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VHHH57H5Hl1lH jVHHH5\7H4HOlZlH 7VHHH5)7H4Hl'lH VHHH56Hg4HllH UHHH56H44HlelH UHHH56H4Hl2lH kUHHH5]6H3HPl^lH ;UHHH5-6H3H l.lH UHHH55Hn3Hl 20.,*(&$"  HHpI+Hl ffffff.UHSHHHVHHEHppYH{ HdHHpP0Hp*HpE[H{ HeHHpP0Hp*HpӰ[H{ HfHHpP0Hp}*Hp>YH{ HgHHpP0HpK*Hp?YH{ HhHHpP0Hp*HpR]H{ HfHHpP0Hp)HpAWH{ HdHHpP0Hp)HTHH;Eu HĈ[]llH RHHH53HH1Hl'ylH RHHH53H1HlFlH RHHH5q3H0HdllH LRHHH5>3H0H1llH RHHH5 3H|0Hl^lH QHHH52HL0Hl.lH QHHH52H0Hl HHp(H]l DUHSHHHRHHEHpg\H{ HHHpP0H Hp'HpaH;HHHpP0Hp'HpWH;HHHpP0Hp['Hp?WH;HHHpP0Hp*'HpXH;HHHpP0Hp&Hp轔XH;HHHpP0Hp&Hp輍aH;HHHpP0Hp&Hp i]H;HHHpP0Hpf&HpznXH;HHHpP0Hp5&HpYYH;HHHpP0Hp&Hp(aH;HHHpP0Hp%Hpg-YH;HHHpP0Hp%Hpv[H;HHHpP0Hpq%Hp腮]H;HHHpP0Hp@%HpaH;HHHpP0Hp%HpmYH;HHHpP0Hp$HpoYH;HHHpP0Hp$Hp!qYH;HHHpP0Hp|$HprYH;HHHpP0HpK$Hp_tYH;HHHpP0Hp$HpN\H;HHHpP0Hp#Hpݡ`H;HHHpP0Hp#HpuYH;HHHpP0Hp#HpTWH;HHHpP0HpV#Hp yYH;HHHpP0Hp%#Hp=`H;HHHpP0Hp"Hp`H;HHHpP0Hp"Hp'`H;HHHpP0Hp"Hp6\H;HHHpP0Hpa"Hp5\H;HHHpP0Hp0"H!MHH;Eu HĈ[]l'lH `KHHH5R,H)HEllH -KHHH5,H)HllH JHHH5+H])HlVlH JHHH5+H*)Hl#[lH JHHH5+H(Hyl(lH aJHHH5S+H(HFllH .JHHH5 +H(HllH IHHH5*H^(HlWlH IHHH5*H+(Hl$\lH IHHH5*H'Hzl)lH bIHHH5T*H'HGllH /IHHH5!*H'HllH HHHH5)H_'HlXlH HHHH5)H,'Hl%]lH HHHH5)H&H{l*lH cHHHH5U)H&HHllH 0HHHH5")H&HllH GHHH5(H`&HlYlH GHHH5(H-&Hl&^lH GHHH5(H%H|l+lH dGHHH5V(H%HIllH 1GHHH5#(H%HllH FHHH5'Ha%HlZlH FHHH5'H.%Hl'_lH FHHH5'H$H}l,lH eFHHH5W'H$HJllH 2FHHH5$'H$HllH EHHH5&Hb$Hl^lH EHHH5&H2$Hl.flH EHHH5&H$Hl 86420.,*(&$"  HHpH l ffff.UHSHHHFHHEHp}`H{ HHHpP0HpqHpaH{ HHHpP0Hp?Hp\H{ HHHpP0Hp HpQ[H{ HHHpP0HpHp?E]H{ HHHpP0HpHpmXH{ HHHpP0HpwHp{@]H{ HHHpP0HpEHpYaH{ HHHpP0HpHpXH{ HHHpP0HpHp՘]H{ HHHpP0HpHpvYH{ HHHpP0Hp}HpsYH{ HHHpP0HpKHpN\H{ HHHpP0HpHpI\H{ HHHpP0HpHp ]H{ HHHpP0HpHp]H{ HHHpP0HpHpW`H{ HHHpP0HpQHp]H{ HHHpP0HpHp`H{ HHHpP0HpHp]H{ HHHpP0HpHBHH;Eu HĈ[] l lH @HHH5!HNH l lH @HHH5!HH lL lH @HHH5w!HHj lX lH R@HHH5D!HH7 l%lH @HHH5!HH llH ?HHH5 HOHllH ?HHH5 HHlMlH ?HHH5x HHklYlH S?HHH5E HH8l&lH ?HHH5 HHllH >HHH5HPHllH >HHH5HHlNlH >HHH5yHHllZlH T>HHH5FHH9l'lH !>HHH5HHllH =HHH5HQHllH =HHH5HHlOlH =HHH5zHHml^lH X=HHH5JHH=l.lH (=HHH5HH l $"  HHptHl f.UHSHHHB>HHEHpVH{ H HHpP0H Hp HpXH;H HHpP0HpHp`H;H HHpP0HpHp/rYH;H HHpP0HpzHp~tYH;H HHpP0HpIHpGYH;H HHpP0HpHpk`H;H HHpP0HpHpXH;H HHpP0HpHpz]XH;H HHpP0HpHp ]H;H HHpP0HpTHp[`H;H HHpP0Hp#Hp'R`H;H HHpP0HpHpfI`H;H HHpP0HpHp蕔[H;H HHpP0HpHp4w`H;H HHpP0Hp_HpCXH;H HHpP0Hp.HptYH;H HHpP0HpHpL`H;H HHpP0HpHp`aH;H HHpP0HpHpU`H;H HHpP0HpjHp>`H;H HHpP0Hp9Hp ?`H;H HHpP0HpHp\`H;H HHpP0HpHp``H;H HHpP0HpHp\H;H HHpP0HpuHpin^H;H HHpP0HpDHpl^H;H HHpP0HpHpC^H;H HHpP0Hp HpD^H;H HHpP0Hp Hp@^H;H HHpP0Hp HpA^H;H! HHpP0HpO Hpf^H;H# HHpP0Hp Hp=^H;H% HHpP0Hp Hpg^H;H' HHpP0Hp Hp>^H;H) HHpP0Hp Hp:^H;H+ HHpP0HpZ Hp;^H;H- HHpP0Hp) Hp}`H;H/ HHpP0Hp Hp3`H;H1 HHpP0Hp HpK)YH;H3 HHpP0Hp Hp'YH;H5 HHpP0Hpe Hp&YH;H7 HHpP0Hp4 HpVH;H9 HHpP0Hp Hp6`H;H; HHpP0Hp Hp \H;H= HHpP0Hp Hp6]H;H? HHpP0Hpp Hp*\H;HA HHpP0Hp? Hp#-\H;HC HHpP0Hp Hpb9\H;HE HHpP0Hp HpAg\H;HG HHpP0Hp Hp_H;HI HHpP0Hp{ Hp_H;HK HHpP0HpJ Hp_H;HM HHpP0Hp Hp]_H;HO HHpP0HpHp_H;HQ HHpP0HpHp_H;HS HHpP0HpHp:_H;HU HHpP0HpUHpi_H;HW HHpP0Hp$Hp`H;HY HHpP0HpHpG*YH;H[ HHpP0HpHpVVH;HZ HHpP0HpHpվ]H;HY HHpP0Hp`HQ2HH;Eu HĈ[]kWkH 0HHH5HHuk $kH ]0HHH5OHHBk kH *0HHH5HHk kH /HHH5HZHk kH /HHH5H'HkP XkH /HHH5H Hvk %kH ^/HHH5PH HCk kH +/HHH5H Hk kH .HHH5H[ Hk kH .HHH5H( HkQ YkH .HHH5H Hwk &kH _.HHH5QH HDk kH ,.HHH5H Hk kH -HHH5H\ Hk kH -HHH5H) HkR ZkH -HHH5H Hxk 'kH `-HHH5RH HEkkH --HHH5H HkkH ,HHH5 H] HkkH ,HHH5 H* HkS[kH ,HHH5 H Hyk (kH a,HHH5S H HFkkH .,HHH5 H HkkH +HHH5 H^ HkkH +HHH5 H+ HkT\kH +HHH5 H Hzk!)kH b+HHH5T H HGkkH /+HHH5! H HkkH *HHH5 H_ HkkH *HHH5 H, HkU]kH *HHH5 HH{k"*kH c*HHH5U HHHkkH 0*HHH5" HHkkH )HHH5 H`HkkH )HHH5 H-HkV^kH )HHH5 HH|k#+kH d)HHH5V HHIkkH 1)HHH5# HHkkH (HHH5 HaHkkH (HHH5 H.HkW_kH (HHH5 HH}k$,kH e(HHH5W HHJkkH 2(HHH5$ HHkkH 'HHH5HbHkkH 'HHH5H/HkX`kH 'HHH5HH~k%-kH f'HHH5XHHKkkH 3'HHH5%HHkkH 'HHH5HcHkkH &HHH5H0HkYakH &HHH5HHk&.kH g&HHH5YHHLkkH 4&HHH5&HHkkH &HHH5HdHkkH %HHH5H1HkZbkH %HHH5HHk'/kH h%HHH5ZHHMkkH 5%HHH5'HHkkH %HHH5HeHkkH $HHH5H2Hk^fkH $HHH5HHk.6kH o$HHH5aHHTk xvtrpnljhfdb`^\ZXVTRPNLJHFDB@><:86420.,*(&$"  HHpgHk ffff.UHSHHH2%HHEHp2^H{ HHHpP0HpHp3^H{ HHHpP0HpHpYH{ HHHpP0HpHp葑ZH{ HHHpP0HpkHp[H{ HHHpP0Hp9Hp$^H{ HHHpP0HpHp%^H{ HHHpP0HpHp艋`H{ HHHpP0HpHpWyZH{ HHHpP0HpqHph\H{ HHHpP0Hp?Hp賡WH{ HHHpP0Hp HpXH{ HHHpP0HpHpYH{ HHHpP0HpHpYH{ HHHpP0HpwHp[nXH{ HHHpP0HpEHpقXH{ HHHpP0HpHpZH{ HHHpP0HpHpeXH{ HHHpP0HpH!HH;Eu HĈ[] kkH HHH5HBHkXskH HHH5HHk%@kH yHHH5kHH^k kH FHHH58HH+kkH HHH5HvHkkH HHH5HCHkYtkH HHH5HHk&AkH zHHH5lHH_kkH GHHH59HH,kkH HHH5HwHkkH HHH5HDHkZukH HHH5HHk'BkH {HHH5mHH`kkH HHHH5:HH-kkH HHH5HxHkkH HHH5HEHk^ykH HHH5HHk.IkH HHH5tHHgk  HHpHk UHSHHHHHEHpGxbH{ HHHpP0HpqHpybH{ HHHpP0Hp?HpzbH{ HHHpP0Hp Hp}bH{ HHHpP0HpHp蟀bH{ HHHpP0HpHp}bH{ HHHpP0HpwHp軃bH{ HHHpP0HpEHp)bH{ HHHpP0HpHp跈bH{ HHHpP0HpHpՋbH{ HHHpP0HpHpӍbH{ HHHpP0Hp}HpabH{ HHHpP0HpKHpobH{ HHHpP0HpHp蝖bH{ HHHpP0HpHp{bH{ HHHpP0HpHpibH{ HHHpP0HpHp距bH{ HHHpP0HpQHp蕡bH{ HHHpP0HpHHH;Eu HĈ[]zkkH OHHH5AHH4kXkH HHH5HHk%kH HHH5HLHk}kH HHH5HHkJkH HHH5uHHhkkH PHHH5BHH5kYkH HHH5HHk&kH HHH5HMHk~kH HHH5HHkKkH HHH5vHHikkH QHHH5CHH6kZkH HHH5HHk'kH HHH5HNHkkH HHH5HHkLkH HHH5wHHjkkH RHHH5DHH7k^kH "HHH5HHk.kH HHH5HUHk  HHpBHk UHAWAVAUATSHH HHEH@IIHL MAHL QHHL kH`(wv{)PHFROM_MODHHH_CONFIG_HHHORT_LOADHf@EL@HHPHAPt H`\kt HMn`HLPHOt HH<0<1)A|$  HoHH5kH QlH{wBHPLE1LHpxBHH5(l-(bHP:PH8H H@H9 fff.Lh@t HxEkLL9uH8 AGAEAAEI~hIwSkAG fAIIw(6kIIw@&kIIwX HM9tIII.MAfA0HDžHkHkIH3QQH5KkH kHPL]THQPH5kHHAHkHPHLVE1H5HL4kmHH/HHHPHXH@HHHkHDžHDžPL%ID$HHHPI@LHFkL-ILXHDžHDžHDžHDžDžH5lHP7_H5kH_H5lHp_H5l.H\_tHH HH-_HHXcHHkHHʧPt H=kt H(kHHHPLLXt HkHXkH5HHPfkHkLP&H2IL/I8AHP HPHDžPHn AAM~AG HkHH5!kH kHJrBHPLE1LHsBHH5kQq]HP6PL8M H@L9DHX@t HxkHI9uH8-HYjHH5^kH kHqBHPLE1LHrBHH5kD\HPB5PL8MH@L9ffHX@t HxkHI9uH8@LL@k t H0kt HkEAbAf`IHXIHPMIHH(kHDEtE1HP8MAA oQDDHPHXHH`fbLHHHL$ HHL$HHL$HHHT$H $H1H!AHHDžIIHtJH褮PH4kHHDžHLPtHvPHkA}t"HDžLPA}EAA fIHIHIHIPDXHƅ\H ckHHEHHH]HeHHmfuwHxHxHL$ HpHL$HhHL$HXH`HT$H $HPHAHPHDžPIIHtIHPHkHPHDžPHtH߬PHokIH;HDžPH%I^{ dHfL8H5!kH kHJmBHPLE1HLnBHH5khqXHP1PL8MH@L9DHX@t HxkHI9uH8wLL@uk t H0`kt HKkH@HAIHAIyAtUA}MLL@k t H0kt HkAEAu1IIIIA0HDL8HEL&kII`LLM贂IMHtH詏HIkMIvLcGIIPHHIIvH6PIHbGA~dtILHDžHDžW)PHDžƅ`AdžAHIIAufIIdž1wkAƆHHII Idž`A0HPHXI(I aAHHDžHtCt H{kHkHHH;EH[A\A]A^A_]HH@k t H0ykt HdkDžPƅTHDžhHDž`HDžXfDžpHDžxHDžƅx(HDžHime_profHHonnxruntHyDžile_ƅHDžHDžHDž(r{DžƅƅDžHDžHDžHDžHDžƅDžHDžHDžHDžHDžHDžHDžƅ ƅ!H^LƅI8ƅHDžƅ HH誱AHtaHWALMHL9HX@t HxEkHI9uHHHLPL茾HHDžHHHCt H{kHkHHDžHtCt H{kHkHHLLku3xu?Xt Hh[kELPH:kxtH%kXuHDžHtHXTHPCPI~hkHxpfAIkHIkHI HI9tIHH< fA0HHDžE1HCt H{8kH0kk4kHH5[nHvkH5H\HNkCt H{kHkkIH@QH5kH 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UHAWAVSHHHĶ}HHEIHHCH+CHEEHHLHHHuHUP0AHK(HC0HHH9uJH{ HH>H?H9wHH@HH9HC HH*HK(HAHC(HC HHHEtH 4HH#4H4EHuHEHUHCH;CtHHCHHCIuFH{Hu HCIt/H@Ht&HHwH !yHL9s _L9rUHc}HH;Eu HH[A^A_]|if|iH }HHH5}H}H|imyi 3|iIH}L{iHiH}1yiHHHMHHPHUHMH@H@HHUL1H5}H}L{i IƳEt9IIEuEu%tL{iLvi H}_{iEtH}P{iufUHAVSH H&}HHEHHCHHHt:H+CHEEHHHHuHUP0t2HCHHCHK(H³}HH;EH [A^]HH}HCH@0MЈ@uH}HHHUHPHMUHKHCHPHSHK(H9tH@8uHXHCpLpHxLs`ziBziH {}HHH5m}Hޏ}H`zifffff.UHAWAVSHXHԲ}HHEW)EHAIEHEHout_of_rHEEangeEH}HuD@HsHEsHESH}TwiHHHMHHPHUHMH@H@HEt H}9yiEt H}*yiEHuHEuH^}HIEwIviH+}HIEt H}xiHα}HH;EuLHX[A^A_]4yiILxiEt2H}'IEtH}xiEu IEt H}xiLsi ffff.UHAWAVSHHAHH>}HHEHC HK(HHHHHEHwH HcHHELuEu=HCH+CHEEHHHHuHUHMP0L{HCH9CtUH@H8HxHGH;GYMȈHMHHEHEHG?E11WEȈEH}HMHMEHEH 1@uHAHUHQHEHHC8HK@HHHHHHK@sIEȈEH}HMHMEHEHKP1@uHAHUHQHEH[PE11viHHH@H@HHEsEjoviH@H@HHE@H=bmߢHE+HuIH@H@HXHAuLH}HH;EuDHHH[A^A_]OviuiH $}HHH5}H}H vi HuLvHpi 'Qfff.UHAWAVAUATSPIIII^H)HHCHH<HMfI)LHHH9wYII9LBMI9vCuiHH5iHQriH}HHH5}H-}HuiILHtiHH 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I8t HH_E1HIALeMt6HEL9t!HX@t Hx_HI9uH}LLe_Et H}|_ptH}j_IALPMt:HXL9t8HX@t Hx7_HI9uHPLX_Et]HP菗AH!_HuHPL_E1H5rHyrL ^B^IHhkYDH5_H s_H}LhO\H5r_HPLAH_HuHPL_E1H5+rHrL~^IAgIAIfIAIAPIHIAIAI*IPt H`^LeMt6HEL9t!HX@t Hx^HI9uH}LLeq^Et H}b^Et H}S^LhMHpL9HX@t Hx ^HI9u]IPt H`^LeMt6HEL9t!HX@t Hx^HI9uH}LLe^Et H}^Et H}^LhMHpL9HX@t Hxk^HI9uIPt H`F^LeMt6HEL9t!HX@t Hx!^HI9uH}LLe^Et H}^Et H}^LhMt:HpL9t=HX@t Hx^HI9uHhLp^Et,L^"LIIEt H}r^L^ UHAVSIx`^HHLH[A^]IH3^Li^ UHAWAVAUATSHIIHsHHEHasHIIaH9HRisIAGXAG`IGhAGpW)EHEEEhighEIOPHhHULBX9HhHDžhHtCt H{d^H\^Et H}M^HW)EEHEElowIOTHhHULW9HhHDžhHtCt H{^H^Et H}^HDžLW)EHEEEseedEHhHUHLLCW9HhHDžhHtCt H{e^H]^Et H}N^Ht^ H,LH IH )))ȹEAOXW)EE HEEdtypfEeHhHUH@L}X9HhHDžhHtCt H{^H^Et H}^Hu$Ip@A?YaV7A?-HXsHH;EuHĘ[A\A]A^A_]^V^IHhSDH5_H b_H}Lh4VHP訐AH_HuHPLXE1H5rHrL$^^IHhRDH5_H ߭_H}LhUHP%AH8_HuHPL8XE1H5NrHrL^BP^IHhRDH5_H \_H}Lh.UH5_HPLxAH_HuHPLWE1H5rHrL^IAgIAIfIAIAPIHIAIAI*IPt H`F^LeMt6HEL9t!HX@t Hx!^HI9uH}LLe^Et H}^Et H}^LhMHpL9HX@t Hx^HI9u]IPt H`^LeMt6HEL9t!HX@t Hxl^HI9uH}LLeR^Et H}C^Et H}4^LhMHpL9HX@t Hx^HI9uIPt H`^LeMt6HEL9t!HX@t Hx^HI9uH}LLe^Et H}^Et H}^LhMt:HpL9t=HX@t HxT^HI9uHhLp9^Et,Lh^"LIIEt H}^L>^ fDUHAVSIx^HHLH[A^]IH^L^ UHAWAVAUATSHIIHsHHEHsHII@9H2bsIAGXAG`IGhW)EHEEHsample_sHEEizeIOPHhHULR9HhHDžhHtCt H{^H^Et H}^HDžLW)EHEEEseedEHhHUHLLHP9HhHDžhHtCt H{j^Hb^Et H}S^Ht^ H,LH IH )))ȹEAOXW)EE HEEdtypfEeHhHUH@LQ9HhHDžhHtCt H{^H^Et H}^Hu@Ap\ZVIpt-A?t'H_sHH;EuHĘ[A\A]A^A_]^]^IHhLDH5_H ʧ_H}Lh;OH5_HPLAH_HuHPLQE1H5rHrL!^Z^IHhKDH5_H =_H}LhNHP"AH[_HuHPL5QE1H5KrH rL^IA(IA}IIPt H`^LeMt6HEL9t!HX@t Hx^HI9uH}LLe^Et H}^Et H}^LhMHpL9HX@t Hx^HI9uIA(IA}IIPt H`B^LeMt6HEL9t!HX@t Hx^HI9uH}LLe^Et H}^Et H}^LhMt:HpL9t=HX@t Hx^HI9uHhLp^Et*L^ LIIEt H}p^L^ fffff.UHAWAVAUATSH8II)EH}L II_;(EIMLH fAnE1仑Annjn%gnW)Mt 1fo"HEf.HH/iȭ)iiG )ȹAB΍TWH*YXXHHH/iȭ)iЏiG )¹AB΍LWH*YXX(Y(YX.\.uQHMMuEo^}uW%bnnbnY^n^EQYY(E(MYXA}IIM9H8[A\A]A^A_]fUHAWAVSHIIAtAGAO%n nnb?pEWfDHH/iȭ)iҏiG )BTWH*HHH/iȭ)iiG )׺Bэ\:WH*:YX^XXHHH/iȭ)iҏiG )BTWH*HHH/iȭ)iiG )׻Bٍ;|;H*YX^XXf(Yf(YXf.fA.uu}EG^MYVn^EQUYAWAGYAYNAXf(H[A^A_]UHAWAVAUATSHHHOL8LA(HLqLQMMI?I=MIIfInJ1fD(-nfD(5nfD(=nf(nfD(nfD(%nf(-nf(5nfo=ªnfHǹHtL9}Lffffff.LZf\(nf]D(nfD_fA(fYnfXnfD: fA(fA(fY%nfAYfD\fD\fA(fYfD(fEYfEXfDYfDXfEYf(fAYfAXfYfXfYfAfXfA\fD^fEXfDXfrffpcfAYf_fALHHHHL9MI?MIM9gfInJfD(-nfD(5nfD(=nf(nfD(nfD(% nf(-nf(5nfo=#nHtIAL9}B ff.B Zf\(nf]D( nfD_fA(fYnfX&nfD: fA(fA(fY%nfAYfD\fD\fA(fYfD(fEYfEXfDYfDXfEYf(fAYfAXfYfXfYfAfXfA\fD^fEXfDXfrffpcfAYf_fC IM9LL)fInHfPRHhHHpHHHHPHLUHx`KLHMK *HLHMHJH)E1f)M@H`HEBZDf\f)Ey^f)E(Eh^(MHEBLHEBZf\Ef)E?^f)E(E.^H`(Uf(MHEBIHlHxt:MHEIZf\f)E^f)E(E^(MC HpH9hLUt9IJffff.BZ\P^CIM9uHĈ[A\A]A^A_]E1HxVfDUHAWAVAUATSHhIH-sHHEп8^HþH.^HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]QK6HxES6I=Q6I5J6I-R6HW)EHE(^HEHH(HMHxHLxL`LhHX HMH5^H}HU t8H5a_Hl8H5G^Hm8HAH5T_Hn8HHHrHHEHiHUHuHuIININ0IFIVIF@H}Ht H}^H]HEHtHGH^HrHH;EuLHh[A\A]A^A_]"^IH}HtH}^IH]HEHtHHs^L^ UHAWAVAUATSHhIH=rHHEпH^HþH>^HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]aH6HxUP6IMN6IEG6I=O6HW)EHE(^HEHH(HMHxHLxL`LhHX HMH5-^H}HU0q8H5q_Hi8H56bHj8HA H5d_Hk8HHHrHHEHHUHuHuIININ0IFIVIF@H}Ht H}^H]HEHtHWH^HrHH;EuLHh[A\A]A^A_]2^IH}HtH}^IH]HEHtHH^L蹾^ UHSHHHUrHHEHU1HH HtH@0@EHuHUHIH P`H讕CHrHH;Eu HH[]i^ffffff.UHAVSHPHIHrHHEEHEHEH}HuH}HuHOH}HuHH}HuHH}HuH_HuL# H]HEHtCt H{;^H3^HrHH;Eu LHP[A^]^IH]HEHtCt H{^H^L^ ff.UHAVSIP^HHrHHH{L+9HLsHH[A^]IHz^L谼^ UHAVSIP`^HHrHHH{L*9HHLsHH[A^]IH^LP^ UHAVSHIHrHHEA>urHH}HEHEI^IFHtACt H{^H^H]HEHt!Ct H{^H^HEIFH]rHH;Eu 1H[A^]^fff.UHAVSHIH#rHHEA>urHH}B HEHEI^IFHtACt H{^H^H]HEHt!Ct H{ʿ^H¿^HEIFHrHH;Eu 1H[A^]^fff.UHAVSHIHcrHHEA>urHH}B HEHEI^IFHtACt H{2^H*^H]HEHt!Ct H{ ^H^HEIFHrHH;Eu 1H[A^]F^fff.UHAVSHIHrHHEA> urHH}HEHEI^IFHtACt H{r^Hj^H]HEHt!Ct H{J^HB^HEIFHrHH;Eu 1H[A^]膾^fff.UHAVSHIHrHHEA>urHH}HEHEI^IFHtACt H{貽^H誽^H]HEHt!Ct H{芽^H肽^HEIFH]rHH;Eu 1H[A^]ƽ^fff.UHAWAVAUATSH8IIHrHHE1LHþLIžLIHCHK H)HtHH8IEIM H)HtHH8Mt%IGIO H)HtH%H8LuHLD0M]LD8EO0N^HEf(.pf)EHbe zero!HH Hcan not HHHperator HHH Range oHHHdelta inH@(HMLuLFCHH5_L}LHLLC`IH5_H]HLvLHC/IH5̐_H]HLELH谊CEt H})^HrHH;ELH8[A\A]A^A_]AA)A*A*^f: L,E1MMIٺ^HEHHHML(HMHU1LT9H<MLu0Iw1HL!fnfpfAnfpf8@ nfBfnfpHyHHVHs 1HutHzH)1fDfD fD0fD@fDPfD`fDpH fHuHt,HtHf.FffH HuI9t"DHDHDHL9rIH}H*H}跹^HH}HtH} HEt H}^HQ^ fUHAWAVAUATSH(HIHHrHHEH8H3rHHHLH@H8LL^HHEEL5rM~LI@L8LQ^L%rILHDž(HDž HDžHDžDž0Lͺ^HLH>HHnS;HHH8CLL8Lt H 趷^H薶^H5rHH%^H8?^HprHH;EuHH([A\A]A^A_]Ϸ^HLL8Lt H 0^H^HbHZHH5rHH芵^HH8蟶^H^ DUHAWAVAUATSH8IIHrHHE1LHþLIžLIHCHK H)HtH&H8IEIM H)HtH2H8(Mt%IGIO H)HtH9H8/HELMmLEgW.ESM0Ƶ^HEf(pf)EHbe zero!HH Hcan not HHHperator HHH Range oHHHdelta inH@(HML„CHH5 _L}LH#LL莄C`IH54_H]HLLH]C/IH5H_H]HLLH,CEt H}襴^HrHH;E7LH8[A\A]A^A_] n\EZMZ^f: L,1MII߿O^HEHHHMHHMHU1L 9HMEMHSكHraHH)f.X@X@X@ X@X@X@X@H XHuHtHHXHuIH}HH}dz^HH}HtH}HEt H}+^Ha^ fUHAWAVAUATSH8IIHrHHE1LHþLIžLIHCHK H)HtHH8 IEIM H)HtHH8Mt%IGIO H)HtH&H8LuHnHLcL0M]LOL8MO0^HEf(pf)EHbe zero!HH Hcan not HHHperator HHH Range oHHHdelta inH@(HMLuLCHH5s_L}LHvLLC`IH5_H]HLELH谀C/IH5_H]HLLHCEt H}^HrHH;ELH8[A\A]A^A_]AI)I*I*^f: L,E1MMI訰^HEHHHML(HMHU1L#9HKMLuPIw11HL!fHnfpDfInfpDfHnfsfoffs ffs ffK?fHnfpDHyHHVH s 1HuuHzH)1ffDfD fD0fD@fDPfD`fDpHfHuHt,HtHf.FffH HuI9t#LHHHHHLHL9rIH}H H}f^HH}HtH} HEt H}ʮ^H^ UHAWAVAUATSH8IIHrHHE1LHþLqIžLaIHCHK H)HtH(H8IEIM H)HtH4H8*Mt%IGIO H)HtH;H81HBEL1MoLEfWf.ESM0褭^HEf(pf)EHbe zero!HH Hcan not HHHperator HHH Range oHHHdelta inH@(HML|CHH5_L}LHLLl|C`IH5_H]HLLH;|C/IH5&_H]HLLH |CEt H}胬^HlrHH;E%LH8[A\A]A^A_] p\EM^f: L,1MII߿5^HEHHHMHHMHU1L9HMEMHSكHrWHH)X@X@X@X@ X@(X@0X@8H@XHuHtHHXHuIH}HH}跫^HH}HtH}HEt H}^HQ^ fUHAWAVAUATSH8IIHrHHE1LHþLIžLIHCHK H)HtHH8 IEIM H)HtH!H8Mt%IGIO H)HtH(H8LuHLD0M^LD8EO0 ^HEf( pf)EHbe zero!HH Hcan not HHHperator HHH Range oHHHdelta inH@(HMLuLyCHH5a~_L}LHdLLxC`IH5u~_H]HL3LHxC/IH5~_H]HLLHmxCEt H}^HrHH;ELH8[A\A]A^A_]AA)A*A*^f: L,E1MMI蓨^HEHHHML(HMHU1L9HMLu;Iw1HL!fnpfpfAnpfpfznfBfnpfpHyHHVH0s 1HupHzH)1@pfDpfDp fDp0fDp@fDpPfDp`fDppH@fHuHt,HtpHf.FffH HuI9t#DHHDfHDHL9rIH}HH}f^HH}HtH} HEt H}ʦ^H^ UHAVSHPIHrHHE螦^HþH蔦^HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H}11U8H(6H5^HHS8H5${_HFJ8H5bHwK8HH HQH5^HL8HHHrHHEH HUHuHuIININ0IFIVIF@H]HEHtHH觤^HrHH;Eu LHP[A^]^IH]HEHtHҳHb^L蘟^ UHAVSHPIH3rHHE>^HþH4^HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H}11S8HG&6H5^HHUQ8H5x_HG8H5bHI8HH HQH5^H7J8HHH&rHHEHHUHuHuIININ0IFIVIF@H]HEHtH跱HG^H0rHH;Eu LHP[A^]蘢^IH]HEHtHrH^L8^ UHAWAVAUATSHxIHrHHEпء^HþHΡ^HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H}11P8Hh#6Hp+6Hx&6I'6I*6I-6HW)EHE0U^HEHH0HMHpHHxHPLhLpLx HX(HMH5^HUHhL8H5u_HD8H5bH$F8HA H5^HKG8HHH:rHHEH+HUHuHuI $IL$IL$0ID$IT$ID$@H}Ht H}d^H]HEHtH賮HC^H,rHH;EuLHx[A\A]A^A_]莟^IH}HtH}^IH]HEHtHOHߞ^L^ ffff.UHAWAVSH8HIHrHHE1HIL}ؾHHEоHpHEIW1H8HEIG0@EH}HuHUHMLE7IH!rHH;EuLH8[A^A_]臞^ffff.UHAWAVAUATSHL(IIHHrHHEH D?Au#H3IUI $H(LHh 'A uTHPH3IUI $H(LAAuH}H3IUI $H(LF!A;H0H3IUI $H(LAIn^IHPDH5m^H s_HhLP7W)0HDž@@輜^H@(jBn)0HispatcheHH(HypeCallDHH Hs in MLTHHHate typeHHHr duplicHHHCheck foHf@0rAH_HhH0LE1H5rHѮrLc^#E1AAu H}H3IUI $H(L%$A u!H}H3IUI $H(Lu*IIIu'HrHH;EuHĸ[A\A]A^A_]^聛^IHP2DH5^H q_HhLP8\H5_H0H R AH _HhH0LE1H5rHrL;^ IALIAILIAIAZII0t H@苚^LeMt6HEL9t!HX@t Hxf^HI9uH}LLeL^Et H}=^ht Hx(^LPMt:HXL9t?HX@t Hx^HI9uHPLX^ELLI0t H@贙^LeMt6HEL9t!HX@t Hx菙^HI9uH}LLeu^Et H}f^ht HxQ^LPMt:HXL9tEHX@t Hx&^HI9uHPLX ^EtL:^L4^ LUHAVSIX^HH{PLMH&rHHHHL 9HrHHH[A^]IH臘^L轓^ DUHAWAVAUATSHHMrHHEW)EEIIHEEminMgHhHUL9HhHDžhHtCt H{^H^A$Et H}ڗ^W)EHEEEmaxHhHULLb9HhHDžhHtCt H{脗^H|^AEt H}f^AA.Gr'HCrHH;EuHĈ[A\A]A^A_]襗^A^IHh DH5l_H Im_H}LhHPKAHm_HuHPLE1H5rH}rL^ IA(IA}IIPt H`胖^LeMt6HEL9t!HX@t Hx^^HI9uH}LLeD^Et H}5^Et H}&^LhMt:HpL9t=HX@t Hx^HI9uHhLp^Et#L^LIEt H}踕^L^ fDUHAWAVAUATSPIIH}1L~HLk1LL8IHIL4#;El$PEd$TL"HI~#;HuڃH9HOII)LH?H>LHH?fAnfAnHrI H9H I9E1HzLtfoC_fo]BLHL9ff.foA_fo]foA_Tfo]\HH9uIIfofoIIHHDGAH"IHH)1fDA$(_(]$Ad(_(]dHHuMtA$_]L9H<I|AfAnfAnHfffff.A(_(])HH9|H9RfAnfAnI)ILI I9sLI L9MIfofoIHIIENAHHM\HtIIL)1ff.Ad(_(]dA$(_(]$HHuMtHA$_]M9twLlj)HPH@tfoA_fo]HOH9tCf.foA_fo]foA_Tfo]\HH9uHEHH[A\A]A^A_]1M'1M=Jffff.UHAVSIPБ^HH"rHHH{L8HsHH[A^]IH芑^L^ UHAVSIPp^HHrHHH{L8HsHH[A^]IH*^L`^ UHAWAVAUATSHMHIHrHHEHtHB H;BHZ\inH)0tHC H;CVH*Oo)@L IILN;LFHIL7;HuڃH9HOII)LH?H>LHH(0(@"HrI H9szH I9sqE1HzLt(C_(]B LHL9f(A_(] (A_D(]LHH9uII((IIHHDGAH IHH)1fff.A(_(]AT(_(]THHuMtA_] L9H<I|1((HfA(_(])HH9|H9>I)ILI I9sLI L9MI((IHIIEOAHHM\HtIIL)1AT(_(]TA(_(]HHuMtHA_] M9tsLlj)HPH@t(A_(] HOH9tAfff.(A_(] (A_D(]LHH9uH\rHH;Eu2HĨ[A\A]A^A_]1M9C1M9F融^:^IHhDH5@e_H e_H}Lh4W)PHDž` ݌^H`(o)PH scalar.HHHld be a HHHmin shouH@AHe_HuHPLJ E1H5`rH!rL賌^b^IHhDH5hd_H d_H}Lh8@W)PHDž` ^H`(Co)PH scalar.HHHld be a HHHmax shouH@AH e_HuHPLrE1H5rHIrLۋ^ IAIIAIIAIA2IuIPt H`.^LeMt6HEL9t!HX@t Hx ^HI9uH}LLe^Et H}^Et H}ъ^LhMHpL9HX@t Hx螊^HI9uIPt H`y^LeMt6HEL9t!HX@t HxT^HI9uH}LLe:^Et H}+^Et H}^LhMt:HpL9tEHX@t Hx^HI9uHhLp։^EtL^L^ LfUHAWAVAUATSHMHIHrHHEHtHB H;BHʜbnH)0tHC H;CH蚜anf)@L|IIL;LHIL;HuH9HOII)LH?LHHf(0f(@HrI H9H I9E1HzLtf(C_f(]B LHL9Eff.f(A_f(] f(A_Df(]LHH9uIIIHHHDOAHBIIH)1fffff.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]ffDL9H<I|/@Af(f_f(f]f)HH9|H9 I)IH IH9s H IH9rqMIITHtJ<1fffff.fTff(f_f(f_f(f]f(f]f\f$HI9uM9tu)HPH@tf(A_f(] HOH9tDff.f(A_f(] f(A_Df(]LHH9uHrHH;Eu"HĨ[A\A]A^A_]1MKv ^覅^IHhWCH5]_H ^_H}Lh4W)PHDž` I^H`(o)PH scalar.HHHld be a HHHmin shouH@AH/^_HuHPLE1H5̛rHrL^΄^IHhCH5\_H ^_H}Lh8W)PHDž` q^H`(o)PH scalar.HHHld be a HHHmax shouH@AHy]_HuHPLE1H5rHrLG^ IAIIAIIAIA2IuIPt H`蚃^LeMt6HEL9t!HX@t Hxu^HI9uH}LLe[^Et H}L^Et H}=^LhMHpL9HX@t Hx ^HI9uIPt H`^LeMt6HEL9t!HX@t Hx^HI9uH}LLe覂^Et H}藂^Et H}舂^LhMt:HpL9tEHX@t Hx]^HI9uHhLpB^EtLq^Lk}^ Lfffff.UHAWAVAUATSHMHIHrHHEHtHB H;BVH D(AHLHtHC H;CHD8ALIILa;HHHIHHC;HH rI I9LHI9E1HpL¨t#C DD8|DA8~C LHL9Rfffff.ADD8|DA8~AA\DD8|DA8~A\HH9uIIAfnff8Afnf8IHHHrHH~H)1fffff.AoAo\f8^ f.UHAWAVAUATSH(MMHMHUHH3HmHCLcLkHC H{8H3HLuL}H}LLMMwV%HCPHEHC`HCXHCPM)t3MILH=uOLNB^HCPN$Lc`HL=B^LcXH3HHHUHMMMbH([A\A]A^A_]H}:?^ IHEH8Ht H{XA^HEH8Ht H{@A^L<^ ffffff.UHAWAVAUATSHH}zrHHEMHIII6IFH)IIH)HI9*ML$IL$L)HI9M)IM9HIL$IBEAHs LMueLH|HH H\H|HH H\H|HH H\H|HH H\H@I9uMt/HtHDI1H^IHhbCH5w`H _H}Lh菸IFI+HHHL@H5,_H Ċ^L_HPHHL@WAHE_HuHPLںE1H5TrHPrLC>^ IAgIAIfIAIAPIHIAIAI*IPt H`x=^LeMt6HEL9t!HX@t HxS=^HI9uH}LLe9=^Et H}*=^Et H}=^LhMHpL9HX@t Hx<^HI9u]IPt H`<^LeMt6HEL9t!HX@t Hx<^HI9uH}LLe<^Et H}u<^Et H}f<^LhMHpL9HX@t Hx3<^HI9uIPt H`<^LeMt6HEL9t!HX@t Hx;^HI9uH}LLe;^Et H};^Et H};^LhMt:HpL9tEHX@t Hx;^HI9uHhLpk;^EtL;^L6^ LUHAWAVAUATSH8MLIHIHHsrHHEH8HbQrHHHHH@H8H{:^LHEEL- rrIEHHI@L8Hy9^L=qrILHDž(HDž HDžHDžDž0L<^HLHHH0H8^L<^HLHHH<^HHHxHH0H8^HHH HHL8Lt H 9^Hc8^H5]rHH7^H8 9^H=rrHH;EuHH8[A\A]A^A_]9^HHHL8Lt H 8^H7^+H(H HHHHH5\rHH87^HH8M8^H3^ UHAWAVAUATSH8MLIHIHH?qrHHEH8HNrHHHHH@H8H7^LHEEL-`orIEHHI@L8H6^L=orILHDž(HDž HDžHDžDž0L5:^HLH HH0H6^L:^HLHٽHH9^HHH踽HH0H5^HHHLHHL8Lt H 6^H5^H5ZrHH25^H8L6^H}orHH;EuHH8[A\A]A^A_]6^HHHL8Lt H 66^H5^+Hh H` HX HP HH HH5ZrHHx4^HH85^H 1^ UHAWAVAUATSH8MLIHIHHnrHHEH8HKrHHHHH@H8H4^LHEEL-lrIEHHI@L8H3^L=\lrILHDž(HDž HDžHDžDž0Lu7^HLHIHH0H]3^LE7^HLHHH$7^HHHHH0H 3^HHH茿HHL8Lt H 4^H2^H5XrHHr2^H83^HlrHH;EuHH8[A\A]A^A_]4^HHHL8Lt H v3^HV2^+H H H H H HH5FWrHH1^HH82^HK.^ UHAVSI`3^HHLH[A^]IH2^L .^ UHAVSH@HIHkrHHEH brHII~ 8H^rHIIFXIFPHEW)EHEEHexclusivHEfEeH}HUHMH٭8Et H}"2^H}uHEHwINPHW)EHEEHEHreverseHEH}HUHMHu8HEHEH]HEHtxCt H{1^H1^H]HEHtCt H{}1^Hu1^Et H}f1^H]Ht1HECt H{F1^H>1^&HEEuHEHwIFXHEHirHH;Eu H@[A^]j1^IEt H}0^H]HEHt&Ct H{0^H IEt H}0^L+^ ffff.UHAWAVAUATSHIH`IH`irHHE1LWxHHC H+CHpL2xIW)EHEH{ HH+{t[HHHH9 0^ILeLeIHEHHpHX H)H~LHa1^ILeHU1LV8HH}v:HHpHHHMLHHH_ HEHLcHH@JHW)PHDž`HHx H+xHHHH9 /^ILXLPIH`HHpHX H)H~LHX0^LHHXKHPq:HxW)0HDž@LPHXL)t\IIHI9 HT.^IL8L0KDH@H~LLH/^IL8W)HDž HH9LpL-^HHHH H5 nHLX/^HH`HxXLXHHYHxPHtHHxH$L0LHHHH HHHHW)HDžLpL-^HHLN4LHL,^LLHJW)HDžL,^ILKHLL,^HHKHxH$HHL0LHH L+,^HHt H,^HeLpfDH`HXPW)HDžL+^IHLxHHIDLHM,LLL+^LLLKW)HDžLj+^ILHIHLLI+^HLhO<W)HDžL+^HHHLrHII~x8H;rHIIFXIFPHEW)EHEEHexclusivHEfEeH}HUHMH虊8Et H}^H}uHEHwINPHW)EHEEHEHreverseHEH}HUHMH58HEHEH]HEHtxCt H{e^H]^H]HEHtCt H{=^H5^Et H}&^H]Ht1HECt H{^H ^&HEEuHEHwIFXHEHFrHH;Eu H@[A^]*^IEt H} ^H]HEHt&Ct H{ ^H IEt H}o ^L^ ffff.UHAWAVAUATSHIH`IH FrHHE1LUHHC H+CHpLTIW)EHEH{ HH+{t[HHHH9 ^ILeLeIHEHHpHX H)H~LH!^ILeHU1Ld8HH}6:HHpHHHMLHHşH_ HEHLcHH@JHW)PHDž`HHx H+xHHHH9 ^ILXLPIH`HHpHX H)H~LH ^LHHXKHP1:HxW)0HDž@LPHXL)t\IIHI9 H ^IL8L0KDH@H~LLHv ^IL8W)HDž HH9LpL ^HHHH H5mHL ^HH`HxXLXHHYHxPHtHHxH$L0LHHHH HHHHW)HDžLpL ^HHLN4LHL ^LLHJW)HDžLk ^ILKHLLQ ^HHKHxH$HHL0LHH L^HHt H^HeLpfDH`HXPW)HDžL^IHLxHHIDLHM,LLLf^LLLKW)HDžL*^ILHIHLL ^HLhO<W)HDžL^HHHLhCH5S^`H 9^H}LhkkIFI+HHHH@H5^H =^L^HPHHL@賵AH^HuHPLmE1H5rHrL]]IHhgCH5]`H z^H}LhjIFI+HHHID$I+D$HH@H5;^H <^Lh^HPHHL@覷AH%^HuHPLlE1H5rHrLR]]IHhfCH5\`H ^H}LhiIFI+HHHL@H5|^H <^L^HPHHL@觹AH^HuHPL*lE1H5@rHrL] IAgIAIfIAIAPIHIAIAI*IPt H`]LeMt6HEL9t!HX@t Hx]HI9uH}LLe]Et H}z]Et H}k]LhMHpL9HX@t Hx8]HI9u]IPt H`]LeMt6HEL9t!HX@t Hx]HI9uH}LLe]Et H}]Et H}]LhMHpL9HX@t Hx]HI9uIPt H`^]LeMt6HEL9t!HX@t Hx9]HI9uH}LLe]Et H}]Et H}]LhMt:HpL9tEHX@t Hx]HI9uHhLp]EtL]L] LUHAVSI`]HHLH[A^]IHc]L] UHAVSH@HIH0%rHHEHrHII~U8HrHIIFXIFPHEW)EHEEHexclusivHEfEeH}HUHMHig8Et H}]H}uHEHwINPHW)EHEEHEHreverseHEH}HUHMHg8HEHEH]HEHtxCt H{5]H-]H]HEHtCt H{ ]H]Et H}]H]Ht1HECt H{]H]&HEEuHEHwIFXHEH#rHH;Eu H@[A^]]IEt H}v]H]HEHt&Ct H{V]H IEt H}?]Lu] ffff.UHAWAVAUATSHIH`IH"rHHE1L1HHC H+CHpL1IW)EHEH{ HH+{t[HHHH9 ]ILeLeIHEHHpHX H)H~LH]ILeHU1L@8HH}w:HHpHHHMLHH|H_ HEHLcHH@JHW)PHDž`HHx H+xHHHH9 ]ILXLPIH`HHpHX H)H~LH]LHHXKHPv:HxW)0HDž@LPHXL)t\IIHI9 H]IL8L0KDH@H~LLHF]IL8W)HDž HH9LpLo]HHHH H5[mHL]HH`HxXLXHHYHxPHtHHxH$L0LHHHH HHHHW)HDžLpL]HHLN4LHLw]LLHJW)HDžL;]ILKHLL!]HHKHxH$HHL0LHH L]HHt H]HeLpfDH`HXPW)HDžLl]IHLxHHIDLHM,LLL6]LLLKW)HDžL]ILHIHLL]HLhO<W)HDžL]HHHLMLHIHHOHHHxHs 1HuuH_H)1fA)AD)DAD )D AD0)D0AD@)D@ADP)DPAD`)D`ADp)DpH HuHtHH@A)HHuMI)IHKH9sJIH9MIIPHHDxAHs1M|I|pHTpI_H)1DLDLDLDLDLDLD D H HuMt0IIIDIADA D H IuM9LD)ICH)HtHfA<u7HH]HtHt!I$I$M|$Mt$[A\A^A_]ÿO]HH]H5qH^qHp]fffff.UHAWAVAUATSPIIIt$IT$IFIFIMnI\$IF IFHt;~1HH>wL<L]Mt HIEIELmI^ Mn(I\$IF0IF(Ht;~1HH>yL<L輼]Mt HIEIEI^0AF8fAF<I$It$IT$I9vuI9VtLIvIVIHII?I>IMIHIIOHHH~Hs 1HulHOH)1()(D)D(D )D (D0)D0(D@)D@(DP)DP(D`)D`(Dp)DpH HuHt$HHfff.(3)0HHuIM)IJ H4H9sH J4H9MIIHHHD^AHs1MN|pJ|pIKH)1ADALDLADALDLADALDLADA D H HuMt6IIILIff.D  D H IuM9MĉD)HJL)HtH@Bj]Ht] fff.UHAWAVSHXIHqHHEI~!L}L}H}HuHUfA~G8AG8I_I9_0uMG(MI(]H~4HH>L4L+]IMt MwMG( IG(E1I_0IIIwH$LMHE$AGL4L袷]Mt HIGIGI_ IGHs1fAAHL)Hxfommfo umfo}mHf.fofGfH HuMtfff.HH9uH~,IGIO(HDHcTt<|4HHAG=H qHH;Eu HX[A^A_]艵]%]HH]H5qH4qHF]H"] f.UHAWAVAUATSPHBHHHAHHMII?MI>IL7IFM~HHEI>L@LHT mHLHJH@(mHff.BTBTXT$0HTXXXHH9|HHHHEH9t"LMrMtMMAT umI~IRAvIFHH&RWWBH9)I)Ht8HIHJH(mTXHHHuIrhHUH)LAMLHHYIHIHI(mTX TXTXB TXHHuLHHEHLuL} I=IHEHHUI>L@LHGT mHLHJH@(mH@B\TBdTX\Tl0HTXXXHH9|HHHHEH9WHHHL @LH)(kmLTX TXB TX ;HTXHH9uHt1HIHH(mTXHHuHUH AFINHMЃMI)HEHHEȻ1A(mLUMt+M:ATIIzH}stWHHHMGH L MTLC\TXCTXCTXBTXHII9uH}HULUt@HHIHHMffff.TXHHuffff._HHI(H9H9)I)Ht9HIHDJH(WmTXHHHuIrlHUH)LAMLHHYIHIHI(m\TXDTX\TXBDTXHHuLpIH}ZHMLiL`IIEHHHhJJ4 MIIx HD[ HPN N40MH0L\[0N<0(?m1HE HXLeH]HHEHTIHEHxIԹHPD$0TB,TX$T48TXXXHLL9|HhLHM0ff.WW_(ffffff.H9H})L`I)Ht.HMHHHEH H@!TXHHHuILeI)HFHUHUHHMHH}HHMHHFHHHHHHH}HMLHM"Ir5HpHXHK >ILH9JH;1LIM^I)LHt?HHHcHI\H  HHuILLLqHA HcHHH t4TTttTTttTT 4t HI9uHLHHpHHDiAHHA4HcHHH4I]H)1fADALT\ATA\DLADA TATAD 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ALLALLAL L ALLALLALLALLHI9uL8H}HXCDH0E11HH`Hu1HXHE~HXDHHHHHHHb;HHH8H HH9HNHx4HEH$D@HED;0W)EHEL`LhM)tOLHHH9OLLZIL}L}IHEM~LLL踃ZML}1HXHU4HH}Ht H}ZE1fff.W)EHEL`LhM)tOLHHH9L蠁ZIL}L}IHEM~LLL ZML}HHuHULHDžH}Ht H}/ZLHxHHHpHH7HpH HHLI2HL(H H9 HeHHHH HHHpE1LI2LIHHEHLxHHIHHxHHHLHLI;u\HHH)HHrAfff.I4HHHHI;uHHH9r@HEHhHXHL I1IHHHHXLLL8M;uSHHH)HHr8I4HXHHHI;uHHH9r@LMMM@uAAAAM9MOHpJHHxL,LhHM$M)MI?I>MIMGI1IGH)LHt%HADAXHHuHHID HHxHL ffff.DXDDXDDDXDDXD HI9u}ffff.J HxIHHHIIH9@I9HHPH9I9@ HPLHHBHHDNAHHHxHL0HhHH\0IAH)1DDLTXDXDD TXXT D0HHu1HP1MHPt&ADALAXADXDI9Lhffff.LO>IIOI9IMHL)LHt%HL@ADA X) HHuH rmHID0HHxHL0DLX) DLX)LDLX)L  X)L0HL9|HHHPL;CM)IHD)H L)Ht'HfCDCXBIHuHHHID HHxHL DBDBXDBBDBXDBDBDBXDBDBBXBD IL9uaLMHpNJ HxI7LHHI41HHHIMHhJI4LhI4H;AL9AH9L9E MIIHHHDxAHIwH)J1fLXDXDD L0T XD0XT D0HHuMt)LADALAXADXDM9u'fffff.HPH;H M1MufDH HHHHHHxHpE11LI2LILLELxHLILHHLHLI;u]HHH)HHrBffff.I4HHHHI;uHHH9r@CLMLXHL I8ILHXLLL8M;u^HHH)HHrCfffff.ILHMqIrKD=II9KI91IEH)LHt!HA X HHuHKD f.LX LXLLXL XL HI9uLhLH(HFHHDBAHtUKD0MXI)1fT\XX\TXXT 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aHH(Hfloat, fHH Hrue for HHHust be tHHHribute mHHHfmod attH@?AHvZHpH@LE1H5ԈmHmL'rYIAgIAIoIAIAVIQIAIAI3I@t HPYqYLeMt6HEL9t!HX@t Hx4qYHI9uH}LLeqYEt H} qYpt H}pYLXMH`L9HX@t HxpYHI9ucI@t HPpYLeMt6HEL9t!HX@t Hx|pYHI9uH}LLebpYEt H}SpYpt H}ApYLXMH`L9HX@t HxpYHI9uI@t HPoYLeMt6HEL9t!HX@t HxoYHI9uH}LLeoYEt H}oYpt H}oYLXMt:H`L9t=HX@t Hx^oYHI9uHXL`CoYEt1LroY'LIptH}oYILCjY ff.UHAWAVAUATSH8MIHIHHmHHEH8HmHHHHH@H8H2nYLHEEL%ץmID$HHI@L8H/mYL5mILHDž(HDž HDžHDžDž0LpYHLHHH0H1L{pYHLHOHH0H蹙1HHHHHL8Lt H ZmYH:lYH5WmHHkYH8lYHmHH;EuHH8[A\A]A^A_]smYHHHL8Lt H lYHkY#HBHBHBHBHH5mHHkYHH8,lYHgY fUHAWAVAUATSHxH=mHHEHHIHHOHVHH$HH HxH HH9HNHEx|HEHh|HEW)HDžL}H]L)tUIILH=aHkYILLKHH~LLH mYILH1L3HHHt H9kYH HEHHILHH5IHHHCH}Ht H}jYHxHt H}jYH`Ht HhjYHHHt HPjYH Ht H(zjYHHt HbjYHHt HJjYH3mHH;EuHx[A\A]A^A_]jY1jYIHx>H5jZH ZHLxp W)`HDžp iYHp(_i)`Hrst outpHHHo get fiHHHfailed tH@ut!AHyYHH`L8E1H5NmH|mLiY HfY IA+IAII`t HpiYLMt?HL9t$HX@t HxhYHI9uHLLhYt HhYt HhYLxMt:HL9t=HX@t HxdhYHI9uHxLIhYEt3LxhY)LIHHtHhYIH}Ht H}gYHxHt H}gYH`Ht HhgYHHHt HPgYH Ht H(gYHHt HgYHHt HrgYLbY UHAWAVAUATSHxH=mHHEHHIHHOHVHHHH HxH HH9HNHEHzHEH8zHEW)HDžL}H]L)tUIILH=aHfYILLKHH~LLH hYILH1L3HHHt H9fYH HEHH%ILHH5IHHHQTH}Ht H}eYHxHt H}eYH`Ht HheYHHHt HPeYH Ht H(zeYHHt HbeYHHt HJeYH3mHH;EuHx[A\A]A^A_]eY1eYIHx>H5eZH ZHLxp W)`HDžp dYHp(_h)`Hrst outpHHHo get fiHHHfailed tH@ut!AHtYHH`L8E1H5N{mHwmLdY HaY IA+IAII`t HpdYLMt?HL9t$HX@t HxcYHI9uHLLcYt HcYt HcYLxMt:HL9t=HX@t HxdcYHI9uHxLIcYEt3LxcY)LIHHtHcYIH}Ht H}bYHxHt H}bYH`Ht HhbYHHHt HPbYH Ht H(bYHHt HbYHHt HrbYL]Y UHAWAVAUATSHH=mHHEH IH=HcH>IH5mH6H;uHHHHĈ[A\A]A^A_]nb>IH5ɚmH6H;uHHHHĈ[A\A]A^A_]>IH5mH6H;uHHHHĈ[A\A]A^A_] 8>IH5KmH6wH;uWHHHHĈ[A\A]A^A_]Q>IH5 mH6aH;uHHHHĈ[A\A]A^A_]>IH5͙mH6tH;uHHHHĈ[A\A]A^A_]T>IH5mH6ZH;uHHHHĈ[A\A]A^A_]T>IH5OmH6@H;u[HHHHĈ[A\A]A^A_]H;u2HHHHĈ[A\A]A^A_]H;u HHHHĈ[A\A]A^A_];H;uHHHHĈ[A\A]A^A_]ڮH;uHHHHĈ[A\A]A^A_]顎H;uHHHHĈ[A\A]A^A_]dH;uuiHHHHĈ[A\A]A^A_]#XH;uuDHHHHĈ[A\A]A^A_]H;uuHHHHĈ[A\A]A^A_]I^_Y^YIHh>H5KYH ZH}Lh8H5rZHPHҁAHZHuHPLUE1H5kumH,qmL^Y IA(IA}IIPt H`2^YLeMt6HEL9t!HX@t Hx ^YHI9uH}LLe]YEt H}]YEt H}]YLhMt:HpL9tEHX@t Hx]YHI9uHhLp]YEtL]YLXY LKGUHAVSIP0]YHHmHHH{L3H0mHHH[A^]IH\YLXY @UHSPHHHH[]DUHAWAVAUATSH8IHHmHHE1LzHþLjHHHHsHPHH HxH HH9HNHElHEHlHELuW)HDžLeH]L)tdLIILH=H[YILLKDHHL~LLH/]YILH1L3HLHIIM Is1Nfffff.H]J K IHuI44H]H4H9AI9H9I9@A@ LMH]LHHQHHDvAHtOL0HUH]LT0H]IVH)1DADALATXADXDADA ATXAXT D0HHuMt%AALAXADXDI9LULMu1LMH]IT$H)LHt(Hfff.AAXHHuHroHUHuHT Kt ffffff.DXDDXDDDXDDXD HI9uOt%HIt$I9IMHL)LHt+HLff.AA X) HHuH rnHUHuHT0Kt0fffff.DLX) DLX)LDLX)L  X)L0HL9|M9L)HUH]JK LI4K M HMHIMLUK JM$L9AH]H9AL9H9E H]HHSIIEiAHH]JT0I]L)E1LUf.BBLBXBDXBTBDBD BL0BT XBD0XBTBIHuMt*MCCLCXCDXBBDHMH9MLMILULMDD)IHL)HtHڐCCXBIHuHrzHMHUHL KT fffff.BDBXDBBDBXDBDBDBXDBDBBXBD IM9uHuHH;Fe 1M!E1LUMHH;F 1f.L_(ILvJ HMHLLH_LW`ILHW`H LH L@I;uWHLgI)IIr?f.IHW`H4HH4I;4uHHL9rCLLHOhLIILHL L LM;uNHLOpI)IIr6IHH4HH4I;4uHHL9r@MuAAAAM9MOO MM)MI?I>MIMoIrKLI H9J I91IMH)LHtHDA X HHuHKL f.LX LXLLXL XL HI9uLH(HJHHDfAHKL0I\$H)1ff.T\XX\TXXT \0HHuMtAA\XX\I9fff.K/I(IMH9HMHL)LHtHL@AX)HHuH r@KL0TX)TX)TTX)T X)T0HH9|L9sM)IH MMO,K42I4H9s L;mMI(It$HHDyAH<H\0IwH)1ɐATA\XXT\AT A\0XXTHHuMtHAA\XX\M9LD)INH)Ht%Hff.A X HHuHrZKL fffff.LX LXLLXL XL HI9uHuHH;F1M1M&H9ucfL_(ILvN LHHMLH_LW`ILHW`H LH L@I;uWHLgI)IIr?f.IHW`H4HH4I;4uHHL9rLLH_hLI$ILHLLLM;EuRHLGpI)IIr:DIHH4HH4I;tuHHL9rCMuAAAAM9MOLeOMM)MI?I>MIMuIrKLI H9J I91IMH)LHt%Hff.A X HHuHKL f.LX LXLLXL XL HI9uLH(HJHHDfAHHMJL0I\$H)1T\XX\TXXT \0HHuMtAA\XX\I9LeK/I(IMH9HMHL)LHtHL@AX)HHuH r@KL0TX)TX)TTX)T X)T0HH9|L9M)IH MMLN$K42H4H9sM9s LeMI(IqHHDyAHSH\0IwH)1ff.AA\XXT\AT A\0XXTHHuMtHAA\XX\M9LeLfffff.D)INH)Ht%Hff.A X HHuHrZKL fffff.LX LXLLXL XL HI9uHuHH;F 1M|1M[A\A]A^A_]ff.UHAWAVAUATSHIIHGmHHEIF@IF8IF0IF(IF IFIFIFIIFHIFXH0IdžIdžIdžIdžIFxIFpIFhIF`IFXIFPIdžMIdžIdžIdžIdžIGI+HHJHH+ HH9HHBH9HCH Ht LHI~HHI~0HHyI~pHHfIHHPHL@MoHH@MH L8DIHIAIIH(HIIuH(L0L9HILH]I9HINHu H1I$L; sH|H8HqH0LH_L; !H(ff.IuH(LqL9HILH]I9HINHu H1I\$H}t!H8H H0LH IH(HIIL; xH(M9/tBI]LHHHxHDž`IFxI;sgHHIFxiHH(H9LyLxHDž`IF I;F(HHIF H0H`HII;tHxHHIHxHII\$IlHDžxHDž`IF I;F(HHIF H`HH0IF8I;F@tHxHHIF8HxHMHLLH(M|$IH`HIF8I;F@tHxHHIF8HxHHDžxHDž`IFxI;sHHIFxH`H.II;tHxHHIHxHdAL; H(IDI\$IIHI9s{HHAH+HH9u1I^H8H H0HH I롐I]H8HHj H0HV ImL8IF8I+F0HHDžxIIVH)HHH)vHxLJ sHIFII+HHDžxINXIV`H)HHH)vHxH0 sHIF`HlymHH;EuH[A\A]A^A_]@Yj@YIH`>H5DZH 1EZHxL`EH5EZH EZHHHUAHSEZHxHHL跼E1H5VmHRmL @Y?YIH`耶>H5?DZH DZHxL`誹H5DZH DZHHHU,AHDZHxHHLE1H52VmHQmL?Y}HA+HAHHHt HX>YL}Mt6HEL9t!L`@t Hx>YLM9uH}LL}>YEt H}>Yxt H}>YL`M HhL9L`@t Hx`>YLM9uHA+HAHHHt HX >YL}Mt6HEL9t!L`@t Hx=YLM9uH}LL}=YEt H}=Yxt H}=YL`Mt:HhL9t=L`@t Hx=YLM9uH`Lhz=YEt*L=Y LH HHL8L@I<$Ht I(=YIHt I=YI~pHt I~xHt I~H5G@ZH 6AZHpLX_貵H5pAZH AZH@HULEAH.AZHpH@L E1H56RmHMmL;Y IA+IAII@t HP:YLeMt6HEL9t!HX@t Hx:YHI9uH}LLe:YEt H}:Ypt H}:YLXMt:H`L9tEHX@t Hxo:YHI9uHXL`T:YEtL:YL}5Y LUHAWAVAUATSH(IHIHHrmHHEH8H`PmHHHLH@H8Ly9YLHEEL=qmMgLI@L8L~8YL-pmILHDž(HDž HDžHDžDž0L;YHLHοH3H7YHH;YHHH螿HHHHLL8Lt H 8YH7YH5\mHH57YH8O8YHqmHH;EuHH([A\A]A^A_]8YHLL8Lt H @8YH 7YHrHjHbHH5 \mHH6YHH87YH%3Y ffff.UHAWAVAUATSHHpmHHEHuHHUHt H9H{HC HHH|H~H;C(t9HKHH~m1H+KHHpH;C(s HHHC Hp` HsHHDžpHC8H;C@sHHHC8H{0Hp HC8HHHMHHHCHHEHCHHomHH;EuHĘ[A\A]A^A_]47Y6YIHX聭>H5@;ZH I)MIIHH=6HI)LHHH9H]H}wtII9LBMI9v^4YHH5uYH1YHYlmHHH5+MmHImH4YH4LV4YIDIF~IJ<54YN$HEJHEL}J4L4YO$MH]~H}HL5YHEIMfHEIFHtHH([A\A]A^A_]3YH([A\A]A^A_]E11yL0YIH3YL.Y ffff.UHAWAVAUATSPIHL3HCL)HL9sjLH=upLcM)J<@3YIKDMMM~LLLI4YLL+L{HCMtLH[A\A]A^A_]2YH[A\A]A^A_]ÿ2YHH5sYH40YHjmHHH5oKmHHmH2YIH2YL-Y ff.UHAWAVAUATSH8MIHIHH6kmHHEH8HHmHHHHH@H8H1YLHEEL%WimID$HHI@L8H0YL5imILHDž(HDž HDžHDžDž0L+4YHLHHH0H%0YL3YHLHϷHH0H/YHHHcHHL8Lt H 0YH/YH5TmHHI/YH8c0YHimHH;EuHH8[A\A]A^A_]0YHHHL8Lt H M0YH-/Y#HHwHoHgHH5%TmHH.YHH8/YH*+Y fUHAWAVAUATSHIIIuMeI)MIIGHH=HMuI)LHHH9waII9LBMLEHuI9vKt/YHH5rpYH,YH+gmHHH5GmHDmH/YLEHuIJ</YHHuLEN4IJN|M~HLI~0YLI]M}MuHtHH[A\A]A^A_].YH[A\A]A^A_]E11L+YIH.YL)Y ff.UHAWAVAUATSHIIIuMeI)MIIGHH=HMuI)LHHH9waII9LBMLEHuI9vK.YHH5oYHR+YHemHHH5FmH.CmH.YLEHuIJ<-YHHuLEN4IJN|M~HLI/YLI]M}MuHtHH[A\A]A^A_]F-YH[A\A]A^A_]E11Lp*YIHY-YLS(Y ff.UHAWAVAUATSHIIIuMeI)MIIGHH=HMuI)LHHH9waII9LBMLEHuI9vK,YHH5mYH)YHKdmHHH5EmHAmH,YLEHuIJ<:,YHHuLEN4IJN|M~HLI-YLI]M}MuHtHH[A\A]A^A_]+YH[A\A]A^A_]E11L)YIH+YL&Y ff.UHAWAVAUATSHILgLoLL)HH9LM)MII6HH=HM)LHHH9H}LEII9LBM/HuI9x +YHH5lYHW(YHbmHHH5CmH3@mH+YLHHIGL9vILHL9IIIPHHAHA~fpDHPH)1AADAD AD0HHuHtA~fpDAADI9t{KHL)LCHHtHIHHHHuIrIIHIHPIHPIHPIHP IHP(IHP0IHP8H@HuIHGHuIJ<)YIHuO LHHIOL9vILHL9HHHHHHDBAH^A~fpDK|0IHH)1fff.DDDHHuMtA~fpDAADH9tI HH)HZHHtH@IHHHHuHrIIHIHAIHAIHAIHA IHA(IHA0IHA8H@HuO4MMIMIs1nfffff.LMJ HUIIHuI44H]H4H9AI9H9I9@A@ LMLHHQHHDvAHHUML0HUH]LT0IVH)1DAoDAoLAoTfAoDfDAoDAo AoTfAofT D0HHu 1LMO1MLMt-AoAoLAofAoDfDI9LUf.IT$H)LHt"Hfff.AAHHuHrMHUHuHT HuIt \\\\\\\\\ HI9uOt%IIt$I9IMHL)LHt'HLAoAo ff HHuH rzHUHuHT0HuIt0fDoDoLff oDoLffLoDoLffL oo ffL0HL9|M9M)IdLMJH]I LI4J M HMHIMLUK JM$L9AH]H9AL9H9E H]HHSIIEiAHH]qJT0I]L)E1LULMfBoBoLBofBoDfBTBDBoD BoL0BoT fBoD0fBTBIHuMt2MCoCoLCofCoDfBBDHMI9ILUDD)IHL)HtHfC4C4B4IHuHrXHMHUHL HUIT BtBtB4BtBtBtBtBtBtB4B4Bt IM9uHuHH;Fm= E1LULMM1HH;F 1fffff.L_(ILvJ 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HI9uHuHH;F#1LHq1M4[A\A]A^A_]UHAVSIPXHHR*mHHH{LOd3H$mHHH[A^]IHXLX @UHSPHHHH[]DUHAWAVAUATSH8IHHS3mHHE1LJBHþL:BHHHHsHP訲HH HxH HH9HNHEHEH HELuW)HDžLeH]L)tdLIILH=HXILLKDHHL~LLHXILH1LP3HLH ^ILHH5IHHHt HXHHHHH}Ht H}XHxHt H}XH`Ht HhXHHHt HPXH Ht H(kXHHt HSXHHt H;XH$1mHH;EuHH8[A\A]A^A_]XHMX H HHHHt HXH}Ht H}XHxHt H}XH`Ht HhXHHHt HPXH Ht H(mXHHt HUXHHt H=XHsX ff.UHAWAVAUATSHpL(IHLLI>LH^L HLH I9 HHMLoH_HW@L)HL_hHOpL)HH(HuLFM`IEL$DƃLHHALIILeL)HHPHHuHLHHuHw`HuLMK4H`J4HhIpHXJ4H0Iq0HHq0HHuH@HIHpIIHx1HMH8HULe*LIHELL H8HUHMHIILO`IEHIEH;uKIEHr=M LO`ILHILH; uIDHH9r@HuHI6HMHHHEHHIHII;$H(uCIHr6I4HI HI I; uIHH9r@H0IHEMHu6HEHH@HML4IsE1offffff.LHEM<HHHN$H`HHhH4H9AL9H9M9AA HuLeH}HHHHUHH1foDoLoTfoDfAADoDo oTfofAT AD0HHuHt-ooLAofAoDfAADHII9HuuE1HuLeHXL)HPt HMKJKIHuHrhHpHMHHxH fff.JTJTKJTJTKTJTJTKTJJKTIM9uHLUI9`1H LHFH I9;HHEH_L_HG@I)ILwhLopM)IHuL~HIGHD@DLHHpALEIPHhH)HxHMHHHHHHEHH0HHEHG`JHXIWH@JH(HQ0HUHMHIH`1HLP1L]HuL8&HHHMLL8HUHIILg`H HH H;uBHIr5HL$Lg`H HH H; uHHL9rIHML LHEHHUHLHMLHIHIH0H;uIIIr<HMHHI HI H; 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uGHIr:DMLH HH I; uHHL9r@HIDHMHHLxIs1fff.LHHH CH(HHHX@t Hx,VHI9uH}Le,VEL,VLIIIH}Ht H}C,VHhHt Hp+,VH@Ht HH,VH(Ht H0+VHHt H+VHHt H+VHHt H+VHHt H+VH}Ht H}+VL&V UHAVSIPp+VHHZjHHH{L返0H(ZjHHH[A^]IH&+VL\&V @UHAWAVAUATSHIIHcjHHEоLrHH H+HH HHH5IH菸2IffEHEM J<HHHH91 *VKLHEHEHHULIJ4HHHrH9JL9HLODI)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)IJDHE1L}pHHHHUHLXHHH@H HH9HNHEHELuffEHELhHpL)t^LIIHI9H'VILmLmKDHEHL~LLHP)VILmHU1LE0IL:HIL[2IH}Ht H}n'VJHLIHML]HLLI)ILK H(INH@IIAPDLHHPHJL)HpHHHH`HHH LINHJ HIKpHxIKHhLIK H01IL8LfDIHLHMHIILHHI; uGHIr:DMLH HH I; uHHL9r@HIDHM0HLIs1fff.LHHH H(HHffEHE@ VHEfo'gfEHis a shaHH0Hor data HH(Ht's tensHH Hnal as iHHHdimensioHHHst be 1 HHHShape muHf@8pe@:AH'+WHHML:E1H5P6jH2jLVH}V H}{V IA%IAIIEt H}VLMt?HL9t$HX@t HxVHI9uHLLVt HVt HVLeMt1HEL9t>HX@t HxjVHI9uH}LeUVELVLIIIH}Ht H}VHhHt HpVH@Ht HHVH(Ht H0VHHt HVHHt HVHHt HVHHt HlVH}Ht H}ZVLV UHAVSIP@VHHLjHHH{L菆0HLjHHH[A^]IHVL,V 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VLK'HLMMLM$LMHHLH)HHK1HPIFH`IIAZDLHHpHCL)HHhHHH8HHHIFH@IApHL0IAHX1LHMHLMHELMILHHI;uDHHr7fM LH4HH4I;4uHHH9r@OT5MH0L$I s1Jf.LIHHH4HPH<H9sLHI9s 1LH``s1LHhHH<H1Lffff.D7L7AD5AL5D7L7AD5 AL50D7L7AD5@AL5PD7 7AD5`AL5pHHuHht5HHf.AD4A 4AD5AL5H HuHpHI9HfDIH@H)H8tHHfA4A\5HHuHHLr>HXD1AD5D1AD5D1AD51AD5HI9uL;M1Hf.LIHLUHEHLHLHHHLI;u]HHH)HHrBffff.I HHHHI;uHHH9r@J7M~ C4 LVH;=HxHHhHt HpVH@Ht HHVH(Ht H0VHHt HgVHHt HOVHHt H7VHHt HVH}Ht H} VH>jHH;Eu"HHĨ[A\A]A^A_]J4K<HVVIH}|;H5XWH GWHLEW)EHE@VHE(gg)EHis a shaHH0Hor data HH(Ht's tensHH Hnal as iHHHdimensioHHHst be 1 HHHShape muHf@8pe@:AHWHHMLŁE1H5jHjL.VH}V H}V IA%IAIIEt H}VLMt?HL9t$HX@t HxdVHI9uHLLDVt H/Vt HVLeMt1HEL9t>HX@t HxVHI9uH}LeVEL VLIIIH}Ht H}VHhHt HpVH@Ht HHoVH(Ht H0WVHHt H?VHHt H'VHHt HVHHt HVH}Ht H}VLU UHAVSIPVHH"2jHHH{Ll0HP1jHHH[A^]IHVLU @UHAWAVAUATSHIIHG;jHHEоL;JHH H+HH HHHJ IH2IffEHEM J<HHHH9 VKLHEHEHHULIJ4HHHrH9JL9HLODI)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 HuL9M)IJDHE1LGHHHHUHOL`HHH@H HH9HNHE3HELuffEHELhHpL)t^LIIHI9<HLUILmLmKDHEHL~LLHVILmHU1LV0IL4HIL軌2IH}Ht H}UJ{HLI$HiMLEHLK pH8INHHIIAzIVHDLHHXIHpHxHOL)HHPHHhHHH0K 6HIHHpLIHH@E1Ifffff.I$IMMHUHIMLHHHH;u\HHH)HHrAfff.MHX@t Hx:UHI9uH}Le%UELPULIIIH}Ht H}UHhHt HpUH@Ht HHUH(Ht H0UHHt HUHHt HlUHHt HTUHHt H0HH}Ht H}UH(HHH8AIL8HHbt2IH@HjHHpLpLeL2t2HuiL.eu z Hnj.eu@z>HbjHHEL}HpL/2H}I9t HtHP(HP H}HtI9tHPH`!HDž`H@H`HPHPH8H@H#H`H9t HtHP(HP H}I9t HtHP(HP HHt H=UHHt H%UHHt H UHHt HUHHt HUHxHt HUH`Ht HhUHjHH;EuHĸ[A\A]A^A_]UH}U (&EIH`H9uHP Ht HP(IH}I9uHP )Ht$HP(IH}HtH}UIHHt HUHHt HUHHt HUHHt HUHHt HUHxHt HUH`Ht HhlULU f.UHAWAVAUATSHIH*jHHEHPLXH(H`HMuL菛HPHHH HH9HNH(;H0HXH8W)EHELHL)t]L0MIILH=HUILeLeKHEHML0~LLHUILeL}1H(L90HH}Ht H}UH(HHHHjHHEL}HpLM:H}I9t HtHP(HP H}HtI9tHPH`!HDž`H@H`HPHPH8H@H/H`H9t HtHP(HP H}I9t HtHP(HP HHt HUHHt HUHHt HkUHHt HSUHHt H;UHxHt H#UH`Ht Hh UHjHH;EuHĸ[A\A]A^A_]VUH}#U (&EIH`H9uHP Ht HP(IH}I9uHP )Ht$HP(IH}HtH}yUIHHt HZUHHt HBUHHt H*UHHt HUHHt HUHxHt HUH`Ht HhULU UHAWAVAUATSHHjHHEHUIL(IHH}L<LI8M $M\$|M9HHXHwLwLW@I)IHH`HOhLHWpH)HHULhIT$H LH_`JHULL(H0L8L@HHLPLxUfff.LIHIH}L(H0L8L@HHLPLxIHHHpHHW`HLHI;LMu]HIL]r\IIHW`HHHI;u*HHL9Lrffffff.L] LHHIIHHH1LH1I;uuBHIr5IHHHHI;\uHHL9rH LmILHMMHXHpL$H`HEu@WA*$W* U,AEM|$HIH}u}ffff.MH}tfHxM4E1WC*'WB* #wU,CD%WC*D'WB*L#UU,CD%KD'IL9uHHUH9HhRM4$ID$HpI9HHxHwLWH_@I)IHHHLHGhLHOpH)HHMHHMH HPHXHH`LhIL$HLG`1HuL H(L0H8L@:fLIMH}HuL H(L0H8L@HUHIMLo`HHHH; uHHILer;fM,Lo`HHHH;uHHL9r DLeHxFHIHuHHHHH0HH0I;3uDHIr7fIHHHHI;uHHL9r@HIHH}HHL<WA*EHXtQH`MWA* $EU,AIIHuHP s f.MHP HEIE1@WC* <E[U,C>WC*L<E?U,CD>WC*L<E"U,CD>WC*L< EU,CD> KD<IH9uLL;pHhHpHM9ufff.L(IIL$LMMM$LLH_LW`ILHG`H3LH3L_@I;3uUHHwH)HHr=IHG`H HH I; uHHH9rOO LLHOhHLI?ILHL)L)HL;(uYHHUHRpH)HHr=I*EMfM)DHt8Hfffff.WA*EMU,AIIHuI 1ffff.WA*DMU,AWA*DMU,ADWA*DMU,ADWA*D MU,AD IDHL9{H[A\A]A^A_]fff.UHHNH1H; {iHD]DUHHi]UHAVSH0IHH jHHEI9H{ H9tIF I9t0HC I~ M9v tAHLPH{ HP IF HC |HHHQI~ HP HC IF H[ \HHuPH{ HP HC I~ HHPI~ HP IF H[ HEH}LPHEH}P Mv H7 jHH;Eu H0[A^]UHpHhUHUH 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HEJHEJL}E1LMf$^$$^$ILLLLIuE1HxLMLhtMB$^C$HMHH;MHM@II9H0[A\A]A^A_]DUHAWAVAUATSHHIH1HeIMw1HL!HEI}PIw I+wHeHIGL$L$HLHMIIHsL$HEIEXHL0"H]HMLhIH譴HEMHEHHAHQHUHqH~HPÃHAHUHD HHHHEIIHRIHXH)H@H8HH]HHHEH}HGHuH)H (dWWL<K&HEIH`IHhHEJ H(LE1Luffffff.LHH}H}MI)HEHLIM ATHH}H@Ls HMN4HXJ H`N,HhN A\TXATXTXATXHMLMMH9uH8L0LuIt2H}HLI Hufff.TXLHu.uH}HMH4Ht[9HH Hc@\EH9|ÃH0JHE11ffffff.LHAHHHHpH4HHhGxGH @\DAHL8ItD(HL=~[LvHcItD(PHLUIFIL;xIƋGHHH;LLLH [TMHLhH @T) (2W)`Ht@HpHE(uG( WQWH [HHH0HHHHH@HHH HEMLLHtH`HMLHl],H1HHH}HEH9t HtHP(HP HHH8tMtILH8PHHt#HHCHuHHPHsGH0tMtI$LH0PMLHIFHILPLRsGtGRtGIH`,H5aIIH IIH}L`90H@(AHIIHuH@LE1H5͊[H[L tG IA(IA}II@t HPsGLeMt6HEL9t!HX@t HxosGHI9uH}LLeUsGEt H}FsGEt H}7sGL`Mt:HhL9tDHX@t Hx sGHI9uH`LhrGELsGLHIHIMIH}HEH9u HP HHP(~MIvIqTILH05LIHHHCt H{?rGH7rGII ILH8LtMtIELH8PLMt#HIFHuILPLpGH0tMtI$LH0PHHt#HHCHuHHPHxpGHHHt#HHCHuHHPHIpGL{lG H{GHsGUHAVSHPIH[HHEqGHþHqGHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]'H5`GH}H4!H5EIH!H5j\KH!HAH5GH!HHH 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LCHHN4LHLCLHHHyHH)H1@ItH9H4H4HL9rH HHx1W)HDžHH)ttHIIHI9 HPCILLKHHPHHH~LIKCMLE~GHHHHE1Affff.I;Du#L4AHHHHH E1AHpjHpH HHH+HH}蕿HH9u AA1H}fffff.HHH@HHH HsHOH;OtCHHHOffDL耽xt51HHMHGH;Gs)HHHG0HH}HucHu;H}HHHH)HH9?H H9HIHHHL(E4`bH蝉 HEHhHHKHxH)HHH)HUH!HHxAE9E)IcW)EHEH.H<H:CILmIDHHEHHHIcIHI)LLCHHEH LI1IDH yA9~/yIȹffffff.H ILHA9HH9uW)HDžHEEFLHECHHHLA3HPM?M1HhHHHpHH~I ˆHHXHH HAH(KMlLHH0KTHHGHH)H8N LHH@E1Lp'f1Hfffff.HhHHpN$M$HHfInHtfH811H0DoT fo fAoD fAoL fHHHuHtHIAofAoTfffpNffI~L(H H9XL@u(fffff.E1AL@HhD)HXL)Ht/H`IIHffffff.LILHuHrlHhL)ID$H`HIILL0IPL$CLL0W)EHEHEL}W)pHEHL$HpH$D$EHuHUHMH?H HC0H0W)PHDž`LpHxL)tVIILH=HkCILXLPKDH`H~LLHCILXI$H@ID$HHHtH@HPH@E1LLH0%< HHHt#HHCHuHHPHCHPHt HXCH HLWIHMLEH(H$?LMHHHpHt Hx>CHuHtHuH}HPH]Ht#HHCHuHHPHCHXHH;EuLH[A\A]A^A_]CCHP C HI2ILHMt#HIFHuILPLCHPHtHXlCI IL"D HpHt HxBCHuHtHuH}HPH]Ht#HHCHuHHPHCL5C H5DUHAVSHPIHXHHECHþHCHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]dH5 CH}HH5EH腆H5*GH趇HH HQH5Q DHֈHHHXHHEH֫HUHuHuIININ0IFIVIF@H]HEHtHVHCHXHH;Eu LHP[A^]7CIH]HEHtHHCLC ffffff.UHAVSHPIHcXHHEnCHþHdCHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]aH5CH}H蔍H5EH%H5GHVHH HQH5DHvHHHeXHHEHֵHUHuHuIININ0IFIVIF@H]HEHtHHCHoXHH;Eu LHP[A^]CIH]HEHtHHACLwC ffffff.UHAVSHPIHXHHECHþHCHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]'`H5`CH}H4H5ZEHŁH5jGHHA H5DHHHH XHHEHMHUHuHuIININ0IFIVIF@H]HEHtHH-CHXHH;Eu LHP[A^]~CIH]HEHtHXHCLC fDUHAVSHPIHXHHECHþHCHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]\H5CH}HH5 EHuH5GH覀HA H5HDH́HHH XHHEHmHUHuHuIININ0IFIVIF@H]HEHtHMHCHXHH;Eu LHP[A^].CIH]HEHtHHCLC fDUHAVSHPIHcXHHEnCHþHdCHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH][H5CH}H蔆H5EH%}H5GHV~HH HQH5DHvHHHe XHHEHHUHuHuIININ0IFIVIF@H]HEHtHHCHoXHH;Eu LHP[A^]CIH]HEHtHHACLwC ffffff.UHAVSHPIHXHHECHþHCHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]'XH5`CH}H4H5cEHzH5jGH{HH HQH5DH}HHHXHHEH6HUHuHuIININ0IFIVIF@H]HEHtHH&CHXHH;Eu LHP[A^]wCIH]HEHtHQHCLC ffffff.UHAVSHPIH XHHECHþHCHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]VH5CH}HԁH5EHexH5 GHyHA H58DHzHHHXHHEHHUHuHuIININ0IFIVIF@H]HEHtH=HCH XHH;Eu LHP[A^]CIH]HEHtHHCLC fDUHAVSHPIHS XHHE^CHþHTCHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]wSH5CH}HH5EHvH5GHFwHA H5DHmxHHH\XHHEH=HUHuHuIININ0IFIVIF@H]HEHtHH}CHf XHH;Eu LHP[A^]CIH]HEHtHH8CLnC fDUHAVSHPIH XHHECHþHCHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]'RH5`CH}H4}H5lEHsH5jGHtHH HQH5 DHvHHHXHHEHVHUHuHuIININ0IFIVIF@H]HEHtHH&CHXHH;Eu LHP[A^]wCIH]HEHtHQHCLC ffffff.UHAVSHPIHXHHECHþHCHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]NH5CH}HzH5 EHeqH5 GHrHH HQH51 DHsHHHWHHEHHUHuHuIININ0IFIVIF@H]HEHtH6HCHXHH;Eu LHP[A^]CIH]HEHtHHCLC ffffff.UHAVSHPIHCXHHENCHþHDCHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]gMH5CH}HtxH5EHoH5GH6pHA H5DH]qHHHLWHHEHHUHuHuIININ0IFIVIF@H]HEHtHHmCHVXHH;Eu LHP[A^]CIH]HEHtHH(CL^C fDUHAVSHPIHXHHECHþHCHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]JH5PCH}H$vH5\EHlH5ZGHmHA H5DH oHHHWHHEHHUHuHuIININ0IFIVIF@H]HEHtHHCHXHH;Eu LHP[A^]nCIH]HEHtHHHCLC fDUHAVSHPIHWHHECHþHCHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]HH5CH}HsH5EHejH5 GHkHH HQH51DHlHHHWHHEHHUHuHuIININ0IFIVIF@H]HEHtH6HCHWHH;Eu LHP[A^]CIH]HEHtHHCL跿C ffffff.UHAVSHPIHCWHHENCHþHDCHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]gEH5CH}HtqH5EHhH5GH6iHH HQH5DHVjHHHEWHHEHHUHuHuIININ0IFIVIF@H]HEHtHHfCHOWHH;Eu LHP[A^]CIH]HEHtHH!CLWC ffffff.UHAVSHPIHWHHECHþHCHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]DH5@CH}HoH5YEHeH5JGHfHA H5xCHgHHHWHHEHHUHuHuIININ0IFIVIF@H]HEHtH}H CHWHH;Eu LHP[A^]^CIH]HEHtH8HȿCLC fDUHAVSHPIHWHHE螿CHþH蔿CHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]@H5CH}HlH5 EHUcH5GHdHA H5(CHeHHHWHHEHHUHuHuIININ0IFIVIF@H]HEHtH-H载CHWHH;Eu LHP[A^]CIH]HEHtHHxCL许C fDUHAVSHPIHCWHHENCHþHDCHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC 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Hr"HHȁAHH;>H9HBL97H[A^A_]fUHAWAVAUATSHHIHMtNLH=J<.>H;HHt>LsINDHMHs-1HuxH;HHt>HCQLH)1ffff.H3HH3HDH3HDH3HDHH9uHtHHHHHuLKMHKMQLutM9r1LIIL#UHJ M9MLuH]M_Lut&M9r 1LIIM9uMifL#]M9tHJ<2IMHEGDDLAtIWI_MLUL]DEHUH]MIAFtINH9t&Iv 'H9tffffff.IvHHAtI u*HtM1AL: uUHI9u3ff.Ht#L>DEL]LUIH]HUuIMHDfI$IH]HJHI$HJL8LuN MMffff.M9MdHH[A\A]A^A_]ÿ{>HH5y:?H>H21SHHH5SHSH>IH^>LX> UHAWAVAUATSH(H1SHHEIHAEM}IuIDIEUH}ĐILcMIt$Lt M9s MIM! 1LIIHJHHHAEt MMM}DHuLMHCL9uWKHtHSL9u]H{HtH{ uYMtz1T A:u6HH9u`ff.LtL9r1IHL9t :H!L9u2HHs$Mt!LLMN>LMMHuu1H0SHH;EuHH([A\A]A^A_]>Hfff.UHAWAVAUATSH(H@0SHHEIHAEM}IuIDIEUH}ILcMIt$Lt M9s MIM! 1LIIHJHHHAEt MMM}DHuLMHCL9uWKHtHSL9u]H{HtH{ uYMtz1T A:u6HH9u`ff.LtL9r1IHL9t :H!L9u2HHs$Mt!LLM>LMMHuu1H.SHH;EuHH([A\A]A^A_]>>H fff.UHAWAVAUATSHIH7LwI)IIFHH;HHWH)HHHH9H}wYHH9HBHDH9vCO>HH5M6?H>H-SHHH5 SHy SH[>HHHEHH>HIN,3LLH]>ID$JD3Mu HEL8L`M9t;M)I@I}L>ID$IEIII uHEL LxMHMHHL(LpHMHHHM9t'ff.I_AGt I4>II9uMtLH[A\A]A^A_]>H[A\A]A^A_]1HE1 8>IH!>EIMM9u*IM9t"fDI^AFt I~>II9uH}Ht>L> fff.UHAWAVSHHa,SHHEHDžpHDžhƅ` fDženeDžaaffiƅgHH`L>fDžW)@HDžPƅ@DžAreluƅEHH@>fDžW) HDž0ƅ IleakyrelL!fDž)uHH >W)ƅHDžHldedreluHfDžHthresholHƅHHP>fDžW)HDžƅDžtanhƅHH>fDžW)HDžƅHscaledtaHfDžnhƅH H>fDž8W)HDžƅHsigmoidHH@He>fDžXW)HDžƅHhardsigmHDžoidH`H>W)`ƅ`fDžxHDžpDžaeluH}H`>W)@fEƅ@HDžPHsoftsignHAƅIH}H@>W) fEƅ HDž0HsoftplusH!ƅ)H}H ?>fEH=UH ]EEE`@  % 1@=`IUamy @`t Hp>H=SH5҈UH۝>W)`ƅ`HDžpLafDžiuHH`>HDž #<W)@HDžPƅ@HADžIoidHH@`>HL>?HW) ƅ HDž0Dž!eluHH >HDž?H=UH @`t Hpq>H="SH5UHtu>H@&SHH;EH[A^A_]H},>EAH}>E8H}>`/Hp>@#HP> H0> H>H>Hp>HW>H>> H0%>@HP >`Hp>H>H>H>H>{Hv> oH0]>@cHPD>`SZH&>lH >`H> TH0>@HHP>`8?>IuIuUt H>E1 t H0i>@uVHJ>tH5>tIA tIA@t3HP>`t'Hp>HuIA1`uHAHf.`t Hp>HuIEEE`@  t H>E1 t H0> @%&H}>EDH}>E;H}>`2Hp~>@&HPe> H0L>H3>H>H>H>IA IA @HP>`Hp>Hh>HO>H6>H>H> H0>@HP>`Hp>HIA`*6B N@Z`fHt+Au%H@`t Hp>HuL1> IAS`IAC\IA3XIA#TIAPIA LIA@HIA1`GUHAVSHPIHSHHE>HþH>HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5NAH}HDH5@H襈H5JBH։HA H5x"?HHHHSHHEHHUHuHuIININ0IFIVIF@H]HEHtH}H >HSHH;Eu LHP[A^]^>IH]HEHtH8H>L> fDUHAWAVAUATSHIIIHSHHE1L{ HHL0L8W)`HDžpHCH+CHH.袋.HH`LLsL{M9t]L`H]fffff.LuHhH;psL0Hhffffff.LHUIXM9uW)EHEIPH@H`LELH0H@HH8t]HHDž@H}Ht H}]>H`Ht HhE>H.SHH;EukHHĨ[A\A]A^A_]H}tCHUHLbGL@HDž@MtAFt I~>L>gHP7>>IH`W$H5 @H j@H}L`ZW)@HDžP v>HP(O)@He input.HHHsequenceHHHptr for HHHGot nullH@AHj?HuH@L\E1H5RHRL>>WHA(HA}HH@t HP>LeMt6HEL9t!Lh@t Hx>LM9uH}LLep>Et H}a>Et H}R>L`Mt:HhL9t=Lh@t Hx'>LM9uH`Lh >EtwL;>mLHL@HDž@MtAFt I~>L>HH}HtH}>HH`Ht Hh>H> fffff.UHAVSI`p>HH SHHH{LGHHPLIHSHH[A^]IH>LL> @UHAVSHPIHSHHE>HþH>HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5N@H}HDHfH5?HHH5#@H苁H50BH輂HA H5^?HHHHSHHEHӠHUHuHuIININ0IFIVIF@H]HEHtHcH>HSHH;Eu LHP[A^]D>IH]HEHtHH>L> fff.UHAWAVAUATSHHIHgSHHE1H^IMW)EHEHU1H2HH}Ht H}(>H<H?IOI+OHH.袋.HHIHSHH;EuLHĈ[A\A]A^A_];>>IHhQ$H5P@H @H}LhTW)PHDž` z>H`(O)PHe input.HHHsequenceHHHptr for HHHGot nullH@AHn?HuHPLVE1H5RHRLB>>IHhP$H5j@H @H}Lh!SW)PHDž`0>H`(sN)PHt tensorHH%Houtput tHH Hptr for HHHGot nullHHHLength: HHHSequenceH@-AH@HuHPLUE1H5RHRL@> IAIIAIIAIA2IuIPt H`>LeMt6HEL9t!HX@t Hxn>HI9uH}LLeT>Et H}E>Et H}6>LhMHpL9HX@t Hx>HI9uIPt H`>LeMt6HEL9t!HX@t Hx>HI9uH}LLe>Et H}>Et H}>LhMt:HpL9t=HX@t HxV>HI9uHhLp;>Et$Lj>LIH}Ht H}>LH> UHAWAVAUATSHAHHSHHE<Ht H1H SH H;MuHĈ[A\A]A^A_]>IǃL>>IHhNM$H5?H @H}LhX{PH{HBIcHH< H5?HPH>HuHP1LRH5RHRLL> IPt H`>E1HIALmMt6HEL9t!HX@t Hx>HI9uH}LLm>Et H}w>EtH}h>IALhMt:HpL9t5HX@t Hx5>HI9uHhLp>Eu LIL?>4>L4> H4@UHAWAVATSH`IH SHHEؿ>HþH>HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5*@H}H HH5>HHIUI]HW)EHEN>HEHHHML HXHMH5?HULH5@HxH5BH?yHA H5?HfzHHHUSHHEHHUHuHuIIOIO0IGIWIG@H}Ht H}>H]HEHtHHd>HM SHH;EuLH`[A\A^A_]>IH}HtH}&>IH]HEHtHpH>L6> fffff.UHHHH9H9Hx]ÐUHAWAVAUATSHIIH SHHE1LHHVLuH&HHHHSH+SHH.袋.HHHH9H9@Hx9Hy HHHHHwIW)EHEI|$ I+|$tQHHHH=n>ILmLmIDHEIt$I\$ H)H~LHE>ILmHU1L:(HH}Ht H}}>L@H L;L{PM4$Mt$PM9MII|$0HPhHxL]H1@LLK>HL]IIH9|HhH5@H @L H?H]HHLhHLH#EtAH}>6Ld]II|$0HPILLH >L@IHGSHH;EuLHĘ[A\A]A^A_]>B>IHhE$H5@H @H}LhT IW)PHDž` >H`(cO)PHe input.HHHsequenceHHHptr for HHHGot nullH@AHٱ?HuHPLDKE1H5ZRHRL>\>IHh E$H5@H @H}LhW:HW)PHDž`0>H`(-N)PHdex tensHHHt for inHHHptr inpuHHHGot nullHf@ or@"AH@HuHPLXJE1H5nRH/RL>p>IHh!D$H5@H @H}LhcNGW)PHDž`0>H`(O)PHut tensoHH Hfor outpHHHnullptr HHHAt: Got HHHSequenceHf@(rAH@HuHPLbIE1H5xRH9RL> H}> IA(IA}IcIPt H`4>LeMt6HEL9t!HX@t Hx>HI9uH}LLe>Et H}>Et H}>LhMHpL9HX@t Hx>HI9uIAIIAIIAIA2IuIPt H`F>LeMt6HEL9t!HX@t Hx!>HI9uH}LLe>Et H}>Et H}>LhMHpL9HX@t Hx>HI9uIPt H`>LeMt6HEL9t!HX@t Hxl>HI9uH}LLeR>Et H}C>Et H}4>LhMt:HpL9t=HX@t Hx >HI9uHhLp>Et5L>+LIH}HtH} IEt H}>L> fUHAWAVAUATSHH}SHHEHG0@Ẽtu9"HcHH ISH H;MuHĘ[A\A]A^A_]>G>IH`?$H5@H %@HxL`C"CH5?HHHUHxHH1LEH5RH}RL> IHt HX>E1H֞IALeMt6HEL9t!HX@t Hxc>HI9uH}LLeI>Et H}:>xtH}(>IAL`Mt:HhL9t5HX@t Hx>HI9uH`Lh>Eu LIL>L> UHAWAVAUATSH8MLHHIHHoSHHEH8HRHHHLH@H8L>LHEEL-RIEHHI@L8L>L=LRILHDž(HDž HDžHDžDž0Le>HLH9MHH0H_>HILL$>HLHLH3H>LHH>HHHLLLvQHHL8Lt H >L>H5RHH`>H8z>HRHH;EuLH8[A\A]A^A_] >IHHL8Lt H d>LH>+H蚛H蒛H芛H肛HzIH58RHH>IH8>L=> DUHAVSHPIHRHHE>HþH>HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5>@H}H4pH5@HhH5:BHiHA H5h?HjHHHRHHEHHUHuHuIININ0IFIVIF@H]HEHtHmH>HRHH;Eu LHP[A^]N>IH]HEHtH(H>L> fDUHAVSH0IHHRHHEHRHHH{+HKSHHCPW)EE HPHEEdtypfEeH}HULH=LuHEMtAFt I~>L>Et H}>MtHHRHH;Eu H0[A^]9>HEt H}>H> UH]fDUHAWAVAUATSHHIIHdRHHEH{H-HH+H1H$HH#HHIGPHHH HcH聽Y:RKD=^6藼/P(!肿; HH.IHuRHH;EuLHĈ[A\A]A^A_]>p>IHh!7$H5@H @H}LhyN:W)PHDž`0>H`(eN)PHsequenceHH&Hutput seHH Htr for oHHHot nullpHHHEmpty: GHHHSequenceH@.AH@HuHPLVn>IHh6$H5@H @H}LhL9IPH5AHPL HuHP1L;H5RHRL9> IPt H`ž>E1HIALeMt6HEL9t!HX@t Hx荾>HI9uH}LLes>Et H}d>EtH}U>IALhM HpL9HX@t Hx>HI9uIIA(IA}IIPt H`ս>LeMt6HEL9t!HX@t Hx谽>HI9uH}LLe薽>Et H}臽>Et H}x>LhMt:HpL9tEHX@t HxM>HI9uHhLp2>EtLa>L[> Lf9Uqj@N2c+\$Gfff.UHAWAVATSH`IHRHHEؿ誼>HþH蠼>HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5 @H}HhI=IEHW)EHEH>HEHHHML HXHMH5m?HULgH5@H`H5BH9aHA H5>H`bHHHORHHEHPHUHuHuIIOIO0IGIWIG@H}Ht H}>H]HEHtHH^>HGRHH;EuLH`[A\A^A_]諺>IIIIH}Ht H}>H]HEHtHdH>L*> fUHAWAVAUATSHHRHHEHIIW)EHEHHI?t#HA@H _@1AL{0$H(IMl$0ID$I\$ W)PHDž`H)tnH8L0IILH=H)>ILXLPKH`HL0H8~LH聺>ILXHEH@HEHHHtH@HhHPH@E1LHHHt#HHCHuHHPHn>HPHt HXR>LhLmM4$Mt$PM9wI|$0HPhI|$Ht=xu7IEH~O1LL;>LEHIIH9|%EHI|$0HPHLLH\>H(H{H;{sHh)HCXHhH胀IHh@H]Ht#HHCHuHHPHP>H5RHH;EuLHĸ[A\A]A^A_]蔷>HP^> I2ILHMt#HIFHuILPL>HPHtOHXŶ>AIIIHt/Ct H{袶>H蚶>IIHhJH]Ht#HHCHuHHPHZ>L茱> @UHAWAVAUATSHIIHRHHE1LIMTLHH!M7H{0HPhI9t{I?OHhH{0?HPH5@H v@L >H]HhLPH LH#EH}i>HLHIOI+OHH.袋.HHHLsL@Ht?HoH@HHH9H9HxфuHy HH@I|$Hj!HH+H1L6HH{HHW)EHEH}L HLHHH@HLE1ffffff.I9H8LLHM1L8HDž8MtAFt I~>L>LLGLH0HLHMH0HDž0HtcCt H{蜳>H蔳>Jf.LLH(HLHMH(HDž(HuIHHH@L9HH9LuDH HMLL!H HDž HtCt H{>H۲>I7HH!LcML{IIH[L9|MI)HèfHXHèIXuI?LUH5x@H @L >H]H@LHHLH讁#II)LM&>IGIGIHHEHCHEIHEHCIEHRHH;EuLH[A\A]A^A_](>ı>IHhu($H5=@H x@H}Lh+W)PHDž` g>H`(O)PHe input.HHHsequenceHHHptr for HHHGot nullH@AHY@HuHPL-E1H5RHRL/>ް>IHh'$H5W@H @H}Lh*W)PHDž` 聰>H`(ouO)PH tensor.HHHinput teHHHptr for HHHGot nullH@AHu?HuHPL,E1H5RHRLI>>IHh&$H5q@H @H}Lh)W)PHDž`0蛯>H`(P)PHsequenceHH'Houtput sHH Hptr for HHHGot nullHHHInsert: HHHSequenceH@/AH@HuHPL+E1H5RHRLG>IPtH`Ю>IALeMt6HEL9t!HX@t Hx裮>HI9uH}LLe艮>Et H}z>EtH}k>IALhMHpL9HX@t Hx0>HI9uIPtH`>IALeMt6HEL9t!HX@t Hx٭>HI9uH}LLe迭>Et H}谭>EtH}衭>IALhMHpL9HX@t Hxf>HI9uIPtH`<>IALeMt6HEL9t!HX@t Hx>HI9uH}LLe>Et H}>EtH}׬>IALhMt?HpL9t$HX@t Hx褬>HI9uHhLLp脬>EutIL讬>gIEtZH}OH蝂ILuMt@H]L9tAMI)Hèfffff.H HèIXuH}Lu >L?> LfUHAWAVAUATSH8MLHHIHHRHHEH8HRHHHHH@H8H'>LHEEL-RM}LI@L8H,>L5RILHDž(HDž HDžHDžDž0L設>HLH|1HHHHL$uHHH5LL8Lt H |>H\>H5yRHH>H8>H6RHH;EuHH8[A\A]A^A_]蕪>HLL8Lt H >H֨> H(HH5RHHX>HH8m>H> UHAWAVAUATSH8MLHHIHH_RHHEH8H¿RHHHLH@H8Lۨ>LHEEL-RIEHHI@L8L٧>L=HLH)/HH0HO>HILL>HLH.H3H>LHHݪ>HHH.LLf3HHL8Lt H ݧ>L>H5RHHP>H8j>HRHH;EuLH8[A\A]A^A_]>IHHL8Lt H T>L8>+H}H}Hz}Hr}Hj}IH5(RHH蚥>IH8详>L-> DUHAWAVAUATSPIM'IGL)HH.袋.HH9H袋.H9MwLL)LkXL蕦>HLM9tCHEM)IƨHIHfHǨLHèHIƨIXuM7MgLHEHI?I_IGM9t"LL)IĨfDLIĨHXuMtLH[A\A]A^A_]>H[A\A]A^A_]ÿ>HH5>H8>HRHHH5sRHRH>IHͥ>LǠ> ffffff.UHAWAVATSH`IHORHHEؿZ>HþHP>HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5@H}HPIX&IP.HW)EHE>HEHHHML HXHMH51V?HULWPH5@HHH5]BHIHA H5>HKHHHRHHEH`iHUHuHuIIOIO0IGIWIG@H}Ht H}/>H]HEHtH~H>HRHH;EuLH`[A\A^A_][>IIIIH}Ht H}Ţ>H]HEHtHH褢>Lڝ> fUHAWAVAUATSHIIHgRHHE1L^IMLEINI+NHH.袋.HHHLkL@Ht?HlH@HHH9H9HxфcHy HH@L(IHaHH+H1L-HHhIMI6LW)EHEH}LzHH~u1fffff.H;@tPLHH8HLHM6L8HDž8MtAEt I}>L>HH9HM|$MMl$MII\$L9L0MI)HèfHhHèIXuL0I}aH5@H @L >H]H@LHHLHo#Et_H}6>TMI#LM> >IEIEIEHEID$HEIHEID$L(I$HRHH;EuLHĸ[A\A]A^A_](>ğ>IHhu$H5=@H }@H}LhW)PHDž`0g>H`(DN)PHsequenceHH&Hutput seHH Htr for oHHHot nullpHHHErase: GHHHSequenceH@.AHZ@HuHPLE1H5RHRL>ž>IHhs$H5;@H {@H}LhW)PHDž` e>H`(O)PHe input.HHHsequenceHHHptr for HHHGot nullH@AHW@HuHPLE1H5ڴRHRL-> IPtH`趝>IALeMt6HEL9t!HX@t Hx艝>HI9uH}LLeo>Et H}`>EtH}Q>IALhMHpL9HX@t Hx>HI9uIPtH`>IALeMt6HEL9t!HX@t Hx远>HI9uH}LLe襜>Et H}薜>EtH}臜>IALhMt?HpL9t$HX@t HxT>HI9uHhLLp4>EudIL^>WIEtNH}CILuMt:H]L9t;MI)HèHHèIXuH}Luɛ>L> LfUHAVSHPIHRHHE螛>HþH蔛>HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]wH5>H}HFH謸H5@HHFH5@H;?H5BHl@HA H5>HAHHHRHHEHC`HUHuHuIININ0IFIVIF@H]HEHtHH裙>HRHH;Eu LHP[A^]>IH]HEHtHΨH^>L蔔> fff.UHAWAVAUATSHIHHRHHEH~HLc(MH(I|$HHH+Hl1LSHYH_HHH0H8E11LLx0f.LDuEL9x0`l>HH](j]O)EPHH5/@ؙ>CPHHHMg#HHHDžHHtCt H{>H>EtH}ߗ>AE9DH8LW)EHEH}L1L}@LH@HLLL@HDž@MtAFt I~R>LJ>D9|L8M|$MtHH0LpHII\$L9t9MI)Hè@HHèIXuI>L8H0H&LM}LΖ>IFIFIHEID$HEHHEID$H(HH yRH H;MuHĸ[A\A]A^A_]ۖ>w>IHh( $H5@H @H}Lh0UW)PHDž`@>H`(HO)PHt sequenHH(Hor outpuHH Hullptr fHHHt: Got nHHHConstrucHHHSequenceHf@0ce@2AH@HuHPLWE1H5mRH.RL>o>IHh $H5@H @H}Lh-MW)PHDž` >H`(.N)PHore inpuHHHe 1 or mHHHMust havHf@ts@AH@HuHPLyE1H5RHPRL>IPtH`k>IALeMt6HEL9t!HX@t Hx>>HI9uH}LLe$>Et H}>EtH}>IALhMHpL9HX@t Hx˓>HI9uIPtH`術>IALeMt6HEL9t!HX@t Hxt>HI9uH}LLeZ>Et H}K>EtH}<>IALhMt?HpL9t$HX@t Hx >HI9uHhLLp>EuiIL>\IEtQH}FILuMt?H]L9t@MI)Hèffff.HXHèIXuH}Luy>L词> LfUHAWAVAUATSHxIH=RHHEпH>HþH>>HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]aIYIQIIHW)EHE >HEHH HML8L`LhHXHMH5D>H}HUG=HH56@HH-=IIHW)`HDžpo>H`HHHpL HXHhH5B?H`L<H5d@H#5H5|BHT6HA H5>H{7HHHjRHHEHVHUHuHuIININ0IFIVIF@H`Ht Hh蔏>H}Ht H}肏>H]HEHtHўHa>HJRHH;EuLHx[A\A]A^A_]謏>I'9I IIH`Ht Hh>H}HtH}>IH]HEHtHFH֎>L > @UHAWAVATSH0IIHRHHEHRHIIHSIfHnfsAGPIG`ffEHEEEaxisEH}HUHML H]E1HuLeHEtCt H{ >H>MgPEt H}>ffEEHEHkeepdimsHEEH}HUHML\ H]Hu'LuHEt)Ct H{莍>H膍>AHEuMwXEt H}a>HJRHH;Eu H0[A\A^A_]豍>HEt H}+>Ha> fUH]FfDUHAWAVAUATSHHIIHRHHE1HIǾHI$IO0IɃ HNHc HH}LHLI\CH}LHLI,H}LHLI>#H}LHLI3HEI$H RHH;EuLHĘ[A\A]A^A_]>>IHh$H5@H 7@H}LhlIG0HHH5@H #AHPHHBHuHP1LhH5RHBRLԋ>IPt H`]>DžDHaIǰDH]Ht6HEH9t!Lh@t Hx>LL9uH}HH]>Et H}>EtH}> IǰDLhMt:HpL9t9HX@t Hx詊>HI9uHhLp莊>Du LIL诊> II$L薅> fGGGGGGGUHAWAVAUATSHIIHIHRHHEHEHCPHEEEEEEW)EHEMtHWRHH;ELH[A\A]A^A_]L0H8LPIEM|$LMd$ W)pHEM)tXMIHI9 L>HHxHpJHEM~HLLP>LHxW)PHDž`LL HXHEHL`LPJ11Hxffffff.HH9uEtE fff.HEHHcH9LcHEHpL<W)@LLH@HI}t0A1ALHg@H @#AIEMd$0HpLxW)HDžI)tfHLHHH9 LV>ILLIHMH~LL赇>MLH@HHHHHtH@E1LLLHH HHt#HHCHuHHPH虄>HHt H}>LMHËEHHcUIHXHxL$HcE9Hu!HHcHHL>HhHL4HHHBHH@HHsE1H)HhHHHRHHHHKvHIIHH HH(E1ffff.HLL>I<HJ4 Lх>H(H<H J4 L賅>HH<HJ4 L蕅>ILLL9uHHHLhH@tNHILLMLHuHH90L`LHH8HxXW)HDžHxH+pHHHELxL+pM~hILuE11ffffff.I9t;HpLHH;tHHHHH"FfHIL9|W)HDžLLM)t[LHHH9LN>ILLIHM~LLL豃>MLHHHQHHt Hہ>HHLtH輁>@HXH;`sH(HXXHPHJHcHxH9E1HHHt#HHCHuHHPHC>EHHHEH9LPI~HHH+H~1LsHt HGIE1It$0LMwMtGMgIGHI_L9zMI)HèfHXHèIXuI<$L`RI_uLPMHXL9MI)HèHHèIXuHPLHL3>ID$ID$I$HPIGHXHH`IGW)PHDž`IEHpH+Hx>LLX>>>IHpI#H5@H 6@HLpsW)PHDž`@8>H`(O)PH be >= 0HH/Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@7AH>@HHPLjE1H5RHARL~> ~>H RHHH5RHRH~>H{>Hu{>벿9~>IHp#H5@H נ@HLpW)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AH@HHPL\E1H5rRH3RL}>Hpz>IPt H`=}>L Mt?H(L9t$HX@t Hx}>HI9uH LL(|>t H|>tH|>IALpM/HxL9HX@t Hx|>HI9u\I IPtH`Q|>IAL Mt?H(L9t$HX@t Hx|>HI9uH LL({>t H{>tH{>IALpMt?HxL9t$HX@t Hx{>HI9uHpLLx{>EuIL{>II8II]IEHCt H{.{>HfIaIHHtHz>IHHtbHz>TIHHt#HHCHuHHPHy>HHtOHz>AIHF0II]IEHtCt H{Yz>HQz>IHHHt#HHCHuHHPH!y>LPMt>HXL9tfMI)HèHHèIXuHPLXy>HpHt Hxy>H}Ht H}y>Lt> LfUHAWAVAUATSHIIHIHaRHHEHEHCPHEEEEEEW)EHEMtHǰRHH;ELH[A\A]A^A_]L0H8LPIEM|$LMd$ W)pHEM)tXMIHI9p LZw>HHxHpJHEM~HLLx>LHxW)PHDž`LLLHXHEHL`LP:11Hxffffff.HH9uEtE fff.HEHHcH9LcHEHpL<W)@LLH@HI}t0A1ALHג@H Q@#1IEMd$0HpLxW)HDžI)tfHLHHH9Lu>ILLIHMH~LL%w>MLH@HHHHHtH@E1LLLHH|HHt#HHCHuHHPH t>HHt Ht>LHËEHHcUIHXHxL$HcE9Hu!HHcHHL1v>HhLN4HHHBHH@HHsE1H)HhHHHRHHHHKvHLHH HH(IE1DHLLhu>I<HJ4 LQu>I<H(J4 L:u>HH<HJ4 Lu>ILH L9uHHHLhH@tQHILLMLHuHH90L`LHH8HxXW)HDžHxH+pHHH5LxL+pM~hILuE11ffffff.I9t;HpLHH;tHHHHH5fHIL9|W)HDžLLM)t[LHHH9Lq>ILLIHM~LLL1s>MLHHHKAHHt H[q>HHLtH@HXH;`sHHXXHPH9HcHxHE1HHHt#HHCHuHHPHo>EHHHEH9LPI~H#HH+H~1LHt HV7IE1It$0LMwMtGMgIGHI_L9zMI)HèfHHèIXuI<$L`RI_uLPMHXL9MI)HèHHèIXuHPLHL3o>ID$ID$I$HPIGHXHH`IGW)PHDž`IEHpH;Hx%o>*LLXo>|o>o>IHp#H5@H !@HLpW)PHDž`@n>H`(O)PH be >= 0HH/Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@7AH@HHPLE1H5RHRLSn> n>H RHHH5xRHaRH#n>Hk>Hj>벿m>IHpj#H52@H ’@HLpW)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AH@HHPLE1H5RHRLEm>Hp"j>IPt H`l>L Mt?H(L9t$HX@t Hxl>HI9uH LL(rl>t H]l>tHHl>IALpM/HxL9HX@t Hx l>HI9u\I IPtH`k>IAL Mt?H(L9t$HX@t Hxk>HI9uH LL(~k>t Hik>tHTk>IALpMt?HxL9t$HX@t Hx!k>HI9uHpLLxk>EuIL(k>II8II]IEHCt H{j>HfIaIHHtHj>IHHtbH`j>TIHHt#HHCHuHHPH+i>HHtOHj>AIH0II]IEHtCt H{i>Hi>IHHHt#HHCHuHHPHh>LPMt>HXL9tfMI)HèH8HèIXuHPLXSi>HpHt Hx;i>H}Ht H})i>L_d> LfUHAWAVAUATSHIIHIHRHHEHEHCPHEEEEEEW)EHEMtHGRHH;ELH[A\A]A^A_]L0H8LPIEM|$LMd$ W)pHEM)tXMIHI9 Lf>HHxHpJHEM~HLL@h>LHxW)PHDž`LLHXHEHL`LPJ11Hxffffff.HH9uEtE fff.HEHHcH9LcHEHpL<W)@LLH@HI}t0A1ALHW@H o@#AIEMd$0HpLxW)HDžI)tfHLHHH9 LFe>ILLIHMH~LLf>MLH@HHHHHtH@E1LLLHHHHt#HHCHuHHPHc>HHt Hmd>LHËEHHcUIHXHxL$HcE9Hu!HHcHHLe>HhHL4HHHBHH@HHsE1H)HhHHHRHHHHKvHIIHH HH(E1ffff.HLLd>I<HJ4 Ld>H(H<H J4 Ld>HH<HJ4 Ld>ILLL9uHHHLhH@tNHILLMLHuHH90L`LHH8HxXW)HDžHxH+pHHH$LxL+pM~hILuE11ffffff.I9t;HpLHH;tHHHHH%fHIL9|W)HDžLLM)t[LHHH9L>a>ILLIHM~LLLb>MLHHH0HHt H`>HHLtH`>@HXH;`sHHXXHPHo)HcHxH)E1HHHt#HHCHuHHPH3_>EHHHEH9LPI~HHH+H~1LcHt H&IE1It$0LMwMtGMgIGHI_L9zMI)HèfHHHèIXuI<$L`RI_uLPMHXL9MI)HèHHèIXuHPLHL3_>ID$ID$I$HPIGHXHH`IGW)PHDž`IEHpH+Hx^>LLX^>^>^>IHp9#H5{@H D@HLpcW)PHDž`@(^>H`(O)PH be >= 0HH/Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@7AH.@HHPLZE1H5ptRH1pRL]> u]>H RHHH5tRHpRH]>HsZ>HeZ>벿)]>IHp#H5y@H @HLpW)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AH@HHPLLE1H5bsRH#oRL\>HpY>IPt H`-\>L Mt?H(L9t$HX@t Hx\>HI9uH LL([>t H[>tH[>IALpM/HxL9HX@t Hx}[>HI9u\I IPtH`A[>IAL Mt?H(L9t$HX@t Hx[>HI9uH LL(Z>t HZ>tHZ>IALpMt?HxL9t$HX@t HxZ>HI9uHpLLxqZ>EuILZ>II8II]IEHCt H{Z>HfIaIHHtHY>IHHtbHY>TIHHt#HHCHuHHPHX>HHtOHY>AIH60II]IEHtCt H{IY>HAY>IHHHt#HHCHuHHPHX>LPMt>HXL9tfMI)HèH訹HèIXuHPLXX>HpHt HxX>H}Ht H}X>LS> LfUHAWAVAUATSHIIHIHQRHHEHEHCPHEEEEEEW)EHEMtHHxHpJHEML~HLLW>LHxW)PHDž`L{H(HEHLX`11H`LHH9uEt EHEHHcH9LcHEHpL<W)@LLH@ζHhI}t0A1ALHr@H _@\#dIEMl$0HpLxW)HDžI)tfHLHHH9LU>ILLIHMH~LLeV>MLH@HHHHHtH@E1LLLHH輫HHt#HHCHuHHPHIS>HHLt H&T>LtIŋuHcUIH`H@H(LIIHuHHH@HP1HxHHtPHxHH@H@IHHH@MtLPfLHkQ>HIIuHxHHHHHHxH9LLrHH9pH8HxXxW)HDžHxH+pHHHLxL+pM~_ILuE11fDI9t;HpLHH;tHHHHHfHIL9|W)HDžLLM)t[LHHH9ILQ>ILLIHM~LLLAS>MLLLH[!HHt HkQ>HHLt HLQ>HXH;`sL輮HXXHPLHcH`LͱE1LHHHt#HHCHuHHPHO>EHhHHEH9LXI~H0HH+H~1LHt HcIE1It$0L莾M~MtTMfIFHhI^L9tBLMI)Hèfff.HذHèIXuI<$L I^,LHhL;O>ID$ID$I$HPIFHXHH`IFW)PHDž`IEHpHt HxrO>L0H}HtI?ZO>HCRHH;EuqLH[A\A]A^A_]LPMtHXL9t0MI)Hèff.HȯHèIXuHPLLXN>OFO>N>IHp#H5[k@H Nu@HLpW)PHDž`@N>H`(PO)PH be >= 0HH/Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@7AHq@HHPLE1H5dRH`RLN> M>H `~RHHH5BeRH+aRHM>HJ>HJ>벿M>IHp4#H5i@H s@HLp^W)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AHip@HHPLE1H5cRH}_RLM>HpI>IPt H`L>H Ht?H(H9t$Lh@t Hx\L>LL9uH HH(t H'L>tHL>IALpMt?HxL9t$HX@t HxK>HI9uHpLLxK>EuILK>IT IPtH`{K>IAL Mt?H(L9t$HX@t HxHK>HI9uH LL((K>t HK>tHJ>IALpMt?HxL9t$HX@t HxJ>HI9uHpLLxJ>EuILJ>II@II]IEHCt H{XJ>HpIkIHHtH)J>IHHHJ>{IHHt#HHCHuHHPHH>HHtSHI>E4II]IEHt/Ct H{I>HI>IIH6HHHt#HHCHuHHPHCH>LPMt@HXL9thMI)HèDHةHèIXuHPLXH>HpHt HxH>H}Ht H}H>LC> LfUHAWAVAUATSH(IHIHH}RHHEH8H^RHHHLH@H8LG>LHEEL=RMgLI@L8LF>L-aRILHDž(HDž HDžHDžDž0LzJ>HLHNH3HsHHJJ>HHHHHHLL8Lt H FG>H&F>H5CkRHHE>H8F>HRHH;EuHH([A\A]A^A_]_G>HLL8Lt H F>HE>HHHHH5jRHHE>HH8'F>HA> ffff.UHAWAVAUATSHXLMDEIIH}L}HRHHEMuM+uIHuH~PL!IIMHH]LHeHcH9KHMI7LHcH9.HM I7HƸL9tL~HcH9Lm@HM(}tPLu8HHIIAHML9tHE0IGHMUIML)HHH)vALMU=MeIEI9HHL)HHH1LsIIEKJ HLHHHHOMDLLIWLHfInfpDHVHHyHP1H HEHffEHEMHHHH9H<FD>IHMHA`LmLmMtLuHrHaI9|I9sLHSHHtHH8H9HHHuH@HHHHQHHQHHQHHQ HHQ(HHQ0HHQ8H@HuuI?I!IPHHwH |f1fHHWH)1ېAADAD AD0AD@ADPAD`ADpAڀAڐAڠAڰAAAAH HkHt(ILHfffff.AH HuI9t)LH)IHfff.L"HHHIMH)HLebHHMcH ZII!fInfpDIHHH{HX1HHVH)f1fDAofAoDfAoL Ao\0Aod@ffAoTPffAoD`fAoLpfHHuHt$ITHfoRfofH HuffpNffH~L9tK ff.HHH9uHUH9uL)HHMHH5b@H b@L O>H]HULEHLeLH&#EH}@>KHOH)1ffff. 0@P`pDހDސDޠDްDDDDH HuHtLILHfAH HuL9L)JHff.LHHHE1E1MHHHzHHNHtB~fpDHyH)1ADADAD AD0HHu1Ht~fpDADADH9ueLuMt#MgL>>IGIGIM/MwMwLeI$HwRHH;EuLHX[A\A]A^A_]ILH)?>>>H 5oRHHH5VRHRRH>>H};> HH}HtH} HEt H}9>>Ho9> UHAWAVAUATSH8MLHHIHHvRHHEH8HBTRHHHLH@H8L[=>LHEEL-uRIEHHI@L8LY<>L=tRILHDž(HDž HDžHDžDž0L?>HLHHH0H;>HILL?>HLHhH3H;>LHH]?>HHH1LLHHL8Lt H ]<>LA;>H5^`RHH:>H8;>HuRHH;EuLH8[A\A]A^A_]z<>IHHL8Lt H ;>L:>+H HHHHIH5_RHH:>IH8/;>L6> DUHAVSIP`;>HHjRHHH{L诤Hp]SHH[A^]IH;>LP6> UHAVSIP;>HHRjRHHH{LOH`]SHH[A^]IH:>L5> UHAVSIX:>HHLwH[A^]IHs:>L5> UHAVSIP`:>HHiRHHH{L诣H]SHH[A^]IH:>LP5> UHAVSIP:>HHRiRHHH{LOH]SHH[A^]IH9>L4> UHAVSIP9>HHhRHHH{LH\SHH[A^]IHZ9>L4> UHAVSIh@9>HHL^H[A^]IH9>LI4> UHAWAVAUATSHHHqRHHEH{Ht4HP8t*HH qRH H;MuHĘ[A\A]A^A_]9>8>IHhe#H5z>H #[@H}Lhi蒲H{HHH5R|>HPHHhAH"|>HuHPLE1H5ORHJRLd8> IAIIPtH`7>IALeMt6HEL9t!HX@t Hx7>HI9uH}LLe7>Et H}7>Et H}x7>LhMt:HpL9tEHX@t HxM7>HI9uHhLp27>EtLa7>L[2> LHV fDUHAWAVAUATSHI袋.HH_H)HI.袋.IHCL9#H}HOH)HIH\tE]tH9wHH9HBIHIkXI6>LILkXO,4IkXLLKL4XHEL0L`M9tGHMM)IĨMIŨLL豓IǨMIĨIXuHEL LpHMML(HHHXM9tLL)IƨfL訖IƨHXuMtLH[A\A]A^A_]5>H[A\A]A^A_]E1E1'2>@UHAWAVAUATSPMIIIHH%8>LHHL8>LLHIH7>LHHλL7>LLH踻H[A\A]A^A_]HQ HI HA H9 fUHAWAVAUATSHHmRHHEHHC0@tu?HRHc H?HH mRH H;MuHĘ[A\A]A^A_]4>4>IHh2#H5P@H BX@H}Lh_H{0 HHH5\X@HPHHHuHP1LаH5JRHFRL<4> IPt H`3>E1H IAkIIALmMt6HEL9t!HX@t Hx3>HI9uH}LLmf3>Et H}W3>Et H}H3>LhMt:HpL9tEHX@t Hx3>HI9uHhLp3>EtL13>L+.> Lfffff.UHAWAVAUATSHIIHkRHHEIILL!IG0@t\L$HMIL}Hc HMIFI;FsHHIF fLL5HIuNLQ=HMt>IDIFI;FtH HHIFfDLHuHIuHjRHH;EuHĘ[A\A]A^A_].2>1>IHh{#H5CN@H U@H}Lh訫I0 HHH5U@HPHHHuHP1LH52HRHCRL1> IPt H`1>E1HLIAkIIALeMt6HEL9t!HX@t Hx0>HI9uH}LLe0>Et H}0>Et H}0>LhMt:HpL9tEHX@t Hxf0>HI9uHhLpK0>EtLz0>Lt+> LUHAWAVAUATSHIIHhRHHEIL̽HLH9u\M9t0I6IVHhRHH;Eu;LH[A\A]A^A_]WHhRHH;EuH[A\A]A^A_]/>/>IHhG#H5>H T@H}LhtLHH />HNT@H1->HHHHHPHHH@H@HH5T@H,>HHHHHPHHH@H@HLoHH^.>tHH HH4,>HHH0HHPH(H H@H@HH5m>H +>HHHHHHPH@H8H@H@HHPH8+>AHR@HuHPL}E1H5DRHT@RL-> IA8IA tvIAtkIAtqIAIAcIIPuG8uS u_ukH`,>8tHH,> tH0,>tHm,>tIAxHC,>lH*,>tH,>IALeMt6HEL9t!HX@t Hx+>HI9uH}LLe+>Et H}+>Et H}+>LhMt:HpL9tEHX@t Hx+>HI9uHhLpj+>EtL+>L&> LfDUHAWAVAUATSHHIHHdRHHEH8HsARHHHLH@H8L*>HEEL58bRM~LI@L8L)>L%aRILHDž(HDž HDžHDžDž0L->HLHHH,>HHH˰HHHuLL8Lt H )>H(>H5MRHHb(>H8|)>HbRHH;EuHH[A\A]A^A_] *>HLL8Lt H m)>HM(>HHHH5UMRHH'>HH8(>HZ$> fUHAVSHPIHaRHHE(>HþH(>HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5(?H}H$H5C@HTH>H5 O@HH5DBHHH HQH5kf>HHHHYRHHEHp*HUHuHuIININ0IFIVIF@H]HEHtHp6H'>H_RHH;Eu LHP[A^]Q'>IH]HEHtH+6H&>L!> fUHAWAVAUATSHIH8H@Hl_RHHE1LcnIMffEHEI|$ I+|$tNIILH=;&>HH]H]JHEIuM} I)M~HL'>LH]HU1L}HHH}Ht H}%>H8@PH IH HcHL{ILkHHHaPMpIrJ ;H9` J H9S 1IWH)LHtH< W*f~HHuHfffff. W*T W*DT W*DT W*f~D HI9uL蔲IL kHHHJMIrJ ;H9J H91IWH)LHtHfff.< fD< | D| D| DHI9uLILYeHHHZmMIrJ ;H9KJ xH9>1IWH)LHtHfff.< fHzH)1f803f80L3pLpf80D3f80L3Dp Lp0f80D3 f80L3(Dp@LpPf80D30f80L38Dp`LppH@HuHt4HHfff.f80D3f80 3Dp pHHuI9~1Htf813f81L3[[LI9M1Ht2ooLfftfoMfftfLI9~>>IHhˊ#H5[;@H ;@H}Lh"ffPHDž`@>H`fouOfPHDataTypeHH(Hof type HH Hrgument HHHe 'to' aHHHmust havHHHCast op H@0HuHP1LH5*RH%RLp>>IHhЉ#H5`:@H :@H}Lh"H8HPH5;@HPHuHP1LzH5)RHT%RL> H}> IPt H`g>E1HIALeMt6HEL9t!HX@t Hx/>HI9uH}LLe>Et H}>EtH}>IALhM HpL9HX@t Hx>HI9uIPt H`>E1HIALeMt6HEL9t!HX@t HxZ>HI9uH}LLe@>Et H}1>EtH}">IALhMt:HpL9t5HX@t Hx>HI9uHhLp>Eu )LIL>IH}Ht H}>L > fDPDpUHAWAVAUATSHH0IRHHEMIIIW)EHULRqI}t H{7@H C@1AL#"IEHMLLL;IEH]Ht#HHCHuHHPH>HHRHH;EuLH[A\A]A^A_]>II]IEHtCt H{[>HS>IH]Ht#HHCHuHHPH&>LX > UHAWAVAUATSHHIHHGRHHEH8HC%RHHHLH@H8L\>HEEL5FRM~LI@L8Lh >L%ERILHDž(HDž HDžHDžDž0L>HLH踔3H >HHHTLL8Lt H >H >H51RHHA >H8[ >HFRHH;EuHH[A\A]A^A_] >HLL8Lt H L >H, >H~HvHH541RHH >HH8 >H9> UHAVSHPIHERHHE >HþH >HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5 ?H}HH5#n@HTHH52@HH5$AH谱HH HQH5KJ>HвHHH=RHHEHpHUHuHuIININ0IFIVIF@H]HEHtHPH >HCRHH;Eu LHP[A^]1 >IH]HEHtH H >L> fUHAWAVAUATSHIH8H@HLCRHHE1LCRIMffEHEI|$ I+|$tNIILH=U >HH]H]JHEIuM} I)M~HL >LH]HU1LtaHHH}Ht H} >H8@PH YIH tHcHL[IL\HHHA4MIX1LIL[HHHiMI1^LIL[HHHsMI|13H@HH8LHHMMH;WH@H6LHHLKLMIL[HHHPMIrJ {H9HLH91IH)LHtHKHHuHKTKTTKTTKTHI9ujL蘕ILMZHHH>bM?Ic1L\ILZHHHBlMI1L ILYHHHVIMIrJ {H9HLH91IH)LHtHfDKHHuHqKTKTTKTTKTHI9u:LhILYHHH~5MIVDH 1H^LILXHHH,MIY1LݓILXHHHSTMIrJ {H9(J xH91IWH)LHtHDHzH)1fDf83sf83LsLf83Dsf83LsD L0f83Ds f83Ls(D@LPf83Ds0f83Ls8D`LpH HuHHt6Hffffff.f83D3f83 3Dp pHHuLHHyHHVHHzH)1fo(No soTsf8f 0f8fT0oLs oTs0f8fL0f8fT0H Hu+LHHyHHVH01H HzH)1@f84sf84LsLf84Dsf84Ls D L0f84Dsf84LsD@LPf84Dsf84LsD`LpHHuHyHt6Hffffff.f84D3f84 3D HHu<LHHyHHVH051H1Hf83sf83Ls[[L1HoSoLSffufoMffufoFNf8fff8fLHzH)1sLspLpDs Ls0Dp Lp0Ds@LsPDp@LpPoDs`oLspDp`LppH@HuHt-Ht6HfDoD3o 3D0 0H HuI9HzH)1sLspLpDs Ls0Dp Lp0Ds@LsPDp@LpPoDs`oLspDp`LppH@HuHt(Ht6HڐoD3o 3D0 0H HuI9b1Ht(osoLsfoNf8f0f8fL0I9%P1Ht(osoLsfoNf8f0f8fL0I9@O=IHhr#H5"@H #@H}Lh#-uffPHDž`@=H`fo\OfPHDataTypeHH(Hof type HH Hrgument HHHe 'to' aHHHmust havHHHCast op H@0HuHP1L9wH5RRH RL=T=IHhq#H5!@H "@H}Lh#2tH8HPH5G"@HPHuHP1LvH5RH RL= H}= IPt H`=E1HIALeMt6HEL9t!HX@t Hxd=HI9uH}LLeJ=Et H};=EtH},=IALhM HpL9HX@t Hx=HI9uIPt H`=E1HIALeMt6HEL9t!HX@t Hx=HI9uH}LLeu=Et H}f=EtH}W=IALhMt:HpL9t5HX@t Hx$=HI9uHhLp =Eu )LIL.=IH}Ht H}=L= KW*f~HI9uWKHI9uFKHHI9u4KHI9u#KHHI9ufH}H财H5V@HTHΠH5@H/H5AH`HH HQH52>H耛HHHo&RHHEH@HUHuHuIININ0IFIVIF@H]HEHtHH=Hy,RHH;Eu LHP[A^]=IH]HEHtHHK=L= fUHAWAVAUATSHIH8H@H+RHHE1L:IMffEHEI|$ I+|$tNIILH==HH]H]JHEIuM} I)M~HL/=LH]HU1L$JHHH}Ht H}c=H8@PH IH HcHL ILPHHHMaI1LILPHHHRM%IrJ H9*J H91IWH)LHtH<M~IVDH1HnZL{ILLHHHM/Ii1 L{ILrLHHHH}HH5#?@HvTHH5@HH5$AH谂HH HQH5K>HЃHHHRHHEHHUHuHuIININ0IFIVIF@H]HEHtHPH=HRHH;Eu LHP[A^]1=IH]HEHtH H=L= fUHAWAVAUATSHIH@HHHLRHHE1LC#IMffEHEI|$ I+|$tNIILH==HH]H]JHEIuM} I)M~HL=LH]HU1Lt2IH}Ht H}=H@@PH %IH HcHL_hMILCHLFMIWDH1H|LhIL6CHL:MUI1LgMILBHLDMIrJ H9 J H9 1IWH)LHtHfHHLI$M I1 LcMIL>HL.*M IP1 LHHyHHVH<HzH)1f.ooLfpffpfDoD oL0fpfDfpfDHHuLHHyHHVHHzH)1DooLfpffpfDoD oL0fpfDfpfDHHuI)1f.H4HxWH*%ffff.HHH WH*XHtHxWH*'ffffff.HHH WH*XDHtHxWH*&fffff.HHH WH*XDHtHxWH*&fffff.HHH WH*XD HI9H H4H1fH#"IEHMLLL IEH]Ht#HHCHuHHPH=H~RHH;EuLH[A\A]A^A_]=II]IEHtCt H{K=HC=IH]Ht#HHCHuHHPH=LH= UHAVSHPIHQHHE=HþH=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]LH5>H}HtH53(@H_TH.rH5?HjH54AHkHH HQH5[>HlHHHQHHEHHUHuHuIININ0IFIVIF@H]HEHtH`H=HQHH;Eu LHP[A^]A=IH]HEHtHH=L= fUHAWAVAUATSHIH8H@H\QHHE1LS IMffEHEI|$ I+|$tNIILH=+=HH]H]JHEIuM} I)M~HL=LH]HU1LHHH}Ht H}=H8@PH IH ,HcHLkQILHHHQMIrJ ;H9p J H9c 1IWH)LHtH< W*f~HHuH&fffff. 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fUHAVSH IH{OHHELH~L)H@B;HE(J)EHlar inpuHH(He on scaHH Ht operatHHHp: CannoHHHements oHHHGatherElHf@0tHMоL L HrL)HHHH9uRH~A1I1fDH9tHH HHIHxiI;cHLH9|I`A;HH](L)EH56=THMC;CTHMоL  ,IH5W=H]HtLH Et H}XA;HAzOHH;Eu LH [A^]A;HEt H}!A;HW<; ffffff.UHAWAVAUATSHHIHHyOHHEH8H3WOHHHLH@H8LL@;HEEL5wOM~LI@L8LX?;L%wOILHDž(HDž HDžHDžDž0LB;HLHH3H>;HHHCLL8Lt H ?;H>;H5cOHH0>;H8J?;H{xOHH;EuHH[A\A]A^A_]?;HLL8Lt H ;?;H>;HmHeHH5#cOHH=;HH8>;H(:; UHAWAVAUATSHHHhH HwOHHE1H裆IMt$H`H臆IMoHhHxPIt$ I+t$HHHxLL(LHCHxHtL IHDžxF LpHhW)EHEI I+tNHHHH=5 =;ILuLuIHEIwMo I)M~LLU?;MLuMHU1H`CIH}HL(Lt H}|=;HC0I;G0txW)EHE`e=;HH](3 L)EH5=UH>;CUHML L E H}<; LHHC0xHbH`L<]HPLc L+cH}1HpRH}LHhHpIv I+vHHLHpMIIOIEIHcHHH{0HPW)EHEMLhcLH=k L<;ILmO|L}LL<;L}Lh)L L#HK LcPIIGPH0H+KH`H}1HpQH}LHhHpLpIv I+vHHLIL`MIIOIEHPHcHHHH{0HPH8W)EHEMLht@LH=b L;;ILmK\H]LL:;IL]LhE1E1LHMT$L9PMV H8IHDȃMHI)1MɹIIHH HHDž@HXL0LHPMM)LHHPI |BHM11f.I9t HI\HHH9|MXfff.1MALL@ILE1L0LH`L8ffffff.HELPLJ4H`ILpLHL:;LHELJtH`ILHL:;LIL9uL+XLhL0LHLH`Ht7HEJtH8HHpHLb:;ILHLhL@LLmL]MM)LHHPIDI H|VH(H HPH HHfff.ItHItHHH;49uIDHHHXH+HXHPHMMRMIH8IHDȃLHH)H1MɹIIHH HHDž@HXL0ffffff.H`LL)HHHQH |BHu1E1fI9t HI\IHH9|Mfff.E1MLPIu#E1LL@LH5LE1L0LL8fHEH`HMJ4J4HPN,8LILpLHL7;LHEH`HMHhH JLJt)ILHL7;LhLIL9lLH+XL0LHLL@HtMHMHUJHTLPIH8LLpHLL67;ILHLhML@LLmL]LL)HHHQIIDH |WH(HHqH HHffff.ITHITHHH;8uIDLHHH)XH`HMMRloE1E1HpLHM9LHEIHPIHDžXLL)HHHPH |.Hu1E1fI9t HI\IHH9|E1MLpLHK[HPLHH@H`H4HT0;IHM9Lh|LmHuHL)HHHQLHMLXILpIIDH |YH(HHqH HHffffff.ITHITHHH;8uIDLHMMIMt LmL?1;H}HL t H}&1;H}Ht H}1;IHxHDžxHtCt H{0;H0;HiOHH;EuLH[A\A]A^A_]'1;H}-; H}-;H}-;<:IJIE'I>I9IEtRH}G IEI@I)IH}Ht H}A0;H}Ht H}/0;H}Ht H}0;HxHDžxHtCt H{/;H/;L%+; ffff.UHAVSIX/;HHLH[A^]IH/;L*; UHAWAVAUATSHIHHghOHHEH^OHHH{јHzZPHW)EHEEEaxisEHKPHhHULªHhHDžhHtCt H{.;H.;Et H}.;Hu'HgOHH;EuHĈ[A\A]A^A_]#/;.;IHhp H5/=H =H}Lh蝨W)PHDž`0b.;H`(p?L)PHte valueHHHttributeHHH'axis' aHHHInvalid HHHMissing/H@&AH݇=HuHPL質E1H5DOH@OL.; IA(IA}IIPt H`-;LeMt6HEL9t!HX@t Hxk-;HI9uH}LLeQ-;Et H}B-;Et H}3-;LhMt:HpL9t=HX@t Hx-;HI9uHhLp,;Et!L-;LIEt H},;L'; DUHAWAVAUATSHIIHIHeOHHEH{YIID$ID$I$LLHC0@L0L(tlHքHME1LuJc HMID$I;D$sHHID$fffff.LLIM9|RH6IM~B1fID$I;D$tIHHID$LLHIL9|Lc(H0H1J+4HHJHSH@MI$H:H8H9|MH9}HfDHy JHLEHH1LLLLHIDH9LsIAHHL9vHI?I!I@HHzH s1H~HGH)1ff.AALLAD AL0D L0AD@ALPD@LPAD`ALpD`LpHHuHt(HHADA D H HuL9J O4M)DHt$Hfffff.IHIHHuI8rc1DIHITHTITHTITHTIT HT IT(HT(IT0HT0IT8HT8IT@H@H9uIJtHIMgIO HLHHH0HHH8H@W)HDžH01HvLLHH0Ht H8;H(W)HDžHHH9L$Lq;HHHHLZ;H1LLfIEHtLHHHH;(|HDžI0HPID$(HxIL$0HID$ HmvHI|$8HDž0H0HH$IIO0IGPyuID$HHHAPID$I$HHHAPID$L0HH8H H@HHHH(HPLXHH`HHhLpH&SOHHEL(H]HMHMWI*W)MEHHt&IHUH]HH H}L9HH'HHIL$8HHI11!L(HHLHHHH MH~wM]HH:LLHHI E1E11Nc LHLLHI9|6M9~1ELIMOMIMM9|fE1ff.LIHL4HH;HMHH9HtSHtHP(HtNH5z=H}H|HMHvEt"H};HP HuHHHt H;HHtHH8;HSOHH;EuHHh[A\A]A^A_];;IH}; 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H3LeMt6HEL9t!HX@t Hx`3HI9uH}LLeF3Et H}73Et H}(3LM HL9HX@t Hx3HI9uIAIAqI@It H3LeMt6HEL9t!HX@t Hx3HI9uH}LLes3Et H}d3Et H}U3LMt:HL9tDHX@t Hx*3HI9uHL3EL:3xLIAIAPILIt H3LeMt6HEL9t!HX@t Hx3HI9uH}LLe3Et H}p3Et H}a3L MH(L9HX@t Hx.3HI9u{IAIA2IyIt H3LeMt6HEL9t!HX@t Hx3HI9uH}LLe3Et H}3Et H}3L MH(L9HX@t Hx[3HI9uIt H63LeMt6HEL9t!HX@t Hx3HI9uH}LLe3Et H}3Et H}3L Mt:H(L9tDHX@t Hx3HI9uH L(3E&L3LIA(IA}II@t HPH3LeMt6HEL9t!HX@t Hx#3HI9uH}LLe 3Et H}3Et H}3L`Mt:HhL9t=HX@t Hx3HI9uH`Lh3EtlL3bL &$I:IH HtH(i3IH@Ht HHL3H`Ht Hh43Lj3 fUHAWAVAUATSHMLHHHHHHHE1HH'H M,$I\$L)IIHH HH+HHI9y W)HDžHtNLH=W H{3ILLKHH~LLH3ILH1LL6IHHt H3HHOL I)LHHLHRL)HH9 HHBH+L9M $It$L)OIIH11f.u IH;t1HH9r HBP]%LHLHHDHHHA`H9 HPHE HH6H ˂6HDH 86HHHfHHH;EHHĨ[A\A]A^A_]M&MfPHH_PL4LHH3HHIHHLϻHHHQIHLHLHHHH06H 6HDȾI =HHAHHHIHIyHHL!HHIHxW)HHLH=HH>HHx~6H ^61ARHHAHHHALMIHIpHHHIHhxAHpDtDILHHHHtH`H9{HPH0}HDž0mHE1HHHHHIHDHDHPH@ HDILHIHHHH`H9HPH`LpHGHHHLH@HpLL3HDžDžHMHLkLH@HpL3L5HILHDž`HDžXHDžPHDžHDžhH5k6H_1HH 2X6H5X6HDHTH_HLzdHH}t H3LHpLHt 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ffffff.UHAWAVSPIIIvIvI_HtHKHtIH9r1HHIdII!IJHtRH8Hu HH?Ht=HGHtH9r1HHL9t ff.H!L9u H9wu1UIGHxH*HHH H*XHH؃H HxH*AO uH*XAO t Y.H HrHS1HH ^f:  C(\H,HH1H,.HCH9HCLI_MFHCHtI9r1LHII!IJHt HIL0DIGIIGMwIJIHt&H@HKHtH9r 1HHH!IL4IGLHH[A^A_]UHAWAVSPHIHuHCHt Hv3HM~L9vLHH[A^A_]Ir IGL1IFHxH*HHH H*XA^F f:  .C(\H,HH1H,.HC„t Hr"HHȁAHHu3H9HBL97H[A^A_]fUHAWAVSPIIMtJLH=J<w3I?IHtw3MwMFDIs-1HusI?IHtuw3IGHLH)1I7HI7HDI7HDI7HDHH9uHtHIHHHuMWMIMJMtM9r1LIIM!IJ<DII:HHGMtL9r1IHL9t @L!L9tIH<tWHHHt*HWHfff.HHH;Qu HHHuHI IH H HIHH8jLII[H[A^A_]ÿ#v3HH5!3Hqs3HڭGHHH5GHMGH/v3IHv3Lq3 UHAWAVAUATSPIL>MnMtIMLtMM9r1LII`IM!IJHtNHHuDHHt5HCL9t!LtL9r1IHL9t H!L9u L9{ur u3HL{CL{HIFHxH*HHH H*XLHDH MxI*AN u"H*XAN tY.wMKL-IrIU1LH ^f:  3C(\H,HH1H,.HCH9HCL/MnIELtM9r1LIII!IJHtHH AIFIVHI^JHHt)H@IMLtL9r 1IHH!HIHIFHHH[A\A]A^A_]IHs3Ln3 fDUHAWAVSHHHNGHHE0Ys3IHPH H}HP IPHuVHGHIIHPx HGHIH۫GHH;EuLHĘ[A^A_]>s3IIHt Ir3IHt Ir3IIH9uHP HtHP(ILH`H9uHP Ht HP(IL@r3Lvm3 fffff.UHAWAVAUATSH(IIHGHHE1HHIMSI_ I+_W)`HDžptCIILH=Hq3H`N4LpHHq3LhW)@HDžPAIH@HEH@I} HH+HupMMOIO L)Ht7IH`H1WI*YH,HHH9rL`LLHALL蝸HA L苸HAHMIGIO H)t6IH`H1WH*YH,HHH9rL`LLLHAfAH{׷HH{IMHH@HHH)HLH)?H@H@4HLMg M+gW) HDž0tCMILH>ILGo3H N4L0HL0o3L(H{I}PHHwH H,IGIO H)H H`H1WH*YH,HHH9rDIw I+wHH@~H@HHHs JHHH~IHHLo3HHHHXHHHlL`yIHHLLHro3IGIw H)HHH`HhH)HH98Ht:H 1fffff.WH*WH* ^HH9rHH L L`HLHH H Ht H( m3LH@Ht HHl3H`Ht Hhl3HGHH;Eu1LH([A\A]A^A_]111H)HWl3Mm3l3IH`OH56H F6H}L`|H@ AHO4HuH@LE1H5GH~GLll3bH`Ii3Q l3IH H5c6H 6H}L H\ AH6HuHLoE1H5GHF~GLk3k3IH 8H5 6H /6H}L eW)HDž@*k3H(WD)Hs input.HH.Hided as HH(H be provHH Hzes mustHHHes or siHHH of scalHHHOnly oneH@6AH6HuHL_E1H5uGH6}GLj3wj3IH (H5 6H 6H}L UW)HDž j3H(D)Ht index.HHHroi inpuHHHInvalid H@AH 6HuHLE1H5GH^|GLi3i3IHPH5 6H G6H}L}W)HDž@Bi3H(C)Has inputHH(Hrovided HH HUST be pHHH sizes MHHHcales orHHHEither sHf@0.AHP 6HuHLE1H5GHZ{GLh3H e3h3IH;H5 6H 26H}LhW)HDž@-h3H(TD)Hs input.HH.Hided as HH(H be provHH Hzes mustHHHes or siHHH of scalHHHOnly oneH@6AH 6HuHLbE1H5x~GH9zGLg3zg3IH+H5 6H "6H}LXW)HDžPg3HH(=D)H5 6DHh3CDAH 6HuHLE1H5}GHsyGLg3 IAIIAIIAIAPIjIt HXf3LeMt6HEL9t!HX@t Hx3f3HI9uH}LLef3Et H} f3Et H}e3LMHL9HX@t Hxe3HI9uIAIAPIIt He3LeMt6HEL9t!HX@t Hx`e3HI9uH}LLeFe3Et H}7e3Et H}(e3LM HL9HX@t Hxd3HI9uIAIAqI@It Hd3LeMt6HEL9t!HX@t Hxd3HI9uH}LLesd3Et H}dd3Et H}Ud3LMt:HL9tDHX@t Hx*d3HI9uHLd3EL:d3xLIAIAPILIt Hc3LeMt6HEL9t!HX@t Hxc3HI9uH}LLec3Et H}pc3Et H}ac3L MH(L9HX@t Hx.c3HI9u{IAIA2IyIt Hb3LeMt6HEL9t!HX@t Hxb3HI9uH}LLeb3Et H}b3Et H}b3L MH(L9HX@t Hx[b3HI9uIt H6b3LeMt6HEL9t!HX@t Hxb3HI9uH}LLea3Et H}a3Et H}a3L Mt:H(L9tDHX@t Hxa3HI9uH L(a3E&La3LIA(IA}II@t HPHa3LeMt6HEL9t!HX@t Hx#a3HI9uH}LLe a3Et H}`3Et H}`3L`Mt:HhL9t=HX@t Hx`3HI9uH`Lh`3EtlL`3bL &$I:IH HtH(i`3IH@Ht HHL`3H`Ht Hh4`3Lj[3 fUHAWAVAUATSHMLHHHHޘGHHE1HHΧH M,$I\$L)IIHH HH+HHI9y W)HDžHtNLH=W H{_3ILLKHH~LLH`3ILH1LLƶ IHHt H_3HHOL I)LHHLHRL)HH9 HHBH+L9M $It$L)OIIH11f.u IH;t1HH9r HBP]%LٶHL躸HHDHHHA`H9 HPHE HH6H 6HDH 86HH0HfGHH;EHHĨ[A\A]A^A_]M&MfPHH_PL薴LHH^3HHIHHLϻHHHQIHLHLHHHH06H 6HDȾI =HHAHHHIHIyHHL!HHIHxW)HHLH HH>HHx5H ^61AHHAHHHALMIHIpHHHIHhxAHpDtDkILHHHHtH`H9{HPH0}HDž0mHE1HHHHHIHDHDHPH@ HD#ILIHHHH`H9HPH`LpHpGHHHLH@HpLLY3HDžDžHMGLkLH@HpLX3L5GILHDž`HDžXHDžPHDžHDžhH5k5H1HH 25H55HDHTHHLzHH}(t HX3LHpLHt HXX3LW3H5|GHH9W3LWX3HHH0HPLLt$(H\$ L|$HHD$AʼnD$Aĉ$HHHHLhLxptH0I9HtHtHP(HXHP HFHuHuHPHHHtHH9tHPHEHEHpHuHPILpLl$(L}L|$ AĉD$ÉD$D$HH$HHHHHMLH}I9t HtHP(HP H}I9tHPHP(EHP :HDž`H@H`HPHH@H\$(HHD$ Lt$L|$AʼnD$HH$HLHLLx&H`H9tHHtHP( HP HHHHt#HHCHuHHPHT3H6 V3U3IHmH55H 5HLTH AH84HHLE1H51lGHgGLU33U3IHH55H S5HLVW)HDž0T3H(1D)Hbe same.HH'Hshould bHH H tensor HHHd outputHHHinput anHHHRank of H@/AHW5HHLE1H5)kGHfGL|T3 H\Q3 HAaHAHHAHAnHHt HS3L8Mt?H@L9t$HX@t HxzS3HI9uH8LL@ZS3 t H0ES3t H0S3LMHL9 HX@t HxR3HI9uHt HR3L8Mt?H@L9t$HX@t HxR3HI9uH8LL@R3 t H0tR3t H_R3LMt:HL9tDHX@t Hx4R3HI9uHLR3ELDR3LHHQ3HHHH`H9u HP H HP(HHHHHCt H{ZQ3HRQ3HLHpLHt HXQ3LP3H5uGHHO3LP3H#HH}I9uHP HtHP(H}HEH9uHP yHH0I9tHtaHP(YHHHtFHHCHu4HHPH]O3!HHHt H6P3HhK3 UHAWAVAUATSHhL@L0HHHEH GH HMHHHzHHL)HqL)HHH0L1HIL)HH9TH8Hf~H(ffEHEH HH=HH8HNO3HEL N3IL`L OtLpLL>3LhKDJHJHJDHI HILIHHL`HIE112ff.HBHAHLPL8H0H9wHMH0HHMHLE1L(H HIHKTHKTLHH40H9|2KDJJI)JJT;IMH!I H*H@HHHH*HHH J*BHMH HBHHBLf~ẺEf~MUUMHE0Hx H1 HH@0HMH $HuHUHMLELMH@HHfn CB. 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I^H;HI{(uHC H/ C(1 HC H@ [A\A^A_]IM&I^HtH IH}'IL讥' fDUHAWAVATSIL%=M&0R'HW)C )C)H襮 I^H2;HI{(uHC Ho C(1Q HC H@ [A\A^A_]IM&I^HtH2 IH轩'IL' fDUHAWAVATSIL%=M&0蒩'HW)C )C)H I^H;HI{(uHC H诪 C(1 HC H@ [A\A^A_]IM&I^HtHr IH'IL.' fDUHAWAVATSIL% =M&0Ҩ'HW)C )C)H% I^HR;HI{(uHC H C(1 HC H@ [A\A^A_]IM&I^HtH貰 IH='ILn' fDUHAWAVATSIL%[=M'0'HW)C )C)He I_H ;HIHK[A\A^A_]IM'I_HtH IH覧'ILע' ffffff.UHAWAVAUATSHIHZ;HHEA(uIG L趨 AG(1' IG H@ :B>HB>H=B>P(IMA(uI_ LX AG(1 HI_ KH{Hu1 HH{H;HH;EudLHĘ[A\A]A^A_]޼ H=A>Ҧ'`H={A>H=;H5hA>HyUz'H=eA>螦')踦'T'IHh H5j*H *H}Lh2 H;HHH{*HPHHAHG*HuHPL"E1H5;Ht;L' IA(IA}IIPt H`z'LeMt6HEL9t!HX@t HxU'HI9uH}LLe;'Et H},'Et H}'LhMt:HpL9t=HX@t Hx'HI9uHhLpפ'EtL'LIH=?>'L' fUHAVSIH =II^HtH H{'L[A^]o'UH]fDUH]DUHHM;]UHH;]UHAWAVAUATSHIHHH;HHEH8HS;HHHLH@H8Ll'HEEL=;MgLI@L8Lx'L-;ILHDž(HDž HDžHDžDž0HH'HHH)Lץ'HLH)HHHU.LL8Lt H Ӣ'H賡'H5;HHB'H8\'H;HH;EuHH[A\A]A^A_]'HLL8Lt H M'H-'HxHwxHH55;HH觠'HH8輡'H:' fUHSPHHtHsH覬HH[]С'H[]ÐUHơ'HHH@H@H]fUHAWAVATSIL% =M'0股'HW)C )C)Hե I_H;HIHK[A\A^A_]IM'I_HtH苩 IH'ILG' ffffff.UHAWAVAUATSHIH;HHEA(uIG L& AG(1 IG H@ "<>H<>H=;>P(IMA(uI_ Lȡ AG(19 HI_ KH{Hu1n HH{H;HH;EudLHĘ[A\A]A^A_]N H=;>B'`H=c;>H=ǰ;H5P;>HN'H=M;>')('ğ'IHhu H5ڟ*H <*H}LhH3;H@HHH*HPHH&AH*HuHPL E1H5;H;Lr' IA(IA}IIPt H`'LeMt6HEL9t!HX@t Hx'HI9uH}LLe觞'Et H}蘞'Et H}艞'LhMt:HpL9t=HX@t Hx^'HI9uHhLpC'EtLr'LIH=9>b'LV' fffff.UHH%;]UHH;]UHSPHHtHsHHH[]'H[]ÐUH趝'HHH@H@H]fUHAWAVATSIL%=M'0r'HW)C )C)Hš I_Hz;HIHK[A\A^A_]IM'I_HtH{ IH'IL7' ffffff.UHAWAVAUATSHIH;HHEA(uIG L AG(1 IG H@ j7>HK7>H=D7>P(IMA(uI_ L踝 AG(1) HI_ KH{Hu1^ HH{H;HH;EudLHĘ[A\A]A^A_]> H=6>2'`H=6>H=;H56>HJڛ'H=6>')'贛'IHhe H5ʛ*H ٝ*H}LhH;H@HHHל*HPHHAH*HuHPLE1H5;HЭ;Lb' IA(IA}IIPt H`֚'LeMt6HEL9t!HX@t Hx豚'HI9uH}LLe藚'Et H}舚'Et H}y'LhMt:HpL9t=HX@t HxN'HI9uHhLp3'EtLb'LIH=4>R'LF' fffff.UHH ;]UHHm;]UHSPHHtHsH{HH[]鰙'H[]ÐUH覙'HHH@H@H]fUHAWAVATSIL%=M'0b'HW)C )C)H赝 I_Hb;HIHK[A\A^A_]IM'I_HtHk IH'IL'' ffffff.UHAWAVAUATSHIH;HHEA(uIG L AG(1w IG H@ 4>H3>H=3>P(IMA(uI_ L訙 AG(1 HI_ KH{Hu1N HH{H;HH;EudLHĘ[A\A]A^A_]. H=3>"'`H=[3>H=o;H5H3>HFʗ'H=E3>')'褗'IHhU H5*H n*H}LhH۴;H@HHHǘ*HPHHAH*HuHPLE1H5;H;LR' IA(IA}IIPt H`Ɩ'LeMt6HEL9t!HX@t Hx衖'HI9uH}LLe臖'Et H}x'Et H}i'LhMt:HpL9t=HX@t Hx>'HI9uHhLp#'EtLR'LIH=1>B'L6' fffff.UHH;]UHHU;]UHSPHHtHsHvwHH[]頕'H[]ÐUH薕'HHH@H@H]fUHAWAVATSIL%<M'0R'HW)C )C)H襙 I_Hj;HIHK[A\A^A_]IM'I_HtH[ IH'IL' ffffff.UHAWAVAUATSHIH;HHEA(uIG L AG(1g IG H@ z/>H[/>H=T/>P(IMA(uI_ L蘕 AG(1 HI_ KH{Hu1> HH{H;HH;EudLHĘ[A\A]A^A_] H=.>'`H=.>.H=G;H5.>HB躓'H=.>ޓ')'蔓'IHhE H5*H *H}Lhr H;;HHH*HPHHAH*HuHPLE1H5;H;LF' IA(IA}IIPt H`躒'LeMt6HEL9t!HX@t Hx蕒'HI9uH}LLe{'Et H}l'Et H}]'LhMt:HpL9t=HX@t Hx2'HI9uHhLp'EtLF'LIH= ->6'L*' fUHH ;]UHHm;]UHSPHHtHsH֧HH[]頑'H[]ÐUH薑'HHH@H@H]fUHAWAVATSIL%<M'0R'HW)C )C)H襕 I_H;HIHK[A\A^A_]IM'I_HtH[ IH'IL' ffffff.UHAWAVAUATSHIH;HHEA(uIG L AG(1g IG H@ +>H+>H=+>P(IMA(uI_ L蘑 AG(1 HI_ KH{Hu1> HH{H;HH;EudLHĘ[A\A]A^A_] H=W+>'`H=3+>H=;H5 +>H>躏'H=+>ޏ')'蔏'IHhE H5*H *H}Lhr H;H@HHH*HPHHAH*HuHPL E1H5;H;LB' IA(IA}IIPt H`趎'LeMt6HEL9t!HX@t Hx葎'HI9uH}LLew'Et H}h'Et H}Y'LhMt:HpL9t=HX@t Hx.'HI9uHhLp'EtLB'LIH=})>2'L&' fffff.UHH;]UHHu;]UHSPHHtHsHHH[]鐍'H[]ÐUH膍'HHH@H@H]fUHAWAVATSIL%<M'0B'HW)C )C)H蕑 I_Hj;HIHK[A\A^A_]IM'I_HtHK IH֌'IL' ffffff.UHAWAVAUATSHIH;HHEA(uIG L AG(1W IG H@ :'>H'>H='>P(IMA(uI_ L舍 AG(1 HI_ KH{Hu1. HH{H;HH;EudLHĘ[A\A]A^A_] H=&>'`H={&>H=W;H5h&>H:誋'H=e&>΋')'脋'IHh5 H5*H **H}LhbHè;H@HHH*HPHHAHs*HuHPLE1H5ߡ;H;L2' IA(IA}IIPt H`覊'LeMt6HEL9t!HX@t Hx聊'HI9uH}LLeg'Et H}X'Et H}I'LhMt:HpL9t=HX@t Hx'HI9uHhLp'EtL2'LIH=$>"'L' fffff.UHH;]UHH];]UHSPHHtHsHHH[]选'H[]ÐUHv'HHH@H@H]fUHAWAVATSIL%{<M'02'HW)C )C)H腍 I_HR;HIHK[A\A^A_]IM'I_HtH; IHƈ'IL' ffffff.UHAWAVAUATSHIHz;HHEA(uIG L։ AG(1G IG H@ #>H#>H=#>P(IMA(uI_ Lx AG(1 HI_ KH{Hu1 HH{H;HH;EudLHĘ[A\A]A^A_] H=O#>'`H=+#>H=?;H5#>H6蚇'H=#>辇')؇'t'IHh% H5*H *H}LhRH;H@HHH*HPHHAHc*HuHPLE1H5ϝ;H;L"' IA(IA}IIPt H`薆'LeMt6HEL9t!HX@t Hxq'HI9uH}LLeW'Et H}H'Et H}9'LhMt:HpL9t=HX@t Hx'HI9uHhLp'EtL"'LIH=u!>'L' fffff.UHH;]UHHE;]UHSPHHtHsHHH[]p'H[]ÐUHf'HHH@H@H]fUHAWAVATSIL%<M'0"'HW)C )C)Hu I_H ;HIHK[A\A^A_]IM'I_HtH+ IH趄'IL' ffffff.UHAWAVAUATSHIHj;HHEЊM>H.>H='>P(IMA(uI_ L蛅 AG(1 HI_ KH{Hu1A HH{H;HH;EudLHĘ[A\A]A^A_]! H=>'`H=>H=j;H5{>H2轃'H=x>')'藃'IHhH H5*H =*H}LhuH֠;H@HHH*HPHHAH[*HuHPLE1H5;H;LE' IA(IA}IIPt H`蹂'LeMt6HEL9t!HX@t Hx蔂'HI9uH}LLez'Et H}k'Et H}\'LhMt:HpL9t=HX@t Hx1'HI9uHhLp'EtLE'LIH=>5'L)}' UHAVSIH<II^HtH+ H軁'L[A^]鯁'UHAWAVATSIL%S<M'0蒁'HW)C )C)H I_Hr;HIHK[A\A^A_]IM'I_HtH蛉 IH&'ILW|' ffffff.UHAWAVAUATSHIHڹ;HHEЊ}>H^>H=W>P(IMA(uI_ L AG(1 HI_ KH{Hu1 HH{H[;HH;EudLHĘ[A\A]A^A_]鑖 H=>腀'`H=>aH=Ґ;H5>H,/-'H=>Q')k''IHh H5*H ,*H}LhH>;H@HHH**HPHHiAH˅*HuHPLLE1H5b;H#;L' IA(IA}IIPt H`)'LeMt6HEL9t!HX@t Hx'HI9uH}LLe~'Et H}~'Et H}~'LhMt:HpL9t=HX@t Hx~'HI9uHhLp~'EtL~'LIH=>~'Ly' UHAWAVATSIL%<M'0B~'HW)C )C)H蕂 I_H;HIHK[A\A^A_]IM'I_HtHK IH}'ILy' ffffff.UHAWAVAUATSHIH;HHEЊ>H>H=>P(IMA(uI_ L~ AG(1 HI_ KH{Hu1a HH{H ;HH;EudLHĘ[A\A]A^A_]A H=>5}'`H=>qH=r;H5>H+|'H=>}')}'|'IHhh H5|*H G*H}LhH֙;H@HHH}*HPHHAH{*HuHPLE1H5;Hӎ;Le|' IA(IA}IIPt H`{'LeMt6HEL9t!HX@t Hx{'HI9uH}LLe{'Et H}{'Et H}|{'LhMt:HpL9t=HX@t HxQ{'HI9uHhLp6{'EtLe{'LIH=@>U{'LIv' UHAWAVATSIL%<M'0z'HW)C )C)HE I_H;HIHK[A\A^A_]IM'I_HtH IHz'ILu' ffffff.UHAWAVAUATSHIH:;HHEЊ}>H^>H=W>P(IMA(uI_ Lk{ AG(1| HI_ KH{Hu1 HH{H;HH;EudLHĘ[A\A]A^A_] H=>y'`H=>AH=B;H5>H(y'H=>y')y'gy'IHh H5}y*H l*H}LhEH;H@HHHz*HPHHAH+*HuHPLE1H5;H;Ly' IA(IA}IIPt H`x'LeMt6HEL9t!HX@t Hxdx'HI9uH}LLeJx'Et H};x'Et H},x'LhMt:HpL9t=HX@t Hxx'HI9uHhLpw'EtLx'LIH=>x'Lr' UHAWAVATSIL%c<M'0w'HW)C )C)H{ I_H;HIHK[A\A^A_]IM'I_HtH IH6w'ILgr' ffffff.UHAWAVAUATSHIH;HHEЊ]>H>>H=7>P(IMA(uI_ Lx AG(1, HI_ KH{Hu1 HH{Hk;HH;EudLHĘ[A\A]A^A_]題 H=>v'`H=>1H= ;H5>H<%=v'H=>av'){v'v'IHh H5-v*H }*H}LhHv;H@HHH:w*HPHHyAH{*HuHPL\E1H5r;H3;Lu' IA(IA}IIPt H`9u'LeMt6HEL9t!HX@t Hxu'HI9uH}LLet'Et H}t'Et H}t'LhMt:HpL9t=HX@t Hxt'HI9uHhLpt'EtLt'LIH=>t'Lo' UHAWAVATSIL%<M'0Rt'HW)C )C)Hx I_Hb;HIHK[A\A^A_]IM'I_HtH[| IHs'ILo' ffffff.UHAWAVAUATSHIH;HHEЊ>H>H=>P(IMA(uI_ Lt AG(1ܿ HI_ KH{Hu1q HH{H;HH;EudLHĘ[A\A]A^A_]Q H=Z>Es'`H=6>H=ƒ;H5#>H!r'H= >s')+s'r'IHhx H5r*H z*H}LhH.;H@HHHs*HPHH)AHx*HuHPL E1H5";H;Lur' IA(IA}IIPt H`q'LeMt6HEL9t!HX@t Hxq'HI9uH}LLeq'Et H}q'Et H}q'LhMt:HpL9t=HX@t Hxaq'HI9uHhLpFq'EtLuq'LIH= >eq'LYl' UHAWAVATSIL%<M'0q'HW)C )C)HUu I_H;HIHK[A\A^A_]IM'I_HtH y IHp'ILk' ffffff.UHAWAVAUATSHIHJ;HHEЊ >H >H= >P(IMA(uI_ L{q AG(1茼 HI_ KH{Hu1! HH{H˨;HH;EudLHĘ[A\A]A^A_] H=z >o'`H=V >qH=Z;H5C >Ho'H=@ >o')o'wo'IHh( H5o*H w*H}LhUHƌ;H@HHHp*HPHHAH;u*HuHPLE1H5҅;H;L%o' IA(IA}IIPt H`n'LeMt6HEL9t!HX@t Hxtn'HI9uH}LLeZn'Et H}Kn'Et H}n'L i' UHAWAVATSIL%s<M'0m'HW)C )C)Hr I_H;HIHK[A\A^A_]IM'I_HtHu IHFm'ILwh' ffffff.UHAWAVAUATSHIH;HHEЊ>H>H=>P(IMA(uI_ L+n AG(1< HI_ KH{Hu1Ѿ HH{H{;HH;EudLHĘ[A\A]A^A_]鱂 H=>l'`H=>衵H=};H5>HLMl'H=>ql')l''l'IHh H5=l*H t*H}LhH~;H@HHHJm*HPHHAHq*HuHPLlE1H5;HC~;Lk' IA(IA}IIPt H`Ik'LeMt6HEL9t!HX@t Hx$k'HI9uH}LLe k'Et H}j'Et H}j'LhMt:HpL9t=HX@t Hxj'HI9uHhLpj'EtLj'LIH=@>j'Le' UHAWAVATSIL%#<M'0bj'HW)C )C)Hn I_Hz;HIHK[A\A^A_]IM'I_HtHkr IHi'IL'e' ffffff.UHAWAVAUATSHIH;HHEЊ5>H>H=>P(IMA(uI_ Lj AG(1 HI_ KH{Hu1聻 HH{H+;HH;EudLHĘ[A\A]A^A_]a H=>Ui'`H=v>H=y;H5c>Hh'H=`>!i');i'h'IHh H5h*H r*H}LhHF;H@HHHi*HPHH9AHn*HuHPLE1H52;Hz;Lh' IA(IA}IIPt H`g'LeMt6HEL9t!HX@t Hxg'HI9uH}LLeg'Et H}g'Et H}g'LhMt:HpL9t=HX@t Hxqg'HI9uHhLpVg'EtLg'LIH=>ug'Lib' UHAWAVATSIL%<M'0g'HW)C )C)Hek I_H2;HIHK[A\A^A_]IM'I_HtHo IHf'ILa' ffffff.UHAWAVAUATSHIHZ;HHEЊ->H>H=>P(IMA(uI_ Lg AG(1蜲 HI_ KH{Hu11 HH{H۞;HH;EudLHĘ[A\A]A^A_]| H=>f'`H=n>!H=v;H5[>He'H=X>e')e'e'IHh8 H5e*H 6o*H}Lhe߿H;H@HHHf*HPHHAHKk*HuHPLE1H5{;Hw;L5e' IA(IA}IIPt H`d'LeMt6HEL9t!HX@t Hxd'HI9uH}LLejd'Et H}[d'Et H}Ld'LhMt:HpL9t=HX@t Hx!d'HI9uHhLpd'EtL5d'LIH==%d'L_' UHAWAVATSIL%<M'0c'HW)C )C)Hh I_H;HIHK[A\A^A_]IM'I_HtHk IHVc'IL^' ffffff.UHAWAVAUATSHIH ;HHEЊ=H=H==P(IMA(uI_ L;d AG(1L HI_ KH{Hu1 HH{H;HH;EudLHĘ[A\A]A^A_]x H=j=b'`H=F=豢H=r;H53=H\]b'H=0=b')b'7b'IHh H5Mb*H il*H}LhܿH^;H@HHHZc*HPHH虽AHg*HuHPL|޿E1H5x;HSt;La' IA(IA}IIPt H`Ya'LeMt6HEL9t!HX@t Hx4a'HI9uH}LLea'Et H} a'Et H}`'LhMt:HpL9t=HX@t Hx`'HI9uHhLp`'EtL`'LIH==`'L[' UHAWAVATSIL%3<M'0r`'HW)C )C)Hd I_H:;HIHK[A\A^A_]IM'I_HtH{h IH`'IL7[' ffffff.UHAWAVAUATSHIH;HHEЊ=H=H==P(IMA(uI_ L` AG(1 HI_ KH{Hu1葱 HH{H;;HH;EudLHĘ[A\A]A^A_]qu H=2=e_'`H==聠H=o;H5=H  _'H==1_')K_'^'IHh H5^*H i*H}LhؿH};HHH`*HPHHMAHd*HuHPL0ۿE1H5Fu;Hq;L^' IA(IA}IIPt H` ^'LeMt6HEL9t!HX@t Hx]'HI9uH}LLe]'Et H}]'Et H}]'LhMt:HpL9t=HX@t 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HHPHH@0Hu1]HPHA0HHWHp:HHHc^HLf Hffffff.M,$HEHt#HcM09})QULtA~(uWeDuff.;uuH}0HE0Ɖ0H}IHEHcMQULtA~(tLX/AF(Mn IHHHWHPHH0HuH 0>5y(HEL0L8HXtH(tu HH Au31LH@D1ۄHPHpHLIc@ ;Fu@1ۅIH(1fHt~(uHv HHH H;4HH9|WH4$HH5"D"H@H 6D"L LHMXt Hh~MHp:LuLPLML(M?M_PHHH$HXHH5C"H@H C"L LHLXIHR H5B"HpH+}H5B"H{HHHMHHPHUHMH@H@HHMLtLEt H}|pt H}|Hĵ2HH;EuLHĸ[A\A]A^A_]#}HEtH}|puHptuH}||jNHXtEHh^|72Hpt5H}E|*H%HHHHHHp9H}xHFw fffff.UHAWAVAUATSH8LLHIIHH2HHEH8H2HHHHH@H8H'{HEEL-Ӳ2MuLI@L8H3zH2HHHDž(HDž HDžHDžDž0L}HLHAIwIEwIEWH]LEHHHHLLHLL8Ht H `zH@yH5]2HHxH8yH2HH;EuLH8[A\A]A^A_]yzILL8Ht H yHxH PHPIH52HH4xIH8IyLt ffffff.UHAWAVAUATSH8MLHHIHH+2HHEH8H2HHHHH@H8HxLHEEL-L2M}LI@L8HwL52ILHDž(HDž HDžHDžDž0L({HLHLMHHHHL0HHHzLL8Lt H wHvH52HHgvH8wH2HH;EuHH8[A\A]A^A_]xHLL8Lt H rwHRv HMHH5b2HHuHH8vHgr ffffff.UHAWAVAUATSH8HIHHܯ2HHEDžxHEHEHEHEE?E9HEHEHEHEAHxIHPHPH}HP H`Ht&@L3Ct H{ 6vH.vLMuHPHDžPHt vIG@HEW)0) Dž@?HDžHLMMffff.AFI^Iv HDIEVH HL(MH\ML$Mt"IL9r 1HIIffff.MI!H JHL(M AFt MVI^ DLL@IEH9t(MtL9r1IHL9tL!L9AMHtIUL9uzI}HtI} u1MS1fDAT : 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H%HH5)HHH0讷H)HH;EuLHX[A\A]A^A_]>HSHeHHHL0HHLt H耷H`H5u)HH=H0 H臲 ffffff.UHAWAVAUATSHhMLHHIHxH)HHEH0L=F)I_@HLH-)HH@H0HfHnfLOLHEEIGHHIhL0HHLFL5)ILHDž HDžHDžHDžDž(LL軸LLHHHL0HHLt H聲HaH5v)HH>H0 H;)HH;EuHHX[A\A]A^A_]薲HSHeHHHL0HHLt HرH踰H5)HH蕰H0aH߬ UHAWAVAUATSH8Hp)HHEIHAMgIwIDIEWH}EIHH]LsMHutI^LtIL9r1HII II!HEHJHL(MAt MOMgDLELMfIEH9t(LtL9r1IHL9tH!L9AMHtIUL9ukI}HtI} u1M1fDAT A: uHIuW))Dž?HPHA@LpHLDHcA8HIH@ff.M>AI_IwHDIEWHD=HLMHXL0SMMMtIL9r1HII DMI!HJHLHL MAt LLPHXDL8fID$H9t(MtL9r1IHL9tL!L9AL$HtIT$L9u|I|$HtI|$ u,Ma 1ɐAT : uQHH9uF M5 HLMMM裥MMML8HX M$$M'LHMĿ(ףILHHH0ޡHXIGIHHx WH*fHHH WH*XLHDH MxI*u#H*Xt Y.KL-IrIU1L ^f: (H%\H,H HH1H,.HCH9HCH0LIELtLXM9r"1LIILXI!ff.HJHtHIVHILHJIHt-H@IMLtL9r 1IHH!HHL8HIL;@wW))Dž?HPHAXLpHLDHcAPHIH@fffff.M>AI_IwHDIEWHxd9HLMHXL0SMMMtIL9r1HII DMI!HJHLHL MAt LLPHXDL8fID$H9t(MtL9r1IHL9tL!L9AL$HtIT$L9u|I|$HtI|$ u,M1ɐAT : uQHH9uMHLMMMáMMML8HXM$$M'LHMĿ(ILHHH0HXIGIHHx WH*fHHH WH*XLHDH MxI*u#H*Xt Y.KL-IrIU1L ^f: (D%\H,H HH1H,.HCH9HCH,LIELtLXM9r"1LIILXI!ff.HJHtHIVHILHJIHt-H@IMLtL9r 1IHH!HHL8HIL;@wHPHApLhHLDHcAhH;IDHXMeID$(HXHHDMc|$ MtUIfDL3AuHu*fIFHtHLNH0HIuLHxHID$@LpHLDIcD$8Ht{MuZILfJIt H uIL5Ixt HܖEuIHIt H誖Eu#IHіIxt H}{EuQIH袖AIH]H@L3Ct H{ 6H.LMuA?It#I *ItvI kIL H&lIHHt)L3Ct H{ 趕H讕LMuHHDžHuIL肕IHHt+fL3Ct H{ VHNLMuHHDžHt* HhkIHHt+fL3Ct H{ HLMuHHDžHt ʔIH`Ht#L3Ct H{ 覔H螔LMuHPHDžPHtzEt H}kH]Ht,f.L3Ct H{ FH>LMuH}HEHt LV fffff.UHAWAVAUATSHhMLHHIHxH)HHEH0L=)I_@HLH)HH@H0HfHnfLLHEEIGHHIhL0HHLL5y)ILHDž HDžHDžHDžDž(LL苕LLHcLxHHgLHH?HHJLHH"HH-LHHHHLHHLHHHL0HHLt HHH5)HH运H0苑H)HH;EuLHh[A\A]A^A_]HSHeHHHL0HHLt H]H=H5R)HHH0Hd fff.UHAWAVAUATSHhMLHHIHxH)HHEH0L=&)I_@HLH )HH@H0HfHnfL/LHEEIGHHIhL0HHL&L5)ILHDž HDžHDžHDžDž(LL蛒LLHsLxHHwLHHOHHZLHH2HH=LHHHH LHHLHHHL0HHLt HHH5)HHύH0蛎H)HH;EuLHh[A\A]A^A_]+HSHeHHHL0HHLt HmHMH5b)HH*H0Ht fff.UHAWAVAUATSHXIIHHH)HHEH0L-D)I]@HLH+)HH@H0HfHnfLMLHEEIEHHIhL0HHLDL%)ILHDž HDžHDžHDžDž(LHH貏LHHAIvIEvIEVLhHHsLHHKHHHHL0HHLt HaHAH5V)HHH0H)HH;EuHHX[A\A]A^A_]vHSHeHHHL0HHLt H踋H蘊H5)HHuH0AH迆 UHAWAVAUATSHXIIHHH9)HHEH0L-)I]@HLH{)HH@H0HfHnfL蝊LHEEIEHHIhL0HHL蔉L%)ILHDž HDžHDžHDžDž(LHHLHHAIvIEvIEVLHHÌLHHHHHHHL0HHLt H豉H葈H5)HHnH0:Hk)HH;EuHHX[A\A]A^A_]ƉHSHeHHHL0HHLt HHH5)HHŇH0葈H UHAWAVAUATSHXIIHHH)HHEH0L-)I]@HLH˞)HH@H0HfHnfLLHEEIEHHIhL0HHLL%G)ILHDž HDžHDžHDžDž(LHHRLHH*AIvIEvIEVLHHLHH HHHHL0HHLt HHH5)HH辅H0芆H)HH;EuHHX[A\A]A^A_]HSHeHHHL0HHLt HXH8H5M)HHH0H_ UHAWAVAUATSHXLLHIIHH˾)HHEH0L5&)M~@LHH )HH@H0HfHnfH/HEEIFHHIhL0LLH-L=)ILHDž HDžHDžHDžDž(HL袇HLHz LA$It$IEt$IET$HI HHTHHH( HHwHEwHEWH HHHHH LHHHL0HHLt HHՂH5)HH貂H0~H)HH;EuLHX[A\A]A^A_]HSHeHHHL0HHLt HPH0H5E)HH H0قHW~ ffffff.UHAWAVAUATSHXIIHHHɻ)HHEH0L-$)I]@HLH )HH@H0HfHnfL-LHEEIEHHIhL0HHL$L%)ILHDž HDžHDžHDžDž(LHH蒄LHHjAIvIEvIEVLHHHSLHH+HH HHL0HHLt HAH!H56)HHH0ʀH)HH;EuHHX[A\A]A^A_]VHSHeHHHL0HHLt H蘀HxH5)HHUH0!H{ UHAWAVAUATSHH(IH#)HHEIHHIcO8HAW)P)@Dž`?IG@LpHLDIHL0IL`A$I\$It$HDIET$H}HLLLHMH8L DMbMtIL9r1HIIfMI!H@JH L(MHt LqHYDffff.IEH9t(MtL9r1IHL9tL!L9AMHtIUL9usI}HtI} u1M71fDAT : uEHH9uMHLML MH8MmM3L0Mſ(#~ILHHH*|H8IFIHXHxWH*&fffff.HHH WH*XH HHH HxH*`u"-ffff.H*X`t Y.H ?HrHW1H ^f: ("%\H,H HH1H,.HCH9HCH@ 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LYHL{ǃAKHH[A^A_]IHh LV fffff.UHAWAVSPIIH KHH;=>p)t LBYHLǃIKHHH[A^A_]IH LU ffff.UHAWAVSPIIHX KHH;=o)t LXHL[ǃKHH;=to)t LxXHL!HH[A^A_]IH( LFU fffff.UHAWAVSPIIH KHH;=n)t LXHL諍ǃKHHu1跡HHL5 HH[A^A_]IH| LT fUHAWAVSPIIH KHH;=^n)t LbWHL ǃK HHu1HHL HH[A^A_]IH LS fUHAWAVAUATSPIIId AL$I$H;=m)t LVI$LcAL$ADŽ$IM~L9tKMt$At$ff.D+A;t$u LTA6ID$ Hc΍VAT$D,HI9uLH[A\A]A^A_]IL LS UHAWAVAUATSPIII AL$I$H;=l)t LUI$L胋AL$ADŽ$IM~L9tKMt$(At$(ff.L+A;t$,u LTA6ID$0Hc΍VAT$(L,HI9uLH[A\A]A^A_]IL L9R UHAWAVATSIHI ANIH;=k)t HUIH詊ANAdžIMgL9t)M~8fLSHHTHI9uL[A\A^A_]HLk HQ UHAWAVATSIHI ANIH;=Lk)t HPTIHANAdž IMgL9M~PDIF`Ht'IcNX09}-QAVXHDUffffff.Av\f.A;v\uL0IF`0Ɖ0I?螝IN`IcVXrAvXHDHH HI9oL[A\A^A_]HLF HdP fff.UHAWAVATSIHI ANIH;=j)t H SIHɈANAdž IMgL9M~hDIFxHt'IcNp09}-QAVpHDUffffff.Avtf.A;vtuLIFx0Ɖ0I?nINxIcVprAvpHDHH? HI9oL[A\A^A_]HL H4O fff.UHSPHH HH[]ÐUHAWAVATSAIIHt KHH;=h)t LQHLwKHH;=h)t LQHLJKDH[A\A^A_]IHH LfN fffff.UHAWAVSH8HH)HHEu܉U؉9/9}+1IHʋ)HH;EuH8[A^A_]/S(RIHEH$H5"H SL SH}LEHOHuLPH^)HIIG IGIG1H5i)He)LR IIEt H}RtLNRLHM UHAWAVAUATSHIHڊ)HHEI$PI $H W)EEHEHn_inputsHUHnum_scanHUHxEHuLLEt H}|QLhHxL)I$LP H`HhH)HXW)@) )HDžPƅHDžHput_axesHHDž0Hscan_inpHƅI$HLHLh0Lcx(W)EHEMEJ<PHH]H]KDN LMJ HHHLH"H9sHLM9I?I!IpHHWH s11H1E1E11iHrH)1ADALLAD AL0D L0AD@ALPD@LPAD`ALpD`LpHHuHt8H4Hffffff.AD5AL5D3 3H HuL9JKtODI)DHtHHH HHHuI8rc1DH<H< H|H| H|H| H|H| H| H| H|(H| (H|0H| 0H|8H| 8H|@H@H9uHL)HLtLuH@Ht'HHMmNMW)@HDžPH@LHLPt H)NHHH+@HH;h(NHHH+@IHHpH3 H$H5HHOLgOH}HpLhdHuL9KH~)HIIF IFIF1H5e)Ha)LMHHHhHEH@HMfW)ƅ HDžHput_axesHHscan_outHƅI$HLHLh0Lcx(W)EHEME J<LHH]H]KDN LMJ HHHLH+H9sHLM9I?I!IpHHWH s11H1E1E11rHrH)1fADALLAD AL0D L0AD@ALPD@LPAD`ALpD`LpHHuHt8H4Hffffff.AD5AL5D3 3H HuL9JKtODI)DHtHHH HHHuI8rc1DH<H< H|H| H|H| H|H| H| H| H|(H| (H|0H| 0H|8H| 8H|@H@H9uHL)HLtLuH Ht'H(MmJMW) HDž0H L(L0t H)JH(H+ HH;X(JH(H+ IHHpH3 H$H5HKLKH}HpLXkHuL9GHz)HIIF IFIF1H5a)H])LI H(HXHEH HMbW)HDžHHx8W)HDžH} HDžHx1Lfffff.I$LPH9vQI$LHPHt?I$LHPH@H(u H@ @uuH@ DhADE1I$LLLPHHjx(`H;hsVLLL舫EtHLHHH;NHHHfDEMH@ LpMuL5,L+hH@B4W)pHEƅpHput_axesHqHAHscan_inpHEAV Hp/pt H}3GIF(HcH|HuaHLpLikHH;skHaKMLpL9t HLW]H0Tffff.HH;uaLHAoLH0^HL_MMLNHHHpHH;s HHLL臃HHHH;xRI$H@0W)pHEƅpDžqbodyƅuHpLIpt H}EMW)pHEHxtn1ALpH1HxHMIHDžH9s"HHxL;xrLHEL;xrIHHHpLLHHpHt HxDI9u41E1I9&L)IIL;`LxHDžH1MtI$LHP(IA(uMo A~(t/(LEAG(1iIMo A~(Mv H;hsJAF_MvA]IEكAMHLH(A(LtL>EAG(1ߎIG AN HH AFL`MnHp^ IcU HH+hH 4W)HDžƅ Hput_axesHHAHscan_outHƅHHXRHt HCtHctE1DIE(N|HEHtHcM09}$QUHDMuff.;uuH}@HE0Ɖ0H}譋HMHcUruHDHL? 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H56' HH H0 HH  UH]f.UH] fDUH H !"H]fDUHHu!"H]UH]f.UH] fDUHAWAVAUATSHHIHm; HHEIELPHHMHW)EHE1L5"L}Le@IELHPx(LuHH I MHEH;EsHHEf.LLHH;]rHEHMH9t(HPH9tHq;Pu HH9u H9{IE1LP(HÃ{(uL{ HC(1NIL{ AOI_Ht {(uL{ 11SHI_{(tHC(1KLIL{ HEAOAG HEH Hc]E1IELPL9IELLPHIELLP H@H(uH@ @uH@ @II9rIE1LPx(uH@ HǾ"HXHuH"AOIHu1JHIHcH}rSL5"L%I"fIELHPx(LuHH HyHuLLRkHH;]rH}Ht H} H8 HH;EuHH[A\A]A^A_] ( IH58H%H}oAHuL Ha0 HIIF IFIFE1H5 H L (B IH5HH}hAHuL H/ HIIF IFIFE1H5 Hz L oHHEt H} Et9L /HHEt H} Et%L HH}Ht H}Y H  UHH H1H9NHD]DUHH="]UHAWAVAUATSHHHIHH6 HHEH0L%;5 M|$@LLH! HH@H0HfHnfLC HEEID$HHIhL0LLL@ L54 ILHDž HDžHDžHDžDž(LL LLH荃LH HHHlLHHHL0HHLt H Hf H5{ HHC H0 H@5 HH;EuLHH[A\A]A^A_] HSHeHHHL0HHLt H H H5 HH H0j H  UHAWAVAUATSHHHIHHp4 HHEH0L%2 M|$@LLH HH@H0HfHnfL HEEID$HHIhL0LLL L532 ILHDž HDžHDžHDžDž(LLE LLHLH( HHHLH譅HHL0HHLt H H H5  HH H0 H2 HH;EuLHH[A\A]A^A_]/ HSHeHHHL0HHLt Hq HQ H5f HH. H0 Hx  UHAWAVAUATSHIIIuMeI)MIIGHH> H?MuI)LHHH9w`II9LBMLEHuI9vK HH59 H H|0 HHH5N H H LEHuIJ<k HHuLEN4ABN|M~HLI LI]M}MuHtHH[A\A]A^A_] H[A\A]A^A_]E11L1 IH L  fff.UH]f.UH] fDUH H "H]fDUHH"H]UH]f.UH]^ fDUHAWAVATSH0IH/0 HHEI1LPx(uH@ H"H@HuH""H["x(HuHH Da DeILPx(uHX C EA91I1LP(HÃ{(uLs H C(1CILs ANI^Ht {(uLs 11HHI^{(tHC(1dAILs ANEf ILPHrI1LPH^I1LPH@H(uH@ @u5,H@ @ILPH ILPHILP@H@H(uH@ @uH@ @I1LPx(uH@ Hȳ"H@HuHP"x(uH@ H}"HXHuH="ANI~Hu1?HI~HILPx(uH@ H("HxHuH="L `H- HH;Eu H0[A\A^A_] ( HH5#H|LH}HMLMAHuH H=% HHHC HCHCE1H5 H Ho  IIEt H} EtH+ L%  ffff.UHH %H1H9NHD]DUHHM"]UHAWAVAUATSHhMLHHIHxH[, HHEH0L=* I_@HLH HH@H0HfHnfL LHEEIGHHIhL0HHL L5* ILHDž HDžHDžHDžDž(LL+ LLHyLxHH LHHxH0L HH LHHxH0L LHQ}HHL0HHLt H H H5 HHw H0C Ht* HH;EuLHh[A\A]A^A_] HSHeHHHL0HHLt H H H5  HH H0 H  @UH]f.UH] fDUH H c"H]fDUHHM"H]UH]f.UH]n fDUHAVSHH1HP(IH1HPx(uH@ H"HpHuH5:"L[A^]^fffff.UHH EH1H9NHD]DUHH"]UH]f.UH] fDUH H "H]fDUHH"H]UH]f.UH]n fDUHAVSHH1HP(IH1HPLH[A^]~fffff.UHH UH1H9NHD]DUHH"]UH]f.UH] fDUH H "H]fDUHHm"H]UH]f.UH] fDUHAVSHH1HQ(IA~(uI^ LAF(1d9HI^ C KH{Hu18HH{[A^]rfUHH 5H1H9NHD]DUHH "]UH]f.UH] fDUH H "H]fDUHH"H]UH]f.UH]~ fDUHAWAVAUATSHhIHM& HHEI$1LPx(uH@ H"Dx I$1LP(HÃ{(uLs HxC(19ILs ANI^Ht {(uHC .1?HI^{(tH-C(17HC HDx I$1LPx(uH@ @u=H۪"@,I$1LPx(uH@ H"LpMuL5r"E~ D}ffEHEEEaxisEI$HuLHt xN1Et H} D}]D9A9yAD}I$LPHuhI$HffEEHEHkeepdimsHMEHuLHEt H}t HHAŻI$LPx(uH@ H"H@I$LPH@ffEHEtL5H}H'kHuHUHMH}rgAHEOH}HaHuHUHMH}H}Ht H}e LmH}I9}!HcEIN(HDx(u#Lx L}I$LPx(uH@ HHHY"H@HuH"x t+H}HEH9tHHHH)HHHs;1HH}HLHHHHHH HIM1I?I!IXHHrH sf1fHutH^H)f1fofoDfoL o\0od@ffoTPffoD`foLpfHHuHt"HTHoRfofH HuffpNffH~L9tJ HHH9uHUI9HH9t$HOH9t'HHpHH;Qu HH9u HH9uHHt I}I E1HxI$1LP(HÃ{(uL{ HC(14IL{ AOI_Ht {(uL{ 11:HI_{(tH9C(12IL{ AOMgMEAF HM|$1fDMH9t}ID$(HtIcL$ 09}QAT$ H|GAt$$A;t$$uL(ID$(0Ɖ0I|$W0HID$(IcL$ QAT$ H|IF(HtߞAF HHcH9l11IMgE7LLHcEIL$(L|AG(HxH~tL誖AG(I_ GuIG AG(0u+Mw L;5tMtAt I~W LO H6 HH;EuHh[A\A]A^A_] (7 IH5H_LH}HMLM޲AHuL H HIIF IFIFE1H5gH`L  ( IH5HH} AHuL H\ HIIF IFIFE1H5HL 늿(@ IH5HH}AHuL H HIIF IFIFE1H5HxL ( IH5HMLH}HMLM0AHuL Hb HIIF IFIFE1H5HL H% HHHEt H} EtPL? FHHEt H} EtML CHH}HtH} HH}HtH}HEt H} H  fDUHH H1H9NHD]DUHH"]UHAWAVAUATSHhMLHHIHxH  HHEH0L=f I_@HLHMHH@H0HfHnfLo LHEEIGHHIhL0HHLf L5 ILHDž HDžHDžHDžDž(LL LLHhLxHH LHHhHH0L HH LHH`hHH0L LHlHHL0HHLt Hh HH H5] HH% H0 H" HH;EuLHh[A\A]A^A_] HSHeHHHL0HHLt H H H5 HH H0L H  fUH]f.UH]n fDUHf H "H]fDUHH"H]UH]f.UH] fDUHAWAVAUATSHHIH HHEI1LPx(uH@ HX"H@HuH"x(uH@ H "Dx I1LP(HÃ{(uHC HC(1*HC HDx ILPHI1LPHI1LPf.H@H(uH@ @uwnH@ @`IHW)EHEEEaxisEHuLHEt H} HEIHW)EEHEHnew_axisHMEHuLHEt H}o Ht DE1I1LPx(uH@ H"H@HuHH"x(uH@ Hu"L`MuL%5"AAD$ 1AE)ЉEuM9U9MyDEI1LP(HÃ{(uLs H C(1(ILs ANMnMu1(IMn}Mu1fIE(HtAM 09}AM LDAu$f.A;u$uLIE(0Ɖ0I}-&IM(IcU rAu HDEH9t2HEIU(H|H9 )ID$(HcHt菔HcEH9H[NH HH;EuHH[A\A]A^A_] ( IHEHD$HH$H5WHLe H}HMLMAHuL H HIIF IFIFE1H5HLG ( IH5ѫHH} AHuLW H HIIF IFIFE1H55H.L r( IH5]HjH}XAHuL H* HIIF IFIFE1H5HL\  H%H HHEt H} EtL HEt H} H  fDUHH H1H9NHD]DUHH!]UHAWAVAUATSHhMLHHIHxH+ HHEH0L= I_@HLHmHH@H0HfHnfL LHEEIGHHIhL0HHL L% ILHDž HDžHDžHDžDž(LL LLH_LxHH LHH_H0L HH LHH_H0L H]H~ LHHV_HE0L` LHcHHL0HHLt Hb HB H5WHH H0 H HH;EuLHh[A\A]A^A_]{ HSHeHHHL0HHLt H H H5HHz H0F H  UHAVSHIHP HHEH`HhH5|HxAHD$$H H L@ 1E1H`9D$$Hn H Lb91E1Ho=H5 H!L  HNH5,9H7!L< HkNH d!H0H0HPHH4H5 HUH5H HH5XHLHHPH9t HtHP(HP H`H HH;EuLH[A^]5 I"IHPH9uHP HtHP(H`L  fffff.UHAWAVSHIHN HHEH`HfD$$HPH eL 1E1H7D$$HtLH 7LvE1HK7D$$HH LG 1E1HZ;HW)HDž fDžTƅM-HH>PW)HDž@c H()Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHX 1E1H7HW)HDž fDžTƅ)HHLW)HDž0 H()Hry type.HHHrbitraryHHH be of aHHHnsor canHHHInput teH@&HHHH8H5T H!L~HGH !H0H0HPHHH5^[HH5uH!HH5HPLH%HPH9t]HtHP(t Ha LMtQHL9t6HX@t Hx5 HI9uHHP uLL t H H`ܞH HH;EuLH[A^A_]/ IkIuS]IIIHPH9tHtHP(uHP t HM LMtGHL9tfffff.HX@t Hx HI9uHL t H H`۞L  LfUHAWAVSHIH HHEH`H_D$$HHH ]L 1E1H/D$$H KH ߲L~ 1E1H3HW)HDž fDžTƅ%HHHW)HDž0 H()Hry type.HHHrbitraryHHH be of aHHHnsor canHHHInput teH@&HHHH4H5 Hb!L H&DH !H0H0HPHHH5WHH5HaHDH5ΰ!HLHeHPH9t]HtHP(t H LMtQHL9t6HX@t Hxu HI9uHHP uLLD t H / H`ٞH  HH;EuLH[A^A_]o IkIuS]IIIHPH9tHtHP(uHP t H LMtGHL9tfffff.HX@t HxU HI9uHL: t H % H`؞LO  LfUHAWAVSHIH HHEH`H@[H5HϯAHD$$HH L 1AH+D$$HH L 1E1H/HW)HDž fDžTƅ!HHDW)HDž0 H()Hnsor typHH Ho any teHHH types tHHHn outputHHHConstraiHf@(e.@*HHHH1H _!H0H0HPHH'H5HHH5H H|H5uHLHHPH9teHtHP(t H LMtYHL9t>fHX@t Hx HI9uHHP uLLt t H _ H`K՞H< HH;EuLH[A^A_] IuXbI]IIIHPH9tHtHP(uHP t H LMtGHL9tfffff.HX@t Hx HI9uHLj t H U H`AԞL  LfUHAWAVSH(IHHHEH`HpWD$$Ht H Ll 1E1H?(D$$H?H L; 1AHK,HW)HDž fDžTƅ>HH/AW)HDž@T H(Ҝ)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHI-HDžH5eH%LHH5r\HE1H>H !H0H0HPHHH54ZH8H5ݰH HlH5HLHݞHPH9teHtHP(t H LMtYHL9t>fHX@t Hx HI9uHHP uLLd t H O H`;ўH,HH;EuLH([A^A_] IkIuS]IIIHPH9tHtHP(uHP t H LMtEHL9tdff.HX@t Hxu HI9uHLZ t H E H`1ОLo  LfUHAWAVSHIHHHEH`H`SD$$HK=H I=L\ 1E1H/$D$$H"QH ?=LBME1H#D$$HQ=H pLME1H#D$$Hi=H LLAH#D$$HUH LLAHY#D$$Hg H /=LU 1E1Hh'HW)HDž fDžTƅ[HHL<W)HDž@q H()Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHf(H5fKH!L(<H7H !H0H0HPHH]H5KH~H5#H HH5<HLH؞HPH9tkHtHP(t H LMt_HL9tDffffff.HX@t Hxվ HI9uHHP uLL褾 t H 菾 H`{̞HlHH;EuLH[A^A_]Ͼ IcIuKUIIHPH9tHtHP(uHP t H LMt?HL9tJDHX@t HxŽ HI9uHL誽 tH 蕽 LIH`w˞L赸  ffff.UHAWAVSHIH>HHEH`HNH5HE1HD$$Hm8H rML~ 1E1HQD$$HH LM 1E1H`#HW)HDž fDžTƅSHHD8W)HDž@i H()Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH^$H /!H0H0HPHHwH5XHH5=HHH5VHLHԞHPH9teHtHP(t H) LMtYHL9t>fHX@t Hx HI9uHHP uLLĺ t H 诺 H`ȞHHH;EuLH[A^A_] IuXbI]IIIHPH9tHtHP(uHP t H LMtGHL9tfffff.HX@t Hxչ HI9uHL躹 t H 襹 H`ǞLϴ  LfUHAWAVSHIH^HHEH`HJHHDž(H5H7L(HD$$Hv4H UL 1E1HZD$$H UH YLmDE1H&D$$HH L" E1HD$$H H L 1E1HHW)HDž fDžTƅHH3W)HDž@ H(])Hsor typeHH(H any tenHH Han be ofHHH types cHHHd outputHHHInput anHf@0.HHHH H5 CH!L3H9/H :!H0H0HPHHH5>H#H5ȣHt HWH5zHLHxОHPH9t`HtHP(t H贶 LMtTHL9t9@HX@t Hx腶 HI9uHHP uLLT t H ? H`+ĞHHH;EuLH[A^A_] IsIIuS]IIIHPH9tHtHP(uHP t H蕵 LMt?HL9t^DHX@t Hxe HI9uHLJ t H 5 H`!ÞL_  LfUHAWAVSHIHHHEH`HPFD$$H;0H PLL 1E1HD$$HPH PLE1HD$$HH L E1HD$$H H LPL 1E1HHW)HDž fDžTƅ HH/W)HDž@ϳ H(mY)Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHHH !H0H0HPHHH5>HH5HV H9H5×HLHZ̞HPH9tbHtHP(t H薲 LMtVHL9t;fDHX@t Hxe HI9uHHP uLL4 t H  H` HHH;EuLH[A^A_]_ IuXbI]IIIHPH9tHtHP(uHP t H} LMtGHL9tfffff.HX@t HxE HI9uHL* t H  H`L?  LfUHAWAVSHIHHHEH`H0BHDž(H5|vHL(HD$$H+H LL 1E1HD$$HLH љL;E1HD$$H_H `L E1HjD$$Hx H bLf 1E1HyHW)HDž fDžTƅlHH]+W)HDž@肯 H(0U)Hsor typeHH(H any tenHH Han be ofHHH types cHHHd outputHHHInput anHf@0.HHHHH5:H!LE+H&H !H0H0HPHHzH55HH5@H HH5Y*HLHǞHPH9thHtHP(t H, LMt\HL9tAfff.HX@t Hx HI9uHHP uLLĭ t H 语 H`蛻HHH;EuLH[A^A_] IkIuS]IIIHPH9tHtHP(uHP t H LMtGHL9tfffff.HX@t Hxլ HI9uHL躬 t H 襬 H`葺Lϧ  LfUHAWAVSHIH^HHEH`H=HDž(H5 rHL(HD$$H~'H (HL 1E1HbD$$HHH Lu7E1H.D$$H< H ML* 1E1H=HW)HDž fDžTƅ0HH!'W)HDž@F H(P)Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHHBH5B6H!L'Hp"H !H0H0HPHH9H5NHZH5H HH5HLHÞHPH9tgHtHP(t H LMt[HL9t@ff.HX@t Hx赩 HI9uHHP uLL脩 t H o H`[HLHH;EuLH[A^A_]诩 IkIuS]IIIHPH9tHtHP(uHP t Hͨ LMtGHL9tfffff.HX@t Hx蕨 HI9uHLz t H e H`QL菣  LfUHAWAVSHIHHHEH`H9HDž(H5mHQL(HcD$$H>#H CLO 1E1H" D$$HCH /L53E1H D$$H H ̓L 1E1H HW)HDž fDžTƅHH"W)HDž@ H(L)Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHHH52H!L"H0H 1!H0H0HPHHH5HH5Hk HNH5؊HLHoHPH9tgHtHP(t H諥 LMt[HL9t@ff.HX@t Hxu HI9uHHP uLLD t H / H`H HH;EuLH[A^A_]o IkIuS]IIIHPH9tHtHP(uHP t H荤 LMtGHL9tfffff.HX@t HxU HI9uHL: t H % H`LO  LfUHAWAVSHIHHHEH`H@5H5HRE1HD$$H H L 1E1HD$$HH L 1E1H HW)HDž fDžTƅHHW)HDž@ H(z)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH H !H0H0HPHHH5/;H8H5ݎH HlH5IHLH荻HPH9teHtHP(t Hɡ LMtYHL9t>fHX@t Hx蕡 HI9uHHP uLLd t H O H`;H,HH;EuLH[A^A_]菡 IuXbI]IIIHPH9tHtHP(uHP t H譠 LMtGHL9tfffff.HX@t Hxu HI9uHLZ t H E H`1Lo  LfUHAWAVSHIHHHEH`H`1H5HAHD$$H*H aL; 1E1HD$$HGH L 1E1HHW)HDž fDžTƅHHW)HDž@& H(v)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH l!H0H0HPHH4H5>HUH5H H艾H5HLH誷HPH9tbHtHP(t H LMtVHL9t;fDHX@t Hx赝 HI9uHHP uLL脝 t H o H`[HLHH;EuLH[A^A_]话 IuXbI]IIIHPH9tHtHP(uHP t H͜ LMtGHL9tfffff.HX@t Hx蕜 HI9uHLz t H e H`QL菗  LfUHAWAVSHIHHHEH`H-H50HAHD$$Hc H L[ 1E1H.D$$H< H {L* 1E1H=HW)HDž fDžTƅ0HH!W)HDž@F H(r)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH;H !H0H0HPHHTH5,HuH5HH詺H53HLHʳHPH9tbHtHP(t H LMtVHL9t;fDHX@t Hxՙ HI9uHHP uLL褙 t H 菙 H`{HlHH;EuLH[A^A_]ϙ IuXbI]IIIHPH9tHtHP(uHP t H LMtGHL9tfffff.HX@t Hx赘 HI9uHL蚘 t H 腘 H`qL诓  LfUHAWAVSH8IH>HHEH`H)H5,HӶAHW)HDž ƅDžDCRH5O7HLHD$$H; H L3 1E1HD$$H H ˶L 1E1HHW)HDžfDžTƅHHW)HDž@ H(n)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH d!H0H0HPHH,H5(HMH5H螾 H聶H5 {6HͼLH袯HPH9tjHtHP(t Hޕ LMt^HL9tCfffff.HX@t Hx襕 HI9uHHP uLLt t H_ t H J H`6H'HH;EuLH8[A^A_]芕 IumwIrIIIIHPH9tHtHP(uHP t H蓔 LMt=HL9tqHX@t Hxe HI9uHLJ t H5 t H H` LJ  Lffff.UHAWAVSHIHHHEH`H0%D$$H4 H L, 1E1HD$$HH L E1HD$$H٢ H FLǢ 1E1HHW)HDž fDžTƅHHW)HDž@ H(aj)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH5t H!LH H !H0H0HPHHϵH5,HH5~HAH$H5vHpLHEHPH9t]HtHP(t H聑 LMtQHL9t6HX@t HxU HI9uHHP uLL$ t H  H`HHH;EuLH[A^A_]O IkIuS]IIIHPH9tHtHP(uHP t Hm LMtGHL9tfffff.HX@t Hx5 HI9uHL t H  H`L/  LfUHAWAVSH8IHHHEH`H !HW)ƅHDž HnearestHH5.H/LHYD$$H˞ H ZLŞ 1E1HD$$H H ,LE1HdD$$Hp H L` 1E1HsHW)HDžfDžTƅfHHW W)HDž@| H(J)Hr types.HH/Hl tensorHH(HY' to alHH Houtput 'HHH'X' and HHHn input HHHConstraiH@7HHHHqH º!H0H0HPHH花H5)0H諲H5PzH H߭H5irH+LHHPH9thHtHP(t H< LMt\HL9tAfff.HX@t Hx HI9uHHP uLLԌ t H迌 t H 誌 H`薚HHH;EuLH8[A^A_] IumwIrIIIIHPH9tHtHP(uHP t H LMt=HL9tqHX@t Hxŋ HI9uHL誋 t H蕋 t H 耋 H`lL誆  Lffff.UHAWAVSHIH.HHEH`HW)ƅHDž HnearestHH5Y*HzLHDž @H5 H{L HHDžH5H|LHW)ƅHDžHhalf_pixHfDželƅH5,HLHW)ƅ$HDžHefer_floHHround_prHfDžorƅH5H|LHDžH5|H~}LHD$$H H ~L 1E1HD$$H}H }L E1HD$$HH ~LE1HTD$$HH LKAHD$$H) H L̈ 1E1H,HW)HDžƅfDžT1ƅHH W)pHDž@. H()pHr types.HH/Hl tensorHH(HY' to alHH Houtput 'HHH'X' and HHHn input HHHConstraiH@7HHHpH#H5 H!L:HQH Ҵ!H0H0HPHHH5H;H5sH茯 HoH5kH軭LH萠HPH9thHtHP(pt H̆ LMt\HL9tAfff.HX@t Hx蕆 HI9uHHP puLLd uTu`ult H 4 H` HHH;EuRLHĈ[A^A_]H tH tHх u3 III UIIpudnIH\ IHPH9tHtHP(puHP pt H LMtDHL9~fDHX@t HxՄ HI9uHL躄 EuEuQt H 膄 H`rL  LItHS tH> uff.UHAWAVSHIHHHEH`H`D$$Hd H )L\ 1E1H/D$$H= H {L+ 1E1H>HW)HDž fDžTƅ1ܞHH"W)HDž@G H(Z)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH xL E1H D$$H H 'L 1E1HHW)HDž fDžTƅ؞HHW)HDž@% H(V)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH5~ H!L[HHH I!H0H0HPHHH5t H2H5jH胦 HfH5b`H貤LH臗HPH9t_HtHP(t H} LMtSHL9t8HX@t Hx} HI9uHHP uLLd} t H O} H`;H,HH;EuLH[A^A_]} IkIuS]IIIHPH9tHtHP(uHP t H| LMtGHL9tfffff.HX@t Hxu| HI9uHLZ| t H E| H`1Low  LfUHAWAVAUATSHIHHHEHPH\ HDžH5AHuLH?D$$HH vL{ 1E1HD$$HPH wL E1HD$$HnH hL@ E1HD$$H H UL@ 1E1HHW)HDžƅfDžT1ƅHHL(W)HDž0z H()Ic types.L`I numericLhI to onlyLpIns inputLxHConstraiH@'HHHHHW)HDžƅfDžT2ƅHHW)HDž0y H(!)L`LhLpLxIConstraiL0@'HHHHHW)`HDžpƅ`fDžaT3ƅcўL(HHHW)0HDž@ y H@(=)0Hor type.HHH tensor HHHn to anyHHL0@H`HHH0H0H !H H H@HHIH5 HjH5eH軠 H螘H5(]HLH近H@H9tgHtHP(0t H@w LHMt[HPL9t@ff.HX@t Hxw HI9uHHHP 0uLLPw `t Hpw t Hjw LMtIHL9t.f.HX@t Hx5w HI9uHLLw t Hw t Hv LMtJHL9t/ff.HX@t Hxv HI9uHLLv t Hv HPlH]HH;EuLH[A\A]A^A_]v I0II!IIryIqI`I+2IIIH@H9tHtHP(0uHP 0t H@lu LHMt?HPL9t$HX@t HxAu HI9uHHLLP!u `t Hp u t Ht LMt?HL9t$HX@t Hxt HI9uHLLt t Ht t Ht LMtH5 H!LpHH !H0H0HPHHH5 HH5_HW H:H5WH膙LH[HPH9t HtHP(HP H`萀HHH;EuLH[A^]r I"IHPH9uHP HtHP(H`9Lwm  ffffff.UHAVSHIHHHEH`HbD$$Hd H _ Lr 1E1H1D$$H= H 8 L3 1E1H@HDž(H5 HToL(HHDž H5 HoL H趤H5rq H!LHH5 HΔ!LnHH c!H0H0HPHH諔H5] H̕H5q]H HH5U6HLLH!HPH9t HtHP(HP H`V~HGHH;EuLH[A^]p I,I'I"IHPH9uHP HtHP(H`}L3k  ff.UHAWAVSHIHHHEH`H D$$HH nL  1E1HD$$H~ H nL~ E1HD$$H~ H nL~ E1HD$$H~ H nL~ 1E1HH5\ H!LnHHW)HDž fDžTƅgǞHHXW)HDž@}n H(E)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHrH Ý!H0H0HPHH苑H55H謒H5QZH HH5jRhH,LHHPH9tiHtHP(t H=m LMt]HL9tBffff.HX@t Hxm HI9uHHP uLLl t H l H`zHHH;EuLH[A^A_]l IuXbI]IIIHPH9tHtHP(uHP t Hl LMtGHL9tfffff.HX@t Hxk HI9uHLk t H k H`yLf  LfUHAWAVSHIHnHHEH`HD$$Hz H z Lz 1E1HD$$Hz H z Li1E1HHW)HDž fDžTƅÞHHW)HDž j H()Hr types.HHH tensor HHHn to allHHHConstraiH@HHHHH !H0H0HPHHH5j HH5VHa HDH5N~H萐LHeHPH9t]HtHP(t Hi LMtQHL9t6HX@t Hxui HI9uHHP uLLDi t H /i H`wH HH;EuLH[A^A_]oi IuXbI]IIIHPH9tHtHP(uHP t Hh LMtGHL9tfffff.HX@t HxUh HI9uHL:h t H %h H`vLOc  LfUHAWAVSH(IHޠHHEH`H@HDž(H5 HgL(H#HDž H5 HhL HD$$Hv H -hLv 1E1HD$$H^iH hLeE1HD$$Hv H 3hL}v 1E1HHW)HDžfDžTƅ胿HHtW)HDž@f H(G )Hsor typeHH(H any tenHH Han be ofHHH types cHHHd outputHHHInput anHf@0.HHHHH !H0H0HPHH賉H5 HԊH5yRH% HH5JHTLH)HPH9taHtHP(t Hee LMtUHL9t:DHX@t Hx5e HI9uHHP uLLe t Hd H`rH̝HH;EuLH([A^A_]/e Iu`jIeIIIIHPH9tHtHP(uHP t HEd LMt?HL9t^DHX@t Hxd HI9uHLc t Hc H`qL_  LfUHAWAVSHIHHHEH`HHDž(H5 HdL(HH5*)H5eE1H%D$$Hr H eLr 1E1HD$$Hr H eLr 1E1HD$$HH ofLJaAH\D$$H@gH IgLaAH%D$$HH hL`AHHW)HDž fDžTƅẞHHݣW)HDž0a H()Hor type.HHHany tensHHHn be of HHHInput caH@ HHHHH !H0H0HPHH/H5HPH5MH衉 H脁H5F HЇLHzHPH9t]HtHP(t H` LMtQHL9t6HX@t Hx` HI9uHHP uLL` t H o` H`[nHLHH;EuLH[A^A_]` IuPZIUIIHPH9tHtHP(uHP t H_ LMt?HL9tJDHX@t Hx_ HI9uHL_ tH u_ LIH`WmLZ  ffff.UHAWAVSHIHHHEH`HHDž(H5a HdL(HcD$$H>H LOn 1E1H"D$$HH L]E1HD$$Hm H Lm 1E1HHW)HDž fDžTƅHH٣W)HDž@^ H()Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHHH S!H0H0HPHHH5 HbLN (N IH5jH\Hx2AHxLK H1HIIF IFIFE1H5eHaLcN (N IH5H\HxIAHxLmK H~HIIF IFIFE1H5KeHDaLM H}J  (M IH5hH=\Hx\AHxLJ H/~HIIF IFIFE1H5dH`LaM 넿(M IH5H[HxFAHxLnJ H}HIIF IFIFE1H5LdHE`LL H6H/H( H!HHxt H}RL Et,LL "H)H$HHHHH}HtL H}Ht H}L H7G  ffffff.UHH 7H1H9NHD]DUHHp!]UHAWAVAUATSHHHHIHHyHHEH0L-ԂMe@LLHaHH@H0HfHnfLJ HHEEIEHHIhL0LLI H>HHHDž HDžHDžHDžDž(LLPM LLH(ѣLL3M LLH ѣHH0L5I HHգHHL0LHt HJ HH H5 nHHH H0I HЂHH;EuHHH[A\A]A^A_]+J ILI^IHHL0LHt HtI HTH H5imHH1H H0H L{D  UH]f.UH]I fDUHI H m!H]fDUHHm!H]UH]f.UH]H fDUHAWAVATSII$1LP(HÃ{(uHC HIC(1術HC H@ I$LPHI$1LPHI$1LPfff.H@H(uH@ @uMDH@ @6I$1LPx(uH@ @uH6"@I$1LP(HÃ{(uL{ HIC(1軒IL{ AOMwMu1IMwIF(HtIcN 09}QAV L|AAv$A;v$uI~IF(0Ɖ0I~]IIF(IcN QAV L|I$1LPx(uH@ HW"H@HuH"HcX A(tLAG(I_ [A\A^A_]ffffff.UHH E3H1H9NHD]DUHHk!]UH]f.UH]nF fDUHfF H k!H]fDUHHk!H]UH]f.UH]F fDUHAVSII1LP(HÃ{(uHC HTGC(1HC H@ I1LP(HÃ{(uLs HGC(1跐ILs ANI~u 1IF[A^]@UHH 2H1H9NHD]DUHHk!]UH]f.UH].E fDUH&E H k!H]fDUHHj!H]UH]f.UH]D fDUHAWAVAUATSHhHH}HHE11H"HH]HPHHMHZHEH Lcu1H}HPH93H}HHPHH}HHP fH@H(uH@ @uDH@ @HL9|H}H1Px(uH@ H."H@HuH"Lcx H}HHW)EHEEEaxisEHuHEt H}C HRDDA9EE9H}u11H}\ DEH}H1P(HÃ{(uLc H{DC(1ILc AL$L}]D!M|$Mu1uIM|$DEAMgIG(HEH]fffff.HEHHtAO 09}AO LfAw$f.A;w$uLHEH0Ɖ0IzIO(IcW rAw HDHuI(L}DuEE1E1fDH}HHPx(H X"uHH LaMuL%"HEA9D$ HxE~V1DI9uID$(JDx(u+Dx ,DHEHHtID$(H|ӵEHI9uHxHH;]MEt,HEHHcMH\Mc{(tHFC(Ls HzHH;EuHh[A\A]A^A_]A (A IH5HxmH}CAHuL~> HqHIIF IFIFE1H5\XHUTL@  (@ IH5HOH}LAHuL > HQqHIIF IFIFE1H5WHSL@ 늿(5@ IH5HNlH}KiAHuL= HpHIIF IFIFE1H5tWHmSL@ H% HHHEt H}? EtL? HEt H}j? H:  UHH -H1H9NHD]DUHH}e!]UH]f.UH]? fDUH? 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LMt=HL9tqHX@t Hxu HI9uHLZ t HE t H 0 H`LZ  Lffff.UHAWAVSH8IHHHEH`H@W)ƅHDž HnearestHH5 *HWLHD$$H H L 1E1HD$$H H TL) E1HD$$H H (L 1E1HHW)HDžfDžTƅHHW)HDž@褉 H(r)Hr types.HH/Hl tensorHH(HY' to alHH Houtput 'HHH'X' and HHHn input HHHConstraiH@7HHHHH H0H0HPHH責H5Q+ HӭH5xuH$ HH5l HSLH(HPH9t`HtHP(t Hd LMtTHL9t9@HX@t Hx5 HI9uHHP uLL t H t H ڇ H`ƕHHH;EuLH8[A^A_] IueoIjIIIHPH9tHtHP(uHP t H+ LMtEHL9tyff.HX@t Hx HI9uHLچ t Hņ t H 谆 H`蜔Lځ  Lffff.UHAWAVSH8IH^HHEH`HW)ƅHDž HnearestHH5%HנLHD$$Hs H Lm 1E1H@D$$Hh H L E1H D$$H H L 1E1HHW)HDžfDžTƅޝHHW)HDž@$ H()Hr types.HH/Hl tensorHH(HY' to alHH Houtput 'HHH'X' and HHHn input HHHConstraiH@7HHHHH H0H0HPHH2H5' HSH5pH褬 H臤H5-HӪLH訝HPH9t`HtHP(t H LMtTHL9t9@HX@t Hx赃 HI9uHHP uLL脃 t Ho t H Z H`FH7HH;EuLH8[A^A_]蚃 IueoIjIIIHPH9tHtHP(uHP t H諂 LMtEHL9tyff.HX@t Hxu HI9uHLZ t HE t H 0 H`LZ}  Lffff.UHAWAVSHIH޺HHEH`H@D$$H+ H ) L< 1E1HH5 HE1HaH5 H AH@H55 HAHD$$H H L 1E1HHW)HDž fDžTƅٝHHW)HDž@ǀ H(EX)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input 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tHxPuE1MH ʻL;1UMDLLLIIELpPM Eu H=MtIEHxXPPt\IMt$IEH@@H uH;tE%_ EeLLIH}LHEIELp@=IEH@pHpHL9It I;utLLLIMH κL;1t MM UԉсDщUԀ}t1H}H}ILIMtSMtNIHGPHtAIuH;FPu7HGXHNXH9tHW`Ht!HH҅tIIuHhHhLHH}HHEL}MgIGL MHEIGH}IE1Aŋ]ԉ%FtAIB ADA‹u) ff.A) !AuMLsHHtJff.LHILHwIHC@ H{HLMuH([A\A]A^A_]fUH1ɸu]ËHt=r3H=c] ƒ t tHH0]VH=l]fUH1ɸu]Ëuu6H=]ጁt tH]H0]H=;]gUHu H=]I]UHt]H=]$@UHSPu H=wӃH[]ÐUHAWAVAUATSHHLMLEHMIHuIAtvHH=4wHRLeEML(LHAԅLmAE1EH]tIIDž@HEIHMXI`HEIhADž(APEL AEt1IMHLILJ/MIpLHILLu1A IGff.DAANƉ)ρ D)΃A7uօu LLA(H]TIHEE1E11fff.LHuHUHM< ÅA(1ffffff.YE'ACuAT$DAtA"uD DAA(tBAP;Eu6IHuIGADž(HED`E'A]"1E'E1fffff.A(t DAA(uEH]LeLutMt-HAԅ"@LeLLLMuӱ1 UAN1ɋMEȉMLEHH[A\A]A^A_]UHHHALLHH$]tH=H]8H]fUH1ɸu]ËHt=r0H=S]ttHH0]IH=_]UHAWAVAUATSPIAFTt#HL8HXLHH[A\A]A^A_]Mf0LANTL-]LuA~u H8IF L8HXLIUIMLH~'HLHHuLZUHAWAVATSHLs0LSTHHu{u H HK H9HqHHHO~VCPL{XMt=Mg0LsuLGLLHL|L$ 1HhKPL[A\A^A_]DUHwH LHIHHL1Ʌ]UHAWAVAUATSHIIIp@HHQHChHC`HCXHCPHCHHC@HC8HC0HC(HC HCHCHCHHH6Lc Lk(CHL=8MIOHHLMLmI~0H}1AFTuA~u L=M~ M/MLLLHM|yLk L{(CHHHHLLP~LsXI~`H,IF`H}OHH[A\A]A^A_]ffff.UHAWAVAUATSPII\$0HrAD$PI|$h3u %M|$XMt(Mw0LuHL*H"H]I|$`HHuuf.LHt^LsI|$`HHMn0LA~PuI|$`LID$`MtM~XI`LIG`IFXH5^1H]HLMt%I`LLIG`ID$XI0LAL$PHLH[A\A]A^A_]UHH`]fUHAVSI1H HHIHHHu ~ L[A^][A^]UHH0HiOHHEHEHEH}HHHHUHUH$111AI1HOHH;UuH0]ff.UHHG HW(]fUH]ffDUHAWAVSPIHL{0L$STHHu{u H HK H9HqHHHm ~H{hIvLHCh1A^ LcH[A^A_]fDUHAWAVSPIHL{0LSTHUHu{u H 1HK H9HqHHH ~ H{hIv'HChAF 1LH[A^A_]ffff.UHAWAVAUATSPIIAWTHHuAu H IO H9HqHHHC AGT%HXIhHIGhC H{(,Ih3HuI`%IMuKff.LMt3I\$I`L"ILk0L3{PuHLI0H51L{MtI~`LIF`IGXH[A\A]A^A_]ÐUHAWAVAUATSHhIIIHGLHHEMHULL5\IINHHHE+mrIL}H}HuEEI\$0H8AL$TLLuRA|$uEHAHKHH;E\ILLHh[A\A]A^A_] AYID$ LxH]L0LhHHILL3I|$hHu ID$hHxHHuLLELHLHmH}LLL Aƃ}xA%>%>%>%>% >% >%>%>%>%>%>%>%>%>%>% >%">%$>%&>%(>%*>%,>%.>%0>%2>%4>%6>%8>%:>%<>%>>%@>%B>%D>%F>%H>%J>%L>%N>%P>%R>%T>%V>%X>%Z>%\>%^>%`>%b>%d>%f>%h>%j>%l>%n>%p>%r>%t>%v>%x>%z>%|>%~>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%>%?%?%?%?%?% ?% ?%?%?%?%?%?%?%?%?%?% ?%"?%$?%&?%(?%*?%,?%.?%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?%|?%~?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%?%@%@%@%@%@% @% @%@%@%@%@%@%@%@%@%@% @%"@%$@%&@%(@%*@%,@%.@%0@%2@%4@%6@%8@%:@%<@%>@%@@%B@%D@%F@%H@%J@%L@%N@%P@%R@%T@L AS% hh<hhh<hhh"hjhhhxh nhldhZhPh Fh/h4h*h hLh hhhAhuh%hohhhhVhhhZhyhvhlhbhfXhNhDh :h80he&hhhh7hXhhhh@hhhhh,hJhhh|hrhhh%^h@Th\Jhx@h6h,h"hh h8hUhohhhhhhhhhh h$h6xhHnhVdhdZhsPhFh hM h[ hg ht h h h h ~h th jh `h Vh Lh!Bh!8h-!.hh"4h"*h" h"h" h#h7#hN#hd#h#h#h#h#h#h $h'$hE$hZ$hv$h$vh$lh$bh$Xh$Nh$Dh$:h%0h%&h'%h7%hL%h[%hj%hy%h%h%h%h%h%h%h%h%h &h&h+&|h;&rhH&hhV&^hh&Thx&Jh&@OrtGetApiBaseOrtSessionOptionsAppendExecutionProvider_CPU/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/session/IOBinding.ccBindInputSynchronizeInputsSynchronizeOutputsonnxruntime::AllocatorPtr onnxruntime::IOBinding::GetCPUAllocator(int, onnxruntime::ProviderType) constp_providerSyncProvidersexecution_mode is not validgraph_optimization_level is not validResult buffer is not large enoughADescription/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/session/custom_ops.ccCreateCustomRegistryUnsupported version '' in custom op 'ComputeAsyncinter-opMemcpyFromHost/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/session/environment.ccXinputTYoutputConstrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.MemcpyToHostInput expected to have tensor typeElement type of input unknownOutput Trying 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/runners/2.168.2/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 &)resultCould not parse model successfully while constructing the inference sessiononnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions &, const onnxruntime::Environment &, const void *, int)~InferenceSessionUnknown error during EndProfiling()Error during EndProfiling(): Received nullptr for exec providerMemory pattern must be disabled before registering DMLExecutionProviderSequential execution must be enabled before registering DMLExecutionProviderReceived nullptr for graph transformerAddCustomOpDomainsReceived nullptr for custom registryLoadThis session already contains a loaded model.Unknown exception in Load()Encountered unknown exception in Load()Exception during loading: ModelProto 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: [: [common::Status onnxruntime::InferenceSession::CreateSubgraphSessionState(onnxruntime::Graph &, onnxruntime::SessionState &)subgraphMain Graph instance should have populated all subgraphs when being resolved.CreateSubgraphSessionStateCUDAExecutionProvidercommon::Status onnxruntime::InferenceSession::InitializeSubgraphSessions(onnxruntime::Graph &, onnxruntime::SessionState &)subgraph_session_stateInitializeSubgraphSessionsp_op_kernelInitializeInitializing session.Model was not loadedModel was not loaded.Session has already been initialized.Adding default CPU execution provider.Parallel execution mode doesn't support CUDA Execution Provider currently.Serializing Optimized ONNX 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.Exception during initialization: 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()ORT_LOAD_CONFIG_FROM_MODELThe 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_parsedProvider has already been registered./Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/framework/execution_providers.hAdd allocator already registered.Unexpected input data type. Actual: () , expected: (/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/framework/feeds_fetches_manager.hFeedsFetchesInfoonnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(const std::vector &, const std::vector &, const onnxruntime::OrtValueNameIdxMap &)Load model from failed:Failed to load model because protobuf parsing failed._.json%Y-%m-%d_%H-%M-%SThe Model Proto has already been checked for the ORT config json.ort_config/Users/runner/runners/2.168.2/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: Json stored in the `ort_config` key cannot be parsed. Error message: 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: 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 size 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 emptyinput array is too shortshape is invalidspace is not enoughopaque(,)/Users/runner/runners/2.168.2/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)1.3.0allocator::allocate(size_t n) 'n' exceeds maximum supported sizeDmlExecutionProviderMem pattern should be disabled when using DML execution provider.Sequential execution should be enabled when using DML execution provider./Users/runner/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/work/1/s/onnxruntime/core/session/ort_env.ccstatic void OrtEnv::Release(OrtEnv *)env_ptr == p_instance_/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/optimizer/attention_fusion.ccshape of layer norm bias tensor not expectedAttention subgraph expects 1 Add and 3 MatMul as children of LayerNormalization.Total fused Attention node count: FuseSubGraphFaild to find path vOutput 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, 3v_reshape initializer value is not expectedreshape initializer value is not expectedFailed in match v_matmul and v_add input shapeFailed 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 matchedFailed to find path for qq root should be layer normalizationq_reshape const not matchedqk_div const not matched.q_transpose perm attribute not matchedq_matmul and q_add shape not matchedFailed to find path for kk root is not layer normk_transpose perm attribute not matchedk_matmul and k_add shape not matchedk_reshape const 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 create mask indexFused an attention node.ProcessMaskMask shape is unknown or not 2D, or data type unknownMask data type is not int32 or int64 or float32qkv_weightsqkv_bias/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/optimizer/bias_gelu_fusion.cc/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/optimizer/constant_folding.ccCould not find a CPU kernel and hence can't constant fold 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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/work/1/s/onnxruntime/core/optimizer/embed_layer_norm_fusion.ccWord embedding shape not expected.Input 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.Failed to match position embedding subgraph.Failed to get position embedding weights.Position embedding shape is not expected.Input id is not valid. Segment id is not valid. Mask is not valid. Input_ids and segment id should have the same shape. Input_ids and mask should have the same shape. Gamma should be of shape (hidden_size). Beta should be of shape (hidden_size). MatchPositionEmbeddingSubgraph1Output edge count not expected for nodes in path1.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.Failed to match position subgraph.MatchPositionSubgraphFailed 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.MatchPositionEmbeddingSubgraph2Failed to find path 1.Output edge count not expected for nodes in path 1.The first input of Range should be a constant with value 0.The third input of Range should be a constant with value 1.CheckInputInput shape is unknown or not 2D, or data type unknownInput data type is not int32 or int64_Int32_Cast/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/optimizer/fast_gelu_fusion.cc/Users/runner/runners/2.168.2/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/runners/2.168.2/work/1/s/onnxruntime/core/optimizer/gelu_approximation.ccTotal Gelu Approximation (FastGelu) node count: /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/optimizer/gelu_fusion.cc/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/optimizer/gemm_activation_fusion.ccfused Gemm /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/optimizer/graph_transformer.ccApply/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/optimizer/graph_transformer_mgr.ccApplyTransformersThis transformer is already registered Level_RuleBasedTransformer/Users/runner/runners/2.168.2/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 std::vector &)Unsupported level RandomUniformRandomNormalRandomUniformLikeRandomNormalLikeMultinomialNot satisfied: tensor_proto.data_type() != ONNX_NAMESPACE::TensorProto_DataType_UNDEFINED && tensor_proto.data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRING /Users/runner/runners/2.168.2/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: CastDef_%d/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/optimizer/insert_cast_transformer.cc/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/optimizer/layer_norm_fusion.cc/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/optimizer/matmul_add_fusion.ccfused Matmul and Add _nchwc_bn_nchwc/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/optimizer/nchwc_transformer.cc/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/optimizer/optimizer_execution_frame.cconnxruntime::OptimizerExecutionFrame::Info::Info(const std::vector &, const onnxruntime::InitializedTensorSet &, std::unique_ptr)cpu_execution_providerallocator_ptr_Info/Users/runner/runners/2.168.2/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/runners/2.168.2/work/1/s/onnxruntime/core/optimizer/reshape_fusion.ccFused reshape node: Total fused reshape node count: Fuse_Subgraph1Cannot replace concat node with initializer:/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/optimizer/rule_based_graph_transformer.ccApplyRulesOnNode/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/optimizer/skip_layer_norm_fusion.cc/Users/runner/runners/2.168.2/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/runners/2.168.2/work/1/s/include/onnxruntime/core/optimizer/graph_transformer.hRecurseauto onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager &, const onnxruntime::InitializedTensorSet &)::(anonymous class)::operator()(const onnxruntime::NodeArg &, size_t) constdup_replacements.find(&arg) == dup_replacements.end()UnsqueezeElimination_/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/optimizer/unsqueeze_elimination.ccUnsqueezeElimination cannot remove node EluHardSigmoidLeakyReluSoftplusReluSigmoidSoftsignTanhThresholdedReluSeluParametricSoftplusScaledTanhunknown kernel type/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/activation/element_wise_ranged_transform.hElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Elu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Elu]input_size < std::numeric_limits::max()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.Attibute name and type don't match/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/activation/activations.hInitgamma/Users/runner/runners/2.168.2/work/1/s/onnxruntime/contrib_ops/cpu/activations.hBVIfthen_branch/Users/runner/runners/2.168.2/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.ComputeSubgraph must have the shape set for all outputs but did not.Failed 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/runners/2.168.2/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/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/controlflow/scan_8.cconnxruntime::Scan<8>::Scan(const onnxruntime::OpKernelInfo &) [OpSet = 8]directionsvirtual onnxruntime::common::Status onnxruntime::Scan<8>::SetupSubgraphExecutionInfo(const onnxruntime::SessionState &, const std::string &, const onnxruntime::SessionState &) [OpSet = 8]virtual onnxruntime::common::Status onnxruntime::Scan<8>::Compute(onnxruntime::OpKernelContext *) const [OpSet = 8]Scan inputs have inconsistent batch size. Previous value was but has batch size of sequence_lens length of did not match batch size of Invalid entries in sequence_lens. Max sequence length was onnxruntime::common::Status onnxruntime::Scan8Impl::CreateLoopStateVariables(std::vector > &)p_mlvalueScan<8> spec does not support transpose of output. This should never be called.gsl::span onnxruntime::Tensor::DataAsSpan() const [T = long long]/Users/runner/runners/2.168.2/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/runners/2.168.2/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()TransposeOutputScan/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/common.hint64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)axis >= -tensor_rank && axis <= tensor_rank - 1axis is not in valid range [-/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/controlflow/scan_utils.hconst OrtValue &onnxruntime::scan::detail::OutputIterator::GetOutput() constfinal_output_mlvalue_/Users/runner/runners/2.168.2/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.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/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/cpu_execution_provider.ccRegisterOnnxOperatorKernelsRegisterOnnxMLOperatorKernelsGetKernelRegistryvirtual std::shared_ptr onnxruntime::CPUExecutionProvider::GetKernelRegistry() constRegisterCPUKernelsCpuConstantOfShape/Users/runner/runners/2.168.2/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=: /Users/runner/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/einsum.ccEinsum op: An implementation for the input type is not supported yet/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/einsum.honnxruntime::Einsum::Einsum(const onnxruntime::OpKernelInfo &)info.GetAttr("equation", &equation_).IsOK()/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/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 *) [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]std::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor &, const std::vector &, const onnxruntime::Tensor &, const std::vector &, onnxruntime::AllocatorPtr, concurrency::ThreadPool *) [T = int]std::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor &, const std::vector &, const onnxruntime::Tensor &, const std::vector &, onnxruntime::AllocatorPtr, concurrency::ThreadPool *) [T = double]std::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor &, const std::vector &, const onnxruntime::Tensor &, const std::vector &, onnxruntime::AllocatorPtr, concurrency::ThreadPool *) [T = long long]std::unique_ptr onnxruntime::EinsumOp::Transpose(const onnxruntime::Tensor &, const std::vector &, const std::vector &, onnxruntime::AllocatorPtr)Length of permutation must match the rank of the input to be permutatedstd::unique_ptr onnxruntime::EinsumOp::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 std::unique_ptr onnxruntime::EinsumOp::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/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/einsum_utils.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: 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 inputbool onnxruntime::EinsumOp::IsTransposeRequired(size_t, const std::vector &)input_rank == permutation.size()void onnxruntime::EinsumOp::FinalizeOutput(const onnxruntime::Tensor &, const std::vector &, const std::vector &, onnxruntime::Tensor &, const onnxruntime::TensorShape &, const onnxruntime::AllocatorPtr &) [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 outputvoid onnxruntime::EinsumOp::CopyOutputCandidateIntoOpOutout(onnxruntime::Tensor &, const onnxruntime::Tensor &)output.SizeInBytes() == candidate.SizeInBytes()Einsum op: The candidate output does not match the actual output's shapestd::unique_ptr onnxruntime::EinsumOp::PairwiseOperandProcess(const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const std::vector &, concurrency::ThreadPool *, const onnxruntime::AllocatorPtr &, const onnxruntime::EinsumComputePreprocessor &, bool, onnxruntime::Tensor &) [T = float]left.Shape().Size() == left_shape_override.Size()right.Shape().Size() == right_shape_override.Size()left_rank == right_rankleft_dim == right_dimEinsum op: Input dimensions must be equal along an axis to be reduced across all inputsvoid onnxruntime::EinsumOp::FinalizeOutput(const onnxruntime::Tensor &, const std::vector &, const std::vector &, onnxruntime::Tensor &, const onnxruntime::TensorShape &, const onnxruntime::AllocatorPtr &) [T = int]std::unique_ptr onnxruntime::EinsumOp::PairwiseOperandProcess(const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const std::vector &, concurrency::ThreadPool *, const onnxruntime::AllocatorPtr &, const onnxruntime::EinsumComputePreprocessor &, bool, onnxruntime::Tensor &) [T = int]void onnxruntime::EinsumOp::FinalizeOutput(const onnxruntime::Tensor &, const std::vector &, const std::vector &, onnxruntime::Tensor &, const onnxruntime::TensorShape &, const onnxruntime::AllocatorPtr &) [T = double]std::unique_ptr onnxruntime::EinsumOp::PairwiseOperandProcess(const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const std::vector &, concurrency::ThreadPool *, const onnxruntime::AllocatorPtr &, const onnxruntime::EinsumComputePreprocessor &, bool, onnxruntime::Tensor &) [T = double]void onnxruntime::EinsumOp::FinalizeOutput(const onnxruntime::Tensor &, const std::vector &, const std::vector &, onnxruntime::Tensor &, const onnxruntime::TensorShape &, const onnxruntime::AllocatorPtr &) [T = long long]std::unique_ptr onnxruntime::EinsumOp::PairwiseOperandProcess(const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const std::vector &, concurrency::ThreadPool *, const onnxruntime::AllocatorPtr &, const onnxruntime::EinsumComputePreprocessor &, bool, onnxruntime::Tensor &) [T = long long]SubMulDivAbsNegFloorCeilReciprocalSqrtT1PowExpLogSumMaxMinLessGreaterEqualMeanBitShiftErfNotAndOrXorUnsupported X type: /Users/runner/runners/2.168.2/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/runners/2.168.2/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)directiononnxruntime::BitShift::BitShift(const onnxruntime::OpKernelInfo &) [T = unsigned char]LEFTRIGHTInvalid direction value of ''. Valid values are 'LEFT' or 'RIGHT'.auto onnxruntime::BitShift::Compute(onnxruntime::OpKernelContext *)::(anonymous class)::operator()(EigenVectorMap, ConstEigenVectorMap, ConstEigenVectorMap) const [T = unsigned char]cur1 == end1cur_out == end_outonnxruntime::BitShift::BitShift(const onnxruntime::OpKernelInfo &) [T = unsigned int]auto onnxruntime::BitShift::Compute(onnxruntime::OpKernelContext *)::(anonymous class)::operator()(EigenVectorMap, ConstEigenVectorMap, ConstEigenVectorMap) const [T = unsigned int]onnxruntime::BitShift::BitShift(const onnxruntime::OpKernelInfo &) [T = unsigned long long]auto onnxruntime::BitShift::Compute(onnxruntime::OpKernelContext *)::(anonymous class)::operator()(EigenVectorMap, ConstEigenVectorMap, ConstEigenVectorMap) const [T = unsigned long long]/Users/runner/runners/2.168.2/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/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:450:7), Input1Scalar = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:451:7), General = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:452:7)]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/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:478:9), Input1Scalar = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:479:9), General = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:480:9)]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = double, TOutput = double, Input0Scalar = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:478:9), Input1Scalar = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:479:9), General = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:480:9)]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/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:478:9), Input1Scalar = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:479:9), General = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:480:9)]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/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:478:9), Input1Scalar = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:479:9), General = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:480:9)]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/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:478:9), Input1Scalar = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:479:9), General = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:480:9)]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/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:478:9), Input1Scalar = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:479:9), General = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:480:9)]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/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:478:9), Input1Scalar = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:479:9), General = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:480:9)]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/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:513:9), Input1Scalar = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:514:9), General = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:515:9)]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = double, TOutput = double, Input0Scalar = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:513:9), Input1Scalar = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:514:9), General = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:515:9)]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = onnxruntime::MLFloat16, TOutput = onnxruntime::MLFloat16, Input0Scalar = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:513:9), Input1Scalar = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:514:9), General = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:515:9)]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = int, TOutput = int, Input0Scalar = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:513:9), Input1Scalar = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:514:9), General = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:515:9)]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = unsigned int, TOutput = unsigned int, Input0Scalar = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:513:9), Input1Scalar = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:514:9), General = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:515:9)]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = long long, TOutput = long long, Input0Scalar = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:513:9), Input1Scalar = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:514:9), General = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:515:9)]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/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:513:9), Input1Scalar = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:514:9), General = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:515:9)]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = float, TOutput = float, Input0Scalar = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:651:7), Input1Scalar = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:652:7), General = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:653:7)]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/runners/2.168.2/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/runners/2.168.2/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 != nullptrc_shape is required if c_data is providedHardmax inputs N, D and N * D must be < . N=, D=HardmaxMatMul/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/matmul.ccNot satisfied: left_num_dims >= 1 && right_num_dims >= 1 /Users/runner/runners/2.168.2/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/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/matmul_integer.ccvirtual onnxruntime::common::Status onnxruntime::MatMulInteger::Compute(onnxruntime::OpKernelContext *) const [T1 = unsigned char, T2 = unsigned char]IsScalarOr1ElementVector(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 1virtual onnxruntime::common::Status onnxruntime::MatMulInteger::Compute(onnxruntime::OpKernelContext *) const [T1 = unsigned char, T2 = signed char]auto onnxruntime::MatMulInteger::Compute(onnxruntime::OpKernelContext *)::(anonymous class)::operator()(onnxruntime::OpKernelContext *, int) const [T1 = unsigned char, T2 = signed char]t->Shape().NumDimensions() <= 1 && t->Shape().Size() == 1Currently only scalar zero_point is supported. TODO: add per channel zero point support.t->IsDataType() || t->IsDataType()QLinearMatMul/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/math/quantize_linear_matmul.ccvirtual onnxruntime::common::Status onnxruntime::QLinearMatMul::Compute(onnxruntime::OpKernelContext *) const [T1 = unsigned char, T2 = unsigned char, T3 = unsigned char]a != nullptr && b != nullptrIsScalarOr1ElementVector(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 = ]helper.called_ < 2SoftmaxLogSoftmaxSoftmaxCPU inputs N, D and N * D must be < /Users/runner/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/nn/batch_norm.honnxruntime::BatchNorm::BatchNorm(const onnxruntime::OpKernelInfo &) [T = float]st.IsOK()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/runners/2.168.2/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.kernel_shapestridesdilationsgroupNOTSETVALIDSAME_UPPERSAME_LOWER/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/nn/autopad_type.honnxruntime::AutoPadType onnxruntime::StringToAutoPadType(const std::string &)/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/nn/conv_attributes.hInferOutputShapeInvalid input shape: onnxruntime::common::Status onnxruntime::ComputePadAndOutputShape(const int64_t, const int64_t, const int64_t, const int64_t, onnxruntime::AutoPadType, int64_t *, int64_t *, int64_t *) [ForceSymmetricAutoPadding = false]dilation == 1Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER.pad type not supported./Users/runner/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/nn/conv_transpose.ccDoConvTransposegroup count is <= 0Only 1D and 2D ConvTranspose is supported.filter number not equal to input channel number. filter_number: num_input_channels: Input channels is not divisible by group./Users/runner/runners/2.168.2/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 >= 0pad type not supported/Users/runner/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/nn/instance_norm.ccepsilon/Users/runner/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/nn/pool.ccUnsupported pooling size.Not satisfied: pooling_dims == pool_attrs_.kernel_shape.size() AveragePoolMaxPoolLpPoolGlobalLpPoolGlobalAveragePoolGlobalMaxPool/Users/runner/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/nn/string_normalizer.cconnxruntime::StringNormalizer::StringNormalizer(const onnxruntime::OpKernelInfo &)LOWERUPPERNONElocalestopwords!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: onnxruntime::string_normalizer::Locale::Locale(const std::string &)Failed to construct locale with name::Please, install necessary language-pack-XX and configure localeswstring_convert: from_bytes errorwstring_convert: to_bytes errorTfIdfVectorizer/Users/runner/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/object_detection/roialign.honnxruntime::RoiAlignBase::RoiAlignBase(const onnxruntime::OpKernelInfo &) [T = float]Invalid mode of value specified. It should be either avg or maxoutput_heightoutput_widthsampling_ratiosampling_ratio_ >= 0Sampling ratio should be >=0, but it was onnxruntime::RoiAlignBase::RoiAlignBase(const onnxruntime::OpKernelInfo &) [T = double]ReduceL1ReduceL2ReduceLogSumReduceLogSumExpReduceMaxReduceMeanReduceMinReduceProdReduceSumReduceSumSquareArgMaxArgMin/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/containers.hOrtStlAllocator onnxruntime::GetAllocator(const onnxruntime::OpKernelContext &) [T = signed char]/Users/runner/runners/2.168.2/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/runners/2.168.2/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_indexonnxruntime::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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/rnn/rnn.ccAffine/Users/runner/runners/2.168.2/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()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 {affinereluleakyreluthresholdedreluscaledtanhhardsigmoidelusoftsignsoftplus/Users/runner/runners/2.168.2/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: onnxruntime::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/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/sequence/concat_from_sequence.ccvirtual onnxruntime::common::Status onnxruntime::ConcatFromSequence::Compute(onnxruntime::OpKernelContext *) const/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu//tensor/concat.hSSequenceLength/Users/runner/runners/2.168.2/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]Cast/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/tensor/cast_op.ccvirtual onnxruntime::common::Status onnxruntime::Cast::Compute(onnxruntime::OpKernelContext *) const [T = unsigned char]Unexpected 'to' argument value: virtual onnxruntime::common::Status onnxruntime::Cast::Compute(onnxruntime::OpKernelContext *) const [T = unsigned short]virtual onnxruntime::common::Status onnxruntime::Cast::Compute(onnxruntime::OpKernelContext *) const [T = unsigned int]virtual onnxruntime::common::Status onnxruntime::Cast::Compute(onnxruntime::OpKernelContext *) const [T = unsigned long long]virtual onnxruntime::common::Status onnxruntime::Cast::Compute(onnxruntime::OpKernelContext *) const [T = signed char]virtual onnxruntime::common::Status onnxruntime::Cast::Compute(onnxruntime::OpKernelContext *) const [T = short]virtual onnxruntime::common::Status onnxruntime::Cast::Compute(onnxruntime::OpKernelContext *) const [T = int]virtual onnxruntime::common::Status onnxruntime::Cast::Compute(onnxruntime::OpKernelContext *) const [T = long long]virtual onnxruntime::common::Status onnxruntime::Cast::Compute(onnxruntime::OpKernelContext *) const [T = bool]virtual onnxruntime::common::Status onnxruntime::Cast::Compute(onnxruntime::OpKernelContext *) const [T = float]virtual onnxruntime::common::Status onnxruntime::Cast::Compute(onnxruntime::OpKernelContext *) const [T = double]virtual onnxruntime::common::Status onnxruntime::Cast::Compute(onnxruntime::OpKernelContext *) const [T = onnxruntime::MLFloat16]Input is missing. The operator Cast expects one and only one inputvirtual onnxruntime::common::Status onnxruntime::Cast >::Compute(onnxruntime::OpKernelContext *) const [T = std::__1::basic_string]onnxruntime::Cast::Cast(const onnxruntime::OpKernelInfo &) [T = unsigned char]onnxruntime::Cast::Cast(const onnxruntime::OpKernelInfo &) [T = unsigned short]onnxruntime::Cast::Cast(const onnxruntime::OpKernelInfo &) [T = unsigned int]onnxruntime::Cast::Cast(const onnxruntime::OpKernelInfo &) [T = unsigned long long]onnxruntime::Cast::Cast(const onnxruntime::OpKernelInfo &) [T = signed char]onnxruntime::Cast::Cast(const onnxruntime::OpKernelInfo &) [T = short]onnxruntime::Cast::Cast(const onnxruntime::OpKernelInfo &) [T = int]onnxruntime::Cast::Cast(const onnxruntime::OpKernelInfo &) [T = long long]onnxruntime::Cast::Cast(const onnxruntime::OpKernelInfo &) [T = bool]onnxruntime::Cast::Cast(const onnxruntime::OpKernelInfo &) [T = float]onnxruntime::Cast::Cast(const onnxruntime::OpKernelInfo &) [T = double]onnxruntime::Cast::Cast(const onnxruntime::OpKernelInfo &) [T = onnxruntime::MLFloat16]onnxruntime::Cast >::Cast(const onnxruntime::OpKernelInfo &) [T = std::__1::basic_string]CastFloat16Datavoid onnxruntime::CastFloat16Data(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::AllocatorPtr &) [SrcType = unsigned char, DstType = onnxruntime::MLFloat16]allocator != nullptrlen > 0buffervoid onnxruntime::CastToStringData(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &) [SrcType = unsigned char]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = unsigned char]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::AllocatorPtr &) [SrcType = unsigned short, DstType = onnxruntime::MLFloat16]void onnxruntime::CastToStringData(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &) [SrcType = unsigned short]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = unsigned short]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::AllocatorPtr &) [SrcType = unsigned int, DstType = onnxruntime::MLFloat16]void onnxruntime::CastToStringData(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &) [SrcType = unsigned int]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = unsigned int]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::AllocatorPtr &) [SrcType = unsigned long long, DstType = onnxruntime::MLFloat16]void onnxruntime::CastToStringData(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &) [SrcType = unsigned long long]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = unsigned long long]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::AllocatorPtr &) [SrcType = signed char, DstType = onnxruntime::MLFloat16]void onnxruntime::CastToStringData(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &) [SrcType = signed char]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::AllocatorPtr &) [SrcType = short, DstType = onnxruntime::MLFloat16]void onnxruntime::CastToStringData(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &) [SrcType = short]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = short]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::AllocatorPtr &) [SrcType = int, DstType = onnxruntime::MLFloat16]void onnxruntime::CastToStringData(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &) [SrcType = int]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::AllocatorPtr &) [SrcType = long long, DstType = onnxruntime::MLFloat16]void onnxruntime::CastToStringData(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &) [SrcType = long long]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::AllocatorPtr &) [SrcType = bool, DstType = onnxruntime::MLFloat16]void onnxruntime::CastToStringData(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &) [SrcType = bool]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = bool]void onnxruntime::CastToStringData(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &) [SrcType = float]NaN-INFINFvoid onnxruntime::CastFloat16Data(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::AllocatorPtr &) [SrcType = double, DstType = onnxruntime::MLFloat16]void onnxruntime::CastToStringData(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &) [SrcType = double]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = double]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::AllocatorPtr &) [SrcType = onnxruntime::MLFloat16, DstType = bool]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::AllocatorPtr &) [SrcType = onnxruntime::MLFloat16, DstType = short]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::AllocatorPtr &) [SrcType = onnxruntime::MLFloat16, DstType = int]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::AllocatorPtr &) [SrcType = onnxruntime::MLFloat16, DstType = long long]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::AllocatorPtr &) [SrcType = onnxruntime::MLFloat16, DstType = unsigned char]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::AllocatorPtr &) [SrcType = onnxruntime::MLFloat16, DstType = unsigned short]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::AllocatorPtr &) [SrcType = onnxruntime::MLFloat16, DstType = unsigned int]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::AllocatorPtr &) [SrcType = onnxruntime::MLFloat16, DstType = unsigned long long]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::AllocatorPtr &) [SrcType = onnxruntime::MLFloat16, DstType = double]void onnxruntime::CastFloat16Data(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &, const onnxruntime::AllocatorPtr &) [SrcType = onnxruntime::MLFloat16, DstType = signed char]void onnxruntime::CastFromStringData(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &) [DstType = short]void onnxruntime::CastFromStringData(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &) [DstType = int]void onnxruntime::CastFromStringData(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &) [DstType = long long]void onnxruntime::CastFromStringData(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &) [DstType = unsigned char]void onnxruntime::CastFromStringData(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &) [DstType = unsigned short]void onnxruntime::CastFromStringData(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &) [DstType = unsigned int]void onnxruntime::CastFromStringData(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &) [DstType = unsigned long long]void onnxruntime::CastFromStringData(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &) [DstType = float]void onnxruntime::CastFromStringData(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &) [DstType = double]void onnxruntime::CastFromStringData(const onnxruntime::Tensor *, onnxruntime::Tensor *, const onnxruntime::TensorShape &) [DstType = signed char]Compress/Users/runner/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/tensor/concat.honnxruntime::ConcatBase::ConcatBase(const onnxruntime::OpKernelInfo &, bool)DynamicQuantizeLinear/Users/runner/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/tensor/gather.cc/Users/runner/runners/2.168.2/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/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/tensor/gather_elements.honnxruntime::GatherElements::GatherElements(const onnxruntime::OpKernelInfo &)/Users/runner/runners/2.168.2/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 /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/tensor/gather_nd.cconnxruntime::common::Status onnxruntime::GatherNDBase::PrepareForCompute(onnxruntime::OpKernelContext *, onnxruntime::GatherNDBase::Prepare &, concurrency::ThreadPool *) const [Tind = int]input_tensor != nullptr && indices_tensor != nullptrlast dimension of indices must not be larger than rank of input tensorinvalid index found, index = onnxruntime::common::Status onnxruntime::GatherNDBase::PrepareForCompute(onnxruntime::OpKernelContext *, onnxruntime::GatherNDBase::Prepare &, concurrency::ThreadPool *) const [Tind = long long]GatherNDbatch_dimsDropoutIdentity/Users/runner/runners/2.168.2/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]T2IsInfdetect_positive/Users/runner/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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 valueconstantreflectedge/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/tensor/pad.honnxruntime::PadBase::PadBase(const onnxruntime::OpKernelInfo &)/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/tensor/pad.ccvirtual onnxruntime::common::Status onnxruntime::Pad::Compute(onnxruntime::OpKernelContext *) const [T = float]pads_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->IsDataType() && value_tensor->Shape().Size() == 1Value tensor should be a 1D tensor of size 1 with the same type as that of the input tensoronnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &, const onnxruntime::Mode &, T) [T = float]data_rank > 0data_rank * 2 == pads.size()PadInputWithDimValueOfZero/Users/runner/runners/2.168.2/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()virtual onnxruntime::common::Status onnxruntime::Pad::Compute(onnxruntime::OpKernelContext *) const [T = double]onnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &, const onnxruntime::Mode &, T) [T = double]virtual onnxruntime::common::Status onnxruntime::Pad::Compute(onnxruntime::OpKernelContext *) const [T = int]onnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &, const onnxruntime::Mode &, T) [T = int]virtual onnxruntime::common::Status onnxruntime::Pad::Compute(onnxruntime::OpKernelContext *) const [T = long long]onnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &, const onnxruntime::Mode &, T) [T = long long]DequantizeLinearQuantizeLinear/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/tensor/quantize_linear.ccvirtual onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext *) const [T = unsigned char]x_scale.Shape().NumDimensions() == 1 && x_scale.Shape().Size() == broadcast_dimx_scale must be 1D tensor with size x_zero_point.Shape().NumDimensions() == 1 && x_zero_point.Shape().Size() == broadcast_dimx_zero_point must be 1D tensor with size IsScalarOr1ElementVector(&x_scale)IsScalarOr1ElementVector(&x_zero_point)virtual onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext *) const [T = signed char]virtual onnxruntime::common::Status onnxruntime::QuantizeLinear::Compute(onnxruntime::OpKernelContext *) const [T = unsigned char]y_scale.Shape().NumDimensions() == 1 && y_scale.Shape().Size() == broadcast_dimy_zero_point.Shape().NumDimensions() == 1 && y_zero_point.Shape().Size() == broadcast_dimIsScalarOr1ElementVector(&y_scale)IsScalarOr1ElementVector(&y_zero_point)virtual onnxruntime::common::Status onnxruntime::QuantizeLinear::Compute(onnxruntime::OpKernelContext *) const [T = signed char]shapeReshapeReshape_1/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/tensor/reshape.hvirtual onnxruntime::common::Status onnxruntime::Reshape::Compute(onnxruntime::OpKernelContext *) constshapeTensor->Shape().NumDimensions() == 1/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/tensor/reshape_helper.honnxruntime::ReshapeHelper::ReshapeHelper(const onnxruntime::TensorShape &, std::vector &)requested_shape[i] >= -1unknown_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/runners/2.168.2/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/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/tensor/reverse_sequence.ccvirtual onnxruntime::common::Status onnxruntime::ReverseSequenceOp::Compute(onnxruntime::OpKernelContext *) constbatch_axis/Users/runner/runners/2.168.2/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 int]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 long long]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = onnxruntime::MLFloat16]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = onnxruntime::MLFloat16]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = onnxruntime::BFloat16]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = onnxruntime::BFloat16]ScatterScatterElementsIndices vs updates dimensions differs at position=Indices must have the same rank as Input. Indices rank=. Input rank=Indices dim= at pos= is greater than input dim=/Users/runner/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/tensor/slice.ccvoid onnxruntime::SliceBase::FillVectorsFromInput(const onnxruntime::Tensor &, const onnxruntime::Tensor &, const onnxruntime::Tensor *, const onnxruntime::Tensor *, std::vector &, std::vector &, std::vector &, std::vector &) conststart_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 != nullptrUnsupported input data type of starts/Users/runner/runners/2.168.2/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 &, std::vector &, std::vector *, const std::vector &, const std::vector &)::(anonymous class)::operator()(SliceIterator &) constoutput == output_endvoid *onnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStep(void *)Unexpected element size of SpaceToDepthDepthToSpace/Users/runner/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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::DataAsSpan() const [T = signed char]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = signed char]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = std::__1::basic_string]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = std::__1::basic_string]Unsqueeze/Users/runner/runners/2.168.2/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 axis/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/providers/cpu/tensor/unsqueeze.honnxruntime::UnsqueezeBase::UnsqueezeBase(const onnxruntime::OpKernelInfo &)info.GetAttrs("axes", axes_).IsOK()Upsample/Users/runner/runners/2.168.2/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.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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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 0 is expected to have 3 dimensions, got Input 0 dimension 2 should be divisiable by value of the num_heads attribute.Input 1 is expected to have 2 dimensions, got Input 1 dimension 0 should have same length as dimension 2 of input 0Input 2 is expected to have 1 dimension, got Input 2 dimension 0 should have same length as dimension 1 of input 1Input 3 is expected to have 1 dimension, got /Users/runner/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/work/1/s/onnxruntime/contrib_ops/cpu/cpu_contrib_kernels.ccRegisterNchwcKernelsRegisterCpuContribKernelsCropborder/Users/runner/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/work/1/s/onnxruntime/contrib_ops/cpu/fused_conv.cconnxruntime::contrib::FusedConvFloat::FusedConvFloat(const onnxruntime::OpKernelInfo &)FusedGemmactivationactivation_/Users/runner/runners/2.168.2/work/1/s/onnxruntime/contrib_ops/cpu/fused_gemm.cconnxruntime::contrib::FusedGemm::FusedGemm(const onnxruntime::OpKernelInfo &) [T = float]/Users/runner/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/work/1/s/onnxruntime/contrib_ops/cpu/maxpool_with_mask.hacross_channels/Users/runner/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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] >= 1SampleOpSkipLayerNormalization/Users/runner/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/work/1/s/onnxruntime/contrib_ops/cpu/transpose_matmul.cconnxruntime::contrib::TransposeMatMul::TransposeMatMul(const onnxruntime::OpKernelInfo &)info.GetAttr("transA", &trans_a_attr_).IsOK()info.GetAttr("transB", &trans_b_attr_).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/runners/2.168.2/work/1/s/onnxruntime/contrib_ops/cpu/word_conv_embedding.ccWordConvEmbeddingembedding_sizeconv_window_sizechar_embedding_size/Users/runner/runners/2.168.2/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]AllocateAllocateStaticallyPreExistingReuseAllocateOutputShareAllocation Plan: (ort_value_idx) output_name : , use fence when asyncIndex out-of-range! Execution Plan: /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/framework/allocation_planner.ccstd::ostream &onnxruntime::operator<<(std::ostream &, std::pair)nullptr != node[Free ml-values: Can not find the node ComputeUseCountsNo suitable kernel definition found for op Can not find the execution provider onnxruntime::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()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 &)kernel_create_info != nullptr && kernel_create_info->kernel_def != nullptrvoid 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)/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/framework/allocator.ccCalcMemSizeForArrayWithAlignmentCudaCudaPinnedSpecified device is not supported.Attempt to use DefaultLogger but none has been registered.OrtMemoryInfo: [ name: id: mem_type: alloc_type:]/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/framework/bfc_arena.ccBFCArenaCreating BFCArena for Creating bins of max chunk size onnxruntime::BFCArena::BFCArena(std::unique_ptr, size_t, onnxruntime::ArenaExtendStrategy)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()bool onnxruntime::BFCArena::Extend(size_t)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:BFC Arena ran out of memory trying to allocate . Current allocation summary follows.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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/work/1/s/onnxruntime/core/framework/data_transfer_manager.ccCopyTensors/Users/runner/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/work/1/s/onnxruntime/core/framework/ex_lib_loader.cc~ExLibLoaderUnloading DSO Failed to unload DSO: Caught exception while destructing CustomOpsLoader with message: A dso with name has already been loaded.LoadExternalLibCaught exception while loading custom ops with message: /Users/runner/runners/2.168.2/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(mem_patterns_->locations[i]) == buffers_.end()Trying 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 behavior, block not found in target location. 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/runners/2.168.2/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_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/runners/2.168.2/work/1/s/onnxruntime/core/framework/execution_provider.ccvoid onnxruntime::IExecutionProvider::InsertAllocator(onnxruntime::AllocatorPtr)/Users/runner/runners/2.168.2/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/runners/2.168.2/work/1/s/onnxruntime/core/framework/fuse_nodes_funcs.ccGetFuncsNo provider specified./Users/runner/runners/2.168.2/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/runners/2.168.2/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 (kernel def can't be NULLFailed to add kernel for : Conflicting with a registered kernel with op versions./Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/framework/kernel_registry.ccTryCreateKernel kernel is not supported in Encountered following errors: (Kernel not foundvoid onnxruntime::(anonymous namespace)::TraverseFormalParametersWithTypeProto(const onnxruntime::Node &, ParamFilterFn, TraverseFn) [ParamFilterFn = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/framework/kernel_registry.cc:71:11), TraverseFn = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/framework/kernel_registry.cc:72:11)]len <= op_schema.inputs().size()void onnxruntime::(anonymous namespace)::TraverseFormalParametersWithTypeProto(const onnxruntime::Node &, ParamFilterFn, TraverseFn) [ParamFilterFn = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/framework/kernel_registry.cc:96:9), TraverseFn = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/framework/kernel_registry.cc:99:9)]The node is not placed on any Execution Provider, therefore, can't find a suitable kernel for Failed to find kernel for found duplicated provider in KernelRegistryManagerThe node is not placed on any Execution Provider (node /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/framework/mldata_type_utils.cconnxruntime::MLDataType onnxruntime::utils::GetMLDataType(const onnxruntime::NodeArg &)nullptr != type_proto/Users/runner/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/work/1/s/onnxruntime/core/framework/op_kernel_info.ccconst OrtMemoryInfo &onnxruntime::OpKernelInfo::GetMemoryInfo(int, OrtMemType) constCould not find OrtValue with name ''/Users/runner/runners/2.168.2/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]No attribute with this name is defined./Users/runner/runners/2.168.2/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/runners/2.168.2/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::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::const_reference onnxruntime::OrtValueTensorSlicer::Iterator::operator*() const [T = const OrtValue]Multiple errors were found./Users/runner/runners/2.168.2/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/runners/2.168.2/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 node ''. Unknown exception was caught by catch-all handler./Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/framework/sequential_executor.ccReleaseNodeMLValues/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/framework/session_state.ccSetGraphSaveMLValueNameIndexMappingDone saving OrtValue mappings.Could not create kernel for node: as there's no execution provider allocated.Kernel creation failed for node: duplicated ort_value index:. Do you have duplicated calls to SessionState::AddInitializedTensor function?Using an input in multiple nodes on different devices is not supported currently. Input: is used by node ) and node Failed to find input name in the mapping: void onnxruntime::SessionState::AddOutputNameToNodeInfoMapping(const std::string &, const onnxruntime::SessionState::NodeInfo &)output_names_to_nodeinfo.empty()Only one node should produce an output. Existing entry for 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 &)Device: [ type: memory_type: device_id:Could not find OrtValue with idx '/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/framework/session_state_initializer.ccCreatePlancommon::Status onnxruntime::SessionStateInitializer::CreatePlan(const onnxruntime::Node *, const ConstPointerContainer > *, ExecutionMode)exec_plan_ptrExecution plan was not found in SessionState. CreatePlan must be called first.SaveInputOutputNamesToNodeMappingSubgraph input with name is not used by any node.ForEachWithIndexoperator()SaveInitializedTensorsSaving initialized tensors.common::Status onnxruntime::SaveInitializedTensors(const onnxruntime::Env &, const std::basic_string &, const onnxruntime::Graph &, const onnxruntime::ExecutionProviders &, const onnxruntime::OrtValueNameIdxMap &, onnxruntime::ITensorAllocator *, const T &, const logging::Logger &, const onnxruntime::DataTransferManager &) [T = (lambda at /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/framework/session_state_initializer.cc:94:3)]ort_value_name_idx_map.MaxIdx() > -1Deserialize tensor failed.Done saving initialized tensorsInvalid allocation info. Provider name = DeserializeTensorProtoInternal error. The preallocated buffer is too small. Requires , Got Failed to copy tensor to execution provider: invalid ort_value_index:/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/framework/simple_tensor_allocator.ccGetPreallocatedBufferFailed to get allocator for initializer '', location: /Users/runner/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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 GetFileContentUnpackTensor: the pre-allocated size does not match the raw data size, expected UnpackTensorWithRawDataNot satisfied: source_bytes.size_bytes() == destination.size_bytes() /Users/runner/runners/2.168.2/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]DnnlExecutionProviderNGRAPHExecutionProviderNupharExecutionProviderOpenVINOExecutionProviderNnapiExecutionProviderRknpuExecutionProviderinvalid allocator/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/framework/utils.ccconst OrtMemoryInfo &onnxruntime::utils::FindMemoryInfoForValue(const onnxruntime::SessionState &, const std::string &)InitializeFeedFetchCopyInfoCopyOneInputAcrossDevicesExecuteGraphFindMemoryInfoForValueconst 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 onnxruntime::SessionState &, const std::vector &, std::vector &)fetch_alloc_info.size() == copy_info.size()CalculateStaticCopyInfoForFeedBatchOrCopyMLValueExecuteGraphImplOnly one thread was configured for parallel execution. Hence will use sequential execution.common::Status onnxruntime::utils::CopyInputsAcrossDevices(const std::vector &, std::vector &, const std::vector &, const onnxruntime::DataTransferManager &)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.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./Users/runner/runners/2.168.2/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 index with shape (batch_size).M3D output tensor with shape (batch_size, sequence_length, hidden_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.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 (http://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).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.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. 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 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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/work/1/s/onnxruntime/core/graph/graph.ccUpdateTypeAndShapeSparseTensor element type mismatch. 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 &, const std::unordered_map &)graph_proto != nullptrConstantGraphInitializer appears in graph inputs and will not be treated as constant value/weight. This may fail 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_argThis 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 (Node () Op () ) 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. This is an invalid model. Error in Node: : Fatal error: is not a registered function/opVerifyNodeAndOpMatch) 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()_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: MergeShapeInfoError merging shape info for output. '' source: target:. Falling back to lenient merge.Output:[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=ai.onnx.mlai.onnx.trainingunordered_map::at: key not found/Users/runner/runners/2.168.2/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/runners/2.168.2/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: /Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/graph/graph_utils.ccconst 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; }) == 0ModelProto does not have a graph.Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported.Unknown model file format version./Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/graph/model.ccModelONNX 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.Failed to load model with error: Protobuf parsing failed.LoadFromBytes less than 0. is less than 0.SaveProtobuf serialization failed.Load model failed. File doesn't exist failedsystem error number SaveModelDomain already set in registry/Users/runner/runners/2.168.2/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 messageonnxruntimeSystemISink must be provided.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.Default logger already set. [VIWEFampm is not a valid date is not a valid year is not a valid day/Users/runner/runners/2.168.2/work/1/s/onnxruntime/core/common/path.ccParsestatic onnxruntime::Path onnxruntime::Path::Parse(const onnxruntime::PathString &)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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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)num_threads >= 1void onnxruntime::concurrency::ThreadPool::Schedule(std::function)fn != nullptrvoid onnxruntime::concurrency::ThreadPool::ParallelFor(std::ptrdiff_t, const onnxruntime::TensorOpCost &, const std::function &)n >= 0Eigen::ThreadPoolInterface *onnxruntime::concurrency::ThreadPool::AsEigenThreadPool() constunderlying_threadpool_ != nullptronnxruntime::(anonymous namespace)::BlockingCounter::BlockingCounter(int)initial_count >= 0/Users/runner/runners/2.168.2/work/1/s/include/onnxruntime/core/common/optional.hT &onnxruntime::optional::value() [T = long long]has_value_T &onnxruntime::optional::value() [T = long]/Users/runner/runners/2.168.2/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/runners/2.168.2/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 &)openfstatGetFileLength: input is not a regular file file "" failed: CleanUpFailed to close file descriptor - error code: Not satisfied: file_path ReadFileIntoBufferNot satisfied: offset >= 0 Not satisfied: length <= buffer.size() lseekreadReadFileIntoBuffer - 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.Field '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: 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.valuessparse_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.DynamicSliceImageScalerScaleWarning: was a removed experimental ops. In the future, we may directly reject this operator. Please update your model as soon as possible.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: rrbValues 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./Users/runner/runners/2.168.2/work/1/s/cmake/external/onnx/onnx/defs/controlflow/defs.ccAn 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].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/runners/2.168.2/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 boolInput type was nullInput 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 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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/work/1/s/cmake/external/onnx/onnx/defs/logical/defs.ccConstrains input types to all numeric tensors.Constrains 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 to boolean tensor.Constrains output to boolean tensor./Users/runner/runners/2.168.2/work/1/s/cmake/external/onnx/onnx/defs/logical/old.ccConstrains input to float tensors.Constrains input to integral tensors.broadcastEnable broadcastingIf set, defines the broadcast dimensions.Left input tensor for the logical operator.Right input tensor for the logical operator.Result tensor.First input operand for the logical operator.Second input operand for the logical operator./Users/runner/runners/2.168.2/work/1/s/cmake/external/onnx/onnx/defs/math/defs.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 signed numeric tensors.Constrain input and output types to all numeric tensors.Coefficient of leakage.Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717).Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946).Coefficient of ELU.The Alpha value in Celu formula which control the shape of the unit. The default value is 1.0.Constrain input and output types to float32 tensors.CeluConstrain input X and output types to float/int tensors.Constrain input Y types to float/int tensors.Value of alpha.Value of beta.The softsign (x/(1+|x|)) values of the input tensor computed element-wiseOptional 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).Dimension 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-wiseA 1-D tensor indicates the shape you want to expand to, following the broadcast ruleThe 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-wiseThe 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.aN-dimensional quantized matrix aa_scalescale of quantized input aa_zero_pointzero point of quantized input abN-dimensional quantized matrix bb_scalescale of quantized input bb_zero_pointzero point of quantized input 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'.Zero point tensor for input 'B'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'.Constrain input A data type to 8-bit integer tensor.Constrain input B data type to 8-bit integer tensor.Constrain output Y data type as 32-bit integer tensor.If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.If set to 1 will perform the sums in reverse direction.An input tensor that is to be processed.(Optional) 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.targetignore_indexreductionnonelossInput tensor of shape (N, C) or (N, C, d1, d2, ..., dk).Target tensor of shape (N) or (N, d1, d2, ..., dk). Target element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the target values should either be in the range [0, C) or have the value ignore_index.Optional rescaling weight tensor. If given, it has to be a tensor of size C. Otherwise, it is treated as if having all ones.The negative log likelihood lossType of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights.Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.Constrain input, weight, and output types to floating-point tensors.Constrain target to integer typesNegativeLogLikelihoodLossNumber 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.InputsOperandsOutputType of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 'mean': the sum of the output will be divided by the number of elements in the output.scoreslog_problabelsThe predicted outputs with shape [batch_size, class_size], or [batch_size, class_size, D1, D2 , ..., Dk], where K is the number of dimensions.The ground truth output tensor, with shape [batch_size], or [batch_size, D1, D2, ..., Dk], where K is the number of dimensions. Labels element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the label values should either be in the range [0, C) or have the value ignore_index.A manual rescaling weight given to each class. If given, it has to be a 1D Tensor assigning weight to each of the classes. Otherwise, it is treated as if having all ones.Weighted loss float Tensor. If reduction is 'none', this has the shape of [batch_size], or [batch_size, D1, D2, ..., Dk] in case of K-dimensional loss. Otherwise, it is a scalar.Log probability tensor. If the output of softmax is prob, its value is log(prob).SoftmaxCrossEntropyLossDescribes 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).'axis' must be in []. Its actual value is: 'shape' input must be 1D tensor of type INT64input 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/runners/2.168.2/work/1/s/cmake/external/onnx/onnx/defs/math/old.cc If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of element size 1 (including a scalar tensor and any tensor with rank equal to or smaller than the first tensor), or having its shape as a contiguous subset of the first tensor's shape. The starting of the mutually equal shape is specified by the argument "axis", and if it is not set, suffix matching is assumed. 1-dim expansion doesn't work yet. For example, the following tensor shapes are supported (with broadcast=1): shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 Attribute `broadcast=1` needs to be passed to enable broadcasting. Input tensor of any shape, base of the exponent.Input tensor of any shape broadcastable to X shape, the exponent component.Pass 1 to enable broadcastingIf set, defines the broadcast dimensions. See doc for details.Output tensor (same size as X)First operand, base of the exponent.Second operand, power of the exponent.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.The exponential of the input tensor computed element-wiseThe natural log of the input tensor computed element-wise1-D input tensorThe hyperbolic tangent values of the input tensor computed element-wiseslopeSlope 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.sumList of tensors for Mean.Minimum value, under which element is replaced by minMaximum value, above which element is replaced by maxInput tensor whose elements to be clippedOutput tensor with clipped input elementsInput 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 int64kNumber 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 retrieveMinimum 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). 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_sizeThe 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).First 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 AConstrain input and output types to high-precision numeric tensors.Invalid 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.List of tensors for 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 dimension/Users/runner/runners/2.168.2/work/1/s/cmake/external/onnx/onnx/defs/nn/defs.ccThe storage order of the tensor. 0 is row major, and 1 is column major.Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.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).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.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.Input tensor must have at least 2 dimensionsRoIs tensor must have 2 dimensionsAttribute pooled_shape has incorrect lengthAttribute pooled_shape must be specifiedInput 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 ...].Scale 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.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. 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 = 0Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.Constrain filter type to 8-bit integer tensor.Constrain output type to 8-bit integer tensor.Constrain bias type to 32-bit integer tensor.The shape of the convolution kernel. If not present, should be inferred from input 'w'.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.number of groups input channels and output channels are divided into. default is 1.Zero point tensor for input 'x'. It's optional and default value is 0. It's a scalar, which means a per-tensor/layer quantization.Zero point tensor for input 'w'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M)Constrain input x and its zero point data type to 8-bit integer tensor.Constrain input w and its zero point data type to 8-bit integer tensor.Constrain output y data type to 32-bit integer tensor.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.momentumFactor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).Input data tensor from the previous operator; dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size, C is the number of channels. Statistics are computed for every channel of C over N and D1 to Dn dimensions. For image data, input dimensions become (N x C x H x W). The op also accepts single dimension input of size N in which case C is assumed to be 1Scale tensor of shape (C).Bias tensor of shape (C).running (training) or estimated (testing) mean tensor of shape (C).varrunning (training) or estimated (testing) variance tensor of shape (C).The output tensor of the same shape as XThe running mean after the BatchNormalization operator.The running variance after the BatchNormalization operator.saved_meanSaved mean used during training to speed up gradient computation.saved_varSaved variance used during training to speed up gradient computation.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 input 1-dimensional scale tensor of size C.The input 1-dimensional bias tensor of size C.The output tensor of the same shape as input.Input matrixMatrix after normalizationThe axis on which to apply normalization, -1 mean last axis.pThe order of the normalization, only 1 or 2 are supported.(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.The lambd value for the Shrink formulation. Default is 0.5.The bias value added to output. Default is 0.A 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.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.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. 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.p value of the Lp norm used to pool over the input data.Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.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]).Optional 1D bias to be added to the convolution, has size of M.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 is 1 along each spatial axis.Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis.number of groups input channels and output channels are divided into.weight and zero_point pair is expected to have same type.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 padsAdditional 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.Ratio of Dropout must be a scalar.training_mode of Dropout must be a scalar.Invalid value() for attribute 'axis'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/Users/runner/runners/2.168.2/work/1/s/cmake/external/onnx/onnx/defs/nn/old.ccDilation value along each spatial axis of filter.Stride along each spatial axis.Padding for the beginning and ending along each axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding. DEPRECATION NOTE: auto_pad is only intended to support legacy uses, and for framework authors, one is explicitly encouraged to use explicit padding specified in the pads attribute.Stride along each axis.p 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 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.Factor 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.The 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 mean used during training to speed up gradient computation. Should not be used for testing.Saved variance used during training to speed up gradient computation. Should not be used for testing.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.The output tensor of the same shape as X.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). If true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch.If spatial is true, the dimension of scale is (C). If spatial is false, the dimensions of scale are (C x D1 x ... x Dn)If spatial is true, the dimension of bias is (C). If spatial is false, the dimensions of bias are (C x D1 x ... x Dn)If spatial is true, the dimension of the running mean (training) or the estimated mean (testing) is (C). If spatial is false, the dimensions of the running mean (training) or the estimated mean (testing) are (C x D1 x ... x Dn).If spatial is true, the dimension of the running variance(training) or the estimated variance (testing) is (C). If spatial is false, the dimensions of the running variance(training) or the estimated variance (testing) are (C x D1 x ... x Dn).dilation value along each spatial axis of the filter.The 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/runners/2.168.2/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/runners/2.168.2/work/1/s/cmake/external/onnx/onnx/defs/object_detection/old.ccScale 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/runners/2.168.2/work/1/s/cmake/external/onnx/onnx/defs/quantization/defs.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.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./Users/runner/runners/2.168.2/work/1/s/cmake/external/onnx/onnx/defs/reduction/defs.ccConstrain input and output types to high-precision and 8 bit numeric tensors.Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index)./Users/runner/runners/2.168.2/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).The axis in which to compute the arg indices.Reduced output tensor with integer data type. 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.The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).First input tensor must have rank 3/Users/runner/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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.Element type of inputs are expected to be the same.Input Sequence and Tensor are expected to have the same elem type. Sequence= Tensor=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=Constrain input types. Casting from complex is not supported.Constrain output types. Casting to complex is not supported./Users/runner/runners/2.168.2/work/1/s/cmake/external/onnx/onnx/defs/tensor/defs.ccSpecified shape for output.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)..Which 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 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).1-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.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 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).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).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.repeats1D int64 tensor of the same length as input's dimension number, includes numbers of repeated copies along input's dimensions.Output tensor of the same dimension and type as tensor input. output_dim[i] = input_dim[i] * repeats[i]Constrain repeat's type to int64 tensors.Three interpolation modes: nearest (default), linear and cubic. The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if "mode" is "cubic".If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0.Four modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.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.(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.Constrain output types to boolean tensors.(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'The number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]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). 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).Target shape may not have multiple -1 dimensionsInvalid position of 0Invalid dimension value: Invalid Target shape product of 0Dimension could not be inferred: incompatible shapesaxis must be in [-rank, rank-1].Invalid 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 splittableInvalid attribute perm {}, input shape = {'axes' attribute must not contain any duplicatesvalues in 'axes' are beyond the bounds of the computed output shape'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.'axis' must be in [-rank(indices), rank(indices)-1]'input' must have rank >= 2'sequence_lens' must have rank of 1'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64toThe 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 strings and complex are not supported.Constrain output types. Casting to strings and complex are not supported./Users/runner/runners/2.168.2/work/1/s/cmake/external/onnx/onnx/defs/tensor/old.ccWhich axis to concat on. Default value is 1.inputsList of tensors for concatenationconcat_resultConcatenated tensorConstrain output types to float tensors.Which axis to concat onThe tensor to splitOptional list of output lengths (see also arg 'split')outputs...One or more outputs forming list of tensors after splittingConstrain input types to float tensors.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 shapereshapedReshaped data.Input tensor of any shape.tilesNumber of repeated copies to make of the input tensor.Axis along which to repeat.Output tensor of same shape and type as input.Constrain tiles and axis's type to int64 tensors.width_scaleThe scale along width dimension. It takes value greater than or equal to 1.height_scaleThe scale along height dimension. It takes value greater than or equal to 1.Two interpolation modes: nearest(default), bilinear4-D tensor, [N,C,H,W]4-D tensor after resizing, [N,C,H,W]Constrain output types to bool, int32, int64, float16, float, double tensors.The scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'.Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)N-D tensorN-D tensor after resizingThe 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 ending indices (exclusive) of corresponding axis in `axes`steps1-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).updatesTensor of rank r >=1 (same rank and shape as indices)Tensor of rank r >= 1 (same rank as input).Blocks 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].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.Tensor of rank q + (r - 1).List of non-negative integers, indicate the dimensions to squeeze.Tensors with at least max(dims) dimensions.squeezedReshaped tensor with same data as input.List of non-negative integers, indicate the dimensions to be insertedOriginal tensorexpanded(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.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.Tensor of rank q + r - indices_shape[-1] - 1.Value of attribute not specified should be of integer type and specify a type. does not specify a valid type.Required attribute axis is missingrank must be greater than axisAll inputs to Concat must have same rankRanks 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 lengthSlice 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/stepsBlocksize must be positiveInput tensor must be 4-dimensionaldata tensor must have rank >= 1axis must be in [-r, r-1]OneHot 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 lengthBoth `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` tensorDimension 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/runners/2.168.2/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/runners/2.168.2/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=type mismatch. existing=Graph has inputs but were providedGraph input # is tensor type, but provided type is (op_type:, name:input is out of boundsGraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. does not contain a graph.output /Users/runner/runners/2.168.2/work/1/b/RelWithDebInfo/onnx/onnx-ml.pb.ccsize_t to int conversion/Users/runner/runners/2.168.2/work/1/s/cmake/external/protobuf/src/google/protobuf/arena.ccCHECK failed: (min_bytes) <= (std::numeric_limits::max() - kBlockHeaderSize): /Users/runner/runners/2.168.2/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/runners/2.168.2/work/1/s/cmake/external/protobuf/src/google/protobuf/extension_set_inl.h/Users/runner/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/work/1/s/cmake/external/protobuf/src/google/protobuf/parse_context.hCan't happen/Users/runner/runners/2.168.2/work/1/s/cmake/external/protobuf/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): /Users/runner/runners/2.168.2/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/runners/2.168.2/work/1/s/cmake/external/protobuf/src/google/protobuf/stubs/int128.ccDivision or mod by zero: dividend.hi=, lo=/Users/runner/runners/2.168.2/work/1/s/cmake/external/protobuf/src/google/protobuf/stubs/stringpiece.ccsize too big: details: /Users/runner/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/work/1/s/cmake/external/re2/re2/onepass.ccCannot use SearchOnePass for unanchored matches.unhandled opcode: /Users/runner/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/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/runners/2.168.2/work/1/s/cmake/external/re2/re2/walker-inl.hStack not empty.NamedCapturesWalker::ShortVisit calledCaptureNamesWalker::ShortVisit calledWalk NULL/Users/runner/runners/2.168.2/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/runners/2.168.2/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}uRRysuh)'..bLLIA #W)'..bLLu % 2X # 3<)'9#+)'LL]TUA (}}  N Q ;(V~)}}   IAs Z~ e5O ZIAs Z~ e5O ZLwրNv H+| S\= HրNv H+| S\= H '$3{D_րN '  ]]eW k' &f  ?. 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iE4TypeEvfE4TypeEvbE4TypeEv̶NSڶaE4TypeEvhE4TypeEvtE4TypeEvsE4TypeEvxE4TypeEv¸dE4TypeEvиjE4TypeEv޸yE4TypeEv39GetSparseTensorTypeI6RegisterDataTypeEPKS0_1GetDataTypeERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE4AllTensorTypesEvGetTensorTypeITypeFromProtoERKN4onnx9TypeProtoE iEEPKS0_vfEEPKS0_vŶbEEPKS0_vӶNSaEEPKS0_vhEEPKS0_vtEEPKS0_vsEEPKS0_vxEEPKS0_vɸdEEPKS0_v׸jEEPKS0_vyEEPKS0_vt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEE4TypeEv_t3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEEEPKS0_v_9MLFloat16EE4TypeEv8BFloat16EE4TypeEv9MLFloat16EEEPKS0_v߹8BFloat16EEEPKS0_v iE4TypeEvfE4TypeEvbE4TypeEvaE4TypeEvμhE4TypeEvܼtE4TypeEvsE4TypeEvxE4TypeEvdE4TypeEvjE4TypeEvyE4TypeEvNS_ iEEPKS0_vfEEPKS0_vbEEPKS0_vǼaEEPKS0_vռhEEPKS0_vtEEPKS0_vsEEPKS0_vxEEPKS0_vdEEPKS0_vjEEPKS0_vyEEPKS0_vNS_“ÓēœƓǓȓɓʓ˓͓̓ΓϓГѓғӓԓՓ֓9MLFloat16EE4TypeEv8BFloat16EE4TypeEvד9MLFloat16EEEPKS0_v8BFloat16EEEPKS0_vؓٓړNS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEExS8_NS1_4lessIS8_EENS6_INS1_4pairIKS8_S8_EEEEEEE4TypeEvxNS1_4lessIS8_EENS6_INS1_4pairIKS8_xEEEEEEE4TypeEvfNS1_4lessIS8_EENS6_INS1_4pairIKS8_fEEEEEEE4TypeEvdNS1_4lessIS8_EENS6_INS1_4pairIKS8_dEEEEEEE4TypeEvۓ3mapI6vectorINS2_3mapI12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEEEPKS0_vNS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEExS9_NS2_4lessIS9_EENS7_INS2_4pairIKS9_S9_EEEEEEEEPKS0_vxNS2_4lessIS9_EENS7_INS2_4pairIKS9_xEEEEEEEEPKS0_vfNS2_4lessIS9_EENS7_INS2_4pairIKS9_fEEEEEEEEPKS0_vdNS2_4lessIS9_EENS7_INS2_4pairIKS9_dEEEEEEEEPKS0_vݓޓߓNS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS1_4lessIxEENS6_INS1_4pairIKxS8_EEEEEEE4TypeEvxNS1_4lessIxEENS1_9allocatorINS1_4pairIKxxEEEEEEE4TypeEvfNS1_4lessIxEENS1_9allocatorINS1_4pairIKxfEEEEEEE4TypeEvdNS1_4lessIxEENS1_9allocatorINS1_4pairIKxdEEEEEEE4TypeEvNS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEENS2_4lessIxEENS7_INS2_4pairIKxS9_EEEEEEEEPKS0_vxNS2_4lessIxEENS2_9allocatorINS2_4pairIKxxEEEEEEEEPKS0_vfNS2_4lessIxEENS2_9allocatorINS2_4pairIKxfEEEEEEEEPKS0_vdNS2_4lessIxEENS2_9allocatorINS2_4pairIKxdEEEEEEEEPKS0_vceTensorTypeItialExecutor7ExecuteERKNS_12SessionStateERKNSt3__16vectorIiNS4_9allocatorIiEEEERKNS5_I8OrtValueNS6_ISB_EEEESA_RSD_RKNS4_13unordered_mapImNS4_8functionIFNS_6common6StatusERKNS_11TensorShapeERK13OrtMemoryInfoRSB_RbEEENS4_4hashImEENS4_8equal_toImEENS6_INS4_4pairIKmSU_EEEEEERKNS_7logging6LoggerE fE4TypeEvdE4TypeEvaE4TypeEvhE4TypeEvsE4TypeEvtE4TypeEviE4TypeEvjE4TypeEvxE4TypeEvyE4TypeEvbE4TypeEvNS128SparseTensorTypeFromONNXEnumEi3AllGetSequenceTensorTypeIAllNumericTensorTypesEv fEEPKS0_vdEEPKS0_vaEEPKS0_vhEEPKS0_vsEEPKS0_vtEEPKS0_viEEPKS0_vjEEPKS0_vxEEPKS0_vyEEPKS0_vbEEPKS0_vNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEE4TypeEv_t3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEEEPKS0_v_9MLFloat16EE4TypeEv8BFloat16EE4TypeEv9MLFloat16EEEPKS0_v8BFloat16EEEPKS0_veparseTensorC̉quenceTypeINSt3__16vectorINS1_3mapIssionStateNS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEfNS1_4lessIS9_EENS7_INS1_4pairIKS9_fEEEEEENS7_ISG_EEEEE4TypeEvxfNS1_4lessIxEENS1_9allocatorINS1_4pairIKxfEEEEEENS6_ISB_EEEEE4TypeEvNS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEfNS2_4lessISA_EENS8_INS2_4pairIKSA_fEEEEEENS8_ISH_EEEEEEPKS0_vxfNS2_4lessIxEENS2_9allocatorINS2_4pairIKxfEEEEEENS7_ISC_EEEEEEPKS0_vĔєԔTensorTypeFromONNXEnumEiAllSequenceTensorTypesEvՔ0SequenceTensorTypeFromONNXEnumEi3All iEfEbENSaEhEtEsExEdEjEyE4TypeEv6DeleteEPvИ4TypeEv6DeleteEPv4TypeEv6DeleteEPvЛt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEE_4TypeEv6DeleteEPv4TypeEv6DeleteEPvО4TypeEv6DeleteEPv4TypeEv6DeleteEPvС4TypeEv6DeleteEPv4TypeEv6DeleteEPvФ4TypeEv6DeleteEPv4TypeEv6DeleteEPvЧ4TypeEv6DeleteEPv9MLFloat16EE8BFloat16EE4TypeEv6DeleteEPvЪ4TypeEv6DeleteEPvFixedSizeTensorExceptHalfTypesEvIEEEFloatTensorExceptHalfTypesEvЭ๕IEEEFloatTensorTypesEvFixedSizeTensorTypesEv•ϕޕ16detail16CopyLittleEndianEmN3gsl4spanIKcEENS3_IcEE2ћ342ONNXTensorElementDataTypeToProtoTensorTypeE25ONNXTensorElementDataTypeŵ 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213dbg_count_check_0dbg_count_check_޸4dbg_count_check_7dbg_count_check_InstanceNormalization_Onnx_ver8dbg_count_check_DynamicQuantizeLinear_Onnx_ver11E59dbg_count_check_convTransposeWithDynamicPadsShapeInferenceERNS_16InferenceContextEdbg_count_check_ҬҬCϸD2ERKNS_5GraphE1ERKNS_5GraphENSt3__110unique_ptrINS_15IndexedSubGraphENS4_14default_deleteIS6_EEEERKNS_7logging6LoggerERKmRKN4onnx13FunctionProtoERKNS_7logging6LoggerE˴NSt3__110unique_ptrINS_15IndexedSubGraphENS4_14default_deleteIS6_EEEERKNS_7logging6LoggerERKmRKN4onnx13FunctionProtoERKNS_7logging6LoggerEѵ2Evл1Ev׻0Ev޻ѵӵӵ8OpSchemaEv4BodyEv1ӵӵ8GetIndexedSubGraphEv¼2GetFuncProtoEvɼӵӵԵC8SetShapeERKN4onnx16TensorShapeProtoEĿ1˿7SetTypeE2ERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKN4onnx9TypeProtoEѾ1ERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKN4onnx9TypeProtoEؾ411TypeAsProtoEv5ShapeEv6ExistsEvNameEvTypeEv0ClearShapeEv8UpdateTypeAndShapeERKN4onnx9TypeProtoEbbRKNS_7logging6LoggerES0_bbRKNS_7logging6LoggerEPKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERKN4onnx9TypeProtoE7EdgeEndC124InitERKNSt3__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_iiii715IndexEv4NameEv628NodeTypeEvEdgeEndToProtoERN4onnx9NodeProtoEb7GetNodeEv14GetึSrcArgIndexEvDstArgIndexEv7NodeConstIterator15SetFunctionBodyERKNS_8FunctionE4C8MutableDefinitionsEv2AddAttributeERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE9UpdateInputArgCountEvCppEvmmEv2ENSt3__121__tree_const_iteratorINS0_7EdgeEndEPNS2_11__tree_nodeIS4_PvEElEE1ENSt3__121__tree_const_iteratorINS0_7EdgeEndEPNS2_11__tree_nodeIS4_PvEElEE71DescriptionEv5GetFunctionBodyEv3GetAttributesEv2GetSubgraphsEv0ForEachDefENSt3__18functionIFvRKNS_7NodeArgEbEEEbNodeConstIteratorGetGraphAttributeERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEeqERKS1_neERKS1_deEvptEvйຶOpTypeEvDomainEvOpEv4GetExecutionProviderTypeEvл໶SetNodeTypeENS0_4TypeEReplaceDefsERKNSt3__13mapIPKNS_7NodeArgEPS3_NS1_4lessIS5_EENS1_9allocatorINS1_4pairIKS5_S6_EEEEEE40MutableRelationshipsEvSetExecutionProviderTypeERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEGetMutableGraphAttributeERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEм1C7329SetInputsERKNSt3__16vectorIPKNS_7NodeArgENS1_9allocatorIS5_EEEED20649GenerateNodeArgNameERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE1ToGraphProtoEvAllocateNodeEvFuseSubGraphENSt3__110unique_ptrINS_15IndexedSubGraphENS1_14default_deleteIS3_EEEERKNS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEƶƶreateSubgraphERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEElearAttributeERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEɶԶԶ1ER2ERS0_RKNS_4NodeERN4onnx10GraphProtoEKNS_5ModelEPN4onnx10GraphProtoERKNSt3__113unordered_mapINS7_12basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEEiNS7_4hashISE_EENS7_8equal_toISE_EENSC_INS7_4pairIKSE_iEEEEEExNS7_10shared_ptrINS_30IOnnxRuntimeOpSchemaCollectionEEEնRKS9_Nfx4onnx1S1_6vectorI4AttributeProtoE0GraphProtoE1TensorProtoE7SparseTensorProtoEضfNS5_IfEEEExNS5_IxEEEES7_NS5_IS7_EEEEN4onnx11TensorProtoENS5_ISC_EEEE0GraphProtoENS5_ISC_EEEE7SparseTensorProtoENS5_ISC_EEEEKNS_5ModelEPN4onnx10GraphProtoERKNSt3__113unordered_mapINS7_12basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEEiNS7_4hashISE_EENS7_8equal_toISE_EENSC_INS7_4pairIKSE_iEEEEEExNS7_10shared_ptrINS_30IOnnxRuntimeOpSchemaCollectionEEES0_RKNS_4NodeERN4onnx10GraphProtoERKNS_7logging6LoggerERKNS8_ISE_PKNS4_13FunctionProtoESG_SI_NSC_INSJ_ISK_SZ_EEEEEEPS0_PKNS_4NodeERKNS_7logging6LoggerERKNS8_ISE_PKNS4_13FunctionProtoESG_SI_NSC_INSJ_ISK_S13_EEEEEERKNS_7logging6LoggerERKNS8_ISE_PKNS4_13FunctionProtoESG_SI_NSC_INSJ_ISK_SZ_EEEEEEPS0_PKNS_4NodeERKNS_7logging6LoggerERKNS8_ISE_PKNS4_13FunctionProtoESG_SI_NSC_INSJ_ISK_S13_EEEEEEԷַAddResolveERKNS0_14ResolveOptionsESetNameERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENodeERKNEdgeEmmii4onnx9NodeProtoERKNSt3__113unordered_mapINS5_12basic_stringIcNS5_11char_traitsIcEENS5_9allocatorIcEEEENS1_9TypeProtoENS5_4hashISC_EENS5_8equal_toISC_EENSA_INS5_4pairIKSC_SD_EEEEEES8InitializeStateFromModelFileGraphProtoEv01GetNodeArgIncludingParentGraphsERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE9PerformTopologicalSortAndCheckIsAcyclicEvComputeOverridableInitializersEvPerformTypeAndShapeInferencingERKNS0_14ResolveOptionsETypeCheckInputsAndInitializersEvVerifyInputAndInitializerNamesEv17InferAndVerifySubgraphTypesERKNS_4NodeERS0_RKNSt3__16vectorIPKN4onnx9TypeProtoENS5_9allocatorISA_EEEERSD_RKNS0_14ResolveOptionsE039InitInputsInitializersOutputsEv2ForThisAndAllSubgraphsERKNSt3__16vectorIPS0_NS1_9allocatorIS3_EEEENS1_8functionIFNS_6common6StatusERS0_EEE4ReplaceInitializedTensorERKN4onnx11TensorProtoE6CleanAllInitializ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29 1ۡ 7 0 6 3 5 4 8 dummyInferenceFunctionERNS_16InferenceContextEconvPoolShapeInferenceERNS_16InferenceContextEbbii OperatorStatus_IsValidEi StringStringEntryProto TypeProto_SparseTensor 1 33IsSupportedOptypeVersionAndDomainERKNS_4NodeERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEERKSt16initializer_listIiESC_އ 2 8FindPathERKNS_4NodeEbRKNSt3__16vectorINS0_14EdgeEndToMatchENS4_9allocatorIS6_EEEERNS5_IPKNS1_7EdgeEndENS7_ISE_EEEERKNS_7logging6LoggerE 6 7 8 9IsSupportedProviderERKNS_4NodeERKNSt3__113unordered_setINS4_12basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEENS4_4hashISB_EENS4_8equal_toISB_EENS9_ISB_EEEE 2 3 0RemoveNodeERNS_5GraphERNS_4NodeE 4AddInitializerERNS_5GraphERKN4onnx11TensorProtoE GetNode ReplaceNodeInputERNS_4NodeEiRNS_7NodeArgE FirstChildByTypeERNS_4NodeERKNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEE InputNameERKNS_4NodeEi AttributeERKNS_4NodeERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEE GetNodeOutputNameERKNS_4NodeEiׇ NodeArgIsConstantERKNS_5GraphERKNS_7NodeArgE FirstParentByTypeERNS_4NodeERKNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEE 1 6Replace 9CanReplaceNodeWithInitializerERKNS_5GraphERKNS_4NodeERKNSt3__112basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEERKNS_7logging6LoggerEݒ 2GetConstantInitializerERKNS_5GraphERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEEb 4AllNodeInputsAreConstantERKNS_5GraphERKNS_4NodeERNSt3__113unordered_mapINS7_12basic_stringIcNS7_11char_traitsIcEENS7_9allocatorIcEEEEPKN4onnx11TensorProtoENS7_4hashISE_EENS7_8equal_toISE_EENSC_INS7_4pairIKSE_SI_EEEEEERKNS7_13unordered_setISE_SK_SM_NSC_ISE_EEEE MatchesOpSinceVersionERKNS_4NodeERK RemoveNodeOutputEdgesERNS_5GraphERNS_4NodeE IsConstantInitializerERKNS_5GraphERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEEb St16initializer_listIiE NSt3__16vectorIiNS4_9allocatorIiEEEE MatchesOpSetDomainERKNS_4NodeERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEE FinalizeNodeFusionERNS_5GraphER Is AddNodeInputERNS_4NodeEiRNS_7NodeArgE GetInput OutputUsedERKNS_4NodeEi GraphInputERKNS_5GraphEPKNS_7NodeArgE CanRemoveNodeERKNS_5GraphERKNS_4NodeERKNS_7logging6LoggerEڑ IsInitializerERKNS_5GraphERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEEb NodeWithInitializerERNS_5GraphERNS_4NodeERNS_7NodeArgE֒ DownstreamNodeInputERNS_5GraphERNS_4NodeEiS4_i NS_4NodeES4_ KNSt3__16vectorINS3_17reference_wrapperINS_4NodeEEENS3_9allocatorIS7_EEEERS6_ EdgeERKNS_4NodeEiܔ NodeERKNS_4NodeEi 2ERKNS_5GraphE 1ERKNS_5GraphE 4NameEv 1 2̖ 9GetInputsEv 30GetInputsIncludingInitializersEv 7GetNodeEmך 5NodesEvޚ 1DescriptionEvŖ 0 2 3NumberOfNodesEv 0GetInitializedTensorERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERPKN4onnx11TensorProtoE 2CanOverrideInitializerEv 6GetNodesInTopologicalOrderEv 4GetAllInitializedTensorsEv 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512F 512F Filter 512FFilter 1Output 2Output 3Output 4Outputٌ 1 2 3 1Output 2Output 3Output 4Output 1 2 3 1 2ً 3 1ҋ 2 3 1 2 3 1 2 3 1Ҍ 2 3 1 2 3 512F Ҁ512F ΃512F ԇӈʑ2 512 2 512ӑ שӰ۶2 512 1OutputϏ 2Output 3Output 4Output 1 2 3 6 1 2 3 6 1ɐ 2А 3א 6ސ 1 2 3 6 څ̑ϜCorȇ Vnni ݡCore Vnni ϕCore Vnni 1Ò 29check_sparse_tensor_indices_ 6check_value_infoERKNS_14ValueInfoProtoERKNS0_14CheckerContextE 2check_tensorERKNS_11TensorProtoERKNS0_14CheckerContextE 9check_sparse_tensorERKNS_17SparseTensorProtoERKNS0_14CheckerContextE 5check_attributeERKNS_14AttributeProtoERKNS0_14CheckerContextERKNS0_19LexicalScopeContextE 1check_ 0check_nodeERKNS_9NodeProtoERKNS0_14CheckerContextERKNS0_19LexicalScopeContextE 4check_functionERKNS_13FunctionProtoERKNS0_14CheckerContextERKNS0_19LexicalScopeContextE 1ERKNS_11TensorProtoERKNS_17SparseTensorProtoEm 2ERKNS_11TensorProtoERKNS_17SparseTensorProtoEm йgraphERKNS_10GraphProtoERKNS0_14CheckerContextERKNS0_19LexicalScopeContextE modelERKNS _10ModelProtoE t3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE RNS0_14CheckerContextE  3 6 9 1 8 7 5 4 0 2 MakeAttributeERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEEӛ Version_ParseERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEEPNS_7VersionE TypeProto_Map FunctionProto auto_pad_docيRK S8_ f x NS _1 0_6vectorIМ 1TensorProtoEœ 0GraphProtoEɜ fNS4_IfEEEE xNS4_IxEEEE S6_NS4_IS6_EEEE NS_1 1TensorProtoENS4_ISA_EEEE۝ 0GraphProtoENS4_ISA_EEEE Ma RNNDocGenerator1EPKcѳ OpSchemaRegistryǺ T keRefAttributeERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEE thDocGeneratorEPKc NS_28AttributeProto_AttributeTypeE S8_NS_28AttributeProto_AttributeTypeE handle_negative_axis_validateERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEEiiԡ ArgReduceDocGenerator_opset11EPKcگ TensorProto_DataLocation_NameENS_24TensorProto_DataLocationE dbg_count_check_‘_ScanInferenceFunctionERNS_16InferenceContextE LoopInferenceFunctionERNS_16InferenceContextE PoolOpSchemaGeneratorEPKcS1_S1_bb ConvOpSchemaGeneratorEPKcޚ ArgReduceDocGeneratorEPKc IfInferenceFunctionERNS_16InferenceContextE einsumRankInferenceERNS_16InferenceContextENSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE OperatorStatus_NameENS_14OperatorStatusE TensorProto_Segment̝ GetOpSchemaINS_ TensorProto 1˥ 2 3׋ 3 5ǧ 4 2 9 7 6 8 If_Onnx_ver11EEENS_8OpSchemaEv A Xor_Onnx_ver N S M Div_Onnx_ver E L Cos_Onnx_ver7EEENS_8OpSchemaEv Tan_Onnx_ver7EEENS_8OpSchemaEv P RNN_Onnx_verñ GRU_Onnx_ver  Loop_Onnx_ver11EEENS_8OpSchemaEv S Equal_Onnx_ver Floor_Onnx_ver C PRelu_Onnx_ver Gemm_Onnx_ver11EEENS_8OpSchemaEv TopK_Onnx_ver1 A IsNaN_Onnx_ver9EEENS_8OpSchemaEv Where_Onnx_ver9EEENS_8OpSchemaEv can_Onnx_ver11EEENS_8OpSchemaEv hape_Onnx_ver1EEENS_8OpSchemaEv plit_Onnx_ver lice_Onnx_ver1EEENS_8OpSchemaEv ScanInferenceFunctionOpset LoopInferenceFunctionOpset8ERNS_16InferenceContextEڰ MathDocGenerator_old_opset6EPKc 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OperatorStatus_ParseERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEEPNS_14OperatorStatusE If_Onnx_ver1EEENS_8OpSchemaEvҵ Or_Onnx_ver Utils1 2dbg_count_check_NegativeLogLikelihoodLoss_Onnx_ver12E0dbg_count_check_1dbg_count_check_MeanVarianceNormalization_Onnx_ver9E3DataTypeUtils 2TypesWrapper 1StringRange 2 1 6ToTypeERKNS 8ToStringERKNS_9TypeProtoERKNSt3__112basic_stringIcNS5_11char_traitsIcEENS5_9allocatorIcEEEESD_ļ 0GetTypeStrToProtoMapEv 1IsValidDataTypeStringERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE 4GetTypeStrLockEv 0FromStringERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEERNS_9TypeProtoE˼ 1ToTypeProtoERKPKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEټ 6ToDataTypeStringEi 8FromDataTypeStringERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEERi _9TypeProtoE t3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEҼ 1 23T C 5GetTypesWrapperEv 9GetAllowedDataTypesEv 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OpSchemaINS_14Mean_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Mean_Onnx_ver8EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Min_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Mod_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Pow_Onnx_ver12EEENS_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_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_15Celu_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Clip_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Clip_Onnx_ver12EEENS_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_15IsNaN_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Loop_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15PRelu_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15PRelu_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15PRelu_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15PRelu_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Scan_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Slice_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Split_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Split_Onnx_ver2EEENS_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_16Expand_Onnx_ver8EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Gather_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_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_16Shrink_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17ArgMax_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17ArgMax_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17ArgMin_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17ArgMin_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_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_17EyeLike_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_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_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_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_18Flatten_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Hardmax_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Identity_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18MaxPool_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18MaxPool_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18MaxPool_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18ReduceL1_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18ReduceL2_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Scaler_OnnxML_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Softmax_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Softplus_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Softsign_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_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_19GatherND_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver12EEENS_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_19ReduceL2_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19ReduceMax_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19ReduceMin_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19ReduceSum_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19Unsqueeze_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20LogSoftmax_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20MaxRoiPool_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20MaxUnpool_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20Reciprocal_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20Reciprocal_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceMax_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceMax_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceMean_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceMin_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceMin_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceProd_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceSum_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21AveragePool_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21AveragePool_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21Binarizer_OnnxML_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21HardSigmoid_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21HardSigmoid_Onnx_ver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1126.LComputeBlockBy1Loop1126.LOutputBlock1126.LMultiplyAlpha2xNBlock1112.LStore2xNBlock1112.LOutput1xNBlock1112.LOutput1xNBlockWithMask1112.LMultiplyAlpha1xNBlockWithMask1112.LStore1xNBlockWithMask1112.LProcessRemainingCountN1112.LComputeBlockBy4Loop1487.LProcessRemainingBlocks1487.LComputeBlockBy1Loop1487.LOutputBlock1487.LProcessCountM6.LProcessNextColumnLoop2xN1580.LComputeBlockBy4Loop1594.LProcessRemainingBlocks1594.LComputeBlockBy1Loop1594.LOutputBlock1594.LMultiplyAlpha2xNBlock1580.LStore2xNBlock1580.LOutput1xNBlock1580.LOutput1xNBlockWithMask1580.LMultiplyAlpha1xNBlockWithMask1580.LStore1xNBlockWithMask1580.LProcessRemainingCountN1580.LComputeBlockBy4Loop1997.LProcessRemainingBlocks1997.LComputeBlockBy1Loop1997.LOutputBlock1997.LExitKernel.LProcessCountM1.LProcessNextColumnLoop2xN2100.LComputeBlockBy4Loop2114.LProcessRemainingBlocks2114.LComputeBlockBy1Loop2114.LOutputBlock2114.LMultiplyAlpha2xNBlock2100.LStore2xNBlock2100.LOutput1xNBlock2100.LOutput1xNBlockWithMask2100.LMultiplyAlpha1xNBlockWithMask2100.LStore1xNBlockWithMask2100.LProcessRemainingCountN2100.LComputeBlockBy4Loop2222.LProcessRemainingBlocks2222.LComputeBlockBy1Loop2222.LOutputBlock2222.LProcessCountM3.LProcessNextColumnLoop2xN2300.LComputeBlockBy4Loop2314.LProcessRemainingBlocks2314.LComputeBlockBy1Loop2314.LOutputBlock2314.LMultiplyAlpha2xNBlock2300.LStore2xNBlock2300.LOutput1xNBlock2300.LOutput1xNBlockWithMask2300.LMultiplyAlpha1xNBlockWithMask2300.LStore1xNBlockWithMask2300.LProcessRemainingCountN2300.LComputeBlockBy4Loop2654.LProcessRemainingBlocks2654.LComputeBlockBy1Loop2654.LOutputBlock2654.LProcessCountM5.LProcessNextColumnLoop2xN2742.LComputeBlockBy4Loop2756.LProcessRemainingBlocks2756.LComputeBlockBy1Loop2756.LOutputBlock2756.LMultiplyAlpha2xNBlock2742.LStore2xNBlock2742.LOutput1xNBlock2742.LOutput1xNBlockWithMask2742.LMultiplyAlpha1xNBlockWithMask2742.LStore1xNBlockWithMask2742.LProcessRemainingCountN2742.LComputeBlockBy4Loop3138.LProcessRemainingBlocks3138.LComputeBlockBy1Loop3138.LOutputBlock3138.LNchw.4.ProcessOutputCount.LNchw.4.ProcessNextOutputCountBy6.LNchw.4.6.ProcessNextRow.LNchw.4.6.ProcessNextColumn.LNchw.4.6.SkipOverPadding.LNchw.4.6.HandlePostProcessing.LNchw.4.ProcessRemainingOutputCount.LNchw.4.3.ProcessNextRow.LNchw.4.3.ProcessNextColumn.LNchw.4.3.SkipOverPadding.LNchw.4.3.HandlePostProcessing.LNchw.4.ProcessRemainingOutputCountLessThan3.LNchw.4.2.ProcessNextRow.LNchw.4.2.ProcessNextColumn.LNchw.4.2.SkipOverPadding.LNchw.4.2.HandlePostProcessing.LNchw.4.ProcessOutputCountRightPadAndRemaining.LNchw.ProcessFilterCount3.LNchw.3.ProcessOutputCount.LNchw.3.ProcessNextOutputCountBy6.LNchw.3.6.ProcessNextRow.LNchw.3.6.ProcessNextColumn.LNchw.3.6.SkipOverPadding.LNchw.3.6.HandlePostProcessing.LNchw.3.ProcessRemainingOutputCount.LNchw.3.3.ProcessNextRow.LNchw.3.3.ProcessNextColumn.LNchw.3.3.SkipOverPadding.LNchw.3.3.HandlePostProcessing.LNchw.3.ProcessRemainingOutputCountLessThan3.LNchw.3.2.ProcessNextRow.LNchw.3.2.ProcessNextColumn.LNchw.3.2.SkipOverPadding.LNchw.3.2.HandlePostProcessing.LNchw.3.ProcessOutputCountRightPadAndRemaining.LNchw.ProcessFilterCountLessThan3.LNchw.2.ProcessOutputCount.LNchw.2.ProcessNextOutputCountBy6.LNchw.2.6.ProcessNextRow.LNchw.2.6.ProcessNextColumn.LNchw.2.6.SkipOverPadding.LNchw.2.6.HandlePostProcessing.LNchw.2.ProcessRemainingOutputCount.LNchw.2.3.ProcessNextRow.LNchw.2.3.ProcessNextColumn.LNchw.2.3.SkipOverPadding.LNchw.2.3.HandlePostProcessing.LNchw.2.ProcessRemainingOutputCountLessThan3.LNchw.2.2.ProcessNextRow.LNchw.2.2.ProcessNextColumn.LNchw.2.2.SkipOverPadding.LNchw.2.2.HandlePostProcessing.LNchw.2.ProcessOutputCountRightPadAndRemaining.LNchw.ProcessFilterCount1.LNchw.1.ProcessOutputCount.LNchw.1.ProcessNextOutputCountBy6.LNchw.1.6.ProcessNextRow.LNchw.1.6.ProcessNextColumn.LNchw.1.6.SkipOverPadding.LNchw.1.6.HandlePostProcessing.LNchw.1.ProcessRemainingOutputCount.LNchw.1.3.ProcessNextRow.LNchw.1.3.ProcessNextColumn.LNchw.1.3.SkipOverPadding.LNchw.1.3.HandlePostProcessing.LNchw.1.ProcessRemainingOutputCountLessThan3.LNchw.1.2.ProcessNextRow.LNchw.1.2.ProcessNextColumn.LNchw.1.2.SkipOverPadding.LNchw.1.2.HandlePostProcessing.LNchw.1.ProcessOutputCountRightPadAndRemaining.LNchw.ExitKernelMlasConvNchwFloatSingleAvx512FFilter1.LNchw.1.1.ProcessNextRow.LNchw.1.1.ProcessNextColumn.LNchw.1.1.SkipOverPadding.LNchw.1.1.HandlePostProcessingMlasConvNchwFloatSingleAvx512FFilter2.LNchw.2.1.ProcessNextRow.LNchw.2.1.ProcessNextColumn.LNchw.2.1.SkipOverPadding.LNchw.2.1.HandlePostProcessingMlasConvNchwFloatSingleAvx512FFilter3.LNchw.3.1.ProcessNextRow.LNchw.3.1.ProcessNextColumn.LNchw.3.1.SkipOverPadding.LNchw.3.1.HandlePostProcessingMlasConvNchwFloatSingleAvx512FFilter4.LNchw.4.1.ProcessNextRow.LNchw.4.1.ProcessNextColumn.LNchw.4.1.SkipOverPadding.LNchw.4.1.HandlePostProcessing.LNchwc.4.ProcessOutputCount.LNchwc.4.ProcessNextOutputCountBy6.LNchwc.4.6.ProcessNextRow.LNchwc.4.6.ProcessNextColumn.LNchwc.4.6.SkipOverPadding.LNchwc.4.6.HandlePostProcessing.LNchwc.4.ProcessRemainingOutputCount.LNchwc.4.3.ProcessNextRow.LNchwc.4.3.ProcessNextColumn.LNchwc.4.3.SkipOverPadding.LNchwc.4.3.HandlePostProcessing.LNchwc.4.ProcessRemainingOutputCountLessThan3.LNchwc.4.2.ProcessNextRow.LNchwc.4.2.ProcessNextColumn.LNchwc.4.2.SkipOverPadding.LNchwc.4.2.HandlePostProcessing.LNchwc.4.ProcessOutputCountRightPadAndRemaining.LNchwc.ProcessFilterCount3.LNchwc.3.ProcessOutputCount.LNchwc.3.ProcessNextOutputCountBy6.LNchwc.3.6.ProcessNextRow.LNchwc.3.6.ProcessNextColumn.LNchwc.3.6.SkipOverPadding.LNchwc.3.6.HandlePostProcessing.LNchwc.3.ProcessRemainingOutputCount.LNchwc.3.3.ProcessNextRow.LNchwc.3.3.ProcessNextColumn.LNchwc.3.3.SkipOverPadding.LNchwc.3.3.HandlePostProcessing.LNchwc.3.ProcessRemainingOutputCountLessThan3.LNchwc.3.2.ProcessNextRow.LNchwc.3.2.ProcessNextColumn.LNchwc.3.2.SkipOverPadding.LNchwc.3.2.HandlePostProcessing.LNchwc.3.ProcessOutputCountRightPadAndRemaining.LNchwc.ProcessFilterCountLessThan3.LNchwc.2.ProcessOutputCount.LNchwc.2.ProcessNextOutputCountBy6.LNchwc.2.6.ProcessNextRow.LNchwc.2.6.ProcessNextColumn.LNchwc.2.6.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.SkipAccumu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