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ddddddddiddidddddiddiddddddddddddddddddddd ddd!dd"dd#dd$d%dddd&d'd(d)d*dd+d,
dd-d.d/d0d1dd2d3d4d5d3d6d5d3d7d5d8d9d:d;dddd d<d)dd=dd"d>dddd?d@dddd&d'd(d)d*dd+d,
dd-d.d/d0d1dd2d3d4d5d3d6d5d3d7d5d8d9dAdBddCdDddidEdFdGdHdIdJdKdLdiddiddMdMddiddiddNdNdOddCdPddidQdRdSdIdTdTddUdVdWddiddXdXdYdZid[d\id]d^d_d5id`dadbddiddidcdddedIdfdfdOdgdDddidhdid5dhdjd5dkd9i dldmddiddidcdddedIdndoddiddidcdddedIdpdpdYdOidqdrdsddtdtddiddiddudvddiddidcdddedIdwdxdydzd{d|ddid}d~dd}d~dd}d~dd|d9ddddiddiddd~did9ddddiddiddd~did9ddddiddddddddiddddddddiddidddddiddidddddiddidddddiddidddddddddddiddidddddiddidi ddddidddddiddiddddddDddidddddiddiddddddddddddd	ddddddddddddddddddddddddOddCdPddidQdRdSdIddddiddiddddOddVdddidddddddUdVddCddddddǜddidIddddd˜ddiddddddϜdddҜdddddiddidddddddUd؜d[diddidIddddCddVdܜddidddddddUd؜d[diddidIdddddddidd^d~did9i ddddiddddddddCdDddidddddiddidddddCdDddidddddCdDddidddddCdDddidddddidddddddddiddddYdiddidddddiddiddddddddiddddddddd dddddiddidddddiddidddddiddidddddiddidddddUdddd	di d
d
ddiddidddddCdDddidddddiddddddddiddid3dd5d3dd5dd9ddddiddiddddYdOid\ddddidIddddddd d!d"d#d$idId%d%ddCd&ddidd'd'ddiddiddidd^d(d5id)d*d+d*id,d,ddiddidd-d-dOd.d/dd\idd0d0dOd.dVd1dd\id2d^d~did`d3d3dOd.dVd1dd\id2d^d~did`d4d4dOd.d/dd\idd5d5dOd.d6dd\idd7d7d8d9d:dd\idi d;d;ddiddidd<d<ddiddidd=d>ddiddid^d?d@dAd^dBdCdAdDd`dEdEddCdDddiddcidIdFdGddd˜ddd ddHdHddiddidcdcidIdIdIddiddidd^d~did9dJdJddKdLddCiddMd+dMidNdNddiddiddOdOdOdPd.dQdRdd\iddSdSdOd.d/dd\idTdGdUdVdWdIdXdXdOd.dVd1dd\id2d^d~did`dYdYdYdOid[d\idUdZd[dId\d\ddUdddidd]d]ddUd^d_d`dadCdbdcdddeddfdfddgdhddidi didjddOiddiddkdkddiddiddldldddmdddndodpddqddrdsddtduddiddiddvdvdYdOiddiddwdwddOiddiddxdxddiddiddydydzdQiddidd{d{ddCdDddidd|d|ddiddid3d}d5d3d~ddd9ddddCdDddidddddCdDddiddddddddQdddddddddd~iid)dd+didddddddddddddddiddddddddiddddi ddddiddidddddidddddidIddddidddddddddCdDddidddddiddiddddddddiddidIddddiddd@ddAidddddCdDddidddddCdDddidddddiddidddddiddidddddiddidddddiddiddidd^d?ddAidddddCdDddidddddiddidddddiddiddidIddddidd~idd~idÜdĜi dŐdddƐdǜddiddȐdddiddbdɜddʐdddiddbdɜddːdddUdddidd̐dddiddbdɜdd͐dddiddbdɜddΐdddϐd_d`dМddbdddcdќddҐdddϐd_d`dМddbdddcdќddӐdddϐdԐd՜ddbd֐dלddؐdؐdِdڐdېdܜdݐdސdߜddddOddVdddidTdGdVddIddddidddddiddidddddiddidddddiddidddddiddidddddiddidd3d~did9i ddddiddidddddCdDddiddddd~ddd~ddddddd~ddd~ddddddd~ddd~dddddddidddddddIddddiddidddddiddidddddCdDddidddddCdDddidddddCdDddidddddiddCidd d ddiddidddddiddidcdddedd^d~didddddidddddiddidd3d~did9ddddd~didi dd	d
ddddddddd+d
ddddddddddddddddd d!d"d#d$d%d&i d'd(d)d*d+d,d-d.d/d0d1d2d3d4d5d6d7d8d9d:d;d<d=d>d?d@dAdBdCdDdEdFdGdHdIdJdCdKddLdLddddUdVddCddddddǜddMdMddNdVdddOdIdDd(d*dCdPddQdRddSdddUdVdTdCddddddǜddUdUdOd.dVdVdWd[d\idTdGdUdVdWdIdXdXdOd.dVdVdYd\dZd[dd\d\dOd.dVdVdWd[d\idTdGdUdVdWdId]d]ddCdDddidd^d^ddCdDddidd_d_ddCdDddidd`d`ddCdDddiddadad͐dbdVdUdcddidddddd͐dedfdVdddgdddhdidjdkdldmdnddododddCdpdUdqdrddddsddtdtddudvdwdxdydzd{d|d$d%d}dd~ddddFdHdddddddddddIdddddddddd!dddd#dddddd8dd@ddd	di ddddCdVdddƐddddddCdƐdddddddddi dddddddddddd dddd!dd"dd#ddddddddddddddddÐdĐdŜdJddƜddǐdddKdLddiddȐdddiddiddɐddddʐdfdVd˜dldhdiddd͜dΐdϐdVdМdIdѐddddʐdfdVddҜddddhdidjdkdldmdӐdԜ
dΐdϐdVdМdIdՐddddVd֜ddd؜ddِddd.dVd1ddڐdۜdܐdݐdސdߜdIddddiddidddddiddiddddddddidd^dd5id9dddddddidddd͐ddddidddddiddd@ddAidddddiddiddddΐdddqdrddddddd$ddIi ddddiddidddddiddidddddCdDddiddddddddd dddddddddddddddd	dd
dddddCdDddidddddCdDddidddddddd\idddddUdVdWdCdddddidIddddiddidddddiddidddddiddidddddiddidddddiddidddddiddiddidIdd ddCdDddidd!d!dd"d#d$didi d%d%ddېd&ddidd'd(ddddVd)d*d+dd,d-d.dd/d/ddCd˜ddNd0dd1d1ddCdDddidd2d2ddiddidd3d3ddiddidd4d4ddd5d6ddidd7d7dd8d9d:d;d<d=ddid>d?d@dAdIdBd+dBidCdCddiddDdDdddddiddEdEdddddiddcidIdFdFddUdVdWdCddŐdGddHdHddOidd\iddIdIddiddiddJdJdOdgdDddidhdid5dhdjd5dkd9dKdLddiddidi dMdNddiddiddOdPddiddiddQdQddRdSddiddTdTddCdDddiddUdUddidddddVdVddiddiddWdWddXdYddiddZd[dddd&d'd)d*dd+d\	dd-d.d0d1dd]dd^d^ddUdVdWdCdddsdd_d_ddiddid`dzidIdސdaddddUd؜d[diddidIddbddiddiddcidd^dddedAidddfdΐddgdqdrdhdddddd$ddIdidjddCddkdldmddddndodpdqdIdrdsddddUd؜d[diddidIdtduddiddddd^d?d@dAid`dvdvddCdېdwddidi dxdxddCdDddiddydyddCdDddiddzdzddiddidd{d|ddddUd؜d[diddidId}d+d}id~d~dydddddidddddCdDddidddddiddidddddiddidddddiddidddddiddiddddd"d˜ddidddddiddidddddiddidddddCdDddidddddiddidddddCdDddidi ddddCdDddidddddiddidddddiddidddddiddiddddd͐dddidddddiddddddddCdDddddddd3d~did9dddddDddddddddddddd dddddQdddCidddddCddddddddiddCdDddidddqdIddddCdDddidddddΐdddd!ddddddiddiddddddddidddddiddidcdddedd^d~didi ddddiddQdddidIddddiddQdddidIddddCdDddidddddiddidddddiddidcdddedd^d~didddddiddiddĐddd~ddd~ddddŐdddiddiddƐddddd&d'd(d)d*ddǜ	dd-d.d/d0d1dd2d3d~dd3d~dd3d~dd8d9dȐddddmdddʜddqdd˜dd̐ddLdiddidd͐dddiddidcdddedd^d~diddϐdddCdDddiddѐdddҐdӜddiddԐdddidddddՐddddmdddʜddqdd˜ddאddd_d`d؜ddbdddcdќdi dِdddiddiddڐdddʐdfdVdVdۜddidΐdϐdݜdIdސdސddΐdddiddߐdߐddΐd!dddddddddOddVddddidddddIddddiddidd^d~did9dddd͐dddidddddCdDddiddddddddidddddidddddd^iid`ddrddddUd؜d[diddidIdddddېdddddddddiddidҐdidId dddiddidddddiddddddddCdDddiddd	ddOiddidi d
dddiddidd^dd5id9ddddiddidddddiddidddddiddidd3d~did9dd+diddddiddiddidd^dd5idddddiddidddddiddidddddidddddiddidddddiddidddddiddidddddiddidddddiddiddidd^d~didddddiddiddidIddddiddid d!id d3d"d5id)d#d#ddQd$ddidi d%d%d&d'iddidd(d(dOd)d*ddrd+dd,d-ddiddidcdddedd^d~didd.d.dd/d!d0ddidd1d1dOdd2d3dd4idd5d6d7dId8d+d8id9d9ddid:d;d<dd=d=dYdOid[d\idUdZd6d>dId?d?ddUd^d_d`dadCdbdcdddedd@d@ddiddd@ddAiddAdAddiddBdBdydzd{d|ddid}d~dd}d~dd}d~dd|d9dCdCddiddiddDdDddiddiddEdEddiddiddFdFddiddiddGdHddCdDddidi dIdIddiddiddJdJddiddidKdcdLdIdMdMddNdOddiddcidIdPdPdYdOid[d\idTdGdQdRdSdIdTdtddiddddd^d?d@dAid`dUdUi dddVdWdXdYdZdqd[d\d]d^d_d`dadbdcdddedfdgdhdidjdkdldmdndodpdqdri ddCdsdtdudvdwdxdydzd{d|d}d~dddddddddddddddddddddddddddddddddddddSdddddddCdddddddddddddddddiddidd^d~did`dddddddd dddd"ddddddddOdddddddddÜddĐddd/d!d0ddiddŐddd/d!d0ddƐdǜddȐdddiddidddd5id`dːdddiddidd̐ddd.d6ddidd͐dddiddiddΐdddidi dϐdddiddid3d}d5d3d~ddd9dАddd͐dѐdҜddiddӐddddѐdҜddiddԐdԐdՐd֐dלddidddddiddؐdddސdٜddڐdۜddܐdddiddiddݐdddiddiddސdddddddddddd5id`ddddCdddddddddd5id`dd+diddddCdDddidddddid dCiddidd^dd5id)ddddiddidddddddddddddd+didddYdiddidi ddddiddddddddiddidddddddddd ddddddddidddddiddidddddiddidddddiddidddddiddidddddiddidd	d	dYdOiddid^d
ddAd^dddAdd`ddddOiddidddddiddidddddiddidddddiddidddddiddidҐdidIddddiddidddddCdDddidi ddddidddddd@ddAidddd^d
ddAd^dddAdddddd^d(d5idddddiddddېdddddidddddiddidd^d~did d^d~did!d"d"dd^d~d(d#idd$d$ddiddidd%d%ddiddidd&d'ddiddddd(idd^d~didd)d)ddiddCidd*d*ddiddidd+d+ddiddidd,d,ddOiddid^d
ddAd^dddAd^d-d.dAd/d`d0d1ddCdDddidd2dJddiddidcddd3d4dd^d~didi d5d5ddidd6d7d8dd9d9ddiddidd:d:ddUdVddd;dCddddddǜddidId<d<dOdd=dd4iddidId>d>ddiddidd?d?ddiddidd@d@ddiddiddAdAddBdCdDdEdFdGdHddIdIddJdKdLdd"dQdMddNdNddiddiddOdOddiddidPd^dQdRdAid`dSdTddiddddUd^dVd5id9dWdXddiddddUd^dVd5id9dYdYddOiddiddZd[ddiddid\didId]d]ddCdDddidd^d^ddiddidi d_d`ddddUd؜d[diddidIdadadYdiddiddbdbddiddcdcdYdiddidddddddiddCiddedfddid3d~dd3d~ddddd@ddAidgdhdiddiddiddjdjddiddddkdlddmdmddidddkdnddododddpddidqdid2d^d~diddrdrdddpddiddsdtddiddddd(idd^dudvdAiddwdwddiddCiddxdydddzd{d|d}dd~ddddddd5id9dd+didddOddddΐddddddddddddddi dddddCdddiddd+didddddddΐddddddiddiddddddddddddddddddddddddddddOdgdd[d\idddddiddidddddiddidddddiddidddddiddiddddddddddddddddd+didddddddddiddddddSddCiddÐddddSdCddĐdŜddƐddOdǐdȜd\dɐdʜdi dːddOd̐dddddΜddddАdѐdҜddӐdddd ddԜdd"d՜dd֐dddאd؜ddiddِdِdddddڜdېdܐdݐdrdސdߜdddd d!d"ddddddddddi dddddddddd&dd'dd)dd*ddddddddddddddddd ddd{idddddddddidddddiddidddddiddddd	d	dd
ddddddddddddYdOiddidddddCdDddddddddCdDddddddddCdVdddidEdFdGdUddddddd
dIddddQdddidd d ddiddidd!d!d"ddQd#d$d%d&d'di d(d(dLd)id*d+idd,d,ddKdLddidd-d-ddCdDd.d/d0dd1dd2d2ddiddidd3d3dOdddVd4d5d6d7dd8dd9d9dOd:ddVd;d<d=dd>dd?d?ddiddidd@dAdddmdddʜddqdd˜dd>dBddCdDddidCd^dBd5id`dDdDddiddEdEddiddiddFdFdydddGdHddiddIdIdd͐dddiddJdJdOddddVdKddd5dLdMddNdNddidddOdPdQdRidIdSdSddiddiddTdTddiddidi dUdUddiddiddVdVdOdddVdWdXdYdZd[d\d]d^d_dd`d`ddiddiddadadbdbiddiddcdcddiddddddedfdgdhdiiddjdjdddkdldmddndodpddqdqdrdsdtduddiddvdvddwdxdydzdd{d|d}d~ddIdddddddidddddiddddddddddddddddddiddddddddidddddd.dddiddܐdݐdސddIddddiddddddddOiddiddddddddddd`dddOiddiddddddddddd`ddddSddiddddCdUdVdddiddd:dVddPdddddddddddddmdddddddqdd˜ddidd^dd5id)dddiddidddddddiddddiddiddddiddiddddiddiddddiddidddYdOiddidddddddiddZ i d dddddddddddddddddddddddddddd%dÐdĐdd;dŐddd@dƐdĐddMdǐdȐddNdɐdddTdʐdȐddXdːd̐ddbd͐dddΐdϐdddАdѐddi dҐdӐdddfdԐdddmd͐dddod͐dddtddՐddvd͐ddd֐dאdؐddِdڐdddېdڐdddܐdݐdؐddސdߐdddddddddddddddddՐddddՐddddՐdi ddddddddddddddddddddddddddddddddddddddddddddddddddɐdddddddddddʐdddddddi dӐddddՐdd̐ddڐddՐddސdd̐ddߐddddddddd ddddddddddddddddddddddddddddddddddddddd	d
ddi ddddddddddddddddddddddddddddddddddddddddddddddddddddddddddd dd!d"d#dd
d$dՐdi dd%dՐddd&d'ddd(dddd)dddd*d+ddd,d-dd%d.dՐdd'd/dՐdd,ddȐdd-d0dՐdd0d1ddd3d2ddd4d3dՐdd5d4ddd5d6ddd7dddd7d8ddi d;dddd<ddddd9d:dd>d;dddEd<dՐddGd=ddd>d?d@ddƐdAdBddːdCdDddEdFdddGdFdddHdHdddIdIdddJdJdȐddKdLdddӐdMdNddNdOddi dOdPddd֐dQdȐddSdRdddSdTdddXd1dddUdVddd\dWdddfdXdddjddՐddِdYdZddkd[dddld\d]ddud^dՐddvd_dddwd`dՐddxddddydaddi dbdcddd{dddՐddddՐdddedfdddgdhdddidjddddkdddldkdddddddmdndddodddpdqdrddsdtdՐdddudddvdwdddxdyddddddi ddddddddddddddzdddd{ddd|d}d~ddddddddddddddŐddddȐddddʐddddːdWddd̐dddd͐ddddΐddddҐdddi dӐddddddՐddddՐddddՐddddddddddddddddddddddddddddddddddddddddddddddddddddȐdi dddddddddddddddddddhddddhdd dddddddd	ddddddddddddddddddddddddddddLddddQdddi ddddddddddddddddddddddddddddddddddddddÐddddĐddddŐdƐdddǐddddǐddddȐdɐdddʐdːddd̐ddi dd͐dΐdd3dϐdАdd9dѐdҐdddӐdՐdddԐdȐdddՐdȐddd֐ddd dddd!dאdؐdd%dِddd(dڐdېdd/dܐdݐddސddddߐdddddddddddd?dddi d1ddȐdd2ddȐdd3dddd4dddd7ddȐddBddȐddCddȐddddddDddՐddEddddddddFdddddd̐ddHddddIddddJddddLdddi dNddddPddddQddddTddՐddUddddVddddWddȐdd[dddd^dddd_ddddvddddzddddDdddd|dd̐dd}dddd~dddddddi dddddddddddddddddd dՐddddddddddddddddddddddddddddddddՐddFddddddddIddȐdddddi dJd	d
ddddddNddddddddddddddddddȐddddddddȐddddddddddddՐddddddd ddd!d"dddd#dddd$dhdi dd%dhdd&d'd(ddd)dddd#dddÐddՐdd*d+d,ddTd-dՐdd.d-dՐddĐd/d0ddŐddddƐd1d2ddɐd3d4dd̐d5d6dd7d'd(ddѐd8dՐddԐd9ddd:d;d<di d=d;d<dd>d?d<dd@dAdBddCdDdEddFdAdBddGdHdIddJdKdBddLdMdNddOdPdQdd֐dRdSddUdӐdddِdTdՐddߐdUdVdddWddddXdՐdddYdՐddZd[d\di dd+d,dd]d^dddrdd̐dd_ddddd`dadddbddddcddddddeddfdgdՐdd	ddhdddidddjdudddkdlddddmdՐdddnddddodՐdddpddi ddqddddrddddrddddsdՐdddtdՐddddՐddudddddvddddwddddxddd#dyddd(dzd{dd-d|dՐdddd}d~dd.ddՐdd1ddddjdddi d9dddddddd@ddddddddqddddAddddvddddddddCddȐddDddddddddddddddddddddEddddFddՐddIdddi dJddddMddddtddddddȐddddՐddddddddddddddĐddՐddŐddddȐddddːdddddddddddd͐ddddϐddddАdddi dӐddՐddԐddddؐddddݐddՐddߐddddddddddddddȐdddd<ddddddddȐdddddddddddÐdddĐdÐddddŐdddddȐdi ddƐdǐdddȐdddddՐdddɐdՐdddddddddddddddddd	dʐdՐddddՐddːdd̐ddd͐dՐdddԐdՐddddՐddddՐdddΐdϐdddАdՐdi ddѐdҐdd"dӐdԐdd$dddd%ddՐdddddd'dՐddd)d֐dȐddאdddd*dؐdddِdڐddddېdȐdd,dܐdՐddJd^ddd5dݐdސddߐdddddddd9dddi ddddddddd<ddՐdd>dddd?dddd@ddՐddAddddIddddddddNddddddddTddddYd`dՐddddՐdd[dddd]ddՐdddddi ddddd`dd̐ddddddddddadÐdddddddbddddcdddddddddfddddidddd	d dȐddm	d	dddo	ddddr	ddddt	ddddw	d	ddi dy	d	d	d	d
dڐdd	dd͐dd	d	dddd	ddd	d	ddՐdd	d	ddd	d	dd	d	d	dd	d	ddd	d	ddd	d	d	dd	d	d 	d!dd	d"ddd	d#	d$dd	d%ddd	d&	d'di d	d(	d)d	d*dudd	d+	d,dd	d-	d.d̐dd	d/	d0d	d1	d2	d3d	d4	d5	d6d	d7	d8	d9d	d:	d;	d<d	d=	d5	d6d	d>	d?ddda	d@dd	dA	dB	d6ddBddddKd5dՐddx	dCdddddՐdi ddddd	dDddd	dE	dFdd|	dGddddddd	dH	dIdddddd	dJdՐd	dK	dL	dMdddddddddd		dN	dOdd	dP	dQddddddddddddddxdddi dyddddi	dRdՐddddddΐd#dddАddddddՐddddddB	dCdddHddȐdd	dSddd	dTddd1dddd	dU	dVd	dW	dXdՐddO	dYdddj	dZ	d[dZ	d\S (]	  absxXoutOut)phi_nameinputsoutputsaccuracyIndicesLabel)r   indiceslabelAccuracyCorrectTotal)r
   correcttotalacosacoshadadelta	adadelta_ParamGradAvgSquaredGradAvgSquaredUpdateLearningRateMasterParam)paramgradavg_squared_gradavg_squared_updatelearning_ratemaster_paramParamOutAvgSquaredGradOutAvgSquaredUpdateOutMasterParamOut)	param_out
moment_outinf_norm_outmaster_param_outadagradadagrad_Moment)r   r   momentr"   r#   	MomentOut)r(   r)   r+   adamadam_Moment1Moment2
Moment2MaxBeta1PowBeta2Pow
SkipUpdate)
r   r   r"   moment1moment2moment2_max	beta1_pow	beta2_powr#   skip_update
Moment1Out
Moment2OutMoment2MaxOutBeta1PowOutBeta2PowOut)r(   moment1_outmoment2_outmoment2_max_outbeta1_pow_outbeta2_pow_outr+   floatBeta1Tensor)	data_typetensor_nameBeta2TensorEpsilonTensor)beta1beta2epsilon)r   r   r	   scalaradamaxadamax_InfNorm)r   r   r"   r/   inf_normr<   r#   
InfNormOutadamwadamw_elementwise_addaddY)r   yScale_xScale_y	Scale_out)scale_xscale_y	scale_out)r   r   r	   attrssumadd_nr   add_position_encodingaddmmInput)inputr   r]   AlphaBeta)alphabetaaffine_channelScaleBias)r   scalebiasaffine_gridrj   ThetaoutputOutputoutput_shapeintOutputShape)r   r   r	   	int_array
reduce_allalldimkeep_dim)axiskeepdimallcloseOtherzstd::stringRtolAtol)rtolatolreduce_amaxamaxreduce_aminaminanchor_generatorAnchors	Variances)anchorsvariances_outangle
reduce_anyanyrangearangeStartEndStep)startendstepdoubleTrue)rK   support_tensorarg_maxargmaxr   int64_targ_minargminargsort)r   r   tensor_array_to_tensorarray_to_tensorOutIndex)r   	out_index
as_complexas_realasinasinhassertCondData)conddata)r   r   assign
assign_posassign_value)r   r	   atanatan2X1X2atanhattention_lstmC0H0AttentionWeightAttentionBiasAttentionScalarAttentionScalarBias
LSTMWeightLSTMBias)	r   c0h0attention_weightattention_biasattention_scalarattention_scalar_biaslstm_weight	lstm_biasHiddenCellAttentionedXAttentionFCOutLSTMXLSTMOUT)hiddencellattentioned_xattention_fc_outlstm_xlstm_outaucPredictStatPosStatNegInsTagWeight)r   r   stat_posstat_negins_tag_weightAUC
StatPosOut
StatNegOut)r   stat_pos_outstat_neg_outbaddbmmbarrierbatch_fcW)rj   wrs   
batch_normMeanVariance)r   meanvariancerr   rs   MeanOutVarianceOut	SavedMeanSavedVarianceReserveSpace)r   mean_outvariance_out
saved_meansaved_variancereserve_spacedata_formatdata_layoutbce_loss)rj   r   beam_search_decodeIdsScores)idsscoresSentenceIdsSentenceScores)sentence_idssentence_scores	bernoullibicubic_interp_v2bicubic_interpOutSize
SizeTensor)r   out_sizesize_tensorscale_tensorbilinear_tensor_productbilinearWeight)r   r]   weightrs   bilinear_interp_v2bilinear_interpbincountWeights)r   weights	minlengthbipartite_matchdist_matDistMatColToRowMatchIndicesColToRowMatchDist)col_to_row_match_indicescol_to_row_match_distbitwise_andbitwise_not
bitwise_orbitwise_xorbmm
bn_act_xpu)r   rd   	box_coderPriorBoxPriorBoxVar	TargetBox)	prior_boxprior_box_var
target_box
output_box	OutputBoxbroadcast_tensorsc_concatc_embedding)r  r   c_softmax_with_cross_entropyLogits)logitsr   SoftmaxLoss)softmaxlossc_splitcastceilcelucheck_finite_and_unscalecheck_finite_and_unscale_)r   rr   FoundInfinite)r   found_infinitecholeskycholesky_solveclass_center_sampler   RemappedLabelSampledLocalClassCenter)remapped_labelsampled_local_class_centerclipMinMax)minmaxclip_by_normcoalesce_tensorFusedOutput)rv   fused_outputsize_of_dtypeuser_defined_size_of_dtypecollect_fpn_proposalsMultiLevelRoisMultiLevelScoresMultiLevelRoIsNum)multi_level_roismulti_level_scoresmulti_level_rois_numFpnRoisRoisNum)fpn_roisrois_numpost_nms_topnpost_nms_topNcomplex)realimagconcat
AxisTensor)r   r   r	   rd   rR   conditional_blockr   conjconv2dFilter)rj   filterconv2d_transpose)r   rf  rs   output_sizeconv2d_transpose_biasconv3dconv3d_transpose)r   rf  correlationInput1Input2)input1input2coscoshcrop_tensorcropShapeShapeTensor)rK   rL   tensors_nameOffsetsOffsetsTensor)shapeoffsetscrosssoftmax_with_cross_entropycross_entropy_with_softmaxcumprodcumsumcvmCVM)r   r  	data_normdecode_jpegdeformable_convOffsetMask)r   offsetrf  maskdepthwise_conv2dScale_inScale_in_eltwiseScale_weights)scale_inrc   scale_in_eltwisescale_weightsdepthwise_conv2d_transpose
dequantizeShift)rr   shiftdequantize_abs_maxdequantize_linear	ZeroPointInAccumInState)r   rr   
zero_pointin_accumin_stateOutScaleOutAccumOutState)r]   	out_scale	out_accum	out_statedequantize_logDict)r   dictdeterminantdetdgc_clip_by_normdgc_momentumVelocitycurrent_stepnranks)r   r   velocityr"   r#   current_step_tensornranks_tensorVelocityOutGrad_out)r(   velocity_outr+   grad_outdiag_v2diag
diag_embeddiagonaldigamma	dirichletrm   dist	div_scaleScaleTensorFalse)rr   rs   elementwise_divdividedotdropoutSeed)r   seed_tensor)r   r  dropout_probis_testdropout_implementationseedfix_seed)pr  moder  r  r  r   
dropout_ndedit_distanceHypsRefs
HypsLength
RefsLength)hypsrefs
hypslength
refslengthSequenceNum)sequencenumr   eigEigenvaluesEigenvectors)out_wout_veigheigvalseigvalsh)eigenvalueseigenvectorsuploUPLOeinsumOperands
InnerCacheXShape)r   inner_cachexshapeelementwise_powelulookup_table_v2	embedding)r   r  sparse	is_sparseemptyrz  ShapeTensorList)r   r	   r{   equal	equal_allerferfinvexp	expand_v2expandexpand_shapes_tensor)r   r   r	   rd   r{   expand_as_v2	expand_asexpm1exponentialexponential_lamlambdaeye)num_rowsnum_columns)r   r	   rR   $fake_channel_wise_dequantize_max_absScales)r   scales"fake_channel_wise_quantize_abs_max)r   r  -fake_channel_wise_quantize_dequantize_abs_maxfake_dequantize_max_absfake_quantize_abs_max fake_quantize_dequantize_abs_max/fake_quantize_dequantize_moving_average_abs_maxInScale)r   in_scaler  r  )r   r  r  r  $fake_quantize_moving_average_abs_maxfake_quantize_range_abs_maxIter)r   r  iter	OutScales)r   r  
out_scalesfaster_tokenizerVocabTextTextPair)vocabtext	text_pairInputIds
SegmentIds)	input_idssegment_idsfc)r  rc   r  feedfetch_barrierfft_c2cfft_c2rfft_r2cfill_anyfillvaluefill_diagonalfill_diagonal_tensorflash_attn_unpadded)max_seqlen_qmax_seqlen_k)r   rR   flash_attn_v3_varlenflash_attn_varlen_qkvpackedflatten_contiguous_rangeflatten)r   r  
start_axis	stop_axis)r1  r2  flipfloorelementwise_floordivfloor_divideelementwise_fmaxfmaxelementwise_fminfminfoldframefrobenius_normfill_constantfullfill_any_like	full_likefull_with_tensor)r   r{   
fused_adamfused_adam_ParamsGradsMoments1Moments2Moments2Max	Beta1Pows	Beta2PowsMasterParams)
paramsgradsr"   moments1moments2moments2_max
beta1_pows
beta2_powsmaster_paramsr>   	ParamsOutMoments1OutMoments2OutMoments2MaxOutBeta1PowsOutBeta2PowsOutMasterParamsOut)
params_outmoments1_outmoments2_outmoments2_max_outbeta1_pows_outbeta2_pows_outmaster_params_outfused_attentionLnScaleLnBiasQKVWQKVBiasCacheKVSrcMask
OutLinearWOutLinearBiasLn2ScaleLn2Bias)r   ln_scaleln_bias
qkv_weightqkv_biascache_kvsrc_maskout_linear_weightout_linear_bias
ln_scale_2	ln_bias_2ln_meanLnMeanln_var
LnVarianceln_outLnOutqkv_outQKVOutqkv_bias_out
QKVBiasOuttranspose_out_2TransposeOut2qk_outQKOutqktv_outQKTVOutsoftmax_out
SoftmaxOutattn_dropout_mask_outAttnDropoutMaskOutattn_dropout_outAttnDropoutOutsrc_mask_out
SrcMaskOutfmha_outFMHAOutout_linear_outOutLinearOutdropout_mask_outDropoutMaskOut	ln_mean_2Ln2Meanln_var_2Ln2VarianceBiasDropoutResidualOut
CacheKVOut)bias_dropout_residual_outcache_kv_outr   fused_batch_norm_act&fused_bias_dropout_residual_layer_normResidual)r   residualrs   rn  ro  )r  r  rx  ln_variancer]   fused_bn_add_activationfused_bn_add_activation_Z)r   zr   r   rr   rs   fused_conv2dResidualData)rj   rf  rs   residual_paramfused_conv2d_add_act)rj   rf  rs   residual_dataOutputs)rv   r	   fused_conv3dfused_elementwise_addfused_elementwise_divfused_elementwise_mulfused_elementwise_sub!fused_embedding_eltwise_layernormEmbs)r   embsrs   rr   fused_embedding_fc_lstm
EmbeddingsWeightH)r   
embeddingsweight_hrs   r   r   XXBatchedInputBatchedHiddenBatchedCellReorderedH0ReorderedC0)r   r   xxbatched_inputbatched_hiddenbatched_cellreordered_h0reordered_c0fused_fc_elementwise_layernormBias0Bias1)r   r   r]   bias0rr   bias1)r   r   r   fused_feedforwardDropout1SeedDropout2SeedLinear1WeightLinear1BiasLinear2WeightLinear2BiasLn1ScaleLn1Bias)r   dropout1_seeddropout2_seedlinear1_weightlinear1_biaslinear2_weightlinear2_bias	ln1_scaleln1_bias	ln2_scaleln2_biasDropout1MaskDropout2MaskLn1MeanLn1Variance
Linear1OutLn1OutDropout1OutDropout2Out)r   dropout1_maskdropout2_maskln1_meanln1_varianceln2_meanln2_variancelinear1_outln1_outdropout1_outdropout2_outr  r  dropout1_ratedropout2_rate)dropout1_seed_valdropout2_seed_valdropout1_probdropout2_probfused_gate_attentionQueryKeyQueryWeight	KeyWeightValueWeight	QKVWeightNonbatchedBias
GateWeightGateBiasOutLinearWeight)querykeyquery_weight
key_weightvalue_weightrp  nonbatched_biasrs  gate_weight	gate_biasrt  ru  QueryTransposeOutKeyTransposeOutValueTransposeOutQKVTransposeOut
SoftmaxLseGateOut)	query_transpose_outkey_transpose_outvalue_transpose_outqkv_transpose_outr  softmax_lser  gate_outr   fused_gemm_epilogue)r   r]   rs   )r   r   fused_gemm_epilogue_gradDOut)r   r]   r   out_gradDXDYDBias)x_grady_grad	bias_gradfused_multi_transformer_int8rn  ro  qkv_wrq  rr  	time_stepTimeSteprs  out_linear_wru  ffn_ln_scale
FFNLnScaleffn_ln_bias	FFNLnBiasffn1_weight
FFN1Weight	ffn1_biasFFN1Biasffn2_weight
FFN2Weight	ffn2_biasFFN2Biasqkv_out_scaleQKVOutScaleOutLinearOutScaleFFN1OutScaleFFN2OutScale)out_linear_out_scaleffn1_out_scaleffn2_out_scale)r  r   fused_seqpool_cvmfused_transpose
fusion_gruWeightX)r   r   weight_xr  rs   
BatchedOut)r  r  r  batched_outr   
Scale_data
Shift_data)
scale_data
shift_datar  fusion_lstm)r   r   r<  r  rs   r   CheckedCell)
r   r   r   r  r  r  r  r  r  checked_cellfusion_repeated_fc_relu)r   r   rs   ReluOut)relu_outr   fusion_seqconv_eltadd_reluColMat)r   col_matcontextLengthcontextStartcontextStride)context_lengthcontext_startcontext_stridefusion_seqpool_concatfusion_transpose_flatten_concatgatherIndex)r   indexAxis	gather_ndgather_treeParents)r   parentsgaussian_randomgaussiangelugenerate_proposals_v2generate_proposals
BboxDeltasImShape)r   bbox_deltasim_shaper   	variancesRpnRoisRpnRoiProbs
RpnRoisNum)rpn_roisrpn_roi_probsrpn_rois_numpre_nms_topN)pre_nms_top_npost_nms_top_nglobal_gatherglobal_scattergrad_addgraph_khop_samplerRowCol_PtrEids)rowcolptrr   eidsOut_SrcOut_DstSample_Index	Reindex_XOut_Eids)out_srcout_dstsample_index	reindex_xout_eidsgraph_sample_neighborsPerm_Buffer)rv  rw  r   rx  perm_buffer	Out_Count)r   	out_countr  greater_equalgreater_thangrid_samplergrid_sampleGrid)r   grid
group_norm)r]   r   r   gumbel_softmaxhard_shrink
hardshrinkhard_sigmoidhardsigmoid
hard_swish	hardswishbreluhardtanhhashruntime_shape&ALL_KERNELS_MUST_COMPUTE_RUNTIME_SHAPEelementwise_heaviside	heaviside
hinge_lossLabels)r1  labelsr5  	histogram)rj   r  hierarchical_sigmoidhsigmoid_loss	PathTablePathCode)r   r   r   rs   pathcodePreOutW_Out)r   pre_outw_out
huber_loss)r   r  im2sequencer_  	increment	index_addAddValue)r   rV  	add_valueindex_elementwise_getrV  
input_dimsinput_strides
index_dimsindex_stride)r   rV  r  r  r  r  slice_offset
accumulateis_combined)r  r  r  index_elementwise_put!index_elementwise_put_with_tensorindex_sampleindex_selectinstance_norm)r]   r   r   inverseis_emptyiscloseisfinite_v2isfiniteisinf_v2isinfisnan_v2isnan
kldiv_lossTarget)r   r   kronkthvaluel1_normlabel_smooth	PriorDist)r   
prior_distlamblamb_)	r   r   r"   r9   r:   r<   r=   r#   r>   )r(   rD   rE   rG   rH   master_param_outs
layer_norm
leaky_relunegative_slopelegacy_bilinear_interplegacy_expandexpand_timesExpandTimesexpand_times_tensorlegacy_generate_proposalsImInfo)r   rc  im_infor   re  matmullegacy_matmulDDXDDY)r   r]   r  x_grad_grady_grad_grad)r   r  r  transpose_Xtranspose_Y)transpose_xtranspose_ynearest_interplegacy_nearest_interpreshapelegacy_reshapelerp)r   r]   r  
less_equal	less_thanlgammalinear_interp_v2linear_interp	linear_v2linspaceStopNum)r   stopnumber	lod_resetloglog10log1plog2log_loss	Predictedlog_softmaxlogcumsumexplogical_andlogical_not
logical_orlogical_xorlogit
logsigmoid	logsumexplookup_table)r   r   lrnMidOut)r   mid_outlstsqSolution	ResidualsRankSingularValues)solution	residualsranksingular_valuesrcond	lu_unpackPivotsPmatLU)pmatlumargin_cross_entropymasked_select)r   r  match_matrix_tensor)r   r]   r   Tmp)r   tmp	matmul_v2trans_xtrans_ymulmatmul_with_flatten
matrix_nmsBBoxes)bboxesr   )r   rV  roisnummatrix_powermatrix_rank	TolTensor)r   
tol_tensor
reduce_maxrI  max_pool2d_with_indexkernel_sizeksizemax_pool3d_with_indexelementwise_maxmaximummaxoutreduce_meanr   mean_allmemory_efficient_attentionmerge_selected_rowsmerged_adam_)	r   r   r"   r9   r:   r;   r<   r=   r#   merged_momentummerged_momentum_)r   r   r  r"   r#   )r(   r  r+   meshgrid
reduce_minrH  elementwise_minminimummish	threshold)r   r  r  momentum	momentum_moving_average_abs_max_scale)r   r  r  	multi_dot	multi_gru)r   r<  r  rs   r  r   )rA  rB  multiclass_nmsmulticlass_nms3)r)  r   rZ  
NmsRoisNum)r   rV  nms_rois_nummultihead_matmulBiasQK)rj   r   rs   bias_qktranspose_Qtranspose_Ktranspose_V)transpose_qtranspose_ktranspose_vmultinomialnum_samples	multiplex)r   rV  elementwise_mulmultiplymvVec)r   vec	nanmedianMedianIndex)r   mediansrK   nearest_interp_v2nll_loss)rj   r   r  Total_weight)r   total_weightnmsBoxesKeepBoxesIdxsiou_thresholdwhere_indexnonzero	condition	ConditionnormNorm)r   rm  	not_equalsizenumel
one_hot_v2one_hotdepthdepth_tensoroverlap_addp_normpad	pad_valuepad2dpad3dpaddingsPaddingspartial_allgatherpartial_concatpartial_recvpartial_sumpixel_shufflepixel_unshufflepoissonpool2dpool3dpowr]   factorFactorTensorprelu)r   rm   printinInr'  Image)rj   image)r   varreduce_prodprod
psroi_poolROIs)r   boxes	boxes_numput_along_axisValue)arrr   valuesResultReduceInclude_self)r   reduceinclude_selfpylayerqrQR)qrquantize)rr   r  r  quantize_linearrandintrandpermrange_v2r^  
reciprocalrelurelu6elementwise_mod	remainderrenormrepeat_interleaveRepeats)repeatsr   #repeat_interleave_with_tensor_indexRepeatTensor)r   r  
requantizeShift_in	Shift_out)r  rc   shift_in	shift_outreshape2resnet_basic_blockfilter1Filter1scale1Scale1r  mean1Mean1var1Var1filter2Filter2scale2Scale2bias2Bias2mean2Mean2var2Var2filter3Filter3scale3Scale3bias3Bias3mean3Mean3var3Var3conv1Conv1saved_mean1
SavedMean1saved_invstd1SavedInvstd1	mean1_outMean1Outvar1_outVar1Outconv2Conv2conv2_input
Conv2Inputsaved_mean2
SavedMean2saved_invstd2SavedInvstd2	mean2_outMean2Outvar2_outVar2Outconv3Conv3saved_mean3
SavedMean3saved_invstd3SavedInvstd3	mean3_outMean3Outvar3_outVar3Out	MaxInput1
MaxFilter1	MaxInput2
MaxFilter2	MaxInput3
MaxFilter3)
max_input1max_filter1
max_input2max_filter2
max_input3max_filter3resnet_unitFilterXScaleXBiasXMeanXVarXFilterZScaleZBiasZMeanZVarZ)r   filter_xra   bias_xmean_xvar_xr  filter_zscale_zbias_zmean_zvar_zBitMaskConvX
SavedMeanXSavedInvstdXRunningMeanXRunningVarXConvZ
SavedMeanZSavedInvstdZRunningMeanZRunningVarZ)r   bit_maskconv_xsaved_mean_xsaved_invstd_xrunning_mean_xrunning_var_xconv_zsaved_mean_zsaved_invstd_zrunning_mean_zrunning_var_zreversermsproprmsprop_
MeanSquareMeanGrad)r   mean_square	mean_gradr"   r   r/   r#   MeanSquareOutMeanGradOut)r(   r)   mean_square_outmean_grad_outr  rnnPreState
WeightListSequenceLength)r   	pre_stateweight_listsequence_lengthDropoutStateStateReserve)r   dropout_state_outstatereserve	roi_alignroi_poolArgmax)r   r   rollshiftsShiftsTensorroundrow_convrsqrtsave_combinerr   scatterUpdates)r   rV  updatesscatter_nd_addsearchsortedSortedSequenceValues)sorted_sequencer  segment_pool)r   r  	SummedIds)r   
summed_idsself_dp_attentionselugraph_send_recvsend_u_recv	Src_index	Dst_index)r   	src_index	dst_index	Dst_count)r   	dst_countr	  Out_sizegraph_send_ue_recvsend_ue_recv)r   r]   rb  rc  graph_send_uvsend_uvsequence_expandsequence_maskmax_lenmaxlenMaxLenTensorsequence_softmaxsgdsgd_)r   r"   r   r#   )r(   r+   shard_indexshare_bufferXOut)r   xout
share_datashare_data_shuffle_batch)r   r  
ShuffleIdxSeedOut)r   shuffle_idxseed_outshuffle_channelgroup)r   r  sigmoidsignsilusinsinhsliceStartsTensorStartsTensorList
EndsTensorEndsTensorList)startsendsslogdeterminantslogdet	soft_relur4  softplus
softshrinksoftsignsolvesparse_batch_normsparse_reshapesparse_slice
sparse_sumsparse_sync_batch_normspectral_normV)r  r  vsplitsections)r   r   r	   rR   r{   split_with_num)rK   r   rL   sqrtsquaresqueeze2squeezeaxesstackstanhstraight_through_estimator_gradstrided_sliceStridesTensorStridesTensorList)r  r  strideselementwise_subsubtract
reduce_sum	out_dtype)r   r   dtypesvdSVH)r  svhswishsync_batch_norm)r   rr   rs   r   r   take_along_axis)r  r   tantanhtanh_shrink	tdm_childTreeInfo
child_numsr  )r   	tree_infor  r  ChildLeafMask)child	leaf_masktdm_samplerTravelLayer)r   travellayer)r   r  r  thresholded_relutilerepeat_timesRepeatTimesrepeat_times_tensortop_k_v2topkkKtop_ktopk_v1trace
transpose2	transposepermtriangular_solve	tril_triutrilinear_interp_v2trilinear_interptrunctruncated_gaussian_randomunbindunfolduniform_randomuniform)r   r	   rR   r{   uniform_random_inplaceuniform_inplaceuniqueCounts)r   r   r  countsunique_consecutive)r   rV  r  unpool)r   r   paddingunpool3d
unsqueeze2	unsqueeze
AxesTensorAxesTensorListunstackupdate_loss_scalingupdate_loss_scaling_PrevLossScalingInGoodSteps
InBadSteps)r   r=  prev_loss_scalingin_good_stepsin_bad_stepsLossScalingOutGoodStepsOutBadSteps)r   loss_scalingout_good_stepsout_bad_stepsstop_updatebool
StopUpdate
view_shapeviterbi_decode
TransitionLength)
potentialstransition_paramslengthsPath)r   r  warpctcLogitsLengthLabelLength)r1  r   logits_lengthlabels_lengthWarpCTCGrad)warpctcgradr5  where)rk  r   r]   whileyolo_boxImgSize)r   img_size)r  r   yolo_box_headyolo_box_postBoxes0Boxes1Boxes2
ImageShape
ImageScale)boxes0boxes1boxes2image_shapeimage_scale)r   rL  yolov3_loss	yolo_lossGTBoxGTLabelGTScore)r   gt_boxgt_labelgt_scoreObjectnessMaskGTMatchMask)r5  objectness_maskgt_match_maskbox_clip)rj   r  c_allreduce_sum
c_identity	c_scatterchannel_shuffle
chunk_eval	Inference	SeqLength)	inferencer   
seq_length	PrecisionRecallzF1-ScoreNumInferChunksNumLabelChunksNumCorrectChunks)	precisionrecallf1_scorenum_infer_chunksnum_label_chunksnum_correct_chunkscomm_init_allcrf_decodingEmission)emission
transitionr   lengthviterbi_pathViterbiPathcross_entropycross_entropy2MatchX)r   x_shapematch_x	ctc_alignInputLength)rj   input_lengthOutputLength)rv   output_length
cudnn_lstmInitHInitC)r   init_hinit_cr   r?  r@  StateOutLastHLastC)rF  	state_outr   last_hlast_cdecayed_adagrad)r   r   r/   r"   )r(   r)   dependDep)r   depdgc)r  r  r   r   U_outV_out
EncodeGrad
GatherBuff)u_outv_outencode_gradr  gather_buffdistribute_fpn_proposalsMultiFpnRoisRestoreIndex)multi_fpn_roisrV  restore_indexdistributed_fused_lamb_init)r   r   fp32_fused_paramFP32FusedParamfp32_fused_gradFP32FusedGradfp16_fused_paramFP16FusedParamfp16_fused_gradFP16FusedGradr9   r:   r<   r=   fused_param_offsetsFusedParamOffsetsfp32_shard_fused_param_offsetsFP32ShardFusedParamOffsetsfp16_shard_fused_param_offsetsFP16ShardFusedParamOffsets
param_info	ParamInfoparam_order
ParamOrderr(   r+   r  GradOutglobal_scaleGlobalScaler   dpsgd)r   r   r"   fetch_v2fetchflatten2)r   rK  ftrlSquaredAccumulatorLinearAccumulator)r   squared_accumulatorlinear_accumulatorr   r"   SquaredAccumOutLinearAccumOut)r(   squared_accum_outlinear_accum_outfill_constant_batch_size_likefull_batch_size_likefused_elemwise_activationIntermediateOut)r   intermediate_outfused_elemwise_add_activationfused_matmul)r   r]   r  fused_reshape_Xfused_transpose_Xfused_reshape_Yfused_transpose_Yfused_reshape_Outfused_transpose_Out)
ra   rb   rc   r  fused_reshape_xfused_transpose_xfused_reshape_yfused_transpose_yfused_reshape_outfused_transpose_outfused_softmax_maskfused_softplusfused_token_pruneAttnNewMask)attnr   r  new_maskSlimmedXCLSInds)	slimmed_xcls_indsfusion_groupInputsoutsOutsfusion_seqpool_cvm_concatfusion_squared_mat_subSquaredXSquaredY	SquaredXY)	squared_x	squared_y
squared_xyr   get_tensor_from_selected_rowsgru)rj   r   r  rs   	BatchGateBatchResetHiddenPrevBatchHidden)
batch_gatebatch_reset_hidden_prevbatch_hiddenr   gru_unit
HiddenPrev)rj   hidden_prevr  rs   GateResetHiddenPrev)gatereset_hidden_prevr   identity_losslars_momentumlars_momentum_legacy_cropr{  limit_by_capacitylod_array_lengthlogspaceBase)r   r  numbaselookup_table_dequantlstm)rj   r   r   r  rs   BatchCellPreAct)r   r   r  batch_cell_pre_actluInfos)r   pivotsinfospivotr  memcpy
memcpy_d2hmp_allreduce_sumnceSampleWeightCustomDistProbsCustomDistAliasCustomDistAliasProbs)rj   r   r  rs   sample_weightcustom_dist_probscustom_dist_aliascustom_dist_alias_probsCostSampleLogitsSampleLabels)costsample_logitssample_labelsnopnumber_countnumberspartial_sendprune_gate_by_capacityGateIdxExpertCount)gate_idxexpert_countout_gate_idx
NewGateIdxpyramid_hash	WhiteList	BlackList)r   r   
white_list
black_listDropPos
X_Temp_Out)r   drop_pos
x_temp_outrandom_routingProb
TopK_ValueTopK_Idx)prob
topk_valuetopk_idxrank_attention
RankOffset	RankParam)r   rank_offset
rank_param	InputHelpInsRank)
input_helpr   ins_rankMaxRankMaxSize)max_rankmax_sizeread_from_arrayI)arrayirecv_v2graph_reindexreindex_graph	NeighborsCountHashTable_ValueHashTable_Index)r   	neighborscounthashtable_valuehashtable_indexReindex_SrcReindex_Dst	Out_Nodes)reindex_srcreindex_dst	out_nodesrreluNoise)r   noisesend_v2sequence_convPaddingData)r   padding_datarf  paddingTrainable)padding_trainablerO  rP  rQ  sequence_poolMaxIndex)r   	max_index	set_value)rK   rw  StepsTensorList)r  r  stepsset_value_with_tensor!sigmoid_cross_entropy_with_logitsskip_layernorm)r   r]   rr   rs   sparse_attentionColumnsKeyPaddingMaskAttnMask)r  r  r  r  columnskey_padding_mask	attn_maskSparseDotSdd)r   sparse_dot_sddr4  sparse_momentum)r   r   r  rV  r   r"   r#   squared_l2_normstftWindow)r   windowsync_calc_streamsync_comm_streamtemporal_shifttransfer_layoutuniform_random_batch_size_likewrite_to_array)r   r  )r<  r=  r>  r?  rH  rI  rJ  c_sync_calc_streamc_sync_comm_streamrO  rP  rQ  rR  zTensor xzTensor(out))argsrv   z&Tensor x, Tensor indices, Tensor labelz0Tensor(accuracy), Tensor(correct), Tensor(total)accuracy_checkzZTensor x, Tensor y, str fn_name, double rtol=1e-5, double atol=1e-8,  bool equal_nan=falsezTensor param, Tensor grad, Tensor avg_squared_grad, Tensor avg_squared_update, Tensor learning_rate, Tensor master_param, float rho = 0.95f, float epsilon = 1.0e-6f, bool multi_precision = falsezUTensor(param_out), Tensor(moment_out), Tensor(inf_norm_out), Tensor(master_param_out)zTensor param, Tensor grad, Tensor moment, Tensor learning_rate, Tensor master_param, float epsilon = 1.0e-6f, bool multi_precision = falsez?Tensor(param_out), Tensor(moment_out), Tensor(master_param_out)a  Tensor param, Tensor grad, Tensor learning_rate, Tensor moment1, Tensor moment2, Tensor moment2_max, Tensor beta1_pow, Tensor beta2_pow, Tensor master_param, Tensor skip_update, Scalar beta1 = 0.9f, Scalar beta2 = 0.999f, Scalar epsilon = 1.0e-8f, bool lazy_mode = false, int64_t min_row_size_to_use_multithread = 1000, bool multi_precision = false, bool use_global_beta_pow = false, bool amsgrad = falsezTensor(param_out), Tensor(moment1_out), Tensor(moment2_out), Tensor(moment2_max_out), Tensor(beta1_pow_out), Tensor(beta2_pow_out), Tensor(master_param_out)zTensor param, Tensor grad, Tensor learning_rate, Tensor moment, Tensor inf_norm, Tensor beta1_pow, Tensor master_param, float beta1 = 0.9f, float beta2 = 0.999f, float epsilon = 1.0e-8f, bool multi_precision = falsea  Tensor param, Tensor grad, Tensor learning_rate, Tensor moment1, Tensor moment2, Tensor moment2_max, Tensor beta1_pow, Tensor beta2_pow, Tensor master_param, Tensor skip_update, Scalar beta1 = 0.9f, Scalar beta2 = 0.999f, Scalar epsilon = 1.0e-8f, float lr_ratio = 1.0f, float coeff = 0.01f, bool with_decay = false, bool lazy_mode = false, int64_t min_row_size_to_use_multithread = 1000, bool multi_precision = false, bool use_global_beta_pow = false, bool amsgrad = falsez/Tensor x, float alpha = 1.0f, float beta = 1.0fzTensor (out)zATensor input, Tensor x, Tensor y, float beta=1.0, float alpha=1.0zBTensor x, Tensor scale, Tensor bias, str data_layout = "AnyLayout"z?Tensor input, IntArray output_shape={}, bool align_corners=truezTensor(output)z/Tensor x, int64_t[] axis={}, bool keepdim=false
all_gatherz'Tensor x, int ring_id = 0, int nranks=0
all_reducez.Tensor x, int ring_id = 0, int reduce_type = 0
all_to_allzTensor x, int ring_id = 0z\Tensor x, Tensor y, Scalar(double) rtol=1e-5, Scalar(double) atol=1e-8, bool equal_nan=falseTensor	ap_facadezTensor[] xs, int64_t num_outputs, str custom_op_name, str infer_meta_func_name, str infer_symbolic_func_name, str serialized_attributeszTensor[](out){num_outputs}ap_trivial_fusion_beginzTensor[] xsap_trivial_fusion_endap_variadiczTensor[] xs, int num_outputs, str code_module_lambda, str infer_symbolic_lambda, str infer_meta_lambda, str rnel_dispatch_lambda, str kernel_dispatch_const_data_lambdaapply_per_channel_scalezTensor x, Tensor scaleszmTensor x, Scalar(int64_t) axis, bool keepdims = false, bool flatten = false, DataType dtype = DataType::INT64z?Tensor x, int axis=-1, bool descending=false, bool stable=falsezTensor(out), Tensor(indices)
as_stridedzLTensor input, int64_t[] dims = {}, int64_t[] stride = {}, int64_t offset = 0asgd_z~Tensor param, Tensor grad, Tensor learning_rate, Tensor d, Tensor y, Tensor n, Tensor master_param, bool multi_precision=falsezITensor(param_out), Tensor(d_out), Tensor(y_out), Tensor(master_param_out)assign_out_zTensor x, Tensor outputz.Tensor x, Tensor cum_count, Tensor eff_num_lenassign_value_zMTensor output, int[] shape, DataType dtype, Scalar[] values, Place place = {}zTensor x, Tensor ya  Tensor x, Tensor c0, Tensor h0, Tensor attention_weight, Tensor attention_bias, Tensor attention_scalar, Tensor attention_scalar_bias, Tensor lstm_weight, Tensor lstm_bias, str gate_activation = "sigmoid", str cell_activation = "tanh", str candidate_activation = "tanh"zuTensor (hidden), Tensor (cell), Tensor (attentioned_x), Tensor (attention_fc_out), Tensor (lstm_x), Tensor (lstm_out)zTensor x, Tensor label, Tensor stat_pos, Tensor stat_neg, Tensor ins_tag_weight, str curve = "ROC", int num_thresholds = (2 << 12) - 1, int slide_steps = 1z7Tensor(auc), Tensor(stat_pos_out), Tensor(stat_neg_out)average_accumulates_zTensor param, Tensor in_sum_1, Tensor in_sum_2, Tensor in_sum_3, Tensor in_num_accumulates, Tensor in_old_num_accumulates, Tensor in_num_updates, float average_window = 0, int64_t max_average_window = INT64_MAX, int64_t min_average_window = 10000LzTensor(out_sum_1), Tensor(out_sum_2), Tensor(out_sum_3), Tensor(out_num_accumulates), Tensor(out_old_num_accumulates), Tensor(out_num_updates)zTensor x, int ring_id=0z#Tensor input, Tensor w, Tensor biaszTensor input, Tensor labelbeam_searchz~Tensor pre_ids, Tensor pre_scores, Tensor ids, Tensor scores, int level, int beam_size, int end_id, bool is_accumulated = truezDTensor (selected_ids), Tensor (selected_scores), Tensor (parent_idx)zTensor x, Tensor out_size, Tensor[] size_tensor, Tensor scale_tensor, str data_format="NCHW", int out_d=0, int out_h=0, int out_w=0, double[] scale={}, str interp_method="bilinear", bool align_corners=true, int align_mode=1z.Tensor x, Tensor y, Tensor weight, Tensor biasz3Tensor x, Tensor weights, Scalar(int) minlength = 0binomialzTensor count, Tensor probzITensor dist_mat, str match_type = "bipartite", float dist_threshold = 0.5zATensor (col_to_row_match_indices), Tensor (col_to_row_match_dist)bitwise_left_shiftz-Tensor x, Tensor y, bool is_arithmetic = truebitwise_right_shiftzTensor input, Tensor im_infozTensor (output)zTensor prior_box, Tensor prior_box_var, Tensor target_box, str code_type = "encode_center_size", bool box_normalized = true, int axis = 0, float[] variance = {}zTensor(output_box)	broadcastz'Tensor x, int ring_id = 0, int root = 0zTensor[] inputzTensor[]{input.size()}"build_src_rank_and_local_expert_idzWTensor expert_num_global_tensor, int64_t[] expert_num_global, int64_t num_local_expertsz'Tensor(vector), Tensor(local_expert_id)zDTensor x, int ring_id, bool use_calc_stream, bool use_model_parallelzZTensor x, int rank, int nranks, int ring_id, bool use_calc_stream, bool use_model_parallelzUTensor x, int ring_id = 0, int root = 0, int nranks = 0, bool use_calc_stream = falsez`Tensor logits, Tensor label,  int64_t ignore_index=-100, int ring_id=0, int rank=0, int nranks=0zTensor(softmax), Tensor(loss)zWTensor x, int rank = 0, int nranks = 1, int ring_id = 0, bool use_model_parallel = truecal_aux_losszTensor gate_prob, Tensor dispatch_mask, Tensor tokens_mask, Tensor dispatch_tokens_mask, int64_t num_experts, bool use_group, int64_t moe_k, float clip_minz4Tensor(l_aux_loss), Tensor(seqlen_float), Tensor(ce)calc_reduced_attn_scoresz&Tensor q, Tensor k, Tensor softmax_lsezTensor(reduced_scores)zTensor x, DataType dtypezTensor x, float alpha = 1.0z,Tensor x, int groups, str data_format="NCHW"zTensor[] x, Tensor scalez/Tensor[](out){x.size()}, Tensor(found_infinite)check_numericszTensor tensor, str op_type = "", str var_name = "", int check_nan_inf_level = 0, int stack_height_limit = -1, str output_dir = ""zTensor(stats), Tensor(values)zTensor x, bool upper=falsez$Tensor x, Tensor y, bool upper=falsezTensor label, int num_classes, int num_samples, int ring_id = 0, int rank = 0, int nranks = 1, bool fix_seed = false, int seed = 0z:Tensor(remapped_label), Tensor(sampled_local_class_center)z.Tensor x, Scalar(float) min, Scalar(float) maxzTensor x, float max_norma  Tensor[] input, DataType dtype, bool copy_data = false, bool set_constant = false, bool persist_output = false, float constant = 0.0, bool use_align = true, int align_size = -1, int size_of_dtype = -1, int64_t[] concated_shapes = {}, int64_t[] concated_ranks = {}z4Tensor[](output){input.size()}, Tensor(fused_output)zhTensor[] multi_level_rois, Tensor[] multi_level_scores, Tensor[] multi_level_rois_num, int post_nms_topnz$Tensor (fpn_rois), Tensor (rois_num)zTensor real, Tensor imagzTensor[] x, Scalar axis=0zTensor input, Tensor filter, int[] strides={1, 1}, int[] paddings={0, 0}, str padding_algorithm="EXPLICIT", int[] dilations={1, 1}, int groups=1, str data_format="NCHW"zTensor x, Tensor filter, int[] strides={1, 1}, int[] paddings={0, 0}, int[] output_padding={}, IntArray output_size={}, str padding_algorithm="EXPLICIT", int groups=1, int[] dilations={1, 1}, str data_format="NCHW"zTensor x, Tensor filter, Tensor bias, int[] strides={1, 1}, int[] paddings={0, 0}, int[] output_padding={}, IntArray output_size={}, str padding_algorithm="EXPLICIT", int groups=1, int[] dilations={1, 1}, str data_format="NCHW"zTensor input, Tensor filter, int[] strides={1, 1, 1}, int[] paddings={0, 0, 0}, str padding_algorithm="EXPLICIT", int groups=1, int[] dilations={1, 1, 1}, str data_format="NCDHW"zTensor x, Tensor filter, int[] strides={1, 1, 1}, int[] paddings={0, 0, 0}, int[] output_padding={}, int[] output_size={}, str padding_algorithm="EXPLICIT", int groups=1, int[] dilations={1, 1, 1}, str data_format="NCHW"copy_toz$Tensor x, Place place, bool blockingcopysignzTensor input1, Tensor input2, int pad_size, int kernel_size, int max_displacement, int stride1, int stride2, int corr_type_multiply=1z?Tensor emission, Tensor transition, Tensor label, Tensor lengthzTensor (viterbi_path)z4Tensor x, IntArray shape = {}, IntArray offsets = {}z Tensor x, Tensor y, int axis = 9zTensor input, Tensor label, bool soft_label=false, bool use_softmax=true, bool numeric_stable_mode=true, int ignore_index=-100, int axis=-1)cross_entropy_with_softmax_bwd_w_downcastz.Tensor label, Tensor softmax, Tensor loss_gradzTensor(input_grad)zcTensor input, Tensor input_length, int blank = 0, bool merge_repeated = true, int padding_value = 0z'Tensor (output), Tensor (output_length)zTensor x, Tensor init_h, Tensor init_c, Tensor w, Tensor[] weight_list, Tensor sequence_length, float dropout_prob = 0.0, bool is_bidirec = false, int hidden_size = 100, int num_layers = 1, bool is_test = false, int seed = 0zTTensor (out), Tensor (last_h), Tensor (last_c), Tensor (reserve), Tensor (state_out)cummaxz7Tensor x, int axis=-1, DataType dtype = DataType::INT64cumminz<Tensor x,  int dim, bool exclusive=false, bool reverse=falsezVTensor x, Scalar axis=-1, bool flatten=false, bool exclusive=false, bool reverse=falsez)Tensor x, Tensor cvm, bool use_cvm = truer   z5str name, IntArray shape, DataType dtype, Place placezlTensor param, Tensor grad, Tensor moment, Tensor learning_rate, float decay = 0.95f, float epsilon = 1.0e-6fz%Tensor(param_out), Tensor(moment_out)zTensor x, str mode, Place placezTensor x, Tensor offset, Tensor filter, Tensor mask, int[] strides, int[] paddings, int[] dilations, int deformable_groups, int groups, int im2col_stepzTensor x, Tensor[] depzTensor input, Tensor filter, int[] strides={1, 1}, int[] paddings={0, 0}, str padding_algorithm="EXPLICIT", int groups=1, int[] dilations={1, 1}, str data_format="NCHW"depthwise_conv2d_biaszTensor input, Tensor filter, Tensor bias, int[] strides={1, 1}, int[] paddings={0, 0}, str padding_algorithm="EXPLICIT", int groups=1, int[] dilations={1, 1}, str data_format="NCHW"depthwise_conv3d_biaszTensor input, Tensor filter, Tensor bias, int[] strides={1, 1, 1}, int[] paddings={0, 0, 0}, str padding_algorithm="EXPLICIT", int groups=1, int[] dilations={1, 1, 1}, str data_format="NCDHW"z'Tensor x, Tensor scale, float max_rangezTensor x, Tensor dictzTensor u, Tensor v, Tensor grad, Tensor param, Tensor current_step, Tensor nranks, float m=0.9, bool use_nesterov=true, float[] sparsity={}, float rampup_begin_step=0.0, float rampup_step=0.0, float regular_coeff=0.0, int regular_type=0zcTensor(u_out), Tensor(v_out), Tensor(encode_grad), Tensor(grad_out), Tensor(k), Tensor(gather_buff)zMTensor x, Tensor current_step, float max_norm, float rampup_begin_step = -1.0aH  Tensor param, Tensor grad, Tensor velocity, Tensor learning_rate, Tensor master_param, Tensor current_step_tensor, Tensor nranks_tensor, float mu, bool use_nesterov = false, str regularization_method = "", float regularization_coeff = 0.0f, bool multi_precision = false, float rescale_grad = 1.0f, float rampup_begin_step = -1.0zWTensor (param_out), Tensor (velocity_out), Tensor (master_param_out), Tensor (grad_out)z3Tensor x, int offset = 0, float padding_value = 0.0z:Tensor input, int offset = 0, int dim1 = -2, int dim2 = -1z6Tensor x, int offset = 0, int axis1 = 0, int axis2 = 1zTensor alphadisable_check_model_nan_infzTensor x, int flag = 0z!Tensor x, Tensor y, float p = 2.0zTensor param, Tensor grad, Tensor learning_rate, float clip = 10.0f, float batch_size = 16.0f, float sigma = 1.0f, int seed = 0zTensor(param_out)zTensor x, Tensor seed_tensor, Scalar p = 0.5f, bool is_test = false, str mode = "downgrade_in_infer", int seed = 0, bool fix_seed = falsezTensor(out), Tensor(mask)zWTensor hyps, Tensor refs, Tensor hypslength, Tensor refslength, bool normalized = falsez Tensor(sequencenum), Tensor(out)zTensor(out_w), Tensor(out_v)zTensor x, str UPLO = "L"z.Tensor x, str uplo = "L", bool is_test = falsez)Tensor(eigenvalues), Tensor(eigenvectors)zTensor x, float alpha = 1.0fembedding_grad_add_toz8Tensor token_indices, Tensor main_grad_, Tensor out_gradzTensor(main_grad_out)embedding_with_scaled_gradientz/Tensor x, Tensor weight, int64_t padding_idx=-1zHIntArray shape, DataType dtype=DataType::FLOAT32, Place place=CPUPlace()
empty_likez@Tensor x, DataType dtype = DataType::UNDEFINED, Place place = {}enable_check_model_nan_infzTensor x, int flag = 1zTensor x, IntArray shape = {}z/Tensor x, Tensor y, int64_t[] target_shape = {}expand_modality_expert_idztTensor expert_id, int64_t num_expert_per_modality, int64_t group_size, int64_t modality_offset, bool is_group_expertzTensor(expert_id_out)zTensor x, float lamzUScalar num_rows, Scalar num_columns, DataType dtype=DataType::FLOAT32, Place place={}z]Tensor x, Tensor[] scales, int[] quant_bits = {8}, int quant_axis = 0, int x_num_col_dims = 1zZTensor x, int bit_length = 8, int round_type = 1, int quant_axis = 0, bool is_test = falsezTensor(out), Tensor(out_scale)zDTensor x, int bit_length = 8, int round_type = 1, int quant_axis = 0z0Tensor x, int bit_length = 8, int round_type = 1zTensor x, Tensor in_scale, Tensor in_accum, Tensor in_state, float moving_rate = 0.9, int bit_length = 8, bool is_test = false, int round_type = 1zDTensor(out), Tensor(out_scale), Tensor(out_state), Tensor(out_accum)z~Tensor x, Tensor in_scale, Tensor iter, int window_size = 10000,  int bit_length = 8, bool is_test = false, int round_type = 1z2Tensor(out), Tensor(out_scale), Tensor(out_scales)z9Tensor x, int64_t[] axes, str normalization, bool forwardzSTensor x, int64_t[] axes, str normalization, bool forward, int64_t last_dim_size=0LzHTensor x, int64_t[] axes, str normalization, bool forward, bool onesidedz Tensor x, Scalar(double) value=0z6Tensor x, float value=0, int offset=0, bool wrap=falsezBTensor x, Tensor y, int64_t offset = 0, int dim1 = 0, int dim2 = 1
flash_attnzTensor q, Tensor k, Tensor v, Tensor fixed_seed_offset, Tensor attn_mask, float dropout = 0.0, bool causal = false, bool return_softmax = false, bool is_test = false, str rng_name = ""zFTensor(out), Tensor(softmax), Tensor(softmax_lse), Tensor(seed_offset)flash_attn_qkvpackedzTensor qkv, Tensor fixed_seed_offset, Tensor attn_mask, float dropout = 0.0, bool causal = false, bool return_softmax = false, bool is_test = false, str rng_name = ""a  Tensor q, Tensor k, Tensor v, Tensor cu_seqlens_q,  Tensor cu_seqlens_k, Tensor fixed_seed_offset, Tensor attn_mask, Scalar max_seqlen_q, Scalar max_seqlen_k, float scale, float dropout = 0.0, bool causal = false, bool return_softmax = false, bool is_test = false, str rng_name = ""flash_attn_v3a  Tensor q, Tensor k, Tensor v, Tensor q_v_, Tensor q_descale_, Tensor k_descale_, Tensor v_descale_, float softmax_scale, bool is_causal, int window_size_left, int window_size_right, float softcap, int num_splits, bool manual_set_pack_gqa, bool pack_gqa_, int sm_marginz Tensor(out), Tensor(softmax_lse)a{  Tensor q, Tensor k, Tensor v, Tensor cu_seqlens_q, Tensor cu_seqlens_k, Tensor seqused_q, Tensor seqused_k, Tensor qv, Tensor q_descale, Tensor k_descale, Tensor v_descale, Scalar max_seqlen_q, Scalar max_seqlen_k, float softmax_scale, bool causal, int window_size_left, int window_size_right, float softcap, int num_splits, bool manual_set_pack_gqa, bool pack_gqa, int sm_margina#  Tensor qkv, Tensor cu_seqlens_q,  Tensor cu_seqlens_k, Tensor fixed_seed_offset, Tensor attn_mask, Scalar max_seqlen_q, Scalar max_seqlen_k, float scale, float dropout = 0.0, bool causal = false, bool return_softmax = false, bool is_test = false, str rng_name = "", bool varlen_padded = trueflashmask_attentionzTensor q, Tensor k, Tensor v, Tensor startend_row_indices,  Tensor fixed_seed_offset, float dropout = 0.0, bool causal = false, bool return_softmax = false, bool is_test = false, str rng_name = ""flashmask_attention_v2zqTensor q, 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float quant_min_boundz2Tensor(out), Tensor(residual_out), Tensor(inv_var)zTensor x, Tensor mask!fused_softmax_mask_upper_trianglezTensor XzTensor(Out)	gammainccgammalnz%Tensor x, Tensor index, Scalar axis=0zTensor x, Tensor indexzTensor ids, Tensor parentszOIntArray shape, float mean, float std, int seed, DataType dtype, Place place={}gaussian_inplacez1Tensor x, float mean=0, float std=1.0, int seed=0z#Tensor x,  bool approximate = falsezTensor scores, Tensor bbox_deltas, Tensor im_shape, Tensor anchors, Tensor variances, int pre_nms_top_n, int post_nms_top_n, float nms_thresh, float min_size, float eta, bool pixel_offset=truez=Tensor(rpn_rois), Tensor(rpn_roi_probs), Tensor(rpn_rois_num)zBTensor x, Tensor local_count, Tensor global_count, int ring_id = 0zVTensor row, Tensor colptr, Tensor x, Tensor eids, int[] sample_sizes, bool return_eidsz[Tensor(out_src), Tensor(out_dst), Tensor(sample_index), Tensor(reindex_x), Tensor(out_eids)z~Tensor row, Tensor colptr, Tensor x, Tensor 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Tensor[] xzTensor bboxes, Tensor scores, Tensor rois_num, float score_threshold, int nms_top_k, int keep_top_k, float nms_threshold=0.3, bool normalized=true, float nms_eta=1.0, int background_label=0z0Tensor(out), Tensor(index), Tensor(nms_rois_num)z?Tensor x, Scalar(int) num_samples = 1, bool replacement = falsezTensor[] inputs, Tensor indexzTensor x, Tensor vecnadam_a$  Tensor param, Tensor grad, Tensor learning_rate, Tensor momentum_decay_pow, Tensor beta2_pow, Tensor mu_product, Tensor moment1, Tensor moment2, Tensor master_param, float beta1 = 0.9f, float beta2 = 0.999f, float epsilon = 1.0e-8f, float momentum_decay = 0.004f, bool multi_precision = falsezTensor(param_out), Tensor(momentum_decay_pow_out), Tensor(beta2_pow_out), Tensor(mu_product_out), Tensor(moment1_out), Tensor(moment2_out), Tensor(master_param_out)nansumzRTensor x, IntArray axis={}, DataType dtype=DataType::UNDEFINED, bool keepdim=false	nextafterz^Tensor input, Tensor label, Tensor weight, int64_t 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 

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 

 5
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 

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 5
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 

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 8D A# l A#  A#"  #A#*  Ui +A#2  ]S 3A#:  g q ;A#B  ji CA#J  l q KA#R A  SA#Z S [A#b V  cA#j Q" kA#r 
A sA#z 9 {A#B @ CA# A#J + KA#R n SA#Z A [A#b A cA#j  kA#r 
A sA#z  Z. {A#B    CA#J  KA#R  z. SA#Z )   [A#b  cA#j  kA#r Q0 sA#z  {A#B  CA#J ^ KA# A# A#R  Q] SA#Z  [A#b  cA#j ) kA#r @ sA#z _ {A#B  CA#J $ KA#R  SA#Z  f J [A#b 
 pK cA#j  J c kA#r S sA#z ) {A#B 5 CA#J , KA#R  QX SA# A# A#Z  [A#b  v" cA#j @ kA#r  v" sA#z E {A#B + CA#J ]U KA#R $ SA#Z ? [A#b  cA#j $ kA#r ? sA#z $ {A#B 
$ CA#J .# KA#R  u& SA#Z 9 [A# A# A#b  * cA#j )i;+ +kA#r V sA#z l {A#B	 V C	A#J	 g K	A#R	 #r1% %S	A#Z	 i [	A#b	  nH c	A#j	 8*! !k	A#r	 * s	A#z	  {	A#B
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 sA#z  {A#B Q) CA#J F KA#R 2 SA#Z ! ^1# #[A#b 0@&2 2cA#j u; kA#r  sh sA#z I0 {A#B I0 CA#J N KA#R h SA#Z 
;  [A#b G cA#j ~9 kA#r 1 sA# A# A#z  j {A#B (  CA#J   KA#R  L SA#Z ! m# #[A#b  V cA#j 9 kA#r ' sA#z 
 {A#B 
 w CA#J _ KA#R  ]k SA#Z E [A#b L cA#j H kA#r  sA#z  {A# A# A#B "($ $CA#J 3 KA#R 
$ SA#Z  R% [A#b  ^- cA#j i4 kA#r 
0 sA#z ,0 {A#B  CA#J B= KA#R 
. SA#Z J) [A#b %A' 'cA#j Z kA#r R sA#z !(# #{A#B $ CA# A# A#J 
 KA#R  SA#Z 
 [A#b / cA#j A kA#r   G)" "sA#z  {A#B % CA#J 
g KA#R +o- -SA#Z )l2+ +[A#b 4V26 6cA#j 9   kA#r B2 sA#z 'B2) ){A#B 6 eX8 8CA#J + eX- -KA# A# A#R " QF$ $SA#Z K [A#b e cA#j Z kA#r 2 sA#z H {A#B T CA#J  MZ KA#R  {Z SA#Z  oZ [A#b  _4 cA#j  N4 kA#r " xZ$ $sA#z  YZ {A#B  D4 CA#J A KA#R &  SA# A# A#Z  [A#b $ cA#j $ kA#r s sA#z x- {A#B {- CA#J I KA#R K SA#Z  mZ [A#b p cA#j  kA#r  Q sA#z [ {A#B ` CA#J P KA#R  A K SA#Z  K K   [A# A# A#b  ZF cA#j ' kA#r (* *sA#z $ {A#B  CA#J 7 KA#R ( SA#Z , [A#b a cA#j C kA#r 5 sA#z  SQ {A#B T CA#J T KA#R ho SA#Z  QD [A#b y cA# A# A#j y= kA#r 
 Kq sA#z  VN {A#B U CA#J 1  KA#R A  SA#Z 9 [A#b $ cA#j .! kA#r y sA#z z? {A#B 91 CA#J 	 KA#R 
 SA#Z 	 [A#b 
 cA#j - kA# A# A#r  F  sA#z   {A#B - CA#J I KA#R  T  SA#Z  m  [A#b ( m * *cA#j Q kA#r ( sA#z 6 {A#B 7 CA#J II KA#R  v" SA#Z  [A#b  cA#j o kA#r  sA# A# A#z  {A#B   C A#J  [ K A#R  $ S A#Z  N0 [ A#b   c A#j  G  k A#r   s Y s A#z  d? { A#B! 8 C!A#J! 3 K!A#R!  S!A#Z! D [!A#b!  v" c!A#j! W k!A#r! W s!A#z! ` {!A# A# A#B" 
 C"A#J"  K"A#R"  S"A#Z"  ["A#b" ; c"A#j" ) k"A#r" e s"A#z" $ {"A#B#  C#A#J# $ K#A#R# $ S#A#Z# - [#A#b#  c#A#j# d k#A#r# V s#A#z# @  {#A#B$  ~ C$A# A# A#J$  Cd K$A#R$ L^ S$A#Z$ 	-> [$A#b$ ? c$A#j$ Z6 k$A#r$  Q1 s$A#z$ 5  {$A#B% " nT$ $C%A#J% '  K%A#R% =. S%A#Z%  f? [%A#b% ! c%A#j% V k%A#r% J s%A#z% _ {%A#B& 
@ C&A#J&  j- K&A# A# A#R&  q- S&A#Z& [3 [&A#b& 4 c&A#j& @ k&A#r&  s&A#z& U0 {&A#B' . C'A#J' . K'A#R' ! }N# #S'A#Z'  ['A#b'  ] a c'A#j'  U F k'A#r' !0 s'A#z' [3 {'A#B( ( C(A#J( A0 K(A#R( H S(A# A# A#Z( H [(A#b( ` c(A#j( kw k(A#r( " A M$ $s(A#z( kw {(A#B) . T _0 0C)A#J)  qw" "K)A#R)  ] b S)A#Z)  sC [)A#b)  KU c)A#j) + k)A#r)  s)A#z)  PD {)A#B* Q C*A#J* / K*A#R* 	& S*A#Z*  w y [*A# A# A#b* U0 c*A#j* d k*A#r*  v" s*A#z* $ {*A#B+ r5 C+A#J+ 
2 K+A#R+ " S+A#Z+ A- [+A#b+ + c+A#j+   k+A#r+ 3 s+A#z+ Z {+A#B, N C,A#J, 7 K,A#R, } S,A#Z, 
< [,A#b, { c,A# A# A#j, A k,A#r, B s,A#z, B {,A#B- J C-A#J- L K-A#R-  S-A#Z- ! [-A#b-  V c-A#j-  W k-A#r- 
) s-A#z- T {-A#B.  J, C.A#J. H K.A#R. ^( S.A#Z.  T [.A#b. x c.A#j.  RH k.A# A# A#r. 	2( s.A#z.  H i {.A#B/ c C/A#J/ 4 K/A#R/ A S/A#Z/ 7 [/A#b/ eE c/A#j/ ] k/A#r/   s/A#z/  {/A#B0 Q C0A#J0 ) K0A#R0 ; S0A#Z0 jO [0A#b0  c0A#j0  k0A#r0 = s0A# A# A#z0 K {0A#B1 *N, ,C1A#J1 * K1A#R1 :  S1A#Z1 ) [1A#b1  c1A#j1  F E k1A#r1 
 Vn s1A#z1  h {1A#B2 |0 C2A#J2 A K2A#R2 - S2A#Z2  il [2A#b2 V. c2A#j2  k2A#r2 Y s2A#z2 M {2A# A# A#B3 8 C3A#J3 c K3A#R3 D3 S3A#Z3 x [3A#b3 p2 c3A#j3  O2 k3A#r3 b s3A#z3  b  {3A#B4 C C4A#J4 a4 K4A#R4 
i  S4A#Z4  X [4A#b4 r? c4A#j4  k4A#r4  s4A#z4 ] {4A#B5   C5A# A# A#J5 ;F K5A#R5 ) S5A#Z5  [5A#b5 (h* *c5A#j5  k5A#r5  s5A#z5 
 {5A#B6  C6A#J6 ~ K6A#R6  S6A#Z6 0 [6A#b6 F c6A#j6 1 k6A#r6  s6A#z6 $ {6A#B7 xK C7A#J7 i K7A# A# A#R7 ?- S7A#Z7 5! [7A#b7  c7A#j7  k7A#r7  s7A#z7 , {7A#B8 *  C8A#J8  K8A#R8 F S8A#Z8 
j [8A#b8 d  c8A#j8 X k8A#r8 
d s8A#z8 
64 {8A#B9  C9A#J9 $ K9A#R9  S9A# A# A#Z9  D K [9A#b9  c9A#j9 6 k9A#r9 
 s9A#z9  {9A#B:  C:A#J: ^6 K:A#R:  d= S:A#Z: ^ [:A#b: D c:A#j: zT k:A#r: e0 s:A#z: H {:A#B; & C;A#J; & K;A#R; e S;A#Z; * [;A# A# A#b; P c;A#j;  v" k;A#r; * s;A#z; N {;A#B<  C<A#J< $ K<A#R<   C" "S<A#Z< ,a [<A#b< ^ c<A#j< f k<A#r<  G s<A#z< % ` ' '{<A#B=  I> C=A#J= | K=A#R=  ] S=A#Z= . [=A#b= 1& c=A# A# A#j=  lp k=A#r= 
j s=A#z= A {=A#B> . C>A#J> 3 K>A#R> B S>A#Z> n0 [>A#b>  E5 c>A#j>  E6 k>A#r> ^ s>A#z>  B {>A#B? _. C?A#J?   DN" "K?A#R? 6 S?A#Z?  d1 [?A#b? 8 c?A#j?  s5 k?A# A# A#r?  jP s?A#z? Z {?A#B@ P C@A#J@ _" K@A#R@  V ^ S@A#Z@ D; [@A#b@ 7- c@A#j@  YR k@A#r@  U` s@A#z@ 7- {@A#BA D CAA#JA 1 KAA#RA ]- SAA#ZA 
$ [AA#bA ! cAA#jA W kAA#rA  sAA# A# A#zA  D K {AA#BB W CBA#JB  F Z! !KBA#RB , SBA#ZB $ [BA#bB *\ cBA#jB $ kBA#rB T sBA#zB e' {BA#BC $ CCA#JC $ KCA#RC  A o SCA#ZC ]6 [CA#bC $ cCA#jC $ kCA#rC  sCA#zC $ {CA# A# A#BD $ CDA#JD X KDA#RD $ SDA#ZD 
@ [DA#bD $ cDA#jD $ kDA#rD $ sDA#zD W {DA#BE $  CEA#JE  k KEA#RE $ SEA#ZE $ [EA#bE -+ cEA#jE H kEA#rE 6 sEA#zE  CQ {EA# A#r  