH9tMLx@t HxTELL9uHhHpSELtEH@TEI>Hu9@HIEuII>Hu#IEt H}SEI>Ht I}SELME fUHAVSIxrSEHH]HHH{LHHPL H1_HH[A^]IHSELME fUHAVSIxSEHH]HHH{L菼HHPLH_HH[A^]IHRELME fUHAWAVAUATSPMLEIIIHLUEHLH艫HLLTEHLHhHEH0H QELTEHLHCHE0HPEH[A\A]A^A_]H~HvHnHfH^HVfDUHAWAVAUATSPMLEIIIHLMTEHLH蹪AuH=PEL+TEHLH藪HEH0H*PELTEHLHrHEH0HPEH[A\A]A^A_]H謮H褮H蜮H蔮H茮H脮@UHAVSHPIH^HHEPEHþHPEHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]7H5d^EH}H411HH5!HHH5IHHH HQH5EH:HH;HHH ]HHMHHUHuHuIIFIF0INIVIF@H]HEHtHHNEHP ^HH;Eu LHP[A^]NEIH]HEHtHʄHlNELHE UHAVSHPIH ^HHE@NEHþH6NEHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]藓H5[EH}H11HH5?HHIH5JIHzHH HQH5gEHHH;HHH ]HHMHOHUHuHuIIFIF0INIVIF@H]HEHtHoHLEH ^HH;Eu LHP[A^]PLEIH]HEHtH*HKEL0FE UHAVSHPIHK ^HHEKEHþHKEHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]H5$YEH}H11HXH5HHH5IHHA H5ΐEHHH;HHH e]HHMH&HUHuHuIIFIF0INIVIF@H]HEHtHHxIEH^HH;Eu LHP[A^]IEIH]HEHtHH3IELCE ffffff.UHAVSIhIEHH"]HHH{L蟲HHPL0H1]HHH[A^]IHHELCE DUHAWAVSHHHIH6^HHEIGIGIW)EHEH;W)EHEEEaxesEH}HULuHLEt H}HEH]HtHECaeH}HuHU)GEHuHUHH9t9HFH9tH>HJHHHxH9tHH9uHH9H9HHH9LBI)DHt(Hfff.HH9tHXHHHuIruH9H98u(HyH98u0HyH98u8HyH98u@H H9uFfHxHHyH98tHxHHyH98tHxHHyH98tHxHH H9uHH9tHEHM9tL该H]HEHtCt H{FEHFEH}Ht H}oFEH^HH;Eu HH[A^A_]FEIH]HEHt&Ct H{,FEH IEt H}FEH}HuI?Hu(Lh@E H}EEI?Ht II?HtIEEL7@E ffffff.UHAWAVAUATSHIHIH<^HHE1L!W)EHHEI}H8HH+HuHLD!H3HHHP H)HHHKHH}H HsPHSXH}eHHpH`HUHW)@HDžPL`HhL)tiLMIILH=wHDEILHL@KHPHML~LLHEEILHH@1LHH@Ht HHDEL+LkPHL8LxPM9Hx0HPhHtExu?HjH~`1LL BEHijHIIH9|2HKjIHHx0HPILLHDEI$H`Ht HhOCEH}Ht H}=CEH^HH;EuLH[A\A]A^A_]sCEH@a@ECEIH@#H5nHH HH`L@JW) ƅ $HDž0Hut is nuH)HAxes inpH!fDž1llƅ3AHHH`H L[E1H5]H]LBEEBEIH@#H5HH HH`L@L.W) HDž00AEH0(aW) H tensor.HHH vector HHHust be aHHHtensor mHHHAn axes H@'AHHH`H LaE1H5]H]LAE HALeMulHAL@MHHAL@MHH t H0@ELeMt6HEL9t!Lh@t Hx@ELM9uH}LLe@EEu`u#L@Mu36H}f@E`tHpQ@EL@MHHL9Lh@t Hx@ELM9uH t H0?ELeMt6HEL9t!Lh@t Hx?ELM9uH}LLe?EEu`u L@Mu,dH}?E`tHp?EL@Mt:HHL9tQLh@t HxT?ELM9uH@LH9?EELX?EH}HLHH}HHH}HHH@HuJHH@Ht;HH>EH`Hu/9HH}Hu6=HH}Hu(/HH`Ht Hht>EH}Ht H}b>EH8E fffff.UHAWAVAUATSHH]HHEHGHGHHFHH+HIM}IEIW)EHEM)H t^HLHHH=L=EILuIH]LL=EH]HLIL9uLmN1E1LIL9t1@I7EHAL}MuPHAL@MHL H(t H89EL}Mt6HEL9t!Lh@t Hxg9ELM9uH}LL}M9ExuXu L@Mu,dH}$9EXtHh9EL@Mt:HHL9thLh@t Hx8ELM9uH@LH8EEL tL8EH}Ht iH}Hu`I>HuiH2E LHH}Ht H}u8EL I>Ht4HH}HtHL H}HtH}88EI>HtI~'8EH2E UHAWAVAUATSHHMLHHHHH]HHEH8H]HHHLH@H8L?7EHEEL-W]M}LI@L8LW6EHEL5]ILHDž(HDž HDžHDžDž0H$HHHHLMHHHLL8Lt H 6EH5EH5]HH+5EH836EH ]HH;EuHHH[A\A]A^A_]6EHLL8Lt H *6EH5EHH5H]HH4EHH85EHU0E ffff.UHAWAVAUATSPMLEIIIHL]8EHLHɎIuH_4EL;8EHLH觎HEH0HL4EL8EHLH肎HuHjH[A\A]A^A_]H迒H跒H诒H角H蟒H藒UHAVSIh5EHH]HHH{L菞HHPL H!]HHH[A^]IH4EL /E DUHAVSIh4EHH]HHH{LHHPLH]HHH[A^]IH94EL.E DUHAWAVAUATSHIH]HHEп4EHþH3EHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]NHHBHP6HX*H`HhHpHxIIIHW)EHEX,2EHEHHXHMHHHHPHPHXHPH`HPHhHP HpHP(HxHP0Lh8Lp@LxHHXPHMH5@EH}HUcH;H5VFHH詷H5HH H5 IH;HH HQH5(xEH[HH;@HHH ]HHMH5HUHuHuI$ID$ID$0IL$IT$ID$@H}Ht H}0EH]HEHtHgH0EHY]HH;EuLHĘ[A\A]A^A_]0ECIH}HuH]HEHu:L*E H}^0EH]HEHtIH]HEHtHfH'0EL*E UHAWAVAUATSHIH]HHEп/EHþH/EHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]>HH2HP&HXH`HhHpHxIIIHW)EHEX.EHEHHXHMHHHHPHPHXHPH`HPHhHP HpHP(HxHP0Lh8Lp@LxHHXPHMH5LL*+EHEH`HIL}ILeHMHMHMHMLeIH'LLpHMHPLmIHMHLet)Hf.HLIL*ELLHuLeLhILmIFHHINHtLxffff.IFHHHI6HvHcH; HIHHHL,HXHRH MLmLLM)HxHPH}H}H]H:HIHHUHIHHUHIHHUHIHHUHIHHULLLEHMLqI)L}fLLL8)EK/HLL&)ELHEJ<8LL)ELHEJ<8LL(ELHEJ<8LL(ELHEJ<8LL(ELHEJ<8LL(ELL}LLL(ELIIMLuML}Mt"IfHLLh(ELIuLeMLpINLhLmHAIFHH7IN H)HINA~LMLHxAFHHH[A\A]A^A_]ff.UHAWAVAUATSHMHIIHf.I|uHHCH}LMHML{LLoNHu3HEHHIHusV1ɸHu4LSIEIDHMHI$HMHHE1~$HH)1ɸIIDIDIDID ID(ID0ID8HH9ut I ̀HfDHHHuHHEH[A\A]A^A_]UHAWAVAUATSHHIHO]HHE1HIMLpMMt$ M+t$HHjIMMIG I+GHLLL`MILHJL9IL0IW)EHEHHx H+xIIM9#EHH]H]JHEHHpLh I)MHL%EHLHuH GL&#H]HH;EL\H G#H GH 'GH JGHpHu&#H]HH;EVHHĘ[A\A]A^A_]11HH1fH I H HL9rW)EHEHH)t]LHHHIH9x"EILmLmKDHELI)M~LHLH#EILmHU1HtHLpH}HHHEHuHH)HHWH H)H9Mu|H9t1H<0H;<2ukHH9uHHA0xHL0LxPHHLgP1LMLHH#ELHH}HH`K|uIID$H}I\$HHIHu6MH1AA H IMu'H OHOlI7HHƅHDžHDžHDžHs H+sHHDž@Ht.HH@`HHHH 11HH)HHFHuƅHHH 1 LHC0@ȃ GH R"HcHLhHHGLo I)IHNtL!LaPH7HwPIL@LLHxL EKLK4HM|5LyHAAHLHs+LMILLMgI)LHLLr ELHLLa ELHLLP ELHLL? ELHLL. ELHLL ELHLL ELHLLELInLMtIHLLELIuHIILhHHAHHHp^LxHHHHHHvHcH;%HI HH It$HDHݲ]HH;EMH}Ht H}DH}Ht H}DH}Ht H}DHH]HH;Etv6DH}Ht H}DH}HHuH}Ht H}DH}Ht H}DH}Ht H}|DHH]HH;EuHH[A\A]A^A_]ÿ^DIH#H5'GH GH0LG?H[rAHGH0HLAE1H5)w]HJs]LDDIH蔩#H5GH GH0L>HqAH GH0HLbAE1H5v]Hr]LD LDIH #H5GH GH0L5>HIqAHGH0HL@E1H5v]H8r]LDDIH肨#H5GH zGH0L=HpAHGH0HLP@E1H5u]Hq]LD IALhM1IALMoRIALhMIALMnIALhMRIALM6IALhM;IALMrIIt H.DLhMt?HpL9t$HX@t HxDHI9uHhLLpDPu0u&LMu6H`D0tH@DLMH L9 HX@t HxlDHI9uIt HGDLhMt?HpL9t$HX@t HxDHI9uHhLLpDPu0u&LMu62H`D0tH@DLMH L9&HX@t HxDHI9uIt H`DLhMt?HpL9t$HX@t Hx5DHI9uHhLLpDPu0u&LMu6KH`D0tH@DLMH L9?HX@t HxDHI9uIt HyDLhMt?HpL9t$HX@t HxNDHI9uHhLLp.DPu0u#LMu/gH`D0tH@DLMt:H L9tcHX@t HxDHI9uHL DEtLDH}Ht gH}Hu^H}HugH}HupwLIEtiH}^IH}Ht(IH}HtIH}Ht IH}HtH}DH}HtH} DH}Ht H}DL_D qhqqq@UHLHWL)t.LH11I I<uHH9r HHH9r]1]f.UHAWAVAUATSHhH]HHEIILz0L}IC0HEI9tnH5GH @EH}HULEH}HM0"H]HEEt H}DI]H]HH;EHJMLHMHDW)EHEL LpM)HpLEtxHuL]LxMILH=L{DHH]H]JHEM~HLLDHLHELxL]HuL6LFM)u\1L6LFM)tNLH11ffffff.I H<uHHHH9rDH9HHH9rM3MsPHpL#LcPM9|ILPhHtFxu@H}AH~Z1f.LLDH}HIIH9|(H}IH{0HPILLHDIEH}Ht H}DH]HH;ELHh[A\A]A^A_]MHpta1@IH9u HH9rHLOL9v5IHLI9 u|E1H9AJLHH9rHLAt 7HAu-H}HLLErH]H}HlH}^HLLEy1H}HuDHMLL9tKIL9u HII9rH9uIII9rHHegADH}2D HH}HuT[HH}HuFMHH}Hu8?HH}Hu*1HEt(H}HHD HH}Ht H}mDHD fUHAWAVAUATSH8MIHIHHΥ]HHEH8Hj]HHHHH@H8HDLHEEL%]ID$HHI@L8HDL5Z]ILHDž(HDž HDžHDžDž0LDHLHo?HH0HALDHLH??HH0HAHHHDHHL8Lt H DHDH5]HH7DH8?DH,]HH;EuHH8[A\A]A^A_]DHHHL8Lt H /DHD#HBHBHBHBHH5-]HHDHH8DH:D fUHAWAVAUATSH8IIIHLkMMEH{0HPHEH HCPHEHMHIIGPHEHMHHEMM9H]LL IIELL4HLyIL HIHIHHMHLHMIHELuMIFHL}OH HcHMLu}fDHLHUHMM$HEHIIuPL1 HEIEN4ILLILL}( HIHIHHMHLHMIHELuMIFHm H HcHMLu@HLHUHM蝷$HEHIIuMHMH}H}}HUHLtMIIHUHrHHuEAH<MIE1fff.N{E1HMHfHIHMt$LHLfHfHHuIrgHuI4pfYf:HYfHYfHYfHY fHY fHYfHHHH9uLJ{HIL;mILUHMHEH AILL;MKMLe>H}3H}(LEMMJ HMHL)HMHHUHHK4HHhL9HX@t HxcDHI9uH`LhHDEtLkDH}HLtH}HLH}HH}HIHHe LIt H迿DLHMt?HPL9t$HX@t Hx蔿DHI9uHHLLPtD0uu#L`Mu/kH@HDtH 3DL`Mt>HhL9HX@t HxDHI9uH`LhDEtL DH}HtH}HH}HH}Ht H}袾DHHHHuHHLD Ct H{cDH[DHHtyLMHHLIH}HRH} DH}HIH}DH}H<@LIHHHIIIIHH'IHHHIHHIH}HLM~PMeMePM9I}0HPhHtJxuDH E1H~^1L fLL9DLHIIH9|.H II}0HPILLH6DE1LM7H@Ht HH腹DH0HDž0HtCt H{_DHWDH`Ht Hh?DH}Ht H}-DHw]HH;E>LH[A\A]A^A_]Mtb1IH9u HH9rJLCL9v4ILM9 1H9MLHH9rHIE0xtuHIE0xuhH(E1LLH(HDž(H0H0HtCt H{TDHLDL0HDž0E1LL]HML9tH9pIHI9sKIL9uHII9rHH9>6DDIH`n#H5GH GH}L`H@6AH/GHuH@LwE1H5;]H7]L覷DH}觴D H@虴D IALeMuFzIAL`MII@t HPDLeMt6HEL9t!HX@t Hx̶DHI9uH}LLe貶DEuEuL`Mu&^H}菶DEtH}耶DL`Mt:HhL9tIHX@t HxUDHI9uH`Lh:DELYDL荰D LIH@HuzIH@HufpIH@HuU_IH}HuIH}HunuIH`HtTIH@HuIH@Ht HH腵DH0HDž0Hu(H`Hu?H}Ht H}PDL贯D Ct H{7DH/DH`HtHhDH}HufUHAWAVAUATSHxLxMIIH`H~s]HHELj HpL+jLHIINH)HHH)vAL#A<$uCMtEHCIHALGI41Lx%sHIFA<$tIMLxM4$IIOH)HHH)vL)rMuKsHIGMtGNHcDCNAHGHuHPLE1H5+]H(]LD IALeMuFzIALhMIIPt H`ODLeMt6HEL9t!HX@t Hx*DHI9uH}LLeDEuEuLhMu&^H}DEtH}ަDLhMt:HpL9tEHX@t Hx賦DHI9uHhLp蘦DEtL軦DLD LfUHAWAVAUATSH(HIMnM~LL)HH9I>I)MIIHH=6HI)LHHH9H]H}wtII9LBMI9v^DHH5DHNDHZ]HHH5,]H\(]HDH4L訥DIDIF~IJ<臥DN$HEJHEL}J4LiDO$MH]~H}HLǦDHEIMfHEIFHtHH([A\A]A^A_] DH([A\A]A^A_]E11yLFDIHDLED ffff.UHAWAVAUATSH8LHHIHH6c]HHEH8H9(]HHHLH@H8LDHEEL%X]Ml$LI@L8L DHW]HHHDž(HDž HDžHDžDž0HLHHL LLLLL8Ht H yDH_DH5=]HHDH8DHa]HH;EuLH8[A\A]A^A_]耣DILL8Ht H DH١DIH5=]HHiDIH8lDLD fDUHAWAVAUATSPMIHIIL1DLLHHsHEsHESL|LDLLHfAIvIEvIEVLDH[A\A]A^A_]HHH}HuDUHAVSIpDHHG]HHH{Lo HHPL0H^HH[A^]IH荡DLD fUHAWAVAUATSHH`]HHEIAEIEIEIEW)EMuHE)PHDž`ƅPDžQpermƅUH}HPHM9Pt H`ҠDH]HtHECHEHMHH)IIIUI}H)HLH)Lv"LHEHMHDžH9uVsJIUHDžH9tH4HHHH9uAEW)0HDž@H*HMgIJ<ҟDIIL0L@L8MIJ4L覟DI?t@D)HHHK!IEIUH9tEfffff.HHHHHIHH IHH9uMtLDH]HEHtCt H{DHDH}Ht H}ܞDH{]]HH;Eu(H[A\A]A^A_]E1IEIUH9LD譞DIHlU#H52zGH tzGHPL4H5zGH 0{GHHEHPH1L-H5n"]H]L_D#DIH0T#H5yGH yGHPL0+HHyGHPHLDžH5!]H]LʝD腝DIH0DT#H5 yGH LyGHPL0-nH5yGH yGHHHPH1LH5F!]Hg]L7D H05DIt H(DDžLeMuRH*IǰLeMt6HEL9t!HX@t HxtDHI9uH}LLeZDpuPu#H0HuOH}.DPtH`DH0Hu IǰH0HH8H9L`@t HxʛDLL9u{IIǰLeMIǰH0HIIJIt HXDDžLeMuRHIǰLeMt6HEL9t!HX@t Hx DHI9uH}LLeDpuPu HHuE}H}ȚDPtH`賚DHHuQIǰHHt:H H9t9L`@t HxoDLL9uHH TDu HILiDH0H<$DH]HEH3aIt H(DLeMt6HEL9t!HX@t Hx͙DHI9uH}LLe賙DpuPu H0Hu,dH}芙DPtH`uDH0Ht:H8H9tRL`@t 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H;MuH@HHHĈ[A\A]A^A_]CCIHh胞"H5XDH FH}Lh3I0H5"YDH2YDH .YDHPM¾AHXDHuHPL;6E1H5yk\Hg\LjC IALeMuFzIALhMIIPt H`CLeMt6HEL9t!HX@t HxCHI9uH}LLeCEuEuLhMu&^H}lCEtH}]CLhMt:HpL9tEHX@t Hx2CHI9uHhLpCEtL:CLnC LؐUHAWAVAUATSH(LMLEIIHIIFIFILLILL;ILILkMMHEIHH @HEIHH@HE1LeHuHMIE1ffffff.H3LI~I;~t ]CIFIIM9LlIIM9HuHHMHMLeI9H([A\A]A^A_]IM&MuOIM&Mu BIM&Mt7IFL9t8HX@t HxCHI9uI>MfsCLC Lf.UHAWAVAUATSHXHMH9HGHEH)HBHEHE1H}IHUHufL9[Bt*@tJL*E>AuDE>AtI^H9ILGMt|At&HEJ|(@HM@uu%HEJ4(DHEJ<(@HM@utJ4*H}HuLCHUHMutHuH}yIDH9Mtb@tJ<*AuHEJ4(HEJ<(AtHEJt(LIHsCHLHUty3ffffff.H9wJD*IIHH;EHu1HX[A\A]A^A_]ff.UHAWAVAUATSH(IHIH\HHEMt$MtxLmLeM|$LeLm IH;HsIV IN(MtIMHufI~ Iv(HHKMtoM~IFHu`Mt$MIHu`@CHIuH{ /HC8HCHLsII$HHt@I$I7:MLeLmIHt1H \HH;Eu6HH([A\A]A^A_]HI|$厾ID$Hן\HH;EtCIHCL|C @UHAWAVSPIIMtLI7LIwLMw MtCIG(L9t.fHX@t HxCHI9uI H[A^A_]LMw(CLH[A^A_]CUHAVSHPIH \HHE`CHþHVCHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]H}11/gH%H5CHH_H5jFHfZH5gYHH[HH HQH5%DH\HH;QHHH \HHMHlHUHuHuIIFIF0INIVIF@H]HEHtHH.CH͜\HH;Eu LHP[A^]mCIH]HEHtHGHCLMC DUHAVSHPIHk\HHECHþHCHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]H}11dH#H54CHH]H5ʽFHWH5VHHXHH HQH5"DHZHH;NHHH {\HHMHHUHuHuIIFIF0INIVIF@H]HEHtHHCH-\HH;Eu LHP[A^]CIH]HEHtHHICLC DUHAVSHPIH˙\HHE CHþHCHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]H}11aHg H5CHHeZH5*FH&UH5'THHWVHA H5K DH~WHH;cLHHH \HHMHsHUHuHuIIFIF0INIVIF@H]HEHtHSHCH\HH;Eu LHP[A^]4CIH]HEHtHHCLC fff.UHAWAVAUATSHIIHIH\HHE1LhHHRW)`HDžpI|$HEHH+HLLu[LHHHC H+CHH4HHIHJH`HH3HSH`L`LhLL)HHHY H+YHW)@HDžPHLHH=H<H7CIL@M4LPLHCLHLM9f.M)I<$ILmHL@LHLL)HH9I<IIM9uLI9Lu"\E1E11M9uI9L?HHIHpL)HHLffff.H:tHHuqffffff.HHHHHuMH FHHK!vH FHH&!QLH}@H:t*Hzt4Hzt?HztJH H9uSf.H1HH2HzuH1HHrHzuH1HHrHzuH1HHrH H9uW) HDž0LL)tUIILH=CHH(H JH0M)M~HLLECLH(H 1L&HHAHHHHHH Ht H(ZCH@Ht HHBCH`Ht Hh*CHɒ\HH;EuHH[A\A]A^A_]`CCIH`͊"H5|FH FH}L`)H@SAH6DHuH@L"E1H5W\HT\LCdH@CSzCIH@9"H5FH +FH}L@0fW) ƅ $HDž0Hut is nuH)HAxes inpH!fDž1llƅ3AHFHuH L!E1H5 W\H*S\LCCIH@t"H5#FH fFH}L@3W) HDž0@XCH0(.Y) H tensor.HH(Hor a 1-DHH H scalar HHHust be aHHHtensor mHHHAn axes H@0AHPFHuH L E1H5V\H(R\LCCIHr"H5!FH dFH}LVHPAH`FHuHLF E1H5U\HQ\LuC H sC HALeMHALM5H2HALeMHAL@MHHAL@MHHt HfCLeMt6HEL9t!Lh@t HxACLM9uH}LLe'CEuEuLMu&^H}CEtH}CLMt:HL9tqLh@t HxCLM9uHLCEtLCH HtSH HCH@HLH t H0OCLeMt6HEL9t!Lh@t Hx*CLM9uH}LLeCEuEu L@Mu-'H}CEtH}CL@MHHL9Lh@t HxCLM9uH t H0CLeMt6HEL9t!Lh@t Hx^CLM9uH}LLeDCEuEuL@Mu&^H}!CEtH}CL@Mt:HHL9tTLh@t HxCLM9uH@LHCELCH`H LHALeMuFzHAL`MHH@t HPUCLeMt6HEL9t!Lh@t Hx0CLM9uH}LLeCEuEuL`Mu&^H}CEtH}CL`Mt:HhL9tILh@t HxCLM9uH`LhCELCHC LHH H+HH@HHH HH(2CH@HufpHH`HumwHH`Hu\fHH`HuKUHH@Hu",HH`Hu)3HH@Ht HHCH`Ht HhCHC UHAWAVAUATSHIH\HHEW)EHPHMI>tDHFH D1A]L6"Hω\HH;ELH[A\A]A^A_]ILeHEL8LxPM,$Ml$PM9t{I|$0HPhHtAxu;ILzH~T1LLCHLZIIH9|*I|$AHI|$0HPHLLHCIH\HH;E4CIIIHtCt H{*CH"CLC fffff.UHAVSIhCHHp\HHH{L3HHPL0H]HH[A^]IHCLC fUHAWAVAUATSHHIH\HHEID$ID$I$H;BuvW)EHEEEaxesEHhHUHLaHhHDžhHtCt H{CHCEt H}CHu'Ht\HH;EuHĘ[A\A]A^A_]CCIHh{"H5FH FH}LhW)PHDž`0_CH`(X)PHte valueHHHttributeHHH'axes' aHHHInvalid HHHMissing/H@&LHFHuHPLDžLH5L\H1H\LCiIǰLLmMuKIǰLHhHIIPt H`XCLmMt6HEL9t!HX@t Hx3CHI9uH}LLmCEuEuHhHu&^H}CEtH}CHhHt:HpH9tJLh@t HxCLL9uHhHpCLt8LCI<$Hu08HIEtH}nCI<$HuII<$Ht I|$MCLC fUHAVSIh2CHHBl\HHH{L/HHPL`H1]HH[A^]IHCLAC fUHAVSIhCHHk\HHH{LO/HHPLH]HH[A^]IHmCLѿC fUHAVSHPIH\HHE@CHþH6CHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChHǃƃHHǃHǃƃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃH]rH5CH}HIH5FHU?H5V>HH@HHHQH5s DHAHH;6HHH o\HHMH[HUHuHuIIFIF0INIVIF@H]HEHtH{HCH\HH;Eu LHP[A^]\CIH]HEHtH6HCLIH`H1ffffff.WH*YH,HHH9rL`LLHHAHH趍HA H蠍IAJMMIGIO H)t1IH`H1fWH*YH,HHH9rL`LLHHaH@Ht HHCH`Ht HhCHo\HH;ELH([A\A]A^A_]A~AH蟌IMII~:HHH@HHH)HHH)H@aH@LMg M+gW) HDž0tCLHHH>9ILCH HH0HLCH(IEPHHHH{hH|MH HH`RIGIO H)H H`H1ff.WH*YH,HHH9r11MTI^HH?L`L6IHHLLHoCIG I+GHhH+`H9 IH`H HL$L L`HLHHH Ht H(`CLH@H:AH@HHMw M+wLHH)HHH)vH@" s HHHMIL@IAMGIv'IIGI I I9stIK H9sg1Kfffff.A?HL9r$s HHHHLHHHHHخCLHHAHHwHs1HIQpKDpH^H)1W( 3VDDLLDDLLDDLLDL H HuHt>J7HW( šVLffffff.D LH HuI9謬CZCIH`c"H5FH YFH}L`FH@Z+AHDHuH@LE1H5+0\HL,\LCH`CƫCIH b"H5FH ŏFH}L W)HDž@iCH(uUX)Hs input.HH.Hided as HH(H be provHH Hzes mustHHHes or siHHH of scalHHHOnly oneH@6AHFHuHLE1H5 /\H++\LC趪CIH ua"H5sFH FH}L W)HDž YCH(Y)Ht index.HHHroi inpuHHHInvalid H@AHFHuHLE1H52.\HS*\L#CީCIH`"H5FH ݍFH}L W)HDž@聩CH(=JV)Has inputHH(Hrovided HH HUST be pHHH sizes MHHHcales orHHHEither sHf@0.AHэFHuHLE1H5.-\HO)\LCH CɨCIH_"H5FH ȌFH}LW)HDžPlCHH(V)H5vFDHCCDAH)FHuHLE1H5W,\Hx(\LHC CIH^"H5FH FH}LW)HDž@覧CH(QX)Hs input.HH.Hided as HH(H be provHH Hzes mustHHHes or siHHH of scalHHHOnly oneH@6AHًFHuHL E1H5G+\Hh'\L8C IALeMIALM[I\IALeMFIALMu I!It HZCLeMt6HEL9t!HX@t Hx5CHI9uH}LLeCEuEu LMu-H}CEtH}CLM|HL9HX@t Hx賥CHI9u9It H莥CLeMt6HEL9t!HX@t HxiCHI9uH}LLeOCEuEu LMu-H})CEtH}CLMHL9HX@t HxCHI9umIALeMIALMMINIALeM~IAL MIIALeM@IAL MhIIt HCLeMt6HEL9t!HX@t HxCHI9uH}LLeңCEuEuLMu&^H}诣CEtH}蠣CLMt:HL9tTHX@t HxuCHI9uHLZCEbLyCH H QLIt HCLeMt6HEL9t!HX@t HxCHI9uH}LLeآCEuEu L Mu-'H}貢CEtH}裢CL MH(L9HX@t HxpCHI9uIt HKCLeMt6HEL9t!HX@t Hx&CHI9uH}LLe CEuEuL Mu&^H}CEtH}ڡCL Mt:H(L9tTHX@t Hx诡CHI9uH L(蔡CE_L賡CH@HSZLIALeMuFzIAL`MII@t HPCLeMt6HEL9t!HX@t HxCHI9uH}LLeޠCEuEuL`Mu&^H}軠CEtH}謠CL`Mt:HhL9tEHX@t Hx聠CHI9uH`LhfCEteL艠CL轚C LTIH`Hu)3IH@Ht HHCH`Ht HhCL`C IH HtH(ןCH@Huffffff.UHAWAVAUATSHMLIHIH2^\HHE1HHz{IMIEI+EIN I+NH9/1HLHXHxHpH$ HL8IFM~ I)LHHLHIL)HH9BH8HJH+ L9MLMMI}L)gMIH11fDu I H; t1HH9r(HIP\LL%}HxHX貖H`HhDXDHHdHC`H9 HPHEHHUHàH9HPHEHH/FH FHDH YFL٠!H\\HH;Eq HXL'LgPII^P։LHH讞CLI$H[\HH;E!L IHHHLϹHpHxHXHDHHHHHHH@HLMIHIXCHHFH FHDȾHH譟!HZ\HH;Eu H@I HHpHHHxHH@HIyH H`HHhL'M,W)HHH3H;HFH ʂF1AHR" HEHHHEHHLHLùHHHH?HH6HpHH0IH@xA`hHˊTHDLyIHXIHHjH`H9HPHL@H\HHHLH@H@LLϙCHDžDžHM\LkLH@H@LCL5M\ILHDž0HDž(HDž HDžDž8H5@PFH~1HH OFH5OFHDHTHHLfHHY!t HCLH@Lt H(CLޗCH53\HHsCLCHHHHHIHHDHPH@ HD@HLHwIHXِIHHH:H`H9HPH`HDžoIBL@HHHpHxLLHuHuHPHH@HpHuHPLpLl$(L}L|$ AƉD$AĉD$hD$H8H$LLHxH`HLpLXH}I9t3Ht4HP(H}I9u.HP HU\HH;E 8HP H}I9tHHP(HgU\HH;EHHHPLLd$(L|$ Lt$H8HD$AʼnD$T$HHH0HpL@Lx`hH.QHI9LtHHP(HHzHH`H9uHP HHHHϋCHHMHHHHH3Ct H{xCHpCHH6HH}HEH9u~HLH@Lt H(CLCH58%\HH萉CL蜊CHJC HHI9tZHHP(HC HH}I9uHP H}HEH9uHtHP(H}HEH9uHP HɄC HHHt#HHCHuHHPH/CH腄C ffff.UHAWAVAUATSHIIHH\HHEHEM$MT$M)trIMH1{V1fff.IMHt#WI*WH*^ffff.(MuHHHuHHL9rHG\HH;EuHHĨ[A\A]A^A_]芉C8CIH`?"H5r:FH qFHxL`N!վI$HIH\$H rFHL$H$H5 rFH>rFH srFL xrFHHLEAHqFHxHHL׾E1H5 \H\L赈C IALeMuFzIAL`MIIHt HXCLeMt6HEL9t!HX@t HxCHI9uH}LLeڇCEuxu L`Mu)aH}贇CxtH}袇CL`Mt:HhL9tEHX@t HxwCHI9uH`Lh\CEtLCL賁C LfDUHAWAVAUATSH8IDUHE\HHEHHHL1LyM)LHIIxH)HHH9iMHLf~xHffEHEHH=HLLLpCHEHL$LeHLXCLeffEHEL5CILmMdLeLLpLCLeHCHHhHH<CIIJ4HH؅CHH?t@)HHHJ!LKDLpI LL|EILH LHhfff.HHIITLHM HU8LE0LmI)HHHH7IE1L0HH7WI*I WH*H*IEBH0EIx HHH@0HMH $HHLHLLW.wHHHHWH*.v HHHfn PvV.0HHE8Hx H<HHHLP0HH HH9|HBHH7fnx@HLLE0uHy1HfnfB~IHHHI9aHjFH jFHkFH CkFEHEʾL识!HA\HH;E5LH8[A\A]A^A_]H,kFH akFEHEʾLHHHPHHXHEH`LhLpxLEIHUIIIH L8ff0HDž@MtHM9N4LJCH0N1MH fox HfnHfB~IIM9|K1fHHfox Hfnf~DHTHfox Hfnf~HI9uIMkffff.IM94 H]L0@HH7WI*I WH*H*IEBH0EIx HHH@0H$HHLHLLHHfn qV.0HHE8Hx HHHHLP0HHHHH9|HJHH7fnx@HLLE0uIHy1HfnLfB~IHHHI9LI~CH}HHL8ff0HDž@MtHM9WN4L~CH0NI4H9K HpH4>I4H91Ht%J4A4HHLIHt H%qCHHt H qCH0Ht H8pCHpCH}Ht H}pCH}Ht H}pCHd/\HH;ELHIHt HpCHHIPE1HIMHfnxHMH0HL0ILH LHhH@HHE1foMx HfBnHfB~LHLHtIL+,LFLDIIO;&|-HDL*LIHIM9LLlIM9LfLIHLIHIQDɃHs1HI)1foMx HfBnf~foMx HfBnf~DfoMx HfBnf~DfoMx HfBnf~D HI9uHt*HHfoMx HfBnf~HHuLIHt HBnCL0M=H8L9t'HXHxHt HxnCHI9uH0LL8KnCmCH !\HHH5:[H[H nC9mCH h!\HHH5 [H[HmC H}jC H0jCHjCH0jCHjCHjCH0jCHzjCHljCH^jC IHH IHH IH0HIHHVHlCL0MF-IHHY7IH0HZIH0H@8IHHu^hIHHtAHkCHHu5?IHHt HkCHHuHHt HkCH0HH8IH}H%IH}HIH}HH}OkCLeC IL0MtMH8L9t3HXHxHt HxkCHI9uH0L8jCLIHjCH}Ht H}jCH}HfL"eC f.UHAWAVAUATSHHLHEH=)\H?H}HuHxHpLhH`_XW)@HDžPH@LW) HDž0H H{H`HhyӽII t8HHýII t(HH=t'ýI#HHH!L HHH!L}@IHL`Ls½1HAIIHI_Ht-HCHCHHCHuHHPHWCHLL`K HLHLxLHHUH4HHHHHH}H HH HJ4HJHHHHHHHM*h.JV1LeLWLm(QEVL@f.WH*HHH;Ps HHH2ffffff.LL/HHW(DVHxHWH*EE.LwL.IFՋfnH,HH9~HHzH9~HWH*\TWH*\TH9u??HpHhHHHpHhHHHH;`HE HHLaI)I*IMH*xIBLBDEUMMEHEHHx HAH]L}f.HH@0HMH $HLHLELMHHH;PsHHHH@H--HxHWH*EE.LwH.AVHFˋ fnH,HH9~HHQH9~HWH*\( BV(THBDWH*\THBLH9uBD?BD?HpHhHJLHpHhHJDH`I9WI*WH*WH*xHE HBLBDEhEUMEHEHHx IH SCH \HHH59[H[H TC HEHp.EVmg1LeLWLm(AVL WH*H(H;0s HH($ffffff.LL*(@VWHpHWH*EE.LwL.IF͋ fnH,HH9~HHQH9~HWH*\THWH*\TH H9u??HhHH HhHHHH;]~HM HH LzI)WH*IMWH*pIBDBLEUMEMHEHHx HALuH]HH@0HMH $LHHLELMH(H;0sHH(H H )HpHWH*EE.HwH.!VIF΋ fnH,HH9~HHQH9~HWH*\( n>V(THBDWH*\THBLH9uBD?BD?HhHJLHhHJDHEI9WI*WH*WH*pHE HBLBDEpEUMEHEHHx IH3OCH \HHH5,[H[HOCHEHt.AVjd1LeLWLm( =VLfDWH*HH;s HH$ffffff.LL&(JHt+HP(HHu%1ILHxHHtHt HHPMt#HIGHuILPLyDCHHu"H Hu.H@Hu:L?C H5ECH HtH(ECH@HtHHECLi?C HyfUHAWAVAUATSH8)LHxHpH(H0H7\HHEW) HDž0H LLֽW)HDžHHHֽW)EHE )EE?)`)PDžp?LxL+8IMIHHWH*HpWH*VYWYVY5VX5VXE1Lmfff.WI*.Q5VuHE HBLBDEULHD@HE8Hx H.HH@0H@H $LHUHLLHLDH(H;0sHH(+H HH BWf: \].Uf~عHEHJH}HLGHIH9r 1HHHHEHHHHItGHCH9uC.uzfDH9r1HHH9unHHud@HCH9t HL!H9uLH9uC.uZHHu-ffff.L!HEHHTfff.)PH}L!(PT5.V(D3VAXA(\ 2V\(DAYDA\YDAXY\(Y\YYAXf:!(Y\YYAX(AYA\YAXY\f:!XHPL#11H=4IL;HHWH*HxWH*UYPWYqUYy1VXy1VXE1Lmfffff.WI*. 11VuHE HBLBDEMLHD@HE8Hx H>HH@0H@H $LHUHLLHLDHH;sHHHHHBWf: \U.Uf~йHEHJH}HLGHIH9r 1HHHHEHHHHItOffffff.HCH9uC.uzfDH9r1HHH9unHHud@HCH9t HL!H9uLH9uC.uRHHu-ffff.L!HEHHLfff.)H}L(T5o*V(D.VAXA(\ .V\(DAYDPA\YDAXY\(Y\YYAXf:!(Y\YYAX(AYA\YAXY\f:!XHPL11H-0IL;H0L Lm0HE(HD}HpHBWH*d]HxHWH*HAWH*`HHWH*ID$HHHLHH(LHHHJHHQH)HHHH1)HH( IEpH1HHxE1H1H HtW..dMAHWf: H,EtJWH*\LH}HLH!HKWHIWDIWH*\LH}HL H+V(HMADMLM MMDIX1WH*THDeXMyWPTLMXIWMXI2QIHWH*XTMXI~!IHWH*P TEXHEHMIHII@HL8L@)PHHI1LHW..`HhWf: L,WI*\LEH}HLINWMWeXPTMXIINWH*XTMXIH(ADHL9H}HLI*)V)WH*TeX&WMXI<WMXI~!INWH*P TEX)LeHPHL3IWHLffffff.HL<$HLHHpLxM(AYE^PX(IH;|HHHhHL8ADL]Aߊ]L@HHL9URI1LHHhWf: L,WI*\LEH}HLINWMWeXPTMXIINWH*XTMXIH}HLI&V)WH*TeXMWMXIcWMXI~!INWH*P TEX)LeHPHLHWLLfIH$HLLHpLxM(AYE^PX(IL;|HHHhHL8ADHLL9]Aߊ]L@HHs1(Hu\H1(fff.DDDDDDDH HuHt*LHHADADH HuHHL9t0LH)LILfffff.(HHuHHHMH;HhL`MtGM7I(Ht@H1CHHuIIGHt1CL1CMMuHDž`HXHҊ]Aߊ]HJЃHs 1Hu]uH)1fHPHHPHDHPHDHPHDHH9uHtHHPHHHuHDžhHIHHHHHHHH;(HHH;0L`MtPf.M7I(Ht@Hb0CHHuIIGHtD0CL<0CMMuHPHDžPHt0CH}HtDH0CHHuH}HEHt/CHHt H/CH Ht H(/CHS[HH;EuH8[A\A]A^A_]/C/CH :[HHH5ܵ[H[H/C.k/CH [HHH5[HU[H}/C IL`MIL`MIHH?IH H?IL`MPIL`M8{IL`M 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HLʻ6IMnMfMtLH[A\A]A^A_]6H[A\A]A^A_]HIHE11kL>6IH 6L=6 DUHAWAVATSIII_IGHH)HHHL9II7H)HHJ3L9H)HHHTUUUUUUH9wHH9HBIHLHH<@76H[HH K4dAHLHILAttHCH07HCHCC HCXHCPHCHHC@HC8HC0C,HC$I_H`I_IFIH 7HCHKHCC HCXHCPHCHHC@HC8HC0C,HC$IWHr`IwB`HBhHJpHBxǂHǂHǂHǂHǂHǂHǂƂHǂI_H`I_HHDH=7H|HDD HDXHDPHDHHD@HD8HD0D,HD$HH`I~HIH37HBHZHBB HBXHBPHBHHB@HB8HB0B,HB$B`HBhHZpHBxǂHǂHǂHǂHǂHǂHǂƂHǂHHHM'IwL9fDH~HyH~HyH~HyH~HyH~H^HYHyHAHAHAFAH~HyHFHFHFHAHAHAFAH~HyHFHFHFH~HHI9H5 DDDLAtHPAtNIwH9HuuLHHAuPuTHtw1L: uVHI9u^fff.H߉HH9t,IwHHAtHxtHt#6tIL;u2LuID$ L;pLutLLHK9t-IHbOHH;ELH8[A\A]A^A_]IHKH HHt;HHfffff.H^L9n HCHCH3HuH9tL9j v{MH I\$HLrLcI@H0HH}1PHUHHLAH]HM3HHCHHHPH;6MH(H{6IH]Ht#HHCHuHHPH6LP6 UHSPHӢ6H NOHHHKHHH[]UHHuNOHHHGHF]@UHSPH_H.3 HHH߉I@,H[]fffff.UHHNH1H; 4OHD]DUHH)O]UHAWAVAUATSHH`OHHEЅx[HcHWHBHRH)HHHH9v2Hj`OHH;UuH IHHHĈ[A\A]A^A_]6觡6IHhfXH5/9H 9H}LhHP AH9HuHPL:E1H5x%OH!OLi6 IALeMuFzIALhMIIPt H`͠6LeMt6HEL9t!HX@t Hx訠6HI9uH}LLe莠6EuEuLhMu&^H}k6EtH}\6LhMt:HpL9tEHX@t Hx16HI9uHhLp6EtL96Lm6 LUHAWAVAUATSHHH`H{^OHHEH{<HKHqH+qH8HHHH} HHH{7IIEIM0L LpM9HPsoHtZLhI$Ht$HHhI}0HHLP0t IM9rIEH@I9u)I<$u IM9rLI9H JOHH@L}HXI$HHEHE(赞6H@HHHHHHMHHHXH8HH HEHhHuL̬H}I9HtHP(LhM H`HM9ILM9HXL}tvHPtLfff.I$Ht,HHhI}0H\HHhP0IM9uI<$udIM9uLffff.IH9]fLHXL}IH9?@HP LhMLIH9vLIH9eL}HuL9x1LH LH`L4[KK;D-HPLXK IT$HRIB2HpJt2 H0H@JD2Jt2LiL9M)u LXHPffff.AG @AG8IHLM8A9D$u6AD$A;Gu+I<$I7辞6uI@A9D$$Md$tLHIt$(Hh%ID$H@HxJL0 HhHpJT0JL0L}HuHHL)HHHH9H`HMHEL9t0fff.HXHxHt Hxd6HI9uH}LL}J6HYOHH;E葛6 36HIHHHILC6H`HH9H 91AAQHhHDžhHtCt H{6H踚6L}MHJYOHH;EaH`HĨ[A\A]A^A_]ÿ茚6H +NOHHH5 OHvOH螚6.\6H MOHHH5 OHFOHn6 H$IH`HHHt;CtH{6IHܙ6HhHDžhHu#IHhHDžhHCt H{蓙6H苙6LuMu2qHLIH}H9t_HtuHP(LuMtAHEL9tSffff.HXHxHt Hx$6HI9uH}Lu6Ls6 HP LuMuLILuMuDUHAWAVAUATSHAAHH\WOHHED9tnH8 D HD* D HDD(HDD(HDPD0D`(HWOHH;EuHĈ[A\A]A^A_]蜘6J6IHh OH59H 9H}Lh6HPJAH9HuHPLE1H5OH<OL 6 IALeMuFzIALhMIIPt H`p6LeMt6HEL9t!HX@t HxK6HI9uH}LLe16EuEuLhMu&^H}6EtH}6LhMt:HpL9tEHX@t HxԖ6HI9uHhLp蹖6EtLܖ6L6 LUHAWAVAUATSHH%UOHHEHuHH_HH)LG LH+WH9u`H9t I9pCH9_t-H3HH_HTOHH;Eu+HĘ[A\A]A^A_]HurHTOHH;EtW66IH`LH59H 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H}HHMI6HLL9DeHcHD9cIIH)HH9L4L7 L LeLHqLE0LDe68uaL6 tUH}6 tHLeI<$HLHEI<$DeHHuH}HtHtLHHMFu fHL91H8[A\A]A^A_]HEM@UHAWAVAUATSH(IIHH;HIM_A TL HHtH߉IL5 tUL HHHH߉P,uup(Hx,1L}LuL{XLs`M9LeHC@HEАIcFHK@HSHH)HH9HL$MtwL HHH߉ HA;MuQH A;M uHHA;Mu?HxIu脐6u.H;HLHtHLHUHMu%MvM9S1H([A\A]A^A_]AFHMȉIINHHINHHKhL脍6H}׊6ffff.UHAWAVAUATSHHKOHHEЅxGHcHO@HWHH)HH9v0HKOHH;UuHHD HĈ[A\A]A^A_]]6 6IHhCH59H 9H}LhرHP AH9HuHPL۱E1H5OH OL͌6 IALeMuFzIALhMIIPt H`16LeMt6HEL9t!HX@t Hx 6HI9uH}LLe6EuEuLhMu&^H}ϋ6EtH}6LhMt:HpL9tEHX@t Hx蕋6HI9uHhLpz6EtL蝋6Lх6 L@UHAWAVAUATSHHIOHHEHuHOH_ HH+HH+GHHHH9u_H9q9H9_(t-H3HH_ HIOHH;Eu/HĘ[A\A]A^A_]HHu+HdIOHH;Et6辊6IH`}AH59H 9HxL`6ֱW)@HDžP ^6HP(*I)@Hrt' entrHHHing 'staHHHNo matchHf@y.@AH9HxH@LرE1H5.OHO OL6ډ6IH`@H59H :9HxL`7ձW)@HDžP0z6HP(K)@Hhan 'staHH Hlarger tHHHalue is HHH'end'. 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H[A^A_]oZ6DUHAWAVAUATSPID&McMnMIMLLM9w 1LIHIHHtyHHtqLt3HCL9tL9r1IHH9uND9cHHu:HCL9tH!H9u(D9cHHuHL!IHHuHuп(?Y6HDcHC HCL{HIFHxH*HHH H*XLHDH MHMxoI*AN tsY.wjIIJHIFIVHI^JHHH@IMLL91IHH*XAN uKL-IrIU1LH ^f:  H(\H,HH1H,.HCH9HCL MnIELt(M9w 1LIIIJHHH I!IJHuH!HIHIFHHH[A\A]A^A_]IL{ Mt#HIGHuILPLXV6HFW6LQ6 fUHAWAVAUATSHHOHHEIIIHsMt$(LH IL$0H9(L8KDsAA M/MGMtI@IcI\$HLH(L@H0LSIHH9w 1HHHI$HHHHt~It;ff.HAH9tH9r1HHH9uSD9qH Hu?HAH9tL!H9u-D9qH HuLH!I$HHzHH(U6IEwMoH@IG L0MwIID$HxH*HHH H*XHH؃H Hx4H*AL$ t8Y.w/LHI $JHH*XAL$ uH HrHS1HH ^f:  H(\H,HH1H,.HCH9HCLI\$HCHt/L9w 1LHII $JHt"HIL8I!I $JHuID$IT$IM|$JIHL8t^H@HKHtCH9rA1HH7ML8t;HI@Hu)ILHPHS6H!HI$L8ID$HUHhLH(H I II?Lt\ID$HI;D$PIHINHHHt HAID$HHID$HHOHH;EH[A\A]A^A_]ÿ8[S6HKHH0H HSHP(HH HMH@HHHIIMH HtIMII|$0HID$8ID$HI;D$PDI@LL HyOHH;E[!S6R6IHh H5S9H ϕ9H}LhO軞W)PƅP(HDž`Hed allocHYHduplicatHQDžaatorƅeHuHP1L'H5hNHNLYR6uIPt H`Q6L}Mt6HEL9t!HX@t HxQ6HI9uH}LL}Q6Et H}Q6Et H}Q6E1LhMuTHIALhMt:HpL9t8HX@t HxGQ6HI9uHhLp,Q6Eu LILBQ6wII_ Ht#HHCHuHHPHO6LP6>IH@t1HH@HAHuH@HHPHO6LJ6 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HIHH0I2HpLIII2HUHFL!L9uII2Hu{IH<tTHHHt"HVH@HHH;Qu HHHuHI IH H HIHH0I2HuLIII2HlH[A^A_]ÿi4HH54HÑ4HHMHHH5HMHMHi4IH@4Lt4 fff.UHLWM LLH DɁHH1Hi-8ߝHHH/H1H1HHH/H1HMZIML9r 1HIIHJHH0HMtCHFH9uL9Nu#mffffff.L9r1IHL9uJH6Hu@@HFH9t HL!L9u(H9uL9NtH6HuM!HJHp1H]ffff.UHAWAVAUATSHIIIuMeI)MIIGHH=HMuI)LHHH9waII9LBMLEHuI9vKV4HH54H谏4HsFMHHH55MHMHV4LEHuIJ<4HHuLEN4IJN|M~HLIN4LI]M}MuHtHH[A\A]A^A_]阑4H[A\A]A^A_]E11LԎ4IH蟑4LӋ4 ff.UHAWAVAUATSH(IIL)HHLWH_LgHL)HH9LM)IILH=HL)HHHH9H}HuLE.HL9IBIM)IHH9讐4HH5Y4H4HDMHHH5MHMH讐4MI)LHH9M,LM9HMM)DHQHLLff.HHHI<$HI<$HuIrfffffff.HHI$HPI$HSHPI$HPI$HSHPI$HPI$HSHPI<$HI<$H L9uMbI MLMHHLH)III)M9JLH)IIIDHHHfIHII $HI $HuIrfffffff.IHI$HHI $IKHHI$HHI $IKHHI$HHI $IKHHI $HI $I M9rHtIL)H4HM9[IEHL)HHHHr HI|H9FHTL98HL)HtHfI>H9IHHuH81IHITHTITHTITHTIT HT IT(HT(IT0HT0IT8HT8IT@H@L9uIM)IHH]H<臍4HJ4M9IGL)HLEHIffffff.IIUIIHuH8rRfDIIEIFIEIFIEIFIEIF IE IF(IE(IF0IE0IF8IE8I@I@M9uHEH+IH8s1HE1J4M9:ILEHMLH)HuII)H~H}LHLI4LH}IHEL4I$L)H~LLHЍ4IHEHHEL8LhLpHtH4HEHInI?I!IPHH_H s%1HLLI5HSH)1DAALLAD AL0D L0AD@ALPD@LPAD`ALpD`LpHHuHt(HHADA D H HuL9uHH([A\A]A^A_]J O4N4IH4LM4 DUHAWAVAUATSHHIHHXIMHHEH8H[MHHHLH@H8L4HEEL50>MM~LI@L8L04L%=MILHDž(HDž HDžHDžDž0L蠌4HLHH膌4HHHHHHLL8Lt H 舉4Hn4H5#MHH4H8 4HGMHH;EuHH[A\A]A^A_]菉4HLL8Lt H 4H4HrHjHH5#MHHh4HH8k4H4 DUHAWAVATSIL%NM&0蔈4HW)C )C)H赔,I^H2#MHI{(uHC H,C(1A,HC H@ [A\A^A_]IM&I^Ht"HB,IH4Lc4 ILV4 fffff.UHAVSIH7NII^HtH,H譇4L[A^]顇4UHVbPt H;bP]H=BbP資4tH2%MHHbPbPH=bP荇4HaP]UHAWAVATSIL%NM&0$4HW)C )C)HE,I^H!MHI{(uHC H,C(1ѽ,HC H@ [A\A^A_]IM&I^Ht"HҖ,IH菆4L4 IL4 fffff.UH>aPt H#aP]H=*aP胆4tH#MHH`P`PH=`P]4H`P]UHAWAVATSIL%cNM&04HW)C )C)H,I^Hr MHI{(uHC Hߎ,C(1衼,HC H@ [A\A^A_]IM&I^Ht"H袕,IH_4L4 IL4 fffff.UH&`Pt H `P]H=`PS4tH"MHH_P_P H=_P-4H_P]UHAWAVATSIL%3NM&0Ą4HW)C )C)H,I^H2MHI{(uHC H词,C(1q,HC H@ [A\A^A_]IM&I^Ht"Hr,IH/4L~4 IL~4 fffff.UH_Pt H^P]H=^P#4tHr!MHH^P^PH=^P4H^P]UHAWAVATSIL%NM&0蔃4HW)C )C)H赏,I^H MHI{(uHC H,C(1A,HC H@ [A\A^A_]IM&I^Ht"HB,IH4Lc}4 ILV}4 fffff.UH]Pt H]P]H=]P4tHJ MHH]P]PH=]P͂4H]P]UHAWAVATSIL%NM&0d4HW)C )C)H腎,I^HMHI{(uHC HO,C(1,HC H@ [A\A^A_]IM&I^Ht"H,IHρ4L3|4 IL&|4 fffff.UH\Pt H\P]H=\PÁ4tH:MHH\P\PH=\P蝁4H\P]UHAWAVATSIL%NM&044HW)C )C)HU,I^HMHI{(uHC H,C(1,HC H@ [A\A^A_]IM&I^Ht"H,IH蟀4L{4 ILz4 fffff.UH[Pt H[P]H=[P蓀4tH*MHH[P[PH=[Pm4Hj[P]UHAWAVATSIL%sNM&04HW)C )C)H%,I^HMHI{(uHC H,C(1豶,HC H@ [A\A^A_]IM&I^Ht"H貏,IHo4Ly4 ILy4 fffff.UHZPt HZP]H=ZPc4tHMHHoZPmZPH=nZP=4HRZP]UHAWAVATSIL%CNM&0~4HW)C )C)H,I^HMHI{(uHC H过,C(1聵,HC H@ [A\A^A_]IM&I^Ht"H肎,IH?~4Lx4 ILx4 fffff.UHYPt H{YP]H=YP3~4tHMHHWYPUYPH=VYP ~4H:YP]UHAWAVATSIL%NM&0}4HW)C )C)Hʼn,I^H*MHI{(uHC H菆,C(1Q,HC H@ [A\A^A_]IM&I^Ht"HR,IH}4Lsw4 ILfw4 fffff.UH~XPt HcXP]H=jXP}4tHjMHH?XP=XP H=>XP|4H"XP]UHAWAVATSIL%NM&0t|4HW)C )C)H蕈,I^HMHI{(uHC H_,C(1!,HC H@ [A\A^A_]IM&I^Ht"H",IH{4LCv4 IL6v4 fffff.UHfWPt HKWP]H=RWP{4tHZMHH'WP%WP H=&WP{4H WP]UHAWAVATSIL%NM&0D{4HW)C )C)He,I^H MHI{(uHC H/,C(1,HC H@ [A\A^A_]IM&I^Ht"H,IHz4Lu4 ILu4 fffff.UHNVPt H3VP]H=:VPz4tHJMHHVP VP H=VP}z4HUP]UHAWAVATSIL%NM&0z4HW)C )C)H5,I^HzMHI{(uHC H,C(1,HC H@ [A\A^A_]IM&I^Ht"H‰,IHy4Ls4 ILs4 fffff.UH6UPt HUP]H="UPsy4tHMHHTPTP H=TPMy4HTP]UHAWAVATSIL%SNM&0x4HW)C )C)H,I^HBMHI{(uHC Hρ,C(1葯,HC H@ [A\A^A_]IM&I^Ht"H蒈,IHOx4Lr4 ILr4 fffff.UHTPt HTP]H= TPCx4tHMHHSPSPH=SPx4HSP]UHAWAVATSIL%NM&0w4HW)C )C)HՃ,I^HMHI{(uHC H蟀,C(1豲,HC H@ [A\A^A_]IM&I^Ht"Hb,IHw4Lq4 ILvq4 fffff.UHAVSIHNII^HtH ,Hv4L[A^]v4UHAWAVATSIL%NM&0v4HW)C )C)Hł,I^HrMHI{(uHC H,C(1衱,HC H@ [A\A^A_]IM&I^Ht"HR,IHv4Lsp4 ILfp4 fffff.UHAWAVATSIL%NM&0u4HW)C )C)H,I^HMHI{(uHC H~,C(1Ѱ,HC H@ [A\A^A_]IM&I^Ht"H肅,IH?u4Lo4 ILo4 fffff.UHAWAVATSIL%NM&0u4HW)C )C)H%,I^HMHI{(uHC H},C(1,HC H@ [A\A^A_]IM&I^Ht"H貄,IHot4Ln4 ILn4 fffff.UHAWAVATSIL%+NM&04t4HW)C )C)HU,I^H MHI{(uHC H},C(11,HC H@ [A\A^A_]IM&I^Ht"H,IHs4Ln4 ILm4 fffff.UHAWAVATSIL%[NM&0ds4HW)C )C)H,I^HZMHI{(uHC HO|,C(1a,HC H@ [A\A^A_]IM&I^Ht"H,IHr4L3m4 IL&m4 fffff.UHAWAVATSIL%NM&0r4HW)C )C)H~,I^HMHI{(uHC H{,C(1葭,HC H@ [A\A^A_]IM&I^Ht"HB,IHq4Lcl4 ILVl4 fffff.UHAWAVATSIL%NM&0q4HW)C )C)H},I^H MHI{(uHC Hz,C(1,HC H@ [A\A^A_]IM&I^Ht"Hr,IH/q4Lk4 ILk4 fffff.UHAWAVATSIL%NM&0p4HW)C )C)H},I^H MHI{(uHC Hy,C(1,HC H@ [A\A^A_]IM&I^Ht"H袀,IH_p4Lj4 ILj4 fffff.UHAWAVATSIL%NM&0$p4HW)C )C)HE|,I^H MHI{(uHC Hy,C(1!,HC H@ [A\A^A_]IM&I^Ht"H,IHo4Li4 ILi4 fffff.UHAWAVATSIL%KNM&0To4HW)C )C)Hu{,I^HZ MHI{(uHC H?x,C(1Q,HC H@ [A\A^A_]IM&I^Ht"H,IHn4L#i4 ILi4 fffff.UHAWAVATSIL%{NM&0n4HW)C )C)Hz,I^H2 MHI{(uHC How,C(1聩,HC H@ [A\A^A_]IM&I^Ht"H2~,IHm4LSh4 ILFh4 fffff.UHAWAVATSIL%NM&0m4HW)C )C)Hy,I^HZ MHI{(uHC Hv,C(1豨,HC H@ [A\A^A_]IM&I^Ht"Hb},IHm4Lg4 ILvg4 fffff.UHAWAVATSIL%NM'0l4HW)C )C)Hy,I_HMHIHK[A\A^A_]IM'I_Ht"H|,IHxl4Lf4 ILf4 UHAWAVAUATSHIH*MHHEA(uIG LVu,AG(1,IG H@ BBPH#BPH=BPP(IMA(uI_ KH{Hu;+Lt,AG(1茥,HI_ KH{Hu1ѩ,HH{H#*MHH;EudLHĘ[A\A]A^A_]Q,H=APk4SH=vAPaH=LH5cAPHl?k4H=`APWk4kk4k4IHh!H5I7H 7H}LhHNLHHHJ7HPHHAH7HuHPL萹E1H5LHLLj4 IALeMuFzIALhMIIPt H`#j4LeMt6HEL9t!HX@t Hxi4HI9uH}LLei4EuEuLhMu&^H}i4EtH}i4LhMt:HpL9tEHX@t Hxi4HI9uHhLpli4Et&Li4Lc4 LIH=?Pwi4Lc4 ffff.UHAVSIHNII^HtH;y,Hh4L[A^]h4UH]fDUH]DUHHL]UHH!M]UHAWAVAUATSHIHHH('MHHEH8H+LHHHLH@H8Lg4HEEL=MMgLI@L8Lg4L-MILHDž(HDž HDžHDžDž0HHmj4HHHLSj4HLHHHHůLL8Lt H Ug4H;f4H5xMHHe4H8f4H%MHH;EuHH[A\A]A^A_]\g4HLL8Lt H f4He4H?ĭH7ĭHH5MHH5e4HH88f4H`4 fUHSPHHtHsH6?HH[]Rf4H[]ÐUHHf4HHH@H@H]fUHAWAVATSIL% NM'0f4HW)C )C)H%r,I_H MHIHK[A\A^A_]IM'I_Ht"Hu,IHe4L_4 IL_4 UHAWAVAUATSHIH$MHHEA(uIG Lvn,AG(1,IG H@ ;PH;PH=;PP(IMA(uI_ KH{Hu;+L n,AG(1謞,HI_ KH{Hu1,HH{HC#MHH;EudLHĘ[A\A]A^A_]q,H=2;Pd4SH=;PqH=LH5:PH_d4H=:Pwd4d49d4IHhH5i7H 7H}Lh%HVLH@HHHf7HPHHAH27HuHPL謲E1H5LH LLc4 IALeMuFzIALhMIIPt H`?c4LeMt6HEL9t!HX@t Hxc4HI9uH}LLec4EuEuLhMu&^H}b4EtH}b4LhMt:HpL9tEHX@t Hxb4HI9uHhLpb4Et&Lb4L\4 LIH= 9Pb4L\4 fUHH-L]UHH%M]UHSPHHtHsH9HH[]b4H[]ÐUHa4HHH@H@H]fUHAWAVATSIL%NM'0a4HW)C )C)Hm,I_HbMHIHK[A\A^A_]IM'I_Ht"Hq,IHHa4L[4 IL[4 UHAWAVAUATSHIHMHHEA(uIG L&j,AG(1ǚ,IG H@ 6PH6PH=6PP(IMA(uI_ KH{Hu;+Li,AG(1\,HI_ KH{Hu1衞,HH{HMHH;EudLHĘ[A\A]A^A_]!,H=:6P[`4SH=6PH=rLH56PH< `4H=6P'`4;`4_4IHhH57H 7H}LhիHLH@HHH7HPHHAH7HuHPL\E1H5LHLL_4 IALeMuFzIALhMIIPt H`^4LeMt6HEL9t!HX@t Hx^4HI9uH}LLe^4EuEuLhMu&^H}^4EtH}~^4LhMt:HpL9tEHX@t HxS^4HI9uHhLp8^4Et&L[^4LX4 LIH=(4PC^4LqX4 fUHHL]UHHM]UHSPHHtHsHV5HH[]]4H[]ÐUH]4HHH@H@H]fUHAWAVATSIL%kNM'0d]4HW)C )C)Hi,I_H MHIHK[A\A^A_]IM'I_Ht"H;m,IH\4L\W4 ILOW4 UHAWAVAUATSHIHbMHHEA(uIG Le,AG(1w,IG H@ R3PH33PH=,3PP(IMA(uI_ KH{Hu;+Lke,AG(1 ,HI_ KH{Hu1Q,HH{HMHH;EudLHĘ[A\A]A^A_]z,H=2P \4SH=2PH=LH5s2PH[4H=p2P[4[4[4IHhXH5ɶ7H m7H}Lh腧H~LH@HHHƷ7HPHHyAH7HuHPL E1H5JLHkLL;[4 IALeMuFzIALhMIIPt H`Z4LeMt6HEL9t!HX@t HxzZ4HI9uH}LLe`Z4EuEuLhMu&^H}=Z4EtH}.Z4LhMt:HpL9tEHX@t HxZ4HI9uHhLpY4Et&L Z4L?T4 LIH=0PY4L!T4 fUHH}L]UHHuM]UHSPHHtHsH1HH[]bY4H[]ÐUHXY4HHH@H@H]fUHAWAVATSIL%NM'0Y4HW)C )C)H5e,I_HLHIHK[A\A^A_]IM'I_Ht"Hh,IHX4L S4 ILR4 UHAWAVAUATSHIHMHHEA(uIG La,AG(1',IG H@ r.PHS.PH=L.PP(IMA(uI_ KH{Hu;+La,AG(1輑,HI_ KH{Hu1,HH{HSMHH;EudLHĘ[A\A]A^A_]v,H=-PW4SH=-PH=LH5-PHoW4H=-PW4W4IW4IHhH5y7H ĵ7H}Lh5H~LHHHz7HPHH-AHF7HuHPLE1H5LHLLV4 IALeMuFzIALhMIIPt H`SV4LeMt6HEL9t!HX@t Hx.V4HI9uH}LLeV4EuEuLhMu&^H}U4EtH}U4LhMt:HpL9tEHX@t HxU4HI9uHhLpU4Et&LU4LO4 LIH=+PU4LO4 ffff.UHHEL]UHH=M]UHSPHHtHsH6:HH[]U4H[]ÐUHU4HHH@H@H]fUHAWAVATSIL%NM'0T4HW)C )C)H`,I_HLHIHK[A\A^A_]IM'I_Ht"Hd,IHXT4LN4 ILN4 UHAWAVAUATSHIHMHHEA(uIG L6],AG(1׍,IG H@ *PH{*PH=t*PP(IMA(uI_ KH{Hu;+L\,AG(1l,HI_ KH{Hu1豑,HH{HMHH;EudLHĘ[A\A]A^A_]1r,H=)PkS4SH=)P1H=LH5)PHLS4H=)P7S4KS4R4IHh H5)7H "7H}Lh垯HLH@HHH&7HPHHAH7HuHPLlE1H5LHLLR4 IALeMuFzIALhMIIPt H`Q4LeMt6HEL9t!HX@t HxQ4HI9uH}LLeQ4EuEuLhMu&^H}Q4EtH}Q4LhMt:HpL9tEHX@t HxcQ4HI9uHhLpHQ4Et&LkQ4LK4 LIH='PSQ4LK4 fUHH L]UHH M]UHSPHHtHsH懨HH[]P4H[]ÐUHP4HHH@H@H]fUHAWAVATSIL%{NM'0tP4HW)C )C)H\,I_HBLHIHK[A\A^A_]IM'I_Ht"HK`,IHP4LlJ4 IL_J4 UHAWAVAUATSHIHrMHHEA(uIG LX,AG(1臉,IG H@ %PH%PH=|%PP(IMA(uI_ KH{Hu;+L{X,AG(1,HI_ KH{Hu1a,HH{H MHH;EudLHĘ[A\A]A^A_]m,H=$PO4SH=$PH=2LH5$PHN4H=$PN4N4N4IHhhH5٩7H Y7H}Lh蕚HLH@HHH֪7HPHHAH7HuHPLE1H5ZLH{LLKN4 IALeMuFzIALhMIIPt H`M4LeMt6HEL9t!HX@t HxM4HI9uH}LLepM4EuEuLhMu&^H}MM4EtH}>M4LhMt:HpL9tEHX@t HxM4HI9uHhLpL4Et&LM4LOG4 LIH="PM4L1G4 fUHHL]UHHM]UHSPHHtHsH薃HH[]rL4H[]ÐUHhL4HHH@H@H]fUHAWAVATSIL%+NM'0$L4HW)C )C)HEX,I_HLHIHK[A\A^A_]IM'I_Ht"H[,IHK4LF4 ILF4 UHAWAVAUATSHIH" MHHEA(uIG LT,AG(17,IG H@ "PH!PH=!PP(IMA(uI_ KH{Hu;+L+T,AG(1̄,HI_ KH{Hu1,HH{Hc MHH;EudLHĘ[A\A]A^A_]i,H=j!PJ4SH=F!PH=LH53!PHJ4H=0!PJ4J4YJ4IHhH57H 7H}LhEH>LH@HHH7HPHH9AHR7HuHPL̘E1H5 LH+LLI4 IALeMuFzIALhMIIPt H`_I4LeMt6HEL9t!HX@t Hx:I4HI9uH}LLe I4EuEuLhMu&^H}H4EtH}H4LhMt:HpL9tEHX@t HxH4HI9uHhLpH4Et&LH4LB4 LIH=XPH4LB4 fUHH]L]UHHUM]UHSPHHtHsHFHH[]"H4H[]ÐUHH4HHH@H@H]fUHAWAVATSIL%SNM'0G4HW)C )C)HS,I_HLHIHK[A\A^A_]IM'I_Ht"HW,IHhG4LA4 ILA4 UHAWAVAUATSHIHMHHEЊ5PHPH=PP(IMA(uI_ KH{Hu;+LP,AG(1?,HI_ KH{Hu1,HH{HFMHH;EudLHĘ[A\A]A^A_]te,H=PF4SH=iP$H=LH5VPHbF4H=SPzF4F44 LIH={PD4L>4 fff.UHAVSIHNII^HtH[T,HD4L[A^]D4UHAWAVATSIL%sNM'0C4HW)C )C)HP,I_HLHIHK[A\A^A_]IM'I_Ht"HS,IHC4L=4 IL=4 UHAWAVAUATSHIHMHHEЊPHPH=PP(IMA(uI_ KH{Hu;+L.L,AG(1_{,HI_ KH{Hu1,HH{HfMHH;EudLHĘ[A\A]A^A_]a,H=mPB4SH=IPH=LH56PHB4H=3PB4B4\B4IHhH57H 7H}LhHHALH@HHH7HPHH4H=P>4?4>4IHh{H57H V7H}Lh訊HLH@HHH7HPHHAH7HuHPL/E1H5mLHLL^>4 IALeMuFzIALhMIIPt H`=4LeMt6HEL9t!HX@t Hx=4HI9uH}LLe=4EuEuLhMu&^H}`=4EtH}Q=4LhMt:HpL9tEHX@t Hx&=4HI9uHhLp =4Et&L.=4Lb74 LIH=CP=4LD74 fff.UHAWAVATSIL%3NM'0<4HW)C )C)HH,I_HLHIHK[A\A^A_]IM'I_Ht"HL,IHH<4L64 IL64 UHAWAVAUATSHIHLHHEЊuPHVPH=OPP(IMA(uI_ KH{Hu;+LD,AG(1t,HI_ KH{Hu1y,HH{H&LHH;EudLHĘ[A\A]A^A_]TZ,H=P;4SH=PĸH=LH5PHoB;4H=PZ;4n;4;4IHhH5L7H +7H}LhHLH@HHHI7HPHHAH7HuHPL菉E1H5;LHLL:4 IALeMuFzIALhMIIPt H`":4LeMt6HEL9t!HX@t Hx94HI9uH}LLe94EuEuLhMu&^H}94EtH}94LhMt:HpL9tEHX@t Hx94HI9uHhLpk94Et&L94L34 LIH=Pv94L34 fff.UHAWAVATSIL%NM'094HW)C )C)H5E,I_H*LHIHK[A\A^A_]IM'I_Ht"HH,IH84L 34 IL24 UHAWAVAUATSHIHLHHEЊPHPH=PP(IMA(uI_ KH{Hu;+LNA,AG(1p,HI_ KH{Hu14v,HH{HLHH;EudLHĘ[A\A]A^A_]V,H=]P74SH=9P脷H=%LH5&PH74H=#P7474|74IHh;H57H 7H}LhhHLH@HHH7HPHH\AHJ7HuHPLE1H5-LHNLL74 IALeMuFzIALhMIIPt H`64LeMt6HEL9t!HX@t Hx]64HI9uH}LLeC64EuEuLhMu&^H} 64EtH}64LhMt:HpL9tEHX@t Hx54HI9uHhLp54Et&L54L"04 LIH=K P54L04 fff.UHAWAVATSIL%NM'0t54HW)C )C)HA,I_HLHIHK[A\A^A_]IM'I_Ht"HKE,IH54Ll/4 IL_/4 UHAWAVAUATSHIHrLHHEЊM PH. 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H HP(H`ş IHH9u$HP H`^ş  H HP(H`1ş IHH9u$HP H`ğp H HP(H`ğp IHݏ+H`ğIHH9u$HP H`dğ 'HHP(H`7ğIHH9u$HP H`ğp H HP(H`ßp IH H9u$HP H`ßHHP(H`wßISIHH9u$HP H`/ß)0HHP(H`ßIH H9u$HP H`ŸxHUHP(H`ŸKRIHPH9u$HP H`oŸ@HHP(H`BŸ@I^ HN+H`ŸN U Is H+H`c j IH`H9u$HP H`0HHP(H`n0IHH9u$HP H`;n u HK HP(H`A H IHPH9u$HP H`0] d H: HP(H`00 7 IHH9u$HP H`{@HHP(H`N@IHZ+H`IH`IH`ҿ0 # IH`豿_ f IH`萿@t HP赊+`t Hp蠊+H=jGʊ+L+ IH`At Hf+t HQ+H=PjG{+L詄+ IH`IH`Ѿ IH`谾 IH`菾0")IH`npelIH`MGNI0t H@]+LHMt:HPL9teHX@t Hx2+HI9uHHLP+`` Hp+H`踽P W LIt HÈ+LMt:HL9teHX@t Hx蘈+HI9uHL}+9 Hd+H`) 0 LIH`@IH`׼ IIH`衼p IH`耼IH`]unxIH`>t Hc+t HN+H=fGx+L要+ IH`ﻟt H+ t H0+H=fG)+LW+ IH`蠻t Hņ+t H谆+H=eGچ+L+ IH`Qt Hv+t H a+H=@eG苆+L蹀+ IH`t H'+t H+H=QdG<+Lj+ IH`賺@t HP؅+Xt HhÅ+H=cG+L+ IH`d@t HP艅+`t Hpt+H=cG螅+L+ IH`@t HP:+`t Hp%+H=teGO+L}+ IH`ƹt H+t Hք+H=eG+L.+ IH`wt H蜄+t H臄+H=dG豄+L~+ IH`(t HM+t H8+H=cGb+L~+ IH`ٸ0t H@+Pt H`+H=xbG+LA~+ IH`芸PIH`i0t H@莃+Pt H`y+H=dG裃+L}+ IIH`pt H*+t H+H=4dG?+Lm}+ IH`趷t Hۂ+t HƂ+H=5cG+L}+ IH`gt H 茂+0t H@w+H=fbG衂+L|+ IH`pt H=+t H(+H=`GR+L|+ IH`ɶt H+t Hف+H=bG+L1|+ IH`z0t H@蟁+Pt H`芁+H=ybG贁+L{+ It H\+H` t H0;+@t HP&+H=`GP+L~{+ It H+H`貵0t H@׀+Pt H`€+H=qaG+L{+ It H蔀+H`Nt Hs+t H ^+H=`G舀+Lz+ IH` IH`޴t H+t H+H=`G+LFz+ IH`菴t H+ t H0+H=_G+Ly+ IH`@t He+t HP+H=?^Gz+Ly+ IH`t H+ t H0+H=p]G++LYy+ IH`袳t H~+t H ~+H=_G~+L y+ IH`St Hx~+ t H0c~+H=\G~+Lx+ It H5~+H`ﲟt H~+t H}+H=]G)~+LWx+ IH`蠲Pt H`}+pt H}+H=]G}+Lx+ IH`Qt H v}+(t H8a}+H=]G}+Lw+ IH`@t HP'}+`t Hp}+H=\G<}+Ljw+ IH`賱t H|+t H |+H=\G|+Lw+ IH`dt H|+t Ht|+H=[G|+Lv+ IH`t H:|+t H%|+H=T[GO|+L}v+ IH`ư@t HP{+`t Hp{+H=ZG|+L.v+ IH`wt H{+t H{+H=YG{+Lu+ It HY{+H`t H8{+t H#{+H=ZGM{+L{u+ IH`įpt Hz+t Hz+H=[Gz+L,u+ It Hz+H``t Hz+t H pz+H=/[Gz+Lt+ IH`t H6z+t H!z+H=PZGKz+Lyt+ IH`®pt Hy+t Hy+H=QXGy+L*t+ IH`st Hy+t Hy+H=2XGy+Ls+ IH`$t HIy+t H 4y+H=WG^y+Ls+ IH`խt Hx+t Hx+H=YGy+L=s+ IH`膭t Hx+ t H0x+H=XGx+Lr+ IH`70t H@\x+Pt H`Gx+H=WGqx+Lr+ IH`謟0t H@ x+Pt H`w+H=wXG"x+LPr+ UHAWAVATSHpHg6DHHEHCt+HL6DHH;E"H0DHp[A\A^A_]H=ICw+tW)pƅpHDžHr(uint8)HvHtensor(uHqƅ~HDžƅH(uint16)HHƅHDžƅH(uint32)HHƅHDžƅH(uint64)HHƅ)ƅHDžHtensor(iHDžnt8)ƅHDžHDžHDžƅHr(int16)HHƅ)HDžƅHr(int32)HHƅHDž(HDž HDžƅHr(int64)HHƅ&)0ƅ0HDž@Hfloat16)H8Htensor(fH1ƅ@HDžXHDžPHDžHƅHHr(float)HNHIƅVHDžpƅ`H(double)HgHtensor(dHaƅoHEƅxH(string)HHtensor(sHyE)EHEEHtensor(bHEEool)EHEHEE"HEHomplex64HEHtensor(cHEE)E)EHEE$Homplex12HMHEfE8)EL=,DIGIGIhs+IGIHHhIOHpHq+HIHIq+HIHIq+HIHIq+HIHIq+HIHIq+HIHIgq+HIHIOq+H0IHI7q+HHIHIq+H`IHIq+HxIHIp+HuIHIp+HuIHIp+HuIHIp+IGEEE x`H'03?KWco{pt Hq+H=CH5!*DHq+H=^Cq+H*0DHH;Eq+H}iq+EH}Vq+EH}Cq+xH}-q+`Hpq+HHXp+0H@p+H(p+Hp+Hp+H~p+Hep+HLp+yH3p+pipIEtNIM'Mt4IGL9PHX@t Hxo+HI9uI?Mgo+EEEx`H0 ".:FR^pt Ho+H=C#IM(IcU rAu HDDH8#H8HHIH9uEIE(HCIcM 09EQAU L|A(yfDD)≕DE1E1HHHpff.Jr D)A9]DHB(HcHtHa#Mu9HEHHHHH9HH#DEtjHdGuHE}H5dGuHuDDDLAtHPt=H~H9tBHvHxAHpuACAH}H? #iH9tQHHRHHxAHptHxu Ht31DT:uHI9uHtf+tAHpH#H8HHIIHH)HI9xHHHL0EIE(AHIcM 09QAU HD5Au$f.A;u$uHPW'IE(0Ɖ0HPH8s#IIE(IcM QAU L|A(tL#AG(I_ H]H#AE9 H"DHH;EuHĨ[A\A]A^A_]\d+( d+IH5W/HCr/HXAHXLqa+H DHIIF IFIFE1H5aCH*CLc+SHHXt Hhec+Et Lc+HHp#HH} #H]+ UHAWAVAUATSHIIIuMeI)MIIGHH=HMuI)LHHH9waII9LBMLEHuI9vKb+HH5a+H`+HDHHH5CHCHb+LEHuIJ<\b+HHuLEN4IJN|M~HLIc+LI]M}MuHtHH[A\A]A^A_]a+H[A\A]A^A_]E11L4_+IHa+L3\+ ff.UHAWAVAUATSHHHIHH8 DHHEH0L%CDM|$@LLHCHH@H0HfHnfL`+HEEID$HHIhL0LLL_+L5DILHDž HDžHDžHDžDž(LLQc+LLH轹LH4c+HHH蜹LH}HHL0HHLt H(`+H_+H5CCHH^+H0_+HDHH;EuLHH[A\A]A^A_]/`+HSHeHHHL0HHLt H_+Hi^+H5CHHF^+H0_+HY+ UHAWAVSH(HDHHEHuHP(HHtvC(t=ujLs ANIFHu 1~#IFH DH H;Mu8H([A^A_]Hh#C(1ɕ#ILs ANIFHu_+(^+HH5Ѵ/H;s+Lr+H}HMAHuH\+H@DHHHC HCHCE1H5CHCHx^+ IIEt H}^+EtH4^+LhX+ UH]f.UH]]+fDUH]+H FH]fDUHHFH]UH]f.UH]]+fDUHAWAVAUATSH8HHDHHE11HbHHP Hr1HDHHPHH1HPHtH1HPH(t$fH@H(uH@ @u;2H@ @$11H2 HHP HH1HPx(uH@ LpMtHxWGLpMuL58WGA~ HHPHHHPHHHPH(tH@H(uH@ @uf]H@ @OHHPx(uH@ LxMtHVGLxMuL=VGA IG(H@ x(IN(HIy(La L` H}#IG(LhHLDMcw MtxIM}HEHtHcM09}QUHD;u;uuH}M'HEȋ0Ɖ0H}#HMHcUruHDHL#IIuHELp A~(tL #AF(Mf HoHuH!#H}z#HCDHH;EuH8[A\A]A^A_]Z+(Z+IH5۫/Hh/H}RAHuLW+H$DHIIF IFIFE1H5CHCL\Z+(Z+IH5d/Hh/H}AHuLW+HDHIIF IFIFE1H5tCH=CLY+LH'HEuHHEt H}nY+EtLY+HH}'#HS+ ffff.UHH U@H1H9NHD]DUHHF]UHAWAVAUATSHHHIHHDHHEH0L% DM|$@LLHiCHH@H0HfHnfL/X+HEEID$HHIhL0LLL8W+L5 DILHDž HDžHDžHDžDž(LLZ+LLH LHZ+HHH찦LH͵HHL0HHLt HxW+H^V+H5CHH;V+H0V+HDHH;EuLHH[A\A]A^A_]W+HSHeHHHL0HHLt HV+HU+H5CHHU+H0VV+HQ+ UH]f.UH]V+fDUHxV+H 3FH]fDUHHFH]UH]f.UH]0V+fDUHSPH1HHHPHtQH1HPHtAH1HPH(tDH@H(u H@ @uu H@ @uH[]11HH[]UHH @H1H9NHD]DUHHF]UH]f.UH]PU+fDUHHU+H FH]fDUHHmFH]UH]f.UH]U+fDUHAWAVAUATSHxHHDHHEо1H1HHHPH0H1HPHH1HPH(u!H@ @H@H(tH@ @H1HPx(BH@ LxMuL=NGA 8HHPx($H@ H@HuHNGx H@(H@x(yH@ HlH`H}F#IG(LhHLDMcg MLhLuIff.M}HEHtHcM09}QUHD:u;uuLE'HEȋ0Ɖ0H}5#HMHcUruHDHL #IIuLhHEHuDu_HMGLxMHMGH@HLuHEHtM09}MLpA~(uFT;uuLE'HEȋ0Ɖ0H}|#HMHcUruHDHELpA~(tL#AF(H`IF 1HHuHi#Hp#IG(LpHEHtHcM09}QUH|>u;uuH}pD'HE0Ɖ0H}͆#HHEHcMQUH|LO #HRHpH#HpW#H}N#HDHH;EuHx[A\A]A^A_]Q+(bQ+IH5/H`/H}fAHuLN+HCHIIF IFIFE1H5CHCL0Q+(P+IH58/Hz_/H}AHuLXN+HCHIIF IFIFE1H5HCHCLP+_H'HEuHHEt H}BP+Et+LeP+!HHp#HH}#HvJ+ fffff.UHH @H1H9NHD]DUHH} F]UHAWAVAUATSHHHIHHHDHHEH0L%SDM|$@LLH)CHH@H0HfHnfLN+HEEID$HHIhL0LLLM+L5DILHDž HDžHDžHDžDž(LLaQ+LLHͧLHDQ+HHH謧LH荬HHL0HHLt H8N+HM+H5SCHHL+H0M+H DHH;EuLHH[A\A]A^A_]?N+HSHeHHHL0HHLt HM+HyL+H5CHHVL+H0M+HG+ UH]f.UH]@M+fDUH8M+H FH]fDUHHFH]UH]f.UH]L+fDUHAWAVAUATSHxIHu DHHE11LIEHW)EE HEEshapfEeHuLHEt H}zL+HtAW)EE HEEshapfEeHu1LmEH}HEEHas_shapeHEHinput_asHEEIEHuLHt x 1Et H}K+H-W)EHEEHextra_shHEEapeIEHuLHHX0Lcx(W)EHEMEJ<jK+ILuLuJKHEu3HEu (HEtH}HH}#MtL"F+H@+ fffff.UHH @H1H9NHD]DUHH F]UHAWAVAUATSHXHUIIH^DHHEI$HHCH}#Lck(MLs0IM>MHEHt+HcM09}1QUH\{(u_lfffff.uff.;uuH}7'HEȋ0Ɖ0H}y#HHEHcMQUH\{(tH"C(L{ IIPLHudHuH #H}o#H8DHH;EuHX[A\A]A^A_]D+(D+IH5Е/HvS/H}7AHuLA+HCHIIF IFIFE1H5CHCLQD+( D+IH5Y/H8S/LLC+4HHEt H}CC+EtLfC+HH}#H=+ fUHAWAVAUATSHHHIHHDHHEH0L%CM|$@LLHyCHH@H0HfHnfL?B+HEEID$HHIhL0LLLHA+L5 CILHDž HDžHDžHDžDž(LLD+LLHLHD+HHHLHݟHHL0HHLt HA+Hn@+H5CHHK@+H0 A+HCHH;EuLHH[A\A]A^A_]A+HSHeHHHL0HHLt H@+H?+H5CHH?+H0f@+H;+ UHAWAVAUATSHXLIHHHHCHHEH0L-%CMu@LLHCHH@H0HfHnfL?+HEEIEHHIhL0LLL>+L=CILHDž HDžHDžHDžDž(LH5B+LHH衘LHHB+HHHyA$It$IEt$IET$HOHHA+HHH.LHHHL0HHLt H>+H=+H5CHH}=+H0=>+H*CHH;EuLHX[A\A]A^A_]>+HSHeHHHL0HHLt H>+H<+H50CHH<+H0=+HJ8+ fUH]f.UH]=+fDUH=+H EH]fDUHHEH]UH]f.UH]p=+fDUHAWAVAUATSHxIHCHHE11LBIEHW)EE HEEshapfEeHuLHEt H}<+HtAW)EE HEEshapfEeHu1LEH}HEEHas_shapeHEHinput_asHEEIEHuLHt x 1Et H}[<+H-W)EHEEHextra_shHEEapeIEHuLHHX0Lcx(W)EHEMEJ<;+ILuLuJKHEu3HEu (HEtH}HH}"MtL6+H1+ fffff.UHH q@H1H9NHD]DUHH E]UH]f.UH]@6+fDUH86+H EH]fDUHHEH]UH]f.UH]5+fDUHAWAVAUATSHIHrCHHE11LI1LP(HÃ{(uLs ANMfMu;+H>#C(1l#ILs ANMfMu1k#IMfILPHt[I1LPHtKI1LPH(t#ff.H@H(u H@ @uuH@ @I\$ID$(HtAL$ 09}AL$ 9|]A;t$$to~At$$A;t$$uH?''ID$(0Ɖ0I|$i#IT$(Ict$ NAL$ HDID$(Ht09}AL$ 9|TA;t$$tfuAt$$H&'ID$(0Ɖ0I|$;i#IT$(Ict$ NAL$ HDID$(Ht09}AL$ 9|TA;t$$tn}At$$H&'ID$(0Ɖ0I|$h#IT$(Ict$ NAL$ HDID$(Ht"09}AL$ HGCHH;EtT At$$H&'ID$(0Ɖ0I|$uh#IL$(IcT$ rAt$ HDHCHH;E Hĸ[A\A]A^A_]I1LPx(uH@ HXHtH.GHXHuH-G{ G HEHEE fEerEbordEIHuLHHHHX0Lch(W)EHEMEF J<2+ILuLuJK1H1E1E1Ey}1E1AEbfHrH)1LAALD L0AD AL0D@LPAD@ALPD`LpAD`ALpHHuHt(H4HD3 3AD6A 6H 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(3(+IHZp+HD$HPHD$Hy8/H$H5]y/Hg+.L(8/H}HxL` AHuLh%+HCHI$ID$ ID$ID$E1H5TCHCL'+Z('+IHo+HD$HXHD$H8/H$H5x/H+.L7/H}HpLh, AHuL$+HCHI$ID$ ID$ID$E1H5CHjCL'+(&+IHn+HD$L|$Hz7/H$H5w/H*.L6/H}HxL`AHuL $+H2CHI$ID$ ID$ID$E1H5CHCLf&+ HrHmHhHcHEu>EHEu3:HEu(/HEu$H2H^HHEt H}%+EuML%+MHEt H}w%+EL%+MHEt H}F%+ELe%+H+ HH}HtEH}%+Eu<_HH}Ht7H}$+Eu.OHMu28HMu(.HEt%H}HEt#H}HMtL$+MtL$+H+ fffff.UHH `@H1H9NHD]DUHHE]UHAWAVAUATSHHHIHHCHHEH0L%CM|$@LLHCHH@H0HfHnfL#+HEEID$HHIhL0LLL"+L5KCILHDž HDžHDžHDžDž(LL%+LLH]|LH%+HHH<|LHHHL0HHLt H"+H!+H5CHH!+H0K"+H8CHH;EuLHH[A\A]A^A_]"+HSHeHHHL0HHLt H#"+H !+H5>CHH +H0!+HX+ UHAWAVAUATSHHHIHHhCHHEH0L%sCM|$@LLHICHH@H0HfHnfL!+HEEID$HHIhL0LLL +L5CILHDž HDžHDžHDžDž(LL#+LLHyLHd#+HHHyLH~HHL0HHLt HX +H>+H5sCHH+H0+HCHH;EuLHH[A\A]A^A_]_ +HSHeHHHL0HHLt H+H+H5ιCHHv+H06+H+ UHAWAVAUATSHHHIHHCHHEH0L%CM|$@LLH٢CHH@H0HfHnfL+HEEID$HHIhL0LLL+L5kCILHDž HDžHDžHDžDž(LL!+LLH}wLH +HHH\wLH=|HHL0HHLt H+H+H5CHH+H0k+HXCHH;EuLHH[A\A]A^A_]+HSHeHHHL0HHLt HC+H)+H5^CHH+H0+Hx+ 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IEDDHAtHPA<$@tUIt$H9L@tNIt$HxAuIuMH 1f\:u`HH9u fH9tMM9Lr 1LIIHpJH@L(M4ML@IEL9tL9r1IHL9IEDDHAtHPA?@t\IwH9L@tVIwHxAuRuVH61ff.\:uZHH9ufH9t6fHxAtHxtH2LLMmM*f.IEL9t L!L9 IEDDHAtHPA?@tVIwH9L@tPIwHxAuLuPH@1D\:u`HH9u"fH9tH0LWHPH0]HPL±H]3HIIG IGIG1H593H53L$(߳HH,>H0LVHPH0ӱAHPH7Hx\3HHHC HCHCE1H5/93H(53H蘳 (VIHSH5<H =HpsAHpL蹰H[3HI$ID$ ID$ID$E1H583H43Ly(ѲHH5;4H<4H :4L<L `4HP}AHPH#Hd[3HHHC HCHCE1H583H43H脲(?HW)PHDž`0H`(R-)PHpty nameHH&Hnon-emptHH H have a HHHers mustHHHnitializHHHTensor iH@.AHPH5HvZ3HHHC HCHCE1H5-73H&33H薱(QHH0HH5;H <L <HPL0L$AHPH蔮HY3HHHC HCHCE1H563H23HX(谰IH:H0LUSHPH0褮HPL HJY3HIIG IGIG1H563H13Lk(&HH51H1H 1L5L 1HP AHPHxHX3HHHC HCHCE1H5p53Hi13Hٯ<(蔯HW)PHDž`@fH`(/)PHpty nameHH-Hon-emptyHH(Hhave a nHH Hrs must HHHitializeHHHensor inHHHSparse tH@5AHPH|HW3HHHC HCHCE1H5t43Hm03Hݮ@(蘮IH5$8H 9HPL6%HPLH>W3HIIG IGIG1H533H/3L_IPH`IP IPunIP&:IPIPuIPH`zIpt H}4EujILOH]HhIPtH`0uIA0t H@ϬEuIHH}HIPtH`萬0uIƳ0t H@ku^IL臬H}HP[IEt H}3EuIHNL肦 IƃL$IHHTH7H1HHH@HHPH8H0H@H@HL֨HHH9HPHHH0hwMIAFu fAFIF IF1LhH`IGHPHXIOI0t H@ t H((HHL蔨HS3HHH{LH503H,3HaZIH^HƪHHI0tH@tuItH(QIqAH42.,IL(H}Hu%3H HIH}HtHHHuHpHDžpHtΩH]Hu;IH]Ht/ffff.L;Ct H{ 蘩H萩LMuH}HEHtrL֣ fffff.UHAWAVAUATSHHg3HHEIIIIt$huHuHFHA|$ u A|$8TLuL H=ߑ6LzIEt H}ǨMH=+3H51@IL$hHqHEqHEQHHHHpHLuLޥH5+3LɥH HQ8AH}賧AH豦H详Hf3HH;EIt$xI}[H@(L+H_ HoHgH_HWHH};H) UHAWAVAUATSHhMLHHIHxH#^3HHEH0L=.S3I_@HLH#3HH@H0HfHnfL˞LHEEIGHHIhL0HHLΝL5R3ILHDž HDžHDžHDžDž(LL7LLHLxHHLHHHHLHHbHH٠LHHEHH輠LHH(LH HHL0HHLt H贝H蚜H573HHwH07H$\3HH;EuLHh[A\A]A^A_]軝HSHeHHHL0HHLt HHH5*73HHқH0蒜HD fff.UHAWAVAUATSHXLLHIIHH3[3HHEH0L5>P3M~@LHH 3HH@H0HfHnfHۛHEEIFHHIhL0LLHL=O3ILHDž HDžHDžHDžDž(HLNHLHLA$It$IEt$IET$HHHHHHhHHwHEwHEWHL5B3ILHDž HDžHDžHDžDž(LL觐LLHLxHH胐LHHHHfLHHHHILHHHH,LHHLHyHHL0HHLt H$H H5?'3HHH0觌HK3HH;EuLHh[A\A]A^A_]+HSHeHHHL0HHLt HHeH5&3HHBH0H贆 fff.UHAWAVAUATSHXLLHIIHHJ3HHEH0L5?3M~@LHH3HH@H0HfHnfHKHEEIFHHIhL0LLHUL=?3ILHDž HDžHDžHDžDž(HL辍HLH*LA$It$IEt$IET$HHHpHHHHHwHEwHEWHHH#HHHLHlHHL0HHLt HHH52$3HHڈH0蚉HH3HH;EuLHX[A\A]A^A_]HSHeHHHL0HHLt HrHXH5#3HH5H0H觃 ffffff.UHAWAVAUATSHXIIHHHG3HHEH0L-<3I]@HLH 3HH@H0HfHnfLILHEEIEHHIhL0HHLLL%<3ILHDž HDžHDžHDžDž(LHH變LHHAIvIEvIEVLHHoLHHHHHHL0HHLt HcHIH5~!3HH&H0HE3HH;EuHHX[A\A]A^A_]fHSHeHHHL0HHLt H躆H蠅H5 3HH}H0=H UHAWAVAUATSHHE3HHEIHHPHH(:HH5HH LxL HxFAHxH范H.3HHHC HCHCE1H5 3H}3H HAHaHP@lL{xH}LTHC0HK8HMHEH}H@H|LXHPLTTHPHLpHLDLcMtkIHP@ILhL` HL5HHHtD9c(IIHPufLȢD IIHPuH(HPSH}H]Ht/ffff.L3Ct H{ XHPLMuHEHUHHJЃHs 1HuTuH)1fff.HuHHuHDHuHDHuHDHH9uHt#Hfff.HUHHHuHEH(HDž(H}HEHtzH0H8H}HDž0H@HEHMH]Ht'H8HuHCHWHtBH9r@1HH6HtHf.L3Ct H{ HLMuH!HUH4HAHH(HDž(Ht谂W))Dž?HXHHPHA(HXHHDLca Mt:ILL;LL貐HtLL>HIuW))Dž?HPHA@HXHHDHcA8HHH8ff.L#A$Mt$It$IDIET$HߔIH0HHLoIyLI9r 1LHHHHHHHvA$YMD$Mt$IHXLHUMHCL9tH9r1HHH9!KHtHSL9uau11ffffff.T A:u@HH9uW IH{ LLLHLHX$ HHY@HCL9tH9r1HHH9qCt)HCH HHuOffffff.Hu LL!HHHDIHXLHMH@@HCL9t L!H9KHtHSL9u\u+1fT A:uAHH9u H{ LLрLHH@HX HHhFfDHCL9tL!H9u/CtHCH HHuHu ILXI(~HHIHL|HXIEIEHHxWH*#ff.HHH WH*XLHHH Hx"H*t&Y.@H*XuH 6HrHV1H DLX^f: (-\H,H HH1H,.HCH9HCHHHFHt0I9r1LHIHJHt HIMgI!HJHu@HIELHJIEHt-H@HNHtH9r 1HHH!HHL(HH0HH;8W))Dž?HPHAXHXHHDHcAPHHH8DL#A$Mt$It$IDIET$HxPڔIH0HHLoIyLI9r 1LHHHHHHHvA$YMD$Mt$IHXLHUMHCL9tH9r1HHH9!KHtHSL9uau11ffffff.T A:u@HH9uIH{ LL|LHLHX_HHY@HCL9tH9r1HHH9qCt)HCHHHuOffffff.HuLL!HHHDIHXLHMH@@HCL9t L!H9KHtHSL9u\u+1fT A:uAHH9uBH{ LL{LHH@HXHHhFfDHCL9tL!H9u/CtHCHHHuHuILXI(yHHIHLwHXIEIEHHxWH*#ff.HHH WH*XLHHH Hx"H*t&Y.@H*XuH 6HrHV1H DLX^f: (o-\H,H HH1H,.HCH9HCHWHHFHt0I9r1LHIHJHt HIMgI!HJHu@HIELHJIEHt-H@HNHtH9r 1HHH!HHL(HH0HH;8HPHApLhHLDHcAhHBIDHXMeID$(HXHHDMc|$ Mt\Iffff.L3AuHu*fIFHtHLXHkHIuLHxHoID$@LpHLDIcD$8Ht{M[It HmEu]IHmHH\yIxIHmLg IH`HIE,0IH]HUL3Ct H{ mHmLMu+QOIt'I} l:II} lwILlHHu"BH6ʓIHHt)L3Ct H{ lHlLMuHHDžHu ILTlHHt0fffff.L3Ct H{ (lH lLMuHHDžHtCkHHuFfIHHuH[ɓIH`HulIHHt*L3Ct H{ kHkLMuHHDžHtlkH`Ht(fDL3Ct H{ HkH@kLMuHPHDžPHtkEt H} kH]Ht,f.L3Ct H{ jHjLMuH}HEHtjL&e fffff.UHAWAVAUATSHhMLHHIHxH)3HHEH0L=3I_@HLH2HH@H0HfHnfLiLHEEIGHHIhL0HHLhL53ILHDž HDžHDžHDžDž(LL'lLLH•LxHHlLHHo•HHkLHHR•HHkLHH5•HHkLHH•LHƕHHL0HHLt HhHgH53HHggH0'hH'3HH;EuLHh[A\A]A^A_]hHSHeHHHL0HHLt HgHfH53HHfH0gH4b fff.UHAWAVAUATSHhMLHHIHxH#&3HHEH0L=.3I_@HLH2HH@H0HfHnfLfLHEEIGHHIhL0HHLeL53ILHDž HDžHDžHDžDž(LL7iLLH裿LxHHiLHHHHhLHHbHHhLHHEHHhLHH(LH ĕHHL0HHLt HeHdH52HHwdH07eH$$3HH;EuLHh[A\A]A^A_]eHSHeHHHL0HHLt HeHcH5*2HHcH0dHD_ fff.UHAWAVAUATSHhLLHIIHxH3#3HHEH0L5>3M~@LHH2HH@H0HfHnfHcHEEIFHHIhLL0LLHbL53ILHDž HDžHDžHDžDž(LLGfLLH購LxA$It$IEt$IET$L膼HHeLHHiHHwHEwHEWLAHHeLHH$HEHpHEpHEPLH]HxeLHH仕HE HpHEpHEPL辻LHHHHH0HHLt HCbH)aH5^2HHaH0aH 3HH;EuLHh[A\A]A^A_]JbHZHlHHHHH0HHLt HaH}`H52HHZ`H0aH[ @UHAWAVAUATSHXIIHHH3HHEH0L-3I]@HLH2HH@H0HfHnfLy`LHEEIEHHIhL0HHL|_L%?3ILHDž HDžHDžHDžDž(LHHbLHHJAIvIEvIEVL(HHbLHH HH轕HHL0HHLt H_Hy^H52HHV^H0_H3HH;EuHHX[A\A]A^A_]_HSHeHHHL0HHLt H^H]H52HH]H0m^HY UHAWAVAUATSHXIIHHH!3HHEH0L-,3I]@HLH2HH@H0HfHnfL]LHEEIEHHIhL0HHL\L%3ILHDž HDžHDžHDžDž(LHH.`LHH蚶AIvIEvIEVLxHH_LHH[HH8HHL0HHLt H\H[H52HH[H0f\HS3HH;EuHHX[A\A]A^A_]\HSHeHHHL0HHLt H:\H [H5U2HHZH0[HoV UHAWAVAUATSHXIIHHHq3HHEH0L-|3I]@HLHS2HH@H0HfHnfL[LHEEIEHHIhL0HHLZL%3ILHDž HDžHDžHDžDž(LHH~]LHH곕AIvIEvIEVLȳHH?]LHH諳HH舸HHL0HHLt H3ZHYH5N2HHXH0YH3HH;EuHHX[A\A]A^A_]6ZHSHeHHHL0HHLt HYHpXH52HHMXH0 YHS UHAWAVAUATSHXLLHIIHH3HHEH0L5 3M~@LHH2HH@H0HfHnfH[XHEEIFHHIhL0LLHeWL=( 3ILHDž HDžHDžHDžDž(HLZHLH:LA$It$IEt$IET$H HHZHHH谕HHwHEwHEWH輰HH3ZHHH蛰LH|HHL0HHLt H'WH VH5B2HHUH0VH3HH;EuLHX[A\A]A^A_].WHSHeHHHL0HHLt HVHhUH52HHEUH0VHP ffffff.UHAWAVAUATSHXIIHHH3HHEH0L- 3I]@HLH2HH@H0HfHnfLYULHEEIEHHIhL0HHL\TL% 3ILHDž HDžHDžHDžDž(LHHWLHH*AIvIEvIEVLHHWLHH뭕HHȲHHL0HHLt HsTHYSH52HH6SH0SH3HH;EuHHX[A\A]A^A_]vTHSHeHHHL0HHLt HSHRH52HHRH0MSHM UHAWAVAUATSHIH3HHEHH H HcO8HH0L W)P)@Dž`?HG@HXHHDHHHLhAEM}IuIDIEUH}°IHHHHLgI{LI9r 1LHHH@HHHHxAE[MEM}IH8L(YM@HCL9tH9r1HHH9!KHtHSL9uau11ffffff.T A:u@HH9uIH{ LL>SL(LH8tHHY@HCL9tH9r1HHH9qCt)HCH+HHuOffffff.HuLL!H@HHDIH8L(MH@HCL9t L!H9KHtHSL9u\u+1fT A:uAHH9uWH{ LLQL(HH8#HHhFfDHCL9tL!H9u/CtHCHHHuHuIL8I(OHHIHLNH8ID$I$HXHxWH*"f.HHH WH*XLHHH Hx"H*`t&Y.@H*X`uH 6HrHV1H DL8^f: (,\H,H HH1H,.HCH9HCH@ǹHHHFHt0I9r1LHIH@JHt HI $gI!H@JHu@HPI$LPHPJI$Ht-H@HNHtH9r 1HHH!HH@L 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HQ.H( H蛋ff.UHAWAVAUATSHH_HtHFHEHvHuDs Au HHuHEDs AtL{( DDHK LmM9MGMt3AHMt Lc0LaH}LL.HMty KL9}rDMt5AtLc0HILHuL.tx3fff.L9}v!HHHJ1H[A\A]A^A_]øꐐUHSHH}2HHEH+6H06H6H56f6/6H=_2H؍,W)HDžƅDžATenƅ)ƅ fDžneDžAffiƅ)) ƅHConstantHDžFillƅHDž8HDž0ƅ(Dž)Cropƅ-)@ƅ@HDžPHDynamicSHADžIliceƅMHDžhHDž`HDžXƅXƅ_tfDž]niDžYGRUUƅ`)pƅpHEHnsorFillHxHGivenTenHqEHEHEEHEHImageScaHEErfEleE)EE$HEHicSoftplHEHParametrHEfEusEHEHEE HEEeEScalE)EEHEHScaledTaHEfEnhEHM6H:6H=+6H5,6H56HHFHH=6H56HFHH=6H56HFH(H=6H56HFH@H=6H56HFHXH=6H56HeFHpH=o6H5p6HHFHUH=U6H5V6H.FHUH=;6H5<6HFHUH=!6H5"6HEHUH=6H56HEEEEEpX@( t Ho(H=2H5_6Hԍs(H2HH;EH[]H}.(E>H}(E5H}(E,H}'p#H}'XHh'@HP'(H8'H {'Hb''HH5G6H=86EEEEpX@(H! 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HDžHDžHDžDž(LL-LLH}LH,HHH\LH=HHL0HHLt H)H(H50HH(H0k)HX0HH;EuLHH[A\A]A^A_])HSHeHHHL0HHLt HC)H)(H5^0HH(H0(Hx# UHAWAVSH(IH0HHEH`HH5HE1HH5H E1HH5HE1HH5(H,E1HH5SHXE1HH5HE1HlH5HE1HNH5HE1H0D$D$$H:H L61E1HG4 HW)HDž fDžTƅ誯HH[uW)HDž@b'H(Q-)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHu5 H 3H0H0HPHHNH5`H_H5 H HH5HLHmHPH9t\HtHP(t H"&LMtPHL9t5HX@t Hx%HI9uHHP uLL%t H %H`kZHD0HH;EuLH([A^A_]%Iu_iILMufIIHPH9tHtHP(uHP t H%LMt@HL9tKfDHX@t Hx$HI9uHL$tH $LIH`WYL ffff.UHAVSHIH0HHEH`HrH5&HAHqD$D$$H7H JL21E1H0 H52H3LSHC H o3H0H0HPHH_H5&]HpH51HHH5 HLHkHPH9t HtHP(HP H`WH0HH;EuLH[A^]p#I"IHPH9uHP HtHP(H`WLA fUHAWAVSH(IHV0HHEH`HH5tHlAHD$D$$HS5H L711E1H. HW)HDž fDžTƅ9HHoW)HDž@!H(=L-)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH0 H u3H0H0HPHHH5ZHH5HO HrH5HnLHhHPH9tkHtHP(t H LMt_HL9tDffffff.HX@t Hxw HI9uHHP uLLF t H 1 H`TH0HH;EuLH([A^A_]_ IugqILMunIIIHPH9tHtHP(uHP t HLMtHHL9tgfffff.HX@t HxGHI9uHL,t H H`SLo LfUHAWAVSH(IH0HHEH`H H5HE1HH5H E1HD$D$$Hh1H LL-1E1H* HW)HDž fDžTƅNHHkW)HDž@H(RH-)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH, H 3H0H0HPHHH5VHH5ėHd HH5)HLHdHPH9t`HtHP(t HLMtTHL9t9@HX@t HxHI9uHHP uLLft H QH` QH0HH;EuLH([A^A_]IugqILMunIIIHPH9tHtHP(uHP t HLMtHHL9tgfffff.HX@t HxgHI9uHLLt H 7H`OL LfUH]f.UH]fDUHH  3H]fDUHH 3H]UH]f.UH]fDUHAWAVAUATSHhIH50HHEIEHW)EE HEEvalufEeHuLHxEt H}BIEHW)EEHEHsparse_vHMEalueEHuLHEEt H}IEHW)EHEEHvalue_inH]fEtHuLHEEt H}IEHW)EEHEH]fEtsEHuLHEEt H}eIEHW)EHEEHvalue_flH]EoatHuLIEt H}IEHW)EEHEH]EoatsEHuLHEEt H}IEHW)EEHEHvalue_stH]EringEHuLIEt H}IEHW)EHEEH_stringsHMH]EHuLELutH}H9HHxHHHHHHMHDHHHH}HHDHHHLMMHDHHHMHDHHH LEMHDщуῃ@MDщуʀHHDHHUH)H33333333HH!HH!HHHHHH!HHH8HHHHuH~41L~81LķILccM%Lk LHHEIMuIG(Ht#IcO 09})QAW H\{(u[hDAw$f.A;w$uH}/ IG(0Ɖ0HEH8KHIG(IcO QAW H\{(tHC(Ls II[gHtZ1L7H}1LŶHuHIH}H0HH;E!AMtdAy(1L(71LnIHELc`(MIIG(HtlIcO 09}hQAW H\{(MAG%1L6H}1LHuH|.Aw$A;w$uLIG(0Ɖ0I>JHIG(IcO QAW H\{(tH*C(Lc Hx0HH;EHh[A\A]A^A_]MtdAx1L51LCIHELc`MIIG(HtmIcO 09}iQAW H\{(MAD$1L5H}1L̴HuHPAw$A;w$uLIG(0Ɖ0I>HHIG(IcO QAW H\{(tHC(Lc HL0HH;EHtmHÃx@1L41LILc{@MIID$(H2IcL$ 09*QAT$ H\{(WaMMMuL%T 4ID$(HuHK 41LK41L葳IA|$LHHEE1ID$ N4IG(HtIcO 09}QAW H\{(uLYAw$A;w$uH} IG(0Ɖ0HEH8gGHIG(IcO QAW H\{(tHC(Ls IIcD$I9bBAt$$A;t$$uLID$(0Ɖ0I>FHID$(IcL$ QAT$ H\{(tHC(L{ H\0HH;E(IH5bHH}AHuLHH0HIIF IFIFE1H50Hؒ0Lr(;IH5bHH}AHuLHѹ0HIIF IFIFE1H50Ha0L (IH5bHeH}XAHuL1HZ0HIIF IFIFE1H5!0H0L(MIH5aH H}葋AHuL H0HIIF IFIFE1H50Hs0L (IH5#aHH}jAHuLC Hl0HIIF IFIFE1H530H0L(_IH5`HH}ÅAHuL H0HIIF IFIFE1H50H0L-(IH55`H?H}| AHuLU H~0HIIF IFIFE1H5E0H0L(qIH5_HH}5AHuL H0HIIF IFIFE1H5Γ0H0L?1HHEupwH{HEu`gHkHEuPWH[HEu@GHKHEu07H;HEu 'H+HEuHHEt H}b EL }yuHH}jeHEuM]HEuBRHEu7GHEu,t H<LMtIHL9t.f.HX@t HxHI9uHLLt HH@I9t HtHP(HP HPm+HF0HH;EuLH[A\A^A_]ILMuOIH@I9Iut~It H-LMtEHL9HX@t HxHI9uHLt HH@I9uHP HtHP(HPb*L LUHAWAVATSH0H0HHEHQs0t+H0HH;EH0H0[A\A^A_]H=s0btW)ƅHDžHr(uint8)HHtensor(uHƅHDžƅH(uint16)HHƅHDžƅH(uint32)HHƅHDžƅH(uint64)HHƅ)ƅHDž Htensor(iHDžnt8)ƅHDž8HDž0HDž(ƅ(Hr(int16)H.H)ƅ6)@HDžPƅ@Hr(int32)HFHAƅNHDžhHDž`HDžXƅXHr(int64)H^HYƅf)pƅpHEHfloat16)HxHtensor(fHqEHEHEHEEHr(float)HUHMEHEEH(double)HMHtensor(dHMEHEE HEHEHEHtensor(bHEEL=0IGIGI aIGIHH IOHHiHIHIQHIHI9HIHI!HIHI H(IHIH@IHIHXIHIHpIHIHuIHIHuIHIHuIHIjIGEEEpX@( $0<t HH=s0H5"0HÜH=_n0H 0HH;EH}JE'H}7EH}$pH}X Hh@HP(H8H HHxH_IEtNIM'Mt4IGL9HX@t HxHI9uI?MgEEEpX@(t HeH=Ll0L H}FEPH}3EGH} p>H} X5Hh@)HP(H8H HHtH[L8UHAWAVATSHIHī0HHEHPH.یH'2H L L@HL HW)HDžfDžTƅ*HH:W)HDž0H(.*)Htensors.HH&Hmeric teHH Ho all nuHHH types tHHHns inputHHHConstraiH@.HHHHH5}HM2LNH H5rH袴H5cfH H&H5vH"LH73t H|LMtIHL9t.f.HX@t HxGHI9uHLL't HH@I9t HtHP(HP HPH0HH;EuLH[A\A^A_]ILMuOIH@I9Iut~It HmLMtEHL9HX@t Hx7HI9uHLt HH@I9uHP HtHP(HPL@ LUHAWAVSHIHV0HHEH`H׌H2H0L0LPHL H5VH2L HG H5 H2LH% H5 HH5cHW HzH5ʣHvLH0HPI9t HtHP(HP H`HY0HH;EuLHĸ[A^A_]HHPI9uHP HtHP(H`,H fUHAVSHIH0HHEH`HR֌D$D$$HH nL1E1HD$D$$HH BL1E1HPH5H2LH H G0HH0H 5i0H8H0HPHHH5 H&H5aH至H誧H5H覱LH.HPH9t HtHP(HP H`H0HH;EuLH[A^]&I"IHPH9uHP HtHP(H`YL ffffff.UHSPH1 HHPHtTH1HPHtDH1HPH(t H@H(u H@ @uu H@ @uH[]11HH[]`UHAVSHIHx0HHEH`HӌD$D$$HH ՠLz1E1HiD$D$$H[H LAE1H-D$D$$HH L1E1HH5H2LH H51HAHH 2H0H0HPHHZH5S HkH5,_H̰ HH5?HLH,HPH9t HtHP(HP H`H΢0HH;EuLH[A^]kI"IHPH9uHP HtHP(H`L< @UHAWAVAUATSHIHR0HHEHPHьH2H H H@H HW)HDžfDžTƅ/HH1W)HDž0#LH(*)Htensors.HH&Hmeric teHH Ho all nuHHH types tHHHns inputHHHConstraiH@.HHHH=H5Hw2L؞H[ HDžHHIW)HDžƅfDžO1ƅIHL8W)@HDžPƅ@DžALessƅE))fDžAƅLHHDžHDžfDžBƅ0IMHLHM~LLHHH@HHHHLHPHH0W)@LHHDžPHHDž(HDž HDž)`HDžpƅ`fDžaO2ƅcIH`LMIW) HDž0ƅ )0)@fDž0Aƅ2Dž!EquafDž%lHDžXHDžPfDžHBƅJ0HIH0HHHL{L HL0L8L@H H(HPHHH0H0HXW) HDž0H`HhHpHDžHDžHDžx)HDž fDžCƅ#IHL=MIW)HDžƅfDžOrƅ))ƅfDžO1ƅHDžHDžƅfDžO2ƅ0HIHHHL{LHLLLHHHHH0HHW)HDžHHHHDžHDžHDž)`HDžp HhH`HH HpHH9$H0HhH`Hh$HHhH`Hh#Hh`HxH`B HxLs LH5HͥH5WH. 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fDžCƅIHLMIW)HDžƅfDžOrƅ))ƅfDžO1ƅHDžHDžƅfDžO2ƅ0"HIHH9HL{L&HLLLHHHHH0HHW)HDžHHHHDžHDžHDž)`HDžp MHhH`HH HpHHH0HhH`HhHHhH`HhHh`HxH`HxLLH5LjHmH5.GHΘ HH5A7HLHLxMtCHL9t(MI)HÀHHHÀIǀuHxLL L`MtAHhL9t&MI)HàHHàI`uH`LLhHH0HHH0`(4@LHH9XHtHP(t HLMOHL90fDHX@t HxHI9uHHHHwH ^HHXE0H@,`HpHHHHH9HP LLt H|H@H H9t HtHP(HP HPH0HH;EuLH[A\A]A^A_]HL`MNHBHAahHLAHAH[HLHAA`HgHLBHAE1HHHH92HP =HLMGHH@H H9HHP(HI9tMoAGt IML9uHzAHI9tMoAGt IEML9uH5AAH")HM9tMoAGt IMM9uLAE1SZH HRYHL`MHhL9MI)HàH]HàI`uHLxMtCHL9t(MI)HÀHHHÀIǀuHxLLL`Mt@HhL9t%MI)HàHHàI`uH`LLhHH0HE1E1E1t HtuMu=HXMt,M9tI\$AD$t I|$7II9uL't H AE1E1Ht HX0uMu:H@Mt)M9tI^AFt I~II9uL`t Hp1E1t HxuMu9H\Mt(L9tLsCt H{?LM9uL/t HMt-Au'KDmHHpMkHHàI`uHHH9HtHP(u t HLMt>HL9t~@HX@t HxwHI9uHL\t HGH@H H9rHP HPH~ L룐UHAWAVATSIM~HMtCI^PL9t.MI)H HxEH IuI~HLM~PM~0Mt:IF8L9t%@HX@t HxHI9uI~0LM~8mAFt I~(]M>Mt+IFL9t+fHX@t Hx7HI9uI> [A\A^A_]LM~[A\A^A_] UH]f.UH]fDUHH 2H]fDUHH2H]UH]f.UH]fDUHAVSH`HH+0HHED$D$$HXH ~L<1E1H+D$D$$HH ~LE1HD$D$$HH }~Ld1E1HfHg2HELuLuHLhH}I9tHtHP(H=0HH;EtHP H%0HH;Eu 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fDUHAWAVSHIHf-HHEH`HH1>0H0L0LPHLYH5HJH5 H HH5DHLHsHPI9t HtHP(HP H``H-HH;EuLHĸ[A^A_]H,HHPI9uHP HtHP(H``H& fDUHAWAVSH(IH6-HHEH`HHDž(H5HL(HcD$D$$H'H L :1E1H2D$D$$HdH L9E1H2D$D$$HH L91E1H57IW)HDž fDžTƅ(wHHIxW)HDžPP*HH())H5CH+CCHHHL8H <0H0H0HPHHH5 QHH5XH HH5HLH,qHPH9tdHtHP(t HZ)LMtXHL9t=HX@t Hx')HI9uHHP uLL(t H (H`]Ht-HH;EuLH([A^A_])IugqILMunIIIHPH9tHtHP(uHP t H0(LMtHHL9tgfffff.HX@t Hx'HI9uHL't H 'H`\L" LfUHAVSHIH8-HHEH`HD$D$$HL:H L:61E1H)/D$D$$H:H L61E1H3H55H00L"HFH -HH0H -H8H0HPHHeH5 KHvH57HHH5pHLH nHPH9t HtHP(HP H`[H-HH;EuLH[A^]v&I"IHPH9uHP HtHP(H`ZLG  ffffff.UHAWAVSH(IHV-HHEH`HD$D$$Hj8H LX41E1HG-D$D$$H98H L41E1H1HW)HDž fDžTƅqHHrW)HDž@$H(e*)Htensors.HH0Hnumeric HH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@8HHHH2H -HH0H -H8H0HPHHH5VHHH5H%HHH5HDLHYkHPH9taHtHP(t H#LMtUHL9t:DHX@t HxW#HI9uHHP uLL&#t H #H`WH-HH;EuLH([A^A_]?#IugqILMunIIIHPH9tHtHP(uHP t H`"LMtHHL9tgfffff.HX@t Hx'"HI9uHL "t H !H`VLO LfUHAVSHIHh-HHEH`HD$D$$H|4H Lj01E1HY)D$D$$HK4H L101E1H-H50H*+0LHAH -HH0H Ţ-H8H0HPHHH5KEHH5gHH*H5 H&LH;hHPH9t HtHP(HP H`0UH -HH;EuLH[A^] I"IHPH9uHP HtHP(H`TLw ffffff.UHAVSHIH-HHEH`H D$D$$H2H  L.1E1Hy'D$D$$Hk2H LQ.1E1H+H5=.Hb)0LH.?H -HH0H -H8H0HPHHH5`CHH5H'HJH5HFLH[fHPH9t HtHP(HP H`PSH)-HH;EuLH[A^]I"IHPH9uHP HtHP(H`RL ffffff.UHAVSHIH-HHEH`H D$D$$H0H .L,1E1H%D$D$$H0H Lq,1E1H*H5],H'0LHN=H -HH0H -H8H0HPHHH5AHH5HGHjH50HfLH{dHPH9t HtHP(HP H`pQHI-HH;EuLH[A^]I"IHPH9uHP HtHP(H`QL ffffff.UHAVSHIH-HHEH`H2 D$D$$H.H NL*1E1H#D$D$$H.H "L*1E1H0(H5}*H%0LHn;H '-HH0H %-H8H0HPHHH5?HH5ǕHgHH5CHLHbHPH9t HtHP(HP H`OHi-HH;EuLH[A^]I"IHPH9uHP HtHP(H`9OL ffffff.UHAVSHIH-HHEH`HRD$D$$H,H nL(1E1H!D$D$$H,H BL(1E1HP&H5(H $0LH9H G-HH0H E-H8H0HPHHH5 H&H5HHH5 VHLH`HPH9t HtHP(HP H`MH-HH;EuLH[A^]&I"IHPH9uHP HtHP(H`YML ffffff.UHAVSHIH-HHEH`HrD$D$$H+H L '1E1HD$D$$H*H bL&1E1Hp$H5&HB"0L;H7H g-HH0H e-H8H0HPHH5H5*HFH5H HH5@zHLH^HPH9t HtHP(HP H`KH-HH;EuLH[A^]FI"IHPH9uHP HtHP(H`yKL ffffff.UHAVSHIH(-HHEH`HD$D$$H>)H L*%1E1HD$D$$H )H L$1E1H"H5$H 0L[H5H -HH0H -H8H0HPHHUH5":HfH5'HHH5`HLH\HPH9t HtHP(HP 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F*HHXI$@HHLxHUHPHtIOLuIFIHHHEH]HJH}H9t HtHP(HP HuH}oHxHpoHg'HH;EuLHx[A\A]A^A_]9IH}LoHxHpoL2 IHpHyoL2 ffffff.UHAWAVSPIHLpHHǃI9t*HtHP(IG Ht%I9t-HIG 0HP IG HuHǃLI HLPHH[A^A_]H2fUH@H]UHAVSIHufHCHC1H5IFHCIINHKHIFIFIH[A^]H豔UHAWAVAUATSH(IIH'HHEW)EHEL9IIIs$DELeMu9C,AuGfANLHHH6ILeHH]LmLLL8C,AtIGIG1L4HEIGHEHMIOIH'HH;EuLH([A\A]A^A_]6H}~3H薓fDUHAWAVATSAHIMgAGu fA$IG(IG 1L3HCID$HHKIL$I$HCHCHEL[A\A^A_]H@UHAWAVAUATSH8AIIH'HHEW)EHEL7HHHs*ELmHuHADI\$AD$uVf`DuIIIL4ILmILuDuH]LLHN6ADI\$AD$tID$(ID$ 1H2HEHCHEHMHKHE$H'HH;EuLH8[A\A]A^A_]4H}1HÑUHAWAVSPHIM~HAFHufAIFXIFP1L$2HCIGHHKIOIHCHCHLH[A^A_]HAUHAWAVAUATSH(IIHB'HHEW)EHEL*6IIIs(DELeMu=C,I_HAGHuKfSLHHH<3ILeHH]LmLLL4C,I_HAGHtIGXIGP1H 1HEHCHEHMHKHHn'HH;EuLH([A\A]A^A_]3H}0Hffff.UHAWAVATSHpHIH'HHEHp0HHHxHHpHW)pHDžLLH?0HHs/0C4C0LLI9tH8LHxMg`HHLHH;tcL=pH'HH;ELHp[A\A^A_]H}HLoHHuHFHHFH3I$HHtI$H3IhމIGpL膳IH0=H01H0p%H|0H'HH;E0HፈHٍHt-\Ht(cHLҲu%u1u=puIHl* H/tH/tH/ptH/H* ffffff.UHAVSH@H+'HHEDH^H]H^H]LVHHFHFHrHuHrHuHrHBHHBDHUH]HHLHHEHEHUHHHHEHEHEDLLèHHL,u(u4H'HH;Eu@HH@[A^]HI.tH4.H'HH;Et.HL躰uHt H-u HL( H-H6( fffff.UHAWAVAUATSHhEĉMHxIHH5'HHEW)EHEL0IIQH]IsDEL}Mu0EHuHUH}MHEu(Eu1H'HH;Eu:HHh[A\A]A^A_]H}7,EtH}(,H'HH;Ets,H}p)H}g) HEt(HEuEuHI& H}+EtH}+H'& ffffff.UHAWAVAUATSHMIHIH)'HHEЃH荥HhH;='Lt H)HhHfpI$HEED#HCHEHCHELkHHCHCAIGHEIGHEMwIIGIGHդHHRDHEHMHHLHEHEHMHHHELHEHEƅLL9HLHHHdL茬t H)t H)HVHJHC'HH;EuHH[A\A]A^A_])()IW)HDž0Z)H(&$)Hismatch.HHHtype misHHHication HHHe specifHHHAttributH@&HL&H'HIIF IFIF1H5v'H?'L(IIt H(t|L(rIL t HQ(t H<(H֪#H謅H褅H蜅H蔅IH襪Lc" ff.UHAWAVAUATSHhLEAHpIHHd'HHEW)EHELL*IIWHxIsDEL}Mu0HuHUHxDLEHEu(Eu1H*'HH;Eu:HHh[A\A]A^A_]H}`&EtH}Q&H'HH;Et&H}#H}# HEt(HEuEuHr  H}%EtH}%HP  UHAWAVAUATSHMIHIHY'HHEЃH轟HhH;='Lt H#HhHjA$EED#HCHEHCHELkHHCHCAIGHEIGHEMwIIGIGHHHDHEHMHHLHEHEHMHHHELHEHEƅLLjHLHHHL车t H$t H#H臦H{Ht'HH;EuHH[A\A]A^A_] $(#IW)HDž0#H('!$)Hismatch.HHHtype misHHHication HHHe specifHHHAttributH@&HL H'HIIF IFIF1H5'Hp'L #IIt H"t|L"rIL=t H"t Hm"H#HHHHIH֤L fff.UHAWAVAUATSHhLEAHpIHH'HHEW)EHEL|$IIWHxIsDEL}Mu0HuHUHxDLEHEu(Eu1HZ'HH;Eu:HHh[A\A]A^A_]H} EtH} H 'HH;Et H}H} HEt(HEuEuH H}+ EtH} H UHAWAVAUATSHMIHIH'HHEЃHHhH;= 't)HHpH;='tL HhHdHpLdLED#HCHEHCHELkHHCHCAIGHEIGHEMwIIGIGHHHDHEHMHHLHEHEHMHHHELHEHEƅLLtHLHHHLǠt H t HH葠H腠H~'HH;EuHH[A\A]A^A_](IW)HDž0H(1$)Hismatch.HHHtype misHHHication HHHe specifHHHAttributH@&HLH'HIIF IFIF1H5'Hz'L*IIt Ht|LrILGt Ht HwH#HyHyHyHyIHL fDUHAWAVAUATSHhLEAHpIHH'HHEW)EHELIIWHxIsDEL}Mu0HuHUHxDLEHEu(Eu1Hj'HH;Eu:HHh[A\A]A^A_]H}EtH}H0'HH;EtH}H} HEt(HEuEuH H};EtH},H UHAWAVAUATSHMIHIH'HHEЃHHhH;=0'HH}Hu1sLHH}LĵLED+HCHEHCHELcHHCHCE7IGHEIGHEI_IIGIGHHИDHEHMHHLHEDHEHMHHHEHHEHEƅLL覒HL'HHHLt H>t H)HÚH跚H'HH;Eu)HH[A\A]A^A_]HhH]$3(IW)HDž0H(O$)Hismatch.HHHtype misHHHication HHHe specifHHHAttributH@&HLH'HIIF IFIF1H5Ϝ'H'LHI It HLIL^t Ht HH(4HsHsIAtH_At LQIHL fff.UHAWAVAUATSHMIHIH'HHEЃH HhH;=@'H H}Hu1GHH}L}LED+HCHEHCHELcHHCHCE7IGHEIGHEI_IIGIGHHDHEHMHHLHEDHEHMHHHEHHEHEƅLL趎HL7HHHL t HNt H9HӖHǖH'HH;Eu)HH[A\A]A^A_]HhH#Y$C(IW)HDž0H(_$)Hismatch.HHHtype misHHHication HHHe specifHHHAttributH@&HLH'HIIF IFIF1H5ߘ'H'LXI It HLILnt Ht HH84HpHpIAtHoAt LaIHL  fff.UHAWAVAUATSHMIIHH'HHEЃ[HHhH;=P'HLt LHhLVEI$Mt$L9tPLfDL;;uLEHHc΍VL_IHML  UHAWAVAUATSHMIIHH'HHEЃ H}{HhH;='Ht L{HhLVFE I]MuL9L8HHHt$Hc@09}'Q@HD^D;DuL> HH0Ɖ0H8e2HHHc@r@HDHHhHI9^E,$ID$HEID$HEMt$I$ID$ID$AIGHEIGHEMgIIGIGHzHH茹DHEHMHHLHEHEHMHHHELHEHEƅLLsyHLHHHLƁt H t HH萁H脁H}'HH;EuHH[A\A]A^A_](IW)HDž0H(0#)Hismatch.HHHtype misHHHication HHHe specifHHHAttributH@&HLH'HIIF IFIF1H5'Hy'L)IIt Ht~LtILFt Ht HvH%HZHZHZHZIHL UHGxH]UHAWAVAUATSHLI׉IDU D]DeH'HHEMIII+H94BHcLH(MED]DU EMDeH(AIOHMIOHMIOIGIIGDHsHuHsHuHsHHCHC0HEH}H8H1H@HEHDž`HDžXHDžPHDžHDžh?DpHEHMHqHxHEHuHEHEHEHEHEDMDeD]DUHcHI}H0EpHXHtfffff.H"HHuHHHDžHHt0t H@H'HH;EuILHĸ[A\A]A^A_]H}pjH}HXHkvUHAWAVAUATSPIM>MnLL)HH9sH)LH[A\A]A^A_]PHIM9u>fI}!AEt.I}I}Hu.D$L$‰$EHULEH}uHEu(Eu1HG'HH;Eu:HHx[A\A]A^A_]H}}EtH}nH 'HH;EtH}H} HEt(HEuEuH H}EtH} Hm DUHAWAVAUATSHLI׉ID] DeDmHn'HHEMII+H9:BHcLMH(EMD] MEDeH(MDmAIOHMIOHMIOIGIIGDHsHuHsHuHsHHCHC0HEH}H8H1H@HEHDž`HDžXHDžPHDžHDžh?DpHEHMHqHxHEHuHEHEHEHEHEDMDmDeD]HcHI8H0EpHXHtfHHHuHHHDžHHt0t H@Hh'HH;EuILHĸ[A\A]A^A_]H}poH}HXHkvUHAWAVAUATSHxELxIՉuHH'HHEW)EHELIIkH]IsDEL}Mu0D$L$‰$EHULEH}u HEu(Eu1H'HH;Eu:HHx[A\A]A^A_]H}EtH}H]'HH;Et H}H} HEt(HEuEuH H}hEtH}YH DUHAWAVAUATSH8HHH˯'HHEHHHH#SH W)E)EE?H;HHCHH9f.H4ILH DH H1Hi-8ߝHHHH/H1H1HII/I1LH]HHsHMI9r 1LHIHEJHHHHt7HAL9tH9r1HHL9ueL9qH HuQHAL9tH!L9u?L9qH Hu+ff.IM!HEJHqfIMwMoIHEHx WH*HHH WH*XHH؃H Hx5H*Mt9Y.w0MHMJHH*XMuH HrHS1H ^f: (!\H,H HH1H,.HCH9HCH}AHH]HCHt)I9r1LHI@HMJHt#HI]I!HMJHuff.HEIL}HEJIHt*H@HKHtH9r 1HHH!HHEL8HEHHH;HHuCHHHLH HHHHDžH8H@HDžHHHLHHHPXHt9HH@HVHtH9r 1HHH!LHGHHCHpHHKHhH`HCHCH H!H0HDHE(HtJIMMHtM~MtLM9r1LIH MLL!HHHH0L M$  L(H0MM@ID$L9tL9r1IHH9AL$HtIT$L9uOu%1ff.AT : u2HH9u`I|$ HLMMH;M$$MfSID$L9tL9r1IHH92AD$tID$HuHM$$MuDMMLID$L9t L!H9AL$HtIT$L9uOu$1f.AT : u3HH9uPI|$ HLLH*M$$MpEID$L9tL!H9u0AD$tID$HuHM$$MuhILH0HC H H(HKHCH`W) HDž0L8LC(HDž8H@H{0HDž@HHHC8Mg8HPHS@XsHHt9H@HWHtH9r 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Hint nameHHHonstrainHHHe type cHHHDuplicatH@AH HzH'HHHC HCHCE1H5bh'H+d'H|I t H0pEuRIHBIAt H{`DI<$HtL?3LMuH{(HC(HtHt H{ H`IPt H`L8Mt?H@L9t$HX@t HxHI9uH8LL@ 0H0I`t HpRHHHtH>HHuH8HDž8Ht uu9HHuEtH0tHa>ItHHHt1HHHuIHHtHHHuHHDžHu'IH}Hu'5ILTH}HuIH}HtH2HHuH}HEHtLx UHAWAVAUATSHL8IHH0H(Hz'HHEW)EHEH}LeMDIff.L#W)EHEL,IIPIDEL}Mf.C7HEH;EHMHHHMHUHPHW)EHEHEHIdffffff.H LMIILIL}ILmLuIH LLL.C7HEH;EXH}HuEt H}sHIW)pHEH0II"IsDpLqMu.>LHHHILuHHpLxLH0LiC>HXHu+W)@HDžPH8SIIIsD@LAMu1ALHHHtILPHH@LHLH8LC>HpHXH@H(I@t HPLXMtIH`L9t.f.HX@t HxHI9uHXLL`pt H}L}Mt?HEL9t*fHX@t HxgHI9uH}LL}MH'HH;EuLHĸ[A\A]A^A_]H} HprH@dIL}MILXMu&qI@t HPLXMtMH`L9t$HX@t HxHI9uHXLL`spu@Ipt4H}IL}Mu+fIEtH}6L}MuIIL}Mt=HEL9t>fff.HX@t HxHI9uH}L}LF LfUHAWAVAUATSHHHX'HHEIL>HFHEI9ILeffffff.W)EI_@)EE?LH<Ht:HH(HUH9HHMHp81H}Bffffff.HILH DH H1Hi-8ߝHHHH/H1H1HHH/H1HLmMIuLtsHL9r 1HIHHEHHHHLtSLeff.HAH9tL9r1IHH9ueL9qH HuQHH!HEHHu'HgqH=*艻H*]@UHSPHHs.H;HHHt[][]ÐUH*t Hk*]H=*#tHf*HS*H@*H-*H5&*<*?H=='Hf豺H=**ɺH*]@UHAWAVAUATSH8IIHLHELHED+HCAH]L{HELDLuLͼIMt 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H L11E1Hw*D$D$$HtH qL<6E1H;*D$D$$HXH ZL11E1H.HW)HDž fDžTƅHHoW)HDž@!H(L#)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH/H 1o)H0H0HPHHH5HH5H+ HNH51HJLH_hHPH9tgHtHP(t H LMt[HL9t@ff.HX@t HxW HI9uHHP uLL& t H  H`TH&HH;EuLH([A^A_]? IugqILMunIIIHPH9tHtHP(uHP t H`LMtHHL9tgfffff.HX@t Hx'HI9uHL t H H`SLO LfUHAWAVSH(IHf&HHEH`H D$D$$H H yLh-1E1HW&D$D$$HTH LB1E1H*HW)HDž fDžTƅ衽HHkW)HDž0H(#)Hry type.HHHrbitraryHHH be of aHHHnsor canHHHInput teH@&HHHH,H5AHi)LH6=H k)H0H0HPHHH5HH5HA HdH5G;H`LHudHPH9t]HtHP(t HLMtQHL9t6HX@t HxwHI9uHHP uLLFt H 1H`PH&HH;EuLH([A^A_]_ILMu|IuS]IIIHPH9tHtHP(uHP t HLMtHHL9tgfffff.HX@t HxGHI9uHL,t H H`OLo LfUHAWAVSH(IH&HHEH`HD$D$$H.H L)1E1Hw"D$D$$H7H L>1E1H&HW)HDž fDžTƅHHhW)HDž0 H(#)Hry type.HHHrbitraryHHH be of aHHHnsor canHHHInput teH@&HHHH8(H5>H"f)LHV9H gh)H0H0HPHHH5HH5Ha HH5gbHLH`HPH9t]HtHP(t HLMtQHL9t6HX@t HxHI9uHHP uLLft H QH` MH&HH;EuLH([A^A_]ILMu|IuS]IIIHPH9tHtHP(uHP t HLMtHHL9tgfffff.HX@t HxgHI9uHLLt H 7H`KL LfUHAWAVSH(IH&HHEH`HH5HAHD$D$$HH L%1AHsD$D$$HH LK%1E1H"HW)HDž fDžTƅ轵HHcW)HDž0H( )Hnsor typHH Ho any teHHH types tHHHn outputHHHConstraiHf@(e.@*HHHH.$H d)H0H0HPHHH5HH5ُHy HH5HLH\HPH9teHtHP(t HLMtYHL9t>fHX@t HxHI9uHHP uLLvt H aH`IH&HH;EuLH([A^A_]IugqILMunIIIHPH9tHtHP(uHP t HLMtHHL9tgfffff.HX@t HxwHI9uHL\t H GH`HL  LfUHAWAVSH(IH&HHEH`H D$D$$H%H L!1E1HD$D$$H2H @Ll&AHhD$D$$HͲH )L@!1AHHW)HDž fDžTƅ诱HH_W)HDž@H(C<#)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH HDžH5DHCLH=H .a)H0H0HPHHH5HH5H( HKH5.JHGLH\XHPH9tdHtHP(t HLMtXHL9t=HX@t HxWHI9uHHP uLL&t H H`DH&HH;EuLH([A^A_]?IuuIugqILMunIIIHPH9tHtHP(uHP t HRLMt:HL9tYHX@t Hx'HI9uHL t H H`CLO  LfUHAWAVSH(IHf&HHEH`HD$D$$HH Lh1E1HWD$D$$HH L1E1HD$D$$HH ѷLE1HD$D$$HH ޷LAHD$D$$HFH ELzAHaD$D$$HU H L91E1HHW)HDž fDžTƅ諬HHZW)HDž@ H(?7#)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH55HX)LH$,H \)H0H0HPHHH5HH5H/ HRH55aHNLHcSHPH9tkHtHP(t H LMt_HL9tDffffff.HX@t HxW HI9uHHP uLL& t H  H`?H&HH;EuLH([A^A_]? ILMutIuKUIIHPH9tHtHP(uHP t Hh LMt@HL9tKfDHX@t Hx7 HI9uHL tH  LIH`>LU ffff.UHAWAVSH(IHf&HHEH`HH5شHִE1HD$D$$HH [LJ1E1H9D$D$$HH L1E1HHW)HDž fDžTƅ胨HHVW)HDž@H(3#)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH /Y)H0H0HPHHH5HH5H) HLH5/HHLH]OHPH9teHtHP(t HLMtYHL9t>fHX@t HxWHI9uHHP uLL&t H H`;H&HH;EuLH([A^A_]?IugqILMunIIIHPH9tHtHP(uHP t H`LMtHHL9tgfffff.HX@t Hx'HI9uHL t H H`:LO LfUHAWAVSH(IHf&HHEH`HH(HDž(H5 HL(HD$D$$HH L31E1H" D$D$$HH LE1H D$D$$HH -LE1H D$D$$HH 'L1E1H!HW)HDž fDžTƅ脌HH5RW)HDž@<H( )Hsor typeHH(H any tenHH Han be ofHHH types cHHHd outputHHHInput anHf@0.HHHH[H5H]P)LHy#H U)H0H0HPHHH5H#H5}H HH5)HLHJHPH9t`HtHP(t HLMtTHL9t9@HX@t HxHI9uHHP uLLt H qH`+7H&HH;EuLH([A^A_]ILMIIuS]IIIHPH9tHtHP(uHP t HLMt)LgHH YE)H0H0HPHHaH5HrH53lHӽ HH5ـ HLH9HPH9t_HtHP(t H5 LMtSHL9t8HX@t Hx HI9uHHP uLL t H H`{%HT&HH;EuLH([A^A_] ILMu|IuS]IIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hx HI9uHL t H H`a$L  LfUHAWAVSH(IH&HHEH`H݂D$D$$HH ܟL 1E1HD$D$$H<H LAHD$D$$HH L 1E1H?HW)HDž fDžTƅHHS<W)HDž@Z H(#)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHmH A)H0H0HPHHFH5 HWH5hH踹 HۭH5|jH׷LH4HPH9tdHtHP(t H LMtXHL9t=HX@t Hx HI9uHHP uLL t H H`[!H4&HH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hx HI9uHL t H H`A L  LfUHAWAVSH(IH&HHEH`H`قD$D$$HH ÜL 1E1HD$D$$HH L E1HD$D$$HeH |L 1E1H"HW)HDž fDžTƅHH68W)HDž@= H(#)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHPH !>)H0H0HPHH)H5H:H5cH蛵 H辩H5xH躳LH0HPH9tgHtHP(t H LMt[HL9t@ff.HX@t Hx HI9uHHP uLL t H H`;H&HH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hx HI9uHL| t H g H`!L  LfUHAWAVSH(IH֥&HHEH`H@ՂH5}HӘAH?D$D$$H H ǘL 1E1HD$D$$H H L~ 1E1HHW)HDž fDžTƅHH14W)HDž@8 H(#)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHKH :)H0H0HPHH$H5H5H5_H薱 H蹥H5t!H赯LH,HPH9tbHtHP(t H LMtVHL9t;fDHX@t Hx HI9uHHP uLL t H H`;H&HH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hx HI9uHL| t H g H`!L  LfUHAWAVSHHIH֡&HHEH`H@тH5}HӔAH?W)HDž ƅDžDCRH5HxLHD$D$$H H Lo 1E1H^D$D$$HR H cL6 1E1HHW)HDžfDžTƅ訁HH/W)HDž@ H(< #)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH 6)H0H0HPHHܨH5rHH5[HN HqH5TpzHmLH(HPH9tjHtHP(t H LMt^HL9tCfffff.HX@t Hxw HI9uHHP uLLF t H1 t H  H`H&HH;EuLHH[A^A_]J IILMIIIIHPH9tHtHP(uHP t HK LMtCHL9twfHX@t Hx HI9uHL t H t H H`L*  Lffff.UHAWAVSH(IH6&HHEH`ĤD$D$$HL H L8 1E1H'D$D$$HH LE1HD$D$$H H L 1E1HbHW)HDž fDžTƅ5}HHv+W)HDž@} H(#)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH5ZH))L-HH 2)H0H0HPHHGH5װHXH5WH蹨 HܜH5kHئLH#HPH9teHtHP(t H LMtYHL9t>fHX@t Hx HI9uHHP uLL t H H`[H4&HH;EuLH([A^A_] ILMu|IuS]IIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hx HI9uHL t H H`AL  LfUHAWAVSHHIH&HHEH`H`ȂH踚W)ƅHDž HnearestHH5H%LH蹽D$D$$H H dL 1E1HD$D$$HH L E1HTD$D$$HF H L, 1E1HHW)HDžfDžTƅ.aHH&W)HDž@ H(2=")Hr types.HH/Hl tensorHH(HY' to alHH Houtput 'HHH'X' and HHHn input HHHConstraiH@7HHHHH .)H0H0HPHHҟH5HH5RHD HgH5Jg HcLHxHPH9t`HtHP(t H LMtTHL9t9@HX@t Hxw HI9uHHP uLLF t H1 t H  H` H&HH;EuLHH[A^A_]J IILMIIIIHPH9tHtHP(uHP t HK LMtCHL9twfHX@t Hx HI9uHL t H t H H` L*  Lffff.UHAWAVSHIH6&HHEH`HÂW)ƅHDž HnearestHH5!HщLHDž @H5THL HHDžH55HLHW)ƅHDžHhalf_pixHfDželƅH5H tLHIW)ƅ$HDžHefer_floHHround_prHfDžorƅH58HԋLHٷDžH5KHЌLH轱D$D$$H H XL21E1HD$D$$H1H .L_AHED$D$$HdH ZcL9 AHD$D$$H*H )L AHD$D$$H H L<1E1H>HW)HDžƅfDžT1ƅ rHHK W)pHDž@R H(6")pHr types.HH/Hl tensorHH(HY' to alHH Houtput 'HHH'X' and HHHn input HHHConstraiH@7HHHpHeH5^H)LHH ()H0H0HPHHH5H-H5KH莝 H豑H5`H譛LHHPH9tjHtHP(pt H LMt^HL9tCfffff.HX@t Hx HI9uHHP puLL uTu`ult H V H`H&HH;EuRLHĘ[A^A_]H tH tH uC ILMI!xIxI IIIpuKUIIHPH9tHtHP(puHP pt H LMtGHL9fHX@t Hx HI9uHL u=u\uht H | H`6L  HZ tLItH2 tH uUHAWAVSH(IH&HHEH`HD$D$$H H L 1E1HD$D$$H{ H L_ 1E1HHW)HDž fDžTƅlHHW)HDž@ H(e")Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH,H %x&HH0H #N&H8H0HPHHH5gHH5FHe H舌H5k[H脖LHHPH9taHtHP(t H LMtUHL9t:DHX@t Hx HI9uHHP uLLf t H Q H` H&HH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hxg HI9uHLL t H 7 H`L  LfUHAWAVSH(IH&HHEH`HH5H[E1HD$D$$H H 7L 1E1HyD$D$$HH \LE1H=D$D$$H1 H L 1E1HHW)HDž fDžTƅQHHW)HDž@ H(")Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH5HD)LHH )H0H0HPHH虏H5VH誐H5kBH H.H5WH*LH?HPH9tgHtHP(t Hm LMt[HL9t@ff.HX@t Hx7 HI9uHHP uLL t H H`H&HH;EuLH([A^A_] ILMu|IuS]IIIHPH9tHtHP(uHP t H@ LMtHHL9tgfffff.HX@t Hx HI9uHL t H H`L/  LfUHAWAVAUATSHIHB&HHEHPH謳HDžH5vH#YLHoD$D$$HH ZL1E1HD$D$$HH [LfQE1HD$D$$HJH LE1HD$D$$H H ڔL1E1HHW)HDžƅfDžT1ƅHHL(W)HDž0 H(d )Ic types.L`I numericLhI to onlyLpIns inputLxHConstraiH@'HHHHAHW)HDžƅfDžT2ƅ HH.W)HDž05 H(c )L`LhLpLxIConstraiL0@'HHHHHW)`HDžpƅ`fDžaT3ƅcJL(HHHrW)0HDž@ y H@(&")0Hor type.HHH tensor HHHn to anyHHL0@H`HHH0HH )H H H@HH虉H5bH誊H5k<H H.H5Q H*LH? H@H9tgHtHP(0t H@m LHMt[HPL9t@ff.HX@t Hx7 HI9uHHHP 0uLLP `uGuSLMtpHL9tUfHX@t Hx HI9uH2Hp tH LMuLLx uIuULMtrHL9tW@HX@t Hx7 HI9uH2H tH LMuLL t Hӿ HPHf~&HH;EuLH[A\A]A^A_] I0ILHM,I.LILMQIILMI`IBTIIIH@H9tHtHP(0uHP 0t H@菾 LHMt?HPL9t$HX@t Hxd HI9uHHLLPD `uu#LMu/lHp tH LMt?HL9t$HX@t Hxؽ HI9uHLL踽 uu#LMu/lH茽 tHw LMt?HL9t^DHX@t HxG HI9uHL, t H HPLo  LfUHAVSHIH{&HHEH`HD$D$$H H L'1E1HyD$D$$Hk H f LH1E1HH5Hz )LpyH.H5HHx )LvyH H )H0H0HPHH襃H5YH趄H5w6H H:|H5K H6LHKHPH9t HtHP(HP H`@Hz&HH;EuLH[A^]趻 I"IHPH9uHP HtHP(H`L臵  ffffff.UHAVSHIHy&HHEH`HD$D$$H H L71E1HD$D$$H{ H v LF1E1HHDž(H5SWHQL(HSHDž H5WH7RL H&H5HX)L&wHH5YFHF)L,wHH )H0H0HPHH[H5VHlH5-4Hͅ HyH5H HLHHPH9t HtHP(HP H`Hw&HH;EuLH[A^]l I,I'I"IHPH9uHP HtHP(H`L3  ff.UHAWAVSH(IHFw&HHEH`H谦D$D$$HH 'QL̅1E1H7D$D$$H! H QL E1HD$D$$H H QL E1H迿D$D$$H H QL 1E1H6H5H)L+QHtHW)HDž fDžTƅw?HH(W)HDž@/ H({")Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHBH )H0H0HPHH~H5H,H50H荂 HvH5EB H謀LHHPH9tiHtHP(t H LMt]HL9tBffff.HX@t Hx践 HI9uHHP uLL膵 t H q H`+Ht&HH;EuLH([A^A_]蟵 IugqILMunIIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hx致 HI9uHLl t H W H`L诮  LfUHAWAVSH(IHr&HHEH`H0D$D$$H H L 1E1H跻D$D$$H H L| 1E1H.HW)HDž fDžTƅSHHBW)HDž I H(EE )Hr types.HHH tensor HHHn to allHHHConstraiH@HHHHH )H0H0HPHH_zH5PHp{H51-H~ HrH5Ah H|LHHPH9t]HtHP(t H3 LMtQHL9t6HX@t Hx HI9uHHP uLLֱ t H H`{HTp&HH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hxװ HI9uHL輰 t H 觰 H`aL  LfUHAWAVSH8IHo&HHEH`H耞HDž(H5tkHIL(HCHDž H5_jH JL HD$D$$H H 6JL 1E1H護D$D$$HCH JLr E1HqD$D$$Hc H ,JLI 1E1HHW)HDžfDžTƅK7HHW)HDž@ H(O )Hsor typeHH(H any tenHH Han be ofHHH types cHHHd outputHHHInput anHf@0.HHHH"H )H0H0HPHHuH5rgH wH5(Hmz HnH5s= HxLHHPH9tiHtHP(t Hϭ LMt]HL9tBffff.HX@t Hx藭 HI9uHHP uLLf t HQ H` Hk&HH;EuLH8[A^A_] IuoyILMuvIIIIHPH9tHtHP(uHP t H蘬 LMt@HL9t_fDHX@t Hxg HI9uHLL t H7 H`L菦  LfUHAWAVSH(IHj&HHEH`HHDž(H5ыHFL(HӃH5HGE1H}D$D$$Ho H GL] 1E1HLD$D$$H> H GL$ 1E1H÷D$D$$HH 8HLؾ AH脷D$D$$HIH IL AHED$D$$H)H ILZ AHHW)HDž fDžTƅi2HHW)HDž0! H(#)Hor type.HHHany tensHHHn be of HHHInput caH@ HHHH^H )H0H0HPHH7qH5PHHrH5 $Hu HiH58 HsLHHPH9teHtHP(t H LMtYHL9t>fHX@t Hxר HI9uHHP uLL覨 t H 葨 H`K݁H$g&HH;EuLH([A^A_]迨 Iu_iILMufIIHPH9tHtHP(uHP t H LMt@HL9tKfDHX@t Hx跧 HI9uHL蜧 tH 臧 LIH`7܁Lա  ffff.UHAWAVSH(IHe&HHEH`HPHDž(H5AHdL(HD$D$$HaH hL 1E1H誮D$D$$HMH dLo E1HnD$D$$Hb H 5RLF 1E1HHW)HDž fDžTƅEHHW)HDž@ H(| )Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHHH )H0H0HPHHlH5>HnH5Heq HeH5k4a HoLHHPH9taHtHP(t HǤ LMtUHL9t:DHX@t Hx藤 HI9uHHP uLLf t H Q H` فHb&HH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t H蠣 LMtHHL9tgfffff.HX@t Hxg HI9uHLL t H 7 H`ׁL菝  LfUHAWAVSHHIHa&HHEH`HW)ƅHDž HconstantHƅH5H.aLHjD$D$$HH 8LR 1E1HAD$D$$HݐH `L E1HD$D$$HbH bLݰ AHƩD$D$$H H L 1E1H=HW)HDžfDžTƅAHHQW)HDž@X H(")Hric typeHH(Hnly numeHH Hput to oHHH and outHHHns inputHHHConstraiHf@0s.@2HHHHsH (H0H0HPHHLhH5YBH]iH5Hl H`H5/ HjLHHPH9tjHtHP(t H LMt^HL9tCfffff.HX@t Hx HI9uHHP uLL趟 t H衟 t H 茟 H`FԁH^&HH;EuLHH[A^A_]躟 Iut~ILMu{IIIHPH9tHtHP(uHP t HΞ LMtFHL9tzfff.HX@t Hx藞 HI9uHL| t Hg t H R H` ӁL誘  Lffff.UH]f.UH] fDUH H {(H]fDUHHe(H]UH]f.UH] fDUHSH(HP\&HHEHW)EHEEfEtoEHu11HZEt H}d HHPHt_H1HPHtOH1HPH(tH@H(u H@ @uuH@ @t 11HmIH[&HH;EuH([]; HEt H}ɜ H-  DUHH #H1H9NHD]DUHH}(]UH]f.UH]p fDUHh H c(H]fDUHHM(H]UH]f.UH] fDUHAWAVAUATSHIHZ&HHE11LFILPHW)EHE@uHPxHc@pH H}1,ׯ0Hu HHAHIHHH}1֯I1LP(HÃ{(uLc H舤C(1JILc AL$ID$HpHu1HpID$I1LPx(uH@ H)HXLuLeW)EHEM)tmMMII]HH<蹚 HHEH]LmMIJ4HH薚 I?t@D)HHHJ!IESLpID$HPE1Hh1H`ID$(HIcL$ 09QAT$ LlKHHEMIADIN $HXAHIHcQ I9HI(JLQ(4HY A}(EN4TfDAt$$fA;t$$uHP ID$(0Ɖ0HPH8AIID$(IcL$ QAT$ LlLuKHHu H`L`tjf.H#A}(uK/f.Lp2LJAE(JHEI] HhHHhILuHEL)HHI9HHY A}(LpuWI} H;=&teH萖 LJAE(ILuI] HhHHhIM!&LpuLBJAE(Hs)IE I LHFL`MHXHh@H@HcH H~_HcULEHx(1fff.Ht~(uHF HH9|+fDH9}PHHI4Hs?HH9|HHhHHiA~(tLWIAF(I^ H}Ht H}Ht H} HU&HH;EuHĈ[A\A]A^A_] D(ɖ IHH]H5HrHxHjAHxL% HN?&HIIF IFIFE1H5&H&L膖 (A IH5HQqHx2AHxL訓 H>&HIIF IFIFE1H5&Ha&L (ĕ IH5HqHxugAHxL+ HT>&HIIF IFIFE1H5&H&L茕 H}荒  (? IH5HpHx@AHxL覒 H=&HIIF IFIFE1H5&H_&L 넿(Ŕ IH5HXpHxFAHxL, HU=&HIIF IFIFE1H5&H&L荔 0HxuCJ Hxu3:Hxu#*H(HxuHxt H} EtL H}HtH}HH}HH  HH}HtvHH}HtzHH}HtlHH}Ht^HH}HtPHH}HtBHH}Ht"HH}HwHH}He H}H`H} Hf  fffff.UHH #H1H9NHD]DUHH-(]UH]f.UH]頒 fDUH蘒 H (H]fDUHH(H]UH]f.UH]P fDUHAWAVATSII$1LP(HÃ{(uHC HOC(1HC H@ I$LPHI$1LPHI$1LPH(t@H@H(uH@ @ud[H@ @MI$1LPx(uH@ @u,HV)@I$1LP(HÃ{(uL{ AOMwMu;+H[C(1IL{ AOMwMu1IMwIF(HtIcN 09}QAV L|AAv$A;v$uI~R IF(0Ɖ0I~IIF(IcN QAV L|I$1LPx(uH@ H@HtH`)H@HuH )HcX A(tLBAG(I_ [A\A^A_]UHH Ր#H1H9NHD]DUHH(]UH]f.UH] fDUH؏ H (H]fDUHH(H]UH]f.UH]鐏 fDUHAVSII1LP(HÃ{(uHC H蔘C(1VHC H@ I1LP(HÃ{(uLs ANI~u+.HIC(1 ILs ANI~t[A^]1lIF[A^]UHH e#H1H9NHD]DUHH(]UH]f.UH]鐎 fDUH舎 H (H]fDUHH(H]UH]f.UH]@ fDUHAWAVAUATSHXHHL&HHE11H9HH]HPHHMHHEH Lcu1H}HPH9H}HHPHiH}HHPH(t!H@H(uH@ @u6D(H@ @HL9|H}H1Px(uH@ H@HtH%)H@HuH)Lc` H}HHW)EHEEEaxisEHuHEt H}ی H$DDA9EE9H}u&11H}9HI1LPH*I1LPH(u!H@ @H@H(tH@ @I1LPx(H@ LhMuL-)E} D}W)EHEEEaxisEIHuLHt x1Et H}ޅ D}̉]D9uA9ly AD}DIE(HcHDx( @ EW)EHEILPHLxILPHILPHH}HJHuHUH})H}Ht H} L}H]ILP 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(F IH5NHH}ZAHuL Hܥ%HIIF IFIFE1H5%Hl~%L r( IH5NHH}ƍAHuL? Hh%HIIF IFIFE1H5/%H}%L  H'HEuEuH}HukrH}) EtLL H}HuJQHHEt H} Et5L HQ  HH}HuHH}Ht H} H$  fff.UHH #H1H9NHD]DUHHW(]UHAVSHIH%HHEH`HrH5HAHqD$D$$H H ϠL1E1HD$D$$H H L81E1HOH59H1W(Lڠ HH5HwW(L HkH Z(H0H0HPHHH5HH5tHv H虺H5͠YHLHAHPH9t HtHP(HP H`.Hx%HH;EuLH[A^] I"IHPH9uHP HtHP(H`H.L  fffff.UHAWAVSH(IH%HHEH`H`D$D$$HH L 1E1HD$D$$HH L E1HD$D$$HȟH ʟL 1E1H"HW)HDž fDžTƅ腀HH6FW)HDž@= H("")Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHPH X(H0H0HPHH)H5sH:H5qHH辷H5HLH>HPH9tgHtHP(t H LMt[HL9t@ff.HX@t Hx HI9uHHP uLL t H H`;+H%HH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hx HI9uHL| t H g H`!*L  LfUHAWAVSH(IHֳ%HHEH`H@D$D$$H~H L 1E1HD$D$$HĢH L<1E1H>HW)HDž fDžTƅ|HHRBW)HDž0Y H(!)Hry type.HHHrbitraryHHH be of aHHHnsor canHHHInput teH@&HHHHH5RHQ(LHH 'U(H0H0HPHH?H5ZHPH5nH豿HԳH5 HнLH:HPH9t]HtHP(t H LMtQHL9t6HX@t Hx HI9uHHP uLL t H H`['H4%HH;EuLH([A^A_] ILMu|IuS]IIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hx HI9uHL t H H`A&L  LfUHAWAVSH(IH%HHEH`H`߁D$D$$HH L 1E1HD$D$$Hr H QL\1E1H^HW)HDž fDžTƅxHHr>W)HDž0y H(!)Hry type.HHHrbitraryHHH be of aHHHnsor canHHHInput teH@&HHHHH5rHBM(LHH Q(H0H0HPHH_H5HpH51jHѻHH5("HLH7HPH9t]HtHP(t H3 LMtQHL9t6HX@t Hx HI9uHHP uLL t H H`{#HT%HH;EuLH([A^A_] ILMu|IuS]IIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hx HI9uHL t H H`a"L  LfUHAWAVSH(IH%HHEH`HہH5UH1AHD$D$$HH L 1AHD$D$$HuH |L 1E1HZHW)HDž fDžTƅtHHn:W)HDž0u H(1)Hnsor typHH Ho any teHHH types tHHHn outputHHHConstraiHf@(e.@*HHHHH _N(H0H0HPHHwH5{H舴H5IfH H H5@vHLH3HPH9teHtHP(t HK LMtYHL9t>fHX@t Hx HI9uHHP uLL t H H`Hd%HH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hx HI9uHL t H H`qL  LfUHAWAVSH(IH&%HHEH`HׁD$D$$H< H L( 1E1HD$D$$H|H ؑL 1AHHW)HDž fDžTƅpHH6W)HDž@ H(")Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHHDžH5HLHH5^H[E1HH J(H0H0HPHHGH50HXH5bH蹳 HܧH5HرLH.HPH9teHtHP(t H LMtYHL9t>fHX@t Hx HI9uHHP uLL t H H`[H4%HH;EuLH([A^A_] IuuILMu|IuS]IIIHPH9tHtHP(uHP t H LMt:HL9tYHX@t Hx HI9uHL t H H`AL  LfUHAWAVSH(IH%HHEH`H`ӁD$D$$HH L 1E1HD$D$$HtH LE1HD$D$$HH aLE1HoD$D$$HH nLIAH0D$D$$H֎H ՎL AHD$D$$H H ZL 1E1HhHW)HDž fDžTƅkHH|1W)HDž@ H( ")Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH5ŧH8@(LSHH 5F(H0H0HPHHMH5vH^H5]H迮 HH5HެLH)HPH9tkHtHP(t H! LMt_HL9tDffffff.HX@t Hx HI9uHHP uLL t H H`[H4%HH;EuLH([A^A_] ILMutIuKUIIHPH9tHtHP(uHP t H LMt@HL9tKfDHX@t Hx HI9uHL tH LIH`GL  ffff.UHAWAVSH(IH%HHEH`H`΁H5hHfE1HbD$D$$HH L 1E1HD$D$$HqH uL 1E1H@HW)HDž fDžTƅgHHT-W)HDž@[ H( ")Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHnH B(H0H0HPHHGH5QHXH5YH蹪HܞH5HبLH%HPH9teHtHP(t H LMtYHL9t>fHX@t Hx HI9uHHP uLL t H H`[H4%HH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hx HI9uHL t H H`AL  LfUHAWAVSH(IH%HHEH`H`ʁD$D$$HH L 1E1HD$D$$HH L E1HD$D$$HqH rL E1HoD$D$$Hc H LG 1E1HHW)HDž fDžTƅIcHH(W)HDž@ H(})Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHHH >(H0H0HPHHH5ƝHH5THf H艚H5rH腤LH!HPH9tbHtHP(t H LMtVHL9t;fDHX@t Hx HI9uHHP uLLf t H Q H` H%HH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hxg HI9uHLL t H 7 H` L  LfUHAWAVSH(IH%HHEH`HƁHDž(H5HуL(HӯD$D$$H!H (L{ 1E1HjD$D$$H H LDE1H.D$D$$HH uL E1HD$D$$H H oL 1E1HiHW)HDž fDžTƅ^HH}$W)HDž@ H(@w)Hsor typeHH(H any tenHH Han be ofHHH types cHHHd outputHHHInput anHf@0.HHHHH5ҚHU3(L`HH :(H0H0HPHHZH5cHkH5,PH̡ HH5#|HLHHPH9thHtHP(t H. LMt\HL9tAfff.HX@t Hx HI9uHHP uLL t H H`k HD%HH;EuLH([A^A_] ILMu|IuS]IIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hx HI9uHL t H H`QL  LfUHAWAVSH(IH%HHEH`HpHDž(H5:HL(H3D$D$$HH L 1E1HD$D$$HmH LE1HD$D$$H H wLf 1E1HHW)HDž fDžTƅhZHH W)HDž@ H()Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHH:H5iH.(LHXH 6(H0H0HPHHH5#fHH5KHc H膑H5wJH肛LHHPH9t_HtHP(t H LMtSHL9t8HX@t Hx HI9uHHP uLLf t H Q H` H%HH;EuLH([A^A_] ILMu|IuS]IIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hxg HI9uHLL t H 7 H`L  LfUHAWAVSH(IH%HHEH`HHDž(H5H|L(HӦD$D$$H!H (L{ 1E1HjD$D$$H H o}LDE1H.D$D$$H" H ~L 1E1HHW)HDž fDžTƅVHHW)HDž@ H(<)Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHHH5 H*(LHH 2(H0H0HPHH葔H5cH袕H5cGH H&H5ZsH"LH7HPH9t_HtHP(t He LMtSHL9t8HX@t Hx7 HI9uHHP uLL t H H`H%HH;EuLH([A^A_] ILMu|IuS]IIIHPH9tHtHP(uHP t H@ LMtHHL9tgfffff.HX@t Hx HI9uHL t H H`L/  LfUHAWAVSH(IHF%HHEH`H谸H5HzE1H貜D$D$$HдH zL* 1E1HD$D$$HzH zL 1E1HHW)HDž fDžTƅQHHW)HDž@ H(!)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH /(H0H0HPHH藐H5^H訑H5iCH H,H5`oH(LH=HPH9teHtHP(t Hk LMtYHL9t>fHX@t Hx7 HI9uHHP uLL t H H`H%HH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t H@ LMtHHL9tgfffff.HX@t Hx HI9uHL t H H`L/  LfUHAWAVSH(IHF%HHEH`H谴H5HwAH诘D$D$$HͰH wL' 1E1HD$D$$HwH vL 1E1HHW)HDž fDžTƅMHHW)HDž@ H(!)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH +(H0H0HPHH蔌H5iH襍H5f?H H)H5]kVH%LH: HPH9tbHtHP(t Hh LMtVHL9t;fDHX@t Hx7 HI9uHHP uLL t H H`H%HH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t H@ LMtHHL9tgfffff.HX@t Hx HI9uHL t H H`L/  LfUHAWAVSH(IHF%HHEH`H谰H5HCtAH诔D$D$$H; H 7tL' 1E1HD$D$$H H stL 1E1HHW)HDž fDžTƅIHHW)HDž@ H(!)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH |((H0H0HPHH蔈H5H襉H5f;HH)H5]gH%LH:HPH9tbHtHP(t Hh LMtVHL9t;fDHX@t Hx7 HI9uHHP uLL t H H`H~%HH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t H@ LMtHHL9tgfffff.HX@t Hx HI9uHL t H ׾ H`L/  LfUHAWAVSHHIHF}%HHEH`H谬H5HCpAH诐W)HDž ƅDžDCRH5HpLHD$D$$H H oL 1E1HD$D$$H H pL 1E1HEHW)HDžfDžTƅEHHY W)HDž@` H(!)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHsH $(H0H0HPHHLH5H]H57H辈 H|H5cH݆LHHPH9tjHtHP(t H LMt^HL9tCfffff.HX@t Hx HI9uHHP uLL趻 t H衻 t H 茻 H`FHz%HH;EuLHH[A^A_]躻 IILMIIIIHPH9tHtHP(uHP t H軺 LMtCHL9twfHX@t Hx臺 HI9uHLl t HW t H B H`L蚴  Lffff.UHAWAVSH(IHx%HHEH`HD$D$$H H $mL 1E1HD$D$$H mH mL E1H[D$D$$HO H PmL3 1E1HHW)HDž fDžTƅ5AHHW)HDž@ H(9!)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH5 H(LlHH (H0H0HPHHH5GHȀH52H)HLxH5^1HHLH]HPH9teHtHP(t H苷 LMtYHL9t>fHX@t HxW HI9uHHP uLL& t H  H`Hu%HH;EuLH([A^A_]? ILMu|IuS]IIIHPH9tHtHP(uHP t H` LMtHHL9tgfffff.HX@t Hx' HI9uHL t H H`LO  LfUHAWAVSHIHft%HHEH`HУW)ƅHDž HnearestHH5QHjLH1Dž @H5hHKkL HHDžH5ehH+lLHW)ƅHDžHhalf_pixHfDželƅH5+gHƉLHyW)ƅ$HDžHefer_floHHround_prHfDžorƅH5hgHlLH DžH5{fHmLHD$D$$H H mLb 1E1H贻D$D$$HamH ^mL@E1HxD$D$$HeH nLl E1HH(LnHH (H0H0HPHHRyH5bHczH5$,H} HqH5XH{LHHPH9t`HtHP(pt H& LMtTHL9t9@HX@t Hx HI9uHHP puLLư uTu`ult H 薰 H`PH)o%HH;EuRLHĘ[A^A_]H] tHH tH3 u胰 ILMI!xIxI IIIpuKUIIHPH9tHtHP(puHP pt H? LMtGHL9fHX@t Hx HI9uHL u=u\uht H 輮 H`vL  H蚮 tLItHr tH] uUHAWAVSH(IHl%HHEH`H@D$D$$H H \Lؼ 1E1HǵD$D$$H H jL 1E1H>HW)HDž fDžTƅ5HHRW)HDž@Y H(!)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHlH eX%HH0H c.%H8H0HPHH3tH5[GHDuH5'HxHlH5RHvLHHPH9taHtHP(t H LMtUHL9t:DHX@t Hx׫ HI9uHHP uLL覫 t H 葫 H`KH$j%HH;EuLH([A^A_]迫 IugqILMunIIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hx觪 HI9uHL茪 t H w H`1߀LϤ  LfUHAVSHIHh%HHEH`HRD$D$$H H L 1E1HٱD$D$$H˼ H Ƽ L961E1HPH5: Hr(LfHH56Hh(LfHlH (H0H0HPHHqH5FHrH5#Hwu HiH5OHsLHHPH9t HtHP(HP H`݀Hyg%HH;EuLH[A^] I"IHPH9uHP HtHP(H`I݀L  ffffff.UHAWAVSH(IHf%HHEH`H`D$D$$H H  L 1E1HD$D$$Hٺ H Ժ L 1E1H^HW)HDž fDžTƅ/HHrW)HDž y H(u9)Hr types.HHH tensor HHHn to allHHHConstraiH@HHHH趵H (H0H0HPHHnH5DHoH5a!Hs H$gH5XMH qLH5HPH9t]HtHP(t Hc LMtQHL9t6HX@t Hx7 HI9uHHP uLL t H H`ڀHd%HH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t H@ LMtHHL9tgfffff.HX@t Hx HI9uHL t H פ H`ـL/  LfUHAWAVSH(IHFc%HHEH`H谒HDž(H5J?HaL(Hs|D$D$$HH ȞL 1E1H D$D$$HH aLϷ E1HΫD$D$$H¶ H OL 1E1HEHW)HDž fDžTƅ+HHYW)HDž@` H(ܔ)Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHHzH ; (H0H0HPHHSjH50<HdkH5%Hn HbH5IHlLHHPH9taHtHP(t H' LMtUHL9t:DHX@t Hx HI9uHHP uLLơ t H 象 H`kրHD`%HH;EuLH([A^A_]ߡ IugqILMunIIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t HxǠ HI9uHL謠 t H 藠 H`QՀL  LfUHAWAVSHHIH_%HHEH`HpW)ƅHDž HconstantHƅH5H^LHʃD$D$$HXH L 1E1H衧D$D$$H=H T^Lf E1HeD$D$$H_H _L= AH&D$D$$H H tL 1E1H蝫HW)HDžfDžTƅHHW)HDž@踞 H(d!)Hric typeHH(Hnly numeHH Hput to oHHH and outHHHns inputHHHConstraiHf@0s.@2HHHHӬH  (H0H0HPHHeH5?HfH5~Hj HA^H5uDH=hLHRHPH9tjHtHP(t H耝 LMt^HL9tCfffff.HX@t HxG HI9uHHP uLL t H t H H`рH[%HH;EuLHH[A^A_] Iut~ILMu{IIIHPH9tHtHP(uHP t H. LMtFHL9tzfff.HX@t Hx HI9uHLܛ t HǛ t H 貛 H`lЀL  Lffff.UHAVSHIHZ%HHEH`H肉H5@H@AHmD$D$$H H @L 1E1HD$D$$Hܭ H @LH'1E1H_H5I H'L[ H蝺H5'H'L[ H{H5&HLcH5 HfHZH5ATHdLHH`΀HX%HH;EuLHİ[A^]k HH`΀H`  UHAVSHIHxX%HHEH`H⇁H5>H>AHkD$D$$Hm H ??L 1E1HHD$D$$H< H ?L%1E1H迥H5 H'LZ HH5r%H'L)Z H۸H \(H0H0HPHHt`H5,%HaH5FHdH YH5=?HcLHHPH9t HtHP(HP H`̀HV%HH;EuLH[A^]腘 I"IHPH9uHP HtHP(H`̀LV  fffff.UHAVSHIHhV%HHEH`H҅H5HYE1HiD$D$$H?H ?LL 1AH8D$D$$H?H ?L 1E1H诣H5 Hq'LXHH5-'H_H5HcHBWH5v=H>aLHSހH`gˀH@U%HH;EuLHİ[A^]ݖ HH`4ˀHҐ  f.UHAWAVSH(IHT%HHEH`HPH5%HWAHOhD$D$$HX>H X>LǤ 1AH賝D$D$$HE>H L>L 1E1H*HW)HDž fDžTƅHH>W)HDž0E H(s)Hnsor typHH Ho any teHHH types tHHHn outputHHHConstraiHf@(e.@*HHHHnH (H0H0HPHHG\H5$HX]H5H`HTH5;H^LHۀHPH9teHtHP(t H LMtYHL9t>fHX@t Hx HI9uHHP uLL趓 t H 術 H`[ȀH4R%HH;EuLH([A^A_]ϓ IugqILMunIIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hx跒 HI9uHL蜒 t H 臒 H`AǀLߌ  LfUHAVSHIHP%HHEH`HbD$D$$H H :L 1E1HD$D$$Ht`H SL AH誙D$D$$HSH k:L 1AHH5k H'LNH\H5aHSE1HcH5_HSE1HpcH5]HYH5 HR]HuQH57 Hq[LH؀H`ŀHsO%HH;EuLHİ[A^] HH`gŀH  ffff.UHAVSHIHO%HHEH`H~H5RHRAHbW)@ƅ@HDžPHconstantHAƅIH5ۍHSL@HsDž<H54HSL<HmD$D$$HzH ]Lw 1E1HfD$D$$HZ H L> 1E1HݛH5* H'LoHH50HWH5 HM[HpOH55N HlYLHր@t HPƎ H`ÀHYM%HH;EuLH[A^] H@uHH@t HPk H`%ÀHÈ  ff.UHAVSHIHL%HHEH`HB|H5<HhRE1HD`H5|nHnE1H&`D$D$$HDxH 4L 1E1H荕D$D$$H4H 4Le 1E1HH5Q H'LmHBH5L;HVH5HtYHMH53k HWLHԀH`HK%HH;EuLHİ[A^]2 HH`H'  ffffff.UHAVSHIH8K%HHEH`HzD$D$$HN H ?L: 1E1H)D$D$$HPH PL E1HD$D$$HH PLŚ E1H豓D$D$$H H cPL 1E1H(H5u HJ'LHHfH5 H@'LGPHDH 'H0H0HPHHRH5m_HSH5HOWHrKH51 HnULHҀHPH9t HtHP(HP H`xHQI%HH;EuLH[A^] IHPH9uHP Ht HP(IH`!L迄  UHAVSHIHH%HHEH`HBxHeRH5&OH+OAH4\H5]OHcOAH\W)@ƅ@HDžPHnearestHAH5tHbOL@HTmD$D$$HN H ]OL< 1E1H+D$D$$H H :OL 1E1H袕H5 H'L6OHH5lHQH5rHUH5IH5i/ H1SLHFЀ@t HP苈 H`EHG%HH;EuLH[A^]軈 HH@t HP> H`H薂  fffff.UHAWAVSHHIHF%HHEH`HvH5:HiNAHZW)ƅHDž HnearestHH5pHNLHPkD$D$$HJ H @L8 1E1H'D$D$$H H CL 1E1H螓HW)HDžfDžTƅHHԇW)HDž@蹆 H(!)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH̔H 'H0H0HPHHMH5iHNH5wHRH:FH5n, H6PLHK̀HPH9tcHtHP(t Hy LMtWHL9t<HX@t HxG HI9uHHP uLL t H t H H`覹HC%HH;EuLHH[A^A_] IILMIIIIHPH9tHtHP(uHP t H LMtCHL9twfHX@t Hx HI9uHL̃ t H跃 t H 袃 H`\L}  Lffff.UHAWAVSHHIHB%HHEH`HpqW)ƅHDž HnearestHH5H=JLHfD$D$$H˕ H |<L 1E1H訊D$D$$H4H *IL E1HlD$D$$H^ H 0?LD 1E1HHW)HDžfDžTƅF HHχW)HDž@ H(J )Hr types.HH/Hl tensorHH(HY' to alHH Houtput 'HHH'X' and HHHn input HHHConstraiH@7HHHHH 'H0H0HPHHHH5eHIH5H\M HAH5'( H{KLHȀHPH9thHtHP(t H辀 LMt\HL9tAfff.HX@t Hx臀 HI9uHHP uLLV t HA t H , H`洀H>%HH;EuLHH[A^A_]Z Iut~ILMu{IIIHPH9tHtHP(uHP t Hn LMtFHL9tzfff.HX@t Hx7 HI9uHL t H t H ~ H`謳LJy  Lffff.UHAWAVSHHIHV=%HHEH`HlW)ƅHDž HnearestHH5A|HELH!bD$D$$H H 7L 1E1HD$D$$H0H wEL E1H輅D$D$$H H :L 1E1H3HW)HDžfDžTƅHHGˇW)HDž@N} H( )Hr types.HH/Hl tensorHH(HY' to alHH Houtput 'HHH'X' and HHHn input HHHConstraiH@7HHHHaH 'H0H0HPHH:DH5-HKEH5 HH HD$D$$H~ H Lz 1E1HsD$D$$H~ H Lnz 1E1H xHW)HDž fDžTƅpHH!W)HDž@(k H(t!)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH;yH |'H0H0HPHH2H53H%3H5H6H*H5. H4LH躱HPH9tbHtHP(t Hi LMtVHL9t;fDHX@t Hxi HI9uHHP uLLi t H qi H`+H(%HH;EuLH([A^A_]i IugqILMunIIIHPH9tHtHP(uHP t Hh LMtHHL9tgfffff.HX@t Hxh HI9uHLlh t H Wh H`Lb  LfUHAWAVSH(IH&%HHEH`H0VHDž(H5nH02L(H?D$D$$HARH HbLv 1E1HoD$D$$H-bH +2Ld,E1HNoD$D$$HBz H 7L&v 1E1HsHW)HDž fDžTƅ(HHٴW)HDž@f H(\X)Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHHtH5)+Hl'LQHH 'H0H0HPHH-H5H.H5H#2HF&H5z  HB0LHWHPH9t_HtHP(t He LMtSHL9t8HX@t HxWe HI9uHHP uLL&e t H e H`˙H#%HH;EuLH([A^A_]?e ILMu|IuS]IIIHPH9tHtHP(uHP t H`d LMtHHL9tgfffff.HX@t Hx'd HI9uHL d t H c H`豘LO^  LfUHAWAVSH(IHf"%HHEH`HQH5NH.E1H5D$D$$HMH LJr 1E1H9kD$D$$HH Lr 1E1HoHW)HDž fDžTƅHHİW)HDž@b H(!)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHpH 'H0H0HPHH)H5~4H*H5H).HL"H5 HH,LH]HPH9teHtHP(t Ha LMtYHL9t>fHX@t HxWa HI9uHHP uLL&a t H a H`˕H%HH;EuLH([A^A_]?a IugqILMunIIIHPH9tHtHP(uHP t H`` LMtHHL9tgfffff.HX@t Hx'` HI9uHL ` t H _ H`豔LOZ  LfUHAWAVSH(IHf%HHEH`HMH5JH)+AH1D$D$$HIH LGn 1E1H6gD$D$$HH Ln 1E1HkHW)HDž fDžTƅHHW)HDž@^ H(!)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHlH 'H0H0HPHH%H5?H&H5H&*HIH5}# HE(LHZHPH9tbHtHP(t H] LMtVHL9t;fDHX@t HxW] HI9uHHP uLL&] t H ] H`ˑH%HH;EuLH([A^A_]?] IugqILMunIIIHPH9tHtHP(uHP t H`\ LMtHHL9tgfffff.HX@t Hx'\ HI9uHL \ t H [ H`豐LOV  LfUHAWAVAUATSHIHb%HHEHPHIHDžH5bH`'LH3D$D$$HVH 'L 1E1H&cD$D$$H)H )LE1HbD$D$$HH *L +E1HbD$D$$Hm H *L*1E1H%gHW)HDžƅfDžT1ƅHH2L(W)HDž02Z H()Ic types.L`I numericLhI to onlyLpIns inputLxHConstraiH@'HHHHahHW)HDžƅfDžT2ƅ-HHNW)HDž0UY H()L`LhLpLxIConstraiL0@'HHHHgHW)`HDžpƅ`fDžaT3ƅcL(HHH蒦W)0HDž@ X H@(ż )0Hor type.HHH tensor HHHn to anyHHL0@H`HHH0HfH !'H H H@HHH5H H5H+$ HNH5 HJ"LH_H@H9tgHtHP(0t H@W LHMt[HPL9t@ff.HX@t HxWW HI9uHHHP 0uLLP&W `uGuSLMtpHL9tUfHX@t HxV HI9uH2HpV tHV LMuLLV uIuULMtrHL9tW@HX@t HxWV HI9uH2H:V tH%V LMuLLV t HU HP譊H%HH;EuLH[A\A]A^A_]V I0ILHM,I.LILMQIILMI`IBTIIIH@H9tHtHP(0uHP 0t H@T LHMt?HPL9t$HX@t HxT HI9uHHLLPdT `uu#LMu/lHp8T tH#T LMt?HL9t$HX@t HxS HI9uHLLS uu#LMu/lHS tHS LMt?HL9t^DHX@t HxgS HI9uHLLS t H7S HPLM  LfUHAWAVSHIH%HHEH`HAH5YH#E1H%D$D$$He H 7MLa 1E1HyZD$D$$H$H $Lv E1H=ZD$D$$H1e H $La 1E1H^HW)@HDžPfDž@TƅBH(HȟW)HDž @Q H (|!)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5H@H(HH_H5u Hd'L$HqH5HH5H2 HUH5 HQLHft H P L(MtHH0L9t-fHX@t HxwP HI9uH(LL0WP @t HPBP H`H%HH;EuLH[A^A_]pP IL(Mu9sI@upzIuIt H O L(MtfHX@t HxL HI9uHHP uLLvL t H aL H`H %HH;EuLH([A^A_]L IuuILMu|IuS]IIIHPH9tHtHP(uHP t HK LMt:HL9tYHX@t HxwK HI9uHL\K t H GK H`LE  LfUHAVSHIH %HHEH`H"9H59H'AH!W)ƅHDž HconstantHƅH5{HHLH[.Dž H5UHOL H?(D$D$$H4H HLY 1E1HRD$D$$H\ H TKLX 1E1H}VH5X H''LH*HiH 'H0H0HPHHTH5aHeH5&HH H5HLHHPH9tKHtHP(t H (I H`}H%HH;Eu LH[A^]HP uGI IuISINIu6@IHPH9uHP u HtHP(t H H H`@}LB  fDUHAWAVSH(IH%HHEH`H`6D$D$$H2H BLV 1E1HOD$D$$HBH L[ E1HOD$D$$HZ H rLV 1E1H"THW)HDž fDžTƅHH6W)HDž@=G H(8)Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHHWUH 'H0H0HPHH0H5 HAH5H HH5;HLH֍HPH9t^HtHP(t HF LMtRHL9t7fHX@t HxE HI9uHHP uLLE t H E H`KzH$%HH;EuLH([A^A_]E IugqILMunIIIHPH9tHtHP(uHP t HD LMtHHL9tgfffff.HX@t HxD HI9uHLD t H wD H`1yL>  LfUH]f.UH]@D fDUH8D H 'H]fDUHH'H]UH]f.UH]C fDUHSH(H%HHEHW)EHEEfEtoEHu11HEt H}C HHPHt_H1HPHtOH1HPH(tH@H(u H@ @uuH@ @t 11HH%HH;EuH([]kC HEt H}B H]=  DUHH \"H1H9NHD]DUHH']UHAWAVATSH AIIHH>%HHEHHxDDP(pB IH5HQLYH}LzHuL? H$HIIF IFIF1H5$H$L6B .EHy%HH;EuHLDH [A\A^A_]c B (A IH5՗HPLsH}LHuL? HF$HIIF IFIF1H5$H$LA z(=A IH5YHLH}LHuL> H$HIIF IFIF1H5$H[$LA  I@I;I6IEuIEu IEt H}|@ u L:  L@ L:  fUHAWAVAUATSHXLIHHHH$HHEH0L-$Mu@LLH$HH@H0HfHnfLr? HEEIEHHIhL0LLL|> L=?$ILHDž HDžHDžHDžDž(LHA LHHQLHHA HHH)A$It$IEt$IET$HHHvA HHHޗLH远HHL0HHLt Hj> HP= H5$HH-= H0= H$HH;EuLHX[A\A]A^A_]q> HSHeHHHL0HHLt H= H< H5$HH< H0H= H7  fUHAWAVAUATSHXLIHHHH$HHEH0L-$Mu@LLH$HH@H0HfHnfL< HEEIEHHIhL0LLL; L=o$ILHDž HDžHDžHDžDž(LH? 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AIIH?wEHI2ffffff.ILΉ A@IIH?wEHLHHcH9k]fUHSPLÉHHC@։H2HC@H[]UHAWAVATSMƉL<Au"8_!L=v!tgAtIWMIAuIII^@H)LcL9|HLLGyLI^@[A\A^A_]LLH.HH=^!wtHv!Hv!Hu!L=u!H=kLH=x^!wABRHH=Y^!\wHq fUHAVSHHtHHPIILHPPE1L[A^]fff.UHAWAVSPIHHPIILHPPHHP8HHLPPILP8ILLPPMtILH[A^A_]`H[A^A_]HMt ILPHp UHAVSHHHtHu HHlH HHQ IILHPPLH[A^]fDUHAWAVSHHIH4HHE ]!wHH=\!HwthAtAH` H],H贃H5 HՃH}HyH}H 4HH;EHH[A^A_]Ês\!L=D!LVtHe\!LHR\!H3HH;EuQLHH[A^A_] tH=!\!"uH=H5&D!H!ytH=[!tuH=[!t]H%s!Hs!Hr!H5r!H=hH=[!tHH=y[!|tHn HH}ƄHn H~H~fff.UHAWAVSPIAuvAIcOH~)1ۉI|Ht AGHHcH9|AG~*Mt1I H9Ht AGHHcH9|AWAH[A^A_]f.UHAWAVATSIIAuDI^@I9wJMgI^@I9v=I^@AwKD#HI^@AubIA~9tbLLH2+HnLH(HI^@AvD DA?wMCHI^@AtIWMA~9uIH)LcL9|NHLLtLI^@[A\A^A_]HʀʀSHÁ?wCI^@A5LLH(Hffffff.UHAWAVAUATSPIIIMt)MgA$AIE@I9EvFIE@wQHILP`AIu@I9uIu@AD>HLH&IE@vـɀ?w HHHʉӀˀXH?wHIE@AILLLH[A\A]A^A_]_gLW&HIu@AcD DA?w FHHʀʀVHƁ?wFIu@ILLPpIE@H[A\A]A^A_]DUHAVSHHIHtHpALw#AIwILP`H w)HрɀA?w&AHI=€ʀ=?wIQH^IωʀAPI?wAHLHKLcH[A^]H։ AH?wQH LH[A^] eff.UHAVSIHHIH9sH)HymIHH9rHI+FIcNH9tL HuIH[A^]UHH!Hm!Hm!Hm!Hm!Hm!]UHH 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Its type info at runtime will be used to infer the type info of this dynamic typed output which is required for the success of the model loading step. More than one dynamic typed inputs are currently not supported as differing types at runtime means the output type cannot be inferred without which model loading cannot proceed.OutputCreateCustomRegistryonnxruntime::CustomOpKernel::CustomOpKernel(const onnxruntime::OpKernelInfo &, const OrtCustomOp &)Unsupported version '' in custom op 'ComputeAsyncNo requested allocator availableEnv is nullOrtMemoryInfo is nullAllocator with this OrtMemoryInfo is already registered.Received invalid value for arena extend strategy. Valid values can be either 0, 1 or -1.inter-opMemcpyFromHostXinputYoutputConstrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.MemcpyToHosttensor(uint8)tensor(uint16)tensor(uint32)tensor(uint64)tensor(int8)tensor(int16)tensor(int32)tensor(int64)tensor(bfloat16)tensor(float16)tensor(float)tensor(double)tensor(string)tensor(bool)Input expected to have type but instead is null expected to have tensor typeElement type of input unknownOutput expected to have sequence 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.TensorrtExecutionProviderAddCustomOpDomainsReceived nullptr for custom registryReceived nullptr for graph transformerRegisterGraphTransformerGraph transformers must be registered before the session is initialized.3SaveToOrtFormatLoadThis session already contains a loaded model.Unknown exception in Load()Encountered unknown exception in Load()ORTModelProto corresponding to the model to be loaded has already been parsed. Invoke Load().ModelProto corresponding to the model to be loaded has not been parsed yet. This API should be called in conjunction with a ctor that takes a model abstraction.TransformGraphCould not find an implementation for the node ( (Node placementsAll nodes have been placed on []., Provider: [: [PartitionOrtFormatModelLoadOrtModelThis session has already been initialized.ORT model verification failed.InferenceSession is null. Invalid ORT format model.Serialized version info is null. Invalid ORT format model.The ORT format model version [] is not supported this build Missing Model. Invalid ORT format model.SessionState is null. Invalid ORT format model.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.tensorValidateInputs is not expected to be of type sparse tensor.sparse_tensor is not expected to be of type tensor sequence.seqOutput vector pointer is NULLAt least one output should be requested.Output vector incorrectly sized: output_names.size(): p_fetches->size(): Invalid Output Name:RunSession was not initializedSession not initialized.Running with tag: Encountered unknown exception in Run()GetModelMetadataGetModelInputsGetOverridableInitializersGetModelOutputsNewIOBindingEndProfilingProfiler is disabled.Could not write a profile because no model was loaded.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 booleandiscardednumberexcessive array size: parse error at line , column while parsing - ; last read: 'unexpected ; expected true literalfalse literalnull literalstring literalnumber literal'[''{'']''}'':'','end of input'[', '{', or a literalunknown tokenkey '' not foundcannot use at() with map::at: key not foundtype must be number, but is FlatBuffers 1.12.0tried 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.The given version [%u] is not supported, only version 1 to %u is supported in this build. 1.7.0allocator::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]void onnxruntime::TensorSeq::SetType(onnxruntime::MLDataType)elem_type_ != nullptr was false. Stacktrace: :/\T *onnxruntime::Tensor::MutableData() [T = std::__1::basic_string]const T *onnxruntime::Tensor::Data() const [T = std::__1::basic_string]/Users/runner/work/1/s/onnxruntime/core/session/ort_env.ccstatic void OrtEnv::Release(OrtEnv *)env_ptr == p_instance_/Users/runner/work/1/s/onnxruntime/core/optimizer/attention_fusion_helper.hbool onnxruntime::AttentionFusionHelper::CheckSliceParameters(const onnxruntime::Graph &, const onnxruntime::Node &, const std::vector &, const std::vector &, const logging::Logger &)input_indices.size() == expected_values.size() && input_indices.size() > 0CheckSliceParametersSlice does not have enough number of inputsSlice ends is less than INT_MAXSlice parameter is not expected. Input index:Expected value:MatchGemmSubgraphStart MatchGemmSubgraphFaild to match gemm pathInput of reshape_before_gemm is not the input of subgraphOutput edge count not expected for nodes in gemm pathGemm does not have 3 inputsGemm bias is not constantGemm bias shape not expectedCheckSliceParameters return falseconcat first input value is not -1Faild to match concat node for Gather pathsconcat_after_gather does not have expected number of inputs or output edgesconcat_after_gather input 2 does not have expected valueFaild to match gemm gather pathOutput edge count not expected for nodes in gemm gather pathunsqueeze_after_gather axes value not expectedgather axis value not expectedgather input 1 value is not expectedPass MatchGemmSubgraphValidateGemmInitializerStart ValidateGemmInitializerGemm bias is not constant initializerGemm bias shape is not expectedGemm weight is not constant initializerGemm weight shape is not expectedPass ValidateGemmInitializerValidateUnidirMaskunidir mask is not constantunidir mask shape not expectedThis optimizer does not support external data for unidirectional mask right nowMask is neither unidirectional nor all onesExpect mask data type is uint8 or floatMatchUnidirMaskSubgraphStart MatchUnidirMaskSubgraphFaild to match the path (Div-->Where-->Add) for unidirectional maskFaild to match path 1 for unidirectional maskOutput edge count not expected for nodes in path 1 of unidirectional maskDiv and Shape1 does not have edgeCheckSliceParameters returns false for last_sliceCheckSliceParameters returns false for mask_sliceValidateUnidirMask returns false for mask_sliceCheckSliceParameters returns false for slice1Faild to match path 2 for unidirectional maskOutput edge count not expected for unsqueeze2 of unidirectional maskFaild to match path 3 for unidirectional maskOutput edge count not expected for unsqueeze3 of unidirectional maskFaild to match path 4 for unidirectional maskDiv and Shape does not have edgeOutput edge count not expected for squeeze_2/slices2/shape2 of unidirectional maskCheckSliceParameters return false for slice2Pass MatchUnidirMaskSubgraphMatchInputMaskSubgraphStart MatchInputMaskSubgraphFailed to find Softmax nodeOutput edge count not expected for SoftmaxFailed to find path for maskOutput edge count not expected for mask nodesSoftmax attribute axis is expected to be 3mask_unsqueeze_1 axes not matched. Expect: 1mask_unsqueeze_2 axes not matched. Expect: 2mask_sub const input not matchedmask_mul const input not matchedPass MatchInputMaskSubgraphStart MatchInputMaskSubgraphDistilBertFailed to find mask pathwhere const not matched.Failed to find shape pathequal const not matched.Failed to find reshape shape path 1Failed to find reshape shape path 2gather indices not matched.Pass MatchInputMaskSubgraphDistilBertMatchPastSubgraphStart MatchPastSubgraphFailed to find path for past_kFailed to find path for present_kFailed to find path for present_v and past_vFailed to match v_concatpast_k_transpose perm attribute not matchedpresent_k_transpose perm attribute not matchedpresent_k_unsqueeze axes value not expectedpresent_v_unsqueeze axes value not expectedpast_v_gather indices != 1past_k_gather indices != 0past_v_gather and past_k_gather does not have same past inputOutput edge count not expected for nodes in past subgraphPass MatchPastSubgraphCheckDistilBertReshapeShapeCheckNodesInPathVStart CheckNodesInPathVOutput edge count not expected for nodes in path vFailed in match Transpose attribute perm. Expected: 0, 2, 1, 3Failed in match v_transpose attribute perm. Expected: 0, 2, 1, 3hidden_size != num_heads * head_sizev_reshape initializer value is not expectedPass CheckNodesInPathVreshape initializer value is not expectedCheckNodesInPathQStart CheckNodesInPathQq_reshape const not matchedqk_div const not matched.q_transpose perm attribute not matchedPass CheckNodesInPathQCheckNodesInPathKStart CheckNodesInPathKk_transpose perm attribute not matchedk_reshape const not matchedPass CheckNodesInPathKFuseGptAttentionStart FuseGptAttentionFaild to find path to qkv_matmulFaild to find path v to SplitCheckNodesInPathV return falseMatchInputMaskSubgraph returns falseMatchUnidirMaskSubgraph returns NULLFailed to find path for qq and v are not from same Split nodeCheckNodesInPathQ returns falseFailed to find path for kk and v are not from same Split nodeCheckNodesInPathK returns falseMatchPastSubgraph returns falseFused an attention node for GPT./Users/runner/work/1/s/onnxruntime/core/optimizer/attention_fusion.ccshape of layer norm bias tensor not expectedTotal fused Attention node count: FuseSubGraphFaild to find path vOutput edge count not expected for Add or MatMul in path vFailed in match v_matmul and v_add input shapeFailed in match input mask subgraphFuseSubGraphQKFused an attention node.FuseSubGraphQKImplq root should be layer normalizationq_matmul and q_add shape not matchedk root is not layer normk_matmul and k_add shape not matchedFailed to load Q, K and V weights, or data type is not float or float16.Failed to load Q, K and V bias tensors, or data type is not float or float16.Failed to convert mask to int32ConvertMaskToInt32Mask shape is unknown or not 2D, or data type unknownMask data type is not int32 or int64 or float32qkv_weightsqkv_biasFuseSubGraphQKDistilBert/Users/runner/work/1/s/onnxruntime/core/optimizer/bias_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 levelstd::vector > onnxruntime::optimizer_utils::GenerateTransformers(onnxruntime::TransformerLevel, const onnxruntime::SessionOptions &, const onnxruntime::IExecutionProvider &, const std::vector &)Unsupported level /Users/runner/work/1/s/onnxruntime/core/optimizer/initializer.ccReadExternalRawDataExternal data type must not be UNDEFINED or STRING.model_path must not be empty. Ensure that a path is provided when the model is created or loaded.TensorProto external data size mismatch. Computed size: /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.ccApplyImplvoid onnxruntime::TransformerMemcpyImpl::ProcessDefs(onnxruntime::Node &, const onnxruntime::KernelRegistryManager &, onnxruntime::InitializedTensorSet &)Execution type '' doesn't support memcpy bool onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager &, const onnxruntime::InitializedTensorSet &)/Users/runner/work/1/s/include/onnxruntime/core/optimizer/graph_transformer.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 MIGraphXExecutionProviderROCMExecutionProviderOpenVINOExecutionProviderDnnlExecutionProviderNupharExecutionProviderVitisAIExecutionProviderNnapiExecutionProviderArmNNExecutionProviderACLExecutionProviderRknpuExecutionProviderEluHardSigmoidLeakyReluSoftplusReluSigmoidSoftsignTanhThresholdedReluSeluParametricSoftplusScaledTanhunknown kernel typeonnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Elu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Elu]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::HardSigmoid]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::HardSigmoid]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::LeakyRelu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::LeakyRelu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Relu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Relu]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Selu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Selu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Sigmoid]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Sigmoid]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Softplus]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Softsign]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Tanh]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Tanh]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::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.gsl::span onnxruntime::Tensor::DataAsSpan() const [T = long long]/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 &, size_t)directions.size() == num_entriesNumber of entries in '' was validInvalid values in ''. 0 == forward. 1 == reverse.Subgraph must have the shape set for all outputs but did not.CreateFeedsFetchesManagerIterateSequencevoid onnxruntime::scan::detail::LoopStateVariable::Next()iteration_num_ < sequence_len_Misuse of LoopStateVariable. Attempt to move beyond end of sequenceFailed to create output tensor for output #AllocateFinalBufferonnxruntime::common::Status onnxruntime::scan::detail::OutputIterator::AllocateFinalOutput(const onnxruntime::TensorShape &)!is_concrete_shape_AllocateFinalOutputOrtValue &onnxruntime::scan::detail::OutputIterator::operator*()cur_iteration_ < num_iterations_is_concrete_shape_Expected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.onnxruntime::scan::detail::OutputIterator &onnxruntime::scan::detail::OutputIterator::operator++()Expected AllocateFinalOutput to have been called to before we increment the iteratorMismatch between expected shape and shape from first output is not compatible with /Users/runner/work/1/s/onnxruntime/core/providers/cpu/cpu_execution_provider.ccRegisterOnnxOperatorKernelsRegisterOnnxMLOperatorKernelsGetKernelRegistryvirtual std::shared_ptr onnxruntime::CPUExecutionProvider::GetKernelRegistry() constRegisterCPUKernelsCpuConstantOfShape/Users/runner/work/1/s/onnxruntime/core/providers/cpu/generator/constant_of_shape_base.honnxruntime::ConstantOfShapeBase >::ConstantOfShapeBase(const onnxruntime::OpKernelInfo &) [EnabledOutputTypeList = onnxruntime::TypeList]t_proto.dims_size() == 1t_proto.dims()[0] == 1void onnxruntime::ConstantOfShapeBase >::SetValueFromTensorProto(const onnx::TensorProto &) [EnabledOutputTypeList = onnxruntime::TypeList]utils::HasDataType(t_proto)ONNX_NAMESPACE::TensorProto::DataType_IsValid(t_proto.data_type())!utils::HasExternalData(t_proto)Tensor proto with external data for value attribute is not supported.SetValueFromTensorProtoUnsupported value attribute datatype: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/generator/constant_of_shape.ccUnsupported output datatype with size: PrepareComputeMust have a valid input shape.RandomNormalRandomUniformRandomNormalLikeRandomUniformLikeMultinomial/Users/runner/work/1/s/onnxruntime/core/providers/cpu/generator/random.ccCould not infer data type from input tensor with data type Empty dimensions for input tensorbatch_size is < 1num_classes is < 1num_samples is < 1Invalid data type of /Users/runner/work/1/s/onnxruntime/core/providers/cpu/generator/random.honnxruntime::RandomNormal::RandomNormal(const onnxruntime::OpKernelInfo &)info.GetAttr("mean", &mean_).IsOK()scaleinfo.GetAttr("scale", &scale_).IsOK()info.GetAttr("dtype", &dtype).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDInvalid dtype of info.GetAttrs("shape", shape).IsOK()onnxruntime::RandomUniform::RandomUniform(const onnxruntime::OpKernelInfo &)info.GetAttr("high", &high_).IsOK()lowinfo.GetAttr("low", &low_).IsOK()onnxruntime::RandomNormalLike::RandomNormalLike(const onnxruntime::OpKernelInfo &)onnxruntime::RandomUniformLike::RandomUniformLike(const onnxruntime::OpKernelInfo &)sample_sizeonnxruntime::Multinomial::Multinomial(const onnxruntime::OpKernelInfo &)info.GetAttr("sample_size", &num_samples_).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::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_/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]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 = onnxruntime::MLFloat16]onnxruntime::common::Status onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopy(const onnxruntime::Tensor &, onnxruntime::Tensor &, void *)output.SizeInBytes() == input.SizeInBytes()Einsum op: The candidate output does not match the actual output's shapestd::unique_ptr onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::Diagonal(const onnxruntime::Tensor &, int64_t, int64_t, onnxruntime::AllocatorPtr, void *)rank >= 2 && dim_1 != dim_2 && input_dims[dim_1] == input_dims[dim_2]Cannot parse the diagonal elements along dims for input shape bool onnxruntime::EinsumOp::IsTransposeRequired(size_t, const std::vector &)input_rank == permutation.size()std::unique_ptr onnxruntime::EinsumOp::Transpose(const onnxruntime::Tensor &, const std::vector &, const std::vector &, onnxruntime::AllocatorPtr, void *, const DeviceHelpers::Transpose &)Length of permutation must match the rank of the input to be permutatedEinsum op: Transpose failed: std::unique_ptr onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDims(const onnxruntime::Tensor &, bool, onnxruntime::AllocatorPtr)input_dims[rank - 2] == input_dims[rank - 1]The innermost dims should have the same dim value to parse the diagonal elementsEinsum op: Unsupported data type for Diagonal /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_compute_preprocessor.ccNumber of subscripts in the input equation does not match number of input tensorsFound a '.' not part of an ellipsis in input: Einsum subscripts string contains too many subscript labels when compared to the rank of the inputEllipsis must indicate a fixed number of dimensions across all inputsFound '.' not part of an ellipsis in input: The only subscript labels allowed are lower-cased letters (a-z) and upper-cased letters (A-Z)Einsum operands could not be broadcast together. Please check input shapes/equation provided.Input shape of operand is incompatible in the dimension . The shape is: Another operand has a dim value of in the same dimensionEinsum subscripts string contains too many subscript labels when compared to the rank of the input Einsum subscripts does not contain enough subscript labels and there is no ellipsis for input onnxruntime::common::Status onnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDims()num_broadcasted_indices < num_of_ellipsis_dims_dim_iter == rank...Inputs have ellipses in them but the provided output subscript does not contain an ellipsisFound a '.' not part of an ellipsis in the output subscript providedFound '.' not part of an ellipsis in the output subscript providedRank of the input must match number of subscript labels corresponding to the input/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_typed_compute_processor.ccstd::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const std::vector &, bool) [T = float]left.Shape().Size() == left_shape_override.Size()The override dims are not compatible with given tensor's shape. Left shape: Left shape override: right.Shape().Size() == right_shape_override.Size()Right shape: Right shape override: left_rank == right_rankRanks of pair-wise operands must be equal. Right shape: left_dim == right_dimEinsum op: Input dimensions must be equal along an axis to be reduced across all inputsvoid onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor &, const std::vector &) [T = float]candidate_output.Shape().Size() == output_shape.Size()Einsum op: The candidate output cannot be reshaped into the op's outputcandidate_output_dims[iter] == 1Not all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: Einsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const std::vector &, bool) [T = int]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor &, const std::vector &) [T = int]std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const std::vector &, bool) [T = double]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor &, const std::vector &) [T = double]std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const std::vector &, bool) [T = long long]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor &, const std::vector &) [T = long long]std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const std::vector &, bool) [T = onnxruntime::MLFloat16]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor &, const std::vector &) [T = onnxruntime::MLFloat16]SubMulDivAbsNegFloorCeilReciprocalSqrtPowT1ExpLogSumMaxMinLessGreaterEqualMeanBitShiftErfNotAndOrXorUnsupported X type: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.ccvirtual onnxruntime::common::Status onnxruntime::Min_6::Compute(onnxruntime::OpKernelContext *) const [T = float]inputCount >= 1Must have 1 or more inputsdata_n.Shape() == shapevirtual onnxruntime::common::Status onnxruntime::Max_6::Compute(onnxruntime::OpKernelContext *) const [T = float]virtual onnxruntime::common::Status onnxruntime::Mean_6::Compute(onnxruntime::OpKernelContext *) const [T = float]data_1.Shape() == shapeSinCosTanAsinAcosAtanSinhCoshAsinhAcoshAtanhPReluExpandModvirtual 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::Reciprocal]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Reciprocal]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Sqrt]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Sqrt]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Sqrt]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Sqrt]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Exp]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Exp]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Exp]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Exp]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Log]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Log]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Log]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Log]virtual onnxruntime::common::Status onnxruntime::Sum_6::Compute(onnxruntime::OpKernelContext *) const [T = float]input_count >= 1virtual onnxruntime::common::Status onnxruntime::Sum_6::Compute(onnxruntime::OpKernelContext *) const [T = double]directiononnxruntime::BitShift::BitShift(const onnxruntime::OpKernelInfo &) [T = unsigned char]LEFTRIGHTInvalid direction value of ''. Valid values are 'LEFT' or 'RIGHT'.auto onnxruntime::BitShift::Compute(onnxruntime::OpKernelContext *)::(anonymous class)::operator()(onnxruntime::BroadcastHelper &) const [T = unsigned char]cur1 == end1cur_out == end_outonnxruntime::BitShift::BitShift(const onnxruntime::OpKernelInfo &) [T = unsigned int]auto onnxruntime::BitShift::Compute(onnxruntime::OpKernelContext *)::(anonymous class)::operator()(onnxruntime::BroadcastHelper &) const [T = unsigned int]onnxruntime::BitShift::BitShift(const onnxruntime::OpKernelInfo &) [T = unsigned long long]auto onnxruntime::BitShift::Compute(onnxruntime::OpKernelContext *)::(anonymous class)::operator()(onnxruntime::BroadcastHelper &) const [T = unsigned long long]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(ptrdiff_t, ptrdiff_t)axis == 1 || axis == largestAttempting to broadcast an axis by a dimension other than 1. by void onnxruntime::BroadcastIterator::Append(ptrdiff_t, ptrdiff_t)void onnxruntime::ExpandBroadcastLooper(onnxruntime::BroadcastHelper &, const onnxruntime::ProcessBroadcastSpanFuncs &)!helper.HaveTwoTensorInputs()ExpandBroadcastLooper should only have a shape for the second input.void onnxruntime::InputBroadcaster::AdvanceBy(size_t)offset % span_size_ == 0onnxruntime::Mod::Mod(const onnxruntime::OpKernelInfo &)(fmod == 0) || (fmod == 1)onnxruntime::OutputBroadcaster::OutputBroadcaster(size_t, onnxruntime::Tensor &, ptrdiff_t, ptrdiff_t)start_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= lenInvalid start/ending offset [) for tensor of length:start_offset % span_size == 0 && real_end % span_size == 0Broadcast Output range [) are not at boundary of span with size:onnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext &)Unsupported Y type: void onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy::operator()(int32_t, Ret &) const [Ret = onnxruntime::common::Status]Unsupported data type: void onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::CheckCalledOnce()called_ == 1void onnxruntime::BroadcastLooper(TBroadcastHelper &, const onnxruntime::ProcessBroadcastSpanFuncs &) [TBroadcastHelper = onnxruntime::BroadcastHelper]helper.HaveTwoTensorInputs()GemmtransA/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/gemm.honnxruntime::Gemm::Gemm(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttr("transA", &temp).IsOK()transBinfo.GetAttr("transB", &temp).IsOK()onnxruntime::Gemm::Gemm(const onnxruntime::OpKernelInfo &) [T = double]/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/gemm.ccvoid onnxruntime::GemmBroadcastBias(int64_t, int64_t, float, const T *, const onnxruntime::TensorShape *, T *) [T = double]c_shape != nullptr/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/gemm_helper.honnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &)left.NumDimensions() == 2 || left.NumDimensions() == 1right.NumDimensions() == 2GEMM: Dimension mismatch, W: K: N:Gemm: Invalid bias shape for broadcastM_ >= 0 && K_ > 0 && N_ >= 0void onnxruntime::GemmBroadcastBias(int64_t, int64_t, float, const T *, const onnxruntime::TensorShape *, T *) [T = float]/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/hardmax.ccHardmax inputs N, D and N * D must be < . N=, D=HardmaxMatMul/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/matmul.cc/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/matmul_helper.hleft_num_dims and right_num_dims must be >= 1MatMul dimension mismatchleft operand cannot broadcast on dim right operand cannot broadcast on dim num_dims_with_pad != num_output_dimsM_ == 1 && N_ == 1 was falsenum_dims_with_pad - 1 != num_output_dimsnum_dims_with_pad - 2 != num_output_dimsMatMulInteger/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/matmul_integer.ccvirtual onnxruntime::common::Status onnxruntime::MatMulInteger::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(a_zero_point)MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 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 1RoundSignSoftmaxLogSoftmax/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/softmax.ccComputeImplOpset13SoftmaxCPU inputs N, D and N * D must be < TopK/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/top_k.ccvoid onnxruntime::TopkOpset9ConstructorCommon(const onnxruntime::OpKernelInfo &, int &, unsigned int &)op_kernel_info.GetAttr("k", &k_temp).IsOK()k_temp > 0op_kernel_info.GetAttr("axis", &axis_temp).IsOK()void onnxruntime::TopkOpset10ConstructorCommon(const onnxruntime::OpKernelInfo &, int &)void onnxruntime::TopkOpset11ConstructorCommon(const onnxruntime::OpKernelInfo &, int &, bool &, bool &)largestop_kernel_info.GetAttr("largest", &largest_temp).IsOK()op_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()input count mismatch, expected 1 input - the tensor to be processedk argument [] should not be greater than specified axis dim value [output count mismatch, expected 2 outputs to be present for TopK operatorinput count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valueArrayFeatureExtractorInvalid argument: X input has empty dimensions.Invalid Y argument: num_indices = 0Invalid Y argument: index is out of range: Y[] () >=BinarizerthresholdInput data with index: is NaNCastMapInvalid input type of value: Expected std::map or std::mapUnexpected CAST_TO value of cast_to/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/cast_map.honnxruntime::ml::CastMap::CastMap(const onnxruntime::OpKernelInfo &)info.GetAttr("cast_to", &attr).IsOK()info.GetAttr("map_form", &attr).IsOK()max_mapinfo.GetAttr("max_map", &max_map_).IsOK()map_form_ != PACK_MAP::SPARSE || max_map_ > 0TO_FLOATTO_STRINGTO_INT64/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/ml_common.honnxruntime::ml::CAST_TO onnxruntime::ml::MakeCast(const std::string &)Invalid CAST_TO value of Expected TO_FLOAT, TO_STRING or TO_INT64DENSESPARSEonnxruntime::ml::PACK_MAP onnxruntime::ml::MakePack(const std::string &)Invalid PACK_MAP value of Expected DENSE or SPARSE/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/cast_map.cconnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext &, TTo) const [TFrom = float, TTo = float]cur_input == end_input || cur_input->first >= 0Negative index values are not permitted. First entry in map has index value of const T *onnxruntime::OpKernelContext::Input(int) const [T = std::__1::map, std::__1::allocator > >]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext &, TTo) const [TFrom = std::__1::basic_string, TTo = float]const T *onnxruntime::OpKernelContext::Input(int) const [T = std::__1::map, std::__1::less, std::__1::allocator > > >]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext &, TTo) const [TFrom = float, TTo = long long]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext &, TTo) const [TFrom = std::__1::basic_string, TTo = long long]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext &, TTo) const [TFrom = float, TTo = std::__1::basic_string]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext &, TTo) const [TFrom = std::__1::basic_string, TTo = std::__1::basic_string]CategoryMapperInput of string must have output of int64Input of int64 must have output of string cats_strings/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/category_mapper.honnxruntime::ml::CategoryMapper::CategoryMapper(const onnxruntime::OpKernelInfo &)info.GetAttrs("cats_strings", string_categories).IsOK()cats_int64sinfo.GetAttrs("cats_int64s", int_categories).IsOK()default_stringinfo.GetAttr("default_string", &default_string_).IsOK()default_int64info.GetAttr("default_int64", &default_int_).IsOK()num_entries == int_categories.size()DictVectorizerstring_vocabulary/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/dictvectorizer.honnxruntime::ml::DictVectorizerOp, long long>::DictVectorizerOp(const onnxruntime::OpKernelInfo &) [AttrType = std::__1::basic_string, TargetType = long long]info.GetAttrs(std::is_same::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()const T *onnxruntime::OpKernelContext::Input(int) const [T = std::__1::map, long long, std::__1::less >, std::__1::allocator, long long> > >]onnxruntime::ml::DictVectorizerOp, float>::DictVectorizerOp(const onnxruntime::OpKernelInfo &) [AttrType = std::__1::basic_string, TargetType = float]const T *onnxruntime::OpKernelContext::Input(int) const [T = std::__1::map, float, std::__1::less >, std::__1::allocator, float> > >]onnxruntime::ml::DictVectorizerOp, double>::DictVectorizerOp(const onnxruntime::OpKernelInfo &) [AttrType = std::__1::basic_string, TargetType = double]const T *onnxruntime::OpKernelContext::Input(int) const [T = std::__1::map, double, std::__1::less >, std::__1::allocator, double> > >]int64_vocabularyonnxruntime::ml::DictVectorizerOp >::DictVectorizerOp(const onnxruntime::OpKernelInfo &) [AttrType = long long, TargetType = std::__1::basic_string]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo &) [AttrType = long long, TargetType = float]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo &) [AttrType = long long, TargetType = double]const T *onnxruntime::OpKernelContext::Input(int) const [T = std::__1::map, std::__1::allocator > >]FeatureVectorizer/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/feature_vectorizer.ccvirtual onnxruntime::common::Status onnxruntime::ml::FeatureVectorizer::Compute(onnxruntime::OpKernelContext *) constinput_count >= 0 && static_cast(input_count) == input_dimensions_.size()Number of inputs () does not match number of inputdimensions values (Invalid input type:inputdimensions/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/feature_vectorizer.honnxruntime::ml::FeatureVectorizer::FeatureVectorizer(const onnxruntime::OpKernelInfo &)status.IsOK() && !input_dimensions_.empty()Imputerimputed_value_floatsimputed_value_int64sreplaced_value_float/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/imputer.cconnxruntime::ml::ImputerOp::ImputerOp(const onnxruntime::OpKernelInfo &)Expected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedreplaced_value_int64Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedimputed_values_float_.empty() ^ imputed_values_int64_.empty()Must provide imputed_values_float_ or imputed_values_int64_ but not both.virtual common::Status onnxruntime::ml::ImputerOp::Compute(onnxruntime::OpKernelContext *) constInvalid typeEmpty value of imputed values.Empty input dimensions.LabelEncoderInput of tensor(string) must have output of tensor(int64)Input of tensor(int64) must have output of tensor(string)keys_floatsvalues_stringskeys_stringsvalues_floatsdefault_floatkeys_int64svalues_int64sclasses_strings/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/label_encoder.honnxruntime::ml::LabelEncoder::LabelEncoder(const onnxruntime::OpKernelInfo &)info.GetAttrs("classes_strings", string_classes).IsOK()onnxruntime::ml::LabelEncoder_2 >::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = float, TValue = std::__1::basic_string]info.GetAttrs(_key_field_name, keys).IsOK()info.GetAttrs(_value_field_name, values).IsOK()num_keys == num_valuesThe attribtues in LabelEncoder (name: ) must have the same length. However, the number of key is and the number of values is onnxruntime::ml::LabelEncoder_2, float>::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = std::__1::basic_string, TValue = float]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = long long, TValue = float]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = float, TValue = long long]onnxruntime::ml::LabelEncoder_2 >::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = long long, TValue = std::__1::basic_string]onnxruntime::ml::LabelEncoder_2, long long>::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = std::__1::basic_string, TValue = long long]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = long long, TValue = long long]LinearClassifiermulti_class/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/linearclassifier.cconnxruntime::ml::LinearClassifier::LinearClassifier(const onnxruntime::OpKernelInfo &)void onnxruntime::ml::LinearClassifier::ComputeImpl(const gsl::span, int64_t, int64_t, int64_t, const std::vector &, const std::vector &, onnxruntime::Tensor &, onnxruntime::Tensor &, onnxruntime::ml::POST_EVAL_TRANSFORM, bool, concurrency::ThreadPool *) constscores_output_data.length() >= scores_output_sizeScores output is incorrect size. Expected: Found:Input shape needs to be at least a single dimension.Unsupported input element type of void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor &, gsl::span &) [SrcType = int]shape_size == out.length()void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor &, gsl::span &) [SrcType = long long]void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor &, gsl::span &) [SrcType = double]LinearRegressorinterceptstargets/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/linearregressor.cconnxruntime::ml::LinearRegressor::LinearRegressor(const onnxruntime::OpKernelInfo &)info.GetAttr("targets", &num_targets_).IsOK()Input shape had more than 2 dimension. Dims=Normalizer/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/normalizer.honnxruntime::ml::Normalizer::Normalizer(const onnxruntime::OpKernelInfo &)info.GetAttr("norm", &norm).IsOK()MAXL1L2onnxruntime::ml::NORMALIZE onnxruntime::ml::MakeNormalize(const std::string &)Invalid normalize value of Rank of input to Normalized must be less than 2. Got Unexpected NORMALIZE value of OneHotEncoderUnknown Category and zeros = 0.zeros/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/onehotencoder.cconnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo &) [T = long long]tmp_cats_int64s.empty() || tmp_cats_strings.empty()One and only one of the 'cats_*' attributes must be definednum_categories_ > 0onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo &) [T = float]onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo &) [T = double]onnxruntime::ml::OneHotEncoderOp >::OneHotEncoderOp(const onnxruntime::OpKernelInfo &) [T = std::__1::basic_string]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 > > > >]MaxUnpool/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/Unpool.ccIndex tensor shape should be same as that of the input data tensor to unpool.Shape must be 1 dimensional as it's tensor data of a shapeoutput_shape is smaller than minimum required. output_shape: inferred output shape:/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/unpool.honnxruntime::MaxUnpool::MaxUnpool(const onnxruntime::OpKernelInfo &)info.GetAttrs("kernel_shape", kernel_shape_).IsOK()kernel_shape_[dim] > 0pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]strides_.size() == kernel_shape_.size()BatchNormalizationspatial/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/batch_norm.honnxruntime::BatchNorm::BatchNorm(const onnxruntime::OpKernelInfo &) [T = float]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_shapestridesonnxruntime::ConvAttributes::ConvAttributes(const OpNodeProtoHelper &)auto_pad == AutoPadType::NOTSETA Conv/ConvTranspose node has both 'auto_pad' and 'pads' attributesdilationsgroupT3ConvInteger/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]!mask || mask->Shape() == X_shapefloat onnxruntime::(anonymous namespace)::GetRatioOrDefault(const onnxruntime::Tensor *) [T2 = float]ratio_tensor->Shape().Size() == 10.0f <= ratio_value && ratio_value < 1.0fvirtual onnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext *) const [T1 = float, T2 = double]float onnxruntime::(anonymous namespace)::GetRatioOrDefault(const onnxruntime::Tensor *) [T2 = double]virtual onnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext *) const [T1 = double, T2 = float]virtual onnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext *) const [T1 = double, T2 = double]Flatten/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/flatten.honnxruntime::Flatten::Flatten(const onnxruntime::OpKernelInfo &)virtual onnxruntime::common::Status onnxruntime::Flatten::Compute(onnxruntime::OpKernelContext *) constgsl::narrow_cast(X_shape.NumDimensions()) >= axisInstanceNormalization/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/instance_norm.ccepsilon/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/instance_norm.honnxruntime::InstanceNorm::InstanceNorm(const onnxruntime::OpKernelInfo &) [T = float]op_kernel_info.GetAttr("epsilon", &epsilon_).IsOK()Invalid input data: number of dimensions is less than 3: Invalid input scale: number of dimensions is not 1: Mismatch between input data and scale: size of scale != input channel count vs. Invalid input B: number of dimensions is not 1: Mismatch between input data and B: size of B != input channel count LpNormalization/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/lp_norm.honnxruntime::LpNorm::LpNorm(const onnxruntime::OpKernelInfo &) [T = float]op_kernel_info.GetAttr("axis", &axis_).IsOK()op_kernel_info.GetAttr("p", &p_).IsOK()p_ == 1 || p_ == 2onnxruntime::LpNorm::LpNorm(const onnxruntime::OpKernelInfo &) [T = double]input count mismatch/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/lrn.ccvirtual onnxruntime::common::Status onnxruntime::LRN::Compute(onnxruntime::OpKernelContext *) const [T = float]X->Shape().NumDimensions() == 4LRN/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/lrn.honnxruntime::LRN::LRN(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttr("size", &size).IsOK()size_ > 0size_ % 2 == 1info.GetAttr("alpha", &alpha_).IsOK()alpha_ > 0.0finfo.GetAttr("beta", &beta_).IsOK()beta_ > 0.0f/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/pool.ccInput dimension cannot be less than 3.Unsupported pooling size.kernel_shape num_dims is not compatible with X num_dims.AveragePoolMaxPoolLpPoolGlobalLpPoolGlobalAveragePoolGlobalMaxPool/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/pool_attributes.hstd::vector onnxruntime::PoolAttributes::SetOutputSize(const onnxruntime::TensorShape &, int64_t, std::vector *) constinput_shape.Size() > 0 || input_shape[0] == 0Invalid input shape. Only N can be zero. Got:void onnxruntime::PoolAttributes::InferOutputSize(const std::vector &, std::vector *, std::vector *) constinput_dims.size() >= 2void onnxruntime::PoolAttributes::ComputeSizePadDilations(const int64_t, const int64_t, const int64_t, int64_t *, int64_t *, int64_t, int64_t *) constonnxruntime::PoolAttributes::PoolAttributes(const OpNodeProtoHelper &, const std::string &, int)info.GetAttrs("kernel_shape", kernel_shape).IsOK()info.GetAttr("auto_pad", &auto_padding).IsOK()ceil_modecount_include_padinfo.GetAttr("count_include_pad", &temp).IsOK()storage_orderinfo.GetAttr("storage_order", &storage_order).IsOK()kernel_shape[dim] > 0pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]strides.size() == kernel_shape.size()dilations.size() == kernel_shape.size()/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/pool_base.hvoid onnxruntime::PoolProcessContext::init(const onnxruntime::OpKernelInfo &)info.GetAttr("p", &p_).IsOK()Unsupported pooling size : T4QLinearConv/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/qlinearconv.ccvirtual onnxruntime::common::Status onnxruntime::QLinearConv::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(X_zero_point)QLinearConv : input zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_zero_point)QLinearConv : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(X_scale)QLinearConv : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_scale)QLinearConv : result scale must be a scalar or 1D tensor of size 1channels_lastMaxRoiPool/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/roi_pool.ccvirtual onnxruntime::common::Status onnxruntime::RoiPool::Compute(onnxruntime::OpKernelContext *) const [T = float]R->Shape()[1] == 5roi_batch_id >= 0roi_batch_id < batch_sizepooled_shape/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/roi_pool.honnxruntime::RoiPool::RoiPool(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttrs("pooled_shape", pooled_shape).IsOK()pooled_shape.size() == 2pooled_height_ > 0pooled_width_ > 0spatial_scaleinfo.GetAttr("spatial_scale", &spatial_scale_).IsOK()spatial_scale_ > 0Shrink/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/shrink.honnxruntime::Shrink::Shrink(const onnxruntime::OpKernelInfo &)op_kernel_info.GetAttr("bias", &bias_temp).IsOK()lambdop_kernel_info.GetAttr("lambd", &lambd_temp).IsOK()StringNormalizer/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/string_normalizer.cconnxruntime::StringNormalizer::StringNormalizer(const onnxruntime::OpKernelInfo &)LOWERUPPERlocalestopwords!sw.empty()wstr != wconv_errorSingle dimension value must be greater than 0Input dimensions are either[C > 0] or [1][C > 0] allowedInput contains invalid utf8 chars at: auto onnxruntime::string_normalizer::Locale::Locale(const std::string &)::(anonymous class)::operator()() constFailed to construct locale with name::Please, install necessary language-pack-XX and configure localeswstring_convert: from_bytes errorwstring_convert: to_bytes errorTfIdfVectorizer/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/tfidfvectorizer.cconnxruntime::TfIdfVectorizer::TfIdfVectorizer(const onnxruntime::OpKernelInfo &)TFIDFTFIDFimpl_->weighting_criteria_ != kNonemode: is unrecognized, acceptable values are TF,IDF,TFIDFmin_gram_lengthimpl_->min_gram_length_ > 0Required min_gram_length must be positive: max_gram_lengthimpl_->max_gram_length_ >= impl_->min_gram_length_min_gram_length >= max_gram_length required: >= max_skip_countimpl_->max_skip_count_ >= 0max_skip_count must be non-negative: ngram_countsstatus.IsOK() && !impl_->ngram_counts_.empty()size_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size()min_gram_length must be inbounds of ngram_counts: <= size_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size()max_gram_length must be inbounds of ngram_counts: ngram_indexesstatus.IsOK() && !impl_->ngram_indexes_.empty()std::all_of(impl_->ngram_indexes_.cbegin(), impl_->ngram_indexes_.cend(), [](int64_t i) { return i >= 0; })weightsimpl_->weights_.size() == impl_->ngram_indexes_.size()Got weights of size: but ngram_indexes size: must be of equal sizepool_strings!pool_strings.empty()pool_int64sstatus.IsOK() && !pool_int64s.empty()end_idx >= start_idx && end_idx <= total_itemsn-gram counts out of bounds for -grams(items % ngram_size == 0)Number of items must compose whole Input shape must have either [C] or [B,C] dimensions with B > 0.size_t onnxruntime::ngram_details::PopulateGrams(ForwardIter, size_t, size_t, size_t, Map &) [K = long long, ForwardIter = const long long *, Map = std::__1::unordered_map, std::__1::default_delete > >, std::__1::hash, std::__1::equal_to, std::__1::allocator, std::__1::default_delete > > > > >]p.first->second->id_ == 0Duplicate ngram detected, size: id: size_t onnxruntime::ngram_details::PopulateGrams(ForwardIter, size_t, size_t, size_t, Map &) [K = std::__1::basic_string, ForwardIter = std::__1::__wrap_iter > *>, Map = std::__1::unordered_map >, std::__1::unique_ptr, std::__1::default_delete > >, std::__1::hash >, std::__1::equal_to >, std::__1::allocator >, std::__1::unique_ptr, std::__1::default_delete > > > > >]NonMaxSuppression/Users/runner/work/1/s/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.ccstatic onnxruntime::common::Status onnxruntime::NonMaxSuppressionBase::PrepareCompute(onnxruntime::OpKernelContext *, onnxruntime::PrepareContext &)boxes_tensorscores_tensorboxes must be a 3D tensor.scores must be a 3D tensor.boxes and scores should have same num_batches.boxes and scores should have same spatial_dimension.The most inner dimension in boxes must have 4 data.GetThresholdsFromInputsiou_threshold must be in range [0, 1].virtual onnxruntime::common::Status onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext *) constoutput != nullptrcenter_point_box/Users/runner/work/1/s/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.honnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBase(const onnxruntime::OpKernelInfo &)0 == center_point_box_ || 1 == center_point_box_RoiAlignNull input X ptrNull rois_ptrNull batch_indices_ptrNumber of dimensions for batch indices should be exactly 1Number of dimensions for rois should be exactly Second dimension for rois should be exactly First dimension (num_rois) of batch_indices and rois don't matchavg/Users/runner/work/1/s/onnxruntime/core/providers/cpu/object_detection/roialign.honnxruntime::RoiAlignBase::RoiAlignBase(const onnxruntime::OpKernelInfo &)Invalid mode of value specified. It should be either avg or maxoutput_heightoutput_widthsampling_ratiosampling_ratio_ >= 0Sampling ratio should be >=0, but it was /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 = double, AGG = onnxruntime::ReduceAggregatorLogSumExp]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = double, 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 = double, AGG = onnxruntime::ReduceAggregatorMax]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = double, 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 = double, AGG = onnxruntime::ReduceAggregatorMean]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = double, 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 = double, AGG = onnxruntime::ReduceAggregatorMin]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = double, 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]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = double, AGG = onnxruntime::ReduceAggregatorArgMinLastIndex]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = double, AGG = onnxruntime::ReduceAggregatorArgMinLastIndex]void onnxruntime::CommonReduce(onnxruntime::OpKernelContext *, const std::vector, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce &, bool) [T = double, AGG = onnxruntime::ReduceAggregatorArgMin]void onnxruntime::NoTransposeReduce(onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const std::vector &, concurrency::ThreadPool *, onnxruntime::ResultsNoTransposePrepareForReduce &) [T = double, 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 {affinereluleakyreluthresholdedreluscaledtanhhardsigmoidelusoftsignsoftplus/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/rnn_helpers.ccstd::string onnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCount(const std::string &, std::vector::const_iterator &, const std::vector::const_iterator &, std::vector::const_iterator &, const std::vector::const_iterator &, float &, float &)Expecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got 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 1.0fonnxruntime::rnn::detail::deepcpu::ActivationFuncPtr onnxruntime::rnn::detail::deepcpu::ActivationFuncByName(const std::string &)Invalid activation function of onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncPtr onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByName(const std::string &)Invalid LSTM merge activation function of onnxruntime::rnn::detail::deepcpu::GruResetGateFuncPtr onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByName(const std::string &)Invalid GRU reset gate activation function: onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncPtr onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByName(const std::string &)Invalid GRU hidden gate activation function: rows[const T *onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span, size_t, size_t) [T = const float]const T *onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span, size_t, size_t) [T = float]ConcatFromSequence/Users/runner/work/1/s/onnxruntime/core/providers/cpu/sequence/concat_from_sequence.ccvirtual onnxruntime::common::Status onnxruntime::ConcatFromSequence::Compute(onnxruntime::OpKernelContext *) 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::(anonymous namespace)::Cast::Cast(const onnxruntime::OpKernelInfo &)NaN-INFINF%.8gtypename std::enable_if::value, void>::type onnxruntime::(anonymous namespace)::CastToString(const SrcType &, std::string &) [SrcType = float]snprintf_result > 0snprintf() failed with return value: snprintf_result > 0 && gsl::narrow_cast(snprintf_result) == buffer_span.size() - 1typename std::enable_if::value, void>::type onnxruntime::(anonymous namespace)::CastToString(const SrcType &, std::string &) [SrcType = double]Compress/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/compress.ccvirtual onnxruntime::common::Status onnxruntime::Compress::Compute(onnxruntime::OpKernelContext *) constaxes_right_stride >= 0 && static_cast(axes_right_stride) < std::numeric_limits::max()Concat/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/concat.ccPrepareForComputeCannot concatenate scalarsonnxruntime::common::Status onnxruntime::ConcatBase::PrepareForCompute(onnxruntime::OpKernelContext *, const std::vector &, onnxruntime::Prepare &) constinputs_n_rank == inputs_0_rankRanks of input data are different, cannot concatenate them. expected rank: got: Non concat axis dimensions must match: Axis has mismatched dimensions of Data type mismatch/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/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_negativeIsNaNNull input ptrMeanVarianceNormalizationInput is expected to have four dimensions corresponding to [N,C,H,W]NonZero/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/nonzero_op.ccvirtual onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext *) const [T = bool]virtual onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext *) const [T = unsigned char]virtual onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext *) const [T = int]virtual onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext *) const [T = long long]virtual onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext *) const [T = float]OneHotInvalid argument for depth; it's not a scalar.Invalid argument for values; either it's rank is more than 1 or it has more than 2 elements/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/onehot.ccDepth is negative.PadCannot use 'reflect' mode to pad dimension with a value of 0. Input shape:Unexpected mode of /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/pad.ccvirtual onnxruntime::common::Status onnxruntime::Pad::Compute(onnxruntime::OpKernelContext *) constpads_tensor.IsDataType()pads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)Pads tensor should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]pads_size == 2 * data_rankPads tensor size should be equal to twice the input dimension count value_tensor->DataType() == data_type && value_tensor->Shape().Size() == 1Value tensor should be a 1D tensor of size 1 with the same type as that of the input tensorUnsupported input data type of valueconstantreflectedge/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/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 virtual onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext *) const [T = int]zero_point == nullptr || std::all_of(zero_point, zero_point + x_zero_point->Shape().Size(), [](int32_t zp) { return zp == 0; })DequantizeLinear with type int32 should have no zero point or all zero points should be 0shapeReshape/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::DataAsSpan() const [T = bool]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = bool]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = double]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = double]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = unsigned char]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned char]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = short]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = short]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = unsigned short]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned short]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = int]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = unsigned int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned int]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = unsigned long long]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned long long]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = onnxruntime::MLFloat16]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = onnxruntime::MLFloat16]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = onnxruntime::BFloat16]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = onnxruntime::BFloat16]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 &)ScatterNDinput tensor and indices tensor must has rank larger than 0. input shape: , indices shape: updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. updates shape: , data shape: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/scatter_nd.ccinvalid indice found, indice = SizeSliceTind'axes' has an axis outside of the tensor dimension count'axes' has duplicates'step' value cannot be 0/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/slice.ccFillVectorsFromInputStarts must be a 1-D arrayEnds must be a 1-D arrayStarts and ends shape mismatchStarts and axes shape mismatchStarts and steps shape mismatchData type for starts and ends inputs' is not supported in this build. Got starts/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/slice.honnxruntime::SliceBase::SliceBase(const onnxruntime::OpKernelInfo &, bool)has_starts && has_ends && attr_starts_.size() == attr_ends_.size()!has_axes || attr_axes_.size() == attr_starts_.size()Invalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributesauto onnxruntime::SliceImpl(onnxruntime::OpKernelContext *, const onnxruntime::Tensor &, SliceOp::PrepareForComputeMetadata &)::(anonymous class)::operator()(SliceIterator &) constoutput == output_endvoid *onnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStep(void *)Unexpected element size of SpaceToDepthDepthToSpace/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/space_depth_ops.ccvirtual onnxruntime::common::Status onnxruntime::SpaceToDepth::Compute(onnxruntime::OpKernelContext *) const [T = float]input_shape.NumDimensions() == 4input_height % this->blocksize_ == 0input_width % this->blocksize_ == 0virtual onnxruntime::common::Status onnxruntime::DepthToSpace::Compute(onnxruntime::OpKernelContext *) const [T = float]input.Shape().NumDimensions() == 4input_depth % (blocksize_ * blocksize_) == 0blocksize/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/space_depth_ops.honnxruntime::SpaceDepthBase::SpaceDepthBase(const onnxruntime::OpKernelInfo &)info.GetAttr("blocksize", &blocksize_).IsOK()CRDDCRonnxruntime::DepthToSpace::DepthToSpace(const onnxruntime::OpKernelInfo &) [T = float]SplitInput cannot be split evenly on selected axis. Input shape= Axis= NumOutputs=Cannot split using values in 'split' attribute. Axis= Input shape= Num entries in 'split' (must equal number of outputs) was Sum of sizes in 'split' (must equal size of selected axis) was /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/split.ccvirtual onnxruntime::common::Status onnxruntime::Split::Compute(onnxruntime::OpKernelContext *) constSplit operator does not support yetsplit/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/split.honnxruntime::SplitBase::SplitBase(const onnxruntime::OpKernelInfo &)std::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value >= 0; })onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &) const [T = float]split_tensor->Shape().NumDimensions() == 1onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &) const [T = int]onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &) const [T = long long]onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &) const [T = unsigned char]onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &) const [T = std::__1::basic_string]Squeeze/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/squeeze.hvirtual onnxruntime::common::Status onnxruntime::Squeeze::Compute(onnxruntime::OpKernelContext *) constaxes_tensor != nullptraxes_tensor->Shape().NumDimensions() == 1static std::vector onnxruntime::SqueezeBase::ComputeOutputShape(const onnxruntime::TensorShape &, const std::vector &)input_shape[i] == 1Dimension of input must be 1 instead of . shape=TileInput count of Tile OP mismatch, the first one is emptyInput count of Tile OP mismatch, the second one is emptythe tensor to be tiled using Tile OP must be atleast 1 dimensional'repeat' input tensor must be 1 dimensional'repeat' input tensor must have the same length as the 'input' tensor/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/tile.ccvirtual onnxruntime::common::Status onnxruntime::Tile::Compute(onnxruntime::OpKernelContext *) const!input_tensor.IsDataType()Tile doesn't have an implementation yet for the type: Transpose of element size not supported in this build. Size=Mismatched data types between input and output Tensors. /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/transpose.ccvirtual onnxruntime::common::Status onnxruntime::Transpose::Compute(onnxruntime::OpKernelContext *) constinput_tensor_ptr != nullptrTransposevoid onnxruntime::DoTransposeEltWise(int64_t, const std::vector &, size_t, const std::vector &, const std::string *, std::string *)num_axes > 0(local_source >= source) && (local_source < source + num_blocks)void onnxruntime::IncrementIndexAndComputeOffsetSetup(onnxruntime::MultiIndex &, size_t, const std::vector &, const std::vector &, size_t)naxes > 0Method IncrementIndexAndComputeOffset assumes this value is strictly positive.void onnxruntime::DoTransposeImpl(int64_t, const std::vector &, size_t, size_t, const std::vector &, const std::string *, std::string *)(local_source >= source) && (local_source < source + num_blocks * num_elts_in_block)void onnxruntime::DoTransposeImpl(int64_t, const std::vector &, size_t, size_t, const std::vector &, const uint8_t *, uint8_t *, size_t)(local_source >= source) && (local_source < source + num_blocks * blocksize)[ perm: does not align with rank of input data: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/transpose.honnxruntime::TransposeBase::TransposeBase(const onnxruntime::OpKernelInfo &)v >= 0 && static_cast(v) <= std::numeric_limits::max()Attribute perm of Transpose has an invalid value. Value is outside range. is repeated.bool onnxruntime::TypedDoTransposeEltWise(int64_t, const std::vector &, size_t, const std::vector &, const uint8_t *, uint8_t *) [T = unsigned long long](local_source >= source) && (local_source < source + sizeof(T) * num_blocks)bool onnxruntime::TypedDoTransposeEltWise(int64_t, const std::vector &, size_t, const std::vector &, const uint8_t *, uint8_t *) [T = unsigned int]bool onnxruntime::TypedDoTransposeEltWise(int64_t, const std::vector &, size_t, const std::vector &, const uint8_t *, uint8_t *) [T = unsigned short]bool onnxruntime::TypedDoTransposeEltWise(int64_t, const std::vector &, size_t, const std::vector &, const uint8_t *, uint8_t *) [T = unsigned char]UniqueUnsupported tensor type of sortedgsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = long long]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = signed char]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = signed char]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = std::__1::basic_string]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = std::__1::basic_string]Unsqueeze/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/unsqueeze.cconnxruntime::common::Status onnxruntime::UnsqueezeBase::PrepareCompute(onnxruntime::OpKernelContext *, onnxruntime::UnsqueezeBase::Prepare &) constaxes_tensor->Shape().NumDimensions() == 0 || axes_tensor->Shape().NumDimensions() == 1'axes' has an out of range axis'axes' has a duplicate axisnullptr != p.output_tensor/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/unsqueeze.honnxruntime::UnsqueezeBase::UnsqueezeBase(const onnxruntime::OpKernelInfo &)info.GetAttrs("axes", axes_).IsOK()Upsample/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/upsample.ccvirtual onnxruntime::common::Status onnxruntime::Upsample::Compute(onnxruntime::OpKernelContext *) const [T = float]roi_input_idx_ > 0sizes == nullptrsizes != nullptr && sizes->Shape().Size() != 0X->Shape().GetDims().size() == output_dims.size()Resize: input tensor's rank does not match the output tensor's rank.onnxruntime::common::Status onnxruntime::Upsample::BaseCompute(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &, const std::vector &) const [T = float]output_dims.size() == dims.size()Resize: input tensor's dimension does not match the scales.Upsample: input tensor's dimension does not match the scales.Resize: size of roi array should be 2 * N where N is the rank of input tensor X.BaseComputeResize: unexpected modeUpsample: unexpected modeResize: input/output value is nullptrUpsample: input/output value is nullptrResize: input/output value's dimension mismatchUpsample: input/output value's dimension mismatchResize: input shape needs to be at least a single dimensionUpsample: input shape needs to be at least a single dimension.void onnxruntime::UpsampleBase::ParseScalesDataFromOutputSize(const std::vector &, const std::vector &, std::vector &) constoutput_dims[i] == 0Input dim is zero but required output dim is non-zero. Cannot scale 0 by any factor to generate a non-zero value. Dimension: Input dim value: Output dim value: virtual onnxruntime::common::Status onnxruntime::Upsample::Compute(onnxruntime::OpKernelContext *) const [T = int]onnxruntime::common::Status onnxruntime::Upsample::BaseCompute(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &, const std::vector &) const [T = int]virtual onnxruntime::common::Status onnxruntime::Upsample::Compute(onnxruntime::OpKernelContext *) const [T = unsigned char]onnxruntime::common::Status onnxruntime::Upsample::BaseCompute(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &, const std::vector &) const [T = unsigned char]WhereGeluonnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::ParametricSoftplus]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::ParametricSoftplus]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::ScaledTanh]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::ScaledTanh]/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/attnlstm/bahdanau_attention.cconnxruntime::contrib::BahdanauAttention::BahdanauAttention(onnxruntime::AllocatorPtr, const logging::Logger &, int, int, int, int, int, bool, concurrency::ThreadPool *) [T = float]!normalize_virtual void onnxruntime::contrib::BahdanauAttention::PrepareMemory(const gsl::span &, const gsl::span &) [T = float]mem_steps <= max_memory_steps_ && mem_steps > 0Real memory steps is not in (0, AttnLSTM/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.ccvirtual onnxruntime::common::Status onnxruntime::contrib::DeepCpuAttnLstmOp::Compute(onnxruntime::OpKernelContext *) constAttention mechanism memory shape error! Expected: {}, actural: Attention mechanism memory sequence lengths must have shape {Attention mechanism memory sequence lengths value must in (0, ], while found!Attention memory layer weight shape error! Expected:{, am_attn_size}, Got:Attention query layer weight shape error! Expected:{}, Got: Attention v weight shape error! Expected:{}. Got: Attention layer weight shape error! Expected: {, aw_attn_size}. Got:Input initial_c must have shape {Input P must have shape {/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.honnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOp(const onnxruntime::OpKernelInfo &)static_cast(activation_func_names.size()) == num_directions_ * 3 [seqno= row[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 (InverseLayerNormalizationSimplifiedLayerNormalization/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/layer_norm.cconnxruntime::contrib::LayerNorm::LayerNorm(const onnxruntime::OpKernelInfo &) [T = float, simplified = false]op_kernel_info.GetAttr("axis", &axis_).IsOK()onnxruntime::contrib::LayerNorm::LayerNorm(const onnxruntime::OpKernelInfo &) [T = float, simplified = true]onnxruntime::contrib::LayerNorm::LayerNorm(const onnxruntime::OpKernelInfo &) [T = double, simplified = false]onnxruntime::contrib::LayerNorm::LayerNorm(const onnxruntime::OpKernelInfo &) [T = double, simplified = true]MatMulInteger16/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/matmul_integer16.ccvirtual onnxruntime::common::Status onnxruntime::contrib::MatMulInteger16::Compute(onnxruntime::OpKernelContext *) const [T1 = short, T2 = short, T3 = int]A != nullptr && B != nullptrMaxpoolWithMask/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/maxpool_with_mask.hacross_channels/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/mean_variance_normalization.honnxruntime::MeanVarianceNormalization_0::MeanVarianceNormalization_0(const onnxruntime::OpKernelInfo &, bool) [T = float]info.GetAttr("across_channels", &across_channels_).IsOK()normalize_varianceinfo.GetAttr("normalize_variance", &normalize_variance_).IsOK()MurmurHash3/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/murmur_hash3.ccvirtual onnxruntime::common::Status onnxruntime::contrib::MurmurHash3::Compute(onnxruntime::OpKernelContext *) constkeyssizeof(uint32_t) == output_element_bytesinput_num_bytes % 4 == 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.output and sum shape must matchonnxruntime::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 givenQLinearGlobalAveragePoolQLinearLeakyReluQLinearSigmoid/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.ccinput scale must be a scalar or 1D tensor of size 1weight must be a scalar or 1D tensor of size 1input zero point must be a scalar or 1D tensor of size 1.weight zero point must be a scalar or 1D tensor of size 1.W_zero_point must have shape {} for per-tensor/layer quantization or shape {, 4*} for per-channel quantization. Actual:R_zero_pointW_scaleR_scaleDynamicQuantizeLSTM : Weight zero point must be zeroWeight point must be constantRecurrentDynamicQuantizeLSTM/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/dynamic_quantize_matmul.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.DynamicQuantizeMatMulMatMulIntegerToFloat/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/nhwc_max_pool.ccvirtual onnxruntime::common::Status onnxruntime::contrib::NhwcMaxPool::Compute(onnxruntime::OpKernelContext *) constinput_shape.Size() > 0 || N == 0NhwcMaxPoolSampleOpSkipLayerNormalization/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/skip_layer_norm.cconnxruntime::contrib::SkipLayerNorm::SkipLayerNorm(const onnxruntime::OpKernelInfo &) [T = float]epsilon_ >= 0input is expected to have 3 dimensions, got gamma is expected to have 1 dimension, got beta is expected to have 1 dimension, got bias is expected to have 1 dimension, got onnxruntime::contrib::SkipLayerNorm::SkipLayerNorm(const onnxruntime::OpKernelInfo &) [T = double]Tokenizer/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/tokenizer.cconnxruntime::contrib::Tokenizer::Tokenizer(const onnxruntime::OpKernelInfo &)pad_valuemincharnummincharnum_ > 0separatorsEither one of the separators OR tokenexp attributes required but none is set!tokenexp.empty()!separators.empty()!char_tokenezation_ || mincharnum_ < 2Can not digest separators: Can not digest tokenexp: Input string contains invalid utf8 chars: Match contains invalid utf8 chars: tensor(string) expected as inputInput dimensions are either [C] or [N][C] 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 void onnxruntime::AllocatorManager::InsertAllocator(onnxruntime::AllocatorPtr)/Users/runner/work/1/s/onnxruntime/core/framework/bfc_arena.ccBFCArenaCreating BFCArena for with following configs: initial_chunk_size_bytes: max_dead_bytes_per_chunk: 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)floatbooldoublestringint8uint8int16uint16int32uint32int64uint64float16bfloat16tensor 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/endian_utils.ccCopyLittleEndiansource and destination buffer size mismatch/Users/runner/work/1/s/onnxruntime/core/framework/ex_lib_loader.cc~ExLibLoaderUnloading DSO Failed to unload DSO: A dso with name has already been loaded.LoadExternalLibCaught exception while destructing CustomOpsLoader with message: Caught exception while loading custom ops with message: /Users/runner/work/1/s/onnxruntime/core/framework/execution_frame.cconnxruntime::IExecutionFrame::IExecutionFrame(const onnxruntime::OrtValueNameIdxMap &, const onnxruntime::NodeIndexInfo &, const std::vector &)node_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx()node_index_info and ort_value_idx_map are out of sync and cannot be usedonnxruntime::common::Status onnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValue(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)TryInsertAllocatorduplicated allocator: IExecutionProvider::Compile with fused Node is not implemented by IExecutionProvider::Compile with fused Node and dll path is not implemented by IExecutionProvider::Compile with FusedNodeAndGraph is not implemented by virtual int onnxruntime::IExecutionProvider::GenerateMetaDefId(const onnxruntime::GraphViewer &, uint64_t &) constmetadef_id_generator_IExecutionProvider constructor must be called with true for use_metadef_id_creator/Users/runner/work/1/s/onnxruntime/core/framework/feeds_fetches_manager.ccMapNamesToMLValueIdxsError mapping feeds: Error mapping output names: void onnxruntime::FeedsFetchesManager::SetDeviceCopyChecks(onnxruntime::DeviceCopyCheck, onnxruntime::DeviceCopyCheck)input_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::Unknownstatic void SafeIntExceptionHandler::SafeIntOnDivZero()func info for node: already exist.Can't use func with null ptr not found./Users/runner/work/1/s/onnxruntime/core/framework/fuse_nodes_funcs.ccGetFuncs/Users/runner/work/1/s/onnxruntime/core/framework/graph_partitioner.ccPartitionOnnxFormatModelNo provider specified.Partitiononnxruntime::common::Status onnxruntime::GraphPartitioner::Partition(onnxruntime::Graph &, bool, onnxruntime::FuncManager &, onnxruntime::GraphPartitioner::Mode, std::unordered_map *) constcompiled_kernel_hashes != nullptrPartitionOnnxFormatModelImplonnxruntime::common::Status onnxruntime::PartitionOnnxFormatModelImpl(onnxruntime::Graph &, bool, onnxruntime::FuncManager &, onnxruntime::KernelRegistryManager &, onnxruntime::KernelRegistry &, onnxruntime::IExecutionProvider &, GraphPartitioner::Mode, int &)fusion_style == IExecutionProvider::FusionStyle::FunctionMust use Function based fusion when exporting compiled nodes to dll. did not return correct number of compiled functionsonnxruntime::Node *onnxruntime::PlaceNode(onnxruntime::Graph &, const onnxruntime::IndexedSubGraph &, const onnxruntime::KernelRegistryManager &, const std::string &, IExecutionProvider::FusionStyle, GraphPartitioner::Mode, int &)1 == capability.nodes.size()/Users/runner/work/1/s/onnxruntime/core/framework/func_kernel.honnxruntime::FunctionKernel::FunctionKernel(const onnxruntime::OpKernelInfo &)compute_info_->create_state_func(&context, &func_state_) == 0InlineNodesPartitionOrtFormatModelImplEP: has Compile error: single_node_compute_func should have 1 elementsonnxruntime::common::Status onnxruntime::PartitionOrtFormatModelImpl(onnxruntime::Graph &, onnxruntime::FuncManager &, onnxruntime::KernelRegistryManager &, onnxruntime::KernelRegistry &, onnxruntime::IExecutionProvider &, std::unordered_map &, int &)Existing entry in compiled kernel hashes for . Execution Provider must generate unique names across the entire model./Users/runner/work/1/s/onnxruntime/core/framework/kernel_def_builder.cconnxruntime::KernelDefBuilder &onnxruntime::KernelDefBuilder::VariadicAlias(int, int)input_offset >= 0 && output_offset >= 0Op with name ( and type ( Version mismatch. node_version: kernel start version: kernel_end_version: Found kernel for Op with name ( in the supported version range (node_version: However the types are incompatible. This op has been implemented only for the following types (), but the node in the model has the following type (/Users/runner/work/1/s/onnxruntime/core/framework/kernel_registry.ccTryCreateKernel kernel not found in No matching hash for kernel is not supported in Encountered following errors: (Kernel not foundkernel def can't be NULLFailed to add kernel for : Conflicting with a registered kernel with op versions.void onnxruntime::(anonymous namespace)::TraverseFormalParametersWithTypeProto(const onnxruntime::Node &, ParamFilterFn, TraverseFn) [ParamFilterFn = (lambda at /Users/runner/work/1/s/onnxruntime/core/framework/kernel_registry.cc: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.libonnxruntime_providers_dnnl.dyliblibonnxruntime_providers_openvino.dyliblibonnxruntime_providers_tensorrt.dylibOrtSessionOptionsAppendExecutionProvider_Dnnl: Failed to load shared libraryOrtSessionOptionsAppendExecutionProvider_Tensorrt: Failed to load shared librarySessionOptionsAppendExecutionProvider_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: /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.CreateSubgraphSessionStateKernel create info is null. Invalid ORT format model.Kernel create info node indices are null. Invalid ORT format model.Kernel create info hashes are null. Invalid ORT format model.Size mismatch for kernel create info node indexes and hashes. Invalid ORT format model.Can't find node with index . Invalid ORT format model.Unable to find compiled kernel hash for node ''.SessionState for subgraphs is null. Invalid ORT format model.Subgraph SessionState entry for is missing. Invalid ORT format model.Subgraph SessionState for is null. Invalid ORT format model.FinalizeSessionStateFinalizeSessionStateImplonnxruntime::common::Status onnxruntime::SessionState::FinalizeSessionStateImpl(const std::basic_string &, onnxruntime::KernelRegistryManager &, const onnxruntime::Node *, const onnxruntime::SessionOptions &, bool, std::unordered_map &)entry != node_to_subgraph_ss.second.cend()Missing session state for subgraph. Node:'' OpType: Index: Attribute:p_op_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 overflow/Users/runner/work/1/s/onnxruntime/core/framework/tensorprotoutils.ccTensorProtoToMLValuestring tensor can not have raw datatensor 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 &, const onnxruntime::Path &, onnx::TensorProto &)Unsupported attribute value type of in 'Constant' node 'SparseTensorProtoToDenseTensorProtocommon::Status onnxruntime::utils::SparseTensorProtoToDenseTensorProto(const onnx::SparseTensorProto &, const onnxruntime::Path &, onnx::TensorProto &)BUG! Report to onnxruntime team.common::Status onnxruntime::utils::DenseTensorToSparseTensorProto(const onnx::TensorProto &, const onnxruntime::Path &, onnx::SparseTensorProto &)HasDataType(dense_proto)DenseTensorToSparseTensorProtoUnpackInitializerDataUnsupported type: UnpackTensorWithExternalDataImplnullptr == p_dataGetExternalDataInfoTensor does not have external data to read from.External data type cannot be UNDEFINED or STRING.TensorProto external data size mismatch. Computed size: , external_data.length: GetFileContentUnsupported sparse tensor data type of ReadExternalDataForTensorUnpackTensor: the pre-allocated size does not match the raw data size, expected CopySparseDataIndicies expected to be INT64Sparse Indicies raw data size does not match expected.Invalid SparseTensor indices. Should either have raw or int64 dataonnxruntime::common::Status onnxruntime::utils::CopySparseData(size_t, const onnx::TensorProto &, gsl::span, std::function) [T = unsigned char]rank == dims.size() && rank > 0cur_index == &*indices_data.cend()Invalid SparseTensor indices. Should be rank 0 or 1. Got:onnxruntime::common::Status onnxruntime::utils::CopySparseData(size_t, const onnx::TensorProto &, gsl::span, std::function) [T = unsigned int]CoreMLExecutionProviderInternalTestingExecutionProvider/Users/runner/work/1/s/onnxruntime/core/framework/utils.ccconst OrtMemoryInfo &onnxruntime::utils::FindMemoryInfoForValue(const onnxruntime::SessionState &, const std::string &)exec_plan_ptrInitializeFeedFetchCopyInfoCopyOneInputAcrossDevicesExecuteGraphFindMemoryInfoForValueconst OrtMemoryInfo &onnxruntime::utils::FindMemoryInfoForValue(const onnxruntime::OrtValueNameIdxMap &, const onnxruntime::SequentialExecutionPlan &, const std::string &)CalculateStaticCopyInfoForFeedsbool onnxruntime::utils::FinalizeCopyInfoForFeeds(const std::vector &, std::vector &)feed_locations.size() == copy_info.size()bool onnxruntime::utils::FinalizeCopyInfoForFetches(const std::vector &, std::vector &)fetch_alloc_info.size() == copy_info.size()CalculateStaticCopyInfoForFeedonnxruntime::common::Status onnxruntime::utils::BatchOrCopyMLValue(const onnxruntime::SessionState &, const onnxruntime::MLValueCopyInfo &, const OrtValue &, OrtValue &, std::vector *)allocator != nullptrFailed to find allocator for device BatchOrCopyMLValueExecuteGraphImplOnly one thread was configured for parallel execution. Hence will use sequential execution.common::Status onnxruntime::utils::CopyInputsAcrossDevices(const onnxruntime::SessionState &, const std::vector &, std::vector &, const std::vector &)copy_info.size() == num_feedsCopyInputsAcrossDevicesCopyOutputsAcrossDevicesNumber of neurons in the hidden layer.Constrain seq_lens to integral tensors.The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]` QWThe weight tensor of the query layer in the attention mechanism. Should be of shape `[num_directions, am_query_depth(hidden_size of lstm), am_attn_size]` MWThe weight tensor of the memory layer in the attention mechanism. Should be of shape `[num_directions, memory_depth, am_attn_size]` The attention_v tensor in the attention mechanism. Should be of shape `[num_directions, am_attn_size]` The sequence of the memory (input) for attention mechanism. Should be of `[batch_size, max_memory_step, memory_depth]` memory_seq_lensThe sequence length of the input memory for the attention mechanism. Should be of `[batch_size]` AWThe weights of attention layer in the attention wrapper. If exists, should be of shape `[num_directions, memory_depth+hidden_size, aw_attn_size]. Please note that attention mechanism context depth is also memory_depth in the attention mechanism.` A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`. 'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64Pads has incorrect number of valuesInput tensors of wrong rank (0).Incompatible dimensions for matrix 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_ndimnormalizedonesidedAinput_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() != nullptrstd::unordered_map onnxruntime::CreateOpsetImportsForFunction(const onnx::FunctionProto &, const std::unordered_map &)result.secondONNX model does not support multiple opset versions for a domain. Model imports opset version for domain and function is trying to import opset version for the same domainvoid onnxruntime::update_subgraphs_within_function_body(onnx::GraphProto &, const onnxruntime::Graph &, const onnx::NodeProto &, const std::unordered_map &, const std::unordered_map &)A node with a function body within a subgraph within another function body is currently not supported in ORT/Users/runner/work/1/s/onnxruntime/core/graph/function_impl.hvirtual const onnxruntime::Graph &onnxruntime::ViewerFunctionImpl::Body() constType mismatch. Current= Input=Tensor element type mismatch. /Users/runner/work/1/s/onnxruntime/core/graph/graph.ccUpdateTypeAndShapeSparseTensor element type mismatch. Serialization of fused function body is not currently supported, Node [] op_type [] does not have the graph for key LoadFromOrtFormatfbs_attr cannot be nullSerialization error. Graph attribute was serialized without Graph instanceNode::LoadFromOrtFormat, input_arg_counts is missingLoadEdgesFromOrtFormatinput index: is not the same as this node's index:ai.onnxThis is an invalid model. The sum of input arg count is not equal to size of input defs in node (onnxruntime::Graph::Graph(const onnxruntime::Model &, onnx::GraphProto *, const std::unordered_map &, onnxruntime::Version, onnxruntime::IOnnxRuntimeOpSchemaCollectionPtr, onnxruntime::Graph *, const onnxruntime::Node *, const logging::Logger &)graph_proto != nullptrConstantDuplicate constant node sparse initializer name: '' Model is invalid.utils::HasName(sparse_tensor)Duplicate sparse_tensor_initializer: 'GraphDuplicate initializer (dense, sparse or ConstantNode): '' the model will use the latest encountered initializer. Please, fix your model.Initializer appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.void onnxruntime::Graph::InitializeStateFromModelFileGraphProto()graph_inputs_excluding_initializers_.empty() && graph_inputs_including_initializers_.empty() && value_info_.empty() && graph_outputs_.empty()node_argGraph ctor should have created NodeArg for initializer. Missing:This is an invalid model. Graph output () does not exist in the graph.This is an invalid model. Error: two nodes with same node name ().This is an invalid model. Error: Duplicate definition of name (SetOuterScopeNodeArgsvoid onnxruntime::Graph::AddEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int)void onnxruntime::Graph::RemoveEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int)BuildConnectionsThis is an invalid model. At top level graph without matching NodeArg that subgraph consumes. Name= Graph may not conform to the ONNX spec and contain initializers that are not graph inputs.This is an invalid model. Failed to find NodeArg in all parent graphs. Name=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!foundReplaceInitializedTensorFailed to find existing initializer with name Replacement tensor's dimensions do not match.Replacement tensor's data type does not match.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 &)dst_implicit_input_idx < (int)node->ImplicitInputDefs().size()Node 's number of inputs is different from function body graph's number of input.onnxruntime::common::Status onnxruntime::Graph::InlineFunction(onnxruntime::Node &)node.MutableOutputDefs().size() == subgraph.GetOutputs().size()'s number of outputs is different from function body graph's number of outputs.InlineFunctionvoid onnxruntime::Graph::SetInputs(const std::vector &)input->Exists()Inputs: : Nodes: : ) -> Outputs: Initializer tensor is missing. Invalid ORT format model.Duplicate initializer (dense or ConstantNode): 'Sparse Initializer tensor is missing. Invalid ORT format model.NodeArg is missing. Invalid ORT format model.Node is missing. Invalid ORT format model.Node index is out of rangeNodeEdge is missing. Invalid ORT format model.MergeShapeInfoError merging shape info for output. '' source: target:. Falling back to lenient merge.[ShapeInferenceError] Mismatch between number of source and target dimensions. Source= Target=Can't merge shape info. Both source and target dimension have values but they differ. Source= Dimension=Output:fbs_node_arg_names cannot be nullnode_arg_name cannot be nullLoadNodeArgsFromOrtFormat: Node [], could not find NodeArg ALLOW_RELEASED_ONNX_OPSET_ONLY/Users/runner/work/1/s/onnxruntime/core/graph/model_load_utils.hbool onnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySet() are '0' and '1'. The environment variable contained the value: ai.onnx.mlai.onnx.training/Users/runner/work/1/s/include/onnxruntime/core/graph/graph.honnxruntime::Node *onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constnode_index < nodes_.size()Validating no unexpected access using an invalid node_index. Got: Max:/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:NodeArg Name is missing. Invalid ORT format model./Users/runner/work/1/s/onnxruntime/core/graph/graph_flatbuffers_utils.ccSaveInitializerOrtFormatSaveSparseInitializerOrtFormatSaveAttributeOrtFormatGraph attribute value was null. Invalid ORT format model.SaveAttributeOrtFormat: Unsupported attribute type: LoadInitializerOrtFormatMissing dimensions for initializer. Invalid ORT format model.Missing string data for initializer. Invalid ORT format model.Missing raw data for initializer. Invalid ORT format model.LoadSparseInitializerOrtFormatMissing values for sparse initializer. Invalid ORT format model.Missing name for SparseTensor initializer. Invalid ORT format model.Missing indicies for sparse initializer: Invalid ORT format model.Missing dims for sparse initializer: LoadAttributeOrtFormatNull string attribute. Invalid ORT format model.Null tensor attribute. Invalid ORT format model.Null graph attribute. Invalid ORT format model.Empty graph proto from deserialization of ORT format modelNull floats attribute. Invalid ORT format model.Null ints attribute. Invalid ORT format model.Null strings attribute. Invalid ORT format model.Null string in strings attribute. Invalid ORT format model.Null tensors attribute. Invalid ORT format model.Null tensor in tensors attribute. Invalid ORT format model.UNDEFINEDFLOATINTSTRINGTENSORGRAPHFLOATSINTSSTRINGSTENSORSGRAPHSSPARSE_TENSORSPARSE_TENSORS/Users/runner/work/1/s/onnxruntime/core/graph/graph_utils.ccint onnxruntime::graph_utils::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.Graph is null. Invalid ORT format model.void onnxruntime::model_load_utils::ValidateOpsetForDomain(const std::unordered_map &, const logging::Logger &, bool, const std::string &, int)ONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions. Opset is under development and support for this is limited. The operator schemas and or other functionality may change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain is till opset ValidateOpsetForDomain is under development and support for this is limited. The operator schemas and or other functionality could possibly change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain Failed to load model with error: Load model failed. File doesn't exist failedsystem error number SaveModelDomain already set in registry/Users/runner/work/1/s/onnxruntime/core/graph/schema_registry.ccRegisterOpSetRegisterOpSchemaInternalknown by the checker., but it its version is higherthan the operator set version [...truncated...] ERROR LOG MSG NOTIFICATION: Failure to successfully parse the messageonnxruntimeSystem/Users/runner/work/1/s/onnxruntime/core/common/logging/logging.cconnxruntime::logging::LoggingManager::LoggingManager(std::unique_ptr, onnxruntime::logging::Severity, bool, const onnxruntime::logging::LoggingManager::InstanceType, const std::string *, int)default_logger_id must be provided if instance_type is InstanceType::DefaultOnly one instance of LoggingManager created with InstanceType::Default can exist at any point in time.void onnxruntime::logging::LoggingManager::CreateDefaultLogger(const std::string &) [VIWEFampm is not a valid date is not a valid year is not a valid day/Users/runner/work/1/s/onnxruntime/core/common/path.ccParsestatic onnxruntime::Path onnxruntime::Path::Parse(const onnxruntime::PathString &)..onnxruntime::Path &onnxruntime::Path::Concat(const onnxruntime::PathString &)first_separator == value.end()Cannot concatenate with a string containing a path separator. String: RelativePathPaths must have the same root to compute a relative path. src root: , dst root: 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.cconnxruntime::concurrency::ThreadPool::ParallelSection::ParallelSection(onnxruntime::concurrency::ThreadPool *)!current_parallel_sectionvoid onnxruntime::concurrency::ThreadPool::ParallelFor(std::ptrdiff_t, const onnxruntime::TensorOpCost &, const std::function &)n >= 0/Users/runner/work/1/s/onnxruntime/core/platform/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: ReadFileIntoBufferfile_path == nullptroffset < 0length > buffer.size()openlseekreadReadFileIntoBuffer - unexpected end of file. File: , offset: , length: MapFileIntoMemorymmapUnmapFilemunmap failed. error code: DeleteFolderDeleteFolder(): nftw() failed with error: nftw_removeremove() failed. Error code: , path: closerealpathFailed to load library with error: Got null library handleFailed to unload library with error: Failed to get symbol lib.so.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.LoadValueInfoOrtFormatNull type info for LoadOpsetImportOrtFormatModel must have opset imports. Invalid ORT format model.opset id is null. Invalid ORT format model.opset import domain is null. Invalid ORT format model.SaveTypeInfoOrtFormatWe do not support type [] for nowSaveSequenceTypeOrtFormatSaveMapTypeOrtFormatLoadTypeInfoOrtFormatNull tensor type info. Invalid ORT format model.Null sequence type info. Invalid ORT format model.Null map type info. Invalid ORT format model.Type: is not supported currentlyLoadTensorTypeAndShapeOrtFormatLoadTensorShapeOrtFormatNull entry in dimensions. Invalid ORT format model.LoadTensorDimensionOrtFormatdim_param value with no name. Invalid ORT format model.LoadSequenceTypeOrtFormatNull value type info in fbs::SequenceType. Invalid ORT format model.LoadMapTypeOrtFormatNull value type info in fbs::MapType. Invalid ORT format model.NONEField 'name' of value_info is required to be non-empty.type is required but missing.elem_typekey_typevalue_typeUnrecognized type value case (value_info name: ): data_typesetting data_type field (tensor name: ) to UNDEFINED is not allowedfloat_dataint32_datastring_dataint64_dataraw_datadouble_datauint64_dataData of TensorProto ( tensor name: ) is stored externally and should not have data field.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_versionONNX models do not support multiple opset version imports for a domain. Function imports opset version where as the model imports opset 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.\/DynamicSliceImageScalerScale ==> Context: Loop 'body' subgraph outputs should all be tensors or sequences but output Loop 'body' subgraph scan outputs should all be tensors but output /Users/runner/work/1/s/cmake/external/onnx/onnx/defs/controlflow/defs.ccFinal N loop carried dependency values then K scan_outputs. Scan outputs must be Tensors.An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].Mismatched type: source= target=Mismatched tensor element type:source sequence type missing element type.target sequence type missing element type.Scan input was not a tensor.bodyGraph attribute inferencing returned type information for outputs. Expected Scan 'body' subgraph outputs should all be tensors but output was not axis value is invalid for a tensor of rank Number of scan input axes specified () is not equal to number of scan inputs (Number of scan output axes specified () is not equal to number of scan outputs (sequence_lensOptional tensor specifying lengths of the sequences in a batch. If this input is not specified, all sequences are assumed to be of the maximum sequence length (the dimension of the sequence axis of the scan_input tensors).initial_state_and_scan_inputsInitial values of the loop's N state variables followed by M scan_inputsfinal_state_and_scan_outputsFinal values of the loop's N state variables followed by K scan_outputsThe graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.An attribute specifying the number of scan_inputs M. An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.Int64 tensor/Users/runner/work/1/s/cmake/external/onnx/onnx/defs/controlflow/old.ccLoop 'body' subgraph outputs should all be tensors but output was A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.condA boolean termination condition. Optional. Pass empty string to skip.v_initialThe initial values of any loop-carried dependencies (values that change across loop iterations)v_final_and_scan_outputsFinal N loop carried dependency values then K scan_outputsThe graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.tensor of int64, which should be a scalar.tensor of bool, which should be a scalar.An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input.An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output.then_branch and else_branch produce different number of outputs. If node has but subgraphs produce Mismatched type for output then= else=Mismatched tensor element type for output Condition for the ifoutputsValues that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same shape and same data type.Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.Only boolValues that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.Input type was nullInput was expected to have either tensor or sequence type. Got Input was expected to have tensor type. Got Element type of input was unknownInput element type of does not match existing output type of Output was expected to have tensor type. Got Input was expected to have sequence type. Got tensor(seq(map(sparse_tensor(mapopaqueFunc_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.Constrain input and output types to floating-point tensors.Number of input tensors does not match the operands in the equation.Ellipsis represents incompatible dimensions.Rank of input does not match the equation indices.equationEinsum expression string.InputsOperandsinputs are expected to have tensor type.input and zero_point pair is expected to have be same type.input and zero_point pair is expected to have same type./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/math/old.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.If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.If set to 1 will perform the sums in reverse direction.An input tensor that is to be processed.A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back.Output tensor of the same type as 'x' with cumulative sums of the x's elementsaxis tensor can be int32 or int64 onlytargetignore_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 typesNegativeLogLikelihoodLossType of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 'mean': the sum of the output will be divided by the number of elements in the output.scoreslog_problabelsThe predicted outputs with shape [batch_size, class_size], or [batch_size, class_size, D1, D2 , ..., Dk], where K is the number of dimensions.The ground truth output tensor, with shape [batch_size], or [batch_size, D1, D2, ..., Dk], where K is the number of dimensions. Labels element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the label values should either be in the range [0, C) or have the value ignore_index.A manual rescaling weight given to each class. If given, it has to be a 1D Tensor assigning weight to each of the classes. Otherwise, it is treated as if having all ones.Weighted loss float Tensor. If reduction is 'none', this has the shape of [batch_size], or [batch_size, D1, D2, ..., Dk] in case of K-dimensional loss. Otherwise, it is a scalar.Log probability tensor. If the output of softmax is prob, its value is log(prob).SoftmaxCrossEntropyLoss If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of element size 1 (including a scalar tensor and any tensor with rank equal to or smaller than the first tensor), or having its shape as a contiguous subset of the first tensor's shape. The starting of the mutually equal shape is specified by the argument "axis", and if it is not set, suffix matching is assumed. 1-dim expansion doesn't work yet. For example, the following tensor shapes are supported (with broadcast=1): shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 Attribute `broadcast=1` needs to be passed to enable broadcasting. Input tensor of any shape, base of the exponent.Input tensor of any shape broadcastable to X shape, the exponent component.Pass 1 to enable broadcastingIf set, defines the broadcast dimensions. See doc for details.Output tensor (same size as X)consumed_inputslegacy optimization attribute.Coefficient of leakage default to 0.01.Coefficient of SELU default to 1.6732.Coefficient of SELU default to 1.0507.Coefficient of ELU default to 1.0.1-D input tensorslopeSlope tensor. If `Slope` is of size 1, the value is sharedacross different channelsSlope tensor. The shape of slope can be smaller then first input X; if so, its shape must be unidirectional broadcastable to XValue of alpha default to 0.2Value of beta default to 0.5data_0List of tensors for Max.Output tensor. Same dimension as inputs.List of tensors for MinList of tensors for Sum.List of tensors for Mean.Minimum value, under which element is replaced by minMaximum value, above which element is replaced by maxInput tensor AInput tensor BInput tensor C, can be inplace.Whether C should be broadcastedScalar multiplier for the product of input tensors A * B, the default value is 1.0.Scalar multiplier for input tensor C, the default value is 1.0.Input tensor CTensor of shape [a_1, a_2, ..., a_n, r]ValuesTensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing top K values from the input tensorIndicesTensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing the corresponding input tensor indices for the top K values.Constrain index tensor to int64Number of top elements to retrieveDimension on which to do the sort.KA 1-D tensor containing a single positive value corresponding to the number of top elements to retrieve Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) where the function `f(x) = x^exponent`, is applied to the data tensor elementwise. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).First operand.Second operand.Result, has same element type as two inputsDescribes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).The input tensor that's coerced into a 2D matrix of size (NxD) as described above.The output values with the same shape as input tensor (the original size without coercion).'axis' must be in []. Its actual value is: List of tensors for 'shape' input must be 1D tensor of type INT64Target rank must be 1 less than the input rank.Input and target dimension value mismatch.Weight rank must be 1.Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_sizeFirst operand, should share the type with the second operand.Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.Result, has same dimensions and type as AInvalid value for attribute axisInvalid value for attribute kK input must be a one-dimensional tensor of size 1.K input must be of type int64.Axis has less than the requested k elements.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = input_shape[i] * strides[i]` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/nn/defs.ccInput tensor must have at least 2 dimensionsRoIs tensor must have 2 dimensionsAttribute pooled_shape has incorrect lengthAttribute pooled_shape must be specifiedx_scaleScale tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.x_zero_pointZero point tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.w_scaleScale tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).w_zero_pointZero point tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).Scale tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.Zero point tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.Optional 1D bias to be added to the convolution, has size of M. Bias must be quantized using scale = x_scale * w_scale and zero_point = 0Constrain input type to 8-bit integer tensor.Constrain filter type to 8-bit integer tensor.Constrain output type to 8-bit integer tensor.Constrain bias type to 32-bit integer tensor.The shape of the convolution kernel. If not present, should be inferred from input 'w'.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.number of groups input channels and output channels are divided into. default is 1.Zero point tensor for input 'x'. It's optional and default value is 0. It's a scalar, which means a per-tensor/layer quantization.Zero point tensor for input 'w'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M)Constrain input x and its zero point data type to 8-bit integer tensor.Constrain input w and its zero point data type to 8-bit integer tensor.Constrain output y data type to 32-bit integer tensor.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.The pads attribute cannot be used simultaneously with auto_pad 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.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.Input tensor must have atleast 2 dimensionsAttribute dilations has incorrect sizeAttribute strides has incorrect sizeAttribute kernel_shape has incorrect sizeAttribute kernel_shape must be specifiedAttribute pads has incorrect sizeSecond input tensor has wrong dimensionWhether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.The storage order of the tensor. 0 is row major, and 1 is column major.Indices tensor from max pooling across the input tensor. The dimensions of indices are the same as output tensor. The values in indices of are the indices of the selected values during pooling. The indices are computed as flatten 1-D tensor, and the indices do not consider padding. So the values in indices are in [0, N x C x D1 x ... x Dn).Dilation value along each spatial axis of filter.Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.MaxUnpool op must have either two or three inputs.Input tensor X must have atleast 2 dimensions.Attribute pads has incorrect size.Attribute strides has incorrect size.Attribute kernel_shape has incorrect size.Attribute kernel_shape must be specified.'output_shape' must be rank 1 tensor.'output_shape' must have same number of elements as the shape of input tensor X.The size of the kernel along each axis.Stride along each spatial axis.Input data tensor that has to be unpooled. This tensor is typically the first output of the MaxPool op.Dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non-image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].Input data tensor containing the indices corresponding to elements in the first input tensor X.This tensor is typically the second output of the MaxPool op.Dimensions must be the same as input tensor X. The indices are linear, i.e. computed considering the tensor as flattened 1-D tensor, assuming row-major storage. Also, the linear indices should not consider padding. So the values in indices are in the range [0, N x C x D1 x ... x Dn).The shape of the output can be explicitly set which will cause pads values to be auto generated. If 'output_shape' is specified, 'pads' values are ignored.Output data tensor that contains the result of the unpooling.Padding for the beginning and ending along each axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding. DEPRECATION NOTE: auto_pad is only intended to support legacy uses, and for framework authors, one is explicitly encouraged to use explicit padding specified in the pads attribute.Stride along each axis.pp value of the Lp norm used to pool over the input data, default is 2.0.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimension are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.Output data tensor from pooling across the input tensor. Dimensions will be N x C x 1 x 1If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.is_testIf set to nonzero, run spatial batch normalization in test mode, default is 0.The epsilon value to use to avoid division by zero, default is 1e-5f.momentumFactor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.The input 4-dimensional tensor of shape NCHW.The scale as a 1-dimensional tensor of size C to be applied to the output.The bias as a 1-dimensional tensor of size C to be applied to the output.The running mean (training) or the estimated mean (testing) as a 1-dimensional tensor of size C.varThe running variance (training) or the estimated variance (testing) as a 1-dimensional tensor of size C.The output 4-dimensional tensor of the same shape as X.The running mean after the BatchNormalization operator. Must be in-place with the input mean. Should not be used for testing.The running variance after the BatchNormalization operator. Must be in-place with the input var. Should not be used for testing.saved_meanSaved mean used during training to speed up gradient computation. Should not be used for testing.saved_varSaved variance used during training to speed up gradient computation. Should not be used for testing.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.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)]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.ONNX models don't support multiple opset version imports for a domain. Function input is out of boundsGraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. does not contain a graph.output /Users/runner/work/1/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}}CF[ ! iq.,",}}TCHF   IAs j~ u*5_ jIAs j~ u*5_ j7 B]; GzLwրNv X+ clM XրNv X+ clM X '$3{D_րN ':  Ň``?hYe}*  svA *e  ]2F: r  4J:c  [  "  e   >B e -< >E [Q ly Y[ "< P7(^i% '?%Bv%&R58YWuh : %   )&d q36 )'ppxIAC O IAC O )'9)'9# b)'bb OlF R})'9)' #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}8€4$3< F`j}7,>)'v5g;  y B  Q - 8p RO 3l Ryt   6 & -S     [ p| }5ꀀhMjCE'F:Fv ')'* 4InB!4"/K4-?ÀAA|0Jz_450(p:"vK{Se"[Kk"V K`TPxxI!Vw"#K("Ki{"Kt450(p:450(p:450(p:450(p:450(p:4s #Kn„%\-ZA^ X\G6t , i h$&: G^ ZD oN   R ;_$- ;   M t 2  -  1 F $ v   %    N7  ',$\p2}.IAC NO 7 AIAC O )'9)'9)'gL[րNK a @ YvNcVF R}^^f  k  !Uv~  k # /NV4 ;  @6 U _@  C 4GO4l4T4<47$?4s {44l_uh][LT; _ Bv*B@b&&99 j&C+n h&x' f&' d&' b& '2 `&<'c ^&m' \&' Z&' X&'' V&1'X T&b' R&' P&' N&' L&&'M J&W'~ H&' F&' D&' B&'B @&L's >&}' <&' :&' 8&'7 6&A'h 4&r' 2&' 0&' .&', ,&6'] *&g' (&' &&' $&'! 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M^d h  f  = ; J' 3& Y v6 +* d *   & Q6  &  K 6 S   :   3 E  ^ {m # . 3    # ( 73 j| # 3   #. u=3& p)';;'bDN C 񃀀Ni ) W m , / "Jl0Y  3 %Mr~    W U F9 E& k 6 +D !~ >  / & g6  &  e 67 m 3  #:)'+3%+ ! # /# i 3  #%4 Y @#> c & r3   #  3- ( : F # i x 3a )';;'bD)';;'bD)';;'bD)';;'bD)';;'bDN C 񃀀LI " P M , / HZ09   M^^ b  `  7  5 A! -& S p6 +$ ^ $   & 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 񃀀NP) W  ,/^"Ln0  'Mt      U &: # 6] D+  1 P:L X&~ 6   v % &[ b D 6   !  :3~  # D 3    4  # >  M 3  # 3   ! # D S 3< )';;'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)';;'bDN C 񃀀L; " P } ? , / HZ0+   M^P T  R  ) ' 6 & E b6 + P    & =6 s &  7 6 o ? }   0 :6 4 8 32 8  .  (0 <# v 3 & 0 2A f M#K p 3 3  + #  3: 5G S#v 3n )';;'bD)';;'bD)';;'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)';;'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 񃀀N_ ) W c , / "Jl0O  ) %Mrt x  v  M K </ ;& a ~6 +: t 4  % & ]6   &  [ 6- c )  :   3   i  r #W  F e3z   #  3z &8 D# g l v3  d #I  1 3 , )';;'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)';;'bDN C 񃀀L; " P } ? , / HZ0+   M^P T  R  ) ' 6 & E b6 + P    & =6 s &  7 6 o ? }   0 :6 4 8 32 8  .  (0 <# v 3 & 0 2A f M#K p 3 3  + #  3: 5G S#v 3n )';;'bD)';;'bD)';;'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)';;'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.) W  , /<"Jl0  { %Mr      3 &  6; "+ i  .* w 6&\ 6f  T  &9 @ " 6 { 񃀀L9 "  P { = ,} / HZ0)   M^N R  P ' % 7 & C `6 + N   & ;6 q &  5 6 m = {   :   /   q  z #_  N m3   # 3 .@ L#o t~3 l#Q 934 h :n l p /j p  f - Ph 6 t#  )3> ^ h jy # k3>  c#H+ 0:3rm (#  3)';;'bD)';;'bD)';;'bD)';;'bD Cf qCn | 񃀀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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7FromDataContainerEPKvmR8OrtValue IsMapCompatibleERKN4onnx9TypeProtoE، ToDataContainerERK8OrtValuemPv  iE4TypeEv fE4TypeEv bE4TypeEv NS aE4TypeEvߒ hE4TypeEv tE4TypeEv sE4TypeEv xE4TypeEv dE4TypeEv jE4TypeEv yE4TypeEv 3 9GetSparseTensorTypeI 6RegisterDataTypeEPKS0_ 1GetDataTypeERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 4AllTensorTypesEv GetTensorTypeI TypeFromProtoERKN4onnx9TypeProtoE iEEPKS0_v fEEPKS0_v bEEPKS0_v NS aEEPKS0_v hEEPKS0_v tEEPKS0_v sEEPKS0_v xEEPKS0_v dEEPKS0_v jEEPKS0_v yEEPKS0_vȓ t3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEE4TypeEv _ϓ t3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEEEPKS0_vؒ _ 9MLFloat16EE4TypeEv 8BFloat16EE4TypeEv 9MLFloat16EEEPKS0_v 8BFloat16EEEPKS0_v”  iE4TypeEv fE4TypeEv bE4TypeEv aE4TypeEv hE4TypeEv tE4TypeEv sE4TypeEv͗ xE4TypeEvۗ dE4TypeEv jE4TypeEv yE4TypeEv NS_  iEEPKS0_v fEEPKS0_v bEEPKS0_v aEEPKS0_v hEEPKS0_v tEEPKS0_vƗ sEEPKS0_vԗ xEEPKS0_v dEEPKS0_v jEEPKS0_v yEEPKS0_v NS_ɘ 9MLFloat16EE4TypeEv˜ 8BFloat16EE4TypeEv 9MLFloat16EEEPKS0_v 8BFloat16EEEPKS0_v NS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEՙ x S8_NS1_4lessIS8_EENS6_INS1_4pairIKS8_S8_EEEEEEE4TypeEv xNS1_4lessIS8_EENS6_INS1_4pairIKS8_xEEEEEEE4TypeEv̞ fNS1_4lessIS8_EENS6_INS1_4pairIKS8_fEEEEEEE4TypeEvڞ dNS1_4lessIS8_EENS6_INS1_4pairIKS8_dEEEEEEE4TypeEv 3mapI 6vectorINS2_3mapI 12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEEEPKS0_v NS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE x S9_NS2_4lessIS9_EENS7_INS2_4pairIKS9_S9_EEEEEEEEPKS0_vŞ xNS2_4lessIS9_EENS7_INS2_4pairIKS9_xEEEEEEEEPKS0_vӞ fNS2_4lessIS9_EENS7_INS2_4pairIKS9_fEEEEEEEEPKS0_v dNS2_4lessIS9_EENS7_INS2_4pairIKS9_dEEEEEEEEPKS0_v NS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS1_4lessIxEENS6_INS1_4pairIKxS8_EEEEEEE4TypeEv xNS1_4lessIxEENS1_9allocatorINS1_4pairIKxxEEEEEEE4TypeEvУ fNS1_4lessIxEENS1_9allocatorINS1_4pairIKxfEEEEEEE4TypeEvޣ dNS1_4lessIxEENS1_9allocatorINS1_4pairIKxdEEEEEEE4TypeEv NS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEENS2_4lessIxEENS7_INS2_4pairIKxS9_EEEEEEEEPKS0_vɣ xNS2_4lessIxEENS2_9allocatorINS2_4pairIKxxEEEEEEEEPKS0_vף fNS2_4lessIxEENS2_9allocatorINS2_4pairIKxfEEEEEEEEPKS0_v dNS2_4lessIxEENS2_9allocatorINS2_4pairIKxdEEEEEEEEPKS0_v ceTensorTypeI tialExecutor7ExecuteERKNS_12SessionStateERKNSt3__16vectorIiNS4_9allocatorIiEEEERKNS5_I8OrtValueNS6_ISB_EEEESA_RSD_RKNS4_13unordered_mapImNS4_8functionIFNS_6common6StatusERKNS_11TensorShapeERK13OrtMemoryInfoRSB_RbEEENS4_4hashImEENS4_8equal_toImEENS6_INS4_4pairIKmSU_EEEEEERKNS_7logging6LoggerE fE4TypeEv̧ dE4TypeEvߩ aE4TypeEv hE4TypeEv sE4TypeEv tE4TypeEv iE4TypeEv jE4TypeEv xE4TypeEv yE4TypeEvϪ bE4TypeEvݪ NS 1 2 8SparseTensorTypeFromONNXEnumEi 3Allɹ GetSequenceTensorTypeI AllNumericTensorTypesEv fEEPKS0_vة dEEPKS0_v aEEPKS0_v hEEPKS0_v sEEPKS0_v tEEPKS0_v iEEPKS0_v jEEPKS0_v xEEPKS0_vȪ yEEPKS0_v֪ bEEPKS0_v NSë t3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEE4TypeEv _ t3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEEEPKS0_v _Ѭ 9MLFloat16EE4TypeEvʬ 8BFloat16EE4TypeEv 9MLFloat16EEEPKS0_v 8BFloat16EEEPKS0_v e parseTensorC quenceTypeINSt3__16vectorINS1_3mapI ssionState NS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEfNS1_4lessIS9_EENS7_INS1_4pairIKS9_fEEEEEENS7_ISG_EEEEE4TypeEv xfNS1_4lessIxEENS1_9allocatorINS1_4pairIKxfEEEEEENS6_ISB_EEEEE4TypeEv NS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEfNS2_4lessISA_EENS8_INS2_4pairIKSA_fEEEEEENS8_ISH_EEEEEEPKS0_v xfNS2_4lessIxEENS2_9allocatorINS2_4pairIKxfEEEEEENS7_ISC_EEEEEEPKS0_v PKS0_ RKNSt3__16vectorIPKS0_NS1_9allocatorIS4_EEEE TensorTypeFromONNXEnumEi AllSequenceTensorTypesEv 0SequenceTensorTypeFromONNXEnumEi 3All 1All  iEȳ fE bE NSô aE hEߵ tE sE xEڶ dE jE yEշ 4TypeEv 6DeleteEPv 4TypeEv 6DeleteEPv 4TypeEv 6DeleteEPv t3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEE _ 4TypeEv 6DeleteEPv Ё4TypeEvѵ 6DeleteEPv 4TypeEv 6DeleteEPv Є4TypeEv 6DeleteEPv 4TypeEv̶ 6DeleteEPv Ї4TypeEv 6DeleteEPv 4TypeEv 6DeleteEPv Њ4TypeEvǷ 6DeleteEPv 4TypeEv 6DeleteEPv Ѝ9MLFloat16EE 8BFloat16EEȸ 4TypeEv 6DeleteEPv 4TypeEv 6DeleteEPv АFixedSizeTensorExceptHalfTypesEv IEEEFloatTensorExceptHalfTypesEv¹ IEEEFloatTensorTypesEv FixedSizeTensorTypesEv FixedSizeSequenceTensorTypesEvں TensorAndSequenceTensorTypesEv 1 6detail16CopyLittleEndianEmN3gsl4spanIKhEENS3_IhEE 2 3 42ONNXTensorElementDataTypeToProtoTensorTypeE25ONNXTensorElementDataType 6 2 7SwapByteOrderCopyEmN3gsl4spanIKhEENS2_IhEE 3 9TensorToTensorProtoERKNS_6TensorERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEE 1DefaultFreeEPvݰ 8ProviderIsCpuBasedERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 5ExecuteSubgraphERKNS_12SessionStateERKNS_19FeedsFetchesManagerERKNSt3__16vectorI8OrtValueNS7_9allocatorIS9_EEEERSC_RKNS7_13unordered_mapImNS7_8functionIFNS_6common6StatusERKNS_11TensorShapeERK13OrtMemoryInfoRS9_RbEEENS7_4hashImEENS7_8equal_toImEENSA_INS7_4pairIKmST_EEEEEE13ExecutionModeRKbRKNS_7logging6LoggerE ContainerCheckerC ReadLittleEndianEmN3gsl4spanIKhEENS2_IhEE 2EPKNS_12DataTypeImplE 1EPKNS_12DataTypeImplE IsOpaqueTypeEPKNS_12DataTypeImplEPKcS5_ UnpackTensorI DefaultAllocEmְ ExecuteGraphERKNS_12SessionStateERNS_19FeedsFetchesManagerERKNSt3__16vectorI8OrtValueNS6_9allocatorIS8_EEEERSB_13ExecutionModeRKbRKNS_7logging6LoggerEb nTensorTypeINSt3__1 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_dEEEEEEE6DeleteEPv 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__invokeEv fNS1_4lessIS8_EENS6_INS1_4pairIKS8_fEEEEEEE13GetCreateFuncEvENUlvE_8__invokeEv dNS1_4lessIS8_EENS6_INS1_4pairIKS8_dEEEEEEE13GetCreateFuncEvENUlvE_8__invokeEv NS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS1_4lessIxEENS6_INS1_4pairIKxS8_EEEEEEE6DeleteEPv xNS1_4lessIxEENS1_9allocatorINS1_4pairIKxxEEEEEEE6DeleteEPv fNS1_4lessIxEENS1_9allocatorINS1_4pairIKxfEEEEEEE6DeleteEPv dNS1_4lessIxEENS1_9allocatorINS1_4pairIKxdEEEEEEE6DeleteEPv NS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS1_4lessIxEENS6_INS1_4pairIKxS8_EEEEEEE13GetCreateFuncEvENUlvE_8__invokeEv xNS1_4lessIxEENS1_9allocatorINS1_4pairIKxxEEEEEEE13GetCreateFuncEvENUlvE_8__invokeEv fNS1_4lessIxEENS1_9allocatorINS1_4pairIKxfEEEEEEE13GetCreateFuncEvENUlvE_8__invokeEv dNS1_4lessIxEENS1_9allocatorINS1_4pairIKxdEEEEEEE13GetCreateFuncEvENUlvE_8__invokeEv ЅЎNS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEfNS1_4lessIS9_EENS7_INS1_4pairIKS9_fEEEEEENS7_ISG_EEEEE6DeleteEPv xfNS1_4lessIxEENS1_9allocatorINS1_4pairIKxfEEEEEENS6_ISB_EEEEE6DeleteEPv NS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEfNS1_4lessIS9_EENS7_INS1_4pairIKS9_fEEEEEENS7_ISG_EEEEE13GetCreateFuncEvENUlvE_8__invokeEv xfNS1_4lessIxEENS1_9allocatorINS1_4pairIKxfEEEEEENS6_ISB_EEEEE13GetCreateFuncEvENUlvE_8__invokeEv ЅЕСЦoOrtStatusERKNS_6common6StatusE ensorShapeC ErrorCodeEPK9OrtStatus ValueTypeEPK8OrtValueP8ONNXType ErrorMessageEPK9OrtStatus MapValueTypeEPK14OrtMapTypeInfoPP11OrtTypeInfoȒ ЩD 15LoadExternalLibERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPPv 2Ev 1Ev 0Ev иC2ERKNS_18OrtValueNameIdxMapERKNS_13NodeIndexInfoERKNSt3__16vectorIiNS7_9allocatorIiEEEE D 34GetMutableNodeInputOrOutputMLValueEi 28GetOrCreateNodeOutputMLValueEiPKNS_11TensorShapeERP8OrtValuem 1 4InitERKNSt3__16vectorIiNS1_9allocatorIiEEEERKNS2_I8OrtValueNS3_IS8_EEEERKNS1_13unordered_mapIiS8_NS1_4hashIiEENS1_8equal_toIiEENS3_INS1_4pairIKiS8_EEEEEESC_ 2Ev 1Ev 0Ev 2 1 8IsOutputEi 7GetNodeInputOrOutputMLValueEi 2GetNodeIdxToMLValueIdxEi 9TryGetInferredShapeEiRNS_11TensorShapeE 2GetAllocatorERK13OrtMemoryInfo 4ReleaseMLValueEi 8ReleaseMLValueImplEi 0GetOutputsERNSt3__16vectorI8OrtValueNS1_9allocatorIS3_EEEE ЈC 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 2Ev 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 neratePatternsEPNS_18MemoryPatternGroupE 2GetAllocatorEi10OrtMemType 3GetCapabilityERKNS_11GraphViewerERKNSt3__16vectorIPKNS_14KernelRegistryENS4_9allocatorIS8_EEEE 7GenerateMetaDefIdERKNS_11GraphViewerERy 1 7CompileERKNSt3__16vectorI 23ModelMetadefIdGenerator10GenerateIdERKNS_11GraphViewerERy 6ReplaceAllocatorENSt3__110shared_ptrINS_10IAllocatorEEE 5InsertAllocatorENSt3__110shared_ptrINS_10IAllocatorEEE 8TryInsertAllocatorENSt3__110shared_ptrINS_10IAllocatorEEE 7RegisterAllocatorENSt3__110shared_ptrINS_16AllocatorManagerEEE PNS_4NodeENS1_9allocatorIS4_EEEERNS NS0_17FusedNodeAndGraphENS1_9allocatorIS3_EEEERNS2_INS_15NodeComputeInfoENS4_IS9_EEEE 2_INS_15NodeComputeInfoENS5_ISA_EEEE 1_12basic_stringIcNS1_11char_traitsIcEENS5_IcEEEE ЀeedsFetchesInfo ullyDefinedTypeERKN4onnx9TypeProtoE 21MapNamesToMLValueIdxsERKNSt3__16vectorINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS6_IS8_EEEERKNS_18OrtValueNameIdxMapERNS2_IiNS6_IiEEEE 14SetMLValueIdxsERKNS_18OrtValueNameIdxMapE 6CreateERKNSt3__16vectorINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS6_IS8_EEEESC_RKNS_18OrtValueNameIdxMapERNS1_10unique_ptrIS0_NS1_14default_deleteIS0_EEEE C 19SetDeviceCopyChecksENS_15DeviceCopyCheckES1_ 2EONS_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 5AliasEԂ RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE PKc ОRKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE PKc RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE PKc У8TypeConstraintImplERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERKNS1_6vectorIPKNS_12DataTypeImplENS5_ISD_EEEEPSG_ 4TypeConstraintE 0MayInplaceE 3VariadicAliasEii RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE PKc RKNS1_6vectorIPKNS_12DataTypeImplENS5_ISD_EEEE PKNS_12DataTypeImplE SH_ RKNSt3__16vectorIPKNS_12DataTypeImplENS3_9allocatorIS7_EEEEہ PKNS_12DataTypeImplE SC_ ЭRKNSt3__16vectorINS1_4pairIiiEENS1_9allocatorIS4_EEEEƂ ii͂ 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 А12CreateKernelERKNS_4NodeERKNS_18IExecutionProviderERKNS_12SessionStateERKNS_16KernelCreateInfoE 20SearchKernelRegistryERKNS_4NodeE 1 22RegisterKernelRegistryENSt3__110shared_ptrINS_14KernelRegistryEEE 5RegisterKernelsERKNS_18ExecutionProvidersE 9HasImplementationOfERKS0_RKNS_4NodeERKNSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEE PPKNS_16KernelCreateInfoE׌ yPPKNS_16KernelCreateInfoEތ 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 еCő 13FromTypeProtoEPKN4onnx9TypeProtoEPPS_ 5CloneEPPS_ 2E25ONNXTensorElementDataTypeP11OrtTypeInfo 1E25ONNXTensorElementDataTypeP11OrtTypeInfo ЈЉC 13FromTypeProtoEPKN4onnx9TypeProtoEPPS_ϓ 5CloneEPPS_֓ 2EP11OrtTypeInfo 1EP11OrtTypeInfoȓ ЋЎC D 1 20ElementTypeFromProtoEiҗ 5CloneEPPS_ٗ 2E8ONNXTypeǔ 1E8ONNXTypeӔ Pߔ P 25OrtTensorTypeAndShapeInfo 1 25OrtTensorTypeAndShapeInfo 1 4OrtMapTypeInfo 9OrtSequenceTypeInfo 4OrtMapTypeInfo 9OrtSequenceTypeInfo 2Ev 1EvƖ Е2FromOrtValueERK8OrtValuePPS_ė 3FromTypeProtoEPKN4onnx9TypeProtoEPPS_˗ ЗC 6OutputEi 1 24GetOrCreateOutputMLValueEi 2EPNS_15IExecutionFrameEPKNS_8OpKernelEPNS_11concurrency10ThreadPoolERKNS_7logging6LoggerEי 1EPNS_15IExecutionFrameEPKNS_8OpKernelEPNS_11concurrency10ThreadPoolERKNS_7logging6LoggerEޙ RK mRKNS_11TensorShapeE NS St16initializer_listIxE _11TensorShapeE t3__16vectorIxNS1_9allocatorIxEEEE 3OutputMLValueEiRKNS_11TensorShapeEm 6GetOutputMLValueEiڠ 2؛ 1ȝ 9 4 5TryGetInferredOutputShapeEiRNS_11TensorShapeE 1GetTempSpaceAllocatorEPNSt3__110shared_ptrINS_10IAllocatorEEE 3GetImplicitInputMLValueEiӠ TryGetInferredInputShapeEiRNS_11TensorShapeE GetImplicitInputArgIndexEi 6GetInputArgIndexEi 7 0 8ImplicitInputFenceEiܟ 1 2GetNodeIndexEv 5GetInputMLValueEi̠ GetOutputArgIndexEi NumVariadicInputsEm InputTypeEi GetOpTypeEvŠ OutputTypeEiΟ InputFenceEi՟ OutputFenceEi Get NodeNameEv OpDomainEv 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_19DataTransferManagerEԤ S0_ 1 2 4nodeEv 3Get 2Get 9TryGetConstantInputEiPPKNS_6TensorE MemoryInfoEi10OrtMemType FusedFuncsERPNS_15NodeComputeInfoE AllocatorEi10OrtMemType KernelDefEv 0GetExecutionProviderEv 2GetDataTransferManagerEv S_22ProtoHelperNodeContextEECݧ 4onnx16InferenceContextEEC 2EPKS1_ 1EPKS1_ ЋS_22ProtoHelperNodeContextEE¨ 4onnx16InferenceContextEEɰ 1 2 7GetAttrIɶ 8GetAttrsI 8GetAttrsStringRefsERKNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEERNS3_6vectorINS3_17reference_wrapperISA_EENS7_ISE_EEEE 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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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tEENS_6common6StatusERKN4onnx11TensorProtoE iEENS_6common6StatusERKN4onnx11TensorProtoE xEENS_6common6StatusERKN4onnx11TensorProtoE yEENS_6common6StatusERKN4onnx11TensorProtoE jEENS_6common6StatusERKN4onnx11TensorProtoE˥ bEENS_6common6StatusERKN4onnx11TensorProtoE NS RKNS_4PathEPT_mŢ PKvmPT_m̢ RKNS_4PathEPT_m PKvmPT_m RKNS_4PathEPT_m PKvmPT_m RKNS_4PathEPT_mң PKvmPT_m٣ RKNS_4PathEPT_m PKvmPT_m RKNS_4PathEPT_m PKvmPT_m RKNS_4PathEPT_mߤ PKvmPT_m ЉRKNS_4PathEPT_m PKvmPT_m RKNS_4PathEPT_m PKvmPT_mĥ RKNS_4PathEPT_m PKvmPT_m RKNS_4PathEPT_m PKvmPT_m _ t3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEEENS_6common6StatusERKN4onnx11TensorProtoE 9MLFloat16EEENS_6common6StatusERKN4onnx11TensorProtoE 8BFloat16EEENS_6common6StatusERKN4onnx11TensorProtoE RKNS_4PathEPT_m PKvmPT_m RKNS_4PathEPT_mߨ PKvmPT_m RKNS_4PathEPT_m PKvmPT_m 256EEENS_6common6StatusERKN4onnx11TensorProtoEPm 0EEENS_6common6StatusERKN4onnx11TensorProtoEPm 4GetTensorShapeFromTensorShapeProtoERKN4onnx16TensorShapeProtoEƫ 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ËRKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKN4onnx9TypeProtoEǾ ON4onnx14ValueInfoProtoEվ NjNjȋ4 11TypeAsProtoEvѿ 5ShapeEvؿ 22HasTensorOrScalarShapeEv߿ 6ExistsEv NameEvÿ TypeEvʿ ȋɋɋɋʋʋ0ClearShapeEv 8UpdateTypeAndShapeERK ̋N4onnx9TypeProtoEbbRKNS_7logging6LoggerE S0_bbRKNS_7logging6LoggerE ΋PKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE RKN4onnx9TypeProtoE ߋ7EdgeEndC 1 2 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_ 1ERKS0_ ii ii 7 1 5 2AddAttributeERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 4C 8MutableDefinitionsEv 9UpdateInputArgCountEv NodeConstIterator LoadFromOrtFormatERKNS_12experimental3fbs4NodeER C ppEv mmEv 2ENSt3__121__tree_const_iteratorINS0_7EdgeEndEPNS2_11__tree_nodeIS4_PvEElEE 1ENSt3__121__tree_const_iteratorINS0_7EdgeEndEPNS2_11__tree_nodeIS4_PvEElEE 1 9ModelPathEv 7ToProtoERN4onnx9NodeProtoEb 20SaveEdgesToOrtFormatERN11flatbuffers17FlatBufferBuilderE 7 5SaveToOrtFormatERN11flatbuffers17FlatBufferBuilderERNS1_6OffsetINS_12experimental3fbs4NodeEEE 2GetSubgraphsEv 0ForEachDefENSt3__18functionIFvRKNS_7NodeArgEbEEEb NodeConstIterator GetGraphAttributeERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE eqERKS1_ neERKS1_ deEv ptEv SetPriorityEi ReplaceDefsERKNSt3__13mapIPKNS_7NodeArgEPS3_NS1_4lessIS5_EENS1_9allocatorINS1_4pairIKS5_S6_EEEEEE 9ModelPathEv 1 2 4NameEv GetFunctionBodyEb SetFunctionBodyERKNS_8FunctionE 2 1 C 7 3 9SetInputsERKNSt3__16vectorIPKNS_7NodeArgENS1_9allocatorIS5_EEEE D̅ 3 1 7InferAndVerifySubgraphTypesERKNS_4NodeERS0_RKNSt3__16vectorIPKN4onnx9TypeProtoENS5_9allocatorISA_EEEERSD_RKNS0_14ResolveOptionsE 0 9InitInputsInitializersOutputsEv 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4GetMutableGraphAttributeERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE ЭreateSubgraphERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE learAttributeERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE ྍ1ER 2ER S0_RKNS_4NodeERN4onnx10GraphProtoE KNS_5ModelE ÍōǍɍˍfNS5_IfEEEE xNS5_IxEEEE S7_NS5_IS7_EEEE N4onnx1 ͍Ѝҍ1TensorProtoENS5_ISC_EEEE 0GraphProtoENS5_ISC_EEEE 7SparseTensorProtoENS5_ISC_EEEE Ս؍܍ߍKNS_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_iEEEEEENS4_10shared_ptrINS_30IOnnxRuntimeOpSchemaCollectionEEEPS0_PKNS_4NodeERKNS_7logging6LoggerE RKNS_7logging6LoggerE 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1GetNodeArgIncludingParentGraphsERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 9PerformTopologicalSortAndCheckIsAcyclicEv ǏComputeOverridableInitializersEv PerformTypeAndShapeInferencingERKNS0_14ResolveOptionsE TypeCheckInputsAndInitializersEv VerifyInputAndInitializerNamesEv ӏVerifyNoDuplicateNameEv Set ֏OuterScopeNodeArgsERKNSt3__113unordered_setINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS1_4hashIS8_EENS1_8equal_toIS8_EENS6_IS8_EEEE GraphInputsOutputsEv Remove SetOutputsERKNSt3__16vectorIPKNS_7NodeArgENS1_9allocatorIS5_EEEE EdgeEmmii NodeEm BuildConnectionsERNSt3__113unordered_setINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS1_4hashIS8_EENS1_8equal_toIS8_EENS6_IS8_EEEE FindAllSubgraphsERNSt3__16vectorIPS0_NS1_9allocatorIS3_EEEE GenerateNodeNameERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE ྐ4ReverseDFSFromERKNSt3__16vectorI 1DescriptionEv 9IsInitializedTensorERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 5SaveToOrtFormatERN11flatbuffers17FlatBufferBuilderERNS1_6OffsetINS_12experimental3fbs5GraphEEE 2ToGraphProtoEv 7GetSchemaRegistryEv mNS1_9allocatorImEEEERKNS1_8functionIFvPKNS_4NodeEEEESF_RKNS8_IFbSB_SB_EEE PKNS_4NodeENS1_9allocatorIS5_EEEERKNS1_8functionIFvS5_EEESF_RKNSB_IFbS5_S5_EEE SJ_ őؑ0 2GetConstantInitializerERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEb KahnsTopologicalSortERKNSt3__18functionIFvPKNS_4NodeEEEERKNS2_IFbS5_S5_EEE GetInitializedTensorERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERPKN4onnx11TensorProtoE ToGraphProtoInternalERN4onnx10GraphProtoE ڑےUpdateShapeInferenceERNS_4NodeE VerifyNodeAndOpMatchERKNS0_14ResolveOptionsE AddInitializedTensorERKN4onnx11TensorProtoE FinalizeFuseSubGraphERKNS_15IndexedSubGraphERNS_4NodeE ݒÓԓޓШޔϕSet CreateNodeArgsERKN6google8protobuf16RepeatedPtrFieldINSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEEEERKNS4_13unordered_mapISA_N4onnx9TypeProtoENS4_4hashISA_EENS4_8equal_toISA_EENS8_INS4_4pairIKSA_SG_EEEEEE AddControlEdgeEmm InlineFunctionERNS_4NodeE DescriptionERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE NodeArgTypeERNS_7NodeArgERKN4onnx9TypeProtoEŅ ϕЕЕЕѕddValueInfoEPKNS_7NodeArgE llocateNodeEv _4NodeE t3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES9_S9_RKNS1_6vectorIPNS_7NodeArgENS5_ISC_EEEESG_PKNS1_13unordered_mapIS7_N4onnx14AttributeProtoENS1_4hashIS7_EENS1_8equal_toIS7_EENS5_INS1_4pairIS8_SJ_EEEEEES9_ ИleanUnusedInitializersEPKNSt3__113unordered_setINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS1_4hashIS8_EENS1_8equal_toIS8_EENS6_IS8_EEEEڃ reateFusedSubGraphNodeERKNS_15IndexedSubGraphERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEE BeginFuseSubGraphERKNS_15IndexedSubGraphERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEE LoadFromOrtFormatERKNS_12experimental3fbs5GraphE Ѐ̘՘՘2Ev 1Ev 0Ev ֘R KNS_5ModelERKNSt3__113unordered_mapINS9_12basic_stringIcNS9_11char_traitsIcEENS9_9allocatorIcEEEEiNS9_4hashISG_EENS9_8equal_toISG_EENSE_INS9_4pairIKSG_iEEEEEENS9_10shared_ptrINS_30IOnnxRuntimeOpSchemaCollectionEEERKNS_7logging6LoggerERNS9_10unique_ptrIS0_NS9_14default_deleteIS0_EEEE S0_RKNS_4NodeERKNS_7logging6LoggerERNSt3__110unique_ptrIS0_NSE_14default_deleteIS0_EEEE Ιљ 2݉ 9 1 7 8 0 6354dummyInferenceFunctionERNS_16InferenceContextEɋ IfInferenceFunction_11ERNS_16InferenceContextE convPoolShapeInferenceERNS_16InferenceContextEbbiiStringStringEntryProtoTypeProto_SparseTensorާOperatorStatus_IsValidEiз2݋ 30 4 2 1 3LoadStringFromOrtFormatERNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEPKN11flatbuffers6StringE SaveInitializerOrtFormatERN11flatbuffers17FlatBufferBuilderERKN4onnx11TensorProtoERKNS_4PathERNS2_6OffsetINS0_3fbs6TensorEEE Load SaveSparseInitializerOrtFormatERN11flatbuffers17FlatBufferBuilderERKN4onnx17SparseTensorProtoERKNS_4PathERNS2_6OffsetINS0_3fbs12SparseTensorEEE LoadSparseInitializerOrtFormatERKNS0_3fbs12SparseTensorERN4onnx17SparseTensorProtoE ßSave Load AttributeOrtFormatERN11flatbuffers17FlatBufferBuilderERKN4onnx14AttributeProtoERNS2_6OffsetINS0_3fbs9AttributeEEERKNS_4PathEPKNS_5GraphE ValueInfoOrtFormatERN11flatbuffers17FlatBufferBuilderERKN4onnx14ValueInfoProtoERNS2_6OffsetINS0_3fbs9ValueInfoEEE ƟInitializerOrtFormatERKNS0_3fbs6TensorERN4onnx11TensorProtoE OpsetImportOrtFormatEPKN11flatbuffers6VectorINS2_6OffsetINS0_3fbs13OperatorSetIdEEEEERNSt3__113unordered_mapINSB_12basic_stringIcNSB_11char_traitsIcEENSB_9allocatorIcEEEEiNSB_4hashISI_EENSB_8equal_toISI_EENSG_INSB_4pairIKSI_iEEEEEE AttributeOrtFormatERKNS0_3fbs9AttributeERN4onnx14AttributeProtoERNSt3__110unique_ptrINS_5GraphENS9_14default_deleteISB_EEEERSB_RNS_4NodeERKNS_7logging6LoggerE ValueInfoOrtFormatERKNS0_3fbs9ValueInfoERN4onnx14ValueInfoProtoE Š 8internal 5Arena21796lsERNSt3__113basic_ostreamIcNS1_11char_traitsIcEEEE4util30WebSafeBase64EscapeWithPaddingENS0_11StringPieceEPNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE2՘ 9ArenaImpl136IsNullILiӦ8byteswapILi1 45RepeatedPrimitiveDefaults16default_instanceEv0369arena_destruct_objectINSś InitializeEnumStringsEPKNS1_9EnumEntryEPKimPNS1_21ExplicitlyConstructedINSt3__112basic_stringIcNS8_11char_traitsIcEENS8_9allocatorIcEEEEEEUnknownGroupLiteParseEPNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEPKcPNS1_12ParseContextE҆t3__1ݛ 0_11_12basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEEEEvPv 3mapIiNS1_12ExtensionSet9ExtensionENS3_4lessIiEENS3_9allocatorINS3_4pairIKiS6_EEEEEEEEvPv3 1 2 8FindPathERس 0GetNodeInputIndexFromInputNameERKNS_4NodeERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEE 3IsSupportedOptypeVersionAndDomainERKNS_4NodeE 2RemoveNodesWithOneOutputBottomUpERNS_5GraphERKNS_4NodeE Д6 7ģ 8 9IsSupportedProviderERKNS_4NodeERKNSt3__113unordered_setINS4_12basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEENS4_4hashISB_EENS4_8equal_toISB_EENS9_ISB_EEEEӭ 2 3 0RemoveNodeERNS_5GraphERNS_4NodeEð 4AddInitializerERNS_5GraphERKN4onnx11TensorProtoE GetNodeƢ ReplaceNodeInputERNS_4NodeEiRNS_7NodeArgEұ FirstChildByTypeERKNS_4NodeERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEE InputNameERKNS_4NodeEi AttributeERKNS_4NodeERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEEڭ GetNodeOutputNameERKNS_4NodeEi NodeArgIsConstantERKNS_5GraphERKNS_7NodeArgE FirstParentByTypeERKNS_4NodeERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEE RKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEERKSt16initializer_listIiESC_ PKcRKSt16initializer_listIiES5_ŭ 1 6Replaceʰ 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_4NodeEƱ IsConstantInitializerERKNS_5GraphERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEEb St16initializer_listIiE NSt3__16vectorIiNS4_9allocatorIiEEEḘ ШMatchesOpSetDomainERKNS_4NodeERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEE FinalizeNodeFusionERNS_5GraphER డбIs AddNodeInputERNS_4NodeEiRNS_7NodeArgE GetInput OutputUsedERKNS_4NodeEi GraphInputERKNS_5GraphEPKNS_7NodeArgEٱ Cӯ IsInitializerERKNS_5GraphERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEEb anRemoveNodeERKNS_5GraphERKNS_4NodeERKNS_7logging6LoggerE reateNodeArgERNS_5GraphERKNS_7NodeArgE ಡơNodeWithInitializerERNS_5GraphERNS_4NodeERNS_7NodeArgE DownstreamNodeInputERNS_5GraphERNS_4NodeEiS4_i ҡաۡi ЌNS_4NodeES4_ KNSt3__16vectorINS3_17reference_wrapperINS_4NodeEEENS3_9allocatorIS7_EEEERS6_ EdgeERKNS_4NodeEiʳ NodeERKNS_4NodeEiѳ 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 ˢТ2ERKNS_5GraphE 1ERKNS_5GraphE PKNS_15IndexedSubGraphE RKNS_15IndexedSubGraphEƷ ţPKNS_15IndexedSubGraphE RKNS_15IndexedSubGraphEͷ ţţţ7GetNodeEm 4NameEv¸ 1ɸ 2 9GetInputsEvӻ 30GetInputsIncludingInitializersEvڻ 5NodesEv Σԣ1DescriptionEv 0 2޼ 3NumberOfNodesEv գ0GetInitializedTensorERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERPKN4onnx11TensorProtoEŻ 2CanOverrideInitializerEv̻ 6GetNodesInTopologicalOrderENS_14ExecutionOrderE 4GetAllInitializedTensorsEvý 1IsConstantInitializerERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEbؽ գ֣֣֣Get IsSubgraphEvѽ OutputsEv׼ NodeArgERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEʽ ףGet MaxNodeIndexEv ValueInfoEv RootNodesEv ףףףأأܣC 1 9 7ToProtoEv 4 2E 1E RKNݿ ON4onnx10ModelProtoERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEEPKNS4_4listINS4_10shared_ptrINS_30IOnnxRuntimeOpSchemaCollectionEEENS8_ISG_EEEERKNS_7logging6LoggerE v 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ProducerNameERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE ModelVersionEx ͤProducerVersionEv SaveToOrtFormatERN11flatbuffers17FlatBufferBuilderERNS1_6OffsetINS_12experimental3fbs5ModelEEE ͤΤΤSetDomainERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE MainGraphEv ϤϤϤФФѤѤѤѤҤLoadE SaveERS0_ R ON4onnx10ModelProtoER iR NSt3__113basic_istreamIcNS1_11char_traitsIcEEEEPN4onnx10ModelProtoE KN Ӥ4onnx10ModelProtoER St3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERN NSt3__110shared_ptrIS0_EEPKNS5_4listINS6_INS_30IOnnxRuntimeOpSchemaCollectionEEENS5_9allocatorISB_EEEERKNS_7logging6LoggerE KNSt3__112basic_stringIcNS5_11char_traitsIcEENS5_9allocatorIcEEEERNS5_10shared_ptrIS0_EEPKNS5_4listINSE_INS_30IOnnxRuntimeOpSchemaCollectionEEENS9_ISJ_EEEERKNS_7logging6LoggerE ֤פNSt3__110shared_ptrIS0_EEPKNS4_4listINS5_INS_30IOnnxRuntimeOpSchemaCollectionEEENS4_9allocatorISA_EEEERKNS_7logging6LoggerE KNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEERNS4_10shared_ptrIS0_EEPKNS4_4listINSD_INS_30IOnnxRuntimeOpSchemaCollectionEEENS8_ISI_EEEERKNS_7logging6LoggerE 4onnx10ModelProtoE S1_10shared_ptrIS0_EEPKNS1_4listINSA_INS_30IOnnxRuntimeOpSchemaCollectionEEENS5_ISF_EEEERKNS_7logging6LoggerE RKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE i N 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 ЅN KNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERNS1_10shared_ptrIS0_EEPKNS1_4listINSA_INS_30IOnnxRuntimeOpSchemaCollectionEEENS5_ISF_EEEERKNS_7logging6LoggerE 4onnx10ModelProtoE 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7Capture 1 8Category13 D CapturePrintfEPKcz ProcessPrintfEPKcP13__va_list_tag 2Ev 1Ev ধ4LoggingManager 1 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 ଧ9CreateDefaultLoggerERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE 2CreateLoggerERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE 7s_default_logger_E2Ev 1Ev NS0_8SeverityEbi 3LogERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEERKNS0_7CaptureE 16SendProfileEventERNS_9profiling11EventRecordE çŧŧGetThreadIdEv 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PRKNScijlmdxyKcvġt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEE0_ġġǡ1Ev2EvȡȡullLogHandlerENS0_8LogLevelEPKciRKNSt3__112basic_stringIcNS5_11char_traitsIcEENS5_9allocatorIcEEEEoLocaleStrtodEPKcPPcɡ11StringPieceE4util6StatusE7uint128Eɡ̡͡ҡӡԡա֡סء١ڡۡۡۡFatalExceptionDDoubleToBufferEdPcBase64UnescapeENS0_11StringPieceEPNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE1Ev2Ev0EvݡetLogHandlerEPFvNS0_8LogLevelEPKciRKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEEEtringStringPrintfEPNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKczݡCD2Ev1Evޡޡ2Ev1EvޡޡCuint128stringslosureDEscapeERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE2Ev1Ev0Evޡޡߡ2Ev1Ev0EvߡߡߡDoNothingEvStrAppendEPNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERKNS0_7strings8AlphaNumEژߡghtonlEjStrCatERKNS0_7strings8AlphaNumES4_ߡߡ10DivModImplES1_S1_PS1_S2_dVERKS1_rMERKS1_RKNS0_7uint128ENS0_11StringPieceE6StatuslsERNSt3__113basic_ostreamIcNS2_11char_traitsIcEEEERKNS1_6StatusECaSERKS2_2OKE9CANCELLEDE7UNKNOWNE1E2EvNS1_5error4CodeENS0_11StringPieceERKS2_vNS1_5error4CodeENS0_11StringPieceERKS2_eqERKS2_8ToStringEv1C7ConsumeES1_4nposE8LogFatalSizeTooBigEmPKc4ConsumeFromEndES1_2ES1_l1ES1_lll148containsES1_5rfindE6substrEmm24AppendToStringEPNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEށ3find_first_ofES1_mՂ7find_first_not_ofE܂6find_last_not_ofECopyToStringEPNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEׁfind_last_ofES1_mcopyEPcmmfindES1_mcmКS1_mǂcm΂S1_mcmࡢS1_mcmЭAppendReplaceERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES9_S9_bVEPNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKcP13__va_list_tagFEPNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKczStringPrintfEPKczsafe_strtoBase64EscapeEGenericScanFastAsciiEPKNS1_19UTF8StateMachineObjEPKciPiSpnStructurallyValidERKNS0_11StringPieceE¢ĢƢUTF8CoerceToStructurallyValidERKNS0_11StringPieceEPccCodedOutputStreamFieldSkipperɢReplaceCharactersEPNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKccՈFastˢStripWhitespaceEPNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEECEscapeInternalEPKciPcibbstrto32_adaptorEPKcPPciߏFastHexToBufferEiPcDelocalizeRadixEPc̢PS7_΢բ֢SplitStringAllowEmptyERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKcPNS1_6vectorIS7_NS5_IS7_EEEEUnescapeCEscapeStringERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEFastIntڒWebSafeBase64UnescapeEߢPNSt3__16vectorINS4_12basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEENS9_ISB_EEEEPS7_ǍPNS1_6vectorIS7_NS5_IS7_EEEEЁ18AlphaNumC×5Utf8SafeCEscapeERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEя0CHexEscapeERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE؏IntHex64ToBufferExPc32ToBufferEiPc64ToBufferEyPc32ToBufferEjPcАВFastUIntGlobalReplaceSubstringERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES9_PS7_Base64UnescapeInternalEPKciPciPKaCleanStringLineEndingsE32ToBufferLeftEjPcӒ64ToBufferLeftEyPcГ32ToBufferLeftEiPc64ToBufferLeftExPcItoaEDtoaEdFtoaEfiܓjlmxy෣bENS0_11StringPieceEPbfEPKcPfdEPKcPdǣȣ32ERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPi64ERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPxʣ32ERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPj64ERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPyϣңף2ENS1_3HexE1ENS1_3HexEۣۣܣS4_ݣS4_ߣS4_S4_S4_S4_ŘS4_ӘSC_SC_SC_bPKciPciNS0_11StringPieceEPNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEciPKcbNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEEbPKcPKhiPNS0_11StringPieceEPNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEciNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEEbPKhiPNS0_11StringPieceEPNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEcibNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEEbRKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPS7_bPNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEbş9130ReadPackedEnumPreserveUnknownsEPNS0_2io16CodedInputStreamEiPFbiEPNS3_17CodedOutputStreamEPNS0_13RepeatedFieldIiEE28EnumSizeERKNS0_13RepeatedFieldIiEESkipFieldEPNS0_2io16CodedInputStreamEjWriteReadBytesEPNS0_2io16CodedInputStreamEPIntЮPNS3_17CodedOutputStreamE 15WriteFloatArrayEPKfiPNS0_2io17CodedOutputStreamE6Ʀ7WriteFixed8WriteSFixed4WriteBoolArrayEPKbiPNS0_2io17CodedOutputStreamE02Write3WriteSFixedЯSkipMessageEPNS0_2io16CodedInputStreamEWriteЫPNS3_17CodedOutputStreamE¤9SkipFieldEPNS0_2io16CodedInputStreamEjҤ1٤Ĥ1SkipMessageEPNS0_2io16CodedInputStreamE5SkipUnknownEnumEiiĤŤ9SkipFieldEPNS0_2io16CodedInputStreamEjޥ1Ť1SkipMessageEPNS0_2io16CodedInputStreamE5SkipUnknownEnumEiiŤƤȤΤWriteDoubleArrayEPKdiPNS0_2io17CodedOutputStreamEVerifyUtf8StringEPKciNS2_9OperationES4_Ϥ32ArrayEPKjiPNS0_2io17CodedOutputStreamE64ArrayEPKyiPNS0_2io17CodedOutputStreamEФФ32ArrayEPKiiPNS0_2io17CodedOutputStreamE64ArrayEPKxiPNS0_2io17CodedOutputStreamEѤҤӤWriteUIntSInt߸IntFloatEifPNS0_2io17CodedOutputStreamEBytesEiRKNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEEPNS0_2io17CodedOutputStreamEGroupEiRKNS0_11MessageLiteEPNS0_2io17CodedOutputStreamE32EiiPNS0_2io17CodedOutputStreamE«64EixPNS0_2io17CodedOutputStreamEɫӤ֤UIntSDoubleEidPNS0_2io17CodedOutputStreamE32EijPNS0_2io17CodedOutputStreamEԬ64EiyPNS0_2io17CodedOutputStreamE۬ؤڤIntԭtringEiRKNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEEPNS0_2io17CodedOutputStreamE32EiiPNS0_2io17CodedOutputStreamE64EixPNS0_2io17CodedOutputStreamEݤߤFixedMessageEiRKNS0_11MessageLiteEPNS0_2io17CodedOutputStreamE32EijPNS0_2io17CodedOutputStreamE¯64EiyPNS0_2io17CodedOutputStreamEɯ32EiiPNS0_2io17CodedOutputStreamE64EixPNS0_2io17CodedOutputStreamEBoolEibPNS0_2io17CodedOutputStreamEEnumEiiPNS0_2io17CodedOutputStreamE327WriteSubMessageMaybeToArrayEiRKNS0_11MessageLiteEPNS0_2io17CodedOutputStreamE4WriteMessageMaybeToArrayEiRKNS0_11MessageLiteEPNS0_2io17CodedOutputStreamE1k0kMessageSetTypeIdTagEWriteStringMaybeAliasedEiRKNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEEPNS0_2io17CodedOutputStreamEkMessageSetItemWritekFieldTypeToCppTypeMapEBytesMaybeAliasedEiRKNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEEPNS0_2io17CodedOutputStreamEGroupMaybeToArrayEiRKNS0_11MessageLiteEPNS0_2io17CodedOutputStreamEЇЊNSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEEPNSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEEЍ32SizeERKNS0_13RepeatedFieldIiEE64SizeERKNS0_13RepeatedFieldIxEEГ32SizeERKNS0_13RepeatedFieldIjEEظ64SizeERKNS0_13RepeatedFieldIyEE32SizeERKNS0_13RepeatedFieldIiEE64SizeERKNS0_13RepeatedFieldIxEEŹЙТ 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120CompileToReverseProgEx5CؚD3RefEv67948ݥ309RemoveLeading7251NumCapturesEv4CompLNamedCapturesEvileToProgExuteSimpleEv೦CompileSetEPNS_6RegexpENS_3RE26AnchorExfirst_byteEvEmptyFlagsERKNS_11StringPieceEPKcDFANFARE2CD1928RWLocker6SearchERKNS_11StringPieceES3_bbbPbPPKcPNS_10SparseSetTIvEE2EPNS_4ProgENS1_9MatchKindEx1EPNS_4ProgENS1_9MatchKindEx2Ev1Ev081CachedStateEPiij2StateToWorkqEPNS0_5StateEPNS0_5WorkqE43AnalyzeSearchEPNS0_12SearchParamsE9AnalyzeSearchHelperEPNS0_12SearchParamsEPNS0_9StartInfoEjClearCacheEvAddToQueueEPNS0_5WorkqEijResetCacheEPNS0_8RWLockerEStateSaverDumpSearchWorkqEPNS0_5WorkqEStateEPNS0_5StateEWorkqToCachedStateEPNS0_5WorkqES2_jPossibleMatchRangeEPNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES8_i1RunWorkqOnEmptyStringEPNS0_5WorkqES2_j2RunStateOnByteUnlockedEPNS0_5StateEiRunSlowSearchLoopEPNS0_12SearchParamsEFastSearchLoopEPNS0_12SearchParamsEBuildAllStatesERKNSt3__18functionIFvPKibEEEWorkqOnByteEPNS0_5WorkqES2_ijPbStateOnByteEPNS0_5StateEiЩધC14LockForWritingEvD2EPNS_5MutexE1EPNS_5MutexE೧2Ev1EvCD7RestoreEv2EPS0_PNS0_5StateE1EPS0_PNS0_5StateEй2Ev1EvмFTFTFEPNS0_12SearchParamsETEPNS0_12SearchParamsEԧFEPNS0_12SearchParamsETEPNS0_12SearchParamsEԧԧFTFEPNS0_12SearchParamsETEPNS0_12SearchParamsEԧէFEPNS0_12SearchParamsETEPNS0_12SearchParamsEէէէ֧֧ڧݧGetDFAENS0_9MatchKindEFanoutEPNS_11SparseArrayIiEEDeleteDFAEPNS_3DFAESearchIsOnePassEvDFAERKNS_11StringPieceES3_NS0_6AnchorENS0_9MatchKindEPS1_PbPNS_10SparseSetTIvEENFAERKNS_11StringPieceES3_NS0_6AnchorENS0_9MatchKindEPS1_iЍЙCD14StepEPNS_11SparseArrayIPNS0_6ThreadEEES5_iRKNS_11StringPieceEPKc6SearchERKNS_11StringPieceES3_bbPS1_i2EPNS_4ProgE1EPNS_4ProgEШب2Ev1Evبڨ2AddToThreadqEPNS_11SparseArrayIPNS0_6ThreadEEEiiRKNS_11StringPieceEPKcS3_3FormatCaptureEPPKcڨ 4367ParseUnicodeGroupEPNS_11StringPieceENS_6Regexp10ParseFlagsEPNS_16CharClassBuilderEPNS_12RegexpStatusE912058num_unicode_groupsEOnePass_ChecksEvLookupCaseFoldEPKNS_8CaseFoldEiiByteMapBuilderCoalesceWalkerSimplifyWalkerէToStringWalkerunicode_groupsESearchOnePassERKNS_11StringPieceES3_NS0_6AnchorENS0_9MatchKindEPS1_iMarkDominatorEiPNS_11SparseArrayIiEES3_PNSt3__16vectorINS5_IiNS4_9allocatorIiEEEENS6_IS8_EEEEPNS_10SparseSetTIvEEPS8_ParseStateNewLiteralEiNS0_10ParseFlagsEРCD189PushCaratEv7PushDotEv20DoLeftParenNoCaptureEv2ENS0_10ParseFlagsERKNS_11StringPieceEPNS_12RegexpStatusE1ENS0_10ParseFlagsERKNS_11StringPieceEPNS_12RegexpStatusEéĩ2Ev1Evĩũ207MaybeConcatStringEiNS0_10ParseFlagsE16P4P3Do5DoConcatenationEvFinishRegexpEPS0_PDoRightParenEvƩPushDoCollapseENS_8RegexpOpERegexpEPS0_DollarEvǩɩIsMarkerENS_8RegexpOpEDoFinishEv̩̩ͩCycleFoldRuneEiSͩPushLiteralEiDoLeftParenERKNS_11StringPieceEϩ֩usharseCCRangeEPNS_11StringPieceEPNS_9RuneRangeERKS2_PNS_12RegexpStatusESimpleOpENS_8RegexpOpERepeatOpENS_8RegexpOpERKNS_11StringPieceEbשushWordBoundaryEbarseCCCharacterEPNS_11StringPieceEPiRKS2_PNS_12RegexpStatusEשة٩۩RepetitionWalkerCharClassBuildernum_posix_groupsEunicode_casefoldE8PreVisitEPNS_6RegexpEiPb9PostVisitEPNS_6RegexpEiiPii10ShortVisitEPNS_6RegexpEiީީushRepetitionEiiRKNS_11StringPieceEbarseVerticalBarEvAlternationEveadingiteralStringEPiiNS0_10ParseFlagsEנRegexpEPS0_StringEPS0_PiPNS0_10ParseFlagsERegexpEPS0_StringEPS0_iFactorAlternationEPPS0_iNS0_10ParseFlagsEConcatOrAlternateENS_8RegexpOpEPPS0_iNS0_10ParseFlagsEbAlternateNoFactorEPPS0_iNS0_10ParseFlagsE1FactorAlternationImpl6Round2MaybeParsePerlCCEscapeEPNS_11StringPieceENS_6Regexp10ParseFlagsE0num_unicode_casefoldE1EPPNS_6RegexpEiNS1_10ParseFlagsEPNSt3__16vectorINS_6SpliceENS5_9allocatorIS7_EEEE2EPPNS_6RegexpEiNS1_10ParseFlagsEPNSt3__16vectorINS_6SpliceENS5_9allocatorIS7_EEEE3EPPNS_6RegexpEiNS1_10ParseFlagsEPNSt3__16vectorINS_6SpliceENS5_9allocatorIS7_EEEEЍ1C84CopyEv6NegateEv3AddRangeFlagsEiiNS_6Regexp10ParseFlagsE20FoldsASCIIEv1RemoveAboveEiП˪CharClassEPNS_11StringPieceEPPS0_PNS_12RegexpStatusEPerlFlagsEPNS_11StringPieceEϪڪConvertLatin1ToUTF8ERKNS_11StringPieceEPNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEEnum_unicode_tolowerEParseERKNS_11StringPieceENS0_10ParseFlagsEPNS_12RegexpStatusEQuestEPS0_NS0_10ParseFlagsEEqualEPS0_S1_9Bitmap25614FindNextSetBitEi3RE21InstDumpEv7Init19InitMatchEi8InitFailEv4DumpEvAltEjjNopEj3InitByteRangeEiiij1InitCaptureEij4InitEmptyWidthENS_7EmptyOpEjД2Ev1EvЗИ2Ev1EvЙЛФШOptimizeEvEmitListEiPNS_11SparseArrayIiEEPNSt3__16vectorINS0_4InstENS4_9allocatorIS6_EEEEPNS_10SparseSetTIvEEPNS5_IiNS7_IiEEEEЩ4MarkEii57RecolorEiMergeEvBuildEPhPi«ѫSperl_groupsEparseArrayIiE4lessERKNS1_10IndexValueES4_tringPiece4nposE7C4InitERKNS_11StringPieceERKNS0_7OptionsED1‹8ConsumeNEPNS_11StringPieceERKS0_PKPKNS0_3ArgEi9QuoteMetaERKNS_11StringPieceEώ3ArgOptions܁ReplaceEPNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERKS0_RKNS_11StringPieceEExtractERKNS_11StringPieceERKS0_S3_PNSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEEȎC14kDefaultMaxMemE2ENS0_13CannedOptionsE1ENS0_13CannedOptionsE६2Eʂ1EPKcۂRKNSবPKcRKNS߃лt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE؃_11StringPieceE໬t3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE_11StringPieceEׄRKNS0_7OptionsERKNS0_7OptionsE¬Ĭ7…1205MatchERKNS_11StringPieceEmmNS0_6AnchorEPS1_iOptions10ParseFlagsEvDoMatchERKNS_11StringPieceENS0_6AnchorEPmPKPKNS0_3ArgEiRewriteEPNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERKNS_11StringPieceEPSA_iƬ18ˈ3ProgramFanoutEPNSt3__13mapIiiNS1_4lessIiEENS1_9allocatorINS1_4pairIKiiEEEEEE9CapturingGroupNamesEvReverseProgEvProgramSizeEvĈά2Ev1EvϬѬѬReverseProgramSizeEvPossibleMatchRangeEPNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES8_i֎CheckRewriteStringERKNS_11StringPieceEPNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEEݎҬӬReverseProgramFanoutEPNSt3__13mapIiiNS1_4lessIiEENS1_9allocatorINS1_4pairIKiiEEEEEENamedCapturingGroupsEv٬۬ܬ0FullMatchNERKNS_11StringPieceERKS0_PKPKNS0_3ArgEi݌35FindAndConsumeNEPNS_11StringPieceERKS0_PKPKNS0_3ArgEi1MaxSubmatchERKNS_11StringPieceEݬݬPartialMatchNERKNS_11StringPieceERKS0_PKPKNS0_3ArgEiGlobalReplaceEPNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERKS0_RKNS_11StringPieceEЏ129parse_intEPKcmPvӘ 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gINS4_24TreeAggregatorClassifierIifEEEEvPNS2_11concurrency10ThreadPoolEPKNS2_6TensorEPSD_SG_RKT_EUllE217TreeAggregatorɰ4TreeAggregatorClassifierIffEEEEvPNS2_11concurrency10ThreadPoolEPKNS2_6TensorEPSD_SG_RKT_EUllE1TreeAggregatorAverageIffEEEEvPNS2_11concurrency10ThreadPoolEPKNS2_6TensorEPSD_SG_RKT_EUllE0_NS_9allocatorISK_EEFvlEEE1_NS_9allocatorISK_EEFvlEEE3_NS_9allocatorISK_EEFvlEEE4_NS_9allocatorISK_EEFvlEEEĘ5_NS_9allocatorISK_EEFvlEEEҘ6_NS_9allocatorISK_EEFvlEEEffE10ComputeAggINS1_dfE10ComputeAggINS1_xfE10ComputeAggINS1_24TreeAggregatorClassifierIxfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllEͥifE10ComputeAggINS1_24TreeAggregatorClassifierIifEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE217TreeAggregator߲4TreeAggregatorClassifierIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1TreeAggregatorAverageIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_1_3_4_˘5_٘6_217TreeAggregator4TreeAggregatorClassifierIdfEEEEvPS4_PKNS2_6TensorEPSE_SH_RKT_EUllE1TreeAggregatorAverageIdfEEEEvPS4_PKNS2_6TensorEPSE_SH_RKT_EUllE_EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEE2_EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEE217TreeAggregator4TreeAggregatorClassifierIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE1TreeAggregatorAverageIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ޜ2_EEvSA_lOSF_lEUllE_217TreeAggregator4TreeAggregatorClassifierIdfEEEEvPNS2_11concurrency10ThreadPoolEPKNS2_6TensorEPSD_SG_RKT_EUllEÞ1TreeAggregatorAverageIdfEEEEvPNS2_11concurrency10ThreadPoolEPKNS2_6TensorEPSD_SG_RKT_EUllE0_NS_9allocatorISK_EEFvlEEE1_NS_9allocatorISK_EEFvlEEE3_NS_9allocatorISK_EEFvlEEE4_NS_9allocatorISK_EEFvlEEE5_NS_9allocatorISK_EEFvlEEEŢ6_NS_9allocatorISK_EEFvlEEEӢ217TreeAggregator4TreeAggregatorClassifierIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1TreeAggregatorAverageIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_1_3_4_5_̢6_ڢ_EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEEˣ2_EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEE_EEvSA_lOSF_lEUllE_2_EEvSA_lOSF_lEUllE_0_NS_9allocatorISK_EEFvlEEEƥ1_NS_9allocatorISK_EEFvlEEE3_NS_9allocatorISK_EEFvlEEE4_NS_9allocatorISK_EEFvlEEE5_NS_9allocatorISK_EEFvlEEE6_NS_9allocatorISK_EEFvlEEE0_1_3_4_5_6_ǦЀЍ_EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEE2_EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEE_EEvSA_lOSF_lEUllE_2_EEvSA_lOSF_lEUllE_0_NS_9allocatorISK_EEFvlEEE1_NS_9allocatorISK_EEFvlEEE3_NS_9allocatorISK_EEFvlEEE4_NS_9allocatorISK_EEFvlEEE5_NS_9allocatorISK_EEFvlEEE6_NS_9allocatorISK_EEFvlEEE0_1_3_4_5_6_УfEE˪dEE_EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEE2_EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEE_EEvSA_lOSF_lEUllE_2_EEvSA_lOSF_lEUllE_0_NS_9allocatorISK_EEFvlEEE1_NS_9allocatorISK_EEFvlEEE3_NS_9allocatorISK_EEFvlEEE4_NS_9allocatorISK_EEFvlEEE5_NS_9allocatorISK_EEFvlEEE6_NS_9allocatorISK_EEFvlEEE0_ݭ1_3_4_5_6_SumIffEEEEvPS4_PKNS2_6TensorEPSE_SH_RKT_EUllEM_EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEEӯ2_EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEESumIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllEM_EEvSA_lOSF_lEUllE_°2_EEvSA_lOSF_lEUllE_SumIffEEEEvPNS2_11concurrency10ThreadPoolEPKNS2_6TensorEPSD_SG_RKT_EUllEM0_NS_9allocatorISK_EEFvlEEEز1_NS_9allocatorISK_EEFvlEEEݳ3_NS_9allocatorISK_EEFvlEEE4_NS_9allocatorISK_EEFvlEEE5_NS_9allocatorISK_EEFvlEEE6_NS_9allocatorISK_EEFvlEEESumIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllEM0_ֳ1_3_4_5_6_inIffEEEEvPS4_PKNS2_6TensorEPSE_SH_RKT_EUllEaxIffEEEEvPS4_PKNS2_6TensorEPSE_SH_RKT_EUllE_EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEE2_EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEEinIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllEݶaxIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_2_EEvSA_lOSF_lEUllE_ƻinIffEEEEvPNS2_11concurrency10ThreadPoolEPKNS2_6TensorEPSD_SG_RKT_EUllEaxIffEEEEvPNS2_11concurrency10ThreadPoolEPKNS2_6TensorEPSD_SG_RKT_EUllE0_NS_9allocatorISK_EEFvlEEE1_NS_9allocatorISK_EEFvlEEE3_NS_9allocatorISK_EEFvlEEEͻ4_NS_9allocatorISK_EEFvlEEEۻ5_NS_9allocatorISK_EEFvlEEE6_NS_9allocatorISK_EEFvlEEEinIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllEaxIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_1_3_Ի4_5_6__EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEE2_EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEE_EEvSA_lOSF_lEUllE_2_EEvSA_lOSF_lEUllE_0_NS_9allocatorISK_EEFvlEEE1_NS_9allocatorISK_EEFvlEEE3_NS_9allocatorISK_EEFvlEEE4_NS_9allocatorISK_EEFvlEEE5_NS_9allocatorISK_EEFvlEEEϿ6_NS_9allocatorISK_EEFvlEEEݿ0_1_3_4_ȿ5_ֿ6__EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEE2_EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEE_EEvSA_lOSF_lEUllE_2_EEvSA_lOSF_lEUllE_0_NS_9allocatorISK_EEFvlEEE1_NS_9allocatorISK_EEFvlEEE3_NS_9allocatorISK_EEFvlEEE4_NS_9allocatorISK_EEFvlEEE5_NS_9allocatorISK_EEFvlEEE6_NS_9allocatorISK_EEFvlEEE0_1_3_4_5_6_ЛSumIdfEEEEvPS4_PKNS2_6TensorEPSE_SH_RKT_EUllEM_EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEE2_EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEESumIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllEM_EEvSA_lOSF_lEUllE_2_EEvSA_lOSF_lEUllE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CEMLpNormISArgMGatherEResizeIUniqueElip_6IfEEumSumIoncatEѓfEEdEEiEExEEetIfEEivIEinsumEMatMulIfEEShrinkEConcatEÓGatherEUniqueEinsumExpandIߓ_6vectorImNS_9allocatorImEEEERKNS2_6TensorERSB_PKNS2_11TensorShapeEPvENS6_ISK_EESJ_EE2_t3__16vectorImNS2_9allocatorImEEEERKNS_6TensorERS9_PKNS_11TensorShapeEPvE_t3__16vectorImNS2_9allocatorImEEEERKNS_6TensorERS9_PKNS_11TensorShapeEPvE_fS6_PfmmmmmmmPNS2_11concurrency10ThreadPoolEPvEEEiS6_PimmmmmmmPNS2_11concurrency10ThreadPoolEPvEEEdS6_PdmmmmmmmPNS2_11concurrency10ThreadPoolEPvEEExS6_PxmmmmmmmPNS2_11concurrency10ThreadPoolEPvEEEfS6_PfmmmmmmmPNS2_11concurrency10ThreadPoolEPvENS_9allocatorISD_EESC_EEiS6_PimmmmmmmPNS2_11concurrency10ThreadPoolEPvENS_9allocatorISD_EESC_EEdS6_PdmmmmmmmPNS2_11concurrency10ThreadPoolEPvENS_9allocatorISD_EESC_EExS6_PxmmmmmmmPNS2_11concurrency10ThreadPoolEPvENS_9allocatorISD_EESC_EEfS3_PfmmmmmmmPNS_11concurrency10ThreadPoolEPvEiS3_PimmmmmmmPNS_11concurrency10ThreadPoolEPvEdS3_PdmmmmmmmPNS_11concurrency10ThreadPoolEPvExS3_PxmmmmmmmPNS_11concurrency10ThreadPoolEPvEfS3_PfmmmmmmmPNS_11concurrency10ThreadPoolEPvEiS3_PimmmmmmmPNS_11concurrency10ThreadPoolEPvEdS3_PdmmmmmmmPNS_11concurrency10ThreadPoolEPvExS3_PxmmmmmmmPNS_11concurrency10ThreadPoolEPvE6TensorERS5_PvENS_9allocatorISB_EESA_EE4NodeERNS2_5GraphERKNS_6vectorIPKN4onnx9TypeProtoENS_9allocatorISE_EEEERSH_RKNS8_14ResolveOptionsEENSF_ISP_EESO_EE6TensorERS2_PvE4NodeERNS_5GraphERKNSt3__16vectorIPKN4onnx9TypeProtoENS7_9allocatorISC_EEEERSF_RKNS5_14ResolveOptionsEE6TensorERS2_PvE4NodeERNS_5GraphERKNSt3__16vectorIPKN4onnx9TypeProtoENS7_9allocatorISC_EEEERSF_RKNS5_14ResolveOptionsEEЀȁЃȄowEadEȅowEadEin_8Eax_8Ein_ax_8E6IfEEІ膖8E6IfEEndEddI؈odEulIfEEdEEiEExEEubIinIfEEdEEiEExEEЍfEEdEEiEExEEfEEdEEiEExEEВ fEEEEdEEEEaEEEEsEEEEiEEEExEEEEhEEEEtEEEEjEEEEyEEEE fEEdEEaEEsEEiEExEEhEEtEEjEEyEE fEEdEEaEEsEEiEExEEhEEtEEjEEyEE fEENS_9allocatorIS5_EEFvllEEEdEENS_9allocatorIS5_EEFvllEEEaEENS_9allocatorIS5_EEFvllEEEsEENS_9allocatorIS5_EEFvllEEEiEENS_9allocatorIS5_EEFvllEEExEENS_9allocatorIS5_EEFvllEEEhEENS_9allocatorIS5_EEFvllEEEtEENS_9allocatorIS5_EEFvllEEEjEENS_9allocatorIS5_EEFvllEEEyEENS_9allocatorIS5_EEFvllEEEИКࠖУХ৖૖ЮfEEEEdEEEEaEEEEiEEEExEEEEаfEEdEEaEEiEExEEfEENS_9allocatorIS5_EEFvllEEEdEENS_9allocatorIS5_EEFvllEEEaEENS_9allocatorIS5_EEFvllEEEiEENS_9allocatorIS5_EEFvllEEExEENS_9allocatorIS5_EEFvllEEEಖз๖к༖ReciprocalIScaledTanhIfEEEEfEEEEdEEEEfEEdEEReciprocalIScaledTanhIfEENS_9allocatorIS5_EEFvllEEEfEENS_9allocatorIS5_EEFvllEEEdEENS_9allocatorIS5_EEFvllEEE–ÖfEEEEdEEEEÖfEEdEEĖfEENS_9allocatorIS5_EEFvllEEEdEENS_9allocatorIS5_EEFvllEEEŖŖƖǖfEEEEdEEEEȖfEEdEEȖfEENS_9allocatorIS5_EEFvllEEEdEENS_9allocatorIS5_EEFvllEEEɖʖʖ˖fEEEEdEEEE̖fEEdEE̖fEENS_9allocatorIS5_EEFvllEEEdEENS_9allocatorIS5_EEFvllEEE͖Ζϖϖum_hapeEplitE6I8IfEEdEEЖіfEEdEEіҖӖӖfEEdEEiEExEEԖԖՖ֖fEEdEEiEExEE֖זؖؖbEEiEExEEfEEdEEٖٖږۖۖean_atMulI6IfEE8IfEEܖݖitShiftIFCArenaEhEEjEEyEEݖޖޖrfIfEEnvEߖfEEdEEsinIfEEcosIfEEtanIfEEnhIfEEgnEzeEshIfEEnvIfEEsinhIfEEcoshIfEEtanhIfEEfEEdEE4GemmI2ml6ExpandIɔ12GatherNDBase17PrepareForComputeI7contribdE17ComputeActivationEPdmPNS2_11concurrency10ThreadPoolEEUlllE_NS_9allocatorIS9_EEFvllEEEfE17ComputeActivationEPfmPNS2_11concurrency10ThreadPoolEEUlllE_NS_9allocatorIS9_EEFvllEEE4GemmI2ml6ExpandI12GatherNDBase17PrepareForComputeI7contribdE17ComputeActivationEPdmPNS_11concurrency10ThreadPoolEEUlllE_fE17ComputeActivationEPfmPNS_11concurrency10ThreadPoolEEUlllE_fEEdEEiEExEEtMulIntegerExPoolNS_9MLFloat16EEEfEEdEEgnEzeEoftmaxIcatterElice10EqueezeEfEEdEEopKILiileE9E1fEEdEE0E1EfEEdEEfEEdEExEE217CastMapE98‡3ArrayFeatureExtractorOpI2TreeEnsembleClassifierI1TreeEnsembleRegressorIfEEdEEiEExEENSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEEE1BinarizerOpIfEE46€7FeatureVectorizerEׂ250NormalizerE†3SVMClassifierE7CastMapE19ImputerOpEނ8ZipMapOpE4CategoryMapperE7FeatureVectorizerEЂ2LabelEncoderE6LinearClassifierE5LinearRegressorE0NormalizerE3SVMClassifierECategoryMapperELabelEncoder_2IЗDictVectorizerOpILinearClassifierENSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEExxEEЁfEEׁdEEށNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEEEfEE‚dEEɂЛ蛗ImputerOpESVMCommonELabelEncoderESVMRegressorIfEEfNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEExNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEEEބxEEمfEExEEfEE҅NSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEEExEEСLinearRegressorEOneHotEncoderOpIɆࣗगxEEfEEdEENSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEEEগScalerOpIއZipMapOpEfEEdEExEEiEEਗ8ScalerOpI6detail18TreeEnsembleCommonIfE7ComputeEPNS2_15OpKernelContextEEUllEdE7ComputeEPNS2_15OpKernelContextEEUllExE7ComputeEPNS2_15OpKernelContextEEUllEiE7ComputeEPNS2_15OpKernelContextEEUllE_NS_9allocatorIS8_EEFvlEEE0_NS_9allocatorIS8_EEFvlEEEЩ8ScalerOpI׊6detail18TreeEnsembleCommonIfE7ComputeEPNS_15OpKernelContextEEUllEdE7ComputeEPNS_15OpKernelContextEEUllExE7ComputeEPNS_15OpKernelContextEEUllEߍiE7ComputeEPNS_15OpKernelContextEEUllEΎ_0_詗Ъ誗_NS_9allocatorIS8_EEFvlEEE0_NS_9allocatorIS8_EEFvlEEE_0_Ȭȭ_NS_9allocatorIS8_EEFvlEEE؍0_NS_9allocatorIS8_EEFvlEEE_0_గ_NS_9allocatorIS8_EEFvlEEEǎ0_NS_9allocatorIS8_EEFvlEEEб_ێ0_豗в貗_NS_9allocatorISE_EEFvfSD_EEE0_NS_9allocatorISE_EEFvfSD_EEE1_NS_9allocatorISE_EEFvfSD_EEE_0_1_fEEՐdEExEEݳiEEʷconcurrency1PlannerImpl0ThreadPool19TryBatchParallelForIÑ5ThreadPoolTemplINS2_3EnvEEZNΒNS_8functionIFvlEEEEEvPS4_lOT_lEUllE_NS_9allocatorISC_EES7_EERNS_8functionIFvlEEEEEvPS4_lOT_lEUllE_NS_9allocatorISD_EES7_EEЬKS2_S2_7contrib20CropAndResizeForwardIfEEvRKNS2_11TensorShapeEPKT_fxxSD_xPSB_RKNS_12basic_stringIcNS_11char_traitsIcEENS_9allocatorIcEEEEPKiPS4_EUllE_EEvSP_lOSB_lEUllE_NSI_ISS_EEFvlEEE2ml6detail18TreeEnsembleCommonI7contribffE10ComputeAggINS7_dfE10ComputeAggINS7_xfE10ComputeAggINS7_24TreeAggregatorClassifierIxfEEEEvPS4_PKNS2_6TensorEPSE_SH_RKT_EUllEifE10ComputeAggINS7_24TreeAggregatorClassifierIifEEEEvPS4_PKNS2_6TensorEPSE_SH_RKT_EUllEѷ2Ɩ17TreeAggregator4TreeAggregatorClassifierIffEEEEvPS4_PKNS2_6TensorEPSE_SH_RKT_EUllEӗ1TreeAggregatorAverageIffEEEEvPS4_PKNS2_6TensorEPSE_SH_RKT_EUllEλ_EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEE2_EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEEconcurrency1ޘPlannerImplݑ0ThreadPool19TryBatchParallelForI5ThreadPoolTemplINS_3EnvEEɿZNNSt3__18functionIFvlEEEEEvPS1_lOT_lEUllE_RNSt3__18functionIFvlEEEEEvPS1_lOT_lEUllE_׬KS_S_7contrib20CropAndResizeForwardIfEEvRKNS_11TensorShapeEPKT_fxxSA_xPS8_RKNSt3__112basic_stringIcNSC_11char_traitsIcEENSC_9allocatorIcEEEEPKiPS1_EUllE_EEvSN_lOS8_lEUllE_2ml6detail18TreeEnsembleCommonI7contribffE10ComputeAggINS4_ԝdfE10ComputeAggINS4_xfE10ComputeAggINS4_24TreeAggregatorClassifierIxfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllEδifE10ComputeAggINS4_24TreeAggregatorClassifierIifEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE217TreeAggregator4TreeAggregatorClassifierIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE1TreeAggregatorAverageIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_2_EEvSA_lOSF_lEUllE_ȺffE10ComputeAggINS4_ҡdfE10ComputeAggINS4_xfE10ComputeAggINS4_24TreeAggregatorClassifierIxfEEEEvPNS2_11concurrency10ThreadPoolEPKNS2_6TensorEPSD_SG_RKT_EUllEifE10ComputeAggINS4_24TreeAggregatorClassifierIifEEEEvPNS2_11concurrency10ThreadPoolEPKNS2_6TensorEPSD_SG_RKT_EUllE217TreeAggregator4TreeAggregatorClassifierIffEEEEvPNS2_11concurrency10ThreadPoolEPKNS2_6TensorEPSD_SG_RKT_EUllE1TreeAggregatorAverageIffEEEEvPNS2_11concurrency10ThreadPoolEPKNS2_6TensorEPSD_SG_RKT_EUllE0_NS_9allocatorISK_EEFvlEEE1_NS_9allocatorISK_EEFvlEEE3_NS_9allocatorISK_EEFvlEEE4_NS_9allocatorISK_EEFvlEEE5_NS_9allocatorISK_EEFvlEEEΩ6_NS_9allocatorISK_EEFvlEEEܩffE10ComputeAggINS1_dfE10ComputeAggINS1_xfE10ComputeAggINS1_24TreeAggregatorClassifierIxfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllEɶifE10ComputeAggINS1_24TreeAggregatorClassifierIifEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE217TreeAggregator4TreeAggregatorClassifierIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1TreeAggregatorAverageIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_1_3_4_ǩ5_թ6_ػؼؽؾؿ—217TreeAggregator4TreeAggregatorClassifierIdfEEEEvPS4_PKNS2_6TensorEPSE_SH_RKT_EUllE1TreeAggregatorAverageIdfEEEEvPS4_PKNS2_6TensorEPSE_SH_RKT_EUllE_EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEE2_EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEE×217TreeAggregator4TreeAggregatorClassifierIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE1TreeAggregatorAverageIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ڭ2_EEvSA_lOSF_lEUllE_×217TreeAggregator4TreeAggregatorClassifierIdfEEEEvPNS2_11concurrency10ThreadPoolEPKNS2_6TensorEPSD_SG_RKT_EUllE1TreeAggregatorAverageIdfEEEEvPNS2_11concurrency10ThreadPoolEPKNS2_6TensorEPSD_SG_RKT_EUllE0_NS_9allocatorISK_EEFvlEEE1_NS_9allocatorISK_EEFvlEEE3_NS_9allocatorISK_EEFvlEEE4_NS_9allocatorISK_EEFvlEEE5_NS_9allocatorISK_EEFvlEEE6_NS_9allocatorISK_EEFvlEEEϳė217TreeAggregator4TreeAggregatorClassifierIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllEܲ1TreeAggregatorAverageIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_1_3_4_5_ȳ6_ֳėŗŗƗƗǗǗȗȗɗɗʗʗ˗_EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEEǴ2_EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEE˗_EEvSA_lOSF_lEUllE_2_EEvSA_lOSF_lEUllE_̗0_NS_9allocatorISK_EEFvlEEE¶1_NS_9allocatorISK_EEFvlEEE3_NS_9allocatorISK_EEFvlEEE4_NS_9allocatorISK_EEFvlEEE5_NS_9allocatorISK_EEFvlEEE6_NS_9allocatorISK_EEFvlEEE̗0_1_3_4_5_6_÷͗͗ΗΗϗϗЗЗїїҗҗӗӗ_EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEE2_EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEEԗ_EEvSA_lOSF_lEUllE_2_EEvSA_lOSF_lEUllE_ԗ0_NS_9allocatorISK_EEFvlEEE1_NS_9allocatorISK_EEFvlEEE3_NS_9allocatorISK_EEFvlEEE4_NS_9allocatorISK_EEFvlEEE5_NS_9allocatorISK_EEFvlEEE6_NS_9allocatorISK_EEFvlEEE՗0_ܺ1_3_4_5_6_՗֗֗חחؗؗٗٗڗڗۗۗfEEǻdEEܗ_EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEE2_EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEEݗ_EEvSA_lOSF_lEUllE_2_EEvSA_lOSF_lEUllE_ݗ0_NS_9allocatorISK_EEFvlEEE1_NS_9allocatorISK_EEFvlEEE3_NS_9allocatorISK_EEFvlEEE4_NS_9allocatorISK_EEFvlEEE5_NS_9allocatorISK_EEFvlEEE6_NS_9allocatorISK_EEFvlEEEޗ0_پ1_3_4_5_6_ޗߗߗSumIffEEEEvPS4_PKNS2_6TensorEPSE_SH_RKT_EUllEM_EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEE2_EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEESumIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllEM_EEvSA_lOSF_lEUllE_2_EEvSA_lOSF_lEUllE_SumIffEEEEvPNS2_11concurrency10ThreadPoolEPKNS2_6TensorEPSD_SG_RKT_EUllEM0_NS_9allocatorISK_EEFvlEEE1_NS_9allocatorISK_EEFvlEEE3_NS_9allocatorISK_EEFvlEEE4_NS_9allocatorISK_EEFvlEEE5_NS_9allocatorISK_EEFvlEEE6_NS_9allocatorISK_EEFvlEEESumIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllEM0_1_3_4_5_6_inIffEEEEvPS4_PKNS2_6TensorEPSE_SH_RKT_EUllEaxIffEEEEvPS4_PKNS2_6TensorEPSE_SH_RKT_EUllE_EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEE2_EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEEinIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllEaxIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_2_EEvSA_lOSF_lEUllE_inIffEEEEvPNS2_11concurrency10ThreadPoolEPKNS2_6TensorEPSD_SG_RKT_EUllEaxIffEEEEvPNS2_11concurrency10ThreadPoolEPKNS2_6TensorEPSD_SG_RKT_EUllE0_NS_9allocatorISK_EEFvlEEE1_NS_9allocatorISK_EEFvlEEE3_NS_9allocatorISK_EEFvlEEE4_NS_9allocatorISK_EEFvlEEE5_NS_9allocatorISK_EEFvlEEE6_NS_9allocatorISK_EEFvlEEEinIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllEaxIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_1_3_4_5_6__EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEE2_EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEE_EEvSA_lOSF_lEUllE_2_EEvSA_lOSF_lEUllE_0_NS_9allocatorISK_EEFvlEEE1_NS_9allocatorISK_EEFvlEEE3_NS_9allocatorISK_EEFvlEEE4_NS_9allocatorISK_EEFvlEEE5_NS_9allocatorISK_EEFvlEEE6_NS_9allocatorISK_EEFvlEEE0_1_3_4_5_6__EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEE2_EEvSD_lOSI_lEUllE_NS_9allocatorISN_EEFvlEEE_EEvSA_lOSF_lEUllE_2_EEvSA_lOSF_lEUllE_0_NS_9allocatorISK_EEFvlEEE1_NS_9allocatorISK_EEFvlEEE3_NS_9allocatorISK_EEFvlEEE4_NS_9allocatorISK_EEFvlE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INS_51kCpuExecutionProvider_Transpose_kOnnxDomain_ver1_12EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_Unsqueeze_kOnnxDomain_ver1_10EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_Where_kOnnxDomain_ver9_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_Where_kOnnxDomain_ver9_stringEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Abs_kOnnxDomain_ver13_uint16_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Abs_kOnnxDomain_ver13_uint32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Abs_kOnnxDomain_ver13_uint64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Abs_kOnnxDomain_ver6_12_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Abs_kOnnxDomain_ver6_12_int8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Add_kOnnxDomain_ver7_12_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_ArgMax_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_ArgMin_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_AveragePool_kOnnxDomain_ver7_9EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Ceil_kOnnxDomain_ver6_12_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_CumSum_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_DepthToSpace_kOnnxDomain_ver13EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Div_kOnnxDomain_ver7_12_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Equal_kOnnxDomain_ver13_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Equal_kOnnxDomain_ver7_10_boolEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Exp_kOnnxDomain_ver6_12_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Expand_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Gemm_kOnnxDomain_ver7_8_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Gemm_kOnnxDomain_ver9_10_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_GlobalMaxPool_kOnnxDomain_ver1EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Less_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Less_kOnnxDomain_ver13_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Less_kOnnxDomain_ver7_8_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Less_kOnnxDomain_ver9_12_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Log_kOnnxDomain_ver6_12_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_MatMul_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Mean_kOnnxDomain_ver8_12_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Mul_kOnnxDomain_ver7_12_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Neg_kOnnxDomain_ver6_12_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Neg_kOnnxDomain_ver6_12_int8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_NonZero_kOnnxDomain_ver13_boolEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_RandomUniform_kOnnxDomain_ver1EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Relu_kOnnxDomain_ver6_12_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Resize_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Round_kOnnxDomain_ver11_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_ScatterND_kOnnxDomain_ver11_12EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_SpaceToDepth_kOnnxDomain_ver13EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Sqrt_kOnnxDomain_ver6_12_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Sub_kOnnxDomain_ver7_12_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Sum_kOnnxDomain_ver8_12_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Tanh_kOnnxDomain_ver6_12_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_TopK_kOnnxDomain_ver11_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_TopK_kOnnxDomain_ver1_9_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Unsqueeze_kOnnxDomain_ver11_12EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Where_kOnnxDomain_ver9_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Where_kOnnxDomain_ver9_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Where_kOnnxDomain_ver9_uint8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Abs_kOnnxDomain_ver6_12_int16_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Abs_kOnnxDomain_ver6_12_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Abs_kOnnxDomain_ver6_12_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Abs_kOnnxDomain_ver6_12_uint8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Add_kOnnxDomain_ver7_12_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Add_kOnnxDomain_ver7_12_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_ArgMax_kOnnxDomain_ver13_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_ArgMin_kOnnxDomain_ver13_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_ConvTranspose_kOnnxDomain_ver11EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_CumSum_kOnnxDomain_ver11_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Div_kOnnxDomain_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_Gemm_kOnnxDomain_ver11_12_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Gemm_kOnnxDomain_ver9_10_doubleEEENS_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_Relu_kOnnxDomain_ver6_12_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_SequenceEmpty_kOnnxDomain_ver11EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_SequenceErase_kOnnxDomain_ver11EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Sigmoid_kOnnxDomain_ver13_floatEEENS_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_53kCpuExecutionProvider_Tanh_kOnnxDomain_ver6_12_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_TopK_kOnnxDomain_ver10_10_floatEEENS_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_Gemm_kOnnxDomain_ver11_12_doubleEEENS_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_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_Sigmoid_kOnnxDomain_ver13_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Softmax_kOnnxDomain_ver13_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_SpaceToDepth_kOnnxDomain_ver1_12EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_TfIdfVectorizer_kOnnxDomain_ver9EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_TopK_kOnnxDomain_ver10_10_doubleEEENS_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_Sigmoid_kOnnxDomain_ver6_12_floatEEENS_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_ver11_12_doubleEEENS_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_56kCpuExecutionProvider_LogSoftmax_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_MatMul_kOnnxDomain_ver9_12_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_MatMul_kOnnxDomain_ver9_12_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_RandomUniformLike_kOnnxDomain_ver1EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_Reciprocal_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ReduceL1_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ReduceL1_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ReduceL2_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ReduceL2_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelC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untime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver13_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_Resize_kOnnxDomain_ver10_10_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_Resize_kOnnxDomain_ver10_10_uint8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_Resize_kOnnxDomain_ver11_12_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_Resize_kOnnxDomain_ver11_12_uint8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_SequenceConstruct_kOnnxDomain_ver11EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_Softmax_kOnnxDomain_ver11_12_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_Upsample_kOnnxDomain_ver7_8_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_Upsample_kOnnxDomain_ver7_8_uint8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_Upsample_kOnnxDomain_ver9_9_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_Upsample_kOnnxDomain_ver9_9_uint8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ConcatFromSequence_kOnnxDomain_ver11EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_Expand_kOnnxDomain_ver8_12_MLFloat16EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_LogSoftmax_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_Reciprocal_kOnnxDomain_ver6_12_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceL1_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceL2_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceLogSum_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver11_11_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver12_12_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver1_10_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceMean_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceMean_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver11_11_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver12_12_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver1_10_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceProd_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceProd_kOnnxDomain_ver13_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceProd_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver11_12_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver1_10_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ScatterElements_kOnnxDomain_ver11_12EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_Dropout_kOnnxDomain_ver13_float_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_LogSoftmax_kOnnxDomain_ver11_12_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_LogSoftmax_kOnnxDomain_ver1_10_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_Reciprocal_kOnnxDomain_ver6_12_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceL1_kOnnxDomain_ver11_12_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceL2_kOnnxDomain_ver11_12_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver11_11_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver12_12_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver12_12_int8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver1_10_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMean_kOnnxDomain_ver11_12_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMean_kOnnxDomain_ver1_10_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver11_11_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver12_12_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver12_12_int8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver1_10_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceProd_kOnnxDomain_ver11_12_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver11_12_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver1_10_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_Dropout_kOnnxDomain_ver13_double_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_Dropout_kOnnxDomain_ver13_float_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_InstanceNormalization_kOnnxDomain_ver6EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_LogSoftmax_kOnnxDomain_ver11_12_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_LpNormalization_kOnnxDomain_ver1_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_NonMaxSuppression_kOnnxDomain_ver10_10EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceLogSum_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceLogSum_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver11_11_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver11_11_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver12_12_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver12_12_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver12_12_uint8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceMean_kOnnxDomain_ver11_12_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceMean_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver11_11_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver11_11_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver12_12_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver12_12_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver12_12_uint8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceProd_kOn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onnx11GetOpSchemaINS_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_15Relu_Onnx_ver14EEENS_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_17CumSum_Onnx_ver14EEENS_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__ZN4onnx11GetOpSchemaINS_20Reciprocal_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20Reciprocal_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceMax_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceMax_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceMax_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceMean_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceMin_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceMin_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceMin_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceProd_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceSum_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceSum_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20Transpose_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21AveragePool_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21AveragePool_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21Binarizer_OnnxML_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21HardSigmoid_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21HardSigmoid_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21LogSoftmax_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21LogSoftmax_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21Multinomial_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21Reciprocal_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21ReduceMean_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21ReduceMean_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21ReduceProd_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21ReduceProd_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21SequenceAt_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_22AveragePool_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_22AveragePool_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_22ConvInteger_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_22DepthToSpace_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_22GlobalLpPool_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_22GlobalLpPool_Onnx_ver2EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_22LessOrEqual_Onnx_ver12EEEN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BuildKernelCreateInfoINS_50kCpuExecutionProvider_Sum_kOnnxDomain_ver13_doubleEEENS_16KernelCreateInfoEvEN4$_908__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Max_kOnnxDomain_ver6_7_floatEEENS_16KernelCreateInfoEvEN4$_918__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_45kCpuExecutionProvider_Max_kOnnxDomain_ver8_11EEENS_16KernelCreateInfoEvEN4$_928__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_46kCpuExecutionProvider_Max_kOnnxDomain_ver12_12EEENS_16KernelCreateInfoEvEN4$_938__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_43kCpuExecutionProvider_Max_kOnnxDomain_ver13EEENS_16KernelCreateInfoEvEN4$_948__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Min_kOnnxDomain_ver6_7_floatEEENS_16KernelCreateInfoEvEN4$_958__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_45kCpuExecutionProvider_Min_kOnnxDomain_ver8_11EEENS_16KernelCreateInfoEvEN4$_968__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_46kCpuExecutionProvider_Min_kOnnxDomain_ver12_12EEENS_16KernelCreateInfoEvEN4$_978__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_43kCpuExecutionProvider_Min_kOnnxDomain_ver13EEENS_16KernelCreateInfoEvEN4$_988__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_Less_kOnnxDomain_ver7_8_floatEEENS_16KernelCreateInfoEvEN4$_998__invokeERKNS_12OpKernelInfoE__ZNK11onnxruntime4LessIfE7ComputeEPNS_15OpKernelContextE__ZZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Less_kOnnxDomain_ver7_8_doubleEEENS_16KernelCreateInfoEvEN5$_1008__invokeERKNS_12OpKernelInfoE__ZNK11onnxruntime4LessIdE7ComputeEPNS_15OpKernelContextE__ZZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Less_kOnnxDomain_ver9_12_floatEEENS_16KernelCreateInfoEvEN5$_1018__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Less_kOnnxDomain_ver9_12_doubleEEENS_16KernelCreateInfoEvEN5$_1028__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Less_kOnnxDomain_ver9_12_int32_tEEENS_16KernelCreateInfoEvEN5$_1038__invokeERKNS_12OpKernelInfoE__ZNK11onnxruntime4LessIiE7ComputeEPNS_15OpKernelContextE__ZZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Less_kOnnxDomain_ver9_12_int64_tEEENS_16KernelCreateInfoEvEN5$_1048__invokeERKNS_12OpKernelInfoE__ZNK11onnxruntime4LessIxE7ComputeEPNS_15OpKernelContextE__ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Less_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEvEN5$_1058__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_Less_kOnnxDomain_ver13_doubleEEENS_16KernelCreateInfoEvEN5$_1068__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Less_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEvEN5$_1078__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Less_kOnnxDomain_ver13_int64_tEEENS_16KernelCreateInfoEvEN5$_1088__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Greater_kOnnxDomain_ver7_8_floatEEENS_16KernelCreateInfoEvEN5$_1098__invokeERKNS_12OpKernelInfoE__ZNK11onnxruntime7GreaterIfE7ComputeEPNS_15OpKernelContextE__ZZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Greater_kOnnxDomain_ver7_8_doubleEEENS_16KernelCreateInfoEvEN5$_1108__invokeERKNS_12OpKernelInfoE__ZNK11onnxruntime7GreaterIdE7ComputeEPNS_15OpKernelContextE__ZZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Greater_kOnnxDomain_ver9_12_floatEEENS_16KernelCreateInfoEvEN5$_1118__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_Greater_kOnnxDomain_ver9_12_doubleEEENS_16KernelCreateInfoEvEN5$_1128__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_Greater_kOnnxDomain_ver9_12_int32_tEEENS_16KernelCreateInfoEvEN5$_1138__invokeERKNS_12OpKernelInfoE__ZNK11onnxruntime7GreaterIiE7ComputeEPNS_15OpKernelContextE__ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_Greater_kOnnxDomain_ver9_12_int64_tEEENS_16KernelCreateInfoEvEN5$_1148__invokeERKNS_12OpKernelInfoE__ZNK11onnxruntime7GreaterIxE7ComputeEPNS_15OpKernelContextE__ZZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Greater_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEvEN5$_1158__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Greater_kOnnxDomain_ver13_doubleEEENS_16KernelCreateInfoEvEN5$_1168__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Greater_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEvEN5$_1178__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime2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