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3HpqH;H|HHpP0Hp2HpmH;H~HHpP0Hp2HpMUjH;HHHpP0Hpx2HpmH;HHHpP0HpG2HpmH;HHHpP0Hp2HpjhH;HHHpP0Hp1HpnH;HHHpP0Hp1HppH;HHHpP0Hp1HpgmH;HHHpP0HpR1HpmH;HHHpP0Hp!1HpoH;HHHpP0Hp0Hp蔠nH;HHHpP0Hp0HpmH;HHHpP0Hp0HpүmH;HHHpP0Hp]0HpmH;HHHpP0Hp,0HpmH;HHHpP0Hp/HpiH;HHHpP0Hp/HpNjH;HHHpP0Hp/Hp=mH;HHHpP0Hph/HpuH;HHHpP0Hp7/Hp軈mH;HHHpP0Hp/HpqH;HHHpP0Hp.Hpi$iH;HHHpP0Hp.Hp*iH;HHHpP0Hps.Hp!iH;HHHpP0HpB.Hp6&iH;HHHpP0Hp.Hp襑mH;HHHpP0Hp-Hp$cqH;HHHpP0Hp-Hp3dqH;HHHpP0Hp~-HpB`qH;HHHpP0HpM-HpQaqH;HHHpP0Hp-Hp`XqH;HHHpP0Hp,Hp/]qH;HHHpP0Hp,Hp>YqH;HHHpP0Hp,Hp ^qH;HHHpP0HpX,HpZqH;HHHpP0Hp',Hp+[qH;HHHpP0Hp+Hp芕mH;HHHpP0Hp+Hp9uH;HHHpP0Hp+HpHmH;HHHpP0Hpc+HpwtH;HHHpP0Hp2+Hp覥mH;HHHpP0Hp+HptH;HHHpP0Hp*HptuH;HHHpP0Hp*HpvmH;HHHpP0Hpn*Hp2jH;HHHpP0Hp=*Hp!jH;HHHpP0Hp *HpPtH;HHHpP0Hp)Hp/tH;HHHpP0Hp)Hp辑mH;HHHpP0Hpy)Hp])uH;HHHpP0HpH)Hp|zmH;HHHpP0Hp)Hp۩mH;HHHpP0Hp(Hp mH;HHHpP0Hp(HptH;HHHpP0Hp(HphmH;HHHpP0HpS(HpGtH;HHHpP0Hp"(Hp,uH;HHHpP0Hp'HpeuH;HHHpP0Hp'HpGjH;HHHpP0Hp'HpHH;Eu HĈ[]m:mH HHH5VHSHRmmH ̃HHH5VHoSHm|lH HHH5SVHQHjKjH hHHH5"TH QHjpjH 5HHH5SHPHj=jH HHH5SHPHUj jH πHHH5SHrPH"jiH HHH5VSH?PHiLiH iHHH5#SH PHiqiH 6HHH5RHOHi>iH HHH5RHOHVi iH HHH5RHsOH#ihH HHH5WRH@OHhMhH jHHH5$RH OHhrhH 7HHH5QHNHh ?hH HHH5QHNHWh  hH ~HHH5QHtNH$h gH ~HHH5XQHANHgN gH k~HHH5%QHNHg sgH 8~HHH5PHMHg @gH ~HHH5PHMHXg  gH }HHH5PHuMH%g fH }HHH5YPHBMHfO fH l}HHH5&PHMHf tfH 9}HHH5OHLHf AfH }HHH5OHLHYf fH |HHH5OHvLH&f eH |HHH5ZOHCLHeP eH m|HHH5'OHLHe ueH :|HHH5NHKHe BeH |HHH5NHKHZe eH {HHH5NHwKH'e dH {HHH5[NHDKHdQ dH n{HHH5(NHKHd vdH ;{HHH5MHJHd CdH {HHH5MHJH[d dH zHHH5MHxJH(d cH zHHH5\MHEJHcR cH ozHHH5)MHJHc wcH xHHH5JHGHaFaH xHHH5JHGH^aaH wHHH5JH{GH+a`H wHHH5_JHHGH`U`H rwHHH5,JHGH`"z`H ?wHHH5IHFH`G`H wHHH5IHFH_``H vHHH5IH|FH,`_H vHHH5`IHIFH_V_H svHHH5-IHFH_#{_H @vHHH5HHEH_H_H vHHH5HHEH`__H uHHH5HH}EH-_^H uHHH5aHHJEH^W^H tuHHH5.HHEH^$|^H AuHHH5GHDH^I^H uHHH5GHDHa^^H tHHH5GH~DH.^]H tHHH5bGHKDH]X]H utHHH5/GHDH]%}]H BtHHH5FHCH]J]H tHHH5FHCHb]]H sHHH5FHCH/]\H sHHH5cFHLCH\Y\H vsHHH50FHCH\&~\H CsHHH5EHBH\K\H sHHH5EHBHc\\H rHHH5EHBH0\[H rHHH5dEHMBH[Z[H wrHHH51EHBH['[H DrHHH5DHAH[L[H rHHH5DHAHd[[H qHHH5DHAH1[ZH qHHH5eDHNAHZ^ZH {qHHH55DHAHZ.ZH KqHHH5DH@HZ  ~|zxvtrpnljhfdb`^\ZXVTRPNLJHFDB@><:86420.,*(&$"  HHpgHT ffff.UHSPHYH FgHHHKHHH[]UHH%gHHHGHF]@UHHH]`fUHHNH1H; ;NHD]DUHHB]UHSHHHqHHEHp׻nH{ HHHpP0HpqHpuoH{ HHHpP0Hp?Hp*uH{ HHHpP0Hp Hpq&uH{ HHHpP0HpHpHH;Eu HĈ[]XWH KnHHH5AH=HWSWH nHHH5@H=HkW^#WH mHHH5@H=H;W.VH mHHH5r@H[=H W HHpHQ f.UHSHHHoHHEHplH{ HHHpP0H Hp}HpAemH;HHHpP0HpLHpP9pH;HHHpP0HpHptH;HHHpP0HpHplH;HHHpP0HpHp蝯mH;HHHpP0HpHpgmH;HHHpP0HpWHp[1pH;HHHpP0Hp&Hp:ejH;HHHpP0HpHp9lH;HHHpP0HpHplH;H HHpP0HpHpǖmH;H HHpP0HpbHpvjH;HHHpP0Hp1Hp赿nH;HHHpP0HpHp]jH;HHHpP0Hp HplH;HHHpP0Hp HpmH;HHHpP0Hpm HpqmH;HHHpP0Hp< Hp耠mH;HHHpP0Hp Hp/dmH;HHHpP0Hp HplH;HHHpP0Hp HpmH;H HHpP0Hpx Hp\lH;H"HHpP0HpG HplH;H$HHpP0Hp Hp_jH;H&HHpP0Hp HplH;H(HHpP0Hp Hp8lH;H*HHpP0Hp HpwamH;H,HHpP0HpR HpvmH;H.HHpP0Hp! Hp%lH;H-HHpP0Hp HpԯtH;H,HHpP0Hp HiHH;Eu HĈ[]PjPH /gHHH59H6HP7PH fHHH59H6HOPPH fHHH59Hl6HPOH fHHH5P9H96HOVOH cfHHH59H6HO#kOH 0fHHH58H5HO8OH eHHH58H5HPOOH eHHH58Hm5HONH eHHH5Q8H:5HNWNH deHHH58H5HN$lNH 1eHHH57H4HN9NH dHHH57H4HQNNH dHHH57Hn4HNMH dHHH5R7H;4HMXMH edHHH57H4HM%mMH 2dHHH56H3HM:MH cHHH56H3HRMMH cHHH56Ho3HMLH cHHH5S6H<3HLYLH fcHHH5 6H 3HL&nLH 3cHHH55H2HL;LH cHHH55H2HSLLH bHHH55Hp2H LKH bHHH5T5H=2HKZKH gbHHH5!5H 2HK'oKH 4bHHH54H1HK1HJ^JH kaHHH5%4H1HJ.vJH ;aHHH53H0HJ :86420.,*(&$"  HHp1HD UHSHHHbHHEHpjH{ H6HHpP0H HpHpqlH;H4HHpP0HpHpнiH;H6HHpP0HpkHpojH;H8HHpP0Hp:HpjH;H:HHpP0Hp HpoH;H<HHpP0HpHpL8pH;H>HHpP0HpHpjH;H@HHpP0HpvHplH;HBHHpP0HpEHp&pH;HDHHpP0HpHp'jH;HFHHpP0HpHpGmH;HHHHpP0HpHp(jH;HJHHpP0HpHpqH;HLHHpP0HpPHp)jH;HNHHpP0HpHpÇqH;HPHHpP0HpHplH;HRHHpP0HpHpaiH;HTHHpP0HpHp]mH;HVHHpP0Hp[Hp~qH;HXHHpP0Hp*Hp莵jH;HZHHpP0HpHp;lH;H\HHpP0HpHpjH;H^HHpP0HpHp;tH;H`HHpP0HpfHphH;HbHHpP0Hp5HpIiH;HaHHpP0HpHphwmH;H`HHpP0HpH]HH;Eu HĈ[]E~DH C[HHH5-H*HD#KDH [HHH5-H*HcDDH ZHHH5-H*H0DCH ZHHH5d-HM*HCCH wZHHH51-H*HCWCH DZHHH5,H)HC$LCH ZHHH5,H)HdCCH YHHH5,H)H1CBH YHHH5e,HN)HBBH xYHHH52,H)HBXBH EYHHH5+H(HB%MBH YHHH5+H(HeBBH XHHH5+H(H2BAH XHHH5f+HO(HAAH yXHHH53+H(HAYAH FXHHH5+H'HA&NAH XHHH5*H'HfAAH WHHH5*H'H3A@H WHHH5g*HP'HA@H zWHHH54*H'H@Z@H GWHHH5*H&H@'O@H WHHH5)H&Hg@@H VHHH5)H&H4@?H VHHH5h)HQ&H@?H {VHHH55)H&H?^?H KVHHH5)H%H?.V?H VHHH5(H%Hn? 20.,*(&$"  HHpH9 ffffff.UHSHHHWHHEHplH{ HdHHpP0HpHpumH{ HeHHpP0HpHpmH{ HfHHpP0HpMHplH{ HgHHpP0HpHp?lH{ HhHHpP0HpHp蝹oH{ HfHHpP0HpHpKhH{ HdHHpP0HpHfVHH;Eu HĈ[]=0=H SHHH5&H#HH='<H SHHH5|&He#H=<H SHHH5I&H2#H<<H \SHHH5&H"H<d<H )SHHH5%H"H|<^4<H RHHH5%H"HL<.<H RHHH5%Hl"H< HHpH6 DUHSHHHTHHEHp'nH{ HHHpP0H HpHp1uH;HHHpP0Hp\Hp`hH;HHHpP0Hp+Hp/hH;HHHpP0HpHpjH;HHHpP0HpHp jH;HHHpP0HpHptH;HHHpP0HpgHp oH;HHHpP0Hp6HpiH;HHHpP0HpHp lH;HHHpP0HpHptH;HHHpP0HpHptjH;HHHpP0HprHpցmH;HHHpP0HpAHp$pH;HHHpP0HpHptH;HHHpP0HpHp蓷jH;HHHpP0HpHpBjH;HHHpP0Hp}HpjH;HHHpP0HpLHp蠼jH;HHHpP0HpHpOjH;HHHpP0HpHpnH;HHHpP0HpHptH;HHHpP0HpHplH;HHHpP0HpWHp=hH;HHHpP0Hp&Hp*lH;HHHpP0HpHpOtH;HHHpP0HpHpsH;HHHpP0HpHptH;HHHpP0HpbHp&nH;HHHpP0Hp1Hp>oH;HHHpP0HpHNHH;Eu HĈ[]-65H pLHHH5*HH5x5H =LHHH5HH5E5H LHHH5HH]5V5H KHHH5HzH*5#4H KHHH5^HGH44H qKHHH5+HH4y4H >KHHH5HH4F4H KHHH5HH^4W4H JHHH5H{H+4$3H JHHH5_HHH33H rJHHH5,HH3z3H ?JHHH5HH3G3H JHHH5HH_3X3H IHHH5H|H,3%2H IHHH5`HIH22H sIHHH5-HH2{2H @IHHH5HH2H2H IHHH5HH`2Y2H HHHH5H}H-2&1H HHHH5aHJH11H tHHHH5.HH1|1H AHHHH5HH1I1H HHHH5HHa1Z1H GHHH5H~H.1'0H GHHH5bHKH00H uGHHH5/HH0}0H BGHHH5HH0J0H GHHH5HHb0^0H FHHH5HH20./H FHHH5iHRH0 86420.,*(&$"  HHpHE* ffff.UHSHHHbHHHEHp藒tH{ HHHpP0HpAHp5tH{ HHHpP0HpHpnH{ HHHpP0HpHp1mH{ HHHpP0HpHp/oH{ HHHpP0HpyHp荮lH{ HHHpP0HpGHpKoH{ HHHpP0HpHptH{ HHHpP0HpHp9jH{ HHHpP0HpHpepH{ HHHpP0HpHp#jH{ HHHpP0HpMHpAjH{ HHHpP0HpHp^nH{ HHHpP0HpHp YnH{ HHHpP0HpHpkpH{ HHHpP0HpHppH{ HHHpP0HpSHp'}sH{ HHHpP0Hp!HpqpH{ HHHpP0HpHpCsH{ HHHpP0HpHpSpH{ HHHpP0HpHlDHH;Eu HĈ[]+6+H AHHH5HHN++H AHHH5HkH+*H AHHH5OH8H*X*H bAHHH5HH*%j*H /AHHH5HH*7*H @HHH5HHO**H @HHH5HlH*)H @HHH5PH9H)Y)H c@HHH5HH)&k)H 0@HHH5HH)8)H ?HHH5HHP))H ?HHH5HmH)(H ?HHH5QH:H(Z(H d?HHH5HH('l(H 1?HHH5HH(9(H >HHH5HHQ((H >HHH5HnH('H >HHH5RH;H'^'H h>HHH5"H H'.s'H 8>HHH5H H' $"  HHpDH! f.UHSHHH@HHEHp)hH{ H HHpP0H HpHpqiH;H HHpP0HpHppsH;H HHpP0Hp{Hp߼jH;H HHpP0HpJHp>jH;H HHpP0HpHpMjH;H HHpP0HpHp,tH;H HHpP0HpHpiH;H HHpP0HpHp xiH;H HHpP0HpUHpspH;H HHpP0Hp$HpotH;H HHpP0HpHpetH;H HHpP0HpHp\tH;H HHpP0HpHp襀mH;H HHpP0Hp`HpDtH;H HHpP0Hp/Hp'iH;H HHpP0HpHpҾjH;H HHpP0HpHpa`tH;H HHpP0HpHptH;H HHpP0HpkHphtH;H HHpP0Hp:Hp^isH;H HHpP0Hp HpQtH;H HHpP0HpHpqtH;H HHpP0HpHpttH;H HHpP0HpvHpj$oH;H HHpP0HpEHpiqH;H HHpP0HpHpqH;H HHpP0HpHpqH;H HHpP0HpHpqH;H HHpP0HpHp%pH;H HHpP0HpPHp4qH;H! 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LLLHHQM'E1L}MLuM9t"LL)IƨLPIƨHXuH}LL}|HHH;ES||IHHn\H5}H I}H`LHhHJHx=KH(H5 J}H0H(SHI}H`H0L1H5뼔HLl| HHLHMIHHH0t'H@|LmMuOHHLmMt6HEL9t!Lp@t Hx|LM9uH}LLmi|Eu`u LHMu,iH}C|`tHp.|LHMt?HPL9t$Lp@t Hx|LM9uHHLLP|HLL|LeMIHMMeMKHII\$ Ht#HHCHuHHPHZ|I\$Ht#HHCHuHHPH-|L'|MLLFHII] Ht#HHCHuHHPH|I]Ht#HHCHuHHPH|L|MLLgHIL肨Hj2 MIII_ Ht#HHCHuHHPH1|I_Ht#HHCHuHHPH|L|LMLeM6QIHH`HIHHA %IHMeMuGIHM4$M_IHLHLMeMtOLII]L9t&MI)HHsH譍HIuI}LMe |LLL|H`H.5IHLeM"MIH]L9t"MI)HèHFPHèIXuH}LLe|LMHAA|HHQHb#LI|H80IHIuLCLM4$MtQLII\$L9t&MI)HHsHCHIuI<$LMt$|LLL|H`Ht Hh|HA!|HHQ H"LI|EHh/UHAWAVAUATSHH}HHEW)EMIIHHEHEHopaque(HMH}H|H5}H|HH|H5}H|H}`6KHHHQ`IMO(|HHC HCHCHCHIELLLHPxIEt H}e|1HHH;EuHHĸ[A\A]A^A_]|G|IHH\H5}H S}H`LH~W)0HDž@`|HH@(^c)0H5w}VHw|CVHJ}H`H0L@ 1H5GHxL|}|IHH\H5K}H }H`LHW)0HDž@0|H@(b)0H type!!!HHH_tensor HHHot a nonHHHype is nHHHOpaque tH@'H}H`H0LH 1H5OHL| IIƳHEH(HIIƳHHH(H'xEIIƳHEH(HgIIƳHHH(H1II*II0t H@|HEH(HtIHEH;(t%Lh@t Hx|LL9(uH}H(H(HEo|Eu!`u*HHH(Hu:H}?|`tHp*|HHH(HVHPH;((Lh@t Hx|LL9(uII0t H@|HEH(HtIHEH;(t%Lh@t Hx|LL9(uH}H(H(HEg|Eu!`u*HHH(Hu6H}7|`tHp"|HHH(HtRHPH;(t(Lh@t Hx|LL9(uHHH(H(HP|tL|EtEAL|HHQ HxLH|IIL{ Mt#HIGHuILPL.|L{Mt#HIGHuILPL|H|EVFIIED4IIE2"IIE IIEH}|AAu.L|HHQHwLH|ILO(ffffff.UH]6fDUHAWAVAUATSHHMHHEW)EMIIHHEHEHopaque(HMH}H|H5 }H|HH|H5 }H|H}0/KHtbHHQ`HHHLLLEt H}q|1HHH;EuHHĸ[A\A]A^A_]|S|IHH\H5! }H K}H`LHW)0HDž@`|HH@(j\)0H5 }VH|CVAHU }H`H0LKE1H5QHL||IHH$\H5U }H  }H`LHW)0HDž@0'|H@([)0H type!!!HHH_tensor HHHot a nonHHHype is nHHHOpaque tH@'AH }H`H0LQE1H5WHL| HIAHEH(HHIAHHH(H)zGHIAHEH(HhHIAHHH(H1HI+HI0t H@|HEH(HtIHEH;(t%Lh@t Hx|LL9(uH}H(H(HEs|Eu!`u*HHH(Hu:H}C|`tHp.|HHH(HVHPH;((Lh@t Hx|LL9(uHI0t H@|HEH(HtIHEH;(t%Lh@t Hx|LL9(uH}H(H(HEk|Eu!`u*HHH(Hu6H};|`tHp&|HHH(HtRHPH;(t(Lh@t Hx|LL9(uHHH(H(HP|EtL|Et`EuZucL|HHQ HLH|fHIEtHIEt HIEtH}C|tu.L[|HHQHLHC|IL!UHAWAVSPII|HHt&|HHtH5M}H|AI1HH[A^A_]Hǃu%|HHQ H{LH|Ńu*|HHQHQLHu|H-!ffff.UHAWAVSPIMt,~ AL@H;HtO|HIuL>|1H[A^A_]DUHAWAVAUATSH8IIIHٔHHE#IIG0xu HٔHH;E"H5} IGI H)HHL9Mt?1I1IHHHH;HLL9rW)EHEHLuALH=H}|ILuKH]LHu|H]EAIufLMHؔHH;E/H5F}HؔHH;EH5U}H8[A\A]A^A_]cLMWILH HHJLDILHHI ILHHHI LLI9uMtILH}t$11f.I I HHL9r1M7I0MPHPMHLHMHH}Ht H}萾|1HהHH;EuHH8[A\A]A^A_]|H}覻| HIH}HtCH}B|t:ucLZ|HHQHLHB|6HIƃuL%|HHQ HLH |JILUHSPZ\HHHHljQ 1HH[]Ãu(H蹽|HHQ H}LH衽|Ƀu(H茽|HHQHPLHt|H.H&fDUHSPHR_HxH1HH[]Ãu(H%|HHQ HLH |Ƀu(H|HHQHLH|HHfUH1H nHB]UHHf}]UH1]HMffff.UHAVSHHtdLs Mt#HIFHuILPL|LsMt#HIFHuILPL|H[A^]ٻ|[A^]UHSPHHtCt H{读|HH[]须|H[]fffff.UHHtH]`]fffff.UHSPHHtH*HH[]H|H[]DUHAWAVSPHH@L<H ҔHQHLtpH@HLpH\H єHHLDHt IX|Hù|H5HLV|LH[A^A_]p|fff.UHAWAVSPHHєHHH LspH@HCpL{HVєHHCCHt H{Xc|LA|H5HHԸ|L|HH[A^A_].|fUHAWAVSPHH@HH єHQHLtpH@HLpL|H ДHHLDHt H{Xչ|L賸|H5HHF|Lj|HH[A^A_]頹|@UHSPHHhДHHC@t H{Pv|HH[]N|fff.UHSPHH(ДHHC@t H{P6|H|HH[] |@UHLV0LNXM9sLVXMDu MtF@u#HF@HMI)Åu E1ۅu1Hy>jHFPMI)ÅtL؃t!uOAuLH+F(Hy;HFH+FHx.I9|)HAtH~tAMHGxHGpHGhHG`HGXHGPHGHHG@HG8HG0HG(HG HGHGHGHHHH]AtHNHHNLN At HcHN(HN0HGxHGpHGhHG`HGXHGPHGHHG@HG8HG0HG(HG HGHGHGHHH]UHSPAHHH1P HH[]ff.UHHG0HOXH9sHGXHG`tHW H9sHO HHOH9s]ffffff.UHHG0HOXH9sHGXHHWH9Ws!tG`u@8ruHHWHO @2]HHWHO 1]UHAWAVAUATSH(AHH:ϔHHEAt&L{Lk0L+{HC8I9t-HKXH{XHs`E1HΔHH;E\AC`HC(HEHCXHEH{@1H}谳|HEHu H7HH1n|HEI)HKXH}H)HUu HHCHHSPHHS(HC8ILk0Hs`HH{XIUHUH9H}HBH?H9t"C@u HK@HHKPIHKL{H{ I9t.HS0EuEH͔HH;Eu]DH([A\A]A^A_]HH@hH ͔H H;Mu4AHH([A\A]A^A_]H袴|裴|H͔HH;Et|fUH]f.UHHHxP ]H(UHHNH 1H; éHD]DUHAWAVAUATSHHICAEAEM}HsL|C fAE Mu(Hs(L|LuMe@Hs@Lѱ|I}XHsX HMHL9AfAIHHH[A\A]A^A_]L}HEIHtXIH9t?fff.Lx@t Hx |LL9uI>I|A$t1HL}HEA$uHEu9HEuUH}褭| I}P诲|HEtL}HEHEtI}8芲|HEtL}HEHEtI}e|H}F| fffff.UHAWAVATSIIIGIGII~I+>HHILHgffffffI9st|HI_IKHIGM&MvM9u7Bfffff.ID$IL$ HK HCI_H(I_I(M9t HL谯|[A\A^A_]L迮| IM'Mu CIM'Mt8IGL9t9HX@t Hx9|HI9uI?Mg%|L| Lf.UHAWAVSPIIHt8L;C(t H{8ڰ|Ct H{ ˰|Hð|LMuIIdžHt蟰|MMtJIL9t/ff.HX@t Hxi|HI9uILMI|AF@uAF(u"AFu+H[A^A_]I~P |AF(tI~8|AFtI~H[A^A_]|f.UHAWAVATSIIIMfI)IID$HH=HI^H)HHHH9wYHH9HBHH9vC良|HH5u|HѬ|HƔHHH5HwH華|HH<7|JLI7IJ4JDM>IvL9H^L)Ht-Hffff.HNHFHvHJHHuH8fHNHFHJHNHFHJHNHFHJHNHFHJHNHFHJHNHFHJHNHFHJHNHFHvHJHI9oM>I^LIIFMFL9t#H{HCH[HtHPI9uMtL[A\A^A_]ǭ|[A\A^A_]11L|IHح|L脨| fff.UHAWAVAUATSHHIHHƔHHEH8HHHHLH@H8LЬ|HEEL5ĔM~LI@L8L֫|L%ÔILHDž(HDž HDžHDžDž0L蚯|HLHhHH}|HHHKHHHLL8Lt H 7|H|H5PHH蠪|H8|H-ŔHH;EuHH[A\A]A^A_]n|HLL8Lt H 豫|H苪|H H HH5HH|HH8 |HJ| fUHAWAVAUATSPHIi|MuHsLY|Me0Hs0LI|M}HHsHL9|HC`IE`I}hHhH?H[A\A]A^A_]HAuA$u3AuGAEu\H豥| I}X輪|A$t HA$tI}@蠪|At HAtI}(膪|AEt HAEtI}j|HL| @UHAWAVAUATSHV|IH}\H5v|H |HXLEW)EE HEHoverflowHEHInteger HEEHXHM1L{H5HL| IEt H}蝩|L}Mt6HEL9t!HX@t Hxx|HI9uH}LL}^|xt H}L|Xt Hh7|E1LmMuUH IALmMt>HEL9t| IALeMuFzIALhMIIPt H`蜤|LeMt6HEL9t!HX@t Hxw|HI9uH}LLe]|EuEuLhMu&^H}:|EtH}+|LhMt:HpL9tEHX@t Hx|HI9uHhLp|EtL|L躞| Lffff.UHAWAVAUATSHHͼHHEHHOH)HH9vFu=Tu3HVH9sNLu DH {H H;Mtѣ|1H dH H;MuHĈ[A\A]A^A_]ÿ+|IHhȏ\H5|H |H}LhW)PHDž`0΢|H`(88)PHts valueHHHy for itHHHype entrHHHissing tHHHMap is mH@'AH&|HuHPLE1H5H2L肢| IALeMuFzIALhMIIPt H`|LeMt6HEL9t!HX@t Hx軡|HI9uH}LLe衡|EuEuLhMu&^H}~|EtH}o|LhMt:HpL9tEHX@t HxD|HI9uHhLp)|EtLR|L| LؐUHAWAVAUATSHHHHHEJH;Cu*HH H H;MuHĘ[A\A]A^A_]B||IHh]\H5|H a|H}Lh,ZUJHHHH|HPHHH]AH|HuHPLE1H5懔HLg| IALhMIIPtH`֟|LeMuEIALeMt6HEL9t!HX@t Hx蠟|HI9uH}LLe膟|EuEuLhMu&^H}c|EtH}T|LhMt:HpL9tEHX@t Hx)|HI9uHhLp|EtL7|L| LfDUHAWAVAUATSH(IIHHHHHEH8H腔HHHLH@H8L-|LHEEL=^MgLI@L8L,|L-!ILHDž(HDž HDžHDžDž0H3HXKL|HLHHH0HXKHHHSLL8Lt H 腝|H_|H5HH|H8|H{HH;EuHH([A\A]A^A_]輝|HLL8Lt H |Hٛ|HHHHH5HHK|HH8f|H萗| UHAWAVAUATSHHHHEHHGH)HH9v7 u.HH9sOH{HH;Eu:HĈ[A\A]A^A_]THUHH;Eu1HĈ[A\A]A^A_]藜||IHh貈\H5|H |H}LhW)PHDž`0踛|H`(B1)PH elementHH&Hor its eHH H entry fHHHing typeHHH is missHHHSequenceH@.AH|HuHPLE1H5݂HL^| IALeMuFzIALhMIIPt H`輚|LeMt6HEL9t!HX@t Hx藚|HI9uH}LLe}|EuEuLhMu&^H}Z|EtH}K|LhMt:HpL9tEHX@t Hx |HI9uHhLp|EtL.|Lڔ| Lffff.UHAWAVAUATSHHHHEHHOH)HH9vFu=Tu3HVH9sNLu DH H H;Mt|1H H H;MuHĈ[A\A]A^A_]ÿK|IHh\H5|H |H}LhW)PHDž`0|H`(X.)PHts valueHHHy for itHHHype entrHHHissing tHHHMap is mH@'AHF|HuHPLE1H5!HR|L袘| IALeMuFzIALhMIIPt H`|LeMt6HEL9t!HX@t Hxۗ|HI9uH}LLe|EuEuLhMu&^H}螗|EtH}菗|LhMt:HpL9tEHX@t Hxd|HI9uHhLpI|EtLr|L| LؐUHAWAVAUATSHHH:HHEJH;Cu*HH H H;MuHĘ[A\A]A^A_]b||IHh}\H5!|H |H}Lh,zuJHHHH=|HPHHH}AH|HuHPLE1H5~H7zL臖| IALhMIIPtH`|LeMuEIALeMt6HEL9t!HX@t Hx|HI9uH}LLe覕|EuEuLhMu&^H}胕|EtH}t|LhMt:HpL9tEHX@t HxI|HI9uHhLp.|EtLW|L| LfDUHAWAVAUATSHIHHHEH8H|HHHLH@H8LZ|HEEHLkLH@H8L`|L5UILHDž(HDž HDžHDžDž0LLLH8Lt H |L|H5 HHp|H8萓|HHH;EuLH[A\A]A^A_]>|ILH8Lt H 聓|L_|IH5HH|IH8|L.| fDUHAWAVAUATSHHMHHEHHOH)HH9vFu=Tu3HVH9sNLu DH H H;MtQ|1H 䫔H H;MuHĈ[A\A]A^A_]ÿ諒|IHhH\H5F|H |H}LhEW)PHDž`0N|H`(')PHts valueHHHy for itHHHype entrHHHissing tHHHMap is mH@'AH|HuHPL{E1H5yHuL| IALeMuFzIALhMIIPt H``|LeMt6HEL9t!HX@t Hx;|HI9uH}LLe!|EuEuLhMu&^H}|EtH}|LhMt:HpL9tEHX@t HxĐ|HI9uHhLp詐|EtLҐ|L~| LؐUHAWAVAUATSHHHHHEJH;Cu*HH ~H H;MuHĘ[A\A]A^A_]|@|IHh|\H5|H |H}Lh,蕺JHHHH|HPHHHAHL|HuHPL`E1H5fwHsL| IALhMIIPtH`V|LeMuEIALeMt6HEL9t!HX@t Hx |HI9uH}LLe|EuEuLhMu&^H}|EtH}Ԏ|LhMt:HpL9tEHX@t Hx詎|HI9uHhLp莎|EtL跎|Lc| LfDUHAWAVAUATSPIM.IFL)HHHH9H H9I^IM)HLupLcM)J<褁|IKDMMM~LLLI<|LL+L{HCMtLH[A\A]A^A_]K|H[A\A]A^A_]ÿJ|HH56|H~|HHHH5gkH8gHP|IH'|L{| ff.UHAWAVAUATSHIIIuMeI)MIIGHH> H?MuI)LHHH9w`II9LBMLEHuI9vKi|HH5U|H}|HHHH5jHWfHo|LEHuIJ<|HHuLEN4ABN|M~HLI蝁|LI]M}MuHtHH[A\A]A^A_]|H[A\A]A^A_]E11L||IH|Ldz| fff.UHAWAVAUATSHH}HHEHHOH)HH9vFu=Tu3HVH9sNLu D H +H H;Mt|1H H H;MuHĈ[A\A]A^A_]ÿ~|IHhxk\H5v|H |H}LhuW)PHDž`0~~|H`()PHts valueHHHy for itHHHype entrHHHissing tHHHMap is mH@'AH|HuHPL諺E1H5eHaL2~| IALeMuFzIALhMIIPt H`}|LeMt6HEL9t!HX@t Hxk}|HI9uH}LLeQ}|EuEuLhMu&^H}.}|EtH}}|LhMt:HpL9tEHX@t Hx||HI9uHhLp||EtL}|Lw| LؐUHAWAVAUATSHHHʕHHE6JH;Cu*HH H H;MuHĘ[A\A]A^A_]||p||IHh i\H5|H |H}Lh, ũJHHHH|HPHHH AH||HuHPL萸E1H5cH_L|| IALhMIIPtH`{|LeMuEIALeMt6HEL9t!HX@t HxP{|HI9uH}LLe6{|EuEuLhMu&^H}{|EtH}{|LhMt:HpL9tEHX@t Hxz|HI9uHhLpz|EtLz|Lu| LfDUHAWAVAUATSPIHL3HCL)HL9sjLH=upLcM)J<dz|IKDMMM~LLLI{|LL+L{HCMtLH[A\A]A^A_] z|H[A\A]A^A_]ÿ z|HH5|HRw|HMHHH5'dH_Hz|IHy|Lt| ff.UHAWAVAUATSHIIIuMeI)MIIGHH=HMuI)LHHH9waII9LBMLEHuI9vK(y|HH5|Hpv|HkHHH5EcH_H.y|LEHuIJ<x|HHuLEN4IJN|M~HLI\z|LI]M}MuHtHH[A\A]A^A_]jx|H[A\A]A^A_]E11Lu|IHwx|L#s| ff.UHAWAVAUATSHH=HHEHHOH)HH9vFu=Tu3HVH9sNLu DH 됔H H;MtAx|1H ԐH H;MuHĈ[A\A]A^A_]ÿw|IHh8d\H56|H |H}Lh5W)PHDž`0>w|H`( )PHts valueHHHy for itHHHype entrHHHissing tHHHMap is mH@'AH|HuHPLkE1H5q^HZLv| IALeMuFzIALhMIIPt H`Pv|LeMt6HEL9t!HX@t Hx+v|HI9uH}LLev|EuEuLhMu&^H}u|EtH}u|LhMt:HpL9tEHX@t Hxu|HI9uHhLpu|EtLu|Lnp| LؐUHAWAVAUATSHHHHHEJH;Cu*HH nH H;MuHĘ[A\A]A^A_]u|0u|IHha\H5q|H |H}Lh,ʮ腣JHHHH|HPHHHAH<|HuHPLPE1H5V\HXLt| IALhMIIPtH`Ft|LeMuEIALeMt6HEL9t!HX@t Hxt|HI9uH}LLes|EuEuLhMu&^H}s|EtH}s|LhMt:HpL9tEHX@t Hxs|HI9uHhLp~s|EtLs|LSn| LfDUHAWAVAUATSHHmHHEHHOH)HH9vFu=Tu3HVH9sNLu DH H H;Mtqs|1H H H;MuHĈ[A\A]A^A_]ÿr|IHhh_\H5f|H z|H}LheW)PHDž`0nr|H`()PHts valueHHHy for itHHHype entrHHHissing tHHHMap is mH@'AH|HuHPL蛮E1H5YHUL"r| IALeMuFzIALhMIIPt H`q|LeMt6HEL9t!HX@t Hx[q|HI9uH}LLeAq|EuEuLhMu&^H}q|EtH}q|LhMt:HpL9tEHX@t Hxp|HI9uHhLpp|EtLp|Lk| 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HxoHI9uH}LL}ѪoEuEuL`Mu&^H}讪oEtH}蟪oL`Mt:HhL9t?HX@t HxtoHI9uH`LhYoELLIAL}MuFzIAL`MIIHt HXoL}Mt6HEL9t!HX@t HxҩoHI9uH}LL}踩oEuEuL`Mu&^H}蕩oEtH}膩oL`Mt:HhL9tWHX@t Hx[oHI9uH`Lh@oELeoHH8HLIH}Ht,H}oH`Hu#IEt H}ڨoH`HtdHh¨oHH8HuXiIHH8HuDUIEu!(IEuIEu IEt H}joHH8HtHHHoL*o fUHAVSIx4oHHLH[A^]IHoLo UHAWAVAUATSHIIHHHEHHIIa@HIAGXAG`IGhAGpW)EHEEEmeanEIOPHhHUL@HhHDžhHtCt H{8oH0oEt H}!oHW)EE HEEscalfEeIOTHhHUL@HhHDžhHtCt H{軦oH賦oEt H}褦oHDžLW)EHEEEseedEHhHUHLL@HhHDžhHtCt H{3oH+oEt H}oHtjo H,LH IH )))ȹEAOXW)EE HEEdtypfEeHhHUH@L'@HhHDžhHtCt H{}oHuoEt H}foHu$Ip@A?f7A?-HbHH;EuHĘ[A\A]A^A_]覥o$oIHhOH5dpH ̐pH}Lh=HPAHƎpHuHPLeE1H5kHLo|衤oIHh>OH5pH IpH}Lh>;HP?AHtpHuHPLE1H5苇HLiooIHh軐OH5^pH ƏpH}LhOH5ŎpHPLbAHBpHuHPLUE1H5[HLܣolIALeMIALhMIIALeMPIALhMIIALeMIALhM:oIlIPt H`躢oLeMt6HEL9t!HX@t Hx蕢oHI9uH}LLe{oEuEu LhMu-H}UoEtH}FoLhMHpL9HX@t HxoHI9uIPt H`oLeMt6HEL9t!HX@t HxɡoHI9uH}LLe诡oEuEu LhMu-'H}艡oEtH}zoLhMHpL9HX@t HxGoHI9uIPt H`"oLeMt6HEL9t!HX@t HxoHI9uH}LLeoEuEuLhMu&^H}oEtH}豠oLhMt:HpL9t=HX@t Hx膠oHI9uHhLpkoEtGL蔠o=LI3IEu!(IEuIEu IEt H}oLo fUHAVSIxoHHLH[A^]IHןoL蹚o UHAWAVAUATSHIIH׸HHEHᨇHII1@H2IAGXAG`IGhAGpW)EHEEEhighEIOPHhHULb@HhHDžhHtCt H{oHoEt H}oHW)EEHEElowIOTHhHUL@HhHDžhHtCt H{葞oH艞oEt H}zoHDžLW)EHEEEseedEHhHUHLLc@HhHDžhHtCt H{ oHoEt H}oHt@o H,LH IH )))ȹEAOXW)EE HEEdtypfEeHhHUH@L@HhHDžhHtCt H{SoHKoEt H}yoL to UHAVSIP$yoHHRHHH{L蟹@HHHH[A^]IHxoLso UHAVSHIH㑇HHEA>urHH}HEHEI^IFHtACt H{vxoHnxoH]HEHt!Ct H{NxoHFxoHEIFH]HH;Eu 1H[A^]xofff.UHAVSHIH#HHEA>urHH}b HEHEI^IFHtACt H{woHwoH]HEHt!Ct H{woHwoHEIFHHH;Eu 1H[A^]wofff.UHAVSHIHcHHEA>urHH} HEHEI^IFHtACt H{voHvoH]HEHt!Ct H{voHvoHEIFHݏHH;Eu 1H[A^](wofff.UHAVSHIHHHEA> urHH}BHEHEI^IFHtACt H{6voH.voH]HEHt!Ct H{voHvoHEIFHHH;Eu 1H[A^]hvofff.UHAVSHIH㎇HHEA>urHH}RHEHEI^IFHtACt H{vuoHnuoH]HEHt!Ct H{NuoHFuoHEIFH]HH;Eu 1H[A^]uofff.UHAWAVAUATSH8IIHHHE1LAHþL1IžL!IHCHK H)HtHH8IEIM H)HtHH8Mt%IGIO H)HtH%H8LuHފLԊD0M]LD8EO0toHEf(~f)EHbe zero!HH Hcan not HHHperator HHH Range oHHHdelta inH@(HMLuLvNHH5WbpL}LHLLBN`IH5kbpH]HLLHN/IH5bpH]HLeLHNEt H}roHHH;ELH8[A\A]A^A_]AA)A*A*^f: L,E1MMIroHEHHHML(HMHU1L脛@H,MLu0Iw1HL!fnfpfAnfpf8@@fBfnfpHyHHVHs 1HutHzH)1fDfD fD0fD@fDPfD`fDpH fHuHt,HtHf.FffH HuI9t"DHDHDHL9rIH}H*H}qoHEu3:HEu(/HEu$HH}HtH} HEt H}poHko fffff.UHAWAVAUATSH(HIHHHHEH8HWHHHLH@H8LooHHEEL5!M~LI@L8LnoL%䆇ILHDž(HDž HDžHDžDž0LroHLH聶HH~CHHH(LL8Lt H ZooH4noH5smHHmoH8noHPHH;EuHH([A\A]A^A_]ooHLL8Lt H noHmoHHHH5lHH(moHH8CnoHmio DUHAWAVAUATSH8IIHHHE1LHþLIžLIHCHK H)HtH&H8IEIM H)HtH2H8(Mt%IGIO H)HtH9H8/H28EL!8MmLE8W.ESM0jmoHEf(f)EHbe zero!HH Hcan not HHHperator HHH Range oHHHdelta inH@(HMLҢNHH5[pL}LH#LL螢N`IH5[pH]HLLHmN/IH5[pH]HLLHOH5oH BpHhLP8H5AqH0H |AHUqHhH0L艎E1H59H5LRo IALeMIALPMIL IALeM7hIALPMII0t H@)QoLeMt6HEL9t!HX@t HxQoHI9uH}LLePoEuhu LPMu,dH}PohtHxPoLPMt:HXL9t?HX@t HxPoHI9uHPLXiPoELLI0t H@;PoLeMt6HEL9t!HX@t HxPoHI9uH}LLeOoEuhu LPMu,dH}OohtHxOoLPMt:HXL9tEHX@t HxOoHI9uHPLX{OoEtLOoLPJo LUHAVSIXTOoHH{PLMHvXHHHHL@HTHHH[A^]IHNoLIo DUHAWAVAUATSHHgHHEW)EEIIHEEminMgHhHULǟ@HhHDžhHtCt H{mNoHeNoA$Et H}NNoW)EHEEEmaxHhHULLR@HhHDžhHtCt H{MoHMoAEt H}MoAA.Gr'HfHH;EuHĈ[A\A]A^A_]7NoMoIHhR:OH5a=pH =pH}LhOHPSAH=pHuHPLE1H54H-1L}Mo IALeMuFzIALhMIIPt H`LoLeMt6HEL9t!HX@t HxLoHI9uH}LLeLoEuEuLhMu&^H}yLoEtH}jLoLhMt:HpL9tEHX@t Hx?LoHI9uHhLp$LoEt4LMLoLFo LIEu IEt H}KoLFo DUHAWAVAUATSPIIH}1LHLk1LLt@IHIL脈CEl$PEd$TL1HI~fCHuڃH9HOII)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.UHAVSIPHoHHBQHHH{L菈@HHH[A^]IHGoLBo UHAVSIPGoHHPHHH{L/@H8HH[A^]IHnGoLPBo UHAWAVAUATSHMHIHd`HHEHtBHB H;BHH)0t0HC H;CpHtbH)0uZ)@LCIIL褃CL,HIL荃CHuڃH9HOII)LH?H>LHH(0(@*HrI H9s!H I9sE1HzLII((IIHHDGAHkIHH)1ff.A(_(]AT(_(]THHuMtA_] L9tfHzLt(C_(]B LHL9t8(A_(] (A_D(]LHH9uH<I|/((HA(_(])HH9|H9>I)ILI I9sLI L9MI((IHIIEOAHHM\HtIIL)1AT(_(]TA(_(]HHuMtHA_] M9tsLlj)HPH@t(A_(] HOH9tAfff.(A_(] (A_D(]LHH9uH\HH;Eu2HĨ[A\A]A^A_]1M1M9FConCoIHh 0OH5S5pH 5pH}Lh4}W)PHDž` CoH`( ,)PH scalar.HHHld be a HHHmin shouH@AH5pHuHPLZE1H5`*H&LBoBoIHh3/OH5{4pH 4pH}Lh80|W)PHDž` 9BoH`(3+)PH scalar.HHHld be a HHHmax shouH@AH5pHuHPL~E1H5)H%L Bo IALeMIALhMIIALeMFIALhMnIIPt H`%AoLeMt6HEL9t!HX@t HxAoHI9uH}LLe@oEuEu LhMu-'H}@oEtH}@oLhMHpL9HX@t Hx~@oHI9uIPt H`Y@oLeMt6HEL9t!HX@t Hx4@oHI9uH}LLe@oEuEuLhMu&^H}?oEtH}?oLhMt:HpL9tEHX@t Hx?oHI9uHhLp?oEtL?oLw:o Lf.UHAWAVAUATSHMHIHXHHEHtBHB H;BH H)0t0HC H;CH ZH)0uR f)@Lr IIL{CL[)HIL{CHuH9HOII)LH?LHHf(0f(@HrI H9s!H I9sE1HzLIIIHHHDOAHIIH)1@fAfA\f(f_f(f_f(f]f(f]ffdfAT fA\0f(f_f(f_f(f]f(f]f\ fd0HHuMt0fAfA\f(f_f_f(f]f]ffDL9tsHzLtf(C_f(]B LHL9tCf.f(A_f(] f(A_Df(]LHH9uH<I|0DAf(f_f(f]f)HH9|H9 I)IH IH9s H IH9rqMIITHtJ<1fffff.fTff(f_f(f_f(f]f(f]f\f$HI9uM9tu)HPH@tf(A_f(] HOH9tDff.f(A_f(] f(A_Df(]LHH9uHTHH;Eu"HĨ[A\A]A^A_]1M;oj;oIHh(OH5O-pH .pH}Lh4uW)PHDž` ;oH`($)PH scalar.HHHld be a HHHmin shouH@AH-pHuHPLVwE1H5\"HL:o:oIHh/'OH5w,pH -pH}Lh8,tW)PHDž` 5:oH`(/#)PH scalar.HHHld be a HHHmax shouH@AH -pHuHPL~vE1H5!HL:o IALeMIALhMIIALeMFIALhMnIIPt H`!9oLeMt6HEL9t!HX@t Hx8oHI9uH}LLe8oEuEu LhMu-'H}8oEtH}8oLhMHpL9HX@t Hxz8oHI9uIPt H`U8oLeMt6HEL9t!HX@t Hx08oHI9uH}LLe8oEuEuLhMu&^H}7oEtH}7oLhMt:HpL9tEHX@t Hx7oHI9uHhLp7oEtL7oLs2o LfDUHAWAVAUATSHMHIHPHHEHt@HB H;BH3D(HLHt*HC H;CDH3D8AHLHuAL3IILsCHHH5IHHsCHH rI I9LHI9E1HpL¨{L9ADD8|DA8~AA\DD8|%DA8#A\HH9uSDA8~݉A\HH9u.IIAfnff8Afnf8IHHHrHH~H)1ff.AoAo\f8oH Q?oLkoHPHHL@AH(oHuHPL )E1H5ԆH@ІLnEnIHhNH5qH oH}Lh%IFI+HHHL@H5oH >oLoHPHHL@AHoHuHPLJ(E1H5PӆHφLn IALeMIALhMIIALeMPIALhMIIALeMIALhM:oIlIPt H`nLeMt6HEL9t!HX@t HxnHI9uH}LLesnEuEu LhMu-H}MnEtH}>nLhMHpL9HX@t Hx nHI9uIPt H`nLeMt6HEL9t!HX@t HxnHI9uH}LLenEuEu LhMu-'H}nEtH}rnLhMHpL9HX@t Hx?nHI9uIPt H`nLeMt6HEL9t!HX@t HxnHI9uH}LLenEuEuLhMu&^H}nEtH}nLhMt:HpL9tEHX@t Hx~nHI9uHhLpcnEtLnL8n Lff.UHAWAVAUATSH8MLIHIHH/HHEH8H*φHHHHH@H8HonLHEEL-IEHHI@L8HgnL=\ILHDž(HDž HDžHDžDž0L+nHLH-HH0HnLnHLH-HHnHHH-HH0HznHHHL2HHL8Lt H wnHQnH5HHnH8nHmHH;EuHH8[A\A]A^A_]nHHHL8Lt H nHn+HEHEHEHEHEHH5HH&nHH8AnHkn UHAWAVAUATSH8MLIHIHHoHHEH8Hj̆HHHHH@H8HnLHEEL-IEHHI@L8HnL=ILHDž(HDž HDžHDžDž0LknHLH9+HH0H nL;nHLH +HHnHHH*HH0HnHHH/HHL8Lt H nHnH5HH nH8@nHHH;EuHH8[A\A]A^A_]nHHHL8Lt H *nHn+HCHCHBHBHBHH5HHfnHH8nHn UHAWAVAUATSH8MLIHIHHHHEH8HɆHHHHH@H8HnLHEEL- IEHHI@L8HnL=ILHDž(HDž HDžHDžDž0LnHLHy(HH0HKnL{nHLHI(HHZnHHH((HH0HnHHH,HHL8Lt H nHnH5߆HH`nH8nHHH;EuHH8[A\A]A^A_].nHHHL8Lt H jnHDn+HH@H@@H8@H0@H(@HH5VކHHnHH8nHn UHAVSI`nHHLH[A^]IHnLn UHAVSH@HIHHHEHHII~* @HcHIIFXIFPHEW)EHEEHexclusivHEfEeH}HUHMH2@Et H}nH}uHEHwINPHW)EHEEHEHreverseHEH}HUHMHE2@HEHEH]HEHCt H{nHnH]HEHtCt H{mnHenEt H}VnH]HtHHECt H{6nH.nHSHH;Eu?H@[A^]HEEuHEHwIFXHEHHH;EtjnIEt H}nH]HEHt&Ct H{nH IEt H}nLsn ff.UHAWAVAUATSHIH`IHHHE1LSHHC H+CHp LSIW)EHEH{ HH+{t[HHHH9 nILeLeIHEHHpHX H)H~LHonILeHU1L@HH}vCHHpHHEHHMLHHHw HEHLcHH@JHW)PHDž`HHx H+xHHHH9 nILXLPIH`HHpHX H)H~LH^nLHHXKHPiCHxW)0HDž@LPHXL)t\IIHI9( H0nIL8L0KDH@H~LLHnIL8W)HDž HH9LpLnHHHH H5HL^nHH`HxXLXHHYHxPHtHHxH$L0LHHHH HHHHW)HDžLpLnHHLN4LHLnLLHJW)HDžLnILKHLLmnHHKHxH$HHL0LHH LnHHt HnH}Lpfffff.H`HXPW)HDžLnIHLxHHIDLHM,LLLznLLLKW)HDžL>nILHIHLLnHLhO<W)HDžLnHHHLnH0Ht H8&nHPHt HXnH}Ht H}nH!HH;EuLHĘ[A\A]A^A_]bnH}nHoH }oH1A膿NnIH0MNH5oH oHPL0G HH4HPH1LH5H&Lvn(HP\nHNn H0@n It H nE1LmMuMH1IALmMt6HEL9t!HX@t HxnHI9uH}LLmnpuPu L0Mu@xH}ZnPtH`EnL0MuLIAL0Mt:H8L9t5HX@t HxnHI9uH0L8nEu /LIL nHHDžHu#@IHHDžH"Ct H{nHnH}H IHH HIHHIHHIHHxILLnHH9@IHH!(IHH!kIH0HkIP*H`IHHIHPHIH}HIHH'nIHhHnHRIHFIHHtOIHHnILLnIHHnHHuOH0Hu[HPHt HXxnH}Ht H}fnLHn IHHtHAnH0HtH8)nHPHuffffff.UHAWAVAUATSHMIHHHEW)EHELH=QH0H(L HN,LnILuKH]LLnH]H(H0IMMOH HHIHEHMHEHMHL$H$H8HLH) L]MLhL`LxLpM)IMH}H@LXIt|HH1fDHJIM9u;KDHLHTHHTH;TuHDITHHuE1HL9uH}HD؃ItKHL)ِHJIM9u KtHE1HJIM9u KTHE1HuHtHJIM9u KDHE1H@LXHt H}nHpHt HxnH}Ht H}nHHH;EuH[A\A]A^A_]nH}n HH}HuHH}Ht H}InH+n UHAWAVAUATSHHMLIIHH>HHEHHJLLKIIHHHHH|$H$HHHvG-I$IL$LLKIIwHEHuHEHuHt$H$HL H}HHHHH LL)HH]L}L)HHHHHEHLeH0HHEHXLhLLxHHHHHSHLE1HHHH IL;uQHITH Hfff.H\HH\I;\uHDI\H HuE1IH`HHHIM9uWHITHHHt8fDH\HH\I;\uHDH`I\HHuE1L;uH0HE1@HHHH IL;uHITH E1IH`HHHIM9uUHI\HHHt6@H\HH\I;\uHDH`I\HHuE1L;]QHXHLLxHt H8nHHt H nH}Ht H}nH}Ht H}nHHH;EuHH[A\A]A^A_]nHH}HuH}HuH:n H}EnH}HtH}3nHn ffff.UHAWAVAUATSHLMIIHLmHHHEHHJLLKIUI}HhHpHhHpH|$H$HHHQC-I$IL$LLKIEIuHhHpHhHpHt$H$HLUIIOLLKIEIuHEHuHEHuHt$H$HhLB-H}HHHHH LL)HLULEM)IHLL)HHH(IHEHHEHHHHHH L]HXLhHHxLHHPHxHXHH`ILLH0IBH@HH8HBHHL0L1Hffffff.XHH HPHH;uSH(IDH Hffffff.IDHIDI;DuIDIDH Hu1HPH`HHHHXHH;uXH0ITHH@Ht:HDIDHIDI;DuIDH`IDHHu1HXHHHHHH`HH; uZH8ITHHHHtdoH QnLkdoHPHHL@AH(doHuHPL E1H5SH@OLknEknIHhWNH5PqH foH}LhߤIFI+HHHL@H5coH nLcoHPHHL@AHcoHuHPLJE1H5PRHNLjn IALeMIALhMIIALeMPIALhMIIALeMIALhM:oIlIPt H`inLeMt6HEL9t!HX@t HxinHI9uH}LLesinEuEu LhMu-H}MinEtH}>inLhMHpL9HX@t Hx inHI9uIPt H`hnLeMt6HEL9t!HX@t HxhnHI9uH}LLehnEuEu LhMu-'H}hnEtH}rhnLhMHpL9HX@t Hx?hnHI9uIPt H`hnLeMt6HEL9t!HX@t HxgnHI9uH}LLegnEuEuLhMu&^H}gnEtH}gnLhMt:HpL9tEHX@t Hx~gnHI9uHhLpcgnEtLgnL8bn Lff.UHAVSHPIHSHHE"gnHþHgnHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]!v<H5rxnH}H>H5/boH>H5sH>HA H5GnH>HH;>HHH sHHMHHUHuHuIIFIF0INIVIF@H]HEHtH!H@enHe~HH;Eu LHP[A^]enIH]HEHtH HdnL_n DUHAVSIPdnHHnHHH{L_?HphHHH[A^]IHdnL|_n @UHAWAVAUATSHIIH}HHE1LIMFIEIM H)HH H FaoHIILHHHHH@HHH;uhL8I L_.IIEJHHhuzH21H8ݐ?HIHHhL^H `oL衜MH|HH;ELLHĸ[A\A]A^A_]L(L0H@H W)EHEH}H@AE1E1@IuJ>HEH;EtHHHE LH}dLIIL;@|HU1H8螎?HHIE~YMcHHHHHE1Lh1ffff.DH(H4LJAHLHL9|H}Ht H}/bnL0IHF{HH;EbnbnIHhNNH58^oH s^oH}Lh谛HPAH^oHuHPLWE1H5]IHELanlHALeMuFzHALhMHHPt H`9anLeMt6HEL9t!Lh@t HxanLM9uH}LLe`nEuEuLhMu&^H}`nEtH}`nLhMt:HpL9t=Lh@t Hx`nLM9uHhLp`nEt\L`nRLHH}Hu8?HH}Hu*1HH}Hu#HH}HuHH}Ht H}!`nH[n ff.UHAWAVSHxHH!yHHEHc7HtzW)EHEH}HOHML]L]MI?I>MMIIIIOHHHxHs91HA|HxHH;EHwH)1fffff.)D)DD )D D0)D0D@)D@DP)DPD`)D`Dp)DpH HuHt$HHfff.)HHuMM)IJJH9sJJH9MIIPHHDpAHs1M}J|pJTpIvH)1DLDLDLDLDLDLD D H HuMt.IIIDID D H IuM9MDD)ICL)HtHB4B4IHuHraM)JDJL1ff.tttttttttttttt44HI9uH}HuLH}?^nEW*HuHMH9HHNHtHHHHL RHHHH9HMLTL@YLYLBYL Y ;HIT2HHuHt-HQHIH Hf.Y HHuY|H}+]nH}"]nH}]nHtHH;Eu|Hx[A^A_]1\nHH}\nHuVn ffff.UHAWAVATSIIIMtMtH1IL9|eLIID$ID$H9t;I<$z\nH~%HH>u7HH\nHtHt!I$I$M|$Mt$[A\A^A_]ÿZnHHZnH5EEHAH[nfffff.UHAWAVAUATSPIIIt$IT$IFIFIMnI\$IF IFHt;~1HH>wL<L[nMt HIEIELmI^ Mn(I\$IF0IF(Ht;~1HH>yL<L[nMt HIEIEI^0AF8fAF<I$It$IT$I9vuI9VtLIvIVIHII?I>IMIHIIOHHH~Hs 1HulHOH)1()(D)D(D )D (D0)D0(D@)D@(DP)DP(D`)D`(Dp)DpH HuHt$HHfff.(3)0HHuIM)IJ H4H9sH J4H9MIIHHHD^AHs1MN|pJ|pIKH)1ADALDLADALDLADALDLADA D H HuMt6IIILIff.D  D H IuM9MĉD)HJL)HtH@BVnH5@H\=HVn+LVnHHVnH5@H/=HgVn HI?HHI}FWnHEH8:WnI>2WnHPn fff.UHAWAVSHXIHnHHEI~/L}L}H}HuHU"fA~G8I_I9_0uMG(_AG8I_I9_0tI(VnH~4HH>L4LVnIMt MMG( IG(E1I_0IIIwH$LMHzE$AGL4LdVnMt HIGI_ IGHs1dAAHL)Hxfo"fo "fo"HfofGfH HuMtfff.HH9uH~,IGIO(HDHcTt<|4HHAG=HmHH;Eu!HX[A^A_]IGI_ 5TnSnHHxSnH5>H:HSnH\Nn @UHAWAVAUATSPHBLpIFHLuLH?II>ML'ID$Ml$HHEI4$L@LHT d1IHLHJH<@(81HDBTBTX8T$HTXXXHH9|HHHHEH9NLMyMtPMAT 0IIIAwIGHHIWWI| MMIIQEGAHHHH4[MOM)MIA($0MސCTIs!LmMuhfffff.LLm@\TXDTX\1TXD9HTXHI9uMt.HLILffff.TXHHu_II(M96] H9)I)Ht:HIHJ<H(/fTXHHHuIrhHMH)LBMLHHZIHIHI(.TX TXTXB TXHHuMIILm 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H}MlLClH05lIH0H9II<$Hu?(IH}HIH]H2PII<$HI|$~lH`H!IHXHHHCHHHPH(lH`HIH0HtlH8lH`Hu`IH}HIEH}IH]HEHIH`HtFHh肔lH}Hu:AIH]Ht#HHCHuHHPHFlH}Ht H}6lH}Ht H}$lH]HEHtH蟠@HlLl ffff.UHH ]UHHGx]fDUHHGp]fDUHH]UHH]UHHP]UHH]UHAWAVATSH@HHHEIHLH~ HtH9tHPHEHEHuHuHPL}LLuH}I9t,HtHP(HðI~ Ht'I9t,HPHE-HP HðI~ HuHE L}HLPLHHH}I9tHtHP(HHH;Et#HP HHH;Eu H@[A\A^A_]lf.UHAWAVAUATSHHIHHPHHEH8HKyHHHLH@H8L萑lHEEL5ȨM~LI@L8L薐lL%ILHDž(HDž HDžHDžDž0LZlHLH(H3HlHHHLL8Lt H lHߏlH5HHnlH8莐lHHH;EuHH[A\A]A^A_]lHhl UHAWAVAUATSPIIM&IFL)HL9LH=I^IM)J<9lIHJL)CHHMHLH{HCH[HyHHuH8fHSHCHQHSHCHQHSHCHQHSHCHQHSHCHQHSHCHQHSHCHQHSHCH[HQHI9oM&I^IMnIFL9t2LsHCH[MtL蓒@LlI9uMt&LH[A\A]A^A_]ׅlM.MnIFMuH[A\A]A^A_]LH8k贅lHH5lHlHHHH5oHkH躅lIH葅lL=l DUHAWAVAUATSPIM.IFL)HHHH9H H9I^IM)HLfffff.Lx@t HxlLL9uI<$I]8lAt!HHEAuAEu(H}}l I}(҂lAEt HEAEtI}赂lH}}l fffff.UHAVSH0IHHHHEI9tH{ H9t.IF I9tKHC I~ HHH;EH0[A^]M9v tTHLPH{ HP IF HC HHHQI~ HP HC IF H[ HHH;EtpHHuPH{ HP HC I~ HHPI~ HP IF H[ HEH}LPHEH}P Mv HHH;E-lH^HVfDUHAVSH0IHHpHHEI9tH{ H9t.IF I9tKHC I~ HCHH;EH0[A^]M9v tTHLPH{ HP 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T\ d0YY TYY\ d0HHuMt TYY TI9t'LH)HYHHuK<I(IsH9HMHL)މLHt)HLffffff.( Y) HHuH rAfD( Y) (LY)L(L Y)L (L0Y)L0HH9|H9M)IMI(IHHHDJAHMJL0IqH)1@LT\$YYLTYY\$HHuMtH TYY TM9t$LH)H DYHHuH(HH ӁH H;Mu/H[A\A]A^A_]1M1MqպiSiIH}IH5jH lkHLEKW)EHE iHE(:H5jH :H56nH :HA H5iHG :HH;<:HHH 3HHMHHUHuHuIIFIF0INIVIF@H]HEHtH|RH蠖iHůHH;Eu LHP[A^]iIH]HEHtH7RH[iL=i DUHAWAVAUATSHIH(HSHHE1Lz HþLj HXH`HhH0HsH@HPH8HHlHXHHH HH9HNH0`H8H` `H@fWf)EHELH L)tJIILH=HLiILmLmKDHEH~LLHiILmHU1L:IH}Ht H}iH0I~=IMHM|$I9LPILzIH0_IH8=HH@_IHHb=H9L9f(r{f)`H?Hp(HH}H9HtHP(HHt H iHHt HiHHt HڒiHHt H’iHHt H誒iHHt H蒒iHhHt HpziH(HH H H;M Hĸ[A\A]A^A_]HP HH!HupL9af(Xp{f)`H?Hp(iH HL`LxLp HEH`H]HPLHH{f(o{f)`H?Hp(薑iH HL`LxLpHEH`H]HPLHWHّiH}舎i LiIH`}IH5jH mXkH}L`HHAHܴjHuHHLE1H5xHtLi{ɐiIH`f}IH51jH WkH}L` cHHgAHtjHuHHL E1H5xHAtL葐iIALeMuFzIAL`MIIHt HXiLeMt6HEL9t!HX@t HxǏiHI9uH}LLe譏iEuEu L`Mu-[H}臏iEtH}xiL`M3HhL9IHX@t HxEiHI9uIALeMuFzIAL`MIIHt HXiLeMt6HEL9t!HX@t HxǎiHI9uH}LLe譎iEuEuL`Mu&^H}芎iEtH}{iL`Mt:HhL9tTHX@t HxPiHI9uH`Lh5iELZiHHLIH}H9tHIH}H9t: IH}H9t,HAHP(HHul:IH}H9uHP HHuIIH}HuIH}HH}kiHHH OiHHH3iHHHiHHHiHHHߌiHHHÌiHhHHp觌iL艇i IHH-HH9HHEHHQHH]HHiHhHuL i fUHAVSHPIH3HHEiHþHiHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]7H5RiH}H:H5&jHo9H5z*nH9HH HQH5 iH:HH;9HHH HHMH5HUHuHuIIFIF0INIVIF@H]HEHtHEHiH>HH;Eu LHP[A^]舊iIH]HEHtHEHԉiL趄i fffff.UHAVSHPIHӢHHE袉iHþH蘉iHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]衘7H5iH}H^:H5̥jH9H5(nH@9HH HQH5iH`9HH;U9HHH LHHMH5HUHuHuIIFIF0INIVIF@H]HEHtHCH蹇iHޠHH;Eu LHP[A^](iIH]HEHtHPCHtiLVi fffff.UHAVSHPIHsHHEBiHþH8iHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]A7H5iH}H9H5ljH9H5%nH9HA H5giH9HH;9HHH HHMHHUHuHuIIFIF0INIVIF@H]HEHtHH iH.HH;Eu LHP[A^]xiIH]HEHtH>HĂiL}i fffff.UHAVSHPIHÛHHE蒂iHþH舂iHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]葙7H5iH}HN9H5jH9H5 !nH09HA H5iHW9HH;L9HHH CHHMHHUHuHuIIFIF0INIVIF@H]HEHtH9H5jH9H5nH 9HA H5iHG9HH;<9HHH 3HHMH=HUHuHuIIFIF0INIVIF@H]HEHtH|.HriHŋHH;Eu LHP[A^]siIH]HEHtH7.H[riL=mi DUHAVSHPIHcHHE2riHþH(riHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]17H5iH}H9H5\jH9H5nH9HH HQH5PiH9HH;9HHH ~HHMH;HUHuHuIIFIF0INIVIF@H]HEHtH%,HIpiHnHH;Eu LHP[A^]piIH]HEHtH+HpiLji fffff.UHAVSHPIHHHEoiHþHoiHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]х7H5"iH}H9H5jH?9H5JnHp9HA H5iH9HH;9HHH |HHMHMHUHuHuIIFIF0INIVIF@H]HEHtH)HmiHHH;Eu LHP[A^]_niIH]HEHtH)HmiLhi DUHAVSHPIHHHEmiHþHxmiHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]聀7H5~iH}H>9H5jH9H5 nH 9HH HQH5iH@9HH;59HHH ,zHHMHKHUHuHuIIFIF0INIVIF@H]HEHtHu'HkiHHH;Eu LHP[A^]liIH]HEHtH0'HTkiL6fi fffff.UHAVSHPIHSHHE"kiHþHkiHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]!~7H5r|iH}H9H5LjH9H5 nH9HA H5GiH9HH;9HHH wHHMHYHUHuHuIIFIF0INIVIF@H]HEHtH%H@iiHeHH;Eu LHP[A^]iiIH]HEHtH$HhiLci DUHAVSHPIHHHEhiHþHhiHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]|7H5"ziH}H9H5jH?9H5JnHp9HH HQH5iH9HH;9HHH |uHHMHWHUHuHuIIFIF0INIVIF@H]HEHtH"HfiHHH;Eu LHP[A^]XgiIH]HEHtH"HfiLai fffff.UHAVSHPIHHHErfiHþHhfiHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]qz7H5wiH}H.9H5jH9H5nH9HA H5iH79HH;,9HHH #sHHMH,jHUHuHuIIFIF0INIVIF@H]HEHtHl HdiH}HH;Eu LHP[A^]diIH]HEHtH' HKdiL-_i DUHAVSHPIHS}HHE"diHþHdiHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]!|7H5ruiH}H9H5LjH9H5nH9HH HQH5@iH9HH;9HHH pHHMH5hHUHuHuIIFIF0INIVIF@H]HEHtHH9biH^{HH;Eu LHP[A^]biIH]HEHtHHaiL\i fffff.UHAVSHPIHzHHEaiHþHaiHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]y7H5siH}H~9H5}jH/9H5:nH`9HA H5iH9HH;|9HHH snHHMHzHUHuHuIIFIF0INIVIF@H]HEHtHH_iHyHH;Eu LHP[A^]O`iIH]HEHtHwH_iL}Zi DUHAVSHPIHxHHEr_iHþHh_iHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]qx7H5piH}H.9H5{jH9H5mH9HH HQH5iH09HH;%9HHH lHHMHxHUHuHuIIFIF0INIVIF@H]HEHtHeH]iHvHH;Eu LHP[A^]]iIH]HEHtH HD]iL&Xi fffff.UHAVSHPIHCvHHE]iHþH]iHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]v7H5bniH}H9H5iHWHH;E>iIt> t! uPA|$PLHHWA|$PtLHH?A|$PLHH #pIPHpLELLH=IHWHH;E[LHĘ[A\A]A^A_]ÿ=iIHXd*IH5@XjH [jHpLX^wW)@HDžP@g=iHP(Qz)@Hle typesHH7Hnd doublHH0Hloat16 aHH(Hfloat, fHH Hrue for HHHust be tHHHribute mHHHfmod attH@?AHY[jHpH@LgyE1H5m$H L7iHI9uHXL`#7iEt1LL7i'LIptH}6iIL1i UHAWAVAUATSH8MIHIHHOHHEH8HHHHHH@H8H6iLHEEL%GMID$HHI@L8H 5iL5MILHDž(HDž HDžHDžDž0L8iHLH|HH0H7L8iHLHo|HH0HY7HHHHHL8Lt H >5iH4iH5W3HH3iH84iH4NHH;EuHH8[A\A]A^A_]u5iHHHL8Lt H 4iH3i#H菔H臔HHwHH52HH2iHH84iH:/i fUHAWAVAUATSHxH]MHHEHHIHHOHVHH-HH HxH HH9HNHEXHEHHHEW)HDžL}H]L)tUIILH=fH3iILLKHH~LLH5iILH1LL\:HHHt H3iHHEHH{o=IH)HHJHHH H}Ht H}2iHxHt H}2iH`Ht Hh2iHHHt HPq2iH Ht H(Y2iHHt HA2iHHt H)2iHNKHH;EuHx[A\A]A^A_]2i2iIHxIH5LjH RjHLxMkW)`HDžp 1iHp(z)`Hrst outpHHHo get fiHHHfailed tH@ut!AHCiHH`LmE1H5H*Lz1i H`.i IALMuLIALxMII`t Hp0iLMt?HL9t$HX@t Hx0iHI9uHLLy0iuu#LxMu/gHM0itH80iLxMt:HL9tIHX@t Hx 0iHI9uHxL/iEtWL0iH}HtOLIHHu+IHHtH/iH}HtlIH}Hu^HxHugH`HupHHHu|H HHHHHL*i H}&/iHxHtH}/iH`HtHh.iHHHtHP.iH HxH(.iHHlH.iHH`H.iLo)i UHAWAVAUATSHxHGHHEHHIHHOHVHH'HH HxH HH9HNHEHEHHEW)HDžL}H]L)tUIILH=fH-iILLKHH~LLHK/iILH1L|V:HHHt HM-iHHEHH{$j=IHHHJHHH H}Ht H},iHxHt H},iH`Ht Hh,iHHHt HP,iH Ht H(,iHHt Hq,iHHt HY,iH~EHH;EuHx[A\A]A^A_],i@,iIHxIH5FjH jHLxMeW)`HDžp +iHp(z)`Hrst outpHHHo get fiHHHfailed tH@ut!AH>iHH`L#hE1H5)HZL+i H(i IALMuLIALxMII`t Hp*iLMt?HL9t$HX@t Hx*iHI9uHLL*iuu#LxMu/gH}*itHh*iLxMt:HL9tIHX@t Hx=*iHI9uHxL"*iEtWLK*iH}HtOLIHHu+IHHtH)iH}HtlIH}Hu^HxHugH`HupHHHu|H HHHHHLK$i H}V)iHxHtH}A)iH`HtHh))iHHHtHP)iH HxH((iHHlH(iHH`H(iL#i UHAWAVAUATSHHAHHEH IH=HcH>IH5AH6H;uHHHHĈ[A\A]A^A_] >IH5IAH6H;uHHHHĈ[A\A]A^A_]?8 >IH5 AH6H;uHHHHĈ[A\A]A^A_]鐎 >IH5@H6wH;uWHHHHĈ[A\A]A^A_]! >IH5@H6aH;uHHHHĈ[A\A]A^A_] >IH5M@H6tH;uHHHHĈ[A\A]A^A_]# >IH5@H6ZH;uHHHHĈ[A\A]A^A_]U >IH5?H6@H;u[HHHHĈ[A\A]A^A_]%q H;u2HHHHĈ[A\A]A^A_]l< H;u HHHHĈ[A\A]A^A_]Ӓ H;uHHHHĈ[A\A]A^A_]z H;uHHHHĈ[A\A]A^A_]! H;uHHHHĈ[A\A]A^A_]8 H;uuiHHHHĈ[A\A]A^A_]部 H;uuDHHHHĈ[A\A]A^A_]nZ H;uuHHHHĈ[A\A]A^A_]u %i>%iIHhIH5oiH jH}Lh8^H5&jHPHO AH>jHuHPLuaE1H5{ HL$i IALeMuFzIALhMIIPt H`Z$iLeMt6HEL9t!HX@t Hx5$iHI9uH}LLe$iEuEuLhMu&^H}#iEtH}#iLhMt:HpL9tEHX@t Hx#iHI9uHhLp#iEtL#iLxi LZVTTTTUHAVSIPD#iHHr,HHH{Lc:H&HHH[A^]IH"iLi @UHSPHW~HHH[]ffff.UHAWAVAUATSHH;HHEIIf.({ga1LژHþLʘHHHHsHPHHHHH HH9HNHEHEHHEHEHfWf)HDžLpHxL)tiLMIILH=H!iILLKHHML~LLH9#iILH1LjJ:HHHEH{3^=IHhHEH\JHEHHt H!iHHuHpHt Hx iHHHt HP iH0Ht H8 iHHt H iHHt H iHHt Hh iHHt HP iIEHm9HH;ELHĈ[A\A]A^A_]1LqHþLaHHHHXHsHhHPH`HpcHHHHH HH9HNHEHEHHEfWf)EHELpHxL)t]LMIILH=5HBiIL}L}KHEHML~LLH iIL}HU1LH:ILH}Ht H}iLuI[=IMMHCHM9uxLHxHXIH`q[=IHhHHpS[=H$HHxLLMIx7Mx WI*LHDH WH*XYu{ZfInfb~f\~f|YHE(iH ,HHHHHLxLpHE1LIHH]HHHhqHH}H9t$HZHP(HpHPWHP HpH5<iHSi H}Hi IH}H9uHP HpHHHP(HpHMIH}Hun=IH}Ht`H}iHpHtWIHHu+5IHpHt,IHHt H5iHpHubHHHunH0HuzHHHHHHHHLi HxiHHHtHPiH0HtH8iHHzH oiHHnHSiHHbH7iHHVHiLi DUHAWAVAUATSHhHG(H8HuH}HH8L>HF~ I9HEHHHMH@hHE21MHu1Mfffff.LvKHxHH]HLH}BIHHH}BLIAuEAAAI9LOOH]HEL4HMHL)II?I>IIMMLmHuIs1[ff.KKLLUI H}J<H]HL;~QHH;^HEHHHMH@hHE)1M1Mc{fLvNLmIMH}<A)EIMH}<MuAAAAM9MOMdMM)MI?I>MIM(]rIrJ ILH9J I91IKH)LHt"HAXHHuHIL f.DXDXDDXDXD HI9uLH(HJHHDVAHaI|0IJH)1fff.LTXX TLXXL T0HHuMtA ATXX TI9fff.KI(I{H9HMHL)߉LHtHL@A X) HHuH r@IL0LX) LX)LLX)L  X)L0HH9|L9sM)IH IMO\J40ItH9s M9MI(IIHHDVAHHL0IzH)1A ATXXLTAL AT0XXLHHuMtHA ATXX TM9LfD)INH)Ht%Hff.AXHHuHrZID fffff.DXDXDDXDXD HI9uHuHH;^I9vHEHHHMH@hHE$1M1Mj@LfKHEHH]HLH}8IHHH}l8LAuDۃL9IOKDMI)MI?I>MIHvHrI ILI9I H91HKH)HHt&Hfff. 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IHH9r~fffff.~nHHILM)IIrRffff.HI4HHHIII H9|HHIHL9rfHILhMI@uAAAI9LOOTHM IM)MI?MIMIr%HIII9KI91D)IFH)HtHِI4H4HHuHfHID8f.HLH HLHLHLHLHLHLHLHL HLHL(HLHL0H HL8HI9uLHHBHHNH s1H|HIDpHYH)1DLLDLD L0DLD@LPfDf fD`fLpHHuHt>HIDHHfff.fD0f 0f7fL7H HuI9HkLO7IIVI9IMHL)LHt#HLffAf)HHuHroHIDpf.DȐ)DȠ)DDȰ)D D)D0D)D@D)DPD)D`ff)DpHL9|LHLI9M)IOJHI LHH HHH9sIKDH9MII@HHVH s1HHHBH)HJ1ff.LDLD L0DLD@LPLfD`fLpfD fL0HHuHHtKHHHDIMIHfBD0fB 0fB7fBL7I HuM9MD)HpL)HtHKJIHuHrqHHHD8JLJ JLJLJLJLJLJLJLJL JLJL(JLJL0J JL8IL9uL;cI9ZLMI@1ff.LI>MMLUHEHLHLHHHLI $H9ufHHH)HHfIqbHI9uH}Le)qbELNqbLkb LIH}HIHhHHppbH@HHHpbH(HH0pbHHH|pbHHH`pbHHHDpbHHH(pbH}HIH}HIH}Ht H}obHhHH@H H(HHH$HH0HHIfMLI HI I; udIHL9rRf~NHHILMLILIII H9|HHIHL9rfHItMHJHIHs1\fff.LHHH4CH0JH@0s1HHHhJ4@Hp1f.DFLFADALDDFLFADD ALD0DFLFADD@ALDPoDF FADD`ALDpH@HuHHtCHXJ AHH`ffffff.oD AALH HuHPHI9fffff.LI)HxtH(D GfA DHHuIrLH8J Afffff.tAfA4DtAfAtDtAfAtD4AfAtDHI9uLL;LIFHHHMIO$6HQH)HHHH1fffff.I HMHEHIMLILII9uE1ffffff.~LHHILJ#M/HByIs1 f.fnpfpHps1HH1f.{D{D{ D{0D{@D{PD{`D{p{{{{{{{{HH{Ht%HH;D;H HuLM9t@fKHI9uH;gH HHhHt HpmNbH@Ht HHUNbH(Ht H0=NbHHt H%NbHHt H NbHHt HMbHHt HMbH}Ht H}MbHfzHH;Eu#HHĸ[A\A]A^A_]J4K|k#NbMbIH}A:BH5hcH BcHLE>ffEHE@KMbHEfowvfEHis a shaHH0Hor data HH(Ht's tensHH Hnal as iHHHdimensioHHHst be 1 HHHShape muHf@8pe@:AH[cHHMLZE1H5`4zH0zLLbH}Ib H}Ib IALMu@}IALeMIIEt H}/LbLMt?HL9t$HX@t HxLbHI9uHLLKbuu LeMu,[HKbtHKbLeMt1HEL9tCHX@t HxKbHI9uH}LelKbELKbL=Fb LIH}HIHhHHpKbH@HHHJbH(HH0JbHHHJbHHHJbHHHJbHHHkJbH}HIH}HIH}Ht H}/JbHhHH@H H(HHH$HH0HHMIMIrHIII9s~KI9su1IFH)LHtH@A44HHuH7HID ff.L LLLL L HI9uLHHBHHNHs1HzHIDpHYH)1DLLDLD L0DLD@LPoD D`LpH HuHt>HIDHHfffff.oD0 07L7H HuI9HLO7IIVI9IMHL)LHt"HLAofHHuHroHIDpf.D)D)DD)D D)D0D)D@D)DPD)D`ofDpH L9|LHLI9M)IOJHI LHH HHH9sIKDH9MII@HHVHs1HHHBH)HJ1ff.LDLD L0DLD@LPLoD`LpD L0H HuHHtKHHHDIMIHf.BoD0B 0B7BL7I HuM9MD)HpL)HtHCBIHuHrIHHHD BLB BLBLBLBLB BL IL9uL;yI9pLMI@1LI6MMLUHEHLHLHHHLI $H9ufHHH)HHfI4HHHHI;HHH9r~fffff.~nHHHLI)IIrRffff.HI4HHHHHIH9|HHHHL9rfG$HHAuDڃH9HOII)MI?I>MIHVHs1<fff.HHfAnfpHNHHxH8s1HH_H)1ffffff.ADADAD AD0AD@ADPAD`ADpAAAAAAAAH@HjHt,HHfffff.AD AD H HuH9Htffff.EdHH9uI4IfAnfpHZH9HMHH)ӉHHtHH@fADHHuHHrDfADfADfAD fAD0fAD@fADPfAD`fADpH H9|OTH9M)IMIfAnfpIKHHxHs 1HuSwH_H)I1DDDDDD D0H HuHt)HLHHDADADH HuM9HtLf.EdHH9uL;HxHHhHt Hp,:bH@Ht HH:bH(Ht H09bHHt H9bHHt H9bHHt H9bHHt H9bH}Ht H}9bHRzHH;Eu#HHx[A\A]A^A_]J4K|n9b`9bIH}&BH5ScH }cHLErffEHE@ 9bHEfo6vfEHis a shaHH0Hor data HH(Ht's tensHH Hnal as iHHHdimensioHHHst be 1 HHHShape muHf@8pe@:AHcHHMLuE1H5 zHPzL8bH}5b H}~5b IALMu@}IALeMIIEt H}7bLMt?HL9t$HX@t Hx7bHI9uHLL7buu LeMu,[Hz7btHe7bLeMt1HEL9tCHX@t Hx@7bHI9uH}Le+7bELP7bL1b LIH}HIHhHHp6bH@HHH6bH(HH06bHHH~6bHHHb6bHHHF6bHHH*6bH}HIH}HIH}Ht H}5bHhHH@H H(HHH$HH0HHzHHH{Lu3H=zHHH[A^]IH 5bL/b @UHAVSIP4bHH">zHHH{Lou3H=zHHH[A^]IH4bL/b @UHAWAVAUATSHIIHMzHHEоL˪HH H+HHHHHIH/q6IffEHEM J<HHHH94bKLHEHEHHULIJ4HHHrH9JL9HLODI)DHtHHHHHHuI8L1ҐHHH\H\H\H\H\H\H\ H\ H\(H\(H\0H\0H\8H\8H\@H@H9uI?I!IPHH_H s 1Hu|HSH)1fDADALLAD AL0D L0AD@ALPD@LPAoD`ALpD`LpHHuHt8HHffff.AoDALD H HuL9 M)IJDHE1LmHHHHUH+L HHH@H HH9HNHEHELuffEHELhHpL)t^LIIHI9Hp1bILmLmKDHEHL~LLH2bILmHU1L5Z3II|$n6ILTHLTIH}Ht H}0bKLIHH H9_ LEHLLI)IIFH0IIAqINHDLHH@HHKH(ILxH8HJ(HI@pHhHFL)HpI@HXHH`HHHI@HPE1IHDIIMLHEHIHHILHHI H9uGHff.MLH HH I; 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D H HuHHI9tffff.HHI9uH;wH HHhHt Hp=%bH@Ht HH%%bH(Ht H0 %bHHt H$bHHt H$bHHt H$bHHt H$bH}Ht H}$bH=zHH;Eu#HHĸ[A\A]A^A_]J4K|$bq$bIH}BH5>cH cHLE^ffEHE@$bHEfoGvfEHis a shaHH0Hor data HH(Ht's tensHH Hnal as iHHHdimensioHHHst be 1 HHHShape muHf@8pe@:AH+cHHML*`E1H50 zHazL#bH} b H} b IALMu@}IALeMIIEt H}"bLMt?HL9t$HX@t Hx"bHI9uHLL"buu LeMu,[H"btHv"bLeMt1HEL9tCHX@t HxQ"bHI9uH}Le<"bELa"bL b LIH}HIHhHHp!bH@HHH!bH(HH0!bHHH!bHHHs!bHHHW!bHHH;!bH}HIH}HIH}Ht H} bHhHH@H H(HHH$HH0HHIfMLI HI I; udIHL9rRf~NHHILMLILIII H9|HHIHL9rfHItMHJHIHs1\fff.LHHH4CH0JH@0s1HHHhJ4@Hp1f.DFLFADALDDFLFADD ALD0DFLFADD@ALDPoDF FADD`ALDpH@HuHHtCHXJ AHH`ffffff.oD AALH HuHPHI9fffff.LI)HxtH(D GfA DHHuIrLH8J Afffff.tAfA4DtAfAtDtAfAtD4AfAtDHI9uLL;LIFHHHMIO$6HQH)HHHH1fffff.I HMHEHIMLILII9uE1ffffff.~LHHILJ#M/HByIs1 f.fnpfpHps1HH1f.{D{D{ D{0D{@D{PD{`D{p{{{{{{{{HH{Ht%HH;D;H HuLM9t@fKHI9uH;gH HHhHt Hp}aH@Ht HHeaH(Ht H0MaHHt H5aHHt HaHHt HaHHt HaH}Ht H}aHzHH;Eu#HHĸ[A\A]A^A_]J4K|k3aaIH}QAH5-cH VcHLEN8ffEHE@[aHEfovfEHis a shaHH0Hor data HH(Ht's tensHH Hnal as iHHHdimensioHHHst be 1 HHHShape muHf@8pe@:AHkcHHMLj:E1H5pyHyLaH}a H}a IALMu@}IALeMIIEt H}?aLMt?HL9t$HX@t HxaHI9uHLLauu LeMu,[HatHaLeMt1HEL9tCHX@t HxaHI9uH}Le|aELaLMa LIH}HIHhHHp#aH@HHHaH(HH0aHHHaHHHaHHHaHHH{aH}HIH}HIH}Ht H}?aHhHH@H H(HHH$HH0HHIfMLI HI I; udIHL9rRf~NHHILMLILIII H9|HHIHL9rfHItMHJIHs1\fff.LHHH4H0JHBIs1ff.fnfpH8s1HHx1ffffff.DD D0D@DPD`DpH@H{Ht%HHfDDH HuHHI9tffff.HI9uL;IJH0IFH@IIAt$I~HDLHHPJHHApHhHFL)HpHAHXHHHH`HxHH(HA H81HIfffff.M HMHELMMLHII H9u1fff.~ HHHIHIHMHJaHHt H&aHHt HaHHt HaHHt HaH}Ht H}aHzHH;Eu#HHĸ[A\A]A^A_]J4K|$aaIH}BAH5cH ՀcHLE?#ffEHE@LaHEfoxxvfEHis a shaHH0Hor data HH(Ht's tensHH Hnal as iHHHdimensioHHHst be 1 HHHShape muHf@8pe@:AH\|cHHML[%E1H5ayHyLaH}a H}a IALMu@}IALeMIIEt H}0aLMt?HL9t$HX@t HxaHI9uHLLauu LeMu,[HatHaLeMt1HEL9tCHX@t HxaHI9uH}LemaELaL>a LIH}HIHhHHpaH@HHHaH(HH0aHHHaHHHaHHHaHHHlaH}HIH}HIH}Ht H}0aHhHH@H H(HHH$HH0HHaILmHU1Lu 3II|$H6IL] HLR IH}Ht H})aKLIHH H9_ LEHLLI)IIFH0IIAqINHDLHH@HHKH(ILxH8HJ(HI@pHhHFL)HpI@HXHH`HHHI@HPE1IHDIIMLHEHIHHILHHI H9uGHff.MLH HH I; udHHL9rRf~NHHHLMLILHHI H9|HHHHL9rfHM M+HxHHIs1mffff.LLIL,H(H4I9sLHH9s1L$H0 sE1H8HHhHHpE1BDBLCCLBDBLCD CL0BDBLCD@CLPBoDB CD`CLpIHuH8tMHHXHIH`ffffff.BoD(B (C,CL,I HuH@HI9LDLMLI)Ht"HHff.H4I4HHuIMLrOHHPHfffff.HLI HLILHLILH ILHI9uL;HH9?HEHHLLI)IIFHxHHLHHLI-N,HQH)HHHH1 ILMHEHIMLHHI4$H9uCHMLH HH I; udHHL9rRf~NHHHLMLILHHI H9|HHHHL9rfN +MH8HJIs 1@fHnfpDHxs1HH1fffff.DD D0D@DPD`DpÀÐàðH H{Ht%HHfDDH HuHHI9tffff.HHI9uI9HI@pHhHFL)HpI@HXHH`HHHI@HPE1ILffff.IIMMHEHIMLHHI H9u1ffff.~HHHHMLMHxJIr/LIH H(J4H9LHH91LMLI)Ht"HHf.H4ItHHuIML{HPJHLILHLILHLILH ILHI9u;LH0 LsE1H8HhJLpE1BDАBLРCDCLBDаBLCD CL0BDBLCD@CLPBoDB CD`CLpIIuH8tMHHXHIH`fffff.BoDB CDCLI HuH@HI9LL>ffff.L;N HQH)HIII1ff.IHMHEHIMLHHI $H9u1ffffff.~HHHJ M%HJIs1fHnfpDHxs1MH1DD D0D@DPD`Dpˀːˠ˰H H{Mt*HLffffff. D H HuHHI9tffff.HHI9uH;wH HHhHt Hp}aH@Ht HHeaH(Ht H0MaHHt H5aHHt HaHHt HaHHt HaH}Ht H}aHyHH;Eu#HHĸ[A\A]A^A_]J4K|3aaIH}QAH5-bH nlcHLENffEHE@[aHEfocvfEHis a shaHH0Hor data HH(Ht's tensHH Hnal as iHHHdimensioHHHst be 1 HHHShape muHf@8pe@:AHkgcHHMLjE1H5pyHyLaH}a H}a IALMu@}IALeMIIEt H}?aLMt?HL9t$HX@t HxaHI9uHLLauu LeMu,[HatHaLeMt1HEL9tCHX@t HxaHI9uH}Le|aELaLMa LIH}HIHhHHp#aH@HHHaH(HH0aHHHaHHHaHHHaHHH{aH}HIH}HIH}Ht H}?aHhHH@H H(HHH$HH0HHoHwH)LL)IHx1LDLDLDLDLDLLoD D L0HHuHtOHHHD0LHI9LLL)HoD DLH HuM9HMffffff.D)HPL)HtHCBIHuHrLHHHDfDBTBBTBTBTBTBBTIL9uL;hI9HHHHHH1 LI ILEHEHLHLHHHLI6H9ujHHH)HHfDI HH4HH4I;4HHH9r~fffff.~nHHHLI)IIrRffff.HI HHHHHIH9|HHHHL9rfC4MIEAHI9HINII)MI?IHtHP(H}Ht9I9tHPH`0H@H`HPHP H}HuHDž`HPH8H@HzH`H9HtHP(H}I9HtHP(HHt H苣aHHt HsaHHt H[aHHt HCaHHt H+aHxHt HaH`Ht HhaH yHH;Eu@Hĸ[A\A]A^A_]HP H}I9HP HH 6aH}a IH}I9tg|IH}I9tYnIH}HIH`H9uHP H}I9t$9HtHP(H}I9t#IH}I9uHP HHtgHtQHP(HHtKIH}Ht.H}aHHt%IHHtqIHHubHHunHHuzHHHHHxHH`HL9a HAaHHtH)aHHtHaHHzHaHHnH٠aHxHbH轠aH`HVHh衠aL胛a ff.UHAWAVAUATSHIHyHHEHPLXH(H`HMuLߙHPHHH HH9HNH(豶H0HXdH8W)EHELHL)t]L0MIILH=H跟aILeLeKHEHML0~LLHCaILeL}1H(Ls2HH}Ht H}JaH(HHH{'5IHH8HݷJH@H yHHpLpLeL5HuVLcHHtHu?HyHyHHEL}HpLE!H}I9t>HtHP(H}Ht9I9tHPH`0H@H`HPHP H}HuHDž`HPH8H@HH`H9HtHP(H}I9HtHP(HHt H؝aHHt HaHHt H訝aHHt H萝aHHt HxaHxHt H`aH`Ht HhHaHmyHH;Eu@Hĸ[A\A]A^A_]HP H}I9HP HH 胝aH}2a IH}I9tg|IH}I9tYnIH}HIH`H9uHP H}I9t$9HtHP(H}I9t#IH}I9uHP HHtgHtQHP(HHtKIH}Ht.H}-aHHt%IHHtqIHHubHHunHHuzHHHHHxHH`HL膖a H莛aHHtHvaHHtH^aHHzHBaHHnH&aHxHbH aH`HVHhaLЕa UHAWAVAUATSHIHyHHEHPLXH(H`HMuL/HPHHH HH9HNH(H0HXdH8W)EHELHL)t]L0MIILH=HaILeLeKHEHML0~LLH蓛aILeL}1H(L2HH}Ht H}蚙aH(HHH{w5IHHtHP(H}Ht9I9tHPH`0H@H`HPHP H}HuHDž`HPH8H@H,H`H9HtHP(H}I9HtHP(HHt HaHHt HaHHt HaHHt H՗aHHt H轗aHxHt H襗aH`Ht Hh荗aHyHH;Eu@Hĸ[A\A]A^A_]HP H}I9HP HH ȗaH}wa IH}I9tg|IH}I9tYnIH}HIH`H9uHP H}I9t$9HtHP(H}I9t#IH}I9uHP HHtgHtQHP(HHtKIH}Ht.H}raHHt%IHHtqIHHubHHunHHuzHHHHHxHH`HLːa HӕaHHtH軕aHHtH裕aHHzH臕aHHnHkaHxHbHOaH`HVHh3aLa ffff.UHAWAVAUATSHIH*yHHEHPLXH(H`HMuLoHPHHH HH9HNH(AH0HXbH8W)EHELHL)t]L0MIILH=HGaILeLeKHEHML0~LLHӕaILeL}1H(L2HH}Ht H}ړaH(HHH{5IH|H8HmJH@HSyHHpLpLeLm5HucL`f. vsu z Hyf.nvu8z6HyHHEL}HpL*H}I9t>HtHP(H}Ht9I9tHPH`0H@H`HPHP H}HuHDž`HPH8H@H*#H`H9HtHP(H}I9HtHP(HHt H[aHHt HCaHHt H+aHHt HaHHt HaHxHt HaH`Ht HhˑaHyHH;Eu@Hĸ[A\A]A^A_]HP H}I9HP HH aH}赎a IH}I9tg|IH}I9tYnIH}HIH`H9uHP H}I9t$9HtHP(H}I9t#IH}I9uHP HHtgHtQHP(HHtKIH}Ht.H}谐aHHt%IHHtqIHHubHHunHHuzHHHHHxHH`HL a HaHHtHaHHtHaHHzHŏaHHnH詏aHxHbH荏aH`HVHhqaLSa ff.UHAWAVAUATSHhHpyHHEHUIHG(H8LmH}[HH8MeIEI9HEHHHMH@hHEfffff.IEIIEH]HLH}YILHH}LW*WA*MI脐aL,A$LyIIHuhIHtXN41fWA*WA*L6a,AWA*DWA*La,ADIDHL9uLmMeM;e{M}M;}mHEHHHMHHhHxHHEfIEIIEH]LH}Hu E4HLHxHHHMHM,WA*EHEL4DHtLHMfDWA* $E a,AIIHuI s fffff.MI HEMtE1WC* ,E諎a,C/WC*L,E菎a,CD/WC*L,Era,CD/WC*L, EUa,CD/ KD,IL9uLmM}M;}~I9HEHHHMH@hHEfff.IEM4MuL}IIH}YHIMHHEH}6ALeLuH]HMHMEHEHx HtDHHuHUHMP0MeM;enHoyHH;EuHh[A\A]A^A_]趋a4aH yHHH5tyHqyHLafff.UHaH ÛyHH]fUHHyHH]fff.UHAWAVAUATSPL*LrM9L>*EMfM)DHt8Hfffff.WA*EMka,AIIHuI 1ffff.WA*DM*a,AWA*DMa,ADWA*DMa,ADWA*D Mԋa,AD IDHL9{H[A\A]A^A_]fff.UHHNH1H; 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AD ADA D AD HI9u[A\A]A^A_]E1H}L}1H}L}%UHH rH1H9NHD]DUHHw]UH]f.UH]2^fDUHSPH(%^H wHHKHSHHHPHKHHHK HH H[]fff.UHHmwHHG HF HGHFHGHOHNHF]UH]f.UH]^fDUHAWAVAUATSHXHwHHEIL6L:I\$HHKHHHHs(HHEHHHMHEIT$It$HILHMMt$HLLIH H`H8LH(I9uyDLILKHHEHLeHH@IHHHH@HLHL I H9HupHHH)HHfff.IH@HHHI;HHH9r~fffff.~nHHHLI)IIrRffff.HIHHH@HHIAD>D; ;AT?VA?VAT> AD>0H@HuHt'A8AL8A<VAD<VA>AD>H9ufffff.1HI9LHOHGH)Ht%Hffff.A A A HHuHHHMIL9LMII)HHHH|HH HH\IT1ffffff.D D D D D D D D D    HI9uIT5IHI9LHOHHHH9HMHljHt HfA0A 4VA) 6HHuH0}HHHD0HHH|90DD7L0VA) 6D7L0VA)L6D7L0VA)L6 7 0VA)L60H@H9|L9IILHI9LLKD L)H HHI9LHOHLH)M|HJ)HH)J<LLHIK +HH)KLLI9I|AHH9@I9IL H9A LIICIIEOAHHHH9HLHH)HH|0/HH)HH\0/I)KD./IIL)1fffff.D3L3T7VD7VT0D0D3 3T7V7VT00H@HuMt*HA0AL0A4VAD4VA6AD6L9LfD)IBH)Ht#Hff.AA AHHuHrsHHHDHHHL1ffffff.\ \A\ \A\\ \A\ A\HI9uLL;>1H*L1MLL;t1fff.LIHHMLLELeHL@ILH@HLHL IMH9upHHH)HHfff.IH@HHHI;THHH9r}ffff.~nHHHLI)IIrRffff.HIHHH@HHI|H9|HHHHL9rG HEHL]HHL`I8ILH}L&L&LxI]I9u[HHPH)HHIAL>D9L9AD> AL>0D9L9AD>@AL>PD9 9AD>`AL>pHHuHt(KDHD8 8A>AL>H 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6w]H,IWI*DWI*Lw]H,IDIDHL9uHuL&L;fbL&L;fUHEHHHMHHhHMLHFIHLmIHEH}L IIIH}MMHHMWJ*JEu-WH*I;v]H,I$LLhIHu [fIHtQN<1fWI*LEu]H,IWI*LEu]H,IDIDHL9uHuL&L;f+I9"HEHHHMH@hHE@HFIHH]HLH}ڜILHH}L轜MO|WH* JMu4WI*u]H,I$MoIHuhffffff.MHtQO41fWI*DMt]H,IWI*DMt]H,IDIDHL9uHuL&L;fH([A\A]A^A_]UHAWAVAUATSH(IHG(H8H}HH8MeIEI9HEHHHMH@hHELmIEIIELmIMH}YIIMH}HHHH~Hx HyH~HI-H5SA_H-H5 bH -HH HQH5]H@-HH;5-HHH ,yuHHMHe0HUHuHuIIFIF0INIVIF@H]HEHtHu&Hj]HuHH;Eu LHP[A^]k]IH]HEHtH0&HTj]L6e] fffff.UHAVSHPIHSuHHE"j]HþHj]HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]!y+H5r{]H}H-H5>_H-H5bH-HA H5G]H-HH;-HHH vuHHMHL.HUHuHuIIFIF0INIVIF@H]HEHtH$H@h]HeuHH;Eu LHP[A^]h]IH]HEHtH#Hg]Lb] DUHAVSIhg]HHLH[A^]IHg]Lb] UHAWAVAUATSHIIHuHHEHpuHI$I|$.HkuHI$ID$`HEHDžxƅp fDžusADžqtranƅwHXHpHML艺.HXHDžXHtCt H{f]Hf]pt H}f]HH}pAD$PHEHDžxƅp fDžusBDžqtranƅwHXHpHML.HXHDžXHtCt H{6f]H.f]pt H}f]HH}pAD$TW)pƅp HEDžqalphfDžuaIL$XHXHpL.HXHDžXHtCt H{e]He]pt H}ze]HW)pHEƅpDžqbetaƅuIL$\HXHpL`.HXHDžXHtCt H{e]Hd]pt H}d]HH~uHH;EuHĨ[A\A]A^A_]Le]d]IHXgQ=H5:_H :_HpLX aH@eIL4IH}HubiILsX]HxHDžxHuEIHxHDžxHt)Ct H{/X]HIH}Ht H}X]LR] ffffff.UHAWAVAUATSH(foHHquHHEHHH^ HU LLLe(Luf.uMf~D@L} MW LL1HuoE.HHHIAuEAAAI9LOIM)MI?I>MIM@fnDIzE1~IIOH)Ht H8HH@fnDHM HKH HALSAAHHH0IT$pLHpH)HH(HH8LHHE1MDHrLHI4L9s*HI L9v1LH)M)LH s1H(L1DAALDLAD AL0DLAD@ALPDLAoD`ALpD H IuH(t6H H8DAoDA AD AL H HuH0HH9It|fff.LH)Mt&Hfffff.AADHHuHr9fAADADADADADAD AD HH9uILML;HJMIfAnfpIqHHPH8 1HHx@fnDHH\HSHCHPLkAAHHH0IL$0LH(HH H)H8LHH1L$1H(ffffff.MHr"ILIDK4H9v$III9sE1LL)MH tfAnfpH81DDDHHuH(tLfAnfpAADH0IH9ttfff.LL)Mt$Hfffff.E:FMIMIrKL9KL91IGH)LHtHAAHHuHTAAADADADADAD AD ADADADADADADADADHI9uKITpHGH)1ffffff.DDDDDDDH HuHt$IMKDH@@H HuM9t%ML)Kffffff.D(HHuHM0HEMuWHL$Ld$H$HHHLfoL('HguHH;E@H[A\A]A^A_]LHHGHHJHs 1Hu}HAH)1AALAALAD AL0AD AL0AD@ALPAD@ALPAoD`ALpAD`ALpH HuHt,HHAoDA ADA H HuI9K<:IIwH9HMHL)LHtHLAofAHHuHriAA)ADA)DAD A)D AD0A)D0AD@A)D@ADPA)DPAD`A)D`AoDpfADpH H9|L9M)IIK H9sKI H9MII@HHDnAHs 1Mu{K :IDpITpIMH)1DLDLDLDLDLDLoD D H HuMt2IMJIAoDA ADA H 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HtH(H8HPH@Ht#HHCHuHHPH`OL\O Hffff.UHAVSIHHjgHHH{ H{P11LlHlgHH[A^]fUHAVSIHH|jgHHH{̡ H{P11LHlgHH[A^]fUHAWAVAUATSHIIIHzgHHEH}LffEfEHEHMHEHtgH@H]HMHEHtYHHCHuGHHPHg_OH]Ht6HHCHu$HHPH;_OHMHEH]HuffPHDž`1LmILEL$hIPHPH$HD$D$HuHxHpLMAHP1L蜋 HLq$IH&IHpHxEt7L}HELUHuH=)hHHMLE1_OCH@H3H@HH]HHPHxHHAOHD OH(HHHBHHLLIIHTHHAHH)H L4IIE1H8LH0H#f1H~f.NÀOfInHtfH 11ɐoTfofAoDfAo fHLHuHtJHHofoTfffpNffI~LHH9(u@AH@D)H(L)Ht.H0IH8H4HLILHuHruH@L)IGH0HLL8I4IGHIHXL9HX@t HxWOHI9uHPLXxWOEuH]HEHu$H]HuNoLWOH]HEHtCt H{/WOH'WOH]Hu4LIH]Ht#HHCHuHHPHUOLQO fUHAWAVAUATSHMHxIHogHHEHD`Lm HHMHm#ILb#I9I]I+]LXW)`HDžpIwI+7HH`LHIIIMgHhL9HPL`Lmf.H;LEXHEHhH;ps!HHHhHI9uDLLmHI9ugII]I+]}LW#HEIINH)HHH)VHUL9IHLHPH`HhH9Lu{1L`LeH]H;pr6ffffff.LLmHL9s*HhH]H;psHHHhHL9rH`HhLHUSOHPL8H`LH)HhHHLHHH)rH9u IFI9GW)@HDžPMMnIJ< TOIIL@LPLHMIJLUOLH?t@)HHK L9LhtlIUHuH HHHHI!4HCHr>@HHHHHI!4HHHHHHI!4HL9uW) HDž0M\HH9LSOH(H HH H0L9MOMI)LHHHr&IMHHL H9III9HI)DHtHfHHHHHuI81ɐH HHT HTHT HTHT HTHT HT HT (HT(HT 0HT0HT 8HT8HT @H@L9u#HsHIFHHHxHHHHjgHH;E+(ROI?I!IHHHzH s 1HunHOH)1LLD L0D L0D@LPD@LPD`LpD`LpHHuHt.H HD  D H HuL9EIJDH(1L L}ffff.L@HHIHsCH]H(H;0s$HHH(HL9rfff.LLuhHL9rL H(L)HHW)HDžHHHhHH9Ls HOOHHHLN4LHOOLLI$Md$LH)H1ҐI4H9 H4H4HH9rH HL`1W)HDžHI)tuHpMIHI9 LOOILLKDHMHHp~LLPOHML~GHhHHHE1AHI;u*L4AHHHHHE1AH;HHL#HHH+HH}HHH9Hpu AM`A1LH]HXs11Mt'IH 1fHOLM9uH`Lh>OEeL?OH9O LHHH nHHHtH>OH}HuHH}HH}j>OHHHN>OHHH2>OH HUSHHHueHH Htb,*(&HH`Hu^hHH`HuMWHHHYHHeH HuNH@HuZH`Ht Hhs=OHU8O HH`HuHH HtH(==OH@Ht,=OH`HuHH`Hu뚐UHAWAVAUATSHHVgHHEЅtKH}HMHuLEH=hH|HMLE1,=OHUgHH;EE+=OHMMHEHxMHHHHPHHzI È]LHMHHxHAHEHMdIIM\HpHGHhH)HEL,II1MH]HXL`#f1Hpf.M<Ҁ}HPHfInHhtfH}11ffff.AoTfAofAoDfAo fHLHuHptI HHofoTfffpNffI~LUHxH9EuAH}D)HEL)Ht$HMII HL9ILHuHreHEL)IJHUHLHH(H.H(HLuHHPHXHHAKHD ?HHHHBHHMLIIITHHAHH)HL,IIE1L HH0Lf1Hu}fOƀ?fInHtfH11ɐoT:fo:fAoD9fAo 9fHLHuHtKHHofoTfffpNffI~LHH9u@AH(D)HL)Ht.H0IH HH5@OH}H.H5QHߡH5SHHH HQH5rOH0HH;%HHH OH}HΥH5BQHH5SH谠HH HQH50pOHСHH;ŖHHH 9gHHMHuHUHuHuIIFIF0INIVIF@H]HEHtHH)+OHNDgHH;Eu LHP[A^]+OIH]HEHtHH*OL%O fffff.UHAVSHPIHCgHHE*OHþH*OHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]9H5OHwpHH;leHHH cgHHMH HUHuHuIIFIF0INIVIF@H]HEHtH謵HNHgHH;Eu LHP[A^]?NIH]HEHtHgHNLmN DUHAVSHPIHgHHEbNHþHXNHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]aH5 OH}HrH5xQHkH5ڗSHmHA H5fHH;Eu LHP[A^]NIH]HEHtH谛HNLN fffff.UHAVSHPIHfHHENHþHNHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H5NH}H^XH5_QHRH5~SH@SHH HQH5"OH`THH;UIHHH LfHHMHuHUHuHuIIFIF0INIVIF@H]HEHtH蕙HNHfHH;Eu LHP[A^](NIH]HEHtHPHtNLVN fffff.UHAVSHPIHsfHHEBNHþH8NHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]AH5NH}HUH5\QHOH5{SHPHH HQH5` OHRHH;FHHH fHHMHHUHuHuIIFIF0INIVIF@H]HEHtH5HYNH~fHH;Eu LHP[A^]NIH]HEHtHHNLN fffff.UHAVSHPIHfHHENHþHNHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H52NH}HSH5VZQHOMH5ZySHNHH HQH5OHOHH;DHHH fHHMHHUHuHuIIFIF0INIVIF@H]HEHtHՔHNHfHH;Eu LHP[A^]hNIH]HEHtH萔HNLN fffff.UHAVSHPIHfHHENHþHxNHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H5NH}H>QH5WQHJH5vSH LHA H5OHGMHH;HH HQH5 OHP?HH;E4HHH H5`EQHO8H5ZdSH9HA H5 OH:HH;/HHH fHHMH,HUHuHuIIFIF0INIVIF@H]HEHtHHNH%fHH;Eu LHP[A^]oNIH]HEHtHHNL蝾N DUHAVSHPIHfHHENHþHNHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H5NH}HN<H5CQH5H5 bSH07HH HQH5OHP8HH;E-HHH QHO1H5Z]SH2HH HQH5OH3HH;(HHH fHHMHŸHUHuHuIIFIF0INIVIF@H]HEHtHxHNHfHH;Eu LHP[A^]hNIH]HEHtHxH贼NL薷N fffff.UHAVSHPIHfHHE肼NHþHxNHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H5NH}H>5H5 .H5 5QH'H5SSH )HH HQH5NH@*HH;5HHH ,fHHMH%HUHuHuIIFIF0INIVIF@H]HEHtHuoH虳NHfHH;Eu LHP[A^]NIH]HEHtH0oHTNL6N fffff.UHAVSHPIHSfHHE"NHþHNHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]!H5rNH}H+H52QH%H5QSH&HA H5GNH'HH;HHH ӿfHHMH<HUHuHuIIFIF0INIVIF@H]HEHtHmH@NHefHH;Eu LHP[A^]诱NIH]HEHtHlHNLݫN DUHAVSHPIHfHHEҰNHþHȰNHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H5"NH}H)H5[0QH?#H5JOSHp$HH HQH5NH%HH;HHH |fHHMHUHUHuHuIIFIF0INIVIF@H]HEHtHjHNHfHH;Eu LHP[A^]XNIH]HEHtHjH褮NL膩N fffff.UHAVSHPIHfHHErNHþHhNHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]qH5¿NH}H.'H5-QH H5LSH"HA H5NH7#HH;,HHH #fHHMHlHUHuHuIIFIF0INIVIF@H]HEHtHlhH萬NHfHH;Eu LHP[A^]NIH]HEHtH'hHKNL-N DUHAVSHPIHSfHHE"NHþHNHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]!H5rNH}H$H5+QHH5JSHHH HQH5@NH HH;HHH ̸fHHMHHUHuHuIIFIF0INIVIF@H]HEHtHfH9NH^fHH;Eu LHP[A^]訪NIH]HEHtHeHNL֤N fffff.UHAVSHPIHfHHE©NHþH踩NHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H5NH}H~"H5U)QH/H5:HSH`HH HQH5NHHH;uHHH lfHHMHմHUHuHuIIFIF0INIVIF@H]HEHtHcH٧NHfHH;Eu LHP[A^]HNIH]HEHtHpcH蔧NLvN fffff.UHAVSHPIHfHHEbNHþHXNHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]aH5NH}H H5&QHH5ESHHA H5NH'HH;HHH fHHMHlHUHuHuIIFIF0INIVIF@H]HEHtH\aH耥NHfHH;Eu LHP[A^]NIH]HEHtHaH;NLN DUHAVSHPIHCfHHENHþHNHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H5bNH}HH5$QHH5CSHHA H57NHHH;HHH ñfHHMHHUHuHuIIFIF0INIVIF@H]HEHtH _H0NHUfHH;Eu LHP[A^]蟣NIH]HEHtH^HNL͝N DUHAVSHPIHfHHE¢NHþH踢NHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H5NH}H~H5U"QH/H5:ASH`HH HQH5NHHH;u HHH lfHHMHHUHuHuIIFIF0INIVIF@H]HEHtH\H٠NHfHH;Eu LHP[A^]HNIH]HEHtHp\H蔠NLvN fffff.UHAVSHPIHfHHEbNHþHXNHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]aH5NH}HH5QHH5>SHHA H5NH'HH; HHH fHHMHHUHuHuIIFIF0INIVIF@H]HEHtH\ZH耞NHfHH;Eu LHP[A^]NIH]HEHtHZH;NLN DUHAVSHPIHCfHHENHþHNHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H5bNH}HH5QHH5H5,QHH5$SH HH HQH5NH@HH;5HHH ,fHHMH%HUHuHuIIFIF0INIVIF@H]HEHtHu@H虄NHfHH;Eu LHP[A^]NIH]HEHtH0@HTNL6N fffff.UHAVSHPIHSfHHE"NHþHNHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]!H5rNH}HH5QHH5"SHHH HQH5@NHHH;HHH ̐fHHMH5HUHuHuIIFIF0INIVIF@H]HEHtH>H9NH^fHH;Eu LHP[A^]訂NIH]HEHtH=HNL|N fffff.UHAVSHPIHfHHENHþH踁NHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H5NH}H~H5lQH/H5: SH`HA H5NHHH;|HHH sfHHMHLHUHuHuIIFIF0INIVIF@H]HEHtH;HNHfHH;Eu LHP[A^]ONIH]HEHtHw;HNL}zN DUHAVSHPIHfHHErNHþHhNHHCHCXHC`HCXHCHHC@HC8HC0HC(HC 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qLH}L` W)@HDžP IHP( ^)@He input.HHHsequenceHHHptr for HHHGot nullH@AHJHuH@L< E1H5BaHsaLIZHALeMuFzHAL`MHH@t HPILeMt6HEL9t!Lh@t HxILM9uH}LLeIEuEuL`Mu&^H}IEtH}IL`Mt:HhL9t=Lh@t HxILM9uH`LhgIEtJLI@LHL@HDž@Mu+H}HuQH`Ht HhIHI AFt I~ILIH}Ht HH}HtH}IH`HuHH`HuHH`Huf.UHAVSI`IHHaHHH{LHHPLkgH|-cHH[A^]IH:ILI @UHAVSHPIHCaHHEIHþHIHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]xH58nLH}HCHH5̘JHHEH5 nLHe?H5pkNH@HA H5JHAHH;6HHH aHHMHHUHuHuIIFIF0INIVIF@H]HEHtHHIH;aHH;Eu LHP[A^]IIH]HEHtH譆HILI ff.UHAWAVAUATSHIIHaHHE1LHHtpHG]1LH<HHKH+KHH.袋.HHIHaaHH;EuLHĈ[A\A]A^A_]I IIHh轶)H5UlLH lLH}LhW)PHDž` IH`(^)PHe input.HHHsequenceHHHptr for HHHGot nullH@AHfJHuHPLE1H5aH5aLI:IIHh׵)H5okLH kLH}Lh!W)PHDž`0IH`(gP[)PHt tensorHH%Houtput tHH Hptr for HHHGot nullHHHLength: HHHSequenceH@-AH|kLHuHPLE1H5aH3aLI IALeMIALhMIIALeMFIALhMnIIPt H`ILeMt6HEL9t!HX@t HxzIHI9uH}LLe`IEuEu LhMu-'H}:IEtH}+ILhMHpL9HX@t HxIHI9uIPt H`ILeMt6HEL9t!HX@t HxIHI9uH}LLeIEuEuLhMu&^H}qIEtH}bILhMt:HpL9tEHX@t Hx7IHI9uHhLpIEtLEILI L@UHAWAVAUATSHAHHaHHEHt*HVH aH H;Mu$HĨ[A\A]A^A_]1H aH H;MtIIǃLIIIHh )H5WKH ImLH}LhnH{HIcHH<"H5U_KH8HIHPH8IHuHP1LH5aHɨaLI IPt H`IE18u4H$IA8tHHzILmMuEIALmMt6HEL9t!HX@t HxDIHI9uH}LLm*IEuEuLhMu:rH}IEtH}ILhMuLIALhMt:HpL9t5HX@t HxIHI9uHhLpIEu LILIILdI Hd#@UHAWAVATSH`IHaHHEؿNIHþHDIHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]oH5tdLH}H9HgH5IHH9IIHW)EHEIHEHHHML HXHMH5JHULw9H5dLHH5H5SaNHy6HA H5JH7HH;,HHH aHHMH5HUHuHuIIGIG0IOIWIG@H}Ht H}IH]HEHtH|HIH aHH;EuLH`[A\A^A_]RIIH]HEHt7Hv|HIL|I IH}HuH]HEHuLUI H}`IH]HEHt룐UHHHH9H9Hx]ÐUHAWAVAUATSHIIH7aHHE1LHHLE6HHHHHSH+SHH.袋.HHHH9H9@HxHy HHHL@HHIM}1LLLHL L`PMuMuPM9I}0HPhHxLH1ff.LL)IHLIIH9|HhH5wbLH bLL JH]HHLhHLHm(EtSH}zIHaHH;EtJILQII}0HPILLHIL@IHUaHH;EuLHĘ[A\A]A^A_]ÿIIHh趪)H5N`LH aLH}LhTW)PHDž` 輽IH`(])PHe input.HHHsequenceHHHptr for HHHGot nullH@AH_JHuHPLE1H5aH.aL~I3IIHhЩ)H5h_LH 7`LH}LhWW)PHDž`0ּIH`([)PHdex tensHHHt for inHHHptr inpuHHHGot nullHf@ or@"AH`LHuHPL E1H5aHBaL蒼IGIIHh)H5|^LH K_LH}LhcW)PHDž`0IH`(M^)PHut tensoHH Hfor outpHHHnullptr HHHAt: Got HHHSequenceHf@(rAH^LHuHPLE1H5aHLaL蜻I IALeMuFzIALhMIIPt H`ILeMt6HEL9t!HX@t HxպIHI9uH}LLe軺IEuEu LhMu-iH}蕺IEtH}膺ILhMAHpL9HHX@t HxSIHI9uIALeMIALhMIIALeMFIALhMnIIPt H`踹ILeMt6HEL9t!HX@t Hx蓹IHI9uH}LLeyIEuEu LhMu-'H}SIEtH}DILhMHpL9HX@t HxIHI9uIPt H`ILeMt6HEL9t!HX@t HxǸIHI9uH}LLe譸IEuEuLhMu&^H}芸IEtH}{ILhMt:HpL9tEHX@t HxPIHI9uHhLp5IEt)L^IL I LIEt H}ILI UHAWAVAUATSHH aHHEHG0@ẼtuIeHcH aH H;Mt*{|HH aH H;MuHĘ[A\A]A^A_] I苷IIH`()H5YLH _LHxL`C"H5pgKHHHU+HxHH1LH5̞aHaLMI IHt HX߶IE1LeMuMHIALeMt6HEL9t!HX@t Hx螶IHI9uH}LLe脶IEuxu L`Mu=uH}^IxtH}LIL`MuLIAL`Mt:HhL9t5HX@t Hx IHI9uH`LhIEu LILIL轰I DUHAWAVAUATSH8MLHHIHHaHHEH8HaHHHLH@H8LILHEEL-0aIEHHI@L8LIL=aILHDž(HDž HDžHDžDž0L軷IHLHHH0HmIHILLzIHLHHH3H!ILHHCIHHHLLHHL8Lt H IL߲IH5aHHnIH8莳IHaHH;EuLH8[A\A]A^A_]!;?T: >F=HH;IHaHH;EuLHĈ[A\A]A^A_]I脮IIHh!)H5PLH JRLH}LhyW)PHDž`0'IH`(C[)PHsequenceHH&Hutput seHH Htr for oHHHot nullpHHHEmpty: GHHHSequenceH@.AHPLHuHPLFE1H5LaH}aLͭI肭IIHh)H5OLH HQLH}LhIPH5LHPL'HuHP1LH5ƔaHaLGI IPt H`٬IE1LeMuMH IALeMt6HEL9t!HX@t Hx蘬IHI9uH}LLe~IEuEu LhMuDzH}XIEtH}IILhMuQIALhM;HpL9BHX@t HxIHI9uIIALeMuFzIALhMIIPt H`螫ILeMt6HEL9t!HX@t HxyIHI9uH}LLe_IEuEuLhMu&^H}IMcLHH0M7H{0HPhI9I?[ HhH{0K HPH5GLH BHLL IH]HhLPH5 LH(Et H}荢IHaHH;ELH[A\A]A^A_]HLIOI+OHH.袋.HHHLsL@Ht?HH@HHH9H9HxфqHy HH@I|$HHH+H1LHH葃HHW)EHEH}L HLHHH@HLE1ff.I9H8LLHML8HDž8MtAFt I~ILILL7LH0HLHMZH0HDž0HtcCt H{蠠IH蘠IJf.LLՔH(HLHMH(HDž(HuIHHH@L9HH9LuDH HMLLH HDž HtCt H{IHߟII7HHaLcML{IIH[L9|MI)HèfH(HèIXuI?LUH5(CLH :CLL cIH]H@LHH# LH(II)LM&!IIGIGIHHEHCHEIHEHCIEH aHH;EW[IٞIIHhv)H5ALH 9CLH}LhsW)PHDž` |IH`(v])PHe input.HHHsequenceHHHptr for HHHGot nullH@AHCLHuHPLE1H5aHaL>I`IIHh萊)H5(@LH SBLH}LhW)PHDž` 薝IH`( ])PH tensor.HHHinput teHHHptr for HHHGot nullH@AH9JHuHPLE1H5ׄaHaLXIz IIHh誉)H5B?LH mALH}LhW)PHDž`0谜IH`( $[)PHsequenceHH'Houtput sHH Hptr for HHHGot nullHHHInsert: HHHSequenceH@/AHO?LHuHPLE1H5ՃaHaLVIxIPtH`ILeMuEIALeMt6HEL9t!HX@t Hx诛IHI9uH}LLe蕛IEuEu LhMuDfH}oIEtH}`ILhMu=IALhM'HpL9HX@t HxIHI9uIPtH`ILeMuEIALeMt6HEL9t!HX@t Hx趚IHI9uH}LLe蜚IEuEu LhMuDmH}vIEtH}gILhMuDIALhM.HpL9HX@t HxIHI9uIPtH`ILeMuEIALeMt6HEL9t!HX@t Hx轙IHI9uH}LLe裙IEuEuLhMu:wH}耙IEtH}qILhMuQIALhMt?HpL9t$HX@t Hx2IHI9uHhLLpIEu{IL3InIEucILuMu%UIEtLH}AHILuMt2H]L9t3MI)HèH8HèIXuH}Lu荘ILoI LfUHAWAVAUATSH8MLHHIHHkaHHEH8HfaHHHHH@H8H諗ILHEEL-ܮaM}LI@L8H誖IL5aILHDž(HDž HDžHDžDž0LnIHLHaIIEIEIEHEID$HEIHEID$L(I$HFaHH;EuLHĸ[A\A]A^A_]ÿ IIHhx)H5?.LH o1LH}LhW)PHDž`0譋IH`(7![)PHsequenceHH&Hutput seHH Htr for oHHHot nullpHHHErase: GHHHSequenceH@.AHL.LHuHPLE1H5raHoaLSIUIIHhw)H5=-LH m0LH}LhW)PHDž` 諊IH`(])PHe input.HHHsequenceHHHptr for HHHGot nullH@AHI/LHuHPLE1H5qaHnaLmIoIPtH`ILeMuEIALeMt6HEL9t!HX@t HxƉIHI9uH}LLe謉IEuEu LhMuDmH}膉IEtH}wILhMuDIALhM.HpL9HX@t Hx-IHI9uIPtH`ILeMuEIALeMt6HEL9t!HX@t Hx͈IHI9uH}LLe賈IEuEuLhMu:wH}萈IEtH}聈ILhMuQIALhMt?HpL9t$HX@t HxBIHI9uHhLLp"IEunILFIaIEtXH}MILuMuDILuMt8H]L9t9MI)HèfDHXHèIXuH}Lu譇IL菂I LfUHAVSHPIHaHHE肇IHþHxIHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]+H5ҘIH}HH63H5(LHHH51,LHH5%NHHA H5IH-HH;"HHH aHHMH"gHUHuHuIIFIF0INIVIF@H]HEHtHbAH膅IHaHH;Eu LHP[A^]IIH]HEHtHAHAIL#I ff.UHAWAVAUATSHIHH7aHHEH~HLc(MH(I|$HHH+Hl1LHYHfHHH0H8E11LLx0f.LDEL9x0`PIHH](])EPHH5*LICPHHHM(HHHDžHHtCt H{ڃIH҃IEtH}ÃIAE9DH8L5W)EHEH}L1L}@LH@HLLL@HDž@MtAFt I~6IL.ID9|L8M|$MtHH0LpHII\$L9t9MI)Hè@HxHèIXuI>L8H0H&LM}L貂IIFIFIHEID$HEHHEID$H(HH aH H;MuHĸ[A\A]A^A_]݂I[IIHhn)H5$LH @(LH}Lh0W)PHDž`@IH`(hk^)PHt sequenHH(Hor outpuHH Hullptr fHHHt: Got nHHHConstrucHHHSequenceHf@0ce@2AH$LHuHPLE1H5iaHNeaL螁IPSIIHhm)H5#LH 8'LH}Lh-W)PHDž` IH`(0[)PHore inpuHHHe 1 or mHHHMust havHf@ts@AH$'LHuHPL9E1H5?haHpdaLIrIPtH`OILeMuEIALeMt6HEL9t!HX@t HxIHI9uH}LLeIEuEu LhMuDmH}IEtH}ILhMuDIALhM.HpL9HX@t HxIHI9uIPtH`VILeMuEIALeMt6HEL9t!HX@t Hx IHI9uH}LLeIEuEuLhMu:wH}~IEtH}~ILhMuQIALhMt?HpL9t$HX@t Hx~IHI9uHhLLpu~IEuqIL~IdILuMuVIEtMH}BILuMt;H]L9t= 0HH/Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@7AHrLHHPLΥE1H5PaHMaLUiI hIH6fIH(fIݿhIIHpU)H5! LH 6LHLp胢W)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AH~LHHPLE1H5OaH"LaLrhIHpUeIIPt H`gIL Mt?H(L9t$HX@t HxgIHI9uH LL(gIuu&LpMuMHvgItHagILpMuIALpMHxL9`HX@t HxgIHI9u7TIHH :IHH>IPt H`fIL MuQIAL Mt?H(L9t$HX@t Hx~fIHI9uH LL(^fIuu&LpMuFH/fItHfILpMuQIALpMt?HxL9t$HX@t HxeIHI9uHpLLxeIEuILeIH}HIHpHV]ILPM+II]IEH+Ct H{;eIH3eIH}HIH}HIHHuXIHHt4HdIHHu,IHHuIHHHdIvIHHHuIHHt#HHCHuHHPHMcIHHH3dIHHHu IHpHHHt|HHCHujHHPHbILPMuYII]IEHt(Ct H{cIHcIHHHuIHHHuLPMtHHXL9tpMI)Hèffff.HoHèIXuHPLX7cIHpHt HxcIH}Ht H} cIL]I LfUHAWAVAUATSHIIHIH|aHHEHEHCPHEEEEEEW)EHEMtN)H}HtHpH8BaIHgzaHH;E LH[A\A]A^A_]L0H8LPIEM|$LMd$ W)pHEM)tXMIHI9 L`IHHxHpJHEM~HLLNbILHxW)PHDž`LL-HXHEHL`LPq11Hxffffff.HH9uEtE fff.HEHHcH9LcHEHpL<W)@LLH@nHI}tpA1ALHLH KlL)HHHHHCHvHHPHO^IEe,IEMd$0HpLxW)HDžI)tfHLHHH9L^IILLIHMH~LLs`IMLH@HHHHHtH@E1LLLHHHcHxHfE1HHHzfDEHHHEH9LPI~H|HH+H~1L̓Ht H;IE1It$0L MwMtPMgIGHI_L9MI)Hèff.HeHèIXuI<$L`RI_uLPMHXL9MI)HèHxeHèIXuHPLHL3XIID$ID$I$HPIGHXHH`IGW)PHDž`IEHpHHxIXIH}HLLX(XIHpHuXI XIIHpD)H5UKH LHLp跑W)PHDž`@WIH`(I^)PH be >= 0HH/Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@7AHrLHHPLΓE1H5>aH;aLUWI VIH6TIH(TIݿVIIHpC)H5!KH LHLp胐W)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AH~KHHPLE1H5=aH":aLrVIHpUSIIPt H`UIL Mt?H(L9t$HX@t HxUIHI9uH LL(UIuu&LpMuMHvUItHaUILpMuIALpMHxL9`HX@t HxUIHI9u7TIHH :IHH>IPt H`TIL MuQIAL Mt?H(L9t$HX@t Hx~TIHI9uH LL(^TIuu&LpMuFH/TItHTILpMuQIALpMt?HxL9t$HX@t HxSIHI9uHpLLxSIEuILSIH}HIHpHV]ILPM+II]IEH+Ct H{;SIH3SIH}HIH}HIHHuXIHHt4HRIHHu,IHHuIHHHRIvIHHHuIHHt#HHCHuHHPHMQIHHH3RIHHHu IH^HHHt|HHCHujHHPHPILPMuYII]IEHt(Ct H{QIHQIHHHuIHHHuLPMtHHXL9tpMI)Hèffff.H]HèIXuHPLX7QIHpHt HxQIH}Ht H} QILKI LfUHAWAVAUATSHIIHIHjaHHEHEHCPHEEEEEEW)EHEMt<)H}HtHpH8BOIHghaHH;E' LH[A\A]A^A_]L0H8LPIEM|$LMd$ W)pHEM)tXMIHI9 LNIHHxHpJHEM~HLLNPILHxW)PHDž`LLdHXHEHL`LP11Hxffffff.HH9uEtE fff.HEHHcH9LcHEHpL<W)@LLH@nHI}tpA1ALHKH Kl:)HHHHHCHHHPHOLIEu<IEMd$0HpLxW)HDžI)tfHLHHH9LLIILLIHMH~LLsNIMLH@HHHHHtH@E1LLLHH= 0HH/Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@7AHbKHHPL辁E1H5,aH(aLEEI DIH&BIHBIݿDIIHpy1)H5KH DKHLps~W)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AHnKHHPLۀE1H5+aH(aLbDIHpEAIIPt H`CIL Mt?H(L9t$HX@t HxCIHI9uH LL(CIuu&LpMuMHfCItHQCILpMuIALpMHxL9`HX@t HxCIHI9u7TIHH :IHH>IPt H`BIL MuQIAL Mt?H(L9t$HX@t HxnBIHI9uH LL(NBIuu&LpMuFHBItH BILpMuQIALpMt?HxL9t$HX@t HxAIHI9uHpLLxAIEuILAIH}HIHpHV]ILPM+II]IEH+Ct H{+AIH#AIH}HIH}HIHHuXIHHt4H@IHHu,IHHuIHHH@IvIHHHuIHHt#HHCHuHHPH=?IHHH#@IHHHu IHLHHHt|HHCHujHHPH>ILPMuYII]IEHt(Ct H{?IH?IHHHuIHHHuLPMtHHXL9tpMI)Hèffff.HKHèIXuHPLX'?IHpHt Hx?IH}Ht H}>IL9I LfUHAWAVAUATSHIIHIHWaHHEHEHCPHEEEEEEW)EHEMt>ILHxW)PHDž`LH(HEHL`11HhLHH9uEtEfHEHHcH9?LcHEHpL<W)@LLH@~sHpI}tpA1ALHKH K|()HHHHHCHHHPH_:IEIEMl$0HpLxW)HDžI)tfHLHHH9!L:IILLIHMH~LLLMI)HèHBHèIXuI<$L I^,LHpL;5IID$ID$I$HPIFHXHH`IFW)PHDž`IEHpHt Hxv5IL8H}H!$LPMtHXL9t4MI)Hèffffff.HAHèIXuHPLLX5IHpHpwx5I4IIHp!)H5+KH KHLpnW)PHDž`@4IH`(&^)PH be >= 0HH/Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@7AHHKHHPLpE1H5aHaL+4I H1IH1I3I3IIHp_ )H5KH KHLpYmW)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AHTKHHPLoE1H5aHaLH3IHp+0IIPt H`2IH Ht?H(H9t$Lh@t Hx2ILL9uH HH({2Iuu&LpMuFHL2ItH72ILpMuQIALpMt?HxL9t$HX@t Hx1IHI9uHpLLx1IEuIL1IH}HIHH :IHH3IPt H`i1IL MuQIAL Mt?H(L9t$HX@t Hx*1IHI9uH LL( 1Iuu&LpMuFH0ItH0ILpMuQIALpMt?HxL9t$HX@t Hx0IHI9uHpLLxg0IEu~IL0IH}HosIHpHBIILPMII]IEHCt H{/IH/IH}HIH}HIHHuXHIHHt4H/IHHu,IHHuIHHHC/IIHHt#HHCHuHHPH.IHHH.IHHHuO{II]IEHtvCt H{.IH.IHHHu]IHHHtNHHCHuaHH;E&IKHOH)1f 0@P`pDހDސDޠDްDDDDH HuHtLILHfAH HuL9L)JHff.LHHHE1E1MHHHzHHNHtB~fpDHyH)1ADADAD AD0HHu1Ht~fpDADADH9uiLuMt#MgL#IIGIGIM/MwMwLeI$HH[A^]IHILI UHAWAVAUATSHHH7aHHEH{Ht4HP8t*HH 6aH H;MuHĘ[A\A]A^A_]:IIIHhU )H5eIH KH}LhlRWH{ɅHHH5gIHPHHtAHpgIHuHPLYE1H5aHaLbI IALhMIIPtH`ILeMuEIALeMt6HEL9t!HX@t HxIHI9uH}LLeIEuEuLhMu&^H}^IEtH}OILhMt:HpL9tEHX@t Hx$IHI9uHhLp IEtL2ILI LH{fUHAWAVAUATSHI袋.HH_H)HI.袋.IHCL9#H}HOH)HIH\tE]tH9wHH9HBIHIkXI`ILILkXO,4IkXLL$KL4XHEL0L`M9tGHMM)IĨMIŨLLQ$IǨMIĨIXuHEL LpHMML(HHHXM9tLL)IƨfLH'IƨHXuMtLH[A\A]A^A_]IH[A\A]A^A_]E1E1'I@UHAWAVAUATSPMIIIHH;ILHH aL%ILLH`IH ILHH`LILLH`H[A\A]A^A_]HyHyHyHyfUHAWAVAUATSHH2aHHEHHC0@t"uOH"0HcH 2aH H;Mt-H5HH 2aH H;MuHĘ[A\A]A^A_]IEIIHh)H5zKH KH}LhRH{0VHHH5KHPHH HuHP1LpUH5yaH`LI IPt H`IE1LmMuIIHh)H5sKH KH}LhOI0O~HHH5ſKHPHH~HuHP1LiRH5r`H`LI IPt H`IE1LeMuKH1ZIHHHHHPHHH@H@HH5KHIHHHHHPHHH@H@HLOHHItHH HHrIHHH0HHPH(H H@H@HH5VXIH "IHHHHHHPH@H8H@H@HHPH8,IAHKHuHPLNE1H5`H`L$I IA8IA IAtwIAt}IAtvIALhMIIPuT8u` uluxLeMH`I8tHHI tH0ItHItIAkHjI_HQISH8ILeMuEIALeMt6HEL9t!HX@t HxIHI9uH}LLeIEuEuLhMu&^H}IEtH}ILhMt:HpL9tEHX@t HxIHI9uHhLppIEtLILE I LUHAWAVAUATSHHIHHP(aHHEH8HK`HHHLH@H8LIHEEL5%aM~LI@L8L IL%%aILHDž(HDž HDžHDžDž0LZIHLH(UHH=IHHH UHHHYLL8Lt H IH IH5 aHH` IH8 IH&aHH;EuHH[A\A]A^A_].IHLL8Lt H q IHK IHOmHGmHH5u aHH IHH8 IH I fUHAVSHPIH3&aHHE IHþH IHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H5&JH dH}蟃H5ڸKH dH艃11H]H5KHNH5YMHHH HQH5OIH蟁HH;vHHH aHHMHd&HUHuHuIIFIF0INIVIF@H]HEHtHH IH$aHH;Eu LHP[A^]g IIH]HEHtHH ILI ffff.UHAVSHPIH#aHHE IHþHx IHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H5#JHdH}H5ZKHdH 11H݄H5;KH|H5٨MH}HA H5MIH&HH;tHHH aHHMH;'HUHuHuIIFIF0INIVIF@H]HEHtH[HIH!aHH;Eu LHP[A^]IIH]HEHtHH:ILI @UHAWAVAUATSHIHHH,!aHHE1LS~IMf1LL0HLDHHI~0DH@PA9uuHL8LxPM.MnPM9HPhHzxpL^DH1LLIL=DHIIH9|YAA tNH6ILLHLCHHrH CI9I H91"]fDK up?ffffff.t88fnXf~fff. AHH9u_EDED0H0HuLHM'LBII~0HPILLHILIEHaHH;E<LH[A\A]A^A_]à /AHXILLfBILM~IrJ I9z K gH9m 1Zfff.ׁGr1@ƃ> (8wfnXf~ 2  fA4OHI9uffHLLLO<I?HKH JI1AWLMT(UA"HILLnHL#AHRHrH CI9C I H96 1HPH)HHtHHHrH CI9I H91#]K up/t88fnXf~ff. 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fUHAWAVAUATSHHC_HHEHW)EHEEEaxisEHhHUH~HhHDžhHtCt H{p*GHh*GEt H}Y*GHu'HyC_HH;EuHĈ[A\A]A^A_]*G;*GIHh'H50IH oIH}LhcW)PHDž`0)GH`(x[)PHte valueHHHttributeHHH'axis' aHHHInvalid HHHMissing/H@&AHIHuHPL fE1H5_HB _L)G IALeMuFzIALhMIIPt H`(GLeMt6HEL9t!HX@t Hx(GHI9uH}LLe(GEuEuLhMu&^H}(GEtH}(GLhMt:HpL9tEHX@t HxT(GHI9uHhLp9(GEt)Lb(GL#G LIEt H} (GL"G DUHAVSIX'GHH"1_HHH{LohHHPLH`HH[A^]IH'GL"G fUHAWAVAUATSH8LLHIIHH{@_HHEH8Hv_HHHHH@H8H&GHEEL-=_MuLI@L8H%GH=_HHHDž(HDž HDžHDžDž0L)GHLHSmA7H%GLMLEHHHHLLHqLL8Ht H &GH$GH5 $_HHp$GH8%GH>_HH;EuLH8[A\A]A^A_]>&GILL8Ht H %GH[$GH_HWIH5#_HH#GIH8$GL G fUHAWAVAUATSPMLEIIIHL'GHLHkIuH#GL'GHLHkHEH0H#GL'GHLHkH[A\A]A^A_]H苄H胄H{HsHkff.UHSPHe$GH 5_HHHKHHH[]UHH5_HHHGHF]@UHH]2fUHHNH1H; _HD]DUHH_]UHAWAVATSIHD:D9LcA)ffff.I>HHwLHH9HHHHWHHWHyHw HcHW(LG0HHHHHA8tVHGHHLG8HG@H81HHH@H4I1HHH@HHH_LHH9HHHHHGH0HGHHHyHW HHW(LG0HHHHHA8tSHWHH2H_8HW@H:1HH@H4H1HHH@HGHhG UHAWAVAUATSHHIHpH0_HHE1H觍IMeHhH茍II_I~PIu I+uHIHxL`LILL.HxHt.LpIHDžxH/_HH;E@GLHLP1HhL?HLHIE0H;C0W)EHE`lGHH](66[)EH5PIUHGCUHMLpLK&Et H} GHxHDžxHtCt H{GHGH/_HH;ELH[A\A]A^A_]LRHIE0xL@LH8H*HIE I+EIH}1H`HXH}LHPH`Iw I+wHHLSILMMIMeHIcHHhHHHx0HPW)EHEMLPLH=v LGHH]N,LmHLGLmLPM~M9KMH@ Hh LXIHHLXI)I |:IIHE1E1ffffff.I9t HHIHL9|E1H`HIHHL,E1HELJLH@H8H4HGIHL;h|H@HH]LmLH)IEHI~DHHQH HHHtHHtHHH;48uHDLHH`LHHLMMLPLXV Lp HMuIM MuPHCPHXI+MHhH}1H`L8H}LHPH`OIw I+wHHLXQILHhIIIL$HH`HcHH(I}0HPIW)EHEHLPLXLH= HGILmK\H]LHhGH]LPLXH`M^L9ML(XMILIHDMHI)JH HH01LHff.HhL)H |,HHHE11I9t HITHHH9|1LHIILE1LXLH`HEHhJ4L`LIL8LHLrGLHEHhJtLILHLJGLIL9HuL+0LPLLXL(IH`HtW)PHDž`06GH`(Е[)PHte valueHHHttributeHHH'axis' aHHHInvalid HHHMissing/H@&AHrIHuHPLc@E1H5i^H^LG IALeMuFzIALhMIIPt H`HGLeMt6HEL9t!HX@t Hx#GHI9uH}LLe GEuEuLhMu&^H}GEtH}GLhMt:HpL9tEHX@t HxGHI9uHhLpGEt)LGLfF LIEt H}cGLEF ffff.UHAWAVAUATSHIIHIHQ_HHEH{ ?IID$ID$I$LL{HC0@L0L(tl(HfHME1LuJc HMID$I;D$sHHID$IM9|}fDLLIM9|bH3IM~R1fID$I;D$t$IHHID$HIL9|LL{HIL9|Lc(H0H1J+4HHJHSH@MI$H:H8H9|MH9}HfDHy JF II<$HuUHAWAVAUATSHHMLHHHHH'_HHEH8H"^HHHLH@H8LgFHEEL-_M}LI@L8LmFHEL5^_ILHDž(HDž HDžHDžDž0H$HHHHLMHHHFLL8Lt H FHFH5^HH;FH8[FH_HH;EuHHH[A\A]A^A_] FHLL8Lt H LFH&FHH5`^HHFHH8FHF ffff.UHAWAVAUATSPMLEIIIHLFHLH@IuHFLFHLHw@HEH0HbFLFHLHR@HEH0H=FH[A\A]A^A_]HLYHDYHLL8Lt H FHFH5^HHNFH8nFH _HH;EuHH[A\A]A^A_]FHLL8Lt H _FH9FH=RH5RHH5c^HHFHH8FHF UHAWAVAUATSH(H _HHEIIHHAHq H):LLHHHyHHHcL$Lxj0HI6LL 6HI6LG0II6L5HpHHIHhW)PHDž`M;LH=HN<LFHPJH`HLFHX1L@IHtLP5HPHHHxH9|WH*H0FH(B[)Hger thanHH Hrank larHHHust has HHHtensor mHHHindices Hf@( 0@*HH%&HWLHDžHHID$(HIL$0HIL$ H耴H@I|$8HDžHIHLHLHhHHpHH@HHxHLHHH H}HPH(L0H_HHEH]H]H]W)EEHULHB&H}H9Ht_HtHP(HHtZH5%IH}HHjHMHd$&Et H}qFHPHu%/HP HHuHHPHt HX4FHY_HH;EuHH([A\A]A^A_]FHPFF IEH}FHPHIHPHIHIHPHulvIH}H9uHP HPHuLVHt9HP(HPHu3=IHPHu",IHPHuIHPHt HXFLF UHAWAVATSH`IH_HHEؿFHþHFHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]]H5>FH}HcIIHW)EHEFHEHHHML HXHMH5IHUL1cH5IH_H5[GH3`HAH5/GHZaHH;OVHHH F^HHMHHUHuHuIIGIG0IOIWIG@H}Ht H}FH]HEHtH}HFH_HH;EuLH`[A\A^A_] FIH]HEHt7H0HTFL6F IH}HuH]HEHuLF H}FH]HEHtff.UHAVSHPIH_HHEFHþHFHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]QH52FH}H~`HH5IHHbH5&IH5\H5@KHf]HH HQH5,GH^HH;{SHHH r^HHMHkHUHuHuIIFIF0INIVIF@H]HEHtH軣HFH_HH;Eu LHP[A^]NFIH]HEHtHvHFL|F @UHAVSHPIH_HHErFHþHhFHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H5FH}H^HVH5HIHH`H5IHYH5ЅKHZHA H5}*GH\HH;QHHH ^HHMHBHUHuHuIIFIF0INIVIF@H]HEHtHRHvFH^HH;Eu LHP[A^]FIH]HEHtH H1FLF ff.UHAWAVAUATSH(HHIH ^HHE1HG[HHH0[HHHzLzHr IM)LHHHHM4HLpPHC H+CHI9~nPsFHH0f(y[f) H5ԼIFHFCFH L& H0HFfWf)HDžHL)HZHHLIILH=jFHLHHJHLM9HHIM)LHHHLr+ID$HIDH9IIIM9HML)Ht"Hfff.IHIHHuH8{1IHITHTITHTITHTIT HT IT(HT(IT0HT0IT8HT8IT@H@L9uL1LM HI?I!I@HHzH s1MHHGH)1MAALLAD AL0D L0AD@ALPD@LPfAD`ALpfD`LpHHuHt8HHfffff.fADA fD H HuL9IJtHILsHK HLHHH LI$H(H0fWf)HDžH 1H IMH Ht H(FfWf)`f)Pf)@f)0f) HLhHH{0HCPuH(IIFPH8H IIFPH0HPHHHPHHQ0RtcL,$HL HIFHHDžHtZCt H{FHFH(uGxL,$HL HIFHHDžHuH(6H HxH`H+XP9[f\A[f|H.H`HEHHxHEH]H]W)MfEHULH3&1fWf)EHE0FHEf(p[f)EHupportedHHH not supHHHata typeHHHtensor dHHHindices H@&HMMLLV&EH}_FHXHH HxH`H+X@[f\ [f|HxF`HEHHxHEH]H]W)MfEHULH1&H}H9MLt HtHP(HP IEHXHt H`FHHt I<${FH^HH;Eu)LH([A\A]A^A_]J O`HW)EHEEHbatch_diHEfEmsEHPH}HULH*LuHEMtAFt I~FLFHEt H}FH ^HH;Eu H0[A^]mFHEt H}FHF DUHAVSIXFHHLH[A^]IHFLiF UHAVSIXtFHHLH[A^]IHGFL)F UHSPH5FH ^HHHKHHH[]UHH^HHHGHF]@UHAWAVAUATSHH9(HwHVHUHVHUHVHHUHV HUHV@HR8HUHcHHEL$IHHH}H?HUHHULM|HH^(L LI LIHU1E1ff.NcH5qjKH?HH HQH5GH@HH;5HHH ^HHMH HUHuHuIIGIG0IOIWIG@H}Ht H}FH]HEHtHڅHFH#^HH;EuLH`[A\A^A_]iFIH]HEHt7H菅HFLF IH}HuH]HEHuLnF H}yFH]HEHtf.UHAVSHPIHs^HHEBFHþH8FHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]mH5FH}H?11HbDH5ߠIH;H5gKH<HAH5[ GH=HH;2HHH ^HHMHp HUHuHuIIFIF0INIVIF@H]HEHtH0HTFHy^HH;Eu LHP[A^]FIH]HEHtHHFLF fUHAVSIPFHH"^HHH{LoH^HHH[A^]IHFLF @UHAWAVAUATSHIH0H^HHE1L^HH莟FH8讠FH^HH;EuHH[A\A]A^A_]\FHLL8Lt H 蟠FHyFH}HuHH5^HHFHH8FH8F UHAVSI`DFHHLNH[A^]IHFLF UHAVSHPIH#^HHEFHþHFHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H5 GH}HH֯H5KIHHH5zIHEH5P>KHvHA H5FHHH; HHH ^HHMHHUHuHuIIFIF0INIVIF@H]HEHtHYHFH^HH;Eu LHP[A^]eFIH]HEHtHYH豝FL蓘F ff.UHAVSHPIH^HHE肝FHþHxFHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]職H5GH}H>HfH5QIIHH$H5fxIHH5;KHHA H5FH-HH;"HHH ^HHMHrHUHuHuIIFIF0INIVIF@H]HEHtHbWH膛FH^HH;Eu LHP[A^]FIH]HEHtHWHAFL#F ff.UHAWAVAUATSPII1LrHHtTII1LLIHkeILL|HI}HDHs%1"H vIL% HHHyHHrHH~H)1W( D{XfoL{Xfff.AAdTTf8f~f8f~dA\ Ad0TTf8f~\f8f~d HHuHu ]1HtVAALf(zXTfozXf8f~Tf8f~LH9tA. HH9uILH[A\A]A^A_]fDUHAWAVATSII1LHH!HIHH1LH%ILzHI|$H HrI GH9HHL91HPH)HHt-Hffff.ALIII|7IIOH?HH?H!H4L譏FM9}HK|IHI)L茏FLmLeLH?H>LIII|7IIOH?HH?H!H4L2FM9}HK|IHI)LFHhIL@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 gIH%H}HtHH8芊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$LH)H D ^ HHuW)pI~PHMLULH?II=MIH>LHMBI(I(AIIIOHHHZHn1(HI~XHuHBH]HpHpHxHH?H>HHH{1D(DaXD( LaX(UaX(%aX(-cX((AYWR(YY(\YYAV(^(f8)HH9|H9L ~|HyHt!Q :dX^ HHH9tJdXffff. Q(^LQ(^THH9uMHhHCH0M]0HHH`LQ0IE HH1LpfnIAzIRH HJ m E1MHhL1U1MHh+KfILOlH(HH@HELHMLXfnfnPIrKI9KTI91AI~H)LHt$H\YHHuHHhDRf\YT\YTT\YTT \YT HI9uLLHfofoHZHHDoAH,MMI)E1f.CdCl\\YYCdClCdC,\\YYCdC,IIuMHhLt)C$Cl\\YYHXB$BlI9֋hDN,0IAfnfnPIVI9IMHL)Ht&Hf.B\YB)IHuH Dr]fffff.B\YC)TBT\YC)TBT \YC)TBT0\YC)IM9|I9fnfnPI)IIH`JHXJH9sH8JKH9LXMIfofoIPHHDpAH6IVH)JHHJ<1$l\\YYdld l0\\YY$lHHuMHht%LA$Al\\YYA$AlM9Mff.D)H0L)Ht'HB\YBIHuHrmfffff.B\YBBT\YBTBT\YBTBT \YBT IL9uH(HHIIHHHHH; L81M1HHh(NfoMHhSHFHM]0HHH`LQ0MM 1L8E1ML0HH#1MfILK|H(HH@HEPI@uAAAAI9LOHXH`JLHMfnPIr!JHXH9JH91LHMIIt$H)LHtH@ \ HHuHLMLH# \ L\LL\LL \L HI9uHHL0LHfoHzHHD~AHCMOI)E1CTC\\\CTC\CTC\\CTCIIuML0HHt#BB\\\HXBB\I9L8fDN< IfnPIT$I9IMHL)HtH@B \B) IHuH rG@B \C)LBL\C)LBL \C)LBL0\C) IM9|HhI9fnPI)ILhH`JHXJH9sKDJH9HHMIfoIVHHD`AHL0IT$H)J4K<1f\\\T\T \0\\\HHuMt)LHH\\\HX\M9HhL0Mfff.HhD)HL)HtHB \B IHuHHhrZff.B \B BL\BLBL\BLBL \BL IL9uH(HHIIHHIH; 4LL)1WL8LPfDHHI X HAHI X HH9uHhMhNLH)1Wf.HHIXHAHIXHH9uHhH_E1M 1MQZ(XL9LH)fDfnXXDXDXD f~HH9uHt(HHfff.fnXf~HHuHh HsH)1(XL XD0XL@XDPXL`XDpXXH HuHwHt$Hfff.X XD0HHvHuXL9~BXHh(XXI9~D)IrH)Ht HHDXHHuHs LHHrCLH)HTXBXBXBXBXBXBXBXH HuWI*^WWMmI( (\YI(a\YIIIOHHDBAH IxH)1ffff.(l (t0\YX(L@\YX(dP\YX\YXHHuHSMt(,\(t0\YXYX((XL9~B($\YXHh(XXI9VIrH)LHt&Hf.\YXHHuHI)HL DYa\YX\YXY\YX \YXHIu\(YIARIBHsHucI)Ҿd\YX\YX\\YXL \YXHI9uHt$HH@\YXHHuHEHL`HMHPHrxII?LH>HH(HI=II(YHIIOHHH{H<1۾H1H|'PHxH&HTfnI~X IHAuDكH9HOII)LH?H>LHHIHrII9IDI91HyH)HHt"HfDA \ALHHuHIffff.A \ALAL\ALAL\ALAL \AL HH9uH)пfnXXTXTXT f~HH9uHHHfnXf~HHuHH(H~HHD@AH(IxH)1AA\\\ATA\AT A\0\\AT A\0HHuHwH)1ffff.XT X\0XT@X\PXT`X\pXXH HuHsHt$Hfff.XX\0HHvHu((XL9~BXHh(XXH9~l)HpH)HtHXHHuHr>H)HLDXQXQXQXQXQXQXQXH HuI~XXWI*^WQ 5OXIHAuDكH9HO^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$\YA)dAd\YA)dAd \YA)d Ad0\YA)d0HH9|H9NI)IsLIDLIeFH~$HH>u1HH5fFHtHtIIM~H[A^A_]ÿhdFHH-dFH5N^HKK^HdFUHAVSHPIHC}^HHEdFHþHdFHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]tH5buFH}HH5?IHH5KHHA H57FHHH;HHH p^HHMH HUHuHuIIFIF0INIVIF@H]HEHtH H0bFHU{^HH;Eu LHP[A^]bFIH]HEHtHHaFL\F DUHAVSHPIHz^HHEaFHþHaFHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]tH5sFH}H~H54=IH/H5:KH`HA H5FHHH;|HHH sn^HHMHHUHuHuIIFIF0INIVIF@H]HEHtHH_FHy^HH;Eu LHP[A^]O`FIH]HEHtHwH_FL}ZF DUHAVSHPIHx^HHEr_FHþHh_FHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]qmH5pFH}H.H5:IHH5JHHA H5FH7HH;,HHH #l^HHMH#HUHuHuIIFIF0INIVIF@H]HEHtHlH]FHv^HH;Eu LHP[A^]]FIH]HEHtH'HK]FL-XF DUHAVSHPIHSv^HHE"]FHþH]FHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]!sH5rnFH}HH58IHH5JHHA H5GFHHH;HHH i^HHMH,,HUHuHuIIFIF0INIVIF@H]HEHtHH@[FHet^HH;Eu LHP[A^][FIH]HEHtHHZFLUF DUHAVSHPIHt^HHEZFHþHZFHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]iH5"lFH}HH5D6IH?H5JJHpHA H5FHHH;HHH g^HHMH4HUHuHuIIFIF0INIVIF@H]HEHtHHXFHr^HH;Eu LHP[A^]_YFIH]HEHtHHXFLSF DUHAVSIPXFHHa^HHH{LHP_^HHH[A^]IHJXFL,SF @UHAWAVAUATSHIIHGq^HHE1LnHHHHHHCLc I)It Iu H8uALW)@HDžPH虔IH@H&HN7IHCHK H)HLt HuFH8u@A?HDž`HHH;PiHHHHW)`HDžpMtALH=nN4LVFH`JHpHLVFHhLH詓IM)HHIH LHE1fA?t!HHH`HhH@IH`HLHHH|HHIHH9t(HOHLH|&HLH9tfff.1HLH}IM9WQH@H`mb1L@A<tHHH`HhLHL9rH`Ht HhhUFLLH@HHH)H1IILeLuHEH`HDžhH`1LBHHxMMINHHHQINHI@I@ LHHуHH)HMIHSLHHMIJ HE1E1H'E1HLof.MMրHt9J K4H4H9J K4H4H91HAu1f1HAt$J HILHH4H4HHH9MLMHNHLH@JJB >LIHIuHLtK MMBBLLHHI9fffff.IHHIM9H>HHH@Ht HHDRFHik^HH;EuHH[A\A]A^A_]RF(RFIH@>&H5.IH .IH`L@.迋W)EE(HEHis requiHEHX input HEEred!EAH dFH`HML8E1H5>9^Ho5^LQFtQFIH}>&H5.IH F.IH`LEbW) HDž0 QFH0(W) Hrst outpHHHo get fiHHHfailed tH@ut!AH=cFH`H L]E1H5c8^H4^LPF H`MF HALeMuiHALeMHHAL@MHH t H0PFLeMt6HEL9t!Lh@t HxOFLM9uH}LLeOFEu`uLeMu)XH}OF`tHpOFLeMt1HEL9tNLh@t HxuOFLM9uH}Le`OFE{LOFH@HovLHEt H}#OFLeMt6HEL9t!Lh@t HxNFLM9uH}LLeNFEu`u L@Mu,dH}NF`tHpNFL@Mt:HHL9tILh@t Hx~NFLM9uH@LHcNFELNFH4IF L(HH`HuJTHH`Hu9CHH@Hu:DHH`HuHH`Ht HhMFH@Ht HHMFHHF fffff.UHAWAVAUATSH(IIIILL)HHMaIyHL)HH9LEM9LL)HHHH=HL)HHHH9LM3HH9HBH6H9MFHH5FHNJFHId^HHH5#7^H2^H MFMM)LHH9ILHUL9M]MH]I)HHItL)HLFLHIH1ITI9sItLHH9LH?L!HUHrIIAQH 1HIHu&RLLLm+HH}H<KFHLEL}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}HuLwKFH}HEHHuI)M~LLTKFH}MHEH8LxHXH}HthIFMJ4O4I|pHrL)1fDϐLϠAALDϰLAD AL0DLAD@ALPD AD`ALpHHuHIHut1H HfD AD A H HuHUI9LEIB >LIHIuHLtK MMBBLLHHI9fffff.IHHIM9H>HHH@Ht HH>FH X^HH;EuHH[A\A]A^A_]J?F>FIH@e+&H5SIH IH`L@._xW)EE(HEHis requiHEHX input HEEred!EAHPFH`HMLzE1H5%^H"^L_>F>FIH}*&H5IH _IH`LEbwW) HDž0 =FH0(W) Hrst outpHHHo get fiHHHfailed tH@ut!AHOFH`H LyE1H5%^H4!^L=F H`j:F HALeMuiHALeMHHAL@MHH t H0B >LIHIuHLtK MMBBLLHHI9fffff.IHHIM9H>HHH@Ht HH3FHM^HH;EuHH[A\A]A^A_]Z4F3FIH@u &H5cIH IH`L@.omW)EE(HEHis requiHEHX input HEEred!EAHEFH`HMLoE1H5^H^Lo3F$3FIH}&H5IH IH`LEblW) HDž0 2FH0(W) Hrst outpHHHo get fiHHHfailed tH@ut!AHDFH`H L oE1H5^HD^L2F H`z/F HALeMuiHALeMHHAL@MHH t H01FLeMt6HEL9t!Lh@t Hx1FLM9uH}LLe1FEu`uLeMu)XH}_1F`tHpJ1FLeMt1HEL9tNLh@t Hx%1FLM9uH}Le1FE{L51FH@HovLHEt H}0FLeMt6HEL9t!Lh@t Hx0FLM9uH}LLe0FEu`u L@Mu,dH}n0F`tHpY0FL@Mt:HHL9tILh@t Hx.0FLM9uH@LH0FEL80FH*F L(HH`HuJTHH`Hu9CHH@Hu:DHH`HuHH`Ht Hh/FH@Ht HHt/FHV*F fffff.UHAVSIPT/FHH8^HHH{LoH06^HHH[A^]IH /FL)F @UHAWAVAUATSHIIHH^HHE1L.HHHHHHCLc I)It Iu H8uALW)@HDžPHYkIH@HHIHCHK H)HLt HuFH8u@I?HDž`HHH;PiHHHHW)`HDžpMtALH=nN4L-FH`JHpHL-FHhLHijIM)HHIH LHE1fI?t!HHH`HhH@iIH`HLHHfDH|HHIHH9t(HOHLH|&HLH9tfff.1HLH}IM9WQH@H`Db1L@I<tHHH`HhLHL9rH`Ht Hh(,FLLH@HHH)H1IILeLuHEH`HDžhH`1LYHHQOMMINHHHQINHI@I@ LHHуHH)HMIHSLHHMIJ HE1E1H'E1HLof.MMրHt9J K4H4H9J K4H4H91HAu1f1HAt$J HILHH4H4HHH9MLMHNHLH@JJB >LIHIuHLtK MMBBLLHHI9fffff.IHHIM9H>HHH@Ht HH)FH)B^HH;EuHH[A\A]A^A_]j)F(FIH@&H5sIH 2IH`L@.bW)EE(HEHis requiHEHX input HEEred!EAH:FH`HMLdE1H5^H/ ^L(F4(FIH}&H5IH IH`LEbaW) HDž0 'FH0(W) Hrst outpHHHo get fiHHHfailed tH@ut!AH9FH`H LdE1H5#^HT ^L'F H`$F HALeMuiHALeMHHAL@MHH t H0&FLeMt6HEL9t!Lh@t Hx&FLM9uH}LLe&FEu`uLeMu)XH}o&F`tHpZ&FLeMt1HEL9tNLh@t Hx5&FLM9uH}Le &FE{LE&FH@HovLHEt H}%FLeMt6HEL9t!Lh@t Hx%FLM9uH}LLe%FEu`u L@Mu,dH}~%F`tHpi%FL@Mt:HHL9tILh@t Hx>%FLM9uH@LH#%FELH%FHF L(HH`HuJTHH`Hu9CHH@Hu:DHH`HuHH`Ht Hh$FH@Ht HH$FHfF fffff.UHAVSIPd$FHH-^HHH{LdH(+^HHH[A^]IH$FLF @UHAWAVAUATSHIIH=^HHE1L>HHHHHHCLc I)It Iu H8uALW)@HDžPHi`IH@HHIHCHK H)HLt HuOH8uIAW.u HDž`HHH;PHHHHW)`HDžpMtALH=N4L"FH`JHpHL"FHhLHp_IMJHHIH LHE1fA.Wu{!HHH`HhH@_IH`HLHHfff.H|HHIHH9t(HOHLH|&HLH9tfff.1HLH}IM9G[H@H`9l1L@A. Wu{HHH`HhLHL9rH`Ht Hh!FLLH@HHH)H1IILeLuHEH`HDžhH`1LMHH7DMMwINHHHQINHI@I@ LHHуHH)HMIHSLHHMIJ HE1E1HE1HLeMMրHt9J K4H4H9J K4H4H91HAu1f1HAt$J HILHH4H4HHH9MLMHNHLH@JJB >LIHIuHLtK MMBBLLHHI9fffff.IHHIM9H>HHH@Ht HHFH7^HH;EuHH[A\A]A^A_]ZFFIH@u &H5cHH HH`L@.oWW)EE(HEHis requiHEHX input HEEred!EAH/FH`HMLYE1H5^H^LoF$FIH} &H5HH HH`LEbVW) HDž0 FH0(W) Hrst outpHHHo get fiHHHfailed tH@ut!AH.FH`H L YE1H5^HD^LF H`zF HALeMuiHALeMHHAL@MHH t H0FLeMt6HEL9t!Lh@t HxFLM9uH}LLeFEu`uLeMu)XH}_F`tHpJFLeMt1HEL9tNLh@t Hx%FLM9uH}LeFE{L5FH@HovLHEt H}FLeMt6HEL9t!Lh@t HxFLM9uH}LLeFEu`u L@Mu,dH}nF`tHpYFL@Mt:HHL9tILh@t Hx.FLM9uH@LHFEL8FHF L(HH`HuJTHH`Hu9CHH@Hu:DHH`HuHH`Ht HhFH@Ht HHtFHVF fffff.UHAVSHPIHs2^HHEBFHþH8FHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]A/H5Y2GH}HH&/H5HHHH /H5]eHHHʑH5LHH{H5JH謌HH HQH5,\FH̍HH;HHH %^HHMH<HUHuHuIIFIF0INIVIF@H]HEHtHH%FHJ0^HH;Eu LHP[A^]FIH]HEHtHHFLF f.UHAVSHPIH/^HHEFHþHFHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH],H5/GH}HnH,H5HHHTH|,H5bHHH:H5HHH5JHHA H5YFHCHH;8HHH /#^HHMHVHUHuHuIIFIF0INIVIF@H]HEHtHxHFH-^HH;Eu LHP[A^] FIH]HEHtH3HWFL9F UHAVSHPIHc-^HHE2FHþH(FHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]1#H5I-GH}HH*H5HHHԌH)H5M`HHH躌H5HfH5QHHH$HLH5SHHH H5HHyH5ƥJHzHA H5sJFH|HH;qHHH ^HHMH(wHUHuHuIIFIF0INIVIF@H]HEHtHHHlFH^HH;Eu LHP[A^]FIH]HEHtHH'FL F UHAVSHPIH3^HHEFHþHFHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H5GH}H}HH5ѰHHH}HH5QHHH}H5 HH;wH5FJHlxHH HQH5GFHyHH;nHHH x^HHMHtHUHuHuIIFIF0INIVIF@H]HEHtHHFH ^HH;Eu LHP[A^]TFIH]HEHtH|HFLE f.UHAVSHPIH^HHErFHþHhFHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]qH5GH}H.{HVH5AHHH{H<H5NHHHzH5|HHtH5JHuHA H5cEFHwHH;kHHH ^HHMHHUHuHuIIFIF0INIVIF@H]HEHtH8H\FH^HH;Eu LHP[A^]FIH]HEHtHHFLE UHAVSHPIH#^HHEEHþHEHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH] H5 GH}HxH H5HHHxHH5 LHHHzxH5HH+rH56JH\sHH HQH5BFH|tHH;qiHHH h ^HHMHHUHuHuIIFIF0INIVIF@H]HEHtH豹HEH^HH;Eu LHP[A^]DEIH]HEHtHlHELrE f.UHAVSHPIH^HHEbEHþHXEHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]a H5yGH}HvHF H51HHHvH, H5}IHHHuH5lHHoH5JHpHA H5S@FHqHH;fHHH ^HHMHXHUHuHuIIFIF0INIVIF@H]HEHtH(HLEHq^HH;Eu LHP[A^]EIH]HEHtHHELE UHAVSHPIH^HHEEHþHEHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H5GH}HsH H5HHHsH H5FHHHjsH5HHmH5&JHLnHH HQH5=FHloHH;adHHH X^HHMHHUHuHuIIFIF0INIVIF@H]HEHtH衴HEH^HH;Eu LHP[A^]4EIH]HEHtH\HELbE f.UHAVSHPIH^HHEREHþHHEHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]QH5iGH}HqH6H5!HHHpHH5mDHHHpH5\HHjH5JHkHA H5C;FHlHH;aHHH ^HHMHHUHuHuIIFIF0INIVIF@H]HEHtHHHH%H;HwHH (F1AH5%Hv]HH;Eu>HH[A\A]A^A_]H {HHG%H8]HH;EtEAwH)1Affffff.L$LdLdLdLd Ld(Ld0Ld8HH9uHt H Hfff.L!HHuHgHIHHHL%HW)PHDž`H H(H)taHIILH=H EILXLPKDH`HH~LHEILXHP1LIHPHHt HXEI~yHHDHLHNIW)HDžHLNME1L@HHx(HH;u6LHO[ff.HHPHH;sHHHIHM9|&LHPIHM9pHH HELeHHEHHuHHHHMHhHMH`HMHUHXHPHpLxHuHEH}HEH@HPHHH@H8HHHHt$HEH HuSHH HHH>H;~|!HH+VHH L9r HVILML;`1W)p)E)E)E)E)EH)HHDžpHHxHLHMHHHUHhHt,HEHhHLHMHTHUHHHMH`WYhtPh1LpL1ff.H9HH9H;y|HHH H9rLH;`|Hs]HH;EuH[A\A]A^A_]跾Eff.UHAVSIXEHHLH[A^]IHELɸE UHAVSIXԽEHHLH[A^]IH觽EL艸E UHAVSH0HIH]HHEH]HII~ H[]HIIFPW)EHEEEaxisEH}HUHMHH]HEHtCt H{EHEEuHt'H]HH;Eu/H0[A^]H}ɼEHuHEIFPH]HH;Et7EHEt H}蕼EHwE ffffff.UHAWAVAUATSHIIHH]HHE1L2HL2IƾL2IIFIN H)HtH:H80IE I+EH'LHHHH;#HL臀HHKHW) HDž0Mt$PHHPHHHL`HX H HH9tH LHL)HHLH蜽HH H4H HP;HHHHS؃H1AHH HHH nHH$H;HHH XF1AHe%H]HH;Eu>HH[A\A]A^A_]H HHw$Hh]HH;Et辺EAwH)1Affffff.L$LdLdLdLd Ld(Ld0Ld8HH9uHt H Hfff.L!HHuHHIHHHLU~HW)PHDž`H H(H)taHIILH=H;EILXLPKDH`HH~LHúEILXHP1LIHPHHt HX辸EI~HH$HLH~IW)HDžHL6MHWH*E1WLf..v7XPHH;r'LHPWCHH;tHHIHM9|#LH6WDIHM9pHHHELeHHEHHuHHHHMHhHMH`HMHUHXHPHpLxHuHEH}HEH@HPHHH@H8HHHHt$HŶEH Hu3HH HH(葶EH]HH;E EHP贳E HHHHHHHHHtrHHPHu\HHPHHXEH Huz*HHL1HMtjAFt I~豵ELPHH Hu:DHHHuHHHt HoEH Ht H(WEH9E UHAWAVAUATSHIIIuMeI)MIIGHH> H?MuI)LHHH9w`II9LBMLEHuI9vKٴEHH5EH!EH]HHH5]Hǚ]HߴELEHuIJ<EHHuLEN4ABN|M~HLI ELI]M}MuHtHH[A\A]A^A_]EH[A\A]A^A_]E11LEEIH(ELԮE fff.UHAWAVAUATSHhH]HHEHHHxIHHHHHH@HHLHGLHDžHDžHDžHDžHDžHDžHDžHDžHDžHHPH8HHHH0HH@H(HH8H HH0HHH HP(HHHHHPH@HHHHDžLLMt{Iǃ?@)»?)ÿHL9EڍK@1HHE1@HEIELH?LѲEHKNANHLLMt{Iǃ?@)»?)ÿHL9EڍK@1HHE1@HEIELH?LBEHKNANHH]HH ]H HHHHGHPHHH@HHDžH(dWHDžpHDžhHDž`HDžXHDžPHDžHHDž8HDž0HDž(HDž HLpDžxHDžHDžHDžL@MLE1LpH`LHHHHLH)HH(HLLI)L9LNHHHhHHHHH)LH0HI)L9LNHPHHHHpH8HHH)H9HNHXHHLLHAD$(ID$ ID$ID$ID$I$HxHtwHHHHItHIؿH9uIH9ɿ)HHDžHHHDžAH@HHL3@tcHxHHHIt$HuIt$I|$H}HuHUHHMHEHEHUH}Hh7DžxIM9HH9t;E11fff.J<73EHHHH)HIH9rHt H豭EH]HH;EHh[A\A]A^A_]H= ]E H]HCHCHHH@H蓖HcHHOH Hc@HHOHKHcH HOHKH=]7E}uEIH=w]EL踧E IHH9t7E11JHH[A\A]A^A_]H KHH$H]HH;Et^EAwH)1Affffff.L$LdLdLdLd Ld(Ld0Ld8HH9uHt H Hfff.L!HHuH7HIHHHLlHW)PHDž`H H(H)taHIILH=HۢEILXLPKDH`HH~LHcEILXHP1LIHPHHt HX^EI~IHHgHLHIW)HDžHLME1L@HHx(HH;u6LH][ff.HHPHH;sHHHIHM9|&LHP觹IHM9pHHcHELeHHEHHuHHHHMHhHMH`HMHUHXHPHpLxHuHEH}HEH@HPHHH@H8HHHHt$H腠EH HuSHH HHH[A\A]A^A_]H wHH$H]HH;EtޗEAwH)1Affffff.L$LdLdLdLd Ld(Ld0Ld8HH9uHt H Hfff.L!HHuHHIHHHL`HW)PHDž`H H(H)taHIILH=H[EILXLPKDH`HH~LHEILXHP1LIHPHHt HXޕEI~HHD`HLHIW)HDžHLMHWH*E1WLf..v7XPHH;r'LHPWCHH;tHHIHM9|#LHWDIHM9pHH]WHELeHHEHHuHHHHMHhHMH`HMHUHXHPHpLxHuHEH}HEH@HPHHH@H8HHHHt$HEH Hu3HH HH(豓EH֬]HH;E EHPԐE HHHHHHHHHtrHHPHu\HHPHHX EH Huz*HHL1HMtjAFt I~ђELPHH Hu:DHHHuHHHt H菒EH Ht H(wEHYE UHAWAVATSHH]HHEHHHHHH(HHH@H HH0HGH8HDžPHDžHHDž@HEHEHEHEHDžxHDžpHHPHMHHHHMHH@HMHH8HMHH0HMHH HP(HUHMHHHPH@HPHHH@HDžhLPL`MtrIƃ?@)»?)ÿHL9EڍK@1HHE1@HEIELH?L臑EHKNANHEUMLHLIHXHtxHƒ?@)ƻ?)ÿHH9EލK@1HHE1@HEIEHH?EHKNANHpx|L@MM~ 1L@LH5HI9uH.]HH;EuH[A\A^A_]tEUHAWAVSH(IIH]HHEIG`AOhAwlLH?HIHLH)HH׉HIGHHMI)MOpAOxEG|LLH?LIIHH)HHDHH*ILIwPH)I.IIHEHJH0H}.EIK vHufHTHD1HEHEHCHEHMHKHH]HH;Eu H([A^A_].EHf.UHAVSIX脎EHHLH[A^]IHWEL9E UHAVSIXDEHHLH[A^]IHELE UHAVSH0HIH ]HHEH*]HII~zH]HIIFPW)EHEEEaxisEH}HUHMHH]HEHtCt H{nEHfEEuHt'H]HH;Eu/H0[A^]H}9EHuHEIFPHQ]HH;Et觍EHEt H}EHE ffffff.UHAWAVAUATSHIIHH]HHE1LHLIƾLIIFIN H)HtH:H80IE I+EH'LHAHHH;#HLPHHKHW) HDž0Mt$PHHPHHHL`HX H HH9tH LH!L)HHLH HH H4H HP諫HHHHS؃H1AHH jHH jHHM$H;HkHH VF1AHw%H]HH;Eu>HH[A\A]A^A_]H kHH$Hأ]HH;Et.EAwH)1Affffff.L$LdLdLdLd 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Hx[DHI9uHLLZDpuPu#LMuC{HZDPtH`ZDLMuLIALMt:HL9tLBD Ct H{ GDHGDHHtFDHHtHFDLAD fUHAWAVAUATSHHIHH_\HHEH8H-\HHHLH@H8LFDHEEL5H]\M~LI@L8LEDL% ]\ILHDž(HDž HDžHDžDž0LHDHLH訌H3HHHHSLL8Lt H EDH_DDH5C\HHCDH8EDH{^\HH;EuHH[A\A]A^A_]EDHLL8Lt H DDHCDHݤHդHH5C\HHSCDHH8nDDH?D UHSPHH{pHt H{xDDH{XHtH{`H[]yDDH[]fDUHSPHH{pHt H{xQDDH{XHt H{`?DDHH[]1DDDUHAVSI$DDHHRM\HHH{L蟄HHPL0H]HH[A^]IHCDL>D fUHAWAVAUATSHIIH\\HHEAEIE0IE(IE IEIEIEW)`M}ƅ` Mu HDžpDžavalufDžeeH}H`HHBH]Ht7W HEtDUHEр҄HHDHu8H %GH}1HA>D+UHEр҄HEHH $GH}1HAa>DA]L蚄x ~AE<EH t HH(H t 1H u&H H UD1HA H=DtA}<7W)`HDžpƅ`DžapadsƅeHHH`LLHHHDžHHtCt H{0@DH(@D`t Hp@DHIMI}HH)HHDž`IU I](H)HHH)vH`LkYIMI}H9uSsHIE(H9tDH)H1@HHxHH9r$ffff.I6HHHH9rEt H}V?DH{X\HH;EuH[A\A]A^A_]û?D3?DHHH+$H5"GH "GH`LH"xW)0HDž@ >DH@( U)0Hte valueHHHttributeHHH'mode' aHHHInvalid H@H`H01H {H5&\HE"\H>DNF>DHHH*$H5!GH !GH`LH1wW)0HDž@ =DH@(U)0Hte valueHHHttributeHHH'pads' aHHHInvalid H@H`H01HzH5'%\HX!\H=DaH 0t H@/=DDž,LeMu"VHH ,LeMt6HEL9t!HX@t HxHt I}(:DI?Ht I}9DH 4D fff.UHAVSI9DHHC\HHH{LOzHHPLH]HH[A^]IH9DLa4D fUHAVSId9DHHB\HHH{LyHHPLpH1]HH[A^]IH9DL3D fUHAWAVAUATSH(HH3H~0CHHFPLEHMIHCHPHCHEHMHC HK(HC0H{HLmLeLE L}(H}LuLLLMM%HC`HEHCpHChHC`M)t3MILH=uLL@8DHC`N,LkpHL/8DLkhHHuHUHMMMsH([A\A]A^A_]H}/5D IHEH8HuHEH8HuL2D H{h7DHEH8HtH{P7DL2D f.UHAWAVAUATSHHP\HHEL>HFL)IIH)HI9RL_ HO(L)HI9:M)IM9*HHGLoIJEAHsLMu|LfHtHIHILHDHIHILHtHIHILHDHIHILH[I9uMt6M|LdI1ff.IHIHI HI9uHGKDHG8M9uKDHG@HCO\HH;EuHĈ[A\A]A^A_]6D6DIHh"$H5PGH EGH}LhoHP裠AHGHuHPLFrE1H5L\H}\L5D IALeMuFzIALhMIIPt H`+5DLeMt6HEL9t!HX@t Hx5DHI9uH}LLe4DEuEuLhMu&^H}4DEtH}4DLhMt:HpL9tEHX@t Hx4DHI9uHhLpt4DEtL4DLI/D Lfff.UHAWAVAUATSHH]M\HHEH^H+MIHGHGHHHtsH@H8L0IILH=H3DIHHL0O$L`LH3DHHL`L0H8H@E1E1HHvH)HHL)HH9M)IL9H~ L9I\M)tXIIHJTHItM9} K HHHKtH)K|IHIuHL\HH;EuHĨ[A\A]A^A_]K3D2DIHhf$H5GH RGH}LhclHPgAHGHuHPL oE1H5\HA\L2D HHw/D IALeMuFzIALhMIIPt H`1DLeMt6HEL9t!HX@t Hx1DHI9uH}LLe1DEuEuLhMu&^H}1DEtH}p1DLhMt:HpL9teHX@t HxE1DHI9uHhLp*1DEtLS1DHHH8Ht0HHH8Hu!L+D LIHHH8HtHHHx0DL+D fDUHAWAVAUATSPIII^I+HH9HFIGIGIHtFHH=H<H}h0DIM'M,MoLHuT0DMoM)uE1E1M)IMFI)MIILL)iKDIraLHtYIqH@tJ|K|K|IYHHt0HI4ĐH HI HLHLH HH[HuHHpMtPM)I41ffff.HtILH HI9uDH I ILHI9uMELL)ɉƒHs 1HuRyII4L)1ff.ILH ILHLILHLILHLHH9uHHt)LI)K Hff.ItH1HHuH[A\A]A^A_]L+D HI?Ht In.DHP)D UHAVSHPIHsG\HHEB.DHþH8.DHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]A@H5?DH}HH5GH诠H5HHHA H5gqDHHH;HHH :\HHMHlHUHuHuIIFIF0INIVIF@H]HEHtHH5B=DH}H认H5GH_H5jHH萟HA H5oDH跠HH;謕HHH 8\HHMH< HUHuHuIIFIF0INIVIF@H]HEHtHH*DH5C\HH;Eu LHP[A^]*DIH]HEHtHH)DL$D DUHAVSHPIHB\HHE)DHþH)DHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH];H5:DH}H^H55GHH5HH@HH HQH5lDH`HH;UHHH L6\HHMH#HUHuHuIIFIF0INIVIF@H]HEHtHH'DH@\HH;Eu LHP[A^]((DIH]HEHtHPHt'DLV"D fffff.UHAVSHPIHs@\HHEB'DHþH8'DHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]A:H58DH}HH5GH诙H5HHHH HQH5`jDHHH;HHH 3\HHMH!HUHuHuIIFIF0INIVIF@H]HEHtH5HY%DH~>\HH;Eu LHP[A^]%DIH]HEHtHH%DLD fffff.UHAVSHPIH>\HHE$DHþH$DHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]3H5=EH}H螝H6H5FHH脝H5l GH5H5@HHfHA H5gDH荙HH;肎HHH y1\HHMHrHUHuHuIIFIF0INIVIF@H]HEHtHH"DH <\HH;Eu LHP[A^]U#DIH]HEHtH}H"DLD ff.UHAVSHPIH;\HHEr"DHþHh"DHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]q1H5;EH}H.HV5H5AFHHH5 GHŔH5HHHA H5}eDHHH;HHH /\HHMHR HUHuHuIIFIF0INIVIF@H]HEHtHRHv DH9\HH;Eu LHP[A^] DIH]HEHtH H1 DLD ff.UHAVSHPIH39\HHE DHþHDHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]/H59EH}H辘H1H5FHH褘H5GHUH5`HH膓HH HQH5cDH覔HH;蛉HHH ,\HHMH+!HUHuHuIIFIF0INIVIF@H]HEHtHHDH$7\HH;Eu LHP[A^]nDIH]HEHtHHDLD @UHAVSHPIH6\HHEDHþHDHHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH],H56EH}HNHv0H5aFHH4H5GHH5HHHH HQH5`DH6HH;+HHH "*\HHMHHUHuHuIIFIF0INIVIF@H]HEHtHkHDH4\HH;Eu LHP[A^]DIH]HEHtH&HJDL,D @UHAVSIX4DHHLH[A^]IHDLD UHAVSH0HIH4\HHEH$\HII~j[H[\HIW)EHEEEaxisEIPH}HUHLnH]HEHtCt H{cDH[DEt H}LDHtIHe3\HH;Eu H0[A^]DHEt H}DHD f.UHAWAVAUATSHXIHuH}H3\HHEE11L)IžLHELILmH]I1LLBHEHEHHPHEH$LELMLHLMt LIHIH}HH}bLuML]M 1LLuffffff.HHUH}HALGMIMUIAJHMHL)HMH1fAnfp(MHUE1ffB8!3fB8!d3ff[[YYBBdfB8!\3fB8!d3 ff[[YYB\ Bd0IHuH}t.fB8!3fB8!d3ff[[YYBB\LJLHM9uQE1H}uHt{E4AIvJ +H9J H91fffff. D)W*YHHHH9rHHL9rffffff.1HL9rLLuHUHL9+fD1HL9{E1LHWE11MfHuHCHs1H 3H9vH H9s1HH(HOHHDJAHtIIH)1f8!f8!\[[YY\f8!Tf8!\ [[YYT \0HHuMt"f8!f8!\[[YY\HHH9t.ffff. W*YHHHH9rIM9LIM9f1IM9HEHH .\H H;MuHX[A\A]A^A_]DUHAWAVAUATSHLHMHIIIHW.\HHEIF I+FHtHI~QHzIEHHHLQHMHMIG I+GHHuILQHDIHhH$H5oFH FH}LhENW)PHDž`@NDH`(X)PH size 1.HH5Hor or siHH0H 1D tensHH(Hcalar orHH Hl or a sHHHt be nulHHHoint musHHHx_zero_pH@=AHFHuHPLQPE1H5W[H[LDIt$I+4$HHHLH0RIEI$HcHLHIEHsLWHMHIFIN H)HuSHI;EuJMtIOIW H)HH9H,\HH;EuHĘ[A\A]A^A_][DDIHhv#H5FH FH}Lh"sLH5FHPHHAHFHuHPL OE1H5[HC[LDHDIHh#H5 FH UFH}Lh%KH5 FHPHHAHsFHuHPL{NE1H5[H[LD IALeMIALhMIIALeMiIALhMIIPt H`DLeMt6HEL9t!HX@t HxDHI9uH}LLeDEuEuLhMu&^H}DEtH}DLhMt:HpL9t?HX@t HxDHI9uHhLpgDELLIALeMFIALhMnIIPt H`DLeMt6HEL9t!HX@t HxDHI9uH}LLeDEuEu LhMu-'H}DEtH}DLhMHpL9HX@t HxWDHI9uIPt H`2DLeMt6HEL9t!HX@t Hx DHI9uH}LLeDEuEuLhMu&^H}DEtH}DLhMt:HpL9tEHX@t HxDHI9uHhLp{DEtLDLP D LUHAWAVAUATSHHIHH`'\HHEH8H[[HHHLH@H8L DHEEL5$\M~LI@L8L DL%$\ILHDž(HDž HDžHDžDž0LjDHLH8TH3H# DHHHXLL8Lt H  DH DH5. \HH~ DH8 DH &\HH;EuHH[A\A]A^A_]L DHLL8Lt H DHi DHmlHelHH5 \HH DHH8 DH(D UHAWAVAUATSHHIHH@%\HHEH8H;[HHHLH@H8L DHEEL5"\M~LI@L8L DL%{"\ILHDž(HDž HDžHDžDž0LJDHLHRH3H DHHHVLL8Lt H DH DH5 \HH^ DH8~ DH#\HH;EuHH[A\A]A^A_], DHLL8Lt H o DHI DHMjHEjHH5s\HHDHH8 DHD UHAVSIX DHHLH[A^]IH DLD UHAVSH0HIH"\HHEH\HII~JJHC \HIW)EHEEEaxisEIPH}HUHL\H]HEHtCt H{C DH; DEt H}, DHtIHE"\HH;Eu H0[A^] DHEt H}DHD f.UHAWAVAUATSHXIHuH}H!\HHEE11L IžL~HEL~ILmH]I1LL|1HEHEHHPHEH$LELMLHLMt Le IHIH}N HH}BLuML]M 1LLuffffff.HHUH}HALGMIMUIAJHMHL)HMH1fAnfp(MHUE1ffB813fB81d3ff[[YYBBdfB81\3fB81d3 ff[[YYB\ Bd0IHuH}t.fB813fB81d3ff[[YYBB\LJLHM9uQE1H}uHt{E4AIvJ +H9J H91fffff. D)W*YHHHH9rHHL9rffffff.1HL9rLLuHUHL9+fD1HL9{E1LHWE11MfHuHCHs1H 3H9vH H9s1HH(HOHHDJAHtIIH)1f81f81\[[YY\f81Tf81\ [[YYT \0HHuMt"f81f81\[[YY\HHH9t.ffff. W*YHHHH9rIM9LIM9f1IM9HEHH \H H;MuHX[A\A]A^A_]DUHAVSIX4DHHLH[A^]IHDLC UHAVSIXDHHLH[A^]IHDLC UHAVSIXDHHLH[A^]IHDLiC UHAVSH0HIH\HHEH \HII~CH\HIW)EHEEEaxisEIPH}HUHLVH]HEHtCt H{DHDEt H}DHtIH\HH;Eu H0[A^]2DHEt H}DHrC f.UHAWAVAUATSHXIHuH}H\HHE1LxIľLxIžLxHMI1LL%+IHEHHPHEH$LELMLLHHt H1HELOHELCILIHEHEHLuHEH}tvJ1ff.HUHtGE1@HEB 8HEBLLLGCMIHEII9r ff.1HUHH;Ur~J HM1L}ffff.HMHtG1DA1LLLCMHHELmH9rHMHH;Mr1HMHH;MrHEHH \H H;MuHX[A\A]A^A_]DfUHAVSIXdDHHLH[A^]IH7DLC UHAVSH0HIH@\HHEHJ \HII~@H;[HIW)EHEEEaxisEIPH}HUHL7SH]HEHtCt H{CHCEt H}|CHtIH\HH;Eu H0[A^]CHEt H}@CH"C f.UHAWAVAUATSHXIHuH}H5\HHE1L\uIľLLuIžLCEuEu LhMu-?H}CEtH} CLhMHpL9HX@t HxCHI9uIPt H`CH0Ht H8CLeMt6HEL9t!HX@t HxtCHI9uH}LLeZCEuEuLhMu&^H}7CEtH}(CLhMt:HpL9tCHX@t HxCHI9uHhLpCEKH(:LIALeMIALhMIIALeMIIALhMqILIPt H`7CLeMt6HEL9t!HX@t HxCHI9uH}LLeCEuEu LhMu-@H}CEtH}CLhMHpL9HX@t HxCHI9uIPt H`kCLeMt6HEL9t!HX@t HxFCHI9uH}LLe,CEuEuLhMu&^H} CEtH}CLhMt:HpL9t?HX@t HxCHI9uHhLpCELLIALeMuFzIALhMIIPt H`RCLeMt6HEL9t!HX@t 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UHAVSHIHt5H3LHsLH{ Ht H{(豧?H[A^]饧?[A^]@UHAWAVAUATSH(MLEIIHHCHCHLHMHILHuILIH MMMIHEIHHE1L}HMLuE1ffff.I4$LHCH;CtHHHHCIIM9HH!IIM9HMHLuLuL}I9H([A\A]A^A_]IH;Hu!IH;Hu IH;Ht H{S?L5? ffff.UHAWAVAUATSH(IHMWHHEMfMM~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 WH H;MuiH([A\A]A^A_]H}LbHuHFHLfI7IHHtII7I~=IFHEH WH H;Mt?ffffff.UHAWAVAUATSPIHIH@C?IM/I_AGMI$IE0IE(IE H{H+;tXLuI IILH=u_?IHMLa(La KIIF0H3H[H)H~LHw?IMf(IF8AGLH[A\A]A^A_]Lɠ? II}Ht HEHx(f?IH}V?L8? UHAWAVAUATSHH]WHHEIIF0xuFII^PILHH,WHH;EuHHHHĈ[A\A]A^A_]g??IHh肏H5@H ]BH}LhI0H5@H@H @HPMgiAH7@HuHPL E1H5WHAWL葢? IALeMuFzIALhMIIPt H`?LeMt6HEL9t!HX@t Hxʡ?HI9uH}LLe谡?EuEuLhMu&^H}荡?EtH}~?LhMt:HpL9tEHX@t HxS?HI9uHhLp8?EtLa?L ? LUHAWAVAUATSHHLMLEIIHuHGHGH}HLLWHELLHILeLuM>IFL)L9H]sMMvL袠?IM)MK\5M~LLL=?LuM.I^MfMH]tLU?HLmPH}EHEHMHHEL1HM1LmE1E1@HEL8MMfIFI9t)AA$IFHH9]fffff.I6I)LHH)ILH?H9wHH9HBHHjHuI腟?IHuMAC&KT&M~LmLIIL?LLmLHEL0IIVMnHLmtH?HH9]LmHMHH9MHH[A\A]A^A_]L)? IHEH8Ht Hx?L裙? ff.UHAWAVAUATSH(IHWHHEMfMM~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 ~WH H;MuiH([A\A]A^A_]H}LbHuHFHLfI7IHHtII7I~>6IFHEH WH H;Mtk?ffffff.UHAWAVAUATSPIHIH@賜?IM'I_AGMuID$0ID$(ID$ I^I+tPHEI HxbHg?IHELh(Lh LHX0I6I^H)H~LH?HEILh(H@8AGLH[A\A]A^A_]LA? HI<$Ht HEHx(ޛ?IH}Λ?H谖? UHAWAVAUATSHHʹWHHEIIF0xuEII^PILnH WH H;MuH@HHHĈ[A\A]A^A_]؛?V?IHhH5x @H BH}LhI0H5 @H @H  @HPMaAH @HuHPL{E1H5WH~WL? IALeMuFzIALhMIIPt H``?LeMt6HEL9t!HX@t Hx;?HI9uH}LLe!?EuEuLhMu&^H}?EtH}?LhMt:HpL9tEHX@t Hxę?HI9uHhLp詙?EtLҙ?L~? LؐUHAWAVATSHIHIH WH HMHuH IIHt&1HrWHH;EHH[A\A^A_]ÿ@+?HI4$H{ 5?HC8HEHCHHCIIHHtII7HI~2IFHWHH;E{??IH褘?L膓? fffff.UHAWAVAUATSH(LMLEIIHIIFIFILLILL ILIL MMHEIHH @HEIHH@HE1LeHuHMIE1ffffff.H3LI~I;~t ݕ?IFIIM9L IIM9HuHHMHMLeI9H([A\A]A^A_]IM&MuOIM&Mu BIM&Mt7IFL9t8HX@t Hx?HI9uI>Mf?L? Lf.UHAWAVAUATSHXHMH9HGHEH)HBHEHE1H}IHUHufL9[Bt*@tJL*E>AuDE>AtI^H9ILGMt|At&HEJ|(@HM@uu%HEJ4(DHEJ<(@HM@utJ4*H}HuL贗?HUHMutHuH}yIDH9Mtb@tJ<*AuHEJ4(HEJ<(AtHEJt(LIHA?HLHUty3ffffff.H9wJD*IIHH;EHu1HX[A\A]A^A_]ff.UHAWAVAUATSH(IHIH'WHHEMt$MtxLmLeM|$LeLm IH;HsIV IN(MtIMHufI~ Iv(HHKMtoM~IFHu`Mt$MIHu`@d?HIuH{ HC8HCHLsII$HHt@I$I7:MLeLmIHt1H%WHH;Eu6HH([A\A]A^A_]HI|$-ID$HWHH;EtE?IH誓?L茎? @UHAWAVSPIIMtLI7LIwLMw MtCIG(L9t.fHX@t HxI?HI9uI H[A^A_]LMw($?LH[A^A_]?UHAVSHPIH#WHHE?HþH?HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H}11)HQ7H52?HH H5BHPH5[1DHHH HQH5?HHH;HHH WHHMHHUHuHuIIFIF0INIVIF@H]HEHtHLH?HWHH;Eu LHP[A^]i?IH]HEHtHLH赐?L藋? ffffff.UHAVSHPIHWHHE肐?HþHx?HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H}11 H4H5¡?HHH5BHH5.DHHH HQH5?H1HH;&HHH WHHMHHUHuHuIIFIF0INIVIF@H]HEHtHfJH芎?HWHH;Eu LHP[A^]?IH]HEHtH!JHE?L'? ffffff.UHAVSHPIHCWHHE?HþH?HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H}11I Hq2H5R?HHH5BHpH5{,DHHA H5(?HHH;HHH WHHMH HUHuHuIIFIF0INIVIF@H]HEHtHGH!?HFWHH;Eu LHP[A^]萌?IH]HEHtHGH܋?L辆? fDUHAWAVAUATSHIHIHԤWHHE1HHHqL#LkLL)HHX H+XHW)`HDžpHHLLHH=H<H"?IL`M4LpLH?LhMHM9|M9HHIpL)HHLH:tHHufff.HHHHHuE1E1E1M9tf.M)I<$IL轌HxML`LhLL)HH9}0I<uNIIM9uMHM9-H +BHH zH &BHHULH}@H:t*Hzt4Hzt?HztJH I9uSf.H1HH2HzuH1HHrHzuH1HHrHzuH1HHrH I9uW)@HDžPLL)tUIILH=?HHHH@JHPM)M~HLL胊?LHHH@1H谱HHAHHHHHH@Ht HHX?H`Ht Hh@?HeWHH;EuHH[A\A]A^A_]覈?$?IH`tH5SBH BH}L`(H@AḦ́@HuH@LeE1H5koWHkWL?H`τ?萇?IH(-tH5BH BH}L(F*H.AHBHuHLE1H5nWHkWLX? H@>? HALeMuFzHAL(MHHt H 訆?LeMt6HEL9t!Lh@t Hx胆?LM9uH}LLei?EuEuL(Mu&^H}F?EtH}7?L(Mt:H0L9tqLh@t Hx ?LM9uH(L0?Eu%H@HH`HL?H@HtaLHALeMuFzHAL`MHH@t HP]?LeMt6HEL9t!Lh@t Hx8?LM9uH}LLe?EuEuL`Mu&^H}?EtH}?L`Mt:HhL9tILh@t Hx?LM9uH`Lh覄?EL˄?Hw? LHH@H$HH`HuOYHH@HqHHA?H`Hu",HH`HuHH`Ht Hh?H~? UHAWAVAUATSHIH WHHEW)EHPHMI>tDHBH yO@1AMLpHǜWHH;ELH[A\A]A^A_]ILeHEL8LxPM,$Ml$PM9t{I|$0HPhHtAxu;IL*H~T1LLY?HL IIH9|*I|$HI|$0HPHLLH襄?IHWHH;E4M?IIIHtCt H{蜂?H蔂?Lv}? fffff.UHAVSIht?HHWHHH{LHHPL0HYHH[A^]IH?L}? fUHAWAVAUATSHIHWHHEID$ID$I$W)EHEEEaxesEHhHUL`HhHDžhHtCt H{v?Hn?Et H}_?Hu'HWHH;EuHĘ[A\A]A^A_]Á?A?IHhmH5BH BH}LhۺW)PHDž`0?H`(~T)PHte valueHHHttributeHHH'axes' aHHHInvalid HHHMissing/H@&LHBHuHPL DžLH5 hWH=IL7q?H N4L0HL q?L(H{I}PHHH HIGIO H)GH H`H1WH*YH,HHH9r Iw I+w]HH@^H@HHHH)HBs JHHH:IHHLq?HHHHXHHHL`4IH¬HLLHq?IG I+GHhH+`H9IH`H HLL L`HLHHeH Ht H(9o?LH@H111H)Ho?uo? o?IH`[H51BH sBH}L`褨H@AHk@HuH@LKE1H5QVWHRWLn?bH`k?Qvn?IH [H5BH ߂BH}L HAHCBHuHL跪E1H5UWHQWL>n?m?IH ZH5BH \BH}L 荧W)HDž@m?H(pS)Hs input.HH.Hided as HH(H be provHH Hzes mustHHHes or siHHH of scalHHHOnly oneH@6AHEBHuHL觩E1H5TWHPWL.m?l?IH YH5 BH LBH}L }W)HDž l?H(hT)Ht index.HHHroi inpuHHHInvalid H@AHHBHuHLϨE1H5SWHPWLVl? l?IHXH52BH tBH}L襥W)HDž@k?H(hQ)Has inputHH(Hrovided HH HUST be pHHH sizes MHHHcales orHHHEither sHf@0.AHzBHuHL˧E1H5RWHOWLRk?H 5h?j?IHWH5BH _BH}L萤W)HDž@j?H(sS)Hs input.HH.Hided as HH(H be provHH Hzes mustHHHes or siHHH of scalHHHOnly oneH@6AHHBHuHL誦E1H5QWHMWL1j?i?IHVH5 ~BH O~BH}L耣W)HDžPi?HH([T)H5BDHk?CDAH~BHuHLE1H5PWHMWLki? IALeMIALM I!IALeMPIALMIIt Hh?LeMt6HEL9t!HX@t Hxbh?HI9uH}LLeHh?EuEu LMu-iH}"h?EtH}h?LMAHL9WHX@t Hxg?HI9uIALeMPIALMIIt Hg?LeMt6HEL9t!HX@t Hx[g?HI9uH}LLeAg?EuEu LMu-bH}g?EtH} g?LM:HL9PHX@t Hxf?HI9uIALeM~IAL MIIt Hyf?LeMt6HEL9t!HX@t HxTf?HI9uH}LLe:f?EuEuLMu&^H}f?EtH}f?LMt:HL9tTHX@t Hxe?HI9uHLe?EiLe?H H]uLIALeMPIAL MIIt HDe?LeMt6HEL9t!HX@t Hxe?HI9uH}LLee?EuEu L Mu-.H}d?EtH}d?L MH(L9HX@t Hxd?HI9uIALeMFIAL MnIIt H=d?LeMt6HEL9t!HX@t Hxd?HI9uH}LLec?EuEu L Mu-'H}c?EtH}c?L MH(L9HX@t Hxc?HI9uIt Hqc?LeMt6HEL9t!HX@t HxLc?HI9uH}LLe2c?EuEuL Mu&^H}c?EtH}c?L Mt:H(L9tTHX@t Hxb?HI9uH L(b?ELb?H@H~LIALeMuFzIAL`MII@t HPCb?LeMt6HEL9t!HX@t Hxb?HI9uH}LLeb?EuEuL`Mu&^H}a?EtH}a?L`Mt:HhL9tIHX@t Hxa?HI9uH`Lha?ELa?L]\? L>H1MH fox HfnHfB~IIM9|K1fHHfox Hfnf~DHTHfox Hfnf~HI9uIMkffff.IM94 H]L0@HH7WI*I WH*H*IEBH0EIx HHH@0H$HHLHLLHHfn #(Q.0HHE8Hx HHHHLP0HHHHH9|HJHH7fnx@HLLE0uIHy1HfnLfB~IHHHI9LID?H}HHL8ff0HDž@MtHM9WN4LD?H0NI4H9K HpH4>I4H91Ht%J4A4HHLIHt H7?HHt H6?H0Ht H86?H6?H}Ht H}6?H}Ht H}6?HOWHH;ELHIHt H|6?HHIPE1HLsIMHfnxHMH0HL0ILH LHhH@HHE1foMx HfBnHfB~LHLHtIL+,LFLDIIO;&|-HDL*LIHIM9LLlIM9LfLIHLIHIQDɃHs1HI)1foMx HfBnf~foMx HfBnf~DfoMx HfBnf~DfoMx HfBnf~D HI9uHt*HHfoMx HfBnf~HHuLIHt H44?L0M=H8L9t'HXHxHt Hx4?HI9uH0LL8m4?3?H JWHHH5jWHSWH4?93?H JWHHH5:WH#WH3? H}0? H00?H0?H00?H0?Hv0?H0h0?HZ0?HL0?H>0? IHH IHH IH0HIHHVH~2?L0MF-IHHY7IH0HZIH0H@8IHHu^hIHHtAH1?HHu5?IHHt H1?HHuHHt H1?H0HH8IH}H%IH}HIH}HH}A1?L#,? IL0MtMH8L9t3HXHxHt Hx1?HI9uH0L80?LIH0?H}Ht H}0?H}HfL+? f.UHAWAVAUATSH8H `HPIHLHHvIWHHEW)EHE)EHELLLy踅IHI t0HMf?L? LUHAWAVAUATSHIIIuMeI)MIIGHH> H?MuI)LHHH9w`II9LBMLEHuI9vKy?HH5eO?H?HWHHH5VHgVH?LEHuIJ<?HHuLEN4ABN|M~HLI?LI]M}MuHtHH[A\A]A^A_]?H[A\A]A^A_]E11L?IH?Lt? fff.UHAWAVAUATSPfnE1W.If~LELJMnMf~MIMMLhM9r 1LIIIJHHHtwLtGHCL9tL9r1IHL9uPC.uHHu6ff.HCL9tH!L9uC.uHHu(?HECHCHCL{HIFHx WH*HHH WH*XLHDH Mx.I*AN t2Y.w)MIJHH*XAN uKL-Ir&IU1LI!IJHH ^f:  zP(\H,HH1H,.HCH9HCL MnIELt$M9r 1LIIIJHtHH PI!IJHuIFIVHI^JHHt)H@IMLtL9r 1IHH!HIHIFHHHH[A\A]A^A_]IH?L> ffffff.UHAWAVAUATSPfnE1W.If~LELJMnMf~MIMLLM9r 1LIHIHHL Mt{LtGID$L9tL9r1IHH9uSAD$.u%M$$Mu6ID$L9tH!H9uAD$.uM$$Mu@?IEAD$ID$0ID$(ID$ ID$AD$8?M|$I$IFHx WH*HHH WH*XIT$LHDH Mx.I*AN t2Y.w)IIJHH*XAN uKL-IHUr&IU1LH!IHHwH ^f:  lP(\H,HH1H,.HCH9HCL MnIELt%M9r 1LIIIJHtHI $RI!IJHuIFIVI$MfJI$Ht)H@IMLtL9r 1IHH!HIL IFILH[A\A]A^A_]II|$(Htf.H?HHuHEH8HHtt?Ll?LN> fDUHAWAVAUATSPfnE1W.If~LELJMnMf~MIMMLhM9r 1LIIIJHHHtwLtGHCL9tL9r1IHL9uPC.uHHu6ff.HCL9tH!L9uC.uHHu(s>HECHCHCL{HIFHx WH*HHH WH*XLHDH Mx.I*AN t2Y.w)MIJHH*XAN uKL-Ir&IU1LI!IJHH ^f:  ZP(\H,HH1H,.HCH9HCLMnIELt$M9r 1LIIIJHtHH PI!IJHuIFIVHI^JHHt)H@IMLtL9r 1IHH!HIHIFHHHH[A\A]A^A_]IH>Lw> ffffff.UHAWAVAUATSHIIH}HWHHELILdLeLMMoMMM9r 1LIIHJHHHMtIfHCL9uL9cu#efff.L9r1IHL9uQHHuG@HCL9t HL!L9u/L9u L9cHHuMM!HJHcH^HL9IO1HHHIIPH9HNHHHHA ^AY AQH9HHNWHHHH^AYH^H9HNXHHHXHAI^AY XHH9HNHHHHAQ ^AYXUHuEHWHH;Eu,H[A\A]A^A_]HuHWHH;Et>DUHAWAVSPHIHuM~L9w2HCHt H>HM~L9vLHH[A^A_]Ir*IGLIFHy"HHH H*X1IFHxH*A^F f:  ~P(\H,HH1H,.HC„t0Hr2HHȁAHH9HBL9:H>H9HBL9 H[A^A_]fUHAVSIHMtJLH=lJ<>H;HHt>LsMFDIs-1HuvH;HHt>HC[A^]LH)1ff.H3HH3HDH3HDH3HDHH9uHtHHHHHuLSMHsMJMtM9r1LIIM!HJ4I2HaMfHFL9r1IHL9uII2Hu0f.H H<t]HHHt$FHHH.Gu z HHHuH I H H H H H HH0I2HnLIII2HSfffff.HFL!L9uII2Hu}H H<tVH>HHt$FHHH.Au z H9HHuH I H H H H H HH0I2HuLIII2Hj[A^]ÿ>HH5??H>HWHHH5VHVH>IH|>L(> UHAWAVSPHIHuM~L9w2HCHt H>HM~L9vLHH[A^A_]Ir*IGLIFHy"HHH H*X1IFHxH*A^F f:  nP(\H,HH1H,.HC„t0Hr2HHȁAHH9HBL9:H>H9HBL9 H[A^A_]fUHAVSIHMtJLH=lJ<>H;HHt>LsMFDIs-1HuvH;HHt>HC[A^]LH)1ff.H3HH3HDH3HDH3HDHH9uHtHHHHHuLSMHsMJMtM9r1LIIM!HJ4I2HaMfHFL9r1IHL9uII2Hu0f.H H<t]HHHt$FHHH.Gu z HHHuH I H H H H H HH0I2HnLIII2HSfffff.HFL!L9uII2Hu}H H<tVH>HHt$FHHH.Au z H9HHuH I H H H H H HH0I2HuLIII2Hj[A^]ÿ>HH5{;?H>H WHHH5VH}VH>IHl>L> UHAWAVAUATSHHUIIIWHHJЃHs 1HuOcH)1ff.I7HI7HDI7HDI7HDHH9uHtHIHHHuIGMoIGMt:@L9eI$IEAD$AEMuLLVIMMuL;eIGHEMw@I$MIvLHL9r 1HIHIHHHHLt2fHAH9tL9r1IHH9ueH9Y#H HuQHAH9tH!H9u?H9YH Hu+ff.HH!IHHwf.H} J>II$IL$IMIEI]IEIGHxWH*'ffffff.HHH WH*XLHDH Mx4WI*AO t8Y.w/H]IHHfWH*XAO uK 6IrIV1L @^f: (6P\H,H HH1H,.HCH9HCLrMwIFLt*L9r1HIHDIHHt$HIM\H!IHHufff.HUHIEL*HIEHt)H@INLtL9r 1IHH!HIL(IGIL;eZI]L`>IHuH[A\A]A^A_]Hk>DI]L0>IHuq> HI>HOHL>H> fffff.UHAWAVSPIIIvIvI_HHKHIH9r 1HHIIJHt_H8HtWHt2fHGH9r1HHL9u:H9wH?Hu&fDHGH!L9uH9wqH?HuIGHxH*HHH H*XHH؃H HxH*AO t Y.H*XAO uH Hr)HS1H II!IJHhH ^f:  nP(\H,HH1H,.HCH9HCLI_MFHCHt'I9r 1LHIIJHtHIL0WI!IJHuIGIIGMwIJIHt*H@HKHtH9r1HH1H!IL4IGLHH[A^A_]@UHAWAVSPHIHuM~L9w2HCHt H5>HM~L9vLHH[A^A_]Ir*IGLIFHy"HHH H*X1IFHxH*A^F f:  ށP(\H,HH1H,.HC„t0Hr2HHȁAHH9HBL9:HY>H9HBL9 H[A^A_]fUHAWAVSPIIMtJLH=]J<Y>I?IHt=>MwMFDIs-1HusI?IHt >IGLH)1I7HI7HDI7HDI7HDHH9uHtHIHHHuMWMIwMJMtM9r1LIIM!IJ4I2HOMfHFL9r1IHL9uII2Huf.IH<t[HHHt"HVH@HHH;Qu HHHuHI IH H HIHH0I2HpLIII2HUHFL!L9uII2Hu{IH<tTHHHt"HVH@HHH;Qu HHHuHI IH H HIHH0I2HuLIII2HlH[A^A_]ÿ >HH50?HS>HNWHHH5(VHVH>IH>L> fff.UHAWAVAUATSPIL>MnMIML;MM9r 1LIIIJHthHHt`Lt6fDHCL9tL9r1IHL9u:L9{HHu&HCL9tH!L9uL9{HHu >HL{CL{HIFHxH*HHH H*XLHDH Mx.I*AN t2Y.w)MIJHH*XAN uKL-Ir)IU1L IM!IJH1H ^f:  |P(\H,HH1H,.HCH9HCLMnIELt$M9r 1LIIIJHtHH PI!IJHuIFIVHI^JHHt)H@IMLtL9r 1IHH!HIHIFHHH[A\A]A^A_]IH!>L> ff.UHAWAVAUATSH8LLHIIHHVHHEH8HVHHHHH@H8H;>HEEL-sVMuLI@L8HA>H6VHHHDž(HDž HDžHDžDž0L>HLH+L>HLH+HE LMLEH$HHHH(LLHB0LL8Ht H t>HN>H5VHH>H8>HjVHH;EuLH8[A\A]A^A_]>ILL8Ht H >H>HCHCIH5VHHB>IH8]>L> ffffff.UHAWAVAUATSPMLEIIIHLW>HLH)*IuH>L5>HLH*HEH0H>L>HLH)HEH0H>H[A\A]A^A_]HBHBHBHBHBHB@UHAWAVSHHHVHHE0>IHPH##H}HP#IPHufHVHIIHP"HVHIH[VHH;EuLHĘ[A^A_]>IIHu'IHu3IIH9u?HP BI>IHtI>IIH9tHtHP(ILH`H9uHP Ht HP(ILe>LG> ffffff.UHAWAVAUATSH(IIHWVHHE1HHwWIM+I_ I+_W)`HDžptCIILH={H>H`N4LpHH>LhW)@HDžPAIH@HEHI}'$HH+HupMMOIO L)Ht7IH`H1WI*YH,HHH9rL`LLHALL-VHA LVHfAIHMIGIO H)t6IH`H1WH*YH,HHH9rL`LLLHeH@Ht HH>H`Ht Hh>H&VHH;E LH([A\A]A^A_]AbAHUHH{IM+HH@HHH)HLH)GH@WMH@<HLMg M+gW) HDž0tCMILH>=IL>H N4L0HL>L(H{I}PHHH H@IGIO H)GH H`H1WH*YH,HHH9r Iw I+w]HH@>LH@HHHH)HBs JHHH蓧IHHL>HHHHXHHHL`荡IHHLLHd>IG I+GHhH+`H9IH`H HL`L L`HLHHeH Ht H(>LH@H111H)H>ul>>IH`H5AH AH}L`H@FAH?HuH@L+E1H51VHbVL>bH`>QV>IH H5}AH AH}L HEAH#AHuHLE1H5VHξVL>>IH pH5AH AH}L mW)HDž@v>H(PS)Hs input.HH.Hided as HH(H be provHH Hzes mustHHHes or siHHH of scalHHHOnly oneH@6AH%AHuHLE1H5VHVL>>IH `H5AH zAH}L ]W)HDž f>H(pS)Ht index.HHHroi inpuHHHInvalid H@AH(AHuHLE1H5VHVL6>>IHH5AH AH}LW)HDž@>H(HnP)Has inputHH(Hrovided HH HUST be pHHH sizes MHHHcales orHHHEither sHf@0.AHZAHuHLE1H5VHVL2>H >>IHsH5AH AH}LpW)HDž@y>H(SS)Hs input.HH.Hided as HH(H be provHH Hzes mustHHHes or siHHH of scalHHHOnly oneH@6AH(AHuHLE1H5VHVL>>IHcH5AH }AH}L`W)HDžPi>HH(pS)H5ADH>CDAHAHuHLE1H5ʽVHVLK> IALeMIALM I!IALeMPIALMIIt Hg>LeMt6HEL9t!HX@t HxB>HI9uH}LLe(>EuEu LMu-iH}>EtH}>LMAHL9WHX@t Hx>HI9uIALeMPIALMIIt H`>LeMt6HEL9t!HX@t Hx;>HI9uH}LLe!>EuEu LMu-bH}>EtH}>LM:HL9PHX@t Hx>HI9uIALeM~IAL MIIt HY>LeMt6HEL9t!HX@t Hx4>HI9uH}LLe>EuEuLMu&^H}>EtH}>LMt:HL9tTHX@t Hx>HI9uHL>EiL>H H]uLIALeMPIAL MIIt H$>LeMt6HEL9t!HX@t Hx>HI9uH}LLe>EuEu L Mu-.H}>EtH}>L MH(L9HX@t Hx}>HI9uIALeMFIAL MnIIt H>LeMt6HEL9t!HX@t Hx>HI9uH}LLe>EuEu L Mu-'H}>EtH}>L MH(L9HX@t Hxv>HI9uIt HQ>LeMt6HEL9t!HX@t Hx,>HI9uH}LLe>EuEuL Mu&^H}>EtH}>L Mt:H(L9tTHX@t Hx>HI9uH L(>EL>H@H~LIALeMuFzIAL`MII@t HP#>LeMt6HEL9t!HX@t Hx>HI9uH}LLe>EuEuL`Mu&^H}>EtH}>L`Mt:HhL9tIHX@t Hx>HI9uH`Lhl>EL>L=> L>H@HuIH@Ht HH>H`Ht Hh>L> UHAWAVAUATSHMLHIHIHVHHE1LCHHd IEI+EHK H+KH9 1LLDHLxL1 HLHKLs I)LHHLHRL)HH9FHHBH+L9QMMIuL)yIIH@11u IH;t1H@H9r@7HBPzH HHHHDHHHA`H9HPHEHHHHLH9HPHEHHAH AHDH AHHHVHH;EHMHL?LPL#LcP6LLH>LIHKVHH;ELHĨ[A\A]A^A_]IHHLϸHHHHQIVHLHLHHHTHAH AHDȾI HHHAHHHIHIyHHL9HHIHxW)HHLHH>HAH A1AI蔶HHzHHCHdHHPHw>HVHH;ESHHAHHHALMIHIpHHHIHhxALA$E$A$tE$H;IHܮLHHHtH`H9HPH0HDž0HEHHvLHEMHHHHHIHDHDHPH@ HDH|IHIHHHH`H9HPH`LpHVHHHLH@HpLL>HDžDžH2VLkLH@HpL>L5VILHDž`HDžXHDžPHDžHDžhH5AH 1HH WAH5@AHDHTH^ HLHHBt HI>LHpLHt HX>L>H5L>HH0HPLLd$(H\$ Ll$HHD$AljD$AƉ$HHHHLhLxt裡H0I9LtHHP( HP HuHuHPHHH/HpHuHPLpLl$(LuLt$ AĉD$D$D$HH$HHHHMLH}I9t:Ht;HP(H}I9u5HP LHVHH;E5>HP H}I9tHLHP(HBVHH;EHDž`H@H`HPH@H\$(HHD$ Ll$Ld$AƉD$HH$HHLHLLx#H`H9Lt'HtHP(IHHHP IHHw >IH软H5GAH OAHL^H-AHƿ?HHL[E1H5aVHVL>>IH4H5AH AHL`.W)HDž07>H(IP)Hbe same.HH'Hshould bHH H tensor HHHd outputHHHinput anHHHRank of H@/AHyAHHLSE1H5YVHVL> HAL8MHALM/H,HAL8MC}HALMHHt H>L8Mt?H@L9t$HX@t Hx>HI9uH8LL@> uu&LMu6OH0Z>tHE>LMHL9-HX@t Hx>HI9uHt H>L8Mt?H@L9t$HX@t Hx辿>HI9uH8LL@螿> uu#LMu/gH0r>tH]>LMt:HL9tMHX@t Hx2>HI9uHL>E$L<>H> LHHӾ>HHHH`H9uHP HH{HaHP(HHWuHIIH,Ct H{6>H.>HH/HLHpLHt HX>LƼ>H5VHHU>Ly>H蟸> HH}HEH9t?dHH}I9uHP H}HEH9t?HtHP(H}HEH9u'HP H8> HH0I9tHtJHP(H > HHHt#HHCHuHHPH>Hȷ> UHAWAVAUATSH(HDUHVHHEHHHL1LyM)LHIIxH)HH9M7HLf~xHffEHEHHH=HLL>HEHL$LeHL>LeffEHEL޻>ILmMdLeLLpL»>LeHCHHhHH<虻>IIJ4HH聻>HHH?Ht@)HHHJ!IDLpI Lfnx|H}H  HH0NL8HfLyfHDž Mt=M9N4L>HNLHHH01"HHH"HHHHL0HD,xLpDMI)HH@HE1E1Hfffff.MO Iu:1HL Dx HB HB III9| HHJ41LMDx HBTHTLDx HHI9uIHHHjfff.II94j L9ff0HDž@MtHM9"N4L?>H0NL8HfLyfHDž MtDM9N4L۳>HNLHLyffHDžMt=M9vN4Lw>HNLHHH01V HHH> HHH& HHHHH H0HpHHD,xHxLqHH@HE11fMHHxHHHHpLI4H9sz1Ht%J4A4HHHL4L HLZ>LML#H01E1HfHL*HHH9|L9ff0HDž@MtHM9N4L٪>H0NL8HfLyfHDž MtDM9N4Lu>HNLHLyffHDžMtHM9:N4L>HNLHfLyfHDžMt=M9N4L譩>HNLHHH01HHHtHHH\HHHDHHHH@HH0HPHHpHHD,xHAHHH@HE11HHff.HHh\HAHOHPHHxLaEAMM)1LfMHHpL,Lx1LDL4MIuCE1MN4Dx HB4HB4IHH9|MHN,E1ffffff.N$MDx HB4CtJtLDx H CLIM9uMMLLMKHH9&HHH;HhHH;@HHqHHHHu4>HHHHt HƦ>HHt H讦>H0Ht H8薦>H芦>H}Ht H}x>H}HH}b>HVHH;E ٦>HHHHt H#>HHAHE1LHHHfnxD,HMH0M>IHH LHHhH IHHE1HDMx HB B LLHfDLLI4$N+,IAHDLHI;|HDL.MIHNlIHI9HsHHHHHHHBӃHs 1HusH)1DMx HBDMx HBDDMx HBDDMx HBD HH9uHt%HHDMx HB HHuHHHt H>L0M[H8L9t.HXHxHt Hxϣ>HI9uH0HLL8 趣>H {VHHH55VHVHΣ>9膣>H KVHHH5VHVH螣> H}臠> H0y>Hk>H0]>HO>HA>H03>H%>H>H >IHH IHH IH0HIHHVHI>L0MF-IHHY7IH0HeIH0HKCIHHu^hIHHtAH赡>HHu5?IHHt H茡>HHuHHt Hh>H0HH8IH}H%IH}HIH}HH} >L> IL0MtXH8L9t>ff.HXHxHt HxƠ>HI9uH0L8諠>LIH菠>H}Ht H}}>H}H[LR> f.UHAWAVAUATSH(XHHIHHH=VHHEW)EHE)EHELhLLH LyuIHI t0HLE8H HpH HHHHL<HhIHHHHHMHHyH)HHHHH8H*H*H@LHDEXE(M0UMEIx HH8HIWH*`1HuL]HH@0HMH $IMLHMLELM|HEH;EsHHEf.H}H|p`ME|.MLwH|.MPLHFƋ|fnH,LHH9LE8LL@~HHPHH9~HWH*\( -{P(TH|WH*\THH9uH?H?HHHHpHHHH HH; HEHWH*BLBDEXE(E0EMEIx H*>H VHHH5sVH\VH >HMHHyH)HHHHhHhWH*HHWH*H@LHDEEMUMEIx HHHHHH0WH*(H HH@HhH H4HpHH`HHHHHXHL$I̻HuLEfHH@0HMH $IMLHMLELM|HEH;EsHHEf.H}H|@(M|.H|MIG.JPLHFE|fnH,H0H9HNIDHH9HNIDILWH*\( xP(THXD|WH*\TH`DH9uHXD?H`D?H;hHU8HH@WH*HEHLDEEEEMEHz HHDޘ>H VHHH5]VHFVH> HH H](L] HHHH8HLhMLHUHXD,HL*HEHHL*HH0LIHHH@K L,LHHLIHHJHpHH@IHIILEWJHH1HHHIH8HH`1fH(HRL`E1fDHh}8HXBD.A.E1DHBD.DA.DHpJH}Ht H}*>H}Ht H}>H=VHH;EuH([A\A]A^A_]联>IHPHpuPIHPHpu7xHIH}HIHPHptCHt9HHHpPHPHRIHPHpuHt#HHCHuHHPH >H}HuH}HuL> H}>H}HtH}>L͍> DUHAWAVAUATSH8IHuHHVHHEHCMcHJHfHKHSH HE11H+2H1HufHS8I*Hs H{(WH*HJ*BHB4H;L ẺEMUuMHz HHHMH $HuHUHMLELMP0{0t-AW.w%HCHJHWH*.AE1HC HK@H *tPB. EEHy HHHuHUP0HKH JH9|HHHUEuHy1HSHJHHEHJIHCHJI9H[VHH;EuH8[A\A]A^A_]袑> >H VHHH5zVHwVH8>UHAWAVSHHHVHHE0轐>IHPHCH}HP3IPHuHVHIIHPHVHIH{VHH;EuLHĘ[A^A_]>IIHu'IHu3IIH9u?HP BI>IHtI֏>IIH9tHtHP(ILH`H9uHP Ht HP(IL腏>Lg> ffffff.UHAWAVAUATSH(IIHwVHHE1HHIM+I_ I+_W)`HDžptCIILH={H>H`N4LpHH>LhW)@HDžPAIH@HEHI}GHH+HupMMOIO L)Ht7IH`H1WI*YH,HHH9rL`LLHALLMHA L;HfAIHMIGIO H)t6IH`H1WH*YH,HHH9rL`LLLHeH@Ht HH9>H`Ht Hh!>HFVHH;E LH([A\A]A^A_]AbAH3HH{IM+HH@HHH)HLH)GH@wH@<HLMg M+gW) HDž0tCMILH>=IL7>H N4L0HL >L(H{I}PHHH HIGIO H)GH H`H1WH*YH,HHH9r Iw I+w]HH@^H@HHHH)HBs JHHHUIHHL>HHHHXHHHL`OIHHLLH脌>IG I+GHhH+`H9IH`H HL:L L`HLHHeH Ht H(9>LH@H111H)H>u茊> >IH`vH51AH AH}L`H@AH?HuH@LKE1H5QqVHmVL҉>bH`赆>Qv>IH vH5AH oAH}L HAHCAHuHLE1H5pVHlVL>>>IH uH5AH AH}L W)HDž@薈>H(pR)Hs input.HH.Hided as HH(H be provHH Hzes mustHHHes or siHHH of scalHHHOnly oneH@6AHEAHuHLE1H5oVHkVL.>>IH tH5 AH ܣAH}L }W)HDž 膇>H(S)Ht index.HHHroi inpuHHHInvalid H@AHHAHuHLE1H5nVHkVLV> >IHsH52AH AH}LW)HDž@讆>H(hP)Has inputHH(Hrovided HH HUST be pHHH sizes MHHHcales orHHHEither sHf@0.AHzAHuHLE1H5mVHjVLR>H 5>>IHrH5AH AH}L萿W)HDž@虅>H(sR)Hs input.HH.Hided as HH(H be provHH Hzes mustHHHes or siHHH of scalHHHOnly oneH@6AHHAHuHLE1H5lVHhVL1>>IHqH5 AH ߠAH}L耾W)HDžP艄>HH(vS)H5ADH>CDAH™AHuHLE1H5kVHhVLk> IALeMIALM I!IALeMPIALMIIt H臃>LeMt6HEL9t!HX@t Hxb>HI9uH}LLeH>EuEu LMu-iH}">EtH}>LMAHL9WHX@t Hx>HI9uIALeMPIALMIIt H耂>LeMt6HEL9t!HX@t Hx[>HI9uH}LLeA>EuEu LMu-bH}>EtH} >LM:HL9PHX@t Hxف>HI9uIALeM~IAL MIIt Hy>LeMt6HEL9t!HX@t HxT>HI9uH}LLe:>EuEuLMu&^H}>EtH}>LMt:HL9tTHX@t Hx݀>HI9uHL€>EiL>H H]uLIALeMPIAL MIIt HD>LeMt6HEL9t!HX@t Hx>HI9uH}LLe>EuEu L Mu-.H}>EtH}>L MH(L9HX@t Hx>HI9uIALeMFIAL MnIIt H=>LeMt6HEL9t!HX@t Hx>HI9uH}LLe~>EuEu L Mu-'H}~>EtH}~>L MH(L9HX@t Hx~>HI9uIt Hq~>LeMt6HEL9t!HX@t HxL~>HI9uH}LLe2~>EuEuL Mu&^H}~>EtH}~>L Mt:H(L9tTHX@t Hx}>HI9uH L(}>EL}>H@H~LIALeMuFzIAL`MII@t HPC}>LeMt6HEL9t!HX@t Hx}>HI9uH}LLe}>EuEuL`Mu&^H}|>EtH}|>L`Mt:HhL9tIHX@t Hx|>HI9uH`Lh|>EL|>L]w> L>H@HuIH@Ht HH|>H`Ht Hh{>Lv> UHAWAVAUATSHMLHIHIHߔVHHE1LHHd IEI+EHK H+KH9 1LLdHLxLQHLHKLs I)LHHLHRL)HH9FHHBH+L9QMMIuL)yIIH@11u IH;t1H@H9r@7HBPzH}HHHHDHHHA`H9HPHEHHHHLH9HPHEHHAH AHDH AHHɲHVHH;EHMHL?LPL#LcPV|LLH{>LIHkVHH;ELHĨ[A\A]A^A_]IHHLϸHHHHQIVHLHLHHHTHAH ŐAHDȾI HHHAHHHIHIyHHL9HHIHxW)HHL賯HH>HHVHH;ESHHAHHHALMIHIpHHHIHhxALA$E$A$tE$H[AIH\LHHHtH`H9HPH0HDž0HEHHvLHEMHHHHHIHDHDHPH@ HDH,yIH={IHHHH`H9HPH`LpH\VHHHLH@HpLL u>HDžDžHRVLkLH@HpL t>L5VILHDž`HDžXHDžPHDžHDžhH5AH贻1HH wgAH5`gAHDHTH~HL?HHbt Hit>LHpLHt HX?t>Ls>H5\rVHHr>Ls>HH0HPLLd$(H\$ Ll$HHD$AljD$AƉ$HHHHLhLxtOH0I9LtHHP( HP HuHuHPHHH/HpHuHPLpLl$(LuLt$ AĉD$D$D$HH$HHHHMLH}I9t:Ht;HP(H}I9u5HP LHVHH;E5r>HP H}I9tHLHP(HbVHH;EHDž`H@H`HPH@H\$(HHD$ Ll$Ld$AƉD$HH$HHLHLLxR H`H9Lt'HtHP(IHHHP IHHw@q>IH]H5gAH ōAHL^תHAHm?HHL{E1H5XVHTVLq>p>IHT]H5ބAH H(O)Hbe same.HH'Hshould bHH H tensor HHHd outputHHHinput anHHHRank of H@/AHAHHLsE1H5yWVHSVLo> HAL8MHALM/H,HAL8MC}HALMHHt Hn>L8Mt?H@L9t$HX@t Hxn>HI9uH8LL@n> uu&LMu6OH0zn>tHen>LMHL9-HX@t Hx2n>HI9uHt H n>L8Mt?H@L9t$HX@t Hxm>HI9uH8LL@m> uu#LMu/gH0m>tH}m>LMt:HL9tMHX@t HxRm>HI9uHL7m>E$L\m>Hh> LHHl>HHHH`H9uHP HH{HaHP(HHWuHIIH,Ct H{Vl>HNl>HH/HLHpLHt HXl>Lj>H5%jVHHuj>Lk>Hf> HH}HEH9t?dHH}I9uHP H}HEH9t?HtHP(H}HEH9u'HP HXf> HH0I9tHtJHP(H,f> HHHt#HHCHuHHPHj>He> UHAWAVAUATSH(LIIIDEHVHHUMYMPHLaI)LHH;HsH)HH98MH`HHxLf~hffEHEHHH=-LpL;j>LLPHELN4LuHILj>LuffEHELLi>IL}O4LuLHi>LuffpHEMt$LHXIJ<i>IHpLuLxMIJ4HHii>HLH?Lt@)HHHJ!IDLI fnhLL`|=IHH LHX@HHI;ITLHM HU8LE0L]ILLP.HHH\,ЉIE1H`oHрAH AHAH 8AEHEʾLeHVVHH;ELH([A\A]A^A_]fff.H?HIWI*I WH*H*If~(EIx H+HH@0HMH $H(HHLLW.wHxHHHWH*.vAHHfn IP.f~(HE8Hx HHH(HP0HxHH H9|HHIHLLE0L]u Hy1ALC HNLLHHfLyfHDžMtDI9aN4LUe>HNe>LLH(H1-!H(H!HLMLp LHD,h1E1Hx~1O 1HLDxATA4HHHH;H|LIM9|1 H`fH?HIWI*I WH*H*If~(EIx HHH@0HMH $H(HHLLHHfn :FP.f~(HE8Hx HbHH(HP0HHxHHH9|HHIHLLE0L]u Hy1AD CHNLLHHfLyfHDžMtUI9N4Lyb>HNLLHHLyffHDžMtDI9:N4Lb>HNLLH(H1H(HH(HHHHA HHLHD,h1E1Hx~aHNE1ff.Hx~.K L1HHExEDE4HHHH;P|IL;p|HLII9| E(Ifn.2CPfn@.CPfn@.CPfn@ .BPH`HHH#HQHHALyLO?LHLIHHHHH HMHH<HIH_ȈLHHhH`HCHH)HXIGHID$ H ID$H(IEH01HHL8L@HHHHHHHI HHHHH0HxH(L H8HL@1ېHHHHHHHIHLLMHLE1E1H`]@LHLHLIIv?HILHH4>HPHH9IHH4>HH9sw1HAt%H4H3H4GA1HA1HHH9 HxD2Dqq2DqDqHI9uf.LHHH4HXE1f.BofofhCDSf`CLSBoDfof`fhCDSC SI HuH`t2IHoJWHfof`fhADALHhHI9I@IMLM9NHHHIHHLIHHHxHH9HH8HH@HHHH H(H0HHHH;LpLpIMCFH@RHE1E1E1HHLHHJ 7JT7H)HHH)LvLkHLKH4s HJD7H(LIILHIL;nffHDžLL)[>HHL4LHL [>LMH1E1Hf.HL2HHH9|L9ffHDž MtYI9N4LZ>HNLLHHfLyfHDžMtUI9fN4LZ>HNLLHHLyffHDžMtYI9N4LY>HNLLHHfLyfHDžMtDI9N4L*Y>HNLLH(H1H(HH(HH(HHHHHHHHL,h11fff.HxHHxH HHE1fHx~nHJHE1@Hx~EO IMHHu1;HIHu4>HLpIHt HW>HHt HV>HH0HV>E1H“Hfnh,HHIHHH LHHXH IH HE1HxMxCD5CLMtBHL9t'HXHxHt HxT>HI9uHLLT>LLT>H}Ht H}T>H}Ht H}T>HmVHH;EuL\U>T>H DkVHHH5=VH:VHT>9OT>H kVHHH5=VH:VHgT> H}PQ> HBQ>H4Q>H&Q>HQ>H Q>HP>HP>HP>HP>IHHIHH%IHHIHH>HS>LM.IHH3IHHKIHHu{5-IHHuFPIHHt)`IHHt HnR>HHu9HHt HJR>HHH.R>HR>HHuIH}HIH}HILMtYHL9t?fff.HXHxHt HxQ>HI9uHLQ>LIHoQ>H}Ht H}]Q>H}Ht H}KQ>L-L> DUHAWAVAUATSH(pMIH@HHHjVHHEW)EHE)EHELLH LyYIHI t0HLE8H LM4HHHMMKJHHHHEHHHRH)HHHHLH*H@H*HPLHHDEpE0M8UMEIx HH@HH(WH*X1LUL]HH@0HMH $LMLHMLELM|HEH;EsHHEf.H}H|PFXME|.MLwH|.OLUIF‹|fnH,H(HH9Hu8ILHHP~HHPHH9~HWH*\( ,P(TH|WH*\THH9uH?H?HhHIHI HH; HEHWH*ËLBDEpE0E8EMEIx H1L>H cVHHH5Z6VHC3VHL>HEHHHQH)HHHLMWI*HhWH*HPLHHDEEMUMEIx HLHhHH(WH*0H HH@K H LHXLHHLHpHJLH8HuLEfHH@0HMH $MLHMLELML|HEH;EsHHEfDH}H|0CL0M|.H|MIG.OHuHFE|fnH,H(H9HNIDHH9HNH8HDILWH*\( (P(THpD|WH*\THXDH9uHpD?HXD?L9HU8HHHPWH*HEHLDEEEEMEHz HH9I>H `VHHH5B3VH+0VHI> HH Hu(LM HhHH@HLHHWH*HL*D,H(HHHH0HH@HJLHHHH@K H LHPLHHLHh1Wffff.HHHIHH8Hp1fHXHOLpE1fDM 1ɀ}Kff.HEB.D.DHE.DA.DKHLLM$HHLH}Ht H}SE>H}Ht H}AE>Hf^VHH;EuH([A\A]A^A_]E>ILH`HMIH`LHMH躤IH}H"IH}HIH}HH}D>H}HH}sD>LU?> IH`HMtHtHHLPH`Ht#HHCHuHHPHC>H}HrH}H{L>> H٣fUHAWAVAUATSH8IHuHH\VHHEHCMcHJHfHKHSH HE11H+2H1HufHS8I*Hs H{(WH*HJ*BHB4H;L ẺEMUuMHz HHHMH $HuHUHMLELMP0{0t-AW.w%HCHJHWH*.AE1HC HK@H *%PB. 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HHH9r~fffff.~nHHHLI)IIrRffff.LMLILHHI H9|HHHHL9rfL(LULXHL I $ILHXL.L.L8MM9uiHHH)HHDI HXHHHI;HHH9r~fffff.~nLHIHLI)IIrOAfHK HHHXJJO4L9|HIJIM9rfN,?Ct4ML(L9(>L;lML(LLKI r)LIH H K48LH9s*LHH9s1HH)HH`s1H|KTpH1D L LD L D L0D L D@LPoD o D`LpHHuHt9HHffffff.oDo D H HuHHI9trfHH)HtHDHHuHr;KDfDTTTTTTHI9uDL;LIH`Ht HhY$>H8Ht H@A$>H Ht H()$>HHt H$>HHt H#>HHt H#>HHt H#>H]HEHtHD0H#>H]HEHtH#0H#>H]Ht#HHCHuHHPHY">H?#>IHPH5@AH @AHLP\ffEHE0">HEfoOOfEHequired!HH Hts are rHHHd Y inpuHHHn, X, anHHHconditioH@(AH@AHHML_E1H5 VHKVL">P">IH}H5?AH @AHPLE[ff0HDž@ !>H@foͧOf0Hrst outpHHHo get fiHHHfailed tH@ut!AH4>HPH0L5^E1H5; VHlVL!> HP> IALMuIIALeMII0t H@ !>LMt?HL9t$HX@t Hx >HI9uHLL >puPu LeMu,[H >PtH` >LeMt1HEL9tNHX@t Hx[ >HI9uH}LeF >ELk >H`HLIALMuFIALPMIIEt H}>LMt?HL9t$HX@t Hx>HI9uHLL>uu#LPMu/gHX>tHC>LPMt:HXL9tIHX@t Hx>HI9uHPLX>E}L">L> LIHPHuIHPHyHX>H`HmHh>H8HaH@r>H HUH(V>HHIH:>HH=H>HH1H>HH%H>H]HEHH]*H>H]HEH H8*H>H]HIH]HEHuIH]HEHIH]HIH`HH8HH HHHHHHHHHH]HEHH]HEHH]Ht#HHCHuHHPHu>LY> UHAWAVAUATSHH}5VHHEHHLAIHHrHQHH HxH HH9HNHEHEHHEffHDžLeL}M)tcLMILH=+L>HHHJHML~HLL'>LHHLHSHHt H'>HEHM>IXHLY ILN HLMMHnH`H9L[I9 DHI)fffff.LMIHEHL}HH@MHHIL@HLHH H;H9ufHHH)HHfML@H HH H; HHH9r~fffff.~nHHHLI)IIrRffff.LMLIL@HHH H9|HHHHL9rfLUL]HHH`LMMLEL;L;LxI9I9uWHHPH)HHLLEHHHI;HHH9rq~nLHHHLPI)IIrOf.LLLILEHHI H9|HHHHL9rfff.HI HH~Hu#1HH9 fHHHTKt1fDD8l u)|A< D8, t&1A| HI9u&1A< D8, u<A| HI9uHtHHD8, uMA A H9K 1A H97 I9H' HAHpHPHqH΃HHHJH)HHxHHH`HHh1H1LLIILLHEHL}HL@ILH@HLHL IH9ufHHH)HHfIH@HHHI;HHH9r~fffff.~nHHHLI)IIrRffff.HIHHH@HHML9|HIHHL9rfHFHEHHEHHL`I IIHHML>L>LxII9ubHHPH)HHfffff.I HMHHHI;HHH9rq~nLHHHLPI)IIrOf.HI HHHMHHML9|HIHHL9rfff.HL<7E8upHLLKH r"HII I0LH9s_HLH9sT1HH)H`G]fffff.HL>L;Hp`s1HxKTpH1fff.D L LD L D L0D L D@LPoD o D`LpHHuHxt9HHffffff.oDo D H HuHHH9fffff.HH)H`tHhDHHuHHrDKDffffff.TTTTTTHH9uDL;2I9)IVHrIFHhDILAfnff8HHFHH)HXHpHHxE1MDLMIMHMLEHH@MIL@HLHH HH9LHHuoHHH)HHff.ML@H HH H; HHH9r~fffff.~nHHHLI)IIrRffff.LMLIL@HHH H9|HHHHL9rfHEH`L]HHL`I:ILH}LLLxII9u`HHPH)HHfff.IHxHt H} >H`Ht Hh >HHHt HP >H Ht H( >HHt H >HHt H >H%VHH;EuHĈ[A\A]A^A_] >H > IHHuIHHH1 >H}HIH}HH} >HxHH} >H`HHh >HHHHP >H HH( >HHH >HHHd >LF> LIHHHtHH5 >H}HHxHH`H(HHH4H H@HHLHHXL> fUHAWAVAUATSHHH#VHHEHCHH}H0BH}u'H#VHH;EuHĘ[A\A]A^A_] >\ >IH`H5(?H (AHxL`CH}CHHH>DH>HxHHLFDžDH5UHUL > IǰDLeMuKIǰDL`MIIHt HXZ >LeMt6HEL9t!Lh@t Hx5 >LM9uH}LLe >Euxu L`Mu)eH}>xtH}>L`Mt>HhL9L`@t Hx>LM9uH`Lh>DuLuHEMu$H[HuOpL>LuHEMtAFt I~K>LC>H[Hu4LIH[Ht#HHCHuHHPH>L> fffff.UHAWAVAUATSHIHIH VHHE蓕 IĿX>IHHEHCHEHtH@HME1LLLe M.H]Ht#HHCHuHHPHP>Hw VHH;EuLH[A\A]A^A_]>IL}Mt#HIGHuILPL>L>L> fffff.UHAVSIP>HHVHHH{LOGHH VHHH[A^]IH>Ll> @UHAWAVAUATSHIHHVHHE1L|IžL|HþL|IMHM LuLL-H}LHM$H}LE1LL$HuHUHHHHHGHHH8H HH9HNHxHEH HEffPHDž`L`HhL)tVIILH=H'>ILXLPKDH`H~LLH>ILXHP1L-IHPHt HX>MHxH(I~AILNHL?JLM*MHuH HH9H9LELHLL)HHHUHL8HH)HHLH(HxH_HHHHHKHH)LHfI HxH HHHHpHHXI@0HHB0HHhHBHL`I@H1HLDM*HLHxLMHILIHII<$H9uRIHHDMLIHII;utIHH9rbfL~WHHIIrI@LMLILIIIMIMI1fffff.L(MLM@uAAAAM9MOHNH N4M)MI?I>MIM|IrK9HI9nKI9a1IAH)LHtHA44HHuH'JD fL LLLL L HI9uLHfAnfpHFHHYH8s1HcHCH)1DD D0D@DPD`DpH@H{HHHff.DH HuLHHJHHpHs1HH JLpH^H)1@DLLDLD L0DLD@LPoDo D`LpH HuHH t7JLHHfff.oDo LH HuI9LOT IIQI9IMHL)ʉLHt!HLAofHHuHrgJLpDA)DDA)DDA)DDA)DDA)DA)DDA)D ofAD0H L9|L;(^M)I4JK 9LHH(J >H H9sIHJH9MIIL$HHpHs1HH HNH)K1LDLD L0DLD@LPLoD`oLpD L0H HuHH t;JLIMIHfDBoD Bo BBLI HuM9 MH(D)HL)HtH@CBIHuHH(JD f.BLB BLBLBLBLB BL IL9ufff.D$HI9uKIfAnfpIQH9HMHL)ʉLHtHLDfHHuHrDfff.ffDfD fD0fD@fDPfD`fDpH H9|H;(M)IMIfAnfpIJHHpHs 1HuMsHVH)I 1DDDDDD D0H HuHt(IMIHDBBDI HuM9t!LH(ff.D$HH9uHH;K H9> H(HHHHHHLX@1HLM*HLHHEH LxHLMMLHLHLI6H9uoHHH)HHff.M,LH HH I; HHH9r~fffff.~nHHHLI)IIrRffff.LM,LILHHI H9|HHHHL9rfLELXLL IILHXMMMML8I6I9uoIEHL)HHf.I HXItHItI;4IDHH9r{ff.~nLHHIULM)IIrNfHI HHHXIDIDIH9|HHITHL9rfDHH GMIMI1fffff.L(MLM@uAAAAM9MOHNH N4M)MI?I>MIM|IrK!HI9nKI9a1IAH)LHtHA44HHuH'JD fL LLLL L HI9uLHfAnfpHFHHYH8s1HcHCH)1DD D0D@DPD`DpH@H{HHHff.DH HuLHHBHHsHs1HH JLpHFH)1@DLLDLD L0DLD@LPoDo D`LpH HuHt>H JDHHfff.oDo LH HuI9H LOT IIqI9IMHL)ΉLHt!HLAofHHuHrgJDpDA)DDA)DDA)DDA)DDA)DA)DDA)D ofAD0H L9|L;(^M)I+JK !LHH(J &H H9sIHJH9MIIOHHpHs 1HuwHNH)K1@LDLD L0DLD@LPLoD`oLpD L0H HuHtBH JDIMIHfDBoDBo BBLI HuM9H )MH(D)HL)Ht Hffff.CBIHuHH(JD f.BLB BLBLBLBLB BL IL9ufff.DMIMhIrK IH9MJI9@1IBH)LHt#Hffffff.ATAHHuHKD LA LALLAL AL HI9uLMAuEAAAM9MOM)MI?I>MIM~J4L=K\5I|9K1DH(HIMHH H Hs31HfHnfpDHsd1Hh(HIHHHHH9H(2HHH9"1H#H1f.AADAD AD0AD@ADPAD`ADpAЀAАAРAаAAAAH HkHht'HHxfDAADH HuHHH(H9Hffff.I HH9uH s1HHH HH1DDӐLӠAALDӰLAD AL0DLAD@ALPoD AD`ALpHHuHtCHH HHHoD AALH HuHHH9(HHHH)HtHpfHOfEHequired!HH Hts are rHHHd Y inpuHHHn, X, anHHHconditioH@(AH@HHMLE1H5ڝUH UL[==IH}谢H5@H H@HPLEff0HDž@ 贵=H@fo;Of0Hrst outpHHHo get fiHHHfailed tH@ut!AH=HPH0LE1H5UH,UL|= HPb= IALMuIIALeMII0t H@ɴ=LMt?HL9t$HX@t Hx螴=HI9uHLL~=puPu LeMu,[HU=PtH`@=LeMt1HEL9tNHX@t Hx=HI9uH}Le=EL+=H`HLIALMuFIALPMIIEt H}菳=LMt?HL9t$HX@t Hxd=HI9uHLLD=uu#LPMu/gH=tH=LPMt:HXL9tIHX@t Hxز=HI9uHPLX轲=EL=L莭= LIHPHuIHPHtqHXn=H`HtiIH]HEHIH]HEHIH]HIH`HH8HH HHHHHHHHHH]HEHH]HEHH]H *HhQ=H8HXH@5=H HLH(=HH@H=HH4H=HH(HŰ=HHH詰=H]HEHH H脰=H]HEHHH_=H]Ht#HHCHuHHPH1=L= ffff.UHAWAVAUATSHH-UHHEHHMAHHHrHQoHH HxH HH9HNHEdHEHdtHEW)HDžLmH]L)tcLIILH=}HS=ILLKHHL~LLHٰ=ILHHHLrHHt H֮=HEHL+I}ILHIL=J LIHHPH`HH:I9 LeHLH HI)IH]HHLxHPH)HHL@HуIHhI)H HXLpIL$HxHHHKHLL`fIMLL`LeIIIL@HLHHH9HuSHIffff.M L@H HH H; utHHL9rbf~^HHHIrPH@LM LIL@HHH H9|HHHHHL9rfHMHLMHHIL]L&L&I]I9u^HH@HLL]H HH I;LuHHH;rif~^LHHHHrI@LHLLIL]HHI\H9|HHHHH;rHXMH~Hu11HhL;PH HxHHHHH1f.D8tu)HLI D84t&1ILHI9u&1I D84uH ILHI9uHhTHHpD84uHHH H 1I L;PH I9 HHHHAHhHPHqH΃HHxHJH)HHpHHHXHH`E1L1LI0ILLHEHL]HH@IHHHH@HLHL M"L9ulHHH)HHI4H@H HH I; HHH9r~fffff.~nHIHLI)IIrRAfff.HK4HHH@JJO L9|HIJIM9rfHHD HEHHEHHL`IMIIHHMLLLxI4$I9uYHHPH)HHDILHMH4HH4I;4HHH9rp~nLHHHLPI)IIrOf.HILHHHMHHML9|HIHHL9rff.HL$E8upLMLLKIr$LIIK4J4H9scLI H9sW1HH)HX.EDHLkH-=L;PqHh s1HpuyKTpH1@DʐLʠLDʰLD L0DLD@LPD D`LpHHuHpt9KTHHfffff.D  LH HuHxHI9tvfDHH)HXtH`DHHHHuHr:KDDHTHHTHTHTHTHHTHI9uѐL;PI9 LMHLH HI)IHMHhHHHxHHPH)HHL@HHZHpуII)HxHHH HXLIIHHL`f.IMML`LMIIML@HLHHHH9uSHIffff.M$L@H HH H; utHHL9rbf~^HHHIrPH@LM$LIL@HHH H9|HHHHHL9rfHMHLMHHIL]LLHHI9uxHHfffff.HLL]H HH HH; HHH;rffffff.~nLHHHHrY@LHLLIL]HHHHH9|HHHHH;rf.HXMHHhHHHps1HxfDHHJ 1D8tHtF1ID8tHuDIDD8tHtF1IDD84HuDIDHI9uHID8tHt1IDD8tHuIDD84Ht1IDHI9oHxt,LHE84Ht1IHHuL;PH}Ht H}膡=HxHt H}q=H`Ht HhY=HHHt HPA=H Ht H()=HHt H=HHt H=HUHH;EuHĘ[A\A]A^A_]b=H= IHHuIHHH葠=H}HIH}HH}i=HxHH}P=H`HHh4=HHHHP=H HH(=HHH=HHHğ=L覚= IHHHHtH1H蕟=H}HHxHH`H(HHH4H H@HHLHHXL = fUHAVSIP=HHBUHHH{LHxUHHH[A^]IHʞ=L謙= @UHAWAVAUATSHIHH÷UHHE1LIžLHþLIMMHDM;LuLLH}LHM$H}LE1LL$HuHUHHHHH臗HHH8H HH9HNHx9hHEH)hHEW)PHDž`L`HhL)tVIILH=Hi=ILXLPKDH`H~LLH=ILXHP1L&IHPHt HX=MHxH(I~ILHLJLM*MHxL I;H9&LELHLL)HHHUHL8HH)HHLH(HxH_HHHHHKLHH)HWWI HxH HHHHpHHXI@0HHB0HHhHBHL`I@H1H LLDM*H ILHHxLMILIHII<$H9uOIHHfM4LIHII;utIHH9rbfL~WHHIIrI@LM4LILIIIMIMI1f.L(MLM@uAAAAM9MOHNH N4M)MI?I>MIM|IrK9HI9|KI9o1IAH)LHtHA44HHuH'JDfL LLLLLL LLLLLL LHI9uLH(HNHHXH8s1HWHSH)1fDTT T0T@TPT`TpH@HuHHHffffff.TH HuLHHJHHpHs1HH }JLpH^H)1fDLT TLTL T0LTL@TPLL`TpH HuHH t'JLHHL TH HuI9fff.LOT IIQI9IMHL)ʉLHtHLA ) HHuHr_JLpL) L)LL)L L)L0L)L@L)LPL)L` )LpH L9|L;(yM)I*JK 9LHH(J >H H9sIHJH9MIIL$HHpHs1HH u|HNH)K1fD TLTL T0LTL@TP TL`TpL T0H HuHH t;JLIMIHf.BL B B BTI HuM9EMH(D)HL)HtHCBIHuHH(JDf.BLB BLBLBLBLBLBL BLBLBLBLBLBLB BLIL9u@ HI9uKI(IIH9HMHL)ɉLHtHL)HHuHrEffff.A)TA)TA)TA)TA)A)TA)T A)T0H H9|H;(M)IMI(IHHHpHs 1HuHtHVH)I1TTTTTT T0H HuHt.IMIHffff.BBTI HuM9t$LH(ffff. HH9uHH;[ H9N H(HHHHHHLX@1HWLM*HLHHEH LxHLMMLHLHLI6H9uoHHH)HHff.M,LH HH I; HHH9r~fffff.~nHHHLI)IIrRffff.LM,LILHHI H9|HHHHL9rfLELXLL IILHXMMMML8I6I9uoIEHL)HHf.I HXItHItI;4IDHH9r{ff.~nLHHIULM)IIrNfHI HHHXIDIDIH9|HHITHL9rfDHHHC .u{aH(I@uAAAAI9LOIM)MI?I>MIMI1f.L(MLM@uAAAAM9MOHNH N4M)MI?I>MIM|IrK!HI9|KI9o1IAH)LHtHA44HHuH'JDfL LLLLLL LLLLLL LHI9uLH(HNHHXH8s1HWHSH)1fDTT T0T@TPT`TpH@HuHHHffffff.TH HuLHHBHHsHs1H}H JLpHFH)1fDLT TLTL T0LTL@TPLL`TpH HuHt.H JDHHL TH HuI9H DLOT IIqI9IMHL)ΉLHtHLA ) HHuHr_JDpL) L)LL)L L)L0L)L@L)LPL)L` )LpH L9|L;(yM)I1JK !LHH(J &H H9sIHJH9MIIOHHpHs 1Hu}HNH)K1fffff. TLTL T0LTL@TP TL`TpL T0H HuHtBH JDIMIHf.BLBB BTI HuM9H >MH(D)HL)HtHِCBIHuHH(JDf.BLB BLBLBLBLBLBL BLBLBLBLBLBLB BLIL9u@ HI9uKI(IIH9HMHL)ɉLHtHL)HHuHrEffff.A)TA)TA)TA)TA)A)TA)T A)T0H H9|H;(M)IMI(IHHHpHs 1HuHtHVH)I1TTTTTT T0H HuHt.IMIHffff.BBTI HuM9t$LH(ffff. HH9uHH;LIH`Ht Hh5=H8Ht H@=H Ht H(=HHt H=HHt HՀ=HHt H轀=HHt H襀=H]HEHtH H脀=H]HEHtHHc=H]Ht#HHCHuHHPH5=H\UHH;EuLHĈ[A\A]A^A_]蝀==IHPlH5@H џ@HLP貹W)EHE0=HE(.O)EHequired!HH Hts are rHHHd Y inpuHHHn, X, anHHHconditioH@(AHÝ@HHMLE1H5fUH+cUL{=0=IH}kH5@H @HPLE͸W)0HDž@ ~=H@(O)0Hrst outpHHHo get fiHHHfailed tH@ut!AH=HPH0LE1H5fUHPbUL~= HP{= IALMuIIALeMII0t H@}=LMt?HL9t$HX@t Hx}=HI9uHLL}=puPu LeMu,[Hy}=PtH`d}=LeMt1HEL9tNHX@t Hx?}=HI9uH}Le*}=ELO}=H`HLIALMuFIALPMIIEt H}|=LMt?HL9t$HX@t Hx|=HI9uHLLh|=uu#LPMu/gH<|=tH'|=LPMt:HXL9tIHX@t Hx{=HI9uHPLX{=E}L|=Lv= LIHPHuIHPHyHX{=H`HmHhr{=H8HaH@V{=H HUH(:{=HHIH{=HH=H{=HH1Hz=HH%Hz=H]HEHHAHz=H]HEH HHz=H]HIH]HEHuIH]HEHIH]HIH`HH8HH HHHHHHHHHH]HEHH]HEHH]Ht#HHCHuHHPHYx=L=t= DUHAWAVAUATSHH]UHHEHHMƉHHHrHQrHH HxH HH9HNHEXHEHACHEffHDžLmH]L)t[MIILH=Hx=ILLKHHM~LLH z=ILLLLLLrHHt Hx=HEHIH{IH^IH ^J HHHHHH'H`HH:H LI9HMLHH L)HHUHpLHHxHHPL)HHL@HVH`II)HhHH8HHxH0HIH@HPHX'DHHLMHPHXL}IIML@ILIH7H9uXIHfHL,L@I HI H; u}IHH9rkff.~^HHIHrPff.LHL,LIL@IIH H9|HHIHH9rfHMHLMHHILUM8M8HH7I9uhIHHff.HLLUI HI H; |IHH9rjf.H~WLHHIHrFLHLLILUIIH4H9|HHIHH9rDLHxMDžHH`s1HhHpHHH@J 011ffffff.8\H<2IE?A<78\H|2IE?A|78\H|2IE?A|78H|2 IE?A|7 HHI9uHhtIL0HHHpH H8fA8LHD6A4HHHuL;H LI9H HHHxL,LIO@H1H1L'HHH0HLLLEL]HL@ILH@HLHL I $H9umHHH)HHfHH4H@HHHI;HHH9rw~nHHHLI)IIrRffff.HHH4HHH@HHIH9|HHHHL9rfGLELMHHL`I $ILHMLLLxI6I9ubHHPH)HHfffff.I HMH4HH4I;4HHH9rq~nLHHHLPI)IIrOf.HI HHHMHHIH9|HHHHL9rfff.K/HD:(HILIAuEAAAI9LOHJ4O4IM)MI?I>MIMIrK IH9JI9|1IBH)LHt"Hffffff.AAHHuHFKDLA LALLALLAL LALLALLAL ALHI9ufff.HIAuEAAAI9LOIM)MI?I>MIM~J4Llo=K ,I|GKIH]HEHIH]HEHIH]H H;IH`HH8HH HHHHHHHHHH]HEHH]HEH,H]H<HHCH&HHPHrO=LVK= Hh^P=H8H,H@BP=H H H(&P=HHH P=HHHO=HHHO=HHHO=H]HEHH-\HO=H]HEHH\HlO=H]HLAJ= fUHAWAVAUATSHHH]hUHHEHHMƉHHHrHQHHHHXH HH9HNHE.HEH!HEW)EHELeH]L)tJIILH= HN=ILmLmKDHEH~LLH"P=ILmHUHHL1H}Ht H}+N=L}HH{IHIHK vH LI2HHt*H@H8I9z KH[I9a JH@HIGH1KHff.LI:LEL]HL ILH HLHLIH9ujHHH)HHfDIH9unHH0H)HHf.I HxHH9u`HH0H)HHf.I HxHH Sb@HxL`{H}zHHH=?=DHW=HxHHL}DžDH5(UH$UL&A= IǰDLeMuKIǰDL`MIIHt HXz@=LeMt6HEL9t!Lh@t HxU@=LM9uH}LLe;@=Euxu L`Mu)eH}@=xtH}@=L`Mt>HhL9L`@t Hx?=LM9uH`Lh?=DuLuHEMu$H[HuOpL?=LuHEMtAFt I~k?=Lc?=H[Hu4LIH[Ht#HHCHuHHPH">=L:= fffff.UHAWAVAUATSHIHIHXUHHE3 IĿX>=IHHEHCHEHtH@HME1LLLAM.H]Ht#HHCHuHHPHp==HWUHH;EuLH[A\A]A^A_]>=IL}Mt#HIGHuILPL==L>=L8= fffff.UHAVSHPIHWUHHE==HþH==HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H}11i HL H5"O=HH菶 H5=H@ H5KAHq HAH5=H蘲 HH;荧 HHH JUHHMHHUHuHuIIFIF0INIVIF@H]HEHtHH;=HUUHH;Eu LHP[A^]`<=IH]HEHtHH;=L6= fDUHAVSHPIHTUHHE;=HþHx;=HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H}11 HqJ H5L=HH/ H5g=H H5AH HAH5~=H8 HH;- HHH $HUHHMHM HUHuHuIIFIF0INIVIF@H]HEHtHmH9=HRUHH;Eu LHP[A^]:=IH]HEHtH(HL9=L.4= fDUHAVSHPIHSRUHHE"9=HþH9=HHCHCXHC`HCXHCHHC@HC8HC0HC(HC HCPHHǃHǃHǃHǃHǃHCxHCpHChHHHǃHǃHǃǃǃHǃH]H}11詳 HH H5bJ=HHϱ H5=H耫 H5AH豬 HH HQH51|=Hѭ HH;Ƣ HHH EUHHMHHUHuHuIIFIF0INIVIF@H]HEHtHH*7=HOPUHH;Eu LHP[A^]7=IH]HEHtHH6=L1= ffffff.UHAVSHPIHOUHHE6=HþH6=HHCHCXHC`HCXHCHHC@HC8HC0HC(HC 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*HH[]m2H[]f.UHSPH;KHH_HtH *HH[]-2H[]f.UHAVSIHKII^HtH *H2L[A^]2UHAWAVAUATSHHHJHHEHP(H9tX{(uvx(Hx HGHuHL1Mx(Hs HJHH;EuLHĈ[A\A]A^A_]8H wJH H;Mu$HĈ[A\A]A^A_]1H SJH H;Mt2'2IHhH5ϋ5H 5H}Lh=HPnAH5HuHPLh@E1H5nIHIL2~2IHhDH5O5H 5H}LhA=HPEnAHs5HuHPL?E1H5IHILo2 IALeMIALhMIIALeMFIALhMnIIPt H`2LeMt6HEL9t!HX@t Hxf2HI9uH}LLeL2EuEu LhMu-'H}&2EtH}2LhMHpL9HX@t Hx2HI9uIPt H`2LeMt6HEL9t!HX@t Hx2HI9uH}LLe2EuEuLhMu&^H}]2EtH}N2LhMt:HpL9tEHX@t Hx#2HI9uHhLp2EtL12L1 LUH ]DUHH]UHAWAVATSIMt5M~MtAI^L9t,MI)HèH( HèIXuI~ [A\A^A_]LM~o2L[A\A^A_]_2UHHG]fDUHHG]fDUHAVSIHKI0*2HW)C )C)H*I^[A^]IH1L1 IL1 fffff.UHSPH3KHH_HtHO*HH[]1H[]f.UH f.UH f.UHHG]fDUHHG]fDUHAWAVAUATSHH]JHHEHGH9tD~(ubx(usHx  Hv H(JHH;EuLHĈ[A\A]A^A_]H JH H;Mu$HĈ[A\A]A^A_]1H JH H;Mt211IHhMH5X5H ։5H}LhJ8HPNiAH5HuHPL:E1H5IH(ILx1~01IHhH5؅5H V5H}Lh7HPhAH5HuHPLq:E1H5wIHIL1 IALeMIALhMIIALeMFIALhMnIIPt H`1LeMt6HEL9t!HX@t Hx1HI9uH}LLe1EuEu LhMu-'H}1EtH}1LhMHpL9HX@t Hxm1HI9uIPt H`H1LeMt6HEL9t!HX@t Hx#1HI9uH}LLe 1EuEuLhMu&^H}1EtH}1LhMt:HpL9tEHX@t Hx1HI9uHhLp1EtL1Lf1 LfUHAWAVAUATSHH}JHHEHGH9tX~(uvx(Hx HGHuH'Mx(Hv H4JHH;EuLHĈ[A\A]A^A_]H JH H;Mu$HĈ[A\A]A^A_]1H JH H;Mt>11IHhYH5d5H 5H}LhV4HPZeAHI5HuHPL6E1H5IH4IL1~<1IHhH55H 5H}Lh3HPdAH5HuHPL}6E1H5IHIL1 IALeMIALhMIIALeMFIALhMnIIPt H` 1LeMt6HEL9t!HX@t Hx1HI9uH}LLe1EuEu LhMu-'H}1EtH}1LhMHpL9HX@t Hxy1HI9uIPt H`T1LeMt6HEL9t!HX@t Hx/1HI9uH}LLe1EuEuLhMu&^H}1EtH}1LhMt:HpL9tEHX@t Hx1HI9uHhLp1EtL1Lr1 LDUHAWAVAUATSHHJHHEHGH9t:~(uXx(uiHx Hv HbJHH;EuLHĈ[A\A]A^A_]H :JH H;Mu$HĈ[A\A]A^A_]1H JH H;Mtl11IHhH5~5H 5H}Lh 0HPaAHL5HuHPL+3E1H51IHbIL1 IALeMuFzIALhMIIPt H`1LeMt6HEL9t!HX@t Hx1HI9uH}LLe1EuEuLhMu&^H}1EtH}1LhMt:HpL9tEHX@t Hxt1HI9uHhLpY1EtL1L.1 LؐUHAWAVAUATSH61IH}H5|5H 5HXLE.W)EEHEHlementedHEHNot implHEEAH 8HXHMLS1E1H5YIHIL1 IALeMIIEt H}R1LeMt6HEL9t!HX@t Hx-1HI9uH}LLe1xuXuLeMu)bH}1XtHh1LeMt;HEL9tIf.HX@t Hx1HI9uH}Le1EtL1Li1 Lfff.UHAWAVAUATSHf1IH}H5{5H 5HXLE-W)EEHEHlementedHEHNot implHEEAH88HXHML/E1H5IHIL 1 IALeMIIEt H}1LeMt6HEL9t!HX@t Hx]1HI9uH}LLeC1xuXuLeMu)bH}1XtHh1LeMt;HEL9tIf.HX@t Hx1HI9uH}Le1EtL1L1 Lfff.UHSPLLtH1LH[]H=3L1tH=L&H=gIH5LHq1f1H=L1HH=Lg1H 1 DUHSPHKHH_HtH)HH[]1H[]f.UHSPLtHqLH[]H=sL1tH=SLfH=IH5@LH01H==L1HH=,L1HM1 DUHSP$LtH LH[]H= Lt1tH=LVH=IH5LH10&1H=LD1HH=L'1H1 DUHSPLtHLH[]H=L1tH=kLֿH=IH5XLH/1H=UL1HH=DL1HM1 DUHSP HLHEH;HHHuP0LZ HLHEH;HHHuP0Lv HLHEH;H~HHuP0L HjLHEH;HOHHuP0L HcLHEH;H HHuP0kL HLLHEH;HHHuP0lL HMLHEH;HHHuP0%LHLHEH;HHHuP0VLH7LHEH;HdHHuP0L:H`LHEH;H5HHuP0LVHLHEH;HHHuP0LrHLHEH;HHHuP0LHLHEH;HHHuP0LHLHEH;HyHHuP0LHLHEH;HJHHuP0LHLHEH;HHHuP0HEIHH;EH[]H=L;1}H=L詂H=IH5LH1H=L1FH=L1bH=cL΃H=IH5PLH螳1H=ML輳1+H=TL襳1JH=0L賄H=4IH5LH^S1H=Lq1H=!LZ1.H=L蘅H=ٔIH5LH1H=L&1H=L1H=ʼL}H=IH5LH轲1H=L۲1H=LIJ1H=LbH=kIH5LH}r1H=L萲1H=Ly1H=dLGH=@IH5QLH2'1H=NLE1H=UL.1H=1L,H=IH5LHܱ1H=L1H="L1H=LH=IH5LH葱1H=L诱1kH=L蘱1H=˻LH=/IH5LHQF1H=Ld1OH=LM1jH=LۋH=IH5LH1H=L13H=L1NH=eLH=ْIH5RLH谰1H=OLΰ1H=VL跰12H=2L襍H=.IH5LHpe1H=L胰1H=#Ll1H=L芎H=ۑIH5LH%1H=L81H=L!1H=̺LoH=IH5LHϯ1H=L1H=L֯1H=L4H=MIH5LH脯1H=L袯1H=L苯1H=fL蹐H=IH5SLHD91H=PLW1H=WL@1H=3L>H=IH5 LH1H=L 1oH=$L1H=LÑH=tIH5LH裮1H=L1SH=L誮1nH=͹LHH=IIH5LHcX1H=Lv17H=L_1RH=L͒H=IH5LH 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H=YLLLHߞLH IH H;MH=ĞLALbH"LH íIH H;MH=LdLxHELH IH H;MunH=.LZLHLH `IH H;Mu@H=xL,hLHILH 2IH H;MuH=2LH0[A^]`Hv1H=L1PH=gLnH=uIH5TLHӮʓ1H=QL1H=XLѓ1:H=4LoH=uIH5!LHӮ1H=L蝓1H=%L膓1$H=LtpH=tIH5LH?Ӯ41H=LR1H=L;1H=ޜLyeH=tIH5˜LHҮ1H=ȜL1H=L1H=ÜLgH=tIH5LHҮ螒1H=L輒1H=$L襒1H=LaH=TtIH5LH^ҮS1H=Lq1H=LZ1H=LxpH=sIH5ڜLHҮ1H=לL&1H=NL1H=*L=jH=sIH5LHѮ轑1H=Lۑ1H=Lđ1H=L2eH=KsIH5lLH}Ѯr1H=iL萑1iH=ЛLy1H=LjH=sIH5LH2Ѯ'1H=LE1SH=ݚL.1tH=L1 H>IHu1L!1 IH=LIH=hLIH=qLIH=LvIH=LjIH=L^IH=VLRIH=™LFIH=~L:IH=L.IH=L"IH=zLIH=VL IH=2L轎1Lc1 R$WN"UHAWAVAUATSH(IHIHH-IHHEH8H(uIHHHLH@H8Lm1LHEEL=IMgLI@L8Ll1L-aIILHDž(HDž HDžHDžDž0L01HLHӵ3HƋ1HH1HHHӵHHHصLL8Lt H 軌1H蕋1H5ԊIHH$1H8D1HIHH;EuHH([A\A]A^A_]1HLL8Lt H 51H1HH HHH51IHH聊1HH8蜋1HƆ1 fffff.UHAVSH0HIHHE}σ HZHc HLHtLH IH H;MH=YLLHLH pIH H;M|H=̘LeLHLH ;IH H;MGH=ǘL0LHLH IH H;MH=ژLǘLHLH ѣIH H;MH=LҗLHLH IH H;MH=LL HƗLH gIH H;MsH=L\XL H9LH 2IH H;M>H=L'L6HLH IH H;M H=qLLLHǖLH ȢIH H;MH=LLbHzLH IH H;MH=_LL IH=L荃1L3~1 R$WN"UHAVSH0HIHHE}σHHc HLH̍L)H=͍L1HLHAcH=ZeIH®H蕂1H=L<aLHJLH=KL茂1H'LH7jH=8eIHI®H;1H=L7LH 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InvaHHH type inHHHNull mapH@-H5-b'H}HUHHMLEELhMH5['H ma'H0uHGH}1Hy$W)HDž @"H ( 7)Ht model.HH(HRT formaHH Hnvalid OHHH info. IHHHsor typeHHHNull tenH@0H5`'H}HUHKHMLE )E5LhM:zH}"H "E{H}"LhM5HpL9fHX@t Hxi"HI9uH}S"2H :"E&H}'"LhMHpL9ff.HX@t Hx"HI9unH}"H "EH}"LhMtEHpL9t*fDHX@t Hxy"HI9uHhLLpY"PH`@"0LAF(1 IM~ HcHH) rmHDHt`HD3ItUAOIWHu1HIWLLI<$ HX'H _'1AbL{ H5hX'H _'H0aHH}1H!W)HDž PO"HH (V7)H5N_'DH"CDH5^'H}HUHlaHMLE# , E8 LhMA LHAF(1 IM~ HcHH)1ɃrHDHt AGƒAWAO Hc HH)roHL HtbHD3ItWAGIWHu1HIWLLI<$ HV'H ^^'1AkL H5V'H ;^'H0jH,H}1H^W)HDž @"H (c4)Ht model.HH7HT formatHH0Hvalid ORHH(HType. InHH Hfbs::MapHHHinfo in HHHue type HHHNull valH@?H5\'H}HUH\_HMLEELhMPL8AF(1IMn HcHH)1ɃrHDHt AEƒAUAM Hc HH)KHL H:HˋH HcHH)6HTH HD9IAEIEHu 1IEHHxA7HAH HIDHIH]E7MH~HHG(Ht)HcO 09}$QW LlAMI}H;=3:ugw$;w$uH'HH@(0Ɖ0HH8wIHHF(HcN QV LlAMI}H;=ʿ:uI]HH5Z >eH;IcLH)H r)ALHt*LHtz"IcLH)H Ƀ9ADH*IAIIc HH) HtH0t7҃LHHA}(҃ r]LHtSHȋHtID3W)0HDž@ID0H1HE?H0H5[R'H 2Y'1HH0W)HDž@J"H(d7)Ht model.HH/HT formatHH(Hvalid ORHH Hname. InHHHwith no HHHm value HHHdim_paraH@7H]HH59X'HH[LHr Eu~LhM1A}(tL}AE(I] I$I$H]Hd}IL; H}"yH"mH "LhMt?HpL9t$HX@t Hx"HI9uHhLLp"Pu0u%I<$3{H`V"0tH@A"I<$JI~HH!H "HHH@HHH0L8HHHL"HB0A}(uI] H;:t'unfvLP|AE(H!>IE I "H@HHH0H8HPHW)0HDž@IEHCHC1H8"H@HCH0H8HKHW)0HDž@I$HN'H PT'1AvLPH}"H "EH}"LhMHpL9HX@t HxM"HI9uHh|H}3"kH "E_H}"LhMHpL9HX@t Hx"HI9uHhkLLp"Pt H`"0t H@"I<$uhHTM'H R'1AzLDLLpL"Pt H`7"0t H@""I<$tCI$H3:HH;E"I$eI$uHL'H KR'1A~L虿KH5L'H S'H0UHH}1H4W)HDž @m"H (Y4)Hrmat modHH(Hd ORT foHH H. 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[A\A^A_]j)衸(IIH5^H `H} AHuLtH2HIIG IGIG1H52H2L (覷HH5YH]H YL:_L YH}38AHuHH%2HHHC HCHCE1H542Hu2Her(IIH5^H ^H}:HuLoH2HIIG IGIG1H52H2L(螶IIH5R]H x^H}<HuLH,2HIIG IGIG1H5<2H}2Lmz("IIH5\H i^H}GHuLwH2HIIG IGIG1H52H2L(覵HIH5&\H ]H}AAAHuHH32HHHC HCHCE1H5B2H2Hs((HIH5[H V]H}sCAHuH|H2HHHC HCHCE1H5ĝ2H2HIHIHIIEuIPIEu>EIEuT[IZIUIHPIEu7>IEu,3IEt H}#Et(HIIEt H}tL,Lخ UHAWAVSH(IH2HHEAGAOPw4@A_Ew0~ EIHaIGHHuHq5AދɃwaHHc HH A9t'KHPA9t>HhA9t 1H8A9u&H@2HH;EH([A^A_]1A9tڿ(IIW@H5GYH H}HuL[H2HIIF IFIF1H52H2Lղ(腲IIW@AGPEH5*XH FXL bXH}LE HuL˯H2HIIF IFIF1H52HU2LE (HH5SHYTH SLxL TH}jAHuHCH|2HHHC HCHCE1H52H̖2H輱r(qIIW@H5WH BTH}HuLɮH2HIIF IFIF1H52HS2LC(IIW@H5VH VH} HuLPH2HIIF IFIF1H52Hڕ2Lʰ(HH5}RHVH |RLvwL RH},AHuHŭH2HHHC HCHCE1H5 2HN2H>IH}IuIpIHkIcIEu!(IEuBIIEu7>IEt H}蓯Et3H)IEuIIEt H}gtL葯L= *@UHAWAVAUATSHXLLHIIHH+2HHEH0L%2I\$@HLH2HH@H0HfHnfLRHEEID$HHIhL0HHLIL5>2ILHDž HDžHDžHDžDž(HLHLHLAIwIEwIEWHHH輰HHHH0HKHH膰HHHTLHHHL0HHLt H2H H5C2HHH0軬H(2HH;EuLHX[A\A]A^A_]iISIeIHHL0HHLt H荬HgH52HHDH0L@ UHAWAVAUATSHhMLHHIHxH;2HHEH0L=2I_@HLH2HH@H0HfHnfLcLHEEIGHHIhL0HHLTL5I2ILHDž HDžHDžHDžDž(LLLLHLxHHLHHHHЭLHHHH賭LHHHH薭LHHhLH)HHL0HHLt HFH H5W2HHH0ϩH<2HH;EuLHh[A\A]A^A_]}HSHeHHHL0HHLt H衩H{H52HHXH0*HT fff.UHAWAVAUATSHXIIHHHY2HHEH0L-2I]@HLH32HH@H0HfHnfL聨LHEEIEHHIhL0HHLrL%g2ILHDž HDžHDžHDžDž(LHH(LHHAIvIEvIEVLHHLHHHHxHHL0HHLt H蕧HoH52HHLH0H2HH;EuHHX[A\A]A^A_]ȧHSHeHHHL0HHLt HHƥH52HH裥H0uH蟡 UHAWAVAUATSHXLLHIIHH2HHEH0L%F2I\$@HLHt2HH@H0HfHnfL¥HEEID$HHIhL0HHL蹤L52ILHDž HDžHDžHDžDž(HLvHLHHLAIwIEwIEWHHH,HHHH0H軣HHHHHLHHHL0HHLt H袤H|H52HHYH0+H2HH;EuLHX[A\A]A^A_]٤ISIeIHHL0HHLt HHעH52HH财H0膣L谞 UHAWAVAUATSHXIIHHH2HHEH0L-d2I]@HLH2HH@H0HfHnfLLHEEIEHHIhL0HHLҡL%ǹ2ILHDž HDžHDžHDžDž(LHH舥LHHZAIvIEvIEVL8HHILHHHHHHL0HHLt HHϠH52HH謠H0~H2HH;EuHHX[A\A]A^A_](HSHeHHHL0HHLt HLH&H5]2HHH0ՠH UHAWAVAUATSHhMLHHIHxH2HHEH0L=2I_@HLHՇ2HH@H0HfHnfL#LHEEIGHHIhL0HHLL5 2ILHDž HDžHDžHDžDž(LLѢLLHLxHH譢LHHHH萢LHHbHHsLHHEHHVLHH(LHHHL0HHLt HHH52HH轝H0菞H2HH;EuLHh[A\A]A^A_]=HSHeHHHL0HHLt HaH;H5r2HHH0H fff.UHAWAVAUATSHXIIHHH2HHEH0L-Ĵ2I]@HLH2HH@H0HfHnfLALHEEIEHHIhL0HHL2L%'2ILHDž HDžHDžHDžDž(LHHLHHAIvIEvIEVLHH詟LHH{HH8HHL0HHLt HUH/H5f2HH H0ޛHK2HH;EuHHX[A\A]A^A_]舜HSHeHHHL0HHLt H講H膚H52HHcH05H_ UHAWAVSHhIH2HHEHU؋~~$HN HwHs$1HueIG H9t,H)ǻ1fDHH\H\H\H\ H\(H\0H\8HH9uHt H Hfff.HHHuIG H9H}LHEHEH}Ht=H1fff.H4HxMH9}HH9HHMHH9rHt H}&HK2HH;Eu Hh[A^A_]薚(IIHH]HHD$HEH$H5@H @L @H}LEHuLCH|2HIIF IFIF1H52H~2L轙(rIIH5&@H /@L X@H}LEHuL輖H2HIIF IFIF1H52HF~2L6 (IIIG HHEHEH$H5[?H l?L l?H}LEHuL%H^2HIIF IFIF1H5n2H}2L蟘dIIEt H}/tOLYL IBIEuI2IEt H}uH}Ht H}ܗL辒 LH}HuUHAWAVAUATSHXLLHIIHH2HHEH0L=V2I_@HLH~2HH@H0HfHnfLӖHEEIGHHIhL0HHL˕L52ILHDž HDžHDžHDžDž(HL舙HLHZݞLA$It$IEt$IET$H)ݞHH:HHHݞHH0HHHHHHܞHEH0H覔LHHHL0HHLt H蜕HvH52HHSH0%H2HH;EuLHX[A\A]A^A_]ӕHSHeHHHL0HHLt HHѓH52HH讓H0耔H誏 fUHAWAVAUATSHhLLHIIHxH2HHEH0L5V2I^@HLH{2HH@H0HfHnfLӓHEEIFHHIhL0HHL˒H2HHHHDž HDžHDžHDžDž(LL聖LLHSڞLxA$It$IEt$IET$L&ڞHH7LHH ڞHH0LߑHHLHHٞHEH0LőH]HߕLHHٞLHrޞHHL0HHHHt H舒HbH52HH?H0H~2HH;EuLHh[A\A]A^A_]迒HZHlHHHL0HHHHt HܑH趐H52HH蓐H0eH菌 UHAWAVAUATSHXLLHIIHH2HHEH0L%62I\$@HLHdx2HH@H0HfHnfL貐HEEID$HHIhL0HHL詏L52ILHDž HDžHDžHDžDž(HLfHLH8מLAIwIEwIEWH מHHHHH֞HH0H輎HHHHH֞LHt۞HHL0HHLt H葏HkH52HHHH0H2HH;EuLHX[A\A]A^A_]ȏISIeIHHL0HHLt HHƍH52HH裍H0uL蟉 UHAWAVAUATSHhH2HHEIIHHA L9 (McnLxM9H}HMHzHEM~H}E|M^ NIE1HE11DHHIH9}~HHH‰UL9|L9ILULIM9r"H}Hu HEIHt H}蜍H2HH;EuHh[A\A]A^A_](膍IHEHH4HD$HEH$H5"4H +4L /H}LEHuL踊H2HIIF IFIF1H5v2HBr2L2 (HHEHH53H 3L U4H}LE AHuH/Hh2HHHC HCHCE1H5wu2Hq2H訌q(HZIHH52H ^3H}AHuL讉H2HI$ID$ ID$ID$E1H5t2H3q2L#(HՋIHH5U2H 3H}AHuL)Hb2HI$ID$ ID$ID$E1H5mt2Hp2L螋gIIIEt H})EtsLRL IEtIIEt H}Et(HIIEt H}ϊtLH}Ht H}豊L蓅 ff.UHAWAVAUATSHXIIHHH2HHEH0L-D2I]@HLHsq2HH@H0HfHnfLLHEEIEHHIhL0HHL貈L%2ILHDž HDžHDžHDžDž(LHHhLHH:ОAIvIEvIEVLОHH)LHHϞHHԞHHL0HHLt HՈH诇H52HH茇H0^Hˡ2HH;EuHHX[A\A]A^A_]HSHeHHHL0HHLt H,HH5=2HHH0赇H߂ UHAWAVAUATSHXIIHHH2HHEH0L-2I]@HLHn2HH@H0HfHnfLLHEEIEHHIhL0HHLL%2ILHDž HDžHDžHDžDž(LHH踉LHH͞AIvIEvIEVLh͞HHyLHHK͞HHҞHHL0HHLt H%HH562HH܄H0讅H2HH;EuHHX[A\A]A^A_]XHSHeHHHL0HHLt H|HVH52HH3H0H/ UHAWAVAUATSHhLLHIIHxH+2HHEH0L5֛2I^@HLHl2HH@H0HfHnfLSHEEIFHHIhL0HHLKH@2HHHHDž HDžHDžHDžDž(LLLLHʞLxA$It$IEt$IET$LʞHH跆LHHʞHH0L_HH舆LHHZʞHE0L"H]H`LHH2ʞLHΞHHL0HHHHt H HH52HHH0蒂H2HH;EuLHh[A\A]A^A_]@HZHlHHHL0HHHHt H]H7H5n2HHH0H} UHAWAVAUATSHXLLHIIHH 2HHEH0L%2I\$@HLHh2HH@H0HfHnfL2HEEID$HHIhL0HHL)L52ILHDž HDžHDžHDžDž(HLHLHǞLAIwIEwIEWHǞHH蜃HHHjǞHH0H<HHeHHH3ǞLH˞HHL0HHLt HH~H5"~2HH~H0H2HH;EuLHX[A\A]A^A_]HISIeIHHL0HHLt HlHF~H5}}2HH#~H0~Lz UHAWAVAUATSHhMLHHIHxH2HHEH0L=ƕ2I_@HLHe2HH@H0HfHnfLC~LHEEIGHHIhL0HHL4}L5)2ILHDž HDžHDžHDžDž(LLLLHĞLxHH̀LHHĞHH谀LHHĞHH蓀LHHeĞHHvLHHHĞLH ɞHHL0HHLt H&}H|H57{2HH{H0|H2HH;EuLHh[A\A]A^A_]]}HSHeHHHL0HHLt H|H[{H5z2HH8{H0 |H4w fff.UHAWAVAUATSHXIIHHH92HHEH0L-2I]@HLHc2HH@H0HfHnfLa{LHEEIEHHIhL0HHLRzL%G2ILHDž HDžHDžHDžDž(LHH~LHHAIvIEvIEVLHH}LHHHHXƞHHL0HHLt HuzHOyH5x2HH,yH0yHk2HH;EuHHX[A\A]A^A_]zHSHeHHHL0HHLt HyHxH5w2HHxH0UyHt UHAWAVAUATSHXIIHHH2HHEH0L-42I]@HLHc`2HH@H0HfHnfLxLHEEIEHHIhL0HHLwL%2ILHDž HDžHDžHDžDž(LHHX{LHH*AIvIEvIEVLHH{LHH뾞HHÞHHL0HHLt HwHvH5u2HH|vH0NwH2HH;EuHHX[A\A]A^A_]wHSHeHHHL0HHLt HwHuH5-u2HHuH0vHq UHAWAVAUATSHXIIHHHُ2HHEH0L-2I]@HLH]2HH@H0HfHnfLvLHEEIEHHIhL0HHLtL%2ILHDž HDžHDžHDžDž(LHHxLHHzAIvIEvIEVLXHHixLHH;HHHHL0HHLt HuHsH5&s2HHsH0tH 2HH;EuHHX[A\A]A^A_]HuHSHeHHHL0HHLt HltHFsH5}r2HH#sH0sHo UHAWAVAUATSHXIIHHH)2HHEH0L-Ԋ2I]@HLH[2HH@H0HfHnfLQsLHEEIEHHIhL0HHLBrL%72ILHDž HDžHDžHDžDž(LHHuLHHʹAIvIEvIEVL訹HHuLHH苹HHHHHL0HHLt HerH?qH5vp2HHqH0qH[2HH;EuHHX[A\A]A^A_]rHSHeHHHL0HHLt HqHpH5o2HHspH0EqHol UHAWAVAUATSHXIIHHHy2HHEH0L-$2I]@HLHSX2HH@H0HfHnfLpLHEEIEHHIhL0HHLoL%2ILHDž HDžHDžHDžDž(LHHHsLHHAIvIEvIEVLHH sLHH۶HH蘻HHL0HHLt HoHnH5m2HHlnH0>oH2HH;EuHHX[A\A]A^A_]oHSHeHHHL0HHLt H oHmH5m2HHmH0nHi UHAWAVAUATSHXIIHHHɇ2HHEH0L-t2I]@HLHU2HH@H0HfHnfLmLHEEIEHHIhL0HHLlL%ׄ2ILHDž HDžHDžHDžDž(LHHpLHHjAIvIEvIEVLHHHYpLHH+HH踞HHL0HHLt HmHkH5k2HHkH0lH2HH;EuHHX[A\A]A^A_]8mHSHeHHHL0HHLt H\lH6kH5mj2HHkH0kHg UHAWAVAUATSHhH02HHEHIIIH(kIH5 H H L:L HxHxL$iH]w2HIIF IFIF1H5mT2HP2Lk HAHcAGA<$|1ttA`ytA ttAttA ttA ƒ@ttA  A~tAA(~tAA@~tAAX~tA Ap~tA PA~tA A|$0u ttILxAGƒ@tIL=AGMl$0 HpA$xH}It$IEIMHMHEH}It$@agEIHuH=X5HxHEt H}iH]Ht#L3Ct H{ hHhLMuH}HEHthIG`HXHHDMcwXMt%Ifff.H;LwHIuIHXHHDMcMt"IfH;LHIuApA$xH}It$IEIMHMHEH}I@L*fEIGxHXHHDMcgpMLpt-ILxf.H;LLHIuEt H}gH]Ht"L3Ct H{ gHgLMuH}HEHtdgH2HH;EuHh[A\A]A^A_]g(NgIH5L H H K LL q Hx HxLdHr2HIIF IFIF1H5O2HL2L g(fIIH5YH dHxHxL dHFr2HIIF IFIF1H5VO2HK2Lf(p2HIIF IFIF1H5NM2HI2Ld(4dIIH5 H Hx HxLaHo2HIIF IFIF1H5L2H I2Lc(cIIH5 H }HxJ HxLaH:o2HIIF IFIF1H5JL2HH2L{c(0cIIH5 H Hx HxL`Hn2HIIF IFIF1H5K2H H2Lb(bIIH5 H yHxF HxL_H6n2HIIF IFIF1H5FK2HG2Lwb(,bIIH5 H Hx HxL{_Hm2HIIF IFIF1H5J2HG2La(aIIH5 H uHxB HxL^H2m2HIIF IFIF1H5BJ2HF2Lsa~((aIIH5 H Hx HxLw^Hl2HIIF IFIF1H5I2HF2L`(`IIH5 H qHx> HxL]H.l2HIIF IFIF1H5>I2HE2Lo`}('`IIH5H HxHxLv]Hk2HIIF IFIF1H5H2HE2L_ Ix2IxIxIxIxIxu|IxufmIxuSZIxu@G~Ixu07nIxu '^IxuNIxt H}^u8Z.IxuIxuIxtILy^L%Y IEt H}'^H]Hfffff.L3Ct H{ ]H]LMuIH]HfL3Ct H{ ]H]LMunIH]HtbL3Ct H{ ]H]LMu>IEt H}f]H]Ht#L3Ct H{ J]HB]LMuH}HEHt$]LX fffff.UHAWAVAUATSHhMLHHIHxHu2HHEH0L=s2I_@HLHC2HH@H0HfHnfL#\LHEEIGHHIhL0HHL[L5 s2ILHDž HDžHDžHDžDž(LL^LLH裢LxHH^LHHHH^LHHbHHs^LHHEHHV^LHH(LH馞HHL0HHLt H[HYH5Y2HHYH0ZHs2HH;EuLHh[A\A]A^A_]=[HSHeHHHL0HHLt HaZH;YH5rX2HHYH0YHU fff.UHAWAVAUATSHhMLHHIHxH s2HHEH0L=p2I_@HLH@2HH@H0HfHnfL3YLHEEIGHHIhL0HHL$XL5p2ILHDž HDžHDžHDžDž(LL[LLH賟LxHH[LHH菟HH[LHHrHH[LHHUHHf[LHH8LHHHL0HHLt HXHVH5'V2HHVH0WH q2HH;EuLHh[A\A]A^A_]MXHSHeHHHL0HHLt HqWHKVH5U2HH(VH0VH$R fff.UHAWAVAUATSHXIIHHH)p2HHEH0L-m2I]@HLH>2HH@H0HfHnfLQVLHEEIEHHIhL0HHLBUL%7m2ILHDž HDžHDžHDžDž(LHHXLHHʜAIvIEvIEVL訜HHXLHH苜HHHHHL0HHLt HeUH?TH5vS2HHTH0TH[n2HH;EuHHX[A\A]A^A_]UHSHeHHHL0HHLt HTHSH5R2HHsSH0ETHoO UHAWAVAUATSHXIIHHHym2HHEH0L-$k2I]@HLHS;2HH@H0HfHnfLSLHEEIEHHIhL0HHLRL%j2ILHDž HDžHDžHDžDž(LHHHVLHHAIvIEvIEVLHH VLHHۙHH蘞HHL0HHLt HRHQH5P2HHlQH0>RHk2HH;EuHHX[A\A]A^A_]RHSHeHHHL0HHLt H RHPH5P2HHPH0QHL UHAWAVAUATSHXIIHHHj2HHEH0L-th2I]@HLH82HH@H0HfHnfLPLHEEIEHHIhL0HHLOL%g2ILHDž HDžHDžHDžDž(LHHSLHHjAIvIEvIEVLHHHYSLHH+HH蛞HHL0HHLt HPHNH5N2HHNH0OHh2HH;EuHHX[A\A]A^A_]8PHSHeHHHL0HHLt H\OH6NH5mM2HHNH0NHJ UHAWAVAUATSHH-h2HHEIIIIH(NIH5HH LL H} HuL'LH`Z2HIIF IFIF1H5p72H32LNHAHiIFXHXHHDMcfPMtIH;LeAHIuIFpHXHHDMcfhMt(Iffffff.H;L%AHIuW)E)EE?L}IFXHXHHDLHMcfPMtDIL}fffff.L3IvLqkHIvLlHIuLHIF@HXHHDMc~8MtaIffffff.L#A}I$H}kHuff.H}LL[HIuIL`HLDMcMt=II$HLQHC(HuHu5HH}IIuIF(HHHHDIcF HfHH@H8H HA(LpHLDHHHcA HtpM<M&A$uHuIfID$Ht6H}LiHu%H]H[(HHLiHtIM9uHHHLHUQHC@L`HLDHcC8Ht|M4M<$Au Hu XIGHtOH}LYiHlH]fff.H[(HtHL,iHt>H}L&IM9uH8HH;@H]Ht.fff.L3Ct H{ JHJLMuH}HEHtJHc2HH;EuHĨ[A\A]A^A_] K(JHIVH5H H}AHuHGHV2HHHC HCHCE1H5*32Hk/2H[J(JIHhHHcH5H L H}LhLHuLHGHU2HIIF IFIF1H522H.2LI9(wIII$H HhpH}HhWGHuLFHT2HIIF IFIF1H522HI.2L9I(HIH5H +H}L HuLGFHT2HIIF IFIF1H512H-2LH8IEtNH}COIEu&@IEtH}6HhuIijht HxHuWIL3HH]HTqIEt H}GEuIHGH]H8IEt H}Gu3ILGLnB IăLGIHPH0aHHP1=EHHHxHHPHpHhH@H@HL2DHEHH}HuHh-nLHAFu fAFIF IF1HDHEHCHEHMHKHht HxFPt H`xF(FILLDH5R2HIIHlDH5S/2H+2LF ZILCLOFHHIhtHxEPuIPtH`EIEH败 IH]Ht.fff.L3Ct H{ EHELMuH}HEHtdELF@ fffff.UHAWAVAUATSHH]^2HHEIIIIt$huHuHFHA|$ u A|$8TLuLCH=^5LrIEt H}DMH=]+2H5@lIL$hHqHEqHEQHEHHHpHLuLAH5+2LAH HQ8AH}CAHBHBH@]2HH;EIt$xI}PH@(H#M2HIIF 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HHH"?ffff.UHAWAVAUATSHXIIHHH1HHEH0L-1I]@HLH1HH@H0HfHnfLLHEEIEHHIhL0HHLL%1ILHDž HDžHDžHDžDž(LHHLHH&AIvIEvIEVLh&HHyLHHK&HH+HHL0HHLt H%HH561HHH0H1HH;EuHHX[A\A]A^A_]XHSHeHHHL0HHLt H|HVH51HH3H0H/ UHAVSHIHP1HHEH]ȹH蠲W)pH]HEHEHEEH4EE)EHpwHpuHpLHpVrH1HH;EuHĀ[A^]HHN HHprH5 ffff.UHAWAVAUATSHXIIHHH91HHEH0L-1I]@HLH1HH@H0HfHnfLaLHEEIEHHIhL0HHLRL%G1ILHDž HDžHDžHDžDž(LHHLHH"AIvIEvIEVL"HHLHH"HHX'HHL0HHLt HuHOH51HH,H0Hk1HH;EuHHX[A\A]A^A_]HSHeHHHL0HHLt HHH51HHH0UH UHAVSHH?1HHH HHhHH@HCH{춚H5]1HLHL[A^]fDUHAWAVSHhHHA1HHEEHEHEHEHEE?ED HEHEHEHEHuHsEt H}H]Ht-ff.L3Ct H{ zHrLMuH}HEHtTHy1HH;Eu Hh[A^A_]IEt H}"H]Ht/ffff.L;Ct H{ HLMuH}HEHtL fffff.UHAWAVAUATSHXIIHHH1HHEH0L5d1I^@HLH1HH@H0HfHnfLLHEEIFHHIhL0HHLL=1ILHDž HDžHDžHDžDž(LHHHLHHWLHHaHHH/A$It$IEt$IET$HLH"HHL0HHLt HHH51HHH0lH1HH;EuLHX[A\A]A^A_]HSHeHHHL0HHLt H>HH5O1HHH0H fUHAVSH1HHHOHHhHH@HH_LHTH51HHL[A^]YDUHAVSHH@HH G1HQHLHQhHH@HLH|豚H5Y1HHL[A^]fUHAVSHH1HHH LHHhHH@HCH{腱H51HHLH[A^]fffff.UHAVSHr1HHHOHHhHH@HH_LHH51HHGLH[A^]Yffff.UHAVSHH@HH 1HQHLHQhHH@HLH|蘰H5 1HHLH[A^]UHAWAVSPIHIIFIFI @HHCHtHCH;C uHHPHu1Mt5IGI;G uILPHtHuZHCH;C t $fHt=E1HCH;C uHHPHLFHCH;C lHHPPeH[A^A_]H!At I~1 H H H2ff.UHAWAVAUATSHH_HtHFHEHvHuDs Au HHuHEDs AtL{( DDHK LmM9MGMt3AHMt Lc0LaH}LLHMty KL9}rDMt5AtLc0HILHuLtx3fff.L9}v!HHHJ1H[A\A]A^A_]øꐐUHSHH1HHEW)HDžƅDžATenƅ)ƅ fDžneDžAffiƅ)) ƅHConstantHDžFillƅHDž8HDž0ƅ(Dž)Cropƅ-)@ƅ@HDžPHDynamicSHADžIliceƅMHDžhHDž`HDžXƅXƅ_tfDž]niDžYGRUUƅ`)pƅpHEHnsorFillHxHGivenTenHqEHEHEEHEHImageScaHEErfEleE)EE$HEHicSoftplHEHParametrHEfEusEHEHEE HEEeEScalE)EEHEHScaledTaHEfEnhEH`4HM4H=>4H5?4H504HH HH=4H54H HH=4H54H H(H=4H54H H@H=4H54H} HXH=4H54H` HpH=4H54HC HUH=h4H5i4H) HUH=N4H5O4H HUH=44H554H HUH=4H54H EEEEpX@( t HlH=1H5r4H{ pHw1HH;EH[]H}+E>H}E5H}E,H}p#H}XHh@HP(H8H xH_HH5Z4H=K4?EEEEpX@(H H}EtH}EkH}vEbH}cpYH}MXPHh4@DHP(8H8,H  HHH UHAWAVSPIIH(9KHH;=v1t LHL ǃAKHH[A^A_]IHHAL fffff.UHAWAVSPIIH8KHH;=1t L HL{ǃIKHHH[A^A_]IH@L ffff.UHAWAVSPIIH8KHH;=V1t LHLǃKHH;=1t LVHLHH[A^A_]IH@L fffff.UHAWAVSPIIHX7KHH;=1t LHL;ǃKHHu1HHLYHH[A^A_]IH\?L* fUHAWAVSPIIH6KHH;=1t L@HL ǃK HHu1HHLu$HH[A^A_]IH>L fUHAWAVAUATSPIII6AL$I$H;=_1t LI$L AL$ADŽ$IM~L9tKMt$At$ff.D+A;t$u LA6ID$ Hc΍VAT$D,HI9uLH[A\A]A^A_]IL=L UHAWAVAUATSPIII45AL$I$H;=1t LI$L AL$ADŽ$IM~L9tKMt$(At$(ff.L+A;t$,u LA6ID$0Hc΍VAT$(L,HI9uLH[A\A]A^A_]IL<L UHAWAVATSIHIW4ANIH;=1t HIH9 ANAdžIMgL9t)M~8fL^HHHI9uL[A\A^A_]HLK<H UHAWAVATSIHI3ANIH;=1t H.IH ANAdž IMgL9M~PDIF`Ht'IcNX09}-QAVXHDUffffff.Av\f.A;v\uLpIF`0Ɖ0I?IN`IcVXrAvXHDHHTHI9oL[A\A^A_]HL&;H fff.UHAWAVATSIHIw2ANIH;=ĩ1t HIHY ANAdž IMgL9M~hDIFxHt'IcNp09}-QAVpHDUffffff.Avtf.A;vtuL@IFx0Ɖ0I?nINxIcVprAvpHDHHHI9oL[A\A^A_]HL9HĽ fff.UHSPHH HH[]ÐUHAWAVATSAIIH$1KHH;=r1t&LKHH;=V1tL HLHLKDH[A\A^A_]IH(9L fffff.UHAWAVSH8HH1HHEu܉U؉9/9}+1IH1HH;EuH8[A^A_]1(IHEH$H5*5H ]tL ctH}LEH{uHuLH1HIIG IGIG1H561HG1Lo I$IEuuL H}tLLʻ fUHAWAVAUATSHIH1HHEI$PI $H W)EEHEHn_inputsHUHnum_scanHUHxEHuLLEt H}PLhHxL)I$LP H`HhH)HXW)@) )HDžPƅHDžHput_axesHHDž0Hscan_inpHƅI$HLHLh0Lcx(W)EHEMEJ<qHH]H]KDN LMJ HHHLH2H9sHLM9I?I!IpHHWH sA1H1E1E11H@HzHrH)1ADALLAD AL0D L0AD@ALPD@LPAD`ALpD`LpHHuHt8H4Hffffff.AD5AL5D3 3H HuL9JKtODI)DHtHHH HHHuI8rc1DH<H< H|H| H|H| H|H| H| H| H|(H| (H|0H| 0H|8H| 8H|@H@H9uHL)HLtLuH@Ht'HHM1MW)@HDžPH@LHLPt HHHH+@HH;h(ԼHHH+@IHHpH~H$H530HoLoH}HpLh]HuLH1HIIF IFIF1H581HI1Lq1HHHhHEH@HM֍W)ƅ HDžHput_axesHHscan_outHƅI$HLHLh0Lcx(W)EHEMEJ<jHH]H]KDN LMJ HHHLH;H9sHLM9'I?I!IpHHWH sA1H1E1E11H HHrH)1fADALLAD AL0D L0AD@ALPD@LPAD`ALpD`LpHHuHt8H4Hffffff.AD5AL5D3 3H HuL9JKtODI)DHtHHH HHHuI8rc1DH<H< H|H| H|H| H|H| H| H| H|(H| (H|0H| 0H|8H| 8H|@H@H9uHL)HLtLuH Ht'H(M!MW) HDž0H L(L0t HݸH(H+ HH;X(ĸH(H+ IHHpHnH$H5#,HkLkH}HpLX]HuLH1HIIF IFIF1H5(1H91La! 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L@UHAWAVAUATSHhMLHHIHxH1HHEH0L=V1I_@HLH{1HH@H0HfHnfLӓLHEEIGHHIhL0HHLĒL51ILHDž HDžHDžHDžDž(LL聖LLHSڝLxHH]LHH/ڝHH0LHH.LHHڝH0LőLHޝHHL0HHLt H͒H觑H5ސ1HH脑H0VHë1HH;EuLHh[A\A]A^A_]HSHeHHHL0HHLt H(HH591HHߐH0豑Hی UHAWAVAUATSHhMLHHIHxH۪1HHEH0L=1I_@HLHx1HH@H0HfHnfLLHEEIGHHIhL0HHLL51ILHDž HDžHDžHDžDž(LL豓LLHםLxHH荓LHH_םHH0L5HH^LHH0םH0LLH۝HHL0HHLt HH׎H51HH贎H0膏H1HH;EuLHh[A\A]A^A_]4HSHeHHHL0HHLt HXH2H5i1HHH0H  UHAWAVATSH@IH,1HHEHPHD$D$$HGH NLW1E1HU D$D$$HNH PLV1AHɣ H5VHNAHXi H5WHOAH7i IW)HDžfDžVƅZ3HHɝHHwɝHHHH!LMtMHL9t2fffff.HX@t Hx艍HI9uHLLiW)HDž VH(-)Hce typesHHHequence HHHor and SHHHAll TensH@HHHLQ H56UH3LNHo H 1HH H H(H H@HH\ H5TH^ H52,Hha HkU H5:H_ LHBLrQAH裙 D$D$$HLBH OBL5QAHd D$D$$HwBH E7LP1AH؝ H5:HBAHgc IW)HDžfDžVƅ-HHÝ&HHÝHHHHLMtMHL9t2fffff.HX@t Hx蹇HI9uHLL虇W)HDž 膇H(-)Hce typesHHHequence HHHor and SHHHAll TensH@HHHL聞 H5THӪ3LBH蟯 H5DOH3LBH} H 1HH H H(H H@HHV H5SHX H5@&Hv[ HyO H54oHY LHJOH@H9tbHtHP(t HLMtVHL9t;fDHX@t HxHI9uHHP uLL踅t H装HP?H1HH;EuLH@[A\A^A_]ILM'iILMtSHL9t$HX@t Hx)HI9uHLL LMu ILMHL9fHX@t Hx蹄HI9uIIu>HIIH@H9tHtHP(uHP t HALMtGHL9tfffff.HX@t Hx HI9uHLt HكHP>L~ LfUHAWAVSH(IHΜ1HHEH`HD$D$$H 8H "8LK1AH D$D$$H68H L8LK1AHh H5P5Ho8AH] H5YH9AH] H5YH9E1H] H5YH>E1H] H5YH1E1H|] H5$ZH2E1H^] H5TOH03Ln:H HW)HDž fDžVƅ_'HH落W)ƅ HDžHor typesHHAll TensHƅHHHH H 1HH0H H8H0HPHHQ H5]HR H5!HGV HJJ H5/4HfT LHJHPH9tcHtHP(t HLMtWHL9t<HX@t Hx蹀HI9uHHP uLL舀t H sH`:H1HH;EuLH([A^A_]рIuKUIIHPH9tHtHP(uHP t HLMtDHL9tOf.HX@t HxHI9uHLtH yLIH`9LEz ffff.UHAWAVAUATSHhMLHHIHxH;1HHEH0L%1I\$@HLHf1HH@H0HfHnfLb~LHEEID$HHIhL0HHLR}L5G1ILHDž HDžHDžHDžDž(LLLLHĝLxHHLHHĝHH΀LHHĝH0Le|HH蠀LHHrĝHE0L:|LH%ɝHHL0HHLt HB}H|H5S{1HH{H0|H81HH;EuLHh[A\A]A^A_]y}ISIeIHHL0HHLt H|Hw{H5z1HHT{H0&|LPw UHAWAVAUATSHhMLHHIHxHK1HHEH0L%1I\$@HLH$c1HH@H0HfHnfLr{LHEEID$HHIhL0HHLbzL5W1ILHDž HDžHDžHDžDž(LL~LLHLxHH}LHHHH}LHHH0LuyHH}LHHHE0LJyLH5ƝHHL0HHLt HRzH,yH5cx1HH yH0yHH1HH;EuLHh[A\A]A^A_]zISIeIHHL0HHLt HyHxH5w1HHdxH06yL`t UHAWAVAUATSHHHIHHp1HHEH0L%1M|$@LLHI`1HH@H0HfHnfLxHEEID$HHIhL0LLLwL51ILHDž HDžHDžHDžDž(LLK{LLHLH.{HHHLHÝHHL0HHLt 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HPHD/HH;EuLH[A\A^A_],ILMuOIH@I9Iut~It H+LMtEHL9HX@t Hx*HI9uHL*t H*H@I9uHP HtHP(HPLp% LUHAWAVSHIHC/HHEH`H耖H![1H0L0LPHL`H5<HY1L HRH5?DHY1LHRH5EDHfH5H HH5[HLHHPI9t HtHP(HP H`HB/HH;EuLHĸ[A^A_])HHPI9uHP HtHP(H`\H# fUHHZ1HH H]fUHHe[1HH H]fUHAWAVSHIHA/HHEH`HДHZ1H0L0LPHLi^H5:HCX1LHQH5BH)X1LKHPH5BHH5HHH51H6LHHPI9t HtHP(HP H`H@/HH;EuLHĸ[A^A_]&(HHPI9uHP HtHP(H`HJ" fUHAWAVSHIHn@/HHEH`H`HY1H0L0LPHL\H5z9HV1LfHOH5AHV1LHuOH5EAHFH5qHHH5HLH{HPI9t HtHP(HP H`Hq?/HH;EuLHĸ[A^A_]&HHPI9uHP HtHP(H`/HHEH`HHW1H0L0LPHL[H5 8HU1LH'NH5?HiU1LkHNH5?HH5H7H:H5QHVLH HPI9t HtHP(HP H` ߔH>/HH;EuLHĸ[A^A_]F%HHPI9uHP HtHP(H`ޔHj fUHAWAVSHIH=/HHEH`H耐H9V1H0L0LPHLZH56H#T1LHLH5?>HT1LHLH5=>HfH5HHH5HLHHPI9t HtHP(HP H`ݔH9/HHEH`H0HyR1H0L0LPHLUH5J2H3P1L}HgHH59H)P1LHEHH59HH5AHwHzH5HLHKHPI9t HtHP(HP H``ٔHA8/HH;EuLHĸ[A^A_]HHPI9uHP HtHP(H` ٔH fUHAWAVSHIH7/HHEH`HH Q1H0L0LPHLYTH50HN1LHFH58HN1L;HFH5}8HH5ѽHH H5!H&LHHPI9t HtHP(HP H`הH6/HH;EuLHĸ[A^A_]HHPI9uHP HtHP(H`הH: fUHAWAVSHIH^6/HHEH`HPHO1H0L0LPHLRH5j/HM1LzHEH57HM1LHeEH57H6H5aHHH5HLHkHPI9t 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H`H/HH;EuLHĸ[A^A_]HHPI9uHP HtHP(H`H fDUHAWAVSHIH>/HHEH`H0rHY?1H0L0LPHL;H5HZH5H HH5HLHΔHPI9t HtHP(HP H`褿H/HH;EuLHĸ[A^A_]HHPI9uHP HtHP(H`PH fDUHAVSHIH/HHEH`HqD$D$$H$H L1E1H9D$D$$HH L1E1HH5H71LH,H /HH0H m.H8H0HPHHUH5HH5H HH5HLH̔HPH9t HtHP(HP H`нH/HH;EuLH[A^]I"IHPH9uHP HtHP(H`yL ffffff.UHAWAVSH(IH./HHEH`H oD$D$$HBH 'L<1E1HWD$D$$HH L1E1HHW)HDž fDžTƅ~HH=W)HDž@+H(u+)Htensors.HH0Hnumeric HH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@8HHHHH /HH0H .H8H0HPHHH5hHH5H5 H8H5 HTLH ʔHPH9taHtHP(t HLMtUHL9t:DHX@t HxHI9uHHP uLLxt H cH`蛺H|/HH;EuLH([A^A_]IugqILMunIIIHPH9tHtHP(uHP t HLMtHHL9tgfffff.HX@t HxyHI9uHL^t H IH`聹L LfUHAVSHIH@/HHEH`H2kD$D$$HTH 9LN1E1HiD$D$$H%H L1E1HH5HB21LH'H /HH0H .H8H0HPHHH5]HH5H HH5H6LHƔHPH9t HtHP(HP H`H/HH;EuLH[A^](I"IHPH9uHP HtHP(H`詷LG ffffff.UHAVSHIH`/HHEH`HRiD$D$$HtH Y Ln1E1H D$D$$HEH - L51E1HH5!H01LH>%H /HH0H .H8H0HPHHH5rHH5H7 H:H5HVLH ŔHPH9t HtHP(HP H` H/HH;EuLH[A^]HI"IHPH9uHP HtHP(H`ɵLg ffffff.UHAVSHIH/HHEH`HrgD$D$$H H y L 1E1H D$D$$He H M LU 1E1H H5A H.1L(H^#H /HH0H .H8H0HPHHH5HH5!HW HZH5/'HvLH+ÔHPH9t HtHP(HP H`@H!/HH;EuLH[A^]hI"IHPH9uHP HtHP(H`鳔L ffffff.UHAVSHIH/HHEH`HeD$D$$H H L 1E1H D$D$$H H mLu 1E1H@H5a H-1LHH~!H /HH0H .H8H0HPHHH5HH5AHw HzH5O=HLHKHPH9t HtHP(HP H``HA/HH;EuLH[A^]I"IHPH9uHP HtHP(H` L ffffff.UHAVSHIH/HHEH`HcD$D$$H H L 1E1HD$D$$H H L 1E1H` H5 HB+1LhHH ?/HH0H .H8H0HPHHH5H6H5aH H蚿H5oSHLHkHPH9t HtHP(HP H`耰Ha/HH;EuLH[A^]I"IHPH9uHP HtHP(H`)L ffffff.UHAVSHIH/HHEH`HaDž, #ItH >0tH@)LpMtwHxL9t\HX@t HxHI9uHp;ILpMt8HxL9tHX@t HxHI9uLLxPJH`9IPu-IL0M;H8L9HX@t HxFHI9uIHHX0H@PH`LPMILPMHXL9HX@t HxHI9uIp/ILMHL9tpMI)H H^J H Iu@ILMtPHL9t.MI)H HJ H IuHLLIH0I HPI HPiGt HLMt?HL9t$HX@t HxuHI9uHLLUt H @LPMHXL9HX@t Hx HI9uHPyI'IHPF,3IILPMt8HXL9tHX@t HxHI9uLLXw0H@ I0uIPH`IL0Mt?H8L9t$HX@t HxHI9uH0LL8H0*P6LPMHXL9HX@t HxHI9uIL0Mt?H8L9t$HX@t HxNHI9uH0LL8.H0PLPMt?HXL9t$HX@t HxHI9uHPLLXptdH}YILPMtOHXL9t4MI)HÀfff.HHHÀIĀuHPLLXOL0MtH .HH0H .H8H0HPHH襛H5H֜H5kH7 H:H5 HVLH HPH9t HtHP(HP H` H.HH;EuLH[A^]HI"IHPH9uHP HtHP(H`ɄLg ffffff.UHAVSHIH.HHEH`Hr6D$D$$HH 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L@HLHW)HDžfDžTƅ9HH=W)HDž@薽H(@S()Hric tensHH(H to numeHH Hut typesHHHand outpHHHn input HHHConstraiH@0ors.@4HHHHnH5IHH5\H HH5HLH贅t H蛼LMtFHL9t+HX@t HxiHI9uHLLIt H4H@I9t HtHP(HP HPMvH..HH;EuLH[A\A^A_]qILMuOIH@I9Iut~It H菻LMtEHL9HX@t HxYHI9uHL>t H)H@I9uHP HtHP(HPBuL LUHAWAVATSHIH.HHEHPH&HO0H HwH(L L@HLyHW)HDžfDžTƅ6HHW)HDž@6H(O()Hric tensHH(H to numeHH Hut typesHHHand outpHHHn input HHHConstraiH@0ors.@4HHHHH5HH5JYH耎 H胂H5XH蟌LHTt H;LMtFHL9t+HX@t Hx HI9uHLLt HԸH@I9t HtHP(HP HPrH.HH;EuLH[A\A^A_]ILMuOIH@I9Iut~It H/LMtEHL9HX@t HxHI9uHL޷t HɷH@I9uHP HtHP(HPqL耲 LUHAWAVSHIH.HHEH`H#H0H0HH8L0LPHLH5H=0LHH5PH芈H5VH HH5ÔH LHHPI9t HtHP(HP H`pH.HH;EuLHĸ[A^A_]HHPI9uHP HtHP(H`pH fDUHAWAVSHIH>.HHEH`H0"H0H0HH8L0LPHLH5<H0L#HYH5H*H5UUH苊 H~H5cH誈LH_~HPI9t HtHP(HP H`toHU.HH;EuLHĸ[A^A_]蚵HHPI9uHP HtHP(H` oH辯 fDUHAWAVSH(IH.HHEH`H D$D$$HH L1E1HD$D$$HH LAHD$D$$HܣH +LtAHD$D$$HGH OL51E1HHW)HDž fDžTƅ/HHW)HDž@]H(+)Htensors.HH0Hnumeric HH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@8HHHH.H .HH0H .H8H0HPHHՂH5GHH51RHg 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HW@HZ4H5YjHv>LH+4HPH9t HtHP(HP H`@%H!,HH;EuLH[A^]hkI"IHPH9uHP HtHP(H`$Le ffffff.UHAVSHIH,HHEH`H֒D$D$$H|H yL|1E1HzD$D$$H|H GZLu|1E1H@H5a|H:.LHpH~H x,HH0H N,H8H0HPHH9H5؃H;H5A Hw>Hz2H5WH<LHK2HPH9t HtHP(HP H``#HA,HH;EuLH[A^]iI"IHPH9uHP HtHP(H` #Lc ffffff.UHAVSHIH,HHEH`HԒD$D$$HzH wLz1E1HxD$D$$HzH XLz1E1H`}H5zHr.LhnH螐H ?v,HH0H M,H8H0HPHH8H5#H69H5aH<H0H5UH:LHk0HPH9t HtHP(HP H`!Ha,HH;EuLH[A^]gI"IHPH9uHP HtHP(H`)!La ffffff.UHAVSHIH,HHEH`HҒD$D$$HxH BWLx1E1H wD$D$$HxH .WL|E1HvD$D$$H6AH WLyx1E1HD{H5exHn.LWH肎H5 Ht.L&W H`H !.H0H0HPHH5H5H 7H55Hk: Hn.H5SH8LH?.HPH9t HtHP(HP H`TH5~,HH;EuLH[A^]|eI"IHPH9uHP HtHP(H`L_ UHAVSHIH},HHEH`HВD$D$$HvH sLv1E1HtD$D$$HvH sLv1E1H`yH5vH .LhjH螌H ?r,HH0H I,H8H0HPHH4H5H65H5aH8H,H5QH6LHk,HPH9t HtHP(HP H`Ha|,HH;EuLH[A^]cI"IHPH9uHP HtHP(H`)L] ffffff.UHH.HHwH H]DUHAWAVATSHIH{,HHEHPHΒHo.H H;RH(L L@HL9HW)HDžfDžTƅ,HH蝝W)HDž@aH(%)Hric tensHH(H to numeHH Hut typesHHHand outpHHHn input HHHConstraiH@0ors.@4HHHHxH5{H2H5 H@6 HC*H5O H_4LH*t H`LMtFHL9t+HX@t Hx`HI9uHLL`t H`H@I9t HtHP(HP HPHy,HH;EuLH[A\A^A_]`ILMuOIH@I9Iut~It H_LMtEHL9HX@t Hx_HI9uHL_t H_H@I9uHP HtHP(HPL@Z LUHAWAVATSHIH\x,HHEHPHN˒H.H HNH(L L@HLٔHW)HDžfDžTƅ,)HH=W)HDž@^H(@%)Hric tensHH(H to numeHH Hut typesHHHand outpHHHn input HHHConstraiH@0ors.@4HHHHnuH5MxH/H5H2 H&H5&LH0LH&t H]LMtFHL9t+HX@t Hxi]HI9uHLLI]t H4]H@I9t HtHP(HP HPMH.v,HH;EuLH[A\A^A_]q]ILMuOIH@I9Iut~It H\LMtEHL9HX@t HxY\HI9uHL>\t H)\H@I9uHP HtHP(HPBLV LUHAWAVSHIHt,HHEH`HǒH.H0HBMH8L0LPHL{H5mH.LaHH5uH,H5HK0HN$H5I Hj.LH$HPI9t HtHP(HP H`4Ht,HH;EuLHĸ[A^A_]Z[HHPI9uHP HtHP(H`H~U fDUHAWAVSHIHs,HHEH`HƒHQ.H0HLgH8L0LPHLH5lHU.L`H蹂H5otH+H5H.H"H51H*H -LH"HPI9t HtHP(HP H`Hr,HH;EuLHĸ[A^A_]YHHPI9uHP HtHP(H`HT fDUHAWAVSH(IH>r,HHEH`H0ŒD$D$$HTkH vJLLk1E1HgiD$D$$HwHH gJLkAH(iD$D$$HAH80Ɖ0H(w HH8Hc0Q0H|H H8Ht#Hc009}Q0H\{(uZg4;4uL@H80Ɖ0H( w HH8Hc0Q0H\{(tH C(HC Hc`HhH4H Hc0H8HtH uuHH;H1 H% H H LL `LE1HhJ\HHtHc09}QHDO;uLX?H0Ɖ0Hu HHcrHDHH( IHc`HI9Z0L1H8L|HHtHc09}QHDO;uL>H0Ɖ0Ht HHcrHDHLl HHc0HH9ZW)`HDžpHHXH`HXHhLXH;ps L0HhH`HXH`He H`Ht HhJH H }Hc`HhH\HHtHc09}QH|V;uH1=H0Ɖ0Hs HHHcQH|H }Hc0H8HHHtHc09}QH|V;uH<H0Ɖ0Hr HHHcQH|H^ I$1LP(HÃ{(uLs ANI~Hu;+HF C(1t ILs ANI~Hu1s HI~H H H H@ Hp H} Ha,HH;EuHĈ[A\A]A^A_] I(HIH5 H{H@hAH@LEHT,HIIF IFIFE1H51,H0-,LXH( HIH5HH,|AHLbEHS,HIIF IFIFE1H50,H,,LGFHt HjGEu HLGH H!H@t HP*GELOGHH`Hu"nHH`Hu]HH`HtNHhF@HG*HXHH GHBHIHPHWH"H)HH H H H H@ Hp H} H&A fffff.UHAWAVAUATSH8IIM9CE}E~0I]Hffffff.H; 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IHpHL7. ffffff.UHAWAVAUATSHxHPL'HHEHxHEHDžxHpIHhM,$IE1M911HH9u#HpHxHIH9HIHtIIVHufLLrI9tAUA9V|]HLxf.IAF9}IMHuD9}(M~IFHu}ffff.IHtHM~I?t^IEHIUL9*IIEHPH9Lhuf.III?uII M 1AMHH@HLpIHpH Ht HpIHxHʒLEILEHxIEHAfHHHuL9\HpLxE1HH]L}LuLEMtMwHpHAHLuLLhHE 0H v)HHXI$@HHLxHUHPHtIOLuIFIHHHEH]HڃH}H9t HtHP(HP HuH}HxHpHOI'HH;EuLHx[A\A]A^A_]0IH}LxHxHpeL* IHpHIL* ffffff.UHAWAVSH8HH'HHEIMpH~ HtH9tHPHEHEHuHuHPH]HL躎H}H9tHtHP(HWH'HH;Et$HP H?H'HH;EuLH8[A^A_]/ff.UH@H]UHAVSIHufHCHC1H,IFHCIINHKHIFIFIH[A^]HaUHAWAVAUATSH(IIHzG'HHEW)EHEL$1IIIs$DELeMu9C,AuGfANLHHH-ILeHH]LmLLL/C,AtIGIG1L+HEIGHEHMIOIHF'HH;EuLH([A\A]A^A_]-H}*HFfDUHAWAVATSAHIMgAGu fA$IG(IG 1L+HCID$HHKIL$I$HCHCHEL[A\A^A_]H贌@UHAWAVAUATSH8AIIHE'HHEW)EHELq/HHHs*ELmHuHADI\$AD$uVf`DuIIIL5,ILmILuDuH]LLH-ADI\$AD$tID$(ID$ 1H)HEHCHEHMHKHE$HD'HH;EuLH8[A\A]A^A_] ,H}(HsUHAWAVSPHIM~HAFHufAIFXIFP1LT)HCIGHHKIOIHCHCHLH[A^A_]HUHAWAVAUATSH(IIH D'HHEW)EHEL-IIIs(DELeMu=C,I_HAGHuKfSLHHH~*ILeHH]LmLLL,C,I_HAGHtIGXIGP1H<(HEHCHEHMHKHH6C'HH;EuLH([A\A]A^A_]z*H}5'H͉ffff.UHAWAVATSHpHIHB'HHEHp'HHHxHHpHW)pHDžLLHo'HHs_'C4C0LL謗I9tH8LH(Mg`HHLHH;tcLpHA'HH;ELHp[A\A^A_]H}HLHHuHFHHFH3I$HHtI$H3IhrIGpL6IH (=H'1H'p%H'H@'HH;E5(H葇H艇Ht-\Ht(cHL肞u%u1u=puIH," H4'tH'tH 'ptH&H! ffffff.UHAVSH@H?'HHEDH^H]H^H]LVHHFHFHrHuHrHuHrHBHHBDHUH]HHLHHEHEHUHHHHEHEHEDLLsHHLܜu(u4H>'HH;Eu@HH@[A^]H%tHv%H>'HH;Et%HLjuHt H3%u H  H%H fffff.UHAWAVAUATSHhEĉMHxIHH='HHEW)EHEL'IIQH]IsDEL}Mu0EHuHUH}MHEu(Eu1H<'HH;Eu:HHh[A\A]A^A_]H}y#EtH}j#H<'HH;Et#H} H}  HEt(HEuEuH  H}#EtH}#H ffffff.UHAWAVAUATSHMIHIH;'HHEЃH=HhH;='Lt H HhHhI$HEED#HCHEHCHELkHHCHCAIGHEIGHEMwIIGIGH腐HHDHEHMHHLHEHEHMHHHELHEHEƅLLHLjHHHdL<t H!t H HHH :'HH;EuHH[A\A]A^A_]L!( IW)HDž0 H(6#)Hismatch.HHHtype misHHHication HHHe specifHHHAttributH@&HLH+'HIIF IFIF1H5'H'L7 IIt Ht|LrIL輖t Ht H~H膖#H\HTHLHDIHUL# ff.UHAWAVAUATSHhLEAHpIHH,8'HHEW)EHEL!IIWHxIsDEL}Mu0HuHUHxDLEHEu(Eu1H6'HH;Eu:HHh[A\A]A^A_]H}EtH}H6'HH;EtH}H} HEt(HEuEuH2 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HHH5&HHhH0:Hd UHAVSHIH1&HHEH`HrH5HAH!D$D$$Hu*H L421E1H(D$D$$HF*H L1E1H,H51H1(LH=@H5H(LH@H (H0H0HPHHH5RHH5H& H)H5fHELHߋHPH9t HtHP(HP H`ыH/&HH;EuLH[A^]7I"IHPH9uHP HtHP(H`ЋLV fffff.UHAWAVSH(IHn/&HHEH`H`D$D$$H%H ?L|(1E1H&D$D$$HH L7E1H[&D$D$$HH L(1E1H*HW)HDž fDžTƅ5HHPW)HDž@/H(Y")Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH,H a(H0H0HPHHH5HH5HK HNH5HjLH݋HPH9tgHtHP(t HLMt[HL9t@ff.HX@t HxHI9uHHP uLLt H sH`͋H,&HH;EuLH([A^A_]IugqILMunIIIHPH9tHtHP(uHP t HLMtHHL9tgfffff.HX@t HxHI9uHLnt H YH`̋L/  LfUHAWAVSH(IHN+&HHEH`H@~D$D$$Hi!H ;L\$1E1Hw"D$D$$HH L+1E1H&HW)HDž fDžTƅ2HHLW)HDž0KH(")Hry type.HHHrbitraryHHH be of aHHHnsor canHHHInput teH@&HHHH8(H5+H*(LHV9H (H0H0HPHHH5HH5+Ha HdH5ʬ9HLH5ًHPH9t]HtHP(t HLMtQHL9t6HX@t HxHI9uHHP uLLt H H`ɋH(&HH;EuLH([A^A_]ILMu|IuS]IIIHPH9tHtHP(uHP t HLMtHHL9tgfffff.HX@t HxHI9uHLt H yH`ȋLO  LfUHAWAVSH(IHn'&HHEH`H`zD$D$$HH 7L| 1E1HD$D$$HlCH `L (1E1H#HW)HDž fDžTƅ!.HHIW)HDž0k H(")Hry type.HHHrbitraryHHH be of aHHHnsor canHHHInput teH@&HHHHX$H5 'HR(LHv5H (H0H0HPHHH5 H H5KH HH5`HLHUՋHPH9t]HtHP(t H% LMtQHL9t6HX@t Hx HI9uHHP uLL t H H`ŋH$&HH;EuLH([A^A_] ILMu|IuS]IIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hx HI9uHL t H H`ċLo LfUHAWAVSH(IH#&HHEH`HvH5EH@AH/D$D$$HH L{1AHD$D$$HH L?1E1H HW)HDž fDžTƅ*HHEW)HDž0g H(")Hnsor typHH Ho any teHHH types tHHHn outputHHHConstraiHf@(e.@*HHHHN H /(H0H0HPHHH5BH8H5cH HH5HLHmыHPH9teHtHP(t H=LMtYHL9t>fHX@t Hx HI9uHHP uLLt H H`H &HH;EuLH([A^A_]!IugqILMunIIIHPH9tHtHP(uHP t HLMtHHL9tgfffff.HX@t HxHI9uHLt H H`L LfUHAWAVSH(IH&HHEH`HrD$D$$HH L1E1HD$D$$HH Ls1AH;HW)HDž fDžTƅN&HH?AW)HDž@H(¿")Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHiHDžH5.@HLHH5HjE1HH (H0H0HPHHH5Q HH53Hi HlH5Ҡ7HLH=͋HPH9teHtHP(t H LMtYHL9t>fHX@t HxHI9uHHP uLLt H H`˽H&HH;EuLH([A^A_]IuuILMu|IuS]IIIHPH9tHtHP(uHP t HLMt:HL9tYHX@t HxHI9uHLt H yH`豼LO  LfUHAWAVSH(IHn&HHEH`H`nD$D$$HH L|1E1HD$D$$HH uLE1H[D$D$$HH pLE1HD$D$$HH }LlAHD$D$$HH L-AHD$D$$H_H ELM1E1HHW)HDž fDžTƅ+!HH<W)HDž@uH(")Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHFH5HH(L>Hd(H (H0H0HPHHH5HH59Ho HrH5؛NHLHCȋHPH9tkHtHP(t H LMt_HL9tDffffff.HX@t Hx HI9uHHP uLL t H H`˸H&HH;EuLH([A^A_] ILMutIuKUIIHPH9tHtHP(uHP t H LMt@HL9tKfDHX@t Hx HI9uHL tH LIH`跷LU  ffff.UHAWAVSH(IHn&HHEH`H`iH5wHuE1HD$D$$Hk H &L^1E1Hy D$D$$HH L%1E1HHW)HDž fDžTƅHH7W)HDž@M H(w")Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH (H0H0HPHHH5I HH53Hi HlH5җHLH=ċHPH9teHtHP(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 y H`豳LO  LfUHAWAVSH(IHn&HHEH`H`eHHHDž(H5]4HL(HD$D$$HTH ,0LG 1E1Hb D$D$$H0H LE1H& D$D$$HH L E1HD$D$$H H L 1E1Ha HW)HDž fDžTƅtHHe3W)HDž@ H(x)Hsor typeHH(H any tenHH Han be ofHHH types cHHHd outputHHHInput anHf@0.HHHHH5=H{(LHH Z(H0H0HPHH2H5HcH5H HǿH5-HLH蘿HPH9t`HtHP(t Hh LMtTHL9t9@HX@t Hx9 HI9uHHP uLL t H H`+H &HH;EuLH([A^A_]Q ILMIIuS]IIIHPH9tHtHP(uHP t H6 LMtD$D$$HH LE1HD$D$$HH L1E1HyHW)HDž fDžTƅHH}*W)HDž@ H()Hsor typeHH(H any tenHH Han be ofHHH types cHHHd outputHHHInput anHf@0.HHHHH5UHr(LHH rx(H0H0HPHHJH5H{H5H H߶H5EHLH谶HPH9thHtHP(t H LMt\HL9tAfff.HX@t HxI HI9uHHP uLL t H  H`;H&HH;EuLH([A^A_]a ILMu|IuS]IIIHPH9tHtHP(uHP t HR LMtHHL9tgfffff.HX@t Hx HI9uHL t H H`!L  LfUHAWAVSH(IH&HHEH`HWHDž(H5&HTL(HCD$D$$HH "L 1E1HD$D$$H"H eL'E1HD$D$$H\ H LJ 1E1HHW)HDž fDžTƅ( HH&W)HDž@r H()Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHHJH5H|n(LBHhH t(H0H0HPHHH5[HH5=Hs HvH5܅H蒼LHGHPH9t_HtHP(t H LMtSHL9t8HX@t Hx HI9uHHP uLL t H H`ۢH&HH;EuLH([A^A_] ILMu|IuS]IIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hx HI9uHL t H H`L_  LfUHAWAVSH(IH~&HHEH`HpSHDž(H5u"HL(HD$D$$HlH DL_ 1E1HzD$D$$H)H ޜLE1H>D$D$$H H sL 1E1HHW)HDž fDžTƅHH!W)HDž@ H({)Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHHH5H,j(LHH p(H0H0HPHH聵H5RH貶H5݄H HH5|H2LH筋HPH9t_HtHP(t H LMtSHL9t8HX@t Hx HI9uHHP uLLX t H C H`{H\%HH;EuLH([A^A_] ILMu|IuS]IIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t HxY HI9uHL> t H ) H`aL  LfUHAWAVSH(IH%HHEH`HOH5HE1H½D$D$$HH ]L 1E1H)D$D$$HWH YL 1E1HHW)HDž fDžTƅHHW)HDž@ H('")Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH /m(H0H0HPHH臱H5H踲H5H HH5}FH8LHHPH9teHtHP(t H LMtYHL9t>fHX@t Hx HI9uHHP uLLX t H C H`{H\%HH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t HxY HI9uHL> t H ) H`aL  LfUHAWAVSH(IH%HHEH`HKH5HAH迹D$D$$HH aL 1E1H&D$D$$H?H VL 1E1HHW)HDž fDžTƅHHW)HDž@ H($")Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH i(H0H0HPHH脭H5|H赮H5|H HH5yH5LHꥋHPH9tbHtHP(t H LMtVHL9t;fDHX@t Hx HI9uHHP uLLX t H C H`{H\%HH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t HxY HI9uHL> t H ) H`aL  LfUHAWAVSH(IH%HHEH`HGH5nHAH迵D$D$$H H L 1E1H&D$D$$H H L 1E1HHW)HDž fDžTƅHHW)HDž@ H($")Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH ,f(H0H0HPHH脩H5H赪H5xH HH5uH5LHꡋHPH9tbHtHP(t H LMtVHL9t;fDHX@t Hx HI9uHHP uLLX t H C H`{H\%HH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t HxY HI9uHL> t H ) H`aL  LfUHAWAVSHHIH%HHEH`HCH5nHAH迱W)HDž ƅDžDCRH53HWLHD$D$$H H ^L 1E1HD$D$$H H BL 1E1HUHW)HDžfDžTƅhHHYW)HDž@ H(܏")Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH db(H0H0HPHHD$D$$H H L 1E1HD$D$$H|H }LE1HkD$D$$H) H L 1E1HHW)HDž fDžTƅHH W)HDž@? H(i")Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH5HbU(L H.H O^(H0H0HPHH觠H5 HءH5pH9 HfHX@t Hx HI9uHHP uLLx t H c H`蛉H|%HH;EuLH([A^A_] ILMu|IuS]IIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hxy HI9uHL^ t H I H`聈L  LfUHAWAVSHHIH>%HHEH`H0:HW)ƅHDž HnearestHH5eHhLH9D$D$$H H L 1E1HD$D$$HH ]L"[E1HD$D$$H H ‰L 1E1HKHW)HDžfDžTƅ^HHOW)HDž@ H(¾")Hr types.HH/Hl tensorHH(HY' to alHH Houtput 'HHH'X' and HHHn input HHHConstraiH@7HHHHyH ZZ(H0H0HPHH2H5#HcH5kHĠ HǔH5-hHLH蘔HPH9t`HtHP(t Hh LMtTHL9t9@HX@t Hx9 HI9uHHP uLL t H t H H`H%HH;EuLHH[A^A_]< IILMIIIIHPH9tHtHP(uHP t H LMtCHL9twfHX@t Hx HI9uHL t H t H H`̃Lj  Lffff.UHAWAVSHIH~%HHEH`Hp5W)ƅHDž HnearestHH5H҅LH聵Dž @H5HL HeHDžH5θHLHhW)ƅHDžHhalf_pixHfDželƅH5 HuLHɴW)ƅ$HDžHefer_floHHround_prHfDžorƅH5ѷHՇLHYDžH5\HшLH=D$D$$H H L1E1HD$D$$H'H $LJtE1HD$D$$HxH cLTE1HD$D$$H&H %L,UAHMD$D$$H H ;L1E1HHW)HDžƅfDžT1ƅHHW)pHDž@ H(4")pHr types.HH/Hl tensorHH(HY' to alHH Houtput 'HHH'X' and HHHn input HHHConstraiH@7HHHpHH5rHEJ(LeH H *T(H0H0HPHH肕H5׳H賖H5dH HH5}aSH3LH荋HPH9t`HtHP(pt H LMtTHL9t9@HX@t Hx HI9uHHP puLLX uTu`ult H ( H``~HA%HH;EuRLHĘ[A^A_]H tH tH uE ILMI!xIxI IIIpuKUIIHPH9tHtHP(puHP pt H LMtGHL9fHX@t Hx HI9uHL~ u=u\uht H N H`|L$  H, tLItH tH uUHAWAVSH(IH%HHEH`H-D$D$$H H L 1E1HD$D$$H H xL 1E1HHW)HDž fDžTƅHHW)HDž@ H({")Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH %HH0H {%H8H0HPHHcH5H蔑H5_H HH5^\pHLHɈHPH9taHtHP(t H虿 LMtUHL9t:DHX@t Hxi HI9uHHP uLL8 t H # H`[yH<%HH;EuLH([A^A_]聿 IugqILMunIIIHPH9tHtHP(uHP t Hr LMtHHL9tgfffff.HX@t Hx9 HI9uHL t H H`AxL߸  LfUHAWAVSH(IH%HHEH`H)H5H[E1H袘D$D$$H H L 1E1H D$D$$HH P\L|E1HD$D$$H H ӖLy 1E1HDHW)HDž fDžTƅbHHHW)HDž@衼 H(v")Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHrH5:H@(LHH 1K(H0H0HPHH H5!H:H5e[H蛐 H螄H5XH躎LHoHPH9tgHtHP(t H? LMt[HL9t@ff.HX@t Hx HI9uHHP uLLغ t H ú H`tH%HH;EuLH([A^A_]! ILMu|IuS]IIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hxٹ HI9uHL边 t H 詹 H`sL  LfUHAWAVAUATSHIH%HHEHPH%HDžH5HiYLHD$D$$H~H _ZLB1E1HD$D$$HԍH Q[LeE1HZD$D$$H^H LE1HD$D$$H H ɏL1E1HHW)HDžƅfDžT1ƅ聂HHL(W)HDž0 H(NM)Ic types.L`I numericLhI to onlyLpIns inputLxHConstraiH@'HHHHHW)HDžƅfDžT2ƅ蝁HHW)HDž0 H(qL)L`LhLpLxIConstraiL0@'HHHHHW)`HDžpƅ`fDžaT3ƅc[L(HHHW)0HDž@ K H@(!)0Hor type.HHH tensor HHHn to anyHHL0@H`HHH0HPH E(H H H@HH H5+H:H5eUH蛊 H~H5Rb H躈LHo~H@H9tgHtHP(0t H@? LHMt[HPL9t@ff.HX@t Hx HI9uHHHP 0uLLPش `uGuSLMtpHL9tUfHX@t Hx虴 HI9uH2Hp| tHg LMuLLJ uIuULMtrHL9tW@HX@t Hx HI9uH2H tH׳ LMuLL躳 t H襳 HPmH%HH;EuLH[A\A]A^A_] I0ILHM,I.LILMQIILMI`IBTIIIH@H9tHtHP(0uHP 0t H@a LHMt?HPL9t$HX@t Hx6 HI9uHHLLP `uu#LMu/lHp tHձ LMt?HL9t$HX@t Hx誱 HI9uHLL花 uu#LMu/lH^ tHI LMt?HL9t^DHX@t Hx HI9uHL t H HP!kL迫  LfUHAVSHIH%HHEH`HD$D$$H H L1E1H D$D$$H H LO]1E1HH5hH5(L\uHH5]H5(LbuHH =@(H0H0HPHHH5HFH5qOH规 HxH5L HƂLH{xHPH9t HtHP(HP H`iHq%HH;EuLH[A^]踯 I"IHPH9uHP HtHP(H`9iLש  ffffff.UHAVSHIH%HHEH`HD$D$$H H L1E1HD$D$$H H L_[1E1HHDž(H5(HQL(HHDž H5H}RL H趎H5H2(LsHtH5ZH2(LsHRH s>(H0H0HPHH}H5hH~H5'MH] H`vH5I H|LH1vHPH9t HtHP(HP H`FgH'%HH;EuLH[A^]n I,I'I"IHPH9uHP HtHP(H`fL胧  ff.UHAWAVSH(IH%HHEH`HD$D$$HH mQLt1E1HǼD$D$$Hy H dQLs E1H苼D$D$$HG H [QL7 E1HOD$D$$H H SQL 1E1HH5sH0(LqQHHW)HDž fDžTƅwPHHW)HDž@ H(+e")Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH ;(H0H0HPHHzH5H{H5IH H sH5F H<}LHrHPH9tiHtHP(t H LMt]HL9tBffff.HX@t Hx艩 HI9uHHP uLLX t H C H`{cH\%HH;EuLH([A^A_]衩 IugqILMunIIIHPH9tHtHP(uHP t H蒨 LMtHHL9tgfffff.HX@t HxY HI9uHL> t H ) H`abL  LfUHAWAVSH(IH%HHEH`HD$D$$H2 H - L, 1E1HGD$D$$H H L51E1H込HW)HDž fDžTƅNjHHW)HDž  H(U.)Hr types.HHH tensor HHHn to allHHHConstraiH@HHHHH w8(H0H0HPHHvH5%HxH5+FHa{ HdoH5B' HyLH5oHPH9t]HtHP(t H LMtQHL9t6HX@t Hx٥ HI9uHHP uLL訥 t H 蓥 H`_H%HH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hx詤 HI9uHL莤 t H y H`^LO  LfUHAWAVSH8IHn%HHEH`H`HDž(H5HEJL(HӄHDž H5ۙHfJL H覄D$D$$H* H |JL" 1E1H=D$D$$H+WH ZJL1E1HD$D$$H H rJL 1E1HxHW)HDžfDžTƅKHHH|ޑW)HDž@բ H(8)Hsor typeHH(H any tenHH Han be ofHHH types cHHHd outputHHHInput anHf@0.HHHH貹H 4(H0H0HPHHkrH5HsH5AHv HkH5f> HuLHjHPH9tiHtHP(t H衡 LMt]HL9tBffff.HX@t Hxi HI9uHHP uLL8 t H# H`[[H<%HH;EuLH8[A^A_]聡 IuoyILMuvIIIIHPH9tHtHP(uHP t Hj LMt@HL9t_fDHX@t Hx9 HI9uHL t H H`AZLߚ  LfUHAWAVSH(IH%HHEH`H HDž(H5nHFL(HcH5H\GE1HuzD$D$$HDZ H HL 1E1HܯD$D$$H H GL 1E1HSD$D$$HRH ~HLC-AHD$D$$HGIH PIL-AHճD$D$$HH JL,AH薳HW)HDž fDžTƅiCHHّW)HDž0 H( ")Hor type.HHHany tensHHHn be of HHHInput caH@ HHHHH O0(H0H0HPHHmH5HnH5=H9r HfHX@t Hx詜 HI9uHHP uLLx t H c H`VH|%HH;EuLH([A^A_] Iu_iILMufIIHPH9tHtHP(uHP t H躛 LMt@HL9tKfDHX@t Hx艛 HI9uHLn tH Y LIH`UL%  ffff.UHAWAVSH(IH>%HHEH`H0HDž(H5sH{`L(H{D$D$$H,H L 1E1H:D$D$$HH k`L(E1HD$D$$H H NL 1E1HuHW)HDž fDžTƅ舺HHyՑW)HDž@ҙ H(|/)Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHH誰H ,(H0H0HPHHciH5pHjH58Hm HaH5^5 HlLHaHPH9taHtHP(t H虘 LMtUHL9t:DHX@t Hxi HI9uHHP uLL8 t H # 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( IH5\HrH}*AHuL H$HIIF IFIFE1H53$HD$Ll r($ IH5[HH}AHuL H$HIIF IFIFE1H5$H$L  H'HEuEuH}HukrH}{ EtL H}HuJQHHEt H}L Et5Lu H!  HH}HuHH}Ht H} H  fff.UHH "H1H9NHD]DUHH {']UHAVSHIH$HHEH`HRH5HAHD$D$$H H OL1E1HD$D$$H H /L.1E1H_H5GHQz'LZ HH5Hz'Lv H{H ~'H0H0HPHHH5H%H5PH膺 H艮H5MWH襸LHZHPH9t HtHP(HP H`oHP$HH;EuLH[A^] I"IHPH9uHP HtHP(H`L  fffff.UHAWAVSH(IH$HHEH`HPD$D$$HH FL 1E1HD$D$$H{H PLrE1HD$D$$H7H 9Lg 1E1H2HW)HDž fDžTƅHH6W)HDž@ H(!)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH`H {'H0H0HPHHH5 HJH5uH諷H讫H5rHʵLHHPH9tgHtHP(t HO LMt[HL9t@ff.HX@t Hx HI9uHHP uLL t H H` H$HH;EuLH([A^A_]1 IugqILMunIIIHPH9tHtHP(uHP t H" LMtHHL9tgfffff.HX@t Hx HI9uHL t H H`L  LfUHAWAVSH(IH$HHEH`HLD$D$$HH & L 1E1HD$D$$H[H dLJ 1E1HNHW)HDž fDžTƅ!HHRW)HDž0 H(Eq!)Hry type.HHHrbitraryHHH be of aHHHnsor canHHHInput teH@&HHHHH5` H:t'LHH Wx'H0H0HPHH/H5H`H5~HHħH5HLH蕧HPH9t]HtHP(t He LMtQHL9t6HX@t Hx9 HI9uHHP uLL t H H`+H $HH;EuLH([A^A_]Q ILMu|IuS]IIIHPH9tHtHP(uHP t HB LMtHHL9tgfffff.HX@t Hx HI9uHL t H H`L  LfUHAWAVSH(IH$HHEH`HHD$D$$HH FL 1E1HD$D$$HH Lj 1E1HnHW)HDž fDžTƅAHHrW)HDž0 H(em!)Hry type.HHHrbitraryHHH be of aHHHnsor canHHHInput teH@&HHHHH5 Hbp'LHH t'H0H0HPHHOH5H耬H5zHHH5 HLH赣HPH9t]HtHP(t H LMtQHL9t6HX@t HxY HI9uHHP uLL( t H  H`KH,$HH;EuLH([A^A_]q ILMu|IuS]IIIHPH9tHtHP(uHP t Hb LMtHHL9tgfffff.HX@t Hx) HI9uHL t H H`1L  LfUHAWAVSH(IH$HHEH`HDH5HAH菳D$D$$HH L 1AHD$D$$HH L 1E1HjHW)HDž fDžTƅ=}HHnW)HDž0 H(q!)Hnsor typHH Ho any teHHH types tHHHn outputHHHConstraiHf@(e.@*HHHHH q'H0H0HPHHgH5H蘨H5vH HH5tHLH͟HPH9teHtHP(t H LMtYHL9t>fHX@t Hxi HI9uHHP uLL8 t H # H`[H<$HH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t Hr LMtHHL9tgfffff.HX@t Hx9 HI9uHL t H H`AL  LfUHAWAVSH(IH$HHEH`H@D$D$$H H lL 1E1H'D$D$$H\H GL 1AHHW)HDž fDžTƅnyHHW)HDž@ H("!)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHHDžH5HaLHH5HʃE1HH m'H0H0HPHH7H5HhH5rHɧ H̛H5HLH蝛HPH9teHtHP(t Hm LMtYHL9t>fHX@t Hx9 HI9uHHP uLL t H H`+H $HH;EuLH([A^A_]Q IuuILMu|IuS]IIIHPH9tHtHP(uHP t H4 LMt:HL9tYHX@t Hx HI9uHL t H H`L  LfUHAWAVSH(IH$HHEH`HfHX@t Hx9 HI9uHHP uLL t H H`+H $HH;EuLH([A^A_]Q IugqILMunIIIHPH9tHtHP(uHP t HB LMtHHL9tgfffff.HX@t Hx HI9uHL t H H`L  LfUHAWAVSH(IH$HHEH`H3H訐HDž(H5HRzL(H+D$D$$HH L 1E1HD$D$$HqH dzLE1HD$D$$HzH zL2 E1HJD$D$$H H zL 1E1HHW)HDž fDžTƅkHHW)HDž@ H([)Hsor typeHH(H any tenHH Han be ofHHH types cHHHd outputHHHInput anHf@0.HHHHH5HZ'LHH a'H0H0HPHH蒕H51HÖH5dH$ H'H5r}HCLHHPH9t`HtHP(t H LMtTHL9t9@HX@t Hx HI9uHHP uLLh t H S H`~Hl$HH;EuLH([A^A_] ILMIIuS]IIIHPH9tHtHP(uHP t H LMt$HHEH`H0&HDž(H55HnL(H裚D$D$$H,H L 1E1H:D$D$$HH nLE1HD$D$$H H FoL 1E1HuHW)HDž fDžTƅH^HHyW)HDž@Ҹ H(|N)Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHHH5LHM'LHH U'H0H0HPHHAH5HrH5WHӌ HրH5eHLH觀HPH9t_HtHP(t Hw LMtSHL9t8HX@t HxI HI9uHHP uLL t H  H`;qH$HH;EuLH([A^A_]a ILMu|IuS]IIIHPH9tHtHP(uHP t HR LMtHHL9tgfffff.HX@t Hx HI9uHL t H H`!pL述  LfUHAWAVSH(IH$HHEH`H!HDž(H5HTjL(HCD$D$$HH L 1E1HD$D$$HH >kL'E1HD$D$$H\ H kLJ 1E1HHW)HDž fDžTƅYHHW)HDž@r H(J)Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHHJH5HfHX@t Hx HI9uHHP uLL踮 t H 裮 H`hH$HH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hx蹭 HI9uHL螭 t H 艭 H`gL_  LfUHAWAVSH(IH~$HHEH`HpH5gHPeAHD$D$$HxH eLk 1E1H膽D$D$$HeH dL2 1E1HHW)HDž fDžTƅQHHW)HDž@Z H(f!)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH+H K'H0H0HPHH{H5ܽH}H5@KHv HytH5=YH~LHJtHPH9tbHtHP(t H LMtVHL9t;fDHX@t Hx HI9uHHP uLL踪 t H 裪 H`dH$HH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hx蹩 HI9uHL螩 t H 艩 H`cL_  LfUHAWAVSH(IH~$HHEH`HpH5ΰHbAHD$D$$Hs H bLk 1E1H膹D$D$$HD H BbL2 1E1HHW)HDž fDžTƅMHHW)HDž@Z H(b!)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH+H G'H0H0HPHHwH5HyH5@GHv|HypH5=UHzLHJpHPH9tbHtHP(t H LMtVHL9t;fDHX@t Hx HI9uHHP uLL踦 t H 裦 H``H$HH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hx蹥 HI9uHL螥 t H 艥 H`_L_  LfUHAWAVSHHIH~$HHEH`HpH5άH^AHW)HDž ƅDžDCRH5H^LHgD$D$$H+ H ]L# 1E1H>D$D$$H H ^L 1E1H赹HW)HDžfDžTƅIHHߐW)HDž@ H(<^!)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH C'H0H0HPHHsH5èHtH5BH.x H1lH5P9HMvLHlHPH9tjHtHP(t HҢ LMt^HL9tCfffff.HX@t Hx虢 HI9uHHP uLLh t HS t H > H`v\HW$HH;EuLHH[A^A_]蜢 IILMIIIIHPH9tHtHP(uHP t Hm LMtCHL9twfHX@t Hx9 HI9uHL t H t H H`,[Lʛ  Lffff.UHAWAVSH(IH޹$HHEH`H D$D$$H H ZL 1E1HD$D$$HZH ZLz E1H˰D$D$$H H [Lw 1E1HBHW)HDž fDžTƅEHHFېW)HDž@蟟 H(Y!)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHpH58 Hr4'LlZHH ?'H0H0HPHHoH5H8pH5c>HsHgH5`LHqLHmgHPH9teHtHP(t H= LMtYHL9t>fHX@t Hx HI9uHHP uLL؝ t H Ý H`WHܶ$HH;EuLH([A^A_]! ILMu|IuS]IIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hxٜ HI9uHL辜 t H 詜 H`VL  LfUHAWAVSHHIH$HHEH`HHxeW)ƅHDž HnearestHH5HWLH虈D$D$$H[ H WLU 1E1HpD$D$$H H WL)E1H4D$D$$H H "XL 1E1H諰HW)HDžfDžTƅ~@HH֐W)HDž@ H("!)Hr types.HH/Hl tensorHH(HY' to alHH Houtput 'HHH'X' and HHHn input HHHConstraiH@7HHHHٱH ;'H0H0HPHHjH5HkH59H$o H'cH5GHCmLHbHPH9t`HtHP(t Hș LMtTHL9t9@HX@t Hx虙 HI9uHHP uLLh t HS t H > H`vSHW$HH;EuLHH[A^A_]蜙 IILMIIIIHPH9tHtHP(uHP t Hm LMtCHL9twfHX@t Hx9 HI9uHL t H t H H`,RLʒ  Lffff.UHAWAVSHIHް$HHEH`HW)ƅHDž HnearestHH5 H2TLHDž @H5MH|UL H}HDžH5.H\VLHwW)ƅHDžHhalf_pixHfDželƅH5lHrLH)W)ƅ$HDžHefer_floHHround_prHfDžorƅH51H5VLH蹂DžH5H1WLH|D$D$$HO H QL 1E1HdD$D$$HWH WLBE1H(D$D$$H؄H AXL:#E1HD$D$$HYH YL#AH譥D$D$$Hi H QL 1E1H$HW)HDžƅfDžT1ƅ9HH!АW)pHDž@z H(!)pHr types.HH/Hl tensorHH(HY' to alHH Houtput 'HHH'X' and HHHn input HHHConstraiH@7HHHpHKH5@HU)'LXHiH 5'H0H0HPHHcH57HeH5>3Hth Hw\H5;AHfLHH\HPH9t`HtHP(pt H LMtTHL9t9@HX@t Hx HI9uHHP puLL踒 uTu`ult H 舒 H`LH$HH;EuRLHĘ[A^A_]HO tH: tH% u襒 ILMI!xIxI IIIpuKUIIHPH9tHtHP(puHP pt H1 LMtGHL9fHX@t Hx HI9uHLސ u=u\uht H 讐 H`JL脋  H茐 tLItHd tHO uUHAWAVSH(IHN$HHEH`H@D$D$$Hd H GL\ 1E1HwD$D$$H5 H TL# 1E1HHW)HDž fDžTƅ4HHʐW)HDž@K H(uI!)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH $HH0H s$H8H0HPHH^H5PgH_H5.HUcHXWH5<$HtaLH)WHPH9taHtHP(t H LMtUHL9t:DHX@t Hxɍ HI9uHHP uLL蘍 t H 胍 H`GH$HH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t HҌ LMtHHL9tgfffff.HX@t Hx虌 HI9uHL~ t H i H`FL?  LfUHAVSHIH`$HHEH`HRD$D$$Ht H o L5 1E1H艜D$D$$HE H @ L81E1HH5 HB!'LPH>H58H8!'LPHH -'H0H0HPHH[H5wgH\H5*H'` H*TH58:HF^LHSHPH9t HtHP(HP H`EH$HH;EuLH[A^]8 I"IHPH9uHP HtHP(H`DLW  ffffff.UHAWAVSH(IHn$HHEH`H`D$D$$H H } L| 1E1H藚D$D$$HS H N L71E1HHW)HDž fDžTƅ.HHŐW)HDž k H()Hr types.HHH tensor HHHn to allHHHConstraiH@HHHHfH +'H0H0HPHHYH5ueHPZH5{(H] HQH5x6PH[LHQHPH9t]HtHP(t HU LMtQHL9t6HX@t Hx) HI9uHHP uLL t H H`BH$HH;EuLH([A^A_]A IugqILMunIIIHPH9tHtHP(uHP t H2 LMtHHL9tgfffff.HX@t Hx HI9uHLކ t H Ɇ H`AL蟁  LfUHAWAVSH(IH$HHEH`HHDž(H5?_HKL(H#gD$D$$HH L 1E1H躖D$D$$HiH KLZE1H~D$D$$H< H X:L* 1E1HHW)HDž fDžTƅ*HHW)HDž@R H()Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHH*H ('H0H0HPHHTH5%\HVH5?$HuY HxMH5<2HWLHIMHPH9taHtHP(t H LMtUHL9t:DHX@t Hx HI9uHHP uLL踃 t H 裃 H`=H$HH;EuLH([A^A_] IugqILMunIIIHPH9tHtHP(uHP t H LMtHHL9tgfffff.HX@t Hx蹂 HI9uHL螂 t H 艂 H`(1E1HoH5W H'L D H譣H5(H'L5D H苣H ,'H0H0HPHHKH5'H5LH5`HOHCH5]( HMLHjCHPH9t HtHP(HP H`4H`$HH;EuLH[A^]z I"IHPH9uHP HtHP(H`(4Lt  fffff.UHAVSHIH$HHEH`HH5H+CE1HTD$D$$H(H (LЋ 1AHD$D$$H(H (L 1E1H_H5 HA'LBH蝡H5xFHnJH5HMHAH5&* HKLHAH`2H$HH;EuLHİ[A^]x HH`2HBs  f.UHAWAVSH(IH^$HHEH`HPH5`HBAHRD$D$$Hg'H g'LK 1AHcD$D$$HT'H ['L 1E1HڌHW)HDž fDžTƅHH޲W)HDž07w H(`!)Hnsor typHH Ho any teHHH types tHHHn outputHHHConstraiHf@(e.@*HHHHH 'H0H0HPHHFH5DHHH53HiKHl?H50$s HILH=?HPH9teHtHP(t H v LMtYHL9t>fHX@t Hxu HI9uHHP uLLu t H u H`/H$HH;EuLH([A^A_]u IugqILMunIIIHPH9tHtHP(uHP t Ht LMtHHL9tgfffff.HX@t Hxt HI9uHLt t H yt H`.LOo  LfUHAVSHIHp$HHEH`HbD$D$$H H #L~ 1E1H虄D$D$$H~H =LE AHZD$D$$H=H z#L 1AHΈH5 H 'L8H H5H=E1H>NH5}H=E1H NH5{HDH5HHH
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^Hr+AHh6H5CkH+AHG6D$D$$Hm H kLm 1E1H^pHW)HDž fDžTƅ1HHbW)HDž@Z H(!)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHqH m'H0H0HPHHE*H5YHv+H5H.H"H5D H,LH"HPH9tcHtHP(t H{Y LMtWHL9t<HX@t HxIY HI9uHHP uLLY t H Y H`;Hr$HH;EuLH([A^A_]aY IugqILMunIIIHPH9tHtHP(uHP t HRX LMtHHL9tgfffff.HX@t HxX HI9uHLW t H W H`!LR  LfUHAWAVSH(IHp$HHEH`HÊD$D$$HfH fLi 1E1HhD$D$$HZH fLTVE1HgD$D$$HfH LVE1HgD$D$$HfH fLUAHPgD$D$$HUH &LUAHgD$D$$Hh H fLh 1E1HkHW)HDž fDžTƅ[HH茑W)HDž@U H(!)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHlH5XTH(&LeH}H &H0H0HPHHM%H5 TH~&H5H) HH50 H'LHHPH9tkHtHP(t HT LMt_HL9tDffffff.HX@t HxIT HI9uHHP uLLT t H T H`;Hm$HH;EuLH([A^A_]aT ILMutIuKUIIHPH9tHtHP(uHP t HZS LMt@HL9tKfDHX@t Hx)S HI9uHLS tH R LIH`' LM  ffff.UHAWAVSH(IHk$HHEH`HоHDž(H5ՍH2#L(HC3D$D$$HaH Ld 1E1HbD$D$$HH .#L'QE1HbD$D$$HH LJd E1HbbD$D$$H d H Ld 1E1HfHW)HDž fDžTƅHH݌W)HDž@6Q H()Hsor typeHH(H any tenHH Han be ofHHH types cHHHd outputHHHInput anHf@0.HHHHhH5OH&L aH1yH &H0H0HPHH H5IKH!H5H<% H?H5x H[#LHHPH9thHtHP(t HO LMt\HL9tAfff.HX@t HxO HI9uHHP uLLxO t H cO H` H|h$HH;EuLH([A^A_]O ILMu|IuS]IIIHPH9tHtHP(uHP t HN LMtHHL9tgfffff.HX@t HxyN HI9uHL^N t H IN H`LI  LfUHAWAVSH(IH>g$HHEH`H0H5UHAH(D$D$$H3` H L+` 1E1HF^D$D$$H` H L_ 1E1HbHW)HDž fDžTƅHHW)HDž@M H(D!)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHcH L&H0H0HPHHH5QHH5H6!H9H5 HULH HPH9tbHtHP(t HK LMtVHL9t;fDHX@t HxK HI9uHHP uLLxK t H cK H`H|d$HH;EuLH([A^A_]K IugqILMunIIIHPH9tHtHP(uHP t HJ LMtHHL9tgfffff.HX@t HxyJ HI9uHL^J t H IJ H`LE  LfUHAWAVSH(IH>c$HHEH`H0HDž(H55H?L(H*D$D$$H,YH L\ 1E1H:ZD$D$$HH :LHE1HYD$D$$H[ H FL[ 1E1Hu^HW)HDž fDžTƅHHHyW)HDž@H H(|)Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHH_H5LGH<&LXHpH i&H0H0HPHHAH5HrH5HHH5 HLHHPH9t_HtHP(t HwG LMtSHL9t8HX@t HxIG HI9uHHP uLLG t H G H`;H`$HH;EuLH([A^A_]aG ILMu|IuS]IIIHPH9tHtHP(uHP t HRF LMtHHL9tgfffff.HX@t HxF HI9uHLE t H E H`!L@  LfUHAWAVSH(IH^$HHEH`HбH5UHE1H D$D$$HTH LW 1E1HUD$D$$HH LW 1E1H`ZHW)HDž fDžTƅ3HHdW)HDž@D H( )Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH[H &H0H0HPHHGH5OHxH5HH H5Y HLH HPH9teHtHP(t H}C LMtYHL9t>fHX@t HxIC HI9uHHP uLLC t H C H`;H\$HH;EuLH([A^A_]aC IugqILMunIIIHPH9tHtHP(uHP t HRB LMtHHL9tgfffff.HX@t HxB HI9uHLA t H A H`!L<  LfUHAWAVSH(IHZ$HHEH`HЭH5QH8AHD$D$$HPH !LS 1E1HQD$D$$HH LS 1E1H]VHW)HDž fDžTƅ0HHa|W)HDž@@ H( )Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHWH l&H0H0HPHHDH5 LMtHHL9tgfffff.HX@t Hx> HI9uHL= t H = H`!L8  LfUHAWAVAUATSHIHV$HHEHPH̩HDžH5xHoLH?D$D$$HtH LW 1E1HMD$D$$HH LE1HMD$D$$H;H $LFE1H^MD$D$$HO H L 1E1HQHW)HDžƅfDžT1ƅHHwL(W)HDž0$< H()Ic types.L`I numericLhI to onlyLpIns inputLxHConstraiH@'HHHHSHW)HDžƅfDžT2ƅHHvW)HDž0G; H()L`LhLpLxIConstraiL0@'HHHH\RHW)`HDžpƅ`fDžaT3ƅcL(HHH2vW)0HDž@ : H@(T )0Hor type.HHH tensor HHHn to anyHHL0@H`HHH0HQH &H H H@HHI H5kHz H5H HH56H LHH@H9tgHtHP(0t H@9 LHMt[HPL9t@ff.HX@t HxI9 HI9uHHHP 0uLLP9 `uGuSLMtpHL9tUfHX@t Hx8 HI9uH2Hp8 tH8 LMuLL8 uIuULMtrHL9tW@HX@t HxI8 HI9uH2H,8 tH8 LMuLL7 t H7 HPHP$HH;EuLH[A\A]A^A_]?8 I0ILHM,I.LILMQIILMI`IBTIIIH@H9tHtHP(0uHP 0t H@6 LHMt?HPL9t$HX@t Hxv6 HI9uHHLLPV6 `uu#LMu/lHp*6 tH6 LMt?HL9t$HX@t Hx5 HI9uHLL5 uu#LMu/lH5 tH5 LMt?HL9t^DHX@t HxY5 HI9uHL>5 t H)5 HPaL/  LfUHAWAVSHIHN$HHEH`HH5 pH E1HD$D$$HG H kLG 1E1H)ED$D$$H H  LN E1HDD$D$$HF H LF 1E1HdIHW)@HDžPfDž@TƅB7H(HhoW)HDž @3 H ( )Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5H@H(HHJH5ZM H4&L' H[H5hHH5H HH5^HLHt H 2 L(MtHH0L9t-fHX@t Hxi2 HI9uH(LL0I2 @t HP42 H`lHMK$HH;EuLH[A^A_]2 IL(Mu9sI@upzIuIt H 1 L(MtfHX@t Hx. HI9uHHP uLLh. t H S. H`HlG$HH;EuLH([A^A_]. IuuILMu|IuS]IIIHPH9tHtHP(uHP t H- LMt:HL9tYHX@t Hxi- HI9uHLN- t H 9- H`qL(  LfUHAVSHIH0F$HHEH`H"H5@H6AHW)ƅHDž HconstantHƅH57gHLH Dž H5H^L HD$D$$H;H gL> 1E1H H jLb> 1E1H-AH5N> H&L52HkTH &H0H0HPHHH5 HH5@HvHyH5=HLHJHPH9tKHtHP(t H + H`RH3D$HH;Eu LH[A^]HP ui+ IuISINIu6@IHPH9uHP u HtHP(t H x* H`LN%  fDUHAWAVSH(IHnC$HHEH`H`D$D$$H9H aaL|< 1E1H:D$D$$HFaH L7E1H[:D$D$$H< H 5L< 1E1H>HW)HDž fDžTƅHHdW)HDž@/) H(پ)Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHH@H &H0H0HPHHH5HH5HR HUH5HqLH&HPH9t^HtHP(t H' LMtRHL9t7fHX@t Hx' HI9uHHP uLL' t H ' H`H@$HH;EuLH([A^A_]' IugqILMunIIIHPH9tHtHP(uHP t H& LMtHHL9tgfffff.HX@t Hx& HI9uHL~& t H i& H`L?!  LfUH]f.UH]2& fDUH*& H k&H]fDUHHU&H]UH]f.UH]% fDUHSH(H>$HHEHW)EHEEfEtoEHu11HEt H}% HHPHt_H1HPHtOH1HPH(tH@H(u H@ @uuH@ @t 11HRH>>$HH;EuH([]% HEt H}$ H  DUHH !H1H9NHD]DUHHm&]UHAWAVATSH AIIHH=$HHEHHxDD谎(b$ IH5>HuLiH}LzHuL! H/$HIIF IFIF1H5 $H $L.$ .EH<$HH;EuHLDH [A\A^A_]-$ (# IH5HLtLH}LHuL H/$HIIF IFIF1H5> $HO$Lw# z(/# IH5 HLH}LHuL H.$HIIF IFIF1H5 $H$L"  I@I;I6IEuIEu IEt H}n" u LL  L" L:  fUHAWAVAUATSHXLIHHHHB;$HHEH0L-8$Mu@LLH $HH@H0HfHnfLj! HEEIEHHIhL0LLLb L=W8$ILHDž HDžHDžHDžDž(LH$ LHHgLHH# HHHgA$It$IEt$IET$HgHH# HHH~gLH?lHHL0HHLt H\ H6 H5m$HH H0 HR9$HH;EuLHX[A\A]A^A_] HSHeHHHL0HHLt H H H5$HHn H0@ Hj  fUHAWAVAUATSHXLIHHHHr8$HHEH0L-6$Mu@LLHL$HH@H0HfHnfL HEEIEHHIhL0LLL L=5$ILHDž HDžHDžHDžDž(LHO! LHH!eLHH+! 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IhHuI`IMuKff.LHMt3I\$I`LILk0L{PuHLI0H51L;MtI~`L*IF`IGXH[A\A]A^A_]ffffff.UH]fDUHAWAVSPIHL{0LSTHڈHu{u H ֈHK H9HqHHH ~H{hIv,HCh1A^ L3H[A^A_]fDUHAWAVSPIHL{0LoSTHEHu{u H AHK H9HqHHH ~ H{hIvHChAF 1LH[A^A_]ÐUHAWAVAUATSHhIIIHw-HHEMHUL3L5IINHHHg E+mrIL}H}HuEEI\$0HXAL$TLLufA|$uYH*UH,HH;EpILLHh[A\A]A^A_] AYH,HH;E'4ID$ LxH]L0LhHHILLI|$hHuID$hHxHHuLL[ELHLHmH}LLLV Aƃ}xA&t 6&t-H=1&&&t%uH=%HH[]UH]Hlffff.UHH]DUHHiH4ׂCHHHH?HHHi6eHHH]DUHHiʚ;HHS㥛 HHH?HH]@UH HiH4ׂCHHH?HHHi6eHHH]UHAWAVAUATSHIIH*%HHEP HiI4ׂCIHH?HHHi6eLLH41Hɚ;L6eLNHIiʚ;LHEH}G HiIHH?HHHi6eH11H)@)t*x911Hq$HH;EuOHH[A\A]A^A_]11L9)хyI$ʚ;1I9IMH)H)H"$HH;Etx fff.UHLH<11Hɚ;H16eHNL]UH1E1H9AD)t]11H9)]DUHH)Hʚ;1H9HMH)H)H]ÐUHSPHH=H5MHH @UHAVSHIH LHH[A^]PfUHH2HG8HG0HG(HG HGHGHGHG@%@%B%D%F%H%J%L%N%P%R%T%V%X%Z%\%^%`%b%d%f%h%j%l%n%p%r%t%v%x%z%|%~%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % % % % % % % % % % % % % % % %" %$ %& %( %* %, %. %0 %2 %4 %6 %8 %: %< %> %@ %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 %| %~ % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %!%!%!%!%!% !% !%!%!%!%!%!%!LAS%hhhh2hThnhh#h#h$hh<xhnhdh<ZhPhFh"hY4h*h hhK hhhh;hwhhh+hghhh hThhNvhlhbhXh5NhDh:h90hX&hyhhhEh|hhhhDhhhhh3hThh|h rh<hh|^hThJh@h(6hF,hd"hhhh!h<hXhthhhh h"h@h[hxhhxhnhdhZhPhFhh$#4h4#*hE# hU#ho# h~#h#h#h#h#h+$h9$hQ$h^$hl$h$h$h$h$h$vh %lh(%bhH%Xhe%Nh%Dh%:h%0h%&h%h&h &h?&hO&h_&ht&h&h&h&h&h&h&h&h&h'h'|h-'rh?'hhP'^h`'Thq'Jh'@h'6h',h'"h'h'h'h'h(h(h'(h8(hI(OrtGetApiBaseOrtSessionOptionsAppendExecutionProvider_CPU/Users/runner/work/1/s/onnxruntime/core/session/IOBinding.ccBindInputSynchronizeInputsSynchronizeOutputsonnxruntime::AllocatorPtr onnxruntime::IOBinding::GetCPUAllocator(int, onnxruntime::ProviderType) constp_providerSyncProviders/Users/runner/work/1/s/onnxruntime/core/session/abi_session_options.ccOrtSessionOptions &OrtSessionOptions::operator=(const OrtSessionOptions &)execution_mode is not validgraph_optimization_level is not validWARNING: Since openmp is enabled in this build, this API cannot be used to configure intra op num threads. Please use the openmp environment variables to control the number of threads. Result buffer is not large enoughADescription/Users/runner/work/1/s/onnxruntime/core/session/custom_ops.ccCreateCustomRegistryonnxruntime::CustomOpKernel::CustomOpKernel(const onnxruntime::OpKernelInfo &, OrtCustomOp &)Unsupported version '' in custom op 'ComputeAsyncNo requested allocator availableEnv is nullOrtMemoryInfo is nullOnly CPU devices are supported for now.Received invalid value for arena extend strategy. Valid values can be either 0, 1 or -1.Allocator with this OrtMemoryInfo is already registered.inter-opMemcpyFromHostXinputTYoutputConstrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.MemcpyToHostInput expected to have type but instead is null expected to have tensor typeElement type of input unknownOutput expected to have sequence typeTrying to register schema with name (domain: version: ) from file line , but it is already registered from file , but it its domain is not known by the checker., but it its version is not in the inclusive range [] (usually, this means you bumped the operator version but forgot to update the version range in DomainToVersionRange in onnx/defs/schema.h).Schema error: com.microsoftcom.microsoft.nchwc/Users/runner/work/1/s/onnxruntime/core/session/inference_session.ccvoid onnxruntime::InferenceSession::ConstructorCommon(const onnxruntime::SessionOptions &, const onnxruntime::Environment &)status.IsOK()Could not finalize session options while constructing the inference session. Error Message: graph_transformation_mgr_.SetSteps(session_options_.max_num_graph_transformation_steps).IsOK()ConstructorCommonCreating and using per session threadpools since use_per_session_threads_ is trueintra-opFailed to create the inter-op thread pool for the parallel executor, setting ExecutionMode to SEQUENTIALUsing global/env threadpools since use_per_session_threads_ is falsesession_env.EnvCreatedWithGlobalThreadPools()When the session is not configured to use per session threadpools, the env must be created with the the CreateEnvWithGlobalThreadPools API.onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions &, const onnxruntime::Environment &, const std::string &)Given model could not be parsed while creating inference session. Error message: onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions &, const onnxruntime::Environment &, std::istream &)st.IsOK()Could not parse model successfully while constructing the inference sessiononnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions &, const onnxruntime::Environment &, const void *, int)result~InferenceSessionUnknown error during EndProfiling()Received nullptr for exec providerRegisterExecutionProviderExecution providers must be registered before the session is initialized. Execution providers must be registered before the session is initialized.DmlExecutionProviderHaving memory pattern enabled is not supported while using the DML Execution Provider. So disabling it for this session since it uses the DML Execution Provider.Parallel execution mode does not support the DML Execution Provider. So making the execution mode sequential for this session since it uses the DML Execution Provider.CUDAExecutionProviderParallel execution mode does not support the CUDA Execution Provider. So making the execution mode sequential for this session since it uses the CUDA Execution Provider.Received nullptr for graph transformerRegisterGraphTransformerGraph transformers must be registered before the session is initialized.AddCustomOpDomainsReceived nullptr for custom registrySaveToOrtFormatLoadThis 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: [: [LoadOrtModelThis session has already been initialized.Satisfied, but should not be: nullptr == fbs_session Satisfied, but should not be: fbs_ort_model_version == nullptr Not satisfied: IsOrtModelVersionSupported(fbs_ort_model_version->str()) The ORT format model version [] is not supported this build Satisfied, but should not be: nullptr == fbs_model Satisfied, but should not be: nullptr == fbs_sess_state InitializeInitializing session.Model was not loadedModel was not loaded.Session has already been initialized.Adding default CPU execution provider.Serializing optimized model with Graph Optimization level greater than ORT_ENABLE_EXTENDED. The generated model may contain hardware and execution provider specific optimizations, and should only be used in the same environment the model was optimized for.Session successfully initialized.Invalid rank for input: Got: Expected: Please fix either the inputs or the model.Got invalid dimensions for input: for the following indices index: Size mismatch: feed_names has elements, but feeds has elements.Invalid Feed Input Name:Input with name: is not expected to be of type tensor.ValidateInputs is not expected to be of type sparse tensor. is not expected to be of type tensor sequence.Output vector pointer is NULLAt least one output should be requested.Output vector incorrectly sized: output_names.size(): p_fetches->size(): Invalid Output Name:RunSession was not initializedSession not initialized.Running with tag: Encountered unknown exception in Run()GetModelMetadataGetModelInputsGetOverridableInitializersGetModelOutputsNewIOBindingEndProfilingProfiler is disabled.Could not write a profile because no model was loaded.const logging::Logger &onnxruntime::InferenceSession::CreateLoggerForRun(const onnxruntime::RunOptions &, std::unique_ptr &)run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: void onnxruntime::InferenceSession::InitLogger(logging::LoggingManager *)session_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: virtual void onnxruntime::InferenceSession::AddPredefinedTransformers(onnxruntime::GraphTransformerManager &, onnxruntime::TransformerLevel, const std::vector &)graph_optimization_level <= TransformerLevel::MaxLevelExceeded max transformer level. Current level is set to WaitForNotificationtimeout_in_ms >0 is not supportedonnxruntime::SessionIOBinding::SessionIOBinding(onnxruntime::InferenceSession *)session->NewIOBinding(&binding_).IsOK()The only supported values for the environment variable are '0' and '1'. The environment variable contained the value: FinalizeSessionOptionsReading the provided model for the ORT configonnxruntime::common::Status onnxruntime::FinalizeSessionOptions(const onnxruntime::SessionOptions &, const onnx::ModelProto &, bool, onnxruntime::SessionOptions &)is_model_proto_parsedError during EndProfiling(): Provider has already been registered./Users/runner/work/1/s/onnxruntime/core/framework/execution_providers.hAddException during loading: ORTMException 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 &)wbrbLoad model from failed:Failed to load model because protobuf parsing failed.LoadOrtModelBytes failed. Only / bytes were able to be read._.json%Y-%m-%d_%H-%M-%SThe Model Proto has already been checked for the ORT config json.ort_config/Users/runner/work/1/s/onnxruntime/core/session/inference_session_utils.ccParseOrtConfigJsonInModelProtoFound session/run/environment configuration in the model file to be used while running the modelORT config json from the model: ParseSessionOptionsFromModelProtoDid not find session options in the model file to be used while running the modelsession_optionsintra_op_num_threadsinter_op_num_threadsexecution_modegraph_optimization_levelgraph_optimization_level option in the model file must be an integerenable_profilingIgnoring unsupported session option in ORT config: Json stored in the `ort_config` key cannot be parsed. Error message: SetIntraOpNumThreadsUnsupported value for intra_op_num_threads: Setting intra_op_num_threads to SetInterOpNumThreadsUnsupported value for inter_op_num_threads: Setting inter_op_num_threads to SetExecutionModeUnsupported execution_mode value in ORT config: Setting execution_mode to Sequential modeParallel modeSetGraphOptimizationLevelSetting graph_optimization_level to ORT_DISABLE_ALLSetting graph_optimization_level to ORT_ENABLE_BASICSetting graph_optimization_level to ORT_ENABLE_EXTENDEDSetting graph_optimization_level to ORT_ENABLE_ALLUnsupported graph_optimization_level value in ORT config: SetEnableProfilingUnsupported value for enable_profiling option: Setting enable_profiling to true[json.exception.invalid BOM; must be 0xEF 0xBB 0xBF if givennullinvalid literalinvalid string: missing closing quoteinvalid string: '\u' must be followed by 4 hex digitsinvalid string: surrogate U+DC00..U+DFFF must be followed by U+DC00..U+DFFFinvalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFFinvalid string: forbidden character after backslashinvalid string: control character U+0000 (NUL) must be escaped to \u0000invalid string: control character U+0001 (SOH) must be escaped to \u0001invalid string: control character U+0002 (STX) must be escaped to \u0002invalid string: control character U+0003 (ETX) must be escaped to \u0003invalid string: control character U+0004 (EOT) must be escaped to \u0004invalid string: control character U+0005 (ENQ) must be escaped to \u0005invalid string: control character U+0006 (ACK) must be escaped to \u0006invalid string: control character U+0007 (BEL) must be escaped to \u0007invalid string: control character U+0008 (BS) must be escaped to \u0008 or \binvalid string: control character U+0009 (HT) must be escaped to \u0009 or \tinvalid string: control character U+000A (LF) must be escaped to \u000A or \ninvalid string: control character U+000B (VT) must be escaped to \u000Binvalid string: control character U+000C (FF) must be escaped to \u000C or \finvalid string: control character U+000D (CR) must be escaped to \u000D or \rinvalid string: control character U+000E (SO) must be escaped to \u000Einvalid string: control character U+000F (SI) must be escaped to \u000Finvalid string: control character U+0010 (DLE) must be escaped to \u0010invalid string: control character U+0011 (DC1) must be escaped to \u0011invalid string: control character U+0012 (DC2) must be escaped to \u0012invalid string: control character U+0013 (DC3) must be escaped to \u0013invalid string: control character U+0014 (DC4) must be escaped to \u0014invalid string: control character U+0015 (NAK) must be escaped to \u0015invalid string: control character U+0016 (SYN) must be escaped to \u0016invalid string: control character U+0017 (ETB) must be escaped to \u0017invalid string: control character U+0018 (CAN) must be escaped to \u0018invalid string: control character U+0019 (EM) must be escaped to \u0019invalid string: control character U+001A (SUB) must be escaped to \u001Ainvalid string: control character U+001B (ESC) must be escaped to \u001Binvalid string: control character U+001C (FS) must be escaped to \u001Cinvalid string: control character U+001D (GS) must be escaped to \u001Dinvalid string: control character U+001E (RS) must be escaped to \u001Einvalid string: control character U+001F (US) must be escaped to \u001Finvalid string: ill-formed UTF-8 byteinvalid number; expected digit after '-'invalid number; expected digit after '.'invalid number; expected '+', '-', or digit after exponentinvalid number; expected digit after exponent signnumber overflow parsing 'arrayobjectexcessive object size: cannot use erase() with stringbooleandiscardednumberexcessive array size: parse error at line , column while parsing - ; last read: 'unexpected ; expected true literalfalse literalnull literalstring literalnumber literal'[''{'']''}'':'','end of input'[', '{', or a literalunknown tokenkey '' not foundcannot use at() with map::at: key not foundtype must be number, but is tried creating tensor with negative value in shapesize overflownot enough space: expected , got type is not supported in this functionRegisterCustomOpsLibrary: Failed to load libraryRegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in libraryinput name cannot be emptyCPUExecutionProvideroutput name cannot be emptylengths allocation failedstring buffer allocation failedOutput buffer allocation failedinput array doesn't equal tensor sizeelement index is out of boundsshape is invalidoffsets buffer is not equal to tensor sizeoutput buffer is too smallbuffer size is too small for stringinput array is too shortopaque(,)/Users/runner/work/1/s/onnxruntime/core/session/onnxruntime_c_api.ccOrtStatusPtr OrtApis::CreateOpaqueValue(const char *, const char *, const void *, size_t, OrtValue **)ml_type != nullptrSpecified domain and type names combination does not refer to a registered opaque typenon_tensor_base != nullptrOrtStatusPtr OrtApis::GetOpaqueValue(const char *, const char *, const OrtValue *, void *, size_t)this API does not support stringslocation dimensions do not match shape sizeinvalid location range1.5.1FlatBuffers 1.12.0allocator::allocate(size_t n) 'n' exceeds maximum supported sizeORT_LOAD_CONFIG_FROM_MODEL1/Users/runner/work/1/s/include/onnxruntime/core/framework/ml_value.honnxruntime::Tensor *OrtValue::GetMutable()IsTensor()Trying to get a Tensor, but got: const onnxruntime::Tensor &OrtValue::Get() constout of index/Users/runner/work/1/s/onnxruntime/core/common/safeint.hstatic void SafeIntExceptionHandler::SafeIntOnOverflow()internal errorindex out of rangeconst onnxruntime::TensorSeq &OrtValue::Get() constIsTensorSequence()Trying to get a TensorSeq, but got: Input is not of one of the supported sequence types.Input is not of type sequence or map./Users/runner/work/1/s/include/onnxruntime/core/framework/data_types_internal.hstatic bool onnxruntime::utils::ContainerChecker::IsContainerOfType, float, std::__1::less >, std::__1::allocator, float> > >, std::__1::allocator, float, std::__1::less >, std::__1::allocator, float> > > > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::vector, float, std::__1::less >, std::__1::allocator, float> > >, std::__1::allocator, float, std::__1::less >, std::__1::allocator, float> > > > >]++index < c.size()static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, float, std::__1::less >, std::__1::allocator, float> > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::map, float, std::__1::less >, std::__1::allocator, float> > >]const T &OrtValue::Get() const [T = std::__1::vector, float, std::__1::less >, std::__1::allocator, float> > >, std::__1::allocator, float, std::__1::less >, std::__1::allocator, float> > > > >]onnxruntime::DataTypeImpl::GetType() == type_ != static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, std::__1::allocator > >, std::__1::allocator, std::__1::allocator > > > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::vector, std::__1::allocator > >, std::__1::allocator, std::__1::allocator > > > >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, std::__1::allocator > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::map, std::__1::allocator > >]const T &OrtValue::Get() const [T = std::__1::vector, std::__1::allocator > >, std::__1::allocator, std::__1::allocator > > > >]/Users/runner/work/1/s/onnxruntime/core/framework/TensorSeq.hconst onnxruntime::Tensor &onnxruntime::TensorSeq::Get(size_t) consti < tensors_.size()Input is not of one of the supported map types.static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, std::__1::basic_string, std::__1::less >, std::__1::allocator, std::__1::basic_string > > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::map, std::__1::basic_string, std::__1::less >, std::__1::allocator, std::__1::basic_string > > >]Invalid index requested for map type.const T &OrtValue::Get() const [T = std::__1::map, std::__1::basic_string, std::__1::less >, std::__1::allocator, std::__1::basic_string > > >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, long long, std::__1::less >, std::__1::allocator, long long> > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::map, long long, std::__1::less >, std::__1::allocator, long long> > >]const T &OrtValue::Get() const [T = std::__1::map, long long, std::__1::less >, std::__1::allocator, long long> > >]const T &OrtValue::Get() const [T = std::__1::map, float, std::__1::less >, std::__1::allocator, float> > >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, double, std::__1::less >, std::__1::allocator, double> > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::map, double, std::__1::less >, std::__1::allocator, double> > >]const T &OrtValue::Get() const [T = std::__1::map, double, std::__1::less >, std::__1::allocator, double> > >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, std::__1::less, std::__1::allocator > > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::map, std::__1::less, std::__1::allocator > > >]const T &OrtValue::Get() const [T = std::__1::map, std::__1::less, std::__1::allocator > > >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, std::__1::allocator > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::map, std::__1::allocator > >]const T &OrtValue::Get() const [T = std::__1::map, std::__1::allocator > >]const T &OrtValue::Get() const [T = std::__1::map, std::__1::allocator > >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, std::__1::allocator > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::map, std::__1::allocator > >]const T &OrtValue::Get() const [T = std::__1::map, std::__1::allocator > >]Number of values should be at least 1.For map type num_values MUST be 2Either the key tensor or the value tensor has NumDimensions > 1Key and value tensors have unequal number of elements.Key type is not supported yet.OrtStatus *OrtCreateValueImplMapHelper(const onnxruntime::Tensor &, const onnxruntime::Tensor &, OrtValue **) [KeyType = std::__1::basic_string]value_type != nullptrTensor must always contain primitive types. Found: OrtStatus *OrtCreateMapMLValue(const onnxruntime::Tensor &, const onnxruntime::Tensor &, OrtValue **) [KeyType = std::__1::basic_string, ValueType = std::__1::basic_string]len >= 0 && static_cast(len) < std::numeric_limits::max()OrtStatus *OrtCreateMapMLValue(const onnxruntime::Tensor &, const onnxruntime::Tensor &, OrtValue **) [KeyType = std::__1::basic_string, ValueType = long long]/Users/runner/work/1/s/include/onnxruntime/core/framework/tensor.hconst T *onnxruntime::Tensor::Data() const [T = long long]utils::IsPrimitiveDataType(dtype_)Tensor type mismatch. T !=OrtStatus *OrtCreateMapMLValue(const onnxruntime::Tensor &, const onnxruntime::Tensor &, OrtValue **) [KeyType = std::__1::basic_string, ValueType = float]const T *onnxruntime::Tensor::Data() const [T = float]OrtStatus *OrtCreateMapMLValue(const onnxruntime::Tensor &, const onnxruntime::Tensor &, OrtValue **) [KeyType = std::__1::basic_string, ValueType = double]const T *onnxruntime::Tensor::Data() const [T = double]OrtStatus *OrtCreateValueImplMapHelper(const onnxruntime::Tensor &, const onnxruntime::Tensor &, OrtValue **) [KeyType = long long]OrtStatus *OrtCreateMapMLValue(const onnxruntime::Tensor &, const onnxruntime::Tensor &, OrtValue **) [KeyType = long long, ValueType = std::__1::basic_string]OrtStatus *OrtCreateMapMLValue(const onnxruntime::Tensor &, const onnxruntime::Tensor &, OrtValue **) [KeyType = long long, ValueType = long long]OrtStatus *OrtCreateMapMLValue(const onnxruntime::Tensor &, const onnxruntime::Tensor &, OrtValue **) [KeyType = long long, ValueType = float]OrtStatus *OrtCreateMapMLValue(const onnxruntime::Tensor &, const onnxruntime::Tensor &, OrtValue **) [KeyType = long long, ValueType = double]Each element of the sequence should be either tensor or map.At least one element in the sequence is of a type different from others.Unsupported input typeOrtStatus *OrtCreateValueImplSeqHelper(const OrtValue *const *, size_t, OrtValue **)in[idx]->IsTensor()Expecting all elements to be tensors. Got: Sequences must have tensors of the same data type. There was at least one tensor in the input that was different.Encountered nullptr.const T *onnxruntime::Tensor::Data() const [T = bool]T *onnxruntime::Tensor::MutableData() [T = bool]T *onnxruntime::Tensor::MutableData() [T = float]T *onnxruntime::Tensor::MutableData() [T = double]const T *onnxruntime::Tensor::Data() const [T = onnxruntime::MLFloat16]T *onnxruntime::Tensor::MutableData() [T = onnxruntime::MLFloat16]const T *onnxruntime::Tensor::Data() const [T = onnxruntime::BFloat16]T *onnxruntime::Tensor::MutableData() [T = onnxruntime::BFloat16]const T *onnxruntime::Tensor::Data() const [T = signed char]T *onnxruntime::Tensor::MutableData() [T = signed char]const T *onnxruntime::Tensor::Data() const [T = unsigned char]T *onnxruntime::Tensor::MutableData() [T = unsigned char]const T *onnxruntime::Tensor::Data() const [T = short]T *onnxruntime::Tensor::MutableData() [T = short]const T *onnxruntime::Tensor::Data() const [T = unsigned short]T *onnxruntime::Tensor::MutableData() [T = unsigned short]const T *onnxruntime::Tensor::Data() const [T = int]T *onnxruntime::Tensor::MutableData() [T = int]const T *onnxruntime::Tensor::Data() const [T = unsigned int]T *onnxruntime::Tensor::MutableData() [T = unsigned int]T *onnxruntime::Tensor::MutableData() [T = long long]const T *onnxruntime::Tensor::Data() const [T = unsigned long long]T *onnxruntime::Tensor::MutableData() [T = unsigned long long]Ret onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper::Get() [Ret = OrtStatus *, UnsupportedPolicy = c_api_internal::UnsupportedReturnFailStatus]called_ < 2void onnxruntime::TensorSeq::SetType(onnxruntime::MLDataType)elem_type_ != nullptr was false. Stacktrace: :/\T *onnxruntime::Tensor::MutableData() [T = std::__1::basic_string]const T *onnxruntime::Tensor::Data() const [T = std::__1::basic_string]/Users/runner/work/1/s/onnxruntime/core/session/ort_env.ccstatic void OrtEnv::Release(OrtEnv *)env_ptr == p_instance_/Users/runner/work/1/s/onnxruntime/core/optimizer/attention_fusion_helper.hbool onnxruntime::AttentionFusionHelper::CheckSliceParameters(const onnxruntime::Graph &, const onnxruntime::Node &, const std::vector &, const std::vector &, const logging::Logger &)input_indices.size() == expected_values.size() && input_indices.size() > 0CheckSliceParametersSlice does not have enough number of inputsSlice ends is less than INT_MAXSlice parameter is not expected. Input index:Expected value:MatchGemmSubgraphStart MatchGemmSubgraphFaild to match gemm pathInput of reshape_before_gemm is not the input of subgraphOutput edge count not expected for nodes in gemm pathGemm does not have 3 inputsGemm bias is not constantGemm bias shape not expectedCheckSliceParameters return falseconcat first input value is not -1Faild to match concat node for Gather pathsconcat_after_gather does not have expected number of inputs or output edgesconcat_after_gather input 2 does not have expected valueFaild to match gemm gather pathOutput edge count not expected for nodes in gemm gather pathunsqueeze_after_gather axes value not expectedgather axis value not expectedgather input 1 value is not expectedPass MatchGemmSubgraphValidateGemmInitializerStart ValidateGemmInitializerGemm bias is not constant initializerGemm bias shape is not expectedGemm weight is not constant initializerGemm weight shape is not expectedPass ValidateGemmInitializerMatchUnidirMaskSubgraphStart 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_sliceCheckSliceParameters returns false for slice1Faild to match path 2 for unidirectional maskOutput edge count not expected for unsqueeze2 of unidirectional maskFaild to match path 3 for unidirectional maskOutput edge count not expected for unsqueeze3 of unidirectional maskFaild to match path 4 for unidirectional maskDiv and Shape does not have edgeOutput edge count not expected for squeeze_2/slices2/shape2 of unidirectional maskCheckSliceParameters return false for slice2Pass MatchUnidirMaskSubgraphMatchInputMaskSubgraphStart MatchInputMaskSubgraphFailed to find Softmax nodeOutput edge count not expected for SoftmaxFailed to find path for maskOutput edge count not expected for mask nodesSoftmax attribute axis is expected to be 3mask_unsqueeze_1 axes not matched. Expect: 1mask_unsqueeze_2 axes not matched. Expect: 2mask_sub const input not matchedmask_mul const input not matchedPass MatchInputMaskSubgraphStart MatchInputMaskSubgraphDistilBertFailed to find mask pathwhere const not matched.Failed to find shape pathequal const not matched.Failed to find reshape shape path 1Failed to find reshape shape path 2gather indices not matched.Pass MatchInputMaskSubgraphDistilBertMatchPastSubgraphStart MatchPastSubgraphFailed to find path for past_kFailed to find path for present_kFailed to find path for present_v and past_vFailed to match v_concatpast_k_transpose perm attribute not matchedpresent_k_transpose perm attribute not matchedpresent_k_unsqueeze axes value not expectedpresent_v_unsqueeze axes value not expectedpast_v_gather indices != 1past_k_gather indices != 0past_v_gather and past_k_gather does not have same past inputOutput edge count not expected for nodes in past subgraphPass MatchPastSubgraphCheckDistilBertReshapeShapeCheckNodesInPathVStart CheckNodesInPathVOutput edge count not expected for nodes in path vFailed in match Transpose attribute perm. Expected: 0, 2, 1, 3Failed in match v_transpose attribute perm. Expected: 0, 2, 1, 3hidden_size != num_heads * head_sizev_reshape initializer value is not expectedPass CheckNodesInPathVreshape initializer value is not expectedCheckNodesInPathQStart CheckNodesInPathQq_reshape const not matchedqk_div const not matched.q_transpose perm attribute not matchedPass CheckNodesInPathQCheckNodesInPathKStart CheckNodesInPathKk_transpose perm attribute not matchedk_reshape const not matchedPass CheckNodesInPathKFuseGptAttentionStart FuseGptAttentionFaild to find path to qkv_matmulFaild to find path v to SplitCheckNodesInPathV return falseMatchInputMaskSubgraph returns falseMatchUnidirMaskSubgraph returns NULLFailed to find path for qq and v are not from same Split nodeCheckNodesInPathQ returns falseFailed to find path for kk and v are not from same Split nodeCheckNodesInPathK returns falseMatchPastSubgraph returns falseFused an attention node for GPT./Users/runner/work/1/s/onnxruntime/core/optimizer/attention_fusion.ccshape of layer norm bias tensor not expectedTotal fused Attention node count: FuseSubGraphFaild to find path vOutput edge count not expected for Add or MatMul in path vFailed in match v_matmul and v_add input shapeFailed in match input mask subgraphFuseSubGraphQKFused an attention node.FuseSubGraphQKImplq root should be layer normalizationq_matmul and q_add shape not matchedk root is not layer normk_matmul and k_add shape not matchedFailed to load Q, K and V weights, or data type is not float or float16.Failed to load Q, K and V bias tensors, or data type is not float or float16.Failed to convert mask to int32ConvertMaskToInt32Mask shape is unknown or not 2D, or data type unknownMask data type is not int32 or int64 or float32qkv_weightsqkv_biasFuseSubGraphQKDistilBert/Users/runner/work/1/s/onnxruntime/core/optimizer/bias_gelu_fusion.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/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 fused Conv with activation auto onnxruntime::(anonymous namespace)::GetClipConstantMinMax(const onnxruntime::Graph &, const onnxruntime::Node &, float &, float &)::(anonymous class)::operator()(const onnxruntime::Node &, size_t, float &) constUnexpected data type for Clip input of /Users/runner/work/1/s/onnxruntime/core/optimizer/conv_add_fusion.ccvirtual onnxruntime::common::Status onnxruntime::ConvAddFusion::Apply(onnxruntime::Graph &, onnxruntime::Node &, onnxruntime::RewriteRule::RewriteRuleEffect &, const logging::Logger &) constadd_B_tensor_protoConvAddFusion_B_ConvAddFusion_Add_B_/Users/runner/work/1/s/onnxruntime/core/optimizer/conv_bn_fusion.ccvirtual onnxruntime::common::Status onnxruntime::ConvBNFusion::Apply(onnxruntime::Graph &, onnxruntime::Node &, onnxruntime::RewriteRule::RewriteRuleEffect &, const logging::Logger &) constbn_scale_tensor_protobn_B_tensor_protobn_mean_tensor_protobn_var_tensor_protoconv_W_tensor_protoconv_B_tensor_protoConvBnFusion_W_ConvBnFusion_BN_B_/Users/runner/work/1/s/onnxruntime/core/optimizer/initializer.honnxruntime::Initializer::Initializer(const onnx::TensorProto &, const onnxruntime::Path &)size_ == sizeInitializerunsupported data type: ReadExternalRawData() failed: ToProtodata type is not supported/Users/runner/work/1/s/onnxruntime/core/optimizer/conv_mul_fusion.ccvirtual onnxruntime::common::Status onnxruntime::ConvMulFusion::Apply(onnxruntime::Graph &, onnxruntime::Node &, onnxruntime::RewriteRule::RewriteRuleEffect &, const logging::Logger &) constmul_B_tensor_protoConvMulFusion_W_ConvMulFusion_Mul_B_/Users/runner/work/1/s/onnxruntime/core/optimizer/dynamic_quantize_matmul_fusion.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/embed_layer_norm_fusion.ccFuseSubGraphDistilBertWord embedding shape not expected.Failed to match position embedding subgraph.Failed to get position embedding weights.Position embedding shape is not expected.Input id is not valid. Gamma should be of shape (hidden_size). Beta should be of shape (hidden_size). MatchPositionEmbeddingSubgraphsFromGatherOutput edge count not expected for nodes in path1.The first input of Range should be a constant with value 0.The third input of Range should be a constant with value 1.Second input of Gather should be a constant with value 1. Failed to match Shape node. The parent of shape nodes are expected to be input_ids.Optional position subgraph nodes number of outputs unexpected.Optional position subgraph nodes Where node is expected to be the parent of Reshape.Failed to match position subgraph.MatchInputToConcatSubgraphFailed to find path 1 of position shape.Output edge count not expected for nodes in path 1 of position shape.Second input of Gather in path 1 of position shape should be a constant with value 0.Failed to find path 2 of position shape.Output edge count not expected for nodes in path 2 of position shape.Gather node in path 2 is not linked to another subgraph.Second input of Gather in path 2 of position shape should be a constant with value 1.The parent of two shape nodes are expected to be input_ids.two paths share the same shapeCheckInputInput shape is unknown or not 2D, or data type unknownInput data type is not int32 or int64_Int32_CastInput is expected to have dim value in all dimensions.Failed to get initializer tensor.Position embedding shape not matched.Position embedding data type shall be float or float16.Segment id is not valid. Input_ids and segment id should have the same shape. /Users/runner/work/1/s/onnxruntime/core/optimizer/fast_gelu_fusion.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/free_dim_override_transformer.cconnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformer(gsl::span)Invalid free dimension override.Conflicting free dimension overrides.The model has input '' with a fixed dimension size which does not equal the specified override of /Users/runner/work/1/s/onnxruntime/core/optimizer/gelu_approximation.ccTotal Gelu Approximation (FastGelu) node count: /Users/runner/work/1/s/onnxruntime/core/optimizer/gelu_fusion.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/gemm_activation_fusion.ccfused Gemm /Users/runner/work/1/s/onnxruntime/core/optimizer/graph_transformer.ccApply/Users/runner/work/1/s/onnxruntime/core/optimizer/graph_transformer_mgr.ccApplyTransformersThis transformer is already registered Level_RuleBasedTransformer/Users/runner/work/1/s/onnxruntime/core/optimizer/graph_transformer_utils.ccstd::vector > onnxruntime::optimizer_utils::GenerateRewriteRules(onnxruntime::TransformerLevel, const std::vector &)Unsupported levelACLExecutionProviderstd::vector > onnxruntime::optimizer_utils::GenerateTransformers(onnxruntime::TransformerLevel, gsl::span, const onnxruntime::IExecutionProvider &, const std::vector &)Unsupported level Not satisfied: tensor_proto.data_type() != ONNX_NAMESPACE::TensorProto_DataType_UNDEFINED && tensor_proto.data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRING /Users/runner/work/1/s/onnxruntime/core/optimizer/initializer.ccReadExternalRawDataSatisfied, but should not be: model_path.IsEmpty() model_path must not be empty. Ensure that a path is provided when the model is created or loaded.Not satisfied: external_data_length == 0 || external_data_length == actual_tensor_data_length TensorProto external data size mismatch. Computed size: , external_data.length: /Users/runner/work/1/s/onnxruntime/core/optimizer/insert_cast_transformer.ccvirtual onnxruntime::common::Status onnxruntime::InsertCastTransformer::ApplyImpl(onnxruntime::Graph &, bool &, int, const logging::Logger &) constdtype_attribute->second.has_i()InsertCastTransformer works on the assumption that `dtype` attribute holds an integer./Users/runner/work/1/s/onnxruntime/core/optimizer/layer_norm_fusion.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/matmul_add_fusion.ccfused Matmul and Add /Users/runner/work/1/s/onnxruntime/core/optimizer/matmul_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: GetScalarConstantInitializerconst T &onnxruntime::optional::value() const [T = float]const T &onnxruntime::optional >::value() const [T = std::__1::pair]std::vector onnxruntime::(anonymous namespace)::GetOutputNodeMerges(onnxruntime::Graph &, onnxruntime::Node &, const std::unordered_set &)output_node.OutputDefs().size() == 1_nchwc_bn_nchwc/Users/runner/work/1/s/onnxruntime/core/optimizer/nchwc_transformer.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/optimizer_execution_frame.cconnxruntime::OptimizerExecutionFrame::Info::Info(const std::vector &, const onnxruntime::InitializedTensorSet &, const onnxruntime::Path &, const onnxruntime::IExecutionProvider &)allocator_ptr_Info/Users/runner/work/1/s/onnxruntime/core/optimizer/relu_clip_fusion.ccvirtual onnxruntime::common::Status onnxruntime::FuseReluClip::Apply(onnxruntime::Graph &, onnxruntime::Node &, onnxruntime::RewriteRule::RewriteRuleEffect &, const logging::Logger &) constUnexpected data type for Clip 'min' input of FuseReluClip__min_zero_constantonnxruntime::Initializer::Initializer(onnx::TensorProto_DataType, const std::string &, const std::vector &)data type is not supported./Users/runner/work/1/s/onnxruntime/core/optimizer/reshape_fusion.ccFused reshape node: Total fused reshape node count: Fuse_SubgraphCannot replace concat node with initializer:/Users/runner/work/1/s/onnxruntime/core/optimizer/rule_based_graph_transformer.ccApplyRulesOnNode/Users/runner/work/1/s/onnxruntime/core/optimizer/skip_layer_norm_fusion.ccArmNNExecutionProvider/Users/runner/work/1/s/onnxruntime/core/optimizer/transformer_memcpy.ccApplyImplTensorrtExecutionProvidervoid onnxruntime::TransformerMemcpyImpl::ProcessDefs(onnxruntime::Node &, const onnxruntime::KernelRegistryManager &, onnxruntime::InitializedTensorSet &)Execution type '' doesn't support memcpy bool onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager &, const onnxruntime::InitializedTensorSet &)/Users/runner/work/1/s/include/onnxruntime/core/optimizer/graph_transformer.hRecurseForEachWithIndexauto onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager &, const onnxruntime::InitializedTensorSet &)::(anonymous class)::operator()(const onnxruntime::NodeArg &, size_t) constdup_replacements.find(&arg) == dup_replacements.end()UnsqueezeElimination_/Users/runner/work/1/s/onnxruntime/core/optimizer/unsqueeze_elimination.ccUnsqueezeElimination cannot remove node EluHardSigmoidLeakyReluSoftplusReluSigmoidSoftsignTanhThresholdedReluSeluParametricSoftplusScaledTanhunknown kernel typeonnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Elu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Elu]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::HardSigmoid]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::HardSigmoid]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::LeakyRelu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::LeakyRelu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Relu]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Selu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Selu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Sigmoid]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Softplus]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Softsign]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Tanh]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::ThresholdedRelu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::ThresholdedRelu]alphaNo attribute with name:''is defined.Attribute name and type don't match for '/Users/runner/work/1/s/onnxruntime/core/providers/cpu/activation/activations.hInitgamma/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/activations.hBVIfthen_branch/Users/runner/work/1/s/onnxruntime/core/providers/cpu/controlflow/if.cconnxruntime::If::If(const onnxruntime::OpKernelInfo &)info.GetAttr("then_branch", &proto).IsOK()else_branchinfo.GetAttr("else_branch", &proto).IsOK()virtual common::Status onnxruntime::If::SetupSubgraphExecutionInfo(const onnxruntime::SessionState &, const std::string &, const onnxruntime::SessionState &)info == nullptrSetupSubgraphExecutionInfo should only be called once for each subgraph.SetupSubgraphExecutionInfovirtual onnxruntime::common::Status onnxruntime::If::Compute(onnxruntime::OpKernelContext *) constthen_feeds_fetches_manager_ && else_feeds_fetches_manager_CreateFeedsFetchesManager must be called prior to execution of graph.session_stateSubgraph SessionState was not found for '' attribute.ComputeFailed to create output tensor for Executeonnxruntime::If::Info::Info(const onnxruntime::Node &, const onnxruntime::GraphViewer &)num_subgraph_outputs == static_cast(num_outputs)'If' node has outputs which doesn't match the subgraph's outputs.Failed to create output tensor for If output ILoop/Users/runner/work/1/s/onnxruntime/core/providers/cpu/controlflow/loop.cconnxruntime::Loop::Loop(const onnxruntime::OpKernelInfo &)info.GetAttr("body", &proto).IsOK()virtual common::Status onnxruntime::Loop::SetupSubgraphExecutionInfo(const onnxruntime::SessionState &, const std::string &, const onnxruntime::SessionState &)info_ == nullptrvirtual onnxruntime::common::Status onnxruntime::Loop::Compute(onnxruntime::OpKernelContext *) constfeeds_fetches_manager_'Loop' input 'M' should be a scalar tensor. Got shape of 'Loop' input 'cond' should be a scalar tensor. Got shape of 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:onnxruntime::Loop::Info::Info(const onnxruntime::Node &, const onnxruntime::GraphViewer &)static_cast(num_subgraph_inputs) == subgraph_inputs.size()Graph in 'body' attribute of Loop should have inputs. Found:num_subgraph_outputs - 1 == num_outputs'Loop' node has outputs so the subgraph requires but has /Users/runner/work/1/s/onnxruntime/core/providers/cpu/controlflow/scan_8.cconnxruntime::Scan<8>::Scan(const onnxruntime::OpKernelInfo &) [OpSet = 8]directionsvirtual onnxruntime::common::Status onnxruntime::Scan<8>::SetupSubgraphExecutionInfo(const onnxruntime::SessionState &, const std::string &, const onnxruntime::SessionState &) [OpSet = 8]virtual onnxruntime::common::Status onnxruntime::Scan<8>::Compute(onnxruntime::OpKernelContext *) const [OpSet = 8]Scan inputs have inconsistent batch size. Previous value was but has batch size of sequence_lens length of did not match batch size of Invalid entries in sequence_lens. Max sequence length was onnxruntime::common::Status onnxruntime::Scan8Impl::CreateLoopStateVariables(std::vector > &)p_mlvalueScan<8> spec does not support transpose of output. This should never be called./Users/runner/work/1/s/onnxruntime/core/framework/ort_value_tensor_slicer.htypename std::enable_if::value, reference>::type onnxruntime::OrtValueTensorSlicer::Iterator::operator*() [T = const OrtValue]position_ >= 0 && position_ < sequence_length_/Users/runner/work/1/s/onnxruntime/core/providers/cpu/controlflow/scan_9.cconnxruntime::Scan<9>::Scan(const onnxruntime::OpKernelInfo &) [OpSet = 9]num_scan_inputsinfo.GetAttr("num_scan_inputs", &num_scan_inputs_).IsOK()scan_input_directionsscan_output_directionsscan_input_axesgsl::narrow_cast(input_axes_.size()) == num_scan_inputs_Number of entries in 'scan_input_axes' was but expected scan_output_axesgsl::narrow_cast(output_axes_.size()) == num_scan_outputsNumber of entries in 'scan_output_axes' was virtual onnxruntime::common::Status onnxruntime::Scan<9>::SetupSubgraphExecutionInfo(const onnxruntime::SessionState &, const std::string &, const onnxruntime::SessionState &) [OpSet = 9]virtual onnxruntime::common::Status onnxruntime::Scan<9>::Compute(onnxruntime::OpKernelContext *) const [OpSet = 9]feeds_fetches_manager_ && info_Invalid scan input: Expected dimensions or more but input had shape of Scan inputs have inconsistent sequence lengths. Previous value was but input '' dimension has length of Invalid value in scan_input_axes for input of . Input tensor rank was ValidateInputSetupInputsSubgraph in 'body' produces outputs but Scan expects AllocateOutputTensorsCreateLoopStateVariablesonnxruntime::common::Status onnxruntime::ScanImpl::CreateLoopStateVariables(std::vector &)output_mlvalueOutput OrtValue has not been created for loop state variable output Invalid value in scan_output_axes for output . Output tensor rank was onnxruntime::common::Status onnxruntime::ScanImpl::TransposeOutput()TransposeOutputScanint64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)axis >= -tensor_rank && axis <= tensor_rank - 1axis is not in valid range [-/Users/runner/work/1/s/onnxruntime/core/providers/cpu/controlflow/scan_utils.hconst OrtValue &onnxruntime::scan::detail::OutputIterator::GetOutput() constfinal_output_mlvalue_/Users/runner/work/1/s/onnxruntime/core/providers/cpu/controlflow/scan_utils.cconnxruntime::scan::detail::Info::Info(const onnxruntime::Node &, const onnxruntime::GraphViewer &, int, bool)num_variadic_inputs == num_subgraph_inputsThe subgraph in 'body' requires inputs but Scan was only given void onnxruntime::scan::detail::ReadDirections(const onnxruntime::OpKernelInfo &, const std::string &, std::vector &, int64_t)num_entries < 0 || gsl::narrow_cast(directions.size()) == num_entriesNumber of entries in '' was validInvalid values in ''. 0 == forward. 1 == reverse.Subgraph must have the shape set for all outputs but did not.CreateFeedsFetchesManagerIterateSequencevoid onnxruntime::scan::detail::LoopStateVariable::Next()iteration_num_ < sequence_len_Misuse of LoopStateVariable. Attempt to move beyond end of sequenceFailed to create output tensor for output #AllocateFinalBufferonnxruntime::common::Status onnxruntime::scan::detail::OutputIterator::AllocateFinalOutput(const onnxruntime::TensorShape &)!is_concrete_shape_AllocateFinalOutputOrtValue &onnxruntime::scan::detail::OutputIterator::operator*()cur_iteration_ < num_iterations_is_concrete_shape_Expected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.onnxruntime::scan::detail::OutputIterator &onnxruntime::scan::detail::OutputIterator::operator++()Expected AllocateFinalOutput to have been called to before we increment the iteratorMismatch between expected shape and shape from first output is not compatible with /Users/runner/work/1/s/onnxruntime/core/providers/cpu/cpu_execution_provider.ccRegisterOnnxOperatorKernelsRegisterOnnxMLOperatorKernelsGetKernelRegistryvirtual std::shared_ptr onnxruntime::CPUExecutionProvider::GetKernelRegistry() constRegisterCPUKernelsCpuConstantOfShape/Users/runner/work/1/s/onnxruntime/core/providers/cpu/generator/constant_of_shape.ccvoid onnxruntime::ConstantOfShapeBase::SetValueFromTensorProto(const onnx::TensorProto &)utils::HasDataType(t_proto)TensorProto::DataType_IsValid(t_proto.data_type())unpack_status.IsOK()Value attribute unpacking failed:Unsupported value attribute datatype: onnxruntime::ConstantOfShapeBase::ConstantOfShapeBase(const onnxruntime::OpKernelInfo &)t_proto.dims_size() == 1t_proto.dims()[0] == 1onnxruntime::common::Status onnxruntime::ConstantOfShapeBase::PrepareCompute(onnxruntime::OpKernelContext *, onnxruntime::Tensor **) constinput_shape.NumDimensions() > 0virtual onnxruntime::common::Status onnxruntime::ConstantOfShape::Compute(onnxruntime::OpKernelContext *) constUnsupported value attribute datatype with sizeof=: RandomNormalRandomUniformRandomNormalLikeRandomUniformLikeMultinomial/Users/runner/work/1/s/onnxruntime/core/providers/cpu/generator/random.ccCould not infer data type from input tensor with data type Empty dimensions for input tensorbatch_size is < 1num_classes is < 1num_samples is < 1Invalid data type of /Users/runner/work/1/s/onnxruntime/core/providers/cpu/generator/random.honnxruntime::RandomNormal::RandomNormal(const onnxruntime::OpKernelInfo &)info.GetAttr("mean", &mean_).IsOK()scaleinfo.GetAttr("scale", &scale_).IsOK()info.GetAttr("dtype", &dtype).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDInvalid dtype of info.GetAttrs("shape", shape).IsOK()onnxruntime::RandomUniform::RandomUniform(const onnxruntime::OpKernelInfo &)info.GetAttr("high", &high_).IsOK()lowinfo.GetAttr("low", &low_).IsOK()onnxruntime::RandomNormalLike::RandomNormalLike(const onnxruntime::OpKernelInfo &)onnxruntime::RandomUniformLike::RandomUniformLike(const onnxruntime::OpKernelInfo &)sample_sizeonnxruntime::Multinomial::Multinomial(const onnxruntime::OpKernelInfo &)info.GetAttr("sample_size", &num_samples_).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDonnxruntime::common::Status onnxruntime::RandomNormalCompute(float, float, std::default_random_engine &, TensorProto::DataType, onnxruntime::Tensor &)onnxruntime::common::Status onnxruntime::RandomUniformCompute(float, float, std::default_random_engine &, TensorProto::DataType, onnxruntime::Tensor &)MultinomialComputeRangestart in Range operator should be scalar like tensor, yet got shape:limit in Range operator should be scalar like tensor, yet got shape:delta in Range operator should be scalar like tensor, yet got shape:delta in Range operator can not be zero!Clipminmax/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/clip.honnxruntime::clip_internal::Clip_6Base::Clip_6Base(const onnxruntime::OpKernelInfo &) [T = float]min_ <= max_void onnxruntime::utils::MLTypeCallDispatcher::Invoke(Args &&...) const [Fn = ComputeImpl, Types = , Args = ]/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/clip.ccvoid onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, onnxruntime::Tensor *) const [T = float]min->Shape().NumDimensions() == 0max->Shape().NumDimensions() == 0void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, onnxruntime::Tensor *) const [T = double]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, onnxruntime::Tensor *) const [T = signed char]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, onnxruntime::Tensor *) const [T = unsigned char]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, onnxruntime::Tensor *) const [T = long long]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, onnxruntime::Tensor *) const [T = unsigned long long]CumSumexclusive/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/cumsum.ccvirtual onnxruntime::common::Status onnxruntime::CumSum::Compute(onnxruntime::OpKernelContext *) const [T = float]void onnxruntime::WritableSliceIterator::Init(const std::vector &, gsl::span, gsl::span) [T = float]dims.size() == starts.size()dims.size()=starts.size()=dims.size() == extents_.size()extents.size()=dims.size() == steps.size()steps.size()=virtual onnxruntime::common::Status onnxruntime::CumSum::Compute(onnxruntime::OpKernelContext *) const [T = double]void onnxruntime::WritableSliceIterator::Init(const std::vector &, gsl::span, gsl::span) [T = double]virtual onnxruntime::common::Status onnxruntime::CumSum::Compute(onnxruntime::OpKernelContext *) const [T = int]void onnxruntime::WritableSliceIterator::Init(const std::vector &, gsl::span, gsl::span) [T = int]virtual onnxruntime::common::Status onnxruntime::CumSum::Compute(onnxruntime::OpKernelContext *) const [T = long long]void onnxruntime::WritableSliceIterator::Init(const std::vector &, gsl::span, gsl::span) [T = long long]Det/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/det.ccvirtual onnxruntime::common::Status onnxruntime::Det::Compute(onnxruntime::OpKernelContext *) const [T = float]X != nullptrInput tensor should have a rank of at least 2Matrix dimensions are not equal. Square matrix is expectedEinsumThere was a problem acquiring temporary memory allocator in Einsum op/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/einsum.ccDeviceComputeEinsum op: An implementation for the input type is not supported yet/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/einsum.honnxruntime::Einsum::Einsum(const onnxruntime::OpKernelInfo &)info.GetAttr("equation", &equation_).IsOK()->/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_auxiliary_ops.ccstd::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor &, const std::vector &, const onnxruntime::Tensor &, const std::vector &, onnxruntime::AllocatorPtr, concurrency::ThreadPool *, void *, const DeviceHelpers::MatMul &) [T = float]input_1.DataType() == input_2.DataType()input_shape_1_override.size() == 3 && input_shape_2_override.size() == 3input_shape_1_override[0] == input_shape_2_override[0]input_shape_1_override[2] == input_shape_2_override[1]Einsum op: Exception during MatMul operation: std::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor &, const std::vector &, const onnxruntime::Tensor &, const std::vector &, onnxruntime::AllocatorPtr, concurrency::ThreadPool *, void *, const DeviceHelpers::MatMul &) [T = int]std::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor &, const std::vector &, const onnxruntime::Tensor &, const std::vector &, onnxruntime::AllocatorPtr, concurrency::ThreadPool *, void *, const DeviceHelpers::MatMul &) [T = double]std::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor &, const std::vector &, const onnxruntime::Tensor &, const std::vector &, onnxruntime::AllocatorPtr, concurrency::ThreadPool *, void *, const DeviceHelpers::MatMul &) [T = long long]onnxruntime::common::Status onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopy(const onnxruntime::Tensor &, onnxruntime::Tensor &)output.SizeInBytes() == input.SizeInBytes()Einsum op: The candidate output does not match the actual output's shapestd::unique_ptr onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::Diagonal(const onnxruntime::Tensor &, int64_t, int64_t, onnxruntime::AllocatorPtr)rank >= 2 && dim_1 != dim_2 && input_dims[dim_1] == input_dims[dim_2]Cannot parse the diagonal elements along dims for input shape bool onnxruntime::EinsumOp::IsTransposeRequired(size_t, const std::vector &)input_rank == permutation.size()std::unique_ptr onnxruntime::EinsumOp::Transpose(const onnxruntime::Tensor &, const std::vector &, const std::vector &, onnxruntime::AllocatorPtr, void *, const DeviceHelpers::Transpose &)Length of permutation must match the rank of the input to be permutatedEinsum op: Transpose failed: std::unique_ptr onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDims(const onnxruntime::Tensor &, bool, onnxruntime::AllocatorPtr)input_dims[rank - 2] == input_dims[rank - 1]The innermost dims should have the same dim value to parse the diagonal elementsEinsum op: Unsupported data type for Diagonal /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_compute_preprocessor.ccNumber of subscripts in the input equation does not match number of input tensorsFound a '.' not part of an ellipsis in input: Einsum subscripts string contains too many subscript labels when compared to the rank of the inputEllipsis must indicate a fixed number of dimensions across all inputsFound '.' not part of an ellipsis in input: The only subscript labels allowed are lower-cased letters (a-z) and upper-cased letters (A-Z)Einsum operands could not be broadcast together. Please check input shapes/equation provided.Input shape of operand is incompatible in the dimension . The shape is: Another operand has a dim value of in the same dimensionEinsum subscripts string contains too many subscript labels when compared to the rank of the input Einsum subscripts does not contain enough subscript labels and there is no ellipsis for input onnxruntime::common::Status onnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDims()num_broadcasted_indices < num_of_ellipsis_dims_dim_iter == rank...Inputs have ellipses in them but the provided output subscript does not contain an ellipsisFound a '.' not part of an ellipsis in the output subscript providedFound '.' not part of an ellipsis in the output subscript providedThe only subscript labels allowed in the output subscript are lowercase letters (a-z)Rank of the input must match number of subscript labels corresponding to the input/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_typed_compute_processor.ccstd::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const std::vector &, bool) [T = float]left.Shape().Size() == left_shape_override.Size()The override dims are not compatible with given tensor's shape. Left shape: Left shape override: right.Shape().Size() == right_shape_override.Size()Right shape: Right shape override: left_rank == right_rankRanks of pair-wise operands must be equal. Right shape: left_dim == right_dimEinsum op: Input dimensions must be equal along an axis to be reduced across all inputsvoid onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor &, const std::vector &) [T = float]candidate_output.Shape().Size() == output_shape.Size()Einsum op: The candidate output cannot be reshaped into the op's outputcandidate_output_dims[iter] == 1Not all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: Einsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const std::vector &, bool) [T = int]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor &, const std::vector &) [T = int]std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const std::vector &, bool) [T = double]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor &, const std::vector &) [T = double]std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const std::vector &, bool) [T = long long]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor &, const std::vector &) [T = long long]SubMulDivAbsNegFloorCeilReciprocalSqrtT1PowExpLogSumMaxMinLessGreaterEqualMeanBitShiftErfNotAndOrXorUnsupported X type: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.ccvirtual onnxruntime::common::Status onnxruntime::Sum_6::Compute(onnxruntime::OpKernelContext *) const [T = float]input_count >= 1data_1.Shape() == shapedata_n.Shape() == shapevirtual onnxruntime::common::Status onnxruntime::Min_6::Compute(onnxruntime::OpKernelContext *) const [T = float]inputCount >= 1virtual onnxruntime::common::Status onnxruntime::Max_6::Compute(onnxruntime::OpKernelContext *) const [T = float]virtual onnxruntime::common::Status onnxruntime::Mean_6::Compute(onnxruntime::OpKernelContext *) const [T = float]SinCosTanAsinAcosAtanSinhCoshAsinhAcoshAtanhPReluExpandModvoid onnxruntime::mod_internal::BroadCastMFloat16FMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *)X and Y input types do not match: vs virtual onnxruntime::common::Status onnxruntime::Mod::Compute(onnxruntime::OpKernelContext *) constfmod_/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.honnxruntime::Broadcaster::Broadcaster(const std::vector &, const std::vector &)largest <= 1Can broadcast 0 by 0 or 1. is invalid.void onnxruntime::BroadcastIterator::Init(int64_t, int64_t)axis == 1 || axis == largestAttempting to broadcast an axis by a dimension other than 1. by void onnxruntime::BroadcastIterator::Append(int64_t, int64_t)onnxruntime::TBroadcastOutput::TBroadcastOutput(size_t, onnxruntime::Tensor &, int64_t, int64_t) [T = float]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:void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = float, TOutput = float, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:223:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:224:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:225:7)]input1_size == output_sizeinput0_size == output_sizevoid onnxruntime::TBroadcaster::AdvanceBy(size_t) [T0 = float, T1 = float]offset % span_size_ == 0onnxruntime::TBroadcastOutput::TBroadcastOutput(size_t, onnxruntime::Tensor &, int64_t, int64_t) [T = double]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = double, TOutput = double, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:223:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:224:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:225:7)]void onnxruntime::TBroadcaster::AdvanceBy(size_t) [T0 = double, T1 = double]onnxruntime::TBroadcastOutput::TBroadcastOutput(size_t, onnxruntime::Tensor &, int64_t, int64_t) [T = int]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = int, TOutput = int, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:223:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:224:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:225:7)]void onnxruntime::TBroadcaster::AdvanceBy(size_t) [T0 = int, T1 = int]onnxruntime::TBroadcastOutput::TBroadcastOutput(size_t, onnxruntime::Tensor &, int64_t, int64_t) [T = long long]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = long long, TOutput = long long, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:223:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:224:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:225:7)]void onnxruntime::TBroadcaster::AdvanceBy(size_t) [T0 = long long, T1 = long long]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = float, TOutput = float, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:233:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:234:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:235:7)]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = double, TOutput = double, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:233:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:234:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:235:7)]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = int, TOutput = int, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:233:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:234:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:235:7)]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = long long, TOutput = long long, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:233:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:234:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:235:7)]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = float, TOutput = float, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:243:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:244:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:245:7)]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = double, TOutput = double, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:243:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:244:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:245:7)]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = int, TOutput = int, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:243:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:244:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:245:7)]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = long long, TOutput = long long, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:243:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:244:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:245:7)]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = float, TOutput = float, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:253:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:254:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:255:7)]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = double, TOutput = double, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:253:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:254:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:255:7)]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = int, TOutput = int, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:253:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:254:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:255:7)]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = long long, TOutput = long long, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:253:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:254:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:255:7)]/Users/runner/work/1/s/onnxruntime/core/providers/cpu/element_wise_ranged_transform.hElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Abs]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Abs]input_size < std::numeric_limits::max()onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Abs]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Abs]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Abs]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Abs]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Abs]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Abs]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Abs]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Abs]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Abs]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Abs]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Neg]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Neg]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Neg]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Neg]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Neg]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Neg]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Floor]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Floor]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Ceil]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Ceil]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Reciprocal]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Reciprocal]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Sqrt]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Sqrt]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Sqrt]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Sqrt]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Exp]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Exp]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Exp]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Exp]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Log]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Log]onnxruntime::TBroadcastOutput::TBroadcastOutput(size_t, onnxruntime::Tensor &, int64_t, int64_t) [T = bool]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = float, TOutput = bool, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:557:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:558:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:559:7)]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = double, TOutput = bool, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:557:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:558:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:559:7)]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = int, TOutput = bool, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:557:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:558:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:559:7)]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = long long, TOutput = bool, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:557:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:558:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:559:7)]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = float, TOutput = bool, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:567:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:568:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:569:7)]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = double, TOutput = bool, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:567:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:568:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:569:7)]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = int, TOutput = bool, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:567:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:568:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:569:7)]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = long long, TOutput = bool, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:567:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:568:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:569:7)]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = bool, TOutput = bool, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:547:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:548:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:549:7)]void onnxruntime::TBroadcaster::AdvanceBy(size_t) [T0 = bool, T1 = bool]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = int, TOutput = bool, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:547:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:548:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:549:7)]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = long long, TOutput = bool, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:547:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:548:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:549:7)]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = float, TOutput = bool, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:547:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:548:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:549:7)]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = double, TOutput = bool, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:547:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:548:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:549:7)]directiononnxruntime::BitShift::BitShift(const onnxruntime::OpKernelInfo &) [T = unsigned char]LEFTRIGHTInvalid direction value of ''. Valid values are 'LEFT' or 'RIGHT'.onnxruntime::TBroadcastOutput::TBroadcastOutput(size_t, onnxruntime::Tensor &, int64_t, int64_t) [T = unsigned char]auto onnxruntime::BitShift::Compute(onnxruntime::OpKernelContext *)::(anonymous class)::operator()(EigenVectorMap, ConstEigenVectorMap, ConstEigenVectorMap) const [T = unsigned char]cur1 == end1cur_out == end_outvoid onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = unsigned char, TOutput = unsigned char, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:636:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:648:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:660:7)]void onnxruntime::TBroadcaster::AdvanceBy(size_t) [T0 = unsigned char, T1 = unsigned char]onnxruntime::BitShift::BitShift(const onnxruntime::OpKernelInfo &) [T = unsigned int]onnxruntime::TBroadcastOutput::TBroadcastOutput(size_t, onnxruntime::Tensor &, int64_t, int64_t) [T = unsigned int]auto onnxruntime::BitShift::Compute(onnxruntime::OpKernelContext *)::(anonymous class)::operator()(EigenVectorMap, ConstEigenVectorMap, ConstEigenVectorMap) const [T = unsigned int]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = unsigned int, TOutput = unsigned int, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:636:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:648:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:660:7)]void onnxruntime::TBroadcaster::AdvanceBy(size_t) [T0 = unsigned int, T1 = unsigned int]onnxruntime::BitShift::BitShift(const onnxruntime::OpKernelInfo &) [T = unsigned long long]onnxruntime::TBroadcastOutput::TBroadcastOutput(size_t, onnxruntime::Tensor &, int64_t, int64_t) [T = unsigned long long]auto onnxruntime::BitShift::Compute(onnxruntime::OpKernelContext *)::(anonymous class)::operator()(EigenVectorMap, ConstEigenVectorMap, ConstEigenVectorMap) const [T = unsigned long long]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = unsigned long long, TOutput = unsigned long long, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:636:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:648:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:660:7)]void onnxruntime::TBroadcaster::AdvanceBy(size_t) [T0 = unsigned long long, T1 = unsigned long long]/Users/runner/work/1/s/include/onnxruntime/core/framework/op_kernel.honnxruntime::Tensor *onnxruntime::OpKernelContext::Output(int)p_ml_valuevirtual onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext *) const [T = float]tensor_shape.Shape().GetDims().size() == 1virtual onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext *) const [T = double]virtual onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext *) const [T = signed char]virtual onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext *) const [T = short]virtual onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext *) const [T = int]virtual onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext *) const [T = long long]virtual onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext *) const [T = unsigned char]virtual onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext *) const [T = unsigned short]virtual onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext *) const [T = unsigned int]virtual onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext *) const [T = unsigned long long]virtual onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext *) const [T = bool]virtual onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext *) const [T = onnxruntime::MLFloat16]onnxruntime::Mod::Mod(const onnxruntime::OpKernelInfo &)(fmod == 0) || (fmod == 1)const T *onnxruntime::OpKernelContext::Input(int) const [T = onnxruntime::Tensor]Missing Input: Unsupported Y type: onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = float, TOutput = float, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:400:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:401:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:402:7)]onnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext &) [T = float]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = float, TOutput = float, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:428:9), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:429:9), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:430:9)]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = double, TOutput = double, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:428:9), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:429:9), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:430:9)]onnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext &) [T = double]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = onnxruntime::MLFloat16, TOutput = onnxruntime::MLFloat16, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:428:9), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:429:9), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:430:9)]onnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext &) [T = onnxruntime::MLFloat16]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = int, TOutput = int, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:428:9), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:429:9), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:430:9)]onnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext &) [T = int]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = unsigned int, TOutput = unsigned int, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:428:9), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:429:9), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:430:9)]onnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext &) [T = unsigned int]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = long long, TOutput = long long, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:428:9), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:429:9), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:430:9)]onnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext &) [T = long long]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = unsigned long long, TOutput = unsigned long long, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:428:9), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:429:9), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:430:9)]onnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext &) [T = unsigned long long]Ret onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper >::Get() [Ret = onnxruntime::common::Status, UnsupportedPolicy = onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy]Ret onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy::operator()(int32_t) const [Ret = onnxruntime::common::Status]Unsupported data type: onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = float, TOutput = float, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:463:9), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:464:9), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:465:9)]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = double, TOutput = double, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:463:9), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:464:9), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:465:9)]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = onnxruntime::MLFloat16, TOutput = onnxruntime::MLFloat16, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:463:9), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:464:9), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:465:9)]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = int, TOutput = int, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:463:9), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:464:9), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:465:9)]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = unsigned int, TOutput = unsigned int, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:463:9), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:464:9), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:465:9)]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = long long, TOutput = long long, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:463:9), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:464:9), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:465:9)]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = unsigned long long, TOutput = unsigned long long, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:463:9), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:464:9), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:465:9)]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = bool, TOutput = bool, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:487:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:493:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:499:7)]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = bool, TOutput = bool, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:507:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:513:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:519:7)]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = bool, TOutput = bool, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:527:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:533:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:539:7)]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = float, TOutput = float, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:607:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:608:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:609:7)]void onnxruntime::BroadcastOneSpan(concurrency::ThreadPool *, double, TOutput *, int64_t, const TInput *, int64_t, const TInput *, int64_t, Input0Scalar, Input1Scalar, General) [TInput = float, TOutput = float, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:948:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:954:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:957:7)]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = float]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = double]void onnxruntime::utils::MLTypeCallDispatcher::Invoke(Args &&...) const [Fn = mod_internal::CallModImpl, Types = , Args = ]helper.called_ == 1void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = unsigned char]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = unsigned char]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = signed char]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = signed char]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = unsigned short]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = unsigned short]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = short]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = short]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = unsigned int]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = unsigned int]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = int]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = int]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = unsigned long long]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = unsigned long long]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = long long]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = long long]GemmtransA/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/gemm.honnxruntime::Gemm::Gemm(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttr("transA", &temp).IsOK()transBinfo.GetAttr("transB", &temp).IsOK()/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/gemm_helper.honnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &)left.NumDimensions() == 2 || left.NumDimensions() == 1right.NumDimensions() == 2GEMM: Dimension mismatch, W: K: N:Gemm: Invalid bias shape for broadcastM_ >= 0 && K_ > 0 && N_ >= 0static void onnxruntime::Gemm::ComputeGemm(CBLAS_TRANSPOSE, CBLAS_TRANSPOSE, int64_t, int64_t, int64_t, float, const T *, const T *, float, const T *, const onnxruntime::TensorShape *, T *, concurrency::ThreadPool *) [T = float]c_shape != nullptrHardmax inputs N, D and N * D must be < . N=, D=HardmaxMatMul/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/matmul.ccNot satisfied: left_num_dims >= 1 && right_num_dims >= 1 /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/matmul_helper.hNot satisfied: K_ == right_shape[right_num_dims - 2] || transb && K_ == right_shape[right_num_dims - 1] Not satisfied: left_padded_dims_[idx_dim] == 1 left operand cannot broadcast on dim Not satisfied: right_padded_dims_[idx_dim] == 1 right operand cannot broadcast on dim Not satisfied: K_ == right_shape[transb ? right_num_dims - 1 : right_num_dims - 2] Not satisfied: num_dims_with_pad == num_output_dims Not satisfied: M_ == 1 && N_ == 1 Not satisfied: num_dims_with_pad - 1 == num_output_dims Not satisfied: num_dims_with_pad - 2 == num_output_dims Not satisfied: K_ == right_shape[0] MatMulInteger/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/matmul_integer.ccvirtual onnxruntime::common::Status onnxruntime::MatMulInteger::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(a_zero_point)MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(b_zero_point)MatmulInteger : input2 zero point must be a scalar or 1D tensor of size 1QLinearMatMul/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/quantize_linear_matmul.ccvirtual onnxruntime::common::Status onnxruntime::QLinearMatMul::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(a_offset)QLinearMatmul : input zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(b_offset)QLinearMatmul : weight zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_offset)QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_scale)QLinearMatmul : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(b_scale)QLinearMatmul : weight scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_scale)QLinearMatmul : result scale must be a scalar or 1D tensor of size 1RoundSignvoid onnxruntime::utils::MLTypeCallDispatcher::Invoke(Args &&...) const [Fn = CallSignImpl, Types = , Args = ]SoftmaxLogSoftmaxSoftmaxCPU inputs N, D and N * D must be < /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/top_k.cconnxruntime::TopK<9, float>::TopK(const onnxruntime::OpKernelInfo &) [OpSet = 9, T = float]op_kernel_info.GetAttr("k", &k_temp).IsOK()k_temp > 0op_kernel_info.GetAttr("axis", &axis_temp).IsOK()input count mismatch, expected 1 input - the tensor to be processedonnxruntime::TopK<10, float>::TopK(const onnxruntime::OpKernelInfo &) [OpSet = 10, T = float]input count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valueTopKvoid onnxruntime::TopkOpset11ConstructorCommon(const onnxruntime::OpKernelInfo &, int &, bool &, bool &)largestop_kernel_info.GetAttr("largest", &largest_temp).IsOK()sortedop_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()k argument [] should not be greater than specified axis dim value [output count mismatch, expected 2 outputs to be present for TopK operatorArrayFeatureExtractorInvalid argument: X input has empty dimensions.Invalid Y argument: num_indices = 0Invalid Y argument: index is out of range: Y[] () >=BinarizerthresholdInput data with index: is NaNCastMapInvalid input type of value: Expected std::map or std::mapUnexpected CAST_TO value of cast_to/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/cast_map.honnxruntime::ml::CastMap::CastMap(const onnxruntime::OpKernelInfo &)info.GetAttr("cast_to", &attr).IsOK()info.GetAttr("map_form", &attr).IsOK()max_mapinfo.GetAttr("max_map", &max_map_).IsOK()map_form_ != PACK_MAP::SPARSE || max_map_ > 0TO_FLOATTO_STRINGTO_INT64/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/ml_common.honnxruntime::ml::CAST_TO onnxruntime::ml::MakeCast(const std::string &)Invalid CAST_TO value of Expected TO_FLOAT, TO_STRING or TO_INT64DENSESPARSEonnxruntime::ml::PACK_MAP onnxruntime::ml::MakePack(const std::string &)Invalid PACK_MAP value of Expected DENSE or SPARSE/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/cast_map.cconnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext &, TTo) const [TFrom = float, TTo = float]cur_input == end_input || cur_input->first >= 0Negative index values are not permitted. First entry in map has index value of const T *onnxruntime::OpKernelContext::Input(int) const [T = std::__1::map, std::__1::allocator > >]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext &, TTo) const [TFrom = std::__1::basic_string, TTo = float]const T *onnxruntime::OpKernelContext::Input(int) const [T = std::__1::map, std::__1::less, std::__1::allocator > > >]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext &, TTo) const [TFrom = float, TTo = long long]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext &, TTo) const [TFrom = std::__1::basic_string, TTo = long long]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext &, TTo) const [TFrom = float, TTo = std::__1::basic_string]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext &, TTo) const [TFrom = std::__1::basic_string, TTo = std::__1::basic_string]CategoryMapperInput of string must have output of int64Input of int64 must have output of string cats_strings/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/category_mapper.honnxruntime::ml::CategoryMapper::CategoryMapper(const onnxruntime::OpKernelInfo &)info.GetAttrs("cats_strings", string_categories).IsOK()cats_int64sinfo.GetAttrs("cats_int64s", int_categories).IsOK()default_stringinfo.GetAttr("default_string", &default_string_).IsOK()default_int64info.GetAttr("default_int64", &default_int_).IsOK()num_entries == int_categories.size()DictVectorizerstring_vocabulary/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/dictvectorizer.honnxruntime::ml::DictVectorizerOp, long long>::DictVectorizerOp(const onnxruntime::OpKernelInfo &) [AttrType = std::__1::basic_string, TargetType = long long]info.GetAttrs(std::is_same::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()const T *onnxruntime::OpKernelContext::Input(int) const [T = std::__1::map, long long, std::__1::less >, std::__1::allocator, long long> > >]onnxruntime::ml::DictVectorizerOp, float>::DictVectorizerOp(const onnxruntime::OpKernelInfo &) [AttrType = std::__1::basic_string, TargetType = float]const T *onnxruntime::OpKernelContext::Input(int) const [T = std::__1::map, float, std::__1::less >, std::__1::allocator, float> > >]onnxruntime::ml::DictVectorizerOp, double>::DictVectorizerOp(const onnxruntime::OpKernelInfo &) [AttrType = std::__1::basic_string, TargetType = double]const T *onnxruntime::OpKernelContext::Input(int) const [T = std::__1::map, double, std::__1::less >, std::__1::allocator, double> > >]int64_vocabularyonnxruntime::ml::DictVectorizerOp >::DictVectorizerOp(const onnxruntime::OpKernelInfo &) [AttrType = long long, TargetType = std::__1::basic_string]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo &) [AttrType = long long, TargetType = float]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo &) [AttrType = long long, TargetType = double]const T *onnxruntime::OpKernelContext::Input(int) const [T = std::__1::map, std::__1::allocator > >]FeatureVectorizer/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/feature_vectorizer.ccvirtual onnxruntime::common::Status onnxruntime::ml::FeatureVectorizer::Compute(onnxruntime::OpKernelContext *) constinput_count >= 0 && static_cast(input_count) == input_dimensions_.size()Number of inputs () does not match number of inputdimensions values (Invalid input type:inputdimensions/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/feature_vectorizer.honnxruntime::ml::FeatureVectorizer::FeatureVectorizer(const onnxruntime::OpKernelInfo &)status.IsOK() && !input_dimensions_.empty()Imputerimputed_value_floatsimputed_value_int64sreplaced_value_float/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/imputer.cconnxruntime::ml::ImputerOp::ImputerOp(const onnxruntime::OpKernelInfo &)Expected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedreplaced_value_int64Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedimputed_values_float_.empty() ^ imputed_values_int64_.empty()Must provide imputed_values_float_ or imputed_values_int64_ but not both.virtual common::Status onnxruntime::ml::ImputerOp::Compute(onnxruntime::OpKernelContext *) constInvalid typeEmpty value of imputed values.Empty input dimensions.LabelEncoderInput of tensor(string) must have output of tensor(int64)Input of tensor(int64) must have output of tensor(string)keys_floatsvalues_stringskeys_stringsvalues_floatsdefault_floatkeys_int64svalues_int64sclasses_strings/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/label_encoder.honnxruntime::ml::LabelEncoder::LabelEncoder(const onnxruntime::OpKernelInfo &)info.GetAttrs("classes_strings", string_classes).IsOK()onnxruntime::ml::LabelEncoder_2 >::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = float, TValue = std::__1::basic_string]info.GetAttrs(_key_field_name, keys).IsOK()info.GetAttrs(_value_field_name, values).IsOK()num_keys == num_valuesThe attribtues in LabelEncoder (name: ) must have the same length. However, the number of key is and the number of values is onnxruntime::ml::LabelEncoder_2, float>::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = std::__1::basic_string, TValue = float]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = long long, TValue = float]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = float, TValue = long long]onnxruntime::ml::LabelEncoder_2 >::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = long long, TValue = std::__1::basic_string]onnxruntime::ml::LabelEncoder_2, long long>::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = std::__1::basic_string, TValue = long long]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = long long, TValue = long long]LinearClassifiermulti_class/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/linearclassifier.cconnxruntime::ml::LinearClassifier::LinearClassifier(const onnxruntime::OpKernelInfo &)void onnxruntime::ml::LinearClassifier::ComputeImpl(const gsl::span, int64_t, int64_t, int64_t, const std::vector &, const std::vector &, onnxruntime::Tensor &, onnxruntime::Tensor &, onnxruntime::ml::POST_EVAL_TRANSFORM, bool, concurrency::ThreadPool *) constscores_output_data.length() >= scores_output_sizeScores output is incorrect size. Expected: Found:Input shape needs to be at least a single dimension.Unsupported input element type of void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor &, gsl::span &) [SrcType = int]shape_size == out.length()void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor &, gsl::span &) [SrcType = long long]void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor &, gsl::span &) [SrcType = double]LinearRegressorinterceptstargets/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/linearregressor.cconnxruntime::ml::LinearRegressor::LinearRegressor(const onnxruntime::OpKernelInfo &)info.GetAttr("targets", &num_targets_).IsOK()Input shape had more than 2 dimension. Dims=Normalizer/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/normalizer.honnxruntime::ml::Normalizer::Normalizer(const onnxruntime::OpKernelInfo &)info.GetAttr("norm", &norm).IsOK()MAXL1L2onnxruntime::ml::NORMALIZE onnxruntime::ml::MakeNormalize(const std::string &)Invalid normalize value of Rank of input to Normalized must be less than 2. Got Unexpected NORMALIZE value of OneHotEncoderUnknown Category and zeros = 0.zeros/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/onehotencoder.cconnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo &) [T = long long]tmp_cats_int64s.empty() || tmp_cats_strings.empty()One and only one of the 'cats_*' attributes must be definednum_categories_ > 0onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo &) [T = float]onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo &) [T = double]onnxruntime::ml::OneHotEncoderOp >::OneHotEncoderOp(const onnxruntime::OpKernelInfo &) [T = std::__1::basic_string]Scaleroffset/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/scaler.cconnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo &) [T = float]!scale_.empty()scale_.size() == offset_.size()Scale size: () != (Invalid argument: input has empty dimensions.Either both scale and offset can be of feature size () or 1onnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo &) [T = double]onnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo &) [T = long long]onnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo &) [T = int]SVMClassifiervectors_per_classprob_aprob_b/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/svmclassifier.cconnxruntime::ml::SVMClassifier::SVMClassifier(const onnxruntime::OpKernelInfo &)proba_.size() == probb_.size()classlabels_intsinfo.GetAttrs("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs("classlabels_ints", classlabels_ints_).IsOK()classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0coefficients_.size() > 0Unsupported data type of void onnxruntime::ml::batched_update_scores_inplace(gsl::span, int64_t, int64_t, onnxruntime::ml::POST_EVAL_TRANSFORM, int, bool, concurrency::ThreadPool *) [T = float]scores.size() == static_cast(expected_num_scores)Unexpected value for 'add_second_class' of SVMRegressorn_supportssupport_vectorspost_transformrho/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/svmregressor.cconnxruntime::ml::SVMRegressor::SVMRegressor(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttrs("rho", rho_).IsOK()coefficientsinfo.GetAttrs("coefficients", coefficients_).IsOK()!coefficients_.empty()one_classkernel_typeLINEARkernel_params/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/svmclassifier.honnxruntime::ml::SVMCommon::SVMCommon(const onnxruntime::OpKernelInfo &)info.GetAttrs("kernel_params", kernel_params).IsOK()POLYRBFLOGISTICSOFTMAXSOFTMAX_ZEROvirtual onnxruntime::common::Status onnxruntime::ml::SVMRegressor::Compute(onnxruntime::OpKernelContext *) const [T = float]num_features == feature_count_Unexpected mode:TreeEnsembleClassifierclass_idsclass_nodeidsclass_treeidsclass_weightsX dims is empty.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::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::TreeAggregatorSum::ProcessTreeNodePrediction(std::vector > &, const TreeNodeElement &) const [ITYPE = long long, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(std::vector > &, const std::vector > &) 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]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::TreeAggregatorSum::ProcessTreeNodePrediction(std::vector > &, const TreeNodeElement &) const [ITYPE = int, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(std::vector > &, const std::vector > &) 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]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(concurrency::ThreadPool *, 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::TreeAggregatorSum::MergePrediction(std::vector > &, const std::vector > &) const [ITYPE = float, OTYPE = float]predictions.size() == predictions2.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::TreeAggregatorMin::MergePrediction(std::vector > &, const std::vector > &) const [ITYPE = float, OTYPE = float]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::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(concurrency::ThreadPool *, 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::TreeAggregatorSum::MergePrediction(std::vector > &, const std::vector > &) 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::TreeAggregatorMin::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::TreeAggregatorMax::MergePrediction(std::vector > &, const std::vector > &) const [ITYPE = double, OTYPE = float]ZipMapclasslabels_int64sclasslabels_strings/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/zipmap.cconnxruntime::ml::ZipMapOp::ZipMapOp(const onnxruntime::OpKernelInfo &)classlabels_strings_.empty() ^ classlabels_int64s_.empty()Must provide classlabels_strings or classlabels_int64s but not both.Zipmap does not support empty dim countZipmap only supports 1D or 2D input tensorsInput features_per_batch[] != number of classlabels[T *OrtValue::GetMutable() [T = std::__1::vector, float, std::__1::less >, std::__1::allocator, float> > >, std::__1::allocator, float, std::__1::less >, std::__1::allocator, float> > > > >]T *OrtValue::GetMutable() [T = std::__1::vector, std::__1::allocator > >, std::__1::allocator, std::__1::allocator > > > >]MaxUnpoolNot satisfied: X_shape.NumDimensions() >= 3 /Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/Unpool.ccNot satisfied: I_shape == X_shape Index tensor shape should be same as that of the input data tensor to unpool.Not satisfied: tensor_shape->Shape().GetDims().size() == 1 Not satisfied: given_shape.Size() >= shape.Size() output_shape is smaller than minimum required. output_shape: inferred output shape:/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/unpool.honnxruntime::MaxUnpool::MaxUnpool(const onnxruntime::OpKernelInfo &)info.GetAttrs("kernel_shape", kernel_shape_).IsOK()kernel_shape_[dim] > 0pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]strides_.size() == kernel_shape_.size()BatchNormalizationspatial/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/batch_norm.honnxruntime::BatchNorm::BatchNorm(const onnxruntime::OpKernelInfo &) [T = float]Invalid input X: The rank of input X must be atleast 2. Got rank: Invalid input scale: NumDimensions() != Invalid input scale: 0th dimension != Invalid input scale: dimension != Invalid input B: NumDimensions() != Invalid input B: 0th dimension != Invalid input B: Invalid input mean: NumDimensions() != Invalid input mean: 0th dimension != Invalid input mean: Invalid input var: NumDimensions() != Invalid input var: 0th dimension != Invalid input var: onnxruntime::BatchNorm::BatchNorm(const onnxruntime::OpKernelInfo &) [T = double]/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/conv.ccConvX num_dims does not match W num_dims. X: W: Input channels C is not equal to kernel channels * group. C: kernel channels: group: Output channels M is not divisible by group. M: kernel_shape num_dims is not compatible with W num_dims. kernel_shape: kernel_shape is not compatible with W shape.Not enough elements in strides. Expected: Not enough elements in kernel shape. Expected: Not enough elements in dilations. Expected: Not enough elements in pads. Expected: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/conv_attributes.hInferOutputShapeInvalid input shape: /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_shapestridesdilationsgroupNOTSETVALIDSAME_UPPERSAME_LOWERonnxruntime::AutoPadType onnxruntime::StringToAutoPadType(const std::string &)/Users/runner/work/1/s/onnxruntime/core/util/math.hvoid onnxruntime::math::Im2colNd::operator()(const T *, const int64_t *, const int64_t *, int64_t, int64_t, const int64_t *, const int64_t *, const int64_t *, const int64_t *, int64_t, T *, bool, T) [T = float, order = 2]d_iter[d_i] < d_maxT3ConvInteger/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)void onnxruntime::math::Im2colNd::operator()(const T *, const int64_t *, const int64_t *, int64_t, int64_t, const int64_t *, const int64_t *, const int64_t *, const int64_t *, int64_t, T *, bool, T) [T = unsigned char, order = 2]ConvTransposeoutput_paddingoutput_shape/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/conv_transpose.ccDoConvTransposegroup count is <= 0filter number not equal to input channel number. filter_number: num_input_channels: Input channels is not divisible by group./Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/conv_transpose_attributes.hvoid onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShape(onnxruntime::TensorShape, int64_t, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const int64_t, std::vector *, std::vector *) constdim_size > 0void onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShape(const int64_t, const int64_t, const int64_t, const int64_t, const int64_t, onnxruntime::AutoPadType, int64_t *, int64_t *, int64_t *) const*out_size >= 0/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/dropout_op.hvirtual onnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext *) const [T1 = float, T2 = float, trainable_dropout = false]!mask || mask->Shape() == X_shapefloat onnxruntime::(anonymous namespace)::GetRatioOrDefault(const onnxruntime::Tensor *) [T2 = float]ratio_tensor->Shape().Size() == 10.0f <= ratio_value && ratio_value < 1.0fvirtual onnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext *) const [T1 = float, T2 = double, trainable_dropout = false]float onnxruntime::(anonymous namespace)::GetRatioOrDefault(const onnxruntime::Tensor *) [T2 = double]virtual onnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext *) const [T1 = double, T2 = float, trainable_dropout = false]virtual onnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext *) const [T1 = double, T2 = double, trainable_dropout = false]Flatten/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/flatten.honnxruntime::Flatten::Flatten(const onnxruntime::OpKernelInfo &)virtual onnxruntime::common::Status onnxruntime::Flatten::Compute(onnxruntime::OpKernelContext *) constgsl::narrow_cast(X_shape.NumDimensions()) >= axisInstanceNormalization/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/instance_norm.ccepsilon/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/instance_norm.honnxruntime::InstanceNorm::InstanceNorm(const onnxruntime::OpKernelInfo &) [T = float]op_kernel_info.GetAttr("epsilon", &epsilon_).IsOK()Invalid input data: number of dimensions is less than 3: Invalid input scale: number of dimensions is not 1: Mismatch between input data and scale: size of scale != input channel count vs. Invalid input B: number of dimensions is not 1: Mismatch between input data and B: size of B != input channel count LpNormalization/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/lp_norm.honnxruntime::LpNorm::LpNorm(const onnxruntime::OpKernelInfo &) [T = float]op_kernel_info.GetAttr("axis", &axis_).IsOK()op_kernel_info.GetAttr("p", &p_).IsOK()p_ == 1 || p_ == 2input count mismatch/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/lrn.ccvirtual onnxruntime::common::Status onnxruntime::LRN::Compute(onnxruntime::OpKernelContext *) const [T = float]X->Shape().NumDimensions() == 4LRN/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/lrn.honnxruntime::LRN::LRN(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttr("size", &size).IsOK()size_ > 0size_ % 2 == 1info.GetAttr("alpha", &alpha_).IsOK()alpha_ > 0.0finfo.GetAttr("beta", &beta_).IsOK()beta_ > 0.0fNot satisfied: input_dims >= 3 /Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/pool.ccUnsupported pooling size.Not satisfied: pooling_dims == pool_attrs_.kernel_shape.size() AveragePoolMaxPoolLpPoolGlobalLpPoolGlobalAveragePoolGlobalMaxPool/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/pool_attributes.hstd::vector onnxruntime::PoolAttributes::SetOutputSize(const onnxruntime::TensorShape &, int64_t, std::vector *) constinput_shape.Size() > 0 || input_shape[0] == 0Invalid input shape. Only N can be zero. Got:void onnxruntime::PoolAttributes::InferOutputSize(const std::vector &, std::vector *, std::vector *) constinput_dims.size() >= 2void onnxruntime::PoolAttributes::ComputeSizePadDilations(const int64_t, const int64_t, const int64_t, int64_t *, int64_t *, int64_t, int64_t *) constonnxruntime::PoolAttributes::PoolAttributes(const OpNodeProtoHelper &, const std::string &, int)info.GetAttrs("kernel_shape", kernel_shape).IsOK()info.GetAttr("auto_pad", &auto_padding).IsOK()ceil_modecount_include_padinfo.GetAttr("count_include_pad", &temp).IsOK()storage_orderinfo.GetAttr("storage_order", &storage_order).IsOK()kernel_shape[dim] > 0pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]strides.size() == kernel_shape.size()dilations.size() == kernel_shape.size()/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/pool_base.hvoid onnxruntime::PoolProcessContext::init(const onnxruntime::OpKernelInfo &)info.GetAttr("p", &p_).IsOK()Not satisfied: x_shape.NumDimensions() >= 3 Unsupported pooling size : T4QLinearConv/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/qlinearconv.ccvirtual onnxruntime::common::Status onnxruntime::QLinearConv::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(X_zero_point)QLinearConv : input zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(W_zero_point)QLinearConv : filter 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(W_scale)QLinearConv : filter 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 1MaxRoiPool/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!impl_->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 = std::__1::__wrap_iter, Map = std::__1::unordered_map, std::__1::default_delete > >, std::__1::hash, std::__1::equal_to, std::__1::allocator, std::__1::default_delete > > > > >]p.first->second->id_ == 0Duplicate ngram detected, size: id: size_t onnxruntime::ngram_details::PopulateGrams(ForwardIter, size_t, size_t, size_t, Map &) [K = std::__1::basic_string, ForwardIter = std::__1::__wrap_iter *>, Map = std::__1::unordered_map >, std::__1::unique_ptr, std::__1::default_delete > >, std::__1::hash >, std::__1::equal_to >, std::__1::allocator >, std::__1::unique_ptr, std::__1::default_delete > > > > >]NonMaxSuppression/Users/runner/work/1/s/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.ccstatic onnxruntime::common::Status onnxruntime::NonMaxSuppressionBase::PrepareCompute(onnxruntime::OpKernelContext *, onnxruntime::PrepareContext &)boxes_tensorscores_tensorNot satisfied: boxes_shape.NumDimensions() == 3 PrepareComputeNot satisfied: scores_shape.NumDimensions() == 3 Not satisfied: boxes_dims[0] == scores_dims[0] Not satisfied: boxes_dims[1] == scores_dims[2] Not satisfied: boxes_dims[2] == 4 Not satisfied: (iou_threshold >= 0 && iou_threshold <= 1.f) GetThresholdsFromInputsNot satisfied: ret.IsOK() virtual onnxruntime::common::Status onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext *) constoutput != nullptrcenter_point_box/Users/runner/work/1/s/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.honnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBase(const onnxruntime::OpKernelInfo &)0 == center_point_box_ || 1 == center_point_box_RoiAlignNull input X ptrNull rois_ptrNull batch_indices_ptrNumber of dimensions for batch indices should be exactly 1Number of dimensions for rois should be exactly Second dimension for rois should be exactly First dimension (num_rois) of batch_indices and rois don't matchavg/Users/runner/work/1/s/onnxruntime/core/providers/cpu/object_detection/roialign.honnxruntime::RoiAlignBase::RoiAlignBase(const onnxruntime::OpKernelInfo &)Invalid mode of value specified. It should be either avg or maxoutput_heightoutput_widthsampling_ratiosampling_ratio_ >= 0Sampling ratio should be >=0, but it was ReduceL1ReduceL2ReduceLogSumReduceLogSumExpReduceMaxReduceMeanReduceMinReduceProdReduceSumReduceSumSquareArgMaxArgMin/Users/runner/work/1/s/onnxruntime/core/providers/cpu/containers.hOrtStlAllocator onnxruntime::GetAllocator(const onnxruntime::OpKernelContext &) [T = signed char]/Users/runner/work/1/s/onnxruntime/core/providers/cpu/reduction/reduction_ops.ccbool onnxruntime::PrepareForReduce(const onnxruntime::Tensor *, FastAllocVector &, int64_t &, int64_t &, const std::vector &, bool, std::vector &, bool, const onnxruntime::TensorShape *) [T = signed char]input_tensor_ptr->Shape().Size() == input_shape_override->Size()The input shape override's size does not match the input tensor's shape sizein_dim != 0Can't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:OrtStlAllocator onnxruntime::GetAllocator(const onnxruntime::OpKernelContext &) [T = unsigned char]bool onnxruntime::PrepareForReduce(const onnxruntime::Tensor *, FastAllocVector &, int64_t &, int64_t &, const std::vector &, bool, std::vector &, bool, const onnxruntime::TensorShape *) [T = unsigned char]/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()select_last_index/Users/runner/work/1/s/include/onnxruntime/core/common/optional.hT &onnxruntime::optional::value() [T = long long]has_value_onnxruntime::ReduceKernelBase::ReduceKernelBase(const onnxruntime::OpKernelInfo &, optional) [allow_multi_axes = true]OrtStlAllocator onnxruntime::GetAllocator(const onnxruntime::OpKernelContext &) [T = float]bool onnxruntime::PrepareForReduce(const onnxruntime::Tensor *, FastAllocVector &, int64_t &, int64_t &, const std::vector &, bool, std::vector &, bool, const onnxruntime::TensorShape *) [T = float]OrtStlAllocator onnxruntime::GetAllocator(const onnxruntime::OpKernelContext &) [T = int]bool onnxruntime::PrepareForReduce(const onnxruntime::Tensor *, FastAllocVector &, int64_t &, int64_t &, const std::vector &, bool, std::vector &, bool, const onnxruntime::TensorShape *) [T = int]OrtStlAllocator onnxruntime::GetAllocator(const onnxruntime::OpKernelContext &) [T = double]bool onnxruntime::PrepareForReduce(const onnxruntime::Tensor *, FastAllocVector &, int64_t &, int64_t &, const std::vector &, bool, std::vector &, bool, const onnxruntime::TensorShape *) [T = double]OrtStlAllocator onnxruntime::GetAllocator(const onnxruntime::OpKernelContext &) [T = long long]bool onnxruntime::PrepareForReduce(const onnxruntime::Tensor *, FastAllocVector &, int64_t &, int64_t &, const std::vector &, bool, std::vector &, bool, const onnxruntime::TensorShape *) [T = long long]GRU/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.ccvirtual onnxruntime::common::Status onnxruntime::DeepCpuGruOp::Compute(onnxruntime::OpKernelContext *) constInvalid data type for GRU operator of /Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.honnxruntime::DeepCpuGruOp::DeepCpuGruOp(const onnxruntime::OpKernelInfo &)info.GetAttr("direction", &direction).IsOK()linear_before_resetinfo.GetAttr("linear_before_reset", &int64_value).IsOK()hidden_sizeinfo.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0activationsactivation_alphaactivation_betaclip_ > 0.fsigmoidtanhactivation_func_names.size() == static_cast(num_directions_) * 2forwardreversebidirectional/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/rnn_helpers.honnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const std::string &)Invalid 'direction' argument of ''. Must be one of 'forward', 'reverse', or 'bidirectional'.ComputeImplgsl::span onnxruntime::Tensor::DataAsSpan() const [T = float]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = float]void onnxruntime::rnn::detail::ComputeGemm(const int, const int, const int, const float, TSpanAIter, TSpanAIter, const int, TSpanBIter, TSpanBIter, const int, const float, TSpanCIter, TSpanCIter, const int, concurrency::ThreadPool *) [TSpanAIter = const float *, TSpanBIter = const float *, TSpanCIter = float *]lda >= K && ldb >= K && ldc >= NA + (M * lda - (lda - K)) <= A_endB + (N * ldb - (ldb - K)) <= B_endC + (M * ldc - (ldc - N)) <= C_endconst T *onnxruntime::rnn::detail::SafeRawConstPointer(typename gsl::span::const_iterator, typename gsl::span::const_iterator, size_t) [T = float]cur + size <= endT *onnxruntime::rnn::detail::SafeRawPointer(typename gsl::span, size_t, size_t) [T = float]offset + size <= size_t(span.size())T *onnxruntime::rnn::detail::SafeRawPointer(typename gsl::span::iterator, typename gsl::span::iterator, size_t) [T = float]void onnxruntime::rnn::detail::ComputeGemm(const int, const int, const int, const float, TSpanAIter, TSpanAIter, const int, TSpanBIter, TSpanBIter, const int, const float, TSpanCIter, TSpanCIter, const int, concurrency::ThreadPool *) [TSpanAIter = float *, TSpanBIter = const float *, TSpanCIter = float *]LSTM/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.ccvirtual onnxruntime::common::Status onnxruntime::DeepCpuLstmOp::Compute(onnxruntime::OpKernelContext *) constInvalid data type for LSTM operator of Input initial_c must have shape {}. Actual:Input P must have shape {/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.honnxruntime::DeepCpuLstmOp::DeepCpuLstmOp(const onnxruntime::OpKernelInfo &)input_forgetactivation_func_names.size() == static_cast(num_directions_) * 3 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]RNN/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 {*Input R must have shape {Input B must have shape {Input sequence_lens must have shape {Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length=Input initial_h must have shape {/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/rnn_helpers.ccstd::string onnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCount(const std::string &, std::vector::const_iterator &, const std::vector::const_iterator &, std::vector::const_iterator &, const std::vector::const_iterator &, float &, float &)Expecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got Dump matrix: reluaffineleakyreluthresholdedreluscaledtanhhardsigmoidelusoftsignsoftplusonnxruntime::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: ConcatFromSequence/Users/runner/work/1/s/onnxruntime/core/providers/cpu/sequence/concat_from_sequence.ccvirtual onnxruntime::common::Status onnxruntime::ConcatFromSequence::Compute(onnxruntime::OpKernelContext *) const/Users/runner/work/1/s/onnxruntime/core/providers/cpu//tensor/concat.hSSequenceLength/Users/runner/work/1/s/onnxruntime/core/providers/cpu/sequence/sequence_ops.ccvirtual onnxruntime::common::Status onnxruntime::SequenceLength::Compute(onnxruntime::OpKernelContext *) constY != nullptrSequenceAtvirtual onnxruntime::common::Status onnxruntime::SequenceAt::Compute(onnxruntime::OpKernelContext *) constI != nullptrInvalid sequence index () specified for sequence of size (SequenceEmptyvirtual onnxruntime::common::Status onnxruntime::SequenceEmpty::Compute(onnxruntime::OpKernelContext *) constSequenceInsertCreateCopyAndAppendCpuTensorvirtual onnxruntime::common::Status onnxruntime::SequenceInsert::Compute(onnxruntime::OpKernelContext *) constS != nullptrData type of the input tensor MUST be same as that of the input sequence. Sequence data type (), input tensor data type (SequenceErasevirtual onnxruntime::common::Status onnxruntime::SequenceErase::Compute(onnxruntime::OpKernelContext *) constSequenceConstructvirtual onnxruntime::common::Status onnxruntime::SequenceConstruct::Compute(onnxruntime::OpKernelContext *) constnum_inputs >= 1Violation of the requirment that all input tensors must have the same data type.SplitToSequencevirtual onnxruntime::common::Status onnxruntime::SplitToSequence::Compute(onnxruntime::OpKernelContext *) constSplitToSequence operator does not support split_size_sum () != split_dim_size (int64_t onnxruntime::GetSeqIdx(const onnxruntime::Tensor &)const T *onnxruntime::OpKernelContext::Input(int) const [T = onnxruntime::TensorSeq]onnxruntime::TensorSeq *OrtValue::GetMutable()onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &, const onnxruntime::Tensor *) const [T = float]split_scalar > 0std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value >= 0; })int64_t onnxruntime::GetScalarSplitInput(const onnxruntime::Tensor &)Invalid data type for split tensor void onnxruntime::GetSplitSizesInput(const onnxruntime::Tensor &, std::vector &)void onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape &)shape_.Size() == new_shape.Size()Tensor size () != new size (onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &, const onnxruntime::Tensor *) const [T = double]onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &, const onnxruntime::Tensor *) const [T = int]onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &, const onnxruntime::Tensor *) const [T = std::__1::basic_string]T2Cast/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/cast_op.cconnxruntime::Cast::Cast(const onnxruntime::OpKernelInfo &)void onnxruntime::utils::MLTypeCallDispatcher::Invoke(Args &&...) const [Fn = SrcDispatcher, Types = , Args = ]helper.called_ < 2void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invoke(Args &&...) const [TCarried = float, Fn = Cast::Dispatcher, Types = , Args = ]void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invoke(Args &&...) const [TCarried = double, Fn = Cast::Dispatcher, Types = , Args = ]void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invoke(Args &&...) const [TCarried = signed char, Fn = Cast::Dispatcher, Types = , Args = ]void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invoke(Args &&...) const [TCarried = unsigned char, Fn = Cast::Dispatcher, Types = , Args = ]void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invoke(Args &&...) const [TCarried = short, Fn = Cast::Dispatcher, Types = , Args = ]void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invoke(Args &&...) const [TCarried = unsigned short, Fn = Cast::Dispatcher, Types = , Args = ]void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invoke(Args &&...) const [TCarried = int, Fn = Cast::Dispatcher, Types = , Args = ]void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invoke(Args &&...) const [TCarried = unsigned int, Fn = Cast::Dispatcher, Types = , Args = ]void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invoke(Args &&...) const [TCarried = long long, Fn = Cast::Dispatcher, Types = , Args = ]void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invoke(Args &&...) const [TCarried = unsigned long long, Fn = Cast::Dispatcher, Types = , Args = ]void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType::Invoke(Args &&...) const [TCarried = bool, Fn = Cast::Dispatcher, Types = , Args = ]void onnxruntime::utils::MLTypeCallDispatcher::Invoke(Args &&...) const [Fn = StringDispatcher, Types = , Args = ]NaN-INFINFgsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = std::__1::basic_string]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = double]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = onnxruntime::MLFloat16]void onnxruntime::(anonymous namespace)::CastFromStringData(const onnxruntime::Tensor &, onnxruntime::Tensor &, const onnxruntime::TensorShape &) [DstType = onnxruntime::MLFloat16]Unsupported type in cast op: from String to gsl::span onnxruntime::Tensor::DataAsSpan() const [T = signed char]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = unsigned char]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = short]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = unsigned short]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = unsigned int]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = long long]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = unsigned long long]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = bool]void onnxruntime::(anonymous namespace)::CastFromStringData(const onnxruntime::Tensor &, onnxruntime::Tensor &, const onnxruntime::TensorShape &) [DstType = bool]Compress/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/compress.ccvirtual onnxruntime::common::Status onnxruntime::Compress::Compute(onnxruntime::OpKernelContext *) constaxes_right_stride >= 0 && static_cast(axes_right_stride) < std::numeric_limits::max()ConcatNot satisfied: input_count >= 1 /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/concat.ccPrepareForComputeNot satisfied: inputs_0_rank > 0 onnxruntime::common::Status onnxruntime::ConcatBase::PrepareForCompute(onnxruntime::OpKernelContext *, const std::vector &, onnxruntime::Prepare &) constinputs_n_rank == inputs_0_rankRanks of input data are different, cannot concatenate them. expected rank: got: Not satisfied: dim_value == inputs_0_dims[axis_index] Non concat axis dimensions must match: Axis has mismatched dimensions of Not satisfied: data_n.DataType() == p.output_tensor->DataType() /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/concat.honnxruntime::ConcatBase::ConcatBase(const onnxruntime::OpKernelInfo &, bool)DynamicQuantizeLinear/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/dynamicquantizelinear.ccvirtual onnxruntime::common::Status onnxruntime::DynamicQuantizeLinear::Compute(onnxruntime::OpKernelContext *) const [T = unsigned char]x_ptr != nullptrEyeLike/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/eye_like.ccvirtual onnxruntime::common::Status onnxruntime::EyeLike::Compute(onnxruntime::OpKernelContext *) constT1 != nullptrUnsupported 'dtype' value: dtypeEyeLike : Input tensor dimension is not 2Gather/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/gather.cc/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/gather.honnxruntime::GatherBase::GatherBase(const onnxruntime::OpKernelInfo &)info.GetAttr("axis", &axis_).IsOK()indices element out of data bounds, idx= must be within the inclusive range [GatherElementsGatherElements op: Rank of input 'data' needs to be equal to rank of input 'indices'GatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: GatherElements op: Data type of input 'data' should match the data type of the output/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/gather_elements.honnxruntime::GatherElements::GatherElements(const onnxruntime::OpKernelInfo &)/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/gather_elements.ccstd::vector onnxruntime::parse_and_validate_indices_tensor(const onnxruntime::Tensor *, int64_t, const onnxruntime::TensorShape &)GatherElements op: Data type for 'indices' tensor must be 'int32_t' and 'int64_t'GatherElements op: Value in indices must be within bounds [ , ]. Actual value is invalid index found, index = GatherND/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/gather_nd.ccvirtual onnxruntime::common::Status onnxruntime::GatherND::Compute(onnxruntime::OpKernelContext *) constinput_tensor != nullptr && indices_tensor != nullptrlast dimension of indices must not be larger than rank of input tensorbatch_dimsDropoutIdentity/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/identity_op.hvirtual onnxruntime::common::Status onnxruntime::IdentityOp::Compute(onnxruntime::OpKernelContext *) const [is_dropout = true]const void *onnxruntime::Tensor::DataRaw(onnxruntime::MLDataType) consttype == dtype_Tensor type mismatch.void *onnxruntime::Tensor::MutableDataRaw(onnxruntime::MLDataType)virtual onnxruntime::common::Status onnxruntime::IdentityOp::Compute(onnxruntime::OpKernelContext *) const [is_dropout = false]IsInfdetect_positive/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/isinf.cconnxruntime::IsInf::IsInf(const onnxruntime::OpKernelInfo &)detect_negativevirtual onnxruntime::common::Status onnxruntime::IsInf::Compute(onnxruntime::OpKernelContext *) constData type X must be float or double, but instead got IsNaNNull input ptrMeanVarianceNormalizationInput is expected to have four dimensions corresponding to [N,C,H,W]NonZero/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/nonzero_op.ccvirtual onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext *) const [T = bool]virtual onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext *) const [T = unsigned char]virtual onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext *) const [T = int]virtual onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext *) const [T = long long]virtual onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext *) const [T = float]OneHotInvalid argument for depth; it's not a scalar.Invalid argument for values; either it's rank is more than 1 or it has more than 2 elements/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/onehot.ccDepth is negative.PadCannot use 'reflect' mode to pad dimension with a value of 0. Input shape:Unexpected mode of /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/pad.ccvirtual onnxruntime::common::Status onnxruntime::Pad::Compute(onnxruntime::OpKernelContext *) constpads_tensor.IsDataType()pads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)Pads tensor should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]pads_size == 2 * data_rankPads tensor size should be equal to twice the input dimension count value_tensor->DataType() == data_type && value_tensor->Shape().Size() == 1Value tensor should be a 1D tensor of size 1 with the same type as that of the input tensorUnsupported input data type of valueconstantreflectedge/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/pad.honnxruntime::PadBase::PadBase(const onnxruntime::OpKernelInfo &)onnxruntime::PadValue onnxruntime::PadValueFromFloat(float, onnxruntime::MLDataType)onnxruntime::common::Status onnxruntime::PadImpl(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &, const onnxruntime::Mode &, T) [T = unsigned int]data_rank > 0data_rank * 2 == pads.size()PadInputWithDimValueOfZero/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/utils.honnxruntime::SliceSkips::SliceSkips(const onnxruntime::TensorShape &, gsl::span, gsl::span)dims.size() == extents.size() && dims.size() >= steps.size()void onnxruntime::SliceIteratorBase::Init(const std::vector &, gsl::span, gsl::span)dims.size() == starts.size() && dims.size() == extents_.size() && dims.size() >= steps.size()onnxruntime::common::Status onnxruntime::PadImpl(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &, const onnxruntime::Mode &, T) [T = unsigned long long]onnxruntime::common::Status onnxruntime::PadImpl(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &, const onnxruntime::Mode &, T) [T = unsigned char]DequantizeLinearQuantizeLinear/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/quantize_linear.ccvoid onnxruntime::PrepareForQDQ(const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const onnxruntime::Tensor *, int64_t, int64_t &, int64_t &, int64_t &)zero_point_ptr == nullptr || IsScalarOr1ElementVector(zero_point_ptr)scale.Shape().NumDimensions() == 1 && scale.Shape()[0] == broadcast_dimscale must be 1D tensor with size zero_point_ptr == nullptr || (zero_point_ptr->Shape().NumDimensions() == 1 && zero_point_ptr->Shape()[0] == broadcast_dim)x_zero_point must be null or 1D tensor with size shapeReshapeReshape_1/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/reshape.hvirtual onnxruntime::common::Status onnxruntime::Reshape::Compute(onnxruntime::OpKernelContext *) constshapeTensor->Shape().NumDimensions() == 1/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/reshape_helper.honnxruntime::ReshapeHelper::ReshapeHelper(const onnxruntime::TensorShape &, std::vector &)requested_shape[i] >= -1A dimension cannot be less than -1, got unknown_dim == -1i < input_shape.NumDimensions()The dimension with value zero exceeds the dimension size of the input tensor.size != 0 && (input_shape.Size() % size) == 0The input tensor cannot be reshaped to the requested shape. Input shape:, requested shape:gsl::narrow_cast(input_shape.Size()) == sizeonnxruntime::Reshape_1::Reshape_1(const onnxruntime::OpKernelInfo &)Resize/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/upsample.honnxruntime::UpsampleBase::UpsampleBase(onnxruntime::OpKernelInfo)info.GetAttr("mode", &mode).IsOK()scalesinfo.GetAttrs("scales", scales_).IsOK()extrapolation_valuecoordinate_transformation_modehalf_pixelasymmetricnearest_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)'Linear' mode and 'Cubic' mode only support 2-D inputs ('Bilinear', 'Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the Resize operatorUpsample operatorpytorch_half_pixeltf_half_pixel_for_nnalign_cornerstf_crop_and_resizeonnxruntime::ResizeCoordinateTransformationMode onnxruntime::UpsampleBase::StringToCoordinateTransformationMode(const std::string &)coordinate_transform_mode:[] is not supportted!round_prefer_ceilfloorceilonnxruntime::ResizeNearestMode onnxruntime::UpsampleBase::StringToNearestMode(const std::string &)nearest_mode:[] is not supported!void onnxruntime::UpsampleBase::ParseScalesData(const onnxruntime::Tensor *, std::vector &) constscales_size > 0ReverseSequencesequence_lens shape must be {batch_size}. Got:. batch_size=/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/reverse_sequence.ccvirtual onnxruntime::common::Status onnxruntime::ReverseSequenceOp::Compute(onnxruntime::OpKernelContext *) constbatch_axis/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/reverse_sequence.honnxruntime::ReverseSequenceOp::ReverseSequenceOp(const onnxruntime::OpKernelInfo &)info.GetAttr("batch_axis", &batch_axis).IsOK()time_axisinfo.GetAttr("time_axis", &time_axis).IsOK()batch_axis < 2Invalid batch_axis of . Must be 0 or 1time_axis < 2Invalid time_axis of batch_axis != time_axistime_axis and batch_axis must have different values but both are gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = bool]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = double]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned char]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = short]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned short]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned long long]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = onnxruntime::MLFloat16]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = onnxruntime::BFloat16]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = onnxruntime::BFloat16]ScatterScatterElementsIndices vs updates dimensions differs at position=Indices must have the same rank as Input. Indices rank=. Input rank=Indices dim= at pos= is greater than input dim=/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/scatter.ccvirtual onnxruntime::common::Status onnxruntime::Scatter::Compute(onnxruntime::OpKernelContext *) constUnknown tensor type of onnxruntime::Scatter::Scatter(const onnxruntime::OpKernelInfo &)/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/scatter_nd.cconnxruntime::common::Status onnxruntime::ScatterNDBase::PrepareForCompute(onnxruntime::OpKernelContext *, onnxruntime::ScatterNDBase::Prepare &) const [Tind = long long]input_tensor != nullptrindice_tensor != nullptrupdate_tensor != nullptrinput tensor and indices tensor must has rank larger than 0. input shape: , indices shape: updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. updates shape: , data shape: invalid indice found, indice = ScatterNDShapeSizeSliceTind'axes' has an axis outside of the tensor dimension count'axes' has duplicates'step' value cannot be 0/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/slice.ccstatic void onnxruntime::SliceBase::FillVectorsFromInput(const onnxruntime::Tensor &, const onnxruntime::Tensor &, const onnxruntime::Tensor *, const onnxruntime::Tensor *, std::vector &, std::vector &, std::vector &, std::vector &)start_tensor.Shape().NumDimensions() == 1ends_tensor.Shape().NumDimensions() == 1start_tensor.Shape() == ends_tensor.Shape()nullptr == axes_tensor || start_tensor.Shape() == axes_tensor->Shape()nullptr == steps_tensor || start_tensor.Shape() == steps_tensor->Shape()Data type for starts and ends inputs' need to be int32_t or int64_t, but instead got onnxruntime::common::Status onnxruntime::SliceBase::Compute(onnxruntime::OpKernelContext *) constinput_tensor_ptr != nullptrstarts/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/slice.honnxruntime::SliceBase::SliceBase(const onnxruntime::OpKernelInfo &, bool)has_starts && has_ends && attr_starts_.size() == attr_ends_.size()!has_axes || attr_axes_.size() == attr_starts_.size()Invalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributesauto onnxruntime::SliceImpl(onnxruntime::OpKernelContext *, const onnxruntime::Tensor &, SliceOp::PrepareForComputeMetadata &)::(anonymous class)::operator()(SliceIterator &) constoutput == output_endvoid *onnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStep(void *)Unexpected element size of SpaceToDepthDepthToSpace/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/space_depth_ops.ccvirtual onnxruntime::common::Status onnxruntime::SpaceToDepth::Compute(onnxruntime::OpKernelContext *) const [T = float]input_shape.NumDimensions() == 4input_height % this->blocksize_ == 0input_width % this->blocksize_ == 0virtual onnxruntime::common::Status onnxruntime::DepthToSpace::Compute(onnxruntime::OpKernelContext *) const [T = float]input.Shape().NumDimensions() == 4input_depth % (blocksize_ * blocksize_) == 0blocksize/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/space_depth_ops.honnxruntime::SpaceDepthBase::SpaceDepthBase(const onnxruntime::OpKernelInfo &)info.GetAttr("blocksize", &blocksize_).IsOK()CRDDCRonnxruntime::DepthToSpace::DepthToSpace(const onnxruntime::OpKernelInfo &) [T = float]SplitInput cannot be split evenly on selected axis. Input shape= Axis= NumOutputs=Cannot split using values in 'split' attribute. Axis= Input shape= Num entries in 'split' (must equal number of outputs) was Sum of sizes in 'split' (must equal size of selected axis) was /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/split.ccvirtual onnxruntime::common::Status onnxruntime::Split::Compute(onnxruntime::OpKernelContext *) constSplit operator does not support yetsplit/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/split.honnxruntime::SplitBase::SplitBase(const onnxruntime::OpKernelInfo &)std::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value >= 0; })Squeeze/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/squeeze.hstatic std::vector onnxruntime::SqueezeBase::ComputeOutputShape(const onnxruntime::TensorShape &, const onnxruntime::TensorShape &)input_shape[i] == 1Dimension of input must be 1 instead of . shape=TileInput count of Tile OP mismatch, the first one is emptyInput count of Tile OP mismatch, the second one is emptythe tensor to be tiled using Tile OP must be atleast 1 dimensional'repeat' input tensor must be 1 dimensional'repeat' input tensor must have the same length as the 'input' tensor/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/tile.ccvirtual onnxruntime::common::Status onnxruntime::Tile::Compute(onnxruntime::OpKernelContext *) constTile doesn't have an implementation yet for the type: Mismatched data types between input and output Tensors. /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/transpose.ccvirtual onnxruntime::common::Status onnxruntime::Transpose::Compute(onnxruntime::OpKernelContext *) constTranspose[ 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.UniqueUnsupported tensor type of gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = long long]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = signed char]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = std::__1::basic_string]Unsqueeze/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/unsqueeze.cconnxruntime::common::Status onnxruntime::UnsqueezeBase::PrepareCompute(onnxruntime::OpKernelContext *, onnxruntime::UnsqueezeBase::Prepare &) const'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_ > 0scales != nullptrsizes == 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.'Linear' mode only support 2-D inputs ('Bilinear') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the 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]Where/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/where_op.ccvirtual onnxruntime::common::Status onnxruntime::Where::Compute(onnxruntime::OpKernelContext *) const [T = unsigned char]condition && X && Yonnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext &) [T = unsigned char]virtual onnxruntime::common::Status onnxruntime::Where::Compute(onnxruntime::OpKernelContext *) const [T = int]virtual onnxruntime::common::Status onnxruntime::Where::Compute(onnxruntime::OpKernelContext *) const [T = long long]virtual onnxruntime::common::Status onnxruntime::Where::Compute(onnxruntime::OpKernelContext *) const [T = float]virtual onnxruntime::common::Status onnxruntime::Where >::Compute(onnxruntime::OpKernelContext *) const [T = std::__1::basic_string]onnxruntime::TensorAllocator >::TensorAllocator(onnxruntime::OpKernelContext &) [T = std::__1::basic_string]Geluonnxruntime::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:/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.honnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOp(const onnxruntime::OpKernelInfo &)static_cast(activation_func_names.size()) == num_directions_ * 3 [seqno= row[Attentionnum_heads/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/bert/attention.cconnxruntime::contrib::AttentionBase::AttentionBase(const onnxruntime::OpKernelInfo &)info.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0unidirectionalInput 'input' is expected to have 3 dimensions, got Input 0 dimension 2 should be divisiable by value of the num_heads attribute.Input 'weights' is expected to have 2 dimensions, got Input 1 dimension 0 should have same length as dimension 2 of input 0Input 'bias' is expected to have 1 dimension, got Input 'bias' dimension 0 should have same length as dimension 1 of input 'weights'Input 'past' is expected to have 5 dimension, got Inputs 'past' dimension 1 shall have same length as dimension 0 of input 0Inputs 'past' dimension 2 shall have length of num_headsInputs 'past' dimension 2 shall have length of Inputs 'mask_index' dimension 0 shall have length of batch_size or 2 * batch_sizeInputs 'mask_index' with raw attention mask shall have shape batch_size x (past_sequence_length + sequence_length)Input 'mask_index' is expected to have 1 or 2 dimensions, got onnxruntime::Tensor *onnxruntime::contrib::AttentionBase::GetPresent(onnxruntime::OpKernelContext *, const onnxruntime::Tensor *, int, int, int, int &) constExpect to have present state output when past state input is given/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/bert/attention_cpu_base.hApplyAttention/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/bert/bias_gelu.ccvirtual onnxruntime::common::Status onnxruntime::contrib::BiasGelu::Compute(onnxruntime::OpKernelContext *) const [T = float, use_approximation = false]use_approximationBiasGeluInput 0 is expected to have 1 or more dimensions, got Input 1 is expected to have 1 dimensions, got Input 1 dimension 0 should have same length as the last dimension of input 0EmbedLayerNormalization/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/bert/embed_layer_norm.cconnxruntime::contrib::EmbedLayerNorm::EmbedLayerNorm(const onnxruntime::OpKernelInfo &) [T = float]input_ids is expected to have 2 dimensions, got word_embedding is expected to have 2 dimensions, got position_embedding is expected to have 2 dimensions, got segment_embedding is expected to have 2 dimensions, got word_embedding and position_embedding shall have same dimension 1beta is expected to have 1 dimensions, got beta is expected to have size of gamma is expected to have 1 dimensions, got gamma is expected to have size of FastGeluCDistmetric/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/cdist.honnxruntime::contrib::CDist::CDist(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttr("metric", &metric).IsOK()sqeuclideaneuclideanThe first input of CDist kernel has wrong shape: The second input of CDist kernel has wrong shape: Input shape dimensions mismatch:onnxruntime::contrib::CDist::CDist(const onnxruntime::OpKernelInfo &) [T = double]ConvTransposeWithDynamicPads/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/cpu_contrib_kernels.ccRegisterNchwcKernelsRegisterQuantizationKernelsRegisterCpuContribKernelsCropborder/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/crop.hAttribute border needs to be specified with four border elements, got Input is expected to have four dimensions corresponding to [N,C,H,W], got input dimensions insteadInput's height () needs to be greater than or equal to the topBorder () + bottomBorder (Input's width () needs to be greater than or equal to the leftBorder () + rightBorder () + scale_[0] () + scale_[1] (CropAndResizebilinear/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/crop_and_resize.honnxruntime::contrib::CropAndResize::CropAndResize(const onnxruntime::OpKernelInfo &) [T = float] specified. It should be either bilinear or nearestNull crop_size_ptrNumber of dimensions for crop size should be exactly 1/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/element_wise_ops.honnxruntime::contrib::Affine::Affine(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttr("alpha", &alpha_).IsOK()info.GetAttr("beta", &beta_).IsOK()onnxruntime::contrib::Scale::Scale(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttr("scale", &scale_).IsOK()axisExpandDims/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/expand_dims.hvirtual onnxruntime::common::Status onnxruntime::contrib::ExpandDims::Compute(onnxruntime::OpKernelContext *) constaxis_tensor->Shape().IsScalar()axis <= X_NumDims && axis >= -X_NumDimsAxis must be within range [ Axis is unimplemented activation: activation_params count mismatchFusedConv/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/fused_conv.cconnxruntime::contrib::FusedConvFloat::FusedConvFloat(const onnxruntime::OpKernelInfo &)FusedGemmactivationactivation_/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/fused_gemm.cconnxruntime::contrib::FusedGemm::FusedGemm(const onnxruntime::OpKernelInfo &) [T = float]/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/image_scaler.honnxruntime::contrib::ImageScaler::ImageScaler(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttrs("bias", bias_).IsOK()Bias size () does not match the number of channels (Inversevoid onnxruntime::utils::MLTypeCallDispatcher::Invoke(Args &&...) const [Fn = ComputeImpl, Types = , Args = ]LayerNormalization/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/layer_norm.cconnxruntime::contrib::LayerNorm::LayerNorm(const onnxruntime::OpKernelInfo &) [T = float]op_kernel_info.GetAttr("axis", &axis_).IsOK()onnxruntime::contrib::LayerNorm::LayerNorm(const onnxruntime::OpKernelInfo &) [T = double]MatMulInteger16/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/matmul_integer16.ccvirtual onnxruntime::common::Status onnxruntime::contrib::MatMulInteger16::Compute(onnxruntime::OpKernelContext *) const [T1 = short, T2 = short, T3 = int]A != nullptr && B != nullptrMaxpoolWithMask/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/maxpool_with_mask.hacross_channels/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/mean_variance_normalization.honnxruntime::MeanVarianceNormalization_0::MeanVarianceNormalization_0(const onnxruntime::OpKernelInfo &, bool) [T = float]info.GetAttr("across_channels", &across_channels_).IsOK()normalize_varianceinfo.GetAttr("normalize_variance", &normalize_variance_).IsOK()MurmurHash3/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/murmur_hash3.ccvirtual onnxruntime::common::Status onnxruntime::contrib::MurmurHash3::Compute(onnxruntime::OpKernelContext *) constkeyssizeof(uint32_t) == output_element_bytesinput_num_bytes % 4 == 0ReorderInputReorderOutput/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/nchwc_ops.ccvirtual onnxruntime::common::Status onnxruntime::contrib::ReorderInput::Compute(onnxruntime::OpKernelContext *) constX_shape.NumDimensions() == 4(X_shape[1] % MlasNchwcGetBlockSize()) == 0virtual onnxruntime::common::Status onnxruntime::contrib::ReorderOutput::Compute(onnxruntime::OpKernelContext *) constX_rank == 4channels_ <= X_shape[1]virtual onnxruntime::common::Status onnxruntime::contrib::NchwcConv::Compute(onnxruntime::OpKernelContext *) const(static_cast(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)Unsupported convolution size.Not satisfied: Y->Shape() == sum_shape onnxruntime::common::Status onnxruntime::contrib::NchwcPoolBase::NchwcPool(onnxruntime::OpKernelContext *, MLAS_POOLING_KIND) constvirtual onnxruntime::common::Status onnxruntime::contrib::NchwcUpsample::Compute(onnxruntime::OpKernelContext *) const/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/nchwc_ops.honnxruntime::contrib::ReorderOutput::ReorderOutput(const onnxruntime::OpKernelInfo &)info.GetAttr("channels", &channels_).IsOK()channels_ > 0invalid channel countchannels_lastinfo.GetAttr("channels_last", &channels_last_).IsOK()onnxruntime::contrib::NchwcConv::NchwcConv(const onnxruntime::OpKernelInfo &)GetFusedActivationAttr(info, activation_).IsOK()onnxruntime::contrib::NchwcPoolBase::NchwcPoolBase(const onnxruntime::OpKernelInfo &)pool_attrs_.kernel_shape.size() == 2onnxruntime::contrib::NchwcUpsample::NchwcUpsample(const onnxruntime::OpKernelInfo &)info.GetAttrs("scales", scales_).IsOK()scales_.size() == 4scales_[0] == 1 && scales_[1] == 1 && scales_[2] >= 1 && scales_[3] >= 1QLinearAddQLinearMul/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/qlinear_binary_op.cconnxruntime::common::Status onnxruntime::contrib::QLinearBroadcastTwo(onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General, double) [T = signed char, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/qlinear_binary_op.cc:119:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/qlinear_binary_op.cc:125:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/qlinear_binary_op.cc:131:7)]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 givenonnxruntime::TBroadcastOutput::TBroadcastOutput(size_t, onnxruntime::Tensor &, int64_t, int64_t) [T = signed char]void onnxruntime::TBroadcaster::AdvanceBy(size_t) [T0 = signed char, T1 = signed char]onnxruntime::common::Status onnxruntime::contrib::QLinearBroadcastTwo(onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General, double) [T = unsigned char, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/qlinear_binary_op.cc:119:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/qlinear_binary_op.cc:125:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/qlinear_binary_op.cc:131:7)]onnxruntime::common::Status onnxruntime::contrib::QLinearBroadcastTwo(onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General, double) [T = signed char, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/qlinear_binary_op.cc:144:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/qlinear_binary_op.cc:150:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/qlinear_binary_op.cc:156:7)]onnxruntime::common::Status onnxruntime::contrib::QLinearBroadcastTwo(onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General, double) [T = unsigned char, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/qlinear_binary_op.cc:144:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/qlinear_binary_op.cc:150:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/qlinear_binary_op.cc:156:7)]QLinearLeakyReluQLinearSigmoid/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/qlinear_lookup_table.ccvoid onnxruntime::contrib::QlinearBuildLookupTable(uint8_t *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::contrib::LookupTableArrayTransformer &) [T = signed char]IsScalarOr1ElementVector(tensor_x_scale)QLinearLeakyRelu : input X_scale must be a scalar or 1D tensor of size 1tensor_x_zero_point == nullptr || IsScalarOr1ElementVector(tensor_x_zero_point)QLinearLeakyRelu : input X_zero_point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_y_scale)QLinearLeakyRelu : input Y_scale must be a scalar or 1D tensor of size 1tensor_y_zero_point == nullptr || IsScalarOr1ElementVector(tensor_y_zero_point)QLinearLeakyRelu : input Y_zero_point must be a scalar or 1D tensor of size 1void onnxruntime::contrib::QlinearBuildLookupTable(uint8_t *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::contrib::LookupTableArrayTransformer &) [T = unsigned char]QAttention/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/attention_quant.ccNot satisfied: IsScalarOr1ElementVector(input_scale_tensor) Not satisfied: IsScalarOr1ElementVector(weight_scale_tensor) Not satisfied: IsScalarOr1ElementVector(i_zp_tensor) Not satisfied: IsScalarOr1ElementVector(w_zp_tensor) /Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/dynamic_quantize_matmul.ccComputeCommonvirtual onnxruntime::common::Status onnxruntime::contrib::DynamicQuantizeMatMul::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(b_scale_tensor)DynamicQuantizeMatMul : input B scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.IsScalarOr1ElementVector(b_zero_point_tensor)DynamicQuantizeMatMul : input B zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.virtual onnxruntime::common::Status onnxruntime::contrib::MatMulIntegerToFloat::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(a_scale_tensor)MatMulIntegerToFloat : input A scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.MatMulIntegerToFloat : input B scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.IsScalarOr1ElementVector(a_zero_point_tensor)MatMulIntegerToFloat : input A zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.MatMulIntegerToFloat : input B zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.DynamicQuantizeMatMulMatMulIntegerToFloatSampleOpSkipLayerNormalization/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/skip_layer_norm.cconnxruntime::contrib::SkipLayerNorm::SkipLayerNorm(const onnxruntime::OpKernelInfo &) [T = float]epsilon_ >= 0input is expected to have 3 dimensions, got gamma is expected to have 1 dimension, got beta is expected to have 1 dimension, got bias is expected to have 1 dimension, got onnxruntime::contrib::SkipLayerNorm::SkipLayerNorm(const onnxruntime::OpKernelInfo &) [T = double]Tokenizer/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/tokenizer.cconnxruntime::contrib::Tokenizer::Tokenizer(const onnxruntime::OpKernelInfo &)pad_valuemincharnummincharnum_ > 0separatorsEither one of the separators OR tokenexp attributes required but none is set!tokenexp.empty()!separators.empty()!char_tokenezation_ || mincharnum_ < 2Can not digest separators: Can not digest tokenexp: Input string contains invalid utf8 chars: Match contains invalid utf8 chars: tensor(string) expected as inputInput dimensions are either [C] or [N][C] allowedTransposeMatMul/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/transpose_scale_matmul.cconnxruntime::contrib::TransposeMatMul::TransposeMatMul(const onnxruntime::OpKernelInfo &)Trilu/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/trilu.ccvirtual onnxruntime::common::Status onnxruntime::contrib::Trilu::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(k)upper/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/trilu.honnxruntime::contrib::Trilu::Trilu(const onnxruntime::OpKernelInfo &)info.GetAttr("upper", &temp).IsOK()Conv filter size does not match embedding_size attribute. embedding_size attribute: conv filter size: Conv kernal size 1 does not match conv_window_size attribute . conv_window_size attribute: conv kernal size 1: Char embedding size does not match char_embedding_size attribute. char_embedding_size attribute: Char embedding size: Char embedding size does not match conv kernal size 2. Conv kernal size 2 : /Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/word_conv_embedding.ccWordConvEmbeddingembedding_sizeconv_window_sizechar_embedding_size/Users/runner/work/1/s/onnxruntime/core/util/math_cpu.ccvoid onnxruntime::math::Gemv(CBLAS_TRANSPOSE, int, int, float, const T *, const T *, float, T *, Provider *) [T = float, Provider = onnxruntime::CPUMathUtil]Gemv found an unexpected CBLAS_TRANSPOSE input ofvoid onnxruntime::math::Gemv(CBLAS_TRANSPOSE, int, int, float, const T *, const T *, float, T *, Provider *) [T = double, Provider = onnxruntime::CPUMathUtil]Received null OrtThreadingOptionsReceived invalid value for allow_spinning. Valid values are 0 or 1AllocateAllocateStaticallyPreExistingReuseAllocateOutputShareAllocation Plan: (ort_value_idx) output_name : , use fence when asyncIndex out-of-range! Execution Plan: /Users/runner/work/1/s/onnxruntime/core/framework/allocation_planner.ccstd::ostream &onnxruntime::operator<<(std::ostream &, std::pair)nullptr != node[Free ml-values: CreatePlanCan not find the node onnxruntime::common::Status onnxruntime::PlannerImpl::ComputeUseCounts()p_kernel_defShould not have entry in kernel create info with nullptr for kernel_defCan not find the execution provider ComputeUseCountsonnxruntime::OrtValueIndex onnxruntime::PlannerImpl::Index(const onnxruntime::OrtValueName &)void onnxruntime::PlannerImpl::ProcessDef(onnxruntime::OrtValueIndex, const onnxruntime::NodeArg *)id >= 0 && static_cast(id) < ort_value_info_.size()int &onnxruntime::PlannerImpl::UseCount(onnxruntime::OrtValueIndex)n >= 0 && static_cast(n) < ort_value_info_.size()const onnxruntime::KernelCreateInfo &onnxruntime::GetKernelCreateInfo(const std::unordered_map > &, onnxruntime::NodeIndex)entry != kernel_create_info_map.cend()SessionState should have saved the KernelCreateInfo prior to this running. NodeIndex:onnxruntime::AllocPlanPerValue &onnxruntime::PlannerImpl::AllocPlan(onnxruntime::OrtValueIndex)n >= 0 && static_cast(n) < plan_.allocation_plan.size()ComputeReusePlanGeneratePlanForWeightsOrtMemoryInfo onnxruntime::PlannerImpl::GetLocationForNodeInput(size_t, const onnxruntime::Node &)void onnxruntime::PlannerImpl::Reuse(onnxruntime::OrtValueIndex, onnxruntime::OrtValueIndex, onnxruntime::AllocKind)reused != reused_forstatic size_t onnxruntime::PlannerImpl::GetElementSize(const onnx::DataType &)nullptr != tensor_type_baseonnxruntime::OrtValueIndex &onnxruntime::PlannerImpl::Buffer(onnxruntime::OrtValueIndex)CudaCudaPinnedSpecified device is not supported./Users/runner/work/1/s/onnxruntime/core/framework/allocator.ccoperator()/Users/runner/work/1/s/include/onnxruntime/core/common/logging/logging.hstatic const onnxruntime::logging::Logger &onnxruntime::logging::LoggingManager::DefaultLogger()OrtMemoryInfo:[name: id: OrtMemType: OrtAllocatorType:]Device:[DeviceType: MemoryType: DeviceId:/Users/runner/work/1/s/onnxruntime/core/framework/allocatormgr.ccCreateAllocatorReceived invalid value of arena_extend_strategy /Users/runner/work/1/s/onnxruntime/core/framework/bfc_arena.ccBFCArenaCreating BFCArena for with following configs: initial_chunk_size_bytes: max_dead_bytes_per_chunk: memory limit: arena_extend_strategy Creating bins of max chunk size onnxruntime::BFCArena::BFCArena(std::unique_ptr, size_t, onnxruntime::ArenaExtendStrategy, int, int)BinForSize(bin_size) == BinFromIndex(b)BinForSize(bin_size + 255) == BinFromIndex(b)BinForSize(bin_size * 2 - 1) == BinFromIndex(b)BinForSize(bin_size * 2) != BinFromIndex(b)BFCArena::Chunk *onnxruntime::BFCArena::ChunkFromHandle(onnxruntime::BFCArena::ChunkHandle)h < chunks_.size()Available memory of is smaller than requested bytes of Failed to allocate memory for requested buffer of size ExtendExtended allocation by bytes.Total allocated bytes: Allocated memory at virtual void *onnxruntime::BFCArena::Reserve(size_t)reserved_chunks_.find(ptr) == reserved_chunks_.end()size_t onnxruntime::BFCArena::RequestedSize(const void *)h != kInvalidChunkHandlesize_t onnxruntime::BFCArena::AllocatedSize(const void *)AllocateRawInternaltried to allocate 0 bytesExtending BFCArena for . bin_num: rounded_bytes:Failed to find a free memory block despite calling Extend. rounded_bytes=BFC Arena ran out of memory trying to allocate . Current allocation summary follows.void *onnxruntime::BFCArena::AllocateRawInternal(size_t, bool)void *onnxruntime::BFCArena::FindChunkPtr(onnxruntime::BFCArena::BinNum, size_t, size_t)!chunk->in_use()void onnxruntime::BFCArena::SplitChunk(BFCArena::ChunkHandle, size_t)!c->in_use() && (c->bin_num == kInvalidBinNum)void onnxruntime::BFCArena::DeallocateRawInternal(void *)void onnxruntime::BFCArena::Merge(BFCArena::ChunkHandle, BFCArena::ChunkHandle)!c1->in_use() && !c2->in_use()c2->prev == h1void onnxruntime::BFCArena::InsertFreeChunkIntoBin(BFCArena::ChunkHandle)void onnxruntime::BFCArena::RemoveFreeChunkIterFromBin(BFCArena::Bin::FreeChunkSet *, const BFCArena::Bin::FreeChunkSet::iterator &)!c->in_use() && (c->bin_num != kInvalidBinNum)void onnxruntime::BFCArena::RemoveFreeChunkFromBin(BFCArena::ChunkHandle)BinFromIndex(c->bin_num)->free_chunks.erase(h) > 0void onnxruntime::BFCArena::FreeAndMaybeCoalesce(BFCArena::ChunkHandle)c->in_use() && (c->bin_num == kInvalidBinNum)std::array onnxruntime::BFCArena::get_bin_debug_info()bin->free_chunks.count(h) == 1c->bin_num == bin_numDumpMemoryLogAllocator:Bin size: Chunks in_use/total (if not zero). Allocated bytes in_use/total. Requested bytes.void onnxruntime::BFCArena::DumpMemoryLog(size_t)b->free_chunks.size() == bin_info.total_chunks_in_bin - bin_info.total_chunks_in_use: Chunks . Bytes . Requested Diff between in-use and requested bytes is Bin for bytes has max bytes of , Chunk State: Overall chunks summary: Chunk Free at of size Summary of in-use chunks by size: chunks of size . Total Sum Total of in-use chunks: Stats: auto onnxruntime::BFCArena::Extend(size_t)::(anonymous class)::operator()(const size_t) constIncorrect arena extend strategy./Users/runner/work/1/s/onnxruntime/core/framework/bfc_arena.honnxruntime::BFCArena::AllocationRegion::AllocationRegion(void *, size_t)0 == memory_size % kMinAllocationSizeRegionForCould not find Region for int onnxruntime::BFCArena::AllocationRegion::IndexFor(const void *) constp_int >= base_intp_int < base_int + memory_size_ Size: | Requested Size: | in_use: , prev: , next: Limit: InUse: TotalAllocated: MaxInUse: NumAllocs: MaxAllocSize: /Users/runner/work/1/s/onnxruntime/core/framework/data_transfer.ccvirtual common::Status onnxruntime::CPUDataTransfer::CopyTensor(const onnxruntime::Tensor &, onnxruntime::Tensor &, int) constsrc.SizeInBytes() == dst.SizeInBytes()data_transfer registered is nullptr.Tensor size mismatchThere's no data transfer registered for copying tensors from to /Users/runner/work/1/s/onnxruntime/core/framework/data_transfer_manager.ccCopyTensors/Users/runner/work/1/s/onnxruntime/core/framework/data_types.ccbool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Map &, const onnx::TypeProto_Map &)bool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Sequence &, const onnx::TypeProto_Sequence &)virtual bool onnxruntime::TensorTypeBase::IsCompatible(const onnx::TypeProto &) constthisProto->value_case() == TypeProto::ValueCase::kTensorTypeutils::HasElemType(thisProto->tensor_type())virtual bool onnxruntime::SparseTensorTypeBase::IsCompatible(const onnx::TypeProto &) constthisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypeutils::HasElemType(thisProto->sparse_tensor_type())virtual bool onnxruntime::SequenceTensorTypeBase::IsCompatible(const onnx::TypeProto &) constthisProto->value_case() == TypeProto::ValueCase::kSequenceTypeutils::HasElemType(thisProto->sequence_type())bool onnxruntime::NonTensorTypeBase::IsMapCompatible(const onnx::TypeProto &) constthisProto->value_case() == TypeProto::ValueCase::kMapTypeutils::HasKeyType(thisProto->map_type())bool onnxruntime::NonTensorTypeBase::IsSequenceCompatible(const onnx::TypeProto &) constbool onnxruntime::NonTensorTypeBase::IsOpaqueCompatible(const onnx::TypeProto &) constthisProto->value_case() == TypeProto::ValueCase::kOpaqueTypevirtual void onnxruntime::NonTensorTypeBase::FromDataContainer(const void *, size_t, OrtValue &) constNot implementedvirtual void onnxruntime::NonTensorTypeBase::ToDataContainer(const OrtValue &, size_t, void *) const(null)tensor(float)tensor(bool)tensor(double)tensor(string)tensor(int8)tensor(uint8)tensor(int16)tensor(uint16)tensor(int32)tensor(uint32)tensor(int64)tensor(uint64)tensor(float16)tensor(bfloat16)tensor type sparse tensor type MLDataType for: is not currently registered or supportedonnxruntime::utils::ContainerChecker::ContainerChecker(onnxruntime::MLDataType)void onnxruntime::data_types_internal::DataTypeRegistry::RegisterDataType(onnxruntime::MLDataType)proto != nullptrp.secondWe do not expect duplicate registration of types for: GetElementType is not implemented/Users/runner/work/1/s/include/onnxruntime/core/framework/data_types.hstatic void onnxruntime::data_types_internal::SetMapTypes, std::__1::basic_string >::Set(onnx::TypeProto &) [K = std::__1::basic_string, V = std::__1::basic_string]value_proto != nullptr expected to be a registered ONNX typestatic void onnxruntime::data_types_internal::SetMapTypes, long long>::Set(onnx::TypeProto &) [K = std::__1::basic_string, V = long long]static void onnxruntime::data_types_internal::SetMapTypes, float>::Set(onnx::TypeProto &) [K = std::__1::basic_string, V = float]static void onnxruntime::data_types_internal::SetMapTypes, double>::Set(onnx::TypeProto &) [K = std::__1::basic_string, V = double]static void onnxruntime::data_types_internal::SetMapTypes >::Set(onnx::TypeProto &) [K = long long, V = std::__1::basic_string]static void onnxruntime::data_types_internal::SetMapTypes::Set(onnx::TypeProto &) [K = long long, V = long long]static void onnxruntime::data_types_internal::SetMapTypes::Set(onnx::TypeProto &) [K = long long, V = float]static void onnxruntime::data_types_internal::SetMapTypes::Set(onnx::TypeProto &) [K = long long, V = double]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = float]elem_proto != nullptrstatic void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = double]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = signed char]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = unsigned char]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = short]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = unsigned short]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = int]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = unsigned int]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = long long]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = unsigned long long]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = bool]static void onnxruntime::data_types_internal::SetSequenceType >::Set(onnx::TypeProto &) [T = std::__1::basic_string]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = onnxruntime::MLFloat16]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = onnxruntime::BFloat16]static void onnxruntime::data_types_internal::SetSequenceType, float, std::__1::less >, std::__1::allocator, float> > > >::Set(onnx::TypeProto &) [T = std::__1::map, float, std::__1::less >, std::__1::allocator, float> > >]static void onnxruntime::data_types_internal::SetSequenceType, std::__1::allocator > > >::Set(onnx::TypeProto &) [T = std::__1::map, std::__1::allocator > >]/Users/runner/work/1/s/onnxruntime/core/framework/ex_lib_loader.cc~ExLibLoaderUnloading DSO Failed to unload DSO: A dso with name has already been loaded.LoadExternalLibCaught exception while destructing CustomOpsLoader with message: Caught exception while loading custom ops with message: /Users/runner/work/1/s/onnxruntime/core/framework/execution_frame.cconnxruntime::IExecutionFrame::IExecutionFrame(const onnxruntime::OrtValueNameIdxMap &, const onnxruntime::NodeIndexInfo &, const std::vector &)node_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx()node_index_info and ort_value_idx_map are out of sync and cannot be usedonnxruntime::common::Status onnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValue(int, const onnxruntime::TensorShape *, OrtValue *&, size_t)shape && tensor.Shape() == *shapeOrtValue shape verification failed. Current shape: Requested shape:invalid index void onnxruntime::IExecutionFrame::Init(const std::vector &, const std::vector &, const std::unordered_map &, const std::vector &)feeds.size() == feed_mlvalue_idxs.size()fetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size()Fetches vector passed to GetOutputs contains entries which doesn't match the number of fetches the frame was initialized with of onnxruntime::ExecutionFrame::ExecutionFrame(const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::unordered_map &, const onnxruntime::SessionState &)buffers_.find(location) == buffers_.end()static_activation_memory_sizes_in_byte_.find(location.name) == static_activation_memory_sizes_in_byte_.end()Memory type should only appear once.ExecutionFrameAllocation of memory pattern buffer for returned nullptrTrying to allocate memory for unused optional inputs/outputsTensor shape cannot contain any negative valueonnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper(OrtValue &, int, onnxruntime::MLDataType, const OrtMemoryInfo &, const onnxruntime::TensorShape &, bool)ort_value.Fence() == nullptrAllocateMLValueTensorSelfOwnBufferHelperFor ort_value with index: , block in memory pattern size is: but the actually size is: , fall back to default allocation behaviorShape mismatch attempting to re-use buffer. . Validate usage of dim_value (values should be > 0) and dim_param (all values with the same string should equate to the same size) in shapes in the model.AllocateMLValueTensorPreAllocateBufferonnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlan(OrtValue &, int, const onnxruntime::TensorShape *, size_t)ort_value_index >= 0 && static_cast(ort_value_index) < alloc_plan.size()Tried to allocate without valid type information, ort_value index=We don't expect custom allocators for non-tensor types, so a shape is mandatory here.AllocateAsPerAllocationPlanInvalid allocation kind: ReleaseMLValueImplconst onnxruntime::AllocPlanPerValue &onnxruntime::ExecutionFrame::GetAllocationPlan(int)ort_value_idx >= 0 && static_cast(ort_value_idx) < alloc_plan.size()TraceAllocateTraceAllocation for ort_value_idx= size= failed: void onnxruntime::ExecutionFrame::TraceFree(int)TraceFreeTraceFree for ort_value_idx=Memory pattern planner is not enabled on this execution framework./Users/runner/work/1/s/onnxruntime/core/framework/execution_frame.hconst OrtValue &onnxruntime::IExecutionFrame::GetMLValue(int) constort_value_index >= 0 && static_cast(ort_value_index) < all_values_size_int onnxruntime::NodeIndexInfo::GetMLValueIndex(int) constoffset >= 0 && static_cast(offset) < node_values_size_ failed. Error:onnxruntime::common::Status onnxruntime::AllocateSparseTensor(OrtValue &, const onnxruntime::DataTypeImpl &, onnxruntime::AllocatorPtr, const onnxruntime::TensorShape &, size_t, bool, const onnxruntime::SessionState &)mlvalue.Fence() == nullptr/Users/runner/work/1/s/onnxruntime/core/framework/execution_provider.ccvoid onnxruntime::IExecutionProvider::InsertAllocator(onnxruntime::AllocatorPtr)/Users/runner/work/1/s/onnxruntime/core/framework/feeds_fetches_manager.ccMapNamesToMLValueIdxsError mapping feeds: Error mapping output names: void onnxruntime::FeedsFetchesManager::SetDeviceCopyChecks(onnxruntime::DeviceCopyCheck, onnxruntime::DeviceCopyCheck)input_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::Unknownstatic void SafeIntExceptionHandler::SafeIntOnDivZero()func info for node: already exist.Can't use func with null ptr not found./Users/runner/work/1/s/onnxruntime/core/framework/fuse_nodes_funcs.ccGetFuncsNo provider specified./Users/runner/work/1/s/onnxruntime/core/framework/graph_partitioner.ccPartitiononnxruntime::common::Status onnxruntime::GraphPartitioner::Partition(onnxruntime::Graph &, bool, onnxruntime::FuncManager &) constnode_compute_funcs.size() == nodes_need_compile.size()onnxruntime::Node *onnxruntime::PlaceNode(onnxruntime::Graph &, std::unique_ptr, const onnxruntime::KernelRegistryManager &, const std::string &, int &)1 == capability->nodes.size()/Users/runner/work/1/s/onnxruntime/core/framework/func_kernel.honnxruntime::FunctionKernel::FunctionKernel(const onnxruntime::OpKernelInfo &)create_func(&context, &func_state_) == 0Op with name ( and type ( Version mismatch. node_version: kernel start version: kernel_end_version: Found kernel for Op with name ( in the supported version range (node_version: However the types are incompatible. This op has been implemented only for the following types (), but the node in the model has the following type (/Users/runner/work/1/s/onnxruntime/core/framework/kernel_registry.ccTryCreateKernel kernel not found in No matching hash for kernel is not supported in Encountered following errors: (Kernel not foundkernel def can't be NULLFailed to add kernel for : Conflicting with a registered kernel with op versions.void onnxruntime::(anonymous namespace)::TraverseFormalParametersWithTypeProto(const onnxruntime::Node &, ParamFilterFn, TraverseFn) [ParamFilterFn = (lambda at /Users/runner/work/1/s/onnxruntime/core/framework/kernel_registry.cc:72:11), TraverseFn = (lambda at /Users/runner/work/1/s/onnxruntime/core/framework/kernel_registry.cc:73:11)]len <= op_schema.inputs().size()void onnxruntime::(anonymous namespace)::TraverseFormalParametersWithTypeProto(const onnxruntime::Node &, ParamFilterFn, TraverseFn) [ParamFilterFn = (lambda at /Users/runner/work/1/s/onnxruntime/core/framework/kernel_registry.cc:97:9), TraverseFn = (lambda at /Users/runner/work/1/s/onnxruntime/core/framework/kernel_registry.cc:100:9)]found duplicated provider in KernelRegistryManager (node ). /Users/runner/work/1/s/onnxruntime/core/framework/mldata_type_utils.cconnxruntime::MLDataType onnxruntime::utils::GetMLDataType(const onnxruntime::NodeArg &)nullptr != type_proto/Users/runner/work/1/s/onnxruntime/core/framework/node_index_info.ccauto onnxruntime::NodeIndexInfo::Init(const onnxruntime::GraphNodes &, onnxruntime::NodeIndex, const onnxruntime::OrtValueNameIdxMap &)::(anonymous class)::operator()(const onnxruntime::NodeArg &, bool) constauto onnxruntime::NodeIndexInfo::Init(const onnxruntime::ValidNodes > > &, onnxruntime::NodeIndex, const onnxruntime::OrtValueNameIdxMap &)::(anonymous class)::operator()(const onnxruntime::NodeArg &, bool) consttype_proto is not of type map!type_proto is not of type sequence!OrtValue is TensorSequence type but has no element Tensor DataType.Tensor types should have been handled alreadynot implementedElementTypeFromProto:tensor type is not supportedconst onnxruntime::SparseTensor &OrtValue::Get() constIsSparseTensor()Trying to get a SparseTensor, but got: /Users/runner/work/1/s/onnxruntime/core/framework/op_kernel.cconnxruntime::OpKernelContext::OpKernelContext(onnxruntime::IExecutionFrame *, const onnxruntime::OpKernel *, concurrency::ThreadPool *, const logging::Logger &)frame != nullptrkernel != nullptrOrtValue *onnxruntime::OpKernelContext::OutputMLValue(int, const onnxruntime::TensorShape &, size_t)int onnxruntime::OpKernelContext::NumVariadicInputs(size_t) constarg_num < arg_counts.size()Invalid arg_num of . Num args is TempSpace allocator not foundOrtValue *onnxruntime::OpKernelContext::GetOrCreateOutputMLValue(int)/Users/runner/work/1/s/onnxruntime/core/framework/node_index_info.hint onnxruntime::NodeIndexInfo::GetNodeOffset(onnxruntime::NodeIndex) constnode_offsets_index < node_offsets_size_onnxruntime::SparseTensor *OrtValue::GetMutable()/Users/runner/work/1/s/onnxruntime/core/framework/op_kernel_info.ccconst OrtMemoryInfo &onnxruntime::OpKernelInfo::GetMemoryInfo(int, OrtMemType) constCould not find OrtValue with name ''/Users/runner/work/1/s/include/onnxruntime/core/framework/op_node_proto_helper.hconst onnx::AttributeProto *onnxruntime::OpNodeProtoHelper::GetAttribute(const std::string &) const [Impl_t = onnxruntime::ProtoHelperNodeContext]attr != nullptrconst onnx::AttributeProto *onnxruntime::OpNodeProtoHelper::GetAttribute(const std::string &) const [Impl_t = onnx::InferenceContext]Attibute name and type don't matchNo attribute with this name is defined./Users/runner/work/1/s/onnxruntime/core/framework/op_node_proto_helper.cconnxruntime::common::Status onnxruntime::OpNodeProtoHelper::GetAttrs(const std::string &, gsl::span) const [Impl_t = onnxruntime::ProtoHelperNodeContext]values.size() == static_cast(attr->floats_size())onnxruntime::common::Status onnxruntime::OpNodeProtoHelper::GetAttrs(const std::string &, gsl::span) const [Impl_t = onnx::InferenceContext]onnxruntime::common::Status onnxruntime::OpNodeProtoHelper::GetAttrs(const std::string &, gsl::span) const [Impl_t = onnxruntime::ProtoHelperNodeContext]values.size() == static_cast(attr->ints_size())onnxruntime::common::Status onnxruntime::OpNodeProtoHelper::GetAttrs(const std::string &, gsl::span) const [Impl_t = onnx::InferenceContext]onnxruntime::common::Status onnxruntime::OpNodeProtoHelper::GetAttrs(const std::string &, gsl::span) const [Impl_t = onnxruntime::ProtoHelperNodeContext]values.size() == static_cast(attr->strings_size())onnxruntime::common::Status onnxruntime::OpNodeProtoHelper::GetAttrs(const std::string &, gsl::span) const [Impl_t = onnx::InferenceContext]onnxruntime::common::Status onnxruntime::OpNodeProtoHelper::GetAttrs(const std::string &, gsl::span) const [Impl_t = onnxruntime::ProtoHelperNodeContext]values.size() == static_cast(attr->tensors_size())onnxruntime::common::Status onnxruntime::OpNodeProtoHelper::GetAttrs(const std::string &, gsl::span) const [Impl_t = onnx::InferenceContext]onnxruntime::common::Status onnxruntime::OpNodeProtoHelper::GetAttrs(const std::string &, gsl::span) const [Impl_t = onnxruntime::ProtoHelperNodeContext]values.size() == static_cast(attr->graphs_size())onnxruntime::common::Status onnxruntime::OpNodeProtoHelper::GetAttrs(const std::string &, gsl::span) const [Impl_t = onnx::InferenceContext]/Users/runner/work/1/s/onnxruntime/core/framework/ort_value_tensor_slicer.ccstatic OrtValueTensorSlicer onnxruntime::OrtValueTensorSlicer::Create(T &, int64_t, int64_t) [T = OrtValue]ort_value.IsTensor()Can't slice a non-tensor OrtValue. Type was ort_value.IsAllocated()gsl::narrow_cast(tensor_shape.NumDimensions()) >= slice_dimensionInsufficient dimensions to slice on . Shape:dim0_offset < dim0_sizeInvalid dim0_offset of . Dimension 0 is onnxruntime::OrtValueTensorSlicer::Iterator::Iterator(T &, size_t, size_t, int64_t, onnxruntime::OrtValueTensorSlicer::Iterator::Direction) [T = OrtValue]onnxruntime::OrtValueTensorSlicer::Iterator::const_reference onnxruntime::OrtValueTensorSlicer::Iterator::operator*() const [T = OrtValue]typename std::enable_if::value, reference>::type onnxruntime::OrtValueTensorSlicer::Iterator::operator*() [T = OrtValue]static OrtValueTensorSlicer onnxruntime::OrtValueTensorSlicer::Create(T &, int64_t, int64_t) [T = const OrtValue]onnxruntime::OrtValueTensorSlicer::Iterator::Iterator(T &, size_t, size_t, int64_t, onnxruntime::OrtValueTensorSlicer::Iterator::Direction) [T = const OrtValue]onnxruntime::OrtValueTensorSlicer::Iterator::const_reference onnxruntime::OrtValueTensorSlicer::Iterator::operator*() const [T = const OrtValue]Multiple errors were found./Users/runner/work/1/s/onnxruntime/core/framework/parallel_executor.ccRunNodeAsyncBegin executionExiting due to terminate flag being set to true.onnxruntime::common::Status onnxruntime::ParallelExecutor::RunNodeAsync(size_t, const onnxruntime::SessionState &, const logging::Logger &)Got nullptr from GetKernel for node: _fence_beforeNon-zero status code returned while running node. Name:'' Status Message: _kernel_time_fence_after/Users/runner/work/1/s/onnxruntime/core/framework/op_kernel_context_internal.honnxruntime::OpKernelContextInternal::OpKernelContextInternal(const onnxruntime::SessionState &, onnxruntime::IExecutionFrame &, const onnxruntime::OpKernel &, const logging::Logger &, const bool &)entry != nullptrAll implicit inputs should have OrtValue instances by now. does not.Exception running nodes starting at '. Unknown exception was caught by catch-all handler./Users/runner/work/1/s/onnxruntime/core/framework/sequential_executor.cc[Memory] ExecutionFrame statically allocates [Memory] ExecutionFrame dynamically allocates ReleaseNodeMLValues/Users/runner/work/1/s/onnxruntime/core/framework/session_options.ccAddConfigEntrySession Config with key [] already exists with value []. It will be overwritten/Users/runner/work/1/s/onnxruntime/core/framework/session_state.ccSetupAllocatorsAllocator already registered for . Ignoring allocator from CreateGraphInfoSaveMLValueNameIndexMappingDone saving OrtValue mappings.PopulateKernelCreateInfoconst onnxruntime::KernelCreateInfo &onnxruntime::SessionState::GetNodeKernelCreateInfo(onnxruntime::NodeIndex) constentry != kernel_create_info_map_.cend()duplicated ort_value index:. Do you have duplicated calls to SessionState::AddInitializedTensor function?PrepackInitializedConstantTensorsUsing an input in multiple nodes on different devices is not supported currently. Input: is used by node ) and node Failed to find input name in the mapping: void onnxruntime::SessionState::AddOutputNameToNodeInfoMapping(const std::string &, const onnxruntime::SessionState::NodeInfo &)output_names_to_nodeinfo.empty()Only one node should produce an output. Existing entry for Failed to find output name in the mapping: void onnxruntime::SessionState::AddSubgraphSessionState(onnxruntime::NodeIndex, const std::string &, std::unique_ptr)existing_entries.find(attribute_name) == existing_entries.cend()Entry exists in node for attribute const onnxruntime::NodeIndexInfo &onnxruntime::SessionState::GetNodeIndexInfo() constnode_index_info_SetGraphAndCreateKernels must be called prior to GetExecutionInfo.void onnxruntime::SessionState::UpdateToBeExecutedNodes(const std::vector &)onnxruntime::common::Status onnxruntime::SessionState::CreateSubgraphSessionState()subgraphMain Graph instance should have populated all subgraphs when being resolved.CreateSubgraphSessionStateSatisfied, but should not be: nullptr == fbs_kcis Satisfied, but should not be: nullptr == node_indices Kernel create info node indices are null. Invalid ORT format model.Satisfied, but should not be: nullptr == kernel_def_hashes Not satisfied: node_indices->size() == kernel_def_hashes->size() Size mismatch for kernel create info node indexes and hashes. Invalid ORT format model.Satisfied, but should not be: node == nullptr Can't find node with index . Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_sub_graph_session_states Satisfied, but should not be: nullptr == fbs_sub_graph_ss Subgraph SessionState entry for is missing. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_sub_session_state Subgraph SessionState for is null. Invalid ORT format model.FinalizeSessionStateFinalizeSessionStateImplonnxruntime::common::Status onnxruntime::SessionState::FinalizeSessionStateImpl(const std::basic_string &, onnxruntime::KernelRegistryManager &, const onnxruntime::Node *, const onnxruntime::SessionOptions &, bool)entry != node_to_subgraph_ss.second.cend()Missing session state for subgraph. Node:'' OpType: Index: Attribute:p_op_kernelCould not find OrtValue with idx 'GetSubGraphSessionStatesOrtFormat/Users/runner/work/1/s/onnxruntime/core/framework/session_state_utils.ccSaveInitializedTensorsSaving initialized tensors.common::Status onnxruntime::session_state_utils::SaveInitializedTensors(const onnxruntime::Env &, const std::basic_string &, const onnxruntime::GraphViewer &, const OrtMemoryInfo &, const onnxruntime::OrtValueNameIdxMap &, onnxruntime::ITensorAllocator &, const std::function &, const logging::Logger &, const onnxruntime::DataTransferManager &)ort_value_name_idx_map.MaxIdx() > -1[Memory] SessionStateInitializer statically allocates bytes for Deserialize tensor failed.Done saving initialized tensorsSaveInputOutputNamesToNodeMappingSubgraph input with name is not used by any node.DeserializeTensorProtoInternal error. The preallocated buffer is too small. Requires , Got Failed to copy tensor to invalid ort_value_index:/Users/runner/work/1/s/onnxruntime/core/framework/simple_tensor_allocator.ccGetPreallocatedBufferFailed to get allocator for initializer '', location: /Users/runner/work/1/s/onnxruntime/core/framework/tensor.cconnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape &, void *, const OrtMemoryInfo &, ptrdiff_t)p_type != nullptronnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape &, std::shared_ptr, 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 lengthchecksum/Users/runner/work/1/s/onnxruntime/core/framework/tensor_shape.ccint64_t onnxruntime::TensorShape::SizeToDimension(size_t) constdimension <= num_dimsInvalid dimension of for SizeFromDimension. Tensor has dimensions.int64_t onnxruntime::TensorShape::SizeFromDimension(size_t) constonnxruntime::TensorShape onnxruntime::TensorShape::Slice(size_t, size_t) constdimstart <= dimend && dimend <= size(){}/Users/runner/work/1/s/onnxruntime/core/framework/tensor_type_and_shape.ccOrtStatusPtr OrtApis::GetTensorTypeAndShape(const OrtValue *, OrtTensorTypeAndShapeInfo **)type != nullptrInvalid TensorProtocorrupted protobuf data: tensor shape size() does not match the data size() in protoUnpackTensor: the pre-allocate size does not match the size in protodata overflowstring tensor can not have raw data/Users/runner/work/1/s/onnxruntime/core/framework/tensorprotoutils.ccTensorProtoToMLValuetensor can't contain negative dimsThe buffer planner is not consistent with tensor buffer size, expected Initialized tensor with unexpected type: ConstantNodeProtoToTensorProtocommon::Status onnxruntime::utils::ConstantNodeProtoToTensorProto(const onnx::NodeProto &, onnx::TensorProto &)Unsupported attribute value type of in 'Constant' node 'SparseTensorProtoToDenseTensorProtoUnsupported sparse tensor data type of Unsupported type: GetFileContentUnpackTensor: the pre-allocated size does not match the raw data size, expected UnpackTensorWithRawDataNot satisfied: source_bytes.size_bytes() == destination.size_bytes() /Users/runner/work/1/s/onnxruntime/core/framework/endian_utils.hReadLittleEndianonnxruntime::common::Status onnxruntime::utils::CopySparseData(size_t, const onnx::TensorProto &, gsl::span, std::function) [T = unsigned int]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 long long]onnxruntime::common::Status onnxruntime::utils::CopySparseData(size_t, const onnx::TensorProto &, gsl::span, std::function) [T = std::__1::basic_string]DnnlExecutionProviderNGRAPHExecutionProviderNupharExecutionProviderVitisAIExecutionProviderOpenVINOExecutionProviderNnapiExecutionProviderRknpuExecutionProvider/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 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/contrib_defs.ccNumber of attention headsWhether every token can only attend to previous tokens. Default value is 0.3D input tensor with shape (batch_size, sequence_length, hidden_size), hidden_size = num_heads * head_sizeweight2D input tensor with shape (hidden_size, 3 * hidden_size)bias1D input tensor with shape (3 * hidden_size)mask_indexAttention mask with shape (batch_size, past_sequence_length + sequence_length), or index with shape (batch_size) or (2 * batch_size).Mpastpast state for key and value with shape (2, batch_size, num_heads, past_sequence_length, head_size).3D output tensor with shape (batch_size, append_length, hidden_size)presentpresent state for key and value with shape (2, batch_size, num_heads, past_sequence_length + sequence_length, head_size)Constrain input and output types to float tensors.Constrain mask index to integer typesinput_scalescale of quantized input tensor. It's a scalar, which means a per-tensor/layer quantization.weight_scalescale of weight scale. It's a scalar, which means a per-tensor/layer quantization.Attention mask index with shape (batch_size)input_zero_pointzero point of quantized input tensor.It's a scalar, which means a per-tensor/layer quantization.weight_zero_pointzero point of quantized weight tensor. It's a scalar, which means a per-tensor/layer quantization.3D output tensor with shape (batch_size, sequence_length, hidden_size)Constrain input and output types to int8 tensors.The 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)mask2D attention mask with shape (batch_size, sequence_length)1D mask_index tensor with shape (batch_size)Constrain input and output integer tensors typesConstrain input and output float tensors types.input tensorbias tensoroutput tensorConstrain input and output types to float or half tensors.3D input tensor with shape (batch_size, sequence_length, hidden_size)skip3D skip tensor with shape (batch_size, sequence_length, hidden_size)1D input tensor with shape (hidden_size)1D skip tensor with shape (hidden_size1D bias tensor with shape (hidden_sizemeanSaved mean used during training to speed up gradient computationUinv_std_varSaved inverse standard variance used during training to speed up gradient computation.Constrain mean and inv_std_var to float tensors.Value of alphaValue of beta1D input tensor1D output tensorBias applied to each channel, same size as C.The scale to apply.Input tensor of shape [N,C,H,W]Result, has same shape and type as inputA 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).A 1-D values of (height, width).Result, has same type as input, with H and W dimensions reduced.Threshold valueInput tensorOutput tensordataTensor of data to extract slices from.1-D tensor of starting indices of corresponding axis in `axes`ends1-D tensor of ending indices (exclusive) of corresponding axis in axesaxes1-D tensor of axes that `starts` and `ends` apply to.Sliced data tensor.Constrain indices to integer typesThe shape of filled tensorThe filled tensorinput_as_shapeextra_shapeInput data to be scaledOutput data after scalingdrop_statesBool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.hidden_prevThe previous GRU hidden state.gatesUnactivated gate outputs from forget, update, and output gates, pre-activation.seq_lengthsArray of sequence lengths. len(seq_lengths) should equal batch size N.The timestep for this operation.hiddenThe new GRU hidden state calculated by this op.If 1, mean and variance are computed across channels. Default is 0.If 0, normalize the mean only. Default is 1.Scaling valueThe scaled hyperbolic tangent values of the input tensor computed element-wiseauto_padpadsConstrain input0 and output types to float tensorssignal_ndimnormalizedonesidedinput_0input_1CWPadsConstrain input and output types to float tensorsactivation_paramsInput tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.Input tensor C. The shape of C should be unidirectional broadcastable to (M, N).Output tensor of shape (M, N).Constrain input and output types to float/int tensors.Whether A should be transposedWhether B should be transposedScalar multiplier for the product of input tensors A * B.Scalar multiplier for input tensor C.activation_gammaSpecified axis to insert a dimensionThe 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.xN-D full precision Input tensor to be quantized.y_scaleScale 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'.y_zero_pointZero 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'.N-D quantized output tensor. It has same shape as 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.x_scaleScale 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'.x_zero_pointZero 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'.N-D full precision output tensor. It has same shape as input 'x'.Constrain 'x' and 'x_zero_point' to 8-bit integer tensors.Constrain 'y', 'x_scale' to float tensors.Strings 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).b_scaleScale 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'.b_zero_pointZero 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'.1D input tensor, whose dimension is same as B's last dimensionConstrain input A, b_scale and output Y data type as float tensor.Constrain input B data type to 8-bit integer tensor.a_scaleScale 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'.a_zero_pointZero 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 data type to 8-bit integer tensor.Constrain input a_scale, b_scale and output Y data type as float tensor.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.reducedReduced output tensor.Constrain input type to 8-bit integer tensor.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 list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.keepdimsKeep the reduced dimension or not, default 1 mean keep reduced dimension.additionC = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_pointmultiplicationC = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_pointdata_scaleInput scale. It's a scalar, which means a per-tensor/layer quantization.data_zero_pointInput zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.reduced_scaleOutput scale. It's a scalar, which means a per-tensor/layer quantization.reduced_zero_pointOutput zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Constrain input types to 8 bit signed and unsigned tensors.First operand.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.Second operand.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 output types to 32 bit tensors.Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.The size of the kernel along each axis.Stride 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.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 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 usedConstrain input and output types to 8 bit tensors.Coefficient of leakage.X_scaleInput X's scale. It's a scalar, which means a per-tensor/layer quantization.X_zero_pointInput X's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Y_scaleOutput Y's scale. It's a scalar, which means a per-tensor/layer quantization.Y_zero_pointOutput Y's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.An 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.A 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).Input data tensor from the previous layer.Scale tensor.Bias tensor.Output data tensor.Constrain input and output types (except mean and inv_std_var) to float tensors.Constrain mean and inv_std_var to be float tensors.The input data as Tensor.The output.The normal input data.The bias input data that is a 1D tensor.Input tensor. Every matrix in the batch must be invertible.Output tensor of the same type and shape as the input tensor.Boolean. Indicates whether upper or lower part of matrix is retained. Default is true.Input tensor of rank 2 or higher.kA 0-D tensor containing a single value corresponding to the number diagonals above or the main diagonal to exclude or include.Default value is 0 if it's not specified.Constrain input and output types to all numeric tensors and bool tensors.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. Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support). {additionalDocumentation} {name}{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_pointResult, has same element type as two inputsConstrain input and output types to 8 bit signed and unsigned tensors.inputs are expected to have tensor type.Incompatible dimensionsInputs 0 shall be 3 dimensionsInputs 4 shall be 5 dimensionsInputs 0 shall be 2 dimensionsword_embedding should have 2 dimensions and dimension size is known.Negative values are not allowed in a shape specificationAttribute should specify a shapeInput's shape must be 4-D'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border)'Scale' must contain exactly 2 values - (height, width)) needs to be greater than or equal to the top_border () + bottom_border () needs to be greater than or equal to the left_border () + right_border () + scale[0] () + scale[1] (First input does not have rank 2Second input does not have rank 2Input axis is invalid: inputs are expected to have tensor type and output type should not be null.Input tensors of wrong rank (0).Incompatible dimensions for matrix multiplicationaxis must be in [-rank, rank-1]. input rank was both data and indices tensor need to have rank larger than zero.last dimension of indices must not be larger and rank of data tensor'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64first input tensor has wrong dimensionrois input tensor has wrong dimensionbatch_indices shape input tensor has wrong dimensioncrop_size shape input tensor has wrong dimensionInput rank must be >= 2.The inner-most 2 dimensions must have the same size (mat_w: != mat_h:/Users/runner/work/1/s/onnxruntime/core/graph/contrib_ops/nchwc_schema_defs.ccchannelstensor rank too smallinvalid scales dimensioninvalid scales valueConstrain 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 &, std::unique_ptr, const logging::Logger &)input_arg->Type() != nullptronnxruntime::FunctionImpl::FunctionImpl(const onnxruntime::Graph &, const onnxruntime::NodeIndex &, const onnx::FunctionProto &, const logging::Logger &)Resolve subgraph failed:void 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 ORTType mismatch. Current= Input=Tensor element type mismatch. /Users/runner/work/1/s/onnxruntime/core/graph/graph.ccUpdateTypeAndShapeSparseTensor element type mismatch. Serialization of fused function body is not currently supported, Node [] op_type [Not satisfied: it != attr_to_subgraph_map_.cend() ] does not have the graph for key LoadFromOrtFormatSatisfied, but should not be: nullptr == fbs_attr Not satisfied: subgraph Serialization error. Graph attribute was serialized without Graph instanceSatisfied, but should not be: nullptr == fbs_input_arg_counts Satisfied, but should not be: fbs_node_edges.node_index() != index_ LoadEdgesFromOrtFormatinput index: is not the same as this node's index:ai.onnxThis is an invalid model. The sum of input arg count is not equal to size of input defs in node (onnxruntime::Graph::Graph(const onnxruntime::Model &, onnx::GraphProto *, const std::unordered_map &, onnxruntime::Version, onnxruntime::IOnnxRuntimeOpSchemaCollectionPtr, onnxruntime::Graph *, const onnxruntime::Node *, const logging::Logger &)graph_proto != nullptrConstantGraphInitializer 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=This is an invalid model. Error: the graph is not acyclic.Size mismatch validating subgraph inputs. Got inputs but subgraph has inputs and requires inputs. Either provide all subgraph inputs, or just the required inputs.Node:InferAndVerifySubgraphTypesonnxruntime::common::Status onnxruntime::Graph::UpdateShapeInference(onnxruntime::Node &)node.GetAttributeNameToMutableSubgraphMap().empty()UpdateTypeShapeInference is not intended to be used with control flow nodes containing subgraphsThis is an invalid model. Node () input arg () does not have type information set by parent node.This is an invalid model. Type Error: Type '' of input parameter () of operator () in node () is invalid.Type Error: Type parameter () bound to different types ( and in node (InferAndVerifyTypeMatchNode () output arg () type inference failedType Error: Type () of output arg () of node () does not match expected type (This is an invalid model. Model input () does not have type information.Type Error: Data in initializer '' has element type but usage of initializer in graph expects Type Error: Shape of initializer does not match. VerifyNodeAndOpMatchFatal error: is not a registered function/op) attribute () is required but not specified.Error: Duplicate definition-site for (InitInputsInitializersOutputsPerformTypeAndShapeInferencingForThisAndAllSubgraphsResolveonnxruntime::common::Status onnxruntime::Graph::Resolve(const onnxruntime::Graph::ResolveOptions &)outer_scope_node_args_consumed.empty()Shouldn't be possible to have NodeArgs that haven't been handled already.void onnxruntime::Graph::AddInitializedTensor(const onnx::TensorProto &)existing->second == &tensorAddInitializedTensor already has tensor with name but different TensorProto.void onnxruntime::Graph::RemoveInitializedTensor(const std::string &)!foundNot satisfied: name_to_initializer_it != name_to_initial_tensor_.end() ReplaceInitializedTensorFailed to find existing initializer with name Not satisfied: dims_eq() Not satisfied: old_initializer.data_type() == new_initializer.data_type() onnxruntime::common::Status onnxruntime::Graph::ReplaceInitializedTensor(const onnx::TensorProto &)existing_entry != mutable_initializers.pointer_end()void onnxruntime::Graph::AddValueInfo(const onnxruntime::NodeArg *)info->Name() != new_value_info->Name()_token_NoOpbool onnxruntime::Graph::RemoveNode(onnxruntime::NodeIndex)node->GetOutputEdgesCount() == 0Can't remove node as it still has output edges.void 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::FuseSubGraph(std::unique_ptr< ::onnxruntime::IndexedSubGraph>, const std::string &)nullptr != sub_graph && nullptr != sub_graph->GetMetaDef()Node 's number of inputs is different from function body graph's number of input.onnxruntime::common::Status onnxruntime::Graph::InlineFunction(onnxruntime::Node &)node.MutableOutputDefs().size() == subgraph.GetOutputs().size()'s number of outputs is different from function body graph's number of outputs.InlineFunctionvoid onnxruntime::Graph::SetInputs(const std::vector &)input->Exists()Inputs: : Nodes: : ) -> Outputs: Satisfied, but should not be: nullptr == fbs_tensor Satisfied, but should not be: nullptr == fbs_value_info Satisfied, but should not be: nullptr == fbs_node Satisfied, but should not be: nullptr == fbs_node_edge MergeShapeInfoError merging shape info for output. '' source: target:. Falling back to lenient merge.[ShapeInferenceError] Mismatch between number of source and target dimensions. Source= Target=Can't merge shape info. Both source and target dimension have values but they differ. Source= Dimension=Output:Satisfied, but should not be: nullptr == fbs_node_arg_names Satisfied, but should not be: nullptr == node_arg_name Satisfied, but should not be: nullptr == node_arg LoadNodeArgsFromOrtFormat: Node [], could not find NodeArg ALLOW_RELEASED_ONNX_OPSET_ONLY/Users/runner/work/1/s/onnxruntime/core/graph/model_load_utils.hbool onnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySet() are '0' and '1'. The environment variable contained the value: ai.onnx.mlai.onnx.trainingunordered_map::at: key not found/Users/runner/work/1/s/include/onnxruntime/core/graph/graph.honnxruntime::Node *onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constnode_index < nodes_.size()Validating no unexpected access using an invalid node_index. Got: Max:/Users/runner/work/1/s/include/onnxruntime/core/common/const_pointer_container.hconst onnxruntime::ConstPointerContainer::T *onnxruntime::ConstPointerContainer > >::at(size_t) const [Container = std::__1::vector >]index < data_.size()[TypeInferenceError] No Graph instance was found for attribute in node Graph attribute inferencing failed: ) Op () This is an invalid model. Error in Node:Satisfied, but should not be: nullptr == fbs_node_arg_name /Users/runner/work/1/s/onnxruntime/core/graph/graph_flatbuffers_utils.ccSaveValueInfoOrtFormatSaveValueInfoOrtFormat: value_info_proto for is missing type info.SaveInitializerOrtFormatSaveAttributeOrtFormatSatisfied, but should not be: nullptr == graph SaveAttributeOrtFormat: Unsupported attribute type: Satisfied, but should not be: nullptr == fbs_dims LoadInitializerOrtFormatSatisfied, but should not be: nullptr == fbs_str_data Satisfied, but should not be: nullptr == fbs_raw_data Missing raw data for initializer. Invalid ORT format model.Satisfied, but should not be: !value_info_proto.name().empty() LoadValueInfoOrtFormatNull type info for Satisfied, but should not be: nullptr == fbs_str LoadAttributeOrtFormatSatisfied, but should not be: nullptr == fbs_graph Empty graph proto from deserialization of ORT format modelSatisfied, but should not be: nullptr == fbs_floats Satisfied, but should not be: nullptr == fbs_ints Satisfied, but should not be: nullptr == fbs_strings Null string in strings attribute. Invalid ORT format model.Satisfied, but should not be: nullptr == fbs_tensors Null tensor in tensors attribute. Invalid ORT format model.SaveTypeInfoOrtFormatWe do not support type [] for nowSaveSequenceTypeOrtFormatSaveMapTypeOrtFormatUNDEFINEDFLOATINTSTRINGTENSORGRAPHFLOATSINTSSTRINGSTENSORSGRAPHSSPARSE_TENSORSPARSE_TENSORSSatisfied, but should not be: nullptr == fbs_tensor_type LoadTypeInfoOrtFormatSatisfied, but should not be: nullptr == fbs_sequence_type Satisfied, but should not be: nullptr == fbs_map_type Type: is not supported currentlySatisfied, but should not be: nullptr == fbs_dim LoadTensorTypeAndShapeOrtFormatSatisfied, but should not be: nullptr == fbs_dim_param LoadTensorDimensionOrtFormatSatisfied, but should not be: nullptr == fbs_type_info LoadSequenceTypeOrtFormatNull value type info in fbs::SequenceType. Invalid ORT format model.LoadMapTypeOrtFormatNONE/Users/runner/work/1/s/onnxruntime/core/graph/graph_utils.ccint onnxruntime::graph_utils::GetNodeInputIndexFromInputName(const onnxruntime::Node &, const std::string &)itr != node.InputDefs().end()const std::string &onnxruntime::graph_utils::GetNodeInputName(const onnxruntime::Node &, int)index >= 0 && static_cast(index) < inputs.size()const std::string &onnxruntime::graph_utils::GetNodeOutputName(const onnxruntime::Node &, int)index >= 0 && static_cast(index) < outputs.size()bool onnxruntime::graph_utils::RemoveNode(onnxruntime::Graph &, onnxruntime::Node &)Should be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.onnxruntime::NodeArg &onnxruntime::graph_utils::AddInitializer(onnxruntime::Graph &, const onnx::TensorProto &)!graph.GetInitializedTensor(new_initializer.name(), existing)Initializer with same name exists. Name:void onnxruntime::graph_utils::ReplaceNodeInput(onnxruntime::Node &, int, onnxruntime::NodeArg &)Invalid input index for node . Index: ExplicitInputs: ImplicitInputs:void onnxruntime::graph_utils::AddNodeInput(onnxruntime::Node &, int, onnxruntime::NodeArg &)num_explicit_inputs == static_cast(target_input_idx)FindPathFailed since multiple edges matched:CanUpdateImplicitInputNameInSubgraphs Implicit input name cannot be safely updated to in one of the subgraphs.bool onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutput(onnxruntime::Graph &, onnxruntime::Node &)std::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })void onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraph(onnxruntime::Node &, const std::string &, const std::string &)std::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0/Users/runner/work/1/s/onnxruntime/core/graph/model.cconnxruntime::Model::Model(onnx::ModelProto &&, const onnxruntime::PathString &, const onnxruntime::IOnnxRuntimeOpSchemaRegistryList *, const logging::Logger &)Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported.ModelONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'. Please upgrade your model to opset 7 or higher. For now, this opset model may run depending upon legacy support of some older opset version operators.Invalid istream object.Null model_proto ptr.No graph was found in the protobuf.Protobuf parsing failed.LoadFromBytes less than 0. is less than 0.SaveProtobuf serialization failed.Satisfied, but should not be: nullptr == fbs_op_set_ids Satisfied, but should not be: nullptr == fbs_domain void onnxruntime::model_load_utils::ValidateOpsetForDomain(const std::unordered_map &, const logging::Logger &, bool, const std::string &, int)ONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions. Opset is under development and support for this is limited. The operator schemas and or other functionality may change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain is till opset ValidateOpsetForDomain is under development and support for this is limited. The operator schemas and or other functionality could possibly change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain Failed to load model with error: Load model failed. File doesn't exist failedsystem error number SaveModelDomain already set in registry/Users/runner/work/1/s/onnxruntime/core/graph/schema_registry.ccRegisterOpSetRegisterOpSchemaInternalknown by the checker., but it its version is higherthan the operator set version [...truncated...] ERROR LOG MSG NOTIFICATION: Failure to successfully parse the message/Users/runner/work/1/s/onnxruntime/core/common/logging/logging.cconnxruntime::logging::LoggingManager::LoggingManager(std::unique_ptr, onnxruntime::logging::Severity, bool, const onnxruntime::logging::LoggingManager::InstanceType, const std::string *, int)default_logger_id must be provided if instance_type is InstanceType::DefaultOnly one instance of LoggingManager created with InstanceType::Default can exist at any point in time.void onnxruntime::logging::LoggingManager::CreateDefaultLogger(const std::string &)onnxruntimeSystem [VIWEFampm is not a valid date is not a valid year is not a valid day/Users/runner/work/1/s/onnxruntime/core/common/path.ccParsestatic onnxruntime::Path onnxruntime::Path::Parse(const onnxruntime::PathString &)onnxruntime::Path &onnxruntime::Path::Concat(const onnxruntime::PathString &)first_separator == value.end()Cannot concatenate with a string containing a path separator. String: Not satisfied: src.GetRootPathString() == dst.GetRootPathString() RelativePathPaths must have the same root to compute a relative path. src root: , dst root: Not satisfied: has_trailing_separator ParsePathRootFailed to parse path root: /Users/runner/work/1/s/onnxruntime/core/common/profiler.cc::onnxruntime::TimePoint onnxruntime::profiling::Profiler::StartTime() constenabled_void onnxruntime::profiling::Profiler::Initialize(const logging::Logger *)session_logger != nullptrvoid onnxruntime::profiling::Profiler::StartProfiling(const logging::Logger *)custom_logger != nullptrEndTimeAndRecordEventMaximum number of events reached, could not record profile event.Writing profiler data to file [ {"cat" : "","pid" :"tid" :"dur" :"ts" :"ph" : "X","name" :""args" : {"" : "} }, ] SessionNodew/Users/runner/work/1/s/onnxruntime/core/common/status.cconnxruntime::common::Status::Status(onnxruntime::common::StatusCategory, int, const std::string &)code != static_cast(common::OK)onnxruntime::common::Status::Status(onnxruntime::common::StatusCategory, int, const char *)SystemError[ONNXRuntimeError]SUCCESSFAILINVALID_ARGUMENTNO_SUCHFILENO_MODELENGINE_ERRORRUNTIME_EXCEPTIONINVALID_PROTOBUFMODEL_LOADEDNOT_IMPLEMENTEDINVALID_GRAPHEP_FAILGENERAL ERROR/Users/runner/work/1/s/onnxruntime/core/common/threadpool.cconnxruntime::concurrency::ThreadPool::ThreadPool(onnxruntime::Env *, const onnxruntime::ThreadOptions &, const onnxruntime::concurrency::ThreadPool::NAME_CHAR_TYPE *, int, bool)degree_of_parallelism >= 1void onnxruntime::concurrency::ThreadPool::Schedule(std::function)fn != nullptrvoid onnxruntime::concurrency::ThreadPool::RunInParallel(std::function, int)void onnxruntime::concurrency::ThreadPool::ParallelFor(std::ptrdiff_t, const onnxruntime::TensorOpCost &, const std::function &)n >= 0static void onnxruntime::concurrency::ThreadPool::TryParallelFor(concurrency::ThreadPool *, std::ptrdiff_t, const onnxruntime::TensorOpCost &, const std::function &)total >= 0;unknown;unknown;0;0;;/Users/runner/work/1/s/onnxruntime/core/platform/env.ccvoid gsl::fail_fast_assert_handler(const char *const, const char *const, const char *const, int)/Users/runner/work/1/s/onnxruntime/core/platform/posix/env.cconnxruntime::(anonymous namespace)::PosixThread::PosixThread(const char *, int, unsigned int (*)(int, Eigen::ThreadPoolInterface *), Eigen::ThreadPoolInterface *, const onnxruntime::ThreadOptions &)CleanUpFailed to close file descriptor - error code: Invalid fd was supplied: fstat file "" failed: Not satisfied: file_path ReadFileIntoBufferNot satisfied: offset >= 0 Not satisfied: length <= buffer.size() openlseekreadReadFileIntoBuffer - unexpected end of file. File: , offset: , length: MapFileIntoMemorymmapUnmapFilemunmap failed. error code: Not satisfied: result == 0 DeleteFolderDeleteFolder(): nftw() failed with error: nftw_removeremove() failed. Error code: , path: closerealpathFailed to load library with error: Got null library handleFailed to unload library with error: Failed to get symbol lib.so.packing unavailablebad dimensionsField 'name' of value_info is required to be non-empty.type is required but missing.elem_typekey_typevalue_typeUnrecognized type value case (value_info name: ): data_typesetting data_type field (tensor name: ) to UNDEFINED is not allowedfloat_dataint32_datastring_dataint64_dataraw_datadouble_datauint64_dataData of TensorProto ( tensor name: ) is stored externally and should not have data field.location) should be stored in , but it doesn't exist or is not accessible.TensorProto ( tensor name: ) is stored externally but doesn't have a location.TensorProto (tensor name: ) is 0-element but contains data!) should contain one and only one value field.STRING data (tensor name: ) should not be stored in raw_data fieldvalues of data_type '' should be stored in field '' instead of 'Unrecognized data_type (tensor name: sequenceSequence ( Structure name: , elem_type: ) is not have a valid element type.setting key_type field (map name: ) to invalid TensorProto key_type is not allowedMap (name: ) should not contain more than one keys field.valuesLength of map keys and map values are not the same (map name: Sparse tensor indices () has values, but NNZ is Sparse tensor () index value at position [] out of range [0, ] not in sorted order.) first dimension size does not equal NNZ.) second dimension size does not match rank of tensor.] out of range.] not in lexicographic sorted order.sparse_tensor_protoSparse tensor values () must have rank 1.) must have a dense-rank > 0) dimensions are not positive.) must have INT64 type.) must have rank 1 or 2.) has no index values.attrtype field and data field mismatch in attribute Attribute (name: ) should not contain more than one value field.) should refer to attribute in parent node.op_typenodeNodeProto (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. in initializer but not in graph inputNodes in a graph must be topologically sorted, however input '' of node: is not output of any previous nodes.Bad node spec: ' has been used as output names multiple times.functionsince_version' has been used multiple times.function () should not have duplicate outputs specified.) should not have duplicate attributes specified.Nodes in a function must be topologically sorted, however input ' is neither output of any previous nodes nor input of the function.Function must be in single static assignment (SSA) form, however 'Unable to open model file:. Please check if it is a valid file.Unable to parse model from file:. Please check if it is a valid protobuf file of model.\/ ==> Context: DynamicSliceImageScalerScalerLoop 'body' subgraph outputs should all be tensors or sequences but output Loop 'body' subgraph scan outputs should all be tensors but output /Users/runner/work/1/s/cmake/external/onnx/onnx/defs/controlflow/defs.ccFinal N loop carried dependency values then K scan_outputs. Scan outputs must be Tensors.An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1].Mismatched type: source= target=Mismatched tensor element type:source sequence type missing element type.target sequence type missing element type.Scan input was not a tensor.bodyGraph attribute inferencing returned type information for outputs. Expected Scan 'body' subgraph outputs should all be tensors but output was not axis value is invalid for a tensor of rank Number of scan input axes specified () is not equal to number of scan inputs (Number of scan output axes specified () is not equal to number of scan outputs (sequence_lensOptional tensor specifying lengths of the sequences in a batch. If this input is not specified, all sequences are assumed to be of the maximum sequence length (the dimension of the sequence axis of the scan_input tensors).initial_state_and_scan_inputsInitial values of the loop's N state variables followed by M scan_inputsfinal_state_and_scan_outputsFinal values of the loop's N state variables followed by K scan_outputsThe graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations.An attribute specifying the number of scan_inputs M. An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction.Int64 tensor/Users/runner/work/1/s/cmake/external/onnx/onnx/defs/controlflow/old.ccLoop 'body' subgraph outputs should all be tensors but output was A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.condA boolean termination condition. Optional. Pass empty string to skip.v_initialThe initial values of any loop-carried dependencies (values that change across loop iterations)v_final_and_scan_outputsFinal N loop carried dependency values then K scan_outputsThe graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.tensor of int64, which should be a scalar.tensor of bool, which should be a scalar.An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input.An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output.then_branch and else_branch produce different number of outputs. If node has but subgraphs produce Mismatched type for output then= else=Mismatched tensor element type for output Condition for the ifoutputsValues that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same shape and same data type.Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.Only boolValues that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible.Input type was nullInput was expected to have either tensor or sequence type. Got Input was expected to have tensor type. Got Element type of input was unknownInput element type of does not match existing output type of Output was expected to have tensor type. Got Input was expected to have sequence type. Got tensor(seq(map(sparse_tensor(seqmapopaquesparse_tensortensorFunc_Input for function node Output for function node The value for the elements of the output tensor.sparse_valueThe value for the elements of the output tensor in sparse format.value_intThe value for the sole element for the scalar, int64, output tensor.value_intsThe values for the elements for the 1D, int64, output tensor.value_floatThe value for the sole element for the scalar, float32, output tensor.value_floatsThe values for the elements for the 1D, float32, output tensor.value_stringThe value for the sole element for the scalar, UTF-8 string, output tensor.value_stringsThe values for the elements for the 1D, UTF-8 string, output tensor.Output tensor containing the same value of the provided tensor./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/generator/defs.cc(Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float321D tensor. The shape of the expected output tensor. If empty tensor is given, the output would be a scalar. All values must be >= 0.Output tensor of shape specified by 'input'.If attribute 'value' is specified, the value and datatype of the output tensor is taken from 'value'.If attribute 'value' is not specified, the value in the output defaults to 0, and the datatype defaults to float32.Constrain input types.Constrain output types to be numerics.(Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal, and k < 0 populates a lower diagonal.(Optional) The data type for the elements of the output tensor. If not specified,the data type of the input tensor T1 is used. If input tensor T1 is also notspecified, then type defaults to 'float'.2D input tensor to copy shape, and optionally, type information from.Output tensor, same shape as input tensor T1.Constrain input types. Strings and complex are not supported.Constrain output types. Strings and complex are not supported.Lower boundary of the output values.highUpper boundary of the output values.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.The shape of the output tensor.Output tensor of random values drawn from uniform distributionThe mean of the normal distribution.The standard deviation of the normal distribution.The data type for the elements of the output tensor. Default is TensorProto::FLOAT.Output tensor of random values drawn from normal distribution(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.Input tensor to copy shape and optionally type information from.Number of times to sample.(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.Input tensor with shape [batch_size, class_size], where class_size is the number of all possible outcomes. Each value along the axis zero represents the unnormalized log-probability of each corresponding outcome in a batch.Output tensor with shape [batch_size, sample_size], where sample_size is the number of times to sample. Each value along the axis zero represents the outcome of the corresponding sample in a batch.Constrain output types to integral tensors.loop_body_attributeiprevcond_outdeltacurrentrangelimitstartScalar. First entry for the range of output values.Scalar. Exclusive upper limit for the range of output values.Scalar. Value to step by.A 1-D tensor with same type as the inputs containing generated range of values.Constrain input types to common numeric type tensors.One and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.Attribute 'value_int' expect an integer.Attribute 'value_ints' expect a list of integers.Attribute 'value_float' expect a float.Attribute 'value_floats' expect a list of floats.Attribute 'value_string' expect a string.Attribute 'value_strings' expect a list of strings.TypeAndShapeInferenceFunction implementation incomplete: this line should never be reached.Shape input must be a one-dimensional tensor.Invalid shape value: Attribute expected to have tensor typeAttribute expected to have a one-dim tensorInput tensor must be 2-dimensionalOutput type must be int32 or int64Input tensor must have rank 2All inputs to 'Range' op must be of the same typeInput to 'Range' op should be scalars (Tensor with only one element and shape empty)/Users/runner/work/1/s/cmake/external/onnx/onnx/defs/generator/old.ccAttribute 'value' of Constant node must exist with 'Tensor' data.Only one of the attributes 'value' or 'sparse_value' must be specified for a Constant node.One of the attributes 'value' or 'sparse_value' must be specified for a Constant node./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/logical/defs.ccConstrains input/output to boolean tensors.First operand, input to be shifted.Second operand, amounts of shift.ZConstrain input and output types to integer tensors.Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).LessOrEqualGreaterOrEqualConstrains input types to all numeric tensors.Constrains output to boolean tensor./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/logical/old.ccConstrains input to boolean tensor.Constrains input to float tensors.Constrains input to integral tensors.First input operand for the logical operator.Second input operand for the logical operator.Result tensor.broadcastEnable broadcastingIf set, defines the broadcast dimensions.Left input tensor for the logical operator.Right input tensor for the logical operator./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/math/defs.ccCoefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717).Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946).Coefficient of ELU.The Alpha value in Celu formula which control the shape of the unit. The default value is 1.0.Constrain input and output types to float32 tensors.CeluValue of alpha.Value of beta.The softsign (x/(1+|x|)) values of the input tensor computed element-wiseDimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Whether to return the top-K largest or smallest elements.Whether to return the elements in sorted order.The sine of the input tensor computed element-wiseThe cosine of the input tensor computed element-wiseThe tangent of the input tensor computed element-wiseThe arcsine of the input tensor computed element-wiseThe arccosine of the input tensor computed element-wiseThe arctangent of the input tensor computed element-wiseThe hyperbolic sine values of the input tensor computed element-wiseThe hyperbolic cosine values of the input tensor computed element-wiseThe hyperbolic arcsine values of the input tensor computed element-wiseThe hyperbolic arccosine values of the input tensor computed element-wiseThe hyperbolic arctangent values of the input tensor computed element-wiseaN-dimensional quantized matrix ascale of quantized input azero point of quantized input abN-dimensional quantized matrix bscale of quantized input bzero point of quantized input bscale of quantized output yzero 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'.Constrain output Y data type as 32-bit integer tensor.If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.If set to 1 will perform the sums in reverse direction.An input tensor that is to be processed.A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back.Output tensor of the same type as 'x' with cumulative sums of the x's elementsaxis tensor can be int32 or int64 onlyConstrain input and output types to floating-point tensors.targetlossInput 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.Constrain input, weight, 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.InputsOperandsOutputinput and zero_point pair is expected to have be same type.input and zero_point pair is expected to have same type.Target rank must be 1 less than the input rank.Input and target dimension value mismatch.Weight rank must be 1./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/math/old.ccfmodWhether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatmentDividend tensorDivisor tensorRemainder tensorConstrain input and output types to high-precision numeric tensors.Constrain input and output types to signed numeric tensors.Constrain input and output types to all numeric tensors.The exponential of the input tensor computed element-wiseThe natural log of the input tensor computed element-wiseThe hyperbolic tangent values of the input tensor computed element-wiseFirst operand, base of the exponent.Second operand, power of the exponent.Output tensor (same size as X)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.scoreslog_problabelsreductionignore_indexNegativeLogLikelihoodLossSpecifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.The 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).Constrain target to integer typesSoftmaxCrossEntropyLossInput 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.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.Output tensor.Whether C should be broadcastedScalar multiplier for the product of input tensors A * B, the default value is 1.0.Scalar multiplier for input tensor C, the default value is 1.0.Input tensor CTensor of shape [a_1, a_2, ..., a_n, r]ValuesTensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing top K values from the input tensorIndicesTensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing the corresponding input tensor indices for the top K values.Constrain index tensor to int64Number of top elements to retrieveDimension on which to do the sort.KA 1-D tensor containing a single positive value corresponding to the number of top elements to retrieveType of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 'mean': the sum of the output will be divided by the number of elements in the output. If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of element size 1 (including a scalar tensor and any tensor with rank equal to or smaller than the first tensor), or having its shape as a contiguous subset of the first tensor's shape. The starting of the mutually equal shape is specified by the argument "axis", and if it is not set, suffix matching is assumed. 1-dim expansion doesn't work yet. For example, the following tensor shapes are supported (with broadcast=1): shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 Attribute `broadcast=1` needs to be passed to enable broadcasting. Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) where the function `f(x) = x^exponent`, is applied to the data tensor elementwise. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).Describes 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 INT64noneDescribes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_sizeFirst operand, should share the type with the second operand.Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.Result, has same dimensions and type as AInvalid value for attribute axisInvalid value for attribute kK input must be a one-dimensional tensor of size 1.K input must be of type int64.Axis has less than the requested k elements./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/nn/defs.ccInput tensor must have at least 2 dimensionsRoIs tensor must have 2 dimensionsAttribute pooled_shape has incorrect lengthAttribute pooled_shape must be specifiedScale tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.Zero 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 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.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 input.Input matrixMatrix after normalizationThe axis on which to apply normalization, -1 mean last axis.The order of the normalization, only 1 or 2 are supported.The lambd value for the Shrink formulation. Default is 0.5.The bias value added to output. Default is 0.Input for n-gram extractionNgram resultsInput is ether string UTF-8 or int32/int641-D tensor of floatsMaximum n-gram length. If this value is 3, 3-grams will be used to generate the output.Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.Maximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=1List of strings n-grams learned from the training set. Either this or pool_int64s attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.List of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.The starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity.list of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor.list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts.The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)UTF-8 strings to normalizeUTF-8 Normalized stringsstring enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"Boolean. Whether the identification of stop words in X is case-sensitive. Default is falseList of stop words. If not set, no word would be removed from X.Environment dependent string that denotes the locale according to which output strings needs to be upper/lowercased.Default en_US or platform specific equivalent as decided by the implementation. Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, there are multiple cases for the number of outputs, which we list below: Output case #1: Y, mean, var, saved_mean, saved_var (training mode) Output case #2: Y (test mode) For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C*D1*D2 ..*Dn) before a BatchNormalization Op. This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. Constrain input and output types to float and 8 bit tensors.ROI pool output shape (height, width).Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.RoIs (Regions of Interest) to pool over. Should be a 2-D tensor of shape (num_rois, 5) given as [[batch_id, x1, y1, x2, y2], ...].RoI pooled output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]).dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis.weight and zero_point pair is expected to have same type.Additional elements added to the side with higher coordinate indices in the output. Each padding value in "output_padding" must be less than the corresponding stride/dilation dimension. By default, this attribute is a zero vector. Note that this attribute doesn't directly affect the computed output values. It only controls the selection of the computed values, so changing this attribute only adds or removes output elements. If "output_shape" is explicitly provided, "output_padding" does not contribute additional size to "output_shape" but participates in the computation of the needed padding amount. This is also called adjs or adjustment in some frameworks.Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.ngram_indexes must be non-empty with no negative valuesInput tensor must have rank 1 or 2Input shape must have either [C] or [1,C] dimensions where C > 0(Optional) Seed to the random generator, if not specified we will auto generate one.ratioThe ratio of random dropout, with value in [0, 1). If this input was not set, or if it was set to 0, the output would be a simple copy of the input. If it's non-zero, output will be a random dropout of the scaled input, which is typically the case during training. It is an optional value, if not specified it will default to 0.5.training_modeIf set to true then it indicates dropout is being used for training. It is an optional value hence unless specified explicitly, it is false. If it is false, ratio is ignored and the operation mimics inference mode where nothing will be dropped from the input data and if mask is requested as output it will contain all ones.The output mask.Constrain input 'ratio' types to float tensors.Constrain output 'mask' types to boolean tensors./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/nn/old.ccA tensor of rank >= axis.A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). sizeThe number of channels to sum overScaling parameter.The exponent.Output tensor, which has the shape and type as input tensorConstrain input and output types to float tensors.A list of integers, along which to reduce. The default is to caculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance.Input tensor must have atleast 2 dimensionsAttribute dilations has incorrect sizeAttribute strides has incorrect sizeAttribute kernel_shape has incorrect sizeAttribute kernel_shape must be specifiedAttribute pads has incorrect sizeSecond input tensor has wrong dimensionThe 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.Stride along each spatial axis.Input data tensor that has to be unpooled. This tensor is typically the first output of the MaxPool op.Dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non-image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].Input data tensor containing the indices corresponding to elements in the first input tensor X.This tensor is typically the second output of the MaxPool op.Dimensions must be the same as input tensor X. The indices are linear, i.e. computed considering the tensor as flattened 1-D tensor, assuming row-major storage. Also, the linear indices should not consider padding. So the values in indices are in the range [0, N x C x D1 x ... x Dn).The shape of the output can be explicitly set which will cause pads values to be auto generated. If 'output_shape' is specified, 'pads' values are ignored.Output data tensor that contains the result of the unpooling.Stride along each axis.pp value of the Lp norm used to pool over the input data, default is 2.0.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimension are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.Output data tensor from pooling across the input tensor. Dimensions will be N x C x 1 x 1If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.is_testIf set to nonzero, run spatial batch normalization in test mode, default is 0.The epsilon value to use to avoid division by zero, default is 1e-5f.momentumFactor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.The input 4-dimensional tensor of shape NCHW.The scale as a 1-dimensional tensor of size C to be applied to the output.The bias as a 1-dimensional tensor of size C to be applied to the output.The running mean (training) or the estimated mean (testing) as a 1-dimensional tensor of size C.varThe running variance (training) or the estimated variance (testing) as a 1-dimensional tensor of size C.The output 4-dimensional tensor of the same shape as X.The running mean after the BatchNormalization operator. Must be in-place with the input mean. Should not be used for testing.The running variance after the BatchNormalization operator. Must be in-place with the input var. Should not be used for testing.saved_meanSaved mean used during training to speed up gradient computation. Should not be used for testing.saved_varSaved variance used during training to speed up gradient computation. Should not be used for testing.The input 1-dimensional scale tensor of size C.The input 1-dimensional bias tensor of size C.The output 4-dimensional tensor of the same shape as input.(float, default 0.5) the ratio of random dropout(int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X.The output mask. If is_test is nonzero, this output is not filled.The ratio of random dropoutConstrain output mask types to boolean tensors.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.The output tensor of the same shape as X.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). If true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).If spatial is true, the dimension of scale is (C). If spatial is false, the dimensions of scale are (C x D1 x ... x Dn)If spatial is true, the dimension of bias is (C). If spatial is false, the dimensions of bias are (C x D1 x ... x Dn)If spatial is true, the dimension of the running mean (training) or the estimated mean (testing) is (C). If spatial is false, the dimensions of the running mean (training) or the estimated mean (testing) are (C x D1 x ... x Dn).If spatial is true, the dimension of the running variance(training) or the estimated variance (testing) is (C). If spatial is false, the dimensions of the running variance(training) or the estimated variance (testing) are (C x D1 x ... x Dn).The output tensor of the same shape as XThe running mean after the BatchNormalization operator.The running variance after the BatchNormalization operator.Saved mean used during training to speed up gradient computation.Saved variance used during training to speed up gradient computation.Padding for the beginning and ending along each axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding. DEPRECATION NOTE: auto_pad is only intended to support legacy uses, and for framework authors, one is explicitly encouraged to use explicit padding specified in the pads attribute.Ratio of Dropout must be a scalar.training_mode of Dropout must be a scalar.Invalid value() for attribute 'axis'p value of the Lp norm used to pool over the input data.Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. X.shape[1] == (W.shape[1] * group) == C (assuming zero based indices for the shape array). Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL. Optional 1D bias to be added to the convolution, has size of M.Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.The shape of the convolution kernel. If not present, should be inferred from input W.dilation value along each spatial axis of the filter.number of groups input channels and output channels are divided into.Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn)The weight tensor that will be used in the convolutions; has size (C x M/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the weight shape will be (C x M/group x k1 x k2 x ... x kn), where (k1 x k2 x ... x kn) is the dimension of the kernel. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, pad lengths and group count. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate padsThe zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f. default 1; Pooled output Y's height.default 1; Pooled output Y's width.Number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly sampling_ratio x sampling_ratio grid points are used. If == 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default is 0.The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.RoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[x1, y1, x2, y2], ...]. The RoIs' coordinates are in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.RoI pooled output, 4-D tensor of shape (num_rois, C, output_height, output_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1]./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/object_detection/defs.ccboxesAn input tensor with shape [num_batches, spatial_dimension, 4]. The single box data format is indicated by center_point_box.An input tensor with shape [num_batches, num_classes, spatial_dimension]max_output_boxes_per_classInteger representing the maximum number of boxes to be selected per batch per class. It is a scalar. Default to 0, which means no output.iou_thresholdFloat representing the threshold for deciding whether boxes overlap too much with respect to IOU. It is scalar. Value range [0, 1]. Default to 0.score_thresholdFloat representing the threshold for deciding when to remove boxes based on score. It is a scalar.selected_indicesselected indices from the boxes tensor. [num_selected_indices, 3], the selected index format is [batch_index, class_index, box_index].Integer indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models. expected to have rank but has rank expected to have rank >Dimension mismatch in unification between /Users/runner/work/1/s/cmake/external/onnx/onnx/defs/object_detection/old.ccScale for doing quantization to get 'y'. It can be a scalar, which means per-tensor/layer quantization, or a 1-D Tensor for per-axis quantization.Zero point for doing quantization to get 'y'. It can be a scalar, which means a per-tensor/layer quantization, or a 1-D tensor for per-axis quantization. Default value is uint8 typed 0 if it's not specified.(Optional) The axis of the quantization dimension of the input tensor. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input)/Users/runner/work/1/s/cmake/external/onnx/onnx/defs/quantization/defs.ccScale for input 'x'. It can be a scalar, which means a per-tensor/layer dequantization, or a 1-D tensor for per-axis dequantization.Zero point for input 'x'. It can be a scalar, which means a per-tensor/layer dequantization, or a 1-D tensor for per-axis dequantization. It's optional. 0 is the default value when it's not specified.(Optional) The axis of the dequantizing dimension of the input tensor. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input)Quantized output tensorOutput 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.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.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.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.cc/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).Constrain input and output types to high-precision and 8 bit numeric tensors.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.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.First input tensor must have rank 3/Users/runner/work/1/s/cmake/external/onnx/onnx/defs/rnn/defs.ccCouple the input and forget gates if 1.A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`.RThe recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`.The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0/Users/runner/work/1/s/cmake/external/onnx/onnx/defs/rnn/old.ccoutput_sequenceOne (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if not specified.The weight tensor for input gate. Concatenation of `Wi` and `WBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, input_size]`.The recurrence weight tensor. Concatenation of `Ri` and `RBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, hidden_size]`.The bias tensor for input gate. Concatenation of `[Wbi, Rbi]` and `[WBbi, RBbi]` (if bidirectional). The tensor has shape `[num_directions, 2*hidden_size]`. Optional: If not specified - assumed to be 0.When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.Couple the input and forget gates if 1, default 0.The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, 4*hidden_size, input_size]`.The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 4*hidden_size, hidden_size]`.The bias tensor for input gate. Concatenation of `[Wb[iofc], Rb[iofc]]`, and `[WBb[iofc], RBb[iofc]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 8*hidden_size]`. Optional: If not specified - assumed to be 0.initial_cOptional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.PThe weight tensor for peepholes. Concatenation of `P[iof]` and `PB[iof]` (if bidirectional) along dimension 0. It has shape `[num_directions, 3*hidde_size]`. Optional: If not specified - assumed to be 0.Y_cThe last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`.Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.Number of neurons in the hidden layerOptional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM.The sequence output for the hidden is optional if 0. Default 0.clipCell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.initial_hOptional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. It is optional if `output_sequence` is 0.Y_hThe last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.Constrain seq_lens to integer tensor.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators. typestr: , has unsupported type: has inconsistent type has unsupported type Operator '' has been deprecated since version ) has input size not in range [min=, max= not in allowed input sizes.) has output size has output size not in allowed output sizes.) has more inputs () than declared () in op definition.)'s input is marked single but has an empty string in the graph) has more outputs ()'s output Attribute '' appeared multiple times.Unrecognized attribute: for operator Mismatched attribute type in '' is expected to have field 't'' is expected to have field 'sparse_tensor'' is expected to have field 'g'' is expected to have field 'floats'' is expected to have field 'ints'' is expected to have field 'strings'' is expected to have field 'tensors'' is expected to have field 'graphs' has unknown expected typeRequired attribute '' is missing.ONNX Schema : failed validating the check: (inputs_.size() - 1) == i(outputs_.size() - 1) == i!(it.GetName().empty())Attributes:Inputs: (no explicit description available)Outputs:(no documentation yet)Defined at (Optional) The data type of the tensors in the output sequence. The default type is 'float'.Empty sequence./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/sequence/defs.ccTensors.Sequence enclosing the input tensors.input_sequenceInput sequence.Input tensor to be inserted into the input sequence.positionPosition in the sequence where the new tensor is inserted. It is optional and default is to insert to the back of the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).Output sequence that contains the inserted tensor at given position.Constrain position to integral tensor. It must be a scalar(tensor of empty shape).Position of the tensor in the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).Output tensor at the specified position in the input sequence.Output sequence that has the tensor at the specified position removed.Length of input sequence. It must be a scalar(tensor of empty shape).Constrain output to integral tensor. It must be a scalar(tensor of empty shape).Length of each output. It can be either a scalar(tensor of empty shape), or a 1-D tensor. All values must be >= 0. One or more outputs forming a sequence of tensors after splittingConstrain split size to integral tensor.Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1].Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored.Which axis to concat on. Accepted range in `[-r, r - 1]`, where `r` is the rank of input tensors. When `new_axis` is 1, accepted range is `[-r - 1, r]`. new_axisInsert and concatenate on a new axis or not, default 0 means do not insert new axis.Sequence of tensors for concatenationAttribute dtype should be of integer type and specify a type.SequenceConstruct is expected to have at least 1 input.Input type for input at index is null. Type info is expected.Element type of inputs are expected to be the same.Input Sequence and Tensor are expected to have type info. Current type is null.Input Sequence and Tensor are expected to have the same elem type. Sequence= Tensor=Input type for input at index 0 is null. Type info is expected.Only supports `int32_t` or `int64_t` inputs for splitInput 'split' can not be empty.Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size= sum of split values=new_axis must be either 0 or 1Invalid value of attribute 'axis'. Accepted range=[], Value=/Users/runner/work/1/s/cmake/external/onnx/onnx/defs/tensor/defs.ccThe 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 specified, then set scales to empty data (zero shape) in this operator's input list.(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length along the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices).Input tensor containing indices. Any entries in the 'indices' input tensor with values outside the range [-depth, depth-1] will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.Scalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [-depth, depth-1]. In case 'depth' is of non-integer type, it will be casted to int64 before use.(Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false.(Optional) Whether map negative infinity to true. Default to 1 so that negative infinity induces true. Set this attribute to 0 if negative infinity should be mapped to false.When True (nonzero), yield X, otherwise yield Yvalues selected at indices where condition is Truevalues selected at indices where condition is FalseTensor of shape equal to the broadcasted shape of condition, X, and Y.Constrain to boolean tensors.(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.Tensor of rank r >= 2.Tensor specifying lengths of the sequences in a batch. It has shape `[batch_size]`.Tensor with same shape of input.(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).(Optional) The dimension to apply unique. If not specified, the unique elements of the flattened input are returned. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).A N-D input tensor that is to be processed.A tensor of the same type as 'X' containing all the unique values or subtensors sliced along a provided 'axis' in 'X', either sorted or maintained in the same order they occur in input 'X'A 1-D INT64 tensor containing indices of 'Y' elements' first occurance in 'X'. When 'axis' is provided, it contains indices to subtensors in input 'X' on the 'axis'. When 'axis' is not provided, it contains indices to values in the flattened input tensor. inverse_indicesA 1-D INT64 tensor containing, for elements of 'X', its corresponding indices in 'Y'. When 'axis' is provided, it contains indices to subtensors in output 'Y' on the 'axis'. When 'axis' is not provided, it contains indices to values in output 'Y'. A 1-D INT64 tensor containing the count of each element of 'Y' in input 'X''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.ccSpecified 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.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.updatesTensor of rank r >=1 (same rank and shape as indices)Tensor of rank r >= 1 (same rank as input).Tensor of rank q + r - indices_shape[-1] - 1.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.Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)N-D tensorThe 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'.N-D tensor after resizingThree 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.roi1-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.Constrain 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 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'.Axes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1].Starting indices of corresponding axis in `axes`Ending indices (exclusive) of corresponding axis in axes`1-D tensor of slice step of corresponding axis in `axes`. Default to 1. Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Tensor of int32/int64 indices, of r >= 1 (same rank as input).Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds. It is an error if any of the index values are out of bounds.List of non-negative integers, indicate the dimensions to squeeze.List of non-negative integers, indicate the dimensions to be inserted(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor.Input tensor containing indices. The values must be non-negative integers. Any entries in the 'indices' input tensor with values outside the range [0, depth) will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.depthScalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [0, depth). In case 'depth' is of non-integer type, it will be casted to int64 before use.Rank 1 tensor containing exactly two elements, in the format [off_value, on_value], where 'on_value' is the value used for filling locations specified in 'indices' input tensor, and 'off_value' is the value used for filling locations other than those specified in 'indices' input tensor. Tensor of rank one greater than input tensor 'indices', i.e. rank(output) = rank(indices) + 1. The data type for the elements of the output tensor is the same as the type of input 'values' is used.(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.conditionRank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length alone the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1.Constrains to boolean tensors.Which axis to split on. List of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D it is the number of pixels. `pads` rank should be double of the input's rank. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.One float, indicates the value to be filled. This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_half_pixel_for_nn",
x_original = (x_resized + 0.5) / scale,
if coordinate_transformation_mode is "tf_crop_and_resize",
x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).Value of attribute not specified should be of integer type and specify a type. does not specify a valid type.Target shape may not have multiple -1 dimensionsInvalid position of 0Invalid dimension value: Invalid Target shape product of 0Dimension could not be inferred: incompatible shapesRequired attribute axis is missingaxis must be in [-rank, rank-1].All inputs to Concat must have same rankInvalid value of attribute 'axis'. Rank= Value=Mismatch between number of splits () and outputs (Mismatch between the sum of 'split' () and the split dimension of the input (The input is not evenly splittableSlice op must have either three, four or five inputs.Incorrect or missing input value for starts and endsInput axes has incorrect lengthInput steps has incorrect lengthInput axes has invalid data'step' cannot be 0Only supports `int32_t` or `int64_t` inputs for starts/ends/axes/stepsInvalid attribute perm {}, input shape = {data tensor must have rank >= 1axis must be in [-r, r-1]'axes' attribute must not contain any duplicatesvalues in 'axes' are beyond the bounds of the computed output shapeBlocksize must be positiveInput tensor must be 4-dimensional'Repeats' input must be 1D tensor of type int64'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions.Both `data` and `indices` input tensors in GatherND op need to have rank larger than 0.Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensor'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64rank must be greater than axisRanks inferred () is not equal to the existing rank value (Number of elements of attribute 'scales' must be same as rank of input 'X'Attribute 'scales' must have floats type.Attribute 'scales' is required.Incorrect or missing attribute value for starts and endsAttribute axes has incorrect lengthOneHot node must have three inputs.Input 'depth' must be a scalar or rank 1 tensor.Input 'depth' must have exactly one element.Input 'values' must be rank 1 tensor.Input 'values' must have exactly two elements.Indices tensor must have rank >= 1'axis' must be in [-rank(indices)-1, rank(indices)]Invalid value of attribute 'axis'Attribute value for pads is requiredAttribute pads has incorrect lengthDimension value inferred () is not equal to the existing dim value (Number of elements of input 'sizes' must be same as rank of input 'X'Input 'sizes' must have int64 element type.Number of elements of input 'scales' must be same as rank of input 'X'Input 'scales' must have float element type.Data to be selectedThe indices, based on 0 as the first index of any dimension.Selected output data as an arrayThe input must be a tensor of a numeric type or string. The output will be of the same tensor type./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/traditionalml/defs.ccData to be binarizedBinarized output dataThe input must be a tensor of a numeric type. The output will be of the same tensor type.Values greater than this are mapped to 1, others to 0.The input map that is to be cast to a tensorA tensor representing the same data as the input map, ordered by their keysmap(int64, string)map(int64, float)The input must be an integer map to either string or float.The output is a 1-D tensor of string, float, or integer.A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.map_formIndicates whether to only output as many values as are in the input (dense), or position the input based on using the key of the map as the index of the output (sparse).
One of 'DENSE', 'SPARSE'.If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.Input dataOutput data. If strings are input, the output values are integers, and vice versa.The input must be a tensor of strings or integers, either [N,C] or [C].The output is a tensor of strings or integers. Its shape will be the same as the input shape.The strings of the map. This sequence must be the same length as the 'cats_int64s' sequenceThe integers of the map. This sequence must be the same length as the 'cats_strings' sequence.A string to use when an input integer value is not found in the map.
One and only one of the 'default_*' attributes must be defined.An integer to use when an input string value is not found in the map.
One and only one of the 'default_*' attributes must be defined.A dictionary.A 1-D tensor holding values from the input dictionary.map(string, int64)map(int64, double)map(string, float)map(string, double)The input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same.The output will be a tensor of the value type of the input map. It's shape will be [1,C], where C is the length of the input dictionary.A string vocabulary array.
One and only one of the vocabularies must be defined.An integer vocabulary array.
One and only one of the vocabularies must be defined.An ordered collection of tensors, all with the same element type.The output array, elements ordered as the inputs.The input type must be a tensor of a numeric type.The size of each input in the input listData to be processed.Imputed output dataThe input type must be a tensor of a numeric type, either [N,C] or [C]. The output type will be of the same tensor type and shape.Value(s) to change toA value that needs replacing.Value(s) to change to.Input data. It can be either tensor or scalar.Output data.The input type is a tensor of any shape.Output type is determined by the specified 'values_*' attribute.A list of strings. One and only one of 'keys_*'s should be set.A list of ints.A list of floats.A list of strings. One and only one of 'value_*'s should be set.A string.An integer.A float.Data to be classified.Classification outputs (one class per example).Classification scores ([N,E] - one score for each class and exampleThe input must be a tensor of a numeric type, and of of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]The output will be a tensor of strings or integers.A collection of weights of the model(s).A collection of intercepts.Indicates whether to do OvR or multinomial (0=OvR is the default).Class labels when using string labels. One and only one 'classlabels' attribute must be defined.Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.Indicates the transform to apply to the scores vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Data to be regressed.Regression outputs (one per target, per example).The input must be a tensor of a numeric type.Indicates the transform to apply to the regression output vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Weights of the model(s).Weights of the intercepts, if used.The total number of regression targets, 1 if not defined.Data to be encoded, a tensor of shape [N,C] or [C]Encoded output datanormOne of 'MAX,' 'L1,' 'L2'Data to be encoded.Encoded output data, having one more dimension than X.List of categories, ints.
One and only one of the 'cats_*' attributes must be defined.List of categories, strings.
One and only one of the 'cats_*' attributes must be defined.If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.Data to be scaled.Scaled output data.First, offset by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.Second, multiply by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.
Must be same length as 'offset'Class scores (one per class per example), if prob_a and prob_b are provided they are probabilities for each class, otherwise they are raw scores.The input must be a tensor of a numeric type, either [C] or [N,C].The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used. Its size will match the bactch size of the input.The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.First set of probability coefficients.Second set of probability coefficients. This array must be same size as prob_a.
If these are provided then output Z are probability estimates, otherwise they are raw scores.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Class labels if using string labels.
One and only one of the 'classlabels_*' attributes must be defined.Class labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined.Regression outputs (one score per target per example).The input type must be a tensor of a numeric type, either [C] or [N,C].Chosen support vectorsFlag indicating whether the regression is a one-class SVM or not.Support vector coefficients.The number of support vectors.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Input of shape [N,F]N, Top class for each pointThe class score for each class, for each point, a tensor of shape [N,E].The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used.Tree id for each node.Node id for each node. Ids may restart at zero for each tree, but it not required to.Feature id for each node.Thresholds to do the splitting on for each node.Popularity of each node, used for performance and may be omitted.The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.
One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'Child node if expression is true.Child node if expression is false.For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.
This attribute may be left undefined, and the defalt value is false (0) for all nodes.The id of the tree that this node is in.node id that this weight is for.The index of the class list that each weight is for.The weight for the class in class_id.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)N classesNode id for each node. Node ids must restart at zero for each tree and increase sequentially.Child node if expression is trueChild node if expression is falseFor each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.
This attribute may be left undefined and the defalt value is false (0) for all nodes.The id of the tree that each node is in.The node id of each weightThe index of the target that each weight is forThe weight for each targetThe total number of targets.Defines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'The input valuesThe output mapseq(map(string, float))seq(map(int64, float))The output will be a sequence of string or integer maps to float.The keys when using string keys.
One and only one of the 'classlabels_*' attributes must be defined.The keys when using int keys.
One and only one of the 'classlabels_*' attributes must be defined.Label encoder has only one input.Label encoder has only one output.Only one of keys_*'s can be set in label encoder.Input type is not string tensor but key_strings is setInput type is not int64 tensor but keys_int64s is setInput type is not float tensor but keys_floats is setOnly one of values_*'s can be set in label encoder.Input's shape should be 1D or 2DInput data.The input type must be a tensor of integers or strings, of any shape.The output type will be a tensor of strings or integers, and will have the same shape as the input.A list of labels./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/traditionalml/old.ccInferred elem type differs from existing elem type: () vs (Inferred shape and existing shape differ in rank: (Inferred shape and existing shape differ in dimension : (type case mismatch. existing= inferred=type case unsupported. existing=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.Shape inference error(s): input is out of boundsGraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. does not contain a graph.output /Users/runner/work/1/b/Release/external/onnx/onnx/onnx-ml.pb.ccsize_t to int conversion/Users/runner/work/1/b/Release/external/onnx/onnx/onnx-operators-ml.pb.cc/Users/runner/work/1/b/Release/external/onnx/onnx/onnx-data.pb.cc/Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/arena.ccCHECK failed: (min_bytes) <= (std::numeric_limits::max() - kBlockHeaderSize): /Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/extension_set.ccCHECK failed: (type) != (WireFormatLite::TYPE_ENUM): CHECK failed: (type) != (WireFormatLite::TYPE_MESSAGE): CHECK failed: (type) != (WireFormatLite::TYPE_GROUP): CHECK failed: (type) == (WireFormatLite::TYPE_ENUM): CHECK failed: type == WireFormatLite::TYPE_MESSAGE || type == WireFormatLite::TYPE_GROUP: Don't lookup extension types if they aren't present (1). Don't lookup extension types if they aren't present (2). CHECK failed: extension != NULL: Index out-of-bounds (field is empty).Extension not found.Non-primitive types can't be packed.Can't get here.Invalid message set extension.Multiple extension registrations for type "", field number can't reach here./Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/extension_set_inl.h/Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/generated_message_util.ccNot implemented field number CHECK failed: (scc->visit_status.load(std::memory_order_relaxed)) == (SCCInfoBase::kRunning): with type /Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/io/coded_stream.ccA protocol message was rejected because it was too big (more than bytes). To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in third_party/protobuf/src/google/protobuf/io/coded_stream.h.CHECK failed: (buffer_size) >= (0): /Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream.ccThis ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug./Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream_impl.ccclose() failed: CHECK failed: !is_closed_: Can't BackUp() after failed Next()./Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream_impl_lite.ccCHECK failed: (last_returned_size_) > (0): BackUp() can only be called after a successful Next().CHECK failed: (count) <= (last_returned_size_): CHECK failed: (count) >= (0): CHECK failed: target_ != NULL: Cannot allocate buffer larger than kint32max for StringOutputStream.CHECK failed: (count) <= (target_->size()): CHECK failed: backup_bytes_ == 0 && buffer_.get() != NULL: BackUp() can only be called after Next().CHECK failed: (count) <= (buffer_used_): Can't back up over more bytes than were returned by the last call to Next(). Parameter to BackUp() can't be negative.CHECK failed: (backup_bytes_) == (0): CHECK failed: (buffer_used_) == (buffer_size_): MessageLite at 0x/Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/message_lite.ccparse exceeded maximum protobuf size of 2GB: Can't message of type "" because it is missing required fields: CHECK failed: (byte_size_before_serialization) == (byte_size_after_serialization): was modified concurrently during serialization.CHECK failed: (bytes_produced_by_serialization) == (byte_size_before_serialization): Byte size calculation and serialization were inconsistent. This may indicate a bug in protocol buffers or it may be caused by concurrent modification of This shouldn't be called if all the sizes are equal.parsing/Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/parse_context.hCan't happen/Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): /Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/stubs/common.ccThis program requires version of the Protocol Buffer runtime library, but the installed version is . Please update your library. If you compiled the program yourself, make sure that your headers are from the same version of Protocol Buffers as your link-time library. (Version verification failed in "".)This program was compiled against version of the Protocol Buffer runtime library, which is not compatible with the installed version (). Contact the program author for an update. If you compiled the program yourself, make sure that your headers are from the same version of Protocol Buffers as your link-time library. (Version verification failed in "%d.%d.%dINFOWARNINGERRORFATAL[libprotobuf %s %s:%d] %s %c%d%u%ld%lu%g%p%lld%llu/Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/stubs/int128.ccDivision or mod by zero: dividend.hi=, lo=/Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/stubs/stringpiece.ccsize too big: details: /Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/stubs/stringprintf.ccCHECK failed: (v.size()) <= (kStringPrintfVectorMaxArgs): StringPrintfVector currently only supports up to arguments. Feel free to add support for more if you need it./Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/stubs/strutil.ccCHECK failed: dest: \x%02x\%03oCHECK failed: i >= 0: FastHexToBuffer() wants non-negative integers, not %.*gCHECK failed: value != nullptr: nullptr output boolean given.tyesyfalsefnon0123456789abcdefCHECK failed: s != nullptr: This can't happen; base64 decoder state = Logic problem? szsrc = %.1fCHECK failed: (temp[0]) == ('1'): CHECK failed: (temp[size - 1]) == ('5'): CHECK failed: (size) <= (6): CHECK failed: result != nullptr: /Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/wire_format_lite.ccCHECK failed: (value.size()) <= (kint32max): '%s'String field contains invalid UTF-8 data when a protocol buffer. Use the 'bytes' type if you intend to send raw bytes. serializing/Users/runner/work/1/s/cmake/external/re2/re2/bitstate.ccGrowStack() failed: njob_ = job_.size() = Unexpected opcode: arg Bad arg in kInstAltMatch: Bad arg in kInstCapture: /Users/runner/work/1/s/cmake/external/re2/re2/compile.ccshould never happenCompiler::Copy called!No ranges in char classMissing case in Compiler: hello, world|%s%d(%p)|| flag=%#x/Users/runner/work/1/s/cmake/external/re2/re2/dfa.ccDeadState in RunStateOnByteNULL state in RunStateOnByteUnexpected special state in RunStateOnByteStateSaver failed to restore state.context does not contain textFailed to analyze start state.RunStateOnByteUnlocked failed after ResetCacheRunStateOnByteUnlocked failed after Reset/Users/runner/work/1/s/cmake/external/re2/re2/nfa.ccunhandled in AddToThreadqUnhandled in step(?,?)(%d,?)(%d,%d)Bad args: nsubmatch=Unexpected opcode in short circuit: in ComputeFirstByte in Prog::Fanout()/Users/runner/work/1/s/cmake/external/re2/re2/onepass.ccCannot use SearchOnePass for unanchored matches.unhandled opcode: /Users/runner/work/1/s/cmake/external/re2/re2/parse.ccRepetitionWalker::ShortVisit calledConcat of unknown round: RE2: unexpected op: Bad call to ParseState::ParsePerlFlagsAddFoldedRange recurses too much.AnyBad hex digit \d\D\s\S\w\W[:alnum:][:^alnum:][:alpha:][:^alpha:][:ascii:][:^ascii:][:blank:][:^blank:][:cntrl:][:^cntrl:][:digit:][:^digit:][:graph:][:^graph:][:lower:][:^lower:][:print:][:^print:][:punct:][:^punct:][:space:][:^space:][:upper:][:^upper:][:word:][:^word:][:xdigit:][:^xdigit:]alt -> %d | %daltmatch -> %d | %dbyte%s [%02x-%02x] -> %d/icapture %d -> %demptywidth %#x -> %dmatch! %dnop -> %dfail[%02x-%02x] -> %d %d. %s %d+ %s /Users/runner/work/1/s/cmake/external/re2/re2/re2.ccUnknown encoding Error parsing '': Error compiling '\x00Invalid RE2: RE2: invalid startpos, endpos pair. [startpos: endpos: text size: DFA out of memory: size bytemap range list count SearchDFA inconsistencySearchOnePass inconsistencySearchBitState inconsistencySearchNFA inconsistencyRewrite schema error: '\' not allowed at end.Rewrite schema error: '\' must be followed by a digit or '\'.Rewrite schema requests %d matches, but the regexp only has %d parenthesized subexpressions.requested group in regexp invalid rewrite pattern: Error reverse compiling '/Users/runner/work/1/s/cmake/external/re2/re2/regexp.ccRegexp not destroyed.Bad reference count Unexpected op in Regexp::Equal: no errorunexpected errorinvalid escape sequenceinvalid character classinvalid character class rangemissing ]missing )trailing \no argument for repetition operatorinvalid repetition sizebad repetition operatorinvalid perl operatorinvalid UTF-8invalid named capture groupNumCapturesWalker::ShortVisit called/Users/runner/work/1/s/cmake/external/re2/re2/walker-inl.hStack not empty.NamedCapturesWalker::ShortVisit calledCaptureNamesWalker::ShortVisit calledWalk NULL/Users/runner/work/1/s/cmake/external/re2/re2/simplify.ccSimplify failed on Case not handled in ComputeSimple: CoalesceWalker::ShortVisit calledDoCoalesce failed: r1->op() is DoCoalesce failed: r2->op() is SimplifyWalker::ShortVisit calledSimplify case not handled: Malformed repeat [truncated](?:/Users/runner/work/1/s/cmake/external/re2/re2/tostring.cckRegexpCapture cap() == 0?P<>[^\x00-\x{10ffff}](?:)Bad final char: ?+{%d,}{%d}{%d,%d}\C^$(?-m:^)(?-m:$)\z\b\B(?HaveMatch:%d)(){}[]*+?|.^$\[]^-\\\r\t\n\f\x{%x}AdlamAhomAnatolian_HieroglyphsArabicArmenianAvestanBalineseBamumBassa_VahBatakBengaliBhaiksukiBopomofoBrahmiBrailleBugineseBuhidCanadian_AboriginalCarianCaucasian_AlbanianCcCfChakmaChamCherokeeCoCommonCopticCsCuneiformCypriotCyrillicDeseretDevanagariDograDuployanEgyptian_HieroglyphsElbasanEthiopicGeorgianGlagoliticGothicGranthaGreekGujaratiGunjala_GondiGurmukhiHanHangulHanifi_RohingyaHanunooHatranHebrewHiraganaImperial_AramaicInheritedInscriptional_PahlaviInscriptional_ParthianJavaneseKaithiKannadaKatakanaKayah_LiKharoshthiKhmerKhojkiKhudawadiLLaoLatinLepchaLimbuLinear_ALinear_BLisuLlLmLoLtLuLycianLydianMahajaniMakasarMalayalamMandaicManichaeanMarchenMasaram_GondiMcMeMedefaidrinMeetei_MayekMende_KikakuiMeroitic_CursiveMeroitic_HieroglyphsMiaoMnModiMongolianMroMultaniMyanmarNNabataeanNdNew_Tai_LueNewaNkoNlNoNushuOghamOl_ChikiOld_HungarianOld_ItalicOld_North_ArabianOld_PermicOld_PersianOld_SogdianOld_South_ArabianOld_TurkicOriyaOsageOsmanyaPahawh_HmongPalmyrenePau_Cin_HauPcPdPePfPhags_PaPhoenicianPiPoPsPsalter_PahlaviRejangRunicSamaritanSaurashtraScSharadaShavianSiddhamSignWritingSinhalaSkSmSoSogdianSora_SompengSoyomboSundaneseSyloti_NagriSyriacTagalogTagbanwaTai_LeTai_ThamTai_VietTakriTamilTangutTeluguThaanaThaiTibetanTifinaghTirhutaUgariticVaiWarang_CitiYiZanabazar_SquareZlZpZsmu held in reader and writer mode simultaneously on entry to nsync_cv_wait_with_deadline() mu not held on entry to nsync_cv_wait_with_deadline() checking a waiter condition while unlocked attempt to nsync_mu_unlock() an nsync_mu held in read mode attempt to nsync_mu_unlock() an nsync_mu not held in write mode attempt to nsync_mu_runlock() an nsync_mu held in write mode attempt to nsync_mu_runlock() an nsync_mu not held in read mode nsync_mu not held in write mode nsync_mu not held in some mode nsync_mu not held in some mode when calling nsync_mu_wait_with_deadline() nsync_mu_wait woke but condition not true panic: unique_lock::lock: references null mutexunique_lock::lock: already lockedunique_lock::unlock: not locked}uWWxl8.l l()'//cMLIA2:-#0W)'//cML1 =u 7% &2g ;#  3C x)'ppxiF R})'OրN!! *#;3)'LL]T3)'LL]T3UA (}}p$[ ! iq.,",}}     IAs j~ u*5_ jIAs j~ u*5_ j7 B]LwրNv X+ clM XրNv X+ clM X '$3{D_րN '7  ]] eW i s g x Fm  Kl w Jj   I b o ~ $  7 : &* 5B UV #    yIAC O IAC O )'9)'9# b)'bb OlF R})' #55E] P\8K@IN0"Nf%Q% K #pN88 O_ÀA""3DiI4= [w])'94""3J;yIAOM ofRn)'9ÀA== QdlրNo/9M)'!!=/#kk w8 # <tG?  0IA/ }Ia_;րNZ()'''?=5'c}€4$3< F`j}ȖIAxI\)'vECAz9 E s yJ 0 Y f-  A9 -  y   - 6 ] P T 7+5:D?"/:D}ꀀhMjC)'* 4InB!4"/K4-?4Ah$B@=450(p:"vK{Se"[Kk"V K`TPxxI!Vw"#K("Ki{"Kt450(p:450(p:450(p:450(p:450(p:„%\-ZA^ X\G6t , i h$&: G^ ZD oN   R ;_$- ;   M t 2  -  1 F $ v   %    N7  ',$\p2}IAC NO 7 AIAC O )'9)'9)'gL[րNK a @ YvNcVF R}^^Ef ! 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Д11TensorShapeE 5GraphE КПGetTensorShapeAndTypeRKN11onnxruntime11TensorShapeE Mlas RKNS_12DataTypeImplEPP25OrtTensorTypeAndShapeInfo PKNSt3__16vectorINS3_12basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEENS8_ISA_EEEERKN4onnx9TypeProtoEPP25OrtTensorTypeAndShapeInfo 5 6MLAS_GEMM_U83MLAS_NCHWC_NN_ALGORITHM1OrtTensorTypeAndShapeInfo5CloneEPPS_ MLAS_GEMM_U8X8_KERNEL_SSE7ToTensorI҅ OpSchema MapProtopads_docEN fEENS_11TensorProtoERK bEENS_11TensorProtoERKɆ iEENS_11TensorProtoERK xEENS_11TensorProtoERK yEENS_11TensorProtoERK dEENS_11TensorProtoERK 11onnxruntime9MLFloat16EEENS_11TensorProtoERK St3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEEENS_11TensorProtoERK T_ NSt3__16vectorIT_NS4_9allocatorIS6_EEEE 9GetSizeInBytesFromTensorProtoILm 8 1 0 4GetNodeInputProviderTypeERKNS_12SessionState8NodeInfoE 2FindMemoryInfoForValueERKNS_12SessionStateERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEE 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13dbg_count_check_0dbg_count_check_7dbg_count_check_InstanceNormalization_Onnx_verǂ8dbg_count_check_DynamicQuantizeLinear_Onnx_ver11E59dbg_count_check_ɛconvTransposeWithDynamicPadsShapeInferenceERNS_16InferenceContextEϞ dbg_count_check_жЊлЖC D 2ERKNS_5GraphEǟ 1ERKNS_5GraphE NSt3__110unique_ptrINS_15IndexedSubGraphENS4_14default_deleteIS6_EEEERKNS_7logging6LoggerE۠ RKmRKN4onnx13FunctionProtoERKNS_7logging6LoggerE NSt3__110unique_ptrINS_15IndexedSubGraphENS4_14default_deleteIS6_EEEERKNS_7logging6LoggerE RKmRKN4onnx13FunctionProtoERKNS_7logging6LoggerE Й2Ev 1Ev 0Ev 8OpSchemaEvآ 4BodyEvߢ 1 8GetIndexedSubGraphEv 2GetFuncProtoEv C 8SetShapeERKN4onnx16TensorShapeProtoE 1 7SetTypeE 2E 1E RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKN4onnx9TypeProtoE ON4onnx14ValueInfoProtoE RKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKN4onnx9TypeProtoE ON4onnx14ValueInfoProtoE 4Ԧ 11TypeAsProtoEv 5ShapeEv 22HasTensorOrScalarShapeEv 6ExistsEv NameEv TypeEv Ь0ClearShapeEv 8UpdateTypeAndShapeERKȧ N4onnx9TypeProtoEbbRKNS_7logging6LoggerE S0_bbRKNS_7logging6LoggerE PKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE RKN4onnx9TypeProtoE 7EdgeEndC 1 2 4InitERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES9_S9_RKNS1_6vectorIPNS_7NodeArgENS5_ISC_EEEESG_PKNS1_13unordered_mapIS7_N4onnx14AttributeProtoENS1_4hashIS7_EENS1_8equal_toIS7_EENS5_INS1_4pairIS8_SJ_EEEEEES9_ 2ERKS0_ 1ERKS0_ƫ ii ٫ iiҫ 7 1 5 2AddAttributeERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 4C 8MutableDefinitionsEv 9UpdateInputArgCountEv NodeConstIteratorЭ LoadFromOrtFormatERKNS_12experimental3fbs4NodeER C ppEvӲ mmEvڲ 2ENSt3__121__tree_const_iteratorINS0_7EdgeEndEPNS2_11__tree_nodeIS4_PvEElEE 1ENSt3__121__tree_const_iteratorINS0_7EdgeEndEPNS2_11__tree_nodeIS4_PvEElEE 1 7ToProtoERN4onnx9NodeProtoEbӽ 20SaveEdgesToOrtFormatERN11flatbuffers17FlatBufferBuilderE 7 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CleanUnusedInitializersEPKNSt3__113unordered_setINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS1_4hashIS8_EENS1_8equal_toIS8_EENS6_IS8_EEEE itFunctionBodyForNodeERNS_4NodeE ferAndVerifyTypeMatchERNS_4NodeERKN4onnx8OpSchemaERKNS0_14ResolveOptionsE 4 2LoadEdgesFromOrtFormatERKNS_12experimental3fbs8NodeEdgeERKNS_5GraphE 0MutableRelationshipsEv SetExecutionProviderTypeERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE̽ GetMutableGraphAttributeERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 2 0 6 4 9GenerateNodeArgNameERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 1ReleaseNodeEm 7LoadFromOrtFormatERKNS_12experimental3fbs5GraphE ToGraphProtoEv A FuseSubGraphENSt3__110unique_ptrINS_15IndexedSubGraphENS1_14default_deleteIS3_EEEERKNS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE NS_5GraphERKNS_7logging6LoggerERNSt3__110unique_ptrIS0_NSC_14default_deleteIS0_EEEE KNS_7logging6LoggerE ЧRK S9_ N f x 4onnx1 S1_6vectorI 4AttributeProtoE 0GraphProtoE 1TensorProtoE 7SparseTensorProtoE reateSubgraphERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE learAttributeERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE Ъ1ER 2ER S0_RKNS_4NodeERN4onnx10GraphProtoE KNS_5ModelE fNS5_IfEEEE xNS5_IxEEEE S7_NS5_IS7_EEEE N4onnx1 1TensorProtoENS5_ISC_EEEE 0GraphProtoENS5_ISC_EEEE 7SparseTensorProtoENS5_ISC_EEEE KNS_5ModelE S0_RKNS_4NodeERN4onnx10GraphProtoE PN4onnx10GraphProtoERKNSt3__113unordered_mapINS7_12basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEEiNS7_4hashISE_EENS7_8equal_toISE_EENSC_INS7_4pairIKSE_iEEEEEExNS7_10shared_ptrINS_30IOnnxRuntimeOpSchemaCollectionEEE RKNSt3__113unordered_mapINS4_12basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEEiNS4_4hashISB_EENS4_8equal_toISB_EENS9_INS4_4pairIKSB_iEEEEEEPS0_PKNS_4NodeERKNS_7logging6LoggerE RKNS_7logging6LoggerE PS0_PKNS_4NodeERKNS_7logging6LoggerE PN4onnx10GraphProtoERKNSt3__113unordered_mapINS7_12basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEEiNS7_4hashISE_EENS7_8equal_toISE_EENSC_INS7_4pairIKSE_iEEEEEExNS7_10shared_ptrINS_30IOnnxRuntimeOpSchemaCollectionEEE RKNSt3__113unordered_mapINS4_12basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEEiNS4_4hashISB_EENS4_8equal_toISB_EENS9_INS4_4pairIKSB_iEEEEEEPS0_PKNS_4NodeERKNS_7logging6LoggerE RKNS_7logging6LoggerE PS0_PKNS_4NodeERKNS_7logging6LoggerE Add ResolveERKNS0_14ResolveOptionsE SetNameERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE NodeERKN EdgeEmmii 4onnx9NodeProtoERKNSt3__113unordered_mapINS5_12basic_stringIcNS5_11char_traitsIcEENS5_9allocatorIcEEEENS1_9TypeProtoENS5_4hashISC_EENS5_8equal_toISC_EENSA_INS5_4pairIKSC_SD_EEEEEE S 8InitializeStateFromModelFileGraphProtoEv 0 1GetNodeArgIncludingParentGraphsERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 9PerformTopologicalSortAndCheckIsAcyclicEv ComputeOverridableInitializersEv PerformTypeAndShapeInferencingERKNS0_14ResolveOptionsE TypeCheckInputsAndInitializersEv VerifyInputAndInitializerNamesEv VerifyNoDuplicateNameEv Set OuterScopeNodeArgsERKNSt3__113unordered_setINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS1_4hashIS8_EENS1_8equal_toIS8_EENS6_IS8_EEEE GraphInputsOutputsEv Remove SetOutputsERKNSt3__16vectorIPKNS_7NodeArgENS1_9allocatorIS5_EEEE EdgeEmmii NodeEm BuildConnectionsERNSt3__113unordered_setINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS1_4hashIS8_EENS1_8equal_toIS8_EENS6_IS8_EEEE FindAllSubgraphsERNSt3__16vectorIPS0_NS1_9allocatorIS3_EEEE GenerateNodeNameERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE В1 2 4NameEv 9ModelPathEv 4ReverseDFSFromERKNSt3__16vectorI 1DescriptionEv 9IsInitializedTensorERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 5SaveToOrtFormatERN11flatbuffers17FlatBufferBuilderERNS1_6OffsetINS_12experimental3fbs5GraphEEE 2ToGraphProtoEv 7GetSchemaRegistryEv mNS1_9allocatorImEEEERKNS1_8functionIFvPKNS_4NodeEEEESF_RKNS8_IFbSB_SB_EEE PKNS_4NodeENS1_9allocatorIS5_EEEERKNS1_8functionIFvS5_EEESF_RKNSB_IFbS5_S5_EEE ЏSJ_ ЩUpdateShapeInferenceERNS_4NodeE VerifyNodeAndOpMatchERKNS0_14ResolveOptionsE AddInitializedTensorERKN4onnx11TensorProtoE 2GetConstantInitializerERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEb 0 ЯбSet CreateNodeArgsERKN6google8protobuf16RepeatedPtrFieldINSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEEEERKNS4_13unordered_mapISA_N4onnx9TypeProtoENS4_4hashISA_EENS4_8equal_toISA_EENS8_INS4_4pairIKSA_SG_EEEEEE AddControlEdgeEmm InlineFunctionERNS_4NodeE DescriptionERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE NodeArgTypeERNS_7NodeArgERKN4onnx9TypeProtoE GetInitializedTensorERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERPKN4onnx11TensorProtoE ToGraphProtoInternalERN4onnx10GraphProtoE ddValueInfoEPKNS_7NodeArgE llocateNodeEv Б_4NodeE 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InitializeEnumStringsEPKNS1_9EnumEntryEPKimPNS1_21ExplicitlyConstructedINSt3__112basic_stringIcNS8_11char_traitsIcEENS8_9allocatorIcEEEEEEUnknownGroupLiteParseEPNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEPKcPNS1_12ParseContextEt3__1 0_11_12basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEEEEvPv 3mapIiNS1_12ExtensionSet9ExtensionENS3_4lessIiEENS3_9allocatorINS3_4pairIKiS6_EEEEEEEEvPv3 1 2 8FindPathER 0GetNodeInputIndexFromInputNameERKNS_4NodeERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEE 3IsSupportedOptypeVersionAndDomainERKNS_4NodeERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEERKSt16initializer_listIiESC_ 2RemoveNodesWithOneOutputBottomUpERNS_5GraphERKNS_4NodeE 6ۀ 7 8݊ 9IsSupportedProviderERKNS_4NodeERKNSt3__113unordered_setINS4_12basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEENS4_4hashISB_EENS4_8equal_toISB_EENS9_ISB_EEEE 2 3 0RemoveNodeERNS_5GraphERNS_4NodeE 4AddInitializerERNS_5GraphERKN4onnx11TensorProtoE GetNode ReplaceNodeInputERNS_4NodeEiRNS_7NodeArgE FirstChildByTypeERNS_4NodeERKNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEE InputNameERKNS_4NodeEi AttributeERKNS_4NodeERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEE GetNodeOutputNameERKNS_4NodeEi NodeArgIsConstantERKNS_5GraphERKNS_7NodeArgE FirstParentByTypeERNS_4NodeERKNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEE 1Ј 6Replace 9CanReplaceNodeWithInitializerERKNS_5GraphERKNS_4NodeERKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEERKNS_7logging6LoggerE 2GetConstantInitializerERKNS_5GraphERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEEb 4AllNodeInputsAreConstantERKNS_5GraphERKNS_4NodeERNSt3__113unordered_mapINS7_12basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEEPKN4onnx11TensorProtoENS7_4hashISE_EENS7_8equal_toISE_EENSC_INS7_4pairIKSE_SI_EEEEEERKNS7_13unordered_setISE_SK_SM_NSC_ISE_EEEE MatchesOpSinceVersionERKNS_4NodeERK 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KNS_4NodeEbRKNSt3__16vectorINS0_14EdgeEndToMatchENS4_9allocatorIS6_EEEERNS5_IPKNS1_7EdgeEndENS7_ISE_EEEERKNS_7logging6LoggerE NS_5GraphERKNS_4NodeEbRKNSt3__16vectorINS0_14EdgeEndToMatchENS6_9allocatorIS8_EEEERNS7_INS6_17reference_wrapperIS3_EENS9_ISF_EEEERKNS_7logging6LoggerE В2ERKNS_5GraphEӔ 1ERKNS_5GraphEڔ 4NameEvȕ 1ϕ 2 9GetInputsEvƘ 30GetInputsIncludingInitializersEv͘ 7GetNodeEm 5NodesEv 1DescriptionEv 0Ԙ 2љ 3NumberOfNodesEv 0GetInitializedTensorERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERPKN4onnx11TensorProtoE 2CanOverrideInitializerEv 6GetNodesInTopologicalOrderEv 4GetAllInitializedTensorsEv 1IsConstantInitializerERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEbҚ Get IsSubgraphEv˚ OutputsEvʙ NodeArgERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEĚ Get MaxNodeIndexEv ValueInfoEv RootNodesEv ‚‚‚‚‚ÂÂC 1 9ݬ 7ToProtoEv 4 2E 1Eԡ RKNל ON4onnx10ModelProtoERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEEPKNS4_4listINS4_10shared_ptrINS_30IOnnxRuntimeOpSchemaCollectionEEENS8_ISG_EEEERKNS_7logging6LoggerEƧ v St3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEbRKNS1_13unordered_mapIS7_S7_NS1_4hashIS7_EENS1_8equal_toIS7_EENS5_INS1_4pairIS8_S7_EEEEEES9_RKNS1_4listINS1_10shared_ptrINS_30IOnnxRuntimeOpSchemaCollectionEEENS5_ISO_EEEERKNSA_IS7_iSC_SE_NS5_INSF_IS8_iEEEEEERKNS1_6vectorIN4onnx13FunctionProtoENS5_IS10_EEEERKNS_7logging6LoggerE 4onnx10ModelProtoERKNSt3__112basic_stringIcNS5_11char_traitsIcEENS5_9allocatorIcEEEEPKNS5_4listINS5_10shared_ptrINS_30IOnnxRuntimeOpSchemaCollectionEEENS9_ISH_EEEERKNS_7logging6LoggerE ǂ9 1ۧ 6DomainEv֬ 8MetaDataEv IrVersionEv͡ DocStringEvӭ MainGraphEv RKN ON4onnx10ModelProtoERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEEPKNS4_4listINS4_10shared_ptrINS_30IOnnxRuntimeOpSchemaCollectionEEENS8_ISG_EEEERKNS_7logging6LoggerEԧ v 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SaveToOrtFormatERN11flatbuffers17FlatBufferBuilderERNS1_6OffsetINS_12experimental3fbs5ModelEEE SetDomainERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE MainGraphEv ОПLoadE SaveERS0_ R ON4onnx10ModelProtoER iRƹ NSt3__113basic_istreamIcNS1_11char_traitsIcEEEEPN4onnx10ModelProtoE KN Р4onnx10ModelProtoER St3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERN NSt3__110shared_ptrIS0_EEPKNS5_4listINS6_INS_30IOnnxRuntimeOpSchemaCollectionEEENS5_9allocatorISB_EEEERKNS_7logging6LoggerE KNSt3__112basic_stringIcNS5_11char_traitsIcEENS5_9allocatorIcEEEERNS5_10shared_ptrIS0_EEPKNS5_4listINSE_INS_30IOnnxRuntimeOpSchemaCollectionEEENS9_ISJ_EEEERKNS_7logging6LoggerE NSt3__110shared_ptrIS0_EEPKNS4_4listINS5_INS_30IOnnxRuntimeOpSchemaCollectionEEENS4_9allocatorISA_EEEERKNS_7logging6LoggerE KNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEERNS4_10shared_ptrIS0_EEPKNS4_4listINSD_INS_30IOnnxRuntimeOpSchemaCollectionEEENS8_ISI_EEEERKNS_7logging6LoggerE 4onnx10ModelProtoE S1_10shared_ptrIS0_EEPKNS1_4listINSA_INS_30IOnnxRuntimeOpSchemaCollectionEEENS5_ISF_EEEERKNS_7logging6LoggerE иRKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEٶ i ʃN KNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEERNS2_10shared_ptrIS0_EEPKNS2_4listINSB_INS_30IOnnxRuntimeOpSchemaCollectionEEENS6_ISG_EEEERKNS_7logging6LoggerE 4onnx10ModelProtoE St3__110shared_ptrIS0_EEPKNS2_4listINS3_INS_30IOnnxRuntimeOpSchemaCollectionEEENS2_9allocatorIS8_EEEERKNS_7logging6LoggerE Ѓу҃N KNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERNS1_10shared_ptrIS0_EEPKNS1_4listINSA_INS_30IOnnxRuntimeOpSchemaCollectionEEENS5_ISF_EEEERKNS_7logging6LoggerE 4onnx10ModelProtoE St3__110shared_ptrIS0_EEPKNS1_4listINS2_INS_30IOnnxRuntimeOpSchemaCollectionEEENS1_9allocatorIS7_EEEERKNS_7logging6LoggerE ؃܃݃35SetBaselineAndOpsetVersionForDomainERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEii 1 24RegisterOpSchemaInternalEON4onnx8OpSchemaE 22GetLatestOpsetVersionsEb 19GetSchemaAndHistoryERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEiS9_PPKN4onnx8OpSchemaEPi ไ3RegisterOpSetERNSt3__16vectorIN4onnx8OpSchemaENS1_9allocatorIS4_EEEERKNS1_12basic_stringIcNS1_11char_traitsIcEENS5_IcEEEEii 6RegisterOpSchemaEON4onnx8OpSchemaE 34GetDomainToVersionMapForRegistriesERNSt3__113unordered_mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEiNS1_4hashIS8_EENS1_8equal_toIS8_EENS6_INS1_4pairIKS8_iEEEEEEb 2 19GetSchemaAndHistoryERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEiS9_PPKN4onnx8OpSchemaEPi 8GetLastReleasedOpsetVersionsEb 2GetLatestOpsetVersionsEb 7Capture 1 8Category¿13 D CapturePrintfEPKcz ProcessPrintfEPKcP13__va_list_tag 2Ev 1Ev 4LoggingManager 1 2GetProcessIdEv 9GetEpochsEv C 1 D 26LogFatalAndCreateExceptionEPKcRKNS_12CodeLocationES3_z 2ENSt3__110unique_ptrINS0_5ISinkENS2_14default_deleteIS4_EEEENS0_8SeverityEbNS1_12InstanceTypeEPKNS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEi 1ENSt3__110unique_ptrINS0_5ISinkENS2_14default_deleteIS4_EEEENS0_8SeverityEbNS1_12InstanceTypeEPKNS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEi 9CreateDefaultLoggerERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE 2CreateLoggerERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE 7s_default_logger_E2Ev 1Ev NS0_8SeverityEbi 3LogERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEERKNS0_7CaptureE 16SendProfileEventERNS_9profiling11EventRecordE ЃGetThreadIdEv OStreamSink8SendImplERKNSt3__16chrono10time_pointINS3_12system_clockENS3_8durationIxNS2_5ratioILl1ELl1000000EEEEEEERKNS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEERKNS0_7CaptureE 5ParseERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE 9NormalizeEv 6 RS0_ ྆1 7IsEmptyEv 2ToPathStringEv 7GetRootPathStringEv 0 ņdžɆIsAbsoluteEv ParentPathEv ʆʆˆAppendERKS0_ ConcatERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE Ԇ߆1 D 2 4StartProfiling 0InitializeEPKNS_7logging6LoggerE 2EndProfilingEv IcEEvRKNSt3__112basic_stringIT_NS3_11char_traitsIS5_EENS3_9allocatorIS5_EEEE EPKNS_7logging6LoggerE 2Ev 1Ev ЫЯ1EndTimeAndRecordEventENS0_13EventCategoryERKNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEERKNS3_6chrono10time_pointINSC_12steady_clockENSC_8durationIxNS3_5ratioILl1ELl1000000000EEEEEEERKSt16initializer_listINS3_4pairIS9_S9_EEEb 2global_max_num_events_EC 11EmptyStringEv 2ENS0_14StatusCategoryEi 1ENS0_14StatusCategoryEi RKNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEE PKc RKNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEE PKc 8 4CodeEv 12ErrorMessageEv CategoryEv ToStringEv C D 35ParallelForFixedBlockSizeSchedulingEllRKNSt3__18functionIFvllEEE 1 8ScheduleENSt3__18functionIFvvEEE 2EPNS_3EnvERKNS_13ThreadOptionsEPKcib 1EPNS_3EnvERKNS_13ThreadOptionsEPKcib Ё2Ev 1Ev 9DegreeOfParallelismEPKS1_ 3RunInParallelENSt3__18functionIFvvEEEi 7SimpleParallelForElRKNSt3__18functionIFvlEEE 1ParallelForEl 4TryParallelForEPS1_lRKNS_12TensorOpCostERKNSt3__18functionIFvllEEE В21ShouldParallelizeLoopEll 1 39NumShardsUsedByFixedBlockSizeSchedulingEll 5CurrentThreadIdEv 0NumThreadsEv ЙRKNS_12TensorOpCostERKNSt3__18functionIFvllEEE dRKNSt3__18functionIFvllEEE ৈC2Ev 7DefaultEv gsl24fail_fast_assert_handlerEPKcS1_S1_i re23MLAS_NCHWC_GROUPED_CONV_ALGORITHM13FilterSetSizeEOrt6GlobalIvE4api_E۬C2Ev 7DefaultEv жз้ԊՊՊՊ׊15timed_wait_implERNSt3__111unique_lockINS_8OrtMutexEEENS1_6chrono10time_pointINS6_12system_clockENS6_8durationIxNS1_5ratioILl1ELl1000000000EEEEEEE 4waitERNSt3__111unique_lockINS_8OrtMutexEEE ؊ييي2 1 1 2DisableTelemetryEventsEv 3LogSessionCreationStartEv 5LogExecutionProviderEventEP5_LUID EnableTelemetryEventsEv SetLanguageProjectionEj ڊڊڊ4Log 7LogEvaluationStopEv 8Log 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2DKernelI20MLAS_ 3DKernelI20MLAS_ MAXIMUM_POOLINGEvPK20MLAS_POOL_WORK_BLOCKmPKfPf AVERAGE_POOLINGEvPK20MLAS_POOL_WORK_BLOCKmPKfPf MAXIMUM_POOLINGEvPK20MLAS_POOL_WORK_BLOCKmPKfPf AVERAGE_POOLINGEvPK20MLAS_POOL_WORK_BLOCKmPKfPf MAXIMUM_POOLINGEvPK20MLAS_POOL_WORK_BLOCKmPKfPf AVERAGE_POOLINGEvPK20MLAS_POOL_WORK_BLOCKmPKfPf ȎюMAXIMUM_POOLINGEvPK20MLAS_POOL_WORK_BLOCKmPKfPf AVERAGE_POOLINGEvPK20MLAS_POOL_WORK_BLOCKmPKfPf ܎2DVectorKernelI20MLAS_Å 3DVectorKernelI20MLAS_ MAXIMUM_POOLINGEvPK20MLAS_POOL_WORK_BLOCKmPKfPf AVERAGE_POOLINGEvPK20MLAS_POOL_WORK_BLOCKmPKfPf MAXIMUM_POOLINGEvPK20MLAS_POOL_WORK_BLOCKmPKfPf AVERAGE_POOLINGEvPK20MLAS_POOL_WORK_BLOCKmPKfPf ŏchwPKxPKfPfև hwcPKxPKfPf݇ ЏConvPKxS0_S0_S0_S0_S0_mPKfS2_S2_PfPK15MLAS_ACTIVATIONbPN11onnxruntime11concurrency10ThreadPoolE Pool17MLAS_POOLING_KINDPKxS1_S1_S1_S1_S1_PKfPfPN11onnxruntime11concurrency10ThreadPoolE֋ ऐThreadedI UpsamplePKxS0_PKfPf 3 25MLAS_NCHWC_POOL_ALGORITHMEvPvi݋ 5MLAS_NCHWC_CONV_ 1MLAS_NCHWC_CONV_NCHWC_ALGORITHMEvPvi 0MLAS_NCHWC_CONV_NCHW_ALGORITHMEvPviϋ POINTWISE_ALGORITHMEvPvi DEPTHWISE_ALGORITHMEvPviȋ Чઐఐ LogisticKernelՌ TanhKernel ErfKernel Co ReduceMގ QLinearAdd Gem Sgemm P Fma3 őőFma3 ˑ̑Fma3 rfPKfPfm xpPKfPfm ԑmpute nv ExpF32Kernel S LogSoftmaxOutputF32Kernel ԑFma3 Avx512F בumExpF32KernelĎ oftmaxOutputF32Kernel בFma3 Avx512F aximumF32Kernel inimumMaximumF32Kernel ܑAvx Avx Avx Avx aEvPKfPT_mfS2_ hEvPKfPT_mfS2_ AddI MulI aEvPKT_fiS2_fifiPS0_mb hEvPKT_fiS2_fifiPS0_mb ЄS8Kernel U8Kernel Avx2 Avx2 hEvPKT_fiS2_fifiPS0_mb aEvPKT_fiS2_fifiPS0_mb Ы֒m vU8S8KernelAvx DoubleKernel FloatKernel͒ U8 Sseƒ Avx Fma3 Sse Avx Fma3 TransposePackB16x4 KernelM1 Sse Avx Nchw DepthwiseFloatKernel PointwiseFloatKernelߔ FloatKernel cFloatKernel Sse Avx Fma3 Sse Avx Fma3 Sseؔ Avx Fma3 Sse Avx Fma3 SseFilter Fma3Filter Avx 1Output1ԕ 2Output1ە 3Output1 4Output1 ֓דؓٓool latformMaximumFloatKernel Average Sse Avxϛ ۓExcludePadFloatKernel IncludePadFloatKernel Sse Avxޛ ݓSse Avx ޓ512F 512FŞ Avxؗ TransposeBAvxߗ Ҍ㘔512F̞ Ц512FӞ 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9check_sparse_tensor_indices_ 4check_is_experimental_opENSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEө 1ERKNS_11TensorProtoERKNS_17SparseTensorProtoEmѨ 2ERKNS_11TensorProtoERKNS_17SparseTensorProtoEmب ȠgraphERKNS_10GraphProtoERKNS0_14CheckerContextERKNS0_19LexicalScopeContextEũ modelERKNS _10ModelProtoE t3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEު RNS0_14CheckerContextEת ˣ 3 6 9ֳ 1 8 7 5 4 0 2auto_pad_docEMakeAttributeERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEE׬ TypeProto_Map FunctionProtoOperatorProtoSequenceProtoauto_pad_docRK S8_ f x NS _1 0_6vectorIԭ 1TensorProtoEƭ 0GraphProtoEͭ fNS4_IfEEEE xNS4_IxEEEE S6_NS4_IS6_EEEE NS_1 1TensorProtoENS4_ISA_EEEE߮ 0GraphProtoENS4_ISA_EEEE Ma RNNDocGenerator1EPKc Op T keRefAttributeERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEE thDocGeneratorEPKc NS_28AttributeProto_AttributeTypeEְ S8_NS_28AttributeProto_AttributeTypeEݰ 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oatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_MaxUnpool_kOnnxDomain_ver9_10EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_Mean_kOnnxDomain_ver6_7_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_NonZero_kOnnxDomain_ver9_boolEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_QLinearConv_kOnnxDomain_ver10EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_RandomNormal_kOnnxDomain_ver1EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_Round_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_SpaceToDepth_kOnnxDomain_ver1EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_Unsqueeze_kOnnxDomain_ver1_10EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_Where_kOnnxDomain_ver9_stringEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_ArgMax_kOnnxDomain_ver12_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_ArgMin_kOnnxDomain_ver12_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_AveragePool_kOnnxDomain_ver7_9EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_CumSum_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_DepthToSpace_kOnnxDomain_ver11EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Equal_kOnnxDomain_ver11_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Equal_kOnnxDomain_ver7_10_boolEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Expand_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_GlobalMaxPool_kOnnxDomain_ver1EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Greater_kOnnxDomain_ver9_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Less_kOnnxDomain_ver7_8_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_MatMul_kOnnxDomain_ver9_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_NonZero_kOnnxDomain_ver9_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_RandomUniform_kOnnxDomain_ver1EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Resize_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Round_kOnnxDomain_ver11_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_TopK_kOnnxDomain_ver11_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Unsqueeze_kOnnxDomain_ver11_12EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Where_kOnnxDomain_ver9_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Where_kOnnxDomain_ver9_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Where_kOnnxDomain_ver9_uint8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_ArgMax_kOnnxDomain_ver12_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_ConvTranspose_kOnnxDomain_ver11EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_CumSum_kOnnxDomain_ver11_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Equal_kOnnxDomain_ver11_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Equal_kOnnxDomain_ver11_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Equal_kOnnxDomain_ver7_10_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Expand_kOnnxDomain_ver13_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Expand_kOnnxDomain_ver13_int8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Expand_kOnnxDomain_ver8_12_boolEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Greater_kOnnxDomain_ver9_doubleEEENS_16KernelCreateInfoEv__ZN11onnx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NS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_TfIdfVectorizer_kOnnxDomain_ver9EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_ArgMax_kOnnxDomain_ver11_11_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_ArgMin_kOnnxDomain_ver11_11_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_ConvTranspose_kOnnxDomain_ver1_10EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Equal_kOnnxDomain_ver7_10_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Equal_kOnnxDomain_ver7_10_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Expand_kOnnxDomain_ver13_uint16_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Expand_kOnnxDomain_ver13_uint32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Expand_kOnnxDomain_ver13_uint64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Expand_kOnnxDomain_ver8_12_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Expand_kOnnxDomain_ver8_12_int8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Greater_kOnnxDomain_ver7_8_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_RandomNormalLike_kOnnxDomain_ver1EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Reciprocal_kOnnxDomain_ver6_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver12_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver12_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Resize_kOnnxDomain_ver10_10_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_ReverseSequence_kOnnxDomain_ver10EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_RoiAlign_kOnnxDomain_ver10_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Round_kOnnxDomain_ver11_MLFloat16EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_ScatterElements_kOnnxDomain_ver11EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Softmax_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_SplitToSequence_kOnnxDomain_ver11EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_ThresholdedRelu_kOnnxDomain_ver10EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Upsample_kOnnxDomain_ver7_9_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ArgMax_kOnnxDomain_ver11_11_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ArgMax_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ArgMin_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_BitShift_kOnnxDomain_ver11_uint8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_Expand_kOnnxDomain_ver13_MLFloat16EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_Expand_kOnnxDomain_ver8_12_int16_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_Expand_kOnnxDomain_ver8_12_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_Expand_kOnnxDomain_ver8_12_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_Expand_kOnnxDomain_ver8_12_uint8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_GlobalAveragePool_kOnnxDomain_ver1EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_LogSoftmax_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_RandomUniformLike_kOnnxDomain_ver1EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ReduceL1_kOnnxDomain_ver11_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ReduceL1_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ReduceL2_kOnnxDomain_ver11_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ReduceL2_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver12_int8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ReduceMean_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver12_int8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ReduceProd_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver11_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_Softmax_kOnnxDomain_ver1_10_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_StringNormalizer_kOnnxDomain_ver10EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ArgMax_kOnnxDomain_ver11_11_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ArgMin_kOnnxDomain_ver11_11_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_BitShift_kOnnxDomain_ver11_uint32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_BitShift_kOnnxDomain_ver11_uint64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_Expand_kOnnxDomain_ver8_12_uint16_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_Expand_kOnnxDomain_ver8_12_uint32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_Expand_kOnnxDomain_ver8_12_uint64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_LogSoftmax_kOnnxDomain_ver11_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_NonMaxSuppression_kOnnxDomain_ver11EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver12_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver12_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver12_uint8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver12_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver12_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver12_uint8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver11_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver11_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_Resize_kOnnxDomain_ver10_10_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_Resize_kOnnxDomain_ver10_10_uint8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_SequenceConstruct_kOnnxDomain_ver11EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_Upsample_kOnnxDomain_ver7_9_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_Upsample_kOnnxDomain_ver7_9_uint8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ConcatFromSequence_kOnnxDomain_ver11EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_Expand_kOnnxDomain_ver8_12_MLFloat16EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_LogSoftmax_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceL1_kOnnxDomain_ver1_10_int32_tEEENS_16Kerne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14Ceil_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Clip_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Clip_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Conv_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Cosh_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Det_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Div_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Erf_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Exp_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14LRN_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14LSTM_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14LSTM_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Less_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Less_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Less_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Log_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Loop_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Max_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Max_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Mean_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Mean_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Mean_Onnx_ver8EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Min_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Min_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Mod_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Mod_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Mul_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Neg_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Pow_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Pow_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Relu_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Relu_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Scan_Onnx_ver8EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Scan_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Selu_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Selu_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Sign_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Sinh_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Sqrt_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Sqrt_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Sub_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Sum_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Tanh_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Tanh_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14TopK_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Acosh_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Asinh_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Atanh_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Cast_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Ceil_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Celu_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Clip_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Clip_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Clip_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Conv_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Equal_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Equal_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Floor_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Floor_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Gemm_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Gemm_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Less_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Loop_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Loop_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Mean_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15PRelu_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15PRelu_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15PRelu_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15PRelu_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Relu_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Scan_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Sign_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Size_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Slice_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Split_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Split_Onnx_ver2EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Sqrt_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Tanh_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Tile_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15TopK_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15TopK_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16ArgMax_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16ArgMin_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Concat_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Concat_Onnx_ver4EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Equal_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Equal_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Expand_Onnx_ver8EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Floor_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Gather_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16IsNaN_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16LpPool_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16LpPool_Onnx_ver2EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16OneHot_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Range_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Round_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Shrink_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17ArgMax_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17ArgMax_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17ArgMax_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17ArgMin_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17ArgMin_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17ArgMin_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17CumSum_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Dropout_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Dropout_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Dropout_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Einsum_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Expand_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17EyeLike_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Greater_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Greater_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Greater_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Hardmax_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17LpPool_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17MatMul_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17MaxPool_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17MaxPool_Onnx_ver8EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Reshape_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Scatter_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Sigmoid_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Sigmoid_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Softmax_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Squeeze_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Compress_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Greater_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Hardmax_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Hardmax_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Identity_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18MaxPool_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18MaxPool_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18MaxPool_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18NonZero_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18ReduceL1_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18ReduceL2_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Reshape_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Scaler_OnnxML_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Scatter_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Sigmoid_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Softmax_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Softmax_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Softplus_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Softsign_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18ZipMap_OnnxML_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19BitShift_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19CastMap_OnnxML_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19Identity_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19Imputer_OnnxML_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19LeakyRelu_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19LeakyRelu_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19MaxUnpool_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19ReduceL1_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19ReduceL1_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19ReduceL2_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19ReduceL2_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19ReduceMax_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19ReduceMin_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19ReduceSum_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19Unsqueeze_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19Upsample_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20LogSoftmax_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20MaxRoiPool_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20MaxUnpool_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20Reciprocal_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20Reciprocal_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceMax_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceMax_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceMax_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceMean_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceMin_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceMin_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceMin_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceProd_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceSum_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceSum_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20Transpose_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver13EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21AveragePool_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21AveragePool_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21Binarizer_OnnxML_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21HardSigmoid_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21HardSigmoid_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21LogSoftmax_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21LogSoftmax_Onnx_ver13EEENS_8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Nchwc.2.6.ProcessNextRow.LNchwc.2.6.ProcessNextColumn.LNchwc.2.6.SkipOverPadding.LNchwc.2.6.HandlePostProcessing.LNchwc.2.ProcessRemainingOutputCount.LNchwc.2.3.ProcessNextRow.LNchwc.2.3.ProcessNextColumn.LNchwc.2.3.SkipOverPadding.LNchwc.2.3.HandlePostProcessing.LNchwc.2.ProcessRemainingOutputCountLessThan3.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.ProcessNextOutputCountBy6.LNchwc.1.6.ProcessNextRow.LNchwc.1.6.ProcessNextColumn.LNchwc.1.6.SkipOverPadding.LNchwc.1.6.HandlePostProcessing.LNchwc.1.ProcessRemainingOutputCount.LNchwc.1.3.ProcessNextRow.LNchwc.1.3.ProcessNextColumn.LNchwc.1.3.SkipOverPadding.LNchwc.1.3.HandlePostProcessing.LNchwc.1.ProcessRemainingOutputCountLessThan3.LNchwc.1.2.ProcessNextRow.LNchwc.1.2.ProcessNextColumn.LNchwc.1.2.SkipOverPadding.LNchwc.1.2.HandlePostProcessing.LNchwc.1.ProcessOutputCountRightPadAndRemaining.LNchwc.ExitKernelMlasConvNchwcFloatSingleAvx512FFilter1.LNchwc.1.1.ProcessNextRow.LNchwc.1.1.ProcessNextColumn.LNchwc.1.1.SkipOverPadding.LNchwc.1.1.HandlePostProcessingMlasConvNchwcFloatSingleAvx512FFilter2.LNchwc.2.1.ProcessNextRow.LNchwc.2.1.ProcessNextColumn.LNchwc.2.1.SkipOverPadding.LNchwc.2.1.HandlePostProcessingMlasConvNchwcFloatSingleAvx512FFilter3.LNchwc.3.1.ProcessNextRow.LNchwc.3.1.ProcessNextColumn.LNchwc.3.1.SkipOverPadding.LNchwc.3.1.HandlePostProcessingMlasConvNchwcFloatSingleAvx512FFilter4.LNchwc.4.1.ProcessNextRow.LNchwc.4.1.ProcessNextColumn.LNchwc.4.1.SkipOverPadding.LNchwc.4.1.HandlePostProcessing.LDepthwise.1.ProcessOutputCount.LDepthwise.1.ProcessNextOutputCountBy6.LDepthwise.1.6.ProcessNextRow.LDepthwise.1.6.ProcessNextColumn.LDepthwise.1.6.SkipOverPadding.LDepthwise.1.6.HandlePostProcessing.LDepthwise.1.ProcessRemainingOutputCount.LDepthwise.1.3.ProcessNextRow.LDepthwise.1.3.ProcessNextColumn.LDepthwise.1.3.SkipOverPadding.LDepthwise.1.3.HandlePostProcessing.LDepthwise.1.ProcessRemainingOutputCountLessThan3.LDepthwise.1.2.ProcessNextRow.LDepthwise.1.2.ProcessNextColumn.LDepthwise.1.2.SkipOverPadding.LDepthwise.1.2.HandlePostProcessing.LDepthwise.1.ProcessOutputCountRightPadAndRemaining.LDepthwise.ExitKernelMlasConvDepthwiseFloatSingleAvx512FFilter1.LDepthwise.1.1.ProcessNextRow.LDepthwise.1.1.ProcessNextColumn.LDepthwise.1.1.SkipOverPadding.LDepthwise.1.1.HandlePostProcessing.LPointwise.4.ProcessNextOutputCountBy6.LPointwise.4.6.ProcessNextInputBlock.LPointwise.4.ProcessRemainingOutputCount.LPointwise.4.3.ProcessNextInputBlock.LPointwise.4.ProcessRemainingOutputCountLessThan3.LPointwise.4.2.ProcessNextInputBlock.LPointwise.4.ProcessRemainingOutputCount1.LPointwise.4.1.ProcessNextInputBlock.LPointwise.ProcessFilterCount3.LPointwise.3.ProcessNextOutputCountBy6.LPointwise.3.6.ProcessNextInputBlock.LPointwise.3.ProcessRemainingOutputCount.LPointwise.3.3.ProcessNextInputBlock.LPointwise.3.ProcessRemainingOutputCountLessThan3.LPointwise.3.2.ProcessNextInputBlock.LPointwise.3.ProcessRemainingOutputCount1.LPointwise.3.1.ProcessNextInputBlock.LPointwise.ProcessFilterCountLessThan3.LPointwise.2.ProcessNextOutputCountBy6.LPointwise.2.6.ProcessNextInputBlock.LPointwise.2.ProcessRemainingOutputCount.LPointwise.2.3.ProcessNextInputBlock.LPointwise.2.ProcessRemainingOutputCountLessThan3.LPointwise.2.2.ProcessNextInputBlock.LPointwise.2.ProcessRemainingOutputCount1.LPointwise.2.1.ProcessNextInputBlock.LPointwise.ProcessFilterCount1.LPointwise.1.ProcessNextOutputCountBy6.LPointwise.1.6.ProcessNextInputBlock.LPointwise.1.ProcessRemainingOutputCount.LPointwise.1.3.ProcessNextInputBlock.LPointwise.1.ProcessRemainingOutputCountLessThan3.LPointwise.1.2.ProcessNextInputBlock.LPointwise.1.ProcessRemainingOutputCount1.LPointwise.1.1.ProcessNextInputBlock.LPointwise.ExitKernel.LPostProcessBlock.1.1.SkipAccumulateOutput.LPostProcessBlock.1.1.SkipBiasAddition.LPostProcessBlock.1.1.SkipReluActivation.LPostProcessBlock.1.2.SkipAccumulateOutput.LPostProcessBlock.1.2.SkipBiasAddition.LPostProcessBlock.1.2.SkipReluActivation.LPostProcessBlock.1.3.SkipAccumulateOutput.LPostProcessBlock.1.3.SkipBiasAddition.LPostProcessBlock.1.3.SkipReluActivation.LPostProcessBlock.1.6.SkipAccumulateOutput.LPostProcessBlock.1.6.SkipBiasAddition.LPostProcessBlock.1.6.SkipReluActivation.LPostProcessBlock.2.1.SkipAccumulateOutput.LPostProcessBlock.2.1.SkipBiasAddition.LPostProcessBlock.2.1.SkipReluActivation.LPostProcessBlock.2.2.SkipAccumulateOutput.LPostProcessBlock.2.2.SkipBiasAddition.LPostProcessBlock.2.2.SkipReluActivation.LPostProcessBlock.2.3.SkipAccumulateOutput.LPostProcessBlock.2.3.SkipBiasAddition.LPostProcessBlock.2.3.SkipReluActivation.LPostProcessBlock.2.6.SkipAccumulateOutput.LPostProcessBlock.2.6.SkipBiasAddition.LPostProcessBlock.2.6.SkipReluActivation.LPostProcessBlock.3.1.SkipAccumulateOutput.LPostProcessBlock.3.1.SkipBiasAddition.LPostProcessBlock.3.1.SkipReluActivation.LPostProcessBlock.3.2.SkipAccumulateOutput.LPostProcessBlock.3.2.SkipBiasAddition.LPostProcessBlock.3.2.SkipReluActivation.LPostProcessBlock.3.3.SkipAccumulateOutput.LPostProcessBlock.3.3.SkipBiasAddition.LPostProcessBlock.3.3.SkipReluActivation.LPostProcessBlock.3.6.SkipAccumulateOutput.LPostProcessBlock.3.6.SkipBiasAddition.LPostProcessBlock.3.6.SkipReluActivation.LPostProcessBlock.4.1.SkipAccumulateOutput.LPostProcessBlock.4.1.SkipBiasAddition.LPostProcessBlock.4.1.SkipReluActivation.LPostProcessBlock.4.2.SkipAccumulateOutput.LPostProcessBlock.4.2.SkipBiasAddition.LPostProcessBlock.4.2.SkipReluActivation.LPostProcessBlock.4.3.SkipAccumulateOutput.LPostProcessBlock.4.3.SkipBiasAddition.LPostProcessBlock.4.3.SkipReluActivation.LPostProcessBlock.4.6.SkipAccumulateOutput.LPostProcessBlock.4.6.SkipBiasAddition.LPostProcessBlock.4.6.SkipReluActivation.LMaximum.ProcessOutputCountLeftPad.LMaximum.ProcessOutputCount.LMaximum.ProcessNextOutputCountBy3.LMaximum.3.ProcessNextRow.LMaximum.3.ProcessNextColumn.LMaximum.3.SkipOverPadding.LMaximum.3.HandlePostProcessing.LMaximum.ProcessRemainingOutputCount.LMaximum.ProcessOutputCountRightPad.LMaximum.ExitKernelMlasPoolMaximumFloatSingleAvx512F.LMaximum.1.ProcessNextRow.LMaximum.1.ProcessNextColumn.LMaximum.1.SkipOverPadding.LMaximum.1.HandlePostProcessing.LAverageExcludePad.ProcessOutputCountLeftPad.LAverageExcludePad.ProcessOutputCount.LAverageExcludePad.ProcessNextOutputCountBy3.LAverageExcludePad.3.ProcessNextRow.LAverageExcludePad.3.ProcessNextColumn.LAverageExcludePad.3.SkipOverPadding.LAverageExcludePad.3.HandlePostProcessing.LAverageExcludePad.ProcessRemainingOutputCount.LAverageExcludePad.ProcessOutputCountRightPad.LAverageExcludePad.ExitKernelMlasPoolAverageExcludePadFloatSingleAvx512F.LAverageExcludePad.1.ProcessNextRow.LAverageExcludePad.1.ProcessNextColumn.LAverageExcludePad.1.SkipOverPadding.LAverageExcludePad.1.HandlePostProcessing.LAverageIncludePad.ProcessOutputCountLeftPad.LAverageIncludePad.ProcessOutputCount.LAverageIncludePad.ProcessNextOutputCountBy3.LAverageIncludePad.3.ProcessNextRow.LAverageIncludePad.3.ProcessNextColumn.LAverageIncludePad.3.SkipOverPadding.LAverageIncludePad.3.HandlePostProcessing.LAverageIncludePad.ProcessRemainingOutputCount.LAverageIncludePad.ProcessOutputCountRightPad.LAverageIncludePad.ExitKernelMlasPoolAverageIncludePadFloatSingleAvx512F.LAverageIncludePad.1.ProcessNextRow.LAverageIncludePad.1.ProcessNextColumn.LAverageIncludePad.1.SkipOverPadding.LAverageIncludePad.1.HandlePostProcessing.LComputeExp.ComputeExpBy16Loop.LComputeExp.ProcessRemainingCount.LComputeExp.ExitKernel.LComputeSumExp.ComputeExpBy48Loop.LComputeSumExp.SkipStoreResultsBy48.LComputeSumExp.ProcessRemainingCount.LComputeSumExp.ComputeExpBy16Loop.LComputeSumExp.ProcessSingleVector.LComputeSumExp.SkipStoreResultsBy16.LComputeSumExp.ReduceAccumulator.LProcessCountM2.LProcessNextColumnLoop32xN1.LComputeBlockBy4Loop22.LProcessRemainingBlocks22.LComputeBlockBy1Loop22.LComputeBlockLoopExit22.LOutput32xNBlock1.LSkipAccumulateOutput32xNBlock1.LOutput16xNBlock1.LOutput16xNBlockWithMask1.LSkipAccumulateOutput16xNBlockWithMask1.LProcessRemainingCountN1.LComputeBlockBy4Loop211.LProcessRemainingBlocks211.LComputeBlockBy1Loop211.LComputeBlockLoopExit211.LProcessNextColumnLoop48xN1.LComputeBlockBy4Loop367.LProcessRemainingBlocks367.LComputeBlockBy1Loop367.LComputeBlockLoopExit367.LSkipAccumulateOutput48xNBlock1.LProcessCountM4.LProcessNextColumnLoop32xN535.LComputeBlockBy4Loop556.LProcessRemainingBlocks556.LComputeBlockBy1Loop556.LComputeBlockLoopExit556.LOutput32xNBlock535.LSkipAccumulateOutput32xNBlock535.LOutput16xNBlock535.LOutput16xNBlockWithMask535.LSkipAccumulateOutput16xNBlockWithMask535.LProcessRemainingCountN535.LComputeBlockBy4Loop765.LProcessRemainingBlocks765.LComputeBlockBy1Loop765.LComputeBlockLoopExit765.LProcessNextColumnLoop48xN535.LComputeBlockBy4Loop931.LProcessRemainingBlocks931.LComputeBlockBy1Loop931.LComputeBlockLoopExit931.LSkipAccumulateOutput48xNBlock535.LProcessCountM6.LProcessNextColumnLoop32xN1129.LComputeBlockBy4Loop1150.LProcessRemainingBlocks1150.LComputeBlockBy1Loop1150.LComputeBlockLoopExit1150.LOutput32xNBlock1129.LSkipAccumulateOutput32xNBlock1129.LOutput16xNBlock1129.LOutput16xNBlockWithMask1129.LSkipAccumulateOutput16xNBlockWithMask1129.LProcessRemainingCountN1129.LComputeBlockBy4Loop1379.LProcessRemainingBlocks1379.LComputeBlockBy1Loop1379.LComputeBlockLoopExit1379.LProcessNextColumnLoop48xN1129.LComputeBlockBy4Loop1555.LProcessRemainingBlocks1555.LComputeBlockBy1Loop1555.LComputeBlockLoopExit1555.LSkipAccumulateOutput48xNBlock1129.LExitKernel.LProcessCountM1.LProcessNextColumnLoop32xN1783.LComputeBlockBy1Loop1804.LComputeBlockLoopExit1804.LOutput32xNBlock1783.LSkipAccumulateOutput32xNBlock1783.LOutput16xNBlock1783.LOutput16xNBlockWithMask1783.LSkipAccumulateOutput16xNBlockWithMask1783.LProcessRemainingCountN1783.LComputeBlockBy1Loop1875.LComputeBlockLoopExit1875.LProcessNextColumnLoop48xN1783.LComputeBlockBy1Loop1922.LComputeBlockLoopExit1922.LSkipAccumulateOutp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