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HHCHHSPHHS(HC8ILk0Hs`HH{XIUHUH9H}HBH?H9t"C@u HK@HHKPIHKL{H{ I9t.HS0EuEHqVmHH;Eu@DH([A\A]A^A_]HH@hH HVmH H;MuAHH([A\A]A^A_]u^[H+^[,^[@UHAWAVSPIHII(][H@H@H TmHHHXLp IG1LHHH[A^A_]<H][HA][ H][HY[ HVffff.UH]f.UHHHxP ]HhVUHHNH 1H; CAmHD]DUHAWAVAUATSPHICAD$A$Mt$HsLZ[C fAD$ M|$(Hs(L~Z[Ml$@Hs@LmZ[HChID$hHCXHK`IL$`ID$XI|$pHpHuH[A\A]A^A_]HAEu Au5At I|$F\[HfX[ I|$P2\[At HAtI|$8\[AuHAu@UHAWAVATSIIIGIGII~I+>tjIILH;uc[[HI_IIIMgM&MvM9u'2ID$HCI_H I_I M9t HL3Y[[A\A^A_]L6X[ IM'MtIvL9H^L)Ht-Hffff.HNHFHvHJHHuH8fHNHFHJHNHFHJHNHFHJHNHFHJHNHFHJHNHFHJHNHFHJHNHFHvHJHI9oM>I^LIIFMFL9t#H{HCH[HtHPI9uMtL[A\A^A_]X[[A\A^A_]11LU[IHX[LT[ fff.UHAWAVAUATSHHIHHPmHHEH8H7mHHHLH@H8LW[HEEL5NmM~LI@L8LV[L%kNmILHDž(HDž HDžHDžDž0LVZ[HLHHH9Z[HHHHHHELL8Lt H ;W[HU[H5@=mHHJU[H8V[H%OmHH;EuHH[A\A]A^A_]TW[HLL8Lt H V[HAU[HVHVHH5.mLR[ IA(IA}IIPt H`lR[LeMt6HEL9t!HX@t HxGR[HI9uH}LLe-R[Et H}R[Et H}R[LhMt:HpL9tEHX@t HxQ[HI9uHhLpQ[EtLQ[LM[ Lffff.UHAWAVAUATSHHImHHEHHOH)HH9v3u*Tu HVH9s@Lu D1H OImH H;MuHĈ[A\A]A^A_]Q[Q[IHhDH5[H Ɨ[H}LhW)PHDž`0P[H`(i)PHts valueHHHy for itHHHype entrHHHissing tHHHMap is mH@'AH[HuHPLE1H5/mH+mLzP[ IA(IA}IIPt H`O[LeMt6HEL9t!HX@t HxO[HI9uH}LLeO[Et H}O[Et H}O[LhMt:HpL9tEHX@t HxfO[HI9uHhLpKO[EtLzO[L^K[ Lffffff.UHAWAVAUATSHHH"GmHHE(9H;Cu*HH GmH H;MuHĘ[A\A]A^A_]8O[N[IHhDH5[H t[H}Lh)躭E(9HHHH0[HPHHH-AHߘ[HuHPL E1H5-mH)mLN[ IAIIPtH`M[IALeMt6HEL9t!HX@t HxM[HI9uH}LLeM[Et H}M[Et H}M[LhMt:HpL9tEHX@t HxjM[HI9uHhLpOM[EtL~M[LbI[ LUHAWAVAUATSH(IIHHH%EmHHEH8HP,mHHHLH@H8LeL[LHEEL=>CmMgLI@L8L"K[L-CmILHDž(HDž HDžHDžDž0H3H 9LN[HLHoHH0Hٝ9HHHӴLL8Lt H K[HUJ[H51mHHI[H8FK[HCmHH;EuHH([A\A]A^A_]K[HLL8Lt H CK[HI[HmVHeVH]VHH5+1mHH5I[HH8J[HG[ UHAWAVAUATSHHBmHHEHHGH)HH9v7 u.HH9sOHBmHH;Eu:HĈ[A\A]A^A_]$HBmHH;Eu1HĈ[A\A]A^A_]J[YJ[IHhRDH5[H [H}Lh?W)PHDž`0I[H`(bi)PH elementHH&Hor its eHH H entry fHHHing typeHHH is missHHHSequenceH@.AHE[HuHPLGE1H5(mH$mLI[ IA(IA}IIPt H`I[LeMt6HEL9t!HX@t HxH[HI9uH}LLeH[Et H}H[Et H}H[LhMt:HpL9tEHX@t HxH[HI9uHhLpyH[EtLH[LD[ Lffff.UHAWAVAUATSHHU@mHHEHHOH)HH9v3u*Tu HVH9s@Lu D1H ?mH H;MuHĈ[A\A]A^A_]1H[G[IHhDH5W[H %[H}Lh賦W)PHDž`0pG[H`(i)PHts valueHHHy for itHHHype entrHHHissing tHHHMap is mH@'AHǍ[HuHPLɨE1H5W&mHp"mL*G[ IA(IA}IIPt H`F[LeMt6HEL9t!HX@t HxyF[HI9uH}LLe_F[Et H}PF[Et H}AF[LhMt:HpL9tEHX@t HxF[HI9uHhLpE[EtL*F[LB[ Lffffff.UHAWAVAUATSHHH=mHHEf 9H;Cu*HH =mH H;MuHĘ[A\A]A^A_]E[E[IHh}DH59[H _[H}Lh)j9HHHH[HPHHHAH[HuHPLЦE1H5^$mHw mL1E[ IAIIPtH`D[IALeMt6HEL9t!HX@t Hx}D[HI9uH}LLecD[Et H}TD[Et H}ED[LhMt:HpL9tEHX@t HxD[HI9uHhLpC[EtL.D[L@[ LUHAWAVAUATSHIH;mHHEH8H #mHHHLH@H8L"C[HEEH:mLkLH@H8LA[L59mILHDž(HDž HDžHDžDž0LLLH8Lt H B[LgA[H5(mHH@[H8XB[H:mHH;EuLH[A\A]A^A_]B[ILH8Lt H UB[L@[IH5Y(mHHc@[IH8A[L@>[ fDUHAWAVAUATSHH:mHHEHHOH)HH9v3u*Tu HVH9s@Lu D1H 9mH H;MuHĈ[A\A]A^A_]A[A[IHhDH5[H [H}LhsW)PHDž`00A[H`(vi)PHts valueHHHy for itHHHype entrHHHissing tHHHMap is mH@'AH[HuHPL艢E1H5 mH0mL@[ IA(IA}IIPt H`^@[LeMt6HEL9t!HX@t Hx9@[HI9uH}LLe@[Et H}@[Et H}@[LhMt:HpL9tEHX@t Hx?[HI9uHhLp?[EtL?[L;[ Lffffff.UHAWAVAUATSHHH7mHHE9H;Cu*HH v7mH H;MuHĘ[A\A]A^A_]?[D?[IHh=DH5[H [H}Lh)*u9HHHH[HPHHHAHO[HuHPL萠E1H5mH7mL>[ IAIIPtH`j>[IALeMt6HEL9t!HX@t Hx=>[HI9uH}LLe#>[Et H}>[Et H}>[LhMt:HpL9tEHX@t Hx=[HI9uHhLp=[EtL=[L9[ LUHAWAVAUATSPIM.IFL)HHHH9H H9I^IM)HLupLcM)J<h1[IKDMMM~LLLI2[LL+L{HCMtLH[A\A]A^A_]1[H[A\A]A^A_]ÿ1[HH5p[H-[H'mHHH5mH mH1[IH0[L,[ ff.UHAWAVAUATSHIIIuMeI)MIIGHH> H?MuI)LHHH9w`II9LBMLEHuI9vK-0[HH5p[H-[H'mHHH5mH?mH90[LEHuIJ</[HHuLEN4ABN|M~HLI11[LI]M}MuHtHH[A\A]A^A_]o/[H[A\A]A^A_]E11L3,[IH/[Lf+[ fff.UHAWAVAUATSHH5'mHHEHHOH)HH9v3u*Tu HVH9s@Lu D 1H &mH H;MuHĈ[A\A]A^A_]/[.[IHhDH57q[H [H}Lh蓍W)PHDž`0P.[H`(xi)PHts valueHHHy for itHHHype entrHHHissing tHHHMap is mH@'AHt[HuHPL詏E1H57 mHP mL .[ IA(IA}IIPt H`~-[LeMt6HEL9t!HX@t HxY-[HI9uH}LLe?-[Et H}0-[Et H}!-[LhMt:HpL9tEHX@t Hx,[HI9uHhLp,[EtL -[L([ Lffffff.UHAWAVAUATSHHH$mHHE8H;Cu*HH $mH H;MuHĘ[A\A]A^A_],[d,[IHh]DH5m[H [H}Lh)J8HHHHv[HPHHHAHov[HuHPL谍E1H5> mHWmL,[ IAIIPtH`+[IALeMt6HEL9t!HX@t Hx]+[HI9uH}LLeC+[Et H}4+[Et H}%+[LhMt:HpL9tEHX@t Hx*[HI9uHhLp*[EtL+[L&[ LUHAWAVAUATSPIHL3HCL)HL9sjLH=upLcM)J<*[IKDMMM~LLLI+[LL+L{HCMtLH[A\A]A^A_]/*[H[A\A]A^A_]ÿ.*[HH5j[H'[H!mHHH5 mH@mH:*[IH*[L%[ ff.UHAWAVAUATSHIIIuMeI)MIIGHH=HMuI)LHHH9waII9LBMLEHuI9vKL)[HH5%i[H4&[H3 mHHH5 mH^mHX)[LEHuIJ<([HHuLEN4IJN|M~HLIP*[LI]M}MuHtHH[A\A]A^A_]([H[A\A]A^A_]E11LR%[IH([L$[ ff.UHAWAVAUATSHHU mHHEHHOH)HH9v3u*Tu HVH9s@Lu D1H mH H;MuHĈ[A\A]A^A_]1(['[IHhDH5Wj[H ܂[H}Lh賆W)PHDž`0p'[H`(qi)PHts valueHHHy for itHHHype entrHHHissing tHHHMap is mH@'AHm[HuHPLɈE1H5WmHpmL*'[ IA(IA}IIPt H`&[LeMt6HEL9t!HX@t Hxy&[HI9uH}LLe_&[Et H}P&[Et H}A&[LhMt:HpL9tEHX@t Hx&[HI9uHhLp%[EtL*&[L"[ Lffffff.UHAWAVAUATSHHHmHHE&8H;Cu*HH mH H;MuHĘ[A\A]A^A_]%[%[IHh}DH59f[H t[H}Lh)j8HHHHo[HPHHHAHo[HuHPLІE1H5^mHwmL1%[ IAIIPtH`$[IALeMt6HEL9t!HX@t Hx}$[HI9uH}LLec$[Et H}T$[Et H}E$[LhMt:HpL9tEHX@t Hx$[HI9uHhLp#[EtL.$[L [ LUHAWAVAUATSHHmHHEHHOH)HH9v3u*Tu HVH9s@Lu D1H mH H;MuHĈ[A\A]A^A_]#[]#[IHhVDH5e[H }LhCW)PHDž`0#[H`(Fmi)PHts valueHHHy for itHHHype entrHHHissing tHHHMap is mH@'AHWi[HuHPLYE1H5mHlL"[ IA(IA}IIPt H`."[LeMt6HEL9t!HX@t Hx "[HI9uH}LLe![Et H}![Et H}![LhMt:HpL9tEHX@t Hx![HI9uHhLp![EtL![L[ Lffffff.UHAWAVAUATSHHHbmHHE8H;Cu*HH FmH H;MuHĘ[A\A]A^A_]x![![IHh DH5a[H e[H}Lh)E8HHHHpk[HPHHHmAHk[HuHPL`E1H5lHlL [ IAIIPtH`: [IALeMt6HEL9t!HX@t Hx [HI9uH}LLe[Et H}[Et H}[LhMt:HpL9tEHX@t Hx[HI9uHhLp[EtL[L[ LUHAWAVAUATSHHHrmHHE8H;Cu*HH VmH H;MuHĘ[A\A]A^A_][$[IHhDH5_[H [H}Lh) ~U8HHHHi[HPHHH}AH/i[HuHPLpE1H5lHlL[ IAIIPtH`J[IALeMt6HEL9t!HX@t Hx[HI9uH}LLe[Et H}[Et H}[LhMt:HpL9tEHX@t Hx[HI9uHhLp[EtL[L[ LUHAWAVAUATSHHmHHEHHOH)HH9v3u*Tu HVH9s@Lu D 1H /mH H;MuHĈ[A\A]A^A_]a[[IHhDH5_[H }[H}Lh{W)PHDž`0[H`(fi)PHts valueHHHy for itHHHype entrHHHissing tHHHMap is mH@'AHb[HuHPL}E1H5lHlLZ[ IA(IA}IIPt H`eMt6HEL9t!HX@t HxI9uH}LLet H}t H}qhMt:HpL9tEHX@t HxFI9uHhLp+tLZ>[ Lffffff.UHAWAVAUATSHHHmHHEV8H;Cu*HH mH H;MuHĘ[A\A]A^A_][[IHh譸DH5i[[H |[H}Lh)y8HHHHe[HPHHH AHd[HuHPL|E1H5lHlLa[ IAIIPtH`[IALeMt6HEL9t!HX@t Hx[HI9uH}LLe[Et H}[Et H}u[LhMt:HpL9tEHX@t HxJ[HI9uHhLp/[EtL^[LB[ LUHAWAVATSH IHIH mH HMHUHMI II$Hu_@[HH{ L[AGC8HEHCHHCI$IHHt II4$HI~CIFHmHH;EuHH [A\A^A_][IH?[L_[ DUHH9FtaHVzuTLBHBHH9tHtAyt"9fHHHyHAH9@Hu]H92u=HNH HtHQHBHFHBHHH9HDH1HVHrHFHIA@HHHHPHtHBHPHQHPHrH9HDHH{H92t=LBIHJHtHQHBI@HBHHH9HDLILBI@IA@HHQHHtHBHPHQHPHrH9HDHHAHH]fUHAVSHIHt2H3LHsLC t H{0[H[A^]|[[A^]UHAWAVAUATSHHIHHhmHHEH8HlHHHLH@H8L[HEEL5 mM~LI@L8Ll[L%K mILHDž(HDž HDžHDžDž0L6[HLHȭHH[HHH諭HHH%~LL8Lt H [H[H5 lHH*[H8[H mHH;EuHH[A\A]A^A_]4[HLL8Lt H [H![H过VH跇VHH5lHH[HH8[Hl[ fUHAWAVATSHIHIH A mH HMHuH II$Ht1P[HH{ L[IG(HCHIGIO HK@HC8IG(IG IGHEHHCHCI$IHHt II4$HI~GIFH mHH;EuHH[A\A^A_][IHA[La[ UHAVSHIHtAH3LHsLC8t H{H[C t H{0[H[A^][[A^]UHAWAVAUATSHH mHHEHHC0xu.HHCPH mH H;MuHĈ[A\A]A^A_][[IHhzDH5w[H Sw[H}LhgqH0H5w[Hw[H w[HPI/AHGw[HuHPLsE1H5`lHylL3[ IA(IA}IIPt H`[LeMt6HEL9t!HX@t Hx[HI9uH}LLeh[Et H}Y[Et H}J[LhMt:HpL9tEHX@t Hx[HI9uHhLp[EtL3[L [ LUHAWAVAUATSH(MHIIHHmHHEH8HlHHHLH@H8L[LHEEL%mID$HHI@L8L[L-mILHDž(HDž HDžHDžDž0L[HLH#Lw[HLH H][HHHHH0HYa9HHHSxHHL8Lt H B[H [H5GlHHQ [H8[H,mHH;EuHH([A\A]A^A_][[HHHL8Lt H [HA [#H߁VHׁVHρVHǁVHH5lHH [HH8[H| [ 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LLLLLLLLLLLLLLHI9uH5C[H[A^A_]9LHHyHHVH`s 1HuyHzH)1fffff.3L30L0D3 L30D0 L00D3@L3PD0@L0PD3`L3pD0`L0pHHuHt.HHfff.D3 3D0 0H HuI9u1HH[A^A_]HHVDUHAWAVAUATSHHlHHEHHC0xu.HHCPH lH H;MuHĈ[A\A]A^A_]ZqZIHhjxDH5>[H MD[H}LhW9H0H5?[H?[H ?[HPIAH7?[HuHPL;E1H5PlHilL#Z IA(IA}IIPt H`ZLeMt6HEL9t!HX@t HxrZHI9uH}LLeXZEt H}IZEt H}:ZLhMt:HpL9tEHX@t HxZHI9uHhLpZEtL#ZLZ LUHAWAVAUATSHHlHHEHHC0xu.HHCPH lH H;MuHĈ[A\A]A^A_]ZZIHhzvDH5=[H B[H}Lhg7H0H5=[H=[H =[HPI/AHG=[HuHPL9E1H5`lHylL3Z IA(IA}IIPt H`ZLeMt6HEL9t!HX@t HxZHI9uH}LLehZEt H}YZEt H}JZLhMt:HpL9tEHX@t HxZHI9uHhLpZEtL3ZLZ LUHAWAVSPIIL(HH8IvIV H)HHL=HHILB=ILMI rJ 3H9J 0H91I~H)LHt"Hfff.HHHHuHSI)11fff.  LLLLLLLLLLLLLLHI9uH5=[H[A^A_]9LHHyHHVH`s 1HuyHzH)1fffff.3L30L0D3 L30D0 L00D3@L3PD0@L0PD3`L3pD0`L0pHHuHt.HHfff.D3 3D0 0H HuI9u1HH[A^A_]HHVDUHAWAVAUATSHHlHHEHHC0xu.HHCPH lH H;MuHĈ[A\A]A^A_]ZQZIHhJrDH58[H >[H}Lh73H0H5d9[Ht9[H p9[HPIAH9[HuHPL5E1H50lHIlLZ IA(IA}IIPt H`wZLeMt6HEL9t!HX@t HxRZHI9uH}LLe8ZEt H})ZEt H}ZLhMt:HpL9tEHX@t HxZHI9uHhLpZEtLZLZ LUHAWAVAUATSHHlHHEHHC0xu.HHCPH lH H;MuHĈ[A\A]A^A_]ZaZIHhZpDH56[H <[H}LhG1H0H5t7[H7[H 7[HPIAH'7[HuHPL3E1H5@lHYlLZ IA(IA}IIPt H`ZLeMt6HEL9t!HX@t HxbZHI9uH}LLeHZEt H}9ZEt H}*ZLhMt:HpL9tEHX@t HxZHI9uHhLpZEtLZLZ LUHAWAVSPIIL(HHW8IvIV H)HHLHHIL<=ILMIrJ sH9J pH91IVH)LHt%Hfff.;f8HHHHuHPI)11f4Sf4PtSftPtSftPtSftPtSftPtS ftP tS ftP tSftPHI9uH57[H[A^A_]ɮ9LHHyHHVH0s 1HuqHzH)1fDsLspLpDs Ls0Dp Lp0Ds@LsPDp@LpPDs`LspDp`LppH@HuHt.Ht6Hff.D3 3D0 0H HuI9u1HH[A^A_]HHHKTUHAWAVAUATSHHlHHEHHC0xu.HHCPH clH H;MuHĈ[A\A]A^A_]Z1ZIHh*lDH52[H 8[H}Lh-H0H5D3[HT3[H P3[HPI߼AH2[HuHPL/E1H5lH)lLZ IA(IA}IIPt H`WZLeMt6HEL9t!HX@t Hx2ZHI9uH}LLeZEt H} ZEt H}ZLhMt:HpL9tEHX@t HxZHI9uHhLpZEtLZLZ LUHAWAVAUATSHHlHHEHHC0xu.HHCPH slH H;MuHĈ[A\A]A^A_]ZAZIHh:jDH50[H B7[H}Lh'+H0H5T1[Hd1[H `1[HPIAH1[HuHPL-E1H5 lH9lLZ IA(IA}IIPt H`gZLeMt6HEL9t!HX@t HxBZHI9uH}LLe(ZEt H}ZEt H} ZLhMt:HpL9tEHX@t HxZHI9uHhLpZEtLZLZ LUHAWAVSPIIL(HHw8IvIV H)HHLHHILu6=ILMIrJ sH9J pH91IVH)LHt%Hfff.;f8HHHHuHPI)11f4Sf4PtSftPtSftPtSftPtSftPtS ftP tS ftP tSftPHI9uH5y1[H[A^A_]驨9LHHyHHVH0s 1HuqHzH)1fDsLspLpDs Ls0Dp Lp0Ds@LsPDp@LpPDs`LspDp`LppH@HuHt.Ht6Hff.D3 3D0 0H HuI9u1HH[A^A_]HHHKTUHAWAVAUATSHHelHHEHHC0xu.HHCPH ClH H;MuHĈ[A\A]A^A_]uZZIHh fDH5,[H D3[H}Lh&H0H5$-[H4-[H 0-[HPI迶AH,[HuHPLb)E1H5lH lLZ IA(IA}IIPt H`7ZLeMt6HEL9t!HX@t HxZHI9uH}LLeZEt H}ZEt H}ZLhMt:HpL9tEHX@t HxZHI9uHhLpZEtLZLZ LUHAWAVAUATSHHulHHEHHC0xu.HHCPH SlH H;MuHĈ[A\A]A^A_]Z!ZIHhdDH5*[H 1[H}Lh%H0H54+[HD+[H @+[HPIϴAH*[HuHPLr'E1H5lHlLZ IA(IA}IIPt H`GZLeMt6HEL9t!HX@t Hx"ZHI9uH}LLeZEt H}ZEt H}ZLhMt:HpL9tEHX@t HxZHI9uHhLpZEtLZLZ LUHAWAVSPIILHH8IvIV H)HHLݘHHILU0=ILMIrJ H9J H91IVH)LHt#Hfff.;8HHHHuHBI)11ff.44ttttt t 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Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]A0Hx50I-0I%0I0HW)EHE( RHEHH(HMHxHLxL`LhHX HMH5RH}HU 2H5\SH聩2H5@>VH貪2HA H5g&SH٫2HHHdHHEHHUHuHuIININ0IFIVIF@H}Ht H}0RH]HEHtHHRH(dHH;EuLHh[A\A]A^A_]ZRIH}HtH}RIH]HEHtHCHRLR UHSHHHdHHEHU1HBHtH@0@EHuHUHIH 8MTH.<HWdHH;Eu HH[]Rffffff.UHAVSHPHIHdHHEEHEHEH}HuH}HuHOH}HuHH}HuHH}HuH_HuLH]HEHtCt H{cRH[RHtdHH;Eu LHP[A^]RIH]HEHtCt H{ RHRL%R ff.UHAVSIPRHH2dHHH{LV3HX*eHH[A^]IHRLR UHAVSIPRHHdHHH{LOV3H)eHH[A^]IHBRLbR UHAVSHIH;dHHEA>urHH}HEHEI^IFHtACt H{RHRH]HEHt!Ct H{RHRHEIFHdHH;Eu 1H[A^]Rfff.UHAVSHIH{dHHEA>urHH}r HEHEI^IFHtACt H{RHRH]HEHt!Ct H{RHRHEIFHdHH;Eu 1H[A^].Rfff.UHAVSHIHdHHEA>urHH}rHEHEI^IFHtACt H{ZRHRRH]HEHt!Ct H{2RH*RHEIFH5dHH;Eu 1H[A^]nRfff.UHAVSHIHdHHEA> urHH}HEHEI^IFHtACt H{RHRH]HEHt!Ct H{rRHjRHEIFHudHH;Eu 1H[A^]Rfff.UHAVSHIH;dHHEA>urHH}HEHEI^IFHtACt H{RHRH]HEHt!Ct H{RHRHEIFHdHH;Eu 1H[A^]Rfff.UHAWAVAUATSHHIHodHHEHCHHt4H[H *RIHI$I|$HHRxH}HH?HEHEL{HCMtuAGt IRLRL}HEMtAGt IRLRWE1M&HdHH;Eu"LHĘ[A\A]A^A_]HER]RIHhV~<H5"SH ɝSH}LhC?W)PHDž`@RH`(v*a)PHispatcheHH(HypeCallDHH Hs in MLTHHHate typeHHHr duplicHHHCheck foH@0rRet@4AHMSHuHPLDAE1H5ҾdHdLR IA(IA}IIPt H`RLeMt6HEL9t!HX@t HxRHI9uH}LLeREt H}REt H}RLhMt:HpL9t=HX@t HxRHI9uHhLpvREtLRLILWRLwR ffff.UHAWAVAUATSH8IIHBdHHE1Lq9HþLa9IžLQ9IHCHK H)HtHH8IEIM H)HtHH8Mt%IGIO H)HtH%H8LuHLD0M]LD8EO0FRHEf(nCbf)EHbe zero!HH Hcan not HHHperator HHH Range oHHHdelta inH@(HMLuL<HH5_SL}LHLLb<`IH5sSH]HLvLH1</IH5SH]HLELH<Et H}!RH:dHH;ELH8[A\A]A^A_]AA)A*A*^f: L,E1MMIRHEHHHML(HMHU1L:3H<MLu0Iw1HL!fnfpfAnfpf8@iOafBfnfpHyHHVHs 1HutHzH)1fDfD fD0fD@fDPfD`fDpH fHuHt,HtHf.FffH HuI9t"DHDHDHL9rIH}H*H}RHH}HtH} HEt H}RH3R fUHAWAVAUATSH(HIHHdHHEH8H#dHHHLH@H8L8RHHEEL5dM~LI@L8LRL%dILHDž(HDž HDžHDžDž0LRHLHQqHHNQ5HHHALL8Lt H RH:RH5dHHRH8+RHdHH;EuHH([A\A]A^A_]RHLL8Lt H (RHRHRKNHJKNHH5dHH"RHH8RHR DUHAWAVAUATSH8IIHdHHE1L3HþL2IžL2IHCHK H)HtH&H8IEIM H)HtH2H8(Mt%IGIO H)HtH9H8/HELMmLEgW.ESM0RHEf(LHH?fAnfAnHrI H9H I9E1HzLtfoC_fo]BLHL9ff.foA_fo]foA_Tfo]\HH9uIIfofoIIHHDGAH"IHH)1fDA$(_(]$Ad(_(]dHHuMtA$_]L9H<I|AfAnfAnHfffff.A(_(])HH9|H9RfAnfAnI)ILI I9sLI L9MIfofoIHIIENAHHM\HtIIL)1ff.Ad(_(]dA$(_(]$HHuMtHA$_]M9twLlj)HPH@tfoA_fo]HOH9tCf.foA_fo]foA_Tfo]\HH9uHEHH[A\A]A^A_]1M'1M=Jffff.UHAVSIPRHHRdHHH{L)3HdHHH[A^]IH辸RL޴R @UHAWAVAUATSHHH@HdHHE1HIľHIƾHILHMl$1HL^3IMtIF I;FL谪R+aM) tIG I;GjL耪"1a)0HH_IL#5L\HIL#5HuڃH9HOII)LH?H>LHH( (0(HrI H9|H I9ssE1HzLt(C_(]B LHL9@(A_(] (A_D(]LHH9uII((IIHHDGAHIHH)1fff.A(_(]AT(_(]THHuMtA_] L9H<I|1((HfA(_(])HH9|H9>I)ILI I9sLI L9MI((IHIIENAH"HM\HtIIL)1AT(_(]TA(_(]HHuMtHA_] M9tsLlj)HPH@t(A_(] HOH9tAfff.(A_(] (A_D(]LHH9uH@HH ƬdH H;Mu2Hĸ[A\A]A^A_]1M+51M+8شRtRIHhmR<H5"tSH uSH}Lh2ZW)PHDž` RH`(b)PH scalar.HHHld be a HHHmin shouH@AHuSHuHPLE1H5dH3dLR蜳RIHhQ<H5JsSH 5tSH}Lh6W)PHDž` ?RH`(b)PH scalar.HHHld be a HHHmax shouH@AHOtSHuHPLE1H5BdH[dLR IAIIAIIAIA2IuIPt H`hRLeMt6HEL9t!HX@t HxCRHI9uH}LLe)REt H}REt H} RLhMHpL9HX@t HxرRHI9uIPt H`賱RLeMt6HEL9t!HX@t Hx莱RHI9uH}LLetREt H}eREt H}VRLhMt:HpL9tEHX@t Hx+RHI9uHhLpREtL?RL#R L@UHAVSHPIHdHHEְRHþH̰RHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]X0H5RH}Hy2H5qSHUp2H5VHq2HA H5;RHr2HHHģdHHEHHUHuHuIININ0IFIVIF@H]HEHtH荓HRHdHH;Eu LHP[A^]FRIH]HEHtHHH谮RLЪR fDUHAVSHPIHdHHE膮RHþH|RHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]W^0H5KRH}Htw2H5nSHn2H5VH6o2HA H5RH]p2HHHtdHHEH}2HUHuHuIININ0IFIVIF@H]HEHtH=H襬RHdHH;Eu LHP[A^]RIH]HEHtHH`RL耨R fDUHAVSHPIH[dHHE6RHþH,RHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]S0H5RH}H$u2H5wlSHk2H5tVHl2HA H5RH n2HHH$dHHEHRHUHuHuIININ0IFIVIF@H]HEHtHHURHndHH;Eu LHP[A^]親RIH]HEHtH討HRL0R fDUHAVSHPIH dHHERHþHܩRHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]X0H5RH}Hr2H5'jSHei2H5$UHj2HA H5KRHk2HHHԜdHHEHqHUHuHuIININ0IFIVIF@H]HEHtH蝌HRHdHH;Eu LHP[A^]VRIH]HEHtHXHRLR fDUHAVSI`訧RHHLH[A^]IH{RL蛣R UHAVSH@HIHxdHHEHdHII~*3HdHIIFXIFPHEW)EHEEHexclusivHEfEeH}HUHMHi,3Et H}ʦRH}uHEHwINPHW)EHEEHEHreverseHEH}HUHMH,3HEHEH]HEHtxCt H{MRHERH]HEHtCt H{%RHREt H}RH]Ht1HECt H{RHR&HEEuHEHwIFXHEHםdHH;Eu H@[A^]RIEt H}莥RH]HEHt&Ct H{nRH IEt H}WRLwR ffff.UHAWAVAUATSH8IIIHRHEH8Ht H{@)RLIR ffffff.UHAWAVAUATSHHdHHEMHIII6IFH)IIH)HI9*ML$IL$L)HI9M)IM9HIL$IBEAHs LMueLH|HH H\H|HH H\H|HH H\H|HH H\H@I9uMt/HtHDI1HVRH IEt H}'VRLGRR ffff.UHAWAVAUATSH8IIIH NdHHE1L;II] I+]HH]ȾLHH H+HHHro URHE(`)EH0D or 1DHHHld be 0DHHHsor shouHHHAxis tenH@HML};%HEHcHHH9H9LLy؉EW)EHEI} LI+}t[HHHH9 TRIHLuLuIHEHqLi I)M~LLVRMLuHU1L蒳2HH}4HLLILc}HH@JHW)EHEI|$ I+|$HHHH9 TRILuLuIHEIt$I\$ H)H~LHaURLHHEKH}޿4HW)pHELuH]L)tYIIHI9HrSRILxLpKDHEH~LLHTRILxLeW)PHDž`MLtNHI9/N4LRRHPJH`H5`HLmTRHXI}XHLsI}PHcUHH$LpLPHLL< HLeHMHEH$H5SH RL SH]HULEHʹLHy;EH}II}PGHcUHH$LpLP1LHLeȸ LLHLcmW)0HDž@HI9N4LnQRIL0KH@LLTQRH8HKW)HDž LQRILKH LLQRHHKHH$H0HLpLPHH LPRH0HLt H8PRHW@LcL}HcEHIFPHHID)0HDž@HI9N4L"PRILL0OdL@LLPRL8LK\W)HDž LORILK\H LLORHHKDW)HDžLyORHHNdHHEHHJLLKIIHHHHH|$H$HXH[#I$IL$LLKIIwHEHuHEHuHt$H$HLH}H 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_f= _ffo _fofX= _XT [ _D\(Y(Y(Y _X _(Y _X _YXYAXYX(Y _X _YAXYXYA(AYXY\XEYDXW(AfE8DVA(fE8E)MIM< IrKI94KDI9&1ICH)LHt!Hffff.AATHHuH DAADADADADADAD AD ADADADADADADADADHI9u| DDH0+L@Hp HL)IVI~ H)H9I9t1fI0H;2HH9uHHLxLIIL2LIH2HHHAuDكH9HOII)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;>HH9uMMIIHzHLMM2LMHHIrJI9H;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 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ff.UHAWAVAUATSH8MIHIHH~@aHHEH8H'aHHHHH@H8HGOLHEEL%>aID$HHI@L8HsFOL5R>aILHDž(HDž HDžHDžDž0L=JOHLHHH0H9-L JOHLHHH0H -HHHHHL8Lt H FOH~EOH5,aHHEOH8oFOH>aHH;EuHH8[A\A]A^A_] GOHHHL8Lt H eFOHDO#H菹JH臹JHJHwJHH5E,aHHODOHH8EOH,BO fUHAWAVAUATSHxH>aHHEHHIHHOHVHH$HH HxH HH9HNHE8HEH8HEW)HDžL}H]L)tUIILH=aHBEOILLKHH~LLHFOILH1L /HHHt HDOH HEHHUUILHHϰ1IHHH H}Ht H}oDOHxHt H}ZDOH`Ht HhBDOHHHt HP*DOH Ht H(DOHHt HCOHHt HCOH;aHH;EuHx[A\A]A^A_]-DOCOIHx8H5 PH 3(PHLx謢W)`HDžp iCOHp(])`Hrst outpHHHo get fiHHHfailed tH@ut!AH SOHH`LؤE1H5f"aHaL9CO H?O IA+IAII`t HpBOLMt?HL9t$HX@t HxqBOHI9uHLLQBOt HOHHt H>OH6aHH;EuHx[A\A]A^A_]-?O>OIHx8H5PH #PHLx謝W)`HDžp i>OHp(])`Hrst outpHHHo get fiHHHfailed tH@ut!AH NOHH`L؟E1H5faHaL9>O H:O IA+IAII`t Hp=OLMt?HL9t$HX@t Hxq=OHI9uHLLQ=Ot H<=Ot H'=OLxMt:HL9t=HX@t 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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.UHAWAVAUATSHIIHaHHEIF@IF8IF0IF(IF IFIFIFIIFHIFXH0IdžIdžIdžIdžIFxIFpIFhIF`IFXIFPIdžMIdžIdžIdžIdžIGI+HHJHH+ HH9HHBH9HCH Ht LHI~HH|I~0HHI~pHHVIHH`HL@MoHH@MH L8DIHIAIIH(HIIuH(L0L9HILH]I9HINHu H1I$L; sH|H8HqH0LH_L; !H(ff.IuH(LqL9HILH]I9HINHu H1I\$H}t!H8H H0LH IH(HIIL; xH(M9/tBI]LHHHxHDž`IFxI;sgHHIFxiHH(H9LyLxHDž`IF I;F(HHIF H0H`HII;tHxHHIHxHII\$IlHDžxHDž`IF I;F(HHIF H`HH0IF8I;F@tHxHHIF8HxH]HLLH(M|$IH`HIF8I;F@tHxHHIF8HxHHDžxHDž`IFxI;sHHIFxH`HII;tHxHHIHxHtAL; H(IDI\$IIHI9s{HHAH+HH9u1I^H8H H0HH I롐I]H8HHj H0HV 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HpVHDžpHC8H;C@sHHHC8H{0HpHC8HHHMHHHCHHEHCHHaHH;EuHĘ[A\A]A^A_]OyOIHXr8H58OH OHpLX[\oH5aOH OH@HULEAHOHpH@LqE1H5X`Hq`L+O IA+IAII@t HPOLeMt6HEL9t!HX@t HxwOHI9uH}LLe]OEt H}NOpt H}<OLXMt:H`L9tEHX@t HxOHI9uHXL`OEtL%OL O Lf.UHAWAVAUATSH(HIMnM~LL)HH9I>I)MIIHH=6HI)LHHH9H]H}wtII9LBMI9v^JOHH5#NOH2 OH1aHHH5`H\`HVOH4L OIDIF~IJ< ON$HEJHEL}J4L OO$MH]~H}HL)OHEIMfHEIFHtHH([A\A]A^A_]c OH([A\A]A^A_]E11yL$ OIHs OLW O ffff.UHAWAVAUATSPIHL3HCL)HL9sjLH=upLcM)J< OIKDMMM~LLLIPOLL+L{HCMtLH[A\A]A^A_] OH[A\A]A^A_]ÿ OHH5gLOHv OHuaHHH5_`H`H OIHq OLUO ff.UHAWAVAUATSH8MIHIHHaHHEH8H9`HHHHH@H8HN OLHEEL%'aID$HHI@L8H OL5aILHDž(HDž HDžHDžDž0L OHLH_HH0Hm OL OHLH/HH0H= OHHHsHHL8Lt H OH OH5`HHOH8 OHlaHH;EuHH8[A\A]A^A_] OHHHL8Lt H OHO#H}JH}JH}JH}JHH5`HHOHH8H OHO fUHAWAVAUATSHIIIuMeI)MIIGHH=HMuI)LHHH9waII9LBMLEHuI9vK OHH5HOHOHaHHH5`H.`H( OLEHuIJ<OHHuLEN4IJN|M~HLI OLI]M}MuHtHH[A\A]A^A_]^OH[A\A]A^A_]E11L"OIHqOLUO ff.UHAWAVAUATSHIIIuMeI)MIIGHH=HMuI)LHHH9waII9LBMLEHuI9vKOHH5GOHOH`HHH5}`H`HOLEHuIJ<ROHHuLEN4IJN|M~HLIOLI]M}MuHtHH[A\A]A^A_]OH[A\A]A^A_]E11LOIHOLO ff.UHAWAVAUATSHILgLoLL)HH9LM)MII6HH=HM)LHHH9H}LEII9LBM/HuI9x!OHH5EOH OH`HHH5`H3`H-OLHHIGL9vILHL9IIIPHHAHA~fpDHPH)1AADAD AD0HHuHtA~fpDAADI9t{KHL)LCHHtHIHHHHuIrIIHIHPIHPIHPIHP IHP(IHP0IHP8H@HuIHGHuIJ<OIHuO 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_]UHAVSIPNHHb`HHH{LF/H(`HHH[A^]IHNLN @UHSPHHHH[]DUHAWAVAUATSH8IHH`HHE1L0HþL0HHHHsHP(HH HxH HH9HNHE=HEH-HELuW)HDžLeH]L)tdLIILH=HNILLKDHHL~LLHNILH1L~3/HLH,ILHHf@1IHHHt HNHHHHH}Ht H}NHxHt H}NH`Ht HhNHHHt HPNH Ht H(NHHt HkNHHt HSNHl`HH;EuHH8[A\A]A^A_]NHN H HHHHt HNH}Ht H}NHxHt H}NH`Ht HhNHHHt HPNH Ht H(NHHt HmNHHt HUNHuN ff.UHAWAVAUATSHpL(IHLLI>LH^L HLH I9 HHMLoH_HW@L)HL_hHOpL)HH(HuLFM`IEL$DƃLHHALIILeL)HHPHHuHLHHuHw`HuLMK4H`J4HhIpHXJ4H0Iq0HHq0HHuH@HIHpIIHx1HMH8HULe*LIHELL H8HUHMHIILO`IEHIEH;uKIEHr=M LO`ILHILH; uIDHH9r@HuHI6HMHHHEHHIHII;$H(uCIHr6I4HI HI I; uIHH9r@H0IHEMHu6HEHH@HML4IsE1offffff.LHEM<HHHN$H`HHhH4H9AL9H9M9AA HuLeH}HHHHUHH1foDoLoTfoDfAADoDo oTfofAT AD0HHuHt-ooLAofAoDfAADHII9HuuE1HuLeHXL)HPt HMKJKIHuHrhHpHMHHxH fff.JTJTKJTJTKTJTJTKTJJKTIM9uHLUI9`1H LHFH I9;HHEH_L_HG@I)ILwhLopM)IHuL~HIGHD@DLHHpALEIPHhH)HxHMHHHHHHEHH0HHEHG`JHXIWH@JH(HQ0HUHMHIH`1HLP1L]HuL8&HHHMLL8HUHIILg`H HH H;uBHIr5HL$Lg`H HH H; uHHL9rIHML LHEHHUHLHMLHIHIH0H;uIIIr<HMHHI HI H; 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.UHAVSIPNHH`HHH{Lo5/H`HHH[A^]IH^NL~N @UHSPHHHH[]DUHAWAVAUATSH8IHH+`HHE1LZHþLJHHHHsHP踢HH HxH HH9HNHE-HEHHELuW)HDžLeH]L)tdLIILH=HLNILLKDHHL~LLHNILH1L"/HLH|ILHH.1IHHHt HNHHHHH}Ht H}pNHxHt H}[NH`Ht HhCNHHHt HP+NH Ht H(NHHt HNHHt HNH`HH;EuHH8[A\A]A^A_]+NH菾N H HHHHt HNH}Ht H}rNHxHt H}]NH`Ht HhENHHHt HP-NH Ht H(NHHt HNHHt HNHN ff.UHAWAVAUATSL_(IHHuHH8HHN` H9uffff.L_(ILFJ HMHHHMLH_HO`IHMHHW`H LH LW@I; 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^DHVDLMuHH8HHt3DI}`Htf.HDHHuI>IHtDLD fUHAWAVSPIHĥVHIIHt+fL;Ct H{ 讻DH覻DLMuI~xIFxHt舻DI~`Htffffff.HhDHHuI~PIFPHHt [A^A_]@D[A^A_]fUHAWAVSPIHVHIIHt+fL;Ct H{ DHDLMuI~xIFxHtȺDI~`Htffffff.H診DHHuI~PIFPHt芺DLH[A^A_]xDUHAVSHPIH{VHHEVDHþHLDHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]'b"H5DH}HD$11H$H5~EHy$H57GHz$HAH5DH!|$HHH8VHHEHHUHuHuIININ0IFIVIF@H]HEHtHHiDHVHH;Eu LHP[A^]躸DIH]HEHtH輜H$DLDD f.UHAVSHPIHVHHEDHþHDHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]g"H5DH}H$11H舂$H5EHiw$H5GHx$HAH5ODHy$HHHتVHHEHqHUHuHuIININ0IFIVIF@H]HEHtH衚H DH"VHH;Eu LHP[A^]ZDIH]HEHtH\HĵDLD f.UHAVSHPIHVHHE薵DHþH茵DHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]gd"H5[DH}H~$11H($H5EH u$H5wGH:v$HAH5DHaw$HHHxVHHEH HUHuHuIININ0IFIVIF@H]HEHtHAH詳DH«VHH;Eu LHP[A^]DIH]HEHtHHdDL脯D f.UHAVSHPIH[VHHE6DHþH,DHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]Z"H5DH}H$|$11H}$H5^EHr$H5GHs$HAH5DHu$HHHVHHEH,HUHuHuIININ0IFIVIF@H]HEHtHHIDHbVHH;Eu LHP[A^]蚱DIH]HEHtH蜕HDL$D f.UHAVSIDHHLH[A^]IH軰DL۬D UHAWAVAUATSHHHIHVHHEHVHIEI}`!%H!VHIEMuPW)`HDžpƅ` DžascalfDžee)EHEH`HMLH< H}Ht H}D`t HpDM}hHDžpHDžhƅ` fDžeetDžaoffsƅgW)EHEH`HMLH%< H}Ht H}sD`t Hp^DIMPIEXH9t;H)IMpI+MhH9HVVHH;EuHH[A\A]A^A_]舯D$DHH}M.H5EH EH`LEC W)@HDžP ƮDHP(oS)@HttributeHHHale in aHHHEmpty scHf@sHEH`H@H-DžH5VH͉VH胮D2DHH}*L.H5EH EH`LEE IuXI+uPHH蓭DH0EH1VDHHHHHPHHH@H@HH5EHDHHHHHPHHH@H@HIupI+uhHHDtHH HHsDHHH HHPHHH@H@HH5DH#DHHH8HHPH0H(H@H@HH@H('DHEH`H@HDžH5VHVHhD H(35H-H %HHHHH HHHAHxH@(+7t HtDLeMt6HEL9t!HX@t HxODHI9uH}LLe5DEt H}&D`t HpDLeMHEL9HX@t HxDHI9uH}Le[HPéD(H8誩DH 葩DHxDH_DHFDLLeH@t HP DLeMt6HEL9t!HX@t HxDHI9uH}LLeͨDEt H}辨D`t Hp詨DH]Ht1HEH9tKL`@t Hx脨DLL9uH}H]oDt H薨DI?Ht9I}p.HHH}Ht H}.D`t HpDI>Ht4I}X)HH}Ht H}D`t HpݧDHD ffffff.UHSPHHhVHHH{hHt H{p藧DH{PHtH{XH[]DH[]UHSPHHVHHH{hHt H{pGDH{PHt H{X5DHH[]'DUHAWAVAUATSH8IIIHVHHE1LKHLk1LL%IH\HL]IHCH;C LL'IHCHS H)HpHHDL.I~hIFpH)HHL9uEI^PIVXH)HL9u1MHEAI1MXuHugIFPIVXH)HuVMHNID1H bEL-HVHH;ECL)H8H VHHHHH@H8HDHEEL%VMt$LI@L8HDL=VILHDž(HDž HDžHDžDž0H5EH5?=HLXDH5EH 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藆DL'DH5lVHH誄DH8DL)1fWI*1HI\YAWI*DHF1I\YADHH9uM~WI*1HI\Y[H)1WI*\YAWI*D\YADHH9uHtWI*\YAHHH}VHH;EuHH8[A\A]A^A_]ŅDHt H;D H=HOIHLL8Lt H DL荃DH5kVHHDH8~DHD UHAVSI踄DHHLH[A^]IH苄DL諀D UHAWAVAUATSHHHIH|VHHEHvVHIEI}0$HnVHIEMuPW)`HDžpƅ` DžascalfDžee)EHEH`HMLH| H}Ht H}ʃD`t Hp赃DM}hHDžpHDžhƅ` fDžeetDžaoffsƅgW)EHEH`HMLH H}Ht H}CD`t Hp.DIMPIEXH9t;H)IMpI+MhH9H&{VHH;EuHH[A\A]A^A_]XDDHH} .H5PEH dEH`LECW)@HDžP 薂DHP(lCS)@HttributeHHHale in aHHHEmpty scHf@sHoEH`H@HDžH5aVH]VHSDDHH}.H5^EH rEH`LEEIuXI+uPHHcDHEH1&DHHHHHPHHH@H@HH5´EH~DHHHHHPHHH@H@HIupI+uhHH赀DtHH HHC~DHHH HHPHHH@H@HH5оDH}DHHH8HHPH0H(H@H@HH@H(}DHdEH`H@HDžH5i_VH[VH8D H(35H-H %HHHHH HHHAHxH@(+7t HD~DLeMt6HEL9t!HX@t Hx~DHI9uH}LLe~DEt H}}D`t Hp}DLeMHEL9HX@t Hx}DHI9uH}Le[HP}D(H8z}DH a}DHH}DH/}DH}DLLeH@t HP|DLeMt6HEL9t!HX@t Hx|DHI9uH}LLe|DEt H}|D`t Hpy|DH]Ht1HEH9tKL`@t HxT|DLL9uH}H]?|Dt Hf|DI?Ht9I}p.HHH}Ht H}{D`t Hp{DI>Ht4I}X)HH}Ht H}{D`t Hp{DHwD ffffff.UHSPHH@fVHHH{hHt H{pg{DH{PHtH{XH[]O{DH[]UHSPHHeVHHH{hHt H{p{DH{PHt 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Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]7H5+|=H}HT6H5S>H,H5@H.HH HQH5ĩ=H6/HHHM`OHHEHHUHuHuIININ0IFIVIF@H]HEHtHPH~k=HcOHH;Eu LHP[A^]k=IH]HEHtHOH9k=LYg= ffffff.UHAVSHPIH+cOHHEk=HþHj=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5y=H}H3H5>H*H5D@H+HH HQH5d=H,HHH]OHHEHHUHuHuIININ0IFIVIF@H]HEHtHMHi=H7aOHH;Eu LHP[A^]oi=IH]HEHtHqMHh=Ld= ffffff.UHAVSHPIH`OHHEh=HþHh=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]wH5kw=H}H1H5>H%(H5@HV)HA H5 =H}*HHH[OHHEHHUHuHuIININ0IFIVIF@H]HEHtH]KHf=H^OHH;Eu LHP[A^]g=IH]HEHtHKHf=Lb= fDUHAVSHPIH{^OHHEVf=HþHLf=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]' H5u=H}HD/H5C>H%H5@H'HA H5=H-(HHHDYOHHEHHUHuHuIININ0IFIVIF@H]HEHtH IHud=H\OHH;Eu LHP[A^]d=IH]HEHtHHH0d=LP`= fDUHAVSHPIH+\OHHEd=HþHc=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH] H5r=H}H,H5>H#H5D@H$HH HQH5d=H%HHHVOHHEHHUHuHuIININ0IFIVIF@H]HEHtHFHb=H7ZOHH;Eu LHP[A^]ob=IH]HEHtHqFHa=L]= ffffff.UHAVSHPIHYOHHEa=HþHa=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]wH5kp=H}H*H5>H%!H5@HV"HH HQH5=Hv#HHHTOHHEHHUHuHuIININ0IFIVIF@H]HEHtHVDH_=HWOHH;Eu LHP[A^]`=IH]HEHtHDHy_=L[= ffffff.UHAVSHPIHkWOHHEF_=HþH<_=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5 n=H}H4(H5<>HH5@HHA H5=H!HHH4ROHHEH HUHuHuIININ0IFIVIF@H]HEHtHAHe]=H~UOHH;Eu LHP[A^]]=IH]HEHtHAH ]=L@Y= fDUHAVSHPIHUOHHE\=HþH\=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5k=H}H%H5>HuH54@HHA H5[=HHHHOOHHEH-HUHuHuIININ0IFIVIF@H]HEHtH?H[=H.SOHH;Eu LHP[A^]f[=IH]HEHtHh?HZ=LV= fDUHAVSHPIHROHHEZ=HþHZ=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]wH5ki=H}H#H5>H%H5@HVHH HQH5=HvHHHMOHHEHFHUHuHuIININ0IFIVIF@H]HEHtHV=HX=HPOHH;Eu LHP[A^]Y=IH]HEHtH=HyX=LT= ffffff.UHAVSHPIHkPOHHEFX=HþHHH5@HHH HQH5=HHHH-KOHHEHHUHuHuIININ0IFIVIF@H]HEHtH:H^V=HwNOHH;Eu LHP[A^]V=IH]HEHtH:HV=L9R= ffffff.UHAVSHPIH NOHHEU=HþHU=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5d=H}HH5>HeH5$@HHA H5K=HHHHHOHHEH=HUHuHuIININ0IFIVIF@H]HEHtH8HT=HLOHH;Eu LHP[A^]VT=IH]HEHtHX8HS=LO= fDUHAVSHPIHKOHHES=HþHS=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]gH5[b=H}HH5>HH5ԧ@HFHA H5=HmHHHFOHHEH]HUHuHuIININ0IFIVIF@H]HEHtHM6HQ=HIOHH;Eu LHP[A^]R=IH]HEHtH6HpQ=LM= fDUHAVSHPIHkIOHHEFQ=HþHHH5@HHH HQH5=HHHH-DOHHEHvHUHuHuIININ0IFIVIF@H]HEHtH3H^O=HwGOHH;Eu LHP[A^]O=IH]HEHtH3HO=L9K= ffffff.UHAVSHPIH GOHHEN=HþHN=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5]=H}HH5>HeH5$@HHH HQH5D=HHHHAOHHEHHUHuHuIININ0IFIVIF@H]HEHtH1HL=HEOHH;Eu LHP[A^]OM=IH]HEHtHQ1HL=LH= ffffff.UHAVSHPIHDOHHEL=HþH|L=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]WH5K[=H}HtH5>H H5Ġ@H6 HA H5=H]HHHt?OHHEH]HUHuHuIININ0IFIVIF@H]HEHtH=/HJ=HBOHH;Eu LHP[A^]J=IH]HEHtH.H`J=LF= fDUHAVSHPIH[BOHHE6J=HþH,J=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5X=H}H$H5I>H H5t@H HA H5=H HHH$=OHHEH}HUHuHuIININ0IFIVIF@H]HEHtH,HUH=Hn@OHH;Eu LHP[A^]H=IH]HEHtH,HH=L0D= fDUHAVSHPIH @OHHEG=HþHG=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5V=H}HH5>HeH5$@HHH HQH5D=H HHH:OHHEHHUHuHuIININ0IFIVIF@H]HEHtH*HE=H>OHH;Eu LHP[A^]OF=IH]HEHtHQ*HE=LA= ffffff.UHAVSHPIH=OHHEE=HþH|E=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]WH5KT=H}HtH5>HH5ę@H6HA H5=H]HHHt8OHHEHmHUHuHuIININ0IFIVIF@H]HEHtH=(HC=H;OHH;Eu LHP[A^]C=IH]HEHtH'H`C=L?= fDUHAVSHPIH[;OHHE6C=HþH,C=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5Q=H}H$ H5S>HH5t@HHH HQH5=HHHH6OHHEHHUHuHuIININ0IFIVIF@H]HEHtH%HNA=Hg9OHH;Eu LHP[A^]A=IH]HEHtH%H A=L)== ffffff.UHAVSHPIH8OHHE@=HþH@=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5O=H}H H5>HUH5@HHH HQH54}=HHHH3OHHEHHUHuHuIININ0IFIVIF@H]HEHtH#H>=H7OHH;Eu LHP[A^]??=IH]HEHtHA#H>=L:= ffffff.UHAVSHPIH6OHHEv>=HþHl>=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]GH5;M=H}HdH5>HH5@H&HA H5z=HMHHHd1OHHEH HUHuHuIININ0IFIVIF@H]HEHtH-!H<=H4OHH;Eu LHP[A^]<=IH]HEHtH HP<=Lp8= fDUHAVSHPIHK4OHHE&<=HþH<=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5J=H}HH5C>HH5d@HHA H5x=HHHH/OHHEH-HUHuHuIININ0IFIVIF@H]HEHtHHE:=H^2OHH;Eu LHP[A^]:=IH]HEHtHH:=L 6= fDUHAVSHPIH1OHHE9=HþH9=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5H=H}HH5>HUH5@HHH HQH54v=HHHH,OHHEHFHUHuHuIININ0IFIVIF@H]HEHtHH7=H0OHH;Eu LHP[A^]?8=IH]HEHtHAH7=L3= ffffff.UHAVSHPIH/OHHEv7=HþHl7=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]GH5;F=H}HdH5>HH5@H&HH HQH5s=HFHHH]*OHHEHHUHuHuIININ0IFIVIF@H]HEHtH&H5=H-OHH;Eu LHP[A^]5=IH]HEHtHHI5=Li1= ffffff.UHAVSHPIH;-OHHE5=HþH 5=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5C=H}HH5>>HH5T@HHA H5{q=HHHH(OHHEH-HUHuHuIININ0IFIVIF@H]HEHtHH53=HN+OHH;Eu LHP[A^]3=IH]HEHtHH2=L/= fDUHAVSHPIH*OHHE2=HþH2=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5A=H}HH5>HEH5@HvHA H5+o=HHHH%OHHEHMHUHuHuIININ0IFIVIF@H]HEHtH}H0=H(OHH;Eu LHP[A^]61=IH]HEHtH8H0=L,= fDUHAVSHPIH(OHHEv0=HþHl0=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]GH5;?=H}HdH5>HH5@H&HH HQH5l=HFHHH]#OHHEHfHUHuHuIININ0IFIVIF@H]HEHtH&H.=H&OHH;Eu LHP[A^].=IH]HEHtHHI.=Li*= ffffff.UHAVSHPIH;&OHHE.=HþH .=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5<=H}HH5>>HH5T@HHA H5{j=HHHH!OHHEHHUHuHuIININ0IFIVIF@H]HEHtHH5,=HN$OHH;Eu LHP[A^],=IH]HEHtHH+=L(= fDUHAVSHPIH#OHHE+=HþH+=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5:=H}HH5>HEH5@HvHH HQH5$h=HHHHOHHEHHUHuHuIININ0IFIVIF@H]HEHtHvH)=H!OHH;Eu LHP[A^]/*=IH]HEHtH1H)=L%= ffffff.UHAVSHPIH!OHHEf)=HþH\)=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]7H5+8=H}HTH5>HH5}@HHH HQH5e=H6HHHMOHHEHVHUHuHuIININ0IFIVIF@H]HEHtH H~'=HOHH;Eu LHP[A^]'=IH]HEHtH H9'=LY#= ffffff.UHAVSHPIH+OHHE'=HþH&=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H55=H}HH58>HH5D{@HHA H5kc=HHHHOHHEHHUHuHuIININ0IFIVIF@H]HEHtH H%%=H>OHH;Eu LHP[A^]v%=IH]HEHtHx H$=L!= fDUHAVSHPIHOHHE$=HþH$=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5{3=H}HH5>H5H5x@HfHA H5a=HHHHOHHEHHUHuHuIININ0IFIVIF@H]HEHtHmH"=HOHH;Eu LHP[A^]&#=IH]HEHtH(H"=L= fDUHAVSHPIHOHHEf"=HþH\"=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]7H5+1=H}HTH5>HH5v@HHH HQH5^=H6HHHMOHHEH6HUHuHuIININ0IFIVIF@H]HEHtHH~ =HOHH;Eu LHP[A^] =IH]HEHtHH9 =LY= ffffff.UHAVSHPIH+OHHE =HþH=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5.=H}HH5C>HH5Dt@HHH HQH5d\=HHHHOHHEHHUHuHuIININ0IFIVIF@H]HEHtHH=H7OHH;Eu LHP[A^]o=IH]HEHtHqH=L= ffffff.UHAVSHPIHOHHE=HþH=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]wH5k,=H}HH5>H%H5q@HVHA H5 Z=H}HHHOHHEHM%HUHuHuIININ0IFIVIF@H]HEHtH]H=HOHH;Eu LHP[A^]=IH]HEHtHH=L= fDUHAVSHPIH{OHHEV=HþHL=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]'H5*=H}HDH5>HH5o@HHA H5W=H-HHHDOHHEHm#HUHuHuIININ0IFIVIF@H]HEHtH Hu=HOHH;Eu LHP[A^]=IH]HEHtHH0=LP= fDUHAVSHPIH+OHHE=HþH=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5'=H}HH5C>HH5Dm@HHH HQH5dU=HHHH OHHEH!HUHuHuIININ0IFIVIF@H]HEHtHH=H7OHH;Eu LHP[A^]o=IH]HEHtHqH=L= ffffff.UHAVSHPIHOHHE=HþH=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]wH5k%=H}HH5>H%H5j@HVHA H5 S=H}HHH OHHEH$HUHuHuIININ0IFIVIF@H]HEHtH]H=H OHH;Eu LHP[A^]=IH]HEHtHH=L= fDUHAVSHPIH{ OHHEV=HþHL=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]'H5#=H}HDH5>HH5h@HHH HQH5P=H&HHH=OHHEH#HUHuHuIININ0IFIVIF@H]HEHtHHn=H OHH;Eu LHP[A^]=IH]HEHtHH)=LI= ffffff.UHAVSHPIH OHHE=HþH=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5 =H}HH53>HuH54f@HHA H5[N=HHHHOHHEHCHUHuHuIININ0IFIVIF@H]HEHtHH=H.OHH;Eu LHP[A^]f=IH]HEHtHhH=L = fDUHAVSHPIHOHHE=HþH=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]wH5k=H}HH5>H%H5c@HVHH HQH5L=HvHHHOHHEHAHUHuHuIININ0IFIVIF@H]HEHtHVH =HOHH;Eu LHP[A^]=IH]HEHtHHy =L = ffffff.UHAVSHPIHkOHHEF =HþH< =HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5 =H}H4H5>HH5a@HHH HQH5I=HHHH-OHHEHEHUHuHuIININ0IFIVIF@H]HEHtHH^ =HwOHH;Eu LHP[A^] =IH]HEHtHH =L9= ffffff.UHAVSHPIH OHHE =HþH =HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]跲H5=H}HH5->HeH5$_@HHA H5KG=HHHHNHHEH-MHUHuHuIININ0IFIVIF@H]HEHtHH =HOHH;Eu LHP[A^]V =IH]HEHtHXH=L= fDUHAVSHPIHOHHE=HþH=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]gH5[=H}HH5ݍ>HH5\@HFHA H5D=HmHHHNHHEHMKHUHuHuIININ0IFIVIF@H]HEHtHMH=HNHH;Eu LHP[A^]=IH]HEHtHHp=L= fDUHAVSHPIHkNHHEF=HþH<=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5 =H}H4H5>HH5Z@HHH HQH5B=HHHH-NHHEHfIHUHuHuIININ0IFIVIF@H]HEHtHH^=HwNHH;Eu LHP[A^]=IH]HEHtHH=L9= ffffff.UHAVSHPIH NHHE=HþH=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]跳H5=H}HH5->HeH5$X@HHA H5K@=HHHHNHHEHMHUHuHuIININ0IFIVIF@H]HEHtHH=HNHH;Eu LHP[A^]V=IH]HEHtHXH=L< fDUHAVSHPIHNHHE=HþH=HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC 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f.UHAWAVAUATSHXH_KHHEHJ\KHHEHMIHIHCKHEHEHEHEH}UA$EEEMcMMADHHuHUHMP0 vG\MYBDCYDXHEBDH}M9}^BECEEEEEIHuf9H X]KHHH5bGKHEKHg9 HEH9t HtHP(HP H^KHH;EuHX[A\A]A^A_]f9HH}HEH9uHP HtHP(Htb9 f.UHAWAVAUATSHXHH^KHHEHZKHHEHMIHIHAKHEHEHEHEH}UA$EEEMcMMADHHuHUHMP0 G\MYBDCYDXHEBDH}M9}^BECEEEEEIHuGe9H [KHHH5EKHCKHee9 HEH9t HtHP(HP H]KHH;EuHX[A\A]A^A_];e9HH}HEH9uHP HtHP(H`9 f.UHAWAVAUATSHXH\KHHEHXKHHEHMIHIH@KHEHEHEHEH}UA$EEEMcMMADHHuHUHMP0 G\MYBDCYDXHEBDH}M9}^BECEEEEEIHuc9H YKHHH5DKH+BKHc9 HEH9t HtHP(HP HV[KHH;EuHX[A\A]A^A_]c9HH}HEH9uHP HtHP(H_9 f.UHAWAVAUATSHXHZKHHEH:WKHHEHMIHIHn>KHEHEHEHEH}UA$EEEMcMMADHHuHUHMP0 fG\MYBDCYDXHEBDH}M9}^BECEEEEEIHua9H HXKHHH5RBKH{@KHb9 HEH9t HtHP(HP HYKHH;EuHX[A\A]A^A_]a9HH}HEH9uHP HtHP(Hd]9 f.UHAWAVAUATSHXH8YKHHEHUKHHEHMIHIHKHU`9 HEH9t HtHP(HP HWKHH;EuHX[A\A]A^A_]+`9HH}HEH9uHP HtHP(H[9 f.UHAWAVAUATSHXHWKHHEHSKHHEHMIHIHN;KHEHEHEHEH}UA$EEEMcMMADHHuHUHMP0 G\MYBDCYDXHEBDH}M9}^BECEEEEEIHu^9H TKHHH5>KH=KH^9 HEH9t HtHP(HP HFVKHH;EuHX[A\A]A^A_]{^9HH}HEH9uHP HtHP(HZ9 f.UHAWAVAUATSHXHUKHHEH*RKHHEHMIHIH9KHEHEHEHEH}UA$EEEMcMMADHHuHUHMP0 VG\MYBDCYDXHEBDH}M9}^BECEEEEEIHu\9H 8SKHHH5B=KHk;KH\9 HEH9t HtHP(HP HTKHH;EuHX[A\A]A^A_]\9HH}HEH9uHP HtHP(HTX9 f.UHAWAVAUATSPMLEIIIHL^9HLH9IuHQZ9L^9HLHHEH0H,Z9L\^9HLHHuHH[A\A]A^A_]H4H4H4H4H4H4UHAWAVAUATSPMLEIIIHL]9HLHiAuH]Y9L]9HLHGHEH0H\Y9L]9HLH"HuH&H[A\A]A^A_]H4H4H4H4H4H4UHAWAVAUATSHHIIHRKHHEI@I@I@IA@ ?HHlHHLI@HEHMLEfffff.A$I\$It$HDIET$H}VHLEMxMHu>MWMtIL9r1HIIMI!IJH L0MA$t M\$I\$DLULmfff.IFH9t(MtL9r1IHL9tL!L9ANHtIVL9uqI~HtI~ u1M1fDAT: uCHH9uMHLM;Z9MLULmHuM6M6fD0X9ILHHL-V9AD$fAF(HEIFILEI@Hx H*HHH H*XLHDH MxI*AH u&H*XAH t Y.K ?IrIW1L^f: (G\H,H HH1H,.HCH9HCLMYM}IGLMtLmM9r 1LIILmI!fff.IJHtHIKfff.HUHIL2JIHt)H@IOLtL9r 1IHH!HIL0I@I L;eHNKHH;EuHH[A\A]A^A_] W9ItI~ V9ILzV9IHEHXHt*L3Ct H{ NV9HFV9LMuHEH8HHt&V9LFR9 HT4@UHAWAVSPHIHuHCHt HTS9HM~L9vLHH[A^A_]Ir IGL1IFHxH*HHH H*XA^F f:  G(\H,HH1H,.HC„t Hr"HHȁAHHR9H9HBL97H[A^A_]fUHAWAVAUATSHHIHMtNLH=J<T9H;HHtT9LsINDHMHs-1HuxH;HHtT9HCQLH)1ffff.H3HH3HDH3HDH3HDHH9uHtHHHHHuLKMHKMQLutM9r1LIIL#UHJ M9MLuH]M_Lut&M9r 1LIIM9uMifL#]M9tHJ<2IMHEGDDLAtIWI_MLUL]DEHUH]MIAFtINH9t&Iv 'H9tffffff.IvHHAtI u*HtM1AL: uUHI9u3ff.Ht#L5T9DEL]LUIH]HUuIMHDfI$IH]HJHI$HJL8LuN MMffff.M9MdHH[A\A]A^A_]ÿ#R9HH59H O9H IKHHH52KH50KH/R9IHR9LM9 UHAWAVAUATSHHIIHIKHHEI@I@I@IA@ ?HHlHHLI@HEHMLEfffff.A$I\$It$HDIET$H}薱HLEMxMHu>MWMtIL9r1HIIMI!IJH L0MA$t M\$I\$DLULmfff.IFH9t(MtL9r1IHL9tL!L9ANHtIVL9uqI~HtI~ u1M1fDAT: uCHH9uMHLM{Q9MLULmHuM6M6fD0O9ILHHLmM9ID$IF(HEIFILEI@Hx H*DHHH H*XLHDH MxI*AH u&H*XAH t Y.K ?IrIW1L^f: (0G\H,H HH1H,.HCH9HCLMYM}IGLMtLmM9r 1LIILmI!fff.IJHtHIKfff.HUHIL2JIHt)H@IOLtL9r 1IHH!HIL0I@I L;eHFKHH;EuHH[A\A]A^A_]JN9ItI~ M9ILM9IHEHXHt*L3Ct H{ M9HM9LMuHEH8HHtfM9LI9 H4@UHAWAVSPHIHuHCHt HJ9HM~L9vLHH[A^A_]Ir IGL1IFHxH*HHH H*XA^F f:  G(\H,HH1H,.HC„t Hr"HHȁAHHI9H9HBL97H[A^A_]fUHAWAVAUATSHHIHMtNLH=J<L9H;HHtK9LsINDHMHs-1HuxH;HHtK9HCQLH)1ffff.H3HH3HDH3HDH3HDHH9uHtHHHHHuLKMHKMQLutM9r1LIIL#UHJ M9MLuH]M_Lut&M9r 1LIIM9uMifL#]M9tHJ<2IMHEGDDLAtIWI_MLUL]DEHUH]MIAFtINH9t&Iv 'H9tffffff.IvHHAtI u*HtM1AL: uUHI9u3ff.Ht#LuK9DEL]LUIH]HUuIMHDfI$IH]HJHI$HJL8LuN MMffff.M9MdHH[A\A]A^A_]ÿcI9HH5<9HKF9HJ@KHHH54*KHu'KHoI9IHFI9L*E9 UHAWAVAUATSH(HAKHHEIHAEM}IuIDIEUH}4ILcMIt$Lt M9s MIM! 1LIIHJHHHAEt MMM}DHuLMHCL9uWKHtHSL9u]H{HtH{ uYMtz1T A:u6HH9u`ff.LtL9r1IHL9t :H!L9u2HHs$Mt!LLM0I9LMMHuu1H?KHH;EuHH([A\A]A^A_]G9H蜺4fff.UHAWAVAUATSH(HX?KHHEIHAEM}IuIDIEUH}脧ILcMIt$Lt M9s MIM! 1LIIHJHHHAEt MMM}DHuLMHCL9uWKHtHSL9u]H{HtH{ uYMtz1T A:u6HH9u`ff.LtL9r1IHL9t :H!L9u2HHs$Mt!LLMG9LMMHuu1H=KHH;EuHH([A\A]A^A_]&F9H4fff.UHAWAVAUATSHIH7LwI)IIFHH;HHWH)HHHH9H}wYHH9HBHDH9vC7E9HH59HB9H9fDžXW)HDžƅHhardsigmHDžoidH`H>9W)`ƅ`fDžxHDžpDžaeluH}H`[>9W)@fEƅ@HDžPHsoftsignHAƅIH}H@>9W) fEƅ HDž0HsoftplusH!ƅ)H}H =9fEH=TMH ]EEE`@  % 1@=`IUamy @`t Hp>9H=YKH5SMH>9W)`ƅ`HDžpLafDžiuHH`3<9HDž #<W)@HDžPƅ@HADžIoidHH@;9HL>?HW) ƅ HDž0Dž!eluHH ;9HDž?H=RMH @`t HpY=9H=KH5RMHd]=9HX5KHH;EH[A^A_]H}=9EAH}=9E8H}<9`/Hp<9@#HP<9 H0<9 H<9Hq<9HX<9H?<9H&<9 H0 <9@HP;9`Hp;9H;9H;9H;9Hw;9{H^;9 oH0E;9@cHP,;9`SZH;9lH:9`H:9 TH0:9@HHP:9`8?;9IuIuUt Hi:9E1 t H0Q:9@uVH2:9tH:9tIA tIA@t3HP99`t'Hp99HuIA1`uHAHf.`t Hpy99HuIEEE`@  t H89E1 t H089 @%&H}89EDH}89E;H}89`2Hpf89@&HPM89 H0489H89H89H79H79IA IA @HP79`Hpi79HP79H779H79H79H69 H069@HP69`Hp69HIA`*6B N@Z`fHt+Au%H@`t Hp59HuL29 IAS`IAC\IA3XIA#TIAPIA LIA@HIA1`GUHAVSHPIH,KHHE49HþH49HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]wH5#:H}HH5:HUH5<HHA H5;q9HHHH'KHHEHHUHuHuIININ0IFIVIF@H]HEHtHH29H+KHH;Eu LHP[A^]F39IH]HEHtHHH29L.9 fDUHAWAVAUATSHIIIH*KHHE1L HHL0L8W)`HDžpHCH+CHH.袋.HH`vNLsL{M9t]L`H]fffff.LuHhH;psL0Hhffffff.LHWIXM9uW)EHEIPH@H`LELH0 H@HH8t]HHDž@H}Ht H}E19H`Ht Hh-19HF)KHH;EukHHĨ[A\A]A^A_]H}tCHUHLbIL@HDž@MtAFt I~09L09gHP1909IH`"H5:H ?:H}L`衏W)@HDžP ^09HP($G)@He input.HHHsequenceHHHptr for HHHGot nullH@AHn9HuH@LőE1H5SKHl KL&09WHA(HA}HH@t HP/9LeMt6HEL9t!Lh@t Hxr/9LM9uH}LLeX/9Et H}I/9Et H}:/9L`Mt:HhL9t=Lh@t Hx/9LM9uH`Lh.9EtwL#/9mLHL@HDž@MtAFt I~.9L.9HH}HtH}.9HH`Ht Hhx.9H*9 fffff.UHAVSI`X.9HH KHHH{LHHPLKHKHH[A^]IH-9L*9 @UHSPHK;uyH 3 KHHH[]ÐUHAVSHPIH%KHHE-9HþH-9HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]wpH5:H}HHLH5Q9HHjH5:HH5<H,HA H5i9HSHHHj KHHEHHUHuHuIININ0IFIVIF@H]HEHtH3H+9H#KHH;Eu LHP[A^]+9IH]HEHtHHV+9Lv'9 fff.UHAWAVAUATSHHIH?#KHHE1H^IMW)EHEHU1HHH}Ht H}*9H<HtIOI+OHH.袋.HHIH"KHH;EuLHĈ[A\A]A^A_]*9*9IHhx"H5:H =:H}LheW)PHDž` "*9H`(G)PHe input.HHHsequenceHHHptr for HHHGot nullH@AH29HuHPL艋E1H5 KH0KL)9)9IHh"H5:H W:H}Lh!W)PHDž`0<)9H`(gG)PHt tensorHH%Houtput tHH Hptr for HHHGot nullHHHLength: HHHSequenceH@-AH:HuHPL臊E1H5KH.KL(9 IAIIAIIAIA2IuIPt H`;(9LeMt6HEL9t!HX@t Hx(9HI9uH}LLe'9Et H}'9Et H}'9LhMHpL9HX@t Hx'9HI9uIPt H`'9LeMt6HEL9t!HX@t Hxa'9HI9uH}LLeG'9Et H}8'9Et H})'9LhMt:HpL9t=HX@t Hx&9HI9uHhLp&9Et$L'9LIH}Ht H}&9L"9 UHAWAVAUATSHAHHKHHEHt H&U1H KH H;MuHĈ[A\A]A^A_]&9IǃLg&9E&9IHh>"H589H L:H}LhZ+H{HIcHH96LdII|$0HPILLH 9L@IHKHH;EuLHĘ[A\A]A^A_]N99IHh"H5P:H /:H}LhT}W)PHDž` 9H`(SG)PHe input.HHHsequenceHHHptr for HHHGot nullH@AH9HuHPLE1H5JHJLU99IHh"H5j:H I:H}LhW|W)PHDž`09H`(}G)PHdex tensHHHt for inHHHptr inpuHHHGot nullHf@ or@"AH :HuHPLE1H5JHJLi99IHh"H5~:H ]:H}Lhc{W)PHDž`09H`(>H)PHut tensoHH Hfor outpHHHnullptr HHHAt: Got HHHSequenceHf@(rAH:HuHPL~E1H5JHJLs9 H}9 IA(IA}IcIPt H`9LeMt6HEL9t!HX@t Hx9HI9uH}LLe9Et H}9Et H}9LhMHpL9HX@t HxL9HI9uIAIIAIIAIA2IuIPt H`9LeMt6HEL9t!HX@t Hx9HI9uH}LLe9Et H}9Et H}9LhMHpL9HX@t Hx^9HI9uIPt H`99LeMt6HEL9t!HX@t Hx9HI9uH}LLe9Et H}9Et H}9LhMt:HpL9t=HX@t Hx9HI9uHhLp9Et5L9+LIH}HtH} IEt H}\9L|9 fUHAWAVAUATSHHUKHHEHG0@Ẽtu9VHc;HH !KH H;MuHĘ[A\A]A^A_]S99IH`"H5U:H :HxL`CwH5A:HHHUHxHH1LSzH5JHJL9 IHt HXC9E1Hv4IALeMt6HEL9t!HX@t Hx 9HI9uH}LLe9Et H}9xtH}9IAL`Mt:HhL9t5HX@t Hx9HI9uH`Lh9Eu LIL9L9 UHAWAVAUATSH8MLHHIHHGKHHEH8HrJHHHLH@H8L9LHEEL-` KIEHHI@L8L=9L= KILHDž(HDž HDžHDžDž0L9HLH虮HH0H9HILL9HLHXH3H[9LHH9HHH!LL~HHL8Lt H 9L%9H5JHH9H89H KHH;EuLH8[A\A]A^A_]9IHHL8Lt H 9L9+H:4H24H*4H"4H4IH5JHH9IH8[9L9 DUHAVSHPIH KHHE9HþH|9HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]gWH5ӵ:H}HH5k:HH5h<H6HA H5P9H]HHHtKHHEHHUHuHuIININ0IFIVIF@H]HEHtH=H9H KHH;Eu LHP[A^]9IH]HEHtHH`9L9 fDUHAVSH0IHHX KHHEHKHHH{ H[KHHCPW)EE HPHEEdtypfEeH}HULHaLuHEMtAFt I~9L9Et H}9MtHH KHH;Eu H0[A^]9HEt H}]9H} 9 UH]fDUHAWAVAUATSHHIIH< KHHEH{HHH+H1H{HH3HHIGPHHH HcH2Y9RS:K ;DE4=561/;(7!48T3 7F6HH{cIHMKHH;EuLHĈ[A\A]A^A_]|99IHh"H5~:H :H}LhynW)PHDž`09H`(!ZG)PHsequenceHH&Hutput seHH Htr for oHHHot nullpHHHEmpty: GHHHSequenceH@.AH:HuHPLqE1H5JHJLg99IHh"H5|:H :H}LhmIPH5U:HPLBHuHP1L}pH5JH'JL9 IPt H`m9E1H蠁4IALeMt6HEL9t!HX@t Hx59HI9uH}LLe9Et H} 9EtH} 9IALhM HpL9HX@t Hx 9HI9uIIA(IA}IIPt H`} 9LeMt6HEL9t!HX@t HxX 9HI9uH}LLe> 9Et H}/ 9Et H} 9LhMt:HpL9tEHX@t Hx 9HI9uHhLp 9EtL 9L9 Lf9Uqj@N2c+\$Gfff.UHAWAVAUATSHHIHHhKHHEH8HJHHHLH@H8L 9HEEL5KM~LI@L8Ll 9L%KKILHDž(HDž HDžHDžDž0L69HLHȣH3H 9HHH3tLL8Lt H ) 9H 9H5.JHH8 9H8 9HKHH;EuHH[A\A]A^A_]B 9HLL8Lt H 9H/ 9H}4H}4HH5JHH9HH8 9Hz9 UHAWAVATSH`IHWKHHEؿ2 9HþH( 9HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]MH5:H}HPIIHW)EHE9HEHHHML HXHMH59HULH5::HXH5^<HHA H5>F9HHHHJHHEH@HUHuHuIIOIO0IGIWIG@H}Ht H}9H]HEHtH~H9HJHH;EuLH`[A\A^A_]39IIIIH}Ht H}9H]HEHtHH|9L9 fUHAWAVAUATSHHuJHHEHIIW)EHEHHoI?t#H:H ԫ:1AL["H(IMl$0ID$I\$ W)PHDž`H)tnH8L0IILH=H9ILXLPKH`HL0H8~LH9ILXHEH@HEHHHtH@HhHPH@E1L9HHHt#HHCHuHHPH9HPHt HX9LhM4$M9ttI|$0HPhI|$Ht:xu4IqH~L1LLs9LqHIIH9|%qHI|$0HPHLLH9H(H{H;{sHh@HCXHhH胀IHhCH]Ht#HHCHuHHPH9HJHH;EuLHĸ[A\A]A^A_],9HP9 I2ILHMt#HIFHuILPL19HPHtOHX]9AIIIHt/Ct H{:9H29IIHhBH]Ht#HHCHuHHPH9L9 @UHAWAVAUATSHIIHJHHE1LIMTL^HH!M7H{0HPhI9t{I?HhH{0HPH5:H :L A9H]HhLPH LH*"EH}9HLH^IOI+OHH.袋.HHHLsL@Ht?H_H@HHH9H9HxфuHy HH@I|$HzvHH+H1LFmHH{HHW)EHEH}L HLHHH@HLE1ffffff.I9H8LLHMAL8HDž8MtAFt I~9L9LL5LH0HLHMH0HDž0HtcCt H{49H,9Jf.LL%5H(HLHMH(HDž(HuIHHH@L9HH9LuDH HMLL1H HDž HtCt H{{9Hs9I7HHSLcML{IIH[L9|MI)HèfH>HèIXuI?LUH5:H :L L>9H]H@LHHLH'"II)LM&8IGIGIHHEHCHEIHEHCIEHJHH;EuLH[A\A]A^A_]8\8IHhU"H5¡:H :H}LhB^W)PHDž` 8H`(űG)PHe input.HHHsequenceHHHptr for HHHGot nullH@AHޣ:HuHPLf`E1H5JH JL8v8IHho"H5ܠ:H :H}Lh\]W)PHDž` 8H`(G)PH tensor.HHHinput teHHHptr for HHHGot nullH@AH)9HuHPL_E1H5JH'JL88IHh艛"H5:H 1:H}Lhv\W)PHDž`038H`(IiH)PHsequenceHH'Houtput sHH Hptr for HHHGot nullHHHInsert: HHHSequenceH@/AH:HuHPL~^E1H5 JH%JL8IPtH`h8IALeMt6HEL9t!HX@t Hx;8HI9uH}LLe!8Et H}8EtH}8IALhMHpL9HX@t Hx8HI9uIPtH`8IALeMt6HEL9t!HX@t Hxq8HI9uH}LLeW8Et H}H8EtH}98IALhMHpL9HX@t Hx8HI9uIPtH`8IALeMt6HEL9t!HX@t Hx8HI9uH}LLe8Et H}~8EtH}o8IALhMt?HpL9t$HX@t Hx<8HI9uHhLLp8EutILF8gIEtZH}OH-m4ILuMt@H]L9tAMI)Hèfffff.Hx8HèIXuH}Lu8L8 LfUHAWAVAUATSH8MLHHIHHsJHHEH8HJHHHHH@H8H8LHEEL-JM}LI@L8Hp8L5OJILHDž(HDž HDžHDžDž0L:8HLH̐HHHHL$uHHHaLL8Lt H 8H8H5JHH#8H88HJHH;EuHH8[A\A]A^A_]-8HLL8Lt H 8H8 Hj4HH5JHH8HH88Hm8 UHAWAVAUATSH8MLHHIHH'JHHEH8HRJHHHLH@H8Lg8LHEEL-@JIEHHI@L8L8L=JILHDž(HDž HDžHDžDž0L8HLHyHH0H8HILL8HLH8H3HM8LHHo8HHHLL^HHL8Lt H u8L8H5~JHH8H88HcJHH;EuLH8[A\A]A^A_]8IHHL8Lt H 8L|8+Hh4Hh4H h4Hh4Hg4IH5JHH8IH8;8L8 DUHAWAVAUATSPIM'IGL)HH.袋.HH9H袋.H9MwLL)LkXL-8HLM9tCHEM)IƨHIHfHǨLt/HèHIƨIXuM7MgLHEHI?I_IGM9t"LL)IĨfDLh2IĨHXuMtLH[A\A]A^A_]8H[A\A]A^A_]ÿ8HH5[39Hj8HiJHHH5SJHJH8IHe8LI8 ffffff.UHAWAVATSH`IHJHHEؿ8HþH8HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]5H5?:H}HI訙I蠡HW)EHE8HEHHHML HXHMH5o9HUL跹H5:HH5F<HIHA H5.9HpHHHJHHEH`iHUHuHuIIOIO0IGIWIG@H}Ht H}8H]HEHtH>H8HJHH;EuLH`[A\A^A_]8IIIIH}Ht H}]8H]HEHtHH<8L\8 fUHAWAVAUATSHIIH/JHHE1LNIMLEKINI+NHH.袋.HHHLkL@Ht?H\H@HHH9H9HxфcHy HH@L(IHqcHH+H1L=ZHHhIMI6LUBW)EHEH}LzHH~u1fffff.H;@tPLH"H8HLHMFL8HDž8MtAEt I}8L8HH9HM|$MMl$MII\$L9L0MI)HèfH,HèIXuL0I}aH5 :H :L [,9H]H@LHHLH"Et_H}8TMI#LM>8IEIEIEHEID$HEIHEID$L(I$HJHH;EuLHĸ[A\A]A^A_]8\8IHhU"H5:H :H}LhBLW)PHDž`08H`(e7G)PHsequenceHH&Hutput seHH Htr for oHHHot nullpHHHErase: GHHHSequenceH@.AHߏ:HuHPLJNE1H5JHJL8Z8IHhS"H5:H :H}Lh@KW)PHDž` 8H`(ÞG)PHe input.HHHsequenceHHHptr for HHHGot nullH@AHܐ:HuHPLdME1H5JH JL8 IPtH`N8IALeMt6HEL9t!HX@t Hx!8HI9uH}LLe8Et H}8EtH}8IALhMHpL9HX@t Hx8HI9uIPtH`8IALeMt6HEL9t!HX@t HxW8HI9uH}LLe=8Et H}.8EtH}8IALhMt?HpL9t$HX@t Hx8HI9uHhLLp8EudIL8WIEtNH}CILuMt:H]L9t;MI)HèH8(HèIXuH}Lua8L8 LfUHAVSHPIH[JHHE68HþH,8HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]$H58H}HTH+H5h:HH:H5:H蛨H5Z=<H̩HA H5%9HHHH JHHEHC`HUHuHuIININ0IFIVIF@H]HEHtHH;8HTJHH;Eu LHP[A^]8IH]HEHtHH8L8 fff.UHAWAVAUATSHIHHJHHEH~ZHLc(MH(I|$HZHH+Hl1LcQHYH_HHH0H8E11LALx0f.LDuAEL9x0`8HH](zG)EPHH5:j8CPHHHM "HHHDžHHtCt H{8H8EtH}w8AE9DH8L8W)EHEH}L1L}@L@H@HLLL@HDž@MtAFt I~8L8D9|L8M|$MtHH0LpHII\$L9t9MI)Hè@HX#HèIXuI>L8H0H&LM}Lf8IFIFIHEID$HEHHEID$H(HH AJH H;MuHĸ[A\A]A^A_]s88IHh"H5u:H 5:H}Lh0BW)PHDž`@8H`(8JH)PHt sequenHH(Hor outpuHH Hullptr fHHHt: Got nHHHConstrucHHHSequenceHf@0ce@2AH:HuHPLDE1H5JHJLX88IHh"H5m:H -:H}Lh-AW)PHDž` 8H`(P!G)PHore inpuHHHe 1 or mHHHMust havHf@ts@AH:HuHPLDE1H5JHJLz8IPtH`8IALeMt6HEL9t!HX@t Hx8HI9uH}LLe8Et H}8EtH}8IALhMHpL9HX@t Hxc8HI9uIPtH`98IALeMt6HEL9t!HX@t Hx 8HI9uH}LLe8Et H}8EtH}8IALhMt?HpL9t$HX@t Hx8HI9uHhLLp8EuiIL8\IEtQH}FILuMt?H]L9t@MI)Hèffff.HHèIXuH}Lu8L18 LfUHAWAVAUATSHxIHJHHEп8HþH8HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]豇I詏I衆I虉HW)EHE 8HEHH HML8L`LhHXHMH5O8H}HU触HO"H5:HH荦I%IHW)`HDžp8H`HHHpL HXHhH5w9H`L"H5:H胞H5B3<H负HA H5i9H۠HHHJHHEHVHUHuHuIININ0IFIVIF@H`Ht Hh,8H}Ht H}8H]HEHtHH8HJHH;EuLHx[A\A]A^A_]D8I'9I IIH`Ht Hh8H}HtH}8IH]HEHtHHn8L8 @UHAWAVATSH0IIHdJHHEHJHIIMHxKIfHnfsAGPIG`ffEHEEEaxisEH}HUHML[aH]E1HuLeHEtCt H{8H8MgPEt H}8ffEEHEHkeepdimsHEEH}HUHML`H]Hu'LuHEt)Ct H{&8H8AHEuMwXEt H}8HJHH;Eu H0[A\A^A_]I8HEt H}8H8 fUH]FfDUHAWAVAUATSHHIIHJHHE1H5IǾH5I$IO0IɃ HNHc HH}LHLI\CH}LHLI,H}LHLI>#H}LHLI3HEI$HJHH;EuLHĘ[A\A]A^A_]88IHhw"H5|:H :H}Lhl8IG0HHH5:H ;HPHHBHuHP1L;H5JHJLl8IPt H`8DžDH!L4IǰDH]Ht6HEH9t!Lh@t Hx8LL9uH}HH]8Et H}8EtH}y8 IǰDLhMt:HpL9t9HX@t HxA8HI9uHhLp&8Du LILG8 II$L8 fGGGGGGGUHAWAVAUATSHIIHIHJHHEHEHCPHEEEEEEW)EHEMt 0HH.Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@6AHu:HHPL .E1H5JHJLk8 8H &JHHH5ȫJHaJH;8H8H8벿8IHpi"H57n:H \t:HLp*W)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AHt:HHPL,E1H5JHJL]8Hp8IPt H`8L Mt?H(L9t$HX@t Hx8HI9uH LL(8t Hu8tH`8IALpM/HxL9HX@t Hx%8HI9u\I IPtH`8IAL Mt?H(L9t$HX@t Hx8HI9uH LL(8t H8tHl8IALpMt?HxL9t$HX@t Hx98HI9uHpLLx8EuIL@8II8II]IEHCt H{8HfIaIHHtH8IHHtbHx8TIHHt#HHCHuHHPH8HHtOH'8AIH0II]IEHtCt H{8H8IHHHt#HHCHuHHPHq8LPMt>HXL9tfMI)HèHHHèIXuHPLXk8HpHt HxS8H}Ht H}A8La8 LfUHAWAVAUATSHIIHIH)JHHEHEHCPHEEEEEEW)EHEMt 0HH.Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@6AHCe:HHPLE1H5JH1JL8 蝻8H JHHH5HJHJH軻8H58H'8벿Q8IHpJY"H5]:H Ff:HLp4W)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AH$d:HHPL|E1H5 JH#JLݺ8HpT8IPt H`U8L Mt?H(L9t$HX@t Hx*8HI9uH LL( 8t H8tH8IALpM/HxL9HX@t Hx襹8HI9u\I IPtH`i8IAL Mt?H(L9t$HX@t Hx68HI9uH LL(8t H8tH8IALpMt?HxL9t$HX@t Hx蹸8HI9uHpLLx虸8EuIL8II8II]IEHCt H{F8HfIaIHHtH8IHHtbH8TIHHt#HHCHuHHPH{8HHtOH觷8AIHV0II]IEHtCt H{q8Hi8IHHHt#HHCHuHHPH8LPMt>HXL9tfMI)HèHHèIXuHPLX8HpHt HxӶ8H}Ht H}8L8 LfUHAWAVAUATSHIIHIHJHHEHEHCPHEEEEEEW)EHEMt 0HH.Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@6AHT:HHPL E1H5JHJL[8  8H JHHH5JHQJH+8H襧8H藧8벿8IHpH"H5'M:H iV:HLp W)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AHS:HHPL E1H5zJHJLM8HpĦ8IPt H`ũ8L Mt?H(L9t$HX@t Hx蚩8HI9uH LL(z8t He8tHP8IALpM/HxL9HX@t Hx8HI9u\I IPtH`٨8IAL Mt?H(L9t$HX@t Hx覨8HI9uH LL(膨8t Hq8tH\8IALpMt?HxL9t$HX@t Hx)8HI9uHpLLx 8EuIL08II8II]IEHCt H{趧8HfIaIHHtH臧8IHHtbHh8TIHHt#HHCHuHHPH8HHtOH8AIH0II]IEHtCt H{8H٦8IHHHt#HHCHuHHPHa8LPMt>HXL9tfMI)HèH8HèIXuHPLX[8HpHt HxC8H}Ht H}18LQ8 LfUHAWAVAUATSHIIHIHJHHEHEHCPHEEEEEEW)EHEMt:H H:HLp]W)PHDž`@8H`(oG)PHt be > 0HH.Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@6AH E:HHPLTE1H5zJHvJL赛8 g8H pJHHH5{JHwJH腛8H8H8벿8IHp9"H5=:H sG:HLpW)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AHC:HHPLFE1H5yJHuJL觚8Hp8IPt H`8H Ht?H(H9t$Lh@t Hx8LL9uH HH(ԙ8t H这8tH誙8IALpMt?HxL9t$HX@t Hxw8HI9uHpLLxW8EuIL~8IT IPtH`8IAL Mt?H(L9t$HX@t Hx8HI9uH LL(8t H諘8tH薘8IALpMt?HxL9t$HX@t Hxc8HI9uHpLLxC8EuILj8II@II]IEHCt H{8HpIkIHHtH8IHHH螗8{IHHt#HHCHuHHPH!8HHtSHM8E4II]IEHt/Ct H{&8H8IIHHHHt#HHCHuHHPH蓕8LPMt@HXL9thMI)HèDHhHèIXuHPLX苖8HpHt Hxs8H}Ht H}a8L聒8 LfUHAWAVAUATSH(IHIHHEJHHEH8HpuJHHHLH@H8L腕8LHEEL=^JMgLI@L8LB8L-!JILHDž(HDž HDžHDžDž0L 8HLH-H3HHHܗ8HHHn-HHHLL8Lt H ޔ8Hj8H5zJHH8H8[8HȌJHH;EuHH([A\A]A^A_]8HLL8Lt H X8H8H4Hz4Hr4HH5@zJHHJ8HH8賓8H'8 ffff.UHAWAVAUATSHXLMDEIIH}L}HJHHEMuM+uIHuH~PL=IIMHH]LHHcH9KHMI7LHcH9.HM I7HƸL9tL^HcH9Lm@HM(}tPLu8HHIIAHML9tHE0IGHMUIML)HHH)vALMU=MeIEI9HHL)HHH1LsIIEKJ HLHHHHOMDLLIWLHfInfpDHVHHyHP1H HEHffEHEMHHHH9H<ޑ8IHMHA`LmLmMtLuHrHaI9|I9sLHSHHtHH8H9HHHuH@HHHHQHHQHHQHHQ HHQ(HHQ0HHQ8H@HuuI?I!IPHHwH |f1fHHWH)1ېAADAD AD0AD@ADPAD`ADpAڀAڐAڠAڰAAAAH HkHt(ILHfffff.AH HuI9t)LH)IHfff.L"HHHIMH)HLebHHMcH ZII!fInfpDIHHH{HX1HHVH)f1fDAofAoDfAoL Ao\0Aod@ffAoTPffAoD`fAoLpfHHuHt$ITHfoRfofH HuffpNffH~L9tK ff.HHH9uHUH9uL)HHMHH5Z6:H d6:L 8H]HULEHLeLH!EH}38KHOH)1ffff. 0@P`pDހDސDޠDްDDDDH HuHtLILHfAH HuL9L)JHff.LHHHE1E1MHHHzHHNHtB~fpDHyH)1ADADAD AD0HHu1Ht~fpDADADH9ueLuMt#MgL萌8IGIGIM/MwMwLeI$H{JHH;EuLHX[A\A]A^A_]ILH)蠌8<8H EJHHH5kJHhJHZ8H}و8 HH}HtH} HEt H}ы8H8 UHAWAVAUATSH8MLHHIHHJHHEH8HjJHHHLH@H8L8LHEEL-JIEHHI@L8L蝉8L=|JILHDž(HDž HDžHDžDž0Lg8HLH"HH0H8HILL&8HLH"H3H͈8LHH8HHH"LLHHL8Lt H 8L腈8H5oJHH8H8v8HJHH;EuLH8[A\A]A^A_]8IHHL8Lt H l8L8+H3H3H3H3Hz3IH5HoJHHR8IH8軈8L/8 DUHAVSIP8HHB{JHHH{LHp%KHH[A^]IH貈8L҄8 UHAVSIP蘈8HHzJHHH{L_H`%KHH[A^]IHR8Lr8 UHAVSIX88HHLuH[A^]IH 8L+8 UHAVSIP8HHBzJHHH{LH%KHH[A^]IH貇8L҃8 UHAVSIP蘇8HHyJHHH{L_H%KHH[A^]IHR8Lr8 UHAVSIP88HHyJHHH{LH$KHH[A^]IH8L8 UHAVSIh؆8HHL^H[A^]IH諆8L˂8 UHAWAVAUATSHHH~JHHEH{Ht4HP8t*HH ~~JH H;MuHĘ[A\A]A^A_]谆8L8IHhE$"H58H .:H}Lhi2H{)HHH5#8HPHHx/AH8HuHPLE1H5)eJHBaJL8 IAIIPtH`u8IALeMt6HEL9t!HX@t HxH8HI9uH}LLe.8Et H}8Et H}8LhMt:HpL9tEHX@t Hx8HI9uHhLpʄ8EtL8L݀8 LH3fDUHAWAVAUATSHI袋.HH_H)HI.袋.IHCL9#H}HOH)HIH\tE]tH9wHH9HBIHIkXI$8LILkXO,4IkXLL{KL4XHEL0L`M9tGHMM)IĨMIŨLLAIǨMIĨIXuHEL LpHMML(HHHXM9tLL)IƨfL8IƨHXuMtLH[A\A]A^A_]S8H[A\A]A^A_]E1E1'8@UHAWAVAUATSPMIIIHH跅8LHHML衅8LLH7IH舅8LHHLr8LLHH[A\A]A^A_]H3H3H3H3fUHAWAVAUATSHHzJHHEHHC0@tu?HHc HeoHH KzJH H;MuHĘ[A\A]A^A_]}88IHh "H5$:H +:H}LhH{0HHH5+:HPHHHuHP1LpH5aJH]JLԁ8 IPt H``8E1H3IAkIIALmMt6HEL9t!HX@t Hx8HI9uH}LLm8Et H}8Et H}8LhMt:HpL9tEHX@t Hx赀8HI9uHhLp蚀8EtLɀ8L|8 Lfffff.UHAWAVAUATSHIIHoxJHHEIoILL1JIG0@t\LĽHMIL}Hc HMIFI;FsHHIF fLLwHIuNLlHMt>IDIFI;FtH HHIFfDLHJHIuHwJHH;EuHĘ[A\A]A^A_]8b8IHh["H5!:H y):H}LhHI0?~HHH5)):HPHHHuHP1LH5J^JHcZJL8 IPt H`~8E1H3IAkIIALeMt6HEL9t!HX@t Hxa~8HI9uH}LLeG~8Et H}8~8Et H})~8LhMt:HpL9tEHX@t Hx}8HI9uHhLp}8EtL~8Ly8 LUHAWAVAUATSHIIHuJHHEILHLH9u\M9t0I6IVHuJHH;Eu;LH[A\A]A^A_]QH`uJHH;EuH[A\A]A^A_]}8.}8IHh'"H58H ':H}LhL HH|8H':H1Tz8HHHHHPHHH@H@HH5':Hy8HHHHHPHHH@H@HL_HH{8tHH HHry8HHH0HHPH(H H@H@HH58H "y8HHHHHHPH@H8H@H@HHPH8&y8AH:&:HuHPLE1H5ZJHVJL~{8 IA8IA tvIAtkIAtqIAIAcIIPuG8uS u_ukH`Dz88tHH/z8 tH0z8tHz8tIAxHy8lHy8tHy8IALeMt6HEL9t!HX@t Hxy8HI9uH}LLefy8Et H}Wy8Et H}Hy8LhMt:HpL9tEHX@t Hxy8HI9uHhLpy8EtL1y8Lu8 LfDUHAWAVAUATSHHIHHpJHHEH8HXJHHHLH@H8Lx8HEEL5nJM~LI@L8Lv8L%nJILHDž(HDž HDžHDžDž0Lz8HLH8HHz8HHHHHHLL8Lt H w8Hv8H5]JHHu8H8w8HuoJHH;EuHH[A\A]A^A_]w8HLL8Lt H w8Hu8H/3H'3HH5\JHHt8HH8hv8Hr8 fUHSPHOJ;u9?H RJHHH[]ÐUHAVSHPIH{nJHHEVv8HþHLv8HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]'"H5O;9H}HD?H5K]:HfLH^=H5M":H5H5~;H6HH HQH58H8HHH'iJHHEHp*HUHuHuIININ0IFIVIF@H]HEHtHXHXt8HqlJHH;Eu LHP[A^]t8IH]HEHtHXHt8L3p8 fUHAWAVAUATSHIH8H@HkJHHE1L#IMffEHEI|$ I+|$tNIILH=s8HH]H]JHEIuM} I)M~HLt8LH]HU1LdHHH}Ht H}+s8H8@PH IH HcHL+ILPHHH聃MpIrJ ;H9` J H9S 1IWH)LHtH< W*f~HHuHfffff. W*T W*DT W*DT W*f~D HI9uLDILiHHH誷MIrJ ;H9J H91IWH)LHtHfff.< MIrJ ;H9'J H91IWH)LHtH< HfD< | D| D| DHI9uLIL蹗HHH躟MIrJ ;H9KJ xH9>1IWH)LHtHfff.< fHzH)1f803f80L3pLpf80D3f80L3Dp Lp0f80D3 f80L3(Dp@LpPf80D30f80L38Dp`LppH@HuHt4HHfff.f80D3f80 3Dp pHHuI9~1Htf813f81L3[[LI9M1Ht2ooLfftfo.FfftfLI9a8ra8IHhk!H5:H :H}Lh"XffPHDž`@a8H`fohGfPHDataTypeHH(Hof type HH Hrgument HHHe 'to' aHHHmust havHHHCast op H@0HuHP1LdH5?JH`8 H}\8 IPt H`_8E1H3IALeMt6HEL9t!HX@t Hx_8HI9uH}LLem_8Et H}^_8EtH}O_8IALhM HpL9HX@t Hx_8HI9uIPt H`^8E1H3IALeMt6HEL9t!HX@t Hx^8HI9uH}LLe^8Et H}^8EtH}z^8IALhMt:HpL9t5HX@t HxG^8HI9uHhLp,^8Eu )LILQ^8IH}Ht H}]8LZ8 fDPDpUHAWAVAUATSHHUJHHEMIIIW)EHUL"I}t H :H :1AL!"IEHMLLL;IEH]Ht#HHCHuHHPH[8HUJHH;EuLH[A\A]A^A_]H]8II]IEHtCt H{\8H\8IH]Ht#HHCHuHHPH6[8LX8 UHAWAVAUATSHHIHHhTJHHEH8H;JHHHLH@H8L[8HEEL5RJM~LI@L8LlZ8L%KRJILHDž(HDž HDžHDžDž0L6^8HLH3HY8HHH4LL8Lt H *[8HY8H5/AJHH9Y8H8Z8HSJHH;EuHH[A\A]A^A_]C[8HLL8Lt H Z8H0Y8H3H3HH5@JHHX8HH8Z8H{V8 UHAVSHPIH[RJHHE6Z8HþH,Z8HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5/9H}H$#H5+A:HFnLH>!H5-:HH5^;HHH HQH5~8HHHHMJHHEHpHUHuHuIININ0IFIVIF@H]HEHtHHzH)1fDf83sf83LsLf83Dsf83LsD L0f83Ds f83Ls(D@LPf83Ds0f83Ls8D`LpH HuHHt6Hffffff.f83D3f83 3Dp pHHuLHHyHHVHHzH)1fo!Go soTsf8f 0f8fT0oLs oTs0f8fL0f8fT0H Hu+LHHyHHVH01H HzH)1@f84sf84LsLf84Dsf84Ls D L0f84Dsf84LsD@LPf84Dsf84LsD`LpHHuHyHt6Hffffff.f84D3f84 3D HHu<LHHyHHVH051H1Hf83sf83Ls[[L1HoSoLSffufoFffufo&Gf8fff8fLHzH)1sLspLpDs Ls0Dp Lp0Ds@LsPDp@LpPoDs`oLspDp`LppH@HuHt-Ht6HfDoD3o 3D0 0H HuI9HzH)1sLspLpDs Ls0Dp Lp0Ds@LsPDp@LpPoDs`oLspDp`LppH@HuHt(Ht6HڐoD3o 3D0 0H HuI9b1Ht(osoLsfoGf8f0f8fL0I9%P1Ht(osoLsfolGf8f0f8fL0I9@H8IHh!H59H 9H}Lh#荧ffPHDž`@HH8H`foGfPHDataTypeHH(Hof type HH Hrgument HHHe 'to' aHHHmust havHHHCast op H@0HuHP1L虩H5*'JHC#JLG8G8IHh!H59H 9H}Lh#蒦H8HPH59HPHuHP1LH5&JH"JLsG8 H}C8 IPt H`F8E1H'3IALeMt6HEL9t!HX@t HxF8HI9uH}LLeF8Et H}F8EtH}F8IALhM HpL9HX@t HxIF8HI9uIPt H`F8E1HR3IALeMt6HEL9t!HX@t HxE8HI9uH}LLeE8Et H}E8EtH}E8IALhMt:HpL9t5HX@t Hx|E8HI9uHhLpaE8Eu )LILE8IH}Ht H}1E8LQA8 KW*f~HI9uWKHI9uFKHHI9u4KHI9u#KHHI9ufH5-9HH5^;HHH HQH5~g8HHHHJHHEHHUHuHuIININ0IFIVIF@H]HEHtH H8)8HQ!JHH;Eu LHP[A^])8IH]HEHtH H(8L%8 fUHAWAVAUATSHIH@HHH JHHE1LIMffEHEI|$ I+|$tNIILH=s(8HH]H]JHEIuM} I)M~HL)8LH]HU1LDIH}Ht H}(8H@@PH %IH HcHLMILuHLf8MIWDH1H|LILuHL+mMUI1L艓MIL[uHL@wMIrJ H9 J H9 1IWH)LHtHfH0HHHoMI rJ ;H9pHLH9a1IWH)LHt"Hffffff.< HHuH>fD< | D| D| DHI9uLtvILy/HHH=MIrJ ;H9KJ xH9>1IWH)LHtHfff.< fHzH)1f8 3f8 L3pLpf8 D3f8 L3Dp Lp0f8 D3 f8 L3(Dp@LpPf8 D30f8 L38Dp`LppH@HuHt4HHfff.f8 D3f8 3Dp pHHuI9~1Htf8!3f8!L3[[LI9M1Ht2ooLfftfouFfftfLI97R7IHhK!H59H 9H}Lh&8^ffPHDž`@7H`foHeGfPHDataTypeHH(Hof type HH Hrgument HHHe 'to' aHHHmust havHHHCast op H@0HuHP1LD`H5IHIL7W7IHhP!H59H 9H}Lh&=]H8HPH569HPHuHP1L_H5KIHdIL7 H}7 IPt H`7E1Hp3IALeMt6HEL9t!HX@t Hxg7HI9uH}LLeM7Et H}>7EtH}/7IALhM HpL9HX@t Hx7HI9uIPt H`7E1Ho3IALeMt6HEL9t!HX@t Hx7HI9uH}LLex7Et H}i7EtH}Z7IALhMt:HpL9t5HX@t Hx'7HI9uHhLp 7Eu )LIL17IH}Ht H}7L7 fDP{8pUHAWAVAUATSHHIHHEMIIIW)EHULdI}t H9H 91AL}!"IEHMLLLIEH]Ht#HHCHuHHPH7HIHH;EuLH[A\A]A^A_](7II]IEHtCt H{7H7IH]Ht#HHCHuHHPH7Lz7 UHAVSHPIH[IHHE67HþH,7HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5/8H}H$H5+9HFLH>H5-9H蟹H5^N;HкHH HQH5~68HHHHIHHEHHUHuHuIININ0IFIVIF@H]HEHtHH87HQIHH;Eu LHP[A^]7IH]HEHtHH7L7 fUHAWAVAUATSHIH8H@HIHHE1LSIMffEHEI|$ I+|$tNIILH=Us7HH]H]JHEIuM} I)M~HL7LH]HU1LDVHHH}Ht H} 7H8@PH YIH tHcHL cILP(HHHaMIX1LbIL(HHH5MI1L`IL&HHHMIrJ {H9HLH91IH)LHtHfDKHHuHsKTKTTKTTKTHI9u<L`IL]%HHHMIVDH 1H^L_IL%HHHMIY1L_IL$HHH&MIrJ {H9(J xH91IWH)LHtHDHzH)1fDf8#sf8#LsLf8#Dsf8#LsD L0f8#Ds f8#Ls(D@LPf8#Ds0f8#Ls8D`LpH HuHHt6Hffffff.f8#D3f8# 3Dp pHHuLHHyHHVHHzH)1foFo soTsf8f 0f8fT0oLs oTs0f8fL0f8fT0H Hu+LHHyHHVH01H HzH)1@f8$sf8$LsLf8$Dsf8$Ls D L0f8$Dsf8$LsD@LPf8$Dsf8$LsD`LpHHuH{Ht6Hffffff.f8$D3f8$ 3D HHu>LHHyHHVH051H1Hf8#sf8#Ls[[L1HoSoLSffufocFffufo&Ff8fff8fLHzH)1sLspLpDs Ls0Dp Lp0Ds@LsPDp@LpPoDs`oLspDp`LppH@HuHt-Ht6HfDoD3o 3D0 0H HuI9HzH)1sLspLpDs Ls0Dp Lp0Ds@LsPDp@LpPoDs`oLspDp`LppH@HuHt(Ht6HڐoD3o 3D0 0H 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Hx/1HI9uKIPt H` 1LeMt6HEL9t!HX@t Hx1HI9uH}LLe1Et H}1Et H}1LhMHpL9HX@t Hxz1HI9uIAIIAIIAIA2IuIPt H`1LeMt6HEL9t!HX@t Hx1HI9uH}LLe1Et H}1Et H}1LhMHpL9HX@t Hx1HI9uIPt H`g1LeMt6HEL9t!HX@t HxB1HI9uH}LLe(1Et H}1Et H} 1LhMt:HpL9tEHX@t Hx1HI9uHhLp1EtL1L1 LHJ-UHAVSHPIHCHHE1HþH|1HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H}11ߢHH5;1HH蕞H-H5S3HHKH5G3HܖH5+5H HAH52H4HHHKCHHEHHUHuHuIININ0IFIVIF@H]HEHtHH|1HCHH;Eu LHP[A^]1IH]HEHtHϹH71LW1 ffff.UHAVSHPIH+CHHE1HþH1HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H}11_HH51HHH53HvH55)5H觕HHHQH5U2HǖHHHCHHEH'HUHuHuIININ0IFIVIF@H]HEHtH觷H1H(CHH;Eu LHP[A^]`1IH]HEHtHbH1L1 UHAVSIP1HHCHHH{LCHCHHH[A^]IHn1L1 @UHAWAVAUATSHIIH_CHHEоL-HHHP H)HL1H;HW)EHEM MJ<IIHI91NHEHEJHUMKNIIIsHHML9MsJH9v HHI?M!IpHHzH s 1HuqHwH)1fDLLD L0D L0D@LPD@LPD`LpD`LpHHuHt.HHD D H HuM9J4JHt I}Xĺ1L1 f.UHHCHHHGPHt HGXH]鈺1]fDUHSPHHCHHH{PHt 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H51HmrHHHCHHEHm?HUHuHuIININ0IFIVIF@H]HEHtHMH赮1HΦCHH;Eu LHP[A^]1IH]HEHtHHp1L萪1 fDUHAVSHPIHkCHHEF1HþH<1HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]UH5 1H}H4wH5~3HmH55HnHA H51HpHHH4CHHEH]=HUHuHuIININ0IFIVIF@H]HEHtHHe1H~CHH;Eu LHP[A^]趬1IH]HEHtH踐H 1L@1 fDUHAVSHPIHCHHE1HþH1HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]WH51H}HtH5.3HukH545HlHA H5[1HmHHHCHHEHM;HUHuHuIININ0IFIVIF@H]HEHtH譎H1H.CHH;Eu LHP[A^]f1IH]HEHtHhHЩ1L1 fDUHAVSI0踩1HHLH[A^]IH苩1L諥1 UHAWAVATSHIHCHHELHL>I_PLeLL+HL0HCHIILHטCHIH!CHH;EuHĠ[A\A^A_]U1IIHt IȨ1IHt I谨1IIH9uHP HtHP(II`I9uHP HtHP(L臤1 ffff.UHSPHHHt H/1HHt H1HHH9t HtHP(HP HH`H9tHtHH[]`(HH[]` H[]ffff.UHSPHHHt H菧1HHt Hw1HHH9t HtHP(HP HC`HH9t HtHP(HP HH[] 1UHSPHnHH[]UHAWAVAUATSH8HCHHEIIIGHIG0IGPHIGpAƇIHIHfALJfALJILJILJILJILJILJILJILJALJLD`A AW)`HDžp)EHEEEmodeEH@HUH`L@-H@HDž@HtCt H{蚥1H蒥1Et H}胥1HH`L AI}/ HHEHEE fEesEscalEH@HULH|4H@HDž@HtCt H{1Hޤ1Et H}Ϥ1H ALHAƇW)EE&HEHation_vaHEHextrapolHEElueH@LuH LL%H@HtW HDž@tCt H{1H1AEt H}1WA )EHE 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HwHLeH(XPH %jIL:,IƄH%H %HEH(H%H %HEHM H8HHHHHGHQMNMNI]I|MЃHHDHHHHHHpH0HHHLhHI9LpHxL9H~H)H ˈHHHHHBHIFrHIErHIEHIFH`1LLHLMH%E1L H0s1HHHHhH AHJ4VH9IHpH AHxJ4VH9*1AtHftJHtHL9rIL M9HHI9LLLH8HHM(HL_MDEIFAf H8AH0 t H0s1HHHhH AHJ4VH9IHpH AHxJ4VH9*1AtHftJHtHL9rIL M9HHI9LLLH8HHM(HL_MDEIFAf H8AH0 t H0s1HHHH HJ4H93H H H(J4H91ɐAttHtHL9rHHL9L`M9LhbLHuLeLHMMPLXM+MHA H}H} Mt Hps1pHMH CMgLI@L8L"1L- CILHDž(HDž HDžHDžDž0L1HLH~H3H1HH1HHHNHHH{LL8Lt H 1HJ1H5BHH1H8;1H CHH;EuHH([A\A]A^A_]1HLL8Lt H 81H1Hb,HZ,HR,HH5 BHH*1HH81H1 ffff.UHAWAVAUATSHHIHH CHHEH8HBHHHLH@H8L1HEEL5CM~LI@L8L1L%CILHDž(HDž HDžHDžDž0L1HLH(H3H=1HHHyLL8Lt H 1H1H5BHH1H81HsCHH;EuHH[A\A]A^A_]1HLL8Lt H 1H1H-,H%,HH5BHH 1HH8f1H 1 UHIHH]fffff.UHHJH]fffff.UHIKHHH]UHHII8LH]UHAWAVAUATSHH5CHHEIIF0x uFII^PIL{HHCHH;EuHHHHĈ[A\A]A^A_]-11IHh¬H5Os1H J3H}LhmI0H5s1Hs1H s1HPMwAHs1HuHPLpE1H5BHBL{1 IA(IA}IIPt H` 1LeMt6HEL9t!HX@t Hx 1HI9uH}LLe 1Et H} 1Et H} 1LhMt:HpL9tEHX@t Hxg 1HI9uHhLpL 1EtL{ 1L_ 1 LUHAWAVAUATSHH5CHHEIIF0x uAII^PILyH CH H;MuHHHĈ[A\A]A^A_]2 1 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LDUHAWAVAUATSHH5BHHEIIF0x uAII^PILoH BH H;MuHHHĈ[A\A]A^A_]211IHhǠH5Tg1H 3H}LhaI0H5g1Hg1H g1HPM|AHg1HuHPLdE1H5BHBL1 IA(IA}IIPt H`1LeMt6HEL9t!HX@t Hx1HI9uH}LLe1Et H}1Et H}1LhMt:HpL9tEHX@t Hxl1HI9uHhLpQ1EtL1Ld0 LDUHAWAVAUATSHH5BHHEIIF0x uAII^PILmH BH H;MuHCHHĈ[A\A]A^A_]211IHhǞH5Te1H 83H}Lh_I0H5e1He1H e1HPM|AHe1HuHPLbE1H5BHBL1 IA(IA}IIPt H`0LeMt6HEL9t!HX@t Hx0HI9uH}LLe0Et H}0Et H}0LhMt:HpL9tEHX@t Hxl0HI9uHhLpQ0EtL0Ld0 LDUHAWAVAUATSHH5BHHEIIF0x uAII^PILkH BH H;MuHCHHĈ[A\A]A^A_]200IHhǜH5Tc1H 3H}Lh]I0H5c1Hc1H c1HPM|AHc1HuHPL`E1H5BHBL0 IA(IA}IIPt H`0LeMt6HEL9t!HX@t Hx0HI9uH}LLe0Et H}0Et H}0LhMt:HpL9tEHX@t Hxl0HI9uHhLpQ0EtL0Ld0 LDUHAWAVAUATSHH5BHHEIIF0xuAII^PILiH BH H;MuHCHHĈ[A\A]A^A_]200IHhǚH5Ta1H 3H}Lh[I0H5a1Ha1H a1HPM|AHa1HuHPL^E1H5BHBL0 IA(IA}IIPt H`0LeMt6HEL9t!HX@t Hx0HI9uH}LLe0Et H}0Et H}0LhMt:HpL9tEHX@t Hxl0HI9uHhLpQ0EtL0Ld0 LDUHAWAVAUATSHH5BHHEIIF0xuAII^PILgH BH H;MuHCHHĈ[A\A]A^A_]200IHhǘH5T_1H : 3H}LhYI0H5_1H_1H _1HPM|AH_1HuHPL\E1H5BHBL0 IA(IA}IIPt H`0LeMt6HEL9t!HX@t Hx0HI9uH}LLe0Et H}0Et H}0LhMt:HpL9tEHX@t Hxl0HI9uHhLpQ0EtL0Ld0 LؐUHAWAVATSH`IH7BHHEؿ0HþH0HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H}11H4H51HH!I蹟I豧HW)EHE0HEHHHML HXHMH5T2HULȿH5T3H)H5L4HZHH HQH551HzHHHBHHEHHUHuHuIIOIO0IGIWIG@H}Ht H}0H]HEHtHHH0HBHH;EuLH`[A\A^A_]0IIIIH}Ht H}g0H]HEHtHHF0Lf0 fff.UHAWAVATSH`IH7BHHEؿ0HþH0HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H}11H1H51HH!I蹜I豤HW)EHE0HEHHHML HXHMH5T2HULȼH5\3H)H5I4HZHA H521H職HHHBHHEHHUHuHuIIOIO0IGIWIG@H}Ht H}0H]HEHtHOH0HBHH;EuLH`[A\A^A_]0IIIIH}Ht H}n0H]HEHtHHM0Lm0 UHAWAVAUATSHIHIHHpLLIM14HpLLIMwfHpLLIMkLHpLLIM f2HpLLIMvHpLLIM蜂HpH8H0_0IHXXH52H 82HpLXBJH52H@HU AHcp3HpH@LLE1H5IBHbBL00IHXĈH5S2H 2HpLXIH52H@HU AHo3HpH@L'LE1H5BHBL0)IAIIAIIAIA8I{I@t HP0LeMt6HEL9t!HX@t Hx0HI9uH}LLe0Et H}0pt H}x0LXMH`L9HX@t HxE0HI9uI@t HP 0LeMt6HEL9t!HX@t Hx0HI9uH}LLe0Et H}0pt H}0LXMt:H`L9t=HX@t Hx0HI9uHXL`z0EtmL0cL<:86420.,*(&$"   IIpt H}0L(0 fx^(zD@*#??W\?"okkfff.UHAWAVAUATSHHMLHHHHHOBHHEH8HzBHHHLH@H8L0HEEL-oBM}LI@L8LS0HEL5.BILHDž(HDž HDžHDžDž0H$HHHHLMf HHHOLL8Lt H 0H0H5 BHH0H80HBHH;EuHHH[A\A]A^A_]0HLL8Lt H 0H 0HH5BHH0HH80Hg0 ffff.UHAWAVAUATSH8MIHIHHBHHEH8HIBHHHHH@H8H^0LHEEL%7BID$HHI@L8H0L5BILHDž(HDž HDžHDžDž0L0HLHo|HH0Hk0L0HLH?|HH0H;0HHHLHHL8Lt H 0H0H5BHH0H80H|BHH;EuHH8[A\A]A^A_]0HHHL8Lt H 0H0#H/V,H'V,HV,HV,HH5BHH0HH8X0H0 fUHAWAVAUATSHHMLHHHHHBHHEH8HBHHHLH@H8L0HEEL-BM}LI@L8L0HEL5^BILHDž(HDž HDžHDžDž0H$HHHHLMfHHH@JLL8Lt H 60H0H5;BHHE0H80H BHH;EuHHH[A\A]A^A_]O0HLL8Lt H 0H<0HH5BHH0HH8#0H0 ffff.UHAWAVAUATSHHIHHXBHHEH8HBHHHLH@H8L0HEEL5xBM~LI@L8L\0L%;BILHDž(HDž HDžHDžDž0L&0HLHwH3H)1HHH#HLL8Lt H 0H0H5BHH(0H80HBHH;EuHH[A\A]A^A_]20HLL8Lt H 0H0HQ,HQ,HH5BHH0HH80Hj0 UHAVSIX80HHLH[A^]IH 0L+0 UHAWAVAUATSHIHHBHHEH1BHHH{NHCHW)EHEEEaxisEHKPHhHULcHhHDžhHtCt H{\0HT0Et H}E0Hu'HYBHH;EuHĈ[A\A]A^A_]0'0IHh {H52H 2H}Lh <W)PHDž`00H`( ?)PHte valueHHHttributeHHH'axis' aHHHInvalid HHHMissing/H@&AH62HuHPL#>E1H5BHʷBL0 IA(IA}IIPt H`0LeMt6HEL9t!HX@t Hx0HI9uH}LLe0Et H}0Et H}0LhMt:HpL9t=HX@t Hxp0HI9uHhLpU0Et!L0LIEt H}/0LO0 DUHAVSIX0HHLH[A^]IH0L 0 UHAWAVAUATSPMLEIIIHLc0HLHrIuH0LA0HLHrHEH0H0L0HLHrHEH0H0H[A\A]A^A_]H|M,HtM,HlM,HdM,H\M,HTM,@UHAWAVAUATSPMLEIIIHL0HLH)rIuHA0Lq0HLHrHEH0H 0LL0HLHqHEH0H0H[A\A]A^A_]HL,HL,HL,HL,HL,HL,@UHAWAVAUATSHLhMH8IIH@H'BHHELII~EHW)EHEH}H>BL`IGHXHHHP~yHXHIcHIIE1CDEHcL9H9!MHEH;Es HHEH}HuHPII9|HXXDIH`H LhH3IMHXH9HK0yHILIMIO<LL0IIIuLpHH]H 0HD$HEH$H52H 2L 1H}HULp7HMH@H EH}HXH0HCH[ H)IIW)EHEHH`LH=H0IL}O,LmLH0LmW)pHEH0HILpHxHHK\H]LHhHv0HxKDIHhIH LP|AHINLH HH`@HHI|H<3I|HHMPII)pHEHxHHH%E1E1LhLPMH8LIHEHhIFH`LH LH01MtKHhLc11fff.LLI9tIIH`HLhH3IMHXH9HK0yHbILIMIO<LL0IIIuLpHH]H|0HD$HEH$H52H 2L (1H}HULpg2HMH@HzEH}HXH 0HCH[ H)IIW)EHEHH`LH=HN0IL}O,LmLH:0LmW)pHEH0HILpHxHHK\H]LHhH0HxKDIHhIH LP|AHINLH HH`@HHI|H<3I|HHMPII)pHEHxHHH%E1E1LhLPMH8LIHEHhIFH`LH LH01MtKHhLc11fff.LLI9tI0HEN0IL}O,LmLH*0LmW)pHEH0HILpHxHHK\H]LHhH֩0HxKDIHhIH LP|AHINLH HH`@HHI|H<3I|HHMPII)pHEHxHHH%E1E1LhLPMH8LIHEHhIFH`LH LH01MtKHhLc11fff.LLI9tI0 HH}Ht H}0H?0 DUHAWAVAUATSHLhMH8IIH@HBHHELxII~HW)EHEH}HL`IGHXHHHP~yHXHIcHIIE1CDEHcL9H9!MHEH;Es HHEH}HugHPII9|HX(IH`HvLhH3IMHXH9HK0yH¿ILWIMIO<LLc0IIIuLpHH]Hܿ0HD$HEH$H5V2H x2L 0H}HULpHMH@HEH}HXH耦0HCH[ H)IIW)EHEHH`LH=H认0IL}O,LmLH蚤0LmW)pHEHv0HILpHxHHK\H]LHhHF0HxKDIHhIH LP|AHINLH HH`@HHI|H<3I|HHMPII)pHEHxHHH%E1E1LhLPMH8LIHEHhIFH`LH LH01MtKHhLc11fff.LLI9tIHINLH HHPHHI|H<3I|HHMSYI)`HDžpHhH8H%E1E1LXL@M H(LIHEHXIFHPLH LH 1ffff.MtKHXL11fff.LLI9tIHINLH HHPHHI|H<3I|HHMSYI)`HDžpHhH8H%E1E1LXL@M H(LIHEHXIFHPLH LH 1ffff.MtKHXL11fff.LLI9tIHINLH HHPHHI|H<3I|HHMSYI)`HDžpHhH8H%E1E1LXL@M H(LIHEHXIFHPLH LH 1ffff.MtKHXL11fff.LLI9tIHINLH HHPHHI|H<3I|HHMSYI)`HDžpHhH8H%E1E1LXL@M H(LIHEHXIFHPLH LH 1ffff.MtKHXL11fff.LLI9tIc0 HH}Ht H}c0H?_0 DUHAWAVAUATSHLXMH(IIH0HZBHHELOII~HW)EHEH}H,LPIGHHHHH@HHHIcHIIE1ffffff.KDH`L9H9HH?H!HHMHEH;EsHHHEf.H}HuYH@II9|HHIHPHVLXH3IMHHH9HK0yH{IL7vIMIO<LLC_0IIIuLuHH]H{0HD$HEH$H59A2H [A2L k0H}H`LEHMH0H轈EH}HHHcb0HCH[ H)IIW)EHEH HPLH=H`0IL}O,LmLH}`0LmW)`HDžpHV`0HIL`HhH8K\HpLHXH#`0HhKDIHXIH L@|>HINLH HHPHHI|H<3I|HHMSYI)`HDžpHhH8H%E1E1LXL@M H(LIHEHXIFHPLH LH 1ffff.MtKHXL11fff.LLI9tIHINLH HHPHHI|H<3I|HHMSYI)`HDžpHhH8H%E1E1LXL@M H(LIHEHXIFHPLH LH 1ffff.MtKHXL11fff.LLI9tIHINLH HHPHHI|H<3I|HHMSYI)`HDžpHhH8H%E1E1LXL@M H(LIHEHXIFHPLH LH 1ffff.MtKHXL11fff.LLI9tIII~謽HW)EHEH}H^LPIGHHHHH@HHHIcHIIE1ffffff.KDH`L9H9HH?H!HHMHEH;EsHHHEf.H}HuHH@II9|HHȼIHPHLXH3IMHHH9HK0yHbjILdIMIO<LLN0IIIuLuHH]Hj0HD$HEH$H5/2H 02L +0H}H`LE芳HMH0H}wEH}HHH#Q0HCH[ H)IIW)EHEH HPLH=HQO0IL}O,LmLH=O0LmW)`HDžpHO0HIL`HhH8K\HpLHXHN0HhKDIHXIH L@|>HINLH HHPHHI|H<3I|HHMSYI)`HDžpHhH8H%E1E1LXL@M H(LIHEHXIFHPLH LH 1ffff.MtKHXL11fff.LLI9tIL0 HH}Ht H}L0H?H0 DUHAWAVAUATSHLXMH(IIH0HCBHHEL8II~HW)EHEH}HLPIGHHHHH@HHHIcHIIE1ffffff.KDH`L9H9HH?H!HHMHEH;EsHHHEf.H}HuBH@II9|HHIHPHV~LXH3IMHHH9HK0yHdIL7_IMIO<LLCH0IIIuLuHH]Hd0HD$HEH$H59*2H [*2L k0H}H`LEʭHMH0HqEH}HHHcK0HCH[ H)IIW)EHEH HPLH=HI0IL}O,LmLH}I0LmW)`HDžpHVI0HIL`HhH8K\HpLHXH#I0HhKDIHXIH L@|>HINLH HHPHHI|H<3I|HHMSYI)`HDžpHhH8H%E1E1LXL@M H(LIHEHXIFHPLH LH 1ffff.MtKHXL11fff.LLI9tIBHHEL83II~,HW)EHEH}HLPIGHHHHH@HHHIcHIIE1ffffff.KDH`L9H9HH?H!HHMHEH;EsHHHEf.H}Hu=H@II9|HHHIHPHxLXH3IMHHH9HK0yH^ILwYIMIO<LLB0IIIuLuHH]H^0HD$HEH$H5y$2H $2L 0H}H`LE HMH0HkEH}HHHE0HCH[ H)IIW)EHEH HPLH=HC0IL}O,LmLHC0LmW)`HDžpHC0HIL`HhH8K\HpLHXHcC0HhKDIHXIH L@|>HINLH HHPHHI|H<3I|HHMSYI)`HDžpHhH8H%E1E1LXL@M H(LIHEHXIFHPLH LH 1ffff.MtKHXL11fff.LLI9tI0IL}O,LmLH=0LmW)`HDžpH=0HIL`HhH8K\HpLHXH=0HhKDIHXIH L@|>HINLH HHPHHI|H<3I|HHMSYI)`HDžpHhH8H%E1E1LXL@M H(LIHEHXIFHPLH LH 1ffff.MtKHXL11fff.LLI9tI00LM9uH}LLe$00Et H}00Et H}00L`MHhL9Lh@t Hx/0LM9u]H@t HP/0LeMt6HEL9t!Lh@t Hx/0LM9uH}LLeo/0Et H}`/0Et H}Q/0L`MHhL9Lh@t Hx/0LM9uH@t HP.0LeMt6HEL9t!Lh@t Hx.0LM9uH}LLe.0Et H}.0Et H}.0L`Mt:HhL9tDLh@t Hxq.0LM9uH`LhV.0EL.0L[YWUHtQH.0C><:86 2+.%#H t7H0) H=HPHHH Ht H(-0H@Ht HH-0H`Ht Hh-0H}Ht H}|-0H)0 fUHAWAVAUATSH8MLHIIHHW%BHHEH8H BHHHHH@H8H,0LHEEL-p#BIEHHI@L8HM+0L5,#BILHDž(HDž HDžHDžDž0L/0HLHL.0HLHHH茤HH.0HHH[HHXHHH”HHL8Lt H +0H=*0H5BHH)0H8.+0H#BHH;EuHH8[A\A]A^A_]+0HHHL8Lt H 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UHAWAVAUATSHIIHPAHHEMT$0IIFHHIH9IT$(fffff.HtHIt$HIY/IT$(@1HID$LSML$8It$@L)HI|Hf:J4VIM9uLK|H4~Mt;Lffff.H\HH\I;\uHDI\H4^HuE1HHL9uH8JkIt$Mt$ LHH@HIIFHHH9 H9u'HNAHH;EuHĨ[A\A]A^A_]@V/U/IHhH5Li1H n1H}LhY´HPAHo1HuHPLIE1H54AH0ALU/ IA(IA}IIPt H`U/LeMt6HEL9t!HX@t HxT/HI9uH}LLeT/Et H}T/Et H}T/LhMt:HpL9tEHX@t HxT/HI9uHhLp{T/EtLT/LP/ Lffffff.UHAWAVAUATSH(LEHMIHH3HƋHCHEHMHCHKHC H{8LmLeLE L}(H}LuLLLMM\HCPHEHC`HCXHCPM)t3MILH=uLLS/HCPN,Lk`HLS/LkXHHuHUHMMMXH([A\A]A^A_]H}2P/ IHEH8Ht H{X4S/HEH8Ht H{@S/L?O/ DUHAWAVAUATSHHKAHHEL>HFL)IIH)HI9?LWHOL)HI9'M)IM9HHGIKEAHsLMutLffff.H\HHXILH\HHXILH\HHXILH\HHXILHvI9uMt/M|HTI1HHHXI HI9uHGKDHG(M9uKDHG0HIAHH;EuHĈ[A\A]A^A_]R/Q/IHhH5DD1H 3l1H}Lh肰HPf AHE1HuHPL E1H50AH,ALjQ/ IA(IA}IIPt H`P/LeMt6HEL9t!HX@t HxP/HI9uH}LLeP/Et H}P/Et H}P/LhMt:HpL9tEHX@t HxVP/HI9uHhLp;P/EtLjP/LNL/ Lffffff.UHAWAVAUATSH(MMHMHUHH3H胇HCLcLkHC H{8H3HLuL}H}LLMMWHCPHEHC`HCXHCPM)t3MILH=uOLvO/HCPN$Lc`HLeO/LcXH3HHHUHMMM"H([A\A]A^A_]H}K/ IHEH8Ht H{XN/HEH8Ht H{@N/L K/ ffffff.UHAVSIN/HHAAHHH{L菿HHP1LH?=AHHH[A^]IHmN/LJ/ UHAWAVAUATSHHIIH\FAHHE1H苩IM'IEI} H9HHHW)EHEH)III9HM/HEN4LuHHM/LuIEI} HW)`HDžpH)IIHI9wHpM/H`N4LpH5 =HHN/LhIEI} uW)EE(HEHlice scaHEHCannot sHEElarsEHMLtEH})`HDžpW)@HDžPH)t`IIHI9L/HHHH@JHPIuMu I)M~HLM/LHHMW) IH HHDž0MIIT$PIL$hHHD$H@HD$H`H\$HEH$L!I?t Hb_1H O/1AL/ILH$H@LMLHL<H Ht H(dK/H@Ht HHLK/H`Ht Hh4K/H}Ht H}"K/H;CAHH;EuLH[A\A]A^A_]jK/K/IH`H5v^1H :f1H}L`qW)@HDžP0J/HP(>)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AHK1HuH@L E1H5)AH%ALjJ/%H}F/H@F/ H`F/ IA(IA}II@t HPI/LeMt6HEL9t!HX@t HxI/HI9uH}LLexI/Et H}iI/Et H}ZI/L`Mt:HhL9tDHX@t Hx/I/HI9uH`LhI/EL?I/LI~IEH}IIIHt&Ct H{H/HH/ I IKIH Ht H(H/H@Ht HHvH/H`Ht Hh^H/H}Ht H}LH/LlD/ fUHAWAVAUATSHXHE@AHHELLHIHW)EHEHHLyI)tJMILH=LG/ILmLmKDHEM~LHL2I/MLmHU1L襦HH}詳HHILuHH腳IHEHuHEHpHEHxHW)PHDž`HLxHX L)IILH=HF/ILXLPO$L`MH~LLHBH/ILXLLXH8HHHHHLLHHHvHPHXHPHXHt$H$HHJ HHpHH Ht H(E/HHHLXE1E1E11HHHRHH)HLL)HHH)v1HP6HHHQLPLXL`sMtLXLHrHqL8L@LHW)PHDž`HHLFHuHH7HHuH}HEHUHEHUHT$HD$HEHUHT$H$HH8HHpHHH Ht H(D/HHt HyD/H8Ht H@aD/HPHtI?MD/HH}Ht H}4D/HM:J4IM9uNK|H4Mt=Lffffff.H\HH\I;\uHDI\H4HuE1HHL9uH8JIt$Mt$ LHH@HIIFHHH9 H9u'H8AHH;EuHĨ[A\A]A^A_]A/@/IHhH5T1H Y1H}LhY蒟HPvAH`Z1HuHPLE1H5AHALz@/ IA(IA}IIPt H`?/LeMt6HEL9t!HX@t Hx?/HI9uH}LLe?/Et H}?/Et H}?/LhMt:HpL9tEHX@t Hxf?/HI9uHhLpK?/EtLz?/L^;/ Lffffff.UHAWAVAUATSH(LEHMIHH3H趂HCHEHMHCHKHC H{8LmLeLE L}(H}LuLLLMMFHCPHEHC`HCXHCPM)t3MILH=uLLy>/HCPN,Lk`HLh>/LkXHHuHUHMMMXH([A\A]A^A_]H};/ IHEH8Ht H{X>/HEH8Ht H{@=/L:/ DUHAWAVAUATSHH5AHHEL>HFL)IIH)HI9?LWHOL)HI9'M)IM9HHGIKEAHsLMutLffff.H\HHILH\HHILH\HHILH\HHILHvI9uMt/M|HTI1HHHI HI9uHGKDHG(M9uKDHG0H4AHH;EuHĈ[A\A]A^A_])@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AHn1HuH@LٖE1H5gAHAL:5/%H}1/H@1/ H`1/ IA(IA}II@t HP4/LeMt6HEL9t!HX@t Hxb4/HI9uH}LLeH4/Et H}94/Et H}*4/L`Mt:HhL9tDHX@t Hx3/HI9uH`Lh3/EL4/LI~IEH}IIIHt&Ct H{3/H3/ I IKIH Ht H(^3/H@Ht HHF3/H`Ht Hh.3/H}Ht H}3/LH:J4IM9uLK|H4Mt;Lffff.H\HH\I;\uHDI\H4HuE1HHL9uH8JIt$Mt$ LHH@HIIFHHH9 H9u'H#AHH;EuHĨ[A\A]A^A_]+/|+/IHhuH5>1H ]D1H}LhYbHPFAH0E1HuHPLE1H5w AHALJ+/ IA(IA}IIPt H`*/LeMt6HEL9t!HX@t Hx*/HI9uH}LLe*/Et H}p*/Et H}a*/LhMt:HpL9tEHX@t Hx6*/HI9uHhLp*/EtLJ*/L.&/ Lffffff.UHAWAVAUATSH(LEHMIHH3HwHCHEHMHCHKHC H{8LmLeLE L}(H}LuLLLMM1HCPHEHC`HCXHCPM)t3MILH=uLLI)/HCPN,Lk`HL8)/LkXHHuHUHMMMXH([A\A]A^A_]H}%/ IHEH8Ht H{X(/HEH8Ht H{@(/L$/ DUHAWAVAUATSHH AHHEL>HFL)IIH)HI9?LWHOL)HI9'M)IM9HHGIKEAHsLMutLffff.H\HHILH\HHILH\HHILH\HHILHvI9uMt/M|HTI1HHHI HI9uHGKDHG(M9uKDHG0HnAHH;EuHĈ[A\A]A^A_]'/<'/IHh5H51H SD1H}Lh"HPAHI1HuHPL詈E1H57AHPAL '/ IA(IA}IIPt H`~&/LeMt6HEL9t!HX@t HxY&/HI9uH}LLe?&/Et H}0&/Et H}!&/LhMt:HpL9tEHX@t Hx%/HI9uHhLp%/EtL &/L!/ Lffffff.UHAWAVAUATSH(MMHMHUHH3HsHCLcLkHC H{8H3HLuL}H}LLMM-HCPHEHC`HCXHCPM)t3MILH=uOL%/HCPN$Lc`HL%/LcXH3HHHUHMMM"H([A\A]A^A_]H}!/ IHEH8Ht H{X$/HEH8Ht H{@$/L / ffffff.UHAVSIh$/HHAHHH{L/HHP1LnHAHHH[A^]IH $/L- / UHAWAVAUATSHHIIHAHHE1H+IM'IEI} H9HHHW)EHEH)III9Hv#/HEN4LuHHe#/LuIEI} HW)`HDžpH)IIHI9wH#/H`N4LpH5=HH$/LhIEI} uW)EE(HEHlice scaHEHCannot sHEElarsEHMLOJEH})`HDžpW)@HDžPH)t`IIHI9-"/HHHH@JHPIuMu I)M~HL#/LHHMW) IH HHDž0MIIT$PIL$hHHD$H@HD$H`H\$HEH$LwI?t H51H /1AL/ILH$H@LMLHL<H Ht H(!/H@Ht HH /H`Ht Hh /H}Ht H} /HAHH;EuLH[A\A]A^A_] !/ /IH`蟾H541H b>1H}L`qW)@HDžP0I /HP(_o>)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AHX1HuH@L詁E1H57@HP@L /%H}/H@y/ H`k/ IA(IA}II@t HPW/LeMt6HEL9t!HX@t Hx2/HI9uH}LLe/Et H} /Et H}/L`Mt:HhL9tDHX@t Hx/HI9uH`Lh/EL/LI~IEH}IIIHt&Ct H{]/HU/ I IKIH Ht H(./H@Ht HH/H`Ht Hh/H}Ht H}/L / fUHAWAVAUATSHXHAHHELLHIHW)EHEHHLyI)tJMILH=Lm/ILmLmKDHEM~LHL/MLmHU1LE|IH}IHL8DHH]IL%HH]HuHEHpHEHxHW)PHDž`HLxHX L)IILH=H/ILXLPO$L`MH~LLH/ILXLLXH?HHHHHLLHHHvHPHXHPHXHt$H$HH HpH(HH Ht H(/HHHLXE1E1E11HHHRHH)HLL)HHH)v8HPe HHHHQLPLXL`HsMtLXLHrHqL8L@LHW)PHDž`HHLFHuHH7HHuH}HEHUHEHUHT$HD$HEHUHT$H$HH8HvHpHH Ht H(+/HHt H/H8Ht H@/HPHtI?/HH}Ht H}/HAHH;EuHHX[A\A]A^A_]/H}}/ HPo/ HHH Ht H(h/HHHL/HHPHt}HX'/oHH Ht H( /HHtH/HH8Ht H@/HPHtI?/ HH}Ht H}/H/ f.UHAWAVAUATSHIIHAHHEI|$0IINL8H I9IT$(HtIt$L/IT$( 1ID$HIMD$8It$@L)HIDHID$HtHFL)IIH)HI9>LWHOL)HI9&M)IM9HHGIKEAHsLMupLffff.H\HHILHDHHILH\HHILHDHHILHvI9uMt2M|HTI1ff.HHHI HI9uHGKDHG(M9uKDHG0H_ AHH;EuHĈ[A\A]A^A_]/-/IHh&H51H 01H}LhqHPAH:1HuHPLsE1H5(@HA@L/ IA(IA}IIPt H`o/LeMt6HEL9t!HX@t HxJ/HI9uH}LLe0/Et H}!/Et H}/LhMt:HpL9tEHX@t Hx/HI9uHhLp/EtL/L / LUHAWAVAUATSH(MMHMHUHH3H5HCLcLkHC H{8H3HLuL}H}LLMMHCPHEHC`HCXHCPM)t3MILH=uOL/HCPN$Lc`HL/LcXH3HHHUHMMM2H([A\A]A^A_]H} / IHEH8Ht H{X/HEH8Ht H{@/L / ffffff.UHAVSIh/HHAHHH{L/HHP1LnH@HHH[A^]IH /L- / UHAWAVAUATSHHIIHAHHE1H+jIM'IEI} H9HHHW)EHEH)III9Hv/HEN4LuHHe/LuIEI} HW)`HDžpH)IIHI9wH/H`N4LpH5=HH/LhIEI} uW)EE(HEHlice scaHEHCannot sHEElarsEHMLO5EH})`HDžpW)@HDžPH)t`IIHI9- /HHHH@JHPIuMu I)M~HL/LHHMW) IH HHDž0MIIT$PIL$hHHD$H@HD$H`H\$HEH$LbI?t H 1H /1AL蝫/ILH$H@LMLHL<H Ht H( /H@Ht HH /H`Ht Hh /H}Ht H} /HAHH;EuLH[A\A]A^A_] / /IH`蟩H51H *1H}L`qjW)@HDžP0I /HP(_Z>)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AHC1HuH@LlE1H57@HP@L /%H}/H@y/ H`k/ IA(IA}II@t HPW /LeMt6HEL9t!HX@t Hx2 /HI9uH}LLe /Et H} /Et H} /L`Mt:HhL9tDHX@t Hx /HI9uH`Lh /EL /LI~IEH}IIIHt&Ct H{] /HU / I IKIH Ht H(. /H@Ht HH /H`Ht Hh/H}Ht H}/L / fUHAWAVAUATSHXHAHHELLHIHW)EHEHHLyI)tJMILH=Lm/ILmLmKDHEM~LHL /MLmHU1LEgHH}ItHHx;ILuHH%tIFHEHuHEHpHEHxHW)PHDž`HLxHX L)IILH=H/ILXLPO$L`MH~LLH/ILXLLXH8HHHHHLLHHHvHPHXHPHXHt$H$HH: HHpHH Ht H(/HHHLXE1E1E11HHHRHH)HLL)HHH)v1HPdHHHQLPLXL`sMtLXLHrHqL8L@LHW)PHDž`HHLFHuHH7HHuH}HEHUHEHUHT$HD$HEHUHT$H$HH8HHpHHH Ht H(1/HHt H/H8Ht H@/HPHtI?/HH}Ht H}/H@HH;EuHHX[A\A]A^A_]/H}/ HPu/ HHH Ht H(n/HHHR/HHPHt}HX-/oHH Ht H(/HHtH/HH8Ht H@/HPHtI?/ HH}Ht H}/H. UHAWAVAUATSHIIH@HHEMT$0IIFHHIH9IT$(fffff.HtHIt$H/IT$(@1HID$LSML$8It$@L)HI|Hf:J4VIM9uLK|H4~Mt;Lffff.H\HH\I;\uHDI\H4^HuE1HHL9uH8JkIt$Mt$ LHH@HIIFHHH9 H9u'H@HH;EuHĨ[A\A]A^A_]/\/IHhUH51H =1H}LhYB`HP&AH1HuHPLbE1H5W@Hp@L*/ IA(IA}IIPt H`/LeMt6HEL9t!HX@t Hxy/HI9uH}LLe_/Et H}P/Et H}A/LhMt:HpL9tEHX@t Hx/HI9uHhLp.EtL*/L. Lffffff.UHAWAVAUATSH(LEHMIHH3H&1HCHEHMHCHKHC H{8LmLeLE L}(H}LuLLLMMHCPHEHC`HCXHCPM)t3MILH=uLL).HCPN,Lk`HL.LkXHHuHUHMMMXH([A\A]A^A_]H}. IHEH8Ht H{X.HEH8Ht H{@.L. DUHAWAVAUATSHH@HHEL>HFL)IIH)HI9?LWHOL)HI9'M)IM9HHGIKEAHsLMutLffff.H\HHXILH\HHXILH\HHXILH\HHXILHvI9uMt/M|HTI1HHHXI HI9uHGKDHG(M9uKDHG0HN@HH;EuHĈ[A\A]A^A_]..IHhH50H 1H}Lh\HPAH)0HuHPL^E1H5@H0@L. IA(IA}IIPt H`^.LeMt6HEL9t!HX@t Hx9.HI9uH}LLe.Et H}.Et H}.LhMt:HpL9tEHX@t Hx.HI9uHhLp.EtL.L. Lffffff.UHAWAVAUATSH(MMHMHUHH3H,HCLcLkHC H{8H3HLuL}H}LLMMgHCPHEHC`HCXHCPM)t3MILH=uOL.HCPN$Lc`HL.LcXH3HHHUHMMM"H([A\A]A^A_]H}|. IHEH8Ht H{X~.HEH8Ht H{@i.L. ffffff.UHAVSIH.HH@HHH{LkHHP1LNzH@HHH[A^]IH.L . UHAWAVAUATSHHIIH@HHE1H UIM'IEI} H9HHHW)EHEH)III9HV.HEN4LuHHE.LuIEI} HW)`HDžpH)IIHI9wH.H`N4LpH5p=HHj.LhIEI} uW)EE(HEHlice scaHEHCannot sHEElarsEHML/ EH})`HDžpW)@HDžPH)t`IIHI9 .HHHH@JHPIuMu I)M~HLb.LHHMW) IH HHDž0MIIT$PIL$hHHD$H@HD$H`H\$HEH$LMI?t H 1H Ϗ/1AL}/ILH$H@LMLHL<H Ht H(.H@Ht HH.H`Ht Hh.H}Ht H}.H@HH;EuLH[A\A]A^A_]..IH`H5 1H 1H}L`qlUW)@HDžP0).HP(?E>)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AH.1HuH@LWE1H5@H0@L.%H}g.H@Y. H`K. IA(IA}II@t HP7.LeMt6HEL9t!HX@t Hx.HI9uH}LLe.Et H}.Et H}.L`Mt:HhL9tDHX@t Hx.HI9uH`Lh.EL.LI~IEH}IIIHt&Ct H{=.H5. I IKIH Ht H(.H@Ht HH.H`Ht Hh.H}Ht H}.L. fUHAWAVAUATSHXH@HHELLHIHW)EHEHHLyI)tJMILH=LM.ILmLmKDHEM~LHL.MLmHU1L%RHH})_HH2ILuHH_IHEHuHEHpHEHxHW)PHDž`HLxHX L)IILH=Hh.ILXLPO$L`MH~LLH.ILXLLXH8HHHHHLLHHHvHPHXHPHXHt$H$HH HHpHH Ht H(x.HHHLXE1E1E11HHHRHH)HLL)HHH)v1HPDHHHQLPLXL`sMtLXLHrHqL8L@LHW)PHDž`HHLFHuHH7HHuH}HEHUHEHUHT$HD$HEHUHT$H$HH8H胅HpHHH Ht H(.HHt H.H8Ht H@.HPHtI?.HH}Ht H}.H@HH;EuHHX[A\A]A^A_].H}c. HPU. HHH Ht H(N.HHH2.HHPHt}HX .oHH Ht H(.HHtH.HH8Ht H@.HPHtI?. HH}Ht H}.H. UHAWAVAUATSHIIH@HHEMT$0IIFHHIH9IT$(fffff.HtHIt$H.IT$(1HID$LML$8It$@L)HI|H:J4IM9uNK|H4Mt=Lffffff.H\HH\I;\uHDI\H4HuE1HHL9uH8JIt$Mt$ LHH@HIIFHHH9 H9u'H^@HH;EuHĨ[A\A]A^A_].,.IHh%H50H 1H}LhYKHPAH1HuHPLME1H5'@H@@L. IA(IA}IIPt H`n.LeMt6HEL9t!HX@t HxI.HI9uH}LLe/.Et H} .Et H}.LhMt:HpL9tEHX@t Hx.HI9uHhLp.EtL.L. Lffffff.UHAWAVAUATSH(MMHMHUHH3H#(HCLcLkHC H{8H3HLuL}H}LLMMwHCPHEHC`HCXHCPM)t3MILH=uOL.HCPN$Lc`HL.LcXH3HHHUHMMMBH([A\A]A^A_]H}. IHEH8Ht H{X.HEH8Ht H{@y.L. ffffff.UHAVSIX.HH@HHH{LZHHP1L^iH@HHH[A^]IH.L. UHAWAVAUATSHHIIH@HHE1HDIM'IEI} H9HHHW)EHEH)III9Hf.HEN4LuHHU.LuIEI} HW)`HDžpH)IIHI9wH.H`N4LpH5_=HHz.LhIEI} uW)EE(HEHlice scaHEHCannot sHEElarsEHML?EH})`HDžpW)@HDžPH)t`IIHI9.HHHH@JHPIuMu I)M~HLr.LHHMW) IH HHDž0MIIT$PIL$hHHD$H@HD$H`H\$HEH$L)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AH1HuH@LFE1H5'@H@@L.%H}w.H@i. H`[. IA(IA}II@t HPG.LeMt6HEL9t!HX@t Hx".HI9uH}LLe.Et H}.Et H}.L`Mt:HhL9tDHX@t Hx.HI9uH`Lh.EL.LI~IEH}IIIHt&Ct H{M.HE. I IKIH Ht H(.H@Ht HH.H`Ht Hh.H}Ht H}.L. fUHAWAVAUATSHXH@HHELLHIHW)EHEHHLyI)tJMILH=L].ILmLmKDHEM~LHL.MLmHU1L5AHH}9NHH+ILuHHNIHEHuHEHpHEHxHW)PHDž`HLxHX L)IILH=Hx.ILXLPO$L`MH~LLH.ILXLLXH8HHHHHLLHHHvHPHXHPHXHt$H$HH HHpHH Ht H(.HHHLXE1E1E11HHHRHH)HLL)HHH)v1HPTHHHQLPLXL`sMtLXLHrHqL8L@LHW)PHDž`HHLFHuHH7HHuH}HEHUHEHUHT$HD$HEHUHT$H$HH8H#HpHHH Ht H(!.HHt H .H8Ht H@.HPHtI?.HH}Ht H}.H@HH;EuHHX[A\A]A^A_] .H}s. HPe. HHH Ht H(^.HHHB.HHPHt}HX.oHH Ht H(.HHtH.HH8Ht H@.HPHtI?. HH}Ht H}.H. UHAWAVAUATSHIIH@HHEMT$0IIFHHIH9IT$(fffff.HtHIt$H.IT$(1HID$LML$8It$@L)HI|HH:J4IM9uLK|H4Mt;Lffff.H\HH\I;\uHDI\H4HuE1HHL9uH8JIt$Mt$ LHH@HIIFHHH9 H9u'Hn@HH;EuHĨ[A\A]A^A_].<.IHh5yH50H 0H}LhY":HPAH0HuHPL)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AH 1HuH@L5E1H57@HP@L .%H}.H@y. H`k. IA(IA}II@t HPW.LeMt6HEL9t!HX@t Hx2.HI9uH}LLe.Et H} .Et H}.L`Mt:HhL9tDHX@t Hx.HI9uH`Lh.EL.LI~IEH}IIIHt&Ct H{].HU. I IKIH Ht H(..H@Ht HH.H`Ht Hh.H}Ht H}.L . fUHAWAVAUATSHXH@HHELLHIHW)EHEHHLyI)tJMILH=Lm.ILmLmKDHEM~LHL.MLmHU1LE0HH}I=HHILuHH%=IHEHuHEHpHEHxHW)PHDž`HLxHX L)IILH=H.ILXLPO$L`MH~LLH.ILXLLXH8HHHHHLLHHHvHPHXHPHXHt$H$HH HHpHH Ht H(.HHHLXE1E1E11HHHRHH)HLL)HHH)v1HPdHHHQLPLXL`sMtLXLHrHqL8L@LHW)PHDž`HHLFHuHH7HHuH}HEHUHEHUHT$HD$HEHUHT$H$HH8HcHpHHH Ht H(1.HHt H.H8Ht H@.HPHtI?.HH}Ht H}.H@HH;EuHHX[A\A]A^A_].H}. HPu. HHH Ht H(n.HHHR.HHPHt}HX-.oHH Ht H(.HHtH.HH8Ht H@.HPHtI?. HH}Ht H}.H. UHAWAVAUATSHIIH@HHEMT$0IIFHHIH9IT$(fffff.HtHIt$H.IT$(1HID$LML$8It$@L)HI|H:J4IM9uNK|H4Mt=Lffffff.H\HH\I;\uHDI\H4HuE1HHL9uH8JIt$Mt$ LHH@HIIFHHH9 H9u'H~@HH;EuHĨ[A\A]A^A_].L.IHhEhH50H -0H}LhY2)HPAH0HuHPL+E1H5G@H`@L. IA(IA}IIPt H`.LeMt6HEL9t!HX@t Hxi.HI9uH}LLeO.Et H}@.Et H}1.LhMt:HpL9tEHX@t Hx.HI9uHhLp.EtL.L. Lffffff.UHAWAVAUATSH(MMHMHUHH3H#HCLcLkHC H{8H3HLuL}H}LLMMHCPHEHC`HCXHCPM)t3MILH=uOL&.HCPN$Lc`HL.LcXH3HHHUHMMM"H([A\A]A^A_]H}. IHEH8Ht H{X.HEH8Ht H{@.L. ffffff.UHAVSIx.HH¹@HHH{L?8HHP1L~GH@HHH[A^]IH.L=. UHAWAVAUATSHHIIH @HHE1H;"IM'IEI} H9HHHW)EHEH)III9H.HEN4LuHHu.LuIEI} HW)`HDžpH)IIHI9wH .H`N4LpH5==HH.LhIEI} uW)EE(HEHlice scaHEHCannot sHEElarsEHML_EH})`HDžpW)@HDžPH)t`IIHI9=.HHHH@JHPIuMu I)M~HL.LHHMW) IH HHDž0MIIT$PIL$hHHD$H@HD$H`H\$HEH$LI?t H0H \/1ALc/ILH$H@LMLHL<H Ht H(.H@Ht HH.H`Ht Hh.H}Ht H}.H@HH;EuLH[A\A]A^A_]..IH`aH5&0H y0H}L`q"W)@HDžP0Y.HP(o>)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AH0HuH@L$E1H5G@H`@L.%H}藿.H@艿. H`{. IA(IA}II@t HPg.LeMt6HEL9t!HX@t HxB.HI9uH}LLe(.Et H}.Et H} .L`Mt:HhL9tDHX@t Hx.HI9uH`Lh.EL.LI~IEH}IIIHt&Ct H{m.He. I IKIH Ht H(>.H@Ht HH&.H`Ht Hh.H}Ht H}.L. fUHAWAVAUATSHXH@HHELLHIHW)EHEHHLyI)tJMILH=L}.ILmLmKDHEM~LHL.MLmHU1LUHH}Y,HHILuHH5,IHEHuHEHpHEHxHW)PHDž`HLxHX L)IILH=H蘿.ILXLPO$L`MH~LLH.ILXLLXH8HHHHHLLHHHvHPHXHPHXHt$H$HH HHpHH Ht H(訾.HHHLXE1E1E11HHHRHH)HLL)HHH)v1HPtHHHQLPLXL`sMtLXLHrHqL8L@LHW)PHDž`HHLFHuHH7HHuH}HEHUHEHUHT$HD$HEHUHT$H$HH8H HpHHH Ht H(A.HHt H).H8Ht H@.HPHtI?.HH}Ht H}.H@HH;EuHHX[A\A]A^A_],.H}蓹. HP腹. HHH Ht H(~.HHHb.HHPHt}HX=.oHH Ht H( .HHtH.HH8Ht H@.HPHtI?׻. HH}Ht H}躻.Hڷ. UHAWAVAUATSHIIH@HHEMT$0IIFHHIH9IT$(fffff.HtHIt$Hؼ.IT$(1HID$LML$8It$@L)HI|HH:J4IM9uLK|H4Mt;Lffff.H\HH\I;\uHDI\H4HuE1HHL9uH8JIt$Mt$ LHH@HIIFHHH9 H9u'H@HH;EuHĨ[A\A]A^A_].\.IHhUWH50H =0H}LhYBHP&uAH0HuHPLE1H5W@Hp@L*. IA(IA}IIPt H`螸.LeMt6HEL9t!HX@t Hxy.HI9uH}LLe_.Et H}P.Et H}A.LhMt:HpL9tEHX@t Hx.HI9uHhLp.EtL*.L. Lffffff.UHAWAVAUATSH(MMHMHUHH3HHCLcLkHC H{8H3HLuL}H}LLMM觿HCPHEHC`HCXHCPM)t3MILH=uOL6.HCPN$Lc`HL%.LcXH3HHHUHMMMH([A\A]A^A_]H}輳. IHEH8Ht H{X辶.HEH8Ht H{@詶.Lɲ. ffffff.UHAVSI舶.HHҨ@HHH{LO'HHP1L6H@HHH[A^]IH-.LM. UHAWAVAUATSHHIIH@HHE1HKIM'IEI} H9HHHW)EHEH)III9H薵.HEN4LuHH腵.LuIEI} HW)`HDžpH)IIHI9wH0.H`N4LpH5,=HH誶.LhIEI} uW)EE(HEHlice scaHEHCannot sHEElarsEHMLoEH})`HDžpW)@HDžPH)t`IIHI9M.HHHH@JHPIuMu I)M~HL袵.LHHMW) IH HHDž0MIIT$PIL$hHHD$H@HD$H`H\$HEH$L I?t H"0H L/1ALR/ILH$H@LMLHL<H Ht H($.H@Ht HH .H`Ht Hh.H}Ht H}.H@HH;EuLH[A\A]A^A_]*.Ʋ.IH`PH560H 0H}L`qW)@HDžP0i.HP(>)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AH 0HuH@LE1H5W@Hp@L*.%H}觮.H@虮. H`苮. IA(IA}II@t HPw.LeMt6HEL9t!HX@t HxR.HI9uH}LLe8.Et H}).Et H}.L`Mt:HhL9tDHX@t Hx.HI9uH`Lh԰.EL.LI~IEH}IIIHt&Ct H{}.Hu. I IKIH Ht H(N.H@Ht HH6.H`Ht Hh.H}Ht H} .L,. fUHAWAVAUATSHXH@HHELLHIHW)EHEHHLyI)tJMILH=L药.ILmLmKDHEM~LHL.MLmHU1LeHH}iHHILuHHEIFHEHuHEHpHEHxHW)PHDž`HLxHX L)IILH=H訮.ILXLPO$L`MH~LLH.ILXLLXH8HHHHHLLHHHvHPHXHPHXHt$H$HH: HHpHH Ht H(踭.HHHLXE1E1E11HHHRHH)HLL)HHH)v1HP脞HHHQLPLXL`sMtLXLHrHqL8L@LHW)PHDž`HHLFHuHH7HHuH}HEHUHEHUHT$HD$HEHUHT$H$HH8HHpHHH Ht H(Q.HHt H9.H8Ht H@!.HPHtI? .HH}Ht H}.H @HH;EuHHX[A\A]A^A_]<.H}裨. HP蕨. HHH Ht H(莫.HHHr.HHPHt}HXM.oHH Ht H(0.HHtH.HH8Ht H@.HPHtI?. HH}Ht H}ʪ.H. UHAWAVAUATSHIIH@HHEMT$0IIFHHIH9IT$(fffff.HtHIt$H.IT$(@1HID$LSML$8It$@L)HI|Hf:J4VIM9uLK|H4~Mt;Lffff.H\HH\I;\uHDI\H4^HuE1HHL9uH8JkIt$Mt$ LHH@HIIFHHH9 H9u'H@HH;EuHĨ[A\A]A^A_].|.IHhuFH50H ]0H}LhYbHPFdAH00HuHPL E1H5w@H@LJ. IA(IA}IIPt H`辧.LeMt6HEL9t!HX@t Hx虧.HI9uH}LLe.Et H}p.Et H}a.LhMt:HpL9tEHX@t Hx6.HI9uHhLp.EtLJ.L.. Lffffff.UHAWAVAUATSH(LEHMIHH3HƿHCHEHMHCHKHC H{8LmLeLE L}(H}LuLLLMM躮HCPHEHC`HCXHCPM)t3MILH=uLLI.HCPN,Lk`HL8.LkXHHuHUHMMMXH([A\A]A^A_]H}Ң. IHEH8Ht H{Xԥ.HEH8Ht H{@迥.Lߡ. DUHAWAVAUATSHH@HHEL>HFL)IIH)HI9?LWHOL)HI9'M)IM9HHGIKEAHsLMutLffff.H\HHXILH\HHXILH\HHXILH\HHXILHvI9uMt/M|HTI1HHHXI HI9uHGKDHG(M9uKDHG0Hn@HH;EuHĈ[A\A]A^A_]蠤.<.IHh5BH50H E0H}Lh"HP`AHI0HuHPLE1H57@HP@L . IA(IA}IIPt H`~.LeMt6HEL9t!HX@t HxY.HI9uH}LLe?.Et H}0.Et H}!.LhMt:HpL9tEHX@t Hx.HI9uHhLpۢ.EtL .L. Lffffff.UHAWAVAUATSH(MMHMHUHH3H胻HCLcLkHC H{8H3HLuL}H}LLMM自HCPHEHC`HCXHCPM)t3MILH=uOL.HCPN$Lc`HL.LcXH3HHHUHMMM"H([A\A]A^A_]H}蜞. IHEH8Ht H{X螡.HEH8Ht H{@艡.L詝. ffffff.UHAVSIh.HH@HHH{L/HHP1Ln!H@HHH[A^]IH .L-. UHAWAVAUATSHHIIH@HHE1H+IM'IEI} H9HHHW)EHEH)III9Hv.HEN4LuHHe.LuIEI} HW)`HDžpH)IIHI9wH.H`N4LpH5=HH芡.LhIEI} uW)EE(HEHlice scaHEHCannot sHEElarsEHMLOEH})`HDžpW)@HDžPH)t`IIHI9-.HHHH@JHPIuMu I)M~HL肠.LHHMW) IH HHDž0MIIT$PIL$hHHD$H@HD$H`H\$HEH$LI?t H0H 6/1AL=/ILH$H@LMLHL<H Ht H(.H@Ht HH.H`Ht Hhԝ.H}Ht H}.Hە@HH;EuLH[A\A]A^A_] .覝.IH`;H50H \0H}L`qW)@HDžP0I.HP(_=)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AH0HuH@LE1H57|@HPx@L .%H}臙.H@y. H`k. IA(IA}II@t HPW.LeMt6HEL9t!HX@t Hx2.HI9uH}LLe.Et H} .Et H}.L`Mt:HhL9tDHX@t Hxϛ.HI9uH`Lh贛.ELߛ.LI~IEH}IIIHt&Ct H{].HU. I IKIH Ht H(..H@Ht HH.H`Ht Hh.H}Ht H}.L . fUHAWAVAUATSHXH@HHELLHIHW)EHEHHLyI)tJMILH=Lm.ILmLmKDHEM~LHLқ.MLmHU1LEIH}IHL舦HH]IL%HH]HuHEHpHEHxHW)PHDž`HLxHX L)IILH=H艙.ILXLPO$L`MH~LLH.ILXLLXH?HHHHHLLHHHvHPHXHPHXHt$H$HH HpH(HH Ht H(虘.HHHLXE1E1E11HHHRHH)HLL)HHH)v8HPeHHHHQLPLXL`HsMtLXLHrHqL8L@LHW)PHDž`HHLFHuHH7HHuH}HEHUHEHUHT$HD$HEHUHT$H$HH8HvHpHH Ht H(+.HHt H.H8Ht H@.HPHtI?.HH}Ht H}Ζ.H@HH;EuHHX[A\A]A^A_].H}}. HPo. HHH Ht H(h.HHHL.HHPHt}HX'.oHH Ht H( .HHtH.HH8Ht H@Օ.HPHtI?. HH}Ht H}褕.Hđ. f.UHAWAVAUATSHIIH@HHEMT$0IIFL9HII9IT$(fffff.HtIt$L輖.IT$( 1ID$HIMD$8It$@L)HIDHID$HtHFL)IIH)HI9>LWHOL)HI9&M)IM9HHGIKEAHsLMupLffff.H\HHILHDHHILH\HHILHDHHILHvI9uMt2M|HTI1ff.HHHI HI9uHGKDHG(M9uKDHG0H_@HH;EuHĈ[A\A]A^A_]葏.-.IHh&-H5Ձ0H r0H}LhHPJAH:0HuHPLE1H5(n@HAj@L. IA(IA}IIPt H`o.LeMt6HEL9t!HX@t HxJ.HI9uH}LLe0.Et H}!.Et H}.LhMt:HpL9tEHX@t Hx.HI9uHhLp̍.EtL.L߉. LUHAWAVAUATSH(MMHMHUHH3HHCLcLkHC H{8H3HLuL}H}LLMM臕HCPHEHC`HCXHCPM)t3MILH=uOL.HCPN$Lc`HL.LcXH3HHHUHMMM2H([A\A]A^A_]H}蜉. IHEH8Ht H{X螌.HEH8Ht H{@艌.L詈. ffffff.UHAVSIh.HH~@HHH{L/HHP1Ln Hoz@HHH[A^]IH .L-. UHAWAVAUATSHHIIH@HHE1H+IM'IEI} H9HHHW)EHEH)III9Hv.HEN4LuHHe.LuIEI} HW)`HDžpH)IIHI9wH.H`N4LpH5=HH芌.LhIEI} uW)EE(HEHlice scaHEHCannot sHEElarsEHMLOEH})`HDžpW)@HDžPH)t`IIHI9-.HHHH@JHPIuMu I)M~HL肋.LHHMW) IH HHDž0MIIT$PIL$hHHD$H@HD$H`H\$HEH$LI?t H0H !/1AL(/ILH$H@LMLHL<H Ht H(.H@Ht HH.H`Ht HhԈ.H}Ht H}ˆ.Hۀ@HH;EuLH[A\A]A^A_] .覈.IH`&H50H t0H}L`qW)@HDžP0I.HP(_=)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AH0HuH@LE1H57g@HPc@L .%H}臄.H@y. H`k. IA(IA}II@t HPW.LeMt6HEL9t!HX@t Hx2.HI9uH}LLe.Et H} .Et H}.L`Mt:HhL9tDHX@t Hxφ.HI9uH`Lh贆.EL߆.LI~IEH}IIIHt&Ct H{].HU. I IKIH Ht H(..H@Ht HH.H`Ht Hh.H}Ht H}.L . fUHAWAVAUATSHXH}@HHELLHIHW)EHEHHLyI)tJMILH=Lm.ILmLmKDHEM~LHL҆.MLmHU1LEHH}IHH踙ILuHH%H@IHEHuHEHpHEHxHW)PHDž`HLxHX L)IILH=H脄.ILXLPO$L`MH~LLHޅ.ILXLLXH8HHHHHLLHHHvHPHXHPHXHt$H$HH HHpHH Ht H(蔃.HHHLXE1E1E11HHHRHH)HLL)HHH)v1HP`tHHHQLPLXL`sMtLXLHrHqL8L@LHW)PHDž`HHLFHuHH7HHuH}HEHUHEHUHT$HD$HEHUHT$H$HH8HHpHHH Ht H(-.HHt H.H8Ht H@.HPHtI?.HH}Ht H}Ё.Hy@HH;EuHHX[A\A]A^A_].H}~. HPq~. HHH Ht H(j.HHHN.HHPHt}HX).oHH Ht H( .HHtH.HH8Ht H@׀.HPHtI?À. HH}Ht H}覀.H|. fff.UHAWAVAUATSHIIHx@HHEI|$0IEIML8H I9FMD$(LHfMl$MtFIO4@Lff.HL}.IHIuMl$MD$(ff.E1J H IIIIL$8IT$@H)HHtH4vItIt$HtFI|$I\$PHDHHDH;Du HDHDH@H4It$HuLHIEL8IEIML8H I93I9*MD$(@MIt$E1ffffff.L|.ID$0H4@HIt$It$ID$ MD$(HID$ L9ID$ IL$8IT$@H)HHDH@H4It$HtUI|$ID$Pff.H\HH\H;\u,HDH\H[H4It$Hufff.IIM90fffff.E1IEL8IEIML8H I9I9u'Hv@HH;EuHĘ[A\A]A^A_]E~.}.IHhH5Q0H –0H}LhYHP9AH0HuHPLNE1H5\@HX@L}. IA(IA}IIPt H`#}.LeMt6HEL9t!HX@t Hx|.HI9uH}LLe|.Et H}|.Et H}|.LhMt:HpL9tEHX@t Hx|.HI9uHhLp|.EtL|.Lx. L@UHAWAVAUATSH(LEHMIHH3HFHCHEHMHCHKHC H{8LmLeLE L}(H}LuLLLMM*HCPHEHC`HCXHCPM)t3MILH=uLL{.HCPN,Lk`HL{.LkXHHuHUHMMMXH([A\A]A^A_]H}Bx. IHEH8Ht H{XD{.HEH8Ht H{@/{.LOw. DUHAWAVAUATSHH%s@HHEL>HFL)IIH)HI9SLWHOL)HI9;M)IM9+HHGIKEAHsLMLfH\HH[HILH\HH[HILH\HH[HILH\HH[HILHvI9uMt3M|HTI1HHH[HI HI9uHGKDHG(M9uKDHG0Hq@HH;EuHĈ[A\A]A^A_]y.y.IHhH5@l0H %0H}Lh~HPb5AHm0HuHPLE1H5X@HT@Lfy. IA(IA}IIPt H`x.LeMt6HEL9t!HX@t Hxx.HI9uH}LLex.Et H}x.Et H}}x.LhMt:HpL9tEHX@t HxRx.HI9uHhLp7x.EtLfx.LJt. Lff.UHAWAVAUATSH(MMHMHUHH3HHCLcLkHC H{8H3HLuL}H}LLMMHCPHEHC`HCXHCPM)t3MILH=uOLvw.HCPN$Lc`HLew.LcXH3HHHUHMMMH([A\A]A^A_]H}s. IHEH8Ht H{Xv.HEH8Ht H{@v.L s. ffffff.UHAVSIv.HHi@HHH{LHǃHǃHǃHCxHCpHChHC`HCXHCPHd@HHH[A^]IH-v.LMr. UHAWAVAUATSHhIH"n@HHE1HHJIMID$I|$ H9HHW)EHEH)IIII9Hu.HEN,LmHHu.LmID$I|$ HW)`HDžpH)IIM9`H9u.H`N,LpH5<HHv.LhID$I|$ xW)EE(HEHlice scaHEHCannot sHEElarsEHMLvEH}I)`HDžpW)@HDžPH)tYHHL9q[t.ILHL@IDHPIt$I\$ H)H~LHu.ILHW) HDž0H H))HDžHDž)HDž)HDžHHLLHLHHHL$ H@HL$L`Ll$HMHL$H$HHLLLI?t%1H0H /ALC IHHt Hr.HHt Hr.HHt Hr.HHt Hr.t(LL,$H@LMLHLH Ht H(Dr.H@Ht HH,r.H`Ht Hhr.H}Ht H}r.Hj@HH;EuLHh[A\A]A^A_]Jr.q.IH`H5V0H -0H}L`qW)@HDžP0q.HP(=)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AH+0HuH@LE1H5wP@HL@LJq.%H}m.H@m. H`m. IA(IA}II@t HPp.LeMt6HEL9t!HX@t Hxrp.HI9uH}LLeXp.Et H}Ip.Et H}:p.L`Mt:HhL9tDHX@t Hxp.HI9uH`Lho.E0Lp.#LIIIEH}IIIHt,Ct H{o.Ho.IIIHHt H]o.HHt HEo.HHt H-o.HHt Ho.H Ht H(n.H@Ht HHn.H`Ht Hhn.H}Ht H}n.Lj. UHAVSIn.HH`@HHH{LoHǃHǃHǃHCxHCpHChHC`HCXHCPH]@HHH[A^]IH n.L-j. UHAWAVAUATSHhIHf@HHE1HH*IMID$I|$ H9HHW)EHEH)IIII9Hwm.HEN,LmHHfm.LmID$I|$ HW)`HDžpH)IIM9`Hm.H`N,LpH5<HHn.LhID$I|$ xW)EE(HEHlice scaHEHCannot sHEElarsEHMLVEH}I)`HDžpW)@HDžPH)tYHHL9q;l.ILHL@IDHPIt$I\$ H)H~LHm.ILHW) HDž0H H))HDžHDž)HDž)HDžHHLLHhLHHHL$ H@HL$L`Ll$HMHL$H$HHLLLI?t%1H~0H x/AL#  IHHt Hj.HHt Hj.HHt Hj.HHt Hhj.t(LL,$H@LMLHL H Ht H($j.H@Ht HH j.H`Ht Hhi.H}Ht H}i.Ha@HH;EuLHh[A\A]A^A_]*j.i.IH`H56}0H 0H}L`qW)@HDžP0ii.HP(=)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AH 0HuH@LE1H5WH@HpD@L*i.%H}e.H@e. H`e. IA(IA}II@t HPwh.LeMt6HEL9t!HX@t HxRh.HI9uH}LLe8h.Et H})h.Et H}h.L`Mt:HhL9tDHX@t Hxg.HI9uH`Lhg.E0Lg.#LIIIEH}IIIHt,Ct H{rg.Hjg.IIIHHt H=g.HHt H%g.HHt H g.HHt Hf.H Ht H(f.H@Ht HHf.H`Ht Hhf.H}Ht H}f.Lb. UHAVSIf.HHX@HHH{LOHǃHǃHǃHCxHCpHChHC`HCXHCPHT@HHH[A^]IHe.L b. UHAWAVAUATSHhIH]@HHE1HH IMID$I|$ H9HHW)EHEH)IIII9HWe.HEN,LmHHFe.LmID$I|$ HW)`HDžpH)IIM9`Hd.H`N,LpH5<HHsf.LhID$I|$ xW)EE(HEHlice scaHEHCannot sHEElarsEHML6EH}I)`HDžpW)@HDžPH)tYHHL9qd.ILHL@IDHPIt$I\$ H)H~LHme.ILHW) HDž0H H))HDžHDž)HDž)HDžHHLLHHLHHHL$ H@HL$L`Ll$HMHL$H$HHLLL|I?t%1Hkv0H X.AL IHHt Hb.HHt Hxb.HHt H`b.HHt HHb.t(LL,$H@LMLHLH Ht H(b.H@Ht HHa.H`Ht Hha.H}Ht H}a.HY@HH;EuLHh[A\A]A^A_] b.a.IH`H5u0H -0H}L`qW)@HDžP0Ia.HP(_=)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AH0HuH@LE1H57@@HP<@L a.%H}].H@y]. H`k]. IA(IA}II@t HPW`.LeMt6HEL9t!HX@t Hx2`.HI9uH}LLe`.Et H} `.Et H}_.L`Mt:HhL9tDHX@t Hx_.HI9uH`Lh_.E0L_.#LIIIEH}IIIHt,Ct H{R_.HJ_.IIIHHt H_.HHt H_.HHt H^.HHt H^.H Ht H(^.H@Ht HH^.H`Ht Hh^.H}Ht H}{^.LZ. UHAVSIh^.HHP@HHH{L/HǃHǃHǃHCxHCpHChHC`HCXHCPHL@HHH[A^]IH].LY. UHAWAVAUATSHhIHU@HHE1HHIMID$I|$ H9HHW)EHEH)IIII9H7].HEN,LmHH&].LmID$I|$ HW)`HDžpH)IIM9`H\.H`N,LpH5t<HHS^.LhID$I|$ xW)EE(HEHlice scaHEHCannot sHEElarsEHMLEH}I)`HDžpW)@HDžPH)tYHHL9q[.ILHL@IDHPIt$I\$ H)H~LHM].ILHW) HDž0H H))HDžHDž)HDž)HDžHHLLH(LHHHL$ H@HL$L`Ll$HMHL$H$HHLLL\I?t%1HKn0H 8.AL IHHt HpZ.HHt HXZ.HHt H@Z.HHt H(Z.t(LL,$H@LMLHL&H Ht H(Y.H@Ht HHY.H`Ht HhY.H}Ht H}Y.HQ@HH;EuLHh[A\A]A^A_]Y.Y.IH`H5l0H 0H}L`qlW)@HDžP0)Y.HP(?=)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AHˑ0HuH@L艺E1H58@H04@LX.%H}gU.H@YU. H`KU. IA(IA}II@t HP7X.LeMt6HEL9t!HX@t HxX.HI9uH}LLeW.Et H}W.Et H}W.L`Mt:HhL9tDHX@t HxW.HI9uH`LhW.E0LW.#LIIIEH}IIIHt,Ct H{2W.H*W.IIIHHt HV.HHt HV.HHt HV.HHt HV.H Ht H(V.H@Ht HHV.H`Ht HhmV.H}Ht H}[V.L{R. UHAVSIHV.HHH@HHH{LHǃHǃHǃHCxHCpHChHC`HCXHCPH'D@HHH[A^]IHU.LQ. UHAWAVAUATSHhIHM@HHE1HHʰIMID$I|$ H9HHW)EHEH)IIII9HU.HEN,LmHHU.LmID$I|$ HW)`HDžpH)IIM9`HT.H`N,LpH5T<HH3V.LhID$I|$ xW)EE(HEHlice scaHEHCannot sHEElarsEHML{EH}I)`HDžpW)@HDžPH)tYHHL9qS.ILHL@IDHPIt$I\$ H)H~LH-U.ILHW) HDž0H H))HDžHDž)HDž)HDžHHLLHLHHHL$ H@HL$L`Ll$HMHL$H$HHLLL.H>.IIIHHt H>.HHt H>.HHt Hm>.HHt HU>.H Ht H(=>.H@Ht HH%>.H`Ht Hh >.H}Ht H}=.L:. UHAVSI=.HH20@HHH{L诮HǃHǃHǃHCxHCpHChHC`HCXHCPHW,@HHH[A^]IHM=.Lm9. UHAWAVAUATSHhIHB5@HHE1HHjIMID$I|$ H9HHW)EHEH)IIII9H<.HEN,LmHH<.LmID$I|$ HW)`HDžpH)IIM9`HY<.H`N,LpH5<HH=.LhID$I|$ xW)EE(HEHlice scaHEHCannot sHEElarsEHMLcEH}I)`HDžpW)@HDžPH)tYHHL9q{;.ILHL@IDHPIt$I\$ H)H~LH<.ILHW) HDž0H H))HDžHDž)HDž)HDžHHLLH訤LHHHL$ H@HL$L`Ll$HMHL$H$HHLLLܚI?t%1HM0H .ALc IHHt H9.HHt H9.HHt H9.HHt H9.t(LL,$H@LMLHLVH Ht H(d9.H@Ht HHL9.H`Ht Hh49.H}Ht H}"9.H;1@HH;EuLHh[A\A]A^A_]j9.9.IH`H5vL0H c0H}L`qW)@HDžP08.HP(=)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AHKq0HuH@L E1H5@H@Lj8.%H}4.H@4. H`4. IA(IA}II@t HP7.LeMt6HEL9t!HX@t Hx7.HI9uH}LLex7.Et H}i7.Et H}Z7.L`Mt:HhL9tDHX@t Hx/7.HI9uH`Lh7.E0L?7.#LIIIEH}IIIHt,Ct H{6.H6.IIIHHt H}6.HHt He6.HHt HM6.HHt H56.H Ht H(6.H@Ht HH6.H`Ht Hh5.H}Ht H}5.L1. UHAVSI5.HH(@HHH{L菦HǃHǃHǃHCxHCpHChHC`HCXHCPH#@HHH[A^]IH-5.LM1. UHAWAVAUATSHhIH"-@HHE1HHJIMID$I|$ H9HHW)EHEH)IIII9H4.HEN,LmHH4.LmID$I|$ HW)`HDžpH)IIM9`H94.H`N,LpH5ԫ<HH5.LhID$I|$ xW)EE(HEHlice scaHEHCannot sHEElarsEHMLv[EH}I)`HDžpW)@HDžPH)tYHHL9q[3.ILHL@IDHPIt$I\$ H)H~LH4.ILHW) HDž0H H))HDžHDž)HDž)HDžHHLLH舜LHHHL$ H@HL$L`Ll$HMHL$H$HHLLL輒I?t%1HE0H .ALC IHHt H1.HHt H1.HHt H1.HHt H1.t(LL,$H@LMLHL|_H Ht H(D1.H@Ht HH,1.H`Ht Hh1.H}Ht H}1.H)@HH;EuLHh[A\A]A^A_]J1.0.IH`H5VD0H [0H}L`q̏W)@HDžP00.HP(=)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AH+i0HuH@LE1H5w@H @LJ0.%H},.H@,. H`,. IA(IA}II@t HP/.LeMt6HEL9t!HX@t Hxr/.HI9uH}LLeX/.Et H}I/.Et H}:/.L`Mt:HhL9tDHX@t Hx/.HI9uH`Lh..E0L/.#LIIIEH}IIIHt,Ct H{..H..IIIHHt H]..HHt HE..HHt H-..HHt H..H Ht H(-.H@Ht HH-.H`Ht Hh-.H}Ht H}-.L). UHAVSI-.HH@HHH{LoHǃHǃHǃHCxHCpHChHC`HCXHCPH@HHH[A^]IH -.L-). UHAWAVAUATSHhIH%@HHE1HH*IMID$I|$ H9HHW)EHEH)IIII9Hw,.HEN,LmHHf,.LmID$I|$ HW)`HDžpH)IIM9`H,.H`N,LpH5<HH-.LhID$I|$ xW)EE(HEHlice scaHEHCannot sHEElarsEHMLVSEH}I)`HDžpW)@HDžPH)tYHHL9q;+.ILHL@IDHPIt$I\$ H)H~LH,.ILHW) HDž0H H))HDžHDž)HDž)HDžHHLLHhLHHHL$ H@HL$L`Ll$HMHL$H$HHLLL蜊I?t%1H=0H x.AL# IHHt H).HHt H).HHt H).HHt Hh).t(LL,$H@LMLHLLhH Ht H($).H@Ht HH ).H`Ht Hh(.H}Ht H}(.H @HH;EuLHh[A\A]A^A_]*).(.IH`H56<0H nT0H}L`q謇W)@HDžP0i(.HP(w=)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AH a0HuH@LɉE1H5W@Hp@L*(.%H}$.H@$. H`$. IA(IA}II@t HPw'.LeMt6HEL9t!HX@t HxR'.HI9uH}LLe8'.Et H})'.Et H}'.L`Mt:HhL9tDHX@t Hx&.HI9uH`Lh&.E0L&.#LIIIEH}IIIHt,Ct H{r&.Hj&.IIIHHt H=&.HHt H%&.HHt H &.HHt H%.H Ht H(%.H@Ht HH%.H`Ht Hh%.H}Ht H}%.L!. UHAVSI%.HH@HHH{LOHǃHǃHǃHCxHCpHChHC`HCXHCPHG@HHH[A^]IH$.L !. UHAWAVAUATSHhIH@HHE1HH IMID$I|$ H9HHW)EHEH)IIII9HW$.HEN,LmHHF$.LmID$I|$ HW)`HDžpH)IIM9`H#.H`N,LpH5<HHs%.LhID$I|$ xW)EE(HEHlice scaHEHCannot sHEElarsEHML6KEH}I)`HDžpW)@HDžPH)tYHHL9q#.ILHL@IDHPIt$I\$ H)H~LHm$.ILHW) HDž0H H))HDžHDž)HDž)HDžHHLLHHLHHHL$ H@HL$L`Ll$HMHL$H$HHLLL|I?t%1Hk50H X.AL IHHt H!.HHt Hx!.HHt H`!.HHt HH!.t(LL,$H@LMLHLqH Ht H(!.H@Ht HH .H`Ht Hh .H}Ht H} .H@HH;EuLHh[A\A]A^A_] !. .IH`蟾H540H L0H}L`qW)@HDžP0I .HP(_o=)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AHX0HuH@L詁E1H57?HP?L .%H}.H@y. H`k. IA(IA}II@t HPW.LeMt6HEL9t!HX@t Hx2.HI9uH}LLe.Et H} .Et H}.L`Mt:HhL9tDHX@t Hx.HI9uH`Lh.E0L.#LIIIEH}IIIHt,Ct H{R.HJ.IIIHHt H.HHt H.HHt H.HHt H.H Ht H(.H@Ht HH.H`Ht Hh.H}Ht H}{.L. UHAVSIh.HH@HHH{L/HǃHǃHǃHCxHCpHChHC`HCXHCPHW @HHH[A^]IH.L. UHAWAVAUATSHhIH@HHE1HHwIMID$I|$ H9HHW)EHEH)IIII9H7.HEN,LmHH&.LmID$I|$ HW)`HDžpH)IIM9`H.H`N,LpH5t<HHS.LhID$I|$ xW)EE(HEHlice scaHEHCannot sHEElarsEHMLCEH}I)`HDžpW)@HDžPH)tYHHL9q.ILHL@IDHPIt$I\$ H)H~LHM.ILHW) HDž0H H))HDžHDž)HDž)HDžHHLLH(LHHHL$ H@HL$L`Ll$HMHL$H$HHLLL\zI?t%1HK-0H 8.AL IHHt Hp.HHt HX.HHt H@.HHt H(.t(LL,$H@LMLHL~H Ht H(.H@Ht HH.H`Ht Hh.H}Ht H}.H@HH;EuLHh[A\A]A^A_]..IH`H5+0H pE0H}L`qlwW)@HDžP0).HP(?g=)@Hbe proceHHHnsor to HHHinput teHHHMissing H@ ssed@$AHP0HuH@LyE1H5?H0?L.%H}g.H@Y. H`K. IA(IA}II@t HP7.LeMt6HEL9t!HX@t Hx.HI9uH}LLe.Et H}.Et H}.L`Mt:HhL9tDHX@t Hx.HI9uH`Lh.E0L.#LIIIEH}IIIHt,Ct H{2.H*.IIIHHt H.HHt H.HHt H.HHt H.H Ht H(.H@Ht HH.H`Ht Hhm.H}Ht H}[.L{. UHAVSIH.HH@HHH{LHǃHǃHǃHCxHCpHChHC`HCXHCPH@HHH[A^]IH.L. 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Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H}11 H' H5[],HH H5.H H5բ/HG HA H5,Hn HHHA>HHEH HUHuHuIININ0IFIVIF@H]HEHtHN1HL,HD>HH;Eu LHP[A^]M,IH]HEHtH 1HqL,LH, UHAWAVAUATSHIHIH\D>HHE1H H脧HL#LkLL)HHX H+XHW)EHEHH8L@L(tMHH=JH<H0K,IL}M4LuLH0K,LuLM9ujE1E11M9t]ff.M)I<$IL}HL}LuLL)HH9}uI<IIM9uLI9H8HIHpL)HtxHLH:u HHHHHuHH .H@HuiH .H@HsuDLHrySH1HH2HzuLH1HHrHzuAH1HHrHzu6H1HHr)fffff.H:tHztHztHztH H9uW)`HDžpLL)tUIILH=XI,HHhH`JHpM)M~HLL,K,LHhH`1H 蕨 H(HAH8HH@HH`Ht Hh:I,H}Ht H}(I,HAA>HH;EuHHĸ[A\A]A^A_]pI, I,IH`H5O.H .H}L`HHAH"-HuHHLyE1H5(>H $>LH,H}WE, H`IE, HA(HA}HHHt HX5H,LeMt6HEL9t!Lh@t HxH,LM9uH}LLeG,Et H}G,Et H}G,L`Mt:HhL9t=Lh@t HxG,LM9uH`LhG,EtGLG,=LHH`HtHh_G,HH}Ht H}FG,HfC, fff.UHAWAVAUATSHIH5?>HHEW)EHPHMI>t#H^.H x,1ACL&ILeHEL8M,$M9I|$0HPhHtGxuAILH~Z1fLLSD,HL蚲IIH9|*I|$聲HI|$0HPHLLHG,IHC>>HH;EuLH[A\A]A^A_]uF,IIIHtCt H{E,HE,LA, UHAVSIhE,HH8>HHH{L菶 HHPL0H ?HH[A^]IHsE,LA, fUHAWAVAUATSHIHb=>HHEID$ID$I$W)EHEEEaxesEHhHUL HhHDžhHtCt H{D,HD,Et H}D,Hu'H<>HH;EuHĘ[A\A]A^A_]D,D,IHhH5.H T.H}Lh{W)PHDž`08D,H`(e;)PHte valueHHHttributeHHH'axes' aHHHInvalid HHHMissing/H@&LH.HuHPL茥DžLH5#>H,>LC, IǰL-IǰL}IIPt H`PC,LmMt6HEL9t!HX@t Hx+C,HI9uH}LLmC,Et H}C,Et H}B,HhHt:HpH9tALh@t HxB,LL9uHhHpB,Lt!LB,HIEt H}B,I<$Ht I|$pB,L>, fDUHAVSIhXB,HH4>HHH{L HHPLH ?HH[A^]IHB,L#>, fUHAVSHPIH9>HHEA,HþHA,HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH] H5P,H}H H5~.HU H5/H HH HQH54~,H HHH4>HHEHVHUHuHuIININ0IFIVIF@H]HEHtH$H?,H8>HH;Eu LHP[A^]?@,IH]HEHtHA$H?,L;, ffffff.UHAVSHPIH7>HHEv?,HþHl?,HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]G H5;N,H}Hd H5|.H H5/H& HH HQH5{,HF HHH]2>HHEHvHUHuHuIININ0IFIVIF@H]HEHtH&"H=,H5>HH;Eu LHP[A^]=,IH]HEHtH!HI=,Li9, ffffff.UHAVSHPIH;5>HHE=,HþH =,HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH] H5K,H}H H52z.H H5T/H HH HQH5ty,H HHH/>HHEH&HUHuHuIININ0IFIVIF@H]HEHtHH.;,HG3>HH;Eu LHP[A^];,IH]HEHtHH:,L 7, ffffff.UHT:D:D:(AX(YD ӳ:DXA\YYA(\D\(AX(Y5/;Y\Y-/;YXYu/;Y\AXYA\YYAXAY\AYXAY\f:!f:!(]fffff.UHAWAVSHHH1>HHE09,IHPH蓪 H}HP胪 IPHu膒H+>HIIHPX 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Ht HP(IL(+LH+ fffff.UHAWAVAUATSH(IIH=HHE1HH7IMSI_ I+_W)`HDžptCIILH=H蜸+H`N4LpHH腸+LhW)@HDžPAIH@HEH@I}7, HH+HupMMOIO L)Ht7IH`H1WI*YH,HHH9rL`LLHALLHA LHAHMIGIO H)t6IH`H1WH*YH,HHH9rL`LLLHAfAH{'HH{"IMHH@HHH)HLH)?H@BH@4HLMg M+gW) HDž0tCMILH>IL/+H N4L0HL+L(H{&"I}PHHwH H5IGIO H)H H`H1WH*YH,HHH9rDIw I+wHH@@H@HHHs JHHH迧IHHL赶+HHHHXH%!HHlL` IH HLLHT+IGIw H)HHH`HhH)HH98Ht:H 1fffff.WH*WH* ^HH9rHH H$L L`HLHH H Ht H(+LH@Ht HHֳ+H`Ht Hh辳+H׫=HH;Eu1LH([A\A]A^A_]111H)HW蔳+M+膳+IH`QH5-H -H}L`lH@PoAHx,HuH@LE1H5=H=LT+bH`˯+Q+IH PH52-H -H}L HnAH-HuHL_E1H5=H=L+o+IH hPH5-H -H}L UW)HDž@+H(:)Hs input.HH.Hided as HH(H be provHH Hzes mustHHHes or siHHH of scalHHHOnly oneH@6AH-HuHLOE1H5ݐ=H=L谱+_+IH XOH5-H -H}L EW)HDž +H((;)Ht index.HHHroi inpuHHHInvalid H@AH-HuHLwE1H5=H=Lذ+臰+IHNH5-H -H}LmW)HDž@*+H(9)Has inputHH(Hrovided HH HUST be pHHH sizes MHHHcales orHHHEither sHf@0.AH-HuHLsE1H5=H=Lԯ+H K+r+IHkMH5-H -H}LXW)HDž@+H(:)Hs input.HH.Hided as HH(H be provHH Hzes mustHHHes or siHHH of scalHHHOnly 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7-1ABHMHAHHHALMIHIpHHHxIHhAHpDtD跔IL輲HHHHtH`H9{HPH0}HDž0mHE1HHHHHIHDHDHPH@ HDILIHHHH`H9HPH`LpHx=HHHLH@HpLL芠+HDžDžHd=LkLH@HpLH+L5'=ILHDž`HDžXHDžPHDžHDžhH5A-H81HH Φ-H5-HDHTHp8HLHHt Hߟ+LHpLHt HX赟+LE+H5=HHȝ+L:+HHH0HPLLl$(H\$ L|$HHD$AƉD$Aĉ$HHHHLhLxptH0I9HtHtHP(HZHP HHHuHuHPHHHtHH9tHPHEHEHpHuHPIMLpL|$(LuLt$ ÉD$AʼnD$AĉD$HH$HHHHHLH}I9t HtHP(HP H}I9tHUHP(JHP ?HDž`H@H`HPHH@H\$(HHD$ L|$Lt$AʼnD$HH$HLHLxL&H`H9tHHtHP( HP HHHHt#HHCHuHHPHm+H;+讜+IH:H5-H -HLNHuXAHa,HHLE1H5{=Hw=Lv+%+IH:H5e-H ,-HLPW)HDž0ś+H(;)Hbe same.HH'Hshould bHH H tensor HHHd outputHHHinput anHHHRank of H@/AH0-HHL E1H5z=Hv=Ln+ H+ HAaHAHHAHAnHHt H藚+L8Mt?H@L9t$HX@t Hxl+HI9uH8LL@L+ t H07+t H"+LMHL9 HX@t Hx+HI9uHt Hƙ+L8Mt?H@L9t$HX@t Hx蛙+HI9uH8LL@{+ t H0f+t HQ+LMt:HL9tDHX@t Hx&+HI9uHL +EL6+LHHИ+HHHH`H9u HP H HP(HHHHHCt H{L+HD+HLHpLHt HX+L螖+H5~=HH!+L蓗+H#HH}I9uHP HtHP(H}HEH9uHP yHH0I9tHtaHP(YHHHtFHHCHu4HHPH+!HHHt H(+HD+ f.UHAWAVAUATSHhL@L0HHHEH =H HMHHHzHHL)HqL)HHH0L1HIL)HH9TH8Hf~H(ffEHEH HH=HH8H6+HEL Nx+.w+H Qn=HHH5[X=HV=Hx+ +'IHXLPMILP\n~|zxvtrpIHXIILPII 3/-+IHXMOILPIHXLP"IH`Ht Hhv+HXv+L}Lv+H}Ht H}v+Lr+ dUHAWAVAUATSH(0`MMHhH HHH]0H7n=HHEW)EHE)EHEK>H;HHH4@QHpHH(H[Ht-HCHCHHCHuHHPHUt+HLE8HpJ HLHHHHJJHJHHMHHQH)HHHMI*H H*H8LH@DE`EHMPUMEIx HH HIWH*X1LUffff.HH@0HMH $HuMLHMLELM|HEH;EsHHEfH}H|pXME|.MLwH|.?9Hu8IL@H8LwHE̋|fnH,HH9~HHPHH9~HWH*\( [9(TH|WH*\THH9uH?H?HhHHpHHHH HL9HEHWH*ËLBDE`EHEPEMEIx H-r+H ,i=HHH56S=H_Q=Hr+HMHHQH)HHHMWI*HhWH*H8LH@DE0EMUMEIx HQHhHHPWH*HJH@K HHpHH`LHHHHXHJHHHuLEffffff.HH@0HMH $IMLHMLELM|HEH;EsHHEf.H}H|PHM|.H|MIG.'9LHFE|fnH,HPH9HNHHDHH9HNHHDHLWH*\( 89(THXD|WH*\TH`DH9uHXD?H`D?L9HU8H@H8WH*HEHLDE0EEEMEHz HH:o+H f=HHH5#P=HLN=Ho+ HH}(Hu MHhIH LMMHED,HWH*HUH8HL*LHXLIHJHRKLLHHLIHILHHNHpIIHWJH@1LHH0HHHHPHh1fH`MFLhE1fDME1@tLB.D.DH8B.DA.DHpJHN$I<W*KILg+H N4L0HLg+L(H{ I}PHHwH HIGIO H)H H`H1WH*YH,HHH9rDIw I+wHH@H@HHHs JHHHXIHHLg+HHHHXH HHlL`RIH HLLHDg+IGIw H)HHH`HhH)HH98Ht:H 1fffff.WH*WH* ^HH9rHH 8L L`HLHH H Ht H(d+LH@Ht HHd+H`Ht Hhd+H\=HH;Eu1LH([A\A]A^A_]111H)HWd+Md+vd+IH`oH5-H G-H}L`\H@@ AH),HuH@LE1H5qC=H?=LDd+bH``+Qc+IH H5"-H -H}L HAHآ-HuHLOE1H5B=H>=Lc+_c+IH XH5-H 0-H}L EW)HDž@c+H(6:)Hs input.HH.Hided as HH(H be provHH Hzes mustHHHes or siHHH of scalHHHOnly oneH@6AHڡ-HuHL?E1H5A=H==Lb+Ob+IH HH5-H -H}L 5W)HDž a+H(:)Ht index.HHHroi inpuHHHInvalid H@AHݠ-HuHLgE1H5@=H==La+wa+IHpH5-H H-H}L]W)HDž@a+H(9)Has inputHH(Hrovided HH HUST be pHHH sizes MHHHcales orHHHEither sHf@0.AH-HuHLcE1H5?=H <=L`+H ;]+b`+IH[H5-H 3-H}LHW)HDž@`+H(3:)Hs input.HH.Hided as HH(H be provHH Hzes mustHHHes or siHHH of scalHHHOnly oneH@6AHݞ-HuHLBE1H5>=H:=L_+R_+IHKH5-H #-H}L8W)HDžP^+HH(9)H5-DHU`+CDAHW-HuHL|E1H5 >=H#:=L^+ IAIIAIIAIAPIjIt H0^+LeMt6HEL9t!HX@t Hx ^+HI9uH}LLe]+Et H}]+Et H}]+LMHL9HX@t Hx]+HI9uIAIAPIIt H]]+LeMt6HEL9t!HX@t Hx8]+HI9uH}LLe]+Et H}]+Et H}]+LM HL9HX@t Hx\+HI9uIAIAqI@It H\+LeMt6HEL9t!HX@t Hxe\+HI9uH}LLeK\+Et H}<\+Et H}-\+LMt:HL9tDHX@t Hx\+HI9uHL[+EL\+xLIAIAPILIt H[+LeMt6HEL9t!HX@t Hxq[+HI9uH}LLeW[+Et H}H[+Et H}9[+L MH(L9HX@t Hx[+HI9u{IAIA2IyIt HZ+LeMt6HEL9t!HX@t HxZ+HI9uH}LLeZ+Et H}uZ+Et H}fZ+L MH(L9HX@t Hx3Z+HI9uIt HZ+LeMt6HEL9t!HX@t HxY+HI9uH}LLeY+Et H}Y+Et H}Y+L Mt:H(L9tDHX@t HxY+HI9uH L(kY+E&LY+LIA(IA}II@t HP Y+LeMt6HEL9t!HX@t HxX+HI9uH}LLeX+Et H}X+Et H}X+L`Mt:HhL9t=HX@t HxX+HI9uH`Lh}X+EtlLX+bL &$I:IH HtH(AX+IH@Ht HH$X+H`Ht Hh X+L,T+ fUHAWAVAUATSHMLHHHHO=HHE1HHH I$Ml$I)MIHH HH+HHI9o W)HDžMtNLH=M LSW+ILLKHM~LHLX+MLH1LL IHHt HV+HHOLw I)LHHLHRL)HH9HHBH+L9M $It$L)JIIH11f.u IH;t1HH9rHBPPLiHLJHHDDHHHA`H9 HPHEHHҖ-H -HDH -HHJHsM=HH;EHHĨ[A\A]A^A_]IL7L賈 HLHV+HHIHHLϻHHHQI HMLHLHHH H-H ږ-HDȾI @HHAHHHIHIyHHL!HHxIHW)HHL袼 HH>HHA-H '-1AHMHAHHHALMIHIpHHHxIHhAHpDtDEILcHHHHtH`H9{HPH0}HDž0mHE1HHHHHIHDHDHPH@ HD}ILIHHHH`H9HPH`LpHh1=HHHLH@HpLLzQ+HDžDžHTH=LkLH@HpL8P+L5H=ILHDž`HDžXHDžPHDžHDžhH51-H1HH W-H5W-HDHTH`HLHHxt HP+LHpLHt HXP+L5O+H56=HHN+L*P+HHH0HPLLl$(H\$ L|$HHD$AƉD$Aĉ$HHHHLhLxptdH0I9HtHtHP(HZHP HHHuHuHPHHHtHH9tHPHEHEHpHuHPIMLpL|$(LuLt$ ÉD$AʼnD$AĉD$HH$HHHHHLH}I9t HtHP(HP H}I9tHUHP(JHP ?HDž`H@H`HPHH@H\$(HHD$ L|$Lt$AʼnD$HH$HLHLxL&H`H9tHHtHP( HP HHHHt#HHCHuHHPH]L+H;N+M+IHH5ދ-H -HLN聬He AH,HHLE1H5,=H(=LfM+M+IHH5U-H r-HLPW)HDž0L+H(˸:)Hbe same.HH'Hshould bHH H tensor HHHd outputHHHinput anHHHRank of H@/AH -HHLE1H5+=H'=L^L+ HH+ HAaHAHHAHAnHHt HK+L8Mt?H@L9t$HX@t Hx\K+HI9uH8LL@ADzADz>ADzAzHH9uMH H0L,0HE1CoDfofhCDCf`CLCCoDfof`fhCDCC CI HuHt2I0H0oKDHfof`fhADALHHH9MLLDHMML9>HH0HIHHHH8HHHH;pHHLHHHH;H`HHIHD,L}1HE11ff@HDžPLL>+IL@KHPLL>+HHKDJHJHHJDHLH HHHHHL@HI1E1*@HHL B1ILL I9_L H~I$HMHLE1HIHff.KTHKTLHH40H9|2KDJJH)JKT'IMH1I WH*H(HHHH*H0H J*BHMH HBHBLf~ẺEf~MUUMHE0Hx HiHH@0HMH $HuHUHMLELMH(HHfn 9B. Ef~EHE8Hx H?HHuHUP0H0H J H9HIHOHɸHHHUHH4:HJ+DHT:HJHJ IH,ɉE1E11H 1GH0L)H8!H WH*H(H WH*I*UHEH@f~@MMUĉUEHE0Hx HdHH@0HMH $H@LHMLELMЀ} L8t*W.wH0HHHWH*.vAMH(Hfn 9.Ef~@HE8Hx HHH@LP0H0H HH9HRHLHҸHHHEHH+0t H3IHHH0HVHIEHHL,>1fffff.H0H HHH8HH*H(H@WH*H*QHMH QI f~@EMUĉUMHE0Hx H HH@0HMH $H@LHMLELMЀ} t0W.wH0HH@HWH*.v H8H(Hfn m9.HEf~@HE8Hx H; HH@LP0H0H HIH9|HHHy1HMHH+QtHHVIHAH8HDu HB !ALH{HH1HVH9oHHDHHH HHH9 HHH@H HH08+ H0L)H8!HWH*H(H WH*I*UHEH@ f~@MMUĉUEHE0Hx HG HH@0HMH $H@HUHMLELMЀ} L8t*W.wH0HHHWH*.vAMH(Hfn u9.Ef~@HE8Hx H HH@HUP0H0H HH9HRHLHIHHEHH+0tH3H HI$HPH>E1 H0HAeI*H(H@WH*H*QHMH QIf~@EMUĉUMHE0Hx HT HH@0HMH $H@HUHMLELMЀ} t+W.wH0HH@HWH*.vAMH(Hfn "9.HEf~@HE8Hx H HH@HUP0H0H HQH9HRHLHIHHEHH+ptHsH HPHHHPH1H0HAeH*H(H@WH*H*QHMH QIf~@EMUĉUMHE0Hx HHH@0HMH $H@HUHMLELMЀ} t+W.wH0HH@HWH*.vAMH(Hfn 9.HEf~@HE8Hx HHH@HUP0H0H HIH9HIHLHIHHEHH+PtHHVH HH1LfHHr/?@ADHQHDʃuL9uHH HB1IHHHHPH9JHIHPI9LHHHH9K5H0L)H8!HWH*H(H WH*I*UHEH@f~@MMUĉUEHE0Hx HY HH@0HMH $H@HUHMLELMЀ} L8t*W.wH0HHHWH*.vAMH(Hfn 9.Ef~@HE8Hx HHH@HUP0H0H HH9HRHLHIHHEHH+0tH3H HI$HPH1 H0HHAeHH*H(H@WH*H*QHMH QIf~@EMUĉUMHE0Hx H]HH@0HMH $H@HUHMLELMЀ} t+W.wH0HH@HWH*.vAMH(Hfn _9.HEf~@HE8Hx HHH@HUP0H0H HQH9HRHLHIHHEHH+ptHsH HPI$HPHCE1 H0HAeI*H(H@WH*H*QHMH QIf~@EMUĉUMHE0Hx HhHH@0HMH $H@HUHMLELMЀ} t+W.wH0HH@HWH*.vAMH(Hfn 9.HEf~@HE8Hx H HH@HUP0H0H HQH9HRHLHIHHEHH+ptHsH HPHHHPH1 H0HAeH*H(H@ WH*H*QHMH Q If~@EMUĉUMHE0Hx HiHH@0HMH $H@HUHMLELMЀ} t+W.wH0HH@HWH*.vAMH(Hfn 9.H Ef~@HE8Hx HHH@HUP0H0H HIH9HIHLHIHHEHH+PtHHVH HH1Lffffff.HHr/?@ADHQHDʃuL9uHH HB1IHHHHPH9=HIHPI9LHHHPHH9FHHHH9HHH8"-+H}Ht H}-+H}Ht H},+H%=HH;EuHS-+,+H P#=HHH5Z =H =H -+2,+H #=HHH5' =HP =H,+,+H "=HHH5 =H =H,+V,+H "=HHH5 =H =Ht,+#,+H "=HHH5 =H =HA,+f++H Q"=HHH5[ =H =H,+3++H "=HHH5( =HQ =H++++H !=HHH5 =H =H++H}"(+I++H !=HHH5 =H =Hg++++H w!=HHH5 =H =H4++Y*+H D!=HHH5N =Hw =H++&*+H !=HHH5 =HD =H*+}*+H =HHH5 =H =H*+J*+H =HHH5 =H=Hh*+*+H x =HHH5 =H=H5*+Z)+H E =HHH5O =Hx=H*+')+H  =HHH5 =HE=H)+~)+H =HHH5 =H=H)+K)+H =HHH5 =H=Hi)+)+H y=HHH5 =H=H6)+^(+H I=HHH5S =H|=H)+.(+H =HHH5# =HL=H(+ }{ywusqomHHHH~HyHtDHmHh8HaH\HWHR"HKHFHAH<H5H0HL8"HH@HL8t HH'+L'+HH}t'+H}Ht H}b'+H#+ wUHAWAVAUATSH(0`MHHH HHH]0H =HHEW)EHE)EHELhK1H;HHH4@QHpHH(H[Ht-HCHCHHCHuHHPH!%+HLE8HpJ HLHHHHhHHHHJHHEHHHQH)HHHMI*H H*HXLIԋDE`E8M@UMEIx HH HIWH*P1LUHH@0HMH $HuMLHMLELM|HEH;EsHHEfH}H|@MPME|.MLwH|.9Hu8IMHXLwHE̋|fnH,HH9~HHPHH9~HWH*\( /9(TH|WH*\THH9uH?H?HHHHpHHHH HHEL9HWH*ËLBDE`E8E@EMEIx H1#+H =HHH5 =H3=H#+HHHQH)HHHHhHhWH*HHWH*HXLH@DE0EMUMEIx HGHHLxWI*8JH@HhH H4HpHH`HHHHHPHHHHHuLEHH@0HMH $IMLHMLELM|HEH;EsHHEf.H}H| J8M|.H|MIG.9LHFE|fnH,I9INHHDHI9INHHDHLWH*\( 9(THPD|WH*\TH`DH9uHPD?H`D?H;hHU8H@HXWH*HEHLDE0EEEMEHz HH= +H =HHH5=H<H + HHM(Hu HHHH HLhLIHWH*HL*D,H`HHHHPHHHJLpLJH@KW*C H<(YYB4YYB6XHYYXYW*YX,CIL9hHHhILL9H`HXHPHXHHHH;dH8HH0H@HH;HpH(tHt HHpPHHt#HHCHuHHPH+H}Ht H}+H}Ht H}+H=HH;EuH([A\A]A^A_]M+H&IWIH(HptHt HHpPHHt#HHCHuHHPH/+H}Ht H}a+H}Ht H}O+Lo+ H}&ffff.UHAVSHPIH;=HHE+HþH +HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5*+H}H H5=a-H H5Tp.H HA H5{X+H HHH=HHEHHUHuHuIININ0IFIVIF@H]HEHtHH5+HN=HH;Eu LHP[A^]+IH]HEHtHH+L+ fDUHAVSHPIH=HHE+HþH+HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5(+H}H H5^-HE H5n.Hv HA H5+V+H HHH =HHEH=;HUHuHuIININ0IFIVIF@H]HEHtH}H+H=HH;Eu LHP[A^]6+IH]HEHtH8H+L+ fDUHAVSHPIH=HHEv+HþHl+HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]GH5;&+H}Hd H5\-H H5k.H& HA H5S+HM HHHd =HHEHaHUHuHuIININ0IFIVIF@H]HEHtH-H+H =HH;Eu LHP[A^]+IH]HEHtHHP+Lp+ fDUHAVSHPIHK =HHE&+HþH+HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5#+H}H H5MZ-H H5di.H HA H5Q+H HHH=HHEHHUHuHuIININ0IFIVIF@H]HEHtHHE+H^ =HH;Eu LHP[A^]+IH]HEHtHH+L + fDUHAVSIP+HH2=HHH{L诃 H=HHH[A^]IH+L+ @UHAWAVAUATSHIHH =HHE1LmIžLmHþLmIMHMLuLL1H}LHM H}LE1LL HuHUHHHHHHHH8H HH9HNHx9OHEH)OHEffPHDž`L`HhL)tVIILH=iH;+ILXLPKDH`H~LLH+ILXHP1Lp HHPHt HX+HHxH(HJPHHH| HH HLMML I>HqLeLLHM)ILMHH8HHH)HHHxH(HrHDVHHHALIJHH)HfHI HxI HHJHpH HHIL$0HII0HLhIIHIL$H1HLLL`LHPHX(ffff.MHLHPHXLMILIMHIMH;IuaIEIHrSM LITHITH;u)IDHL9rffffff.HHXIHIHHXHHHHH;uIHHr<I HXHHHH;uHHH9r@HHI HH(L H`LLHhH H HH(H1LHH(tu HAHHH9ptHH H HH|u|A<MIMI1pL(MLM̨uAAAAM9MOHNHNMIM~IrK H I9pK I9c1IL$H)LHtH@A44HHuH'JL fTTTTTT HI9uLHfAnfpHJHH^H8s1HSHKH)1DD D0D@DPD`DpH@H{HHHff.0D0H HuLHHKHHzHs1HHJtpHOH)1@DLLDLD L0DLD@LPoDo D`LpH HuHt>HJLHHfff.oDo LH HuI9HLO IIt$I9IMHL)LHt!HLAofHHuHrgJLpDA)DDA)DDA)DDA)DDA)DA)DDA)D ofAD0H L9|L;(NM)I$J KLHHMIM;I11fffff.L(MLMuAAAAM9MOHN,HH LMIMIr-H I HH I9KLI9}1INH)LHt!Hffffff.A44HHuHGHH HL @TTTTTT HI9uLHfAnfpHJHH^H8s1HHKH)1DD D0D@DPD`DpH@H{H)HHff.0D0H HuILHHSHHyHs1HHH HtpHWH)1f.DLLDLD L0DLD@LPoDo D`LpH HuHt>HH HTHHDoD o LH HuI9LDO0IIvI9IMHL)LHt%HLff.AofHHuHrwHH HTpDA)DDA)DDA)DDA)DDA)DA)DDA)D ofAD0H L9|L;(M)I[HOlH ILHHHX@t Hx*HI9uH}Le*EQL *DLIA%IAIIEt H}*LMt?HL9t$HX@t Hxk*HI9uHLLK*t H6*t H!*LPMt:HXL9tDHX@t Hx*HI9uHPLX*EdL*WLIHPHt+HX*IIIIH`Ht Hhl*H8Ht H@T*H Ht H(<*HHt H$*HHt H *HHt H*HHt H*H]HEHtH0 H*H]HEHtHb0 H*H]Ht#HHCHuHHPH**L* @UHAWAVAUATSHHe<HHEHHMĉIHHrHQHH HxH 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HAH9HYuHL9TLHs1HDžPI>LH+0HHAHHHA@HHLPL6HDž@DžHH0HXHHLh@LLHHHHPHH@H@H@H@Dž,HHH5>!TrHLhLH8G tAHPH@?HxPH`HHHHq HH5ߨ!qHH8 t HPH@HHHqHH5!qHH`t HPH@HHHNqHLAA XH2HLH5!H !/GE1HPLHMHtHH HHpHPLMHL9t|HX@t Hx-HI9uHSHh_HxPSH`HCJLLt HhLt HxH`HAHHPDH*HPHDžPL#HM%AD$t I|$'LHPHDžPHML@D4tCt H{Ht HHHHHLHHPt HHPHHHHLPLHHHt H =D4AH LHDžPML@D4!HHHQIW)PHDž`LXHHHsPLQHu1=IIILzIL`ILPHXLLHADHHHPHHHDžHH`HHHtPCt H{HHHHDžHHt-Ct H{HPu HDžHPt H`L`LPL@LL{ Hc2L0H5!H i!HHBHPLHE1HLDHH5F!0HlHP|LMt>HL98HX@t HxHI9uHLht HxHt HXxIHHqH5(!H ơ!HPLHi2W)HDž@&H(|9.)Hto true.HH(Hing set HH H flag beHHHerminateHHHdue to tHHHExiting H@0HPH1Lv4H50H 0L H0HH;EuH`H[A\A]A^A_]^IHHWpH5!H !HPLHA1H8UH5!HHzHPH1L3H5H0Ha0LHPLI@It HpE1HDIALMt?HL9t$HX@t Hx2HI9uHLLpt HPtH`IALHMt?HPL9t$HX@t HxHI9uHHLLPEL`MuIL`LIIPt H`HyL`LPHhCIt HE1HCCIALMt?HL9t$HX@t HxHI9uHLLpt HPtH`IALHMtBHPL9tJHX@t HxMHI9uHHLP2EL`MuLIL`LCIHP胹ILMt@HL9tjfDHX@t HxHI9uHLht HxHt HXxL`LIHPILMt@HL9tbfDHX@t HxHI9uHLht HxHHXuLidI=ZItHu&ItHlhuIht HxIPdIHP讷+ILMtFHL9tyfff.HX@t HxHI9uHLt HhHHt:Hxu2=LI ILPHHHJIHdL`IHHHAL`LPIIHIIhtHxIPL`t$H`HuIL`HHX IIL`dIHHHHLHHPt HLHHH,HIL`AIL`LP&IPL`LPt H`LHHt H IIHtCt H{bHZLz UHH0HHHGHHt HGPH](]fDUH]FfDUHSPH2HH[]@UHAWAVAUATSHMIHIH0HHEHHL`$H0HIEI]8Mu@I]HIEXIEPIEHI=IM$M+$IIcH8H[E~M1L}L6HEHt[IMPI;MXtHIEPfH8LHD9|H0HH;EuHĨ[A\A]A^A_]DIH`fH5O!H !HxL`#'I$H<H5O!H !HHHuDH!HxHHL *DžDH50H0Lz IİD0IİDIIHt HXLuMt6HEL9t!HX@t HxHI9uH}LLuEt H}xt H}~H`Ht:HhH9tALp@t HxSLL9uH`Hh8DtLc HIH8H8Ht I}PL# fUHAWAVAUATSPIHL3HCL)HL9sjLH=upLcM)J<IKDMMM~LLLI LL+L{HCMtLH[A\A]A^A_]_H[A\A]A^A_]ÿ^HH57HFHE0HHH5/0Hp0HjIHAL% ff.UHAWAVAUATSH(HHIHH0HHEH8H 0HHHLH@H8L!HEEL=0MgLI@L8LL-Ļ0ILHDž(HDž HDžHDžDž0LHLHA]HsHEsHESH]HHiHHH\HHHu-LL8Lt H kHH5p0HHzH8HU0HH;EuHH([A\A]A^A_]HLL8Lt H HqH7H7H6HH5ͩ0HHHH8@H贿 f.UHSPHH0HHH{HHt H{PWHH[]IfUHAWAVAUATSHIIIuMeI)MIIGHH=HMuI)LHHH9waII9LBMLEHuI9vKHH5H贿H0HHH50Hޠ0HLEHuIJ<rHHuLEN4IJN|M~HLILI]M}MuHtHH[A\A]A^A_]H[A\A]A^A_]E11LҾIH!L ff.UHAWAVATSIHK1IMMtXIL9t=LcHCH[MtAD$t I|$yLqI9uILMTIZI~pYI~(YI~Ht I~$I~IFHtH[A\A^A_]`[A\A^A_]fff.UH]f.UH]fDUHSPH(H 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H{HM9uMtLH[A\A]A^A_]ԵH[A\A]A^A_]E113蘲IL(IH}蝵H}u IH}t H}聵L衱 UHAWAVSPIIH~HE~HHLHɲH]ILH[A^A_]fff.UHAWAVAUATSPIH~HE~肷IL葵IID$ID$I$LjHHsyHsA$MIHu2>LuIIIL菴IMl$IM4$LuI\$LLHADLzLH[A\A]A^A_]L"UHSPH()W))@H@ H@H@H@ H@H1HH[]Ãu(HHHQ HHɃu(H̳HHQHH贳H&H&fff.UHw1]DUH71]fDUHHt H辰1]Hb&fUHG1]UH1]@UHGu HHHH>1]fUHG 1]@UHG 1]ÐUHAWAVAUATSHMLXHIHPIL}H0HHELx>@@HDžHDžHDžtLB>H:HL,$HH(HHXLLEMXHE x H2HH5h!H JHCA HPLE1Hu H!HH5!IHPLMtBHL9t'HX@t Hx]HI9uHLL=t H(t HL.ILHM~ IV(I9cH50HH`HK0HHxHٖ0HHLH0H8HPH@8!IHHHhLpIEIMH)HH9LI$H0LW)PHDž`HXHLhL9tzLE1ffff.H{HuHP0gHݶHHHXH;`s HHXHPLH(I9u0菞IIF(IF IFIFIFIHLLHI<$Ho!H 71AL_=M~MMMf M9fI|$IHtfH؝HHuI<$I$Ht躝M9uI~HhH5l!HPH:FHPLAHE x Hg2HH5n!H D6HY HPLE1Hu H HH5Ik!0K5HPLM%HL9HX@t Hx輜HI9uHI$LHPHLs*LHDžMM~MMMf M9}I|$IHtffff.H(HHuI<$I$Ht M9uI~1LM~ MI>Ht I~ܛLԛA[LL躛t H襛t H萛W)PHDž`@}H`(.)PHto true.HH(Hing set HH H flag beHHHerminateHHHdue to tHHHExiting H@0HPLPTH`ӚCLM~ šMI>Ht I~讚L覚I<$t&AHl!H 31AL9 I$E1HPHt HXVELL1&HW)PHDž` H`(-)PHor::ExecHHHalExecutHHHSequentiH@uteL-HPH1E1HXPt H`蓙I$H蟴H0HH;EuLHĸ[A\A]A^A_]ǙHP7 I"IIPt H` Q"HN II$I$HICt H{Hߘ-IHP[ILM4HL9RfDHX@t Hx荘HI9uILHDžMM~MMf M9I|$IHtfH(HHuI<$I$Ht M9uII$I$HtCt H{ӗH˗IM~MtUMf M9tj@I|$IHtfH蘗HHuI<$I$HtzM9uI~M~ hI>Ht I~WLOLIPH`II$I$HCt H{HoIHPjILMtGHL9tiffff.HX@t Hx蝖HI9uHL肖t HmHLII$I$H2Ct H{HgzIt/Ht#H̕ht,IuhuPu1LIhtHx艕Pu)IPtH`fudITH<CIHPILMt@HL9tsfDHX@t HxHI9uHLҔt H轔Ht5H衔u-@L IHH _I:I@IhuPu1uRHxPt IPtH`uEI5H$I!;9IIHPH(HXHIIIIIIHHHLHxHt HH諑HH2H蠒H(HDž(HtBCt H{ΒH)I)I$IIIPt H`蛒H`H0HxHt HuH艭L艎 ffffff.UHHG]fDUHAWAVAUATSHHIHH80HHEп8ILHI\$M|$Ht,H{ Ht H{(ؑH{Ht H{ƑH辑M$MuH2L8M0A H2HH5nc!H c!LLHXE1LHMJHH5c!)HX{L@MtMHHL9t2fffff.HX@t HxHI9uH@LLH͐(t H8踐t H裐LI|$HLxL9t"Mt$(H;LHHI9uI|$HL0HHI9sff.I>u II9rLI9:HLh(HILHLM9t.fI$HtHhLH IM9uHHLL9t5f.L#LEtLLHHI9uHHLL9t6ff.L#LtLɮLHnHI9uHI9s)II9t)I>uIL9uffffff.I9LIHLpL9t3I(H;htH;<LHHI9uH{ H2L0H5b`!H `!LCLHXE1HLM>HH5`!v&HXxL@MtAHHL9t&fHX@t HxHI9uH@LLH͍(t H8踍t H裍HHH 0H H;MuH[A\A]A^A_]|IH5lLXH5Kn0Hk0L薍ILmIHXwIL@MHHL9HX@t Hx݌HI9uNIHXTwIL@MtAHHL9t\HX@t Hx荌HI9uH@LHr(t H8]tH LIL;L[ 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^HVLMuH}HEHt8HQ#HH;EuHĨ[A\A]A^A_](HIVH59H 9H}AHuH(HK#HHHC HCHCE1H5#Hs#H(IHhHHvH59H 9L 9H}LhLN$HuLH#HIIF IFIF1H5#Hڵ#L\( II$H8HhH}HhHuLH(#HIIF IFIF1H5x#HQ#L(IH57H 9H}L]&HuLH#HIIF IFIF1H5#Hٴ#L[IEtEH}:F@IEtH}huIijht Hxu8IL(IEt H}Eu IHIEt H}Wu&ILIăLZIHPHH7HP1HHHxHHPHpHhH@H@HLHEH9H}HuHhjLHAFu fAFIF IF1H/HEHCHEHMHKHht HxIPt H`4(BILLpH#HIIHH5#HIJ#LF ZIL=LHH I IhtHxPuIPtH`IHH IH]Ht&@L3Ct H{ NHFLMuH}HEHt(LH fffff.UHAWAVAUATSHH#HHEIIIINhuHu(HAHA~ u A~8%IvhH=%HH=W#H52 lINhHqHEqHEQHlH52HlH521HplH527H\lH52 HHlHHHpHLLH5Ų#LH HQ8AHAHH_IvxI}#H@(\IFXHXHHDMcfPMt(Ifff.H;LLrHIuI}8HIvhINx\PHt _HLcI~x$HOHHDHu3H n(1HE1I~xHO$҄HHDH u6H 1HA ^lI~xHO$҄HEHu#H R1HA*HQ#HH;EHĈ[A\A]A^A_]H=%N_W)HDžƅDžATenƅ)ƅ fDžneDžAffiƅ))ƅHConstantHDžFillƅHDžHDžƅDž Cropƅ ) ƅ HDž0HDynamicSH!Dž)liceƅ-HDžHHDž@HDž8ƅ8ƅ?tfDž=niDž9GRUUƅ@)PƅPHDž`HnsorFillHXHGivenTenHQƅ`HDžxHDžhƅhHDžpHImageScaHiƅsrfDžqleƅt)EE$HEHicSoftplHEHParametrHEfEusEHEHEE HEEeEScalE)EEHEHScaledTaHEfEnhEH%H%H=%H5%H5r%HHHH=U%H5V%HnHH=8%H59%HQHH=%H5%H4H H=%H5%HH8H=%H5%HHPH=%H5%HݿHhH=%H5%HHUH=%H5%H覿HUH=s%H5t%H茿HUH=Y%H5Z%HrEEEhP8  t HH=#H5%HeH=%H}ECH}xE:H}eh1HxLP%H`38HH  H0HHH(IH5$H*H $L*L $HHAHLHǿ#HIIF IFIFE1H5#H#Lq( IIV`MFhH5/*H :*L <*H AHLH;#HIIG IGIGE1H5#Hc#L(IIVxH5u*H2H5HHHHHHPHHH@H@HHHAHLAHd#HIIG IGIGE1H5#H#L(IIVhH5)Hx[H5)HxHHHHHPHHH@H@HH`\`HpHh(IIVhH5)HxH5)HxUHHHHHPHHH@H@HH`\F`HpHhHaHHHHHHPHHH@H@HHHAHLGHj#HIIG IGIGE1H5#H#LHaH!HHHHHPHHH@H@HHHAHLH#HIIG IGIGE1H5#Hڤ#L\ 4J`HAHA`HAHAxSHAEMT@HAHA HHHu)u5`uAuMxuYHtH`tHptHxttHfHuu u,KHtHmt!HHt HCE LnH!Ht HEL;HH5%H=%$EEEhP8 t HVH=_%H}<EbH})EYH}hPHxPDH`88HH ,H0 HHHgH7 H7 H}7 HHtHN @UHAWAVAUATSHhMLHHIHxH#HHEH0L=V#I_@HLH #HH@H0HfHnfL+LHEEIGHHIhL0HHLL5#ILHDž HDžHDžHDžDž(LLLLH3[LxHHyLHH[HH\LHHZHH?LHHZHH"LHHZLH9+HHL0HHLt HHH5#HHwH0H#HH;EuLHh[A\A]A^A_]3HSHeHHHL0HHLt HuHH5r#HHҿH0Hf fff.UHAWAVAUATSHXLLHIIHH#HHEH0L5f#M~@LHH#HH@H0HfHnfH;HEEIFHHIhL0LLHL=ж#ILHDž HDžHDžHDžDž(HLHLHJXLA$It$IEt$IET$HXHHfHHHWHHwHEwHEWHWHHHHHWLH,(HHL0HHLt H H虽H5 #HHjH0芾H#HH;EuLHX[A\A]A^A_]&HSHeHHHL0HHLt HhHH5e#HHżH0HY ffffff.UHAWAVAUATSHhMLHHIHxH#HHEH0L=V#I_@HLH #HH@H0HfHnfL+LHEEIGHHIhL0HHLڻL5#ILHDž HDžHDžHDžDž(LL蝿LLH3ULxHHyLHHUHH\LHHTHH?LHHTHH"LHHTLH9%HHL0HHLt HH覺H5#HHwH0藻H#HH;EuLHh[A\A]A^A_]3HSHeHHHL0HHLt HuHH5r#HHҹH0Hf fff.UHAWAVAUATSHXIIHHH!#HHEH0L-t#I]@HLH+#HH@H0HfHnfLILHEEIEHHIhL0HHLL%װ#ILHDž HDžHDžHDžDž(LHH贼LHHJRAIvIEvIEVL(RHHuLHH RHH"HHL0HHLt HiHH5f#HHƷH0HS#HH;EuHHX[A\A]A^A_]~HSHeHHHL0HHLt HHLH5#HHH0=H豴 UHAWAVAUATSH8H#HHEIHAMgIwIDIEWH}HH]LsMHutI^LtIL9r1HII II!HEHJHL(MAt MOMgDLELMfIEH9t(LtL9r1IHL9tH!L9AMHtIUL9ukI}HtI} u1M1fDAT A: uHSHeHHHL0HHLt H耯H H5}#HHݭH0Hq UHAWAVAUATSHHE#HHEHHOxH( HH5/H0H .LL 1H}AHuHH$#HHHC HCHCE1H5s#HL#HήoHAHgGB W)E)EE?HUHG(HXHHDILcg MtCILuffffff.L;LL;HL LLnHIuW)E)pE?MIG@LpHLDIcG8HLIHfffff.M&A$I\$It$HDIET$H@ HLLxMHLXMNMtIL9r1HIIf.MI!HpJHL(MA$t LLPHXDLIEH9t(MtL9r1IHL9tL!L9AMHtIUL9uI}HtI} u1M1fDAT : uQHH9uvMeHLMMM襭MMMLH2MmM'LMſ(߫ILHHH膩HID$I$HEHx WH*HHH WH*XLHDH MxI*Mu#fDH*XMt Y.K 6IrIV1Lff.^f: (F\H,H HH1H,.HCH9HCHpLxIFLtLM9r"1LIILI!ff.HpJHtHI $NHEI$LeHEJI$Ht-H@INLtL9r 1IHH!HHpL HELIL;jW)P)@Dž`?IGXLpHLDIcGPHIHM>AI_IwHDIEWH( HLLHMHLL5MrMtIL9r1HII MI!H@JHL(MAt MgI_ DIEH9t(MtL9r1IHL9tL!L9AMHtIUL9usI}HtI} u1MS1fDAT : uEHH9u3M"HLM۩LMHMmM3Mſ(ILHHHƥHID$I$HXHx WH*HHH WH*XLHHHH HxH*`u&fDH*X`t Y.H ?HrHW1H ^f: (v\H,H HH1H,.HCH9HCH@.HHHGHtLI9r"1LHILI!ff.H@JHtHI $WHPI$LPHPJI$Ht-H@HOHtH9r 1HHH!HH@L HXLIL;nIGpL`HLDIcGhH6IHM<$IG(HXHHDMcw MtSIL+AEuHu&IEHtH}L3HHIuLHHUeIG@LhHLDIcG8H|MtM}Au Hu WIGHtNH}L3HH]ff.H[(HtHL2HtH}LfIM9uIL;HPHt"L3Ct H{ 认H覤LMuH@HDž@Ht肤H]Ht+fL3Ct H{ ^HVLMuHpHDžpHt2H]Ht+fL3Ct H{ HLMuH}HEHtH#HH;EH[A\A]A^A_]ÿ(УHHHPxH5H H@0AH@H̠H#HHHC HCHCE1H5>#H#H虣:解(CHHHPxH5-H `H(SAH(H?Hb#HHHC HCHCE1H5#H#H (転IH5*H HpLs HpLH#HIIF IFIF1H53#H #L莢/(=IH5%H H(LH(LBHe#HIIF IFIF1H5#H~#L(迡IHL蕘H5H L EH(LLjH(L觞Hʔ#HIIF IFIF1H5#H}#Lu($HH5JHH ILL oH}AHuHH?#HHHC HCHCE1H5#Hg}#HI(tH8hu1Idzt#H !I(t H8,uILTIpt H}uIL&I(t H8͟EuIHI@t HP蛟EuIHŸIEt H}oEu]IH薟MCAIt%I|$ ?+ItqI|$ )eIL HP IHPHt#L3Ct H{ HLMuH@HDž@HuIL躞IH]Ht.fff.L3Ct H{ 莞H膞LMuHpHDžpHtb H IH]Ht.fff.L3Ct H{ .H&LMuH}HEHtL( fffff.UHAWAVAUATSHhMLHHIHxHӕ#HHEH0L=&#I_@HLH|#HH@H0HfHnfLLHEEIGHHIhL0HHL誛L5#ILHDž HDžHDžHDžDž(LLmLLH5LxHHILHH4HH,LHH4HHLHH4HHLHH4LH HHL0HHLt HHvH5#HHGH0gHԓ#HH;EuLHh[A\A]A^A_]HSHeHHHL0HHLt HEHљH5B#HH袙H0šH6 fff.UHAWAVAUATSHhMLHHIHxH#HHEH0L=6#I_@HLHy#HH@H0HfHnfL LHEEIGHHIhL0HHL躘L5#ILHDž HDžHDžHDžDž(LL}LLH2LxHHYLHH1HH<LHH1HHLHH1HHLHH1LHHHL0HHLt HH膗H5~#HHWH0wH#HH;EuLHh[A\A]A^A_]HSHeHHHL0HHLt HUHH5R~#HH貖H0җHF fff.UHAWAVAUATSHXIIHHH#HHEH0L-T#I]@HLH w#HH@H0HfHnfL)LHEEIEHHIhL0HHLؕL%#ILHDž HDžHDžHDžDž(LHH蔙LHH*/AIvIEvIEVL/HHULHH.HHhHHL0HHLt HIHՔH5F|#HH覔H0ƕH3#HH;EuHHX[A\A]A^A_]^HSHeHHHL0HHLt H蠕H,H5{#HHH0H葑 UHAWAVAUATSHXIIHHHQ#HHEH0L-#I]@HLH[t#HH@H0HfHnfLyLHEEIEHHIhL0HHL(L%#ILHDž HDžHDžHDžDž(LHHLHHz,AIvIEvIEVLX,HH襖LHH;,HHHHL0HHLt H虓H%H5y#HHH0H#HH;EuHHX[A\A]A^A_]讓HSHeHHHL0HHLt HH|H5x#HHMH0mH UHAWAVAUATSHXIIHHH#HHEH0L-#I]@HLHq#HH@H0HfHnfLɑLHEEIEHHIhL0HHLxL%W#ILHDž HDžHDžHDžDž(LHH4LHH)AIvIEvIEVL)HHLHH)HHHHL0HHLt HHuH5v#HHFH0fHӈ#HH;EuHHX[A\A]A^A_]HSHeHHHL0HHLt H@H̎H5=v#HH蝎H0轏H1 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UHAWAVSHxIH)!HHEHH0H"HpHPHxLpLHL{H5H|H5 H͈H0H5-lHLHqHI9t HtHP(HP H6H/)!HH;EuLHx[A^A_]b1HHI9uHP HtHP(H⯯H, UHAWAVSHxIH(!HHEHHHQ"HpHHxLpLHL;H5kH<H5˅H荇H~H5qH輅LH1HI9t HtHP(HP HH'!HH;EuLHx[A^A_]"0HHI9uHP HtHP(H袮H+ UHAWAVSHxIHv'!HHEHH谝H"HpHhHxLpLHLH5IHH5HMH}H5vH|LHHI9t HtHP(HP H趭H&!HH;EuLHx[A^A_].HHI9uHP HtHP(HbHb* UHAVSHIH8&!HHEHHrD$$H=H L=1E1H聿D$$H`H L=AHJD$$H\H Lq=AHD$$HL=H L:=1E1HH5&=H"L !HH !!HHpH !HxHpHHH'H5HȀH5WH H|{H5yHHLHHH9t HtHP(HP H肫H{$!HH;EuLHĐ[A^],I"IHH9uHP HtHP(H+L+( UHAWAVSHxIH$!HHEHH@H"HpHfHnHfHnflxLpLHLuH5,HvH5Hǂ H*zH5'HLHkHI9t HtHP(HP H0H)#!HH;EuLHx[A^A_]\+HHI9uHP HtHP(HܩH& fUHAWAVSHxIH"!HHEHHH1"HpHfHnHfHnflxLpLHL%H5w+H&~H5Hw HxH5׽HLHHI9t HtHP(HP HਯH!!HH;EuLHx[A^A_] *HHI9uHP HtHP(H茨H% fUHAWAVSHxIHf!!HHEHH蠗H"HpHfHnHfHnflxLpLHLH5?H|H5e~H' HwH5HV~LHHI9t HtHP(HP H萧H !HH;EuLHx[A^A_](HHI9uHP HtHP(HH !HHpH 5!HxHpHHHwH56HFyH5zH|HsH5HzLH;HH9t HtHP(HP HH!HH;EuLHĐ[A^].%I"IHH9uHP HtHP(H詣L  ffffff.UHAWAVSHIHv!HHEH賒H5BHDH虼H?H1!HHH8HHPH0H(H@H@HH5H(H!HHHPHHPHHH@H@H@H`#IH5WL %AƇWH@WL HHHhHHPH`HXH@H@HHHXvD$$H1H !L21E1H苴D$$HH !L2E1HWD$$H"H {L~2AH D$$HW2H "LG21E1H蟹H532Hq{"L"HHDžH5`H$"LH`HDžH5H"LH3Dž?H5H "LH跉Dž?H5H"LH苉H \"HpHpHHHsH5H5uH5vHx HoH5:HvLH*HH9HtHP(Xt Hh L @uK(uWt H H诟H!HH;EuULH[A^A_]HPb (tH8M uHP XY` IXII@I(H8IIIHH9uHP Xu$.HtHP(XuIXt HhTLL@t HP7(Mt H H)L) ffffff.UHAWAVAUATSHAIH!HHEHcI$PH9I$LHPHI$LHPH /# fHȋP(uH@ @H@Hu܃usH@ @tiMcI$LPL9vWI$LLPHtEI$LLPH m/#HȋP(uH@ @tH@Huu H@ @u&H!HH;E Hĸ[A\A]A^A_]I$LHPx(uH@ H6.#HpHuH5-#H} I$LLPx(uH@ H-#HpHuH5-#Hpy }Q }G H@ H ]LXu>HhHHc`09Q`H\{(D`E~)HhHH;1 HIuDž`]3HEHHL IHhLc(Hc`L@H8I)HP~5E~0HcPAHHJH7!HH;EuLHĐ[A^]lI"IHH9uHP HtHP(H獯L  ffff.UHAWAVSHIH!HHEHH|D$$H]H LW1E1HD$$HH L E1H˟D$$HIH IL1E1HJD$$HH LҧE1HHW)PHDž`fDžPTƅRmH8HJnW) HDž0@g H0(W) Hric tensHH(H to numeHH Hut typesHHHand outpHHHn input HHHConstraiH@0ors.@4HPH8H H+H5ЦH}f"LHYHDžH5q HLHnHDžH5NH}LHnHDžH5jHLHnH m"HpHpHHHK^H5gH_H5{aH=c HZH5HlaLHHH9tiHtHP( t H0 L8Mt]H@L9tBffff.HX@t HxM HI9uH8HP uLL@ Pt H` HH!HH;EuLH[A^A_]G  IkI uS]IPIIHH9tHtHP( uHP t H0_ L8MtAH@L9t`HX@t Hx- HI9uH8L@ Pt H` HL LfUHAVSHIH!HHEHH"xD$$HH bL1E1H1D$$HjH LX1E1H谠H5DH c"L*H޲H HHpH HxHpHHHE[H5H\H5u^H7`HWH51Hf^LHHH9t HtHP(HP H蠇H!HH;EuLHĐ[A^]I"IHH9uHP HtHP(HILI ffffff.UHAVSHIH!HHEHHRvD$$HH L1E1HaD$$HH KL1E1HH5tHRa"LZHH HHpH  HxHpHHHuYH5H[H5\Hg^HUH5ǚGH\LH HH9t HtHP(HP HЅH HH;EuLHĐ[A^]I"IHH9uHP HtHP(HyLy ffffff.UHAVSHIHH HHEHHtD$$HH L1E1H著D$$HH L1E1HH5H_"LH>H  HHpH 5 HxHpHHHWH5QHFYH5ZH\HSH5]HZLH;HH9t HtHP(HP HH HH;EuLHĐ[A^].I"IHH9uHP HtHP(H詃L ffffff.UHAVSHIHx HHEHHrD$$H!H L1E1HD$$HH L1E1H@H5H]"LHnH O HHpH e HxHpHHHUH5HvWH5YHZH*RH5'sHXLHkHH9t HtHP(HP H0H) HH;EuLHĐ[A^]^I"IHH9uHP HtHP(HفL ffffff.UHAVSHIH HHEHHpD$$HQH "LI1E1HD$$H*H |L1E1HpH5H*\"LH螫H  HHpH HxHpHHHTH5HUH55WHXHZPH5WH&WLHHH9t HtHP(HP H`HY HH;EuLHĐ[A^]I"IHH9uHP HtHP(H L  ffffff.UHAVSHIH HHEHHoD$$HH RLy1E1H!D$$HZH LH1E1H蠗H54HrZ"LHΩH HHpH HxHpHHH5RH5HSH5eUH'WHNH5HVULHHH9t HtHP(HP H~H HH;EuLHĐ[A^]I"IHH9uHP HtHP(H9~L9 ffffff.UHAWAVSHIH HHEHH@mD$$HH L1E1HOD$$HH KLYE1HD$$HTH (LB1E1H蚕HW)PHDž`fDžPTƅRm"H8H^W) HDž0@H0(Ad) Htensors.HH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@0HPH8H H趗H ^"HpHpHHHOH5`H QH5RHqTHKH5ѐHRLHHH9t]HtHP( t H0L8MtQH@L9t6HX@t HxHI9uH8HP uLL@\Pt H`GH[{HT HH;EuLH[A^A_]I uXbI]IPIIHH9tHtHP( uHP t H0L8MtGH@L9tfffff.HX@t HxmHI9uH8L@RPt H`=HQzLQ LfUHAVSHIH( HHEHHbiD$$H H L 1E1HqD$$H H •L 1E1HH5 HT"LjHH HHpH  HxHpHHHLH5DH&NH5OHwQ HHH5׍HOLHHH9t HtHP(HP HxH HH;EuLHĐ[A^]I"IHH9uHP HtHP(HxL ffffff.UHAVSHIHX HHEHHgD$$H H L1E1H衊D$$HH 7L1E1H H5H"S"LHNH / HHpH E HxHpHHHJH5yHVLH5MHO H GH5HMLHKHH9t HtHP(HP HwH HH;EuLHĐ[A^]>I"IHH9uHP HtHP(HvL ffffff.UHAVSHIH HHEHHeD$$H1H L)1E1HшD$$H H L1E1HPH5HjQ"LH~H _ HHpH u HxHpHHHHH5HJH5LHM H:EH57HLLH{HH9t HtHP(HP H@uH9 HH;EuLHĐ[A^]nI"IHH9uHP HtHP(HtL ffffff.UHAVSHIH HHEHHcD$$HaH 2LY1E1HD$$H:H &L(1E1H而H5HO"LH讞H HHpH HxHpHHHGH5HHH5EJHL HjCH5g2H6JLHHH9t HtHP(HP HpsHi HH;EuLHĐ[A^]I"IHH9uHP HtHP(HsL ffffff.UHAVSHIH HHEHH"bD$$HH bL1E1H1D$$HjH LX1E1H谊H5DHM"L*HޜH HHpH HxHpHHHEEH5HFH5uHH7J HAH5HHfHLHHH9t HtHP(HP HqH HH;EuLHĐ[A^]I"IHH9uHP HtHP(HIqLI ffffff.UHAWAVSHIH HHEHHP`D$$HH L1E1H_D$$HH L1E1HވHW)PHDž`fDžPTƅRQH8HRW) HDž0@/H0(uW) Htensors.HH0Hnumeric HH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@8HPH8H HH  HHpH 3 HxHpHHHBH5HDDH5EHG H>H5_HELH9ޮHH9taHtHP( t H0L8MtUH@L9t:DHX@t HxHI9uH8HP uLL@|Pt H`gH{nHt HH;EuLH[A^A_]I uXbI]IPIIHH9tHtHP( uHP t H0L8MtGH@L9tfffff.HX@t HxHI9uH8L@rPt H`]HqmLq LfUHAWAVSHIHF HHEHH\D$$HH L1E1HD$$HH L1E1HHW)PHDž`fDžPTƅRMH8HBNW) HDž0@_H0(S) Htensors.HH0Hnumeric HH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@8HPH8H HH M HHpH c HxHpHHH>H5ϲHt@H5BHC H(;H5%uHALHiڮHH9taHtHP( t H0 L8MtUH@L9t:DHX@t HxHI9uH8HP uLL@Pt H`HjH HH;EuLH[A^A_]I uXbI]IPIIHH9tHtHP( uHP t H0L8MtGH@L9tfffff.HX@t HxHI9uH8L@Pt H`HiL LfUHAVSHIHx HHEHHXD$$HH zLM1E1H{D$$HqH rLE1H{D$$H`H fLE1HY{D$$HYH TLE1H%{D$$HHH ILME1HzD$$H7H =L[E1HzD$$HXH )LE1HzD$$HH LCE1HUzD$$HH LZC1E1HH5H6C"LHH5H$C"L(HH5CHC"LNH辑H I"HpHpHHH7:H5H;H5g=H)? H6H5{HX=LHծHH9t HtHP(HP HfH HH;EuLHĐ[A^]IHH9uHP Ht HP(IH;fL; UHAVSHIH HHEHHRUD$$HH L1E1HaxD$$H{H LE1H-xD$$HH ܅LAHwD$$H<H L`AHwD$$HH L@1E1H>}H5H@"LGHlH5H@"LZHJH5l@H@"LmH(H G"HpHpHHH7H5HB9H5:H< H3H5xH:LH7ӮHH9t HtHP(HP HcH HH;EuLHĐ[A^]*IHH9uHP Ht HP(IHcL ff.UHAVSHIHx HHEHHRHDžhH5HHLhHFHDž`H5rHL`HXFD$$HH L1E1HguD$$H{H LE1H3uD$$HH NLZ1E1HzH5FHl>"LHH5gHz>"LRH辌H HHpH HxHpHHH%5H5XH6H5U8H: Hz1H5wv.HF8LHЮHH9t HtHP(HP HaHy HH;EuLHĠ[A^]$IHH9uHP Ht HP(IH'aL' ffff.UHAVSHIH HHEHH2PD$$HH rL1E1HAsD$$HxH NLh1E1HxH5TH<"L:HH HHpH HxHpHHHU3H5H4H56HG8 H/H5tKHv6LHήHH9t HtHP(HP H_H HH;EuLHĐ[A^]I"IHH9uHP HtHP(HY_LY ffffff.UHAVSHIH( HHEHHbND$$HH L1E1HqqD$$HH ~L1E1HvH5H;"LكHH _B"HpHpHHH1H5H83H54H6 H-H5rH4LH-ͮHH9t HtHP(HP H]H HH;EuLHĐ[A^] I"IHH9uHP HtHP(H]L UH]f.UH]VfDUHSPHIH 9"HHKHHH[]@UHH9"HHGHF]UH]f.UH]fDUHAWAVSHIIH HHE`HH](dj)EH5_H$C_IWH5nH}vHH5pHƃH5 pH}H詩H1HxHuHxL"0xt H}D$$HH 5L1E1LnD$$HqH LE1LKnD$$HH Lr1E1LsW)`HDžpfDž`TƅbHHH=W)0HDž@PHH@(1B)0H53CH~CCH`HHH0L%v0t H@LHMtGHPL9t,HX@t Hx}HI9uHHLLP]`t HpHHi7"HEH]H]LHz-H}H9t8HtHP(Et H}H HH;EuH[A^A_]HP EuHIH}H9uHP EHHP(EI0tH@ILHMttHPL9t;fHX@t HxMHI9uHHLP2`u8MLIxtH +"H]fDUHH*"H]UH]f.UH]fDUHAWAVAUATSH8HH HHE11HHHPHH1HPHH1HPH " HȋP(uH@ @H@Hu܃H@ @HHPH~HHPHgHHPH ^"HȋP(uH@ @7H@Hu܃#H@ @H1HPx(uH@ HC"L`MuL%"HHPx(uH@ H"LhMuL-"H1HP(HÃ{(uL{ H( C(1Z( IL{ AOMwMu1' IMwW)EHELeH}HuGdHELmH;Es L(HE H}Hu"dH}L^H}Ht H}H HH;EuH8[A\A]A^A_]HH}Ht H}wH ffff.UHH [H1H9NHD]DUHHm("]UH]f.UH]fDUHH S("H]fDUHH=("H]UH]f.UH]fDUHAWAVAUATSH8HH HHE11HHHPHH1HPHH1HPH " HȋP(uH@ @H@Hu܃H@ @HHPH~HHPHgHHPH ."HȋP(uH@ @7H@Hu܃#H@ @H1HPx(uH@ H"L`MuL%"HHPx(uH@ H"LhMuL-"H1HP(HÃ{(uL{ H C(1*% IL{ AOMwMu1$ IMwW)EHELeH}HuaHELmH;Es L(HE H}Hu`H}L[H}Ht H}H HH;EuH8[A\A]A^A_]HH}Ht H}GHg ffff.UHH XH1H9NHD]DUHH%"]UH]f.UH]fDUHSPHH %"HHKHHH[]@UHH}%"HHGHF]UH]f.UH]vfDUHAWAVAUATSHIIHo HHEпJHH]()EH5:qHƃIVH5bWH}mHH5YHtƃH5XH}H2HHhHuvHhLht HxHDž`HDžXƅP fDžU_0DžQdataƅWM~W) HDž0LHHHs L!HuEQLMIIILIL0IL H(MLLLHJADH[pH 13HHHMHHPHUHMH@H@HH5L}LHHHHHHPH@H8H@H@HW)HDžfDžTƅD$$HPH8LALP8E Pt H`}IVD$$H zLW1E1LZH5CHy""L)LlH""HEL}LLcH}I9HtHP(Et H}H HH;EH[A\A]A^A_]H8HHEH} H0uPHP E^bH + IPIH}I9uHP EHHP(EIu&8u2EuI u`PulH8tHHEt IEtH}z t I tH0WPt?H`Iht*HxIH%EuIEu IEt H}L  fDUHH 5TH1H9NHD]DUHH "]UH]f.UH]fDUHH "H]fDUHH"H]UH]f.UH]VfDUHAWAVAUATSH8IHU HHE11LILPW)EHEILc1L}LefLHQHx(H@ @H@HuH"HEHMH;MsHHEfffff.LLXHIL9|1LQ(HÃ{(uLs H; C(1m ILs ANIvHu1 HIvH}ASH}Ht H}H( HH;EuH8[A\A]A^A_]]HH}Ht H}ҾH UHH QH1H9NHD]DUHH]"]UH]f.UH]vfDUHnH S"H]fDUHH="H]UH]f.UH]&fDUHAWAVAUATSHhHH% HHE11HHHPH`H1HPHLH1HPH " HȋP(uH@ @H@Hu܃H@ @HHPHHHPHHHPH "HȋP(uH@ @H@Hu܃H@ @yHHHEHDžxƅp fDžusADžqtranƅwHpHIpt H}螼E1Mt AAHHHEHDžxƅp fDžusBDžqtranƅwHpHIpt H}/MtE1AAAH1HPx(uH@ H"LpMuL5M"HHPx(uH@ H\"LhMuL-"A~ MA} IF(JtHpI~ IE(JtLuL4~ 1HzHIIHC(HtHcK 09}QS H|(IH5EHnHpAHpLH HIIF IFIFE1H5^ H LtH!HHpt H}DEtFLs<#H)H0Hpt"H}HL| Hp| H ffffff.UHH LH1H9NHD]DUHH "]UH]f.UH]閸fDUH莸H "H]fDUHH"H]UH]f.UH]FfDUHSPH11H1HH[]&fDUHH LH1H9NHD]DUHH"]UH]f.UH]ƷfDUH辷H "H]fDUHH"H]UH]f.UH]vfDUHAWAVAUATSHHIHu HHE11L2L:ILPH#I1LPHI1LPH P" fDHȋP(uH@ @H@Hu܃H@ @I1LPx(uH@ H/"LpMuL5"Mcf W)EHEEEaxisEIHuLHHt xyHEt H}1HH?L!HGL9>IF(LcJ\ILPHti{(ucxHH HywH}HN[H}L/Ht H}詵{(3HC L91E11LIľLHA~ gLHHEIIID$(HtAL$ 09}AL$ PfAt$$fA;t$$uH} ID$(0Ɖ0I|$) IL$(IcT$ rAt$ HDHC(HtK 09} K Nfs$ff.;s$uL豯 HC(0Ɖ0H{ HK(HcS rs HDAE;n q1L9H}L腄 HEJ\{(tH.x C(Lk 1L9HuH݋ L HuHċ H} H HH;EuHH[A\A]A^A_]I(IH5=HxH}#AHuLH HIIF IFIFE1H5\ H L迳(nIH5H[xH},AHuL{H HIIF IFIFE1H5 H LHN(IH5OHxH}AHuLH HIIF IFIFE1H5n H LѲ(耲IH5HwH}讱AHuL药H HIIF IFIFE1H5 H LZcH,H' HHHEt H}ұEt*L HEtH}豱 HH}裂 Hí fUHH UGH1H9NHD]DUHH"]UH]f.UH]FfDUH>H "H]fDUHH"H]UH]f.UH]fDUHAWAVAUATSHIH HHE11LI$LPII$LPHYI$1LPHDI$1LPH "HȋP(uH@ @H@Hu܃MH@ @I$LPx(uH@ H"H@HuH`"x AI$1LPx(uH@ HY"H@HuH"HXH`L+UH}b~ L`LhM9ff.HEHtHcM09}QUH\>u;uuH}K HEȋ0Ɖ0H}X HHEHcMQUH\M>{(tHr C(L{ IM9y1LHW)EHEHXHxH}HxGHEHMHxH;Es HHEH}HxGH}HBH}Ht H}bH}Y H`Ht HhAHZ HH;EuHĈ[A\A]A^A_]茮((IH5HYH}AHuL5HH HIIF IFIFE1H5 HH LtHHEt H}菭EtPL辭FHH}HtH}g H HH}R~ H`Ht Hh:HZ UHH CH1H9NHD]DUHH"]UHAWAVAUATSHHHIHH HHEH0L%K M|$@LLH HH@H0HfHnfLHEEID$HHIhL0LLLԪL5 ILHDž HDžHDžHDžDž(LL藮LLH-DLHzHHH DLHHHL0HHLt HnHH5k HH˩H0HX HH;EuLHH[A\A]A^A_]臫HSHeHHHL0HHLt HɪHUH5Ɛ HH&H0FH躦 UH]f.UH]vfDUHnH S "H]fDUHH= "H]UH]f.UH]&fDUHAWAVSH(HH) HHEH1HPIHHPIMMA(A~(HHPH!H 5"x(HuHP B A(uIO ;A HHPHSH "x(HuHP B A~(uIN ;A (1HHA HH;Eu1HH([A^A_]l(HH5HTH}AHuHH( HHHC HCHCE1H5 H( H(葨HH5HTH}AHuH螥H HHHC HCHCE1H5 H Hkr(HH50HRTH}AHuH*H= HHHC HCHCE1H5 H= H I%I IIEt H}vEtH襧L艣 ffffff.UHH >H1H9NHD]DUHH} "]UHAWAVAUATSHHHIHH HHEH0L%k M|$@LLH! 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uLGI0Ɖ0I?IIcrAHDHLeLWHI9KHDHH;EuLHĈ[A\A]A^A_]L HHPWHbH  UH1HHO]fDUHSPHH~ Ht HHP0HH[]ff.UHAWAVATSIAIH;=)t LI ILAAIv0I H;=)t^I I IcI(AA )A0IML9t/Mfff.LHHH HxI9uIML9t-Mf.LؚHHH HxI9uI^`M~hL9t_ILc L褚HLwH HKHtHHHu#HCH9tHH HAH9HYuHL9u[A\A^A_]ffffff.UHAWAVSHXHLBHHEIIMLH)HHHHrHxt1fff._t7t"uRAAA9DA+H9XAAALJHHxH9rIMLH)HHHLGHJt1f.Yt7t"uRAAA9DA+I9oAAALJHHxH9rL9tAff.uHufHHHHxI9uL9t;DuHuTf.HBH=HxI9uLHMLLLA~"H(@HH;Eu-LHX[A^A_] H@HH;Eu HX[A^A_]?H G IH5pH}L}G H5jH}E HHHMHHPHUHMH@H@HH5H}D HHHMHHPHUHMH@H@HHuL/D 1H5(H[%LeG PG IH5H}LF H5H}TD HHHMHHPHUHMH@H@HH5H}D HHHMHHPHUHMH@H@HHuLhC 1H5S'H$LF MF IH5H}LE H5ܻH}C HHHMHHPHUHMH@H@HH5H}OC HHHMHHPHUHMH@H@HHuLB 1H5&H#LE E IH5H}L(E H5H}B HHHMHHPHUHMH@H@HH5H}B HHHMHHPHUHMH@H@HHuLA 1H5%H#LE  (IPIG,*I:IdzEt%NIdzEtPIIEu"Eu+Eu4tLD Ls@  H}@D EtH}1D EtH}"D ufff.UHAWAVAUATSHIH<HHEHhH~pL`H5 LܮHHHpHLuL@ H5"L@ H HQ8AH}B AHA HA HhLp`LhhM9<H]fff.LH5n|ۮAN8Iv9IEvHIEV@HXۮHH5y DۮANPIvQIEv`IEVXH ۮIIHpLH? 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H  UHAWAVAUATSH8HHHEIII^IFHH)HHHL9swI"""""""I>H)HHJ4#L9w IVH)HHHH9HH9ILBL}HEHUME11ID$LHHf.HC8HCpHChHC`HCXHCPHCHHC0HC(HC HCHCHCHHC@C8?I^HxI^IHjHBHC8HCpHChHC`HCXHCPHCHHC0HC(HC HCHCHCHHC@C8?IFHHxINHǀHǀHǀHǀHǀHǀHǀHǀHǀHǀHǀHǀHǀH@xHǀǀ?IFHHxINHǀHǀHǀHǀHǀHǀHǀHǀHǀHǀHǀHǀHǀH@xHǀǀ?IFHHxINHǀHǀHǀHǀHǀHǀHǀHǀHǀHǀHǀHǀHǀH@xHǀǀ?I^HxI^IDL}HEHUIkxb HEHkxHHMHMIkxHHUIT$LHLHHHLHF8HFpHFhHF`HFXHFPHFHHF0HF(HF HFHFHFHHF@F8?HxHHrHHHfffff.HF8HFpHFhHF`HFXHFPHFHHF0HF(HF HFHFHFHHF@F8?HǂHǂHǂHǂHǂHǂHǂHǂHǂHǂHǂHǂHǂHǂHǂǂ?HBHǂxHǂpHǂhHǂ`HǂXHǂPHǂHHǂ@Hǂ8Hǂ0Hǂ(Hǂ HǂHǂǂH?HǂxHǂpHǂhHǂ`HǂXHǂPHBHBHBHBHBHBHBHBHBHBHBHBHBHBHBB?HBHBHBHBHBHBHHH"IkxHHHEMI~L9H@HGHAHGHWHQHAHGHGHGHwHGHqHGHAHGHGHALWHWHQ_YHtHLIH@HYHSHtH9r1HHffff.H!L HGHGHGHAHGHWHQHAHGHGHGHGHAHGHWHQHAHGHGHGHGHAHMHHMLM9MI~HEHMILEIFH}IFIINILEH}.HHH;EuH8[A\A]A^A_]< L fUHAWAVAUATSPIMwfI} MoM9tmMeMgAEt I}v AEt I}f I}Htffff.HH HHuI}IEHt* A$tI?HHt[A\A]A^A_] [A\A]A^A_]ff.UHAWAVAUATSH(HHHEIHAEM}IuIDIEUH}$wILcMIt$Lt M9s MIM! 1LIIHJHHHAEt MMM}DHuLMHCL9uWKHtHSL9u]H{HtH{ uYMtz1T A:u6HH9u`ff.LtL9r1IHL9t :H!L9u2HHs$Mt!LLM  LMMHuu1H HH;EuHH([A\A]A^A_] H茈fff.UHAWAVAUATSH8HIHB HHEI88IMnI)IH988LIML9pIFIvH)HHHqqH9s)HH9ILBL}HEHEMuE11!L}HEHEJH< HEKLHHUHUKHHUHSHTHHsHtHHCHCHHD(HD CDHS(HT(HC(HC HCHS@HT@HS0Hs8Ht8HT0HC@HC8HC0HuHHHuIINH9HUfff.HqHrHqHyHzHrHAHAHAHBHBHBABHqHrHAHAHAHqHrHqHyHzHrHAHAHAHIHUH¸HUH9GIINHuHUIHEIvHMINIIVIHEH},H HH;EuH8[A\A]A^A_] LR UHAWAVAUATSPIMwMoM9Luf.MeMgAEt I}( I]HtKIEH9t6fDLp@t Hx LL9uI}Luff.HI] A$t I} MoM9wI?HHt[A\A]A^A_] [A\A]A^A_]ÐUHAWAVAUATSPIIIHHUHOHs 1HuRfH)1fffff.I7HI7HDI7HDI7HDHH9uHtHIHHHuIGMwIGMtG@L;et*ID$IFM.LL#M$$MMuDIL IHuLuM9Ii-8ߝfDr HID$HCHH %HH1IHH/H1H1IHH/H1IHKHLHzM$$M9uH[A\A]A^A_]H f.IL IHu  I HIH L  ffffff.UHAWAVSPIIGHH %HH1Hi-8ߝHHH/H1H1HII/I1LM_II^IFHxH*HHH H*XHH؃H HxH*AN uH*XAN t Y.vnH HrHS1HH ^f:  H(\H,HH1H,.HCH9HCL I^M_LKItMI9r1LHIMM!IJ4HHHtwE1fHHyHIt!H9r1HHL9tOffff.L!L9u:D$L9uHQI;W1҄t9u9tAHHHuHIL>IHtgH@ItJH9r1HHL9uEJIFIIFM~IJIHt,H@ItH9r1HH L!L9u L!ILfHX@t Hx HI9uHHP uLL t H t H L(MtIH0L9t.f.HX@t Hx] HI9uH(LL0= @t HP( HHW)PHDž`fDžPSƅR!H8HrIW) HDž0 H0() Hor type.HHH tensor HHHn to anyHHHConstraiH@HPH8H HvH5 H8wLcH褑H yHpHpHHH:H5K H;H5M= H? Hr6H5^CH>=LHթHH9tkHtHP( t H0W L8Mt_H@L9tDffffff.HX@t Hx HI9uH8HP uLL@ Pt H` HeHHH;EuLH[A^A_] IkI uS]IPIIHH9tHtHP( uHP t H05 L8MtGH@L9tfffff.HX@t Hx HI9uH8L@ Pt H` HdL  LfUHAWAVSH8IHHHEHHSD$$H_ H 9LW 1E1HvD$$H H taL7 AHvD$$HGH aLw 1E1HG|HW)PHDž`fDžPTƅR H8H{EW) HDž00 H0(μ) Hsor typeHH H all tenHHHtypes toHHHn input HHIConstraiL8f@(s.@*HPH8H Hk~H5~ H5sL`H虍HW)HDžfDžSƅ,HH}DW)HDž0 H()Hnsor typHH Ho all teHHH types tHHHn outputHHL8@(es.HHHHz}HDžH5 H&`LH]EHDžH5 Hn`LH0EH !uHpHpHHH4H5 H6H58 H9 H>1H5nYH 8LHЩHH9tgHtHP(t H# LMt[HL9t@ff.HX@t Hx HI9uHHP uLL t H t H0 L8MtIH@L9t.f.HX@t Hx] HI9uH8LL@= Pt H`( H<`H5HH;EuLH8[A^A_]h IIIII IP!IIHH9tHtHP(uHP t HF LMt?HL9t$HX@t Hx HI9uHLL t H t H0 L8MtCH@L9tbfHX@t Hx HI9uH8L@ Pt H`m H^L  LfUHAWAVSH(IHVHHEHHMH5 H7]AH;HDžhH5]H]LhHBAD$$HHVH ]L1 1E1HQpD$$HH Lx 1E1HuHW)PHDž`fDžPSƅRH8H?W) HDž00! H0() Hsor typeHH H any tenHHHtypes toHHHn input HHIConstraiL8f@(.HPH8H HwHW)HDžfDžTƅHH,>W)HDž0I H()Hnsor typHH Ho any teHHH types tHHHn outputHHL8f@(e.@*HHHH&wH oHpHpHHH.H5} H0H52 H3 HD+H5tSaH2LHʩHH9t]HtHP(t H) LMtQHL9t6HX@t Hx HI9uHHP uLL t H t H0 L8MtIH@L9t.f.HX@t Hxm HI9uH8LL@M Pt H`8 HLZHEHH;EuLH([A^A_]x III IIGIIPIIHH9tHtHP(uHP t HR LMt?HL9t$HX@t Hx' HI9uHLL t H t H0 L8Mt?H@L9t^DHX@t Hx HI9uH8L@ Pt H`} HXL  LfUH]f.UH]F fDUH> H hH]fDUHHmhH]UH]f.UH] fDUHAWAVSH(IHHHEIHW)EE HEEdtypfEeHuLHEt H} Ht{DAI1LP(HÃ{(uLs H1C(16ILs ANI^Ht {(uHC .1[<HI^{(tHC(15HC HDx HHH;Eu H([A^A_]> ( IH5 HVH}AHuL HHIIF IFIFE1H5QHL  HHEt H}A EtLp HEt H} H@  fDUHH H1H9NHD]DUHHf]UHAWAVAUATSHHHIHHHHEH0L%+M|$@LLHHH@H0HfHnfL HEEID$HHIhL0LLL L5ILHDž HDžHDžHDžDž(LLw LLH nLHZ HHHmLHm>HHL0HHLt HN H H5KHH H0 H8HH;EuLHH[A\A]A^A_]g HSHeHHHL0HHLt H H5 H5HH H0& H  UH]f.UH]V fDUHN H dH]fDUHHdH]UH]f.UH] fDUHAWAVAUATSHHIHHHEIELPHHMHW)EHE1L5DL}Le@IELHPx(LuHH I MHEH;EsHHEf.LLHH;]rHEHMH9t(HPH9tHq;Pu HH9u H9{IE1LP(HÃ{(uL{ HC(1v1IL{ AOI_Ht {(uL{ 116HI_{(tHC(1/IL{ HEAOAG HEH LcuE1HIELPL9 IELLPHIELLPH؋H(uH@ @H@Hu܃H@ @IM9rIE1LPx(uH@ HfHXHuH&AOIHu1I.HIHZH}rJL5L%IELHPx(LuHH HyHuLLrHH;]rH}Ht H}7 HPHH;EuHH[A\A]A^A_] (! IH54 HyPH}oAHuL. HAHIIF IFIFE1H5HAL ( IH5 H:PH}hAHuL HHIIF IFIFE1H5!HʬL oHHEt H} Et9L@ /HHEt H} Et%L HH}Ht H} H  UHH uH1H9NHD]DUHH_]UHAWAVAUATSHHHIHHxHHEH0L%M|$@LLHHH@H0HfHnfL HEEID$HHIhL0LLLT L53ILHDž HDžHDžHDžDž(LL LLHfLH HHHfLH 7HHL0HHLt H Hz H5HHK H0k HHH;EuLHH[A\A]A^A_] HSHeHHHL0HHLt HI H H5FHH H0 H:  UHAWAVAUATSHHHIHHHHEH0L%[M|$@LLHHH@H0HfHnfL/ HEEID$HHIhL0LLL L5ILHDž HDžHDžHDžDž(LL LLH=dLH HHHdLH4HHL0HHLt H~ H H5{HH H0 HhHH;EuLHH[A\A]A^A_] HSHeHHHL0HHLt H He H5ְHH6 H0V H  UHAWAVAUATSHIIIuMeI)MIIGHH> H?MuI)LHHH9w`II9LBMLEHuI9vK- HH5 H HHHH5H?H9 LEHuIJ< HHuLEN4ABN|M~HLI1 LI]M}MuHtHH[A\A]A^A_]o H[A\A]A^A_]E11L3 IH Lf  fff.UH]f.UH] fDUH H cYH]fDUHHMYH]UH]f.UH] fDUHAWAVATSH0IHHHEI1LPx(uH@ HBH@HuHHx(HuHH Da DeILPx(uHX C EA9VI1LP(HÃ{(uLs HC(1&ILs ANI^Ht {(uLs 11,HI^{(tHC(1$ILs ANEf ILPHI1LPHI1LPH  f.HȋP(uH@ @LH@Hu܃8H@ @*ILPHILPHILPH >HȋP(uH@ @H@Hu܃H@ @I1LPx(uH@ HKH@HuHx(uH@ HHXHuHANI~Hu1"HI~HILPx(uH@ HHxHuH=kLHHH;Eu H0[A\A^A_]K ( HH5 HELEH}HMLMAHuH HHHHC HCHCE1H5OHH  IIEt H}? EtHn LR  UHH ՚H1H9NHD]DUHHU]UHAWAVAUATSHhMLHHIHxHӼHHEH0L=&I_@HLHݣHH@H0HfHnfL LHEEIGHHIhL0HHL L5ILHDž HDžHDžHDžDž(LLm LLH\LxHHI LHH[H0L HH LHH[H0L LH!,HHL0HHLt H H H5HH_ H0 HHH;EuLHh[A\A]A^A_] HSHeHHHL0HHLt H] H H5ZHH H0 HN  @UH]f.UH] fDUH H RH]fDUHHRH]UH]f.UH] fDUHAVSHH1HP(IH1HPx(uH@ H:HpHuH5L[A^]fffff.UHH H1H9NHD]DUHHmR]UH]f.UH] fDUH H SRH]fDUHH=RH]UH]f.UH] fDUHAVSHH1HP(IH1HPLH[A^]fffff.UHH H1H9NHD]DUHH R]UH]f.UH]& fDUH H QH]fDUHHQH]UH]f.UH]ֿ fDUHAVSHH1HQ(IA~(uI^ LAF(1HI^ C KH{Hu1HH{[A^]ҐfUHH H1H9NHD]DUHH}Q]UH]f.UH] fDUH H cQH]fDUHHMQH]UH]f.UH]ƾ fDUHAWAVAUATSHhIHŶHHEI$1LPx(uH@ HDx I$1LP(HÃ{(uLs H8C(1ILs ANI^Ht {(uHC .1b"HI^{(tHC(1HC HDx I$1LPx(uH@ @u=Hc@,I$1LPx(uH@ H:LpMuL5E~ D}ffEHEEEaxisEI$HuLHt xN1Et H}= D}]D9A9yAD}I$LPHuhI$HffEEHEHkeepdimsHMEHuLHEt H}輼 HHAŻI$LPx(uH@ HH@I$LPH@ffEHEtL5H}HaHuHUHMH}RAHEOH}HDXHuHUHMH}H}Ht H}譻 LmH}I9}!HcEIN(HDx(u#Lx L}I$LPx(uH@ HHHH@HuHx t+H}HEH9tHHHH)HHHs;1HH}HLHHHHHH HIM1I?I!IXHHrH sf1fHutH^H)f1fofoDfoL o\0od@ffoTPffoD`foLpfHHuHt"HTHoRfofH HuffpNffH~L9tJ HHH9uHUI9HH9t$HOH9t'HHpHH;Qu HH9u HH9uHHt I}葹 E1HxI$1LP(HÃ{(uL{ HDC(1IL{ AOI_Ht {(uL{ 11nHI_{(tHC(1+IL{ AOMgMEAF HM|$1fDMH9t}ID$(HtIcL$ 09}QAT$ H|GAt$$A;t$$uL蜳ID$(0Ɖ0I|$HID$(IcL$ QAT$ H|IF(Ht/AF HHcH9l1IMgE7LLՏHcEIL$(L|AG(HxH~tL{AG(I_ GuIG AG(0u+Mw L;5ŕtMtAt I~蟷 L藷 HHH;EuHh[A\A]A^A_] ( IH5 HlcLcH}HMLMNAHuL} HHIIF IFIFE1H5HLJ  ( IH5T H7H}AHuL HHIIF IFIFE1H5sHLֶ 늿(舶 IH5 H6H}ֱAHuL蕳 HHIIF IFIFE1H5HLb ( IH5i H6L6H}HMLM0AHuL H"HIIF IFIFE1H5yH"Lܵ H% HHHEt H}X EtPL臵 FHHEt H}2 EtMLa CHH}HtH} HH}HtH}HEt H} H  fDUHH EH1H9NHD]DUHHmG]UHAWAVAUATSHhMLHHIHxHHHEH0L=֪I_@HLHHH@H0HfHnfL諳 LHEEIGHHIhL0HHLZ L59ILHDž HDžHDžHDžDž(LL LLHKLxHH LHHKHH0L衱 HHʵ LHH`KHH0Lr LHHHL0HHLt H谲 H< H5HH H0- HHH;EuLHh[A\A]A^A_]ɲ HSHeHHHL0HHLt H H藰 H5HHh H0舱 H  fUH]f.UH]鶱 fDUH讱 H DH]fDUHHmDH]UH]f.UH]f fDUHAWAVAUATSHHIHeHHEI1LPx(uH@ HH@HuHhx(uH@ HDx I1LP(HÃ{(uHC HC(1 HC HDx ILPHI1LPHI1LPH HȋP(uH@ @yH@Hu܃eH@ @WIHW)EHEEEaxisEHuLHEt H} HEIHW)EEHEHnew_axisHMEHuLHEt H}讯 Ht DE1I1LPx(uH@ H?H@HuHx(uH@ HL`MuL%AAD$ 1AE)ЉEuM9L9DyDEI1LP(HÃ{(uLs HC(1 ILs ANMnMu1W IMn}Mu1IE(HtAM 09}AM LDAu$f.A;u$uLpIE(0Ɖ0I}} IM(IcU rAu HDEH9t2HEIU(H|H9 )ID$(HcHtxHcEH9H[NHHH;EuHH[A\A]A^A_]< (ح IHEHD$H.H$H5 H.L H}HMLMAHuLª HՠHIIF IFIFE1H5,HՉL菭 (> IH5 HuH}AHuLK H^HIIF IFIFE1H5H^L r(ʬ IH5" H-H}ȴAHuLש HHIIF IFIFE1H5AHL褬  H%H HHEt H}# EtLR HEt H} H  fDUHH UH1H9NHD]DUHH ?]UHAWAVAUATSHhMLHHIHxHHHEH0L=I_@HLHHH@H0HfHnfL˪ LHEEIGHHIhL0HHLz L%YILHDž HDžHDžHDžDž(LL= LLHBLxHH LHHBH0L螨 HH LHHBH0Lp H]H LHHVBHE0LH LHHHL0HHLt H誩 H6 H5HH H0' HHH;EuLHh[A\A]A^A_]é HSHeHHHL0HHLt H H葧 H5HHb H0肨 H  UHAVSHIHȠHHEHHH5WHWAHAD$$HP H XL|n 1E1H9D$$H) H XLZ 1E1Ho?H57n Ha;LT) HQH5$ H;Lp) H{QH <HpHpHHHH5T HH5$ H HIH5G)WHLH芕HH9t HtHP(HP HO&HHHH;EuLHĐ[A^]} I"IHH9uHP HtHP(H%L  fffff.UHAWAVSHIHƞHHEHHD$$Ho H Ŵ Lg 1E1H8D$$Hv H k(L E1H7D$$HZH ZL 1E1HZ=HW)PHDž`fDžPTƅR-ʩH8HW) HDž0@諥 H0(у) Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HPH8H Hh?H :HpHpHHH1H5 HH5a H#HH5&HRLHǒHH9t_HtHP( t H0k L8MtSH@L9t8HX@t Hx= HI9uH8HP uLL@ Pt H` H #HHH;EuLH[A^A_]7 I uXbI]IPIIHH9tHtHP( uHP t H0U L8MtGH@L9tfffff.HX@t Hx HI9uH8L@ Pt H` H"L  LfUHAWAVSHIH֚HHEHHD$$H H հ Lw 1E1H4D$$H H $Lzh 1E1H9HW)PHDž`fDžPTƅRqƩH8HW) HDž00 H0(E) Hry type.HHHrbitraryHHH be of aHHHnsor canHHHInput teH@&HPH8H H;H5g H5L#HJH W7HpHpHHHoH5= HH5 HaHH5"HLHHH9t]HtHP( t H0詠 L8MtQH@L9t6HX@t Hx} HI9uH8HP uLL@L Pt H`7 HKHDHH;EuLH[A^A_]w IkI uS]IPIIHH9tHtHP( uHP t H0蕟 L8MtGH@L9tfffff.HX@t Hx] HI9uH8L@B Pt H`- HALA  LfUHAWAVSHIHHHEHHP D$$H H  L 1E1H_0D$$H> H !Ld 1E1H5HW)PHDž`fDžPTƅR©H8HW) HDž00/ H0() Hry type.HHHrbitraryHHH be of aHHHnsor canHHHInput teH@&HPH8H H8H5c H1LA H6GH 4HpHpHHHH5 HPH5 HHH5 HLHEHH9t]HtHP( t H0 L8MtQH@L9t6HX@t Hx轜 HI9uH8HP uLL@茜 Pt H`w HHHH;EuLH[A^A_]跜 IkI uS]IPIIHH9tHtHP( uHP t H0՛ L8MtGH@L9tfffff.HX@t Hx蝛 HI9uH8L@肛 Pt H`m HL聗  LfUHAWAVSHIHVHHEHH H5 HAHD$$H3LH 3LL֪ 1AH{,D$$H(LH /LL 1E1H1HW)PHDž`fDžPTƅR;H8H.W) HDž00K H0(r) Hnsor typHH Ho any teHHH types tHHHn outputHHHConstraiHf@(e.@*HPH8H H4H 0HpHpHHHH5r HH5 H HfHX@t Hx HI9uH8HP uLL@輘 Pt H`觘 HHHH;EuLH[A^A_] I uXbI]IPIIHH9tHtHP( uHP t H0 L8MtGH@L9tfffff.HX@t Hx͗ HI9uH8L@貗 Pt H`蝗 HL豓  LfUHAWAVSHIHHHEHHD$$H/ H IL' 1E1H(D$$H H .IL 1AHK.HW)PHDž`fDžPTƅRH8HW) HDž0@蜖 H0(t) Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HPH8H HY0HDžH5 HYLHfHX@t Hxݔ HI9uH8HP uLL@謔 Pt H`藔 HHHH;EuLH[A^A_]ה IkI uS]IPIIHH9tHtHP( uHP t H0 L8MtEH@L9tdff.HX@t Hx轓 HI9uH8L@袓 Pt H`荓 HL衏  LfUHAWAVSHIHvHHEHHD$$H H  L 1E1H$D$$HK H  Lq E1H$D$$H% H LLq E1HW$D$$H= H Lcq AH $D$$HLH LL,q AH#D$$H" H  L 1E1Hh)HW)PHDž`fDžPTƅR;H8HW) HDž0@蹑 H0(o) Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HPH8H Hv+H5o HH%L H:H )HpHpHHHH5 HH5M H HrH5pH>LH~HH9tkHtHP( t H0W L8Mt_H@L9tDffffff.HX@t Hx HI9uH8HP uLL@ Pt H`׏ HHHH;EuLH[A^A_] IcI uKUIPIHH9tHtHP( uHP t H0= L8Mt?H@L9tJDHX@t Hx HI9uH8L@ PtH`ݎ LIH L  ffff.UHAWAVSHIHHHEHHH5HE1H2D$$HA H L9 1E1HD$$H6H :L 1E1H`%HW)PHDž`fDžPTƅR3H8HW) HDž0@豍 H0(k) Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HPH8H Hn'H %HpHpHHH7H5 HH5g H)HH5HXLHzHH9teHtHP( t H0q L8MtYH@L9t>fHX@t Hx= HI9uH8HP uLL@ Pt H` H HHH;EuLH[A^A_]7 I uXbI]IPIIHH9tHtHP( uHP t H0U L8MtGH@L9tfffff.HX@t Hx HI9uH8L@ Pt H` H L  LfUHAWAVSHIHւHHEHHHxHDžhH5` HLhHD$$HJ H LB 1E1HD$$H H Lh E1HD$$H4EH 5ELݙ E1HD$$H H 7EL 1E1H!HW)PHDž`fDžPTƅRԭH8H5W) HDž0@R H0() Hsor typeHH(H any tenHH Han be ofHHH types cHHHd outputHHHInput anHf@0.HPH8H H#H5g HL HI2H !HpHpHHHH5^ HcH5 H HH5 KHLHXvHH9t`HtHP( t H0 L8MtTH@L9t9@HX@t Hx͇ HI9uH8HP uLL@蜇 Pt H`臇 HHHH;EuLH[A^A_]LJ IsII uS]IPIIHH9tHtHP( uHP t H0݆ L8Mt?H@L9t^DHX@t Hx譆 HI9uH8L@蒆 Pt H`} HL葂  LfUHAWAVSHIHf~HHEHHD$$H H L 1E1HD$$H H L E1H{D$$H@H 9 L E1HGD$$H H # Ln 1E1HHW)PHDž`fDžPTƅR虩H8HW) HDž0@ H0() Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HPH8H HH HpHpHHHH5h HEH5 H HH5HLH:rHH9tbHtHP( t H0ރ L8MtVH@L9t;fDHX@t Hx譃 HI9uH8HP uLL@| Pt H`g H{Ht{HH;EuLH[A^A_]觃 I uXbI]IPIIHH9tHtHP( uHP t H0ł L8MtGH@L9tfffff.HX@t Hx荂 HI9uH8L@r Pt H`] HqLq~  LfUHAWAVSHIHFzHHEHHHDžhH5؂ H0LhHSD$$H{ H o L 1E1HbD$$H\ H RLn` E1H.D$$H<H <LU E1HD$$H3 H <L! 1E1HyHW)PHDž`fDžPTƅRLH8HW) HDž0@ʀ H0(P) Hsor typeHH(H any tenHH Han be ofHHH types cHHHd outputHHHInput anHf@0.HPH8H HH5 _ HL{ H)H "HpHpHHH:H5ގ HH5j H, HH5H[LHmHH9thHtHP( t H0t L8Mt\H@L9tAfff.HX@t Hx= HI9uH8HP uLL@  Pt H`~ H HwHH;EuLH[A^A_]7 IkI uS]IPIIHH9tHtHP( uHP t H0U~ L8MtGH@L9tfffff.HX@t Hx~ HI9uH8L@~ Pt H`} HLz  LfUHAWAVSHIHuHHEHHHDžhH5h~ H@LhHD$$HRw H LJ 1E1HD$$H H aL[ E1HD$$H H :L 1E1H=HW)PHDž`fDžPTƅRH8HqݮW) HDž0@| H0() Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HPH8H HRH5Z HTLv H%H aHpHpHHHH5Z HH5) H HNH5L tHLHiHH9tgHtHP( t H03{ L8Mt[H@L9t@ff.HX@t Hxz HI9uH8HP uLL@z Pt H`z HHrHH;EuLH[A^A_]z IkI uS]IPIIHH9tHtHP( uHP t H0z L8MtGH@L9tfffff.HX@t Hxy HI9uH8L@y Pt H`y HLu  LfUHAWAVSHIHqHHEHHHDžhH5(z HLhHD$$Hs H L 1E1H D$$H H LW E1H~ D$$H H {L 1E1HHW)PHDž`fDžPTƅRМH8H1ٮW) HDž0@Nx H0() Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HPH8H HH5V H$ Lr H@!H HpHpHHHH5W HZH5 H HH5 HLHOeHH9tgHtHP( t H0v L8Mt[H@L9t@ff.HX@t Hxv HI9uH8HP uLL@v Pt H`wv HHnHH;EuLH[A^A_]v IkI uS]IPIIHH9tHtHP( uHP t H0u L8MtGH@L9tfffff.HX@t Hxu HI9uH8L@u Pt H`mu HLq  LfUHAWAVSHIHVmHHEHHH5o H E1HD$$Hn H 2Lل 1E1HD$$H2H 2L 1E1H HW)PHDž`fDžPTƅRӘH8H4ծW) HDž0@Qt H0(wR) Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HPH8H HH ?HpHpHHHH5B HxH5 H H,H5* HLHmaHH9teHtHP( t H0s L8MtYH@L9t>fHX@t Hxr HI9uH8HP uLL@r Pt H`r HHjHH;EuLH[A^A_]r I uXbI]IPIIHH9tHtHP( uHP t H0q L8MtGH@L9tfffff.HX@t Hxq HI9uH8L@q Pt H`q HLm  LfUHAWAVSHIHviHHEHHߩH5k H AHD$$Hj H /L 1E1HD$$H/H /Lŀ 1E1HHW)PHDž`fDžPTƅRH8HQѮW) HDž0@np H0(N) Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HPH8H H+ H HpHpHHHH5 HH5$ H HIH5G HLH]HH9tbHtHP( t H0.o L8MtVH@L9t;fDHX@t Hxn HI9uH8HP uLL@n Pt H`n HHfHH;EuLH[A^A_]n I uXbI]IPIIHH9tHtHP( uHP t H0n L8MtGH@L9tfffff.HX@t Hxm HI9uH8L@m Pt H`m HLi  LfUHAWAVSHIHeHHEHH۩H5՝ H)AHD$$H} H (L} 1E1HD$$H| H * L| 1E1H=HW)PHDž`fDžPTƅRH8HqͮW) HDž0@l H0(J) Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HPH8H HKH |HpHpHHHH5& H赿H5D HHiH5g H5LHYHH9tbHtHP( t H0Nk L8MtVH@L9t;fDHX@t Hxk HI9uH8HP uLL@j Pt H`j HHbHH;EuLH[A^A_]k I uXbI]IPIIHH9tHtHP( uHP t H05j L8MtGH@L9tfffff.HX@t Hxi HI9uH8L@i Pt H`i HLe  LfUHAWAVSHIHaHHEHHשH5 H %AH/W)PHDž`ƅPDžQDCRH5l~ H LPHD$$Hx H $Lx 1E1HD$$Hx H %Lx 1E1HHW)0HDž@fDž0Tƅ2茩HHIɮW)HDž@fh H(F)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5H0HHH#H HpHpHHHH5 H荻H5 H޾ HAH5? H LHUHH9tjHtHP(t H&g LMt^H L9tCfffff.HX@t Hxf HI9uHHP uLL f 0t H@f Pt H`f HH^HH;EuLH[A^A_]f IumwIrII0IPIHH9tHtHP(uHP t He LMt=H L9tqHX@t Hxe HI9uHL e 0t H@}e Pt H`he H|L|a  Lffff.UHAWAVSHIHF]HHEHHөD$$Ht H Lt 1E1HD$$H H L* E1H[D$$Ht H Lt 1E1HHW)PHDž`fDžPTƅR譈H8HŮW) HDž0@+d H0(QB) Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HPH8H HH5) H LB H H HpHpHHH菵H5. H0H5 H聺HH5 [H谸LH%QHH9t]HtHP( t H0b L8MtQH@L9t6HX@t Hxb HI9uH8HP uLL@lb Pt H`Wb HkHdZHH;EuLH[A^A_]b IkI uS]IPIIHH9tHtHP( uHP t H0a L8MtGH@L9tfffff.HX@t Hx}a HI9uH8L@ba Pt H`Ma HaLa]  LfUHAWAVSHIH6YHHEHHpϩHدW)PƅPHDž`HnearestHQH5u H|LPHD$$Hp H Lp 1E1H(D$$Hc H yLPq E1HD$$H+p H MLp 1E1HsHW)0HDž@fDž0Tƅ2FHHW)HDž@_ H(Z)Hr types.HH/Hl tensorHH(HY' to alHH Houtput 'HHH'X' and HHHn input HHHConstraiH@7H0HHHH 2HpHpHHHJH5X HH5z H< H蟭H5 |HkLHLHH9thHtHP(t H^ LMt\H L9tAfff.HX@t HxM^ HI9uHHP uLL ^ 0t H@^ Pt H`] HݩHUHH;EuLH[A^A_]2^ IumwIrII0IPIHH9tHtHP(uHP t H;] LMt=H L9tqHX@t Hx ] HI9uHL \ 0t H@\ Pt H`\ H۩LX  Lffff.UHAWAVSHHIHTHHEHHʩW)PƅPHDž`HnearestHQH5vq H. LPHDžL@H5` Hx LLHHDž@H5_ HX L@H8W) ƅ HDž0Hhalf_pixH!fDž)elƅ+H5!_ HL HYW)ƅ$HDžHefer_floH Hround_prHfDžorƅH5^ H1 LHDžH5q^ H- LHD$$Hj H L 1E1HLD$$H H LB E1HD$$H] H U L@k E1HD$$H H Lj AHD$$Hi H L 1E1H,HW)HDžƅfDžT1ƅ}HHYW)HDž@vY H( )Hr types.HH/Hl tensorHH(HY' to alHH Houtput 'HHH'X' and HHHn input HHHConstraiH@7HHHH3H5 A H]L HaH BHpHpHHHڪH5[ H{H5 H̯ H/H5- HLHpFHH9thHtHP(t HX LMt\HL9tAfff.HX@t HxW HI9uHHP uLLW uTu` ulPt H`|W H֩HOHH;EuRLHH[A^A_]HCW tH.W tH0W Pu{W III  UIPIudnIHV IHH9tHtHP(uHP t HRV LMtDHL9~fDHX@t HxV HI9uHLV EuE uQPt H`U HԩLQ  LItHU tH0U Puff.UHAWAVSHIHvMHHEHHéD$$He H N Le 1E1HD$$Hd H D Ld 1E1H>HW)PHDž`fDžPTƅRyH8HrW) HDž0@T H0(2) Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HPH8H HLH }HHHpH 0HxHpHHHH59 H褧H53 HHXH5V H$LHAHH9taHtHP( t H0=S L8MtUH@L9t:DHX@t Hx S HI9uH8HP uLL@R Pt H`R HѩHJHH;EuLH[A^A_]S I uXbI]IPIIHH9tHtHP( uHP t H0%R L8MtGH@L9tfffff.HX@t HxQ HI9uH8L@Q Pt H`Q HЩLM  LfUHAWAVSHIHIHHEHH࿩H5CR H E1H"D$$H1a H b L)a 1E1HD$$HH M L, E1HD$$H` H tL` 1E1HHW)PHDž`fDžPTƅRtH8HPW) HDž0@mP H0(.) Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HPH8H H*H5 HlLHXH HpHpHHHѡH5' HrH5 Hæ H&H5$ /HLHg=HH9t_HtHP( t H0 O L8MtSH@L9t8HX@t HxN HI9uH8HP uLL@N Pt H`N HͩHFHH;EuLH[A^A_]N IkI uS]IPIIHH9tHtHP( uHP t H0M L8MtGH@L9tfffff.HX@t HxM HI9uH8L@M Pt H`M H̩LI  LfUHAWAVAUATSHIHrEHHEHH謻HDžXH5N H LXHD$$H^ H L 1E1HD$$H H ~ L4 E1HZD$$HG H L_ E1H&D$$H_\ H L+ 1E1HHW)@HDžPƅ@fDžAT1ƅCQH(HҬLhW)HDž 0K H (.)Ic types.L`I numericLhI to onlyLpIns inputLxHConstraiH@'H@H(HHHW)HDžƅfDžT2ƅmHHW)HDž0 K H(Q)L`LhLpLxIConstraiL0@'HHHH HW)HDžƅfDžT3ƅnLhHH2W)pHDž OJ H()pHor type.HHH tensor HHHn to anyHHL0@HHHpH@H qH`H`HHH H56 H誝H59 H H^H5\ H*LH7HH9tgHtHP(pt HCI LMt[HL9t@ff.HX@t Hx I HI9uHHP puLLH t HH t HH LMtIHL9t.f.HX@t Hx}H HI9uHLL]H t HHH t H 3H L(MtJH0L9t/ff.HX@t HxG HI9uH(LL0G @t HPG HƩH?HH;EuLHĈ[A\A]A^A_]H IpII!IIryIqII+2I@IIHH9tHtHP(puHP pt HF LMt?HL9t$HX@t HxF HI9uHLLiF t HTF t H?F LMt?HL9t$HX@t HxF HI9uHLLE t HE t H E L(MtL` HH HpHpHHHkH5U* H H5 H] HH5 H茗LH0HH9t HtHP(HP HH9HH;EuLHĠ[A^]A I,I'I"IHH9uHP HtHP(HeLe=  ff.UHAWAVSHIH69HHEHHpD$$HjH } L 1E1HD$$HP H | LP E1HKD$$HP H { LrP E1HD$$HPP H { L>P 1E1HH5J HL HHW)PHDž`fDžPTƅRGdH8H訠W) HDž0@? H0() Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HPH8H HH 3HpHpHHHKH5d HH5{ H= H蠍H5 7HlLH,HH9tiHtHP( t H0> L8Mt]H@L9tBffff.HX@t HxM> HI9uH8HP uLL@> Pt H`> HH6HH;EuLH[A^A_]G> I uXbI]IPIIHH9tHtHP( uHP t H0e= L8MtGH@L9tfffff.HX@t Hx-= HI9uH8L@= Pt H`< HL9  LfUHAWAVSHIH4HHEHH D$$HL H L LL 1E1H/D$$HfL H L9M 1E1HHW)PHDž`fDžPTƅR`H8H✮W) HDž0 ; H0(Uz) Hr types.HHH tensor HHHn to allHHHConstraiH@HPH8H HH HpHpHHH词H5b% HPH5ߐ H衒 HH5 MHАLHE)HH9t]HtHP( t H0: L8MtQH@L9t6HX@t Hx: HI9uH8HP uLL@: Pt H`w: H苹H2HH;EuLH[A^A_]: I uXbI]IPIIHH9tHtHP( uHP t H09 L8MtGH@L9tfffff.HX@t Hx9 HI9uH8L@9 Pt H`m9 H聸L5  LfUHAWAVSHIHV1HHEHH萧HDžhH5C H LhHcHDž`H5B H L`H6D$$HH H LH 1E1HED$$H H LOI E1HD$$HHH H L8H 1E1HHW)@HDžPfDž@TƅBc\H(HĘW)HDž @7 H (g)Hsor typeHH(H any tenHH Han be ofHHH types cHHHd outputHHHInput anHf@0.H@H(HHH [HpHpHHHsH5? 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LFAtHsLWHs@u1H5 u HHVHvLH[A^A_]dHfffff.HGH UHAWAVAUATSPuDLxGLgID$Hy H}uHXEl$ LMt;E1ff.ID$(McJ|~HÃDIHAE9uA\$؃DIIN|8H}ЋGt G xDI IDLH[A\A]A^A_]HH3@UH]fDUHAWAVAUATSPIII\$t7IFHH8@HHsHEsHESOA\$ANM~Mu1?SIM~LuID$HL-LEMuAt4IGIH8@tkHAIvIEvIEVIILLLut AD$ AF A ^H[A\A]A^A_]HC`HH3`Hfffff.UHAVSIHI9tHHL[A^][A^]fUH]UHAVSH HHHHEH9HCHNHKHFC N K F CNKFHCLvD tZHAtiItwHHHHMHHPHUHMINHHIIVHPHHEIFHEHMINIHHH;Eu)H [A^]HH_IHuH^fUHAVSH HHHHEHCHNHKHFC N K F CNKFHCLvD tZHAtiItwHHHHMHHPHUHMINHHIIVHPHHEIFHEHMINIH HH;Eu)H [A^]HH ^IHuH]$fUHSPHHCHCH HC(+iHparseTenHHHeProto.SHHHonnx.TypH@sorHH[]fffff.UH]f.UHAWAVSPHH HHCHC{u~fpDCH[A^A_]L{H=O袗IIt%HtHCHut H{HIL ffffff.UH~tfpDG]UHAWAVSPHH<HHCHCu~'fpDCH[A^A_]L{H=ҖIIt%HtHCHut H{HIL ffffff.UHAWAVAUATSPIIHIEIEAGAEMuAEI_t.L[H HsHEsHESHL%MeAOtIwH;5t I}JAOMe tIw H;5t I L~JH[A\A]A^A_]IIt%HtHCHut H{HIL fffff.UH]fDUHAVSIH_II^H;atHtt H{<H4I^ H;9tHtt H{H I^t%HtHCHut H{HIF[A^]ÐUHAVSII^H;tHtt H{HI^ H;tHtt H{~H[A^]r[A^]ffff.UH]fDUHSPHHH[]4@UHw]uH?UHH=ޓ]H&DUHGtJtHOu fu2HQHAtHG 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6HcHH{ HtHPC(Mt C(Ls [A^]ffff.UHAWAVSPHHLHHCHC3uHHCC(H[A^A_]L{H=BIIt%HtHCHut H{8H0ILI ffffff.UHH}HGG(]UHAWAVSPHH|HHCHCcuH0HCC(H[A^A_]L{H=7rIIt%HtHCHut H{hH`ILy ffffff.UHAWAVATSIIHȣI$ID$AGAD$Mt$AD$I_t.L3LH HsHEsHESHQH>ID$AGtIwH;5t I|$C;AD$(AG(ȃH XHcHAD$(1>ID$ A(Iw AD$(1?ID$ A(u}Iw ~AD$(1AID$ A(ulIw mAD$(1CID$ A(u[Iw \AD$(1ILW @fff.UHAVSIHIWI^t%HtHCHut H{HIF[A^]HILmHRDUHAVSII^H;tHtt H{fH^AF(HwH %HcHI~ HtHPAF([A^]@UH]fDUHSPHHH[]@UHw]ZuHUHH=C~]HDUHAWAVAUATSHhHIHHHEIFHEHuHEHEHEIFtHHNHMHHVHUHMHHHHNHHPHVHHMHHHMHUHPHH}H}Hu1oIIFHpIF Hxffffff.HH;Cs"0@~HHL Lu/DHH1҃H H LȃH LHcH@ AF(ȃw%H HcHHxH8HtHPAF(1:IMn H H;Kg\HH a@".AF(ȃw%H 4HcHHxH8HtHPAF(1;IHxL(H H;K=2HH 7@*AF(ȃw%H HcHHxH8HtHPAF(1;IHxL(H H;KHH @2<ANIvH;5̹uHpH5$G5HpH0H%>DY@:AF(ȃw%H HcHHxH8HtHPAF(1 >IHxL(H H;KHH @ButAF(ȃw%H HcHHxH8HtHPAF(1%<IHxL(H H;KpeHH jD)HHUH=H߉=ILH !LH萺HD=H谕H߉ILH LHHDIHaH߉蟈ILH LHpHD蟉HPH߉PILH 4LH!HDPHÔH߉ILH LHHD\Mn I~ HBCHxL(PI~ HHxL(I~ HEFHxL(SI~ HHxL(H}脛H}KCHdHH;EuHh[A\A]A^A_]1H HH}>H}CH% 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UH~dfpDGxLJHLJ]UH]&fDUHAWAVAUATSH(IHIEIIEHEIEAFAEM}IvAE,IE$IEIEL/I]0IE@IE8IE0Iv0HL}/H]MeHIEXIEPIEHIvHLn/M}`Iv`IEpIEhIE`L3I^t7HEH8@HHHsHEsHESzHgI]xANtIvxH;5t I}xlANItIH;5t LH@AAIIH([A\A]A^A_]H},HLHEdHEHEHE_HEMupMt4I?u.A~II>HtHPIHuMupLIEpMuXMtGI<$u@E&E~0IM>MtAt ILIIuMuXLIEXMu@MtIHEH8u?A~0IM>MtAt ImLeIHuMu@LPIE@Mu(MtSHEH8uIA~:If.M>MtAt I LIHuMu(LIE(HEHt%HtHCHut H{HHEHH}Ǽ ffff.UH]ffDUHAWAVATSIHKII^xH;mtHtt H{HH@IH;BtHtt H{HM~pMt?I~`u8A~)If.I?HtHPIHuM~pLͿIFpMfXMtQI~HuJA$~:IfM<$MtAt I舿L耿IHuMfXLkIFXMf@MtOI~0uHA$~8IM<$MtAt I(L IHuMf@L IF@Mf(MtOI~uHA$~8IM<$MtAt IȾLIHuMf(L諾IF(I^t%HtHCHut H{}HuIF[A\A^A_]@UHAVSII^xH;[tHtt H{6H.IH;0tHtt H{ H[A^][A^]f.UH]ffDUHSPHRHH[]Ľ@UHw]uHwUHH=snb]H^DUHAWAVSPIAN IF(ʃu 1Hu{H)1H|ufffffff.H_HGH|Huffff.H_HGH9uHt HDufHHH@AF AN8IF@ʃu 1HuwH)1@H|ufffffff.H_HGH|Huffff.H_HGH9uHt HDufHHH@AF8ANPIFXʃu 1HuwH)1@H|ufffffff.H_HGH|Huffff.H_HGH9uHt HDufHHH@AFPE~hE~/I^pHfffff.H;HIuAFhAFttINxu]fug tAdžIdžAFIFtHufHHH@H[A^A_]HQHAtIu f uHQHA ofUHAWAVAUATSHHIH?HHEIFHEHuHEHEHEIFtHHNHMHHVHUHMHHHHNHHPHVHHMHHHMHUHPHH}蛊H}Hu1{IIFxHpIFHPIF0HXIFHH`IF`HhLHHx I@HH;Cs0@~HHL Lu4 1HvH1҃H H LȃH HcH@ ANIvxH;5uHpH5 ,HpH0H f.8G@YANH H;K`UHH @'HH;CD EHHA @"HPHHgb@*HXHHA<@2H`HH@:|IFpHIcNh09QAVhLd@BuJANIH;5GuHxH5HxH0Hff.HHU,wHoAvlA;vluHhDZIFp0Ɖ0HhH8 IIFpIcNhQAVhLdH H;KsxHH HqCH߉5eILH 'LHHD5fE1HDpIMAwANE}|nHEHEEȉE|lHEHAr.Dfffff.ÀD!HAԁ?wAD!HH)HcHE)EKH}}E}H}D}$H}yxH}@ HYHH;EuHĈ[A\A]A^A_]1腵HHH}.xH}H -HÉH%HHK^{HH4^H[A^]H5oHH,IH;L[ UHAWAVSPHHt[H{pHD0H$HHcHHCHC<tFIIH=*P10ūHHOcHHCHCuA~fpDCHC(HH[A^A_]H5n0HV+>IIH=PIM?At&It IGHuAt IL H7IIt%HtHCHut H{تHЪIL ffffff.UHAVSHHt(H{pu>H+H#HH{HHԭH[A^]H5nHH*IH;L[ UHAVSHHt(H{pu>H#H#HHHHtH[A^]H5mH)IH軩Lۥ UHAWAVSPHHt[H{pH (H"HHbHHCHCtFIIH=%N1(EHHbHHCHCu9~fpDCHH[A^A_]H5l(H(FIIH=sMIM?At&It IGHuAt I蚨L蒨H芨7IIt%HtHCHut H{`HXILq UHAVSHHt(H{pu>Hc8H!HH8 HHH[A^]H5k8H'IH˧L UHAVSHHt(H{pu>HH HH苧HHH[A^]H5kHX'IHKLk UHAWAVSPHHtXH{pH(H HHebHHCHCĥtCL{H=K1(ئHH"bHHCHCu7HC HCHH[A^A_]H5j(Hs&KIIH=8;KIM?At&It IGHuAt I/L'H7IIt%HtHCHut H{HIL 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AEIEKT>LHP(IfDI]HKHS(HBHC(HH+sHHHkUHHHDH)H+s HHrHy/.HCI]HC(H&HsHK HLHHHHHHkUH)HIHHt(HtHLHcDL$Ld HLDA$KD>({HKL>(ǃuKD> HHq-HHfff.HHsHCH)HHHkUHHHDHK(HK H9IHWLHsHK HK(H %HH;EuLH([A\A]A^A_];-HHDH( UHAWAVAUATSHXIH$HHEIG HUHIG MoMwIUI]I_IGI9I?HH)HHAH?LlIJII)I)LHLMt*ILHUHLH-HHUIMoLM4MwIGIGIOHI+_HHI+HHH9H9+HEHuLMoMwIUI]I_IGI9I?HH)H)HHHALEMHUH}LH=J<'+H}HHHALEIGHEHEMLH=J<*g*HEHuLH)HHHALEMHUH}LH=J<y*H}1HEH HMHMJHEL*HH]H}HuIGI_H9t+Luff.HLH IGH9uI_I?(EH}AHE(EH]IOAGHMH9tHKH)HHHHMH)1NIN LL9INL)Ht,HLff.HHHHHuH8rbLH8rWfff.H HHKHNHKHNHKHNHK HN HK(HN(HK0HN0HK8HN8H@H@L9uI?IMOIwMGHt (MwIHUIIGH HH;EHX[A\A]A^A_]1NIN LL9tINL)HtHLΐHHHHHuLH8Yfffff.H HHKHNHKHNHKHNHK HN HK(HN(HK0HN0HK8HN8H@H@L9uM('IH5gL$'IH5gL$HHIH5HL''IH5dgLs$IL'BIHC'IHMHEH9tHPH)HHHHUH}Ht 'L,# fUHAWAVAUATSPIMgIGI9M/I_HL)vpHHAH?LtIJII)I)LHLMt*ILHuHLH!(HHuIMwLMdMgIGL)HHHALEMHutLH=J<1&1NIN LL9IL$H)ىHHLf.HHHHHuH8rRH HHKHNHKHNHKHNHK HN HK(HN(HK0HN0HK8HN8H@H@L9uM/IMOIwMGMtLV%MgIHuHI$IGH[A\A]A^A_]LH8a몿-%HH5eH"HHHH5H?H9%IH%L  f.UHAWAVAUATSPIIII_H9tIMgIGI9sGL)HHHH?LtIKI)teIM)LHLL%IIMwLAH)HHHALEMt2MLH=+J<$MIMoIG1INHL,NL9It$H)މHHLfff.H H HHHuH8rRH H HKHJHKHJHKHJHK HJ HK(HJ(HK0HJ0HK8HJ8H@H@L9uILIMoIWMGHtHL#IMoIIEIGH[A\A]A^A_]LH8_먿"HH5bHHHHH5H H#IH"L UHAWAVAUATSPIMgIGI9M/I_HL)vpHHAH?LtIJII)I)LHLMt*ILHuHLH#HHuIMwLMdMgIGL)HHHALEMHutLH=J<!1NIN LL9IL$H)ىHHLf.HHHHHuH8rRH HHKHNHKHNHKHNHK HN HK(HN(HK0HN0HK8HN8H@H@L9uM/IMOIwMGMtL MgIHuHI$IGH[A\A]A^A_]LH8a몿 HH5`HHHHH5HH IH L f.UHAWAVAUATSPIIII_H9tIMgIGI9sGL)HHHH?LtIKI)teIM)LHLL!IIMwLAH)HHHALEMt2MLH=+J<MIMoIG1INHL,NL9It$H)މHHLfff.H H HHHuH8rRH H HKHJHKHJHKHJHK HJ HK(HJ(HK0HJ0HK8HJ8H@H@L9uILIMoIWMGHtHLIMoIIEIGH[A\A]A^A_]LH8_먿HH5d^HsHrHHH5\HHIHnLR UHSPH_H)H9HGHHHHHH[]ÐUHHGH9HBH)H9HBH7H]ffffff.UHSHOHH9LLH~HtcM LH)H9|QAI)MI9t@H6DD8u-Ht2f:uHH9uffff.HI9uLL)HH9HHF[]fffff.UHLGHMt@LH)v8HHIff.@82t HHuLHH)L9HHE]f.UHAWAVATSH_HNHII)HL7I9LGMILL)H9MLM9LIt E\ H)LyI)HI9IGLzM)I)H)LMfIHE8\$Md$u.L9tB1\:\uHH9uff.M9uH9HGHL)IM9tM)L[A\A^A_]UHLGHMtKHJI9IBHt;HIHII)M9MGL)ff.@849t HHu]H]f.UHSPHHHVH8HH[]ÐUHAVSH0HHHEIHHCHCHHHHEȿ0HEEH HMH@(H@ H@H@H@HH]EܠH}Ⱥ1LA}tH5]2HH}C HLHH;Eu HH0[A^]IIH} t H{L fUH]fDUHAWAVSHHIHHHEE1<wAH 'HcH*A~ AI~H51HyHH;EDH[A^A_]I~H5j3{uHLpHLDiHE9}LH5-PLDGHHcGHHGII9uHH;xt=mLIH5 /DAt_mH5-IIIcT$H5R-At9GHOHW| |H5-AuIH5 H# HH;E1H8[A\A]A^A_]MwAT$H5,H14 HHEHELSH5L,H1 HHEHELIrH(AD$@bmHwHH|AYvbHtHHH*H IU&.J@UHAWAVSH(A׉IHd HHEt94H=1+Ht\LӾCE[LcоLOW)EEHE]HuELEt H}]LH HH;Eu/H([A^A_]Hw HH;EuLH([A^A_]HEt H}#HC  fUHSPHHH[]@UHAWAVSPHH4HH`LsMtiI^M~LH)tdIN HHH4ӉσHH>MF(IN(HHHH H~HH9t$H HH+HuH[A^A_]MF(IHHr6fffff.H;(I^M~HI^LH)HHwHt Hu@IF L9t:H;HI9uINIFH9tHPH)HHHIVI>HtLH[A^A_]HƂfDUHAVSIC^w9H=z(HtH5h(L ӾL BCw?H `HcHH54(H5.(H5((H5(Lo [A^]Á H5(H5(1L[A^]D UHyF5t]Àwr rVu€w Ɂr Ѹ뫀wDFADƁu  fD cfff.UHHcHw ]ÁwHH ?ɀO]ÁHFHH=w'H HH?ɀO$? G]HHH ?ɀOHH?ɀO$? G]ffffff.UHr%r1Ɂ)]UH1~#y|r|ur1]ÐUH1 Hxu ff.w@tɀwr r߹Wùw t냀wWu t[]fUH]iLcfff.Hx&L9fffff.Wt9uw#r сrӸ9uDWAAuw% ɁrD Ѹ9LSwDOAD%j D JD ɸ91H]ÐUHAWAVAUATSHHILkN$L M8LuL3ML=L1HEfHAʀw(IcLf\tHffff.\u f\\Hoʀ_r^H-H<1H(s HEH7f\nH+f\rHf\'Hf\"H HÐILH)M9.HLuIFIFIHHs4AMfHuFMfIFIFI15LkIL. IMfIM.I^LHuH HEAHLH[A\A]A^A_]LuIFIFILlfff.UHLGfDHGHHGHGu&Ht`u%L!fHWHu;DHWHwD%@%B%D%F%H%J%L%N%P%R%T%V%X%Z%\%^%`%b%d%f%h%j%l%n%p%r%t%v%x%z%|%~%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % %%%%%%%%%% %"%$%&%(%*%,%.%0%2%4%6%8%:%<%>%@%B%D%F%H%J%L%N%P%R%T%V%X%Z%\%^%`%b%d%f%h%j%l%n%p%r%t%v%x%z%|%~%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % %%%%%%%%%% %"%$%&%(%*%,%.%0%2%4%6%8%:%<%>%@%B%D%F%H%JLAS% hh<hhh0hwhhhh@hahxhnhBdhaZhPhFhh4h *h\ hh hh#hahhhh,hvhhhh-hZhhvh]lhbhXhNhDhh:h0h&h.hehhh4hVhvhhhhhMh~hhhh|hDrhhh^hThJh-@hP6hs,h"hhhh3hQhohhh h%hFhwhhhhhxh7nhTdhnZhPhFhh"4h"*h" h"h # h#h(#h7#hP#h_#hl#h{#h#h#h#h#h#h#h$h $vh<$lh[$bhu$Xh$Nh$Dh$:h$0h$&h %h)%hF%he%h%h%h%h%h%h &h(&h5&hD&hP&h]&hk&h|&|h&rh&hh&^h&Th&Jh&@h&6h&,h '"h'h*'h:'hK'h['hh'hu'h'h'h'h'OrtGetApiBaseOrtSessionOptionsAppendExecutionProvider_CPU/Users/runner/runners/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/session/custom_ops.ccCreateCustomRegistryUnsupported version '' in custom op 'ComputeAsyncMemcpyFromHost/Users/runner/runners/2.163.1/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: com.microsoftcom.microsoft.nchwc/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/session/inference_session.ccvoid onnxruntime::InferenceSession::ConstructorCommon(const onnxruntime::SessionOptions &, logging::LoggingManager *)Environment::IsInitialized()Environment must be initialized before creating an InferenceSession.status.IsOK()Could not finalize session options while constructing the inference session. Error Message: onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions &, const std::string &, logging::LoggingManager *)Given model could not be parsed while creating inference session. Error message: onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions &, std::istream &, logging::LoggingManager *)resultCould not parse model successfully while constructing the inference sessiononnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions &, const void *, int, logging::LoggingManager *)~InferenceSessionUnknown error during EndProfiling()Error during EndProfiling(): Received nullptr for exec providerMemory pattern must be disabled before registering DMLExecutionProviderSequential execution must be enabled before registering DMLExecutionProviderReceived nullptr for graph transformerAddCustomOpDomainsReceived nullptr for custom registryLoadThis session already contains a loaded model.Unknown exception in Load()Encountered unknown exception in Load()Exception during loading: ModelProto corresponding to the model to be loaded has already been parsed. Invoke Load().ModelProto corresponding to the model to be loaded has not been parsed yet. This API should be called in conjunction with a ctor that takes a model abstraction.TransformGraphCould not find an implementation for the node ( (Node placementsAll nodes have been placed on []. Provider: []: [common::Status onnxruntime::InferenceSession::CreateSubgraphSessionState(onnxruntime::Graph &, onnxruntime::SessionState &)subgraphMain Graph instance should have populated all subgraphs when being resolved.CreateSubgraphSessionStateCUDAExecutionProvidercommon::Status onnxruntime::InferenceSession::InitializeSubgraphSessions(onnxruntime::Graph &, onnxruntime::SessionState &)subgraph_session_stateInitializeSubgraphSessionsp_op_kernelInitializeInitializing session.Model was not loadedModel was not loaded.Session has already been initialized.Adding default CPU execution provider.Parallel execution is currently not supported for the registered CUDA Execution Provider.Serializing Optimized ONNX model with Graph Optimization level greater than ORT_ENABLE_EXTENDED. The generated model may contain hardware and execution provider specific optimizations, and should only be used in the same environment the model was optimized for.Session successfully initialized.Exception during initialization: Invalid rank for input: Got: Expected: Please fix either the inputs or the model.Got invalid dimensions for input: for the following indices index: Size mismatch: feed_names has elements, but feeds has elements.Invalid Feed Input Name:Input with name: is not expected to be of type tensor.ValidateInputs is not expected to be of type sparse tensor. is not expected to be of type tensor sequence.Output vector pointer is NULLAt least one output should be requested.Output vector incorrectly sized: output_names.size(): p_fetches->size(): Invalid Output Name:RunSession was not initializedSession not initialized.Running with tag: Encountered unknown exception in Run()GetModelMetadataGetModelInputsGetOverridableInitializersGetModelOutputsNewIOBindingEndProfilingProfiler is disabled.Could not write a profile because no model was loaded.const logging::Logger &onnxruntime::InferenceSession::CreateLoggerForRun(const onnxruntime::RunOptions &, std::unique_ptr &)run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: void onnxruntime::InferenceSession::InitLogger(logging::LoggingManager *)session_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: void onnxruntime::InferenceSession::AddPredefinedTransformers(onnxruntime::GraphTransformerManager &, onnxruntime::TransformerLevel, const std::vector &)graph_optimization_level <= TransformerLevel::MaxLevelExceeded max transformer level. Current level is set to WaitForNotificationtimeout_in_ms >0 is not supportedThe only supported values for the environment variable are '0' and '1'. The environment variable contained the value: FinalizeSessionOptionsReading the provided model for the ORT configonnxruntime::common::Status onnxruntime::FinalizeSessionOptions(const onnxruntime::SessionOptions &, const onnx::ModelProto *, onnxruntime::SessionOptions &)model_protoProvider has already been registered./Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/framework/execution_providers.hAdd allocator already registered.Unexpected input data type. Actual: () , expected: (/Users/runner/runners/2.163.1/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-%SThe Model Proto has already been checked for the ORT config json./Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/session/inference_session_utils.ccParseOrtConfigJsonInModelProtoFound session/run/environment configuration in the model file to be used while running the modelORT config json from the model: Json stored in the `ort_config` key cannot be parsed. Error message: ParseSessionOptionsFromModelProtoDid not find session options in the model file to be used while running the modelintra_op_num_threadsinter_op_num_threadsexecution_modegraph_optimization_levelgraph_optimization_level option in the model file must be an integerenable_profilingIgnoring unsupported session option in ORT config: SetIntraOpNumThreadsUnsupported value for intra_op_num_threads: Setting intra_op_num_threads to SetInterOpNumThreadsUnsupported value for inter_op_num_threads: Setting inter_op_num_threads to SetExecutionModeUnsupported execution_mode value in ORT config: Setting execution_mode to Sequential modeParallel modeSetGraphOptimizationLevelSetting graph_optimization_level to ORT_DISABLE_ALLSetting graph_optimization_level to ORT_ENABLE_BASICSetting graph_optimization_level to ORT_ENABLE_EXTENDEDSetting graph_optimization_level to ORT_ENABLE_ALLUnsupported graph_optimization_level value in ORT config: SetEnableProfilingUnsupported value for enable_profiling option: Setting enable_profiling to true[json.exception.invalid BOM; must be 0xEF 0xBB 0xBF if givennullinvalid literalinvalid string: missing closing quoteinvalid string: '\u' must be followed by 4 hex digitsinvalid string: surrogate U+DC00..U+DFFF must be followed by U+DC00..U+DFFFinvalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFFinvalid string: forbidden character after backslashinvalid string: control character U+0000 (NUL) must be escaped to \u0000invalid string: control character U+0001 (SOH) must be escaped to \u0001invalid string: control character U+0002 (STX) must be escaped to \u0002invalid string: control character U+0003 (ETX) must be escaped to \u0003invalid string: control character U+0004 (EOT) must be escaped to \u0004invalid string: control character U+0005 (ENQ) must be escaped to \u0005invalid string: control character U+0006 (ACK) must be escaped to \u0006invalid string: control character U+0007 (BEL) must be escaped to \u0007invalid string: control character U+0008 (BS) must be escaped to \u0008 or \binvalid string: control character U+0009 (HT) must be escaped to \u0009 or \tinvalid string: control character U+000A (LF) must be escaped to \u000A or \ninvalid string: control character U+000B (VT) must be escaped to \u000Binvalid string: control character U+000C (FF) must be escaped to \u000C or \finvalid string: control character U+000D (CR) must be escaped to \u000D or \rinvalid string: control character U+000E (SO) must be escaped to \u000Einvalid string: control character U+000F (SI) must be escaped to \u000Finvalid string: control character U+0010 (DLE) must be escaped to \u0010invalid string: control character U+0011 (DC1) must be escaped to \u0011invalid string: control character U+0012 (DC2) must be escaped to \u0012invalid string: control character U+0013 (DC3) must be escaped to \u0013invalid string: control character U+0014 (DC4) must be escaped to \u0014invalid string: control character U+0015 (NAK) must be escaped to \u0015invalid string: control character U+0016 (SYN) must be escaped to \u0016invalid string: control character U+0017 (ETB) must be escaped to \u0017invalid string: control character U+0018 (CAN) must be escaped to \u0018invalid string: control character U+0019 (EM) must be escaped to \u0019invalid string: control character U+001A (SUB) must be escaped to \u001Ainvalid string: control character U+001B (ESC) must be escaped to \u001Binvalid string: control character U+001C (FS) must be escaped to \u001Cinvalid string: control character U+001D (GS) must be escaped to \u001Dinvalid string: control character U+001E (RS) must be escaped to \u001Einvalid string: control character U+001F (US) must be escaped to \u001Finvalid string: ill-formed UTF-8 byteinvalid number; expected digit after '-'invalid number; expected digit after '.'invalid number; expected '+', '-', or digit after exponentinvalid number; expected digit after exponent signnumber overflow parsing 'arrayobjectexcessive object size: cannot use erase() with stringbooleandiscardednumberexcessive array size: parse error: at line , column while parsing - ; last read: 'unexpected ; expected true literalfalse literalnull literalstring literalnumber literal'[''{'']''}'':'','end of input'[', '{', or a literalunknown tokenkey '' not foundcannot use at() with map::at: key not foundtype must be number, but is size overflownot enough space: expected , got type is not supported in this functionRegisterCustomOpsLibrary: Failed to load libraryRegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in libraryinput name cannot be emptyCPUExecutionProvideroutput name cannot be emptyinput array is too shortshape is invalidspace is not enoughopaque(,)/Users/runner/runners/2.163.1/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)1.1.0: allocator::allocate(size_t n) 'n' exceeds maximum supported size/\DmlExecutionProviderMem pattern should be disabled when using DML execution provider.Sequential execution should be enabled when using DML execution provider./Users/runner/runners/2.163.1/work/1/s/include/onnxruntime/core/framework/ml_value.honnxruntime::Tensor *OrtValue::GetMutable()IsTensor()Trying to get a Tensor, but got: const onnxruntime::Tensor &OrtValue::Get() constout of indexinternal errorindex out of rangeconst onnxruntime::TensorSeq &OrtValue::Get() constIsTensorSequence()Trying to get a TensorSeq, but got: Input is not of one of the supported sequence types.Input is not of type sequence or map./Users/runner/runners/2.163.1/work/1/s/include/onnxruntime/core/framework/data_types_internal.hstatic bool onnxruntime::utils::ContainerChecker::IsContainerOfType, float, std::__1::less >, std::__1::allocator, float> > >, std::__1::allocator, float, std::__1::less >, std::__1::allocator, float> > > > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::vector, float, std::__1::less >, std::__1::allocator, float> > >, std::__1::allocator, float, std::__1::less >, std::__1::allocator, float> > > > >]++index < c.size()static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, float, std::__1::less >, std::__1::allocator, float> > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::map, float, std::__1::less >, std::__1::allocator, float> > >]const T &OrtValue::Get() const [T = std::__1::vector, float, std::__1::less >, std::__1::allocator, float> > >, std::__1::allocator, float, std::__1::less >, std::__1::allocator, float> > > > >]onnxruntime::DataTypeImpl::GetType() == type_ != static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, std::__1::allocator > >, std::__1::allocator, std::__1::allocator > > > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::vector, std::__1::allocator > >, std::__1::allocator, std::__1::allocator > > > >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, std::__1::allocator > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::map, std::__1::allocator > >]const T &OrtValue::Get() const [T = std::__1::vector, std::__1::allocator > >, std::__1::allocator, std::__1::allocator > > > >]/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/framework/TensorSeq.hconst onnxruntime::Tensor &onnxruntime::TensorSeq::Get(size_t) consti < tensors_.size()Input is not of one of the supported map types.static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, std::__1::basic_string, std::__1::less >, std::__1::allocator, std::__1::basic_string > > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::map, std::__1::basic_string, std::__1::less >, std::__1::allocator, std::__1::basic_string > > >]Invalid index requested for map type.const T &OrtValue::Get() const [T = std::__1::map, std::__1::basic_string, std::__1::less >, std::__1::allocator, std::__1::basic_string > > >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, long long, std::__1::less >, std::__1::allocator, long long> > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::map, long long, std::__1::less >, std::__1::allocator, long long> > >]const T &OrtValue::Get() const [T = std::__1::map, long long, std::__1::less >, std::__1::allocator, long long> > >]const T &OrtValue::Get() const [T = std::__1::map, float, std::__1::less >, std::__1::allocator, float> > >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, double, std::__1::less >, std::__1::allocator, double> > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::map, double, std::__1::less >, std::__1::allocator, double> > >]const T &OrtValue::Get() const [T = std::__1::map, double, std::__1::less >, std::__1::allocator, double> > >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, std::__1::less, std::__1::allocator > > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::map, std::__1::less, std::__1::allocator > > >]const T &OrtValue::Get() const [T = std::__1::map, std::__1::less, std::__1::allocator > > >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, std::__1::allocator > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::map, std::__1::allocator > >]const T &OrtValue::Get() const [T = std::__1::map, std::__1::allocator > >]const T &OrtValue::Get() const [T = std::__1::map, std::__1::allocator > >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, std::__1::allocator > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::map, std::__1::allocator > >]const T &OrtValue::Get() const [T = std::__1::map, std::__1::allocator > >]Number of values should be at least 1.For map type num_values MUST be 2Either the key tensor or the value tensor has NumDimensions > 1Key and value tensors have unequal number of elements.Key type is not supported yet.OrtStatus *OrtCreateValueImplMapHelper(const onnxruntime::Tensor &, const onnxruntime::Tensor &, OrtValue **) [KeyType = std::__1::basic_string]value_type != nullptrTensor must always contain primitive types. Found: /Users/runner/runners/2.163.1/work/1/s/include/onnxruntime/core/framework/tensor.hconst T *onnxruntime::Tensor::Data() const [T = long long]utils::IsPrimitiveDataType(dtype_)Tensor type mismatch. T !=const T *onnxruntime::Tensor::Data() const [T = float]const T *onnxruntime::Tensor::Data() const [T = double]OrtStatus *OrtCreateValueImplMapHelper(const onnxruntime::Tensor &, const onnxruntime::Tensor &, OrtValue **) [KeyType = long long]Each element of the sequence should be either tensor or map.At least one element in the sequence is of a type different from others.Unsupported input typeOrtStatus *OrtCreateValueImplSeqHelper(const OrtValue *const *, size_t, OrtValue **)in[idx]->IsTensor()Expecting all elements to be tensors. Got: Sequences must have tensors of the same data type. There was at least one tensor in the input that was different.Encountered nullptr.const T *onnxruntime::Tensor::Data() const [T = bool]T *onnxruntime::Tensor::MutableData() [T = bool]T *onnxruntime::Tensor::MutableData() [T = float]T *onnxruntime::Tensor::MutableData() [T = double]const T *onnxruntime::Tensor::Data() const [T = onnxruntime::MLFloat16]T *onnxruntime::Tensor::MutableData() [T = onnxruntime::MLFloat16]const T *onnxruntime::Tensor::Data() const [T = onnxruntime::BFloat16]T *onnxruntime::Tensor::MutableData() [T = onnxruntime::BFloat16]const T *onnxruntime::Tensor::Data() const [T = signed char]T *onnxruntime::Tensor::MutableData() [T = signed char]const T *onnxruntime::Tensor::Data() const [T = unsigned char]T *onnxruntime::Tensor::MutableData() [T = unsigned char]const T *onnxruntime::Tensor::Data() const [T = short]T *onnxruntime::Tensor::MutableData() [T = short]const T *onnxruntime::Tensor::Data() const [T = unsigned short]T *onnxruntime::Tensor::MutableData() [T = unsigned short]const T *onnxruntime::Tensor::Data() const [T = int]T *onnxruntime::Tensor::MutableData() [T = int]const T *onnxruntime::Tensor::Data() const [T = unsigned int]T *onnxruntime::Tensor::MutableData() [T = unsigned int]T *onnxruntime::Tensor::MutableData() [T = long long]const T *onnxruntime::Tensor::Data() const [T = unsigned long long]T *onnxruntime::Tensor::MutableData() [T = unsigned long long]Ret onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper::Get() [Ret = OrtStatus *, UnsupportedPolicy = c_api_internal::UnsupportedReturnFailStatus]called_ < 2void onnxruntime::TensorSeq::SetType(onnxruntime::MLDataType)elem_type_ != nullptr was false. Stacktrace: static void OrtEnv::Release(OrtEnv *)env_ptr == p_instance_T *onnxruntime::Tensor::MutableData() [T = std::__1::basic_string]const T *onnxruntime::Tensor::Data() const [T = std::__1::basic_string]/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/optimizer/attention_fusion.ccshape of layer norm bias tensor not expectedAttention subgraph expects 1 Add and 3 MatMul as children of LayerNormalization.Total fused Attention node count: FuseSubGraphFaild to find path vOutput edge count not expected for nodes in path vFailed in match Transpose attribute perm. Expected: 0, 2, 1, 3Failed in match v_transpose attribute perm. Expected: 0, 2, 1, 3v_reshape initializer value is not expectedreshape initializer value is not expectedFailed in match v_matmul and v_add input shapeFailed to find path for maskOutput edge count not expected for mask nodesSoftmax attribute axis is expected to be 3mask_unsqueeze_1 axes not matched. Expect: 1mask_unsqueeze_2 axes not matched. Expect: 2mask_sub const input not matchedmask_mul const input not matchedFailed to find path for qq root should be layer normalizationq_reshape const not matchedqk_div const not matched.q_transpose perm attribute not matchedq_matmul and q_add shape not matchedFailed to find path for kk root is not layer normk_transpose perm attribute not matchedk_matmul and k_add shape not matchedk_reshape const not matchedFailed to load Q, K and V weights, or data type is not float or float16.Failed to load Q, K and V bias tensors, or data type is not float or float16.Failed to create mask indexFused an attention node.ProcessMaskMask shape is unknown or not 2D, or data type unknownMask data type is not int32 or int64qkv_weightsqkv_bias/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/optimizer/bias_gelu_fusion.cc/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/optimizer/constant_folding.ccvirtual onnxruntime::common::Status onnxruntime::ConstantFolding::ApplyImpl(onnxruntime::Graph &, bool &, int, const logging::Logger &) constfetches.size() == node->OutputDefs().size()Unsupported output type of . Can't constant fold /Users/runner/runners/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/optimizer/conv_add_fusion.ccvirtual onnxruntime::common::Status onnxruntime::ConvAddFusion::Apply(onnxruntime::Graph &, onnxruntime::Node &, onnxruntime::RewriteRule::RewriteRuleEffect &, const logging::Logger &) constadd_B_tensor_protoConvAddFusion_B_ConvAddFusion_Add_B_/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/optimizer/conv_bn_fusion.ccvirtual onnxruntime::common::Status onnxruntime::ConvBNFusion::Apply(onnxruntime::Graph &, onnxruntime::Node &, onnxruntime::RewriteRule::RewriteRuleEffect &, const logging::Logger &) constbn_scale_tensor_protobn_B_tensor_protobn_mean_tensor_protobn_var_tensor_protoconv_W_tensor_protoconv_B_tensor_protoConvBnFusion_W_ConvBnFusion_BN_B_ToProtodata type is not supported/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/optimizer/initializer.honnxruntime::Initializer::Initializer(const onnx::TensorProto &)size_ == sizeInitializer/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/optimizer/conv_mul_fusion.ccvirtual onnxruntime::common::Status onnxruntime::ConvMulFusion::Apply(onnxruntime::Graph &, onnxruntime::Node &, onnxruntime::RewriteRule::RewriteRuleEffect &, const logging::Logger &) constmul_B_tensor_protoConvMulFusion_W_ConvMulFusion_Mul_B_/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/optimizer/embed_layer_norm_fusion.ccWord embedding shape not expected.Input is expected to have dim value in all dimensions.Failed to get initializer tensor.Position embedding shape not matched.Position embedding data type shall be float or float16.Failed to match position embedding subgraph.Failed to get position embedding weights.Position embedding shape is not expected.Input id is not valid. Segment id is not valid. Mask is not valid. Input_ids and segment id should have the same shape. Input_ids and mask should have the same shape. Gamma should be of shape (hidden_size). Beta should be of shape (hidden_size). MatchPositionEmbeddingSubgraph1Output edge count not expected for nodes in path1.Second input of Gather should be a constant with value 1. Failed to match Shape node. The parent of shape nodes are expected to be input_ids.Failed to match position subgraph.MatchPositionSubgraphFailed to find path 1 of position shape.Output edge count not expected for nodes in path 1 of position shape.Second input of Gather in path 1 of position shape should be a constant with value 0.Failed to find path 2 of position shape.Output edge count not expected for nodes in path 2 of position shape.Gather node in path 2 is not linked to another subgraph.Second input of Gather in path 2 of position shape should be a constant with value 1.The parent of two shape nodes are expected to be input_ids.MatchPositionEmbeddingSubgraph2Failed to find path 1.Output edge count not expected for nodes in path 1.The first input of Range should be a constant with value 0.The third input of Range should be a constant with value 1.CheckInputInput shape is unknown or not 2D, or data type unknownInput data type is not int32 or int64_Int32_Cast/Users/runner/runners/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/optimizer/gelu_approximation.ccTotal Gelu Approximation (FastGelu) node count: /Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/optimizer/gelu_fusion.cc/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/optimizer/gemm_activation_fusion.ccfused Gemm leaky_relu_/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/optimizer/graph_transformer.ccApply/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/optimizer/graph_transformer_mgr.ccApplyTransformersThis transformer is already registered Level_RuleBasedTransformer/Users/runner/runners/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/optimizer/insert_cast_transformer.cc/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/optimizer/layer_norm_fusion.cc/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/optimizer/matmul_add_fusion.ccfused Matmul and Add _nchwc/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/optimizer/nchwc_transformer.cc/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/optimizer/optimizer_execution_frame.cconnxruntime::OptimizerExecutionFrame::Info::Info(const std::vector &, const onnxruntime::InitializedTensorSet &)allocator_ptr_ != nullptrInfo/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/optimizer/relu_clip_fusion.ccvirtual onnxruntime::common::Status onnxruntime::FuseReluClip::Apply(onnxruntime::Graph &, onnxruntime::Node &, onnxruntime::RewriteRule::RewriteRuleEffect &, const logging::Logger &) constUnexpected data type for Clip 'min' input of FuseReluClip__min_zero_constantonnxruntime::Initializer::Initializer(onnx::TensorProto_DataType, const std::string &, const std::vector &)data type is not supported./Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/optimizer/reshape_fusion.ccFused reshape node: Total fused reshape node count: Fuse_Subgraph1Cannot replace concat node with initializer:/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/optimizer/rule_based_graph_transformer.ccApplyRulesOnNode/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/optimizer/skip_layer_norm_fusion.ccACLExecutionProvider/Users/runner/runners/2.163.1/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/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/optimizer/unsqueeze_elimination.ccUnsqueezeElimination cannot remove node EluHardSigmoidLeakyReluReluSeluSigmoidSoftplusSoftsignTanhThresholdedRelualphagammaBVIfthen_branch/Users/runner/runners/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/providers/cpu/controlflow/loop.cconnxruntime::Loop::Loop(const onnxruntime::OpKernelInfo &)info.GetAttr("body", &proto).IsOK()virtual common::Status onnxruntime::Loop::SetupSubgraphExecutionInfo(const onnxruntime::SessionState &, const std::string &, const onnxruntime::SessionState &)info_ == nullptrvirtual onnxruntime::common::Status onnxruntime::Loop::Compute(onnxruntime::OpKernelContext *) constfeeds_fetches_manager_'Loop' input 'M' should be a scalar tensor. Got shape of 'Loop' input 'cond' should be a scalar tensor. Got shape of ConcatenateLoopOutputLoop had zero iterations and the shape of subgraph output was not found. Defaulting to a rank 1 shape of {0}.Inconsistent shape in loop output for output. Expected: Got:onnxruntime::Loop::Info::Info(const onnxruntime::Node &, const onnxruntime::GraphViewer &)static_cast(num_subgraph_inputs) == subgraph_inputs.size()Graph in 'body' attribute of Loop should have inputs. Found:num_subgraph_outputs - 1 == num_outputs'Loop' node has outputs so the subgraph requires but has /Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/providers/cpu/controlflow/scan_8.cconnxruntime::Scan<8>::Scan(const onnxruntime::OpKernelInfo &) [OpSet = 8]directionsvirtual onnxruntime::common::Status onnxruntime::Scan<8>::SetupSubgraphExecutionInfo(const onnxruntime::SessionState &, const std::string &, const onnxruntime::SessionState &) [OpSet = 8]virtual onnxruntime::common::Status onnxruntime::Scan<8>::Compute(onnxruntime::OpKernelContext *) const [OpSet = 8]Scan inputs have inconsistent batch size. Previous value was but has batch size of sequence_lens length of did not match batch size of Invalid entries in sequence_lens. Max sequence length was onnxruntime::common::Status onnxruntime::Scan8Impl::CreateLoopStateVariables(std::vector > &)p_mlvalueScan<8> spec does not support transpose of output. This should never be called.gsl::span onnxruntime::Tensor::DataAsSpan() const [T = long long]/Users/runner/runners/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/providers/cpu/controlflow/scan_utils.hconst OrtValue &onnxruntime::scan::detail::OutputIterator::GetOutput() constfinal_output_mlvalue_/Users/runner/runners/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/providers/cpu/cpu_execution_provider.ccRegisterOnnxOperatorKernelsRegisterOnnxMLOperatorKernelsGetKernelRegistryvirtual std::shared_ptr onnxruntime::CPUExecutionProvider::GetKernelRegistry() constRegisterCPUKernelsCpuUsed is not implementedMaxConstantOfShape/Users/runner/runners/2.163.1/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/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/providers/cpu/generator/random.honnxruntime::RandomNormal::RandomNormal(const onnxruntime::OpKernelInfo &)info.GetAttr("mean", &mean_).IsOK()scaleinfo.GetAttr("scale", &scale_).IsOK()info.GetAttr("dtype", &dtype).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDInvalid dtype of info.GetAttrs("shape", shape).IsOK()onnxruntime::RandomUniform::RandomUniform(const onnxruntime::OpKernelInfo &)info.GetAttr("high", &high_).IsOK()lowinfo.GetAttr("low", &low_).IsOK()onnxruntime::RandomNormalLike::RandomNormalLike(const onnxruntime::OpKernelInfo &)onnxruntime::RandomUniformLike::RandomUniformLike(const onnxruntime::OpKernelInfo &)sample_sizeonnxruntime::Multinomial::Multinomial(const onnxruntime::OpKernelInfo &)info.GetAttr("sample_size", &num_samples_).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDonnxruntime::common::Status onnxruntime::RandomNormalCompute(float, float, std::default_random_engine &, TensorProto::DataType, onnxruntime::Tensor &)onnxruntime::common::Status onnxruntime::RandomUniformCompute(float, float, std::default_random_engine &, TensorProto::DataType, onnxruntime::Tensor &)MultinomialComputeRangestart in Range operator should be scalar like tensor, yet got shape:limit in Range operator should be scalar like tensor, yet got shape:delta in Range operator should be scalar like tensor, yet got shape:delta in Range operator can not be zero!Ret onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper >::Get() [Ret = onnxruntime::common::Status, UnsupportedPolicy = onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy]Ret onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy::operator()(int32_t) const [Ret = onnxruntime::common::Status]Clipmax/Users/runner/runners/2.163.1/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/2.163.1/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/2.163.1/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_/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.honnxruntime::Broadcaster::Broadcaster(const std::vector &, const std::vector &)largest <= 1Can broadcast 0 by 0 or 1. is invalid.void onnxruntime::BroadcastIterator::Init(int64_t, int64_t)axis == 1 || axis == largestAttempting to broadcast an axis by a dimension other than 1. by void onnxruntime::BroadcastIterator::Append(int64_t, int64_t)onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = float, TOutput = float, Input0Scalar = (lambda at /Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:376:7), Input1Scalar = (lambda at /Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:377:7), General = (lambda at /Users/runner/runners/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:376:7), Input1Scalar = (lambda at /Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:377:7), General = (lambda at /Users/runner/runners/2.163.1/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/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:322:7), Input1Scalar = (lambda at /Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:323:7), General = (lambda at /Users/runner/runners/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:349:7), Input1Scalar = (lambda at /Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:350:7), General = (lambda at /Users/runner/runners/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:507:7), Input1Scalar = (lambda at /Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:508:7), General = (lambda at /Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:509:7)]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = float]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = double]void onnxruntime::utils::MLTypeCallDispatcher::Invoke(Args &&...) const [Fn = mod_internal::CallModImpl, Types = , Args = ]helper.called_ == 1Unsupported data type: 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/2.163.1/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/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/providers/cpu/math/matmul.ccNot satsified: left_num_dims >= 1 && right_num_dims >= 1 /Users/runner/runners/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/providers/cpu/math/matmul_integer.ccvirtual onnxruntime::common::Status onnxruntime::MatMulInteger::Compute(onnxruntime::OpKernelContext *) const [T1 = unsigned char, T2 = unsigned char]IsScalarOr1ElementVector(a_zero_point)MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(b_zero_point)MatmulInteger : input2 zero point must be a scalar or 1D tensor of size 1virtual onnxruntime::common::Status onnxruntime::MatMulInteger::Compute(onnxruntime::OpKernelContext *) const [T1 = unsigned char, T2 = signed char]auto onnxruntime::MatMulInteger::Compute(onnxruntime::OpKernelContext *)::(anonymous class)::operator()(onnxruntime::OpKernelContext *, int) const [T1 = unsigned char, T2 = signed char]t->Shape().NumDimensions() <= 1 && t->Shape().Size() == 1Currently only scalar zero_point is supported. TODO: add per channel zero point support.t->IsDataType() || t->IsDataType()QLinearMatMul/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/providers/cpu/math/quantize_linear_matmul.ccvirtual onnxruntime::common::Status onnxruntime::QLinearMatMul::Compute(onnxruntime::OpKernelContext *) const [T1 = unsigned char, T2 = unsigned char, T3 = unsigned char]a != nullptr && b != nullptrIsScalarOr1ElementVector(a_offset)QLinearMatmul : input zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(b_offset)QLinearMatmul : weight zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_offset)QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_scale)QLinearMatmul : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(b_scale)QLinearMatmul : weight scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_scale)QLinearMatmul : result scale must be a scalar or 1D tensor of size 1RoundSignvoid onnxruntime::utils::MLTypeCallDispatcher::Invoke(Args &&...) const [Fn = CallSignImpl, Types = , Args = ]helper.called_ < 2SoftmaxLogSoftmax/Users/runner/runners/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/providers/cpu/ml/normalizer.ccvirtual onnxruntime::common::Status onnxruntime::ml::Normalizer::Compute(onnxruntime::OpKernelContext *) const/Users/runner/runners/2.163.1/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::utils::MLTypeCallDispatcher::Invoke(Args &&...) const [Fn = CallNormalizerImpl, Types = , Args = ]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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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.onnxruntime::ml::TreeEnsembleRegressor::TreeEnsembleRegressor(const onnxruntime::OpKernelInfo &) [T = double]ZipMapclasslabels_int64sclasslabels_strings/Users/runner/runners/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/providers/cpu/nn/batch_norm.ccepsilon/Users/runner/runners/2.163.1/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/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/providers/cpu/nn/autopad_type.honnxruntime::AutoPadType onnxruntime::StringToAutoPadType(const std::string &)/Users/runner/runners/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/util/math.hvoid onnxruntime::math::Im2colNd::operator()(const T *, const int64_t *, const int64_t *, int64_t, int64_t, const int64_t *, const int64_t *, const int64_t *, const int64_t *, int64_t, T *, bool, T) [T = float, order = 2]d_iter[d_i] < d_maxT3ConvInteger/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/providers/cpu/nn/conv_integer.ccvirtual onnxruntime::common::Status onnxruntime::ConvInteger::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(X_Zero_Point)IsScalarOr1ElementVector(W_Zero_Point)void onnxruntime::math::Im2colNd::operator()(const T *, const int64_t *, const int64_t *, int64_t, int64_t, const int64_t *, const int64_t *, const int64_t *, const int64_t *, int64_t, T *, bool, T) [T = unsigned char, order = 2]ConvTransposeoutput_paddingoutput_shape/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/providers/cpu/nn/conv_transpose.ccDoConvTransposegroup count is <= 0Only 1D and 2D ConvTranspose is supported.filter number not equal to input channel number. filter_number: num_input_channels: Input channels is not divisible by group./Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/providers/cpu/nn/conv_transpose_attributes.hvoid onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShape(onnxruntime::TensorShape, int64_t, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const int64_t, std::vector *, std::vector *) constdim_size > 0void onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShape(const int64_t, const int64_t, const int64_t, const int64_t, const int64_t, onnxruntime::AutoPadType, int64_t *, int64_t *, int64_t *) const*out_size >= 0pad type not supportedFlatten/Users/runner/runners/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/providers/cpu/nn/instance_norm.cc/Users/runner/runners/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/providers/cpu/nn/roi_pool.honnxruntime::RoiPool::RoiPool(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttrs("pooled_shape", pooled_shape).IsOK()pooled_shape.size() == 2pooled_height_ > 0pooled_width_ > 0spatial_scaleinfo.GetAttr("spatial_scale", &spatial_scale_).IsOK()spatial_scale_ > 0Shrink/Users/runner/runners/2.163.1/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/2.163.1/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/2.163.1/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.NonMaxSuppression/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.ccstatic onnxruntime::common::Status onnxruntime::NonMaxSuppressionBase::PrepareCompute(onnxruntime::OpKernelContext *, onnxruntime::PrepareContext &)boxes_tensorscores_tensorNot 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: (iou_threshold >= 0 && iou_threshold <= 1.f) GetThresholdsFromInputsNot satsified: ret.IsOK() virtual onnxruntime::common::Status onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext *) constoutput != nullptrcenter_point_box/Users/runner/runners/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/providers/cpu/rnn/rnn.ccAffineScaledTanh/Users/runner/runners/2.163.1/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/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/providers/cpu/sequence/concat_from_sequence.ccvirtual onnxruntime::common::Status onnxruntime::ConcatFromSequence::Compute(onnxruntime::OpKernelContext *) const/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/providers/cpu//tensor/concat.hSSequenceLength/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/providers/cpu/sequence/sequence_ops.ccvirtual onnxruntime::common::Status onnxruntime::SequenceLength::Compute(onnxruntime::OpKernelContext *) constY != nullptrSequenceAtvirtual onnxruntime::common::Status onnxruntime::SequenceAt::Compute(onnxruntime::OpKernelContext *) constI != nullptrInvalid sequence index () specified for sequence of size (SequenceEmptyvirtual onnxruntime::common::Status onnxruntime::SequenceEmpty::Compute(onnxruntime::OpKernelContext *) constSequenceInsertCreateCopyAndAppendCpuTensorvirtual onnxruntime::common::Status onnxruntime::SequenceInsert::Compute(onnxruntime::OpKernelContext *) constS != nullptrData type of the input tensor MUST be same as that of the input sequence. Sequence data type (), input tensor data type (SequenceErasevirtual onnxruntime::common::Status onnxruntime::SequenceErase::Compute(onnxruntime::OpKernelContext *) constSequenceConstructvirtual onnxruntime::common::Status onnxruntime::SequenceConstruct::Compute(onnxruntime::OpKernelContext *) constnum_inputs >= 1Violation of the requirment that all input tensors must have the same data type.SplitToSequencevirtual onnxruntime::common::Status onnxruntime::SplitToSequence::Compute(onnxruntime::OpKernelContext *) constSplitToSequence operator does not support split_size_sum () != split_dim_size (int64_t onnxruntime::GetSeqIdx(const onnxruntime::Tensor &)const T *onnxruntime::OpKernelContext::Input(int) const [T = onnxruntime::TensorSeq]onnxruntime::TensorSeq *OrtValue::GetMutable()onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &, const onnxruntime::Tensor *) const [T = float]split_scalar > 0std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value > 0; })int64_t onnxruntime::GetScalarSplitInput(const onnxruntime::Tensor &)Invalid data type for split tensor void onnxruntime::GetSplitSizesInput(const onnxruntime::Tensor &, std::vector &)void onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape &)shape_.Size() == new_shape.Size()Tensor size () != new size (onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &, const onnxruntime::Tensor *) const [T = double]onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &, const onnxruntime::Tensor *) const [T = int]onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &, const onnxruntime::Tensor *) const [T = std::__1::basic_string]Cast/Users/runner/runners/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/providers/cpu/tensor/compress.ccvirtual onnxruntime::common::Status onnxruntime::Compress::Compute(onnxruntime::OpKernelContext *) constaxes_right_stride >= 0 && static_cast(axes_right_stride) < std::numeric_limits::max()ConcatNot satsified: input_count >= 1 /Users/runner/runners/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/providers/cpu/tensor/concat.honnxruntime::ConcatBase::ConcatBase(const onnxruntime::OpKernelInfo &, bool)DynamicQuantizeLinear/Users/runner/runners/2.163.1/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/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/providers/cpu/tensor/gather.cc/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/providers/cpu/tensor/gather.honnxruntime::GatherBase::GatherBase(const onnxruntime::OpKernelInfo &)info.GetAttr("axis", &axis_).IsOK()indices element out of data bounds, idx= must be within the inclusive range [GatherElementsGatherElements op: Rank of input 'data' needs to be equal to rank of input 'indices'GatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: GatherElements op: Data type of input 'data' should match the data type of the output/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/providers/cpu/tensor/gather_elements.honnxruntime::GatherElements::GatherElements(const onnxruntime::OpKernelInfo &)/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/providers/cpu/tensor/gather_elements.ccstd::vector onnxruntime::parse_and_validate_indices_tensor(const onnxruntime::Tensor *, int64_t, const onnxruntime::TensorShape &)GatherElements op: Data type for 'indices' tensor must be 'int32_t' and 'int64_t'GatherElements op: Value in indices must be within bounds [ , ]. Actual value is /Users/runner/runners/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/providers/cpu/tensor/pad.honnxruntime::PadBase::PadBase(const onnxruntime::OpKernelInfo &)/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/providers/cpu/tensor/pad.ccvirtual onnxruntime::common::Status onnxruntime::Pad::Compute(onnxruntime::OpKernelContext *) const [T = float]pads_tensor.IsDataType()pads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)Pads tensor should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]pads_size == 2 * data_rankPads tensor size should be equal to twice the input dimension count value_tensor->IsDataType() && value_tensor->Shape().Size() == 1Value tensor should be a 1D tensor of size 1 with the same type as that of the input tensoronnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &, const onnxruntime::Mode &, T) [T = float]data_rank > 0data_rank * 2 == pads.size()PadInputWithDimValueOfZero/Users/runner/runners/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/providers/cpu/tensor/upsample.honnxruntime::UpsampleBase::UpsampleBase(onnxruntime::OpKernelInfo)info.GetAttr("mode", &mode).IsOK()scalesinfo.GetAttrs("scales", scales_).IsOK()extrapolation_valuecoordinate_transformation_modehalf_pixelasymmetricnearest_moderound_prefer_floorcubic_coeff_aexclude_outsideexclude_outside can be set to 1 only when mode is CUBIC. Current mode is set to nearestlinearcubiconnxruntime::UpsampleMode onnxruntime::UpsampleBase::StringToUpsampleMode(const std::string &)mode attribute is . It can only be (default) or or void onnxruntime::UpsampleBase::ScalesValidation(const std::vector &, const onnxruntime::UpsampleMode) constscale >= 1scale > 0scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1)'Linear' mode and 'Cubic' mode only support 2-D inputs ('Bilinear', 'Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the Resize operatorUpsample operatorpytorch_half_pixeltf_half_pixel_for_nnalign_cornerstf_crop_and_resizeonnxruntime::ResizeCoordinateTransformationMode onnxruntime::UpsampleBase::StringToCoordinateTransformationMode(const std::string &)coordinate_transform_mode:[] is not supportted!round_prefer_ceilfloorceilonnxruntime::ResizeNearestMode onnxruntime::UpsampleBase::StringToNearstMode(const std::string &)nearst_mode:[void onnxruntime::UpsampleBase::ParseScalesData(const onnxruntime::Tensor *, std::vector &) constscales_size > 0ReverseSequencesequence_lens shape must be {batch_size}. Got:. batch_size=/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/providers/cpu/tensor/reverse_sequence.ccvirtual onnxruntime::common::Status onnxruntime::ReverseSequenceOp::Compute(onnxruntime::OpKernelContext *) constbatch_axis/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/providers/cpu/tensor/reverse_sequence.honnxruntime::ReverseSequenceOp::ReverseSequenceOp(const onnxruntime::OpKernelInfo &)info.GetAttr("batch_axis", &batch_axis).IsOK()time_axisinfo.GetAttr("time_axis", &time_axis).IsOK()batch_axis < 2Invalid batch_axis of . Must be 0 or 1time_axis < 2Invalid time_axis of batch_axis != time_axistime_axis and batch_axis must have different values but both are gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = bool]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = double]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = short]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned short]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned long long]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = onnxruntime::MLFloat16]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = onnxruntime::MLFloat16]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = onnxruntime::BFloat16]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = onnxruntime::BFloat16]ScatterScatterElementsIndices vs updates dimensions differs at position=Indices must have the same rank as Input. Indices rank=. Input rank=Indices dim= at pos= is greater than input dim=/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/providers/cpu/tensor/scatter.ccvirtual onnxruntime::common::Status onnxruntime::Scatter::Compute(onnxruntime::OpKernelContext *) constUnknown tensor type of onnxruntime::Scatter::Scatter(const onnxruntime::OpKernelInfo &)/Users/runner/runners/2.163.1/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/2.163.1/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/2.163.1/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]auto onnxruntime::SliceImpl(onnxruntime::OpKernelContext *, const onnxruntime::Tensor &, std::vector &, std::vector *, const std::vector &, const std::vector &)::(anonymous class)::operator()(SliceIterator &) constoutput == output_endvirtual 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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/providers/cpu/tensor/unsqueeze.honnxruntime::UnsqueezeBase::UnsqueezeBase(const onnxruntime::OpKernelInfo &)info.GetAttrs("axes", axes_).IsOK()Upsample/Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/providers/cpu/tensor/upsample.ccvirtual onnxruntime::common::Status onnxruntime::Upsample::Compute(onnxruntime::OpKernelContext *) const [T = float]roi_input_idx_ > 0scales != nullptrsizes == nullptrsizes != nullptr && sizes->Shape().Size() != 0X->Shape().GetDims().size() == output_dims.size()Resize: input tensor's rank does not match the output tensor's rank.onnxruntime::common::Status onnxruntime::Upsample::BaseCompute(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &, const std::vector &) const [T = float]output_dims.size() == dims.size()Resize: input tensor's dimension does not match the scales.Upsample: input tensor's dimension does not match the scales.Resize: size of roi array should be 2 * N where N is the rank of input tensor X.'Linear' mode only support 2-D inputs ('Bilinear') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the BaseComputeResize: unexpected modeUpsample: unexpected modeResize: input/output value is nullptrUpsample: input/output value is nullptrResize: input/output value's dimension mismatchUpsample: input/output value's dimension mismatchResize: input shape needs to be at least a single dimensionUpsample: input shape needs to be at least a single dimension.virtual onnxruntime::common::Status onnxruntime::Upsample::Compute(onnxruntime::OpKernelContext *) const [T = int]onnxruntime::common::Status onnxruntime::Upsample::BaseCompute(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &, const std::vector &) const [T = int]virtual onnxruntime::common::Status onnxruntime::Upsample::Compute(onnxruntime::OpKernelContext *) const [T = unsigned char]onnxruntime::common::Status onnxruntime::Upsample::BaseCompute(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &, const std::vector &) const [T = unsigned char]Where/Users/runner/runners/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/work/1/s/onnxruntime/contrib_ops/cpu/bert/attention.cconnxruntime::contrib::AttentionBase::AttentionBase(const onnxruntime::OpKernelInfo &)info.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0Input 0 is expected to have 3 dimensions, got Input 0 dimension 2 should be divisiable by value of the num_heads attribute.Input 1 is expected to have 2 dimensions, got Input 1 dimension 0 should have same length as dimension 2 of input 0Input 2 is expected to have 1 dimension, got Input 2 dimension 0 should have same length as dimension 1 of input 1Input 3 is expected to have 1 dimension, got BiasGeluShape of Input 0 is expected to have at least 1 dimension, got Input 1 is expected to have 1 dimension, got dimension 0 of Input 1 should have same length as the last dimension of input 0/Users/runner/runners/2.163.1/work/1/s/onnxruntime/contrib_ops/cpu/bert/bias_gelu_fusion.cc/Users/runner/runners/2.163.1/work/1/s/onnxruntime/contrib_ops/cpu/bert/embed_layer_norm.ccinput_ids is expected to have 2 dimensions, got word_embedding is expected to have 2 dimensions, got position_embedding is expected to have 2 dimensions, got segment_embedding is expected to have 2 dimensions, got word_embedding and position_embedding shall have same dimension 1beta is expected to have 1 dimensions, got beta is expected to have size of gamma is expected to have 1 dimensions, got gamma is expected to have size of /Users/runner/runners/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/work/1/s/onnxruntime/contrib_ops/cpu/fused_conv.cconnxruntime::contrib::FusedConvFloat::FusedConvFloat(const onnxruntime::OpKernelInfo &)/Users/runner/runners/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/work/1/s/onnxruntime/contrib_ops/cpu/maxpool_with_mask.h/Users/runner/runners/2.163.1/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/2.163.1/work/1/s/onnxruntime/contrib_ops/cpu/murmur_hash3.ccvirtual onnxruntime::common::Status onnxruntime::contrib::MurmurHash3::Compute(onnxruntime::OpKernelContext *) constkeyssizeof(uint32_t) == output_element_bytes/Users/runner/runners/2.163.1/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/2.163.1/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() == 2SampleOpinput is expected to have 3 dimensions, got gamma is expected to have 1 dimension, got beta is expected to have 1 dimension, got bias is expected to have 1 dimension, got /Users/runner/runners/2.163.1/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/2.163.1/work/1/s/onnxruntime/contrib_ops/cpu/word_conv_embedding.cc/Users/runner/runners/2.163.1/work/1/s/onnxruntime/contrib_ops/cpu_contrib_kernels.ccRegisterNchwcKernelsRegisterCpuContribKernelsallocation failuregemmlowp error: %s SSE, 12x4, depth 2/Users/runner/runners/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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())virtual bool onnxruntime::SequenceTensorTypeBase::IsCompatible(const onnx::TypeProto &) constthisProto->value_case() == TypeProto::ValueCase::kSequenceTypeutils::HasElemType(thisProto->sequence_type())bool onnxruntime::NonTensorTypeBase::IsMapCompatible(const onnx::TypeProto &) constthisProto->value_case() == TypeProto::ValueCase::kMapTypeutils::HasKeyType(thisProto->map_type())bool onnxruntime::NonTensorTypeBase::IsSequenceCompatible(const onnx::TypeProto &) constbool onnxruntime::NonTensorTypeBase::IsOpaqueCompatible(const onnx::TypeProto &) constthisProto->value_case() == TypeProto::ValueCase::kOpaqueTypevirtual void onnxruntime::NonTensorTypeBase::FromDataContainer(const void *, size_t, OrtValue &) constNot implementedvirtual void onnxruntime::NonTensorTypeBase::ToDataContainer(const OrtValue &, size_t, void *) const(null)tensor(bfloat16)tensor type sparse tensor type MLDataType for: is not currently registered or supportedonnxruntime::utils::ContainerChecker::ContainerChecker(onnxruntime::MLDataType)void onnxruntime::data_types_internal::DataTypeRegistry::RegisterDataType(onnxruntime::MLDataType)proto != nullptrp.secondWe do not expect duplicate registration of types for: GetElementType/Users/runner/runners/2.163.1/work/1/s/include/onnxruntime/core/framework/data_types.hstatic void onnxruntime::data_types_internal::SetMapTypes, std::__1::basic_string >::Set(onnx::TypeProto &) [K = std::__1::basic_string, V = std::__1::basic_string]value_proto != nullptr expected to be a registered ONNX typestatic void onnxruntime::data_types_internal::SetMapTypes, long long>::Set(onnx::TypeProto &) [K = std::__1::basic_string, V = long long]static void onnxruntime::data_types_internal::SetMapTypes, float>::Set(onnx::TypeProto &) [K = std::__1::basic_string, V = float]static void onnxruntime::data_types_internal::SetMapTypes, double>::Set(onnx::TypeProto &) [K = std::__1::basic_string, V = double]static void onnxruntime::data_types_internal::SetMapTypes >::Set(onnx::TypeProto &) [K = long long, V = std::__1::basic_string]static void onnxruntime::data_types_internal::SetMapTypes::Set(onnx::TypeProto &) [K = long long, V = long long]static void onnxruntime::data_types_internal::SetMapTypes::Set(onnx::TypeProto &) [K = long long, V = float]static void onnxruntime::data_types_internal::SetMapTypes::Set(onnx::TypeProto &) [K = long long, V = double]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = float]elem_proto != nullptrstatic void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = double]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = signed char]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = unsigned char]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = short]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = unsigned short]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = int]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = unsigned int]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = long long]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = unsigned long long]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = bool]static void onnxruntime::data_types_internal::SetSequenceType >::Set(onnx::TypeProto &) [T = std::__1::basic_string]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = onnxruntime::MLFloat16]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = onnxruntime::BFloat16]static void onnxruntime::data_types_internal::SetSequenceType, float, std::__1::less >, std::__1::allocator, float> > > >::Set(onnx::TypeProto &) [T = std::__1::map, float, std::__1::less >, std::__1::allocator, float> > >]static void onnxruntime::data_types_internal::SetSequenceType, std::__1::allocator > > >::Set(onnx::TypeProto &) [T = std::__1::map, std::__1::allocator > >]/Users/runner/runners/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/framework/execution_provider.ccvoid onnxruntime::IExecutionProvider::InsertAllocator(onnxruntime::AllocatorPtr)/Users/runner/runners/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/framework/fuse_nodes_funcs.ccGetFuncsNo provider specified./Users/runner/runners/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/framework/kernel_registry.cc:71:11), TraverseFn = (lambda at /Users/runner/runners/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/framework/kernel_registry.cc:96:9), TraverseFn = (lambda at /Users/runner/runners/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/framework/mldata_type_utils.cconnxruntime::MLDataType onnxruntime::utils::GetMLDataType(const onnxruntime::NodeArg &)nullptr != type_proto/Users/runner/runners/2.163.1/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 onnxruntime::SparseTensor &OrtValue::Get() constIsSparseTensor()Trying to get a SparseTensor, but got: /Users/runner/runners/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/framework/node_index_info.hint onnxruntime::NodeIndexInfo::GetNodeOffset(onnxruntime::NodeIndex) constnode_offsets_index < node_offsets_size_onnxruntime::SparseTensor *OrtValue::GetMutable()/Users/runner/runners/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/framework/sequential_executor.ccReleaseNodeMLValues/Users/runner/runners/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/framework/simple_tensor_allocator.ccGetPreallocatedBufferFailed to get allocator for initializer '', location: /Users/runner/runners/2.163.1/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)dtype_ != nullptrTensor is expected to contain one of the primitive data types. Got: /Users/runner/runners/2.163.1/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/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/framework/tensor_type_and_shape.ccOrtStatus *OrtApis::GetTensorTypeAndShape(const OrtValue *, OrtTensorTypeAndShapeInfo **)type != nullptrInvalid TensorProtocorrupted protobuf data: tensor shape size() does not match the data size() in protoUnpackTensor: the pre-allocate size does not match the size in protodata overflowstring tensor can not have raw data/Users/runner/runners/2.163.1/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: GetFileContentUnpackTensor: the pre-allocated size does not match the raw data size, expected UnpackTensorWithRawDataNot satsified: source_bytes.size_bytes() == destination.size_bytes() /Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/framework/endian_utils.hReadLittleEndianDnnlExecutionProviderNGRAPHExecutionProviderNupharExecutionProviderOpenVINOExecutionProviderNnapiExecutionProviderinvalid allocator/Users/runner/runners/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/graph/contrib_ops/contrib_defs.ccConstrain input and output types to float/quantized tensorsReorderOutputchannelsactivationactivation_paramsWAttentionnum_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 typesEmbedLayerNormalizationinput_ids2D words IDs with shape (batch_size, sequence_length)segment_ids2D segment IDs with shape (batch_size, sequence_length)word_embedding2D with shape (,hidden_size)position_embedding2D with shape (, hidden_size)segment_embedding1D gamma tensor for layer normalization with shape (hidden_size)beta1D beta tensor for layer normalization with shape (hidden_size)mask2D attention mask with shape (batch_size, sequence_length)1D mask_index tensor with shape (batch_size)Constrain input and output integer tensors typesConstrain input and output float tensors types.input tensorbias tensoroutput tensorConstrain input and output types to float or half tensors.SkipLayerNormalization3D input tensor with shape (batch_size, sequence_length, hidden_size)skip3D skip tensor with shape (batch_size, sequence_length, hidden_size)1D input tensor with shape (hidden_size)1D skip tensor with shape (hidden_size1D bias tensor with shape (hidden_sizeValue 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.The normal input data.The bias input data that is a 1D tensor.invalid channel counttensor rank too smallInputs 0 shall be 2 dimensionsword_embedding should have 2 dimensions and dimension size is known.Negative values are not allowed in a shape specificationAttribute should specify a shapeInput's shape must be 4-D'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border)'Scale' must contain exactly 2 values - (height, width)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/2.163.1/work/1/s/onnxruntime/core/graph/function.cconnxruntime::FunctionImpl::FunctionImpl(const onnxruntime::Graph &, std::unique_ptr, const logging::Logger &)input_arg->Type() != nullptronnxruntime::FunctionImpl::FunctionImpl(const onnxruntime::Graph &, const onnxruntime::NodeIndex &, const onnx::FunctionProto &, const logging::Logger &)Resolve subgraph failed:void onnxruntime::update_subgraphs_within_function_body(onnx::GraphProto &, const onnxruntime::Graph &, const onnx::NodeProto &, const std::unordered_map &, const std::unordered_map &)A node with a function body within a subgraph within another function body is currently not supported in ORTType mismatch. Current= Input=Tensor element type mismatch. /Users/runner/runners/2.163.1/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 logging::Logger &, 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 () Op () ) output arg () type inference failedType Error: Type () of output arg () of node () does not match expected type (This is an invalid model. Model input () does not have type information.Type Error: 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.void onnxruntime::Graph::AddInitializedTensor(const onnx::TensorProto &)existing->second == &tensorAddInitializedTensor already has tensor with name but different TensorProto.void onnxruntime::Graph::RemoveInitializedTensor(const std::string &)!foundNot satsified: name_to_initializer_it != name_to_initial_tensor_.end() ReplaceInitializedTensorFailed to find existing initializer with name Not satsified: dims_eq() Not satsified: old_initializer.data_type() == new_initializer.data_type() onnxruntime::common::Status onnxruntime::Graph::ReplaceInitializedTensor(const onnx::TensorProto &)existing_entry != mutable_initializers.pointer_end()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/2.163.1/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/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/graph/graph_utils.ccconst std::string &onnxruntime::graph_utils::GetNodeInputName(const onnxruntime::Node &, int)index >= 0 && static_cast(index) < inputs.size()const std::string &onnxruntime::graph_utils::GetNodeOutputName(const onnxruntime::Node &, int)index >= 0 && static_cast(index) < outputs.size()bool onnxruntime::graph_utils::RemoveNode(onnxruntime::Graph &, onnxruntime::Node &)Should be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.onnxruntime::NodeArg &onnxruntime::graph_utils::AddInitializer(onnxruntime::Graph &, const onnx::TensorProto &)!graph.GetInitializedTensor(new_initializer.name(), existing)Initializer with same name exists. Name:void onnxruntime::graph_utils::ReplaceNodeInput(onnxruntime::Node &, int, onnxruntime::NodeArg &)Invalid input index for node . Index: ExplicitInputs: ImplicitInputs:void onnxruntime::graph_utils::AddNodeInput(onnxruntime::Node &, int, onnxruntime::NodeArg &)num_explicit_inputs == static_cast(target_input_idx)FindPathFailed since multiple edges matched:->CanUpdateImplicitInputNameInSubgraphs Implicit input name cannot be safely updated to in one of the subgraphs.bool onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutput(onnxruntime::Graph &, onnxruntime::Node &)std::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })void onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraph(onnxruntime::Node &, const std::string &, const std::string &)std::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0ModelProto 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/2.163.1/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/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/common/profiler.cc::onnxruntime::TimePoint onnxruntime::profiling::Profiler::StartTime() constenabled_void onnxruntime::profiling::Profiler::Initialize(const logging::Logger *)session_logger != nullptrvoid onnxruntime::profiling::Profiler::StartProfiling(const logging::Logger *)custom_logger != nullptrEndTimeAndRecordEventMaximum number of events reached, could not record profile event.Writing profiler data to file [ {"cat" : "","pid" :"tid" :"dur" :"ts" :"ph" : "X","name" :""args" : {"" : "} }, ] SessionNodew/Users/runner/runners/2.163.1/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/2.163.1/work/1/s/onnxruntime/core/platform/env.ccvoid gsl::fail_fast_assert_handler(const char *const, const char *const, const char *const, int)openfstatGetFileLength: input is not a regular file file "" failed: /Users/runner/runners/2.163.1/work/1/s/onnxruntime/core/platform/posix/env.ccCleanUpFailed to close file descriptor - error code: Not satsified: file_path ReadFileIntoBufferNot satsified: offset >= 0 Not satsified: length <= buffer.size() lseekreadReadFileIntoBuffer - unexpected end of file. File: , offset: , length: MapFileIntoMemorymmapUnmapFilemunmap 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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/work/1/s/cmake/external/onnx/onnx/defs/generator/old.ccAttribute 'value' of Constant node must exist with 'Tensor' data./Users/runner/runners/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/work/1/b/RelWithDebInfo/onnx/onnx-ml.pb.cc/Users/runner/runners/2.163.1/work/1/s/cmake/external/protobuf/src/google/protobuf/arena.ccCHECK failed: (min_bytes) <= (std::numeric_limits::max() - kBlockHeaderSize): /Users/runner/runners/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/work/1/s/cmake/external/protobuf/src/google/protobuf/stubs/int128.ccDivision or mod by zero: dividend.hi=, lo=/Users/runner/runners/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/work/1/s/cmake/external/re2/re2/onepass.ccCannot use SearchOnePass for unanchored matches.unhandled opcode: /Users/runner/runners/2.163.1/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/2.163.1/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/2.163.1/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/2.163.1/work/1/s/cmake/external/re2/re2/walker-inl.hStack not empty.NamedCapturesWalker::ShortVisit calledCaptureNamesWalker::ShortVisit calledWalk NULL/Users/runner/runners/2.163.1/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/2.163.1/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\%03o}uRRysuh)'..bLLIA #W)'..bLLu % 2X # 3<)'9#+)'LL]TUA -@|E(mt}}8  N Q ;(V~)}}н    IAs ?~u J54 ?Lw)'}}tրNv H+| S\= HրNv H+| S\= H '$3{D_րN '  ]]eW k' &f  ?. 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2Ȟ9 1 7 0 6 3 5 4 8 dummyInferenceFunctionERNS_16InferenceContextEconvPoolShapeInferenceERNS_16InferenceContextEbbiiИ OperatorStatus_IsValidEi StringStringEntryProtoސ TypeProto_SparseTensor 2Š9ArenaImpl 1 8InfinityEv۷ 3 6IsNullILi 1arena_destruct_objectINˡ4 5RepeatedPrimitiveDefaults16default_instanceEvگ 0 3 6 2DeleteLogSilencerCountEv 9 Sۡ4onnx t3__11_ 0_1 12basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEEEEvPv3mapIiNS1_12ExtensionSet9ExtensionENS3_4lessIiEENS3_9allocatorINS3_4pairIKiS6_EEEEEEEEvPv 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2 3 ڣ1Output 2Outputş 3Output 4OutputŠ 1 2 3 ڣ⤮⤮1 2ݟ 3 ɥ1֟ 2 3 ɥ1 2 3 Ǩ1 2 3 Ǩתת1֠ 2 3 Ȭ1 2 3 Ȭ̱512F 512F 512F Ṯ2ڡ 512 Ǯ2 512 ٮ2 512ɥ ǯӓưʰ刱ϱٖ1Output 2Output 3Output 4OutputƤ 1ƣ 2ͣ 3ԣ 6ۣ ᙵ1 2 3 6 1 2 3 6 1ܤ 2 3 6 ɭڮﰵBW Vnni µBW Vnni¥ BW٥ Vnni ڂ1 29check_sparse_tensor_indices_ 6check_value_infoERKNS_14ValueInfoProtoERKNS0_14CheckerContextE 2check_tensorERKNS_11TensorProtoERKNS0_14CheckerContextE 9check_sparse_tensorERKNS_17SparseTensorProtoERKNS0_14CheckerContextE 5check_attributeERKNS_14AttributeProtoERKNS0_14CheckerContextERKNS0_19LexicalScopeContextE 1check_ 0check_nodeERKNS_9NodeProtoERKNS0_14CheckerContextERKNS0_19LexicalScopeContextE 4check_functionERKNS_13FunctionProtoERKNS0_14CheckerContextERKNS0_19LexicalScopeContextE طЏ1ERKNS_11TensorProtoERKNS_17SparseTensorProtoEm 2ERKNS_11TensorProtoERKNS_17SparseTensorProtoEm ЩڹgraphERKNS_10GraphProtoERKNS0_14CheckerContextERKNS0_19LexicalScopeContextE modelERKNS Х_10ModelProtoEά t3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE RNS0_14CheckerContextE ֻ 3ĭ 6 9 1ص 8 7 5Ѳ 4õ 0 2auto_pad_docEMakeAttributeERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEE̮ TypeProto_Map FunctionProto auto_pad_docRKۮ S8_ f x NS І_1 0_6vectorIɯ 1TensorProtoE 0GraphProtoE¯ fNS4_IfEEEE xNS4_IxEEEE S6_NS4_IS6_EEEE NS_1 1TensorProtoENS4_ISA_EEEE԰ 0GraphProtoENS4_ISA_EEEE۰ Ma RNNDocGenerator1EPKc OpSchemaRegistry T keRefAttributeERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEENS_28AttributeProto_AttributeTypeE thDocGeneratorEPKc handle_negative_axis_validateERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEEii dbg_count_check__ĊScanInferenceFunctionERNS_16InferenceContextE LoopInferenceFunctionERNS_16InferenceContextEѵ PoolOpSchemaGeneratorEPKcS1_S1_bט ConvOpSchemaGeneratorEPKc ArgReduceDocGeneratorEPKc IfInferenceFunctionERNS_16InferenceContextEʵ TensorProto_Segment ƼԼGetOpSchemaINS_ TensorProto 1 2 3 3 5 4Ծ 2 9 7 6 8 If_Onnx_ver11EEENS_8OpSchemaEv A Xor_Onnx_ver N S M Div_Onnx_ver E L P Cos_Onnx_ver7EEENS_8OpSchemaEv Tan_Onnx_ver7EEENS_8OpSchemaEv RNN_Onnx_ver GRU_Onnx_verγ  Loop_Onnx_ver11EEENS_8OpSchemaEv S Equal_Onnx_ver Floor_Onnx_ver PRelu_Onnx_verх Cӊ Gemm_Onnx_ver11EEENS_8OpSchemaEv TopK_Onnx_ver1 A IsNaN_Onnx_ver9EEENS_8OpSchemaEv Where_Onnx_ver9EEENS_8OpSchemaEv can_Onnx_ver11EEENS_8OpSchemaEv hape_Onnx_ver1EEENS_8OpSchemaEv plit_Onnx_ver lice_Onnx_ver1EEENS_8OpSchemaEv ScanInferenceFunctionOpset LoopInferenceFunctionOpset8ERNS_16InferenceContextE MathDocGenerator_old_opset6EPKc convTransposeShapeInferenceERNS_16InferenceContextE 8ERNS_16InferenceContextE 9ERNS_16InferenceContextE; handle_negative_axis_validate_opset9ERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEEiiƾ AttributeProto_AttributeType_IsValidEi dbg_count_check__SparseTensorProto_default_instance_E Sܿ L M C Relu_Onnx_verˀ T A Det_Onnx_ver11EEENS_8OpSchemaEv̓ Gemm_Onnx_ver Pad_Onnx_ver11EEENS_8OpSchemaEv can_Onnx_ver qrt_Onnx_ver elu_Onnx_ver i 8EEENS_8OpSchemaEv 9EEENS_8OpSchemaEv oop_Onnx_ver1EEENS_8OpSchemaEv ess_Onnx_ver STM_Onnx_ver IfInferenceFunction1ERNS_16InferenceContextE FunctionExpandHelperERKNS_9NodeProtoERKNS_13FunctionProtoERNS_10GraphProtoERKNSt3__112basic_stringIcNS8_11char_traitsIcEENS8_9allocatorIcEEEE build_nodes_range_opEv matmulShapeInferenceERNS_16InferenceContextEii MathDocGenerator_oldEPKc resizeShapeInferenceERNS_16InferenceContextEb žIf_Onnx_ver1EEENS_8OpSchemaEv Or_Onnx_ver Utils1 1dbg_count_check_MeanVarianceNormalization_Onnx_ver9E0dbg_count_check_TreeEnsembleClassifier_OnnxML_ver1E3DataTypeUtils 2TypesWrapper 1StringRange 2 1 6ToTypeERKNS 8ToStringERKNS_9TypeProtoERKNSt3__112basic_stringIcNS5_11char_traitsIcEENS5_9allocatorIcEEEESD_ 0GetTypeStrToProtoMapEv 1IsValidDataTypeStringERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEE 4GetTypeStrLockEv 0FromStringERKNSt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEERNS_9TypeProtoE 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30ReadPackedEnumPreserveUnknownsEPNS0_2io16CodedInputStreamEiPFbiEPNS3_17CodedOutputStreamEPNS0_13RepeatedFieldIiEE 8EnumSizeERKNS0_13RepeatedFieldIiEE SkipFieldEPNS0_2io16CodedInputStreamEj Write ReadBytesEPNS0_2io16CodedInputStreamEP Int PNS3_17CodedOutputStreamE 1 5WriteFloatArrayEPKfiPNS0_2io17CodedOutputStreamE 6 7WriteFixed 8WriteSFixed 4WriteBoolArrayEPKbiPNS0_2io17CodedOutputStreamEш 0؈ 2Write 3WriteSFixed SkipMessageEPNS0_2io16CodedInputStreamEŁ Write PNS3_17CodedOutputStreamE 9SkipFieldEPNS0_2io16CodedInputStreamEj 1 1SkipMessageEPNS0_2io16CodedInputStreamE 5SkipUnknownEnumEii 9SkipFieldEPNS0_2io16CodedInputStreamEj 1 1SkipMessageEPNS0_2io16CodedInputStreamE 5SkipUnknownEnumEii 2 3 4WriteMessageMaybeToArrayEiRKNS0_11MessageLiteEPNS0_2io17CodedOutputStreamE 1k0kMessageSetTypeIdTagEReadPackedEnumNoInlineEPNS0_2io16CodedInputStreamEPFbiEPNS0_13RepeatedFieldIiEE Write kFieldTypeToCppTypeMapEWriteDoubleArrayEPKdiPNS0_2io17CodedOutputStreamE VerifyUtf8StringEPKciNS2_9OperationES4_ 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EnumEiiPNS0_2io17CodedOutputStreamE WriteStringMaybeAliasedEiRKNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEEPNS0_2io17CodedOutputStreamE kMessageSetItemЅBytesMaybeAliasedEiRKNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEEPNS0_2io17CodedOutputStreamE˓ GroupMaybeToArrayEiRKNS0_11MessageLiteEPNS0_2io17CodedOutputStreamE NSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEE PNSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEE 32SizeERKNS0_13RepeatedFieldIiEEؕ 64SizeERKNS0_13RepeatedFieldIxEE 32SizeERKNS0_13RepeatedFieldIjEE 64SizeERKNS0_13RepeatedFieldIyEE 32SizeERKNS0_13RepeatedFieldIiEE 64SizeERKNS0_13RepeatedFieldIxEE  8 4Prog 9 6 3 1 2 lsERNSt3__113basic_ostreamIcNS0_11char_traitsIcEEEERKNS_11StringPieceE7BitState Compiler֟ fullruneEPKciC 9 4PushEiPKciޙ 6SearchERKNS_11StringPieceES3_bbPS1_i 2EPNS_4ProgE 1EPNS_4ProgE GrowStackEvי TrySearchEiPKc о 1̚ 6 9ƹ 30TEST_dfa_should_bail_when_slowEb 4 C D 8 7FlattenEv 4 0 8PossibleMatchRangeEPNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES8_i 6ComputeFirstByteEv 3 1DumpByteMapEv SearchBitStateERKNS_11StringPieceES3_NS0_6AnchorENS0_9MatchKindEPS1_i BuildEntireDFAENS0_9MatchKindERKNSt3__18functionIFvPKibEEE DumpUnanchoredEv ComputeByteMapEv MarkSuccessorsEPNS_11SparseArrayIiEES3_PNSt3__16vectorINS5_IiNS4_9allocatorIiEEEENS6_IS8_EEEEPNS_10SparseSetTIvEEPS8_ PatchListΞ ApplyFoldEPKNS_8CaseFoldEi CharClass 2MkEj 5 6AppendEPNS_4Prog4InstES0_S0_ϟ DerefEPNS_4Prog4InstES0_ PatchEPNS_4Prog4InstES0_jȟ  C 9 D 7ɡ 3 4 5 1 2 8 6FinishEv 2Ev 1Ev A ByteRangeEiib PostVisitEPNS_6RegexpENS_4FragES3_PS3_i llocInstEi ddSuffixEi 2Ev 1Ev 0Ev¡ NoMatchEv Cѥ LiteralEib DotStarEv CatENS_4FragES1_ AltENS_4FragES1_ NopEv StarENS_4FragEb PlusENS_4FragEb CopyENS_4FragEѨ QuestENS_4FragEbڣ MatchEi SetupENS_6Regexp10ParseFlagsExNS_3RE26AnchorE 0Τ 8Add 3ç 4ByteRangeEqualEii 2AddRuneRangeEiib 6AddRuneRangeUTF8Eiib EmptyWidthENS_7EmptyOpEʥ BeginRangeEv 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4StepEPNS_11SparseArrayIPNS0_6ThreadEEES5_iRKNS_11StringPieceEPKc 6SearchERKNS_11StringPieceES3_bbPS1_i 2EPNS_4ProgEݼ 1EPNS_4ProgE 2Ev 1Ev 2AddToThreadqEPNS_11SparseArrayIPNS0_6ThreadEEEiiRKNS_11StringPieceEPKcS3_ 3FormatCaptureEPPKc  4Ϳ 3 6 7ParseUnicodeGroupEPNS_11StringPieceENS_6Regexp10ParseFlagsEPNS_16CharClassBuilderEPNS_12RegexpStatusE 9 1 2 05׊8num_unicode_groupsEOnePass_ChecksEv LookupCaseFoldEPKNS_8CaseFoldEii ByteMapBuilder CoalesceWalkerڃSimplifyWalkerToStringWalkerunicode_groupsESearchOnePassERKNS_11StringPieceES3_NS0_6AnchorENS0_9MatchKindEPS1_i MarkDominatorEiPNS_11SparseArrayIiEES3_PNSt3__16vectorINS5_IiNS4_9allocatorIiEEEENS6_IS8_EEEEPNS_10SparseSetTIvEEPS8_ ParseState NewLiteralEiNS0_10ParseFlagsE C D 1 8 9PushCaratEv 7PushDotEv 20DoLeftParenNoCaptureEv 2ENS0_10ParseFlagsERKNS_11StringPieceEPNS_12RegexpStatusE 1ENS0_10ParseFlagsERKNS_11StringPieceEPNS_12RegexpStatusE У2Ev 1Ev 2 0 7MaybeConcatStringEiNS0_10ParseFlagsE 1 6P 4P 3Do 5DoConcatenationEv FinishRegexpEPS0_ P 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0num_unicode_casefoldE1EPPNS_6RegexpEiNS1_10ParseFlagsEPNSt3__16vectorINS_6SpliceENS5_9allocatorIS7_EEEE 2EPPNS_6RegexpEiNS1_10ParseFlagsEPNSt3__16vectorINS_6SpliceENS5_9allocatorIS7_EEEE 3EPPNS_6RegexpEiNS1_10ParseFlagsEPNSt3__16vectorINS_6SpliceENS5_9allocatorIS7_EEEE 1 C84CopyEv6NegateEv3AddRangeFlagsEiiNS_6Regexp10ParseFlagsE 20FoldsASCIIEv1RemoveAboveEiCharClassEPNS_11StringPieceEPPS0_PNS_12RegexpStatusE PerlFlagsEPNS_11StringPieceE ConvertLatin1ToUTF8ERKNS_11StringPieceEPNSt3__112basic_stringIcNS3_11char_traitsIcEENS3_9allocatorIcEEEE num_unicode_tolowerEParseERKNS_11StringPieceENS0_10ParseFlagsEPNS_12RegexpStatusE QuestEPS0_NS0_10ParseFlagsE EqualEPS0_S1_ 9Bitmap25614FindNextSetBitEi 3RE2 1 Inst DumpEv 7Init 1 9InitMatchEi 8InitFailEv 4DumpEv AltEjj NopEj 3InitByteRangeEiiij 1InitCaptureEij 4InitEmptyWidthENS_7EmptyOpEj 2Ev 1Ev 2Ev 1Ev OptimizeEv EmitListEiPNS_11SparseArrayIiEEPNSt3__16vectorINS0_4InstENS4_9allocatorIS6_EEEEPNS_10SparseSetTIvEEPNS5_IiNS7_IiEEEE 4MarkEii 5 7RecolorEi MergeEv BuildEPhPi S perl_groupsEparseArrayIiE4lessERKNS1_10IndexValueES4_ tringPiece4nposEЭ7 C 4InitERKNS_11StringPieceERKNS0_7OptionsE D 1 8ConsumeNEPNS_11StringPieceERKS0_PKPKNS0_3ArgEi 9QuoteMetaERKNS_11StringPieceE 3Arg Options ReplaceEPNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERKS0_RKNS_11StringPieceE ExtractERKNS_11StringPieceERKS0_S3_PNSt3__112basic_stringIcNS6_11char_traitsIcEENS6_9allocatorIcEEEE C 14kDefaultMaxMemE2ENS0_13CannedOptionsE 1ENS0_13CannedOptionsE 2E 1E PKc RKNS PKc RKNS t3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE _11StringPieceE t3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE _11StringPieceE RKNS0_7OptionsE RKNS0_7OptionsE 7 1 20 5MatchERKNS_11StringPieceEmmNS0_6AnchorEPS1_i Options10ParseFlagsEv DoMatchERKNS_11StringPieceENS0_6AnchorEPmPKPKNS0_3ArgEi 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_9allocatorIcEEEE ReverseProgramFanoutEPNSt3__13mapIiiNS1_4lessIiEENS1_9allocatorINS1_4pairIKiiEEEEEE NamedCapturingGroupsEv Џ0FullMatchNERKNS_11StringPieceERKS0_PKPKNS0_3ArgEi 3 5FindAndConsumeNEPNS_11StringPieceERKS0_PKPKNS0_3ArgEi 1MaxSubmatchERKNS_11StringPieceE PartialMatchNERKNS_11StringPieceERKS0_PKPKNS0_3ArgEi GlobalReplaceEPNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEERKS0_RKNS_11StringPieceE ЗЙ1 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_ 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 int_ short_hexEPKcmPv radixEPKcmPvi octalEPKcmPv 0parse_longlong_ 1parse_ 2parse_ulonglong_cradixEPKcmPv radixEPKcmPvi octalEPKcmPv ulonglong_ longlong_cradixEPKcmPv radixEPKcmPvi octalEPKcmPv short_cradixEPKcmPv longlong_hexEPKcmPv uint_hexEPKcmPv long _hexEPKcmPv longEPKcmPv _hexEPKcmPv longEPKcmPv 2ENS_8RegexpOpENS0_10ParseFlagsE 1ENS_8RegexpOpENS0_10ParseFlagsE 2Ev 1Ev 6 3NewEi8ContainsEiDeleteEv NegateEvQuickDestroyEv NewCharClassEPNS_9CharClassENS0_10ParseFlagsE CaptureNamesEv IncrefEv DecrefEv ConcatEPPS0_iNS0_10ParseFlagsE RepeatEPS0_NS0_10ParseFlagsEii DestroyEv CaptureEPS0_NS0_10ParseFlagsEi AddRuneToStringEi 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1StringPiece݇RequiredPrefixEPNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPbPPS0_SimplifyRegexpERKNS_11StringPieceENS0_10ParseFlagsEPNSt3__112basic_stringIcNS5_11char_traitsIcEENS5_9allocatorIcEEEEPNS_12RegexpStatusE2Ev1EvAddRangeEiiContainsEiAddCharClassEPS0_GetCharClassEvSimplifyEṽToStringEv4CopyEPNS_6RegexpE19PostVisitEPNS_6RegexpES2_S2_PS2_i0˄1CanCoalesceEPNS_6RegexpES2_ShortVisitEPNS_6RegexpES2_DoCoalesceEPPNS_6RegexpES3_เƀȀ4CopyEPNS_6RegexpE18PreVisitEPNS_6RegexpES2_Pb9PostVisitEPNS_6RegexpES2_S2_PS2_i7Concat2EPNS_6RegexpES2_NS1_10ParseFlagsEևπ0ShortVisitEPNS_6RegexpES2_4SimplifyRepeatEPNS_6RegexpEiiNS1_10ParseFlagsEȇ7SimplifyCharClassEPNS_6RegexpEχπЀрڀ46substrEmm5rfindEÈcopyEPcmmfindERKS0_mcmЕRKS0_mՈcm܈8PreVisitEPNS_6RegexpEiPb9PostVisitEPNS_6RegexpEiiPiichartoruneEPiPKcrunetocharEPcPKirunelenEiutfruneEPKciɊCEscapeERKNS_11StringPieceEЊPrefixSuccessorEPNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEE֋num_perl_groupsEunicode_tolowerEStringPrintfEPNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEEPKcztringAppendFEPNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEEPKczŁƁN1ۗPFFZSt3__111onnxruntime˕34onnx5Eigen186google8protobuf213mapI0__shared_ptr_Վ1__basic_string_commonILb1EEEemplaceINpointerIP11onnxruntimeʏS_ɪ8nlohmann6detail20input_buffer_adapterENS_9allocatorIS3_EEEE125ModelENS_9allocatorIS2_EEEE4Ր6AllocatorWrapperENS_9allocatorIS2_EEEE8CpuProviderFactoryENS_9allocatorIS2_EEEECustomRegistryENS_9allocatorIS2_EEEEKernelRegistryENS_9allocatorIS2_EEEE7OnnxRuntimeOpSchemaRegistryENS_9allocatorIS2_EEEE1SchemaRegistryManagerENS_9allocatorIS2_EEEEྺ0__function6__8basic_stringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEEҜ7bad_function_callE43basic_f9basic_ostringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEE5basic_stringbufIcNS_11char_traitsIcEENS_9allocatorIcEEEE2baseIFfuncIØPN11onnxruntime8OpKernelERKNS2_12OpKernelInfoEEEEĕbvN۵llEEEffffxfbEEEiPN11onnxruntime14ComputeContextEPPvEEE 182739456 416ɦ0532789CustomOpKernelEESGatherElementsEQuantizeLinearITensorTypeBaseEFunctionKernelEOrt9ExceptionEԗgsl15narrowing_errorEre2ú9OrtDefaultAllocator6OrtAllocatorImpl2OrtAllocator4LoggingWrapperúúúN՘PFZ4onnx8OpSchema11onnxruntime19num_inputs_allowed_MUliE_ENS_9allocatorIS4_EEFbiEEE20num_outputs_allowed_MUliE_ENS_9allocatorIS4_EEFbiEEEĺiEEEPKN11onnxruntime4NodeES5_EEE״ź8OpSchema17checker15ValidationErrorE9219num_inputs_allowed_MUliE_E20num_outputs_allowed_MUliE_EźƺǺ 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xp_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Exp_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13GRU_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13GRU_Onnx_ver3EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13GRU_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13If_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13LRN_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Log_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Log_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Max_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Max_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Max_Onnx_ver8EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Min_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Min_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Min_Onnx_ver8EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Mul_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Mul_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Mul_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Neg_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Neg_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Not_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Pad_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Pad_Onnx_ver2EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Pow_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Pow_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13RNN_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13RNN_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Sin_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Sub_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Sub_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Sub_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Sum_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Sum_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Sum_Onnx_ver8EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Tan_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Xor_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_13Xor_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Acos_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Asin_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Atan_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_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__ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21AveragePool_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21AveragePool_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21Binarizer_OnnxML_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21HardSigmoid_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21HardSigmoid_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21LogSoftmax_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21Multinomial_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21ReduceMean_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21ReduceProd_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_21SequenceAt_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_22AveragePool_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_22AveragePool_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_22ConvInteger_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_22DepthToSpace_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_22GlobalLpPool_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_22GlobalLpPool_Onnx_ver2EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_22Normalizer_OnnxML_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_22QLinearConv_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_22RandomNormal_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_22ReduceLogSum_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_23ConvTranspose_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_23GlobalMaxPool_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_23RandomUniform_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_23ReduceLogSum_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_24ConvTranspose_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_24LabelEncoder_OnnxML_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_24LabelEncoder_OnnxML_ver2EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_24MatMulInteger_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_24QLinearMatMul_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_24SVMRegressor_OnnxML_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_24SequenceEmpty_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_24SequenceErase_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_25ConstantOfShape_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_25LpNormalization_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_25OneHotEncoder_OnnxML_ver1EEENS_8OpSchemaEv__ZN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Padding.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.Pro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