__TEXT__text__TEXT $un $__stubs__TEXTnn__stub_helper__TEXTn n__cstring__TEXTpogpo__gcc_except_tab__TEXTؗt8M ؗt__const__TEXT ~] ~__unwind_info__TEXTBMB__eh_frame__TEXT8__DATA __nl_symbol_ptr__DATAY__got__DATA,[__la_symbol_ptr__DATA __mod_init_func__DATAƃhƃ __const__DATA@ǃ\@ǃ__data__DATAP#pP#__thread_vars__DATA(H(__thread_ptrs__DATA))__thread_data__DATA)0)__thread_bss__DATA@)__bss__DATAP)0__common__DATA>HH__LINKEDIT`a90a9 @@rpath/libonnxruntime.1.4.0.dylib"00``KXeX&!Dǐuw8_H2' PUU!vWh>4OIO0MޗJ/\$  * 0 /usr/lib/libc++.1.dylib 8/usr/lib/libSystem.B.dylib&f0Z) UHSPHH5@nntH5>nHnuH-9 H11H[]ÐUHH7HGxHGpHGhHG`HGXHGPHGHHG@HG8HG0HG(HG HGHGHG]ffffff.UHH7HGxHGpHGhHG`HGXHGPHGHHG@HG8HG0HG(HG HGHGHG]ffffff.UHAWAVAUATSHHIIIH}HKHHEI_HLUAHUI}HHP0W)E)EHEI7LEH}LLIWKI}AHznH n1A$LSH]Ht#HHCHuHHPH0nH]Ht#HHCHuHHPHnEL?Au II;LnIGM HEHIIUIMHtHAHMtILMt#HIFHuILPLynIEIDIDIUIM HtHAHMt IL MLmHIFHuvILPLncHL+IG(I;G0t;IMHIMHHHtHAAE@IM HH HtHAIG((I LLLmIEHHH;EiLHH[A\A]A^A_]IEAu II;LnIGMg 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HHHIHH0tH@jHHLmMt6HEL9t!Lp@t HxjLM9uH}LLmjEt H}j`t Hp|jLHMt?HPL9t$Lp@t HxQjLM9uHHLLP1jLHlLIj_HIMHII\$ Ht#HHCHuHHPHjI\$Ht#HHCHuHHPHjLjMHpHII] Ht#HHCHuHHPHHjI]Ht#HHCHuHHPHjLjMHHIL HT &MIII_ Ht#HHCHuHHPHjI_Ht#HHCHuHHPHyjLmjML(HIhHIuHI)HIvHILH趎LMeMtIHI]L9t&MI)HHsH耎HIuI}LMejHLHILeMIH]L9t"MI)HèHEHèIXuH}LLehjLHSHIIuL_LM4$MtKHI\$L9t&MI)HHsH_HIuI<$LMt$jHLjH`Ht HhjLu(jHHQ Hx7BIju-jHHQHK7BIjHR@UHAWAVAUATSHH~HHEW)EMIIHHEHEHopaque(HMH}HjH5YjHjHHjH5YjHjH}UAHHHQ`IMO(jHHC HCHCHCHIELLLHPxIEt H}Yj1H~HH;EuHHĸ[A\A]A^A_]j;jIHH4>OH5XjH 9YjH`LHLW)0HDž@`jHH@(z)0H5]YjVH;jCVH0YjH`H0L1H5~H~LjqjIHHj=OH51XjH oXjH`LHNW)0HDž@0jH@(z)0H type!!!HHH_tensor HHHot a nonHHHype is nHHHOpaque tH@'HXjH`H0L1H5~H~Lj IIƳOIIƳIIƳIIƳIIII0t H@jHEH(HtIHEH;(t%Lh@t HxjLL9(uH}H(H(HEjEt H}j`t HpjHHH(H5HPH;(Lh@t HxZjLL9(uII0t H@.jHEH(HtIHEH;(t%Lh@t HxjLL9(uH}H(H(HEjEt H}j`t HpjHHH(HtRHPH;(t(Lh@t Hx{jLL9(uHHH(H(HPLjtLvjEtEALCjHHQ H1BH+jIIL{ Mt#HIGHuILPLjL{Mt#HIGHuILPLjHjEVIIE>H}`jA5Au.LrjHHQH1BHZjILKf.UH]VfDUHAWAVAUATSHHE~HHEW)EMIIHHEHEHopaque(HMH}HzjH5SjHkjHH`jH5mSjHQjH}NAHtbHHQ`HHHLLLEt H}5j1H~HH;EuHHĸ[A\A]A^A_]ojjIHH8OH5RjH TjH`LH]W)0HDž@`jHH@(z)0H59SjVHjCVAH SjH`H0LE1H5ٳ~Hb~LjKjIHHD7OH5 RjH 5SjH`LH_W)0HDž@0jH@(z)0H type!!!HHH_tensor HHHot a nonHHHype is nHHHOpaque tH@'AH{RjH`H0LE1H5߲~Hh~Lj HIARHIAHIAHIAHIHI0t H@jHEH(HtIHEH;(t%Lh@t HxjLL9(uH}H(H(HEjEt H}j`t HpljHHH(H5HPH;(Lh@t Hx.jLL9(uHI0t H@jHEH(HtIHEH;(t%Lh@t HxjLL9(uH}H(H(HEjEt H}j`t HpjHHH(HtRHPH;(t(Lh@t HxOjLL9(uHHH(H(HP jEtLIjEtHEuBuKL"jHHQ H+BH jNHIEtH}jtu.LjHHQHb+BHjRIL*Ff.UHAWAVSPIIjHHt-jHHtH5MjHjH@AI1H[A^A_]f.UHAWAVSPIMt,~ AL@H;HtjHIuLj1H[A^A_]DUH1H ~HB]UHHOj]UH]HEffff.UHAVSHHtdLs Mt#HIFHuILPL) jLsMt#HIFHuILPL jH[A^] j[A^]UHSPHHtCt H{ jHH[] jH[]fffff.UHHtH]`]fffff.UHSPHHtHHH[]\ jH[]DUHAWAVSPHH@L<H ~HQHLtpH@HLpH\H ~HHLDHt IX jH jH5»~HLp jLH[A^A_] jfff.UHAWAVSPHH~HHH LspH@HCpL{HN~HHCCHt H{Xw jL[ jH5@~HH jL jHH[A^A_]B jfUHAWAVSPHH@HH ~HQHLtpH@HLpL|H ~HHLDHt H{X 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Lf.UHAWAVATSIIIMfI)IID$HH=HI^H)HHHH9wYHH9HBHH9vCjHH5cCjHKiH$~HHH5N~H~HjHH<jJLI7IJ4JDM>IvL9H^L)Ht-Hffff.HNHFHvHJHHuH8fHNHFHJHNHFHJHNHFHJHNHFHJHNHFHJHNHFHJHNHFHJHNHFHvHJHI9oM>I^LIIFMFL9t#H{HCH[HtHPI9uMtL[A\A^A_];j[A\A^A_]11LiiIHLjLi fff.UHAWAVAUATSHHIHH8~HHEH8Hà~HHHLH@H8LDiHEEL5`~M~LI@L8LPiL%#~ILHDž(HDž HDžHDžDž0LjHLHHHHjHHH+HHHLL8Lt H iHiH5p~HHiH84iH~HH;EuHH[A\A]A^A_]iHLL8Lt H %iHiH5H5HH5խ~HHiHH8iHi fUHAWAVAUATSPHIiMuHsLiMe0Hs0LiM}HHsHLiHC`IE`I}hHhHH[A\A]A^A_]HAu'A$ujH}LhsW)PHDž`0iH`(vy)PHts valueHHHy for itHHHype entrHHHissing tHHHMap is mH@'AH=jHuHPLE1H5~H~LDi IA(IA}IIPt H`iLeMt6HEL9t!HX@t HxiHI9uH}LLeiEt H}piEt H}aiLhMt:HpL9tEHX@t Hx6iHI9uHhLpiEtLDiLi Lffffff.UHAWAVAUATSHHH2~HHE& AH;Cu*HH ~H H;MuHĘ[A\A]A^A_]iiIHhOH55jH C>jH}Lh)* AHHHH?jHPHHH-AH?jHuHPLE1H5~H~LKi IAIIPtH`iIALeMt6HEL9t!HX@t HxiHI9uH}LLeiEt H}tiEt H}eiLhMt:HpL9tEHX@t Hx:iHI9uHhLpiEtLHiLi LUHAWAVAUATSH(IIHHH5~HHEH8H~HHHLH@H8LAiLHEEL=V~MgLI@L8LFiL-~ILHDž(HDž HDžHDžDž0H3HyALiHLH/HH0HIAHHHLL8Lt H iHyiH5^~HHiH8"iHõ~HH;EuHH([A\A]A^A_]iHLL8Lt H iHiH'H'H'HH5~HHeiHH8ziHi UHAWAVAUATSHH~HHEHHGH)HH9v7 u.HH9sOHô~HH;Eu:HĈ[A\A]A^A_]$H~HH;Eu1HĈ[A\A]A^A_]i)iIHh"OH53jH ;jH}LhW)PHDž`0iH`(y)PH elementHH&Hor its eHH H entry fHHHing typeHHH is missHHHSequenceH@.AH7jHuHPLE1H5~H>~Lri IA(IA}IIPt H`iLeMt6HEL9t!HX@t HxiHI9uH}LLeiEt H}iEt H}iLhMt:HpL9tEHX@t HxdiHI9uHhLpIiEtLriL*i Lffff.UHAWAVAUATSHHe~HHEHHOH)HH9v3u*Tu HVH9s@Lu D1H ~H H;MuHĈ[A\A]A^A_]iiIHhOH561jH ;jH}Lh#W)PHDž`0@iH`(&y)PHts valueHHHy for itHHHype entrHHHissing tHHHMap is mH@'AH4jHuHPL9E1H57~H~Li IA(IA}IIPt H`niLeMt6HEL9t!HX@t HxIiHI9uH}LLe/iEt H} iEt H}iLhMt:HpL9tEHX@t HxiHI9uHhLpiEtLiLi Lffffff.UHAWAVAUATSHHH~HHEAH;Cu*HH Ư~H H;MuHĘ[A\A]A^A_]iTiIHhMOH5,jH .;jH}Lh)eAHHHH6jHPHHHAH^6jHuHPL@E1H5>~Hdž~Li IAIIPtH`ziIALeMt6HEL9t!HX@t HxMiHI9uH}LLe3iEt H}$iEt H}iLhMt:HpL9tEHX@t HxiHI9uHhLpiEtLiLi LUHAWAVAUATSHIH~HHEH8H}~HHHLH@H8LiHEEH~LkLH@H8L iL5ݫ~ILHDž(HDž HDžHDžDž0LLLH8Lt H iLiH5p~HHiH84iHլ~HH;EuLH[A\A]A^A_]iILH8Lt H %iL iIH5~HHiIH8iLi fDUHAWAVAUATSHH%~HHEHHOH)HH9v3u*Tu HVH9s@Lu D1H ϫ~H H;MuHĈ[A\A]A^A_]i]iIHhV OH5*jH A9jH}LhW)PHDž`0iH`(y)PHts valueHHHy for itHHHype entrHHHissing tHHHMap is mH@'AHV.jHuHPLE1H5~H~Li IA(IA}IIPt H`.iLeMt6HEL9t!HX@t Hx iHI9uH}LLeiEt H}iEt H}iLhMt:HpL9tEHX@t HxiHI9uHhLpiEtLiLli Lffffff.UHAWAVAUATSHHH~HHEV@H;Cu*HH ~H H;MuHĘ[A\A]A^A_]liiIHh OH5S&jH s9jH}Lh)@HHHHo0jHPHHHAH0jHuHPLE1H5~H~Li IAIIPtH`:iIALeMt6HEL9t!HX@t Hx iHI9uH}LLeiEt H}iEt H}iLhMt:HpL9tEHX@t HxiHI9uHhLpiEtLiLpi LUHAWAVAUATSPIM.IFL)HHHH9H H9I^IM)HLiHH5jHiHe~HHH5}~Hz~HDiIHiLi ff.UHAWAVAUATSHIIIuMeI)MIIGHH=HMuI)LHHH9waII9LBMLEHuI9vK\iHH5jHiH~HHH5|~Hy~HbiLEHuIJ<iHHuLEN4IJN|M~HLI`iLI]M}MuHtHH[A\A]A^A_]iH[A\A]A^A_]E11LiIHiLci ff.UHAWAVAUATSHHH~HHEV@H;Cu*HH ~H H;MuHĘ[A\A]A^A_]liiIHh NH5SjH c1jH}Lh)蚴@HHHHo$jHPHHHAH$jHuHPLE1H5x~Ht~Li IAIIPtH`:iIALeMt6HEL9t!HX@t Hx iHI9uH}LLeiEt H}iEt H}iLhMt:HpL9tEHX@t HxiHI9uHhLpiEtLiLpi LUHAWAVAUATSPIHL3HCL)HL9sjLH>upLcM)J<8iIKDMMM~LLLIiLL+L{HCMtLH[A\A]A^A_]iH[A\A]A^A_]ÿiHH5DjH,iH~HHH5/y~Hu~HiIHiLsi ff.UHAWAVAUATSHIIIuMeI)MIIGHH> H?MuI)LHHH9w`II9LBMLEHuI9vKiHH5cjHKiH$~HHH5Nx~Ht~HiLEHuIJ<iHHuLEN4ABN|M~HLIiLI]M}MuHtHH[A\A]A^A_]?iH[A\A]A^A_]E11LiiIHLiLi fff.UHAWAVAUATSHHE~HHEHHOH)HH9v3u*Tu HVH9s@Lu D 1H ~H H;MuHĈ[A\A]A^A_]i}iIHhvNH5jH -jH}LhW)PHDž`0 iH`(y)PHts valueHHHy for itHHHype entrHHHissing tHHHMap is mH@'AHvjHuHPLE1H5t~Ho~Li IA(IA}IIPt H`NiLeMt6HEL9t!HX@t Hx)iHI9uH}LLeiEt H}iEt H}iLhMt:HpL9tEHX@t HxiHI9uHhLpiEtLiLi Lffffff.UHAWAVAUATSHHH–~HHEv@H;Cu*HH ~H H;MuHĘ[A\A]A^A_]i4iIHh-NH5sjH W-jH}Lh)躭@HHHHjHPHHHAH>jHuHPL E1H5r~Hm~Li IAIIPtH`ZiIALeMt6HEL9t!HX@t Hx-iHI9uH}LLeiEt H}iEt H}iLhMt:HpL9tEHX@t HxiHI9uHhLpiEtLiLi LUHAWAVAUATSPIHL3HCL)HL9sjLH=upLcM)J<XiIKDMMM~LLLIiLL+L{HCMtLH[A\A]A^A_]iH[A\A]A^A_]ÿiHH5djHLiH%~HHH5Or~Hn~HiIHiLi ff.UHAWAVAUATSHIIIuMeI)MIIGHH=HMuI)LHHH9waII9LBMLEHuI9vKiHH5jHjiHC~HHH5mq~Hm~H"iLEHuIJ<iHHuLEN4IJN|M~HLI iLI]M}MuHtHH[A\A]A^A_]^iH[A\A]A^A_]E11LiIHkiL#i ff.UHAWAVAUATSHHe~HHEHHOH)HH9v3u*Tu HVH9s@Lu D1H ~H H;MuHĈ[A\A]A^A_]iiIHhNH56jH 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fA(fA(fY%>ofAYfD\fD\fA(fYfD(fEYfEXfDYfDXfEYf(fAYfAXfYfXfYfAfXfA\fD^fEXfDXfrffpcfAYf_fALHHHHL9MI?MIM9gfInJfD(-=ofD(5=ofD(=>of( >ofD(>ofD(%>of(-#>of(5+>ofo=3>oHtIAL9}B ff.B Zf\(,=of]D(0=ofD_fA(fY.=ofX6=ofD: fA(fA(fY%-=ofAYfD\fD\fA(fYfD(fEYfEXfDYfDXfEYf(fAYfAXfYfXfYfAfXfA\fD^fEXfDXfrffpcfAYf_fC IM9LL)fInHfPRHhHHpHHHHPHLUHx`KLHMK *HLHMHJH)E1f)M@H`HEBZDf\f)E^f)E(E^(MHEBLHEBZf\Ef)E^f)E(E^H`(Uf(MHEBIHlHxt:MHEIZf\f)EI^f)E(E8^(MC HpH9hLUt9IJffff.BZ\P^CIM9uHĈ[A\A]A^A_]E1HxVfDUHAWAVAUATSHhIHsHHEп^HþH^HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]!5Hx5I 5I5I5HW)EHE(M^HEHH(HMHxHLxL`LhHX HMH5_H}HU@=8H5_H58H5H_H68HAH5D2_H78HHHPsHHEHiHUHuHuIININ0IFIVIF@H}Ht H}p^H]HEHtHcHO^HsHH;EuLHh[A\A]A^A_]^IH}HtH}^IH]HEHtHcH^L^ UHAWAVAUATSHhIH%sHHEп^HþH^HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]15Hx%5I5I5I 5HW)EHE(]^HEHH(HMHxHLxL`LhHX HMH5.^H}HUP:8H5_H28H5=bH38HA H5T/_H 58HHH`sHHEHHUHuHuIININ0IFIVIF@H}Ht H}^H]HEHtH`H_^HsHH;EuLHh[A\A]A^A_]^IH}HtH}!^IH]HEHtH`H^L^ UHSHHH=sHHEHU1HiHtH@0@EHuHUHIH |`HjCHsHH;Eu HH[]^ffffff.UHAVSHPHIHsHHEEHEHEH}HuH}HuHOH}HuHH}HuHH}HuH_HuL H]HEHtCt H{^H^HsHH;Eu LHP[A^]^IH]HEHtCt H{]^HU^LC^ ff.UHAVSIP8^HHZsHHH{Lo8HtHH[A^]IH^L^ UHAVSIP^HHsHHH{L8HtHH[A^]IH^L^ UHAVSHIH˱sHHEA>urHH}HEHEI^IFHtACt H{*^H"^H]HEHt!Ct H{^H^HEIFHEsHH;Eu 1H[A^]2^fff.UHAVSHIH sHHEA>urHH}B HEHEI^IFHtACt H{j^Hb^H]HEHt!Ct H{B^H:^HEIFHsHH;Eu 1H[A^]r^fff.UHAVSHIHKsHHEA>urHH}B HEHEI^IFHtACt H{^H^H]HEHt!Ct H{^Hz^HEIFHůsHH;Eu 1H[A^]^fff.UHAVSHIHsHHEA> urHH}HEHEI^IFHtACt H{^H^H]HEHt!Ct H{^H^HEIFHsHH;Eu 1H[A^]^fff.UHAVSHIHˮsHHEA>urHH}HEHEI^IFHtACt H{*^H"^H]HEHt!Ct H{^H^HEIFHEsHH;Eu 1H[A^]2^fff.UHAWAVAUATSH8IIHsHHE1LcHþLcIžLcIHCHK H)HtHH8IEIM H)HtHH8Mt%IGIO H)HtH%H8LuH>L4D0M]L D8EO0^HEf(pf)EHbe zero!HH Hcan not HHHperator HHH Range oHHHdelta inH@(HMLuLV`CHH5F_L}LHLL"`C`IH5Z_H]HLvLH_C/IH5n_H]HLELH_CEt H}^HsHH;ELH8[A\A]A^A_]AA)A*A*^f: L,E1MMIQ^HEHHHML(HMHU1L8H\MLu0Iw1HL!fnfpfAnfpf8@IofBfnfpHyHHVHs 1HutHzH)1fDfD fD0fD@fDPfD`fDpH fHuHt,HtHf.FffH HuI9t"DHDHDHL9rIH}H*H}#^HH}HtH} HEt H}^H^ fUHAWAVAUATSH(HIHHsHHEH8HCsHHHLH@H8L^HHEEL5٧sM~LI@L8L^L%sILHDž(HDž HDžHDžDž0L?^HLHHH:HHHXLL8Lt H .^H^H5sHH^H8^HXsHH;EuHH([A\A]A^A_];^HLL8Lt H ^H^HBH:HH5XsHH^HH8^HM^ DUHAWAVAUATSH8IIHsHHE1L1]HþL!]IžL]IHCHK H)HtH&H8IEIM H)HtH2H8(Mt%IGIO H)HtH9H8/HTELTMmLETW.ESM0>^HEf(&pf)EHbe zero!HH Hcan not HHHperator HHH Range oHHHdelta inH@(HMLYCHH5_L}LH#LLYC`IH5_H]HLLHmYC/IH5_H]HLLHLHH?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.UHAVSIPH^HHjxsHHH{L8HhsHH[A^]IH^L^ UHAVSIP^HH xsHHH{L8HsHH[A^]IH袼^L萷^ UHAWAVAUATSHMHIH́sHHEHtHB H;BHz/nH)0tHC H;CVHJ/o)@L-/IIL螬:LfIHIL臬:HuڃH9HOII)LH?H>LHH(0(@"HrI H9szH I9sqE1HzLt(C_(]B LHL9f(A_(] (A_D(]LHH9uII((IIHHDGAH IHH)1fff.A(_(]AT(_(]THHuMtA_] L9H<I|1((HfA(_(])HH9|H9>I)ILI I9sLI L9MI((IHIIEOAHHM\HtIIL)1AT(_(]TA(_(]HHuMtHA_] M9tsLlj)HPH@t(A_(] HOH9tAfff.(A_(] (A_D(]LHH9uHD~sHH;Eu2HĨ[A\A]A^A_]1M9C1M9F ^貸^IHhCH5Ҝ_H _H}Lh48W)PHDž` U^H`(p)PH scalar.HHHld be a HHHmin shouH@AHE_HuHPLjE1H5hYsHTsL%^ڷ^IHhCH5_H 6_H}Lh8`W)PHDž` }^H`(ӝp)PH scalar.HHHld be a HHHmax shouH@AH_HuHPL蒖E1H5XsHTsLM^ IAIIAIIAIA2IuIPt H`覶^LeMt6HEL9t!HX@t Hx聶^HI9uH}LLeg^Et H}X^Et H}I^LhMHpL9HX@t Hx^HI9uIPt H`^LeMt6HEL9t!HX@t Hx̵^HI9uH}LLe貵^Et H}裵^Et H}蔵^LhMt:HpL9tEHX@t Hxi^HI9uHhLpN^EtLw^L/^ LfUHAWAVAUATSHMHIHlzsHHEHtHB H;BH*DnH)0tHC H;CH*nf)@L*IIL=:L5FHIL&:HuH9HOII)LH?LHHf(0f(@HrI H9H I9E1HzLtf(C_f(]B LHL9Eff.f(A_f(] f(A_Df(]LHH9uIIIHHHDOAHBIIH)1fffff.fAfA\f(f_f(f_f(f]f(f]ffdfAT fA\0f(f_f(f_f(f]f(f]f\ fd0HHuMt0fAfA\f(f_f_f(f]f]ffDL9H<I|/@Af(f_f(f]f)HH9|H9 I)IH IH9s H IH9rqMIITHtJ<1fffff.fTff(f_f(f_f(f]f(f]f\f$HI9uM9tu)HPH@tf(A_f(] HOH9tDff.f(A_f(] f(A_Df(]LHH9uHvsHH;Eu"HĨ[A\A]A^A_]1MKvv^^IHhCH5>_H u_H}Lh4褍W)PHDž` ^H`(p)PH scalar.HHHld be a HHHmin shouH@AH_HuHPL֏E1H5QsH]MsL葰^F^IHh?CH5f_H _H}Lh8̌W)PHDž` ^H`(?p)PH scalar.HHHld be a HHHmax shouH@AH_HuHPLE1H5PsHLsL蹯^ IAIIAIIAIA2IuIPt H`^LeMt6HEL9t!HX@t Hx^HI9uH}LLeӮ^Et H}Į^Et H}赮^LhMHpL9HX@t Hx肮^HI9uIPt H`]^LeMt6HEL9t!HX@t Hx8^HI9uH}LLe^Et H}^Et H}^LhMt:HpL9tEHX@t Hxխ^HI9uHhLp躭^EtL^L蛨^ Lfffff.UHAWAVAUATSHMHIHrsHHEHtHB H;BVH*PD(AHLHtHC H;CHPD8ALOIIL豝:HHHQIHH蓝:HH rI I9LHI9E1HpL¨t#C DD8|DA8~C LHL9Rfffff.ADD8|DA8~AA\DD8|DA8~A\HH9uIIAfnff8Afnf8IHHHrHH~H)1fffff.AoAo\f8^H6^&HEEuHEHwIFXHEHgMsHH;Eu H@[A^]V^IEt H}އ^H]HEHt&Ct H{辇^H IEt H}觇^L蕂^ ffff.UHAWAVAUATSHIH`IHLsHHE1LgHHC H+CHpLBIW)EHEH{ HH+{t[HHHH9 ^ILeLeIHEHHpHX H)H~LHS^ILeHU1L6v8HH}Fw:HHpHHHMLHHH_ HEHLcHH@JHW)PHDž`HHx H+xHHHH9 ^ILXLPIH`HHpHX H)H~LHJ^LHHXKHPAv:HxW)0HDž@LPHXL)t\IIHI9 HL^IL8L0KDH@H~LLH訆^IL8W)HDž HH9LpLׄ^HHHH H5[nHLJ^HH`HxXLXHHYHxPHtHHxH$L0LHHHH HHHHW)HDžLpL^HHLN4LHL߃^LLHJW)HDžL裃^ILKHLL艃^HHKHxH$HHL0LHH L#^HHt H ^HeLpfDH`HXPW)HDžLԂ^IHLxHHIDLHM,LLL螂^LLLKW)HDžLb^ILHIHLLA^HLhO<W)HDžL ^HHHLsHHEW)EHELH=H0H(L HN,L,y^ILuKH]LLy^H]H(H0IMMOH HHIHEHMHEHMHL$H$H8HLHI LEMLhL`LxLpM)IMQH}H@LHHX1f.J4HL9uKKDH4Mt;Lffff.HTHHTI;TuHDITH4Hu1HL9uH@HXHt H}w^HpHt Hxw^H}Ht H}w^H\^H IEt H}'\^LW^ ffff.UHAWAVAUATSHIH`IHH!sHHE1LHHC H+CHpLIW)EHEH{ HH+{t[HHHH9 |[^ILeLeIHEHHpHX H)H~LH\^ILeHU1LJ8HH}K:HHpHHHMLHHH_ HEHLcHH@JHW)PHDž`HHx H+xHHHH9 |Z^ILXLPIH`HHpHX H)H~LH[^LHHXKHPJ:HxW)0HDž@LPHXL)t\IIHI9 HY^IL8L0KDH@H~LLH([^IL8W)HDž HH9LpLWY^HHHH H5۟nHLZ^HH`HxXLXHHYHxPHtHHxH$L0LHHHH HHHHW)HDžLpLX^HHLN4LHL_X^LLHJW)HDžL#X^ILKHLL X^HHKHxH$HHL0LHH LW^HHt HW^HeLpfDH`HXPW)HDžLTW^IHLxHHIDLHM,LLLW^LLLKW)HDžLV^ILHIHLLV^HLhO<W)HDžLV^HHHL^>^IHhcCH5Ҫ`H D*_H}Lh/IFI+HHHL@H5)_H ^Lw)_HPHHL@AH7)_HuHPLzE1H5xrHrL5>^ IAgIAIfIAIAPIHIAIAI*IPt H`p=^LeMt6HEL9t!HX@t HxK=^HI9uH}LLe1=^Et H}"=^Et H}=^LhMHpL9HX@t Hx<^HI9u]IPt H`<^LeMt6HEL9t!HX@t Hx<^HI9uH}LLe|<^Et H}m<^Et H}^<^LhMHpL9HX@t Hx+<^HI9uIPt H`<^LeMt6HEL9t!HX@t Hx;^HI9uH}LLe;^Et H};^Et H};^LhMt:HpL9tEHX@t Hx~;^HI9uHhLpc;^EtL;^LD6^ LUHAVSI`8;^HHLH[A^]IH ;^L5^ UHAVSH@HIHHsHHEHrHII~*<8HrHIIFXIFPHEW)EHEEHexclusivHEfEeH}HUHMHM8Et H}Z:^H}uHEHwINPHW)EHEEHEHreverseHEH}HUHMHM8HEHEH]HEHtxCt H{9^H9^H]HEHtCt H{9^H9^Et H}9^H]Ht1HECt H{~9^Hv9^&HEEuHEHwIFXHEHrHH;Eu H@[A^]9^IEt H}9^H]HEHt&Ct H{8^H IEt H}8^L3^ ffff.UHAWAVAUATSHIH`IHrHHE1L觳HHC H+CHpL肳IW)EHEH{ HH+{t[HHHH9 <8^ILeLeIHEHHpHX H)H~LH9^ILeHU1Lv'8HH}(:HHpHHHMLHHşH_ HEHLcHH@JHW)PHDž`HHx H+xHHHH9 <7^ILXLPIH`HHpHX H)H~LH8^LHHXKHP':HxW)0HDž@LPHXL)t\IIHI9 H6^IL8L0KDH@H~LLH7^IL8W)HDž HH9LpL6^HHHH H5|nHL7^HH`HxXLXHHYHxPHtHHxH$L0LHHHH HHHHW)HDžLpL@5^HHLN4LHL5^LLHJW)HDžL4^ILKHLL4^HHKHxH$HHL0LHH Lc4^HHt HK4^HeLpfDH`HXPW)HDžL4^IHLxHHIDLHM,LLL3^LLLKW)HDžL3^ILHIHLL3^HLhO<W)HDžLL3^HHHLrHH;EuH[A\A]A^A_]H}Hu)^H}%^ HH}Ht H}(^H#^ UHAWAVAUATSHHMLIIHHrHHEHHJLLKIIHHHHH|$H$HHH&I$IL$LLKIIwHEHuHEHuHt$H$HLH}6HHHHH HH)HHHHJHHMHLmH0HLH]HuH)HHHHSHLeLXLhLLxE1ff.AHH IL;uRHHTH HHt3H\HH\I;\uHDH\H HuE1IH`HHIIM9uWHHTIHHt8fDI\HI\I;\uIDH`H\IHuE1L;ELXHLLxHt H8%^HHt H %^H}Ht H}%^H}Ht H}%^HrHH;Eu HH[A\A]A^A_]H0Hu%^HH}Ht H}^%^H}Ht H}L%^H: ^ 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XA HHuHr^KDfffff.LXA LXALLXAL XALHI9uHuL>L;~Hh[A\A]A^A_]DUHAVSIP(XHHJmHHH{L_2HmHHH[A^]IHXLX @UHSPHHHH[]DUHAWAVAUATSH8IHHmHHE1LFHþLzFHHHHsHPHH HxH HH9HNHEmHEH]HELuW)HDžLeH]L)tdLIILH=HXILLKDHHL~LLH!XILH1L2HLH{ 4IH萇HH聇JHHHt HXHHHHH}Ht H}XHxHt H}XH`Ht HhXHHHt HPXH Ht H(XHHt HvXHHt H^XHmHH;EuHH8[A\A]A^A_]XHpX H HHHHt HXH}Ht H}XHxHt H}XH`Ht HhXHHHt HPXH Ht H(XHHt HxXHHt H`XHNX fDUHAWAVAUATSHXHG(H8HuH} HH8L>HFz I9HEHHHMH@hHE2M1M 1LeMff.LvKHEHH]HLH}IHHH}LHAuDރH9HOLmOHEL4HMII)MI?I>MIHMIHs 1ffDIHEH 0LUI HEH<0I|I9AI9I9I9A  LU HHHWHHDQAHHUHEH\0MHEMl0IRH)1oDoLAoTfAoDfAADoDo AoTfAoDfAT AD0HHuMt/AoAoLAofAoDfAADH9LUMu1LUffff.HNH)HHt$Hffff.AAAHHuHrPHEIL HEIT @\\A\\A\\\A\A\ HH9uI1ILeHNH9HMHH)HHt2HHffff.AoAo ffA HHuH r}HEIL0KT0ffff.oDoLffA oDoLffALoDoLffAL oo ffAL0HH9|H}H9M)IeI I4LLM,IL;~e4 HH;^' HEHHHMH@hHE-1M 1Mffff.LvJHEHLeI$M$H}E,I$M$H}MuAAAAM9MOM$MM)MI?I>MIMuIrJ I H9J I91IKH)LHt!Hfff.AMIMIr"HMJ I I9K I91IKH)LHtHfA|AL;~HX[A\A]A^A_]ffff.UHAVSIPȸXHHsmHHH{L2HmmHHH[A^]IH~XLlX 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BfALEHHuILMLr?H0J yTAfATETAfATETAfATEAfATEHI9uH;H9HEHHLLI)ILIFHxHHLHHO,6HQH)HHHH1fILMHEHIMLHHI<$H9uSHIffff.MLH HH I; utHHL9rbf~^HHHIrPff.LMLILHHI H9|HHHHL9rfN +M?HBxIs1f.fnpfpHxps1HH1f.CDCDC DC0DC@DCPDC`DCpCCCCCCCCHH{Ht%HHDH HuHHI9tffff.fCHI9uL;HPHHhHt HpVH@Ht HHVH(Ht H0ъVHHt H蹊VHHt H衊VHHt H艊VHHt HqVH}Ht H}_VHOkHH;Eu#HHĸ[A\A]A^A_]J4K|k荊V5VIH}1;H5WH WHLEfffEHE@߉VHEforhfEHis a shaHH0Hor data HH(Ht's tensHH Hnal as iHHHdimensioHHHst be 1 HHHShape muHf@8pe@:AHWHHMLhE1H5*kHA&kLuVH}^V H}SV IA%IAIIEt H}܈VLMt?HL9t$HX@t Hx豈VHI9uHLL葈Vt H|Vt HgVLeMt1HEL9t>HX@t HxBVHI9uH}Le-VELRVLIIIH}Ht H}VHhHt HpԇVH@Ht HH輇VH(Ht H0複VHHt H茇VHHt HtVHHt H\VHHt HDVH}Ht H}2VL V UHAVSIPVHH:BkHHH{LO0HAkHHH[A^]IHΆVL輁V @UHAWAVAUATSHxIHxHKkHHEоLHH H+HH/HHIH,IHw2IffEHEMJ<HHL9#VKLHEHEHHULIJ4HHHrH9JL9HLODI)DHtH@HHHHHuI8L1ҐHHH\H\H\H\H\H\H\ H\ H\(H\(H\0H\0H\8H\8H\@H@H9uI?I!IPHH_H s 1Hu|HSH)1fDADALLAD AL0D L0AD@ALPD@LPAoD`ALpD`LpHHuHt8HHffff.AoDALD H HuL9 M)IJDHE1L=HHHHUHOMHHH@H HH9HNHE1?HEHEHffEHELhHpL)tEIIM9k H袃VILeLeKHEH~LLHVILeHU1Lr0IIs2IL@ILy@IH}HHt H}VKLI6HH@I9 HCHpHHIE@HE1L5fLI6LhIHHHLL]HELHHHHHHIILIH9uqIHL)HHffff.I4HI HI I; IHH9r~fffff.~nHHILM)IIrRffff.HI4HHHIII H9|HHIHL9rfHILhMI@uAAAAI9LOOTHM IM)MI?I>MIMIrHIII9s~KI9su1IFH)LHtH@A44HHuH7HID ff.L LLLL L HI9uLHHBHHNHs1HzHIDpHYH)1DLLDLD L0DLD@LPoD D`LpH HuHt>HIDHHfffff.oD0 07L7H HuI9HLO7IIVI9IMHL)LHt"HLAofHHuHroHIDpf.D)D)DD)D D)D0D)D@D)DPD)D`ofDpH L9|LHLI9M)I?JHI LHH HHH9sIKDH9MII@HHVHs1|HBH)HJ1fffff.LDLD L0DLD@LPLoD`LpD L0H HuHHtKHHHDIMIHf.BoD0B 0B7BL7I HuM9MD)HpL)HtHCBIHuHrIHHHD BLB BLBLBLBLB BL IL9uL;yI9pLMI@1LI6MMLUHEHLHLHHHLI $H9ufHHH)HHfI4HHHHI;HHH9r~fffff.~nHHHLI)IIrRffff.HI4HHHHHIH9|HHHHL9rfG$HHAuDڃH9HOII)MI?I>MIHVHs1<fff.HHfAnfpHNHHxH8s1HH_H)1ffffff.ADADAD AD0AD@ADPAD`ADpAAAAAAAAH@HjHt,HHfffff.AD AD H HuH9Htffff.EdHH9uI4IfAnfpHZH9HMHH)ӉHHtHH@fADHHuHHrDfADfADfAD fAD0fAD@fADPfAD`fADpH H9|OTH9M)IMIfAnfpIKHHxHs 1HuSwH_H)I1DDDDDD D0H HuHt)HLHHDADADH HuM9HtLf.EdHH9uL;HxHHhHt Hp xVH@Ht HHxVH(Ht H0wVHHt HwVHHt HwVHHt HwVHHt HwVH}Ht H}~wVHHX@t HxauVHI9uH}LeLuVELquVLIIIH}Ht H} uVHhHt HptVH@Ht HHtVH(Ht H0tVHHt HtVHHt HtVHHt H{tVHHt HctVH}Ht H}QtVL?oV UHAVSIP8tVHHZ/kHHH{Lou0H.kHHH[A^]IHsVLnV @UHAWAVAUATSHIIH9kHHEоLHH H+HHR HHHZIH/d2IffEHEM J<HHHH9 GsVKLHEHEHHULIJ4HHHrH9JL9HLODI)DHtHHHHHHuI8L1ҐHHH\H\H\H\H\H\H\ H\ H\(H\(H\0H\0H\8H\8H\@H@H9uI?I!IPHH_H s 1Hu|HSH)1fDADALLAD AL0D L0AD@ALPD@LPAoD`ALpD`LpHHuHt8HHffff.AoDALD H HuL9 M)IJDHE1L]HHHHUHopLPHHH@H HH9HNHEHELuffEHELhHpL)t^LIIHI9| HpVILmLmKDHEHL~LLHrVILmHU1L_0II|$a2IL7HL7IH}Ht H}-pVKLIHHH9LEHLLI)ILKH(IFH8IIAqDLHHHHFL)MHhH@HHXHHH IFHJHxI@pHpI@H`LI@H01HILffff.IHIMHEHIMLHHI $H9uLHIfDMLH HH I; utHHL9rbf~^HHHIrPff.LMLILHHI4H9|HHHHL9rfHxItMHJIs 1SILHH H(J4H9sLHH9s 1LH8 sLE1H@HpLJHhE1ffff.BDȐBLȠCDCLBDȰBLCD CL0BDBLCD@CLPBoDB CD`CLpIHuH@t=H`HIHXDBoDB CD CL I HuHHHI9ILDMIMLI)Ht$H fff.H ILHHuILMLrHX@t HxbfVHI9uH}LeMfVELrfVLIIIH}Ht H} fVHhHt HpeVH@Ht HHeVH(Ht H0eVHHt HeVHHt HeVHHt H|eVHHt HdeVH}Ht H}ReVL@`V UHAVSIP8eVHHZ kHHH{Lof0HkHHH[A^]IHdVL_V @UHAWAVAUATSHIIH*kHHEоLHH H+HHG HHHZIH/U2IW)EHEMJ<HHHH9 IdVK HEHEHHULIJ4HHHrH9JL9HLODI)DHt!Hff.HHHHHuI891ҐHHH\H\H\H\H\H\H\ H\ H\(H\(H\0H\0H\8H\8H\@H@H9uI?I!IPHH_H s 1HuyHSH)1fDAALLAD AL0D L0AD@ALPD@LPAD`ALpD`LpHHuHt(HHADA D H HuL9M)IJDHE1LpHHHHUHaLLHHH@H HH9HNHE HEL}W)EHELhLpM)tOLHHH9LaVILuLuIHEM~LLL/cVMLuHU1HQ0HH{"R2IHW HHH IH}Ht H}CaVMLM$M~HL9LXIGHxHPIwH`DLHHHzH)HHHHHhHHpE1IfLM$IILEHELLLMLIEIEHH H9utIEHL)HHffffff.MLITHITH;IDHH9r{ff.~nHHIUHL)HHrQfff.LMLILIDIDH4H9|HHITHH9rfDK>MHKI r*LIH4 K<8LH9LHH9|1H`H)Hht"Hpff.2A6HHuH&KLT1A6T1AT6T1AT61AT6HI9uHx`s1HKLpH1ffffff.D1L1A6AL6D1L1AD6 AL60D1L1AD6@AL6PD1 1AD6`AL6pHHuHt3HHff.D2 2AD6A 6H HuHHI9H;X]HL9M1f.LI $HLEHELLHLHIEIELIH9ugIEHL)HHfI HItHItI;4IDHH9r{ff.~nHHIULM)IIrQfff.HI HHHIDIDI4H9|HHITHL9rfDJ?M~ C4L]VL9HHHhHt Hp+\VH@Ht HH\VH(Ht H0[VHHt H[VHHt H[VHHt H[VHHt H[VH}Ht H}[VH kHH;Eu"HHĈ[A\A]A^A_]J4KHX@t HxqYVHI9uH}Le\YVELYVLIIIH}Ht H}YVHhHt HpYVH@Ht HHXVH(Ht H0XVHHt HXVHHt HXVHHt HXVHHt HsXVH}Ht H}aXVLOSV UHAVSIPHXVHHjkHHH{LY0HkHHH[A^]IHWVLRV @UHAWAVAUATSHIIH/kHHEоLHH H+HHB HHHjIH?H2IffEHEM J<HHHH9 WWVKLHEHEHHULIJ4HHHrH9JL9HLODI)DHtHHHHHHuI8L1ҐHHH\H\H\H\H\H\H\ H\ H\(H\(H\0H\0H\8H\8H\@H@H9uI?I!IPHH_H s 1Hu|HSH)1fDADALLAD AL0D L0AD@ALPD@LPAoD`ALpD`LpHHuHt8HHffff.AoDALD H HuL9M)IJDHE1LmHHHHUHTLPHHH@H HH9HNHEM HELuffEHELhHpL)t^LIIHI9l HTVILmLmKDHEHL~LLH"VVILmHU1LD0II|$E2IL HL IH}Ht H}=TVK|LIHHH9LEHLLI)ILKpH(IFH8IIAqDLHHHHFL)MHhH@HHXHHH IFHK6HxI@pHpI@H`LI@H01HILIHIMHEHIMLHHI $H9uLHIfDMLH HH I; utHHL9rbf~^HHHIrPff.LMLILHHI4H9|HHHHL9rfHxItMHJHIs 1SILHH CH(J4NH9sLHCH9s 1LH80sLE1H@HpLJHHhE1ffff.BDHBLHCDMCLMBDHBLHCDM CLM0BDHBLHCDM@CLMPBoDHB HCDM`CLMpI@HuH@t=H`HpMHXfDBoDB CD CL I HuHHHI9ILDMIMLI)Ht%H fff. BfALEHHuILMLr?H0J yTAfATETAfATETAfATEAfATEHI9uH;H9HEHHLLI)ILIFHxHHLHHO,6HQH)HHHH1fILMHEHIMLHHI<$H9uSHIffff.MLH HH I; utHHL9rbf~^HHHIrPff.LMLILHHI H9|HHHHL9rfN +M?HBxIs1f.fnpfpHxps1HH1f.CDCDC DC0DC@DCPDC`DCpCCCCCCCCHH{Ht%HHDH HuHHI9tffff.fCHI9uL;HPHHhHt HpAMVH@Ht HH)MVH(Ht H0MVHHt HLVHHt HLVHHt HLVHHt HLVH}Ht H}LVHkHH;Eu#HHĸ[A\A]A^A_]J4K|kLVuLVIH}qq;H5XWH fWHLE(ffEHE@LVHEfoG4hfEHis a shaHH0Hor data HH(Ht's tensHH Hnal as iHHHdimensioHHHst be 1 HHHShape muHf@8pe@:AHbWHHML*E1H5jHjLKVH}HV H}HV IA%IAIIEt H}KVLMt?HL9t$HX@t HxJVHI9uHLLJVt HJVt HJVLeMt1HEL9t>HX@t HxJVHI9uH}LemJVELJVLIIIH}Ht H},JVHhHt HpJVH@Ht HHIVH(Ht H0IVHHt HIVHHt HIVHHt HIVHHt HIVH}Ht H}rIVL`DV UHAVSIPXIVHHzkHHH{LJ0HkHHH[A^]IHIVLCV @UHAWAVAUATSHIIH?kHHEоLHH H+HHQ HHHzIHO92IffEHEM J<HHHH9 gHVKLHEHEHHULIJ4HHHrH9JL9HLODI)DHtHHHHHHuI8L1ҐHHH\H\H\H\H\H\H\ H\ H\(H\(H\0H\0H\8H\8H\@H@H9uI?I!IPHH_H s 1Hu|HSH)1fDADALLAD AL0D L0AD@ALPD@LPAoD`ALpD`LpHHuHt8HHffff.AoDALD H HuL9 M)IJDHE1L}HHHHUHELPHHH@H HH9HNHE}HELuffEHELhHpL)t^LIIHI9{ HEVILmLmKDHEHL~LLH2GVILmHU1L50II|$(62ILHLIH}Ht H}MEVKLIHHH9LEHLLI)ILKH(IFH8IIAqDLHHHHFL)MHhH@HHXHHH IFHJHxI@pHpI@H`LI@ H01HILffff.IHIMHEHIMLHHI $H9uLHIfDMLH HH I; utHHL9rbf~^HHHIrPff.LMLILHHI4H9|HHHHL9rfHxItMHJIs 1SILHH H(J4H9sLHH9s 1LH8sLE1H@HpLJHhE1ffff.BDBLCDCLBDBLCD CL0BDBLCD@CLPBoDB CD`CLpI HuH@t=H`HIHXDBoDB CD CL I HuHHHI9ILDMIMLI)Ht#H fff. ALHHuILMLr9H0J fTATTATTATAT HI9uH;H9HEHHLLI)ILIFHxHHLHHN,HQH)HHHH1ffff.ILMHEHIMLHHI<$H9uSHIffff.MLH HH I; utHHL9rbf~^HHHIrPff.LMLILHHI H9|HHHHL9rfN +M>HBIs1ff.fnfpHx8s1HH1ffffff.DD D0D@DPD`DpH@H{Ht%HHfDDH HuHHI9tffff.HI9uL;HPHHhHt HpB>VH@Ht HH*>VH(Ht H0>VHHt H=VHHt H=VHHt H=VHHt H=VH}Ht H}=VHkHH;Eu#HHĸ[A\A]A^A_]J4K|\=Vv=VIH}rb;H5IWH kXWHLEffEHE@ =VHEfoH%hfEHis a shaHH0Hor data HH(Ht's tensHH Hnal as iHHHdimensioHHHst be 1 HHHShape muHf@8pe@:AHSWHHMLE1H5jHjLHX@t Hx;VHI9uH}Len;VEL;VLIIIH}Ht H}-;VHhHt Hp;VH@Ht HH:VH(Ht H0:VHHt H:VHHt H:VHHt H:VHHt H:VH}Ht H}s:VLa5V fUHAVSIPX:VHHzjHHH{L;0HjHHH[A^]IH:VL4V @UHAWAVAUATSHIIH?jHHEоL۴HH H+HHR HHHzIHO*2IffEHEM J<HHHH9 g9VKLHEHEHHULIJ4HHHrH9JL9HLODI)DHtHHHHHHuI8L1ҐHHH\H\H\H\H\H\H\ H\ H\(H\(H\0H\0H\8H\8H\@H@H9uI?I!IPHH_H s 1Hu|HSH)1fDADALLAD AL0D L0AD@ALPD@LPAoD`ALpD`LpHHuHt8HHffff.AoDALD H HuL9 M)IJDHE1L}HHHHUH6LPHHH@H HH9HNHEHELuffEHELhHpL)t^LIIHI9| H6VILmLmKDHEHL~LLH28VILmHU1L&0II|$('2IL HLIH}Ht H}M6VKLIHHH9LEHLLI)ILKH(IFH8IIAqDLHHHHFL)MHhH@HHXHHH IFHJHxI@pHpI@H`LI@H01HILffff.IHIMHEHIMLHHI $H9uLHIfDMLH HH I; utHHL9rbf~^HHHIrPff.LMLILHHI4H9|HHHHL9rfHxItMHJIs 1SILHH H(J4H9sLHH9s 1LH8 sLE1H@HpLJHhE1ffff.BDȐBLȠCDCLBDȰBLCD CL0BDBLCD@CLPBoDB CD`CLpIHuH@t=H`HIHXDBoDB CD CL I HuHHHI9ILDMIMLI)Ht$H fff.H ILHHuILMLrHX@t Hx,VHI9uH}Lem,VEL,VLIIIH}Ht H},,VHhHt Hp,VH@Ht HH+VH(Ht H0+VHHt H+VHHt H+VHHt H+VHHt H+VH}Ht H}r+VL`&V UHAVSIPX+VHHzjHHH{L,0HjHHH[A^]IH+VL%V @UHAWAVAUATSHIIH?jHHEоLۥHH H+HH HHHzIHO2IW)EHEMJ<HHHH9T i*VK HEHEHHULIJ4HHHrH9JL9HLODI)DHt!Hff.HHHHHuI891ҐHHH\H\H\H\H\H\H\ H\ H\(H\(H\0H\0H\8H\8H\@H@H9uI?I!IPHH_H s 1HuyHSH)1fDAALLAD AL0D L0AD@ALPD@LPAD`ALpD`LpHHuHt(HHADA D H HuL9vM)IJDHE1L萣HHHHUH'LLxHHH@H HH9HNHE虯HEL}W)EHELhLpM)tOLHHH9L'VILuLuIHEM~LLLO)VMLuHU1H.0HH{B2IHHHذIH}Ht H}c'VMLI$HL9.LLEHLLI)IHK8HPIGH`IIAZDLHHpHCL)HHhHHH0HHHIGH8I@pHL(I@HXE1LL@fffff.I$ILMHEHIILHHIMH9uOHIfMLH HH I;LusHHL9ra~^HHHIrPff.LMLILHHI\H9|HHHHL9rO>MH(H0I s1Kff.LLIHLHPH3I9sLHH9s1LH``s1HhHH 0L1ff.DLAALDLAD AL0DLAD@ALPD AD`ALpHIuHht3HHff.D ADA H HuHpHI9LfMML8I)H0t&HHffffff. A HHuIMML@rHX@t Hx1VHI9uH}LeVELAVLIIIH}Ht H}VHhHt HpVH@Ht HHVH(Ht H0VHHt H{VHHt HcVHHt HKVHHt H3VH}Ht H}!VLV UHAVSIPVHH*jHHH{L?0HXjHHH[A^]IHVLV @UHAWAVAUATSHIIHjHHEоL苘HH H+HH2 HHH*IH 2IffEHEM J<HHHH9 VKLHEHEHHULIJ4HHHrH9JL9HLODI)DHtHHHHHHuI8L1ҐHHH\H\H\H\H\H\H\ H\ H\(H\(H\0H\0H\8H\8H\@H@H9uI?I!IPHH_H s 1Hu|HSH)1fDADALLAD AL0D L0AD@ALPD@LPAoD`ALpD`LpHHuHt8HHffff.AoDALD H HuL9M)IJDHE1L-HHHHUH?LXHHH@H HH9HNHEmHELuffEHELhHpL)t^LIIHI9\ HVILmLmKDHEHL~LLHVILmHU1L 0II|$ 2IL譲HL袲IH}Ht H}VK|LIHHH9LELHLKpH0IFH@IIAqINHDLHHPHFL)HpHHHH`HHH(I@pHxK6HI@HhLI@H81HIfIHMMHEHIMLHHI H9umHHH)HHfM,LHHHI;HHH9r~fffff.~nHHHLI)IIrRffff.LM,LILHHI4H9|HHHHL9rfHM MHJPIr3LHH CH0J4VH9LHCH91LI)Ht#H(ff. BfA DHHuIUH8J Qf.TAfADTAfATDTAfATDAfATDHI9uH@0sE1HHHxJPHpE1BD@BL@CDCLDBD@BL@CDD CLD0BD@BL@CDD@CLDPBoD@B @CDD`CLDpI@HuHHt=MHhJPH`BoDB CCLI HuHPHI9vfff.L;H9HEHLHLIFHHHMIO$6HQH)HHHH1 fILMHEHIMLHHI H9udHHH)HHM,LHHHI;HHH9r~fffff.~nHHHLI)IIrRffff.LM,LILHHI H9|HHHHL9rfN #M/HBPIs1 f.fnpfpHps1HH1f.CDCDC DC0DC@DCPDC`DCpCCCCCCCCHH{Ht%HHDH HuLM9t@fCHI9uL;HXHHhHt HpVH@Ht HHVH(Ht H0VHHt HVHHt HVHHt HVHHt HVH}Ht H}oVHjHH;Eu#HHĸ[A\A]A^A_]J4K|{VEVIH}A7;H5WH .WHLEffEHE@VHEfogfEHis a shaHH0Hor data HH(Ht's tensHH Hnal as iHHHdimensioHHHst be 1 HHHShape muHf@8pe@:AH(WHHMLE1H5ȲjHQjLVH}nV H}cV IA%IAIIEt H}VLMt?HL9t$HX@t HxVHI9uHLLVt HVt HwVLeMt1HEL9t>HX@t HxRVHI9uH}Le=VELbVLIIIH}Ht H}VHhHt HpVH@Ht HHVH(Ht H0VHHt HVHHt HVHHt HlVHHt HTVH}Ht H}BVL0 V UHAVSIX(VHHLH[A^]IHVL V UHAWAVAUATSHHIH/jHHEHjHII~0H21kIAFPHEW)pHEƅpDžqfmodƅuH}HpHMH!0pt H}GVH]Ht!HECt H{'VHVHEHs8HAFPHEH[jHH;EuHĘ[A\A]A^A_]AV VIHX2;H5<WH +WHpLXPlW)@HDžP0 VHP(OUf)@Hher 0 orHHHalue eitHHHt have vHHHfmod musHf@ 1@"AH*WHpH@LE1H5jHjLB V IA+IAII@t HP VLeMt6HEL9t!HX@t Hx VHI9uH}LLez VEt H}k Vpt H}Y VLXMt:H`L9taHX@t Hx. 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HH9HNHEHEHHEW)HDžL}H]L)tUIILH=fH5SILLKHH~LLHo6SILH1LL$-HHHt H4SHHEHH{4%/IHHHJHHHH}Ht H}:4SHxHt H}%4SH`Ht Hh 4SHHHt HP3SH Ht H(3SHHt H3SHHt H3SHgHH;EuHx[A\A]A^A_]3S3SIHxX8H5?TH THLxW)`HDžp 43SHp(&c)`Hrst outpHHHo get fiHHHfailed tH@ut!AHASHH`LCE1H5AgHgL2S H/S IA+IAII`t Hpg2SLMt?HL9t$HX@t Hx<2SHI9uHLL2St H2St H1SLxMt:HL9t=HX@t Hx1SHI9uHxL1SEt3L1S)LIHHtH{1SIH}Ht H}b1SHxHt H}M1SH`Ht Hh51SHHHt HP1SH Ht H(1SHHt H0SHHt H0SL+S ff.UHAWAVAUATSH(HG(H8HuH}]HH8L&HFI9HEHHHMH@hHE@HFIHH]HLH}jOILHH}LMOMKLLIAWH*WH*0SLH,A$LiIIHu pfIHtfN41fADA WH*WH*0SH,AADALWH*WH*l0SH,ADIDHL9uHuL&L;f|L&L;foHEHHHMHHhHML@HFIHLLuMH}NIIIH}NMMH CDWH*JEu1WH*I/SLH,A$LiIHuc@IHtWN<1fADWH*EK/SH,AADWH*E+/SH,ADIDHL9uHuL&L;f;I92HEHHHMH@hHEfffff.HFIHH]HLH}LILHH}LLMO|WH*JMu2AWH*T.SH,A$MoIHuif.MHtWO41fADWH*M .SH,AADWH*M-SH,ADIDHL9uHuL&L;fH([A\A]A^A_]ÐUHAWAVAUATSHXIHG(H8LeH}HH8M,$ID$I92 HEHHHMH@hHEffffff.ID$IDI$H]HLH}GKILHH}L*KLMMLJ HHyHOTH4 ItHIHLI9@M9AI9M9DP HLuH}HIHI!K|OLEKL}IM|MDE1Co\CoCodf:Co f:1f~f~1fnf:"f:f:1f:"f:f:1f:"f:f:1f~f~1fnf:"f:f:1f:"f:f:1f:"CTC\IM9#L9ULEL}LLuuufff.LLLILH)u1A3HIHHr8K 1f31A437D31At3T7HD3HH9uLeM,$M;l$M<$M;|$HEHHHMHHhHMHM-SHH`LE1H5gHgLNS H4S IA+IAII`t HpSLMt?HL9t$HX@t HxSHI9uHLLlSt HWSt HBSLxMt:HL9t=HX@t HxSHI9uHxLSEt3L%S)LIHHtHSIH}Ht H}SHxHt H}SH`Ht HhSHHHt HPmSH Ht H(USHHt H=SHHt H%SLS ff.UHAWAVAUATSHxHUgHHEHHIHHOHVHHHH HxH HH9HNHEHEHHEW)HDžL}H]L)tUIILH=fHRSILLKHH~LLHSILH1L -HHHt HSHHEHH{t /IHHHJHHHlH}Ht H}zSHxHt H}eSH`Ht HhMSHHHt HP5SH Ht H(SHHt HSHHt HSHFgHH;EuHx[A\A]A^A_],SSIHx>8H5'&TH gTHLxWW)`HDžp tSHp( c)`Hrst outpHHHo get fiHHHfailed tH@ut!AH.(SHH`LE1H5gH gL>S H$S IA+IAII`t HpSLMt?HL9t$HX@t Hx|SHI9uHLL\St HGSt H2SLxMt:HL9t=HX@t HxSHI9uHxLSEt3LS)LIHHtHSIH}Ht H}SHxHt H}SH`Ht HhuSHHHt HP]SH Ht H(ESHHt H-SHHt HSLS ff.UHAWAVAUATSH(HG(H8HuH}OHH8L&HFnI9HEHHHMH@hHE@HFIHH]HLH}5ILHH}L5MKLLW*WA*I)SL,A$LiIIHuifIHtXN41fWA*DWA* S,AWA*DWA*LS,ADIDHL9uHuL&L;fRL&L;fEHEHHHMHHhHMLfHFIHLmIHEH}Lj4IIIH}P4MMHHMWB*JEu-W*IS,A$LLhIHu[@IHtON<1fWA*LES,AWA*LES,ADIDHL9uHuL&L;fI9HEHHHMH@hHEfDHFIHH]HLH}:3ILHH}L3MO|W* JMu%WA*S,A$MoIHu XMHtOO41fWA*DM~S,AWA*DMbS,ADIDHL9uHuL&L;fH([A\A]A^A_]fUHAWAVAUATSH8IHHC(H8LeH]8HH8M<$ID$*I9HCHEHChHEID$II$HLH}1ILHH}L1Lfo5YcfvMNH4J HLQI%O I<H|HHDI9M9I9L9@DŽ@ HULUMHI!O$N KE1ffffff.CoBof:f:f~f~Ǚfnf:"f:f:f:"f:f:f:"fofffoffffofffoffffffC$IM9]L9]HUu JMILMJ H)1ff.3A<1~xy~A4HH9uLeM<$M;|$H]M,$M;l$HCHEHChHEHHUMHI!O|NfAnfpE1fDBo f:f:f~f~fnf:"f:f:f:"f:f:f:"fofffoffffofffoffffffClIM9bM9LeHUfff.ID$IDI$HHEH}.IHLH}.Lfo=UcfvIHHtJ HEF HHLXIrLHDI9IDH9uMIIJL)1A :Dș~xy~A?HH9uM,$M;l$H]I9}HCHEHChHEfDID$II$HLH}-ILHH}L-LfMN JHLHIrLHDI9KI9MMMJtL)@u0AҸO!Dž҉HNAIIHsO1ffff.A>ȅ1!˅xÅOA;AD>ȅ1!ƉȅxOAt;ID>HL9uHU1MHI!OO4fnfpfnfpfnfp1fAo$f:f:f~f~Ùfnf:"f:f:f:"f:f:f:"fffffofffffrfrffA,HI9iM9HUNfff.LeM<$M;|$H]H8[A\A]A^A_]f.UHAWAVAUATSHxHUgHHEHHIHHOHVHH HH HxH HH9HNHEHHEH8HEW)HDžL}H]L)tUIILH=fHR SILLKHH~LLH SILH1L,HHHt H SHHEHH{t.IHYHHJJHHHH}Ht H}z SHxHt H}e SH`Ht HhM SHHHt HP5 SH Ht H( SHHt H SHHt H SHFgHH;EuHx[A\A]A^A_], S SIHx.8H5'TH XTHLxqWW)`HDžp t SHp(b)`Hrst outpHHHo get fiHHHfailed tH@ut!AH.SHH`LE1H5gH gL> S H$S IA+IAII`t HpSLMt?HL9t$HX@t Hx|SHI9uHLL\St HGSt H2SLxMt:HL9t=HX@t HxSHI9uHxLSEt3LS)LIHHtHSIH}Ht H}SHxHt H}SH`Ht HhuSHHHt HP]SH Ht H(ESHHt H-SHHt HSLS ff.UHAWAVAUATSHxHEgHHEHHIHHOHVHHHH HxH HH9HNHE8HEH(HEW)HDžL}H]L)tUIILH=fHBSILLKHH~LLHSILH1L|,HHHt HSHHEHH{d.IHIHH:JHHHl H}Ht H}jSHxHt H}USH`Ht Hh=SHHHt HP%SH Ht H( SHHt HSHHt HSH6gHH;EuHx[A\A]A^A_]SSIHx)8H5TH TTHLxGW)`HDžp dSHp(b)`Hrst outpHHHo get fiHHHfailed tH@ut!AHSHH`LsE1H5qgHgL.S HS IA+IAII`t HpSLMt?HL9t$HX@t HxlSHI9uHLLLSt H7St H"SLxMt:HL9t=HX@t HxSHI9uHxLSEt3LS)LIHHtHSIH}Ht H}SHxHt H}}SH`Ht HheSHHHt HPMSH Ht H(5SHHt HSHHt HSLR ff.UHAWAVAUATSH8HG(H8HuH}lHH8L&HFI9HEHHHMH@hHEHf.HFIHH]HHHEH}I ILHH}Lf Lf(Kdf(3dMHMJ LWH*WI*IHRLH,I$LiIIHuiIHtZN41fWI*DWI* RH,IWI*DWI*LRH,IDIDHL9uHuL&L;fbL&L;fUHEHHHMHHhHMLHFIHLmIHEH}L IIIH}p MMHHMWJ*JEu-WH*IRH,I$LLhIHu [fIHtQN<1fWI*LERH,IWI*LERH,IDIDHL9uHuL&L;f+I9"HEHHHMH@hHE@HFIHH]HLH}Z ILHH}L= MO|WH* JMu4WI*RH,I$MoIHuhffffff.MHtQO41fWI*DMRH,IWI*DMqRH,IDIDHL9uHuL&L;fH([A\A]A^A_]UHAWAVAUATSH(IHG(H8H}HH8MeIELmI9 HEHHHMH@hHEIEIIELmIMH}IIMH}HHtHOItH1I8H >HHH~Hx HyH~HIRHW)pHEƅpDžqbetaƅuIL$\HXHpL,HXHDžXHtCt H{RHRpt H}RHHgHH;EuHĨ[A\A]A^A_]RRIHX8H5,TH ,THpLX H@THpH@LѸDž<H5zgHQvgLR Iư<Iư<IgIư<8Iư<I?Iư<Iư<IIưMIM@fnD%IZE1IIOH)Ht H8tHH@fnDM HKH HALSAAHHH0IT$pLHpH)HH(HH8LHHE1Mfffff.HwHvLHIL9svHIL9vj1LH)MtHAADHHuH%DAADADADADADAD AD HH9uLH s1H(zL1AALDLAD AL0DLAD@ALPDLAoD`ALpD H IuH(t6H H8DAoDA AD AL H HuH0HH9IILML;HjMIfAnfpIqHHPH81H8[Hx@fnDHH<HCHPLkAAHHH0IL$0LH(HH H)H8LHH1LHrH)1AADAD AD0AD@ADPAD`ADpAAAAAAAAH@HkHt(IDHfffff.@H HuM9t%LL)Kffffff.D*HHuO<IfAnIsI9IMHL)މLHtHLfDA)HHuHrDfff.A)A)DA)D A)D0A)D@A)DPA)D`A)DpH L9|I9;M)I MIfAnfpIAHH~HV1H1H(HMHv"ILKItH9vuIII9shE1LL)MtHfDE:FMIMIrKL9KL91IGH)LHtHAAHHuHSAAADADADADAD AD ADADADADADADADADHI9uKITpHGH)1fffff.DDDDDDDH HuHt$IMKDH@@H HuM9t%ML)Kffffff.D(HHuHM0HEMuWHL$Ld$H$HHHLfoL(&HߋgHH;E@H[A\A]A^A_]LHHGHHJHs 1Hu}HAH)1AALAALAD AL0AD AL0AD@ALPAD@ALPAoD`ALpAD`ALpH HuHt,HHAoDA ADA H HuI9K<:IIwH9HMHL)LHtHLAofAHHuHriAA)ADA)DAD A)D AD0A)D0AD@A)D@ADPA)DPAD`A)D`AoDpfADpH H9|L9M)IIK H9sKI H9MII@HHDnAHs 1Mu{K :IDpITpIMH)1DLDLDLDLDLDLoD D H HuMt2IMJIAoDA ADA H IuM9LD)ICH)HtHAAHHuHI)IDIL1ҋtttttttttttttt44HI9u^R;RIHh47H5mTH TH}Lh8ffPHDž`0RH`foWdfPH provideHH H_data isHHHred if cHHHis requiHHHc_shape Hf@(dAH?THuHPLѡE1H5cgHX_gLR IA(IA}IIPt H`RLeMt6HEL9t!HX@t HxRHI9uH}LLeREt H}REt H}RLhMt:HpL9tEHX@t Hx~RHI9uHhLpcREtLRLDR LUHAWAVAUATSHHgHHEMIIGH>HvH)HHHHHHYH)H9tHuLgM'HIW3HuL'HL'M'IWAM'HIWHHpEHEHI_AH1H9HhHo.IwH0%RHTH016RHHH`HHPHXHPH@H@HIwHɿRHTH1ڽRHHH(HHPH HH@H@HH5$TH}HhHPLbHHHMx87HHHDžHI_IGH@Ct H{6RH.RHHHDžHHtCt H{RHREP'03ht Hx譾RM'I_IFI+HHH 5TH}:7HEHEI_IGHCt H{KRHCRH]HEHCt H{RHRI?x}I~vIxoH\gHH;EH[A\A]A^A_]L腮.Ht5IINH)HHFHH9HEI?y袽RIHh7H5TH TH}Lh,(HPHTHuHPL誜DžH5^gH*ZgL^RHDžHEH}RH(μRH赼RPH`蜼R0H@胼RhHL9uHPHH9HH9X蚼RBRIHh;7H5gTH TH}Lh ȘHP<HTHuHPLJDžH5A]gHXgLRz賻RIHh7H5 TH TH}Lh9HPHTHuHPL軚DžH5\gH;XgLoRIưXIưI|IưIư2ITIPt H`豺RLeMt6HEL9t!HX@t Hx茺RHI9uH}LLerREt H}cREt H}TRHhHHpH9L`@t Hx!RLL9uIPt H`RLeMt6HEL9t!HX@t Hx׹RHI9uH}LLe轹REt H}讹REt H}蟹RHhH HpH9L`@t HxlRLL9uIư-Iư}IIPt H`%RLeMt6HEL9t!HX@t HxRHI9uH}LLeREt H}׸REt H}ȸRHhHt:HpH9tHL`@t Hx蝸RLL9uHhHp肸RL裸R HIEu6uMugPus0uhH}Rt ItH(Rt ItHշRPtH`R0tH@諷Rhu4E@IPOI0FIhtHxbRII_IGHtCt H{;RH3RL!R fUHAWAVAUATSH8LHHIHHF|gHHEH8HWgHHHLH@H8LRRHEEL%nzgMl$LI@L8L]RH0zgHHHDž(HDž HDžHDžDž0HLHHL LLLLL8Ht H ϵRH说RH5egHH>RH8XRHzgHH;EuLH8[A\A]A^A_]ܵRILL8Ht H IRH)RIH5 egHH賳RIH8ȴRLR fDUHAWAVAUATSPMIHIIL臷RLLH HsHEsHESLA$It$IEt$IET$LƝAIvIEvIEVL褝H[A\A]A^A_]HHHHDUHSPH)RH qgHHHKHHH[]UHHqgHHHGHF]@UHH6HHHH]fUHHNH1H; #]gHD]DUHH-Ug]UHAVSIh舳RHHLH[A^]IH[RLIR UHAVSIhHRHHLH[A^]IHRL R UHAWAVAUATSHXIIHHHxgHHE1L-IL%HIc~PIw I+wMgHbILL.ILLLL6.IILL H HH0H[SgHHHLH@H0LܱRHHEEL-ugIEHHI@L0LڰRL%ugILHDž HDžHDžHDžDž(H5TH(ԚH7RH5TH賚HH*RH5TH蔚HL RL0HH/HHH*7t HRHHL0Lt HɰRH詯RH5`gHH8RLVRUI W)HDžLtMHLcLH>FIL`RILOdLLLERLE1LE1DDHL&1HH臟,Ho=IH萠.HQtgWHL&E~\IcDH1fDE~3AD1f .u{HH9|f.A?HIHH9uHHHHt HDRHtgHH;EuHHX[A\A]A^A_]耯RHVR RPNHtiHR[VL0HH5^gHH[R L0HL~HHHttH蝮RfL0HHHL0Lt H`RH@RH5%^gHHϬRLRH#R ff.UHAVSHPIHksgHHERHþHRHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]w)H5RH}H+H5THe+H5VH+HH HQH5RH+HHH kgHHEHHUHuHuIININ0IFIVIF@H]HEHtHHRHwqgHH;Eu LHP[A^]cRIH]HEHtHqH٫RLǦR ffffff.UHAVSHPIH qgHHE覫RHþH蜫RHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH])H5RH}Ht+H5SH+H5VH6+HA H5RH]+HHHhgHHEHHUHuHuIININ0IFIVIF@H]HEHtH]HũRHogHH;Eu LHP[A^] RIH]HEHtHH耩RLnR fDUHAVSIXhRHHLH[A^]IH;RL)R UHAVSH0HIHxngHHEHBdgHII~Z,HagHIAFPW)EHEEEaxisEH}HUHMH2,Et H}裨RH]Ht!HECt H{胨RH{REAFPHEHmgHH;Eu H0[A^]貨RHEt H}:RH(R UHAVSIX(RHHLH[A^]IHRLR UHAVSHPIH;mgHHE֧RHþḨRHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]G)H5RH}H+H5SH5+H5VHf+HHHQH5RH+HHHdgHHEHHUHuHuIININ0IFIVIF@H]HEHtHHRHGkgHH;Eu LHP[A^]3RIH]HEHtHAH詥RL藠R ffffff.UHAVSHPIHjgHHEvRHþHlRHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH])H5QRH}HD+H5SH+H5aVH+HHHQH5qRH&+HHH}bgHHEHOHUHuHuIININ0IFIVIF@H]HEHtH&H莣RHhgHH;Eu LHP[A^]ӣRIH]HEHtHHIRL7R ffffff.UHAVSHPIH{hgHHERHþH RHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]臈)H5RH}H+H5 SHu+H5VH+HA H5RH+HHH$`gHHEH-PHUHuHuIININ0IFIVIF@H]HEHtHH5RHfgHH;Eu LHP[A^]zRIH]HEHtHHRLޛR fDUHAVSHPIH+fgHHEƠRHþH輠RHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]7)H5RH}H+H5SH%+H5VHV+HA H5RH}+HHH]gHHEH=NHUHuHuIININ0IFIVIF@H]HEHtH}HRH>dgHH;Eu LHP[A^]*RIH]HEHtH8H蠞RL莙R fDUHAVSHPIHcgHHEvRHþHlRHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH])H5QRH}HD+H5SH+H5aVH+HA H5xRH-+HHH[gHHEHMLHUHuHuIININ0IFIVIF@H]HEHtH-H蕜RHagHH;Eu LHP[A^]ڜRIH]HEHtH HPRL>R fDUHAVSHPIHagHHE&RHþHRHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]藊)H5RH}H+H50SH+H5VH+HA H5(RH+HHH4YgHHEHLHUHuHuIININ0IFIVIF@H]HEHtH HERH_gHH;Eu LHP[A^]芚RIH]HEHtH HRLR fDUHAVSHPIH;_gHHE֙RHþH̙RHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC 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H{XtNHH[]tNUHAWAVAUATSH8IIHH9cHHE1LwHLc1LLc(IHILIHCH;C Ld*HHCHS H)HpHHDL&I}hIEpH)HHL9u>I]PIUXH)HL9u*HAAH1MNHu^IEPIUXH)HuMHʃH71HvH PHH2qH8H@cHHHHH@H8HrNHEEL-6cM}LI@L8HqNL56cILHDž(HDž HDžHDžDž0H5)PH5[HLPqNH5<PH[HIHV`HHHF2t H!rNLL8Lt H qNLpNH5!cHHjpNH8qNL)1fWI*1HI\YAWI*DHF1I\YADHH9uM~WI*1HI\Y[H)1WI*\YAWI*D\YADHH9uHtWI*\YAHHH66cHH;EuHH8[A\A]A^A_]qNHt HpN H=HOIHLL8Lt H ]pNLAoNH5& cHHnNH8oNH kN UHAVSIpNHHLH[A^]IHoNLjN UHAWAVAUATSHHHIH5cHHEH*cHIEI}q(H$cHIEMuPW)`HDžpƅ` DžascalfDžee)EHEH`HMLHLH H}Ht H}*oN`t HpoNM}hHDžpHDžhƅ` fDžeetDžaoffsƅgW)EHEH`HMLHG H}Ht H}nN`t HpnNIMPIEXH9t;H)IMpI+MhH9H3cHH;EuHH[A\A]A^A_]nNTnNHH}L3H5OH OH`LECJW)@HDžP mNHP(v_)@HttributeHHHale in aHHHEmpty scHf@sHOH`H@HLDžH5cH} cHmNbmNHH}Z3H5OH OH`LEEIIuXI+uPHHlNHFOH1jNHHHHHPHHH@H@HH5OHxjNHHHHHPHHH@H@HIupI+uhHH!lNtHH HHiNHHH HHPHHH@H@HH5ͫNHiNHHH8HHPH0H(H@H@HH@H(iNHOH`H@HJDžH5 cHbcHkN H(35H-H %HHHHH HHHAHxH@(+7t HiNLeMt6HEL9t!HX@t HxiNHI9uH}LLeeiNEt H}ViN`t HpAiNLeMHEL9HX@t HxiNHI9uH}Le[HPhN(H8hNH hNHhNHhNHvhNLLeH@t 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H}FN@?HP.H(H}Ht H}FN@!HPHH}Ht H}hFN@t HPSFNH-H%H(M&Mt>IPL9 @HX@t Hx FNHI9uI>MPENHH8Ht I8ENHH8HLHt I ENHH8Ht IENHH8Ht I|ENH;Ht IhENHH8Ht IMENI?Ht I9ENI>Ht I%ENH(@N L ffffff.UH]vfDUHAWAVAUATSHXIHIH/ cHHE1LοILHfWf)EIG0D`DeAH]u%MMIL( HIE1MMGH}5*HHULLu=(I>t#HXOH BO1AsLqi3wLuILu1HEHM1H0$tIHuLPHEDeAt>ALutbA LɹHLML}H1ELLuHLML}WH10LAHLML}-HSكH1H H5OH}HUN3HML#2ETH}CNFHHHyHHVHXHzH)1L[[AALD L0[[AD AL0HHu HH)1WH*AWH*DADWH*DADWH*DAD HH9uHHI1fWH*HH9uHHHyHHVHHzH)1ff.ffLfZfZfAfALfD fL0fZfZfADfALHHu,1H?L[[AAL 1HffLfZfZfAfALIE/H}AN! 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RIHc^HH;EuHpHĘ[A\A]A^A_]BRIQIIH`v.H5KH KH}L`*p.W)@HDžP0QIHP(s9[)@H size 1.HH Hensor orHHH or 1D tHHHa scalarHHHMust be H@(AHKHuH@L0E1H5]H ]LAQIPIIH`u.H5KH ,KH}L`/|-W)@HDžP@PIHP(PY)@Hrted nowHH(Hot suppoHH Hion is nHHHuantizatHHHtensor qHHHNon per-Hf@0.AHKHuH@L/E1H5]H ]L=PI H`#MIH@MIH MIHLIHLIIA(IA}II@t HPqOILeMt6HEL9t!HX@t HxLOIHI9uH}LLe2OIEt H}#OIEt H}OIL`MHhL9 HX@t HxNIHI9uIA(IA}II@t HPNILeMt6HEL9t!HX@t HxNIHI9uH}LLeeNIEt H}VNIEt H}GNIL`Mt:HhL9tDHX@t HxNIHI9uH`LhNIEWL&NIJLL`LXIkHyCIHpHHHt-Ct H{MIHMI^I1Hx1H`1HXHHHHCHHHPH3LIq31IHHHMI IIIIIHpHHHCt H{LIHhIH HH(IH@HHHIH`HHhIII(IHpHHH\Ct H{LIH@I=IHpHHH2Ct H{KIHIHHLXH`t HKIHxtHtHHHxPMt#HIGHuILPLPJIHHt H4KIHHt HKIHHt HKIH HtHH8JIH@HtHH8JIH`HtHH8JIH}Ht H}JILEI H?ffffff.UHAWAVAUATSHHHHHU0LEH^HHEH~HJЃHs1ɾHu^|uH)1ɾfffff.I4ItItItIt It(It0It8HH9uHt I Hfff.H1HHuHHHW)EHEL}0MHLH=cN4L^IIHEJH]HHHLCIIH]W)EHEL"IIHEN@HH HtH9HH^H9uH\HIL;VHtH}HFIHHt H}FIH ^HH;EuH[A\A]A^A_]FIFIIH(k.H5HKH KH@L(4#H]AH/KH@HL%E1H5]H!]LUFI H}>CIIA+IAIIt H EILxMt9HEL9t$HX@t HxEIHI9uHxLLe|EI`t HpgEI@t HPREIL(Mt:H0L9tOHX@t Hx'EIHI9uH(L0 EIEtL5EIH}HtH}DI LIH}Ht H}DIL?I DUHHm^HHP]UHSPHHH^HH{PHH[]vDIfDUHAVSIhDIHH]HHH{LE#H^HHHPLVH[A^]IHDIL?I fUHAVSHPIHK ^HHECIHþHCIHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]W) H5QIH}H贎"H5 KHE"H5IMHv"HH HQH5IH薇"HHH^HHEHHUHuHuIININ0IFIVIF@H]HEHtH薳HAIHW^HH;Eu LHP[A^]CBIIH]HEHtHQHAILIfUHSPH1HH[]fDUHAWAVSHHHIHF^HHE HEEH_paddingHEHoutput_pHEMEW)EHEHUHMLHH}Ht H}s=IEt H}d=IW)EEHEHoutput_sHEEhapeEI)EHEHUHMH腿H}Ht H}=IEt H}Ht-IHH}Ht H}t@;H"HHHH!H HHH!LAHHHHHt-HCHCHHCHuHHPH6IHH 蟪HHH(腪HH8HW)HDž(7IHHHHHHHHXH`HHH HH H+HH jH@HHHXIUHrMuLHHpHHH@HHHHNHhHFH`H)Hx1LHLPLLHXHHLL1DH(HHHHHHL$LLl$L$$poWHHI}YH I HH8HPHH~XH0HIHYHAHHpHH8LNHuLL^H}LHwHWHHEHH@HD$8HL$0H|$(HT$ Ht$LD$LL\$L$HL]HD$HLt$@H0L8LHHjmH(HIOXL@LHH HH9H0HLLPLLLPoMLLzIJ0LE11Hh4M7MMJIvKJ|H9vtKTIH9sf1HAtICXCHHI9fff.XXDDHI9uH`:Hx1DLT\$XXLTXX\$HHuHht'LA ATXXA ATHpHI9ILLM9HHHXHHHHHHHHMLHXHH9@JlH8HH/%HHHHHyXHH@LHIXIUHrM]LHHhHHH8HH@HJH0H`HFHXH)Hp1LHLPLxHHLHAHLL1fffff.H(HHHHHHL$HHL$L$$poWHHIxYH I,LLHb!%LEHUHuLpHxL)HHcH ]HL$(Ll$ Ht$HT$L$H|$HLLHLyH(HHHHXL8L@H HH9H0HLLLPLLP MLLx~IJ0LE11H`1@M7MMJIvKJ|H9vtKTIH9sf1HAtICXCHHI9fff.XXDDHI9uHX=Hp1DLT\$XXLTXX\$HHuH`t'LA ATXXA ATHhHI9IH0HHM9IOXHHHHHHHHMHHHH9@HHt H-II$HHt H-IHHHHt HtHPHHHCHHHPHt,I-IH*I HeIH8II$I$H"Ct H{-IH-II^IHHH,I864II$I$HCt H{,IH,IIIH"IHHHt HS,IHHt H;,IHHHt HtHPHt#HHCHuHHPH*IHHt#HHCHuHHPH*IH}Ht H}+IH}Ht H}+IH}Ht H}+IHpHt Hxx+IHXHt H``+ILN&I Hbfffff.UHAWAVAUATSHELPAHHHHg]HHEE11HIƾHHhDEtHӥILEtIH该I1HpMIH}LY'%IvHFHEHLIHYHHhIIHOLXIV H)H H)H9H9HHHH5JHJLJH}HMHMHHߢ-H5eJHXJH}L0 HMHH裢-dHpL&%pHtXHEYH5JHEJLOJH}LM:HMHH=-EH}HqH0HPL &%Pt H`HQHH5JHJL JH}H0LHMH諡-EuPupu)0H}x(IPtH`c(Ipt H}Q(IH}Ht H}?(IH]HH;E HHĨ[A\A]A^A_]L6HYIW)EHEHMHHHLH;t4H JH |K1AVHL.H}H]H}OLxHHHLHW)pHEL)L`tpIII99HN'IHLHxHHxHpJHEH~ALIH(ILHHHxHuiHxH1HLHHMH+MHHDžPH)HHH)vHpHPA s HHxW)PHDž`HuH+uHHPKtuM}HXHE1LP1HI1L%H9}hHۓLHXH;`tHHHHXLH LHHp0HP8HP%HXHHPHXH9HuRHMH+MHHDž0H)HHH)vHPH0y@ s HHXHW)0HDž@HLxHHXPL)tiIII9H %IIH8HL8L0KH@H~7LLHZ&ILHH8HubH8H1ILHMH+MHHDžL)HHH)vH0H}? s IH8HW)HDž HLxHX L)tiIII9H$IIHHLLKH H~7LLH^%ILHHHubHH1ILHMH+MHHDžL)HHH)vHH> s IHW)HDž)HDžLuH]L)t[IIHI9H#IILLKHH~LLHe$IILHHD$HPHD$HxHD$HpH$HHMLL0HL HHt HT"IW)HDžLHL)t[IIHI99H"IILLKHH~LLHs#IILH1H`L#HPHXH HhHKHpHKHCHxHC H{8HEH9t HuHU|fHEHC(Lc0H{PHt$LsPH{XJ!IIFIFIHEHCPHEHCXHEHC`W)EHEH{hHLLt'LshH{p IWIFIFIHPHChIHCpH`HCx)PHDž`HHt*LH IIFIFIHHI$HH HW)HDž HHt-IIH IWIFIFIH0HLIHH@H)0HDž@HHHHt HIHHt HwIHHt I<$bIH0HtI>NIHPHtI?:IHpHoHH8I[wIHpMI6H0?I(H1IH#I HI HHHw]HHHHHH0HH8HHpHHxHHHt$HHHHt HIHHt HIHHtHH8IH0HHH8IHHL1HMAFt I~ILHHvHHEuPu.puHH}EIPt HPtH`"IpusHptgH}\H\HEtSH}HHHHHPHt HXIHpHtHH8IH}Ht H}IH}Ht H}IHI UHAWAVAUATSH(IIIHH]HHEH8H@]HHHHH@H8HILHEEL=]IGHHI@L8HIL-]ILHDž(HDž HDžHDžDž0L5IHLHLIHLHHH0HIHHH1 HHL8Lt H IHIH5]HHoIH8IH*]HH;EuHH([A\A]A^A_] IHHHL8Lt H sIHSIH RHRHQHH5]HHIHH8IHI UHAWAVAUATSH8MLHIIHH7]HHEH8Hº]HHHHH@H8HCILHEEL-X]IEHHI@L8HAIL5]ILHDž(HDž HDžHDžDž0LIHLH9LIHLHHH0HIHHfIHHHHH0HfIHHH|HHL8Lt H KIH+IH5]HHIH8IHu]HH;EuHH8[A\A]A^A_]XIHHHL8Lt H IHI+HXOHPOHHOH@OH8OHH5V]HHIHH8IHKI UHAWAVAUATSH8MLHIIHHw]HHEH8H]HHHHH@H8HILHEEL-]IEHHI@L8HIL5T]ILHDž(HDž HDžHDžDž0LIHLHyLIHLH_HH0HIHHIHHH(HH0HIHHHHHL8Lt H IHkIH5P]HHIH8IH]HH;EuHH8[A\A]A^A_]IHHHL8Lt H IHI+HLHLHLHLHxLHH5]HH@IHH8UIHI UHAWAVAUATSHIH]HHEH}(II+HH]HH]LLHHHUHMHUHKHCH H+H(mH(HHHE1E1E1ffff.HDžxHt(IFHH;(IDIHHxHE HHHIH)HL9 H HH~H)HLH9LHH4 HH JHH J HHNH]HN EUHHIA@IAIIHt HX~I0t H@iILeMt6HEL9t!HX@t HxDIHI9uH}LLe*IEt H}IEt H} IL`Mt:HhL9tEHX@t HxIHI9uH`LhIEtLIL I Lf.UHAWAVAUATSHH](LU L]EH=]H?H}H;Ht>HHHHIIJT 1H)HIHHu@H)I I_t<ȃIIHHHIIJD 'IH)I HHHIIJD I+I+HH"]HH;EuHĈ[A\A]A^A_]IIIHh4.H5JH :JH}Lh6HP&AHJHuHPLE1H5]HD]LxI 0IHH5JHFH5]H(]HDIIA(IA}IIPt H`ILeMt6HEL9t!HX@t HxIHI9uH}LLeIEt H}rIEt H}cILhMt:HpL9tEHX@t Hx8IHI9uHhLpIEtLFILI LؐUHAWAVAUATSHHIHH8]HHEH8Hî]HHHLH@H8LD IHEEL5`]M~LI@L8LP IL%#]ILHDž(HDž HDžHDžDž0LIHLHHHsHEsHESH#HHHLL8Lt H IH IH5h]HH IH8, IH]HH;EuHH[A\A]A^A_] IHLL8Lt H  IH IHCHCHH5ͻ]HHw IHH8 IHI f.UHSPH* IH]HHH[]HECDUHAVSI IHH]HHH{L #H8]HHHHPLBH[A^]IH/ ILI DUHAVSHPIHk]HHE IHþH IHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]wH5IH}HU"H\H5JHHU"HBH5nkKHHU"H5DiKH1L"H5MHbM"HA H5HIHN"HHH]HHEHHUHuHuIININ0IFIVIF@H]HEHtHzHIHJ]HH;Eu LHP[A^]6 IIH]HEHtHDzHILI fUHAVSHPIH]HHEIHþH|IHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5aIH}HTS"HH5RJHH:S"HH5hKHH S"H5fKHI"H5=MHJ"HA H5TFIH L"HHH`]HHEHHUHuHuIININ0IFIVIF@H]HEHtH xHqIH]HH;Eu LHP[A^]IIH]HEHtHwH,ILI fUHAVSHPIHk]HHEIHþHIHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]wH5IH}HP"H\H5JHHP"HBH5nfKHHP"H5DdKH1G"H5 MHbH"HA H5CIHI"HHH]HHEH9HUHuHuIININ0IFIVIF@H]HEHtHuHIHJ]HH;Eu LHP[A^]6IIH]HEHtHDuHILH fUHAVSHPIH]HHEIHþH|IHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5aIH}HTN"HH5RJHH:N"HH5cKHH N"H5aKHD"H5= MHE"HA H5TAIH G"HHH`]HHEH"HUHuHuIININ0IFIVIF@H]HEHtH sHqIH]HH;Eu LHP[A^]IIH]HEHtHrH,ILH fUHAVSIXIHHLH[A^]IHILH UHAWAVSH8HIH&]HHEH]HII#H)]HIIGPHEW)EHEEEseedEH}HUHMH#H]HEHtCt H{8IH0IEt H}!IHu#IHMHIPIGPHtHHR]HH;Eu H8[A^A_]?IIIEt H}HIPIGPHtHLH fUHHH ]HHHxPH@PHt]tH]fUHSPHH]HHH{PHCPHtCHHH[]5HDUHAWAVAUATSHIH0HHd]HHE1LzILHHH LyHDI\$1LH;"HH8H(LH"I1MtpHHLdII]Iu HH)IT$I|$ H)H9L8H91H<3H;<2HH9u1HHIjHHH$I1HHLHHLIHLHHHx$LII1HHL8I1HHLxLWD.uHHԄ8DH0H@PHu .#DL(MM)M)HMH@H )))¿E;/YWLDHH/iȭ)iϏiG )B\WH*YX.<HIuMLH kDY(\HrII9=M941HVHAt"A<uWAY ^ALHHH9 DYfDA<<(uWAY^ATA|<(uWAYT^ATHH9u[M9H tL)t LL9HLL)H Àu,ID$L)HHHOITL)LHHHHHHt7HH]HH;EHH[A\A]A^A_]HH(HHHHzHH_H)1Wfo%>YfA81,fvfA4Y^AtfA81lfvfAtY^AtHHuHt(fA81ffvf%X>YAY^A\H91HuHIHIHhB.H5JH JH}LhAW)PHDž`0HH`(Z)PHme shapeHHHhe same HHHd have tHHHsk shoulHHHX and maH@%AH-JHuHPLE1H5]Hl]LH IA(IA}IIPt H`HLeMt6HEL9t!HX@t HxHHI9uH}LLeHEt H}HEt H}HLhMt:HpL9t=HX@t HxHHI9uHhLpwHEt'LHLHHIHtEHL9H UHAWAVAUATSHHH]HHEH H{$H<H'kW.r ?Y.HIHh.H5xJH JH}Lh)nW)PHDž`0HH`(X)PHge [0, 1HHH the ranHHHst be inHHHratio muHf@ )AHJHuHPLE1H5]H]LKH YHC]HH;EuHĈ[A\A]A^A_])HHIHh.H5aJH sJH}Lh$WW)PHDž`0tHH`(ZX)PHe value.HHHa singleHHHld have HHHput shouHHHratio inH@'AH=JHuHPLmE1H5k]H]L(H IA(IA}IIPt H`HLeMt6HEL9t!HX@t Hx}HHI9uH}LLecHEt H}THEt H}EHLhMHpL9 HX@t HxHHI9uIA(IA}IIPt H`HLeMt6HEL9t!HX@t HxHHI9uH}LLeHEt H}HEt H}xHLhMt:HpL9tEHX@t HxMHHI9uHhLp2HEtL[HLH LfDUHAVSIXHHHLH[A^]IHHLH UHAWAVSH8HIH]HHEH]HII"H]HIIGPHEW)EHEEEseedEH}HUHMH#H]HEHtCt H{(HH HEt H}HHu#HHMHIPIGPHtHHB]HH;Eu H8[A^A_]/HIIEt H}HIPIGPHtHLH fUHHH ]HHHxPH@PHt]dH]fUHSPHHЪ]HHH{PHCPHt3HHH[]%HDUHAWAVAUATSHIH0HHT]HHE1LlIL8HH LlHDI\$1LH+"HH8H(LH"I1MtpHHLWII]Iu HH)IT$I|$ H)H9L8H91H<3H;<2HH9u1HHIjHHH$I1HHLHHLIHLHHHh$LII1HHL8I1HHLkLWD.uHHw8DH0H@PHu #DL(MM)M)HMH@H )))¿E+"YWLDHH/iȭ)iϏiG )B\WH*YX.<HIuMLH [7Y(\HrII9=M941HVHAt"A<uWAY ^ALHHH9 6YfDA<<(uWAY^ATA|<(uWAYT^ATHH9u[M9H tL)t LL)HLL)H Àu,ID$L)HHHOITL)LHHHHHHt'HH]HH;EHH[A\A]A^A_]HH(HHHHzHH_H)1Wfo%1YfA81,fvfA4Y^AtfA81lfvfAtY^AtHHuHt(fA81ffvf%H1YAY^A\H91HuH9HIHh2.H5ɻJH JH}LhAW)PHDž`0HH`(Z)PHme shapeHHHhe same HHHd have tHHHsk shoulHHHX and maH@%AHJHuHPLE1H5Ӎ]H\]LH IA(IA}IIPt H` HLeMt6HEL9t!HX@t HxHHI9uH}LLeHEt H}HEt H}HLhMt:HpL9t=HX@t HxHHI9uHhLpgHEt'LHLHHIHt5HL)H UHAWAVAUATSHHHr]HHEH$H{$H@H`ZW.r 2Y.HIHh.H5dJH ۻJH}Lh)ZW)PHDž`0wHH`(X)PHge [0, 1HHH the ranHHHst be inHHHratio muHf@ )AHnJHuHPL|E1H5z]H]L7HXH/]HH;EuHĈ[A\A]A^A_]HHIHh.H5MJH ĺJH}Lh$CW)PHDž`0`HH`(FX)PHe value.HHHa singleHHHld have HHHput shouHHHratio inH@'AH)JHuHPLYE1H5W]H]LH IA(IA}IIPt H`HLeMt6HEL9t!HX@t HxiHHI9uH}LLeOHEt H}@HEt H}1HLhMHpL9 HX@t HxHHI9uIA(IA}IIPt H`HLeMt6HEL9t!HX@t HxHHI9uH}LLeHEt H}sHEt H}dHLhMt:HpL9tEHX@t Hx9HHI9uHhLpHEtLGHLH LfUHAVSIXHHHLH[A^]IHHLH UHAWAVSH8HIH]HHEHС]HII"H]HIIGPHEW)EHEEEseedEH}HUHMH"H]HEHtCt H{HHHEt H}HHu#HHMHIPIGPHtHH2]HH;Eu H8[A^A_]HIIEt H}HIPIGPHtHLzH fUHHH ]HHHxPH@PHt]TH]fUHSPHH]HHH{PHCPHt#HHH[]HDUHAWAVAUATSHHH H0HD]HHE1H_ILx H8HH_HeLMt$1HL"HLH(HHHL"I1MtmH@LJIIIGIw HH)IT$I|$ H)H9L(E1H9tu1H<0H;<2HH9uRH@L$H1H@HHILHHL@LW$MI1ML(H^WL.u4H+Hj8LH H@PHu '#LLM)M)HMH@H )))E7YWLꐉHH/iȭ)iiG )B\7WH*YX.47HIuMH0H8/LH]*Y\ZIu~1HH8L9Ht H)tLHLL)H ÀH0IEL)HHHOITL)LuHH)1 _Zffffff.A| 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Hh#EHE fUHAWAVAUATSHHUYHHEЅHhL`t|LeHpHxHMHuLEHEM,MH`HNHpHHeLAI>A3HPM?M1HhHHHpHH~I ˆHHXHH HAH(KMlLHH0KTHHGHH)H8N LHH@E1Lp'f1Hfffff.HhHHpN$M$HHfInHtfH811H0DoT fo fAoD fAoL fHHHuHtHIAofAoTfffpNffI~L(H H9XL@u(fffff.E1AL@HhD)HXL)Ht/H`IIHffffff.LILHuHrlHhL)ID$H`HIILL0IPLDLL0W)EHEHEL}W)pHEHL$HpH$D$EHuHUHMH?H HC0H0W)PHDž`LpHxL)tVIILH=H#DILXLPKDH`H~LLHDILXI$H@ID$HHHtH@HPH@E1LLH0 HHHt#HHCHuHHPHjDHPHt HXNDH gHLHMLEH(H$?LMHHHpHt HxDHuHtHuH}HPH]Ht#HHCHuHHPHDH YHH;EuLH[A\A]A^A_]DHPD H#0I2ILHMt#HIFHuILPL@DHPHtHX$DI IL HpHt HxDHuHtHuH}HPH]Ht#HHCHuHHPHDLD HU/DUHAVSHPIHYHHEDHþH|DHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5aEH}HTBH5FH8H5qHH:HH HQH55EH6;HHHYHHEH֫HUHuHuIININ0IFIVIF@H]HEHtH6gHDHYHH;Eu LHP[A^]DIH]HEHtHfHYDLGD ffffff.UHAVSHPIHYHHE&DHþHDHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5EH}H?H5FFH6H5HH7HH HQH5!3EH8HHH-YHHEHֵHUHuHuIININ0IFIVIF@H]HEHtHdH>DHYHH;Eu LHP[A^]DIH]HEHtHdHDLD ffffff.UHAVSHPIH+YHHEDHþHDHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]7H5EH}H=H5FH%4H5HHV5HA H50EH}6HHHԯYHHEHMHUHuHuIININ0IFIVIF@H]HEHtH}bHDH>YHH;Eu LHP[A^]*DIH]HEHtH8bHDLD fDUHAVSHPIH۵YHHEvDHþHlDHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5QDH}HD;H5FH1H5aHH3HA H5x.EH-4HHHYHHEHmHUHuHuIININ0IFIVIF@H]HEHtH-`HDHYHH;Eu LHP[A^]DIH]HEHtH_HPDL>D fDUHAVSHPIHYHHE&DHþHDHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5DH}H8H5OFH/H5HH0HH HQH5!,EH1HHH-YHHEHHUHuHuIININ0IFIVIF@H]HEHtH]H>DHYHH;Eu LHP[A^]DIH]HEHtH]HDLD ffffff.UHAVSHPIH+YHHEDHþHDHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]7H5DH}H6H5FH%-H5HHV.HH HQH5)EHv/HHHͨYHHEH6HUHuHuIININ0IFIVIF@H]HEHtHv[HDH7YHH;Eu LHP[A^]#DIH]HEHtH1[HDLD ffffff.UHAVSHPIHˮYHHEfDHþH\DHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5ADH}H44H5FH*H5QHH+HA H5h'EH-HHHtYHHEHHUHuHuIININ0IFIVIF@H]HEHtHYHDHެYHH;Eu LHP[A^]DIH]HEHtHXH@DL.D fDUHAVSHPIH{YHHEDHþH DHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5DH}H1H5?FHu(H5HH)HA H5%EH*HHH$YHHEH=HUHuHuIININ0IFIVIF@H]HEHtHVH5DHYHH;Eu LHP[A^]zDIH]HEHtHVHDLD fDUHAVSHPIH+YHHEDHþHDHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]7H5DH}H/H5FH%&H5HHV'HH HQH5"EHv(HHH͡YHHEHVHUHuHuIININ0IFIVIF@H]HEHtHvTHDH7YHH;Eu LHP[A^]#DIH]HEHtH1THDLD ffffff.UHAVSHPIH˧YHHEfDHþH\DHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5ADH}H4-H5FH#H5QHH$HH HQH5a EH&HHHmYHHEHHUHuHuIININ0IFIVIF@H]HEHtHRH~DHץYHH;Eu LHP[A^]DIH]HEHtHQH9DL'D ffffff.UHAVSHPIHkYHHEDHþHDHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]wH5DH}H*H58FHe!H5HH"HA H5EH#HHHYHHEHHUHuHuIININ0IFIVIF@H]HEHtHOH%DH~YHH;Eu LHP[A^]jDIH]HEHtHxOHDLD fDUHAVSHPIHYHHEDHþHDHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]'H5DH}H(H5FHH5HHF HA H5EHm!HHHĚYHHEHHUHuHuIININ0IFIVIF@H]HEHtHmMHDH.YHH;Eu LHP[A^]DIH]HEHtH(MHDL~D fDUHAVSHPIHˠYHHEfDHþH\DHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5ADH}H4&H5FHH5QHHHH HQH5aEHHHHmYHHEHHUHuHuIININ0IFIVIF@H]HEHtHKH~DHמYHH;Eu LHP[A^]DIH]HEHtHJH9DL'D ffffff.UHAVSHPIHkYHHEDHþHDHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]wH5DH}H#H5EFHeH5HHHH HQH5EHHHH YHHEHHUHuHuIININ0IFIVIF@H]HEHtHHHDHwYHH;Eu LHP[A^]cDIH]HEHtHqHHDLD ffffff.UHAVSHPIH YHHEDHþHDHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5DH}Ht!H5FHH5HH6HA H5EH]HHHYHHEHHUHuHuIININ0IFIVIF@H]HEHtH]FHDHYHH;Eu LHP[A^] DIH]HEHtHFHDLnD fDUHAVSHPIHYHHEVDHþHLDHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]ǸH51DH}H$H5FHH5AHHHA H5XEH HHHdYHHEHHUHuHuIININ0IFIVIF@H]HEHtH DHuDHΗYHH;Eu LHP[A^]DIH]HEHtHCH0DLD fDUHAVSHPIHkYHHEDHþHDHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]wH5DH}HH5UFHeH5HHHH HQH5EHHHH YHHEHHUHuHuIININ0IFIVIF@H]HEHtHAHDHwYHH;Eu LHP[A^]cDIH]HEHtHqAHDLD ffffff.UHAVSHPIH YHHEDHþHDHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]H5DH}HtH5FHH5HH6HH HQH5 EHVHHHYHHEHHUHuHuIININ0IFIVIF@H]HEHtHV?HDHYHH;Eu LHP[A^]DIH]HEHtH?HyDLgD ffffff.UHAVSHPIHYHHEFDHþHYHHEFyDHþHbDH'YHH;Eu LHP[A^]bDIH]HEHtHHaDL\D ffffff.UHAVSHPIH+'YHHEaDHþHaDHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]7GH5oDH}H蔬H5Y]FH%H5gHHVHH HQH5DHvHHHYHHEH&HUHuHuIININ0IFIVIF@H]HEHtHvH_DH7%YHH;Eu LHP[A^]#`DIH]HEHtH1H_DLZD ffffff.UHAVSHPIH$YHHEf_DHþH\_DHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]CH5AmDH}H4H5ZFHŠH5QeHHHH HQH5aDHHHHmYHHEH6HUHuHuIININ0IFIVIF@H]HEHtHH~]DH"YHH;Eu LHP[A^]]DIH]HEHtHH9]DL'XD ffffff.UHAVSHPIHk"YHHE]DHþH\DHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]wBH5jDH}HԧH5XFHeH5bHH薟HA H5DH轠HHHYHHEHMHUHuHuIININ0IFIVIF@H]HEHtHH%[DH~ YHH;Eu LHP[A^]j[DIH]HEHtHxHZDLUD fDUHAVSHPIH YHHEZDHþHZDHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]'?H5hDH}H脥H5IVFHH5`HHFHA H5DHmHHHYHHEHmHUHuHuIININ0IFIVIF@H]HEHtHmHXDH.YHH;Eu LHP[A^]YDIH]HEHtH(HXDL~SD fDUHAVSHPIHYHHEfXDHþH\XDHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]=H5AfDH}H4H5TFHřH5Q^HHHH HQH5aDHHHHmYHHEHHUHuHuIININ0IFIVIF@H]HEHtHH~VDHYHH;Eu LHP[A^]VDIH]HEHtHH9VDL'QD ffffff.UHAVSHPIHkYHHEVDHþHUDHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]w:H5cDH}HԠH5QFHeH5[HH薘HH HQH5DH趙HHH YHHEHfHUHuHuIININ0IFIVIF@H]HEHtHHTDHwYHH;Eu LHP[A^]cTDIH]HEHtHqHSDLND ffffff.UHAVSHPIH YHHESDHþHSDHHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]9H5aDH}HtH5@OFHH5YHH6HH HQH5DHVHHHYHHEHHUHuHuIININ0IFIVIF@H]HEHtHVHQDHYHH;Eu LHP[A^]RDIH]HEHtHHyQDLgLD ffffff.UHAVSHPIHYHHEFQDHþHIILbHBIAMOIIArHL)H0ӃHH)HHHH HHFL)HH8HH((THBHL,E1LXHULuL}H@uH@؃H9HOHH)HH?H>H8HHHH0HEoMWAHHHL[MM)MI1(`TMffff.Mt+A TIIsMuZW{LθTX TXBTX >HTXHI9uMt.HHILTXHHuffff.H@ HIH;00H0HH0HHH8I_HHHCHHHIDHuHtHuH}HPH]Ht#HHCHuHHPH=DHYHH;EuLH[A\A]A^A_]>DHP;D +HuIHPHtHX*>DIHpHt Hx >DHuHtHuH}HPH]Ht#HHCHuHHPHHHP11L<HXHHH[A^]IHK=DL98D UHAWAVAUATSHIIIH|YHHEH}Lff0f HDž@HMHEHtDH@H@H8H@Ht3HHCHu!HHPH;DH8H@H]Ht#HHCHuHHPHc;DffHDž1LNLPENhIPHH $HD$D$H 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UHHH5TH9THD@ HEH9t HtHP(HP H UHH;EuHX[A\A]A^A_]C@HH}HEH9uHP HtHP(HP>@ UHAWAVAUATSHXHUHHEHUHHEIIHHTHEHEHEHEH}McAUEEEMMAf.HHuHUHMP0MYCLH}M9}^CEBEEEEEIHuuB@H UHHH5(THTHB@ HEH9t HtHP(HP HtUHH;EuHX[A\A]A^A_]]B@HH}HEH9uHP HtHP(H<@ UHAWAVAUATSHXHUHHEHBUHHEIIHHTHEHEHEHEH}McAUEEEMMAf.HHuHUHMP0MYCLH}M9}^CEBEEEEEIHu@@H UHHH5THTH@@ HEH9t HtHP(HP HUHH;EuHX[A\A]A^A_]@@HH}HEH9uHP HtHP(H0;@ UHAWAVAUATSHXHxUHHEHTHHEIIHH*THEHEHEHEH}McAUEEEMMAf.HHuHUHMP0MYCLH}M9}^CEBEEEEEIHuU?@H UHHH5THTHm?@ HEH9t HtHP(HP HTUHH;EuHX[A\A]A^A_]=?@HH}HEH9uHP HtHP(H9@ UHAWAVAUATSHXHUHHEH"THHEIIHHTHEHEHEHEH}McAUEEEMMAf.HHuHUHMP0MYCLH}M9}^CEBEEEEEIHu=@H nUHHH5xTHTH=@ HEH9t HtHP(HP HUHH;EuHX[A\A]A^A_]=@HH}HEH9uHP HtHP(H8@ UHAWAVAUATSHXHXUHHEHTHHEIIHH"THEHEHEHEH}McAUEEEMMAf.HHuHUHMP0MYCLH}M9}^CEBEEEEEIHu5<@H THHH5THiTHM<@ HEH9t HtHP(HP H4UHH;EuHX[A\A]A^A_]<@HH}HEH9uHP HtHP(H6@ UHAWAVAUATSHXHUHHEHTHHEIIHHTHEHEHEHEH}McAUEEEMMAf.HHuHUHMP0MYCLH}M9}^CEBEEEEEIHu:@H NTHHH5XTHTH:@ HEH9t HtHP(HP HTHH;EuHX[A\A]A^A_]:@HH}HEH9uHP HtHP(H4@ UHAWAVAUATSHXH8THHEHrTHHEIIHH*THEHEHEHEH}McAUEEEMMAf.HHuHUHMP0MYCLH}M9}^CEBEEEEEIHu9@H THHH5THITH-9@ HEH9t HtHP(HP HTHH;EuHX[A\A]A^A_]8@HH}HEH9uHP HtHP(H`3@ UHAWAVAUATSHXHTHHEHTHHEIIHHTHEHEHEHDžH}LcUAEEEMMAff.HHuHUHMP0MYCLH}M9}^BECEEEEEIHu7@H .THHH58THTH7@ HEH9t HtHP(HP HTHH;EuHX[A\A]A^A_]m7@HH}HEH9uHP HtHP(H1@ UHAWAVAUATSHXHTHHEHRTHHEIIHHTHEHEHEHDžH}LcUAEEEMMAff.HHuHUHMP0MYCLH}M9}^BECEEEEEIHu5@H THHH5TH)TH 6@ HEH9t HtHP(HP HTHH;EuHX[A\A]A^A_]5@HH}HEH9uHP HtHP(H@0@ UHAWAVAUATSHXHTHHEHTHHEIIHHJTHEHEHEHDžH}LcUAEEEMMAff.HHuHUHMP0MYCLH}M9}^BECEEEEEIHue4@H THHH5THTH}4@ HEH9t HtHP(HP HdTHH;EuHX[A\A]A^A_]M4@HH}HEH9uHP HtHP(H.@ UHAWAVAUATSHXHTHHEH2THHEIIHHTHEHEHEHDžH}LcUAEEEMMAff.HHuHUHMP0MYCLH}M9}^BECEEEEEIHu2@H ~THHH5TH TH2@ HEH9t HtHP(HP HTHH;EuHX[A\A]A^A_]2@HH}HEH9uHP HtHP(H -@ UHAWAVAUATSHXHhTHHEHTHHEIIHH"THEHEHEHDžH}LcUAEEEMMAff.HHuHUHMP0MYCLH}M9}^BECEEEEEIHuE1@H THHH5THyTH]1@ HEH9t HtHP(HP HDTHH;EuHX[A\A]A^A_]-1@HH}HEH9uHP HtHP(H+@ UHAWAVAUATSHXHTHHEHTHHEIIHHTHEHEHEHDžH}LcUAEEEMMAff.HHuHUHMP0MYCLH}M9}^BECEEEEEIHu/@H ^THHH5hTHTH/@ HEH9t HtHP(HP HTHH;EuHX[A\A]A^A_]/@HH}HEH9uHP HtHP(H*@ UHAWAVAUATSHXHHTHHEHTHHEIIHHBTHEHEHEHDžH}LcUAEEEMMAff.HHuHUHMP0MYCLH}M9}^BECEEEEEIHu%.@H THHH5THYTH=.@ HEH9t HtHP(HP H$THH;EuHX[A\A]A^A_] .@HH}HEH9uHP HtHP(Hp(@ UHAWAVAUATSHXHTHHEHTHHEIIHHTHEHEHEHDžH}LcUAEEEMMAff.HHuHUHMP0MYCLH}M9}^BECEEEEEIHu,@H >THHH5HTHTH,@ HEH9t HtHP(HP HTHH;EuHX[A\A]A^A_]},@HH}HEH9uHP HtHP(H&@ UHAWAVAUATSHXH(THHEHbTHHEHMIHIHTHEHEHEHEH}UA$EEEMcMMADHHuHUHMP0 6sP\MYBDCYDXHEBDH}M9}^BECEEEEEIHu*@H THHH5THTH*@ HEH9t HtHP(HP HTHH;EuHX[A\A]A^A_]*@HH}HEH9uHP HtHP(H2%@ f.UHAWAVAUATSHXHxTHHEHTHHEHMIHIHvTHEHEHEHEH}UA$EEEMcMMADHHuHUHMP0 qP\MYBDCYDXHEBDH}M9}^BECEEEEEIHu7)@H THHH5THkTHO)@ HEH9t HtHP(HP H6THH;EuHX[A\A]A^A_])@HH}HEH9uHP HtHP(H#@ f.UHAWAVAUATSHXHTHHEHTHHEHMIHIHTHEHEHEHEH}UA$EEEMcMMADHHuHUHMP0 oP\MYBDCYDXHEBDH}M9}^BECEEEEEIHu'@H 0THHH5:THTH'@ HEH9t HtHP(HP HTHH;EuHX[A\A]A^A_]o'@HH}HEH9uHP HtHP(H!@ f.UHAWAVAUATSHXHTHHEHRTHHEHMIHIHTHEHEHEHEH}UA$EEEMcMMADHHuHUHMP0 &nP\MYBDCYDXHEBDH}M9}^BECEEEEEIHu%@H THHH5TH TH%@ HEH9t HtHP(HP HTHH;EuHX[A\A]A^A_]%@HH}HEH9uHP HtHP(H" @ f.UHAWAVAUATSHXHhTHHEHTHHEHMIHIHTHEHEHEHEH}UA$EEEMcMMADHHuHUHMP0 vlP\MYBDCYDXHEBDH}M9}^BECEEEEEIHu'$@H THHH5TH[TH?$@ HEH9t HtHP(HP H&THH;EuHX[A\A]A^A_]$@HH}HEH9uHP HtHP(Hr@ f.UHAWAVAUATSHXHTHHEHTHHEHMIHIH~THEHEHEHEH}UA$EEEMcMMADHHuHUHMP0 jP\MYBDCYDXHEBDH}M9}^BECEEEEEIHuw"@H THHH5*THTH"@ HEH9t HtHP(HP HvTHH;EuHX[A\A]A^A_]_"@HH}HEH9uHP HtHP(H@ f.UHAWAVAUATSHXHTHHEHBTHHEHMIHIHTHEHEHEHEH}UA$EEEMcMMADHHuHUHMP0 iP\MYBDCYDXHEBDH}M9}^BECEEEEEIHu @H pTHHH5zTHTH @ HEH9t HtHP(HP HTHH;EuHX[A\A]A^A_] @HH}HEH9uHP HtHP(H@ f.UHAWAVAUATSHXHXTHHEHTHHEHMIHIHFTHEHEHEHEH}UA$EEEMcMMADHHuHUHMP0 fgP\MYBDCYDXHEBDH}M9}^BECEEEEEIHu@H THHH5THKTH/@ HEH9t HtHP(HP HTHH;EuHX[A\A]A^A_]@HH}HEH9uHP HtHP(Hb@ f.UHAWAVAUATSPMLEIIIHL @HLHiIuH@L @HLHGHEH0H@L @HLH"HuH&H[A\A]A^A_]HoUHgUH_UHWUHOUHGUUHAWAVAUATSPMLEIIIHL @HLHAuH@L@HLHwHEH0H@L@HLHRHuHVH[A\A]A^A_]HTHTHTHTHTHwTUHAWAVAUATSHHIIHTHHEI@I@I@IA@ ?HHlHHLI@HEHMLEfffff.A$I\$It$HDIET$H}HLEMxMHu>MWMtIL9r1HIIMI!IJH L0MA$t M\$I\$DLULmfff.IFH9t(MtL9r1IHL9tL!L9ANHtIVL9uqI~HtI~ u1M1fDAT: uCHH9uMHLM{@MLULmHuM6M6fD0@ILHHL@AD$fAF(HEIFILEI@Hx H*HHH H*XLHDH MxI*AH u&H*XAH t Y.K ?IrIW1L^f: (@P\H,H HH1H,.HCH9HCLMYM}IGLMtLmM9r 1LIILmI!fff.IJHtHIKfff.HUHIL2JIHt)H@IOLtL9r 1IHH!HIL0I@I L;eHUTHH;EuHH[A\A]A^A_]>@ItI~ @IL@IHEHXHt*L3Ct H{ @H@LMuHEH8HHtf@LT@ HP@UHAWAVSPHIHuHCHt H@HM~L9vLHH[A^A_]Ir IGL1IFHxH*HHH H*XA^F f:  P(\H,HH1H,.HC„t Hr"HHȁAHH#@H9HBL97H[A^A_]fUHAWAVAUATSHHIHMtNLH=J<@H;HHt@LsINDHMHs-1HuxH;HHt@HCQLH)1ffff.H3HH3HDH3HDH3HDHH9uHtHHHHHuLKMHKMQLutM9r1LIIL#UHJ M9MLuH]M_Lut&M9r 1LIIM9uMifL#]M9tHJ<2IMHEGDDLAtIWI_MLUL]DEHUH]MIAFtINH9t&Iv 'H9tffffff.IvHHAtI u*HtM1AL: uUHI9u3ff.Ht#Lu@DEL]LUIH]HUuIMHDfI$IH]HJHI$HJL8LuN MMffff.M9MdHH[A\A]A^A_]ÿc@HH5U@H@HTHHH5TH%THi@IH@@L@ UHAWAVAUATSHHIIHBTHHEI@I@I@IA@ ?HHlHHLI@HEHMLEfffff.A$I\$It$HDIET$H} HLEMxMHu>MWMtIL9r1HIIMI!IJH L0MA$t M\$I\$DLULmfff.IFH9t(MtL9r1IHL9tL!L9ANHtIVL9uqI~HtI~ u1M1fDAT: uCHH9uMHLM@MLULmHuM6M6fD0@ILHHL @ID$IF(HEIFILEI@Hx H*DHHH H*XLHDH MxI*AH u&H*XAH t Y.K ?IrIW1L^f: (P\H,H HH1H,.HCH9HCLMYM}IGLMtLmM9r 1LIILmI!fff.IJHtHIKfff.HUHIL2JIHt)H@IOLtL9r 1IHH!HIL0I@I L;eHTHH;EuHH[A\A]A^A_]~@ItI~ @IL@IHEHXHt*L3Ct H{ @H@LMuHEH8HHt@L @ HDG@UHAWAVSPHIHuHCHt H( @HM~L9vLHH[A^A_]Ir IGL1IFHxH*HHH H*XA^F f:  NP(\H,HH1H,.HC„t Hr"HHȁAHHc @H9HBL97H[A^A_]fUHAWAVAUATSHHIHMtNLH=J<V@H;HHt:@LsINDHMHs-1HuxH;HHt@HCQLH)1ffff.H3HH3HDH3HDH3HDHH9uHtHHHHHuLKMHKMQLutM9r1LIIL#UHJ M9MLuH]M_Lut&M9r 1LIIM9uMifL#]M9tHJ<2IMHEGDDLAtIWI_MLUL]DEHUH]MIAFtINH9t&Iv 'H9tffffff.IvHHAtI u*HtM1AL: uUHI9u3ff.Ht#L @DEL]LUIH]HUuIMHDfI$IH]HJHI$HJL8LuN MMffff.M9MdHH[A\A]A^A_]ÿ @HH5 M@H@HTHHH5THeTH @IH @L8@ UHAWAVAUATSH(HTHHEIHAEM}IuIDIEUH}ILcMIt$Lt M9s MIM! 1LIIHJHHHAEt MMM}DHuLMHCL9uWKHtHSL9u]H{HtH{ uYMtz1T A:u6HH9u`ff.LtL9r1IHL9t :H!L9u2HHs$Mt!LLMp @LMMHuu1H$THH;EuHH([A\A]A^A_] @HLAfff.UHAWAVAUATSH(HTHHEIHAEM}IuIDIEUH}ILcMIt$Lt M9s MIM! 1LIIHJHHHAEt MMM}DHuLMHCL9uWKHtHSL9u]H{HtH{ uYMtz1T A:u6HH9u`ff.LtL9r1IHL9t :H!L9u2HHs$Mt!LLM @LMMHuu1HtTHH;EuHH([A\A]A^A_]Z@H?fff.UHAWAVAUATSHIH7LwI)IIFHH;HHWH)HHHH9H}wYHH9HBHDH9vCw@HH5H@H@HTHHH5ȪTH9TH}@HHHEHH@HIN,3LLH]%@ID$JD3Mu HEL8L`M9t;M)I@I}L@ID$IEIII uHEL LxMHMHHL(LpHMHHHM9t'ff.I_AGt I\@II9uMtLH[A\A]A^A_]9@H[A\A]A^A_]1HE1 `@IHC@EIMM9u*IM9t"fDI^AFt I~@II9uH}Ht@L@ fff.UHAWAVSHHTHHEHDžpHDžhƅ` fDženeDžaaffiƅgHH`t@fDžW)@HDžPƅ@DžAreluƅEHH@+@fDžW) HDž0ƅ IleakyrelL!fDž)uHH @W)ƅHDžHldedreluHfDžHthresholHƅHHx@fDžW)HDžƅDžtanhƅHH/@fDžW)HDžƅHscaledtaHfDžnhƅH H@fDž8W)HDžƅHsigmoidHH@H@fDžXW)HDžƅHhardsigmHDžoidH`H:@W)`ƅ`fDžxHDžpDžaeluH}H`@W)@fEƅ@HDžPHsoftsignHAƅIH}H@@W) fEƅ HDž0HsoftplusH!ƅ)H}H g@fEH=>6WH ]EEE`@  % 1@=`IUamy @`t Hp@H=ATH55WHK @W)`ƅ`HDžpLafDžiuHH`?HDž #<W)@HDžPƅ@HADžIoidHH@?HL>?HW) ƅ HDž0Dž!eluHH >?HDž?H=84WH @`t Hp?H=TH53WH?HTHH;EH[A^A_]H}T?EAH}A?E8H}.?`/Hp?@#HP? 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IAIIAIIAIA2IuIPt H`?LeMt6HEL9t!HX@t Hx?HI9uH}LLe|?Et H}m?Et H}^?LhMHpL9HX@t Hx+?HI9uIPt H`?LeMt6HEL9t!HX@t Hx?HI9uH}LLe?Et H}?Et H}?LhMt:HpL9t=HX@t Hx~?HI9uHhLpc?Et$L?LIH}Ht H}:?L(? UHAWAVAUATSHAHHoTHHEHt H֑1H KTH H;MuHĈ[A\A]A^A_]1?IǃL??IHh %H5@H BH}LhXKH{HIcHH<H5YAHPH!?HuHP1LH5TH:TLn? IPt H`?E1HIALmMt6HEL9t!HX@t Hx?HI9uH}LLm?Et H}?EtH}?IALhMt:HpL9t5HX@t Hx]?HI9uHhLpB?Eu LILa?V?L? H@UHAWAVATSH`IHWTHHEؿ?HþH?HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]sgH5~BH}H/H`H5?HH/I.I&HW)EHEv?HEHHHML HXHMH5@HUL}/H5BH'H5jCH)HA H5$@H6*HHHTHHEHHUHuHuIIOIO0IGIWIG@H}Ht H}?H]HEHtH$VH?HTHH;EuLH`[A\A^A_]?IH}HtH}N?IH]HEHtHUH(?L? fffff.UHHHH9H9Hx]ÐUHAWAVAUATSHIIH/THHE1LHHVL^H&HHHHSH+SHH.袋.HHHH9H9@Hx9Hy HHHHHwIW)EHEI|$ I+|$tQHHHH=n ?ILmLmIDHEIt$I\$ H)H~LHg?ILmHU1LJHH}Ht H}?L@H L;L{PM4$Mt$PM9MII|$0HPhHxLH1@LLs?HLIIH9|HhH5+BH =BL !@H]HHLhHLHZ$EtAH}?6LdII|$0HPILLH,?L@IHߦTHH;EuLHĘ[A\A]A^A_]?j?IHhc%H5AH AH}LhTW)PHDž` ?H`(3Q)PHe input.HHHsequenceHHHptr for HHHGot nullH@AH@HuHPLE1H5TH}TL??IHh}%H5)AH AH}LhW W)PHDž`0'?H`('P)PHdex tensHHHt for inHHHptr inpuHHHGot nullHf@ or@"AHAHuHPL(E1H5&TH|TL??IHh%H5=AH AH}LhcW)PHDž`0;?H`(qsQ)PHut tensoHH Hfor outpHHHnullptr HHHAt: Got HHHSequenceHf@(rAHVAHuHPL2E1H50TH{TL? H}? IA(IA}IcIPt H`\?LeMt6HEL9t!HX@t Hx7?HI9uH}LLe?Et H}?Et H}?LhMHpL9HX@t Hx?HI9uIAIIAIIAIA2IuIPt H`n?LeMt6HEL9t!HX@t HxI?HI9uH}LLe/?Et H} ?Et H}?LhMHpL9HX@t Hx?HI9uIPt H`?LeMt6HEL9t!HX@t Hx?HI9uH}LLez?Et H}k?Et H}\?LhMt:HpL9t=HX@t Hx1?HI9uHhLp?Et5L??+LIH}HtH} IEt H}?L? fUHAWAVAUATSHHTHHEHG0@Ẽtu9Hc[IHH TH H;MuHĘ[A\A]A^A_]?o?IH`h%H5AH iAHxL`CH5AHHHUK|HxHH1LsH5t|THwTL1? IHt HX?E1HfIALeMt6HEL9t!HX@t Hx?HI9uH}LLeq?Et H}b?xtH}P?IAL`Mt:HhL9t5HX@t Hx?HI9uH`Lh?Eu LIL!?L? UHAWAVAUATSH8MLHHIHHTHHEH8HzTHHHLH@H8L?LHEEL-(TIEHHI@L8L?L=TILHDž(HDž HDžHDžDž0L?HLH HH0H?HILLF?HLHH3H;?LHH?HHHLLFHHL8Lt H ?L?H5އTHH?H8?HCTHH;EuLH8[A\A]A^A_]&?IHHL8Lt H ?Lp?+H*H"HHH IH5(THH?IH8?L? DUHAVSHPIHkTHHE?HþH?HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]WH5AH}H H5AHeH5CHHA H5@HHHHTHHEHHUHuHuIININ0IFIVIF@H]HEHtHFH%?H~THH;Eu LHP[A^]j?IH]HEHtHxFH?L? fDUHAVSH0IHHTHHEHTHHH{HUHHCPW)EE HPHEEdtypfEeH}HULHLuHEMtAFt I~0?L(?Et H}?MtHHfTHH;Eu H0[A^]U?HEt H}?H? UH]fDUHAWAVAUATSHHIIHTHHEH{HHH+H1HHH#HHIGPHHH HcH3Y9Rs:K,;De4=562/;( 8!58t3 -7f6HH蛣IH THH;EuLHĈ[A\A]A^A_]??IHh$H5=AH AH}LhyW)PHDž`0;?H`(AO)PHsequenceHH&Hutput seHH Htr for oHHHot nullpHHHEmpty: GHHHSequenceH@.AHJAHuHPL&E1H5$sTHnTL??IHh$H5;AH AH}LhIPH5*BHPL袞HuHP1L蝰H5rTH'nTL[? IPt H`?E1HIALeMt6HEL9t!HX@t Hx?HI9uH}LLe?Et H}?EtH}}?IALhM HpL9HX@t HxB?HI9uIIA(IA}IIPt H`?LeMt6HEL9t!HX@t Hx?HI9uH}LLe?Et H}?Et H}?LhMt:HpL9tEHX@t Hxu?HI9uHhLpZ?EtL?L;? Lf9Uqj@N2c+\$Gfff.UHAWAVATSH`IH7THHEؿ?HþH?HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]SOH5^AH}HI(I HW)EHEp?HEHHHML HXHMH5~@HULwH5 AHH5dCH HA H5{ @H0HHHTHHEHPHUHuHuIIOIO0IGIWIG@H}Ht H}?H]HEHtH>H?HߑTHH;EuLH`[A\A^A_]?IIIIH}Ht H}=?H]HEHtH=H?L ? fUHAWAVAUATSHHUTHHEHIIW)EHEHHI?t#HAH A1AL$H(IMl$0ID$I\$ W)PHDž`H)tnH8L0IILH=HQ?ILXLPKH`HL0H8~LH?ILXHEH@HEHHHtH@HhHPH@E1LAHHHt#HHCHuHHPH?HPHt HXz?LhLmM4$Mt$PM9wI|$0HPhI|$Ht=xu7IH~O1LLc?LͺHIIH9|%趺HI|$0HPHLLH~?H(H{H;{sHh艊HCXHhH胀IHh蠍H]Ht#HHCHuHHPHx?H͎THH;EuLHĸ[A\A]A^A_]?HP? I2ILHMt#HIFHuILPL ?HPHtOHX?AIIIHt/Ct H{?H?IIHh誌H]Ht#HHCHuHHPH?Ll? @UHAWAVAUATSHIIHTHHE1LIMTL5CHH!M7H{0HPhI9t{I?HhH{0HPH5bAH AL @H]HhLPH LH@$EH}?HLBIOI+OHH.袋.HHHLsL@Ht?HoH@HHH9H9HxфuHy HH@I|$HjHH+H1LFHH{HHW)EHEH}L HLHHH@HLE1ffffff.I9H8LLHM1L8HDž8MtAFt I~?L?LLGtLH0HLHMH0HDž0HtcCt H{?H?Jf.LLsH(HLHMH(HDž(HuIHHH@L9HH9LuDH HMLL!H HDž HtCt H{ ?H?I7HHѕLcML{IIH[L9|MI)HèfH踈HèIXuI?LUH5AH AL y@H]H@LHHLHn=$II)LM&E?IGIGIHHEHCHEIHEHCIEHaTHH;EuLH[A\A]A^A_]D??IHh$H5AH AH}LhrW)PHDž` ?H`(EQ)PHe input.HHHsequenceHHHptr for HHHGot nullH@AHAHuHPL薢E1H5dTH`TLQ??IHh$H5AH AH}Lh茟W)PHDž` ?H`(P)PH tensor.HHHinput teHHHptr for HHHGot nullH@AH@HuHPL谡E1H5cTH7_TLk? ?IHh$H5AH AH}Lh覞W)PHDž`0?H`()Q)PHsequenceHH'Houtput sHH Hptr for HHHGot nullHHHInsert: HHHSequenceH@/AHAHuHPL讠E1H5bTH5^TLi?IPtH`?IALeMt6HEL9t!HX@t Hx?HI9uH}LLe?Et H}?EtH}?IALhMHpL9HX@t HxX?HI9uIPtH`.?IALeMt6HEL9t!HX@t Hx?HI9uH}LLe?Et H}ؿ?EtH}ɿ?IALhMHpL9HX@t Hx莿?HI9uIPtH`d?IALeMt6HEL9t!HX@t Hx7?HI9uH}LLe?Et H}?EtH}?IALhMt?HpL9t$HX@t Hx̾?HI9uHhLLp謾?EutILо?gIEtZH}OH-ILuMt@H]L9tAMI)Hèfffff.HHHèIXuH}Lu1?L? LfUHAWAVAUATSH8MLHHIHHCTHHEH8H^THHHHH@H8HO?LHEEL-dTM}LI@L8HT?L5'TILHDž(HDž HDžHDžDž0Lʿ?HLHLHHHHL$uHHHΪLL8Lt H 褼?H脻?H5ilTHH?H8-?H΁THH;EuHH8[A\A]A^A_]豼?HLL8Lt H ?H? HHH5kTHH耺?HH8蕻?H˶? UHAWAVAUATSH8MLHHIHHTHHEH8H\THHHLH@H8L?LHEEL-TIEHHI@L8L?L=~TILHDž(HDž HDžHDžDž0Lw?HLHHH0Hw?HILL6?HLH踣H3H=?LHH?HHH聣LL6HHL8Lt H ?L?H5iTHHx?H8蒹?H3THH;EuLH8[A\A]A^A_]?IHHL8Lt H |?L`?+HHH HHIH5iTHH·?IH8׸?L ? DUHAWAVAUATSPIM'IGL)HH.袋.HH9H袋.H9MwLL)LkXL轸?HLM9tCHEM)IƨHIHfHǨLDyHèHIƨIXuM7MgLHEHI?I_IGM9t"LL)IĨfDL8|IĨHXuMtLH[A\A]A^A_]?H[A\A]A^A_]ÿ?HH5x?H`?H9|THHH5c[THWTH?IH?L觲? ffffff.UHAWAVATSH`IH|THHEؿ肷?HþHx?HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]8H5AH}HI؛IУHW)EHE ?HEHHHML HXHMH5.u@HUL'H5AHH5CHHA H5+?HHHH7tTHHEH`iHUHuHuIIOIO0IGIWIG@H}Ht H}W?H]HEHtH&H6?HzTHH;EuLH`[A\A^A_]w?IIIIH}Ht H}?H]HEHtHd&H̴?L躯? fUHAWAVAUATSHIIHyTHHE1L^IML/INI+NHH.袋.HHHLkL@Ht?HlH@HHH9H9HxфcHy HH@L(IHaHH+H1L=HHhIMI6L腄W)EHEH}LzHH~u1fffff.H;@tPLHaH8HLHM6L8HDž8MtAEt I}?L?HH9HM|$MMl$MII\$L9L0MI)HèfHvHèIXuL0I}aH5AH AL ?H]H@LHHLH}+$Et_H}^?TMI#LM>H?IEIEIEHEID$HEIHEID$L(I$HawTHH;EuLHĸ[A\A]A^A_]D??IHh$H5AH AH}LhrW)PHDž`0菱?H`(O)PHsequenceHH&Hutput seHH Htr for oHHHot nullpHHHErase: GHHHSequenceH@.AHAHuHPLzE1H5xRTHNTL5??IHh$H5AH AH}LhpW)PHDž` 荰?H`(CQ)PHe input.HHHsequenceHHHptr for HHHGot nullH@AHAHuHPL蔏E1H5QTHMTLO? IPtH`ޯ?IALeMt6HEL9t!HX@t Hx豯?HI9uH}LLe藯?Et H}舯?EtH}y?IALhMHpL9HX@t Hx>?HI9uIPtH`?IALeMt6HEL9t!HX@t Hx?HI9uH}LLeͮ?Et H}辮?EtH}诮?IALhMt?HpL9t$HX@t Hx|?HI9uHhLLp\?EudIL耮?WIEtNH}CILuMt:H]L9t;MI)HèHrHèIXuH}Lu?Lߨ? LfUHAVSHPIH+sTHHEƭ?HþH輭?HHCHChHC`HCXHCPHCHHC@HC8HC0HC(HC Cp?HHǃHǃHǃHǃHǃHǃHǃHCxHHHǃHǃHǃǃǃH]&H5?H}HH,.H57AHHH5AH H5CH<HA H5?HcHHHjTHHEHC`HUHuHuIININ0IFIVIF@H]HEHtHcH˫?H$qTHH;Eu LHP[A^]?IH]HEHtHH膫?Lt? fff.UHAWAVAUATSHIHHpTHHEH~误HLc(MH(I|$H臯HH+Hl1LcHYH_HHH0H8E11L%Lx0f.LD%EL9x0`蔪?HH](P)EPHH5sA?CPHHHMT#$HHHDžHHtCt H{?H?EtH}?AE9DH8LzW)EHEH}L1L}@L$H@HLLL@HDž@MtAFt I~z?Lr?D9|L8M|$MtHH0LpHII\$L9t9MI)Hè@H(mHèIXuI>L8H0H&LM}L?IFIFIHEID$HEHHEID$H(HH nTH H;MuHĸ[A\A]A^A_]?蟨?IHh$H5DAH AH}Lh0%W)PHDž`@B?H`(xQ)PHt sequenHH(Hor outpuHH Hullptr fHHHt: Got nHHHConstrucHHHSequenceHf@0ce@2AHKAHuHPL'E1H5%ITHDTL?藧?IHh$H5#H}LHLI3HEI$HcTHH;EuLHĘ[A\A]A^A_]蛞?C?IHh<$H5AH {AH}LhlzIG0HHH5AH 2BHPHHBHuHP1L8}H59?TH:TL?IPt H`腝?DžDH!IǰDH]Ht6HEH9t!Lh@t HxA?LL9uH}HH]'?Et H}?EtH} ? IǰDLhMt:HpL9t9HX@t Hxќ?HI9uHhLp趜?Du LILќ? II$Lv? fGGGGGGGUHAWAVAUATSHIIHIHaTHHEHEHCPHEEEEEEW)EHEMt= 0HH/Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@7AHAHHPL:pE1H582TH-TL? 譐?H MTHHH52TH.THŐ?H諍?H蝍?벿a?IHpZ$H5AH AHLplW)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AHcAHHPL,oE1H5*1TH,TL?Hpʌ?IPt H`e?L Mt?H(L9t$HX@t Hx:?HI9uH LL(?t H?tH?IALpM/HxL9HX@t Hx赎?HI9u\I IPtH`y?IAL Mt?H(L9t$HX@t HxF?HI9uH LL(&?t H?tH?IALpMt?HxL9t$HX@t Hxɍ?HI9uHpLLx詍?EuILʍ?II8II]IEHCt H{V?HfIaIHHtH'?IHHtbH?TIHHt#HHCHuHHPHӋ?HHtOH跌?AIHP0II]IEHtCt H{职?Hy?IHHHt#HHCHuHHPHI?LPMt>HXL9tfMI)HèHPHèIXuHPLX?HpHt Hx?H}Ht H}ы?L迆? LfUHAWAVAUATSHIIHIHPTHHEHEHCPHEEEEEEW)EHEMt?ILH L9uHHHLhH@tQHIL= 0HH/Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@7AHAHHPL_E1H5!THATLu? -?H 0AHAHHPL^E1H5 TH3TLg?HpJ|?IPt H`~?L Mt?H(L9t$HX@t Hx~?HI9uH LL(~?t H~?tHp~?IALpM/HxL9HX@t Hx5~?HI9u\I IPtH`}?IAL Mt?H(L9t$HX@t Hx}?HI9uH LL(}?t H}?tH|}?IALpMt?HxL9t$HX@t HxI}?HI9uHpLLx)}?EuILJ}?II8II]IEHCt H{|?HfIaIHHtH|?IHHtbH|?TIHHt#HHCHuHHPHS{?HHtOH7|?AIH&@0II]IEHtCt H{|?H{?IHHHt#HHCHuHHPHz?LPMt>HXL9tfMI)HèH?HèIXuHPLX{{?HpHt Hxc{?H}Ht H}Q{?L?v? LfUHAWAVAUATSHIIHIHy@THHEHEHCPHEEEEEEW)EHEMtTHH;ELH[A\A]A^A_]L0H8LPIEM|$LMd$ W)pHEM)tXMIHI9 Ly?HHxHpJHEM~HLLbz?LHxW)PHDž`LL3HXHEHL`LPJ11Hxffffff.HH9uEtE fff.HEHHcH9LcHEHpL<W)@LLH@qHI}t0A1ALHAH mA $AIEMd$0HpLxW)HDžI)tfHLHHH9 Lnw?ILLIHMH~LLx?MLH@HHHHHtH@E1LLLHH\1HHt#HHCHuHHPHu?HHt Hv?L3HËEHHcUIHXHxL$HcE9Hu!HHcHHLw?HhHL4HHHBHH@HHsE1H)HhHHHRHHHHKvHIIHH HH(E1ffff.HLLv?I<HJ4 Lv?H(H<H J4 Lv?HH<HJ4 Lv?ILLL9uHHHLhH@tNHIL= 0HH/Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@7AHrAHHPL*OE1H5(TH TLo? o?H +THHH5TH THo?Hl?Hl?벿Qo?IHpJ$H5AH )AHLpKW)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AHSAHHPLNE1H5TH TLn?Hpk?IPt H`Un?L Mt?H(L9t$HX@t Hx*n?HI9uH LL( n?t Hm?tHm?IALpM/HxL9HX@t Hxm?HI9u\I IPtH`im?IAL Mt?H(L9t$HX@t Hx6m?HI9uH LL(m?t Hm?tHl?IALpMt?HxL9t$HX@t Hxl?HI9uHpLLxl?EuILl?II8II]IEHCt H{Fl?HfIaIHHtHl?IHHtbHk?TIHHt#HHCHuHHPHj?HHtOHk?AIH/0II]IEHtCt H{qk?Hik?IHHHt#HHCHuHHPH9j?LPMt>HXL9tfMI)HèH/HèIXuHPLXj?HpHt Hxj?H}Ht H}j?Le? LfUHAWAVAUATSHIIHIH/THHEHEHCPHEEEEEEW)EHEMt= 0HH/Hes must HH(HAll valuHH H input. HHH 'split'HHHvalue inHHHInvalid H@7AH̄AHHPL?E1H5TH SL?`? _?H XTHHH5THSH`?H\?H\?벿_?IHp褄$H5P}AH 3AHLp.<W)PHDž`ƅP&Hould be HYHSplit shHQDža> 0AHAHHPLv>E1H5tTHSL1_?Hp\?IPt H`^?H Ht?H(H9t$Lh@t Hx^?LL9uH HH(d^?t HO^?tH:^?IALpMt?HxL9t$HX@t Hx^?HI9uHpLLx]?EuIL^?IT IPtH`]?IAL Mt?H(L9t$HX@t Hxp]?HI9uH LL(P]?t H;]?tH&]?IALpMt?HxL9t$HX@t Hx\?HI9uHpLLx\?EuIL\?II@II]IEHCt H{\?HpIkIHHtHQ\?IHHH.\?{IHHt#HHCHuHHPHZ?HHtSH[?E4II]IEHt/Ct H{[?H[?IIHHHHt#HHCHuHHPHkZ?LPMt@HXL9thMI)HèDH8HèIXuHPLX[?HpHt Hx[?H}Ht H}Z?LU? 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UHAWAVAUATSH8MLHHIHHwTHHEH8HSHHHLH@H8LO?LHEEL-TIEHHI@L8LN?L=TTILHDž(HDž HDžHDžDž0LQ?HLHy8HH0HM?HILLQ?HLH88H3HM?LHHQ?HHH8LLIDIFI;FtH HHIFfDLHEjHIuHd THH;EuHĘ[A\A]A^A_]JD?C?IHhh$H5aAH 9iAH}Lhx I0oHHH5hAHPHHHuHP1L"H5SHsSLC? IPt H`9C?E1HzIAkIIALeMt6HEL9t!HX@t HxB?HI9uH}LLeB?Et H}B?Et H}B?LhMt:HpL9tEHX@t HxB?HI9uHhLpsB?EtLB?LT=? LUHAWAVAUATSHIIHTHHEIL2HL2H9u\M9t0I6IVHYTHH;Eu;LH[A\A]A^A_]H0THH;EuH[A\A]A^A_]B?A?IHhf$H5ީ?H _gAH}LhDL2HH3A?HgAH1D??HHHHHPHHH@H@HH5TgAH>?HHHHHPHHH@H@HLo1HH@?tHH HH\>?HHH0HHPH(H H@H@HH5,?H >?HHHHHHPH@H8H@H@HHPH8>?AHeAHuHPLME1H5KSHSL@? IA8IA tvIAtkIAtqIAIAcIIPuG8uS u_ukH`>?8tHH>? tH0>?tH>?tIAxHk>?lHR>?tH=>?IALeMt6HEL9t!HX@t Hx>?HI9uH}LLe=?Et H}=?Et H}=?LhMt:HpL9tEHX@t Hx=?HI9uHhLp=?EtL=?Ls8? 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fUHAWAVAUATSHhIHIH}H[=QHHE1HIM4$H]HIMl$I^ I+^HI?H'ID$W)EHEIE LpI+EHHxHtH}觜M|$MLH}~bHE1E1fDLHIvHEH;EtHHHHEff.H}GH}ILIM9|MHpHXL` L9tILuL}DH HMHEH;EtHHHEfLLH}HI9uLMwHxI9}aII MLHIuHEH;EtHHHHEff.H}wIMI9|H]ILuHMH]HEHEIHEHHEW)EHEHU1H}eHMHAH}Ht H}u<HH}HtI>u<H;QHH;EuHHh[A\A]A^A_]v<HH}HtH}u< HH}Ht H}hu<HVp< fffff.UHAWAVAUATSHIHH:QHHEHPHMH;tGH>H 3=1AjH7!HP:QHH;E*HH[A\A]A^A_]H]HHEH`LXLpHx0GEHPHL}IwL:lILLLfHxLeI|$eHhLpLLkLhIIMMH`HHQPH}HHOPHXHI\$0[t]E1}AH|$8L|$0HpH|$(LT$ HD$L\$HxHD$L,$LH]H<E1}AH|$8L|$0HpH|$(LT$ HD$L\$HxHD$L,$LH]H\0Us<HE(L)EHin GatheHH Hted yet HHHt supporHHH Tind noHHHType forHf@(r.@*HM H]H EH}r<s<IEt0H}%IL;HMtAGt Ir<L{r<Lim< UHAWAVAUATSHHLe0Lm(L} HEHHc]@Lu8H7QHHEHxHpDoL`HT-ILXIHHPM~;HH1ffffff.Ic4LH9H9HL9|HE HHE(HHE0HHXHHPHHEH HoH(HpH0H`H8HxH@HH*H)3QHHEHHMH}HHEHEHuW)MIEHHUHM!H}HEH9HHHP(xHMHHEHAm?HD$HEH$H56>H X>L Z<LHLLEL HHLJ H!p<DLc1,ffff.HMHHQL8L*HAL HALHHIHLLAyHQ HHQ(Hq0HHLHL>t@HQHH2LA8HI@H 1HH@H4M1HHH@IHHp<HL9>iHP HHH~4QHH;EuHH[A\A]A^A_]ao<Ht3Hn<%HH}HEH9uHP HtHP(Hi< fUHAWAVAUATSHHLe0Lm(L} HEHHc]@Lu8H3QHHEHxHpDoL`HILXIHHPM~9HH1ffffff.I4HuH9H9HL9|HE HHE(HHE0HHXHHPHHEH HoH(HpH0H`H8HxH@HH*Hs/QHHEHHMH}HHEHEHuW)MIEHHUHM!H}HEH9HHHP(zHMHHHHi?HD$HHH$H5r>H >L <LHULEL HHL H`l<ALc1+fff.HMHHQL8L*HAL HALHHIHLLIHyHQ HHQ(Hq0HHLHL>t@HQHH2LA8HI@H 1HH@H4M1HHH@IHH"m<HL9>gHP HHH0QHH;EuHH[A\A]A^A_]k<Ht3H#k<%HH}HEH9uHP HtHP(He< fUHAVSIXj<HH &QHHH{LlHHPL0HY&RHH[A^]IHj<Le< fUHAWAVAUATSHH/QHHEHW)EHEEEaxisEHhHUH}HhHDžhHtCt H{j<Hi<Et H}i<Hu'HA/QHH;EuHĈ[A\A]A^A_]'j<i<IHhȎ!H5>H >H}LhUFW)PHDž`0ri<H`(8M)PHte valueHHHttributeHHH'axis' aHHHInvalid HHHMissing/H@&AH>HuHPLkHE1H5i QHQL&i< IA(IA}IIPt H`h<LeMt6HEL9t!HX@t Hx{h<HI9uH}LLeah<Et H}Rh<Et H}Ch<LhMt:HpL9t=HX@t Hxh<HI9uHhLpg<Et!L&h<LIEt H}g<Lb< ffff.UHAVSIXg<HH"QHHH{LhHHPLH)#RHH[A^]IHcg<LQb< fUHAWAVAUATSH8LLHIIHHs,QHHEH8HQHHHHH@H8Hf<HEEL-*QMuLI@L8He<H^*QHHHDž(HDž HDžHDžDž0Li<HLHOA7Hd<LMLEHHHHLLHSLL8Ht H e<Hd<H5QHH:d<H8Te<H*QHH;EuLH8[A\A]A^A_]e<ILL8Ht H Ee<H%d<HߜHלIH5QHHc<IH8d<L_< 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>HPHHL@-HuHP1L"H5PH2PLfC<HfQHH;EuLHĸ[A\A]A^A_]IC<B<IHhg!H5>H >H}Lh`wW)PHDž``B<HH`(xM)PH5>QHC<CQHuHP1L!H5PHMPLB<IPt H`B<Dž0HyIư0H]Ht6HEH9t!Lh@t HxA<LL9uH}HH]A<Et H}A<EtH}A< Iư0LhMHpL9HX@t HxTA<HI9uIPt H`*A<Dž0HxIư0H]Ht6HEH9t!Lh@t Hx@<LL9uH}HH]@<Et H}@<EtH}@< Iư0LhMt:HpL9t9HX@t Hxv@<HI9uHhLp[@<0u LILv@< II<$Ht I|$ @<L;< fDUHAWAVAUATSHHMLHHHHH/QHHEH8HPHHHLH@H8L;?<HEEL-WQM}LI@L8LG><HEL5QILHDž(HDž HDžHDžDž0H$HHHHLMHHH,LL8Lt H ><H=<H5kPHH=<H8/><HQHH;EuHHH[A\A]A^A_]><HLL8Lt H ><H=<HH5PHH<<HH8=<H8< ffff.UHAWAVAUATSPMLEIIIHLS@<HLH&IuHa<<L1@<HLH&HEH0H<<<L @<HLH&HEH0H<<H[A\A]A^A_]HtHtHtHtHtHtUHAWAVAUATSH(HEQHHEIIHHAHq H)ULLHHHyHHHcL,Lx.HI4$LL(4HI4$L.II4$L4HpHHHIHhW)PHDž`MtwLH=&N<L<<HPJH`HL;<HX1fff.I$HtLo3HPHHH;x|HDžHHHHC(HHK0HHK HH@H{8HDžLLLLLHhHHpHH@HHxHLHHH HPH(H0HuPHHELxL}HEHEWI*W)MH}EHIHUH]LHK H}L0q:<H(oL)Hger thanHH Hrank larHHHust has HHHtensor mHHHindices Hf@( 0@*HH HHEH@HHC8HIDHE1I11LLxHHLHHHhHpMH~gM<$LPLHHI 1E1DNcLHL$LHI9|6M9~1EMIMO$LIMM9|f1fff.LHHHHH}HH HMHf Et"H}G8<HP HHuHHPHt 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HxW5HI9uHLV5ELW5LHHV5HHHH`H9u HP HHP(HL3HMAFt I~ H?MuI)LHHH9w`II9LBMLEHuI9vK5HH5T5H5HIHHH5IH_IH5LEHuIJ<C5HHuLEN4ABN|M~HLI5LI]M}MuHtHH[A\A]A^A_]5H[A\A]A^A_]E11L 5IH5L 5 fff.UHAWAVAUATSPfnE1W.If~LELJMnMf~MtIMMLtM9r1LIIkI!IJHt\HHuRffffff.HHt;HCL9t!LtL9r1IHL9t H!L9uC.uz(5HECHCHCL{HIFHx WH*HHH WH*XLHDH MxI*AN u"H*XAN tY.wMKL-IrIU1LH ^f:  hE(\H,HH1H,.HCH9HCLMnIELtM9r1LIII!IJHtHH AIFIVHI^JHHt)H@IMLtL9r 1IHH!HIHIFHHHH[A\A]A^A_]IH15L 5 UHAWAVAUATSPfnE1W.If~LELJMnMf~MtIMLLtM9r1LIHoH!IHHt`L MuVffffff.M$$Mt>ID$L9t!LtL9r1IHH9t H!H9uAD$.uz@[5IEAD$ID$0ID$(ID$ ID$AD$8?M|$I$IFHx WH*HHH WH*XIT$LHDH MxI*AN u"H*XAN tY.wIKL-IHUrIU1LH ^f:  E(\H,HH1H,.HCH9HCLvMnIELtM9r1LIII!IJHt HI $CIFIVI$MfJI$Ht)H@IMLtL9r 1IHH!HIL IFILH[A\A]A^A_]II|$(HtfHx 5HHuHEH8HHtX 5LP 5L>5 fDUHAWAVAUATSPfnE1W.If~LELJMnMf~MtIMMLtM9r1LIIkI!IJHt\HHuRffffff.HHt;HCL9t!LtL9r1IHL9t H!L9uC.uz( 5HECHCHCL{HIFHx WH*HHH WH*XLHDH MxI*AN u"H*XAN tY.wMKL-IrIU1LH ^f:   E(\H,HH1H,.HCH9HCLMnIELtM9r1LIII!IJHtHH AIFIVHI^JHHt)H@IMLtL9r 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HHH9r~fffff.~nHHHLI)IIrRffff.LMLILHHI H9|HHHHL9rfL(LULXHL I $ILHXL.L.L8MM9uiHHH)HHDI HXHHHI;HHH9r~fffff.~nLHIHLI)IIrOAfHK HHHXJJO4L9|HIJIM9rfN,?Ct4ML(LM:4L;lML(LLKI r)LIH H K48LH9szLHH9so1HH)HtHfHHuH'KDfTTTTTTHI9uH`s1H|KTpH1D L LD L D L0D L D@LPoD o D`LpHHuHt9HHffffff.oDo D H HuHHI9fffff.L;LIH`Ht Hh64H8Ht H@64H Ht H(}64HHt He64HHt HM64HHt H564HHt H64H]HEHtHH54H]HEHtHH54H]Ht#HHCHuHHPH44HHHH;EuLH[A\A]A^A_]5454IHPZH56H 6HLPffEHE0754HEfoFfEHequired!HH Hts are rHHHd Y inpuHHHn, X, anHHHconditioH@(AH6HHML4E1H52HHHL4444IH}YH56H "6HPLE-ff0HDž@ H44H@fo'Df0Hrst outpHHHo get fiHHHfailed tH@ut!AHC4HPH0LUE1H5SHHHL44 HP04 IA+IAII0t H@y34LMt?HL9t$HX@t HxN34HI9uHLL.34pt H34Pt H`34LeMt1HEL9t>HX@t Hx24HI9uH}Le24EQL24DLIA%IAIIEt H}24LMt?HL9t$HX@t HxW24HI9uHLL724t H"24t H 24LPMt:HXL9tDHX@t Hx14HI9uHPLX14EdL14WLIHPHt+HX14IIIIH`Ht HhX14H8Ht H@@14H Ht H((14HHt H14HHt H04HHt H04HHt H04H]HEHtHH04H]HEHtHH04H]Ht#HHCHuHHPH^/4LH+4 UHAWAVAUATSHHHHHEHHLAIHHrHQ?/HH HxH HH9HNHEDHEHHEffHDžLmLeM)LXt\MILH=L/4HHHJHMLX~HLL04LHHLHHHt H /4HEHM'I|$HLILHLMMH`LH`H9LI9 DHI)ffff.LMIHEHLeHH@MHHIL@HLHH H;H9ufHHH)HHfML@H HH H; 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HH=?p(HH=\?p(Hk( ffff.UHAVSIHtHH@HXHs(LHHH5+LY[A^]f.UHAWAVAUATSHIH5=HHEID$ID$I$HHP`Ht7HHQ(HHL-LhLEH=e?H5?.fDžhEID$I;D$U?HC(ȃIcDLfDžhEID$I;D$sEHID$hL{ fDžhAO MID$I;D$sNffHHID$bILMLML{(LMML-*H5?u.L{ (LMLLH7H5?H=Y?IMIIH fDžhEID$I;D$sP(fDžhHC H MID$I;D$s>ffHHID$=HhHUL$(HhHULHhHULzHc3=HH;EuHĘ[A\A]A^A_]In(m(IHh H5z+H C+H}LhwJW)PHDž`0m(H`(:)PHefinitioHH HeProto dHHHImpl TypHHHDataTypeHHHInvalid Hf@(nLH-HuHPLLDžLH5}=H =L:m(IǰL-IǰL}IIPt H`l(LmMt6HEL9t!HX@t Hxl(HI9uH}LLmhl(Et H}Yl(Et H}Jl(HhHt:HpH9tALh@t Hxl(LL9uHhHpl(LtL)l(H II<$Ht I|$k(Lf( FJw"@UH]VfDUHAWAVATSIHHP`HtXHHQ(x(uIL` M|$Hn(At IOH9t %H9u1HLHIbh(t 1[A\A^A_]I\$ Lm( t HKH9u H9u1HHLIh(HrHjf.UH1]UHH]UHAVSH j(HH5~+H+H}a HuHg(H=HHH5 =Hi=Hj( IEtH}j(HáIH>j(Ld( fffff.UHAVSH i(HH5\~+Hd~+H}HuH3g(H=HHH50 =H=Hi( IEtH}}i(H#IHi(LVd( fffff.UHAVSH Si(HH5}+H}+H}!HuHf(HN=HHH5 =H)=HEi( IEtH}h(H胠IHh(Lc( fffff.UHAWAVSPIIh(HHLf(IH[A^A_]IHoh(L]c( DUHAVSII~Htffff.H8h(HHuI>IHt [A^]h([A^]UHAWAVATSH@HZ-=HHEIID$ID$ID$I$AD$ ?H)=HHELeL}L}L聀H}I9t HtHP(HP H,=HH;Eu H@[A\A^A_]g(IH}I9t HtHP(HP I|$Htf.H(g(HHuI<$I$Ht g(La( UHSPHf(H &)=HHHKHHH[]UHH)=HHHGHF]@UHH6H]鿛ffffff.UHHNH1H; =HD]DUHH =]UHAWAVAUATSHHIHH+=HHEH8H#=HHHLH@H8Le(HEEL5)=M~LI@L8Ld(L%)=ILHDž(HDž HDžHDžDž0L&h(HLHNH3Hc(HHHCSLL8Lt H e(Hc(H5=HHc(H8d(HC*=HH;EuHH[A\A]A^A_]&e(HLL8Lt H d(Hsc(H-H%HH5C=HHb(HH8d(H8_( UHAWAVSPHIHuHCHt Ha(HM~L9vLHH[A^A_]Ir IGL1IFHxH*HHH H*XA^F f:  c8(\H,HH1H,.HC„t Hr"HHȁAHHa(H9HBL97H[A^A_]fUHAWAVSPIIMtJLH=J< c(I?IHtb(MwMFDIs-1HusI?IHtb(IGHLH)1I7HI7HDI7HDI7HDHH9uHtHIHHHuMWMIMJMtM9r1LIIM!IJ<DII:HHGMtL9r1IHL9t @L!L9tIH<tWHHHt*HWHfff.HHH;Qu HHHuHI IH H HIHH8jLII[H[A^A_]ÿka(HH5Ѣ(H^(H%=HHH5=H-=Hqa(IHHa(L\( UHAWAVAUATSHHIHH8&=HHEH8H=HHHLH@H8LD`(HEEL5`$=M~LI@L8LP_(L%#$=ILHDž(HDž HDžHDžDž0Lb(HLHHIHb(HHH.IHHHMLL8Lt H _(H^(H5s=HH^(H87_(H$=HH;EuHH[A\A]A^A_]_(HLL8Lt H (_(H^(H–H躖HH5=HH](HH8^(HY( DUHAWAVATSIL%^>M&0^(HW)C )C)Hb!I^H=HI{(uHC H_!C(1!!HC H@ [A\A^A_]IM&I^HtHBf!IH%^(ILY( fDUHAVSIH7^>II^HtHe!H](L[A^]](UH^?t HC?]H=J?](tH=HH??H=?](ffffff.UHAWAVATSIL%]>M&0Z](HW)C )C)HUa!I^H =HI{(uHC H^!C(1!HC H@ [A\A^A_]IM&I^HtHd!IH\(ILW( fDUHV?t H;?]H=B?\(tH=HH??H=?\(ffffff.UHAWAVATSIL%s\>M&0:\(HW)C )C)H5`!I^HZ =HI{(uHC H\!C(1衦!HC H@ [A\A^A_]IM&I^HtHc!IH[(ILV( fDUHN?t H3?]H=:?[(tH =HH? ? 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HH!C(1a!HC H@ [A\A^A_]IM&I^HtHBO!IH%G(ILB( fDUHAWAVATSIL%G>M&0F(HW)C )C)HJ!I^H<HI{(uHC HG!C(1衕!HC H@ [A\A^A_]IM&I^HtHN!IHeF(ILNA( fDUHAWAVATSIL%F>M&0:F(HW)C )C)H5J!I^Hz<HI{(uHC HF!C(1!HC H@ [A\A^A_]IM&I^HtHM!IHE(IL@( fDUHAWAVATSIL%;F>M&0zE(HW)C )C)HuI!I^H<HI{(uHC H?F!C(1!!HC H@ [A\A^A_]IM&I^HtHM!IHD(IL?( fDUHAWAVATSIL%F>M'0D(HW)C )C)HH!I_HR<HIHK[A\A^A_]IM'I_HtHkL!IHND(IL7?( ffffff.UHAWAVAUATSHIHr =HHEA(uIG LE!AG(1w!IG H@ x?Hx?H=x?P(IMA(uI_ LD!AG(1!HI_ KH{Hu1N!HH{H=HH;EudLHĘ[A\A]A^A_].Y!H=Ox?tC(`H=+x?H=<H5x?HiL"C(H=x?@C()TC(B(IHhg H5|W+H W+H}LhH<HHH}X+HPHHAHIX+HuHPL!E1H5<Ht<LB( IA(IA}IIPt H`"B(LeMt6HEL9t!HX@t HxA(HI9uH}LLeA(Et H}A(Et H}A(LhMt:HpL9t=HX@t HxA(HI9uHhLpA(EtLA(LIH=yv?A(LJ<( fUHAVSIHC>II^HtH;I!H#A(L[A^]A(UH]fDUH]DUHHU<]UHH=]UHAWAVAUATSHIHHH=HHEH8H<HHHLH@H8L@(HEEL=0=MgLI@L8L ?(L-=ILHDž(HDž HDžHDžDž0HHB(HHH)LyB(HLH(HHH-LL8Lt H {?(H[>(H5@<HH=(H8?(H=HH;EuHH[A\A]A^A_]?(HLL8Lt H >(H=(HvHvHH5<HHO=(HH8d>(H9( fUHSPHHtHsHHH[]x>(H[]ÐUHn>(HHH@H@H]fUHAWAVATSIL%?>M'0*>(HW)C )C)H%B!I_H<HIHK[A\A^A_]IM'I_HtHE!IH=(IL8( ffffff.UHAWAVAUATSHIH=HHEA(uIG Lv>!AG(1!IG H@ r?Hr?H=r?P(IMA(uI_ L>!AG(1艊!HI_ KH{Hu1辎!HH{H0=HH;EudLHĘ[A\A]A^A_]R!H=7r?<(`H=r?H=<H5r?HE<(H=q?<()<(l<(IHhea H5P+H >R+H}LhH<H@HHHQ+HPHH&AHQ+HuHPLYE1H5W<H<L<( IA(IA}IIPt H`;(LeMt6HEL9t!HX@t Hxi;(HI9uH}LLeO;(Et H}@;(Et H}1;(LhMt:HpL9t=HX@t Hx;(HI9uHhLp:(EtL;(LIH=]p?;(L5( fffff.UHH-<]UHH<]UHSPHHtHsH~HH[]h:(H[]ÐUH^:(HHH@H@H]fUHAWAVATSIL%;>M'0:(HW)C )C)H>!I_H<HIHK[A\A^A_]IM'I_HtHA!IH9(IL4( ffffff.UHAWAVAUATSHIH<HHEA(uIG Lf:!AG(1׆!IG H@ n?Hm?H=m?P(IMA(uI_ L:!AG(1y!HI_ KH{Hu1變!HH{H <HH;EudLHĘ[A\A]A^A_]N!H=m?8(`H=[m?H=<H5Hm?HA8(H=Em?8()8(\8(IHhU] H5L+H N+H}LhH<H@HHHM+HPHHAHM+HuHPLIE1H5G<H<L8( IA(IA}IIPt H`~7(LeMt6HEL9t!HX@t HxY7(HI9uH}LLe?7(Et H}07(Et H}!7(LhMt:HpL9t=HX@t Hx6(HI9uHhLp6(EtL7(LIH=k?6(L1( fffff.UHH<]UHH<]UHSPHHtHsHzHH[]X6(H[]ÐUHN6(HHH@H@H]fUHAWAVATSIL%7>M'0 6(HW)C )C)H:!I_H<HIHK[A\A^A_]IM'I_HtH=!IH5(IL0( ffffff.UHAWAVAUATSHIH<HHEA(uIG LV6!AG(1ǂ!IG H@ j?Hj?H=j?P(IMA(uI_ L5!AG(1i!HI_ KH{Hu1螆!HH{H<HH;EudLHĘ[A\A]A^A_]~J!H=/j?4(`H= j?H=w<H5i?H=r4(H=i?4()4(L4(IHhEY H5H+H pK+H}LhH<H@HHHI+HPHHAHI+HuHPL9E1H57<H<L3( IA(IA}IIPt H`n3(LeMt6HEL9t!HX@t HxI3(HI9uH}LLe/3(Et H} 3(Et H}3(LhMt:HpL9t=HX@t Hx2(HI9uHhLp2(EtL2(LIH=Uh?2(L-( fffff.UHH<]UHHm<]UHSPHHtHsHvHH[]H2(H[]ÐUH>2(HHH@H@H]fUHAWAVATSIL%3>M'01(HW)C )C)H5!I_H<HIHK[A\A^A_]IM'I_HtH9!IH1(ILw,( ffffff.UHAWAVAUATSHIH<HHEA(uIG LF2!AG(1~!IG H@ *f?H f?H=f?P(IMA(uI_ L1!AG(1Y~!HI_ KH{Hu1莂!HH{H<HH;EudLHĘ[A\A]A^A_]nF!H=e?0(`H=ke?.H=O<H5Xe?H9b0(H=Ue?0()0(<0(IHh5U H5D+H H+H}Lh H <HHHE+HPHHAHE+HuHPL-E1H5+<H<L/( IA(IA}IIPt H`b/(LeMt6HEL9t!HX@t Hx=/(HI9uH}LLe#/(Et H}/(Et H}/(LhMt:HpL9t=HX@t Hx.(HI9uHhLp.(EtL.(LIH=c?.(L)( fUHH<]UHH<]UHSPHHtHsH&HH[]H.(H[]ÐUH>.(HHH@H@H]fUHAWAVATSIL%/>M'0-(HW)C )C)H1!I_H<HIHK[A\A^A_]IM'I_HtH5!IH-(ILw(( ffffff.UHAWAVAUATSHIH<HHEA(uIG LF.!AG(1z!IG H@ b?Hb?H=|b?P(IMA(uI_ L-!AG(1Yz!HI_ KH{Hu1~!HH{H<HH;EudLHĘ[A\A]A^A_]nB!H=b?,(`H=a?H=<H5a?H5b,(H=a?,(),(<,(IHh5Q H5@+H D+H}LhH<H@HHHA+HPHHAHA+HuHPL) E1H5'<H<L+( 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*I]@HLH*HH@H0HfHnfLLHEEIEHHIhL0HHLL%o*ILHDž HDžHDžHDžDž(LHHLHHȬAIvIEvIEVLhȬHHLHHKȬHH̬HHL0HHLt HHH5v*HHvH0BH*HH;EuHHX[A\A]A^A_]HSHeHHHL0HHLt HHH5͍*HHH0H UHAWAVAUATSHXIIHHH*HHEH0L-\*I]@HLHk~*HH@H0HfHnfLLHEEIEHHIhL0HHLL%*ILHDž HDžHDžHDžDž(LHHTLHHŬAIvIEvIEVLŬHHLHHŬHHHʬHHL0HHLt H HH5Ƌ*HHH0H3*HH;EuHHX[A\A]A^A_]HSHeHHHL0HHLt H`H@H5*HHH0H UHAWAVAUATSHXIIHHHQ*HHEH0L-*I]@HLH{*HH@H0HfHnfLELHEEIEHHIhL0HHL<L%*ILHDž HDžHDžHDžDž(LHHLHH*ìAIvIEvIEVLìHHeLHH¬HHǬHHL0HHLt HYH9H5*HHH0H*HH;EuHHX[A\A]A^A_]bHSHeHHHL0HHLt HHH5m*HHmH09Ho UHAWAVAUATSHH*HHEIIIIH(4IH5HH LL H}] HuLH*HIIF IFIF1H5y*H)v*LHAHiIFXHXHHDMcfPMtIH;LEbHIuIFpHXHHDMcfhMt(Iffffff.H;LbHIuW)E)EE?L}IFXHXHHDLHMcfPMtDIL}fffff.L3IvL!HIvL$HIuLHIF@HXHHDMc~8MtaIffffff.L#A}I$H}Huff.H}G$LL||HIuIL`HLDMcMt=II$HLQHC(HuH,-HH}#IIuIF(HHHHDIcF HfHH@H8H HA(LpHLDHHHcA HtpM<M&A$uHuIfID$Ht6H}LHu%H]H[(HHLxHtIM9uHHHLHU!HC@L`HLDHcC8Ht|M4M<$Au Hu XIGHtOH}L HlH]fff.H[(HtHLHt>H}Lv"IM9uH8HH;@H]Ht.fff.L3Ct H{ HLMuH}HEHtHQ*HH;EuHĨ[A\A]A^A_]7(HIVH5H H} AHuH<Hk*HHHC 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H,1HHIHtHEH4H}Ht H}"H}Ht H}Hiv(HH;EuHHX[A^A_]S(IH5H^H}yAHuL\Hsm(HIIF IFIFE1H5R(HN(LϰZHHEt H}bEt6L苰,HH}HtH}>HH}Ht H}'H ffff.UHAWAVSHXIHHH Su(H HMx{A~H}H9 H}H9 H}L9 H}HE\HE^f: H,1HHIHtHEHlH}Ht H}ZH}Ht H}HHt(HH;EuHHX[A^A_]苯(3IH5*HH}AHuL蔬Hk(HIIF IFIFE1H5P(HM(LZHHEt H}蚮Et6Lî,HH}HtH}vHH}Ht H}_HM DUHAWAVAUATSHHHIHHs(HHEH0L%q(M|$@LLHN(HH@H0HfHnfL{HEEID$HHIhL0LLLxL5Kq(ILHDž HDžHDžHDžDž(LLLLHmLHʯHHHLLHHHL0HHLt H辬H螫H5{\(HH{H0GHq(HH;EuLHH[A\A]A^A_]ˬHSHeHHHL0HHLt HHH5[(HH֪H0被Hئ UHAVSHIH(q(HHEH`HH5 HAHD$$HH L~1E1HH5jH2*L 1H"H ?*H0H0HPHHH5HH5DH HH5%H8LH&HPH9t HtHP(HP H`Ho(HH;EuLH[A^]I"IHPH9uHP HtHP(H`kL9 UHAWAVSHIHo(HHEH`HPH5gH1AHoD$$HH eLܸ1E1HHW)HDž fDžTƅ1eHHW)HDž@oH(%$)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH H U*H0H0HPHH%H5HFH5үH HzH5TCHLH$HPH9tcHtHP(t H/LMtWHL9t<HX@t HxHI9uHHP uLĻt H 跧H`Hm(HH;EuLH[A^A_]IuXbI]IIIHPH9tHtHP(uHP t HLMtGHL9tfffff.HX@t HxݦHI9uHL¦t H 警H`L菡 LfUHAWAVSHIHk(HHEH`H蠚H5HE1HH5H E1HD$$H#H L1E1HC HW)HDž fDžTƅfaHH'W)HDž@褥H(Z$)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHA H *H0H0HPHHZH5{H{H5H HH5HLHP HPH9thHtHP(t HdLMt\HL9tAfff.HX@t Hx-HI9uHHP uLLt H H`H4i(HH;EuLH[A^A_]IuXbI]IIIHPH9tHtHP(uHP t HELMtGHL9tfffff.HX@t Hx HI9uHLt H ݢH`L违 LfUH]f.UH]馢fDUH螢H *H]fDUHH}*H]UH]f.UH]VfDUHAWAVAUATSH8IHg(HHEIHW)EE HEEvalufEeHuLHEt H}HCH1LS{1L ILccMLk LHHEIfDMuIG(Ht#IcO 09})QAW H\{(uXeDAw$f.A;w$uH}IG(0Ɖ0I HIG(IcO QAW H\{(tHP C(Ls II^HGf(HH;EuH8[A\A]A^A_]0HEt H}踠H覛 fffff.UHH 5%H1H9NHD]DUHH*]UH]f.UH]VfDUHNH *H]fDUHH*H]UH]f.UH]fDUHAWAVAUATSH8IHEe(HHEIHW)EE HEEvalufEeHuLHEt H}蝟HCH1Ly1LILccMLk LHHEIfDMuIG(Ht#IcO 09})QAW H\{(uXeDAw$f.A;w$uH}OIG(0Ɖ0I HIG(IcO QAW H\{(tHgN C(Ls II^Hc(HH;EuH8[A\A]A^A_](舞IH5HH}AHuLH[(HIIF IFIFE1H5O@(HX<(L\ HHEt H}EtLHEt H}ΝH輘 @UHH 5%H1H9NHD]DUHH*]UHAWAVAUATSHHHIHHb(HHEH0L%#a(M|$@LLH1>(HH@H0HfHnfL軜HEEID$HHIhL0LLL踛L5`(ILHDž HDžHDžHDžDž(LL'LLH譅LH HHH茅LH=HHL0HHLt HHޚH5K(HH軚H0臛H(a(HH;EuLHH[A\A]A^A_] HSHeHHHL0HHLt HYH9H5K(HHH0H UH]f.UH]fDUHH *H]fDUHH*H]UH]f.UH]鶚fDUHAWAVAUATSH8IH_(HHEIEHW)EE HEEvalufEeHuLIEt H}LIEHW)EEHEHsparse_vHMEalueEHuLIEt H}Mt M9MIHuH*1LTs1L ILccMLk LHHEIMuIG(Ht#IcO 09})QAW H\{(uXeDAw$f.A;w$uH}IG(0Ɖ0I HIG(IcO QAW H\{(tHH C(Ls II^MMMuL%*ID$(HuH*1LH HH5fH2LH t H貍LMtIHL9t.f.HX@t Hx}HI9uHLL]t HHH@I9t HtHP(HP HP=HvR(HH;EuLH[A\A^A_][I1IIuszIt H迌LMtAHL9{HX@t Hx荌HI9uHLrt H]H@I9uHP HtHP(HPRL  LUHAWAVATSHHgQ(HHEH&(t+HLQ(HH;EvHP(H[A\A^A_]H=i&(tW)ƅHDžHr(uint8)HHtensor(uHƅHDžƅH(uint16)HHƅHDžƅH(uint32)HHƅHDž(ƅH(uint64)HHƅ')0ƅ0HDž@Htensor(iH1Dž9nt8)ƅ=HDžXHDžPHDžHƅHHr(int16)HNHIƅV)`HDžpƅ`Hr(int32)HfHaƅnHEHEHDžxƅxHr(int64)H~HyE)EEHEHfloat16)HEHtensor(fHEEHEHEHEEHr(float)HMHEEHEEH(double)HEHtensor(dHEEL=N(IGIGI>IGIHHIOHH:HIHI"HIHI HIHIH0IHIچHHIHI†H`IHI誆HxIHI蒆HuIHI}HuIHIhHuIHISIGEEEx`H0t H蔇H=5((H56L(Hߐ蘇H="(趇vʇH}[ECH}HE:H}5x1H}`(HpHHX0H@ԆH(軆H袆H艆IEtNIM'Mt4IGL9HX@t Hx>HI9uI?Mg*EEEx`H0t H蜅H== (̅L~ H}}E]H}jETH}WxKH}A`BHp(H6HX0*H@H(݄HĄH諄L^UHAWAVATSHIHI(HHEHPHxH)H L L@HLgHW)HDžfDžTƅHHkbW)HDž0H(#)Htensors.HH&Hmeric teHH Ho all nuHHH types tHHHns inputHHHConstraiH@.HHHHH5iH )LHH5bHҧH5^H# HH5tHRLHt H҂LMtIHL9t.f.HX@t Hx蝂HI9uHLL}t HhH@I9t HtHP(HP HP]HG(HH;EuLH[A\A^A_]{I1IIuszIt H߁LMtAHL9{HX@t Hx譁HI9uHL蒁t H}H@I9uHP HtHP(HPrL@| LUHAWAVSHIHF(HHEH`HPuH)H0L0LPHLH5H)L5 HWH5݌H)LH5H5HFH5҇H藨 HzH5HƦLHHPI9t HtHP(HP H`PHE(HH;EuLHĸ[A^A_]pHHPI9uHP HtHP(H`Hz fUHAVSHIHE(HHEH`HsD$$HH 5L1E1HD$$HnH L^1E1HH5JHj)LHH =(HH0H e(H8H0HPHHeH5;H膣H5HצH躞H5ϽHLH[HPH9t HtHP(HP H`HC(HH;EuLH[A^]~I"IHPH9uHP HtHP(H`9Ly ffffff.UHSPH1 {WHHPHtDH1HPHt4H1HPH@H(u H@ @uu H@ @uH[]11HH[]pFUHAVSHIHB(HHEH`HqD$$HEH L?1E1H1D$$HH LE1HD$$HټH Lڋ1E1H H5ƋH)LH*H5ÎHżAHH )H0H0HPHHҟH5HH5HD H'H5<HsLHHPH9t HtHP(HP H`H6A(HH;EuLH[A^]|I"IHPH9uHP HtHP(H`Ltv fff.UHAWAVAUATSHIH@(HHEHPH|oH)H H H@HHHW)HDžfDžTƅHHLYW)HDž0zH(z#)Htensors.HH&Hmeric teHH Ho all nuHHH types tHHHns inputHHHConstraiH@.HHHHtH5JH)LHHDžLHLuHW)HDžƅfDžO1ƅ)@HDžPyHHH@HHHPHHwHHW) HDž0ƅ Dž!Lessƅ%))fDžAƅHDžHDžfDžBƅ)HDž0xHHHH0HHHvHHHHvHHH@H Hh&W)`HDžpƅ`fDžaO2ƅc)HDžMt+IFL9t+fHX@t HxmPHI9uI> [A\A^A_]LM~[A\A^A_]CPUH]f.UH]&PfDUHPH )H]fDUHHu)H]UH]f.UH]OfDUHAVSHPHH(HHED$$H]H ґL^1E1H蓱D$$H H ϑLp^E1H_D$$HH ^LH[1E1HnH)HELuLuHLrH}I9t HtHP(HP H=(HH;Eu HP[A^],OHH}I9uHP Ht HP(HHI UHH a%H1H9NHD]DUHH)]UH]f.UH]FNfDUH>NH #)H]fDUHH )H]UH]f.UH]MfDUHAWAVAUATSH8HH5(HHE1 HO'HHPHH1HPHH1HPfff.H@H(uH@ @uH@ @HHPHrHHPH[HHP@H@H(uH@ @u,#H@ @H1HPx(uH@ H8*L`MuL%*HHPx(uH@ H*LhMuL-Ǥ*H1HP(HÃ{(uL{ HmM C(1 IL{ AOMwMu1s IMwW)EHELeH}Hu\wHELmH;Es L(HE H}Hu7wH}LqH}Ht H}KH"(HH;EuH8[A\A]A^A_] LHH}Ht H}KHzF fUHH E^%H1H9NHD]DUHH)]UH]f.UH]6KfDUH.KH )H]fDUHH)H]UH]f.UH]JfDUHAWAVAUATSH8HH%(HHE11HHHPHH1HPHH1HPffffff.H@H(uH@ @uH@ @HHPHrHHPH[HHP@H@H(uH@ @u,#H@ @H1HPx(uH@ H(*L`MuL%*HHPx(uH@ H*LhMuL-*H1HP(HÃ{(uL{ H]J C(1 IL{ AOMwMu1c IMwW)EHELeH}HuLtHELmH;Es L(HE H}Hu'tH}LnH}Ht H}HH(HH;EuH8[A\A]A^A_]HHH}Ht H}|HHjC fUHH e\%H1H9NHD]DUHH-)]UHAWAVAUATSPHI&M}HsLCFMe0Hs0Lw&I}HHHHH[A\A]A^A_]HEI$HtBIE8H9t!Lp@t HxGLL9uI<$HI]8GAu HEAtI}(GHEMuMt3IEL9t5fHX@t Hx]GHI9uI}MuHGH}5B LUHAWAVAUATSPIIIGIGII~I+>t{HHHm۶m۶mHH%I$I$H9sfFHII?HiHIGIMvL9t.AfHx HILII9uH[A\A]A^A_]LC IM'Mt?I_L9t@MI)H DH H IuI?MgFL A L萐UHH)HH H]fUHHu)HH H]fUHAWAVSHIH (HHEH`H9H)H0L0LPHLH5sTH[)L H׼H5]QHA)L H赼H5QHiH5RLHmHdH5XHFkLHHPI9t HtHP(HP H`дH (HH;EuLHĸ[A^A_]DHHPI9uHP HtHP(H`|HJ? fUHAWAVSHIH (HHEH`H`8H!)H0L0LPHL)H5SH)LHgH5OH)LHEH5PHVhH5JHkHcH5fHiLH+HPI9t HtHP(HP H``H(HH;EuLHĸ[A^A_]CHHPI9uHP HtHP(H` H= fUHAWAVSHIH&(HHEH`H6H)H0L0LPHLH5QH)L)HH5}NH)L+HչH5NHfH5rIH7jHbH5tHfhLH軽HPI9t HtHP(HP H`H)(HH;EuLHĸ[A^A_]BHHPI9uHP HtHP(H`蜱Hj< fUHAWAVSHIH(HHEH`H5HA)H0L0LPHLIH5#PH;)LFH臸H5 MH1)LHeH5 MHveH5HHhH`H5HfLHKHPI9t HtHP(HP H`耰H(HH;EuLHĸ[A^A_]@HHPI9uHP HtHP(H`,H: fUHAWAVSHIHF(HHEH`H4Hѷ)H0L0LPHLH5NH)LրHH5KH)LKHH5KHdH5FHWgH:_H51HeLHۺHPI9t HtHP(HP H`HI(HH;EuLHĸ[A^A_]0?HHPI9uHP HtHP(H`輮H9 fUHAWAVSHIH(HHEH`H2Ha)H0L0LPHLiH5CMH)LH觵H5-JH)L~H腵H53JHbH5"EHeH]H5~HdLHkHPI9t HtHP(HP H`蠭H(HH;EuLHĸ[A^A_]=HHPI9uHP HtHP(H`LH8 fUHAWAVSHIHf(HHEH`H01H)H0L0LPHLH5KHK)L~H7H5HHA)Lk}HH5HH&aH5CHwdHZ\H5Q}HbLHHPI9t HtHP(HP H`0Hi(HH;EuLHĸ[A^A_]P<HHPI9uHP HtHP(H`ܫH6 fUHAWAVSHIH(HHEH`H/H)H0L0LPHL艾H5cJH)L|HDzH5MGH)L{H襲H5KGH_H5BBHcHZH5{H6aLH苶HPI9t HtHP(HP H`H'HH;EuLHĸ[A^A_]:HHPI9uHP HtHP(H`lH:5 fUHAWAVSHIH'HHEH`HP.H)H0L0LPHLH5HH)L{HWH5EH)LzH5H5EHF^H5@HaHzYH5qzH_LHHPI9t HtHP(HP H`PH'HH;EuLHĸ[A^A_]p9HHPI9uHP HtHP(H`H3 fUH]f.UH]8fDUH8H )H]fDUHH)H]UH]f.UH]f8fDUHAVSHPHH'HHEHEH5yHyLuHM:kH5'HyE1H|eD$$HBFH yLF1E1HۙD$$HOH yLFE1H觙D$$HH yLC1E1H趝H'HHEH'HELuHLZH}I9t HtHP(HP Hz'HH;Eu HP[A^]i7HH}I9uHP Ht HP(HH1 UHH K%H1H9NHD]DUHH%)]UHSPH1 HHPHtDH1HPHt4H1HPH@H(u H@ @uu H@ @uH[]11HH[]UH]f.UH]6fDUH5H )H]fDUHHu)H]UH]f.UH]5fDUHAVSHPHH'HHED$$HCH wLD1E1HsD$$HH wLPDE1H?D$$HjH >wL(A1E1HNH)HELuLuHL`XH}I9t HtHP(HP H'HH;Eu HP[A^] 5HH}I9uHP Ht HP(HHp/ UHH K%H1H9NHD]DUHHͭ)]UH]f.UH]&4fDUH4H #)H]fDUHH )H]UH]f.UH]3fDUHAWAVAUATSH8HH'HHE1 H/ HHPHH1HPHH1HPfff.H@H(uH@ @uH@ @HHPHrHHPH[HHP@H@H(uH@ @u,#H@ @H1HPx(uH@ H*L`MuL%؊*HHPx(uH@ H*LhMuL-*H1HP(HÃ{(uL{ HM3 C(1| IL{ AOMwMu1S| IMwW)EHELeH}Hu<]HELmH;Es L(HE H}Hu]H}LWH}Ht 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LM51E1H?D$$H,5H ϴL51E1HNHW)HDž fDžTƅ豙HH2W)HDž@%H( %)Htensors.HH0Hnumeric HH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@8HHHHLH 'HH0H c'H8H0HPHHSHH50HtIH5,HLHDH59gHJLHIHPH9taHtHP(t H]$LMtUHL9t:DHX@t Hx-$HI9uHHP uLL#t H #H`H4'HH;EuLH[A^A_]$IuXbI]IIIHPH9tHtHP(uHP t HE#LMtGHL9tfffff.HX@t Hx #HI9uHL"t H "H`L LfUHAVSHIH'HHEH`HD$$H1H %L11E1HqD$$H^1H LN11E1H耈H5:1H)LܧH螙H 'HH0H e'H8H0HPHHUEH5-HvFH5)HIHAH5;dHGLHKHPH9t HtHP(HP H`耑H'HH;EuLH[A^]!I"IHPH9uHP HtHP(H`)L ffffff.UHAVSHIH8'HHEH`HD$$H/H UL/1E1H衂D$$H/H 1L~/1E1H谆H5j/HB)L HΗH 'HH0H 'H8H0HPHHCH5,HDH52'HGH?H5kbH&FLH{HPH9t HtHP(HP H`谏H'HH;EuLH[A^]I"IHPH9uHP HtHP(H`YL' ffffff.UHAVSHIHh'HHEH`H2D$$H-H L-1E1HрD$$H-H aL-1E1HH5-H)L<HH 'HH0H ż'H8H0HPHHAH56*HBH5b%H'FH >H5`HVDLH諙HPH9t HtHP(HP H`H'HH;EuLH[A^]I"IHPH9uHP HtHP(H`艍LW ffffff.UHAVSHIH'HHEH`HbD$$H,H L,1E1HD$$H+H L+1E1HH5+HҖ)LlH.H O'HH0H 'H8H0HPHH?H5v(HAH5#HWDH:<H5^-HBLHۗHPH9t HtHP(HP H`HI'HH;EuLH[A^]2I"IHPH9uHP HtHP(H`蹋L ffffff.UHAVSHIH'HHEH`HD$$HE*H L?*1E1H1}D$$H*H L*1E1H@H5)H)LH^H 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H(a)Hry type.HHHrbitraryHHH be of aHHHnsor canHHHInput teH@&HHHH4H5 H"LH&DH "H0H0HPHHH5sUHH5HaHDH5!HLHGHPH9t]HtHP(t H LMtQHL9t6HX@t Hx HI9uHHP uLL t H H`;HԐ HH;EuLH[A^A_] IkIuS]IIIHPH9tHtHP(uHP t H LMtGHL9tfffff.HX@t Hx HI9uHL t H } H`:L_  LfUHAWAVSHIH HHEH`HpH5=HAHD$$HH L 1AH+D$$HH L 1E1H/HW)HDž fDžTƅHHާW)HDž0[ H()Hnsor typHH Ho any teHHH types tHHHn outputHHHConstraiHf@(e.@*HHHH1H "H0H0HPHH'H5HHH5H H|H5uHLHDHPH9teHtHP(t H1 LMtYHL9t>fHX@t Hx HI9uHHP uLL t H H`7H HH;EuLH[A^A_] IuXbI]IIIHPH9tHtHP(uHP t H LMtGHL9tfffff.HX@t Hx HI9uHL t H H`6L  LfUHAWAVSH(IH֋ HHEH`H蠺D$$HU H LM 1E1H?(D$$HH L 1AHK,HW)HDž fDžTƅnHH/W)HDž@ H(b)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHI-HDžH5 H LHH5YHrE1H>H "H0H0HPHHH5WH8H5H HlH5HLH @HPH9teHtHP(t H! LMtYHL9t>fHX@t Hx HI9uHHP uLL t H H`3H HH;EuLH([A^A_] IkIuS]IIIHPH9tHtHP(uHP t H LMtEHL9tdff.HX@t Hx HI9uHL t H H`2L  LfUHAWAVSHIHƇ HHEH`H萶D$$HQH QL= 1E1H/$D$$HNH PLKE1H#D$$H QH DLJE1H#D$$H#QH lLJAH#D$$H)H LrJAHY#D$$HH H PL6 1E1Hh'HW)HDž fDžTƅ|HHLW)HDž@ H()Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHf(H55IH(}"LOH7H "H0H0HPHH]H5HH~H5 H HH50HLHS;HPH9tkHtHP(t Hg LMt_HL9tDffffff.HX@t Hx- HI9uHHP uLL t H H`.H4 HH;EuLH[A^A_] IcIuKUIIHPH9tHtHP(uHP t HM LMt?HL9tJDHX@t Hx HI9uHL tH LIH`-LŸ  ffff.UHAWAVSHIH HHEH`HбH5H~E1HD$$H'LH SfL_ 1E1HQD$$HH L. 1E1H`#HW)HDž fDžTƅxHHDW)HDž@ H(w)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH^$H }"H0H0HPHHwH5UHH5$HHH5JHLHm7HPH9teHtHP(t H聻 LMtYHL9t>fHX@t HxM HI9uHHP uLL t H  H`+HT HH;EuLH[A^A_]; IuXbI]IIIHPH9tHtHP(uHP t He LMtGHL9tfffff.HX@t Hx- HI9uHL t H H`*Lߴ  LfUHAWAVSHIH& HHEH`HHHDž(H5HL(HD$$H0HH oLh 1E1HZD$$HoH =LH&H r"H0H0HPHHzH53HH5'H HH5M*HLHp*HPH9thHtHP(t H脮 LMt\HL9tAfff.HX@t HxM HI9uHHP uLL t H  H`HTs HH;EuLH[A^A_]; IkIuS]IIIHPH9tHtHP(uHP t He LMtGHL9tfffff.HX@t Hx- HI9uHL t H H`Lߧ  LfUHAWAVSHIH&r HHEH`HHDž(H5rHuL(HD$$H8;H bLp 1E1HbD$$HbH LD5E1H.D$$H H !L 1E1H=HW)HDž fDžTƅ`gHH!W)HDž@螫 H(ī)Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHHBH54H4h"L:Hp"H An"H0H0HPHH9H5bHZH5H HH5 HLH/&HPH9tgHtHP(t HC LMt[HL9t@ff.HX@t Hx HI9uHHP uLLܩ t H ǩ H`Ho HH;EuLH[A^A_] IkIuS]IIIHPH9tHtHP(uHP t H% LMtGHL9tfffff.HX@t Hx HI9uHLҨ t H 轨 H`L蟣  LfUHAWAVSHIHm HHEH`H谜HDž(H5rnH5L(HcD$$H6H ^L0 1E1H" D$$H^H L1E1H D$$Hݶ H L˶ 1E1H HW)HDž fDžTƅ cHHᅢW)HDž@^ H()Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHHH5/Hd"L~6H0H j"H0H0HPHHH5yHH5Hk HNH5̢HLH!HPH9tgHtHP(t H LMt[HL9t@ff.HX@t Hxͥ HI9uHHP uLL蜥 t H 臥 H`Hj HH;EuLH[A^A_]軥 IkIuS]IIIHPH9tHtHP(uHP t H LMtGHL9tfffff.HX@t Hx譤 HI9uHL蒤 t H } H`L_  LfUHAWAVSHIHi HHEH`HpH53H6E1HD$$H2H tL 1E1HD$$HvH xLβ 1E1H HW)HDž fDžTƅ#_HH䁢W)HDž@a H()Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH H g"H0H0HPHHH5~8H8H5ĩH HlH5IHLH HPH9teHtHP(t H! LMtYHL9t>fHX@t Hx HI9uHHP uLL輡 t H 觡 H`Hf HH;EuLH[A^A_]ۡ IuXbI]IIIHPH9tHtHP(uHP t H LMtGHL9tfffff.HX@t Hx͠ HI9uHL負 t H 蝠 H`L  LfUHAWAVSHIHe HHEH`H萔H5/HAHD$$H.H 5L 1E1HD$$HH L 1E1HHW)HDž fDžTƅ@[HH~W)HDž@~ H(4)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH c"H0H0HPHH4H5;HUH5H H艾H5HLH*HPH9tbHtHP(t H> LMtVHL9t;fDHX@t Hx HI9uHHP uLLܝ t H ǝ H` Hc HH;EuLH[A^A_] IuXbI]IIIHPH9tHtHP(uHP t H% LMtGHL9tfffff.HX@t Hx HI9uHLҜ t H 轜 H` L蟗  LfUHAWAVSHIHa HHEH`H谐H5@.HAHD$$HD H L< 1E1H.D$$H H _L 1E1H=HW)HDž fDžTƅ`WHH!zW)HDž@螛 H(T)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHH;H \`"H0H0HPHHTH5*HuH5HH詺H5'HLHJHPH9tbHtHP(t H^ LMtVHL9t;fDHX@t Hx- HI9uHHP uLL t H H` H4_ HH;EuLH[A^A_] IuXbI]IIIHPH9tHtHP(uHP t HE LMtGHL9tfffff.HX@t Hx HI9uHL t H ݘ H`L迓  LfUHAWAVSH8IH^ HHEH`HЌH5`*HAHW)HDž ƅDžDCRH5RHLHD$$H H [L 1E1HD$$H H L 1E1HHW)HDžfDžTƅ8SHHuW)HDž@v H(,)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH \"H0H0HPHH,H5e&HMH5ٝH螾 H聶H56HͼLH"HPH9tjHtHP(t H6 LMt^HL9tCfffff.HX@t Hx HI9uHHP uLL̕ t H跕 t H 袕 H`HZ HH;EuLH8[A^A_]֕ IumwIrIIIIHPH9tHtHP(uHP t H LMt=HL9tqHX@t Hx轔 HI9uHL袔 t H荔 t H x H`LZ  Lffff.UHAWAVSHIHY HHEH`H`D$$H H TL 1E1HD$$HߙH L E1HD$$H H *L 1E1HHW)HDž fDžTƅNHHqW)HDž@; H()Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH5 HO"LwH H X"H0H0HPHHϵH5Z)HH5|HAH$H5HpLH HPH9t]HtHP(t Hّ LMtQHL9t6HX@t Hx譑 HI9uHHP uLL| t H g H`{HV HH;EuLH[A^A_]蛑 IkIuS]IIIHPH9tHtHP(uHP t HŐ LMtGHL9tfffff.HX@t Hx荐 HI9uHLr t H ] H`qL?  LfUHAWAVSH8IHU HHEH`HPHW)ƅHDž HnearestHH5IHLHYD$$H H .L 1E1HD$$HH LáE1HdD$$HQ H LA 1E1HsHW)HDžfDžTƅJHHWmW)HDž@Ԏ H(J)Hr types.HH/Hl tensorHH(HY' to alHH Houtput 'HHH'X' and HHHn input HHHConstraiH@7HHHHqH U"H0H0HPHH花H5-H諲H57H H߭H5]H+LH HPH9thHtHP(t H蔍 LMt\HL9tAfff.HX@t Hx] HI9uHHP uLL, t H t H  H`HOR HH;EuLH8[A^A_]6 IumwIrIIIIHPH9tHtHP(uHP t HK LMt=HL9tqHX@t Hx HI9uHL t H t H ؋ H`L躆  Lffff.UHAWAVSHIHP HHEH`HW)ƅHDž HnearestHH5EHcLHDž @H5HL HHDžH5HLHW)ƅHDžHhalf_pixHfDželƅH5;HتLHW)ƅ$HDžHefer_floHHround_prHfDžorƅH5HfLHDžH5HbLHD$$HИ H RL֕ 1E1HD$$HȕH ŕL[ E1HD$$HH LE1HTD$$HחH ֗L AHD$$H H L 1E1H,HW)HDžƅfDžT1ƅHDHH gW)pHDž@膈 H()pHr types.HH/Hl tensorHH(HY' to alHH Houtput 'HHH'X' and HHHn input HHHConstraiH@7HHHpH#H5 H=E"LHQH "O"H0H0HPHHH5.H;H5ǎH茯 HoH5H軭LHHPH9thHtHP(pt H$ LMt\HL9tAfff.HX@t Hx HI9uHHP puLL輆 uTu`ult H 茆 H`HK HH;EuRLHĈ[A^A_]HS tH> tH) u III UIIpudnIH贅 IHPH9tHtHP(puHP pt Hb LMtDHL9~fDHX@t Hx- HI9uHL EuEuQt H ބ H`L  LItH諄 tH薄 uff.UHAWAVSHIHI HHEH`HxD$$HE H L= 1E1H/D$$H H yL 1E1H>HW)HDž fDžTƅa?HH"bW)HDž@蟃 H(U)Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHH5z H@1"LψHH m<"H0H0HPHHH5y HH5zHW H:H5oH膙LHHPH9t HtHP(HP H`HI8 HH;EuLH[A^]2s I"IHPH9uHP HtHP(H`Lm  ffffff.UHAVSHIH7 HHEH`HfD$$HE H @ LK~ 1E1H1D$$H H  L 1E1H@HDž(H5 H8L(HHDž H5 HL H趤H5} H0/"L`HH5@ H/"LHH :"H0H0HPHH諔H5 H̕H5XxH HH5~m6HLLHHPH9t HtHP(HP H`H6 HH;EuLH[A^]p I,I'I"IHPH9uHP HtHP(H`uLCk  ff.UHAWAVSHIH5 HHEH`HPdD$$HH L* 1E1HD$$H~ H L~ E1HD$$H~ H L~ E1HD$$Hv~ H Ld~ 1E1HH5) H,"LΆHHW)HDž fDžTƅ*HHXMW)HDž@n H()Hr types.HH-Htensor tHH(H to all HH Hut typesHHHand outpHHHn input HHHConstraiH@5HHHHrH 8"H0H0HPHH苑H5eH謒H58uH HH5^jhH,LHHPH9tiHtHP(t Hm LMt]HL9tBffff.HX@t Hx]m HI9uHHP uLL,m t H m H`+ݜHd2 HH;EuLH[A^A_]Km IuXbI]IIIHPH9tHtHP(uHP t Hul LMtGHL9tfffff.HX@t Hx=l HI9uHL"l t H l H`!ܜLf  LfUHAWAVSHIH61 HHEH`H`D$$Hz H z Lz 1E1HD$$Hz H z LX~1E1HHW)HDž fDžTƅ&HHIW)HDž k H(_)Hr types.HHH tensor HHHn to allHHHConstraiH@HHHHH 4"H0H0HPHHH5 HH5qHa HDH5f~H萐LHHPH9t]HtHP(t Hi LMtQHL9t6HX@t Hxi HI9uHHP uLLi t H i H`ٜH. HH;EuLH[A^A_]i IuXbI]IIIHPH9tHtHP(uHP t Hh LMtGHL9tfffff.HX@t Hxh HI9uHLh t H }h H`؜L_c  LfUHAWAVSH(IH- HHEH`Hp\HDž(H5 HL(H#HDž H5 HL HD$$Hv H Lv 1E1HD$$HH LnzE1HD$$Hnv H L^v 1E1HHW)HDžfDžTƅ"HHtEW)HDž@f H('g)Hsor typeHH(H any tenHH Han be ofHHH types cHHHd outputHHHInput anHf@0.HHHHH ;1"H0H0HPHH賉H5 HԊH5`mH% HH5bHTLHHPH9taHtHP(t He LMtUHL9t:DHX@t Hxe HI9uHHP uLL\e t HGe H`[՜H* HH;EuLH([A^A_]{e Iu`jIeIIIIHPH9tHtHP(uHP t Hd LMt?HL9t^DHX@t Hxmd HI9uHLRd t H=d H`QԜL_  LfUHAWAVSHIHf) HHEH`H0XHDž(H5# H|L(HH5)H}E1H%D$$Hr H }Lr 1E1HD$$Hqr H }Lar 1E1HD$$HH S~L vAH\D$$H$H -LuAH%D$$H>!H LuAHHW)HDž fDžTƅHH@W)HDž0Ob H(])Hor type.HHHany tensHHHn be of HHHInput caH@ HHHHH 7-"H0H0HPHH/H5* HPH5hH衉 H脁H5^ HЇLH%ݜHPH9t]HtHP(t H9a LMtQHL9t6HX@t Hx a HI9uHHP uLL` t H ` H`МH& HH;EuLH[A^A_]` IuPZIUIIHPH9tHtHP(uHP t H-` LMt?HL9tJDHX@t Hx_ HI9uHL_ tH _ LIH`ϜLZ  ffff.UHAWAVSHIH$ HHEH`HSHDž(H5 H|L(HcD$$HH L0n 1E1H"D$$HH mLqE1HD$$Hm H ߡLm 1E1HHW)HDž fDžTƅ HH<W)HDž@^^ H(^)Htensor tHH(H to any HH Hut typesHHHand outpHHHn input HHHConstraiH@0ype.@4HHHHH )"H0H0HPHHH5 H HHIIF IFIFE1H5<HELIA  (A IH5+HgH}_AHuLb> HyHIIF IFIFE1H5HL@ 늿(@ IH5+H"H}KiAHuL= HHIIF IFIFE1H5TH]La@ H% HHHEt H}? EtL @ HEt H}? H:  UHH H1H9NHD]DUHH!]UH]f.UH]f? fDUH^? 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tHxPuE1MH VL;1UMDLLLIIELpPM Eu H=MtIEHxXPPt\IMt$IEH@@H UH;tE%_ EeLLIH}LHEIELp@=IEH@pHpHL9It I;utLLLIMH UL;1t MM UԉсDщUԀ}t1H}H}ILIMtSMtNIHGPHtAIuH;FPu7HGXHNXH9tHW`Ht!HH҅tIIuHhHhLHH}HHEL}MgIGL MHEIGH}IE1Aŋ]ԉ%FtAIB ADA‹u) ff.A) !AuMLsHHtJff.LHILHwIHC@ H{HLMuH([A\A]A^A_]fUH1ɸu]ËHt=r3H=3] ƒ t tHRH0]VH=<]fUH1ɸu]Ëuu6H=]ጁt tHRH0]H= ]gUHu H=l]I]UHt]H=h]$@UHSPu H=GӃH[]ÐUHAWAVAUATSHHLMLEHMIHuIAtvHQH=wHQLeEML(LHAԅLmAE1EH]tIIDž@HEIHMXI`HEIhADž(APEL AEt1IMHLILJ/MIpLHILLu1A IGff.DAANƉ)ρ D)΃A7uօu LLA(H]TIHEE1E11fff.LHuHUHM< ÅA(1ffffff.YE'ACuAT$DAtA"uD DAA(tBAP;Eu6IHuIGADž(HED`E'A]"1E'E1fffff.A(t DAA(uEH]LeLutMt-HAԅ"@LeLLLMuӱ1 UAN1ɋMEȉMLEHH[A\A]A^A_]UHHH1hLLHH$]tH=H]8H]fUH1ɸu]ËHt=r0H=#]ttHMH0]IH=/]UHAWAVAUATSPIAFTt#HgL8HXLHH[A\A]A^A_]Mf0LANTL-MgLuA~u H(gIF L8HXLIUIMLH~'HLHHuLZUHAWAVATSHLs0LSTHfHu{u H fHK H9HqHHHO~VCPL{XMt=Mg0LsuLGLLHL|L$ 1HhKPL[A\A^A_]DUHwH fLHIHHL1Ʌ]UHAWAVAUATSHIIIpHHQHChHC`HCXHCPHCHHC@HC8HC0HC(HC HCHCHCHHH6Lc Lk(CHL=(eMIOHHLMLmI~0H}1AFTuA~u L=dM~ M/MLLLHM|yLk L{(CHdHHHLLP~LsXI~`H,IF`H}OHH[A\A]A^A_]ffff.UHAWAVAUATSPII\$0HrAD$PI|$h3u %M|$XMt(Mw0LuHL*H"H]I|$`HHuuf.LHt^LsI|$`HHMn0LA~PuI|$`LID$`MtM~XI`LIG`IFXH5^1H]HLMt%I`LLIG`ID$XI0LAL$PHLH[A\A]A^A_]@UHH`]fUHAVSI1H bHHIHHHu ~ L[A^][A^]UHH0H!HHEHEHEH}HHH9CHUHUH$111AI1HHH;UuH0]ff.UHHG HW(]fUH]ffDUHAWAVSPIHL{0L$STHaHu{u H aHK H9HqHHHm ~H{hIvLHCh1A^ LcH[A^A_]fDUHAWAVSPIHL{0LSTHEaHu{u H !aHK H9HqHHH ~ H{hIv'HChAF 1LH[A^A_]ffff.UHAWAVAUATSPIIAWTH`HuAu H `IO H9HqHHHC AGT%HXIhHIGhC H{(,Ih3HuI`%IMuKff.LMt3I\$I`L"ILk0L3{PuHLI0H51L{MtI~`LIF`IGXH[A\A]A^A_]ÐUHAWAVAUATSHhIIIHHHEMHULL5L_IINHHHE+mrIL}H}HuEEI\$0H8AL$TLLuRA|$uEH^AHaHH;E\ILLHh[A\A]A^A_] AYID$ LxH]L0LhHHILL3I|$hHu ID$hHxHHuLLELHLHmH}LLL Aƃ}xA%@%B%D%F%H%J%L%N%P%R%T%V%X%Z%\%^%`%b%d%f%h%j%l%n%p%r%t%v%x%z%|%~%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % %%%%%%%%%% %"%$%&%(%*%,%.%0%2%4%6%8%:%<%>%@%B%D%F%H%J%L%N%P%R%T%V%X%Z%\%^%`%b%d%f%h%j%l%n%p%r%t%v%x%z%|%~%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % %%%%%%%%%% %"%$LUAS%Ehh<hhh<hhh"hjhhhxh nhldhZhPh Fh/h4h*h hLh hhhAhuh%hohhhhVhhhZhyhvhlhbhfXhNhDh :h80he&hhhh7hXhhhh@hhhhh,hJhhh|hrhhh%^h@Th\Jhx@h6h,h"h h!h>hXhthhhhhhhhhhhh(xh6nhEdhSZhbPhpFhh"4h"*h" h"h" h#h/#hE#ha#hv#h#h#h#h#h$h&$h;$hW$hs$h$vh$lh$bh$Xh$Nh$Dh$:h$0h%&h%h-%h<%hK%hZ%hk%h~%h%h%h%h%h%h%h%h%h &h&|h,&rh9&hhG&^hY&Thi&Jhy&@OrtGetApiBaseOrtSessionOptionsAppendExecutionProvider_CPU/Users/runner/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/work/1/s/onnxruntime/core/session/custom_ops.ccCreateCustomRegistryUnsupported version '' in custom op 'ComputeAsyncinter-opMemcpyFromHostXinputTYoutputConstrain to any tensor type. If the dtype attribute is not provided this must be a valid output type.MemcpyToHostInput expected to have tensor typeElement type of input unknownOutput Trying to register schema with name (domain: version: ) from file line , but it is already registered from file , but it its domain is not known by the checker., but it its version is not in the inclusive range [] (usually, this means you bumped the operator version but forgot to update the version range in DomainToVersionRange in onnx/defs/schema.h).Schema error: com.microsoftcom.microsoft.nchwc/Users/runner/work/1/s/onnxruntime/core/session/inference_session.ccvoid onnxruntime::InferenceSession::ConstructorCommon(const onnxruntime::SessionOptions &, const onnxruntime::Environment &)status.IsOK()Could not finalize session options while constructing the inference session. Error Message: graph_transformation_mgr_.SetSteps(session_options_.max_num_graph_transformation_steps).IsOK()ConstructorCommonCreating and using per session threadpools since use_per_session_threads_ is trueintra-opFailed to create the inter-op thread pool for the parallel executor, setting ExecutionMode to SEQUENTIALUsing global/env threadpools since use_per_session_threads_ is falsesession_env.EnvCreatedWithGlobalThreadPools()When the session is not configured to use per session threadpools, the env must be created with the the CreateEnvWithGlobalThreadPools API.onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions &, const onnxruntime::Environment &, const std::string &)Given model could not be parsed while creating inference session. Error message: onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions &, const onnxruntime::Environment &, std::istream &)st.IsOK()Could not parse model successfully while constructing the inference sessiononnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions &, const onnxruntime::Environment &, const void *, int)result~InferenceSessionUnknown error during EndProfiling()Error during EndProfiling(): Received nullptr for exec providerRegisterExecutionProviderExecution providers must be registered before the session is initialized. Execution providers must be registered before the session is initialized.Memory pattern must be disabled before registering DMLExecutionProviderSequential execution must be enabled before registering DMLExecutionProviderReceived nullptr for graph transformerRegisterGraphTransformerGraph transformers must be registered before the session is initialized.AddCustomOpDomainsReceived nullptr for custom registryLoadThis session already contains a loaded model.Unknown exception in Load()Encountered unknown exception in Load()Exception during loading: ModelProto corresponding to the model to be loaded has already been parsed. Invoke Load().ModelProto corresponding to the model to be loaded has not been parsed yet. This API should be called in conjunction with a ctor that takes a model abstraction.TransformGraphCould not find an implementation for the node ( (Node placementsAll nodes have been placed on []., Provider: [: [common::Status onnxruntime::InferenceSession::CreateSubgraphSessionState(onnxruntime::Graph &, onnxruntime::SessionState &)subgraphMain Graph instance should have populated all subgraphs when being resolved.CreateSubgraphSessionStateCUDAExecutionProvidercommon::Status onnxruntime::InferenceSession::InitializeSubgraphSessions(onnxruntime::Graph &, onnxruntime::SessionState &)subgraph_session_stateInitializeSubgraphSessionsp_op_kernelInitializeInitializing session.Model was not loadedModel was not loaded.Session has already been initialized.Adding default CPU execution provider.Parallel execution mode doesn't support CUDA Execution Provider currently.Serializing Optimized ONNX model with Graph Optimization level greater than ORT_ENABLE_EXTENDED. The generated model may contain hardware and execution provider specific optimizations, and should only be used in the same environment the model was optimized for.Session successfully initialized.Exception during initialization: Invalid rank for input: Got: Expected: Please fix either the inputs or the model.Got invalid dimensions for input: for the following indices index: Size mismatch: feed_names has elements, but feeds has elements.Invalid Feed Input Name:Input with name: is not expected to be of type tensor.ValidateInputs is not expected to be of type sparse tensor. is not expected to be of type tensor sequence.Output vector pointer is NULLAt least one output should be requested.Output vector incorrectly sized: output_names.size(): p_fetches->size(): Invalid Output Name:RunSession was not initializedSession not initialized.Running with tag: Encountered unknown exception in Run()GetModelMetadataGetModelInputsGetOverridableInitializersGetModelOutputsNewIOBindingEndProfilingProfiler is disabled.Could not write a profile because no model was loaded.const logging::Logger &onnxruntime::InferenceSession::CreateLoggerForRun(const onnxruntime::RunOptions &, std::unique_ptr &)run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: void onnxruntime::InferenceSession::InitLogger(logging::LoggingManager *)session_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast(logging::Severity::kFATAL)Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: virtual void onnxruntime::InferenceSession::AddPredefinedTransformers(onnxruntime::GraphTransformerManager &, onnxruntime::TransformerLevel, const std::vector &)graph_optimization_level <= TransformerLevel::MaxLevelExceeded max transformer level. Current level is set to WaitForNotificationtimeout_in_ms >0 is not supportedonnxruntime::SessionIOBinding::SessionIOBinding(onnxruntime::InferenceSession *)session->NewIOBinding(&binding_).IsOK()ORT_LOAD_CONFIG_FROM_MODELThe only supported values for the environment variable are '0' and '1'. The environment variable contained the value: FinalizeSessionOptionsReading the provided model for the ORT configonnxruntime::common::Status onnxruntime::FinalizeSessionOptions(const onnxruntime::SessionOptions &, const onnx::ModelProto &, bool, onnxruntime::SessionOptions &)is_model_proto_parsedProvider has already been registered./Users/runner/work/1/s/onnxruntime/core/framework/execution_providers.hAddUnexpected input data type. Actual: () , expected: (/Users/runner/work/1/s/onnxruntime/core/framework/feeds_fetches_manager.hFeedsFetchesInfoonnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(const std::vector &, const std::vector &, const onnxruntime::OrtValueNameIdxMap &)Load model from failed:Failed to load model because protobuf parsing failed._.json%Y-%m-%d_%H-%M-%SThe Model Proto has already been checked for the ORT config json.ort_config/Users/runner/work/1/s/onnxruntime/core/session/inference_session_utils.ccParseOrtConfigJsonInModelProtoFound session/run/environment configuration in the model file to be used while running the modelORT config json from the model: Json stored in the `ort_config` key cannot be parsed. Error message: ParseSessionOptionsFromModelProtoDid not find session options in the model file to be used while running the modelsession_optionsintra_op_num_threadsinter_op_num_threadsexecution_modegraph_optimization_levelgraph_optimization_level option in the model file must be an integerenable_profilingIgnoring unsupported session option in ORT config: SetIntraOpNumThreadsUnsupported value for intra_op_num_threads: Setting intra_op_num_threads to SetInterOpNumThreadsUnsupported value for inter_op_num_threads: Setting inter_op_num_threads to SetExecutionModeUnsupported execution_mode value in ORT config: Setting execution_mode to Sequential modeParallel modeSetGraphOptimizationLevelSetting graph_optimization_level to ORT_DISABLE_ALLSetting graph_optimization_level to ORT_ENABLE_BASICSetting graph_optimization_level to ORT_ENABLE_EXTENDEDSetting graph_optimization_level to ORT_ENABLE_ALLUnsupported graph_optimization_level value in ORT config: SetEnableProfilingUnsupported value for enable_profiling option: Setting enable_profiling to true[json.exception.] invalid BOM; must be 0xEF 0xBB 0xBF if givennullinvalid literalinvalid string: missing closing quoteinvalid string: '\u' must be followed by 4 hex digitsinvalid string: surrogate U+DC00..U+DFFF must be followed by U+DC00..U+DFFFinvalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFFinvalid string: forbidden character after backslashinvalid string: control character U+0000 (NUL) must be escaped to \u0000invalid string: control character U+0001 (SOH) must be escaped to \u0001invalid string: control character U+0002 (STX) must be escaped to \u0002invalid string: control character U+0003 (ETX) must be escaped to \u0003invalid string: control character U+0004 (EOT) must be escaped to \u0004invalid string: control character U+0005 (ENQ) must be escaped to \u0005invalid string: control character U+0006 (ACK) must be escaped to \u0006invalid string: control character U+0007 (BEL) must be escaped to \u0007invalid string: control character U+0008 (BS) must be escaped to \u0008 or \binvalid string: control character U+0009 (HT) must be escaped to \u0009 or \tinvalid string: control character U+000A (LF) must be escaped to \u000A or \ninvalid string: control character U+000B (VT) must be escaped to \u000Binvalid string: control character U+000C (FF) must be escaped to \u000C or \finvalid string: control character U+000D (CR) must be escaped to \u000D or \rinvalid string: control character U+000E (SO) must be escaped to \u000Einvalid string: control character U+000F (SI) must be escaped to \u000Finvalid string: control character U+0010 (DLE) must be escaped to \u0010invalid string: control character U+0011 (DC1) must be escaped to \u0011invalid string: control character U+0012 (DC2) must be escaped to \u0012invalid string: control character U+0013 (DC3) must be escaped to \u0013invalid string: control character U+0014 (DC4) must be escaped to \u0014invalid string: control character U+0015 (NAK) must be escaped to \u0015invalid string: control character U+0016 (SYN) must be escaped to \u0016invalid string: control character U+0017 (ETB) must be escaped to \u0017invalid string: control character U+0018 (CAN) must be escaped to \u0018invalid string: control character U+0019 (EM) must be escaped to \u0019invalid string: control character U+001A (SUB) must be escaped to \u001Ainvalid string: control character U+001B (ESC) must be escaped to \u001Binvalid string: control character U+001C (FS) must be escaped to \u001Cinvalid string: control character U+001D (GS) must be escaped to \u001Dinvalid string: control character U+001E (RS) must be escaped to \u001Einvalid string: control character U+001F (US) must be escaped to \u001Finvalid string: ill-formed UTF-8 byteinvalid number; expected digit after '-'invalid number; expected digit after '.'invalid number; expected '+', '-', or digit after exponentinvalid number; expected digit after exponent signnumber overflow parsing 'arrayobjectexcessive object size: cannot use erase() with stringbooleandiscardednumberexcessive array size: parse error at line , column while parsing - ; last read: 'unexpected ; expected true literalfalse literalnull literalstring literalnumber literal'[''{'']''}'':'','end of input'[', '{', or a literalunknown tokenkey '' not foundcannot use at() with map::at: key not foundtype must be number, but is tried creating tensor with negative value in shapesize overflownot enough space: expected , got type is not supported in this functionRegisterCustomOpsLibrary: Failed to load libraryRegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in libraryinput name cannot be emptyCPUExecutionProvideroutput name cannot be emptyinput array is too shortshape is invalidspace is not enoughopaque(,)/Users/runner/work/1/s/onnxruntime/core/session/onnxruntime_c_api.ccOrtStatusPtr OrtApis::CreateOpaqueValue(const char *, const char *, const void *, size_t, OrtValue **)ml_type != nullptrSpecified domain and type names combination does not refer to a registered opaque typenon_tensor_base != nullptrOrtStatusPtr OrtApis::GetOpaqueValue(const char *, const char *, const OrtValue *, void *, size_t)1.4.0allocator::allocate(size_t n) 'n' exceeds maximum supported sizeDmlExecutionProviderMem pattern should be disabled when using DML execution provider.Sequential execution should be enabled when using DML execution provider./Users/runner/work/1/s/include/onnxruntime/core/framework/ml_value.honnxruntime::Tensor *OrtValue::GetMutable()IsTensor()Trying to get a Tensor, but got: const onnxruntime::Tensor &OrtValue::Get() constout of index/Users/runner/work/1/s/onnxruntime/core/common/safeint.hstatic void SafeIntExceptionHandler::SafeIntOnOverflow()internal errorindex out of rangeconst onnxruntime::TensorSeq &OrtValue::Get() constIsTensorSequence()Trying to get a TensorSeq, but got: Input is not of one of the supported sequence types.Input is not of type sequence or map./Users/runner/work/1/s/include/onnxruntime/core/framework/data_types_internal.hstatic bool onnxruntime::utils::ContainerChecker::IsContainerOfType, float, std::__1::less >, std::__1::allocator, float> > >, std::__1::allocator, float, std::__1::less >, std::__1::allocator, float> > > > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::vector, float, std::__1::less >, std::__1::allocator, float> > >, std::__1::allocator, float, std::__1::less >, std::__1::allocator, float> > > > >]++index < c.size()static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, float, std::__1::less >, std::__1::allocator, float> > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::map, float, std::__1::less >, std::__1::allocator, float> > >]const T &OrtValue::Get() const [T = std::__1::vector, float, std::__1::less >, std::__1::allocator, float> > >, std::__1::allocator, float, std::__1::less >, std::__1::allocator, float> > > > >]onnxruntime::DataTypeImpl::GetType() == type_ != static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, std::__1::allocator > >, std::__1::allocator, std::__1::allocator > > > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::vector, std::__1::allocator > >, std::__1::allocator, std::__1::allocator > > > >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, std::__1::allocator > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::map, std::__1::allocator > >]const T &OrtValue::Get() const [T = std::__1::vector, std::__1::allocator > >, std::__1::allocator, std::__1::allocator > > > >]/Users/runner/work/1/s/onnxruntime/core/framework/TensorSeq.hconst onnxruntime::Tensor &onnxruntime::TensorSeq::Get(size_t) consti < tensors_.size()Input is not of one of the supported map types.static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, std::__1::basic_string, std::__1::less >, std::__1::allocator, std::__1::basic_string > > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::map, std::__1::basic_string, std::__1::less >, std::__1::allocator, std::__1::basic_string > > >]Invalid index requested for map type.const T &OrtValue::Get() const [T = std::__1::map, std::__1::basic_string, std::__1::less >, std::__1::allocator, std::__1::basic_string > > >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, long long, std::__1::less >, std::__1::allocator, long long> > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::map, long long, std::__1::less >, std::__1::allocator, long long> > >]const T &OrtValue::Get() const [T = std::__1::map, long long, std::__1::less >, std::__1::allocator, long long> > >]const T &OrtValue::Get() const [T = std::__1::map, float, std::__1::less >, std::__1::allocator, float> > >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, double, std::__1::less >, std::__1::allocator, double> > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::map, double, std::__1::less >, std::__1::allocator, double> > >]const T &OrtValue::Get() const [T = std::__1::map, double, std::__1::less >, std::__1::allocator, double> > >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, std::__1::less, std::__1::allocator > > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::map, std::__1::less, std::__1::allocator > > >]const T &OrtValue::Get() const [T = std::__1::map, std::__1::less, std::__1::allocator > > >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, std::__1::allocator > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::map, std::__1::allocator > >]const T &OrtValue::Get() const [T = std::__1::map, std::__1::allocator > >]const T &OrtValue::Get() const [T = std::__1::map, std::__1::allocator > >]static bool onnxruntime::utils::ContainerChecker::IsContainerOfType, std::__1::allocator > > >::check(const onnxruntime::utils::ContainerChecker::Cont &, size_t) [T = std::__1::map, std::__1::allocator > >]const T &OrtValue::Get() const [T = std::__1::map, std::__1::allocator > >]Number of values should be at least 1.For map type num_values MUST be 2Either the key tensor or the value tensor has NumDimensions > 1Key and value tensors have unequal number of elements.Key type is not supported yet.OrtStatus *OrtCreateValueImplMapHelper(const onnxruntime::Tensor &, const onnxruntime::Tensor &, OrtValue **) [KeyType = std::__1::basic_string]value_type != nullptrTensor must always contain primitive types. Found: OrtStatus *OrtCreateMapMLValue(const onnxruntime::Tensor &, const onnxruntime::Tensor &, OrtValue **) [KeyType = std::__1::basic_string, ValueType = std::__1::basic_string]len >= 0 && static_cast(len) < std::numeric_limits::max()OrtStatus *OrtCreateMapMLValue(const onnxruntime::Tensor &, const onnxruntime::Tensor &, OrtValue **) [KeyType = std::__1::basic_string, ValueType = long long]/Users/runner/work/1/s/include/onnxruntime/core/framework/tensor.hconst T *onnxruntime::Tensor::Data() const [T = long long]utils::IsPrimitiveDataType(dtype_)Tensor type mismatch. T !=OrtStatus *OrtCreateMapMLValue(const onnxruntime::Tensor &, const onnxruntime::Tensor &, OrtValue **) [KeyType = std::__1::basic_string, ValueType = float]const T *onnxruntime::Tensor::Data() const [T = float]OrtStatus *OrtCreateMapMLValue(const onnxruntime::Tensor &, const onnxruntime::Tensor &, OrtValue **) [KeyType = std::__1::basic_string, ValueType = double]const T *onnxruntime::Tensor::Data() const [T = double]OrtStatus *OrtCreateValueImplMapHelper(const onnxruntime::Tensor &, const onnxruntime::Tensor &, OrtValue **) [KeyType = long long]OrtStatus *OrtCreateMapMLValue(const onnxruntime::Tensor &, const onnxruntime::Tensor &, OrtValue **) [KeyType = long long, ValueType = std::__1::basic_string]OrtStatus *OrtCreateMapMLValue(const onnxruntime::Tensor &, const onnxruntime::Tensor &, OrtValue **) [KeyType = long long, ValueType = long long]OrtStatus *OrtCreateMapMLValue(const onnxruntime::Tensor &, const onnxruntime::Tensor &, OrtValue **) [KeyType = long long, ValueType = float]OrtStatus *OrtCreateMapMLValue(const onnxruntime::Tensor &, const onnxruntime::Tensor &, OrtValue **) [KeyType = long long, ValueType = double]Each element of the sequence should be either tensor or map.At least one element in the sequence is of a type different from others.Unsupported input typeOrtStatus *OrtCreateValueImplSeqHelper(const OrtValue *const *, size_t, OrtValue **)in[idx]->IsTensor()Expecting all elements to be tensors. Got: Sequences must have tensors of the same data type. There was at least one tensor in the input that was different.Encountered nullptr.const T *onnxruntime::Tensor::Data() const [T = bool]T *onnxruntime::Tensor::MutableData() [T = bool]T *onnxruntime::Tensor::MutableData() [T = float]T *onnxruntime::Tensor::MutableData() [T = double]const T *onnxruntime::Tensor::Data() const [T = onnxruntime::MLFloat16]T *onnxruntime::Tensor::MutableData() [T = onnxruntime::MLFloat16]const T *onnxruntime::Tensor::Data() const [T = onnxruntime::BFloat16]T *onnxruntime::Tensor::MutableData() [T = onnxruntime::BFloat16]const T *onnxruntime::Tensor::Data() const [T = signed char]T *onnxruntime::Tensor::MutableData() [T = signed char]const T *onnxruntime::Tensor::Data() const [T = unsigned char]T *onnxruntime::Tensor::MutableData() [T = unsigned char]const T *onnxruntime::Tensor::Data() const [T = short]T *onnxruntime::Tensor::MutableData() [T = short]const T *onnxruntime::Tensor::Data() const [T = unsigned short]T *onnxruntime::Tensor::MutableData() [T = unsigned short]const T *onnxruntime::Tensor::Data() const [T = int]T *onnxruntime::Tensor::MutableData() [T = int]const T *onnxruntime::Tensor::Data() const [T = unsigned int]T *onnxruntime::Tensor::MutableData() [T = unsigned int]T *onnxruntime::Tensor::MutableData() [T = long long]const T *onnxruntime::Tensor::Data() const [T = unsigned long long]T *onnxruntime::Tensor::MutableData() [T = unsigned long long]Ret onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper::Get() [Ret = OrtStatus *, UnsupportedPolicy = c_api_internal::UnsupportedReturnFailStatus]called_ < 2void onnxruntime::TensorSeq::SetType(onnxruntime::MLDataType)elem_type_ != nullptr was false. Stacktrace: :/\T *onnxruntime::Tensor::MutableData() [T = std::__1::basic_string]const T *onnxruntime::Tensor::Data() const [T = std::__1::basic_string]/Users/runner/work/1/s/onnxruntime/core/session/ort_env.ccstatic void OrtEnv::Release(OrtEnv *)env_ptr == p_instance_/Users/runner/work/1/s/onnxruntime/core/optimizer/attention_fusion_helper.hbool onnxruntime::AttentionFusionHelper::CheckSliceParameters(const onnxruntime::Graph &, const onnxruntime::Node &, const std::vector &, const std::vector &, const logging::Logger &)input_indices.size() == expected_values.size() && input_indices.size() > 0CheckSliceParametersSlice does not have enough number of inputsSlice ends is less than INT_MAXSlice parameter is not expected. Input index:Expected value:MatchGemmSubgraphStart MatchGemmSubgraphFaild to match gemm pathInput of reshape_before_gemm is not the input of subgraphOutput edge count not expected for nodes in gemm pathGemm does not have 3 inputsGemm bias is not constantGemm bias shape not expectedCheckSliceParameters return falseconcat first input value is not -1Faild to match concat node for Gather pathsconcat_after_gather does not have expected number of inputs or output edgesconcat_after_gather input 2 does not have expected valueFaild to match gemm gather pathOutput edge count not expected for nodes in gemm gather pathunsqueeze_after_gather axes value not expectedgather axis value not expectedgather input 1 value is not expectedPass MatchGemmSubgraphValidateGemmInitializerStart ValidateGemmInitializerGemm bias is not constant initializerGemm bias shape is not expectedGemm weight is not constant initializerGemm weight shape is not expectedPass ValidateGemmInitializerMatchUnidirMaskSubgraphStart MatchUnidirMaskSubgraphFaild to match the path (Div-->Where-->Add) for unidirectional maskFaild to match path 1 for unidirectional maskOutput edge count not expected for nodes in path 1 of unidirectional maskDiv and Shape1 does not have edgeCheckSliceParameters returns false for last_sliceCheckSliceParameters returns false for mask_sliceCheckSliceParameters returns false for slice1Faild to match path 2 for unidirectional maskOutput edge count not expected for unsqueeze2 of unidirectional maskFaild to match path 3 for unidirectional maskOutput edge count not expected for unsqueeze3 of unidirectional maskFaild to match path 4 for unidirectional maskDiv and Shape does not have edgeOutput edge count not expected for squeeze_2/slices2/shape2 of unidirectional maskCheckSliceParameters return false for slice2Pass MatchUnidirMaskSubgraphMatchInputMaskSubgraphStart MatchInputMaskSubgraphFailed to find Softmax nodeOutput edge count not expected for SoftmaxFailed to find path for maskOutput edge count not expected for mask nodesSoftmax attribute axis is expected to be 3mask_unsqueeze_1 axes not matched. Expect: 1mask_unsqueeze_2 axes not matched. Expect: 2mask_sub const input not matchedmask_mul const input not matchedPass MatchInputMaskSubgraphMatchPastSubgraphStart MatchPastSubgraphFailed to find path for past_kFailed to find path for present_kFailed to find path for present_v and past_vFailed to match v_concatpast_k_transpose perm attribute not matchedpresent_k_transpose perm attribute not matchedpresent_k_unsqueeze axes value not expectedpresent_v_unsqueeze axes value not expectedpast_v_gather indices != 1past_k_gather indices != 0past_v_gather and past_k_gather does not have same past inputOutput edge count not expected for nodes in past subgraphPass MatchPastSubgraphCheckNodesInPathVStart CheckNodesInPathVOutput edge count not expected for nodes in path vFailed in match Transpose attribute perm. Expected: 0, 2, 1, 3Failed in match v_transpose attribute perm. Expected: 0, 2, 1, 3hidden_size != num_heads * head_sizev_reshape initializer value is not expectedreshape initializer value is not expectedPass CheckNodesInPathVCheckNodesInPathQStart CheckNodesInPathQq_reshape const not matchedqk_div const not matched.q_transpose perm attribute not matchedPass CheckNodesInPathQCheckNodesInPathKStart CheckNodesInPathKk_transpose perm attribute not matchedk_reshape const not matchedPass CheckNodesInPathKFuseGptAttentionStart FuseGptAttentionFaild to find path to qkv_matmulFaild to find path v to SplitCheckNodesInPathV return falseMatchInputMaskSubgraph returns falseMatchUnidirMaskSubgraph returns NULLFailed to find path for qq and v are not from same Split nodeCheckNodesInPathQ returns falseFailed to find path for kk and v are not from same Split nodeCheckNodesInPathK returns falseMatchPastSubgraph returns falseFused an attention node for GPT./Users/runner/work/1/s/onnxruntime/core/optimizer/attention_fusion.ccshape of layer norm bias tensor not expectedTotal fused Attention node count: FuseSubGraphFaild to find path vOutput edge count not expected for Add or MatMul in path vFailed in match v_matmul and v_add input shapeFailed in match input mask subgraphq root should be layer normalizationq_matmul and q_add shape not matchedk root is not layer normk_matmul and k_add shape not matchedFailed to load Q, K and V weights, or data type is not float or float16.Failed to load Q, K and V bias tensors, or data type is not float or float16.Failed to create mask indexFused an attention node.ProcessMaskMask shape is unknown or not 2D, or data type unknownMask data type is not int32 or int64 or float32qkv_weightsqkv_bias/Users/runner/work/1/s/onnxruntime/core/optimizer/bias_gelu_fusion.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/constant_folding.ccCould not find a CPU kernel and hence can't constant fold virtual onnxruntime::common::Status onnxruntime::ConstantFolding::ApplyImpl(onnxruntime::Graph &, bool &, int, const logging::Logger &) constfetches.size() == node->OutputDefs().size()Unsupported output type of . Can't constant fold /Users/runner/work/1/s/onnxruntime/core/optimizer/conv_activation_fusion.ccfused fused Conv with activation auto onnxruntime::(anonymous namespace)::GetClipConstantMinMax(const onnxruntime::Graph &, const onnxruntime::Node &, float &, float &)::(anonymous class)::operator()(const onnxruntime::Node &, size_t, float &) constUnexpected data type for Clip input of /Users/runner/work/1/s/onnxruntime/core/optimizer/conv_add_fusion.ccvirtual onnxruntime::common::Status onnxruntime::ConvAddFusion::Apply(onnxruntime::Graph &, onnxruntime::Node &, onnxruntime::RewriteRule::RewriteRuleEffect &, const logging::Logger &) constadd_B_tensor_protoConvAddFusion_B_ConvAddFusion_Add_B_/Users/runner/work/1/s/onnxruntime/core/optimizer/conv_bn_fusion.ccvirtual onnxruntime::common::Status onnxruntime::ConvBNFusion::Apply(onnxruntime::Graph &, onnxruntime::Node &, onnxruntime::RewriteRule::RewriteRuleEffect &, const logging::Logger &) constbn_scale_tensor_protobn_B_tensor_protobn_mean_tensor_protobn_var_tensor_protoconv_W_tensor_protoconv_B_tensor_protoConvBnFusion_W_ConvBnFusion_BN_B_/Users/runner/work/1/s/onnxruntime/core/optimizer/initializer.honnxruntime::Initializer::Initializer(const onnx::TensorProto &, const onnxruntime::Path &)size_ == sizeInitializerunsupported data type: ReadExternalRawData() failed: ToProtodata type is not supported/Users/runner/work/1/s/onnxruntime/core/optimizer/conv_mul_fusion.ccvirtual onnxruntime::common::Status onnxruntime::ConvMulFusion::Apply(onnxruntime::Graph &, onnxruntime::Node &, onnxruntime::RewriteRule::RewriteRuleEffect &, const logging::Logger &) constmul_B_tensor_protoConvMulFusion_W_ConvMulFusion_Mul_B_/Users/runner/work/1/s/onnxruntime/core/optimizer/dynamic_quantize_matmul_fusion.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/embed_layer_norm_fusion.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). MatchPositionEmbeddingSubgraphsFromGatherOutput edge count not expected for nodes in path1.The first input of Range should be a constant with value 0.The third input of Range should be a constant with value 1.Second input of Gather should be a constant with value 1. Failed to match Shape node. The parent of shape nodes are expected to be input_ids.Optional position subgraph nodes number of outputs unexpected.Optional position subgraph nodes Where node is expected to be the parent of Reshape.Failed to match position subgraph.MatchInputToConcatSubgraphFailed to find path 1 of position shape.Output edge count not expected for nodes in path 1 of position shape.Second input of Gather in path 1 of position shape should be a constant with value 0.Failed to find path 2 of position shape.Output edge count not expected for nodes in path 2 of position shape.Gather node in path 2 is not linked to another subgraph.Second input of Gather in path 2 of position shape should be a constant with value 1.The parent of two shape nodes are expected to be input_ids.CheckInputInput shape is unknown or not 2D, or data type unknownInput data type is not int32 or int64_Int32_Cast/Users/runner/work/1/s/onnxruntime/core/optimizer/fast_gelu_fusion.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/free_dim_override_transformer.cconnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformer(gsl::span)Invalid free dimension override.Conflicting free dimension overrides.The model has input '' with a fixed dimension size which does not equal the specified override of /Users/runner/work/1/s/onnxruntime/core/optimizer/gelu_approximation.ccTotal Gelu Approximation (FastGelu) node count: /Users/runner/work/1/s/onnxruntime/core/optimizer/gelu_fusion.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/gemm_activation_fusion.ccfused Gemm /Users/runner/work/1/s/onnxruntime/core/optimizer/graph_transformer.ccApply/Users/runner/work/1/s/onnxruntime/core/optimizer/graph_transformer_mgr.ccApplyTransformersThis transformer is already registered Level_RuleBasedTransformer/Users/runner/work/1/s/onnxruntime/core/optimizer/graph_transformer_utils.ccstd::vector > onnxruntime::optimizer_utils::GenerateRewriteRules(onnxruntime::TransformerLevel, const std::vector &)Unsupported levelACLExecutionProviderstd::vector > onnxruntime::optimizer_utils::GenerateTransformers(onnxruntime::TransformerLevel, gsl::span, const std::vector &)Unsupported level RandomUniformRandomNormalRandomUniformLikeRandomNormalLikeMultinomialNot satisfied: tensor_proto.data_type() != ONNX_NAMESPACE::TensorProto_DataType_UNDEFINED && tensor_proto.data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRING /Users/runner/work/1/s/onnxruntime/core/optimizer/initializer.ccReadExternalRawDataSatisfied, but should not be: model_path.IsEmpty() model_path must not be empty. Ensure that a path is provided when the model is created or loaded.Not satisfied: external_data_length == 0 || external_data_length == actual_tensor_data_length TensorProto external data size mismatch. Computed size: , external_data.length: CastDef_%d/Users/runner/work/1/s/onnxruntime/core/optimizer/insert_cast_transformer.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/layer_norm_fusion.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/matmul_add_fusion.ccfused Matmul and Add _nchwc_bn_nchwc/Users/runner/work/1/s/onnxruntime/core/optimizer/nchwc_transformer.cc/Users/runner/work/1/s/onnxruntime/core/optimizer/optimizer_execution_frame.cconnxruntime::OptimizerExecutionFrame::Info::Info(const std::vector &, const onnxruntime::InitializedTensorSet &, std::unique_ptr)cpu_execution_providerallocator_ptr_Info/Users/runner/work/1/s/onnxruntime/core/optimizer/relu_clip_fusion.ccvirtual onnxruntime::common::Status onnxruntime::FuseReluClip::Apply(onnxruntime::Graph &, onnxruntime::Node &, onnxruntime::RewriteRule::RewriteRuleEffect &, const logging::Logger &) constUnexpected data type for Clip 'min' input of FuseReluClip__min_zero_constantonnxruntime::Initializer::Initializer(onnx::TensorProto_DataType, const std::string &, const std::vector &)data type is not supported./Users/runner/work/1/s/onnxruntime/core/optimizer/reshape_fusion.ccFused reshape node: Total fused reshape node count: Fuse_SubgraphCannot replace concat node with initializer:/Users/runner/work/1/s/onnxruntime/core/optimizer/rule_based_graph_transformer.ccApplyRulesOnNode/Users/runner/work/1/s/onnxruntime/core/optimizer/skip_layer_norm_fusion.ccArmNNExecutionProvider/Users/runner/work/1/s/onnxruntime/core/optimizer/transformer_memcpy.ccApplyImplTensorrtExecutionProvidervoid onnxruntime::TransformerMemcpyImpl::ProcessDefs(onnxruntime::Node &, const onnxruntime::KernelRegistryManager &, onnxruntime::InitializedTensorSet &)Execution type '' doesn't support memcpy bool onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager &, const onnxruntime::InitializedTensorSet &)/Users/runner/work/1/s/include/onnxruntime/core/optimizer/graph_transformer.hRecurseauto onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager &, const onnxruntime::InitializedTensorSet &)::(anonymous class)::operator()(const onnxruntime::NodeArg &, size_t) constdup_replacements.find(&arg) == dup_replacements.end()UnsqueezeElimination_/Users/runner/work/1/s/onnxruntime/core/optimizer/unsqueeze_elimination.ccUnsqueezeElimination cannot remove node EluHardSigmoidLeakyReluSoftplusReluSigmoidSoftsignTanhThresholdedReluSeluParametricSoftplusScaledTanhunknown kernel type/Users/runner/work/1/s/onnxruntime/core/providers/cpu/activation/element_wise_ranged_transform.hElementWiseKernelonnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Elu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Elu]input_size < std::numeric_limits::max()onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::HardSigmoid]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::HardSigmoid]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::LeakyRelu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::LeakyRelu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Relu]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::Selu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Selu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Sigmoid]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Softplus]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Softsign]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::Tanh]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::ThresholdedRelu]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::ThresholdedRelu]alphaNo attribute with name:''is defined.Attibute name and type don't match/Users/runner/work/1/s/onnxruntime/core/providers/cpu/activation/activations.hInitgamma/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/activations.hBVIfthen_branch/Users/runner/work/1/s/onnxruntime/core/providers/cpu/controlflow/if.cconnxruntime::If::If(const onnxruntime::OpKernelInfo &)info.GetAttr("then_branch", &proto).IsOK()else_branchinfo.GetAttr("else_branch", &proto).IsOK()virtual common::Status onnxruntime::If::SetupSubgraphExecutionInfo(const onnxruntime::SessionState &, const std::string &, const onnxruntime::SessionState &)info == nullptrSetupSubgraphExecutionInfo should only be called once for each subgraph.SetupSubgraphExecutionInfovirtual onnxruntime::common::Status onnxruntime::If::Compute(onnxruntime::OpKernelContext *) constthen_feeds_fetches_manager_ && else_feeds_fetches_manager_CreateFeedsFetchesManager must be called prior to execution of graph.session_stateSubgraph SessionState was not found for '' attribute.ComputeFailed to create output tensor for Executeonnxruntime::If::Info::Info(const onnxruntime::Node &, const onnxruntime::GraphViewer &)num_subgraph_outputs == static_cast(num_outputs)'If' node has outputs which doesn't match the subgraph's outputs.Failed to create output tensor for If output ILoop/Users/runner/work/1/s/onnxruntime/core/providers/cpu/controlflow/loop.cconnxruntime::Loop::Loop(const onnxruntime::OpKernelInfo &)info.GetAttr("body", &proto).IsOK()virtual common::Status onnxruntime::Loop::SetupSubgraphExecutionInfo(const onnxruntime::SessionState &, const std::string &, const onnxruntime::SessionState &)info_ == nullptrvirtual onnxruntime::common::Status onnxruntime::Loop::Compute(onnxruntime::OpKernelContext *) constfeeds_fetches_manager_'Loop' input 'M' should be a scalar tensor. Got shape of 'Loop' input 'cond' should be a scalar tensor. Got shape of ConcatenateLoopOutputLoop had zero iterations and the shape of subgraph output was not found. Defaulting to a rank 1 shape of {0}.Inconsistent shape in loop output for output. Expected: Got:onnxruntime::Loop::Info::Info(const onnxruntime::Node &, const onnxruntime::GraphViewer &)static_cast(num_subgraph_inputs) == subgraph_inputs.size()Graph in 'body' attribute of Loop should have inputs. Found:num_subgraph_outputs - 1 == num_outputs'Loop' node has outputs so the subgraph requires but has /Users/runner/work/1/s/onnxruntime/core/providers/cpu/controlflow/scan_8.cconnxruntime::Scan<8>::Scan(const onnxruntime::OpKernelInfo &) [OpSet = 8]directionsvirtual onnxruntime::common::Status onnxruntime::Scan<8>::SetupSubgraphExecutionInfo(const onnxruntime::SessionState &, const std::string &, const onnxruntime::SessionState &) [OpSet = 8]virtual onnxruntime::common::Status onnxruntime::Scan<8>::Compute(onnxruntime::OpKernelContext *) const [OpSet = 8]Scan inputs have inconsistent batch size. Previous value was but has batch size of sequence_lens length of did not match batch size of Invalid entries in sequence_lens. Max sequence length was onnxruntime::common::Status onnxruntime::Scan8Impl::CreateLoopStateVariables(std::vector > &)p_mlvalueScan<8> spec does not support transpose of output. This should never be called.gsl::span onnxruntime::Tensor::DataAsSpan() const [T = long long]/Users/runner/work/1/s/onnxruntime/core/framework/ort_value_tensor_slicer.htypename std::enable_if::value, reference>::type onnxruntime::OrtValueTensorSlicer::Iterator::operator*() [T = const OrtValue]position_ >= 0 && position_ < sequence_length_/Users/runner/work/1/s/onnxruntime/core/providers/cpu/controlflow/scan_9.cconnxruntime::Scan<9>::Scan(const onnxruntime::OpKernelInfo &) [OpSet = 9]num_scan_inputsinfo.GetAttr("num_scan_inputs", &num_scan_inputs_).IsOK()scan_input_directionsscan_output_directionsscan_input_axesgsl::narrow_cast(input_axes_.size()) == num_scan_inputs_Number of entries in 'scan_input_axes' was but expected scan_output_axesgsl::narrow_cast(output_axes_.size()) == num_scan_outputsNumber of entries in 'scan_output_axes' was virtual onnxruntime::common::Status onnxruntime::Scan<9>::SetupSubgraphExecutionInfo(const onnxruntime::SessionState &, const std::string &, const onnxruntime::SessionState &) [OpSet = 9]virtual onnxruntime::common::Status onnxruntime::Scan<9>::Compute(onnxruntime::OpKernelContext *) const [OpSet = 9]feeds_fetches_manager_ && info_Invalid scan input: Expected dimensions or more but input had shape of Scan inputs have inconsistent sequence lengths. Previous value was but input '' dimension has length of Invalid value in scan_input_axes for input of . Input tensor rank was ValidateInputSetupInputsSubgraph in 'body' produces outputs but Scan expects AllocateOutputTensorsCreateLoopStateVariablesonnxruntime::common::Status onnxruntime::ScanImpl::CreateLoopStateVariables(std::vector &)output_mlvalueOutput OrtValue has not been created for loop state variable output Invalid value in scan_output_axes for output . Output tensor rank was onnxruntime::common::Status onnxruntime::ScanImpl::TransposeOutput()TransposeOutputScanint64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t)axis >= -tensor_rank && axis <= tensor_rank - 1axis is not in valid range [-/Users/runner/work/1/s/onnxruntime/core/providers/cpu/controlflow/scan_utils.hconst OrtValue &onnxruntime::scan::detail::OutputIterator::GetOutput() constfinal_output_mlvalue_/Users/runner/work/1/s/onnxruntime/core/providers/cpu/controlflow/scan_utils.cconnxruntime::scan::detail::Info::Info(const onnxruntime::Node &, const onnxruntime::GraphViewer &, int, bool)num_variadic_inputs == num_subgraph_inputsThe subgraph in 'body' requires inputs but Scan was only given void onnxruntime::scan::detail::ReadDirections(const onnxruntime::OpKernelInfo &, const std::string &, std::vector &, int64_t)num_entries < 0 || gsl::narrow_cast(directions.size()) == num_entriesNumber of entries in '' was validInvalid values in ''. 0 == forward. 1 == reverse.Subgraph must have the shape set for all outputs but did not.CreateFeedsFetchesManagerIterateSequencevoid onnxruntime::scan::detail::LoopStateVariable::Next()iteration_num_ < sequence_len_Misuse of LoopStateVariable. Attempt to move beyond end of sequenceFailed to create output tensor for output #AllocateFinalBufferonnxruntime::common::Status onnxruntime::scan::detail::OutputIterator::AllocateFinalOutput(const onnxruntime::TensorShape &)!is_concrete_shape_AllocateFinalOutputOrtValue &onnxruntime::scan::detail::OutputIterator::operator*()cur_iteration_ < num_iterations_is_concrete_shape_Expected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator.onnxruntime::scan::detail::OutputIterator &onnxruntime::scan::detail::OutputIterator::operator++()Expected AllocateFinalOutput to have been called to before we increment the iteratorMismatch between expected shape and shape from first output is not compatible with /Users/runner/work/1/s/onnxruntime/core/providers/cpu/cpu_execution_provider.ccRegisterOnnxOperatorKernelsRegisterOnnxMLOperatorKernelsGetKernelRegistryvirtual std::shared_ptr onnxruntime::CPUExecutionProvider::GetKernelRegistry() constRegisterCPUKernelsCpuConstantOfShape/Users/runner/work/1/s/onnxruntime/core/providers/cpu/generator/constant_of_shape.ccvoid onnxruntime::ConstantOfShapeBase::SetValueFromTensorProto(const onnx::TensorProto &)utils::HasDataType(t_proto)TensorProto::DataType_IsValid(t_proto.data_type())unpack_status.IsOK()Value attribute unpacking failed:Unsupported value attribute datatype: onnxruntime::ConstantOfShapeBase::ConstantOfShapeBase(const onnxruntime::OpKernelInfo &)t_proto.dims_size() == 1t_proto.dims()[0] == 1onnxruntime::common::Status onnxruntime::ConstantOfShapeBase::PrepareCompute(onnxruntime::OpKernelContext *, onnxruntime::Tensor **) constinput_shape.NumDimensions() > 0virtual onnxruntime::common::Status onnxruntime::ConstantOfShape::Compute(onnxruntime::OpKernelContext *) constUnsupported value attribute datatype with sizeof=: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/generator/random.ccCould not infer data type from input tensor with data type Empty dimensions for input tensorbatch_size is < 1num_classes is < 1num_samples is < 1Invalid data type of /Users/runner/work/1/s/onnxruntime/core/providers/cpu/generator/random.honnxruntime::RandomNormal::RandomNormal(const onnxruntime::OpKernelInfo &)info.GetAttr("mean", &mean_).IsOK()scaleinfo.GetAttr("scale", &scale_).IsOK()info.GetAttr("dtype", &dtype).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDInvalid dtype of info.GetAttrs("shape", shape).IsOK()onnxruntime::RandomUniform::RandomUniform(const onnxruntime::OpKernelInfo &)info.GetAttr("high", &high_).IsOK()lowinfo.GetAttr("low", &low_).IsOK()onnxruntime::RandomNormalLike::RandomNormalLike(const onnxruntime::OpKernelInfo &)onnxruntime::RandomUniformLike::RandomUniformLike(const onnxruntime::OpKernelInfo &)sample_sizeonnxruntime::Multinomial::Multinomial(const onnxruntime::OpKernelInfo &)info.GetAttr("sample_size", &num_samples_).IsOK()ONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINEDonnxruntime::common::Status onnxruntime::RandomNormalCompute(float, float, std::default_random_engine &, TensorProto::DataType, onnxruntime::Tensor &)onnxruntime::common::Status onnxruntime::RandomUniformCompute(float, float, std::default_random_engine &, TensorProto::DataType, onnxruntime::Tensor &)MultinomialComputeRangestart in Range operator should be scalar like tensor, yet got shape:limit in Range operator should be scalar like tensor, yet got shape:delta in Range operator should be scalar like tensor, yet got shape:delta in Range operator can not be zero!Clipminmax/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/clip.honnxruntime::clip_internal::Clip_6Base::Clip_6Base(const onnxruntime::OpKernelInfo &) [T = float]min_ <= max_void onnxruntime::utils::MLTypeCallDispatcher::Invoke(Args &&...) const [Fn = ComputeImpl, Types = , Args = ]/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/clip.ccvoid onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, onnxruntime::Tensor *) const [T = float]min->Shape().NumDimensions() == 0max->Shape().NumDimensions() == 0void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, onnxruntime::Tensor *) const [T = double]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, onnxruntime::Tensor *) const [T = signed char]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, onnxruntime::Tensor *) const [T = unsigned char]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, onnxruntime::Tensor *) const [T = long long]void onnxruntime::Clip::ComputeImpl::operator()(const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, onnxruntime::Tensor *) const [T = unsigned long long]CumSumexclusive/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/cumsum.ccvirtual onnxruntime::common::Status onnxruntime::CumSum::Compute(onnxruntime::OpKernelContext *) const [T = float]void onnxruntime::WritableSliceIterator::Init(const std::vector &, gsl::span, gsl::span) [T = float]dims.size() == starts.size()dims.size()=starts.size()=dims.size() == extents_.size()extents.size()=dims.size() == steps.size()steps.size()=virtual onnxruntime::common::Status onnxruntime::CumSum::Compute(onnxruntime::OpKernelContext *) const [T = double]void onnxruntime::WritableSliceIterator::Init(const std::vector &, gsl::span, gsl::span) [T = double]virtual onnxruntime::common::Status onnxruntime::CumSum::Compute(onnxruntime::OpKernelContext *) const [T = int]void onnxruntime::WritableSliceIterator::Init(const std::vector &, gsl::span, gsl::span) [T = int]virtual onnxruntime::common::Status onnxruntime::CumSum::Compute(onnxruntime::OpKernelContext *) const [T = long long]void onnxruntime::WritableSliceIterator::Init(const std::vector &, gsl::span, gsl::span) [T = long long]Det/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/det.ccvirtual onnxruntime::common::Status onnxruntime::Det::Compute(onnxruntime::OpKernelContext *) const [T = float]X != nullptrInput tensor should have a rank of at least 2Matrix dimensions are not equal. Square matrix is expectedEinsumThere was a problem acquiring temporary memory allocator in Einsum op/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/einsum.ccDeviceComputeEinsum op: An implementation for the input type is not supported yet/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/einsum.honnxruntime::Einsum::Einsum(const onnxruntime::OpKernelInfo &)info.GetAttr("equation", &equation_).IsOK()/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_auxiliary_ops.ccstd::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor &, const std::vector &, const onnxruntime::Tensor &, const std::vector &, onnxruntime::AllocatorPtr, concurrency::ThreadPool *, void *, const DeviceHelpers::MatMul &) [T = float]input_1.DataType() == input_2.DataType()input_shape_1_override.size() == 3 && input_shape_2_override.size() == 3input_shape_1_override[0] == input_shape_2_override[0]input_shape_1_override[2] == input_shape_2_override[1]Einsum op: Exception during MatMul operation: std::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor &, const std::vector &, const onnxruntime::Tensor &, const std::vector &, onnxruntime::AllocatorPtr, concurrency::ThreadPool *, void *, const DeviceHelpers::MatMul &) [T = int]std::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor &, const std::vector &, const onnxruntime::Tensor &, const std::vector &, onnxruntime::AllocatorPtr, concurrency::ThreadPool *, void *, const DeviceHelpers::MatMul &) [T = double]std::unique_ptr onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor &, const std::vector &, const onnxruntime::Tensor &, const std::vector &, onnxruntime::AllocatorPtr, concurrency::ThreadPool *, void *, const DeviceHelpers::MatMul &) [T = long long]onnxruntime::common::Status onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopy(const onnxruntime::Tensor &, onnxruntime::Tensor &)output.SizeInBytes() == input.SizeInBytes()Einsum op: The candidate output does not match the actual output's shapestd::unique_ptr onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::Diagonal(const onnxruntime::Tensor &, int64_t, int64_t, onnxruntime::AllocatorPtr)rank >= 2 && dim_1 != dim_2 && input_dims[dim_1] == input_dims[dim_2]Cannot parse the diagonal elements along dims for input shape bool onnxruntime::EinsumOp::IsTransposeRequired(size_t, const std::vector &)input_rank == permutation.size()std::unique_ptr onnxruntime::EinsumOp::Transpose(const onnxruntime::Tensor &, const std::vector &, const std::vector &, onnxruntime::AllocatorPtr, void *, const DeviceHelpers::Transpose &)Length of permutation must match the rank of the input to be permutatedEinsum op: Transpose failed: std::unique_ptr onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDims(const onnxruntime::Tensor &, bool, onnxruntime::AllocatorPtr)input_dims[rank - 2] == input_dims[rank - 1]The innermost dims should have the same dim value to parse the diagonal elementsEinsum op: Unsupported data type for Diagonal /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_compute_preprocessor.ccNumber of subscripts in the input equation does not match number of input tensorsFound a '.' not part of an ellipsis in input: Einsum subscripts string contains too many subscript labels when compared to the rank of the inputEllipsis must indicate a fixed number of dimensions across all inputsFound '.' not part of an ellipsis in input: Einsum operands could not be broadcast together. Please check input shapes/equation provided.Input shape of operand is incompatible in the dimension . The shape is: Another operand has a dim value of in the same dimensionEinsum subscripts string contains too many subscript labels when compared to the rank of the input Einsum subscripts does not contain enough subscript labels and there is no ellipsis for input onnxruntime::common::Status onnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDims()num_broadcasted_indices < num_of_ellipsis_dims_dim_iter == rank...Inputs have ellipses in them but the provided output subscript does not contain an ellipsisFound a '.' not part of an ellipsis in the output subscript providedFound '.' not part of an ellipsis in the output subscript providedThe only subscript labels allowed in the output subscript are lowercase letters (a-z)Rank of the input must match number of subscript labels corresponding to the input/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_typed_compute_processor.ccstd::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const std::vector &, bool) [T = float]left.Shape().Size() == left_shape_override.Size()The override dims are not compatible with given tensor's shape. Left shape: Left shape override: right.Shape().Size() == right_shape_override.Size()Right shape: Right shape override: left_rank == right_rankRanks of pair-wise operands must be equal. Right shape: left_dim == right_dimEinsum op: Input dimensions must be equal along an axis to be reduced across all inputsvoid onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor &, const std::vector &) [T = float]candidate_output.Shape().Size() == output_shape.Size()Einsum op: The candidate output cannot be reshaped into the op's outputcandidate_output_dims[iter] == 1Not all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: Einsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const std::vector &, bool) [T = int]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor &, const std::vector &) [T = int]std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const std::vector &, bool) [T = double]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor &, const std::vector &) [T = double]std::unique_ptr onnxruntime::EinsumTypedComputeProcessor::PairwiseOperandProcess(const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const onnxruntime::Tensor &, const onnxruntime::TensorShape &, const std::vector &, bool) [T = long long]void onnxruntime::EinsumTypedComputeProcessor::FinalizeOutput(const onnxruntime::Tensor &, const std::vector &) [T = long long]SubMulDivAbsNegFloorCeilReciprocalSqrtT1PowExpLogSumMaxMinLessGreaterEqualMeanBitShiftErfNotAndOrXorUnsupported X type: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.ccvirtual onnxruntime::common::Status onnxruntime::Sum_6::Compute(onnxruntime::OpKernelContext *) const [T = float]input_count >= 1data_1.Shape() == shapedata_n.Shape() == shapevirtual onnxruntime::common::Status onnxruntime::Min_6::Compute(onnxruntime::OpKernelContext *) const [T = float]inputCount >= 1virtual onnxruntime::common::Status onnxruntime::Max_6::Compute(onnxruntime::OpKernelContext *) const [T = float]virtual onnxruntime::common::Status onnxruntime::Mean_6::Compute(onnxruntime::OpKernelContext *) const [T = float]SinCosTanAsinAcosAtanSinhCoshAsinhAcoshAtanhPReluExpandModvoid onnxruntime::mod_internal::BroadCastMFloat16FMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *)X and Y input types do not match: vs virtual onnxruntime::common::Status onnxruntime::Mod::Compute(onnxruntime::OpKernelContext *) constfmod_/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.honnxruntime::Broadcaster::Broadcaster(const std::vector &, const std::vector &)largest <= 1Can broadcast 0 by 0 or 1. is invalid.void onnxruntime::BroadcastIterator::Init(int64_t, int64_t)axis == 1 || axis == largestAttempting to broadcast an axis by a dimension other than 1. by void onnxruntime::BroadcastIterator::Append(int64_t, int64_t)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/work/1/s/include/onnxruntime/core/framework/op_kernel.honnxruntime::Tensor *onnxruntime::OpKernelContext::Output(int)p_ml_valuevirtual onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext *) const [T = float]tensor_shape.Shape().GetDims().size() == 1virtual onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext *) const [T = double]virtual onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext *) const [T = signed char]virtual onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext *) const [T = short]virtual onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext *) const [T = int]virtual onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext *) const [T = long long]virtual onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext *) const [T = unsigned char]virtual onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext *) const [T = unsigned short]virtual onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext *) const [T = unsigned int]virtual onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext *) const [T = unsigned long long]virtual onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext *) const [T = bool]virtual onnxruntime::common::Status onnxruntime::Expand_8::Compute(onnxruntime::OpKernelContext *) const [T = onnxruntime::MLFloat16]onnxruntime::Mod::Mod(const onnxruntime::OpKernelInfo &)(fmod == 0) || (fmod == 1)const T *onnxruntime::OpKernelContext::Input(int) const [T = onnxruntime::Tensor]Missing Input: Unsupported Y type: onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = float, TOutput = float, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:451:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:452:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:453:7)]onnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext &) [T = float]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = float, TOutput = float, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:479:9), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:480:9), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:481:9)]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = double, TOutput = double, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:479:9), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:480:9), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:481:9)]onnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext &) [T = double]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = onnxruntime::MLFloat16, TOutput = onnxruntime::MLFloat16, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:479:9), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:480:9), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:481:9)]onnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext &) [T = onnxruntime::MLFloat16]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = int, TOutput = int, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:479:9), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:480:9), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:481:9)]onnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext &) [T = int]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = unsigned int, TOutput = unsigned int, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:479:9), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:480:9), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:481:9)]onnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext &) [T = unsigned int]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = long long, TOutput = long long, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:479:9), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:480:9), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:481:9)]onnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext &) [T = long long]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = unsigned long long, TOutput = unsigned long long, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:479:9), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:480:9), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:481:9)]onnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext &) [T = unsigned long long]Ret onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper >::Get() [Ret = onnxruntime::common::Status, UnsupportedPolicy = onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy]Ret onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy::operator()(int32_t) const [Ret = onnxruntime::common::Status]Unsupported data type: onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = float, TOutput = float, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:514:9), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:515:9), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:516:9)]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = double, TOutput = double, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:514:9), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:515:9), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:516:9)]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = onnxruntime::MLFloat16, TOutput = onnxruntime::MLFloat16, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:514:9), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:515:9), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:516:9)]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = int, TOutput = int, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:514:9), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:515:9), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:516:9)]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = unsigned int, TOutput = unsigned int, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:514:9), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:515:9), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:516:9)]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = long long, TOutput = long long, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:514:9), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:515:9), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:516:9)]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = unsigned long long, TOutput = unsigned long long, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:514:9), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:515:9), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:516:9)]onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node &, onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General) [TInput = float, TOutput = float, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:652:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:653:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/element_wise_ops.cc:654:7)]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = float]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = double]void onnxruntime::utils::MLTypeCallDispatcher::Invoke(Args &&...) const [Fn = mod_internal::CallModImpl, Types = , Args = ]helper.called_ == 1void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = unsigned char]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = unsigned char]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = signed char]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = signed char]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = unsigned short]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = unsigned short]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = short]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = short]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = unsigned int]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = unsigned int]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = int]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = int]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = unsigned long long]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = unsigned long long]void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = long long]void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor &, const onnxruntime::Tensor &, onnxruntime::OpKernelContext *) [T = long long]GemmtransA/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/gemm.honnxruntime::Gemm::Gemm(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttr("transA", &temp).IsOK()transBinfo.GetAttr("transB", &temp).IsOK()/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/gemm_helper.honnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &, bool, const onnxruntime::TensorShape &)left.NumDimensions() == 2 || left.NumDimensions() == 1right.NumDimensions() == 2GEMM: Dimension mismatch, W: K: N:Gemm: Invalid bias shape for broadcastM_ >= 0 && K_ > 0 && N_ >= 0static void onnxruntime::Gemm::ComputeGemm(CBLAS_TRANSPOSE, CBLAS_TRANSPOSE, int64_t, int64_t, int64_t, float, const T *, const T *, float, const T *, const onnxruntime::TensorShape *, T *, concurrency::ThreadPool *) [T = float]c_shape != nullptrc_shape is required if c_data is providedHardmax inputs N, D and N * D must be < . N=, D=HardmaxMatMul/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/matmul.ccNot satisfied: left_num_dims >= 1 && right_num_dims >= 1 /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/matmul_helper.hNot satisfied: K_ == right_shape[right_num_dims - 2] || transb && K_ == right_shape[right_num_dims - 1] Not satisfied: left_padded_dims_[idx_dim] == 1 left operand cannot broadcast on dim Not satisfied: right_padded_dims_[idx_dim] == 1 right operand cannot broadcast on dim Not satisfied: K_ == right_shape[transb ? right_num_dims - 1 : right_num_dims - 2] Not satisfied: num_dims_with_pad == num_output_dims Not satisfied: M_ == 1 && N_ == 1 Not satisfied: num_dims_with_pad - 1 == num_output_dims Not satisfied: num_dims_with_pad - 2 == num_output_dims Not satisfied: K_ == right_shape[0] MatMulInteger/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/matmul_integer.ccvirtual onnxruntime::common::Status onnxruntime::MatMulInteger::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(a_zero_point)MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(b_zero_point)MatmulInteger : input2 zero point must be a scalar or 1D tensor of size 1QLinearMatMul/Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/quantize_linear_matmul.ccvirtual onnxruntime::common::Status onnxruntime::QLinearMatMul::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(a_offset)QLinearMatmul : input zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(b_offset)QLinearMatmul : weight zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_offset)QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(a_scale)QLinearMatmul : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(b_scale)QLinearMatmul : weight scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(y_scale)QLinearMatmul : result scale must be a scalar or 1D tensor of size 1RoundSignvoid onnxruntime::utils::MLTypeCallDispatcher::Invoke(Args &&...) const [Fn = CallSignImpl, Types = , Args = ]helper.called_ < 2SoftmaxLogSoftmaxSoftmaxCPU inputs N, D and N * D must be < /Users/runner/work/1/s/onnxruntime/core/providers/cpu/math/top_k.cconnxruntime::TopK<9, float>::TopK(const onnxruntime::OpKernelInfo &) [OpSet = 9, T = float]op_kernel_info.GetAttr("k", &k_temp).IsOK()k_temp > 0op_kernel_info.GetAttr("axis", &axis_temp).IsOK()input count mismatch, expected 1 input - the tensor to be processedonnxruntime::TopK<10, float>::TopK(const onnxruntime::OpKernelInfo &) [OpSet = 10, T = float]input count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k valueTopKvoid onnxruntime::TopkOpset11ConstructorCommon(const onnxruntime::OpKernelInfo &, int &, bool &, bool &)largestop_kernel_info.GetAttr("largest", &largest_temp).IsOK()sortedop_kernel_info.GetAttr("sorted", &sorted_temp).IsOK()k argument [] should not be greater than specified axis dim value [output count mismatch, expected 2 outputs to be present for TopK operatorArrayFeatureExtractorInvalid argument: X input has empty dimensions.Invalid Y argument: num_indices = 0Invalid Y argument: index is out of range: Y[] () >=BinarizerthresholdInput data with index: is NaNCastMapInvalid input type of value: Expected std::map or std::mapUnexpected CAST_TO value of cast_to/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/cast_map.honnxruntime::ml::CastMap::CastMap(const onnxruntime::OpKernelInfo &)info.GetAttr("cast_to", &attr).IsOK()info.GetAttr("map_form", &attr).IsOK()max_mapinfo.GetAttr("max_map", &max_map_).IsOK()map_form_ != PACK_MAP::SPARSE || max_map_ > 0TO_FLOATTO_STRINGTO_INT64/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/ml_common.honnxruntime::ml::CAST_TO onnxruntime::ml::MakeCast(const std::string &)Invalid CAST_TO value of Expected TO_FLOAT, TO_STRING or TO_INT64DENSESPARSEonnxruntime::ml::PACK_MAP onnxruntime::ml::MakePack(const std::string &)Invalid PACK_MAP value of Expected DENSE or SPARSE/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/cast_map.cconnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext &, TTo) const [TFrom = float, TTo = float]cur_input == end_input || cur_input->first >= 0Negative index values are not permitted. First entry in map has index value of const T *onnxruntime::OpKernelContext::Input(int) const [T = std::__1::map, std::__1::allocator > >]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext &, TTo) const [TFrom = std::__1::basic_string, TTo = float]const T *onnxruntime::OpKernelContext::Input(int) const [T = std::__1::map, std::__1::less, std::__1::allocator > > >]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext &, TTo) const [TFrom = float, TTo = long long]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext &, TTo) const [TFrom = std::__1::basic_string, TTo = long long]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext &, TTo) const [TFrom = float, TTo = std::__1::basic_string]onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext &, TTo) const [TFrom = std::__1::basic_string, TTo = std::__1::basic_string]CategoryMapperInput of string must have output of int64Input of int64 must have output of string cats_strings/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/category_mapper.honnxruntime::ml::CategoryMapper::CategoryMapper(const onnxruntime::OpKernelInfo &)info.GetAttrs("cats_strings", string_categories).IsOK()cats_int64sinfo.GetAttrs("cats_int64s", int_categories).IsOK()default_stringinfo.GetAttr("default_string", &default_string_).IsOK()default_int64info.GetAttr("default_int64", &default_int_).IsOK()num_entries == int_categories.size()DictVectorizerstring_vocabulary/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/dictvectorizer.honnxruntime::ml::DictVectorizerOp, long long>::DictVectorizerOp(const onnxruntime::OpKernelInfo &) [AttrType = std::__1::basic_string, TargetType = long long]info.GetAttrs(std::is_same::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK()const T *onnxruntime::OpKernelContext::Input(int) const [T = std::__1::map, long long, std::__1::less >, std::__1::allocator, long long> > >]onnxruntime::ml::DictVectorizerOp, float>::DictVectorizerOp(const onnxruntime::OpKernelInfo &) [AttrType = std::__1::basic_string, TargetType = float]const T *onnxruntime::OpKernelContext::Input(int) const [T = std::__1::map, float, std::__1::less >, std::__1::allocator, float> > >]onnxruntime::ml::DictVectorizerOp, double>::DictVectorizerOp(const onnxruntime::OpKernelInfo &) [AttrType = std::__1::basic_string, TargetType = double]const T *onnxruntime::OpKernelContext::Input(int) const [T = std::__1::map, double, std::__1::less >, std::__1::allocator, double> > >]int64_vocabularyonnxruntime::ml::DictVectorizerOp >::DictVectorizerOp(const onnxruntime::OpKernelInfo &) [AttrType = long long, TargetType = std::__1::basic_string]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo &) [AttrType = long long, TargetType = float]onnxruntime::ml::DictVectorizerOp::DictVectorizerOp(const onnxruntime::OpKernelInfo &) [AttrType = long long, TargetType = double]const T *onnxruntime::OpKernelContext::Input(int) const [T = std::__1::map, std::__1::allocator > >]FeatureVectorizer/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/feature_vectorizer.ccvirtual onnxruntime::common::Status onnxruntime::ml::FeatureVectorizer::Compute(onnxruntime::OpKernelContext *) constinput_count >= 0 && static_cast(input_count) == input_dimensions_.size()Number of inputs () does not match number of inputdimensions values (Invalid input type:inputdimensions/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/feature_vectorizer.honnxruntime::ml::FeatureVectorizer::FeatureVectorizer(const onnxruntime::OpKernelInfo &)status.IsOK() && !input_dimensions_.empty()Imputerimputed_value_floatsimputed_value_int64sreplaced_value_float/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/imputer.cconnxruntime::ml::ImputerOp::ImputerOp(const onnxruntime::OpKernelInfo &)Expected 'replaced_value_float' attribute since 'imputed_value_floats' is specifiedreplaced_value_int64Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specifiedimputed_values_float_.empty() ^ imputed_values_int64_.empty()Must provide imputed_values_float_ or imputed_values_int64_ but not both.virtual common::Status onnxruntime::ml::ImputerOp::Compute(onnxruntime::OpKernelContext *) constInvalid typeEmpty value of imputed values.Empty input dimensions.LabelEncoderInput of tensor(string) must have output of tensor(int64)Input of tensor(int64) must have output of tensor(string)keys_floatsvalues_stringskeys_stringsvalues_floatsdefault_floatkeys_int64svalues_int64sclasses_strings/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/label_encoder.honnxruntime::ml::LabelEncoder::LabelEncoder(const onnxruntime::OpKernelInfo &)info.GetAttrs("classes_strings", string_classes).IsOK()onnxruntime::ml::LabelEncoder_2 >::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = float, TValue = std::__1::basic_string]info.GetAttrs(_key_field_name, keys).IsOK()info.GetAttrs(_value_field_name, values).IsOK()num_keys == num_valuesThe attribtues in LabelEncoder (name: ) must have the same length. However, the number of key is and the number of values is onnxruntime::ml::LabelEncoder_2, float>::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = std::__1::basic_string, TValue = float]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = long long, TValue = float]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = float, TValue = long long]onnxruntime::ml::LabelEncoder_2 >::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = long long, TValue = std::__1::basic_string]onnxruntime::ml::LabelEncoder_2, long long>::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = std::__1::basic_string, TValue = long long]onnxruntime::ml::LabelEncoder_2::LabelEncoder_2(const onnxruntime::OpKernelInfo &) [TKey = long long, TValue = long long]LinearClassifiermulti_class/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/linearclassifier.cconnxruntime::ml::LinearClassifier::LinearClassifier(const onnxruntime::OpKernelInfo &)void onnxruntime::ml::LinearClassifier::ComputeImpl(const gsl::span, int64_t, int64_t, int64_t, const std::vector &, const std::vector &, onnxruntime::Tensor &, onnxruntime::Tensor &, onnxruntime::ml::POST_EVAL_TRANSFORM, bool, concurrency::ThreadPool *) constscores_output_data.length() >= scores_output_sizeScores output is incorrect size. Expected: Found:Input shape needs to be at least a single dimension.Unsupported input element type of void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor &, gsl::span &) [SrcType = int]shape_size == out.length()void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor &, gsl::span &) [SrcType = long long]void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor &, gsl::span &) [SrcType = double]LinearRegressorinterceptstargets/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/linearregressor.cconnxruntime::ml::LinearRegressor::LinearRegressor(const onnxruntime::OpKernelInfo &)info.GetAttr("targets", &num_targets_).IsOK()Input shape had more than 2 dimension. Dims=Normalizer/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/normalizer.honnxruntime::ml::Normalizer::Normalizer(const onnxruntime::OpKernelInfo &)info.GetAttr("norm", &norm).IsOK()MAXL1L2onnxruntime::ml::NORMALIZE onnxruntime::ml::MakeNormalize(const std::string &)Invalid normalize value of Rank of input to Normalized must be less than 2. Got Unexpected NORMALIZE value of OneHotEncoderUnknown Category and zeros = 0.zeros/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/onehotencoder.cconnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo &) [T = long long]tmp_cats_int64s.empty() || tmp_cats_strings.empty()One and only one of the 'cats_*' attributes must be definednum_categories_ > 0onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo &) [T = float]onnxruntime::ml::OneHotEncoderOp::OneHotEncoderOp(const onnxruntime::OpKernelInfo &) [T = double]onnxruntime::ml::OneHotEncoderOp >::OneHotEncoderOp(const onnxruntime::OpKernelInfo &) [T = std::__1::basic_string]Scaleroffset/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/scaler.cconnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo &) [T = float]!scale_.empty()scale_.size() == offset_.size()Scale size: () != (Invalid argument: input has empty dimensions.Either both scale and offset can be of feature size () or 1onnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo &) [T = double]onnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo &) [T = long long]onnxruntime::ml::ScalerOp::ScalerOp(const onnxruntime::OpKernelInfo &) [T = int]SVMClassifiervectors_per_classprob_aprob_b/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/svmclassifier.cconnxruntime::ml::SVMClassifier::SVMClassifier(const onnxruntime::OpKernelInfo &)proba_.size() == probb_.size()classlabels_intsinfo.GetAttrs("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs("classlabels_ints", classlabels_ints_).IsOK()classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0coefficients_.size() > 0Unsupported data type of void onnxruntime::ml::batched_update_scores_inplace(gsl::span, int64_t, int64_t, onnxruntime::ml::POST_EVAL_TRANSFORM, int, bool, concurrency::ThreadPool *) [T = float]scores.size() == static_cast(expected_num_scores)Unexpected value for 'add_second_class' of SVMRegressorn_supportssupport_vectorspost_transformrho/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/svmregressor.cconnxruntime::ml::SVMRegressor::SVMRegressor(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttrs("rho", rho_).IsOK()coefficientsinfo.GetAttrs("coefficients", coefficients_).IsOK()!coefficients_.empty()one_classkernel_typeLINEARkernel_params/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/svmclassifier.honnxruntime::ml::SVMCommon::SVMCommon(const onnxruntime::OpKernelInfo &)info.GetAttrs("kernel_params", kernel_params).IsOK()POLYRBFLOGISTICSOFTMAXSOFTMAX_ZEROvirtual onnxruntime::common::Status onnxruntime::ml::SVMRegressor::Compute(onnxruntime::OpKernelContext *) const [T = float]num_features == feature_count_Unexpected mode:TreeEnsembleClassifierclass_idsclass_nodeidsclass_treeidsclass_weightsX dims is empty.void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(std::vector > &, OTYPE *, int, int64_t *) const [ITYPE = float, OTYPE = float]predictions.size() == 2int64_t onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binary(int &, const std::vector > &) const [ITYPE = float, OTYPE = float]classes.size() == 2 || classes.size() == 1void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(std::vector > &, OTYPE *, int, int64_t *) const [ITYPE = double, OTYPE = float]int64_t onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binary(int &, const std::vector > &) const [ITYPE = double, OTYPE = float]onnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommon(int, int, const std::string &, const std::vector &, int64_t, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::string &, const std::vector &, const std::vector &, const std::vector &, const std::vector &) [ITYPE = long long, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(std::vector > &, const TreeNodeElement &) const [ITYPE = long long, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(std::vector > &, const std::vector > &) const [ITYPE = long long, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(std::vector > &, OTYPE *, int, int64_t *) const [ITYPE = long long, OTYPE = float]int64_t onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binary(int &, const std::vector > &) const [ITYPE = long long, OTYPE = float]onnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommon(int, int, const std::string &, const std::vector &, int64_t, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::string &, const std::vector &, const std::vector &, const std::vector &, const std::vector &) [ITYPE = int, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(std::vector > &, const TreeNodeElement &) const [ITYPE = int, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(std::vector > &, const std::vector > &) const [ITYPE = int, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorClassifier::FinalizeScores(std::vector > &, OTYPE *, int, int64_t *) const [ITYPE = int, OTYPE = float]int64_t onnxruntime::ml::detail::TreeAggregatorClassifier::_set_score_binary(int &, const std::vector > &) const [ITYPE = int, OTYPE = float]TreeEnsembleRegressoraggregate_functionSUMbase_valuesn_targetsnodes_falsenodeidsnodes_featureidsnodes_hitratesnodes_missing_value_tracks_truenodes_modesnodes_nodeidsnodes_treeidsnodes_truenodeidsnodes_valuestarget_idstarget_nodeidstarget_treeidstarget_weights/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/tree_ensemble_common.honnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommon(int, int, const std::string &, const std::vector &, int64_t, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::string &, const std::vector &, const std::vector &, const std::vector &, const std::vector &) [ITYPE = float, OTYPE = float]n_targets_or_classes > 0nodes_falsenodeids.size() == nodes_featureids.size()nodes_falsenodeids.size() == nodes_modes.size()nodes_falsenodeids.size() == nodes_nodeids.size()nodes_falsenodeids.size() == nodes_treeids.size()nodes_falsenodeids.size() == nodes_truenodeids.size()nodes_falsenodeids.size() == nodes_values.size()target_class_ids.size() == target_class_nodeids.size()target_class_ids.size() == target_class_treeids.size() in tree is already there.Unable to find node - (truenode).One falsenode is pointing either to itself, either to another tree. (falsenode). (weights).AVERAGEMINBRANCH_LEQLEAFBRANCH_LTBRANCH_GTEBRANCH_GTBRANCH_EQvoid onnxruntime::ml::detail::TreeEnsembleCommon::compute(concurrency::ThreadPool *, const onnxruntime::Tensor *, onnxruntime::Tensor *, onnxruntime::Tensor *) const [ITYPE = float, OTYPE = float]/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/tree_ensemble_aggregator.hvoid onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(std::vector > &, const TreeNodeElement &) const [ITYPE = float, OTYPE = float]it->i < (int64_t)predictions.size()void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(std::vector > &, const std::vector > &) const [ITYPE = float, OTYPE = float]predictions.size() == predictions2.size()void onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScores(std::vector > &, OTYPE *, int, int64_t *) const [ITYPE = float, OTYPE = float]this->base_values_.size() == predictions.size()void onnxruntime::ml::detail::TreeAggregatorMin::MergePrediction(std::vector > &, const std::vector > &) const [ITYPE = float, OTYPE = float]void onnxruntime::ml::detail::TreeAggregator::FinalizeScores(std::vector > &, OTYPE *, int, int64_t *) const [ITYPE = float, OTYPE = float]predictions.size() == (size_t)n_targets_or_classes_void onnxruntime::ml::detail::TreeAggregatorMax::MergePrediction(std::vector > &, const std::vector > &) const [ITYPE = float, OTYPE = float]onnxruntime::ml::detail::TreeEnsembleCommon::TreeEnsembleCommon(int, int, const std::string &, const std::vector &, int64_t, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::string &, const std::vector &, const std::vector &, const std::vector &, const std::vector &) [ITYPE = double, OTYPE = float]void onnxruntime::ml::detail::TreeEnsembleCommon::compute(concurrency::ThreadPool *, const onnxruntime::Tensor *, onnxruntime::Tensor *, onnxruntime::Tensor *) const [ITYPE = double, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorSum::ProcessTreeNodePrediction(std::vector > &, const TreeNodeElement &) const [ITYPE = double, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorSum::MergePrediction(std::vector > &, const std::vector > &) const [ITYPE = double, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorAverage::FinalizeScores(std::vector > &, OTYPE *, int, int64_t *) const [ITYPE = double, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorMin::MergePrediction(std::vector > &, const std::vector > &) const [ITYPE = double, OTYPE = float]void onnxruntime::ml::detail::TreeAggregator::FinalizeScores(std::vector > &, OTYPE *, int, int64_t *) const [ITYPE = double, OTYPE = float]void onnxruntime::ml::detail::TreeAggregatorMax::MergePrediction(std::vector > &, const std::vector > &) const [ITYPE = double, OTYPE = float]ZipMapclasslabels_int64sclasslabels_strings/Users/runner/work/1/s/onnxruntime/core/providers/cpu/ml/zipmap.cconnxruntime::ml::ZipMapOp::ZipMapOp(const onnxruntime::OpKernelInfo &)classlabels_strings_.empty() ^ classlabels_int64s_.empty()Must provide classlabels_strings or classlabels_int64s but not both.Zipmap does not support empty dim countZipmap only supports 1D or 2D input tensorsInput features_per_batch[] != number of classlabels[T *OrtValue::GetMutable() [T = std::__1::vector, float, std::__1::less >, std::__1::allocator, float> > >, std::__1::allocator, float, std::__1::less >, std::__1::allocator, float> > > > >]T *OrtValue::GetMutable() [T = std::__1::vector, std::__1::allocator > >, std::__1::allocator, std::__1::allocator > > > >]MaxUnpoolNot satisfied: X_shape.NumDimensions() >= 3 /Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/Unpool.ccNot satisfied: I_shape == X_shape Index tensor shape should be same as that of the input data tensor to unpool.Not satisfied: tensor_shape->Shape().GetDims().size() == 1 Not satisfied: given_shape.Size() >= shape.Size() output_shape is smaller than minimum required. output_shape: inferred output shape:/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/unpool.honnxruntime::MaxUnpool::MaxUnpool(const onnxruntime::OpKernelInfo &)info.GetAttrs("kernel_shape", kernel_shape_).IsOK()kernel_shape_[dim] > 0pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim]strides_.size() == kernel_shape_.size()BatchNormalizationspatial/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/batch_norm.honnxruntime::BatchNorm::BatchNorm(const onnxruntime::OpKernelInfo &) [T = float]Invalid input X: The rank of input X must be atleast 2. Got rank: Invalid input scale: NumDimensions() != Invalid input scale: 0th dimension != Invalid input scale: dimension != Invalid input B: NumDimensions() != Invalid input B: 0th dimension != Invalid input B: Invalid input mean: NumDimensions() != Invalid input mean: 0th dimension != Invalid input mean: Invalid input var: NumDimensions() != Invalid input var: 0th dimension != Invalid input var: onnxruntime::BatchNorm::BatchNorm(const onnxruntime::OpKernelInfo &) [T = double]/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/conv.ccConvX num_dims does not match W num_dims. X: W: Input channels C is not equal to kernel channels * group. C: kernel channels: group: Output channels M is not divisible by group. M: kernel_shape num_dims is not compatible with W num_dims. kernel_shape: kernel_shape is not compatible with W shape.kernel_shapestridesdilationsgroupNOTSETVALIDSAME_UPPERSAME_LOWER/Users/runner/work/1/s/onnxruntime/core/providers/common.honnxruntime::AutoPadType onnxruntime::StringToAutoPadType(const std::string &)/Users/runner/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/work/1/s/onnxruntime/core/util/math.hvoid onnxruntime::math::Im2colNd::operator()(const T *, const int64_t *, const int64_t *, int64_t, int64_t, const int64_t *, const int64_t *, const int64_t *, const int64_t *, int64_t, T *, bool, T) [T = float, order = 2]d_iter[d_i] < d_maxT3ConvInteger/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/conv_integer.ccvirtual onnxruntime::common::Status onnxruntime::ConvInteger::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(X_Zero_Point)IsScalarOr1ElementVector(W_Zero_Point)void onnxruntime::math::Im2colNd::operator()(const T *, const int64_t *, const int64_t *, int64_t, int64_t, const int64_t *, const int64_t *, const int64_t *, const int64_t *, int64_t, T *, bool, T) [T = unsigned char, order = 2]ConvTransposeoutput_paddingoutput_shape/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/conv_transpose.ccDoConvTransposegroup count is <= 0filter number not equal to input channel number. filter_number: num_input_channels: Input channels is not divisible by group./Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/conv_transpose_attributes.hvoid onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShape(onnxruntime::TensorShape, int64_t, const std::vector &, const std::vector &, const std::vector &, const std::vector &, const int64_t, std::vector *, std::vector *) constdim_size > 0void onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShape(const int64_t, const int64_t, const int64_t, const int64_t, const int64_t, onnxruntime::AutoPadType, int64_t *, int64_t *, int64_t *) const*out_size >= 0pad type not supported/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/dropout_op.hvirtual onnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext *) const [T1 = float, T2 = float, trainable_dropout = false]!mask || mask->Shape() == X_shapefloat onnxruntime::(anonymous namespace)::GetRatioOrDefault(const onnxruntime::Tensor *) [T2 = float]ratio_tensor->Shape().Size() == 10.0f <= ratio_value && ratio_value < 1.0fvirtual onnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext *) const [T1 = float, T2 = double, trainable_dropout = false]float onnxruntime::(anonymous namespace)::GetRatioOrDefault(const onnxruntime::Tensor *) [T2 = double]virtual onnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext *) const [T1 = double, T2 = float, trainable_dropout = false]virtual onnxruntime::common::Status onnxruntime::Dropout::Compute(onnxruntime::OpKernelContext *) const [T1 = double, T2 = double, trainable_dropout = false]Flatten/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/flatten.honnxruntime::Flatten::Flatten(const onnxruntime::OpKernelInfo &)virtual onnxruntime::common::Status onnxruntime::Flatten::Compute(onnxruntime::OpKernelContext *) constgsl::narrow_cast(X_shape.NumDimensions()) >= axisInstanceNormalization/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/instance_norm.ccepsilon/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/instance_norm.honnxruntime::InstanceNorm::InstanceNorm(const onnxruntime::OpKernelInfo &) [T = float]op_kernel_info.GetAttr("epsilon", &epsilon_).IsOK()Invalid input data: number of dimensions is less than 3: Invalid input scale: number of dimensions is not 1: Mismatch between input data and scale: size of scale != input channel count vs. Invalid input B: number of dimensions is not 1: Mismatch between input data and B: size of B != input channel count LpNormalization/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/lp_norm.honnxruntime::LpNorm::LpNorm(const onnxruntime::OpKernelInfo &) [T = float]op_kernel_info.GetAttr("axis", &axis_).IsOK()op_kernel_info.GetAttr("p", &p_).IsOK()p_ == 1 || p_ == 2input count mismatch/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/lrn.ccvirtual onnxruntime::common::Status onnxruntime::LRN::Compute(onnxruntime::OpKernelContext *) const [T = float]X->Shape().NumDimensions() == 4LRN/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/lrn.honnxruntime::LRN::LRN(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttr("size", &size).IsOK()size_ > 0size_ % 2 == 1info.GetAttr("alpha", &alpha_).IsOK()alpha_ > 0.0finfo.GetAttr("beta", &beta_).IsOK()beta_ > 0.0fNot satisfied: input_dims >= 3 /Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/pool.ccUnsupported pooling size.Not satisfied: pooling_dims == pool_attrs_.kernel_shape.size() AveragePoolMaxPoolLpPoolGlobalLpPoolGlobalAveragePoolGlobalMaxPool/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/pool_attributes.hstd::vector onnxruntime::PoolAttributes::SetOutputSize(const onnxruntime::TensorShape &, int64_t, std::vector *) constinput_shape.Size() > 0 || input_shape[0] == 0Invalid input shape. Only N can be zero. Got:void onnxruntime::PoolAttributes::InferOutputSize(const std::vector &, std::vector *, std::vector *) constinput_dims.size() >= 2void onnxruntime::PoolAttributes::ComputeSizePadDilations(const int64_t, const int64_t, const int64_t, int64_t *, int64_t *, int64_t, int64_t *) constonnxruntime::PoolAttributes::PoolAttributes(const OpNodeProtoHelper &, const std::string &, int)info.GetAttrs("kernel_shape", kernel_shape).IsOK()info.GetAttr("auto_pad", &auto_padding).IsOK()ceil_modecount_include_padinfo.GetAttr("count_include_pad", &temp).IsOK()storage_orderinfo.GetAttr("storage_order", &storage_order).IsOK()kernel_shape[dim] > 0pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim]strides.size() == kernel_shape.size()dilations.size() == kernel_shape.size()/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/pool_base.hvoid onnxruntime::PoolProcessContext::init(const onnxruntime::OpKernelInfo &)info.GetAttr("p", &p_).IsOK()Not satisfied: x_shape.NumDimensions() >= 3 Unsupported pooling size : T4QLinearConv/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/qlinearconv.ccvirtual onnxruntime::common::Status onnxruntime::QLinearConv::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(X_zero_point)QLinearConv : input zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(W_zero_point)QLinearConv : filter zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_zero_point)QLinearConv : result zero point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(X_scale)QLinearConv : input scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(W_scale)QLinearConv : filter scale must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(Y_scale)QLinearConv : result scale must be a scalar or 1D tensor of size 1MaxRoiPool/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/roi_pool.ccvirtual onnxruntime::common::Status onnxruntime::RoiPool::Compute(onnxruntime::OpKernelContext *) const [T = float]R->Shape()[1] == 5roi_batch_id >= 0roi_batch_id < batch_sizepooled_shape/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/roi_pool.honnxruntime::RoiPool::RoiPool(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttrs("pooled_shape", pooled_shape).IsOK()pooled_shape.size() == 2pooled_height_ > 0pooled_width_ > 0spatial_scaleinfo.GetAttr("spatial_scale", &spatial_scale_).IsOK()spatial_scale_ > 0Shrink/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/shrink.honnxruntime::Shrink::Shrink(const onnxruntime::OpKernelInfo &)op_kernel_info.GetAttr("bias", &bias_temp).IsOK()lambdop_kernel_info.GetAttr("lambd", &lambd_temp).IsOK()StringNormalizer/Users/runner/work/1/s/onnxruntime/core/providers/cpu/nn/string_normalizer.cconnxruntime::StringNormalizer::StringNormalizer(const onnxruntime::OpKernelInfo &)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/work/1/s/onnxruntime/core/providers/cpu/nn/tfidfvectorizer.cconnxruntime::TfIdfVectorizer::TfIdfVectorizer(const onnxruntime::OpKernelInfo &)TFIDFTFIDFimpl_->weighting_criteria_ != kNonemode: is unrecognized, acceptable values are TF,IDF,TFIDFmin_gram_lengthimpl_->min_gram_length_ > 0Required min_gram_length must be positive: max_gram_lengthimpl_->max_gram_length_ >= impl_->min_gram_length_min_gram_length >= max_gram_length required: >= max_skip_countimpl_->max_skip_count_ >= 0max_skip_count must be non-negative: ngram_countsstatus.IsOK() && !impl_->ngram_counts_.empty()size_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size()min_gram_length must be inbounds of ngram_counts: <= size_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size()max_gram_length must be inbounds of ngram_counts: ngram_indexesstatus.IsOK() && !impl_->ngram_indexes_.empty()std::all_of(impl_->ngram_indexes_.cbegin(), impl_->ngram_indexes_.cend(), [](int64_t i) { return i >= 0; })weightsimpl_->weights_.size() == impl_->ngram_indexes_.size()Got weights of size: but ngram_indexes size: must be of equal sizepool_strings!impl_->pool_strings_.empty()pool_int64sstatus.IsOK() && !pool_int64s.empty()end_idx >= start_idx && end_idx <= total_itemsn-gram counts out of bounds for -grams(items % ngram_size == 0)Number of items must compose whole Input shape must have either [C] or [B,C] dimensions with B > 0.size_t onnxruntime::ngram_details::PopulateGrams(ForwardIter, size_t, size_t, size_t, Map &) [K = long long, ForwardIter = std::__1::__wrap_iter, Map = std::__1::unordered_map, std::__1::default_delete > >, std::__1::hash, std::__1::equal_to, std::__1::allocator, std::__1::default_delete > > > > >]p.first->second->id_ == 0Duplicate ngram detected, size: id: size_t onnxruntime::ngram_details::PopulateGrams(ForwardIter, size_t, size_t, size_t, Map &) [K = std::__1::basic_string, ForwardIter = std::__1::__wrap_iter *>, Map = std::__1::unordered_map >, std::__1::unique_ptr, std::__1::default_delete > >, std::__1::hash >, std::__1::equal_to >, std::__1::allocator >, std::__1::unique_ptr, std::__1::default_delete > > > > >]NonMaxSuppression/Users/runner/work/1/s/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.ccstatic onnxruntime::common::Status onnxruntime::NonMaxSuppressionBase::PrepareCompute(onnxruntime::OpKernelContext *, onnxruntime::PrepareContext &)boxes_tensorscores_tensorNot satisfied: boxes_shape.NumDimensions() == 3 PrepareComputeNot satisfied: scores_shape.NumDimensions() == 3 Not satisfied: boxes_dims[0] == scores_dims[0] Not satisfied: boxes_dims[1] == scores_dims[2] Not satisfied: boxes_dims[2] == 4 Not satisfied: (iou_threshold >= 0 && iou_threshold <= 1.f) GetThresholdsFromInputsNot satisfied: ret.IsOK() virtual onnxruntime::common::Status onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext *) constoutput != nullptrcenter_point_box/Users/runner/work/1/s/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.honnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBase(const onnxruntime::OpKernelInfo &)0 == center_point_box_ || 1 == center_point_box_RoiAlignNull input X ptrNull rois_ptrNull batch_indices_ptrNumber of dimensions for batch indices should be exactly 1Number of dimensions for rois should be exactly Second dimension for rois should be exactly First dimension (num_rois) of batch_indices and rois don't matchavg/Users/runner/work/1/s/onnxruntime/core/providers/cpu/object_detection/roialign.honnxruntime::RoiAlignBase::RoiAlignBase(const onnxruntime::OpKernelInfo &) [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/work/1/s/onnxruntime/core/providers/cpu/containers.hOrtStlAllocator onnxruntime::GetAllocator(const onnxruntime::OpKernelContext &) [T = signed char]/Users/runner/work/1/s/onnxruntime/core/providers/cpu/reduction/reduction_ops.ccbool onnxruntime::PrepareForReduce(const onnxruntime::Tensor *, FastAllocVector &, int64_t &, int64_t &, const std::vector &, bool, std::vector &, bool, const onnxruntime::TensorShape *) [T = signed char]input_tensor_ptr->Shape().Size() == input_shape_override->Size()The input shape override's size does not match the input tensor's shape sizein_dim != 0Can't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape:OrtStlAllocator onnxruntime::GetAllocator(const onnxruntime::OpKernelContext &) [T = unsigned char]bool onnxruntime::PrepareForReduce(const onnxruntime::Tensor *, FastAllocVector &, int64_t &, int64_t &, const std::vector &, bool, std::vector &, bool, const onnxruntime::TensorShape *) [T = unsigned char]/Users/runner/work/1/s/onnxruntime/core/providers/cpu/reduction/reduction_ops.honnxruntime::ReduceKernelBase::ReduceKernelBase(const onnxruntime::OpKernelInfo &, optional) [allow_multi_axes = false]info.GetAttr("keepdims", &keepdims).IsOK()select_last_indexonnxruntime::ReduceKernelBase::ReduceKernelBase(const onnxruntime::OpKernelInfo &, optional) [allow_multi_axes = true]OrtStlAllocator onnxruntime::GetAllocator(const onnxruntime::OpKernelContext &) [T = float]bool onnxruntime::PrepareForReduce(const onnxruntime::Tensor *, FastAllocVector &, int64_t &, int64_t &, const std::vector &, bool, std::vector &, bool, const onnxruntime::TensorShape *) [T = float]OrtStlAllocator onnxruntime::GetAllocator(const onnxruntime::OpKernelContext &) [T = int]bool onnxruntime::PrepareForReduce(const onnxruntime::Tensor *, FastAllocVector &, int64_t &, int64_t &, const std::vector &, bool, std::vector &, bool, const onnxruntime::TensorShape *) [T = int]OrtStlAllocator onnxruntime::GetAllocator(const onnxruntime::OpKernelContext &) [T = double]bool onnxruntime::PrepareForReduce(const onnxruntime::Tensor *, FastAllocVector &, int64_t &, int64_t &, const std::vector &, bool, std::vector &, bool, const onnxruntime::TensorShape *) [T = double]OrtStlAllocator onnxruntime::GetAllocator(const onnxruntime::OpKernelContext &) [T = long long]bool onnxruntime::PrepareForReduce(const onnxruntime::Tensor *, FastAllocVector &, int64_t &, int64_t &, const std::vector &, bool, std::vector &, bool, const onnxruntime::TensorShape *) [T = long long]GRU/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.ccvirtual onnxruntime::common::Status onnxruntime::DeepCpuGruOp::Compute(onnxruntime::OpKernelContext *) constInvalid data type for GRU operator of /Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.honnxruntime::DeepCpuGruOp::DeepCpuGruOp(const onnxruntime::OpKernelInfo &)info.GetAttr("direction", &direction).IsOK()linear_before_resetinfo.GetAttr("linear_before_reset", &int64_value).IsOK()hidden_sizeinfo.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0activationsactivation_alphaactivation_betaclip_ > 0.fsigmoidtanhactivation_func_names.size() == static_cast(num_directions_) * 2forwardreversebidirectional/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/rnn_helpers.honnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const std::string &)Invalid 'direction' argument of ''. Must be one of 'forward', 'reverse', or 'bidirectional'.ComputeImplgsl::span onnxruntime::Tensor::DataAsSpan() const [T = float]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = int]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = float]void onnxruntime::rnn::detail::ComputeGemm(const int, const int, const int, const float, TSpanAIter, TSpanAIter, const int, TSpanBIter, TSpanBIter, const int, const float, TSpanCIter, TSpanCIter, const int, concurrency::ThreadPool *) [TSpanAIter = const float *, TSpanBIter = const float *, TSpanCIter = float *]lda >= K && ldb >= K && ldc >= NA + (M * lda - (lda - K)) <= A_endB + (N * ldb - (ldb - K)) <= B_endC + (M * ldc - (ldc - N)) <= C_endconst T *onnxruntime::rnn::detail::SafeRawConstPointer(typename gsl::span::const_iterator, typename gsl::span::const_iterator, size_t) [T = float]cur + size <= endT *onnxruntime::rnn::detail::SafeRawPointer(typename gsl::span, size_t, size_t) [T = float]offset + size <= size_t(span.size())T *onnxruntime::rnn::detail::SafeRawPointer(typename gsl::span::iterator, typename gsl::span::iterator, size_t) [T = float]void onnxruntime::rnn::detail::ComputeGemm(const int, const int, const int, const float, TSpanAIter, TSpanAIter, const int, TSpanBIter, TSpanBIter, const int, const float, TSpanCIter, TSpanCIter, const int, concurrency::ThreadPool *) [TSpanAIter = float *, TSpanBIter = const float *, TSpanCIter = float *]LSTM/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.ccvirtual onnxruntime::common::Status onnxruntime::DeepCpuLstmOp::Compute(onnxruntime::OpKernelContext *) constInvalid data type for LSTM operator of Input initial_c must have shape {}. Actual:Input P must have shape {/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.honnxruntime::DeepCpuLstmOp::DeepCpuLstmOp(const onnxruntime::OpKernelInfo &)input_forgetactivation_func_names.size() == static_cast(num_directions_) * 3 rows[..const T *onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span, size_t, size_t) [T = const float]const T *onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span, size_t, size_t) [T = float]RNN/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/rnn.ccAffine/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/rnn.honnxruntime::RNN::RNN(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttr("direction", &direction_).IsOK()allowed_directions.find(direction_) != allowed_directions.end()info.GetAttrs("activations", activations_).IsOK()info.GetAttr("hidden_size", &hidden_size_).IsOK()activations_.size() == static_cast(num_directions)allowed_activations.find(activations_[direction]) != allowed_activations.end()RNN op: Invalid activation attribute - Input X must have 3 dimensions only. Actual:Input W must have shape {*Input R must have shape {Input B must have shape {Input sequence_lens must have shape {Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length=Input initial_h must have shape {affinereluleakyreluthresholdedreluscaledtanhhardsigmoidelusoftsignsoftplus/Users/runner/work/1/s/onnxruntime/core/providers/cpu/rnn/rnn_helpers.ccstd::string onnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCount(const std::string &, std::vector::const_iterator &, const std::vector::const_iterator &, std::vector::const_iterator &, const std::vector::const_iterator &, float &, float &)Expecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got 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/work/1/s/onnxruntime/core/providers/cpu/sequence/concat_from_sequence.ccvirtual onnxruntime::common::Status onnxruntime::ConcatFromSequence::Compute(onnxruntime::OpKernelContext *) const/Users/runner/work/1/s/onnxruntime/core/providers/cpu//tensor/concat.hSSequenceLength/Users/runner/work/1/s/onnxruntime/core/providers/cpu/sequence/sequence_ops.ccvirtual onnxruntime::common::Status onnxruntime::SequenceLength::Compute(onnxruntime::OpKernelContext *) constY != nullptrSequenceAtvirtual onnxruntime::common::Status onnxruntime::SequenceAt::Compute(onnxruntime::OpKernelContext *) constI != nullptrInvalid sequence index () specified for sequence of size (SequenceEmptyvirtual onnxruntime::common::Status onnxruntime::SequenceEmpty::Compute(onnxruntime::OpKernelContext *) constSequenceInsertCreateCopyAndAppendCpuTensorvirtual onnxruntime::common::Status onnxruntime::SequenceInsert::Compute(onnxruntime::OpKernelContext *) constS != nullptrData type of the input tensor MUST be same as that of the input sequence. Sequence data type (), input tensor data type (SequenceErasevirtual onnxruntime::common::Status onnxruntime::SequenceErase::Compute(onnxruntime::OpKernelContext *) constSequenceConstructvirtual onnxruntime::common::Status onnxruntime::SequenceConstruct::Compute(onnxruntime::OpKernelContext *) constnum_inputs >= 1Violation of the requirment that all input tensors must have the same data type.SplitToSequencevirtual onnxruntime::common::Status onnxruntime::SplitToSequence::Compute(onnxruntime::OpKernelContext *) constSplitToSequence operator does not support split_size_sum () != split_dim_size (int64_t onnxruntime::GetSeqIdx(const onnxruntime::Tensor &)const T *onnxruntime::OpKernelContext::Input(int) const [T = onnxruntime::TensorSeq]onnxruntime::TensorSeq *OrtValue::GetMutable()onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &, const onnxruntime::Tensor *) const [T = float]split_scalar > 0std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value >= 0; })int64_t onnxruntime::GetScalarSplitInput(const onnxruntime::Tensor &)Invalid data type for split tensor void onnxruntime::GetSplitSizesInput(const onnxruntime::Tensor &, std::vector &)void onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape &)shape_.Size() == new_shape.Size()Tensor size () != new size (onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &, const onnxruntime::Tensor *) const [T = double]onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &, const onnxruntime::Tensor *) const [T = int]onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext &, const onnxruntime::Tensor &, const onnxruntime::Tensor *) const [T = std::__1::basic_string]Cast/Users/runner/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/work/1/s/onnxruntime/core/providers/cpu/tensor/compress.ccvirtual onnxruntime::common::Status onnxruntime::Compress::Compute(onnxruntime::OpKernelContext *) constaxes_right_stride >= 0 && static_cast(axes_right_stride) < std::numeric_limits::max()ConcatNot satisfied: input_count >= 1 /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/concat.ccPrepareForComputeNot satisfied: inputs_0_rank > 0 onnxruntime::common::Status onnxruntime::ConcatBase::PrepareForCompute(onnxruntime::OpKernelContext *, const std::vector &, onnxruntime::Prepare &) constinputs_n_rank == inputs_0_rankRanks of input data are different, cannot concatenate them. expected rank: got: Not satisfied: dim_value == inputs_0_dims[axis_index] Non concat axis dimensions must match: Axis has mismatched dimensions of Not satisfied: data_n.DataType() == p.output_tensor->DataType() /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/concat.honnxruntime::ConcatBase::ConcatBase(const onnxruntime::OpKernelInfo &, bool)DynamicQuantizeLinear/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/dynamicquantizelinear.ccvirtual onnxruntime::common::Status onnxruntime::DynamicQuantizeLinear::Compute(onnxruntime::OpKernelContext *) const [T = unsigned char]x_ptr != nullptrEyeLike/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/eye_like.ccvirtual onnxruntime::common::Status onnxruntime::EyeLike::Compute(onnxruntime::OpKernelContext *) constT1 != nullptrUnsupported 'dtype' value: dtypeEyeLike : Input tensor dimension is not 2Gather/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/gather.cc/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/gather.honnxruntime::GatherBase::GatherBase(const onnxruntime::OpKernelInfo &)info.GetAttr("axis", &axis_).IsOK()indices element out of data bounds, idx= must be within the inclusive range [GatherElementsGatherElements op: Rank of input 'data' needs to be equal to rank of input 'indices'GatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: GatherElements op: Data type of input 'data' should match the data type of the output/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/gather_elements.honnxruntime::GatherElements::GatherElements(const onnxruntime::OpKernelInfo &)/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/gather_elements.ccstd::vector onnxruntime::parse_and_validate_indices_tensor(const onnxruntime::Tensor *, int64_t, const onnxruntime::TensorShape &)GatherElements op: Data type for 'indices' tensor must be 'int32_t' and 'int64_t'GatherElements op: Value in indices must be within bounds [ , ]. Actual value is invalid index found, index = GatherND/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/gather_nd.ccvirtual onnxruntime::common::Status onnxruntime::GatherND::Compute(onnxruntime::OpKernelContext *) constinput_tensor != nullptr && indices_tensor != nullptrlast dimension of indices must not be larger than rank of input tensorbatch_dimsDropoutIdentity/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/identity_op.hvirtual onnxruntime::common::Status onnxruntime::IdentityOp::Compute(onnxruntime::OpKernelContext *) const [is_dropout = true]const void *onnxruntime::Tensor::DataRaw(onnxruntime::MLDataType) consttype == dtype_Tensor type mismatch.void *onnxruntime::Tensor::MutableDataRaw(onnxruntime::MLDataType)virtual onnxruntime::common::Status onnxruntime::IdentityOp::Compute(onnxruntime::OpKernelContext *) const [is_dropout = false]T2IsInfdetect_positive/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/isinf.cconnxruntime::IsInf::IsInf(const onnxruntime::OpKernelInfo &)detect_negativevirtual onnxruntime::common::Status onnxruntime::IsInf::Compute(onnxruntime::OpKernelContext *) constData type X must be float or double, but instead got IsNaNNull input ptrMeanVarianceNormalizationInput is expected to have four dimensions corresponding to [N,C,H,W]NonZero/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/nonzero_op.ccvirtual onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext *) const [T = bool]virtual onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext *) const [T = unsigned char]virtual onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext *) const [T = int]virtual onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext *) const [T = long long]virtual onnxruntime::common::Status onnxruntime::NonZero::Compute(onnxruntime::OpKernelContext *) const [T = float]OneHotInvalid argument for depth; it's not a scalar.Invalid argument for values; either it's rank is more than 1 or it has more than 2 elements/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/onehot.ccDepth is negative.PadCannot use 'reflect' mode to pad dimension with a value of 0. Input shape:Unexpected mode of /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/pad.ccvirtual onnxruntime::common::Status onnxruntime::Pad::Compute(onnxruntime::OpKernelContext *) constpads_tensor.IsDataType()pads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1)Pads tensor should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]pads_size == 2 * data_rankPads tensor size should be equal to twice the input dimension count value_tensor->DataType() == data_type && value_tensor->Shape().Size() == 1Value tensor should be a 1D tensor of size 1 with the same type as that of the input tensorUnsupported input data type of valueconstantreflectedge/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/pad.honnxruntime::PadBase::PadBase(const onnxruntime::OpKernelInfo &)onnxruntime::PadValue onnxruntime::PadValueFromFloat(float, onnxruntime::MLDataType)onnxruntime::common::Status onnxruntime::PadImpl(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &, const onnxruntime::Mode &, T) [T = unsigned int]data_rank > 0data_rank * 2 == pads.size()PadInputWithDimValueOfZero/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/utils.honnxruntime::SliceSkips::SliceSkips(const onnxruntime::TensorShape &, gsl::span, gsl::span)dims.size() == extents.size() && dims.size() >= steps.size()void onnxruntime::SliceIteratorBase::Init(const std::vector &, gsl::span, gsl::span)dims.size() == starts.size() && dims.size() == extents_.size() && dims.size() >= steps.size()onnxruntime::common::Status onnxruntime::PadImpl(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &, const onnxruntime::Mode &, T) [T = unsigned long long]onnxruntime::common::Status onnxruntime::PadImpl(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &, const onnxruntime::Mode &, T) [T = unsigned char]DequantizeLinearQuantizeLinear/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/quantize_linear.ccvirtual onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext *) const [T = unsigned char]x_scale.Shape().NumDimensions() == 1 && x_scale.Shape().Size() == broadcast_dimx_scale must be 1D tensor with size x_zero_point != nullptr && x_zero_point->Shape().NumDimensions() == 1 && x_zero_point->Shape().Size() == broadcast_dimx_zero_point must be 1D tensor with size IsScalarOr1ElementVector(&x_scale)x_zero_point == nullptr || IsScalarOr1ElementVector(x_zero_point)virtual onnxruntime::common::Status onnxruntime::DequantizeLinear::Compute(onnxruntime::OpKernelContext *) const [T = signed char]virtual onnxruntime::common::Status onnxruntime::QuantizeLinear::Compute(onnxruntime::OpKernelContext *) const [T = unsigned char]y_scale.Shape().NumDimensions() == 1 && y_scale.Shape().Size() == broadcast_dimy_scale must be 1D tensor with size y_zero_point != nullptr && y_zero_point->Shape().NumDimensions() == 1 && y_zero_point->Shape().Size() == broadcast_dimy_zero_point must be 1D tensor with size IsScalarOr1ElementVector(&y_scale)y_zero_point == nullptr || IsScalarOr1ElementVector(y_zero_point)virtual onnxruntime::common::Status onnxruntime::QuantizeLinear::Compute(onnxruntime::OpKernelContext *) const [T = signed char]shapeReshapeReshape_1/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/reshape.hvirtual onnxruntime::common::Status onnxruntime::Reshape::Compute(onnxruntime::OpKernelContext *) constshapeTensor->Shape().NumDimensions() == 1/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/reshape_helper.honnxruntime::ReshapeHelper::ReshapeHelper(const onnxruntime::TensorShape &, std::vector &)requested_shape[i] >= -1unknown_dim == -1i < input_shape.NumDimensions()The dimension with value zero exceeds the dimension size of the input tensor.size != 0 && (input_shape.Size() % size) == 0The input tensor cannot be reshaped to the requested shape. Input shape:, requested shape:gsl::narrow_cast(input_shape.Size()) == sizeonnxruntime::Reshape_1::Reshape_1(const onnxruntime::OpKernelInfo &)Resize/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/upsample.honnxruntime::UpsampleBase::UpsampleBase(onnxruntime::OpKernelInfo)info.GetAttr("mode", &mode).IsOK()scalesinfo.GetAttrs("scales", scales_).IsOK()extrapolation_valuecoordinate_transformation_modehalf_pixelasymmetricnearest_moderound_prefer_floorcubic_coeff_aexclude_outsideexclude_outside can be set to 1 only when mode is CUBIC. Current mode is set to nearestlinearcubiconnxruntime::UpsampleMode onnxruntime::UpsampleBase::StringToUpsampleMode(const std::string &)mode attribute is . It can only be (default) or or void onnxruntime::UpsampleBase::ScalesValidation(const std::vector &, const onnxruntime::UpsampleMode) constscale >= 1scale > 0scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1)'Linear' mode and 'Cubic' mode only support 2-D inputs ('Bilinear', 'Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the Resize operatorUpsample operatorpytorch_half_pixeltf_half_pixel_for_nnalign_cornerstf_crop_and_resizeonnxruntime::ResizeCoordinateTransformationMode onnxruntime::UpsampleBase::StringToCoordinateTransformationMode(const std::string &)coordinate_transform_mode:[] is not supportted!round_prefer_ceilfloorceilonnxruntime::ResizeNearestMode onnxruntime::UpsampleBase::StringToNearestMode(const std::string &)nearest_mode:[] is not supported!void onnxruntime::UpsampleBase::ParseScalesData(const onnxruntime::Tensor *, std::vector &) constscales_size > 0ReverseSequencesequence_lens shape must be {batch_size}. Got:. batch_size=/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/reverse_sequence.ccvirtual onnxruntime::common::Status onnxruntime::ReverseSequenceOp::Compute(onnxruntime::OpKernelContext *) constbatch_axis/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/reverse_sequence.honnxruntime::ReverseSequenceOp::ReverseSequenceOp(const onnxruntime::OpKernelInfo &)info.GetAttr("batch_axis", &batch_axis).IsOK()time_axisinfo.GetAttr("time_axis", &time_axis).IsOK()batch_axis < 2Invalid batch_axis of . Must be 0 or 1time_axis < 2Invalid time_axis of batch_axis != time_axistime_axis and batch_axis must have different values but both are gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = bool]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = double]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = unsigned 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/work/1/s/onnxruntime/core/providers/cpu/tensor/scatter.ccvirtual onnxruntime::common::Status onnxruntime::Scatter::Compute(onnxruntime::OpKernelContext *) constUnknown tensor type of onnxruntime::Scatter::Scatter(const onnxruntime::OpKernelInfo &)/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/scatter_nd.cconnxruntime::common::Status onnxruntime::ScatterNDBase::PrepareForCompute(onnxruntime::OpKernelContext *, onnxruntime::ScatterNDBase::Prepare &) const [Tind = long long]input_tensor != nullptrindice_tensor != nullptrupdate_tensor != nullptrinput tensor and indices tensor must has rank larger than 0. input shape: , indices shape: updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. updates shape: , data shape: invalid indice found, indice = ScatterNDShapeSizeSliceTind'axes' has an axis outside of the tensor dimension count'axes' has duplicates'step' value cannot be 0/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/slice.ccvoid onnxruntime::SliceBase::FillVectorsFromInput(const onnxruntime::Tensor &, const onnxruntime::Tensor &, const onnxruntime::Tensor *, const onnxruntime::Tensor *, std::vector &, std::vector &, std::vector &, std::vector &) conststart_tensor.Shape().NumDimensions() == 1ends_tensor.Shape().NumDimensions() == 1start_tensor.Shape() == ends_tensor.Shape()nullptr == axes_tensor || start_tensor.Shape() == axes_tensor->Shape()nullptr == steps_tensor || start_tensor.Shape() == steps_tensor->Shape()Data type for starts and ends inputs' need to be int32_t or int64_t, but instead got onnxruntime::common::Status onnxruntime::SliceBase::Compute(onnxruntime::OpKernelContext *) constinput_tensor_ptr != nullptrstarts/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/slice.honnxruntime::SliceBase::SliceBase(const onnxruntime::OpKernelInfo &, bool)has_starts && has_ends && attr_starts_.size() == attr_ends_.size()!has_axes || attr_axes_.size() == attr_starts_.size()Invalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributesauto onnxruntime::SliceImpl(onnxruntime::OpKernelContext *, const onnxruntime::Tensor &, std::vector &, std::vector *, const std::vector &, const std::vector &)::(anonymous class)::operator()(SliceIterator &) constoutput == output_endvoid *onnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStep(void *)Unexpected element size of SpaceToDepthDepthToSpace/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/space_depth_ops.ccvirtual onnxruntime::common::Status onnxruntime::SpaceToDepth::Compute(onnxruntime::OpKernelContext *) const [T = float]input_shape.NumDimensions() == 4input_height % this->blocksize_ == 0input_width % this->blocksize_ == 0virtual onnxruntime::common::Status onnxruntime::DepthToSpace::Compute(onnxruntime::OpKernelContext *) const [T = float]input.Shape().NumDimensions() == 4input_depth % (blocksize_ * blocksize_) == 0blocksize/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/space_depth_ops.honnxruntime::SpaceDepthBase::SpaceDepthBase(const onnxruntime::OpKernelInfo &)info.GetAttr("blocksize", &blocksize_).IsOK()CRDDCRonnxruntime::DepthToSpace::DepthToSpace(const onnxruntime::OpKernelInfo &) [T = float]SplitInput cannot be split evenly on selected axis. Input shape= Axis= NumOutputs=Cannot split using values in 'split' attribute. Axis= Input shape= Num entries in 'split' (must equal number of outputs) was Sum of sizes in 'split' (must equal size of selected axis) was /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/split.ccvirtual onnxruntime::common::Status onnxruntime::Split::Compute(onnxruntime::OpKernelContext *) constSplit operator does not support yetsplit/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/split.honnxruntime::SplitBase::SplitBase(const onnxruntime::OpKernelInfo &)std::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value >= 0; })Squeeze/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/squeeze.hstatic std::vector onnxruntime::SqueezeBase::ComputeOutputShape(const onnxruntime::TensorShape &, const onnxruntime::TensorShape &)input_shape[i] == 1Dimension of input must be 1 instead of . shape=TileInput count of Tile OP mismatch, the first one is emptyInput count of Tile OP mismatch, the second one is emptythe tensor to be tiled using Tile OP must be atleast 1 dimensional'repeat' input tensor must be 1 dimensional'repeat' input tensor must have the same length as the 'input' tensor/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/tile.ccvirtual onnxruntime::common::Status onnxruntime::Tile::Compute(onnxruntime::OpKernelContext *) constTile doesn't have an implementation yet for the type: Mismatched data types between input and output Tensors. /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/transpose.ccvirtual onnxruntime::common::Status onnxruntime::Transpose::Compute(onnxruntime::OpKernelContext *) constTranspose[ perm: does not align with rank of input data: /Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/transpose.honnxruntime::TransposeBase::TransposeBase(const onnxruntime::OpKernelInfo &)v >= 0 && static_cast(v) <= std::numeric_limits::max()Attribute perm of Transpose has an invalid value. Value is outside range. is repeated.UniqueUnsupported tensor type of gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = long long]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = signed char]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = signed char]gsl::span onnxruntime::Tensor::DataAsSpan() const [T = std::__1::basic_string]gsl::span onnxruntime::Tensor::MutableDataAsSpan() [T = std::__1::basic_string]Unsqueeze/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/unsqueeze.cconnxruntime::common::Status onnxruntime::UnsqueezeBase::PrepareCompute(onnxruntime::OpKernelContext *, onnxruntime::UnsqueezeBase::Prepare &) const'axes' has an out of range axis'axes' has a duplicate axis/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/unsqueeze.honnxruntime::UnsqueezeBase::UnsqueezeBase(const onnxruntime::OpKernelInfo &)info.GetAttrs("axes", axes_).IsOK()Upsample/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/upsample.ccvirtual onnxruntime::common::Status onnxruntime::Upsample::Compute(onnxruntime::OpKernelContext *) const [T = float]roi_input_idx_ > 0scales != nullptrsizes == nullptrsizes != nullptr && sizes->Shape().Size() != 0X->Shape().GetDims().size() == output_dims.size()Resize: input tensor's rank does not match the output tensor's rank.onnxruntime::common::Status onnxruntime::Upsample::BaseCompute(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &, const std::vector &) const [T = float]output_dims.size() == dims.size()Resize: input tensor's dimension does not match the scales.Upsample: input tensor's dimension does not match the scales.Resize: size of roi array should be 2 * N where N is the rank of input tensor X.'Linear' mode only support 2-D inputs ('Bilinear') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the BaseComputeResize: unexpected modeUpsample: unexpected modeResize: input/output value is nullptrUpsample: input/output value is nullptrResize: input/output value's dimension mismatchUpsample: input/output value's dimension mismatchResize: input shape needs to be at least a single dimensionUpsample: input shape needs to be at least a single dimension.void onnxruntime::UpsampleBase::ParseScalesDataFromOutputSize(const std::vector &, const std::vector &, std::vector &) constoutput_dims[i] == 0Input dim is zero but required output dim is non-zero. Cannot scale 0 by any factor to generate a non-zero value. Dimension: Input dim value: Output dim value: virtual onnxruntime::common::Status onnxruntime::Upsample::Compute(onnxruntime::OpKernelContext *) const [T = int]onnxruntime::common::Status onnxruntime::Upsample::BaseCompute(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &, const std::vector &) const [T = int]virtual onnxruntime::common::Status onnxruntime::Upsample::Compute(onnxruntime::OpKernelContext *) const [T = unsigned char]onnxruntime::common::Status onnxruntime::Upsample::BaseCompute(onnxruntime::OpKernelContext *, const std::vector &, const std::vector &, const std::vector &) const [T = unsigned char]Where/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/where_op.ccvirtual onnxruntime::common::Status onnxruntime::Where::Compute(onnxruntime::OpKernelContext *) const [T = unsigned char]condition && X && Yonnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext &) [T = unsigned char]virtual onnxruntime::common::Status onnxruntime::Where::Compute(onnxruntime::OpKernelContext *) const [T = int]virtual onnxruntime::common::Status onnxruntime::Where::Compute(onnxruntime::OpKernelContext *) const [T = long long]virtual onnxruntime::common::Status onnxruntime::Where::Compute(onnxruntime::OpKernelContext *) const [T = float]virtual onnxruntime::common::Status onnxruntime::Where >::Compute(onnxruntime::OpKernelContext *) const [T = std::__1::basic_string]onnxruntime::TensorAllocator >::TensorAllocator(onnxruntime::OpKernelContext &) [T = std::__1::basic_string]Geluonnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::ParametricSoftplus]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::ParametricSoftplus]onnxruntime::ElementWiseKernel >::ElementWiseKernel(const onnxruntime::OpKernelInfo &) [F = onnxruntime::functors::ScaledTanh]virtual onnxruntime::common::Status onnxruntime::ElementWiseKernel >::Compute(onnxruntime::OpKernelContext *) const [F = onnxruntime::functors::ScaledTanh]/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/attnlstm/bahdanau_attention.cconnxruntime::contrib::BahdanauAttention::BahdanauAttention(onnxruntime::AllocatorPtr, const logging::Logger &, int, int, int, int, int, bool, concurrency::ThreadPool *) [T = float]!normalize_virtual void onnxruntime::contrib::BahdanauAttention::PrepareMemory(const gsl::span &, const gsl::span &) [T = float]mem_steps <= max_memory_steps_ && mem_steps > 0Real memory steps is not in (0, AttnLSTM/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.ccvirtual onnxruntime::common::Status onnxruntime::contrib::DeepCpuAttnLstmOp::Compute(onnxruntime::OpKernelContext *) constAttention mechanism memory shape error! Expected: {}, actural: Attention mechanism memory sequence lengths must have shape {Attention mechanism memory sequence lengths value must in (0, ], while found!Attention memory layer weight shape error! Expected:{, am_attn_size}, Got:Attention query layer weight shape error! Expected:{}, Got: Attention v weight shape error! Expected:{}. Got: Attention layer weight shape error! Expected: {, aw_attn_size}. Got:/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.honnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOp(const onnxruntime::OpKernelInfo &)static_cast(activation_func_names.size()) == num_directions_ * 3 [seqno= row[Attentionnum_heads/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/bert/attention.cconnxruntime::contrib::AttentionBase::AttentionBase(const onnxruntime::OpKernelInfo &)info.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0unidirectionalInput 'input' is expected to have 3 dimensions, got Input 0 dimension 2 should be divisiable by value of the num_heads attribute.Input 'weights' is expected to have 2 dimensions, got Input 1 dimension 0 should have same length as dimension 2 of input 0Input 'bias' is expected to have 1 dimension, got Input 'bias' dimension 0 should have same length as dimension 1 of input 'weights'Input 'past' is expected to have 5 dimension, got Inputs 'past' dimension 1 shall have same length as dimension 0 of input 0Inputs 'past' dimension 2 shall have length of num_headsInputs 'past' dimension 2 shall have length of Inputs 'mask_index' dimension 0 shall have length of batch_size or 2 * batch_sizeInputs 'mask_index' with raw attention mask shall have shape batch_size x (past_sequence_length + sequence_length)Input 'mask_index' is expected to have 1 or 2 dimensions, got onnxruntime::Tensor *onnxruntime::contrib::AttentionBase::GetPresent(onnxruntime::OpKernelContext *, const onnxruntime::Tensor *, int, int, int, int &) constExpect to have present state output when past state input is given/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/bert/attention_cpu_base.hApplyAttention/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/bert/bias_gelu.ccvirtual onnxruntime::common::Status onnxruntime::contrib::BiasGelu::Compute(onnxruntime::OpKernelContext *) const [T = float, use_approximation = false]use_approximationBiasGeluInput 0 is expected to have 1 or more dimensions, got Input 1 is expected to have 1 dimensions, got Input 1 dimension 0 should have same length as the last dimension of input 0EmbedLayerNormalization/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/bert/embed_layer_norm.cconnxruntime::contrib::EmbedLayerNorm::EmbedLayerNorm(const onnxruntime::OpKernelInfo &) [T = float]input_ids is expected to have 2 dimensions, got word_embedding is expected to have 2 dimensions, got position_embedding is expected to have 2 dimensions, got segment_embedding is expected to have 2 dimensions, got word_embedding and position_embedding shall have same dimension 1beta is expected to have 1 dimensions, got beta is expected to have size of gamma is expected to have 1 dimensions, got gamma is expected to have size of FastGeluCDistmetric/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/cdist.honnxruntime::contrib::CDist::CDist(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttr("metric", &metric).IsOK()sqeuclideaneuclideanThe first input of CDist kernel has wrong shape: The second input of CDist kernel has wrong shape: Input shape dimensions mismatch:onnxruntime::contrib::CDist::CDist(const onnxruntime::OpKernelInfo &) [T = double]ConvTransposeWithDynamicPads/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/cpu_contrib_kernels.ccRegisterNchwcKernelsRegisterQuantizationKernelsRegisterCpuContribKernelsCropborder/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/crop.hAttribute border needs to be specified with four border elements, got Input is expected to have four dimensions corresponding to [N,C,H,W], got input dimensions insteadInput's height () needs to be greater than or equal to the topBorder () + bottomBorder (Input's width () needs to be greater than or equal to the leftBorder () + rightBorder () + scale_[0] () + scale_[1] (CropAndResizebilinear/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/crop_and_resize.honnxruntime::contrib::CropAndResize::CropAndResize(const onnxruntime::OpKernelInfo &) [T = float] specified. It should be either bilinear or nearestNull crop_size_ptrNumber of dimensions for crop size should be exactly 1/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/element_wise_ops.honnxruntime::contrib::Affine::Affine(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttr("alpha", &alpha_).IsOK()info.GetAttr("beta", &beta_).IsOK()onnxruntime::contrib::Scale::Scale(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttr("scale", &scale_).IsOK()axisExpandDims/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/expand_dims.hvirtual onnxruntime::common::Status onnxruntime::contrib::ExpandDims::Compute(onnxruntime::OpKernelContext *) constaxis_tensor->Shape().IsScalar()axis <= X_NumDims && axis >= -X_NumDimsAxis must be within range [ Axis is unimplemented activation: activation_params count mismatchFusedConv/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/fused_conv.cconnxruntime::contrib::FusedConvFloat::FusedConvFloat(const onnxruntime::OpKernelInfo &)FusedGemmactivationactivation_/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/fused_gemm.cconnxruntime::contrib::FusedGemm::FusedGemm(const onnxruntime::OpKernelInfo &) [T = float]/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/image_scaler.honnxruntime::contrib::ImageScaler::ImageScaler(const onnxruntime::OpKernelInfo &) [T = float]info.GetAttrs("bias", bias_).IsOK()Bias size () does not match the number of channels (Inversevoid onnxruntime::utils::MLTypeCallDispatcher::Invoke(Args &&...) const [Fn = ComputeImpl, Types = , Args = ]LayerNormalization/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/layer_norm.cconnxruntime::contrib::LayerNorm::LayerNorm(const onnxruntime::OpKernelInfo &) [T = float]op_kernel_info.GetAttr("axis", &axis_).IsOK()onnxruntime::contrib::LayerNorm::LayerNorm(const onnxruntime::OpKernelInfo &) [T = double]MatMulInteger16/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/matmul_integer16.ccvirtual onnxruntime::common::Status onnxruntime::contrib::MatMulInteger16::Compute(onnxruntime::OpKernelContext *) const [T1 = short, T2 = short, T3 = int]A != nullptr && B != nullptrMaxpoolWithMask/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/maxpool_with_mask.hacross_channels/Users/runner/work/1/s/onnxruntime/core/providers/cpu/tensor/mean_variance_normalization.honnxruntime::MeanVarianceNormalization_0::MeanVarianceNormalization_0(const onnxruntime::OpKernelInfo &, bool) [T = float]info.GetAttr("across_channels", &across_channels_).IsOK()normalize_varianceinfo.GetAttr("normalize_variance", &normalize_variance_).IsOK()MurmurHash3/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/murmur_hash3.ccvirtual onnxruntime::common::Status onnxruntime::contrib::MurmurHash3::Compute(onnxruntime::OpKernelContext *) constkeyssizeof(uint32_t) == output_element_bytesinput_num_bytes % 4 == 0ReorderInputReorderOutput/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/nchwc_ops.ccvirtual onnxruntime::common::Status onnxruntime::contrib::ReorderInput::Compute(onnxruntime::OpKernelContext *) constX_shape.NumDimensions() == 4(X_shape[1] % MlasNchwcGetBlockSize()) == 0virtual onnxruntime::common::Status onnxruntime::contrib::ReorderOutput::Compute(onnxruntime::OpKernelContext *) constX_rank == 4channels_ <= X_shape[1]virtual onnxruntime::common::Status onnxruntime::contrib::NchwcConv::Compute(onnxruntime::OpKernelContext *) const(static_cast(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0)Unsupported convolution size.Not satisfied: Y->Shape() == sum_shape onnxruntime::common::Status onnxruntime::contrib::NchwcPoolBase::NchwcPool(onnxruntime::OpKernelContext *, MLAS_POOLING_KIND) constvirtual onnxruntime::common::Status onnxruntime::contrib::NchwcUpsample::Compute(onnxruntime::OpKernelContext *) const/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/nchwc_ops.honnxruntime::contrib::ReorderOutput::ReorderOutput(const onnxruntime::OpKernelInfo &)info.GetAttr("channels", &channels_).IsOK()channels_ > 0invalid channel countchannels_lastinfo.GetAttr("channels_last", &channels_last_).IsOK()onnxruntime::contrib::NchwcConv::NchwcConv(const onnxruntime::OpKernelInfo &)GetFusedActivationAttr(info, activation_).IsOK()onnxruntime::contrib::NchwcPoolBase::NchwcPoolBase(const onnxruntime::OpKernelInfo &)pool_attrs_.kernel_shape.size() == 2onnxruntime::contrib::NchwcUpsample::NchwcUpsample(const onnxruntime::OpKernelInfo &)info.GetAttrs("scales", scales_).IsOK()scales_.size() == 4scales_[0] == 1 && scales_[1] == 1 && scales_[2] >= 1 && scales_[3] >= 1QLinearAdd/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/qlinear_binary_op.cconnxruntime::common::Status onnxruntime::contrib::QLinearBroadcastTwo(onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General, double) [T = signed char, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/qlinear_binary_op.cc:119:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/qlinear_binary_op.cc:125:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/qlinear_binary_op.cc:131:7)]IsScalarOr1ElementVector(tensor_a_scale)MatmulInteger : input1 A_scale must be a scalar or 1D tensor of size 1tensor_a_zero_point == nullptr || IsScalarOr1ElementVector(tensor_a_zero_point)MatmulInteger : input1 A_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_b_scale)MatmulInteger : input1 B_scale must be a scalar or 1D tensor of size 1tensor_b_zero_point == nullptr || IsScalarOr1ElementVector(tensor_b_zero_point)MatmulInteger : input1 B_zero_point must be a scalar or 1D tensor of size 1 if givenIsScalarOr1ElementVector(tensor_c_scale)MatmulInteger : input1 C_scale must be a scalar or 1D tensor of size 1tensor_c_zero_point == nullptr || IsScalarOr1ElementVector(tensor_c_zero_point)MatmulInteger : input1 C_zero_point must be a scalar or 1D tensor of size 1 if givenonnxruntime::TBroadcastOutput::TBroadcastOutput(size_t, onnxruntime::Tensor &, int64_t, int64_t) [T = signed char]start_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= lenInvalid start/ending offset [) for tensor of length:start_offset % span_size == 0 && real_end % span_size == 0Broadcast Output range [) are not at boundary of span with size:void onnxruntime::TBroadcaster::AdvanceBy(size_t) [T0 = signed char, T1 = signed char]offset % span_size_ == 0onnxruntime::common::Status onnxruntime::contrib::QLinearBroadcastTwo(onnxruntime::OpKernelContext &, Input0Scalar, Input1Scalar, General, double) [T = unsigned char, Input0Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/qlinear_binary_op.cc:119:7), Input1Scalar = (lambda at /Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/qlinear_binary_op.cc:125:7), General = (lambda at /Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/qlinear_binary_op.cc:131:7)]onnxruntime::TBroadcastOutput::TBroadcastOutput(size_t, onnxruntime::Tensor &, int64_t, int64_t) [T = unsigned char]void onnxruntime::TBroadcaster::AdvanceBy(size_t) [T0 = unsigned char, T1 = unsigned char]QLinearLeakyRelu/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/qlinear_lookup_table.ccvoid onnxruntime::contrib::BuildQLinearLeakyReluLookupTable(uint8_t *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, float) [T = signed char]IsScalarOr1ElementVector(tensor_x_scale)QLinearLeakyRelu : input X_scale must be a scalar or 1D tensor of size 1tensor_x_zero_point == nullptr || IsScalarOr1ElementVector(tensor_x_zero_point)QLinearLeakyRelu : input X_zero_point must be a scalar or 1D tensor of size 1IsScalarOr1ElementVector(tensor_y_scale)QLinearLeakyRelu : input Y_scale must be a scalar or 1D tensor of size 1tensor_y_zero_point == nullptr || IsScalarOr1ElementVector(tensor_y_zero_point)QLinearLeakyRelu : input Y_zero_point must be a scalar or 1D tensor of size 1void onnxruntime::contrib::BuildQLinearLeakyReluLookupTable(uint8_t *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, const onnxruntime::Tensor *, float) [T = unsigned char]QAttention/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/attention_quant.ccNot satisfied: IsScalarOr1ElementVector(input_scale_tensor) Not satisfied: IsScalarOr1ElementVector(weight_scale_tensor) Not satisfied: IsScalarOr1ElementVector(i_zp_tensor) Not satisfied: IsScalarOr1ElementVector(w_zp_tensor) /Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/quantization/dynamic_quantize_matmul.ccComputeCommonvirtual onnxruntime::common::Status onnxruntime::contrib::DynamicQuantizeMatMul::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(b_scale_tensor)DynamicQuantizeMatMul : input B scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.IsScalarOr1ElementVector(b_zero_point_tensor)DynamicQuantizeMatMul : input B zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.virtual onnxruntime::common::Status onnxruntime::contrib::MatMulIntegerToFloat::Compute(onnxruntime::OpKernelContext *) constIsScalarOr1ElementVector(a_scale_tensor)MatMulIntegerToFloat : input A scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.MatMulIntegerToFloat : input B scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.IsScalarOr1ElementVector(a_zero_point_tensor)MatMulIntegerToFloat : input A zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.MatMulIntegerToFloat : input B zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet.DynamicQuantizeMatMulMatMulIntegerToFloatSampleOpSkipLayerNormalization/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/skip_layer_norm.cconnxruntime::contrib::SkipLayerNorm::SkipLayerNorm(const onnxruntime::OpKernelInfo &) [T = float]epsilon_ >= 0input is expected to have 3 dimensions, got gamma is expected to have 1 dimension, got beta is expected to have 1 dimension, got bias is expected to have 1 dimension, got onnxruntime::contrib::SkipLayerNorm::SkipLayerNorm(const onnxruntime::OpKernelInfo &) [T = double]Tokenizer/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/tokenizer.cconnxruntime::contrib::Tokenizer::Tokenizer(const onnxruntime::OpKernelInfo &)pad_valuemincharnummincharnum_ > 0separatorsEither one of the separators OR tokenexp attributes required but none is set!tokenexp.empty()!separators.empty()!char_tokenezation_ || mincharnum_ < 2Can not digest separators: Can not digest tokenexp: Input string contains invalid utf8 chars: Match contains invalid utf8 chars: tensor(string) expected as inputInput dimensions are either [C] or [N][C] allowedTransposeMatMul/Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/transpose_matmul.cconnxruntime::contrib::TransposeMatMul::TransposeMatMul(const onnxruntime::OpKernelInfo &)info.GetAttr("transA", &trans_a_attr_).IsOK()info.GetAttr("transB", &trans_b_attr_).IsOK()Conv filter size does not match embedding_size attribute. embedding_size attribute: conv filter size: Conv kernal size 1 does not match conv_window_size attribute . conv_window_size attribute: conv kernal size 1: Char embedding size does not match char_embedding_size attribute. char_embedding_size attribute: Char embedding size: Char embedding size does not match conv kernal size 2. Conv kernal size 2 : /Users/runner/work/1/s/onnxruntime/contrib_ops/cpu/word_conv_embedding.ccWordConvEmbeddingembedding_sizeconv_window_sizechar_embedding_size/Users/runner/work/1/s/onnxruntime/core/util/math_cpu.ccvoid onnxruntime::math::Gemv(CBLAS_TRANSPOSE, int, int, float, const T *, const T *, float, T *, Provider *) [T = float, Provider = onnxruntime::CPUMathUtil]Gemv found an unexpected CBLAS_TRANSPOSE input ofvoid onnxruntime::math::Gemv(CBLAS_TRANSPOSE, int, int, float, const T *, const T *, float, T *, Provider *) [T = double, Provider = onnxruntime::CPUMathUtil]AllocateAllocateStaticallyPreExistingReuseAllocateOutputShareAllocation Plan: (ort_value_idx) output_name : , use fence when asyncIndex out-of-range! Execution Plan: /Users/runner/work/1/s/onnxruntime/core/framework/allocation_planner.ccstd::ostream &onnxruntime::operator<<(std::ostream &, std::pair)nullptr != node[Free ml-values: CreatePlanCan not find the node ComputeUseCountsNo suitable kernel definition found for op Can not find the execution provider onnxruntime::OrtValueIndex onnxruntime::PlannerImpl::Index(const onnxruntime::OrtValueName &)void onnxruntime::PlannerImpl::ProcessDef(onnxruntime::OrtValueIndex, const onnxruntime::NodeArg *)id >= 0 && static_cast(id) < ort_value_info_.size()int &onnxruntime::PlannerImpl::UseCount(onnxruntime::OrtValueIndex)n >= 0 && static_cast(n) < ort_value_info_.size()onnxruntime::AllocPlanPerValue &onnxruntime::PlannerImpl::AllocPlan(onnxruntime::OrtValueIndex)n >= 0 && static_cast(n) < plan_.allocation_plan.size()ComputeReusePlanGeneratePlanForWeightsOrtMemoryInfo onnxruntime::PlannerImpl::GetLocationForNodeInput(size_t, const onnxruntime::Node &)kernel_create_info != nullptr && kernel_create_info->kernel_def != nullptrvoid onnxruntime::PlannerImpl::Reuse(onnxruntime::OrtValueIndex, onnxruntime::OrtValueIndex, onnxruntime::AllocKind)reused != reused_forstatic size_t onnxruntime::PlannerImpl::GetElementSize(const onnx::DataType &)nullptr != tensor_type_baseonnxruntime::OrtValueIndex &onnxruntime::PlannerImpl::Buffer(onnxruntime::OrtValueIndex)/Users/runner/work/1/s/onnxruntime/core/framework/allocator.ccCalcMemSizeForArrayWithAlignmentCudaCudaPinnedSpecified device is not supported.Attempt to use DefaultLogger but none has been registered.OrtMemoryInfo:[name: id: OrtMemType: OrtAllocatorType:]Device:[DeviceType: MemoryType: DeviceId:/Users/runner/work/1/s/onnxruntime/core/framework/bfc_arena.ccBFCArenaCreating BFCArena for Creating bins of max chunk size onnxruntime::BFCArena::BFCArena(std::unique_ptr, size_t, onnxruntime::ArenaExtendStrategy)BinForSize(bin_size) == BinFromIndex(b)BinForSize(bin_size + 255) == BinFromIndex(b)BinForSize(bin_size * 2 - 1) == BinFromIndex(b)BinForSize(bin_size * 2) != BinFromIndex(b)BFCArena::Chunk *onnxruntime::BFCArena::ChunkFromHandle(onnxruntime::BFCArena::ChunkHandle)h < chunks_.size()Available memory of is smaller than requested bytes of Failed to allocate memory for requested buffer of size ExtendExtended allocation by bytes.Total allocated bytes: Allocated memory at virtual void *onnxruntime::BFCArena::Reserve(size_t)reserved_chunks_.find(ptr) == reserved_chunks_.end()size_t onnxruntime::BFCArena::RequestedSize(const void *)h != kInvalidChunkHandlesize_t onnxruntime::BFCArena::AllocatedSize(const void *)AllocateRawInternaltried to allocate 0 bytesExtending BFCArena for . bin_num: rounded_bytes:Failed to find a free memory block despite calling Extend. rounded_bytes=BFC Arena ran out of memory trying to allocate . Current allocation summary follows.void *onnxruntime::BFCArena::AllocateRawInternal(size_t, bool)void *onnxruntime::BFCArena::FindChunkPtr(onnxruntime::BFCArena::BinNum, size_t, size_t)!chunk->in_use()void onnxruntime::BFCArena::SplitChunk(BFCArena::ChunkHandle, size_t)!c->in_use() && (c->bin_num == kInvalidBinNum)void onnxruntime::BFCArena::DeallocateRawInternal(void *)void onnxruntime::BFCArena::Merge(BFCArena::ChunkHandle, BFCArena::ChunkHandle)!c1->in_use() && !c2->in_use()c2->prev == h1void onnxruntime::BFCArena::InsertFreeChunkIntoBin(BFCArena::ChunkHandle)void onnxruntime::BFCArena::RemoveFreeChunkIterFromBin(BFCArena::Bin::FreeChunkSet *, const BFCArena::Bin::FreeChunkSet::iterator &)!c->in_use() && (c->bin_num != kInvalidBinNum)void onnxruntime::BFCArena::RemoveFreeChunkFromBin(BFCArena::ChunkHandle)BinFromIndex(c->bin_num)->free_chunks.erase(h) > 0void onnxruntime::BFCArena::FreeAndMaybeCoalesce(BFCArena::ChunkHandle)c->in_use() && (c->bin_num == kInvalidBinNum)std::array onnxruntime::BFCArena::get_bin_debug_info()bin->free_chunks.count(h) == 1c->bin_num == bin_numDumpMemoryLogAllocator:Bin size: Chunks in_use/total (if not zero). Allocated bytes in_use/total. Requested bytes.void onnxruntime::BFCArena::DumpMemoryLog(size_t)b->free_chunks.size() == bin_info.total_chunks_in_bin - bin_info.total_chunks_in_use: Chunks /. Bytes . Requested Diff between in-use and requested bytes is Bin for bytes has max bytes of , Chunk State: Overall chunks summary: Chunk Free at of size Summary of in-use chunks by size: chunks of size . Total Sum Total of in-use chunks: Stats: auto onnxruntime::BFCArena::Extend(size_t)::(anonymous class)::operator()(const size_t) constIncorrect arena extend strategy./Users/runner/work/1/s/onnxruntime/core/framework/bfc_arena.honnxruntime::BFCArena::AllocationRegion::AllocationRegion(void *, size_t)0 == memory_size % kMinAllocationSizeRegionForCould not find Region for int onnxruntime::BFCArena::AllocationRegion::IndexFor(const void *) constp_int >= base_intp_int < base_int + memory_size_ Size: | Requested Size: | in_use: , prev: , next: Limit: InUse: TotalAllocated: MaxInUse: NumAllocs: MaxAllocSize: /Users/runner/work/1/s/onnxruntime/core/framework/data_transfer.ccvirtual common::Status onnxruntime::CPUDataTransfer::CopyTensor(const onnxruntime::Tensor &, onnxruntime::Tensor &, int) constsrc.SizeInBytes() == dst.SizeInBytes()data_transfer registered is nullptr.Tensor size mismatchThere's no data transfer registered for copying tensors from to /Users/runner/work/1/s/onnxruntime/core/framework/data_transfer_manager.ccCopyTensors/Users/runner/work/1/s/onnxruntime/core/framework/data_types.ccbool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Map &, const onnx::TypeProto_Map &)bool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Sequence &, const onnx::TypeProto_Sequence &)virtual bool onnxruntime::TensorTypeBase::IsCompatible(const onnx::TypeProto &) constthisProto->value_case() == TypeProto::ValueCase::kTensorTypeutils::HasElemType(thisProto->tensor_type())virtual bool onnxruntime::SparseTensorTypeBase::IsCompatible(const onnx::TypeProto &) constthisProto->value_case() == TypeProto::ValueCase::kSparseTensorTypeutils::HasElemType(thisProto->sparse_tensor_type())virtual bool onnxruntime::SequenceTensorTypeBase::IsCompatible(const onnx::TypeProto &) constthisProto->value_case() == TypeProto::ValueCase::kSequenceTypeutils::HasElemType(thisProto->sequence_type())bool onnxruntime::NonTensorTypeBase::IsMapCompatible(const onnx::TypeProto &) constthisProto->value_case() == TypeProto::ValueCase::kMapTypeutils::HasKeyType(thisProto->map_type())bool onnxruntime::NonTensorTypeBase::IsSequenceCompatible(const onnx::TypeProto &) constbool onnxruntime::NonTensorTypeBase::IsOpaqueCompatible(const onnx::TypeProto &) constthisProto->value_case() == TypeProto::ValueCase::kOpaqueTypevirtual void onnxruntime::NonTensorTypeBase::FromDataContainer(const void *, size_t, OrtValue &) constNot implementedvirtual void onnxruntime::NonTensorTypeBase::ToDataContainer(const OrtValue &, size_t, void *) const(null)tensor(float)tensor(bool)tensor(double)tensor(string)tensor(int8)tensor(uint8)tensor(int16)tensor(uint16)tensor(int32)tensor(uint32)tensor(int64)tensor(uint64)tensor(float16)tensor(bfloat16)tensor type sparse tensor type MLDataType for: is not currently registered or supportedonnxruntime::utils::ContainerChecker::ContainerChecker(onnxruntime::MLDataType)void onnxruntime::data_types_internal::DataTypeRegistry::RegisterDataType(onnxruntime::MLDataType)proto != nullptrp.secondWe do not expect duplicate registration of types for: GetElementType is not implemented/Users/runner/work/1/s/include/onnxruntime/core/framework/data_types.hstatic void onnxruntime::data_types_internal::SetMapTypes, std::__1::basic_string >::Set(onnx::TypeProto &) [K = std::__1::basic_string, V = std::__1::basic_string]value_proto != nullptr expected to be a registered ONNX typestatic void onnxruntime::data_types_internal::SetMapTypes, long long>::Set(onnx::TypeProto &) [K = std::__1::basic_string, V = long long]static void onnxruntime::data_types_internal::SetMapTypes, float>::Set(onnx::TypeProto &) [K = std::__1::basic_string, V = float]static void onnxruntime::data_types_internal::SetMapTypes, double>::Set(onnx::TypeProto &) [K = std::__1::basic_string, V = double]static void onnxruntime::data_types_internal::SetMapTypes >::Set(onnx::TypeProto &) [K = long long, V = std::__1::basic_string]static void onnxruntime::data_types_internal::SetMapTypes::Set(onnx::TypeProto &) [K = long long, V = long long]static void onnxruntime::data_types_internal::SetMapTypes::Set(onnx::TypeProto &) [K = long long, V = float]static void onnxruntime::data_types_internal::SetMapTypes::Set(onnx::TypeProto &) [K = long long, V = double]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = float]elem_proto != nullptrstatic void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = double]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = signed char]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = unsigned char]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = short]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = unsigned short]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = int]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = unsigned int]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = long long]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = unsigned long long]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = bool]static void onnxruntime::data_types_internal::SetSequenceType >::Set(onnx::TypeProto &) [T = std::__1::basic_string]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = onnxruntime::MLFloat16]static void onnxruntime::data_types_internal::SetSequenceType::Set(onnx::TypeProto &) [T = onnxruntime::BFloat16]static void onnxruntime::data_types_internal::SetSequenceType, float, std::__1::less >, std::__1::allocator, float> > > >::Set(onnx::TypeProto &) [T = std::__1::map, float, std::__1::less >, std::__1::allocator, float> > >]static void onnxruntime::data_types_internal::SetSequenceType, std::__1::allocator > > >::Set(onnx::TypeProto &) [T = std::__1::map, std::__1::allocator > >]/Users/runner/work/1/s/onnxruntime/core/framework/ex_lib_loader.cc~ExLibLoaderUnloading DSO Failed to unload DSO: 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/work/1/s/onnxruntime/core/framework/execution_frame.cconnxruntime::IExecutionFrame::IExecutionFrame(const onnxruntime::OrtValueNameIdxMap &, const onnxruntime::NodeIndexInfo &, const std::vector &)node_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx()node_index_info and ort_value_idx_map are out of sync and cannot be usedonnxruntime::common::Status onnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValue(int, const onnxruntime::TensorShape *, OrtValue *&, size_t)shape && tensor.Shape() == *shapeOrtValue shape verification failed. Current shape: Requested shape:invalid index void onnxruntime::IExecutionFrame::Init(const std::vector &, const std::vector &, const std::unordered_map &, const std::vector &)feeds.size() == feed_mlvalue_idxs.size()fetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size()Fetches vector passed to GetOutputs contains entries which doesn't match the number of fetches the frame was initialized with of onnxruntime::ExecutionFrame::ExecutionFrame(const std::vector &, const std::vector &, const std::vector &, const std::vector &, const std::unordered_map &, const onnxruntime::SessionState &)buffers_.find(location) == buffers_.end()ExecutionFrameAllocation of memory pattern buffer for returned nullptr failed. Error: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 behaviorShape mismatch attempting to re-use buffer. . Validate usage of dim_value (values should be > 0) and dim_param (all values with the same string should equate to the same size) in shapes in the model.AllocateMLValueTensorPreAllocateBufferonnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlan(OrtValue &, int, const onnxruntime::TensorShape *, size_t)ort_value_index >= 0 && static_cast(ort_value_index) < alloc_plan.size()Tried to allocate without valid type information, ort_value index=We don't expect custom allocators for non-tensor types, so a shape is mandatory here.AllocateAsPerAllocationPlanInvalid allocation kind: ReleaseMLValueImplconst onnxruntime::AllocPlanPerValue &onnxruntime::ExecutionFrame::GetAllocationPlan(int)ort_value_idx >= 0 && static_cast(ort_value_idx) < alloc_plan.size()TraceAllocateTraceAllocation for ort_value_idx= size= failed: void onnxruntime::ExecutionFrame::TraceFree(int)TraceFreeTraceFree for ort_value_idx=Memory pattern planner is not enabled on this execution framework./Users/runner/work/1/s/onnxruntime/core/framework/execution_frame.hconst OrtValue &onnxruntime::IExecutionFrame::GetMLValue(int) constort_value_index >= 0 && static_cast(ort_value_index) < all_values_size_int onnxruntime::NodeIndexInfo::GetMLValueIndex(int) constoffset >= 0 && static_cast(offset) < node_values_size_onnxruntime::common::Status onnxruntime::AllocateSparseTensor(OrtValue &, const onnxruntime::DataTypeImpl &, onnxruntime::AllocatorPtr, const onnxruntime::TensorShape &, size_t, bool, const onnxruntime::SessionState &)mlvalue.Fence() == nullptr/Users/runner/work/1/s/onnxruntime/core/framework/execution_provider.ccvoid onnxruntime::IExecutionProvider::InsertAllocator(onnxruntime::AllocatorPtr)/Users/runner/work/1/s/onnxruntime/core/framework/feeds_fetches_manager.ccMapNamesToMLValueIdxsError mapping feeds: Error mapping output names: void onnxruntime::FeedsFetchesManager::SetDeviceCopyChecks(onnxruntime::DeviceCopyCheck, onnxruntime::DeviceCopyCheck)input_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::Unknown/Users/runner/work/1/s/onnxruntime/core/framework/finalize_session_state.ccFinalizeSessionStateSaveInputOutputNamesToNodeMappingSubgraph 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::GraphViewer &, const OrtMemoryInfo &, const onnxruntime::OrtValueNameIdxMap &, onnxruntime::ITensorAllocator &, const T &, const logging::Logger &, const onnxruntime::DataTransferManager &) [T = (lambda at /Users/runner/work/1/s/onnxruntime/core/framework/finalize_session_state.cc:87:3)]ort_value_name_idx_map.MaxIdx() > -1Deserialize tensor failed.Done saving initialized tensorsDeserializeTensorProtoInternal error. The preallocated buffer is too small. Requires , Got Failed to copy tensor to static void SafeIntExceptionHandler::SafeIntOnDivZero()func info for node: already exist.Can't use func with null ptr not found./Users/runner/work/1/s/onnxruntime/core/framework/fuse_nodes_funcs.ccGetFuncsNo provider specified./Users/runner/work/1/s/onnxruntime/core/framework/graph_partitioner.ccPartitiononnxruntime::common::Status onnxruntime::GraphPartitioner::Partition(onnxruntime::Graph &, bool, onnxruntime::FuncManager &) constnode_compute_funcs.size() == nodes_need_compile.size()onnxruntime::Node *onnxruntime::PlaceNode(onnxruntime::Graph &, std::unique_ptr, const onnxruntime::KernelRegistryManager &, const std::string &, int &)1 == capability->nodes.size()/Users/runner/work/1/s/onnxruntime/core/framework/func_kernel.honnxruntime::FunctionKernel::FunctionKernel(const onnxruntime::OpKernelInfo &)create_func(&context, &func_state_) == 0Op with name ( and type ( Version mismatch. node_version: kernel start version: kernel_end_version: Found kernel for Op with name ( in the supported version range (node_version: However the types are incompatible. This op has been implemented only for the following types (), but the node in the model has the following type (kernel def can't be NULLFailed to add kernel for : Conflicting with a registered kernel with op versions./Users/runner/work/1/s/onnxruntime/core/framework/kernel_registry.ccTryCreateKernel kernel is not supported in Encountered following errors: (Kernel not foundvoid onnxruntime::(anonymous namespace)::TraverseFormalParametersWithTypeProto(const onnxruntime::Node &, ParamFilterFn, TraverseFn) [ParamFilterFn = (lambda at /Users/runner/work/1/s/onnxruntime/core/framework/kernel_registry.cc:71:11), TraverseFn = (lambda at /Users/runner/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/work/1/s/onnxruntime/core/framework/kernel_registry.cc:96:9), TraverseFn = (lambda at /Users/runner/work/1/s/onnxruntime/core/framework/kernel_registry.cc:99:9)]The node is not placed on any Execution Provider, therefore, can't find a suitable kernel for Failed to find kernel for found duplicated provider in KernelRegistryManagerThe node is not placed on any Execution Provider (node /Users/runner/work/1/s/onnxruntime/core/framework/mldata_type_utils.cconnxruntime::MLDataType onnxruntime::utils::GetMLDataType(const onnxruntime::NodeArg &)nullptr != type_proto/Users/runner/work/1/s/onnxruntime/core/framework/node_index_info.ccauto onnxruntime::NodeIndexInfo::Init(const onnxruntime::GraphNodes &, onnxruntime::NodeIndex, const onnxruntime::OrtValueNameIdxMap &)::(anonymous class)::operator()(const onnxruntime::NodeArg &, bool) constauto onnxruntime::NodeIndexInfo::Init(const onnxruntime::ValidNodes > > &, onnxruntime::NodeIndex, const onnxruntime::OrtValueNameIdxMap &)::(anonymous class)::operator()(const onnxruntime::NodeArg &, bool) consttype_proto is not of type map!type_proto is not of type sequence!OrtValue is TensorSequence type but has no element Tensor DataType.Tensor types should have been handled alreadynot implementedElementTypeFromProto:tensor type is not supportedconst onnxruntime::SparseTensor &OrtValue::Get() constIsSparseTensor()Trying to get a SparseTensor, but got: /Users/runner/work/1/s/onnxruntime/core/framework/op_kernel.cconnxruntime::OpKernelContext::OpKernelContext(onnxruntime::IExecutionFrame *, const onnxruntime::OpKernel *, concurrency::ThreadPool *, const logging::Logger &)frame != nullptrkernel != nullptrOrtValue *onnxruntime::OpKernelContext::OutputMLValue(int, const onnxruntime::TensorShape &, size_t)int onnxruntime::OpKernelContext::NumVariadicInputs(size_t) constarg_num < arg_counts.size()Invalid arg_num of . Num args is TempSpace allocator not foundOrtValue *onnxruntime::OpKernelContext::GetOrCreateOutputMLValue(int)/Users/runner/work/1/s/onnxruntime/core/framework/node_index_info.hint onnxruntime::NodeIndexInfo::GetNodeOffset(onnxruntime::NodeIndex) constnode_offsets_index < node_offsets_size_onnxruntime::SparseTensor *OrtValue::GetMutable()/Users/runner/work/1/s/onnxruntime/core/framework/op_kernel_info.ccconst OrtMemoryInfo &onnxruntime::OpKernelInfo::GetMemoryInfo(int, OrtMemType) constCould not find OrtValue with name ''/Users/runner/work/1/s/include/onnxruntime/core/framework/op_node_proto_helper.hconst onnx::AttributeProto *onnxruntime::OpNodeProtoHelper::GetAttribute(const std::string &) const [Impl_t = onnxruntime::ProtoHelperNodeContext]attr != nullptrconst onnx::AttributeProto *onnxruntime::OpNodeProtoHelper::GetAttribute(const std::string &) const [Impl_t = onnx::InferenceContext]No attribute with this name is defined./Users/runner/work/1/s/onnxruntime/core/framework/op_node_proto_helper.cconnxruntime::common::Status onnxruntime::OpNodeProtoHelper::GetAttrs(const std::string &, gsl::span) const [Impl_t = onnxruntime::ProtoHelperNodeContext]values.size() == static_cast(attr->floats_size())onnxruntime::common::Status onnxruntime::OpNodeProtoHelper::GetAttrs(const std::string &, gsl::span) const [Impl_t = onnx::InferenceContext]onnxruntime::common::Status onnxruntime::OpNodeProtoHelper::GetAttrs(const std::string &, gsl::span) const [Impl_t = onnxruntime::ProtoHelperNodeContext]values.size() == static_cast(attr->ints_size())onnxruntime::common::Status onnxruntime::OpNodeProtoHelper::GetAttrs(const std::string &, gsl::span) const [Impl_t = onnx::InferenceContext]onnxruntime::common::Status onnxruntime::OpNodeProtoHelper::GetAttrs(const std::string &, gsl::span) const [Impl_t = onnxruntime::ProtoHelperNodeContext]values.size() == static_cast(attr->strings_size())onnxruntime::common::Status onnxruntime::OpNodeProtoHelper::GetAttrs(const std::string &, gsl::span) const [Impl_t = onnx::InferenceContext]onnxruntime::common::Status onnxruntime::OpNodeProtoHelper::GetAttrs(const std::string &, gsl::span) const [Impl_t = onnxruntime::ProtoHelperNodeContext]values.size() == static_cast(attr->tensors_size())onnxruntime::common::Status onnxruntime::OpNodeProtoHelper::GetAttrs(const std::string &, gsl::span) const [Impl_t = onnx::InferenceContext]onnxruntime::common::Status onnxruntime::OpNodeProtoHelper::GetAttrs(const std::string &, gsl::span) const [Impl_t = onnxruntime::ProtoHelperNodeContext]values.size() == static_cast(attr->graphs_size())onnxruntime::common::Status onnxruntime::OpNodeProtoHelper::GetAttrs(const std::string &, gsl::span) const [Impl_t = onnx::InferenceContext]/Users/runner/work/1/s/onnxruntime/core/framework/ort_value_tensor_slicer.ccstatic OrtValueTensorSlicer onnxruntime::OrtValueTensorSlicer::Create(T &, int64_t, int64_t) [T = OrtValue]ort_value.IsTensor()Can't slice a non-tensor OrtValue. Type was ort_value.IsAllocated()gsl::narrow_cast(tensor_shape.NumDimensions()) >= slice_dimensionInsufficient dimensions to slice on . Shape:dim0_offset < dim0_sizeInvalid dim0_offset of . Dimension 0 is onnxruntime::OrtValueTensorSlicer::Iterator::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/work/1/s/onnxruntime/core/framework/parallel_executor.ccRunNodeAsyncBegin executionExiting due to terminate flag being set to true.onnxruntime::common::Status onnxruntime::ParallelExecutor::RunNodeAsync(size_t, const onnxruntime::SessionState &, const logging::Logger &)Got nullptr from GetKernel for node: _fence_beforeNon-zero status code returned while running node. Name:'' Status Message: _kernel_time_fence_after/Users/runner/work/1/s/onnxruntime/core/framework/op_kernel_context_internal.honnxruntime::OpKernelContextInternal::OpKernelContextInternal(const onnxruntime::SessionState &, onnxruntime::IExecutionFrame &, const onnxruntime::OpKernel &, const logging::Logger &, const bool &)entry != nullptrAll implicit inputs should have OrtValue instances by now. does not.Exception running nodes starting at node ''. Unknown exception was caught by catch-all handler./Users/runner/work/1/s/onnxruntime/core/framework/sequential_executor.ccReleaseNodeMLValues/Users/runner/work/1/s/onnxruntime/core/framework/session_state.ccSetupAllocatorsAllocator already registered for . Ignoring allocator from CreateGraphInfoSaveMLValueNameIndexMappingDone saving OrtValue mappings.Could not create kernel for node: as there's no execution provider allocated.Kernel creation failed for node: duplicated ort_value index:. Do you have duplicated calls to SessionState::AddInitializedTensor function?Using an input in multiple nodes on different devices is not supported currently. Input: is used by node ) and node Failed to find input name in the mapping: void onnxruntime::SessionState::AddOutputNameToNodeInfoMapping(const std::string &, const onnxruntime::SessionState::NodeInfo &)output_names_to_nodeinfo.empty()Only one node should produce an output. Existing entry for Failed to find output name in the mapping: void onnxruntime::SessionState::AddSubgraphSessionState(onnxruntime::NodeIndex, const std::string &, std::unique_ptr)existing_entries.find(attribute_name) == existing_entries.cend()Entry exists in node for attribute const onnxruntime::NodeIndexInfo &onnxruntime::SessionState::GetNodeIndexInfo() constnode_index_info_SetGraphAndCreateKernels must be called prior to GetExecutionInfo.void onnxruntime::SessionState::UpdateToBeExecutedNodes(const std::vector &)Could not find OrtValue with idx 'invalid ort_value_index:/Users/runner/work/1/s/onnxruntime/core/framework/simple_tensor_allocator.ccGetPreallocatedBufferFailed to get allocator for initializer '', location: /Users/runner/work/1/s/onnxruntime/core/framework/tensor.cconnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape &, void *, const OrtMemoryInfo &, ptrdiff_t)p_type != nullptronnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape &, std::shared_ptr, ptrdiff_t)size_t onnxruntime::Tensor::SizeInBytes() constvoid onnxruntime::Tensor::Init(onnxruntime::MLDataType, const onnxruntime::TensorShape &, void *, onnxruntime::AllocatorPtr, ptrdiff_t)dtype_ != nullptrTensor is expected to contain one of the primitive data types. Got: /Users/runner/work/1/s/onnxruntime/core/framework/tensor_allocator_with_mem_pattern.hFinalizePlanFailed to get allocator for location: duplicated locationMem pattern for initializer is not foundWeight buffer for initializer '' is not foundGet preallocated buffer for initializer '' failedTracemodel format error! Need a value for the external data infoparsing lengthchecksum/Users/runner/work/1/s/onnxruntime/core/framework/tensor_shape.ccint64_t onnxruntime::TensorShape::SizeToDimension(size_t) constdimension <= num_dimsInvalid dimension of for SizeFromDimension. Tensor has dimensions.int64_t onnxruntime::TensorShape::SizeFromDimension(size_t) constonnxruntime::TensorShape onnxruntime::TensorShape::Slice(size_t, size_t) constdimstart <= dimend && dimend <= size(){}/Users/runner/work/1/s/onnxruntime/core/framework/tensor_type_and_shape.ccOrtStatusPtr OrtApis::GetTensorTypeAndShape(const OrtValue *, OrtTensorTypeAndShapeInfo **)type != nullptrInvalid TensorProtocorrupted protobuf data: tensor shape size() does not match the data size() in protoUnpackTensor: the pre-allocate size does not match the size in protodata overflowstring tensor can not have raw data/Users/runner/work/1/s/onnxruntime/core/framework/tensorprotoutils.ccTensorProtoToMLValuetensor can't contain negative dimsThe buffer planner is not consistent with tensor buffer size, expected Initialized tensor with unexpected type: ConstantNodeProtoToTensorProtocommon::Status onnxruntime::utils::ConstantNodeProtoToTensorProto(const onnx::NodeProto &, onnx::TensorProto &)Unsupported attribute value type of in 'Constant' node 'SparseTensorProtoToDenseTensorProtoUnsupported sparse tensor data type of GetFileContentUnpackTensor: the pre-allocated size does not match the raw data size, expected UnpackTensorWithRawDataNot satisfied: source_bytes.size_bytes() == destination.size_bytes() /Users/runner/work/1/s/onnxruntime/core/framework/endian_utils.hReadLittleEndianonnxruntime::common::Status onnxruntime::utils::CopySparseData(size_t, const onnx::TensorProto &, gsl::span, std::function) [T = unsigned int]rank == dims.size() && rank > 0cur_index == &*indices_data.cend()Invalid SparseTensor indices. Should be rank 0 or 1. Got:onnxruntime::common::Status onnxruntime::utils::CopySparseData(size_t, const onnx::TensorProto &, gsl::span, std::function) [T = unsigned long long]onnxruntime::common::Status onnxruntime::utils::CopySparseData(size_t, const onnx::TensorProto &, gsl::span, std::function) [T = std::__1::basic_string]DnnlExecutionProviderNGRAPHExecutionProviderNupharExecutionProviderVitisAIExecutionProviderOpenVINOExecutionProviderNnapiExecutionProviderRknpuExecutionProvider/Users/runner/work/1/s/onnxruntime/core/framework/utils.ccconst OrtMemoryInfo &onnxruntime::utils::FindMemoryInfoForValue(const onnxruntime::SessionState &, const std::string &)exec_plan_ptrInitializeFeedFetchCopyInfoCopyOneInputAcrossDevicesExecuteGraphFindMemoryInfoForValueconst OrtMemoryInfo &onnxruntime::utils::FindMemoryInfoForValue(const onnxruntime::OrtValueNameIdxMap &, const onnxruntime::SequentialExecutionPlan &, const std::string &)CalculateStaticCopyInfoForFeedsbool onnxruntime::utils::FinalizeCopyInfoForFeeds(const std::vector &, std::vector &)feed_locations.size() == copy_info.size()bool onnxruntime::utils::FinalizeCopyInfoForFetches(const std::vector &, std::vector &)fetch_alloc_info.size() == copy_info.size()CalculateStaticCopyInfoForFeedonnxruntime::common::Status onnxruntime::utils::BatchOrCopyMLValue(const onnxruntime::SessionState &, const onnxruntime::MLValueCopyInfo &, const OrtValue &, OrtValue &, std::vector *)Failed to find allocator for device BatchOrCopyMLValueExecuteGraphImplOnly one thread was configured for parallel execution. Hence will use sequential execution.common::Status onnxruntime::utils::CopyInputsAcrossDevices(const onnxruntime::SessionState &, const std::vector &, std::vector &, const std::vector &)copy_info.size() == num_feedsCopyInputsAcrossDevicesCopyOutputsAcrossDevicesNumber of neurons in the hidden layer.Constrain seq_lens to integral tensors.The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]` QWThe weight tensor of the query layer in the attention mechanism. Should be of shape `[num_directions, am_query_depth(hidden_size of lstm), am_attn_size]` MWThe weight tensor of the memory layer in the attention mechanism. Should be of shape `[num_directions, memory_depth, am_attn_size]` The attention_v tensor in the attention mechanism. Should be of shape `[num_directions, am_attn_size]` The sequence of the memory (input) for attention mechanism. Should be of `[batch_size, max_memory_step, memory_depth]` memory_seq_lensThe sequence length of the input memory for the attention mechanism. Should be of `[batch_size]` AWThe weights of attention layer in the attention wrapper. If exists, should be of shape `[num_directions, memory_depth+hidden_size, aw_attn_size]. Please note that attention mechanism context depth is also memory_depth in the attention mechanism.` A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`. 'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64Pads has incorrect number of valuesInput data type does not match the expected data typeInput data type does not match the expected data type. Current data type is Scale and Zero-point must be a scalarScale and Zero-point must be of rank 1Scale and Zero-point must be of rank 1 and the number of elements should be equal to the number of rows of the corresponding input.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding./Users/runner/work/1/s/onnxruntime/core/graph/contrib_ops/contrib_defs.ccNumber of attention headsWhether every token can only attend to previous tokens. Default value is 0.3D input tensor with shape (batch_size, sequence_length, hidden_size), hidden_size = num_heads * head_sizeweight2D input tensor with shape (hidden_size, 3 * hidden_size)bias1D input tensor with shape (3 * hidden_size)mask_indexAttention mask with shape (batch_size, past_sequence_length + sequence_length), or index with shape (batch_size) or (2 * batch_size).Mpastpast state for key and value with shape (2, batch_size, num_heads, past_sequence_length, head_size).3D output tensor with shape (batch_size, append_length, hidden_size)presentpresent state for key and value with shape (2, batch_size, num_heads, past_sequence_length + sequence_length, head_size)Constrain input and output types to float tensors.Constrain mask index to integer typesinput_scalescale of quantized input tensor. It's a scalar, which means a per-tensor/layer quantization.weight_scalescale of weight scale. It's a scalar, which means a per-tensor/layer quantization.Attention mask index with shape (batch_size)input_zero_pointzero point of quantized input tensor.It's a scalar, which means a per-tensor/layer quantization.weight_zero_pointzero point of quantized weight tensor. It's a scalar, which means a per-tensor/layer quantization.3D output tensor with shape (batch_size, sequence_length, hidden_size)Constrain input and output types to int8 tensors.The epsilon value to use to avoid division by zero.input_ids2D words IDs with shape (batch_size, sequence_length)segment_ids2D segment IDs with shape (batch_size, sequence_length)word_embedding2D with shape (,hidden_size)position_embedding2D with shape (, hidden_size)segment_embedding1D gamma tensor for layer normalization with shape (hidden_size)beta1D beta tensor for layer normalization with shape (hidden_size)mask2D attention mask with shape (batch_size, sequence_length)1D mask_index tensor with shape (batch_size)Constrain input and output integer tensors typesConstrain input and output float tensors types.input tensorbias tensoroutput tensorConstrain input and output types to float or half tensors.3D input tensor with shape (batch_size, sequence_length, hidden_size)skip3D skip tensor with shape (batch_size, sequence_length, hidden_size)1D input tensor with shape (hidden_size)1D skip tensor with shape (hidden_size1D bias tensor with shape (hidden_sizemeanSaved mean used during training to speed up gradient computationUinv_std_varSaved inverse standard variance used during training to speed up gradient computation.Constrain mean and inv_std_var to float tensors.Value of alphaValue of beta1D input tensor1D output tensorBias applied to each channel, same size as C.The scale to apply.Input tensor of shape [N,C,H,W]Result, has same shape and type as inputA 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder).A 1-D values of (height, width).Result, has same type as input, with H and W dimensions reduced.Threshold valueInput tensorOutput tensordataTensor of data to extract slices from.1-D tensor of starting indices of corresponding axis in `axes`ends1-D tensor of ending indices (exclusive) of corresponding axis in axesaxes1-D tensor of axes that `starts` and `ends` apply to.Sliced data tensor.Constrain indices to integer typesThe shape of filled tensorThe filled tensorinput_as_shapeextra_shapeInput data to be scaledOutput data after scalingdrop_statesBool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length.hidden_prevThe previous GRU hidden state.gatesUnactivated gate outputs from forget, update, and output gates, pre-activation.seq_lengthsArray of sequence lengths. len(seq_lengths) should equal batch size N.The timestep for this operation.hiddenThe new GRU hidden state calculated by this op.If 1, mean and variance are computed across channels. Default is 0.If 0, normalize the mean only. Default is 1.Scaling valueThe scaled hyperbolic tangent values of the input tensor computed element-wiseauto_padpadsConstrain input0 and output types to float tensorssignal_ndimnormalizedonesidedinput_0input_1CWPadsConstrain input and output types to float tensorsactivation_paramsInput tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.Input tensor C. The shape of C should be unidirectional broadcastable to (M, N).Output tensor of shape (M, N).Constrain input and output types to float/int tensors.Whether A should be transposedWhether B should be transposedScalar multiplier for the product of input tensors A * B.Scalar multiplier for input tensor C.activation_gammaSpecified axis to insert a dimensionThe axis along which same quantization parameters are applied. It's optional.If it's not specified, it means per-tensor quantization and input 'x_scale' and 'x_zero_point' must be scalars.If it's specified, it means per 'axis' quantization and input 'x_scale' and 'x_zero_point' must be 1-D tensors.xN-D full precision Input tensor to be quantized.y_scaleScale for doing quantization to get 'y'. It could be a scalar or a 1-D tensor,which means a per-tensor or per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.y_zero_pointZero point for doing quantization to get 'y'. It could be a scalar or a 1-D tensor, which means a per-tensoror per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.N-D quantized output tensor. It has same shape as input 'x'.Constrain 'x', 'y_scale' to float tensors.Constrain 'y_zero_point' and 'y' to 8-bit integer tensors.N-D quantized Input tensor to be de-quantized.x_scaleScale for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.x_zero_pointZero point for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'.N-D full precision output tensor. It has same shape as input 'x'.Constrain 'x' and 'x_zero_point' to 8-bit integer tensors.Constrain 'y', 'x_scale' to float tensors.Strings to tokenizeTokenized stringsInput/Output is a string tensormarkBoolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03).The string used to pad output tensors when the tokens extracted doesn't match the maximum number of tokens found. If start/end markers are needed, padding will appear outside the markers.tokenexpAn optional string. Token's regular expression in basic POSIX format (http://pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap09.html#tag_09_03). If set, tokenizer may produce tokens matching the specified pattern. Note that one and only of 'tokenexp' and 'separators' should be set.an optional list of strings attribute that contains a list of separators - regular expressions to match separators Two consecutive segments in X connected by a separator would be divided into two tokens. For example, if the input is "Hello World!" and this attribute contains only one space character, the corresponding output would be ["Hello", "World!"]. To achieve character-level tokenization, one should set the 'separators' to [""], which contains an empty string.Minimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignoredN-dimensional matrix AN-dimensional matrix BMatrix multiply results from A * BConstrain input A data types as 16-bit integer tensorConstrain input B data types as 16-bit integer tensorConstrain output Y data types as 32-bit integer tensor.T3 must be tensor(uint32) when both T1 and T2 are tensor(uint16),or must be tensor(int32) when either T1 or T2 is tensor(int16).b_scaleScale of quantized input 'B'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'.b_zero_pointZero point tensor for input 'B'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'.1D input tensor, whose dimension is same as B's last dimensionConstrain input A, b_scale and output Y data type as float tensor.Constrain input B data type to 8-bit integer tensor.a_scaleScale of quantized input 'A'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'A'.a_zero_pointZero point tensor for input 'A'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'A'.Constrain input A data type to 8-bit integer tensor.Constrain input a_scale, b_scale and output Y data type as float tensor.Whether A should be transposed on the last two dimensions before doing multiplicationWhether B should be transposed on the last two dimensions before doing multiplicationMatrix multiply resultsAn input tensor.reducedReduced output tensor.Constrain input type to 8-bit integer tensor.Constrain output data type to 32-bit integer tensor.T2 must be tensor(uint32) when T1 is tensor(uint8),or must be tensor(int32) when T1 is tensor(int8).A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor.keepdimsKeep the reduced dimension or not, default 1 mean keep reduced dimension.additionC = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_pointmultiplicationC = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_pointdata_scaleInput scale. It's a scalar, which means a per-tensor/layer quantization.data_zero_pointInput zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.reduced_scaleOutput scale. It's a scalar, which means a per-tensor/layer quantization.reduced_zero_pointOutput zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Constrain input types to 8 bit signed and unsigned tensors.First operand.A_zero_pointInput A zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Second operand.B_zero_pointInput B zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Constrain output to 32 bit tensorConstrain output types to 32 bit tensors.Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad.The size of the kernel along each axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis.Whether to use ceil or floor (default) to compute the output shape.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is usedConstrain input and output types to 8 bit tensors.Coefficient of leakage.X_scaleInput X's scale. It's a scalar, which means a per-tensor/layer quantization.X_zero_pointInput X's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.Y_scaleOutput Y's scale. It's a scalar, which means a per-tensor/layer quantization.Y_zero_pointOutput Y's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.An input tensor to hash.32-bit hash value.Constrain input type to unsigned or signed 32-bit integer tensor, or string tensor. It should be utf-8 encoded if using unicode.Constrain output type to unsigned and signed 32-bit integer tensor.seedSeed for the hashing algorithm, unsigned 32-bit integer, default to 0.positiveIf value is 1, output type is uint32_t, else int32_t. Default value is 1.Tensor of rank r >= 1.indicesTensor of rank q >= 1.Tensor of rank q-1+r-indices[-1].Constrain indice type to int32 or int64Integer representing the embedding vector size for each word.If not provide, use the fileter size of conv weightThis operator applies convolution to word from left to right with window equal to conv_window_size and stride to 1.Take word 'example' for example, with conv_window_size equal to 2, conv is applied to [ex],[xa], [am], [mp]...If not provide, use the first dimension of conv kernal shape.Integer representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector.SequenceSpecify batchs of sequence words to embeddingSpecify weights of convSpecify bias of convSpecify embedding vector of charConstrain to tensor(int32).Constrain to tensor(float).modeThree modes: `constant`(default) - pads with a given constant value, `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis, `edge` - pads with the edge values of arrayInput tensor.Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]. `pads` format (1D example) should be as follow [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.(Optional) A scalar or rank 1 tensor containing a single value to be filled if the mode chosen is `constant` (by default it is 0.0).Tensor after padding.A 1-D input tensor that is to be processed.A 1-D tensor of the same type as 'x' containing all the unique values in 'x' sorted in the same order that they occur in the input 'x'idxA 1-D INT64 tensor of the same size as 'x' containing the indices for each value in 'x' in the output 'uniques'countsA 1-D INT64 tensor containing the the count of each element of 'uniques' in the input 'x'Input can be of any tensor type.The distance metric to use. If a string, the distance function can be "braycurtis", "canberra", "chebyshev", "cityblock", "correlation", "cosine", "dice", "euclidean", "hamming", "jaccard", "jensenshannon", "kulsinski", "mahalanobis", "matching", "minkowski", "rogerstanimoto", "russellrao", "seuclidean", "sokalmichener", "sokalsneath", "sqeuclidean", "wminkowski", "yule".2D matrix with shape (M,N)2D matrix with shape (K,N)A 2D Matrix that represents the distance between each pair of the two collections of inputs.Constrains input to only numeric types.The pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'.Value used for extrapolation, when applicable. Default is 0.0f. Input data tensor from the previous operator; 4-D feature map of shape (N, C, H, W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.roisRoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[y1, x1, y2, x2], ...]. The RoIs' coordinates are normalized in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.batch_indices1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch.crop_size1-D tensor of 2 elements: [crop_height, crop_width]. All cropped image patches are resized to this size. Both crop_height and crop_width need to be positive.RoI pooled output, 4-D tensor of shape (num_rois, C, crop_height, crop_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1].Constrain types to float tensors.Constrain types to int tensors.The first normalization dimension: normalization will be performed along dimensions axis : rank(inputs).Input data tensor from the previous layer.Scale tensor.Bias tensor.Output data tensor.Constrain input and output types (except mean and inv_std_var) to float tensors.Constrain mean and inv_std_var to be float tensors.The input data as Tensor.The output.The normal input data.The bias input data that is a 1D tensor.Input tensor. Every matrix in the batch must be invertible.Output tensor of the same type and shape as the input tensor. Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support). {additionalDocumentation} {name}{additionalDocumentation}A_scaleInput A's scale. It's a scalar, which means a per-tensor/layer quantization.B_scaleInput B's scale. It's a scalar, which means a per-tensor/layer quantization.C_scaleC_zero_pointResult, has same element type as two inputsConstrain input and output types to 8 bit signed and unsigned tensors.inputs are expected to have tensor type.Incompatible dimensionsInputs 0 shall be 3 dimensionsInputs 4 shall be 5 dimensionsInputs 0 shall be 2 dimensionsword_embedding should have 2 dimensions and dimension size is known.Negative values are not allowed in a shape specificationAttribute should specify a shapeInput's shape must be 4-D'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border)'Scale' must contain exactly 2 values - (height, width)) needs to be greater than or equal to the top_border () + bottom_border () needs to be greater than or equal to the left_border () + right_border () + scale[0] () + scale[1] (First input does not have rank 2Second input does not have rank 2Input axis is invalid: inputs are expected to have tensor type and output type should not be null.Input tensors of wrong rank (0).Incompatible dimensions for matrix multiplicationaxis must be in [-rank, rank-1]. input rank was both data and indices tensor need to have rank larger than zero.last dimension of indices must not be larger and rank of data tensor'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64first input tensor has wrong dimensionrois input tensor has wrong dimensionbatch_indices shape input tensor has wrong dimensioncrop_size shape input tensor has wrong dimensionInput rank must be >= 2.The inner-most 2 dimensions must have the same size (mat_w: != mat_h:/Users/runner/work/1/s/onnxruntime/core/graph/contrib_ops/nchwc_schema_defs.ccchannelstensor rank too smallinvalid scales dimensioninvalid scales valueConstrain input and output types.Tensor(scalar, or dims=[1]). First entry in the range.Tensor(scalar, or dims=[1]). Upper limit of sequence, exclusive.Tensor(scalar, or dims=[1]). Number that increments start. Defaults to 1.1-D Tensor of the range.Unsupported type:Can not get shape initializer data!Unsupported non-raw-data data type!inout/Users/runner/work/1/s/onnxruntime/core/graph/function.cconnxruntime::FunctionImpl::FunctionImpl(const onnxruntime::Graph &, std::unique_ptr, const logging::Logger &)input_arg->Type() != nullptronnxruntime::FunctionImpl::FunctionImpl(const onnxruntime::Graph &, const onnxruntime::NodeIndex &, const onnx::FunctionProto &, const logging::Logger &)Resolve subgraph failed:void onnxruntime::update_subgraphs_within_function_body(onnx::GraphProto &, const onnxruntime::Graph &, const onnx::NodeProto &, const std::unordered_map &, const std::unordered_map &)A node with a function body within a subgraph within another function body is currently not supported in ORTType mismatch. Current= Input=Tensor element type mismatch. /Users/runner/work/1/s/onnxruntime/core/graph/graph.ccUpdateTypeAndShapeSparseTensor element type mismatch. ai.onnxThis is an invalid model. The sum of input arg count is not equal to size of input defs in node (onnxruntime::Graph::Graph(const onnxruntime::Model &, onnx::GraphProto *, const std::unordered_map &, onnxruntime::Version, onnxruntime::IOnnxRuntimeOpSchemaCollectionPtr, onnxruntime::Graph *, const onnxruntime::Node *, const logging::Logger &, const std::unordered_map &)graph_proto != nullptrConstantGraphInitializer appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py.void onnxruntime::Graph::InitializeStateFromModelFileGraphProto()graph_inputs_excluding_initializers_.empty() && graph_inputs_including_initializers_.empty() && value_info_.empty() && graph_outputs_.empty()node_argGraph ctor should have created NodeArg for initializer. Missing:This is an invalid model. Graph output () does not exist in the graph.This is an invalid model. Error: two nodes with same node name ().This is an invalid model. Error: Duplicate definition of name (SetOuterScopeNodeArgsvoid onnxruntime::Graph::AddEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int)void onnxruntime::Graph::RemoveEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int)BuildConnectionsThis is an invalid model. At top level graph without matching NodeArg that subgraph consumes. Name= Graph may not conform to the ONNX spec and contain initializers that are not graph inputs.This is an invalid model. Failed to find NodeArg in all parent graphs. Name=This is an invalid model. Error: the graph is not acyclic.Size mismatch validating subgraph inputs. Got inputs but subgraph has inputs and requires inputs. Either provide all subgraph inputs, or just the required inputs.Node:InferAndVerifySubgraphTypesonnxruntime::common::Status onnxruntime::Graph::UpdateShapeInference(onnxruntime::Node &)node.GetAttributeNameToMutableSubgraphMap().empty()UpdateTypeShapeInference is not intended to be used with control flow nodes containing subgraphsThis is an invalid model. Node () input arg () does not have type information set by parent node.This is an invalid model. Type Error: Type '' of input parameter () of operator () in node () is invalid.Type Error: Type parameter () bound to different types ( and in node (Node () Op () ) output arg () type inference failedType Error: Type () of output arg () of node () does not match expected type (This is an invalid model. Model input () does not have type information.Type Error: Data in initializer '' has element type but usage of initializer in graph expects Type Error: Shape of initializer does not match. This is an invalid model. Error in Node: : Fatal error: is not a registered function/opVerifyNodeAndOpMatch) attribute () is required but not specified.Error: Duplicate definition-site for (InitInputsInitializersOutputsPerformTypeAndShapeInferencingForThisAndAllSubgraphsResolveonnxruntime::common::Status onnxruntime::Graph::Resolve(const onnxruntime::Graph::ResolveOptions &)outer_scope_node_args_consumed.empty()Shouldn't be possible to have NodeArgs that haven't been handled already.void onnxruntime::Graph::AddInitializedTensor(const onnx::TensorProto &)existing->second == &tensorAddInitializedTensor already has tensor with name but different TensorProto.void onnxruntime::Graph::RemoveInitializedTensor(const std::string &)!foundNot satisfied: name_to_initializer_it != name_to_initial_tensor_.end() ReplaceInitializedTensorFailed to find existing initializer with name Not satisfied: dims_eq() Not satisfied: old_initializer.data_type() == new_initializer.data_type() onnxruntime::common::Status onnxruntime::Graph::ReplaceInitializedTensor(const onnx::TensorProto &)existing_entry != mutable_initializers.pointer_end()void onnxruntime::Graph::AddValueInfo(const onnxruntime::NodeArg *)info->Name() != new_value_info->Name()_token_NoOpbool onnxruntime::Graph::RemoveNode(onnxruntime::NodeIndex)node->GetOutputEdgesCount() == 0Can't remove node as it still has output edges.void onnxruntime::Graph::ToGraphProtoInternal(onnx::GraphProto &) constOuter scope node arg name ''was added but does not exist. CleanUnusedInitializersRemoving initializer ''. It is not used by any node and should be removed from the model.'. It is no longer used by any node. must be either specified in graph inputs or graph initializers.gsl::not_null onnxruntime::Graph::AllocateNode()nodes_.size() < static_cast(std::numeric_limits::max())onnxruntime::Node &onnxruntime::Graph::FuseSubGraph(std::unique_ptr< ::onnxruntime::IndexedSubGraph>, const std::string &)nullptr != sub_graph && nullptr != sub_graph->GetMetaDef()Node 's number of inputs is different from function body graph's number of input.onnxruntime::common::Status onnxruntime::Graph::InlineFunction(onnxruntime::Node &)node.MutableOutputDefs().size() == subgraph.GetOutputs().size()'s number of outputs is different from function body graph's number of outputs.InlineFunctionvoid onnxruntime::Graph::SetInputs(const std::vector &)input->Exists()Inputs: Nodes: : ) -> Outputs: MergeShapeInfoError merging shape info for output. '' source: target:. Falling back to lenient merge.Output:[ShapeInferenceError] Mismatch between number of source and target dimensions. Source= Target=Can't merge shape info. Both source and target dimension have values but they differ. Source= Dimension=ai.onnx.mlai.onnx.trainingunordered_map::at: key not found/Users/runner/work/1/s/include/onnxruntime/core/graph/graph.honnxruntime::Node *onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) constnode_index < nodes_.size()Validating no unexpected access using an invalid node_index. Got: Max:/Users/runner/work/1/s/include/onnxruntime/core/common/const_pointer_container.hconst onnxruntime::ConstPointerContainer::T *onnxruntime::ConstPointerContainer > >::at(size_t) const [Container = std::__1::vector >]index < data_.size()[TypeInferenceError] No Graph instance was found for attribute in node Graph attribute inferencing failed: /Users/runner/work/1/s/onnxruntime/core/graph/graph_utils.ccint onnxruntime::graph_utils::GetNodeInputIndexFromInputName(const onnxruntime::Node &, const std::string &)itr != node.InputDefs().end()const std::string &onnxruntime::graph_utils::GetNodeInputName(const onnxruntime::Node &, int)index >= 0 && static_cast(index) < inputs.size()const std::string &onnxruntime::graph_utils::GetNodeOutputName(const onnxruntime::Node &, int)index >= 0 && static_cast(index) < outputs.size()bool onnxruntime::graph_utils::RemoveNode(onnxruntime::Graph &, onnxruntime::Node &)Should be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here.onnxruntime::NodeArg &onnxruntime::graph_utils::AddInitializer(onnxruntime::Graph &, const onnx::TensorProto &)!graph.GetInitializedTensor(new_initializer.name(), existing)Initializer with same name exists. Name:void onnxruntime::graph_utils::ReplaceNodeInput(onnxruntime::Node &, int, onnxruntime::NodeArg &)Invalid input index for node . Index: ExplicitInputs: ImplicitInputs:void onnxruntime::graph_utils::AddNodeInput(onnxruntime::Node &, int, onnxruntime::NodeArg &)num_explicit_inputs == static_cast(target_input_idx)FindPathFailed since multiple edges matched:->CanUpdateImplicitInputNameInSubgraphs Implicit input name cannot be safely updated to in one of the subgraphs.bool onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutput(onnxruntime::Graph &, onnxruntime::Node &)std::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; })void onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraph(onnxruntime::Node &, const std::string &, const std::string &)std::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0ModelProto does not have a graph.Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported.Unknown model file format version./Users/runner/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/work/1/s/onnxruntime/core/graph/schema_registry.ccRegisterOpSetRegisterOpSchemaInternalknown by the checker., but it its version is higherthan the operator set version [...truncated...] ERROR LOG MSG NOTIFICATION: Failure to successfully parse the messageonnxruntimeSystemISink must be provided.default_logger_id must be provided if instance_type is InstanceType::DefaultOnly one instance of LoggingManager created with InstanceType::Default can exist at any point in time.Default logger already set. [VIWEFampm is not a valid date is not a valid year is not a valid day/Users/runner/work/1/s/onnxruntime/core/common/path.ccParsestatic onnxruntime::Path onnxruntime::Path::Parse(const onnxruntime::PathString &)Not satisfied: src.GetRootPathString() == dst.GetRootPathString() RelativePathPaths must have the same root to compute a relative path. src root: , dst root: Not satisfied: has_trailing_separator ParsePathRootFailed to parse path root: /Users/runner/work/1/s/onnxruntime/core/common/profiler.cc::onnxruntime::TimePoint onnxruntime::profiling::Profiler::StartTime() constenabled_void onnxruntime::profiling::Profiler::Initialize(const logging::Logger *)session_logger != nullptrvoid onnxruntime::profiling::Profiler::StartProfiling(const logging::Logger *)custom_logger != nullptrEndTimeAndRecordEventMaximum number of events reached, could not record profile event.Writing profiler data to file [ {"cat" : "","pid" :"tid" :"dur" :"ts" :"ph" : "X","name" :""args" : {"" : "} }, ] SessionNodew/Users/runner/work/1/s/onnxruntime/core/common/status.cconnxruntime::common::Status::Status(onnxruntime::common::StatusCategory, int, const std::string &)code != static_cast(common::OK)onnxruntime::common::Status::Status(onnxruntime::common::StatusCategory, int, const char *)SystemError[ONNXRuntimeError]SUCCESSFAILINVALID_ARGUMENTNO_SUCHFILENO_MODELENGINE_ERRORRUNTIME_EXCEPTIONINVALID_PROTOBUFMODEL_LOADEDNOT_IMPLEMENTEDINVALID_GRAPHEP_FAILGENERAL ERROR/Users/runner/work/1/s/onnxruntime/core/common/threadpool.cconnxruntime::concurrency::ThreadPool::ThreadPool(onnxruntime::Env *, const onnxruntime::ThreadOptions &, const onnxruntime::concurrency::ThreadPool::NAME_CHAR_TYPE *, int, bool)degree_of_parallelism >= 1void onnxruntime::concurrency::ThreadPool::Schedule(std::function)fn != nullptrvoid onnxruntime::concurrency::ThreadPool::RunInParallel(std::function, int)void onnxruntime::concurrency::ThreadPool::ParallelFor(std::ptrdiff_t, const onnxruntime::TensorOpCost &, const std::function &)n >= 0/Users/runner/work/1/s/include/onnxruntime/core/common/optional.hT &onnxruntime::optional::value() [T = long long]has_value_T &onnxruntime::optional::value() [T = long]/Users/runner/work/1/s/onnxruntime/core/platform/env.ccvoid gsl::fail_fast_assert_handler(const char *const, const char *const, const char *const, int)/Users/runner/work/1/s/onnxruntime/core/platform/posix/env.cconnxruntime::(anonymous namespace)::PosixThread::PosixThread(const char *, int, unsigned int (*)(int, Eigen::ThreadPoolInterface *), Eigen::ThreadPoolInterface *, const onnxruntime::ThreadOptions &)CleanUpFailed to close file descriptor - error code: Invalid fd was supplied: fstat file "" failed: Not satisfied: file_path ReadFileIntoBufferNot satisfied: offset >= 0 Not satisfied: length <= buffer.size() openlseekreadReadFileIntoBuffer - unexpected end of file. File: , offset: , length: MapFileIntoMemorymmapUnmapFilemunmap failed. error code: Not satisfied: result == 0 DeleteFolderDeleteFolder(): nftw() failed with error: nftw_removeremove() failed. Error code: , path: closerealpathFailed to load library with error: Got null library handleFailed to unload library with error: Failed to get symbol lib.so.packing unavailableField 'name' of value_info is required to be non-empty.type is required but missing.elem_typekey_typevalue_typeUnrecognized type value case (value_info name: ): data_typesetting data_type field (tensor name: ) to UNDEFINED is not allowedfloat_dataint32_datastring_dataint64_dataraw_datadouble_datauint64_dataData of TensorProto ( tensor name: ) is stored externally and should not have data field.location) should be stored in , but it doesn't exist or is not accessible.TensorProto ( tensor name: ) is stored externally but doesn't have a location.TensorProto (tensor name: ) is 0-element but contains data!) should contain one and only one value field.STRING data (tensor name: ) should not be stored in raw_data fieldvalues of data_type '' should be stored in field '' instead of 'Unrecognized data_type (tensor name: Sparse tensor indices () has values, but NNZ is Sparse tensor () index value at position [] out of range [0, ] not in sorted order.) first dimension size does not equal NNZ.) second dimension size does not match rank of tensor.] out of range.] not in lexicographic sorted order.valuessparse_tensor_protoSparse tensor values () must have rank 1.) must have a dense-rank > 0) dimensions are not positive.) must have INT64 type.) must have rank 1 or 2.) has no index values.attrtype field and data field mismatch in attribute Attribute (name: ) should not contain more than one value field.) should refer to attribute in parent node.op_typenodeNodeProto (name: , type: ) has zero input and zero output.DynamicSliceImageScalerScaleWarning: was a removed experimental ops. In the future, we may directly reject this operator. Please update your model as soon as possible.No opset import for domain 'No Op registered for with domain_version of Op registered for is deprecated in domain_version of graphGraph must be in single static assignment (SSA) form, however '' has been used as graph input names multiple times. in initializer but not in graph inputNodes in a graph must be topologically sorted, however input '' of node: is not output of any previous nodes.Bad node spec: ' has been used as output names multiple times.functionsince_version' has been used multiple times.function () should not have duplicate outputs specified.) should not have duplicate attributes specified.Nodes in a function must be topologically sorted, however input ' is neither output of any previous nodes nor input of the function.Function must be in single static assignment (SSA) form, however 'Unable to open model file:. Please check if it is a valid file.Unable to parse model from file:. Please check if it is a valid protobuf file of model.\/ ==> Context: rrbValues that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible./Users/runner/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/work/1/s/cmake/external/onnx/onnx/defs/controlflow/old.ccLoop 'body' subgraph outputs should all be tensors but output was A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip.condA boolean termination condition. Optional. Pass empty string to skip.v_initialThe initial values of any loop-carried dependencies (values that change across loop iterations)v_final_and_scan_outputsFinal N loop carried dependency values then K scan_outputsThe graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations.tensor of int64, which should be a scalar.tensor of bool, which should be a scalar.An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration.An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input.An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output.then_branch and else_branch produce different number of outputs. If node has but subgraphs produce Mismatched type for output then= else=Mismatched tensor element type for output Condition for the ifoutputsValues that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same shape and same data type.Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch.Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch.Only boolInput type was nullInput was expected to have tensor type. Got Element type of input was unknownInput element type of does not match existing output type of Output was expected to have tensor type. Got tensor(seq(map(sparse_tensor(seqmapopaquesparse_tensortensorFunc_Input for function node Output for function node The value for the elements of the output tensor.sparse_valueThe value for the elements of the output tensor in sparse format.value_intThe value for the sole element for the scalar, int64, output tensor.value_intsThe values for the elements for the 1D, int64, output tensor.value_floatThe value for the sole element for the scalar, float32, output tensor.value_floatsThe values for the elements for the 1D, float32, output tensor.value_stringThe value for the sole element for the scalar, UTF-8 string, output tensor.value_stringsThe values for the elements for the 1D, UTF-8 string, output tensor.Output tensor containing the same value of the provided tensor./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/generator/defs.cc(Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float321D tensor. The shape of the expected output tensor. If empty tensor is given, the output would be a scalar. All values must be >= 0.Output tensor of shape specified by 'input'.If attribute 'value' is specified, the value and datatype of the output tensor is taken from 'value'.If attribute 'value' is not specified, the value in the output defaults to 0, and the datatype defaults to float32.Constrain input types.Constrain output types to be numerics.(Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal, and k < 0 populates a lower diagonal.(Optional) The data type for the elements of the output tensor. If not specified,the data type of the input tensor T1 is used. If input tensor T1 is also notspecified, then type defaults to 'float'.2D input tensor to copy shape, and optionally, type information from.Output tensor, same shape as input tensor T1.Constrain input types. Strings and complex are not supported.Constrain output types. Strings and complex are not supported.Lower boundary of the output values.highUpper boundary of the output values.The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT.The shape of the output tensor.Output tensor of random values drawn from uniform distributionThe mean of the normal distribution.The standard deviation of the normal distribution.The data type for the elements of the output tensor. Default is TensorProto::FLOAT.Output tensor of random values drawn from normal distribution(Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor.Input tensor to copy shape and optionally type information from.Number of times to sample.(Optional) The data type for the elements of the output tensor, if not specified, we will use int32.Input tensor with shape [batch_size, class_size], where class_size is the number of all possible outcomes. Each value along the axis zero represents the unnormalized log-probability of each corresponding outcome in a batch.Output tensor with shape [batch_size, sample_size], where sample_size is the number of times to sample. Each value along the axis zero represents the outcome of the corresponding sample in a batch.Constrain output types to integral tensors.loop_body_attributeiprevcond_outdeltacurrentrangelimitstartScalar. First entry for the range of output values.Scalar. Exclusive upper limit for the range of output values.Scalar. Value to step by.A 1-D tensor with same type as the inputs containing generated range of values.Constrain input types to common numeric type tensors.One and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node.Attribute 'value_int' expect an integer.Attribute 'value_ints' expect a list of integers.Attribute 'value_float' expect a float.Attribute 'value_floats' expect a list of floats.Attribute 'value_string' expect a string.Attribute 'value_strings' expect a list of strings.TypeAndShapeInferenceFunction implementation incomplete: this line should never be reached.Shape input must be a one-dimensional tensor.Invalid shape value: Attribute expected to have tensor typeAttribute expected to have a one-dim tensorInput tensor must be 2-dimensionalOutput type must be int32 or int64Input tensor must have rank 2All inputs to 'Range' op must be of the same typeInput to 'Range' op should be scalars (Tensor with only one element and shape empty)/Users/runner/work/1/s/cmake/external/onnx/onnx/defs/generator/old.ccAttribute 'value' of Constant node must exist with 'Tensor' data.Only one of the attributes 'value' or 'sparse_value' must be specified for a Constant node.One of the attributes 'value' or 'sparse_value' must be specified for a Constant node./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/logical/defs.ccConstrains input types to all numeric tensors.Constrains input/output to boolean tensors.First operand, input to be shifted.Second operand, amounts of shift.ZConstrain input and output types to integer tensors.Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift).LessOrEqualGreaterOrEqualConstrains input to boolean tensor.Constrains output to boolean tensor./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/logical/old.ccConstrains input to float tensors.Constrains input to integral tensors.broadcastEnable broadcastingIf set, defines the broadcast dimensions.Left input tensor for the logical operator.Right input tensor for the logical operator.Result tensor.First input operand for the logical operator.Second input operand for the logical operator./Users/runner/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 SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717).Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946).Coefficient of ELU.The Alpha value in Celu formula which control the shape of the unit. The default value is 1.0.Constrain input and output types to float32 tensors.CeluConstrain input X and output types to float/int tensors.Constrain input Y types to float/int tensors.Value of alpha.Value of beta.The softsign (x/(1+|x|)) values of the input tensor computed element-wiseOptional input tensor C. If not specified, the computation is done as if C is a scalar 0. The shape of C should be unidirectional broadcastable to (M, N).Dimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Whether to return the top-K largest or smallest elements.Whether to return the elements in sorted order.The sine of the input tensor computed element-wiseThe cosine of the input tensor computed element-wiseThe tangent of the input tensor computed element-wiseThe arcsine of the input tensor computed element-wiseThe arccosine of the input tensor computed element-wiseThe arctangent of the input tensor computed element-wiseA 1-D tensor indicates the shape you want to expand to, following the broadcast ruleThe hyperbolic sine values of the input tensor computed element-wiseThe hyperbolic cosine values of the input tensor computed element-wiseThe hyperbolic arcsine values of the input tensor computed element-wiseThe hyperbolic arccosine values of the input tensor computed element-wiseThe hyperbolic arctangent values of the input tensor computed element-wiseThe sign of the input tensor computed element-wise. It has the same shape and type of the input.The error function of the input tensor computed element-wise. It has the same shape and type of the input.aN-dimensional quantized matrix ascale of quantized input azero point of quantized input abN-dimensional quantized matrix bscale of quantized input bzero point of quantized input bscale of quantized output yzero point of quantized output yQuantized matrix multiply results from a * bConstrain input a and its zero point data type to 8-bit integer tensor.Constrain input b and its zero point data type to 8-bit integer tensor.Constrain output y and its zero point data type to 8-bit integer tensor.Zero point tensor for input 'A'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-row quantization. If it's a 1-D tensor, its number of elements should be equal to the number of rows of input 'A'.Constrain output Y data type as 32-bit integer tensor.If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements.If set to 1 will perform the sums in reverse direction.An input tensor that is to be processed.(Optional) A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back.Output tensor of the same type as 'x' with cumulative sums of the x's elementsaxis tensor can be int32 or int64 onlyConstrain input and output types to floating-point tensors.targetignore_indexreductionnonelossInput tensor of shape (N, C) or (N, C, d1, d2, ..., dk).Target tensor of shape (N) or (N, d1, d2, ..., dk). Target element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the target values should either be in the range [0, C) or have the value ignore_index.Optional rescaling weight tensor. If given, it has to be a tensor of size C. Otherwise, it is treated as if having all ones.The negative log likelihood lossType of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights.Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value.Constrain input, weight, and output types to floating-point tensors.Constrain target to integer typesNegativeLogLikelihoodLossNumber of input tensors does not match the operands in the equation.Ellipsis represents incompatible dimensions.Rank of input does not match the equation indices.equationEinsum expression string.InputsOperandsOutputType of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 'mean': the sum of the output will be divided by the number of elements in the output.scoreslog_problabelsThe predicted outputs with shape [batch_size, class_size], or [batch_size, class_size, D1, D2 , ..., Dk], where K is the number of dimensions.The ground truth output tensor, with shape [batch_size], or [batch_size, D1, D2, ..., Dk], where K is the number of dimensions. Labels element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the label values should either be in the range [0, C) or have the value ignore_index.A manual rescaling weight given to each class. If given, it has to be a 1D Tensor assigning weight to each of the classes. Otherwise, it is treated as if having all ones.Weighted loss float Tensor. If reduction is 'none', this has the shape of [batch_size], or [batch_size, D1, D2, ..., Dk] in case of K-dimensional loss. Otherwise, it is a scalar.Log probability tensor. If the output of softmax is prob, its value is log(prob).SoftmaxCrossEntropyLossDescribes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).'axis' must be in []. Its actual value is: 'shape' input must be 1D tensor of type INT64input and zero_point pair is expected to have be same type.input and zero_point pair is expected to have same type.Target rank must be 1 less than the input rank.Input and target dimension value mismatch.Weight rank must be 1./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/math/old.cc If necessary the right-hand-side argument will be broadcasted to match the shape of left-hand-side argument. When broadcasting is specified, the second tensor can either be of element size 1 (including a scalar tensor and any tensor with rank equal to or smaller than the first tensor), or having its shape as a contiguous subset of the first tensor's shape. The starting of the mutually equal shape is specified by the argument "axis", and if it is not set, suffix matching is assumed. 1-dim expansion doesn't work yet. For example, the following tensor shapes are supported (with broadcast=1): shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor shape(A) = (2, 3, 4, 5), shape(B) = (5,) shape(A) = (2, 3, 4, 5), shape(B) = (4, 5) shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1 shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0 Attribute `broadcast=1` needs to be passed to enable broadcasting. Input tensor of any shape, base of the exponent.Input tensor of any shape broadcastable to X shape, the exponent component.Pass 1 to enable broadcastingIf set, defines the broadcast dimensions. See doc for details.Output tensor (same size as X)First operand, base of the exponent.Second operand, power of the exponent.consumed_inputslegacy optimization attribute.Coefficient of leakage default to 0.01.Coefficient of SELU default to 1.6732.Coefficient of SELU default to 1.0507.Coefficient of ELU default to 1.0.The exponential of the input tensor computed element-wiseThe natural log of the input tensor computed element-wise1-D input tensorThe hyperbolic tangent values of the input tensor computed element-wiseslopeSlope tensor. If `Slope` is of size 1, the value is sharedacross different channelsSlope tensor. The shape of slope can be smaller then first input X; if so, its shape must be unidirectional broadcastable to XValue of alpha default to 0.2Value of beta default to 0.5data_0List of tensors for Max.Output tensor. Same dimension as inputs.List of tensors for MinList of tensors for Sum.sumList of tensors for Mean.Minimum value, under which element is replaced by minMaximum value, above which element is replaced by maxInput tensor whose elements to be clippedOutput tensor with clipped input elementsInput tensor AInput tensor BInput tensor C, can be inplace.Output tensor.Whether C should be broadcastedScalar multiplier for the product of input tensors A * B, the default value is 1.0.Scalar multiplier for input tensor C, the default value is 1.0.Input tensor CTensor of shape [a_1, a_2, ..., a_n, r]ValuesTensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing top K values from the input tensorIndicesTensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing the corresponding input tensor indices for the top K values.Constrain index tensor to int64kNumber of top elements to retrieveDimension on which to do the sort.KA 1-D tensor containing a single positive value corresponding to the number of top elements to retrieveMinimum value, under which element is replaced by min. It must be a scalar(tensor of empty shape).Maximum value, above which element is replaced by max. It must be a scalar(tensor of empty shape). Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) where the function `f(x) = x^exponent`, is applied to the data tensor elementwise. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md).Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_sizeThe input tensor that's coerced into a 2D matrix of size (NxD) as described above.The output values with the same shape as input tensor (the original size without coercion).First operand, should share the type with the second operand.Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size.Result, has same dimensions and type as AConstrain input and output types to high-precision numeric tensors.Invalid value for attribute axisInvalid value for attribute kK input must be a one-dimensional tensor of size 1.K input must be of type int64.Axis has less than the requested k elements.List of tensors for Input tensor must have atleast 2 dimensionsAttribute dilations has incorrect sizeAttribute strides has incorrect sizeAttribute kernel_shape has incorrect sizeAttribute kernel_shape must be specifiedAttribute pads has incorrect sizeSecond input tensor has wrong dimension/Users/runner/work/1/s/cmake/external/onnx/onnx/defs/nn/defs.ccThe storage order of the tensor. 0 is row major, and 1 is column major.Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis.Indices tensor from max pooling across the input tensor. The dimensions of indices are the same as output tensor. The values in indices of are the indices of the selected values during pooling. The indices are computed as flatten 1-D tensor, and the indices do not consider padding. So the values in indices are in [0, N x C x D1 x ... x Dn).MaxUnpool op must have either two or three inputs.Input tensor X must have atleast 2 dimensions.Attribute pads has incorrect size.Attribute strides has incorrect size.Attribute kernel_shape has incorrect size.Attribute kernel_shape must be specified.'output_shape' must be rank 1 tensor.'output_shape' must have same number of elements as the shape of input tensor X.Input data tensor that has to be unpooled. This tensor is typically the first output of the MaxPool op.Dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non-image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].Input data tensor containing the indices corresponding to elements in the first input tensor X.This tensor is typically the second output of the MaxPool op.Dimensions must be the same as input tensor X. The indices are linear, i.e. computed considering the tensor as flattened 1-D tensor, assuming row-major storage. Also, the linear indices should not consider padding. So the values in indices are in the range [0, N x C x D1 x ... x Dn).The shape of the output can be explicitly set which will cause pads values to be auto generated. If 'output_shape' is specified, 'pads' values are ignored.Output data tensor that contains the result of the unpooling.Input tensor must have at least 2 dimensionsRoIs tensor must have 2 dimensionsAttribute pooled_shape has incorrect lengthAttribute pooled_shape must be specifiedInput data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].Scale tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.Zero point tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. X.shape[1] == (W.shape[1] * group) == C (assuming zero based indices for the shape array). Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL. w_scaleScale tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).w_zero_pointZero point tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).Scale tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.Zero point tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.Optional 1D bias to be added to the convolution, has size of M. Bias must be quantized using scale = x_scale * w_scale and zero_point = 0Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.Constrain filter type to 8-bit integer tensor.Constrain output type to 8-bit integer tensor.Constrain bias type to 32-bit integer tensor.The shape of the convolution kernel. If not present, should be inferred from input 'w'.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis.Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis.number of groups input channels and output channels are divided into. default is 1.Zero point tensor for input 'x'. It's optional and default value is 0. It's a scalar, which means a per-tensor/layer quantization.Zero point tensor for input 'w'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M)Constrain input x and its zero point data type to 8-bit integer tensor.Constrain input w and its zero point data type to 8-bit integer tensor.Constrain output y data type to 32-bit integer tensor.dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis.Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.momentumFactor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum).Input data tensor from the previous operator; dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size, C is the number of channels. Statistics are computed for every channel of C over N and D1 to Dn dimensions. For image data, input dimensions become (N x C x H x W). The op also accepts single dimension input of size N in which case C is assumed to be 1Scale tensor of shape (C).Bias tensor of shape (C).running (training) or estimated (testing) mean tensor of shape (C).varrunning (training) or estimated (testing) variance tensor of shape (C).The output tensor of the same shape as XThe running mean after the BatchNormalization operator.The running variance after the BatchNormalization operator.saved_meanSaved mean used during training to speed up gradient computation.saved_varSaved variance used during training to speed up gradient computation.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.The input 1-dimensional scale tensor of size C.The input 1-dimensional bias tensor of size C.The output tensor of the same shape as input.Input matrixMatrix after normalizationThe axis on which to apply normalization, -1 mean last axis.pThe order of the normalization, only 1 or 2 are supported.(Optional) Seed to the random generator, if not specified we will auto generate one.ratioThe ratio of random dropout, with value in [0, 1). If this input was not set, or if it was set to 0, the output would be a simple copy of the input. If it's non-zero, output will be a random dropout of the scaled input, which is typically the case during training. It is an optional value, if not specified it will default to 0.5.training_modeIf set to true then it indicates dropout is being used for training. It is an optional value hence unless specified explicitly, it is false. If it is false, ratio is ignored and the operation mimics inference mode where nothing will be dropped from the input data and if mask is requested as output it will contain all ones.The output mask.Constrain input 'ratio' types to float tensors.Constrain output 'mask' types to boolean tensors.The lambd value for the Shrink formulation. Default is 0.5.The bias value added to output. Default is 0.A tensor of rank >= axis.A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). sizeThe number of channels to sum overScaling parameter.The exponent.Output tensor, which has the shape and type as input tensorConstrain input and output types to float tensors.Input for n-gram extractionNgram resultsInput is ether string UTF-8 or int32/int641-D tensor of floatsMaximum n-gram length. If this value is 3, 3-grams will be used to generate the output.Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams.Maximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=1List of strings n-grams learned from the training set. Either this or pool_int64s attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.List of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector.The starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity.list of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor.list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts.The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF)UTF-8 strings to normalizeUTF-8 Normalized stringsstring enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE"Boolean. Whether the identification of stop words in X is case-sensitive. Default is falseList of stop words. If not set, no word would be removed from X.Environment dependent string that denotes the locale according to which output strings needs to be upper/lowercased.Default en_US or platform specific equivalent as decided by the implementation.A list of integers, along which to reduce. The default is to caculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance. Carries out batch normalization as described in the paper https://arxiv.org/abs/1502.03167. Depending on the mode it is being run, there are multiple cases for the number of outputs, which we list below: Output case #1: Y, mean, var, saved_mean, saved_var (training mode) Output case #2: Y (test mode) For previous (depreciated) non-spatial cases, implementors are suggested to flatten the input shape to (N x C*D1*D2 ..*Dn) before a BatchNormalization Op. This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted. Constrain input and output types to float and 8 bit tensors.p value of the Lp norm used to pool over the input data.Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes.ROI pool output shape (height, width).Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data.RoIs (Regions of Interest) to pool over. Should be a 2-D tensor of shape (num_rois, 5) given as [[batch_id, x1, y1, x2, y2], ...].RoI pooled output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]).Optional 1D bias to be added to the convolution, has size of M.The shape of the convolution kernel. If not present, should be inferred from input W.dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis.Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis.number of groups input channels and output channels are divided into.weight and zero_point pair is expected to have same type.Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn)The weight tensor that will be used in the convolutions; has size (C x M/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the weight shape will be (C x M/group x k1 x k2 x ... x kn), where (k1 x k2 x ... x kn) is the dimension of the kernel. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, pad lengths and group count. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array)The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate padsAdditional elements added to the side with higher coordinate indices in the output. Each padding value in "output_padding" must be less than the corresponding stride/dilation dimension. By default, this attribute is a zero vector. Note that this attribute doesn't directly affect the computed output values. It only controls the selection of the computed values, so changing this attribute only adds or removes output elements. If "output_shape" is explicitly provided, "output_padding" does not contribute additional size to "output_shape" but participates in the computation of the needed padding amount. This is also called adjs or adjustment in some frameworks.Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1.Ratio of Dropout must be a scalar.training_mode of Dropout must be a scalar.Invalid value() for attribute 'axis'ngram_indexes must be non-empty with no negative valuesInput tensor must have rank 1 or 2Input shape must have either [C] or [1,C] dimensions where C > 0/Users/runner/work/1/s/cmake/external/onnx/onnx/defs/nn/old.ccDilation value along each spatial axis of filter.Stride along each spatial axis.Padding for the beginning and ending along each axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute.auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding. DEPRECATION NOTE: auto_pad is only intended to support legacy uses, and for framework authors, one is explicitly encouraged to use explicit padding specified in the pads attribute.Stride along each axis.p value of the Lp norm used to pool over the input data, default is 2.0.Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimension are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size.Output data tensor from pooling across the input tensor. Dimensions will be N x C x 1 x 1If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1.is_testIf set to nonzero, run spatial batch normalization in test mode, default is 0.The epsilon value to use to avoid division by zero, default is 1e-5f.Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f.The input 4-dimensional tensor of shape NCHW.The scale as a 1-dimensional tensor of size C to be applied to the output.The bias as a 1-dimensional tensor of size C to be applied to the output.The running mean (training) or the estimated mean (testing) as a 1-dimensional tensor of size C.The running variance (training) or the estimated variance (testing) as a 1-dimensional tensor of size C.The output 4-dimensional tensor of the same shape as X.The running mean after the BatchNormalization operator. Must be in-place with the input mean. Should not be used for testing.The running variance after the BatchNormalization operator. Must be in-place with the input var. Should not be used for testing.Saved mean used during training to speed up gradient computation. Should not be used for testing.Saved variance used during training to speed up gradient computation. Should not be used for testing.The output 4-dimensional tensor of the same shape as input.(float, default 0.5) the ratio of random dropout(int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X.The output mask. If is_test is nonzero, this output is not filled.The ratio of random dropoutConstrain output mask types to boolean tensors.The output tensor of the same shape as X.Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). If true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch.If spatial is true, the dimension of scale is (C). If spatial is false, the dimensions of scale are (C x D1 x ... x Dn)If spatial is true, the dimension of bias is (C). If spatial is false, the dimensions of bias are (C x D1 x ... x Dn)If spatial is true, the dimension of the running mean (training) or the estimated mean (testing) is (C). If spatial is false, the dimensions of the running mean (training) or the estimated mean (testing) are (C x D1 x ... x Dn).If spatial is true, the dimension of the running variance(training) or the estimated variance (testing) is (C). If spatial is false, the dimensions of the running variance(training) or the estimated variance (testing) are (C x D1 x ... x Dn).dilation value along each spatial axis of the filter.The zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks.Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f. default 1; Pooled output Y's height.default 1; Pooled output Y's width.Number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly sampling_ratio x sampling_ratio grid points are used. If == 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default is 0.The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'.RoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[x1, y1, x2, y2], ...]. The RoIs' coordinates are in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input.RoI pooled output, 4-D tensor of shape (num_rois, C, output_height, output_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1]./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/object_detection/defs.ccboxesAn input tensor with shape [num_batches, spatial_dimension, 4]. The single box data format is indicated by center_point_box.An input tensor with shape [num_batches, num_classes, spatial_dimension]max_output_boxes_per_classInteger representing the maximum number of boxes to be selected per batch per class. It is a scalar. Default to 0, which means no output.iou_thresholdFloat representing the threshold for deciding whether boxes overlap too much with respect to IOU. It is scalar. Value range [0, 1]. Default to 0.score_thresholdFloat representing the threshold for deciding when to remove boxes based on score. It is a scalar.selected_indicesselected indices from the boxes tensor. [num_selected_indices, 3], the selected index format is [batch_index, class_index, box_index].Integer indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models. expected to have rank but has rank expected to have rank >Dimension mismatch in unification between /Users/runner/work/1/s/cmake/external/onnx/onnx/defs/object_detection/old.ccScale for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization.Zero point for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization. Default value is uint8 typed 0 if it's not specified.Constrain 'x' to float or int32 tensor.Constrain 'y_zero_point' and 'y' to 8-bit integer tensor./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/quantization/defs.ccN-D quantized input tensor to be de-quantized.Scale for input 'x'. It's a scalar, which means a per-tensor/layer quantization.Zero point for input 'x'. It's a scalar, which means a per-tensor/layer quantization. It's optional. 0 is the default value when it's not specified.Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor.Quantized output tensorOutput zero point. It's a scalar, which means a per-tensor/layer quantization.Constrain 'x' to float tensor.Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/reduction/defs.ccConstrain input and output types to high-precision and 8 bit numeric tensors.Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index)./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/reduction/old.ccA list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data).The axis in which to compute the arg indices.Reduced output tensor with integer data type. Computes the indices of the {name} elements of the input tensor's element along the provided axis. The resulting tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then the resulting tensor have the reduced dimension pruned. The type of the output tensor is integer.The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data).First input tensor must have rank 3/Users/runner/work/1/s/cmake/external/onnx/onnx/defs/rnn/defs.ccCouple the input and forget gates if 1.A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`.RThe recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`.The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0/Users/runner/work/1/s/cmake/external/onnx/onnx/defs/rnn/old.ccoutput_sequenceOne (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if not specified.The weight tensor for input gate. Concatenation of `Wi` and `WBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, input_size]`.The recurrence weight tensor. Concatenation of `Ri` and `RBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, hidden_size]`.The bias tensor for input gate. Concatenation of `[Wbi, Rbi]` and `[WBbi, RBbi]` (if bidirectional). The tensor has shape `[num_directions, 2*hidden_size]`. Optional: If not specified - assumed to be 0.When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate.A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified.Couple the input and forget gates if 1, default 0.The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, 4*hidden_size, input_size]`.The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 4*hidden_size, hidden_size]`.The bias tensor for input gate. Concatenation of `[Wb[iofc], Rb[iofc]]`, and `[WBb[iofc], RBb[iofc]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 8*hidden_size]`. Optional: If not specified - assumed to be 0.initial_cOptional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.PThe weight tensor for peepholes. Concatenation of `P[iof]` and `PB[iof]` (if bidirectional) along dimension 0. It has shape `[num_directions, 3*hidde_size]`. Optional: If not specified - assumed to be 0.Y_cThe last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`.Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional.Number of neurons in the hidden layerOptional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM.The sequence output for the hidden is optional if 0. Default 0.clipCell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified.The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`.Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`.initial_hOptional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`.A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. It is optional if `output_sequence` is 0.Y_hThe last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`.Constrain seq_lens to integer tensor.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01.Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators. typestr: , has unsupported type: has inconsistent type has unsupported type Operator '' has been deprecated since version ) has input size not in range [min=, max= not in allowed input sizes.) has output size has output size not in allowed output sizes.) has more inputs () than declared () in op definition.)'s input is marked single but has an empty string in the graph) has more outputs ()'s output Attribute '' appeared multiple times.Unrecognized attribute: for operator Mismatched attribute type in '' is expected to have field 't'' is expected to have field 'sparse_tensor'' is expected to have field 'g'' is expected to have field 'floats'' is expected to have field 'ints'' is expected to have field 'strings'' is expected to have field 'tensors'' is expected to have field 'graphs' has unknown expected typeRequired attribute '' is missing.ONNX Schema : failed validating the check: (inputs_.size() - 1) == i(outputs_.size() - 1) == i!(it.GetName().empty())Attributes:Inputs: (no explicit description available)Outputs:(no documentation yet)Defined at (Optional) The data type of the tensors in the output sequence. The default type is 'float'.Empty sequence./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/sequence/defs.ccTensors.Sequence enclosing the input tensors.input_sequenceInput sequence.Input tensor to be inserted into the input sequence.positionPosition in the sequence where the new tensor is inserted. It is optional and default is to insert to the back of the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).Output sequence that contains the inserted tensor at given position.Constrain position to integral tensor. It must be a scalar(tensor of empty shape).Position of the tensor in the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).Output tensor at the specified position in the input sequence.Output sequence that has the tensor at the specified position removed.Length of input sequence. It must be a scalar(tensor of empty shape).Constrain output to integral tensor. It must be a scalar(tensor of empty shape).Length of each output. It can be either a scalar(tensor of empty shape), or a 1-D tensor. All values must be >= 0. One or more outputs forming a sequence of tensors after splittingConstrain split size to integral tensor.Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1].Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored.Which axis to concat on. Accepted range in `[-r, r - 1]`, where `r` is the rank of input tensors. When `new_axis` is 1, accepted range is `[-r - 1, r]`. new_axisInsert and concatenate on a new axis or not, default 0 means do not insert new axis.Sequence of tensors for concatenationAttribute dtype should be of integer type and specify a type.SequenceConstruct is expected to have at least 1 input.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/work/1/s/cmake/external/onnx/onnx/defs/tensor/defs.ccSpecified shape for output.Shape of the input tensorConstrain output to int64 tensor.Total number of elements of the input tensorConstrain output to int64 tensor, which should be a scalar though.Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs)..Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input).length of each output. Values should be >= 0.1-D tensor of axes that `starts` and `ends` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).1-D tensor of slice step of corresponding axis in `axes`. Negative value means slicing backward. 'steps' cannot be 0. Defaults to 1.permA list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given.transposedTransposed output.Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of int32/int64 indices, with the same rank r as the input. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of the same shape as indices.List of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).List of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded).Output tensor of [N, C * blocksize * blocksize, H/blocksize, W/blocksize].DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order.repeats1D int64 tensor of the same length as input's dimension number, includes numbers of repeated copies along input's dimensions.Output tensor of the same dimension and type as tensor input. output_dim[i] = input_dim[i] * repeats[i]Constrain repeat's type to int64 tensors.Three interpolation modes: nearest (default), linear and cubic. The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor).The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if "mode" is "cubic".If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0.Four modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest".When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f.roi1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize"The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified. If 'size' is needed, the user can use an empty string as the name of 'scales' in this operator's input list.sizesThe size of the output tensor. The number of elements of 'sizes' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified.Constrain roi type to float or double.Tensor to copy input into.(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length along the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices).Input tensor containing indices. Any entries in the 'indices' input tensor with values outside the range [-depth, depth-1] will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.Scalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [-depth, depth-1]. In case 'depth' is of non-integer type, it will be casted to int64 before use.Constrain output types to boolean tensors.(Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false.(Optional) Whether map negative infinity to true. Default to 1 so that negative infinity induces true. Set this attribute to 0 if negative infinity should be mapped to false.When True (nonzero), yield X, otherwise yield Yvalues selected at indices where condition is Truevalues selected at indices where condition is FalseTensor of shape equal to the broadcasted shape of condition, X, and Y.Constrain to boolean tensors.(Optional) Specify which axis is time axis. Must be one of 0 (default), or 1.(Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0.Tensor of rank r >= 2.Tensor specifying lengths of the sequences in a batch. It has shape `[batch_size]`.Tensor with same shape of input.(Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default).(Optional) The dimension to apply unique. If not specified, the unique elements of the flattened input are returned. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).A N-D input tensor that is to be processed.A tensor of the same type as 'X' containing all the unique values or subtensors sliced along a provided 'axis' in 'X', either sorted or maintained in the same order they occur in input 'X'A 1-D INT64 tensor containing indices of 'Y' elements' first occurance in 'X'. When 'axis' is provided, it contains indices to subtensors in input 'X' on the 'axis'. When 'axis' is not provided, it contains indices to values in the flattened input tensor. inverse_indicesA 1-D INT64 tensor containing, for elements of 'X', its corresponding indices in 'Y'. When 'axis' is provided, it contains indices to subtensors in output 'Y' on the 'axis'. When 'axis' is not provided, it contains indices to values in output 'Y'. A 1-D INT64 tensor containing the count of each element of 'Y' in input 'X'The number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:]Supported modes: `constant`(default), `reflect`, `edge`Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank]. `pads` format should be: [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `i` and xi_end, the number of pad values added at the end of axis `i`.constant_value(Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0). This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor.
The coordinate of each dimension is transformed individually. Let's describe a case using axis x as an example. Denote x_resized as the coordinate of axis x in the resized tensor, x_original as the coordinate of axis x in the original tensor, length_original as the length of the original tensor in axis x, length_resized as the length of the resized tensor in axis x, roi_x = (start_x, end_x) of the axis x in input "roi", scale = length_resized / length_original,
if coordinate_transformation_mode is "half_pixel",
x_original = (x_resized + 0.5) / scale - 0.5,
if coordinate_transformation_mode is "pytorch_half_pixel",
x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0,
if coordinate_transformation_mode is "align_corners",
x_original = x_resized * (length_original - 1) / (length_resized - 1),
if coordinate_transformation_mode is "asymmetric",
x_original = x_resized / scale,
if coordinate_transformation_mode is "tf_half_pixel_for_nn",
x_original = (x_resized + 0.5) / scale,
if coordinate_transformation_mode is "tf_crop_and_resize",
x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1).Target shape may not have multiple -1 dimensionsInvalid position of 0Invalid dimension value: Invalid Target shape product of 0Dimension could not be inferred: incompatible shapesaxis must be in [-rank, rank-1].Invalid value of attribute 'axis'. Rank= Value=Mismatch between number of splits () and outputs (Mismatch between the sum of 'split' () and the split dimension of the input (The input is not evenly splittableInvalid attribute perm {}, input shape = {'axes' attribute must not contain any duplicatesvalues in 'axes' are beyond the bounds of the computed output shape'Repeats' input must be 1D tensor of type int64'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions.'axis' must be in [-rank(indices), rank(indices)-1]'input' must have rank >= 2'sequence_lens' must have rank of 1'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64toThe data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProtoInput tensor to be cast.Output tensor with the same shape as input with type specified by the 'to' argumentConstrain input types. Casting from strings and complex are not supported.Constrain output types. Casting to strings and complex are not supported./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/tensor/old.ccWhich axis to concat on. Default value is 1.inputsList of tensors for concatenationconcat_resultConcatenated tensorConstrain output types to float tensors.Which axis to concat onThe tensor to splitOptional list of output lengths (see also arg 'split')outputs...One or more outputs forming list of tensors after splittingConstrain input types to float tensors.Which axis to split onlength of each outputpaddingsList of integers indicate the padding element count at the beginning and end of each axis, for 2D it is the number of pixel. `paddings` rank should be double of the input's rank. `paddings` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.Three modes: constant(default), reflect, edgeOne float, indicates the value to be filled, default is 0New shapereshapedReshaped data.Input tensor of any shape.tilesNumber of repeated copies to make of the input tensor.Axis along which to repeat.Output tensor of same shape and type as input.Constrain tiles and axis's type to int64 tensors.width_scaleThe scale along width dimension. It takes value greater than or equal to 1.height_scaleThe scale along height dimension. It takes value greater than or equal to 1.Two interpolation modes: nearest(default), bilinear4-D tensor, [N,C,H,W]4-D tensor after resizing, [N,C,H,W]Constrain output types to bool, int32, int64, float16, float, double tensors.The scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'.Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc)N-D tensorN-D tensor after resizingThe scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'.Axes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1].Starting indices of corresponding axis in `axes`Ending indices (exclusive) of corresponding axis in axes`1-D tensor of ending indices (exclusive) of corresponding axis in `axes`steps1-D tensor of slice step of corresponding axis in `axes`. Default to 1. Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Tensor of int32/int64 indices, of r >= 1 (same rank as input).updatesTensor of rank r >=1 (same rank and shape as indices)Tensor of rank r >= 1 (same rank as input).Blocks of [blocksize, blocksize] are moved.Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width.Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize].Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds. It is an error if any of the index values are out of bounds.Tensor of rank q + (r - 1).List of non-negative integers, indicate the dimensions to squeeze.Tensors with at least max(dims) dimensions.squeezedReshaped tensor with same data as input.List of non-negative integers, indicate the dimensions to be insertedOriginal tensorexpanded(Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor.Input tensor containing indices. The values must be non-negative integers. Any entries in the 'indices' input tensor with values outside the range [0, depth) will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use.depthScalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [0, depth). In case 'depth' is of non-integer type, it will be casted to int64 before use.Rank 1 tensor containing exactly two elements, in the format [off_value, on_value], where 'on_value' is the value used for filling locations specified in 'indices' input tensor, and 'off_value' is the value used for filling locations other than those specified in 'indices' input tensor. Tensor of rank one greater than input tensor 'indices', i.e. rank(output) = rank(indices) + 1. The data type for the elements of the output tensor is the same as the type of input 'values' is used.(Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected.conditionRank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length alone the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded.Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1.Constrains to boolean tensors.Which axis to split on. List of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D it is the number of pixels. `pads` rank should be double of the input's rank. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.One float, indicates the value to be filled.Tensor of rank q >= 1. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.Tensor of rank q + r - indices_shape[-1] - 1.Value of attribute not specified should be of integer type and specify a type. does not specify a valid type.Required attribute axis is missingrank must be greater than axisAll inputs to Concat must have same rankRanks inferred () is not equal to the existing rank value (Number of elements of attribute 'scales' must be same as rank of input 'X'Attribute 'scales' must have floats type.Attribute 'scales' is required.Incorrect or missing attribute value for starts and endsAttribute axes has incorrect lengthSlice op must have either three, four or five inputs.Incorrect or missing input value for starts and endsInput axes has incorrect lengthInput steps has incorrect lengthInput axes has invalid data'step' cannot be 0Only supports `int32_t` or `int64_t` inputs for starts/ends/axes/stepsBlocksize must be positiveInput tensor must be 4-dimensionaldata tensor must have rank >= 1axis must be in [-r, r-1]OneHot node must have three inputs.Input 'depth' must be a scalar or rank 1 tensor.Input 'depth' must have exactly one element.Input 'values' must be rank 1 tensor.Input 'values' must have exactly two elements.Indices tensor must have rank >= 1'axis' must be in [-rank(indices)-1, rank(indices)]Invalid value of attribute 'axis'Attribute value for pads is requiredAttribute pads has incorrect lengthBoth `data` and `indices` input tensors in GatherND op need to have rank larger than 0.Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensorDimension value inferred () is not equal to the existing dim value (Number of elements of input 'sizes' must be same as rank of input 'X'Input 'sizes' must have int64 element type.Number of elements of input 'scales' must be same as rank of input 'X'Input 'scales' must have float element type.Data to be selectedThe indices, based on 0 as the first index of any dimension.Selected output data as an arrayThe input must be a tensor of a numeric type or string. The output will be of the same tensor type./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/traditionalml/defs.ccData to be binarizedBinarized output dataThe input must be a tensor of a numeric type. The output will be of the same tensor type.Values greater than this are mapped to 1, others to 0.The input map that is to be cast to a tensorA tensor representing the same data as the input map, ordered by their keysmap(int64, string)map(int64, float)The input must be an integer map to either string or float.The output is a 1-D tensor of string, float, or integer.A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.map_formIndicates whether to only output as many values as are in the input (dense), or position the input based on using the key of the map as the index of the output (sparse).
One of 'DENSE', 'SPARSE'.If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.Input dataOutput data. If strings are input, the output values are integers, and vice versa.The input must be a tensor of strings or integers, either [N,C] or [C].The output is a tensor of strings or integers. Its shape will be the same as the input shape.The strings of the map. This sequence must be the same length as the 'cats_int64s' sequenceThe integers of the map. This sequence must be the same length as the 'cats_strings' sequence.A string to use when an input integer value is not found in the map.
One and only one of the 'default_*' attributes must be defined.An integer to use when an input string value is not found in the map.
One and only one of the 'default_*' attributes must be defined.A dictionary.A 1-D tensor holding values from the input dictionary.map(string, int64)map(int64, double)map(string, float)map(string, double)The input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same.The output will be a tensor of the value type of the input map. It's shape will be [1,C], where C is the length of the input dictionary.A string vocabulary array.
One and only one of the vocabularies must be defined.An integer vocabulary array.
One and only one of the vocabularies must be defined.An ordered collection of tensors, all with the same element type.The output array, elements ordered as the inputs.The input type must be a tensor of a numeric type.The size of each input in the input listData to be processed.Imputed output dataThe input type must be a tensor of a numeric type, either [N,C] or [C]. The output type will be of the same tensor type and shape.Value(s) to change toA value that needs replacing.Value(s) to change to.Input data. It can be either tensor or scalar.Output data.The input type is a tensor of any shape.Output type is determined by the specified 'values_*' attribute.A list of strings. One and only one of 'keys_*'s should be set.A list of ints.A list of floats.A list of strings. One and only one of 'value_*'s should be set.A string.An integer.A float.Data to be classified.Classification outputs (one class per example).Classification scores ([N,E] - one score for each class and exampleThe input must be a tensor of a numeric type, and of of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]The output will be a tensor of strings or integers.A collection of weights of the model(s).A collection of intercepts.Indicates whether to do OvR or multinomial (0=OvR is the default).Class labels when using string labels. One and only one 'classlabels' attribute must be defined.Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.Indicates the transform to apply to the scores vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Data to be regressed.Regression outputs (one per target, per example).The input must be a tensor of a numeric type.Indicates the transform to apply to the regression output vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Weights of the model(s).Weights of the intercepts, if used.The total number of regression targets, 1 if not defined.Data to be encoded, a tensor of shape [N,C] or [C]Encoded output datanormOne of 'MAX,' 'L1,' 'L2'Data to be encoded.Encoded output data, having one more dimension than X.List of categories, ints.
One and only one of the 'cats_*' attributes must be defined.List of categories, strings.
One and only one of the 'cats_*' attributes must be defined.If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.Data to be scaled.Scaled output data.First, offset by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.Second, multiply by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.
Must be same length as 'offset'Class scores (one per class per example), if prob_a and prob_b are provided they are probabilities for each class, otherwise they are raw scores.The input must be a tensor of a numeric type, either [C] or [N,C].The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used. Its size will match the bactch size of the input.The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.First set of probability coefficients.Second set of probability coefficients. This array must be same size as prob_a.
If these are provided then output Z are probability estimates, otherwise they are raw scores.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'Class labels if using string labels.
One and only one of the 'classlabels_*' attributes must be defined.Class labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined.Regression outputs (one score per target per example).The input type must be a tensor of a numeric type, either [C] or [N,C].Chosen support vectorsFlag indicating whether the regression is a one-class SVM or not.Support vector coefficients.The number of support vectors.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Input of shape [N,F]N, Top class for each pointThe class score for each class, for each point, a tensor of shape [N,E].The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used.Tree id for each node.Node id for each node. Ids may restart at zero for each tree, but it not required to.Feature id for each node.Thresholds to do the splitting on for each node.Popularity of each node, used for performance and may be omitted.The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.
One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF'Child node if expression is true.Child node if expression is false.For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.
This attribute may be left undefined, and the defalt value is false (0) for all nodes.The id of the tree that this node is in.node id that this weight is for.The index of the class list that each weight is for.The weight for the class in class_id.Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)N classesNode id for each node. Node ids must restart at zero for each tree and increase sequentially.Child node if expression is trueChild node if expression is falseFor each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.
This attribute may be left undefined and the defalt value is false (0) for all nodes.The id of the tree that each node is in.The node id of each weightThe index of the target that each weight is forThe weight for each targetThe total number of targets.Defines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.'The input valuesThe output mapseq(map(string, float))seq(map(int64, float))The output will be a sequence of string or integer maps to float.The keys when using string keys.
One and only one of the 'classlabels_*' attributes must be defined.The keys when using int keys.
One and only one of the 'classlabels_*' attributes must be defined.Label encoder has only one input.Label encoder has only one output.Only one of keys_*'s can be set in label encoder.Input type is not string tensor but key_strings is setInput type is not int64 tensor but keys_int64s is setInput type is not float tensor but keys_floats is setOnly one of values_*'s can be set in label encoder.Input's shape should be 1D or 2DInput data.The input type must be a tensor of integers or strings, of any shape.The output type will be a tensor of strings or integers, and will have the same shape as the input.A list of labels./Users/runner/work/1/s/cmake/external/onnx/onnx/defs/traditionalml/old.ccInferred elem type differs from existing elem type: () vs (Inferred shape and existing shape differ in rank: (Inferred shape and existing shape differ in dimension : (type case mismatch. existing= inferred=type case unsupported. existing=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/work/1/b/RelWithDebInfo/onnx/onnx-ml.pb.ccsize_t to int conversion/Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/arena.ccCHECK failed: (min_bytes) <= (std::numeric_limits::max() - kBlockHeaderSize): /Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/extension_set.ccCHECK failed: (type) != (WireFormatLite::TYPE_ENUM): CHECK failed: (type) != (WireFormatLite::TYPE_MESSAGE): CHECK failed: (type) != (WireFormatLite::TYPE_GROUP): CHECK failed: (type) == (WireFormatLite::TYPE_ENUM): CHECK failed: type == WireFormatLite::TYPE_MESSAGE || type == WireFormatLite::TYPE_GROUP: Don't lookup extension types if they aren't present (1). Don't lookup extension types if they aren't present (2). CHECK failed: extension != NULL: Index out-of-bounds (field is empty).Extension not found.Non-primitive types can't be packed.Can't get here.Invalid message set extension.Multiple extension registrations for type "", field number can't reach here./Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/extension_set_inl.h/Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/generated_message_util.ccNot implemented field number CHECK failed: (scc->visit_status.load(std::memory_order_relaxed)) == (SCCInfoBase::kRunning): with type /Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/io/coded_stream.ccA protocol message was rejected because it was too big (more than bytes). To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in third_party/protobuf/src/google/protobuf/io/coded_stream.h.CHECK failed: (buffer_size) >= (0): /Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream.ccThis ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug./Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream_impl.ccclose() failed: CHECK failed: !is_closed_: Can't BackUp() after failed Next()./Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream_impl_lite.ccCHECK failed: (last_returned_size_) > (0): BackUp() can only be called after a successful Next().CHECK failed: (count) <= (last_returned_size_): CHECK failed: (count) >= (0): CHECK failed: target_ != NULL: Cannot allocate buffer larger than kint32max for StringOutputStream.CHECK failed: (count) <= (target_->size()): CHECK failed: backup_bytes_ == 0 && buffer_.get() != NULL: BackUp() can only be called after Next().CHECK failed: (count) <= (buffer_used_): Can't back up over more bytes than were returned by the last call to Next(). Parameter to BackUp() can't be negative.CHECK failed: (backup_bytes_) == (0): CHECK failed: (buffer_used_) == (buffer_size_): MessageLite at 0x/Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/message_lite.ccparse exceeded maximum protobuf size of 2GB: Can't message of type "" because it is missing required fields: CHECK failed: (byte_size_before_serialization) == (byte_size_after_serialization): was modified concurrently during serialization.CHECK failed: (bytes_produced_by_serialization) == (byte_size_before_serialization): Byte size calculation and serialization were inconsistent. This may indicate a bug in protocol buffers or it may be caused by concurrent modification of This shouldn't be called if all the sizes are equal.parsing/Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/parse_context.hCan't happen/Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): /Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/stubs/common.ccThis program requires version of the Protocol Buffer runtime library, but the installed version is . Please update your library. If you compiled the program yourself, make sure that your headers are from the same version of Protocol Buffers as your link-time library. (Version verification failed in "".)This program was compiled against version of the Protocol Buffer runtime library, which is not compatible with the installed version (). Contact the program author for an update. If you compiled the program yourself, make sure that your headers are from the same version of Protocol Buffers as your link-time library. (Version verification failed in "%d.%d.%dINFOWARNINGERRORFATAL[libprotobuf %s %s:%d] %s %c%d%u%ld%lu%g%p%lld%llu/Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/stubs/int128.ccDivision or mod by zero: dividend.hi=, lo=/Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/stubs/stringpiece.ccsize too big: details: /Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/stubs/stringprintf.ccCHECK failed: (v.size()) <= (kStringPrintfVectorMaxArgs): StringPrintfVector currently only supports up to arguments. Feel free to add support for more if you need it./Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/stubs/strutil.ccCHECK failed: dest: \x%02x\%03oCHECK failed: i >= 0: FastHexToBuffer() wants non-negative integers, not %.*gCHECK failed: value != nullptr: nullptr output boolean given.tyesyfalsefnon0123456789abcdefCHECK failed: s != nullptr: This can't happen; base64 decoder state = Logic problem? szsrc = %.1fCHECK failed: (temp[0]) == ('1'): CHECK failed: (temp[size - 1]) == ('5'): CHECK failed: (size) <= (6): CHECK failed: result != nullptr: /Users/runner/work/1/s/cmake/external/protobuf/src/google/protobuf/wire_format_lite.ccCHECK failed: (value.size()) <= (kint32max): '%s'String field contains invalid UTF-8 data when a protocol buffer. Use the 'bytes' type if you intend to send raw bytes. serializing/Users/runner/work/1/s/cmake/external/re2/re2/bitstate.ccGrowStack() failed: njob_ = job_.size() = Unexpected opcode: arg Bad arg in kInstAltMatch: Bad arg in kInstCapture: /Users/runner/work/1/s/cmake/external/re2/re2/compile.ccshould never happenCompiler::Copy called!No ranges in char classMissing case in Compiler: hello, world|%s%d(%p)|| flag=%#x/Users/runner/work/1/s/cmake/external/re2/re2/dfa.ccDeadState in RunStateOnByteNULL state in RunStateOnByteUnexpected special state in RunStateOnByteStateSaver failed to restore state.context does not contain textFailed to analyze start state.RunStateOnByteUnlocked failed after ResetCacheRunStateOnByteUnlocked failed after Reset/Users/runner/work/1/s/cmake/external/re2/re2/nfa.ccunhandled in AddToThreadqUnhandled in step(?,?)(%d,?)(%d,%d)Bad args: nsubmatch=Unexpected opcode in short circuit: in ComputeFirstByte in Prog::Fanout()/Users/runner/work/1/s/cmake/external/re2/re2/onepass.ccCannot use SearchOnePass for unanchored matches.unhandled opcode: /Users/runner/work/1/s/cmake/external/re2/re2/parse.ccRepetitionWalker::ShortVisit calledConcat of unknown round: RE2: unexpected op: Bad call to ParseState::ParsePerlFlagsAddFoldedRange recurses too much.AnyBad hex digit \d\D\s\S\w\W[:alnum:][:^alnum:][:alpha:][:^alpha:][:ascii:][:^ascii:][:blank:][:^blank:][:cntrl:][:^cntrl:][:digit:][:^digit:][:graph:][:^graph:][:lower:][:^lower:][:print:][:^print:][:punct:][:^punct:][:space:][:^space:][:upper:][:^upper:][:word:][:^word:][:xdigit:][:^xdigit:]alt -> %d | %daltmatch -> %d | %dbyte%s [%02x-%02x] -> %d/icapture %d -> %demptywidth %#x -> %dmatch! %dnop -> %dfail[%02x-%02x] -> %d %d. %s %d+ %s /Users/runner/work/1/s/cmake/external/re2/re2/re2.ccUnknown encoding Error parsing '': Error compiling '\x00Invalid RE2: RE2: invalid startpos, endpos pair. [startpos: endpos: text size: DFA out of memory: size bytemap range list count SearchDFA inconsistencySearchOnePass inconsistencySearchBitState inconsistencySearchNFA inconsistencyRewrite schema error: '\' not allowed at end.Rewrite schema error: '\' must be followed by a digit or '\'.Rewrite schema requests %d matches, but the regexp only has %d parenthesized subexpressions.requested group in regexp invalid rewrite pattern: Error reverse compiling '/Users/runner/work/1/s/cmake/external/re2/re2/regexp.ccRegexp not destroyed.Bad reference count Unexpected op in Regexp::Equal: no errorunexpected errorinvalid escape sequenceinvalid character classinvalid character class rangemissing ]missing )trailing \no argument for repetition operatorinvalid repetition sizebad repetition operatorinvalid perl operatorinvalid UTF-8invalid named capture groupNumCapturesWalker::ShortVisit called/Users/runner/work/1/s/cmake/external/re2/re2/walker-inl.hStack not empty.NamedCapturesWalker::ShortVisit calledCaptureNamesWalker::ShortVisit calledWalk NULL/Users/runner/work/1/s/cmake/external/re2/re2/simplify.ccSimplify failed on Case not handled in ComputeSimple: CoalesceWalker::ShortVisit calledDoCoalesce failed: r1->op() is DoCoalesce failed: r2->op() is SimplifyWalker::ShortVisit calledSimplify case not handled: Malformed repeat [truncated](?:/Users/runner/work/1/s/cmake/external/re2/re2/tostring.cckRegexpCapture cap() == 0?P<>[^\x00-\x{10ffff}](?:)Bad final char: ?+{%d,}{%d}{%d,%d}\C^$(?-m:^)(?-m:$)\z\b\B(?HaveMatch:%d)(){}[]*+?|.^$\[]^-\\\r\t\n\f\x{%x}AdlamAhomAnatolian_HieroglyphsArabicArmenianAvestanBalineseBamumBassa_VahBatakBengaliBhaiksukiBopomofoBrahmiBrailleBugineseBuhidCanadian_AboriginalCarianCaucasian_AlbanianCcCfChakmaChamCherokeeCoCommonCopticCsCuneiformCypriotCyrillicDeseretDevanagariDograDuployanEgyptian_HieroglyphsElbasanEthiopicGeorgianGlagoliticGothicGranthaGreekGujaratiGunjala_GondiGurmukhiHanHangulHanifi_RohingyaHanunooHatranHebrewHiraganaImperial_AramaicInheritedInscriptional_PahlaviInscriptional_ParthianJavaneseKaithiKannadaKatakanaKayah_LiKharoshthiKhmerKhojkiKhudawadiLLaoLatinLepchaLimbuLinear_ALinear_BLisuLlLmLoLtLuLycianLydianMahajaniMakasarMalayalamMandaicManichaeanMarchenMasaram_GondiMcMeMedefaidrinMeetei_MayekMende_KikakuiMeroitic_CursiveMeroitic_HieroglyphsMiaoMnModiMongolianMroMultaniMyanmarNNabataeanNdNew_Tai_LueNewaNkoNlNoNushuOghamOl_ChikiOld_HungarianOld_ItalicOld_North_ArabianOld_PermicOld_PersianOld_SogdianOld_South_ArabianOld_TurkicOriyaOsageOsmanyaPahawh_HmongPalmyrenePau_Cin_HauPcPdPePfPhags_PaPhoenicianPiPoPsPsalter_PahlaviRejangRunicSamaritanSaurashtraScSharadaShavianSiddhamSignWritingSinhalaSkSmSoSogdianSora_SompengSoyomboSundaneseSyloti_NagriSyriacTagalogTagbanwaTai_LeTai_ThamTai_VietTakriTamilTangutTeluguThaanaThaiTibetanTifinaghTirhutaUgariticVaiWarang_CitiYiZanabazar_SquareZlZpZsmu held in reader and writer mode simultaneously on entry to nsync_cv_wait_with_deadline() mu not held on entry to nsync_cv_wait_with_deadline() checking a waiter condition while unlocked attempt to nsync_mu_unlock() an nsync_mu held in read mode attempt to nsync_mu_unlock() an nsync_mu not held in write mode attempt to nsync_mu_runlock() an nsync_mu held in write mode attempt to nsync_mu_runlock() an nsync_mu not held in read mode nsync_mu not held in write mode nsync_mu not held in some mode nsync_mu not held in some mode when calling nsync_mu_wait_with_deadline() nsync_mu_wait woke but condition not true panic: unique_lock::lock: references null mutexunique_lock::lock: already lockedunique_lock::unlock: not locked}uRRysuh)'..bLLIA #W)'..bLL1 =u % 2X # 3<)'O)'9#+)'LL]TUA (}} N Q ;(V~)}}`   IAs Z~ e5O ZIAs Z~ e5O ZLwրNv H+| S\= HրNv H+| S\= H '$3{D_րN '  ]]eW k' &f  ?. 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fEEhEEaEEtEEsEEiEExEENSbEEdEEjEEyEE210SetTensorElementTypeERN4onnx9TypeProtoE6SetSparseTensorElementTypeERN4onnx9TypeProtoEʖ˖3SetMapKeyTypeERN4onnx9TypeProtoE4GetElementTypeEv̖̖210SetTensorElementTypeERN4onnx9TypeProtoE6SetSparseTensorElementTypeERN4onnx9TypeProtoE̖͖3SetMapKeyTypeERN4onnx9TypeProtoE4GetElementTypeEvΖΖ210SetTensorElementTypeERN4onnx9TypeProtoE6SetSparseTensorElementTypeERN4onnx9TypeProtoEΖϖ3SetMapKeyTypeERN4onnx9TypeProtoE4GetElementTypeEvЖЖ210SetTensorElementTypeERN4onnx9TypeProtoE6SetSparseTensorElementTypeERN4onnx9TypeProtoEЖі3SetMapKeyTypeERN4onnx9TypeProtoE4GetElementTypeEvҖҖ210SetTensorElementTypeERN4onnx9TypeProtoE6SetSparseTensorElementTypeERN4onnx9TypeProtoEҖӖ3SetMapKeyTypeERN4onnx9TypeProtoE4GetElementTypeEvԖԖ210SetTensorElementTypeERN4onnx9TypeProtoE6SetSparseTensorElementTypeERN4onnx9TypeProtoEԖՖ3SetMapKeyTypeERN4onnx9TypeProtoE4GetElementTypeEv֖֖210SetTensorElementTypeERN4onnx9TypeProtoE6SetSparseTensorElementTypeERN4onnx9TypeProtoE֖ז3SetMapKeyTypeERN4onnx9TypeProtoE4GetElementTypeEvؖؖt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEEE_210SetTensorElementTypeERN4onnx9TypeProtoE6SetSparseTensorElementTypeERN4onnx9TypeProtoEٖؖ3SetMapKeyTypeERN4onnx9TypeProtoE4GetElementTypeEvږږ210SetTensorElementTypeERN4onnx9TypeProtoE6SetSparseTensorElementTypeERN4onnx9TypeProtoEږۖ3SetMapKeyTypeERN4onnx9TypeProtoE4GetElementTypeEvܖܖ9MLFloat16EEE8BFloat16EEE210SetTensorElementTypeERN4onnx9TypeProtoE6SetSparseTensorElementTypeERN4onnx9TypeProtoEܖݖ3SetMapKeyTypeERN4onnx9TypeProtoE4GetElementTypeEvޖޖ210SetTensorElementTypeERN4onnx9TypeProtoE6SetSparseTensorElementTypeERN4onnx9TypeProtoEޖߖ3SetMapKeyTypeERN4onnx9TypeProtoE4GetElementTypeEv210SetTensorElementTypeERN4onnx9TypeProtoE6SetSparseTensorElementTypeERN4onnx9TypeProtoE3SetMapKeyTypeERN4onnx9TypeProtoE4GetElementTypeEv210SetTensorElementTypeERN4onnx9TypeProtoE6SetSparseTensorElementTypeERN4onnx9TypeProtoE3SetMapKeyTypeERN4onnx9TypeProtoE4GetElementTypeEv210SetTensorElementTypeERN4onnx9TypeProtoE6SetSparseTensorElementTypeERN4onnx9TypeProtoE3SetMapKeyTypeERN4onnx9TypeProtoE4GetElementTypeEv4TypeEvCD18mutable_type_protoEvTensorSeqEEEPKS0_vMLFloat16EEEPKS0_v4TypeEvCD18mutable_type_protoEv9CopyMutableMapValueERKN4onnx9TypeProtoERS2_2IsCompatibleERKN4onnx7RegisterAllProtosERKNSt3__18functionIFvPKNS_12DataTypeImplEEEEȔ122TypeProto_SparseTensorES4_6TypeProto_3TypeProto_MapES4_8TypeProto_SequenceES4_TensorES4_OpaqueES4_14SizeEv23GetDeleteFuncEvGetTypeProtoEvIsCompatibleERKN4onnx9TypeProtoE2Ev1Ev2Ev1Ev0Ev2Ev1Ev2Ev1Ev0Ev14SizeEv23GetDeleteFuncEvIsCompatibleERKN4onnx9TypeProtoEGetTypeProtoEv2Ev1Ev2Ev1Ev0EvВГSequenceTensorTypeBaseProtoHelperNodeContext114SizeEv23GetDeleteFuncEvIsCompatibleERKN4onnx9TypeProtoEGetTypeProtoEvC2EvD18mutable_type_protoEv2Ev1Ev0Ev120IsSequenceCompatibleERKN4onnx9TypeProtoE2GetTypeProtoEv58IsOpaqueCompatibleERKN4onnx9TypeProtoE7FromDataContainerEPKvmR8OrtValueIsMapCompatibleERKN4onnx9TypeProtoEToDataContainerERK8OrtValuemPvг iE4TypeEvfE4TypeEvbE4TypeEvNSaE4TypeEvhE4TypeEvtE4TypeEvsE4TypeEvxE4TypeEvdE4TypeEvjE4TypeEvyE4TypeEv39GetSparseTensorTypeI6RegisterDataTypeEPKS0_ϔ1GetDataTypeERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE֔4AllTensorTypesEvGetTensorTypeITypeFromProtoERKN4onnx9TypeProtoE iEEPKS0_vfEEPKS0_vbEEPKS0_vNSaEEPKS0_vhEEPKS0_vtEEPKS0_vsEEPKS0_vxEEPKS0_vdEEPKS0_vjEEPKS0_vyEEPKS0_vt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEE4TypeEv_t3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEEEPKS0_v_—×ėŗƗǗȗɗʗ˗̗͗Η9MLFloat16EE4TypeEv8BFloat16EE4TypeEvϗ9MLFloat16EEEPKS0_v8BFloat16EEEPKS0_vЗїҗ iE4TypeEvfE4TypeEvbE4TypeEvaE4TypeEvhE4TypeEvtE4TypeEvsE4TypeEvxE4TypeEvdE4TypeEvjE4TypeEvyE4TypeEvNS_ӗ iEEPKS0_vfEEPKS0_vbEEPKS0_vaEEPKS0_vhEEPKS0_vtEEPKS0_vsEEPKS0_vxEEPKS0_vdEEPKS0_vjEEPKS0_vyEEPKS0_vNS_՗֗חؗٗڗۗܗݗޗߗ9MLFloat16EE4TypeEv8BFloat16EE4TypeEv9MLFloat16EEEPKS0_v8BFloat16EEEPKS0_vNS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEExS8_NS1_4lessIS8_EENS6_INS1_4pairIKS8_S8_EEEEEEE4TypeEvxNS1_4lessIS8_EENS6_INS1_4pairIKS8_xEEEEEEE4TypeEvfNS1_4lessIS8_EENS6_INS1_4pairIKS8_fEEEEEEE4TypeEvdNS1_4lessIS8_EENS6_INS1_4pairIKS8_dEEEEEEE4TypeEv3mapI6vectorINS2_3mapI12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEEEPKS0_vNS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEx؄S9_NS2_4lessIS9_EENS7_INS2_4pairIKS9_S9_EEEEEEEEPKS0_vxNS2_4lessIS9_EENS7_INS2_4pairIKS9_xEEEEEEEEPKS0_vfNS2_4lessIS9_EENS7_INS2_4pairIKS9_fEEEEEEEEPKS0_vdNS2_4lessIS9_EENS7_INS2_4pairIKS9_dEEEEEEEEPKS0_vNS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS1_4lessIxEENS6_INS1_4pairIKxS8_EEEEEEE4TypeEvфxNS1_4lessIxEENS1_9allocatorINS1_4pairIKxxEEEEEEE4TypeEvfNS1_4lessIxEENS1_9allocatorINS1_4pairIKxfEEEEEEE4TypeEvdNS1_4lessIxEENS1_9allocatorINS1_4pairIKxdEEEEEEE4TypeEvNS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEENS2_4lessIxEENS7_INS2_4pairIKxS9_EEEEEEEEPKS0_vxNS2_4lessIxEENS2_9allocatorINS2_4pairIKxxEEEEEEEEPKS0_vfNS2_4lessIxEENS2_9allocatorINS2_4pairIKxfEEEEEEEEPKS0_vdNS2_4lessIxEENS2_9allocatorINS2_4pairIKxdEEEEEEEEPKS0_vceTensorTypeItialExecutor7ExecuteERKNS_12SessionStateERKNSt3__16vectorIiNS4_9allocatorIiEEEERKNS5_I8OrtValueNS6_ISB_EEEESA_RSD_RKNS4_13unordered_mapImNS4_8functionIFNS_6common6StatusERKNS_11TensorShapeERK13OrtMemoryInfoRSB_RbEEENS4_4hashImEENS4_8equal_toImEENS6_INS4_4pairIKmSU_EEEEEERKNS_7logging6LoggerE fE4TypeEvdE4TypeEvaE4TypeEvhE4TypeEvsE4TypeEvtE4TypeEv̍iE4TypeEvڍjE4TypeEvxE4TypeEvyE4TypeEvbE4TypeEvNS128SparseTensorTypeFromONNXEnumEiە3AllGetSequenceTensorTypeIAllNumericTensorTypesEv fEEPKS0_vdEEPKS0_vaEEPKS0_vhEEPKS0_vsEEPKS0_vōtEEPKS0_vӍiEEPKS0_vjEEPKS0_vxEEPKS0_vyEEPKS0_vbEEPKS0_vNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEE4TypeEv_Џt3__112basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEEEPKS0_vɏ_9MLFloat16EE4TypeEv8BFloat16EE4TypeEv9MLFloat16EEEPKS0_v8BFloat16EEEPKS0_vÐeparseTensorCquenceTypeINSt3__16vectorINS1_3mapIssionStateNS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEfNS1_4lessIS9_EENS7_INS1_4pairIKS9_fEEEEEENS7_ISG_EEEEE4TypeEvxfNS1_4lessIxEENS1_9allocatorINS1_4pairIKxfEEEEEENS6_ISB_EEEEE4TypeEvNS2_12basic_stringIcNS2_11char_traitsIcEENS2_9allocatorIcEEEEfNS2_4lessISA_EENS8_INS2_4pairIKSA_fEEEEEENS8_ISH_EEEEEEPKS0_vxfNS2_4lessIxEENS2_9allocatorINS2_4pairIKxfEEEEEENS7_ISC_EEEEEEPKS0_v֘TensorTypeFromONNXEnumEiAllSequenceTensorTypesEv0SequenceTensorTypeFromONNXEnumEiԕ3AllܛЏ iEfEܖbENSaEhEʘtEsExEřdEjEyE4TypeEvΖ6DeleteEPvઙ4TypeEv6DeleteEPv4TypeEv6DeleteEPv୙t3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEE_4TypeEv6DeleteEPv4TypeEv6DeleteEPvఙ4TypeEv6DeleteEPv4TypeEv6DeleteEPv೙4TypeEv6DeleteEPv4TypeEv6DeleteEPv඙4TypeEv6DeleteEPv4TypeEv6DeleteEPv๙4TypeEvۚ6DeleteEPv9MLFloat16EE8BFloat16EE4TypeEv6DeleteEPv·༙4TypeEvΛ6DeleteEPvɷFixedSizeTensorExceptHalfTypesEvIEEEFloatTensorExceptHalfTypesEv࿙˙IEEEFloatTensorTypesEvFixedSizeTensorTypesEvϙԙ16detail16CopyLittleEndianEmN3gsl4spanIKcEENS3_IcEE׷2342ONNXTensorElementDataTypeToProtoTensorTypeE25ONNXTensorElementDataType6ContainerCheckerC2ͣ7SwapByteOrderCopyEmN3gsl4spanIKcEENS2_IcEEз39TensorToTensorProtoERKNS_6TensorERKNSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEE1DefaultFreeEPv8ProviderIsCpuBasedERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE5ExecuteSubgraphERKNS_12SessionStateERKNS_19FeedsFetchesManagerERKNSt3__16vectorI8OrtValueNS7_9allocatorIS9_EEEERSC_RKNS7_13unordered_mapImNS7_8functionIFNS_6common6StatusERKNS_11TensorShapeERK13OrtMemoryInfoRS9_RbEEENS7_4hashImEENS7_8equal_toImEENSA_INS7_4pairIKmST_EEEEEE13ExecutionModeRKbRKNS_7logging6LoggerE2EPKNS_12DataTypeImplE1EPKNS_12DataTypeImplEƣIsOpaqueTypeEPKNS_12DataTypeImplEPKcS5_UnpackTensorIDefaultAllocEmExecuteGraphERKNS_12SessionStateERNS_19FeedsFetchesManagerERKNSt3__16vectorI8OrtValueNS6_9allocatorIS8_EEEERSB_13ExecutionModeRKbRKNS_7logging6LoggerEbnTensorTypeINSt3__1deIndexInfoC3mapI6vectorINS1_3mapINS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEЦxS8_NS1_4lessIS8_EENS6_INS1_4pairIKS8_S8_EEEEEEE6DeleteEPvxNS1_4lessIS8_EENS6_INS1_4pairIKS8_xEEEEEEE6DeleteEPv֬fNS1_4lessIS8_EENS6_INS1_4pairIKS8_fEEEEEEE6DeleteEPvdNS1_4lessIS8_EENS6_INS1_4pairIKS8_dEEEEEEE6DeleteEPv13NonTensorTypeINSt3__12ml7logging14LoggingManager12GetTimestampEvE6epochsֶ3mapI6vectorINS1_3mapI״NS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEExS8_NS1_4lessIS8_EENS6_INS1_4pairIKS8_S8_EEEEEEE13GetCreateFuncEvENUlvE_8__invokeEvϬxNS1_4lessIS8_EENS6_INS1_4pairIKS8_xEEEEEEE13GetCreateFuncEvENUlvE_8__invokeEvݬfNS1_4lessIS8_EENS6_INS1_4pairIKS8_fEEEEEEE13GetCreateFuncEvENUlvE_8__invokeEvdNS1_4lessIS8_EENS6_INS1_4pairIKS8_dEEEEEEE13GetCreateFuncEvENUlvE_8__invokeEvЀNS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS1_4lessIxEENS6_INS1_4pairIKxS8_EEEEEEE6DeleteEPvxNS1_4lessIxEENS1_9allocatorINS1_4pairIKxxEEEEEEE6DeleteEPvֲfNS1_4lessIxEENS1_9allocatorINS1_4pairIKxfEEEEEEE6DeleteEPvdNS1_4lessIxEENS1_9allocatorINS1_4pairIKxdEEEEEEE6DeleteEPvNS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEENS1_4lessIxEENS6_INS1_4pairIKxS8_EEEEEEE13GetCreateFuncEvENUlvE_8__invokeEvϲxNS1_4lessIxEENS1_9allocatorINS1_4pairIKxxEEEEEEE13GetCreateFuncEvENUlvE_8__invokeEvݲfNS1_4lessIxEENS1_9allocatorINS1_4pairIKxfEEEEEEE13GetCreateFuncEvENUlvE_8__invokeEvdNS1_4lessIxEENS1_9allocatorINS1_4pairIKxdEEEEEEE13GetCreateFuncEvENUlvE_8__invokeEvРਛNS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEfNS1_4lessIS9_EENS7_INS1_4pairIKS9_fEEEEEENS7_ISG_EEEEE6DeleteEPvдxfNS1_4lessIxEENS1_9allocatorINS1_4pairIKxfEEEEEENS6_ISB_EEEEE6DeleteEPvNS1_12basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEfNS1_4lessIS9_EENS7_INS1_4pairIKS9_fEEEEEENS7_ISG_EEEEE13GetCreateFuncEvENUlvE_8__invokeEvٶxfNS1_4lessIxEENS1_9allocatorINS1_4pairIKxfEEEEEENS6_ISB_EEEEE13GetCreateFuncEvENUlvE_8__invokeEvЎИЙজoOrtStatusERKNS_6common6StatusEensorShapeCErrorCodeEPK9OrtStatus߸ValueTypeEPK8OrtValueP8ONNXTypeErrorMessageEPK9OrtStatusMapValueTypeEPK14OrtMapTypeInfoPP11OrtTypeInfoD15LoadExternalLibERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEEPPvں2Ev1Evź0Ev̺ЩйC2ERKNS_18OrtValueNameIdxMapERKNS_13NodeIndexInfoERKNSt3__16vectorIiNS7_9allocatorIiEEEE׽D޽34GetMutableNodeInputOrOutputMLValueEi28GetOrCreateNodeOutputMLValueEiPKNS_11TensorShapeERP8OrtValuem14InitERKNSt3__16vectorIiNS1_9allocatorIiEEEERKNS2_I8OrtValueNS3_IS8_EEEERKNS1_13unordered_mapIiS8_NS1_4hashIiEENS1_8equal_toIiEENS3_INS1_4pairIKiS8_EEEEEESC_Ĝ2Ev1Ev0Ev̜ΜΜ2ľ12GetAllocatorERK13OrtMemoryInfo8IsOutputEi7GetNodeInputOrOutputMLValueEi2GetNodeIdxToMLValueIdxEiΜϜϜϜۜ4ReleaseMLValueEi8ReleaseMLValueImplEi0GetOutputsERNSt3__16vectorI8OrtValueNS1_9allocatorIS3_EEEEۜܜCD341279TraceFreeEi2ERKNSt3__16vectorIiNS1_9allocatorIiEEEERKNS2_I8OrtValueNS3_IS8_EEEES7_SC_RKNS1_13unordered_mapImNS1_8functionIFNS_6common6StatusERKNS_11TensorShapeERK13OrtMemoryInfoRS8_RbEEENS1_4hashImEENS1_8equal_toImEENS3_INS1_4pairIKmSQ_EEEEEERKNS_12SessionStateE1ERKNSt3__16vectorIiNS1_9allocatorIiEEEERKNS2_I8OrtValueNS3_IS8_EEEES7_SC_RKNS1_13unordered_mapImNS1_8functionIFNS_6common6StatusERKNS_11TensorShapeERK13OrtMemoryInfoRS8_RbEEENS1_4hashImEENS1_8equal_toImEENS3_INS1_4pairIKmSQ_EEEEEERKNS_12SessionStateE2Ev1Ev0Evа0CopyTensorERKNS_6TensorERS1_6Ge4AllocateMLValueTensorSelfOwnBufferER8OrtValueiPKNS_12DataTypeImplERK13OrtMemoryInfoRKNS_11TensorShapeEb8AllocateMLValueTensorPreAllocateBufferER8OrtValueiPKNS_12DataTypeImplERK13OrtMemoryInfoRKNS_11TensorShapeEb0A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FindAllSubgraphsERNSt3__16vectorIPS0_NS1_9allocatorIS3_EEEEʭ GenerateNodeNameERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE Խ1 4NameEv 9ModelPathEv 2α 4ReverseDFSFromERKNSt3__16vectorIʪ 1DescriptionEv 2 7GetSchemaRegistryEv mNS1_9allocatorImEEEERKNS1_8functionIFvPKNS_4NodeEEEESF_RKNS8_IFbSB_SB_EEE PKNS_4NodeENS1_9allocatorIS5_EEEERKNS1_8functionIFvS5_EEESF_RKNSB_IFbS5_S5_EEE лSJ_ оҾUpdateShapeInferenceERNS_4NodeE VerifyNodeAndOpMatchERKNS0_14ResolveOptionsEí AddInitializedTensorERKN4onnx11TensorProtoE РSet CreateNodeArgsERKN6google8protobuf16RepeatedPtrFieldINSt3__112basic_stringIcNS4_11char_traitsIcEENS4_9allocatorIcEEEEEERKNS4_13unordered_mapISA_N4onnx9TypeProtoENS4_4hashISA_EENS4_8equal_toISA_EENS8_INS4_4pairIKSA_SG_EEEEEE AddControlEdgeEmm InlineFunctionERNS_4NodeE DescriptionERKNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEE NodeArgTypeERNS_7NodeArgERKN4onnx9TypeProtoE 0 4GetAllInitializedTensorsEv 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120CompileToReverseProgEx5CD3RefEv67948309RemoveLeading7251NumCapturesEv4CompǞLNamedCapturesEvileToProgExuteSimpleEvCompileSetEPNS_6RegexpENS_3RE26AnchorExޟfirst_byteEvEmptyFlagsERKNS_11StringPieceEPKcDFANFARE2CD1ʡ92٥8RWLocker6SearchERKNS_11StringPieceES3_bbbPbPPKcPNS_10SparseSetTIvEEެ2EPNS_4ProgENS1_9MatchKindEx1EPNS_4ProgENS1_9MatchKindExЗ2Evá1Ev081CachedStateEPiijĥ2StateToWorkqEPNS0_5StateEPNS0_5WorkqE˥43AnalyzeSearchEPNS0_12SearchParamsEЬ9AnalyzeSearchHelperEPNS0_12SearchParamsEPNS0_9StartInfoEj׬ClearCacheEvڣAddToQueueEPNS0_5WorkqEijҥResetCacheEPNS0_8RWLockerEStateSaverХDumpSearchWorkqEPNS0_5WorkqEStateEPNS0_5StateEੲWorkqToCachedStateEPNS0_5WorkqES2_jPossibleMatchRangeEPNSt3__112basic_stringIcNS1_11char_traitsIcEENS1_9allocatorIcEEEES8_i1RunWorkqOnEmptyStringEPNS0_5WorkqES2_j2RunStateOnByteUnlockedEPNS0_5StateEiIJRunSlowSearchLoopEPNS0_12SearchParamsE¬FastSearchLoopEPNS0_12SearchParamsEɬBuildAllStatesERKNSt3__18functionIFvPKibEEEWorkqOnByteEPNS0_5WorkqES2_ijPbStateOnByteEPNS0_5StateEiŲʲ̲C14LockForWritingEvD2EPNS_5MutexE٨1EPNS_5MutexEԲԲԲ2Ev1EvղղֲCD7RestoreEv2EPS0_PNS0_5StateE۩1EPS0_PNS0_5StateEڲ۲2Ev1EvܲݲݲFTFTFEPNS0_12SearchParamsEܪTEPNS0_12SearchParamsE߲FEPNS0_12SearchParamsETEPNS0_12SearchParamsEFTFEPNS0_12SearchParamsETEPNS0_12SearchParamsEFEPNS0_12SearchParamsETEPNS0_12SearchParamsEGetDFAENS0_9MatchKindEFanoutEPNS_11SparseArrayIiEEDeleteDFAEPNS_3DFAEڭSearchIsOnePassEvDFAERKNS_11StringPieceES3_NS0_6AnchorENS0_9MatchKindEPS1_PbPNS_10SparseSetTIvEENFAERKNS_11StringPieceES3_NS0_6AnchorENS0_9MatchKindEPS1_iЃЇਲ਼CDͰ14StepEPNS_11SparseArrayIPNS0_6ThreadEEES5_iRKNS_11StringPieceEPKcٱ6SearchERKNS_11StringPieceES3_bbPS1_i2EPNS_4ProgE1EPNS_4ProgEư2Evݰ1Ev2AddToThreadqEPNS_11SparseArrayIPNS0_6ThreadEEEiiRKNS_11StringPieceEPKcS3_ұ3FormatCaptureEPPKc 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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_14Max_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Mean_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Mean_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Mean_Onnx_ver8EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Min_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Mod_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Pow_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Relu_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Relu_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Scan_Onnx_ver8EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Scan_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Selu_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Selu_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Sign_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Sinh_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Sqrt_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Sqrt_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Tanh_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Tanh_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_14TopK_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Acosh_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Asinh_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Atanh_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Celu_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Clip_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Clip_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Conv_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Equal_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Equal_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Floor_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Floor_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Gemm_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Loop_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15PRelu_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15PRelu_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15PRelu_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15PRelu_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Scan_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Slice_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Split_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Split_Onnx_ver2EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15TopK_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15TopK_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16ArgMax_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16ArgMin_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Concat_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Concat_Onnx_ver4EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Equal_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Expand_Onnx_ver8EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Gather_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16LpPool_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16LpPool_Onnx_ver2EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16OneHot_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Range_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Round_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Shrink_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17ArgMax_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17ArgMax_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17ArgMin_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17ArgMin_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17CumSum_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Dropout_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Dropout_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Dropout_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Einsum_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17EyeLike_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Greater_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Greater_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Greater_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Hardmax_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17LpPool_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17MaxPool_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17MaxPool_Onnx_ver8EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Reshape_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Scatter_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Sigmoid_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Sigmoid_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Softmax_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Squeeze_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Compress_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Hardmax_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Identity_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18MaxPool_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18MaxPool_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18MaxPool_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18ReduceL1_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18ReduceL2_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Scaler_OnnxML_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Softmax_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Softplus_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Softsign_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver7EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_18ZipMap_OnnxML_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19BitShift_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19CastMap_OnnxML_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19Imputer_OnnxML_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19LeakyRelu_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19LeakyRelu_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19MaxUnpool_Onnx_ver9EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19ReduceL1_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19ReduceL2_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19ReduceMax_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19ReduceMin_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19ReduceSum_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19Unsqueeze_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20LogSoftmax_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20MaxRoiPool_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20MaxUnpool_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20Reciprocal_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20Reciprocal_Onnx_ver6EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceMax_Onnx_ver11EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceMax_Onnx_ver12EEENS_8OpSchemaEv__ZN4onnx11GetOpSchemaINS_20ReduceMean_Onnx_ver1EEENS_8OpSchemaEv__ZN4onnx11Ge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chw.3.2.SkipOverPadding.LNchw.3.2.HandlePostProcessing.LNchw.3.ProcessRemainingOutputCount.LNchw.3.ProcessOutputCountRightPadAndRemaining.LNchw.ProcessFilterCountLessThan3.LNchw.2.ProcessOutputCount.LNchw.2.ProcessNextOutputCountBy2.LNchw.2.2.ProcessNextRow.LNchw.2.2.ProcessNextColumn.LNchw.2.2.SkipOverPadding.LNchw.2.2.HandlePostProcessing.LNchw.2.ProcessRemainingOutputCount.LNchw.2.ProcessOutputCountRightPadAndRemaining.LNchw.ProcessFilterCount1.LNchw.1.ProcessOutputCount.LNchw.1.ProcessNextOutputCountBy2.LNchw.1.2.ProcessNextRow.LNchw.1.2.ProcessNextColumn.LNchw.1.2.SkipOverPadding.LNchw.1.2.HandlePostProcessing.LNchw.1.ProcessRemainingOutputCount.LNchw.1.ProcessOutputCountRightPadAndRemaining.LNchw.ExitKernelMlasConvNchwFloatSingleAvxFilter1.LNchw.1.1.ProcessNextRow.LNchw.1.1.ProcessNextColumn.LNchw.1.1.SkipOverPadding.LNchw.1.1.HandlePostProcessingMlasConvNchwFloatSingleAvxFilter2.LNchw.2.1.ProcessNextRow.LNchw.2.1.ProcessNextColumn.LNchw.2.1.SkipOverPadding.LNchw.2.1.HandlePostProcessingMlasConvNchwFloatSingleAvxFilter3.LNchw.3.1.ProcessNextRow.LNchw.3.1.ProcessNextColumn.LNchw.3.1.SkipOverPadding.LNchw.3.1.HandlePostProcessingMlasConvNchwFloatSingleAvxFilter4.LNchw.4.1.ProcessNextRow.LNchw.4.1.ProcessNextColumn.LNchw.4.1.SkipOverPadding.LNchw.4.1.HandlePostProcessing.LNchwc.4.ProcessOutputCount.LNchwc.4.ProcessNextOutputCountBy2.LNchwc.4.2.ProcessNextRow.LNchwc.4.2.ProcessNextColumn.LNchwc.4.2.SkipOverPadding.LNchwc.4.2.HandlePostProcessing.LNchwc.4.ProcessRemainingOutputCount.LNchwc.4.ProcessOutputCountRightPadAndRemaining.LNchwc.ProcessFilterCount3.LNchwc.3.ProcessOutputCount.LNchwc.3.ProcessNextOutputCountBy2.LNchwc.3.2.ProcessNextRow.LNchwc.3.2.ProcessNextColumn.LNchwc.3.2.SkipOverPadding.LNchwc.3.2.HandlePostProcessing.LNchwc.3.ProcessRemainingOutputCount.LNchwc.3.ProcessOutputCountRightPadAndRemaining.LNchwc.ProcessFilterCountLessThan3.LNchwc.2.ProcessOutputCount.LNchwc.2.ProcessNextOutputCountBy2.LNchwc.2.2.ProcessNextRow.LNchwc.2.2.ProcessNextColumn.LNchwc.2.2.SkipOverPadding.LNchwc.2.2.HandlePostProcessing.LNchwc.2.ProcessRemainingOutputCount.LNchwc.2.ProcessOutputCountRightPadAndRemaining.LNchwc.ProcessFilterCount1.LNchwc.1.ProcessOutputCount.LNchwc.1.ProcessNextOutputCountBy2.LNchwc.1.2.ProcessNextRow.LNchwc.1.2.ProcessNextColumn.LNchwc.1.2.SkipOverPadding.LNchwc.1.2.HandlePostProcessing.LNchwc.1.ProcessRemainingOutputCount.LNchwc.1.ProcessOutputCountRightPadAndRemaining.LNchwc.ExitKernelMlasConvNchwcFloatSingleAvxFilter1.LNchwc.1.1.ProcessNextRow.LNchwc.1.1.ProcessNextColumn.LNchwc.1.1.SkipOverPadding.LNchwc.1.1.HandlePostProcessingMlasConvNchwcFloatSingleAvxFilter2.LNchwc.2.1.ProcessNextRow.LNchwc.2.1.ProcessNextColumn.LNchwc.2.1.SkipOverPadding.LNchwc.2.1.HandlePostProcessingMlasConvNchwcFloatSingleAvxFilter3.LNchwc.3.1.ProcessNextRow.LNchwc.3.1.ProcessNextColumn.LNchwc.3.1.SkipOverPadding.LNchwc.3.1.HandlePostProcessingMlasConvNchwcFloatSingleAvxFilter4.LNchwc.4.1.ProcessNextRow.LNchwc.4.1.ProcessNextColumn.LNchwc.4.1.SkipOverPadding.LNchwc.4.1.HandlePostProcessing.LDepthwise.1.ProcessOutputCount.LDepthwise.1.ProcessNextOutputCountBy2.LDepthwise.1.2.ProcessNextRow.LDepthwise.1.2.ProcessNextColumn.LDepthwise.1.2.SkipOverPadding.LDepthwise.1.2.HandlePostProcessing.LDepthwise.1.ProcessRemainingOutputCount.LDepthwise.1.ProcessOutputCountRightPadAndRemaining.LDepthwise.ExitKernelMlasConvDepthwiseFloatSingleAvxFilter1.LDepthwise.1.1.ProcessNextRow.LDepthwise.1.1.ProcessNextColumn.LDepthwise.1.1.SkipOverPadding.LDepthwise.1.1.HandlePostProcessing.LPointwise.4.ProcessNextOutputCountBy2.LPointwise.4.2.ProcessNextInputBlock.LPointwise.4.ProcessRemainingOutputCount.LPointwise.4.1.ProcessNextInputBlock.LPointwise.ProcessFilterCount3.LPointwise.3.ProcessNextOutputCountBy2.LPointwise.3.2.ProcessNextInputBlock.LPointwise.3.ProcessRemainingOutputCount.LPointwise.3.1.ProcessNextInputBlock.LPointwise.ProcessFilterCountLessThan3.LPointwise.2.ProcessNextOutputCountBy2.LPointwise.2.2.ProcessNextInputBlock.LPointwise.2.ProcessRemainingOutputCount.LPointwise.2.1.ProcessNextInputBlock.LPointwise.ProcessFilterCount1.LPointwise.1.ProcessNextOutputCountBy2.LPointwise.1.2.ProcessNextInputBlock.LPointwise.1.ProcessRemainingOutputCount.LPointwise.1.1.ProcessNextInputBlock.LPointwise.ExitKernel.LPostProcessBlock.1.1.SkipAccumulateOutput.LPostProcessBlock.1.1.SkipBiasAddition.LPostProcessBlock.1.1.SkipReluActivation.LPostProcessBlock.1.2.SkipAccumulateOutput.LPostProcessBlock.1.2.SkipBiasAddition.LPostProcessBlock.1.2.SkipReluActivation.LPostProcessBlock.1.3.SkipAccumulateOutput.LPostProcessBlock.1.3.SkipBiasAddition.LPostProcessBlock.1.3.SkipReluActivation.LPostProcessBlock.2.1.SkipAccumulateOutput.LPostProcessBlock.2.1.SkipBiasAddition.LPostProcessBlock.2.1.SkipReluActivation.LPostProcessBlock.2.2.SkipAccumulateOutput.LPostProcessBlock.2.2.SkipBiasAddition.LPostProcessBlock.2.2.SkipReluActivation.LPostProcessBlock.2.3.SkipAccumulateOutput.LPostProcessBlock.2.3.SkipBiasAddition.LPostProcessBlock.2.3.SkipReluActivati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.LSkipAccumulateOutputMasked8xNBlock815.LProcessCountM2.LProcessNextColumnLoop2xN2.LComputeBlockBy4Loop4.LProcessRemainingBlocks4.LComputeBlockBy1Loop4.LOutputBlock4.LMultiplyAlpha2xNBlock2.LStore2xNBlock2.LProcessRemainingCountN2.LComputeBlockBy4Loop198.LProcessRemainingBlocks198.LComputeBlockBy1Loop198.LOutputBlock198.LMultiplyAlpha1xNBlock2.LStore1xNBlock2.LOutputMasked2xNBlock2.LMultiplyAlphaMasked2xNBlock2.LStoreMasked2xNBlock2.LOutputMasked1xNBlock2.LMultiplyAlphaMasked1xNBlock2.LStoreMasked1xNBlock2.LProcessCountM4.LProcessNextColumnLoop2xN374.LComputeBlockBy4Loop376.LProcessRemainingBlocks376.LComputeBlockBy1Loop376.LOutputBlock376.LMultiplyAlpha2xNBlock374.LStore2xNBlock374.LProcessRemainingCountN374.LComputeBlockBy4Loop612.LProcessRemainingBlocks612.LComputeBlockBy1Loop612.LOutputBlock612.LMultiplyAlpha1xNBlock374.LStore1xNBlock374.LOutputMasked2xNBlock374.LMultiplyAlphaMasked2xNBlock374.LStoreMasked2xNBlock374.LOutputMasked1xNBlock374.LMultiplyAlphaMasked1xNBlock374.LStoreMasked1xNBlock374.LProcessCountM6.LProcessNextColumnLoop2xN828.LComputeBlockBy4Loop830.LProcessRemainingBlocks830.LComputeBlockBy1Loop830.LOutputBlock830.LMultiplyAlpha2xNBlock828.LStore2xNBlock828.LProcessRemainingCountN828.LComputeBlockBy4Loop1108.LProcessRemainingBlocks1108.LComputeBlockBy1Loop1108.LOutputBlock1108.LMultiplyAlpha1xNBlock828.LStore1xNBlock828.LOutputMasked2xNBlock828.LMultiplyAlphaMasked2xNBlock828.LStoreMasked2xNBlock828.LOutputMasked1xNBlock828.LMultiplyAlphaMasked1xNBlock828.LStoreMasked1xNBlock828.LExitKernelAndZeroUpper.LExitKernel.LProcessCountM1.LProcessNextColumnLoop2xN1364.LComputeBlockBy4Loop1366.LProcessRemainingBlocks1366.LComputeBlockBy1Loop1366.LOutputBlock1366.LMultiplyAlpha2xNBlock1364.LStore2xNBlock1364.LProcessRemainingCountN1364.LComputeBlockBy4Loop1439.LProcessRemainingBlocks1439.LComputeBlockBy1Loop1439.LOutputBlock1439.LMultiplyAlpha1xNBlock1364.LStore1xNBlock1364.LOutputMasked2xNBlock1364.LMultiplyAlphaMasked2xNBlock1364.LStoreMasked2xNBlock1364.LOutputMasked1xNBlock1364.LMultiplyAlphaMasked1xNBlock1364.LStoreMasked1xNBlock1364.LProcessCountM3.LProcessNextColumnLoop2xN1530.LComputeBlockBy4Loop1532.LProcessRemainingBlocks1532.LComputeBlockBy1Loop1532.LOutputBlock1532.LMultiplyAlpha2xNBlock1530.LStore2xNBlock1530.LProcessRemainingCountN1530.LComputeBlockBy4Loop1747.LProcessRemainingBlocks1747.LComputeBlockBy1Loop1747.LOutputBlock1747.LMultiplyAlpha1xNBlock1530.LStore1xNBlock1530.LOutputMasked2xNBlock1530.LMultiplyAlphaMasked2xNBlock1530.LStoreMasked2xNBlock1530.LOutputMasked1xNBlock1530.LMultiplyAlphaMasked1xNBlock1530.LStoreMasked1xNBlock1530.LProcessCountM5.LProcessNextColumnLoop2xN1943.LComputeBlockBy4Loop1945.LProcessRemainingBlocks1945.LComputeBlockBy1Loop1945.LOutputBlock1945.LMultiplyAlpha2xNBlock1943.LStore2xNBlock1943.LProcessRemainingCountN1943.LComputeBlockBy4Loop2202.LProcessRemainingBlocks2202.LComputeBlockBy1Loop2202.LOutputBlock2202.LMultiplyAlpha1xNBlock1943.LStore1xNBlock1943.LOutputMasked2xNBlock1943.LMultiplyAlphaMasked2xNBlock1943.LStoreMasked2xNBlock1943.LOutputMasked1xNBlock1943.LMultiplyAlphaMasked1xNBlock1943.LStoreMasked1xNBlock1943.LProcessCountM2.LProcessNextColumnLoop2xN2.LComputeBlockBy4Loop4.LProcessRemainingBlocks4.LComputeBlockBy1Loop4.LOutputBlock4.LMultiplyAlpha2xNBlock2.LStore2xNBlock2.LProcessRemainingCountN2.LComputeBlockBy4Loop198.LProcessRemainingBlocks198.LComputeBlockBy1Loop198.LOutputBlock198.LMultiplyAlpha1xNBlock2.LStore1xNBlock2.LOutputMasked2xNBlock2.LMultiplyAlphaMasked2xNBlock2.LStoreMasked2xNBlock2.LOutputMasked1xNBlock2.LMultiplyAlphaMasked1xNBlock2.LStoreMasked1xNBlock2.LProcessCountM4.LProcessNextColumnLoop2xN374.LComputeBlockBy4Loop376.LProcessRemainingBlocks376.LComputeBlockBy1Loop376.LOutputBlock376.LMultiplyAlpha2xNBlock374.LStore2xNBlock374.LProcessRemainingCountN374.LComputeBlockBy4Loop612.LProcessRemainingBlocks612.LComputeBlockBy1Loop612.LOutputBlock612.LMultiplyAlpha1xNBlock374.LStore1xNBlock374.LOutputMasked2xNBlock374.LMultiplyAlphaMasked2xNBlock374.LStoreMasked2xNBlock374.LOutputMasked1xNBlock374.LMultiplyAlphaMasked1xNBlock374.LStoreMasked1xNBlock374.LProcessCountM6.LProcessNextColumnLoop2xN828.LComputeBlockBy4Loop830.LProcessRemainingBlocks830.LComputeBlockBy1Loop830.LOutputBlock830.LMultiplyAlpha2xNBlock828.LStore2xNBlock828.LProcessRemainingCountN828.LComputeBlockBy4Loop1108.LProcessRemainingBlocks1108.LComputeBlockBy1Loop1108.LOutputBlock1108.LMultiplyAlpha1xNBlock828.LStore1xNBlock828.LOutputMasked2xNBlock828.LMultiplyAlphaMasked2xNBlock828.LStoreMasked2xNBlock828.LOutputMasked1xNBlock828.LMultiplyAlphaMasked1xNBlock828.LStoreMasked1xNBlock828.LExitKernelAndZeroUpper.LExitKernel.LProcessCountM1.LProcessNextColumnLoop2xN1364.LComputeBlockBy4Loop1366.LProcessRemainingBlocks1366.LComputeBlockBy1Loop1366.LOutputBlock1366.LMultiplyAlpha2xNBlock1364.LStore2xNBlock1364.LProcessRemainingCountN1364.LComputeBlockBy4Loop1439.LProcessRemainingBlocks1439.LComputeBlockBy1Loop1439.LOutputBlock1439.LMultiplyAlpha1xNBlock1364.LStore1xNBl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ocessNextRow.LNchw.2.3.ProcessNextColumn.LNchw.2.3.SkipOverPadding.LNchw.2.3.HandlePostProcessing.LNchw.2.ProcessRemainingOutputCountLessThan3.LNchw.2.2.ProcessNextRow.LNchw.2.2.ProcessNextColumn.LNchw.2.2.SkipOverPadding.LNchw.2.2.HandlePostProcessing.LNchw.2.ProcessOutputCountRightPadAndRemaining.LNchw.ProcessFilterCount1.LNchw.1.ProcessOutputCount.LNchw.1.ProcessNextOutputCountBy6.LNchw.1.6.ProcessNextRow.LNchw.1.6.ProcessNextColumn.LNchw.1.6.SkipOverPadding.LNchw.1.6.HandlePostProcessing.LNchw.1.ProcessRemainingOutputCount.LNchw.1.3.ProcessNextRow.LNchw.1.3.ProcessNextColumn.LNchw.1.3.SkipOverPadding.LNchw.1.3.HandlePostProcessing.LNchw.1.ProcessRemainingOutputCountLessThan3.LNchw.1.2.ProcessNextRow.LNchw.1.2.ProcessNextColumn.LNchw.1.2.SkipOverPadding.LNchw.1.2.HandlePostProcessing.LNchw.1.ProcessOutputCountRightPadAndRemaining.LNchw.ExitKernelMlasConvNchwFloatSingleAvx512FFilter1.LNchw.1.1.ProcessNextRow.LNchw.1.1.ProcessNextColumn.LNchw.1.1.SkipOverPadding.LNchw.1.1.HandlePostProcessingMlasConvNchwFloatSingleAvx512FFilter2.LNchw.2.1.ProcessNextRow.LNchw.2.1.ProcessNextColumn.LNchw.2.1.SkipOverPadding.LNchw.2.1.HandlePostProcessingMlasConvNchwFloatSingleAvx512FFilter3.LNchw.3.1.ProcessNextRow.LNchw.3.1.ProcessNextColumn.LNchw.3.1.SkipOverPadding.LNchw.3.1.HandlePostProcessingMlasConvNchwFloatSingleAvx512FFilter4.LNchw.4.1.ProcessNextRow.LNchw.4.1.ProcessNextColumn.LNchw.4.1.SkipOverPadding.LNchw.4.1.HandlePostProcessing.LNchwc.4.ProcessOutputCount.LNchwc.4.ProcessNextOutputCountBy6.LNchwc.4.6.ProcessNextRow.LNchwc.4.6.ProcessNextColumn.LNchwc.4.6.SkipOverPadding.LNchwc.4.6.HandlePostProcessing.LNchwc.4.ProcessRemainingOutputCount.LNchwc.4.3.ProcessNextRow.LNchwc.4.3.ProcessNextColumn.LNchwc.4.3.SkipOverPadding.LNchwc.4.3.HandlePostProcessing.LNchwc.4.ProcessRemainingOutputCountLessThan3.LNchwc.4.2.ProcessNextRow.LNchwc.4.2.ProcessNextColumn.LNchwc.4.2.SkipOverPadding.LNchwc.4.2.HandlePostProcessing.LNchwc.4.ProcessOutputCountRightPadAndRemaining.LNchwc.ProcessFilterCount3.LNchwc.3.ProcessOutputCount.LNchwc.3.ProcessNextOutputCountBy6.LNchwc.3.6.ProcessNextRow.LNchwc.3.6.ProcessNextColumn.LNchwc.3.6.SkipOverPadding.LNchwc.3.6.HandlePostProcessing.LNchwc.3.ProcessRemainingOutputCount.LNchwc.3.3.ProcessNextRow.LNchwc.3.3.ProcessNextColumn.LNchwc.3.3.SkipOverPadding.LNchwc.3.3.HandlePostProcessing.LNchwc.3.ProcessRemainingOutputCountLessThan3.LNchwc.3.2.ProcessNextRow.LNchwc.3.2.ProcessNextColumn.LNchwc.3.2.SkipOverPadding.LNchwc.3.2.HandlePostProcessing.LNchwc.3.ProcessOutputCountRightPadAndRemaining.LNchwc.ProcessFilterCountLessThan3.LNchwc.2.ProcessOutputCount.LNchwc.2.ProcessNextOutputCountBy6.LNchwc.2.6.ProcessNextRow.LNchwc.2.6.ProcessNextColumn.LNchwc.2.6.SkipOverPadding.LNchwc.2.6.HandlePostProcessing.LNchwc.2.ProcessRemainingOutputCount.LNchwc.2.3.ProcessNextRow.LNchwc.2.3.ProcessNextColumn.LNchwc.2.3.SkipOverPadding.LNchwc.2.3.HandlePostProcessing.LNchwc.2.ProcessRemainingOutputCountLessThan3.LNchwc.2.2.ProcessNextRow.LNchwc.2.2.ProcessNextColumn.LNchwc.2.2.SkipOverPadding.LNchwc.2.2.HandlePostProcessing.LNchwc.2.ProcessOutputCountRightPadAndRemaining.LNchwc.ProcessFilterCount1.LNchwc.1.ProcessOutputCount.LNchwc.1.ProcessNextOutputCountBy6.LNchwc.1.6.ProcessNextRow.LNchwc.1.6.ProcessNextColumn.LNchwc.1.6.SkipOverPadding.LNchwc.1.6.HandlePostProcessing.LNchwc.1.ProcessRemainingOutputCount.LNchwc.1.3.ProcessNextRow.LNchwc.1.3.ProcessNextColumn.LNchwc.1.3.SkipOverPadding.LNchwc.1.3.HandlePostProcessing.LNchwc.1.ProcessRemainingOutputCountLessThan3.LNchwc.1.2.ProcessNextRow.LNchwc.1.2.ProcessNextColumn.LNchwc.1.2.SkipOverPadding.LNchwc.1.2.HandlePostProcessing.LNchwc.1.ProcessOutputCountRightPadAndRemaining.LNchwc.ExitKernelMlasConvNchwcFloatSingleAvx512FFilter1.LNchwc.1.1.ProcessNextRow.LNchwc.1.1.ProcessNextColumn.LNchwc.1.1.SkipOverPadding.LNchwc.1.1.HandlePostProcessingMlasConvNchwcFloatSingleAvx512FFilter2.LNchwc.2.1.ProcessNextRow.LNchwc.2.1.ProcessNextColumn.LNchwc.2.1.SkipOverPadding.LNchwc.2.1.HandlePostProcessingMlasConvNchwcFloatSingleAvx512FFilter3.LNchwc.3.1.ProcessNextRow.LNchwc.3.1.ProcessNextColumn.LNchwc.3.1.SkipOverPadding.LNchwc.3.1.HandlePostProcessingMlasConvNchwcFloatSingleAvx512FFilter4.LNchwc.4.1.ProcessNextRow.LNchwc.4.1.ProcessNextColumn.LNchwc.4.1.SkipOverPadding.LNchwc.4.1.HandlePostProcessing.LDepthwise.1.ProcessOutputCount.LDepthwise.1.ProcessNextOutputCountBy6.LDepthwise.1.6.ProcessNextRow.LDepthwise.1.6.ProcessNextColumn.LDepthwise.1.6.SkipOverPadding.LDepthwise.1.6.HandlePostProcessing.LDepthwise.1.ProcessRemainingOutputCount.LDepthwise.1.3.ProcessNextRow.LDepthwise.1.3.ProcessNextColumn.LDepthwise.1.3.SkipOverPadding.LDepthwise.1.3.HandlePostProcessing.LDepthwise.1.ProcessRemainingOutputCountLessThan3.LDepthwise.1.2.ProcessNextRow.LDepthwise.1.2.ProcessNextColumn.LDepthwise.1.2.SkipOverPadding.LDepthwise.1.2.HandlePostProcessing.LDepthwise.1.ProcessOutputCountRightPadAndRemaining.LDepthwise.ExitKernelMlasConvDepthwiseFloatSingleAvx512FFilter1.LDepthwise.1.1.ProcessN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