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Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]-H5\YH}H7/H5r[ZH-/H5\H./HAH5ZH//HHH_HHEH HUHuHuIININ0IFIVIF@H]HEHtHHNH_HH;Eu LHP[A^]NIH]HEHtHXH`NLzN fDUHAVSHPIH_HHE6NHþH,NHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH] -H5 YH}H4/H5"YZHU+/H5{\H,/HAH5VYH-/HHH_HHEH}HUHuHuIININ0IFIVIF@H]HEHtHMHUNH_HH;Eu LHP[A^]NIH]HEHtHHNL*N fDUHAVSHPIH[_HHENHþHNHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]G -H5YH}Ht2/H5VZH)/H5+\H6*/HAH5YH]+/HHH|_HHEHHUHuHuIININ0IFIVIF@H]HEHtHHNHn_HH;Eu LHP[A^]VNIH]HEHtHHNLN fDUHAVSHPIH _HHENHþHNHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]-H5lYH}H$0/H5TZH&/H5\H'/HAH5YH )/HHH,_HHEHHUHuHuIININ0IFIVIF@H]HEHtHHNH_HH;Eu LHP[A^]NIH]HEHtHhHpNLN fDUHAVSHPIH_HHEFNHþH_HH;Eu LHP[A^]&NIH]HEHtHHNLN fDUHAVSHPIH_HHEfNHþH\NHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH],H5MIMpIrK H9J I91IT$H)LHtHAf: HHuHffff.Af: ADf: DADf: DAD f: D HI9uLHHWHHDAAHIpH)1fff.AALf: f: LAD AL0f: f: D L0HHuMt AALf: f: LI9K<#IIL$H9HMHL)LHtHLAf:)HHuH rMfAf:)ADf:)DAD f:)D AD0f:)D0HH9|H9M)IH IH9sH IH9MIIPHHDIAHZMOd0Jt0IQH)1ADALf: f: DLADA f: f: D HHuMt#HAALf: f: LM9Llj)HHH)Ht(Hfff.Af: HHuHrTAf: ADf: DADf: DAD f: D HH9uILH[A\A]A^A_]1M1M (@UHAWAVAUATSHAHH^_HHEcU/Ht H&1H ]_H H;MuHĈ[A\A]A^A_]bNIǃLbNebNIHh 8H5.YH YH}LhZH{Hn[/IcHH<3H5YHPHaNHuHP1LPH5F_HJC_LbN IPt H`aNE1HsIIALmMt6HEL9t!HX@t HxhaNHI9uH}LLmNaNEt H}?aNEtH}0aNIALhMt:HpL9t5HX@t Hx`NHI9uHhLp`NEu LILaN`NL\N HI@UHAWAVAUATSPII1LHLc1LLD/IH胃ILK1LHI}K1IuAAAAI9LOIM)MI?I>MIMpIrK H9J I91IT$H)LHtHAf: HHuHffff.Af: ADf: DADf: DAD f: D HI9uLHHWHHDAAHIpH)1fff.AALf: f: LAD AL0f: f: D L0HHuMt AALf: f: LI9K<#IIL$H9HMHL)LHtHLAf:)HHuH rMfAf:)ADf:)DAD f:)D AD0f:)D0HH9|H9M)IH IH9sH IH9MIIPHHDIAHZMOd0Jt0IQH)1ADALf: f: DLADA f: f: D HHuMt#HAALf: f: LM9Llj)HHH)Ht(Hfff.Af: HHuHrTAf: ADf: DADf: DAD f: D HH9uILH[A\A]A^A_]1M1M (@UHAWAVAUATSPII1LRHLc1LL@/IHILhG1L蠭HI}TG1HuڃH9HOII)LH?H>LHHHrI H9swH I9snE1HzLtK6WC^BLHL9'6W(A^  (A^LLHH9uIIIyHHDAAH!IxH)1(0WA (^AL(^THHuMtA( 0W^ L9LI|tLBM9LIMHH)H@uA( r0W^) LHr;([0Wff.A (^)AL(^)THL9|I9%I)IJ ILHHEH3HH]LmrII9II9E1LkM)Ht*Hfffff.C3YNCIHuIH]LmDCYNCCDXNCDCDXNCDCD XNCD IL9ubIIIL$HHPH HUsE1HZH)E1C)EpXN)EEbXN(Mf:!)M(EHXN(Mf:! )M(E-XN(Mf:!0C CD)EXN)EEXN(Mf:!)M(EWN(Mf:! )M(EWN(Mf:!0CLCD )EWN)EEWN(Mf:!)M(EWN(Mf:! )M(EjWN(Mf:!0CL CD0)EKWN)EE=WN(Mf:!)M(E#WN(Mf:! )M(EWN(Mf:!0CL0IHlH]Ht|IHfff.C/)EVN)EEVN(Mf:!)M(EVN(Mf:! )M(E}VN(Mf:!0C .IHuH]L9LmHUL$Idfo- +W(%+WD(+WD( +WHD(+WD(+WEWD(-+WD(5+Wfffff.A(=*W_fofrD[DX=|*Wf=*Wffo *WfofX=*WXT +*WD\(Y(Y(Y *WX *W(Y*WX*WYXYAXYX(Y*WX*WYAXYXYA(AYXY\XEYDXW(AfE8DVA(fE8E)SN(Mf:! )M(E#SN(Mf:!0HELAD)ESN)EERN(Mf:!)M(ERN(Mf:! )M(ERNH`(Mf:!0HE HHIHxHLmILmIHC/)EH]RN)EEORN(Mf:!)M(E5RN(Mf:! )M(ERNH(Mf:!0C .IHuHpH9EHuIĉD)IIM)HtHfCQNCIHuIH]r`ffff.CQNCCDQNCDCDnQNCDCD [QNCD IL9uHhHHx[A\A]A^A_]UHAWAVAUATSHhHHH K_HHEH~H/HD8E1HIMI1HL3/H0HHIH{:1HAHHL-rIL:1IAuEAAAI9LOIM)MI?I>MIM< IrKI94KDI9&1ICH)LHt!Hffff.AATHHuH DAADADADADADAD AD ADADADADADADADADHI9u| DDH0+L@Hp HL)IVI~ H)H9I9t1fI0H;2HH9uHHLxLIpIL91LpIH81HHHAuDكH9HOII)LH?H>LHHH1HAH)HHtHAAXADHHuHmff.AAXADADAXDADADAXDADAD AXD AD HH9uItIIHHN$0HH)MFIIvI9IMEHHL)HLQIEALHHAH8HAzLIQLL)HLILHHHJD0 H HHGLL)H(O|IDHKDHIFHHCHHI HL-1H B1HH0@,HHHx HH)HxHsH[ H)H9H9t%1fff.H9H;>HH9uMMIIHiHLMM11LMHHIrJI9H;1HH)Ht1Hffffff.AXDADHHuH+ff.AXDADDAXDADDAXDADD AXD AD HI9uHHH1fff.LATXADXAL A\0ATADD XL0XAD AL0HHuHt'LATXADXATADHHI9HLHt.HLff.AXDA)DHHuH rYffff.AXDA)DDAXDA)DD AXD A)D D0AXD0A)D0HL9|I9LH8HI9sJ LH;HH H H(1ff.DLAXADXAL A\0AADDX XAD AL0HHuHt*LLATXADXATADHHH98f.)HH)Ht'HfAXDADHHuHrgffffff.AXDADDAXDADDAXDADD AXD AD HH9u@;DD7HGH)1fDAALADALAD AL0AD AL0AD@ALPAD@ALPAD`ALpAD`ALpH HuHt2HHADA ADALH HuI9K4II[H9HMHL)ۉLHtHLfAA)DHHuHrnfDAA)DADA)DAD A)D AD0A)D0AD@A)D@ADPA)DPAD`A)D`ADpA)DpH H9|HHH9M)IIDI H9sIDI H9MII@HH_Hs1HK IDpITpHKH)1fDDLDLDLDLDLDLD D H HuHt3IMJHfADA ADALH HuM9HHLƉ)HGH)HtHfAATHHuHrafAADADADADADAD AD ADADADADADADADADHH9uHHH s:_H H;Mu2Hh[A\A]A^A_]1MA1M'5?N>NIHh7H5FYH YH}Lh6WW)PHDž`0t>NH`(X)PH same shHHHhave theHHHts must HHHAll inpuH@ apeAHYHuHPLxE1H5#_Ho_L9>N =NIHh7H5`YH YH}Lh1qW)PHDž`0=NH`(4X)PH same shHHHhave theHHHts must HHHAll inpuH@ apeAHYHuHPLE1H58"_H_LS=N=NIHh7H5wYH ϳYH}Lh(W)PHDž` MIMIrK H9J I91IKH)LHtH@A44HHuHmA ALLALLAL L ALLALLALLALLHI9uL8H}HXŘCDH0E11HH`Hu1HXHE~HXDHHHHHHH貨HHH8H HH9HNHxDZHEH4ZD@HED;0W)EHEL`LhM)tOLHHH9OL6NIL}L}IHEM~LLL.8NML}1HXHU /HH}Ht H}t6NE1fff.W)EHEL`LhM)tOLHHH9L(6NIL}L}IHEM~LLL7NML}HHuHUۅLHDžH}Ht H}5NLHxHHNHpHH 1HpH HHLI2HL(H H9 HeHHHH HHHpE1LI2LIHHEHLxHHIHHxHHHLHLI;u\HHH)HHrAfff.I4HHHHI;uHHH9r@HEHhHXHL I1IHHHHXLLL8M;uSHHH)HHr8I4HXHHHI;uHHH9r@LMMM@uAAAAM9MOHpJHHxL,LhHM$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 \0HIu1MLhtAA\XX\I9J4+I(IUH9HMHL)LHtHL@AX)HHuH r@KD0TX)TX)TTX)T X)T0HH9|HxLH;I)IIMIKHJ?IH9sIHpJH9MI(ICHHDbAH6ID$H)H1ffffff.\XX\T \0XXT \0HHuMtHAA\XX\M9LfDH)HH)HtH@A X HHuHHrSKD LX LXLLXL XL HH9uL;H1M5HHHHHhHHHpE1LI2LxILL}LxHHIHHHHHLHLI;uSHHH)HHr8I4HHHHI;uHHH9r@LELXHL IILHXLLL8M;uTHHH)HHr9@IHXH4HH4I;4uHHH9r@HILxMCHu؃H9HOHpN4MHM II)MI?I>MIHrHr%HHII9II91HxH)HHtHfA X HHuHHIT LX LXLLXL XL HH9uHH(H~HHDBAHHI\0MXI)1DT\XX\TXXT \0HIuMtAA\XX\H9MfItI(HXH9HMHH)ÉHHtHHAX)HHuH rPHIT0fTX)TX)TTX)T X)T0HH9|HLH;M)IM4HHLIHHII9sIHpJH9MI(IFHHDbAH<ID$H)H1\XX\T \0XXT \0HHuMtHAA\XX\M9LfDH)HhH)HtH@A X HHuHHrSHID LX LXLLXL XL HH9uL;H|*1M`u1M(f.H`HtH0H!NH`HL(@t Hh!NH8Ht H@!NH Ht H(o!NHHt HW!NHHt H?!NHHt H'!NHHt H!NAL;D@L8ILuMt#HIFHuILPLNMCL0L N.LHHWHHNHs 1HuuHQH)1fAALLAD AL0D L0AD@ALPD@LPAD`ALpD`LpH HuHt(HHADA D H HuI9cK<IIsH9HMHL)މLHtHLDA)HHuHrgA)AD)DAD )D AD0)D0AD@)D@ADP)DPAD`)D`ADp)DpH H9|H9M)IH IH9sH IH9MIIHHHD~AHs1MK ITpHLpMgI)1fDDLDLDLDLDLDLD D H IuMt0IMJ IfADA D  H IuM9Llj)HHH)HtHA44HHuHrZff.A ALLALLAL L ALLALLALLALLHH9uMIH_HH;EuLH[A\A]A^A_]N[NIH}7H5pYH YHLEqW)EHE NHE(W)EHore inpuHHHe 1 or mHHHMust havHf@ts@AH*YHHMLE1H5_H^LNH}fN H}[N IA%IAIIEt H}4NLMt?HL9t$HX@t Hx NHI9uHLLNt HNt HNLeMt1HEL9t>HX@t HxNHI9uH}LeNE4LN'LIIE1MIML@{IrJI9KH91ICH)LHtHًAHHuH-ADADDADD AD DADDADDADDADHI9uLHHNHHxHs 1Hu|HOH)1fLAALD L0AD AL0D@LPAD@ALPD`LpAD`ALpH HuHt(HHD ADA H HuI9K4IICH9HMHL)؉LHtHLDA)HHuHrgA)DA)DD A)D D0A)D0D@A)D@DPA)DPD`A)D`DpA)DpH H9|H@H9M)I IH H9sIH H9MIIHHHDxAHs1zK HTpILpIH)1ff.DLDLDLDLDLDLD D H HuMH@t9IMJIff.D ADA H IuM9LƉ)HGH)HtHAHHuHrZff.ADADDADD AD DADDADDADDADHH9uLDۃH9HOAHEHH)HH?H>HHL$HH)LKIEAHqHDVHHHAH0HALIPLL)HHD H LIBHH)H(O,IHIHHCHHGHHI HAH61H1Hfff.HD1HHL@ LH)HHwH H)H9L9t1H9H;>nHH9uDMIM/IrK H9HJ I9;1IKH)LHtH@A44HHuHA ALLALLAL L ALLALLALLALLHI9uL H}HHkC,HE11HHPHu1HH HAvHH(HHHHHHHzHHH8H HH9HNHx,HEH,HED;W)EHEL`LhM)tOLHHH9L( NIL}L}IHEM~LLL NML}1HHHU].HH}Ht H}N1W)EHEL`LhM)tOLHHH9.LNIL}L}IHEM~LLL NML}HHuHU;XHHDžH}Ht H}NHH@HxHHYHxHHT0HxH H0LI2HH H9D HHHHHL,HxE1L`LI2LILHEHhLxHLILHHLHLI;uVHHH)HHr;fDI4HHHHI;uHHH9r@HEHHXHL IIHHHHXLLL8M;uSHHH)HHr8IHXH4HH4I;4uHHH9r@HILMI@LhuAAAAI9LOHxJH8LpKHHL$IM)LH?H>LHMIE1IWLAtCB]BLHL9HHHTHpItff.D]D]DHI9uaLL8KTHpI L HI4HH4M9AH9AI9I9EA L` MIISIIEMAHLXHpIt0HHHT0IM)1DLT]D]DD T]]T D0HIuE1L`W1HXL`t#LA]AD]DM9N ;LI|A ]) HL9|N/L; I)ILXMLMHxNH8J HpI7HITI42MlHIILKI42I4L9AL9AH9L9@E@ LHH8HqHHDyAHhIwH)N1L`LhLXf.L]D]AADD L0T ]D0]AT AD0HHuMt&LLA]AD]DH8I9IL`LhLXHD)LtCB]BIIL9HtJHHHDHpITD]D]DHH9uL;0d 1L`LhLXMffffff.H' HHHHhHHHxE11LI LILLELxHLILHHLHLI;u]HHH)HHrBffff.I HHHHI;uHHH9r@CLULXHL I0ILHXLLL8M;u^HHH)HHrCfffff.I4HXHHHI;uHHH9r@LMLML@uكL9IOHxN OLHHHrJ1II9II91HAH)HHt$Hff.(A]  HHuH0KD ff.(]L (]LL(]LL(] L HH9uHH(HFHHDBAHLpO\0I@H)E1CTC\(](]B$BTCTC(](]Bd BT0IHuMtCC\(]]B$BLH9Lpfff.H4 I|.(Hfff.A(])HH9|HLH;I)IILHIHJ 2I H9sIHxJH9MI(IAHHDaAHFID$H)H1f\(](]$TT \0(](]d T0HHuMt HAA\(]]$LM9LH)HhH)HtH@(A]  HHuHHrPKD (]L (]LL(]LL(] L HH9uL;0E1M1M 'HHHHHpHHhHxE1@LI2LILHEHLxHLILHHLHLI;uVHHH)HHr;fDI4HHHHI;uHHH9r@LELXHL I ILHXLLL8M;uTHHH)HHr9@I HXHHHI;uHHH9r@HILMCH@u؃H9HOHxN LOLHHHrJ 0I I9I I91HHH)HHt$Hff.(A]  HHuH0KL ff.(]L (]LL(]LL(] L HH9uHH(HNHHDBAHO\0IHH)E1ffffff.CTC\(](]B$BTCTC(](]Bd BT0IHuMtCC\(]]B$BLH9Lfff.H4I|.(Hfff.A(])HH9|HhLH;I)II LHIHJ2IH9sIHxJH9MI(IIHHD`AHCIL$H)H1f\(](]$TT \0(](]d T0HHuMt HAA\(]]$LM9LH)HpH)HtH@(A]  HHuHHrPKD (]L (]LL(]LL(] L HH9uL;0.E1M1M*ffff.HPHtH輳0HMH`Ht HhMH8HH@t H@}MH Ht H(eMHHt HMMHHt H5MHHt HMHHt HM(AH;,H HLuMt#HIFHuILPLoML@MLL蛲0LM7LHHWHHNHs 1Hu~HQH)1ff.AALLAD AL0D L0AD@ALPD@LPAD`ALpD`LpH HuHt(HHADA D H HuI9K<IIsH9HMHL)މLHtHLDA)HHuHrgA)AD)DAD )D AD0)D0AD@)D@ADP)DPAD`)D`ADp)DpH H9|H9M)IH IH9sH IH9MIIHHHD~AHs1MK ITpHLpMgI)1fDDLDLDLDLDLDLD D H IuMt0IMJ IfADA D  H IuM9Llj)HHH)HtHA44HHuHrZff.A ALLALLAL L ALLALLALLALLHH9uLHH^HH;EuHH[A\A]A^A_]M;MIH}7H5PlYH YHLEqĂW)EHE MHE(pW)EHore inpuHHHe 1 or mHHHMust havHf@ts@AH hYHHMLE1H5^H^LMH}FM H};M IA%IAIIEt H}MLMt?HL9t$HX@t HxMHI9uHLLMt HMt HMLeMt1HEL9t>HX@t HxzMHI9uH}LeeME?LM2LI%I1LI1E1IH}Ht H}M1$IxIE1HPrIH@H`Ht HhMH8Ht H@MH Ht H(MHHt HMHHt HuMHHt H]MHHHAMIH`Ht Hh!MH8Ht H@ MH Ht H(MHHt HMHHt HMHHt HMHHt HM1HPIH]Ht)HHCHuHIHPHMLMtLIH@0H8MLHtHH%0HML7M UHAWAVAUATSHHHHH[^HHEH~;.HLv 1HIM}1HHL.HHD>IH{0ILLHL0MAuEAAAM9MOMM)MI?I>MIML@{IrJI9KH91ICH)LHtHًAHHuH-ADADDADD AD DADDADDADDADHI9uLHHNHHxHs 1Hu|HOH)1fLAALD L0AD AL0D@LPAD@ALPD`LpAD`ALpH HuHt(HHD ADA H HuI9K4IICH9HMHL)؉LHtHLDA)HHuHrgA)DA)DD A)D D0A)D0D@A)D@DPA)DPD`A)D`DpA)DpH H9|H@H9M)I IH H9sIH H9MIIHHHDxAHs1zK HTpILpIH)1ff.DLDLDLDLDLDLD D H HuMH@t9IMJIff.D ADA H IuM9LƉ)HGH)HtHAHHuHrZff.ADADDADD AD DADDADDADDADHH9uLDۃH9HOAHEHH)HH?H>HHL$HH)LKIEAHqHDVHHHAH0HALIPLL)HHD H LIBHH)H(O,IHIHHCHHGHHI HAH61H1Hfff.HDHHL@ LH)HHwH H)H9L9t1H9H;>nHH9uD}H LuLHH L)HL}LHHPL)HHHLhIEEÃHHHHAL@IHL8L)HPHHHHxHHMHL@I HI HH@HIF0HXIG0H`LIGH LIFH(1HLHpHH$fM HMHHHLMML@IHIH;u_IHLHrRffffff.MfIHEH HH I;LuHHH9rfDHL4C<t.HHML;hfDHItKI r)LIHH K4LH9s|LHH9sq1HH)HxtH@07HHuHKDfL0 7L0L7L0L7 0L7HI9uH`s1Hu|KLpHp1D1L17L7D1L1D7 L70D1L1D7@L7PD1 1D7`L7pHHuHt)HHD0 0D7 7H HuHHI9fL;hH0HH}Ht H}MHxHt H}MH`Ht HhvMHHHt HP^MH Ht H(FMHHt H.MHHt HMH^HH;EuHH[A\A]A^A_]^MHμM HHHHt H輿MH}Ht H}調MHxHt H}蕿MH`Ht Hh}MHHHt HPeMH Ht H(MMHHt H5MHHt HMH7M UHAWAVAUATSHIHH[^HHE1L \HþL[HHHHsHP/HH HxH HH9HNHEHEHHELuW)HDžLeH]L)tdLIILH=lH,MILLKDHHL~LLHuMILH1LN.HHHHH 0HHHt H蕽MHHHLIHLPH`HH8 HLMHHH H)HLULHLPM)IMfIE|$DLHH0AL(IGL L)H8HHHhHxHHEHL@K1HK2HIFHIA0H@IB0HHL`IBHpLIAHx1HLHHL-ff.IHMHHLHHIML@HHHH; HuQHHrDffffff.ML@HHHH;uHHH9r@L}HHHLHMIHIHH;uBIIr5H HMIMIMDLIrKI9KDI91ICH)LHt Hfff.AATHHuHDAADADADADADAD AD ADADADADADADADADHI9u\)FL@Hp HL)IVI~ H)H9M*I9t&1ffff.I0H;2HH9uHHL`LIIL0LIH0LH0HH(L@uDكL9IOMI)LH?H>LHHDLH1HAH)HHt)Hff.AAXADHHuHmff.AAXADADAXDADADAXDADAD AXD AD HH9uItIIHHN$0HhH)MFII~I9IMEHHL)HLQIDLHHAHHArLIQLL)HHxHHHHHJD0 HHHFLL)HO|KDHKDHIFHICHHI HLp=H81H$F1HH$;HHHx HH)H`HsH[ 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(L0Y)L0HH9|H9M)IMI(IHHHDJAHMJL0IqH)1@LT\$YYLTYY\$HHuMtH TYY TM9t$LH)H DYHHuH0HH m^H H;Mu/H[A\A]A^A_]1M1MqrM7rMIH}7H5LXH ?YHLEqW)EHE qMHE(l/W)EHore inpuHHHe 1 or mHHHMust havHf@ts@AHXHHMLE1H5V^HR^LqMH}BnM H}7nM IA%IAIIEt H}qMLMt?HL9t$HX@t HxpMHI9uHLLpMt HpMt HpMLeMt1HEL9t>HX@t HxvpMHI9uH}LeapME4LpM'LIIE1jM f.UHAWAVAUATSHHei^HHEIa.Ht.H Mi^H H;MuHHĈ[A\A]A^A_](nMmMIHh}7H5XH XH}LhJW)PHDž`@gmMH`(-+W)PHd shape.HH(HspecifieHH Hor with HHHput tensHHHetch outHHHPlease fH@0AHqXHuHPLRE1H5Q^HIN^LmM IA(IA}IIPt H`lMLeMt6HEL9t!HX@t HxblMHI9uH}LLeHlMEt H}9lMEt H}*lMLhMt:HpL9tEHX@t HxkMHI9uHhLpkMEtLlMLgM LUHAVSHPIHg^HHEkMHþHkMHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]+H5|qXH}H4-H5XHŲ-H5b[H-HAH5ƅXH-HHH^^HH;Eu LHP[A^]&cMIH]HEHtHrHbML^M fDUHAVSHPIH]^HHEfbMHþH\bMHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]dž+H5MIMI$1HILIuAAAAI9LOHNK4IM)LH?H>LHMIrK'II9KH91IOH)LHt!HYHHuHHKL @TYTYTTYTYT HI9uDLH(HNHHxH81H-LLH(HHHHDZAHKL0MCI)1fff.\dYYd\$YY\ d0HIuCHWH)1fDTT T0T@TPT`TpH@HuHtHH T H HuI9Htf HI9uK4I(IKH9HMHL)ىLHtHL)HHuHHr6fD))T)T )T0)T@)TP)T`)TpH H9|H9wM)IMI(IHHHxHs 1HuT}HWH)HH1DTTTTTT T0H HuHt.IMIHffff.BBTI HuM9HLf HH9u1MLtdYYdI9H-N?I(IWI9IMHL)LHtHL@Y)HHuH HrJKL0f.\Y)\Y)\\Y)\ Y)\0HL9|I9I)IKMIKMHxHt H}=MH`Ht Hh=MHHHt HP=MH Ht H(=MHHt H=MHHt Hv=MH8^HH;EuHH[A\A]A^A_]=MH.:M HHHHt H=MH}Ht H} =MHxHt H}H.MH>*^HH;Eu LHP[A^]&/MIH]HEHtH>H.ML*M fDUHAVSHPIH)^HHEf.MHþH\.MHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]Y+H5<4XH}H~-H5XHu-H5%[Hv-HAH5HXHw-HHH$^HHEH=&HUHuHuIININ0IFIVIF@H]HEHtH}?+Hh2I+Hp&F+HxE+HC+I D+IK+IW)EHEXbMHEHHXHMHHHHPHPHXHPH`HPHhHP HpHP(HxHP0HX8Lp@LxHL`PHMH5 XH}HUi-H5ГXH4b-H5Z[Hec-HA H555XHd-HHH^HHEHIM9uLPL;H\HMLP LILmL}HL@ILH@HLHH H;u_HHH)HHrDffffff.IH@H HH H; uHHH9r@LELMHHL`I:ILH}HLHHxH;uWHHPH)HHr<ILLLA.LLL_LLLxKLLLhUH0HHS^HH;EuHHĨ[A\A]A^A_]2 M MIH`致6H5CXH YXHxL`XQW)@HDžP@n MHP(TV)@Hle typesHH7Hnd doublHH0Hloat16 aHH(Hfloat, fHH Hrue for HHHust be tHHHribute mHHHfmod attH@?AHXHxH@L:E1H5]H1]L Mp MIH`c6H5XH 5XHxL`[-W)@HDžP@J MHP(0V)@Hle typesHH7Hnd doublHH0Hloat16 aHH(Hfloat, fHH Hrue for HHHust be tHHHribute mHHHfmod attH@?AHЃXHxH@LE1H5]H ]LMLMIH`?6H5~XH XHxL`^ W)@HDžP@&MHP( V)@Hle typesHH7Hnd doublHH0Hloat16 aHH(Hfloat, fHH Hrue for HHHust be tHHHribute mHHHfmod attH@?AHXHxH@LE1H5]H]LM(bMIH`6H5}XH XHxL`H58XH@HUn`AH1ZHxH@L^E1H5]HU]LMIA+IAIBI@t HPMLeMt6HEL9t!HX@t HxhMHI9uH}LLeNMEt H}?Mxt H}-ML`MHhL9HX@t HxMHI9urIAIIAIoIAIAVIQI@t HPMLeMt6HEL9t!HX@t HxwMHI9uH}LLe]MEt H}NMxt H}<ML`MHhL9HX@t Hx MHI9uIAIA8I{I@t HPMLeMt6HEL9t!HX@t HxMHI9uH}LLeMEt H}xMxt H}fML`MHhL9HX@t Hx3MHI9uI@t HPMLeMt6HEL9t!HX@t HxMHI9uH}LLeMEt H}Mxt H}ML`Mt:HhL9t=HX@t HxMHI9uH`LhhMEt/LM%LIxtH}?M ILNL f.UHAWAVAUATSH8MIHIHH^]HHEH8H]HHHHH@H8HNMLHEEL%w]ID$HHI@L8HML52]ILHDž(HDž HDžHDžDž0LMHLHOHH0H9+LMHLHHH0H +HHHHHL8Lt H MHMH5/]HHLH8MH]HH;EuHH8[A\A]A^A_]MHHHL8Lt H MHL#HsHHsHHsHHsHHH5}]HHLHH8HMHL fUHAWAVAUATSHxH]HHEHHIHHOHVHHqHH HxH HH9HNHE(#HEH#HEW)HDžL}H]L)tUIILH=aHLILLKHH~LLH#MILH1L-HHHt HaLH HEHHPILHH/IHHH\H}Ht H}LHxHt H}LH`Ht HhLHHHt HPLH Ht H(LHHt HLHHt HrLH]HH;EuHx[A\A]A^A_]LYLIHx6H5tXH ܓXHLx܏W)`HDžp LHp(/U)`Hrst outpHHHo get fiHHHfailed tH@ut!AHXHH`LE1H5]H]LL HOL IA+IAII`t Hp,LLMt?HL9t$HX@t HxLHI9uHLLLt HLt HLLxMt:HL9t=HX@t HxLHI9uHxLqLEt3LL)LIHHtH@LIH}Ht H}'LHxHt H}LH`Ht HhLHHHt HPLH Ht H(LHHt HLHHt HLLL UHAWAVAUATSHxH]HHEHHIHHOHVHHlHH HxH HH9HNHE("HEH"HEW)HDžL}H]L)tUIILH=aHLILLKHH~LLH#LILH1L-HHHt HaLH HEHHRILHH/IHHHmH}Ht H}LHxHt H}LH`Ht HhLHHHt HPLH Ht H(LHHt HLHHt HrLH]HH;EuHx[A\A]A^A_]LYLIHx6H5oXH pXHLx܊W)`HDžp LHp(/U)`Hrst outpHHHo get fiHHHfailed tH@ut!AHWHH`LE1H5]H]LL HOL IA+IAII`t Hp,LLMt?HL9t$HX@t HxLHI9uHLLLt HLt HLLxMt:HL9t=HX@t HxLHI9uHxLqLEt3LL)LIHHtH@LIH}Ht H}'LHxHt H}LH`Ht HhLHHHt HPLH Ht H(LHHt HLHHt HLLL UHAWAVAUATSHxH]HHEHHIHHOHVHHgHH HxH HH9HNHE(!HEH!HEW)HDžL}H]L)tUIILH=`HLILLKHH~LLH#LILH1L-IHHt HaLM HEHLZHHIL/HHHHzH}Ht H}LHxHt H}LH`Ht HhLHHHt HPLH Ht H(LHHt HLHHt HsLH]HH;EuHx[A\A]A^A_]LZLIHx6H5jXH XHLx݅W)`HDžp LHp(0U)`Hrst outpHHHo get fiHHHfailed tH@ut!AHWHH`L E1H5]H]LL HPL IA+IAII`t Hp-LLMt?HL9t$HX@t HxLHI9uHLLLt HLt HLLxMt:HL9t=HX@t HxLHI9uHxLrLEt3LL)LIHHtHALIH}Ht H}(LHxHt H}LH`Ht HhLHHHt HPLH Ht H(LHHt HLHHt HLLL UHAWAVAUATSHxH]HHEHHIHHOHVHHbHH HxH HH9HNHE(HEHHEW)HDžL}H]L)tUIILH=`HLILLKHH~LLH#LILH1L-IHHt HaLM HEHLUHHIL/HHHHr|H}Ht H}LHxHt H}LH`Ht HhLHHHt HPLH Ht H(LHHt HLHHt HsLH]HH;EuHx[A\A]A^A_]LZLIHx6H5eXH XHLx݀W)`HDžp LHp(0U)`Hrst outpHHHo get fiHHHfailed tH@ut!AHWHH`L E1H5]H]LL HPL IA+IAII`t Hp-LLMt?HL9t$HX@t HxLHI9uHLLLt HLt HLLxMt:HL9t=HX@t HxLHI9uHxLrLEt3LL)LIHHtHALIH}Ht H}(LHxHt H}LH`Ht HhLHHHt HPLH Ht H(LHHt HLHHt HLLL UHAWAVAUATSHxH]HHEHHIHHOHVHH]HH HxH HH9HNHE(HEHHEW)HDžL}H]L)tUIILH=`HLILLKHH~LLH#LILH1L-IHHt HaLM HEHLJHHIL/HHHH2H}Ht H}LHxHt H}LH`Ht HhLHHHt HPLH Ht H(LHHt HLHHt HsLH]HH;EuHx[A\A]A^A_]LZLIHx6H5`XH =XHLx{W)`HDžp LHp(0U)`Hrst outpHHHo get fiHHHfailed tH@ut!AHWHH`L ~E1H5]H]LL HPL IA+IAII`t Hp-LLMt?HL9t$HX@t HxLHI9uHLLLt HLt HLLxMt:HL9t=HX@t HxLHI9uHxLrLEt3LL)LIHHtHALIH}Ht H}(LHxHt H}LH`Ht HhLHHHt HPLH Ht H(LHHt HLHHt HLLL UHAWAVAUATSHxH]HHEHHIHHOHVHHXHH HxH HH9HNHE(HEHHEW)HDžL}H]L)tUIILH=`HLILLKHH~LLH#LILH1L-IHHt HaLM HEHLEHHIL/HHHHH}Ht H}LHxHt H}LH`Ht HhLHHHt HPLH Ht H(LHHt HLHHt HsLH]HH;EuHx[A\A]A^A_]LZLIHx6H5[XH }XHLxvW)`HDžp LHp(0U)`Hrst outpHHHo get fiHHHfailed tH@ut!AHWHH`L yE1H5]H]LL HPL IA+IAII`t Hp-LLMt?HL9t$HX@t HxLHI9uHLLLt HLt HLLxMt:HL9t=HX@t HxLHI9uHxLrLEt3LL)LIHHtHALIH}Ht H}(LHxHt H}LH`Ht HhLHHHt HPLH Ht H(LHHt HLHHt HLLL UHAWAVAUATSHxH]HHEHHIHHOHVHHSHH HxH HH9HNHE(HEHHEW)HDžL}H]L)tUIILH=aHLILLKHH~LLH#LILH1L-HHHt HaLH HEHHSILHH/IFHHHAH}Ht H}LHxHt H}LH`Ht HhLHHHt HPLH Ht H(LHHt HLHHt HrLH]HH;EuHx[A\A]A^A_]LYLIHx6H5VXH oyXHLxqW)`HDžp LHp(/U)`Hrst outpHHHo get fiHHHfailed tH@ut!AHWHH`LtE1H5]H]LL HOL IA+IAII`t Hp,LLMt?HL9t$HX@t HxLHI9uHLLLt HLt HLLxMt:HL9t=HX@t HxLHI9uHxLqLEt3LL)LIHHtH@LIH}Ht H}'LHxHt H}LH`Ht HhLHHHt HPLH Ht H(LHHt HLHHt HLLL UHAWAVAUATSHxH]HHEHHIHHOHVHHNHH HxH HH9HNHE( HEH HEW)HDžL}H]L)tUIILH=aHLILLKHH~LLH#LILH1L-HHHt HaLH HEHHNILHH/IFHHHH}Ht H}LHxHt H}LH`Ht HhLHHHt HPLH Ht H(LHHt HLHHt HrLH]HH;EuHx[A\A]A^A_]LYLIHx6H5QXH uXHLxlW)`HDžp LHp(/U)`Hrst outpHHHo get fiHHHfailed tH@ut!AHWHH`LoE1H5]H]LL HOL IA+IAII`t Hp,LLMt?HL9t$HX@t HxLHI9uHLLLt HLt HLLxMt:HL9t=HX@t HxLHI9uHxLqLEt3LL)LIHHtH@LIH}Ht H}'LHxHt H}LH`Ht HhLHHHt HPLH Ht H(LHHt HLHHt HLLL UHAWAVAUATSHxH]HHEHHIHHOHVHHIHH HxH HH9HNHE(HEHHEW)HDžL}H]L)tUIILH=aHLILLKHH~LLH#LILH1L-HHHt HaLH HEHH5CILHH/IFHHHH}Ht H}LHxHt H}LH`Ht HhLHHHt HPLH Ht H(LHHt HLHHt HrLH]HH;EuHx[A\A]A^A_]LYLIHx6H5LXH pXHLxgW)`HDžp LHp(/U)`Hrst outpHHHo get fiHHHfailed tH@ut!AHWHH`LjE1H5]H]LL HOL IA+IAII`t Hp,LLMt?HL9t$HX@t HxLHI9uHLLLt HLt HLLxMt:HL9t=HX@t HxLHI9uHxLqLEt3LL)LIHHtH@LIH}Ht H}'LHxHt H}LH`Ht HhLHHHt HPLH Ht H(LHHt HLHHt HLLL UHAWAVAUATSHxH]HHEHHIHHOHVHHDHH HxH HH9HNHE(HEHHEW)HDžL}H]L)tUIILH=aHLILLKHH~LLH#LILH1L-HHHt HaLH HEHH5>ILHH蟽/IFHHH豲H}Ht H}LHxHt H}LH`Ht HhLHHHt HPLH Ht H(LHHt HLHHt HrLH]HH;EuHx[A\A]A^A_]LYLIHx{6H5GXH IIM 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.UHAWAVAUATSHIIH]HHEIF@IF8IF0IF(IF IFIFIFIIFHIFXH0IdžIdžIdžIdžIFxIFpIFhIF`IFXIFPIdžMIdžIdžIdžIdžIGI+HHJHH+ HH9HHBH9HCH Ht LHI~HH|I~0HHI~pHHVIHHHL@MoHH@MH L8DIHIAIIH(HIIuH(L0L9HILH]I9HINHu H1I$L; sH|H8HqH0LH_L; !H(ff.IuH(LqL9HILH]I9HINHu H1I\$H}t!H8H H0LH IH(HIIL; xH(M9/tBI]LHHHxHDž`IFxI;sgHHIFxiHH(H9LyLxHDž`IF I;F(HHIF H0H`HII;tHxHHIHxHRII\$IlHDžxHDž`IF I;F(HHIF H`HH0IF8I;F@tHxHHIF8HxH轑HLLH(M|$IH`HIF8I;F@tHxHHIF8HxHUHDžxHDž`IFxI;sHHIFxH`HII;tHxHHIHxHԐAL; H(IDI\$IIHI9s{HHAH+HH9u1I^H8H H0HH I롐I]H8HHj H0HV ImL8IF8I+F0HHDžxIIVH)HHH)vHxL sHIFII+HHDžxINXIV`H)HHH)vHxH0w sHIF`H$]HH;EuH[A\A]A^A_]L袇LIH`[16H5XH XHxL`%H5[XH pXHHHUAH-XHxHHLE1H5=l]Hh]LXLLIH`06H5XH pXHxL`H5XH XHHHU,AHXHxHHLE1H5k]Hg]L轆L}HA+HAHHHt HX+LL}Mt6HEL9t!L`@t HxLLM9uH}LL}LEt H}݅Lxt H}˅LL`M HhL9L`@t Hx蘅LLM9uHA+HAHHHt HXXLL}Mt6HEL9t!L`@t Hx3LLM9uH}LL}LEt H} Lxt H}LL`Mt:HhL9t=L`@t Hẍ́LLM9uH`Lh貄LEt*LL LH HHL8L@I<$Ht I`LIHt IHLI~pHt I~x6LH0H8Ht I~`LI~0Ht I~8 LI~Ht I~ LI>Ht I~LHL ffffff.UHAWAVAUATSHH%]HHEHuHHUHt H91HHpHK H;K(s HHHK H{Hp- HC8H;C@tHMHHHC8 H{0HuHCHHEHCHH~]HH;EuHĘ[A\A]A^A_]sLLIHX,6H5!WH WHpLXSH5JWH WH@HULEAHWHpH@LE1H5g]Hc]LL IA+IAII@t HP2LLeMt6HEL9t!HX@t Hx LHI9uH}LLeLEt H}Lpt H}ҁLLXMt:H`L9tEHX@t Hx见LHI9uHXL`茁LEtL軁LL}L LUHAWAVAUATSH(IHIHH|]HHEH8HHf]HHHLH@H8L襀LLHEEL=z]MgLI@L8LbLL-z]ILHDž(HDž HDžHDžDž0L&LHLHH3H~LHHLHHH~HHHX"LL8Lt H LH~LH5j]HH ~LH8{LH8{]HH;EuHH([A\A]A^A_]LHLL8Lt H xLH~LHBGH:GH2GHH5j]HHj}LHH8~LHA{L ffff.UHAWAVAUATSHHez]HHEHuHHUHt H9H{HC HHH| HmHKH H~b1H+KHHpH;C(s HHHC HpVHDžpHC8H;C@sHHHC8H{0HpHC8HHHMHHHCHHEHCHHy]HH;EuHĘ[A\A]A^A_]}~L~LIHX'6H5+WH WHpLX[H5TWH WH@HULEAHWHpH@L E1H5b]H_]L}L IA+IAII@t HP<}LLeMt6HEL9t!HX@t Hx}LHI9uH}LLe|LEt H}|Lpt H}|LLXMt:H`L9tEHX@t Hx|LHI9uHXL`|LEtL|LLxL Lf.UHAWAVAUATSH(HIMnM~LL)HH9I>I)MIIHH=6HI)LHHH9H]H}wtII9LBMI9v^{LHH5WHxLHv]HHH5+b]H_]H{LH4L{LIDIF~IJ<}{LN$HEJHEL}J4L_{LO$MH]~H}HL|LHEIMfHEIFHtHH([A\A]A^A_]{LH([A\A]A^A_]E11yLwLIH{LLvL ffff.UHAWAVAUATSPIHL3HCL)HL9sjLH=upLcM)J<zLIKDMMM~LLLI{LLL+L{HCMtLH[A\A]A^A_]/zLH[A\A]A^A_]ÿ.zLHH5+WH"wLH]t]HHH5o`]H]]H:zLIHzLLuL ff.UHAWAVAUATSH8MIHIHHt]HHEH8H^]HHHHH@H8HxLLHEEL%s]ID$HHI@L8HwLL5r]ILHDž(HDž HDžHDžDž0Lg{LHLHHH0H wLL7{LHLHHH0HvLHHHHHL8Lt H "xLHvLH5b]HH1vLH8wLH\s]HH;EuHH8[A\A]A^A_];xLHHHL8Lt H wLH!vL#H_GHWGHOGHGGHH5b]HHuLHH8vLHVsL fUHAWAVAUATSHIIIuMeI)MIIGHH=HMuI)LHHH9waII9LBMLEHuI9vKvLHH5WHsLHp]HHH5\]HZ]HvLLEHuIJ<bvLHHuLEN4IJN|M~HLIwLLI]M}MuHtHH[A\A]A^A_]uLH[A\A]A^A_]E11LrLIHvLLqL ff.UHAWAVAUATSHIIIuMeI)MIIGHH=HMuI)LHHH9waII9LBMLEHuI9vKLuLHH5IWH@rLH{o]HHH5[]HY]HXuLLEHuIJ<tLHHuLEN4IJN|M~HLIDvLLI]M}MuHtHH[A\A]A^A_]tLH[A\A]A^A_]E11L^qLIHtLLpL ff.UHAWAVAUATSHILgLoLL)HH9LM)MII6HH=HM)LHHH9H}LEII9LBM/HuI9xsLHH5WHpLHm]HHH5Z]HW]HsLLHHIGL9vILHL9IIIPHHAHA~fpDHPH)1AADAD AD0HHuHtA~fpDAADI9t{KHL)LCHHtHIHHHHuIrIIHIHPIHPIHPIHP IHP(IHP0IHP8H@HuIHGHuIJ<:rLIHuO 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$L9AH]H9AL9H9E H]HHSIIEiAHH]qJT0I]L)E1LULMfBoBoLBofBoDfBTBDBoD BoL0BoT fBoD0fBTBIHuMt2MCoCoLCofCoDfBBDHMI9ILUDD)IHL)HtHfC4C4B4IHuHrXHMHUHL HUIT BtBtB4BtBtBtBtBtBtB4B4Bt IM9uHuHH;Fm= E1LULMM1HH;F 1fffff.L_(ILvJ HMHLLH_LO`ILHW`H LH LW@I; uWHLgI)IIr?f.IHW`H4HH4I;4uHHL9rG HHMHHOhLIIHHUHHL9L9LM;:uUHLGpI)IIr=IHH4HH4I;4uHHL9r@MuAAAAM9MOLEHMM,MM)MI?I>MIMwIr"HMI I H9J I91IT$H)LHtHfA\DˉHHuHHUIT f.tDΉ4tDΉttDΉt4DΉt HI9uLHfAnfpHYHHrHH]MT0LFI)1ff.AoLAoTff TAoLAoffL T0HIuHt!AoLAoTff TI9LEK 'IfAnfpI\$H9HMHL)LHtHLAoLff HHuH rXHUIT0oLff oLffLoLffL o ffL0HH9|L9M)IHHuIMO$J46I4H9s L;eMIfAnfpIRHHD{AHVHt0IWH)1ffffff.Ao AoTffLTAoL AoT0ffLHHuMt$HAoLAoTff TM9Lffffff.D)IVH)Ht!Hff.A\DˉHHuHrJHUIT fffff.tDΉ4tDΉttDΉt4DΉt HI9uHuHH;F1H]y1M ,H9urffff.L_(ILvJ HMHLLH_Hw`IHHMHHW`H LH LG@I;uPHLI)IIr8IHW`H4HH4I;4uHHL9rLLH_hLI$ILHLLLM;uSHLWpI)IIr;fDIHH4HH4I;4uHHL9r@CMuAAAAM9MOHMM,MM)MI?I>MIMwIr"HMI I H9J I91MD$I)LHtHfAt։4HHuIHuIt ff.\Ӊ\Ӊ\\Ӊ\Ӊ\ HI9uLMLHfnfpHqIIEAAHHuH]LT0LLFM)1AoLAoTff TAoLAoffL T0HIuHt!AoLAoTff TI9LMK 'IfnfpMD$L9LMIM)DLHtHLAoLff HHuI rXH]I\0oLff oLffLoLffL o ffL0HH9|L9M)IH4H]IMO$J3IH9s L;eMIfnfpIrHHD{AHCLD0IwH)1Ao AoTffALATAoL AoT0ffALAHHuMt$HAoLAoTff TM9Lff.D)MFI)Ht Hff.At։4HHuIrGHuIt ffffff.\Ӊ\Ӊ\\Ӊ\Ӊ\ HI9uHuHH;F#1LHq1M4[A\A]A^A_]UHAVSIPCLHH:]HHH{L9-H2]HHH[A^]IHnCLL?L @UHSPHHHH[]DUHAWAVAUATSH8IHH>]HHE1L:HþL*HHHHsHP(HH HxH HH9HNHEwHEHwHELuW)HDžLeH]L)tdLIILH=H\BLILLKDHHL~LLHCLILH1L~&-HLHILHHF-/IHHHt HALHHHHH}Ht H}ALHxHt H}kALH`Ht HhSALHHHt HP;ALH Ht H(#ALHHt H ALHHt H@LH\<]HH;EuHH8[A\A]A^A_];ALH=L H HHHHt H@LH}Ht H}@LHxHt H}m@LH`Ht HhU@LHHHt HP=@LH Ht H(%@LHHt H @LHHt H?LHLH^L HLH I9 HHMLoH_HW@L)HL_hHOpL)HH(HuLFM`IEL$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; uIHL9rH(MMLL]HEHML,Is1fff.MLHHPLHXHI9sLHI9s1HuMfInfpDHhMtdHEHLx1DoLoTffA AToLoffAL AT0HIu1H}Hut#AoLAoTffA ATHpHI9L]ff.L@I)H}t#HHITLIHHuIL]rMH`HUHHLLI HLLILHLLILH LILHI9uL; HEH I9HHMLoHWH_@L)HLghLOpM)IHuHvL^IECAAHHHpALEILhM)LxL@ILXHHMHHMHHML_`LEI HMHNHPH HMIH0HMIHH`1HMLHH(H0L8=ffff.LIHEMML H(H0L8HMHILHEMLG`IEHIEH;uGIEHr9@MLG`ILHILH; uIDHH9r@LIHUHLHI $HI $H]H; uPI$IrBffff.IHIHIH;uIHL9r@HEMHHEHML HEJHsE1 ffff.MHHELHM4HUHI9HsHII9s E1MfHnfpDHhtmHEHHx1fffff.oLoTffA AToLoffAL AT0HHu1H}Mt"Ao AoTffA ATHpIH9HPL)H@tHXDKHKIHuHrRH`HMHJLHK JLHKLJLHKLJ HKLIL9uHI9HEHHp[A\A]A^A_]ffffff.UHAVSIPH2LHH(]HHH{L_(-H"]HHH[A^]IH1LL.L @UHSPHHHH[]DUHAWAVAUATSH8IHH-]HHE1LHþLHHHHsHP踢HH HxH HH9HNHEMTHEH=THELuW)HDžLeH]L)tdLIILH=H0LILLKDHHL~LLH52LILH1L-HLH,ILHH/IHHHt HC0LHHHHH}Ht H}0LHxHt H}/LH`Ht Hh/LHHHt HP/LH Ht H(/LHHt H/LHHt H/LH*]HH;EuHH8[A\A]A^A_]/LH;,L H HHHHt H$/LH}Ht H}/LHxHt H}.LH`Ht Hh.LHHHt HP.LH Ht H(.LHHt H.LHHt H.LH*L ff.UHAWAVAUATSL_(IHHuHH8HHN` H9uffff.L_(ILFJ HMHHHMLH_HO`IHMHHW`H LH LW@I; uOHLwI)IIr7fIHW`H4HH4I;4uHHL9rHHMHHOhLIIHHUHHL L LM; uYHLOpI)IIrAfff.IHH4HH4I;4uHHL9r@MĨuAAAAM9MOLULMOHMHULIIM Is1Nfffff.H]J K IHuI44H]H4H9AI9H9I9@A@ LMH]LHHQHHDvAHtOL0HUH]LT0H]IVH)1DADALAT\AT\LADA AT\A\D L0HHuMt%AALA\AT\LI9LULMu1LMH]IT$H)LHt(Hfff.AA\HHuHroHUHuHT Kt ffffff.D\DD\DDD\DD\D HI9uOt%HIt$I9IMHL)LHt+HLff.AA \)HHuH rnHUHuHT0Kt0fffff.DL\)DL\)DDL\)D  \)D0HL9|M9L)HUH]JK LI4K M HMHIMLUK JM$L9AH]H9AL9H9E H]HHSIIEiAHH]JT0I]L)E1LUf.BBLB\BT\BDBLBD BL0BT \BT0\BDB IHuMt*MCCLC\CT\BBLHMH9MLMILULMDD)IHL)HtHڐCC\BIHuHrzHMHUHL KT fffff.BDB\DBBDB\DBDBDB\DBDBB\BD 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>MIMIrKLI H9J I91IMH)LHtHD(A\  HHuH KL ff.(\L (\LL(\LL(\ L HI9uLH(HJHHDfAHKL0I\$H)1ffffff.T\(\(\$TT(\(\d T0HHuMtAA\(\\$LI9ffff.K/I(IMH9HMHL)LHt"HL@A(\)HHuH rYKL0ffff.T(\)T(\)\T(\)\ (\)\0HH9|L9M)IH MMO,K42I4H9s L;mMI(It$HHDyAHLH\0IwH)1DATA\(\(\dTAT A\0(\(\dHHuMt HAA\(\\$LM9LfD)INH)Ht$Hff.(A\  HHuHrWKL ffffff.(\L (\LL(\LL(\ L HI9uHuHH;F1Mi1M)H9ugffff.L_(ILvN LHHMLH_LW`ILHW`H LH L@I;uWHLgI)IIr?f.IHW`H4HH4I;4uHHL9rLLH_hLI$ILHLLLM;EuRHLGpI)IIr:DIHH4HH4I;tuHHL9rCMuAAAAM9MOLeOMM)MI?I>MIMuIrKLI H9J I91IMH)LHt%Hff.A \ HHuHKL f.L\ L\LL\L \L 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;LIHHt H;H0HDž0HtCt H{Ō;H轌;L׈; UH]f.UH]閌;fDUHSPHp艌;H MHHsHx HH[]fff.UHHMHHGH~ HH]ffff.UH]f.UH];fDUHAWAVAUATSHHULHHEHcLgLOMLIHw H6H_(H HH_8ZY\YLYYD \$`Df(_(\_WH*IMI $HH0HW0H^WI*^H`HO@H @Wf: L,HLOXWf: H,HOHH8LuHLLxMfWf)EHEMt>LMLH:IL薊;HEHLH]HL胊;H]LMIHpHL2LPH`HAHHQPHHIHHHEL*M*L*H*HL*HLZM*E1MIIHHED-RDfEW%]D1f.HHHheEWL*DY@DX(1fffff.H}HWH*YXX MI1ffff.MWH*AXWZY@A^AXf(AfUL,MGL9LNLNfA(WI*\ ]D\HLLLE1f.vZH}HD78HD70HD7(HD7 HD7HD7HD7H7f.WI*AXWZYXA^Xf.wfA.lf.bf(AfUH,H_I9LLfA(WH*\\D\ff(fYfYJ?LN4;LLeI4Mt4I|4I\4fAL4 fAT40LIH@M9MHLUH;UHHH;HHhHH;poH`L`LPI$HPHH0IH0HxWH*H`HBHJH0HHpHBXHHHBHHJPH HHH8HPH81HxMIIHHXLmL}LEH ffffff.HpHxHHpH(H8HHHLE11Hhffffff.HH(HhHHH0HH`H@`H1fffff.HHHHH@*H}LeHELHLL8fW1E1E1ffffff.MLMΐIFIVI~IvAfAnA^AAY$eAY,mf.LwHEAY]f.LwHAYUf.LwHH0AuHAfHnf.wHHfHnI@HMLLuILXL;]fH}HELHLL0fWE1fffff.M~dLLDHAHQHqHYQYAYAYXAfAf!fYXXXH@HuMILXL;]u D1fWLe^fH~H`H@hHHH H@HH H9LeHhHHHHhH;p@HxHHHHxH;PH}Ht H};Hc~LHH;EuH[A\A]A^A_]E;H}; HH}Ht H}賂;H~; fUHH DH1H9NHD]DUHH=M]UHAVSHPIH}LHHEV;HþHL;HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]跦H5,FH}HH50GHuH5yIHHH HQH5oFHHHHxLHHEH6HUHuHuIININ0IFIVIF@H]HEHtHfHn;H{LHH;Eu LHP[A^]迀;IH]HEHtH!H);LC|; ffffff.UHAVSHPIHk{LHHE;HþH;HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]WH5̅FH}HH5гGHH5;wIHFHH HQH5FHfHHHvLHHEHfHUHuHuIININ0IFIVIF@H]HEHtHH~;HwyLHH;Eu LHP[A^]_~;IH]HEHtHH};Ly; ffffff.UHAVSHPIH yLHHE};HþH};HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5lFH}H$H5pGHH5tIHHA H5FH HHH,tLHHEHHUHuHuIININ0IFIVIF@H]HEHtH譋H{;HwLHH;Eu LHP[A^]|;IH]HEHtHhHp{;Lw; fDUHAVSHPIHvLHHEF{;HþH<{;HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]觞H5FH}HH5 GHeH5rIHHA H5fFHHHHqLHHEHHUHuHuIININ0IFIVIF@H]HEHtH]Hey;HtLHH;Eu LHP[A^]y;IH]HEHtHH y;L:u; fDUHAVSHPIHktLHHEx;HþHx;HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]WH5~FH}HH5٬GHH5;pIHFHH HQH5FHfHHHoLHHEHHUHuHuIININ0IFIVIF@H]HEHtHHw;HwrLHH;Eu LHP[A^]_w;IH]HEHtHHv;Lr; ffffff.UHAVSHPIH rLHHEv;HþHv;HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5l|FH}H$H5yGH赽H5mIHHH HQH5FHHHH%mLHHEHHUHuHuIININ0IFIVIF@H]HEHtH覄Ht;HpLHH;Eu LHP[A^]t;IH]HEHtHaHit;Lp; ffffff.UHAVSHPIHoLHHE6t;HþH,t;HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]藘H5 zFH}HH5GHUH5{kIH膼HA H5VFH譽HHHjLHHEHHUHuHuIININ0IFIVIF@H]HEHtHMHUr;HmLHH;Eu LHP[A^]r;IH]HEHtHHr;L*n; fDUHAVSHPIH[mLHHEq;HþHq;HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]GH5wFH}HtH5ɥGHH5+iIH6HA H5FH]HHH|hLHHEHHUHuHuIININ0IFIVIF@H]HEHtHHp;HnkLHH;Eu LHP[A^]Vp;IH]HEHtHHo;Lk; fDUHAVSHPIH kLHHEo;HþHo;HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5luFH}H$H5GH赶H5fIHHH HQH5FHHHH%fLHHEHHUHuHuIININ0IFIVIF@H]HEHtH}Hm;HiLHH;Eu LHP[A^]m;IH]HEHtHa}Him;Li; ffffff.UHAVSHPIHhLHHE6m;HþH,m;HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]藐H5 sFH}HĽH5"GHUH5{dIH膵HH HQH5OFH覶HHHcLHHEHHUHuHuIININ0IFIVIF@H]HEHtHF{HNk;HfLHH;Eu LHP[A^]k;IH]HEHtH{H k;L#g; 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fDUHAVSHPIHZLHHE&_;HþH_;HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]臂H5dFH}H贯H5GHEH5kVIHvHA H5FyFH蝨HHHULHHEH]HUHuHuIININ0IFIVIF@H]HEHtH=mHE];HXLHH;Eu LHP[A^]];IH]HEHtHlH];LY; fDUHAVSHPIHKXLHHE\;HþH\;HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]7H5bFH}HdH5ߐGHH5TIH&HH HQH5vFHFHHHeSLHHEHvHUHuHuIININ0IFIVIF@H]HEHtHjHZ;HWVLHH;Eu LHP[A^]?[;IH]HEHtHjHZ;LV; ffffff.UHAVSHPIHULHHEvZ;HþHlZ;HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]}H5L`FH}HH5GH蕡H5QIHƢHH HQH5tFHHHHQLHHEHHUHuHuIININ0IFIVIF@H]HEHtHhHX;HSLHH;Eu LHP[A^]X;IH]HEHtHAhHIX;LcT; ffffff.UHAVSHPIHSLHHEX;HþH X;HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]w|H5]FH}H褨H5GH5H5[OIHfHA H56rFH荡HHHNLHHEH]HUHuHuIININ0IFIVIF@H]HEHtH-fH5V;HQLHH;Eu LHP[A^]V;IH]HEHtHeHU;L R; fDUHAVSHPIH;QLHHEU;HþHU;HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]'yH5[FH}HTH5ωGHH5 MIHHA H5oFH=HHH\LLHHEH}HUHuHuIININ0IFIVIF@H]HEHtHcHS;HNOLHH;Eu LHP[A^]6T;IH]HEHtHcHS;LO; fDUHAVSHPIHNLHHEvS;HþHlS;HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]~H5LYFH}HH5GH蕚H5JIHƛHH HQH5mFHHHHJLHHEHHUHuHuIININ0IFIVIF@H]HEHtHaHQ;HLLHH;Eu LHP[A^]Q;IH]HEHtHAaHIQ;LcM; ffffff.UHAVSHPIHLLHHEQ;HþH Q;HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]w|H5VFH}H褡H5GH5H5[HIHfHA H56kFH荚HHHGLHHEHmHUHuHuIININ0IFIVIF@H]HEHtH-_H5O;HJLHH;Eu LHP[A^]O;IH]HEHtH^HN;L K; fDUHAVSHPIH;JLHHEN;HþHN;HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]'sH5TFH}HTH5قGHH5 FIHHH HQH5hFH6HHHUELHHEHHUHuHuIININ0IFIVIF@H]HEHtH\HL;HGHLHH;Eu LHP[A^]/M;IH]HEHtH\HL;LH; ffffff.UHAVSHPIHGLHHEfL;HþH\L;HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]oH5IHHA H5aFH-HHHL>LHHEH-HUHuHuIININ0IFIVIF@H]HEHtHUHE;H>ALHH;Eu LHP[A^]&F;IH]HEHtHUHE;LA; fDUHAVSHPIH@LHHEfE;HþH\E;HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]iH5LHH;Eu LHP[A^]C;IH]HEHtH1SH9C;LS?; ffffff.UHAVSHPIH{>LHHEC;HþHB;HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC 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Hr"HHȁAHHU7H9HBL97H[A^A_]fUHAWAVAUATSHHIHMtNLH=J<VW7H;HHt:W7LsINDHMHs-1HuxH;HHtW7HCQLH)1ffff.H3HH3HDH3HDH3HDHH9uHtHHHHHuLKMHKMQLutM9r1LIIL#UHJ M9MLuH]M_Lut&M9r 1LIIM9uMifL#]M9tHJ<2IMHEGDDLAtIWI_MLUL]DEHUH]MIAFtINH9t&Iv 'H9tffffff.IvHHAtI u*HtM1AL: uUHI9u3ff.Ht#LV7DEL]LUIH]HUuIMHDfI$IH]HJHI$HJL8LuN MMffff.M9MdHH[A\A]A^A_]ÿT7HH5wBHQ7HNHHHH5:HHm8HHT7IHT7LdP7 UHAWAVAUATSH(HOHHHEIHAEM}IuIDIEUH}ILcMIt$Lt M9s MIM! 1LIIHJHHHAEt MMM}DHuLMHCL9uWKHtHSL9u]H{HtH{ uYMtz1T A:u6HH9u`ff.LtL9r1IHL9t :H!L9u2HHs$Mt!LLMdT7LMMHuu1H4NHHH;EuHH([A\A]A^A_]S7H|2fff.UHAWAVAUATSH(HMHHHEIHAEM}IuIDIEUH}$ILcMIt$Lt M9s MIM! 1LIIHJHHHAEt MMM}DHuLMHCL9uWKHtHSL9u]H{HtH{ uYMtz1T A:u6HH9u`ff.LtL9r1IHL9t :H!L9u2HHs$Mt!LLMR7LMMHuu1HLHHH;EuHH([A\A]A^A_]fQ7H2fff.UHAWAVAUATSHIH7LwI)IIFHH;HHWH)HHHH9H}wYHH9HBHDH9vCwP7HH5tsBHkM7HJHHHH56HHA4HHP7HHHEHHP7HIN,3LLH]M7ID$JD3Mu HEL8L`M9t;M)I@I}LM7ID$IEIII uHEL LxMHMHHL(LpHMHHHM9t'ff.I_AGt I\O7II9uMtLH[A\A]A^A_]9O7H[A\A]A^A_]1HE1 L7IHIO7EIMM9u*IM9t"fDI^AFt I~N7II9uH}HtN7LJ7 fff.UHAWAVSHH JHHHEHDžpHDžhƅ` fDženeDžaaffiƅgHH`L7fDžW)@HDžPƅ@DžAreluƅEHH@K7fDžW) HDž0ƅ IleakyrelL!fDž)uHH yK7W)ƅHDžHldedreluHfDžHthresholHƅHHK7fDžW)HDžƅDžtanhƅHHJ7fDžW)HDžƅHscaledtaHfDžnhƅH HvJ7fDž8W)HDžƅHsigmoidHH@H-J7fDžXW)HDžƅHhardsigmHDžoidH`HI7W)`ƅ`fDžxHDžpDžaeluH}H`I7W)@fEƅ@HDžPHsoftsignHAƅIH}H@QI7W) fEƅ HDž0HsoftplusH!ƅ)H}H I7fEH=HJH ]EEE`@  % 1@=`IUamy @`t HpJ7H=Q.HH5GJHJ7W)`ƅ`HDžpLafDžiuHH`sG7HDž #<W)@HDžPƅ@HADžIoidHH@(G7HL>?HW) ƅ HDž0Dž!eluHH F7HDž?H=FJH @`t HpH7H=,HH5sFJHH7HCHHH;EH[A^A_]H}TH7EAH}AH7E8H}.H7`/HpH7@#HPG7 H0G7 HG7HG7HG7HG7HfG7 H0MG7@HP4G7`HpG7HG7HF7HF7HF7{HF7 oH0F7@cHPlF7`SZHNF7lH5F7`HF7 TH0F7@HHPE7`8?EF7IuIuUt HE7E1 t H0E7@uVHrE7tH]E7tIA tIA@t3HP!E7`t'Hp E7HuIA1`uHAHf.`t HpD7HuIEEE`@  t HD7E1 t H0D7 @%&H}C7EDH}C7E;H}C7`2HpC7@&HPC7 H0tC7H[C7HBC7H)C7HC7IA IA @HPB7`HpB7HB7HwB7H^B7HEB7H,B7 H0B7@HPA7`HpA7HIA`*6B N@Z`fHt+Au%H@`t Hp)A7HuL==7 IAS`IAC\IA3XIA#TIAPIA LIA@HIA1`GUHAVSHPIH;HHHE@7HþH @7HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]gH5CH}HԎH5CH5H5[7EHfHA H56ZBH草HHH6HHHEHHUHuHuIININ0IFIVIF@H]HEHtH-NH5>7H9HHH;Eu LHP[A^]>7IH]HEHtHMH=7L :7 fDUHAWAVAUATSHIIIH,9HHHE1L HHL0L(L8HCH+CHI.袋.LW)`HDžpIcH`JE~bMcE1LuE1f.HCLHEHhH;psHHhH`LQTIIXM9|W)EHEH(HPH@H`LEHH0jH@HL8t]IHDž@H}Ht H}w<7H`Ht Hh_<7H7HHH;EukLHĸ[A\A]A^A_]H}tCHULHEL@HDž@MtAFt I~;7L;7gIPQ<7;7IH` H5CH gCH}L`sW)@HDžP ;7HP(O@)@He input.HHHsequenceHHHptr for HHHGot nullH@AHݰBHuH@LE1H5= HHHLX;7WHA(HA}HH@t HP:7LeMt6HEL9t!Lh@t Hx:7LM9uH}LLe:7Et H}{:7Et H}l:7L`Mt:HhL9t=Lh@t HxA:7LM9uH`Lh&:7EtwLU:7mLHL@HDž@MtAFt I~97L97HH}HtH}97HH`Ht Hh97H57 UHAVSI`97HH/HHHH{L/HHPL{HHLHHH[A^]IH>97LX57 @UHSPH#H;u)H SHHHH[]ÐUHAVSHPIHK4HHHE87HþH87HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]' H5ˆCH}H蔇HdH5?BHHJH5CHH50EH HA H5RBH3HHHR/HHHEHÚHUHuHuIININ0IFIVIF@H]HEHtHFH67HD2HHH;Eu LHP[A^],77IH]HEHtHFH67L27 fff.UHAWAVAUATSHHIH1HHHE1H^IMW)EHEHU1HHH}Ht H}67H<H/IOI+OHH.袋.HHIHD1HHH;EuLHĈ[A\A]A^A_]#6757IHhx H5ĆCH CH}LhEW)PHDž` b57H`(XI@)PHe input.HHHsequenceHHHptr for HHHGot nullH@AHBHuHPLiE1H5HH`HL*5747IHh H5ޅCH 6CH}Lh!_W)PHDž`0|47H`(b?)PHt tensorHH%Houtput tHH Hptr for HHHGot nullHHHLength: HHHSequenceH@-AHCHuHPLgE1H5 HH^HL(47 IAIIAIIAIA2IuIPt H`{37LeMt6HEL9t!HX@t HxV37HI9uH}LLe<37Et H}-37Et H}37LhMHpL9HX@t Hx27HI9uIPt H`27LeMt6HEL9t!HX@t Hx27HI9uH}LLe27Et H}x27Et H}i27LhMt:HpL9t=HX@t Hx>27HI9uHhLp#27Et$LR27LIH}Ht H}17L.7 UHAWAVAUATSHAHH?-HHHE$Ht H1H -HH H;MuHĈ[A\A]A^A_]17IǃL1717IHh> H5NBH +CH}LhZ H{H*IcHH<H5ȿBHPH07HuHP1LpH5HHjHL417 IPt H`07E1H蓣2IALmMt6HEL9t!HX@t Hx07HI9uH}LLmn07Et H}_07EtH}P07IALhMt:HpL9t5HX@t Hx07HI9uHhLp07Eu LIL'0707L,7 H财2@UHAWAVATSH`IH'+HHHEؿ/7HþH/7HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5CH}Hp~HH5m5BHHV~IRIZHW)EHE6.7HEHHHML HXHMH5zBHUL}H5CH^vH5&EHwHA H5_IBHxHHH%HHHEHHUHuHuIIOIO0IGIWIG@H}Ht H}m-7H]HEHtHD=HL-7H(HHH;EuLH`[A\A^A_]-7IH}HtH}-7IH]HEHtH ILHMH@H1HH)H~LH-7ILmHU1LHH}Ht H}O+7H#L#LM,M9L@IL0H(H8H0LuHH0H9(H8ID0H8HPHLLHS,7HhH5E}CH W}CL FBH]HHLhH,LHG{ EtRH}h*7GH~41ffffff.LL#(7HLIIH9|L IH%HHH;EuLHĸ[A\A]A^A_]g*7*7IHh H5{CH {CH}LhT艻W)PHDž` )7H`(=@)PHe input.HHHsequenceHHHptr for HHHGot nullH@AHBHuHPL譽E1H5SHH HLn)7)7IHh H5"zCH {CH}LhW裺W)PHDž`0(7H`(6@)PHdex tensHHHt for inHHHptr inpuHHHGot nullHf@ or@"AHzCHuHPLE1H5g HH HL(71(7IHh H56yCH zCH}Lhc跹W)PHDž`0'7H`(X@)PHut tensoHH Hfor outpHHHnullptr HHHAt: Got HHHSequenceHf@(rAH_yCHuHPL˻E1H5q HHHL'7 H}$7 IA(IA}IcIPt H`&7LeMt6HEL9t!HX@t Hx&7HI9uH}LLe&7Et H}&7Et H}&7LhMHpL9HX@t Hxe&7HI9uIAIIAIIAIA2IuIPt H`&7LeMt6HEL9t!HX@t Hx%7HI9uH}LLe%7Et H}%7Et H}%7LhMHpL9HX@t Hxw%7HI9uIPt H`R%7LeMt6HEL9t!HX@t Hx-%7HI9uH}LLe%7Et H}%7Et H}$7LhMt:HpL9t=HX@t Hx$7HI9uHhLp$7Et5L$7+LIH}HtH} IEt H}u$7L 7 ff.UHAWAVAUATSHHHHHHEVkẼtu?H1UHc H$YHH zHH H;MuHĘ[A\A]A^A_]\$7#7IH` H5tCH bzCHxL`C{H5QBHHHUHxHH1LH5HHHL#7 IHt HXL#7E1H2IALeMt6HEL9t!HX@t Hx#7HI9uH}LLe"7Et H}"7xtH}"7IAL`Mt:HhL9t5HX@t Hx"7HI9uH`Lh"7Eu LIL"7L7 f.UHAWAVAUATSH8MLHHIHHHHHEH8H*HHHHLH@H8L!7LHEEL-HIEHHI@L8L= 7L=lHILHDž(HDž HDžHDžDž0L$7HLH艿HH0H7HILL#7HLHHH3H[7LHH#7HHHLLHHL8Lt H 7L%7H5F HHH7H8 7HHHH;EuLH8[A\A]A^A_] 7IHHL8Lt H 7L7+Hڒ2HҒ2Hʒ2H’2H躒2IH5 HHH7IH8[7L7 DUHAVSHPIHHHHE7HþH|7HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5roCH}HDnH5 qCHfH5EHgHA H59BHhHHHHHHEHmHUHuHuIININ0IFIVIF@H]HEHtH-H7HHHH;Eu LHP[A^]7IH]HEHtHX-H`7Lz7 fDUHAVSH0IHHHHHEHHHHH{ZH{HHHCPW)EE HPHEEdtypfEeH}HULH'LuHEMtAFt I~7L7Et H}7MtHHHHH;Eu H0[A^]7HEt H}]7Hw7 UH]fDUHAWAVAUATSHHIIHHHHEH{HhHH+H1H4 HHHHIGPHHH HcHqYRcKDU=N6/@(!+ HIHHHH;EuLHĈ[A\A]A^A_]7 7IHh H5%lCH mCH}Lhy覬W)PHDž`07H`(<@)PHsequenceHH&Hutput seHH Htr for oHHHot nullpHHHEmpty: GHHHSequenceH@.AHBlCHuHPL议E1H5TGHGLo77IHh H5#kCH lCH}Lh褫IPH5&CHPL:HuHP1L%H5GHGL7 IPt H`u7E1HH2IALeMt6HEL9t!HX@t Hx=7HI9uH}LLe#7Et H}7EtH}7IALhM HpL9HX@t Hx7HI9uIIA(IA}IIPt H`7LeMt6HEL9t!HX@t Hx`7HI9uH}LLeF7Et H}77Et H}(7LhMt:HpL9tEHX@t Hx7HI9uHhLp7EtL7L7 LfA]yrHV:k3d%,O@UHAWAVAUATSHHIHHHHHEH8H[GHHHLH@H8L7HEEL5HM~LI@L8L|7L%HILHDž(HDž HDžHDžDž0L@7HLHȴH3H7HHH蓸LL8Lt H 97H7H5HHHH7H87HsHHH;EuHH[A\A]A^A_]R7HLL8Lt H 7H?7H}2Hu2HH5KHHH7HH87H7 UHAWAVATSH`IHHHHEؿB7HþH87HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5.eCH}HdI8I@HW)EHE7HEHHHML HXHMH5m`BHULcH5fCH\H5. EH9]HA H5 /BH`^HHH HHHEHzHUHuHuIIOIO0IGIWIG@H}Ht H}7H]HEHtH"H7H_HHH;EuLH`[A\A^A_]C7IIIIH}Ht H}7H]HEHtH"H7L7 fUHAWAVAUATSHH HHHEHIIW)EHEHH?I?t#HecCH eC1AL+ H(IMl$0ID$I\$ W)PHDž`H)tnH8L0IILH=H7ILXLPKH`HL0H8~LH7ILXHEH@HEHHHtH@HhHPH@E1L!HHHt#HHCHuHHPH7HPHt HX7LhM4$M9tRI\$0H0I|$H8(H;0I|$0HHQHHLLH7H(H{H;{sHh+HCXHHhHazIHh>H]Ht#HHCHuHHPH7H{ HHH;EuZLHĸ[A\A]A^A_]HL8U1ff.LL 7L-HIIH9| 7HP 7 I2ILHMt#HIFHuILPL7HPHtOHXF7AIIIHt/Ct H{#7H7IIHhH]Ht#HHCHuHHPH 7L 7 ffff.UHAWAVAUATSHHIH HHHE1HHHHH蚫HH{LI>H;{0txɊHhH{0蹊HPH5bCH tbCL '*BH]HhLPHLH^ EH} 7HHMfM+fLHH.袋.HHHL{L@MHtBHIL@HHI9I9MxȄ=My IL@HHxHHH+H1HYHHuHHHHHHHHL M(LE1E1M9H8HHHH8HDž8HtCt H{` 7HX 7HHpLH0HHH0HDž0HtYCt H{ 7H 7@HHpLH(HHXH(HDž(HuILHL@IXM9ILM9uOH HHHH HDž HtCt H{L 7HD 7IEHHHH;Eu^LH[A\A]A^A_]H5]CH ]CL 'BH]H@LHHLH[ ; 7 7IHh萴 H5[CH ^CH}Lh]W)PHDž` z 7H`(p@)PHe input.HHHsequenceHHHptr for HHHGot nullH@AH]CHuHPL聞E1H5'GHxGLB 7 7IHh誳 H5ZCH 1]CH}LhwW)PHDž` 7H`(J>@)PH tensor.HHHinput teHHHptr for HHHGot nullH@AH~BHuHPL蛝E1H5AGHGL\ 7 7IHhIJ H5ZCH K\CH}Lh葚W)PHDž`07H`(4@)PHsequenceHH'Houtput sHH Hptr for HHHGot nullHHHInsert: HHHSequenceH@/AH-ZCHuHPL虜E1H5?GHGLZ7 IAgIAIfIAIAPIHIAIAI*IPt H`7LeMt6HEL9t!HX@t Hxj7HI9uH}LLeP7Et H}A7Et H}27LhMHpL9HX@t Hx7HI9u]IPt H`7LeMt6HEL9t!HX@t Hx7HI9uH}LLe7Et H}7Et H}}7LhMHpL9HX@t HxJ7HI9uIPt H`%7LeMt6HEL9t!HX@t Hx7HI9uH}LLe7Et H}7Et H}7LhMt:HpL9t=HX@t Hx7HI9uHhLp7Et#L7LIEt H}Z7Lt7 UHAWAVAUATSH8MLHHIHHHHHEH8HGHHHHH@H8Hs7LHEEL-GM}LI@L8H07L5_GILHDž(HDž HDžHDžDž0L7HLH|HHHHLoHHH.LL8Lt H 7H`7H5GHH7H8Q7HGHH;EuHH8[A\A]A^A_]7HLL8Lt H N7H7 Hv2HH5GHHP7HH87H'6 UHAWAVAUATSH8MLHHIHH7GHHEH8HGHHHLH@H8L'7LHEEL-PGIEHHI@L8L7L= GILHDž(HDž HDžHDžDž0L7HLH)HH0HG7HILL`7HLHH3H 7LHH)7HHH豟LL薣HHL8Lt H 57L6H5GHHH6H87HsGHH;EuLH8[A\A]A^A_]R7IHHL8Lt H 7L<6+Hzs2Hrs2Hjs2Hbs2HZs2IH50GHH6IH86Li6 DUHAWAVAUATSPIM'IGL)HH.袋.HH9H袋.H9MwLL)LkXL6HLM9tCHEM)IƨHIHfHǨLtHèHIƨIXuM7MgLHEHI?I_IGM9t"LL)IĨfDLhIĨHXuMtLH[A\A]A^A_]C6H[A\A]A^A_]ÿB6HH5?"BH66HqGHHH5GH GHN6IH%6L6 ffffff.UHAWAVATSH`IH'GHHEؿ6HþH6HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5NCH}HpMI!I)HW)EHEP6HEHHHML HXHMH5IBHULMH5|QCHxEH5DHFHA H5yBHGHHHGHHEHcHUHuHuIIOIO0IGIWIG@H}Ht H}6H]HEHtH^ Hf6HGHH;EuLH`[A\A^A_]6IIIIH}Ht H}6H]HEHtH H6L6 fUHAWAVAUATSHHIH?GHHE1HIMH0HΘMl$M+l$LHI.袋.LLHI^H@H HtBHHH@LHH9L9HxȄ=Hy LH@L(H0HxHdHH+HS1H0,H<HbIM(I$ILHH eMxE1E1I9taIt$LH8H0LH8HDž8Ht+Ct H{L6HD6fH@IXI9uIL9HL(IHGHH;EujLHĸ[A\A]A^A_]H5{LCH LCL BH]H@LHHLH}J EtH}6 66IHh^ H5JCH MCH}Lh+W)PHDž`0H6H`(.@)PHsequenceHH&Hutput seHH Htr for oHHHot nullpHHHErase: GHHHSequenceH@.AHJCHuHPL3E1H5GH*GL66IHh\ H5ICH LCH}Lh)W)PHDž` F6H`(< @)PHe input.HHHsequenceHHHptr for HHHGot nullH@AHKCHuHPLME1H5GHDGL6 IA(IA}IIPt H`6LeMt6HEL9t!HX@t Hx]6HI9uH}LLeC6Et H}46Et H}%6LhMHpL9HX@t Hx6HI9uIA(IA}IIPt H`6LeMt6HEL9t!HX@t Hx6HI9uH}LLev6Et H}g6Et H}X6LhMt:HpL9t=HX@t Hx-6HI9uHhLp6Et!LA6LIEt H}6L6 fUHAVSHPIH;GHHE6HþH6HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5AH}HDHH5ECHHjDH5KICH<H5DH=HA H5BH#?HHHBGHHEH[HUHuHuIININ0IFIVIF@H]HEHtHH6H4GHH;Eu LHP[A^]6IH]HEHtH~H6L6 fff.UHAWAVAUATSHIHHGHHEH~HLc8MH0I}HxHH+H1LDHH[H8H11LL`0LL9`0`6ILu(l'@)EPLH5GC6AFPHHHM%C LHHDžHMtAFt I~.6L&6EtH}6D9EH8L HHL1L@LLHLH8AL@HDž@MtAD$t I|$6L6A9uH0HH GH H;MuHĨ[A\A]A^A_]6b6IHh H5gBCH 'FCH}Lh,W)PHDž`@6H`( @)PHt sequenHH(Hor outpuHH Hullptr fHHHt: Got nHHHConstrucHHHSequenceHf@0ce@2AH~BCHuHPLE1H5GHGL6Z6IHh H5_ACH ECH}Lh)W)PHDž` 6H`(@)PHore inpuHHHe 1 or mHHHMust havHf@ts@AH ECHuHPL E1H5GHGL6 HA(HA}HHPt H`A6LeMt6HEL9t!Lh@t Hx6LM9uH}LLe6Et H}6Et H}6LhMHpL9Lh@t Hx6LM9uHA(HA}HHPt H`t6LeMt6HEL9t!Lh@t HxO6LM9uH}LLe56Et H}&6Et H}6LhMt:HpL9t=Lh@t Hx6LM9uHhLp6Et!L6LHEt H}6H6 UHAWAVAUATSHxIHGHHEп6HþHv6HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]IIIHW)EHE )6HEHH HML8L`LhHXHMH5AH}HU;H_H56ILXLPIH`MH~LL6MLXH@HHHHHtH@E1LLLHPH蜕HHt#HHCHuHHPH96HPHt HXe6L 2HËEHHcUIHHL$HcEH9Hu!HHcHHL6HL<HHHBAAHHhsE1L)HHHHRHHHH KH(HHH0HH8HH@E1fHLL6I<HJ4 L6H@H<H8J4 L6H(H<HJ4 Lk6IL H0L9uMHHhtQHIL$LLHJHIIfLHL6MLIuHH9HLPLLHXHxXW)PHDž`HxH+pHHHPLxL+pM~hILuE11ffffff.I9t;HpLHXH;`tHHHHXHPBfHIL9|W)HDžLPLXM)t[LHHH9L66ILLIHM~LLL6MLLLHMHHt H6HPHLtHX6@HxH{H;{sLGHCXH`LFHcHL[E1HHHt#HHCHuHHPH6Eu&HHHEH9LpIEHpH@Hx6/J66IHp蟅 H5,CH 3CHLpimW)PHDž`@6H`(\@)PHt be > 0HH.Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@6AH 3CHHPL`oE1H5GHWGL!6 6H GHHH56GHGH6HPw6Hi6벿6IHp@ H5+CH 1CHLp lW)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AH2CHHPLRnE1H5GHIGL6Hp6IAI*IIPt H`k6L Mt?H(L9t$HX@t Hx@6HI9uH LL( 6t H 6t H6LpM#HxL9)HX@t Hx6HI9u IA+IAIIPt H`~6L Mt?H(L9t$HX@t HxS6HI9uH LL(36t H6t H 6LpMt:HxL9tDHX@t Hx6HI9uHpLx6EnL6aLI8II]IEH8Ct H{u6HIIHHtHF6IHPHtbHX'6TIHHt#HHCHuHHPH6HPHtOHX6AIHŔ0II]IEHtCt H{6H6IHHHt#HHCHuHHPH 6HpHt HxL6H}Ht H}:6LT6 UHAWAVAUATSHIIHIHyGHHEHEHCPHEEEEEEW)EHEMtHHt H6HPHLtHX6@HxH{H;{sL7HCXH`Lq7HcHLKE1HHHt#HHCHuHHPH6Eu&HHHEH9LpIEHpHPHx6?:66IHpv H5CH r&CHLpY^W)PHDž`@v6H`(L@)PHt be > 0HH.Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@6AH$CHHPLP`E1H5GHGGL6 6H GHHH5&GHGH6HPg6HY6벿w6IHp0u H5|CH %CHLp\W)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AH"CHHPLB_E1H5GH9GL6Hp6IAI*IIPt H`[6L Mt?H(L9t$HX@t Hx06HI9uH LL(6t H6t H6LpM#HxL9)HX@t Hx6HI9u IA+IAIIPt H`n6L Mt?H(L9t$HX@t HxC6HI9uH LL(#6t H6t H6LpMt:HxL9tDHX@t Hx6HI9uHpLx6EnL6aLI8II]IEH8Ct H{e6HIIHHtH66IHPHtbHX6TIHHt#HHCHuHHPH6HPHtOHX6AIH赅0II]IEHtCt H{6H6IHHHt#HHCHuHHPH6HpHt Hx<6H}Ht H}*6LD6 UHAWAVAUATSHIIHIHiGHHEHEHCPHEEEEEEW)EHEMt 0HH.Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@6AHCHHPL0QE1H5֡GH'GL6 裼6H GHHH5GHǞGH6HPG6H96벿W6IHpf H5\ CH CHLpMW)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AHCHHPL"PE1H5ȠGHGL6Hpf6IAI*IIPt H`;6L Mt?H(L9t$HX@t Hx6HI9uH LL(6t Hۺ6t Hƺ6LpM#HxL9)HX@t Hx蓺6HI9u IA+IAIIPt H`N6L Mt?H(L9t$HX@t Hx#6HI9uH LL(6t H6t Hٹ6LpMt:HxL9tDHX@t Hx讹6HI9uHpLx蓹6EnL边6aLI8II]IEH8Ct H{E6HIIHHtH6IHPHtbHX6TIHHt#HHCHuHHPHz6HPHtOHX覸6AIHv0II]IEHtCt H{p6Hh6IHHHt#HHCHuHHPH6HpHt Hx6H}Ht H} 6L$6 UHAWAVAUATSHIIHIHIGHHEHEHCPHEEEEEEW)EHEMt 0HH.Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@6AHCHHPLBE1H5GHGL议6 `6H iGHHH5ÓGHGH~6H6HP6벿6IHpW H5BH  CHLp?W)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AHCHHPLAE1H5GH֎GL蠭6Hp#6IAII"IPt H`6L Mt?H(L9t$HX@t HxȬ6HI9uH LL(訬6t H蓬6t H~6LpMHxL9$HX@t HxK6HI9uIA+IAIIPt H` 6L Mt?H(L9t$HX@t Hx6HI9uH LL(6t H諫6t H薫6LpMt:HxL9tDHX@t Hxk6HI9uHpLxP6EvL{6iLI@II]IEH@Ct H{6H$IIHHtHӪ6IHPHHX谪6{IHHt#HHCHuHHPH36HPHtSHX_6E4II]IEHt/Ct H{86H06IIHhHHHt#HHCHuHHPH襨6HpHt Hxѩ6H}Ht H}迩6L٥6 DUHAWAVAUATSH(IHIHHGHHEH8HGHHHLH@H8L6LHEEL=GMgLI@L8L袧6L-ѢGILHDž(HDž HDžHDžDž0Lf6HLHFH3HߊHH66HHHFHHHJLL8Lt H >6Hʦ6H5GHHM6H8軧6HxGHH;EuHH([A\A]A^A_]W6HLL8Lt H 踧6HD6H2Hz2Hr2HH5HGHH誥6HH86H聣6 ffff.UHAWAVAUATSHXLMDEIIH}L}HGHHEMuM+uIHuH~PL迨IIMHH]LH5HcH9KHMI7L(HcH9.HM I7HƸL9tLHcH9Lm@HM(}tPLu8HHIIAHML9tHE0IGHMUIML)HHH)vAL)MU=MeIEI9HHL)HHH1LsIIEKJ HLHHHHOMDLLIWLHfInfpDHVHHyHP1H HEHffEHEMHHHH9H<>6IHMHA`LmLmMtLuHrHaI9|I9sLHSHHtHH8H9HHHuH@HHHHQHHQHHQHHQ HHQ(HHQ0HHQ8H@HuuI?I!IPHHwH |f1fHHWH)1ېAADAD AD0AD@ADPAD`ADpAڀAڐAڠAڰAAAAH HkHt(ILHfffff.AH HuI9t)LH)IHfff.L"HHHIMH)HLebHHMcH ZII!fInfpDIHHH{HX1HHVH)f1fDAofAoDfAoL Ao\0Aod@ffAoTPffAoD`fAoLpfHHuHt$ITHfoRfofH HuffpNffH~L9tK ff.HHH9uHUH9uL)HHMHH5YBH cBL ݽAH]HULEHLeLHvEH}蓡6KHOH)1ffff. 0@P`pDހDސDޠDްDDDDH HuHtLILHfAH HuL9L)JHff.LHHHE1E1MHHHzHHNHtB~fpDHyH)1ADADAD AD0HHu1Ht~fpDADADH9ueLuMt#MgL6IGIGIM/MwMwLeI$H+GHH;EuLHX[A\A]A^A_]ILH)6蜟6H GHHH5GHGH躟6H}E6 HH}HtH} HEt H}16HK6 UHAWAVAUATSH8MLHHIHHWGHHEH8HGHHHLH@H8LG6LHEEL-pGIEHHI@L8L6L=,GILHDž(HDž HDžHDžDž0L6HLHIIH]IcHMIFI;FsHHIF fLHIIuH$GHH;EuHĘ[A\A]A^A_]6袐6IHh[: H5BH `BH}Lh("L HHH5BHPHHHuHP1L$H5CuGHqGL^6 IPt H`6E1H2IAkIIALeMt6HEL9t!HX@t Hx袏6HI9uH}LLe舏6Et H}y6Et H}j6LhMt:HpL9tEHX@t Hx?6HI9uHhLp$6EtLS6L16 LUHAWAVAUATSHIIHOGHHEILH54BHH5~DHHH HQH5AHHHH~GHHEHp*HUHuHuIININ0IFIVIF@H]HEHtH萕H蘅6HGHH;Eu LHP[A^]6IH]HEHtHKHS6Lm6 fUHAWAVAUATSHIH8H@HGHHE1L3"IMffEHEI|$ I+|$tNIILH=ӄ6HH]H]JHEIuM} I)M~HL%6LH]HU1LiHHH}Ht H}k6H8@PH IH HcHLoIL@HHHMpIrJ ;H9` J H9S 1IWH)LHtH< W*f~HHuHfffff. W*T W*DT W*DT W*f~D HI9uLnILYHHHMIrJ ;H9J H91IWH)LHtHfff.< M]IrJ ;H9J H91IWH)LHtH< HfD< | D| D| DHI9uLiIL詨HHHzMIrJ ;H9KJ xH9>1IWH)LHtHfff.< fHzH)1f803f80L3pLpf80D3f80L3Dp Lp0f80D3 f80L3(Dp@LpPf80D30f80L38Dp`LppH@HuHt4HHfff.f80D3f80 3Dp pHHuI9~1Htf813f81L3[[LI9M1Ht2ooLfftfoJ?fftfLI9s6r6IHhk H5BH BH}Lh"8ffPHDž`@Sr6H`fo0@fPHDataTypeHH(Hof type HH Hrgument HHHe 'to' aHHHmust havHHHCast op H@0HuHP1LDH5VGH>SGLr6q6IHhp H5BH BH}Lh"=H8HPH5=BHPHuHP1LH5cVGHRGL~q6 H}n6 IPt H`p6E1H1IALeMt6HEL9t!HX@t Hxp6HI9uH}LLep6Et H}p6EtH}p6IALhM HpL9HX@t HxTp6HI9uIPt H`*p6E1H1IALeMt6HEL9t!HX@t Hxo6HI9uH}LLeo6Et H}o6EtH}o6IALhMt:HpL9t5HX@t Hxo6HI9uHhLplo6Eu )LILo6IH}Ht H}HzH)1fDf83sf83LsLf83Dsf83LsD L0f83Ds f83Ls(D@LPf83Ds0f83Ls8D`LpH HuHHt6Hffffff.f83D3f83 3Dp pHHuLHHyHHVHHzH)1fo?o soTsf8f 0f8fT0oLs oTs0f8fL0f8fT0H Hu+LHHyHHVH01H HzH)1@f84sf84LsLf84Dsf84Ls D L0f84Dsf84LsD@LPf84Dsf84LsD`LpHHuHyHt6Hffffff.f84D3f84 3D HHu<LHHyHHVH051H1Hf83sf83Ls[[L1HoSoLSffufof6?ffufo?f8fff8fLHzH)1sLspLpDs Ls0Dp Lp0Ds@LsPDp@LpPoDs`oLspDp`LppH@HuHt-Ht6HfDoD3o 3D0 0H HuI9HzH)1sLspLpDs Ls0Dp Lp0Ds@LsPDp@LpPoDs`oLspDp`LppH@HuHt(Ht6HڐoD3o 3D0 0H HuI9b1Ht(osoLsfo9?f8f0f8fL0I9%P1Ht(osoLsfo?f8f0f8fL0I9@Y6IHh H5BH BH}Lh#mffPHDž`@Y6H`foM@fPHDataTypeHH(Hof type HH Hrgument HHHe 'to' aHHHmust havHHHCast op H@0HuHP1LyH5">GHs:GL=Y6X6IHh H5BH BH}Lh#rH8HPH5rBHPHuHP1LH5=GH9GLX6 H}6Eu )LIL?6IH}Ht H}>6L:6 WH*f~HI9uVHHI9uEHHI9u4<HI9u"fHHI9ufHHI9uH@HH9GHH;EuH@HĨ[A\A]A^A_]}>65`x$g`q$UHAWAVAUATSHH(9GHHEMIIIW)EHUL+I}t HBH B1AL}"IEHMLLL(IEH]Ht#HHCHuHHPH;6H8GHH;EuLH[A\A]A^A_]h=6II]IEHtCt H{<6H<6IH]Ht#HHCHuHHPHV;6L86 UHAVSHPIH7GHHEv<6HþHl<6HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]jH5AH}HH54BH69IHH5BHH53DH谄HH HQH5yVAHЅHHH2GHHEHHUHuHuIININ0IFIVIF@H]HEHtHpJHx:6H5GHH;Eu LHP[A^]:6IH]HEHtH+JH3:6LM66 fUHAWAVAUATSHIH@HHHd5GHHE1LIMffEHEI|$ I+|$tNIILH=96HH]H]JHEIuM} I)M~HL;6LH]HU1LIH}Ht H}O96H@@PH %IH HcHL$MILHL越MIWDH1H|LA$ILFHL[MUI1L $MIL HLMIrJ H9 J H9 1IWH)LHtHfHfD< | D| D| DHI9uLILDHHHZMIrJ ;H9KJ 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fUHAWAVAUATSH8LLHIIHHDHHEH8H&DHHHHH@H8H胬3HEEL-DMuLI@L8HG3HvDHHHDž(HDž HDžHDžDž0L 3HLHJA7H蔪3LMLEHHHHLLH5NLL8Ht H ۫3Hg3H5DHH3H8X3HDHH;EuLH8[A\A]A^A_]3ILL8Ht H U3H3H/H/IH5DHHO3IH8踪3L&3 fUHAWAVAUATSPMLEIIIHL}3HLH IIuH13L[3HLHHHEH0H 3L63HLHHH[A\A]A^A_]HK/HC/H;/H3/H+/ff.UHAWAVAUATSH8LLHIIHHsDHHEH8HDHHHHH@H8Hc3HEEL-DMuLI@L8H'3HVDHHHDž(HDž HDžHDžDž0L3HLHsGI7H蘧3LMLEHHHHLLHKLL8Ht H 軨3HG3H5hDHHʦ3H883HDHH;EuLH8[A\A]A^A_]Ԩ3ILL8Ht H 53H3H/H/IH5͒DHH/3IH8蘧3L3 UHAWAVATSH`IH7DHHEؿ§3HþH踧3HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]sH5>H}HIIHW)EHE`3HEHHHML HXHMH53@HUL'H5E5@HH5AHHA H5>HHHHDHHEH0HUHuHuIIOIO0IGIWIG@H}Ht H}藥3H]HEHtHnHv3HߠDHH;EuLH`[A\A^A_]å3IIIIH}Ht H}-3H]HEHtHH 3L&3 fUHHwH+7HH]8UHAWAVAUATSHIIIH,DHHE1LAII_ I+_LAH@苤3HE(<)EHlar inpuHH(He on scaHH Ht operatHHHp: CannoHHHements oHHHGatherElHf@0tHMLaLpHx H+xIIHL9HxLW)EHEHtNLH=a 豣3ILeLeKHEHxHpLp I)M~LL3MLeHU1LۇIH}HLt H}?3I_0I;^0`33HH](<)EH52@UH荤3CUHMLJ`3HH](<)EH51@THA3CTHMLkEt H}茢3HDHH;E* LH[A\A]A^A_]LxILH LHHhHpHxPIw 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H}K3H}H1GDHH;Eu(HĘ[A\A]A^A_]HDžXpuK3K3IHXRH5?H ?HpLX#W)@HDžP09K3HP(<)@LxH detect_HHHo obtainHHHFailed tH@ AHAHpH@LGE1H5/DH>,DLK3J3IHXpH5?H ?HpLX%:W)@HDžP0WJ3HP(-<)@LxH detect_HHHo obtainHHHFailed tH@ AH/AHpH@LeE1H5 /DH\+DL&J3 IAIIAIIAIA8I{I@t HPyI3LeMt6HEL9t!HX@t HxTI3HI9uH}LLe:I3Et H}+I3pt H}I3LXMH`L9HX@t HxH3HI9uI@t HPH3LeMt6HEL9t!HX@t HxH3HI9uH}LLeH3Et H}sH3pt H}aH3LXMt:H`L9t=HX@t Hx6H3HI9uHXL`H3Et$LJH3LIpt H}G3H]HEHt)Ct H{G3HIpt H}G3LC3 fffff.UH]&fDUHAWAVAUATSHHIIHBDHHE1HIIU1H+II]0H] H9t+H9uq1IP1IXLL1IP1IXLLIHMBDHH;EuLHĘ[A\A]A^A_],G3F3IH`H5?H V?HxL`UKH5?HHHU HxHH1LH5u+DH'DLF3 IHt HXF3E1H.IALeMt6HEL9t!HX@t HxE3HI9uH}LLeE3Et H}E3xtH}E3IAL`Mt:HhL9t5HX@t HxvE3HI9uH`Lh[E3Eu LILE3L^A3 f.UHAWAVAUATSPAIIMl$Lz0HEL辏EtHtDLgILR0L蚏HIL;0HH1EtH}H贈Hl]H5?HH蚈H5"?H+H5Q/AH\HA H5,R>H胁HHH.DHHEHHUHuHuIININ0IFIVIF@H]HEHtH#FH+63H1DHH;Eu LHP[A^]|63IH]HEHtHEH53L23 fff.UHAVSHPIH+1DHHE53HþH53HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]eH5>H}HDHZH5l?HH*H5?H|H5,AH}HA H5O>HHHH2,DHHEHsHUHuHuIININ0IFIVIF@H]HEHtHCH33H$/DHH;Eu LHP[A^] 43IH]HEHtHnCHv33L/3 fff.UHAWAVAUATSPII1LrHHtTII1LLIH+VILL}HI}lHDHs%1"H D?L轆 HHHyHHrHH~H)1W( <fo <fff.AAdTTf8f~f8f~dA\ Ad0TTf8f~\f8f~d HHuHu ]1HtVAALf(0 <Tfo1 <f8f~Tf8f~LH9tA. 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HW)EHE-HEHHHML HXHMH5|:HULDH5:H(=H5N;HY>HH HQH5"9Hy?HHH>HHEH)4HUHuHuIIOIO0IGIWIG@H}Ht H}0-H]HEHtHH-Hx>HH;EuLH`[A\A^A_]\-IIIIH}Ht H}-H]HEHtHH-L- ff.UHAWAVATSH`IH>HHEؿr-HþHh-HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH] H5H8H}HDI I HW)EHE-HEHHHML HXHMH5:HULAH5Ͳ:H8:H5^;Hi;HH HQH52 9H<HHH>HHEH8HUHuHuIIOIO0IGIWIG@H}Ht H}@-H]HEHtHH-H>HH;EuLH`[A\A^A_]l-IIIIH}Ht H}-H]HEHtHH-L- ff.UHAWAVATSH`IH>HHEؿ-HþHx-HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH] H5X8H}HAI I HW)EHE -HEHHHML HXHMH5|:HUL>H5ݯ:HH7H5n;Hy8HH HQH5B 9H9HHH>HHEH=HUHuHuIIOIO0IGIWIG@H}Ht H}P-H]HEHtH'H/-H>HH;EuLH`[A\A^A_]|-IIIIH}Ht H}-H]HEHtHH-L- ff.UHAWAVATSH`IH>HHEؿ-HþH-HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH] H5h8H}H >I I HW)EHE0-HEHHHML HXHMH5y:HUL;H5:HX4H5~;H5HH HQH5R9H6HHH>HHEHBHUHuHuIIOIO0IGIWIG@H}Ht H}`-H]HEHtH7H?-H>HH;EuLH`[A\A^A_]-IIIIH}Ht H}-H]HEHtHH-L- ff.UHAWAVATSH`IH>HHEؿ-HþH-HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH] H5x8H}H0;I I HW)EHE@-HEHHHML HXHMH5v:HUL9H5:Hh1H5;H2HH HQH5b9H3HHH>HHEHiGHUHuHuIIOIO0IGIWIG@H}Ht H}p-H]HEHtHGHO-H>HH;EuLH`[A\A^A_]-IIIIH}Ht H}-H]HEHtHH-L- ff.UHAWAVATSH`IH'>HHEؿ-HþH-HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH] H58H}H@8I I HW)EHEP-HEHHHML HXHMH5s:HUL6H5 :Hx.H5;H/HH HQH5r9H0HHH>HHEH9LHUHuHuIIOIO0IGIWIG@H}Ht H}-H]HEHtHWH_-H>HH;EuLH`[A\A^A_]-IIIIH}Ht H}-H]HEHtHH-L- ff.UHAWAVATSH`IH7>HHEؿ-HþH-HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]# H58H}HP5I I HW)EHE`-HEHHHML HXHMH5p:HUL'3H5:H+H5;H,HH HQH58H-HHH>HHEH QHUHuHuIIOIO0IGIWIG@H}Ht H}-H]HEHtHgHo-H>HH;EuLH`[A\A^A_]-IIIIH}Ht H}&-H]HEHtHH-L- ff.UHAWAVATSH`IHG>HHEؿ-HþH-HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]3 H58H}H`2I I HW)EHEp-HEHHHML HXHMH5m:HUL70H5-:H(H5;H)HA H58H*HHH>HHEHUHUHuHuIIOIO0IGIWIG@H}Ht H}-H]HEHtH~H-H>HH;EuLH`[A\A^A_]-IIIIH}Ht H}=-H]HEHtHH-L6- fUHAWAVATSH`IHg>HHEؿ-HþH-HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]S H58H}H/I8 I0 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Hx{-HI9uH`Lh{-EL{-LI\IEt}H}rIIIHt#Ct H{}{-Hu{- I3IH@Ht HHQ{-H`Ht Hh9{-H}Ht H}'{-LAw- ffff.UHAWAVAUATSHHev>HHELL IHHH0W)EHEMeM}M)tWH(LHHH=Lz-ILuLuIHEMH(~LLL{-MLuHU1H^IH}eH<LHILeIH HHHMEMMHHHvHEHuHEHuHt$H$H8HMN{LhL`LxLpM)IMHH}H@HHHXffffff.fJ YHL9uKKTH QMt;Lffff.HTHHTH;TuHDITH QHu1HL9uH@HXH0H8H}H0HHt H}x-HpHt Hxx-H}Ht H}x-H t>HH;EuHH[A\A]A^A_]x-H}[u- HH}Ht H}Nx-Hht- @UHAWAVAUATSH(MMHMHUHH3H#HCLcLkHC H{8H3HLuL}H}LLMMgcHCPHEHC`HCXHCPM)t3MILH=uOLw-HCPN$Lc`HLw-LcXH3HHHUHMMMbH([A\A]A^A_]H}8t- IHEH8Ht H{X.w-HEH8Ht H{@w-L3s- ffffff.UHAWAVAUATSHHUr>HHEL>HFL)IIH)HI9?LWHOL)HI9'M)IM9HHGIKEAHsLMutLffff.H\HHXILH\HHXILH\HHXILH\HHXILHvI9uMt/M|HTI1HHHXI HI9uHGKDHG(M9uKDHG0Hq>HH;EuHĈ[A\A]A^A_]u-u-IHhEH5/:H <:H}LhHPV AH:HuHPL E1H5?Z>HV>LZu- IA(IA}IIPt H`t-LeMt6HEL9t!HX@t Hxt-HI9uH}LLet-Et H}t-Et H}qt-LhMt:HpL9tEHX@t HxFt-HI9uHhLp+t-EtLZt-L8p- Lffffff.UHAVSIs-HHBj>HHH{LjHHP1LHf>HHH[A^]IHs-Lo- UHAWAVAUATSHIIHn>HHE1H8HIMIFI~ H9HHW)EHEH)III9nH s-HEN,LmHHr-LmIFI~ IW)`HDžpI)LHHH9!Lr-H`HHpH5K6HLt-HhIFI~ 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ffffff.UHAWAVAUATSHH%e>HHEL>HFL)IIH)HI9?LWHOL)HI9'M)IM9HHGIKEAHsLMutLffff.H\HHILH\HHILH\HHILH\HHILHvI9uMt/M|HTI1HHHI HI9uHGKDHG(M9uKDHG0Hc>HH;EuHĈ[A\A]A^A_]h-\h-IHhH5 :H 0:H}LhHP&r AHd :HuHPLiE1H5M>H`I>L*h- IA(IA}IIPt H`g-LeMt6HEL9t!HX@t Hxyg-HI9uH}LLe_g-Et H}Pg-Et H}Ag-LhMt:HpL9tEHX@t Hxg-HI9uHhLpf-EtL*g-Lc- Lffffff.UHAVSIf-HH]>HHH{L\HHP1LnHY>HHH[A^]IHmf-Lb- UHAWAVAUATSHIIHa>HHE1H8HWIMIFI~ H9HHW)EHEH)III9nHe-HEN,LmHHe-LmIFI~ IW)`HDžpI)LHHH9!Lse-H`HHpH5>6HLf-HhIFI~ uW)EE(HEHlice scaHEHCannot sHEElarsEHML貵E]H}O)`HDžpW)@HDžPH)taHHHH9-d-ILHL@IDHPIvI^ H)H~LHe-ILHMIMIIT$PIL$hH@HD$HEH$L~I?t Hl%:H :81ACL+IH@LEL`LH8L H@Ht HHc-H`Ht Hhc-H}Ht H}{c-H^>HH;EuLHĸ[A\A]A^A_]c-_c-IH` H5$:H ,:H}L`,W)@HDžP0c-HP( 7)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AH^H:HuH@LE1H5G>HC>Lb-%H}L_-H@>_- H`0_- IA(IA}II@t HPb-LeMt6HEL9t!HX@t Hxa-HI9uH}LLea-Et H}a-Et H}a-L`Mt:HhL9tDHX@t Hxa-HI9uH`Lhma-ELa-LI\IEt}H}rIIIHt#Ct H{a-Ha- I3IH@Ht HH`-H`Ht Hh`-H}Ht H}`-L\- ffff.UHAWAVAUATSHH\>HHELL IHHH0W)EHEMeM}M)tWH(LHHH=L9`-ILuLuIHEMH(~LLLa-MLuHU1HkDIH}OKH<LN,HIL/KIH HHHMEMMHHHvHEHuHEHuHt$H$H8HMNLhL`LxLpM)IMHH}H@HHHXffffff.HHJ HL9uKKTH Mt;Lffff.HTHHTH;TuHDITH Hu1HL9uH@HXH0H8H}H0HHt H}j^-HpHt HxR^-H}Ht H}@^-HY>HH;EuHH[A\A]A^A_]^-H}Z- HH}Ht H}]-HZ- @UHAWAVAUATSH(MMHMHUHH3H3(HCLcLkHC H{8H3HLuL}H}LLMMIHCPHEHC`HCXHCPM)t3MILH=uOLF]-HCPN$Lc`HL5]-LcXH3HHHUHMMMbH([A\A]A^A_]H}Y- IHEH8Ht H{X\-HEH8Ht H{@\-LX- ffffff.UHAWAVAUATSHHW>HHEL>HFL)IIH)HI9?LWHOL)HI9'M)IM9HHGIKEAHsLMutLffff.H\HHILH\HHILH\HHILH\HHILHvI9uMt/M|HTI1HHHI HI9uHGKDHG(M9uKDHG0HV>HH;EuHĈ[A\A]A^A_][-,[-IHhH59H $:H}LhHPd AH49HuHPL9E1H5?>H0<>LZ- IA(IA}IIPt H`nZ-LeMt6HEL9t!HX@t HxIZ-HI9uH}LLe/Z-Et H} Z-Et H}Z-LhMt:HpL9tEHX@t HxY-HI9uHhLpY-EtLY-LU- Lffffff.UHAVSIY-HHO>HHH{LOHHP1L>HK>HHH[A^]IH=Y-LWU- UHAWAVAUATSHIIHT>HHE1H8H'IMIFI~ H9HHW)EHEH)III9nHX-HEN,LmHHX-LmIFI~ IW)`HDžpI)LHHH9!LCX-H`HHpH5~16HLY-HhIFI~ uW)EE(HEHlice scaHEHCannot sHEElarsEHML肨E]H}O)`HDžpW)@HDžPH)taHHHH9-`W-ILHL@IDHPIvI^ H)H~LHX-ILHMIMIIT$PIL$hH@HD$HEH$LNI?t H<:H 81ACL+IH@LEL`LH8L H@Ht HHuV-H`Ht Hh]V-H}Ht H}KV-HQ>HH;EuLHĸ[A\A]A^A_]V-/V-IH`H5l:H :H}L`,W)@HDžP0U-HP(6)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AH.;:HuH@LE1H5x:>H6>LU-%H}R-H@R- H`R- IA(IA}II@t HPT-LeMt6HEL9t!HX@t HxT-HI9uH}LLeT-Et H}T-Et H}T-L`Mt:HhL9tDHX@t HxXT-HI9uH`Lh=T-ELhT-LI\IEt}H}rIIIHt#Ct H{S-HS- I3IH@Ht HHS-H`Ht HhS-H}Ht H}S-LO- ffff.UHAWAVAUATSHHN>HHELL IHIH0W)EHEM.I^L)tWL(IILH=H S-ILeLeKHEHL(~LLH]T-ILeHU1L<7IH} >HvLoIIL>IH HHHMMNHHHvHEHuHEHuHt$H$H8HML0MH@@A$H@HhHXH@HHXH;`u{HDžXHpHxH)HHLHH@HtGHHH}fff.H\HH\H;\uHDHLH@HufIM9DIH}Ht H}KQ-HpHtHx3Q-L0IH}Ht H}Q-HzL>HH;EuLH[A\A]A^A_]YQ-H}M- HH}Ht H}P-HL- DUHAWAVAUATSH(MMHMHUHH3HyHCLcLkHC H{8H3HLuL}H}LLMM;HCPHEHC`HCXHCPM)t3MILH=uOLP-HCPN$Lc`HLP-LcXH3HHHUHMMMbH([A\A]A^A_]H}L- IHEH8Ht H{XO-HEH8Ht H{@O-LK- ffffff.UHAWAVAUATSHHJ>HHEL>HFL)IIH)HI9>LWHOL)HI9&M)IM9HHGIKEAHsLMupLffff.H\HHILHDHHILH\HHILHDHHILHvI9uMt2M|HTI1ff.HHHI HI9uHGKDHG(M9uKDHG0HI>HH;EuHĈ[A\A]A^A_]aN-M-IHhH59H :H}LhHPW AH9HuHPL E1H52>H/>LM- IA(IA}IIPt H`?M-LeMt6HEL9t!HX@t HxM-HI9uH}LLeM-Et H}L-Et H}L-LhMt:HpL9tEHX@t HxL-HI9uHhLpL-EtLL-LH- LUHAVSIxL-HHB>HHH{LBHHP1LH/?>HHH[A^]IHL-L7H- UHAWAVAUATSHIIH_G>HHE1H8HIMIFI~ H9HHW)EHEH)III9nHK-HEN,LmHHxK-LmIFI~ IW)`HDžpI)LHHH9!L#K-H`HHpH5^$6HLL-HhIFI~ uW)EE(HEHlice scaHEHCannot sHEElarsEHMLbE]H}O)`HDžpW)@HDžPH)taHHHH9-@J-ILHL@IDHPIvI^ H)H~LHK-ILHMIMIIT$PIL$hH@HD$HEH$L.I?t H :H 81ACL+IH@LEL`LH8L H@Ht HHUI-H`Ht Hh=I-H}Ht H}+I-HD>HH;EuLHĸ[A\A]A^A_]sI-I-IH`H5L :H :H}L`,W)@HDžP0H-HP(x6)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AH.:HuH@LE1H5X->H)>LsH-%H}D-H@D- H`D- IA(IA}II@t HPG-LeMt6HEL9t!HX@t HxG-HI9uH}LLeG-Et H}rG-Et H}cG-L`Mt:HhL9tDHX@t Hx8G-HI9uH`LhG-ELHG-LI\IEt}H}rIIIHt#Ct H{F-HF- I3IH@Ht HHF-H`Ht HhF-H}Ht H}wF-LB- ffff.UHAWAVAUATSHHA>HHELL IHHH0W)EHEMeM}M)tWH(LHHH=LE-ILuLuIHEMH(~LLLHH;EuHH[A\A]A^A_]8D-H}@- HH}Ht H}C-H?- @UHAWAVAUATSH(MMHMHUHH3HspHCLcLkHC H{8H3HLuL}H}LLMM.HCPHEHC`HCXHCPM)t3MILH=uOLB-HCPN$Lc`HLB-LcXH3HHHUHMMMbH([A\A]A^A_]H}?- IHEH8Ht H{X~B-HEH8Ht H{@iB-L>- ffffff.UHAWAVAUATSHH=>HHEL>HFL)IIH)HI9?LWHOL)HI9'M)IM9HHGIKEAHsLMutLffff.H\HHXILH\HHXILH\HHXILH\HHXILHvI9uMt/M|HTI1HHHXI HI9uHGKDHG(M9uKDHG0H^<>HH;EuHĈ[A\A]A^A_]@A-@-IHhH59H  :H}LhbHPJ AH9HuHPLE1H5%>H!>L@- IA(IA}IIPt H`@-LeMt6HEL9t!HX@t Hx?-HI9uH}LLe?-Et H}?-Et H}?-LhMt:HpL9tEHX@t Hx?-HI9uHhLp{?-EtL?-L;- Lffffff.UHAVSIH?-HH5>HHH{L_5HHP1LH1>HHH[A^]IH>-L;- UHAWAVAUATSHIIH/:>HHE1H8HIMIFI~ H9HHW)EHEH)III9nHY>-HEN,LmHHH>-LmIFI~ IW)`HDžpI)LHHH9!L=-H`HHpH5.6HLa?-HhIFI~ uW)EE(HEHlice scaHEHCannot sHEElarsEHML2E]H}O)`HDžpW)@HDžPH)taHHHH9-=-ILHL@IDHPIvI^ H)H~LHX>-ILHMIMIIT$PIL$hH@HD$HEH$LI?t H9H 81ACLz+IH@LEL`LH8L H@Ht HH%<-H`Ht Hh <-H}Ht H};-Hd7>HH;EuLHĸ[A\A]A^A_]C<-;-IH`H59H :H}L`,eW)@HDžP0;-HP(H6)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AH :HuH@LE1H5( >Hy>LC;-%H}7-H@7- H`7- IA(IA}II@t HP:-LeMt6HEL9t!HX@t Hxk:-HI9uH}LLeQ:-Et H}B:-Et H}3:-L`Mt:HhL9tDHX@t Hx:-HI9uH`Lh9-EL:-LI\IEt}H}rIIIHt#Ct H{9-H9- I3IH@Ht HHq9-H`Ht HhY9-H}Ht H}G9-La5- ffff.UHAWAVAUATSHH4>HHELL IHHH0W)EHEMeM}M)tWH(LHHH=L8-ILuLuIHEMH(~LLL :-MLuHU1HIH}#H<L^HIL#IH HHHMEMMHHHvHEHuHEHuHt$H$H8HMNLhL`LxLpM)IMHH}H@HHHXffffff.J HL9uMKTH Mt=Lffffff.HTHHTH;TuHDITH Hu1HL9uH@HXH0H8H}H0HHt H}6-HpHt Hx6-H}Ht H}6-H)2>HH;EuHH[A\A]A^A_]7-H}{3- HH}Ht H}n6-H2- @UHAWAVAUATSH(MMHMHUHH3HCgHCLcLkHC H{8H3HLuL}H}LLMM!HCPHEHC`HCXHCPM)t3MILH=uOL5-HCPN$Lc`HL5-LcXH3HHHUHMMMүH([A\A]A^A_]H}X2- IHEH8Ht H{XN5-HEH8Ht H{@95-LS1- ffffff.UHAVSI5-HHb+>HHH{L/+HHP1L辟H'>HHH[A^]IH4-L0- UHAWAVAUATSHIIH/>HHE1H8HIMIFI~ H9HHW)EHEH)III9nH)4-HEN,LmHH4-LmIFI~ IW)`HDžpI)LHHH9!L3-H`HHpH5 6HL15-HhIFI~ uW)EE(HEHlice scaHEHCannot sHEElarsEHMLE]H}O)`HDžpW)@HDžPH)taHHHH9-2-ILHL@IDHPIvI^ H)H~LH(4-ILHMIMIIT$PIL$hH@HD$HEH$LuI?t H9H }81ACLJ+IH@LEL`LH8L H@Ht HH1-H`Ht Hh1-H}Ht H}1-H4->HH;EuLHĸ[A\A]A^A_]2-1-IH`hH59H 9H}L`,5W)@HDžP0R1-HP(6)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AH:HuH@LRE1H5>HI>L1-%H}--H@-- H`-- IA(IA}II@t HP`0-LeMt6HEL9t!HX@t Hx;0-HI9uH}LLe!0-Et H}0-Et H}0-L`Mt:HhL9tDHX@t Hx/-HI9uH`Lh/-EL/-LI\IEt}H}rIIIHt#Ct H{m/-He/- I3IH@Ht HHA/-H`Ht Hh)/-H}Ht H}/-L1+- ffff.UHAWAVAUATSHHU*>HHELL IHHH0W)EHEMeM}M)tWH(LHHH=L.-ILuLuIHEMH(~LLL/-MLuHU1HIH}H<LNHILIH HHHMEMMHHHvHEHuHEHuHt$H$H8HMNLhL`LxLpM)IMHH}H@HHHXffffff.HHJ HL9uKKTH Mt;Lffff.HTHHTH;TuHDITH Hu1HL9uH@HXH0H8H}H0HHt H},-HpHt Hx,-H}Ht H},-H'>HH;EuHH[A\A]A^A_],-H}K)- HH}Ht H}>,-HX(- @UHAWAVAUATSH(MMHMHUHH3HaHCLcLkHC H{8H3HLuL}H}LLMMWHCPHEHC`HCXHCPM)t3MILH=uOL+-HCPN$Lc`HL+-LcXH3HHHUHMMMBH([A\A]A^A_]H}((- IHEH8Ht H{X+-HEH8Ht H{@ +-L#'- ffffff.UHAVSI*-HH2!>HHH{L HHP1L莕H_>HHH[A^]IH*-L&- UHAWAVAUATSHIIH%>HHE1H8HwIMIFI~ H9HHW)EHEH)III9nH)-HEN,LmHH)-LmIFI~ IW)`HDžpI)LHHH9!L)-H`HHpH56HL+-HhIFI~ uW)EE(HEHlice scaHEHCannot sHEElarsEHMLyE]H}O)`HDžpW)@HDžPH)taHHHH9-(-ILHL@IDHPIvI^ H)H~LH)-ILHMIMIIT$PIL$hH@HD$HEH$LkI?t H9H Zs81ACL+IH@LEL`LH8L H@Ht HH'-H`Ht Hh'-H}Ht H}'-H#>HH;EuLHĸ[A\A]A^A_]'-'-IH`8H59H k9H}L`,W)@HDžP0"'-HP(6)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AH~ :HuH@L"E1H5 >H>L&-%H}l#-H@^#- H`P#- IA(IA}II@t HP0&-LeMt6HEL9t!HX@t Hx &-HI9uH}LLe%-Et H}%-Et H}%-L`Mt:HhL9tDHX@t Hx%-HI9uH`Lh%-EL%-LI\IEt}H}rIIIHt#Ct H{=%-H5%- I3IH@Ht HH%-H`Ht Hh$-H}Ht H}$-L!- ffff.UHAWAVAUATSHH% >HHELL IHHH0W)EHEMeM}M)tWH(LHHH=LY$-ILuLuIHEMH(~LLL%-MLuHU1HIH}oH<LuHILOIH HHHMEMMHHHvHEHuHEHuHt$H$H8HMNLhL`LxLpM)IMHH}H@HHHXffffff.J HL9uMKTH Mt=Lffffff.HTHHTH;TuHDITH Hu1HL9uH@HXH0H8H}H0HHt H}"-HpHt Hxr"-H}Ht H}`"-H>HH;EuHH[A\A]A^A_]"-H}- HH}Ht H}"-H(- @UHAWAVAUATSH(MMHMHUHH3HDHCLcLkHC H{8H3HLuL}H}LLMM' HCPHEHC`HCXHCPM)t3MILH=uOLf!-HCPN$Lc`HLU!-LcXH3HHHUHMMM H([A\A]A^A_]H}- IHEH8Ht H{X -HEH8Ht H{@ -L- ffffff.UHAVSI -HH>HHH{LHHP1L^H>HHH[A^]IH] -Lw- UHAWAVAUATSHIIH>HHE1H8HGIMIFI~ H9HHW)EHEH)III9nH-HEN,LmHH-LmIFI~ IW)`HDžpI)LHHH9!Lc-H`HHpH55HL -HhIFI~ uW)EE(HEHlice scaHEHCannot sHEElarsEHMLoE]H}O)`HDžpW)@HDžPH)taHHHH9--ILHL@IDHPIvI^ H)H~LH-ILHMIMIIT$PIL$hH@HD$HEH$LnaI?t H\9H *i81ACL+IH@LEL`LH8L H@Ht HH-H`Ht Hh}-H}Ht H}k-H>HH;EuLHĸ[A\A]A^A_]-O-IH`H59H 9H}L`,ծW)@HDžP0-HP(6)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AHN:HuH@LE1H5>H=L-%H}<-H@.- H` - IA(IA}II@t HP-LeMt6HEL9t!HX@t Hx-HI9uH}LLe-Et H}-Et H}-L`Mt:HhL9tDHX@t Hxx-HI9uH`Lh]-EL-LI\IEt}H}rIIIHt#Ct H{ -H- I3IH@Ht HH-H`Ht Hh-H}Ht H}-L- ffff.UHAWAVAUATSHH>HHELL IHHH0W)EHEMeM}M)tWH(LHHH=L)-ILuLuIHEMH(~LLL|-MLuHU1H[ IH}?H<LqHILIH HHHMEMMHHHvHEHuHEHuHt$H$H8HMNLhL`LxLpM)IMHH}H@HHHXffffff.HHJ HL9uKKTH Mt;Lffff.HTHHTH;TuHDITH Hu1HL9uH@HXH0H8H}H0HHt H}Z-HpHt HxB-H}Ht H}0-H>HH;EuHH[A\A]A^A_]x-H}- HH}Ht H}-H- @UHAWAVAUATSH(MMHMHUHH3H>HCLcLkHC H{8H3HLuL}H}LLMMHCPHEHC`HCXHCPM)t3MILH=uOL6-HCPN$Lc`HL%-LcXH3HHHUHMMMJH([A\A]A^A_]H}- IHEH8Ht H{X-HEH8Ht H{@-L- ffffff.UHAVSI-HH >HHH{L HHP1L.H>HHH[A^]IH--LG- UHAWAVAUATSHIIHo>HHE1H8HIMIFI~ H9HHW)EHEH)III9nH-HEN,LmHH-LmIFI~ IW)`HDžpI)LHHH9!L3-H`HHpH5n5HL-HhIFI~ uW)EE(HEHlice scaHEHCannot sHEElarsEHMLreE]H}O)`HDžpW)@HDžPH)taHHHH9-P-ILHL@IDHPIvI^ H)H~LH-ILHMIMIIT$PIL$hH@HD$HEH$L>WI?t H,9H ^81ACL躾+IH@LEL`LH8L H@Ht HHe-H`Ht HhM-H}Ht H};-H>HH;EuLHĸ[A\A]A^A_]--IH`ؼH5\9H /9H}L`,襤W)@HDžP0-HP(6)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AH9HuH@L¦E1H5h=H=L-%H} -H@- H`- IA(IA}II@t HP-LeMt6HEL9t!HX@t Hx-HI9uH}LLe-Et H}-Et H}s-L`Mt:HhL9tDHX@t HxH-HI9uH`Lh--ELX-LI\IEt}H}rIIIHt#Ct H{-H- I3IH@Ht HH-H`Ht Hh-H}Ht H}-L - ffff.UHAWAVAUATSHH >HHELL IHHH0W)EHEMeM}M)tWH(LHHH=L-ILuLuIHEMH(~LLLL-MLuHU1H+ IH}H<LNkHILIH HHHMEMMHHHvHEHuHEHuHt$H$H8HMN{LhL`LxLpM)IMHH}H@HHHXffffff.fJ YHL9uKKTH QMt;Lffff.HTHHTH;TuHDITH QHu1HL9uH@HXH0H8H}H0HHt H}*-HpHt Hx-H}Ht H}-Hi >HH;EuHH[A\A]A^A_]H-H} - HH}Ht H} -H - @UHAWAVAUATSH(MMHMHUHH3H2HCLcLkHC H{8H3HLuL}H}LLMMHCPHEHC`HCXHCPM)t3MILH=uOL -HCPN$Lc`HL -LcXH3HHHUHMMMbH([A\A]A^A_]H} - IHEH8Ht H{X -HEH8Ht H{@y -L- ffffff.UHAWAVAUATSHH>HHEL>HFL)IIH)HI9?LWHOL)HI9'M)IM9HHGIKEAHsLMutLffff.H\HHXILH\HHXILH\HHXILH\HHXILHvI9uMt/M|HTI1HHHXI HI9uHGKDHG(M9uKDHG0Hn>HH;EuHĈ[A\A]A^A_]P - -IHh襴H59H 9H}LhrHP AH9HuHPLE1H5=H=L - IA(IA}IIPt H`. -LeMt6HEL9t!HX@t Hx -HI9uH}LLe -Et H} -Et H} -LhMt:HpL9tEHX@t Hx -HI9uHhLp -EtL -L- Lffffff.UHAVSIX -HH=HHH{Lo HHP1LsH=HHH[A^]IH-L- UHAWAVAUATSHIIH?>HHE1H8HIMIFI~ H9HHW)EHEH)III9nHi-HEN,LmHHX-LmIFI~ IW)`HDžpI)LHHH9!L-H`HHpH5>5HLq -HhIFI~ uW)EE(HEHlice scaHEHCannot sHEElarsEHMLBXE]H}O)`HDžpW)@HDžPH)taHHHH9- -ILHL@IDHPIvI^ H)H~LHh-ILHMIMIIT$PIL$hH@HD$HEH$LJI?t H9H Q81ACL花+IH@LEL`LH8L H@Ht HH5-H`Ht Hh-H}Ht H} -Ht>HH;EuLHĸ[A\A]A^A_]S--IH`訯H5,9H _9H}L`,uW)@HDžP0-HP(X6)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AH9HuH@L蒙E1H58=H=LS-%H}-H@- H`- IA(IA}II@t HP-LeMt6HEL9t!HX@t Hx{-HI9uH}LLea-Et H}R-Et H}C-L`Mt:HhL9tDHX@t Hx-HI9uH`Lh-EL(-LI\IEt}H}rIIIHt#Ct H{-H- I3IH@Ht HH-H`Ht Hhi-H}Ht H}W-Lq, ffff.UHAWAVAUATSHH=HHELL IHIH0W)EHEM.I^L)tWL(IILH=H-ILeLeKHEHL(~LLH-ILeHU1L IH}H(LMIILIH HHHMMNHHHvHEHuHEHuHt$H$H8HnML0MLhL`LxHpI)IMHH}H@HHHXff.A$LHL9u8JtMt,LfH\HH\H;\uHDHtHu1IM9uH@HXL0I1H}IHt H}-HpHt Hx-H}Ht H}-HU=HH;EuLH[A\A]A^A_]4-H}, HH}Ht H}-H, UHAWAVAUATSH(MMHMHUHH3HHCLcLkHC H{8H3HLuL}H}LLMMHCPHEHC`HCXHCPM)t3MILH=uOL,HCPN$Lc`HL,LcXH3HHHUHMMMbH([A\A]A^A_]H}, IHEH8Ht H{X~,HEH8Ht H{@i,L, ffffff.UHAWAVAUATSHH=HHEL>HFL)IIH)HI9>LWHOL)HI9&M)IM9HHGIKEAHsLMupLffff.H\HHILHDHHILH\HHILHDHHILHvI9uMt2M|HTI1ff.HHHI HI9uHGKDHG(M9uKDHG0H_=HH;EuHĈ[A\A]A^A_]A,,IHh薧H59H 9H}LhcHP AH9HuHPLE1H5=H=L, IA(IA}IIPt H`,LeMt6HEL9t!HX@t Hx,HI9uH}LLe,Et H},Et H},LhMt:HpL9tEHX@t Hx,HI9uHhLp|,EtL,L, LUHAVSIX,HH=HHH{Lo HHP1LfH=HHH[A^]IH,L, UHAWAVAUATSHIIH?=HHE1H8HIMIFI~ H9HHW)EHEH)III9nHi,HEN,LmHHX,LmIFI~ IW)`HDžpI)LHHH9!L,H`HHpH5>5HLq,HhIFI~ uW)EE(HEHlice scaHEHCannot sHEElarsEHMLBKE]H}O)`HDžpW)@HDžPH)taHHHH9- ,ILHL@IDHPIvI^ H)H~LHh,ILHMIMIIT$PIL$hH@HD$HEH$L=I?t H9H D81ACL芤+IH@LEL`LH8L H@Ht HH5,H`Ht Hh,H}Ht H} ,Ht=HH;EuLHĸ[A\A]A^A_]S,,IH`訢H5,9H w9H}L`,uW)@HDžP0,HP(X6)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AH9HuH@L蒌E1H58=H=LS,%H},H@, H`, IA(IA}II@t HP,LeMt6HEL9t!HX@t Hx{,HI9uH}LLea,Et H}R,Et H}C,L`Mt:HhL9tDHX@t Hx,HI9uH`Lh,EL(,LI\IEt}H}rIIIHt#Ct H{,H, I3IH@Ht HH,H`Ht Hhi,H}Ht H}W,Lq, ffff.UHAWAVAUATSHH=HHELL IHHH0W)EHEMeM}M)tWH(LHHH=FL,ILuLuIHEMH(~LLL,MLuHU1H IH}HLznIILIH HHHMEMMHHHvHEHuHEHuHt$H$H8HMKM4H@@L,HhH4@HH@HXH@HHXH;`HDžXHpHxH)HHTHRH4H@HtTHHH}fH\HH\H;\u,HDH\H[H4H@Huf.IM9#H0HH}Ht H},HpHtHx,H0HH}Ht H},H=HH;EuHH[A\A]A^A_],H}d, H-HH}Ht H}R,HpHt Hx:,H}Ht H}(,HB, fffff.UHAWAVAUATSH(MMHMHUHH3HcqHCLcLkHC H{8H3HLuL}H}LLMM7HCPHEHC`HCXHCPM)t3MILH=uOLv,HCPN$Lc`HLe,LcXH3HHHUHMMMbH([A\A]A^A_]H}, IHEH8Ht H{X,HEH8Ht H{@,L, ffffff.UHAWAVAUATSHH%=HHEL>HFL)IIH)HI9SLWHOL)HI9;M)IM9+HHGIKEAHsLMLfH\HH[HILH\HH[HILH\HH[HILH\HH[HILHvI9uMt3M|HTI1HHH[HI HI9uHGKDHG(M9uKDHG0H=HH;EuHĈ[A\A]A^A_],H,IHhH59H 9H}Lh΁HP AHP9HuHPLUE1H5=HL=L, IA(IA}IIPt H`,LeMt6HEL9t!HX@t Hxe,HI9uH}LLeK,Et H}<,Et H}-,LhMt:HpL9tEHX@t Hx,HI9uHhLp,EtL,L, Lff.UHAVSI,HH=HHH{L HǃHǃHǃHCxHCpHChHC`HCXHCPH=HHH[A^]IH,L7, UHAWAVAUATSHHIHb=HHE1HH IMID$I|$ H9HHW)EHEH)IIII9]H,HEN,LmHHv,LmID$I|$ HW)`HDžpH)IIM9H),H`N,LpH5d5HH,LhID$I|$ xW)EE(HEHlice scaHEHCannot sHEElarsEHMLf=ERH}DI)`HDžpW)@HDžPH)tYHHL9)K,ILHL@IDHPIt$I\$ H)H~LH,ILHW) )HDž0HDž)HDž)HDžH HLLHX@LHH@HL$H`HL$HMHL$H$H HLLL6I?t%1H9H b68A>L IHHt H,HHt H,HHt H,H Ht H(,t$H@LEL`LHLLbH@Ht HHd,H`Ht HhL,H}Ht H}:,H=HH;EuLHH[A\A]A^A_],,IH`דH5[9H 9H}L`,{W)@HDžP0,HP(6)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AH9HuH@L}E1H5g=H=L,%H} ,H@, H`, IA(IA}II@t HP,LeMt6HEL9t!HX@t Hx,HI9uH}LLe,Et H},Et H}r,L`Mt:HhL9tDHX@t HxG,HI9uH`Lh,,ELW,LIAIEH}IIIHt)Ct H{,H,IkIIHHt H,HHt H,HHt Hn,H Ht H(V,H@Ht HH>,H`Ht Hh&,H}Ht H},L., f.UHAVSI,HHB=HHH{L HǃHǃHǃHCxHCpHChHC`HCXHCPH=HHH[A^]IH],Lw, UHAWAVAUATSHHIH=HHE1HHJIMID$I|$ H9HHW)EHEH)IIII9]H,HEN,LmHH,LmID$I|$ HW)`HDžpH)IIM9Hi,H`N,LpH55HH,LhID$I|$ xW)EE(HEHlice scaHEHCannot sHEElarsEHML5ERH}DI)`HDžpW)@HDžPH)tYHHL9),ILHL@IDHPIt$I\$ H)H~LH,ILHW) )HDž0HDž)HDž)HDžH HLLH8LHH@HL$H`HL$HMHL$H$H HLLL.I?t%1HԤ9H .8A>L_ IHHt H,,HHt H,HHt H,H Ht H(,t$H@LEL`LHLgH@Ht HH,H`Ht Hh,H}Ht H}z,H=HH;EuLHH[A\A]A^A_],^,IH`H59H 9H}L`,sW)@HDžP0,HP(Lj6)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AH]9HuH@LvE1H5=H=L,%H}K,H@=, H`/, IA(IA}II@t HP,LeMt6HEL9t!HX@t Hx,HI9uH}LLe,Et H},Et H},L`Mt:HhL9tDHX@t Hx,HI9uH`Lhl,EL,LIAIEH}IIIHt)Ct H{,H,IkIIHHt H,HHt H,HHt H,H Ht H(,H@Ht HH~,H`Ht Hhf,H}Ht H}T,Ln, f.UHAVSI8,HH=HHH{LO HǃHǃHǃHCxHCpHChHC`HCXHCPH=HHH[A^]IH,L, UHAWAVAUATSHHIH=HHE1HH{IMID$I|$ H9HHW)EHEH)IIII9]H,HEN,LmHH,LmID$I|$ HW)`HDžpH)IIM9H,H`N,LpH55HH,LhID$I|$ xW)EE(HEHlice scaHEHCannot sHEElarsEHML-ERH}DI)`HDžpW)@HDžPH)tYHHL9),ILHL@IDHPIt$I\$ H)H~LH,ILHW) )HDž0HDž)HDž)HDžH HLLH0LHH@HL$H`HL$HMHL$H$H HLLL('I?t%1H9H &8A>L蟆 IHHt Hl,HHt HT,HHt H<,H Ht H($,t$H@LEL`LHLmH@Ht HH,H`Ht Hh,H}Ht H},H#=HH;EuLHH[A\A]A^A_],,IH`WH5ۛ9H ߮9H}L`,$lW)@HDžP0A,HP(6)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AH9HuH@LAnE1H5=H8=L,%H},H@}, H`o, IA(IA}II@t HPO,LeMt6HEL9t!HX@t Hx*,HI9uH}LLe,Et H},Et H},L`Mt:HhL9tDHX@t Hx,HI9uH`Lh,EL,LIAIEH}IIIHt)Ct H{P,HH,IkIIHHt H,HHt H,HHt H,H Ht H(,H@Ht HH,H`Ht Hh,H}Ht H},L, f.UHAVSIx,HH=HHH{L HǃHǃHǃHCxHCpHChHC`HCXHCPH'=HHH[A^]IH,L, UHAWAVAUATSHHIH"=HHE1HHsIMID$I|$ H9HHW)EHEH)IIII9]HG,HEN,LmHH6,LmID$I|$ HW)`HDžpH)IIM9H,H`N,LpH5$5HHW,LhID$I|$ xW)EE(HEHlice scaHEHCannot sHEElarsEHML&&ERH}DI)`HDžpW)@HDžPH)tYHHL9) ,ILHL@IDHPIt$I\$ H)H~LHQ,ILHW) )HDž0HDž)HDž)HDžH HLLH)LHH@HL$H`HL$HMHL$H$H HLLLhI?t%1HT9H "8A>L~ IHHt H,HHt H,HHt H|,H Ht H(d,t$H@LEL`LHLrH@Ht HH$,H`Ht Hh ,H}Ht H},Hc=HH;EuLHH[A\A]A^A_]B,,IH`|H59H 9H}L`,ddW)@HDžP0,HP(Gy6)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AHݷ9HuH@LfE1H5'=Hx=LB,%H},H@, H`, IA(IA}II@t HP,LeMt6HEL9t!HX@t Hxj,HI9uH}LLeP,Et H}A,Et H}2,L`Mt:HhL9tDHX@t Hx,HI9uH`Lh,EL,LIAIEH}IIIHt)Ct H{,H,IkIIHHt H^,HHt HF,HHt H.,H Ht H(,H@Ht HH,H`Ht Hh,H}Ht H},L, f.UHAVSI,HH=HHH{L HǃHǃHǃHCxHCpHChHC`HCXHCPH=HHH[A^]IH,L7, UHAWAVAUATSHHIHb=HHE1HH lIMID$I|$ H9HHW)EHEH)IIII9]H,HEN,LmHHv,LmID$I|$ HW)`HDžpH)IIM9H),H`N,LpH5d5HH,LhID$I|$ xW)EE(HEHlice scaHEHCannot sHEElarsEHMLfERH}DI)`HDžpW)@HDžPH)tYHHL9)K,ILHL@IDHPIt$I\$ H)H~LH,ILHW) )HDž0HDž)HDž)HDžH HLLHX!LHH@HL$H`HL$HMHL$H$H HLLLI?t%1H9H b8A>Lw IHHt H,HHt H,HHt H,H Ht H(,t$H@LEL`LHLwH@Ht HHd,H`Ht HhL,H}Ht H}:,H=HH;EuLHH[A\A]A^A_],,IH`tH5[9H 9H}L`,\W)@HDžP0,HP(q6)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AH9HuH@L^E1H5g=H=L,%H} ,H@, H`, IA(IA}II@t HP,LeMt6HEL9t!HX@t Hx,HI9uH}LLe,Et H},Et H}r,L`Mt:HhL9tDHX@t HxG,HI9uH`Lh,,ELW,LIAIEH}IIIHt)Ct H{,H,IkIIHHt H,HHt H,HHt Hn,H Ht H(V,H@Ht HH>,H`Ht Hh&,H}Ht H},L., f.UHAVSI,HHB=HHH{L HǃHǃHǃHCxHCpHChHC`HCXHCPHw=HHH[A^]IH],Lw, UHAWAVAUATSHHIH=HHE1HHJdIMID$I|$ H9HHW)EHEH)IIII9]H,HEN,LmHH,LmID$I|$ HW)`HDžpH)IIM9Hi,H`N,LpH55HH,LhID$I|$ xW)EE(HEHlice scaHEHCannot sHEElarsEHMLERH}DI)`HDžpW)@HDžPH)tYHHL9),ILHL@IDHPIt$I\$ H)H~LH,ILHW) )HDž0HDž)HDž)HDžH HLLHLHH@HL$H`HL$HMHL$H$H HLLLI?t%1Hԅ9H 8A>L_o IHHt H,,HHt H,HHt H,H Ht H(,t$H@LEL`LHL\}H@Ht HH,H`Ht Hh,H}Ht H}z,H=HH;EuLHH[A\A]A^A_],^,IH`mH59H 9H}L`,TW)@HDžP0,HP(i6)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AH]9HuH@LWE1H5=H=L,%H}K,H@=, H`/, IA(IA}II@t HP,LeMt6HEL9t!HX@t Hx,HI9uH}LLe,Et H},Et H},L`Mt:HhL9tDHX@t Hx,HI9uH`Lhl,EL,LIAIEH}IIIHt)Ct H{,H,IkIIHHt H,HHt H,HHt H,H Ht H(,H@Ht HH~,H`Ht Hhf,H}Ht H}T,Ln, f.UHAVSI8,HH=HHH{LO HǃHǃHǃHCxHCpHChHC`HCXHCPH=HHH[A^]IH蝿,L跻, UHAWAVAUATSHHIH=HHE1HH\IMID$I|$ H9HHW)EHEH)IIII9]H,HEN,LmHH,LmID$I|$ HW)`HDžpH)IIM9H詾,H`N,LpH55HH,LhID$I|$ xW)EE(HEHlice scaHEHCannot sHEElarsEHMLERH}DI)`HDžpW)@HDžPH)tYHHL9)˽,ILHL@IDHPIt$I\$ H)H~LH,ILHW) )HDž0HDž)HDž)HDžH HLLHLHH@HL$H`HL$HMHL$H$H HLLL(I?t%1H~9H 8A>Lg IHHt Hl,HHt HT,HHt H<,H Ht H($,t$H@LEL`LHL̂H@Ht HH,H`Ht Hh̻,H}Ht H}躻,H#=HH;EuLHH[A\A]A^A_],螻,IH`WeH5|9H O9H}L`,$MW)@HDžP0A,HP(b6)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AH9HuH@LAOE1H5=H8=L,%H}苷,H@}, H`o, IA(IA}II@t HPO,LeMt6HEL9t!HX@t Hx*,HI9uH}LLe,Et H},Et H},L`Mt:HhL9tDHX@t Hxǹ,HI9uH`Lh謹,EL׹,LIAIEH}IIIHt)Ct H{P,HH,IkIIHHt H,HHt H,HHt H,H Ht H(ָ,H@Ht HH辸,H`Ht Hh覸,H}Ht H}蔸,L讴, f.UHAVSIx,HH®=HHH{L菮 HǃHǃHǃHCxHCpHChHC`HCXHCPH=HHH[A^]IHݷ,L, UHAWAVAUATSHHIH"=HHE1HHTIMID$I|$ H9HHW)EHEH)IIII9]HG,HEN,LmHH6,LmID$I|$ HW)`HDžpH)IIM9H,H`N,LpH5$5HHW,LhID$I|$ xW)EE(HEHlice scaHEHCannot sHEElarsEHML&ERH}DI)`HDžpW)@HDžPH)tYHHL9) ,ILHL@IDHPIt$I\$ H)H~LHQ,ILHW) )HDž0HDž)HDž)HDžH HLLH LHH@HL$H`HL$HMHL$H$H HLLLhI?t%1HTv9H "8A>L_ IHHt H謴,HHt H蔴,HHt H|,H Ht H(d,t$H@LEL`LHLLX IHHt H,HHt HԬ,HHt H輬,H Ht H(褬,t$H@LEL`LHL謇H@Ht HHd,H`Ht HhL,H}Ht H}:,H=HH;EuLHH[A\A]A^A_]肬,,IH`UH5[m9H 9H}L`,=W)@HDžP0,HP(R6)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AH9HuH@L?E1H5g=H=L肫,%H} ,H@, H`, IA(IA}II@t HPϪ,LeMt6HEL9t!HX@t Hx說,HI9uH}LLe萪,Et H}聪,Et H}r,L`Mt:HhL9tDHX@t HxG,HI9uH`Lh,,ELW,LIAIEH}IIIHt)Ct H{Щ,Hȩ,IkIIHHt H螩,HHt H膩,HHt Hn,H Ht H(V,H@Ht HH>,H`Ht Hh&,H}Ht H},L., f.UHAVSI,HHB=HHH{L HǃHǃHǃHCxHCpHChHC`HCXHCPH'=HHH[A^]IH],Lw, UHAWAVAUATSHHIH=HHE1HHJEIMID$I|$ H9HHW)EHEH)IIII9]Hǧ,HEN,LmHH趧,LmID$I|$ HW)`HDžpH)IIM9Hi,H`N,LpH55HHר,LhID$I|$ xW)EE(HEHlice scaHEHCannot sHEElarsEHMLERH}DI)`HDžpW)@HDžPH)tYHHL9)苦,ILHL@IDHPIt$I\$ H)H~LHѧ,ILHW) )HDž0HDž)HDž)HDžH HLLHLHH@HL$H`HL$HMHL$H$H HLLLI?t%1Hf9H 7A>L_P IHHt H,,HHt H,HHt H,H Ht H(,t$H@LEL`LHLH@Ht HH褤,H`Ht Hh茤,H}Ht H}z,H=HH;EuLHH[A\A]A^A_]¤,^,IH`NH5e9H |9H}L`,5W)@HDžP0,HP(J6)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AH]9HuH@L8E1H5=H=L£,%H}K,H@=, H`/, IA(IA}II@t HP,LeMt6HEL9t!HX@t Hx,HI9uH}LLeТ,Et H},Et H}貢,L`Mt:HhL9tDHX@t Hx臢,HI9uH`Lhl,EL藢,LIAIEH}IIIHt)Ct H{,H,IkIIHHt Hޡ,HHt Hơ,HHt H计,H Ht H(薡,H@Ht HH~,H`Ht Hhf,H}Ht H}T,Ln, f.UHAVSI8,HH=HHH{LO HǃHǃHǃHCxHCpHChHC`HCXHCPH'=HHH[A^]IH蝠,L跜, UHAWAVAUATSHHIH=HHE1HH=IMID$I|$ H9HHW)EHEH)IIII9]H,HEN,LmHH,LmID$I|$ HW)`HDžpH)IIM9H詟,H`N,LpH5x5HH,LhID$I|$ xW)EE(HEHlice scaHEHCannot sHEElarsEHMLERH}DI)`HDžpW)@HDžPH)tYHHL9)˞,ILHL@IDHPIt$I\$ H)H~LH,ILHW) )HDž0HDž)HDž)HDžH HLLHLHH@HL$H`HL$HMHL$H$H HLLL(I?t%1H_9H 7A>LH IHHt Hl,HHt HT,HHt H<,H Ht H($,t$H@LEL`LHL茌H@Ht HH,H`Ht Hh̜,H}Ht H}躜,H#=HH;EuLHH[A\A]A^A_],螜,IH`WFH5]9H u9H}L`,$.W)@HDžP0A,HP(C6)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AH9HuH@LA0E1H5=H8}=L,%H}苘,H@}, H`o, IA(IA}II@t HPO,LeMt6HEL9t!HX@t Hx*,HI9uH}LLe,Et H},Et H},L`Mt:HhL9tDHX@t Hxǚ,HI9uH`Lh謚,ELך,LIAIEH}IIIHt)Ct H{P,HH,IkIIHHt H,HHt H,HHt H,H Ht H(֙,H@Ht HH辙,H`Ht Hh覙,H}Ht H}蔙,L讕, f.UHAVSIx,HH=HHH{L菏 HǃHǃHǃHCxHCpHChHC`HCXHCPH=HHH[A^]IHݘ,L, UHAWAVAUATSHHIH"=HHE1HH5IMID$I|$ H9HHW)EHEH)IIII9]HG,HEN,LmHH6,LmID$I|$ HW)`HDžpH)IIM9H,H`N,LpH5$q5HHW,LhID$I|$ xW)EE(HEHlice scaHEHCannot sHEElarsEHML&ERH}DI)`HDžpW)@HDžPH)tYHHL9) ,ILHL@IDHPIt$I\$ H)H~LHQ,ILHW) )HDž0HDž)HDž)HDžH HLLHLHH@HL$H`HL$HMHL$H$H HLLLhI?t%1HTW9H "7A>L@ IHHt H謕,HHt H蔕,HHt H|,H Ht H(d,t$H@LEL`LHLH@Ht HH$,H`Ht Hh ,H}Ht H},Hc=HH;EuLHH[A\A]A^A_]B,ޔ,IH`>H5V9H n9H}L`,d&W)@HDžP0联,HP(G;6)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AHy9HuH@L(E1H5'y=Hxu=LB,%H}ː,H@轐, H`诐, IA(IA}II@t HP菓,LeMt6HEL9t!HX@t Hxj,HI9uH}LLeP,Et H}A,Et H}2,L`Mt:HhL9tDHX@t Hx,HI9uH`Lh,EL,LIAIEH}IIIHt)Ct H{萒,H舒,IkIIHHt H^,HHt HF,HHt H.,H Ht H(,H@Ht HH,H`Ht Hh,H}Ht H}ԑ,L, f.UHAVSI踑,HH=HHH{Lχ HǃHǃHǃHCxHCpHChHC`HCXHCPH=HHH[A^]IH,L7, UHAWAVAUATSHHIHb=HHE1HH .IMID$I|$ H9HHW)EHEH)IIII9]H臐,HEN,LmHHv,LmID$I|$ HW)`HDžpH)IIM9H),H`N,LpH5di5HH藑,LhID$I|$ xW)EE(HEHlice scaHEHCannot sHEElarsEHMLfERH}DI)`HDžpW)@HDžPH)tYHHL9)K,ILHL@IDHPIt$I\$ H)H~LH葐,ILHW) )HDž0HDž)HDž)HDžH HLLHXLHH@HL$H`HL$HMHL$H$H HLLLI?t%1HO9H b7A>L9 IHHt H,HHt Hԍ,HHt H輍,H Ht H(褍,t$H@LEL`LHL,H`Ht Hh&,H}Ht H},L., f.UHAVSI,HHB=HHH{L HǃHǃHǃHCxHCpHChHC`HCXHCPHG|=HHH[A^]IH],Lw, UHAVSIH,HH=HHH{L_ HǃHǃHǃHCxHCpHChHC`HCXHCPH{=HHH[A^]IH譈,LDŽ, UHAVSI蘈,HH~=HHH{L~ HǃHǃHǃHCxHCpHChHC`HCXHCPH{=HHH[A^]IH,L, UHAVSI,HH2~=HHH{L} HǃHǃHǃHCxHCpHChHC`HCXHCPHz=HHH[A^]IHM,Lg, UHAVSI8,HH}=HHH{LO} HǃHǃHǃHCxHCpHChHC`HCXHCPHGy=HHH[A^]IH蝆,L跂, UHAVSI舆,HH|=HHH{L| HǃHǃHǃHCxHCpHChHC`HCXHCPHy=HHH[A^]IH,L, UHAVSI؅,HH"|=HHH{L{ HǃHǃHǃHCxHCpHChHC`HCXHCPH7x=HHH[A^]IH=,LW, UHAVSI(,HHr{=HHH{L?{ HǃHǃHǃHCxHCpHChHC`HCXHCPHw=HHH[A^]IH荄,L觀, UHAVSIx,HHz=HHH{Lz HǃHǃHǃHCxHCpHChHC`HCXHCPHv=HHH[A^]IH݃,L, UHAVSIȃ,HHz=HHH{Ly HǃHǃHǃHCxHCpHChHC`HCXHCPHu=HHH[A^]IH-,LG, UHAVSI,HHby=HHH{L/y HǃHǃHǃHCxHCpHChHC`HCXHCPHu=HHH[A^]IH},L~, UHAVSIh,HHx=HHH{Lx HǃHǃHǃHCxHCpHChHC`HCXHCPHt=HHH[A^]IH́,L}, UHAVSI踁,HHx=HHH{Lw HǃHǃHǃHCxHCpHChHC`HCXHCPHs=HHH[A^]IH,L7}, UHAWAVSPHIHuHCHt HT~,HM~L9vLHH[A^A_]Ir IGL1IFHxH*HHH H*XA^F f:  ^*5(\H,HH1H,.HC„t Hr"HHȁAHH},H9HBL97H[A^A_]fUHAWAVSPIIMtJLH=J<,I?IHt,MwMFDIs-1HusI?IHt,IGHLH)1I7HI7HDI7HDI7HDHH9uHtHIHHHuMWMIMJMtM9r1LIIM!IJ<DII:HHGMtL9r1IHL9t @L!L9tIH<tWHHHt*HWHfff.HHH;Qu HHHuHI IH H HIHH8jLII[H[A^A_]ÿ;~,HH587H/{,Hjx=HHH5|d=Hb=HG~,IH~,Ly, UHAVSHPIH+y=HHE},HþH},HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH] H57H}HD H5W9H H5t:H HAH5֗7H- HHHLt=HHEHHUHuHuIININ0IFIVIF@H]HEHtH͋H{,H>w=HH;Eu LHP[A^]&|,IH]HEHtH舋H{,Lw, fDUHAVSHPIHv=HHEf{,HþH\{,HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]ǟ H5<7H}H H5MU9H H5r:H HH HQH57H HHHq=HHEHHUHuHuIININ0IFIVIF@H]HEHtHvH~y,Ht=HH;Eu LHP[A^]y,IH]HEHtH1H9y,LSu, ffffff.UHAVSHPIH{t=HHEy,HþHx,HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]g H5~7H}H H5R9H% H5Kp:HV HA H5&7H} HHHo=HHEHHUHuHuIININ0IFIVIF@H]HEHtHH%w,Hr=HH;Eu LHP[A^]vw,IH]HEHtH؆Hv,Lr, fDUHAWAVAUATSH(IHHHr=HHE1LHH3HKHC H)H hHQHLhPHHHIHHHAHHIHHL!LyW)EHE v,LLIMIHEHP HUL8HHHHHHHHHUHU1L4Z HH}Ht H}u,H裘HHH{PLLHHHLLHHHHLPLLXH`HhHpHxHHHEfHnfsE(f5E(k5EHHH{PLHHHLHHHLH H(L0H8H@HHHEHHHHHH o=H H;Mt5EH 8HH$Ho=HH;EuHH([A\A]A^A_]|t,t,IHHH5'O9H O9H}LH)H} AHO9HuHL%E1H5X=HU=Ls,s,IHHNH5N9H N9H}LH.H_} AHuO9HuHLE1H5HX=HT=Lcs,~s,IHHH5$N9H }N9H}LH/H| AHO9HuHL"E1H5W=HT=Lr, IAgIAIfIAIAPIHIAIAI*It Hr,LeMt6HEL9t!HX@t Hxq,HI9uH}LLeq,Et H}q,Et H}q,LHMHPL9HX@t Hxq,HI9u]It Hcq,LeMt6HEL9t!HX@t Hx>q,HI9uH}LLe$q,Et H}q,Et H}q,LHMHPL9HX@t Hxp,HI9uIt Hp,LeMt6HEL9t!HX@t Hxp,HI9uH}LLeop,Et H}`p,Et H}Qp,LHMt:HPL9t=HX@t Hx&p,HI9uHHLP p,Et$L:p,LIH}Ht H}o,Lk, UHAWAVAUATSHXHHHk=HHE1HH HHHKHC H)H ULaHLpPLHLHHHHHLiLyW)EHE (o,HLLILLIMLHIHILHMHQ HUL1HAHyHqHUHU1H5S HH}Ht H}n,LA}X,H蒑HIuPMLHHHHHHLPHXH`HhHHpLLxHHHEfHnfsE(5E(5EH蛿HIuPLHHHHHLH H(H0L8H@H'H Ҁ8HHjHh=HH;EHH.HfHIuPMLHHHHHHLPHXH`HhHHpLLxHHHEfHnfsE(z5E(5EHoHIuPLHHHHHLHHHLHHHHEHHH HHH g=H H;MuHX[A\A]A^A_]l,'l,IHHH56G9H {H9H}LHFHu AHG9HuHL4E1H5P=H+M=Lk,~k,IHH`H5F9H G9H}LHL-Hqu AHPH9HuHLE1H5ZP=HL=Luk, IAIIAIIAIA2IuIt Hj,LeMt6HEL9t!HX@t Hxj,HI9uH}LLej,Et H}zj,Et H}kj,LHMHPL9HX@t Hx8j,HI9uIt Hj,LeMt6HEL9t!HX@t Hxi,HI9uH}LLei,Et H}i,Et H}i,LHMt:HPL9t=HX@t Hxi,HI9uHHLPpi,Et$Li,LIH}Ht H}Gi,Lae, ffff.UHAVSIX(i,HHL.HT=HHH[A^]IHh,Le, UHAWAVAUATSHIHH/d=HHEH_=HHH{^ HT=HHW)EHEEHblocksizHEfEeHKPHhHUL%s HhHDžhHtCt H{/h,H'h,Et H}h,Hu'H|c=HH;EuHĈ[A\A]A^A_]^h,g,IHhH5 E9H dE9H}LhW)PHDž` g,H`({5)PHnot set.HHHize is nHHHe blocksHHHAttributH@AH%E9HuHPLE1H5JL=HH=Leg, IA(IA}IIPt H`f,LeMt6HEL9t!HX@t Hxf,HI9uH}LLef,Et H}f,Et H}|f,LhMt:HpL9t=HX@t HxQf,HI9uHhLp6f,Et!Lef,LIEt H}f,L*b, fDUHAVSI`e,HHLH[A^]IHe,La, UHAWAVAUATSHHIHa=HHEH,Q=HIAFXW)EHE)`HDžpƅ`DžamodeƅeHHH`HMHq HHHDžHHtCt H{e,Hd,`t Hpd,HuE$HMHHDHu3H B9H}1HAa,tpUHM$҄HEHufH B9H}1HARa,uCEt H}Wd,H_=HH;EuHĨ[A\A]A^A_]AFXEud,1d,IHH H5CA9H B9H`LH*W)0HDž@@c,H@(/5)0HsupporteHH0Hdes are HH(H'CRD' moHH HCR' and HHH only 'DHHHpace op:HHHDepthToSHf@8dH`H01LH5ZH=HD=Luc, I0t H@c,E1H'IALeMt6HEL9t!HX@t Hxb,HI9uH}LLeb,Et H}b,`tHpb,IALHMt:HPL9t5HX@t HxXb,HI9uHHLP=b,Eu @LILbb,Eu07H'H'I`t Hpa,Et H}a,L^, ffff.UHAVSI`a,HHLH[A^]IHa,L], UHAWAVAUATSHIH\=HHEIHHPHH0HIHH(HxHH HpHHHhHHH@H`HXLHIvLOHLHLHHH@=LILLH[=L(IM9}LL W)HDžHHXLHI9t?AMiL)IIIEBAH u AMIA(M)¹LAf.xAtDDDHpH9HLދxAtDDDL(L9|IHH LI9uMt$HID I9LLADA< DHHHF(HXHF HPHFHHHFH@HHNH8H0HHNHHHFHHFHHF HHF(HHH0L HHHHHH HDHHHHHHD>HHIHHLJD(HIILLKDHIIHLJD6HIHLHID?HHHLIIIHHHHDžHDž@HXHL8HhHLHHHH0LHxHL(IH(HHL0L`MLLpMLHHH LPHLML8MLHLLIH@HILL^,HMtHHE1HLLHL LL(L0HH8H@LHLHHHHHH HHHpLLH(HHHhHH`LH0HxHHXHHPLH8HHHLH@HLHIHHI)L9LNLHLL)IHHHHHH)H9HNHI)HHK&HHpHLHI)M9MNHI)HHK&HHhHH`HHI)L9LNHI)HxHK&HHXHHPHHI)L9LNHI)HHMHLM)L9LNLLIHIIHLILHHHLLLLH H(H0H8L@HDžHHHHA(HPHC(HA HC HAHCHAHCHAHCHHHHHPHL0HKH8HHN(HKHN HKHNHKHNHKHNHKHHK(HNXHK HNPHKHNHHKHN@HKHN8H HN0HK(HHK HHKHNxJHKHNpH HSHVhHN`HEHH0}fff.HH]LPfHnfsf0(xN5)@( 5)PHLHH0H/(IL;H/Y,HHt HW,HTS=HH;EH[A\A]A^A_]H=6=HHW,HHHfR=H@H@HHH0HHcHHOHR=HH Hc0HHOHJHcH HOHJH=@6=QW,HH-]W,V,HHV,H5w==H;=HW, HH=5=V,HHHt HV,HR, fUHAWAVAUATSHHQ=HHEHHHHA0H HHQ(HHQ HHQHHQHIHHH(H0HwHH?H8H0H@HHHPHXII?MILMIM9}%Hf.HLEIM9|M9<`LHHH(HHHH 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LHHmHEH0L H]H LHHmLHqHHL0HHHHt H H H5HHj H0 HGHH;EuLHh[A\A]A^A_]& HZHlHHHL0HHHHt Ha H H5HH H0 HL  UHAWAVAUATSHXLLHIIHHSHHEH0L%I\$@HLHIJHH@H0HfHnfL* HEEID$HHIhL0HHL L5ILHDž HDžHDžHDžDž(HL HLH(kLAIwIEwIEWHjHHR HHHjH0H HH HHHjLHnHHL0HHLt H H H5HHs H0 HPHH;EuLHX[A\A]A^A_]/ ISIeIHHL0HHLt Hq H H5HH H0 L\  UHAWAVAUATSHhLLHIIHxHcHHEH0L5I^@HLHկHH@H0HfHnfL; HEEIFHHIhL0HHL H HHHHDž HDžHDžHDžDž(LL LLH3hLxA$It$IEt$IET$LhHH] LHHgH0L HH/ LHHgHEH0L H]H LHHgLHskHHL0HHHHt H H H5HHZ H0z H7HH;EuLHh[A\A]A^A_] HZHlHHHL0HHHHt HQ H H5HH H0 H<  UHAWAVAUATSHXLLHIIHHCHHEH0L%I\$@HLHHH@H0HfHnfL HEEID$HHIhL0HHL L5ILHDž HDžHDžHDžDž(HL HLHeLAIwIEwIEWHdHHB HHHdH0H HH HHHdLHuhHHL0HHLt H H H5HHc H0 H@HH;EuLHX[A\A]A^A_] ISIeIHHL0HHLt Ha H H5HH H0 LL  UHAWAVAUATSHXIIHHHaHHEH0L-I]@HLHөHH@H0HfHnfL9 LHEEIEHHIhL0HHL L%ILHDž HDžHDžHDžDž(LHH LHH*bAIvIEvIEVLbHH_ LHHaHHeHHL0HHLt HY H H5HH H0 HHH;EuHHX[A\A]A^A_]n HSHeHHHL0HHLt H H< H5UHH H0- H蛾  UHAWAVAUATSHXLHIIHHHHEH0L-I]@HLHHH@H0HfHnfL HEEIEHHIhL0HHL8 L=gILHDž HDžHDžHDžDž(HL HLH_LAIvIEvIEVHT_HH HHH3_HHwHEwHEWH_LHbHHL0HHLt Hy H H5HH־ H0 HHH;EuLHX[A\A]A^A_] HSHeHHHL0HHLt HԿ H` H5yHH1 H0Q H迻  ff.UHAWAVAUATSHXIIHHHѺHHEH0L-$I]@HLHCHH@H0HfHnfL詾 LHEEIEHHIhL0HHLX L%ILHDž HDžHDžHDžDž(LHH LHH\AIvIEvIEVLx\HH LHH[\HH8`HHL0HHLt Hɽ HU H5nHH& H0F HHH;EuHHX[A\A]A^A_]޽ HSHeHHHL0HHLt H H謻 H5ŧHH} H0蝼 H  UHAWAVAUATSHXIIHHH!HHEH0L-tI]@HLHHH@H0HfHnfL LHEEIEHHIhL0HHL診 L%׵ILHDž HDžHDžHDžDž(LHH^ LHHYAIvIEvIEVLYHH LHHYHH]HHL0HHLt H H襹 H5HHv H0薺 HSHH;EuHHX[A\A]A^A_]. HSHeHHHL0HHLt Hp H H5HH͸ H0 H[  UHAWAVAUATSHXIIHHHqHHEH0L-ijI]@HLHHH@H0HfHnfLI LHEEIEHHIhL0HHL L%'ILHDž HDžHDžHDžDž(LHH讻 LHH:WAIvIEvIEVLWHHo LHHVHHZHHL0HHLt Hi H H5HHƶ H0 HHH;EuHHX[A\A]A^A_]~ HSHeHHHL0HHLt H HL H5eHH H0= H諳  UHAWAVAUATSHXIIHHHHHEH0L-I]@HLH3HH@H0HfHnfL虶 LHEEIEHHIhL0HHLH L%wILHDž HDžHDžHDžDž(LHH LHHTAIvIEvIEVLhTHH迸 LHHKTHH(XHHL0HHLt H蹵 HE H5^HH H06 HHH;EuHHX[A\A]A^A_]ε HSHeHHHL0HHLt H H蜳 H5HHm H0荴 H  UHAWAVAUATSHXIIHHHHHEH0L-dI]@HLHHH@H0HfHnfL LHEEIEHHIhL0HHL蘲 L%ǭILHDž HDžHDžHDžDž(LHHN LHHQAIvIEvIEVLQHH LHHQHHxUHHL0HHLt H H蕱 H5HHf H0膲 HCHH;EuHHX[A\A]A^A_] HSHeHHHL0HHLt H` H H5HH轰 H0ݱ HK  UHAWAVAUATSHXIIHHHaHHEH0L-I]@HLHӖHH@H0HfHnfL9 LHEEIEHHIhL0HHL L%ILHDž HDžHDžHDžDž(LHH螳 LHH*OAIvIEvIEVLOHH_ LHHNHHRHHL0HHLt HY H H5HH趮 H0֯ HHH;EuHHX[A\A]A^A_]n HSHeHHHL0HHLt H谯 H< H5UHH H0- H蛫  UHAWAVAUATSHXIIHHHHHEH0L-I]@HLH#HH@H0HfHnfL艮 LHEEIEHHIhL0HHL8 L%gILHDž HDžHDžHDžDž(LHH LHHzLAIvIEvIEVLXLHH诰 LHH;LHHPHHL0HHLt H詭 H5 H5NHH H0& HHH;EuHHX[A\A]A^A_]辭 HSHeHHHL0HHLt H H茫 H5HH] H0} H  UH剷H]ÐUHƇ H]UHAWAVATSHpHHHEIHHpHxL~LxHNHMHt!IGHNHHFHFHM Hp1HH]L}LeHMHtMgHpH@HLeLHE H #HHXIHHLxHUHPHtIOLeID$I$HHHEH]H$H}H9t HtHP(HP HuH}[HxHpHHHH;EuLHp[A\A^A_]蕫 IH}LHxHpL  UHAWAVATSHpHJHHEIHHpHxL~LxHNHMHt!IGHNHHFHFHM Hp1HH]L}LeHMHtMgHpH@HLeLHE < H a"HHXI@HHLxHUHPHtIOLeID$I$HHHEH]Ht~H}H9t HtHP(HP HuH}諺HxHp蘺HHH;EuLHp[A\A^A_] IH}LdHxHpQLk  UHAWAVSH8HHHEIMpH~ HtH9tHPHEHEHuHuHPH]HLZH}H9t HtHP(HP H'HH;EuLH8[A^A_] ff.UH@H]UHAWAVSPHIM~0AF0ufAIF@IF81L HCIGHHKIOIHCHCHLH[A^A_]HUHAVSIHufHCHC1H舥 IFHCIINHKHIFIFIH[A^]HqUHAWAVAUATSH(IIHHHEW)EHEL IIIsDELeMu(4LHHH' ILeHH]LmLLL膨 C,AufAIGIG1L舤 HEIGHEHMIOIH HH;EuLH([A\A]A^A_] H}聣 H_ffffff.UHAWAVATSAHIMgAGu fA$IG(IG 1L HCID$HHKIL$I$HCHCHEL[A\A^A_]H@UHAWAVAUATSH8AIIH/HHEW)EHELG HHHsELmHu0EHuHUH}MHEt H}e Et H}V HHH;EuHHh[A\A]A^A_]衝 H} H}  HEu!HEt H} Et H} H  DUHAWAVAUATSHMIHIH)HHEЃHMRHhH;=xLt HU HhHvI$HEED#HCHEHCHELkHHCHCAIGHEIGHEMwIIGIGHQHH蒉DHEHMHHLHEHEHMHHHELHEHEƅLLPHLHHHL,Zt H t H HYHYHCHH;EuHH[A\A]A^A_]" (辚 IW)HDž0萚 H()Hismatch.HHHtype misHHHication HHHe specifHHHAttributH@&HLi HnHIIF IFIF1H5~HO|L1 IIt H赙 t|L rILXt H臙 t Hr HvX#H< H4 H, H$ IHEXLO  ff.UHAWAVAUATSHhLEAHpIHHdHHEW)EHEL| IIAHxIsDEL}Mu0HuHUHxDLEHEt H}辗 Et H}诗 HHH;EuHHh[A\A]A^A_] H}y H}p  HEu!HEt H}W Et H}H Hb  fffff.UHAWAVAUATSHMIHIHyHHEЃHLHhH;=zLt H襔 HhH{A$EED#HCHEHCHELkHHCHCAIGHEIGHEMwIIGIGHKHHDHEHMHHLHEHEHMHHHELHEHEƅLLJKHLKHHHeL}Tt HX t HC HGTH;THHH;EuHH[A\A]A^A_]s ( IW)HDž0 H()Hismatch.HHHtype misHHHication HHHe specifHHHAttributH@&HL躑 HHIIF IFIF1H5yHvL肔 IIt H t|L6 rILRt Hؓ t HÓ HR#HHH}HuIHRL蠏  fff.UHAWAVAUATSHhLEAHpIHHHHEW)EHEL̕ IIAHxIsDEL}Mu0HuHUHxDLEHEt H} Et H} HhHH;EuHHh[A\A]A^A_]J H}Ɏ H}  HEu!HEt H}觑 Et H}蘑 H貍  fffff.UHAWAVAUATSHMIHIHɌHHEЃHFHhH;=ut H HhHvHpH;=tt L̎ HpLuLED#HCHEHCHELkHHCHCAIGHEIGHEMwIIGIGHFHH ~DHEHMHHLHEHEHMHHHELHEHEƅLLtEHLu}HHHLNt H肏 t Hm HqNHeNHHH;EuHH[A\A]A^A_]蝏 (9 IW)HDž0 H(1 )Hismatch.HHHtype misHHHication HHHe specifHHHAttributH@&HL HHIIF IFIF1H5sHpL謎 IIt H0 t|L` rIL'Mt H t H HL#HHHHIHLLʉ  fDUHAWAVAUATSHhLEAHpIHHHHEW)EHEL IIAHxIsDEL}Mu0HuHUHxDLEHEt H}> Et H}/ HHH;EuHHh[A\A]A^A_]z H} H}  HEu!HEt H}׋ Et H}ȋ H  fffff.UHAWAVAUATSHMIHIHHHEЃ HAHhH;=Hot H, HhHMpH}Hu1HH}L:LED+HCHEHCHELcHHCHCE7IGHEIGHEI_IIGIGHHCFDHEHMHHLHEDHEHMHHHEHHEHEƅLL?HLwHHHLHt Hlj t H貉 HHHHHHH;EuHH[A\A]A^A_] (~ IW)HDž0P H(v)Hismatch.HHHtype misHHHication HHHe specifHHHAttributH@&HL) H.HIIF IFIF1H5>nHkL I It Hu L衈 ILeGt H@ t H+ H/G4HHIAtH At L IHFL  UHAWAVAUATSHMIHIHHHEЃ H==HhH;=hkt HL HhHml H}Hu1HH}LLED+HCHEHCHELcHHCHCE7IGHEIGHEI_IIGIGHHcBDHEHMHHLHEDHEHMHHHEHHEHEƅLL;HLsHHHL Et H t H҅ HDHDH#HH;EuHH[A\A]A^A_] (螅 IW)HDž0p H()Hismatch.HHHtype misHHHication HHHe specifHHHAttributH@&HLI HN|HIIF IFIF1H5^jH/gL I It H蕄 L ILCt H` t HK HOC4HH IAtH At L IH CL  UHAWAVAUATSHMIIHH9HHEЃ\H]9HhH;=gHLt L^ HhLhEI$Mt$L9tQLfDL;;uLk(HHc΍VL|HI9uEeIEHEIEHEMuIEIEIEHHAHEHAHELiHHAHAHB8HH?pDHEHMHHLHEHEHMHHHELHEHEƅLL7HLoHHHL@t H贁 t H蟁 H@H@H|HH;EuHH[A\A]A^A_]ρ (k IW)HDž0= H(c)Hismatch.HHHtype misHHHication HHHe specifHHHAttributH@&HL~ HxHIIF IFIF1H5+fHbLހ IIt Hb t~L蒀 tILY?t H4 t H H#?%HHHHIH>L{  fDUHAWAVAUATSHMIIHH{HHEЃ\H=5HhH;=hcHLt L>} HhL_dEI$Mt$L9tQLfDD;;uL;#HHc΍VD|HI9uEeIEHEIEHEMuIEIEIEHHAHEHAHELiHHAHAH"4HHlDHEHMHHLHEHEHMHHHELHEHEƅLL3HLkHHHL<t H} t H} H<Hw<HxHH;EuHH[A\A]A^A_]} (K} IW)HDž0} H(C)Hismatch.HHHtype misHHHication HHHe specifHHHAttributH@&HLy HsHIIF IFIF1H5 bH^L| IIt HB| t~Lr| tIL9;t H| t H{ H;%HHHHIH:Lw  fDUHAWAVAUATSHMIIHHvHHEЃ$H1HhH;=H_Ht L%y HhLF`EI$Mt$L9t&LLX-HHx HI9uEuIEHEIEHEMeIEIEIEAIGHEIGHEMoIIGIGH:0HH7hDHEHMHHLHEHEHMHHHELHEHEƅLL/HLgHHHL8t Hy t Hy H8H8HtHH;EuHH[A\A]A^A_]y (cy IW)HDž05y H([)Hismatch.HHHtype misHHHication HHHe specifHHHAttributH@&HLv HpHIIF IFIF1H5#^HZLx IIt HZx t~Lx tILQ7t H,x t Hx H7%HHHHIH6Ls  fffff.UHAWAVAUATSHMIIHH sHHEЃ H--HhH;=X[Ht L5u HhLV\E I]MuL9L H0Ht$Hc(09}'Q(HD^,;,uL~qH00Ɖ0H uH0Hc(r(HDHH.&HI9^E,$ID$HEID$HEMt$I$ID$ID$AIGHEIGHEMgIIGIGH+HHcDHEHMHHLHEHEHMHHHELHEHEƅLL#+HL$cHHH>LV4t H1u t Hu H 4H4HmpHH;EuHH[A\A]A^A_]Lu (t IW)HDž0t H()Hismatch.HHHtype misHHHication HHHe specifHHHAttributH@&HLq HkHIIF IFIF1H5YHyVL[t IIt Hs t~Lt tIL2t Hs t Hs H2%HfH^HVHNIHm2Lwo  UHAWAVAUATSHMIIHHnHHEЃ H(HhH;=VHt Lp HhLWE I]MuL9L8HHHt$Hc@09}'Q@HD^D;DuLmHH0Ɖ0H8HHHc@r@HDHH~HI9^E,$ID$HEID$HEMt$I$ID$ID$AIGHEIGHEMgIIGIGHO'HHL_DHEHMHHLHEHEHMHHHELHEHEƅLL&HL^HHHL/t Hp t Hp H/H/HkHH;EuHH[A\A]A^A_]p (xp IW)HDž0Jp H(p)Hismatch.HHHtype misHHHication HHHe specifHHHAttributH@&HL#m H(gHIIF IFIF1H58UH RLo IIt Hoo t~Lo tILf.t HAo t H,o H0.%HHHHIH-Lk  UHGxH]UHAWAVAUATSHLII׉D]HjHHEDeIHHH+IHi9*BHcLD$I)D]LDeD$AIGHEIGHEIIGIIGEIFHEIFHEMVIIFIFHsHuHsHuHsHCHHC(HMH]H0H)H8HEHEHDžXHDžPHDžHHDž@Dž`?hHEHMHiHpHxHEHEDEHEHMHMHELUHEHEDMDeD]HcHkxI}H(Et H}l ht Hxl HPHtHl HHuH@HDž@Httl (t H8_l HgHH;EuHH[A\A]A^A_]l UHAWAVAUATSPIM>MnLL)HHHH9sH)LH[A\A]A^A_]NHkxIM9trAEt I}k AEt I}k I}HtHk HHuMeI}IEHtvk A$t I}fk MM9uM~H[A\A]A^A_]ff.UHAWAVSPIHufHCHC1Hh IFHCIINHKHIFIFIIvH{>L{@C@ufAHCPHCH1L`h IF@HHIOHHPIWIH@H@HL{XCXufAHChHC`1Lh IFXHHIOHHPIWIH@H@HIFpHCpHH[A^A_]HHHff.UHAWAVAUATSHEMHPIAH`HeHHEW)EHEL3l HHLXHDlsELeHu.:EIIILPi ILeILmEH]LLHj AW)EHELPLk 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teH@&HPH8H H8H5HLH6GH WHpHpHHHH5YHPH5vHHH5 HLH5HH9t]HtHP( t H0y L8MtQH@L9t6HX@t HxM HI9uH8HP uLL@ Pt H` H{BHdHH;EuLH[A^A_]G IkI uS]IPIIHH9tHtHP( uHP t H0e L8MtGH@L9tfffff.HX@t Hx- HI9uH8L@ Pt H` HqAL  LfUHAWAVSHIH6HHEHH0H5;HAHD$$HH Lw1AH{,D$$HH LC1E1H1HW)PHDž`fDžPTƅRH8H^)W) HDž00ە H0(a) Hnsor typHH Ho any teHHH types tHHHn outputHHHConstraiHf@(e.@*HPH8H H4H HpHpHHHH5JHH5H HfHX@t Hx} HI9uH8HP uLL@L Pt H`7 H>HHH;EuLH[A^A_]w I uXbI]IPIIHH9tHtHP( uHP t H0蕓 L8MtGH@L9tfffff.HX@t Hx] HI9uH8L@B Pt H`- H=L;  LfUHAWAVSHIHfHHEHH,D$$HЙH Lș1E1H(D$$H%H L1AHK.HW)PHDž`fDžPTƅRH8H%W) HDž0@, H0() Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HPH8H HY0HDžH56HLHfHX@t Hxm HI9uH8HP uLL@< Pt H`' H:HHH;EuLH[A^A_]g IkI uS]IPIIHH9tHtHP( uHP t H0胏 L8MtEH@L9tdff.HX@t HxM HI9uH8L@2 Pt H` H9L+  LfUHAWAVSHIHVHHEHH(D$$H,H ,L1E1H$D$$HRH ,L$E1H$D$$H,H ^LE1HW$D$$H,H /LAH $D$$HCH BLAH#D$$HÔH ,L1E1Hh)HW)PHDž`fDžPTƅR+ݮH8H W) HDž0@I H0() Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HPH8H Hv+H5HH L+H:H EHpHpHHHH5SMHH5H HrH5H>LH裥HH9tkHtHP( t H0 L8Mt_H@L9tDffffff.HX@t Hx譋 HI9uH8HP uLL@| Pt H`g H5HĆHH;EuLH[A^A_]觋 IcI uKUIPIHH9tHtHP( uHP t H0͊ L8Mt?H@L9tJDHX@t Hx蝊 HI9uH8L@肊 PtH`m LIH4Lq  ffff.UHAWAVSHIHHHEHH#H5HE1H2D$$H(H Y<Lڐ1E1HD$$HװH ۰L1E1H`%HW)PHDž`fDžPTƅR#ٮH8HW) HDž0@A H0() Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HPH8H Hn'H HpHpHHH7H5UnHH5H)HH5+HXLH轡HH9teHtHP( t H0 L8MtYH@L9t>fHX@t Hx͇ HI9uH8HP uLL@蜇 Pt H`臇 H1HHH;EuLH[A^A_]LJ I uXbI]IPIIHH9tHtHP( uHP t H0 L8MtGH@L9tfffff.HX@t Hx譆 HI9uH8L@蒆 Pt H`} H0L苂  LfUHAWAVSHIHHHEHH HxHDžhH5"+HYLhHD$$H $H ;L1E1HD$$H;H {LOE1HD$$HH L~E1HD$$H\H LJ1E1H!HW)PHDž`fDžPTƅRԮH8HeW) HDž0@ H0(() Hsor typeHH(H any tenHH Han be ofHHH types cHHHd outputHHHInput anHf@0.HPH8H H#H5H=Lc#HI2H HpHpHHHH5@HcH5|H HH5KHLHHHH9t`HtHP( t H0范 L8MtTH@L9t9@HX@t Hx] HI9uH8HP uLL@, Pt H` H-Ht~HH;EuLH[A^A_]W IsII uS]IPIIHH9tHtHP( uHP t H0m L8Mt?H@L9t^DHX@t Hx= HI9uH8L@" Pt H` H,L~  LfUHAWAVSHIHF}HHEHHD$$HH ~7L1E1HD$$Hk7H l7LZE1H{D$$HH کLCE1HGD$$H!H 6L1E1HHW)PHDž`fDžPTƅRЮH8H*W) HDž0@觀 H0(]) Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HPH8H HH LHpHpHHHH5@HEH5kxH HH5HLH*HH9tbHtHP( t H0n L8MtVH@L9t;fDHX@t Hx= HI9uH8HP uLL@  Pt H`~ Hk)HTzHH;EuLH[A^A_]7 I uXbI]IPIIHH9tHtHP( uHP t H0U~ L8MtGH@L9tfffff.HX@t Hx~ HI9uH8L@~ Pt H`} Ha(Ly  LfUHAWAVSHIH&yHHEHHpHDžhH5"HѤLhHSD$$HH 13L[1E1HbD$$H3H L E1H.D$$HMH NLE1HD$$HԃH PLƒ1E1HyHW)PHDž`fDžPTƅR<̮H8HW) HDž0@Z| H0() Hsor typeHH(H any tenHH Han be ofHHH types cHHHd outputHHHInput anHf@0.HPH8H HH5e HLH)H bHpHpHHH:H5S8HH5tH, HH5.H[LHHH9thHtHP( t H0{ L8Mt\H@L9tAfff.HX@t Hxz HI9uH8HP uLL@z Pt H`z H$HuHH;EuLH[A^A_]z IkI uS]IPIIHH9tHtHP( uHP t H0y L8MtGH@L9tfffff.HX@t Hxy HI9uH8L@y Pt H`}y H#Lu  LfUHAWAVSHIHtHHEHHHDžhH5*HLhHD$$HH .L1E1HD$$H.H LWE1HD$$HH ;L1E1H=HW)PHDž`fDžPTƅRȮH8H W) HDž0@x H0(Ԝ) Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HPH8H HRH5$HLH%H HpHpHHHH5HH5oH HNH5tHLHHH9tgHtHP( t H0v L8Mt[H@L9t@ff.HX@t Hxv HI9uH8HP uLL@\v Pt H`Gv H HqHH;EuLH[A^A_]v IkI uS]IPIIHH9tHtHP( uHP t H0u L8MtGH@L9tfffff.HX@t Hxmu HI9uH8L@Ru Pt H`=u HLKq  LfUHAWAVSHIHvpHHEHHHDžhH5HLhHD$$HH *L{1E1H D$$Hn*H LE1H~ D$$HX{H LF{1E1HHW)PHDž`fDžPTƅRîH8HaW) HDž0@s H0() Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HPH8H HH5HdLZH@!H HpHpHHHH5HZH5kH HH5HLH?HH9tgHtHP( t H0r L8Mt[H@L9t@ff.HX@t HxMr HI9uH8HP uLL@r Pt H`r H{HdmHH;EuLH[A^A_]Gr IkI uS]IPIIHH9tHtHP( uHP t H0eq L8MtGH@L9tfffff.HX@t Hx-q HI9uH8L@q Pt H`p HqL m  LfUHAWAVSHIH6lHHEHH H5HE1HD$$HH Lzw1E1HD$$HH LIw1E1H HW)PHDž`fDžPTƅRÿH8HdW) HDž0@o H0(W) Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HPH8H HH HpHpHHHH5PHxH5gH H,H5˒HLH]HH9teHtHP( t H0n L8MtYH@L9t>fHX@t Hxmn HI9uH8HP uLL@H荻H5[H޾ HAH5H LHr|HH9tjHtHP(t Hb LMt^H L9tCfffff.HX@t Hx}b HI9uHHP uLL Lb 0t H@7b Pt H`"b H H]HH;EuLH[A^A_]bb IumwIrII0IPIHH9tHtHP(uHP t Hka LMt=H L9tqHX@t Hx=a HI9uHL "a 0t H@ a Pt H`` Hl L]  Lffff.UHAWAVSHIH&\HHEHHpD$$HgH nLg1E1HD$$HKH LLE1H[D$$H5gH L#g1E1HHW)PHDž`fDžPTƅR蝯H8H>W) HDž0@_ H0(1) Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HPH8H HH5}HJLH H 7HpHpHHH菵H5xAH0H5VWH聺HH5[H谸LHxHH9t]HtHP( t H0Y^ L8MtQH@L9t6HX@t Hx-^ HI9uH8HP uLL@] Pt H`] H[HDYHH;EuLH[A^A_]'^ IkI uS]IPIIHH9tHtHP( uHP t H0E] L8MtGH@L9tfffff.HX@t Hx ] HI9uH8L@\ Pt H`\ HQLX  LfUHAWAVSHIHXHHEHH`HدW)PƅPHDž`HnearestHQH5HLPHD$$H'cH HL!c1E1H(D$$H@H LcE1HD$$HbH Lb1E1HsHW)0HDž@fDž0Tƅ26HHW)HDž@T[ H(j)Hr types.HH/Hl tensorHH(HY' to alHH Houtput 'HHH'X' and HHHn input HHHConstraiH@7H0HHHH rHpHpHHHJH5EHH5SH< H蟭H5>~|HkLHsHH9thHtHP(t HZ LMt\H L9tAfff.HX@t HxY HI9uHHP uLL Y 0t H@Y Pt H`Y HHTHH;EuLH[A^A_]Y IumwIrII0IPIHH9tHtHP(uHP t HX LMt=H L9tqHX@t HxX HI9uHL X 0t H@mX Pt H`XX HLfT  Lffff.UHAWAVSHHIHSHHEHHW)PƅPHDž`HnearestHQH58HυLPHDžL@H5 HLLHHDž@H5c HL@H8W) ƅ HDž0Hhalf_pixH!fDž)elƅ+H5 HL HYW)ƅ$HDžHefer_floH Hround_prHfDžorƅH5y H҇LHDžH5HΈLHD$$HK]H lL1E1HLD$$H4H 1LE1HD$$H0H L]E1HD$$HCH BL]AHD$$H\H L1E1H,HW)HDžƅfDžT1ƅ褮HHW)HDž@U H()Hr types.HH/Hl tensorHH(HY' to alHH Houtput 'HHH'X' and HHHn input HHHConstraiH@7HHHH3H5cHLHaH HpHpHHHڪH5H{H5LH̯ H/H5wHLH`mHH9thHtHP(t HS LMt\HL9tAfff.HX@t HxmS HI9uHHP uLLHW)PHDž`fDžPTƅRH8HW) HDž0@P H0() Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HPH8H HLH GHHpH 2HxHpHHHH5#H褧H5GHHXH5rH$LHhHH9taHtHP( t H0N L8MtUH@L9t:DHX@t HxN HI9uH8HP uLL@lN Pt H`WN HHIHH;EuLH[A^A_]N I uXbI]IPIIHH9tHtHP( uHP t H0M L8MtGH@L9tfffff.HX@t Hx}M HI9uH8L@bM Pt H`MM HL[I  LfUHAWAVSHIHHHHEHHH5HpE1H"D$$HSH LS1E1HD$$H,H LE1HD$$HwSH LeS1E1HHW)PHDž`fDžPTƅRߛH8H߲W) HDž0@K H0(s) Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HPH8H H*H5HLIHXH HpHpHHHѡH5HrH5CHæ H&H5n/HLHWdHH9t_HtHP( t H0J L8MtSH@L9t8HX@t HxmJ HI9uH8HP uLL@H LQE1HZD$$HH VLOE1H&D$$HOH CLO1E1HHW)@HDžPƅ@fDžAT1ƅC衳H(H۲LhW)HDž 0xG H (n)Ic types.L`I numericLhI to onlyLpIns inputLxHConstraiH@'H@H(HHHW)HDžƅfDžT2ƅ轲HHڲW)HDž0F H()L`LhLpLxIConstraiL0@'HHHH HW)HDžƅfDžT3ƅȕLhHHbٲW)pHDž E H(z)pHor type.HHH tensor HHHn to anyHHL0@HHHpH@H H`H`HHH H5H誝H5=H H^H5hH*LH^HH9tgHtHP(pt HD LMt[HL9t@ff.HX@t HxD HI9uHHP puLLlD t HWD t HBD LMtIHL9t.f.HX@t Hx D HI9uHLLC t HC t H C L(MtJH0L9t/ff.HX@t HxC HI9uH(LL0mC @t HPXC HH>HH;EuLHĈ[A\A]A^A_]C IpII!IIryIqII+2I@IIHH9tHtHP(puHP pt HDB LMt?HL9t$HX@t HxB HI9uHLLA t HA t HA LMt?HL9t$HX@t HxA HI9uHLLA t HoA t H ZA L(Mt;1E1HED$$HJAH }sL;E1HD$$H:H sL:1E1HHW)@HDžPfDž@TƅBSH(HƲW)HDž @q3 H ()Hsor typeHH(H any tenHH Han be ofHHH types cHHHd outputHHHInput anHf@0.H@H(HHH HpHpHHHsH5HH5:+He HȅH5gVH蔌LHKHH9taHtHP(t H =2 L(MtUH0L9t:DHX@t Hx 2 HI9uH(HP uLL01 @t HP1 H;ܮH$-HH;EuLH[A^A_]2 Iu`jIeII@IIHH9tHtHP(uHP t H 1 L(Mt?H0L9t^DHX@t Hx0 HI9uH(L00 @t HP0 H1ۮL,  LfUHAWAVSHIH+HHEHH@ʮHDžhH5HApLhH#H5HHpE1HeD$$H7H RqL 71E1HD$$H6H MqL61E1HD$$HH qL7AH\D$$HrH rLW7AH%D$$HfH usL 7AHHW)PHDž`fDžPTƅR~H8HR²W) HDž00. 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H1H9NHD]DUHH]UH]f.UH]fDUHH H]fDUHHH]UH]f.UH]ƋfDUHAWAVSH(HHHHE11HHHPHH1HPHH1HPH >fff.HȋP(uH@ @H@Hu܃urH@ @thHHPHvYHHPHtFHHPH ƅHȋP(uH@ @tH@Huu H@ @uHHH;EH([A^A_]H1HPx(uH@ HH@HuHXx ~fHHPx(uH@ HaH@HuH!x HHH;Eu11HH([A^A_]n( HH5lHH}ȴAHuHH*HHHC HCHCE1H59oH lHr(薉HH5"lHiH}XAHuH詆HHHHC HCHCE1H5nHkHp IIIEt H}EtH%L ffffff.UHH H1H9NHD]DUHH]UH]f.UH]醈fDUH~H H]fDUHHH]UH]f.UH]6fDUHAWAVAUATSH8IHHHE11LRI1LPII1LP(HEILP IIZILP(HþL|r{(uLs HC(1:ILs ANI^Hu1HI^HC(HtK 09}K IsJs$;s$uH{HC(0Ɖ0H{HK(HcS rs HDIILP(HþLq{(uLs H9C(1kILs ANI^Hu1HI^HC(HtK 09}K IsJs$;s$uH{HC(0Ɖ0H{HK(HcS rs HDIILP(HþLp{(uLs HjC(1ILs ANI^Hu1HI^HC(HtK 09} K ;s$;s$uH{KHC(0Ɖ0H{HHK(HcS rs HDIHW)EHEEEaxisEHuLHEt H}6HtA}(uIM H]{(5Ls IH LyMH]uL=~AW !9{AE1{(u Ls !@HC(1JILs ANI^Hu1HI^HC(HtHcK 09}$QS HDMs$ff.;s$uH{~HC(0Ɖ0H{HK(HcS rs HDM9H]tIO(JtH`NIIcG I9 HCC(1uILs ANI^Hu1HI^HC(HtK 09} K ;s$;s$uH{$~HC(0Ɖ0H{!HK(HcS rs HDH~HH;EuH8[A\A]A^A_]蜃(8IH5eHH}V@AHuLKHXzHIIF IFIFE1H5ghH8eL HHEt H}蟂EtL΂HEt H}~H~ @UHH eH1H9NHD]DUHH]UH]f.UH]&fDUHH H]fDUHHH]UH]f.UH]ցfDUHAWAVAUATSHHHH%}HHE11HHHPHH1HPHlH1HPH J| HȋP(uH@ @7H@Hu܃#H@ @HHPHHHPHHHPH {HȋP(uH@ @H@Hu܃H@ @H1HPx(uH@ HzH@HuHkzHELch HHPx(uH@ HrzHxHuH=2zEMW BHcHG(H4ȃ~( Hv HuL9H}HɉHMAH@(IJHEH1HP(IA(uMg L(AG(1YIMg AL$M|$Mu1IM|$IG(HtIcO 09}QAW H|AAw$A;w$uIyIG(0Ɖ0IHIG(IcO QAW H|HuIL9uHE HEA9A)HL4HEH@(J0HEH1HP(IA(uMg L-AG(1^IMg AL$M|$Mu1IM|$IG(HtIcO 09}QAW H|AAw$A;w$uIyIG(0Ɖ0IHIG(IcO QAW H|HuHIA HyyHH;EuHH[A\A]A^A_]^~(}HH5`HH}8AHuH {HuHHHC HCHCE1H5)cH_H} (}HH5`HH}4AHuHzHtHHHC HCHCE1H5bH_H`}IIIEt H}|EtH}Lx ffffff.UHH H1H9NHD]DUHH ]UHAWAVAUATSHHHIHHwHHEH0L%+vM|$@LLHIaHH@H0HfHnfL{HEEID$HHIhL0LLLdzL5uILHDž HDžHDžHDžDž(LL!~LLHLH~HHHLHmHHL0HHLt HzHyH5eHH[yH0{zH8vHH;EuLHH[A\A]A^A_]{HSHeHHHL0HHLt HYzHxH5dHHxH0yHDv UHAWAVAUATSHHHIHHhuHHEH0L%sM|$@LLH^HH@H0HfHnfL?yHEEID$HHIhL0LLLwL5#sILHDž HDžHDžHDžDž(LL{LLH=LH{HHHLHHHL0HHLt HxHwH53cHHvH0 xHsHH;EuLHH[A\A]A^A_]xHSHeHHHL0HHLt HwHuvH5bHHFvH0fwHs UH]f.UH]wfDUHwH H]fDUHHH]UH]f.UH]FwfDUHAWAVAUATSHXHHrHHE11Hb٭HHPHtSH1HPHtCH1HPH qHȋP(uH@ @tH@Huu H@ @u#HrHH;EHX[A\A]A^A_]H1HPx(uH@ HpLxMuL=PpMco HHPHt^xREH}HHEH+EHKL-H9H1HP(HÃ{(Ls 1HHE$IIfffff.HC(HtK 09}K Ius$;s$uL;pHC(0Ɖ0H{8HK(HcS rs HDIuHÎC(1ILs ANMfMu1YIMfL}EID$HEN4E1Lmffff.HEH@(NlID$(Ht>IcL$ 09}:QAT$ H\A}(tpHEJ J 0HL ?}At$$A;t$$uH}2oID$(0Ɖ0HEH8+HID$(IcL$ QAT$ H\A}(uHELN,Li N,0{(tH7C(Lk IIL;}H}HOH}sA_t(sIH5VHH}iAHuLqHkHIIF IFIFE1H5*YHULsr(sIH5VHH}BAHuLpHjHIIF IFIFE1H5XHULas HHEt H}rEt5Ls+HHEt H}rEt!LrHH}Ht H}rHn UHH UH1H9NHD]DUHH ]UHAWAVAUATSHHHIHHmHHEH0L%kM|$@LLHWHH@H0HfHnfLqHEEID$HHIhL0LLL4pL5ckILHDž HDžHDžHDžDž(LLsLLH}LHsHHH\LH=HHL0HHLt HpHZoH5s[HH+oH0KpHlHH;EuLHH[A\A]A^A_]pHSHeHHHL0HHLt H)pHnH5ZHHnH0oHl UHAVSH`IHHkHHEHH H5 HAHD$$HvH \L1E1HD$$HjvH DLS1E1H H5HLo H=H5H/L HH5H\H5gHHH5@HLHAH%HjHH;EuLH`[A^]nHHHj UHAVSHIHiHHEHHH5zHvAHQD$$HuH ̿LY1E1HD$$HtH L1E1H H5H!L߿ HH5H_L HH HpHpHHHH5:HH5eHHYH5~H%LH芆HH9t HtHP(HP HOH8hHH;EuLHĐ[A^]mI"IHH9uHP HtHP(HLh fffff.UHAVSH`IHgHHEHHH57HQE1HTD$$HYH YLr1AHD$$HNH ULr1E1HH5rHL@HH5HH5dH?H袾H55HnLHӄHHfHH;EuLH`[A^]kHHHg f.UHAWAVSHIHFfHHEHHH5HAHD$$HH Lq1AHD$$HٽH LSq1E1H HW)PHDž`fDžPTƅR͹H8HnW) HDž00iH0(q) Hnsor typHH Ho any teHHH types tHHHn outputHHHConstraiHf@(e.@*HPH8H H.H 7HpHpHHHH5ZHH5aHHLH5߻HLH}HH9teHtHP( t H0hL8MtYH@L9t>fHX@t HxhHI9uH8HP uLL@\hPt H`GhHHcHH;EuLH[A^A_]hI uXbI]IPIIHH9tHtHP( uHP t H0gL8MtGH@L9tfffff.HX@t HxmgHI9uH8L@RgPt H`=gHLKc LfUHAVSH`IHxbHHEHHD$$HmH Lm1E1HD$$H&GH LmAHD$$HH Lrm1AH&H5[mHLHTH59 HǺE1HVH5FHE1H8H5sDHYH5^HH H5HٿLH>H"H aHH;EuLH`[A^]eHHHa DUHAVSHIH`HHEHHH5HAHQW)ƅHDžHconstantHƅH5BH0LHDž|H5H2L|HD$$HH Lwk1E1H~D$$HXkH +!LFk1E1HH52kHLH+H5HlH5\H轿H H52HLHQ}t HcH H _HH;EuLHĐ[A^]cHHt H]cH Hk_ UHAVSH`IH^HHEHHH53H͹E1H4H5׷HE1HD$$HH =Li1E1HD$$HbH dLi1E1HDH5yiHLHrH5yH賺H5ZHHgH5OH3LH{H| He]HH;EuLH`[A^]JbHHI H] ffffff.UHAVSHIH]HHEHHRD$$HrhH PLjh1E1HqD$$HAH @L9hE1H=D$$HH CLhE1H D$$HgH +Lg1E1HH5gHJLHH5H@LHH HpHpHHH H5BH许H5XHHbH5H.LHyHH9t HtHP(HP HX HA[HH;EuLHĐ[A^]&`IHH9uHP Ht HP(IH L[ UHAVSHIHZHHEHHH襶H5HAHTH5%H+AH3W)ƅHDžHnearestHH5+H*LHD$$HeH -Le1E1HD$$HkeH L[e1E1HH5GeHLH@ H58IH聶H5VHҹH5H5ȰHLHfwt H]H5HYHH;EuLHĀ[A^]^HHt Ht]HHY fffff.UHAWAVSHIHXHHEHHH5gHAAH?W)PƅPHDž`HnearestHQH57HLPHD$$HcH Lc1E1HD$$HwcH Lgc1E1HHW)0HDž@fDž0Tƅ2᫭HHW)HDž@[H(u)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5H0HHH,H =HpHpHHHH5MFH薳H5SHHJH5ݭHLH{tHH9tcHtHP(t HZLMtWH L9t<HX@t HxZHI9uHHP uLL \Z0t H@GZPt H`2ZHHUHH;EuLH[A^A_]rZIumwIrII0IPIHH9tHtHP(uHP t H{YLMt=H L9tqHX@t HxMYHI9uHL 2Y0t H@YPt H`YH|LU Lffff.UHAWAVSHIH6THHEHHW)PƅPHDž`HnearestHQH5HELPHD$$HO_H pLI_1E1HPD$$Hh H BL`E1HD$$H^H L^1E1HHW)0HDž@fDž0Tƅ2^HHW)HDž@|WH()Hr types.HH/Hl tensorHH(HY' to alHH Houtput 'HHH'X' and HHHn input HHHConstraiH@7H0HHHH :HpHpHHHrH5AHH59OHd HǩH5Z H蓰LHoHH9t`HtHP(t H LfUHAWAVSHIH=HHEHH0ܭH5HAHD$$H/IH L'I1E1H.D$$HIH QLH1E1HHW)PHDž`fDžPTƅRpH8HձW) HDž0@AH0() Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HPH8H HH HpHpHHH脗H5H%H5K9HvHٓH5lH襚LH ZHH9tbHtHP( t H0N@L8MtVH@L9t;fDHX@t Hx@HI9uH8HP uLL@?Pt H`?HKH4;HH;EuLH[A^A_]@I uXbI]IPIIHH9tHtHP( uHP t H05?L8MtGH@L9tfffff.HX@t Hx>HI9uH8L@>Pt 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H P X >AZazptvwz}/1VYY` Jnoq/M$$((@X`j 9 = = P P X a q     ( * 0 2 3 5 6 8 9 Y \ ^ ^ r t     ( * 0 2 3 5 9 = = \ ] _ a q q     ( * 9 = = X Z ` a     : = = N N T V _ a z  023@F@GIl*??PUZ]aaefnpuHJMPVXXZ]`Zlo  1@Q`lnp xPmpt T3EK#MOZ} EHMPWYY[[]]_}q q   !!!! !!!!!!$!$!&!&!(!(!*!-!/!9!>@ACDF=Pptv!:AZf  &(:<=?MP]-@BIPu'0c6@U`g 578<<?U`v   9            5 ` |        5 @ U ` r    H      # ''0E7&DDPrvv+ (*02359==PP]a4GJ/DD+ 2::PP\ .@@r  0FF`eghj #$C%0.4DFFh8j@j^jjjk/k@kCkckwk}kk@nnoDoPoPoooooppjp|TV  9;>@DFFJPR46NPnpC!"$$'')24799;;BBGGIIKKMOQRTTWWYY[[]]__abddgjlrtwy|~~֦4@ AZaz%,\bekwyq q   *!+!2!2!N!N!`!!`,,"0Z\d!:AZ7;IMO +0;@@DO6@U`g  &(:<=?MP]Фaz      !!##%%''))++--//11335578::<<>>@@BBDDFFHIKKMMOOQQSSUUWWYY[[]]__aacceeggiikkmmooqqssuuwwzz||~      !!##%%''))++--//1139<<?@BBGGIIKKMMOqqssww{}0_aacceeggiikkmmooqqssuuwwyy{{}}      !!##%%''))++--//`+kwy      !!##%%''))++--//1133557799;;==??AACCEEGGIIKKMMOOQQSSUUWWYY[[]]__aacceeggiikkmmooqqssuuwwyy{{}} '07@EPW`gp} ! !!!!!/!/!4!4!9!9!>@ACDF=Pptvfoq  &(:<=?MP]-@BIPuP'0c6@U`g 578<<?U`v   9            5 ` |        5 @ U ` r    H  # ''0E7&DDPrvv+ (*02359==PP]a4GJ/DD+ 2::PP\ .@@r  0FF`eghj #$C%0.4DFFh8j@j^jjjk/kckwk}kkoDoPoPoppjp|!"$$'')24799;;BBGGIIKKMOQRTTWWYY[[]]__abddgjlrtwy|~~֦4@ AZ      ""$$&&((**,,..0022446699;;==??AACCEEGGJJLLNNPPRRTTVVXXZZ\\^^``bbddffhhjjllnnpprrttvvxy{{}}      ""$$&&((**,,..0022:;=>AACFHHJJLLNNpprrvv/``bbddffhhjjllnnpprrttvvxxzz||~~      ""$$&&((**,,..1V      ""$$&&((**,,..0022446688::<<>>@@BBDDFFHHJJLLNNPPRRTTVVXXZZ\\^^``bbddffhhjjllnnpprrttvvxxzz||~~(/8?HMYY[[]]__ho!!!! ! !!!!!!!$!$!&!&!(!(!*!-!0!3!>!?!E!E!!!,.,`,`,b,d,g,g,i,i,k,k,m,p,r,r,u,u,~,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,@@BBDDFFHHJJLLNNPPRRTTVVXXZZ\\^^``bbddffhhjjll""$$&&((**,,..22446688::<<>>@@BBDDFFHHJJLLNNPPRRTTVVXXZZ\\^^``bbddffhhjjllnnyy{{}~!:'  @n_n4Mh  89;>@DFFJPl!<Up4Vn! 9 ? ? oK_pp0J#%')-Y[ : < > O Q W b c   < < > B G H K M Q Q p q u u   < < > D G H K M V W b c  > D F H J M U V b c  ; < > D F H J M W W b c 114:GN557799>?q+>VY^`bdgmqt]_24RSrs   +0;U^`|4Dks$7 ,,----*0/000ort} #'Ũ&-GS)6CCLM{} /vz      8 : ? ?   $ ' FP8F'4EFss,7>>;<>DGHKMWWbcflpt5F^^0@+,: 39;>GGQ[/68?16::<=?EGGjj0k6kQo~oooeimr{BD6;luu!#$&*DJPv      D F H J O T c f  @[^^    p  6::<=?GPY  ; ; > @ I L N O   > @   > > @ @ G H K L W W   A D   > @ F H J L W W >?+,1188;<VWbdgm#&)+0138UUWWaacdmr55;;=ACD$+45.0/0#$''èRS/034MM{{}},,EF,.2355>?ADGHKMWWbc57@AEE02;<>> !&&,.8899WX//>>Qo~oefmr pr@nn        oDoPo~ooooK_pp0J#%')-Y[ : : < < A H M M Q W b c   < < A B G H K M Q Q p q u u   < < ? ? A D M M V V b c   > @ F H J M U V b c  ; < A D M M b c 114:GN557799q~-0279:=>XY^`qt]_24RSrs   "'(229;VVX^``bbels|446:<<BBks,367 ,,----*0-000oot} %&ĨŨ&-GQ).1256CCLL|| /vz      8 : ? ?   $ ' FP8F'+-4ss/14467>>;<@@flpt8?BDFF^^3:==?@"%'+/79: 38;>GGQVY[068=??16::<=?EGGjj0k6koogi{BD6;luu!#$&*DJDPY x`l@j^j`jijnjoj`09`if o f o f o r w f o x ~ X ^ f x PY 3@Ii|FOPY@IPYp p t y P!!!!`$$$$v'',,00!0)080:011 2)2H2O2Q2_22222 )05Ш٨ Щ٩PY3@x #AAJJX_y        @ H } ~     X _ x      0 9 `~&QTRo6?PYPY0;PlPY$n$`jijPkYk[kaknn`xPYq 09`if o f o f o f o f o PY )@IFOPY@IPY )Ш٨ Щ٩PY0 9 fo6?PYPY09PYPY`jijPkYkPYY[[]^`!!!!00!0)080:0@tAAJJ$n$ r w x ~ X ^ p x *3i|p p t y P!_!!!`$$$$v'',,11 2)2H2O2Q2_22222053ux #X_y        @ H } ~     X _ x      `~&QTRe:;Zl[kaknn`xq oopP      #-/  Pz'`   H       ( * 0 2 3 5 9 < D G H K M V W \ ] _ c f w !#%*,/:;?@[]__{{}}~~Z_    jm 0>^^d e p p v v OOZ[:=JO`hmn56 DEZ`;?~ ' 0 C E Q S ^ } ~ # #)#*#h'u''''')))))),,,,p-p-...0.N.0000000000=0=00000 ss~~twΨϨ./__ͩީߩ\_ުߪ>?0RTacchhjk  ;=??[[]]_eooWW  ? ? P X     9 ?   UYGM@Ctu8=KO[[]]AC`l<>;;?FAEpqp$t$njojjj7k;kDkDknn^_kEkPkYk[kakckwk}kk`__? @ T T 34MO??--  ....:.;.@.@.0000000012XXcc ))]]}};;==F F ~ ~ # # # #*#*#i'i'k'k'm'm'o'o'q'q's's'u'u'''''''''''''))))))))))))))))))))))))))))#.#.%.%.'.'.).). 0 0 0 0 0 000000000000000>>6688::<<>>@@BBDDHHZZ\\^^ ==]]``cc    : : .... . . . ...!.!.@w          9 9 .... . . . ... . .!#%'**,,./:;?@\\~~Z_    jm 0>^^d e p p v v OOZ[JO`hmn56 DEZ`;?~  ' 0 8 ; > A C G Q S S U ^ ,,,,p-p-.... . .........*...0.9.<.?.A.A.C.N.00=0=000 ss~~twΨϨ./__ͩީߩ\_ުߪ00EFILPRTW_ahhjk  <;;?FAEpqp$t$njojjj7k;kDkDknn^_(([[{{::<<    E E } } ## # #)#)#h'h'j'j'l'l'n'n'p'p'r'r't't'''''''''''''))))))))))))))))))))))))))))".".$.$.&.&.(.(.B.B.00 0 0 0 000000000000000??557799;;==??AACCGGYY[[]];;[[__bb      0S__$$++<>^^``||~~uu   p p   O O y y ??446688@@ajt|D D R R z | !!!!! !!!!!!#!%!%!'!'!)!)!.!.!:!;!@!D!J!M!O!O!!!!# #(#+#&$@$J$$$%g''''''))))))s+v++++++,,....////0000 0 06070>0?00011111122*2G2P2P2`22222233MMƤ !(+69wy[[))bbdfii >>@@\\^^7?ywx  ??@psvzz|`m-0>ŨΨ٨$$   ?? 88iiP ^^``uu00 ![[>>@@++<>||~~D D R R z | !!@!D!K!K!!!!!!!!!!!!!!!!!!!!" #!#|#|#####%%%%%%o&o&'''''')))))))*0+D+G+L+))bbdf \\^^55OOoo p p   O O y y 446688@@ajt|!!!!! !!!!!!#!%!%!'!'!)!)!.!.!:!;!J!J!L!M!O!O!!!!!!!!!!!!!!!!!!!!!## ##"#(#+#{#}#####&$@$J$$$%%%%%%&n&p&g'''((+/+E+F+M+s+v++++++,,....////0000 0 06070>0?011111122*2G2P2P2`22222233MMƤ(+6799wy7?ywx  ??@psvzz|`m0YP+ JMO`j `lnprsPmpt ^`|ª۪ߪ oop    ( * 9 = D F H J M U V X Z ` c f o x  :@[GIlq0-g-o-p---+Ƥ  ( ) / / _ _ 00G( ( ) )  / / _ _ 00/Users/runner/runners/2.159.2/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/2.159.2/work/1/s/onnxruntime/core/session/custom_ops.ccCreateCustomRegistryUnsupported version '' in custom op 'ComputeAsyncMemcpyFromHost/Users/runner/runners/2.159.2/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.MemcpyToHosttensor(uint8)tensor(uint16)tensor(uint32)tensor(uint64)tensor(int8)tensor(int16)tensor(int32)tensor(int64)tensor(float16)tensor(float)tensor(double)tensor(string)tensor(bool)[TypeInferenceError] Input 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: unknowncom.microsoftcom.microsoft.nchwc/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/session/inference_session.cconnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions &, logging::LoggingManager *)Environment::IsInitialized()Environment must be initialized before creating an InferenceSession.~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: 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.CreateSubgraphSessionStatecommon::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.CUDAExecutionProviderParallel execution is currently not supported for the registered CUDA Execution Provider.Serializing Optimized ONNX model with Graph Optimization level greater than 2 is not supported.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.ValidateInputsOutput 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: void onnxruntime::InferenceSession::AddPredefinedTransformers(onnxruntime::GraphTransformerManager &, onnxruntime::TransformerLevel, const std::vector &)graph_optimization_level < TransformerLevel::MaxTransformerLevelAllowed values are 1 and 2. Current level is set to WaitForNotificationtimeout_in_ms >0 is not supportedProvider has already been registered./Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/framework/execution_providers.hAdd allocator already registered.Unexpected input data type. Actual: () , expected: (/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/framework/feeds_fetches_manager.hFeedsFetchesInfoonnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(const std::vector &, const std::vector &, const onnxruntime::OrtValueNameIdxMap &)Attempt to use DefaultLogger but none has been registered.Load model from failed:Failed to load model because protobuf parsing failed._.json%Y-%m-%d_%H-%M-%Stype 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/2.159.2/work/1/s/onnxruntime/core/session/onnxruntime_c_api.ccOrtStatus *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 != nullptrOrtStatus *OrtApis::GetOpaqueValue(const char *, const char *, const OrtValue *, void *, size_t)1.0.0out of indexinternal errorindex out of rangeInput is not of one of the supported sequence types.Input is not of type sequence or map./Users/runner/runners/2.159.2/work/1/s/include/onnxruntime/core/framework/ml_value.hconst 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_ != const T &OrtValue::Get() const [T = std::__1::vector, std::__1::allocator > >, std::__1::allocator, std::__1::allocator > > > >]Input is not of one of the supported map types.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 > > >]allocator::allocate(size_t n) 'n' exceeds maximum supported sizeconst 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> > >]const T &OrtValue::Get() const [T = std::__1::map, double, std::__1::less >, std::__1::allocator, double> > >]const T &OrtValue::Get() const [T = std::__1::map, std::__1::less, 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 > >]const T &OrtValue::Get() const [T = std::__1::map, std::__1::allocator > >]Invalid tensor element type in the input./Users/runner/runners/2.159.2/work/1/s/include/onnxruntime/core/framework/tensor.hconst T *onnxruntime::Tensor::Data() const [T = bool]DataTypeImpl::GetType() == dtype_Tensor type mismatch. !=const T *onnxruntime::Tensor::Data() const [T = float]const T *onnxruntime::Tensor::Data() const [T = onnxruntime::MLFloat16]const T *onnxruntime::Tensor::Data() const [T = double]const T *onnxruntime::Tensor::Data() const [T = signed char]const T *onnxruntime::Tensor::Data() const [T = unsigned char]const T *onnxruntime::Tensor::Data() const [T = short]const T *onnxruntime::Tensor::Data() const [T = unsigned short]const T *onnxruntime::Tensor::Data() const [T = int]const T *onnxruntime::Tensor::Data() const [T = unsigned int]const T *onnxruntime::Tensor::Data() const [T = long long]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.Value type is not supported yet.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 typeSequences must have tensors of the same data type. There was at least one tensor in the input that was different.Unsupported data type: Encountered nullptr.T *onnxruntime::Tensor::MutableData() [T = bool]T *onnxruntime::Tensor::MutableData() [T = float]T *onnxruntime::Tensor::MutableData() [T = double]T *onnxruntime::Tensor::MutableData() [T = signed char]T *onnxruntime::Tensor::MutableData() [T = unsigned char]T *onnxruntime::Tensor::MutableData() [T = short]T *onnxruntime::Tensor::MutableData() [T = unsigned short]T *onnxruntime::Tensor::MutableData() [T = int]T *onnxruntime::Tensor::MutableData() [T = unsigned int]T *onnxruntime::Tensor::MutableData() [T = long long] was false. Stacktrace: :/\T *OrtValue::GetMutable() [T = onnxruntime::Tensor]size overflownot enough space: expected , got DmlExecutionProviderMem pattern should be disabled when using DML execution provider.Sequential execution should be enabled when using DML execution provider.T *onnxruntime::Tensor::MutableData() [T = std::__1::basic_string]const T &OrtValue::Get() const [T = onnxruntime::Tensor]const T *onnxruntime::Tensor::Data() const [T = std::__1::basic_string]const T &OrtValue::Get() const [T = onnxruntime::TensorSeq]/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/optimizer/constant_folding.ccvirtual onnxruntime::common::Status onnxruntime::ConstantFolding::ApplyImpl(onnxruntime::Graph &, bool &, int) constfetches.size() == node->OutputDefs().size()Unsupported output type of . Can't constant fold /Users/runner/runners/2.159.2/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/2.159.2/work/1/s/onnxruntime/core/optimizer/conv_add_fusion.ccvirtual onnxruntime::common::Status onnxruntime::ConvAddFusion::Apply(onnxruntime::Graph &, onnxruntime::Node &, onnxruntime::RewriteRule::RewriteRuleEffect &) constadd_B_tensor_protoConvAddFusion_B_ConvAddFusion_Add_B_/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/optimizer/conv_bn_fusion.ccvirtual onnxruntime::common::Status onnxruntime::ConvBNFusion::Apply(onnxruntime::Graph &, onnxruntime::Node &, onnxruntime::RewriteRule::RewriteRuleEffect &) constbn_scale_tensor_protobn_B_tensor_protobn_mean_tensor_protobn_var_tensor_protoconv_W_tensor_protoconv_B_tensor_protoConvBnFusion_W_ConvBnFusion_BN_B_ToProto/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/optimizer/conv_mul_fusion.ccvirtual onnxruntime::common::Status onnxruntime::ConvMulFusion::Apply(onnxruntime::Graph &, onnxruntime::Node &, onnxruntime::RewriteRule::RewriteRuleEffect &) constmul_B_tensor_protoConvMulFusion_W_ConvMulFusion_Mul_B_/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/optimizer/free_dim_override_transformer.ccThe model has input '' with a fixed dimension denotation 'but the size of this dimension does not equal the specified override ofInvalid free dimension override./Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/optimizer/gelu_fusion.cc/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/optimizer/initializer.honnxruntime::Initializer::Initializer(const onnx::TensorProto &)size_ == sizeInitializerdata type is not supported/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/optimizer/gemm_activation_fusion.ccfused Gemm leaky_relu_/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/optimizer/graph_transformer.ccApply/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/optimizer/graph_transformer_mgr.ccApplyTransformersThis transformer is already registered Level_RuleBasedTransformer/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/optimizer/graph_transformer_utils.ccstd::vector > onnxruntime::optimizer_utils::GenerateRewriteRules(onnxruntime::TransformerLevel, const std::vector &)Unsupported levelstd::vector > onnxruntime::optimizer_utils::GenerateTransformers(onnxruntime::TransformerLevel, gsl::span, const std::vector &)Unsupported level RandomUniformRandomNormalRandomUniformLikeRandomNormalLikeMultinomialCastDef_%d/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/optimizer/insert_cast_transformer.cc/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/optimizer/matmul_add_fusion.ccfused Matmul and Add _nchwc/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/optimizer/nchwc_transformer.cc/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/optimizer/optimizer_execution_frame.cconnxruntime::OptimizerExecutionFrame::Info::Info(const std::vector &, const onnxruntime::InitializedTensorSet &)allocator_ptr_ != nullptr/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/optimizer/relu_clip_fusion.ccvirtual onnxruntime::common::Status onnxruntime::FuseReluClip::Apply(onnxruntime::Graph &, onnxruntime::Node &, onnxruntime::RewriteRule::RewriteRuleEffect &) 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/2.159.2/work/1/s/onnxruntime/core/optimizer/rule_based_graph_transformer.ccApplyRulesOnNode/Users/runner/runners/2.159.2/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/2.159.2/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/2.159.2/work/1/s/onnxruntime/core/optimizer/unsqueeze_elimination.ccUnsqueezeElimination cannot remove node EluHardSigmoidLeakyReluReluSeluSigmoidSoftplusSoftsignTanhThresholdedRelualphagammaBVIfthen_branch/Users/runner/runners/2.159.2/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/2.159.2/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 Inconsistent shape in loop output for output Expected: Got:Loop had zero iterations and the shape of subgraph output was not found. Defaulting to a rank 1 shape of {0}.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/2.159.2/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_mlvaluegsl::span onnxruntime::Tensor::DataAsSpan() const [T = long long]/Users/runner/runners/2.159.2/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/2.159.2/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/2.159.2/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/2.159.2/work/1/s/onnxruntime/core/providers/cpu/controlflow/scan_utils.hconst OrtValue &onnxruntime::scan::detail::OutputIterator::GetOutput() constfinal_output_mlvalue_/Users/runner/runners/2.159.2/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/2.159.2/work/1/s/onnxruntime/core/providers/cpu/cpu_execution_provider.ccRegisterOnnxOperatorKernelsRegisterOnnxMLOperatorKernelsGetKernelRegistryvirtual std::shared_ptr onnxruntime::CPUExecutionProvider::GetKernelRegistry() constRegisterCPUKernelsCpuUsed is not implementedMaxConstantOfShape/Users/runner/runners/2.159.2/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/2.159.2/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/2.159.2/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 &)MultinomialComputeRangeRange op: Unsupported tensor data type:start 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!Clipmax/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/math/clip.honnxruntime::Clip_6::Clip_6(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttr("max", &max_).IsOK()mininfo.GetAttr("min", &min_).IsOK()virtual onnxruntime::common::Status onnxruntime::Clip::Compute(onnxruntime::OpKernelContext *) const [T = float]min->Shape().NumDimensions() == 0max->Shape().NumDimensions() == 0CumSumexclusiveAxis should be in the range [) but got: 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()=void onnxruntime::WritableSliceIterator::Init(const std::vector &, gsl::span, gsl::span) [T = double]void onnxruntime::WritableSliceIterator::Init(const std::vector &, gsl::span, gsl::span) [T = int]void onnxruntime::WritableSliceIterator::Init(const std::vector &, gsl::span, gsl::span) [T = long long]Det/Users/runner/runners/2.159.2/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 expectedSubMulDivAbsNegFloorCeilReciprocalSqrtPowExpLogSumMinT1LessGreaterEqualMeanBitShiftErfNotAndOrXor/Users/runner/runners/2.159.2/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]SinCosTanAsinAcosAtanSinhCoshAsinhAcoshAtanhPReluExpandvirtual onnxruntime::common::Status onnxruntime::Erf::Compute(onnxruntime::OpKernelContext *) const [T = float]X_ptr != nullptrModvoid 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_Unsupported data type/Users/runner/runners/2.159.2/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)const T *onnxruntime::Tensor::Data() const [T = unsigned long long]T *onnxruntime::Tensor::MutableData() [T = unsigned long long]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = float, TOutput = float, Input0Scalar = (lambda at /Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:376:7), Input1Scalar = (lambda at /Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:377:7), General = (lambda at /Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:378:7)]onnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext &) [T = float]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = double, TOutput = double, Input0Scalar = (lambda at /Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:376:7), Input1Scalar = (lambda at /Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:377:7), General = (lambda at /Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:378:7)]onnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext &) [T = double]directiononnxruntime::BitShift::BitShift(const onnxruntime::OpKernelInfo &) [T = unsigned char]LEFTRIGHTInvalid direction value of ''. Valid values are 'LEFT' or 'RIGHT'.auto onnxruntime::BitShift::Compute(onnxruntime::OpKernelContext *)::(anonymous class)::operator()(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/2.159.2/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: onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = float, TOutput = float, Input0Scalar = (lambda at /Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:322:7), Input1Scalar = (lambda at /Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:323:7), General = (lambda at /Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:324:7)]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = float, TOutput = float, Input0Scalar = (lambda at /Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:349:7), Input1Scalar = (lambda at /Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:350:7), General = (lambda at /Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:351:7)]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = float, TOutput = float, Input0Scalar = (lambda at /Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:507:7), Input1Scalar = (lambda at /Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:508:7), General = (lambda at /Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:509:7)]T *onnxruntime::Tensor::MutableData() [T = onnxruntime::MLFloat16]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::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/2.159.2/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/2.159.2/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_ >= 0Not implemented fused activation: Hardmax inputs N, D and N * D must be < . N=, D=HardmaxMatMul/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/math/matmul.ccNot satsified: left_num_dims >= 1 && right_num_dims >= 1 /Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/math/matmul_helper.hNot satsified: K_ == right_shape[right_num_dims - 2] MatMul dimension mismatch, Left: Right:Not satsified: left_padded_dims_[idx_dim] == 1 left operand cannot broadcast on dim Not satsified: right_padded_dims_[idx_dim] == 1 right operand cannot broadcast on dim Not satsified: num_dims_with_pad == num_output_dims Not satsified: M_ == 1 && N_ == 1 Not satsified: num_dims_with_pad - 1 == num_output_dims Not satsified: num_dims_with_pad - 2 == num_output_dims Not satsified: K_ == right_shape[0] MatMulInteger/Users/runner/runners/2.159.2/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->DataType() == DataTypeImpl::GetType() || t->DataType() == DataTypeImpl::GetType()QLinearMatMul/Users/runner/runners/2.159.2/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 1RoundSignconst T *onnxruntime::Tensor::Data() const [T = onnxruntime::BFloat16]T *onnxruntime::Tensor::MutableData() [T = onnxruntime::BFloat16]SoftmaxLogSoftmax/Users/runner/runners/2.159.2/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 valueonnxruntime::TopK<11, float>::TopK(const onnxruntime::OpKernelInfo &) [OpSet = 11, T = float]largestop_kernel_info.GetAttr("largest", &largest_temp).IsOK()sortedop_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()TopKk 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/2.159.2/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/2.159.2/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/2.159.2/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/2.159.2/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/2.159.2/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/2.159.2/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/2.159.2/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/2.159.2/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/2.159.2/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]LinearClassifiermulti_class/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/ml/linearclassifier.cconnxruntime::ml::LinearClassifier::LinearClassifier(const onnxruntime::OpKernelInfo &) [T = float]10onnxruntime::ml::LinearClassifier::LinearClassifier(const onnxruntime::OpKernelInfo &) [T = double]onnxruntime::ml::LinearClassifier::LinearClassifier(const onnxruntime::OpKernelInfo &) [T = long long]onnxruntime::ml::LinearClassifier::LinearClassifier(const onnxruntime::OpKernelInfo &) [T = int]LinearRegressorInput shape needs to be at least a single dimension.interceptstargets/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/ml/linearregressor.cconnxruntime::ml::LinearRegressor::LinearRegressor(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttr("targets", &targets_).IsOK()void onnxruntime::ml::write_scores(std::vector &, onnxruntime::ml::POST_EVAL_TRANSFORM, int64_t, onnxruntime::Tensor *, int) [T = float]Normalizer/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/ml/normalizer.ccvirtual onnxruntime::common::Status onnxruntime::ml::Normalizer::Compute(onnxruntime::OpKernelContext *) constInvalid input type of /Users/runner/runners/2.159.2/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 void onnxruntime::ml::Normalizer::Normalize(onnxruntime::OpKernelContext *) const [T = float]Unexpected NORMALIZE value of void onnxruntime::ml::Normalizer::Normalize(onnxruntime::OpKernelContext *) const [T = double]void onnxruntime::ml::Normalizer::Normalize(onnxruntime::OpKernelContext *) const [T = long long]void onnxruntime::ml::Normalizer::Normalize(onnxruntime::OpKernelContext *) const [T = int]OneHotEncoderUnknown Category and zeros = 0.zeros/Users/runner/runners/2.159.2/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/2.159.2/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/2.159.2/work/1/s/onnxruntime/core/providers/cpu/ml/svmclassifier.cconnxruntime::ml::SVMClassifier::SVMClassifier(const onnxruntime::OpKernelInfo &) [T = float]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() > 0No support vectors.onnxruntime::ml::SVMClassifier::SVMClassifier(const onnxruntime::OpKernelInfo &) [T = double]onnxruntime::ml::SVMCommon::SVMCommon(const onnxruntime::OpKernelInfo &) [T = double]onnxruntime::ml::SVMClassifier::SVMClassifier(const onnxruntime::OpKernelInfo &) [T = long long]onnxruntime::ml::SVMCommon::SVMCommon(const onnxruntime::OpKernelInfo &) [T = long long]onnxruntime::ml::SVMClassifier::SVMClassifier(const onnxruntime::OpKernelInfo &) [T = int]onnxruntime::ml::SVMCommon::SVMCommon(const onnxruntime::OpKernelInfo &) [T = int]SVMRegressorn_supportssupport_vectorspost_transformrho/Users/runner/runners/2.159.2/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/2.159.2/work/1/s/onnxruntime/core/providers/cpu/ml/svmclassifier.honnxruntime::ml::SVMCommon::SVMCommon(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttrs("kernel_params", kernel_params).IsOK()POLYRBFLOGISTICSOFTMAXSOFTMAX_ZEROTreeEnsembleClassifierclass_nodeidsclass_treeidsclass_idsclass_weights/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/ml/tree_ensemble_classifier.cconnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo &) [T = float]class_nodeids_.size() == class_ids_.size()class_nodeids_.size() == class_weights_.size()nodes_nodeids_.size() == nodes_featureids_.size()nodes_nodeids_.size() == nodes_modes_names_.size()nodes_nodeids_.size() == nodes_values_.size()nodes_nodeids_.size() == nodes_truenodeids_.size()nodes_nodeids_.size() == nodes_falsenodeids_.size()(nodes_nodeids_.size() == nodes_hitrates_.size()) || (nodes_hitrates_.empty())std::all_of( std::begin(missing_tracks_true_), std::end(missing_tracks_true_), [](int64_t elem) { return elem >= 0; })void onnxruntime::ml::TreeEnsembleClassifier::Initialize() [T = float]base_values_.empty() || base_values_.size() == static_cast(class_count_) || base_values_.size() == weights_classes_.size()X dims is empty.Invalid mode of value: common::Status onnxruntime::ml::TreeEnsembleClassifier::ProcessTreeNode(std::map &, int64_t, const T *, int64_t) const [T = float]treeindex >= 0onnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo &) [T = double]void onnxruntime::ml::TreeEnsembleClassifier::Initialize() [T = double]common::Status onnxruntime::ml::TreeEnsembleClassifier::ProcessTreeNode(std::map &, int64_t, const T *, int64_t) const [T = double]onnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo &) [T = long long]void onnxruntime::ml::TreeEnsembleClassifier::Initialize() [T = long long]common::Status onnxruntime::ml::TreeEnsembleClassifier::ProcessTreeNode(std::map &, int64_t, const T *, int64_t) const [T = long long]onnxruntime::ml::TreeEnsembleClassifier::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo &) [T = int]void onnxruntime::ml::TreeEnsembleClassifier::Initialize() [T = int]common::Status onnxruntime::ml::TreeEnsembleClassifier::ProcessTreeNode(std::map &, int64_t, const T *, int64_t) const [T = int]TreeEnsembleRegressornodes_treeidsnodes_nodeidsnodes_featureidsnodes_valuesnodes_hitratesnodes_truenodeidsnodes_falsenodeidsnodes_missing_value_tracks_truetarget_nodeidstarget_treeidstarget_idstarget_weightsbase_valuesaggregate_functionSUMn_targets/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/ml/treeregressor.cconnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressor(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttr("n_targets", &n_targets_).IsOK()!nodes_treeids_.empty()nodes_modestarget_nodeids_.size() == target_ids_.size()target_nodeids_.size() == target_weights_.size()nodes_id_size == nodes_featureids_.size()nodes_id_size == nodes_values_.size()nodes_id_size == nodes_modes_.size()nodes_id_size == nodes_truenodeids_.size()nodes_id_size == nodes_falsenodeids_.size()(nodes_id_size == nodes_hitrates_.size()) || (nodes_hitrates_.empty())it != parents.end()it != indices.end()base_values_.empty() || base_values_.size() == static_cast(n_targets_)AVERAGEMINBRANCH_LEQLEAFBRANCH_LTBRANCH_GTEBRANCH_GTBRANCH_EQtreeindex evaluated to a negative value, which should not happen.ZipMapclasslabels_int64sclasslabels_strings/Users/runner/runners/2.159.2/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 satsified: X_shape.NumDimensions() >= 3 /Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/nn/Unpool.ccNot satsified: I_shape == X_shape Index tensor shape should be same as that of the input data tensor to unpool.Not satsified: tensor_shape->Shape().GetDims().size() == 1 Not satsified: given_shape.Size() >= shape.Size() output_shape is smaller than minimum required. output_shape: inferred output shape:/Users/runner/runners/2.159.2/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()meanvarBatchNormalization/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/nn/batch_norm.ccepsilon/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/nn/batch_norm.honnxruntime::BatchNorm::BatchNorm(const onnxruntime::OpKernelInfo &) [T = float]st.IsOK()spatialInvalid 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: /Users/runner/runners/2.159.2/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/2.159.2/work/1/s/onnxruntime/core/providers/cpu/nn/autopad_type.honnxruntime::AutoPadType onnxruntime::StringToAutoPadType(const std::string &)/Users/runner/runners/2.159.2/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/2.159.2/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 *, Provider *, bool, T) [T = float, Provider = onnxruntime::CPUMathUtil, order = 2]d_iter[d_i] < d_maxT3ConvInteger/Users/runner/runners/2.159.2/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 *, Provider *, bool, T) [T = unsigned char, Provider = onnxruntime::CPUMathUtil, order = 2]ConvTransposeoutput_paddingoutput_shape/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/nn/conv_transpose.ccDoConvTransposegroup count is <= 0Input X must be 4-dimensional.filter number not equal to input channel number. filter_number: num_input_channels: Input channels is not divisible by group./Users/runner/runners/2.159.2/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 &, std::vector *, std::vector *) constoutput_height >= Houtput_width >= Wvoid 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 supportedFlatten/Users/runner/runners/2.159.2/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/2.159.2/work/1/s/onnxruntime/core/providers/cpu/nn/instance_norm.cc/Users/runner/runners/2.159.2/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/2.159.2/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/2.159.2/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/2.159.2/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 satsified: input_dims >= 3 /Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/nn/pool.ccUnsupported pooling size.Not satsified: pooling_dims == pool_attrs_.kernel_shape.size() Not satsified: x_shape.NumDimensions() >= 3 Unsupported pooling size : AveragePoolMaxPoolLpPoolGlobalLpPoolGlobalAveragePoolGlobalMaxPool/Users/runner/runners/2.159.2/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_orderkernel_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/2.159.2/work/1/s/onnxruntime/core/providers/cpu/nn/pool_base.hvoid onnxruntime::PoolProcessContext::init(const onnxruntime::OpKernelInfo &)info.GetAttr("p", &p_).IsOK()T4QLinearConv/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/nn/qlinearconv.ccvirtual onnxruntime::common::Status onnxruntime::QLinearConv::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(input_offset)QLinearConv : input zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(filter_offset)QLinearConv : filter zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(result_offset)QLinearConv : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(input_scale)QLinearConv : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(filter_scale)QLinearConv : filter scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(result_scale)QLinearConv : result scale must be a scalar or 1D tensor of size 1MaxRoiPool/Users/runner/runners/2.159.2/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/2.159.2/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_ > 0ShrinkInput types for the Shrink operator are constrained to all numeric types only. Got bool type here.Input types for the Shrink operator are constrained to all numeric types only. Got std::string type here./Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/nn/shrink.ccvirtual onnxruntime::common::Status onnxruntime::Shrink::Compute(onnxruntime::OpKernelContext *) constUnknown tensor type of /Users/runner/runners/2.159.2/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/2.159.2/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/2.159.2/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 (before_insert + ngrams) == impl_->int64_set_.size()pool_int64s duplicate -grams detected(before_insert + ngrams) == impl_->str_set_.size()poll_strings duplicate Invalid type of the input argumentInput shape must have either [C] or [B,C] dimensions with B > 0.NonMaxSuppressionNot satsified: (iou_threshold >= 0 && iou_threshold <= 1.f) /Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.ccGetThresholdsFromInputsstatic onnxruntime::common::Status onnxruntime::NonMaxSuppressionBase::PrepareCompute(onnxruntime::OpKernelContext *, onnxruntime::PrepareContext &)boxes_tensorscores_tensorNot satsified: boxes_shape.NumDimensions() == 3 PrepareComputeNot satsified: scores_shape.NumDimensions() == 3 Not satsified: boxes_dims[0] == scores_dims[0] Not satsified: boxes_dims[1] == scores_dims[2] Not satsified: boxes_dims[2] == 4 Not satsified: ret.IsOK() virtual onnxruntime::common::Status onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext *) constoutput != nullptrcenter_point_box/Users/runner/runners/2.159.2/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/2.159.2/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/2.159.2/work/1/s/onnxruntime/core/providers/cpu/reduction/reduction_ops.honnxruntime::ReduceKernelBase::ReduceKernelBase(const onnxruntime::OpKernelInfo &) [allow_multi_axes = true]info.GetAttr("keepdims", &keepdims).IsOK()/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/reduction/reduction_ops.ccbool onnxruntime::PrepareForReduce(onnxruntime::OpKernelContext *, std::vector &, onnxruntime::Tensor **, int64_t &, int64_t &, const std::vector &, bool, bool) [T = float]in_dim != 0Can't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:bool onnxruntime::PrepareForReduce(onnxruntime::OpKernelContext *, std::vector &, onnxruntime::Tensor **, int64_t &, int64_t &, const std::vector &, bool, bool) [T = int]bool onnxruntime::PrepareForReduce(onnxruntime::OpKernelContext *, std::vector &, onnxruntime::Tensor **, int64_t &, int64_t &, const std::vector &, bool, bool) [T = long long]bool onnxruntime::PrepareForReduce(onnxruntime::OpKernelContext *, std::vector &, onnxruntime::Tensor **, int64_t &, int64_t &, const std::vector &, bool, bool) [T = double]onnxruntime::ReduceKernelBase::ReduceKernelBase(const onnxruntime::OpKernelInfo &) [allow_multi_axes = false]GRU/Users/runner/runners/2.159.2/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/2.159.2/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/2.159.2/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/2.159.2/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/2.159.2/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/2.159.2/work/1/s/onnxruntime/core/providers/cpu/rnn/rnn.ccAffineScaledTanh/Users/runner/runners/2.159.2/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/2.159.2/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/2.159.2/work/1/s/onnxruntime/core/providers/cpu/sequence/concat_from_sequence.ccvirtual onnxruntime::common::Status onnxruntime::ConcatFromSequence::Compute(onnxruntime::OpKernelContext *) constSSequenceLength/Users/runner/runners/2.159.2/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]T *OrtValue::GetMutable() [T = onnxruntime::TensorSeq]onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &, const onnxruntime::Tensor *) const [T = float]split_scalar > 0std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value > 0; })int64_t onnxruntime::GetScalarSplitInput(const onnxruntime::Tensor &)Invalid data type for split tensor void onnxruntime::GetSplitSizesInput(const onnxruntime::Tensor &, std::vector &)void onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape &)shape_.Size() == new_shape.Size()Tensor size () != new size (onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &, const onnxruntime::Tensor *) const [T = double]onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &, const onnxruntime::Tensor *) const [T = int]onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &, const onnxruntime::Tensor *) const [T = std::__1::basic_string]Cast/Users/runner/runners/2.159.2/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/2.159.2/work/1/s/onnxruntime/core/providers/cpu/tensor/compress.ccvirtual onnxruntime::common::Status onnxruntime::Compress::Compute(onnxruntime::OpKernelContext *) constaxis < static_cast(rank)axes_right_stride >= 0 && static_cast(axes_right_stride) < std::numeric_limits::max()ConcatNot satsified: input_count >= 1 /Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/tensor/concat.ccPrepareForComputeNot satsified: 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 satsified: dim_value == inputs_0_dims[axis_index] Non concat axis dimensions must match: Axis has mismatched dimensions of Not satsified: data_n.DataType() == p.output_tensor->DataType() /Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/tensor/concat.honnxruntime::ConcatBase::ConcatBase(const onnxruntime::OpKernelInfo &, bool)DynamicQuantizeLinear/Users/runner/runners/2.159.2/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/2.159.2/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 2TindGather/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/tensor/gather.cc/Users/runner/runners/2.159.2/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: Data type of input 'data' should match the data type of the output/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/tensor/gather_elements.honnxruntime::GatherElements::GatherElements(const onnxruntime::OpKernelInfo &)/Users/runner/runners/2.159.2/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: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: 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/2.159.2/work/1/s/onnxruntime/core/providers/cpu/tensor/gather_nd.cconnxruntime::common::Status onnxruntime::GatherNDBase::PrepareForCompute(onnxruntime::OpKernelContext *, onnxruntime::GatherNDBase::Prepare &) 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 &) const [Tind = long long]GatherNDDropoutIdentity/Users/runner/runners/2.159.2/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/2.159.2/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/2.159.2/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/2.159.2/work/1/s/onnxruntime/core/providers/cpu/tensor/onehot.ccDepth is negative.'axis' attribute must have a value in the range [PadCannot use 'reflect' mode to pad dimension with a value of 0. Input shape:Unexpected mode of valueconstantreflectedge/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/tensor/pad.honnxruntime::PadBase::PadBase(const onnxruntime::OpKernelInfo &)/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/tensor/pad.ccvirtual onnxruntime::common::Status onnxruntime::Pad::Compute(onnxruntime::OpKernelContext *) const [T = float]pads_tensor.DataType() == DataTypeImpl::GetType()pads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)Pads tensor should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]pads_size == 2 * data_rankPads tensor size should be equal to twice the input dimension count value_tensor->DataType() == DataTypeImpl::GetType() && 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/2.159.2/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::SliceIterator::Init(const std::vector &, gsl::span, gsl::span) [T = float]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]void onnxruntime::SliceIterator::Init(const std::vector &, gsl::span, gsl::span) [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]void onnxruntime::SliceIterator::Init(const std::vector &, gsl::span, gsl::span) [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]void onnxruntime::SliceIterator::Init(const std::vector &, gsl::span, gsl::span) [T = long long]xx_scalex_zero_pointyDequantizeLineary_zero_pointQuantizeLinear/Users/runner/runners/2.159.2/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() == broadcastDimx_scale must be 1D tensor with size x_zero_point.Shape().NumDimensions() == 1 && x_zero_point.Shape().Size() == broadcastDimx_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]IsScalarOr1ElementVector(&y_scale)IsScalarOr1ElementVector(&y_zero_point)y_scale.Shape().NumDimensions() == 1 && y_scale.Shape().Size() == broadcastDimy_zero_point.Shape().NumDimensions() == 1 && y_zero_point.Shape().Size() == broadcastDimvirtual onnxruntime::common::Status onnxruntime::QuantizeLinear::Compute(onnxruntime::OpKernelContext *) const [T = signed char]shapeReshapeReshape_1/Users/runner/runners/2.159.2/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/2.159.2/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.(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/2.159.2/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_modeasymmetrictf_crop_and_resizenearest_modecubic_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 only support 2-D inputs ('Bilinear') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the Resize operatorUpsample operatorpytorch_half_pixeltf_half_pixel_for_nnalign_cornersround_prefer_ceilfloorceilvoid onnxruntime::UpsampleBase::ParseScalesData(const onnxruntime::Tensor *, std::vector &) constscales_size > 0ReverseSequencesequence_lens shape must be {batch_size}. Got:. batch_size=/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/tensor/reverse_sequence.ccvirtual onnxruntime::common::Status onnxruntime::ReverseSequenceOp::Compute(onnxruntime::OpKernelContext *) constbatch_axis/Users/runner/runners/2.159.2/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 = double]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = short]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned char]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned short]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned long long]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = bool]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/2.159.2/work/1/s/onnxruntime/core/providers/cpu/tensor/scatter.ccvirtual onnxruntime::common::Status onnxruntime::Scatter::Compute(onnxruntime::OpKernelContext *) constonnxruntime::Scatter::Scatter(const onnxruntime::OpKernelInfo &)/Users/runner/runners/2.159.2/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 = ScatterNDShapeSizeSlice'axes' has an axis outside of the tensor dimension count'axes' has duplicates'step' value cannot be 0/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/tensor/slice.ccvoid onnxruntime::SliceBase::FillVectorsFromInput(const onnxruntime::OpKernelContext *, std::vector &, std::vector &, std::vector &, std::vector &) constnullptr != start_tensor && start_tensor->Shape().NumDimensions() == 1nullptr != ends_tensor && ends_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 starts/Users/runner/runners/2.159.2/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 attributesvirtual onnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext *) const [T = unsigned char, dynamic = false]void onnxruntime::SliceIterator::Init(const std::vector &, gsl::span, gsl::span) [T = unsigned char]virtual onnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext *) const [T = unsigned short, dynamic = false]void onnxruntime::SliceIterator::Init(const std::vector &, gsl::span, gsl::span) [T = unsigned short]virtual onnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext *) const [T = unsigned int, dynamic = false]void onnxruntime::SliceIterator::Init(const std::vector &, gsl::span, gsl::span) [T = unsigned int]virtual onnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext *) const [T = unsigned long long, dynamic = false]void onnxruntime::SliceIterator::Init(const std::vector &, gsl::span, gsl::span) [T = unsigned long long]virtual onnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext *) const [T = signed char, dynamic = false]void onnxruntime::SliceIterator::Init(const std::vector &, gsl::span, gsl::span) [T = signed char]virtual onnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext *) const [T = short, dynamic = false]void onnxruntime::SliceIterator::Init(const std::vector &, gsl::span, gsl::span) [T = short]virtual onnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext *) const [T = int, dynamic = false]virtual onnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext *) const [T = long long, dynamic = false]virtual onnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext *) const [T = float, dynamic = false]virtual onnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext *) const [T = double, dynamic = false]virtual onnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext *) const [T = onnxruntime::MLFloat16, dynamic = false]void onnxruntime::SliceIterator::Init(const std::vector &, gsl::span, gsl::span) [T = onnxruntime::MLFloat16]virtual onnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext *) const [T = bool, dynamic = false]void onnxruntime::SliceIterator::Init(const std::vector &, gsl::span, gsl::span) [T = bool]virtual onnxruntime::common::Status onnxruntime::Slice, false>::Compute(onnxruntime::OpKernelContext *) const [T = std::__1::basic_string, dynamic = false]void onnxruntime::SliceIterator >::Init(const std::vector &, gsl::span, gsl::span) [T = std::__1::basic_string]virtual onnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext *) const [T = unsigned char, dynamic = true]virtual onnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext *) const [T = unsigned short, dynamic = true]virtual onnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext *) const [T = unsigned int, dynamic = true]virtual onnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext *) const [T = unsigned long long, dynamic = true]virtual onnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext *) const [T = signed char, dynamic = true]virtual onnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext *) const [T = short, dynamic = true]virtual onnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext *) const [T = int, dynamic = true]virtual onnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext *) const [T = long long, dynamic = true]virtual onnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext *) const [T = float, dynamic = true]virtual onnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext *) const [T = double, dynamic = true]virtual onnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext *) const [T = onnxruntime::MLFloat16, dynamic = true]virtual onnxruntime::common::Status onnxruntime::Slice::Compute(onnxruntime::OpKernelContext *) const [T = bool, dynamic = true]virtual onnxruntime::common::Status onnxruntime::Slice, true>::Compute(onnxruntime::OpKernelContext *) const [T = std::__1::basic_string, dynamic = true]SpaceToDepthDepthToSpace/Users/runner/runners/2.159.2/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_depth % (blocksize_ * blocksize_) == 0blocksize/Users/runner/runners/2.159.2/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/2.159.2/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/2.159.2/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/2.159.2/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/2.159.2/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/2.159.2/work/1/s/onnxruntime/core/providers/cpu/tensor/transpose.ccvirtual onnxruntime::common::Status onnxruntime::Transpose::Compute(onnxruntime::OpKernelContext *) constinput_tensor_ptr != nullptrTranspose[ perm: does not align with rank of input data: /Users/runner/runners/2.159.2/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/2.159.2/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/2.159.2/work/1/s/onnxruntime/core/providers/cpu/tensor/unsqueeze.honnxruntime::UnsqueezeBase::UnsqueezeBase(const onnxruntime::OpKernelInfo &)info.GetAttrs("axes", axes_).IsOK()Upsample/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/tensor/upsample.ccvirtual onnxruntime::common::Status onnxruntime::Upsample::Compute(onnxruntime::OpKernelContext *) const [T = float]scales != nullptrsizes != nullptr && sizes->Shape().Size() != 0Either scales input or sizes input must be provided and the number of elements of scales or sizes should be the same as the rank of the input.sizes == nullptrroi_input_idx_ > 0onnxruntime::common::Status onnxruntime::Upsample::BaseCompute(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &) const [T = float]Resize: input tensor's dimension does not match the scales.Upsample: input tensor's dimension does not match the scales.Resize: size of roi array should be 2 * N where N is the rank of input tensor X.BaseComputeResize: unexpected modeUpsample: unexpected modeResize: input/output value is nullptrUpsample: input/output value is nullptrResize: input/output value's dimension mismatchUpsample: input/output value's dimension mismatchResize: input shape needs to be at least a single dimensionUpsample: input shape needs to be at least a single dimension.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 [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 [T = unsigned char]Where/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/providers/cpu/tensor/where_op.ccvirtual onnxruntime::common::Status onnxruntime::Where::Compute(onnxruntime::OpKernelContext *) const [T = int]condition && X && Yonnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext &) [T = int]virtual onnxruntime::common::Status onnxruntime::Where::Compute(onnxruntime::OpKernelContext *) const [T = long long]onnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext &) [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]Gelu/Users/runner/runners/2.159.2/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/2.159.2/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/2.159.2/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[/Users/runner/runners/2.159.2/work/1/s/onnxruntime/contrib_ops/cpu/cdist.honnxruntime::contrib::CDist::CDist(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttr("metric", &metric).IsOK()euclideanThe 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]Crop/Users/runner/runners/2.159.2/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 instead) needs to be greater than or equal to the topBorder () + bottomBorder () needs to be greater than or equal to the leftBorder () + rightBorder () + scale_[0] () + scale_[1] (bilinear/Users/runner/runners/2.159.2/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/2.159.2/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()/Users/runner/runners/2.159.2/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 mismatch/Users/runner/runners/2.159.2/work/1/s/onnxruntime/contrib_ops/cpu/fused_conv.cconnxruntime::contrib::FusedConvFloat::FusedConvFloat(const onnxruntime::OpKernelInfo &)/Users/runner/runners/2.159.2/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 (/Users/runner/runners/2.159.2/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]/Users/runner/runners/2.159.2/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 != nullptr/Users/runner/runners/2.159.2/work/1/s/onnxruntime/contrib_ops/cpu/maxpool_with_mask.h/Users/runner/runners/2.159.2/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()info.GetAttr("normalize_variance", &normalize_variance_).IsOK()/Users/runner/runners/2.159.2/work/1/s/onnxruntime/contrib_ops/cpu/murmur_hash3.ccvirtual onnxruntime::common::Status onnxruntime::contrib::MurmurHash3::Compute(onnxruntime::OpKernelContext *) constkeys/Users/runner/runners/2.159.2/work/1/s/onnxruntime/contrib_ops/cpu/nchwc_ops.ccvirtual onnxruntime::common::Status onnxruntime::contrib::NchwcConv::Compute(onnxruntime::OpKernelContext *) constX_shape.NumDimensions() == 4(static_cast(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)Unsupported convolution size.Not satsified: Y->Shape() == sum_shape onnxruntime::common::Status onnxruntime::contrib::NchwcPoolBase::NchwcPool(onnxruntime::OpKernelContext *, MLAS_POOLING_KIND) const(X_shape[1] % MlasNchwcGetBlockSize()) == 0virtual onnxruntime::common::Status onnxruntime::contrib::ReorderInput::Compute(onnxruntime::OpKernelContext *) const [T = float]/Users/runner/runners/2.159.2/work/1/s/onnxruntime/contrib_ops/cpu/nchwc_ops.honnxruntime::contrib::ReorderOutput::ReorderOutput(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttr("channels", &channels_).IsOK()channels_ > 0virtual onnxruntime::common::Status onnxruntime::contrib::ReorderOutput::Compute(onnxruntime::OpKernelContext *) const [T = float]channels_ <= Y_shape[1]onnxruntime::contrib::NchwcConv::NchwcConv(const onnxruntime::OpKernelInfo &)GetFusedActivationAttr(info, activation_).IsOK()onnxruntime::contrib::NchwcPoolBase::NchwcPoolBase(const onnxruntime::OpKernelInfo &)pool_attrs_.kernel_shape.size() == 2SampleOp/Users/runner/runners/2.159.2/work/1/s/onnxruntime/contrib_ops/cpu/tokenizer.cconnxruntime::contrib::Tokenizer::Tokenizer(const onnxruntime::OpKernelInfo &)mincharnum_ > 0Either 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 inputConv 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/2.159.2/work/1/s/onnxruntime/contrib_ops/cpu/word_conv_embedding.cc/Users/runner/runners/2.159.2/work/1/s/onnxruntime/contrib_ops/cpu_contrib_kernels.ccRegisterNchwcKernelsRegisterCpuContribKernelsallocation failuregemmlowp error: %s SSE, 12x4, depth 2/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/util/math_cpu.ccvoid onnxruntime::math::Gemv(const CBLAS_TRANSPOSE, int, int, float, const float *, const float *, float, float *, onnxruntime::CPUMathUtil *)Gemv float found an unexpected CBLAS_TRANSPOSE input of/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/util/protobuf_parsing_utils.ccclose() failed: CHECK failed: !is_closed_: Can't BackUp() after failed Next().AllocateAllocateStaticallyPreExistingReuseAllocateOutputShareAllocation Plan: (ort_value_idx) output_name : ) , use fence when asyncIndex out-of-range! Execution Plan: /Users/runner/runners/2.159.2/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)CudaCudaPinnedSpecified device is not supported.OrtMemoryInfo: [ name: id: mem_type: type:/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/framework/bfc_arena.ccBFCArenaCreating bin of max chunk size onnxruntime::BFCArena::BFCArena(std::unique_ptr, size_t)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()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 bytesBFC 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_numvoid onnxruntime::BFCArena::DumpMemoryLog(size_t)b->free_chunks.size() == bin_info.total_chunks_in_bin - bin_info.total_chunks_in_useDumpMemoryLogBin (): Total Chunks: , Chunks in use: . allocated for chunks. in use in bin. client-requested in use in bin.Bin for was , Chunk State: ChunkFree at of size Summary of in-use Chunks by size: Chunks of size totalling Sum Total of in-use chunks: Stats: /Users/runner/runners/2.159.2/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/2.159.2/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/2.159.2/work/1/s/onnxruntime/core/framework/data_types.ccbool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Map &, const onnx::TypeProto_Map &)falsebool 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())bool onnxruntime::NonTensorTypeBase::IsMapCompatible(const onnx::TypeProto &) constthisProto->value_case() == TypeProto::ValueCase::kMapTypeutils::HasKeyType(thisProto->map_type())bool onnxruntime::NonTensorTypeBase::IsSequenceCompatible(const onnx::TypeProto &) constthisProto->value_case() == TypeProto::ValueCase::kSequenceTypeutils::HasElemType(thisProto->sequence_type())bool 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 *) consttensor(MLFloat16)tensor(bfloat16)tensor type sparse tensor type static onnxruntime::MLDataType onnxruntime::DataTypeImpl::TypeFromProto(const onnx::TypeProto &)utils::HasElemType(tensor_type)utils::HasElemType(sparse_tensor_type)type != nullptrMap with key type: value type: is not registeredtype: is not currently registered or supported(null)void onnxruntime::data_types_internal::DataTypeRegistry::RegisterDataType(onnxruntime::MLDataType)proto != nullptrp.secondWe do not expect duplicate registration of types for: GetElementType/Users/runner/runners/2.159.2/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 = onnxruntime::Tensor]elem_proto != nullptrstatic void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = int]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = float]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 = 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 = unsigned short]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = short]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = long long]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = double]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = unsigned int]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 = 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/2.159.2/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/2.159.2/work/1/s/onnxruntime/core/framework/execution_frame.cconnxruntime::IExecutionFrame::IExecutionFrame(const std::vector &, const std::vector &, const std::unordered_map &, const std::vector &, const std::vector &, const onnxruntime::OrtValueNameIdxMap &, const onnxruntime::NodeIndexInfo &)feeds.size() == feed_mlvalue_idxs.size()fetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size()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 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/2.159.2/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/2.159.2/work/1/s/onnxruntime/core/framework/execution_provider.ccvoid onnxruntime::IExecutionProvider::InsertAllocator(onnxruntime::AllocatorPtr)/Users/runner/runners/2.159.2/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::Unknownfunc info for node: already exist.Can't use func with null ptr not found./Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/framework/fuse_nodes_funcs.ccGetFuncsNo provider specified./Users/runner/runners/2.159.2/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/2.159.2/work/1/s/onnxruntime/core/framework/func_kernel.honnxruntime::FunctionKernel::FunctionKernel(const onnxruntime::OpKernelInfo &)create_func(&context, &func_state_) == 0Op: Version mismatch. node_version: kernel start version: kernel_end_version: kernel def can't be NULLFailed to add kernel for : Conflicting with a registered kernel with op versions.Failed to find kernel for /Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/framework/kernel_registry.ccTryFindKernelFailed to create kernel for op: since it was ill-formed during registration kernel is not supported in Encountered following errors: (void onnxruntime::(anonymous namespace)::TraverseFormalParametersWithTypeProto(const onnxruntime::Node &, ParamFilterFn, TraverseFn) [ParamFilterFn = (lambda at /Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/framework/kernel_registry.cc:71:11), TraverseFn = (lambda at /Users/runner/runners/2.159.2/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/2.159.2/work/1/s/onnxruntime/core/framework/kernel_registry.cc:96:9), TraverseFn = (lambda at /Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/framework/kernel_registry.cc:99:9)] Incompatible types.The node is not placed on any Execution Provider, therefore, can't find a suitable kernel for found duplicated provider in KernelRegistryManagerThe node is not placed on any Execution Provider (node /Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/framework/mldata_type_utils.cconnxruntime::MLDataType onnxruntime::utils::GetMLDataType(const onnxruntime::NodeArg &)nullptr != type_proto/Users/runner/runners/2.159.2/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) constTensor types should have been handled alreadynot implementedElementTypeFromProto:tensor type is not supportedconst T &OrtValue::Get() const [T = onnxruntime::SparseTensor]/Users/runner/runners/2.159.2/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/2.159.2/work/1/s/onnxruntime/core/framework/node_index_info.hint onnxruntime::NodeIndexInfo::GetNodeOffset(onnxruntime::NodeIndex) constnode_offsets_index < node_offsets_size_T *OrtValue::GetMutable() [T = onnxruntime::SparseTensor]/Users/runner/runners/2.159.2/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/2.159.2/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 name:''is defined.Attibute name and type don't matchNo attribute with this name is defined./Users/runner/runners/2.159.2/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/2.159.2/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/2.159.2/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/2.159.2/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/2.159.2/work/1/s/onnxruntime/core/framework/sequential_executor.ccReleaseNodeMLValues/Users/runner/runners/2.159.2/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.Device: [ memory_type: device_id:/Users/runner/runners/2.159.2/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/2.159.2/work/1/s/onnxruntime/core/framework/session_state_initializer.cc:95:3)]ort_value_name_idx_map.MaxIdx() > 0Deserialize 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/2.159.2/work/1/s/onnxruntime/core/framework/simple_tensor_allocator.ccGetPreallocatedBufferFailed to get allocator for initializer '', location: /Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/framework/tensor.cconnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape &, void *, const OrtMemoryInfo &, int64_t)p_type != nullptronnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape &, std::shared_ptr, int64_t)size_t onnxruntime::Tensor::SizeInBytes() constvoid onnxruntime::Tensor::Init(onnxruntime::MLDataType, const onnxruntime::TensorShape &, void *, onnxruntime::AllocatorPtr, int64_t)/Users/runner/runners/2.159.2/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 checksum/Users/runner/runners/2.159.2/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/2.159.2/work/1/s/onnxruntime/core/framework/tensor_type_and_shape.ccOrtStatus *OrtApis::GetTensorTypeAndShape(const OrtValue *, OrtTensorTypeAndShapeInfo **)Invalid 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/2.159.2/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: UnpackTensor: the pre-allocated size does not match the raw data size, expected MKLDNNExecutionProviderNGRAPHExecutionProviderNupharExecutionProviderOpenVINOExecutionProviderNnapiExecutionProviderinvalid allocator/Users/runner/runners/2.159.2/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()CalculateStaticCopyInfoForFeedCopyMLValueExecuteGraphImplOnly 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]`. auto_padpadsConstrain input and output types to float tensorsReorderInput/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/graph/contrib_ops/contrib_defs.ccConstrain input and output types to float/quantized tensorsReorderOutputchannelsactivationactivation_paramsWnum_headsNumber of attention heads3D 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_ids2D words IDs with shape (batch_size, sequence_length)segment_ids2D segment IDs with shape (batch_size, sequence_length)mask2D attention mask 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)1D mask_index tensor with shape (batch_size)Constrain input and output integer tensors typesConstrain input and output float tensors types.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_sizeConstrain input and output types to float or half tensors.Value of alphaValue of beta1D input tensor1D output tensorParametricSoftplusImageScalerBias 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 inputborderA 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 tensorDynamicSlicedataTensor 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 tensorvaluesinput_as_shapeextra_shapeScaleInput 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.tThe timestep for this operation.hiddenThe new GRU hidden state calculated by this op.across_channelsIf 1, mean and variance are computed across channels. Default is 0.normalize_varianceIf 0, normalize the mean only. Default is 1.Scaling valueThe scaled hyperbolic tangent values of the input tensor computed element-wiseMaxpoolWithMaskConstrain input0 and output types to float tensorsConvTransposeWithDynamicPadsPadsFusedConvFusedGemmInput 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.CInput 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.leaky_relu_alphaExpandDimsaxisSpecified 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.N-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'.Zero point for doing quantization to get 'y'. It could be a scalar or a 1-D tensor, which means a per-tensoror per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.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.Scale for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.Zero point for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.N-D full precision output tensor. It has same shape as input 'x'.Constrain 'y', 'x_scale' to float tensors.Constrain 'x_zero_point' and 'x' to 8-bit integer tensors.TokenizerStrings 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).pad_valueThe 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.separatorsan 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.mincharnumMinimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignoredMatMulInteger16N-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).An 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.MurmurHash3An 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 int64WordConvEmbeddingembedding_sizeInteger representing the embedding vector size for each word.If not provide, use the fileter size of conv weightconv_window_sizeThis 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.char_embedding_sizeInteger 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.CDistmetricThe 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".sqeuclidean2D 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.CropAndResizeThe 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.LayerNormalizationThe first normalization dimension: normalization will be performed along dimensions axis : rank(inputs).The epsilon value to use to avoid division by zero.Input data tensor from the previous layer.Scale tensor.Bias tensor.Output data tensor.Saved mean used during training to speed up gradient computationUinv_std_varSaved inverse standard variance used during training to speed up gradient computation.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.invalid channel counttensor rank too smallInputs 0, 1 and 2 shall be 2 dimensionsInputs 0, 1 and 2 shall have same value in dimension 1Inputs 0, 1 and 2 shall have value in dimension 1word_embedding should have 2 dimensions and dimension size is known.position_embedding should have 2 dimensionsThe last dimension of word_embedding and position_embedding does not match.segment_embedding should have 2 dimensionsThe last dimension of word_embedding and segment_embedding does not match.gamma should have 1 dimensionThe last dimension of word_embedding and gamma does not match.beta should have 1 dimensionThe last dimension of word_embedding and beta does not match.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)Input's height () needs to be greater than or equal to the top_border () + bottom_border (Input's width () 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: Input dimensions are either [C] or [N][C] allowedinputs are expected to have tensor type and output type should not be null.both data and indices tensor need to have rank larger than zero.last dimension of indices must not be larger and rank of data tensor'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64first input tensor has wrong dimensionrois input tensor has wrong dimensionbatch_indices shape input tensor has wrong dimensioncrop_size shape input tensor has wrong dimensionConstrain 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/2.159.2/work/1/s/onnxruntime/core/graph/function.cconnxruntime::FunctionImpl::FunctionImpl(const onnxruntime::Graph &, std::unique_ptr)input_arg->Type() != nullptrstatus.IsOK()onnxruntime::FunctionImpl::FunctionImpl(const onnxruntime::Graph &, const onnxruntime::NodeIndex &, const onnx::FunctionProto &)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/2.159.2/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(onnx::GraphProto *, const std::unordered_map &, onnxruntime::Version, onnxruntime::IOnnxRuntimeOpSchemaCollectionPtr, onnxruntime::Graph *, const onnxruntime::Node *, const std::unordered_map &)graph_proto != nullptrConstantconstant_attribute.has_t()Only 'value' attribute is supported within a 'Constant' node in ORTGraphThis 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:InferAndVerifySubgraphTypesThis 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 () 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: Value of initializer does not match its type.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(bool)outer_scope_node_args_consumed.empty()Shouldn't be possible to have NodeArgs that haven't been handled already.NoOpbool onnxruntime::Graph::RemoveNode(onnxruntime::NodeIndex)node->GetOutputEdgesCount() == 0Can't remove node as it still has output edges.void onnxruntime::Graph::ToGraphProtoInternal(onnx::GraphProto &) constnode_argOuter 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.onnxruntime::common::Status onnxruntime::Graph::SetGraphInputsOutputs()This is an invalid model. Graph output () does not exist in the graph. 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()InlineFunctionvoid onnxruntime::Graph::SetInputs(const std::vector &)This API is not supported when model is loaded from proto file right now.void onnxruntime::Graph::SetOutputs(const std::vector &)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.mlunordered_map::at: key not found/Users/runner/runners/2.159.2/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/2.159.2/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()No Graph instance was found for attribute in node Graph attribute inferencing failed: /Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/graph/graph_utils.ccCanUpdateImplicitInputNameInSubgraphs Implicit input name cannot be safely updated to in one of the subgraphs.const std::string &onnxruntime::graph_utils::GetNodeInputName(const onnxruntime::Node &, int)index >= 0 && static_cast(index) < inputs.size()const std::string &onnxruntime::graph_utils::GetNodeOutputName(const onnxruntime::Node &, int)index >= 0 && static_cast(index) < outputs.size()bool onnxruntime::graph_utils::RemoveNode(onnxruntime::Graph &, onnxruntime::Node &)Should be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.onnxruntime::NodeArg &onnxruntime::graph_utils::AddInitializer(onnxruntime::Graph &, const onnx::TensorProto &)!graph.GetInitializedTensor(new_initializer.name(), existing)Initializer with same name exists. Name:void onnxruntime::graph_utils::ReplaceNodeInput(onnxruntime::Node &, int, onnxruntime::NodeArg &)Invalid input index for node . Index: ExplicitInputs: ImplicitInputs:void onnxruntime::graph_utils::AddNodeInput(onnxruntime::Node &, int, onnxruntime::NodeArg &)num_explicit_inputs == static_cast(target_input_idx)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 was null.ModelProto 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./Users/runner/runners/2.159.2/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/2.159.2/work/1/s/onnxruntime/core/graph/schema_registry.ccRegisterOpSetknown 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/2.159.2/work/1/s/onnxruntime/core/common/profiler.ccvoid 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/2.159.2/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 ERRORthread constructor failed/Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/platform/env.ccvoid gsl::fail_fast_assert_handler(const char *const, const char *const, const char *const, int)ReadFileAsString: 'fname' cannot be NULLReadFileAsString: offset must be non-negativeopenfstatReadFileAsString: input is not a regular file file "" failed: read file '' fail, error code = /Users/runner/runners/2.159.2/work/1/s/onnxruntime/core/platform/posix/env.ccUnmapFilemunmap failed. error code:closeFailed to load library with error: Got null library handleFailed to unload library with error: Failed to get symbol lib.so.mutex lock failedOrtCondVar wait failed: mutex not lockedOrtCondVar wait failed, error code=OrtCondVar timed_wait failed, error code=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.sparse_tensor_protoSparse tensor values () must have rank 1.) must have a dense-rank > 0) dimensions are not positive.) must have INT64 type.) must have rank 1 or 2.) has no index values.attrtype field and data field mismatch in attribute Attribute (name: ) should not contain more than one value field.) should refer to attribute in parent node.op_typenodeNodeProto (name: , type: ) has zero input and zero output.Warning: 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/2.159.2/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/2.159.2/work/1/s/cmake/external/onnx/onnx/defs/controlflow/old.ccLoop 'body' subgraph outputs should all be tensors but output 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_tensorFunc_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.Output tensor containing the same value of the provided tensor./Users/runner/runners/2.159.2/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.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.(Optional) Seed to the random generator, if not specified we will auto generate one.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 usethe 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.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.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/2.159.2/work/1/s/cmake/external/onnx/onnx/defs/generator/old.ccAttribute 'value' of Constant node must exist with 'Tensor' data./Users/runner/runners/2.159.2/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). Bitwise shift operator performs element-wise operation. For each input element, if the attribute "direction" is "RIGHT", this operator moves its binary representation toward the right side so that the input value is effectively decreased. If the attribute "direction" is "LEFT", bits of binary representation moves toward the left side, which results the increase of its actual value. The input X is the tensor to be shifted and another input Y specifies the amounts of shifting. For example, if "direction" is "Right", X is [1, 4], and S is [1, 1], the corresponding output Z would be [0, 2]. If "direction" is "LEFT" with X=[1, 2] and S=[1, 2], the corresponding output Y would be [2, 8]. Because this operator supports Numpy-style broadcasting, X's and Y's shapes are not necessarily identical. andConstrains input to boolean tensor.Constrains output to boolean tensor./Users/runner/runners/2.159.2/work/1/s/cmake/external/onnx/onnx/defs/logical/old.ccorxorgreaterConstrains input to float tensors.lessequalConstrains input to integral tensors. Returns the tensor resulted from performing the `{name}` logical operation elementwise on the input tensors `A` and `B`. If broadcasting is enabled, the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. See the doc of `Add` for a detailed description of the broadcasting rules. {name}broadcastEnable broadcastingIf set, defines the broadcast dimensions.Left input tensor for the logical operator.Right input tensor for the logical operator.Result tensor. Returns the tensor resulted from performing the `{name}` logical operation elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support). {broadcast_doc} {broadcast_doc}First input operand for the logical operator.Second input operand for the logical operator.This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).Incompatible dimensions/Users/runner/runners/2.159.2/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.First operand, base of the exponent.Second operand, power of the exponent.Value of alpha.Value of beta.Minimum 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).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'.Scale 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.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. Performs element-wise binary {name} (with Numpy-style broadcasting support). {broadcast_doc} First operand.Second operand.Result, has same element type as two inputs The operator computes the {name} ({description}) values for each layer in the batch of the given input. The input does not need to explicitly be a 2D vector; rather, it will be coerced into one. For an arbitrary n-dimensional tensor input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is the axis provided, then input will be coerced into a 2-dimensional tensor with dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default case where axis=1, this means the input tensor will be coerced into a 2D tensor of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size. In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D. Each of these dimensions must be matched correctly, or else the operator will throw errors. The output tensor has the same shape and contains the {name} values of the corresponding input. Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).'axis' must be in []. Its actual value is: Element-wise {name} of each of the input tensors (with Numpy-style broadcasting support). All inputs and outputs must have the same data type. {broadcast_doc} List of tensors for 'shape' input must be 1D tensor of type INT64inputs are expected to have tensor type.input and zero_point pair is expected to have be same type.input and zero_point pair is expected to have same type.Input rank must be >= 2.The inner-most 2 dimensions must have the same size (mat_w: != mat_h:softmaxnormalized exponential/Users/runner/runners/2.159.2/work/1/s/cmake/external/onnx/onnx/defs/math/old.cclogsoftmaxlog of softmaxhardmax1 for the first maximum value, and 0 for all others 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. additionsubtractionmultiplicationdivisionInput tensor of any shape, base of the exponent.Input tensor of any shape broadcastable to X shape, the exponent component.Pass 1 to enable broadcastingIf set, defines the broadcast dimensions. See doc for details.Output tensor (same size as X)consumed_inputslegacy optimization attribute.Coefficient of leakage default to 0.01.Coefficient of SELU default to 1.6732.Coefficient of SELU default to 1.0507.Coefficient of ELU default to 1.0.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 channelstensor slopeinput tensor XSlope 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 Ctensor A * BTensor 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 retrieve Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) where the function `f(x) = x^exponent`, is applied to the data tensor elementwise. PRelu takes input data (Tensor) and slope tensor as input, and produces one output data (Tensor) where the function `f(x) = slope * x for x < 0`, `f(x) = x for x >= 0`., is applied to the data tensor elementwise. should be unidirectional broadcastable to ); for more details please check [the doc](Broadcasting.md).General Matrix multiplication: https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3 A' = transpose(A) if transA else A B' = transpose(B) if transB else B Compute Y = alpha * A' * B' + beta * C, where input tensor A has shape (M, K) or (K, M), input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N), and output tensor Y has shape (M, N). A will be transposed before doing the computation if attribute transA is non-zero, same for B and transB. The operator computes the {name} ({description}) values for each layer in the batch of the given input. The input is a 2-D tensor (Tensor) of size (batch_size x input_feature_dimensions). The output tensor has the same shape and contains the {name} values of the corresponding input. Input does not need to explicitly be a 2D vector; rather, it will be coerced into one. For an arbitrary n-dimensional tensor input \in [a_0, a_1, ..., a_{k-1}, a_k, ..., a_{n-1}] and k is the axis provided, then input will be coerced into a 2-dimensional tensor with dimensions [a_0 * ... * a_{k-1}, a_k * ... * a_{n-1}]. For the default case where axis=1, this means the input tensor will be coerced into a 2D tensor of dimensions [a_0, a_1 * ... * a_{n-1}], where a_0 is often the batch size. In this situation, we must have a_0 = N and a_1 * ... * a_{n-1} = D. Each of these dimensions must be matched correctly, or else the operator will throw errors. {description}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). Performs element-wise binary {name} (with limited broadcast support). {broadcast_doc}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.Input tensors of wrong rank (0).Incompatible dimensions for matrix multiplicationInvalid 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.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding.Input tensor must have atleast 2 dimensionsAttribute dilations has incorrect sizeAttribute strides has incorrect sizeAttribute kernel_shape has incorrect sizeAttribute kernel_shape must be specifiedAttribute pads has incorrect sizeSecond input tensor has wrong dimensionaverageThe output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero).Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad./Users/runner/runners/2.159.2/work/1/s/cmake/external/onnx/onnx/defs/nn/defs.ccThe output of each pooling window is maximum number of elements exclude pad.The 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.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.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 specifieda filterInput 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_pointScale 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.Output 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.Scale 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.lp poolmomentumFactor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).Input data tensor from the previous operator; dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size, C is the number of channels. Statistics are computed for every channel of C over N and D1 to Dn dimensions. For image data, input dimensions become (N x C x H x W). The op also accepts single dimension input of size N in which case C is assumed to be 1Scale tensor of shape (C).Bias tensor of shape (C).running (training) or estimated (testing) mean tensor of shape (C).running (training) or estimated (testing) variance tensor of shape (C).The output tensor of the same shape as XThe running mean after the BatchNormalization operator.The running variance after the BatchNormalization operator.saved_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.ratioThe ratio of random dropoutThe output mask.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.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].Output tensor, which has the shape and type as input tensorConstrain input and output types to float tensors.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. Dropout takes one input floating tensor and produces two tensor outputs, output (floating tensor) and mask (`Tensor`). Depending on whether it is in test mode or not, the output Y will either be a random dropout, or a simple copy of the input. Note that our implementation of Dropout does scaling in the training phase, so during testing nothing needs to be done. {name} consumes an input tensor X and applies {opName} pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. {opName} pooling consisting of computing the {opName} on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape will be following: ``` output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1) ``` or ``` output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - {kernelSpatialShape}) / strides_spatial_shape[i] + 1) ``` if ceil_mode is enabled ``` * pad_shape[i] is sum of pads along axis i ``` `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following: ``` VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - {kernelSpatialShape} + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) ``` And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: ``` pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + {kernelSpatialShape} - input_spatial_shape[i] ``` {additionalDescription} {opName}{additionalDescription}{kernelSpatialShape}((kernel_spatial_shape[i] - 1) * dilations[i] + 1)kernel_spatial_shape[i]Wether to use ceil or floor (default) to compute the output shape.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 used {name} consumes an input tensor X and applies Lp pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. Lp pooling consisting of computing the Lp norm on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing.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 {name} pool consumes an input tensor X and region of interests (RoIs) to apply {name} pooling across each RoI, to produce output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]).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]). The convolution operator consumes an input tensor and {filter_desc}, and computes the output.{filter_desc}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. The convolution transpose operator consumes an input tensor and {filter_desc}, and computes the output. If the pads parameter is provided the shape of the output is calculated via the following equation: output_shape[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - pads[start_i] - pads[end_i] output_shape can also be explicitly specified in which case pads values are auto generated using these equations: total_padding[i] = stride[i] * (input_size[i] - 1) + output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - output_shape[i] If (auto_pads != SAME_UPPER): pads[start_i] = total_padding[i]/2; pads[end_i] = total_padding[i] - (total_padding[i]/2) Else: pads[start_i] = total_padding[i] - (total_padding[i]/2); pads[end_i] = (total_padding[i]/2). 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. Global{op_type} consumes an input tensor X and applies {op} pooling across the values in the same channel. This is equivalent to {op_type} with kernel size equal to the spatial dimension of input tensor.{op_type}{op}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.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 > 0The output of each pooling window is divided by the number of elements exclude pad./Users/runner/runners/2.159.2/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 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). Dropout takes one input data (Tensor) and produces two Tensor outputs, output (Tensor) and mask (Tensor). Depending on whether it is in test mode or not, the output Y will either be a random dropout, or a simple copy of the input. Note that our implementation of Dropout does scaling in the training phase, so during testing nothing needs to be done. 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) {name} consumes an input tensor X and applies {opName} pooling across the tensor according to kernel sizes, stride sizes, and pad lengths. {opName} pooling consisting of computing the {opName} on all values of a subset of the input tensor according to the kernel size and downsampling the data into the output tensor Y for further processing. The output spatial shape will be following: ``` output_spatial_shape[i] = floor((input_spatial_shape[i] + pad_shape[i] - kernel_spatial_shape[i]) / strides_spatial_shape[i] + 1) * pad_shape[i] is sum of pads along axis i ``` `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following: ``` VALID: output_spatial_shape[i] = ceil((input_spatial_shape[i] - kernel_spatial_shape[i] + 1) / strides_spatial_shape[i]) SAME_UPPER or SAME_LOWER: output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides_spatial_shape[i]) ``` And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`: ``` pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + kernel_spatial_shape[i] - input_spatial_shape[i] ``` {additionalDescription} 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/2.159.2/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.scoresAn 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/2.159.2/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/2.159.2/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 scale. It's a scalar, which means a per-tensor/layer quantization.Output zero point. It's a scalar, which means a per-tensor/layer quantization.Constrain 'x' to float tensor.Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor./Users/runner/runners/2.159.2/work/1/s/cmake/external/onnx/onnx/defs/reduction/defs.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).axis must be in [-rank, rank-1]. input rank was Computes the indices of the {name} elements of the input tensor's element along the provided axis. The resulted tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulted 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)./Users/runner/runners/2.159.2/work/1/s/cmake/external/onnx/onnx/defs/reduction/old.ccsum squareproductlog sumlog sum exponentL1 normL2 norm Computes the {name} of the input tensor's element along the provided axes. The resulted tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. The above behavior is similar to numpy, with the exception that numpy default keepdims to False instead of True. Computes the indices of the {name} elements of the input tensor's element along the provided axis. The resulted tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulted tensor have the reduced dimension pruned. The type of the output tensor is integer.The axis in which to compute the arg indices.Reduced output tensor with integer data type.First input tensor must have rank 3/Users/runner/runners/2.159.2/work/1/s/cmake/external/onnx/onnx/defs/rnn/defs.ccCouple the input and forget gates if 1. Computes an one-layer simple RNN. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `t` - time step (t-1 means previous time step) `Wi` - W parameter weight matrix for input gate `Ri` - R recurrence weight matrix for input gate `Wbi` - W parameter bias vector for input gate `Rbi` - R parameter bias vector for input gate `WBi` - W parameter weight matrix for backward input gate `RBi` - R recurrence weight matrix for backward input gate `WBbi` - WR bias vectors for backward input gate `RBbi` - RR bias vectors for backward input gate `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Tanh): - Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi) Computes an one-layer GRU. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `z` - update gate `r` - reset gate `h` - hidden gate `t` - time step (t-1 means previous time step) `W[zrh]` - W parameter weight matrix for update, reset, and hidden gates `R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates `Wb[zrh]` - W bias vectors for update, reset, and hidden gates `Rb[zrh]` - R bias vectors for update, reset, and hidden gates `WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates `RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates `WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates `RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh): - zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz) - rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr) - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0 - ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0 - Ht = (1 - zt) (.) ht + zt (.) Ht-1 Computes an one-layer LSTM. This operator is usually supported via some custom implementation such as CuDNN. Notations: `X` - input tensor `i` - input gate `o` - output gate `f` - forget gate `c` - cell gate `t` - time step (t-1 means previous time step) `W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates `R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates `Wb[iofc]` - W bias vectors for input, output, forget, and cell gates `Rb[iofc]` - R bias vectors for input, output, forget, and cell gates `P[iof]` - P peephole weight vector for input, output, and forget gates `WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates `RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates `WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates `RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates `PB[iof]` - P peephole weight vector for backward input, output, and forget gates `H` - Hidden state `num_directions` - 2 if direction == bidirectional else 1 Activation functions: Relu(x) - max(0, x) Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x}) Sigmoid(x) - 1/(1 + e^{-x}) (NOTE: Below are optional) Affine(x) - alpha*x + beta LeakyRelu(x) - x if x >= 0 else alpha * x ThresholdedRelu(x) - x if x >= alpha else 0 ScaledTanh(x) - alpha*Tanh(beta*x) HardSigmoid(x) - min(max(alpha*x + beta, 0), 1) Elu(x) - x if x >= 0 else alpha*(e^x - 1) Softsign(x) - x/(1 + |x|) Softplus(x) - log(1 + e^x) Equations (Default: f=Sigmoid, g=Tanh, h=Tanh): - it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi) - ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf) - ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc) - Ct = ft (.) Ct-1 + it (.) ct - ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo) - Ht = ot (.) h(Ct) 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/2.159.2/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.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/2.159.2/work/1/s/cmake/external/onnx/onnx/defs/sequence/defs.ccTensors.Sequence enclosing the input tensors.input_sequenceInput sequence.tensorInput 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.lengthLength 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 positive. 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/2.159.2/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).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).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.Tensor of rank q + r - indices_shape[-1] - 1.Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of 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.output (always 2D tensor)(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'Tensor of rank q >= 1. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Supported modes: `constant`(default), `reflect`, `edge`Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank]. `pads` format should be: [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `i` and xi_end, the number of pad values added at the end of axis `i`.constant_value(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0). 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=Invalid 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 1Both `data` and `indices` input tensors in GatherND op need to have rank larger than 0.Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensor'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64Pads has incorrect number of valuestoThe 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/2.159.2/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.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 lengthDimension value inferred () is not equal to the existing dim value (Number of elements of input 'sizes' must be same as rank of input 'X'Input 'sizes' must have int64 element type.Number of elements of input 'scales' must be same as rank of input 'X'Input 'scales' must have float element type.Data to be selectedThe indices, based on 0 as the first index of any dimension.Selected output data as an arrayThe input must be a tensor of a numeric type or string. The output will be of the same tensor type./Users/runner/runners/2.159.2/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/2.159.2/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/2.159.2/work/1/b/RelWithDebInfo/onnx/onnx-ml.pb.cc/Users/runner/runners/2.159.2/work/1/s/cmake/external/protobuf/src/google/protobuf/arena.ccCHECK failed: (min_bytes) <= (std::numeric_limits::max() - kBlockHeaderSize): /Users/runner/runners/2.159.2/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.Multiple extension registrations for type "", field number can't reach here./Users/runner/runners/2.159.2/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/2.159.2/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 google/protobuf/io/coded_stream.h.CHECK failed: (buffer_size) >= (0): /Users/runner/runners/2.159.2/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/2.159.2/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_): /Users/runner/runners/2.159.2/work/1/s/cmake/external/protobuf/src/google/protobuf/message_lite.ccExceeded maximum protobuf size of 2GB: CHECK failed: !coded_out.HadError(): parseCan'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./Users/runner/runners/2.159.2/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/2.159.2/work/1/s/cmake/external/protobuf/src/google/protobuf/stubs/int128.ccDivision or mod by zero: dividend.hi=, lo=/Users/runner/runners/2.159.2/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/2.159.2/work/1/s/cmake/external/protobuf/src/google/protobuf/wire_format_lite.ccCHECK failed: (value.size()) <= (kint32max): parsingserializing '%s'String field contains invalid UTF-8 data when a protocol buffer. Use the 'bytes' type if you intend to send raw bytes. /Users/runner/runners/2.159.2/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/2.159.2/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/2.159.2/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/2.159.2/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/2.159.2/work/1/s/cmake/external/re2/re2/onepass.ccCannot use SearchOnePass for unanchored matches.unhandled opcode: /Users/runner/runners/2.159.2/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/2.159.2/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/2.159.2/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/2.159.2/work/1/s/cmake/external/re2/re2/walker-inl.hStack not empty.NamedCapturesWalker::ShortVisit calledCaptureNamesWalker::ShortVisit calledWalk NULL/Users/runner/runners/2.159.2/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/2.159.2/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%02x\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_SquareZlZpZs\%03oOrtGetApiBaseOrtSessionOptionsAppendExecutionProvider_CPU PT XQ!QaQX QaQ! 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ХetLogHandlerEPFvNS0_8LogLevelEPKciRKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEEE tringAppend StringPrintfEPNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKcz C D 2Ev 1Ev 2Ev 1Ev ШClosureD uint128 2Ev 1Ev 0Ev 2Ev 1Ev 0Ev Ъа10DivModImplES1_S1_PS1_S2_ dVERKS1_ rMERKS1_ 6Status lsERNSt3__113basic_ostreamIcNS2_11char_traitsIcEEEERKNS1_6StatusE C aSERKS2_ 2OKE9CANCELLEDE7UNKNOWNE1E 2E v NS1_5error4CodeENS0_11StringPieceE RKS2_ v NS1_5error4CodeENS0_11StringPieceE RKS2_ eqERKS2_ 8ToStringEv VEPNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKcP13__va_list_tag FEPNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPKcz GenericScanFastAsciiEPKNS1_19UTF8StateMachineObjEPKciPi SpnStructurallyValidERKNS0_11StringPieceE UTF8CoerceToStructurallyValidERKNS0_11StringPieceEPcc CodedOutputStreamFieldSkipper Ѐ9 1 2 30ReadPackedEnumPreserveUnknownsEPNS0_2io16CodedInputStreamEiPFbiEPNS3_17CodedOutputStreamEPNS0_13RepeatedFieldIiEE 8EnumSizeERKNS0_13RepeatedFieldIiEE SkipFieldEPNS0_2io16CodedInputStreamEj Write ReadBytesEPNS0_2io16CodedInputStreamEP Int PNS3_17CodedOutputStreamE 1 5WriteFloatArrayEPKfiPNS0_2io17CodedOutputStreamE 6 7WriteFixed 8WriteSFixed 4WriteBoolArrayEPKbiPNS0_2io17CodedOutputStreamE 0 2Write 3WriteSFixed SkipMessageEPNS0_2io16CodedInputStreamE Write PNS3_17CodedOutputStreamE 9SkipFieldEPNS0_2io16CodedInputStreamEj 1 1SkipMessageEPNS0_2io16CodedInputStreamE 5SkipUnknownEnumEii 9SkipFieldEPNS0_2io16CodedInputStreamEj 1 1SkipMessageEPNS0_2io16CodedInputStreamE 5SkipUnknownEnumEii 2 3 4WriteMessageMaybeToArrayEiRKNS0_11MessageLiteEPNS0_2io17CodedOutputStreamE 1k0kMessageSetTypeIdTagEReadPackedEnumNoInlineEPNS0_2io16CodedInputStreamEPFbiEPNS0_13RepeatedFieldIiEE Write kFieldTypeToCppTypeMapEnjWriteDoubleArrayEPKdiPNS0_2io17CodedOutputStreamE VerifyUtf8StringEPKciNS2_9OperationES4_ 32ArrayEPKjiPNS0_2io17CodedOutputStreamE 64ArrayEPKyiPNS0_2io17CodedOutputStreamE 32ArrayEPKiiPNS0_2io17CodedOutputStreamE 64ArrayEPKxiPNS0_2io17CodedOutputStreamE Write UInt SInt Int FloatEifPNS0_2io17CodedOutputStreamE BytesEiRKNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEEPNS0_2io17CodedOutputStreamE GroupEiRKNS0_11MessageLiteEPNS0_2io17CodedOutputStreamE 32EiiPNS0_2io17CodedOutputStreamE 64EixPNS0_2io17CodedOutputStreamE UInt S DoubleEidPNS0_2io17CodedOutputStreamE 32EijPNS0_2io17CodedOutputStreamE 64EiyPNS0_2io17CodedOutputStreamE Int tringEiRKNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEEPNS0_2io17CodedOutputStreamE 32EiiPNS0_2io17CodedOutputStreamE 64EixPNS0_2io17CodedOutputStreamE ХFixed MessageEiRKNS0_11MessageLiteEPNS0_2io17CodedOutputStreamE 32EijPNS0_2io17CodedOutputStreamE 64EiyPNS0_2io17CodedOutputStreamE 32EiiPNS0_2io17CodedOutputStreamE 64EixPNS0_2io17CodedOutputStreamE BoolEibPNS0_2io17CodedOutputStreamE EnumEiiPNS0_2io17CodedOutputStreamE аWriteStringMaybeAliasedEiRKNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEEPNS0_2io17CodedOutputStreamE kMessageSetItemеBytesMaybeAliasedEiRKNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEEPNS0_2io17CodedOutputStreamE GroupMaybeToArrayEiRKNS0_11MessageLiteEPNS0_2io17CodedOutputStreamE NSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEE PNSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEE 32SizeERKNS0_13RepeatedFieldIiEE 64SizeERKNS0_13RepeatedFieldIxEE 32SizeERKNS0_13RepeatedFieldIjEE 64SizeERKNS0_13RepeatedFieldIyEE 32SizeERKNS0_13RepeatedFieldIiEE 64SizeERKNS0_13RepeatedFieldIxEE  8 4Prog 9 6 3 1 2 lsERNSt3__113basic_ostreamIcNS0_11char_traitsIcEEEERKNS_11StringPieceE 7 BitState Compiler fullruneEPKci C 9 4PushEiPKci 6SearchERKNS_11StringPieceES3_bbPS1_i 2EPNS_4ProgE 1EPNS_4ProgE GrowStackEv TrySearchEiPKc  1 6 9ۈ 30TEST_dfa_should_bail_when_slowEb 4 C D 8ר 7FlattenEv̪ 4 0 8PossibleMatchRangeEPNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES8_iʊ 6ComputeFirstByteEv 3 1DumpByteMapEvɨ SearchBitStateERKNS_11StringPieceES3_NS0_6AnchorENS0_9MatchKindEPS1_i BuildEntireDFAENS0_9MatchKindERKNSt3__18functionIFvPKibEEE DumpUnanchoredEv¨ ComputeByteMapEvŪ MarkSuccessorsEPNS_11SparseArrayIiEES3_PNSt3__16vectorINS5_IiNS4_9allocatorIiEEEENS6_IS8_EEEEPNS_10SparseSetTIvEEPS8_Ӫ PatchList ApplyFoldEPKNS_8CaseFoldEi CharClass 2MkEj 5 6AppendEPNS_4Prog4InstES0_S0_ DerefEPNS_4Prog4InstES0_ PatchEPNS_4Prog4InstES0_j  C 9 D 7 3 4 5 1 2 8 6FinishEv 2Ev 1Ev A ByteRangeEiib PostVisitEPNS_6RegexpENS_4FragES3_PS3_i llocInstEi ddSuffixEi Ѐ2Ev 1Ev 0Ev ІNoMatchEv C LiteralEib DotStarEv CatENS_4FragES1_ AltENS_4FragES1_ NopEv StarENS_4FragEb PlusENS_4FragEb CopyENS_4FragE QuestENS_4FragEb MatchEi SetupENS_6Regexp10ParseFlagsExNS_3RE26AnchorE 0 8Add 3 4ByteRangeEqualEii 2AddRuneRangeEiib 6AddRuneRangeUTF8Eiib EmptyWidthENS_7EmptyOpE BeginRangeEv 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4StepEPNS_11SparseArrayIPNS0_6ThreadEEES5_iRKNS_11StringPieceEPKc 6SearchERKNS_11StringPieceES3_bbPS1_i 2EPNS_4ProgE 1EPNS_4ProgE 2Ev 1Ev 2AddToThreadqEPNS_11SparseArrayIPNS0_6ThreadEEEiiRKNS_11StringPieceEPKcS3_ 3FormatCaptureEPPKc  4 3 6 7ParseUnicodeGroupEPNS_11StringPieceENS_6Regexp10ParseFlagsEPNS_16CharClassBuilderEPNS_12RegexpStatusEݢ 9ڣ 1ڪ 2 0 5 8num_unicode_groupsEҏOnePass_ChecksEv LookupCaseFoldEPKNS_8CaseFoldEii ByteMapBuilder CoalesceWalker SimplifyWalker ToStringWalker unicode_groupsESearchOnePassERKNS_11StringPieceES3_NS0_6AnchorENS0_9MatchKindEPS1_i MarkDominatorEiPNS_11SparseArrayIiEES3_PNSt3__16vectorINS5_IiNS4_9allocatorIiEEEENS6_IS8_EEEEPNS_10SparseSetTIvEEPS8_ ParseState NewLiteralEiNS0_10ParseFlagsE CВ Dړ 1 8 9PushCaratEv 7PushDotEv 20DoLeftParenNoCaptureEv 2ENS0_10ParseFlagsERKNS_11StringPieceEPNS_12RegexpStatusE̓ 1ENS0_10ParseFlagsERKNS_11StringPieceEPNS_12RegexpStatusEӓ 2Ev 1Ev 2۔ 0 7MaybeConcatStringEiNS0_10ParseFlagsEڕ 1 6P 4P 3Do 5DoConcatenationEvӛ FinishRegexpEPS0_ P DoRightParenEvڛ Push DoCollapseENS_8RegexpOpEϢ RegexpEPS0_ӕ DollarEv IsMarkerENS_8RegexpOpE DoFinishEv CycleFoldRuneEi S PushLiteralEi DoLeftParenERKNS_11StringPieceE ushח arseCCRangeEPNS_11StringPieceEPNS_9RuneRangeERKS2_PNS_12RegexpStatusE SimpleOpENS_8RegexpOpE RepeatOpENS_8RegexpOpERKNS_11StringPieceEb ushWordBoundaryEb arseCCCharacterEPNS_11StringPieceEPiRKS2_PNS_12RegexpStatusE RepetitionWalker CharClassBuilderɡ num_posix_groupsEunicode_casefoldE8PreVisitEPNS_6RegexpEiPb̚ 9PostVisitEPNS_6RegexpEiiPiiӚ 10ShortVisitEPNS_6RegexpEiښ ushRepetitionEiiRKNS_11StringPieceEb arse VerticalBarEv̛ AlternationEv eading iteralStringEPiiNS0_10ParseFlagsE RegexpEPS0_Ԝ StringEPS0_PiPNS0_10ParseFlagsE RegexpEPS0_ StringEPS0_i FactorAlternationEPPS0_iNS0_10ParseFlagsE ConcatOrAlternateENS_8RegexpOpEPPS0_iNS0_10ParseFlagsEb AlternateNoFactorEPPS0_iNS0_10ParseFlagsE 1FactorAlternationImpl6Round 2MaybeParsePerlCCEscapeEPNS_11StringPieceENS_6Regexp10ParseFlagsE֢ 0num_unicode_casefoldE1EPPNS_6RegexpEiNS1_10ParseFlagsEPNSt3__16vectorINS_6SpliceENS5_9allocatorIS7_EEEE 2EPPNS_6RegexpEiNS1_10ParseFlagsEPNSt3__16vectorINS_6SpliceENS5_9allocatorIS7_EEEE 3EPPNS_6RegexpEiNS1_10ParseFlagsEPNSt3__16vectorINS_6SpliceENS5_9allocatorIS7_EEEE¡ И1 C 8 4CopyEv 6NegateEv 3AddRangeFlagsEiiNS_6Regexp10ParseFlagsEȢ 2 0FoldsASCIIEv 1RemoveAboveEi CharClassEPNS_11StringPieceEPPS0_PNS_12RegexpStatusẸ PerlFlagsEPNS_11StringPieceEӣ ConvertLatin1ToUTF8ERKNS_11StringPieceEPNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEE num_unicode_tolowerEˏParseERKNS_11StringPieceENS0_10ParseFlagsEPNS_12RegexpStatusEڥ QuestEPS0_NS0_10ParseFlagsE EqualEPS0_S1_ 9Bitmap25614FindNextSetBitEi 3RE2 1 Inst DumpEv 7Init 1 9InitMatchEi 8InitFailEv 4DumpEv AltEjj NopEj 3InitByteRangeEiiijΧ 1InitCaptureEijէ 4InitEmptyWidthENS_7EmptyOpEjܧ Б2Ev 1Ev ДЕ2Ev 1Ev ЖУOptimizeEvߩ EmitListEiPNS_11SparseArrayIiEEPNSt3__16vectorINS0_4InstENS4_9allocatorIS6_EEEEPNS_10SparseSetTIvEEPNS5_IiNS7_IiEEEE« Ф4MarkEii 5 7RecolorEi MergeEv BuildEPhPi S perl_groupsEparseArrayIiE4lessERKNS1_10IndexValueES4_ tringPiece4nposE7 C 4InitERKNS_11StringPieceERKNS0_7OptionsEȯ D 1 8ConsumeNEPNS_11StringPieceERKS0_PKPKNS0_3ArgEi 9QuoteMetaERKNS_11StringPieceE 3Argʻ Options® ReplaceEPNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERKS0_RKNS_11StringPieceE ExtractERKNS_11StringPieceERKS0_S3_PNSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEE Cޮ 14kDefaultMaxMemE2ENS0_13CannedOptionsE 1ENS0_13CannedOptionsE Г2E 1Eϯ PKc RKNS ДЗPKc RKNSŰ t3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE _11StringPieceE t3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE _11StringPieceE RKNS0_7OptionsEױ RKNS0_7OptionsEޱ 7 1 20 5MatchERKNS_11StringPieceEmmNS0_6AnchorEPS1_i Options10ParseFlagsEv DoMatchERKNS_11StringPieceENS0_6AnchorEPmPKPKNS0_3ArgEiʹ RewriteEPNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERKNS_11StringPieceEPSA_i 1 8 3ProgramFanoutEPNSt3__13mapIiiNS1_4lessIiEENS1_9allocatorINS1_4pairIKiiEEEEEE 9CapturingGroupNamesEv ReverseProgEv ProgramSizeEv 2Ev 1Ev ReverseProgramSizeEv PossibleMatchRangeEPNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES8_i CheckRewriteStringERKNS_11StringPieceEPNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEEû ReverseProgramFanoutEPNSt3__13mapIiiNS1_4lessIiEENS1_9allocatorINS1_4pairIKiiEEEEEE NamedCapturingGroupsEv 0FullMatchNERKNS_11StringPieceERKS0_PKPKNS0_3ArgEiù 3ѹ 5FindAndConsumeNEPNS_11StringPieceERKS0_PKPKNS0_3ArgEi 1MaxSubmatchERKNS_11StringPieceE PartialMatchNERKNS_11StringPieceERKS0_PKPKNS0_3ArgEi GlobalReplaceEPNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERKS0_RKNS_11StringPieceE 1 2 9parse_intEPKcmPv 0parse_ 2parse_ 7parse_ 1parse_ 6parse_ 8parse_ 5parse_ 9parse_u 3parse_int_hexEPKcmPv 4parse_ nullEPKcmPv charEPKcmPv uintEPKcmPv longEPKcmPv stringEPKcmPv doubleEPKcmPv ushortEPKcmPv s u long_cradixEPKcmPv tringpieceEPKcmPv hort_ s׾ u floatEPKcmPv charEPKcmPv hortEPKcmPv charEPKcmPv longEPKcmPv long_ʿ u int_cradixEPKcmPv radixEPKcmPvi octalEPKcmPv long_ int_cradixEPKcmPv radixEPKcmPvi octalEPKcmPv radixEPKcmPvi octalEPKcmPv u short_cradixEPKcmPv longlong_hexEPKcmPv short_ long_cradixEPKcmPv radixEPKcmPvi octalEPKcmPv int_ short_hexEPKcmPv ulong radixEPKcmPvi octalEPKcmPv Њint_ short_hexEPKcmPv radixEPKcmPvi octalEPKcmPv 0parse_longlong_ 1parse_ 2parse_ulonglong_cradixEPKcmPv radixEPKcmPvi octalEPKcmPv ulonglong_ longlong_cradixEPKcmPv radixEPKcmPvi octalEPKcmPv Кshort_cradixEPKcmPv longlong_hexEPKcmPv uint_hexEPKcmPv long _hexEPKcmPv longEPKcmPv _hexEPKcmPv longEPKcmPv ФХ2ENS_8RegexpOpENS0_10ParseFlagsE 1ENS_8RegexpOpENS0_10ParseFlagsE 2Ev 1Ev 6 3NewEi 8ContainsEi DeleteEv NegateEv QuickDestroyEv NewCharClassEPNS_9CharClassENS0_10ParseFlagsE CaptureNamesEv IncrefEv DecrefEv 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7Concat2EPNS_6RegexpES2_NS1_10ParseFlagsE 0ShortVisitEPNS_6RegexpES2_ 4SimplifyRepeatEPNS_6RegexpEiiNS1_10ParseFlagsE 7SimplifyCharClassEPNS_6RegexpE ЄЗ4 6substrEmm 5rfindE copyEPcmm findE RKS0_m cm RKS0_m cm 8PreVisitEPNS_6RegexpEiPb 9PostVisitEPNS_6RegexpEiiPii chartoruneEPiPKc runetocharEPcPKi runelenEi utfruneEPKci CEscapeERKNS_11StringPieceE PrefixSuccessorEPNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEE num_perl_groupsEunicode_tolowerEďStringPrintfEPNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEEPKcz tringAppendFEPNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEEPKcz N 1 PF F Z8OrtValueSt3__1 11onnxruntime 3 4onnx 5Eigen1 8gemmlowp6google8protobuf2 1 3mapI0__shared_ptr_ 1__basic_string_commonILb1EEEemplaceIN pointerIP11onnxruntime S_ 1 2 5ModelENS_9allocatorIS2_EEEE4 6AllocatorWrapperENS_9allocatorIS2_EEEE8CpuProviderFactoryENS_9allocatorIS2_EEEECustomRegistryENS_9allocatorIS2_EEEE KernelRegistryENS_9allocatorIS2_EEEE Џя7OnnxRuntimeOpSchemaRegistryENS_9allocatorIS2_EEEE 1SchemaRegistryManagerENS_9allocatorIS2_EEEEҏ0__function6__ 8basic_stringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE 7bad_function_callE 4 3basic_f 9basic_ostringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEEՔ5basic_stringbufIcNS_11char_traitsIcEENS_9allocatorIcEEEEܔ2baseIF funcI PN11onnxruntime8OpKernelERKNS2_12OpKernelInfoEEEE b v NllEEEffffxfbEEEiPN11onnxruntime14ComputeContextEPPvEEEԏ 1 8 2ڐ739456 4 1 6 0532ܙ78۟9CustomOpKernelE ESGatherElementsEQuantizeLinearITensorTypeBaseEFunctionKernelEՏOrt9ExceptionE gsl15narrowing_errorEԈre2֏9OrtDefaultAllocator 6OrtAllocatorImpl 2OrtAllocator 4LoggingWrapper׏׏׏N PF Z 4onnx8OpSchema 11onnxruntime19num_inputs_allowed_MUliE_ENS_9allocatorIS4_EEFbiEEE 20num_outputs_allowed_MUliE_ENS_9allocatorIS4_EEFbiEEE ؏iEEE PKN11onnxruntime4NodeES5_EEE؏8OpSchema 1 7checker15ValidationErrorE9219num_inputs_allowed_MUliE_E 20num_outputs_allowed_MUliE_E ُُڏ 4 1 650ρ9TensorProto_SegmentE7SparseTensorProtoE83InferenceErrorE AttributeProtoEValueInfoProtoEڏۏSchemaErrorE TensorProtoEۏv PN11onnxruntime8OpKernelERKNS2_12OpKernelInfoEENS_9allocatorIS9_EES8_EEllENS_9allocatorIS3_EES2_EEffffENS_9allocatorIS3_EES2_EEڌN11onnxruntime6common6StatusERKNS2_4NodeERNS2_5GraphERKNS_6vectorIPKN4onnx9TypeProtoENS_9allocatorISE_EEEERSH_ENSF_ISM_EESL_EERN4onnx ON4onnx8OpSchemaEENS_9allocatorIS6_EES5_EE 16InferenceContextEENS_9allocatorIS6_EES5_EE 8OpSchemaEENS_9allocatorIS6_EES5_EE܏R ON4onnx8OpSchemaEEEE N5Eigen3MapINS2_6MatrixIllEEEiEEEPvEEEŌxxEEEN4onnx KN11onnxruntime7NodeArgEbEEE16InferenceContextEEEE 8OpSchemaEEEE܏v PN11onnxruntime8OpKernelERKNS_12OpKernelInfoEEllEffffEN11onnxruntime6common6StatusERKNS_4NodeERNS_5GraphERKNSt3__16vectorIPKN4onnx9TypeProtoENS7_9allocatorISC_EEEERSF_ERN4onnx ON4onnx8OpSchemaEE PvE16InferenceContextEE 8OpSchemaEEݏv 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InfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_ArgMax_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_ArgMin_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_AveragePool_kOnnxDomain_ver7_9EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Cast_kOnnxDomain_ver6_9_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Cast_kOnnxDomain_ver6_9_int8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_CumSum_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_DepthToSpace_kOnnxDomain_ver11EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Equal_kOnnxDomain_ver7_10_boolEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Expand_kOnnxDomain_ver8_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Expand_kOnnxDomain_ver8_int8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_GlobalMaxPool_kOnnxDomain_ver1EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Less_kOnnxDomain_ver7_9_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_LogSoftmax_kOnnxDomain_ver1_10EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_MatMul_kOnnxDomain_ver9_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_NonZero_kOnnxDomain_ver9_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_RandomUniform_kOnnxDomain_ver1EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Resize_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Round_kOnnxDomain_ver11_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Slice_kOnnxDomain_ver11_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Slice_kOnnxDomain_ver11_int8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Slice_kOnnxDomain_ver11_stringEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Slice_kOnnxDomain_ver1_9_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Where_kOnnxDomain_ver9_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_Where_kOnnxDomain_ver9_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Cast_kOnnxDomain_ver6_9_int16_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Cast_kOnnxDomain_ver6_9_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Cast_kOnnxDomain_ver6_9_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Cast_kOnnxDomain_ver6_9_uint8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_ConvTranspose_kOnnxDomain_ver11EEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_CumSum_kOnnxDomain_ver11_doubleEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Equal_kOnnxDomain_ver11_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Equal_kOnnxDomain_ver11_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Expand_kOnnxDomain_ver8_int16_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Expand_kOnnxDomain_ver8_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Expand_kOnnxDomain_ver8_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Expand_kOnnxDomain_ver8_uint8_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_MatMul_kOnnxDomain_ver1_8_floatEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_MatMul_kOnnxDomain_ver9_int32_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_MatMul_kOnnxDomain_ver9_int64_tEEENS_16KernelCreateInfoEv__ZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_QLinearMatMul_kOnnxDomain_ver10EEENS_16KernelCreateInfoEv__ZN11onnxruntime2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OpSchemaINS_14Cast_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Ceil_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Ceil_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Clip_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Clip_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Conv_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Cosh_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Det_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14LSTM_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14LSTM_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Less_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Less_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Less_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Loop_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Mean_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Mean_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Mean_Onnx_ver8EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Mod_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_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_15Clip_Onnx_ver11EEENS_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_17ArgMin_Onnx_ver11EEENS_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_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_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_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_19GatherND_Onnx_ver11EEENS_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_20ReduceMean_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceMin_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceProd_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceSum_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEv__ZN4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ExitKernel.LProcessCountM1.LProcessNextColumnLoop16xN266.LComputeBlockBy1Loop286.LSkipAccumulateOutput16xNBlock266.LProcessRemainingCountN266.LComputeBlockBy1Loop331.LSkipAccumulateOutput8xNBlock266.LOutputMasked16xNBlock266.LSkipAccumulateOutputMasked16xNBlock266.LOutputMasked8xNBlock266.LSkipAccumulateOutputMasked8xNBlock266.LProcessCountM3.LProcessNextColumnLoop16xN375.LComputeBlockBy1Loop395.LSkipAccumulateOutput16xNBlock375.LProcessRemainingCountN375.LComputeBlockBy1Loop452.LSkipAccumulateOutput8xNBlock375.LOutputMasked16xNBlock375.LSkipAccumulateOutputMasked16xNBlock375.LOutputMasked8xNBlock375.LSkipAccumulateOutputMasked8xNBlock375.LProcessRowLoop4.LProcessColumnLoop4By32.LSkipAccumulateOutput4By32.LStoreOutput4By32.LAdvanceRowLoop4.LProcessRemainingRows.LExitKernel.LProcessColumnLoop4By8.LSkipAccumulateOutput4By8.LProcessColumn4By4.LSkipAccumulateOutput4By4.LProcessColumn4BySmallN.LProcessColumn4By1.LComputeOutput4BySmallN.LStoreOutput4BySmallN.LProcessRemainingSmallK.LLoadVectorASingleRemainingByte.LBroadcastVectorARemainingBytes.LProcessColumnLoopSmallKBy4.LComputeOutputSmallKBy4.LSkipAccumulateOutputSmallKBy4.LProcessColumnLoopSmallKBySmallN.LComputeOutputSmallKBySmallN.LSkipAccumulateOutputSmallKBySmallN.LCopyPackA.ProcessNextRowM4.LCopyPackA.ProcessNextColumnLoopM4.LCopyPackA.ProcessRemainingColumnsM4.LCopyPackA.CopyRemainingCountKLessThan8M4.LCopyPackA.CopyRemainingCountKLessThan4M4.LCopyPackA.CopyRemainingCountKLessThan2M4.LCopyPackA.ProcessPaddedMatrixADataM4.LCopyPackA.ReduceRowSumVectorM4.LCopyPackA.ProcessRemainingRows.LCopyPackA.ProcessNextRowM1.LCopyPackA.ProcessNextColumnLoopM1.LCopyPackA.ProcessRemainingColumnsM1.LCopyPackA.CopyRemainingCountKLessThan8M1.LCopyPackA.CopyRemainingCountKLessThan4M1.LCopyPackA.CopyRemainingCountKLessThan2M1.LCopyPackA.ProcessPaddedMatrixADataM1.LCopyPackA.ReduceRowSumVectorM1.LCopyPackA.ExitRoutine.LCopyPackB.ProcessNextColumnN16.LCopyPackB.ProcessNextRowLoopN16.LCopyPackB.ProcessRemainingRowsN16.LCopyPackB.ReduceColumnSumVectorN16.LCopyPackB.ProcessRemainingColumns.LCopyPackB.ExitRoutine.LCopyPackB.ProcessColumnNUnaligned.LCopyPackB.ProcessNextRowLoopNUnaligned.LCopyPackB.CopyRemainingCountNLessThan8K2.LCopyPackB.CopyRemainingCountNLessThan4K2.LCopyPackB.CopyRemainingCountNLessThan2K2.LCopyPackB.ProcessPaddedMatrixBDataK2.LCopyPackB.ProcessRemainingRowsNUnaligned.LCopyPackB.CopyRemainingCountNLessThan8K1.LCopyPackB.CopyRemainingCountNLessThan4K1.LCopyPackB.CopyRemainingCountNLessThan2K1.LCopyPackB.ProcessPaddedMatrixBDataK1.LCopyPackB.ReduceColumnSumVectorNUnaligned.LProcessCountM2.LProcessNextColumnLoop16xN0.LComputeBlockBy2Loop20.LProcessRemainingBlocks20.LComputeBlockLoopExit20.LSkipAccumulateOutput16xNBlock0.LProcessRemainingCountN0.LComputeBlockBy1Loop112.LComputeBlockLoopExit112.LSkipAccumulateOutput8xNBlock0.LOutputMasked16xNBlock0.LSkipAccumulateOutputMasked16xNBlock0.LOutputMasked8xNBlock0.LSkipAccumulateOutputMasked8xNBlock0.LProcessCountM4.LProcessNextColumnLoop16xN171.LComputeBlockBy2Loop191.LProcessRemainingBlocks191.LComputeBlockLoopExit191.LSkipAccumulateOutput16xNBlock171.LProcessRemainingCountN171.LComputeBlockBy1Loop289.LComputeBlockLoopExit289.LSkipAccumulateOutput8xNBlock171.LOutputMasked16xNBlock171.LSkipAccumulateOutputMasked16xNBlock171.LOutputMasked8xNBlock171.LSkipAccumulateOutputMasked8xNBlock171.LProcessCountM6.LProcessNextColumnLoop16xN350.LComputeBlockBy2Loop370.LProcessRemainingBlocks370.LComputeBlockLoopExit370.LSkipAccumulateOutput16xNBlock350.LProcessRemainingCountN350.LComputeBlockBy1Loop474.LComputeBlockLoopExit474.LSkipAccumulateOutput8xNBlock350.LOutputMasked16xNBlock350.LSkipAccumulateOutputMasked16xNBlock350.LOutputMasked8xNBlock350.LSkipAccumulateOutputMasked8xNBlock350.LExitKernel.LProcessCountM1.LProcessNextColumnLoop16xN537.LComputeBlockBy1Loop557.LComputeBlockLoopExit557.LSkipAccumulateOutput16xNBlock537.LProcessRemainingCountN537.LComputeBlockBy1Loop616.LComputeBlockLoopExit616.LSkipAccumulateOutput8xNBlock537.LOutputMasked16xNBlock537.LSkipAccumulateOutputMasked16xNBlock537.LOutputMasked8xNBlock537.LSkipAccumulateOutputMasked8xNBlock537.LProcessCountM3.LProcessNextColumnLoop16xN674.LComputeBlockBy1Loop694.LComputeBlockLoopExit694.LSkipAccumulateOutput16xNBlock674.LProcessRemainingCountN674.LComputeBlockBy1Loop755.LComputeBlockLoopExit755.LSkipAccumulateOutput8xNBlock674.LOutputMasked16xNBlock674.LSkipAccumulateOutputMasked16xNBlock674.LOutputMasked8xNBlock674.LSkipAccumulateOutputMasked8xNBlock674.LProcessCountM5.LProcessNextColumnLoop16xN815.LComputeBlockBy1Loop835.LComputeBlockLoopExit835.LSkipAccumulateOutput16xNBlock815.LProcessRemainingCountN815.LComputeBlockBy1Loop898.LComputeBlockLoopExit898.LSkipAccumulateOutput8xNBlock815.LOutputMasked16xNBlock815.LSkipAccumulateOutputMasked16xNBlock815.LOutputMasked8xNBlock815.LSkipAccumulateOutputMasked8xNBlock815.LProcessCountM2.LProcessNextColumnLoop2xN2.LComputeBlockBy4Loop4.LProcessRemainingBlocks4.LComputeBlockBy1Loop4.LOutputBlock4.LMultiplyAlpha2xNBlock2.LStore2xNBlock2.LProcessRemainingCountN2.LComputeBlockBy4Loop198.LProcessRemainingBlocks198.LComputeBlockBy1Loop198.LOutputBlock198.LMultiplyAlpha1xNBlock2.LStore1xNBlock2.LOutputMasked2xNBlock2.LMultiplyAlphaMasked2xNBlock2.LStoreMasked2xNBlock2.LOutputMasked1xNBlock2.LMultiplyAlphaMasked1xNBlock2.LStoreMasked1xNBlock2.LProcessCountM4.LProcessNextColumnLoop2xN374.LComputeBlockBy4Loop376.LProcessRemainingBlocks376.LComputeBlockBy1Loop376.LOutputBlock376.LMultiplyAlpha2xNBlock374.LStore2xNBlock374.LProcessRemainingCountN374.LComputeBlockBy4Loop612.LProcessRemainingBlocks612.LComputeBlockBy1Loop612.LOutputBlock612.LMultiplyAlpha1xNBlock374.LStore1xNBlock374.LOutputMasked2xNBlock374.LMultiplyAlphaMasked2xNBlock374.LStoreMasked2xNBlock374.LOutputMasked1xNBlock374.LMultiplyAlphaMasked1xNBlock374.LStoreMasked1xNBlock374.LProcessCountM6.LProcessNextColumnLoop2xN828.LComputeBlockBy4Loop830.LProcessRemainingBlocks830.LComputeBlockBy1Loop830.LOutputBlock830.LMultiplyAlpha2xNBlock828.LStore2xNBlock828.LProcessRemainingCountN828.LComputeBlockBy4Loop1108.LProcessRemainingBlocks1108.LComputeBlockBy1Loop1108.LOutputBlock1108.LMultiplyAlpha1xNBlock828.LStore1xNBlock828.LOutputMasked2xNBlock828.LMultiplyAlphaMasked2xNBlock828.LStoreMasked2xNBlock828.LOutputMasked1xNBlock828.LMultiplyAlphaMasked1xNBlock828.LStoreMasked1xNBlock828.LExitKernelAndZeroUpper.LExitKernel.LProcessCountM1.LProcessNextColumnLoop2xN1364.LComputeBlockBy4Loop1366.LProcessRemainingBlocks1366.LComputeBlockBy1Loop1366.LOutputBlock1366.LMultiplyAlpha2xNBlock1364.LStore2xNBlock1364.LProcessRemainingCountN1364.LComputeBlockBy4Loop1439.LProcessRemainingBlocks1439.LComputeBlockBy1Loop1439.LOutputBlock1439.LMultiplyAlpha1xNBlock1364.LStore1xNBlock1364.LOutputMasked2xNBlock1364.LMultiplyAlphaMasked2xNBlock1364.LStoreMasked2xNBlock1364.LOutputMasked1xNBlock1364.LMultiplyAlphaMasked1xNBlock1364.LStoreMasked1xNBlock1364.LProcessCountM3.LProcessNextColumnLoop2xN1530.LComputeBlockBy4Loop1532.LProcessRemainingBlocks1532.LComputeBlockBy1Loop1532.LOutputBlock1532.LMultiplyAlpha2xNBlock1530.LStore2xNBlock1530.LProcessRemainingCountN1530.LComputeBlockBy4Loop1747.LProcessRemainingBlocks1747.LComputeBlockBy1Loop1747.LOutputBlock1747.LMultiplyAlpha1xNBlock1530.LStore1xNBlock1530.LOutputMasked2xNBlock1530.LMultiplyAlphaMasked2xNBlock1530.LStoreMasked2xNBlock1530.LOutputMasked1xNBlock1530.LMultiplyAlphaMasked1xNBlock1530.LStoreMasked1xNBlock1530.LProcessCountM5.LProcessNextColumnLoop2xN1943.LComputeBlockBy4Loop1945.LProcessRemainingBlocks1945.LComputeBlockBy1Loop1945.LOutputBlock1945.LMultiplyAlpha2xNBlock1943.LStore2xNBlock1943.LProcessRemainingCountN1943.LComputeBlockBy4Loop2202.LProcessRemainingBlocks2202.LComputeBlockBy1Loop2202.LOutputBlock2202.LMultiplyAlpha1xNBlock1943.LStore1xNBlock1943.LOutputMasked2xNBlock1943.LMultiplyAlphaMasked2xNBlock1943.LStoreMasked2xNBlock1943.LOutputMasked1xNBlock1943.LMultiplyAlphaMasked1xNBlock1943.LStoreMasked1xNBlock1943.LProcessCountM2.LProcessNextColumnLoop2xN2.LComputeBlockBy4Loop4.LProcessRemainingBlocks4.LComputeBlockBy1Loop4.LOutputBlock4.LMultiplyAlpha2xNBlock2.LStore2xNBlock2.LProcessRemainingCountN2.LComputeBlockBy4Loop198.LProcessRemainingBlocks198.LComputeBlockBy1Loop198.LOutputBlock198.LMultiplyAlpha1xNBlock2.LStore1xNBlock2.LOutputMasked2xNBlock2.LMultiplyAlphaMasked2xNBlock2.LStoreMasked2xNBlock2.LOutputMasked1xNBlock2.LMultiplyAlphaMasked1xNBlock2.LStoreMasked1xNBlock2.LProcessCountM4.LProcessNextColumnLoop2xN374.LComputeBlockBy4Loop376.LProcessRemainingBlocks376.LComputeBlockBy1Loop376.LOutputBlock376.LMultiplyAlpha2xNBlock374.LStore2xNBlock374.LProcessRemainingCountN374.LComputeBlockBy4Loop612.LProcessRemainingBlocks612.LComputeBlockBy1Loop612.LOutputBlock612.LMultiplyAlpha1xNBlock374.LStore1xNBlock374.LOutputMasked2xNBlock374.LMultiplyAlphaMasked2xNBlock374.LStoreMasked2xNBlock374.LOutputMasked1xNBlock374.LMultiplyAlphaMasked1xNBlock374.LStoreMasked1xNBlock374.LProcessCountM6.LProcessNextColumnLoop2xN828.LComputeBlockBy4Loop830.LProcessRemainingBlocks830.LComputeBlockBy1Loop830.LOutputBlock830.LMultiplyAlpha2xNBlock828.LStore2xNBlock828.LProcessRemainingCountN828.LComputeBlockBy4Loop1108.LProcessRemainingBlocks1108.LComputeBlockBy1Loop1108.LOutputBlock1108.LMultiplyAlpha1xNBlock828.LStore1xNBlock828.LOutputMasked2xNBlock828.LMultiplyAlphaMasked2xNBlock828.LStoreMasked2xNBlock828.LOutputMasked1xNBlock828.LMultiplyAlphaMasked1xNBlock828.LStoreMasked1xNBlock828.LExitKernelAndZeroUpper.LExitKernel.LProcessCountM1.LProcessNextColumnLoop2xN1364.LComputeBlockBy4Loop1366.LProcessRemainingBlocks1366.LComputeBlockBy1Loop1366.LOutputBlock1366.LMultiplyAlpha2xNBlock1364.LStore2xNBlock1364.LProcessRemainingCountN1364.LComputeBlockBy4Loop1439.LProcessRemainingBlocks1439.LComputeBlockBy1Loop1439.LOutputBlock1439.LMultiplyAlpha1xNBlock1364.LStore1xNBlock1364.LOutputMasked2xNBlock1364.LMultiplyAlphaMasked2xNBlock1364.LStoreMasked2xNBlock1364.LOutputMasked1xNBlock1364.LMultiplyAlphaMasked1xNBlock1364.LStoreMasked1xNBlock1364.LProcessCountM3.LProcessNextColumnLoop2xN1530.LComputeBlockBy4Loop1532.LProcessRemainingBlocks1532.LComputeBlockBy1Loop1532.LOutputBlock1532.LMultiplyAlpha2xNBlock1530.LStore2xNBlock1530.LProcessRemainingCountN1530.LComputeBlockBy4Loop1747.LProcessRemainingBlocks1747.LComputeBlockBy1Loop1747.LOutputBlock1747.LMultiplyAlpha1xNBlock1530.LStore1xNBlock1530.LOutputMasked2xNBlock1530.LMultiplyAlphaMasked2xNBlock1530.LStoreMasked2xNBlock1530.LOutputMasked1xNBlock1530.LMultiplyAlphaMasked1xNBlock1530.LStoreMasked1xNBlock1530.LProcessCountM5.LProcessNextColumnLoop2xN1943.LComputeBlockBy4Loop1945.LProcessRemainingBlocks1945.LComputeBlockBy1Loop1945.LOutputBlock1945.LMultiplyAlpha2xNBlock1943.LStore2xNBlock1943.LProcessRemainingCountN1943.LComputeBlockBy4Loop2202.LProcessRemainingBlocks2202.LComputeBlockBy1Loop2202.LOutputBlock2202.LMultiplyAlpha1xNBlock1943.LStore1xNBlock1943.LOutputMasked2xNBlock1943.LMultiplyAlphaMasked2xNBlock1943.LStoreMasked2xNBlock1943.LOutputMasked1xNBlock1943.LMultiplyAlphaMasked1xNBlock1943.LStoreMasked1xNBlock1943.LNchw.4.ProcessOutputCount.LNchw.4.ProcessNextOutputCountBy3.LNchw.4.3.ProcessNextRow.LNchw.4.3.ProcessNextColumn.LNchw.4.3.SkipOverPadding.LNchw.4.3.HandlePostProcessing.LNchw.4.ProcessRemainingOutputCount.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.ProcessNextOutputCountBy3.LNchw.3.3.ProcessNextRow.LNchw.3.3.ProcessNextColumn.LNchw.3.3.SkipOverPadding.LNchw.3.3.HandlePostProcessing.LNchw.3.ProcessRemainingOutputCount.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.ProcessNextOutputCountBy3.LNchw.2.3.ProcessNextRow.LNchw.2.3.ProcessNextColumn.LNchw.2.3.SkipOverPadding.LNchw.2.3.HandlePostProcessing.LNchw.2.ProcessRemainingOutputCount.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.ProcessNextOutputCountBy3.LNchw.1.3.ProcessNextRow.LNchw.1.3.ProcessNextColumn.LNchw.1.3.SkipOverPadding.LNchw.1.3.HandlePostProcessing.LNchw.1.ProcessRemainingOutputCount.LNchw.1.2.ProcessNextRow.LNchw.1.2.ProcessNextColumn.LNchw.1.2.SkipOverPadding.LNchw.1.2.HandlePostProcessing.LNchw.1.ProcessOutputCountRightPadAndRemaining.LNchw.ExitKernelMlasConvNchwFloatSingleFma3Filter1.LNchw.1.1.ProcessNextRow.LNchw.1.1.ProcessNextColumn.LNchw.1.1.SkipOverPadding.LNchw.1.1.HandlePostProcessingMlasConvNchwFloatSingleFma3Filter2.LNchw.2.1.ProcessNextRow.LNchw.2.1.ProcessNextColumn.LNchw.2.1.SkipOverPadding.LNchw.2.1.HandlePostProcessingMlasConvNchwFloatSingleFma3Filter3.LNchw.3.1.ProcessNextRow.LNchw.3.1.ProcessNextColumn.LNchw.3.1.SkipOverPadding.LNchw.3.1.HandlePostProcessingMlasConvNchwFloatSingleFma3Filter4.LNchw.4.1.ProcessNextRow.LNchw.4.1.ProcessNextColumn.LNchw.4.1.SkipOverPadding.LNchw.4.1.HandlePostProcessing.LNchwc.4.ProcessOutputCount.LNchwc.4.ProcessNextOutputCountBy3.LNchwc.4.3.ProcessNextRow.LNchwc.4.3.ProcessNextColumn.LNchwc.4.3.SkipOverPadding.LNchwc.4.3.HandlePostProcessing.LNchwc.4.ProcessRemainingOutputCount.LNchwc.4.2.ProcessNextRow.LNchwc.4.2.ProcessNextColumn.LNchwc.4.2.SkipOverPadding.LNchwc.4.2.HandlePostProcessing.LNchwc.4.ProcessOutputCountRightPadAndRemaining.LNchwc.ProcessFilterCount3.LNchwc.3.ProcessOutputCount.LNchwc.3.ProcessNextOutputCountBy3.LNchwc.3.3.ProcessNextRow.LNchwc.3.3.ProcessNextColumn.LNchwc.3.3.SkipOverPadding.LNchwc.3.3.HandlePostProcessing.LNchwc.3.ProcessRemainingOutputCount.LNchwc.3.2.ProcessNextRow.LNchwc.3.2.ProcessNextColumn.LNchwc.3.2.SkipOverPadding.LNchwc.3.2.HandlePostProcessing.LNchwc.3.ProcessOutputCountRightPadAndRemaining.LNchwc.ProcessFilterCountLessThan3.LNchwc.2.ProcessOutputCount.LNchwc.2.ProcessNextOutputCountBy3.LNchwc.2.3.ProcessNextRow.LNchwc.2.3.ProcessNextColumn.LNchwc.2.3.SkipOverPadding.LNchwc.2.3.HandlePostProcessing.LNchwc.2.ProcessRemainingOutputCount.LNchwc.2.2.ProcessNextRow.LNchwc.2.2.ProcessNextColumn.LNchwc.2.2.SkipOverPadding.LNchwc.2.2.HandlePostProcessing.LNchwc.2.ProcessOutputCountRightPadAndRemaining.LNchwc.ProcessFilterCount1.LNchwc.1.ProcessOutputCount.LNchwc.1.ProcessNextOutputCountBy3.LNchwc.1.3.ProcessNextRow.LNchwc.1.3.ProcessNextColumn.LNchwc.1.3.SkipOverPadding.LNchwc.1.3.HandlePostProcessing.LNchwc.1.ProcessRemainingOutputCount.LNchwc.1.2.ProcessNextRow.LNchwc.1.2.ProcessNextColumn.LNchwc.1.2.SkipOverPadding.LNchwc.1.2.HandlePostProcessing.LNchwc.1.ProcessOutputCountRightPadAndRemaining.LNchwc.ExitKernelMlasConvNchwcFloatSingleFma3Filter1.LNchwc.1.1.ProcessNextRow.LNchwc.1.1.ProcessNextColumn.LNchwc.1.1.SkipOverPadding.LNchwc.1.1.HandlePostProcessingMlasConvNchwcFloatSingleFma3Filter2.LNchwc.2.1.ProcessNextRow.LNchwc.2.1.ProcessNextColumn.LNchwc.2.1.SkipOverPadding.LNchwc.2.1.HandlePostProcessingMlasConvNchwcFloatSingleFma3Filter3.LNchwc.3.1.ProcessNextRow.LNchwc.3.1.ProcessNextColumn.LNchwc.3.1.SkipOverPadding.LNchwc.3.1.HandlePostProcessingMlasConvNchwcFloatSingleFma3Filter4.LNchwc.4.1.ProcessNextRow.LNchwc.4.1.ProcessNextColumn.LNchwc.4.1.SkipOverPadding.LNchwc.4.1.HandlePostProcessing.LDepthwise.1.ProcessOutputCount.LDepthwise.1.ProcessNextOutputCountBy3.LDepthwise.1.3.ProcessNextRow.LDepthwise.1.3.ProcessNextColumn.LDepthwise.1.3.SkipOverPadding.LDepthwise.1.3.HandlePostProcessing.LDepthwise.1.ProcessRemainingOutputCount.LDepthwise.1.2.ProcessNextRow.LDepthwise.1.2.ProcessNextColumn.LDepthwise.1.2.SkipOverPadding.LDepthwise.1.2.HandlePostProcessing.LDepthwise.1.ProcessOutputCountRightPadAndRemaining.LDepthwise.ExitKernelMlasConvDepthwiseFloatSingleFma3Filter1.LDepthwise.1.1.ProcessNextRow.LDepthwise.1.1.ProcessNextColumn.LDepthwise.1.1.SkipOverPadding.LDepthwise.1.1.HandlePostProcessing.LPointwise.4.ProcessNextOutputCountBy3.LPointwise.4.3.ProcessNextInputBlock.LPointwise.4.ProcessRemainingOutputCount.LPointwise.4.2.ProcessNextInputBlock.LPointwise.4.ProcessRemainingOutputCount1.LPointwise.4.1.ProcessNextInputBlock.LPointwise.ProcessFilterCount3.LPointwise.3.ProcessNextOutputCountBy3.LPointwise.3.3.ProcessNextInputBlock.LPointwise.3.ProcessRemainingOutputCount.LPointwise.3.2.ProcessNextInputBlock.LPointwise.3.ProcessRemainingOutputCount1.LPointwise.3.1.ProcessNextInputBlock.LPointwise.ProcessFilterCountLessThan3.LPointwise.2.ProcessNextOutputCountBy3.LPointwise.2.3.ProcessNextInputBlock.LPointwise.2.ProcessRemainingOutputCount.LPointwise.2.2.ProcessNextInputBlock.LPointwise.2.ProcessRemainingOutputCount1.LPointwise.2.1.ProcessNextInputBlock.LPointwise.ProcessFilterCount1.LPointwise.1.ProcessNextOutputCountBy3.LPointwise.1.3.ProcessNextInputBlock.LPointwise.1.ProcessRemainingOutputCount.LPointwise.1.2.ProcessNextInputBlock.LPointwise.1.ProcessRemainingOutputCount1.LPointwise.1.1.ProcessNextInputBlock.LPointwise.ExitKernel.LComputeLogisticBy8Loop.LProcessRemainingCount.LExitKernel.LComputeTanhBy8Loop.LProcessRemainingCount.LExitKernel.LComputeErf4x8Loop.BiggerNumbers.LErfProcessRemainingCount.LErfProcess1x8.BiggerNumbersRemaining.LErfBatchExp.LProcessNextColumnLoop2xN2.LComputeBlockBy4Loop28.LProcessRemainingBlocks28.LComputeBlockBy1Loop28.LOutputBlock28.LMultiplyAlpha2xNBlock2.LStore2xNBlock2.LOutput1xNBlock2.LOutput1xNBlockWithMask2.LMultiplyAlpha1xNBlockWithMask2.LStore1xNBlockWithMask2.LProcessRemainingCountN2.LComputeBlockBy4Loop563.LProcessRemainingBlocks563.LComputeBlockBy1Loop563.LOutputBlock563.LProcessCountMLessThan12.LProcessCountM2.LProcessNextColumnLoop2xN696.LComputeBlockBy4Loop710.LProcessRemainingBlocks710.LComputeBlockBy1Loop710.LOutputBlock710.LMultiplyAlpha2xNBlock696.LStore2xNBlock696.LOutput1xNBlock696.LOutput1xNBlockWithMask696.LMultiplyAlpha1xNBlockWithMask696.LStore1xNBlockWithMask696.LProcessRemainingCountN696.LComputeBlockBy4Loop1029.LProcessRemainingBlocks1029.LComputeBlockBy1Loop1029.LOutputBlock1029.LProcessCountM4.LProcessNextColumnLoop2xN1112.LComputeBlockBy4Loop1126.LProcessRemainingBlocks1126.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.LProcessNextColumnLoop2xN2.LComputeBlockBy4Loop28.LProcessRemainingBlocks28.LComputeBlockBy1Loop28.LOutputBlock28.LMultiplyAlpha2xNBlock2.LStore2xNBlock2.LOutput1xNBlock2.LOutput1xNBlockWithMask2.LMultiplyAlpha1xNBlockWithMask2.LStore1xNBlockWithMask2.LProcessRemainingCountN2.LComputeBlockBy4Loop563.LProcessRemainingBlocks563.LComputeBlockBy1Loop563.LOutputBlock563.LProcessCountMLessThan12.LProcessCountM2.LProcessNextColumnLoop2xN696.LComputeBlockBy4Loop710.LProcessRemainingBlocks710.LComputeBlockBy1Loop710.LOutputBlock710.LMultiplyAlpha2xNBlock696.LStore2xNBlock696.LOutput1xNBlock696.LOutput1xNBlockWithMask696.LMultiplyAlpha1xNBlockWithMask696.LStore1xNBlockWithMask696.LProcessRemainingCountN696.LComputeBlockBy4Loop1029.LProcessRemainingBlocks1029.LComputeBlockBy1Loop1029.LOutputBlock1029.LProcessCountM4.LProcessNextColumnLoop2xN1112.LComputeBlockBy4Loop1126.LProcessRemainingBlocks1126.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.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