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8HxHGH;GMȈHMHHEHEHGE11Hm4HH;EEȈEH}HMHMEHEH 1@uHAHUHQHELHHC8HK@HHHHHHK@sFEȈEH}HMHMEHEHKP1@uHAHUHQHEH[P#E11HuIH@H@HXHAuLHu3HH;EuDHHH[A^A_]4⻂H 2HHH51HLH HuLGHո ffff.UHAVSIHHLQHAFCH{IvHFRHIF HC [A^]fffff.UHSPHHQHFCH{H趸HQHH[]fUHSPHHPHFCH{HvH QHH[]fUHSPHHPHFCH{H6H QHH[]fUHSPHH@PHFCH{HHOHH[]fUHSPHHPHH{طHHH[]fffff.UHAWAVSHhIIHV1HHEIwHH]H脹Hv1H豷HHHMHHPHUHMH@H@HH5AH}UHHHMHHPHUHMH@H@HIwH}Et HuHU HuH}HHINHHPIVIH@H@HEu0Eu9EuBEuKHC0HH;EuTLHh[A^A_]H}衸EtH}蒸EtH}胸EtH}tH/HH;Et迸HEuEu0EuDEuXH脵 H}1Et HEtH}Et HEtH}Et HEtH}㷂H# ff.UHAWAVATSH0H:/HHEIID$I9D$tCH@8ugLpI^I;^HwH WHcHHC]LuHwHJHc HHED>D}LuIwHJc HHE$LH@H@HHEUѶH@H@HHE觶HHH@H@HHEEI$0@uHHHUHPHML I$H -H H;MH0[A\A^A_]ÿ2H@H@H HHH@H@HHEEID$ 0D8@uHHHUHPHMLj ID$ H .-H H;Mb蜵HHH@H@HHCCIFID$H@H@H@HH ,H H;M렐.4oo[...YUHAWAVAUATSHIIIuMeI)MIIGHH=HMuI)LHHH9waII9LBMLEHuI9vKjHH5HбH+HHH5M*H^)HjLEHuIJ<HHuLEN4IJN|M~HLI\LI]M}MuHtHH[A\A]A^A_]鬳H[A\A]A^A_]E11LIH賳Lð ff.UHAWAVAUATSHL7LgMM)IIEHH<HLM)LHHH9H}w\II9LBMHI9vFIH5LhH3*HIH5(H'LHILH讲HI6B4(JT(HwH 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HHEHHCH9Ct0H@8usHxHGH;GH@HGEH}HEH0@uHHHUHPHMHH H H;MuNH([]EH}HEHC 0@uHHHUHPHMHC H rH H;MtB/HCH@H@H@HH DH H;MtfUHAWAVAUATSPHL3L{MM)IIEHH<HLcM)LHHH9wYII9LBMI9vCIHH5H诤HzHHH5,H=HIILHIJ (IB(IJD(J|(LL)HHt9Hfff.AGAIGHAAGIGMHHuH0fff.AGAIGHAAGIGAGAIGHAAGIGAGЈAIGHAAGIGAGAIGHAAGHIIGMM9{L;LsH H{LcM9t+LL)If.AvLSIHuMtLH[A\A]A^A_]|H[A\A]A^A_]E11wHIH耥L萢 UHSH(HHHEHHCH9Ct7H@8uyHxHGH;GH@HHHHGHEH}HEH0@uHHHUHPHMdHH *H H;MuMH([]HEH}HEHC 0@uHHHUHPHMHC H H H;Mt譤*HCH@H@H@HH H H;Mt@UHAWAVAUATSPL7LMM)IID$HH<*HLoM)LHHH9w`II9LBMH}HI9vJ责HH5IHHHHH5HH责H}HILH\HH}IJ IIHB JT J\ LL)HHt7Hf.AGAIGHAAGIGMHHuH0fff.AGAIGHAAGIGAGAIGHAAGIGAGЈAIGHAAGIGAGAIGHAAGHIIGMM9{L?LwHH_LoM9t+LL)If.AvLIHuMtLH[A\A]A^A_]ܡH[A\A]A^A_]E11z$IH㡂L ff.UHAWAVAUATSH(HHHEIHHCH9CtRH@8LxMoM;o IEAEEILLWMeIGHEEILL%H}L}H0@uHHHUHPHMHH GH H;MurH([A\A]A^A_]HEE裠ILL赞H}L}HC 0@uHHHUHPHMHC H H H;Mt襠LL\HCH@H@H@HH H H;MVHLHE HLH. fDUHAWAVAUATSPIIM~I)IIGHH<HMnI)LHHH9HuwYII9LBMI9vCxHH5 HޜHHHH5[HlHxILH'HIJD;B;ILHuJ;IIJL;L!HHM>IVL9HrL)Ht,HߐZXHZHXBHBHRHHuH0r{fZXHrHpBHBZXHrHpBHBZЈXHrHpBHBZXHrHpBH@HBHRI9uM>MfMIINMnM9t#LL)IAt$LIHuMtLH[A\A]A^A_]髝H[A\A]A^A_]E11zLIH话L迚 ILjHu IHtHSL蓚 ff.UHSH(HHHEHHCH9Ct7H@8uyHxHGH;GH@HHHHGHEH}HEH0@uHHHUHPHMdHH *H H;MuMH([]HEH}HEHC 0@uHHHUHPHMHC H H H;Mt譜*HCH@H@H@HH H H;Mt@UHAWAVAUATSPL7LMM)IID$HH<*HLoM)LHHH9w`II9LBMH}HI9vJ贛HH5IHHHHH5HH贛H}HILH\HH}IJ IIHB JT J\ LL)HHt7Hf.AGAIGHAAGIGMHHuH0fff.AGAIGHAAGIGAGAIGHAAGIGAGЈAIGHAAGIGAGAIGHAAGHIIGMM9{L?LwHH_LoM9t+LL)If.AvLIHuMtLH[A\A]A^A_]ܙH[A\A]A^A_]E11z$IH㙂L ff.UHAWAVSHH HHEIHIIFII90dH@H@H c0HHH 0HHHHX HLDLx(HuHEH}1H(EW)MI^AHt#HHCHuHHPHЗH]Ht#HHCHuHHPH褗HHH;EuVH[A^A_]ÿ0臘H@H@H /HHHH /HHH@(H@ 蕘Hu Hk UH]f.UH]fDUHSPHHH[]◂@UHHGH]`UH]ėfDUHHGH;Gs HHHO]ø]fffff.UH]f.UH]tfDUHAWAVSH(HHHEHH;uI<t<uHGH;FHGH;FHGH;FH H H;Mu H([A^A_]b IW)EHE@薂HE(Ӗ)EHntainersHH(Herent coHH H of diffHHHteratorsHHHompare iHHHcannot cH@0HUL1H5|,H%L虖 I$IEuuLw H}$tLHLX UHAWAVSH(H| HHEH8u(HGH b H H;MuH H([A^A_]# ѕIW)EHE0評HE()EHiteratorHH H-object HHH for nonHHHse key()HHHcannot uHf@(sHULr1H5I+HLf IIEt H}tL#L3 ff.UHAWAVSH(HL HHEHH 觔W)EE HEHet valueHMHHcannot gHEEAHUHE1H5]*HHzHGH H H;Mt#HGH8H H H;Mu H([A^A_]I W)EE HEHet valueHMHHcannot gHEEAHUHE1H5)HVHʓ IEu IEt H}]Eu L蘐 HvL膐 fffff.UHAWAVAUATSH8H HHEH<wIH HcHC C,CHZ HH;EuH8[A\A]A^A_] ɒIHHw H o+L<L= ԂW)EHELHHrH}ُHsELeHu(4IIILCILeILmH]LLH蜓AHՂH}1HHHMHHPHUHMH@H@HHU.L1H5(HpL IdzEt1I9IEuEuu&L迎 H}lEtH}]tL聑L葎 ZUU`UHAWAVSH8HHHEH}HuȉULHMHEH}Hu13HH}`RIH]HEHtCt H{軐H賐H.HH;EuLH8[A^A_]IIH]HEHtCt H{jHbAu(LxHHQ HRI`Au0LJHHQHRI2PILP ׏DUHAWAVSH8HLHHEW)E}HHuHEH}Hu11HH}RIH]HEHtCt H{lHdHHH;EuLH8[A^A_]衏IIH]HEHtCt H{HAu(L)HHQ HRIAu0LHHQHRI㎂PILfUHAWAVSH8H HHEW)E}HHuHEH}Hu0HH}RIH]HEHtCt H{.H&HHH;EuLH8[A^A_]cIIH]HEHtCt H{ݍHՍAu(L덂HHQ HRIӍAu0L轍HHQHURI襍PIL{ff.UHAWAVSH8HHHEH}HuȉULHMHEH}HuLK/HH}RIH]HEHtCt H{ꌂH⌂H]HH;EuLH8[A^A_]IIH]HEHtCt H{虌H葌Au(L觌HHQ H?RI菌Au0LyHHQHRIaPIL7UHSPueHHHHQ1HH[]Ãu(H HHQ HRHɃu(H܋HHQHtRHċHHf.UHSPeHHHHQ1HH[]Ãu(HiHHQ 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inH@(HMH]HQTEH}AA)A*A*^f: L,E1MMI/qHEHHHML(HMHU1L蔪DHMEIw1&HL!fnpfpfAnpfpfefBfnpfpHyHHVH0s 1HuzHzH)1fffff.pfDpfDp fDp0fDp@fDpPfDp`fDppH@fHuHt,HtpHf.FffH HuI9t#DHHDfHDHL9rH]HH}Ht H}qH BHH;EuHH8[A\A]A^A_]qHEtH}HH}Ht H}DqHq fff.UHAVSHPIHAHHE(qHþ H qHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H}11eCH@H5qHH+CH5rH,CH5uH]CHH HQH5t rH}CHH;BCHHH 1HMHHUHuHuIIFIF0INIVIF@H]HEHtHvEHqH7?HH;Eu LHP[A^]qIH]HEHtH1EHwqLq ffffff.UHAWAVSHxIH>HHE(BqHþ H8qHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H}11CI@HW)EHEqHEHHHMHHM@HW)pHE_qHpHHHMHHxH5IqHUHpLCH5rHCH5kuHCHH HQH5( rH1CHH;CHHH HMH:HUHuHuIIGIG0IOIWIG@HpHt HxyqH}Ht H}gqH]HEHtHEHFqH;HH;EuLHx[A^A_]qIH}Ht0^IH]HEHt+bIHpHu$H}Hu0H]HEHu9Lq HxqH}HtH}qH]HEHtHEEHqLq UHAWAVAUATSHHPH:HHEп(WqHþ HMqHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H}11CHH@HX@I@I@H@I0@IW)EHE0~qHEHH0HMHXHLhLpHXL` Lx(HM^@HXR@I@IB@Iz@I@HW)`HDžp0qH`HH0HpHXHLhL`LpLx HX(HhH5qHUH`HHHCH5GrHYCH5uHCHH HQH5rHCHH;oCHHH ^HMHHUHuHuHPHHCHC0HKHSHC@H`Ht HhqH}Ht H}ٿqLuHEMtLrEL踿qH37HH;EuHHĘ[A\A]A^A_]qrIH`Hu$H}Hu>H]HEHu]L裼q HhMqH}Ht IH}HtH}-qH]HEHtIH]HEHtHEHqL6q fffff.UHAWAVAUATSHHPH>6HHEп(跾qHþ H譾qHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H}11CHH<@HX0@Ih@I @HX@I@IW)EHE0޼qHEHH0HMHXHLhLpHXL` Lx(HM@HX@I@I@I@I@HW)`HDžp0ZqH`HH0HpHXHLhL`LpLx HX(HhH5&qHUH`HHCH5rH蹻CH5DuHCHA H5rHCHH;ֲCHHH HMH HUHuHuHPHHCHC0HKHSHC@H`Ht HhRqH}Ht H}@qLuHEMtLELqH2HH;EuHHĘ[A\A]A^A_]UqrIH`Hu$H}Hu>H]HEHu]L q Hh贺qH}Ht IH}HtH}蔺qH]HEHtIH]HEHtHEH]qL蝷q DUHAWAVSH8HIH1HHE1HUEIL}ؾHAEHEоH0EHEIW1H~DHEIG0@EH}HuHUHMLEg IH91HH;EuLH8[A^A_]qffff.UHHH "HHxH@Ht]fq]fDUHSPHHHH{HCHt5qHH[]'qfUHAVSIqHH{LMHHHHLTDH孉HH[A^]IHŸqLq ffff.UHAWAVAUATSHH0HHEW)EEIIHEEminMgHhHULDHhHDžhHtCt H{/qH'qA$Et H}qW)EHEEEmaxHhHULL肰DHhHDžhHtCt H{躷qH買qAEt H}蜷qAA.Gr'H /HH;EuHĈ[A\A]A^A_]ɷqwqIHh"eUH5rH IrH}LhHPH HAHrHuLHG E1H5=PHL:q IALhMIIPt H`诶qLeMt6HEL9t!HX@t Hx芶qHI9uH}LLepqEuEuLhMu&^H}MqEtH}>qLhMt:HpL9tEHX@t HxqHI9uHhLpqEt4LqL+q LIEu IEt H}迵qLq UHHH BHHxH@Ht]醵q]fDUHSPHHHH{HCHtUqHH[]GqfUHAWAVAUATSPIIH}1LNEHLk1LL蚓DIHoIL7HEl$Ed$LRHI~7HHuڃH9HOII)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.UHAVSIvqHHHH{LÊDHHH[A^]IH4qLtq fff.UHAVSIqHHHH{LcDHHH[A^]IH԰qLq fff.UHAVSI趰qHHBHH{LDH<HH[A^]IHtqL购q fff.UHAWAVATSHPMIIHH'HHE؋EHEtm  uH3I$IMH}V$HEH}5 HR'HH;EHP[A\A^A_]H3I$IMH}HEЋEȃ vH3I$IMH}HEЋEȃYH3I$IMH}HEЋEȃLHH(0(@,HrI H9s!H I9sE1HzLII((IIHHDGAHmIHH)1ffff.A(_(]AT(_(]THHuMtA_] L9tfHzLt(C_(]B LHL9t8(A_(] (A_D(]LHH9uH<I|/((HA(_(])HH9|H9>I)ILI I9sLI L9MI((IHIIEOAHHM\HtIIL)1AT(_(]TA(_(]HHuMtHA_] M9tsLlj)HPH@t(A_(] HOH9tAfff.(A_(] (A_D(]LHH9uH"HH;Eu2HĨ[A\A]A^A_]1M1M9F"qЪqIHh{XUH5rH [rH}Lh\W)PHDž` sqH`({#)PH scalar.HHHld be a HHHmin shouH@AHrHuHPLJE1H5@CHL=qqIHhWUH5GrH rH}Lh` W)PHDž` 蛩qH`(")PH scalar.HHHld be a HHHmax shouH@AHҩrHuHPLrE1H5hBHLeq IALeMIALhMIIALeMFIALhMnIIPt H`臨qLeMt6HEL9t!HX@t HxbqHI9uH}LLeHqEuEu LhMu-'H}"qEtH}qLhMHpL9HX@t HxqHI9uIPt H`軧qLeMt6HEL9t!HX@t Hx薧qHI9uH}LLe|qEuEuLhMu&^H}YqEtH}JqLhMt:HpL9tEHX@t HxqHI9uHhLpqEtL'qL7q Lf.UHAWAVAUATSHMIHH<HHEHtpHBHJ H)HtHH8HM)0tGIFIN H)HtHH8L֠M)0ud@f)@H褠IHH(HLݻHIL(HHuH9HOII)LH?LHHf(0f(@HrI H9s!H I9sE1HzLIIIHHHDOAHIIH)1fDfAfA\f(f_f(f_f(f]f(f]ffdfAT fA\0f(f_f(f_f(f]f(f]f\ fd0HHuMt0fAfA\f(f_f_f(f]f]ffDL9tsHzLtf(C_f(]B LHL9tCf.f(A_f(] f(A_Df(]LHH9uH<I|0DAf(f_f(f]f)HH9|H9 I)IH IH9s H IH9rqMIITHtJ<1fffff.fTff(f_f(f_f(f]f(f]f\f$HI9uM9tu)HPH@tf(A_f(] HOH9tDff.f(A_f(] f(A_Df(]LHH9uH@HH;Eu"HĨ[A\A]A^A_]1Mq蜢qIHhGPUH5rH rH}Lh\W)PHDž` ?qH`(G)PH scalar.HHHld be a HHHmin shouH@AH^rHuHPL E1H5 ;HL qġqIHhoOUH5rH 6rH}Lh`W)PHDž` gqH`(o)PH scalar.HHHld be a HHHmax shouH@AHrHuHPL> E1H54:HL1q IALeMIALhMIIALeMFIALhMnIIPt H`SqLeMt6HEL9t!HX@t Hx.qHI9uH}LLeqEuEu LhMu-'H}qEtH}ߟqLhMHpL9HX@t Hx謟qHI9uIPt H`臟qLeMt6HEL9t!HX@t HxbqHI9uH}LLeHqEuEuLhMu&^H}%qEtH}qLhMt:HpL9tEHX@t HxqHI9uHhLpОqEtLqLq LfDUHAWAVAUATSHLIIH HHEHtgHBHJ H)HtHH8HD Mt:IFIN H)HtHJH8@LnD8 AMuALVIIL HHoIHH HHH rI I9LHI9E1HpL¨L9f.ADD8|DA8~A\A\DD8|$DA8"A\HH9uUDA8~މA\HH9u1IIAfnff8Afnf8IHHHrHH~H)1ff.AoAo\f8H84L>D8 E1MuAL&IILHH?IHHHHH rI I9LHI9E1HpL¨xL9f.ADD8rDA8rA\A\DD8r$DA8s"A\HH9uIDA8rމA\HH9u%IIAfnff8Afnf8IHHHrHH~H)1ff.AoAo\ffffATA\AoT Ao\0ffffAT A\0H@HuHt+AoAo\ffffATA\L9tzCH5ɧuHo{CHH HQH5qH|CHH;TqCHHH CHMHjHUHuHuIIGIG0IOIWIG@H}Ht H}yqH]HEHtHvEHyqH7HH;EuLH`[A\A^A_]yqIH]HEHt7H)EHoyqLvq IH}HuH]HEHuLvq H}5yqH]HEHt@UHAWAVATSH`IHHHEؿ(yqHþ HxqHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]٧@H5qH}H&CI辡@I趧@HW)EHEDwqHEHHHML HXHMH5>sHUL={CH5~{rHvCH5YuHwCHH HQH5qHyCHH;mCHHH ӮHMHhHUHuHuIIGIG0IOIWIG@H}Ht H}mvqH]HEHtHEHLvqHHH;EuLH`[A\A^A_]vqIH]HEHt7H蹏EHuqL?sq IH}HuH]HEHuLsq H}uqH]HEHt@UHAWAVATSH`IHHHEؿ(uqHþ HuqHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H])@H5qH}H|CIN@IF@HW)EHEsqHEHHHML HXHMH5ΣsHULwCH5xrH^sCH5uHtCHAH5qHuCHH;{jCHHH jHMHHUHuHuIIGIG0IOIWIG@H}Ht H}sqH]HEHtH蝌EHrqH^HH;EuLH`[A\A^A_]sqIH]HEHt7HPEHrqLoq IH}HuH]HEHuLoq H}\rqH]HEHtff.UHAWAVATSH`IHHHEؿ( rqHþ HrqHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]蹡@H5}qH}HFyCIޚ@I֠@HW)EHEdpqHEHHHML HXHMH5^sHUL]tCH5trHoCH5yuHqCHAH5=qHFrCHH; gCHHH HMHHUHuHuIIGIG0IOIWIG@H}Ht H}oqH]HEHtH-EHsoqHHH;EuLH`[A\A^A_]oqIH]HEHt7HEH&oqLflq IH}HuH]HEHuL?lq H}nqH]HEHtff.UHAWAVATSH`IH7HHEؿ(nqHþ HnqHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]艗@H59zqH}HuCIn@If@HW)EHElqHEHHHML HXHMH5sHULpCH5.qrH~lCH5 uHmCHAH5ͰqHnCHH;cCHHH HMHHUHuHuIIGIG0IOIWIG@H}Ht H}$lqH]HEHtH轅EHlqH~HH;EuLH`[A\A^A_]>lqIH]HEHt7HpEHkqLhq IH}HuH]HEHuLhq H}|kqH]HEHtff.UHAWAVATSH`IHHHEؿ(@kqHþ H6kqHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]@H5vqH}HfrCI@I@HW)EHEiqHEHHHML HXHMH5~sHUL}mCH5mrHiCH5uH?jCHAH5]qHfkCHH;+`CHHH HMH_HUHuHuIIGIG0IOIWIG@H}Ht H}hqH]HEHtHMEHhqHHH;EuLH`[A\A^A_]hqIH]HEHt7HEHFhqLeq IH}HuH]HEHuL_eq H} hqH]HEHtff.UHAVSI gqHHLH[A^]IHgqLdq UHAWAVATSH@HIH߈HHEL%&]M&I~@DHS]IIFIFHEW)EHEEHexclusivHEfEeH}HUHMH`DEt H} gqH}uHEHwINHW)EHEEHEHreverseHEH}HUHMH`DHEHEH]HEHCt H{fqHfqH]HEHtCt H{cfqH[fqEt H}LfqH]HtLHECt H{,fqH$fqH݈HH;EuCH@[A\A^A_]HEEuHEHwIFHEH\݈HH;Et,fqIEt H}eqH]HEHt&Ct H{eqH IEt H}eqM&I~IFHtjeqLbq fUHHH ZHHxH@Ht]6eq]fDUHSPHHZHH{HCHteqHH[]dqfUHAWAVAUATSHIH`IHH܈HHE1LEHHC H+CHp L§EIW)EHEH{ HH+{t[HHHH9 ZdqILeLeIHEHHpHX H)H~LHeqILeHU1LBDHH}GHHpHHEHHMLHHHw HEHLcHH@JHW)PHDž`HHx H+xHHHH9 RcqILXLPIH`HHpHX H)H~LHdqLHHXKHPGHxW)0HDž@LPHXL)t\IIHI9' HbqIL8L0KDH@H~LLHcqIL8W)HDž HH9LpL-bqHHHH H5HLcqHH`HxLXHHYHxHtHHxH$L0LHHHH HHHHW)HDžLpLVaqHHLN4LHL5aqLLHJW)HDžL`qILKHLL`qHHKHxH$HHL0LHH Ly`qHHt Ha`qH}Lpfffff.H`HXW)HDžL"`qIHLxHHIDLHM,LLL_qLLLKW)HDžL_qILHIHLL_qHLhO<W)HDžLZ_qHHHLu^H"HHH!H Hy9HH9w2HHy1I)r17DHHH!HxHHy-1Is!E1H L`HHJ\IHH MtHfHELMLH tI DALMsMGxDMLMfffff.DHH L`HNfLMjDALMHHBH HNH+HHLHN(H;u)HLHDH HA(HIH HA0H~HH;EuH[A\A]A^A_]qHI9uI8t HH>qLmMt6HEL9t!HX@t Hx>qHI9uH}LLm>qEuhu LPMu,dH}b>qhtHxM>qLPMt:HXL9t?HX@t Hx">qHI9uHPLX>qELLI8t HH=qLmMt6HEL9t!HX@t Hx=qHI9uH}LLm=qEuhu LPMu,dH}t=qhtHx_=qLPMt:HXL9tEHX@t Hx4=qHI9uHPLX=qEtL<=qLL:q Lffffff.UHAWAVAUATSH8LLHHIHH3HHEH8HnHHHLH@H8LqHHH8HH0H:qHHH<>qHHHHHH>qHHHڮHH0H$:qHHH莴LL8Lt H ";qH:qH5HH9qH8:qHnHH;EuHH8[A\A]A^A_]);qHLL8Lt H :qH|9q+H蘪H萪H航H耪HxHH5fHH8qH89qHJHH89qH3UHAVSI :qHHLH[A^]IH9qL)7q UHAWAVATSH@HIHDHHEL%V/M&I~DH/IIFIFHEW)EHEEHexclusivHEfEeH}HUHMH-3DEt H}<9qH}uHEHwINHW)EHEEHEHreverseHEH}HUHMH2DHEHEH]HEHCt H{8qH8qH]HEHtCt H{8qH8qEt H}|8qH]HtLHECt H{\8qHT8qHϯHH;EuCH@[A\A^A_]HEEuHEHwIFHEHHH;Et\8qIEt H}7qH]HEHt&Ct H{7qH IEt H}7qM&I~IFHt7qL4q fUHHH "-HHxH@Ht]f7q]fDUHSPHH,HH{HCHt57qHH[]'7qfUHAWAVAUATSHIH`IHxHHE1LzEHHC H+CHp LyEIW)EHEH{ HH+{t[HHHH9 6qILeLeIHEHHpHX H)H~LH7qILeHU1LDHH}GHHpHHEHHMLHHHw HEHLcHH@JHW)PHDž`HHx H+xHHHH9 5qILXLPIH`HHpHX H)H~LH6qLHHXKHPGHxW)0HDž@LPHXL)t\IIHI9' H4qIL8L0KDH@H~LLH6qIL8W)HDž HH9LpL]4qHHHH H5HL5qHH`HxLXHHYHxHtHHxH$L0LHHHH HHHHW)HDžLpL3qHHLN4LHLe3qLLHJW)HDžL)3qILKHLL3qHHKHxH$HHL0LHH L2qHHt H2qH}Lpfffff.H`HXW)HDžLR2qIHLxHHIDLHM,LLL2qLLLKW)HDžL1qILHIHLL1qHLhO<W)HDžL1qHHHLq IHEH8HuHEH8HuLq H{XqHEH8HtH{@qLq fUHAWAVAUATSHH又HHEMIHFHH+HHHI)IL9HOH HWH)HH9M)IL9HLAHIffff.HJ\Mt HyeMI HH Mt"HDffffff.HELMwLH t I DHLsfH L`HYHHHLHH tIHH=u^H#HHH!H Hy9HH9w2HHy1I)r17DHHH!HxHHy]1IsQE1H L`HHJ\IHH MtHfHELMLH tI DALMsѓMGxDMLMfffff.DHH L`H~fLMjDALMHHBH HNH+HHLHN(H;u)HLHDH HA(HIH HA0HHH;EuH[A\A]A^A_]lqqIHPTH5'^sH rHhLP?{HHAH+HH0L(HrHEHhqHEHrHEH8HuH0HMLEL(HuAH6rHhLH}E1H5H(VL|q7qIHPTH5D]sH rHhLP\zHHAH+HH0H HAH+AHH(HrHEH gqHEHrHEH8HuH0HMLEL(HAHyrHhLH|E1H5H2ULqAqIHPTH5N\sH rHhLPfyHHAH+HH0L(HrHEH)fqHEHrHEH8HuH0HMLEL(HAHrHhLH{E1H5HOTLq IALPMILIALPMILIALPMnII8t HHqLmMt6HEL9t!HX@t HxqHI9uH}LLmqEuhu#LPMu36H}dqhtHxOqLPMHXL9 HX@t HxqHI9uI8t HHqLmMt6HEL9t!HX@t HxqHI9uH}LLmqEuhu LPMu,dH}qhtHx}qLPMt:HXL9t?HX@t 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HxOnHI9uH}LLmOnEuEuL`Mu&^H}OnEtH}OnL`Mt:HhL9tIHX@t HxkOnHI9uH`LhPOnELoOnLLn LIt HOnL}Mt6HEL9t!HX@t HxNnHI9uH}LL}NnEuEuLMu&~H}NnEtH}NnLMtZHL9wHX@t HxxNnHI9uIAL}MuRIALME=IIt HNnL}Mt6HEL9t!HX@t HxMnHI9uH}LL}MnEELMoHL9HX@t HxMnHI9uHLrMnEnLMnHH HH}IH}Ht#0IE1H}Ht H}H+@HH   IEHIEHHtH@MIE0H$HH@H LHHHt#HHCHuHHPHm8nHIH`IHHHL`HP0HeHHDžHtlH螖DH8nH Hu\fIH`IHHHL`HHP0H<H Ht H(8nH@HtI>l8nH}Ht H}Z8nHկHH;EuH8[A\A]A^A_]8nA8nIHQH5CUoH \oH`L.fHsYoHEHHuHL*AH.YoH`LHE1H5ЅHyL7n 7nIH@UQH5ToH a\oH`L@ϝW) HDž0PJ7nHH0() H5HL9Lh@t Hx 4nLM9uHL3nEu7LHDžMu?LHDžML3nLHDžMtAFt I~3nL{3nLHDžM9LYHt H;3nLeMt6HEL9t!Lh@t Hx3nLM9uH}LLe2nEu`u LMu,dH}2n`tHp2nLMt:HL9toLh@t Hx2nLM9uHL{2nEtL2nLHDžM1AFI~?2nLHALeMuFzHAL@MHH t H01nLeMt6HEL9t!Lh@t Hx1nLM9uH}LLe1nEu`u L@Mu,dH}1n`tHpo1nL@Mt:HHL9tQLh@t HxD1nLM9uH@LH)1nE.LH1nH}H"&LHALMHHt H0nLeMt6HEL9t!Lh@t Hx0nLM9uH}LLe0nEu`u LMu,dH}a0n`tHpL0nLMt:HL9tTLh@t Hx!0nLM9uHL0nEL%0nH HLHLHDžMLWDL/nH HHLMtgHIFHuUILPLj.nH HuDN1/HH}Hu_fHH@Hu7AHH}Hu>EHH Ht H( /nH@Ht HH.nH}Ht H}.nH ,n UHAWAVAUATSHMMHHIH"HHEHHzHdDIHHRDI9LLIH+DHL DH9HL8LpM)LHM,$LID$L)HHH9TW)`HDžp@-nILH@L`LhHpW)@HDžP@-nHHH@H@HHHPW) HDž0@g-nHUHH H(H@H0LBL+IMHHHh1۸HHxHpHE11H1HLOrDHnH HDž Ht,HrDHmnHHu HDž HHHHCHHHPHnH@H`nnIHQH51oH 7oH}L6zW)HDž0nH(R)Hdo not aHHH shapes HHHp: InputHHHEinsum oH@ lign@$AH03oHuHLs|E1H5iHULfns!nIHQH5#0oH 6oH}LIyW)HDž`nHH(I)H52oWHnCWAHj2oHuHL{E1H5HLTLn[nIH`QH5]/oH 6oH}L`gxHDHH0oH H1oHH1oHH@H HLLHHUAH@0oHuLHzE1H5HjSLnynIH`$QH5{.oH 5oH}L`iwL DHH0oH H0oHH@H HLHH+AH0oHuLHzE1H5HRLnnIH`[QH5-oH W4oH}L`xvH0oH Hl/oHH 0oHH@H HLHHLAH/oHuLH3yE1H5)HQL&n6H 0 n(H" nH n H  n IAL`MPIIALmMIAL`M9I:IALmM_IAL`MIIAL}MDIALMlI@t HPnLuMt6HEL9t!HX@t HxnHI9uH}LLuvnEuEuL`Mu&^H}SnEtH}DnL`Mt:HhL9tIHX@t HxnHI9uH`Lh nE LnL- n LI@t HP nLmMt6HEL9t!HX@t Hx nHI9uH}LLm nEuEuL`Mu&^H}d nEtH}U nL`Mt:HhL9tIHX@t Hx* nHI9uH`Lh nE L. nL> n LI@t HP nLmMt6HEL9t!HX@t Hx nHI9uH}LLm nEuEuL`Mu&^H}u nEtH}f nL`Mt:HhL9tIHX@t Hx; nHI9uH`Lh nEL? nLO n LIt H nL}Mt6HEL9t!HX@t Hx nHI9uH}LL} nEuEuLMu&~H} nEtH}w nLMtZHL9wHX@t HxH nHI9uIAL}MuRIALME=IIt H nL}Mt6HEL9t!HX@t Hx nHI9uH}LL} nEELMoHL9HX@t Hx] nHI9uHLB nEnLa nHH HH} nErH} nLMiLL nEIHHHHCHHHPHnIH HjIH HRIHHIHHIH@HAHIHHttHHCHubHHPHnOIHHtwHHCHueHHPHnRIHHt HnHHHIHHt HYnHHHIH@HSZIH@H;BIH@H#*IH@H IH@HIH HuIH HH(nIHHt#HHCHuHHPH_nHHtHCnH HuRIH HtCH(5IH@Hu7AIHHHHtHdDHnH@Ht HHnH`HHhnLHHIHHIHHIHHIHHIHHkrIHHyIHHahIHHIPIHH18I11HH H3I11HH@H I11HH`H IHHIHHIHHt6{IHHt#HHCHuHHPHnH}Ht H}nHH HJ8IHHIHH HH(nnH@HHHRnH`HHh6nHHaDHnHHIHHu)3ILHHuIHHt HnLHHt HnHHHHt HnH}Hu_H HH@HH`HHHHtH`DH%nLen H}nH HtUHAWAVAUATSHHHhzHHEIIMt$0H~ HLEHMtH9tHPHEHEHuHuHPL}LLH}I9t*HtHP(I\$`I} Ht%I9t*HPHE+HP I\$`I} HuHE L}HLPLHOH}I9t HtHP(HP I$HEHx HtH9tHPHEHE L}HLPLH0CH}I9t HtHP(HP IHEHx HtH9tHPHEHE L}HLPLL DH}I9tHtHP(HxHH;Et%HP HxHH;EuHH[A\A]A^A_]knffff.UHAWAVAUATSHHH8H[xHHEH{ IH{ HPH{ HpH{ H`H{ IHXHHx9@LL+W)EHE)EHEE1H}LE1H}LHEM1H]LeIMH<t%HMH;Mt;HHHMNfffff.HMH;Mt#HHHM)fLH腠H}HwHEHHEL9|H}HH;EHhu0HPHL MqHIAhHPHH0Hu HpHH0H`HHXHAHxHAHEHtH@HXLIHHHL$H$H}HMLxILeHEH]HtHHCHIALMu HpHH0HUHXKH8HH}Ht H}ImH}HtHhH81mMtL[DLmHuHH;EHHĸ[A\A]A^A_]E1IATH}Ht H}mH}HtHhH8mA|*AGMHHIcH01AJLH8HIM?JHEH}HH@koW)EHEH}LHE1ff.IML9IH}Ht#0IE1H}Ht H}jmH}HuMuLm H}ImMtLVDL4mLtm fff.UHAWAVAUATSH8HHIHxpHHEI} HI} IW)EHEII+?tVIILH=mILeLeKHEI7MwI)M~LLmMLeMwM+7E1I}1L@HHLxL{DIH}{DI9HHp H+pHW)@HDžPH@×W) HDž0HDž`MtIH H`Lt>HLcI)LIE1E1H f.HJHHHtLH L,J4I7IHHH;Pt3HHHHHIM9r7IJ<t H@ԗHIM9qLLHI>H+@HH  IEHIEHHtH@MIE0H$HH@H LHjHHt#HHCHuHHPH=mHIH`IHHHL`HP0HeHHDžHtlHnSDHmH Hu\fIH`IHHHL`HHP0H<H Ht H(PmH@HtI>HmmmH jHHH5BiH Hm:mIHQH5<oH oH`LE_YHsPH oHMHHuHHBAHUnH`LH[E1H5܋H4LmmIH?QH5oH oH`L?XHPH oHMHHuHHAHnH`LH@[E1H56H3L3mrHALMH;HALM"WHTHt HmLeMt6HEL9t!Lh@t Hx]mLM9uH}LLeCmEu`u LMu,hH}m`tHpmLMt>HL9Lh@t HxmLM9uHLmEu7LHDžMu?LHDžMLmLHDžMtAFt I~SmLKmLHDžM9LYHt H mLeMt6HEL9t!Lh@t HxmLM9uH}LLemEu`u LMu,dH}m`tHpmLMt:HL9toLh@t HxfmLM9uHLKmEtLnmLHDžM1AFI~mLHALeMuFzHAL@MHH t H0mLeMt6HEL9t!Lh@t HxmLM9uH}LLezmEu`u L@Mu,dH}Tm`tHp?mL@Mt:HHL9tQLh@t HxmLM9uH@LHmE.LmH}H"&LHALMHHt HmLeMt6HEL9t!Lh@t HxqmLM9uH}LLeWmEu`u LMu,dH}1m`tHpmLMt:HL9tTLh@t HxmLM9uHLmELmH HLHLHDžML'JDL}mH HHLMtgHIFHuUILPL:mH HuDN1/HH}Hu_fHH@Hu7AHH}Hu>EHH Ht H(mH@Ht HHmH}Ht H}mHm UHAWAVAUATSHMMHHIHbHHEHHzH4nDIHH"nDI9LLIHmDHLmDH9HL8LpM)LHM,$LID$L)HHH9TW)`HDžp@mILH@L`LhHpW)@HDžP@umHHH@H@HHHPW) HDž0@7mHUHH H(H@H0LBL+IMHHHh1۸HHxHpHE11H1HLOLIHLLIL;DH@Hu/>LHxIHxLIL;@H Hu>LHpIHpLIL;L`HhH@HHHEHLGH H(L+ILNH1HHHLHHpHxHH)H)L)W)E)HEHDžHHHLHHHHXLLLHH`HhHyAHH@HHHXAHHEHHHHIHH H(HAHHBHHLvHN HIEHDH9H+:HH諳HHLLH)tCI6H11@H H<uHHHH9rDH9MHHH9rHML9t-HHpHP H}H?DHLI9uH]?HLIH]I)I1H H H HL9rW)HDžL}LH)t\IILH=lmILLKHI)ML~LHLmMLHH6BHHHt HmHH HHHAHHAHHtH@HLH@0H$HHLLyHHHDžHHthH=DHBmHHDžHtH=DHmHHtmH@HHH"mH`HHhmHHDHmHHIHHu)3ILHHuIHHt HmLHHt HymHHHHt HSmH}Hu_H HH@HH`HHHHtHDHmL5m H}mH HtUHAVSH0IHH87HHEI9tH{ H9t.IF I9tKHC I~ H 7HH;EH0[A^]M9v tTHLPH{ HP IF HC HHHQI~ HP HC IF H[ H6HH;EtpHHuPH{ HP HC I~ HHPI~ HP IF H[ HEH}LPHEH}P Mv H86HH;E-mH.H.fDUHAVSH0IHH5HHEI9tH{ H9t.IF I9tKHC I~ H5HH;EH0[A^]M9v tTHLPH{ HP IF HC HHHQI~ HP HC IF H[ Hf5HH;EtpHHuPH{ HP HC I~ HHPI~ HP IF H[ HEH}LPHEH}P Mv H4HH;E-ĽmHn-Hf-fDUHAVSH0IHH4HHEI9tH{ H9t.IF I9tKHC I~ H4HH;EH0[A^]M9v tTHLPH{ HP IF HC HHHQI~ HP HC IF H[ H&4HH;EtpHHuPH{ HP HC I~ HHPI~ HP IF H[ HEH}LPHEH}P Mv H3HH;E-脼mH.,H&,fDUHAWAVAUATSH8LLHHIHHS3HHEH8H1HHHLH@H8L7mHHEEL-2MuLI@L8LBmL=2ILHDž(HDž HDžHDžDž0I$H薽mHHHX.HHHrmHHH4.HHIDHHH;mHHH-HH0HYmHHH3LL8Lt H EmH%mH5<1HHmH8ȹmH1HH;EuHH8[A\A]A^A_]LmHLL8Lt H 迹mH蟸m+H)H)H)H)H)HH50HH mH8mHm)HH8mHV)fDUHAWAVAUATSHILgLoLL)HH9LM)MII6HH=HM)LHHH9H}LE II9LBMDHuI9诸mHH5DnHmH/HHH5.H-H诸mLHHr IGL9`ILHL9MLCHHtHIHHHHuI@IHIHPIHPIHPIHP IHP(IHP0IHP8H@HuTHuIJ<谷mIHuO LHHr IOL9)ILHL9HZHHt$Hffffff.IHHHHuHj@IHIHAIHAIHAIHA IHA(IHA0IHA8H@Hu IIIPHHAHA~fpDHPH)1fDAADAD AD0HHuHtA~fpDAADI9IHGHHHHHHDBAH%A~fpDK|0IHH)1fffff.DDDHHuMtA~fpDAADH9O4M!H6!fDUHH)HHGHHwHH]n%Sfffff.UHAVSHPIH[(HHE(԰mHþ HʰmHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]m<H5]mH}H?H5mH?H5qH,?HH HQH5CmHL?HH;?HHH HMHsHUHuHuIIFIF0INIVIF@H]HEHtHEAH苮mH&HH;Eu LHP[A^]ʮmIH]HEHtHAHFmL膫m fffff.UHAVSHPIH%HHE(mHþ H mHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]<H5mH}H:?H5mH;?H5qHl?HH HQH5mH茮?HH;Q?HHH @HMHHUHuHuIIFIF0INIVIF@H]HEHtHAH˫mHF#HH;Eu LHP[A^] mIH]HEHtH@AH膫mLƨm fffff.UHAVSHPIH"HHE(TmHþ HJmHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]-<H5ݶmH}Hz?H5HmH{?H5qH謪?HH HQH5mH̫?HH;葠?HHH HMHHUHuHuIIFIF0INIVIF@H]HEHtHAH mH HH;Eu LHP[A^]JmIH]HEHtHAHƨmLm fffff.UHAVSHPIH HHE(蔨mHþ H芨mHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]m<H5mH}H躯?H5mH軦?H5FqH?HH HQH5mH ?HH;ѝ?HHH ޅHMHHUHuHuIIFIF0INIVIF@H]HEHtHAHKmHHH;Eu LHP[A^]芦mIH]HEHtHAHmLFm fffff.UHAVSHPIH[HHE(ԥmHþ HʥmHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]m<H5]mH}H?H5mH?H5qH,?HH HQH5CmHL?HH;?HHH ܅HMH5HUHuHuIIFIF0INIVIF@H]HEHtHEAH苣mHHH;Eu LHP[A^]ʣmIH]HEHtHAHFmL膠m fffff.UHAVSHPIHHHE(mHþ H mHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]<H5mH}H:?H5mH;?H5qHl?HH HQH5mH茣?HH;Q?HHH @مHMHգHUHuHuIIFIF0INIVIF@H]HEHtH腺AHˠmHFHH;Eu LHP[A^] mIH]HEHtH@AH膠mLƝm fffff.UHAVSHPIHHHE(TmHþ HJmHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]-<H5ݫmH}Hz?H5HmH{?H5qH謟?HH HQH5mH̠?HH;葕?HHH օHMHuHUHuHuIIFIF0INIVIF@H]HEHtHŷAH mHHH;Eu LHP[A^]JmIH]HEHtH耷AHƝmLm fffff.UHAVSHPIHHHE(蔝mHþ H芝mHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]m<H5mH}H躤?H5mH軛?H5FqH?HH HQH5mH ?HH;ђ?HHH ӅHMHHUHuHuIIFIF0INIVIF@H]HEHtHAHKmHHH;Eu LHP[A^]芛mIH]HEHtHAHmLFm fffff.UHAVSHPIH[HHE(ԚmHþ HʚmHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]m<H5]mH}H?H5mH?H5qH,?HAH5JmHS?HH;?HHH хHMHHUHuHuIIFIF0INIVIF@H]HEHtHLAH蒘mH HH;Eu LHP[A^]јmIH]HEHtHAHMmL荕m DUHAVSHPIHHHE($mHþ HmHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]<H5mH}HJ?H5mHK?H5qH|?HAH5mH裘?HH;h?HHH W΅HMHlHUHuHuIIFIF0INIVIF@H]HEHtH蜯AHmH] HH;Eu LHP[A^]!mIH]HEHtHWAH蝕mLݒm DUHAVSHPIH HHE(tmHþ HjmHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]M<H5mH}H蚜?H5hmH蛓?H5&qH̔?HAH5mH?HH;踊?HHH ˅HMHHUHuHuIIFIF0INIVIF@H]HEHtHAH2mH HH;Eu LHP[A^]qmIH]HEHtH觬AHmL-m DUHAVSHPIHK HHE(ĒmHþ H躒mHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]<H5MmH}H?H5mH?H5vqH?HAH5:mHC?HH;?HHH ȅHMH̕HUHuHuIIFIF0INIVIF@H]HEHtH?HHH @HMHHUHuHuIIFIF0INIVIF@H]HEHtH`AHFmHFHH;Eu LHP[A^] GmIH]HEHtH@`AHFmLCm fffff.UHAVSHPIH۽HHE(TFmHþ HJFmHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]-u<H5QmH}HzM?H5knH{D?H5rqHE?HH HQH5ÈmHF?HH;;?HHH |HMHHUHuHuIIFIF0INIVIF@H]HEHtH]AH DmHHH;Eu LHP[A^]JDmIH]HEHtH]AHCmLAm fffff.UHAVSHPIHHHE(CmHþ HCmHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]-m<H5OmH}HJ?H5inHA?H5FoqHB?HH HQH5mH D?HH;8?HHH yHMHHUHuHuIIFIF0INIVIF@H]HEHtH[AHKAmHƸHH;Eu LHP[A^]AmIH]HEHtHZAHAmLF>m fffff.UHAVSHPIH[HHE(@mHþ H@mHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]mp<H5]LmH}HG?H5]fnH>?H5lqH,@?HH HQH5CmHLA?HH;6?HHH wHMHHUHuHuIIFIF0INIVIF@H]HEHtHEXAH>mHHH;Eu LHP[A^]>mIH]HEHtHXAHF>mL;m fffff.UHAVSHPIHHHE(>mHþ H >mHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]f<H5ImH}H:E?H5cnH;?HH;Q3?HHH @tHMH5HUHuHuIIFIF0INIVIF@H]HEHtHUAH;mHFHH;Eu LHP[A^] mH}HZ:?H5XnH[1?H5^qH2?HAH5umH3?HH;x(?HHH giHMHHUHuHuIIFIF0INIVIF@H]HEHtHJAH0mHmHH;Eu LHP[A^]11mIH]HEHtHgJAH0mL-m DUHAVSHPIH HHE(0mHþ Hz0mHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]]_<H5 qH?HA H5TmH?HH;?HHH HHMHFHUHuHuIIFIF0INIVIF@H]HEHtH)AHmHHH;Eu LHP[A^]QmIH]HEHtH)AHmL m DUHAVSHPIH+HHE(mHþ HmHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]=?<H5-mH}H?H515nH ?H5V;qH?HA H5RmH#?HH;?HHH EHMH,DHUHuHuIIFIF0INIVIF@H]HEHtH'AHb mH݄HH;Eu LHP[A^] mIH]HEHtH&AH mL] m DUHAVSHPIH{HHE( mHþ H mHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]8<H5}mH}H?H52nH ?H58qHL ?HA H5jOmHs ?HH;8?HHH 'CHMHAHUHuHuIIFIF0INIVIF@H]HEHtHl$AH mH-HH;Eu LHP[A^] mIH]HEHtH'$AHm mLm DUHAVSHPIHˁHHE(D mHþ H: mHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]]8<H5mH}Hj?H5/nHk?H55qH ?HA H5LmH ?HH;>HHH w@HMHL?HUHuHuIIFIF0INIVIF@H]HEHtH!AHmH}HH;Eu LHP[A^]AmIH]HEHtHw!AHmLm DUHAVSHPIHHHE(mHþ HmHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]m0<H5mH}H?H5!-nH?H5F3qH?HA H5 JmH?HH;>HHH =HMH<HUHuHuIIFIF0INIVIF@H]HEHtH AHRmH|HH;Eu LHP[A^]mIH]HEHtHAH mLMm DUHAVSHPIHk|HHE(mHþ HmHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]3<H5mmH}H ?H5q*nH ?H50qH<?HA H5ZGmHc?HH;(>HHH ;HMHl:HUHuHuIIFIF0INIVIF@H]HEHtH\AHmHzHH;Eu LHP[A^]mIH]HEHtHAH]mLl DUHAVSHPIHyHHE(4mHþ H*mHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H].<H5 mH}HZ ?H5'nH[?H5-qH?HA H5DmH?HH;x>HHH g8HMH7HUHuHuIIFIF0INIVIF@H]HEHtHAHlHmwHH;Eu LHP[A^]1mIH]HEHtHgAHlLl DUHAVSHPIH wHHE(lHþ HzlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H],<H5 mH}H?H5%nH>H56+qH>HA H5AmH?HH;>HHH 5HMH5HUHuHuIIFIF0INIVIF@H]HEHtHAHBlHtHH;Eu LHP[A^]lIH]HEHtHAHlL=l DUHAVSHPIH[tHHE(lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]--<H5]mH}H?H5a"nH>H5(qH,>HA H5J?mHS>HH;>HHH 3HMH3HUHuHuIIFIF0INIVIF@H]HEHtHLAHlH rHH;Eu LHP[A^]lIH]HEHtHAHMlLl DUHAVSHPIHqHHE($lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]=+<H5mH}HJ?H5nHK>H5%qH|>HA H5HH;h>HHH W0HMH0HUHuHuIIFIF0INIVIF@H]HEHtHAHlH]oHH;Eu LHP[A^]!lIH]HEHtHWAHlLl DUHAVSHPIHnHHE(tlHþ HjlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] !<H5mH}H>H5nH>H5&#qH>HH HQH59mH>HH;>HHH -HMH5.HUHuHuIIFIF0INIVIF@H]HEHtHAH+lHlHH;Eu LHP[A^]jlIH]HEHtHAHlL&l fffff.UHAVSHPIH;lHHE(lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]M$<H5=mH}H>H5EnH>H5f qH >HH HQH5#7mH,>HH;>HHH *HMH<HUHuHuIIFIF0INIVIF@H]HEHtH% AHklHiHH;Eu LHP[A^]lIH]HEHtH AH&lLfl fffff.UHAVSHPIH{iHHE(lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]<H5}lH}H>H5nH>H5qHL>HH HQH5c4mHl>HH;1>HHH (HMHEKHUHuHuIIFIF0INIVIF@H]HEHtHe AHlH&gHH;Eu LHP[A^]lIH]HEHtH AHflLl fffff.UHAVSHPIHfHHE(4lHþ H*lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] <H5lH}HZ>H5nH[>H5qH>HH HQH51mH>HH;q>HHH `%HMHeTHUHuHuIIFIF0INIVIF@H]HEHtHAHlHfdHH;Eu LHP[A^]*lIH]HEHtH`AHlLl fffff.UHAVSHPIHcHHE(tlHþ HjlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]M<H5lH}H>H5nH>H5&qH>HH HQH5.mH>HH;>HHH "HMHbHUHuHuIIFIF0INIVIF@H]HEHtHAH+lHaHH;Eu LHP[A^]jlIH]HEHtHAHlL&l fffff.UHAVSHPIH;aHHE(lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]M<H5=lH}H>H5EnH>H5fqH >HA H5*,mH3>HH;>HHH HMHkHUHuHuIIFIF0INIVIF@H]HEHtH,AHrlH^HH;Eu LHP[A^]lIH]HEHtHAH-lLml DUHAVSHPIH^HHE(lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]<H5lH}H*>H5 nH+>H5qH\>HA H5z)mH>HH;H>HHH 7HMH|iHUHuHuIIFIF0INIVIF@H]HEHtH|@HlH=\HH;Eu LHP[A^]lIH]HEHtH7@H}lLl DUHAVSHPIH[HHE(TlHþ HJlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]-<H5lH}Hz>H5 nH{>H5qH>HA H5&mH>HH;>HHH HMH gHUHuHuIIFIF0INIVIF@H]HEHtH@HlHYHH;Eu LHP[A^]QlIH]HEHtH@HlL l DUHAVSHPIH+YHHE(lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]} <H5-lH}H>H55nH>H5V qH>HA H5$mH#>HH;>HHH HMHdHUHuHuIIFIF0INIVIF@H]HEHtH@HblHVHH;Eu LHP[A^]lIH]HEHtH@HlL]l DUHAVSHPIH{VHHE(lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] <H5}lH}H>H5nH>H5 qHL>HA H5j!mHs>HH;8>HHH 'HMH,bHUHuHuIIFIF0INIVIF@H]HEHtHl@HlH-THH;Eu LHP[A^]lIH]HEHtH'@HmlLl DUHAVSHPIHSHHE(DlHþ H:lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]<H5lH}Hj>H5nHk>H5qH>HH HQH5mH>HH;>HHH pHMH_HUHuHuIIFIF0INIVIF@H]HEHtH@HlHvQHH;Eu LHP[A^]:lIH]HEHtHp@HlLl fffff.UHAVSHPIH QHHE(lHþ HzlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]<H5 lH}H>H5mH>H56qH>HA H5mH>HH;>HHH HMHnHUHuHuIIFIF0INIVIF@H]HEHtH@HBlHNHH;Eu LHP[A^]lIH]HEHtH@HlL=l DUHAVSHPIH[NHHE(lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]m<H5]lH}H>H5omH>H5qH,>HH HQH5CmHL>HH;>HHH HMHkHUHuHuIIFIF0INIVIF@H]HEHtHE@HlHLHH;Eu LHP[A^]lIH]HEHtH@HFlLl fffff.UHAVSHPIHKHHE(lHþ H lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H];H5lH}H:>H5mH;>H5pHl>HA H5mH>HH;X>HHH G HMH zHUHuHuIIFIF0INIVIF@H]HEHtH@HlHMIHH;Eu LHP[A^]lIH]HEHtHG@HlLl DUHAVSHPIHHHHE(dlHþ HZlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H];H5lH}H>H5mH>H5pH>HH HQH5mH>HH;>HHH HMHwHUHuHuIIFIF0INIVIF@H]HEHtH@HlHFHH;Eu LHP[A^]ZlIH]HEHtH@HlLl fffff.UHAVSHPIH+FHHE(lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]=;H5-lH}H>H5DmH>H5VpH>HH HQH5mH>HH;>HHH HMHuHUHuHuIIFIF0INIVIF@H]HEHtH@H[lHCHH;Eu LHP[A^]lIH]HEHtH@HlLVl fffff.UHAVSHPIHkCHHE(lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]};H5mlH}H >H5mH >H5pH<>HA H5ZmHc>HH;(>HHH HMHHUHuHuIIFIF0INIVIF@H]HEHtH\@HlHAHH;Eu LHP[A^]lIH]HEHtH@H]lLl DUHAVSHPIH@HHE(4lHþ H*lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H];H5lH}HZ>H5mH[>H5pH>HA H5 mH>HH;x>HHH gHMH<HUHuHuIIFIF0INIVIF@H]HEHtH@HlHm>HH;Eu LHP[A^]1lIH]HEHtHg@HlLl DUHAVSHPIH >HHE(lHþ HzlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H];H5 lH}H>H5/mH>H56pH>HH HQH5mH>HH;>HHH HMHŌHUHuHuIIFIF0INIVIF@H]HEHtH@H;lH;HH;Eu LHP[A^]zlIH]HEHtH@HlL6l fffff.UHAVSHPIHK;HHE(lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]];H5MlH}H>H5omH>H5vpH>HH HQH53mH<>HH;>HHH HMHHUHuHuIIFIF0INIVIF@H]HEHtH5@H{lH8HH;Eu LHP[A^]lIH]HEHtH@H6lLvl fffff.UHAVSHPIH8HHE(lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H];H5lH}H*>H5mH+>H5pH\>HA H5zmH>HH;H>HHH 7HMH̟HUHuHuIIFIF0INIVIF@H]HEHtH|@H¾lH=6HH;Eu LHP[A^]lIH]HEHtH7@H}lL轻l DUHAVSHPIH5HHE(TlHþ HJlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H];H5lH}Hz>H5mH{>H5pH謽>HA H5mHӾ>HH;蘳>HHH HMH\HUHuHuIIFIF0INIVIF@H]HEHtH@HlH3HH;Eu LHP[A^]QlIH]HEHtH@HͻlL l DUHAVSHPIH+3HHE(褻lHþ H蚻lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H52lHTH eH}贿>H5NmHŹ>H5PpH>HH HQH5 lH>HH;۰>HHH HMHߚHUHuHuIIFIF0INIVIF@H]HEHtH@HUlH0HH;Eu LHP[A^]蔹lIH]HEHtH@HlLPl UHAVSHPIHk0HHE(lHþ HڸlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5rlHėH 헇H}>H5mHH 症H׼>H5qmH>H5spH>HH HQH50lH9>HH;>HHH HMHbHUHuHuIIFIF0INIVIF@H]HEHtH2@HxlH-HH;Eu LHP[A^]跶lIH]HEHtH@H3lLsl ff.UHAVSHPIH-HHE(lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5lH䔇H H}>H5mHޔH H>H5mH>H5pH9>HA H5WlH`>HH;%>HHH HMHHUHuHuIIFIF0INIVIF@H]HEHtHY@H蟳lH+HH;Eu LHP[A^]޳lIH]HEHtH@HZlL蚰l fUHAVSHPIH*HHE(4lHþ H*lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H];H5lH}HZ>H5mH[>H5pH茲>HH HQH5lH謳>HH;q>HHH `HMHHUHuHuIIFIF0INIVIF@H]HEHtH@HlHf(HH;Eu LHP[A^]*lIH]HEHtH`@H覰lLl fffff.UHAVSHPIH'HHE(tlHþ HjlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] ;H5lH}H蚷>H5+mH蛮>H5&pH̯>HH HQH5lH>HH;豥>HHH HMHUHUHuHuIIFIF0INIVIF@H]HEHtH@H+lH%HH;Eu LHP[A^]jlIH]HEHtH@HlL&l fffff.UHAVSHPIH;%HHE(购lHþ H読lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]M;H5=lH}Hڴ>H5kmH۫>H5fpH >HA H5*lH3>HH;>HHH HMHLHUHuHuIIFIF0INIVIF@H]HEHtH,@HrlH"HH;Eu LHP[A^]豫lIH]HEHtH@H-lLml DUHAVSHPIH"HHE(lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H];H5lH}H*>H5mH+>H5pH\>HA H5zlH胫>HH;H>HHH 7HMHܫHUHuHuIIFIF0INIVIF@H]HEHtH|@H¨lH= HH;Eu LHP[A^]lIH]HEHtH7@H}lL轥l DUHAVSHPIHHHE(TlHþ HJlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H];H5ݳlH}Hz>H5mH{>H5pH謧>HH HQH5lH̨>HH;葝>HHH ބHMHeHUHuHuIIFIF0INIVIF@H]HEHtHſ@H lHHH;Eu LHP[A^]JlIH]HEHtH耿@HƥlLl fffff.UHAVSHPIHHHE(蔥lHþ H芥lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]-;H5lH}H躬>H5OmH軣>H5FpH>HH HQH5lH >HH;њ>HHH ۄHMHHUHuHuIIFIF0INIVIF@H]HEHtH@HKlHHH;Eu LHP[A^]芣lIH]HEHtH@HlLFl fffff.UHAVSHPIH[HHE(ԢlHþ HʢlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]m;H5]lH}H>H5mH>H5pH,>HA H5JlHS>HH;>HHH لHMHHUHuHuIIFIF0INIVIF@H]HEHtHL@H蒠lH HH;Eu LHP[A^]ѠlIH]HEHtH@HMlL荝l DUHAVSHPIHHHE($lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H];H5lH}HJ>H5mHK>H5pH|>HA H5lH裠>HH;h>HHH WքHMHHUHuHuIIFIF0INIVIF@H]HEHtH蜷@HlH]HH;Eu LHP[A^]!lIH]HEHtHW@H蝝lLݚl DUHAVSHPIHHHE(tlHþ HjlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] ;H5lH}H蚤>H53mH蛛>H5&pH̜>HHHQH5lH>HH;豒>HHH ӄHMH5HUHuHuIIFIF0INIVIF@H]HEHtH@H+lHHH;Eu LHP[A^]jlIH]HEHtH蠴@HlL&l fffff.UHAVSHPIH;HHE(贚lHþ H誚lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]M;H5=lH}Hڡ>H5smHۘ>H5fpH >HHHQH5#lH,>HH;>HHH ЄHMH%HUHuHuIIFIF0INIVIF@H]HEHtH%@HklHHH;Eu LHP[A^]誘lIH]HEHtH@H&lLfl fffff.UHAVSHPIH{HHE(lHþ HlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H];H5}lH}H>H5mH>H5pHL>HH HQH5clHl>HH;1>HHH ΄HMHHUHuHuIIFIF0INIVIF@H]HEHtHe@H諕lH& HH;Eu LHP[A^]lIH]HEHtH @HflL覒l fffff.UHAVSHPIH HHE(4lHþ H*lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H];H5lH}HZ>H5mH[>H5pH茔>HH HQH5lH謕>HH;q>HHH `˄HMH HUHuHuIIFIF0INIVIF@H]HEHtH襬@HlHf HH;Eu LHP[A^]*lIH]HEHtH`@H覒lLl fffff.UHAVSHPIH HHE(tlHþ HjlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] ;H5lH}H蚙>H53mH蛐>H5&pH̑>HA H5lH>HH;踇>HHH ȄHMH<HUHuHuIIFIF0INIVIF@H]HEHtH@H2lHHH;Eu LHP[A^]qlIH]HEHtH觩@HlL-l DUHAVSHPIHKHHE(ďlHþ H躏lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]];H5MlH}H>H5mH>H5vpH>HA H5:lHC>HH;>HHH ńHMHHUHuHuIIFIF0INIVIF@H]HEHtH<@H肍lHHH;Eu LHP[A^]lIH]HEHtH@H=lL}l DUHAVSHPIHHHE(lHþ H lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]譶;H5lH}H:>H5ײmH;>H5ƸpHl>HHHQH5lH茍>HH;Q>HHH @ÄHMHHUHuHuIIFIF0INIVIF@H]HEHtH腤@HˊlHFHH;Eu LHP[A^] lIH]HEHtH@@H膊lLƇl fffff.UHAVSHPIHHHE(TlHþ HJlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5lHiH iH}d>H5mHu>H5pH覉>HH HQH5lHƊ>HH;>HHH zHMHHUHuHuIIFIF0INIVIF@H]HEHtH迡@HlHHH;Eu LHP[A^]DlIH]HEHtHz@HlLl UHAVSHPIHHHE(蔇lHþ H芇lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5"lHfH gH}褋>H5QmH赅>H5@pH>HH HQH5lH>HH;|>HHH HMH?HUHuHuIIFIF0INIVIF@H]HEHtH@HElHHH;Eu LHP[A^]脅lIH]HEHtH躞@HlL@l UHAVSHPIH[HHE(ԄlHþ HʄlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5blH,dH UdH}>H5mH>H5pH&>HA H5DlHM>HH;z>HHH HMHHUHuHuIIFIF0INIVIF@H]HEHtHF@H茂lHHH;Eu LHP[A^]˂lIH]HEHtH@HGlLl ffffff.UHAVSHPIHHHE(lHþ H lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]譫;H5lH}H:>H5ۧmH;>H5ƭpHl>HHHQH5lH茂>HH;Qw>HHH @HMH HUHuHuIIFIF0INIVIF@H]HEHtH腙@HlHFHH;Eu LHP[A^] lIH]HEHtH@@HlL|l fffff.UHAVSHPIHHHE(TlHþ HJlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5lH^H _H}d>H5mHu}>H5pH~>HH HQH5lH>HH;t>HHH zHMH HUHuHuIIFIF0INIVIF@H]HEHtH迖@H}lHHH;Eu LHP[A^]D}lIH]HEHtHz@H|lLzl UHAVSHPIHHHE(|lHþ H|lHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5"lHL\H u\H}褀>H5UmHz>H5@pH{>HH HQH5lH}>HH;q>HHH HMH/HUHuHuIIFIF0INIVIF@H]HEHtH@HEzlHHH;Eu LHP[A^]zlIH]HEHtH躓@HzlL@wl UHAVSHPIH[HHE(ylHþ HylHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5blHYH YH}}>H5mHw>H5pH&y>HA H5DlHMz>HH;o>HHH HMHHUHuHuIIFIF0INIVIF@H]HEHtHF@HwlHHH;Eu LHP[A^]wlIH]HEHtH@HGwlLtl ffffff.UHAVSHPIHHHE(wlHþ H wlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]譠;H5lH}H:~>HR;H5mHH ~>H5ŜmH!u>H5pHRv>HHHQH5ilHrw>HH;7l>HHH &HMH[HUHuHuIIFIF0INIVIF@H]HEHtHk@HtlH,HH;Eu LHP[A^]tlIH]HEHtH&@HltlLql @UHAVSHPIHHHE(DtlHþ H:tlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]ݣ;H5lH}Hj{>H肞;H5mHHP{>H5mHQr>H5ܟpHs>HHHQH5lHt>HH;gi>HHH VHMHHUHuHuIIFIF0INIVIF@H]HEHtH蛋@HqlH\HH;Eu LHP[A^] rlIH]HEHtHV@HqlLnl @UHAVSHPIHHHE(tqlHþ HjqlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] ;H5|lH}Hx>H貛;H5 mHHx>H5%mHo>H5 pHp>HH HQH5ɳlHq>HH;f>HHH HMH HUHuHuIIFIF0INIVIF@H]HEHtHˈ@HolHHH;Eu LHP[A^]PolIH]HEHtH膈@HnlL ll @UHAVSHPIH+HHE(nlHþ HnlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]=;H5-zlH}Hu>H;H5PmHHu>H5UmHl>H5HH HQH5lHo>HH;c>HHH HMH[HUHuHuIIFIF0INIVIF@H]HEHtH@HAllHHH;Eu LHP[A^]llIH]HEHtH超@HklLH;H5mHHr>H5mHi>H5lpHk>HH HQH5)lH2l>HH;`>HHH 桄HMHHUHuHuIIFIF0INIVIF@H]HEHtH+@HqilHHH;Eu LHP[A^]ilIH]HEHtH@H,ilLlfl @UHAVSHPIHHHE(ilHþ HhlHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]ݗ;H5tlH}H*p>HB;H5mHHp>H5mHg>H5pHBh>HH HQH5YlHbi>HH;'^>HHH HMH HUHuHuIIFIF0INIVIF@H]HEHtH[@HflHރHH;Eu LHP[A^]flIH]HEHtH@H\flLcl @UHAVSHPIH݃HHE(4flHþ H*flHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]͏;H5qlH}HZm>Hr;H5mHH@m>H5mHAd>H5̑pHre>HA H5lHf>HH;^[>HHH MHMH HUHuHuIIFIF0INIVIF@H]HEHtH}@HclHSۃHH;Eu 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fff.UHAWAVSPIH\LLMM`MMHuE1uMI!IEJH1HIIIEJHE1H]xHH;EV`DMHuE1fHCH9tHL!L9H9KHtHSL9udH{HtH{ u'Mt[1ff.T A:u5HH9u9Mt4LLMLM`LMMHutDHHTIH\xHH;ELH([A\A]A^A_]HUUHAVSHPIHk\xHHE(`Hþ H`HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]=0H5 bH}H 2H0H5`cHH2H5\bH2H5@dH"2HAH5@'aHI2HH;2HHH yHMHHUHuHuIIFIF0INIVIF@H]HEHtHB4H`HZxHH;Eu LHP[A^]`IH]HEHtH4HC`L` ff.UHAVSHPIHYxHHE(`Hþ H `HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]-0H5bH}H:2H 0H5cHH 2H5ZbH!2H5pdHR2HAH5p$aHy2HH;>2HHH -yHMHHUHuHuIIFIF0INIVIF@H]HEHtHr4H`H3WxHH;Eu LHP[A^]`IH]HEHtH-4Hs`L` ff.UHAVSHPIHVxHHE(D`Hþ H:`HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]0H5 bH}Hj2H0H5 cHHP2H5EWbHQ2H5dH2HAH5!aH2HH;n2HHH ]yHMHbHUHuHuIIFIF0INIVIF@H]HEHtH4H`HcTxHH;Eu LHP[A^]'`IH]HEHtH]4H`L` ff.UHAVSHPIHSxHHE(t`Hþ Hj`HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] 0H5;bH}H2Hr0H5 cHH2H5uTbH2H5dH2HAH5aH2HH;2HHH yHMH$HUHuHuIIFIF0INIVIF@H]HEHtH4H`HQxHH;Eu LHP[A^]W`IH]HEHtH4H`L` ff.UHAVSHPIH+QxHHE(`Hþ H`HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]0H5kaH}H2H"0H5 cHH2H5QbH2H5dH2HAH5aH 2HH;2HHH yHMH'HUHuHuIIFIF0INIVIF@H]HEHtH4HH`HNxHH;Eu LHP[A^]`IH]HEHtH4H`LC` ff.UHAVSHPIH[NxHHE(`Hþ H`HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]0H5aH}H2HR0H5PcHH2H5NbH2H50dH2HAH50aH92HH;2HHH yHMH"*HUHuHuIIFIF0INIVIF@H]HEHtH24Hx`HKxHH;Eu LHP[A^]`IH]HEHtH4H3`Ls` ff.UHAVSI(`HHLH[A^]IH`L)` UHAWAVAUATSHHIH?KxHHEL%QxMeI}3H'yIEIE IEIEW)EMuE"HEHocabularHEHstring_vHEfEyHhHUHL3HhHDžhHtCt H{ `H`Et H}`Hu'HiJxHH;EuHĨ[A\A]A^A_]'``IHh耀DH5LbH LbH}Lh8HPH~cDHXMbHuLH;DžDH5kxH8L`H8DHhHHHH8H8Pt H``HEHHHtIHEH;Ht%HX@t Hx`HH9HuH}HHHHHE`EuQEuZHhHHHtHpH;HtUHX@t HxK`HH9HuHh4H}-`EtH}`HhHHHuHHHHHp`Dt(L`IHu ]H8Et H}`IHt?IEH9t^ffffff.Lx@t Hx`LL9uI>I]w`MeI}IEHt]`H8` Hfff.UHAWAVSPIH<$yIM~Mt=IFL9t(HX@t Hx`HI9uI~LM~`H|xII~IFHHt [A^A_]`[A^A_]fUHAWAVSPIH#yIM~Mt=IFL9t(HX@t Hxk`HI9uI~LM~Q`HxII~IFHt1`LH[A^A_]`UHAWAVAUATSHHIHH}H~FxHHE1LHEHEHCH+CHIIHEHEHEHEHU1L腱3HIHsH]H[H)teHIL}IAE1fffff.HEHpIILH}L9tH@8KDI9r 1KDI9rHEHH ExH H;MuHH[A\A]A^A_]S`DUHAWAVAUATSHAHHOExHHE#3Ht*HVkH /ExH H;Mu$HĨ[A\A]A^A_]1H ExH H;Mt`IǃL`u`IHh {DH5dH HbH}Lh$3H{3IcHH<<H5dH8H`HPH8`HuHP1L6H5 fxHL ` IPt H``E18uWHI]W`MeI}IEHt=`H8y` Hfff.UHAWAVSPIH|yIM~Mt=IFL9t(HX@t Hx`HI9uI~LM~`H\xII~IFHHt [A^A_]`[A^A_]fUHAWAVSPIHyIM~Mt=IFL9t(HX@t HxK`HI9uI~LM~1`H̺xII~IFHt`LH[A^A_]`UHAWAVAUATSHHIHH}H^1fffff.UHAWAVAUATSPIII^IHAt MgM IDLuMf.C tLs( fDM9LIGHt9tH{0L`u5#H{ HL`uffff.1M9BHKHILIHHvLuM9t=AE tI](I9LHGHtt*Iu0L`u-I9sMLH[A\A]A^A_]Iu HLZ`tx@UHAVSI(ƿ`HHLH[A^]IH虿`Lټ` UHAWAVAUATSHHIH6xHHEL%xMeI}Ę3HyIEIE IEIEW)EMuE"HEHocabularHEHstring_vHEfEyHhHUHLR3HhHDžhHtCt H{躾`H貾`Et H}裾`Hu'H6xHH;EuHĨ[A\A]A^A_]׾`腾`IHh0lDH568bH ZI]'`MeI}IEHt `H8I` Hfff.UHAWAVSPIHyIM~Mt=IFL9t(HX@t Hx諻`HI9uI~LM~葻`H,xII~IFHHt [A^A_]g`[A^A_]fUHAWAVSPIHyIM~Mt=IFL9t(HX@t Hx`HI9uI~LM~`HxII~IFHt`LH[A^A_]Ϻ`UHAWAVAUATSHHIHH}H.2xHHE1L HEHEHCH+CHIIHEHEHEHEHU1L53H-IHsH]H[H)txHIL}IAE1fffff.HEHpIILH}L9t @8CDI9rffff.WCDI9rHEHH /1xH H;MuHH[A\A]A^A_]`fUHAWAVAUATSHAHH0xHHEæ3Ht*HbH 0xH H;Mu$HĨ[A\A]A^A_]1H 0xH H;Mt{`IǃL+``IHhfDH5,dH 7bH}Lh$=H{脒3IcHH<1<H5dH8Hu`HPH8貶`HuHP1L!H5QxHUL詸` IPt H`A`E18uWH=(IA8t@HH`LmMu4hIALhMIIALmMt6HEL9t!HX@t Hx跷`HI9uH}LLm蝷`EuEuLhMu&^H}z`EtH}k`LhMt:HpL9tJHX@t Hx@`HI9uHhLp%`EtLH`=`LS` LH'fffff.UHAWAVAUATSPIII^IHAt MgM IDLuMf.C tLs( fDM9LIGHt9tH{0L`u5#H{ HLϷ`uffff.1M9BHKHILIHHvLuM9t=AE tI](I9LHGHtt*Iu0LW`u-I9sMLH[A\A]A^A_]Iu HL*`tx@UHAVSI(薵`HHLH[A^]IHi`L該` UHAWAVAUATSHHIH,xHHEL=ѪxM}I}蔎3HE yIEIE IEIEW)EE HEHcabularyHEHint64_voHEMuEHhHUHL3HhHDžhHtCt H{茴`H脴`Et H}u`Hu'H+xHH;EuHĨ[A\A]A^A_]詴`W`H8HhaDH5.bH 3bH}Lh{HPHDLH.bHuH8HDžLH5 MxHH8` H@LHhHH@H@Pt H`f`LeMt6HEL9t!HX@t HxA`HI9uH}LLe'`EuEuHhHu&^H}`EtH}`HhHt:HpH9tML`@t Hxʲ`LL9uHhHp诲`Lt1H8ʲ`I>Hu%,HH@Et H}u`I>Ht I}d`M}I}IEHtJ`H@膯` fffff.UHSPHHHyHH{Ht H{ `HxHH{HCHHt[]`[]fUHSPHHyHH{Ht H{蹱`HTxHH{HCHt虱`HH[]鋱`ffff.UHAWAVAUATSH8HIH}H(xHHE1HMHEIOI+OIHHMHEHEHEHU1H3H܆HMoM+oIIE1I $Ht:IGJLfff.HQH9A HMHMH HuL9tH;F }SHEHEufHCHC1H菮`HEHMHKHCHC H(H芮`HIM9NHEHH 'xH H;MuH8[A\A]A^A_]m`H UHAVSI(`HHLH[A^]IHɯ`L ` UHAWAVAUATSHHIH'xHHEL=1xM}I}3HyIEIE IEIEW)EE HEHcabularyHEHint64_voHEMuEHhHUHLd3HhHDžhHtCt H{`H`Et H}ծ`Hu'HK&xHH;EuHĨ[A\A]A^A_] `跮`H8Hh^\DH5d(bH /bH}LhHPH\?LH6)bHuH8HzDžLH5iGxHH8b` H@LHhHH@H@Pt H`ƭ`LeMt6HEL9t!HX@t Hx衭`HI9uH}LLe臭`EuEuHhHu&^H}d`EtH}U`HhHt:HpH9tML`@t Hx*`LL9uHhHp`Lt1H8*`I>Hu%,HH@Et H}լ`I>Ht I}Ĭ`M}I}IEHt説`H@` fffff.UHSPHHyHH{Ht H{i`HxHH{HCHHt[]C`[]fUHSPHHyHH{Ht H{`HxHH{HCHt`HH[]`ffff.UHAWAVAUATSH(IIIH?#xHHE1LHEIOI+OIHHMHEHEHEHU1LU3HݼMGMWM)IMMMt\I1fILLfDHNH9V HMHMH1HuWL9t H;S |C(HHL9rIIGHH`IHL"xHH;EuLH([A\A]A^A_] `fff.UHAVSI(薪`HHLH[A^]IHi`L詧` UHAWAVAUATSHHIH!xHHEL=џxM}I}蔃3HyIEIE IEIEW)EE HEHcabularyHEHint64_voHEMuEHhHUHL3HhHDžhHtCt H{茩`H脩`Et H}u`Hu'H xHH;EuHĨ[A\A]A^A_]詩`W`H8HhVDH5#bH @*bH}Lh{HPH9LH#bHuH8HDžLH5 BxHH8` H@LHhHH@H@Pt H`f`LeMt6HEL9t!HX@t 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ܻYH}Lh)EW)PHDž`0NWH`(*i)PHge [0, 1HHH the ranHHHst be inHHHratio muHf@ )AHYHuHPLHE1H5 xoH LWWIHhn;H5YH YH}Lh$DW)PHDž`0fWH`((i)PHe value.HHHa singleHHHld have HHHput shouHHHratio inH@'AH{YHuHPL!GE1H5woHLW IALeMuFzIALhMIIPt H`xWLeMt6HEL9t!HX@t HxSWHI9uH}LLe9WEuEu LhMu-[H}WEtH}WLhM3HpL9:HX@t HxWHI9uIALeMuFzIALhMIIPt H`xWLeMt6HEL9t!HX@t HxSWHI9uH}LLe9WEuEuLhMu&^H}WEtH}WLhMt:HpL9tEHX@t HxWHI9uHhLpWEtLWLW LUHAVSIWHHLH[A^]IHiWLW UHAWAVATSH0HIHRoHHEL%oM'I蚴*H{vpIIGHEW)EHEEEseedEH}HUHMH*H]HEHtCt H{WHWEt H}WHu#WHMHIIGHt}WHQoHH;Eu H0[A\A^A_]WIIIGHu%(IEt H}3WIIGHtWM'IIGHtWLDW fff.UHSPHH8upHH{HCHtWH`oHH{HCHHt[]W[]fffff.UHSPHHtpHH{HCHteWHoHH{HCHtEWHH[]7WfUHAWAVAUATSHHH H0HPoHHE1H#,ILhH8HH,HLMt$1HL;*HcH(HHHL*I1MtmH@L蕗IIIGIw HH)IT$I|$ H)H9L(%E1H9tu1H<0H;<2HH9uRH@LZ.H1H@HWILHWL@LZ.MI1ML(H,WL.u8H/H]8LH H@Hu G2,LLM)M)HMH@H )))E7piWLꐉHH/iȭ)iiG )B\7WH*YX.47HIuMH0H8?LH-i\ZI1H H8L9Ht H)tLWLL)H ÀH0IEL)HHHOITL)L}WHMH)1 _lff.A| f(ufWY^AA| f(ufWYT^ATHH9uHt%A| ufW D_lY ^A HMtL1WHLoHH;EuHH[A\A]A^A_]mWWIHhƂ;H5YH YH}LhAC;W)PHDž`0WH`(hl)PHme shapeHHHhe same HHHd have tHHHsk shoulHHHX and maH@%AHKYHuHPLy=E1H5omoHLlWMIALeMuFzIALhMIIPt H`WLeMt6HEL9t!HX@t HxWHI9uH}LLeWEuEuLhMu&^H}kWEtH}\WLhMt:HpL9t=HX@t Hx1WHI9uHhLpWEt=L9W3LLIHu!LIHuH@IHtWLW fff.UHAVSIWHHLH[A^]IHWLW UHAWAVATSH0HIHIoHHEL%oM'I身*HmpIIGHEW)EHEEEseedEH}HUHMH*H]HEHtCt H{WHWEt H}WHu#WHMHIIGHtWHIoHH;Eu H0[A\A^A_]WIIIGHu%(IEt H}SWIIGHt=WM'IIGHt$WLdW fff.UHSPHHlpHH{HCHtWHoHH{HCHHt[]W[]fffff.UHSPHHXlpHH{HCHtWH oHH{HCHteWHH[]WWfUHAWAVAUATSHHH H0HGoHHE1HC,IL舑H8HH,HLMt$1HL[*H胓H(HHHL.*I1MtmH@L赎IIIGIw HH)IT$I|$ H)H9L(%E1H9tu1H<0H;<2HH9uRH@LQ.H1H@HWILHWL@LQ.MI1ML(H,WL.u8H/H}8LH H@Hu g),LLM)M)HMH@H )))EWgiWLꐉHH/iȭ)iiG )B\7WH*YX.47HIuMH0H8?LHM~i\ZI1H H8L9Ht H)tLWLL)H ÀH0IEL)HHHOITL)LWHMH)1 Vlff.A| f(ufWY^AA| f(ufWYT^ATHH9uHt%A| ufW dVlY ^A HMtLQWHCoHH;EuHH[A\A]A^A_]W;WIHhy;H59YH dYH}LhAc2W)PHDž`0WH`(`l)PHme shapeHHHhe same HHHd have tHHHsk shoulHHHX and maH@%AHkYHuHPL4E1H5doH8 LWMIALeMuFzIALhMIIPt H`WLeMt6HEL9t!HX@t HxWHI9uH}LLeWEuEuLhMu&^H}WEtH}|WLhMt:HpL9t=HX@t HxQWHI9uHhLp6WEt=LYW3LLIHu!LIHuH@IHtWL4W fff.UHAVSIWHHL>H[A^]IHWLW UHAVSIWHHLH[A^]IHiWLW UHAVSIVWHHLH[A^]IH)WLiW UHAVSIWHHL~H[A^]IHWL)W UHAVSHPIHK@oHHE(WHþ HWHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H}11)HL'H5=WHHK)H5YH)H5g[H )HHHQH5$ XH-)HH;)HHH oHMH HUHuHuIIFIF0INIVIF@H]HEHtH&+HlWH=oHH;Eu LHP[A^]WIH]HEHtH+H'WLgW ffffff.UHAVSHPIH{=oHHE(WHþ HWHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H}11)HJ'H5mWHH{)H53YH )H5[H=)HH HQH5TXH])HH;")HHH oHMH HUHuHuIIFIF0INIVIF@H]HEHtHV+HWH;oHH;Eu LHP[A^]WIH]HEHtH+HWWLW ffffff.UHAVSHPIH:oHHE($WHþ HWHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H}115)HMG'H5WHH)H5cYH<)H5[Hm)HH HQH5XH)HH;R)HHH AoHMH HUHuHuIIFIF0INIVIF@H]HEHtH+HWHG8oHH;Eu LHP[A^] WIH]HEHtHA+HWLǽW ffffff.UHAVSHPIH7oHHE(TWHþ HJWHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H}11e)H}D'H5WHH)H5YHl)H5[H蝿)HA H5XH)HH;艵)HHH xoHMHmHUHuHuIIFIF0INIVIF@H]HEHtH+HWH~5oHH;Eu LHP[A^]BWIH]HEHtHx+H辽WLW fDUHAVSI覽WHHLH[A^]IHyWL蹺W UHAWAVAUATSHHIH4oHHEL-oM,$I|$裖*HLYpI$W)EHEEEaxisEIL$HhHUHҶ*HhHDžhHtCt H{ʼWH¼WEt H}購WHu'H)4oHH;EuHĘ[A\A]A^A_]W蕼WH@Hh.HELHNA.HEHEHhHDžpHh1LC*L(LhPM4$Mt$PM9I|$0HPhHtmxugL <.H~1ېLLQWHL;.IIH9|VH YL:H[0oHH;Eu]LFL;.II|$0HPILLH)WHHHH 0oH H;MuHĘ[A\A]A^A_]θW|WIH}*f;H5JYH ǖYHhLE W)PHDž`0"WH`(2l)PH >= axisHH H must beHHHt tensorHHH of inpuHHHThe rankH@(AHYHhHPL E1H5PoHyLͷW IALeMuCwIALeMIIPt H`4WLeMt6HEL9t!HX@t HxWHI9uH}LLeWEuhuLeMu)XH}ҶWhtHx轶WLeMt1HEL9t?HX@t Hx蘶WHI9uH}Le胶WEtL覶WL足W LfUHAVSIVWHHLH[A^]IH)WLiW UHAVSIWHHLH[A^]IHWL)W UHAVSIֵWHHLNH[A^]IH詵WLW UHAVSHPIH -oHHE(脵WHþ HzWHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]&H5 WH}H誼)H5YH諳)H56[Hܴ)HAH5WH)HH;Ȫ)HHH oHMHHUHuHuIIFIF0INIVIF@H]HEHtH+HBWH*oHH;Eu LHP[A^]聳WIH]HEHtH+HWL=W DUHAWAVAUATSHXHHPI1H+IǾH+IľH+ILLLeLLI>t#HYH X1ALY`;lH]LPLLIMILIGHHHHHپL9.I1H}L蒐*HHH I*ELH?HH=LHH>LHHHHOIOHHrHHEH)HȃHxDMI)HHHLH)HHHHHHFHH)HHHHIGHIGHE1HEиHHHXHpHE H`AHh1LD1ML 6 U 1M ? 1HgL@MH8LH诧IOLAuDڃL9IOLH)HCHHH?II>IIIAHH=HHL ALHzL9|HHHHuI4ffffff.XTXHHuHOH"DA+IL~H}sHu^cHEI fDX,XlXlXl XlXlXlXlHI9uH HEIHff.X(HHuHHrbHuIffff.XD XT0\@XAD0XXTPXD`XLpXHOH L9|((XL9~C X(XXHHJփHs 1HuZxHH)HHI1XlXlXlXlXlXlXlX,HH9uHt HMI HX)HHuL9}ND)D)HxH)L)Ht8HUIHLDX,HHuHskff.L[LHrRLH)HXIXhXhXhXhX(XhXhXh H Huffffff.^mML]Lu H A(\YI6AS\YIHHpI H1f.\d\YXD\YX\YX\YXHHuHHHtA\Ad0\YXYX((XHMHH9~tA\YXdff.WH]@A\YI H}HHH]^HM(XXI9Ht,HEIH@ \YXHHuHH]%LH)HMI IQ\YX\YX \YXA\YXHHufffff.H]HEI f. T\YX\YXL\YXD \YXHH9uHH]t2HEIH\YXHHuM^EXGQ gZi^)MH) IHEMtHHH8HHH(EY)EHɠM(m( YMAuEAAAM9MO\MM)MI?I>MIML@Ir$HEII I9IDH9E1IFH)LHt6HMIIMtAGt I迣WL跣WHW ffffff.UHAWAVAUATSH8IIIHHoHHEIF I+FHHQH8HoHHHLH@H8LâWHEEH_oLkLH@H8LաWL=oILHDž(HDž HDžHDžDž0H5 YH9Iv I+vHH=WHLHLL.r:t H7WLH8Lt H WLID$ I+D$HumIL$.INH;AIE I+EHIL$.INH;ALIHoHH;E[H8HLoHHHLH@H8LWHEEHoLkLH@H8LWL=LoILHDž(HDž HDžHDžDž0H5vYH4#It$ I+t$HHmWHLHHap:t HjWLH8Lt H @WL$WH5;oHH连WH8ǟWLHoHH;EUWH8HoHHHHH@H8HbWHEEL-oIEHHI@L8HmWHoHHHDž(HDž HDžHDžDž0H5YHLIL!.LHWH50YH_INHqHWHLLHLLn:t H袞WHHL8Ht H qWLUWH5loHHWH8WHoHH;E8LH8[A\A]A^A_]H8HoHHHLH@H8L芝WHEEH&oLcLH@H8L蜜WL=oILHDž(HDž HDžHDžDž0H5~YH0Iu I+uHHWHLuHHl:t HWLH8LH8HoHHHHH@H8HGWHEEL%oID$HHI@L8HQWHoHHHDž(HDž HDžHDžDž0H5y}YHDmIL.LHɚWH5}YHCINHqH襚WHLLHLL}k:t H膛WHHL8HHH8WbHHt(HW1HHLH8LHH螚WHeHHHL8HHt%H2WeHHLH8Lt H WLHtpH˙WbYHHHHHL8Ht H wWH4HLH8Lt H =WL!WH58oHH輗WH8ĘWHNW fDUHAVSIWHHLH[A^]IHɘWL W UHAWAVAUATSHHIHoHHEL-1oM,$I|$q*H4pI$W)EHEEHepsilonHEIL$HhHUHߐ*HhHDžhHtCt H{WHWEt H}WHu'HvoHH;EuHĘ[A\A]A^A_]4WWH@HhE;H58wYH zwYH}LhHPH(LHwYHuH@HDžLH50oH=H@荗W IǰLHhHIIPt H`WLuMt6HEL9t!HX@t HxؖWHI9uH}LLu辖WEuEuHhHu&^H}蛖WEtH}茖WHhHt:HpH9tELp@t HxaWLL9uHhHpFWLt%H@aWHIEt H}WM,$I|$ID$HtWLoM,$I|$3"*HToI$W)pHEƅpDžqsizeƅuHXHpHMLTB*HXHDžXHtCt H{LHWHDHWpt H}2HWH/EAD$WW)pƅp HEDžqalphfDžuaM|$HXHpLLi@*HXHDžXHtCt H{GWHGWpt H}GWHdAW.W)pHEƅpDžqbetaƅuM|$HXHpLL?*HXHDžXHtCt H{FWHFWpt H}FWHAW.W)pHEƅpDžqbiasƅuM|$HXHpLL%?*pt H}qFWHXHtKA?HDžXCt H{DFWHWLL9u I@t HP>WL}Mt6HEL9t!HX@t Hx>WHI9uH}LL}>WEupu#HXHu0H}a>WptH}O>WHXH{H`H9Lx@t Hx>WLL9u8I@t HP=WL}Mt6HEL9t!HX@t Hx=WHI9uH}LL}=WEupu#HXHu0H}=WptH}}=WHXHH`H9Lx@t HxJ=WLL9ufI@t HP%=WL}Mt6HEL9t!HX@t Hx=WHI9uH}LL}+H%WLF"W fffff.UHAVSHPIH[nHHE($WHþ H$WHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]mN&H5]0WH}H+)H55 YH")H5P[H,$)HH HQH5CgWHL%)HH;)HHH [oHMHPHUHuHuIIFIF0INIVIF@H]HEHtHE<+H"WHnHH;Eu LHP[A^]"WIH]HEHtH<+HF"WLW fffff.UHAVSHPIHnHHE("WHþ H "WHHCHCXHC`HCXHCHHC@HC8HC0HC(HC 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fffff.UHAVSHPIH@kHHE(4SHþ H*SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] "H5SH}HZ%H5]UH[%H5WH%HH HQH5 TH%HH;q%HHH `kHMHՖHUHuHuIIFIF0INIVIF@H]HEHtH'HSHf>kHH;Eu LHP[A^]*SIH]HEHtH`'HSLS fffff.UHAVSHPIH=kHHE(tSHþ HjSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] "H5SH}H%H5UH%H5&WH%HA H5TH%HH;踻%HHH kHMHܔHUHuHuIIFIF0INIVIF@H]HEHtH'H2SH;kHH;Eu LHP[A^]qSIH]HEHtH'HSL-S DUHAVSHPIHK;kHHE(SHþ HSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]"H5MSH}H%H5UH%H5vWH%HA H5:THC%HH;%HHH kHMHHUHuHuIIFIF0INIVIF@H]HEHtH<'HSH8kHH;Eu LHP[A^]SIH]HEHtH'H=SL}S DUHAVSHPIH8kHHE(SHþ H SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]"H5SH}H:%H5FUH;%H5WHl%HH HQH5TH%HH;Q%HHH @kHMHHUHuHuIIFIF0INIVIF@H]HEHtH'H˾SHF6kHH;Eu LHP[A^] SIH]HEHtH@'H膾SLƻS fffff.UHAVSHPIH5kHHE(TSHþ HJSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]-"H5SH}Hz%H5UH{%H5WH謽%HH HQH5TH̾%HH;葳%HHH kHMHHUHuHuIIFIF0INIVIF@H]HEHtH'H SH3kHH;Eu LHP[A^]JSIH]HEHtH'HƻSLS fffff.UHAVSHPIH3kHHE(蔻SHþ H芻SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]-"H5SH}H%H5UH軹%H5FWH%HH HQH5SH %HH;Ѱ%HHH kHMHEHUHuHuIIFIF0INIVIF@H]HEHtH'HKSH0kHH;Eu LHP[A^]芹SIH]HEHtH'HSLFS fffff.UHAVSHPIH[0kHHE(ԸSHþ HʸSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]"H5]SH}H%H5UH%H5WH,%HH HQH5CSHL%HH;%HHH kHMHEHUHuHuIIFIF0INIVIF@H]HEHtHE'H苶SH.kHH;Eu LHP[A^]ʶSIH]HEHtH'HFSL膳S fffff.UHAVSHPIH-kHHE(SHþ H SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]"H5SH}H:%H5FUH;%H5WHl%HA H5SH蓶%HH;X%HHH GkHMHLHUHuHuIIFIF0INIVIF@H]HEHtH'HҳSHM+kHH;Eu LHP[A^]SIH]HEHtHG'H荳SLͰS DUHAVSHPIH*kHHE(dSHþ HZSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]="H5SH}H芺%H5UH英%H5WH輲%HA H5SH%HH;訨%HHH kHMH\HUHuHuIIFIF0INIVIF@H]HEHtH'H"SH(kHH;Eu LHP[A^]aSIH]HEHtH'HݰSLS DUHAVSHPIH;(kHHE(贰SHþ H誰SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]M"H5=SH}Hڷ%H5UHۮ%H5fWH %HH HQH5#SH,%HH;%HHH kHMHeHUHuHuIIFIF0INIVIF@H]HEHtH%'HkSH%kHH;Eu LHP[A^]誮SIH]HEHtH'H&SLfS fffff.UHAVSHPIH{%kHHE(SHþ HSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]"H5}SH}H%H53UH%H5WHL%HH HQH5cSHl%HH;1%HHH kHMHUHUHuHuIIFIF0INIVIF@H]HEHtHe'H諫SH&#kHH;Eu LHP[A^]SIH]HEHtH 'HfSL覨S fffff.UHAVSHPIH"kHHE(4SHþ H*SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]"H5SH}HZ%H5sUH[%H5WH茪%HH HQH5SH謫%HH;q%HHH `kHMHHUHuHuIIFIF0INIVIF@H]HEHtH'HSHf kHH;Eu LHP[A^]*SIH]HEHtH`'H覨SLS fffff.UHAVSHPIHkHHE(tSHþ HjSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]M"H5SH}H蚯%H5UH蛦%H5&WḨ%HH HQH5SH%HH;豝%HHH kHMHHUHuHuIIFIF0INIVIF@H]HEHtH'H+SHkHH;Eu LHP[A^]jSIH]HEHtH蠿'HSL&S fffff.UHAVSHPIH;kHHE(败SHþ H誥SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]M"H5=SH}Hڬ%H5UHۣ%H5fWH %HA H5*SH3%HH;%HHH kHMHHUHuHuIIFIF0INIVIF@H]HEHtH,'HrSHkHH;Eu LHP[A^]豣SIH]HEHtH'H-SLmS DUHAVSHPIHkHHE(SHþ HSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]"H5SH}H*%H5CUH+%H5WH\%HA H5zSH胣%HH;H%HHH 7kHMHHUHuHuIIFIF0INIVIF@H]HEHtH|'H SH=kHH;Eu LHP[A^]SIH]HEHtH7'H}SL轝S DUHAVSHPIHkHHE(TSHþ HJSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]"H5ݫSH}Hz%H5UH{%H5WH謟%HH HQH5SH̠%HH;葕%HHH kHMHHUHuHuIIFIF0INIVIF@H]HEHtHŷ'H SHkHH;Eu LHP[A^]JSIH]HEHtH耷'HƝSLS fffff.UHAVSHPIHkHHE(蔝SHþ H芝SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]-"H5SH}H躤%H5ӢUH軛%H5FWH%HH HQH5SH %HH;ђ%HHH kHMHHUHuHuIIFIF0INIVIF@H]HEHtH'HKSHkHH;Eu LHP[A^]芛SIH]HEHtH'HSLFS fffff.UHAVSHPIH[kHHE(ԚSHþ HʚSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]m"H5]SH}H%H5UH%H5WH,%HA H5JSHS%HH;%HHH kHMHHUHuHuIIFIF0INIVIF@H]HEHtHL'H蒘SH kHH;Eu LHP[A^]јSIH]HEHtH'HMSL荕S DUHAVSHPIHkHHE($SHþ HSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]"H5SH}HJ%H5sUHK%H5WH|%HH HQH5SH蜘%HH;a%HHH PkHMHHUHuHuIIFIF0INIVIF@H]HEHtH蕯'HەSHV kHH;Eu LHP[A^]SIH]HEHtHP'H薕SL֒S fffff.UHAVSHPIH kHHE(dSHþ HZSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]="H5SH}H芜%H5UH苓%H5WH輔%HH HQH5SHܕ%HH;衊%HHH kHMHUHUHuHuIIFIF0INIVIF@H]HEHtHլ'HSH kHH;Eu LHP[A^]ZSIH]HEHtH萬'H֒SLS fffff.UHAVSHPIH+ kHHE(褒SHþ H蚒SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]}"H5-SH}Hʙ%H5UHː%H5VWH%HH HQH5SH%HH;%HHH kHMH%,HUHuHuIIFIF0INIVIF@H]HEHtH'H[SHkHH;Eu LHP[A^]蚐SIH]HEHtHЩ'HSLVS fffff.UHAVSHPIHkkHHE(SHþ HڏSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]}"H5mSH}H %H53UH %H5WH<%HH HQH5SSH\%HH;!%HHH kHMHEKHUHuHuIIFIF0INIVIF@H]HEHtHU'H蛍SHkHH;Eu LHP[A^]ڍSIH]HEHtH'HVSL薊S fffff.UHAVSHPIHkHHE($SHþ HSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]轶"H5SH}HJ%H5sUHK%H5ָWH|%HH HQH5SH蜍%HH;a%HHH PkHMHEuHUHuHuIIFIF0INIVIF@H]HEHtH蕤'HۊSHVkHH;Eu LHP[A^]SIH]HEHtHP'H薊SLևS fffff.UHAVSHPIHkHHE(dSHþ HZSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]="H5SH}H芑%H5UH苈%H5WH載%HH HQH5SH܊%HH;%HHH kHMHEsHUHuHuIIFIF0INIVIF@H]HEHtHա'HSHjHH;Eu LHP[A^]ZSIH]HEHtH萡'HևSLS fffff.UHAVSHPIH+jHHE(複SHþ H蚇SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]}"H5-SH}Hʎ%H5UH˅%H5VWH%HH HQH5SH%HH;|%HHH нkHMHEqHUHuHuIIFIF0INIVIF@H]HEHtH'H[SHjHH;Eu LHP[A^]蚅SIH]HEHtHО'HSLVS fffff.UHAVSHPIHkjHHE(SHþ HڄSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]}"H5mSH}H %H53UH %H5WH<%HH HQH5SSH\%HH;!z%HHH kHMHEoHUHuHuIIFIF0INIVIF@H]HEHtHU'H蛂SHjHH;Eu LHP[A^]ڂSIH]HEHtH'HVSLS fffff.UHAVSHPIHjHHE($SHþ HSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]轫"H5SH}HJ%H5sUHK%H5֭WH|%HH HQH5SH蜂%HH;aw%HHH PkHMHEmHUHuHuIIFIF0INIVIF@H]HEHtH蕙'HSHVjHH;Eu LHP[A^]SIH]HEHtHP'HSL|S fffff.UHAVSHPIHjHHE(dSHþ HZSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]="H5SH}H芆%H5UH}%H5WH~%HH HQH5SH%HH;t%HHH kHMHEkHUHuHuIIFIF0INIVIF@H]HEHtHՖ'H}SHjHH;Eu LHP[A^]Z}SIH]HEHtH萖'H|SLzS fffff.UHAVSHPIH+jHHE(|SHþ H|SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]}"H5-SH}Hʃ%H5UHz%H5VWH{%HH HQH5SH}%HH;q%HHH вkHMHEiHUHuHuIIFIF0INIVIF@H]HEHtH'H[zSHjHH;Eu LHP[A^]zSIH]HEHtHГ'HzSLVwS fffff.UHAVSHPIHkjHHE(ySHþ HySHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]}"H5mSH}H %H53UH x%H5WH%HHH kHMHHUHuHuIIFIF0INIVIF@H]HEHtHe`'HFSH&jHH;Eu LHP[A^]FSIH]HEHtH `'HfFSLCS fffff.UHAVSHPIHjHHE(4FSHþ H*FSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] o"H5QSH}HZM%H5KUH[D%H5qWHE%HH HQH5SHF%HH;q;%HHH `|kHMH5HUHuHuIIFIF0INIVIF@H]HEHtH]'HCSHfjHH;Eu LHP[A^]*DSIH]HEHtH`]'HCSL@S fffff.UHAVSHPIHjHHE(tCSHþ HjCSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]Mr"H5NSH}HJ%H5HUHA%H5&oWHB%HH HQH5SHC%HH;8%HHH ykHMH$HUHuHuIIFIF0INIVIF@H]HEHtHZ'H+ASHjHH;Eu LHP[A^]jASIH]HEHtHZ'H@SL&>S fffff.UHAVSHPIH;jHHE(@SHþ H@SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]Mp"H5=LSH}HG%H5FUH>%H5flWH @%HH HQH5#SH,A%HH;5%HHH vkHMH%CHUHuHuIIFIF0INIVIF@H]HEHtH%X'Hk>SHjHH;Eu LHP[A^]>SIH]HEHtHW'H&>SLf;S fffff.UHAVSHPIH{jHHE(=SHþ H=SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]g"H5}ISH}HE%H5XCUH<%H5iWHL=%HH HQH5cSHl>%HH;13%HHH tkHMH%mHUHuHuIIFIF0INIVIF@H]HEHtHeU'H;SH&jHH;Eu LHP[A^];SIH]HEHtH U'Hf;SL8S fffff.UHAVSHPIHjHHE(4;SHþ H*;SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] d"H5FSH}HZB%H5@UH[9%H5fWH:%HH HQH5}SH;%HH;q0%HHH `qkHMH%kHUHuHuIIFIF0INIVIF@H]HEHtHR'H8SHfjHH;Eu LHP[A^]*9SIH]HEHtH`R'H8SL5S fffff.UHAVSHPIHjHHE(t8SHþ Hj8SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]Mg"H5CSH}H?%H5=UH6%H5&dWH7%HH HQH5zSH8%HH;-%HHH nkHMH%iHUHuHuIIFIF0INIVIF@H]HEHtHO'H+6SHjHH;Eu LHP[A^]j6SIH]HEHtHO'H5SL&3S fffff.UHAVSHPIH;jHHE(5SHþ H5SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]Me"H5=ASH}H<%H5;UH3%H5faWH 5%HH HQH5#xSH,6%HH;*%HHH kkHMH%gHUHuHuIIFIF0INIVIF@H]HEHtH%M'Hk3SHjHH;Eu LHP[A^]3SIH]HEHtHL'H&3SLf0S fffff.UHAVSHPIH{jHHE(2SHþ H2SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]\"H5}>SH}H:%H5X8UH1%H5^WHL2%HH HQH5cuSHl3%HH;1(%HHH ikHMH%eHUHuHuIIFIF0INIVIF@H]HEHtHeJ'H0SH&jHH;Eu LHP[A^]0SIH]HEHtH J'Hf0SL-S fffff.UHAVSHPIHjHHE(40SHþ H*0SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] Y"H5;SH}HZ7%H55UH[.%H5[WH/%HH HQH5rSH0%HH;q%%HHH `fkHMH%cHUHuHuIIFIF0INIVIF@H]HEHtHG'H-SHfjHH;Eu LHP[A^]*.SIH]HEHtH`G'H-SL*S fffff.UHAVSHPIHjHHE(t-SHþ Hj-SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]M\"H58SH}H4%H52UH+%H5&YWH,%HH HQH5oSH-%HH;"%HHH ckHMH%aHUHuHuIIFIF0INIVIF@H]HEHtHD'H++SHjHH;Eu LHP[A^]j+SIH]HEHtHD'H*SL&(S fffff.UHAVSHPIH;jHHE(*SHþ H*SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]MZ"H5=6SH}H1%H50UH(%H5fVWH *%HH HQH5#mSH,+%HH;%HHH `kHMH%_HUHuHuIIFIF0INIVIF@H]HEHtH%B'Hk(SHjHH;Eu LHP[A^](SIH]HEHtHA'H&(SLf%S fffff.UHAVSHPIH{jHHE('SHþ H'SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]S"H5}3SH}H/%H5X-UH&%H5SWHL'%HH HQH5cjSHl(%HH;1%HHH ^kHMH%]HUHuHuIIFIF0INIVIF@H]HEHtHe?'H%SH&jHH;Eu LHP[A^]%SIH]HEHtH ?'Hf%SL"S fffff.UHAVSHPIHjHHE(4%SHþ H*%SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]Q"H50SH}HZ,%H5*UH[#%H5PWH$%HH HQH5gSH%%HH;q%HHH `[kHMHU~HUHuHuIIFIF0INIVIF@H]HEHtH<'H"SHfjHH;Eu LHP[A^]*#SIH]HEHtH`<'H"SLS fffff.UHAVSHPIHjHHE(t"SHþ Hj"SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] L"H5-SH}H)%H5'UH %H5&NWH!%HA H5dSH"%HH;%HHH XkHMH<HUHuHuIIFIF0INIVIF@H]HEHtH9'H2 SHjHH;Eu LHP[A^]q SIH]HEHtH9'HSL-S DUHAVSHPIHKjHHE(SHþ HSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H"H5M+SH}H&%H5(%UH%H5vKWH%HA H5:bSHC %HH;%HHH UkHMHLHUHuHuIIFIF0INIVIF@H]HEHtH<7'HSHjHH;Eu LHP[A^]SIH]HEHtH6'H=SL}S DUHAVSHPIHjHHE(SHþ H SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]K"H5(SH}H:$%H5x"UH;%H5HWHl%HA H5_SH%HH;X%HHH GSkHMH\HUHuHuIIFIF0INIVIF@H]HEHtH4'HSHMjHH;Eu LHP[A^]SIH]HEHtHG4'HSLS DUHAVSHPIHjHHE(dSHþ HZSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]I"H5%SH}H!%H5UH%H5FWH%HA H5\SH%HH;%HHH PkHMHlHUHuHuIIFIF0INIVIF@H]HEHtH1'H"SHjHH;Eu LHP[A^]aSIH]HEHtH1'HSLS DUHAVSHPIH;jHHE(SHþ HSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]C"H5=#SH}H%H5UH%H5fCWH %HA H5*ZSH3%HH; %HHH MkHMH|HUHuHuIIFIF0INIVIF@H]HEHtH,/'HrSHjHH;Eu LHP[A^]SIH]HEHtH.'H-SLmS DUHAVSHPIHjHHE(SHþ HSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]A"H5 SH}H*%H5hUH+%H5@WH\%HA H5zWSH%HH;H %HHH 7KkHMHHUHuHuIIFIF0INIVIF@H]HEHtH|,'HSH=jHH;Eu LHP[A^]SIH]HEHtH7,'H}SLS DUHAVSHPIHۉjHHE(TSHþ HJSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H];"H5SH}Hz%H5UH{%H5>WH%HH HQH5TSH%HH;%HHH HkHMHHUHuHuIIFIF0INIVIF@H]HEHtH)'H SHjHH;Eu LHP[A^]JSIH]HEHtH)'HSL S fffff.UHAVSHPIHjHHE(SHþ HSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]m8"H5SH}H%H5UH %H5F;WH%HH HQH5RSH %HH;%HHH EkHMHHUHuHuIIFIF0INIVIF@H]HEHtH''HK SHƄjHH;Eu LHP[A^] SIH]HEHtH&'H SLF S fffff.UHAVSHPIH[jHHE( SHþ H SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H];"H5]SH}H%H5BUH %H58WH, %HH HQH5COSHL %HH;%HHH CkHMHHUHuHuIIFIF0INIVIF@H]HEHtHE$'H SHjHH;Eu LHP[A^] SIH]HEHtH$'HF SLS fffff.UHAVSHPIHjHHE( SHþ H SHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]3"H5SH}H:%H5UH;%H55WHl %HH HQH5LSH %HH;Q$HHH @@kHMHHUHuHuIIFIF0INIVIF@H]HEHtH!'HSHFjHH;Eu LHP[A^] SIH]HEHtH@!'HSLS fffff.UHAVSHPIH~jHHE(TSHþ HJSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]-0"H5SH}Hz%H5 UH{%H53WH%HH HQH5ISH%HH;$HHH =kHMHHUHuHuIIFIF0INIVIF@H]HEHtH'H SH|jHH;Eu LHP[A^]JSIH]HEHtH'HSLS fffff.UHAVSHPIH|jHHE(SHþ HSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]m3"H5SH}H %H5 UH%H5F0WH%HH HQH5GSH %HH;$HHH :kHMHHUHuHuIIFIF0INIVIF@H]HEHtH'HKSHyjHH;Eu LHP[A^]SIH]HEHtH'HSLFR fffff.UHAVSHPIH[yjHHE(SHþ HSHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]m+"H5] SH}H%H5BUH$H5-WH,%HA H5JDSHS%HH;$HHH 8kHMHHUHuHuIIFIF0INIVIF@H]HEHtHL'HRH wjHH;Eu LHP[A^]RIH]HEHtH'HMRLR DUHAVSHPIHvjHHE($RHþ HRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]'"H5 SH}HJ%H5UHK$H5*WH|$HA H5ASH$HH;h$HHH W5kHMH HUHuHuIIFIF0INIVIF@H]HEHtH'HRH]tjHH;Eu LHP[A^]!RIH]HEHtHW'HRLR DUHAVSHPIHsjHHE(tRHþ HjRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]M+"H5SH}H%H5UH$H5&(WH$HA H5>SH$HH;$HHH 2kHMHHUHuHuIIFIF0INIVIF@H]HEHtH'H2RHqjHH;Eu LHP[A^]qRIH]HEHtH'HRL-R DUHAVSHPIHKqjHHE(RHþ HRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]]#"H5MSH}H%H5=TH$H5v%WH$HH HQH53TH˨$H5VVH$HH HQH5RH$HH;$HHH jHMHUHUHuHuIIFIF0INIVIF@H]HEHtH&H[RHjHH;Eu LHP[A^]蚨RIH]HEHtH&HRLVR fffff.UHAVSHPIHkjHHE(RHþ HڧRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]!H5mRH}H $H5~TH $H5VH<$HH HQH5SRH\$HH;!$HHH jHMHU HUHuHuIIFIF0INIVIF@H]HEHtHU&H蛥RHjHH;Eu LHP[A^]ڥRIH]HEHtH&HVRL薢R fffff.UHAVSHPIHjHHE($RHþ HRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]!H5RH}HJ$H5THK$H5VH|$HH HQH5RH蜥$HH;a$HHH PjHMH#HUHuHuIIFIF0INIVIF@H]HEHtH蕼&HۢRHVjHH;Eu LHP[A^]RIH]HEHtHP&H薢RL֟R fffff.UHAVSHPIHjHHE(dRHþ HZRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]=!H5RH}H芩$H5TH苠$H5VH輡$HH HQH5RHܢ$HH;街$HHH jHMH!HUHuHuIIFIF0INIVIF@H]HEHtHչ&HRHjHH;Eu LHP[A^]ZRIH]HEHtH萹&H֟RLR fffff.UHAVSHPIH+jHHE(褟RHþ H蚟RHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]=!H5-RH}Hʦ$H5>TH˝$H5VVH$HH HQH5RH$HH;$HHH jHMHHUHuHuIIFIF0INIVIF@H]HEHtH&H[RHjHH;Eu LHP[A^]蚝RIH]HEHtHж&HRLVR fffff.UHAVSHPIHkjHHE(RHþ HڜRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]}!H5mRH}H $H5~TH $H5VH<$HA H5ZRHc$HH;($HHH jHMH<6HUHuHuIIFIF0INIVIF@H]HEHtH\&H袚RHjHH;Eu LHP[A^]RIH]HEHtH&H]RL蝗R DUHAVSHPIHjHHE(4RHþ H*RHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] !H5RH}HZ$H5ΟTH[$H5VH茙$HA H5RH賚$HH;x$HHH gjHMHL4HUHuHuIIFIF0INIVIF@H]HEHtH謱&HRHmjHH;Eu LHP[A^]1RIH]HEHtHg&H譗RLR DUHAVSHPIH jHHE(脗RHþ HzRHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]!H5 RH}H語$H5TH諕$H56VHܖ$HA H5RH$HH;Ȍ$HHH jHMH\2HUHuHuIIFIF0INIVIF@H]HEHtH&HBRH jHH;Eu LHP[A^]聕RIH]HEHtH跮&HRL=R DUHAWAVAUATSHHU jHHEHH~@HpHG8H;G0(H' jHH;EuHĈ[A\A]A^A_]R蓔RIHh>B6H5TH TH}LhHPH<%AHTHuLHcE1H5Y-jHLVRRIHhA6H5'TH qTH}Lh9HPH$AHTHuLHE1H5,jHLԓR}蒓RIHh=A6H5TH TH}LhHPH;$AH7THuLHbE1H5X,jHLUR IALhMIIALhMIIALhM:oIlIPt H`脒RLmMt6HEL9t!HX@t Hx_RHI9uH}LLmEREuEu LhMu-H}REtH}RLhMHpL9HX@t HxݑRHI9uIPt H`踑RLmMt6HEL9t!HX@t Hx蓑RHI9uH}LLmyREuEu LhMu-'H}SREtH}DRLhMHpL9HX@t HxRHI9uIPt H`RLmMt6HEL9t!HX@t HxǐRHI9uH}LLm譐REuEuLhMu&^H}芐REtH}{RLhMt:HpL9tEHX@t HxPRHI9uHhLp5REtLXRLhR Lff.UHUHAWAVAUATSHRIH}=6H5TH ~THXLE#W)EHEE&HoverloadHEHmust be HEEed.AHtXHXHMLE1H5(jH?L蓏R 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UHAWAVAUATSHHIIHRAcHHEHGHGHGHG ?M IMHGHELmH}DA$Mt$It$IDIET$H}F:HH}LMHuLeMWM]IL9r 1HIIHJHZHHNA$&ML$Mt$M"MfDHCH9tL9r1IHL9KHtHSL9ueu11ffffff.T A:uDHH9uH{ LLMMKMMLmLeHuqHHUsHCH9tL9r1IHL9uUCtHCH0HHu6fDHuMI!HJHf.Mſ0KIMLHHL}LKIGIF(HEIFIH}HGHxH*!fff.HHH H*XLHHH Hx5H*O t9Y.w0LLmLeHHHH*XO uH 6HrHV1HLe^f: (]\H,H HH1H,.HCH9HCIIuHFHLt=H]H9Lmr1HHHfffff.HHHt$HIH]H!LmHHHufHUHIL2HIHOH@HNH1H9+1HHDMMLUHCH9t L!L9KHtHSL9u_u+1fT A:uDHH9uH{ LLMMKLUMMLmLeHutaHHe`HCH9t L!L9KCt#HCHt*HHu-ffff.HufDI M93H!HHL0HGI M9H M<LuH}ffffff.I_Lt'L9r 1HIHH9uLL>MufH!H9tI$H<HMHuIMHAWHtIwHuLIAVt"IvHMH9HHtSt'IN (HMH9t"ff.INH1AT7:1HH9uILHuH4LHL>MMHfIAEt4IMHUH9I t;Iu pKt>fHUH9thfffff.IuH2KuJIMMHuMH([A\A]A^A_]ÿ(KHH5LH莼KHY6cHHH5 5cH4cH(KIHKLK UHAWAVAUATSH(H(6cHHEIIA$M|$It$IDIET$H}a/HMuM]MNML9+IIEJH3HH'A$7Ml$M|$MHu3E1DHCH9ugKHtHSL9ubH{HtH{ uaM1ffffff.T A:u+HH9uvL9r1IHL9[HHnJM>LLMM;KMMHuE1uMI!IEJH1HIIIEJHE1Hz4cHH;EFKDMHuE1fHCH9tHL!L9H9KHtHSL9udH{HtH{ u'Mt[1ff.T A:u5HH9u9Mt4LLMLM½KLMMHutDHHTIH3cHH;ELH([A\A]A^A_]H,UHAWAVAUATSH(HX3cHHEIIA$M|$It$IDIET$H},HMuM]MNML9+IIEJH3HH'A$7Ml$M|$MHu3E1DHCH9ugKHtHSL9ubH{HtH{ uaM1ffffff.T A:u+HH9uvL9r1IHL9[HHnJM>LLMMKMMHuE1uMI!IEJH1HIIIEJHE1H1cHH;EvKDMHuE1fHCH9tHL!L9H9KHtHSL9udH{HtH{ u'Mt[1ff.T A:u5HH9u9Mt4LLMLMKLMMHutDHHTIH0cHH;ELH([A\A]A^A_]H1)UHAWAVAUATSHIH7LwI)IIFHH;HHWH)HHHH9H}wYHH9HBHDH9vC赸KHH5JLHKH/cHHH5.cH-cH赸KHHHEHH]KHIN,3LLH]cKID$JD3Mu HEL8L`M9t;M)I@I}L.KID$IEIII uHEL LxMHMHHL(LpHMHHHM9t'ff.I_AGt I蚷KII9uMtLH[A\A]A^A_]wKH[A\A]A^A_]1HE1 輴KIH{KL苴K IMM9u2IM9t*fffff.I^AFt I~ KII9uH}HtKL4K fff.UHAWAVSHHI.cHHEHDžpHDžhƅ` fDženeDžaaffiƅgHH`袴KfDžW)@HDžPƅ@DžAreluƅEHH@YKfDžW) HDž0ƅ IleakyrelL!fDž)uHH KW)ƅHDžHldedreluHfDžHthresholHƅHH観KfDžW)HDžƅDžtanhƅHH]KfDžW)HDžƅHscaledtaHfDžnhƅH HKfDž8W)HDžƅHsigmoidHH@H軲KfDžXW)HDžƅHhardsigmHDžoidH`HhKW)`ƅ`fDžxHDžpDžaeluH}H`)KW)@fEƅ@HDžPHsoftsignHAƅIH}H@߱KW) fEƅ HDž0HsoftplusH!ƅ)H}H 蕱KfEH=>fH -EEE`@  % 1@=`IUamy @`t Hp.KH=yPH5fHf2KW)`ƅ`HDžpLafDžiuHH`KHDž #<W)@HDžPƅ@HADžIoidHH@趯KHL>?HW) ƅ HDž0Dž!eluHH lKHDž?H=8fH/ @`t HpǰKH=OH5ÒfHd˰KH((cHH;EH[A^A_]H}肰KEAH}oKE8H}\K`/HpCK@#HP*K H0K HKH߯KHƯKH譯KH蔯K H0{K@HPbK`HpIKH0KHKHKHK{ḪK oH0賮K@cHP蚮K`SZH|KlHcK`HJK TH01K@HHPK`8?aKIu u,u8E1 uU_HɭKtH购KtH蟭KE1 uIA t H0vK@u3IA@tHPFK`tIA1`u Hu_HpKHJA@Hffffff.`t Hp׬KHu IEEE`@ E1  H}%KEfH}KE]H}K`THpK@HHPͫK AE$~6HcI<уAN΍DH4iKHLHH9|FA])DHHcq$HA9u@AЅHcHEHDIcHHHGHHs tHHGMtHII9s ILfHHZHMƉHH9HLHHHGHHtH@HcwHHDCHH0H8LMHt+HIEHuIELPL^KHHGHH0H8HH HOHH H(HHHHHHHHHHLmDWW$A҉O(u u҉IUIuHXH`H9HHHDGHDO HHxH|$ DL$DT$D$D$LLIIHLLtHHIMIELHI9IrH9ABA;IMLIMIBH9L)I HLHtULJZ>9}I9|IA;|M9|IA;|IMHHLHuMBH;}I A;LMIA0ALAM9NȉDž?DžAE$|A]Lz |AU MIIIxHD$(H|$t$HL$L$HD$ LMMDž?HHpHHhEuDdAM4DtDA}8ÉdtÉdLHHHdHHHHHHHH|HHHHHHHHHHEH HH(HEH0HpH8HhH@HHHHHPHE HX|AE$***XYMxLHL\K۹ODD1HcH'MdHELe]H]H]fWf)EEHULHf.H}H9t+HHt,HP($IxH1UHP HHc;HLHHLHEH<HMH HPHxHQHHPtHhHQtHpHHIHHH@HE1AfG EMcp$MADDAB HHLHcHLAAAiIAMGIIyLHfDA@$$AHcHH<уAN͍DH4DYKHLII9BIIGHxH4H<LI|H9sLHHFHH_Hs1AHDAHHhH DHHpHHCH)1ADDLDLDLDLDLDLDf Df H HuHtNDAAIcHHHHcHHH H1Df Df H HuI9LHt:MMH1fff.ALALHLHL9rfII94_I1AA<u>A@$~6HcI<уAN΍DH4VKHLHH9|FAX)DHHcq$HA9u@AЅHcHEHDIcHHHGHHs tHHGMtHII9s IA9uH[A^A_]fff.UHH U^H1H9NHD]DUHHKLeMt6HEL9t!Lh@t Hx>KLM9uH}LLe>KEuEuL`Mu&^H}>KEtH}>KL`Mt:HhL9t=Lh@t HxT>KLM9uH`Lh9>KEtJL\>K@LHL@HDž@Mu+H}HuQH`Ht Hh=KH*;K AFt I~=KL=KH}Ht HH}HtH}=KH`HuHH`HuHH`Huf.UHHH 3cHHxH@Ht]F=K]fDUHSPHH2cHH{HCHt=KHH[]=KfUHAWAVSPI HkH5LHH`CH5`MHa:H5gOH;HA H5~KH<HH;~1HHH mrcHMHRHUHuHuIIFIF0INIVIF@H]HEHtHSH9KHsbHH;Eu LHP[A^]7:KIH]HEHtHmSH9KL6K ff.UHAWAVAUATSHIIHbHHE1L螖HHtpHǪ^1L5H<H4HKH+KHH.袋.HHIHbHH;EuLHĈ[A\A]A^A_]T9K9KIHh.H5^MH 6_MH}Lh *W)PHDž` 8KH`(v\)PHe input.HHHsequenceHHHptr for HHHGot nullH@AHBLHuHPLnE1H5dbH zLa8K8KIHh.H5^MH P^MH}Lh#DW)PHDž`07KH`(v\)PHt tensorHH%Houtput tHH Hptr for HHHGot nullHHHLength: HHHSequenceH@-AH^MHuHPLlE1H5bbH yL_7K IALeMIALhMIIALeMFIALhMnIIPt H`6KLeMt6HEL9t!HX@t Hx\6KHI9uH}LLeB6KEuEu LhMu-'H}6KEtH} 6KLhMHpL9HX@t Hx5KHI9uIPt H`5KLeMt6HEL9t!HX@t Hx5KHI9uH}LLev5KEuEuLhMu&^H}S5KEtH}D5KLhMt:HpL9tEHX@t Hx5KHI9uHhLp4KEtL!5KL12K L@UHAWAVATSH`IHGbHHEؿ(4KHþ H4KHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H59YMH}HV7H޸H5.@KHH<7Id]I\cHW)EHE2KHEHHHML HXHMH5 LHUL6H5YMHt2H5_OH3HA H5vKH4HH;)HHH jcHMHūHUHuHuIIGIG0IOIWIG@H}Ht H}2KH]HEHtHKH1KHtbHH;EuLH`[A\A^A_]42KIH]HEHt7HfKH1KL.K IH}HuH]HEHuL.K H}r1KH]HEHt룐UHAWAVAUATSHIIHbHHE1L^HH LEtHH$H8HSH+SHH.袋.HHHH9H9@HxHy HH8L(HH8IM}1LL,HLL L`PMuMuPM9#I}0HPhHxL&!H1ff.LLa.KHL!IIH9|H0H`WMH`HkWMH@HuKHEHxH`H8H@L0LMHLHv-xtSH}/KHbHH;EtJ/KLG!II}0HPILLH0KL(IHbHH;EuLHĸ[A\A]A^A_]ÿ!/KIH`.H5 UMH UMHxL`VFW)@HDžP .KHP(m\)@He input.HHHsequenceHHHptr for HHHGot nullH@AH8LHxH@L臗E1H5}bH&pLz.K5.KIH`.H5!TMH TMHxL`YZW)@HDžP0-KHP(_)@Hdex tensHHHt for inHHHptr inpuHHHGot nullHf@ or@"AHTMHxH@L蕖E1H5bH4oL-KC-KIH`.H5/SMH SMHxL`ehW)@HDžP0,KHP(?_)@Hut tensoHH Hfor outpHHHnullptr HHHAt: Got HHHSequenceHf@(rAHESMHxH@L處E1H5bH8nL,K IALeMuFzIAL`MII@t HP+KLeMt6HEL9t!HX@t Hx+KHI9uH}LLe+KEuxu#L`Mu0xH}+KxtH}v+KL`MMHhL9THX@t HxC+KHI9u IALeMIAL`MIIALeMLIAL`MzII@t HP*KLeMt6HEL9t!HX@t Hx*KHI9uH}LLei*KEuxu#L`Mu00H}@*KxtH}.*KL`MHhL9 HX@t Hx)KHI9uI@t HP)KLeMt6HEL9t!HX@t Hx)KHI9uH}LLe)KEuxu L`Mu)aH}q)KxtH}_)KL`Mt:HhL9tEHX@t Hx4)KHI9uH`Lh)KEt,L<)KLL&K LIxt H}(KL(&K UHAWAVAUATSHHEbHHEHG0@EătuIekHcH bH H;Mt*KHH bH H;MuHĘ[A\A]A^A_](Km(KIHX.H5YNMH ZSMHpLXE蒎HLHEH@HuHUH耘Hp1LH,H5%bHiL"(K I@t HP'KLeMt6HEL9t!HX@t Hx'KHI9uH}LLe{'KEt H}l'Kpt H}Z'KE1LXMuTHSIALXMt:H`L9t5HX@t Hx'KHI9uHXL`&KEu LIL'KL$K fDUHAVSHPIH;bHHE(&KHþ H&KHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5-KMH}HJ)H5LMH$H5fROH &HA H5*iKH3'HH;HHH \cHMHHUHuHuIIFIF0INIVIF@H]HEHtH,>Hr$KHbHH;Eu LHP[A^]$KIH]HEHtH=H-$KLm!K DUHAWAVATSH0IHHbHHEL%cL#H{ZHdHHCW)EE LsHEEdtypfEeH}HULLH]HEHtCt H{#KHw#KEt H}h#KHtIHךbHH;Eu H0[A\A^A_]#KIEt H}(#KL#H{HCHt#KLO K UHAWAVAUATSHHIIH\bHHEH{HH+H1HH HHwHHIGHHH [HcH赘Y螜RKpDy=R6/ĝ(荛!ƙؘ 芚HH|IHqbHH;EuLHĘ[A\A]A^A_],"K!KIH`.H5GMH WIMHxL`{W)@HDžP0z!KHP(2l\)@HsequenceHH&Hutput seHH Htr for oHHHot nullpHHHEmpty: GHHHSequenceH@.AHGMHxH@L$E1H5bHbL!K KIH`}.H5FMH OHMHxL`IHHMHEH@HuHLr|Hx1LH莉H5bH0bL K I@t HP KL}Mt6HEL9t!HX@t HxKHI9uH}LL}KEt H}Kxt H}KE1L`MuTH赏IAL`Mt:HhL9t8HX@t HxrKHI9uH`LhWKEu &LILIALeMuFzIAL`MII@t HPKLeMt6HEL9t!HX@t HxKHI9uH}LLeKEuxu L`Mu)aH}KxtH}zKL`Mt:HhL9tEHX@t HxOKHI9uH`Lh4KEtLWKLgK Lf RRUHAWAVATSH`IH7bHHEؿ(KHþ HKHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5)BMH}HF InFIfLHW)EHEKHEHHHML HXHMH5KHULH5CMH~H5 IOHHA H5_KHHH;HHH ScHMHoHUHuHuIIGIG0IOIWIG@H}Ht H}$KH]HEHtH4HKH~bHH;EuLH`[A\A^A_]>KIH]HEHt7Hp4HKLK IH}HuH]HEHuLK H}|KH]HEHtff.UHAWAVAUATSHHőbHHEHIIW)EHEHHoI?tkH*@MH TBM1AL.H]HKHHCH5HHPHKH@bHH;E 0H(IMl$0ID$I\$ W)PHDž`H)tnH8L0IILH=HWKILXLPKH`HL0H8~LHKILXHEH@HEHHHtH@HhHPH@Ll!HHHt#HHCHuHHPHKHPHt HXKLhLmM4$Mt$PM9tvI|$0HPhI|$HtMHXH?MH@Hl[KHEHpHXH8H@L0LMHLHW-pt H}cKHތbHH;ELH[A\A]A^A_]þL_XIOI+OHH.袋.HH8H0HHHHH9H9HxH0H9ttupH,6KIEIEIEHpID$HxIHEID$LI$HkzbHH;EuLH[A\A]A^A_]ÿKIHX脰.H5(MH ,MHpLXhW)@HDžP0yKHP(1M\)@HsequenceHH&Hutput seHH Htr for oHHHot nullpHHHErase: GHHHSequenceH@.AH(MHpH@L#kE1H5bHCLKKIHX|.H5'MH +MHpLXgW)@HDžP qKHP(?\)@He input.HHHsequenceHHHptr for HHHGot nullH@AH)MHpH@L7jE1H5-bHBL*KI@tHPKLeMuEIALeMt6HEL9t!HX@t HxKHI9uH}LLeoKEupu#LXMuGvH}FKptH}4KLXMuJIALXM4H`L9HX@t HxJHI9u I@tHPJLeMuEIALeMt6HEL9t!HX@t HxJHI9uH}LLepJEupu LXMu=zH}JJptH}8JLXMuQIALXMt?H`L9t$HX@t HxJHI9uHXLL`JEuILJyIptmH}bILpMuVILpMtGHxL9tEMI)Hèfff.H\!HèIXuHpLxIJLJ Lfff.UHAVSHPIHubHHE(JHþ H JHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]MH5 KH}HH2H5r"MHHH52&MH!H5)OHRHA H5p@KHyHH;>HHH -4cHMHvHUHuHuIIFIF0INIVIF@H]HEHtHrHJH3sbHH;Eu LHP[A^]JIH]HEHtH-HsJLJ ff.UHAWAVAUATSHIHHrbHHEHHLc(MH(I|$HH+Hq1LH^H~HMH0H8E11L=Lx0ffffff.LD=EL9x0`JHH](k^)EPHH5$MJCPHHHM-HHHDžHHtCt H{ JHJEtH}JAE9DH8LSW)EHEH}L1L}@L<H@HLL1L@HDž@MtAFt I~hJL`JD9|L8M|$MtHH0LpHII\$L9t9MI)Hè@HV!HèIXuI>L8H0H&LM}LJIFIFIHEID$HEHHEID$H(HH !pbH H;MuHĸ[A\A]A^A_]JJIHh8.H5yMH E"MH}Lh2^W)PHDž`@0JH`(8s_)PHt sequenHH(Hor outpuHH Hullptr fHHHt: Got nHHHConstrucHHHSequenceHf@0ce@2AHMHuHPL`E1H5͐bHv9LJPJIHh0.H5qMH =!MH}Lh/]W)PHDž` (JH`(A\)PHore inpuHHHe 1 or mHHHMust havHf@ts@AH)!MHuHPL_E1H5bH8LJrIPtH`JLeMuEIALeMt6HEL9t!HX@t HxKJHI9uH}LLe1JEuEu LhMuDmH} JEtH}JLhMuDIALhM.HpL9HX@t HxJHI9uIPtH`JLeMuEIALeMt6HEL9t!HX@t HxRJHI9uH}LLe8JEuEuLhMu:wH}JEtH}JLhMuQIALhMt?HpL9t$HX@t HxJHI9uHhLLpJEuqILJdILuMuVIEtMH}BILuMt;H]L9tJHEHH(HML8L`LhHXLp HM)I!#IYIQ"IHW)`HDžp(JH`HH(HpL8L`LhLpHX HhH5JH}HUH`-H5H5uMHHII!HW)@HDžP;JH@HHHPL0HXHHH5KH@L(H5MHH5DOHHA H55KHHH;HHH (cHMHkHUHuHuH8HHCHC0HKHSHC@H@Ht HHRJH`Ht Hh:JH}Ht H}(JLuHEMtL LJHgbHH;EuHHĨ[A\A]A^A_]=JIH`Ht?yIH`Ht.hIH`HtWIH@Hu0H`Hu!HèIXuHPLHL3JID$ID$I$HPIGHXHH`IGW)PHDž`IEHpHHx{JH}H LLXZJHpHuJRJIHp.H5>MH { MHLpwFW)PHDž`@JH`(z_)PH be >= 0HH/Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@7AH MHHPLHE1H5xbH-!LJ JHJHxJݿJIHpɌ.H5 MH G MHLpCEW)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AH MHHPLGE1H5wbHJ LJHpJIPt H`"JL Mt?H(L9t$HX@t HxJHI9uH LL(Juu&LpMuMHJtHJLpMuIALpMHxL9`HX@t HxIJHI9u7TIHH :IHH>IPt H`JL MuQIAL Mt?H(L9t$HX@t HxJHI9uH LL(Juu&LpMuFHaJtHLJLpMuQIALpMt?HxL9t$HX@t Hx JHI9uHpLLxJEuILJH}HIHpHV]ILPM+II]IEH+Ct H{mJHeJH}HIH}HIHHuXIHHt4H JHHu,IHHuIHHHJvIHHHuIHHt#HHCHuHHPHJHHHeJHHHu IH7!HHHt|HHCHujHHPHJLPMuYII]IEHt(Ct H{JHJHHHuIHHHuLPMtHHXL9tpMI)Hèffff.H(7!HèIXuHPLXiJHpHt HxQJH}Ht H}?JLJ LfUHAWAVAUATSHIHpIIHPbHHEHEID$HEEEEEEW)EHEMt5I@ I;@uDL'YHHi L}H]HEHxpHEHx1E11WHuL/[L}H]HUI9Hxt)Lffffff.H8 HH9u11LIVHuHt$0HuHt$(HuHt$ HuHt$HuHt$HuHt$HuH4$DLMLLOEI}t_HLH XL1AL芅.H}HtHxH8JHNbHH;E LH[A\A]A^A_]H8L0L@IEM~LMf W)pHEM)tXMIHI9 LJHHxHpJHEM~HLLLJLHxW)PHDž`LL*HXHEHL`_11H@HH9uEtE fff.HEHHcH9LcHEHpL<W)@LHpH@ZHI}tlA1ALHLH Lȃ.HHHHHCHrHHPHJEa(IEMd$0HpLxW)HDžI)tfHLHHH9L= 0HH/Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@7AHLHHPL6E1H5fbHMLJ @JHJHJݿ>JIHpz.H5*LH NLHLpc3W)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AHLHHPL5E1H5ebHjLJHpJIPt H`BJL Mt?H(L9t$HX@t HxJHI9uH LL(Juu&LpMuMHJtHJLpMuIALpMHxL9`HX@t HxiJHI9u7TIHH :IHH>IPt H`JL MuQIAL Mt?H(L9t$HX@t HxJHI9uH LL(Juu&LpMuFHJtHlJLpMuQIALpMt?HxL9t$HX@t Hx-JHI9uHpLLx JEuIL(JH}HIHpHV]ILPM+II]IEH+Ct H{JHJH}HIH}HIHHuXIHHt4H-JHHu,IHHuIHHHJvIHHHuIHHt#HHCHuHHPHJHHHJHHHu IH&!HHHt|HHCHujHHPH:JLPMuYII]IEHt(Ct H{JHJHHHuIHHHuLPMtHHXL9tpMI)Hèffff.HH%!HèIXuHPLXJHpHt HxqJH}Ht H}_JLJ LfUHAWAVAUATSHIHpIIH>bHHEHEID$HEEEEEEW)EHEMt5I@ I;@uDLGGHHi L}H]HEHxpHEHx1E11WHuLOIL}H]HUI9Hxt)Lffffff.H8 HH9u11LIVHuHt$0HuHt$(HuHt$ HuHt$HuHt$HuHt$HuH4$DLMLLo3I}t_HLH xL1ALs.H}HtHxH8JH=bHH;E LH[A\A]A^A_]H8L0L@IEM~LMf W)pHEM)tXMIHI9 LJHHxHpJHEM~HLLlJLHxW)PHDž`LLzHXHEHL`_11H@HH9uEtE fff.HEHHcH9LcHEHpL<W)@LHpH@zHI}tlA1ALHLH Lq.HHHHHCHrHHPHJEa(IEMd$0HpLxW)HDžI)tfHLHHH9L\JILLIHMH~LLJMLH@HHHHHtH@LLLHH!HHt#HHCHuHHPHJHHt HJLpHËEHHcUIHXHL$HcE9Hu!HHcHHLJHhHL4HHHBHHHHPsE1H)HhHHHRHHHHKvHIIHH HH(E1HLLJI<HJ4 LJH(H<H J4 LJHH<HJ4 LJILLL9uHPHLhHHtNHIL= 0HH/Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@7AHLHHPL$E1H5TbHmLJ `JHƸJH踸Jݿ^JIHp i.H5JLH !LHLp!W)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AHLHHPL#E1H5SbHL޺JHpJIPt H`bJL Mt?H(L9t$HX@t Hx7JHI9uH LL(Juu&LpMuMHJtHӹJLpMuIALpMHxL9`HX@t Hx艹JHI9u7TIHH :IHH>IPt H`/JL MuQIAL Mt?H(L9t$HX@t HxJHI9uH LL(иJuu&LpMuFH衸JtH茸JLpMuQIALpMt?HxL9t$HX@t HxMJHI9uHpLLx-JEuILHJH}HIHpHV]ILPM+II]IEH+Ct H{護JH襷JH}HIH}HIHHuXIHHt4HMJHHu,IHHuIHHH JvIHHHuIHHt#HHCHuHHPHŵJHHH襶JHHHu IH'!HHHt|HHCHujHHPHZJLPMuYII]IEHt(Ct H{"JHJHHHuIHHHuLPMtHHXL9tpMI)Hèffff.Hh!HèIXuHPLX詵JHpHt Hx葵JH}Ht H}JL迲J LfUHAWAVAUATSHIHpIIH,bHHEHEID$HEEEEEEW)EHEMt5I@ I;@uDLg5HHi L}H]HEHxpHEHx1E11WHuLo7L}H]HUI9Hxt)Lffffff.H8 HH9u11LIVHuHt$0HuHt$(HuHt$ HuHt$HuHt$HuHt$HuH4$DLMLL!I}t_H LH L1ALa.H}HtHxH8JH;+bHH;E LH[A\A]A^A_]H8L0L@IEM~LMf W)pHEM)tXMIHI9 L>JHHxHpJHEM~HLL茴JLHxW)PHDž`LL蚢HXHEHL`_11H@HH9uEtE fff.HEHHcH9LcHEHpL<W)@LHpH@蚚HI}tlA1ALH= 0HH/Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@7AHLHHPLE1H5BbHLJ 耩JHJHئJݿ~JIHp)W.H5jLH LHLpW)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AHLHHPL E1H5BbHLJHpJIPt H`肨JL Mt?H(L9t$HX@t HxWJHI9uH LL(7Juu&LpMuMHJtHJLpMuIALpMHxL9`HX@t Hx詧JHI9u7TIHH :IHH>IPt H`OJL MuQIAL Mt?H(L9t$HX@t HxJHI9uH LL(Juu&LpMuFHJtH謦JLpMuQIALpMt?HxL9t$HX@t HxmJHI9uHpLLxMJEuILhJH}HIHpHV]ILPM+II]IEH+Ct H{ͥJHťJH}HIH}HIHHuXIHHt4HmJHHu,IHHuIHHH)JvIHHHuIHHt#HHCHuHHPHJHHHŤJHHHu IHG!HHHt|HHCHujHHPHzJLPMuYII]IEHt(Ct H{BJH:JHHHuIHHHuLPMtHHXL9tpMI)Hèffff.H!HèIXuHPLXɣJHpHt Hx豣JH}Ht H}蟣JLߠJ LfUHAWAVAUATSHIHHIHbHHEHEHCHEEEEEEW)EHEMt5I@ I;@uDL#HH L}HEHLulLu1HE11SHuL%L}HULuI9t(LH8HH9uH1 1LIT$HuHt$0HuHt$(HuHt$ HuHt$HuHt$HuHt$HuH4$DLMLHI}tXH-LH L1ALO.H}HtI>JHfbHH;E8LH[A\A]A^A_]L@H8LHIEM|$Mt$ W)pHEM)Lt_MIHI9 LgJHHxHpJHEML~HLL订JLHxW)PHDž`L}H0HEH11HhLpHH9uEtEfHEHHcH9?LcHEHpL<W)@LHH@ʈH(I}tlA1ALHlLH L8N.HHHHHCHHHPHJEIEMl$0HpLxW)HDžI)tfHxLHHH9!L謟JILLIHMHx~LLJMLH@HHHHHtH@LLLHHo HHt#HHCHuHHPHJHHLt HϞJLtIŋULcuMHhH@H0LLpMI)HèHh HèIXuI<$Lp I^,LHL;藚JID$ID$I$HPIFHXHH`IFW)PHDž`IEHpHt Hx(JLHH}H!$LPMtHXL9t4MI)Hèffffff.Hx HèIXuHPLLX贙JHpHpwJ訙JIHpSG.H5LH LHLpW)PHDž`@HJH`(^)PH be >= 0HH/Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@7AHLHHPLE1H51bHLטJ HJHӕJZJtJIHpF.H5`LH LHLpW)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AHLHHPLE1H50bHLJHpJIPt H`xJH Ht?H(H9t$Lh@t HxMJLL9uH HH(-Juu&LpMuFHJtHJLpMuQIALpMt?HxL9t$HX@t Hx誖JHI9uHpLLx芖JEuIL襖JH}HIHH :IHH3IPt H`JL MuQIAL Mt?H(L9t$HX@t HxܕJHI9uH LL(輕Juu&LpMuFH荕JtHxJLpMuQIALpMt?HxL9t$HX@t Hx9JHI9uHpLLxJEu~IL4JH}HosIHpHBIILPMII]IEHCt H{虔JH葔JH}HIH}HIHHuXHIHHt4H9JHHu,IHHuIHHHJIHHt#HHCHuHHPH轒JHHH蝓JHHHuO{II]IEHtvCt H{gJH_JHHHu]IHHHtNHHCHuL{J IHt HX*{JE1LmMuMH&IALmMt6HEL9t!HX@t HxzJHI9uH}LLmzJEuxu L`Mu=uH}zJxtH}zJL`MuLIAL`Mt:HhL9t5HX@t HxXzJHI9uH`Lh=zJEu LILVzJLfwJ fffff.UHAVSHPIH{aHHE(yJHþ HyJHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5KHZeH ZeH}~H5rLHZeH ZeH}11H H5LLHwH5wNHyHH HQH54JH=zHH;oHHH bHMHHUHuHuIIFIF0INIVIF@H]HEHtH6H|wJHaHH;Eu LHP[A^]wJIH]HEHtHH7wJLwtJ ffffff.UHAVSHPIHaHHE(wJHþ HvJHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5МKHWeH WeH}{H5LHWeH WeHz11HH5\LHtH5NH-vHA H5KJHTwHH;lHHH bHMH&HUHuHuIIFIF0INIVIF@H]HEHtHMHtJHaHH;Eu LHP[A^]tJIH]HEHtHHNtJLqJ fDUHAVSI6tJHHLH[A^]IH tJLIqJ UHAWAVAUATSHHIH_aHHEL-qibM,$I|$3MHlicI$W)pHEƅpfDžqtoƅsH}HpHMHXmpt H}dsJH}u/EAD$HaHH;EuHĨ[A\A]A^A_]sJIM}1HLNIL HI}0GA;D$u]M&MfPMuMuPM9HPhHtSxuML H~g1LLmJHLj IIH9|@ILLLMD'L: HI}0HPHLLHpJHEHH[A\A]A^A_]ÐUHAWAVAUATSHHaHHEЉ LHHHDž(HD> NDpHDžxAu@H IHryHHsM~hI1E1AAH* IHyHHMnIs\10AHDžxAA|LHHyHHVH1H/!LHHyHHrHH~H)1ffo +\ff.f81f81\fvfvff\f81Tf81\ fvfvffT \0HHuHt4f81f81Lffvfvfo\ffLI9t1fn\fffff.< fouff~ HI9uILxA rA Hk IH\wHHM2Is1LHHyHHVH 1HCHzH)1fff.f813f81L3Lf81D3f81L3 D L0f81D3f81L3D@LPf81D3f81L3D`LpH HuHHHf81D3f81 3D HHuHzH)1f823f82L3Lf82D3f82L3D L0f82D3f82L3 D@LPf82D3 f82L3D`LpHHuHHHf82D3f82 3D HHu1H HI9uDpHDžxAAHM IH>uHH?MIs1LHHyHHVH s1HHzH)1ff823f82L3Lf82D3f82L3D L0f82D3f82L3 D@LPf82D3 f82L3D`LpHHuHt9HH@f82D3f82 3D HHu  HHI9uILxAA H IHsHH~M'IVDHs1HI)1~^ffffff.<fouVff Ѐ|fotRfL|fouVffL|fotRfLHI9uXff.f Ѐ|fouffL|fotfL|fouffLHI9SHt9HH1~^ffff.<fouff HH9uILxA *A =H IH|rHH M~ Is&1bA ILxALHHyHHVHs1HHzH)1Df813f81L3Lf81D3f81L3 D L0f81D3f81L3D@LPf81D3f81L3D`LpH HuHHHf81D3f81 3D HHukA&HD IH5qHHMcIs1FLHHyHHVH0s1H#HzH)1ff803f80L3pLpf80D3f80L3Dp Lp0f80D3 f80L3(Dp@LpPf80D30f80L38Dp`LppH@HuHHHf80D3f80 3Dp pHHukA&H IHpHHvM\Is1?LHHyHHVH0s1HHzH)1ff803f80L3pLpf80D3f80L3Dp Lp0f80D3 f80L3(Dp@LpPf80D30f80L38Dp`LppH@HuHHHf80D3f80 3Dp pHHudAcH IHnHHfMI rJ #H9HLH91I|$H)LHtHD HHuH/ T TT TT THI9uAILxALHHyHHVH`s 1Hu}HzH)1fffff.3L30L0D3 L30D0 L00D3@L3PD0@L0PoD3`oL3pD0`L0pHHuHt.HHoD3o 3D0 0H HuI9LxDpILxAA fH IHlHHBM%IrJ #H91J `H9$1IT$H)LHt4H< tfH( HD8DpHDžxA u@H* IHHH 28wfnXf~f. 2  f4HHI9uLxDpILxAAH IHIHHME1Lff.C4LHJufHCHC1HFJHHCHHHKHIHM9|LxILxHpH(HD8 7DpHDžxA uWH IH7HHUM$IVDH1HE1AA^H IHm7HHYM IVDHx1HA ILxAI)1ffff.H<H|DH|DH|DHI9uHt)H4H1ffffff.H<HH9uHDžxAAH IHi6HH芋M~I1QAILxAA H4 IH6HH\MIVDH1H:_I)1@WH*WH*DDWH*DDWH*Df~D HI9uHt*H4H1f.WH*f~HH9uILxA A H@ IH5HHқMIrJ H9J H9u1IT$H)LHtHH Affffff.8wfnXf~f. 2  f4HHI9sLxDpILxA"A"A ILxAuLHHyHHVHHzH)1fo[Do oTf8f: 0f8f:T0oL oT0f8f:L0f8f:T0HHu1Ht,ooLfo-[f8f:0f8f:L0I9LxDpILxA AHӻ IH(HH\M~DIu/1HAILxA[I)1WH*f~Ɖ>f4HWH*Df~Ɖ>ftHHI9uHt#WH*f~‰2fHLxDpILxAHɺ IH'IH HM{E1LDK4Lr7JufHCHC1H5JHHCHHHKHIHM9|LxILxHp޼H( HD8DHDžA uCEHǹ IHH.IHCMIsu1fE1AAHp IH-HHbM{IVDH1H6WLHHyHHrHH~H)1f( [fo[AAdTTf8f~f8f~dA\ Ad0TTf8f~\f8f~d HHuHtAAALf()[Tfo*[f8f~Tf8f~LI9t%ffA.ӈHI9uHDžAEAdH޷ IH_,HHyM~IY1@AILAA Hz IH+HH\JMIy1:@I)1ffff.H,4H4H,tHtH,tHtH,t HtHI9uHt&H4H1f.H,LHHyHHVHHzH)1fDZZLLZDZLD L0HHuHi>M)1s}[Hf (\H,H1H,.HFH L(\H,H1H,.HFH|L(\H,H1H,.HFH|L (\H,H1H,.HFH|HI9]MtTH4H1|[Hfff. (\H,H1H,.HFHHI9uILA AH IH(HHhM"Is1=LHHyHHVHHzH)1fff.L[fk[fkfpfLpD L0[fk[fkfDpfLpHHuHT<1H 1H!<L[[LI9;DILAw^1H;ZZLLI9;ILA A xH蜲 IH'IH{M~ Iso1;A ILA,AH< IH&HHmMIe1C;LHHyHHDBAHIxH)1A H,H,fnf:"(H,f:"H,ALf:"H,H,fnf:"(H,f:"H,f:"TAL H,H,fnf:"(H,f:"H,AL0f:"H,H,fnf:"(H,f:"H,f:"D T0HHM9LHHyHHVH HzH)1fff.L[f8+[f8+fpfLpD L0[f8+[f8+fDpfLpHHuHU91H49oL[fkfp[fkfDpI98ILAuAH觯 IH($HH)WMIrJ H9HLH91IT$H)LHtH,<@<HHuH$D,,TT,TT,T THI9uAILALHHyHHrHlH~H)1fo{[ff. T[f8f~ [f8f~LL T0[[f8f~Lf8f~T HHu1MG7A H,H,fnf:"(H,f:"H,ALf:"H,H,fnf:"(H,f:"H,f:"TI96DILA,S1H6oL[f8+fp[f8+fDpI9}6ILAwAAH IH!HH6[MIrJ H9HLH91IT$H)LHtHD,<@<HHuH2D,,TT,TT,T THI9uAILA hLHHyHHrHzH~H)1fo[ff. 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HHHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]-H5HH}HH5JHH5FMHHH HQH5.IH HH;HHH !aHMHWHUHuHuIIFIF0INIVIF@H]HEHtHHKHH``HH;Eu LHP[A^]HIH]HEHtHHHLFH fffff.UHAVSHPIH[``HHE(HHþ HHHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]mH5]HH}HH5VJHH5MH,HA H5J+IHSHH;HHH aHMHLiHUHuHuIIFIF0INIVIF@H]HEHtHLHHH ^`HH;Eu LHP[A^]HIH]HEHtHHMHLH DUHAVSHPIH]`HHE($HHþ HHHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5HH}HJH5JHKH5MH|HH HQH5(IHHH;aHHH PaHMHfHUHuHuIIFIF0INIVIF@H]HEHtHHHHV[`HH;Eu LHP[A^]HIH]HEHtHPHHLH fffff.UHAVSHPIHZ`HHE(dHHþ HZHHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]=H5HH}HH5 JHH5MHHA H5%IHHH;HHH aHMHxHUHuHuIIFIF0INIVIF@H]HEHtHH"HHX`HH;Eu LHP[A^]aHIH]HEHtHHHLH DUHAVSHPIH;X`HHE(HHþ HHHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5=HH}HH56 JHH5f MH HH HQH5##IH,HH;HHH aHMHvHUHuHuIIFIF0INIVIF@H]HEHtH%HkHHU`HH;Eu LHP[A^]HIH]HEHtHH&HLfH fffff.UHAVSHPIH{U`HHE(HHþ HHHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] H5}HH}HH5vJHH5 MHLHA H5j IHsHH;8HHH 'aHMHHUHuHuIIFIF0INIVIF@H]HEHtHlHHH-S`HH;Eu LHP[A^]HIH]HEHtH'HmHLH DUHAVSHPIHR`HHE(DHHþ H:HHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5HH}HjH5JHkH5MHHH HQH5IHHH;HHH paHMHeHUHuHuIIFIF0INIVIF@H]HEHtHHHHvP`HH;Eu LHP[A^]:HIH]HEHtHpHHLH fffff.UHAVSHPIH P`HHE(HHþ HzHHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]]H5 HH}HH5JHH56MHHA H5IHHH;HHH aHMHHUHuHuIIFIF0INIVIF@H]HEHtHHBHHM`HH;Eu LHP[A^]HIH]HEHtHHHL=H DUHAVSHPIH[M`HHE(HHþ HHHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5]HH}HH5VIHH5MH,HH HQH5CIHLHH;HHH aHMH5HUHuHuIIFIF0INIVIF@H]HEHtHEHHHK`HH;Eu LHP[A^]HIH]HEHtHHFHLH fffff.UHAVSHPIHJ`HHE(HHþ H HHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5HH}H:H5IH;H5LHlHA H5IHHH;XHHH G aHMHlHUHuHuIIFIF0INIVIF@H]HEHtHHHHMH`HH;Eu LHP[A^]HIH]HEHtHGHHLH DUHAVSHPIHG`HHE(dHHþ HZHHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5HH}HH5IHH5LHHH HQH5IHHH;HHH aHMHHUHuHuIIFIF0INIVIF@H]HEHtHHHHE`HH;Eu LHP[A^]ZHIH]HEHtHHHLH fffff.UHAVSHPIH+E`HHE(HHþ HHHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]=H5-HH}HH5&IHH5VLHHA H5IH#HH;HHH aHMHHUHuHuIIFIF0INIVIF@H]HEHtHHbHHB`HH;Eu LHP[A^]HIH]HEHtHHHL]H DUHAVSHPIH{B`HHE(HHþ HHHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]MH5}HH}HH5vIHH5LHLHH HQH5c IHlHH;1HHH aHMHŵHUHuHuIIFIF0INIVIF@H]HEHtHeHHH&@`HH;Eu LHP[A^]HIH]HEHtH HfHLH fffff.UHAVSHPIH?`HHE(4HHþ H*HHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5HH}HZH5IH[H5LHHA H5 IHHH;xHHH g`HMHHUHuHuIIFIF0INIVIF@H]HEHtHHHHm=`HH;Eu LHP[A^]1HIH]HEHtHgHHLH DUHAVSHPIH =`HHE(HHþ HzHHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5 HH}HH5IHH56LHHH HQH5IHHH;HHH `HMHHUHuHuIIFIF0INIVIF@H]HEHtHH;HH:`HH;Eu LHP[A^]zHIH]HEHtHHHL6H fffff.UHAVSHPIHK:`HHE(HHþ HHHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5MHH}HH5FIHH5vLHHA H5:IHCHH;HHH `HMHHUHuHuIIFIF0INIVIF@H]HEHtH<HHH7`HH;Eu LHP[A^]HIH]HEHtHH=HL}H DUHAVSHPIH7`HHE(HHþ H HHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]-H5HH}H:H5IH;H5LHlHH HQH5IHHH;QHHH @`HMHUHUHuHuIIFIF0INIVIF@H]HEHtHH˽HHF5`HH;Eu LHP[A^] HIH]HEHtH@H膽HLƺH fffff.UHAVSHPIH4`HHE(THHþ HJHHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]mH5HH}HzH5IH{H5LH謼HA H5HHӽHH;蘲HHH `HMHHUHuHuIIFIF0INIVIF@H]HEHtHHHH2`HH;Eu LHP[A^]QHIH]HEHtHHͺHL H DUHAVSHPIH+2`HHE(褺HHþ H蚺HHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5-HH}HH5&IH˸H5VLHHH HQH5HHHH;HHH `HMH5HUHuHuIIFIF0INIVIF@H]HEHtHH[HH/`HH;Eu LHP[A^]蚸HIH]HEHtHHHLVH fffff.UHAVSHPIHk/`HHE(HHþ HڷHHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]=H5mHH}H H5fIH H5LH<HA H5ZHHcHH;(HHH `HMH<HUHuHuIIFIF0INIVIF@H]HEHtH\H袵HH-`HH;Eu LHP[A^]HIH]HEHtHH]HL蝲H DUHAVSHPIH,`HHE(4HHþ H*HHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] H5HH}HZH5IH[H5LH茴HH HQH5HH謵HH;qHHH ``HMHHUHuHuIIFIF0INIVIF@H]HEHtHHHHf*`HH;Eu LHP[A^]*HIH]HEHtH`H覲HLH fffff.UHAVSHPIH)`HHE(tHHþ HjHHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]MH5HH}H蚹H5IH蛰H5&LH̱HA H5HHHH;踧HHH `HMHHUHuHuIIFIF0INIVIF@H]HEHtHH2HH'`HH;Eu LHP[A^]qHIH]HEHtHHHL-H DUHAVSI֯HHHb`HH{L#HaHH[A^]IH蔯HLԬH fff.UHHH `HHxH@Ht]VH]fDUHSPHH`HH{HCHt%HHH[]HfUHAWAVAUATSH8HHHk&`HHE1H IL?HxID$HM|$ HHILXHI~l1IfWf)`HDžpMJ<IIHI9 LHNHhH`J HpMKNIIIrL9J H9HHNDI)DHtHHHHHHuI8Y1ɐHHHTHTHTHTHTHTHT HT HT(HT(HT0HT0HT8HT8HT@H@L9uHhH11I?M!IpHHyH s 1Hu|HwH)1ffffff.LLD L0D L0D@LPD@LPfD`LpfD`LpHHuHt.H HfDfD  fD H HuM92HhHI)IJ|HhH)IL$It$ HH)HHH)v%HEH`HMH IL$It$ H9tdL`LhH)LL)1fDH9tQH|I\H9t&HuI|HH9u,fDHHH9uHL L`fWf)@HDžPLL)t^LIIHI9CHILHL@KHPHI)M~LL苬HMLHH@1HxHPL+H8IH`HL!I)MIM9LILH0HyHHHILALxHHH(LbHHH LLHILHMLLL'IžLMKDHHKTHHHHIt uIIIf.HCHI|ItHHHHHHIHIII_pffff.HHYHHHDžHx HHH@HIL0IHK|IfWf)HDžMHtJI9hMN<L蠨HHJHHL艨HHMLKDHHHHHxa+HcLHHIIHLpHHEPHH NaHHHHHHHH0HHH(HH H HH(H8HH0HxHH8HHH@HHHHHEfHnfbgSZf\oSZf|W)MfEHUH]LLHY+H}H91H.HP(#ME1fff.HIHHxL0L9}.L(L ESA)IKAH9uH1MuZi1efff.M)H1HHIHH(HDž(Ht"HH@Ht HHHH`HtHH8HHp`HH;EHH8[A\A]A^A_]fWf)EE(HEHexpand sHEHinvalid HEEhapeEHMHHUr+EYH}K転HH`赟HH@觟H H號H IEH}HH`HIH}H9HuvHHHHDžHH HDž HH(HDž(HH@HHP(HHt`IHHpHIHHX0IHH@IH`HIH@HelIH HDž H# IH(HDž(H IH@HIH`HHhIH@HIH@HIH}I99HP HH8IHH#HHHHDžHҟHH HDž H貟HH(HDž(H 蒟HH@Ht HH耟HH`HtHH8eHL襜H ffff.UHAWAVAUATSH(IIIIMISMcLH)HL9LML]IHUHH)HLHH=HI)LHHH9II9LBMI9讞HHH5CHHHH`HHH5`H`H讞HIM)LHHHML9ILI)LLIIFL9vKL"M9IFINH0IHHxINLL9}.IN IV(L LESA)IKAH9uH1MuZi1efff.M)H1HHIHHJHHL'HHMLKDHHHHHx+HcLHHIIHLpHHEP譊HH TaHHHHHHHH0HHH(HH H HH(H8HH0HxHH8HHH@HHHHHEfHnfb6Zf\ 6Zf|W)MfEHUH]LLH+H}H9/H,HP(!ME1f.HIHHxL0L9}.L(L ESA)IKAH9uH1MuZi1efff.M)H1HHIL"M9IFINH0IHHxINLL9}.IN IV(L LESA)IKAH9uH1MuZi1efff.M)H1HHIL"M9IFINH0IHHxINLL9}.IN IV(L LESA)IKAH9uH1MuZi1efff.M)H1HHI|fff.ILHjHHKoMLL9vLuM9sGO4/M9vIHMM9r'LHuHUjHMM9rfHuHH;uH([A\A]A^A_]UHH U[H1H9NHD]DUHHmda]UHAVSIfhHHH]`HH{LAHbaHH[A^]IH$hHLdeH fff.UHAVSIhHHH]`HH{LSAHcaHH[A^]IHgHLeH fff.UHHH B]`HHxH@Ht]gH]fDUHSPHH]`HH{HCHtUgHHH[]GgHfUHAWAVAUATSH8HHH_HHE1H:IL菜HxID$HM|$ HHILVHI~IfWf)`HDžpMJ<IIHI9 |fHNHhH`J HpMKNIIIrL9J H9HHNDI)DHtHHHHHHuI8Y1ɐHHHTHTHTHTHTHTHT HT HT(HT(HT0HT0HT8HT8HT@H@L9uHhH11I?M!IpHHyH s 1Hu|HwH)1ffffff.LLD L0D L0D@LPD@LPfD`LpfD`LpHHuHt.H HfDfD  fD H HuM9-HhHI)IJ|HhH)IL$It$ HH)HHH)v%HEH`HMHIL$It$ H9tdL`LhH)LL)1fDH9tQH|I\H9t&HuI|HH9u,fDHHH9uHL L`fWf)@HDžPLL)t^LIIHI9scHILHL@KHPHI)M~LLdHMLHH@1HAHКL+H8IH`HL!I)MIM9LILH0HyHHHILALbHHH(LbHHH L|bHILHMLLL)IžLMKDHHKTHHHHIt uIIIf.HCHI|ItHHHHHHIHIII_rffff.HHYHHHDžHx fHHH@HIL0IHK|IfWf)HDžMHtJI9fMN<L`HHJHHL`HHMLKDHHHHHx\+HcLHHIIHLpHHEP>`HH \aHHHHHHHH0HHH(HH H HH(H8HH0HxHH8HHH@HHHHHEfHnfb Zf\ Zf|W)MfEHUH]LLHk+H}H9+H(HP(ME1ff.HIHHxL0L9}.L(L ESA)IKAH9uH1MuZi1efff.M)H1HHIL"M9IFINH0IHHxINLL9}.IN IV(L LESA)IKAH9uH1MuZi1efff.M)H1HHIRaHHKHSHHHPHKHHHK HH HK(HH(H[]@UHHQaHHG(HF(HG HF HGHFHGHOHNHF]UH]f.UH]THfDUHAWAVAUATSH(H}H6HHEH9 HUHBHJHHuHHRH2H H HHHHHUHB LB(HHHHFHH(HDž(Ht"FHH@Ht HHFHH`HtHH8EHHp_HH;EHH8[A\A]A^A_]fWf)EE(HEHexpand sHEHinvalid HEEhapeEHMHHU+EYH}KEHH`BHH@BH HBH IEH}EHH`HIH}H9HuvHHHHDžHH HDž HH(HDž(HH@HHP(HHt`IHHpHIHHX0IHH@IH`HIH@HelIH HDž H# IH(HDž(H IH@HIH`HHhIH@HIH@HIH}I99HP HH8IHH#HBHHHDžHBHH HDž HBHH(HDž(H BHH@Ht HHBHH`HtHH8eBHL?H ffff.UH]f.UH]4BHfDUHSPHP'BHH ?aHHsHx HH[]fff.UHH?aHHGH~ HH]ffff.UH]f.UH]AHfDUHAWAVATSIL>L"M9IFINH0IHHxINLL9}.IN IV(L LESA)IKAH9uH1MuZi1efff.M)H1HHIa]UH]f.UH]4@HfDUHSPH0'@HH n>aHHKHSHHHPHKHHHK HH HK(HH(H[]@UHH->aHHG(HF(HG HF HGHFHGHOHNHF]UH]f.UH]?HfDUHAWAVAUATSH(H}H6HHEH9HUHBHJHHuHHRH2H H HHHHHUHB LB(HHHH+H}I9tHtHP(fffff.HP HHHHH;OfDM!1 HHHHH H IHHHHHHHL"M9IFINH0IHHxINLL9}.IN IV(L LESA)IKAH9uH1MuZi1efff.M)H1HHIL"M9IFINH0IHHxINLL9}.IN IV(L LESA)IKAH9uH1MuZi1efff.M)H1HHI|fff.ILHHHKoMLL9vLuM9sGO4/M9vIHMM9r'LHuHuHMM9rfHuHH;uH([A\A]A^A_]UHH u[H1H9NHD]DUHH a]UHAVSIHHH `HH{LHaHH[A^]IHDHLH fff.UHAVSI&HHH`HH{LsHaHH[A^]IHHL$H fff.UHHH b`HHxH@Ht]H]fDUHSPHH0`HH{HCHtuHHH[]gHfUHAWAVAUATSH8HHH_HHE1HZUILONHxID$HM|$ HH#UILHI~輔IfWf)`HDžpMJ<IIHI9 HNHhH`J HpMKNIIIrL9J H9HHNDI)DHtHHHHHHuI8Y1ɐHHHTHTHTHTHTHTHT HT HT(HT(HT0HT0HT8HT8HT@H@L9uHhH11I?M!IpHHyH s 1Hu|HwH)1ffffff.LLD L0D L0D@LPD@LPfD`LpfD`LpHHuHt.H HfDfD  fD H HuM9-HhHI)IJ|HhH)IL$It$ HH)HHH)v%HEH`HMHoIL$It$ H9tdL`LhH)LL)1fDH9tQH|I\H9t&HuI|HH9u,fDHHH9uHL L`fWf)@HDžPLL)t^LIIHI9HILHL@KHPHI)M~LLHMLHH@1HHLL+H8IH`HL!I)MIM9LILH0HyHHHILAL HHH(L HHH L HILHMLLL)IžLMKDHHKTHHHHIt uIIIf.HCHI|ItHHHHHHIHIII_rffff.HHYHHHDžHx fHHH@HIL0IHK|IfWf)HDžMHtJI9fMN<L HHJHHL HHMLKDHHHHHx+HcLHHIIHLpHHEP^ HH E aHHHHHHHH0HHH(HH H HH(H8HH0HxHH8HHH@HHHHHEfHnfbYf\Yf|W)MfEHUH]LLH+H}H9+H(HP(ME1ff.HIHHxL0L9}.L(L ESA)IKAH9uH1MuZi1efff.M)H1HHIL"M9IFINH0IHHxINLL9}.IN IV(L LESA)IKAH9uH1MuZi1efff.M)H1HHIL"M9IFINH0IHHxINLL9}.IN IV(L LESA)IKAH9uH1MuZi1efff.M)H1HHIL"M9IFINH0IHHxINLL9}.IN IV(L LESA)IKAH9uH1MuZi1efff.M)H1HHI0IL$It$ H9tdL`LhH)LL)1fDH9tQH|I\H9t&HuI|HH9u,fDHHH9uHL L`fWf)@HDžPLL)t^LIIHI9GILHL@KHPHI)M~LLGMLHH@1HHL+H8IH`HL!I)MIM9LILH0HyHHHILALGHH(LGHH LGILHMLLL'IžLMKDHHKTHHHHIt uIIIf.HCHI|ItHHHHHHIHIII_pffff.HHYHHHDžHx HHH@HIL0IHK|IfWf)HDžMHtJI9XMN<L0GHJHHLGHMLKDHHHHx*HcLHHIIHLpHHEPGH `HHHHHHHH0HHH(HH H HH(H8HH0HxHH8HHH@HHHHHEfHnfbvYf\wYf|W)MfEHUH]LLH*H}H9HHP(ME1HIHHxL0L9}.L(L ESA)IKAH9uH1MuZi1efff.M)H1HHI_HH;EHH8[A\A]A^A_]fWf)EE(HEHexpand sHEHinvalid HEEhapeEHMHH*EYH}K[GH`UGH@GG H9G IEH}GH`HIH}H9HuvHHHHDžHH HDž HH(HDž(HH@HHP(HHt`IHHpHIHHX0IHH@IH`HIH@HelIH HDž H# IH(HDž(H IH@HIH`HHhIH@HIH@HIH}I99HP HH8IHH#HGHHDžHrGH HDž HRGH(HDž(H 2GH@Ht HH GH`HtHH8GLEG ffff.UH]f.UH]GfDUHSPHPGH `HHsHx HH[]fff.UHH`HHGH~ HH]ffff.UH]f.UH]DGfDUHAWAVATSIL>L"M9IFINH0IHHxINLL9}.IN IV(L LESA)IKAH9uH1MuZi1efff.M)H1HHI|fff.ILHGHKoMLL9vLuM9sGO4/M9vIHMM9r'LHuHGMM9rfHuHH;uH([A\A]A^A_]UHH G[H1H9NHD]DUHH`]UHAVSI覾GHH2_HH{LH4`HH[A^]IHdGL褻G fff.UHAVSIFGHHҳ_HH{L蓗H4`HH[A^]IHGLDG fff.UHHH _HHxH@Ht]ƽG]fDUHSPHHP_HH{HCHt蕽GHH[]釽GfUHAWAVAUATSH8HHH4_HHE1HzILOHxID$HM|$ HHCILȬHI~?IfWf)`HDžpMJ<IIHI9 輼GNHhH`J HpMKNIIIrL9J H9HHNDI)DHtHHHHHHuI8Y1ɐHHHTHTHTHTHTHTHT HT HT(HT(HT0HT0HT8HT8HT@H@L9uHhH11I?M!IpHHyH s 1Hu|HwH)1ffffff.LLD L0D L0D@LPD@LPfD`LpfD`LpHHuHt.H HfDfD  fD H HuM9-HhHI)IJ|HhH)IL$It$ HH)HHH)v%HEH`HMHIL$It$ H9tdL`LhH)LL)1fDH9tQH|I\H9t&HuI|HH9u,fDHHH9uHL L`fWf)@HDžPLL)t^LIIHI9賹GILHL@KHPHI)M~LLGMLHH@1HHL+H8IH`HL!I)MIM9LILH0HyHHHILALGHH(LҸGHH L輸GILHMLLL)IžLMKDHHKTHHHHIt uIIIf.HCHI|ItHHHHHHIHIII_rffff.HHYHHHDžHx fHHH@HIL0IHK|IfWf)HDžMHtJI9fMN<LGHJHHLGHMLKDHHHHHxв*HcLHHIIHLpHHEP~GH `HHHHHHHH0HHH(HH H HH(H8HH0HxHH8HHH@HHHHHEfHnfbaYf\aYf|W)MfEHUH]LLH*H}H9+H(HP(ME1ff.HIHHxL0L9}.L(L ESA)IKAH9uH1MuZi1efff.M)H1HHIL"M9IFINH0IHHxINLL9}.IN IV(L LESA)IKAH9uH1MuZi1efff.M)H1HHIGHEHH(HML0L`LhLxHX HM)I!IIQIIHW)`HDžp(ѦGH`HH(HpL0L`LhLxHX HhH5HH}HUH`-HIIIIHW)@HDžP(9GH@HH(HPL LhLpLxHX HHIIHCI;IW) HDž0(åGH HH(H0L LhHXLpLx H(H5IH@H HH5IH*H5KH[HA H5yGH肧HH;GHHH 6_HMH HUHuHuLIIFIF0INIVIF@H Ht H(äGH@Ht HH諤GH`Ht Hh蓤GH}Ht H}聤GH]HEHtHH`GH_HH;EuLH[A\A]A^A_]薤GIH`Ht,IH HuDH@HuaH`Hu~H}HH]HEHLG H(ãGH@HtIH@HtHH蚣GH`HtIH`HtHhqGH}HvIH}HdH}IGH]HEH[IH]HEHAHļH GLJG fUHAWAVAUATSHxIIHpH[_HHE1LHHC H+CHu%HS1LIAAG0蔢GHE(o[)EH is not HH HimensionHHHtensor dHHH: Input HHHEyeLike Hf@(2HMHpq*E3 H}GH|_HH;E& 7 HC0@EHEIO LuL}EHML$INHLiLmHxLMHEuLeAAAM9MOMM)LH?H>LHM~J4H}[GN4#I}_M9HUHLuIFH9PL}L]I)HxI9LOM1҃ OHEJLHHH)LxIHHuHJ4HfDHHHuHUHHUHK E tKHUILIINHLaLHH~ HLRGMx%INL9yMM)I9LOM.LHINH91LL9IOIMMI1MLOAMIEAItUM)IIL)ILI\LHHLILIT11DH H HHI9uMtIIMMKDHUHHUHEHH EHUILIINHLaLHH~ HLGMx%INL9yMM)I9LOM.LHINH91LL9IOIMMI1MLOAMIEAItXM)IIL)ILI\LHHLILIT11H H HHI9uMtIIMMKDHUHHUHEHH E HUHMLIIFHLxL}HMLMAuEAAAM9MOMM)LH?H>LHML]~J4L葙GL]N4#I}HM9HUHHEH@H9PPHMH)HEH9HOH0K_GH_HH;EuHHh[A\A]A^A_]蘊GHH}HuH}HuTHcG H}GH}Ht6HH}Ht(HH}HtHH}Ht HH}HtH}ƉGHG fffff.UHAWAVAUATSHIHH_HHEHHMH;tGHIH aH1AHG7+H_HH;EHH[A\A]A^A_]H]HHEH`LXLpHx0GEHPHL}IwLILLL HxLeI|$ HhLpLLjLhIIMMH`HHQPH}HHOPHXHI\$0[tnE1}AH|$8L|$0HpH|$(LT$ HD$L\$HxHD$L,$LH]H|H^HH;EcE1}AH|$8L|$0HpH|$(LT$ HD$L\$HxHD$L,$LH]HH ^HH;EPG0莇GHE(X)EHs build.HH%Hn this bHH Hported iHHH not supHHHind typeHHHGather TH@-HM H]HV*EH}GHj^HH;EEIEt0H}%IL;HMtAGt I襆GL蝆GL݃G DUHAWAVAUATSHIH] L}Lce@Lm8H^HHEHhH`D_LPHHHIMJ H@H~0HH1@Hc<LHeQ*t+HnGH^HH;Et2HP IH^HH;EuLH[A\A]A^A_]腁GHt=H GHM~G HH}I9uHP H1~G HtHP(H~G @UHHH bv_HHxH@Ht]馀G]fDUHSPHH0v_HH{HCHtuGHH[]gGfUHAWAVSPISGHL=u_L;H{LYHHLQH`HHH[A^A_]L;IH{HCHt GIHGL}G fDUHAWAVAUATSHH5^HHEHW)EHEEEaxisEHhHUHZyHhHDžhHtCt H{RGHJGEt H};GHu'H^HH;EuHĈ[A\A]A^A_]oGGIHh,+H5IH #IH}LhEW)PHDž`0~GH`([)PHte valueHHHttributeHHH'axis' aHHHInvalid HHHMissing/H@&AHζIHuHPL{E1H5q_HLn~G IALeMuFzIALhMIIPt H`}GLeMt6HEL9t!HX@t Hx}GHI9uH}LLe}GEuEuLhMu&^H}p}GEtH}a}GLhMt:HpL9tEHX@t Hx6}GHI9uHhLp}GEt)L>}GLNzG LIEt H}|GL-zG DUHAWAVSPI|GHL=_r_L;H{L VHHLHz`HHH[A^A_]L;IH{HCHt k|GIH^|GLyG fDUHAWAVSPIC|GHL=q_L;H{LUHHLAH`HHH[A^A_]L;IH{HCHt {GIH{GLyG fDUHAWAVAUATSPLMLIIIL6L#~GLLHIIuLLzGI$H}GLHHI6L'zGHEHH}GLHHH[A\A]A^A_]H1H)H!HHUHAWAVAUATSH8LLHHIHHC^HHEH8H~^HHHLH@H8L'zGHHEEL5^MnLI@L8L2yGL=w^ILHDž(HDž HDžHDžDž0I$H|GHHHHH0HxGHLMLEHHHHHHLL8Lt H hyGHHxGH5_^HHwGH8xGH^HH;EuHH8[A\A]A^A_]oyGHLL8Lt H xGHwGHHHH5^HHHwGH8PxGHHH89xGHUH]f.UH]dxGfDUHSPHWxGH `HHKHHH[]@UHH`HHGHF]UH]f.UH]wGfDUHH]2fUHH [H1H9NHD]DUHH`]UHAWAVATSIHD:D9LcA)ffff.I>HHwLHH9HHHHWHHWHyHw HcHW(LG0HHHHHA8tVHGHHLG8HG@H81HHH@H4I1HHH@H<(uGIA`0fffff.HG8HHHH7H>xGIA.[A\A^A_]DUH]f.UH]vGfDUHSPHwvGH ^`HHKHHH[]@UHH=`HHGHF]UH]f.UH]vGfDUHH]2fUHH [H1H9NHD]DUHH-`]UHAWAVATSIHD:D9LcA)ffff.I>HH_LHH9HHHHHGH0HGHHHyHW HHW(LG0HHHHHA8tSHWHH2H_8HW@H:1HH@H4H1HHH@HHH8Lp L+pHx1HHHPIH HHH Hc@H HHL`HhHqHpH~=LhHp1Ic|`H9 0H90HH9|HPHp H+pHHL LFHPLIMfIIcHHHH8Hx0HPHW)@HDžPIHI92L gGHH@N4LPHLgGLHHIHHXHDžXHM4$Md$PL07H8HLh HHPHL+hHx1HHHP"HH 3HHH Hc@H HHH`HhHqHpH~>LhLp1f.Hc<`H9,H9,HH9|H(HPHp H+pHHL LpHPLIIMl$IIcHHHH8Hx0HPHHW)@HDžPHqI90IL?eGHH@N$LPHL%eGLH6H8AHL:HH8Lp L+pHx1HHHPwTIH HHH Hc@H HHL`HhHqHpH~ELhHp1ffffff.I|H`H9H-H9?-HH9|HPHp H+pHHL LHPLIMfIIcHHHH8Hx0HPHW)@HDžPIH9 I9z/LcGHH@N4LPHLcGLH H8HLh HHPHL+hHx1HHHPRHH HHH Hc@H HHH`HhHqHpH~8LhLp1HLPL}HEL9tMt ILP(HHu!ILP HHt HXGHHt HXGH Ht H(XGHHt HwXGH HH@LHLH)ID$H I|DHHQH HHHtHHtHHH;48uHDLHHDILH0HH 1E1L0HPHxL;@HHH HHHE1I)I |>IIHx11ffff.H9@t H4H4HHL9|1HHLPLHHHH(HLH@HHH@H@HH8LpLx M)H0LtfLHHH9#LVGILLIHM~0LLLWGMMLMLE1E11E1 MLMHHHHHHHHHHxHHHpHhHHAHAHLH)t`HHHH9"UGILLIHM)M~%LLLVGMML E1E1MHEX4UGIHd`I$HID$8HID$0HID$(HID$ HxID$HhHpIL$ID$ID$PID$HID$@M)tSLHHH9LTGIMt$HMt$@IID$PM~LLLUGMMt$HLeLHI'KMLPI~_HEI$LHuP0L9};LeH]HMu1HLHUa9LPILeHEL9tMt"I$LP(HHu"I$LP HHt HSGHHt HjSGH HH@LHLH)ID$H |DHHQH HHHtHHtHHH;48uHDLHLMLH0HHN HHHHHHE1I)IH0~;IILx11f.H9@t IHHHL9| Lx1HHLPLHHH@H@HLuLL)t[HHHH9QGILLIHM)M~LLL>SGMLHHH(HLH@HH H@H@HH8LpLx M)t[LHHH9L5QGIL(L IH0M~LLLRGML(HhHQHEpPGILH$_`IHxIGHhHpIOIGIG HIG0IG(IG LHL)tPIIHI9HJPGIMo(Mo KDIG0H~LLHQGIMo(HHHAIGPHAIGHHHIIO@IG8IGhIG`IGXLLM)tPLHHH9LOGIMo`MoXIDIGhM~LLLPGMMo`L}LHI'CMLPLe~SHEILLP0L9}1L}H]HMu1HLHU4LPL}HEL9tMt ILP(HHu!ILP HHt HNGHHt HNGH Ht H(NGHHt HgNGH HH@LHLH)ID$H I|DHHQH HHHtHHtHHH;48uHDLHHLILH0HHHtHHHMGHxHLt H}MGIHXHDžXHfx1E1HPHxL;@L(HH1HPI)I |8IIHx11H9@t H4H4HHL9|1HHHHHLHPH@HHH@H@HH8LpLx M)H0tfLHHH9LYLGILLIHM~0LLLMGMMLMLE1E11E1 MLMHPHHHHhHpHHxHHHHHHHHAHAHLH)t`HHHH9sXKGILLIHM)M~%LLLLGMML E1E1MHPHHEXJGIHV`I$HID$0HID$(HID$ HxID$HhHpIL$ID$ID$HID$@ID$8M)tSLHHH9LTJGIMt$@Mt$8IID$HM~LLLKGMMt$@HPID$PLeLHI'DML(L}~THEI$LLP0L9}1LeH]HMu1HLHU /L(LeHEL9tMt"I$LP(HHu"I$LP HHt HKIGHHLIt H"IGH HH@LHLH)IFH |DHHQH HHHtHHtHHH;48uHDLHHPHHHPH0HHY HH1HPI)IH0~GMLLLPLH@HHH@H@HH8LpLx M)t[LHHH9 L>GML L0HhH+HEpIAH*GHXHDžXH1CIHXHDžXH ID72IHH8Ht/I()G$IHH8Ht2I(e)G'ILV)GHH4IL4)GHHIHHIH@HIH@HIH@HIH@HIL(GHHILr(GHHDIHH?IHH'IHHIHHIH@HIH@HIH@HIH@HIHHx,IHH`IHHIHHIHH/IHHIH HIH HsIHH,IHHIHH|HIHHd0IH}HEH9uHHP HHtGHM&GHHt;H5&GHH-HuIHHuHHuHHH %GHHHP(HHteIH}HEH9u_HP HHt^H%GHHtRHv%GH HtFH(^%GHHu>GHu@HHuHHuH HuHHHHP(HHtWIH}HEH9u0HP HHt/H$GHH!Hu%HHuHHHzHP(HHtIH}HEH9u,HP HHt/HA$GHHt/!HHHuHHt H$GH@Ht HH#GHxHt H}#GHXHDžXHtCt H{#GH#GL G HP(HHm9UHHH _HHxH@Ht]V#G]fDUHSPHH_HH{HCHt%#GHH[]#GfUHAVSI#GHHLH[A^]IH"GL G UHAWAVAUATSHHIH/^HHEL-A_M,$I|$H -`I$W)EHEEEaxisEIL$HhHUH2HhHDžhHtCt H{*"GH""GEt H}"GHu'H^HH;EuHĘ[A\A]A^A_]G"G!GH@Hh*H5[IH <\IH}LhW)PHDž`0!GH`(\4[)PHte valueHHHttributeHHH'axis' aHHHInvalid HHHMissing/H@&LHYIHuHPH@FDžLH55^HbH@.!G IǰLLuMuKIǰLHhHIIPt H` GLuMt6HEL9t!HX@t Hxc GHI9uH}LLuI GEuEuHhHu&^H}& GEtH} GHhHt:HpH9tELp@t HxGLL9uHhHpGLt%H@GHIEt H}GM,$I|$ID$HtGLG ffffff.UHAVSIfGHHL~H[A^]IH9GLyG UHAWAVAUATSPIHIGHCIIOHKHHLsHC(HC HCI I+t^III9GILc LcKHC(IwMo I)MH~LL GHMLc IG@HC@IG0IO8HK8HC0HCXHCPHCHIPI+HtOLuLcHIII9w\/GILkPLkHKDHCXIwHMwPI)M~LLzGMLkPIG`HC`H[A\A]A^A_]L4GL*G II<$Ht H{PGHEH8Hu II>Ht H{ GLG UHAWAVAUATSH8LLHIIHHǔ^HHEH8H^HHHLH@H8LGHHEEL-@^IEHHI@L8LGL5^ILHDž(HDž HDžHDžDž0I$HGHHHŏI7H(GHHHGHHH蒏HH0HGHHHGHHHXHE0HGHHHHHL8Lt H GH}GH5^HHGH8 GH^HH;EuHH8[A\A]A^A_]GHHHL8Lt H GHG3H HHHHHHH5ґ^HHVGH8^GH越HH8GGH蟊ffffff.UHAWAVATSHPIH^HHEL}IIHMMtHM9IuwHEI|$ H?HHuP0H^HH;EM1LufDH]I|$ HHLP0HH;]|H~8HP1LufDH]I|$ HHLP0HH9]L*HL9LNL}LH~HE$`HEHEHEHEHELeH]H]LHH*H}H9tHtHP(H^HH;Et#HP H^HH;Eu HP[A\A^A_]GGGH ^HHH5ʎ^HHGIH}H9uHP LG HtHP(LG f.UH]f.UH]dGfDUHSPH WGH &#`HHKHSHHHPHKHHH[]@UHH"`HHGHFHGHOHNHF]UH]f.UH]GfDUHAWAVATSHH:^HHEIHIvIFHHH>IH9~I\$HNd#L9|BIHIHL9}0L}f.IFH]Hx Ht3HLP0HL9|H^HH;Eu H[A\A^A_]zG(GH C^HHH5^HƧH:Gfffff.UHH եZH1H9NHD]DUHH!`]UHSPHH"`HH{PHt H{XGH{ HtH{(H[]qGH[]fff.UHSPHH!`HH{PHt H{X9GH{ Ht H{('GHH[]Gff.UHAVSIpGHIvHV!`HH{H[A^]IHGL G DUHHGH !`HH~H]qUHSPHH{PHt H{XGH{ HtH{(H[]kGH[]fDUHSPHH{PHt H{XCGH{ Ht H{(1GHH[]#GDUHHLGHWLO8HcO@HcHLWHIc4Hy LOPIcI4HO J4HHH RI4HGhH@HHGH]Gff.UHH ZH1H9NHD]DUHHM `]UHH] `HHG8Ht HG@H]ZG]f.UHSPHH( `HH{8Ht H{@)GHH[]Gffff.UHAWAVAUATSHIXGHH`HIG0HC0IG(HC(IG HC IIGHCIGIOHKHCHCHHC@HC8IG8Mg@I)tZHELuH8MILH=uXLsGIHMLi@Li8KDIIFHMHu~LLGMMn@IGPIFPLH[A\A]A^A_]HuG IH;Ht HEHx@GH}GL,G @UHAWAVAUATSPHH`HIIF0HC0IF(HC(IF HC IFHCIFINHKHCHCHHC@HC8I~@I+~8tNL{8IILH=uQaGILc@Lc8KHCHIv8M~@I)M~LLGMLc@IFPHCPH[A\A]A^A_]LgG II?Ht H{@GL+G UHHHx8Ht Hx@]G]@UHSPHH{8Ht H{@GHH[]GUHHLGLOHw HcO(HcHHc4Hy HcO0HW8H4LH vI4HGPH@HHGH]vGf.UHH բZH1H9NHD]DUHHM`]UHAWAVAUATSPIHIGHCIIOHKHHLsHC(HC HCI I+t^III9GILc LcKHC(IwMo I)MH~LLGHMLc IG@HC@IG0IO8HK8HC0HCXHCPHCHIPI+HtOLuLcHIII9w\GILkPLkHKDHCXIwHMwPI)M~LLJGMLkPIG`HC`H[A\A]A^A_]L GL G II<$Ht H{P}GHEH8Hu II>Ht H{ [GL G UHAWAVAUATSH8LLHIIHH^HHEH8H҄^HHHLH@H8L{GHHEEL-^IEHHI@L8L GL5ą^ILHDž(HDž HDžHDžDž0I$HGHHH蕁I7H GHHHGHHHbHH0H GHHHfGHHH(HEH0H GHHH߆HHL8Lt H l GHL GH5c^HH GH8 GH^HH;EuHH8[A\A]A^A_]s GHHHL8Lt H GH G3H|H|H|H|H|H|HH5^HH% GH8- GH|HH8 GHn|fffff.UHSPHH`HH{PHt H{X) GH{ HtH{(H[] GH[]fff.UHSPHHX`HH{PHt H{X GH{ Ht H{( GHH[] Gff.UHAVSIp GHIvH`HH{H[A^]IHm GLG DUHHGH `HH~H]AUHSPHH{PHt H{X# GH{ HtH{(H[] GH[]fDUHSPHH{PHt H{X GH{ Ht H{( GHH[] GDUHHLGHWHw8HO@HLOHH4Hy LWPIcI4HO J4HHH RI4HGhH@HHGH]Gfffff.UHH eZH1H9NHD]DUHH`]UHH`HHG8Ht HG@H] G]f.UHSPHH`HH{8Ht H{@ GHH[] Gffff.UHAWAVAUATSHIX GHHp`HIG0HC0IG(HC(IG HC IIGHCIGIOHKHCHCHHC@HC8IG8Mg@I)tZHELuH8MILH=uXL GIHMLi@Li8KDIIFHMHu~LL^ GMMn@IGPIFPLH[A\A]A^A_]HG IH;Ht HEHx@GH}GLG @UHAWAVAUATSPHHP`HIIF0HC0IF(HC(IF HC IFHCIFINHKHCHCHHC@HC8I~@I+~8tNL{8IILH=uQGILc@Lc8KHCHIv8M~@I)M~LLM GMLc@IFPHCPH[A\A]A^A_]LG II?Ht H{@GLG UHHHx8Ht Hx@]dG]@UHSPHH{8Ht H{@CGHH[]5GUHHLGHWHw HO(HH4Hy LcO0HO8J4HH vI4HGPH@HHGH]Gffff.UHH UZH1H9NHD]DUHH`]UHAWAVAUATSPHIHCIGHHKIOIHMwIG(IG IGH{ H+{t^III9;GIMg MgKIG(HsLk I)MH~LL}GHMMg HCHIGHHC@IG@HC0HK8IO8IG0IG`IGXIGPH{XH+{PtOLuMgPIII9wTGIMoXMoPKDIG`HsPH[XH)H~LHGIMoXH[A\A]A^A_]LGLG HI<$Ht IXGHEH8Hu HI>Ht I GH;G UHSPHHH`HH{XHt H{`GH{ HtH{(H[]GH[]fff.UHSPHH`HH{XHt H{`yGH{ Ht H{(gGHH[]YGff.UHAVSIpFGHIvH`HH{H[A^]IH GLMG DUHHGH Y`HH~H]AUHSPHH{XHt H{`GH{ HtH{(H[]GH[]fDUHSPHH{XHt H{`GH{ Ht H{(qGHH[]cGDHLHP8HIHxHpHHLH@LcPHMcMLPPOc MyUHSLXXIcM []H@ N LLHHGff.UHH ZH1H9NHD]DUHH`]UHH`HHG@Ht HGHH]G]f.UHSPHHh`HH{@Ht H{HiGHH[][Gffff.UHAWAVAUATSPHX?GIH`I$HC8ID$8HC0ID$0HC(ID$(HC ID$ MHCID$HCHKIL$ID$ID$PID$HID$@HC@L{HI)tOHEI@LHHH=uILGIMnHMn@IDIFPMHu~LLGMMnHLH[A\A]A^A_]LF II<$Ht I~H;GL3GLsF ff.UHAWAVAUATSPIH`IHHC8IF8HC0IF0HC(IF(HC IF HCIFHCHKINIFIFPIFHIF@H{HH+{@tNM~@IILH=uIGIMfHMf@KIFPHs@H[HH)H~LHGIMfHH[A\A]A^A_]LF HI?Ht I~H+GHkF UHHHx@Ht HxH]G]@UHSPHH{@Ht H{HFHH[]FUHHHHP HHHxHpL@LH(LcP0HcLIc Hy LcH8H@@J LHH]Gfff.UHH 5ZH1H9NHD]DUHH `]UHAWAVAUATSPHIHCIGHHKIOIHMwIG(IG IGH{ H+{t^III9FIMg MgKIG(HsLk I)MH~LLGHMMg HCHIGHHC@IG@HC0HK8IO8IG0IG`IGXIGPH{XH+{PtOLuMgPIII9wT7FIMoXMoPKDIG`HsPH[XH)H~LHFIMoXH[A\A]A^A_]LDFL:F HI<$Ht IXFHEH8Hu HI>Ht I FHF UHSPHH `HH{XHt H{`iFH{ HtH{(H[]QFH[]fff.UHSPHH `HH{XHt H{`FH{ Ht H{(FHH[]Fff.UHAVSIpFHIvH6 `HH{H[A^]IHFLF DUHHGH `HH~H]AUHSPHH{XHt H{`cFH{ HtH{(H[]KFH[]fDUHSPHH{XHt H{`#FH{ Ht H{(FHH[]FDHLHP8HIHxHpHHLH@LXHMLPPO MyUHSLXXIcM []H@ N LLHH"Ffffff.UHH UZH1H9NHD]DUHH- `]UHH= `HHG@Ht HGHH]:F]f.UHSPHH `HH{@Ht H{H FHH[]Fffff.UHAWAVAUATSPHXFIH `I$HC8ID$8HC0ID$0HC(ID$(HC ID$ MHCID$HCHKIL$ID$ID$PID$HID$@HC@L{HI)tOHEI@LHHH=uILGFIMnHMn@IDIFPMHu~LLFMMnHLH[A\A]A^A_]LXF II<$Ht I~HFLFLF ff.UHAWAVAUATSPIH`IHHC8IF8HC0IF0HC(IF(HC IF HCIFHCHKINIFIFPIFHIF@H{HH+{@tNM~@IILH=uI9FIMfHMf@KIFPHs@H[HH)H~LHFIMfHH[A\A]A^A_]LGF HI?Ht I~HFH F UHHHx@Ht HxH]F]@UHSPHH{@Ht H{HFHH[]uFUHHHHP HHHxHpL@LH(HH0I Hy LcH8H@@J LHH]FfUHH ZH1H9NHD]DUHH=`]ÐUHAWAVAUATSH(HUo^HHEIIHHAHq H):LLHHHyHHHcL$Lx:|HI6LL)HI6L|II6L HpHHIHhW)PHDž`M;LH= HN<LFHPJH`HLFHX1L@IHtLp~HPHHHxH9|WH*H0zFH(X)Hger thanHH Hrank larHHHust has HHHtensor mHHHindices Hf@( 0@*HH)HWLHDžHHID$(HIL$0HIL$ HP8H@I|$8HDžHIHLHaLHhHHpHH@HHxHLHHH HPH(H}L0H`HEH]H]H]W)EEHULH*H}H9HtiHtHP(HHtdH0IHELuHuHHLPHL>)Et H}MFHPHu%/HP HHuHHPHt HXFHk^HH;EuHH([A\A]A^A_]FFHP@F IEH}FHPHIHIHPHulvIH}H9uHP HPHuLVHt9HP(HPHu3=IHPHu",IHPHuIHPHt HX FLJF fUHAWAVATSHIII?IGHH)HL9sxHt"IFIGIGI1LH=u@HHHHH9HML9IH9LFMWMI)IM9LIBHpHrHSH9HH9HL@HHtHf.H HHHHuI@H HH HNH HNH HNH HN H HN(H HN0H HN8H@HuIJ<FIGIJ IOHLIrHKH9J4HLH9vyLBHHtHH3H1HHHuI@H3H1H3HqH3HqH3HqH3Hq H3Hq(H3Hq0H3Hq8H@Hu}LHH~HHJH`~fpDHyH)1DD D0HHuHt~fpDDL9IJIIIPHHDNAH~fpDIQH)1ff.DD D0HHuMt~fpDDL9IM97M)LLIHCL9vKLLH9LHHFHHJHT~fpDHAH)1fffff.AADAD AD0HHuHt~fpDAADI9t|I LH)HrHHtHؐH;H9HHHuHrIHHHHAHHAHHAHHA HHA(HHA0HHA8H@HuKJI[A\A^A_]H LH)LBHH+1HJ4L)L@HH$>1M7F1Hfff.UHAWAVAUATSH(Hee^HHEIIHHAHq H):LLHHHyHHHcL$LxJrHI6LL9uHI6L'rII6LuHpHHIHhW)PHDž`M;LH= HN<L FHPJH`HLFHX1L@IHtLtHPHHHxH9|WH*H0FH("X)Hger thanHH Hrank larHHHust has HHHtensor mHHHindices Hf@( 0@*HH)HWLHDžHHID$(HIL$0HIL$ H`H@I|$8HDžHIHLHqLHhHHpHH@HHxHLHHH HPH(H}L0H_HEH]H]H]W)EEHULH)H}H9HtiHtHP(HHtdH&IHELuHuHHLFHLN)Et H}]FHPHu%/HP HHuHHPHt HX FHa^HH;EuHH([A\A]A^A_]VFHPPF IEH}FHPHIHIHPHulvIH}H9uHP HPHuLVHt9HP(HPHu3=IHPHu",IHPHuIHPHt HXFLZF fUHAWAVATSH`IHw`^HHEؿ(FHþ HFHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H])mH5yFH}HIIHW)EHE4FHEHHHML HXHMH5PIHUL-H5F#IHH5FHHAH5 +GHHH;HHH _HMH/HUHuHuIIGIG0IOIWIG@H}Ht H}dFH]HEHtHHCFH]^HH;EuLH`[A\A^A_]~FIH]HEHt7HHFL6F IH}HuH]HEHuLF H}FH]HEHtff.UHAVSHPIH ]^HHE(FHþ HzFHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]iH5 FH}HHBH5LIHHH5 IHH5KHHH HQH5'GHHH;HHH _HMH[HUHuHuIIFIF0INIVIF@H]HEHtHH!FHZ^HH;Eu LHP[A^]`FIH]HEHtHHFLF @UHAVSHPIH;Z^HHE(FHþ HFHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]fH5=FH}HJHrH5JIHHH5IIHH5LKHHH HQH5 %GHHH;HHH _HMHHUHuHuIIFIF0INIVIF@H]HEHtH HQFHW^HH;Eu LHP[A^]FIH]HEHtHH FLLF @UHAVSHPIHkW^HHE(FHþ HFHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]dH5mFH}HzHH5LGIHHH5yIHH5| KH"HA H5@"GHIHH;HHH _HMHBHUHuHuIIFIF0INIVIF@H]HEHtHBHFHU^HH;Eu LHP[A^]FIH]HEHtHHCFLF ff.UHAWAVAUATSH(HHIHT^HHE1H' HHH HHHzLzHr IM)LHHHHM4HLpHC H+CHI9~nPFHH0f(uZf) H5IFHFCFH L) H0HFfWf)HDžHL)HZHHLIILH=jFHLHHJHLM9HHIM)LHHHLr+ID$HIDH9IIIM9HML)Ht"Hfff.IHIHHuH8{1IHITHTITHTITHTIT HT IT(HT(IT0HT0IT8HT8IT@H@L9uL1LM HI?I!I@HHzH s1MHHGH)1MAALLAD AL0D L0AD@ALPD@LPfAD`ALpfD`LpHHuHt8HHfffff.fADA fD H HuL9IJtHILsHK HL]HHH LI$H(H0fWf)HDžH 1H胷IMH Ht H(FfWf)`f)Pf)@f)0f) HLhHH{0HCPuH(IIFPH8H IIFPH0HPHHHHHQ0RtcL,$HL HI#HHDžHtZCt H{FHFH(uGxL,$HL HIHHDžHuH(6H HxH`H+XPIXf\QXf|H_HEHHxHEH]H]W)MfEHULH )1fWf)EHE0 FHEf(Zf)EHupportedHHH not supHHHata typeHHHtensor dHHHindices H@&HMMLLf)EH}qFHXHH HxH`H+X@Xf\Xf|H(_HEHHxHEH]H]W)MfEHULH)H}H9MLt HtHP(HP IEHXHt H`FHHt I<$FHM^HH;Eu)LH([A\A]A^A_]J O^HHEL%^L#H{jH[_HW)EHEEHbatch_diHEfEmsELsH}HULLH]HEHtCt H{FHFIEt H}nFH=^HH;Eu H0[A\A^A_]FIEt H}:FL#H{HCHt!FLaF fUHAVSIFHHLH[A^]IHFLF UHAVSIFHHL~H[A^]IHFLF UHAVSIFHHL>H[A^]IHYFLF UH]f.UH]4FfDUHSPH'FH _HHKHHH[]@UHH_HHGHF]UH]f.UH]FfDUHAWAVAUATSHH9(HwHVHUHVHUHVHHUHV HUHV@HR8HUHcHHEL$IHHH}H?HUHHULM|HH^(L LI LIHU1E1ff.NcFIH]HEHtHtH躱FLF fUHAVSHPIH)^HHE(蔱FHþ H花FHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]EH5?GH}H*11H莺H5HH误H5:JHHAH5FHHH;̦HHH ^HMHHUHuHuIIFIF0INIVIF@H]HEHtHHFFH&^HH;Eu LHP[A^]腯FIH]HEHtHHFLAF fUHAVSIFHHr^HH{L3H_HH[A^]IH褮FLF fff.UHHH "^HHxH@Ht]fF]fDUHSPHH^HH{HCHt5FHH[]'FfUHAWAVAUATSHIIH%^HHE1LNHH9L1L IMI|$vHH+H~1L6Ht Hi11I6HHW)EHxHUL評HxW)EHEM~IFHI9L ;ff.HHPHFH}Hffff.Mo0W)EHEI I+tNHHHH=贬FILuLuIHEIwI_ H)H~LHFILuHEHHEHHtH@LLHUHHHtHHCH H}Ht H} FLH HpI7IwPHjFH}H;}sLHEXffff.H}LLL IXL9HLaMLyLqHYL9tMI)HèHHèIXuI?K1LlHHRII1LLL襉ILk0HLILLLILM9MHC0xubHfIH臀IL-HtfHH@ffffff.LLFIIHu4LqLD-HIELPHLHHūFHL轈HLHHXPHHHFLM&FIGIGIHHEHAHEIHEHAHxHDžxHLtCt H{菩FH臩FH]Ht#HHCHuHHPH_FIH ^HH;EuLH[A\A]A^A_]芩FH}臦F*FIH}V*H5HH HH LE@UH]H9AH,GH LHE1H5A^HLF;諨FIH}YV*H5&HH jHH LEGW)EHE WFHE(W)EH allocatHHHo get anHHHUnable tHf@or@H HM1L6H5/A^HL,FzFIH}U*H5eHH HH LEH]H8AHGH LHE1H5@^H\L谧F IEt H}NFHXHt?H`H9t$Lh@t Hx#FLL9uHXHH`F@u u#LmMuGHPצF tH0¦FLmMuoIALmM\HEL9CHX@t Hx聦FHI9u4IEt H}]FE1HXHuYHVIAHXHt?H`H9t$Lh@t HxFLL9uHXHH`F@u u LmMu=qHPʥF tH0赥FLmMuEIALmMt6HEL9t!HX@t HxFHI9uH}LLmeFEuIL耥FHxHDžxHIEt H}FHXHt?H`H9t$Lh@t HxFLL9uHXHH`ӤF@u u LmMu=qHP誤F tH0蕤FLmMuEIALmMt6HEL9t!HX@t Hx_FHI9uH}LLmEFEuIL`FLpF IH}H 0IH]HILuMtAH]L9MI)Hèff.HhHèIXuH}Lu诣FHxHDžxHtCt H{艣FH聣FH]Ht#HHCHuHHPHYFL蕠F LIHHt#HHCHuHHPHFH}Ht*H}FLuMAIH LuM UHAWAVAUATSHAHH/^HHEHt*H覥H ^H H;Mu$HĨ[A\A]A^A_]1H ^H H;Mt転FIǃLkFUFIHhP*H5lIH HH}Lh$}H{{IcHHLIII|7IIOH?HH?H!H4Lo`FM9}HK|IHI)LN`FLmLeLH?H>LIII|7IIOH?HH?H!H4L_FM9}HK|IHI)L_FHhIL@MHXLMHhL gHrLKHCH(EAL4IM)LHHHE1H`LI0@LHHH`HIuAAAAI9LOIM)IBHJLH?II>MIKBIH=IIK<BLIHH9IDHDHXTXHHuHQIrZfffff.XD XT0\@XD0XXTPXD`XLpXHQH H9|((XH9~ X(XXMHhIPDHsP1Hfffff.HHH(M LH)1fDXXDXDXD XDXDXDXDHH9uHt HHfff.XHHuH9}ND)D)LL)L)HHL@fXHHuH@L@ffffff.XXDXDXD XDXDXDXDHI9uMtsHHHXHHuQL@HrD@XXDXDXD XDXDXDXDHH9uLHIH}XILL; L8L LPHXLLL)HHH HH.)H}HtHH8L[FH]HH;EHH[A\A]A^A_]H}L8LP?H}EAI]1L8LPMtHHIWXHWH*HMII?I>IIH|l(IIOHHD@AHZIpH)1D((\^)^)\HHuMt (^)HL)HryII(IpHHDJAH-J\IqH)1@T^T^HHuMtL^L9t+ML)Jfff.^HHuMHhHHH?II=IIH>HHL{LCHCH(IAMOIIABLH)HXAAAIM)LHHHH0HL)HLHHL1L`#1H0fHHHHH@H`HH}HHH|x(HHOHHVH! HZH)1fff.((\^)^)\HHuHHht (^)II)IMI(IPHHDNAHH\IQH)1fT^T^HHuMtH^M9Hht$LH)H D ^ HHuW)pI~HMLULH?II=MIH>LHMBI(I(AIIIOHHHZHn1(HI~HuHBH]HpXHpHxHH?H>HHH{1D(WD( W(W(%W(-X((AYWR(YY(\YYAV(^(f8)HH9|H9L ~|HyHt!Q :X^ HHH9tJXffff. Q(^LQ(^THH9uMHhHCH0M]0HHH`LQ0IE HH1LpfnIAzIRH HJ m E1MHhL1U1MHh+KfILOlH(HH@HELHMLXfnfnPIrKI9KTI91AI~H)LHt$H\YHHuHHhDRf\YT\YTT\YTT \YT HI9uLLHfofoHZHHDoAH,MMI)E1f.CdCl\\YYCdClCdC,\\YYCdC,IIuMHhLt)C$Cl\\YYHXB$BlI9֋hDN,0IAfnfnPIVI9IMHL)Ht&Hf.B\YB)IHuH Dr]fffff.B\YC)TBT\YC)TBT \YC)TBT0\YC)IM9|I9fnfnPI)IIH`JHXJH9sH8JKH9LXMIfofoIPHHDpAH6IVH)JHHJ<1$l\\YYdld l0\\YY$lHHuMHht%LA$Al\\YYA$AlM9Mff.D)H0L)Ht'HB\YBIHuHrmfffff.B\YBBT\YBTBT\YBTBT \YBT IL9uH(HHIIHHHHH; L81M1HHh(NfoMHhSHFHM]0HHH`LQ0MM 1L8E1ML0HH#1MfILK|H(HH@HEPI@uAAAAI9LOHXH`JLHMfnPIr!JHXH9JH91LHMIIt$H)LHtH@ \ HHuHLMLH# \ L\LL\LL \L HI9uHHL0LHfoHzHHD~AHCMOI)E1CTC\\\CTC\CTC\\CTCIIuML0HHt#BB\\\HXBB\I9L8fDN< IfnPIT$I9IMHL)HtH@B \B) IHuH rG@B \C)LBL\C)LBL \C)LBL0\C) IM9|HhI9fnPI)ILhH`JHXJH9sKDJH9HHMIfoIVHHD`AHL0IT$H)J4K<1f\\\T\T \0\\\HHuMt)LHH\\\HX\M9HhL0Mfff.HhD)HL)HtHB \B IHuHHhrZff.B \B BL\BLBL\BLBL \BL IL9uH(HHIIHHIH; 4LL)1WL8LPfDHHI X HAHI X HH9uHhMhNLH)1Wf.HHIXHAHIXHH9uHhH_E1M 1MQZ(XL9LH)fDfnXXDXDXD f~HH9uHt(HHfff.fnXf~HHuHh HsH)1(XL XD0XL@XDPXL`XDpXXH HuHwHt$Hfff.X XD0HHvHuXL9~BXHh(XXI9~D)IrH)Ht HHDXHHuHs LHHrCLH)HTXBXBXBXBXBXBXBXH HuWI*^WWMmI( (\YI(a\YIIIOHHDBAH IxH)1ffff.(l (t0\YX(L@\YX(dP\YX\YXHHuHSMt(,\(t0\YXYX((XL9~B($\YXHh(XXI9VIrH)LHt&Hf.\YXHHuHI)HL DYa\YX\YXY\YX \YXHIu\(YIARIBHsHucI)Ҿd\YX\YX\\YXL \YXHI9uHt$HH@\YXHHuHEHL`HMHPHrxII?LH>HH(HI=II(YHIIOHHH{H<1۾H1H|'PHxH&HTfnI~ IHAuDكH9HOII)LH?H>LHHIHrII9IDI91HyH)HHt"HfDA \ALHHuHIffff.A \ALAL\ALAL\ALAL \AL HH9uH)пfnXXTXTXT f~HH9uHHHfnXf~HHuHH(H~HHD@AH(IxH)1AA\\\ATA\AT A\0\\AT A\0HHuHwH)1ffff.XT X\0XT@X\PXT`X\pXXH HuHsHt$Hfff.XX\0HHvHu((XL9~BXHh(XXH9~l)HpH)HtHXHHuHr>H)HLDXQXQXQXQXQXQXQXH HuI~XWI*^WQ 5WIHAuDكH9HO^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$\YA)dAd\YA)dAd \YA)d Ad0\YA)d0HH9|H9NI)IsLIDLIFH}H9H5PqHH0H56^JH1HH HQH5tFH2HH;'HHH h^HMH%*HUHuHuIIFIF0INIVIF@H]HEHtHIH;0FH]HH;Eu LHP[A^]z0FIH]HEHtHIH/FL6-F fffff.UHAVSHPIHK]HHE(/FHþ H/FHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]XH5M;FH}H6H5nHH-H5v[JH/HH HQH53rFH<0HH;%HHH e^HMH2HUHuHuIIFIF0INIVIF@H]HEHtH5GH{-FH]HH;Eu LHP[A^]-FIH]HEHtHFH6-FLv*F fffff.UHAVSHPIH]HHE(-FHþ H,FHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H][H58FH}H*4H5kHH++H5XJH\,HH HQH5soFH|-HH;A"HHH 0c^HMHe;HUHuHuIIFIF0INIVIF@H]HEHtHuDH*FH6]HH;Eu LHP[A^]*FIH]HEHtH0DHv*FL'F fffff.UHAVSHPIHˡ]HHE(D*FHþ H:*FHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]SH55FH}Hj1H5iHHk(H5UJH)HH HQH5lFH*HH;HHH p`^HMHDHUHuHuIIFIF0INIVIF@H]HEHtHAH'FHv]HH;Eu LHP[A^]:(FIH]HEHtHpAH'FL$F fffff.UHAVSHPIH ]HHE('FHþ Hz'FHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]QH5 3FH}H.H5PfHH%H56SJH&HA H5iFH(HH;HHH ]^HMHLHUHuHuIIFIF0INIVIF@H]HEHtH>HB%FH]HH;Eu LHP[A^]%FIH]HEHtH>H$FL="F DUHAVSHPIH[]HHE($FHþ H$FHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]mQH5]0FH}H+H5cHH"H5PJH,$HA H5JgFHS%HH;HHH [^HMH|JHUHuHuIIFIF0INIVIF@H]HEHtHL<H"FH ]HH;Eu LHP[A^]"FIH]HEHtH<HM"FLF DUHAVSHPIH]HHE($"FHþ H"FHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]JH5-FH}HJ)H5`HHK H5MJH|!HA H5dFH"HH;hHHH WX^HMH,HHUHuHuIIFIF0INIVIF@H]HEHtH9HFH]]HH;Eu LHP[A^]! FIH]HEHtHW9HFLF DUHAVSHPIH]HHE(tFHþ HjFHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]MNH5*FH}H&H5@^HHH5&KJHHA H5aFHHH;HHH U^HMHEHUHuHuIIFIF0INIVIF@H]HEHtH6H2FH]HH;Eu LHP[A^]qFIH]HEHtH6HFL-F DUHAVSHPIHK]HHE(FHþ HFHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]]FH5M(FH}H#H5[HHH5vHJHHA H5:_FHCHH;HHH R^HMHCHUHuHuIIFIF0INIVIF@H]HEHtH<4HFH]HH;Eu LHP[A^]FIH]HEHtH3H=FL}F DUHAVSI&FHH^HH{LsHl._HH[A^]IHFL$F fff.UHHH b^HHxH@Ht]F]fDUHSPHH0^HH{HCHtuFHH[]gFfUHAWAVAUATSHIIH]HHE1L^\HHHHHHCLc I)It Iu H8uALW)@HDžPH詛IH@H膸H#IHCHK H)HLt HuFH8u@A?HDž`HHH;PiHHHHW)`HDžpMtALH=nN4LFH`JHpHLFHhLH蹚IM)HHIH LHE1fA?t!HHH`HhH@IH`HLHHH|HHIHH9t(HOHLH|&HLH9tfff.1HLH}IM9WQH@H`Brb1L@A<tHHH`HhLHL9rH`Ht HhFLLH@HHH)H1IILeLuHEH`HDžhH`1LHHfMMINHHHQINHI@I@ LHHуHH)HMIHSLHHMIJ HE1E1H'E1HLof.MMրHt9J K4H4H9J K4H4H91HAu1f1HAt$J HILHH4H4HHH9MLMHNHLH@JJB >LIHIuHLtK MMBBLLHHI9fffff.IHHIM9H>HHH@Ht HHfFH]HH;EuHH[A\A]A^A_]FJFIH@)H5SHH SHH`L@9oyW)EE(HEHis requiHEHX input HEEred!EAH#FH`HML{E1H5ޫ]HTLFFIH}D)H5RHH &SHH`LEmxW) HDž0 <FH0(?W) Hrst outpHHHo get fiHHHfailed tH@ut!AH#FH`H L {E1H5]HSLF H` F HALeMuiHALeMHHAL@MHH t H03FLeMt6HEL9t!Lh@t HxFLM9uH}LLeFEu`uLeMu)XH}F`tHpFLeMt1HEL9tNLh@t HxFLM9uH}LeFE{LFH@HovLHEt H}EFLeMt6HEL9t!Lh@t Hx FLM9uH}LLeFEu`u L@Mu,dH}F`tHpFL@Mt:HHL9tILh@t HxFLM9uH@LHFELFH F L(HH`HuJTHH`Hu9CHH@Hu:DHH`HuHH`Ht HhFH@Ht HHFH& F fffff.UHAWAVAUATSH(IIIILL)HHMaIyHL)HH9LEM9LL)HHHH=HL)HHHH9LM3HH9HBH6H9(FHH5VFH FHY]HHH5 ]H]H(FMM)LHH9ILHUL9M]MH]I)HHItL)HLFLHIH1ITI9sItLHH9LH?L!HUHrIIAQH 1HIHu&RLLLm+HH}H< FHLEL}M)LHHIM9H}MUMM)IMKIr KDH9JHDL9HM)DHt Hf.IHHIHuI8c1I H ILHLILHLILHLIL HL IL(HL(IL0HL0IL8HL8IL@H@L9u1HE1I?M!IGHHqH s1HH}HDpH~H)1fff.AALDȐLȠAD AL0DȰLAD@ALPDLAD`ALpD HHuHH}t2IDHHLH1DD D H HuM9ulN|IM~H}HuLW FH}HEHHuI)M~LL4 FH}MHEH8LxHXH}Ht FMJ4O4I|pHrL)1fDϐLϠAALDϰLAD AL0DLAD@ALPD AD`ALpHHuHIHut1H HfD AD A H HuHUI9LEIB >LIHIuHLtK MMBBLLHHI9fffff.IHHIM9H>HHH@Ht HHEHw]HH;EuHH[A\A]A^A_]EzEIH@%)H5?HH @HH`L@9eW)EE(HEHis requiHEHX input HEEred!EAHFH`HMLhE1H5]H@L EEIH}t)H5?HH ?HH`LEmdW) HDž0 lEH0(+W) Hrst outpHHHo get fiHHHfailed tH@ut!AH?FH`H L=gE1H53]H?L0E H`:E HALeMuiHALeMHHAL@MHH t H0cELeMt6HEL9t!Lh@t Hx>ELM9uH}LLe$EEu`uLeMu)XH}E`tHpELeMt1HEL9tNLh@t HxELM9uH}LeEE{LEH@HovLHEt H}uELeMt6HEL9t!Lh@t HxPELM9uH}LLe6EEu`u L@Mu,dH}E`tHpEL@Mt:HHL9tILh@t HxELM9uH@LHEELEHE L(HH`HuJTHH`Hu9CHH@Hu:DHH`HuHH`Ht Hh.EH@Ht HHEHVE fffff.UHAVSIEHH]HH{LCH_HH[A^]IHELE fff.UHHH 2]HHxH@Ht]vE]fDUHSPHH]HH{HCHtEEHH[]7EfUHAWAVAUATSHIIHq]HHE1L.=HHHHHHCLc I)It Iu H8uALW)@HDžPHy|IH@HVH>B >LIHIuHLtK MMBBLLHHI9fffff.IHHIM9H>HHH@Ht HH6EHk]HH;EuHH[A\A]A^A_]lEEIH@š)H5c4HH 5HH`L@9?ZW)EE(HEHis requiHEHX input HEEred!EAHFH`HML\E1H5]HW5LEfEIH})H53HH 4HH`LEmYW) HDž0 EH0(d W) Hrst outpHHHo get fiHHHfailed tH@ut!AHFH`H L[E1H5Ӌ]H|4LE H`E HALeMuiHALeMHHAL@MHH t H0ELeMt6HEL9t!Lh@t HxELM9uH}LLeEEu`uLeMu)XH}E`tHpELeMt1HEL9tNLh@t HxgELM9uH}LeREE{LqEH@HovLHEt H}ELeMt6HEL9t!Lh@t HxELM9uH}LLeEEu`u L@Mu,dH}E`tHpEL@Mt:HHL9tILh@t HxpELM9uH@LHUEELtEHE L(HH`HuJTHH`Hu9CHH@Hu:DHH`HuHH`Ht HhEH@Ht HHEHE fffff.UHAVSIEHH"]HH{LH_HH[A^]IHTELE fff.UHHH ]HHxH@Ht]E]fDUHSPHH]HH{HCHtEHH[]EfUHAWAVAUATSHIIH/f]HHE1L1HHHHHHCLc I)It Iu H8uALW)@HDžPHqIH@HHIHCHK H)HLt HuFH8u@I?HDž`HHH;PiHHHHW)`HDžpMtALH=nN4LEH`JHpHLsEHhLH)pIM)HHIH LHE1fI?t!HHH`HhH@IH`HLHHfDH|HHIHH9t(HOHLH|&HLH9tfff.1HLH}IM9WQH@H`Gb1L@I<tHHH`HhLLHL9rH`Ht HhELLH@HHH)H1IILeLuHEH`HDžhH`1LHH;MMINHHHQINHI@I@ LHHуHH)HMIHSLHHMIJ HE1E1H'E1HLof.MMրHt9J K4H4H9J K4H4H91HAu1f1HAt$J HILHH4H4HHH9MLMHNHLH@JJB >LIHIuHLtK MMBBLLHHI9fffff.IHHIM9H>HHH@Ht HHEHQ`]HH;EuHH[A\A]A^A_] EEIH@e)H5)HH *HH`L@9NW)EE(HEHis requiHEHX input HEEred!EAHOEH`HMLXQE1H5N]H)LKEEIH}贕)H5R(HH *HH`LEm1NW) HDž0 EH0(W) Hrst outpHHHo get fiHHHfailed tH@ut!AHEH`H L}PE1H5s]H)LpE H`zE HALeMuiHALeMHHAL@MHH t H0ELeMt6HEL9t!Lh@t Hx~ELM9uH}LLedEEu`uLeMu)XH}AE`tHp,ELeMt1HEL9tNLh@t HxELM9uH}LeEE{LEH@HovLHEt H}ELeMt6HEL9t!Lh@t HxELM9uH}LLevEEu`u L@Mu,dH}PE`tHp;EL@Mt:HHL9tILh@t HxELM9uH@LHEELEH$E L(HH`HuJTHH`Hu9CHH@Hu:DHH`HuHH`Ht HhnEH@Ht HHVEHE fffff.UHAVSI6EHH]HH{L能H^HH[A^]IHEL4E fff.UHHH r]HHxH@Ht]E]fDUHSPHH@]HH{HCHtEHH[]wEfUHAWAVAUATSHIIHZ]HHE1Ln&HHHHHHCLc I)It Iu H8uALW)@HDžPHeIH@H薂H.IHCHK H)HLt HuOH8uIAW.u HDž`HHH;PHHHHW)`HDžpMtALH=N4L!EH`JHpHL EHhLHdIMJHHIH LHE1fA.YWu{!HHH`HhH@IH`HLHHfff.H|HHIHH9t(HOHLH|&HLH9tfff.1HLH}IM9G[H@H`2<l1L@A.YWu{HHH`HhLHL9rH`Ht HhpELLH@HHH)H1IILeLuHEH`HDžhH`1LHH/MMwINHHHQINHI@I@ LHHуHH)HMIHSLHHMIJ HE1E1HE1HLeMMրHt9J K4H4H9J K4H4H91HAu1f1HAt$J HILHH4H4HHH9MLMHNHLH@JJB >LIHIuHLtK MMBBLLHHI9fffff.IHHIM9H>HHH@Ht HHVEHT]HH;EuHH[A\A]A^A_]E:EIH@)H5HH HH`L@9_CW)EE(HEHis requiHEHX input HEEred!EAHEH`HMLEE1H5u]HwLEEIH}4)H5HH HH`LEmBW) HDž0 ,EH0( W) Hrst outpHHHo get fiHHHfailed tH@ut!AHEH`H LDE1H5t]HLE H`E HALeMuiHALeMHHAL@MHH t H0#ELeMt6HEL9t!Lh@t HxELM9uH}LLeEEu`uLeMu)XH}E`tHpELeMt1HEL9tNLh@t HxELM9uH}LerEE{LEH@HovLHEt H}5ELeMt6HEL9t!Lh@t HxELM9uH}LLeEEu`u L@Mu,dH}E`tHpEL@Mt:HHL9tILh@t HxELM9uH@LHuEELEHE L(HH`HuJTHH`Hu9CHH@Hu:DHH`HuHH`Ht HhEH@Ht HHEHE fffff.UHAVSIEHHB]HH{LH^HH[A^]IHtELE fff.UHAVSIVEHH]HH{L裱H^HH[A^]IHELTE fff.UHAVSIEHH]HH{LCH^HH[A^]IHELE fff.UHAVSIEHH"]HH{LH^HH[A^]IHTELE fff.UHAVSI6EHH]HH{L胰H^HH[A^]IHEL4E fff.UHAVSHPIHKN]HHE(EHþ HEHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5FH}HHH5@HHHHhH5GHHH5(HHH5BJHHH HQH5FHHH;HHH ^HMHBHUHuHuIIFIF0INIVIF@H]HEHtHHGEHK]HH;Eu LHP[A^]EIH]HEHtHHELBE f.UHAVSHPIH[K]HHE(EHþ HEHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5FH}HHH5PHHHHxH5GHHH58HHH5RIHHA H5FHHH;HHH ^HMH\HUHuHuIIFIF0INIVIF@H]HEHtHH^EHH]HH;Eu LHP[A^]EIH]HEHtHHELYE UHAVSHPIH{H]HHE(EHþ HEHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5FH}HHH5pGHHHH5GHHH5XHHH5rIHHH HQH5/FH8HH;HHH ^HMHQZHUHuHuIIFIF0INIVIF@H]HEHtH1HwEHE]HH;Eu LHP[A^]EIH]HEHtHH2ELrE f.UHAVSHPIHE]HHE(EHþ HEHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H5FH}H*HH5GHHHH5ϡGHHH5hHHH5IH(HA H5FFHOHH;HHH 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^CHhL0oVHHAHf_CHhLH2YE1H5(XH1L%@ IAL0MIIAL0MIIt Hw@LmMt6HEL9t!HX@t HxR@HI9uH}LLm8@Euhu#L0Mu36H}@htHx@L0MH8L9HX@t Hx@HI9uIt H@LmMt6HEL9t!HX@t Hx}@HI9uH}LLmc@Euhu L0Mu,dH}=@htHx(@L0Mt:H8L9tEHX@t Hx@HI9uH0L8@EtL@L@ LUHAVSI@HHL.HZHH[A^]IH@L@ UHAWAVAUATSHHIHdXHHEL-XM,$I|$HZI$W)EHEEHblocksizHEfEeIL$HhHUHHhHDžhHtCt H{@H@Et H}@Hu'H dXHH;EuHĘ[A\A]A^A_]@@H@Hh3$H5[CH =\CH}LhRW)PHDž` +@H`(*R)PHnot set.HHHize is nHHHe blocksHHHAttributH@LH[CHuHPH@TDžLH5ڄXH-H@@ IǰLLuMuKIǰLHhHIIPt H`-@LuMt6HEL9t!HX@t Hx@HI9uH}LLu@EuEuHhHu&^H}@EtH}@HhHt:HpH9tELp@t Hx@LL9uHhHpv@Lt%H@@HIEt H}H@M,$I|$ID$Ht,@Ll@ @UHHH XHHxH@Ht]@]fDUHSPHHXHH{HCHt@HH[]@fUH f.UH f.UHAVSI@HHLHZHH[A^]IHO@L@ UHAVSI 6@HHLH[A^]IH @LI@ UHAWAVAUATSHHIH_`XHHEcHZI$AD$W)EHE)`HDžpƅ`DžamodeƅeHHH`HMHHHHDžHHtCt H{G@H?@`t Hp*@HuE$HMHHDHu3H hXCH}1HAI@tpUHM$҄HEHugH -XCH}1HA @uDEt H}@H_XHH;Eu Hĸ[A\A]A^A_]AD$Eu@p@IHH$H5VCH WCH`LH*MW)0HDž@@@H@(XbU)0HsupporteHH0Hdes are HH(H'CRD' moHH HCR' and HHH only 'DHHHpace op:HHHDepthToSHf@8dH`H01LOH5XHT(L@ I0t H@@@Dž,H]HuRH5VIǰ,H]Ht6HEH9t!Lh@t Hx@LL9uH}HH]@Eu`u LHMuE}H}@`tHp@LHMuQIǰ,LHMt:HPL9t9HX@t HxZ@HI9uHHLP?@,u @LILT@Eu07H&UHUI`t Hp@Et H}@HXI$I|$ID$Ht@L@ f.UHHH BXHHxH@Ht]@]fDUHSPHHXHH{HCHtU@HH[]G@fUHAVSI 6@HHLH[A^]IH @LI@ UHAVSI @HHLH[A^]IH@L @ UHAWAVAUATSHH%[XHHEHHHHH0HIHH(HHH HHHHHHH@HHLHHwHLY[bH2[HHHHG1Ƀ`Wt-THS(HHS HHSHHSHHHsHHH ( R(8HDžhHDž`HDžXHDžPHDžHHDž@HDžHDžHDžHDžHDžHDžHDžHDžHDžHDžHDžHDžxH L@DžHHDž`HDžXHDžPLpMLE1L@@HLHHHHLH)HH@HLI)L9LNLHxHHHHHH)ID HHHI)L9LNHHHHHHHH)HPHLI)L9LNID HHHHHHHH)ID HXHLI)L9LNHHHHHHHH)LH`HHH)H9HNHHHHHHhHHH)H9HNHHHhLpLxLLHHAG@IG8IG0IG(IG IGIGIGIHHhLDžHIM9HPH9Xt:E11ff.J<7)@HHPHXH)HIH9rHt HX@H^VXHH;EHĘ[A\A]A^A_]H=[@HȮ[H[H[HHhH@wHcHHOH n[HchHHOH \[Hc@H HOH J[H=K[V@j@IH=2[1@L;@ IHPH9Xt:E11ff.JHI|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$9J HH H9sEJ/HJ H9s4Lp{ff.1JOH7LhLHH`HHHHpHs1HHHH|pHHhLdpHNH)1ADALDLADALDLADALDLADA D H HuHtVHHHH HhHHHH޿fD8 8D9 9H HuH`I9HLpI@D)HL)HtHB4C4IHuHrqHJDHJLfBtBtBtBtBtBtBtBtBtBtBtBtBtBtB4B4IL9ux~@H1HHpHHpH;p|/H@L+HL+(HH0L9|fHLHLhHHHH;M*I HHHH`LL^0HHIHMzEAMM)LJ<HLHMI1HfDHIs 1MuHvHJ1ff.C40C4B4C4B4C44HHI9uMt0ILHHLffff.C40HHux~CH1HqHHqH;q|!HAL+IL+)HH0H;|LILiHHHHHHH;! MBLHIJHH)HDЃJ HLLHLHHHHT A<|A||A||A| K\0HyL)IHI HL4LE1CDC BDBLCDCL BDB IHuHt*MHIT HLCCLHI9LLHI̐D)HL)HtHCCIHuHHrBIL fffff.CBTCTBTCTBTCT BIL9uLI܃tNH1Lfffff.H9HH9H;y|1HH+qHH L9rL HqHHHIL;HXHH;EuHX[A\A]A^A_]R@@UHAWAVAUATSHHXHNXHHEп(ǖ@Hþ H轖@HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]`I蘿IIHHIW)EHE(@HEHH(HML8L`LhHXLp HM9I1IIIYHW)`HDžp(衔@H`HH(HpL8L`LhLpHX HhH5x@H}HUH`H5CHH5DH?HH HQH5V@H_HH;$HHH XHMH(GHUHuHuHXHHCHC0HKHSHC@H`Ht Hh蠓@H}Ht H}莓@LuHEMtL'Lm@H XHH;EuHHĈ[A\A]A^A_]裓@IH`Hu$H}Hu>H]HEHu]LZ@ Hh@H}Ht IH}HtH}@H]HEHtIH]HEHtHgH譒@L@ DUHAWAVAUATSHHXH XHHEп(w@Hþ Hm@HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]IHI@IHpIW)EHE(辐@HEHH(HML8L`LhHXLp HMII虻I葾I HW)`HDžp(Q@H`HH(HpL8L`LhLpHX HhH5(@H}HUH`譕H5CH辏H5IDHHH HQH5@HHH;ԆHHH XHMHIHUHuHuHXHHCHC0HKHSHC@H`Ht HhP@H}Ht H}>@LuHEMtLרL@HXHH;EuHHĈ[A\A]A^A_]S@IH`Hu$H}Hu>H]HEHu]L @ Hh贎@H}Ht IH}HtH}蔎@H]HEHtIH]HEHtHH]@L蝋@ DUHAWAVAUATSHHXHXHHEп('@Hþ H@HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]III診H IW)EHE(n@HEHH(HML8L`LhHXLp HM虶I葼IIIAI蹸HW)`HDžp(@H`HH(HpL8L`LhLpHX HhH5ؘ@H}HUH`]H5qBHnH5DH蟌HA H5@HƍHH;苂HHH zXHMHOEHUHuHuHXHHCHC0HKHSHC@H`Ht Hh@H}Ht H}@LuHEMtL莤LԊ@HOXHH;EuHHĈ[A\A]A^A_] @IH`Hu$H}Hu>H]HEHu]L@ Hhk@H}Ht IH}HtH}K@H]HEHtIH]HEHtHΣH@LT@ fff.UHAWAVAUATSHHeXHHEMMAIHhDmI_I+HH`H>H)IIH HXLHNHcH9`A$I6L3HcH9EHMI6HƸH9tL HcH9HMHu L6HFI9t>H`Hq HxH.HHL)HHH1LMc1HXHIHtlHBHEHBHEHBHEHEH$H]HuHMLMHLL`CLhLHKX#[HHIIffEHEEbJ<8@HH]H]JHHEAIHLhHu [H?H!HZHHwH sf1fHH^H)f1ffffff.AofAoDfAoL Ao\0Aod@ffAoTPffAoD`fAoLpfHHuHt$I|HfoWfofH HuffpNffH~H9tI ff.H1HH9uHxL)HIcH9uH;Xu LheHpHBHEHBHEHBHEHBHEH BHEHxHD$ HEHD$HpHD$HEHD$HEH$H]HuHMLMHH`MCLhLHU#EH}@HqWHH;EE11LhMBfLHfInfpDHpHHJHLhs1HHqH)1ffff.DD D0D@DPD`DpӀӐӠӰH H{Ht(HTHfffff.BH HuL9Hu t!H I)fffff.L9HIuIL}Mt)LvLIw@LHFHFHHL~L~IEHWHH;EuLHĨ[A\A]A^A_]耄@@H}x@ HH}Ht#H}HEu HEt H}@H!@ fUHAWAVAUATSHHIIH,WHHE1HII$IG0@ȃH HcHHpLHLZHpLHL2CHpLHLk!,HpLHL HpLHL+HpI$HkWHH;EuLHĨ[A\A]A^A_]&@Ԃ@IHX0$H5BH BHpLXrIG0H8HBHEHBHEH@HuH8HMHCHp1LHvH5oXHľLl@!I@t HP@LmMt6HEL9t!HX@t Hx܁@HI9uH}LLm@Et H}賁@pt H}衁@Dž4HXHu!YHIǰ4HXHt:H`H9t9Lh@t HxK@LL9uHXH`0@4u HILE@ II$L@~@ 000UHAWAVAUATSHHHIIH%WHHEH{UZLL+IGHEEEEW)EHEHHwHt2HHHP H)HL1HoJH}HIwIW H}HHPIIHEHD$HEHD$HEH$LELMLLDI}tYHBH UB1AL-$H}Ht H}@HWHH;ELHĘ[A\A]A^A_]LIEHLxLh W)PHDž`M)t[MIHI9nL @HHXHPJH`M~HLLW@LHXH-vHEHEHHcEHcMHHHHUIcHHH8ƒHpHH)HhHHH@HpHHHxHHHHH11HfDHEHL$IcL9,LPHM$W)0HDž@LXM)t\LHHH9L}@IL8L0IDH@M~LLL~@ML8HHH0[IH0Ht H8 }@LwIDHHL4D9u1LLHS~@IcHHH;IcL$HHLs1H!K dHHHHXHH`1Lffffff.LLL}@K<,HJ40L}@H`J<(K47Lx}@HJ<(HpJ40LZ}@HLxLXH9huHtZHHHHL4HHHLLLHL|@MLIuHHHHH;BBE1E1HEN,IcL9cHPHL,W)0HDž@LXI)ttHLMIHI9 Lz@HH8H0JH@MLH~HL{@LH8HDH0XHH0Ht H82z@H蜋DD9uHJ4HH{@IcIIL;LIEHPH5HXy@H}H%)z@y@H0v@y@IH0J'$H5UBH BHPL0߿W)HDž 0?y@H (T)H tensor.HH Ha vectorHHHmust be HHH tensor HHHAn splitH@(AHBHPHLE1H5XHLx@ HPu@ HALeMuFzHAL0MHHt H @x@LeMt6HEL9t!Lh@t Hxx@LM9uH}LLex@puPu L0Mu,dH}w@PtH`w@L0Mt:H8L9tQLh@t Hxw@LM9uH0L8}w@ELw@H}HLHH0HdbHMuIEMAFt I~w@L w@H}Huv}HH}HufmHH0Hu$.HHPHu)3HH0Ht H8v@HPHt HXv@H}Ht H}v@Hs@ fUHAWAVAUATSHHHIIHWHHEH{OLL+IGHEEEEW)EHEHHHt2HHHP H)HL1HeJH}H責IwIW H}?HHPIIHEHD$HEHD$HEH$LELMLLD\I}tYH0BH B1AL'#$H}Ht H}#u@HWHH;ELHĘ[A\A]A^A_]LIEHLxLh W)PHDž`M)t[MIHI9nLt@HHXHPJH`M~HLLu@LHXHHEHEHHcEHcMHHHHUIcHHH8ƒHpHH)HhHHH@HpHHHxHHHHH11HfDHEHL$IcL9,LPHM$W)0HDž@LXM)t\LHHH9L*s@IL8L0IDH@M~LLLtt@ML8HHH0ZQIH0Ht H8r@L藷IDHHL4D9u1LLHs@IcHHH;IcL$HHLs1H!K dHHHHXHH`1Lffffff.LLLFs@K<,HJ40L/s@H`J<(K47Ls@HJ<(HpJ40Lr@HLxLXH9huHtZHHHHL4HHHLLLHLr@MLIuHHHHH;BBE1E1HEN,IcL9cHPHL,W)0HDž@LXI)ttHLMIHI9 LSp@HH8H0JH@MLH~HLq@LH8HDH0NHH0Ht H8o@H輴DD9uHJ4HH%q@IcIIL;LIEHPH5HX^o@H}H%)o@6o@H0l@?o@IH0$H5BH hBHPL0dտW)HDž 0n@H (T)H tensor.HH Ha vectorHHHmust be HHH tensor HHHAn splitH@(AHBHPHL׿E1H5XH6Ln@ HPk@ HALeMuFzHAL0MHHt H m@LeMt6HEL9t!Lh@t Hxm@LM9uH}LLem@puPu L0Mu,dH}xm@PtH`cm@L0Mt:H8L9tQLh@t Hx8m@LM9uH0L8m@ELWHH;ELHĘ[A\A]A^A_]LIEHLxLh W)PHDž`M)t[MIHI9^LLj@HHXHPJH`M~HLLk@LHXHYHEHEHHcEHcMHHHHUIcHHH(ƒHpHH)HhHHH@HpHHHxHHHHH11Hf.HEHL,IcL9LPHM,W)0HDž@LXM)t[LHHH9Lh@IL8L0IH@M~LLLj@ML8HHH0FIH0Ht H8Nh@L8HDHHL4D9u2HLHi@IcHHH;IcL$HHHs1HK dHHHHXHH`I1LLLLh@K<,HJ40Lh@H`J<(K47Lh@HJ<(HpJ40Lh@HLxLXH9huHtZHHHHL4HHHLLLHL6h@MLIuHHHHH;RBE1E1HEN,IcL9cHPHL,W)0HDž@LXI)ttHLMIHI9 Lf@HH8H0JH@MLH~HLCg@LH8HDH0/DHH0Ht H8e@HlDD9uHJ4HHf@IcIIL;LIEHPHEHXe@H}H59We@d@H0Lb@d@IH0$H5BH BHPL0˿W)HDž 0d@H (gT)H tensor.HH Ha vectorHHHmust be HHH tensor HHHAn splitH@(AHNBHPHLGͿE1H5=WH楾L:d@ HPDa@ HALeMuFzHAL0MHHt H c@LeMt6HEL9t!Lh@t Hxkc@LM9uH}LLeQc@puPu L0Mu,dH}(c@PtH`c@L0Mt:H8L9tQLh@t Hxb@LM9uH0L8b@ELb@H}HLHH0HdbHMuIEMAFt I~ab@LYb@H}Huv}HH}HufmHH0Hu$.HHPHu)3HH0Ht H8a@HPHt HXa@H}Ht H}a@H_@ fUHAWAVAUATSHHHIIHWHHEH{E;LL+IGHEEEEW)EHEHHgHt2HHHP H)HL1HPJH}HIwIW H}HHPIIHEHD$HEHD$HEH$LELMLLDI}tYHBH ErB1ALw$H}Ht H}s`@HWHH;ELHĈ[A\A]A^A_]LIEHLxLh W)PHDž`M)t[MIHI9L_@HHXHPJH`M~HLLGa@LHXH蝎HEHEHHcEHcMHHHUIcHHH[ƒHpHH)HxHIHHHH HHHH1E1fLHEHL,IcL9eLPHM,W)0HDž@LXM)t[LHHH9L^@IL8L0IH@M~LLL_@ML8HHH0<IH0Ht H8^@L1ILDHN48D9u;LLH]_@McMHH;fffff.IcHHs,E1LH[ffffff.HH @HH HhHHpME1fffff.LLHH^@HJ40J<+H^@HJ40HpJ<(Hj^@HJ40HJ<(HL^@ILLhL9xyLHttLMMMLMLHfffff.LLL]@MLHuHMHH;)iHLMHH;CE1E1HEN,IcL9cHPHL,W)0HDž@LXI)ttHLMIHI9 L[@HH8H0JH@MLH~HL\@LH8HDH09HH0Ht H8[@HDD9uHJ4!HHU\@IcIIL;LIEHPHHXZ@H}H Z@fZ@H0W@oZ@IH0$H5%BH BHPL0W)HDž 0Z@H (T)H tensor.HH Ha vectorHHHmust be HHH tensor HHHAn splitH@(AHBHPHL¿E1H5WHfLY@ HPV@ HALeMuFzHAL0MHHt H Y@LeMt6HEL9t!Lh@t HxX@LM9uH}LLeX@puPu L0Mu,dH}X@PtH`X@L0Mt:H8L9tQLh@t HxhX@LM9uH0L8MX@ELlX@H}HLHH0HdbHMuIEMAFt I~W@LW@H}Huv}HH}HufmHH0Hu$.HHPHu)3HH0Ht H8{W@HPHt HXcW@H}Ht H}QW@HT@ fUHAWAVAUATSHhHHIIHWHHEH{0LL+IGHEEEEW)EHEHHHt2HHHP H)HL1HRFJH}H肍IwIW H}HHPIIHEHD$HEHD$HEH$LELMLLD,I}tYHBH gB1AL$H}Ht H}U@HnWHH;E8LHh[A\A]A^A_]LIEHLxHX W)PHDž`L)t\IIHI9H|U@ILXLPKDH`H~LLHV@ILXH1HEL6HEHHcEHcUHHHЋMIcHHHfHHH@H1W1HHEH HcH9LPHHI )0HDž@LXM)HtiLHHH9L=T@IL8L0IH@MLH~LLLzU@ML8HH0g2HH0Ht H8S@H)ILDHH@HLL@L~I@ fDUHAWAVAUATSHIIHWHHEMuIEIEIEIE ffEHEEEaxisEHPHUHhEHPE1HuLhHDžPtCt H{yK@HqK@MeEt H}^K@I?CffEE HEEsplifEtHhHULLgMHhHDžhHtCt H{J@HJ@Et H}J@HpIEIMH9tHQH)HHHs+1H&IE HWHH;E9FI?I!IpHH{H sf1fH|HwH)f1fofoDfoL o\0od@ffoTPffoD`foLpfHHuHt,HtHf.oVfofH HuffpNffH~L9tJff.H2HH9uIu H8x0HH9uHWHH;EuHĘ[A\A]A^A_]I@FI@H@Hh#H5лBH BH}Lh"jffPHDž`@H@H`foYTfPHust be >HH0Hvalues mHH(Hte. All HH H attribuHHH 'split'HHHvalue inHHHInvalid Hf@8 0@:LHBHuHPH@qDžLH5`WH H@YH@tIİLL}MuKIİLHhHIIPt H`G@L}Mt6HEL9t!HX@t HxG@HI9uH}LL}qG@EuEuHhHu&^H}NG@EtH}?G@HhHt:HpH9tMLx@t HxG@LL9uHhHpF@LtEH@G@I>Hu9@HIEuII>Hu#IEt H}F@I>Ht I}F@LC@ fUHAWAVSPI8F@HL=@H8?@HHH8?@HUHAWAVAUATSPLMMIIIH6>@I]H:B@LHHLLIHB@LHHܲHEЋ0L>@HEHHA@LHH豲HEH0L>@HE HHA@LHH腲HE(H0L=@H[A\A]A^A_]HHHHH﮽H箽H߮H׮HϮffffff.UHAWAVAUATSH(HHIHHWHHEH8H11HEH5BH/:H5gDH`;HH HQH5w~@H<HH;E1HHH 4rXHMHHUHuHuIIFIF0INIVIF@H]HEHtHySH9@H:WHH;Eu LHP[A^]9@IH]HEHtH4SHz9@L6@ fUHAVSHPIH۰WHHE(T9@Hþ HJ9@HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]荽H5D@H}H;11HNBH5PBHo7H5dDH8HH HQH5{@H9HH;.HHH toXHMH HUHuHuIIFIF0INIVIF@H]HEHtHPH6@HzWHH;Eu LHP[A^]>7@IH]HEHtHtPH6@L3@ fUHAVSHPIHWHHE(6@Hþ H6@HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]ͺH5B@H}H*911H?H5BH4H5:bDH5HA H5x@H7HH;+HHH lXHMHHUHuHuIIFIF0INIVIF@H]HEHtHNHF4@HWHH;Eu LHP[A^]4@IH]HEHtHMH4@LA1@ fUHAWAVSPI(3@HL=o)XL;H{L0 HHLQH_YHHH[A^A_]L;IH{HCHt {3@IHn3@L0@ fDUHAWAVSHHHIHƪWHHEIGIGIW)EHEH;q+W)EHEEEaxesEH}HULuHL#5Et H}2@H]HtHECaeH}HuHU1@HuHUHH9t9HFH9tH>HJHHHxH9tHH9uHH9H9HHH9LBI)DHt(Hfff.HH9tHXHHHuIruH9H98u(HyH98u0HyH98u8HyH98u@H H9uFfHxHHyH98tHxHHyH98tHxHHyH98tHxHH H9uHH9tHEHM9tLH]HEHtCt H{=1@H51@H}Ht H}#1@HWHH;Eu HH[A^A_]c1@IH]HEHt&Ct H{0@H IEt H}0@H}HuI?Hu(L-@ H}0@I?Ht II?HtI0@L-@ ffffff.UHSPHH{Ht H{S0@H%XHH{HCHHt[]-0@[]fff.UHSPHH{Ht H{0@H%XHH{HCHt/@HH[]/@UHAWAVAUATSHIHIH,WHHE1LrHW)EHEI}< HH+HuHLrH7HHHP H)HHHHH}HfHsHSH}謫HHpH`HUHzW)@HDžPL`HhL)tiLMIILH={H.@ILHL@KHPHML~LLH/@ILHH@1L HH@Ht HH+.@L+LkPHL8LxPM9Hx0HPhHtIxuCHΰH~d1LL,@H詰HIIH9|2H苰IHHx0HPILLH /@I$H`Ht Hhc-@H}Ht H}Q-@H̤WHH;EuLH[A\A]A^A_]-@H@*@$-@IH@#H5BH BH`L@MIW) ƅ $HDž0Hut is nuH)HAxes inpH!fDž1llƅ3AHBH`H L諕E1H5WHJnL,@Y,@IH@#H5ޢBH BH`L@O~W) HDž00+@H0(vQ) H tensor.HHH vector HHHust be aHHHtensor mHHHAn axes H@'AHBH`H L豔E1H5WHPmL+@ HALeMulHAL@MHHAL@MHH t H0*@LeMt6HEL9t!Lh@t Hx*@LM9uH}LLe*@Eu`u#L@Mu36H}z*@`tHpe*@L@MHHL9Lh@t Hx2*@LM9uH t H0 *@LeMt6HEL9t!Lh@t Hx)@LM9uH}LLe)@Eu`u L@Mu,dH})@`tHp)@L@Mt:HHL9tQLh@t Hxh)@LM9uH@LHM)@ELl)@H}HLHH}HHH@HuXHH@HtIHH(@H`Hu=GHH}HuDKHH}Hu6=HH}Hu(/HH`Ht Hh(@H}Ht H}}(@H%@ DUHAWAVAUATSHH՟WHHEHGHGHHFHH+HIM}IEIW)EHEM)Ht^HLHHH=L'@ILuIH]LL'@H]HLIL9uLmN1E1LIL9t1@IHuiH@ LHH}Ht H}"@LI>Ht4HH}HtHLH}HtH}V"@I>HtI~E"@H@ ffff.UHAWAVAUATSH8LLHIIHHwWHHEH8HWHHHLH@H8L[!@HHEEL-WIEHHI@L8L_ @L5WILHDž(HDž HDžHDžDž0I$H#@HHHuI7H@HHH#@HHHBHH0H@HHHF#@HHHHuHȯHHH™HHL8Lt H O @H/@H5FWHH@H8@HWHH;EuHH8[A\A]A^A_]V @HHHL8Lt H @H@3H辏H趏H讏H規H螏H薏HH5WHH@H8@HhHH8@HQUHAWAVSPI(3@HL=XL;H{LHHLHKYHHH[A^A_]L;IH{HCHt @IH@L@ fDUHAWAVSPI(@HL=/XL;H{LHHLHJYHHH[A^A_]L;IH{HCHt ;@IH.@Ln@ fDUHAWAVAUATSHIHWHHEп(@Hþ H@HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]GHHMHPIHXKH`FHhLHpLJHxKIMINIGHW)EHEX@HEHHXHMHHHHPHPHXHPH`HPHhHP HpHP(HxHP0Lh8Lp@LxHHXPHMH5(@H}HUHKH5BAHH$H5BHH5HDHAHH HQH5X_@HaHH;&HHH SXHMH -HUHuHuI$ID$ID$0IL$IT$ID$@H}Ht H}@H]HEHtHB4H@HWHH;EuLHĘ[A\A]A^A_]@CIH}HuH]HEHu:L@ H},@H]HEHtIH]HEHtH3H@L5@ ffff.UHAWAVAUATSHIHBWHHEп(@Hþ H@HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]TCHHHIHP|EHXGH`dBHhXHHp FHxFIIIpJICHW)EHEX@HEHHXHMHHHHPHPHXHPH`HPHhHP HpHP(HxHP0Lh8Lp@LxHHXPHMH5M$@H}HUYHGH5o>AHHH5zBHH5[DDHHA H5[@H(HH; HHH NXHMH1)HUHuHuI$ID$ID$0IL$IT$ID$@H}Ht H}p@H]HEHtH 0HO@HʍWHH;EuLHĘ[A\A]A^A_]@CIH}HuH]HEHu:LF@ H}@H]HEHtIH]HEHtHv/H@L@ @UHAWAVAUATSHHIHWHHE1HXIMLpMMt$ M+t$HHzXIMMIG I+GHLLL`MILHۗL9ILIW)EHEHHx H+xIIM9@HH]H]JHEHHpLh I)MHL@HLHuH ̌BLc"HWHH;EL\H ҌB#H BH 2BH UBHpH"H^WHH;EVHHĘ[A\A]A^A_]11HH1fH I H HL9rW)EHEHH)t]LHHHIH9\@ILmLmKDHELI)M~LHLH@ILmHU1HHLpH}ΕHHEHuHH)HHWH H)H9Mu|H9t1H<0H;<2ukHH9uHHA0xHL0LxPHHLgP!iLMLHH@LHH}HH`K|uIID$H}I\$HHHu6MH1AA H IMu'HkHOlI7HHƅHDžHDžHDžHs H+sHHDž@Ht.HH@bHHHH 11HH)HHדHuƅHHH 1"zLHC0@ȃ GH ."HcHLhHHGLo I)IHNtL!LaPH7HwPIL@LLHxL@KLK4HM|5LyHAAHLHs+LMILLMgI)LHLLP@LHLL?@LHLL.@LHLL@LHLL @LHLL@LHLL@LHLL@LInLMtIHLL@LIuHIILhHHAHHHp^LxHHHHHHvHcH;%HI HH It$H?MHH<LL%?MHPH<LL ?MHH<LL?MHH<LL?MHHLL?LI3HhH MHLLL?MHuHMIMIEEAHLM)HLLC?LHLL2?LHLL!?LHLL?LHLL?LHLL?LHLL?LHLL?LInLMtIHLL?LIuHpHOLƅHHH Ht H(?HHtH?H}Hu H}Ht H}?H}HH}?HmWHH;E??IH-#H5nBH 1oBH@L[HH@0HHoBHEHHuHH0H@1LH,^H5%WH6L"?H},?H}??IHl#H52nBH pnBH@LZW) HDž00a?H0(y3Q) Hing typeHHHport strHHHsn't supHHHTile doeH@ yet@$AH=nBH@H L ]E1H5WH5L??IHy#H5?mBH }mBH@LYW) HDž00n?H0(2Q) Hing typeHHHport strHHHsn't supHHHTile doeH@ yet@$AHJmBH@H L-\E1H5#WH4L ? 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H{(>H[A^]w>[A^]@UHAVSHIHt#H3LHsLH[A^]9>[A^]fDUHAWAVAUATSH(MLEIIHHCHCHLHMHVILHuSILIHnMMMIHEIHHE1L}HMLuE1ffff.I4$LHCH;CtHHHHCIIM9HoIIM9HMHLuLuL}I9H([A\A]A^A_]IH;Hu!IH;Hu IH;Ht H{>L%> ffff.UHAWAVAUATSH(IH5EVHHEMfMM~LLVM9t!M)I|$ M\$(L9uQ}ffff.ID$ I;D$(MM'MuMffff.II|$ M\$(L9t.MI)1fDI9t{I4HH9|nH9| HI9uI)1ff.I9t+H4IH9|H9|RHI9uGfffff.M|$ID$Hm$ff.I$MHPMfMIHt!1H CVH H;MuiH([A\A]A^A_]H}LbHuHFHLfI7IHHtII7I~IFHEH CVH H;Mt]>ffffff.UHAWAVAUATSPIHIH@>IM/I_AGMI$IE0IE(IE H{H+;tXLuI IILH=u_>IHMLa(La KIIF0H3H[H)H~LH>IMf(IF8AGLH[A\A]A^A_]Ly> II}Ht HEHx(>IH}>L(> UHAVSHIHt5H3LHsLH{ Ht H{(>H[A^]>[A^]@UHAWAVAUATSHHAVHHEIIF0xuFII^PIL.MHHAVHH;EuHHHHĨ[A\A]A^A_]y>'>IHPw"H58?H OAHhLPL0Hp9?HEH|9?HEI0Hp9?HEH8HuHUHMHM艻AH9?HhLH2E1H5bVH\ L> IALPMII8t HH%>LmMt6HEL9t!HX@t Hx>HI9uH}LLm>Euhu LPMu,dH}>htHx>LPMt:HXL9tEHX@t Hx>HI9uHPLXe>EtL>L> Lff.UHAWAVAUATSHH?VHHEIIF0xuFII^PILJHHt?VHH;EuHHHHĨ[A\A]A^A_])>>IHPu"H56?H MAHhLP-H 7?HEH,7?HEI0H 7?HEH8HuHUHMHM9AH6?HhLHm0E1H5c`VH L`> IALPMII8t HH>LmMt6HEL9t!HX@t Hx>HI9uH}LLm>Euhu LPMu,dH}p>htHx[>LPMt:HXL9tEHX@t Hx0>HI9uHPLX>EtL8>LH> Lff.UHAVSHIHt#H3LHsLH[A^]>[A^]fDUHAWAVAUATSHHLMLEIIHuHGHGH}HLL'MHELLJHILeLuM>IFL)L9H]sMMvL4>IM)MK\5M~LLL>LuM.I^MfMH]tL>HLmPH}EHEHMHHEL1HM1LmE1E1@HEL8MMfIFI9t)AA$IFHH9]fffff.I6I)LHH)ILH?H9wHH9HBHHjHuI>IHuMAC&KT&M~LmLIIL_>LLmLHEL0IIVMnHLmtH>HH9]LmHMHH9MHH[A\A]A^A_]L> IHEH8Ht HxS>L> ff.UHAWAVAUATSH(IH:VHHEMfMM~LLVM9t!M)I|$ M\$(L9uQ}ffff.ID$ I;D$(MM'MuMffff.II|$ M\$(L9t.LH)1fDI9t{E,A8|mD8|HH9uI)1ff.I9t+4A@8|@8|QHI9uGfffff.M|$ID$Hm$ff.I$MHPMfMIHt!1H f9VH H;MuiH([A\A]A^A_]H}LbHuHFHLfI7IHHtII7I~~IFHEH 8VH H;Mt>ffffff.UHAWAVAUATSPIHIH@E>IM'I_AGMuID$0ID$(ID$ I^I+tPHEI HxbH>IHELh(Lh LHX0I6I^H)H~LHC>HEILh(H@8AGLH[A\A]A^A_]L> HI<$Ht HEHx(p>IH}`>H蠽> UHAVSHIHt5H3LHsLH{ Ht H{(>H[A^]>[A^]@UHAWAVAUATSHHe7VHHEIIF0xuEII^PILBH 77VH H;MuH@HHHĨ[A\A]A^A_]>蘿>IHPCm"H5c.?H EAHhLP%H.?HEH.?HEI0H.?HEH8HuHUHMHMAHu.?HhLH.(E1H5$XVHL!> IALPMII8t HH薾>LmMt6HEL9t!HX@t Hxq>HI9uH}LLmW>Euhu LPMu,dH}1>htHx>LPMt:HXL9tEHX@t Hx>HI9uHPLXֽ>EtL>L > Lfff.UHAWAVAUATSPIII^IHAt MgM IDLuMf.C tLs( fDM9LIGHt9tH{0L觾>u5#H{ HL菾>uffff.1M9BHKHILIHHvLuM9t=AE tI](I9LHGHtt*Iu0L>u-I9sMLH[A\A]A^A_]Iu HL>tx@UHAWAVATSHIHIH 3VH HMHuHIIHt&1H3VHH;EHH[A\A^A_]ÿ@>HI4$H{ >HC8HEHCHHCIIHHtII7HI~蒀IFH3VHH;E{>IHv>L趸> fffff.UHAWAVAUATSH(LwHMt*HuȊH}t$HBHEHRHUE~ Au;HH>HHUHEE~ Au$@H}IE~ AtMf(fDDIN H]L9IGHt3AHMt Mn0LiH}LH >HMtx 8L9esILH{\fHt;AtMn0HILHuH踻>tyI~IFH3 L9ewHEL0H}HEL0LHEL0HH([A\A]A^A_]UHAWAVAUATSHHE1VHHEIIF0xuEII^PIL~<H 1VH H;MuH@HHHĨ[A\A]A^A_]ʹ>x>IHP#g"H5C(?H &@AHhLPH(?HEH(?HEI0H(?HEH8HuHUHMHMڪAHU(?HhLH"E1H5RVHL> IALPMII8t HHv>LmMt6HEL9t!HX@t HxQ>HI9uH}LLm7>Euhu LPMu,dH}>htHx>LPMt:HXL9tEHX@t Hxѷ>HI9uHPLX趷>EtLٷ>L> Lfff.UHAVSHIHt2H3LHsLC t H{0V>H[A^]J>[A^]UHAWAVAUATSH(LMLEIIHIIFIFILL>ILL;ILILNMMHEIHH @HEIHH@HE1LeHuHMIE1ffffff.H3LI~I;~t 蕴>IFIIM9LOIIM9HuHHMHMLeI9H([A\A]A^A_]IM&MuOIM&Mu BIM&Mt7IFL9t8HX@t Hx˵>HI9uI>Mf践>L> Lf.UHAWAVAUATSHXHMH9HGHEH)HBHEHE1H}IHUHufL9[Bt*@tJL*E>AuDE>AtI^H9ILGMt|At&HEJ|(@HM@uu%HEJ4(DHEJ<(@HM@utJ4*H}HuL$>HUHMutHuH}yIDH9Mtb@tJ<*AuHEJ4(HEJ<(AtHEJt(LIH豵>HLHUty3ffffff.H9wJD*IIHH;EHu1HX[A\A]A^A_]ff.UHAWAVAUATSH(IHIH/+VHHEMt$MtxLmLeM|$LeLm IH;HsIV IN(MtIMHufI~ Iv(HHKMtoM~IFHu`Mt$MIHu`@>HIuH{ HC8HCHLsII$HHt@I$I7:MLeLmIHt1H-*VHH;Eu6HH([A\A]A^A_]HI|$uwID$H)VHH;EtDz>IH\>L蜯> @UHAWAVSPIIMtLI7LIwLMw MtCIG(L9t.fHX@t Hx>HI9uI H[A^A_]LMw(ֱ>LH[A^A_]ı>fDUHAVSHPIH+)VHHE(褱>Hþ H蚱>HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H}11赺H5H5>HH+H5.7AH輯H5GBHHH HQH5>H HH;ҦHHH VHMH&HUHuHuIIFIF0INIVIF@H]HEHtHHL>H&VHH;Eu LHP[A^]苯>IH]HEHtHH>LG> ffffff.UHAVSHPIH[&VHHE(Ԯ>Hþ Hʮ>HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H}11H2H5M>HH[H5^4AHH5wBHHH HQH54>H=HH;HHH VHMHFHUHuHuIIFIF0INIVIF@H]HEHtH6H|>H#VHH;Eu LHP[A^]軬>IH]HEHtHH7>Lw> ffffff.UHAVSHPIH#VHHE(>Hþ H>HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]H}11H-0H5}>HH苮H51AHH5BHMHA H5k>HtHH;9HHH (VHMH HUHuHuIIFIF0INIVIF@H]HEHtHmH賩>H.!VHH;Eu LHP[A^]>IH]HEHtH(Hn>L讦> fDUHAWAVAUATSHIIHIH VHHE1LXHH@W)`HDžpI}躂HH+Hu[LHHHC H+CHH4HH+IH]JH`HH3HSH`%LLL`LhLL)HHHY H+YHW)@HDžPHLHH=H<H>IL@M4LPLH>LHLM9t}M)I<$ILGHL@LHLL)HH9I<IIM9uLI9Lu"\E1E11M9uI9L?HHIHpL)HHLffff.H:tHHuqffffff.HHHHHuMH =/AHHy!tH 8/AHHy!OLH}@H:t*Hzt4Hzt?HztJH H9uSf.H1HH2HzuH1HHrHzuH1HHrHzuH1HHrH H9uW) HDž0LL)tUIILH=˥>HH(H JH0M)M~HLL>LH(H 1HIEHHHIEHH Ht H(0>H@Ht HH>H`Ht Hh>H{VHH;EuHH[A\A]A^A_]6>>IH`R"H5+AH ?,AH}L`) H@H5AH?HuLH E1H5=VHSL觤>cH@计>RQ>IH@Q"H5i+AH +AH}L@0y W) ƅ $HDž0Hut is nuH)HAxes inpH!fDž1llƅ3AHAHuH L E1H5茣>IH@7Q"H5*AH *AH}L@3 W) HDž0@/>H0(R) H tensor.HH(Hor a 1-DHH H scalar HHHust be aHHHtensor mHHHAn axes H@0AH*AHuH L E1H5;VH{LϢ>芢>IH5P"H5)AH )AH}LVHH33AH*AHuLHZ E1H5P;VHLM> H W> HALM5H2HALeMHAL@MHHAL@MHHt HV>LeMt6HEL9t!Lh@t Hx1>LM9uH}LLe>EuEuLMu&^H}>EtH}>LMt:HL9tqLh@t Hx躠>LM9uHL蟠>EtL >H HtBH H2H@HLH t H0?>LeMt6HEL9t!Lh@t Hx>LM9uH}LLe>EuEu L@Mu-'H}ڟ>EtH}˟>L@MHHL9Lh@t Hx蘟>LM9uH t H0s>LeMt6HEL9t!Lh@t HxN>LM9uH}LLe4>EuEuL@Mu&^H}>EtH}>L@Mt:HHL9tTLh@t Hxמ>LM9uH@LH輞>EL۞>H`HLHAL`MHH@t HPV>LeMt6HEL9t!Lh@t Hx1>LM9uH}LLe>EuEuL`Mu&^H}>EtH}>L`Mt:HhL9tILh@t Hx躝>LM9uH`Lh蟝>EL辝>HΚ> LHH H+HH@HHH HH(3>H@HufpHH`HumwHH`Hu\fHH@Hu3=HH`Hu:DHH`Hu)3HH@Ht HH赜>H`Ht Hh蝜>Hݙ> DUHAWAVAUATSHIHVHHEW)EHHMI>tDH#AH t?1A]L&J"HVHH;ELH[A\A]A^A_]ILeHEL8LxPM,$Ml$PM9t{I|$0HPhHtAxu;ILH~T1LL>HLIIH9|*I|$qHI|$0HPHLLH>IHVHH;E4诛>IIIHtCt H{.>H&>Lf> fffff.UHSPHH{Ht H{>HVHH{HCHHt[]͚>[]fff.UHSPHH{Ht H{裚>H>VHH{HCHt胚>HH[]u>UHAWAVSPI(c>HL=VL;H{LsHHLQHWHHH[A^A_]L;IH{HCHt >IH>L.> fDUHAWAVAUATSHHIH?VHHEID$ID$I$H;uvW)EHEEEaxesEHhHUHL譛HhHDžhHtCt H{5>H->Et H}>Hu'HVHH;EuHĘ[A\A]A^A_]R>>IHhF"H5!AH !AH}Lh(W)PHDž`0裘>H`(kR)PHte valueHHHttributeHHH'axes' aHHHInvalid HHHMissing/H@&LH!AHuHPLYDžLH5H1VHټLE>iIǰLLmMuKIǰLHhHIIPt H`蜗>LmMt6HEL9t!HX@t Hxw>HI9uH}LLm]>EuEuHhHu&^H}:>EtH}+>HhHt:HpH9tJLh@t Hx>LL9uHhHp>Lt8L>I<$Hu08HIEtH}貖>I<$HuII<$Ht I|$葖>Lѓ> fUHAWAVSPI(s>HL=VL;H{LoHHLaHWHHH[A^A_]L;IH{HCHt >IH>L>> fDUHAWAVSPI(>HL=oVL;H{L0oHHLHWHHH[A^A_]L;IH{HCHt {>IHn>L讒> fDUHAVSHPIH VHHE(D>Hþ H:>HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]ݾ H5͠>H}HjH5-AHkH5BH蜔HHHQH5>H輕HH;聊HHH pVHMHeHUHuHuIIFIF0INIVIF@H]HEHtH赬H>Hv VHH;Eu LHP[A^]:>IH]HEHtHpH趒>L> fffff.UHAVSHPIH VHHE(脒>Hþ Hz>HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]] H5 >H}H誙H5mAH諐H56BHܑHHHQH5>HHH;HHH VHMHHUHuHuIIFIF0INIVIF@H]HEHtHH;>HVHH;Eu LHP[A^]z>IH]HEHtH谩H>L6> fffff.UHAVSHPIHKVHHE(ď>Hþ H躏>HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]] H5M>H}HH5AHH5vBHHHHQH53>H<HH;HHH VHMHu<HUHuHuIIFIF0INIVIF@H]HEHtH5H{>HVHH;Eu LHP[A^]躍>IH]HEHtHH6>Lv> fffff.UHAVSHPIHVHHE(>Hþ H>HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H]蝶 H5>H}H*H5AH+H5BH\HH HQH5s>H|HH;AHHH 0VHMHHUHuHuIIFIF0INIVIF@H]HEHtHuH車>H6VHH;Eu LHP[A^]>IH]HEHtH0Hv>L趇> fffff.UHAVSHPIHVHHE(D>Hþ H:>HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] H5͕>H}HjH5-AHkH5BH蜉HH HQH5>H輊HH;HHH pVHMHeHUHuHuIIFIF0INIVIF@H]HEHtH赡H>HvUHH;Eu LHP[A^]:>IH]HEHtHpH趇>L> fffff.UHAVSHPIH UHHE(脇>Hþ Hz>HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHCpHCxHCpHChƃHǃfǃHǃHǃƃHHǃHǃHǃHǃHǃHǃHǃHǃHǃHHHǃHǃHǃǃǃHǃ H] H5 >H}H誎H5mAH諅H56BH܆HH HQH5>HHH;|HHH VHMHHUHuHuIIFIF0INIVIF@H]HEHtHH;>HUHH;Eu LHP[A^]z>IH]HEHtH谞H>L6> fffff.UHAWAVSHXHHIUHHE„>IH}HYuIHu~H5zVIIHu]HnWIHUHH;EuLHX[A^A_]趄>IIHu$IHu0IIG`H9uIHtI>IIG`H9tHtHP(I@LH H9uHP Ht HP(IL贃>L> fff.UHSPHH0yVHH{HCHtu>HHt H]>HHt HE>HC`HH9t)HtHP(H{@H H9t$Ht+HH[]`(HP H{@H H9uHH[]` H[]UHSPHHpxVHH{HCHt赂>HHt H蝂>HHt H腂>HC`HH9t%HtHP(HC H{@H9t Ht!HP(HP HC H{@H9uHP HH[])>ff.UHAWAVAUATSH(IIHUHHE1HHIMI_ I+_W)`HDžptCIILH=H誁>H`N4LpHH蓁>LhW)@HDžPAIH@HEHLZHH+HuhMIGIO H)t/IH`H1WH*YH,HHH9rL`LLHHAHHHAHIAJMMIGIO H)t1IH`H1fWH*YH,HHH9rL`LLHHQH@Ht HH>H`Ht Hh>HZUHH;ELH([A\A]A^A_]A~AHIMII~ZHH.H@HHH)HHH)H@H@LMg M+gW) HDž0tCLHHH>8IL~>H HH0HL~>H(IEHHHH{H|MH HH IGIO H)H H`H1ff.WH*YH,HHH9r11MSI^HH>L`LmIHHLLH]>IG I+GHhH+`H9IH`H HLy$L L`HLHHH Ht H(T}>LH@H:AH@HHMw M+wLHH)HHH)vH@~ s HHHMIL@IAMGIv'IIGI I I9stIK H9sg1Kfffff.A?HL9r4s HHHHLsHHHH}>LHHAHHwHs1HIQpKDpH^H)1W( (PDDLLDDLLDDLLDL H HuHt>J7HW( "(PLffffff.D LH HuI9 {>N{>IH`("H5AH AH}L`vH@H AHJ?HuLHE1H5VHL{>H`x>z>IH f("H5"AH dAH}L W)HDž@^z>H(5R)Hs input.HH.Hided as HH(H be provHH Hzes mustHHHes or siHHH of scalHHHOnly oneH@6AH;AHuHLE1H5VHLy>y>IH V'"H5AH TAH}L ߽W)HDž Ny>H( S)Ht index.HHHroi inpuHHHInvalid H@AHPAHuHL%E1H5VHĺLy>x>IH~&"H5:AH |AH}L ޽W)HDž@vx>H(NO)Has inputHH(Hrovided HH HUST be pHHH sizes MHHHcales orHHHEither sHf@0.AHpAHuHL!E1H5VHLx>H u>w>IHi%"H5%AH gAH}LݽW)HDžPaw>HH(V'P)H5ADHx>CDAHAHuHLJE1H5@VH鸼L=w> v>IH$"H5_AH AH}L ݽW)HDž@v>H(C1R)Hs input.HH.Hided as HH(H be provHH Hzes mustHHHes or siHHH of scalHHHOnly oneH@6AHxAHuHL:߽E1H50VHٷL-v> IALeMIALM[I\IALeMFIALMu I!It HOu>LeMt6HEL9t!HX@t Hx*u>HI9uH}LLeu>EuEu LMu-H}t>EtH}t>LM|HL9HX@t Hxt>HI9u9It Ht>LeMt6HEL9t!HX@t Hx^t>HI9uH}LLeDt>EuEu LMu-H}t>EtH}t>LMHL9HX@t Hxs>HI9umIALeMIALMMINIALeM~IAL MIIALeM@IAL MhIIt Hs>LeMt6HEL9t!HX@t Hxr>HI9uH}LLer>EuEuLMu&^H}r>EtH}r>LMt:HL9tTHX@t Hxjr>HI9uHLOr>ELnr>H H&LIt H r>LeMt6HEL9t!HX@t Hxq>HI9uH}LLeq>EuEu L Mu-'H}q>EtH}q>L MH(L9HX@t Hxeq>HI9uIt H@q>LeMt6HEL9t!HX@t Hxq>HI9uH}LLeq>EuEuL Mu&^H}p>EtH}p>L Mt:H(L9tTHX@t Hxp>HI9uH L(p>E}Lp>H@HqxLIAL`MII@t HP#p>LeMt6HEL9t!HX@t Hxo>HI9uH}LLeo>EuEuL`Mu&^H}o>EtH}o>L`Mt:HhL9tIHX@t Hxo>HI9uH`Lhlo>ELo>Ll> LD@>H@HuIH@Ht HHn>H`Ht Hhn>Ll> ff.UHAWAVAUATSHMLIHIHUHHE1HH誱IMIEI+EIN I+NH91HLOHXHxHp H HL8IFM~ I)LHHLHIL)HH9<H8HJH+ L9GLMMI}L)aMIH11fDu I H; t1HH9r"HIVLLdHxHX~H`HhDXDH{@HaHC H9HPHEHHRH`H9HPHEHH@H @HDH @L?!HUHH;Ek HXL'LgPII^PLHHm>LI$HUHH;EC IHHHLϹHpHxHXHAHHHHHHH@HLMIHIXCHHh@H I@HDȾHHc>!HUHH;El H:I HHpHHHxHH@HIyH H`HHhL'M,W)HHH SH;H;@H ~@1AH" HEHHHEHHLHLùHHHH?HH6HpHH0IH@xA`hHˊTHDL"aIHX{IH{@HgH H9HPHL@H6UHHHLH@H@LLh>HDžDžHrULkLH@H@Lg>L5-UILHDž0HDž(HDž HDžDž8H5@H~ܽ1HH F@H5/@HDHTH۽HLHH"8!t H+h>LH@Lt H(h>Lf>H5UHHf>Lg>HHHHHIHHDHPH@ HD@HL^IHXxIHHx@H:H H9HPH`HDžoIBL@HHHpHxLLHuHuHPHHFHpHuHPLpLl$(L}L|$ AƉD$AĉD$hD$H8H$LLHxH`HLpLXH}I9t3Ht4HP(H}I9u.HP HUHH;E 5HP H}I9tHHP(HSUHH;EHHHPLLd$(L|$ Lt$H8HD$AʼnD$T$HHH0HpL@Lx`hH:QHI9LtHDžDžHEULkLH@H@L`>L5UILHDž0HDž(HDž HDžDž8H5@HԽ1HH @H5@HDHTHԽHLrڽHH0!HDž0LIHA}*HH0HPLIHAHH@HD$@HHD$0L|$(Lt$ ÉD$H8HD$Ld$L,$HH\$8HpHxLHL`H@Hh,T06H0H9HP HHHHt#HHCHuHHPH^>LHPUHH;E`>_>IHu "H51@H @HLSŽHHpAHi?HLHȽE1H5UH3L_>B_>IH "H5@H 1@HLUgŽW)HDž0^>H(:qR)Hbe same.HH'Hshould bHH H tensor HHHd outputHHHinput anHHHRank of H@/AHR@HHLǽE1H5UH+L^> HALMHALM%H"HALM H Ht H]>LMt?HL9t$HX@t Hxs]>HI9uHLLS]>uu#LMu/gH']>tH]>LMt:HL9tMHX@t Hx\>HI9uHL\>EL\>HY> LHt H\>LMt?HL9t$HX@t Hxa\>HI9uHLLA\>uu#LMu/gH\>tH\>LMt:HL9tMHX@t Hx[>HI9uHL[>EL[>HX> LH6*HiHH0H9t>HHP(HHzHH`H9uHP HHHHZ>HHMHHHHH3Ct H{Z>H|Z>HH6HH}HEH9u~HLH@Lt H(Z>LY>H5UHHX>LY>H2W> HHI9tZHHP(HW> HH}I9uHP H}HEH9uHtHP(H}HEH9uHP HV> HHHt#HHCHuHHPH5X>HmV> DUHAWAVAUATSHIIHUHHEHEM$MT$M)trIMH1~ P1fff.IMHt#WI*WH*^ffff.(MuHHHuHHL9rHUHH;EuHH[A\A]A^A_]UoX>LX>IH8"H5@H _@HPL8PqI$HIH @HMH @HMH >@HMH ?@HMH G@HMH\$HMHL$H$H HuHUHMLELMH{AH]@HPLHE1H5UHNLW> IAL8MII t H0W>LmMt6HEL9t!HX@t HxV>HI9uH}LLmV>puPu L8Mu,dH}V>PtH`V>L8Mt:H@L9tEHX@t HxoV>HI9uH8L@TV>EtLwV>LS> Lf.UHAWAVAUATSH8IDUHUHHEHHHL1LyM)LHIIxH)HHH9iMHLf~xHffEHEHH=HLLLdU>HEHL$LeHLLU>LeffEHEL)U>ILmMdLeLLpL U>LeHCHHhHH<T>IIJ4HHT>HH?t@)HHHJ!LKDLpI LL|EILH LHhfff.HHIITLHM HU8LE0LmI)HHHH7IE1L0HH7WI*I WH*H*IEBH0EIx HHH@0HMH $HHLHLLW.wHHHHWH*.v HHHfn P.0HHE8Hx H<HHHLP0HH HH9|HBHH7fnx@HLLE0uHy1HfnfB~IHHHI9aHz@H @H@H @EHEʾLO%!HUHH;E5LH8[A\A]A^A_]H@H @EHEʾLHHHPHHXHEH`LhLpxLEIHUIIIH L8ff0HDž@MtHM9N4L>Q>H0NL8HfLxfHDž Mt=M9xN4LP>HNLHHH01'UHHHUHHLM L0HLpEAMM)HLHE1E1fnxMK Iu>1MH fox HfnHfB~IIM9|K1fHHfox Hfnf~DHTHfox Hfnf~HI9uIMkffff.IM94 H]L0@HH7WI*I WH*H*IEBH0EIx HHH@0H$HHLHLLHHfn cO.0HHE8Hx HHHHLP0HHHHH9|HJHH7fnx@HLLE0uIHy1HfnLfB~IHHHI9LIM>H}HHL8ff0HDž@MtHM9WN4LM>H0NL8HfLxfHDž MtDM9N4LGM>HNLHLxffHDžMt=M9N4LL>HNLHHH010QHHHQHHHQHHHHH 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MhIIt H趸=LeMt6HEL9t!HX@t Hx葸=HI9uH}LLew=EuEuLMu&^H}T=EtH}E=LMt:HL9tTHX@t Hx=HI9uHL=EL=H H&LIt H輷=LeMt6HEL9t!HX@t Hx藷=HI9uH}LLe}=EuEu L Mu-'H}W=EtH}H=L MH(L9HX@t Hx=HI9uIt H=LeMt6HEL9t!HX@t Hx˶=HI9uH}LLe豶=EuEuL Mu&^H}莶=EtH}=L Mt:H(L9tTHX@t HxT=HI9uH L(9=E}LX=H@HqxLIAL`MII@t HPӵ=LeMt6HEL9t!HX@t Hx讵=HI9uH}LLe蔵=EuEuL`Mu&^H}q=EtH}b=L`Mt:HhL9tIHX@t Hx7=HI9uH`Lh=EL;=LK= LD@>@H P>@HDH >@L? H)UHH;Ek HXL'LgPII^PLHHb=LI$HH)UHH;EC IHHHLϹHpHxHXHAHHHHHHH@HLMIHIXCHH>@H =@HDȾHH Hl(UHH;El H:I HHpHHHxHH@HIyH H`HHhL'M,W)HHH蹘H;H9@H .=@1AH1^! HEHHHEHHLHLùHHHH?HH6HpHH0IH@xA`hHˊTHDLҦIHXIH{@HgH H9HPHL@H$UHHHLH@H@LL茮=HDžDžH"&ULkLH@H@L蘭=L5%UILHDž0HDž(HDž HDžDž8H5@H~!1HH @H5@HDHTH~!HLO'HH} t Hۭ=LH@Lt H(豭=L蕬=H5$UHH0=L<=HHHHHIHHDHPH@ HD@HLIHXIHHx@H:H H9HPH`HDžoIBL@HHHpHxLLHuHuHPHHFHpHuHPLpLl$(L}L|$ AƉD$AĉD$hD$H8H$LLHxH`HLpLX H}I9t3Ht4HP(H}I9u.HP H:#UHH;E 5HP H}I9tHHP(H#UHH;EHHHPLLd$(L|$ Lt$H8HD$AʼnD$T$HHH0HpL@Lx`hHHI9LtHLHDE1H5:>UHL7==IHR!H5Y.@H 4@HLU W)HDž0蒤=H(Q)Hbe same.HH'Hshould bHH H tensor HHHd outputHHHinput anHHHRank of H@/AH0@HHL< E1H52=UHL/= HALMHALM%H"HALM H Ht HN=LMt?HL9t$HX@t Hx#=HI9uHLL=uu#LMu/gHע=tH¢=LMt:HL9tMHX@t Hx藢=HI9uHL|=EL蛢=H觟= LHt H<=LMt?HL9t$HX@t Hx=HI9uHLL=uu#LMu/gHš=tH谡=LMt:HL9tMHX@t Hx腡=HI9uHLj=EL艡=H蕞= LH6*HiHH0H9t>HHP(HHzHH`H9uHP HHHH苠=HHMHHHHH3Ct H{4=H,=HH6HH}HEH9u~HLH@Lt H(͟=L豞=H5UHHL=LX=H= HHI9tZHHP(H貜= HH}I9uHP H}HEH9uHtHP(H}HEH9uHP Ha= HHHt#HHCHuHHPH=H= DUHAWAVAUATSH(HDUH*UHHEHHHL1LyM)LHIIxH)HH9M7HLf~xHffEHEHHH=HLL =HEHL$LeHL=LeffEHELН=ILmMdLeLLpL贝=LeHCHHhHH<苝=IIJ4HHs=HHH?Ht@)HHHJ!IDLpI Lfnx|I4H9sz1Ht%J4A4HHHHHt H踈=HHt H蠈=H0Ht H8興=H|=H}Ht H}j=H}HH}T=HTHH;E 蛈=HHHHt H=HHAHE1L HHHfnxD,HMH0M>IHH LHHhH IHHE1HDMx HB B LLHfDLLI4$N+,IAHDLHI;|HDL.MIHNlIHI9HsHHHHHHHBӃHs 1HusH)1DMx HBDMx HBDDMx HBDDMx HBD HH9uHt%HHDMx HB HHuHHHt H=L0M[H8L9t.HXHxHt Hx=HI9uH0HLL8 訅=H THHH5}THFH躅=9x=H THHH5MTHH芅= H}藂= H0艂=H{=H0m=H_=HQ=H0C=H5=H'=H=IHH IHH IH0HIHHVH;=L0MF-IHHY7IH0HeIH0HKCIHHu^hIHHtAH觃=HHu5?IHHt H~=HHuHHt HZ=H0HH8IH}H%IH}HIH}HH}=L>= IL0MtXH8L9t>ff.HXHxHt Hx踂=HI9uH0L8蝂=LIH聂=H}Ht H}o=H}H[L= f.UHAWAVAUATSH8LHEH=TH?H}HuHxHpLhH`_XW)@HDžPH@L/&W) HDž0H H &H`Hhy6II t8HH|=H`H.~,Oga1LeLWLm('OL WH*H(H;0s HH($ffffff.LL(^'OWHpHWH*EE.LwL.IF͋ fnH,H9~HHHHHpHH9~HHH H4WH*\THWH*\TH H9u??HH;`HMHH LbI)WH*IMWH*pIBDBLEUMEMHE8Hx HAH]L}f.HH@0HMH $HLHLELMH(H;0sHH(H H=HpHWH*EE.LwH.NHFˋ fnH,H9~HHJDHHpHH9~HHJLJtWH*\( $O(THBDWH*\THBLH9uBD?BD?H`I9WI*WH*WH*pHEHBLBDEEUMEHE8Hx IH5!x=H JHt+HP(HHu%1ILHxHHtHt HHPMt#HIGHuILPL'\=HHu"H Hu.H@Hu:L?Z= H\=H HtH(\=H@HtHH\=LY= H̺fUHAWAVAUATSH8IHuHHTHHEHC HB. OuKzIHKIcH H<HSHHuH61f.JHHHH;<|HKMcH JHuHKHsH H6E11H+>H9H}ffffff.HC HWI*Hs(WH*HS8J*BHB4H;L ẺEMUuMHz H HHMH $HuHUHMLELMP0{0t*AW.w"HCHJHWH*.AE1HC HK@H OB. 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H@诤2Hs LҠ2 H8萤2L輠2 @UHAWAVAUATSH(IHML@MI?MIHL"H8LBIIOHEH ЃHH)H0LQIHAAzID ȈEHHHEHHHPMILHHxH}HGLhL)HEHAH`HAHXHF HEID$HELEN E1HEE1E1LeHHLp1H}:fff.LH}t4IMKH`H I JH]HLIHHHHH9Pu()f1HuHH9PLMI)HSHMHIHEHHLHMHHEH LE1ff.NLYJ<H9MHIuffffff.HpHUH]1C ADALBD;B ;ADA HHHuHxLt H8H}LAALLH9MHMHIIHHIHEHEL;@LM IIO1H}HEL9H([A\A]A^A_]ffff.UHAWAVAUATSH MI?I>MMILEIHMHLHULZIIOHEK[M,MIKIMH~HHME1f.1HMf.M<L|HHTIHTITHHHuIMMLMH}M9|HMHEHHEHULuL;u'HILLBLmID HxMJHhHAHEHXσHHHUJHUIO\ pHSH)HUMH]HH]HHHHXHAHPNdpHH8L`JH@NtHFHpE1MLU#HEMHPH)HHuxfHrRLHH`IHhH<IHHHpLH?LHMILHH)LMLH1HHHOHML I_ HEHHEHE0HH(HE8HHHEL)H01L@LHfHEHH9HHNHEH}HM1HEH;MHEHHHHHHHHHHxHHMHHuHfDHIH(LHA HPH HzILMIILIHHHHHHM$BL BL HXL L  M"LLHhH`1E1E111E11Hu1E1fff.HUH8LeHEL8HEHPHIHHILLvHLMMMH~LLuIMILFHLML}LMHULRHUHJHV IMIH^(HHIMMHF0HLIMMLN8HHMMLMHMLy HMLY(LF@LILHMH~HMIIMMHNPILHIMMH^XHHLMHLHELH0HELh8HF`HIHUHULVhILLILHULvpLLIMMMLFxLIMMLIHMHQ@HMLaHHHHH]HIHILL]LILLHLHELMMILMMHEL@PHELpXHIMIHIHIMMHIHIML]LILMIMMHMLq`HMHQhHIMMLHHLILHELLILILLLIMMLMH}HWpH}HxH}LLHLLLIMLLMH8LLEMLELeLHHHLIMHEHLLILLL@MHIHL9CHMH`HhLLE111HEE111HEE1E1JH`JHhHXHHXHH8L;uHu1L0LMfDI MItHY XHLHL9|AYCX C IIL;UL]HEL)HIIIIHHVHHhJt HA5H ;B51ALnILLI>t HA5H B51A Lt6ILL0I>t@HA5H A51A L<HIHH;Eu@LH[A\A]A^A_]IL"L&IHIHH;EtUb2IIIHtCt H{a2Ha2L_2 UHAWAVATSIIIID$@IL$HH)HL9sI|$@LH)-vLHHID$HI|$HPL)I\$I|$Hh Hc1Hr*M|$MgI_HL)HHHL9sII)LL[A\A^A_]0vPKvHII9tH*0tBY.w9MHJHf.H*X0uH 6HrHV1H^f: (+C\H,H HH1H,.HCH9HCH#HHFHt.I9r1LHIHJHt HII!HJHufH IL H JIHeH@HNHCH9=1HH0DIMLPL(ff.HCL9t L!L9KHtHSL9ugu+1fT A:uLHH9uH{ MLLMM&]2MMML(LP}HH]HHCL9t L!L9+Ct#HCHt>HHu ffff.HuH!HHL8H(H8HH;@LXI}dL8L`M9tBM7LESLH/LL2L!4IM9uIE L8L`M9t@fDM7LRLHj/LL1L3IM9uI}M LxML0tXffff.I_LH/AI}HB LDHd1LH.L_3M?MuIEI}LpPHXXI9UHPI6IMHXHx0Iu 5HHHHXHX(M0H{XLl1HH@(H HHxHƅwIHHE0Y2H KHHHHHxHHHHHHwHH HXHH(HELHH]H膒H}H9tHHptHP(I<$tHP HpI<$I$ƅwLHHHE0PX2H KHHHHHxHHHHHHwHH HXHH(HELHH近H}H9tHtHP(I<$tHP I<$I$IuIHXHx0I+3L@HHtHuw1HlHHE1H8H(Hffffff.IN<:ItI {N2%ILlN2H Hu"B HdIH Ht'DL3Ct H{ ,N2H$N2LMuHHDžHtN2L@K2 UHAWAVAUATSHIH HKIHHEIFHI~0HLhL9tE@L;LFLH"L0IA$L}ID$HI9uIF HLhL9tPffffff.L;LELH*"L`0IA$LID$HI9uH HL=2H;tGH,5H 051AHHBIHH;EHH[A\A]A^A_]HI~pHHHAPH9AXINXH1LH8HHH4I~ELIHy0I6'HHt,fDž,IIH)L0(HLH HE1Hf.IN$LTLDLLH ALHLD.HX,tELCHxHxx(LDu.ff.HHHIH9tffff.L9 t HH9uFH9tALD .I~ H0A HA(HL2CHxHxx(I>HPH0u+LDHM=tu̹LD6I>HPuILLHU @t6u̹LDs6uLAL<-8u3LLD%-LD-Hx0H8T8L0HII9Hu1HAHH 4F2uI~G t HG(Hu HuH 1HAH H"3F2gH0HHI~HH;AH}ALHL&,8L{fffff.MMM9LLff.IHQtqH@LHnLD@tML!u? OH2XH8HHL(IN`HHHIF`IFhLW+8tfIM9s1L7+Hx0H8AIM9Jf.H0LLM9fIHPtqH@LHLd?tML u? oG2XH8HHL(IN`HHHIF`IFhLw*8tfIM9s1LW*Hx0H8@IM9Jf.H0HLL9fL;LOtqL9?LHL>D8LD uB F2DxH8HHL(IN`HHHIF`IFhLD)8tHI9s1LDv)Hx0H8@HI9IfH8HHHAPHIXH)HH9H HHRIHH;EF2E2IH`wH5$5H a)5HxL`W)@HDžP@lE2HP(dF)@Hfor now.HH0Houtputs HH(Hxternal HH Hed for eHHH supportHHHsors areHHHOnly tenH@8AH(5HxH@LE1H5IHLD2 IALeMuFzIAL`MII@t HP_D2LeMt6HEL9t!HX@t Hx:D2HI9uH}LLe D2Euxu L`Mu)aH}C2xtH}C2L`Mt:HhL9tEHX@t HxC2HI9uH`LhC2EtALC2L@2 LIL3HMtAFt I~dC2L\C2L@2 H\HT@UHAWAVAUATSHHIH}HIHHEIEHXPH@XHEH9I}H3IMH]M$M$1M9fɱtIM9ufff.IHtjHKtoH;LHyL%X,@Ƌ@uup(L%x,IM9p.1IM9_1IM9Nfffff.I$M$L9uiL3MtlLJtqL=:LHL$P,@Nj0@uup(L$x,fDHI9u%1HI9v1HI9eDI$M$L9ftiL3MtqL)JteL}9LHL($H,@Nj0@uup(L#x,fDHI9uf1HI9nIEH]H H@hHHHtHH#2=2IHhaH55H $5H}LhޣHPH_ΰAH$5HuLH膦E1H5|IH%Ly=2 IALhMIIPt H`<2LmMt6HEL9t!HX@t Hx<2HI9uH}LLm<2EuEuLhMu&^H}<2EtH}}<2LhMt:HpL9tEHX@t HxR<2HI9uHhLp7<2EtLZ<2Lj92 Lffff.UHAWAVAUATSHHuIHHEHEHOHAHIH9t78uHx0Hp8H9tGH)HxPH+xHH9u7HUH`HH9uHIHH;EuHĘ[A\A]A^A_];2;2IHX.H5|5H #5HpLX}訡Hb#5HEH@HuHUHvAH#5HpLH:E1H50IH|L-;2 IALXMII@t HP:2LmMt6HEL9t!HX@t Hx}:2HI9uH}LLmc:2Eupu LXMu)aH}=:2ptH}+:2LXMt:H`L9tEHX@t Hx:2HI9uHXL`92EtL:2L72 Lff.UHAWAVAUATSHLPLXIHIHHLe HIHHEп|92IHХKIIg92I<$M<$Ht HP M<$HEH`LhLpHxLmHXHEHPHEHEHEHEHEHEL}L}L}HEH`HHH H}tFHEH}HHHHJH@HHEL9tfffff.Lwu82LM9uH}Ht H}[82H֯IHH;EuHHĘ[A\A]A^A_]82IH}t:HEH}HHHHJH@HHEL9tfH_72HL9uH}Ht H}72L52 UHAWAVAUATSPHIM7IGL)HH9sfHH32HH5{2H02HoIHHH5!IH2IH>32IGLHt/HfHCHC I^HI^IHuHfff.HCHC IFHHINH@H@@IFHHINH@H@@IFHHINH@H@@I^HI^IkxILH22HHIWLH`HHfHAHA HIHuIHHAHA HAHAAHA(HA A,HA8HA0AIVL)H)H~ HL822IMnMfMtLH[A\A]A^A_]02H[A\A]A^A_]HIHE11kL-2IH}02L-2 DUHAWAVATSIII_IGHH)HHHL9II7H)HHJ3L9H)HHHTUUUUUUH9wHH9HBIHLHH<@/2H[HH K4dAHLHILAttHCH!3HCHCC HCXHCPHCHHC@HC8HC0C,HC$I_H`I_IFIH !3HCHKHCC HCXHCPHCHHC@HC8HC0C,HC$IWHr`IwB`HBhHJpHBxǂHǂHǂHǂHǂHǂHǂƂHǂI_H`I_HHDH= 3H|HDD HDXHDPHDHHD@HD8HD0D,HD$HH`I~HIH3HBHZHBB HBXHBPHBHHB@HB8HB0B,HB$B`HBhHZpHBxǂHǂHǂHǂHǂHǂHǂƂHǂHHHM'IwL9fDH~HyH~HyH~HyH~HyH~H^HYHyHAHAHAFAH~HyHFHFHFHAHAHAFAH~HyHFHFHFH~HHI9H`L9HX@t Hx)2HI9uHXL`(2EuH]HEHu#L&2 L(2H]HEHtCt H{(2H(2L%2 LUHAWAVAUATSHHIHHEЅxRHcHO@HHH)HH9v;HDt HHIHH;EuHĈ[A\A]A^A_]v(2$(2IHhH55H 5H}LhLHPH͸AH5HuLHE1H5IHiL'2 IALhMIIPt H`\'2LmMt6HEL9t!HX@t Hx7'2HI9uH}LLm'2EuEuLhMu&^H}&2EtH}&2LhMt:HpL9tEHX@t Hx&2HI9uHhLp&2EtL&2L#2 Lff.UHAWAVAUATSHHIHHEЅxGHcHO@HWHH)HH9v0HIHH;UuHHDHĈ[A\A]A^A_]q&2&2IHhH55H Y5H}LhGHPHȶAHo5HuLHE1H5IHgL%2 IALhMIIPt H`W%2LmMt6HEL9t!HX@t Hx2%2HI9uH}LLm%2EuEuLhMu&^H}$2EtH}$2LhMt:HpL9tEHX@t Hx$2HI9uHhLp$2EtL$2L!2 LfDUHAWAVAUATSHHIHHEHuH_HLCIt$HH9w"1HHHHHHuLH!HHHtyH8HtqItAHGH9uH9wu#fff.H9r1HHH9u9H?Hu/@HGH9t HL!H9uH9u H9wH?Huտ#2IHXLH55H Y5HpLXrƉH5HEH@HuHUHAH5HpLHXE1H5NIHdLK#2 HGH rIH H;MuHĘ[A\A]A^A_]0#2IALXMII@t HP"2LmMt6HEL9t!HX@t Hxp"2HI9uH}LLmV"2Eupu LXMu)aH}0"2ptH}"2LXMt:H`L9tEHX@t Hx!2HI9uHXL`!2EtL!2L 2 Lfffff.UH]f.UH]!2fDUHSPH0!2H FKHHKHSHHHPHKHHHK HH HK(HH(H[]@UHHKHHG(HF(HG HF HGHFHGHOHNHF]UH]f.UH] 2fDUHSPHHH ;HH[]ÐUHH .FH1H9NHD]DUHH͋K]UHAWAVAUATSH8IHIHIHHELc HILLNLH;L~HLuID$ L0H@HEI9LHHEI>HoDDDLAtHPAtNIwH9HuuLHHAuPuTHtw1L: uVHI9u^fff.H߉HH9t,IwHHAtHxtHt# 2tIL;u2LuID$ L;pLutLLHK9t-IH}IHH;ELH8[A\A]A^A_]IHKH HHt;HHfffff.H^L9n HCHCH3HuH9tL9j v{MH I\$HLrLcI@H0HH}1PHUHHLAH]HM3HHCHHHPH2MH(H_2IH]Ht#HHCHuHHPH2L2 UH]f.UH]2fDUHSPH2H ވKHHKHHH[]@UHHKHHGHF]UH]f.UH]42fDUHSPH_HHHH߉I@,H[]fffff.UHH +FH1H9NHD]DUHHK]UHAWAVAUATSHH%IHHEЅx[HcHWHBHRH)HHHH9v2HIHH;UuH IHHHĈ[A\A]A^A_]2K2IHhH5D4H /4H}LhsHPHAHa4HuLHE1H5IH]L2 IALhMIIPt H`2LmMt6HEL9t!HX@t Hx^2HI9uH}LLmD2EuEuLhMu&^H}!2EtH}2LhMt:HpL9tEHX@t Hx2HI9uHhLp2EtL2L2 LfUHAWAVAUATSHHH`H IHHEH{HKHqH+qH@HHHH}HPH{]IIEIM0L LpM9HXsoHtZLhI$Ht$HHhI}0HHLP0t IM9rIEH@I9u)I<$u IM9rLI9L}HHfI$HHEHE(y2H PKHHPHHHMHHHXH@HH HEHhHuLRH}I9HtHP(LhM H`HM9ILM9HHL}tvHXtLfff.I$Ht,HHhI}0H\HHhP0IM9uI<$udIM9uLffff.IH9]fLHHL}IH9?@HP LhMLIH9vLIH9eL}HuL9x1LP LP`L4[KK;D-H8LXK IT$HRIB2HpJt2 H0H@JD2Jt2LiL9M)u LXH8ffff.AG HAG@IHLM@A9D$u6AD$A;Gu+I<$I7d2uIHA9D$$Md$tLPIt$(HhuID$H@HxJL0 HhHpJT0JL0L}HuHHL)HHHH9H`HMHEL9t0fff.HXHxHt Hx(2HI9uH}LL}2HIHH;EU2 2HIHHHIL2H`HH4H 41A`HhHDžhHtCt H{2H|2L}MHIHH;EaH`HĨ[A\A]A^A_]ÿP2H kIHHH5%IHHb2. 2H ;IHHH5IHH22 H섯$IH`HHHt;CtH{2IH2HhHDžhHu#IHhHDžhHCt H{W2HO2LuMu2qHILIH}H9t_HtuHP(LuMtAHEL9tSffff.HXHxHt Hx2HI9uH}Lu2L2 HP LuMuLILuMuDUHAWAVAUATSHAAHHIHHED9tnHH D HD: D HDD(HDD(HDpD0D`(HIHH;EuHĈ[A\A]A^A_]`22IHhH54H 4H}Lh6yHPH跣AH4HuLH{E1H5ԫIH}TL2 IALhMIIPt H`F2LmMt6HEL9t!HX@t Hx!2HI9uH}LLm2EuEuLhMu&^H}2EtH}2LhMt:HpL9tEHX@t Hx2HI9uHhLp2EtL2L2 LDUHAWAVAUATSHHՈIHHEHuHH_HH)LG LH+WH9u`H9t I9pCH9_t-H3HH_HIHH;Eu+HĘ[A\A]A^A_]HuH[IHH;Et+22IH`脾H54H $4HxL`0vW)@HDžP0y2HP(F)@HerminateHHHas not tHHH entry wHHHPreviousHf@ d.@"AH4HxH@L9yE1H5/IHQL,22IH`蒽H54H 24HxL`1 vW)@HDžP@2HP(/E)@Hs 'end'.HH.Hrevious HH(Hr than pHH Hs smalleHHH Value iHHH'start'.HHHInvalid H@6AH4HxH@L#xE1H5IHPL2 IALeMIAL`MIIALeMLIAL`MzII@t HP82LeMt6HEL9t!HX@t Hx2HI9uH}LLe 2Euxu#L`Mu00H} 2xtH} 2L`MHhL9 HX@t Hx 2HI9uI@t HPf 2LeMt6HEL9t!HX@t HxA 2HI9uH}LLe' 2Euxu L`Mu)aH} 2xtH} 2L`Mt:HhL9tEHX@t Hx 2HI9uH`Lh 2EtL 2L 2 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VHHHInvalid H@(rt'.@,AH4HxH@LlE1H5IHEL2 IALeMIAL`MIIALeMLIAL`MzII@t HP2LeMt6HEL9t!HX@t Hx2HI9uH}LLe2Euxu#L`Mu00H}2xtH}2L`MHhL9 HX@t HxW2HI9uI@t HP22LeMt6HEL9t!HX@t Hx 2HI9uH}LLe2Euxu L`Mu)aH}2xtH}2L`Mt:HhL9tEHX@t Hx2HI9uH`Lhu2EtL2L1 Lff.UHAVSH0HxIHHEW)EHIE(HEHtionProvHEHCPUExecuHEEiderEH{XHuةH@(H H4HH}11PEt H}2HEHHINHHH@IFIH]Ht#HHCHuHHPH}1HwIHH;Eu LH0[A^]2HEt H}F2H1 fffff.UHAWAVATSHIIFIFIH(H H9HH<@1HINIH[HIFHCHHt3HDHAHAHINHINHHuHff.HAHAHIFHHINH@(H@ H@IFHHINH@(H@ H@IFHHINH@(H@ H@INHINHg[A\A^A_]L*1 IM&Mt3IFL9t4HXHxHt Hx1HI9uI>Mf1L1 LUH]f.UH]T1fDUHSPH(G1H jKHHKHSHHHPHKHHHK HH H[]fff.UHHiKHHG HF HGHFHGHOHNHF]UH]f.UH]1fDUHSPHHH ;HH[]ÐUHH % FH1H9NHD]DUHHiK]UHAWAVAUATSHXIHIHtIHHEL#HEH IH{LFmHtjLuLLAHCL0HKH}LLIcH@ITMdIDI9soHEID$HEHMIL$I$HjIH]HEHaCt H{z1Hr1HsIHH;EJ1I1LhMt:HpL9tEHX@t Hx1HI9uHhLp1EtL1L+1 Lfffff.UHAWAVAUATSPLcoD9n;E2L Lf 1IDILP(t= y(H@ H;A HL9|ffffff.y(H@ HHq HDDALpDLIDDAt2H~H9At/HvHxAu.0ffffff.DH9tXHHxAtHxu-Ht1fDT:u-HI9uMM&Ht 1u M 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H!HH;EuLHh[A\A]A^A_]远 HSHeHHHL0HHLt H H H5!HHК H0薛 H$  UHAVSHIH8!HHEH`HRH5HAHAD$D$$H H L 1E1HX;D$D$$HX H LA 1E1H?H5] H1#L"H SH5 H#L>HRH #H0H0HPHHH5 HH5 Hf H H5hHLHzHPH9t HtHP(HP H`_ҁH!HH;EuLH[A^]i I"IHPH9uHP HtHP(H`ҁL  fffff.UHAWAVSH(IH&!HHEH`H@HDž(H5 HL(HcD$D$$H, H $LN 1E1H:9D$D$$Ht H L E1H8D$D$$HH L٥ 1E1Hu=HW)HDž fDžTƅ8HHW)HDž@d H(̪)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH>H 4#H0H0HPHH| H5 H H5 H^HH5H}LHr߁HPH9tjHtHP(t H$ LMt^HL9tCfffff.HX@t Hx HI9uHHP uLL躖 t H 襖 H`΁H!HH;EuLH([A^A_]Ӗ IugqILMunIIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hx軕 HI9uHL蠕 t H 苕 H`́L迒  LfUHAWAVSH(IH !HHEH`HD$D$$H( H  L+ 1E1H5D$D$$HQ H L0 1E1H9HW)HDž fDžTƅ4HHW)HDž0} H(E)Hry type.HHHrbitraryHHH be of aHHHnsor canHHHInput teH@&HHHH:H5F H#L( HKH #H0H0HPHHH5ڬ H H5 Hq HH5MH LHہHPH9t]HtHP(t H7 LMtQHL9t6HX@t Hx HI9uHHP uLLڒ t H Œ H`ʁH4 !HH;EuLH([A^A_] ILMu|IuS]IIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hxۑ HI9uHL t H 諑 H`ɁLߎ  LfUHAWAVSH(IH!HHEH`HD$D$$H$ H ! LK 1E1H71D$D$$H]H i LP 1E1H5HW)HDž fDžTƅ0HHW)HDž0蝐 H(e)Hry type.HHHrbitraryHHH be of aHHHnsor canHHHInput teH@&HHHH6H5f H"#LHHH g#H0H0HPHHH5 HH5 H HH5tHLHׁHPH9t]HtHP(t HW LMtQHL9t6HX@t Hx+ HI9uHHP uLL t H H` ǁHT!HH;EuLH([A^A_] ILMu|IuS]IIIHPH9tHtHP(uHP t H4 LMtHHL9tgfffff.HX@t Hx HI9uHL t H ˍ H`ŁL  LfUHAWAVSH(IH!HHEH`H0~H5: HIAH D$D$$HH LJ 1AH3-D$D$$HH L 1E1H1HW)HDž fDžTƅ,HHW)HDž0虌 H(1()Hnsor typHH Ho any teHHH types tHHHn outputHHHConstraiHf@(e.@*HHHH2H #H0H0HPHHH5п HH53 H HH5HLHӁHPH9teHtHP(t Ho LMtYHL9t>fHX@t Hx; HI9uHHP uLL t H H`ÁHd!HH;EuLH([A^A_]# IugqILMunIIIHPH9tHtHP(uHP t HD LMtHHL9tgfffff.HX@t Hx HI9uHL t H ۉ H`L  LfUHAWAVSH(IH&!HHEH`H@zD$D$$H H L{ 1E1Hg)D$D$$H H L8 AH()D$D$$H H L 1AH-HW)HDž fDžTƅ(HHW)HDž@苈 H()Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH.HDžH575 HLH H .#H0H0HPHHvH5Z HH5 HX HH5]HwLHlρHPH9tdHtHP(t H LMtXHL9t=HX@t Hx HI9uHHP uLL躆 t H 襆 H`˾H HH;EuLH([A^A_]ӆ IuuIugqILMunIIIHPH9tHtHP(uHP t H LMt:HL9tYHX@t Hx軅 HI9uHL蠅 t H 苅 H`豽L迂  LfUHAWAVSH(IH HHEH`HuD$D$$H H  L+ 1E1H%D$D$$H H } L E1H$D$D$$H H YL E1H$D$D$$H H fLt AH`$D$D$$HH L5 AH!$D$D$$H! H M L 1E1H(HW)HDž fDžTƅ#HHW)HDž@臃 H()Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH)H5 H#LF H:H #H0H0HPHH}H5 HH5 H_ HH5wH~LHsʁHPH9tkHtHP(t H% LMt_HL9tDffffff.HX@t Hx HI9uHHP uLL躁 t H 襁 H`˹H HH;EuLH([A^A_]Ӂ ILMutIuKUIIHPH9tHtHP(uHP t H LMt@HL9tKfDHX@t Hxˀ HI9uHL谀 tH 蛀 LIH`跸L}  ffff.UHAWAVSH(IH HHEH`HpH5`H^E1HD$D$$Hs H L 1E1HD$D$$HiH mLԌ 1E1Hp$HW)HDž fDžTƅHHW)HDž@_ H(Ǒ)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH%H /#H0H0HPHHwH5| HH5 HY HH5HxLHmƁHPH9teHtHP(t H~ LMtYHL9t>fHX@t Hx} HI9uHHP uLL} t H } H`˵H HH;EuLH([A^A_]} IugqILMunIIIHPH9tHtHP(uHP t H| LMtHHL9tgfffff.HX@t Hx| HI9uHL| t H | H`豴Ly  LfUHAWAVSH(IH HHEH`HlHxHDž(H5) HL(H D$D$$H\ H e L 1E1HD$D$$HJ H #L E1HD$D$$H4H L E1HjD$D$$Hj H LE 1E1HHW)HDž fDžTƅ4yHHW)HDž@z H()Hsor typeHH(H any tenHH Han be ofHHH types cHHHd outputHHHInput anHf@0.HHHH!H5E H]#L H92H #H0H0HPHHH5{ HH5> H HH5?HLHHPH9t`HtHP(t Hzy LMtTHL9t9@HX@t HxKy HI9uHHP uLLy t H y H`+Ht HH;EuLH([A^A_]3y ILMIIuS]IIIHPH9tHtHP(uHP t HHx LMtfHX@t Hx{R HI9uHHP uLLJR t H 5R H`[H HH;EuLH([A^A_]cR ILMu|IuS]IIIHPH9tHtHP(uHP t HQ LMtHHL9tgfffff.HX@t HxKQ HI9uHL0Q t H Q H`ALON  LfUHAWAVSHHIHf HHEH`HAHW)ƅHDž HnearestHH5^ HLHD$D$$Ha H Ld] 1E1HPD$D$$HȤ H LPb E1HD$D$$Ha H L\ 1E1HHW)HDžfDžTƅMHH请W)HDž@zO H()Hr types.HH/Hl tensorHH(HY' to alHH Houtput 'HHH'X' and HHHn input HHHConstraiH@7HHHHH ʵ#H0H0HPHHH55 HH5{ Ht H跿H5'HLH舖HPH9t`HtHP(t H:N LMtTHL9t9@HX@t Hx N HI9uHHP uLLM t HM t H M H`օH HH;EuLHH[A^A_]M IILMIIIIHPH9tHtHP(uHP t HL LMtCHL9twfHX@t HxL HI9uHLL t H{L t H fL H`茄LI  Lffff.UHAWAVSHIH HHEH`H<W)ƅHDž HnearestHH5 HYLHADž @H5m HL H5HDžH5N HLHhW)ƅHDžHhalf_pixHfDželƅH5 HLHW)ƅ$HDžHefer_floHHround_prHfDžorƅH5Q H\LHDžH5d HXLH D$D$$Hs[ H Lq 1E1HDD$D$$HH L-z AHD$D$$H} H L\ AHD$D$$HH L[ AHD$D$$HZ H Lp 1E1HHW)HDžƅfDžT1ƅ HHW)pHDž@H H(n)pHr types.HH/Hl tensorHH(HY' to alHH Houtput 'HHH'X' and HHHn input HHHConstraiH@7HHHpH%H5cx H#L HCH #H0H0HPHHܼH5ћ HH5Hu H HH5 HݾLHҏHPH9tjHtHP(pt HG LMt^HL9tCfffff.HX@t HxKG HI9uHHP puLLG uTu`ult H F H`HY HH;EuRLHĘ[A^A_]HF tHF tHF uF ILMI!xIxI IIIpuKUIIHPH9tHtHP(puHP pt HE LMtGHL9fHX@t Hx[E HI9uHL@E u=u\uht H E H`6}LDB  HD tLItHD tHD uUHAWAVATSH@IH HHEHPH5D$D$$HvU H L{ 1E1HED$D$$HEU H LB 1E1HIW)HDžfDžVƅHHૈkHH̫HHHH{LMtBHL9t'HX@t Hx[C HI9uHLL;C W)HDžP(C HH(})H5BHvD CBHHHLH $#H H H(H H@HH|H50 H荸H5o H^H衳H5H}LHrH@H9tjHtHP(t H$B LMt^HL9tCfffff.HX@t HxA HI9uHHP uLLA t HA HPyH HH;EuLH@[A\A^A_]A IILMTILMtSHL9t$HX@t HxA HI9uHLL@ LMuILMHL9@HX@t Hx@ HI9uIIIH@H9tHtHP(uHP t HA@ LMtEHL9tdff.HX@t Hx @ HI9uHL? t H? HPxL=  LfUHAWAVSH(IH& HHEH`H@0H5 H{E1H2D$D$$HzP H L]L 1E1HID$D$$HH Lbf E1H D$D$$H P H |LK 1E1HHW)HDž fDžTƅ H(P)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH5 e HT#LHH #H0H0HPHHiH5Op HzH5j HK H莮H5 HjLH_HPH9tgHtHP(t H= LMt[HL9t@ff.HX@t Hx< HI9uHHP uLL< t H < H`tH HH;EuLH([A^A_]< ILMu|IuS]IIIHPH9tHtHP(uHP t H; LMtHHL9tgfffff.HX@t Hx; HI9uHL; t H {; H`sL8  LfUHAWAVAUATSHIH² HHEHPH+HDžH5y HLHD$D$$Hw H L(b 1E1HD$D$$H}H Lj E1HD$D$$Hq H L E1H^D$D$$H^K H rL 1E1HHW)HDžƅfDžT1ƅQHHL(W)HDž09 H(N)Ic types.L`I numericLhI to onlyLpIns inputLxHConstraiH@'HHHHHW)HDžƅfDžT2ƅmHHW)HDž08 H(q)L`LhLpLxIConstraiL0@'HHHH\HW)`HDžpƅ`fDžaT3ƅc6L(HHHRW)0HDž@ 8 H@(E)0Hor type.HHH tensor HHHn to anyHHL0@H`HHH0HH !#H H H@HHiH5z HzH5d HK H莨H5 HjLH_H@H9tgHtHP(0t H@7 LHMt[HPL9t@ff.HX@t Hx6 HI9uHHHP 0uLLP6 `uGuSLMtpHL9tUfHX@t Hxk6 HI9uH2HpN6 tH96 LMuLL6 uIuULMtrHL9tW@HX@t Hx5 HI9uH2H5 tH5 LMuLL5 t Hw5 HPmH HH;EuLH[A\A]A^A_]5 I0ILHM,I.LILMQIILMI`IBTIIIH@H9tHtHP(0uHP 0t H@34 LHMt?HPL9t$HX@t Hx4 HI9uHHLLP3 `uu#LMu/lHp3 tH3 LMt?HL9t$HX@t Hx|3 HI9uHLL\3 uu#LMu/lH03 tH3 LMt?HL9t^DHX@t Hx2 HI9uHL2 t H2 HPjL/  LfUHAVSHIH HHEH`H"#D$D$$HxC H sC LY 1E1HID$D$$HGC H BC L2b 1E1HH5NY H#LHH5a H#LHH #H0H0HPHHuH5q H膧H5^ HW H蚢H5 HvLHkyHPH9t HtHP(HP H`PiH HH;EuLH[A^]Z1 I"IHPH9uHP HtHP(H`hL.  ffffff.UHAVSHIH HHEH`H2!D$D$$HA H A LW 1E1HYD$D$$HWA H RA LB` 1E1HHDž(H5To HL(HHDž H5o HL H趴H5W Hh#LHH5_ HV#LĽHH #H0H0HPHH+H5n H H L: E1HD$D$$H> H L: E1HD$D$$H> H Lj: 1E1HH5v H#L HDHW)HDž fDžTƅ7+HHW)HDž@, H(;?)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH ##H0H0HPHHH5G HH5WY Hͤ HH5b HLHsHPH9tiHtHP(t H+ LMt]HL9tBffff.HX@t Hx[+ HI9uHHP uLL*+ t H + H`;cH HH;EuLH([A^A_]C+ IugqILMunIIIHPH9tHtHP(uHP t Hd* LMtHHL9tgfffff.HX@t Hx+* HI9uHL* t H ) H`!bL/'  LfUHAWAVSH(IHF HHEH`H`D$D$$H: H : L6 1E1HD$D$$H: H : LX; 1E1HHW)HDž fDžTƅɁHH"W)HDž ( H(g)Hr types.HHH tensor HHHn to allHHHConstraiH@HHHHVH #H0H0HPHH/H5h H@H5U H HTH5^ H0LH%pHPH9t]HtHP(t H' LMtQHL9t6HX@t Hx' HI9uHHP uLLz' t H e' H`_HԞ HH;EuLH([A^A_]' IugqILMunIIIHPH9tHtHP(uHP t H& LMtHHL9tgfffff.HX@t Hx{& HI9uHL`& t H K& H`q^L#  LfUHAWAVSH8IH HHEH`HHDž(H5 HގL(HӪHDž H5 HL H親D$D$$H6 H L2 1E1H}D$D$$Hh H LN7 E1HAD$D$$H?6 H L2 1E1HHW)HDžfDžTƅ #HH܌W)HDž@$ H(o)Hsor typeHH(H any tenHH Han be ofHHH types cHHHd outputHHHInput anHf@0.HHHHH #H0H0HPHH˘H5 HܙH57Q H譜 HH5 H̚LHkHPH9tiHtHP(t Hs# LMt]HL9tBffff.HX@t Hx;# HI9uHHP uLL # t H" H`d HH;EuLH8[A^A_]## IuoyILMuvIIIIHPH9tHtHP(uHP t H<" LMt@HL9t_fDHX@t Hx " HI9uHL! t H! H`ZL  LfUHAWAVSH(IH& HHEH`H@HDž(H5 HL(HcH5 HE1HD$D$$HK2 H L0. 1E1HD$D$$H2 H L- 1E1HD$D$$HK H L2 AHTD$D$$HH Lu2 AHD$D$$H H L62 AHHW)HDž fDžTƅ)HHW)HDž0 H(m)Hor type.HHHany tensHHHn be of HHHInput caH@ HHHH.H #H0H0HPHHH5 HH5sL H H,H56| HLHfHPH9teHtHP(t H LMtYHL9t>fHX@t Hx{ HI9uHHP uLLJ t H 5 H`[VH HH;EuLH([A^A_]c Iu_iILMufIIHPH9tHtHP(uHP t H LMt@HL9tKfDHX@t Hx[ HI9uHL@ tH + LIH`GULU  ffff.UHAWAVSH(IHf HHEH`H HDž(H5FZ H'L(H裡D$D$$H H L) 1E1HzD$D$$H H LK. E1H>D$D$$H>- H ͘L) 1E1HHW)HDž fDžTƅȻHHكW)HDž@ H()Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHHH #H0H0HPHHÏH5,W HԐH5/H H襓 HH5w HđLHbHPH9taHtHP(t Hk LMtUHL9t:DHX@t Hx; HI9uHHP uLL  t H  H`RHd HH;EuLH([A^A_]# IugqILMunIIIHPH9tHtHP(uHP t HD LMtHHL9tgfffff.HX@t Hx  HI9uHL t H  H`QL  LfUHAWAVSHHIH& HHEH`H@ W)ƅHDž HconstantHƅH5 HƧLH躨D$D$$H H Ϳ L%% 1E1HD$D$$Hp H L) E1HշD$D$$HH _L$ AH薷D$D$$H( H Lq$ 1E1H HW)HDžfDžTƅ HH1W)HDž@ H(d))Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH;H ̂#H0H0HPHHH5RZ H%H5C H H9H5Cs= HLH ^HPH9tbHtHP(t H LMtVHL9t;fDHX@t Hx HI9uHHP uLLZ t HE t H 0 H`VMH HH;EuLHH[A^A_]^ Iut~ILMu{IIIHPH9tHtHP(uHP t Hr LMtFHL9tzfff.HX@t Hx; HI9uHL  t H  t H  H`LL*  Lffff.UHAWAVSH(IH6 HHEH`HPHDž(H5W H~ L(HsD$D$$H{$ H L^ 1E1HJD$D$$H H GL% AH D$D$$H $ H v L 1E1H肷HW)HDž fDžTƅ蕲HHzW)HDž@q H($)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH谸H ~#H0H0HPHH艆H5 H蚇H5> HkH讂H5n H芈LHYHPH9tgHtHP(t H1 LMt[HL9t@ff.HX@t Hx HI9uHHP uLL t H  H`HH$ HH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hx HI9uHL t H  H`GL  LfUH]f.UH]d fDUH\ H ;m#H]fDUHH%m#H]UH]f.UH] fDUHAVSH H~ HHEHW)EHEEfEtoEHu11HrEt H} HHPHtuH1HPHteH1HPH(tDH@H(u H@ @u3u.H@ @t$H1HP(IH1HPHLҁH HH;Eu H [A^]u HEt H} HC  ff.UHH e1H1H9NHD]DUHHl#]UH]f.UH] fDUH H l#H]fDUHHk#H]UH]f.UH]T fDUHAWAVAUATSHIH HHE11LILPHHW)EHEEHallowzerHEfEoIHuLHt x1H`Et H} W)EHECuHSpHcChH H}1/4HHu HHAHIHHH}1I1LP(HÃ{(uLc -HC(H{@HIILc AL$ID$HpHu$I|$@HsHpID$I1LPx(uH@ Ha#HPLuLeW)EHEM)tmMMII]HH<k HHEH]LmMIJ4HHH I?t@D)HHHJ!IEnLpID$HHE1Hh1HX<HEMIADIN $HPAt8HIHcQI9{HI JLQ(t"uHY A}(uJN4\LpHY A}(umI} LpHIUHHRL?AE(ILuI] HhHHhIM!&LpZLp?LpAE(I} HN$IE HIUxu HH1Lp`DID$ HtfIcL$;}]QAT$LlKKHuu`A}(tL>AE(JHEIE fDHHH8QHHHILuKKHtHuHXLXtIDfHA}(tLH>AE(JHEI] HhHHhILuHEL)HHI9LXMHPHh@H@HcHH~rHcULEHx 1fHt~(uHF HH9|HfDH9}mHHI4Hs\HH9|"HH`E$(HHhHHA~(tL*=AF(I^ H}Ht H}Ht H}| H~ HH;EuDHĘ[A\A]A^A_]@uHH?<@uHH?`1#1R D(. IHH]H5 HqHxH AHxL H6#IIF IFIFE1H5"HIL ( IH5y H{Hx7AHxL H5#IIF IFIFE1H5i"HrILv (4 IH5 Hf{HxLAHxL H(5#IIF IFIFE1H5"HHL H}   ( IH5 HzHx`AHxL& H4#IIF IFIFE1H5r"H{HL 눿(= IH5 HıHxAHxL H14#IIF IFIFE1H5"HHL  0Hxu5< Hxu%,HxuHHxt H}j EtL H}HtH}HH}HHxtHH}HtHH}HHH}HHH}HuovHH}HuahHEt_H}THH}HuDKHH}H\"HH}HJHH}H8u H}Ht H}c H  ff.UHH 'H1H9NHD]DUHHa#]UHAWAVAUATSHHHIHHxz HHEH0L%#z M|$@LLHx HH@H0HfHnfLC HEEID$HHIhL0LLLF L5y ILHDž HDžHDžHDžDž(LL LLH]uLHz HHHH芙HI|$H7ILLP(IA}(u$Me AL$ID$HukLfffff.LnAE(I}@H蹙IMe AL$ID$HuI|$@HID$HcMH@ H|"IILP I9Hc HH;E,Hh[A\A]A^A_]ÿ(IH5 H0LRH}HMLM舍AHuLqH#IIF IFIFE1H5"H.L(IH5T HbH}AHuLH#IIF IFIFE1H5J"HS.LW(IHEH+EHHEILP HEH0 H$H5 HDLaH}HMLM;AHuLTH#IIG IGIGE1H5"H-LH`HEt H}CEuHL^H}HHEuHHEt H}EtyLH}HuqxHHEt H}Et\LRHH}Ht4H}H}Hu+2HEtH}HH}Ht H}jH fUHH H1H9NHD]DUHH J#]UH]f.UH]fDUH H I#H]fDUHHI#H]UH]f.UH]fDUHAWAVAUATSHIH"` HHEILPHH*11LXILPHtOI1LPHt?I1LPH(uH@ @tH@H(tu H@ @u&H_ HH;EH[A\A]A^A_]ILPIILPIILPHr]ILPHtJILP1ۋH(ufH@ @t!H@H(tuH@ @1ILPHrgILPHtTILPE1H(u$f.H@ @H@H(tH@ @E1MMILPHreILPHtRILPH(uH@ @t2H@H(tu!H@ @tILPHKILPHreILPHtRILPH(uH@ @t2H@H(tu!H@ @tILPHAD$HXLH@LH`H+XHHH+@H9I1LPx(H@ HHHHuH #HcAHLXL`Lff HDž0L)HLLMIILH=H IL OL\AG(I@H豇IMg AL$ID$HuI|$@HID$H@ N|A(MI Iw@H^$AG(%HyH9HNHHIH@HLO[AG(I@H耆IMg AL$ID$HuI|$@H豅ID$H@ HH\{(tHnC(Lk HHp HCH;H HLII?HI!II9(MU@uKHH?D@uJHH?"@u?HH68@u>HH?7@u=HH?!1H11H1H1H}HtHHHuH}HEHtHHtH Ht H(H@Ht HHHXHdH`qHN HH;EX(fIH5~ HfHpÙAHpLHZ#IIF IFIFE1H5%"H.L2 (IH5 H@ Hp}OAHpLcH#IIG IGIGE1H5"HL눿(zIH5I H&HpgAHpLHn#IIG IGIGE1H59"HBLF(IH5 H7HpޭAHpLtH#IIF IFIFE1H5"HL(IH5W HPHAHLH|#IIG IGIGE1H5G"HPLTH [ (IH5 HHpAHpLqH#IIG IGIGE1H5"HLHpIpt H}QEuILlHHIt HEuIL* Ipt H}Eu^ILH@HOVIpt H}Eu`H}HtDHHHuH}HEHdHHIptILdH}HuIpt H}EuIL/L? 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@uHHH;EH([A^A_]H1HPx(uH@ H@HtH6#H@HuH6#x~CH1HP(IH1HPH }H H;MuHLH([A^A_]o"8^(]HH5a HE" H}vAHuH_[H"HHC HCHCE1H5="HH] I&IEuEuLZ H}B]EtHe]LuZ ffff.UHH uH1H9NHD]DUHHM"]UHAVSHIHXHHEH`HrMH5 H AHaD$D$$HmH 7 Lʃ 1E1HxD$D$$HxmH  La 1E1HH5} H"LB H-H5+ H"L^ H H D"H0H0HPHHH5 HH5 H HH55 YHLH蚣HPH9t HtHP(HP H`HHH;EuLH[A^][I"IHPH9uHP HtHP(H`(L6X fffff.UHAWAVSH(IHFHHEH`H`KD$D$$H H q Lg1E1HD$D$$H H I LXlE1HKD$D$$H0 H 2 L&g1E1HHW)HDž fDžTƅHHW)HDž@YH(l)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH "H0H0HPHHH5B HH55 H HH5Z HLH迠HPH9tgHtHP(t HqXLMt[HL9t@ff.HX@t Hx;XHI9uHHP uLL Xt H WH`HdHH;EuLH([A^A_]#XIugqILMunIIIHPH9tHtHP(uHP t HDWLMtHHL9tgfffff.HX@t Hx WHI9uHLVt H VH`LT LfUHAWAVSH(IH&HHEH`H@GD$D$$H H Q L{c1E1HgD$D$$H H ) L8hE1H+D$D$$H H  Lc1E1HHW)HDž fDžTƅSHHƽW)HDž@UH(g)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH Q"H0H0HPHHH5" HH5 HHH5: HLH蟜HPH9tgHtHP(t HQTLMt[HL9t@ff.HX@t HxTHI9uHHP uLLSt H SH`HDHH;EuLH([A^A_]TIugqILMunIIIHPH9tHtHP(uHP t H$SLMtHHL9tgfffff.HX@t HxRHI9uHLRt H RH`ኀLO LfUHAWAVSH(IHHHEH`H CD$D$$H H 1 L[_1E1HGD$D$$H H = L`y 1E1HHW)HDž fDžTƅPHH⹇W)HDž0QH(ud)Hry type.HHHrbitraryHHH be of aHHHnsor canHHHInput teH@&HHHHH5vx HZ"LX H& H "H0H0HPHHH5 j HH5+~ HHH5P HLH赘HPH9t]HtHP(t HgPLMtQHL9t6HX@t Hx;PHI9uHHP uLL Pt H OH`HdHH;EuLH([A^A_]#PILMu|IuS]IIIHPH9tHtHP(uHP t HDOLMtHHL9tgfffff.HX@t Hx OHI9uHLNt H NH`LL LfUHAWAVSH(IH&HHEH`H@?D$D$$H H Q L{[1E1HgD$D$$H H Lu 1E1HHW)HDž fDžTƅ1LHHW)HDž0MH(`)Hry type.HHHrbitraryHHH be of aHHHnsor canHHHInput teH@&HHHH(H5t H"L HFH "H0H0HPHHH5 HH5Kz HHH5p HLHՔHPH9t]HtHP(t HLLMtQHL9t6HX@t Hx[LHI9uHHP uLL*Lt H LH`;HHH;EuLH([A^A_]CLILMu|IuS]IIIHPH9tHtHP(uHP t HdKLMtHHL9tgfffff.HX@t Hx+KHI9uHLKt H JH`!L/H LfUHAWAVSH(IHFHHEH`H`;H5 Hy AHOD$D$$H H LzW1AHcD$D$$H H L>W1E1HHW)HDž fDžTƅ-HHHW)HDž0IH(a)Hnsor typHH Ho any teHHH types tHHHn outputHHHConstraiHf@(e.@*HHHHH "H0H0HPHHH5} HH5cv H HH5 HLH퐀HPH9teHtHP(t HHLMtYHL9t>fHX@t HxkHHI9uHHP uLL:Ht H %HH`KHHH;EuLH([A^A_]SHIugqILMunIIIHPH9tHtHP(uHP t HtGLMtHHL9tgfffff.HX@t Hx;GHI9uHL Gt H GH`1L?D LfUHAWAVSH(IHVHHEH`Hp7D$D$$HWH E LS1E1HD$D$$H H LrS1AH HW)HDž fDžTƅ^DHH/W)HDž@EH(bX)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH9HDžH5 H: LH,H5ٷ H E1HH o"H0H0HPHHǹH5 HغH53r H詽 HH5X HȻLH轌HPH9teHtHP(t HoDLMtYHL9t>fHX@t Hx;DHI9uHHP uLL Dt H CH`|HdHH;EuLH([A^A_]#DIuuILMu|IuS]IIIHPH9tHtHP(uHP t H6CLMt:HL9tYHX@t Hx CHI9uHLBt H BH`{L@ LfUHAWAVSH(IH&HHEH`H@3D$D$$H H L{O1E1HgD$D$$H H L< E1H+D$D$$H H L E1HD$D$$H H LĪ AHD$D$$H H  L AHqD$D$$HqRH LLN1E1HHW)HDž fDžTƅ;?HH W)HDž@@H(?S)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH5@ Hx"L H4H "H0H0HPHHʹH5 H޵H59m H诸 HH5^ HζLHÇHPH9tkHtHP(t Hu?LMt_HL9tDffffff.HX@t Hx;?HI9uHHP uLL ?t H >H`wHdHH;EuLH([A^A_]#?ILMutIuKUIIHPH9tHtHP(uHP t HL>LMt@HL9tKfDHX@t Hx>HI9uHL>tH =LIH`vL; ffff.UHAWAVSH(IH&HHEH`H@.H5 H E1H2D$D$$H H 3 L]J1E1HID$D$$H H L$J1E1HHW)HDž fDžTƅ;HH䤇W)HDž@<H(O)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH o"H0H0HPHHǰH5̷ HرH53i H詴HH5X HȲLH轃HPH9teHtHP(t Ho;LMtYHL9t>fHX@t Hx;;HI9uHHP uLL ;t H :H`sHdHH;EuLH([A^A_]#;IugqILMunIIIHPH9tHtHP(uHP t HD:LMtHHL9tgfffff.HX@t Hx :HI9uHL9t H 9H`rL7 LfUHAWAVSH(IH&HHEH`H@*D$D$$H H L{F1E1HgD$D$$H H L8KE1H+D$D$$H H LFE1HD$D$$HIH 9 LE1E1HfHW)HDž fDžTƅ6HH芠W)HDž@U8H(=)Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHHH "H0H0HPHHtH5A H腭H5d HV H虨H5 HuLHjHPH9tbHtHP(t H7LMtVHL9t;fDHX@t Hx6HI9uHHP uLL6t H 6H`nHHH;EuLH([A^A_]6IugqILMunIIIHPH9tHtHP(uHP t H5LMtHHL9tgfffff.HX@t Hx5HI9uHL5t H 5H`mL2 LfUHAWAVSH(IH֬HHEH`H%HDž(H5 H L(HD$D$$Hd H m LA1E1HD$D$$HR H + L E1HD$D$$H< H LAE1HrD$D$$HrEH LMA1E1HHW)HDž fDžTƅ<2HH W)HDž@3H(~)Hsor typeHH(H any tenHH Han be ofHHH types cHHHd outputHHHInput anHf@0.HHHH#H5M H"L HAH "H0H0HPHHڧH5 HH5F` H輫 HH5k rH۩LHzHPH9thHtHP(t H2LMt\HL9tAfff.HX@t HxK2HI9uHHP uLL2t H 2H`+jHtHH;EuLH([A^A_]32ILMu|IuS]IIIHPH9tHtHP(uHP t HT1LMtHHL9tgfffff.HX@t Hx1HI9uHL1t H 0H`iL. LfUHAWAVSH(IH6HHEH`HP!HDž(H5 H- L(HsD$D$$H H L^=1E1HJD$D$$H H > L E1HD$D$$HAH L<1E1HHW)HDž fDžTƅ-HH詗W)HDž@t/H(\)Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHHH5 H<"L: HH "H0H0HPHHqH5+f H肤H5[ HS H薟H5 HrLHgvHPH9t_HtHP(t H.LMtSHL9t8HX@t Hx-HI9uHHP uLL-t H -H`eHHH;EuLH([A^A_]-ILMu|IuS]IIIHPH9tHtHP(uHP t H,LMtHHL9tgfffff.HX@t Hx,HI9uHL,t H ,H`dL) LfUHAWAVSH(IH֣HHEH`HHDž(H5 Hͪ L(HD$D$$Hd H m L81E1HD$D$$HR H L E1HD$D$$H<H L L81E1H%HW)HDž fDžTƅx)HHIW)HDž@+H()Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHHZH5 H"Lھ HxH "H0H0HPHHH5&c H"H5}W H H6H5 KHLHrHPH9t_HtHP(t H)LMtSHL9t8HX@t Hx)HI9uHHP uLLZ)t H E)H`kaHHH;EuLH([A^A_]s)ILMu|IuS]IIIHPH9tHtHP(uHP t H(LMtHHL9tgfffff.HX@t Hx[(HI9uHL@(t H +(H`Q`L_% LfUHAWAVSH(IHvHHEH`HH5 Hʨ E1H肦D$D$$H H 6 L41E1HD$D$$H0 H 2 Lt41E1HHW)HDž fDžTƅc%HH4W)HDž@&H(g9)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH>H ?"H0H0HPHHH5ٜ H(H5S H H

fHX@t Hx%HI9uHHP uLLZ%t H E%H`k]HHH;EuLH([A^A_]s%IugqILMunIIIHPH9tHtHP(uHP t H$LMtHHL9tgfffff.HX@t Hx[$HI9uHL@$t H +$H`Q\L_! LfUHAWAVSH(IHvHHEH`HH5 Hɥ AHD$D$$H H : L01E1HD$D$$H H / Lq01E1H HW)HDž fDžTƅ`!HH1W)HDž@"H(d5)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH;H "H0H0HPHHH5g H%H5O H H9H5 HLH jHPH9tbHtHP(t H!LMtVHL9t;fDHX@t Hx!HI9uHHP uLLZ!t H E!H`kYHHH;EuLH([A^A_]s!IugqILMunIIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hx[ HI9uHL@ t H + H`QXL_ LfUHAWAVSH(IHvHHEH`HH5h H AHD$D$$H0H  L,1E1H薿D$D$$H0H Lq,1E1H HW)HDž fDžTƅ`HH1W)HDž@H(d1)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH;H <"H0H0HPHHH5 H%H5K HH9H5 /HLH fHPH9tbHtHP(t HLMtVHL9t;fDHX@t HxHI9uHHP uLLZt H EH`kUHHH;EuLH([A^A_]sIugqILMunIIIHPH9tHtHP(uHP t HLMtHHL9tgfffff.HX@t Hx[HI9uHL@t H +H`QTL_ LfUHAWAVSHHIHvHHEH`H H5h H AHW)HDž ƅDžDCRH5\ H0 LHD$D$$H,H 7 Lb(1E1HND$D$$HN,H  L)(1E1HſHW)HDžfDžTƅHH邇W)HDž@H(-)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH t"H0H0HPHH̎H5] HݏH58G H讒 HH5] H͐LHaHPH9tjHtHP(t HtLMt^HL9tCfffff.HX@t Hx;HI9uHHP uLL t Ht H H`QHOHH;EuLHH[A^A_]IILMIIIIHPH9tHtHP(uHP t HLMtCHL9twfHX@t HxHI9uHLt Ht H H`OL Lffff.UHAWAVSH(IH֎HHEH`HD$D$$HH(H l L+$1E1HD$D$$HU H V L0> E1H۶D$D$$H'H L#1E1HRHW)HDž fDžTƅHHv~W)HDž@AH(()Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH耼H5< H""L HH _"H0H0HPHH7H5 HHH5B HH\H5Ȍ H8LH-]HPH9teHtHP(t HLMtYHL9t>fHX@t HxHI9uHHP uLLzt H eH`LHԋHH;EuLH([A^A_]ILMu|IuS]IIIHPH9tHtHP(uHP t HLMtHHL9tgfffff.HX@t Hx{HI9uHL`t H KH`qKL LfUHAWAVSHIHHHEH`HW)ƅHDž HnearestHH5 HI LH1Dž @H5]h H L H%HDžH5>h Hs LHXW)ƅHDžHhalf_pixHfDželƅH5g H LHyW)ƅ$HDžHefer_floHHround_prHfDžorƅH5Ag HL LH DžH5Tf HH LHD$D$$Hc"H Л L8 1E1H4D$D$$H H LA E1HD$D$$Hpe H c L"E1H輰D$D$$H H L"AH}D$D$$H{!H L7 1E1HHW)HDžƅfDžT1ƅ@HHxW)pHDž@H(dL)pHr types.HH/Hl tensorHH(HY' to alHH Houtput 'HHH'X' and HHHn input HHHConstraiH@7HHHpHH5Y? H}"L H9H z"H0H0HPHH҃H5b HH5>< H贇 HH5c @HӅLHVHPH9t`HtHP(pt HzLMtTHL9t9@HX@t HxKHI9uHHP puLLuTu`ult H H`FHYHH;EuRLHĘ[A^A_]H tH tH u ILMI!xIxI IIIpuKUIIHPH9tHtHP(puHP pt H LMtGHL9fHX@t Hx[ HI9uHL@ u=u\uht H  H`6DLD  H tLItH tH uUHAWAVSH(IHHHEH`H D$D$$HxH L[1E1HGD$D$$HGH  L"1E1H辯HW)HDž fDžTƅѪHHrW)HDž@ H()Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH E!H0H 'H8H0HPHH~H5kG HH5#7 H虂 HzH5H ]H踀LHQHPH9teHtHP(t H_ LMtYHL9t>fHX@t Hx+ HI9uHHP uLLt H H` AHTHH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t H4LMtHHL9tgfffff.HX@t HxHI9uHLt H H`?L LfUHAWAVSH(IHHHEH`H0D$D$$HH Lk1E1HWD$D$$HWH $ L21E1HΫHW)HDž fDžTƅ!HHnW)HDž@H(%)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH U!H0H 7H8H0HPHHzH5{C H{H533 H~HvH5X} jH|LHMHPH9teHtHP(t HoLMtYHL9t>fHX@t Hx;HI9uHHP uLL t H H`=Hd|HH;EuLH([A^A_]#IugqILMunIIIHPH9tHtHP(uHP t HDLMtHHL9tgfffff.HX@t Hx HI9uHLt H H`gLH38H`7(HgHH;EuLHİ[A^]AHH`(H f.UHAWAVSH(IH&gHHEH`H@H5 HL AH/nD$D$$Hh H h LZ1AHCD$D$$Hh H h L1E1H躓HW)HDž fDžTƅ HHVW)HDž0H(A)Hnsor typHH Ho any teHHH types tHHHn outputHHHConstraiHf@(e.@*HHHHH k"H0H0HPHHbH5! HcH5C HfH^H5he HdLH5HPH9teHtHP(t HLMtYHL9t>fHX@t HxKHI9uHHP uLLt H H`+%HtdHH;EuLH([A^A_]3IugqILMunIIIHPH9tHtHP(uHP t HTLMtHHL9tgfffff.HX@t HxHI9uHLt H H`$L LfUHAVSHIH8cHHEH`HR܀D$D$$HH 'e L1E1HyD$D$$H] H &~ LTAH:D$D$$H%~ H d L1AH讏H5HH["Lx HH5$ H} E1HniH5[] H} E1HPiH5T[ H`H5 HRcH[H5b HqaLHf2H`j"HaHH;EuLHİ[A^]tHH`7"HE ffff.UHAVSHIHXaHHEH`HrڀH5 } H} AHahW)@ƅ@HDžPHconstantHAƅIH50 HC~ L@HyDž<H5v0 HE~ L<HsD$D$$Hp| H L 1E1HD$D$$HH L1E1HmH5HY"LCp H諠H5, H|^H5 HMaHYH5_  Hl_LHa0@t HP*H`P H_HH;EuLH[A^]ZH@uHH@t HPH`H ff.UHAVSHIH_HHEH`H2؀H57 H| E1H$fH5qw Hzw E1HfD$D$$Hz H _ L11E1HD$D$$H_ H _ L1E1H蔋H5H^W"Ljn HҞH5 7 H\H5 Ht_HWH5#^ 9 H]LH.H`H]HH;EuLHİ[A^]HH`YHg ffffff.UHAVSHIHx]HHEH`HրD$D$$HH j L1E1H蹅D$D$$Hz H z LE1H}D$D$$H H z LXE1HAD$D$$HAH z L1E1H踉H5HU"Ls HH5$ HU"Lz HԜH b"H0H0HPHHmYH5\ H~ZH5 HO]HUH5[ p Hn[LHc,HPH9t HtHP(HP H`HH[HH;EuLH[A^]RIHPH9uHP Ht HP(IH`L UHAVSHIH[HHEH`H2ԀHXH5~y Hy AHbH5y Hy AHaW)@ƅ@HDžPHnearestHAH5ɉ Hy L@HdsD$D$$HH y L1E1H軂D$D$$HH y L1E1H2H5H4S"Ly HpH5k HAXH5 H[HUSH5Y H1YLH&*@t HPH`H^YHH;EuLH[A^]HH@t HPH`H fffff.UHAWAVSHHIHXHHEH`HҀH55 Hx AH_W)ƅHDž HnearestHH5Ň Hy LH`qD$D$$HH Sk L1E1H跀D$D$$HH Cn L1E1H.HW)HDžfDžTƅHHRHW)HDž@H()Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH\H ]"H0H0HPHH5TH5h HFUH5 HXHZPH5V H6VLH+'HPH9tcHtHP(t HLMtWHL9t<HX@t HxHI9uHHP uLLzt Het H PH`vHUHH;EuLHH[A^A_]~IILMIIIIHPH9tHtHP(uHP t HLMtCHL9twfHX@t HxKHI9uHL0t Ht H H`,L: Lffff.UHAWAVSHHIHFTHHEH`H`̀W)ƅHDž HnearestHH5F Ht LHlD$D$$HgH f LL1E1H8|D$D$$H0 H s L8E1H{D$D$$HH i L1E1HsHW)HDžfDžTƅHHCW)HDž@bH()Hr types.HH/Hl tensorHH(HY' to alHH Houtput 'HHH'X' and HHHn input HHHConstraiH@7HHHH衁H Y"H0H0HPHHzOH5d HPH5 H\S HKH5 R H{QLHp"HPH9thHtHP(t H"LMt\HL9tAfff.HX@t HxHI9uHHP uLLt Ht H H`HPHH;EuLHH[A^A_]Iut~ILMu{IIIHPH9tHtHP(uHP t HLMtFHL9tzfff.HX@t HxHI9uHLt Hkt H VH`|L Lffff.UHAWAVSHHIHOHHEH`HȀW)ƅHDž HnearestHH5~ Ho LH1hD$D$$HH $b L1E1HwD$D$$H, H o LE1HLwD$D$$HJH d L'1E1H{HW)HDžfDžTƅHH>W)HDž@H(:)Hr types.HH/Hl tensorHH(HY' to alHH Houtput 'HHH'X' and HHHn input HHHConstraiH@7HHHH|H rU"H0H0HPHHJH5) HKH56 HN HFH5[M  HLLHHPH9thHtHP(t HrLMt\HL9tAfff.HX@t Hx;HI9uHHP uLL t Ht H H` HOLHH;EuLHH[A^A_]Iut~ILMu{IIIHPH9tHtHP(uHP t H"LMtFHL9tzfff.HX@t HxHI9uHLt Ht H H` L Lffff.UHAWAVSH(IHJHHEH`HĀD$D$$Hf H f L;1E1H'sH56g H_l E1HQH5= Hl AHQH5f Hl AHwQD$D$$HH f L1E1H>wHW)HDž fDžTƅHHb:W)HDž@-H()Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHlxH mQ"H0H0HPHHEFH5i: HVGH5 H'JHjBH5H HFHLH;HPH9tcHtHP(t HLMtWHL9t<HX@t HxHI9uHHP uLLt H uH`HGHH;EuLH([A^A_]IugqILMunIIIHPH9tHtHP(uHP t HLMtHHL9tgfffff.HX@t HxHI9uHLpt H [H`L LfUHAWAVSH(IHFHHEH`HD$D$$Hab H _b L1E1HnD$D$$Ho9 H Mb L7 E1HnD$D$$HWb H )I L7 E1HonD$D$$Hgb H eb LD7 AH0nD$D$$HI H 6h L7 AHmD$D$$HH b L1E1HhrHW)HDž fDžTƅHH5W)HDž@WH()Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHsH55 H="La H贄H L"H0H0HPHHMAH5q5 H^BH5 H/E Hr=H5C HNCLHCHPH9tkHtHP(t HLMt_HL9tDffffff.HX@t HxHI9uHHP uLLt H uH`HBHH;EuLH([A^A_]ILMutIuKUIIHPH9tHtHP(uHP t HLMt@HL9tKfDHX@t HxHI9uHLtH kLIH`L ffff.UHAWAVSH(IHAHHEH`HHDž(H5]w H{d L(HND$D$$H4] H =m L1E1HiD$D$$H"m H wd L2 E1H~iD$D$$H F H G LYE1HBiD$D$$HBH G L1E1HmHW)HDž fDžTƅ HH0W)HDž@H()Hsor typeHH(H any tenHH Han be ofHHH types cHHHd outputHHHInput anHf@0.HHHHnH51 H9"Ls\ HH H"H0H0HPHHLHHPH9thHtHP(t HRLMt\HL9tAfff.HX@t HxHI9uHHP uLLt H H`HD>HH;EuLH([A^A_]ILMu|IuS]IIIHPH9tHtHP(uHP t H$LMtHHL9tgfffff.HX@t HxHI9uHLt H H`L LfUHAWAVSH(IH=HHEH`H H54 HH AHDD$D$$HWH H L:1E1H&eD$D$$H&H H L1E1HiHW)HDž fDžTƅHH,W)HDž@H()Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHjH LE"H0H0HPHH8H551 H9H5 H<H4H55; H:LH HPH9tbHtHP(t HLLMtVHL9t;fDHX@t HxHI9uHHP uLLt H H`HD:HH;EuLH([A^A_]IugqILMunIIIHPH9tHtHP(uHP t H$LMtHHL9tgfffff.HX@t HxHI9uHLt H H`L LfUHAWAVSH(IH9HHEH`H HDž(H5n H\ L(HCFD$D$$HT H d L.1E1HaD$D$$Hd H \ L) E1H`D$D$$HH @ L1E1HUeHW)HDž fDžTƅ訾HHy(W)HDž@DH(,p)Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHHfH5( H0"L T HwH iA"H0H0HPHHA4H5 HR5H5 H#8Hf0H56 o HB6LH7HPH9t_HtHP(t HLMtSHL9t8HX@t Hx軾HI9uHHP uLL芾t H uH`H5HH;EuLH([A^A_]裾ILMu|IuS]IIIHPH9tHtHP(uHP t HĽLMtHHL9tgfffff.HX@t Hx苽HI9uHLpt H [H`L菺 LfUHAWAVSH(IH4HHEH`HH5/Q H>Y E1H;D$D$$HCP H f> L1E1H\D$D$$H`> H b> L1E1H@aHW)HDž fDžTƅ蓺HHd$W)HDž@/H()Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHnbH ="H0H0HPHHG0H5 2 HX1H5 H)4Hl,H52 HH2LH=HPH9teHtHP(t HLMtYHL9t>fHX@t Hx軺HI9uHHP uLL芺t H uH`H1HH;EuLH([A^A_]裺IugqILMunIIIHPH9tHtHP(uHP t HĹLMtHHL9tgfffff.HX@t Hx苹HI9uHLpt H [H`L菶 LfUHAWAVSH(IH0HHEH`HH5/M HU AH7D$D$$H@L H j; L1E1HXD$D$$HH; H _: L1E1H=]HW)HDž fDžTƅ萶HHa W)HDž@,H()Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHk^H l:"H0H0HPHHD,H5> HU-H5 H&0Hi(H5. HE.LH:HPH9tbHtHP(t HLMtVHL9t;fDHX@t Hx軶HI9uHHP uLL芶t H uH`H-HH;EuLH([A^A_]裶IugqILMunIIIHPH9tHtHP(uHP t HĵLMtHHL9tgfffff.HX@t Hx苵HI9uHLpt H [H`L菲 LfUHAWAVAUATSHIH,HHEHPH輥HDžH5Yb HQ LH9D$D$$HWX H AR L1E1HTD$D$$H]S H \S L E1HzTD$D$$H H mT L̉ E1H>TD$D$$H>H RU L 1E1HXHW)HDžƅfDžT1ƅ1YHHL(W)HDž0薳H(.)Ic types.L`I numericLhI to onlyLpIns inputLxHConstraiH@'HHHHYHW)HDžƅfDžT2ƅMXHHW)HDž0蹲H(Q)L`LhLpLxIConstraiL0@'HHHHfHX@t Hx HI9uHHP uLLڥt H ťH`H4HH;EuLH([A^A_]IuuILMu|IuS]IIIHPH9tHtHP(uHP t HLMt:HL9tYHX@t HxۤHI9uHLt H 諤H`Lߡ LfUHAVSHIHHHEH`HH5; HH AH#W)ƅHDž HconstantHƅH5J H8 LHk4Dž H5 HI L H_.D$D$$H7 H RK L1E1HCD$D$$HH M Lq1E1H HH5]Hw"L* HK[H '"H0H0HPHHH5" HH5P HH H5u HLHHPH9tKHtHP(t H 茢H`HHH;Eu LH[A^]HP u諢IuISINIu6@IHPH9uHP u HtHP(t H H`L fDUHAWAVSH(IH6HHEH`HPD$D$$H4 H D L1E1HwAD$D$$HD H 0 LHE1H;AD$D$$H;H  L1E1HEHW)HDž fDžTƅHHW)HDž@衠H(P)Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHHFH $"H0H0HPHHH5) HH5, H HH5Q HLHHPH9t^HtHP(t HhLMtRHL9t7fHX@t Hx;HI9uHHP uLL t H H`HdHH;EuLH([A^A_]#IugqILMunIIIHPH9tHtHP(uHP t HDLMtHHL9tgfffff.HX@t Hx HI9uHLt H ۝H`L LfUH]f.UH]餝fDUH蜝H "H]fDUHH"H]UH]f.UH]TfDUHAVSH HHHEHW)EHEEfEtoEHu11HEt H}HHPHtuH1HPHteH1HPH(tDH@H(u H@ @u3u.H@ @t$H1HP(IH1HPHLaHHH;Eu H [A^]赜HEt H}CH胙 ff.UHH eH1H9NHD]DUHH "]UHAWAVATSH AIIHH^HHEHHxDD0(贛IH5̢ H&\ LG H}LzHuL$H!IIF IFIF1H5q{!HzL~&EHHH;EuHLDH [A\A^A_]fS(IH5 Hs[ LF H}LHuLqH!IIF IFIF1H5z!HL˚v(艚IH5 HF L(F H}LHuLHz!IIF IFIF1H5Fz!HOLS I@I;I6IEuIEu IEt H}̙u L LL fffff.UHAWAVAUATSHXLIHHHHHHEH0L-Mu@LLHHH@H0HfHnfL趘HEEIEHHIhL0LLL躗L=ILHDž HDžHDžHDžDž(LH LHH LHHHHH A$It$IEt$IET$H HH蜚HHH^ LH/HHL0HHLt H讗H莖H5HHkH01HHH;EuLHX[A\A]A^A_]赗HSHeHHHL0HHLt H HH5 HHƕH0茖H fUHAWAVAUATSHXLIHHHHHHEH0L- Mu@LLH, HH@H0HfHnfLHEEIEHHIhL0LLLL=/ ILHDž HDžHDžHDžDž(LH;LHH LHHHHHA$It$IEt$IET$HHH̗HHHLH_HHL0HHLt HޔH输H5 HH蛓H0aH* HH;EuLHX[A\A]A^A_]HSHeHHHL0HHLt H9HH5 HHH0輓HJ fUHAWAVAUATSHXLIHHHHJ HHEH0L- Mu@LLH\ HH@H0HfHnfLHEEIEHHIhL0LLLL=_ ILHDž HDžHDžHDžDž(LHkLHH1LHHGHHH A$It$IEt$IET$HHHHHHLH HHL0HHLt HHH5HHːH0葑HZ 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AI~H5-H;HٴHH;EDH[A^A_]I~H5\;{uIA|$~+1fIID$ 4AT$HIcD$H9|AAIHW| |H5*H/uHH5@+gIwH9HdA~PkIH5VIH5 IH5*8AD$@tIH5*8E IH5;!AL$LAT$Mg9aH5*H1` HHEHEL8ZIH57AIH5Gi7Aq|IH5)*7AR]IH5 *7A3>IH5)y7AIH5)Z7AIH5);7AIH5)7AAD$ IH5z)6AID$xIH5M6Mt$LɧDLLtLئIIH5(}6A~LtlLgE<$El$E9>HLpHLDHE9}LH5O*6LDHHcGHHGII9uHH;xt=yLIH5!5DAr}H5'5AWbIIcT$H5*(5A3>HOHW| |WH5'X5AMwAT$H5'H1SHHEHEL5NSH5L'H1HHEHEL4IH6AD$@HwHHe4AAuIH5K4HܭHH;Eu1H8[A\A]A^A_]6Hu?Hu1Ht%H6HD3 HH_H+3 4v W ?^}fff.UHAWAVSH(A׉IHHHEt94H=%6Ht\L3ӾCE[L2оL2W)EEHE]HuEL2Et H}4]L2HHH;Eu/H([A^A_]HHH;EuLH([A^A_]4HEt H}A4H1 fUHSPH"HH[]4@UH1]UHAWAVSPHHHԦLsMtmI^M~LH)thIN HHH4ӉσHH>MF(IN(HHHH @H~HH9t$H HH+HuH[A^A_]MF(IHHr6fffff.H;63I^M~HI^LH)HHwHt Hu@IF L9t:H;2HI9uINIFH9tHPH)HHHIVI>Ht2LH[A^A_]2H覢}fDUHAVSIC^wIIHHSIH7H]IGAuyGIIPHHIHH]IGAu;ff.ASƉ)сE)΃A7u!ffff.AK)ʃAuLutA(u#@LHA(vIHEE1E11fffff.LHuHUHMÅA(1E'ACu,iE'ACuAT$DAtA"uD DAA(tXAP;EuLIHuIGADž(HED`E'A]A(uDM1A(u5>E'E1A(u"+ffff.A(t DAA(uELeLuH]tMt)LAօ@LLHmMuױ1 UԻ1ɋMEȉML^EHH[A\A]A^A_]fff.UHHHLLHH$tH=<H]H]fUHAWAVAUATSPIAFTt#HL8HXLHH[A\A]A^A_]Mf0LXANTL-MLuA~u H(IF L8HXLsIUIMLH~HLHHuLZUHAWAVATSHLs0LSTHHu{u H HK H9HqHHH~VCPL{XMt=Mg0L3uLL_LWHL|L 1HhKPL[A\A^A_]uDUHH`]fUHAVSIaH HHIHHH~ L[A^][A^]UHAWAVAUATSPIIAWTHHuAu H IO H9HqHHHsAGTIhZHt7DHXIhH`IGhC H{(L Ih#HuI`IMuKff.LXMt3I\$I`LILk0L{PuHLI0H51LMtI~`LIF`IGXH[A\A]A^A_]ÐUHAWAVAUATSHhIIIHHHEMHULL5<IINHHHE+mrIL}H}HuEEI\$0HAL$TLLufA|$uYHUHQHH;EpILLHh[A\A]A^A_]wAYHHH;E'4ID$ LxH]L0LhHHILL?I|$hHuID$hHxHHuLL ELHLHmH}TLLLAƃ}xA{%@{%B{%D{%F{%H{%J{%L{%N{%P{%R{%T{%V{%X{%Z{%\{%^{%`{%b{%d{%f{%h{%j{%l{%n{%p{%r{%t{%v{%x{%z{%|{%~{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%{%|%|%|%|%|% |% |%|%|%|%|%|%|%|%|%|% |L!qAS%qhh<hhh@hhhhDhrhhxhnhxdhZhPhFh@h04hn*h hRh hhh-hhhhh5hhhh2h_hhvh$lhCbhdXhNhDh:h0hI&hxhhh h-h\hhhhh h)hGhhhh|h%rh[hhu^hThJh@h6h,h"hhhFh&h6hIhbhthhhhhhhhhxhnhdh*Zh8PhIFhYh 4h *h h!h;! hP!hl!h!h!h!h!h!h!h!h"h"h"h0"hE"hT"vhc"lht"bh"Xh"Nh"Dh":h"0h"&h"h"h#h#h##h0#h>#hP#h`#hp#OrtGetApiBaseOrtSessionOptionsAppendExecutionProvider_CPU/Users/runner/work/1/s/onnxruntime/core/session/IOBinding.ccBindInputp_providerexecution_mode is not validgraph_optimization_level is not validResult buffer is not large enoughInputT/Users/runner/work/1/s/onnxruntime/core/session/custom_ops.cccommon::Status onnxruntime::CreateCustomRegistry(const std::vector &, std::shared_ptr &)type_id_counter == 1There must be one (and only one) dynamic typed input to the custom op. Its type info at runtime will be used to infer the type info of this dynamic typed output which is required for the success of the model loading step. More than one dynamic typed inputs are currently not supported as differing types at runtime means the output type cannot be inferred without which model loading cannot proceed.OutputCreateCustomRegistryonnxruntime::CustomOpKernel::CustomOpKernel(const onnxruntime::OpKernelInfo &, const OrtCustomOp &)Unsupported version '' in custom op 'ComputeAsyncNo requested allocator availableEnv is nullOrtMemoryInfo is nullAllocator with this OrtMemoryInfo is already registered.Received invalid value for arena extend strategy. Valid values can be either 0, 1 or -1.intra-opinter-opMemcpyFromHostXinputYoutputConstrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.MemcpyToHosttensor(uint8)tensor(uint16)tensor(uint32)tensor(uint64)tensor(int8)tensor(int16)tensor(int32)tensor(int64)tensor(bfloat16)tensor(float16)tensor(float)tensor(double)tensor(string)tensor(bool)Input expected to have type but instead is null expected to have tensor typeElement type of input unknownOutput expected to have sequence typeMismatch between source and target type. Source=Trying to register schema with name (domain: version: ) from file line , but it is already registered from file , but its domain is not known by the checker., but its version is not in the inclusive range [] (usually, this means you bumped the operator version but forgot to update the version range in DomainToVersionRange in onnx/defs/schema.h).Schema error: com.microsoftcom.microsoft.nchwc/Users/runner/work/1/s/onnxruntime/core/session/inference_session.ccvoid onnxruntime::InferenceSession::ConstructorCommon(const onnxruntime::SessionOptions &, const onnxruntime::Environment &)status.IsOK()Could not finalize session options while constructing the inference session. Error Message: graph_transformation_mgr_.SetSteps(session_options_.max_num_graph_transformation_steps).IsOK()ConstructorCommonCreating and using per session threadpools since use_per_session_threads_ is true-session--intra-op-inter-opFailed to create the inter-op thread pool for the parallel executor, setting ExecutionMode to SEQUENTIALUsing global/env threadpools since use_per_session_threads_ is falsesession_env.EnvCreatedWithGlobalThreadPools()When the session is not configured to use per session threadpools, the env must be created with the the CreateEnvWithGlobalThreadPools API.onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions &, const onnxruntime::Environment &, const std::string &)Given model could not be parsed while creating inference session. Error message: st.IsOK()Could not parse model successfully while constructing the inference sessiononnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions &, const onnxruntime::Environment &, const void *, int)result~InferenceSessionUnknown error during EndProfiling()Received nullptr for exec providerRegisterExecutionProviderExecution providers must be registered before the session is initialized. Execution providers must be registered before the session is initialized.DmlExecutionProviderHaving memory pattern enabled is not supported while using the DML Execution Provider. So disabling it for this session since it uses the DML Execution Provider.Parallel execution mode does not support the DML Execution Provider. So making the execution mode sequential for this session since it uses the DML Execution Provider.CUDAExecutionProviderParallel execution mode does not support the CUDA Execution Provider. So making the execution mode sequential for this session since it uses the CUDA Execution Provider.TensorrtExecutionProviderAddCustomOpDomainsReceived nullptr for custom registry4SaveToOrtFormatLoadThis session already contains a loaded model.Unknown exception in Load()Encountered unknown exception in Load()ORTModelProto corresponding to the model to be loaded has already been parsed. Invoke Load().ModelProto corresponding to the model to be loaded has not been parsed yet. This API should be called in conjunction with a ctor that takes a model abstraction.TransformGraphCould not find an implementation for the node ( (Node placementsAll nodes have been placed on []., Provider: [: [PartitionOrtFormatModelLoadOrtModelThis session has already been initialized.ORT model verification failed.InferenceSession is null. Invalid ORT format model.Serialized version info is null. Invalid ORT format model.The ORT format model version [] is not supported this build Missing Model. Invalid ORT format model.SessionState is null. Invalid ORT format model.The provided PrePackedWeightsContainer instance to be added to the session is nullInitializeInitializing session.Model was not loadedModel was not loaded.Session has already been initialized.Adding default CPU execution provider.This session will use the allocator registered with the environment.Unable to serialize model as it contains compiled nodes. Please disable any execution providers which generate compiled nodes.Serializing optimized model with Graph Optimization level greater than ORT_ENABLE_EXTENDED and the NchwcTransformer enabled. The generated model may contain hardware specific optimizations, and should only be used in the same environment the model was optimized in.Session successfully initialized.Invalid rank for input: Got: Expected: Please fix either the inputs or the model.Got invalid dimensions for input: for the following indices index: Size mismatch: feed_names has elements, but feeds has elements.Invalid Feed Input Name:Input with name: is not expected to be of type tensor.tensorValidateInputs is not expected to be of type sparse tensor.sparse_tensor is not expected to be of type tensor sequence.seqOutput vector pointer is NULLAt least one output should be requested.Output vector incorrectly sized: output_names.size(): p_fetches->size(): Invalid Output Name:RunSession was not initializedSession not initialized.Running with tag: Encountered unknown exception in Run()GetModelMetadataGetModelInputsGetOverridableInitializersGetModelOutputsNewIOBindingEndProfilingProfiler is disabled.Could not write a profile because no model was loaded.cpugpuUnsupported device specified in the memory arena shrink list: Unsupported device id in the memory arena shrink list: Did not find an arena based allocator registered for device-id combination in the memory arena shrink list: The registered allocator for device-id combination is not an arena based allocator: ShrinkMemoryArenasUnable to shrink arena: error message: const logging::Logger &onnxruntime::InferenceSession::CreateLoggerForRun(const onnxruntime::RunOptions &, std::unique_ptr &)run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: void onnxruntime::InferenceSession::InitLogger(logging::LoggingManager *)session_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: The only supported values for the environment variable are '0' and '1'. The environment variable contained the value: FinalizeSessionOptionsReading the provided model for the ORT configonnxruntime::common::Status onnxruntime::FinalizeSessionOptions(const onnxruntime::SessionOptions &, const onnx::ModelProto &, bool, onnxruntime::SessionOptions &)is_model_proto_parsedError during EndProfiling(): Provider has already been registered./Users/runner/work/1/s/onnxruntime/core/framework/execution_providers.hAddORTMException during loading: Exception during initialization: Unexpected input data type. Actual: ()) , expected: ())/Users/runner/work/1/s/onnxruntime/core/framework/feeds_fetches_manager.hFeedsFetchesInfoonnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(const std::vector &, const std::vector &, const onnxruntime::OrtValueNameIdxMap &)Flush-to-zero and denormal-as-zero are onoffwbrbLoad model from failed:Failed to load model because protobuf parsing failed.LoadOrtModelBytes failed. Only / bytes were able to be read._.json%Y-%m-%d_%H-%M-%SThe Model Proto has already been checked for the ORT config json.ort_config/Users/runner/work/1/s/onnxruntime/core/session/inference_session_utils.ccParseOrtConfigJsonInModelProtoFound session/run/environment configuration in the model file to be used while running the modelORT config json from the model: ParseSessionOptionsFromModelProtoDid not find session options in the model file to be used while running the modelsession_optionsintra_op_num_threadsinter_op_num_threadsexecution_modegraph_optimization_levelgraph_optimization_level option in the model file must be an integerenable_profilingIgnoring unsupported session option in ORT config: Json stored in the `ort_config` key cannot be parsed. Error message: SetIntraOpNumThreadsUnsupported value for intra_op_num_threads: Setting intra_op_num_threads to SetInterOpNumThreadsUnsupported value for inter_op_num_threads: Setting inter_op_num_threads to SetExecutionModeUnsupported execution_mode value in ORT config: Setting execution_mode to Sequential modeParallel modeSetGraphOptimizationLevelSetting graph_optimization_level to ORT_DISABLE_ALLSetting graph_optimization_level to ORT_ENABLE_BASICSetting graph_optimization_level to ORT_ENABLE_EXTENDEDSetting graph_optimization_level to ORT_ENABLE_ALLUnsupported graph_optimization_level value in ORT config: SetEnableProfilingUnsupported value for enable_profiling option: Setting enable_profiling to true[json.exception.invalid BOM; must be 0xEF 0xBB 0xBF if givennullinvalid literalinvalid string: missing closing quoteinvalid string: '\u' must be followed by 4 hex digitsinvalid string: surrogate U+DC00..U+DFFF must be followed by U+DC00..U+DFFFinvalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFFinvalid string: forbidden character after backslashinvalid string: control character U+0000 (NUL) must be escaped to \u0000invalid string: control character U+0001 (SOH) must be escaped to \u0001invalid string: control character U+0002 (STX) must be escaped to \u0002invalid string: control character U+0003 (ETX) must be escaped to \u0003invalid string: control character U+0004 (EOT) must be escaped to \u0004invalid string: control character U+0005 (ENQ) must be escaped to \u0005invalid string: control character U+0006 (ACK) must be escaped to \u0006invalid string: control character U+0007 (BEL) must be escaped to \u0007invalid string: control character U+0008 (BS) must be escaped to \u0008 or \binvalid string: control character U+0009 (HT) must be escaped to \u0009 or \tinvalid string: control character U+000A (LF) must be escaped to \u000A or \ninvalid string: control character U+000B (VT) must be escaped to \u000Binvalid string: control character U+000C (FF) must be escaped to \u000C or \finvalid string: control character U+000D (CR) must be escaped to \u000D or \rinvalid string: control character U+000E (SO) must be escaped to \u000Einvalid string: control character U+000F (SI) must be escaped to \u000Finvalid string: control character U+0010 (DLE) must be escaped to \u0010invalid string: control character U+0011 (DC1) must be escaped to \u0011invalid string: control character U+0012 (DC2) must be escaped to \u0012invalid string: control character U+0013 (DC3) must be escaped to \u0013invalid string: control character U+0014 (DC4) must be escaped to \u0014invalid string: control character U+0015 (NAK) must be escaped to \u0015invalid string: control character U+0016 (SYN) must be escaped to \u0016invalid string: control character U+0017 (ETB) must be escaped to \u0017invalid string: control character U+0018 (CAN) must be escaped to \u0018invalid string: control character U+0019 (EM) must be escaped to \u0019invalid string: control character U+001A (SUB) must be escaped to \u001Ainvalid string: control character U+001B (ESC) must be escaped to \u001Binvalid string: control character U+001C (FS) must be escaped to \u001Cinvalid string: control character U+001D (GS) must be escaped to \u001Dinvalid string: control character U+001E (RS) must be escaped to \u001Einvalid string: control character U+001F (US) must be escaped to \u001Finvalid string: ill-formed UTF-8 byteinvalid number; expected digit after '-'invalid number; expected digit after '.'invalid number; expected '+', '-', or digit after exponentinvalid number; expected digit after exponent signnumber overflow parsing 'arrayobjectexcessive object size: cannot use erase() with booleandiscardednumberexcessive array size: parse error at line , column while parsing - ; last read: 'unexpected ; expected true literalfalse literalnull literalstring literalnumber literal'[''{'']''}'':'','end of input'[', '{', or a literalunknown tokenkey '' not foundcannot use at() with map::at: key not foundtype must be number, but is tried creating tensor with negative value in shapesize overflownot enough space: expected , got type is not supported in this functionRegisterCustomOpsLibrary: Failed to load libraryRegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in libraryinput name cannot be emptyCPUExecutionProvideroutput name cannot be emptylengths allocation failedstring buffer allocation failedOutput buffer allocation failedinput array doesn't equal tensor sizeelement index is out of boundsshape is invalidoffsets buffer is not equal to tensor sizeoutput buffer is too smallbuffer size is too small for stringinput array is too shortopaque(,)/Users/runner/work/1/s/onnxruntime/core/session/onnxruntime_c_api.ccOrtStatusPtr OrtApis::CreateOpaqueValue(const char *, const char *, const void *, size_t, OrtValue **)ml_type != nullptrSpecified domain and type names combination does not refer to a registered opaque typenon_tensor_base != nullptrOrtStatusPtr OrtApis::GetOpaqueValue(const char *, const char *, const OrtValue *, void *, size_t)this API does not support stringslocation dimensions do not match shape sizeinvalid location rangeROCM execution provider is not enabled.max_memarena_extend_strategyinitial_chunk_size_bytesmax_dead_bytes_per_chunkinitial_growth_chunk_size_bytesInvalid key found: The given version [%u] is not supported, only version 1 to %u is supported in this build. 1.8.0allocator::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_dropout_fusion.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/bias_gelu_fusion.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/bias_softmax_fusion.ccfused into softmax(input + bias)/Users/runner/work/1/s/onnxruntime/core/optimizer/common_subexpression_elimination.ccvirtual onnxruntime::common::Status onnxruntime::CommonSubexpressionElimination::ApplyImpl(onnxruntime::Graph &, bool &, int, const logging::Logger &) constrepresentative.output_index != kInvalidOutputIndexNot eliminating output of node ] because it's the graph's output./Users/runner/work/1/s/onnxruntime/core/optimizer/constant_folding.ccCould not find a CPU kernel and hence can't constant fold node 'virtual onnxruntime::common::Status onnxruntime::ConstantFolding::ApplyImpl(onnxruntime::Graph &, bool &, int, const logging::Logger &) constfetches.size() == node->OutputDefs().size()Unsupported output type of . Can't constant fold /Users/runner/work/1/s/onnxruntime/core/optimizer/conv_activation_fusion.ccfused Conv with activation auto onnxruntime::(anonymous namespace)::GetClipConstantMinMax(const onnxruntime::Graph &, const onnxruntime::Node &, float &, float &)::(anonymous class)::operator()(const onnxruntime::Node &, size_t, float &) constUnexpected data type for Clip input of /Users/runner/work/1/s/onnxruntime/core/optimizer/conv_add_fusion.ccvirtual onnxruntime::common::Status onnxruntime::ConvAddFusion::Apply(onnxruntime::Graph &, onnxruntime::Node &, onnxruntime::RewriteRule::RewriteRuleEffect &, const logging::Logger &) constadd_B_tensor_protoConvAddFusion_B_ConvAddFusion_Add_B_/Users/runner/work/1/s/onnxruntime/core/optimizer/conv_bn_fusion.ccvirtual onnxruntime::common::Status onnxruntime::ConvBNFusion::Apply(onnxruntime::Graph &, onnxruntime::Node &, onnxruntime::RewriteRule::RewriteRuleEffect &, const logging::Logger &) constbn_scale_tensor_protobn_B_tensor_protobn_mean_tensor_protobn_var_tensor_protoconv_W_tensor_protoconv_B_tensor_protoConvBnFusion_W_ConvBnFusion_BN_B_/Users/runner/work/1/s/onnxruntime/core/optimizer/initializer.honnxruntime::Initializer::Initializer(const onnx::TensorProto &, const onnxruntime::Path &)size_ == sizeInitializerunsupported data type: ReadExternalRawData() failed: ToProtodata type is not supported/Users/runner/work/1/s/onnxruntime/core/optimizer/conv_mul_fusion.ccvirtual onnxruntime::common::Status onnxruntime::ConvMulFusion::Apply(onnxruntime::Graph &, onnxruntime::Node &, onnxruntime::RewriteRule::RewriteRuleEffect &, const logging::Logger &) constmul_B_tensor_protoConvMulFusion_W_ConvMulFusion_Mul_B_/Users/runner/work/1/s/onnxruntime/core/optimizer/dynamic_quantize_matmul_fusion.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/embed_layer_norm_fusion.ccFuseSubGraphDistilBertWord embedding shape not expected.Failed to match position embedding subgraph.Failed to get position embedding weights.Position embedding shape is not expected.Input id is not valid. Gamma should be of shape (hidden_size). Beta should be of shape (hidden_size). MatchPositionEmbeddingSubgraphsFromGatherOutput edge count not expected for nodes in path1.The first input of Range should be a constant with value 0.The third input of Range should be a constant with value 1.Second input of Gather should be a constant with value 1. Failed to match Shape node. The parent of shape nodes are expected to be input_ids.Optional position subgraph nodes number of outputs unexpected.Optional position subgraph nodes Where node is expected to be the parent of Reshape.Failed to match position subgraph.MatchInputToConcatSubgraphFailed to find path 1 of position shape.Output edge count not expected for nodes in path 1 of position shape.Second input of Gather in path 1 of position shape should be a constant with value 0.Failed to find path 2 of position shape.Output edge count not expected for nodes in path 2 of position shape.Gather node in path 2 is not linked to another subgraph.Second input of Gather in path 2 of position shape should be a constant with value 1.The parent of two shape nodes are expected to be input_ids.two paths share the same shapeCheckInputInput shape is unknown or not 2D, or data type unknownInput data type is not int32 or int64_Int32_CastInput is expected to have dim value in all dimensions.Failed to get initializer tensor.Position embedding shape not matched.Position embedding data type shall be float or float16.Segment id is not valid. Input_ids and segment id should have the same shape. /Users/runner/work/1/s/onnxruntime/core/optimizer/fast_gelu_fusion.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/free_dim_override_transformer.cconnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformer(gsl::span)Invalid free dimension override.Conflicting free dimension overrides.The model has input '' with a fixed dimension size which does not equal the specified override of /Users/runner/work/1/s/onnxruntime/core/optimizer/gelu_approximation.ccTotal Gelu Approximation (FastGelu) node count: /Users/runner/work/1/s/onnxruntime/core/optimizer/gelu_fusion.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/gemm_activation_fusion.ccfused Gemm _transformed/Users/runner/work/1/s/onnxruntime/core/optimizer/graph_transformer.ccApply/Users/runner/work/1/s/onnxruntime/core/optimizer/graph_transformer_mgr.ccApplyTransformersThis transformer is already registered Level_RuleBasedTransformer/Users/runner/work/1/s/onnxruntime/core/optimizer/graph_transformer_utils.ccstd::vector > onnxruntime::optimizer_utils::GenerateRewriteRules(onnxruntime::TransformerLevel, const std::unordered_set &)Unsupported levelstd::vector > onnxruntime::optimizer_utils::GenerateTransformers(onnxruntime::TransformerLevel, const onnxruntime::SessionOptions &, const onnxruntime::IExecutionProvider &, const std::unordered_set &)Unsupported level YieldOp/Users/runner/work/1/s/onnxruntime/core/optimizer/initializer.ccReadExternalRawDataExternal data type must not be UNDEFINED or STRING.model_path must not be empty. Ensure that a path is provided when the model is created or loaded.TensorProto external data size mismatch. Computed size: /Users/runner/work/1/s/onnxruntime/core/optimizer/insert_cast_transformer.ccvirtual onnxruntime::common::Status onnxruntime::InsertCastTransformer::ApplyImpl(onnxruntime::Graph &, bool &, int, const logging::Logger &) constdtype_attribute->second.has_i()InsertCastTransformer works on the assumption that `dtype` attribute holds an integer./Users/runner/work/1/s/onnxruntime/core/optimizer/layer_norm_fusion.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/matmul_add_fusion.ccfused Matmul and Add /Users/runner/work/1/s/onnxruntime/core/optimizer/matmul_integer_to_float.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/matmul_scale_fusion.cc_FusedMatMulAndScalestd::vector onnxruntime::(anonymous namespace)::GetInputNodeMerges(onnxruntime::Graph &, onnxruntime::Node &, const std::unordered_set &)input_node.InputDefs().size() == 2 && scale_and_index.value().second < 2optional > onnxruntime::(anonymous namespace)::GetScaleFromNode(const onnxruntime::Graph &, const onnxruntime::Node &, const std::unordered_set &)div_inputs.size() == 2mul_inputs.size() == 2optional onnxruntime::(anonymous namespace)::GetScalarConstantInitializer(const onnxruntime::Graph &, const onnxruntime::NodeArg &)Constant initializer NodeArg shape should not be null. NodeArg: GetScalarConstantInitializerstd::vector onnxruntime::(anonymous namespace)::GetOutputNodeMerges(onnxruntime::Graph &, onnxruntime::Node &, const std::unordered_set &)output_node.OutputDefs().size() == 1/Users/runner/work/1/s/onnxruntime/core/optimizer/matmul_transpose_fusion.ccbool onnxruntime::GetTransposePerms(const onnxruntime::Node &, std::vector &)transpose_node.InputDefs().size() == 1onnxruntime::Node *onnxruntime::ReorderCastAndTranspose(onnxruntime::Graph &, onnxruntime::Node *, std::unordered_map &, std::deque &)cast != nullptrCreated a new Transpose node to interchange Cast and Transpose nodessize_t onnxruntime::UpdateConsumerCount(onnxruntime::Graph &, onnxruntime::NodeArg *, std::unordered_map &)!node_consumers.empty()_nchwc_bn_nchwc/Users/runner/work/1/s/onnxruntime/core/optimizer/nchwc_transformer.cc_nhwc/Users/runner/work/1/s/onnxruntime/core/optimizer/nhwc_transformer.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/optimizer_execution_frame.cconnxruntime::OptimizerExecutionFrame::Info::Info(const std::vector &, const onnxruntime::InitializedTensorSet &, const onnxruntime::Path &, const onnxruntime::IExecutionProvider &)allocator_ptr_InfoQLinear855dd0fa-cd7b-4b10-ae5a-df64cabfe1f835b188f7-c464-43e3-8692-97ac832bb14a/Users/runner/work/1/s/onnxruntime/core/optimizer/qdq_transformer/qdq_propagation.ccvoid onnxruntime::SwapAdjacentNodes(onnxruntime::Graph &, onnxruntime::Node &, onnxruntime::Node &)optimizer_utils::CheckOutputEdges(graph, up_node, 1)up_node should have only one Edge that points to down_node and its output is not graph outputedge_it->GetDstArgIndex() == 0 && edge_it->GetSrcArgIndex() == 0 && edge_it->GetNode().Index() == down_node.Index()up_node should be parent of down_node and NodeArg slots of the edge between up_node and down_node should be (0, 0)./Users/runner/work/1/s/onnxruntime/core/optimizer/qdq_transformer/qdq_s8_to_u8.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/qdq_transformer/qdq_transformer.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/relu_clip_fusion.ccvirtual onnxruntime::common::Status onnxruntime::FuseReluClip::Apply(onnxruntime::Graph &, onnxruntime::Node &, onnxruntime::RewriteRule::RewriteRuleEffect &, const logging::Logger &) constUnexpected data type for Clip 'min' input of FuseReluClip__min_zero_constantonnxruntime::Initializer::Initializer(onnx::TensorProto_DataType, const std::string &, const std::vector &)data type is not supported./Users/runner/work/1/s/onnxruntime/core/optimizer/reshape_fusion.ccFused reshape node: Total fused reshape node count: Fuse_SubgraphCannot replace concat node with initializer:/Users/runner/work/1/s/onnxruntime/core/optimizer/rule_based_graph_transformer.ccApplyRulesOnNode/Users/runner/work/1/s/onnxruntime/core/optimizer/skip_layer_norm_fusion.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/transformer_memcpy.ccApplyImplvoid onnxruntime::TransformerMemcpyImpl::ProcessDefs(onnxruntime::Node &, const onnxruntime::KernelRegistryManager &, onnxruntime::InitializedTensorSet &)Execution type '' doesn't support memcpy bool onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager &, const onnxruntime::InitializedTensorSet &)/Users/runner/work/1/s/include/onnxruntime/core/optimizer/graph_transformer.hRecurseauto onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager &, const onnxruntime::InitializedTensorSet &)::(anonymous class)::operator()(const onnxruntime::NodeArg &, size_t) constdup_replacements.find(&arg) == dup_replacements.end()UnsqueezeElimination_/Users/runner/work/1/s/onnxruntime/core/optimizer/unsqueeze_elimination.ccUnsqueezeElimination cannot remove node MIGraphXExecutionProviderROCMExecutionProviderOpenVINOExecutionProviderDnnlExecutionProviderNupharExecutionProviderVitisAIExecutionProviderNnapiExecutionProviderArmNNExecutionProviderACLExecutionProviderRknpuExecutionProviderCeluEluHardSigmoidLeakyReluSoftplusReluSigmoidSoftsignTanhThresholdedReluSeluParametricSoftplusScaledTanhunknown kernel typeonnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Elu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Elu]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::HardSigmoid]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::HardSigmoid]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::LeakyRelu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::LeakyRelu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Relu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Relu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Relu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Relu]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Selu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Selu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Sigmoid]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Sigmoid]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Softplus]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Softsign]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Tanh]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Tanh]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Celu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Celu]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::ThresholdedRelu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::ThresholdedRelu]alphaNo attribute with name:''is defined.Attribute name and type don't match for '/Users/runner/work/1/s/onnxruntime/core/providers/cpu/activation/activations.hInitgamma/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/activations.hBVIf/Users/runner/work/1/s/onnxruntime/core/providers/cpu/controlflow/if.cconnxruntime::If::Info::Info(const onnxruntime::Node &, const onnxruntime::GraphViewer &)num_subgraph_outputs == static_cast(num_outputs)'If' node has outputs which doesn't match the subgraph's outputs.then_branchvoid onnxruntime::If::Init(const onnxruntime::OpKernelInfo &)info.GetAttr("then_branch", &proto).IsOK()else_branchinfo.GetAttr("else_branch", &proto).IsOK()virtual common::Status onnxruntime::If::SetupSubgraphExecutionInfo(const onnxruntime::SessionState &, const std::string &, const onnxruntime::SessionState &)info == nullptrSetupSubgraphExecutionInfo should only be called once for each subgraph.SetupSubgraphExecutionInfovirtual onnxruntime::common::Status onnxruntime::If::Compute(onnxruntime::OpKernelContext *) constthen_feeds_fetches_manager_ && else_feeds_fetches_manager_CreateFeedsFetchesManager must be called prior to execution of graph.session_stateSubgraph SessionState was not found for '' attribute.ComputeFailed to create output tensor for Executeonnxruntime::TensorSeq *OrtValue::GetMutable()Failed to create output tensor for If output ILoop/Users/runner/work/1/s/onnxruntime/core/providers/cpu/controlflow/loop.cconnxruntime::Loop::Info::Info(const onnxruntime::Node &, const onnxruntime::GraphViewer &)static_cast(num_subgraph_inputs) == subgraph_inputs.size()Graph in 'body' attribute of Loop should have inputs. Found:num_subgraph_outputs - 1 == num_outputs'Loop' node has outputs so the subgraph requires but has void onnxruntime::Loop::Init(const onnxruntime::OpKernelInfo &)info.GetAttr("body", &proto).IsOK()virtual common::Status onnxruntime::Loop::SetupSubgraphExecutionInfo(const onnxruntime::SessionState &, const std::string &, const onnxruntime::SessionState &)info_ == nullptrvirtual onnxruntime::common::Status onnxruntime::Loop::Compute(onnxruntime::OpKernelContext *) constfeeds_fetches_manager_'Loop' input 'M' should be a scalar tensor. Got shape of 'Loop' input 'cond' should be a scalar tensor. Got shape of Loop subgraph input 0 has unknown shape: Loop subgraph input 1 has unknown shape: void onnxruntime::LoopImpl::SaveOutputsAndUpdateFeeds(const std::vector &, std::vector &)last_outputs[j + 1].IsTensor()ConcatenateLoopOutputLoop had zero iterations and the shape of subgraph output was not found. Defaulting to a rank 1 shape of {0}.Inconsistent shape in loop output for output. Expected: Got:auto onnxruntime::LoopImpl::Execute(const onnxruntime::FeedsFetchesManager &)::(anonymous class)::operator()(const OrtValue &, int) const/Users/runner/work/1/s/onnxruntime/core/providers/cpu/controlflow/scan_8.ccvoid onnxruntime::Scan<8>::Init(const onnxruntime::OpKernelInfo &) [OpSet = 8]directionsvirtual onnxruntime::common::Status onnxruntime::Scan<8>::SetupSubgraphExecutionInfo(const onnxruntime::SessionState &, const std::string &, const onnxruntime::SessionState &) [OpSet = 8]virtual onnxruntime::common::Status onnxruntime::Scan<8>::Compute(onnxruntime::OpKernelContext *) const [OpSet = 8]Scan inputs have inconsistent batch size. Previous value was but has batch size of sequence_lens length of did not match batch size of Invalid entries in sequence_lens. Max sequence length was onnxruntime::common::Status onnxruntime::Scan8Impl::CreateLoopStateVariables(std::vector > &)p_mlvalueScan<8> spec does not support transpose of output. This should never be called./Users/runner/work/1/s/onnxruntime/core/framework/ort_value_tensor_slicer.htypename std::enable_if::value, reference>::type onnxruntime::OrtValueTensorSlicer::Iterator::operator*() [T = const OrtValue]position_ >= 0 && position_ < sequence_length_/Users/runner/work/1/s/onnxruntime/core/providers/cpu/controlflow/scan_9.ccvoid onnxruntime::Scan<9>::Init(const onnxruntime::OpKernelInfo &) [OpSet = 9]num_scan_inputsinfo.GetAttr("num_scan_inputs", &num_scan_inputs_).IsOK()scan_input_directionsscan_output_directionsscan_input_axesgsl::narrow_cast(input_axes_.size()) == num_scan_inputs_Number of entries in 'scan_input_axes' was but expected scan_output_axesgsl::narrow_cast(output_axes_.size()) == num_scan_outputsNumber of entries in 'scan_output_axes' was virtual onnxruntime::common::Status onnxruntime::Scan<9>::SetupSubgraphExecutionInfo(const onnxruntime::SessionState &, const std::string &, const onnxruntime::SessionState &) [OpSet = 9]virtual onnxruntime::common::Status onnxruntime::Scan<9>::Compute(onnxruntime::OpKernelContext *) const [OpSet = 9]feeds_fetches_manager_ && info_Invalid scan input: Expected dimensions or more but input had shape of Scan inputs have inconsistent sequence lengths. Previous value was but input '' dimension has length of Invalid value in scan_input_axes for input of . Input tensor rank was ValidateInputSetupInputsSubgraph in 'body' produces outputs but Scan expects AllocateOutputTensorsCreateLoopStateVariablesonnxruntime::common::Status onnxruntime::ScanImpl::CreateLoopStateVariables(std::vector &)output_mlvalueOutput OrtValue has not been created for loop state variable output Invalid value in scan_output_axes for output . Output tensor rank was onnxruntime::common::Status onnxruntime::ScanImpl::TransposeOutput()TransposeOutputScanint64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)axis >= -tensor_rank && axis <= tensor_rank - 1axis is not in valid range [-/Users/runner/work/1/s/onnxruntime/core/providers/cpu/controlflow/scan_utils.hconst OrtValue &onnxruntime::scan::detail::OutputIterator::GetOutput() constfinal_output_mlvalue_/Users/runner/work/1/s/onnxruntime/core/providers/cpu/controlflow/scan_utils.cconnxruntime::scan::detail::Info::Info(const onnxruntime::Node &, const onnxruntime::GraphViewer &, int, bool)num_variadic_inputs == num_subgraph_inputsThe subgraph in 'body' requires inputs but Scan was only given void onnxruntime::scan::detail::ReadDirections(const onnxruntime::OpKernelInfo &, const std::string &, std::vector &, size_t)directions.size() == num_entriesNumber of entries in '' was validInvalid values in ''. 0 == forward. 1 == reverse.Subgraph must have the shape set for all outputs but did not.CreateFeedsFetchesManagerIterateSequencevoid onnxruntime::scan::detail::LoopStateVariable::Next()iteration_num_ < sequence_len_Misuse of LoopStateVariable. Attempt to move beyond end of sequenceFailed to create output tensor for output #AllocateFinalBufferonnxruntime::common::Status onnxruntime::scan::detail::OutputIterator::AllocateFinalOutput(const onnxruntime::TensorShape &)!is_concrete_shape_AllocateFinalOutputOrtValue &onnxruntime::scan::detail::OutputIterator::operator*()cur_iteration_ < num_iterations_is_concrete_shape_Expected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.onnxruntime::scan::detail::OutputIterator &onnxruntime::scan::detail::OutputIterator::operator++()Expected AllocateFinalOutput to have been called to before we increment the iteratorMismatch between expected shape and shape from first output is not compatible with /Users/runner/work/1/s/onnxruntime/core/providers/cpu/cpu_execution_provider.ccRegisterOnnxOperatorKernelsRegisterOnnxMLOperatorKernelsRegisterCPUKernelsGetKernelRegistryvirtual std::shared_ptr onnxruntime::CPUExecutionProvider::GetKernelRegistry() constCpuConstantOfShape/Users/runner/work/1/s/onnxruntime/core/providers/cpu/generator/constant_of_shape_base.honnxruntime::ConstantOfShapeBase >::ConstantOfShapeBase(const onnxruntime::OpKernelInfo &) [EnabledOutputTypeList = onnxruntime::TypeList]t_proto_p->dims_size() == 1t_proto_p->dims()[0] == 1void onnxruntime::ConstantOfShapeBase >::SetValueFromTensorProto(const onnx::TensorProto &) [EnabledOutputTypeList = onnxruntime::TypeList]utils::HasDataType(t_proto)ONNX_NAMESPACE::TensorProto::DataType_IsValid(t_proto.data_type())!utils::HasExternalData(t_proto)Tensor proto with external data for value attribute is not supported.SetValueFromTensorProtoUnsupported value attribute datatype: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/generator/constant_of_shape.ccUnsupported output datatype with size: PrepareComputeMust have a valid input shape.RandomNormalRandomUniformRandomNormalLikeRandomUniformLikeMultinomial/Users/runner/work/1/s/onnxruntime/core/providers/cpu/generator/random.ccCould not infer data type from input tensor with data type Empty dimensions for input tensorbatch_size is < 1num_classes is < 1num_samples is < 1Invalid data type of /Users/runner/work/1/s/onnxruntime/core/providers/cpu/generator/random.honnxruntime::RandomNormal::RandomNormal(const onnxruntime::OpKernelInfo &)info.GetAttr("mean", &mean_).IsOK()scaleinfo.GetAttr("scale", &scale_).IsOK()info.GetAttr("dtype", &dtype).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDInvalid dtype of info.GetAttrs("shape", shape).IsOK()onnxruntime::RandomUniform::RandomUniform(const onnxruntime::OpKernelInfo &)info.GetAttr("high", &high_).IsOK()lowinfo.GetAttr("low", &low_).IsOK()onnxruntime::RandomNormalLike::RandomNormalLike(const onnxruntime::OpKernelInfo &)onnxruntime::RandomUniformLike::RandomUniformLike(const onnxruntime::OpKernelInfo &)sample_sizeonnxruntime::Multinomial::Multinomial(const onnxruntime::OpKernelInfo &)info.GetAttr("sample_size", &num_samples_).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDOutput type not supported in this build: MultinomialComputeRangestart in Range operator should be scalar like tensor, yet got shape:limit in Range operator should be scalar like tensor, yet got shape:delta in Range operator should be scalar like tensor, yet got shape:delta in Range operator can not be zero!Clipminmax/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/clip.honnxruntime::clip_internal::Clip_6Base::Clip_6Base(const onnxruntime::OpKernelInfo &) [T = float]min_ <= max_/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/clip.ccvoid onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, onnxruntime::Tensor *) const [T = float]min->Shape().IsScalar()max->Shape().IsScalar()void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, onnxruntime::Tensor *) const [T = double]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, onnxruntime::Tensor *) const [T = signed char]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, onnxruntime::Tensor *) const [T = unsigned char]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, onnxruntime::Tensor *) const [T = long long]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, onnxruntime::Tensor *) const [T = unsigned long long]CumSumexclusive/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/cumsum.ccvirtual onnxruntime::common::Status onnxruntime::CumSum::Compute(onnxruntime::OpKernelContext *) const [T = float]void onnxruntime::WritableSliceIterator::Init(const std::vector &, gsl::span, gsl::span) [T = float]dims.size() == starts.size()dims.size()=starts.size()=dims.size() == extents_.size()extents.size()=dims.size() == steps.size()steps.size()=virtual onnxruntime::common::Status onnxruntime::CumSum::Compute(onnxruntime::OpKernelContext *) const [T = double]void onnxruntime::WritableSliceIterator::Init(const std::vector &, gsl::span, gsl::span) [T = double]virtual onnxruntime::common::Status onnxruntime::CumSum::Compute(onnxruntime::OpKernelContext *) const [T = int]void onnxruntime::WritableSliceIterator::Init(const std::vector &, gsl::span, gsl::span) [T = int]virtual onnxruntime::common::Status onnxruntime::CumSum::Compute(onnxruntime::OpKernelContext *) const [T = long long]void onnxruntime::WritableSliceIterator::Init(const std::vector &, gsl::span, gsl::span) [T = long long]Det/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/det.ccvirtual onnxruntime::common::Status onnxruntime::Det::Compute(onnxruntime::OpKernelContext *) const [T = float]X != nullptrInput tensor should have a rank of at least 2Matrix dimensions are not equal. Square matrix is expectedEinsumThere was a problem acquiring temporary memory allocator in Einsum op/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/einsum.ccDeviceComputeEinsum op: An implementation for the input type is not supported yet/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/einsum.honnxruntime::Einsum::Einsum(const onnxruntime::OpKernelInfo &)info.GetAttr("equation", &equation_).IsOK()->/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_auxiliary_ops.ccstd::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor &, const std::vector &, const onnxruntime::Tensor &, const std::vector &, onnxruntime::AllocatorPtr, concurrency::ThreadPool *, void *, const DeviceHelpers::MatMul &) [T = float]input_1.DataType() == input_2.DataType()input_shape_1_override.size() == 3 && input_shape_2_override.size() == 3input_shape_1_override[0] == input_shape_2_override[0]input_shape_1_override[2] == input_shape_2_override[1]Einsum op: Exception during MatMul operation: std::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor &, const std::vector &, const onnxruntime::Tensor &, const std::vector &, onnxruntime::AllocatorPtr, concurrency::ThreadPool *, void *, const DeviceHelpers::MatMul &) [T = int]std::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor &, const std::vector &, const onnxruntime::Tensor &, const std::vector &, onnxruntime::AllocatorPtr, concurrency::ThreadPool *, void *, const DeviceHelpers::MatMul &) [T = double]std::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor &, const std::vector &, const onnxruntime::Tensor &, const std::vector &, onnxruntime::AllocatorPtr, concurrency::ThreadPool *, void *, const DeviceHelpers::MatMul &) [T = long long]onnxruntime::common::Status onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopy(const onnxruntime::Tensor &, onnxruntime::Tensor &, void *)output.SizeInBytes() == input.SizeInBytes()Einsum op: The candidate output does not match the actual output's shapestd::unique_ptr onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::Diagonal(const onnxruntime::Tensor &, int64_t, int64_t, onnxruntime::AllocatorPtr, void *)rank >= 2 && dim_1 != dim_2 && input_dims[dim_1] == input_dims[dim_2]Cannot parse the diagonal elements along dims for input shape bool onnxruntime::EinsumOp::IsTransposeRequired(size_t, const std::vector &)input_rank == permutation.size()std::unique_ptr onnxruntime::EinsumOp::Transpose(const onnxruntime::Tensor &, const std::vector &, const std::vector &, onnxruntime::AllocatorPtr, void *, const DeviceHelpers::Transpose &)Length of permutation must match the rank of the input to be permutatedEinsum op: Transpose failed: std::unique_ptr onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDims(const onnxruntime::Tensor &, bool, onnxruntime::AllocatorPtr)input_dims[rank - 2] == input_dims[rank - 1]The innermost dims should have the same dim value to parse the diagonal elementsEinsum op: Unsupported data type for Diagonal /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_compute_preprocessor.ccNumber of subscripts in the input equation does not match number of input tensorsFound a '.' not part of an ellipsis in input: Einsum subscripts string contains too many subscript labels when compared to the rank of the inputEllipsis must indicate a fixed number of dimensions across all inputsFound '.' not part of an ellipsis in input: The only subscript labels allowed are lower-cased letters (a-z) and upper-cased letters (A-Z)Einsum operands could not be broadcast together. Please check input shapes/equation provided.Input shape of operand is incompatible in the dimension . The shape is: Another operand has a dim value of in the same dimensionEinsum subscripts string contains too many subscript labels when compared to the rank of the input Einsum subscripts does not contain enough subscript labels and there is no ellipsis for input onnxruntime::common::Status onnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDims()num_broadcasted_indices < num_of_ellipsis_dims_dim_iter == rank...Inputs have ellipses in them but the provided output subscript does not contain an ellipsisFound a '.' not part of an ellipsis in the output subscript providedFound '.' not part of an ellipsis in the output subscript providedRank of the input must match number of subscript labels corresponding to the input/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_typed_compute_processor.ccstd::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const std::vector &, bool) [T = float]left.Shape().Size() == left_shape_override.Size()The override dims are not compatible with given tensor's shape. Left shape: Left shape override: right.Shape().Size() == right_shape_override.Size()Right shape: Right shape override: left_rank == right_rankRanks of pair-wise operands must be equal. Right shape: left_dim == right_dimEinsum op: Input dimensions must be equal along an axis to be reduced across all inputsvoid onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor &, const std::vector &) [T = float]candidate_output.Shape().Size() == output_shape.Size()Einsum op: The candidate output cannot be reshaped into the op's outputcandidate_output_dims[iter] == 1Not all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: Einsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const std::vector &, bool) [T = int]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor &, const std::vector &) [T = int]std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const std::vector &, bool) [T = double]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor &, const std::vector &) [T = double]std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const std::vector &, bool) [T = long long]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor &, const std::vector &) [T = long long]SubMulDivAbsNegFloorCeilReciprocalSqrtPowT1ExpLogSumMaxMinLessGreaterEqualLessOrEqualGreaterOrEqualMeanBitShiftErfNotAndOrXorUnsupported X type: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.ccvirtual onnxruntime::common::Status onnxruntime::Min_6::Compute(onnxruntime::OpKernelContext *) const [T = float]inputCount >= 1Must have 1 or more inputsdata_n.Shape() == shapevirtual onnxruntime::common::Status onnxruntime::Max_6::Compute(onnxruntime::OpKernelContext *) const [T = float]virtual onnxruntime::common::Status onnxruntime::Mean_6::Compute(onnxruntime::OpKernelContext *) const [T = float]data_1.Shape() == shapeSinCosTanAsinAcosAtanSinhCoshAsinhAcoshAtanhPReluExpandMod/Users/runner/work/1/s/onnxruntime/core/providers/cpu/element_wise_ranged_transform.hElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Abs]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Abs]input_size < std::numeric_limits::max()onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Abs]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Abs]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Abs]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Abs]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Abs]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Abs]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Abs]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Abs]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Abs]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Neg]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Neg]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Neg]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Neg]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Neg]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Floor]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Floor]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Ceil]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Ceil]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Reciprocal]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Reciprocal]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Reciprocal]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Reciprocal]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Sqrt]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Sqrt]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Sqrt]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Sqrt]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Exp]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Exp]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Exp]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Exp]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Log]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Log]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Log]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Log]virtual onnxruntime::common::Status onnxruntime::Sum_6::Compute(onnxruntime::OpKernelContext *) const [T = float]input_count >= 1virtual onnxruntime::common::Status onnxruntime::Sum_6::Compute(onnxruntime::OpKernelContext *) const [T = double]directiononnxruntime::BitShift::BitShift(const onnxruntime::OpKernelInfo &) [T = unsigned char]LEFTRIGHTInvalid direction value of ''. Valid values are 'LEFT' or 'RIGHT'.auto onnxruntime::BitShift::Compute(onnxruntime::OpKernelContext *)::(anonymous class)::operator()(onnxruntime::BroadcastHelper &) const [T = unsigned char]cur1 == end1cur_out == end_outonnxruntime::BitShift::BitShift(const onnxruntime::OpKernelInfo &) [T = unsigned int]auto onnxruntime::BitShift::Compute(onnxruntime::OpKernelContext *)::(anonymous class)::operator()(onnxruntime::BroadcastHelper &) const [T = unsigned int]onnxruntime::BitShift::BitShift(const onnxruntime::OpKernelInfo &) [T = unsigned long long]auto onnxruntime::BitShift::Compute(onnxruntime::OpKernelContext *)::(anonymous class)::operator()(onnxruntime::BroadcastHelper &) const [T = unsigned long long]void onnxruntime::UntypedExpand(onnxruntime::OpKernelContext &, const onnxruntime::ProcessBroadcastSpanFuncs &)shape_data_tensor.Shape().GetDims().size() == 1/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.honnxruntime::Broadcaster::Broadcaster(const std::vector &, const std::vector &)largest <= 1Can broadcast 0 by 0 or 1. is invalid.void onnxruntime::BroadcastIterator::Init(ptrdiff_t, ptrdiff_t)axis == 1 || axis == largestAttempting to broadcast an axis by a dimension other than 1. by void onnxruntime::BroadcastIterator::Append(ptrdiff_t, ptrdiff_t)void onnxruntime::ExpandBroadcastLooper(onnxruntime::BroadcastHelper &, const onnxruntime::ProcessBroadcastSpanFuncs &)!helper.HaveTwoTensorInputs()ExpandBroadcastLooper should only have a shape for the second input.void onnxruntime::InputBroadcaster::AdvanceBy(size_t)offset % span_size_ == 0fmodonnxruntime::Mod::Mod(const onnxruntime::OpKernelInfo &)(fmod == 0) || (fmod == 1)onnxruntime::OutputBroadcaster::OutputBroadcaster(size_t, onnxruntime::Tensor &, ptrdiff_t, ptrdiff_t)start_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= lenInvalid start/ending offset [) for tensor of length:start_offset % span_size == 0 && real_end % span_size == 0Broadcast Output range [) are not at boundary of span with size:onnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext &)Unsupported Y type: void onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy::operator()(int32_t, Ret &) const [Ret = onnxruntime::common::Status]Unsupported data type: void onnxruntime::mod_internal::CallModImpl::operator()(bool, onnxruntime::OpKernelContext *) const [T = float, Enable = void]void onnxruntime::mod_internal::CallModImpl::operator()(bool, onnxruntime::OpKernelContext *) const [T = double, Enable = void]void onnxruntime::mod_internal::CallModImpl::operator()(bool, onnxruntime::OpKernelContext *) constvoid onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::CheckCalledOnce()called_ == 1void onnxruntime::BroadcastLooper(TBroadcastHelper &, const onnxruntime::ProcessBroadcastSpanFuncs &) [TBroadcastHelper = onnxruntime::BroadcastHelper]helper.HaveTwoTensorInputs()GemmtransA/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/gemm.honnxruntime::Gemm::Gemm(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttr("transA", &temp).IsOK()transBinfo.GetAttr("transB", &temp).IsOK()onnxruntime::Gemm::Gemm(const onnxruntime::OpKernelInfo &) [T = double]/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/gemm.ccvoid onnxruntime::GemmBroadcastBias(int64_t, int64_t, float, const T *, const onnxruntime::TensorShape *, T *) [T = double]c_shape != nullptr/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/gemm_helper.honnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &)left.NumDimensions() == 2 || left.NumDimensions() == 1right.NumDimensions() == 2GEMM: Dimension mismatch, W: K: N:Gemm: Invalid bias shape for broadcastM_ >= 0 && K_ > 0 && N_ >= 0void onnxruntime::GemmBroadcastBias(int64_t, int64_t, float, const T *, const onnxruntime::TensorShape *, T *) [T = float]/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/hardmax.ccHardmax inputs N, D and N * D must be < . N=, D=HardmaxMatMul/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/matmul.cc/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/matmul_helper.hleft_num_dims and right_num_dims must be >= 1MatMul dimension mismatchleft operand cannot broadcast on dim right operand cannot broadcast on dim num_dims_with_pad != num_output_dimsM_ == 1 && N_ == 1 was falsenum_dims_with_pad - 1 != num_output_dimsnum_dims_with_pad - 2 != num_output_dimsMatMulInteger/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/matmul_integer.ccvirtual onnxruntime::common::Status onnxruntime::MatMulInteger::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(a_zero_point)MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1IsBQuantParamSupported(b_zero_point->Shape(), b ? b->Shape() : b_shape_)QLinearMatMul/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/quantize_linear_matmul.ccvirtual onnxruntime::common::Status onnxruntime::QLinearMatMul::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(a_offset)QLinearMatmul : input zero point must be a scalar or 1D tensor of size 1IsBQuantParamSupported(b_offset->Shape(), b ? b->Shape() : b_shape_)QLinearMatmul : weight zero point must be a scalar, 1D tensor of size 1, or last to second dimension is 1IsScalarOr1ElementVector(y_offset)QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_scale)QLinearMatmul : input scale must be a scalar or 1D tensor of size 1IsBQuantParamSupported(b_scale->Shape(), b ? b->Shape() : b_shape_)QLinearMatmul : weight scale must be a scalar, 1D tensor of size 1, or last to second dimension is 1IsScalarOr1ElementVector(y_scale)QLinearMatmul : result scale must be a scalar or 1D tensor of size 1Per-column quantization parameter of batched matrix should have same dimension as the matrix,and its size by K should be equal to the matrix's size.RoundSignSoftmaxLogSoftmax/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/softmax.ccComputeImplOpset13SoftmaxCPU inputs N, D and N * D must be < TopK/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/top_k.ccvoid onnxruntime::TopkOpset9ConstructorCommon(const onnxruntime::OpKernelInfo &, int &, unsigned int &)op_kernel_info.GetAttr("k", &k_temp).IsOK()k_temp > 0op_kernel_info.GetAttr("axis", &axis_temp).IsOK()void onnxruntime::TopkOpset10ConstructorCommon(const onnxruntime::OpKernelInfo &, int &)void onnxruntime::TopkOpset11ConstructorCommon(const onnxruntime::OpKernelInfo &, int &, bool &, bool &)largestop_kernel_info.GetAttr("largest", &largest_temp).IsOK()op_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()input count mismatch, expected 1 input - the tensor to be processedk argument [] should not be greater than specified axis dim value [output count mismatch, expected 2 outputs to be present for TopK operatorinput count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valueArrayFeatureExtractorInvalid argument: X input has empty dimensions.Invalid Y argument: num_indices = 0Invalid Y argument: index is out of range: Y[] () >=BinarizerthresholdInput data with index: is NaNCastMapInvalid input type of value: Expected std::map or std::mapUnexpected CAST_TO value of cast_to/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/cast_map.honnxruntime::ml::CastMap::CastMap(const onnxruntime::OpKernelInfo &)info.GetAttr("cast_to", &attr).IsOK()info.GetAttr("map_form", &attr).IsOK()max_mapinfo.GetAttr("max_map", &max_map_).IsOK()map_form_ != PACK_MAP::SPARSE || max_map_ > 0TO_FLOATTO_STRINGTO_INT64/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/ml_common.honnxruntime::ml::CAST_TO onnxruntime::ml::MakeCast(const std::string &)Invalid CAST_TO value of Expected TO_FLOAT, TO_STRING or TO_INT64DENSESPARSEonnxruntime::ml::PACK_MAP onnxruntime::ml::MakePack(const std::string &)Invalid PACK_MAP value of Expected DENSE or SPARSE/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/cast_map.cconnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext &, TTo) const [TFrom = float, TTo = float]cur_input == end_input || cur_input->first >= 0Negative index values are not permitted. First entry in map has index value of const T *onnxruntime::OpKernelContext::Input(int) const [T = std::__1::map, std::__1::allocator > >]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext &, TTo) const [TFrom = std::__1::basic_string, TTo = float]const T *onnxruntime::OpKernelContext::Input(int) const [T = std::__1::map, std::__1::less, std::__1::allocator > > >]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext &, TTo) const [TFrom = float, TTo = long long]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext &, TTo) const [TFrom = std::__1::basic_string, TTo = long long]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext &, TTo) const [TFrom = float, TTo = std::__1::basic_string]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext &, TTo) const [TFrom = std::__1::basic_string, TTo = std::__1::basic_string]CategoryMapperInput of string must have output of int64Input of int64 must have output of string cats_strings/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/category_mapper.honnxruntime::ml::CategoryMapper::CategoryMapper(const onnxruntime::OpKernelInfo &)info.GetAttrs("cats_strings", string_categories).IsOK()cats_int64sinfo.GetAttrs("cats_int64s", int_categories).IsOK()default_stringinfo.GetAttr("default_string", &default_string_).IsOK()default_int64info.GetAttr("default_int64", &default_int_).IsOK()num_entries == int_categories.size()DictVectorizerstring_vocabulary/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/dictvectorizer.honnxruntime::ml::DictVectorizerOp, long long>::DictVectorizerOp(const onnxruntime::OpKernelInfo &) [AttrType = std::__1::basic_string, TargetType = long long]info.GetAttrs(std::is_same::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()const T *onnxruntime::OpKernelContext::Input(int) const [T = std::__1::map, long long, std::__1::less >, std::__1::allocator, long long> > >]onnxruntime::ml::DictVectorizerOp, float>::DictVectorizerOp(const onnxruntime::OpKernelInfo &) [AttrType = std::__1::basic_string, TargetType = float]const T *onnxruntime::OpKernelContext::Input(int) const [T = std::__1::map, float, std::__1::less >, std::__1::allocator, float> > >]onnxruntime::ml::DictVectorizerOp, double>::DictVectorizerOp(const onnxruntime::OpKernelInfo &) [AttrType = std::__1::basic_string, TargetType = double]const T *onnxruntime::OpKernelContext::Input(int) const [T = std::__1::map, double, std::__1::less >, std::__1::allocator, double> > >]int64_vocabularyonnxruntime::ml::DictVectorizerOp >::DictVectorizerOp(const onnxruntime::OpKernelInfo &) [AttrType = long long, TargetType = std::__1::basic_string]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo &) [AttrType = long long, TargetType = float]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo &) [AttrType = long long, TargetType = double]const T *onnxruntime::OpKernelContext::Input(int) const [T = std::__1::map, std::__1::allocator > >]FeatureVectorizer/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/feature_vectorizer.ccvirtual onnxruntime::common::Status onnxruntime::ml::FeatureVectorizer::Compute(onnxruntime::OpKernelContext *) constinput_count >= 0 && static_cast(input_count) == input_dimensions_.size()Number of inputs () does not match number of inputdimensions values (Invalid input type:inputdimensions/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/feature_vectorizer.honnxruntime::ml::FeatureVectorizer::FeatureVectorizer(const onnxruntime::OpKernelInfo &)status.IsOK() && !input_dimensions_.empty()Imputerimputed_value_floatsimputed_value_int64sreplaced_value_float/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/imputer.cconnxruntime::ml::ImputerOp::ImputerOp(const onnxruntime::OpKernelInfo &)Expected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedreplaced_value_int64Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedimputed_values_float_.empty() ^ imputed_values_int64_.empty()Must provide imputed_values_float_ or imputed_values_int64_ but not both.virtual common::Status onnxruntime::ml::ImputerOp::Compute(onnxruntime::OpKernelContext *) constInvalid typeEmpty value of imputed values.Empty input dimensions.LabelEncoderInput of tensor(string) must have output of tensor(int64)Input of tensor(int64) must have output of tensor(string)keys_floatsvalues_stringskeys_stringsvalues_floatsdefault_floatkeys_int64svalues_int64sclasses_strings/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/label_encoder.honnxruntime::ml::LabelEncoder::LabelEncoder(const onnxruntime::OpKernelInfo &)info.GetAttrs("classes_strings", string_classes).IsOK()onnxruntime::ml::LabelEncoder_2 >::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = float, TValue = std::__1::basic_string]info.GetAttrs(_key_field_name, keys).IsOK()info.GetAttrs(_value_field_name, values).IsOK()num_keys == num_valuesThe attribtues in LabelEncoder (name: ) must have the same length. However, the number of key is and the number of values is onnxruntime::ml::LabelEncoder_2, float>::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = std::__1::basic_string, TValue = float]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = long long, TValue = float]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = float, TValue = long long]onnxruntime::ml::LabelEncoder_2 >::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = long long, TValue = std::__1::basic_string]onnxruntime::ml::LabelEncoder_2, long long>::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = std::__1::basic_string, TValue = long long]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = long long, TValue = long long]LinearClassifiermulti_class/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/linearclassifier.cconnxruntime::ml::LinearClassifier::LinearClassifier(const onnxruntime::OpKernelInfo &)void onnxruntime::ml::LinearClassifier::ComputeImpl(const gsl::span, int64_t, int64_t, int64_t, const std::vector &, const std::vector &, onnxruntime::Tensor &, onnxruntime::Tensor &, onnxruntime::ml::POST_EVAL_TRANSFORM, bool, concurrency::ThreadPool *) constscores_output_data.length() >= scores_output_sizeScores output is incorrect size. Expected: Found:Input shape needs to be at least a single dimension.Unsupported input element type of void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor &, gsl::span &) [SrcType = int]shape_size == out.length()void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor &, gsl::span &) [SrcType = long long]void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor &, gsl::span &) [SrcType = double]LinearRegressorinterceptstargets/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/linearregressor.cconnxruntime::ml::LinearRegressor::LinearRegressor(const onnxruntime::OpKernelInfo &)info.GetAttr("targets", &num_targets_).IsOK()Input shape had more than 2 dimension. Dims=Normalizer/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/normalizer.honnxruntime::ml::Normalizer::Normalizer(const onnxruntime::OpKernelInfo &)info.GetAttr("norm", &norm).IsOK()MAXL1L2onnxruntime::ml::NORMALIZE onnxruntime::ml::MakeNormalize(const std::string &)Invalid normalize value of Rank of input to Normalized must be less than 2. Got Unexpected NORMALIZE value of OneHotEncoderUnknown Category and zeros = 0.zeros/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/onehotencoder.cconnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo &) [T = long long]tmp_cats_int64s.empty() || tmp_cats_strings.empty()One and only one of the 'cats_*' attributes must be definednum_categories_ > 0onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo &) [T = float]onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo &) [T = double]onnxruntime::ml::OneHotEncoderOp >::OneHotEncoderOp(const onnxruntime::OpKernelInfo &) [T = std::__1::basic_string]Scaler/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/scaler.cconnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo &) [T = float]!scale_.empty()scale_.size() == offset_.size()Scale size: () != (Invalid argument: input has empty dimensions.Either both scale and offset can be of feature size () or 1onnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo &) [T = double]onnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo &) [T = long long]onnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo &) [T = int]SVMClassifiervectors_per_classprob_aprob_b/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/svmclassifier.cconnxruntime::ml::SVMClassifier::SVMClassifier(const onnxruntime::OpKernelInfo &)proba_.size() == probb_.size()classlabels_intsinfo.GetAttrs("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs("classlabels_ints", classlabels_ints_).IsOK()classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0coefficients_.size() > 0Unsupported data type of void onnxruntime::ml::batched_update_scores_inplace(gsl::span, int64_t, int64_t, onnxruntime::ml::POST_EVAL_TRANSFORM, int, bool, concurrency::ThreadPool *) [T = float]scores.size() == static_cast(expected_num_scores)Unexpected value for 'add_second_class' of SVMRegressorn_supportssupport_vectorspost_transformrho/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/svmregressor.cconnxruntime::ml::SVMRegressor::SVMRegressor(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttrs("rho", rho_).IsOK()coefficientsinfo.GetAttrs("coefficients", coefficients_).IsOK()!coefficients_.empty()one_classkernel_typeLINEARkernel_params/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/svmclassifier.honnxruntime::ml::SVMCommon::SVMCommon(const onnxruntime::OpKernelInfo &)info.GetAttrs("kernel_params", kernel_params).IsOK()POLYRBFLOGISTICSOFTMAXSOFTMAX_ZEROvirtual onnxruntime::common::Status onnxruntime::ml::SVMRegressor::Compute(onnxruntime::OpKernelContext *) const [T = float]num_features == feature_count_Unexpected mode:TreeEnsembleClassifierclass_idsclass_nodeidsclass_treeidsclass_weightsX dims is empty.computevoid onnxruntime::ml::detail::TreeEnsembleCommonClassifier::compute(onnxruntime::OpKernelContext *, const onnxruntime::Tensor *, onnxruntime::Tensor *, onnxruntime::Tensor *) const [ITYPE = float, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(std::vector > &, OTYPE *, int, int64_t *) const [ITYPE = float, OTYPE = float]predictions.size() == 2int64_t onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binary(int &, const std::vector > &) const [ITYPE = float, OTYPE = float]classes.size() == 2 || classes.size() == 1void onnxruntime::ml::detail::TreeEnsembleCommonClassifier::compute(onnxruntime::OpKernelContext *, const onnxruntime::Tensor *, onnxruntime::Tensor *, onnxruntime::Tensor *) const [ITYPE = double, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(std::vector > &, OTYPE *, int, int64_t *) const [ITYPE = double, OTYPE = float]int64_t onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binary(int &, const std::vector > &) const [ITYPE = double, OTYPE = float]onnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommon(int, int, const std::string &, const std::vector &, int64_t, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::string &, const std::vector &, const std::vector &, const std::vector &, const std::vector &) [ITYPE = long long, OTYPE = float]void onnxruntime::ml::detail::TreeEnsembleCommonClassifier::compute(onnxruntime::OpKernelContext *, const onnxruntime::Tensor *, onnxruntime::Tensor *, onnxruntime::Tensor *) const [ITYPE = long long, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(std::vector > &, const TreeNodeElement &) const [ITYPE = long long, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(std::vector > &, OTYPE *, int, int64_t *) const [ITYPE = long long, OTYPE = float]int64_t onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binary(int &, const std::vector > &) const [ITYPE = long long, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(std::vector > &, const std::vector > &) const [ITYPE = long long, OTYPE = float]onnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommon(int, int, const std::string &, const std::vector &, int64_t, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::string &, const std::vector &, const std::vector &, const std::vector &, const std::vector &) [ITYPE = int, OTYPE = float]void onnxruntime::ml::detail::TreeEnsembleCommonClassifier::compute(onnxruntime::OpKernelContext *, const onnxruntime::Tensor *, onnxruntime::Tensor *, onnxruntime::Tensor *) const [ITYPE = int, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(std::vector > &, const TreeNodeElement &) const [ITYPE = int, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(std::vector > &, OTYPE *, int, int64_t *) const [ITYPE = int, OTYPE = float]int64_t onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binary(int &, const std::vector > &) const [ITYPE = int, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(std::vector > &, const std::vector > &) const [ITYPE = int, OTYPE = float]TreeEnsembleRegressoraggregate_functionSUMbase_valuesn_targetsnodes_falsenodeidsnodes_featureidsnodes_hitratesnodes_missing_value_tracks_truenodes_modesnodes_nodeidsnodes_treeidsnodes_truenodeidsnodes_valuestarget_idstarget_nodeidstarget_treeidstarget_weights/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/tree_ensemble_common.honnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommon(int, int, const std::string &, const std::vector &, int64_t, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::string &, const std::vector &, const std::vector &, const std::vector &, const std::vector &) [ITYPE = float, OTYPE = float]n_targets_or_classes > 0nodes_falsenodeids.size() == nodes_featureids.size()nodes_falsenodeids.size() == nodes_modes.size()nodes_falsenodeids.size() == nodes_nodeids.size()nodes_falsenodeids.size() == nodes_treeids.size()nodes_falsenodeids.size() == nodes_truenodeids.size()nodes_falsenodeids.size() == nodes_values.size()target_class_ids.size() == target_class_nodeids.size()target_class_ids.size() == target_class_treeids.size() in tree is already there.Unable to find node (truenode).One falsenode is pointing either to itself, either to another tree. (falsenode). (weights).AVERAGEMINBRANCH_LEQLEAFBRANCH_LTBRANCH_GTEBRANCH_GTBRANCH_EQvoid onnxruntime::ml::detail::TreeEnsembleCommon::compute(onnxruntime::OpKernelContext *, const onnxruntime::Tensor *, onnxruntime::Tensor *, onnxruntime::Tensor *) const [ITYPE = float, OTYPE = float]/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/tree_ensemble_aggregator.hvoid onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(std::vector > &, const TreeNodeElement &) const [ITYPE = float, OTYPE = float]it->i < (int64_t)predictions.size()void onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScores(std::vector > &, OTYPE *, int, int64_t *) const [ITYPE = float, OTYPE = float]this->base_values_.size() == predictions.size()void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(std::vector > &, const std::vector > &) const [ITYPE = float, OTYPE = float]predictions.size() == predictions2.size()void onnxruntime::ml::detail::TreeAggregator::FinalizeScores(std::vector > &, OTYPE *, int, int64_t *) const [ITYPE = float, OTYPE = float]predictions.size() == (size_t)n_targets_or_classes_void onnxruntime::ml::detail::TreeAggregatorMin::MergePrediction(std::vector > &, const std::vector > &) const [ITYPE = float, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorMax::MergePrediction(std::vector > &, const std::vector > &) const [ITYPE = float, OTYPE = float]onnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommon(int, int, const std::string &, const std::vector &, int64_t, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::string &, const std::vector &, const std::vector &, const std::vector &, const std::vector &) [ITYPE = double, OTYPE = float]void onnxruntime::ml::detail::TreeEnsembleCommon::compute(onnxruntime::OpKernelContext *, const onnxruntime::Tensor *, onnxruntime::Tensor *, onnxruntime::Tensor *) const [ITYPE = double, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(std::vector > &, const TreeNodeElement &) const [ITYPE = double, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScores(std::vector > &, OTYPE *, int, int64_t *) const [ITYPE = double, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(std::vector > &, const std::vector > &) const [ITYPE = double, OTYPE = float]void onnxruntime::ml::detail::TreeAggregator::FinalizeScores(std::vector > &, OTYPE *, int, int64_t *) const [ITYPE = double, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorMin::MergePrediction(std::vector > &, const std::vector > &) const [ITYPE = double, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorMax::MergePrediction(std::vector > &, const std::vector > &) const [ITYPE = double, OTYPE = float]ZipMapclasslabels_int64sclasslabels_strings/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/zipmap.cconnxruntime::ml::ZipMapOp::ZipMapOp(const onnxruntime::OpKernelInfo &)classlabels_strings_.empty() ^ classlabels_int64s_.empty()Must provide classlabels_strings or classlabels_int64s but not both.Zipmap does not support empty dim countZipmap only supports 1D or 2D input tensorsInput features_per_batch[] != number of classlabels[T *OrtValue::GetMutable() [T = std::__1::vector, float, std::__1::less >, std::__1::allocator, float> > >, std::__1::allocator, float, std::__1::less >, std::__1::allocator, float> > > > >]T *OrtValue::GetMutable() [T = std::__1::vector, std::__1::allocator > >, std::__1::allocator, std::__1::allocator > > > >]MaxUnpool/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/Unpool.ccIndex tensor shape should be same as that of the input data tensor to unpool.Shape must be 1 dimensional as it's tensor data of a shapeoutput_shape is smaller than minimum required. output_shape: inferred output shape:/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/unpool.honnxruntime::MaxUnpool::MaxUnpool(const onnxruntime::OpKernelInfo &)info.GetAttrs("kernel_shape", kernel_shape_).IsOK()kernel_shape_[dim] > 0pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]strides_.size() == kernel_shape_.size()BatchNormalizationspatial/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/batch_norm.honnxruntime::BatchNorm::BatchNorm(const onnxruntime::OpKernelInfo &) [T = float]!is_train_Invalid input X: The rank of input X must be atleast 2. Got rank: Invalid input scale: NumDimensions() != Invalid input scale: 0th dimension != Invalid input scale: dimension != Invalid input B: NumDimensions() != Invalid input B: 0th dimension != Invalid input B: Invalid input mean: NumDimensions() != Invalid input mean: 0th dimension != Invalid input mean: Invalid input var: NumDimensions() != Invalid input var: 0th dimension != Invalid input var: onnxruntime::BatchNorm::BatchNorm(const onnxruntime::OpKernelInfo &) [T = double]/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/conv.ccConvX num_dims does not match W num_dims. X: W: Input channels C is not equal to kernel channels * group. C: kernel channels: group: Output channels M is not divisible by group. M: kernel_shape num_dims is not compatible with W num_dims. kernel_shape: kernel_shape is not compatible with W shape.Not enough elements in strides. Expected: Not enough elements in kernel shape. Expected: Not enough elements in dilations. Expected: Not enough elements in pads. Expected: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/conv_attributes.hInferOutputShapeInvalid input shape: /Users/runner/work/1/s/onnxruntime/core/providers/common.hComputePadAndOutputShapeDilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER.ComputePad: pad type not supported.kernel_shapestridesonnxruntime::ConvAttributes::ConvAttributes(const onnxruntime::OpKernelInfo &)auto_pad == AutoPadType::NOTSETA Conv/ConvTranspose node has both 'auto_pad' and 'pads' attributesdilationsgroupNOTSETVALIDSAME_UPPERSAME_LOWERonnxruntime::AutoPadType onnxruntime::StringToAutoPadType(const std::string &)T3ConvInteger/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/conv_integer.ccvirtual onnxruntime::common::Status onnxruntime::ConvInteger::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(X_Zero_Point)IsScalarOr1ElementVector(W_Zero_Point)ConvTranspose/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/conv_transpose.ccDoConvTransposeoutput_paddingoutput_shapegroup count is <= 0filter number not equal to input channel number. filter_number: num_input_channels: Input channels is not divisible by group./Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/conv_transpose_attributes.hvoid onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShape(onnxruntime::TensorShape, int64_t, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const int64_t, std::vector *, std::vector *) constdim_size > 0void onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShape(const int64_t, const int64_t, const int64_t, const int64_t, const int64_t, onnxruntime::AutoPadType, int64_t *, int64_t *, int64_t *) const*out_size >= 0/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/dropout_op.hvirtual onnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext *) const [T1 = float, T2 = float]!mask || mask->Shape() == X_shapefloat onnxruntime::(anonymous namespace)::GetRatioOrDefault(const onnxruntime::Tensor *) [T2 = float]ratio_tensor->Shape().Size() == 10.0f <= ratio_value && ratio_value < 1.0fvirtual onnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext *) const [T1 = float, T2 = double]float onnxruntime::(anonymous namespace)::GetRatioOrDefault(const onnxruntime::Tensor *) [T2 = double]virtual onnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext *) const [T1 = double, T2 = float]virtual onnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext *) const [T1 = double, T2 = double]Flatten/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/flatten.honnxruntime::Flatten::Flatten(const onnxruntime::OpKernelInfo &)virtual onnxruntime::common::Status onnxruntime::Flatten::Compute(onnxruntime::OpKernelContext *) constgsl::narrow_cast(X_shape.NumDimensions()) >= axisInstanceNormalization/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/instance_norm.ccepsilon/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/instance_norm.honnxruntime::InstanceNorm::InstanceNorm(const onnxruntime::OpKernelInfo &) [T = float]op_kernel_info.GetAttr("epsilon", &epsilon_).IsOK()Invalid input data: number of dimensions is less than 3: Invalid input scale: number of dimensions is not 1: Mismatch between input data and scale: size of scale != input channel count vs. Invalid input B: number of dimensions is not 1: Mismatch between input data and B: size of B != input channel count LpNormalization/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/lp_norm.honnxruntime::LpNorm::LpNorm(const onnxruntime::OpKernelInfo &) [T = float]op_kernel_info.GetAttr("axis", &axis_).IsOK()op_kernel_info.GetAttr("p", &p_).IsOK()p_ == 1 || p_ == 2onnxruntime::LpNorm::LpNorm(const onnxruntime::OpKernelInfo &) [T = double]input count mismatch/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/lrn.ccvirtual onnxruntime::common::Status onnxruntime::LRN::Compute(onnxruntime::OpKernelContext *) const [T = float]X->Shape().NumDimensions() == 4LRN/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/lrn.honnxruntime::LRN::LRN(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttr("size", &size).IsOK()size_ > 0size_ % 2 == 1info.GetAttr("alpha", &alpha_).IsOK()alpha_ > 0.0finfo.GetAttr("beta", &beta_).IsOK()beta_ > 0.0f/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/pool.ccInput dimension cannot be less than 3.Unsupported pooling size.kernel_shape num_dims is not compatible with X num_dims.AveragePoolMaxPoolLpPoolGlobalLpPoolGlobalAveragePoolGlobalMaxPool/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/pool_attributes.hstd::vector onnxruntime::PoolAttributes::SetOutputSize(const onnxruntime::TensorShape &, int64_t, std::vector *) constinput_shape.Size() > 0 || input_shape[0] == 0Invalid input shape. Only N can be zero. Got:void onnxruntime::PoolAttributes::InferOutputSize(const std::vector &, std::vector *, std::vector *) constinput_dims.size() >= 2void onnxruntime::PoolAttributes::ComputeSizePadDilations(const int64_t, const int64_t, const int64_t, int64_t *, int64_t *, int64_t, int64_t *) constonnxruntime::PoolAttributes::PoolAttributes(const OpNodeProtoHelper &, const std::string &, int)info.GetAttrs("kernel_shape", kernel_shape).IsOK()info.GetAttr("auto_pad", &auto_padding).IsOK()ceil_modecount_include_padinfo.GetAttr("count_include_pad", &temp).IsOK()storage_orderinfo.GetAttr("storage_order", &storage_order).IsOK()kernel_shape[dim] > 0pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]strides.size() == kernel_shape.size()dilations.size() == kernel_shape.size()/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/pool_base.hvoid onnxruntime::PoolProcessContext::init(const onnxruntime::OpKernelInfo &)info.GetAttr("p", &p_).IsOK()Unsupported pooling size : T4QLinearConv/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/qlinearconv.ccvirtual onnxruntime::common::Status onnxruntime::QLinearConv::UseSharedPrePackedBuffers(std::vector &, int, bool &)prepacked_buffers[0].get() == nullptrvirtual onnxruntime::common::Status onnxruntime::QLinearConv::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(X_zero_point)QLinearConv : input zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_zero_point)QLinearConv : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(X_scale)QLinearConv : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_scale)QLinearConv : result scale must be a scalar or 1D tensor of size 1channels_lastMaxRoiPool/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/roi_pool.ccvirtual onnxruntime::common::Status onnxruntime::RoiPool::Compute(onnxruntime::OpKernelContext *) const [T = float]R->Shape()[1] == 5roi_batch_id >= 0roi_batch_id < batch_sizepooled_shape/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/roi_pool.honnxruntime::RoiPool::RoiPool(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttrs("pooled_shape", pooled_shape).IsOK()pooled_shape.size() == 2pooled_height_ > 0pooled_width_ > 0spatial_scaleinfo.GetAttr("spatial_scale", &spatial_scale_).IsOK()spatial_scale_ > 0Shrink/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/shrink.honnxruntime::Shrink::Shrink(const onnxruntime::OpKernelInfo &)op_kernel_info.GetAttr("bias", &bias_temp).IsOK()lambdop_kernel_info.GetAttr("lambd", &lambd_temp).IsOK()StringNormalizer/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/string_normalizer.cconnxruntime::StringNormalizer::StringNormalizer(const onnxruntime::OpKernelInfo &)LOWERUPPERlocalestopwords!sw.empty()wstr != wconv_errorSingle dimension value must be greater than 0Input dimensions are either[C > 0] or [1][C > 0] allowedInput contains invalid utf8 chars at: auto onnxruntime::string_normalizer::Locale::Locale(const std::string &)::(anonymous class)::operator()() constFailed to construct locale with name::Please, install necessary language-pack-XX and configure localeswstring_convert: from_bytes errorwstring_convert: to_bytes errorTfIdfVectorizer/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/tfidfvectorizer.cconnxruntime::TfIdfVectorizer::TfIdfVectorizer(const onnxruntime::OpKernelInfo &)TFIDFTFIDFimpl_->weighting_criteria_ != kNonemode: is unrecognized, acceptable values are TF,IDF,TFIDFmin_gram_lengthimpl_->min_gram_length_ > 0Required min_gram_length must be positive: max_gram_lengthimpl_->max_gram_length_ >= impl_->min_gram_length_min_gram_length >= max_gram_length required: >= max_skip_countimpl_->max_skip_count_ >= 0max_skip_count must be non-negative: ngram_countsstatus.IsOK() && !impl_->ngram_counts_.empty()size_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size()min_gram_length must be inbounds of ngram_counts: <= size_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size()max_gram_length must be inbounds of ngram_counts: ngram_indexesstatus.IsOK() && !impl_->ngram_indexes_.empty()std::all_of(impl_->ngram_indexes_.cbegin(), impl_->ngram_indexes_.cend(), [](int64_t i) { return i >= 0; })weightsimpl_->weights_.size() == impl_->ngram_indexes_.size()Got weights of size: but ngram_indexes size: must be of equal sizepool_strings!pool_strings.empty()pool_int64sstatus.IsOK() && !pool_int64s.empty()end_idx >= start_idx && end_idx <= total_itemsn-gram counts out of bounds for -grams(items % ngram_size == 0)Number of items must compose whole Input shape must have either [C] or [B,C] dimensions with B > 0.size_t onnxruntime::ngram_details::PopulateGrams(ForwardIter, size_t, size_t, size_t, Map &) [K = long long, ForwardIter = const long long *, Map = std::__1::unordered_map, std::__1::default_delete > >, std::__1::hash, std::__1::equal_to, std::__1::allocator, std::__1::default_delete > > > > >]p.first->second->id_ == 0Duplicate ngram detected, size: id: size_t onnxruntime::ngram_details::PopulateGrams(ForwardIter, size_t, size_t, size_t, Map &) [K = std::__1::basic_string, ForwardIter = std::__1::__wrap_iter > *>, Map = std::__1::unordered_map >, std::__1::unique_ptr, std::__1::default_delete > >, std::__1::hash >, std::__1::equal_to >, std::__1::allocator >, std::__1::unique_ptr, std::__1::default_delete > > > > >]NonMaxSuppression/Users/runner/work/1/s/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.ccstatic onnxruntime::common::Status onnxruntime::NonMaxSuppressionBase::PrepareCompute(onnxruntime::OpKernelContext *, onnxruntime::PrepareContext &)boxes_tensorscores_tensorboxes must be a 3D tensor.scores must be a 3D tensor.boxes and scores should have same num_batches.boxes and scores should have same spatial_dimension.The most inner dimension in boxes must have 4 data.GetThresholdsFromInputsiou_threshold must be in range [0, 1].virtual onnxruntime::common::Status onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext *) constoutput != nullptrcenter_point_box/Users/runner/work/1/s/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.honnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBase(const onnxruntime::OpKernelInfo &)0 == center_point_box_ || 1 == center_point_box_RoiAlignNull input X ptrNull rois_ptrNull batch_indices_ptrNumber of dimensions for batch indices should be exactly 1Number of dimensions for rois should be exactly Second dimension for rois should be exactly First dimension (num_rois) of batch_indices and rois don't matchavg/Users/runner/work/1/s/onnxruntime/core/providers/cpu/object_detection/roialign.honnxruntime::RoiAlignBase::RoiAlignBase(const onnxruntime::OpKernelInfo &)Invalid mode of value specified. It should be either avg or maxoutput_heightoutput_widthsampling_ratiosampling_ratio_ >= 0Sampling ratio should be >=0, but it was ReduceL1ReduceL2ReduceLogSumReduceLogSumExpReduceMaxReduceMeanReduceMinReduceProdReduceSumReduceSumSquareArgMaxArgMin/Users/runner/work/1/s/onnxruntime/core/providers/cpu/reduction/reduction_ops.ccvoid onnxruntime::ResultsNoTransposePrepareForReduce::ValidateNotEmpty()last_loop_red_size > 0last_loop_size > 0projected_index.size() > 0void onnxruntime::ValidateNoTransposeReduce(int64_t)count == 1void onnxruntime::ValidateCommonFastReduce(const onnxruntime::Tensor *)keepdims/Users/runner/work/1/s/onnxruntime/core/providers/cpu/reduction/reduction_ops.honnxruntime::ReduceKernelBase::ReduceKernelBase(const onnxruntime::OpKernelInfo &, optional) [allow_multi_axes = false]info.GetAttr("keepdims", &keepdims).IsOK()noop_with_empty_axesselect_last_indexvoid onnxruntime::ValidateFastReduceKR(const std::vector &, const onnxruntime::Tensor &)fast_shape.size() == 2fast_shape[0] == output.Shape().Size()void onnxruntime::ValidateFastReduceRK(const std::vector &, const onnxruntime::Tensor &)fast_shape[1] == output.Shape().Size()void onnxruntime::ValidateFastReduceKRK(const std::vector &, const onnxruntime::Tensor &)fast_shape.size() == 3fast_shape[0] * fast_shape[2] == output.Shape().Size()void onnxruntime::ValidateMustBeOverloaded()void onnxruntime::ValidateKeepDims(const onnxruntime::TensorShape &, int64_t)Can't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:onnxruntime::ReduceKernelBase::ReduceKernelBase(const onnxruntime::OpKernelInfo &, optional) [allow_multi_axes = true]GRU/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.ccvirtual onnxruntime::common::Status onnxruntime::DeepCpuGruOp::Compute(onnxruntime::OpKernelContext *) constInvalid data type for GRU operator of /Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.honnxruntime::DeepCpuGruOp::DeepCpuGruOp(const onnxruntime::OpKernelInfo &)info.GetAttr("direction", &direction).IsOK()linear_before_resetinfo.GetAttr("linear_before_reset", &int64_value).IsOK()hidden_sizeinfo.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0activationsactivation_alphaactivation_betaclip_ > 0.fsigmoidtanhactivation_func_names.size() == static_cast(num_directions_) * 2layoutlayout_ == 0Batchwise recurrent operations (layout == 1) are not supported. If you need support create a github issue with justification.forwardreversebidirectional/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/rnn_helpers.honnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const std::string &)Invalid 'direction' argument of ''. Must be one of 'forward', 'reverse', or 'bidirectional'.ComputeImplgsl::span onnxruntime::Tensor::DataAsSpan() const [T = float]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = float]void onnxruntime::rnn::detail::ComputeGemm(const int, const int, const int, const float, TSpanAIter, TSpanAIter, const int, TSpanBIter, TSpanBIter, const int, const float, TSpanCIter, TSpanCIter, const int, concurrency::ThreadPool *) [TSpanAIter = const float *, TSpanBIter = const float *, TSpanCIter = float *]lda >= K && ldb >= K && ldc >= NA + (M * lda - (lda - K)) <= A_endB + (N * ldb - (ldb - K)) <= B_endC + (M * ldc - (ldc - N)) <= C_endconst T *onnxruntime::rnn::detail::SafeRawConstPointer(typename gsl::span::const_iterator, typename gsl::span::const_iterator, size_t) [T = float]cur + size <= endT *onnxruntime::rnn::detail::SafeRawPointer(typename gsl::span, size_t, size_t) [T = float]offset + size <= size_t(span.size())T *onnxruntime::rnn::detail::SafeRawPointer(typename gsl::span::iterator, typename gsl::span::iterator, size_t) [T = float]void onnxruntime::rnn::detail::ComputeGemm(const int, const int, const int, const float, TSpanAIter, TSpanAIter, const int, TSpanBIter, TSpanBIter, const int, const float, TSpanCIter, TSpanCIter, const int, concurrency::ThreadPool *) [TSpanAIter = float *, TSpanBIter = const float *, TSpanCIter = float *]LSTM/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.ccPrePackvirtual onnxruntime::common::Status onnxruntime::DeepCpuLstmOp::Compute(onnxruntime::OpKernelContext *) constInvalid data type for LSTM operator of /Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/lstm_base.honnxruntime::LSTMBase::LSTMBase(const onnxruntime::OpKernelInfo &)input_forgetactivation_func_names.size() == static_cast(num_directions_) * 3/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/lstm_base.ccRNN/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/rnn.ccAffine/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/rnn.honnxruntime::RNN::RNN(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttr("direction", &direction_).IsOK()allowed_directions.find(direction_) != allowed_directions.end()info.GetAttrs("activations", activations_).IsOK()info.GetAttr("hidden_size", &hidden_size_).IsOK()activations_.size() == static_cast(num_directions)allowed_activations.find(activations_[direction]) != allowed_activations.end()RNN op: Invalid activation attribute - Input X must have 3 dimensions only. Actual:Input W must have shape {*}. Actual:Input R must have shape {Input B must have shape {Input sequence_lens must have shape {Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length=Input initial_h must have shape {affinereluleakyreluthresholdedreluscaledtanhhardsigmoidelusoftsignsoftplus/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/rnn_helpers.ccstd::string onnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCount(const std::string &, std::vector::const_iterator &, const std::vector::const_iterator &, std::vector::const_iterator &, const std::vector::const_iterator &, float &, float &)Expecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got void onnxruntime::rnn::detail::ComputeGemm(const int, const int, const int, const float, const float *, const float *, const GemmWeights &, const float, float *, float *, const int, uint8_t *, int32_t *, concurrency::ThreadPool *)A + (M * K) <= A_endvoid onnxruntime::rnn::detail::ComputeGemm(const int, const int, const int, const float, const float *, const float *, const GemmWeights &, const float, float *, float *, const int, uint8_t *, int32_t *, concurrency::ThreadPool *)weights.quant_para_alpha == 1.0f && (beta == 0.0f || beta == 1.0f)Quantized GEMM only support alpha equal to 1.0f and beta equal to 0.0f or 1.0fonnxruntime::rnn::detail::deepcpu::ActivationFuncPtr onnxruntime::rnn::detail::deepcpu::ActivationFuncByName(const std::string &)Invalid activation function of onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncPtr onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByName(const std::string &)Invalid LSTM merge activation function of onnxruntime::rnn::detail::deepcpu::GruResetGateFuncPtr onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByName(const std::string &)Invalid GRU reset gate activation function: onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncPtr onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByName(const std::string &)Invalid GRU hidden gate activation function: rows[const T *onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span, size_t, size_t) [T = const float]const T *onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span, size_t, size_t) [T = float]ConcatFromSequence/Users/runner/work/1/s/onnxruntime/core/providers/cpu/sequence/concat_from_sequence.ccvirtual onnxruntime::common::Status onnxruntime::ConcatFromSequence::Compute(onnxruntime::OpKernelContext *) constSSequenceLength/Users/runner/work/1/s/onnxruntime/core/providers/cpu/sequence/sequence_ops.ccvirtual onnxruntime::common::Status onnxruntime::SequenceLength::Compute(onnxruntime::OpKernelContext *) constY != nullptrSequenceAtvirtual onnxruntime::common::Status onnxruntime::SequenceAt::Compute(onnxruntime::OpKernelContext *) constI != nullptrInvalid sequence index () specified for sequence of size (SequenceEmptyvirtual onnxruntime::common::Status onnxruntime::SequenceEmpty::Compute(onnxruntime::OpKernelContext *) constUnsupported 'dtype' value: SequenceInsertCreateCopyAndAppendCpuTensorvirtual onnxruntime::common::Status onnxruntime::SequenceInsert::Compute(onnxruntime::OpKernelContext *) constS != nullptrData type of the input tensor MUST be same as that of the input sequence. Sequence data type (), input tensor data type (SequenceErasevirtual onnxruntime::common::Status onnxruntime::SequenceErase::Compute(onnxruntime::OpKernelContext *) constSequenceConstructvirtual onnxruntime::common::Status onnxruntime::SequenceConstruct::Compute(onnxruntime::OpKernelContext *) constnum_inputs >= 1Violation of the requirment that all input tensors must have the same data type.SplitToSequenceSplitToSequence operator does not support split_size_sum () != split_dim_size (int64_t onnxruntime::GetSeqIdx(const onnxruntime::Tensor &)onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &, const onnxruntime::Tensor *) const [T = float]split_scalar > 0std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value >= 0; })int64_t onnxruntime::GetScalarSplitInput(const onnxruntime::Tensor &)Invalid data type for split tensor void onnxruntime::GetSplitSizesInput(const onnxruntime::Tensor &, std::vector &)onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &, const onnxruntime::Tensor *) const [T = double]onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &, const onnxruntime::Tensor *) const [T = int]onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &, const onnxruntime::Tensor *) const [T = long long]onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &, const onnxruntime::Tensor *) const [T = std::__1::basic_string]T2Cast/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/cast_op.cconnxruntime::(anonymous namespace)::Cast::Cast(const onnxruntime::OpKernelInfo &)NaN-INFINF%.8gtypename std::enable_if::value, void>::type onnxruntime::(anonymous namespace)::CastToString(const SrcType &, std::string &) [SrcType = float]snprintf_result > 0snprintf() failed with return value: snprintf_result > 0 && gsl::narrow_cast(snprintf_result) == buffer_span.size() - 1typename std::enable_if::value, void>::type onnxruntime::(anonymous namespace)::CastToString(const SrcType &, std::string &) [SrcType = double]Compress/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/compress.ccvirtual onnxruntime::common::Status onnxruntime::Compress::Compute(onnxruntime::OpKernelContext *) constaxes_right_stride >= 0 && static_cast(axes_right_stride) < std::numeric_limits::max()Concat/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/concat.ccPrepareForComputeCannot concatenate scalarsonnxruntime::common::Status onnxruntime::ConcatBase::PrepareForCompute(onnxruntime::OpKernelContext *, const std::vector &, onnxruntime::Prepare &) constinputs_n_rank == inputs_0_rankRanks of input data are different, cannot concatenate them. expected rank: got: Non concat axis dimensions must match: Axis has mismatched dimensions of Data type mismatch/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/concatbase.honnxruntime::ConcatBase::ConcatBase(const onnxruntime::OpKernelInfo &, bool)DynamicQuantizeLinear/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/dynamicquantizelinear.ccvirtual onnxruntime::common::Status onnxruntime::DynamicQuantizeLinear::Compute(onnxruntime::OpKernelContext *) const [T = unsigned char]x_ptr != nullptrEyeLikedtypeGather/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/gather.cc/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/gatherbase.honnxruntime::GatherBase::GatherBase(const onnxruntime::OpKernelInfo &)info.GetAttr("axis", &axis_).IsOK()indices element out of data bounds, idx= must be within the inclusive range [GatherElementsGatherElements op: Rank of input 'data' needs to be equal to rank of input 'indices'GatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: GatherElements op: Data type of input 'data' should match the data type of the output/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/gather_elements.honnxruntime::GatherElements::GatherElements(const onnxruntime::OpKernelInfo &)/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/gather_elements.ccauto onnxruntime::core_impl(const onnxruntime::Tensor *, const onnxruntime::Tensor *, onnxruntime::Tensor *, int64_t, concurrency::ThreadPool *)::(anonymous class)::operator()(ptrdiff_t) constGatherElements op: Value in indices must be within bounds [ , ]. Actual value is invalid index found, index = GatherND/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/gather_nd.ccvirtual onnxruntime::common::Status onnxruntime::GatherND::Compute(onnxruntime::OpKernelContext *) constinput_tensor != nullptr && indices_tensor != nullptrlast dimension of indices must not be larger than rank of input tensorbatch_dimsDropoutIdentity/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/identity_op.hvirtual onnxruntime::common::Status onnxruntime::IdentityOp::Compute(onnxruntime::OpKernelContext *) const [is_dropout = true]const T *onnxruntime::OpKernelContext::Input(int) const [T = onnxruntime::TensorSeq]virtual onnxruntime::common::Status onnxruntime::IdentityOp::Compute(onnxruntime::OpKernelContext *) const [is_dropout = false]IsInfdetect_positive/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/isinf.cconnxruntime::IsInf::IsInf(const onnxruntime::OpKernelInfo &)detect_negativeIsNaNNull input ptrMeanVarianceNormalizationInput is expected to have four dimensions corresponding to [N,C,H,W]NonZero/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/nonzero_op.ccvirtual onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext *) const [T = bool]virtual onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext *) const [T = unsigned char]virtual onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext *) const [T = int]virtual onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext *) const [T = long long]virtual onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext *) const [T = float]OneHotInvalid argument for depth; it's not a scalar.Invalid argument for values; either it's rank is more than 1 or it has more than 2 elements/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/onehot.ccDepth is negative.PadCannot use 'reflect' mode to pad dimension with a value of 0. Input shape:Unexpected mode of /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/pad.ccvirtual onnxruntime::common::Status onnxruntime::Pad::Compute(onnxruntime::OpKernelContext *) constpads_tensor.IsDataType()pads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)Pads tensor should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]pads_size == 2 * data_rankPads tensor size should be equal to twice the input dimension count value_tensor->DataType() == data_type && value_tensor->Shape().Size() == 1Value tensor should be a 1D tensor of size 1 with the same type as that of the input tensorUnsupported input data type of valueconstantreflectedge/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/padbase.honnxruntime::PadBase::PadBase(const onnxruntime::OpKernelInfo &)onnxruntime::PadValue onnxruntime::PadValueFromFloat(float, onnxruntime::MLDataType)onnxruntime::common::Status onnxruntime::PadImpl(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &, const onnxruntime::Mode &, T) [T = unsigned int]data_rank > 0data_rank * 2 == pads.size()PadInputWithDimValueOfZero/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/utils.honnxruntime::SliceSkips::SliceSkips(const onnxruntime::TensorShape &, gsl::span, gsl::span)dims.size() == extents.size() && dims.size() >= steps.size()void onnxruntime::SliceIteratorBase::Init(const std::vector &, gsl::span, gsl::span)dims.size() == starts.size() && dims.size() == extents_.size() && dims.size() >= steps.size()onnxruntime::common::Status onnxruntime::PadImpl(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &, const onnxruntime::Mode &, T) [T = unsigned long long]onnxruntime::common::Status onnxruntime::PadImpl(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &, const onnxruntime::Mode &, T) [T = unsigned char]DequantizeLinearQuantizeLinear/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/quantize_linear.ccvoid onnxruntime::PrepareForQDQ(const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const onnxruntime::Tensor *, int64_t, int64_t &, int64_t &, int64_t &)zero_point_ptr == nullptr || IsScalarOr1ElementVector(zero_point_ptr)scale.Shape().NumDimensions() == 1 && scale.Shape()[0] == broadcast_dimscale must be 1D tensor with size zero_point_ptr == nullptr || (zero_point_ptr->Shape().NumDimensions() == 1 && zero_point_ptr->Shape()[0] == broadcast_dim)x_zero_point must be null or 1D tensor with size virtual onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext *) const [T = int]zero_point == nullptr || std::all_of(zero_point, zero_point + x_zero_point->Shape().Size(), [](int32_t zp) { return zp == 0; })DequantizeLinear with type int32 should have no zero point or all zero points should be 0Reshapeshape/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/reshape.honnxruntime::Reshape_1::Reshape_1(const onnxruntime::OpKernelInfo &)/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/reshape_helper.honnxruntime::ReshapeHelper::ReshapeHelper(const onnxruntime::TensorShape &, std::vector &, bool)requested_shape[i] >= -1A dimension cannot be less than -1, got !allow_zeroThe input tensor cannot be reshaped to the requested shape. Input shape:, requested shape:unknown_dim == -1i < input_shape.NumDimensions()The dimension with value zero exceeds the dimension size of the input tensor.size != 0 && (input_shape.Size() % size) == 0gsl::narrow_cast(input_shape.Size()) == sizeallowzerovirtual onnxruntime::common::Status onnxruntime::Reshape::Compute(onnxruntime::OpKernelContext *) constshapeTensor->Shape().NumDimensions() == 1Resize/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/upsample.honnxruntime::UpsampleBase::UpsampleBase(const onnxruntime::OpKernelInfo &)info.GetAttr("mode", &mode).IsOK()scalesinfo.GetAttrs("scales", scales_).IsOK()extrapolation_valuecoordinate_transformation_modehalf_pixelasymmetricUpsampleBase`tf_half_pixel_for_nn` is deprecated since opset 13, yet this opset model uses the deprecated attributenearest_moderound_prefer_floorcubic_coeff_aexclude_outsideexclude_outside can be set to 1 only when mode is CUBIC. Current mode is set to nearestlinearcubiconnxruntime::UpsampleMode onnxruntime::UpsampleBase::StringToUpsampleMode(const std::string &)mode attribute is . It can only be (default) or or void onnxruntime::UpsampleBase::ScalesValidation(const std::vector &, const onnxruntime::UpsampleMode) constscale >= 1scale > 0scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1) || scales.size() == 3 || (scales.size() == 5 && scales[0] == 1 && scales[1] == 1)'Linear' mode only support 2-D inputs or 3-D inputs ('Bilinear', 'Trilinear') or 4-D inputs or 5-D inputs with the corresponding outermost 2 scale values being 1 in the Resize operatorUpsample operatorscales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1)'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the pytorch_half_pixeltf_half_pixel_for_nnalign_cornerstf_crop_and_resizeonnxruntime::ResizeCoordinateTransformationMode onnxruntime::UpsampleBase::StringToCoordinateTransformationMode(const std::string &)coordinate_transform_mode:[] is not supportted!round_prefer_ceilfloorceilonnxruntime::ResizeNearestMode onnxruntime::UpsampleBase::StringToNearestMode(const std::string &)nearest_mode:[] is not supported!void onnxruntime::UpsampleBase::ParseScalesData(const onnxruntime::Tensor *, std::vector &) constscales_size > 0ReverseSequencesequence_lens shape must be {batch_size}. Got:. batch_size=/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/reverse_sequence.ccvirtual onnxruntime::common::Status onnxruntime::ReverseSequenceOp::Compute(onnxruntime::OpKernelContext *) constUnknown tensor type of batch_axis/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/reverse_sequence.honnxruntime::ReverseSequenceOp::ReverseSequenceOp(const onnxruntime::OpKernelInfo &)info.GetAttr("batch_axis", &batch_axis).IsOK()time_axisinfo.GetAttr("time_axis", &time_axis).IsOK()batch_axis < 2Invalid batch_axis of . Must be 0 or 1time_axis < 2Invalid time_axis of batch_axis != time_axistime_axis and batch_axis must have different values but both are Invalid sequence length: . Value must be in range [0,gsl::span onnxruntime::Tensor::DataAsSpan() const [T = bool]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = bool]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = double]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = double]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = unsigned char]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned char]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = unsigned short]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned short]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = short]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = short]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = int]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = unsigned int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned int]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = unsigned long long]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned long long]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = onnxruntime::MLFloat16]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = onnxruntime::MLFloat16]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = onnxruntime::BFloat16]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = onnxruntime::BFloat16]ScatterScatterElements/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/scatter.cconnxruntime::Scatter > >::Scatter(const onnxruntime::OpKernelInfo &) [EnabledDataTypes = onnxruntime::TypeList >]Indices vs updates dimensions differs at position= vs Indices must have the same rank as Input. Indices rank=. Input rank=Indices dim= at pos= is greater than input dim=ScatterNDinput tensor and indices tensor must has rank larger than 0. input shape: , indices shape: updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. updates shape: , data shape: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/scatter_nd.ccinvalid indice found, indice = SizeSliceTind'axes' has an axis outside of the tensor dimension count'axes' has duplicates'step' value cannot be 0/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/slice.ccFillVectorsFromInputStarts must be a 1-D arrayEnds must be a 1-D arrayStarts and ends shape mismatchStarts and axes shape mismatchStarts and steps shape mismatchData type for starts and ends inputs' is not supported in this build. Got starts/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/slice.honnxruntime::SliceBase::SliceBase(const onnxruntime::OpKernelInfo &, bool)has_starts && has_ends && attr_starts_.size() == attr_ends_.size()!has_axes || attr_axes_.size() == attr_starts_.size()Invalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributesauto onnxruntime::SliceImpl(onnxruntime::OpKernelContext *, const onnxruntime::Tensor &, SliceOp::PrepareForComputeMetadata &)::(anonymous class)::operator()(SliceIterator &) constoutput == output_endvoid *onnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStep(void *)Unexpected element size of SpaceToDepthDepthToSpace/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/space_depth_ops.ccvirtual onnxruntime::common::Status onnxruntime::SpaceToDepth::Compute(onnxruntime::OpKernelContext *) const [T = float]input_shape.NumDimensions() == 4input_height % this->blocksize_ == 0input_width % this->blocksize_ == 0virtual onnxruntime::common::Status onnxruntime::DepthToSpace::Compute(onnxruntime::OpKernelContext *) const [T = float]input.Shape().NumDimensions() == 4input_depth % (blocksize_ * blocksize_) == 0blocksize/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/space_depth_ops.honnxruntime::SpaceDepthBase::SpaceDepthBase(const onnxruntime::OpKernelInfo &)info.GetAttr("blocksize", &blocksize_).IsOK()CRDDCRonnxruntime::DepthToSpace::DepthToSpace(const onnxruntime::OpKernelInfo &) [T = float]SplitInput cannot be split evenly on selected axis. Input shape= Axis= NumOutputs=Cannot split using values in 'split' attribute. Axis= Input shape= Num entries in 'split' (must equal number of outputs) was Sum of sizes in 'split' (must equal size of selected axis) was /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/split.ccvirtual onnxruntime::common::Status onnxruntime::Split::Compute(onnxruntime::OpKernelContext *) constSplit operator does not support yetsplit/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/split.honnxruntime::SplitBase::SplitBase(const onnxruntime::OpKernelInfo &)std::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value >= 0; })onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &) const [T = float]split_tensor->Shape().NumDimensions() == 1onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &) const [T = int]onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &) const [T = long long]onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &) const [T = unsigned char]onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &) const [T = std::__1::basic_string]Squeeze/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/squeeze.hvirtual onnxruntime::common::Status onnxruntime::Squeeze::Compute(onnxruntime::OpKernelContext *) constaxes_tensor != nullptraxes_tensor->Shape().NumDimensions() == 1static std::vector onnxruntime::SqueezeBase::ComputeOutputShape(const onnxruntime::TensorShape &, const std::vector &)input_shape[i] == 1Dimension of input must be 1 instead of . shape=TileInput count of Tile OP mismatch, the first one is emptyInput count of Tile OP mismatch, the second one is emptythe tensor to be tiled using Tile OP must be atleast 1 dimensional'repeat' input tensor must be 1 dimensional'repeat' input tensor must have the same length as the 'input' tensor/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/tile.ccvirtual onnxruntime::common::Status onnxruntime::Tile::Compute(onnxruntime::OpKernelContext *) const!input_tensor.IsDataType()Tile doesn't have an implementation yet for the type: Transpose of element size not supported in this build. Size=Mismatched data types between input and output Tensors. /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/transpose.ccvirtual onnxruntime::common::Status onnxruntime::Transpose::Compute(onnxruntime::OpKernelContext *) constinput_tensor_ptr != nullptrTransposevoid onnxruntime::DoTransposeEltWise(int64_t, const std::vector &, size_t, const std::vector &, const std::string *, std::string *)num_axes > 0(local_source >= source) && (local_source < source + num_blocks)void onnxruntime::IncrementIndexAndComputeOffsetSetup(onnxruntime::MultiIndex &, size_t, const std::vector &, const std::vector &, size_t)naxes > 0Method IncrementIndexAndComputeOffset assumes this value is strictly positive.void onnxruntime::DoTransposeImpl(int64_t, const std::vector &, size_t, size_t, const std::vector &, const std::string *, std::string *)(local_source >= source) && (local_source < source + num_blocks * num_elts_in_block)void onnxruntime::DoTransposeImpl(int64_t, const std::vector &, size_t, size_t, const std::vector &, const uint8_t *, uint8_t *, size_t)(local_source >= source) && (local_source < source + num_blocks * blocksize)[ perm: does not align with rank of input data: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/transpose.honnxruntime::TransposeBase::TransposeBase(const onnxruntime::OpKernelInfo &)v >= 0 && static_cast(v) <= std::numeric_limits::max()Attribute perm of Transpose has an invalid value. Value is outside range. is repeated.bool onnxruntime::TypedDoTransposeEltWise(int64_t, const std::vector &, size_t, const std::vector &, const uint8_t *, uint8_t *) [T = unsigned long long](local_source >= source) && (local_source < source + sizeof(T) * num_blocks)bool onnxruntime::TypedDoTransposeEltWise(int64_t, const std::vector &, size_t, const std::vector &, const uint8_t *, uint8_t *) [T = unsigned int]bool onnxruntime::TypedDoTransposeEltWise(int64_t, const std::vector &, size_t, const std::vector &, const uint8_t *, uint8_t *) [T = unsigned short]bool onnxruntime::TypedDoTransposeEltWise(int64_t, const std::vector &, size_t, const std::vector &, const uint8_t *, uint8_t *) [T = unsigned char]Trilu/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/trilu.ccvirtual onnxruntime::common::Status onnxruntime::Trilu::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(k)upper/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/trilu.honnxruntime::Trilu::Trilu(const onnxruntime::OpKernelInfo &)info.GetAttr("upper", &temp).IsOK()UniqueUnsupported tensor type of sortedgsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = long long]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = signed char]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = signed char]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = std::__1::basic_string]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = std::__1::basic_string]Unsqueeze/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/unsqueeze.cconnxruntime::common::Status onnxruntime::UnsqueezeBase::PrepareCompute(onnxruntime::OpKernelContext *, onnxruntime::UnsqueezeBase::Prepare &) constaxes_tensor->Shape().NumDimensions() == 0 || axes_tensor->Shape().NumDimensions() == 1'axes' has an out of range axis'axes' has a duplicate axisnullptr != p.output_tensor/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/unsqueeze.honnxruntime::UnsqueezeBase::UnsqueezeBase(const onnxruntime::OpKernelInfo &)info.GetAttrs("axes", axes_).IsOK()Upsample/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/upsample.ccvirtual onnxruntime::common::Status onnxruntime::Upsample::Compute(onnxruntime::OpKernelContext *) const [T = float]roi_input_idx_ > 0sizes == nullptrsizes != nullptr && sizes->Shape().Size() != 0X->Shape().GetDims().size() == output_dims.size()Resize: input tensor's rank does not match the output tensor's rank.onnxruntime::common::Status onnxruntime::Upsample::BaseCompute(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &, const std::vector &) const [T = float]output_dims.size() == dims.size()Resize: input tensor's dimension does not match the scales.Upsample: input tensor's dimension does not match the scales.Resize: size of roi array should be 2 * N where N is the rank of input tensor X.BaseComputeResize: unexpected modeUpsample: unexpected modeResize: input/output value is nullptrUpsample: input/output value is nullptrResize: input/output value's dimension mismatchUpsample: input/output value's dimension mismatchResize: input shape needs to be at least a single dimensionUpsample: input shape needs to be at least a single dimension.void onnxruntime::UpsampleBase::ParseScalesDataFromOutputSize(const std::vector &, const std::vector &, std::vector &) constoutput_dims[i] == 0Input dim is zero but required output dim is non-zero. Cannot scale 0 by any factor to generate a non-zero value. Dimension: Input dim value: Output dim value: virtual onnxruntime::common::Status onnxruntime::Upsample::Compute(onnxruntime::OpKernelContext *) const [T = int]onnxruntime::common::Status onnxruntime::Upsample::BaseCompute(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &, const std::vector &) const [T = int]virtual onnxruntime::common::Status onnxruntime::Upsample::Compute(onnxruntime::OpKernelContext *) const [T = unsigned char]onnxruntime::common::Status onnxruntime::Upsample::BaseCompute(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &, const std::vector &) const [T = unsigned char]WhereGeluonnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::ParametricSoftplus]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::ParametricSoftplus]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::ScaledTanh]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::ScaledTanh]/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/attnlstm/bahdanau_attention.cconnxruntime::contrib::BahdanauAttention::BahdanauAttention(onnxruntime::AllocatorPtr, const logging::Logger &, int, int, int, int, int, bool, concurrency::ThreadPool *) [T = float]!normalize_virtual void onnxruntime::contrib::BahdanauAttention::PrepareMemory(const gsl::span &, const gsl::span &) [T = float]mem_steps <= max_memory_steps_ && mem_steps > 0Real memory steps is not in (0, AttnLSTM/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.ccvirtual onnxruntime::common::Status onnxruntime::contrib::DeepCpuAttnLstmOp::Compute(onnxruntime::OpKernelContext *) constAttention mechanism memory shape error! Expected: {}, actural: Attention mechanism memory sequence lengths must have shape {Attention mechanism memory sequence lengths value must in (0, ], while found!Attention memory layer weight shape error! Expected:{, am_attn_size}, Got:Attention query layer weight shape error! Expected:{}, Got: Attention v weight shape error! Expected:{}. Got: Attention layer weight shape error! Expected: {, aw_attn_size}. Got:Input initial_c must have shape {Input P must have shape {/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.honnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOp(const onnxruntime::OpKernelInfo &)static_cast(activation_func_names.size()) == num_directions_ * 3 [seqno= row[AttentionInput 'input' is expected to have 3 dimensions, got Input 'weights' is expected to have 2 dimensions, got Input 1 dimension 0 should have same length as dimension 2 of input 0Input 'bias' is expected to have 1 dimension, got Input 'bias' dimension 0 should have same length as dimension 1 of input 'weights'Input 'past' is expected to have 5 dimension, got Inputs 'past' dimension 1 shall have same length as dimension 0 of input 0Inputs 'past' dimension 2 shall have length of num_headsInputs 'past' dimension 2 shall have length of Inputs 'mask_index' with 1D data shall have length of batch_size or 2 * batch_sizeInputs 'mask_index' with 2D data shall have shape batch_size x (past_sequence_length + sequence_length)Inputs 'mask_index' with 3D data shall have shape batch_size x sequence_length x (past_sequence_length + sequence_length)Inputs 'mask_index' with 4D data shall have shape batch_size x 1 x max_sequence_length x max_sequence_length)Inputs 'mask_index' with 4D data shall have is_unidirectional_ set to falseInput 'mask_index' is expected to have 1, 2, 3 or 4 dimensions, got /Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/bert/attention.cconnxruntime::Tensor *onnxruntime::contrib::AttentionBase::GetPresent(onnxruntime::OpKernelContext *, const onnxruntime::Tensor *, int, int, int, int &) constExpect to have present state output when past state input is givennum_heads/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/bert/attention_base.honnxruntime::contrib::AttentionBase::AttentionBase(const onnxruntime::OpKernelInfo &)info.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0unidirectional/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/bert/attention_cpu_base.hApplyAttention/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/bert/bias_gelu.ccvirtual onnxruntime::common::Status onnxruntime::contrib::BiasGelu::Compute(onnxruntime::OpKernelContext *) const [T = float, use_approximation = false]use_approximationBiasGeluInput 0 is expected to have 1 or more dimensions, got Input 1 is expected to have 1 dimensions, got Input 1 dimension 0 should have same length as the last dimension of input 0EmbedLayerNormalization/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/bert/embed_layer_norm.cconnxruntime::contrib::EmbedLayerNorm::EmbedLayerNorm(const onnxruntime::OpKernelInfo &) [T = float]input_ids is expected to have 2 dimensions, got word_embedding is expected to have 2 dimensions, got position_embedding is expected to have 2 dimensions, got segment_embedding is expected to have 2 dimensions, got word_embedding and position_embedding shall have same dimension 1beta is expected to have 1 dimensions, got beta is expected to have size of gamma is expected to have 1 dimensions, got gamma is expected to have size of FastGeluCDistmetric/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/cdist.honnxruntime::contrib::CDist::CDist(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttr("metric", &metric).IsOK()sqeuclideaneuclideanThe first input of CDist kernel has wrong shape: The second input of CDist kernel has wrong shape: Input shape dimensions mismatch:onnxruntime::contrib::CDist::CDist(const onnxruntime::OpKernelInfo &) [T = double]ConvTransposeWithDynamicPads/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/cpu_contrib_kernels.ccRegisterNchwcKernelsRegisterQuantizationKernelsRegisterCpuContribKernelsCropborder/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/crop.hAttribute border needs to be specified with four border elements, got Input is expected to have four dimensions corresponding to [N,C,H,W], got input dimensions insteadInput's height () needs to be greater than or equal to the topBorder () + bottomBorder (Input's width () needs to be greater than or equal to the leftBorder () + rightBorder () + scale_[0] () + scale_[1] (CropAndResizebilinear/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/crop_and_resize.honnxruntime::contrib::CropAndResize::CropAndResize(const onnxruntime::OpKernelInfo &) [T = float] specified. It should be either bilinear or nearestNull crop_size_ptrNumber of dimensions for crop size should be exactly 1/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/element_wise_ops.honnxruntime::contrib::Affine::Affine(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttr("alpha", &alpha_).IsOK()info.GetAttr("beta", &beta_).IsOK()onnxruntime::contrib::Scale::Scale(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttr("scale", &scale_).IsOK()axisExpandDims/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/expand_dims.hvirtual onnxruntime::common::Status onnxruntime::contrib::ExpandDims::Compute(onnxruntime::OpKernelContext *) constaxis_tensor->Shape().IsScalar()axis <= X_NumDims && axis >= -X_NumDimsAxis must be within range [ Axis is unimplemented activation: activation_params count mismatchFusedConv/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/fused_conv.cconnxruntime::contrib::FusedConvFloat::FusedConvFloat(const onnxruntime::OpKernelInfo &)FusedGemmactivationactivation_/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/fused_gemm.cconnxruntime::contrib::FusedGemm::FusedGemm(const onnxruntime::OpKernelInfo &) [T = float]TransposeMatMulFusedMatMul/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/image_scaler.honnxruntime::contrib::ImageScaler::ImageScaler(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttrs("bias", bias_).IsOK()Bias size () does not match the number of channels (InverseLayerNormalizationSimplifiedLayerNormalization/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/layer_norm.cconnxruntime::contrib::LayerNorm::LayerNorm(const onnxruntime::OpKernelInfo &) [T = float, simplified = false]op_kernel_info.GetAttr("axis", &axis_).IsOK()onnxruntime::contrib::LayerNorm::LayerNorm(const onnxruntime::OpKernelInfo &) [T = float, simplified = true]onnxruntime::contrib::LayerNorm::LayerNorm(const onnxruntime::OpKernelInfo &) [T = double, simplified = false]onnxruntime::contrib::LayerNorm::LayerNorm(const onnxruntime::OpKernelInfo &) [T = double, simplified = true]MatMulInteger16/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/matmul_integer16.ccvirtual onnxruntime::common::Status onnxruntime::contrib::MatMulInteger16::Compute(onnxruntime::OpKernelContext *) const [T1 = short, T2 = short, T3 = int]A != nullptr && B != nullptrMaxpoolWithMask/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/maxpool_with_mask.hacross_channels/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/mean_variance_normalization.honnxruntime::MeanVarianceNormalization_0::MeanVarianceNormalization_0(const onnxruntime::OpKernelInfo &, bool) [T = float]info.GetAttr("across_channels", &across_channels_).IsOK()normalize_varianceinfo.GetAttr("normalize_variance", &normalize_variance_).IsOK()MurmurHash3/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/murmur_hash3.ccvirtual onnxruntime::common::Status onnxruntime::contrib::MurmurHash3::Compute(onnxruntime::OpKernelContext *) constkeyssizeof(uint32_t) == output_element_bytesinput_num_bytes % 4 == 0/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/nchwc_ops.ccvirtual onnxruntime::common::Status onnxruntime::contrib::ReorderInput::Compute(onnxruntime::OpKernelContext *) constX_rank == 4(channels % 4) == 0virtual onnxruntime::common::Status onnxruntime::contrib::ReorderOutput::Compute(onnxruntime::OpKernelContext *) constchannels_ <= X_shape[1]virtual onnxruntime::common::Status onnxruntime::contrib::NchwcConv::Compute(onnxruntime::OpKernelContext *) constX_shape.NumDimensions() == 4(static_cast(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)Unsupported convolution size.output and sum shape must matchonnxruntime::common::Status onnxruntime::contrib::NchwcPoolBase::NchwcPool(onnxruntime::OpKernelContext *, MLAS_POOLING_KIND) const(X_shape[1] % MlasNchwcGetBlockSize()) == 0virtual onnxruntime::common::Status onnxruntime::contrib::NchwcUpsample::Compute(onnxruntime::OpKernelContext *) constX_shape.size() == 4ReorderInputReorderOutput/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/nchwc_ops.honnxruntime::contrib::ReorderInput::ReorderInput(const onnxruntime::OpKernelInfo &)info.GetAttr("channels_last", &channels_last_).IsOK()onnxruntime::contrib::ReorderOutput::ReorderOutput(const onnxruntime::OpKernelInfo &)info.GetAttr("channels", &channels_).IsOK()channels_ > 0invalid channel countonnxruntime::contrib::NchwcConv::NchwcConv(const onnxruntime::OpKernelInfo &)GetFusedActivationAttr(info, activation_).IsOK()onnxruntime::contrib::NchwcPoolBase::NchwcPoolBase(const onnxruntime::OpKernelInfo &)pool_attrs_.kernel_shape.size() == 2onnxruntime::contrib::NchwcUpsample::NchwcUpsample(const onnxruntime::OpKernelInfo &)info.GetAttrs("scales", scales_).IsOK()scales_.size() == 4scales_[0] == 1 && scales_[1] == 1 && scales_[2] >= 1 && scales_[3] >= 1info.GetAttr("coordinate_transformation_mode", &transformation_mode).IsOK()Unsupported transformation mode '' for NCHWc Upsampletransformation_mode_ == TransformationMode::ASYMMETRICUnsupported mode 'QLinearAddQLinearMul/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/qlinear_binary_op.ccvoid onnxruntime::contrib::(anonymous namespace)::QLinearImpl(onnxruntime::OpKernelContext &, double, const onnxruntime::ProcessBroadcastSpanFuncs &) [T = signed char]IsScalarOr1ElementVector(tensor_a_scale)MatmulInteger : input1 A_scale must be a scalar or 1D tensor of size 1tensor_a_zero_point == nullptr || IsScalarOr1ElementVector(tensor_a_zero_point)MatmulInteger : input1 A_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_b_scale)MatmulInteger : input1 B_scale must be a scalar or 1D tensor of size 1tensor_b_zero_point == nullptr || IsScalarOr1ElementVector(tensor_b_zero_point)MatmulInteger : input1 B_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_c_scale)MatmulInteger : input1 C_scale must be a scalar or 1D tensor of size 1tensor_c_zero_point == nullptr || IsScalarOr1ElementVector(tensor_c_zero_point)MatmulInteger : input1 C_zero_point must be a scalar or 1D tensor of size 1 if givenvoid onnxruntime::BroadcastLooper(TBroadcastHelper &, const onnxruntime::ProcessBroadcastSpanFuncs &) [TBroadcastHelper = onnxruntime::contrib::(anonymous namespace)::QLinearBroadcastHelper]void onnxruntime::contrib::(anonymous namespace)::QLinearImpl(onnxruntime::OpKernelContext &, double, const onnxruntime::ProcessBroadcastSpanFuncs &) [T = unsigned char]/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/qlinear_concat.cconnxruntime::contrib::QLinearConcat::QLinearConcat(const onnxruntime::OpKernelInfo &)input_def_count >= 8 && (input_def_count - 2) % 3 == 0At least two inputs are needed, and each input must be (tensor, scale, zero_point) tuple!tensor_x_scale->IsDataType()Input scale is not float for input def @tensor_x_zero_point->GetElementType() == tensor_y_zero_point->GetElementType()Wrong input type encountered for zero point input def @virtual onnxruntime::common::Status onnxruntime::contrib::QLinearConcat::Compute(onnxruntime::OpKernelContext *) constinput_count_x3 >= 6 && input_count_x3 % 3 == 0Input scale is not float for quantized input @Wrong input type encountered for zero point of quantized input @QLinearConcat/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/qlinear_global_average_pool.ccvirtual onnxruntime::common::Status onnxruntime::contrib::QLinearGlobalAveragePool::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(tensor_x_zero_point)IsScalarOr1ElementVector(tensor_y_zero_point)QLinearGlobalAveragePoolQLinearLeakyReluQLinearSigmoid/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/qlinear_lookup_table.ccvoid onnxruntime::contrib::QlinearBuildLookupTable(uint8_t *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::contrib::LookupTableArrayTransformer &) [T = unsigned char]QlinearBuildLookupTable : input X_scale must be a scalar or 1D tensor of size 1QlinearBuildLookupTable : input X_zero_point must be a scalar or 1D tensor of size 1QlinearBuildLookupTable : input Y_scale must be a scalar or 1D tensor of size 1QlinearBuildLookupTable : input Y_zero_point must be a scalar or 1D tensor of size 1void onnxruntime::contrib::QlinearBuildLookupTable(uint8_t *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::contrib::LookupTableArrayTransformer &) [T = signed char]/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/qlinear_pool.ccvirtual onnxruntime::common::Status onnxruntime::contrib::QLinearAveragePool::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(tensor_x_scale)tensor_x_zero_point == nullptr || IsScalarOr1ElementVector(tensor_x_zero_point)input x_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_y_scale)tensor_y_zero_point == nullptr || IsScalarOr1ElementVector(tensor_y_zero_point)input y_zero_point must be a scalar or 1D tensor of size 1 if givenUnsupported 'dtype' in QLinear Pooling:QLinear Pooling unsupported pooling size!QLinearAveragePoolQAttention/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/attention_quant.ccinput scale must be a scalar or 1D tensor of size 1input zero point must be a scalar or 1D tensor of size 1./Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/dynamic_quantize_lstm.ccW_zero_point must have shape {} for per-tensor/layer quantization or shape {, 4*} for per-channel quantization. Actual:R_zero_pointW_scaleR_scaleDynamicQuantizeLSTM : Weight zero point must be zeroWeight point must be constantRecurrentDynamicQuantizeLSTM/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/dynamic_quantize_matmul.ccComputeCommononnxruntime::common::Status onnxruntime::contrib::MatMulIntegerToFloatBase::ComputeCommon(onnxruntime::OpKernelContext *, const uint8_t *, const onnxruntime::TensorShape &, float, uint8_t, const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *) constIsBQuantParamSupported(b_zp_tensor->Shape(), b_tensor ? b_tensor->Shape() : b_shape_)virtual onnxruntime::common::Status onnxruntime::contrib::MatMulIntegerToFloat::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(a_zero_point_tensor)MatMulIntegerToFloat : input a zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.DynamicQuantizeMatMulMatMulIntegerToFloat/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/nhwc_max_pool.ccvirtual onnxruntime::common::Status onnxruntime::contrib::NhwcMaxPool::Compute(onnxruntime::OpKernelContext *) constinput_shape.Size() > 0 || N == 0NhwcMaxPoolSampleOpSkipLayerNormalization/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/skip_layer_norm.cconnxruntime::contrib::SkipLayerNorm::SkipLayerNorm(const onnxruntime::OpKernelInfo &) [T = float]epsilon_ >= 0input is expected to have 3 dimensions, got gamma is expected to have 1 dimension, got beta is expected to have 1 dimension, got bias is expected to have 1 dimension, got onnxruntime::contrib::SkipLayerNorm::SkipLayerNorm(const onnxruntime::OpKernelInfo &) [T = double]Tokenizer/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/tokenizer.cconnxruntime::contrib::Tokenizer::Tokenizer(const onnxruntime::OpKernelInfo &)pad_valuemincharnummincharnum_ > 0separatorsEither one of the separators OR tokenexp attributes required but none is set!tokenexp.empty()!separators.empty()!char_tokenezation_ || mincharnum_ < 2Can not digest separators: Can not digest tokenexp: Input string contains invalid utf8 chars: Match contains invalid utf8 chars: tensor(string) expected as inputInput dimensions are either [C] or [N][C] allowedConv filter size does not match embedding_size attribute. embedding_size attribute: conv filter size: Conv kernal size 1 does not match conv_window_size attribute . conv_window_size attribute: conv kernal size 1: Char embedding size does not match char_embedding_size attribute. char_embedding_size attribute: Char embedding size: Char embedding size does not match conv kernal size 2. Conv kernal size 2 : /Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/word_conv_embedding.ccWordConvEmbeddingembedding_sizeconv_window_sizechar_embedding_size/Users/runner/work/1/s/onnxruntime/core/util/math_cpu.ccGemv found an unexpected CBLAS_TRANSPOSE input ofvoid onnxruntime::math::Gemv(CBLAS_TRANSPOSE, int, int, float, const T *, const T *, float, T *, Provider *) [T = double, Provider = onnxruntime::CPUMathUtil]bool onnxruntime::math::NextPosition(int64_t, const int64_t *, int64_t *)dims[d_i] < d_maxReceived null OrtThreadingOptionsReceived invalid value for allow_spinning. Valid values are 0 or 1/Users/runner/work/1/s/onnxruntime/core/framework/allocation_planner.cc[CreatePlanCan not find the node onnxruntime::common::Status onnxruntime::PlannerImpl::ComputeUseCounts()p_kernel_defShould not have entry in kernel create info with nullptr for kernel_defCan not find the execution provider ComputeUseCountsallocatoronnxruntime::OrtValueIndex onnxruntime::PlannerImpl::Index(const onnxruntime::OrtValueName &)void onnxruntime::PlannerImpl::ProcessDef(onnxruntime::OrtValueIndex, const onnxruntime::NodeArg *)id >= 0 && static_cast(id) < ort_value_info_.size()int &onnxruntime::PlannerImpl::UseCount(onnxruntime::OrtValueIndex)n >= 0 && static_cast(n) < ort_value_info_.size()const onnxruntime::KernelCreateInfo &onnxruntime::GetKernelCreateInfo(const std::unordered_map > &, onnxruntime::NodeIndex)entry != kernel_create_info_map.cend()SessionState should have saved the KernelCreateInfo prior to this running. NodeIndex:onnxruntime::AllocPlanPerValue &onnxruntime::PlannerImpl::AllocPlan(onnxruntime::OrtValueIndex)n >= 0 && static_cast(n) < plan_.allocation_plan.size()ComputeReusePlanonnxruntime::common::Status onnxruntime::PlannerImpl::ComputeReusePlan()!IsNonTensor(*node_output)GeneratePlanForWeightsOrtMemoryInfo onnxruntime::PlannerImpl::GetLocationForNodeInput(size_t, const onnxruntime::Node &)void onnxruntime::PlannerImpl::Reuse(onnxruntime::OrtValueIndex, onnxruntime::OrtValueIndex, onnxruntime::AllocKind)reused != reused_for/Users/runner/work/1/s/onnxruntime/core/framework/sequential_execution_plan.hvoid onnxruntime::AllocPlanPerValue::ProgramCounter::AddStart(size_t)starts_.size() == ends_.size()starts_.empty() || start > ends_.back()static size_t onnxruntime::PlannerImpl::GetElementSize(const onnx::DataType &)nullptr != tensor_type_baseonnxruntime::OrtValueIndex &onnxruntime::PlannerImpl::Buffer(onnxruntime::OrtValueIndex)void onnxruntime::AllocPlanPerValue::ProgramCounter::AddEnd(size_t)starts_.size() == ends_.size() + 1end >= starts_.back()void onnxruntime::PlannerImpl::GenerateDeallocationPlan()AllocPlan(ml_value_idx).program_counter.Ends().back() == program_countervoid onnxruntime::PlannerImpl::VerifyMemoryTimeSchedule()entry.program_counter.HasValidEntries()Invalid program_counter entries at index CudaCudaPinnedSpecified device is not supported./Users/runner/work/1/s/onnxruntime/core/framework/allocator.ccoperator()/Users/runner/work/1/s/include/onnxruntime/core/common/logging/logging.hstatic const onnxruntime::logging::Logger &onnxruntime::logging::LoggingManager::DefaultLogger()OrtMemoryInfo:[name: id: OrtMemType: OrtAllocatorType:]Device:[DeviceType: MemoryType: DeviceId:/Users/runner/work/1/s/onnxruntime/core/framework/allocatormgr.ccCreateAllocatorReceived invalid value of arena_extend_strategy void onnxruntime::AllocatorManager::InsertAllocator(onnxruntime::AllocatorPtr)/Users/runner/work/1/s/onnxruntime/core/framework/bfc_arena.ccBFCArenaCreating BFCArena for with following configs: initial_chunk_size_bytes: max_dead_bytes_per_chunk: initial_growth_chunk_size_bytes: memory limit: arena_extend_strategy: Creating bins of max chunk size onnxruntime::BFCArena::BFCArena(std::unique_ptr, size_t, onnxruntime::ArenaExtendStrategy, int, int, int)BinForSize(bin_size) == BinFromIndex(b)BinForSize(bin_size + 255) == BinFromIndex(b)BinForSize(bin_size * 2 - 1) == BinFromIndex(b)BinForSize(bin_size * 2) != BinFromIndex(b)BFCArena::Chunk *onnxruntime::BFCArena::ChunkFromHandle(onnxruntime::BFCArena::ChunkHandle)h < chunks_.size()Available memory of is smaller than requested bytes of Failed to allocate memory for requested buffer of size ExtendExtended allocation by bytes.Total allocated bytes: Allocated memory at ReserveReserving memory in BFCArena for size: virtual void *onnxruntime::BFCArena::Reserve(size_t)reserved_chunks_.find(ptr) == reserved_chunks_.end()h != kInvalidChunkHandleAllocateRawInternaltried to allocate 0 bytesExtending BFCArena for . bin_num: (requested) num_bytes: (actual) rounded_bytes:Failed to find a free memory block despite calling Extend. rounded_bytes=BFC Arena ran out of memory trying to allocate . Current allocation summary follows.void *onnxruntime::BFCArena::AllocateRawInternal(size_t, bool)void *onnxruntime::BFCArena::FindChunkPtr(onnxruntime::BFCArena::BinNum, size_t, size_t)!chunk->in_use()void onnxruntime::BFCArena::SplitChunk(BFCArena::ChunkHandle, size_t)!c->in_use() && (c->bin_num == kInvalidBinNum) BFC Arena shrunk by bytes. The total allocated bytes is now void onnxruntime::BFCArena::DeallocateRawInternal(void *)void onnxruntime::BFCArena::Merge(BFCArena::ChunkHandle, BFCArena::ChunkHandle)!c1->in_use() && !c2->in_use()c2->prev == h1void onnxruntime::BFCArena::InsertFreeChunkIntoBin(BFCArena::ChunkHandle)void onnxruntime::BFCArena::RemoveFreeChunkIterFromBin(BFCArena::Bin::FreeChunkSet *, const BFCArena::Bin::FreeChunkSet::iterator &)!c->in_use() && (c->bin_num != kInvalidBinNum)void onnxruntime::BFCArena::RemoveFreeChunkFromBin(BFCArena::ChunkHandle)BinFromIndex(c->bin_num)->free_chunks.erase(h) > 0void onnxruntime::BFCArena::FreeAndMaybeCoalesce(BFCArena::ChunkHandle)c->in_use() && (c->bin_num == kInvalidBinNum)std::array onnxruntime::BFCArena::get_bin_debug_info()bin->free_chunks.count(h) == 1c->bin_num == bin_numDumpMemoryLogAllocator:Bin size: Chunks in_use/total (if not zero). Allocated bytes in_use/total. Requested bytes.void onnxruntime::BFCArena::DumpMemoryLog(size_t)b->free_chunks.size() == bin_info.total_chunks_in_bin - bin_info.total_chunks_in_use: Chunks . Bytes . Requested Diff between in-use and requested bytes is Bin for bytes has max bytes of , Chunk State: Overall chunks summary: Chunk Free at of size Summary of in-use chunks by size: chunks of size . Total Sum Total of in-use chunks: Stats: auto onnxruntime::BFCArena::Extend(size_t)::(anonymous class)::operator()(const size_t) constIncorrect arena extend strategy./Users/runner/work/1/s/onnxruntime/core/framework/bfc_arena.honnxruntime::BFCArena::AllocationRegion::AllocationRegion(void *, size_t, int64_t)0 == memory_size % kMinAllocationSizeRegionForCould not find Region for int onnxruntime::BFCArena::AllocationRegion::IndexFor(const void *) constp_int >= base_intp_int < base_int + memory_size_void onnxruntime::BFCArena::RegionManager::RemoveAllocationRegion(void *)entry != regions_.end()Could not find Region for: Size: | Requested Size: | in_use: , prev: , next: Limit: InUse: TotalAllocated: MaxInUse: NumAllocs: NumReserves: NumArenaExtensions: NumArenaShrinkages: MaxAllocSize: /Users/runner/work/1/s/onnxruntime/core/framework/config_options.ccAddConfigEntryConfig with key [] already exists with value []. It will be overwritten/Users/runner/work/1/s/onnxruntime/core/framework/data_transfer.ccvirtual common::Status onnxruntime::CPUDataTransfer::CopyTensor(const onnxruntime::Tensor &, onnxruntime::Tensor &, int) constsrc.SizeInBytes() == dst.SizeInBytes()data_transfer registered is nullptr.Tensor size mismatchThere's no data transfer registered for copying tensors from to /Users/runner/work/1/s/onnxruntime/core/framework/data_transfer_manager.ccCopyTensors/Users/runner/work/1/s/onnxruntime/core/framework/data_types.ccbool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Map &, const onnx::TypeProto_Map &)bool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Sequence &, const onnx::TypeProto_Sequence &)virtual bool onnxruntime::TensorTypeBase::IsCompatible(const onnx::TypeProto &) constthisProto->value_case() == TypeProto::ValueCase::kTensorTypeutils::HasElemType(thisProto->tensor_type())virtual bool onnxruntime::SparseTensorTypeBase::IsCompatible(const onnx::TypeProto &) constthisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypeutils::HasElemType(thisProto->sparse_tensor_type())virtual bool onnxruntime::SequenceTensorTypeBase::IsCompatible(const onnx::TypeProto &) constthisProto->value_case() == TypeProto::ValueCase::kSequenceTypeutils::HasElemType(thisProto->sequence_type())bool onnxruntime::NonTensorTypeBase::IsMapCompatible(const onnx::TypeProto &) constthisProto->value_case() == TypeProto::ValueCase::kMapTypeutils::HasKeyType(thisProto->map_type())bool onnxruntime::NonTensorTypeBase::IsSequenceCompatible(const onnx::TypeProto &) constvirtual void onnxruntime::NonTensorTypeBase::FromDataContainer(const void *, size_t, OrtValue &) constNot implementedvirtual void onnxruntime::NonTensorTypeBase::ToDataContainer(const OrtValue &, size_t, void *) const(null)floatbooldoublestringint8uint8int16uint16int32uint32int64uint64float16bfloat16tensor type MLDataType for: is not currently registered or supportedonnxruntime::utils::ContainerChecker::ContainerChecker(onnxruntime::MLDataType)void onnxruntime::data_types_internal::DataTypeRegistry::RegisterDataType(onnxruntime::MLDataType)proto != nullptrp.secondWe do not expect duplicate registration of types for: GetElementType is not implemented/Users/runner/work/1/s/include/onnxruntime/core/framework/data_types.hstatic void onnxruntime::data_types_internal::SetMapTypes, std::__1::basic_string >::Set(onnx::TypeProto &) [K = std::__1::basic_string, V = std::__1::basic_string]value_proto != nullptr expected to be a registered ONNX typestatic void onnxruntime::data_types_internal::SetMapTypes, long long>::Set(onnx::TypeProto &) [K = std::__1::basic_string, V = long long]static void onnxruntime::data_types_internal::SetMapTypes, float>::Set(onnx::TypeProto &) [K = std::__1::basic_string, V = float]static void onnxruntime::data_types_internal::SetMapTypes, double>::Set(onnx::TypeProto &) [K = std::__1::basic_string, V = double]static void onnxruntime::data_types_internal::SetMapTypes >::Set(onnx::TypeProto &) [K = long long, V = std::__1::basic_string]static void onnxruntime::data_types_internal::SetMapTypes::Set(onnx::TypeProto &) [K = long long, V = long long]static void onnxruntime::data_types_internal::SetMapTypes::Set(onnx::TypeProto &) [K = long long, V = float]static void onnxruntime::data_types_internal::SetMapTypes::Set(onnx::TypeProto &) [K = long long, V = double]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = float]elem_proto != nullptrstatic void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = double]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = signed char]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = unsigned char]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = short]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = unsigned short]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = int]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = unsigned int]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = long long]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = unsigned long long]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = bool]static void onnxruntime::data_types_internal::SetSequenceType >::Set(onnx::TypeProto &) [T = std::__1::basic_string]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = onnxruntime::MLFloat16]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = onnxruntime::BFloat16]static void onnxruntime::data_types_internal::SetSequenceType, float, std::__1::less >, std::__1::allocator, float> > > >::Set(onnx::TypeProto &) [T = std::__1::map, float, std::__1::less >, std::__1::allocator, float> > >]static void onnxruntime::data_types_internal::SetSequenceType, std::__1::allocator > > >::Set(onnx::TypeProto &) [T = std::__1::map, std::__1::allocator > >]/Users/runner/work/1/s/onnxruntime/core/framework/endian_utils.ccCopyLittleEndiansource and destination buffer size mismatch/Users/runner/work/1/s/onnxruntime/core/framework/ex_lib_loader.cc~ExLibLoaderUnloading DSO Failed to unload DSO: A dso with name has already been loaded.LoadExternalLibCaught exception while destructing CustomOpsLoader with message: Caught exception while loading custom ops with message: /Users/runner/work/1/s/onnxruntime/core/framework/execution_frame.cconnxruntime::IExecutionFrame::IExecutionFrame(const onnxruntime::OrtValueNameIdxMap &, const onnxruntime::NodeIndexInfo &, const std::vector &)node_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx()node_index_info and ort_value_idx_map are out of sync and cannot be usedonnxruntime::common::Status onnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValue(const int, int, const onnxruntime::TensorShape *, OrtValue *&, const onnxruntime::Node &, size_t)shape && tensor.Shape() == *shapeOrtValue shape verification failed. Current shape: Requested shape:invalid index void onnxruntime::IExecutionFrame::Init(const std::vector &, const std::vector &, const std::unordered_map &, const std::vector &)feeds.size() == feed_mlvalue_idxs.size()fetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size()Fetches vector passed to GetOutputs contains entries which doesn't match the number of fetches the frame was initialized with of onnxruntime::ExecutionFrame::ExecutionFrame(const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::unordered_map &, const onnxruntime::SessionState &)buffers_.find(location) == buffers_.end()static_activation_memory_sizes_in_byte_.find(location.name) == static_activation_memory_sizes_in_byte_.end()Memory type should only appear once.ExecutionFrameAllocation of memory pattern buffer for returned nullptrTrying to allocate memory for unused optional inputs/outputsTensor shape cannot contain any negative valueonnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper(OrtValue &, int, onnxruntime::MLDataType, const OrtMemoryInfo &, const onnxruntime::TensorShape &, bool)ort_value.Fence() == nullptrAllocateMLValueTensorSelfOwnBufferHelperFor ort_value with index: , block in memory pattern size is: but the actually size is: , fall back to default allocation behaviorShape mismatch attempting to re-use buffer. . Validate usage of dim_value (values should be > 0) and dim_param (all values with the same string should equate to the same size) in shapes in the model.AllocateMLValueTensorPreAllocateBufferonnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlan(OrtValue &, int, const onnxruntime::TensorShape *, size_t)ort_value_index >= 0 && static_cast(ort_value_index) < alloc_plan.size()Tried to allocate without valid type information, ort_value index=We don't expect custom allocators for non-tensor types, so a shape is mandatory here.AllocateAsPerAllocationPlanInvalid allocation kind: VerifyOutputSizesExpected shape from model of does not match actual shape of for output ReleaseMLValueImplconst onnxruntime::AllocPlanPerValue &onnxruntime::ExecutionFrame::GetAllocationPlan(int)ort_value_idx >= 0 && static_cast(ort_value_idx) < alloc_plan.size()TraceAllocateTraceAllocation for ort_value_idx= size= failed: void onnxruntime::ExecutionFrame::TraceFree(int)TraceFreeTraceFree for ort_value_idx=Memory pattern planner is not enabled on this execution framework./Users/runner/work/1/s/onnxruntime/core/framework/execution_frame.hconst OrtValue &onnxruntime::IExecutionFrame::GetMLValue(int) constort_value_index >= 0 && static_cast(ort_value_index) < all_values_size_int onnxruntime::NodeIndexInfo::GetMLValueIndex(int) constoffset >= 0 && static_cast(offset) < node_values_size_ failed. Error:onnxruntime::common::Status onnxruntime::AllocateSparseTensor(OrtValue &, const onnxruntime::DataTypeImpl &, onnxruntime::AllocatorPtr, const onnxruntime::TensorShape &, size_t, bool, const onnxruntime::SessionState &)mlvalue.Fence() == nullptr/Users/runner/work/1/s/onnxruntime/core/framework/execution_provider.ccvoid onnxruntime::IExecutionProvider::InsertAllocator(onnxruntime::AllocatorPtr)TryInsertAllocatorduplicated allocator: IExecutionProvider::Compile with fused Node is not implemented by IExecutionProvider::Compile with fused Node and dll path is not implemented by IExecutionProvider::Compile with FusedNodeAndGraph is not implemented by virtual int onnxruntime::IExecutionProvider::GenerateMetaDefId(const onnxruntime::GraphViewer &, uint64_t &) constmetadef_id_generator_IExecutionProvider constructor must be called with true for use_metadef_id_creator/Users/runner/work/1/s/onnxruntime/core/framework/fallback_cpu_capability.ccstd::unordered_set onnxruntime::GetCpuPreferredNodes(const onnxruntime::GraphViewer &, const std::string &, const std::vector &, const std::vector &)kernel_info != nullptrGetCpuPreferredNodesORT optimization- Force fallback to CPU execution for node: because the CPU execution path is deemed faster than overhead involved with execution on other EPs capable of executing this nodeForEachWithIndexCandidate for fallback CPU execution: /Users/runner/work/1/s/onnxruntime/core/framework/feeds_fetches_manager.ccMapNamesToMLValueIdxsError mapping feeds: Error mapping output names: void onnxruntime::FeedsFetchesManager::SetDeviceCopyChecks(onnxruntime::DeviceCopyCheck, onnxruntime::DeviceCopyCheck)input_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::Unknownstatic void SafeIntExceptionHandler::SafeIntOnDivZero()func info for node: already exist.Can't use func with null ptr not found./Users/runner/work/1/s/onnxruntime/core/framework/fuse_nodes_funcs.ccGetFuncs/Users/runner/work/1/s/onnxruntime/core/framework/graph_partitioner.ccPartitionOnnxFormatModelNo provider specified.Partitiononnxruntime::common::Status onnxruntime::GraphPartitioner::Partition(onnxruntime::Graph &, bool, onnxruntime::FuncManager &, onnxruntime::GraphPartitioner::Mode, std::unordered_map *) constcompiled_kernel_hashes != nullptrPartitionOnnxFormatModelImplonnxruntime::common::Status onnxruntime::PartitionOnnxFormatModelImpl(onnxruntime::Graph &, bool, onnxruntime::FuncManager &, onnxruntime::KernelRegistryManager &, onnxruntime::KernelRegistry &, onnxruntime::IExecutionProvider &, GraphPartitioner::Mode, int &)fusion_style == IExecutionProvider::FusionStyle::FunctionMust use Function based fusion when exporting compiled nodes to dll. did not return correct number of compiled functionsonnxruntime::Node *onnxruntime::PlaceNode(onnxruntime::Graph &, const onnxruntime::IndexedSubGraph &, const onnxruntime::KernelRegistryManager &, const std::string &, IExecutionProvider::FusionStyle, GraphPartitioner::Mode, int &)1 == capability.nodes.size()/Users/runner/work/1/s/onnxruntime/core/framework/func_kernel.honnxruntime::FunctionKernel::FunctionKernel(const onnxruntime::OpKernelInfo &)compute_info_->create_state_func(&context, &func_state_) == 0InlineNodesPartitionOrtFormatModelImplEP: has Compile error: single_node_compute_func should have 1 elementsonnxruntime::common::Status onnxruntime::PartitionOrtFormatModelImpl(onnxruntime::Graph &, onnxruntime::FuncManager &, onnxruntime::KernelRegistryManager &, onnxruntime::KernelRegistry &, onnxruntime::IExecutionProvider &, std::unordered_map &, int &)Existing entry in compiled kernel hashes for . Execution Provider must generate unique names across the entire model./Users/runner/work/1/s/onnxruntime/core/framework/kernel_def_builder.cconnxruntime::KernelDefBuilder &onnxruntime::KernelDefBuilder::VariadicAlias(int, int)input_offset >= 0 && output_offset >= 0Op with name ( and type ( Version mismatch. node_version: kernel start version: kernel_end_version: Found kernel for Op with name ( in the supported version range (node_version: However the types are incompatible. This op has been implemented only for the following types (), but the node in the model has the following type (/Users/runner/work/1/s/onnxruntime/core/framework/kernel_registry.ccTryCreateKernel kernel not found in No matching hash for kernel is not supported in Encountered following errors: (Kernel not foundkernel def can't be NULLFailed to add kernel for : Conflicting with a registered kernel with op versions.void onnxruntime::(anonymous namespace)::TraverseFormalParametersWithTypeProto(const onnxruntime::Node &, ParamFilterFn, TraverseFn) [ParamFilterFn = (lambda at /Users/runner/work/1/s/onnxruntime/core/framework/kernel_registry.cc:75:11), TraverseFn = (lambda at /Users/runner/work/1/s/onnxruntime/core/framework/kernel_registry.cc:76:11)]len <= op_schema.inputs().size()void onnxruntime::(anonymous namespace)::TraverseFormalParametersWithTypeProto(const onnxruntime::Node &, ParamFilterFn, TraverseFn) [ParamFilterFn = (lambda at /Users/runner/work/1/s/onnxruntime/core/framework/kernel_registry.cc:100:9), TraverseFn = (lambda at /Users/runner/work/1/s/onnxruntime/core/framework/kernel_registry.cc:103:9)]found duplicated provider in KernelRegistryManager (node ). /Users/runner/work/1/s/onnxruntime/core/framework/mldata_type_utils.cconnxruntime::MLDataType onnxruntime::utils::GetMLDataType(const onnxruntime::NodeArg &)nullptr != type_proto/Users/runner/work/1/s/onnxruntime/core/framework/node_index_info.ccauto onnxruntime::NodeIndexInfo::Init(const onnxruntime::ConstGraphNodes &, onnxruntime::NodeIndex, const onnxruntime::OrtValueNameIdxMap &)::(anonymous class)::operator()(const onnxruntime::NodeArg &, bool) constauto onnxruntime::NodeIndexInfo::Init(const onnxruntime::ValidNodes > > &, onnxruntime::NodeIndex, const onnxruntime::OrtValueNameIdxMap &)::(anonymous class)::operator()(const onnxruntime::NodeArg &, bool) consttype_proto is not of type map!type_proto is not of type sequence!OrtValue is TensorSequence type but has no element Tensor DataType.Tensor types should have been handled alreadynot implemented is not supportedconst onnxruntime::SparseTensor &OrtValue::Get() constIsSparseTensor()Trying to get a SparseTensor, but got: /Users/runner/work/1/s/onnxruntime/core/framework/op_kernel.cconnxruntime::OpKernelContext::OpKernelContext(onnxruntime::IExecutionFrame *, const onnxruntime::OpKernel *, concurrency::ThreadPool *, const logging::Logger &)frame != nullptrkernel != nullptrOrtValue *onnxruntime::OpKernelContext::OutputMLValue(int, const onnxruntime::TensorShape &, size_t)int onnxruntime::OpKernelContext::NumVariadicInputs(size_t) constarg_num < arg_counts.size()Invalid arg_num of . Num args is TempSpace allocator not foundOrtValue *onnxruntime::OpKernelContext::GetOrCreateOutputMLValue(int)/Users/runner/work/1/s/onnxruntime/core/framework/node_index_info.hint onnxruntime::NodeIndexInfo::GetNodeOffset(onnxruntime::NodeIndex) constnode_offsets_index < node_offsets_size_/Users/runner/work/1/s/onnxruntime/core/framework/op_kernel_info.ccconst OrtMemoryInfo &onnxruntime::OpKernelInfo::GetMemoryInfo(int, OrtMemType) constNo attribute with name: is defined.Requested attribute: is expected to have type: but is of type: Attibute name and type don't matchNo attribute with this name is defined.Attribute: expected to be of type: /Users/runner/work/1/s/onnxruntime/core/framework/mem_pattern_planner.hvoid onnxruntime::MemPatternPlanner::TraceAllocation(int, size_t)!using_counters_current <= buffer_size_/Users/runner/work/1/s/onnxruntime/core/framework/ort_value_tensor_slicer.ccstatic OrtValueTensorSlicer onnxruntime::OrtValueTensorSlicer::Create(T &, int64_t, int64_t) [T = OrtValue]ort_value.IsTensor()Can't slice a non-tensor OrtValue. Type was ort_value.IsAllocated()gsl::narrow_cast(tensor_shape.NumDimensions()) >= slice_dimensionInsufficient dimensions to slice on . Shape:dim0_offset < dim0_sizeInvalid dim0_offset of . Dimension 0 is onnxruntime::OrtValueTensorSlicer::Iterator::Iterator(T &, size_t, size_t, int64_t, onnxruntime::OrtValueTensorSlicer::Iterator::Direction) [T = OrtValue]typename std::enable_if::value, reference>::type onnxruntime::OrtValueTensorSlicer::Iterator::operator*() [T = OrtValue]static OrtValueTensorSlicer onnxruntime::OrtValueTensorSlicer::Create(T &, int64_t, int64_t) [T = const OrtValue]onnxruntime::OrtValueTensorSlicer::Iterator::Iterator(T &, size_t, size_t, int64_t, onnxruntime::OrtValueTensorSlicer::Iterator::Direction) [T = const OrtValue]Multiple errors were found./Users/runner/work/1/s/onnxruntime/core/framework/parallel_executor.ccRunNodeAsyncBegin executionExiting due to terminate flag being set to true.onnxruntime::common::Status onnxruntime::ParallelExecutor::RunNodeAsync(size_t, const onnxruntime::SessionState &, const logging::Logger &)Got nullptr from GetKernel for node: _fence_beforeNon-zero status code returned while running node. Name:'' Status Message: _kernel_time_fence_after/Users/runner/work/1/s/onnxruntime/core/framework/op_kernel_context_internal.honnxruntime::OpKernelContextInternal::OpKernelContextInternal(const onnxruntime::SessionState &, onnxruntime::IExecutionFrame &, const onnxruntime::OpKernel &, const logging::Logger &, const bool &)entry != nullptrAll implicit inputs should have OrtValue instances by now. does not.Exception running nodes starting at '. Unknown exception was caught by catch-all handler./Users/runner/work/1/s/onnxruntime/core/framework/prepacked_weights.ccuint64_t onnxruntime::PrePackedWeights::GetHash() constbuffers_.size() == buffer_sizes_.size()/Users/runner/work/1/s/onnxruntime/core/framework/prepacked_weights_container.cconnxruntime::AllocatorPtr onnxruntime::PrepackedWeightsContainer::GetOrCreateAllocator(const std::string &)Unsupported device allocator in the context of pre-packed weights caching: libonnxruntime_providers_cuda.dyliblibonnxruntime_providers_dnnl.dyliblibonnxruntime_providers_openvino.dyliblibonnxruntime_providers_tensorrt.dylib/Users/runner/work/1/s/onnxruntime/core/framework/provider_bridge_ort.ccGetProviderInfo_CUDAGetProviderInfo_CUDA called, returning nullptrProviderInfo_CUDA *onnxruntime::GetProviderInfo_CUDA()SessionOptionsAppendExecutionProvider_Tensorrt: Failed to load shared librarySessionOptionsAppendExecutionProvider_OpenVINO: Failed to load shared libraryCUDA execution provider is not enabled.OrtSessionOptionsAppendExecutionProvider_Cuda: Failed to load shared libraryEnsureGetunordered_map::at: key not found/Users/runner/work/1/s/include/onnxruntime/core/framework/op_kernel_context.hconst T *onnxruntime::OpKernelContext::Input(int) const [T = onnxruntime::Tensor]Missing Input: const T &onnxruntime::OpKernelContext::RequiredInput(int) const [T = onnxruntime::Tensor]input_ptrRequired input at index is not present.onnxruntime::Tensor *onnxruntime::OpKernelContext::Output(int)p_ml_valueonnxruntime::Tensor &onnxruntime::OpKernelContext::RequiredOutput(int, const onnxruntime::TensorShape &)output_ptrRequired output at index gsl::span onnxruntime::Tensor::DataAsSpan() const [T = long long]void *onnxruntime::Tensor::MutableDataRaw(onnxruntime::MLDataType)type == dtype_Tensor type mismatch.const void *onnxruntime::Tensor::DataRaw(onnxruntime::MLDataType) constvoid onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape &)shape_.Size() == new_shape.Size()Tensor size () != new size (/Users/runner/work/1/s/onnxruntime/core/framework/sequential_executor.cc[Memory] ExecutionFrame statically allocates [Memory] ExecutionFrame dynamically allocates ReleaseNodeMLValues/Users/runner/work/1/s/onnxruntime/core/framework/session_state.ccSetupAllocatorsAllocator already registered for . Ignoring allocator from CreateGraphInfoSaveMLValueNameIndexMappingDone saving OrtValue mappings.PopulateKernelCreateInfoconst onnxruntime::KernelCreateInfo &onnxruntime::SessionState::GetNodeKernelCreateInfo(onnxruntime::NodeIndex) constentry != kernel_create_info_map_.cend()duplicated ort_value index:. Do you have duplicated calls to SessionState::AddInitializedTensor function?Using an input in multiple nodes on different devices is not supported currently. Input: is used by node ) and node Failed to find input name in the mapping: void onnxruntime::SessionState::AddOutputNameToNodeInfoMapping(const std::string &, const onnxruntime::SessionState::NodeInfo &)output_names_to_nodeinfo.empty()Only one node should produce an output. Existing entry for void onnxruntime::SessionState::AddSubgraphSessionState(onnxruntime::NodeIndex, const std::string &, std::unique_ptr)existing_entries.find(attribute_name) == existing_entries.cend()Entry exists in node for attribute const onnxruntime::NodeIndexInfo &onnxruntime::SessionState::GetNodeIndexInfo() constnode_index_info_SetGraphAndCreateKernels must be called prior to GetExecutionInfo.void onnxruntime::SessionState::UpdateToBeExecutedNodes(const std::vector &)onnxruntime::common::Status onnxruntime::SessionState::CreateSubgraphSessionState()subgraphMain Graph instance should have populated all subgraphs when being resolved.CreateSubgraphSessionStateKernel create info is null. Invalid ORT format model.Kernel create info node indices are null. Invalid ORT format model.Kernel create info hashes are null. Invalid ORT format model.Size mismatch for kernel create info node indexes and hashes. Invalid ORT format model.Can't find node with index . Invalid ORT format model.Unable to find compiled kernel hash for node ''.SessionState for subgraphs is null. Invalid ORT format model.Subgraph SessionState entry for is missing. Invalid ORT format model.Subgraph SessionState for is null. Invalid ORT format model.FinalizeSessionStateFinalizeSessionStateImplonnxruntime::common::Status onnxruntime::SessionState::FinalizeSessionStateImpl(const std::basic_string &, onnxruntime::KernelRegistryManager &, const onnxruntime::Node *, const onnxruntime::SessionOptions &, bool, std::unordered_map &)entry != node_to_subgraph_ss.second.cend()Missing session state for subgraph. Node:'' OpType: Index: Attribute:p_op_kernelauto onnxruntime::SessionState::PrepackConstantInitializedTensors(std::unordered_map &, const std::unordered_map &)::(anonymous class)::operator()(bool) constallocator_for_caching.get() != nullptrweights_to_be_filled_in.buffers_.size() > 0The kernel corresponding to the node doesn't have an implementation that can cache computed pre-packed weights!op_type.empty()Using cached version of pre-packed weight for constant initializer: used in the node: which is of op type: Unable to write the provided PrePackedWeights instance into the containerCould not find OrtValue with name ''+KernelUseSharedPrePackedBuffers doesn't have an implementation that can consume provided pre-packed weightsCould not find OrtValue with idx 'GetSubGraphSessionStatesOrtFormat/Users/runner/work/1/s/onnxruntime/core/framework/session_state_utils.ccSaveInitializedTensorsSaving initialized tensors.common::Status onnxruntime::session_state_utils::SaveInitializedTensors(const onnxruntime::Env &, const std::basic_string &, const onnxruntime::GraphViewer &, const onnxruntime::AllocatorPtr &, const onnxruntime::OrtValueNameIdxMap &, const std::vector &, onnxruntime::ITensorAllocator &, const std::function &, const logging::Logger &, const onnxruntime::DataTransferManager &, const onnxruntime::ExecutionPlanBase &, const onnxruntime::SessionOptions &)ort_value_name_idx_map.MaxIdx() > -1entry != initialized_tensors_to_allocate.end() && entry->second->data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRING[Memory] SessionStateInitializer statically allocates bytes for Using user supplied initializer with name (Deserialize tensor failed.Done saving initialized tensorsSaveInputOutputNamesToNodeMappingSubgraph input with name is not used by any node.Cannot use user supplied initializer with name: () because the ORT planned memory location device ) is different from what is supplied (DeserializeTensorProto() takes either pre-allocated buffer or an allocator!Internal error. The preallocated buffer is too small. Requires DeserializeTensorProtoFailed to copy tensor to /Users/runner/work/1/s/onnxruntime/core/framework/tensor.cconnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape &, void *, const OrtMemoryInfo &, ptrdiff_t)p_type != nullptronnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape &, std::shared_ptr)onnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape &, void *, std::shared_ptr, ptrdiff_t)size_t onnxruntime::Tensor::SizeInBytes() constvoid onnxruntime::Tensor::Init(onnxruntime::MLDataType, const onnxruntime::TensorShape &, void *, onnxruntime::AllocatorPtr, ptrdiff_t)dtype_ != nullptrTensor is expected to contain one of the primitive data types. Got: /Users/runner/work/1/s/onnxruntime/core/framework/tensor_allocator_with_mem_pattern.hFinalizePlanFailed to get allocator for location: duplicated locationMem pattern for initializer is not foundWeight buffer for initializer '' is not foundGet preallocated buffer for initializer '' failedTracemodel format error! Need a value for the external data infoparsing checksum/Users/runner/work/1/s/onnxruntime/core/framework/tensor_shape.ccint64_t onnxruntime::TensorShape::SizeToDimension(size_t) constdimension <= num_dimsInvalid dimension of for SizeFromDimension. Tensor has dimensions.int64_t onnxruntime::TensorShape::SizeFromDimension(size_t) constonnxruntime::TensorShape onnxruntime::TensorShape::Slice(size_t, size_t) constdimstart <= dimend && dimend <= size(){}/Users/runner/work/1/s/onnxruntime/core/framework/tensor_type_and_shape.ccOrtStatusPtr OrtApis::GetTensorTypeAndShape(const OrtValue *, OrtTensorTypeAndShapeInfo **)type != nullptrInvalid TensorProtocorrupted protobuf data: tensor shape size() does not match the data size() in protoUnpackTensor: the pre-allocate size does not match the size in protodata overflowTensorProtoToTensor() tensor shape mismatch!TensorProto type can not be writen into Tensor type /Users/runner/work/1/s/onnxruntime/core/framework/tensorprotoutils.ccTensorProtoToTensorstring tensor can not have raw datatensor can't contain negative dimsInitialized tensor with unexpected type: TensorProtoToMLValue() must take a pre-allocated MemBuffer!string tensor can not use pre-allocated bufferThe preallocated buffer is too small. Requires , Got ConstantNodeProtoToTensorProtocommon::Status onnxruntime::utils::ConstantNodeProtoToTensorProto(const onnx::NodeProto &, const onnxruntime::Path &, onnx::TensorProto &)Unsupported attribute value type of in 'Constant' node 'SparseTensorProtoToDenseTensorProto BUG! Report to onnxruntime team. element_size of: is not supported. type: common::Status onnxruntime::utils::DenseTensorToSparseTensorProto(const onnx::TensorProto &, const onnxruntime::Path &, onnx::SparseTensorProto &)HasDataType(dense_proto)DenseTensorToSparseTensorProto data_type: UnpackInitializerDataUnsupported type: UnpackTensorWithExternalDataImplnullptr == p_dataGetExternalDataInfoTensor does not have external data to read from.External data type cannot be UNDEFINED or STRING.TensorProto external data size mismatch. Computed size: , external_data.length: GetFileContentUnsupported sparse tensor data type of ReadExternalDataForTensorUnpackTensor: the pre-allocated size does not match the raw data size, expected CopySparseDataIndicies expected to be INT64Sparse Indicies raw data size does not match expected.Invalid SparseTensor indices. Should either have raw or int64 dataonnxruntime::common::Status onnxruntime::utils::CopySparseData(size_t, const onnx::TensorProto &, gsl::span, std::function) [T = unsigned char]rank == dims.size() && rank > 0cur_index == &*indices_data.cend()Invalid SparseTensor indices. Should be rank 0 or 1. Got:onnxruntime::common::Status onnxruntime::utils::CopySparseData(size_t, const onnx::TensorProto &, gsl::span, std::function) [T = unsigned short]onnxruntime::common::Status onnxruntime::utils::CopySparseData(size_t, const onnx::TensorProto &, gsl::span, std::function) [T = unsigned int]onnxruntime::common::Status onnxruntime::utils::CopySparseData(size_t, const onnx::TensorProto &, gsl::span, std::function) [T = unsigned long long]CoreMLExecutionProviderInternalTestingExecutionProvider/Users/runner/work/1/s/onnxruntime/core/framework/utils.ccconst OrtMemoryInfo &onnxruntime::utils::FindMemoryInfoForValue(const onnxruntime::SessionState &, const std::string &)exec_plan_ptrInitializeFeedFetchCopyInfoCopyOneInputAcrossDevicesExecuteGraphFindMemoryInfoForValueconst OrtMemoryInfo &onnxruntime::utils::FindMemoryInfoForValue(const onnxruntime::OrtValueNameIdxMap &, const onnxruntime::SequentialExecutionPlan &, const std::string &)CalculateStaticCopyInfoForFeedsbool onnxruntime::utils::FinalizeCopyInfoForFeeds(const std::vector &, std::vector &)feed_locations.size() == copy_info.size()bool onnxruntime::utils::FinalizeCopyInfoForFetches(const std::vector &, std::vector &)fetch_alloc_info.size() == copy_info.size()CalculateStaticCopyInfoForFeedonnxruntime::common::Status onnxruntime::utils::BatchOrCopyMLValue(const onnxruntime::SessionState &, const onnxruntime::MLValueCopyInfo &, const OrtValue &, OrtValue &, std::vector *)allocator != nullptrFailed to find allocator for device BatchOrCopyMLValueExecuteGraphImplOnly one thread was configured for parallel execution. Hence will use sequential execution.common::Status onnxruntime::utils::CopyInputsAcrossDevices(const onnxruntime::SessionState &, const std::vector &, std::vector &, const std::vector &)copy_info.size() == num_feedsCopyInputsAcrossDevicesCopyOutputsAcrossDevicesNumber of neurons in the hidden layer.Constrain seq_lens to integral tensors.The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]` QWThe weight tensor of the query layer in the attention mechanism. Should be of shape `[num_directions, am_query_depth(hidden_size of lstm), am_attn_size]` MWThe weight tensor of the memory layer in the attention mechanism. Should be of shape `[num_directions, memory_depth, am_attn_size]` The attention_v tensor in the attention mechanism. Should be of shape `[num_directions, am_attn_size]` The sequence of the memory (input) for attention mechanism. Should be of `[batch_size, max_memory_step, memory_depth]` memory_seq_lensThe sequence length of the input memory for the attention mechanism. Should be of `[batch_size]` AWThe weights of attention layer in the attention wrapper. If exists, should be of shape `[num_directions, memory_depth+hidden_size, aw_attn_size]. Please note that attention mechanism context depth is also memory_depth in the attention mechanism.` A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`. 'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64Pads has incorrect number of valuesInput tensors of wrong rank (0).Incompatible dimensions for matrix multiplicationInputs 0 shall be 3 dimensionsInvalid bias shapeInputs 4 shall be 5 dimensions/Users/runner/work/1/s/onnxruntime/core/graph/contrib_ops/contrib_defs.ccNumber of attention headsWhether every token can only attend to previous tokens. Default value is 0.3D input tensor with shape (batch_size, sequence_length, input_hidden_size)weight2D input tensor with shape (input_hidden_size, 3 * hidden_size), where hidden_size = num_heads * head_sizebias1D input tensor with shape (3 * hidden_size)mask_indexAttention mask with shape (batch_size, 1, max_sequence_length, max_sequence_length), (batch_size, past_sequence_length + sequence_length)or (batch_size, sequence_length, past_sequence_length + sequence_length), or index with shape (batch_size) or (2 * batch_size).Mpastpast state for key and value with shape (2, batch_size, num_heads, past_sequence_length, head_size).3D output tensor with shape (batch_size, append_length, hidden_size)presentpresent state for key and value with shape (2, batch_size, num_heads, past_sequence_length + sequence_length, head_size)Constrain input and output types to float tensors.Constrain mask index to integer types2D input tensor with shape (input_hidden_size, 3 * hidden_size), hidden_size = num_heads * head_sizeinput_scalescale of quantized input tensor. It's a scalar, which means a per-tensor/layer quantization.weight_scalescale of weight scale. It's a scalar or a 1D tensor, which means a per-tensor/per-column quantization.Its size should be 3 * hidden_size if it is per-column quantizationAttention mask index with shape (batch_size)input_zero_pointzero point of quantized input tensor.It's a scalar, which means a per-tensor/layer quantization.weight_zero_pointzero point of quantized weight tensor. It's a scalar or a 1D tensor, which means a per-tensor/per-column quantization.Its size should be 3 * hidden_size if it is per-column quantization3D output tensor with shape (batch_size, sequence_length, hidden_size)Constrain input and output types to int8 tensors.windowOne sided attention windows length W, or half of total window length3D input tensor with shape (batch_size, sequence_length, hidden_size), hidden_size = num_heads * head_size2D input tensor with shape (hidden_size, 3 * hidden_size)maskAttention mask with shape (batch_size, sequence_length)global_weightglobal_biasglobalGlobal attention flags with shape (batch_size, sequence_length)GConstrain to integer typesThe epsilon value to use to avoid division by zero.input_ids2D words IDs with shape (batch_size, sequence_length)segment_ids2D segment IDs with shape (batch_size, sequence_length)word_embedding2D with shape (,hidden_size)position_embedding2D with shape (, hidden_size)segment_embedding1D gamma tensor for layer normalization with shape (hidden_size)beta1D beta tensor for layer normalization with shape (hidden_size)2D attention mask with shape (batch_size, sequence_length)1D mask_index tensor with shape (batch_size)Constrain input and output integer tensors typesConstrain input and output float tensors types.input tensorbias tensoroutput tensorConstrain input and output types to float or half tensors.3D input tensor with shape (batch_size, sequence_length, hidden_size)skip3D skip tensor with shape (batch_size, sequence_length, hidden_size)1D input tensor with shape (hidden_size)1D skip tensor with shape (hidden_size1D bias tensor with shape (hidden_sizemeanSaved mean used during training to speed up gradient computationUinv_std_varSaved inverse standard variance used during training to speed up gradient computation.Constrain mean and inv_std_var to float tensors.Value of alphaValue of beta1D input tensor1D output tensorBias applied to each channel, same size as C.The scale to apply.Input tensor of shape [N,C,H,W]Result, has same shape and type as inputA 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).A 1-D values of (height, width).Result, has same type as input, with H and W dimensions reduced.Threshold valueInput tensorOutput tensordataTensor of data to extract slices from.1-D tensor of starting indices of corresponding axis in `axes`ends1-D tensor of ending indices (exclusive) of corresponding axis in axesaxes1-D tensor of axes that `starts` and `ends` apply to.Sliced data tensor.Constrain indices to integer typesThe shape of filled tensorThe filled tensorinput_as_shapeextra_shapeInput data to be scaledOutput data after scalingdrop_statesBool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.hidden_prevThe previous GRU hidden state.gatesUnactivated gate outputs from forget, update, and output gates, pre-activation.seq_lengthsArray of sequence lengths. len(seq_lengths) should equal batch size N.The timestep for this operation.hiddenThe new GRU hidden state calculated by this op.If 1, mean and variance are computed across channels. Default is 0.If 0, normalize the mean only. Default is 1.Scaling valueThe scaled hyperbolic tangent values of the input tensor computed element-wiseauto_padpadsConstrain input0 and output types to float tensorssignal_ndimnormalizedonesidedAinput_0input_1CWPadsConstrain input and output types to float tensorsactivation_paramsZInput tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.Input tensor C. The shape of C should be unidirectional broadcastable to (M, N).Output tensor of shape (M, N).Constrain input and output types to float/int tensors.Whether A should be transposedWhether B should be transposedScalar multiplier for the product of input tensors A * B.Scalar multiplier for input tensor C.activation_gammaSpecified axis to insert a dimensionStrings to tokenizeTokenized stringsInput/Output is a string tensormarkBoolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03).The string used to pad output tensors when the tokens extracted doesn't match the maximum number of tokens found. If start/end markers are needed, padding will appear outside the markers.tokenexpAn optional string. Token's regular expression in basic POSIX format (pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap09.html#tag_09_03). If set, tokenizer may produce tokens matching the specified pattern. Note that one and only of 'tokenexp' and 'separators' should be set.an optional list of strings attribute that contains a list of separators - regular expressions to match separators Two consecutive segments in X connected by a separator would be divided into two tokens. For example, if the input is "Hello World!" and this attribute contains only one space character, the corresponding output would be ["Hello", "World!"]. To achieve character-level tokenization, one should set the 'separators' to [""], which contains an empty string.Minimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignoredN-dimensional matrix AN-dimensional matrix BMatrix multiply results from A * BConstrain input A data types as 16-bit integer tensorConstrain input B data types as 16-bit integer tensorConstrain output Y data types as 32-bit integer tensor.T3 must be tensor(uint32) when both T1 and T2 are tensor(uint16),or must be tensor(int32) when either T1 or T2 is tensor(int16).Scalar multiplier for the product of the input tensors.Whether A should be transposed on the last two dimensions before doing multiplicationWhether B should be transposed on the last two dimensions before doing multiplicationMatrix multiply resultsAn input tensor to hash.32-bit hash value.Constrain input type to unsigned or signed 32-bit integer tensor, or string tensor. It should be utf-8 encoded if using unicode.Constrain output type to unsigned and signed 32-bit integer tensor.seedSeed for the hashing algorithm, unsigned 32-bit integer, default to 0.positiveIf value is 1, output type is uint32_t, else int32_t. Default value is 1.Tensor of rank r >= 1.indicesTensor of rank q >= 1.Tensor of rank q-1+r-indices[-1].Constrain indice type to int32 or int64Integer representing the embedding vector size for each word.If not provide, use the fileter size of conv weightThis operator applies convolution to word from left to right with window equal to conv_window_size and stride to 1.Take word 'example' for example, with conv_window_size equal to 2, conv is applied to [ex],[xa], [am], [mp]...If not provide, use the first dimension of conv kernal shape.Integer representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector.SequenceSpecify batchs of sequence words to embeddingSpecify weights of convSpecify bias of convSpecify embedding vector of charConstrain to tensor(int32).Constrain to tensor(float).modeThree modes: `constant`(default) - pads with a given constant value, `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis, `edge` - pads with the edge values of arrayInput tensor.Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]. `pads` format (1D example) should be as follow [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.(Optional) A scalar or rank 1 tensor containing a single value to be filled if the mode chosen is `constant` (by default it is 0.0).Tensor after padding.xA 1-D input tensor that is to be processed.A 1-D tensor of the same type as 'x' containing all the unique values in 'x' sorted in the same order that they occur in the input 'x'idxA 1-D INT64 tensor of the same size as 'x' containing the indices for each value in 'x' in the output 'uniques'countsA 1-D INT64 tensor containing the the count of each element of 'uniques' in the input 'x'Input can be of any tensor type.The distance metric to use. If a string, the distance function can be "braycurtis", "canberra", "chebyshev", "cityblock", "correlation", "cosine", "dice", "euclidean", "hamming", "jaccard", "jensenshannon", "kulsinski", "mahalanobis", "matching", "minkowski", "rogerstanimoto", "russellrao", "seuclidean", "sokalmichener", "sokalsneath", "sqeuclidean", "wminkowski", "yule".2D matrix with shape (M,N)2D matrix with shape (K,N)A 2D Matrix that represents the distance between each pair of the two collections of inputs.Constrains input to only numeric types.The pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'.Value used for extrapolation, when applicable. Default is 0.0f. Input data tensor from the previous operator; 4-D feature map of shape (N, C, H, W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.roisRoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[y1, x1, y2, x2], ...]. The RoIs' coordinates are normalized in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.batch_indices1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.crop_size1-D tensor of 2 elements: [crop_height, crop_width]. All cropped image patches are resized to this size. Both crop_height and crop_width need to be positive.RoI pooled output, 4-D tensor of shape (num_rois, C, crop_height, crop_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1].Constrain types to float tensors.Constrain types to int tensors.The first normalization dimension: normalization will be performed along dimensions axis : rank(inputs).stash_typetype used for stash mean/inv_std_varInput data tensor from the previous layer.Scale tensor.Bias tensor.Output data tensor.InvStdDevSaved inverse standard deviation used during training to speed up gradient computation.Constrain input types and output Y type to float tensors.Type of Mean and InvStdDev tensors.Constrain input and output types (except mean and inv_std_var) to float tensors.Constrain mean and inv_std_var to be float tensors.The input data as Tensor.The output.The normal input data.The bias input data that is a 1D tensor.Input tensor. Every matrix in the batch must be invertible.Output tensor of the same type and shape as the input tensor.The embedding matrix of size N x M. 'N' is equal to the maximum possible index + 1, and 'M' is equal to the embedding sizeLong tensor containing the indices to extract from embedding matrix.padding_idxA 0-D scalar tensor. If specified, the entries at `padding_idx` do not contribute to the gradient; therefore, the embedding vector at `padding_idx` is not updated during training, i.e. it remains as a fixed pad.scale_grad_by_freqA 0-D bool tensor. If given, this will scale gradients by the inverse of frequency of the indices (words) in the mini-batch. Default is ``False``Output tensor of the same type as the input tensor. Shape of the output is * x M, where '*' is the shape of input indices, and 'M' is the embedding size.Constrain input and output types to all numeric tensors.Boolean. Indicates whether upper or lower part of matrix is retained. Default is true.Input tensor of rank 2 or higher.kA 0-D tensor containing a single value corresponding to the number diagonals above or the main diagonal to exclude or include.Default value is 0 if it's not specified.Constrain input and output types to all numeric tensors and bool tensors.softmax_axisapply softmax to elements for dimensions softmax_axis or higherbroadcast_axisbroadcast bias across input for dimensions broadcast_axis to softmax_axis-1The bias (or mask) as Tensor.(Optional) Seed to the random generator, if not specified we will auto generate one.The bias input, a vector with the same shape as last dim of dataresidualThe residual input, must have the same shape as dataratioThe ratio of random dropout, with value in [0, 1). If this input was not set, or if it was set to 0, the output would be a simple copy of the input. If it's non-zero, output will be a random dropout of input, which is typically the case during training.training_modeIf set to true then it indicates dropout is being used for training. It is an optional value hence unless specified explicitly, it is false. If it is false, ratio is ignored and the operation mimics inference mode where nothing will be dropped from the input data and if mask is requested as output it will contain all ones.The output mask of dropout.Constrain input 'ratio' types to float tensors.Constrain output 'mask' types to boolean tensors.isinf_onlyIf true, check only for Inf, -Inf.isnan_onlyIf true, check only for NaN.Constrain the output to a boolean tensor.Input tensors to check.The output scalar. Its value is true if all input tensors are finite. Otherwise, the output value would be false.Incompatible dimensionsInputs 0 shall be 2 dimensionsword_embedding should have 2 dimensions and dimension size is known.X_biasNegative values are not allowed in a shape specificationAttribute should specify a shapeInput's shape must be 4-D'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border)'Scale' must contain exactly 2 values - (height, width)) needs to be greater than or equal to the top_border () + bottom_border () needs to be greater than or equal to the left_border () + right_border () + scale[0] () + scale[1] (First input does not have rank 2Second input does not have rank 2Input axis is invalid: inputs are expected to have tensor type and output type should not be null.both data and indices tensor need to have rank larger than zero.last dimension of indices must not be larger and rank of data tensor'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64first input tensor has wrong dimensionrois input tensor has wrong dimensionbatch_indices shape input tensor has wrong dimensioncrop_size shape input tensor has wrong dimensionInput rank must be >= 2.The inner-most 2 dimensions must have the same size (mat_w: != mat_h: expected to have rank > but has rank Dimension mismatch in unification between auto onnxruntime::contrib::RegisterContribSchemas()::(anonymous class)::operator()(onnx::InferenceContext &) const!(isinf_only && isnan_only)Both attributes isinf_only and isnan_only cannot be set. Unset both to check for both conditions./Users/runner/work/1/s/onnxruntime/core/graph/contrib_ops/nchwc_schema_defs.ccchannelstensor rank too smallinvalid scales dimensioninvalid scales value/Users/runner/work/1/s/onnxruntime/core/graph/contrib_ops/nhwc_schema_defs.ccInput data tensor from the previous operator; According to channels_last, dimensions for image case are (N x C x H x W), or (N x H x W x C) where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), or (N x D1 X D2 ... Dn x C) where N is the batch size.Scale of quantized input 'X'. It must be a scalar.Zero point tensor for input 'X'. It must be a scalar.Scale of quantized output 'Y'. It must be a scalar.Zero point tensor for output 'Y'. It must be a scalar.Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. with the N and C value keep it value, while the otherdimensions are all 1.Constrain input and output types to singed/unsigned int8 tensors.Works on NHWC layout or not? Default not.Input scale. It's a scalar, which means a per-tensor/layer quantization.Input zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Output zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Constrain input and output types to 8 bit tensors.Output tensor must have at least 2 dimensionsInput data type does not match the expected data typeInput data type does not match the expected data type. Current data type is Scale and Zero-point must be a scalarScale and Zero-point must be of rank 1Scale and Zero-point must be of rank 1 and the number of elements should be equal to the number of rows of the corresponding input./Users/runner/work/1/s/onnxruntime/core/graph/contrib_ops/quantization_defs.ccThe axis along which same quantization parameters are applied. It's optional.If it's not specified, it means per-tensor quantization and input 'x_scale' and 'x_zero_point' must be scalars.If it's specified, it means per 'axis' quantization and input 'x_scale' and 'x_zero_point' must be 1-D tensors.Scale for doing quantization to get 'y'. It could be a scalar or a 1-D tensor,which means a per-tensor or per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.Zero point for doing quantization to get 'y'. It could be a scalar or a 1-D tensor, which means a per-tensoror per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.Constrain 'x', 'y_scale' to float tensors.Constrain 'y_zero_point' and 'y' to 8-bit integer tensors.N-D quantized Input tensor to be de-quantized.Scale for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.Zero point for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.Constrain 'x' and 'x_zero_point' to 8-bit integer tensors.Constrain 'y', 'x_scale' to float tensors.Constrain output data type to 32-bit integer tensor.T2 must be tensor(uint32) when T1 is tensor(uint8),or must be tensor(int32) when T1 is tensor(int8).A_zero_pointInput A zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.B_zero_pointInput B zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Constrain output to 32 bit tensorConstrain input types to 8 bit signed and unsigned tensors.Constrain output types to 32 bit tensors.Scale of quantized input 'B'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'.1D input tensor, whose dimension is same as B's last dimensionConstrain input A, b_scale and output Y data type as float tensor.Scale of quantized input 'A'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'A'.Zero point tensor for input 'A'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'A'.Constrain input a_scale, b_scale and output Y data type as float tensor.additionC = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_pointmultiplicationC = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_pointdata_scaledata_zero_pointreduced_scalereduced_zero_pointX_scaleInput X's scale. It's a scalar, which means a per-tensor/layer quantization.X_zero_pointInput X's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Y_scaleOutput Y's scale. It's a scalar, which means a per-tensor/layer quantization.Y_zero_pointOutput Y's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, input_size, 4*hidden_size]`.The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, hidden_size, 4*hidden_size]`.W's scale. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size.W's zero point. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size.R's scale. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size.R's zero point. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size.Constrain weights types to 8 bit tensors.Y's scale.Y's zero point.T8List of tensors/scale/zero_point for concatenationTVConstrain input and output types to 8 bit signed and unsigned tensors.Constrain scale types to any float tensor type.Sequence of (Tensor, Scale, ZeroPoint) tuples. The type is sequence of (T8, TF, T8). Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support). {additionalDocumentation} {additionalDocumentation}A_scaleInput A's scale. It's a scalar, which means a per-tensor/layer quantization.B_scaleInput B's scale. It's a scalar, which means a per-tensor/layer quantization.C_scaleC_zero_pointaxis must be in [-rank, rank)Constrain input and output types.Tensor(scalar, or dims=[1]). First entry in the range.Tensor(scalar, or dims=[1]). Upper limit of sequence, exclusive.Tensor(scalar, or dims=[1]). Number that increments start. Defaults to 1.1-D Tensor of the range.Unsupported type:Can not get shape initializer data!Unsupported non-raw-data data type!inout/Users/runner/work/1/s/onnxruntime/core/graph/function.cconnxruntime::FunctionImpl::FunctionImpl(const onnxruntime::Graph &, const onnxruntime::IndexedSubGraph &, const logging::Logger &)_dummyonnxruntime::FunctionImpl::FunctionImpl(const onnxruntime::Graph &, const onnxruntime::NodeIndex &, const onnx::FunctionProto &, const logging::Logger &)Resolve subgraph failed:std::unique_ptr onnxruntime::CreateSchema(const onnxruntime::Graph &, const onnxruntime::IndexedSubGraph &)input_arg->Type() != nullptrstd::unordered_map onnxruntime::CreateOpsetImportsForFunction(const onnx::FunctionProto &, const std::unordered_map &)(result.first->second == opset_version)ONNX model does not support multiple opset versions for a domain. Model imports opset version for domain and function is trying to import opset version for the same domainvoid onnxruntime::update_subgraphs_within_function_body(onnx::GraphProto &, const onnxruntime::Graph &, const onnx::NodeProto &, const std::unordered_map &, const std::unordered_map &)A node with a function body within a subgraph within another function body is currently not supported in ORT/Users/runner/work/1/s/onnxruntime/core/graph/function_impl.hvirtual const onnxruntime::Graph &onnxruntime::ViewerFunctionImpl::Body() constType mismatch. Current= Input=Tensor element type mismatch. /Users/runner/work/1/s/onnxruntime/core/graph/graph.ccUpdateTypeAndShapeSparseTensor element type mismatch. Serialization of fused function body is not currently supported, Node [] op_type [] does not have the graph for key LoadFromOrtFormatfbs_attr cannot be nullSerialization error. Graph attribute was serialized without Graph instanceNode::LoadFromOrtFormat, input_arg_counts is missingLoadEdgesFromOrtFormatinput index: is not the same as this node's index:ai.onnxThis is an invalid model. The sum of input arg count is not equal to size of input defs in node (onnxruntime::Graph::Graph(const onnxruntime::Model &, onnx::GraphProto *, const std::unordered_map &, onnxruntime::Version, onnxruntime::IOnnxRuntimeOpSchemaCollectionPtr, onnxruntime::Graph *, const onnxruntime::Node *, const logging::Logger &)graph_proto != nullptrConstantDuplicate constant node sparse initializer name: '' Model is invalid.utils::HasName(sparse_tensor)Duplicate sparse_tensor_initializer: 'GraphDuplicate initializer (dense, sparse or ConstantNode): '' the model will use the latest encountered initializer. Please, fix your model.Initializer appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.void onnxruntime::Graph::InitializeStateFromModelFileGraphProto()graph_inputs_excluding_initializers_.empty() && graph_inputs_including_initializers_.empty() && value_info_.empty() && graph_outputs_.empty()node_argGraph ctor should have created NodeArg for initializer. Missing:This is an invalid model. Graph output () does not exist in the graph.This is an invalid model. Error: two nodes with same node name ().This is an invalid model. Error: Duplicate definition of name (SetOuterScopeNodeArgsvoid onnxruntime::Graph::AddEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int)void onnxruntime::Graph::RemoveEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int)BuildConnectionsThis is an invalid model. At top level graph without matching NodeArg that subgraph consumes. Name= Graph may not conform to the ONNX spec and contain initializers that are not graph inputs.This is an invalid model. Failed to find NodeArg in all parent graphs. Name=Invalid model. Node input '' is not a graph input, initializer, or output of a previous node.void onnxruntime::Graph::KahnsTopologicalSort(const std::function &, const std::function &) constSome nodes are not included in the topological sort, graph have a cycle.This is an invalid model. Error: the graph is not acyclic.Size mismatch validating subgraph inputs. Got inputs but subgraph has inputs and requires inputs. Either provide all subgraph inputs, or just the required inputs.Node:InferAndVerifySubgraphTypesonnxruntime::common::Status onnxruntime::Graph::UpdateShapeInference(onnxruntime::Node &)node.GetAttributeNameToMutableSubgraphMap().empty()UpdateTypeShapeInference is not intended to be used with control flow nodes containing subgraphsThis is an invalid model. Node () input arg () does not have type information set by parent node.This is an invalid model. Type Error: Type '' of input parameter () of operator () in node () is invalid.Type Error: Type parameter () of Optype () bound to different types ( and in node (InferAndVerifyTypeMatchNode () output arg () type inference failedType Error: Type () of output arg () of node () does not match expected type (This is an invalid model. Model input () does not have type information.Type Error: Data in initializer '' has element type but usage of initializer in graph expects Type Error: Shape of initializer does not match. VerifyNodeAndOpMatchFatal error: is not a registered function/op) attribute () is required but not specified.Error: Duplicate definition-site for (InitInputsInitializersOutputsPerformTypeAndShapeInferencingForThisAndAllSubgraphsResolveonnxruntime::common::Status onnxruntime::Graph::Resolve(const onnxruntime::Graph::ResolveOptions &)outer_scope_node_args_consumed.empty()Shouldn't be possible to have NodeArgs that haven't been handled already.void onnxruntime::Graph::AddInitializedTensor(const onnx::TensorProto &)existing->second == &tensorAddInitializedTensor already has tensor with name but different TensorProto.void onnxruntime::Graph::RemoveInitializedTensor(const std::string &)sparse_tensor_names_.count(tensor_name) == 0!found_token_NoOpbool onnxruntime::Graph::RemoveNode(onnxruntime::NodeIndex)node->GetOutputEdgesCount() == 0Can't remove node as it still has output edges.onnx::GraphProto onnxruntime::Graph::ToGraphProto() constlocationoffsetlengthvoid onnxruntime::Graph::ToGraphProtoInternal(onnx::GraphProto &) constOuter scope node arg name ''was added but does not exist. CleanUnusedInitializersRemoving initializer ''. It is not used by any node and should be removed from the model.'. It is no longer used by any node. must be either specified in graph inputs or graph initializers.gsl::not_null onnxruntime::Graph::AllocateNode()nodes_.size() < static_cast(std::numeric_limits::max())onnxruntime::Node &onnxruntime::Graph::CreateFusedSubGraphNode(const onnxruntime::IndexedSubGraph &, const std::string &)nullptr != func_meta_defvoid onnxruntime::Graph::FinalizeFuseSubGraph(const onnxruntime::IndexedSubGraph &, onnxruntime::Node &)dst_implicit_input_idx < (int)node->ImplicitInputDefs().size()Node 's number of inputs is different from function body graph's number of input.onnxruntime::common::Status onnxruntime::Graph::InlineFunction(onnxruntime::Node &)node.MutableOutputDefs().size() == subgraph.GetOutputs().size()'s number of outputs is different from function body graph's number of outputs.InlineFunctionvoid onnxruntime::Graph::SetInputs(const std::vector &)input->Exists() : : Initializer tensor is missing. Invalid ORT format model.Duplicate initializer (dense or ConstantNode): 'Sparse Initializer tensor is missing. Invalid ORT format model.NodeArg is missing. Invalid ORT format model.Node is missing. Invalid ORT format model.Node index is out of rangeNodeEdge is missing. Invalid ORT format model.MergeShapeInfoError merging shape info for output. '' source: target:. Falling back to lenient merge.[ShapeInferenceError] Mismatch between number of source and target dimensions. Source= Target=Can't merge shape info. Both source and target dimension have values but they differ. Source= Dimension=Output:fbs_node_arg_names cannot be nullnode_arg_name cannot be nullLoadNodeArgsFromOrtFormat: Node [], could not find NodeArg ALLOW_RELEASED_ONNX_OPSET_ONLY/Users/runner/work/1/s/onnxruntime/core/graph/model_load_utils.hbool onnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySet() are '0' and '1'. The environment variable contained the value: ai.onnx.mlai.onnx.training/Users/runner/work/1/s/include/onnxruntime/core/graph/graph.honnxruntime::Node *onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constnode_index < nodes_.size()Validating no unexpected access using an invalid node_index. Got: Max:[TypeInferenceError] No Graph instance was found for attribute in node Graph attribute inferencing failed: ) Op () This is an invalid model. Error in Node:NodeArg Name is missing. Invalid ORT format model./Users/runner/work/1/s/include/onnxruntime/core/common/const_pointer_container.hconst onnxruntime::ConstPointerContainer::T *onnxruntime::ConstPointerContainer > >::at(size_t) const [Container = std::__1::vector >]index < data_.size()/Users/runner/work/1/s/onnxruntime/core/graph/graph_flatbuffers_utils.ccSaveInitializerOrtFormatSaveSparseInitializerOrtFormatSaveAttributeOrtFormatGraph attribute value was null. Invalid ORT format model.SaveAttributeOrtFormat: Unsupported attribute type: LoadInitializerOrtFormatMissing dimensions for initializer. Invalid ORT format model.Missing string data for initializer. Invalid ORT format model.Missing raw data for initializer. Invalid ORT format model.LoadSparseInitializerOrtFormatMissing values for sparse initializer. Invalid ORT format model.Missing name for SparseTensor initializer. Invalid ORT format model.Missing indicies for sparse initializer: Invalid ORT format model.Missing dims for sparse initializer: LoadAttributeOrtFormatNull string attribute. Invalid ORT format model.Null tensor attribute. Invalid ORT format model.Null graph attribute. Invalid ORT format model.Empty graph proto from deserialization of ORT format modelNull floats attribute. Invalid ORT format model.Null ints attribute. Invalid ORT format model.Null strings attribute. Invalid ORT format model.Null string in strings attribute. Invalid ORT format model.Null tensors attribute. Invalid ORT format model.Null tensor in tensors attribute. Invalid ORT format model.UNDEFINEDFLOATINTSTRINGTENSORGRAPHFLOATSINTSSTRINGSTENSORSGRAPHSSPARSE_TENSORSPARSE_TENSORS/Users/runner/work/1/s/onnxruntime/core/graph/graph_utils.ccint onnxruntime::graph_utils::GetIndexFromName(const onnxruntime::Node &, const std::string &, bool)itr != node_args.end()Attempting to get index by a name which does not exist:for node: const std::string &onnxruntime::graph_utils::GetNodeInputName(const onnxruntime::Node &, int)index >= 0 && static_cast(index) < inputs.size()const std::string &onnxruntime::graph_utils::GetNodeOutputName(const onnxruntime::Node &, int)index >= 0 && static_cast(index) < outputs.size()bool onnxruntime::graph_utils::RemoveNode(onnxruntime::Graph &, onnxruntime::Node &)Should be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.onnxruntime::NodeArg &onnxruntime::graph_utils::AddInitializer(onnxruntime::Graph &, const onnx::TensorProto &)!graph.GetInitializedTensor(new_initializer.name(), existing)Initializer with same name exists. Name:void onnxruntime::graph_utils::ReplaceNodeInput(onnxruntime::Node &, int, onnxruntime::NodeArg &)Invalid input index for node . Index: ExplicitInputs: ImplicitInputs:void onnxruntime::graph_utils::AddNodeInput(onnxruntime::Node &, int, onnxruntime::NodeArg &)num_explicit_inputs == static_cast(target_input_idx)FindPathFailed since multiple edges matched:CanUpdateImplicitInputNameInSubgraphs Implicit input name cannot be safely updated to in one of the subgraphs.bool onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutput(onnxruntime::Graph &, onnxruntime::Node &)std::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })void onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraph(onnxruntime::Node &, const std::string &, const std::string &)std::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0/Users/runner/work/1/s/onnxruntime/core/graph/graph_viewer.cconnxruntime::GraphViewer::GraphViewer(const onnxruntime::Graph &, const onnxruntime::IndexedSubGraph *)graph_->GetNode(idx) != nullptrnodeargMismatch between Graph and IndexedSubGraph. Input not found:Mismatch between Graph and IndexedSubGraph. Output not found:nodeMismatch between Graph and IndexedSubGraph. Node not found: const std::vector &onnxruntime::GraphViewer::GetNodesInTopologicalOrder(onnxruntime::ExecutionOrder) constconst std::vector &onnxruntime::GraphViewer::GetRootNodes() constfilter_info_ == nullptrShape/Users/runner/work/1/s/onnxruntime/core/graph/model.cconnxruntime::Model::Model(onnx::ModelProto &&, const onnxruntime::PathString &, const onnxruntime::IOnnxRuntimeOpSchemaRegistryList *, const logging::Logger &)Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported.ModelONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'. Please upgrade your model to opset 7 or higher. For now, this opset model may run depending upon legacy support of some older opset version operators.No graph was found in the protobuf.Protobuf parsing failed. less than 0. is less than 0.SaveProtobuf serialization failed.Graph is null. Invalid ORT format model.void onnxruntime::model_load_utils::ValidateOpsetForDomain(const std::unordered_map &, const logging::Logger &, bool, const std::string &, int)ONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions. Opset is under development and support for this is limited. The operator schemas and or other functionality may change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain is till opset ValidateOpsetForDomain is under development and support for this is limited. The operator schemas and or other functionality could possibly change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain Failed to load model with error: Load model failed. File doesn't exist failedsystem error number SaveModelDomain already set in registry/Users/runner/work/1/s/onnxruntime/core/graph/schema_registry.ccRegisterOpSetRegisterOpSchemaInternal, but it its domain is notknown by the checker., but it its version is higherthan the operator set version onnxruntime/Users/runner/work/1/s/onnxruntime/core/common/logging/logging.cconnxruntime::logging::LoggingManager::LoggingManager(std::unique_ptr, onnxruntime::logging::Severity, bool, const onnxruntime::logging::LoggingManager::InstanceType, const std::string *, int)default_logger_id must be provided if instance_type is InstanceType::DefaultOnly one instance of LoggingManager created with InstanceType::Default can exist at any point in time.void onnxruntime::logging::LoggingManager::CreateDefaultLogger(const std::string &) [VIWEFampm is not a valid date is not a valid year is not a valid day/Users/runner/work/1/s/onnxruntime/core/common/path.ccParsestatic onnxruntime::Path onnxruntime::Path::Parse(const onnxruntime::PathString &)..ParsePathRootFailed to parse path root: /Users/runner/work/1/s/onnxruntime/core/common/profiler.cc::onnxruntime::TimePoint onnxruntime::profiling::Profiler::StartTime() constvoid onnxruntime::profiling::Profiler::Initialize(const logging::Logger *)session_logger != nullptrEndTimeAndRecordEventMaximum number of events reached, could not record profile event.Writing profiler data to file [ {"cat" : "","pid" :"tid" :"dur" :"ts" :"ph" : "X","name" :""args" : {" : " : "} }, ] SessionNodew/Users/runner/work/1/s/onnxruntime/core/common/status.cconnxruntime::common::Status::Status(onnxruntime::common::StatusCategory, int, const std::string &)code != static_cast(common::OK)onnxruntime::common::Status::Status(onnxruntime::common::StatusCategory, int, const char *)SystemError[ONNXRuntimeError]SUCCESSFAILINVALID_ARGUMENTNO_SUCHFILENO_MODELENGINE_ERRORRUNTIME_EXCEPTIONINVALID_PROTOBUFMODEL_LOADEDNOT_IMPLEMENTEDINVALID_GRAPHEP_FAILGENERAL ERRORunnamed_thread_pool/Users/runner/work/1/s/onnxruntime/core/common/threadpool.ccstd::string onnxruntime::concurrency::ThreadPoolProfiler::Stop()enabled_{"main_thread": {"thread_pool_name": "", }, "sub_threads": {}}void onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEnd(onnxruntime::concurrency::ThreadPoolProfiler::ThreadPoolEvent)!points_.empty()void onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEndAndStart(onnxruntime::concurrency::ThreadPoolProfiler::ThreadPoolEvent)std::string onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::Reset()points_.empty()"thread_id": "", "block_size": [], "core": "": DistributionDistributionEnqueueWaitWaitRevoke": {"num_run": "core": onnxruntime::concurrency::ThreadPool::ParallelSection::ParallelSection(onnxruntime::concurrency::ThreadPool *)!current_parallel_sectionvoid onnxruntime::concurrency::ThreadPool::ParallelFor(std::ptrdiff_t, const onnxruntime::TensorOpCost &, const std::function &)n >= 0/Users/runner/work/1/s/onnxruntime/core/platform/posix/env.cconnxruntime::(anonymous namespace)::PosixThread::PosixThread(const char *, int, unsigned int (*)(int, Eigen::ThreadPoolInterface *), Eigen::ThreadPoolInterface *, const onnxruntime::ThreadOptions &)CleanUpFailed to close file descriptor - error code: Invalid fd was supplied: fstat file "" failed: ReadFileIntoBufferfile_path == nullptroffset < 0length > buffer.size()openlseekreadReadFileIntoBuffer - unexpected end of file. File: , offset: , length: MapFileIntoMemorymmapUnmapFilemunmap failed. error code: DeleteFolderDeleteFolder(): nftw() failed with error: nftw_removeremove() failed. Error code: , path: closerealpathFailed to load library with error: Got null library handleFailed to unload library with error: Failed to get symbol lib.so.bad dimensionsElementSize must be power of 2 and less or equal than 16!QLinearGlobalAveragePool ImageSize too large!QLinearGlobalAveragePool parameter out of computation range!/Users/runner/work/1/s/onnxruntime/core/flatbuffers/flatbuffers_utils.ccSaveValueInfoOrtFormatSaveValueInfoOrtFormat: value_info_proto for is missing type info.LoadValueInfoOrtFormatNull type info for LoadOpsetImportOrtFormatModel must have opset imports. Invalid ORT format model.opset id is null. Invalid ORT format model.opset import domain is null. Invalid ORT format model.SaveTypeInfoOrtFormatWe do not support type [] for nowSaveSequenceTypeOrtFormatSaveMapTypeOrtFormatLoadTypeInfoOrtFormatNull tensor type info. Invalid ORT format model.Null sequence type info. Invalid ORT format model.Null map type info. Invalid ORT format model.Type: is not supported currentlyLoadTensorTypeAndShapeOrtFormatLoadTensorShapeOrtFormatNull entry in dimensions. Invalid ORT format model.LoadTensorDimensionOrtFormatdim_param value with no name. Invalid ORT format model.LoadSequenceTypeOrtFormatNull value type info in fbs::SequenceType. Invalid ORT format model.LoadMapTypeOrtFormatNull value type info in fbs::MapType. Invalid ORT format model.NONEField 'name' of value_info is required to be non-empty.type is required but missing.elem_typekey_typevalue_typeUnrecognized type value case (value_info name: ): data_typesetting data_type field (tensor name: ) to UNDEFINED is not allowedfloat_dataint32_datastring_dataint64_dataraw_datadouble_datauint64_dataData of TensorProto ( tensor name: ) is stored externally and should not have data field.) should be stored in , but it doesn't exist or is not accessible.TensorProto ( tensor name: ) is stored externally but doesn't have a location.TensorProto (tensor name: ) is 0-element but contains data!) should contain one and only one value field.STRING data (tensor name: ) should not be stored in raw_data fieldvalues of data_type '' should be stored in field '' instead of 'Unrecognized data_type (tensor name: valuesSparse tensor indices () has values, but NNZ is Sparse tensor () index value at position [] out of range [0, ] not in sorted order.) first dimension size does not equal NNZ.) second dimension size does not match rank of tensor.] out of range.] not in lexicographic sorted order.sparse_tensor_protoSparse tensor values () must have rank 1.) must have a dense-rank > 0) dimensions are not positive.) must have INT64 type.) must have rank 1 or 2.) has no index values.attrtype field and data field mismatch in attribute Attribute (name: ) should not contain more than one value field.) should refer to attribute in parent node.op_typeNodeProto (name: , type: ) has zero input and zero output.Warning: Checker does not support models with experimental ops: No opset import for domain 'No Op registered for with domain_version of Op registered for is deprecated in domain_version of graphGraph must be in single static assignment (SSA) form, however '' has been used as graph input names multiple times.init initializer name is not unique in initializer but not in graph input sparse initializer name is not unique across initializers and sparse_initializersNodes in a graph must be topologically sorted, however input '' of node: name: OpType: is not output of any previous nodes.' has been used as output names multiple times. imports opset version where as the model imports opset version DynamicSliceImageScalerScaleBad node spec for node. Name: ==> Context: Loop 'body' subgraph outputs should all be tensors or sequences but output Loop 'body' subgraph scan outputs should all be tensors but output /Users/runner/work/1/s/cmake/external/onnx/onnx/defs/controlflow/defs.ccFinal N loop carried dependency values then K scan_outputs. Scan outputs must be Tensors.An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].Mismatched type: source= target=Mismatched tensor element type:source sequence type missing element type.target sequence type missing element type.Scan input was not a tensor.bodyGraph attribute inferencing returned type information for outputs. Expected Scan 'body' subgraph outputs should all be tensors but output was not axis value is invalid for a tensor of rank Number of scan input axes specified () is not equal to number of scan inputs (Number of scan output axes specified () is not equal to number of scan outputs (sequence_lensOptional tensor specifying lengths of the sequences in a batch. If this input is not specified, all sequences are assumed to be of the maximum sequence length (the dimension of the sequence axis of the scan_input tensors).initial_state_and_scan_inputsInitial values of the loop's N state variables followed by M scan_inputsfinal_state_and_scan_outputsFinal values of the loop's N state variables followed by K scan_outputsThe graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.An attribute specifying the number of scan_inputs M. An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.Int64 tensor/Users/runner/work/1/s/cmake/external/onnx/onnx/defs/controlflow/old.ccLoop 'body' subgraph outputs should all be tensors but output was A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.condA boolean termination condition. Optional. Pass empty string to skip.v_initialThe initial values of any loop-carried dependencies (values that change across loop iterations)v_final_and_scan_outputsFinal N loop carried dependency values then K scan_outputsThe graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.tensor of int64, which should be a scalar.tensor of bool, which should be a scalar.An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input.An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output.then_branch and else_branch produce different number of outputs. If node has but subgraphs produce Mismatched type for output then= else=Mismatched tensor element type for output Condition for the ifoutputsValues that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same shape and same data type.Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.Only boolValues that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.Input type was nullInput was expected to have either tensor or sequence type. Got Input was expected to have tensor type. Got Element type of input was unknownInput element type of does not match existing output type of Output was expected to have tensor type. Got Input was expected to have sequence type. Got Invalid data type tensor(seq(map(sparse_tensor(Unsuported type proto value case.Invalid tensor data type mapopaqueDataTypeUtils::FromDataTypeString - Received invalid data type string The value for the elements of the output tensor.sparse_valueThe value for the elements of the output tensor in sparse format.value_intThe value for the sole element for the scalar, int64, output tensor.value_intsThe values for the elements for the 1D, int64, output tensor.value_floatThe value for the sole element for the scalar, float32, output tensor.value_floatsThe values for the elements for the 1D, float32, output tensor.value_stringThe value for the sole element for the scalar, UTF-8 string, output tensor.value_stringsThe values for the elements for the 1D, UTF-8 string, output tensor.Output tensor containing the same value of the provided tensor./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/generator/defs.cc(Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float321D tensor. The shape of the expected output tensor. If empty tensor is given, the output would be a scalar. All values must be >= 0.Output tensor of shape specified by 'input'.If attribute 'value' is specified, the value and datatype of the output tensor is taken from 'value'.If attribute 'value' is not specified, the value in the output defaults to 0, and the datatype defaults to float32.Constrain input types.Constrain output types to be numerics.(Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal, and k < 0 populates a lower diagonal.(Optional) The data type for the elements of the output tensor. If not specified,the data type of the input tensor T1 is used. If input tensor T1 is also notspecified, then type defaults to 'float'.2D input tensor to copy shape, and optionally, type information from.Output tensor, same shape as input tensor T1.Constrain input types. Strings and complex are not supported.Constrain output types. Strings and complex are not supported.Lower boundary of the output values.highUpper boundary of the output values.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.The shape of the output tensor.Output tensor of random values drawn from uniform distributionThe mean of the normal distribution.The standard deviation of the normal distribution.The data type for the elements of the output tensor. Default is TensorProto::FLOAT.Output tensor of random values drawn from normal distribution(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.Input tensor to copy shape and optionally type information from.Number of times to sample.(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.Input tensor with shape [batch_size, class_size], where class_size is the number of all possible outcomes. Each value along the axis zero represents the unnormalized log-probability of each corresponding outcome in a batch.Output tensor with shape [batch_size, sample_size], where sample_size is the number of times to sample. Each value along the axis zero represents the outcome of the corresponding sample in a batch.Constrain output types to integral tensors.loop_body_attributeiprevcond_outdeltacurrentrangelimitstartScalar. First entry for the range of output values.Scalar. Exclusive upper limit for the range of output values.Scalar. Value to step by.A 1-D tensor with same type as the inputs containing generated range of values.Constrain input types to common numeric type tensors.One and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.Attribute 'value_int' expect an integer.Attribute 'value_ints' expect a list of integers.Attribute 'value_float' expect a float.Attribute 'value_floats' expect a list of floats.Attribute 'value_string' expect a string.Attribute 'value_strings' expect a list of strings.TypeAndShapeInferenceFunction implementation incomplete: this line should never be reached.Shape input must be a one-dimensional tensor.Invalid shape value: Attribute expected to have tensor typeAttribute expected to have a one-dim tensorInput tensor must be 2-dimensionalOutput type must be int32 or int64Input tensor must have rank 2All inputs to 'Range' op must be of the same typeInput to 'Range' op should be scalars (Tensor with only one element and shape empty)/Users/runner/work/1/s/cmake/external/onnx/onnx/defs/generator/old.ccAttribute 'value' of Constant node must exist with 'Tensor' data.Only one of the attributes 'value' or 'sparse_value' must be specified for a Constant node.One of the attributes 'value' or 'sparse_value' must be specified for a Constant node./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/logical/defs.ccConstrains input/output to boolean tensors.First operand, input to be shifted.Second operand, amounts of shift.Constrain input and output types to integer tensors.Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).Constrains input types to all numeric tensors.Constrains output to boolean tensor./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/logical/old.ccConstrains input to boolean tensor.Constrains input to float tensors.Constrains input to integral tensors.First input operand for the logical operator.Second input operand for the logical operator.Result tensor.broadcastEnable broadcastingIf set, defines the broadcast dimensions.Left input tensor for the logical operator.Right input tensor for the logical operator./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/math/defs.ccCoefficient of leakage.Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717).Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946).Coefficient of ELU.The Alpha value in Celu formula which control the shape of the unit. The default value is 1.0.Constrain input and output types to float32 tensors.Value of alpha.Value of beta.HardSwishThe softsign (x/(1+|x|)) values of the input tensor computed element-wiseDimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Whether to return the top-K largest or smallest elements.Whether to return the elements in sorted order.The sine of the input tensor computed element-wiseThe cosine of the input tensor computed element-wiseThe tangent of the input tensor computed element-wiseThe arcsine of the input tensor computed element-wiseThe arccosine of the input tensor computed element-wiseThe arctangent of the input tensor computed element-wiseThe hyperbolic sine values of the input tensor computed element-wiseThe hyperbolic cosine values of the input tensor computed element-wiseThe hyperbolic arcsine values of the input tensor computed element-wiseThe hyperbolic arccosine values of the input tensor computed element-wiseThe hyperbolic arctangent values of the input tensor computed element-wiseaN-dimensional quantized matrix aa_scalescale of quantized input aa_zero_pointzero point of quantized input abN-dimensional quantized matrix bb_scalescale of quantized input bb_zero_pointzero point of quantized input by_scalescale of quantized output yy_zero_pointzero point of quantized output yQuantized matrix multiply results from a * bConstrain input a and its zero point data type to 8-bit integer tensor.Constrain input b and its zero point data type to 8-bit integer tensor.Constrain output y and its zero point data type to 8-bit integer tensor.Zero point tensor for input 'A'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-row quantization. If it's a 1-D tensor, its number of elements should be equal to the number of rows of input 'A'.Zero point tensor for input 'B'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'.Constrain input A data type to 8-bit integer tensor.Constrain input B data type to 8-bit integer tensor.Constrain output Y data type as 32-bit integer tensor.Constrain input and output types to floating-point tensors.Number of input tensors does not match the operands in the equation.Ellipsis represents incompatible dimensions.Rank of input does not match the equation indices.equationEinsum expression string.InputsOperandsinputs are expected to have tensor type.input and zero_point pair is expected to have be same type.input and zero_point pair is expected to have same type./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/math/old.ccWhether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatmentDividend tensorDivisor tensorRemainder tensorConstrain input and output types to high-precision numeric tensors.Constrain input and output types to signed numeric tensors.The exponential of the input tensor computed element-wiseThe natural log of the input tensor computed element-wiseThe hyperbolic tangent values of the input tensor computed element-wiseFirst operand, base of the exponent.Second operand, power of the exponent.Output tensor.Constrain input X and output types to float/int tensors.Constrain input Y types to float/int tensors.sumInput tensor whose elements to be clippedMinimum value, under which element is replaced by min. It must be a scalar(tensor of empty shape).Maximum value, above which element is replaced by max. It must be a scalar(tensor of empty shape).Output tensor with clipped input elementsOptional input tensor C. If not specified, the computation is done as if C is a scalar 0. The shape of C should be unidirectional broadcastable to (M, N).A 1-D tensor indicates the shape you want to expand to, following the broadcast ruleThe sign of the input tensor computed element-wise. It has the same shape and type of the input.The error function of the input tensor computed element-wise. It has the same shape and type of the input.If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.If set to 1 will perform the sums in reverse direction.An input tensor that is to be processed.A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back.Output tensor of the same type as 'x' with cumulative sums of the x's elementsaxis tensor can be int32 or int64 onlytargetignore_indexreductionnonelossconst_zero_floatconst_zero_castedconst_one_floatconst_one_castedInput tensor of shape (N, C) or (N, C, d1, d2, ..., dk).Target tensor of shape (N) or (N, d1, d2, ..., dk). Target element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the target values should either be in the range [0, C) or have the value ignore_index.Optional rescaling weight tensor. If given, it has to be a tensor of size C. Otherwise, it is treated as if having all ones.The negative log likelihood lossType of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights.Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.Constrain input, weight, and output types to floating-point tensors.Constrain target to integer typesNegativeLogLikelihoodLossType of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 'mean': the sum of the output will be divided by the number of elements in the output.scoreslog_problabelsThe predicted outputs with shape [batch_size, class_size], or [batch_size, class_size, D1, D2 , ..., Dk], where K is the number of dimensions.The ground truth output tensor, with shape [batch_size], or [batch_size, D1, D2, ..., Dk], where K is the number of dimensions. Labels element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the label values should either be in the range [0, C) or have the value ignore_index.A manual rescaling weight given to each class. If given, it has to be a 1D Tensor assigning weight to each of the classes. Otherwise, it is treated as if having all ones.Weighted loss float Tensor. If reduction is 'none', this has the shape of [batch_size], or [batch_size, D1, D2, ..., Dk] in case of K-dimensional loss. Otherwise, it is a scalar.Log probability tensor. If the output of softmax is prob, its value is log(prob).SoftmaxCrossEntropyLoss If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of element size 1 (including a scalar tensor and any tensor with rank equal to or smaller than the first tensor), or having its shape as a contiguous subset of the first tensor's shape. The starting of the mutually equal shape is specified by the argument "axis", and if it is not set, suffix matching is assumed. 1-dim expansion doesn't work yet. For example, the following tensor shapes are supported (with broadcast=1): shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 Attribute `broadcast=1` needs to be passed to enable broadcasting. Input tensor of any shape, base of the exponent.Input tensor of any shape broadcastable to X shape, the exponent component.Pass 1 to enable broadcastingIf set, defines the broadcast dimensions. See doc for details.Output tensor (same size as X)consumed_inputslegacy optimization attribute.Coefficient of leakage default to 0.01.Coefficient of SELU default to 1.6732.Coefficient of SELU default to 1.0507.Coefficient of ELU default to 1.0.1-D input tensorslopeSlope tensor. If `Slope` is of size 1, the value is sharedacross different channelsSlope tensor. The shape of slope can be smaller then first input X; if so, its shape must be unidirectional broadcastable to XValue of alpha default to 0.2Value of beta default to 0.5data_0List of tensors for Max.Output tensor. Same dimension as inputs.List of tensors for MinList of tensors for Sum.List of tensors for Mean.Minimum value, under which element is replaced by minMaximum value, above which element is replaced by maxInput tensor AInput tensor BInput tensor C, can be inplace.Whether C should be broadcastedScalar multiplier for the product of input tensors A * B, the default value is 1.0.Scalar multiplier for input tensor C, the default value is 1.0.Input tensor CTensor of shape [a_1, a_2, ..., a_n, r]ValuesTensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing top K values from the input tensorIndicesTensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing the corresponding input tensor indices for the top K values.Constrain index tensor to int64Number of top elements to retrieveDimension on which to do the sort.KA 1-D tensor containing a single positive value corresponding to the number of top elements to retrieve Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) where the function `f(x) = x^exponent`, is applied to the data tensor elementwise. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).First operand.Second operand.Result, has same element type as two inputsDescribes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).The input tensor that's coerced into a 2D matrix of size (NxD) as described above.The output values with the same shape as input tensor (the original size without coercion).'axis' must be in []. Its actual value is: List of tensors for 'shape' input must be 1D tensor of type INT64Target rank must be 1 less than the input rank.Input and target dimension value mismatch.Weight rank must be 1.Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_sizeFirst operand, should share the type with the second operand.Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.Result, has same dimensions and type as AInvalid value for attribute axisInvalid value for attribute kK input must be a one-dimensional tensor of size 1.K input must be of type int64.Axis has less than the requested k elements.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that `output_shape[i] = input_shape[i] * strides[i]` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/nn/defs.ccInput tensor must have at least 2 dimensionsRoIs tensor must have 2 dimensionsAttribute pooled_shape has incorrect lengthAttribute pooled_shape must be specifiedx_scaleScale tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.x_zero_pointZero point tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.w_scaleScale tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).w_zero_pointZero point tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).Scale tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.Zero point tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.Optional 1D bias to be added to the convolution, has size of M. Bias must be quantized using scale = x_scale * w_scale and zero_point = 0Constrain input type to 8-bit integer tensor.Constrain filter type to 8-bit integer tensor.Constrain output type to 8-bit integer tensor.Constrain bias type to 32-bit integer tensor.The shape of the convolution kernel. If not present, should be inferred from input 'w'.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.number of groups input channels and output channels are divided into. default is 1.Zero point tensor for input 'x'. It's optional and default value is 0. It's a scalar, which means a per-tensor/layer quantization.Zero point tensor for input 'w'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M)Constrain input x and its zero point data type to 8-bit integer tensor.Constrain input w and its zero point data type to 8-bit integer tensor.Constrain output y data type to 32-bit integer tensor.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.The pads attribute cannot be used simultaneously with auto_pad attributeIf set to true, it indicates BatchNormalization is being used for training, and outputs 1, 2, 3, and 4 would be populated.input_meaninput_varrunning_meanrunning_varThe running variance after the BatchNormalization operator. This op uses the population size (N) for calculating variance, and not the sample size N-1.Constrain mean and variance types to float tensors. It allows all float type for U.The output tensor of the same shape as input.Input matrixMatrix after normalizationThe axis on which to apply normalization, -1 mean last axis.The order of the normalization, only 1 or 2 are supported.The lambd value for the Shrink formulation. Default is 0.5.The bias value added to output. Default is 0.Input for n-gram extractionNgram resultsInput is ether string UTF-8 or int32/int641-D tensor of floatsMaximum n-gram length. If this value is 3, 3-grams will be used to generate the output.Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.Maximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=1List of strings n-grams learned from the training set. Either this or pool_int64s attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.List of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.The starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity.list of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor.list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts.The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)UTF-8 strings to normalizeUTF-8 Normalized stringsstring enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"Boolean. Whether the identification of stop words in X is case-sensitive. Default is falseList of stop words. If not set, no word would be removed from X.Environment dependent string that denotes the locale according to which output strings needs to be upper/lowercased.Default en_US or platform specific equivalent as decided by the implementation. Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, There are five required inputs 'X', 'scale', 'B', 'input_mean' and 'input_var'. Note that 'input_mean' and 'input_var' are expected to be the estimated statistics in inference mode (training_mode=False, default), and the running statistics in training mode (training_mode=True). There are multiple cases for the number of outputs, which we list below: Output case #1: Y, running_mean, running_var (training_mode=True) Output case #2: Y (training_mode=False) When training_mode=False, extra outputs are invalid. The outputs are updated as follows when training_mode=True: ``` running_mean = input_mean * momentum + current_mean * (1 - momentum) running_var = input_var * momentum + current_var * (1 - momentum) Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B where: current_mean = ReduceMean(X, axis=all_except_channel_index) current_var = ReduceVar(X, axis=all_except_channel_index) Notice that ReduceVar refers to the population variance, and it equals to sum(sqrd(x_i - x_avg)) / N where N is the population size (this formula does not use sample size N - 1). ``` When training_mode=False: ``` Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B ``` For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C * D1 * D2 * ... * Dn) before a BatchNormalization Op. Constrain input and output types to float and 8 bit tensors.ROI pool output shape (height, width).Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.RoIs (Regions of Interest) to pool over. Should be a 2-D tensor of shape (num_rois, 5) given as [[batch_id, x1, y1, x2, y2], ...].RoI pooled output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]).dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis.weight and zero_point pair is expected to have same type.Additional elements added to the side with higher coordinate indices in the output. Each padding value in "output_padding" must be less than the corresponding stride/dilation dimension. By default, this attribute is a zero vector. Note that this attribute doesn't directly affect the computed output values. It only controls the selection of the computed values, so changing this attribute only adds or removes output elements. If "output_shape" is explicitly provided, "output_padding" does not contribute additional size to "output_shape" but participates in the computation of the needed padding amount. This is also called adjs or adjustment in some frameworks.Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.This number of op outputs should be 3 when Training_mode = True, but it is not.This number of op outputs should be 1 when Training_mode = False, but it is not.ngram_indexes must be non-empty with no negative valuesInput tensor must have rank 1 or 2Input shape must have either [C] or [1,C] dimensions where C > 0The ratio of random dropout, with value in [0, 1). If this input was not set, or if it was set to 0, the output would be a simple copy of the input. If it's non-zero, output will be a random dropout of the scaled input, which is typically the case during training. It is an optional value, if not specified it will default to 0.5.The output mask./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/nn/old.ccA tensor of rank >= axis.A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). sizeThe number of channels to sum overScaling parameter.The exponent.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].Output tensor, which has the shape and type as input tensorConstrain input and output types to float tensors.A list of integers, along which to reduce. The default is to caculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.Input tensor must have atleast 2 dimensionsAttribute dilations has incorrect sizeAttribute strides has incorrect sizeAttribute kernel_shape has incorrect sizeAttribute kernel_shape must be specifiedAttribute pads has incorrect sizeSecond input tensor has wrong dimensionWhether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.The storage order of the tensor. 0 is row major, and 1 is column major.Indices tensor from max pooling across the input tensor. The dimensions of indices are the same as output tensor. The values in indices of are the indices of the selected values during pooling. The indices are computed as flatten 1-D tensor, and the indices do not consider padding. So the values in indices are in [0, N x C x D1 x ... x Dn).Dilation value along each spatial axis of filter.Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.MaxUnpool op must have either two or three inputs.Input tensor X must have atleast 2 dimensions.Attribute pads has incorrect size.Attribute strides has incorrect size.Attribute kernel_shape has incorrect size.Attribute kernel_shape must be specified.'output_shape' must be rank 1 tensor.'output_shape' must have same number of elements as the shape of input tensor X.The size of the kernel along each axis.Stride along each spatial axis.Input data tensor that has to be unpooled. This tensor is typically the first output of the MaxPool op.Dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non-image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].Input data tensor containing the indices corresponding to elements in the first input tensor X.This tensor is typically the second output of the MaxPool op.Dimensions must be the same as input tensor X. The indices are linear, i.e. computed considering the tensor as flattened 1-D tensor, assuming row-major storage. Also, the linear indices should not consider padding. So the values in indices are in the range [0, N x C x D1 x ... x Dn).The shape of the output can be explicitly set which will cause pads values to be auto generated. If 'output_shape' is specified, 'pads' values are ignored.Output data tensor that contains the result of the unpooling.Padding for the beginning and ending along each axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding. DEPRECATION NOTE: auto_pad is only intended to support legacy uses, and for framework authors, one is explicitly encouraged to use explicit padding specified in the pads attribute.Stride along each axis.pp value of the Lp norm used to pool over the input data, default is 2.0.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimension are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.Output data tensor from pooling across the input tensor. Dimensions will be N x C x 1 x 1If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.is_testIf set to nonzero, run spatial batch normalization in test mode, default is 0.The epsilon value to use to avoid division by zero, default is 1e-5f.momentumFactor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.The input 4-dimensional tensor of shape NCHW.The scale as a 1-dimensional tensor of size C to be applied to the output.The bias as a 1-dimensional tensor of size C to be applied to the output.The running mean (training) or the estimated mean (testing) as a 1-dimensional tensor of size C.varThe running variance (training) or the estimated variance (testing) as a 1-dimensional tensor of size C.The output 4-dimensional tensor of the same shape as X.The running mean after the BatchNormalization operator. Must be in-place with the input mean. Should not be used for testing.The running variance after the BatchNormalization operator. Must be in-place with the input var. Should not be used for testing.saved_meanSaved mean used during training to speed up gradient computation. Should not be used for testing.saved_varSaved variance used during training to speed up gradient computation. Should not be used for testing.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).Input data tensor from the previous operator; dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size, C is the number of channels. Statistics are computed for every channel of C over N and D1 to Dn dimensions. For image data, input dimensions become (N x C x H x W). The op also accepts single dimension input of size N in which case C is assumed to be 1Scale tensor of shape (C).Bias tensor of shape (C).running (training) or estimated (testing) mean tensor of shape (C).running (training) or estimated (testing) variance tensor of shape (C).The output tensor of the same shape as XThe running mean after the BatchNormalization operator.The running variance after the BatchNormalization operator.Saved mean used during training to speed up gradient computation.Saved variance used during training to speed up gradient computation.The input 1-dimensional scale tensor of size C.The input 1-dimensional bias tensor of size C.The output 4-dimensional tensor of the same shape as input.(float, default 0.5) the ratio of random dropout(int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X.The output mask. If is_test is nonzero, this output is not filled.The ratio of random dropoutConstrain output mask types to boolean tensors.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.The output tensor of the same shape as X.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). If true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch.If spatial is true, the dimension of scale is (C). If spatial is false, the dimensions of scale are (C x D1 x ... x Dn)If spatial is true, the dimension of bias is (C). If spatial is false, the dimensions of bias are (C x D1 x ... x Dn)If spatial is true, the dimension of the running mean (training) or the estimated mean (testing) is (C). If spatial is false, the dimensions of the running mean (training) or the estimated mean (testing) are (C x D1 x ... x Dn).If spatial is true, the dimension of the running variance(training) or the estimated variance (testing) is (C). If spatial is false, the dimensions of the running variance(training) or the estimated variance (testing) are (C x D1 x ... x Dn). Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, there are multiple cases for the number of outputs, which we list below: Output case #1: Y, mean, var, saved_mean, saved_var (training mode) Output case #2: Y (test mode) For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C*D1*D2 ..*Dn) before a BatchNormalization Op. Ratio of Dropout must be a scalar.training_mode of Dropout must be a scalar.Invalid value() for attribute 'axis'Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is usedStride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.Whether to use ceil or floor (default) to compute the output shape.p value of the Lp norm used to pool over the input data.Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. X.shape[1] == (W.shape[1] * group) == C (assuming zero based indices for the shape array). Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL. Optional 1D bias to be added to the convolution, has size of M.Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.The shape of the convolution kernel. If not present, should be inferred from input W.dilation value along each spatial axis of the filter.number of groups input channels and output channels are divided into.Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn)The weight tensor that will be used in the convolutions; has size (C x M/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the weight shape will be (C x M/group x k1 x k2 x ... x kn), where (k1 x k2 x ... x kn) is the dimension of the kernel. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, pad lengths and group count. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate padsThe zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f. default 1; Pooled output Y's height.default 1; Pooled output Y's width.Number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly sampling_ratio x sampling_ratio grid points are used. If == 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default is 0.The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.RoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[x1, y1, x2, y2], ...]. The RoIs' coordinates are in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.RoI pooled output, 4-D tensor of shape (num_rois, C, output_height, output_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1]./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/object_detection/defs.ccboxesAn input tensor with shape [num_batches, spatial_dimension, 4]. The single box data format is indicated by center_point_box.An input tensor with shape [num_batches, num_classes, spatial_dimension]max_output_boxes_per_classInteger representing the maximum number of boxes to be selected per batch per class. It is a scalar. Default to 0, which means no output.iou_thresholdFloat representing the threshold for deciding whether boxes overlap too much with respect to IOU. It is scalar. Value range [0, 1]. Default to 0.score_thresholdFloat representing the threshold for deciding when to remove boxes based on score. It is a scalar.selected_indicesselected indices from the boxes tensor. [num_selected_indices, 3], the selected index format is [batch_index, class_index, box_index].Integer indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models. expected to have rank /Users/runner/work/1/s/cmake/external/onnx/onnx/defs/object_detection/old.ccScale for doing quantization to get 'y'. It can be a scalar, which means per-tensor/layer quantization, or a 1-D Tensor for per-axis quantization.Zero point for doing quantization to get 'y'. It can be a scalar, which means a per-tensor/layer quantization, or a 1-D tensor for per-axis quantization. Default value is uint8 typed 0 if it's not specified.(Optional) The axis of the quantization dimension of the input tensor. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input)/Users/runner/work/1/s/cmake/external/onnx/onnx/defs/quantization/defs.ccScale for input 'x'. It can be a scalar, which means a per-tensor/layer dequantization, or a 1-D tensor for per-axis dequantization.Zero point for input 'x'. It can be a scalar, which means a per-tensor/layer dequantization, or a 1-D tensor for per-axis dequantization. It's optional. 0 is the default value when it's not specified.(Optional) The axis of the dequantizing dimension of the input tensor. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input)Quantized output tensorOutput scale. It's a scalar, which means a per-tensor/layer quantization.Output zero point. It's a scalar, which means a per-tensor/layer quantization.Constrain 'x' to float tensor.Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor.N-D full precision Input tensor to be quantized.Scale for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization.Zero point for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization. Default value is uint8 typed 0 if it's not specified.N-D quantized output tensor. It has same shape as input 'x'.Constrain 'x' to float or int32 tensor.Constrain 'y_zero_point' and 'y' to 8-bit integer tensor./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/quantization/old.ccN-D quantized input tensor to be de-quantized.Scale for input 'x'. It's a scalar, which means a per-tensor/layer quantization.Zero point for input 'x'. It's a scalar, which means a per-tensor/layer quantization. It's optional. 0 is the default value when it's not specified.N-D full precision output tensor. It has same shape as input 'x'.Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/reduction/defs.ccDefines behaviour if 'axes' is empty. Default behaviour with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor.Optional input list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor if 'noop_with_empty_axes' is false, else act as an Identity op when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data).axes as an input and attribute cannot be specified at the same time./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/reduction/old.ccA list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).Keep the reduced dimension or not, default 1 mean keep reduced dimension.reducedReduced output tensor.Constrain input and output types to high-precision and 8 bit numeric tensors.axis must be in [-rank, rank-1]. input rank was The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index).Reduced output tensor with integer data type.A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.The axis in which to compute the arg indices. Computes the indices of the {name} elements of the input tensor's element along the provided axis. The resulting tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulting tensor have the reduced dimension pruned. The type of the output tensor is integer.{name}/Users/runner/work/1/s/cmake/external/onnx/onnx/defs/rnn/defs.ccThe shape format of inputs X, initial_h, initial_c and outputs Y, Y_h, Y_c. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [num_directions, batch_size, hidden_size].The shape format of inputs X, initial_h and outputs Y, Y_h. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = [batch_size, num_directions, hidden_size].A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`.RThe recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`.The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0/Users/runner/work/1/s/cmake/external/onnx/onnx/defs/rnn/old.ccoutput_sequenceOne (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if not specified.The weight tensor for input gate. Concatenation of `Wi` and `WBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, input_size]`.The recurrence weight tensor. Concatenation of `Ri` and `RBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, hidden_size]`.The bias tensor for input gate. Concatenation of `[Wbi, Rbi]` and `[WBbi, RBbi]` (if bidirectional). The tensor has shape `[num_directions, 2*hidden_size]`. Optional: If not specified - assumed to be 0.When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.Couple the input and forget gates if 1, default 0.The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, 4*hidden_size, input_size]`.The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 4*hidden_size, hidden_size]`.The bias tensor for input gate. Concatenation of `[Wb[iofc], Rb[iofc]]`, and `[WBb[iofc], RBb[iofc]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 8*hidden_size]`. Optional: If not specified - assumed to be 0.initial_cOptional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.PThe weight tensor for peepholes. Concatenation of `P[iof]` and `PB[iof]` (if bidirectional) along dimension 0. It has shape `[num_directions, 3*hidde_size]`. Optional: If not specified - assumed to be 0.Y_cThe last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`.First input tensor must have rank 3Couple the input and forget gates if 1. Computes an one-layer simple RNN. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `t` - time step (t-1 means previous time step) `Wi` - W parameter weight matrix for input gate `Ri` - R recurrence weight matrix for input gate `Wbi` - W parameter bias vector for input gate `Rbi` - R parameter bias vector for input gate `WBi` - W parameter weight matrix for backward input gate `RBi` - R recurrence weight matrix for backward input gate `WBbi` - WR bias vectors for backward input gate `RBbi` - RR bias vectors for backward input gate `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Tanh): - Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi) This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. Computes an one-layer GRU. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `z` - update gate `r` - reset gate `h` - hidden gate `t` - time step (t-1 means previous time step) `W[zrh]` - W parameter weight matrix for update, reset, and hidden gates `R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates `Wb[zrh]` - W bias vectors for update, reset, and hidden gates `Rb[zrh]` - R bias vectors for update, reset, and hidden gates `WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates `RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates `WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates `RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh): - zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz) - rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr) - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0 - ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0 - Ht = (1 - zt) (.) ht + zt (.) Ht-1 Computes an one-layer LSTM. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `o` - output gate `f` - forget gate `c` - cell gate `t` - time step (t-1 means previous time step) `W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates `R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates `Wb[iofc]` - W bias vectors for input, output, forget, and cell gates `Rb[iofc]` - R bias vectors for input, output, forget, and cell gates `P[iof]` - P peephole weight vector for input, output, and forget gates `WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates `RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates `WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates `RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates `PB[iof]` - P peephole weight vector for backward input, output, and forget gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh, h=Tanh): - it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi) - ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf) - ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc) - Ct = ft (.) Ct-1 + it (.) ct - ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo) - Ht = ot (.) h(Ct) Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.Number of neurons in the hidden layerOptional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM.The sequence output for the hidden is optional if 0. Default 0.clipCell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.initial_hOptional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. It is optional if `output_sequence` is 0.Y_hThe last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.Constrain seq_lens to integer tensor.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. typestr: , has unsupported type: has inconsistent type has unsupported type Operator '' has been deprecated since version ) has input size not in range [min=, max= not in allowed input sizes.) has output size has output size not in allowed output sizes.) has more inputs () than declared () in op definition.)'s input is marked single but has an empty string in the graph) has more outputs ()'s output Attribute '' appeared multiple times.Unrecognized attribute: for operator Mismatched attribute type in '' is expected to have field 't'' is expected to have field 'sparse_tensor'' is expected to have field 'g'' is expected to have field 'floats'' is expected to have field 'ints'' is expected to have field 'strings'' is expected to have field 'tensors'' is expected to have field 'graphs' has unknown expected typeRequired attribute '' is missing.ONNX Schema : failed validating the check: (inputs_.size() - 1) == i(outputs_.size() - 1) == i!(it.GetName().empty())(Optional) The data type of the tensors in the output sequence. The default type is 'float'.Empty sequence./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/sequence/defs.ccTensors.Sequence enclosing the input tensors.input_sequenceInput sequence.Input tensor to be inserted into the input sequence.positionPosition in the sequence where the new tensor is inserted. It is optional and default is to insert to the back of the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).Output sequence that contains the inserted tensor at given position.Constrain position to integral tensor. It must be a scalar(tensor of empty shape).Position of the tensor in the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).Output tensor at the specified position in the input sequence.Output sequence that has the tensor at the specified position removed.Length of input sequence. It must be a scalar(tensor of empty shape).Constrain output to integral tensor. It must be a scalar(tensor of empty shape).Length of each output. It can be either a scalar(tensor of empty shape), or a 1-D tensor. All values must be >= 0. One or more outputs forming a sequence of tensors after splittingConstrain split size to integral tensor.Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1].Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored.Which axis to concat on. Accepted range in `[-r, r - 1]`, where `r` is the rank of input tensors. When `new_axis` is 1, accepted range is `[-r - 1, r]`. new_axisInsert and concatenate on a new axis or not, default 0 means do not insert new axis.Sequence of tensors for concatenationAttribute dtype should be of integer type and specify a type.SequenceConstruct is expected to have at least 1 input.Input type for input at index is null. Type info is expected.Element type of inputs are expected to be the same.Input Sequence and Tensor are expected to have type info. Current type is null.Input Sequence and Tensor are expected to have the same elem type. Sequence= Tensor=Input type for input at index 0 is null. Type info is expected.Only supports `int32_t` or `int64_t` inputs for splitInput 'split' can not be empty.Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size= sum of split values=new_axis must be either 0 or 1Invalid value of attribute 'axis'. Accepted range=[], Value=/Users/runner/work/1/s/cmake/external/onnx/onnx/defs/tensor/defs.cc(Optional) By default, when any value in the 'shape' input is equal to zero the corresponding dimension value is copied from the input tensor dynamically. allowzero=1 indicates that if any value in the 'shape' input is set to zero, the zero value is honored, similar to NumPy.Optional length of each output. Values should be >= 0.Sum of the values must be equal to the dim value at 'axis' specified.The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. One of 'scales' and 'sizes' MUST be specified and it is an error if both are specified. If 'sizes' is needed, the user can use an empty string as the name of 'scales' in this operator's input list.Constrain input and output types to all tensor and sequence types.(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length along the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices).Input tensor containing indices. Any entries in the 'indices' input tensor with values outside the range [-depth, depth-1] will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.Scalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [-depth, depth-1]. In case 'depth' is of non-integer type, it will be casted to int64 before use.(Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false.(Optional) Whether map negative infinity to true. Default to 1 so that negative infinity induces true. Set this attribute to 0 if negative infinity should be mapped to false.When True (nonzero), yield X, otherwise yield Yvalues selected at indices where condition is Truevalues selected at indices where condition is FalseTensor of shape equal to the broadcasted shape of condition, X, and Y.Constrain to boolean tensors.(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.Tensor of rank r >= 2.Tensor specifying lengths of the sequences in a batch. It has shape `[batch_size]`.Tensor with same shape of input.(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).(Optional) The dimension to apply unique. If not specified, the unique elements of the flattened input are returned. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).A N-D input tensor that is to be processed.A tensor of the same type as 'X' containing all the unique values or subtensors sliced along a provided 'axis' in 'X', either sorted or maintained in the same order they occur in input 'X'A 1-D INT64 tensor containing indices of 'Y' elements' first occurance in 'X'. When 'axis' is provided, it contains indices to subtensors in input 'X' on the 'axis'. When 'axis' is not provided, it contains indices to values in the flattened input tensor. inverse_indicesA 1-D INT64 tensor containing, for elements of 'X', its corresponding indices in 'Y'. When 'axis' is provided, it contains indices to subtensors in output 'Y' on the 'axis'. When 'axis' is not provided, it contains indices to values in output 'Y'. A 1-D INT64 tensor containing the count of each element of 'Y' in input 'X'(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0, empty string or False).A 0-D tensor containing a single value corresponding to the number diagonals above or below the main diagonal to exclude or include. Default value is 0 if it's not specified. This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_crop_and_resize",
x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).Target shape may not have multiple -1 dimensions.Invalid position of 0.Invalid Target shape product of 0. Product cannot be 0 in combination with -1'axis' must be in [-rank(indices), rank(indices)-1]'input' must have rank >= 2'sequence_lens' must have rank of 1toThe data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProtoInput tensor to be cast.Output tensor with the same shape as input with type specified by the 'to' argumentConstrain input types. Casting from complex is not supported.Constrain output types. Casting to complex is not supported./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/tensor/old.ccAn input tensor.Specified shape for output.reshapedReshaped data.Shape of the input tensorConstrain output to int64 tensor.Total number of elements of the input tensorConstrain output to int64 tensor, which should be a scalar though.Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..inputsList of tensors for concatenationconcat_resultConcatenated tensorThe tensor to splitOne or more outputs forming list of tensors after splittingWhich axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).length of each output. Values should be >= 0.1-D tensor of ending indices (exclusive) of corresponding axis in `axes`1-D tensor of axes that `starts` and `ends` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).steps1-D tensor of slice step of corresponding axis in `axes`. Negative value means slicing backward. 'steps' cannot be 0. Defaults to 1.permA list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.transposedTransposed output.updatesTensor of rank q + r - indices_shape[-1] - 1.Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of rank r >=1 (same rank and shape as indices)Tensor of rank r >= 1 (same rank as input).Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of rank q + (r - 1).Tensor of int32/int64 indices, with the same rank r as the input. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of the same shape as indices.List of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensors with at least max(dims) dimensions.squeezedReshaped tensor with same data as input.List of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded).Original tensorexpandedBlocks of [blocksize, blocksize] are moved.Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.Output tensor of [N, C * blocksize * blocksize, H/blocksize, W/blocksize].DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize].Input tensor of any shape.repeats1D int64 tensor of the same length as input's dimension number, includes numbers of repeated copies along input's dimensions.Output tensor of the same dimension and type as tensor input. output_dim[i] = input_dim[i] * repeats[i]Constrain repeat's type to int64 tensors.Three interpolation modes: nearest (default), linear and cubic. The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if "mode" is "cubic".If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0.Four modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.N-D tensorroi1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize"The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified. If 'size' is needed, the user can use an empty string as the name of 'scales' in this operator's input list.sizesThe size of the output tensor. The number of elements of 'sizes' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified.N-D tensor after resizingConstrain roi type to float or double.Tensor to copy input into.Constrain input types to float tensors.Constrain output types to boolean tensors.The number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]Tensor of rank q >= 1. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Supported modes: `constant`(default), `reflect`, `edge`Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank]. `pads` format should be: [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `i` and xi_end, the number of pad values added at the end of axis `i`.constant_value(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0).Constrain input types. Casting from strings and complex are not supported.Constrain output types. Casting to strings and complex are not supported.Which axis to concat on. Default value is 1.Constrain output types to float tensors.Which axis to concat onOptional list of output lengths (see also arg 'split')outputs...Which axis to split onlength of each outputpaddingsList of integers indicate the padding element count at the beginning and end of each axis, for 2D it is the number of pixel. `paddings` rank should be double of the input's rank. `paddings` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.Three modes: constant(default), reflect, edgeOne float, indicates the value to be filled, default is 0New shapetilesNumber of repeated copies to make of the input tensor.Axis along which to repeat.Output tensor of same shape and type as input.Constrain tiles and axis's type to int64 tensors.width_scaleThe scale along width dimension. It takes value greater than or equal to 1.height_scaleThe scale along height dimension. It takes value greater than or equal to 1.Two interpolation modes: nearest(default), bilinear4-D tensor, [N,C,H,W]4-D tensor after resizing, [N,C,H,W]Constrain output types to bool, int32, int64, float16, float, double tensors.The scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'.Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'.Axes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1].Starting indices of corresponding axis in `axes`Ending indices (exclusive) of corresponding axis in axes`1-D tensor of slice step of corresponding axis in `axes`. Default to 1. Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Tensor of int32/int64 indices, of r >= 1 (same rank as input).Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds. It is an error if any of the index values are out of bounds.List of non-negative integers, indicate the dimensions to squeeze.List of non-negative integers, indicate the dimensions to be inserted(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor.Input tensor containing indices. The values must be non-negative integers. Any entries in the 'indices' input tensor with values outside the range [0, depth) will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.depthScalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [0, depth). In case 'depth' is of non-integer type, it will be casted to int64 before use.Rank 1 tensor containing exactly two elements, in the format [off_value, on_value], where 'on_value' is the value used for filling locations specified in 'indices' input tensor, and 'off_value' is the value used for filling locations other than those specified in 'indices' input tensor. Tensor of rank one greater than input tensor 'indices', i.e. rank(output) = rank(indices) + 1. The data type for the elements of the output tensor is the same as the type of input 'values' is used.(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.conditionRank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length alone the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1.Constrains to boolean tensors.Which axis to split on. List of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D it is the number of pixels. `pads` rank should be double of the input's rank. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.One float, indicates the value to be filled. This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_half_pixel_for_nn",
x_original = (x_resized + 0.5) / scale,
if coordinate_transformation_mode is "tf_crop_and_resize",
x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).Value of attribute not specified should be of integer type and specify a type. does not specify a valid type.Target shape may not have multiple -1 dimensionsInvalid position of 0Invalid dimension value: Invalid Target shape product of 0Dimension could not be inferred: incompatible shapesRequired attribute axis is missingaxis must be in [-rank, rank-1].All inputs to Concat must have same rankInvalid value of attribute 'axis'. Rank= Value=Mismatch between number of splits () and outputs (Mismatch between the sum of 'split' () and the split dimension of the input (The input is not evenly splittableSlice op must have either three, four or five inputs.Incorrect or missing input value for starts and endsInput axes has incorrect lengthInput steps has incorrect lengthInput axes has invalid data'step' cannot be 0Only supports `int32_t` or `int64_t` inputs for starts/ends/axes/stepsInvalid attribute perm {}, input shape = {data tensor must have rank >= 1axis must be in [-r, r-1]'axes' attribute must not contain any duplicatesvalues in 'axes' are beyond the bounds of the computed output shapeBlocksize must be positiveInput tensor must be 4-dimensional'Repeats' input must be 1D tensor of type int64'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions.Both `data` and `indices` input tensors in GatherND op need to have rank larger than 0.Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensor'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64rank must be greater than axisRanks inferred () is not equal to the existing rank value (Number of elements of attribute 'scales' must be same as rank of input 'X'Attribute 'scales' must have floats type.Attribute 'scales' is required.Incorrect or missing attribute value for starts and endsAttribute axes has incorrect lengthOneHot node must have three inputs.Input 'depth' must be a scalar or rank 1 tensor.Input 'depth' must have exactly one element.Input 'values' must be rank 1 tensor.Input 'values' must have exactly two elements.Indices tensor must have rank >= 1'axis' must be in [-rank(indices)-1, rank(indices)]Attribute value for pads is requiredAttribute pads has incorrect lengthDimension value inferred () is not equal to the existing dim value (Number of elements of input 'sizes' must be same as rank of input 'X'Input 'sizes' must have int64 element type.Number of elements of input 'scales' must be same as rank of input 'X'Input 'scales' must have float element type.Data to be selectedThe indices, based on 0 as the first index of any dimension.Selected output data as an arrayThe input must be a tensor of a numeric type or string. The output will be of the same tensor type./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/traditionalml/defs.ccData to be binarizedBinarized output dataThe input must be a tensor of a numeric type. The output will be of the same tensor type.Values greater than this are mapped to 1, others to 0.The input map that is to be cast to a tensorA tensor representing the same data as the input map, ordered by their keysmap(int64, string)map(int64, float)The input must be an integer map to either string or float.The output is a 1-D tensor of string, float, or integer.A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.map_formIndicates whether to only output as many values as are in the input (dense), or position the input based on using the key of the map as the index of the output (sparse).
One of 'DENSE', 'SPARSE'.If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.Input dataOutput data. If strings are input, the output values are integers, and vice versa.The input must be a tensor of strings or integers, either [N,C] or [C].The output is a tensor of strings or integers. Its shape will be the same as the input shape.The strings of the map. This sequence must be the same length as the 'cats_int64s' sequenceThe integers of the map. This sequence must be the same length as the 'cats_strings' sequence.A string to use when an input integer value is not found in the map.
One and only one of the 'default_*' attributes must be defined.An integer to use when an input string value is not found in the map.
One and only one of the 'default_*' attributes must be defined.A dictionary.A 1-D tensor holding values from the input dictionary.map(string, int64)map(int64, double)map(string, float)map(string, double)The input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same.The output will be a tensor of the value type of the input map. It's shape will be [1,C], where C is the length of the input dictionary.A string vocabulary array.
One and only one of the vocabularies must be defined.An integer vocabulary array.
One and only one of the vocabularies must be defined.An ordered collection of tensors, all with the same element type.The output array, elements ordered as the inputs.The input type must be a tensor of a numeric type.The size of each input in the input listData to be processed.Imputed output dataThe input type must be a tensor of a numeric type, either [N,C] or [C]. The output type will be of the same tensor type and shape.Value(s) to change toA value that needs replacing.Value(s) to change to.Input data. It can be either tensor or scalar.Output data.The input type is a tensor of any shape.Output type is determined by the specified 'values_*' attribute.A list of strings. One and only one of 'keys_*'s should be set.A list of ints.A list of floats.A list of strings. One and only one of 'value_*'s should be set.A string.An integer.A float.Data to be classified.Classification outputs (one class per example).Classification scores ([N,E] - one score for each class and exampleThe input must be a tensor of a numeric type, and of of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]The output will be a tensor of strings or integers.A collection of weights of the model(s).A collection of intercepts.Indicates whether to do OvR or multinomial (0=OvR is the default).Class labels when using string labels. One and only one 'classlabels' attribute must be defined.Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.Indicates the transform to apply to the scores vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Data to be regressed.Regression outputs (one per target, per example).The input must be a tensor of a numeric type.Indicates the transform to apply to the regression output vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Weights of the model(s).Weights of the intercepts, if used.The total number of regression targets, 1 if not defined.Data to be encoded, a tensor of shape [N,C] or [C]Encoded output datanormOne of 'MAX,' 'L1,' 'L2'Data to be encoded.Encoded output data, having one more dimension than X.List of categories, ints.
One and only one of the 'cats_*' attributes must be defined.List of categories, strings.
One and only one of the 'cats_*' attributes must be defined.If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.Data to be scaled.Scaled output data.First, offset by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.Second, multiply by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.
Must be same length as 'offset'Class scores (one per class per example), if prob_a and prob_b are provided they are probabilities for each class, otherwise they are raw scores.The input must be a tensor of a numeric type, either [C] or [N,C].The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used. Its size will match the bactch size of the input.The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.First set of probability coefficients.Second set of probability coefficients. This array must be same size as prob_a.
If these are provided then output Z are probability estimates, otherwise they are raw scores.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Class labels if using string labels.
One and only one of the 'classlabels_*' attributes must be defined.Class labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined.Regression outputs (one score per target per example).The input type must be a tensor of a numeric type, either [C] or [N,C].Chosen support vectorsFlag indicating whether the regression is a one-class SVM or not.Support vector coefficients.The number of support vectors.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Input of shape [N,F]N, Top class for each pointThe class score for each class, for each point, a tensor of shape [N,E].The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used.Tree id for each node.Node id for each node. Ids may restart at zero for each tree, but it not required to.Feature id for each node.Thresholds to do the splitting on for each node.Popularity of each node, used for performance and may be omitted.The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.
One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'Child node if expression is true.Child node if expression is false.For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.
This attribute may be left undefined, and the defalt value is false (0) for all nodes.The id of the tree that this node is in.node id that this weight is for.The index of the class list that each weight is for.The weight for the class in class_id.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)N classesNode id for each node. Node ids must restart at zero for each tree and increase sequentially.Child node if expression is trueChild node if expression is falseFor each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.
This attribute may be left undefined and the defalt value is false (0) for all nodes.The id of the tree that each node is in.The node id of each weightThe index of the target that each weight is forThe weight for each targetThe total number of targets.Defines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'The input valuesThe output mapseq(map(string, float))seq(map(int64, float))The output will be a sequence of string or integer maps to float.The keys when using string keys.
One and only one of the 'classlabels_*' attributes must be defined.The keys when using int keys.
One and only one of the 'classlabels_*' attributes must be defined.Label encoder has only one input.Label encoder has only one output.Only one of keys_*'s can be set in label encoder.Input type is not string tensor but key_strings is setInput type is not int64 tensor but keys_int64s is setInput type is not float tensor but keys_floats is setOnly one of values_*'s can be set in label encoder.Input's shape should be 1D or 2DInput data.The input type must be a tensor of integers or strings, of any shape.The output type will be a tensor of strings or integers, and will have the same shape as the input.A list of labels./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/traditionalml/old.ccInferred elem type differs from existing elem type: () vs (Inferred shape and existing shape differ in rank: (Inferred shape and existing shape differ in dimension : (type case mismatch. existing= inferred=type case unsupported. existing=Graph has inputs but were provided, node name: (op_type:Warning: Shape inference does not support models with experimental operators: Warning: Unsupported operator . No schema registered for this operator.ONNX models don't support multiple opset version imports for a domain. Function input is out of boundsGraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. does not contain a graph.output /Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/arena.ccCHECK failed: (min_bytes) <= (std::numeric_limits::max() - SerialArena::kBlockHeaderSize): /Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream.ccThis ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug./Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream_impl.ccclose() failed: CHECK failed: !is_closed_: /Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream_impl_lite.ccCHECK failed: (count) >= (0): CHECK failed: backup_bytes_ == 0 && buffer_.get() != NULL: BackUp() can only be called after Next().CHECK failed: (count) <= (buffer_used_): Can't back up over more bytes than were returned by the last call to Next(). Parameter to BackUp() can't be negative.CHECK failed: (backup_bytes_) == (0): CHECK failed: (buffer_used_) == (buffer_size_): /Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/message_lite.ccparse exceeded maximum protobuf size of 2GB: Can't message of type "" because it is missing required fields: /Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/parse_context.hCan't happenINFOWARNINGERRORFATAL[libprotobuf %s %s:%d] %s %lu\x%02xtyfalse/Users/runner/work/1/s/cmake/external/re2/re2/bitstate.ccGrowStack() failed: njob_ = job_.size() = Unexpected opcode: arg Bad arg in kInstAltMatch: Bad arg in kInstCapture: /Users/runner/work/1/s/cmake/external/re2/re2/compile.ccshould never happenCompiler::Copy called!No ranges in char classMissing case in Compiler: |/Users/runner/work/1/s/cmake/external/re2/re2/dfa.ccDeadState in RunStateOnByteNULL state in RunStateOnByteUnexpected special state in RunStateOnByteStateSaver failed to restore state.context does not contain textFailed to analyze start state.RunStateOnByteUnlocked failed after ResetCacheRunStateOnByteUnlocked failed after Reset/Users/runner/work/1/s/cmake/external/re2/re2/nfa.ccunhandled in AddToThreadqUnhandled in stepBad args: nsubmatch=Unexpected opcode in short circuit: in ComputeFirstByte/Users/runner/work/1/s/cmake/external/re2/re2/onepass.ccCannot use SearchOnePass for unanchored matches.unhandled opcode: /Users/runner/work/1/s/cmake/external/re2/re2/parse.ccRepetitionWalker::ShortVisit calledConcat of unknown round: RE2: unexpected op: Bad call to ParseState::ParsePerlFlagsAddFoldedRange recurses too much.AnyBad hex digit \d\D\s\S\w\W[:alnum:][:^alnum:][:alpha:][:^alpha:][:ascii:][:^ascii:][:blank:][:^blank:][:cntrl:][:^cntrl:][:digit:][:^digit:][:graph:][:^graph:][:lower:][:^lower:][:print:][:^print:][:punct:][:^punct:][:space:][:^space:][:upper:][:^upper:][:word:][:^word:][:xdigit:][:^xdigit:]/Users/runner/work/1/s/cmake/external/re2/re2/re2.ccUnknown encoding Error parsing '': Error compiling 'Invalid RE2: RE2: invalid startpos, endpos pair. [startpos: endpos: text size: DFA out of memory: size bytemap range list count SearchDFA inconsistencySearchOnePass inconsistencySearchBitState inconsistencySearchNFA inconsistencyError reverse compiling '/Users/runner/work/1/s/cmake/external/re2/re2/regexp.ccRegexp not destroyed.Bad reference count Unexpected op in Regexp::Equal: no errorunexpected errorinvalid escape sequenceinvalid character classinvalid character class rangemissing ]missing )trailing \no argument for repetition operatorinvalid repetition sizebad repetition operatorinvalid perl operatorinvalid UTF-8invalid named capture groupNumCapturesWalker::ShortVisit called/Users/runner/work/1/s/cmake/external/re2/re2/walker-inl.hStack not empty.Walk NULL/Users/runner/work/1/s/cmake/external/re2/re2/simplify.ccCase not handled in ComputeSimple: CoalesceWalker::ShortVisit calledDoCoalesce failed: r1->op() is DoCoalesce failed: r2->op() is SimplifyWalker::ShortVisit calledSimplify case not handled: Malformed repeat [truncated](?:/Users/runner/work/1/s/cmake/external/re2/re2/tostring.cckRegexpCapture cap() == 0?P<>[^\x00-\x{10ffff}](?:)Bad final char: ?{%d,}{%d}{%d,%d}\C^$(?-m:^)(?-m:$)\z\b\B(?HaveMatch:%d)(){}[]*+?|.^$\[]^-\\\r\t\n\f\x{%x}AdlamAhomAnatolian_HieroglyphsArabicArmenianAvestanBalineseBamumBassa_VahBatakBengaliBhaiksukiBopomofoBrahmiBrailleBugineseBuhidCanadian_AboriginalCarianCaucasian_AlbanianCcCfChakmaChamCherokeeCoCommonCopticCsCuneiformCypriotCyrillicDeseretDevanagariDograDuployanEgyptian_HieroglyphsElbasanEthiopicGeorgianGlagoliticGothicGranthaGreekGujaratiGunjala_GondiGurmukhiHanHangulHanifi_RohingyaHanunooHatranHebrewHiraganaImperial_AramaicInheritedInscriptional_PahlaviInscriptional_ParthianJavaneseKaithiKannadaKatakanaKayah_LiKharoshthiKhmerKhojkiKhudawadiLLaoLatinLepchaLimbuLinear_ALinear_BLisuLlLmLoLtLuLycianLydianMahajaniMakasarMalayalamMandaicManichaeanMarchenMasaram_GondiMcMeMedefaidrinMeetei_MayekMende_KikakuiMeroitic_CursiveMeroitic_HieroglyphsMiaoMnModiMongolianMroMultaniMyanmarNNabataeanNdNew_Tai_LueNewaNkoNlNoNushuOghamOl_ChikiOld_HungarianOld_ItalicOld_North_ArabianOld_PermicOld_PersianOld_SogdianOld_South_ArabianOld_TurkicOriyaOsageOsmanyaPahawh_HmongPalmyrenePau_Cin_HauPcPdPePfPhags_PaPhoenicianPiPoPsPsalter_PahlaviRejangRunicSamaritanSaurashtraScSharadaShavianSiddhamSignWritingSinhalaSkSmSoSogdianSora_SompengSoyomboSundaneseSyloti_NagriSyriacTagalogTagbanwaTai_LeTai_ThamTai_VietTakriTamilTangutTeluguThaanaThaiTibetanTifinaghTirhutaUgariticVaiWarang_CitiYiZanabazar_SquareZlZpZsmu held in reader and writer mode simultaneously on entry to nsync_cv_wait_with_deadline() mu not held on entry to nsync_cv_wait_with_deadline() checking a waiter condition while unlocked attempt to nsync_mu_unlock() an nsync_mu held in read mode attempt to nsync_mu_unlock() an nsync_mu not held in write mode attempt to nsync_mu_runlock() an nsync_mu held in write mode attempt to nsync_mu_runlock() an nsync_mu not held in read mode nsync_mu not held in some mode when calling nsync_mu_wait_with_deadline() nsync_mu_wait woke but condition not true panic: unique_lock::lock: references null mutexunique_lock::lock: already lockedunique_lock::unlock: not locked}uWWxl8.l l(1 =F R})'OUA 3o8(`g}}{{[ ! iq.,",}}{{   IAs j~ u*5_ jIAs j~ u*5_ j8 C]; GzLwրNv X+ clM XրNv X+ clM X 3 3 '$3{D_րN ''d ;1 '}u * ! w/ ;a }u * ! w/ ;a ߇\\dY\e<} G EsavA teT? 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 88   N11onnxruntime15AttentionFusionE   N11onnxruntime17BiasDropoutFusionE N11onnxruntime14BiasGeluFusionEN11onnxruntime17BiasSoftmaxFusionE  N11onnxruntime15CastEliminationEN11onnxruntime30CommonSubexpressionEliminationEN11onnxruntime15ConstantFoldingE        N11onnxruntime20ConvActivationFusionE  N11onnxruntime13ConvAddFusionE  N11onnxruntime12ConvBNFusionE  N11onnxruntime13ConvMulFusionE  N11onnxruntime12DivMulFusionE N11onnxruntime16EliminateDropoutE N11onnxruntime27DynamicQuantizeMatMulFusionEN11onnxruntime20EmbedLayerNormFusionE̼+N11onnxruntime17ExpandEliminationE'7=               N11onnxruntime14FastGeluFusionEN11onnxruntime32FreeDimensionOverrideTransformerEN11onnxruntime17GeluApproximationE ?((      N11onnxruntime10GeluFusionE N11onnxruntime20GemmActivationFusionE       N11onnxruntime19GemmTransposeFusionEN11onnxruntime11RewriteRuleEN11onnxruntime17EliminateIdentityEN11onnxruntime21InsertCastTransformerEN11onnxruntime30RemoveDuplicateCastTransformerE          '7       N11onnxruntime15LayerNormFusionEN11onnxruntime25SimplifiedLayerNormFusionE  N11onnxruntime15MatMulAddFusionE00 N11onnxruntime26MatMulIntegerToFloatFusionEN11onnxruntime17MatMulScaleFusionE    N11onnxruntime21MatmulTransposeFusionE             N11onnxruntime16NchwcTransformerE   N11onnxruntime15NhwcTransformerEN11onnxruntime15NoopEliminationE  N11onnxruntime14NotWhereFusionEN11onnxruntime23OptimizerExecutionFrameENSt3__110__function6__funcIZN11onnxruntime23OptimizerExecutionFrame4InfoC1ERKNS_6vectorIPKNS2_4NodeENS_9allocatorIS8_EEEERKNS_13unordered_mapINS_12basic_stringIcNS_11char_traitsIcEENS9_IcEEEEPKN4onnx11TensorProtoENS_4hashISJ_EENS_8equal_toISJ_EENS9_INS_4pairIKSJ_SN_EEEEEERKNS2_4PathERKNS2_18IExecutionProviderEE3$_0NS9_IS15_EEFNS2_6common6StatusERKNS2_7NodeArgEmEEEZN11onnxruntime23OptimizerExecutionFrame4InfoC1ERKNSt3__16vectorIPKNS_4NodeENS2_9allocatorIS6_EEEERKNS2_13unordered_mapINS2_12basic_stringIcNS2_11char_traitsIcEENS7_IcEEEEPKN4onnx11TensorProtoENS2_4hashISH_EENS2_8equal_toISH_EENS7_INS2_4pairIKSH_SL_EEEEEERKNS_4PathERKNS_18IExecutionProviderEE3$_0NSt3__110__function6__funcIZN11onnxruntime50Register_AveragePool_qdq_QDQAveragePoolTransformerEvE3$_0NS_9allocatorIS3_EEFNS_10unique_ptrINS2_22QDQOperatorTransformerENS_14default_deleteIS7_EEEERNS2_4NodeERNS2_5GraphEEEEN11onnxruntime25QDQAveragePoolTransformerEZN11onnxruntime50Register_AveragePool_qdq_QDQAveragePoolTransformerEvE3$_0NSt3__110__function6__funcIZN11onnxruntime39Register_Add_qdq_QDQBinaryOpTransformerEvE3$_0NS_9allocatorIS3_EEFNS_10unique_ptrINS2_22QDQOperatorTransformerENS_14default_deleteIS7_EEEERNS2_4NodeERNS2_5GraphEEEEN11onnxruntime22QDQBinaryOpTransformerEZN11onnxruntime39Register_Add_qdq_QDQBinaryOpTransformerEvE3$_0NSt3__110__function6__funcIZN11onnxruntime39Register_Mul_qdq_QDQBinaryOpTransformerEvE3$_1NS_9allocatorIS3_EEFNS_10unique_ptrINS2_22QDQOperatorTransformerENS_14default_deleteIS7_EEEERNS2_4NodeERNS2_5GraphEEEEZN11onnxruntime39Register_Mul_qdq_QDQBinaryOpTransformerEvE3$_1NSt3__110__function6__funcIZN11onnxruntime40Register_Concat_qdq_QDQConcatTransformerEvE3$_0NS_9allocatorIS3_EEFNS_10unique_ptrINS2_22QDQOperatorTransformerENS_14default_deleteIS7_EEEERNS2_4NodeERNS2_5GraphEEEEN11onnxruntime20QDQConcatTransformerEZN11onnxruntime40Register_Concat_qdq_QDQConcatTransformerEvE3$_0NSt3__110__function6__funcIZN11onnxruntime36Register_Conv_qdq_QDQConvTransformerEvE3$_0NS_9allocatorIS3_EEFNS_10unique_ptrINS2_22QDQOperatorTransformerENS_14default_deleteIS7_EEEERNS2_4NodeERNS2_5GraphEEEENSt3__110__function6__baseIFNS_10unique_ptrIN11onnxruntime22QDQOperatorTransformerENS_14default_deleteIS4_EEEERNS3_4NodeERNS3_5GraphEEEEN11onnxruntime18QDQConvTransformerEZN11onnxruntime36Register_Conv_qdq_QDQConvTransformerEvE3$_0NSt3__110__function6__funcIZN11onnxruntime40Register_MatMul_qdq_QDQMatMulTransformerEvE3$_0NS_9allocatorIS3_EEFNS_10unique_ptrINS2_22QDQOperatorTransformerENS_14default_deleteIS7_EEEERNS2_4NodeERNS2_5GraphEEEEN11onnxruntime20QDQMatMulTransformerEZN11onnxruntime40Register_MatMul_qdq_QDQMatMulTransformerEvE3$_0N11onnxruntime22QDQOperatorTransformerEs 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EvE4$_12NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_18Squeeze_Onnx_ver11EEENS2_8OpSchemaEvE4$_13NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEvE4$_13NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_20Unsqueeze_Onnx_ver11EEENS2_8OpSchemaEvE4$_14NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEvE4$_14NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_22SpaceToDepth_Onnx_ver1EEENS2_8OpSchemaEvE4$_15NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvE4$_15NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_23DepthToSpace_Onnx_ver11EEENS2_8OpSchemaEvE4$_16NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvE4$_16NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_14Tile_Onnx_ver6EEENS2_8OpSchemaEvE4$_17NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEvE4$_17NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_17Resize_Onnx_ver11EEENS2_8OpSchemaEvE4$_18NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEvE4$_18NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_15IsNaN_Onnx_ver9EEENS2_8OpSchemaEvE4$_19NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_8OpSchemaEvE4$_19NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_17NonZero_Onnx_ver9EEENS2_8OpSchemaEvE4$_20NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEvE4$_20NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_19GatherND_Onnx_ver12EEENS2_8OpSchemaEvE4$_21NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver12EEENS_8OpSchemaEvE4$_21NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_14Pad_Onnx_ver11EEENS2_8OpSchemaEvE4$_22NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEvE4$_22NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_14Cast_Onnx_ver6EEENS2_8OpSchemaEvE4$_23NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver6EEENS_8OpSchemaEvE4$_23NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_16Concat_Onnx_ver4EEENS2_8OpSchemaEvE4$_24NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_16Concat_Onnx_ver4EEENS_8OpSchemaEvE4$_24NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_14Tile_Onnx_ver1EEENS2_8OpSchemaEvE4$_25NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver1EEENS_8OpSchemaEvE4$_25NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_18Upsample_Onnx_ver7EEENS2_8OpSchemaEvE4$_26NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver7EEENS_8OpSchemaEvE4$_26NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_18Upsample_Onnx_ver9EEENS2_8OpSchemaEvE4$_27NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver9EEENS_8OpSchemaEvE4$_27NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_17Resize_Onnx_ver10EEENS2_8OpSchemaEvE4$_28NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver10EEENS_8OpSchemaEvE4$_28NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_15Slice_Onnx_ver1EEENS2_8OpSchemaEvE4$_29NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_15Slice_Onnx_ver1EEENS_8OpSchemaEvE4$_29NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_16Slice_Onnx_ver10EEENS2_8OpSchemaEvE4$_30NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver10EEENS_8OpSchemaEvE4$_30NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_17Scatter_Onnx_ver9EEENS2_8OpSchemaEvE4$_31NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_17Scatter_Onnx_ver9EEENS_8OpSchemaEvE4$_31NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_22DepthToSpace_Onnx_ver1EEENS2_8OpSchemaEvE4$_32NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_22DepthToSpace_Onnx_ver1EEENS_8OpSchemaEvE4$_32NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_16Gather_Onnx_ver1EEENS2_8OpSchemaEvE4$_33NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_16Gather_Onnx_ver1EEENS_8OpSchemaEvE4$_33NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_17Squeeze_Onnx_ver1EEENS2_8OpSchemaEvE4$_34NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_17Squeeze_Onnx_ver1EEENS_8OpSchemaEvE4$_34NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_19Unsqueeze_Onnx_ver1EEENS2_8OpSchemaEvE4$_35NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_19Unsqueeze_Onnx_ver1EEENS_8OpSchemaEvE4$_35NSt3__110__function6__funcIZN4onnx11GetOpSchemaINS2_16OneHot_Onnx_ver9EEENS2_8OpSchemaEvE4$_36NS_9allocatorIS6_EEFvRNS2_16InferenceContextEEEEZN4onnx11GetOpSchemaINS_16One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ntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Less_kOnnxDomain_ver9_12_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Less_kOnnxDomain_ver9_12_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Less_kOnnxDomain_ver9_12_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Less_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_Less_kOnnxDomain_ver13_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Less_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Less_kOnnxDomain_ver13_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Greater_kOnnxDomain_ver7_8_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Greater_kOnnxDomain_ver7_8_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Greater_kOnnxDomain_ver9_12_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_Greater_kOnnxDomain_ver9_12_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_Greater_kOnnxDomain_ver9_12_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_Greater_kOnnxDomain_ver9_12_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Greater_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Greater_kOnnxDomain_ver13_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Greater_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Greater_kOnnxDomain_ver13_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Equal_kOnnxDomain_ver7_10_boolEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Equal_kOnnxDomain_ver7_10_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Equal_kOnnxDomain_ver7_10_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Equal_kOnnxDomain_ver7_10_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Equal_kOnnxDomain_ver7_10_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Equal_kOnnxDomain_ver11_12_boolEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_Equal_kOnnxDomain_ver11_12_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_Equal_kOnnxDomain_ver11_12_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Equal_kOnnxDomain_ver11_12_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Equal_kOnnxDomain_ver11_12_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Equal_kOnnxDomain_ver13_boolEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Equal_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Equal_kOnnxDomain_ver13_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_Equal_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Equal_kOnnxDomain_ver13_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_LessOrEqual_kOnnxDomain_ver12_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_LessOrEqual_kOnnxDomain_ver12_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_LessOrEqual_kOnnxDomain_ver12_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_LessOrEqual_kOnnxDomain_ver12_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_GreaterOrEqual_kOnnxDomain_ver12_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_61kCpuExecutionProvider_GreaterOrEqual_kOnnxDomain_ver12_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_62kCpuExecutionProvider_GreaterOrEqual_kOnnxDomain_ver12_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_62kCpuExecutionProvider_G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ED1Ev__ZN11onnxruntime4LessIxED0Ev__ZNK11onnxruntime4LessIxE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime4LessIxE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime4LessIxE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime4LessIxE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Less_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEvEN5$_1218__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_Less_kOnnxDomain_ver13_doubleEEENS_16KernelCreateInfoEvEN5$_1228__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Less_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEvEN5$_1238__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Less_kOnnxDomain_ver13_int64_tEEENS_16KernelCreateInfoEvEN5$_1248__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Greater_kOnnxDomain_ver7_8_floatEEENS_16KernelCreateInfoEvEN5$_1258__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime7GreaterIfED1Ev__ZN11onnxruntime7GreaterIfED0Ev__ZNK11onnxruntime7GreaterIfE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime7GreaterIfE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime7GreaterIfE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime7GreaterIfE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Greater_kOnnxDomain_ver7_8_doubleEEENS_16KernelCreateInfoEvEN5$_1268__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime7GreaterIdED1Ev__ZN11onnxruntime7GreaterIdED0Ev__ZNK11onnxruntime7GreaterIdE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime7GreaterIdE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime7GreaterIdE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime7GreaterIdE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Greater_kOnnxDomain_ver9_12_floatEEENS_16KernelCreateInfoEvEN5$_1278__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_Greater_kOnnxDomain_ver9_12_doubleEEENS_16KernelCreateInfoEvEN5$_1288__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_Greater_kOnnxDomain_ver9_12_int32_tEEENS_16KernelCreateInfoEvEN5$_1298__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime7GreaterIiED1Ev__ZN11onnxruntime7GreaterIiED0Ev__ZNK11onnxruntime7GreaterIiE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime7GreaterIiE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime7GreaterIiE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime7GreaterIiE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_Greater_kOnnxDomain_ver9_12_int64_tEEENS_16KernelCreateInfoEvEN5$_1308__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime7GreaterIxED1Ev__ZN11onnxruntime7GreaterIxED0Ev__ZNK11onnxruntime7GreaterIxE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime7GreaterIxE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime7GreaterIxE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime7GreaterIxE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Greater_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEvEN5$_1318__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Greater_kOnnxDomain_ver13_doubleEEENS_16KernelCreateInfoEvEN5$_1328__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Greater_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEvEN5$_1338__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Greater_kOnnxDomain_ver13_int64_tEEENS_16KernelCreateInfoEvEN5$_1348__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Equal_kOnnxDomain_ver7_10_boolEEENS_16KernelCreateInfoEvEN5$_1358__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime5EqualIbED1Ev__ZN11onnxruntime5EqualIbED0Ev__ZNK11onnxruntime5EqualIbE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime5EqualIbE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime5EqualIbE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime5EqualIbE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8_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NUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime11LessOrEqualIdE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime11LessOrEqualIdE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_LessOrEqual_kOnnxDomain_ver12_int32_tEEENS_16KernelCreateInfoEvEN5$_1528__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime11LessOrEqualIiED1Ev__ZN11onnxruntime11LessOrEqualIiED0Ev__ZNK11onnxruntime11LessOrEqualIiE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime11LessOrEqualIiE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime11LessOrEqualIiE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime11LessOrEqualIiE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_LessOrEqual_kOnnxDomain_ver12_int64_tEEENS_16KernelCreateInfoEvEN5$_1538__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime11LessOrEqualIxED1Ev__ZN11onnxruntime11LessOrEqualIxED0Ev__ZNK11onnxruntime11LessOrEqualIxE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime11LessOrEqualIxE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime11LessOrEqualIxE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime11LessOrEqualIxE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_GreaterOrEqual_kOnnxDomain_ver12_floatEEENS_16KernelCreateInfoEvEN5$_1548__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime14GreaterOrEqualIfED1Ev__ZN11onnxruntime14GreaterOrEqualIfED0Ev__ZNK11onnxruntime14GreaterOrEqualIfE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime14GreaterOrEqualIfE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime14GreaterOrEqualIfE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime14GreaterOrEqualIfE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_61kCpuExecutionProvider_GreaterOrEqual_kOnnxDomain_ver12_doubleEEENS_16KernelCreateInfoEvEN5$_1558__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime14GreaterOrEqualIdED1Ev__ZN11onnxruntime14GreaterOrEqualIdED0Ev__ZNK11onnxruntime14GreaterOrEqualIdE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime14GreaterOrEqualIdE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime14GreaterOrEqualIdE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime14GreaterOrEqualIdE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_62kCpuExecutionProvider_GreaterOrEqual_kOnnxDomain_ver12_int32_tEEENS_16KernelCreateInfoEvEN5$_1568__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime14GreaterOrEqualIiED1Ev__ZN11onnxruntime14GreaterOrEqualIiED0Ev__ZNK11onnxruntime14GreaterOrEqualIiE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime14GreaterOrEqualIiE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime14GreaterOrEqualIiE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime14GreaterOrEqualIiE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_62kCpuExecutionProvider_GreaterOrEqual_kOnnxDomain_ver12_int64_tEEENS_16KernelCreateInfoEvEN5$_1578__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime14GreaterOrEqualIxED1Ev__ZN11onnxruntime14GreaterOrEqualIxED0Ev__ZNK11onnxruntime14GreaterOrEqualIxE7ComputeEPNS_15OpKernelContextE__ZZNK11onnxruntime14GreaterOrEqualIxE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE_8__invokeES5___ZZNK11onnxruntime14GreaterOrEqualIxE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE0_8__invokeES5___ZZNK11onnxruntime14GreaterOrEqualIxE7ComputeEPNS_15OpKernelContextEENUlRNS_15BroadcastHelperEE1_8__invokeES5___ZZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_Mean_kOnnxDomain_ver6_7_floatEEENS_16KernelCreateInfoEvEN5$_1588__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime6Mean_6IfED1Ev__ZN11onnxruntime6Mean_6IfED0Ev__ZZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Mean_kOnnxDomain_ver8_12_floatEEENS_16KernelCreateInfoEvEN5$_1598__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime6Mean_8IfED1Ev__ZN11onnxruntime6Mean_8IfED0Ev__ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Mean_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEvEN5$_1608__invokeERKNS_12OpKernelInfoE__ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_BitShift_kOnnxDomain_ver11_uint8_tEEENS_16KernelCreateInfoEvEN5$_1618__invokeERKNS_12OpKernelInfoE__ZN11onnxruntime8BitShiftIhEC2ERKNS_12OpKernelInfoE__ZN11onnxruntime8BitShiftIhED1Ev__ZN11onn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Filter2.LNchw.2.1.ProcessNextRow.LNchw.2.1.ProcessNextColumn.LNchw.2.1.SkipOverPadding.LNchw.2.1.HandlePostProcessingMlasConvNchwFloatSingleFma3Filter3.LNchw.3.1.ProcessNextRow.LNchw.3.1.ProcessNextColumn.LNchw.3.1.SkipOverPadding.LNchw.3.1.HandlePostProcessingMlasConvNchwFloatSingleFma3Filter4.LNchw.4.1.ProcessNextRow.LNchw.4.1.ProcessNextColumn.LNchw.4.1.SkipOverPadding.LNchw.4.1.HandlePostProcessing.LNchwc.4.ProcessOutputCount.LNchwc.4.ProcessNextOutputCountBy3.LNchwc.4.3.ProcessNextRow.LNchwc.4.3.ProcessNextColumn.LNchwc.4.3.SkipOverPadding.LNchwc.4.3.HandlePostProcessing.LNchwc.4.ProcessRemainingOutputCount.LNchwc.4.2.ProcessNextRow.LNchwc.4.2.ProcessNextColumn.LNchwc.4.2.SkipOverPadding.LNchwc.4.2.HandlePostProcessing.LNchwc.4.ProcessOutputCountRightPadAndRemaining.LNchwc.ProcessFilterCount3.LNchwc.3.ProcessOutputCount.LNchwc.3.ProcessNextOutputCountBy3.LNchwc.3.3.ProcessNextRow.LNchwc.3.3.ProcessNextColumn.LNchwc.3.3.SkipOverPadding.LNchwc.3.3.HandlePostProcessing.LNchwc.3.ProcessRemainingOutputCount.LNchwc.3.2.ProcessNextRow.LNchwc.3.2.ProcessNextColumn.LNchwc.3.2.SkipOverPadding.LNchwc.3.2.HandlePostProcessing.LNchwc.3.ProcessOutputCountRightPadAndRemaining.LNchwc.ProcessFilterCountLessThan3.LNchwc.2.ProcessOutputCount.LNchwc.2.ProcessNextOutputCountBy3.LNchwc.2.3.ProcessNextRow.LNchwc.2.3.ProcessNextColumn.LNchwc.2.3.SkipOverPadding.LNchwc.2.3.HandlePostProcessing.LNchwc.2.ProcessRemainingOutputCount.LNchwc.2.2.ProcessNextRow.LNchwc.2.2.ProcessNextColumn.LNchwc.2.2.SkipOverPadding.LNchwc.2.2.HandlePostProcessing.LNchwc.2.ProcessOutputCountRightPadAndRemaining.LNchwc.ProcessFilterCount1.LNchwc.1.ProcessOutputCount.LNchwc.1.ProcessNextOutputCountBy3.LNchwc.1.3.ProcessNextRow.LNchwc.1.3.ProcessNextColumn.LNchwc.1.3.SkipOverPadding.LNchwc.1.3.HandlePostProcessing.LNchwc.1.ProcessRemainingOutputCount.LNchwc.1.2.ProcessNextRow.LNchwc.1.2.ProcessNextColumn.LNchwc.1.2.SkipOverPadding.LNchwc.1.2.HandlePostProcessing.LNchwc.1.ProcessOutputCountRightPadAndRemaining.LNchwc.ExitKernelMlasConvNchwcFloatSingleFma3Filter1.LNchwc.1.1.ProcessNextRow.LNchwc.1.1.ProcessNextColumn.LNchwc.1.1.SkipOverPadding.LNchwc.1.1.HandlePostProcessingMlasConvNchwcFloatSingleFma3Filter2.LNchwc.2.1.ProcessNextRow.LNchwc.2.1.ProcessNextColumn.LNchwc.2.1.SkipOverPadding.LNchwc.2.1.HandlePostProcessingMlasConvNchwcFloatSingleFma3Filter3.LNchwc.3.1.ProcessNextRow.LNchwc.3.1.ProcessNextColumn.LNchwc.3.1.SkipOverPadding.LNchwc.3.1.HandlePostProcessingMlasConvNchwcFloatSingleFma3Filter4.LNchwc.4.1.ProcessNextRow.LNchwc.4.1.ProcessNextColumn.LNchwc.4.1.SkipOverPadding.LNchwc.4.1.HandlePostProcessing.LDepthwise.1.ProcessOutputCount.LDepthwise.1.ProcessNextOutputCountBy3.LDepthwise.1.3.ProcessNextRow.LDepthwise.1.3.ProcessNextColumn.LDepthwise.1.3.SkipOverPadding.LDepthwise.1.3.HandlePostProcessing.LDepthwise.1.ProcessRemainingOutputCount.LDepthwise.1.2.ProcessNextRow.LDepthwise.1.2.ProcessNextColumn.LDepthwise.1.2.SkipOverPadding.LDepthwise.1.2.HandlePostProcessing.LDepthwise.1.ProcessOutputCountRightPadAndRemaining.LDepthwise.ExitKernelMlasConvDepthwiseFloatSingleFma3Filter1.LDepthwise.1.1.ProcessNextRow.LDepthwise.1.1.ProcessNextColumn.LDepthwise.1.1.SkipOverPadding.LDepthwise.1.1.HandlePostProcessing.LPointwise.4.ProcessNextOutputCountBy3.LPointwise.4.3.ProcessNextInputBlock.LPointwise.4.ProcessRemainingOutputCount.LPointwise.4.2.ProcessNextInputBlock.LPointwise.4.ProcessRemainingOutputCount1.LPointwise.4.1.ProcessNextInputBlock.LPointwise.ProcessFilterCount3.LPointwise.3.ProcessNextOutputCountBy3.LPointwise.3.3.ProcessNextInputBlock.LPointwise.3.ProcessRemainingOutputCount.LPointwise.3.2.ProcessNextInputBlock.LPointwise.3.ProcessRemainingOutputCount1.LPointwise.3.1.ProcessNextInputBlock.LPointwise.ProcessFilterCountLessThan3.LPointwise.2.ProcessNextOutputCountBy3.LPointwise.2.3.ProcessNextInputBlock.LPointwise.2.ProcessRemainingOutputCount.LPointwise.2.2.ProcessNextInputBlock.LPointwise.2.ProcessRemainingOutputCount1.LPointwise.2.1.ProcessNextInputBlock.LPointwise.ProcessFilterCount1.LPointwise.1.ProcessNextOutputCountBy3.LPointwise.1.3.ProcessNextInputBlock.LPointwise.1.ProcessRemainingOutputCount.LPointwise.1.2.ProcessNextInputBlock.LPointwise.1.ProcessRemainingOutputCount1.LPointwise.1.1.ProcessNextInputBlock.LPointwise.ExitKernel.LComputeExp.ComputeExpBy8Loop.LComputeExp.ProcessRemainingCount.LComputeExp.ExitKernel.LComputeSumExp.ComputeExpBy24Loop.LComputeSumExp.SkipStoreResultsBy24.LComputeSumExp.ProcessRemainingCount.LComputeSumExp.ComputeExpBy8Loop.LComputeSumExp.LoadPartialVector.LComputeSumExp.ProcessSingleVector.LComputeSumExp.SkipStoreResultsBy8.LComputeSumExp.StorePartialVector.LComputeSumExp.ReduceAccumulator.LComputeLogisticBy8Loop.LProcessRemainingCount.LExitKernel.LComputeTanhBy8Loop.LProcessRemainingCount.LExitKernel.LComputeErf4x8Loop.BiggerNumbers.LErfProcessRemainingCount.LErfProcess1x8.BiggerNumbersRemaining.LErfBatchExp_MlasQLinearAddS8KernelAvx2_MlasQLinearAddU8KernelAvx2__Z27MlasConvDepthwiseKernelAvx2IaEvPKPKhhP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ocessRemainingCountN2101.LComputeBlockBy4Loop2223.LProcessRemainingBlocks2223.LComputeBlockBy1Loop2223.LOutputBlock2223.LProcessCountM3.LProcessNextColumnLoop2xN2301.LComputeBlockBy4Loop2315.LProcessRemainingBlocks2315.LComputeBlockBy1Loop2315.LOutputBlock2315.LMultiplyAlpha2xNBlock2301.LStore2xNBlock2301.LOutput1xNBlock2301.LOutput1xNBlockWithMask2301.LMultiplyAlpha1xNBlockWithMask2301.LStore1xNBlockWithMask2301.LProcessRemainingCountN2301.LComputeBlockBy4Loop2655.LProcessRemainingBlocks2655.LComputeBlockBy1Loop2655.LOutputBlock2655.LProcessCountM5.LProcessNextColumnLoop2xN2743.LComputeBlockBy4Loop2757.LProcessRemainingBlocks2757.LComputeBlockBy1Loop2757.LOutputBlock2757.LMultiplyAlpha2xNBlock2743.LStore2xNBlock2743.LOutput1xNBlock2743.LOutput1xNBlockWithMask2743.LMultiplyAlpha1xNBlockWithMask2743.LStore1xNBlockWithMask2743.LProcessRemainingCountN2743.LComputeBlockBy4Loop3139.LProcessRemainingBlocks3139.LComputeBlockBy1Loop3139.LOutputBlock3139.LNchw.4.ProcessOutputCount.LNchw.4.ProcessNextOutputCountBy6.LNchw.4.6.ProcessNextRow.LNchw.4.6.ProcessNextColumn.LNchw.4.6.SkipOverPadding.LNchw.4.6.HandlePostProcessing.LNchw.4.ProcessRemainingOutputCount.LNchw.4.3.ProcessNextRow.LNchw.4.3.ProcessNextColumn.LNchw.4.3.SkipOverPadding.LNchw.4.3.HandlePostProcessing.LNchw.4.ProcessRemainingOutputCountLessThan3.LNchw.4.2.ProcessNextRow.LNchw.4.2.ProcessNextColumn.LNchw.4.2.SkipOverPadding.LNchw.4.2.HandlePostProcessing.LNchw.4.ProcessOutputCountRightPadAndRemaining.LNchw.ProcessFilterCount3.LNchw.3.ProcessOutputCount.LNchw.3.ProcessNextOutputCountBy6.LNchw.3.6.ProcessNextRow.LNchw.3.6.ProcessNextColumn.LNchw.3.6.SkipOverPadding.LNchw.3.6.HandlePostProcessing.LNchw.3.ProcessRemainingOutputCount.LNchw.3.3.ProcessNextRow.LNchw.3.3.ProcessNextColumn.LNchw.3.3.SkipOverPadding.LNchw.3.3.HandlePostProcessing.LNchw.3.ProcessRemainingOutputCountLessThan3.LNchw.3.2.ProcessNextRow.LNchw.3.2.ProcessNextColumn.LNchw.3.2.SkipOverPadding.LNchw.3.2.HandlePostProcessing.LNchw.3.ProcessOutputCountRightPadAndRemaining.LNchw.ProcessFilterCountLessThan3.LNchw.2.ProcessOutputCount.LNchw.2.ProcessNextOutputCountBy6.LNchw.2.6.ProcessNextRow.LNchw.2.6.ProcessNextColumn.LNchw.2.6.SkipOverPadding.LNchw.2.6.HandlePostProcessing.LNchw.2.ProcessRemainingOutputCount.LNchw.2.3.ProcessNextRow.LNchw.2.3.ProcessNextColumn.LNchw.2.3.SkipOverPadding.LNchw.2.3.HandlePostProcessing.LNchw.2.ProcessRemainingOutputCountLessThan3.LNchw.2.2.ProcessNextRow.LNchw.2.2.ProcessNextColumn.LNchw.2.2.SkipOverPadding.LNchw.2.2.HandlePostProcessing.LNchw.2.ProcessOutputCountRightPadAndRemaining.LNchw.ProcessFilterCount1.LNchw.1.ProcessOutputCount.LNchw.1.ProcessNextOutputCountBy6.LNchw.1.6.ProcessNextRow.LNchw.1.6.ProcessNextColumn.LNchw.1.6.SkipOverPadding.LNchw.1.6.HandlePostProcessing.LNchw.1.ProcessRemainingOutputCount.LNchw.1.3.ProcessNextRow.LNchw.1.3.ProcessNextColumn.LNchw.1.3.SkipOverPadding.LNchw.1.3.HandlePostProcessing.LNchw.1.ProcessRemainingOutputCountLessThan3.LNchw.1.2.ProcessNextRow.LNchw.1.2.ProcessNextColumn.LNchw.1.2.SkipOverPadding.LNchw.1.2.HandlePostProcessing.LNchw.1.ProcessOutputCountRightPadAndRemaining.LNchw.ExitKernelMlasConvNchwFloatSingleAvx512FFilter1.LNchw.1.1.ProcessNextRow.LNchw.1.1.ProcessNextColumn.LNchw.1.1.SkipOverPadding.LNchw.1.1.HandlePostProcessingMlasConvNchwFloatSingleAvx512FFilter2.LNchw.2.1.ProcessNextRow.LNchw.2.1.ProcessNextColumn.LNchw.2.1.SkipOverPadding.LNchw.2.1.HandlePostProcessingMlasConvNchwFloatSingleAvx512FFilter3.LNchw.3.1.ProcessNextRow.LNchw.3.1.ProcessNextColumn.LNchw.3.1.SkipOverPadding.LNchw.3.1.HandlePostProcessingMlasConvNchwFloatSingleAvx512FFilter4.LNchw.4.1.ProcessNextRow.LNchw.4.1.ProcessNextColumn.LNchw.4.1.SkipOverPadding.LNchw.4.1.HandlePostProcessing.LNchwc.4.ProcessOutputCount.LNchwc.4.ProcessNextOutputCountBy6.LNchwc.4.6.ProcessNextRow.LNchwc.4.6.ProcessNextColumn.LNchwc.4.6.SkipOverPadding.LNchwc.4.6.HandlePostProcessing.LNchwc.4.ProcessRemainingOutputCount.LNchwc.4.3.ProcessNextRow.LNchwc.4.3.ProcessNextColumn.LNchwc.4.3.SkipOverPadding.LNchwc.4.3.HandlePostProcessing.LNchwc.4.ProcessRemainingOutputCountLessThan3.LNchwc.4.2.ProcessNextRow.LNchwc.4.2.ProcessNextColumn.LNchwc.4.2.SkipOverPadding.LNchwc.4.2.HandlePostProcessing.LNchwc.4.ProcessOutputCountRightPadAndRemaining.LNchwc.ProcessFilterCount3.LNchwc.3.ProcessOutputCount.LNchwc.3.ProcessNextOutputCountBy6.LNchwc.3.6.ProcessNextRow.LNchwc.3.6.ProcessNextColumn.LNchwc.3.6.SkipOverPadding.LNchwc.3.6.HandlePostProcessing.LNchwc.3.ProcessRemainingOutputCount.LNchwc.3.3.ProcessNextRow.LNchwc.3.3.ProcessNextColumn.LNchwc.3.3.SkipOverPadding.LNchwc.3.3.HandlePostProcessing.LNchwc.3.ProcessRemainingOutputCountLessThan3.LNchwc.3.2.ProcessNextRow.LNchwc.3.2.ProcessNextColumn.LNchwc.3.2.SkipOverPadding.LNchwc.3.2.HandlePostProcessing.LNchwc.3.ProcessOutputCountRightPadAndRemaining.LNchwc.ProcessFilterCountLessThan3.LNchwc.2.ProcessOutputCount.LNchwc.2.ProcessNextOutputCountBy6.LNchwc.2.6.ProcessNextRow.LNchwc.2.6.ProcessNextColumn.LNchwc.2.6.SkipOverPa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